Creating core properties and classes... Creating primitive properties... Creating primitive classes... Done Loading RDF file /local/ferre/data/ontologies/FB15k-237/train.rdf Processing triples RDF/XML file 100000 triples loaded 200000 triples loaded Performing PageRank iterations Loading RDF file /local/ferre/data/ontologies/FB15k-237/valid.ttl Performing PageRank iterations EVAL Entity Relation Value Algo Hits@1 Hits@3 Hits@10 MRR $nb_concepts $nb_concepts_used $max_measure Relation (long) Max priority: 3 Max branching: Inverse triples: true #22850-03xpsrx PRED entity: 03xpsrx PRED relation: nominated_for PRED expected values: 0l76z => 86 concepts (41 used for prediction) PRED predicted values (max 10 best out of 164): 05h43ls (0.44 #1622, 0.44 #380, 0.25 #37272), 02ljhg (0.25 #37272, 0.24 #40516, 0.23 #66461), 0l76z (0.22 #705), 063ykwt (0.11 #571, 0.04 #2193, 0.01 #29738), 01b_lz (0.11 #500, 0.01 #29667), 0fphf3v (0.11 #1219), 02x3lt7 (0.11 #81), 01jc6q (0.11 #23), 0g60z (0.09 #1662, 0.02 #29207, 0.02 #24347), 0kfv9 (0.07 #1888, 0.04 #3508, 0.03 #29433) >> Best rule #1622 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0g476; >> query: (?x2841, ?x2586) <- film(?x2841, ?x2586), ?x2586 = 05h43ls, nominated_for(?x2841, ?x2660) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #705 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0g476; *> query: (?x2841, 0l76z) <- film(?x2841, ?x2586), ?x2586 = 05h43ls, nominated_for(?x2841, ?x2660) *> conf = 0.22 ranks of expected_values: 3 EVAL 03xpsrx nominated_for 0l76z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 41.000 0.444 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22849-01qqwp9 PRED entity: 01qqwp9 PRED relation: group! PRED expected values: 085jw => 110 concepts (95 used for prediction) PRED predicted values (max 10 best out of 116): 03bx0bm (0.71 #2039, 0.71 #4297, 0.69 #2124), 03_vpw (0.71 #4297, 0.33 #124, 0.26 #1858), 03qjg (0.70 #969, 0.65 #1334, 0.60 #766), 0l14qv (0.70 #969, 0.50 #730, 0.47 #1298), 04rzd (0.70 #969, 0.48 #565, 0.47 #484), 026t6 (0.70 #969, 0.48 #565, 0.47 #484), 02fsn (0.70 #969, 0.48 #565, 0.47 #484), 03gvt (0.70 #969, 0.48 #565, 0.47 #484), 0l14j_ (0.70 #969, 0.45 #2996, 0.45 #3972), 01wy6 (0.70 #969, 0.45 #2996, 0.45 #3972) >> Best rule #2039 for best value: >> intensional similarity = 8 >> extensional distance = 29 >> proper extension: 01wv9xn; 01s560x; >> query: (?x3207, 03bx0bm) <- group(?x6947, ?x3207), group(?x227, ?x3207), role(?x6947, ?x212), instrumentalists(?x2460, ?x6947), artist(?x3888, ?x6947), role(?x74, ?x2460), role(?x2460, ?x75), currency(?x6947, ?x170) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #532 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 05563d; 07m4c; *> query: (?x3207, 085jw) <- group(?x6947, ?x3207), group(?x1495, ?x3207), role(?x6947, ?x212), award_winner(?x1854, ?x6947), ?x1495 = 013y1f, profession(?x6947, ?x220), award(?x6947, ?x2420) *> conf = 0.25 ranks of expected_values: 50 EVAL 01qqwp9 group! 085jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 110.000 95.000 0.710 http://example.org/music/performance_role/regular_performances./music/group_membership/group #22848-0g5gq PRED entity: 0g5gq PRED relation: nutrient! PRED expected values: 0f25w9 0971v 0dj75 => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 5): 0f25w9 (0.89 #103, 0.89 #45, 0.89 #270), 0971v (0.89 #103, 0.89 #45, 0.89 #37), 0dj75 (0.89 #103, 0.89 #45, 0.89 #37), 06x4c (0.89 #103, 0.89 #45, 0.89 #37), 0dcfv (0.89 #103, 0.89 #45, 0.89 #37) >> Best rule #103 for best value: >> intensional similarity = 130 >> extensional distance = 12 >> proper extension: 025sf0_; 025rw19; >> query: (?x10891, ?x3264) <- nutrient(?x10612, ?x10891), nutrient(?x9732, ?x10891), nutrient(?x9489, ?x10891), nutrient(?x9005, ?x10891), nutrient(?x8298, ?x10891), nutrient(?x7057, ?x10891), nutrient(?x6285, ?x10891), nutrient(?x6191, ?x10891), nutrient(?x6159, ?x10891), nutrient(?x6032, ?x10891), nutrient(?x5009, ?x10891), nutrient(?x4068, ?x10891), nutrient(?x3900, ?x10891), nutrient(?x3468, ?x10891), nutrient(?x2701, ?x10891), nutrient(?x1303, ?x10891), nutrient(?x1257, ?x10891), ?x9489 = 07j87, ?x1303 = 0fj52s, ?x9005 = 04zpv, ?x8298 = 037ls6, ?x6285 = 01645p, ?x5009 = 0fjfh, ?x7057 = 0fbdb, ?x6191 = 014j1m, ?x1257 = 09728, nutrient(?x3468, ?x14210), nutrient(?x3468, ?x13944), nutrient(?x3468, ?x13545), nutrient(?x3468, ?x13126), nutrient(?x3468, ?x12902), nutrient(?x3468, ?x12083), nutrient(?x3468, ?x11758), nutrient(?x3468, ?x11409), nutrient(?x3468, ?x11270), nutrient(?x3468, ?x10709), nutrient(?x3468, ?x10453), nutrient(?x3468, ?x10098), nutrient(?x3468, ?x9949), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x9840), nutrient(?x3468, ?x9733), nutrient(?x3468, ?x9619), nutrient(?x3468, ?x9490), nutrient(?x3468, ?x9436), nutrient(?x3468, ?x9426), nutrient(?x3468, ?x9365), nutrient(?x3468, ?x8442), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x7720), nutrient(?x3468, ?x7652), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7364), nutrient(?x3468, ?x7362), nutrient(?x3468, ?x7219), nutrient(?x3468, ?x6586), nutrient(?x3468, ?x6286), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x6160), nutrient(?x3468, ?x6033), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5526), nutrient(?x3468, ?x5451), nutrient(?x3468, ?x5337), nutrient(?x3468, ?x5010), nutrient(?x3468, ?x4069), nutrient(?x3468, ?x3469), nutrient(?x3468, ?x3203), nutrient(?x3468, ?x2702), nutrient(?x3468, ?x2018), nutrient(?x3468, ?x1960), nutrient(?x3468, ?x1304), nutrient(?x3468, ?x1258), ?x9949 = 02kd0rh, ?x3900 = 061_f, ?x10453 = 075pwf, ?x10709 = 0h1sz, ?x2701 = 0hkxq, ?x7364 = 09gvd, ?x13126 = 02kc_w5, ?x9619 = 0h1tg, ?x7431 = 09gwd, ?x11270 = 02kc008, ?x5526 = 09pbb, ?x6286 = 02y_3rf, ?x6159 = 033cnk, ?x6033 = 04zjxcz, ?x9915 = 025tkqy, ?x4069 = 0hqw8p_, ?x11758 = 0q01m, ?x2018 = 01sh2, ?x11409 = 0h1yf, ?x10098 = 0h1_c, ?x9840 = 02p0tjr, ?x5451 = 05wvs, ?x14210 = 0f4k5, ?x9426 = 0h1yy, ?x4068 = 0fbw6, ?x9490 = 0h1sg, ?x2702 = 0838f, ?x1258 = 0h1wg, ?x9733 = 0h1tz, ?x13944 = 0f4kp, ?x5010 = 0h1vz, ?x9732 = 05z55, ?x6192 = 06jry, ?x9436 = 025sqz8, ?x10612 = 0frq6, ?x6032 = 01nkt, ?x12902 = 0fzjh, ?x6160 = 041r51, ?x7362 = 02kc5rj, ?x13545 = 01w_3, ?x7219 = 0h1vg, ?x6586 = 05gh50, ?x3203 = 04kl74p, ?x12083 = 01n78x, ?x8442 = 02kcv4x, ?x9365 = 04k8n, ?x1960 = 07hnp, ?x5337 = 06x4c, ?x8413 = 02kc4sf, nutrient(?x5373, ?x1304), nutrient(?x1959, ?x1304), nutrient(?x3264, ?x5549), ?x7652 = 025s0s0, ?x1959 = 0f25w9, ?x7720 = 025s7x6, ?x3469 = 0h1zw, ?x5373 = 0971v >> conf = 0.89 => this is the best rule for 5 predicted values ranks of expected_values: 1, 2, 3 EVAL 0g5gq nutrient! 0dj75 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.891 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0g5gq nutrient! 0971v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.891 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0g5gq nutrient! 0f25w9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.891 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #22847-01jfsb PRED entity: 01jfsb PRED relation: titles PRED expected values: 0fg04 011yqc 07j8r 0jdr0 027r7k => 56 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1840): 07cw4 (0.75 #2789, 0.73 #2786, 0.69 #2787), 0fjyzt (0.75 #2789, 0.73 #2786, 0.69 #2787), 0cmc26r (0.75 #2789, 0.73 #2786, 0.69 #2787), 01vfqh (0.75 #2789, 0.73 #2786, 0.69 #2787), 01y9r2 (0.75 #2789, 0.73 #2786, 0.69 #2787), 07nt8p (0.75 #2789, 0.73 #2786, 0.69 #2787), 09sr0 (0.75 #2789, 0.73 #2786, 0.69 #2787), 011yth (0.75 #2789, 0.73 #2786, 0.69 #2787), 07j94 (0.75 #2789, 0.73 #2786, 0.69 #2787), 0hx4y (0.75 #2789, 0.73 #2786, 0.69 #2787) >> Best rule #2789 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 03mqtr; >> query: (?x812, ?x324) <- genre(?x8605, ?x812), genre(?x5388, ?x812), genre(?x3614, ?x812), genre(?x324, ?x812), ?x8605 = 01jmyj, nominated_for(?x323, ?x324), ?x3614 = 0fy66, award(?x5388, ?x350) >> conf = 0.75 => this is the best rule for 231 predicted values *> Best rule #8543 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 02n4kr; *> query: (?x812, 011yqc) <- genre(?x5185, ?x812), genre(?x2896, ?x812), genre(?x1710, ?x812), ?x5185 = 0dl9_4, titles(?x812, ?x394), film_crew_role(?x1710, ?x137), film_release_region(?x2896, ?x87) *> conf = 0.50 ranks of expected_values: 350, 717, 778, 786, 866 EVAL 01jfsb titles 027r7k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 43.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 01jfsb titles 0jdr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 43.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 01jfsb titles 07j8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 43.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 01jfsb titles 011yqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 43.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 01jfsb titles 0fg04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 43.000 0.754 http://example.org/media_common/netflix_genre/titles #22846-02vjp3 PRED entity: 02vjp3 PRED relation: country PRED expected values: 09c7w0 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 100): 09c7w0 (0.90 #1721, 0.81 #2196, 0.81 #4095), 03k9fj (0.51 #769, 0.07 #1008, 0.06 #2727), 0345h (0.14 #440, 0.13 #262, 0.13 #381), 0f8l9c (0.12 #1678, 0.12 #727, 0.11 #2093), 03_3d (0.10 #836, 0.10 #777, 0.09 #1489), 0d060g (0.07 #304, 0.07 #599, 0.07 #658), 0chghy (0.06 #248, 0.06 #3618, 0.06 #426), 0d05w3 (0.06 #3618, 0.05 #456, 0.05 #397), 03rt9 (0.06 #3618, 0.04 #723, 0.02 #770), 06mkj (0.06 #3618, 0.03 #453, 0.03 #394) >> Best rule #1721 for best value: >> intensional similarity = 4 >> extensional distance = 731 >> proper extension: 047svrl; >> query: (?x7480, 09c7w0) <- country(?x7480, ?x205), film(?x1207, ?x7480), produced_by(?x7480, ?x4294), award_nominee(?x1286, ?x4294) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vjp3 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.898 http://example.org/film/film/country #22845-0bpm4yw PRED entity: 0bpm4yw PRED relation: film_release_region PRED expected values: 0jgd 03rt9 07twz => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 162): 0jgd (0.87 #1359, 0.85 #794, 0.85 #1246), 03rt9 (0.85 #799, 0.82 #1364, 0.80 #1251), 01mjq (0.66 #364, 0.61 #816, 0.60 #138), 06qd3 (0.63 #813, 0.61 #1265, 0.60 #1378), 0h7x (0.60 #132, 0.56 #810, 0.50 #358), 07f1x (0.55 #531, 0.53 #870, 0.49 #1322), 06npd (0.53 #125, 0.42 #803, 0.36 #351), 07twz (0.39 #398, 0.37 #850, 0.34 #511), 0j5g9 (0.31 #8031), 02jx1 (0.31 #8031) >> Best rule #1359 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 07g_0c; >> query: (?x4336, 0jgd) <- film_crew_role(?x4336, ?x137), film_release_region(?x4336, ?x1475), ?x1475 = 05qx1 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 8 EVAL 0bpm4yw film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 82.000 82.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bpm4yw film_release_region 03rt9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bpm4yw film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22844-0pkr1 PRED entity: 0pkr1 PRED relation: nominated_for PRED expected values: 065ym0c => 119 concepts (44 used for prediction) PRED predicted values (max 10 best out of 373): 0gl02yg (0.30 #9729, 0.30 #37298, 0.29 #50272), 030z4z (0.29 #2944, 0.08 #4565), 04jwjq (0.21 #1711, 0.06 #3332), 0233bn (0.20 #1162, 0.04 #4405, 0.02 #4865), 047q2k1 (0.14 #1653, 0.04 #3274, 0.01 #6517), 02tcgh (0.14 #3162, 0.04 #4783), 09yxcz (0.14 #3138, 0.04 #4759), 052_mn (0.14 #2874, 0.04 #4495), 01p3ty (0.14 #2006, 0.04 #3627), 01f85k (0.10 #4268, 0.02 #4865, 0.02 #15620) >> Best rule #9729 for best value: >> intensional similarity = 4 >> extensional distance = 175 >> proper extension: 0bz5v2; 07ymr5; 04smkr; 0d02km; 0n8bn; >> query: (?x10695, ?x5826) <- award_winner(?x5038, ?x10695), profession(?x10695, ?x319), film(?x10695, ?x5826), category(?x10695, ?x134) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pkr1 nominated_for 065ym0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 44.000 0.304 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22843-03spz PRED entity: 03spz PRED relation: film_release_region! PRED expected values: 0ds35l9 0gx1bnj 017gm7 04n52p6 0gvrws1 08052t3 06wbm8q 0gh8zks 0gh65c5 03cw411 07s846j 0dlngsd 0gg5qcw 0bt3j9 03yvf2 0gbfn9 064lsn 0gfh84d 03np63f 0cmf0m0 0cp0t91 0fpgp26 0gwgn1k 0gvt53w 0g57wgv 024lt6 02wtp6 => 168 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1537): 0fpgp26 (0.85 #13166, 0.78 #19832, 0.76 #4277), 017gm7 (0.83 #19012, 0.82 #12346, 0.80 #1234), 07s846j (0.82 #12613, 0.76 #3724, 0.73 #1501), 02vr3gz (0.80 #1475, 0.76 #12587, 0.76 #3698), 06wbm8q (0.79 #12463, 0.71 #3574, 0.71 #19129), 0bq6ntw (0.79 #12877, 0.71 #19543, 0.67 #3988), 04n52p6 (0.79 #12373, 0.71 #19039, 0.67 #1261), 0cmf0m0 (0.79 #13091, 0.71 #19757, 0.60 #1979), 0bc1yhb (0.76 #12778, 0.73 #1666, 0.68 #19444), 0407yj_ (0.76 #12502, 0.66 #19168, 0.60 #1390) >> Best rule #13166 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 05qx1; 06t2t; >> query: (?x4743, 0fpgp26) <- film_release_region(?x6520, ?x4743), film_release_region(?x1999, ?x4743), ?x6520 = 02bg55, ?x1999 = 0gd0c7x >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 7, 8, 11, 13, 14, 15, 18, 21, 22, 26, 27, 28, 51, 57, 58, 59, 60, 82, 88, 122, 123, 125, 157 EVAL 03spz film_release_region! 02wtp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 024lt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0g57wgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0gvt53w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0gwgn1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0fpgp26 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0cp0t91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0cmf0m0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 03np63f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0gfh84d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0gbfn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 03yvf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0bt3j9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0gg5qcw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0dlngsd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 07s846j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 03cw411 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0gh65c5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0gh8zks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 06wbm8q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 08052t3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0gvrws1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 04n52p6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 017gm7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0gx1bnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03spz film_release_region! 0ds35l9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 168.000 132.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22842-03_1pg PRED entity: 03_1pg PRED relation: student! PRED expected values: 01xk7r => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 52): 06182p (0.20 #825, 0.17 #1352, 0.07 #1879), 017d77 (0.20 #562, 0.17 #1089, 0.04 #1616), 017z88 (0.07 #1663, 0.06 #2190, 0.04 #6933), 053mhx (0.07 #1876, 0.02 #2403, 0.01 #13470), 01bm_ (0.07 #1827, 0.01 #22135, 0.01 #7097), 0bwfn (0.06 #2383, 0.06 #2910, 0.05 #18720), 015nl4 (0.04 #1648, 0.03 #7972, 0.03 #13769), 07tg4 (0.04 #1667, 0.02 #3775, 0.02 #3248), 017j69 (0.04 #1726, 0.02 #9631, 0.02 #6996), 02ldmw (0.04 #1866, 0.01 #2393) >> Best rule #825 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01rzqj; 05dtsb; 04wf_b; >> query: (?x4933, 06182p) <- award_nominee(?x5542, ?x4933), film(?x4933, ?x5347), ?x5542 = 05dtwm, type_of_union(?x4933, ?x566) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_1pg student! 01xk7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 82.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #22841-039c26 PRED entity: 039c26 PRED relation: nominated_for! PRED expected values: 0fbtbt => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 197): 0fbtbt (0.73 #861, 0.33 #3905, 0.29 #1797), 0m7yy (0.70 #4919, 0.69 #7729, 0.69 #1873), 0bdw1g (0.53 #734, 0.22 #2841, 0.20 #3778), 0bp_b2 (0.47 #719, 0.23 #2826, 0.21 #3529), 0gkts9 (0.47 #824, 0.20 #3868, 0.20 #2931), 0gq9h (0.39 #12708, 0.38 #12005, 0.38 #12943), 0cqh6z (0.37 #757, 0.16 #1693, 0.15 #3801), 0gs9p (0.35 #12007, 0.35 #12710, 0.34 #12945), 019f4v (0.34 #12699, 0.33 #11996, 0.33 #12934), 05p1dby (0.33 #82, 0.25 #10070, 0.19 #17569) >> Best rule #861 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 03j63k; 0m123; >> query: (?x3303, 0fbtbt) <- actor(?x3303, ?x818), nominated_for(?x2041, ?x3303), ?x2041 = 0bdx29 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039c26 nominated_for! 0fbtbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.733 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22840-03_js PRED entity: 03_js PRED relation: religion PRED expected values: 07x21 => 206 concepts (206 used for prediction) PRED predicted values (max 10 best out of 40): 0c8wxp (0.39 #2738, 0.36 #2562, 0.36 #5298), 02rsw (0.33 #24, 0.31 #553, 0.18 #376), 0kpl (0.32 #1465, 0.23 #1597, 0.23 #3402), 03_gx (0.25 #58, 0.21 #1469, 0.21 #3406), 0631_ (0.24 #581, 0.21 #846, 0.17 #802), 019cr (0.22 #805, 0.16 #1246, 0.16 #894), 01lp8 (0.22 #265, 0.14 #177, 0.09 #353), 03j6c (0.20 #506, 0.11 #3105, 0.09 #5844), 051kv (0.17 #799, 0.14 #2165, 0.14 #1769), 07x21 (0.14 #214, 0.14 #170, 0.14 #1537) >> Best rule #2738 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 06hgj; >> query: (?x8991, 0c8wxp) <- place_of_death(?x8991, ?x4989), county(?x4989, ?x4990), profession(?x8991, ?x3342), location(?x8991, ?x2020), religion(?x8991, ?x14467) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #214 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0dj5q; *> query: (?x8991, 07x21) <- gender(?x8991, ?x231), basic_title(?x8991, ?x346), ?x346 = 060c4, place_of_birth(?x8991, ?x4989) *> conf = 0.14 ranks of expected_values: 10 EVAL 03_js religion 07x21 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 206.000 206.000 0.386 http://example.org/people/person/religion #22839-0jmmn PRED entity: 0jmmn PRED relation: draft PRED expected values: 038981 => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 16): 038c0q (0.83 #152, 0.80 #184, 0.80 #168), 038981 (0.77 #525, 0.74 #342, 0.72 #258), 02pq_rp (0.53 #468, 0.43 #516, 0.38 #65), 0g3zpp (0.48 #494, 0.38 #544, 0.37 #561), 09l0x9 (0.45 #503, 0.38 #553, 0.37 #570), 092j54 (0.45 #501, 0.38 #551, 0.37 #568), 05vsb7 (0.43 #493, 0.36 #543, 0.35 #560), 04f4z1k (0.43 #475, 0.39 #523, 0.35 #540), 02r6gw6 (0.43 #472, 0.39 #520, 0.32 #537), 047dpm0 (0.41 #476, 0.39 #524, 0.35 #541) >> Best rule #152 for best value: >> intensional similarity = 26 >> extensional distance = 16 >> proper extension: 0jmfv; 0jmjr; >> query: (?x5419, 038c0q) <- team(?x6848, ?x5419), team(?x4570, ?x5419), draft(?x5419, ?x12852), draft(?x5419, ?x4979), ?x12852 = 06439y, position(?x11420, ?x4570), position(?x9995, ?x4570), position(?x7158, ?x4570), position(?x7136, ?x4570), position(?x6128, ?x4570), position(?x5154, ?x4570), position(?x4571, ?x4570), position(?x2820, ?x4570), position(?x1347, ?x4570), ?x4979 = 0f4vx0, ?x6128 = 0jm64, ?x5154 = 0jm8l, ?x7158 = 0jm4v, ?x4571 = 0jm6n, ?x11420 = 0jmhr, ?x9995 = 0jm9w, school(?x5419, ?x1087), ?x2820 = 0jmj7, ?x6848 = 02_ssl, ?x7136 = 0jm74, ?x1347 = 0jmfv >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #525 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 59 *> proper extension: 05m_8; 01yhm; 05g76; 02d02; *> query: (?x5419, ?x2569) <- team(?x1348, ?x5419), draft(?x5419, ?x12852), school(?x12852, ?x4599), draft(?x11168, ?x12852), draft(?x5483, ?x12852), draft(?x2820, ?x12852), major_field_of_study(?x4599, ?x6870), major_field_of_study(?x4599, ?x1695), student(?x4599, ?x3273), school(?x2820, ?x10297), school(?x2820, ?x9745), school(?x2820, ?x8202), sport(?x11168, ?x4833), currency(?x4599, ?x170), teams(?x8993, ?x11168), ?x9745 = 01jpqb, draft(?x5483, ?x2569), colors(?x2820, ?x332), company(?x2998, ?x4599), ?x1695 = 06ms6, ?x6870 = 01540, ?x10297 = 02rv1w, ?x8202 = 06fq2 *> conf = 0.77 ranks of expected_values: 2 EVAL 0jmmn draft 038981 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 49.000 49.000 0.833 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #22838-0l2nd PRED entity: 0l2nd PRED relation: adjoins! PRED expected values: 0kq2g => 131 concepts (53 used for prediction) PRED predicted values (max 10 best out of 496): 0n6mc (0.86 #1568, 0.86 #3138, 0.86 #3137), 0l2nd (0.33 #736, 0.26 #25135, 0.26 #33787), 0l2sr (0.26 #25135, 0.26 #33787, 0.25 #29851), 0kq1l (0.26 #25135, 0.26 #33787, 0.25 #29851), 0kq2g (0.26 #25135, 0.25 #7068, 0.25 #21994), 0kpzy (0.26 #33787, 0.25 #29851, 0.18 #41651), 0kq0q (0.26 #33787, 0.25 #29851, 0.18 #41651), 0l34j (0.26 #33787, 0.25 #29851, 0.18 #41651), 0235l (0.26 #33787, 0.25 #7068, 0.25 #21994), 0kv4k (0.12 #1260, 0.11 #2045, 0.08 #3615) >> Best rule #1568 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 0kpys; 0l2l_; 0l2hf; 0l380; 0l34j; 0l2vz; 0l2v0; 0bxqq; 0kq39; 0kpzy; ... >> query: (?x13522, ?x5892) <- adjoins(?x13522, ?x5892), second_level_divisions(?x94, ?x13522), contains(?x1227, ?x13522), ?x94 = 09c7w0, ?x1227 = 01n7q >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #25135 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 286 *> proper extension: 0mrf1; *> query: (?x13522, ?x7520) <- adjoins(?x5892, ?x13522), adjoins(?x5892, ?x7520), source(?x5892, ?x958), currency(?x5892, ?x170) *> conf = 0.26 ranks of expected_values: 5 EVAL 0l2nd adjoins! 0kq2g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 131.000 53.000 0.859 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #22837-0l76z PRED entity: 0l76z PRED relation: honored_for! PRED expected values: 09p30_ => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 84): 02yvhx (0.43 #413, 0.02 #7784, 0.02 #7901), 03nnm4t (0.40 #294, 0.22 #2049, 0.22 #1230), 0clfdj (0.29 #353, 0.01 #7724, 0.01 #7841), 02q690_ (0.28 #2041, 0.25 #3679, 0.25 #637), 05c1t6z (0.26 #1414, 0.25 #1180, 0.24 #3637), 0gvstc3 (0.26 #1430, 0.25 #143, 0.24 #1196), 0lp_cd3 (0.25 #133, 0.20 #250, 0.16 #1186), 09g90vz (0.25 #220, 0.08 #2092, 0.08 #688), 0g55tzk (0.25 #232, 0.07 #817, 0.06 #1285), 0275n3y (0.20 #529, 0.20 #295, 0.12 #1231) >> Best rule #413 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0344gc; 017gl1; 011ywj; >> query: (?x4588, 02yvhx) <- award(?x4588, ?x678), honored_for(?x3460, ?x4588), ?x3460 = 092t4b, nominated_for(?x3381, ?x4588) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #8894 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 844 *> proper extension: 01jc6q; 028_yv; 0c0yh4; 0yyg4; 05jf85; 0g5qs2k; 016z5x; 01h7bb; 0dqytn; 0dj0m5; ... *> query: (?x4588, ?x1193) <- award(?x4588, ?x678), nominated_for(?x4775, ?x4588), award_winner(?x1193, ?x4775) *> conf = 0.11 ranks of expected_values: 22 EVAL 0l76z honored_for! 09p30_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 107.000 107.000 0.429 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #22836-07f1x PRED entity: 07f1x PRED relation: olympics PRED expected values: 0jdk_ => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 35): 0jdk_ (0.72 #197, 0.69 #582, 0.68 #57), 0lgxj (0.63 #58, 0.60 #583, 0.59 #806), 0l998 (0.55 #181, 0.50 #566, 0.49 #706), 018ctl (0.55 #182, 0.42 #42, 0.36 #567), 0lbbj (0.52 #190, 0.48 #575, 0.47 #50), 0lbd9 (0.50 #587, 0.47 #62, 0.47 #727), 0l98s (0.48 #180, 0.48 #565, 0.47 #40), 0ldqf (0.48 #206, 0.48 #591, 0.47 #66), 0lv1x (0.47 #47, 0.45 #572, 0.45 #187), 09x3r (0.47 #44, 0.45 #569, 0.45 #184) >> Best rule #197 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 06qd3; 03rj0; >> query: (?x7747, 0jdk_) <- film_release_region(?x9194, ?x7747), film_release_region(?x1498, ?x7747), ?x1498 = 04jkpgv, ?x9194 = 0fpgp26 >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07f1x olympics 0jdk_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.724 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #22835-044crp PRED entity: 044crp PRED relation: sport PRED expected values: 02vx4 => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 7): 02vx4 (0.88 #93, 0.88 #129, 0.88 #120), 018jz (0.13 #59, 0.10 #195, 0.09 #159), 03tmr (0.11 #55, 0.07 #191, 0.07 #155), 0jm_ (0.08 #157, 0.08 #166, 0.08 #175), 018w8 (0.06 #58, 0.05 #158, 0.05 #167), 039yzs (0.03 #224, 0.03 #215, 0.03 #197), 09xp_ (0.01 #223) >> Best rule #93 for best value: >> intensional similarity = 14 >> extensional distance = 133 >> proper extension: 0ytc; 0f6cl2; >> query: (?x3719, 02vx4) <- position(?x3719, ?x60), colors(?x3719, ?x663), position(?x3719, ?x63), ?x60 = 02nzb8, ?x63 = 02sdk9v, colors(?x2574, ?x663), colors(?x7338, ?x663), colors(?x6824, ?x663), team(?x935, ?x2574), contains(?x94, ?x6824), teams(?x1860, ?x2574), institution(?x4981, ?x7338), ?x935 = 06b1q, ?x4981 = 03bwzr4 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044crp sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.881 http://example.org/sports/sports_team/sport #22834-0g1rw PRED entity: 0g1rw PRED relation: production_companies! PRED expected values: 0gcrg 0gl3hr => 117 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1172): 0n83s (0.47 #5484, 0.44 #8773, 0.44 #13158), 0cq8nx (0.47 #5484, 0.44 #8773, 0.44 #13158), 02x0fs9 (0.47 #5484, 0.44 #13158, 0.41 #18641), 0gxfz (0.47 #5484, 0.44 #13158, 0.41 #18641), 043n1r5 (0.39 #24124, 0.38 #14255, 0.38 #2194), 01v1ln (0.39 #24124, 0.38 #14255, 0.38 #2194), 0d61px (0.39 #24124, 0.38 #14255, 0.38 #2194), 0140g4 (0.39 #24124, 0.38 #14255, 0.38 #2194), 011yhm (0.39 #24124, 0.38 #14255, 0.38 #2194), 02fwfb (0.39 #24124, 0.38 #14255, 0.38 #2194) >> Best rule #5484 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0p51w; 0l15n; >> query: (?x788, ?x1804) <- award(?x788, ?x720), ?x720 = 018wng, nominated_for(?x788, ?x1804) >> conf = 0.47 => this is the best rule for 4 predicted values *> Best rule #24124 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 05xbx; *> query: (?x788, ?x6013) <- film(?x788, ?x6013), award_nominee(?x1172, ?x788), nominated_for(?x112, ?x6013) *> conf = 0.39 ranks of expected_values: 43, 1123 EVAL 0g1rw production_companies! 0gl3hr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 117.000 84.000 0.467 http://example.org/film/film/production_companies EVAL 0g1rw production_companies! 0gcrg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 117.000 84.000 0.467 http://example.org/film/film/production_companies #22833-01l_vgt PRED entity: 01l_vgt PRED relation: profession PRED expected values: 02hrh1q => 179 concepts (102 used for prediction) PRED predicted values (max 10 best out of 92): 02hrh1q (0.90 #4221, 0.86 #12191, 0.86 #11889), 09jwl (0.69 #3173, 0.66 #13701, 0.64 #12347), 0nbcg (0.57 #4389, 0.50 #13714, 0.49 #5596), 01d_h8 (0.57 #1357, 0.39 #3610, 0.36 #4817), 016z4k (0.46 #1805, 0.45 #12028, 0.45 #9625), 0dz3r (0.44 #3155, 0.41 #14886, 0.40 #4662), 0d1pc (0.43 #802, 0.40 #502, 0.26 #3205), 0n1h (0.41 #1963, 0.34 #6465, 0.33 #312), 0dxtg (0.37 #2866, 0.36 #2415, 0.33 #314), 03gjzk (0.36 #2417, 0.36 #3470, 0.34 #2868) >> Best rule #4221 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 0738b8; >> query: (?x3382, 02hrh1q) <- participant(?x3382, ?x4620), place_of_birth(?x3382, ?x2645), influenced_by(?x4620, ?x1029), award(?x4620, ?x1565) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l_vgt profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 102.000 0.900 http://example.org/people/person/profession #22832-026qnh6 PRED entity: 026qnh6 PRED relation: nominated_for! PRED expected values: 099flj => 104 concepts (87 used for prediction) PRED predicted values (max 10 best out of 234): 0gq_v (0.47 #490, 0.32 #6368, 0.31 #12719), 0gq9h (0.42 #6410, 0.39 #12761, 0.38 #4764), 02r0csl (0.41 #475, 0.16 #2590, 0.16 #945), 0gr0m (0.39 #529, 0.25 #2644, 0.25 #6407), 0gs9p (0.37 #6412, 0.35 #12763, 0.33 #4766), 099c8n (0.37 #526, 0.29 #1231, 0.28 #1936), 019f4v (0.37 #6401, 0.33 #4755, 0.33 #5930), 0k611 (0.32 #6421, 0.29 #2423, 0.29 #12772), 040njc (0.30 #6355, 0.28 #10818, 0.28 #2357), 0f4x7 (0.28 #10818, 0.26 #6373, 0.25 #10819) >> Best rule #490 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 011yr9; 0gltv; >> query: (?x4810, 0gq_v) <- country(?x4810, ?x390), nominated_for(?x2489, ?x4810), film(?x574, ?x4810), ?x2489 = 02x2gy0 >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #20460 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1299 *> proper extension: 015qsq; 047q2k1; 090s_0; 02py4c8; 02z9hqn; 06krf3; 02bg8v; 0b76kw1; 085bd1; 032016; ... *> query: (?x4810, ?x143) <- country(?x4810, ?x390), nominated_for(?x2393, ?x4810), award(?x1199, ?x2393), nominated_for(?x143, ?x1199) *> conf = 0.07 ranks of expected_values: 134 EVAL 026qnh6 nominated_for! 099flj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 104.000 87.000 0.469 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22831-02t4yc PRED entity: 02t4yc PRED relation: colors PRED expected values: 06fvc => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 20): 01l849 (0.29 #141, 0.29 #121, 0.28 #681), 01g5v (0.27 #623, 0.27 #1564, 0.26 #1624), 019sc (0.18 #207, 0.18 #1008, 0.18 #1628), 06fvc (0.17 #1003, 0.16 #1623, 0.16 #1563), 0jc_p (0.14 #4, 0.11 #164, 0.10 #244), 038hg (0.13 #112, 0.13 #552, 0.09 #1013), 09ggk (0.11 #56, 0.06 #1317, 0.06 #276), 036k5h (0.10 #1186, 0.10 #5, 0.10 #145), 03wkwg (0.10 #15, 0.09 #75, 0.09 #235), 067z2v (0.10 #9, 0.09 #129, 0.08 #89) >> Best rule #141 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 0ks67; >> query: (?x3696, 01l849) <- student(?x3696, ?x9650), institution(?x1368, ?x3696), school(?x8133, ?x3696), ?x1368 = 014mlp >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1003 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 225 *> proper extension: 071_8; 0jpkw; 01zh3_; *> query: (?x3696, 06fvc) <- institution(?x1200, ?x3696), citytown(?x3696, ?x5525), colors(?x3696, ?x663), state_province_region(?x3696, ?x4061) *> conf = 0.17 ranks of expected_values: 4 EVAL 02t4yc colors 06fvc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 160.000 160.000 0.286 http://example.org/education/educational_institution/colors #22830-05qx1 PRED entity: 05qx1 PRED relation: teams PRED expected values: 03_lsr => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 97): 03dj48 (0.11 #247, 0.07 #607, 0.04 #1327), 03zb6t (0.07 #698, 0.03 #3578, 0.02 #5738), 03z8bw (0.07 #578, 0.03 #3458, 0.02 #5618), 01l3vx (0.04 #764, 0.04 #1124, 0.04 #1484), 03xh50 (0.04 #872, 0.04 #1232, 0.04 #1952), 04h54p (0.04 #971, 0.04 #1331, 0.04 #2051), 03zrhb (0.04 #894, 0.04 #1254, 0.04 #1974), 02ltg3 (0.04 #803, 0.04 #1163, 0.04 #1883), 01352_ (0.04 #1020, 0.04 #1740, 0.04 #2100), 086x3 (0.04 #1080, 0.04 #1800, 0.04 #2160) >> Best rule #247 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0fb18; >> query: (?x1475, 03dj48) <- contains(?x8483, ?x1475), contains(?x1475, ?x14641), ?x8483 = 059g4 >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05qx1 teams 03_lsr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 100.000 0.111 http://example.org/sports/sports_team_location/teams #22829-0fq9zcx PRED entity: 0fq9zcx PRED relation: nominated_for PRED expected values: 089j8p => 55 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1349): 0g9lm2 (0.71 #654, 0.63 #5406, 0.46 #3822), 089j8p (0.71 #1007, 0.32 #5759, 0.15 #4175), 03hmt9b (0.69 #3759, 0.42 #5343, 0.26 #18015), 09p0ct (0.62 #3357, 0.32 #4941, 0.29 #189), 0y_9q (0.62 #3993, 0.32 #5577, 0.17 #18249), 0hfzr (0.62 #3801, 0.26 #5385, 0.19 #18057), 095zlp (0.57 #52, 0.53 #4804, 0.46 #3220), 05c46y6 (0.57 #391, 0.53 #5143, 0.45 #1975), 09z2b7 (0.57 #209, 0.47 #4961, 0.18 #1793), 04qw17 (0.57 #261, 0.42 #5013, 0.27 #1845) >> Best rule #654 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 09qwmm; 094qd5; 0fq9zdn; 0gqwc; 099cng; >> query: (?x13107, 0g9lm2) <- nominated_for(?x13107, ?x4756), award(?x11983, ?x13107), ?x4756 = 0462hhb, ?x11983 = 0bwgc_ >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1007 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 09qwmm; 094qd5; 0fq9zdn; 0gqwc; 099cng; *> query: (?x13107, 089j8p) <- nominated_for(?x13107, ?x4756), award(?x11983, ?x13107), ?x4756 = 0462hhb, ?x11983 = 0bwgc_ *> conf = 0.71 ranks of expected_values: 2 EVAL 0fq9zcx nominated_for 089j8p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 55.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22828-0gffmn8 PRED entity: 0gffmn8 PRED relation: film! PRED expected values: 0h7pj 063g7l => 90 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1142): 0h5g_ (0.33 #74, 0.12 #4231, 0.09 #12545), 055c8 (0.33 #543, 0.12 #4700, 0.07 #15092), 01wy5m (0.33 #858, 0.12 #5015, 0.06 #25798), 07lt7b (0.33 #114, 0.12 #4271, 0.04 #22975), 01f6zc (0.33 #943, 0.12 #5100, 0.04 #40431), 05xf75 (0.33 #1488, 0.12 #5645, 0.04 #28506), 02nb2s (0.33 #85, 0.12 #4242, 0.03 #16712), 05zbm4 (0.33 #152, 0.12 #4309, 0.03 #16779), 0b1q7c (0.33 #1858, 0.12 #6015, 0.03 #18485), 02vntj (0.33 #734, 0.12 #4891, 0.03 #17361) >> Best rule #74 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 0bpm4yw; >> query: (?x3217, 0h5g_) <- genre(?x3217, ?x225), film_release_region(?x3217, ?x8593), film_release_region(?x3217, ?x2316), film_release_region(?x3217, ?x1023), film_release_region(?x3217, ?x1003), ?x2316 = 06t2t, film_regional_debut_venue(?x3217, ?x362), ?x1023 = 0ctw_b, ?x1003 = 03gj2, ?x225 = 02kdv5l, ?x8593 = 01crd5 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5698 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 0dtfn; 0bh8yn3; 0by1wkq; 03yvf2; 01mgw; *> query: (?x3217, 0h7pj) <- genre(?x3217, ?x225), film_release_region(?x3217, ?x2316), film_release_region(?x3217, ?x1023), film_release_region(?x3217, ?x1003), ?x2316 = 06t2t, film_regional_debut_venue(?x3217, ?x362), ?x1023 = 0ctw_b, ?x1003 = 03gj2, ?x225 = 02kdv5l *> conf = 0.12 ranks of expected_values: 45, 718 EVAL 0gffmn8 film! 063g7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 90.000 49.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 0gffmn8 film! 0h7pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 90.000 49.000 0.333 http://example.org/film/actor/film./film/performance/film #22827-02y_j8g PRED entity: 02y_j8g PRED relation: award_winner PRED expected values: 0psss => 56 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1933): 015nhn (0.67 #6694, 0.50 #1788, 0.38 #11602), 0bdt8 (0.67 #6325, 0.50 #1419, 0.38 #11233), 0mz73 (0.50 #6599, 0.50 #1693, 0.42 #13961), 0h1mt (0.50 #5120, 0.50 #214, 0.33 #7573), 0h1nt (0.50 #5148, 0.50 #242, 0.33 #12510), 01csvq (0.50 #5030, 0.50 #124, 0.25 #12392), 0l6px (0.50 #10303, 0.50 #7848, 0.17 #5395), 015q43 (0.50 #6045, 0.42 #13407, 0.25 #10953), 0159h6 (0.50 #4984, 0.38 #9892, 0.33 #7437), 020_95 (0.50 #1225, 0.33 #6131, 0.25 #13493) >> Best rule #6694 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0gqwc; 02z1nbg; >> query: (?x7521, 015nhn) <- award(?x1009, ?x7521), award_winner(?x7521, ?x8612), award_winner(?x7521, ?x2551), ?x2551 = 0h0wc, ?x8612 = 01jw4r >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #26991 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 0fm3b5; 054knh; *> query: (?x7521, ?x200) <- award(?x8769, ?x7521), disciplines_or_subjects(?x7521, ?x373), ?x373 = 02vxn, nominated_for(?x200, ?x8769) *> conf = 0.12 ranks of expected_values: 153 EVAL 02y_j8g award_winner 0psss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 56.000 34.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #22826-044zvm PRED entity: 044zvm PRED relation: gender PRED expected values: 02zsn => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #21, 0.84 #35, 0.81 #61), 02zsn (0.46 #34, 0.46 #28, 0.45 #32) >> Best rule #21 for best value: >> intensional similarity = 2 >> extensional distance = 211 >> proper extension: 02qggqc; 0bs1yy; >> query: (?x12041, 05zppz) <- nominated_for(?x12041, ?x238), executive_produced_by(?x4501, ?x12041) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 347 *> proper extension: 01t07j; *> query: (?x12041, 02zsn) <- nominated_for(?x12041, ?x238), type_of_union(?x12041, ?x566), participant(?x496, ?x12041) *> conf = 0.46 ranks of expected_values: 2 EVAL 044zvm gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.859 http://example.org/people/person/gender #22825-0fqpg6b PRED entity: 0fqpg6b PRED relation: ceremony PRED expected values: 0h98b3k => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 140): 05c1t6z (0.55 #296, 0.50 #437, 0.47 #577), 02q690_ (0.48 #346, 0.41 #487, 0.41 #627), 0gvstc3 (0.46 #315, 0.43 #456, 0.39 #596), 03nnm4t (0.45 #355, 0.40 #496, 0.38 #636), 0gx_st (0.41 #318, 0.38 #459, 0.35 #599), 0hn821n (0.29 #411, 0.26 #552, 0.24 #692), 02yvhx (0.26 #217, 0.13 #3369, 0.10 #780), 02hn5v (0.26 #182, 0.13 #3369, 0.10 #745), 0n8_m93 (0.26 #257, 0.10 #820, 0.10 #1101), 0bzm81 (0.26 #162, 0.10 #725, 0.10 #1006) >> Best rule #296 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 09v7wsg; 02py_sj; >> query: (?x14647, 05c1t6z) <- nominated_for(?x14647, ?x14197), honored_for(?x13189, ?x14197), languages(?x14197, ?x254), ?x254 = 02h40lc >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #844 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 206 *> proper extension: 02wwsh8; 0468g4r; *> query: (?x14647, ?x13189) <- award(?x14197, ?x14647), honored_for(?x13189, ?x14197), ceremony(?x3245, ?x13189), award_winner(?x13189, ?x8415) *> conf = 0.22 ranks of expected_values: 53 EVAL 0fqpg6b ceremony 0h98b3k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 32.000 32.000 0.554 http://example.org/award/award_category/winners./award/award_honor/ceremony #22824-07twz PRED entity: 07twz PRED relation: form_of_government PRED expected values: 01fpfn => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 4): 01fpfn (0.50 #6, 0.47 #38, 0.46 #174), 01q20 (0.40 #23, 0.32 #47, 0.31 #7), 018wl5 (0.38 #17, 0.35 #173, 0.35 #29), 026wp (0.20 #4, 0.11 #40, 0.09 #84) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 082fr; >> query: (?x4737, 01fpfn) <- film_release_region(?x2104, ?x4737), countries_spoken_in(?x2502, ?x4737), ?x2104 = 0j_tw >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07twz form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.500 http://example.org/location/country/form_of_government #22823-02yw0y PRED entity: 02yw0y PRED relation: artists PRED expected values: 01mxt_ => 53 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1072): 0p76z (0.70 #4174, 0.45 #2168, 0.33 #920), 01w8n89 (0.50 #3573, 0.46 #2167, 0.45 #2168), 050z2 (0.50 #1443, 0.46 #2167, 0.45 #2168), 01pny5 (0.50 #4313, 0.46 #2167, 0.45 #2168), 0pkyh (0.50 #3496, 0.46 #2167, 0.45 #2168), 01k47c (0.50 #4093, 0.46 #2167, 0.45 #2168), 01vsyjy (0.50 #3919, 0.46 #2167, 0.45 #2168), 0134pk (0.50 #4159, 0.45 #2168, 0.33 #905), 04k05 (0.50 #4214, 0.45 #2168, 0.25 #2044), 0130sy (0.50 #1700, 0.30 #3870, 0.07 #6041) >> Best rule #4174 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 0xhtw; 0dl5d; 03lty; 02yv6b; 016jny; >> query: (?x9645, 0p76z) <- artists(?x9645, ?x10744), artists(?x9645, ?x10039), parent_genre(?x9645, ?x597), ?x10744 = 01t8399, instrumentalists(?x75, ?x10039), artists(?x597, ?x8849), award(?x8849, ?x724), artist(?x7793, ?x10039) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2167 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 06by7; 02v2lh; *> query: (?x9645, ?x2693) <- artists(?x9645, ?x10744), artists(?x9645, ?x10039), parent_genre(?x9645, ?x597), ?x10744 = 01t8399, ?x10039 = 0ftqr, artists(?x597, ?x2693), profession(?x2693, ?x563) *> conf = 0.46 ranks of expected_values: 27 EVAL 02yw0y artists 01mxt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 53.000 20.000 0.700 http://example.org/music/genre/artists #22822-03ryzs PRED entity: 03ryzs PRED relation: film PRED expected values: 07cdz => 28 concepts (9 used for prediction) PRED predicted values (max 10 best out of 1780): 016fyc (0.50 #46, 0.31 #4843, 0.12 #12845), 03mh_tp (0.38 #449, 0.31 #5246, 0.21 #13248), 0k0rf (0.38 #798, 0.31 #5595, 0.19 #10395), 01dvbd (0.38 #443, 0.23 #5240, 0.20 #6838), 047gpsd (0.38 #1067, 0.23 #5864, 0.19 #10664), 035xwd (0.38 #101, 0.23 #4898, 0.18 #11299), 05b6rdt (0.38 #983, 0.23 #5780, 0.17 #13782), 0jqj5 (0.38 #797, 0.23 #5594, 0.14 #10394), 0bz6sq (0.38 #1353, 0.23 #6150, 0.14 #12551), 011yhm (0.38 #1040, 0.23 #5837, 0.12 #13839) >> Best rule #46 for best value: >> intensional similarity = 12 >> extensional distance = 6 >> proper extension: 0g1rw; 0gfmc_; >> query: (?x13579, 016fyc) <- film(?x13579, ?x1903), language(?x1903, ?x254), titles(?x512, ?x1903), honored_for(?x5873, ?x1903), genre(?x1903, ?x53), nominated_for(?x1307, ?x1903), nominated_for(?x749, ?x1903), ?x1307 = 0gq9h, ?x749 = 094qd5, nominated_for(?x294, ?x1903), film_release_distribution_medium(?x1903, ?x81), ?x81 = 029j_ >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3196 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 9 *> proper extension: 0jz9f; 016tt2; 03xq0f; 025jfl; 05qd_; 016tw3; 061dn_; 0fqy4p; 0fvppk; *> query: (?x13579, ?x531) <- film(?x13579, ?x2329), film(?x13579, ?x1903), language(?x1903, ?x254), titles(?x512, ?x1903), honored_for(?x5873, ?x1903), genre(?x1903, ?x162), film_crew_role(?x1903, ?x2178), ?x162 = 04xvlr, award(?x1903, ?x3722), film_release_region(?x1903, ?x94), nominated_for(?x3722, ?x531), music(?x2329, ?x806) *> conf = 0.08 ranks of expected_values: 1233 EVAL 03ryzs film 07cdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 9.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #22821-04m_zp PRED entity: 04m_zp PRED relation: story_by! PRED expected values: 0419kt => 83 concepts (78 used for prediction) PRED predicted values (max 10 best out of 75): 015g28 (0.10 #689, 0.10 #4137, 0.10 #1723), 0pb33 (0.10 #689, 0.10 #4137, 0.10 #1723), 0dyb1 (0.02 #447, 0.01 #1826, 0.01 #1481), 0443v1 (0.02 #338), 02qjv1p (0.02 #5862, 0.02 #6897, 0.02 #11721), 02kk_c (0.02 #5862, 0.02 #6897, 0.02 #11721), 02mc5v (0.01 #609), 02c7k4 (0.01 #567), 05zy2cy (0.01 #431), 07h9gp (0.01 #400) >> Best rule #689 for best value: >> intensional similarity = 3 >> extensional distance = 138 >> proper extension: 027l0b; 06z4wj; 01y8d4; 014hdb; >> query: (?x4036, ?x1450) <- profession(?x4036, ?x353), written_by(?x1450, ?x4036), award_winner(?x4036, ?x496) >> conf = 0.10 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 04m_zp story_by! 0419kt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 78.000 0.104 http://example.org/film/film/story_by #22820-01540 PRED entity: 01540 PRED relation: major_field_of_study! PRED expected values: 02_xgp2 => 61 concepts (29 used for prediction) PRED predicted values (max 10 best out of 17): 02_xgp2 (0.88 #306, 0.83 #340, 0.79 #356), 01ysy9 (0.50 #137, 0.50 #119, 0.49 #401), 0bjrnt (0.50 #89, 0.49 #401, 0.48 #456), 02m4yg (0.50 #113, 0.49 #401, 0.48 #456), 01gkg3 (0.50 #112, 0.49 #401, 0.48 #456), 071tyz (0.49 #401, 0.48 #456, 0.47 #86), 022h5x (0.47 #86, 0.44 #103, 0.44 #455), 01rr_d (0.47 #86, 0.44 #103, 0.44 #455), 013zdg (0.47 #86, 0.44 #103, 0.44 #455), 027f2w (0.47 #86, 0.44 #103, 0.44 #455) >> Best rule #306 for best value: >> intensional similarity = 9 >> extensional distance = 15 >> proper extension: 0pf2; 04g51; 04g7x; >> query: (?x6870, 02_xgp2) <- major_field_of_study(?x9803, ?x6870), major_field_of_study(?x7660, ?x6870), major_field_of_study(?x3485, ?x6870), major_field_of_study(?x9803, ?x742), ?x742 = 05qjt, organization(?x346, ?x9803), colors(?x7660, ?x4557), student(?x7660, ?x2390), ?x3485 = 01mpwj >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01540 major_field_of_study! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 29.000 0.882 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #22819-0154j PRED entity: 0154j PRED relation: time_zones PRED expected values: 02llzg => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 12): 02llzg (0.64 #1667, 0.64 #2048, 0.63 #2034), 042g7t (0.63 #2062, 0.17 #11, 0.14 #76), 02hcv8 (0.35 #2023, 0.35 #2037, 0.35 #2051), 03plfd (0.21 #23, 0.17 #114, 0.14 #166), 02lcqs (0.16 #252, 0.15 #2025, 0.15 #2053), 02fqwt (0.16 #1094, 0.16 #1654, 0.16 #782), 02hczc (0.12 #249, 0.12 #1095, 0.11 #1056), 03bdv (0.12 #1555, 0.09 #748, 0.08 #1529), 0gsrz4 (0.09 #307, 0.08 #359, 0.07 #958), 052vwh (0.07 #90, 0.05 #142, 0.05 #168) >> Best rule #1667 for best value: >> intensional similarity = 3 >> extensional distance = 241 >> proper extension: 0g14f; >> query: (?x172, ?x2864) <- contains(?x172, ?x4826), place_of_birth(?x2691, ?x4826), time_zones(?x4826, ?x2864) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0154j time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.639 http://example.org/location/location/time_zones #22818-011k_j PRED entity: 011k_j PRED relation: role! PRED expected values: 04rzd => 71 concepts (55 used for prediction) PRED predicted values (max 10 best out of 108): 026t6 (0.88 #2585, 0.88 #2482, 0.87 #2375), 0342h (0.87 #2382, 0.86 #310, 0.86 #102), 0dwtp (0.87 #2391, 0.86 #310, 0.86 #102), 0l14qv (0.86 #310, 0.86 #102, 0.85 #515), 0395lw (0.86 #310, 0.86 #102, 0.85 #515), 01vdm0 (0.86 #310, 0.86 #102, 0.85 #515), 04rzd (0.86 #310, 0.86 #102, 0.85 #515), 0cfdd (0.86 #310, 0.86 #102, 0.85 #515), 06rvn (0.86 #310, 0.86 #102, 0.85 #515), 01vnt4 (0.86 #310, 0.86 #102, 0.85 #515) >> Best rule #2585 for best value: >> intensional similarity = 17 >> extensional distance = 15 >> proper extension: 0mbct; >> query: (?x4078, 026t6) <- role(?x3409, ?x4078), role(?x2764, ?x4078), role(?x1574, ?x4078), role(?x432, ?x4078), role(?x314, ?x4078), role(?x6947, ?x4078), ?x1574 = 0l15bq, award_winner(?x2139, ?x6947), ?x314 = 02sgy, role(?x1715, ?x3409), ?x2764 = 01s0ps, profession(?x6947, ?x220), role(?x74, ?x432), ceremony(?x2139, ?x139), artist(?x3888, ?x6947), instrumentalists(?x432, ?x133), role(?x432, ?x645) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #310 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 2 *> proper extension: 07brj; *> query: (?x4078, ?x227) <- role(?x1574, ?x4078), role(?x432, ?x4078), role(?x6947, ?x4078), role(?x4052, ?x4078), ?x1574 = 0l15bq, ?x6947 = 01vrnsk, ?x4052 = 050z2, performance_role(?x212, ?x4078), role(?x4078, ?x1166), role(?x4078, ?x227), role(?x75, ?x432), role(?x11689, ?x432), instrumentalists(?x432, ?x8152), role(?x2799, ?x432), role(?x2237, ?x432), ?x8152 = 04m2zj, ?x11689 = 06p03s, award_winner(?x2729, ?x2799), award_nominee(?x959, ?x2237) *> conf = 0.86 ranks of expected_values: 7 EVAL 011k_j role! 04rzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 71.000 55.000 0.882 http://example.org/music/performance_role/track_performances./music/track_contribution/role #22817-01dwyd PRED entity: 01dwyd PRED relation: current_club! PRED expected values: 03d8m4 => 112 concepts (66 used for prediction) PRED predicted values (max 10 best out of 30): 01_lhg (0.36 #223, 0.36 #191, 0.33 #130), 02s9vc (0.29 #82, 0.14 #114, 0.11 #479), 02s2lg (0.17 #372, 0.17 #36, 0.17 #6), 03y_f8 (0.14 #218, 0.14 #95, 0.08 #125), 02ltg3 (0.14 #67, 0.08 #614, 0.08 #129), 032jlh (0.14 #242, 0.08 #149, 0.07 #210), 0329r5 (0.14 #197, 0.08 #136, 0.07 #229), 02rqxc (0.14 #101, 0.08 #162, 0.07 #192), 02w64f (0.14 #122, 0.08 #183, 0.06 #914), 01l3wr (0.13 #269, 0.12 #631, 0.12 #299) >> Best rule #223 for best value: >> intensional similarity = 11 >> extensional distance = 12 >> proper extension: 049bmk; 04999m; 049msk; 049m_l; >> query: (?x11991, 01_lhg) <- position(?x11991, ?x203), position(?x11991, ?x63), position(?x11991, ?x60), team(?x530, ?x11991), team(?x5471, ?x11991), ?x203 = 0dgrmp, ?x60 = 02nzb8, ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x5471 = 03zv9, position(?x11991, ?x63) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 15 *> proper extension: 01j_jh; *> query: (?x11991, 03d8m4) <- position(?x11991, ?x63), teams(?x14229, ?x11991), position(?x11991, ?x530), ?x63 = 02sdk9v, ?x530 = 02_j1w, country(?x14229, ?x205), category(?x14229, ?x134), film_release_region(?x11839, ?x205), ?x11839 = 072hx4 *> conf = 0.06 ranks of expected_values: 21 EVAL 01dwyd current_club! 03d8m4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 112.000 66.000 0.357 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #22816-0177z PRED entity: 0177z PRED relation: location_of_ceremony! PRED expected values: 04ztj => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.89 #61, 0.87 #77, 0.86 #69), 0jgjn (0.20 #20, 0.09 #28, 0.06 #60), 01g63y (0.20 #18, 0.06 #34, 0.03 #58), 01bl8s (0.03 #51, 0.03 #59, 0.03 #83) >> Best rule #61 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 04jpl; 02cl1; 02_286; 06y57; 08966; 0chgzm; 02z0j; 07dfk; >> query: (?x4826, 04ztj) <- mode_of_transportation(?x4826, ?x8731), ?x8731 = 01bjv, month(?x4826, ?x1459), citytown(?x2106, ?x4826), contains(?x172, ?x4826) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0177z location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.886 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #22815-02j9lm PRED entity: 02j9lm PRED relation: award PRED expected values: 0bdw1g 09td7p => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 273): 09sb52 (0.67 #442, 0.33 #19289, 0.33 #14477), 05pcn59 (0.20 #6497, 0.14 #8903, 0.14 #8502), 0cqhk0 (0.19 #8057, 0.19 #6052, 0.17 #7255), 0fbvqf (0.17 #850, 0.17 #449, 0.15 #28877), 0f4x7 (0.17 #432, 0.12 #2036, 0.12 #10457), 027dtxw (0.17 #405, 0.08 #2009, 0.07 #10430), 0gq9h (0.15 #28877, 0.15 #36498, 0.13 #38103), 02x4w6g (0.15 #28877, 0.15 #36498, 0.13 #38103), 03qgjwc (0.15 #28877, 0.15 #36498, 0.13 #38103), 02x4x18 (0.15 #28877, 0.14 #130, 0.13 #38103) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0dlglj; 030h95; 04smkr; 028r4y; 0crvfq; >> query: (?x2900, 09sb52) <- award_nominee(?x8147, ?x2900), nominated_for(?x2900, ?x3610), ?x8147 = 01tnxc >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 06151l; 083chw; 013knm; 03_l8m; 05p5nc; *> query: (?x2900, 0bdw1g) <- award_nominee(?x5662, ?x2900), type_of_union(?x2900, ?x566), ?x5662 = 02nwxc *> conf = 0.14 ranks of expected_values: 19, 20 EVAL 02j9lm award 09td7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 108.000 108.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02j9lm award 0bdw1g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 108.000 108.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22814-0gps0z PRED entity: 0gps0z PRED relation: artist! PRED expected values: 01cl0d => 180 concepts (174 used for prediction) PRED predicted values (max 10 best out of 121): 015_1q (0.55 #8778, 0.27 #1550, 0.26 #4330), 01w40h (0.39 #6007, 0.21 #8787, 0.13 #1559), 043g7l (0.38 #588, 0.13 #11571, 0.13 #1562), 0mzkr (0.36 #6004, 0.19 #8923, 0.13 #1417), 02bh8z (0.33 #22, 0.08 #2803, 0.07 #6000), 011k1h (0.31 #8907, 0.20 #845, 0.20 #11549), 04fc6c (0.30 #910, 0.07 #11539, 0.07 #11679), 017l96 (0.28 #8916, 0.11 #4190, 0.11 #10723), 03rhqg (0.27 #11415, 0.27 #11555, 0.19 #5994), 01cl0d (0.26 #6310, 0.10 #4364, 0.09 #2835) >> Best rule #8778 for best value: >> intensional similarity = 4 >> extensional distance = 267 >> proper extension: 089tm; 01t_xp_; 0m19t; 025xt8y; 07_3qd; 01v0sx2; 03xgm3; 01wp8w7; 07z542; 03g5jw; ... >> query: (?x9639, 015_1q) <- artist(?x7793, ?x9639), artists(?x671, ?x9639), artist(?x7793, ?x12880), ?x12880 = 011xhx >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #6310 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 148 *> proper extension: 0fp_v1x; 0ftps; 01p9hgt; 01w724; 016h9b; 01l_vgt; 06gd4; 03f0vvr; 0d9xq; 01dwrc; ... *> query: (?x9639, 01cl0d) <- artist(?x7793, ?x9639), artists(?x671, ?x9639), artist(?x7793, ?x5550), ?x5550 = 01bczm *> conf = 0.26 ranks of expected_values: 10 EVAL 0gps0z artist! 01cl0d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 180.000 174.000 0.554 http://example.org/music/record_label/artist #22813-01t04r PRED entity: 01t04r PRED relation: artist PRED expected values: 01p0vf => 108 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1044): 0kzy0 (0.50 #4895, 0.38 #5706, 0.17 #3273), 03f0fnk (0.50 #5186, 0.38 #5997, 0.11 #13297), 01vsykc (0.50 #5076, 0.38 #5887, 0.11 #13187), 02cpp (0.50 #3658, 0.33 #6902, 0.33 #5280), 01323p (0.50 #3789, 0.33 #7033, 0.33 #548), 0bk1p (0.50 #3890, 0.33 #7134, 0.33 #649), 0p76z (0.50 #3945, 0.33 #7189, 0.33 #704), 02vcp0 (0.50 #3817, 0.33 #7061, 0.33 #576), 01309x (0.50 #3485, 0.33 #6729, 0.33 #244), 0132k4 (0.50 #6160, 0.33 #5349, 0.25 #2106) >> Best rule #4895 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 03rhqg; 015_1q; 01cl2y; 02y21l; >> query: (?x9286, 0kzy0) <- artist(?x9286, ?x10043), artist(?x9286, ?x9287), artist(?x9286, ?x7653), group(?x227, ?x7653), award(?x7653, ?x3647), ?x9287 = 033s6, group(?x2363, ?x7653), group(?x3214, ?x10043), artists(?x1000, ?x7653), ?x1000 = 0xhtw, ?x3214 = 02snj9 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4863 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 027f3ys; *> query: (?x9286, ?x475) <- artist(?x9286, ?x13578), artist(?x9286, ?x10198), artist(?x9286, ?x7653), artist(?x9286, ?x4082), ?x7653 = 0b_xm, artists(?x8187, ?x10198), type_of_union(?x10198, ?x1873), location(?x4082, ?x4627), group(?x227, ?x13578), type_of_union(?x4082, ?x566), artists(?x8187, ?x475) *> conf = 0.05 ranks of expected_values: 797 EVAL 01t04r artist 01p0vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 108.000 67.000 0.500 http://example.org/music/record_label/artist #22812-04yqlk PRED entity: 04yqlk PRED relation: nominated_for PRED expected values: 0g60z => 75 concepts (32 used for prediction) PRED predicted values (max 10 best out of 195): 0g60z (0.36 #41, 0.18 #1662, 0.08 #22693), 08r4x3 (0.36 #1765, 0.27 #144, 0.01 #26079), 03tps5 (0.26 #29180, 0.25 #21069, 0.24 #27559), 09g8vhw (0.26 #29180, 0.25 #21069, 0.24 #27559), 04vr_f (0.13 #1780, 0.09 #159, 0.01 #19607), 02b6n9 (0.09 #1412, 0.09 #3033, 0.01 #20860), 02prw4h (0.09 #170, 0.09 #1791), 0180mw (0.09 #1038, 0.07 #30803, 0.04 #2659), 049xgc (0.09 #885, 0.07 #30803, 0.04 #2506), 0124k9 (0.08 #22693, 0.07 #30803, 0.05 #221) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 01ggc9; >> query: (?x4408, 0g60z) <- award_nominee(?x4408, ?x1870), ?x1870 = 0hvb2, actor(?x2660, ?x4408) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04yqlk nominated_for 0g60z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 32.000 0.364 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22811-01tvz5j PRED entity: 01tvz5j PRED relation: film PRED expected values: 06__m6 => 91 concepts (50 used for prediction) PRED predicted values (max 10 best out of 625): 0180mw (0.65 #7155, 0.64 #5366, 0.64 #3577), 05sy_5 (0.20 #1055, 0.02 #11787, 0.01 #17153), 0gffmn8 (0.20 #523, 0.01 #2311, 0.01 #4100), 053rxgm (0.20 #176, 0.01 #1964, 0.01 #3753), 033g4d (0.20 #178, 0.01 #1966, 0.01 #5544), 03qcfvw (0.20 #9, 0.01 #3586, 0.01 #5375), 01v1ln (0.20 #1229, 0.01 #11961), 0170xl (0.20 #1717), 02qd04y (0.20 #1532), 0gtx63s (0.20 #1410) >> Best rule #7155 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 041h0; >> query: (?x426, ?x6482) <- award(?x426, ?x1254), award_winner(?x6482, ?x426), friend(?x426, ?x2818) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01tvz5j film 06__m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 50.000 0.647 http://example.org/film/actor/film./film/performance/film #22810-0j582 PRED entity: 0j582 PRED relation: student! PRED expected values: 02q636 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 127): 0bwfn (0.10 #1853, 0.09 #5536, 0.08 #19212), 03ksy (0.09 #632, 0.04 #35876, 0.04 #1158), 01mpwj (0.07 #633, 0.03 #1159), 04b_46 (0.07 #1805, 0.04 #5488, 0.03 #6014), 015nl4 (0.06 #2697, 0.05 #5854, 0.05 #21634), 06thjt (0.05 #923, 0.03 #1449, 0.03 #3027), 017hnw (0.05 #1034, 0.03 #1560, 0.01 #3138), 065y4w7 (0.05 #1592, 0.05 #20003, 0.05 #24211), 017z88 (0.04 #20071, 0.04 #19019, 0.03 #24279), 01w5m (0.04 #35875, 0.04 #105, 0.04 #31667) >> Best rule #1853 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 02hy9p; >> query: (?x1548, 0bwfn) <- award(?x1548, ?x435), executive_produced_by(?x1547, ?x1548), film(?x1548, ?x4749) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 02n9k; 05vzql; *> query: (?x1548, 02q636) <- languages(?x1548, ?x5607), nationality(?x1548, ?x94), ?x5607 = 064_8sq *> conf = 0.02 ranks of expected_values: 54 EVAL 0j582 student! 02q636 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 120.000 120.000 0.096 http://example.org/education/educational_institution/students_graduates./education/education/student #22809-02c0mv PRED entity: 02c0mv PRED relation: profession PRED expected values: 0dxtg => 107 concepts (65 used for prediction) PRED predicted values (max 10 best out of 66): 02hrh1q (0.78 #4188, 0.77 #2996, 0.75 #3592), 0dxtg (0.71 #1057, 0.70 #610, 0.69 #2399), 01d_h8 (0.61 #6, 0.57 #155, 0.55 #751), 02jknp (0.56 #8208, 0.32 #8, 0.30 #4628), 0cbd2 (0.43 #1640, 0.33 #7455, 0.31 #8798), 02krf9 (0.33 #2710, 0.31 #2412, 0.30 #325), 018gz8 (0.27 #2998, 0.24 #762, 0.20 #17), 09jwl (0.22 #4788, 0.22 #4341, 0.19 #6279), 0np9r (0.20 #766, 0.18 #319, 0.17 #468), 0kyk (0.16 #2266, 0.15 #2862, 0.15 #3011) >> Best rule #4188 for best value: >> intensional similarity = 4 >> extensional distance = 368 >> proper extension: 0jfx1; 0jrqq; 08vr94; 033jkj; 01t110; 01mqc_; 03f7jfh; 024qwq; 037q1z; 016z1c; >> query: (?x8545, 02hrh1q) <- gender(?x8545, ?x231), award_winner(?x9793, ?x8545), location(?x8545, ?x8771), people(?x12950, ?x8545) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1057 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 112 *> proper extension: 02j8nx; 0988cp; 020ffd; 0g69lg; *> query: (?x8545, 0dxtg) <- award_winner(?x8545, ?x9793), producer_type(?x8545, ?x632), nationality(?x8545, ?x512), student(?x892, ?x9793) *> conf = 0.71 ranks of expected_values: 2 EVAL 02c0mv profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 65.000 0.778 http://example.org/people/person/profession #22808-06fc0b PRED entity: 06fc0b PRED relation: profession PRED expected values: 09jwl => 125 concepts (123 used for prediction) PRED predicted values (max 10 best out of 75): 0dxtg (0.48 #6821, 0.40 #2973, 0.36 #4601), 02jknp (0.46 #6815, 0.35 #303, 0.27 #451), 09jwl (0.43 #166, 0.31 #4310, 0.29 #462), 03gjzk (0.40 #2974, 0.38 #310, 0.36 #7266), 0nbcg (0.29 #179, 0.23 #4323, 0.20 #5507), 016z4k (0.29 #152, 0.20 #4, 0.19 #4296), 0dz3r (0.28 #4294, 0.25 #3850, 0.23 #5478), 0np9r (0.25 #16579, 0.20 #7864, 0.19 #6532), 0d1pc (0.23 #1382, 0.23 #2566, 0.21 #2862), 018gz8 (0.20 #2976, 0.17 #4604, 0.15 #312) >> Best rule #6821 for best value: >> intensional similarity = 3 >> extensional distance = 610 >> proper extension: 01g4zr; 022_lg; 0ksf29; 06w33f8; 01c58j; 01n8_g; 0c_mvb; 04b19t; 06pwf6; 03m_k0; ... >> query: (?x7823, 0dxtg) <- place_of_birth(?x7823, ?x9341), profession(?x7823, ?x319), ?x319 = 01d_h8 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 02r3cn; *> query: (?x7823, 09jwl) <- participant(?x2352, ?x7823), location(?x7823, ?x1131), ?x2352 = 01pgzn_ *> conf = 0.43 ranks of expected_values: 3 EVAL 06fc0b profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 123.000 0.479 http://example.org/people/person/profession #22807-02r3zy PRED entity: 02r3zy PRED relation: artist! PRED expected values: 0181dw => 92 concepts (57 used for prediction) PRED predicted values (max 10 best out of 135): 01trtc (0.33 #353, 0.16 #1893, 0.11 #2033), 01cszh (0.29 #151, 0.16 #431, 0.12 #1411), 015_1q (0.25 #2259, 0.25 #2119, 0.24 #1559), 03rhqg (0.24 #156, 0.22 #2537, 0.21 #436), 033hn8 (0.24 #154, 0.17 #994, 0.17 #294), 0g768 (0.20 #37, 0.15 #3260, 0.15 #3400), 01t04r (0.18 #205, 0.17 #1465, 0.16 #485), 0229rs (0.18 #158, 0.16 #438, 0.10 #718), 01cl2y (0.18 #170, 0.13 #730, 0.12 #1430), 03mp8k (0.17 #347, 0.13 #2307, 0.12 #1047) >> Best rule #353 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 01q7cb_; 01gx5f; 0fpj4lx; 0p3r8; 0dw3l; >> query: (?x1060, 01trtc) <- artist(?x3240, ?x1060), artists(?x3753, ?x1060), artists(?x1572, ?x1060), ?x1572 = 06by7, ?x3753 = 01_bkd >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #182 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 02mq_y; *> query: (?x1060, 0181dw) <- group(?x2798, ?x1060), group(?x745, ?x1060), ?x745 = 01vj9c, artists(?x302, ?x1060), ?x2798 = 03qjg *> conf = 0.12 ranks of expected_values: 19 EVAL 02r3zy artist! 0181dw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 92.000 57.000 0.333 http://example.org/music/record_label/artist #22806-0d3mlc PRED entity: 0d3mlc PRED relation: team PRED expected values: 01l0__ => 64 concepts (57 used for prediction) PRED predicted values (max 10 best out of 727): 07s8qm7 (0.83 #536, 0.81 #2676, 0.81 #3209), 01kj5h (0.25 #90, 0.12 #359, 0.08 #6161), 01kwhf (0.13 #576, 0.12 #39, 0.08 #3249), 03_44z (0.13 #764, 0.12 #496, 0.09 #1831), 0182r9 (0.12 #14, 0.11 #1083, 0.11 #1618), 01rly6 (0.12 #191, 0.10 #728, 0.08 #3133), 02b10g (0.12 #54, 0.10 #591, 0.08 #6161), 02b0_6 (0.12 #77, 0.08 #3019, 0.08 #3287), 03d0d7 (0.12 #242, 0.08 #537, 0.08 #6161), 0mmd6 (0.12 #255, 0.08 #6161, 0.08 #524) >> Best rule #536 for best value: >> intensional similarity = 9 >> extensional distance = 23 >> proper extension: 0c11mj; 09r1j5; 0d3f83; 02zbjwr; 07zr66; >> query: (?x12509, ?x1026) <- team(?x12509, ?x6871), team(?x12509, ?x1026), team(?x12509, ?x59), teams(?x2863, ?x6871), position(?x6871, ?x530), ?x530 = 02_j1w, position(?x59, ?x63), athlete(?x471, ?x12509), current_club(?x59, ?x6477) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #537 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 23 *> proper extension: 0c11mj; 09r1j5; 0d3f83; 02zbjwr; 07zr66; *> query: (?x12509, ?x6477) <- team(?x12509, ?x6871), team(?x12509, ?x59), teams(?x2863, ?x6871), position(?x6871, ?x530), ?x530 = 02_j1w, position(?x59, ?x63), athlete(?x471, ?x12509), current_club(?x59, ?x6477) *> conf = 0.08 ranks of expected_values: 56 EVAL 0d3mlc team 01l0__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 64.000 57.000 0.832 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #22805-06wm0z PRED entity: 06wm0z PRED relation: nationality PRED expected values: 09c7w0 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 65): 09c7w0 (0.83 #4413, 0.79 #1, 0.78 #1604), 07ssc (0.30 #7326, 0.10 #1819, 0.09 #4327), 0l2v0 (0.27 #9532, 0.01 #3810, 0.01 #3608), 02jx1 (0.10 #5451, 0.10 #333, 0.10 #6655), 03rk0 (0.08 #4057, 0.08 #4157, 0.08 #4358), 0d060g (0.06 #3716, 0.05 #3313, 0.05 #3514), 04hqz (0.05 #79, 0.03 #7124), 0chghy (0.04 #911, 0.03 #7124, 0.03 #1814), 03rjj (0.03 #405, 0.03 #305, 0.03 #7124), 0f8l9c (0.03 #7124, 0.03 #523, 0.03 #222) >> Best rule #4413 for best value: >> intensional similarity = 2 >> extensional distance = 1113 >> proper extension: 09fqd3; >> query: (?x5058, 09c7w0) <- location(?x5058, ?x5174), county(?x5174, ?x5173) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06wm0z nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.829 http://example.org/people/person/nationality #22804-03t5kl PRED entity: 03t5kl PRED relation: ceremony PRED expected values: 013b2h => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 128): 01c6qp (0.78 #1589, 0.60 #410, 0.57 #1720), 01mh_q (0.75 #1654, 0.60 #475, 0.57 #999), 01bx35 (0.74 #1578, 0.67 #792, 0.54 #1709), 013b2h (0.73 #1646, 0.60 #467, 0.53 #1777), 01s695 (0.73 #1575, 0.57 #920, 0.54 #1706), 01mhwk (0.71 #1609, 0.60 #430, 0.60 #299), 01xqqp (0.66 #1661, 0.60 #482, 0.60 #351), 0jzphpx (0.64 #1607, 0.60 #428, 0.60 #297), 09qvms (0.53 #2098, 0.33 #263, 0.23 #2885), 05c1t6z (0.33 #263, 0.23 #1455, 0.23 #2885) >> Best rule #1589 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 054knh; >> query: (?x4837, 01c6qp) <- ceremony(?x4837, ?x5656), award_winner(?x5656, ?x1794), locations(?x5656, ?x1523), participant(?x4080, ?x1794) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 97 *> proper extension: 054knh; *> query: (?x4837, 013b2h) <- ceremony(?x4837, ?x5656), award_winner(?x5656, ?x1794), locations(?x5656, ?x1523), participant(?x4080, ?x1794) *> conf = 0.73 ranks of expected_values: 4 EVAL 03t5kl ceremony 013b2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 42.000 42.000 0.778 http://example.org/award/award_category/winners./award/award_honor/ceremony #22803-0mzww PRED entity: 0mzww PRED relation: place_of_death! PRED expected values: 01qbjg => 181 concepts (137 used for prediction) PRED predicted values (max 10 best out of 724): 0443c (0.20 #757, 0.20 #744, 0.04 #42309), 034cj9 (0.20 #736, 0.03 #4515, 0.03 #3760), 01dbhb (0.20 #726, 0.03 #4505, 0.03 #3750), 0f3nn (0.20 #707, 0.03 #4486, 0.03 #3731), 0164y7 (0.20 #706, 0.03 #4485, 0.03 #3730), 01fxfk (0.20 #704, 0.03 #4483, 0.03 #3728), 06y7d (0.20 #668, 0.03 #4447, 0.03 #3692), 0cpvcd (0.20 #630, 0.03 #4409, 0.03 #3654), 0p9qb (0.20 #587, 0.03 #4366, 0.03 #3611), 0gqrb (0.20 #582, 0.03 #4361, 0.03 #3606) >> Best rule #757 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02_286; 0cr3d; 01m1zk; >> query: (?x6987, ?x13779) <- location(?x13779, ?x6987), place_of_birth(?x6549, ?x6987), ?x13779 = 0443c >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0mzww place_of_death! 01qbjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 137.000 0.200 http://example.org/people/deceased_person/place_of_death #22802-01hwkn PRED entity: 01hwkn PRED relation: entity_involved PRED expected values: 03gyl => 58 concepts (41 used for prediction) PRED predicted values (max 10 best out of 3001): 0cdbq (0.51 #4691, 0.43 #1124, 0.40 #1869), 01k6y1 (0.51 #4691, 0.40 #1869, 0.33 #3284), 01flgk (0.51 #4691, 0.33 #393, 0.29 #1170), 05pq3_ (0.51 #4691, 0.33 #577, 0.25 #887), 02c4s (0.51 #4691, 0.33 #322, 0.24 #3439), 06mkj (0.51 #4691, 0.33 #22, 0.24 #3439), 07ssc (0.51 #4691, 0.24 #3439, 0.19 #2501), 0dbxy (0.50 #1027, 0.33 #250, 0.24 #3439), 034rd (0.50 #981, 0.33 #204, 0.20 #2079), 024pcx (0.44 #1796, 0.25 #1712, 0.25 #1638) >> Best rule #4691 for best value: >> intensional similarity = 7 >> extensional distance = 26 >> proper extension: 0c6cwg; >> query: (?x12673, ?x512) <- entity_involved(?x12673, ?x6830), combatants(?x12673, ?x1778), entity_involved(?x12777, ?x6830), gender(?x6830, ?x231), people(?x6734, ?x6830), nationality(?x6830, ?x789), entity_involved(?x12777, ?x512) >> conf = 0.51 => this is the best rule for 7 predicted values No rule for expected values ranks of expected_values: EVAL 01hwkn entity_involved 03gyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 41.000 0.509 http://example.org/base/culturalevent/event/entity_involved #22801-0gy7bj4 PRED entity: 0gy7bj4 PRED relation: story_by PRED expected values: 01tz6vs => 73 concepts (39 used for prediction) PRED predicted values (max 10 best out of 41): 07h07 (0.06 #3460, 0.04 #7595, 0.03 #8469), 041h0 (0.05 #437, 0.04 #5, 0.02 #653), 081k8 (0.04 #87, 0.03 #1816, 0.03 #1599), 0ff2k (0.04 #194, 0.02 #626, 0.01 #842), 09v6tz (0.04 #130, 0.02 #562, 0.01 #778), 02zjd (0.04 #108, 0.02 #540, 0.01 #756), 013tcv (0.04 #160, 0.01 #808), 0bv7t (0.04 #92, 0.01 #740), 079vf (0.04 #1082, 0.03 #1298, 0.02 #1947), 040dv (0.03 #374, 0.01 #806) >> Best rule #3460 for best value: >> intensional similarity = 3 >> extensional distance = 350 >> proper extension: 025x1t; 0gxsh4; >> query: (?x9839, ?x4008) <- nominated_for(?x4008, ?x9839), profession(?x4008, ?x987), influenced_by(?x4008, ?x4028) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gy7bj4 story_by 01tz6vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 39.000 0.056 http://example.org/film/film/story_by #22800-03csqj4 PRED entity: 03csqj4 PRED relation: gender PRED expected values: 05zppz => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #31, 0.86 #47, 0.86 #33), 02zsn (0.24 #80, 0.24 #122, 0.24 #98) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 211 >> proper extension: 017yfz; 03mv0b; 03k1vm; 05dxl_; >> query: (?x12010, 05zppz) <- place_of_death(?x12010, ?x1248), profession(?x12010, ?x1078), film_crew_role(?x148, ?x1078) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03csqj4 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.859 http://example.org/people/person/gender #22799-027j9wd PRED entity: 027j9wd PRED relation: film! PRED expected values: 08vr94 07m77x => 97 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1237): 041c4 (0.38 #892, 0.11 #5048, 0.10 #19594), 0f7hc (0.38 #828, 0.09 #13296, 0.07 #4984), 0436kgz (0.25 #1162, 0.09 #28176, 0.07 #30254), 0p__8 (0.25 #1055, 0.07 #5211, 0.07 #15601), 0bksh (0.25 #852, 0.07 #5008, 0.07 #7086), 0kftt (0.25 #1465, 0.07 #5621, 0.07 #7699), 03f1r6t (0.25 #928, 0.05 #13396, 0.04 #19630), 020ffd (0.25 #1085, 0.04 #19787, 0.03 #34333), 02_p5w (0.24 #2721, 0.10 #35969, 0.07 #46359), 06ltr (0.15 #5100, 0.12 #11334, 0.12 #19646) >> Best rule #892 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02vw1w2; >> query: (?x6000, 041c4) <- prequel(?x6000, ?x3088), prequel(?x9194, ?x6000), genre(?x6000, ?x2540), ?x2540 = 0hcr >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1540 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02vw1w2; *> query: (?x6000, 07m77x) <- prequel(?x6000, ?x3088), prequel(?x9194, ?x6000), genre(?x6000, ?x2540), ?x2540 = 0hcr *> conf = 0.12 ranks of expected_values: 17, 197 EVAL 027j9wd film! 07m77x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 97.000 53.000 0.375 http://example.org/film/actor/film./film/performance/film EVAL 027j9wd film! 08vr94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 97.000 53.000 0.375 http://example.org/film/actor/film./film/performance/film #22798-02_286 PRED entity: 02_286 PRED relation: place_founded! PRED expected values: 04htfd 05s34b => 219 concepts (218 used for prediction) PRED predicted values (max 10 best out of 184): 05njw (0.32 #2443, 0.16 #1831, 0.12 #4067), 02l48d (0.32 #2443, 0.16 #1831, 0.12 #4067), 01pf21 (0.32 #2443, 0.16 #1831, 0.12 #4067), 03qbm (0.32 #2443, 0.12 #4067, 0.12 #4068), 0sxdg (0.32 #2443, 0.12 #4067, 0.12 #4068), 016tw3 (0.17 #410, 0.11 #5, 0.10 #207), 09d5h (0.16 #1831, 0.12 #4067, 0.12 #4068), 0bwfn (0.16 #1831, 0.12 #4067, 0.12 #4068), 058j2 (0.16 #1831, 0.12 #4067, 0.12 #4068), 0cv_2 (0.16 #1831, 0.12 #4067, 0.12 #4068) >> Best rule #2443 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 024bqj; >> query: (?x739, ?x12013) <- citytown(?x12013, ?x739), contains(?x739, ?x1005), place_founded(?x12013, ?x4271) >> conf = 0.32 => this is the best rule for 5 predicted values *> Best rule #642 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 026mj; *> query: (?x739, 04htfd) <- place_founded(?x2549, ?x739), contains(?x739, ?x1005), adjoins(?x739, ?x3670) *> conf = 0.13 ranks of expected_values: 23 EVAL 02_286 place_founded! 05s34b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 218.000 0.323 http://example.org/organization/organization/place_founded EVAL 02_286 place_founded! 04htfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 219.000 218.000 0.323 http://example.org/organization/organization/place_founded #22797-03p9hl PRED entity: 03p9hl PRED relation: film PRED expected values: 03phtz => 126 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1008): 02qr46y (0.56 #39349, 0.56 #41138, 0.49 #84064), 0sxns (0.40 #1076, 0.33 #2864, 0.04 #121633), 02cbhg (0.29 #4979, 0.04 #121633, 0.03 #80101), 01y9r2 (0.29 #4921, 0.04 #121633, 0.02 #33540), 09jcj6 (0.20 #798, 0.17 #2586, 0.06 #7951), 03wy8t (0.20 #1586, 0.17 #3374, 0.04 #10528), 0blpg (0.20 #656, 0.17 #2444, 0.02 #29274), 03m4mj (0.20 #202, 0.17 #1990, 0.02 #12723), 0gtvpkw (0.20 #566, 0.17 #2354, 0.02 #13087), 06lpmt (0.20 #685, 0.17 #2473, 0.02 #13206) >> Best rule #39349 for best value: >> intensional similarity = 3 >> extensional distance = 243 >> proper extension: 01309x; >> query: (?x13793, ?x11829) <- spouse(?x5335, ?x13793), gender(?x13793, ?x514), nominated_for(?x13793, ?x11829) >> conf = 0.56 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03p9hl film 03phtz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 73.000 0.565 http://example.org/film/actor/film./film/performance/film #22796-02rchht PRED entity: 02rchht PRED relation: produced_by! PRED expected values: 02vjp3 => 92 concepts (48 used for prediction) PRED predicted values (max 10 best out of 274): 02ppg1r (0.36 #5685, 0.36 #6633, 0.35 #4737), 0h03fhx (0.03 #2319, 0.02 #5162, 0.02 #6110), 084qpk (0.03 #3860, 0.02 #5756, 0.02 #4808), 03cp4cn (0.02 #5342, 0.02 #2499, 0.02 #6290), 01cssf (0.02 #1949, 0.02 #5740, 0.02 #3844), 0gg5qcw (0.02 #2372, 0.02 #5215, 0.01 #6163), 03h3x5 (0.02 #2126, 0.02 #4969, 0.01 #5917), 0b6l1st (0.02 #2573, 0.01 #6364, 0.01 #11101), 03tbg6 (0.02 #2772, 0.01 #6563), 0gd0c7x (0.02 #2063, 0.01 #5854) >> Best rule #5685 for best value: >> intensional similarity = 3 >> extensional distance = 119 >> proper extension: 030pr; >> query: (?x264, ?x4581) <- award_nominee(?x163, ?x264), film(?x264, ?x4581), student(?x263, ?x264) >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02rchht produced_by! 02vjp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 48.000 0.364 http://example.org/film/film/produced_by #22795-013n0n PRED entity: 013n0n PRED relation: time_zones PRED expected values: 02fqwt => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 11): 02fqwt (0.79 #40, 0.68 #27, 0.60 #625), 02hczc (0.60 #625, 0.53 #1003, 0.19 #1266), 02lcqs (0.48 #213, 0.33 #135, 0.32 #239), 02hcv8 (0.44 #484, 0.43 #1373, 0.43 #1360), 03bdv (0.20 #6, 0.05 #878, 0.05 #956), 02llzg (0.08 #550, 0.08 #1020, 0.07 #1164), 03plfd (0.03 #1026, 0.02 #1170, 0.02 #1236), 0gsrz4 (0.02 #1024), 042g7t (0.02 #1027, 0.02 #766, 0.01 #987), 02lcrv (0.02 #137, 0.01 #371, 0.01 #176) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0mrs1; 0d1xh; 013mj_; 0mq17; 0mqs0; 0fxwx; 0mrhq; 0mpzm; 0mskq; 0ms1n; ... >> query: (?x12222, 02fqwt) <- source(?x12222, ?x958), ?x958 = 0jbk9, contains(?x3634, ?x12222), ?x3634 = 07b_l >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013n0n time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.788 http://example.org/location/location/time_zones #22794-02tr7d PRED entity: 02tr7d PRED relation: award_nominee PRED expected values: 03v3xp => 103 concepts (42 used for prediction) PRED predicted values (max 10 best out of 985): 09fqtq (0.81 #80846, 0.81 #53122, 0.80 #9238), 02k6rq (0.81 #80846, 0.81 #53122, 0.80 #9238), 0cjsxp (0.81 #80846, 0.81 #53122, 0.80 #9238), 02l4pj (0.81 #80846, 0.81 #53122, 0.80 #9238), 03v3xp (0.76 #64672, 0.75 #55433, 0.75 #57743), 01ksr1 (0.76 #64672, 0.75 #55433, 0.75 #57743), 02qgqt (0.27 #78535, 0.17 #85465, 0.08 #4639), 02tr7d (0.27 #78535, 0.17 #85465, 0.08 #2652), 0dgskx (0.27 #78535, 0.17 #85465, 0.04 #3799), 02w9895 (0.27 #78535, 0.17 #85465, 0.04 #2543) >> Best rule #80846 for best value: >> intensional similarity = 3 >> extensional distance = 1164 >> proper extension: 07sgfsl; 0fwy0h; 06s6hs; 04j_gs; >> query: (?x1669, ?x368) <- award_winner(?x472, ?x1669), award_nominee(?x368, ?x1669), award_nominee(?x1669, ?x374) >> conf = 0.81 => this is the best rule for 4 predicted values *> Best rule #64672 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1011 *> proper extension: 02f9wb; *> query: (?x1669, ?x368) <- award_winner(?x472, ?x1669), award_winner(?x368, ?x1669), ceremony(?x451, ?x472) *> conf = 0.76 ranks of expected_values: 5 EVAL 02tr7d award_nominee 03v3xp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 103.000 42.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22793-0dwcl PRED entity: 0dwcl PRED relation: industry PRED expected values: 020mfr => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 33): 020mfr (0.67 #1101, 0.59 #960, 0.50 #63), 02vxn (0.60 #332, 0.50 #379, 0.42 #803), 04rlf (0.24 #850, 0.18 #2453, 0.14 #815), 03qh03g (0.24 #850, 0.18 #2453, 0.12 #759), 02jjt (0.24 #850, 0.18 #2453, 0.10 #338), 029g_vk (0.18 #2453, 0.15 #614, 0.14 #1321), 01mf0 (0.18 #2453, 0.08 #974, 0.07 #1115), 019z7b (0.18 #2453, 0.05 #952, 0.04 #1093), 0hz28 (0.18 #2453, 0.04 #2293, 0.04 #2434), 01mfj (0.18 #2453, 0.02 #2300, 0.02 #2441) >> Best rule #1101 for best value: >> intensional similarity = 5 >> extensional distance = 73 >> proper extension: 02bm1v; 01tt27; 046qpy; 01_30_; >> query: (?x13241, 020mfr) <- industry(?x13241, ?x245), industry(?x12074, ?x245), industry(?x9469, ?x245), ?x12074 = 02rfft, service_language(?x9469, ?x254) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dwcl industry 020mfr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.667 http://example.org/business/business_operation/industry #22792-01lsl PRED entity: 01lsl PRED relation: nominated_for! PRED expected values: 0f4x7 => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 204): 019f4v (0.56 #52, 0.42 #984, 0.39 #751), 040njc (0.44 #7, 0.33 #706, 0.31 #2098), 0gqyl (0.42 #74, 0.27 #307, 0.25 #773), 0f4x7 (0.38 #257, 0.37 #24, 0.31 #2098), 0gr4k (0.38 #258, 0.35 #491, 0.29 #1889), 04dn09n (0.37 #34, 0.28 #966, 0.24 #2365), 0gqy2 (0.35 #117, 0.30 #350, 0.26 #583), 04ljl_l (0.31 #2098, 0.06 #2567, 0.06 #3266), 02grdc (0.31 #2098), 0gs96 (0.31 #317, 0.25 #550, 0.23 #1016) >> Best rule #52 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0j_tw; 0jqzt; >> query: (?x9185, 019f4v) <- genre(?x9185, ?x53), featured_film_locations(?x9185, ?x739), list(?x9185, ?x3004) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #257 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 08cfr1; *> query: (?x9185, 0f4x7) <- genre(?x9185, ?x53), film_art_direction_by(?x9185, ?x8402), award(?x9185, ?x1862) *> conf = 0.38 ranks of expected_values: 4 EVAL 01lsl nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 48.000 48.000 0.558 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22791-09bymc PRED entity: 09bymc PRED relation: honored_for PRED expected values: 072kp 03ln8b 02wgk1 04qk12 => 27 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1004): 03ln8b (0.67 #3035, 0.60 #2453, 0.50 #121), 05lfwd (0.40 #2671, 0.40 #1505, 0.33 #3253), 09p0ct (0.40 #2407, 0.40 #1241, 0.33 #2989), 092vkg (0.40 #2386, 0.40 #1220, 0.33 #2968), 05jzt3 (0.40 #2378, 0.40 #1212, 0.33 #2960), 0fhzwl (0.40 #1072, 0.17 #3986, 0.16 #5740), 0d68qy (0.33 #3644, 0.29 #4816, 0.29 #5398), 017f3m (0.33 #5833, 0.29 #8165, 0.29 #6999), 034fl9 (0.33 #5833, 0.29 #8165, 0.29 #6999), 02r2j8 (0.33 #5833, 0.29 #8165, 0.29 #6999) >> Best rule #3035 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 03nnm4t; >> query: (?x8762, 03ln8b) <- honored_for(?x8762, ?x8664), honored_for(?x8762, ?x5810), ?x5810 = 0828jw, award_winner(?x8762, ?x828), film(?x194, ?x8664), nominated_for(?x2489, ?x8664), titles(?x53, ?x8664), award(?x828, ?x112), participant(?x91, ?x828), film(?x828, ?x857), country(?x8664, ?x94), award_winner(?x4836, ?x828), award_winner(?x157, ?x91) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 481 EVAL 09bymc honored_for 04qk12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 27.000 20.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 09bymc honored_for 02wgk1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 20.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 09bymc honored_for 03ln8b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 27.000 20.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 09bymc honored_for 072kp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 27.000 20.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #22790-09pnw5 PRED entity: 09pnw5 PRED relation: honored_for PRED expected values: 0gpx6 => 32 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1141): 08jgk1 (0.40 #1272, 0.14 #4227, 0.14 #2455), 0cs134 (0.40 #1735, 0.11 #7643, 0.10 #9419), 0hz55 (0.33 #884, 0.20 #1474, 0.17 #2657), 02k_4g (0.33 #42, 0.20 #1223, 0.12 #4178), 0g60z (0.33 #17, 0.20 #1198, 0.09 #4153), 0330r (0.33 #519, 0.20 #1700, 0.07 #4655), 02hct1 (0.33 #145, 0.20 #1326, 0.07 #1918), 039c26 (0.33 #195, 0.20 #1376, 0.07 #1968), 02py4c8 (0.33 #37, 0.20 #1218, 0.05 #2991), 01g03q (0.33 #1105, 0.11 #3468, 0.08 #6421) >> Best rule #1272 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 02q690_; 027n06w; >> query: (?x7452, 08jgk1) <- award_winner(?x7452, ?x3762), award_winner(?x7452, ?x2965), award_winner(?x7452, ?x538), ?x3762 = 04x4s2, award_nominee(?x2965, ?x7730), profession(?x538, ?x1041), honored_for(?x7452, ?x675), ?x1041 = 03gjzk, nominated_for(?x538, ?x2755), award_nominee(?x538, ?x772), award(?x538, ?x2379), nationality(?x7730, ?x94), nominated_for(?x2379, ?x89), people(?x9428, ?x538) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1771 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 3 *> proper extension: 02q690_; 027n06w; *> query: (?x7452, ?x89) <- award_winner(?x7452, ?x3762), award_winner(?x7452, ?x2965), award_winner(?x7452, ?x538), ?x3762 = 04x4s2, award_nominee(?x2965, ?x7730), profession(?x538, ?x1041), honored_for(?x7452, ?x675), ?x1041 = 03gjzk, nominated_for(?x538, ?x2755), award_nominee(?x538, ?x772), award(?x538, ?x2379), nationality(?x7730, ?x94), nominated_for(?x2379, ?x89), people(?x9428, ?x538) *> conf = 0.03 ranks of expected_values: 426 EVAL 09pnw5 honored_for 0gpx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 31.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #22789-05kms PRED entity: 05kms PRED relation: role! PRED expected values: 0g33q => 76 concepts (44 used for prediction) PRED predicted values (max 10 best out of 106): 05148p4 (0.90 #3589, 0.90 #3509, 0.90 #3374), 04rzd (0.85 #1675, 0.85 #3269, 0.85 #3207), 0l14md (0.85 #1675, 0.84 #4119, 0.84 #97), 02hnl (0.84 #97, 0.84 #1886, 0.84 #406), 0g33q (0.84 #97, 0.84 #406, 0.83 #2841), 01c3q (0.84 #97, 0.84 #406, 0.83 #2841), 0mkg (0.78 #1579, 0.75 #1369, 0.73 #2644), 02sgy (0.78 #1575, 0.67 #621, 0.67 #2432), 01vj9c (0.76 #4137, 0.73 #2333, 0.72 #3823), 042v_gx (0.73 #2328, 0.67 #4132, 0.67 #2434) >> Best rule #3589 for best value: >> intensional similarity = 23 >> extensional distance = 19 >> proper extension: 02g9p4; >> query: (?x6039, ?x1166) <- role(?x6039, ?x2956), role(?x6039, ?x2309), role(?x6039, ?x1969), role(?x6039, ?x1495), role(?x6039, ?x1166), role(?x6039, ?x227), role(?x2956, ?x74), role(?x6039, ?x214), instrumentalists(?x2956, ?x2964), ?x1969 = 04rzd, role(?x2956, ?x314), ?x227 = 0342h, performance_role(?x1495, ?x212), role(?x1495, ?x316), role(?x5141, ?x1495), role(?x2662, ?x1495), performance_role(?x1260, ?x1495), group(?x1495, ?x997), ?x2662 = 045zr, ?x2309 = 06ncr, role(?x642, ?x1495), ?x1166 = 05148p4, ?x5141 = 01qgry >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 1 *> proper extension: 013y1f; *> query: (?x6039, ?x75) <- role(?x6039, ?x4583), role(?x6039, ?x2956), role(?x6039, ?x227), role(?x6039, ?x75), ?x2956 = 0myk8, role(?x4311, ?x6039), role(?x74, ?x6039), role(?x2662, ?x6039), ?x4583 = 0bmnm, role(?x745, ?x6039), family(?x6039, ?x3156), ?x4311 = 01xqw, ?x2662 = 045zr, instrumentalists(?x6039, ?x5141), role(?x74, ?x314), ?x314 = 02sgy, group(?x6039, ?x5838), role(?x1818, ?x74), ?x227 = 0342h, award_winner(?x139, ?x5141), award_winner(?x9945, ?x5141), ?x5838 = 02dw1_ *> conf = 0.84 ranks of expected_values: 5 EVAL 05kms role! 0g33q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 76.000 44.000 0.905 http://example.org/music/performance_role/regular_performances./music/group_membership/role #22788-07b1gq PRED entity: 07b1gq PRED relation: currency PRED expected values: 09nqf => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.79 #92, 0.79 #22, 0.78 #57), 01nv4h (0.02 #23, 0.02 #233, 0.02 #303), 02gsvk (0.01 #139) >> Best rule #92 for best value: >> intensional similarity = 2 >> extensional distance = 242 >> proper extension: 03rtz1; 01f7gh; 02725hs; 07024; 016kv6; 0blpg; 0g9lm2; 0ktpx; 03hxsv; 04tng0; ... >> query: (?x3640, 09nqf) <- film(?x3651, ?x3640), nominated_for(?x3640, ?x3330) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07b1gq currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.791 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #22787-06pjs PRED entity: 06pjs PRED relation: student! PRED expected values: 01_r9k 03818y => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 211): 0bwfn (0.14 #274, 0.12 #12373, 0.11 #4482), 065y4w7 (0.14 #14, 0.09 #4222, 0.08 #4748), 017j69 (0.14 #145, 0.05 #1197, 0.04 #2249), 021w0_ (0.14 #323, 0.03 #1375, 0.02 #1901), 01qd_r (0.12 #806, 0.03 #2910, 0.03 #3436), 07wrz (0.11 #1640, 0.03 #3744, 0.02 #10583), 08815 (0.09 #3684, 0.08 #10523, 0.07 #11575), 01w5m (0.07 #3787, 0.05 #10626, 0.05 #11678), 07tg4 (0.07 #1664, 0.05 #1138, 0.04 #2190), 0gl5_ (0.07 #1821, 0.03 #5503, 0.03 #3925) >> Best rule #274 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 06pj8; 0693l; 0gyx4; 04sry; 06t8b; >> query: (?x9153, 0bwfn) <- award_winner(?x5398, ?x9153), participant(?x2499, ?x9153), ?x5398 = 02w_6xj >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #4061 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 05fg2; 016h4r; 06whf; 047c9l; 04pp9s; *> query: (?x9153, 01_r9k) <- award_winner(?x5398, ?x9153), student(?x373, ?x9153), student(?x1368, ?x9153) *> conf = 0.03 ranks of expected_values: 50 EVAL 06pjs student! 03818y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 129.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06pjs student! 01_r9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 129.000 129.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #22786-01kkx2 PRED entity: 01kkx2 PRED relation: type_of_union PRED expected values: 04ztj => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.91 #45, 0.90 #9, 0.88 #25), 01g63y (0.20 #106, 0.18 #94, 0.18 #186), 01bl8s (0.01 #51, 0.01 #47) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 012cph; 057d89; 02whj; 0h1m9; 02knnd; 01c58j; 0177s6; 014dq7; 05x2t7; 0379s; ... >> query: (?x12037, 04ztj) <- profession(?x12037, ?x987), people(?x4322, ?x12037), place_of_burial(?x12037, ?x3153) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kkx2 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.906 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22785-081hvm PRED entity: 081hvm PRED relation: profession PRED expected values: 02hrh1q => 125 concepts (94 used for prediction) PRED predicted values (max 10 best out of 55): 02hrh1q (0.89 #1665, 0.88 #8418, 0.88 #3916), 01d_h8 (0.49 #1356, 0.41 #1206, 0.40 #156), 02jknp (0.38 #8, 0.34 #1358, 0.27 #2408), 0dxtg (0.30 #9317, 0.29 #11117, 0.29 #8717), 015cjr (0.27 #351, 0.21 #951, 0.21 #801), 0cbd2 (0.24 #2858, 0.20 #3158, 0.19 #2557), 03gjzk (0.23 #2867, 0.22 #5717, 0.21 #6318), 09jwl (0.19 #3171, 0.18 #3471, 0.17 #13375), 0np9r (0.16 #8875, 0.15 #5423, 0.15 #6624), 0kyk (0.15 #2882, 0.15 #2581, 0.13 #3182) >> Best rule #1665 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 07yw6t; 0fr7nt; 0cvbb9q; 0cct7p; 045n3p; >> query: (?x12062, 02hrh1q) <- award(?x12062, ?x4687), nationality(?x12062, ?x2146), ?x4687 = 03rbj2, ?x2146 = 03rk0 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 081hvm profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 94.000 0.894 http://example.org/people/person/profession #22784-02tqm5 PRED entity: 02tqm5 PRED relation: language PRED expected values: 02h40lc => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 32): 02h40lc (0.90 #594, 0.90 #475, 0.89 #120), 064_8sq (0.20 #81, 0.17 #22, 0.14 #2278), 04306rv (0.20 #64, 0.10 #242, 0.09 #537), 04h9h (0.17 #43, 0.04 #575, 0.04 #457), 06nm1 (0.13 #484, 0.13 #425, 0.12 #603), 03_9r (0.11 #128, 0.06 #1908, 0.06 #2980), 05qqm (0.10 #100, 0.01 #455, 0.01 #693), 02bjrlw (0.09 #415, 0.08 #593, 0.08 #533), 012w70 (0.07 #427, 0.05 #486, 0.04 #725), 06b_j (0.07 #260, 0.07 #853, 0.06 #1208) >> Best rule #594 for best value: >> intensional similarity = 4 >> extensional distance = 204 >> proper extension: 0413cff; 04jn6y7; >> query: (?x3246, 02h40lc) <- genre(?x3246, ?x604), country(?x3246, ?x94), ?x604 = 0lsxr, ?x94 = 09c7w0 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tqm5 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.903 http://example.org/film/film/language #22783-0fphf3v PRED entity: 0fphf3v PRED relation: film_release_region PRED expected values: 04gzd 07ssc 05v8c 015fr 0k6nt 059j2 0jgx => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 105): 07ssc (0.88 #12, 0.78 #589, 0.78 #1454), 059j2 (0.85 #606, 0.84 #1327, 0.82 #29), 015fr (0.85 #14, 0.75 #1312, 0.74 #591), 0k6nt (0.82 #600, 0.79 #1321, 0.76 #1609), 0b90_r (0.74 #3, 0.67 #580, 0.66 #1301), 04gzd (0.68 #7, 0.46 #1305, 0.44 #584), 01p1v (0.68 #47, 0.41 #1345, 0.40 #624), 05v8c (0.62 #13, 0.53 #590, 0.53 #1311), 01mjq (0.62 #40, 0.51 #617, 0.49 #1338), 0ctw_b (0.59 #24, 0.47 #601, 0.47 #1322) >> Best rule #12 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 05zvzf3; >> query: (?x7832, 07ssc) <- film_crew_role(?x7832, ?x137), film_release_region(?x7832, ?x7413), film_release_region(?x7832, ?x390), ?x7413 = 04hqz, ?x390 = 0chghy >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 8, 29 EVAL 0fphf3v film_release_region 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 77.000 77.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fphf3v film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fphf3v film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fphf3v film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fphf3v film_release_region 05v8c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fphf3v film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fphf3v film_release_region 04gzd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 77.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22782-0k0sv PRED entity: 0k0sv PRED relation: official_language! PRED expected values: 012m_ => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 255): 05r7t (0.50 #486, 0.40 #859, 0.40 #673), 0d060g (0.33 #195, 0.29 #1126, 0.25 #381), 06dfg (0.33 #308, 0.29 #1239, 0.25 #494), 01nln (0.33 #307, 0.29 #1238, 0.25 #493), 03_xj (0.33 #287, 0.29 #1218, 0.25 #473), 07z5n (0.33 #239, 0.29 #1170, 0.25 #425), 0366c (0.33 #364, 0.29 #1295, 0.25 #550), 06tw8 (0.33 #286, 0.29 #1217, 0.25 #472), 02khs (0.33 #226, 0.29 #1157, 0.25 #412), 077qn (0.33 #76, 0.27 #4291, 0.04 #6166) >> Best rule #486 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 06nm1; >> query: (?x5814, 05r7t) <- countries_spoken_in(?x5814, ?x1790), language(?x6719, ?x5814), language(?x6332, ?x5814), nominated_for(?x1243, ?x6332), nominated_for(?x6332, ?x7304), film_format(?x6332, ?x10390), film_crew_role(?x6332, ?x137), ?x1243 = 0gr0m, film(?x981, ?x6332), film(?x3477, ?x6719), country(?x6719, ?x94), production_companies(?x6719, ?x6560), nominated_for(?x1336, ?x7304) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 0k0sb; *> query: (?x5814, 012m_) <- countries_spoken_in(?x5814, ?x1790), language(?x6332, ?x5814), languages_spoken(?x3584, ?x5814), film_distribution_medium(?x6332, ?x81), genre(?x6332, ?x600), official_language(?x6435, ?x5814), titles(?x8581, ?x6332), ?x6435 = 0166b, titles(?x600, ?x2676), ?x2676 = 0f4m2z, film(?x981, ?x6332) *> conf = 0.33 ranks of expected_values: 72 EVAL 0k0sv official_language! 012m_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 49.000 49.000 0.500 http://example.org/location/country/official_language #22781-028kk_ PRED entity: 028kk_ PRED relation: specialization_of! PRED expected values: 029bkp => 48 concepts (32 used for prediction) PRED predicted values (max 10 best out of 113): 01xr66 (0.33 #41, 0.12 #485, 0.04 #706), 0mbx4 (0.33 #104, 0.12 #548, 0.04 #658), 0g7nc (0.33 #95, 0.12 #539, 0.04 #649), 0w7c (0.33 #39, 0.12 #483, 0.04 #593), 021wpb (0.33 #30, 0.12 #474, 0.04 #584), 0np9r (0.33 #10, 0.12 #454, 0.04 #564), 01c72t (0.25 #232, 0.12 #455, 0.04 #565), 0nbcg (0.25 #236, 0.12 #459, 0.04 #569), 01c8w0 (0.25 #225, 0.12 #448, 0.04 #558), 05vyk (0.25 #284, 0.12 #507, 0.04 #617) >> Best rule #41 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 02hrh1q; >> query: (?x8353, 01xr66) <- profession(?x8532, ?x8353), profession(?x6835, ?x8353), profession(?x4184, ?x8353), profession(?x3890, ?x8353), ?x4184 = 01m3x5p, ?x6835 = 06mt91, ?x8532 = 05yzt_, award_winner(?x1079, ?x3890) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #111 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 02hrh1q; *> query: (?x8353, ?x131) <- profession(?x8532, ?x8353), profession(?x6835, ?x8353), profession(?x4184, ?x8353), profession(?x3890, ?x8353), profession(?x954, ?x8353), ?x4184 = 01m3x5p, ?x6835 = 06mt91, ?x8532 = 05yzt_, award_winner(?x1079, ?x3890), profession(?x954, ?x131) *> conf = 0.03 ranks of expected_values: 38 EVAL 028kk_ specialization_of! 029bkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 48.000 32.000 0.333 http://example.org/people/profession/specialization_of #22780-02frhbc PRED entity: 02frhbc PRED relation: place! PRED expected values: 02frhbc => 161 concepts (106 used for prediction) PRED predicted values (max 10 best out of 196): 0zgfm (0.33 #306, 0.04 #3398), 0d23k (0.20 #693, 0.04 #3270, 0.03 #4818), 02frhbc (0.19 #33019, 0.14 #47990), 05kj_ (0.19 #33019, 0.14 #47990), 09c7w0 (0.19 #33019, 0.14 #47990), 0d22f (0.07 #24244, 0.05 #6706, 0.05 #32503), 0mx3k (0.07 #26308, 0.05 #5157, 0.03 #21666), 01cx_ (0.06 #2125, 0.05 #2641, 0.03 #3673), 02_286 (0.06 #2075, 0.05 #2591, 0.03 #3623), 0rh6k (0.06 #2063, 0.03 #3611, 0.03 #5159) >> Best rule #306 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0zgfm; >> query: (?x9605, 0zgfm) <- contains(?x94, ?x9605), county(?x9605, ?x3067), ?x3067 = 0d22f >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #33019 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 194 *> proper extension: 0_wm_; *> query: (?x9605, ?x94) <- citytown(?x12728, ?x9605), contains(?x94, ?x12728), colors(?x12728, ?x663) *> conf = 0.19 ranks of expected_values: 3 EVAL 02frhbc place! 02frhbc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 161.000 106.000 0.333 http://example.org/location/hud_county_place/place #22779-01lbcqx PRED entity: 01lbcqx PRED relation: written_by PRED expected values: 0ff3y => 79 concepts (34 used for prediction) PRED predicted values (max 10 best out of 172): 0c12h (0.33 #666, 0.21 #8029, 0.19 #9035), 05kfs (0.33 #351, 0.02 #3026, 0.02 #5037), 04gcd1 (0.33 #62, 0.02 #1734, 0.01 #2070), 0fx02 (0.32 #2008, 0.23 #2675, 0.20 #5018), 05kh_ (0.21 #8029, 0.19 #9035, 0.14 #1000), 0hw1j (0.17 #439, 0.03 #2782, 0.02 #4791), 06b_0 (0.17 #562, 0.01 #2905), 01j5ts (0.14 #1000, 0.09 #1336, 0.08 #1671), 012v9y (0.14 #1000, 0.07 #665, 0.06 #999), 0h0jz (0.09 #1336, 0.08 #1671, 0.08 #1001) >> Best rule #666 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 01gglm; >> query: (?x8461, ?x6239) <- film(?x6239, ?x8461), film(?x6239, ?x6181), award(?x6239, ?x2532), award(?x6239, ?x1862), award(?x6239, ?x601), ?x2532 = 02x4wr9, ?x1862 = 0gr51, ?x601 = 0gr4k >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01lbcqx written_by 0ff3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 34.000 0.333 http://example.org/film/film/written_by #22778-07_l6 PRED entity: 07_l6 PRED relation: instrumentalists PRED expected values: 0k4gf 03_f0 016wvy => 76 concepts (52 used for prediction) PRED predicted values (max 10 best out of 856): 01vrnsk (0.71 #5927, 0.62 #8391, 0.57 #5310), 09prnq (0.71 #5658, 0.62 #8122, 0.57 #5041), 01sb5r (0.70 #11942, 0.64 #13794, 0.57 #5781), 032t2z (0.62 #6797, 0.60 #3102, 0.50 #2489), 017l4 (0.62 #7203, 0.60 #3508, 0.45 #13987), 0zjpz (0.60 #3179, 0.57 #5645, 0.57 #5028), 01t110 (0.60 #3441, 0.57 #5290, 0.44 #9601), 01vw20_ (0.60 #11869, 0.56 #9402, 0.50 #2629), 0c9d9 (0.60 #3088, 0.56 #9248, 0.50 #6783), 01vvycq (0.60 #11736, 0.56 #9269, 0.45 #13588) >> Best rule #5927 for best value: >> intensional similarity = 24 >> extensional distance = 5 >> proper extension: 04rzd; >> query: (?x3296, 01vrnsk) <- role(?x614, ?x3296), role(?x432, ?x3296), role(?x228, ?x3296), role(?x75, ?x3296), role(?x3296, ?x1267), role(?x3296, ?x716), role(?x1473, ?x3296), ?x614 = 0mkg, ?x1473 = 0g2dz, ?x228 = 0l14qv, instrumentalists(?x3296, ?x3890), ?x432 = 042v_gx, ?x75 = 07y_7, place_of_birth(?x3890, ?x4335), ?x716 = 018vs, nominated_for(?x3890, ?x4007), role(?x1267, ?x1750), role(?x1267, ?x4913), award_nominee(?x3890, ?x4693), role(?x433, ?x1267), group(?x3296, ?x3109), ?x4913 = 03ndd, artists(?x284, ?x3890), ?x1750 = 02hnl >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3005 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 2 *> proper extension: 013y1f; *> query: (?x3296, 016wvy) <- role(?x2944, ?x3296), role(?x2798, ?x3296), role(?x614, ?x3296), role(?x432, ?x3296), role(?x228, ?x3296), role(?x75, ?x3296), role(?x3296, ?x1267), role(?x3296, ?x716), role(?x1473, ?x3296), ?x614 = 0mkg, ?x1473 = 0g2dz, ?x228 = 0l14qv, instrumentalists(?x3296, ?x7053), instrumentalists(?x3296, ?x3890), ?x432 = 042v_gx, ?x75 = 07y_7, place_of_birth(?x3890, ?x4335), ?x716 = 018vs, nominated_for(?x3890, ?x4007), ?x1267 = 07brj, award(?x3890, ?x462), ?x2944 = 0l14j_, ?x7053 = 01p0vf, artists(?x284, ?x3890), category(?x3890, ?x134), music(?x3742, ?x3890), group(?x2798, ?x997) *> conf = 0.50 ranks of expected_values: 34, 359, 431 EVAL 07_l6 instrumentalists 016wvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 76.000 52.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 07_l6 instrumentalists 03_f0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 52.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 07_l6 instrumentalists 0k4gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 52.000 0.714 http://example.org/music/instrument/instrumentalists #22777-03txms PRED entity: 03txms PRED relation: legislative_sessions PRED expected values: 070m6c 02bn_p 02bqn1 02bp37 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 42): 070m6c (0.70 #48, 0.64 #90, 0.49 #506), 02bn_p (0.70 #50, 0.64 #92, 0.49 #506), 02bp37 (0.70 #54, 0.55 #96, 0.49 #506), 02gkzs (0.60 #62, 0.55 #104, 0.49 #506), 02bqn1 (0.60 #52, 0.55 #94, 0.49 #506), 03rtmz (0.55 #100, 0.50 #58, 0.49 #506), 032ft5 (0.49 #506, 0.45 #93, 0.42 #337), 03ww_x (0.49 #506, 0.45 #89, 0.42 #337), 02glc4 (0.49 #506, 0.42 #337, 0.40 #69), 077g7n (0.49 #506, 0.42 #337, 0.40 #46) >> Best rule #48 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 012v1t; >> query: (?x7961, 070m6c) <- gender(?x7961, ?x231), legislative_sessions(?x7961, ?x845), religion(?x7961, ?x2769), ?x845 = 07p__7 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5 EVAL 03txms legislative_sessions 02bp37 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.700 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 03txms legislative_sessions 02bqn1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 142.000 142.000 0.700 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 03txms legislative_sessions 02bn_p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.700 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 03txms legislative_sessions 070m6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.700 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #22776-0kjrx PRED entity: 0kjrx PRED relation: award PRED expected values: 05p09zm 0cqgl9 => 125 concepts (110 used for prediction) PRED predicted values (max 10 best out of 296): 05ztrmj (0.73 #41500, 0.72 #2768, 0.72 #791), 09sb52 (0.40 #3598, 0.38 #15454, 0.37 #1226), 05pcn59 (0.29 #5215, 0.28 #1659, 0.28 #6401), 05p09zm (0.29 #1699, 0.24 #512, 0.23 #5255), 05zr6wv (0.18 #411, 0.18 #5154, 0.17 #5549), 0gr51 (0.18 #2072, 0.13 #28059, 0.12 #41896), 0gqwc (0.17 #1257, 0.16 #2443, 0.16 #5604), 0gr4k (0.17 #2009, 0.06 #9913, 0.06 #20583), 04kxsb (0.15 #4862, 0.14 #5652, 0.13 #28059), 05b1610 (0.15 #2015, 0.12 #41896, 0.05 #43480) >> Best rule #41500 for best value: >> intensional similarity = 3 >> extensional distance = 2245 >> proper extension: 044mz_; 07nznf; 012ljv; 02s2ft; 05vsxz; 05bnp0; 016qtt; 04qvl7; 01k7d9; 02p65p; ... >> query: (?x8134, ?x3508) <- award_winner(?x3508, ?x8134), award(?x123, ?x3508), award(?x8134, ?x154) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1699 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 84 *> proper extension: 01wxyx1; 02hhtj; 017m2y; *> query: (?x8134, 05p09zm) <- vacationer(?x126, ?x8134), participant(?x8134, ?x2499) *> conf = 0.29 ranks of expected_values: 4, 88 EVAL 0kjrx award 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 125.000 110.000 0.728 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0kjrx award 05p09zm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 125.000 110.000 0.728 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22775-02bj22 PRED entity: 02bj22 PRED relation: film_release_region PRED expected values: 09c7w0 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 161): 09c7w0 (0.76 #1262, 0.75 #2524, 0.73 #1803), 06mkj (0.30 #2776, 0.29 #1876, 0.29 #2057), 0d0vqn (0.30 #2712, 0.28 #1993, 0.27 #1631), 07ssc (0.30 #1440, 0.30 #1981, 0.29 #2880), 059j2 (0.30 #2745, 0.28 #1845, 0.28 #2026), 0345h (0.29 #1847, 0.29 #2028, 0.28 #2747), 0f8l9c (0.29 #1832, 0.29 #2732, 0.28 #1651), 03_3d (0.29 #2710, 0.28 #1629, 0.28 #1810), 0jgd (0.28 #2705, 0.27 #1805, 0.27 #1986), 03gj2 (0.28 #2737, 0.26 #1296, 0.26 #1837) >> Best rule #1262 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 0170z3; 0ds35l9; 03qcfvw; 03g90h; 01gc7; 011yxg; 0ds11z; 0872p_c; 047msdk; 0dtfn; ... >> query: (?x9193, 09c7w0) <- titles(?x1510, ?x9193), region(?x9193, ?x512), film(?x2564, ?x9193), currency(?x9193, ?x170) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bj22 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.756 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22774-07z1_q PRED entity: 07z1_q PRED relation: nationality PRED expected values: 09c7w0 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 19): 09c7w0 (0.89 #101, 0.88 #4209, 0.87 #601), 02jx1 (0.35 #4208, 0.12 #1833, 0.12 #4943), 07ssc (0.35 #4208, 0.09 #4925, 0.08 #4624), 0d060g (0.35 #4208, 0.06 #907, 0.06 #1007), 03_3d (0.06 #1106, 0.06 #1206, 0.03 #5618), 03rk0 (0.05 #10367, 0.05 #10567, 0.05 #10967), 0f8l9c (0.04 #122, 0.03 #3427, 0.03 #822), 0345h (0.03 #4339, 0.03 #4640, 0.03 #331), 0chghy (0.03 #3717, 0.03 #3617, 0.02 #2914), 03rjj (0.03 #805, 0.02 #4614, 0.02 #4413) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 04nw9; 03ywyk; >> query: (?x3272, 09c7w0) <- award_nominee(?x3272, ?x444), award(?x3272, ?x2603), ?x2603 = 09qs08 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07z1_q nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.893 http://example.org/people/person/nationality #22773-02bpy_ PRED entity: 02bpy_ PRED relation: student PRED expected values: 01w20rx => 109 concepts (89 used for prediction) PRED predicted values (max 10 best out of 929): 02x8z_ (0.06 #773, 0.04 #4959, 0.04 #2866), 06hx2 (0.06 #1070, 0.04 #3163, 0.03 #13628), 0194xc (0.06 #1642, 0.04 #3735, 0.02 #14200), 02sb1w (0.06 #1108, 0.04 #3201, 0.01 #7387), 052hl (0.06 #1171, 0.04 #3264, 0.01 #7450), 01pcbg (0.06 #536, 0.04 #2629, 0.01 #6815), 02779r4 (0.06 #1163, 0.04 #3256, 0.01 #9535), 084w8 (0.06 #10, 0.04 #2103, 0.01 #8382), 03r1pr (0.06 #462, 0.04 #2555, 0.01 #15113), 08chdb (0.06 #1759, 0.04 #3852) >> Best rule #773 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 0dzt9; 0fwc0; 013h9; 0k1jg; >> query: (?x11559, 02x8z_) <- category(?x11559, ?x134), contains(?x1426, ?x11559), contains(?x94, ?x11559), ?x1426 = 07z1m, ?x94 = 09c7w0 >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02bpy_ student 01w20rx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 89.000 0.062 http://example.org/education/educational_institution/students_graduates./education/education/student #22772-03kxj2 PRED entity: 03kxj2 PRED relation: film! PRED expected values: 02j4sk => 92 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1019): 01j5ts (0.62 #41523, 0.50 #91345, 0.47 #105874), 04kj2v (0.50 #91345, 0.47 #105874, 0.44 #51904), 05b4rcb (0.44 #51904, 0.43 #110026, 0.41 #97572), 026lyl4 (0.44 #51904, 0.43 #110026, 0.41 #97572), 0h96g (0.29 #851, 0.04 #5002, 0.02 #2926), 046qq (0.29 #4892, 0.02 #6967, 0.02 #31883), 0fvf9q (0.20 #4151, 0.11 #80966, 0.11 #66435), 030xr_ (0.20 #5739, 0.03 #110027, 0.02 #78890), 0p_pd (0.14 #54, 0.06 #91346, 0.02 #2129), 09qh1 (0.14 #619, 0.04 #2694, 0.02 #8922) >> Best rule #41523 for best value: >> intensional similarity = 3 >> extensional distance = 663 >> proper extension: 01f3p_; 03g9xj; >> query: (?x2231, ?x241) <- nominated_for(?x241, ?x2231), film(?x241, ?x407), languages(?x241, ?x254) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #10036 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 88 *> proper extension: 0h3k3f; *> query: (?x2231, 02j4sk) <- film_sets_designed(?x2230, ?x2231), film(?x7487, ?x2231), film(?x3815, ?x2231), profession(?x7487, ?x1032), award_winner(?x678, ?x3815) *> conf = 0.01 ranks of expected_values: 784 EVAL 03kxj2 film! 02j4sk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 92.000 53.000 0.619 http://example.org/film/actor/film./film/performance/film #22771-09tqx3 PRED entity: 09tqx3 PRED relation: profession PRED expected values: 02hrh1q => 120 concepts (44 used for prediction) PRED predicted values (max 10 best out of 62): 02hrh1q (0.86 #6234, 0.85 #1791, 0.83 #1495), 0dxtg (0.69 #4899, 0.68 #1346, 0.67 #4603), 0d1pc (0.50 #50, 0.25 #198, 0.16 #791), 0cbd2 (0.33 #4301, 0.17 #5782, 0.17 #4745), 03gjzk (0.33 #2680, 0.30 #1348, 0.30 #3272), 02krf9 (0.23 #1360, 0.22 #5506, 0.21 #3876), 0kyk (0.19 #4324, 0.14 #5805, 0.11 #6250), 018gz8 (0.19 #460, 0.14 #4311, 0.13 #2682), 09jwl (0.18 #3572, 0.16 #5942, 0.16 #6090), 025352 (0.15 #355, 0.12 #503, 0.10 #651) >> Best rule #6234 for best value: >> intensional similarity = 5 >> extensional distance = 536 >> proper extension: 01wbgdv; 0bt4r4; 0154qm; 02vntj; 02yplc; 03hh89; 01tnbn; 020ffd; 013sg6; 01kgg9; ... >> query: (?x8622, 02hrh1q) <- location(?x8622, ?x9315), religion(?x8622, ?x8967), profession(?x8622, ?x319), profession(?x2687, ?x319), ?x2687 = 09ftwr >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09tqx3 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 44.000 0.855 http://example.org/people/person/profession #22770-05cw8 PRED entity: 05cw8 PRED relation: month! PRED expected values: 049d1 0ply0 0947l 03902 => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 338): 049d1 (0.89 #30, 0.89 #57, 0.88 #41), 0947l (0.89 #30, 0.89 #57, 0.88 #41), 03902 (0.89 #30, 0.89 #57, 0.88 #41), 0ply0 (0.89 #30, 0.89 #57, 0.88 #41), 03czqs (0.89 #30, 0.89 #57, 0.88 #41), 0l0mk (0.89 #57, 0.33 #24, 0.33 #10), 03pzf (0.64 #14, 0.24 #82), 018lc_ (0.64 #14), 0jpkg (0.64 #14), 0171b8 (0.64 #14) >> Best rule #30 for best value: >> intensional similarity = 108 >> extensional distance = 1 >> proper extension: 03_ly; >> query: (?x3270, ?x3106) <- month(?x12674, ?x3270), month(?x11237, ?x3270), month(?x11197, ?x3270), month(?x10143, ?x3270), month(?x9605, ?x3270), month(?x8977, ?x3270), month(?x8602, ?x3270), month(?x8252, ?x3270), month(?x6960, ?x3270), month(?x6959, ?x3270), month(?x6703, ?x3270), month(?x6494, ?x3270), month(?x6458, ?x3270), month(?x6054, ?x3270), month(?x5267, ?x3270), month(?x5168, ?x3270), month(?x5036, ?x3270), month(?x4826, ?x3270), month(?x4698, ?x3270), month(?x3501, ?x3270), month(?x3269, ?x3270), month(?x3052, ?x3270), month(?x3026, ?x3270), month(?x2645, ?x3270), month(?x2474, ?x3270), month(?x2316, ?x3270), month(?x2277, ?x3270), month(?x2254, ?x3270), month(?x1860, ?x3270), month(?x1658, ?x3270), month(?x1646, ?x3270), month(?x1523, ?x3270), month(?x1458, ?x3270), month(?x739, ?x3270), month(?x659, ?x3270), month(?x362, ?x3270), seasonal_months(?x9905, ?x3270), seasonal_months(?x7298, ?x3270), seasonal_months(?x4869, ?x3270), seasonal_months(?x2140, ?x3270), seasonal_months(?x1650, ?x3270), seasonal_months(?x1459, ?x3270), ?x4826 = 0177z, ?x10143 = 0h3tv, ?x362 = 04jpl, ?x2316 = 06t2t, ?x3052 = 01cx_, ?x2645 = 03h64, ?x6054 = 0fn2g, ?x5168 = 06mxs, ?x1523 = 030qb3t, ?x1458 = 05ywg, month(?x8956, ?x2140), month(?x3373, ?x2140), month(?x3106, ?x2140), ?x8252 = 0k3p, seasonal_months(?x2255, ?x2140), ?x1860 = 01_d4, ?x3501 = 0f2v0, ?x11197 = 05l64, ?x12674 = 0g6xq, ?x8977 = 02z0j, ?x1650 = 06vkl, ?x1658 = 0h7h6, ?x2254 = 0dclg, ?x2277 = 013yq, ?x2474 = 052p7, ?x3026 = 0cv3w, ?x9905 = 028kb, ?x8956 = 0947l, ?x1459 = 04w_7, ?x11237 = 03khn, ?x6494 = 02sn34, ?x8602 = 0chgzm, ?x2255 = 040fv, ?x5267 = 0d9jr, ?x1646 = 0156q, ?x4698 = 056_y, ?x7298 = 04wzr, ?x659 = 02cl1, ?x4869 = 02xx5, ?x6959 = 06c62, ?x5036 = 06y57, ?x3373 = 0ply0, ?x9605 = 02frhbc, ?x3269 = 0vzm, contains(?x6960, ?x1659), location(?x4782, ?x6960), location(?x4284, ?x6960), location(?x4039, ?x6960), origin(?x5391, ?x6960), ?x4284 = 02fn5, ?x739 = 02_286, ?x6458 = 08966, ?x6703 = 0f04v, award_nominee(?x7830, ?x4782), award_nominee(?x3101, ?x4782), place_of_birth(?x1182, ?x6960), place_of_death(?x5806, ?x6960), mode_of_transportation(?x6960, ?x4272), nominated_for(?x4782, ?x1811), ?x3101 = 0dvmd, ?x7830 = 01p4vl, gender(?x4782, ?x514), featured_film_locations(?x8302, ?x6960), award_winner(?x8089, ?x4782), award_nominee(?x4039, ?x2028), award_nominee(?x450, ?x5806) >> conf = 0.89 => this is the best rule for 5 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 05cw8 month! 03902 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.895 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 05cw8 month! 0947l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.895 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 05cw8 month! 0ply0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.895 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 05cw8 month! 049d1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.895 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #22769-02hct1 PRED entity: 02hct1 PRED relation: titles! PRED expected values: 07c52 => 82 concepts (56 used for prediction) PRED predicted values (max 10 best out of 62): 07c52 (0.82 #544, 0.82 #441, 0.68 #1686), 07s9rl0 (0.37 #1241, 0.27 #5525, 0.27 #5420), 0cjdk (0.27 #722, 0.21 #410, 0.16 #828), 04xvlr (0.25 #1244, 0.19 #4379, 0.18 #4586), 03mdt (0.21 #351, 0.16 #663, 0.12 #559), 024qqx (0.20 #183, 0.14 #285, 0.08 #3512), 01z4y (0.18 #3467, 0.17 #1276, 0.15 #5037), 01jfsb (0.14 #224, 0.08 #3451, 0.08 #5021), 0hfjk (0.14 #284, 0.04 #804, 0.03 #1320), 02n4kr (0.14 #218, 0.03 #1254, 0.03 #5643) >> Best rule #544 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 02r5qtm; >> query: (?x2436, 07c52) <- actor(?x2436, ?x10127), genre(?x2436, ?x8534), nationality(?x10127, ?x94), ?x8534 = 0c4xc >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hct1 titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 56.000 0.825 http://example.org/media_common/netflix_genre/titles #22768-0495ys PRED entity: 0495ys PRED relation: legislative_sessions! PRED expected values: 024_vw => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 23): 024_vw (0.76 #514, 0.72 #307, 0.71 #541), 0bymv (0.76 #514, 0.72 #307, 0.68 #358), 0d3qd0 (0.76 #514, 0.72 #307, 0.68 #358), 012v1t (0.76 #514, 0.72 #307, 0.68 #358), 0d06m5 (0.76 #514, 0.72 #307, 0.68 #358), 03txms (0.76 #514, 0.72 #307, 0.68 #358), 016lh0 (0.76 #514, 0.72 #307, 0.68 #358), 02mjmr (0.76 #514, 0.63 #151, 0.62 #27), 01lct6 (0.66 #386, 0.63 #151, 0.62 #27), 06hx2 (0.54 #540, 0.51 #673, 0.45 #777) >> Best rule #514 for best value: >> intensional similarity = 60 >> extensional distance = 4 >> proper extension: 070m6c; >> query: (?x355, ?x652) <- district_represented(?x355, ?x335), legislative_sessions(?x6933, ?x355), legislative_sessions(?x6139, ?x355), legislative_sessions(?x5339, ?x355), legislative_sessions(?x4821, ?x355), legislative_sessions(?x3463, ?x355), legislative_sessions(?x1829, ?x355), legislative_sessions(?x1137, ?x355), legislative_sessions(?x1028, ?x355), legislative_sessions(?x845, ?x355), legislative_sessions(?x605, ?x355), ?x4821 = 02bqm0, ?x845 = 07p__7, legislative_sessions(?x355, ?x2861), legislative_sessions(?x355, ?x606), ?x5339 = 02glc4, district_represented(?x6933, ?x7405), district_represented(?x6933, ?x6521), district_represented(?x6933, ?x4198), district_represented(?x6933, ?x3818), district_represented(?x6933, ?x3086), district_represented(?x6933, ?x2831), district_represented(?x6933, ?x2713), district_represented(?x6933, ?x2256), district_represented(?x6933, ?x1755), district_represented(?x6933, ?x1138), district_represented(?x6933, ?x1024), district_represented(?x6933, ?x961), district_represented(?x6933, ?x953), ?x7405 = 07_f2, ?x2713 = 06btq, legislative_sessions(?x9334, ?x6933), legislative_sessions(?x8607, ?x6933), legislative_sessions(?x6742, ?x6933), legislative_sessions(?x2357, ?x6933), legislative_sessions(?x652, ?x6933), ?x3086 = 0846v, ?x2831 = 0gyh, ?x8607 = 0226cw, ?x9334 = 02hy5d, ?x2861 = 03tcbx, ?x953 = 0hjy, ?x3463 = 02bqmq, ?x6742 = 06bss, ?x605 = 077g7n, ?x1755 = 01x73, ?x1829 = 02bp37, ?x6139 = 060ny2, ?x2256 = 07srw, ?x1024 = 05fhy, ?x1137 = 02bqn1, ?x335 = 059rby, ?x2357 = 0bymv, ?x606 = 03ww_x, legislative_sessions(?x2860, ?x1028), ?x3818 = 03v0t, ?x6521 = 05mph, ?x1138 = 059_c, ?x961 = 03s0w, ?x4198 = 05fky >> conf = 0.76 => this is the best rule for 8 predicted values ranks of expected_values: 1 EVAL 0495ys legislative_sessions! 024_vw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.765 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #22767-0d1t3 PRED entity: 0d1t3 PRED relation: country PRED expected values: 0d0vqn => 42 concepts (41 used for prediction) PRED predicted values (max 10 best out of 391): 06bnz (0.92 #7110, 0.92 #5196, 0.91 #6730), 0d05w3 (0.88 #4255, 0.83 #5026, 0.80 #3680), 0d0vqn (0.88 #5167, 0.80 #2679, 0.79 #191), 0jgd (0.81 #4205, 0.80 #3630, 0.80 #3056), 01mjq (0.81 #4235, 0.80 #3660, 0.75 #2128), 06qd3 (0.81 #3845, 0.78 #2324, 0.73 #3655), 035qy (0.81 #4229, 0.75 #2122, 0.73 #3654), 0163v (0.80 #3674, 0.78 #2343, 0.75 #3864), 047lj (0.80 #2871, 0.75 #4212, 0.73 #3637), 0b90_r (0.79 #191, 0.78 #2300, 0.77 #3248) >> Best rule #7110 for best value: >> intensional similarity = 32 >> extensional distance = 47 >> proper extension: 096f8; 03krj; >> query: (?x4876, 06bnz) <- sports(?x391, ?x4876), country(?x4876, ?x1273), country(?x4876, ?x205), country(?x5429, ?x205), country(?x5481, ?x205), film_release_region(?x6394, ?x205), film_release_region(?x5109, ?x205), film_release_region(?x4643, ?x205), film_release_region(?x2168, ?x205), film_release_region(?x1259, ?x205), nationality(?x101, ?x205), ?x5109 = 0b44shh, location_of_ceremony(?x2182, ?x205), ?x2168 = 0bx0l, olympics(?x205, ?x418), adjoins(?x291, ?x1273), country(?x4673, ?x1273), ?x4643 = 080lkt7, olympics(?x205, ?x358), ?x6394 = 0cmdwwg, second_level_divisions(?x205, ?x7191), country(?x5396, ?x205), country(?x2315, ?x205), combatants(?x5503, ?x205), administrative_area_type(?x205, ?x2792), contains(?x205, ?x1356), member_states(?x7416, ?x205), ?x5396 = 0486tv, ?x4673 = 07jbh, ?x5429 = 02psgq, ?x2315 = 06wrt, ?x1259 = 04hwbq >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #5167 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 22 *> proper extension: 01sgl; *> query: (?x4876, 0d0vqn) <- olympics(?x4876, ?x778), country(?x4876, ?x1023), country(?x4876, ?x205), country(?x4876, ?x87), ?x1023 = 0ctw_b, film_release_region(?x9565, ?x87), film_release_region(?x7678, ?x87), film_release_region(?x7379, ?x87), film_release_region(?x6882, ?x87), film_release_region(?x5576, ?x87), film_release_region(?x5016, ?x87), film_release_region(?x4453, ?x87), film_release_region(?x3745, ?x87), film_release_region(?x2656, ?x87), film_release_region(?x2163, ?x87), film_release_region(?x1602, ?x87), film_release_region(?x791, ?x87), film_release_region(?x504, ?x87), film_release_region(?x186, ?x87), film_release_region(?x80, ?x87), ?x1602 = 0gxtknx, ?x7678 = 0gvvf4j, film_release_region(?x1185, ?x87), ?x2163 = 0j6b5, ?x504 = 0g5qs2k, ?x791 = 087wc7n, ?x4453 = 0dr_9t7, ?x80 = 0b76d_m, ?x3745 = 03cw411, ?x186 = 02vxq9m, ?x5016 = 062zm5h, combatants(?x5503, ?x87), ?x205 = 03rjj, olympics(?x87, ?x1931), ?x6882 = 043tvp3, jurisdiction_of_office(?x182, ?x87), ?x5576 = 0gbfn9, ?x9565 = 0hz6mv2, ?x7379 = 032clf, contains(?x87, ?x7809), ?x2656 = 03qnc6q *> conf = 0.88 ranks of expected_values: 3 EVAL 0d1t3 country 0d0vqn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 42.000 41.000 0.918 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #22766-027mvrc PRED entity: 027mvrc PRED relation: season! PRED expected values: 03lpp_ 01d5z 049n7 0x2p 04mjl => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 405): 01d5z (0.85 #19, 0.82 #97, 0.80 #106), 049n7 (0.85 #19, 0.71 #17, 0.60 #55), 04mjl (0.85 #19, 0.67 #65, 0.62 #83), 0x2p (0.85 #19, 0.60 #56, 0.60 #46), 03lpp_ (0.85 #19, 0.55 #78, 0.55 #10), 051wf (0.85 #19, 0.41 #85, 0.34 #67), 0jmk7 (0.71 #17, 0.55 #10, 0.53 #33), 05tfm (0.71 #17, 0.55 #10, 0.50 #18), 05g49 (0.71 #17, 0.55 #10, 0.50 #18), 0jmj7 (0.71 #17, 0.53 #33, 0.53 #26) >> Best rule #19 for best value: >> intensional similarity = 79 >> extensional distance = 1 >> proper extension: 0dx84s; >> query: (?x11501, ?x662) <- season(?x10279, ?x11501), season(?x8901, ?x11501), season(?x8111, ?x11501), season(?x6823, ?x11501), season(?x2174, ?x11501), season(?x2067, ?x11501), season(?x1823, ?x11501), season(?x1438, ?x11501), season(?x580, ?x11501), season(?x260, ?x11501), ?x6823 = 07l8f, ?x1438 = 0512p, ?x2067 = 05g76, draft(?x2174, ?x10600), draft(?x2174, ?x8786), draft(?x2174, ?x8499), school(?x2174, ?x9676), school(?x2174, ?x6953), school(?x2174, ?x6333), school(?x2174, ?x4161), school(?x2174, ?x3777), school(?x2174, ?x1884), school(?x2174, ?x735), ?x6953 = 01jq0j, team(?x11844, ?x2174), ?x580 = 05m_8, team(?x8520, ?x2174), team(?x5727, ?x2174), ?x8499 = 02r6gw6, team(?x8110, ?x2174), organization(?x5510, ?x9676), ?x1823 = 01yhm, ?x8520 = 01z9v6, student(?x9676, ?x2259), major_field_of_study(?x9676, ?x2981), ?x2981 = 02j62, colors(?x2174, ?x332), ?x260 = 01ypc, fraternities_and_sororities(?x3777, ?x3697), institution(?x3437, ?x9676), ?x3437 = 02_xgp2, category(?x1884, ?x134), currency(?x6333, ?x170), organization(?x346, ?x3777), season(?x2174, ?x2406), ?x10279 = 04wmvz, state_province_region(?x9676, ?x3818), institution(?x1526, ?x1884), student(?x1884, ?x1815), state_province_region(?x6333, ?x3634), state_province_region(?x3777, ?x4776), contains(?x94, ?x9676), school(?x6462, ?x3777), school(?x1883, ?x1884), ?x5727 = 02wszf, major_field_of_study(?x1884, ?x2014), colors(?x3777, ?x3315), student(?x6333, ?x5350), citytown(?x1884, ?x8322), major_field_of_study(?x3777, ?x1154), ?x6462 = 09l0x9, ?x8901 = 07l4z, position(?x2174, ?x4244), ?x2014 = 04rjg, ?x2406 = 03c6sl9, school(?x8786, ?x4211), student(?x4161, ?x1583), ?x1526 = 0bkj86, draft(?x662, ?x8786), ?x10600 = 04f4z1k, ?x8111 = 07147, state_province_region(?x4161, ?x3908), school_type(?x4161, ?x1507), sport(?x2174, ?x5063), student(?x735, ?x65), company(?x5309, ?x735), major_field_of_study(?x6333, ?x6870), institution(?x7636, ?x735), major_field_of_study(?x735, ?x254) >> conf = 0.85 => this is the best rule for 6 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 027mvrc season! 04mjl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.848 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 027mvrc season! 0x2p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.848 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 027mvrc season! 049n7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.848 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 027mvrc season! 01d5z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.848 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 027mvrc season! 03lpp_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.848 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #22765-0gcrg PRED entity: 0gcrg PRED relation: production_companies PRED expected values: 0g1rw => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 70): 0g1rw (0.49 #416, 0.49 #340, 0.45 #583), 05qd_ (0.22 #426, 0.18 #593, 0.17 #677), 086k8 (0.17 #2, 0.13 #1086, 0.13 #1502), 017s11 (0.17 #3, 0.11 #1503, 0.10 #252), 016tt2 (0.16 #671, 0.14 #587, 0.13 #420), 017jv5 (0.14 #351, 0.09 #518, 0.02 #1020), 016tw3 (0.12 #261, 0.09 #1595, 0.09 #2096), 030_1_ (0.11 #851, 0.11 #1351, 0.08 #1267), 04rcl7 (0.11 #1073, 0.04 #1489, 0.03 #2239), 01gb54 (0.10 #287, 0.08 #872, 0.08 #1621) >> Best rule #416 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 03mh_tp; 0ckrgs; 02v_r7d; >> query: (?x3909, ?x788) <- film(?x788, ?x3909), film_crew_role(?x3909, ?x12763), ?x788 = 0g1rw >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gcrg production_companies 0g1rw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.490 http://example.org/film/film/production_companies #22764-03mck3c PRED entity: 03mck3c PRED relation: sport PRED expected values: 02vx4 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 7): 02vx4 (0.74 #67, 0.73 #332, 0.65 #104), 0z74 (0.31 #606, 0.30 #596, 0.27 #707), 018w8 (0.13 #326, 0.07 #571, 0.06 #600), 0jm_ (0.08 #325, 0.07 #570, 0.06 #589), 018jz (0.06 #327, 0.05 #572, 0.04 #591), 03tmr (0.04 #698, 0.02 #568, 0.02 #587), 039yzs (0.02 #329, 0.02 #574, 0.01 #178) >> Best rule #67 for best value: >> intensional similarity = 15 >> extensional distance = 33 >> proper extension: 0k_l4; 01z1r; 027ffq; >> query: (?x13980, 02vx4) <- position(?x13980, ?x203), position(?x13980, ?x63), position(?x13980, ?x60), ?x63 = 02sdk9v, team(?x10955, ?x13980), ?x203 = 0dgrmp, team(?x10955, ?x6179), team(?x10955, ?x5159), place_of_birth(?x10955, ?x10174), current_club(?x4972, ?x5159), ?x60 = 02nzb8, colors(?x6179, ?x1101), teams(?x1658, ?x6179), gender(?x10955, ?x231), type_of_union(?x10955, ?x566) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mck3c sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.743 http://example.org/sports/sports_team/sport #22763-025l5 PRED entity: 025l5 PRED relation: award_winner! PRED expected values: 01mh_q => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 127): 01c6qp (0.38 #158, 0.29 #575, 0.17 #1270), 09n4nb (0.33 #47, 0.32 #2225, 0.13 #464), 0gx1673 (0.33 #118, 0.07 #3316, 0.06 #3872), 08pc1x (0.33 #137, 0.02 #3196, 0.02 #2501), 013b2h (0.32 #2225, 0.23 #635, 0.16 #2164), 02cg41 (0.32 #2225, 0.22 #263, 0.15 #680), 02rjjll (0.32 #2225, 0.20 #422, 0.17 #1256), 0466p0j (0.32 #2225, 0.17 #631, 0.16 #214), 0gpjbt (0.32 #2225, 0.16 #168, 0.15 #585), 01mh_q (0.32 #2225, 0.15 #505, 0.10 #2173) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0g_g2; >> query: (?x4584, 01c6qp) <- award_winner(?x2420, ?x4584), ?x2420 = 026mfs, artists(?x505, ?x4584) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2225 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 203 *> proper extension: 0308kx; *> query: (?x4584, ?x486) <- award_winner(?x4584, ?x2638), role(?x2638, ?x227), award_winner(?x486, ?x2638) *> conf = 0.32 ranks of expected_values: 10 EVAL 025l5 award_winner! 01mh_q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 101.000 101.000 0.375 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22762-071vr PRED entity: 071vr PRED relation: place_of_birth! PRED expected values: 029q_y => 246 concepts (184 used for prediction) PRED predicted values (max 10 best out of 2199): 02whj (0.35 #470379, 0.33 #418405, 0.32 #36383), 02fn5 (0.35 #470379, 0.33 #418405, 0.32 #36383), 03mp9s (0.35 #470379, 0.33 #418405, 0.32 #36383), 0br1w (0.35 #470379, 0.33 #418405, 0.32 #36383), 035rnz (0.35 #470379, 0.33 #418405, 0.32 #36383), 01520h (0.35 #470379, 0.33 #418405, 0.32 #36383), 06jzh (0.35 #470379, 0.33 #418405, 0.32 #36383), 0259r0 (0.35 #470379, 0.33 #418405, 0.32 #36383), 06688p (0.35 #470379, 0.33 #418405, 0.32 #36383), 03h_fqv (0.31 #252087, 0.29 #192319, 0.28 #358631) >> Best rule #470379 for best value: >> intensional similarity = 3 >> extensional distance = 312 >> proper extension: 01vskn; >> query: (?x6960, ?x194) <- category(?x6960, ?x134), place_of_birth(?x1182, ?x6960), location(?x194, ?x6960) >> conf = 0.35 => this is the best rule for 9 predicted values No rule for expected values ranks of expected_values: EVAL 071vr place_of_birth! 029q_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 246.000 184.000 0.354 http://example.org/people/person/place_of_birth #22761-01m65sp PRED entity: 01m65sp PRED relation: artist! PRED expected values: 017l96 => 136 concepts (135 used for prediction) PRED predicted values (max 10 best out of 135): 037h1k (0.33 #625, 0.33 #199, 0.20 #483), 017l96 (0.33 #161, 0.28 #1865, 0.20 #303), 0fb0v (0.33 #7, 0.20 #859, 0.16 #2137), 0mcf4 (0.33 #60, 0.11 #770, 0.09 #1196), 0dd2f (0.33 #267, 0.11 #693, 0.03 #3249), 011k1h (0.24 #1856, 0.23 #2850, 0.20 #862), 015_1q (0.23 #3002, 0.22 #588, 0.21 #4707), 0181dw (0.22 #753, 0.20 #895, 0.20 #469), 0g768 (0.21 #1742, 0.17 #2310, 0.16 #2594), 0n85g (0.20 #916, 0.17 #2336, 0.14 #2478) >> Best rule #625 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01k_yf; 01w5n51; >> query: (?x3206, 037h1k) <- artists(?x2996, ?x3206), artists(?x2542, ?x3206), ?x2542 = 03xnwz, ?x2996 = 01243b >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 02vnpv; *> query: (?x3206, 017l96) <- artists(?x2996, ?x3206), artists(?x2542, ?x3206), artists(?x1572, ?x3206), ?x2542 = 03xnwz, ?x2996 = 01243b, ?x1572 = 06by7 *> conf = 0.33 ranks of expected_values: 2 EVAL 01m65sp artist! 017l96 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 135.000 0.333 http://example.org/music/record_label/artist #22760-02b25y PRED entity: 02b25y PRED relation: place_of_birth PRED expected values: 064xp => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 170): 056_y (0.22 #2984, 0.12 #6504, 0.12 #2280), 0rh6k (0.14 #706, 0.12 #1410, 0.09 #4226), 01_d4 (0.14 #770, 0.09 #4290, 0.07 #5698), 02hrh0_ (0.14 #894, 0.09 #4414, 0.05 #7934), 0fw4v (0.13 #5232, 0.12 #6640, 0.12 #2416), 0d6hn (0.12 #2525, 0.11 #3229, 0.10 #3933), 07ypt (0.12 #2437, 0.11 #3141, 0.10 #3845), 0fr0t (0.12 #1552, 0.02 #10704), 0cr3d (0.11 #7134, 0.06 #9950, 0.06 #19806), 02_286 (0.10 #3539, 0.08 #44375, 0.07 #66911) >> Best rule #2984 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 03c602; >> query: (?x2584, 056_y) <- artists(?x12082, ?x2584), ?x12082 = 08vlns, instrumentalists(?x227, ?x2584) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02b25y place_of_birth 064xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 128.000 0.222 http://example.org/people/person/place_of_birth #22759-03r0g9 PRED entity: 03r0g9 PRED relation: film_crew_role PRED expected values: 02r96rf => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 24): 02r96rf (0.75 #218, 0.73 #407, 0.73 #56), 089g0h (0.61 #229, 0.59 #418, 0.58 #202), 0d2b38 (0.50 #423, 0.49 #234, 0.44 #207), 01xy5l_ (0.47 #225, 0.45 #414, 0.42 #198), 015h31 (0.36 #61, 0.29 #7, 0.17 #412), 0263ycg (0.29 #12, 0.18 #66, 0.16 #228), 02rh1dz (0.24 #116, 0.19 #658, 0.18 #332), 02ynfr (0.20 #389, 0.18 #119, 0.18 #661), 05smlt (0.18 #68, 0.08 #230, 0.07 #122), 01d_h8 (0.14 #1, 0.09 #55, 0.02 #82) >> Best rule #218 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 047svrl; >> query: (?x3693, 02r96rf) <- film(?x1018, ?x3693), film_crew_role(?x3693, ?x4305), ?x4305 = 0215hd, music(?x3693, ?x7027) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03r0g9 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22758-0127xk PRED entity: 0127xk PRED relation: influenced_by! PRED expected values: 029_3 => 152 concepts (50 used for prediction) PRED predicted values (max 10 best out of 418): 016_mj (0.26 #5699, 0.12 #2106, 0.06 #567), 01j7rd (0.26 #5716, 0.04 #2123, 0.03 #13931), 01xwv7 (0.24 #6068, 0.12 #2475, 0.06 #936), 01x4r3 (0.24 #6025, 0.12 #17967, 0.08 #2432), 0126rp (0.24 #5715, 0.08 #2122, 0.03 #8282), 0q5hw (0.21 #5747, 0.12 #17967, 0.07 #3180), 014z8v (0.21 #5802, 0.08 #2209, 0.06 #6672), 01xwqn (0.21 #6087, 0.08 #2494, 0.04 #19438), 049fgvm (0.21 #5909, 0.06 #777, 0.04 #2316), 03g5jw (0.16 #5176, 0.14 #6202, 0.14 #3122) >> Best rule #5699 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 04sd0; >> query: (?x11334, 016_mj) <- influenced_by(?x1725, ?x11334), producer_type(?x1725, ?x632), person(?x424, ?x1725) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #17967 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 142 *> proper extension: 02wh0; *> query: (?x11334, ?x1593) <- people(?x4322, ?x11334), influenced_by(?x4066, ?x11334), influenced_by(?x1593, ?x4066) *> conf = 0.12 ranks of expected_values: 21 EVAL 0127xk influenced_by! 029_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 152.000 50.000 0.265 http://example.org/influence/influence_node/influenced_by #22757-0165v PRED entity: 0165v PRED relation: form_of_government PRED expected values: 01fpfn => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 4): 01fpfn (0.46 #127, 0.45 #107, 0.43 #171), 018wl5 (0.40 #1, 0.37 #134, 0.34 #126), 01q20 (0.32 #236, 0.30 #35, 0.30 #3), 026wp (0.11 #24, 0.10 #8, 0.10 #4) >> Best rule #127 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 027rn; 09c7w0; 0160w; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0h3y; ... >> query: (?x9816, 01fpfn) <- administrative_area_type(?x9816, ?x2792), adjustment_currency(?x9816, ?x170), form_of_government(?x9816, ?x48) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0165v form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.458 http://example.org/location/country/form_of_government #22756-09gq0x5 PRED entity: 09gq0x5 PRED relation: language PRED expected values: 02h40lc => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.90 #239, 0.90 #894, 0.89 #180), 064_8sq (0.26 #141, 0.21 #200, 0.20 #259), 0jzc (0.20 #20, 0.07 #79, 0.04 #375), 02hwyss (0.20 #42, 0.04 #101, 0.02 #220), 04306rv (0.11 #124, 0.11 #838, 0.09 #1316), 03_9r (0.11 #129, 0.06 #188, 0.06 #247), 06nm1 (0.11 #844, 0.11 #725, 0.10 #1021), 06b_j (0.09 #142, 0.08 #915, 0.07 #737), 02bjrlw (0.07 #834, 0.07 #1251, 0.06 #1131), 0653m (0.06 #131, 0.05 #249, 0.04 #190) >> Best rule #239 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 05dy7p; 02phtzk; 02zk08; 0c5qvw; >> query: (?x1813, 02h40lc) <- nominated_for(?x1443, ?x1813), ?x1443 = 054krc, production_companies(?x1813, ?x3462) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09gq0x5 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.900 http://example.org/film/film/language #22755-02704ff PRED entity: 02704ff PRED relation: production_companies PRED expected values: 054lpb6 => 84 concepts (75 used for prediction) PRED predicted values (max 10 best out of 63): 086k8 (0.44 #327, 0.28 #409, 0.27 #245), 024rbz (0.34 #2933, 0.34 #2769, 0.32 #3668), 046b0s (0.19 #349, 0.11 #431, 0.06 #1405), 0283xx2 (0.15 #553, 0.06 #960, 0.06 #390), 054lpb6 (0.14 #96, 0.12 #1396, 0.11 #503), 016tw3 (0.14 #93, 0.11 #582, 0.09 #3272), 01gb54 (0.14 #119, 0.08 #851, 0.08 #1582), 08wjc1 (0.14 #109, 0.07 #271, 0.06 #353), 04rtpt (0.14 #129, 0.07 #291, 0.06 #373), 0gfmc_ (0.14 #130, 0.07 #292, 0.06 #456) >> Best rule #327 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 05fgt1; 02rrfzf; 0660b9b; >> query: (?x5694, 086k8) <- film(?x2646, ?x5694), film(?x286, ?x5694), film(?x826, ?x5694), award_nominee(?x92, ?x2646), ?x286 = 014zcr >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #96 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0872p_c; 065z3_x; 02qr69m; 01qb559; 01z452; *> query: (?x5694, 054lpb6) <- film(?x6279, ?x5694), nominated_for(?x68, ?x5694), ?x6279 = 017r13, featured_film_locations(?x5694, ?x108) *> conf = 0.14 ranks of expected_values: 5 EVAL 02704ff production_companies 054lpb6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 84.000 75.000 0.438 http://example.org/film/film/production_companies #22754-023vcd PRED entity: 023vcd PRED relation: titles! PRED expected values: 09q17 037hz => 91 concepts (58 used for prediction) PRED predicted values (max 10 best out of 58): 09q17 (0.38 #75, 0.11 #382, 0.08 #687), 07s9rl0 (0.35 #3078, 0.34 #2871, 0.34 #4823), 07ssc (0.32 #1133, 0.13 #3087, 0.11 #2365), 04xvlr (0.23 #3081, 0.22 #4826, 0.21 #5032), 05p553 (0.22 #2974, 0.21 #5853, 0.17 #4822), 04t36 (0.19 #1029, 0.10 #519, 0.06 #3085), 024qqx (0.16 #591, 0.11 #284, 0.10 #181), 01jfsb (0.15 #4842, 0.14 #1860, 0.14 #2684), 01hmnh (0.12 #27, 0.12 #1765, 0.11 #5778), 09blyk (0.10 #455, 0.06 #4868, 0.06 #1886) >> Best rule #75 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 02hxhz; 02qdrjx; >> query: (?x10246, 09q17) <- film_release_distribution_medium(?x10246, ?x81), film(?x9204, ?x10246), film_crew_role(?x10246, ?x137), ?x81 = 029j_, ?x9204 = 06rq2l >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023vcd titles! 037hz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 58.000 0.375 http://example.org/media_common/netflix_genre/titles EVAL 023vcd titles! 09q17 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 58.000 0.375 http://example.org/media_common/netflix_genre/titles #22753-0fsv2 PRED entity: 0fsv2 PRED relation: source PRED expected values: 0jbk9 => 210 concepts (210 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #105, 0.93 #84, 0.93 #131) >> Best rule #105 for best value: >> intensional similarity = 4 >> extensional distance = 168 >> proper extension: 0mp08; >> query: (?x13739, 0jbk9) <- category(?x13739, ?x134), ?x134 = 08mbj5d, county(?x13739, ?x12253), currency(?x12253, ?x170) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fsv2 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 210.000 0.935 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #22752-0f502 PRED entity: 0f502 PRED relation: award PRED expected values: 09sb52 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 283): 027986c (0.74 #23855, 0.70 #39504, 0.70 #33632), 027c95y (0.74 #23855, 0.70 #39504, 0.70 #33632), 027b9j5 (0.74 #23855, 0.70 #39504, 0.70 #33632), 09sb52 (0.42 #38, 0.40 #3948, 0.40 #7077), 05p1dby (0.39 #2057, 0.15 #37938, 0.14 #10169), 0gq9h (0.35 #16890, 0.30 #2029, 0.15 #37938), 01by1l (0.31 #17705, 0.20 #20833, 0.18 #23570), 040njc (0.27 #16823, 0.14 #38721, 0.13 #47329), 01bgqh (0.24 #17638, 0.17 #23503, 0.16 #20766), 02x4w6g (0.21 #109, 0.15 #37938, 0.14 #10169) >> Best rule #23855 for best value: >> intensional similarity = 2 >> extensional distance = 745 >> proper extension: 0kc6x; 065y4w7; 01y67v; 02p10m; 01fkr_; 0cv_2; 06v99d; 0381pn; 01bfjy; >> query: (?x4360, ?x401) <- category(?x4360, ?x134), award_winner(?x401, ?x4360) >> conf = 0.74 => this is the best rule for 3 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0x3n; 01f5q5; *> query: (?x4360, 09sb52) <- spouse(?x5197, ?x4360), award(?x4360, ?x3019), ?x3019 = 057xs89 *> conf = 0.42 ranks of expected_values: 4 EVAL 0f502 award 09sb52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 138.000 138.000 0.740 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22751-0gywn PRED entity: 0gywn PRED relation: artists PRED expected values: 01l1b90 0j1yf 0bqsy 01qgry 044mfr 0k1bs 02z4b_8 01w5jwb => 71 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1127): 06mt91 (0.78 #9193, 0.67 #6304, 0.60 #10156), 01vtj38 (0.67 #9243, 0.67 #6354, 0.60 #11170), 01jfr3y (0.67 #6238, 0.60 #5275, 0.57 #8164), 09qr6 (0.67 #8746, 0.60 #4894, 0.57 #7783), 03f5spx (0.67 #5829, 0.56 #8718, 0.50 #11610), 0bqsy (0.67 #6085, 0.56 #8974, 0.50 #9937), 043zg (0.67 #6195, 0.56 #9084, 0.50 #10047), 02vwckw (0.67 #6436, 0.56 #9325, 0.50 #10288), 0j1yf (0.67 #5899, 0.56 #8788, 0.50 #9751), 047sxrj (0.67 #5933, 0.56 #8822, 0.50 #9785) >> Best rule #9193 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 035wcs; >> query: (?x3928, 06mt91) <- artists(?x3928, ?x5906), artists(?x3928, ?x2925), artists(?x3928, ?x1953), participant(?x5906, ?x1896), artists(?x3996, ?x1953), ?x3996 = 02lnbg, award_winner(?x342, ?x5906), ?x2925 = 01vx5w7 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6085 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 064t9; 02lnbg; *> query: (?x3928, 0bqsy) <- artists(?x3928, ?x3384), artists(?x3928, ?x1974), artists(?x3928, ?x1953), ?x1953 = 019g40, ?x1974 = 0136p1, gender(?x3384, ?x231), artist(?x3265, ?x3384), award(?x3384, ?x567) *> conf = 0.67 ranks of expected_values: 6, 9, 19, 31, 43, 49, 220, 495 EVAL 0gywn artists 01w5jwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 71.000 26.000 0.778 http://example.org/music/genre/artists EVAL 0gywn artists 02z4b_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 71.000 26.000 0.778 http://example.org/music/genre/artists EVAL 0gywn artists 0k1bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 26.000 0.778 http://example.org/music/genre/artists EVAL 0gywn artists 044mfr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 71.000 26.000 0.778 http://example.org/music/genre/artists EVAL 0gywn artists 01qgry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 26.000 0.778 http://example.org/music/genre/artists EVAL 0gywn artists 0bqsy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 71.000 26.000 0.778 http://example.org/music/genre/artists EVAL 0gywn artists 0j1yf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 71.000 26.000 0.778 http://example.org/music/genre/artists EVAL 0gywn artists 01l1b90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 71.000 26.000 0.778 http://example.org/music/genre/artists #22750-068p2 PRED entity: 068p2 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.80 #81, 0.79 #25, 0.77 #13), 01g63y (0.45 #329, 0.24 #423, 0.06 #10), 0jgjn (0.04 #84, 0.03 #116, 0.03 #200), 01bl8s (0.02 #191, 0.02 #195, 0.01 #83) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 04vmp; 01d26y; >> query: (?x4499, 04ztj) <- place_of_birth(?x1887, ?x4499), citytown(?x3351, ?x4499), featured_film_locations(?x5116, ?x4499) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 068p2 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 179.000 0.797 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #22749-02jyr8 PRED entity: 02jyr8 PRED relation: colors PRED expected values: 01l849 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 18): 01g5v (0.28 #1029, 0.28 #1181, 0.27 #839), 01l849 (0.26 #837, 0.26 #400, 0.25 #210), 019sc (0.20 #26, 0.19 #900, 0.18 #1033), 06fvc (0.17 #78, 0.16 #116, 0.16 #135), 0jc_p (0.14 #4, 0.11 #23, 0.10 #42), 036k5h (0.11 #290, 0.10 #328, 0.10 #271), 067z2v (0.10 #9, 0.09 #28, 0.05 #275), 04mkbj (0.09 #409, 0.09 #846, 0.09 #1017), 03wkwg (0.08 #14, 0.08 #33, 0.06 #223), 09ggk (0.07 #471, 0.07 #72, 0.07 #110) >> Best rule #1029 for best value: >> intensional similarity = 3 >> extensional distance = 405 >> proper extension: 0pz6q; >> query: (?x1845, 01g5v) <- colors(?x1845, ?x663), organization(?x346, ?x1845), company(?x346, ?x94) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #837 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 342 *> proper extension: 01t38b; 0gjv_; 0bsnm; 0283sdr; 01zh3_; *> query: (?x1845, 01l849) <- colors(?x1845, ?x663), organization(?x346, ?x1845), school_type(?x1845, ?x3092) *> conf = 0.26 ranks of expected_values: 2 EVAL 02jyr8 colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 126.000 0.278 http://example.org/education/educational_institution/colors #22748-01kp_1t PRED entity: 01kp_1t PRED relation: gender PRED expected values: 02zsn => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #182, 0.75 #81, 0.73 #110), 02zsn (0.46 #268, 0.42 #30, 0.42 #2) >> Best rule #182 for best value: >> intensional similarity = 3 >> extensional distance = 1543 >> proper extension: 0gry51; >> query: (?x9528, 05zppz) <- profession(?x9528, ?x2225), profession(?x6166, ?x2225), ?x6166 = 051z6rz >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #268 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4244 *> proper extension: 0dhrqx; *> query: (?x9528, ?x231) <- profession(?x9528, ?x2225), profession(?x862, ?x2225), gender(?x862, ?x231) *> conf = 0.46 ranks of expected_values: 2 EVAL 01kp_1t gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 134.000 134.000 0.793 http://example.org/people/person/gender #22747-0bk4s PRED entity: 0bk4s PRED relation: nationality PRED expected values: 07ssc => 119 concepts (86 used for prediction) PRED predicted values (max 10 best out of 133): 02jx1 (0.99 #4931, 0.44 #7255, 0.43 #528), 09c7w0 (0.86 #6716, 0.81 #7019, 0.81 #6212), 07ssc (0.78 #5013, 0.67 #708, 0.39 #2102), 0cdbq (0.60 #1055, 0.07 #1156, 0.06 #1255), 01zst8 (0.35 #5907, 0.35 #4897, 0.35 #5604), 03rk0 (0.35 #4193, 0.33 #145, 0.19 #3091), 06q1r (0.26 #7721, 0.13 #7522, 0.11 #769), 0h924 (0.24 #6714, 0.24 #6209, 0.24 #4293), 0n5yv (0.23 #2789), 059f4 (0.23 #2789) >> Best rule #4931 for best value: >> intensional similarity = 5 >> extensional distance = 348 >> proper extension: 05vsxz; 027dtv3; 0134w7; 07_3qd; 01fwj8; 0fv6dr; 016ntp; 02pq9yv; 0p3r8; 013_vh; ... >> query: (?x6779, 02jx1) <- gender(?x6779, ?x231), nationality(?x6779, ?x6371), split_to(?x6371, ?x512), form_of_government(?x6371, ?x6065), ?x6065 = 01q20 >> conf = 0.99 => this is the best rule for 1 predicted values *> Best rule #5013 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 374 *> proper extension: 0c9d9; 0hl3d; 0h5f5n; 0z4s; 01zkxv; 04r7jc; 07lt7b; 0487c3; 035gjq; 0b68vs; ... *> query: (?x6779, 07ssc) <- gender(?x6779, ?x231), nationality(?x6779, ?x6371), combatants(?x12673, ?x6371), combatants(?x1679, ?x6371), combatants(?x12673, ?x13906), ?x13906 = 01s47p *> conf = 0.78 ranks of expected_values: 3 EVAL 0bk4s nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 86.000 0.991 http://example.org/people/person/nationality #22746-098n_m PRED entity: 098n_m PRED relation: location PRED expected values: 02frhbc => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 93): 0r0ss (0.49 #22524, 0.48 #23329, 0.48 #26549), 02_286 (0.23 #1647, 0.21 #3255, 0.19 #842), 030qb3t (0.20 #83, 0.14 #10539, 0.13 #9734), 0vzm (0.20 #173, 0.12 #978, 0.10 #1783), 0n95v (0.20 #594), 0f2rq (0.20 #281), 04tgp (0.20 #240), 04jpl (0.08 #822, 0.07 #1627, 0.05 #2431), 0cc56 (0.08 #862, 0.07 #1667, 0.04 #9708), 01n7q (0.08 #868, 0.07 #1673, 0.03 #3281) >> Best rule #22524 for best value: >> intensional similarity = 4 >> extensional distance = 956 >> proper extension: 04sx9_; 04n_g; 050t68; 01vw917; 02784z; >> query: (?x5371, ?x12250) <- film(?x5371, ?x9154), place_of_birth(?x5371, ?x12250), nominated_for(?x500, ?x9154), music(?x9154, ?x2392) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1274 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 076_74; 037d35; *> query: (?x5371, 02frhbc) <- nominated_for(?x5371, ?x6200), award(?x5371, ?x2902), ?x2902 = 02x4sn8, place_of_birth(?x5371, ?x12250) *> conf = 0.04 ranks of expected_values: 17 EVAL 098n_m location 02frhbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 109.000 109.000 0.487 http://example.org/people/person/places_lived./people/place_lived/location #22745-01gvsn PRED entity: 01gvsn PRED relation: nominated_for! PRED expected values: 0gq_v 0gs96 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 218): 0gqz2 (0.78 #5695, 0.77 #6883, 0.67 #5694), 0gq9h (0.54 #297, 0.52 #1720, 0.44 #3618), 0gq_v (0.54 #257, 0.41 #20, 0.36 #1680), 019f4v (0.48 #1713, 0.39 #3611, 0.36 #765), 0gs9p (0.46 #1722, 0.39 #299, 0.39 #3620), 040njc (0.42 #719, 0.37 #1667, 0.31 #3565), 0gs96 (0.41 #88, 0.31 #325, 0.27 #563), 0k611 (0.40 #1731, 0.34 #3629, 0.29 #4340), 02pqp12 (0.38 #770, 0.27 #1718, 0.24 #58), 04dn09n (0.36 #748, 0.33 #1696, 0.31 #36) >> Best rule #5695 for best value: >> intensional similarity = 3 >> extensional distance = 597 >> proper extension: 02nf2c; 0m123; 02_1ky; 06mmr; >> query: (?x10948, ?x1323) <- award(?x10948, ?x1323), award_winner(?x10948, ?x538), ceremony(?x1323, ?x78) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #257 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 06wzvr; 02q_4ph; *> query: (?x10948, 0gq_v) <- award(?x10948, ?x1079), film(?x538, ?x10948), film_sets_designed(?x13444, ?x10948) *> conf = 0.54 ranks of expected_values: 3, 7 EVAL 01gvsn nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 87.000 87.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01gvsn nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22744-05b1610 PRED entity: 05b1610 PRED relation: nominated_for PRED expected values: 03rtz1 0f2sx4 => 51 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1385): 0f2sx4 (0.67 #8688, 0.60 #7179, 0.57 #10198), 0gzlb9 (0.67 #8751, 0.60 #7242, 0.57 #10261), 08mg_b (0.60 #6972, 0.57 #9991, 0.50 #8481), 026n4h6 (0.57 #9253, 0.50 #7743, 0.50 #1709), 03rtz1 (0.50 #7681, 0.50 #3155, 0.43 #9191), 0bshwmp (0.50 #7673, 0.43 #9183, 0.40 #6164), 02z9rr (0.50 #4145, 0.40 #7162, 0.40 #5653), 074rg9 (0.50 #2329, 0.40 #5345, 0.33 #821), 02wgbb (0.43 #10174, 0.33 #8664, 0.33 #1122), 060v34 (0.43 #9118, 0.33 #7608, 0.33 #66) >> Best rule #8688 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 05b4l5x; >> query: (?x688, 0f2sx4) <- award_winner(?x688, ?x800), nominated_for(?x688, ?x6053), nominated_for(?x688, ?x1372), award(?x702, ?x688), ?x6053 = 05qbbfb, ?x1372 = 01kff7 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 05b1610 nominated_for 0f2sx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 19.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05b1610 nominated_for 03rtz1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 51.000 19.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22743-02cvcd PRED entity: 02cvcd PRED relation: colors PRED expected values: 01l849 => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.82 #405, 0.38 #4, 0.30 #64), 083jv (0.41 #1144, 0.40 #82, 0.40 #1204), 01l849 (0.28 #1183, 0.28 #422, 0.28 #1163), 036k5h (0.23 #46, 0.20 #66, 0.13 #647), 019sc (0.21 #369, 0.21 #990, 0.19 #429), 06fvc (0.19 #965, 0.18 #985, 0.18 #1165), 03wkwg (0.18 #35, 0.15 #55, 0.15 #115), 038hg (0.15 #72, 0.12 #12, 0.11 #112), 0jc_p (0.15 #65, 0.12 #245, 0.12 #326), 04d18d (0.12 #19, 0.08 #139, 0.07 #2803) >> Best rule #405 for best value: >> intensional similarity = 5 >> extensional distance = 91 >> proper extension: 0ym1n; >> query: (?x12530, 01g5v) <- citytown(?x12530, ?x6769), major_field_of_study(?x12530, ?x2981), colors(?x12530, ?x7179), colors(?x11722, ?x7179), ?x11722 = 019vv1 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1183 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 221 *> proper extension: 0kz2w; 01k2wn; 0lfgr; 022xml; 07vht; 065r8g; 04344j; 01c333; 02zd2b; 017v71; ... *> query: (?x12530, 01l849) <- school_type(?x12530, ?x1044), currency(?x12530, ?x170), ?x170 = 09nqf, major_field_of_study(?x12530, ?x2981), colors(?x12530, ?x7179) *> conf = 0.28 ranks of expected_values: 3 EVAL 02cvcd colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 203.000 203.000 0.817 http://example.org/education/educational_institution/colors #22742-01ycbq PRED entity: 01ycbq PRED relation: award_nominee PRED expected values: 04bdzg => 98 concepts (35 used for prediction) PRED predicted values (max 10 best out of 925): 04bdzg (0.81 #74770, 0.81 #58411, 0.81 #18693), 014g22 (0.43 #3299, 0.04 #67760, 0.02 #5635), 034zc0 (0.41 #3702, 0.04 #6038, 0.02 #22392), 021vwt (0.38 #2693, 0.14 #79444, 0.02 #5029), 02ch1w (0.38 #3714, 0.14 #79444, 0.02 #6050), 02jsgf (0.38 #3283, 0.14 #79444, 0.02 #5619), 042z_g (0.38 #3546, 0.14 #79444, 0.02 #5882), 057_yx (0.38 #4551, 0.14 #79444, 0.02 #6887), 03q1vd (0.38 #2939, 0.14 #79444, 0.02 #5275), 0z4s (0.35 #2419, 0.14 #79444, 0.03 #56154) >> Best rule #74770 for best value: >> intensional similarity = 3 >> extensional distance = 1212 >> proper extension: 0m2wm; 02zq43; 04wqr; 07lmxq; 03m8lq; 01j5x6; 01v3s2_; 04cf09; 02knnd; 02zyy4; ... >> query: (?x2033, ?x434) <- film(?x2033, ?x253), award_nominee(?x434, ?x2033), award_nominee(?x2033, ?x262) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ycbq award_nominee 04bdzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 35.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22741-0jzw PRED entity: 0jzw PRED relation: country PRED expected values: 09c7w0 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 45): 09c7w0 (0.86 #1228, 0.84 #2147, 0.84 #2209), 07ssc (0.47 #78, 0.34 #1978, 0.30 #997), 03rjj (0.38 #5706, 0.10 #190, 0.08 #926), 03rt9 (0.38 #5706, 0.05 #688, 0.03 #995), 0f8l9c (0.14 #203, 0.10 #693, 0.10 #877), 0345h (0.12 #4565, 0.12 #1866, 0.12 #3952), 0chghy (0.10 #747, 0.07 #442, 0.06 #2833), 0d060g (0.07 #682, 0.06 #2031, 0.06 #1785), 03_3d (0.06 #376, 0.06 #314, 0.06 #130), 03rk0 (0.06 #162, 0.03 #1081, 0.02 #1632) >> Best rule #1228 for best value: >> intensional similarity = 4 >> extensional distance = 130 >> proper extension: 0140g4; 011yxg; 0ds3t5x; 0g5qs2k; 0ds33; 06_wqk4; 04tc1g; 0344gc; 026390q; 069q4f; ... >> query: (?x810, 09c7w0) <- film(?x166, ?x810), nominated_for(?x2375, ?x810), honored_for(?x3943, ?x810), award(?x157, ?x2375) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jzw country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.864 http://example.org/film/film/country #22740-03x31g PRED entity: 03x31g PRED relation: location PRED expected values: 04vmp => 113 concepts (87 used for prediction) PRED predicted values (max 10 best out of 111): 030qb3t (0.39 #17758, 0.34 #36236, 0.22 #16152), 04vmp (0.31 #5172, 0.30 #1959, 0.27 #2762), 02_286 (0.19 #49847, 0.18 #20927, 0.18 #44224), 0cvw9 (0.18 #2806, 0.12 #5216, 0.10 #7625), 049lr (0.17 #451, 0.12 #6073, 0.05 #7679), 09f07 (0.17 #597, 0.08 #3809, 0.07 #4612), 01_yvy (0.17 #1302, 0.06 #40974, 0.05 #6924), 0byh8j (0.15 #3559, 0.13 #4362, 0.09 #2756), 059rby (0.14 #15281, 0.05 #27334, 0.05 #20906), 0rh6k (0.13 #15269, 0.05 #27322, 0.04 #12054) >> Best rule #17758 for best value: >> intensional similarity = 3 >> extensional distance = 405 >> proper extension: 03lh3v; 047g6; >> query: (?x11170, 030qb3t) <- people(?x5025, ?x11170), location(?x11170, ?x5384), administrative_division(?x5384, ?x11812) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #5172 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 090gpr; *> query: (?x11170, 04vmp) <- gender(?x11170, ?x514), award(?x11170, ?x10156), ?x10156 = 03r8v_, ?x514 = 02zsn *> conf = 0.31 ranks of expected_values: 2 EVAL 03x31g location 04vmp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 87.000 0.388 http://example.org/people/person/places_lived./people/place_lived/location #22739-0kbws PRED entity: 0kbws PRED relation: olympics! PRED expected values: 06f32 056vv => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 181): 0d060g (0.80 #662, 0.71 #1320, 0.71 #661), 03spz (0.80 #662, 0.71 #1320, 0.71 #661), 06t8v (0.80 #662, 0.71 #661, 0.70 #660), 03rt9 (0.80 #662, 0.71 #661, 0.70 #660), 01mjq (0.80 #662, 0.71 #661, 0.70 #660), 03shp (0.71 #1320, 0.71 #661, 0.70 #660), 06c1y (0.71 #1320, 0.71 #661, 0.70 #660), 07ylj (0.71 #1320, 0.71 #661, 0.70 #660), 0j4b (0.71 #1320, 0.71 #661, 0.70 #660), 07f5x (0.71 #1320, 0.71 #661, 0.70 #660) >> Best rule #662 for best value: >> intensional similarity = 15 >> extensional distance = 2 >> proper extension: 06sks6; >> query: (?x1931, ?x142) <- olympics(?x8588, ?x1931), olympics(?x3635, ?x1931), olympics(?x3432, ?x1931), olympics(?x142, ?x1931), participating_countries(?x1931, ?x7833), ?x8588 = 0jhd, olympics(?x4673, ?x1931), ?x3635 = 019pcs, medal(?x1931, ?x422), ?x4673 = 07jbh, film_release_region(?x4441, ?x142), olympics(?x1122, ?x1931), ?x4441 = 0125xq, nationality(?x2259, ?x7833), olympics(?x3432, ?x778) >> conf = 0.80 => this is the best rule for 5 predicted values *> Best rule #1320 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 4 *> proper extension: 0l6m5; 0l6mp; *> query: (?x1931, ?x183) <- olympics(?x8948, ?x1931), olympics(?x8588, ?x1931), olympics(?x4059, ?x1931), olympics(?x3432, ?x1931), olympics(?x1353, ?x1931), olympics(?x1273, ?x1931), participating_countries(?x1931, ?x183), ?x1273 = 04wgh, organization(?x8588, ?x127), olympics(?x150, ?x1931), taxonomy(?x3432, ?x939), film_release_region(?x124, ?x4059), adjustment_currency(?x8948, ?x170), ?x1353 = 035qy, administrative_area_type(?x3432, ?x2792), adjoins(?x2000, ?x8588), countries_within(?x455, ?x8588) *> conf = 0.71 ranks of expected_values: 11, 107 EVAL 0kbws olympics! 056vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 61.000 61.000 0.804 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 0kbws olympics! 06f32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 61.000 61.000 0.804 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #22738-0bv7t PRED entity: 0bv7t PRED relation: influenced_by PRED expected values: 084w8 => 166 concepts (68 used for prediction) PRED predicted values (max 10 best out of 423): 081k8 (0.36 #2756, 0.25 #8385, 0.23 #4921), 0p_47 (0.30 #107, 0.13 #4006, 0.12 #9203), 01svq8 (0.30 #423, 0.10 #3456, 0.10 #4755), 0448r (0.29 #8490, 0.16 #2861, 0.14 #12128), 028p0 (0.28 #2630, 0.21 #8259, 0.15 #6094), 03_87 (0.28 #2802, 0.17 #4967, 0.15 #6266), 07g2b (0.24 #2613, 0.17 #4778, 0.15 #6077), 014z8v (0.23 #4020, 0.20 #9217, 0.20 #121), 032l1 (0.21 #8663, 0.20 #2689, 0.16 #5287), 045bg (0.21 #8663, 0.14 #12128, 0.10 #25563) >> Best rule #2756 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 06whf; >> query: (?x5261, 081k8) <- award_winner(?x921, ?x5261), profession(?x5261, ?x3746), ?x3746 = 05z96, influenced_by(?x5261, ?x1235) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #2603 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 06whf; *> query: (?x5261, 084w8) <- award_winner(?x921, ?x5261), profession(?x5261, ?x3746), ?x3746 = 05z96, influenced_by(?x5261, ?x1235) *> conf = 0.12 ranks of expected_values: 39 EVAL 0bv7t influenced_by 084w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 166.000 68.000 0.360 http://example.org/influence/influence_node/influenced_by #22737-0gg8z1f PRED entity: 0gg8z1f PRED relation: film_release_region PRED expected values: 047yc 01pj7 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 101): 0f8l9c (0.84 #300, 0.83 #1153, 0.83 #868), 06t2t (0.79 #335, 0.78 #193, 0.78 #761), 03rj0 (0.68 #191, 0.66 #759, 0.66 #333), 05v8c (0.65 #153, 0.65 #721, 0.64 #579), 06t8v (0.59 #350, 0.58 #208, 0.55 #776), 01p1v (0.59 #611, 0.58 #753, 0.57 #327), 015qh (0.57 #744, 0.57 #602, 0.56 #318), 047yc (0.57 #731, 0.55 #873, 0.54 #1158), 016wzw (0.54 #339, 0.54 #197, 0.49 #765), 01ls2 (0.54 #150, 0.53 #718, 0.51 #576) >> Best rule #300 for best value: >> intensional similarity = 7 >> extensional distance = 89 >> proper extension: 047svrl; 0gh8zks; >> query: (?x6321, 0f8l9c) <- film_release_region(?x6321, ?x1229), film_release_region(?x6321, ?x1023), film_release_region(?x6321, ?x512), ?x1023 = 0ctw_b, ?x1229 = 059j2, nominated_for(?x7670, ?x6321), nationality(?x111, ?x512) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #731 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 131 *> proper extension: 014lc_; 0g56t9t; 02vxq9m; 03g90h; 011yrp; 0gtv7pk; 0dscrwf; 05p1tzf; 02x3lt7; 0gx9rvq; ... *> query: (?x6321, 047yc) <- film_release_region(?x6321, ?x2513), film_release_region(?x6321, ?x1023), ?x1023 = 0ctw_b, genre(?x6321, ?x53), film(?x2353, ?x6321), ?x2513 = 05b4w *> conf = 0.57 ranks of expected_values: 8, 12 EVAL 0gg8z1f film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 76.000 76.000 0.835 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gg8z1f film_release_region 047yc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 76.000 76.000 0.835 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22736-01y64 PRED entity: 01y64 PRED relation: school_type! PRED expected values: 02cw8s => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1328): 01pl14 (0.44 #2283, 0.44 #1712, 0.36 #4563), 04rwx (0.44 #2318, 0.36 #3458, 0.36 #2887), 02sjgpq (0.40 #850, 0.33 #2559, 0.33 #1988), 012mzw (0.40 #864, 0.33 #2573, 0.33 #2002), 02bhj4 (0.40 #849, 0.33 #2558, 0.33 #1987), 02km0m (0.40 #810, 0.33 #2519, 0.33 #1948), 03hdz8 (0.40 #848, 0.33 #1986, 0.33 #279), 017d77 (0.40 #604, 0.33 #1742, 0.33 #35), 037njl (0.40 #733, 0.33 #2442, 0.31 #5860), 03fgm (0.40 #975, 0.33 #406, 0.27 #4964) >> Best rule #2283 for best value: >> intensional similarity = 22 >> extensional distance = 7 >> proper extension: 05pcjw; 01_9fk; 01_srz; 05jxkf; 06cs1; 07tf8; >> query: (?x9240, 01pl14) <- school_type(?x13639, ?x9240), school_type(?x12028, ?x9240), school_type(?x10686, ?x9240), school_type(?x9239, ?x9240), currency(?x10686, ?x170), state_province_region(?x10686, ?x177), student(?x13639, ?x13200), student(?x13639, ?x2728), company(?x346, ?x13639), ?x346 = 060c4, category(?x10686, ?x134), participant(?x4394, ?x13200), award_winner(?x2728, ?x628), contains(?x2254, ?x12028), award_nominee(?x230, ?x2728), citytown(?x9239, ?x362), location(?x2728, ?x4030), student(?x12028, ?x6399), ?x170 = 09nqf, contains(?x94, ?x10686), profession(?x2728, ?x1032), registering_agency(?x10686, ?x1982) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01y64 school_type! 02cw8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 19.000 19.000 0.444 http://example.org/education/educational_institution/school_type #22735-02wt0 PRED entity: 02wt0 PRED relation: participating_countries! PRED expected values: 09n48 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 40): 0lgxj (0.73 #188, 0.55 #388, 0.48 #1708), 018ctl (0.60 #168, 0.59 #1128, 0.54 #1688), 09x3r (0.60 #372, 0.59 #292, 0.53 #172), 09n48 (0.49 #1123, 0.45 #363, 0.45 #1923), 016r9z (0.41 #301, 0.40 #381, 0.40 #181), 0blfl (0.40 #189, 0.25 #1149, 0.25 #389), 0sx8l (0.40 #174, 0.25 #374, 0.25 #1694), 0jdk_ (0.33 #106, 0.20 #386, 0.20 #186), 06sks6 (0.30 #384, 0.27 #184, 0.24 #3081), 0c_tl (0.27 #183, 0.25 #383, 0.18 #703) >> Best rule #188 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 012wgb; >> query: (?x2290, 0lgxj) <- vacationer(?x2290, ?x6187), film_release_region(?x5877, ?x2290), ?x5877 = 02qyv3h >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1123 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 0d0vqn; 01znc_; 0d05w3; 05b4w; 04xn_; 02k8k; 05b7q; *> query: (?x2290, 09n48) <- country(?x1352, ?x2290), administrative_parent(?x2290, ?x551), ?x1352 = 0w0d *> conf = 0.49 ranks of expected_values: 4 EVAL 02wt0 participating_countries! 09n48 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 124.000 124.000 0.733 http://example.org/olympics/olympic_games/participating_countries #22734-02f2dn PRED entity: 02f2dn PRED relation: award_nominee! PRED expected values: 0c6qh => 97 concepts (34 used for prediction) PRED predicted values (max 10 best out of 672): 0154qm (0.81 #46413, 0.81 #46412, 0.81 #44090), 0c6qh (0.81 #46413, 0.81 #46412, 0.81 #44090), 027n4zv (0.47 #1844, 0.19 #78903, 0.14 #46414), 03v1jf (0.47 #1222, 0.19 #78903, 0.14 #46414), 08pth9 (0.47 #1057, 0.19 #78903, 0.14 #46414), 0d810y (0.40 #1338, 0.19 #78903, 0.14 #46414), 07s95_l (0.40 #1221, 0.19 #78903, 0.14 #46414), 048q6x (0.40 #1189, 0.19 #78903, 0.01 #15111), 09btt1 (0.40 #1058, 0.14 #46414), 05lb87 (0.40 #271, 0.02 #14193, 0.01 #55966) >> Best rule #46413 for best value: >> intensional similarity = 3 >> extensional distance = 1229 >> proper extension: 0m2wm; 02zq43; 04wqr; 07lmxq; 0f830f; 03m8lq; 08w7vj; 01j5x6; 01v3s2_; 0bz5v2; ... >> query: (?x2646, ?x7242) <- award_nominee(?x2646, ?x7242), film(?x2646, ?x964), award_nominee(?x7242, ?x4507) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02f2dn award_nominee! 0c6qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 34.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22733-01q2sk PRED entity: 01q2sk PRED relation: major_field_of_study PRED expected values: 02822 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 115): 01mkq (0.49 #387, 0.48 #1132, 0.43 #1256), 02j62 (0.44 #1148, 0.40 #1023, 0.38 #1770), 04rjg (0.41 #144, 0.37 #1137, 0.34 #1261), 01tbp (0.39 #433, 0.26 #1053, 0.24 #1178), 03g3w (0.38 #1144, 0.33 #1019, 0.33 #1268), 0g26h (0.36 #415, 0.31 #1035, 0.24 #1160), 01540 (0.33 #434, 0.23 #1054, 0.22 #1303), 02ky346 (0.33 #388, 0.22 #1008, 0.17 #1257), 062z7 (0.33 #1145, 0.32 #400, 0.30 #1020), 05qjt (0.32 #380, 0.31 #1125, 0.29 #1249) >> Best rule #387 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 01jssp; 05krk; 06pwq; 065y4w7; 01w3v; 07w0v; 024y8p; 04rwx; 03v6t; 0bthb; ... >> query: (?x3351, 01mkq) <- currency(?x3351, ?x170), ?x170 = 09nqf, major_field_of_study(?x3351, ?x1154), ?x1154 = 02lp1 >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 02mw6c; *> query: (?x3351, 02822) <- colors(?x3351, ?x3364), school_type(?x3351, ?x1044), ?x3364 = 036k5h, student(?x3351, ?x10075) *> conf = 0.14 ranks of expected_values: 28 EVAL 01q2sk major_field_of_study 02822 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 145.000 145.000 0.493 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #22732-05kjlr PRED entity: 05kjlr PRED relation: category_of! PRED expected values: 05kjlr => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 10): 01ppdy (0.04 #938, 0.02 #1905, 0.02 #2227), 02tzwd (0.04 #947, 0.02 #2397, 0.01 #2880), 0j6j8 (0.04 #931, 0.01 #2864, 0.01 #3025), 058vy5 (0.02 #1909, 0.02 #2231, 0.01 #2875), 02v1ws (0.02 #1933, 0.01 #2899, 0.01 #3060), 04jhhng (0.02 #1930, 0.01 #3057, 0.01 #3379), 01tgwv (0.02 #2398, 0.01 #2881, 0.01 #3042), 01cd7p (0.01 #2897, 0.01 #3058, 0.01 #3380), 02r0d0 (0.01 #3059, 0.01 #3381), 01b8bn (0.01 #3034, 0.01 #3356) >> Best rule #938 for best value: >> intensional similarity = 6 >> extensional distance = 26 >> proper extension: 0j6j8; 0154yf; >> query: (?x13257, 01ppdy) <- award_winner(?x13257, ?x5254), profession(?x5254, ?x353), influenced_by(?x2608, ?x5254), student(?x6919, ?x5254), ?x353 = 0cbd2, people(?x6260, ?x5254) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05kjlr category_of! 05kjlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 59.000 0.036 http://example.org/award/award_category/category_of #22731-01skmp PRED entity: 01skmp PRED relation: category PRED expected values: 08mbj5d => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.38 #12, 0.34 #19, 0.34 #8) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 165 >> proper extension: 02r3cn; >> query: (?x6702, 08mbj5d) <- participant(?x6702, ?x300), participant(?x6187, ?x6702), location(?x6702, ?x739) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01skmp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.377 http://example.org/common/topic/webpage./common/webpage/category #22730-015rkw PRED entity: 015rkw PRED relation: location PRED expected values: 01xd9 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 67): 030qb3t (0.23 #4903, 0.18 #8918, 0.17 #9721), 02_286 (0.22 #59471, 0.19 #61880, 0.17 #35376), 0cr3d (0.08 #59579, 0.07 #61988, 0.05 #17011), 05qtj (0.06 #240, 0.03 #1043, 0.03 #35340), 0f2wj (0.06 #33, 0.03 #4854, 0.03 #2442), 0hyxv (0.06 #210, 0.03 #35340, 0.02 #3423), 09ctj (0.06 #759, 0.03 #35340), 0nq_b (0.06 #700, 0.03 #35340), 01qs54 (0.06 #490, 0.03 #35340), 0n9r8 (0.06 #327, 0.03 #35340) >> Best rule #4903 for best value: >> intensional similarity = 2 >> extensional distance = 355 >> proper extension: 02wb6yq; >> query: (?x1739, 030qb3t) <- languages(?x1739, ?x254), nominated_for(?x1739, ?x1813) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #3297 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 332 *> proper extension: 05_6_y; 032t2z; 05fg2; 03f5vvx; 0135nb; 026y23w; 08304; 01_k0d; 01sxd1; 01w9mnm; ... *> query: (?x1739, 01xd9) <- nationality(?x1739, ?x512), ?x512 = 07ssc *> conf = 0.01 ranks of expected_values: 54 EVAL 015rkw location 01xd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 109.000 109.000 0.230 http://example.org/people/person/places_lived./people/place_lived/location #22729-06m6z6 PRED entity: 06m6z6 PRED relation: executive_produced_by! PRED expected values: 034qzw => 97 concepts (55 used for prediction) PRED predicted values (max 10 best out of 113): 0h95927 (0.07 #1601, 0.06 #533, 0.06 #534), 01pgp6 (0.06 #97, 0.01 #2763), 01bb9r (0.06 #533, 0.06 #7999, 0.05 #1600), 047d21r (0.06 #533, 0.05 #1600, 0.04 #9599), 040_lv (0.06 #533, 0.05 #1600, 0.04 #9599), 0fh694 (0.04 #572, 0.03 #38, 0.03 #1105), 02rqwhl (0.04 #605, 0.01 #1138), 01z452 (0.04 #9599, 0.04 #8000, 0.03 #12795), 04jwly (0.03 #157, 0.03 #1224, 0.02 #691), 0407yfx (0.03 #115, 0.03 #1182, 0.01 #2781) >> Best rule #1601 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 0dr5y; >> query: (?x3961, ?x7651) <- profession(?x3961, ?x319), film(?x3961, ?x7651), film_release_region(?x7651, ?x456), ?x456 = 05qhw >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06m6z6 executive_produced_by! 034qzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 55.000 0.067 http://example.org/film/film/executive_produced_by #22728-085ccd PRED entity: 085ccd PRED relation: film_release_region PRED expected values: 0d0vqn 0chghy => 145 concepts (134 used for prediction) PRED predicted values (max 10 best out of 237): 0d0vqn (0.94 #1652, 0.93 #996, 0.92 #832), 0chghy (0.92 #837, 0.87 #1657, 0.87 #1986), 05qhw (0.90 #1005, 0.88 #841, 0.85 #3141), 035qy (0.90 #1027, 0.88 #863, 0.84 #2012), 03rt9 (0.90 #1004, 0.88 #840, 0.81 #1660), 06t2t (0.88 #894, 0.87 #1058, 0.81 #2043), 01znc_ (0.88 #872, 0.83 #1036, 0.79 #3172), 0b90_r (0.86 #3128, 0.86 #1977, 0.80 #992), 0154j (0.86 #1978, 0.80 #3129, 0.77 #993), 06bnz (0.85 #877, 0.80 #1041, 0.77 #2026) >> Best rule #1652 for best value: >> intensional similarity = 6 >> extensional distance = 60 >> proper extension: 0j43swk; 0gwjw0c; >> query: (?x2434, 0d0vqn) <- film(?x382, ?x2434), executive_produced_by(?x2434, ?x8563), film_release_region(?x2434, ?x2645), film_release_region(?x2434, ?x1229), ?x2645 = 03h64, ?x1229 = 059j2 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 085ccd film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 134.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 085ccd film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 134.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22727-03m5y9p PRED entity: 03m5y9p PRED relation: film! PRED expected values: 071ywj 024bbl 023zsh => 75 concepts (50 used for prediction) PRED predicted values (max 10 best out of 863): 03q43g (0.29 #7374, 0.29 #5299, 0.05 #11525), 05dtsb (0.25 #1174, 0.06 #9476, 0.02 #11551), 05txrz (0.25 #766, 0.04 #15295, 0.03 #25671), 09fb5 (0.25 #58, 0.03 #12512, 0.02 #29113), 07r1h (0.25 #1088, 0.03 #13542, 0.02 #61271), 014v6f (0.25 #968, 0.03 #66409, 0.03 #85087), 026c1 (0.25 #359, 0.02 #14888, 0.02 #23189), 034zc0 (0.25 #1027, 0.02 #11404, 0.02 #13481), 04954 (0.25 #1304, 0.02 #13758, 0.01 #22059), 019pm_ (0.25 #470, 0.02 #12924) >> Best rule #7374 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 084qpk; >> query: (?x8218, 03q43g) <- film(?x4670, ?x8218), film(?x2414, ?x8218), ?x2414 = 03n_7k, award_nominee(?x193, ?x4670), currency(?x8218, ?x170) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #8811 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 09rfh9; 02pcq92; *> query: (?x8218, 071ywj) <- production_companies(?x8218, ?x1914), currency(?x8218, ?x170), ?x170 = 09nqf, ?x1914 = 03xsby *> conf = 0.06 ranks of expected_values: 116, 256, 656 EVAL 03m5y9p film! 023zsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 75.000 50.000 0.286 http://example.org/film/actor/film./film/performance/film EVAL 03m5y9p film! 024bbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 75.000 50.000 0.286 http://example.org/film/actor/film./film/performance/film EVAL 03m5y9p film! 071ywj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 75.000 50.000 0.286 http://example.org/film/actor/film./film/performance/film #22726-0c1pj PRED entity: 0c1pj PRED relation: participant PRED expected values: 0pmhf => 131 concepts (74 used for prediction) PRED predicted values (max 10 best out of 324): 0c9c0 (0.20 #2109, 0.05 #5309, 0.03 #4029), 014zcr (0.17 #17, 0.10 #7697, 0.09 #10899), 0c6qh (0.17 #165, 0.10 #2085, 0.07 #7845), 0bq2g (0.17 #245, 0.04 #6005, 0.03 #9205), 016z2j (0.17 #153, 0.02 #5913, 0.02 #32011), 0jfx1 (0.12 #801, 0.03 #10403, 0.03 #11043), 0h0wc (0.12 #810, 0.01 #10412, 0.01 #11052), 0pz91 (0.11 #1366, 0.10 #3926, 0.04 #10328), 0bl2g (0.11 #1303, 0.07 #3863, 0.05 #8343), 0q5hw (0.11 #1472, 0.07 #4032, 0.03 #8512) >> Best rule #2109 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0gz5hs; 01vs_v8; 0g2lq; 05g7q; >> query: (?x556, 0c9c0) <- participant(?x262, ?x556), film(?x556, ?x299), organizations_founded(?x556, ?x10629) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #7854 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 01wyy_; *> query: (?x556, 0pmhf) <- participant(?x262, ?x556), profession(?x556, ?x524), written_by(?x174, ?x556) *> conf = 0.02 ranks of expected_values: 213 EVAL 0c1pj participant 0pmhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 131.000 74.000 0.200 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #22725-0b05xm PRED entity: 0b05xm PRED relation: award PRED expected values: 0fbtbt => 79 concepts (77 used for prediction) PRED predicted values (max 10 best out of 221): 0fbtbt (0.50 #1046, 0.50 #234, 0.40 #1452), 0cjyzs (0.33 #3355, 0.33 #3761, 0.32 #2137), 09sb52 (0.31 #6131, 0.27 #6943, 0.27 #8567), 0gr4k (0.30 #2469, 0.30 #4499, 0.29 #4093), 04dn09n (0.28 #2480, 0.26 #4104, 0.26 #4510), 0gr51 (0.27 #2537, 0.26 #4567, 0.25 #4161), 0ck27z (0.25 #1717, 0.21 #6589, 0.20 #7401), 03hkv_r (0.23 #2452, 0.21 #4076, 0.20 #4482), 0gs9p (0.21 #4546, 0.21 #2516, 0.20 #4140), 0gq9h (0.21 #2514, 0.19 #4544, 0.17 #4138) >> Best rule #1046 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 03y9ccy; >> query: (?x3570, 0fbtbt) <- award_nominee(?x3570, ?x10215), award_nominee(?x3570, ?x4671), gender(?x3570, ?x231), ?x4671 = 027hnjh, profession(?x10215, ?x524) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b05xm award 0fbtbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 77.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22724-01jz6x PRED entity: 01jz6x PRED relation: award PRED expected values: 09sb52 => 80 concepts (76 used for prediction) PRED predicted values (max 10 best out of 284): 09sb52 (0.36 #4900, 0.32 #7735, 0.29 #3685), 0ck27z (0.16 #4952, 0.14 #7787, 0.13 #3737), 0cjyzs (0.15 #511, 0.12 #916, 0.09 #24709), 0fbtbt (0.15 #638, 0.12 #1043, 0.05 #11979), 05pcn59 (0.13 #23493, 0.13 #9721, 0.12 #14177), 0gqyl (0.13 #23493, 0.13 #9721, 0.12 #14177), 0bdwqv (0.13 #23493, 0.13 #9721, 0.12 #14177), 027dtxw (0.13 #23493, 0.13 #9721, 0.12 #14177), 04ljl_l (0.13 #23493, 0.13 #9721, 0.12 #14177), 09sdmz (0.13 #23493, 0.13 #9721, 0.12 #14177) >> Best rule #4900 for best value: >> intensional similarity = 3 >> extensional distance = 1045 >> proper extension: 03zqc1; 01v42g; 06lgq8; 0f6_dy; 080knyg; 02xb2bt; 0308kx; 06lht1; 017khj; 02nwxc; ... >> query: (?x10488, 09sb52) <- nominated_for(?x10488, ?x631), film(?x10488, ?x2102), award_nominee(?x1871, ?x10488) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jz6x award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 76.000 0.361 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22723-0fy59t PRED entity: 0fy59t PRED relation: ceremony! PRED expected values: 0f4x7 0gr4k => 39 concepts (37 used for prediction) PRED predicted values (max 10 best out of 363): 018wng (0.90 #3712, 0.89 #3467, 0.89 #3221), 0gvx_ (0.89 #1104, 0.89 #3561, 0.88 #5528), 0gr4k (0.88 #1494, 0.88 #3704, 0.88 #1248), 0f4x7 (0.87 #5424, 0.86 #5914, 0.86 #2965), 0gr07 (0.85 #2859, 0.81 #3104, 0.80 #2368), 0gs96 (0.83 #5971, 0.83 #5481, 0.82 #4251), 0gqng (0.83 #2213, 0.83 #1721, 0.80 #3195), 018wdw (0.76 #4913, 0.67 #2874, 0.66 #1891), 0gqxm (0.76 #4913, 0.52 #2821, 0.50 #2330), 0gqzz (0.76 #4913, 0.27 #2252, 0.24 #1760) >> Best rule #3712 for best value: >> intensional similarity = 14 >> extensional distance = 46 >> proper extension: 0fzrhn; >> query: (?x8259, 018wng) <- ceremony(?x1972, ?x8259), ceremony(?x1862, ?x8259), ceremony(?x1245, ?x8259), award_winner(?x8259, ?x382), ?x1972 = 0gqyl, award(?x361, ?x1862), award(?x697, ?x1862), nominated_for(?x1862, ?x69), ceremony(?x1862, ?x5924), ?x5924 = 0bzknt, ?x1245 = 0gqwc, ?x361 = 0h5f5n, nominated_for(?x112, ?x697), genre(?x697, ?x53) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1494 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 23 *> proper extension: 02pgky2; *> query: (?x8259, 0gr4k) <- ceremony(?x1972, ?x8259), ceremony(?x1862, ?x8259), award_winner(?x8259, ?x382), ?x1972 = 0gqyl, ?x1862 = 0gr51, honored_for(?x8259, ?x2721), nominated_for(?x382, ?x7864), award_nominee(?x382, ?x8590), award_winner(?x2451, ?x382), nominated_for(?x8590, ?x4384), instance_of_recurring_event(?x8259, ?x3459), friend(?x2451, ?x8898), location(?x2451, ?x578), country(?x7864, ?x94) *> conf = 0.88 ranks of expected_values: 3, 4 EVAL 0fy59t ceremony! 0gr4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 39.000 37.000 0.896 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0fy59t ceremony! 0f4x7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 39.000 37.000 0.896 http://example.org/award/award_category/winners./award/award_honor/ceremony #22722-01cbt3 PRED entity: 01cbt3 PRED relation: category PRED expected values: 08mbj5d => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.77 #19, 0.74 #52, 0.72 #25) >> Best rule #19 for best value: >> intensional similarity = 2 >> extensional distance = 227 >> proper extension: 04cr6qv; >> query: (?x5251, 08mbj5d) <- film(?x5251, ?x1261), artists(?x4910, ?x5251) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cbt3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.769 http://example.org/common/topic/webpage./common/webpage/category #22721-07ww5 PRED entity: 07ww5 PRED relation: official_language PRED expected values: 02h40lc => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 35): 06nm1 (0.60 #536, 0.39 #1064, 0.38 #1328), 02h40lc (0.57 #178, 0.52 #618, 0.50 #134), 064_8sq (0.20 #104, 0.16 #1908, 0.15 #1028), 05zjd (0.20 #108, 0.11 #240, 0.05 #812), 01r2l (0.17 #151, 0.14 #195, 0.03 #767), 03x42 (0.17 #168, 0.14 #212, 0.03 #828), 0jzc (0.12 #1290, 0.10 #630, 0.10 #1950), 0653m (0.11 #317, 0.09 #493, 0.08 #801), 04306rv (0.11 #313, 0.09 #489, 0.07 #1501), 02bjrlw (0.11 #309, 0.09 #485, 0.05 #793) >> Best rule #536 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 06n3y; >> query: (?x1317, 06nm1) <- contains(?x7273, ?x1317), adjoins(?x1317, ?x11553), ?x7273 = 07c5l, contains(?x9729, ?x11553) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #178 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 034tl; *> query: (?x1317, 02h40lc) <- country(?x4045, ?x1317), ?x4045 = 06z6r, country(?x1317, ?x94), currency(?x1317, ?x170) *> conf = 0.57 ranks of expected_values: 2 EVAL 07ww5 official_language 02h40lc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 123.000 123.000 0.600 http://example.org/location/country/official_language #22720-01v42g PRED entity: 01v42g PRED relation: profession PRED expected values: 02hrh1q => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 61): 02hrh1q (0.88 #765, 0.88 #2415, 0.88 #1815), 01d_h8 (0.40 #6, 0.36 #306, 0.33 #5857), 0dxtg (0.31 #5865, 0.30 #3314, 0.29 #3764), 09jwl (0.25 #6752, 0.25 #170, 0.21 #1370), 0q04f (0.25 #6752, 0.03 #1601, 0.02 #701), 03gjzk (0.25 #166, 0.24 #4216, 0.24 #3616), 0np9r (0.25 #172, 0.17 #3022, 0.17 #2422), 0nbcg (0.24 #483, 0.16 #1383, 0.14 #1233), 02jknp (0.23 #5859, 0.21 #308, 0.20 #9012), 0d1pc (0.20 #52, 0.13 #802, 0.11 #952) >> Best rule #765 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 057hz; 01bmlb; >> query: (?x1289, 02hrh1q) <- film(?x1289, ?x1640), location_of_ceremony(?x1289, ?x6408), people(?x743, ?x1289) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v42g profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.880 http://example.org/people/person/profession #22719-04sj3 PRED entity: 04sj3 PRED relation: organization PRED expected values: 041288 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 48): 041288 (0.61 #454, 0.55 #134, 0.52 #94), 0_2v (0.50 #43, 0.48 #283, 0.47 #183), 01rz1 (0.45 #401, 0.43 #461, 0.42 #361), 0j7v_ (0.37 #1144, 0.33 #165, 0.33 #45), 04k4l (0.32 #344, 0.32 #1346, 0.30 #504), 018cqq (0.32 #1346, 0.30 #169, 0.30 #289), 02jxk (0.32 #1346, 0.21 #322, 0.21 #362), 085h1 (0.32 #1346, 0.18 #521, 0.17 #50), 059dn (0.32 #1346, 0.17 #53, 0.10 #233), 034h1h (0.18 #1333, 0.02 #1740, 0.02 #1760) >> Best rule #454 for best value: >> intensional similarity = 2 >> extensional distance = 57 >> proper extension: 05g2v; >> query: (?x8781, 041288) <- contains(?x2467, ?x8781), ?x2467 = 0dg3n1 >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sj3 organization 041288 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.610 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #22718-0qm8b PRED entity: 0qm8b PRED relation: honored_for! PRED expected values: 02yvhx => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 117): 02yvhx (0.17 #65, 0.12 #187, 0.09 #6230), 09qvms (0.10 #253, 0.03 #6963, 0.03 #497), 04n2r9h (0.10 #280, 0.03 #524, 0.02 #1867), 0418154 (0.10 #337, 0.03 #581, 0.02 #703), 058m5m4 (0.10 #289, 0.03 #533, 0.02 #655), 0bq_mx (0.10 #360, 0.01 #5375), 09gkdln (0.09 #594, 0.03 #2547, 0.03 #2670), 05qb8vx (0.09 #536, 0.02 #2688, 0.02 #2689), 05zksls (0.09 #516, 0.02 #2469, 0.02 #2592), 09k5jh7 (0.09 #559, 0.02 #1170, 0.02 #681) >> Best rule #65 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 09ps01; >> query: (?x1586, 02yvhx) <- film(?x6730, ?x1586), film(?x4043, ?x1586), ?x6730 = 01kgv4, award_nominee(?x539, ?x4043), nominated_for(?x298, ?x1586) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qm8b honored_for! 02yvhx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #22717-04bs3j PRED entity: 04bs3j PRED relation: gender PRED expected values: 02zsn => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #55, 0.86 #67, 0.85 #19), 02zsn (0.48 #24, 0.48 #90, 0.48 #52) >> Best rule #55 for best value: >> intensional similarity = 2 >> extensional distance = 291 >> proper extension: 01d494; 099bk; 07c37; 03j90; >> query: (?x545, 05zppz) <- student(?x7545, ?x545), influenced_by(?x545, ?x3917) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 02hhtj; 01fxck; *> query: (?x545, 02zsn) <- participant(?x2437, ?x545), participant(?x989, ?x545), languages(?x545, ?x254) *> conf = 0.48 ranks of expected_values: 2 EVAL 04bs3j gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.870 http://example.org/people/person/gender #22716-0bs0bh PRED entity: 0bs0bh PRED relation: award! PRED expected values: 048q6x 016zp5 => 41 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2267): 03mg35 (0.81 #10003, 0.76 #26673, 0.68 #40013), 02vg0 (0.81 #10003, 0.76 #26673, 0.68 #40013), 02zfg3 (0.81 #10003, 0.76 #26673, 0.68 #40013), 014kg4 (0.81 #10003, 0.76 #26673, 0.68 #40013), 026l37 (0.81 #10003, 0.76 #26673, 0.68 #40013), 017149 (0.71 #13449, 0.33 #6780, 0.33 #3445), 016ggh (0.71 #16395, 0.33 #9726, 0.33 #6391), 0170pk (0.71 #13775, 0.33 #7106, 0.33 #3771), 01fh9 (0.67 #10500, 0.43 #13835, 0.33 #7166), 03ym1 (0.57 #14992, 0.44 #18326, 0.33 #8323) >> Best rule #10003 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 09sb52; >> query: (?x1921, ?x1922) <- award(?x10743, ?x1921), award(?x1975, ?x1921), award(?x1250, ?x1921), ?x10743 = 0301yj, ?x1975 = 04y9dk, ?x1250 = 01tcf7, award_winner(?x1921, ?x1922) >> conf = 0.81 => this is the best rule for 5 predicted values *> Best rule #14935 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 027dtxw; 02x73k6; *> query: (?x1921, 016zp5) <- award(?x10743, ?x1921), award(?x3841, ?x1921), award(?x3138, ?x1921), award(?x1250, ?x1921), ?x1250 = 01tcf7, gender(?x3138, ?x231), award_winner(?x494, ?x3841), actor(?x273, ?x10743) *> conf = 0.57 ranks of expected_values: 17, 653 EVAL 0bs0bh award! 016zp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 41.000 15.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bs0bh award! 048q6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 15.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22715-0h9c PRED entity: 0h9c PRED relation: profession! PRED expected values: 0j0pf 03s9v => 69 concepts (31 used for prediction) PRED predicted values (max 10 best out of 4202): 0fb1q (0.53 #68803, 0.36 #43354, 0.33 #29683), 0gz_ (0.50 #9608, 0.50 #5368, 0.47 #21202), 0372p (0.50 #9712, 0.50 #5472, 0.40 #13952), 0dx97 (0.50 #10181, 0.50 #5941, 0.40 #14421), 015pxr (0.50 #43012, 0.47 #68461, 0.33 #17566), 04jzj (0.50 #4560, 0.47 #21202, 0.40 #13040), 0drdv (0.50 #46306, 0.42 #71755, 0.33 #20860), 0pnf3 (0.50 #45807, 0.42 #71256, 0.33 #20361), 03s9v (0.50 #6612, 0.40 #15092, 0.33 #2372), 05d1y (0.50 #11213, 0.33 #2733, 0.25 #6973) >> Best rule #68803 for best value: >> intensional similarity = 7 >> extensional distance = 17 >> proper extension: 015cjr; 02dsz; >> query: (?x3801, 0fb1q) <- profession(?x5131, ?x3801), place_of_birth(?x5131, ?x1841), citytown(?x893, ?x1841), time_zones(?x1841, ?x5327), notable_people_with_this_condition(?x6484, ?x5131), location(?x6319, ?x1841), administrative_parent(?x1841, ?x2235) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #6612 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 04s2z; *> query: (?x3801, 03s9v) <- profession(?x9903, ?x3801), profession(?x9531, ?x3801), profession(?x7341, ?x3801), profession(?x5131, ?x3801), ?x5131 = 01tdnyh, ?x9531 = 01t_z, ?x9903 = 034ks, ?x7341 = 0m93, specialization_of(?x3801, ?x3802) *> conf = 0.50 ranks of expected_values: 9, 1900 EVAL 0h9c profession! 03s9v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 69.000 31.000 0.526 http://example.org/people/person/profession EVAL 0h9c profession! 0j0pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 69.000 31.000 0.526 http://example.org/people/person/profession #22714-036gdw PRED entity: 036gdw PRED relation: award PRED expected values: 05b4l5x => 132 concepts (108 used for prediction) PRED predicted values (max 10 best out of 307): 05b4l5x (0.50 #6, 0.23 #2837, 0.15 #29979), 09sb52 (0.41 #10574, 0.33 #851, 0.33 #19082), 07bdd_ (0.35 #2496, 0.23 #2837, 0.18 #19852), 05p09zm (0.30 #2555, 0.10 #10658, 0.10 #5392), 0gkts9 (0.27 #1384, 0.23 #1789, 0.21 #979), 03c7tr1 (0.25 #464, 0.25 #59, 0.23 #2837), 01by1l (0.25 #518, 0.20 #15507, 0.19 #9836), 05pcn59 (0.25 #892, 0.19 #1702, 0.19 #1297), 0gq9h (0.25 #78, 0.15 #2508, 0.11 #17498), 07cbcy (0.25 #484, 0.15 #2509, 0.08 #10612) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03xmy1; 01_f_5; >> query: (?x2827, 05b4l5x) <- award_winner(?x3064, ?x2827), award_winner(?x2826, ?x2827), type_of_union(?x2827, ?x566), ?x3064 = 05q5t0b >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 036gdw award 05b4l5x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 108.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22713-01cx_ PRED entity: 01cx_ PRED relation: location! PRED expected values: 01vsy95 04vq3h 0d3k14 02lm0t => 272 concepts (226 used for prediction) PRED predicted values (max 10 best out of 2398): 0gd_b_ (0.54 #396237, 0.48 #421004, 0.47 #557207), 06nns1 (0.54 #396237, 0.48 #421004, 0.47 #557207), 0136p1 (0.54 #396237, 0.47 #361566, 0.47 #165919), 02lt8 (0.54 #396237, 0.47 #165919, 0.46 #321944), 02bkdn (0.48 #421004, 0.47 #557207, 0.47 #165919), 01309x (0.47 #361566, 0.47 #165919, 0.46 #321944), 023slg (0.47 #361566, 0.47 #165919, 0.46 #321944), 05bnx3j (0.47 #165919, 0.46 #321944, 0.46 #264985), 026g4l_ (0.47 #165919, 0.46 #321944, 0.46 #264985), 0c4qzm (0.47 #165919, 0.46 #321944, 0.46 #264985) >> Best rule #396237 for best value: >> intensional similarity = 3 >> extensional distance = 158 >> proper extension: 05sb1; 0r0ss; 050tt8; >> query: (?x3052, ?x5853) <- contains(?x3052, ?x1151), place_of_birth(?x5853, ?x3052), people(?x1050, ?x5853) >> conf = 0.54 => this is the best rule for 4 predicted values *> Best rule #7120 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 05k7sb; *> query: (?x3052, 0d3k14) <- location(?x12258, ?x3052), ?x12258 = 019fz, contains(?x3052, ?x1151) *> conf = 0.20 ranks of expected_values: 38, 2161 EVAL 01cx_ location! 02lm0t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 272.000 226.000 0.544 http://example.org/people/person/places_lived./people/place_lived/location EVAL 01cx_ location! 0d3k14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 272.000 226.000 0.544 http://example.org/people/person/places_lived./people/place_lived/location EVAL 01cx_ location! 04vq3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 272.000 226.000 0.544 http://example.org/people/person/places_lived./people/place_lived/location EVAL 01cx_ location! 01vsy95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 272.000 226.000 0.544 http://example.org/people/person/places_lived./people/place_lived/location #22712-072x7s PRED entity: 072x7s PRED relation: film_crew_role PRED expected values: 02r96rf 04pyp5 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 31): 02r96rf (0.68 #549, 0.64 #723, 0.64 #515), 0dxtw (0.44 #555, 0.43 #521, 0.39 #729), 01vx2h (0.36 #556, 0.35 #487, 0.32 #522), 01pvkk (0.29 #626, 0.27 #1763, 0.27 #557), 02rh1dz (0.25 #720, 0.21 #1514, 0.18 #554), 089g0h (0.25 #720, 0.21 #1514, 0.17 #512), 0d2b38 (0.25 #720, 0.21 #1514, 0.17 #512), 0215hd (0.21 #1514, 0.17 #512, 0.14 #392), 015h31 (0.21 #1514, 0.17 #512, 0.12 #519), 02_n3z (0.21 #1514, 0.17 #512, 0.11 #376) >> Best rule #549 for best value: >> intensional similarity = 4 >> extensional distance = 265 >> proper extension: 0gtsx8c; >> query: (?x1685, 02r96rf) <- language(?x1685, ?x732), country(?x1685, ?x94), crewmember(?x1685, ?x1622), film(?x1018, ?x1685) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1, 13 EVAL 072x7s film_crew_role 04pyp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 74.000 74.000 0.678 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 072x7s film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.678 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22711-0bl2g PRED entity: 0bl2g PRED relation: student! PRED expected values: 04gd8j => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 150): 01ljpm (0.11 #221, 0.03 #747, 0.03 #1273), 0bwfn (0.09 #24470, 0.08 #29204, 0.08 #21840), 01d34b (0.07 #1307, 0.05 #3411, 0.05 #3937), 065y4w7 (0.07 #10533, 0.06 #8955, 0.06 #2643), 08815 (0.06 #1, 0.05 #527, 0.04 #4209), 015nl4 (0.06 #66, 0.05 #22158, 0.05 #28470), 09f2j (0.06 #158, 0.05 #5418, 0.04 #2262), 07w0v (0.06 #19, 0.03 #8961, 0.02 #10539), 033gn8 (0.06 #377, 0.03 #903, 0.03 #1429), 025v3k (0.06 #119, 0.03 #1171, 0.02 #2223) >> Best rule #221 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0d1mp3; >> query: (?x398, 01ljpm) <- award_nominee(?x2444, ?x398), award_winner(?x3001, ?x398), ?x3001 = 026kq4q >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #893 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 014zfs; *> query: (?x398, 04gd8j) <- award_nominee(?x2444, ?x398), award_winner(?x1588, ?x398), diet(?x398, ?x3130) *> conf = 0.03 ranks of expected_values: 35 EVAL 0bl2g student! 04gd8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 133.000 133.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #22710-01n8gr PRED entity: 01n8gr PRED relation: award PRED expected values: 01by1l => 133 concepts (100 used for prediction) PRED predicted values (max 10 best out of 282): 0c4z8 (0.50 #71, 0.29 #2465, 0.25 #4061), 0gqz2 (0.50 #80, 0.22 #14765, 0.19 #4390), 01c427 (0.37 #483, 0.17 #84, 0.15 #16844), 09sb52 (0.35 #26778, 0.20 #38749, 0.19 #39548), 01by1l (0.33 #111, 0.33 #12880, 0.32 #14476), 025m8l (0.33 #118, 0.19 #4390, 0.15 #26338), 01cky2 (0.33 #189, 0.13 #5776, 0.11 #12958), 0f4x7 (0.27 #3622, 0.24 #1228, 0.24 #829), 03qbh5 (0.24 #12969, 0.23 #8181, 0.22 #14565), 054krc (0.22 #14765, 0.21 #7270, 0.18 #28334) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01k98nm; 02dbp7; 01l3mk3; 01jgkj2; >> query: (?x3358, 0c4z8) <- category(?x3358, ?x134), award_nominee(?x3358, ?x3235), artists(?x505, ?x3358), ?x3235 = 02v3yy >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #111 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 01k98nm; 02dbp7; 01l3mk3; 01jgkj2; *> query: (?x3358, 01by1l) <- category(?x3358, ?x134), award_nominee(?x3358, ?x3235), artists(?x505, ?x3358), ?x3235 = 02v3yy *> conf = 0.33 ranks of expected_values: 5 EVAL 01n8gr award 01by1l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 133.000 100.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22709-02d42t PRED entity: 02d42t PRED relation: award PRED expected values: 099t8j => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 247): 02y_rq5 (0.58 #877, 0.50 #89, 0.41 #483), 094qd5 (0.50 #43, 0.47 #831, 0.47 #437), 0cqgl9 (0.49 #971, 0.16 #17731, 0.15 #21278), 0bdwft (0.47 #853, 0.16 #17731, 0.15 #21278), 09qwmm (0.38 #33, 0.35 #427, 0.30 #821), 05pcn59 (0.25 #76, 0.18 #470, 0.16 #17731), 09td7p (0.24 #508, 0.20 #902, 0.19 #114), 01by1l (0.20 #2469, 0.19 #4439, 0.19 #3651), 099t8j (0.19 #133, 0.18 #527, 0.17 #921), 027b9k6 (0.19 #202, 0.18 #596, 0.06 #990) >> Best rule #877 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 01l2fn; 02jt1k; 06lj1m; 028d4v; 01gv_f; 086sj; 01nms7; >> query: (?x4872, 02y_rq5) <- award(?x4872, ?x1008), award(?x5330, ?x1008), ?x5330 = 02f2p7 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #133 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0psss; 014g9y; *> query: (?x4872, 099t8j) <- award(?x4872, ?x1008), ?x1008 = 05zvq6g, award_nominee(?x91, ?x4872) *> conf = 0.19 ranks of expected_values: 9 EVAL 02d42t award 099t8j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 100.000 100.000 0.583 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22708-026w398 PRED entity: 026w398 PRED relation: team! PRED expected values: 0b_71r 0b_6mr => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 12): 0bqthy (0.76 #156, 0.76 #155, 0.74 #28), 0b_6v_ (0.76 #156, 0.76 #155, 0.74 #28), 0b_71r (0.76 #156, 0.76 #155, 0.74 #28), 0b_6mr (0.76 #156, 0.76 #155, 0.74 #28), 0b_6qj (0.76 #156, 0.76 #155, 0.74 #28), 0b_770 (0.76 #156, 0.76 #155, 0.74 #28), 0b_756 (0.76 #156, 0.76 #155, 0.74 #28), 05g_nr (0.76 #156, 0.76 #155, 0.74 #28), 0b_734 (0.76 #156, 0.76 #155, 0.74 #28), 02z6gky (0.67 #157, 0.57 #234) >> Best rule #156 for best value: >> intensional similarity = 35 >> extensional distance = 4 >> proper extension: 03y9p40; >> query: (?x10171, ?x9146) <- team(?x11210, ?x10171), team(?x10736, ?x10171), team(?x9974, ?x10171), team(?x8992, ?x10171), team(?x7378, ?x10171), team(?x7042, ?x10171), team(?x6583, ?x10171), team(?x3797, ?x10171), locations(?x8992, ?x8993), locations(?x8992, ?x3786), locations(?x8992, ?x2740), locations(?x8992, ?x2504), ?x2740 = 0f__1, team(?x8992, ?x9147), team(?x8992, ?x8528), team(?x8992, ?x4938), ?x9974 = 0b_6pv, ?x8993 = 0fsb8, ?x6583 = 0b_75k, ?x7378 = 0bzrxn, ?x8528 = 091tgz, ?x11210 = 0b_6q5, ?x3797 = 0b_6zk, team(?x10736, ?x6847), ?x7042 = 0b_72t, instance_of_recurring_event(?x10736, ?x10863), place_of_birth(?x1870, ?x2504), team(?x9146, ?x4938), ?x6847 = 02r2qt7, contains(?x94, ?x2504), position(?x4938, ?x1579), location_of_ceremony(?x566, ?x2504), contains(?x2504, ?x2388), ?x9147 = 0263cyj, ?x3786 = 071cn >> conf = 0.76 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 4 EVAL 026w398 team! 0b_6mr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 42.000 42.000 0.763 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 026w398 team! 0b_71r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 42.000 42.000 0.763 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #22707-07ddz9 PRED entity: 07ddz9 PRED relation: award_nominee PRED expected values: 02bkdn 016fjj => 74 concepts (29 used for prediction) PRED predicted values (max 10 best out of 913): 02bkdn (0.81 #23379, 0.81 #16365, 0.81 #32729), 0f502 (0.81 #23379, 0.81 #16365, 0.81 #32729), 07ddz9 (0.62 #2110, 0.16 #67800, 0.02 #51434), 016fjj (0.62 #835, 0.16 #67800, 0.02 #51434), 043js (0.31 #9351, 0.02 #7598, 0.02 #51434), 0306ds (0.31 #9351, 0.02 #14601, 0.01 #19277), 03061d (0.31 #9351), 07k2p6 (0.31 #9351), 0306bt (0.31 #9351), 01k8rb (0.31 #9351) >> Best rule #23379 for best value: >> intensional similarity = 3 >> extensional distance = 1358 >> proper extension: 044mz_; 0q9kd; 0184jc; 04bdxl; 02s2ft; 06qgvf; 0grwj; 03qcq; 05bnp0; 016qtt; ... >> query: (?x10167, ?x1871) <- gender(?x10167, ?x231), award_nominee(?x1871, ?x10167), type_of_union(?x10167, ?x566) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1, 4 EVAL 07ddz9 award_nominee 016fjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 29.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 07ddz9 award_nominee 02bkdn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 29.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22706-0kvf3b PRED entity: 0kvf3b PRED relation: film! PRED expected values: 031sg0 => 81 concepts (40 used for prediction) PRED predicted values (max 10 best out of 749): 02wb6d (0.47 #72939, 0.45 #14591, 0.43 #79191), 02qx1m2 (0.47 #72939, 0.45 #14591, 0.43 #79191), 0p51w (0.22 #14592, 0.15 #62519, 0.15 #56268), 044qx (0.08 #734, 0.05 #2818, 0.05 #4902), 0z4s (0.06 #8407, 0.05 #10491, 0.04 #18827), 0j_c (0.06 #411, 0.05 #2495, 0.05 #4579), 0c0k1 (0.06 #1511, 0.04 #3595, 0.04 #5679), 039bp (0.06 #181, 0.03 #2265, 0.03 #4349), 013sg6 (0.06 #1639, 0.03 #3723, 0.03 #5807), 0chsq (0.06 #79, 0.03 #2163, 0.03 #4247) >> Best rule #72939 for best value: >> intensional similarity = 5 >> extensional distance = 867 >> proper extension: 0456zg; >> query: (?x10549, ?x6971) <- genre(?x10549, ?x53), nominated_for(?x9095, ?x10549), nominated_for(?x6971, ?x10549), film(?x9095, ?x4504), award_winner(?x9095, ?x1357) >> conf = 0.47 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0kvf3b film! 031sg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 40.000 0.471 http://example.org/film/actor/film./film/performance/film #22705-0j_tw PRED entity: 0j_tw PRED relation: titles! PRED expected values: 01z4y => 80 concepts (77 used for prediction) PRED predicted values (max 10 best out of 81): 01z4y (0.45 #2306, 0.42 #2099, 0.37 #4688), 07s9rl0 (0.38 #513, 0.36 #410, 0.33 #205), 01jfsb (0.33 #20, 0.19 #944, 0.14 #1875), 03mqtr (0.33 #46, 0.09 #455, 0.08 #558), 04xvlr (0.29 #310, 0.27 #413, 0.23 #516), 02l7c8 (0.23 #409, 0.19 #4446, 0.18 #5380), 05p553 (0.23 #409, 0.19 #4446, 0.18 #5380), 04t36 (0.20 #110, 0.17 #212, 0.14 #314), 09blyk (0.20 #149, 0.07 #1489, 0.07 #971), 07ssc (0.17 #214, 0.14 #316, 0.11 #728) >> Best rule #2306 for best value: >> intensional similarity = 4 >> extensional distance = 226 >> proper extension: 08cfr1; >> query: (?x2104, 01z4y) <- genre(?x2104, ?x258), featured_film_locations(?x2104, ?x4980), film(?x919, ?x2104), ?x258 = 05p553 >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j_tw titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 77.000 0.452 http://example.org/media_common/netflix_genre/titles #22704-0dfjb8 PRED entity: 0dfjb8 PRED relation: languages PRED expected values: 03k50 09bnf => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 16): 03k50 (0.55 #213, 0.55 #178, 0.54 #248), 09bnf (0.38 #140, 0.31 #280, 0.30 #350), 02hxcvy (0.18 #233, 0.10 #163, 0.09 #198), 055qm (0.18 #231, 0.10 #161, 0.09 #196), 01c7y (0.15 #273, 0.12 #133, 0.09 #238), 03_9r (0.14 #74, 0.02 #1229, 0.02 #564), 064_8sq (0.12 #118, 0.12 #293, 0.11 #328), 0121sr (0.10 #170, 0.09 #240, 0.09 #205), 0688f (0.10 #166, 0.09 #201, 0.02 #411), 0jzc (0.09 #2311, 0.07 #2487, 0.03 #362) >> Best rule #213 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 084z0w; 02n1p5; 02wmbg; 050llt; >> query: (?x5120, 03k50) <- languages(?x5120, ?x5121), profession(?x5120, ?x319), ?x5121 = 07c9s, film(?x5120, ?x2892) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0dfjb8 languages 09bnf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.545 http://example.org/people/person/languages EVAL 0dfjb8 languages 03k50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.545 http://example.org/people/person/languages #22703-0hz35 PRED entity: 0hz35 PRED relation: source PRED expected values: 0jbk9 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #45, 0.91 #28, 0.91 #30) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x11730, 0jbk9) <- category(?x11730, ?x134), ?x134 = 08mbj5d, place(?x11730, ?x11730) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hz35 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #22702-06w2sn5 PRED entity: 06w2sn5 PRED relation: instrumentalists! PRED expected values: 07gql => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 114): 05148p4 (0.48 #106, 0.37 #623, 0.35 #451), 018vs (0.34 #3200, 0.32 #529, 0.31 #3976), 026t6 (0.25 #3, 0.16 #606, 0.14 #520), 0l14md (0.25 #7, 0.15 #438, 0.14 #610), 0dwtp (0.25 #16, 0.04 #102, 0.04 #274), 03qjg (0.19 #481, 0.18 #2202, 0.18 #2461), 0l14qv (0.15 #608, 0.13 #522, 0.11 #436), 018j2 (0.09 #4001, 0.09 #4087, 0.09 #3225), 03gvt (0.09 #150, 0.06 #3252, 0.06 #4028), 0mkg (0.09 #96, 0.04 #1732, 0.04 #3198) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 01hrqc; >> query: (?x1462, 05148p4) <- profession(?x1462, ?x220), artists(?x3562, ?x1462), ?x220 = 016z4k, friend(?x1462, ?x6577) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2711 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 318 *> proper extension: 01nqfh_; 0274ck; 0k4gf; 01p45_v; 01qkqwg; 01ky2h; 012zng; 02jg92; 01tp5bj; 02ck1; ... *> query: (?x1462, 07gql) <- profession(?x1462, ?x131), artists(?x3562, ?x1462), instrumentalists(?x227, ?x1462), place_of_birth(?x1462, ?x9699) *> conf = 0.04 ranks of expected_values: 29 EVAL 06w2sn5 instrumentalists! 07gql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 125.000 125.000 0.478 http://example.org/music/instrument/instrumentalists #22701-034qzw PRED entity: 034qzw PRED relation: executive_produced_by PRED expected values: 06m6z6 => 84 concepts (56 used for prediction) PRED predicted values (max 10 best out of 61): 06pj8 (0.10 #1320, 0.07 #2836, 0.07 #3089), 0343h (0.09 #295, 0.05 #1307, 0.05 #2319), 079vf (0.06 #1519, 0.05 #1772, 0.04 #1014), 05hj_k (0.05 #1868, 0.05 #1110, 0.05 #605), 02q_cc (0.05 #1293, 0.04 #2305, 0.03 #2809), 06q8hf (0.05 #674, 0.04 #6741, 0.04 #6487), 04pqqb (0.05 #624, 0.02 #1382, 0.02 #1634), 02mt4k (0.05 #626, 0.02 #2141), 02q42j_ (0.05 #644, 0.01 #3675, 0.01 #5446), 0b13g7 (0.05 #593, 0.01 #3624, 0.01 #5395) >> Best rule #1320 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0crh5_f; >> query: (?x2102, 06pj8) <- genre(?x2102, ?x258), film_distribution_medium(?x2102, ?x2099), film_crew_role(?x2102, ?x137), production_companies(?x2102, ?x4564) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 034qzw executive_produced_by 06m6z6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 56.000 0.104 http://example.org/film/film/executive_produced_by #22700-043ljr PRED entity: 043ljr PRED relation: category PRED expected values: 08mbj5d => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.81 #110, 0.81 #109, 0.81 #42) >> Best rule #110 for best value: >> intensional similarity = 6 >> extensional distance = 517 >> proper extension: 0gkkf; 07tl0; 018m5q; 0143hl; 0c_zj; 01rr31; 01sjz_; 0d07s; 01f2xy; 01wv24; ... >> query: (?x3006, ?x134) <- state_province_region(?x3006, ?x2020), contains(?x2020, ?x13869), contains(?x2020, ?x4989), place_of_birth(?x8991, ?x4989), category(?x13869, ?x134), country(?x2020, ?x94) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043ljr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.813 http://example.org/common/topic/webpage./common/webpage/category #22699-04_j5s PRED entity: 04_j5s PRED relation: institution! PRED expected values: 02cq61 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.66 #334, 0.66 #378, 0.61 #1157), 014mlp (0.64 #1382, 0.60 #1338, 0.58 #1072), 019v9k (0.56 #384, 0.54 #340, 0.51 #1385), 03bwzr4 (0.51 #389, 0.51 #345, 0.39 #147), 016t_3 (0.49 #379, 0.47 #335, 0.44 #4), 0bkj86 (0.43 #339, 0.42 #383, 0.31 #8), 02cq61 (0.38 #17, 0.17 #150, 0.15 #194), 07s6fsf (0.33 #178, 0.32 #134, 0.32 #376), 04zx3q1 (0.33 #333, 0.31 #2, 0.31 #377), 027f2w (0.31 #10, 0.29 #385, 0.29 #341) >> Best rule #334 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 0gl5_; 0yl_3; 0yldt; >> query: (?x11711, 02h4rq6) <- citytown(?x11711, ?x739), institution(?x3437, ?x11711), ?x3437 = 02_xgp2 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 07szy; 01wrwf; *> query: (?x11711, 02cq61) <- contains(?x94, ?x11711), institution(?x7636, ?x11711), institution(?x1519, ?x11711), ?x1519 = 013zdg, ?x7636 = 01rr_d *> conf = 0.38 ranks of expected_values: 7 EVAL 04_j5s institution! 02cq61 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 122.000 122.000 0.660 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22698-016yxn PRED entity: 016yxn PRED relation: award PRED expected values: 027986c => 113 concepts (93 used for prediction) PRED predicted values (max 10 best out of 183): 02rdyk7 (0.32 #1472, 0.29 #536, 0.27 #771), 09d28z (0.29 #893, 0.22 #1594, 0.21 #658), 02rdxsh (0.29 #935, 0.27 #5605, 0.27 #1636), 0gs9p (0.29 #935, 0.27 #5605, 0.27 #1636), 019f4v (0.29 #935, 0.27 #5605, 0.27 #1636), 0gr4k (0.29 #935, 0.27 #5605, 0.27 #1636), 03hkv_r (0.29 #935, 0.27 #5605, 0.27 #1636), 02w_6xj (0.25 #859, 0.21 #1560, 0.17 #624), 0gr0m (0.21 #525, 0.19 #760, 0.16 #1929), 0gq9h (0.20 #1932, 0.19 #763, 0.14 #528) >> Best rule #1472 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 0jyx6; 0gvt53w; >> query: (?x11942, 02rdyk7) <- award(?x11942, ?x591), nominated_for(?x1063, ?x11942), ?x1063 = 02rdxsh, film(?x516, ?x11942) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1440 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 0jyx6; 0gvt53w; *> query: (?x11942, 027986c) <- award(?x11942, ?x591), nominated_for(?x1063, ?x11942), ?x1063 = 02rdxsh, film(?x516, ?x11942) *> conf = 0.12 ranks of expected_values: 26 EVAL 016yxn award 027986c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 113.000 93.000 0.319 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22697-09tqkv2 PRED entity: 09tqkv2 PRED relation: language PRED expected values: 02h40lc => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.90 #4177, 0.89 #4117, 0.89 #2441), 064_8sq (0.30 #200, 0.22 #911, 0.20 #674), 04306rv (0.20 #183, 0.12 #1071, 0.11 #1965), 06b_j (0.14 #498, 0.11 #141, 0.10 #201), 06nm1 (0.12 #604, 0.11 #900, 0.11 #426), 02hwyss (0.11 #160, 0.10 #220, 0.09 #338), 02hxcvy (0.11 #152, 0.09 #330, 0.09 #271), 0cjk9 (0.11 #122, 0.09 #300, 0.09 #241), 06mp7 (0.11 #134, 0.09 #312, 0.09 #253), 02bjrlw (0.10 #179, 0.09 #1961, 0.09 #238) >> Best rule #4177 for best value: >> intensional similarity = 4 >> extensional distance = 1170 >> proper extension: 0gtsx8c; 0dq626; 0gx9rvq; 09p35z; 0crfwmx; 0jjy0; 07sc6nw; 02qrv7; 04zyhx; 018nnz; ... >> query: (?x2052, 02h40lc) <- film(?x1414, ?x2052), film(?x2028, ?x2052), country(?x2052, ?x94), award_winner(?x2028, ?x1384) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09tqkv2 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.897 http://example.org/film/film/language #22696-0dy04 PRED entity: 0dy04 PRED relation: institution! PRED expected values: 03bwzr4 => 160 concepts (109 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.82 #318, 0.81 #403, 0.78 #467), 014mlp (0.80 #321, 0.79 #406, 0.74 #788), 019v9k (0.76 #473, 0.72 #324, 0.72 #578), 03bwzr4 (0.72 #328, 0.71 #413, 0.64 #75), 016t_3 (0.61 #256, 0.60 #468, 0.60 #404), 07s6fsf (0.48 #402, 0.47 #317, 0.42 #254), 0bjrnt (0.41 #69, 0.35 #90, 0.33 #429), 013zdg (0.33 #430, 0.31 #472, 0.30 #746), 01rr_d (0.30 #438, 0.30 #331, 0.29 #416), 071tyz (0.28 #2349, 0.23 #72, 0.22 #93) >> Best rule #318 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 06pwq; 01w3v; 01w5m; 08qnnv; >> query: (?x2637, 02h4rq6) <- major_field_of_study(?x2637, ?x742), ?x742 = 05qjt, student(?x2637, ?x2800), institution(?x1526, ?x2637), ?x1526 = 0bkj86 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #328 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 06pwq; 01w3v; 01w5m; 08qnnv; *> query: (?x2637, 03bwzr4) <- major_field_of_study(?x2637, ?x742), ?x742 = 05qjt, student(?x2637, ?x2800), institution(?x1526, ?x2637), ?x1526 = 0bkj86 *> conf = 0.72 ranks of expected_values: 4 EVAL 0dy04 institution! 03bwzr4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 160.000 109.000 0.825 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22695-058frd PRED entity: 058frd PRED relation: award_winner! PRED expected values: 05b1610 => 99 concepts (95 used for prediction) PRED predicted values (max 10 best out of 199): 05p1dby (0.37 #15483, 0.37 #15914, 0.37 #18066), 07bdd_ (0.37 #15483, 0.37 #15914, 0.37 #18066), 0f_nbyh (0.37 #15483, 0.37 #15914, 0.37 #18066), 0cjyzs (0.22 #2685, 0.18 #3115, 0.16 #4405), 05f4m9q (0.18 #443, 0.14 #873, 0.10 #27962), 09sb52 (0.16 #6921, 0.10 #9071, 0.10 #9501), 0fbtbt (0.14 #2809, 0.12 #3239, 0.10 #3669), 027c924 (0.14 #10, 0.09 #5600, 0.09 #440), 01l78d (0.14 #285, 0.05 #4155, 0.04 #2005), 0j298t8 (0.14 #404) >> Best rule #15483 for best value: >> intensional similarity = 3 >> extensional distance = 1372 >> proper extension: 086k8; 03zqc1; 04lgymt; 017s11; 016tt2; 04rcr; 0g1rw; 0785v8; 0kx4m; 05qd_; ... >> query: (?x6086, ?x198) <- award_nominee(?x6086, ?x123), award_winner(?x6086, ?x10064), award(?x6086, ?x198) >> conf = 0.37 => this is the best rule for 3 predicted values *> Best rule #6881 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 348 *> proper extension: 04dyqk; 03c9pqt; 024c1b; *> query: (?x6086, ?x102) <- produced_by(?x7800, ?x6086), nominated_for(?x102, ?x7800) *> conf = 0.06 ranks of expected_values: 69 EVAL 058frd award_winner! 05b1610 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 99.000 95.000 0.370 http://example.org/award/award_category/winners./award/award_honor/award_winner #22694-0dnkmq PRED entity: 0dnkmq PRED relation: film_crew_role PRED expected values: 09vw2b7 0dxtw => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 34): 09vw2b7 (0.80 #109, 0.76 #348, 0.72 #1212), 0dxtw (0.58 #44, 0.51 #732, 0.47 #180), 01pvkk (0.44 #793, 0.32 #733, 0.30 #594), 02ynfr (0.44 #793, 0.25 #83, 0.24 #980), 02rh1dz (0.44 #793, 0.21 #731, 0.21 #214), 02_n3z (0.44 #793, 0.20 #1, 0.15 #104), 033smt (0.44 #793, 0.20 #129, 0.10 #2898), 089fss (0.44 #793, 0.17 #74, 0.12 #555), 01xy5l_ (0.44 #793, 0.16 #183, 0.15 #115), 0215hd (0.44 #793, 0.15 #983, 0.15 #120) >> Best rule #109 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 01pj_5; >> query: (?x10515, 09vw2b7) <- language(?x10515, ?x254), film_crew_role(?x10515, ?x137), featured_film_locations(?x10515, ?x11240), featured_film_locations(?x10515, ?x1036), ?x1036 = 080h2, state(?x11240, ?x1426) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0dnkmq film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dnkmq film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22693-044n3h PRED entity: 044n3h PRED relation: place_of_birth PRED expected values: 0hsqf => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 24): 02_286 (0.27 #45776, 0.27 #17607, 0.27 #47889), 0pfd9 (0.05 #628), 0nq_b (0.05 #589), 01rmjw (0.05 #314), 0chrx (0.05 #305), 0f2tj (0.05 #248), 0f2w0 (0.05 #62), 0156q (0.05 #57), 0r7fy (0.05 #49), 0cr3d (0.04 #7136, 0.03 #16996, 0.03 #2207) >> Best rule #45776 for best value: >> intensional similarity = 2 >> extensional distance = 2301 >> proper extension: 0854hr; 0qkj7; >> query: (?x10401, ?x739) <- gender(?x10401, ?x514), location(?x10401, ?x739) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 044n3h place_of_birth 0hsqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.275 http://example.org/people/person/place_of_birth #22692-02c6d PRED entity: 02c6d PRED relation: genre PRED expected values: 07s9rl0 => 86 concepts (52 used for prediction) PRED predicted values (max 10 best out of 98): 01jfsb (0.97 #3946, 0.70 #358, 0.69 #473), 07s9rl0 (0.73 #4864, 0.73 #4168, 0.71 #1963), 02kdv5l (0.61 #4982, 0.50 #5213, 0.48 #5328), 02l7c8 (0.56 #131, 0.56 #16, 0.40 #2673), 0lsxr (0.56 #239, 0.39 #1390, 0.34 #3942), 03k9fj (0.40 #5221, 0.39 #5336, 0.27 #4990), 05p553 (0.35 #3822, 0.34 #3357, 0.34 #2431), 04xvlr (0.35 #4169, 0.33 #233, 0.23 #1964), 06n90 (0.30 #359, 0.24 #704, 0.24 #819), 060__y (0.24 #4184, 0.22 #248, 0.22 #2327) >> Best rule #3946 for best value: >> intensional similarity = 6 >> extensional distance = 367 >> proper extension: 05r3qc; 06_sc3; >> query: (?x1252, 01jfsb) <- film_crew_role(?x1252, ?x137), genre(?x1252, ?x3613), genre(?x9786, ?x3613), genre(?x6520, ?x3613), ?x9786 = 06bc59, ?x6520 = 02bg55 >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #4864 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 742 *> proper extension: 08g_jw; *> query: (?x1252, 07s9rl0) <- film_crew_role(?x1252, ?x137), film_release_distribution_medium(?x1252, ?x81), genre(?x1252, ?x600), genre(?x3482, ?x600), ?x3482 = 017z49 *> conf = 0.73 ranks of expected_values: 2 EVAL 02c6d genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 52.000 0.970 http://example.org/film/film/genre #22691-01pgzn_ PRED entity: 01pgzn_ PRED relation: profession PRED expected values: 02hrh1q => 142 concepts (141 used for prediction) PRED predicted values (max 10 best out of 84): 02hrh1q (0.91 #2808, 0.91 #5015, 0.90 #14), 09jwl (0.60 #6343, 0.57 #2372, 0.54 #4285), 0dxtg (0.59 #896, 0.41 #4867, 0.40 #3985), 01d_h8 (0.53 #888, 0.43 #1182, 0.41 #3830), 0nbcg (0.49 #2385, 0.48 #620, 0.47 #6356), 0cbd2 (0.44 #7212, 0.44 #4860, 0.44 #7359), 0dz3r (0.44 #2355, 0.40 #4268, 0.40 #590), 03gjzk (0.43 #898, 0.27 #5016, 0.27 #6927), 018gz8 (0.41 #900, 0.25 #14858, 0.19 #4871), 01c72t (0.32 #1348, 0.24 #8701, 0.22 #12233) >> Best rule #2808 for best value: >> intensional similarity = 2 >> extensional distance = 174 >> proper extension: 02mhfy; 02fb1n; 0143wl; 01vzxmq; 01kmd4; 015g_7; 02pzck; 01507p; 01nglk; >> query: (?x2352, 02hrh1q) <- participant(?x1416, ?x2352), actor(?x416, ?x2352) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pgzn_ profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 141.000 0.909 http://example.org/people/person/profession #22690-02km0m PRED entity: 02km0m PRED relation: institution! PRED expected values: 016t_3 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 22): 03bwzr4 (0.59 #107, 0.57 #14, 0.52 #132), 019v9k (0.57 #1074, 0.57 #1426, 0.56 #1191), 016t_3 (0.57 #3, 0.44 #96, 0.40 #1421), 02_xgp2 (0.49 #105, 0.45 #1430, 0.43 #130), 07s6fsf (0.43 #1, 0.30 #1067, 0.29 #94), 0bkj86 (0.42 #100, 0.37 #125, 0.34 #1306), 022h5x (0.36 #44, 0.29 #20, 0.17 #113), 04zx3q1 (0.34 #95, 0.32 #120, 0.29 #1516), 028dcg (0.29 #1516, 0.17 #275, 0.17 #160), 013zdg (0.29 #286, 0.29 #6, 0.23 #495) >> Best rule #107 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 06pwq; 0kz2w; 0gkkf; 0ks67; 0c5x_; 0ymcz; >> query: (?x6541, 03bwzr4) <- school_type(?x6541, ?x4994), ?x4994 = 07tf8, major_field_of_study(?x6541, ?x4268), student(?x4268, ?x906) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02gr81; *> query: (?x6541, 016t_3) <- school_type(?x6541, ?x4994), ?x4994 = 07tf8, major_field_of_study(?x6541, ?x4268), ?x4268 = 02822 *> conf = 0.57 ranks of expected_values: 3 EVAL 02km0m institution! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 140.000 0.593 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22689-06196 PRED entity: 06196 PRED relation: award! PRED expected values: 04r68 0210f1 03hpr => 91 concepts (48 used for prediction) PRED predicted values (max 10 best out of 3417): 06jcc (0.81 #97916, 0.80 #77658, 0.80 #81035), 034bs (0.81 #97916, 0.80 #77658, 0.80 #81035), 07w21 (0.81 #97916, 0.80 #77658, 0.80 #81035), 03j90 (0.81 #97916, 0.80 #77658, 0.80 #81035), 01dzz7 (0.60 #13953, 0.37 #37596, 0.35 #30839), 09dt7 (0.60 #13812, 0.35 #30698, 0.33 #37455), 0c3kw (0.60 #13943, 0.35 #30829, 0.33 #37586), 0gd_s (0.60 #16163, 0.33 #2660, 0.31 #33049), 01g6bk (0.60 #16702, 0.33 #3199, 0.30 #40345), 01963w (0.60 #13836, 0.33 #333, 0.25 #44234) >> Best rule #97916 for best value: >> intensional similarity = 6 >> extensional distance = 64 >> proper extension: 04ljl_l; 05b4l5x; 05f4m9q; 05zr6wv; 05zkcn5; 0gkvb7; 0f4x7; 0gr4k; 05b1610; 09qvc0; ... >> query: (?x10270, ?x476) <- award_winner(?x10270, ?x476), award(?x6796, ?x10270), award(?x442, ?x10270), award(?x10269, ?x10270), place_of_birth(?x6796, ?x12190), origin(?x442, ?x1658) >> conf = 0.81 => this is the best rule for 4 predicted values *> Best rule #14984 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 040vk98; *> query: (?x10270, 04r68) <- award_winner(?x10270, ?x477), award_winner(?x10270, ?x476), disciplines_or_subjects(?x10270, ?x1013), peers(?x477, ?x6796), profession(?x477, ?x353), ?x476 = 07w21 *> conf = 0.60 ranks of expected_values: 11, 23, 172 EVAL 06196 award! 03hpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 91.000 48.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 06196 award! 0210f1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 91.000 48.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 06196 award! 04r68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 91.000 48.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22688-01tfck PRED entity: 01tfck PRED relation: film PRED expected values: 02ny6g => 83 concepts (66 used for prediction) PRED predicted values (max 10 best out of 388): 017f3m (0.48 #23275, 0.47 #57299, 0.41 #28647), 09y6pb (0.48 #23275, 0.47 #57299, 0.41 #28647), 0418wg (0.12 #401, 0.03 #109235, 0.03 #111026), 06z8s_ (0.12 #130, 0.03 #109235, 0.03 #112817), 02qr3k8 (0.12 #1289, 0.02 #34020, 0.02 #6659), 0dj0m5 (0.12 #97), 01shy7 (0.07 #2213, 0.04 #4003, 0.03 #12954), 08r4x3 (0.06 #154, 0.03 #109235, 0.03 #111026), 027pfg (0.06 #1223, 0.03 #109235, 0.03 #111026), 0284b56 (0.06 #984, 0.03 #109235, 0.03 #111026) >> Best rule #23275 for best value: >> intensional similarity = 2 >> extensional distance = 886 >> proper extension: 02lq10; 01m7f5r; >> query: (?x2200, ?x1820) <- people(?x1423, ?x2200), nominated_for(?x2200, ?x1820) >> conf = 0.48 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01tfck film 02ny6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 66.000 0.476 http://example.org/film/actor/film./film/performance/film #22687-02lq5w PRED entity: 02lq5w PRED relation: medal! PRED expected values: 0chghy 01ls2 05qhw 06mzp 0k6nt 09pmkv 01p1v 035dk 0166v 05r7t 06m_5 0jhd => 3 concepts (3 used for prediction) PRED predicted values (max 10 best out of 461): 05qhw (0.75 #68, 0.75 #106, 0.70 #66), 0k6nt (0.75 #68, 0.75 #106, 0.70 #66), 01p1v (0.75 #68, 0.75 #106, 0.70 #66), 0chghy (0.75 #68, 0.75 #106, 0.70 #66), 05sb1 (0.75 #68, 0.75 #106, 0.70 #66), 01ls2 (0.75 #68, 0.75 #106, 0.70 #66), 06mzp (0.75 #68, 0.75 #106, 0.67 #57), 05r7t (0.75 #68, 0.70 #66, 0.50 #48), 034m8 (0.75 #68, 0.70 #66, 0.49 #109), 0165b (0.75 #68, 0.70 #66, 0.49 #109) >> Best rule #68 for best value: >> intensional similarity = 381 >> extensional distance = 1 >> proper extension: 02lpp7; >> query: (?x1242, ?x456) <- medal(?x10801, ?x1242), medal(?x9251, ?x1242), medal(?x8593, ?x1242), medal(?x8197, ?x1242), medal(?x7747, ?x1242), medal(?x7287, ?x1242), medal(?x6305, ?x1242), medal(?x5360, ?x1242), medal(?x5114, ?x1242), medal(?x4954, ?x1242), medal(?x4059, ?x1242), medal(?x3855, ?x1242), medal(?x3635, ?x1242), medal(?x2984, ?x1242), medal(?x2843, ?x1242), medal(?x2513, ?x1242), medal(?x2152, ?x1242), medal(?x2146, ?x1242), medal(?x2000, ?x1242), medal(?x1892, ?x1242), medal(?x1781, ?x1242), medal(?x1536, ?x1242), medal(?x1499, ?x1242), medal(?x1471, ?x1242), medal(?x1453, ?x1242), medal(?x1264, ?x1242), medal(?x1241, ?x1242), medal(?x1229, ?x1242), medal(?x1023, ?x1242), medal(?x1003, ?x1242), medal(?x583, ?x1242), medal(?x404, ?x1242), medal(?x205, ?x1242), medal(?x151, ?x1242), medal(?x47, ?x1242), medal(?x7688, ?x1242), medal(?x5176, ?x1242), medal(?x4424, ?x1242), medal(?x3971, ?x1242), medal(?x2630, ?x1242), medal(?x2233, ?x1242), medal(?x2134, ?x1242), medal(?x2131, ?x1242), medal(?x2043, ?x1242), medal(?x867, ?x1242), medal(?x778, ?x1242), medal(?x584, ?x1242), medal(?x418, ?x1242), ?x9251 = 07tp2, ?x3635 = 019pcs, ?x5114 = 05vz3zq, adjustment_currency(?x5360, ?x170), ?x867 = 0l6ny, combatants(?x10801, ?x1611), ?x404 = 047lj, contains(?x9006, ?x10801), ?x778 = 0kbvb, sports(?x7688, ?x6150), sports(?x7688, ?x5182), sports(?x7688, ?x4833), sports(?x7688, ?x4045), sports(?x7688, ?x1967), sports(?x7688, ?x1121), sports(?x7688, ?x779), sports(?x7688, ?x471), administrative_parent(?x5360, ?x551), ?x551 = 02j71, ?x2513 = 05b4w, ?x2630 = 0swff, ?x1003 = 03gj2, ?x4045 = 06z6r, teams(?x7747, ?x11379), film_release_region(?x11839, ?x7747), film_release_region(?x10080, ?x7747), film_release_region(?x9657, ?x7747), film_release_region(?x9216, ?x7747), film_release_region(?x9194, ?x7747), film_release_region(?x9002, ?x7747), film_release_region(?x8292, ?x7747), film_release_region(?x8137, ?x7747), film_release_region(?x7554, ?x7747), film_release_region(?x6932, ?x7747), film_release_region(?x6556, ?x7747), film_release_region(?x6520, ?x7747), film_release_region(?x6446, ?x7747), film_release_region(?x6422, ?x7747), film_release_region(?x6376, ?x7747), film_release_region(?x6270, ?x7747), film_release_region(?x6095, ?x7747), film_release_region(?x6014, ?x7747), film_release_region(?x5016, ?x7747), film_release_region(?x4684, ?x7747), film_release_region(?x4464, ?x7747), film_release_region(?x4290, ?x7747), film_release_region(?x3748, ?x7747), film_release_region(?x3745, ?x7747), film_release_region(?x3606, ?x7747), film_release_region(?x3498, ?x7747), film_release_region(?x2933, ?x7747), film_release_region(?x2896, ?x7747), film_release_region(?x2889, ?x7747), film_release_region(?x2695, ?x7747), film_release_region(?x2441, ?x7747), film_release_region(?x2155, ?x7747), film_release_region(?x1927, ?x7747), film_release_region(?x1915, ?x7747), film_release_region(?x1904, ?x7747), film_release_region(?x1724, ?x7747), film_release_region(?x1421, ?x7747), film_release_region(?x972, ?x7747), film_release_region(?x86, ?x7747), religion(?x7747, ?x492), ?x583 = 015fr, ?x7287 = 05b7q, ?x2155 = 0407yfx, ?x6520 = 02bg55, organization(?x5360, ?x5701), organization(?x5360, ?x4403), organization(?x5360, ?x312), ?x584 = 0l98s, film_release_region(?x11209, ?x151), film_release_region(?x11074, ?x151), film_release_region(?x10095, ?x151), film_release_region(?x9832, ?x151), film_release_region(?x9501, ?x151), film_release_region(?x8193, ?x151), film_release_region(?x7887, ?x151), film_release_region(?x7680, ?x151), film_release_region(?x7629, ?x151), film_release_region(?x7126, ?x151), film_release_region(?x6882, ?x151), film_release_region(?x6283, ?x151), film_release_region(?x6181, ?x151), film_release_region(?x6078, ?x151), film_release_region(?x5704, ?x151), film_release_region(?x5644, ?x151), film_release_region(?x4950, ?x151), film_release_region(?x4448, ?x151), film_release_region(?x4047, ?x151), film_release_region(?x3599, ?x151), film_release_region(?x3566, ?x151), film_release_region(?x3423, ?x151), film_release_region(?x3226, ?x151), film_release_region(?x3137, ?x151), film_release_region(?x3035, ?x151), film_release_region(?x2961, ?x151), film_release_region(?x2783, ?x151), film_release_region(?x2655, ?x151), film_release_region(?x2318, ?x151), film_release_region(?x1803, ?x151), film_release_region(?x1744, ?x151), film_release_region(?x1602, ?x151), film_release_region(?x1525, ?x151), film_release_region(?x1470, ?x151), film_release_region(?x1259, ?x151), film_release_region(?x1202, ?x151), film_release_region(?x1080, ?x151), film_release_region(?x1012, ?x151), film_release_region(?x781, ?x151), film_release_region(?x641, ?x151), film_release_region(?x66, ?x151), adjoins(?x151, ?x8260), adjoins(?x151, ?x1227), ?x10095 = 0267wwv, ?x3599 = 0kxf1, official_language(?x151, ?x2502), ?x6014 = 031ldd, ?x6095 = 0bq6ntw, ?x2152 = 06mkj, ?x3423 = 09g7vfw, olympics(?x7479, ?x7688), olympics(?x985, ?x7688), olympics(?x456, ?x7688), ?x1536 = 06c1y, religion(?x5360, ?x109), ?x3498 = 02fqrf, ?x2000 = 0d0kn, medal(?x7747, ?x422), ?x4424 = 0blfl, ?x1915 = 0fq7dv_, ?x6078 = 04pk1f, ?x9194 = 0fpgp26, entity_involved(?x6982, ?x10801), nationality(?x6406, ?x3855), ?x9657 = 07jqjx, ?x1229 = 059j2, ?x1499 = 01znc_, ?x1744 = 035yn8, ?x3035 = 0j43swk, ?x6150 = 07_53, ?x1803 = 0g9wdmc, ?x1012 = 0bwfwpj, countries_spoken_in(?x5607, ?x8197), ?x2933 = 0407yj_, country(?x668, ?x7747), ?x5176 = 0sx92, administrative_area_type(?x8593, ?x2792), vacationer(?x151, ?x12047), vacationer(?x151, ?x9585), vacationer(?x151, ?x7025), adjoins(?x5360, ?x8742), ?x5704 = 0h95zbp, contains(?x8593, ?x10757), locations(?x7241, ?x3855), geographic_distribution(?x9148, ?x7747), ?x1471 = 07t21, religion(?x7833, ?x492), religion(?x3038, ?x492), religion(?x2256, ?x492), ?x6446 = 089j8p, ?x7833 = 0jdx, contains(?x151, ?x3285), place_of_birth(?x2335, ?x4954), form_of_government(?x7479, ?x1926), ?x1121 = 0bynt, olympics(?x766, ?x7688), country(?x4310, ?x151), country(?x3598, ?x151), ?x1264 = 0345h, country(?x14195, ?x8197), ?x766 = 01hp22, ?x2131 = 0lk8j, member_states(?x7695, ?x7747), ?x1080 = 01c22t, ?x2984 = 082fr, combatants(?x3918, ?x7747), ?x9002 = 0ndsl1x, ?x5701 = 0b6css, ?x2783 = 0879bpq, form_of_government(?x8197, ?x48), ?x1724 = 02r8hh_, contains(?x3855, ?x13391), ?x781 = 0gkz15s, ?x3226 = 0gyfp9c, titles(?x812, ?x2655), ?x47 = 027rn, ?x3598 = 03rbzn, ?x1259 = 04hwbq, contains(?x4954, ?x10174), contains(?x1144, ?x7479), religion(?x111, ?x492), ?x312 = 07t65, ?x8193 = 03z9585, nominated_for(?x2183, ?x2655), titles(?x53, ?x6422), jurisdiction_of_office(?x3119, ?x10801), ?x1023 = 0ctw_b, ?x3745 = 03cw411, ?x4403 = 0j7v_, ?x2889 = 040b5k, jurisdiction_of_office(?x182, ?x5360), ?x1453 = 06qd3, nominated_for(?x1641, ?x2655), ?x3038 = 0d0x8, official_language(?x5360, ?x5003), ?x471 = 02vx4, ?x2695 = 047svrl, vacationer(?x3026, ?x12047), ?x10080 = 065ym0c, ?x6376 = 01f85k, ?x86 = 0ds35l9, ?x6882 = 043tvp3, ?x418 = 09n48, film(?x9585, ?x97), ?x2896 = 0645k5, olympics(?x151, ?x6464), ?x4464 = 05pdh86, ?x11209 = 04fjzv, ?x9832 = 01xlqd, film_release_distribution_medium(?x7680, ?x81), adjoins(?x8742, ?x6437), ?x7629 = 02825nf, ?x6305 = 07t_x, ?x1202 = 0gj8t_b, adjoins(?x7747, ?x1122), contains(?x2467, ?x8742), award(?x9585, ?x154), ?x2183 = 02x4w6g, ?x1892 = 02vzc, ?x53 = 07s9rl0, contains(?x1227, ?x191), service_location(?x1540, ?x151), ?x2256 = 07srw, religion(?x1227, ?x962), ?x6181 = 0hv27, film_release_region(?x4668, ?x3855), ?x8292 = 0cmf0m0, ?x3566 = 04jpk2, combatants(?x326, ?x151), ?x1927 = 0by1wkq, ?x1602 = 0gxtknx, ?x779 = 096f8, ?x11839 = 072hx4, ?x1470 = 03twd6, ?x6556 = 05dss7, award_winner(?x4047, ?x163), country(?x2631, ?x3855), ?x3606 = 0gh65c5, nominated_for(?x1243, ?x4047), nominated_for(?x1107, ?x4047), nominated_for(?x277, ?x4047), film_crew_role(?x2961, ?x4305), ?x4059 = 077qn, ?x2134 = 0blg2, ?x11074 = 0jqzt, ?x66 = 014lc_, ?x668 = 07gyv, ?x4448 = 01k60v, ?x2318 = 06v9_x, jurisdiction_of_office(?x900, ?x1227), ?x4305 = 0215hd, ?x3137 = 0htww, ?x326 = 081pw, ?x5182 = 0crlz, ?x4310 = 064vjs, ?x1243 = 0gr0m, adjoins(?x1781, ?x311), ?x8137 = 0gtx63s, taxonomy(?x1227, ?x939), ?x3971 = 0jhn7, ?x4684 = 03nm_fh, spouse(?x2435, ?x9585), adjoins(?x4954, ?x3720), ?x3748 = 05zlld0, ?x2441 = 0cc5mcj, languages(?x1515, ?x5003), ?x6932 = 027pfg, ?x2146 = 03rk0, country(?x3106, ?x1122), ?x5016 = 062zm5h, ?x1107 = 019f4v, ?x4668 = 0bh8x1y, ?x4833 = 018w8, exported_to(?x4164, ?x1122), ?x277 = 0f_nbyh, ?x5644 = 0dll_t2, ?x1421 = 07qg8v, ?x9216 = 08j7lh, countries_spoken_in(?x2890, ?x1122), ?x2233 = 0l6mp, nominated_for(?x2379, ?x7554), ?x1241 = 05cgv, film_release_region(?x1956, ?x8593), ?x1967 = 01cgz, ?x9501 = 0g5qmbz, ?x7126 = 0ds1glg, time_zones(?x8260, ?x2088), film(?x6211, ?x7554), country(?x14657, ?x1781), contains(?x8260, ?x448), ?x4950 = 07k2mq, ?x2379 = 02qvyrt, ?x2043 = 0lv1x, ?x7887 = 04z_3pm, film_release_region(?x6121, ?x7479), ?x1525 = 03qnvdl, ?x641 = 08720, ?x972 = 017gl1, ?x3026 = 0cv3w, ?x205 = 03rjj, film_regional_debut_venue(?x7554, ?x12806), entity_involved(?x12976, ?x1781), film(?x4832, ?x4047), film_format(?x7680, ?x6392), ?x2843 = 016wzw, ?x12806 = 0prpt, ?x4290 = 0gtxj2q, profession(?x7025, ?x967), actor(?x10731, ?x9585), featured_film_locations(?x7554, ?x12926), ?x6283 = 0gmd3k7, ?x422 = 02lq67, ?x1904 = 09146g, ?x6270 = 0g9zljd, ?x939 = 04n6k, language(?x89, ?x2502), organization(?x3855, ?x1062), contains(?x7708, ?x4954), contains(?x1122, ?x12910), ?x985 = 0k6nt, participant(?x9374, ?x12047) >> conf = 0.75 => this is the best rule for 13 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 7, 8, 14, 84, 105, 109, 110 EVAL 02lq5w medal! 0jhd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 06m_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 05r7t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 0166v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 035dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 01p1v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 09pmkv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 06mzp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 01ls2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.754 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #22686-0d060g PRED entity: 0d060g PRED relation: organization PRED expected values: 0_2v 04k4l => 235 concepts (213 used for prediction) PRED predicted values (max 10 best out of 16): 0_2v (0.68 #183, 0.62 #304, 0.62 #673), 018cqq (0.61 #149, 0.61 #577, 0.57 #455), 01rz1 (0.57 #406, 0.56 #161, 0.53 #955), 04k4l (0.50 #44, 0.48 #796, 0.45 #552), 02jxk (0.39 #407, 0.37 #448, 0.36 #570), 041288 (0.38 #2676, 0.35 #3129, 0.35 #3171), 059dn (0.32 #1645, 0.29 #33, 0.29 #13), 0gkjy (0.32 #1645, 0.28 #2751, 0.25 #2668), 085h1 (0.21 #2296, 0.21 #2910, 0.21 #2909), 034h1h (0.18 #4153, 0.02 #1045, 0.02 #3798) >> Best rule #183 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 04ty8; >> query: (?x279, 0_2v) <- country(?x150, ?x279), form_of_government(?x279, ?x1926), region(?x280, ?x279) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0d060g organization 04k4l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 235.000 213.000 0.684 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0d060g organization 0_2v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 235.000 213.000 0.684 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #22685-0fp_v1x PRED entity: 0fp_v1x PRED relation: artists! PRED expected values: 06by7 => 147 concepts (78 used for prediction) PRED predicted values (max 10 best out of 259): 06by7 (0.60 #949, 0.60 #1882, 0.59 #3123), 017_qw (0.53 #5651, 0.52 #7828, 0.51 #9692), 05bt6j (0.44 #3146, 0.35 #1905, 0.28 #22425), 0xhtw (0.44 #1878, 0.39 #635, 0.33 #9025), 016clz (0.40 #3108, 0.39 #624, 0.32 #6527), 03lty (0.39 #646, 0.26 #3750, 0.22 #6549), 0cx7f (0.38 #2001, 0.16 #3242, 0.15 #14435), 05r6t (0.35 #3185, 0.33 #701, 0.19 #3805), 06j6l (0.34 #1289, 0.25 #16516, 0.25 #18380), 011j5x (0.33 #3134, 0.09 #4064, 0.09 #4688) >> Best rule #949 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 0pmw9; 02fybl; >> query: (?x460, 06by7) <- location(?x460, ?x461), role(?x460, ?x212), role(?x460, ?x1166), ?x1166 = 05148p4 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fp_v1x artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 78.000 0.600 http://example.org/music/genre/artists #22684-029v40 PRED entity: 029v40 PRED relation: prequel! PRED expected values: 01npcx => 89 concepts (44 used for prediction) PRED predicted values (max 10 best out of 75): 02sg5v (0.11 #197, 0.06 #738, 0.06 #557), 02vxq9m (0.11 #184, 0.06 #725, 0.06 #544), 0164qt (0.11 #196, 0.06 #737, 0.06 #556), 08nvyr (0.11 #80), 0642ykh (0.06 #657, 0.03 #1199, 0.01 #1741), 05t54s (0.06 #836, 0.01 #3364), 06x43v (0.06 #849, 0.01 #1752, 0.01 #1933), 031ldd (0.06 #825, 0.01 #1728, 0.01 #4440), 03r0g9 (0.06 #607, 0.01 #2233), 08c6k9 (0.06 #871) >> Best rule #197 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0bq8tmw; 0299hs; >> query: (?x10088, 02sg5v) <- film(?x788, ?x10088), genre(?x10088, ?x604), genre(?x10088, ?x225), ?x604 = 0lsxr, ?x788 = 0g1rw, ?x225 = 02kdv5l >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 029v40 prequel! 01npcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 44.000 0.111 http://example.org/film/film/prequel #22683-0n_hp PRED entity: 0n_hp PRED relation: film! PRED expected values: 0jfx1 => 98 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1226): 0bj9k (0.20 #2409, 0.13 #19053, 0.10 #25293), 01csvq (0.20 #2190, 0.07 #12594, 0.06 #18834), 01vy_v8 (0.18 #9056, 0.14 #4894, 0.10 #17378), 02g8h (0.15 #64497, 0.13 #41610, 0.12 #2081), 0f5xn (0.14 #5131, 0.14 #970, 0.12 #9293), 0k269 (0.14 #4771, 0.12 #8933, 0.10 #2691), 09l3p (0.14 #4910, 0.12 #9072, 0.07 #17394), 016ypb (0.14 #4659, 0.12 #8821, 0.06 #37947), 017r13 (0.14 #5273, 0.12 #9435, 0.04 #11516), 057_yx (0.14 #6000, 0.12 #10162, 0.04 #55933) >> Best rule #2409 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 0h6r5; 0y_yw; 01jr4j; >> query: (?x9129, 0bj9k) <- nominated_for(?x2288, ?x9129), titles(?x53, ?x9129), genre(?x9129, ?x1509), genre(?x9129, ?x604), ?x604 = 0lsxr, ?x1509 = 060__y, film(?x539, ?x9129) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 0cq806; *> query: (?x9129, 0jfx1) <- genre(?x9129, ?x6887), genre(?x9129, ?x1509), genre(?x9129, ?x53), ?x1509 = 060__y, produced_by(?x9129, ?x318), ?x53 = 07s9rl0, film(?x539, ?x9129), ?x6887 = 03bxz7 *> conf = 0.14 ranks of expected_values: 28 EVAL 0n_hp film! 0jfx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 98.000 49.000 0.200 http://example.org/film/actor/film./film/performance/film #22682-0jkhr PRED entity: 0jkhr PRED relation: school! PRED expected values: 03lsq => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 86): 04wmvz (0.36 #72, 0.15 #667, 0.12 #1347), 07147 (0.36 #61, 0.14 #996, 0.12 #826), 02c_4 (0.36 #59, 0.11 #1871, 0.11 #1872), 05m_8 (0.27 #3, 0.24 #598, 0.21 #1278), 07l8x (0.27 #60, 0.15 #995, 0.14 #825), 05tfm (0.27 #16, 0.11 #1871, 0.11 #1872), 02d02 (0.18 #63, 0.14 #148, 0.13 #658), 01d5z (0.18 #10, 0.14 #1285, 0.13 #775), 061xq (0.18 #31, 0.13 #796, 0.12 #1136), 01yjl (0.18 #27, 0.13 #1132, 0.12 #1302) >> Best rule #72 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 04bfg; >> query: (?x6856, 04wmvz) <- school(?x1632, ?x6856), school_type(?x6856, ?x1507), institution(?x865, ?x6856), ?x1632 = 0cqt41 >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1871 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 112 *> proper extension: 06mkj; 0d05w3; *> query: (?x6856, ?x1438) <- school(?x8499, ?x6856), draft(?x1438, ?x8499), school(?x1438, ?x466) *> conf = 0.11 ranks of expected_values: 60 EVAL 0jkhr school! 03lsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 161.000 161.000 0.364 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #22681-016kjs PRED entity: 016kjs PRED relation: artists! PRED expected values: 0glt670 06j6l 036jv => 116 concepts (91 used for prediction) PRED predicted values (max 10 best out of 208): 0glt670 (0.71 #965, 0.54 #2201, 0.40 #3743), 02lnbg (0.65 #2219, 0.34 #3761, 0.29 #4993), 0ggx5q (0.58 #2239, 0.30 #3781, 0.29 #2856), 06j6l (0.56 #2209, 0.43 #973, 0.36 #3751), 0gywn (0.44 #2218, 0.27 #3760, 0.23 #10543), 0mhfr (0.36 #332, 0.33 #640, 0.33 #24), 01lyv (0.36 #342, 0.33 #650, 0.31 #1266), 05bt6j (0.33 #10529, 0.24 #4978, 0.23 #3746), 02w4v (0.25 #1277, 0.24 #1586, 0.18 #353), 016clz (0.23 #14190, 0.23 #18507, 0.22 #4939) >> Best rule #965 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 016ksk; 011z3g; 02vwckw; 01wlt3k; 03f0qd7; >> query: (?x1125, 0glt670) <- artists(?x8184, ?x1125), award(?x1125, ?x2139), ?x8184 = 016_v3 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 12 EVAL 016kjs artists! 036jv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 116.000 91.000 0.714 http://example.org/music/genre/artists EVAL 016kjs artists! 06j6l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 91.000 0.714 http://example.org/music/genre/artists EVAL 016kjs artists! 0glt670 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 91.000 0.714 http://example.org/music/genre/artists #22680-09gnn PRED entity: 09gnn PRED relation: influenced_by PRED expected values: 03_hd => 164 concepts (74 used for prediction) PRED predicted values (max 10 best out of 416): 06myp (0.57 #3837, 0.25 #26448, 0.23 #25577), 03_hd (0.50 #1865, 0.33 #567, 0.14 #3598), 0h25 (0.50 #2517, 0.29 #3382, 0.21 #7283), 03_87 (0.43 #3665, 0.25 #1932, 0.18 #8000), 02wh0 (0.43 #3845, 0.25 #26448, 0.24 #29871), 099bk (0.43 #3574, 0.25 #26448, 0.23 #25577), 03sbs (0.36 #14089, 0.33 #11485, 0.33 #654), 04hcw (0.33 #865, 0.33 #656, 0.33 #25576), 01bpn (0.33 #865, 0.33 #25576, 0.29 #25575), 0j3v (0.33 #492, 0.29 #3523, 0.25 #1790) >> Best rule #3837 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0dzkq; 04hcw; 0c1fs; >> query: (?x10499, 06myp) <- peers(?x4309, ?x10499), influenced_by(?x10499, ?x11837), people(?x4322, ?x10499), location(?x11837, ?x1355), ?x1355 = 0h7x >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1865 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 034bs; *> query: (?x10499, 03_hd) <- peers(?x4309, ?x10499), influenced_by(?x10499, ?x11837), influenced_by(?x10499, ?x1857), ?x11837 = 032r1, interests(?x1857, ?x713) *> conf = 0.50 ranks of expected_values: 2 EVAL 09gnn influenced_by 03_hd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 164.000 74.000 0.571 http://example.org/influence/influence_node/influenced_by #22679-02jt1k PRED entity: 02jt1k PRED relation: location PRED expected values: 0lhql => 132 concepts (117 used for prediction) PRED predicted values (max 10 best out of 262): 0f94t (0.75 #32908, 0.71 #28893, 0.71 #42544), 030qb3t (0.25 #10512, 0.19 #11314, 0.19 #8104), 0cc56 (0.22 #1660, 0.13 #5672, 0.10 #3265), 013yq (0.22 #1721, 0.10 #3326, 0.09 #4128), 0h7h6 (0.20 #3297, 0.18 #4099, 0.02 #8111), 04jpl (0.19 #36139, 0.13 #5633, 0.09 #6435), 01m1zk (0.17 #212, 0.10 #2619), 0k049 (0.17 #810, 0.07 #7228, 0.06 #10439), 080h2 (0.17 #855, 0.01 #10484), 01b8jj (0.17 #1393) >> Best rule #32908 for best value: >> intensional similarity = 4 >> extensional distance = 608 >> proper extension: 01h2_6; 011zwl; >> query: (?x1700, ?x1005) <- place_of_birth(?x1700, ?x1005), people(?x2510, ?x1700), location(?x1700, ?x739), citytown(?x166, ?x739) >> conf = 0.75 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02jt1k location 0lhql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 117.000 0.749 http://example.org/people/person/places_lived./people/place_lived/location #22678-07qy0b PRED entity: 07qy0b PRED relation: profession PRED expected values: 09jwl => 119 concepts (118 used for prediction) PRED predicted values (max 10 best out of 72): 02hrh1q (0.86 #16726, 0.72 #2847, 0.70 #1804), 01c72t (0.69 #621, 0.64 #2410, 0.64 #919), 09jwl (0.56 #6888, 0.54 #7486, 0.54 #7038), 0nbcg (0.44 #6901, 0.44 #7051, 0.42 #7499), 01d_h8 (0.40 #752, 0.40 #1498, 0.38 #1199), 0dz3r (0.39 #6421, 0.38 #6571, 0.38 #6721), 0dxtg (0.37 #15978, 0.30 #1952, 0.29 #1505), 016z4k (0.36 #7023, 0.36 #6273, 0.35 #6423), 02jknp (0.29 #754, 0.27 #1201, 0.27 #14176), 03gjzk (0.29 #761, 0.25 #1507, 0.25 #1208) >> Best rule #16726 for best value: >> intensional similarity = 3 >> extensional distance = 2906 >> proper extension: 0806vbn; 01wgxtl; 07sgfsl; 057hz; 027r8p; 0fr7nt; 01jb26; 01wbsdz; 0cvbb9q; 0gs6vr; ... >> query: (?x3371, 02hrh1q) <- profession(?x3371, ?x563), profession(?x5720, ?x563), ?x5720 = 01l1rw >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #6888 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 520 *> proper extension: 0f0y8; 05cljf; 01l1b90; 01vw87c; 0c9d9; 01vrx3g; 0m2l9; 026ps1; 06cc_1; 01vvy; ... *> query: (?x3371, 09jwl) <- type_of_union(?x3371, ?x566), ?x566 = 04ztj, artists(?x4910, ?x3371) *> conf = 0.56 ranks of expected_values: 3 EVAL 07qy0b profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 118.000 0.859 http://example.org/people/person/profession #22677-04p3w PRED entity: 04p3w PRED relation: people PRED expected values: 09p06 03zyvw 012dtf 0d6d2 => 53 concepts (41 used for prediction) PRED predicted values (max 10 best out of 2917): 02dth1 (0.60 #2077, 0.33 #781, 0.25 #1429), 01vyv9 (0.50 #1446, 0.33 #798, 0.20 #4038), 015wfg (0.40 #2086, 0.33 #790, 0.25 #1438), 01938t (0.33 #913, 0.33 #264, 0.25 #1561), 0h1_w (0.33 #659, 0.25 #1307, 0.20 #3899), 099p5 (0.33 #1059, 0.25 #1707, 0.20 #4299), 0gr36 (0.33 #741, 0.25 #1389, 0.20 #2037), 01vs4f3 (0.33 #1009, 0.25 #1657, 0.20 #2305), 03f3_p3 (0.33 #966, 0.25 #1614, 0.20 #2262), 029m83 (0.33 #975, 0.25 #1623, 0.20 #2271) >> Best rule #2077 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 01qqwn; >> query: (?x4659, 02dth1) <- people(?x4659, ?x12052), people(?x4659, ?x10914), people(?x4659, ?x7684), people(?x4659, ?x2452), nominated_for(?x2452, ?x12173), award_winner(?x435, ?x7684), gender(?x10914, ?x231), nominated_for(?x686, ?x12173), place_of_birth(?x7684, ?x12931), profession(?x12052, ?x987), place_of_death(?x7684, ?x4151), ?x435 = 0bp_b2 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6592 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 17 *> proper extension: 012hw; *> query: (?x4659, 09p06) <- people(?x4659, ?x12052), people(?x4659, ?x7684), people(?x4659, ?x3261), people(?x4659, ?x2452), award_winner(?x2222, ?x2452), type_of_union(?x3261, ?x566), place_of_birth(?x12052, ?x2850), place_of_birth(?x7684, ?x12931), place_of_death(?x12052, ?x1523), place_of_burial(?x7684, ?x1879), religion(?x12052, ?x1985), gender(?x12052, ?x231) *> conf = 0.05 ranks of expected_values: 552, 685, 1212, 2092 EVAL 04p3w people 0d6d2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 41.000 0.600 http://example.org/people/cause_of_death/people EVAL 04p3w people 012dtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 41.000 0.600 http://example.org/people/cause_of_death/people EVAL 04p3w people 03zyvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 41.000 0.600 http://example.org/people/cause_of_death/people EVAL 04p3w people 09p06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 41.000 0.600 http://example.org/people/cause_of_death/people #22676-02q42j_ PRED entity: 02q42j_ PRED relation: produced_by! PRED expected values: 065_cjc => 138 concepts (103 used for prediction) PRED predicted values (max 10 best out of 581): 0gy7bj4 (0.39 #25955, 0.39 #20398, 0.30 #16690), 027tbrc (0.39 #25955, 0.39 #20398, 0.30 #16690), 0ggbhy7 (0.29 #11125, 0.25 #5561, 0.17 #268), 03p2xc (0.17 #654, 0.11 #1580, 0.05 #38002), 025rxjq (0.17 #716, 0.11 #1642, 0.05 #38002), 065_cjc (0.17 #633, 0.11 #1559, 0.05 #38002), 01sxdy (0.17 #319), 0bwfwpj (0.11 #1943, 0.02 #3795, 0.02 #4723), 08phg9 (0.11 #2325, 0.01 #4177, 0.01 #11597), 02r1c18 (0.06 #8343, 0.05 #4632, 0.05 #4633) >> Best rule #25955 for best value: >> intensional similarity = 3 >> extensional distance = 337 >> proper extension: 0n6f8; >> query: (?x5973, ?x2447) <- produced_by(?x2029, ?x5973), film(?x100, ?x2029), nominated_for(?x5973, ?x2447) >> conf = 0.39 => this is the best rule for 2 predicted values *> Best rule #633 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 06rrzn; *> query: (?x5973, 065_cjc) <- award_nominee(?x1039, ?x5973), award_nominee(?x5973, ?x647), ?x647 = 04r7jc *> conf = 0.17 ranks of expected_values: 6 EVAL 02q42j_ produced_by! 065_cjc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 138.000 103.000 0.393 http://example.org/film/film/produced_by #22675-084m3 PRED entity: 084m3 PRED relation: award_winner! PRED expected values: 07z31v => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 127): 019bk0 (0.14 #856, 0.12 #996, 0.06 #1416), 01mhwk (0.12 #881, 0.10 #1021, 0.07 #1441), 01bx35 (0.12 #847, 0.10 #987, 0.06 #1407), 01mh_q (0.11 #89, 0.08 #1069, 0.08 #929), 05c1t6z (0.11 #15, 0.08 #1835, 0.04 #7295), 0gkxgfq (0.11 #106, 0.05 #1926, 0.03 #246), 0jt3qpk (0.11 #43, 0.04 #1863, 0.03 #183), 02rjjll (0.11 #845, 0.09 #985, 0.07 #1405), 0jzphpx (0.09 #879, 0.08 #1019, 0.06 #1439), 09qvms (0.09 #5053, 0.05 #2813, 0.05 #1133) >> Best rule #856 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 01wz_ml; 06lxn; >> query: (?x7489, 019bk0) <- inductee(?x9953, ?x7489), category(?x7489, ?x134), award_winner(?x435, ?x7489) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #7311 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 559 *> proper extension: 06jntd; *> query: (?x7489, 07z31v) <- award_winner(?x782, ?x7489), genre(?x782, ?x53) *> conf = 0.03 ranks of expected_values: 69 EVAL 084m3 award_winner! 07z31v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 114.000 114.000 0.138 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22674-0r4xt PRED entity: 0r4xt PRED relation: jurisdiction_of_office! PRED expected values: 01q24l => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.33 #1191, 0.32 #1235, 0.31 #1169), 01q24l (0.30 #299, 0.22 #475, 0.21 #519), 0fkvn (0.29 #884, 0.24 #620, 0.24 #400), 060bp (0.28 #1189, 0.27 #1233, 0.26 #1167), 0f6c3 (0.27 #403, 0.22 #623, 0.21 #887), 09n5b9 (0.24 #407, 0.19 #891, 0.17 #539), 04syw (0.10 #204, 0.09 #182, 0.08 #138), 0fkzq (0.09 #412, 0.07 #632, 0.07 #544), 0fj45 (0.08 #217, 0.08 #151, 0.07 #195), 0p5vf (0.08 #210, 0.07 #672, 0.05 #1244) >> Best rule #1191 for best value: >> intensional similarity = 3 >> extensional distance = 447 >> proper extension: 05r4w; 087vz; 01mk6; 06srk; 05br2; 01gh6z; 018jmn; >> query: (?x3883, 060c4) <- jurisdiction_of_office(?x1195, ?x3883), jurisdiction_of_office(?x1195, ?x5267), citytown(?x3543, ?x5267) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #299 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 03l2n; *> query: (?x3883, 01q24l) <- county(?x3883, ?x9472), second_level_divisions(?x94, ?x9472), jurisdiction_of_office(?x1195, ?x3883) *> conf = 0.30 ranks of expected_values: 2 EVAL 0r4xt jurisdiction_of_office! 01q24l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.330 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #22673-06cqb PRED entity: 06cqb PRED relation: artists PRED expected values: 0lbj1 01w61th 01kx_81 02qwg 012z8_ 016fnb 01s1zk => 54 concepts (23 used for prediction) PRED predicted values (max 10 best out of 975): 01kx_81 (0.75 #7475, 0.50 #5364, 0.40 #10644), 07mvp (0.75 #7969, 0.50 #5858, 0.38 #6913), 01vrncs (0.67 #5347, 0.62 #7458, 0.50 #6402), 016fmf (0.67 #9713, 0.60 #4435, 0.50 #3379), 01w60_p (0.67 #5436, 0.50 #6491, 0.38 #7547), 01vsksr (0.62 #7954, 0.50 #5843, 0.38 #6898), 01p95y0 (0.62 #8296, 0.50 #6185, 0.38 #7240), 01vtj38 (0.60 #11208, 0.33 #1704, 0.33 #648), 0lbj1 (0.56 #9518, 0.33 #1071, 0.33 #15), 07s3vqk (0.50 #10571, 0.50 #7402, 0.50 #5291) >> Best rule #7475 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 07sbbz2; 01lyv; 05w3f; 06j6l; 02yv6b; 0155w; >> query: (?x283, 01kx_81) <- artists(?x283, ?x4918), artists(?x283, ?x2521), award(?x2521, ?x9828), ?x4918 = 01mwsnc, artist(?x5744, ?x2521), ?x9828 = 01ckcd >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9, 16, 73, 98, 127, 335 EVAL 06cqb artists 01s1zk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 54.000 23.000 0.750 http://example.org/music/genre/artists EVAL 06cqb artists 016fnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 54.000 23.000 0.750 http://example.org/music/genre/artists EVAL 06cqb artists 012z8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 54.000 23.000 0.750 http://example.org/music/genre/artists EVAL 06cqb artists 02qwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 54.000 23.000 0.750 http://example.org/music/genre/artists EVAL 06cqb artists 01kx_81 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 23.000 0.750 http://example.org/music/genre/artists EVAL 06cqb artists 01w61th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 23.000 0.750 http://example.org/music/genre/artists EVAL 06cqb artists 0lbj1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 54.000 23.000 0.750 http://example.org/music/genre/artists #22672-0c8tk PRED entity: 0c8tk PRED relation: location_of_ceremony! PRED expected values: 06c53w => 213 concepts (136 used for prediction) PRED predicted values (max 10 best out of 263): 01kgg9 (0.20 #470, 0.06 #1478, 0.05 #1730), 05_2h8 (0.20 #411, 0.06 #1419, 0.05 #1671), 054k_8 (0.14 #640, 0.06 #1396, 0.04 #1901), 02m30v (0.08 #2525, 0.08 #2270, 0.06 #1512), 02n1gr (0.08 #1213, 0.06 #1465, 0.04 #2478), 02n1p5 (0.08 #1183, 0.06 #1435, 0.04 #2448), 01933d (0.08 #2205, 0.06 #3727, 0.06 #4995), 0dvld (0.08 #2166, 0.06 #3688, 0.06 #4449), 03m2fg (0.08 #1191, 0.05 #8029, 0.04 #8535), 01nglk (0.06 #1500, 0.05 #1752, 0.04 #2005) >> Best rule #470 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0fn2g; >> query: (?x4335, 01kgg9) <- place_of_birth(?x2873, ?x4335), country(?x4335, ?x2146), featured_film_locations(?x3257, ?x4335), ?x3257 = 0192hw >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c8tk location_of_ceremony! 06c53w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 213.000 136.000 0.200 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #22671-01kym3 PRED entity: 01kym3 PRED relation: nationality PRED expected values: 03_3d => 109 concepts (106 used for prediction) PRED predicted values (max 10 best out of 28): 03_3d (0.80 #306, 0.50 #6, 0.40 #7334), 09c7w0 (0.76 #2711, 0.76 #3112, 0.76 #4115), 07ssc (0.40 #7334, 0.35 #2206, 0.23 #2018), 02jx1 (0.37 #634, 0.35 #734, 0.30 #2036), 05b4w (0.35 #2206), 0chghy (0.20 #110, 0.11 #210, 0.07 #410), 03rk0 (0.16 #1548, 0.15 #1848, 0.14 #1949), 0d060g (0.09 #808, 0.08 #1008, 0.08 #908), 01znc_ (0.07 #438, 0.06 #538), 0345h (0.05 #632, 0.05 #732, 0.03 #2338) >> Best rule #306 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 03fghg; 01kymm; 01wphh2; 0392kz; 01nsyf; 01rddlc; 03cz9_; 0f8grf; 02t1dv; 03d29b; >> query: (?x13574, 03_3d) <- profession(?x13574, ?x1032), gender(?x13574, ?x514), category(?x13574, ?x134), special_performance_type(?x13574, ?x296), ?x296 = 01kyvx >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kym3 nationality 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 106.000 0.800 http://example.org/people/person/nationality #22670-02301 PRED entity: 02301 PRED relation: student PRED expected values: 06z9yh => 159 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1669): 0641g8 (0.29 #7094, 0.29 #5014, 0.08 #11254), 037lyl (0.17 #8978, 0.08 #25618, 0.06 #29778), 041mt (0.17 #8649, 0.06 #29449, 0.05 #37770), 0405l (0.17 #10163, 0.06 #30963, 0.05 #39284), 015wc0 (0.17 #10008, 0.06 #30808, 0.05 #39129), 0306ds (0.17 #8724, 0.06 #29524, 0.05 #37845), 02vntj (0.17 #9020, 0.06 #29820, 0.05 #38141), 01l1rw (0.17 #9315, 0.06 #30115, 0.05 #38436), 03rs8y (0.17 #8367, 0.06 #29167, 0.05 #37488), 0drc1 (0.17 #9753, 0.06 #30553, 0.05 #38874) >> Best rule #7094 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01x5fb; >> query: (?x2730, 0641g8) <- currency(?x2730, ?x170), major_field_of_study(?x2730, ?x1154), featured_film_locations(?x153, ?x2730), film(?x382, ?x153) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02301 student 06z9yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 93.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #22669-0284jb PRED entity: 0284jb PRED relation: contains! PRED expected values: 0kpys => 106 concepts (60 used for prediction) PRED predicted values (max 10 best out of 215): 02_286 (0.60 #6295, 0.28 #47349, 0.12 #24160), 059rby (0.47 #6272, 0.44 #25924, 0.30 #32179), 07ssc (0.44 #20577, 0.39 #23256, 0.26 #25042), 02jx1 (0.34 #51009, 0.33 #51902, 0.32 #20631), 0kpys (0.33 #2859, 0.26 #1965, 0.23 #4645), 013yq (0.28 #47349, 0.21 #3717, 0.02 #24261), 04jpl (0.28 #47349, 0.19 #24139, 0.19 #25926), 0h7h6 (0.28 #47349, 0.02 #24222, 0.02 #26009), 018dk_ (0.28 #47349), 0dqyw (0.28 #47349) >> Best rule #6295 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 0h095; >> query: (?x1358, 02_286) <- contains(?x1523, ?x1358), location(?x5597, ?x1523), ?x5597 = 02pk6x >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2859 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 0fn2g; *> query: (?x1358, 0kpys) <- place_of_death(?x9339, ?x1358), place_of_birth(?x6402, ?x1358), profession(?x9339, ?x1041), ?x1041 = 03gjzk *> conf = 0.33 ranks of expected_values: 5 EVAL 0284jb contains! 0kpys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 106.000 60.000 0.600 http://example.org/location/location/contains #22668-01kjr0 PRED entity: 01kjr0 PRED relation: film! PRED expected values: 01vsy7t => 49 concepts (25 used for prediction) PRED predicted values (max 10 best out of 808): 01x1fq (0.41 #35359, 0.40 #31199, 0.35 #37439), 0jfx1 (0.20 #406, 0.10 #10802, 0.09 #14961), 01xcfy (0.20 #493, 0.05 #27039, 0.05 #4652), 016ywr (0.20 #298, 0.05 #27039, 0.05 #4457), 0509bl (0.20 #322, 0.05 #27039, 0.05 #4481), 014g22 (0.20 #718, 0.05 #27039, 0.02 #11114), 063g7l (0.20 #1894, 0.05 #27039, 0.02 #6053), 01jrp0 (0.20 #1889, 0.05 #27039, 0.02 #6048), 05hdf (0.20 #423, 0.05 #27039, 0.02 #4582), 03ds3 (0.20 #137, 0.05 #27039, 0.02 #4296) >> Best rule #35359 for best value: >> intensional similarity = 4 >> extensional distance = 535 >> proper extension: 0275kr; >> query: (?x6209, ?x9891) <- award_winner(?x6209, ?x9891), student(?x2767, ?x9891), school_type(?x2767, ?x1044), location(?x9891, ?x2850) >> conf = 0.41 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kjr0 film! 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 25.000 0.406 http://example.org/film/actor/film./film/performance/film #22667-0gcs9 PRED entity: 0gcs9 PRED relation: profession PRED expected values: 0nbcg => 149 concepts (127 used for prediction) PRED predicted values (max 10 best out of 99): 02hrh1q (0.87 #4724, 0.84 #5607, 0.81 #5460), 0dxtg (0.74 #3252, 0.55 #16797, 0.48 #11349), 0dz3r (0.63 #884, 0.51 #2948, 0.50 #5890), 0nbcg (0.59 #5918, 0.58 #2976, 0.57 #2384), 039v1 (0.53 #2981, 0.39 #5923, 0.38 #770), 01c72t (0.50 #4586, 0.47 #1051, 0.42 #3409), 0n1h (0.50 #10, 0.33 #157, 0.30 #10160), 0cbd2 (0.49 #5746, 0.48 #7511, 0.47 #7952), 02jknp (0.46 #3246, 0.46 #12226, 0.46 #11343), 03gjzk (0.41 #5608, 0.39 #3254, 0.36 #16799) >> Best rule #4724 for best value: >> intensional similarity = 3 >> extensional distance = 175 >> proper extension: 07ymr5; >> query: (?x2963, 02hrh1q) <- people(?x1446, ?x2963), award_winner(?x139, ?x2963), participant(?x2963, ?x496) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #5918 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 201 *> proper extension: 02fybl; 09g0h; *> query: (?x2963, 0nbcg) <- profession(?x2963, ?x220), role(?x2963, ?x227), role(?x2963, ?x314) *> conf = 0.59 ranks of expected_values: 4 EVAL 0gcs9 profession 0nbcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 149.000 127.000 0.870 http://example.org/people/person/profession #22666-052nd PRED entity: 052nd PRED relation: institution! PRED expected values: 014mlp => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 14): 014mlp (0.72 #171, 0.70 #18, 0.67 #697), 03bwzr4 (0.59 #161, 0.58 #176, 0.52 #145), 07s6fsf (0.43 #169, 0.43 #154, 0.30 #530), 04zx3q1 (0.43 #155, 0.40 #139, 0.33 #33), 013zdg (0.28 #1207, 0.26 #157, 0.24 #141), 071tyz (0.28 #1207, 0.11 #36, 0.09 #142), 02cq61 (0.28 #1207, 0.10 #147, 0.10 #344), 01ysy9 (0.28 #1207, 0.07 #498, 0.06 #60), 01gkg3 (0.28 #1207, 0.01 #448, 0.01 #795), 03mkk4 (0.24 #159, 0.19 #52, 0.18 #21) >> Best rule #171 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 0p5wz; 01trxd; >> query: (?x481, 014mlp) <- major_field_of_study(?x481, ?x3490), institution(?x865, ?x481), ?x3490 = 05qfh >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 052nd institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.722 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22665-01kym3 PRED entity: 01kym3 PRED relation: profession PRED expected values: 0cbd2 => 106 concepts (52 used for prediction) PRED predicted values (max 10 best out of 114): 01c72t (0.87 #4128, 0.69 #463, 0.64 #901), 016z4k (0.83 #4255, 0.55 #882, 0.43 #1028), 0dxtg (0.78 #6455, 0.46 #1477, 0.33 #1331), 09jwl (0.76 #605, 0.73 #897, 0.68 #1043), 0nbcg (0.68 #909, 0.64 #1055, 0.56 #471), 01d_h8 (0.47 #2346, 0.43 #3815, 0.43 #6447), 018gz8 (0.43 #603, 0.33 #2651, 0.28 #3238), 02jknp (0.37 #6449, 0.27 #1471, 0.24 #2348), 0dz3r (0.32 #1026, 0.31 #442, 0.31 #4253), 039v1 (0.32 #914, 0.29 #1060, 0.26 #4287) >> Best rule #4128 for best value: >> intensional similarity = 6 >> extensional distance = 226 >> proper extension: 01w923; 01vyp_; 01qdjm; 050z2; 0kxbc; 0c8hct; 02bgmr; 063tn; 0cj2w; 0420y; >> query: (?x13574, 01c72t) <- type_of_union(?x13574, ?x566), profession(?x13574, ?x6476), profession(?x12947, ?x6476), profession(?x6947, ?x6476), ?x6947 = 01vrnsk, ?x12947 = 0164y7 >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #6448 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 874 *> proper extension: 03qcq; 05g8ky; 0h5f5n; 050023; 07w21; 041h0; 08f3b1; 02773nt; 012t1; 057d89; ... *> query: (?x13574, 0cbd2) <- type_of_union(?x13574, ?x566), profession(?x13574, ?x6476), profession(?x6947, ?x6476), profession(?x5988, ?x6476), ?x5988 = 0h0p_, gender(?x6947, ?x231) *> conf = 0.24 ranks of expected_values: 13 EVAL 01kym3 profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 106.000 52.000 0.868 http://example.org/people/person/profession #22664-02qfv5d PRED entity: 02qfv5d PRED relation: titles PRED expected values: 07j8r 02x2jl_ => 53 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1666): 05k2xy (0.74 #1539, 0.74 #1538, 0.74 #3078), 032sl_ (0.74 #1539, 0.74 #1538, 0.74 #3078), 02qhlwd (0.74 #1539, 0.74 #1538, 0.74 #3078), 0gy4k (0.74 #1539, 0.74 #1538, 0.74 #3078), 0dmn0x (0.74 #1538, 0.73 #3077, 0.32 #21548), 035zr0 (0.50 #13419, 0.40 #7258, 0.33 #4180), 07z6xs (0.50 #13063, 0.33 #3824, 0.33 #746), 0191n (0.50 #13045, 0.33 #3806, 0.33 #728), 01q7h2 (0.50 #13644, 0.33 #4405, 0.33 #1327), 03h_yy (0.50 #12380, 0.33 #3141, 0.25 #4680) >> Best rule #1539 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 0c3351; >> query: (?x11405, ?x11148) <- genre(?x11148, ?x11405), genre(?x1685, ?x11405), film(?x1104, ?x11148), titles(?x11405, ?x8605), ?x8605 = 01jmyj, film(?x2499, ?x11148), film(?x1018, ?x1685), film_format(?x1685, ?x909) >> conf = 0.74 => this is the best rule for 4 predicted values *> Best rule #7645 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 07ssc; *> query: (?x11405, 02x2jl_) <- titles(?x11405, ?x5074), titles(?x11405, ?x2814), award_winner(?x2814, ?x286), nominated_for(?x112, ?x2814), film_crew_role(?x2814, ?x137), ?x112 = 027dtxw, film(?x92, ?x2814), ?x5074 = 05mrf_p *> conf = 0.40 ranks of expected_values: 20, 328 EVAL 02qfv5d titles 02x2jl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 53.000 21.000 0.745 http://example.org/media_common/netflix_genre/titles EVAL 02qfv5d titles 07j8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 21.000 0.745 http://example.org/media_common/netflix_genre/titles #22663-01v3s2_ PRED entity: 01v3s2_ PRED relation: cast_members! PRED expected values: 04h07s => 70 concepts (32 used for prediction) PRED predicted values (max 10 best out of 4): 01v3s2_ (0.85 #1, 0.07 #5, 0.06 #22), 04h07s (0.69 #4, 0.07 #8, 0.05 #25), 086nl7 (0.62 #3, 0.07 #7, 0.04 #24), 07ymr5 (0.46 #2, 0.04 #23) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 086nl7; 04h07s; 05drr9; 04s430; 03q45x; 030wkp; >> query: (?x905, 01v3s2_) <- profession(?x905, ?x1032), film(?x905, ?x2102), cast_members(?x906, ?x905) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 086nl7; 04h07s; 05drr9; 04s430; 03q45x; 030wkp; *> query: (?x905, 04h07s) <- profession(?x905, ?x1032), film(?x905, ?x2102), cast_members(?x906, ?x905) *> conf = 0.69 ranks of expected_values: 2 EVAL 01v3s2_ cast_members! 04h07s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 32.000 0.846 http://example.org/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members #22662-02gm9n PRED entity: 02gm9n PRED relation: award! PRED expected values: 015_30 => 51 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2814): 03h_fk5 (0.81 #20308, 0.80 #13538, 0.80 #33849), 0dvmd (0.70 #855, 0.09 #31318, 0.08 #55012), 01vvb4m (0.60 #843, 0.13 #30463, 0.08 #27921), 0c6qh (0.60 #665, 0.13 #31128, 0.10 #54822), 0dzf_ (0.60 #1318, 0.12 #31781, 0.10 #55475), 07r1h (0.60 #1811, 0.10 #32274, 0.10 #55968), 0pmhf (0.60 #697, 0.10 #31160, 0.09 #54854), 03h_9lg (0.60 #190, 0.10 #17113, 0.08 #27268), 01_xtx (0.60 #1075, 0.08 #17998, 0.07 #31538), 0bl2g (0.50 #72, 0.21 #30462, 0.14 #60927) >> Best rule #20308 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 02f77l; >> query: (?x12940, ?x2807) <- award(?x2444, ?x12940), award_winner(?x12940, ?x2807), influenced_by(?x2444, ?x7717), origin(?x2444, ?x4061) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #17385 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 02f77l; *> query: (?x12940, 015_30) <- award(?x2444, ?x12940), award_winner(?x12940, ?x2807), influenced_by(?x2444, ?x7717), origin(?x2444, ?x4061) *> conf = 0.11 ranks of expected_values: 305 EVAL 02gm9n award! 015_30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 51.000 18.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22661-0cjyzs PRED entity: 0cjyzs PRED relation: nominated_for PRED expected values: 03ln8b 05zr0xl 07zhjj 02rkkn1 => 60 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1404): 072kp (0.73 #18806, 0.66 #36054, 0.65 #32918), 01s81 (0.73 #18806, 0.66 #36054, 0.65 #32918), 015pnb (0.73 #18806, 0.66 #36054, 0.65 #32918), 05zr0xl (0.56 #9085, 0.33 #7518, 0.33 #1257), 05h43ls (0.50 #5060, 0.29 #10964, 0.27 #37625), 075wx7_ (0.50 #4929, 0.10 #9629, 0.09 #17469), 047csmy (0.50 #5513, 0.10 #10213, 0.06 #18053), 0dfw0 (0.50 #5450, 0.10 #10150, 0.06 #17990), 0ch3qr1 (0.50 #5562, 0.10 #10262, 0.06 #18102), 033f8n (0.50 #5434, 0.10 #10134, 0.06 #17974) >> Best rule #18806 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 06196; >> query: (?x2016, ?x631) <- award(?x8163, ?x2016), award_winner(?x2016, ?x906), award(?x631, ?x2016), influenced_by(?x8163, ?x986) >> conf = 0.73 => this is the best rule for 3 predicted values *> Best rule #9085 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 09qvc0; 09qj50; 09qv3c; 09qvf4; 027gs1_; 0cqhmg; *> query: (?x2016, 05zr0xl) <- award(?x6443, ?x2016), nominated_for(?x2016, ?x6341), ?x6341 = 01rp13, award_nominee(?x3917, ?x6443) *> conf = 0.56 ranks of expected_values: 4, 29, 56, 227 EVAL 0cjyzs nominated_for 02rkkn1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 60.000 25.000 0.732 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0cjyzs nominated_for 07zhjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 60.000 25.000 0.732 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0cjyzs nominated_for 05zr0xl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 60.000 25.000 0.732 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0cjyzs nominated_for 03ln8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 60.000 25.000 0.732 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22660-06nz46 PRED entity: 06nz46 PRED relation: nationality PRED expected values: 02jx1 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 113): 09c7w0 (0.78 #2102, 0.72 #2603, 0.72 #2804), 02jx1 (0.60 #1433, 0.43 #33, 0.16 #733), 07ssc (0.33 #1415, 0.29 #15, 0.12 #2016), 0b90_r (0.14 #3, 0.01 #6724), 03rjj (0.12 #105, 0.11 #13239, 0.11 #205), 0chghy (0.11 #13239, 0.11 #910, 0.11 #1010), 0d05w3 (0.11 #13239, 0.06 #13940, 0.06 #950), 0f8l9c (0.11 #13239, 0.06 #13940, 0.05 #1222), 0d0vqn (0.11 #13239, 0.06 #13940, 0.05 #5619), 03gj2 (0.11 #13239, 0.06 #13940, 0.05 #526) >> Best rule #2102 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 01csrl; >> query: (?x4561, 09c7w0) <- award_winner(?x1243, ?x4561), location(?x4561, ?x362), nominated_for(?x4561, ?x2168), people(?x9771, ?x4561) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1433 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 01pcql; 0h10vt; *> query: (?x4561, 02jx1) <- award_winner(?x1243, ?x4561), location(?x4561, ?x362), award_winner(?x2168, ?x4561), ?x362 = 04jpl *> conf = 0.60 ranks of expected_values: 2 EVAL 06nz46 nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 144.000 0.776 http://example.org/people/person/nationality #22659-04bdlg PRED entity: 04bdlg PRED relation: profession PRED expected values: 02hrh1q => 115 concepts (80 used for prediction) PRED predicted values (max 10 best out of 82): 02hrh1q (0.92 #765, 0.90 #1065, 0.89 #1965), 0np9r (0.50 #922, 0.30 #2724, 0.22 #3474), 01d_h8 (0.40 #4209, 0.34 #1056, 0.33 #156), 02jknp (0.36 #4211, 0.25 #308, 0.23 #3760), 0dxtg (0.36 #4217, 0.35 #614, 0.26 #11870), 018gz8 (0.25 #2720, 0.25 #768, 0.16 #1968), 089fss (0.22 #467, 0.21 #12007, 0.19 #5254), 03gjzk (0.22 #2718, 0.21 #12007, 0.20 #11872), 01c72t (0.21 #12007, 0.20 #25, 0.19 #5254), 01c8w0 (0.21 #12007, 0.20 #9, 0.19 #5254) >> Best rule #765 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 03h_9lg; 02pb53; 01cwcr; 0sw6g; 03lmzl; 02ct_k; 033jj1; 01tsbmv; >> query: (?x11913, 02hrh1q) <- film(?x11913, ?x2924), award(?x11913, ?x6878), nationality(?x11913, ?x94), ?x6878 = 08_vwq >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bdlg profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 80.000 0.917 http://example.org/people/person/profession #22658-03f2_rc PRED entity: 03f2_rc PRED relation: award PRED expected values: 03tk6z => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 300): 019f4v (0.82 #775, 0.78 #17037, 0.77 #19749), 01by1l (0.82 #775, 0.78 #17037, 0.77 #19749), 02qkk9_ (0.82 #775, 0.78 #17037, 0.77 #19749), 054krc (0.50 #5111, 0.34 #8982, 0.31 #5885), 040njc (0.42 #6202, 0.27 #17431, 0.25 #14721), 0gs9p (0.41 #6265, 0.20 #70, 0.20 #15558), 0f4x7 (0.40 #29, 0.16 #7386, 0.15 #6999), 0l8z1 (0.40 #5092, 0.28 #5866, 0.26 #8963), 09sb52 (0.38 #13204, 0.37 #7396, 0.35 #13591), 02f73b (0.36 #1820, 0.18 #2594, 0.16 #3368) >> Best rule #775 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 013tjc; >> query: (?x538, ?x1107) <- award_winner(?x1107, ?x538), award_winner(?x537, ?x538), award_winner(?x6595, ?x538), ?x537 = 0gkvb7 >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #54625 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2309 *> proper extension: 0565cz; 0phx4; 03n93; 0jn5l; 05qhnq; 0564mx; 076df9; 019n7x; *> query: (?x538, ?x1079) <- award_nominee(?x7955, ?x538), award(?x7955, ?x1079) *> conf = 0.13 ranks of expected_values: 96 EVAL 03f2_rc award 03tk6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 151.000 151.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22657-04hhv PRED entity: 04hhv PRED relation: country! PRED expected values: 0bynt => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 56): 0bynt (0.85 #514, 0.83 #1466, 0.83 #1802), 03_8r (0.72 #415, 0.70 #975, 0.69 #527), 071t0 (0.67 #416, 0.66 #528, 0.60 #304), 01cgz (0.62 #350, 0.62 #462, 0.62 #966), 01lb14 (0.54 #296, 0.53 #520, 0.50 #408), 06f41 (0.53 #519, 0.52 #407, 0.52 #463), 0194d (0.52 #329, 0.50 #441, 0.49 #553), 06wrt (0.51 #521, 0.50 #409, 0.50 #297), 0w0d (0.50 #124, 0.47 #516, 0.46 #404), 07jbh (0.49 #539, 0.48 #483, 0.48 #427) >> Best rule #514 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 09c7w0; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0h3y; 0j1z8; 0chghy; 03rt9; ... >> query: (?x8033, 0bynt) <- exported_to(?x2346, ?x8033), jurisdiction_of_office(?x182, ?x8033), organization(?x8033, ?x312) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04hhv country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.847 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #22656-01k8q5 PRED entity: 01k8q5 PRED relation: student PRED expected values: 03dq9 => 150 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1028): 05y5kf (0.22 #9214, 0.20 #2938, 0.12 #5030), 07fpm3 (0.22 #8967, 0.04 #154833, 0.04 #25705), 04myfb7 (0.22 #8661, 0.01 #92353), 041c4 (0.20 #2958, 0.11 #9234, 0.10 #15511), 0ff3y (0.20 #2069, 0.10 #12530, 0.07 #27175), 0bq2g (0.20 #565, 0.04 #154833), 09v6tz (0.20 #1341, 0.04 #26447, 0.02 #95494), 01g257 (0.20 #239, 0.04 #25345, 0.01 #92299), 048_p (0.20 #959, 0.04 #26065), 0hwbd (0.20 #1020, 0.02 #93080, 0.01 #105635) >> Best rule #9214 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 031ns1; >> query: (?x1848, 05y5kf) <- student(?x1848, ?x3718), award_nominee(?x5205, ?x3718), ?x5205 = 08_83x, award(?x3718, ?x1670) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #10128 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 031ns1; *> query: (?x1848, 03dq9) <- student(?x1848, ?x3718), award_nominee(?x5205, ?x3718), ?x5205 = 08_83x, award(?x3718, ?x1670) *> conf = 0.11 ranks of expected_values: 98 EVAL 01k8q5 student 03dq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 150.000 86.000 0.222 http://example.org/education/educational_institution/students_graduates./education/education/student #22655-015xp4 PRED entity: 015xp4 PRED relation: type_of_union PRED expected values: 04ztj => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.85 #201, 0.84 #109, 0.84 #221), 01g63y (0.25 #50, 0.17 #306, 0.17 #6), 01bl8s (0.05 #15, 0.02 #47, 0.02 #63) >> Best rule #201 for best value: >> intensional similarity = 4 >> extensional distance = 277 >> proper extension: 0443c; >> query: (?x5140, 04ztj) <- award_winner(?x1088, ?x5140), people(?x3799, ?x5140), people(?x3799, ?x5146), story_by(?x8677, ?x5146) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015xp4 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.846 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22654-0146pg PRED entity: 0146pg PRED relation: award_nominee PRED expected values: 02zft0 => 139 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1301): 02lfp4 (0.82 #9366, 0.81 #142822, 0.80 #124091), 02zft0 (0.82 #9366, 0.81 #142822, 0.80 #124091), 020jqv (0.82 #9366, 0.81 #142822, 0.80 #124091), 03n0pv (0.82 #9366, 0.81 #142822, 0.80 #124091), 02ryx0 (0.75 #35120, 0.74 #107702, 0.74 #114726), 016szr (0.75 #35120, 0.74 #107702, 0.74 #114726), 01vttb9 (0.75 #35120, 0.74 #114726, 0.74 #149849), 05ccxr (0.75 #35120, 0.74 #114726, 0.74 #149849), 0178rl (0.14 #1238, 0.07 #10604, 0.05 #17627), 019x62 (0.11 #3953, 0.03 #44485, 0.02 #67168) >> Best rule #9366 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0jn5l; >> query: (?x669, ?x2641) <- music(?x670, ?x669), award_nominee(?x2641, ?x669), location(?x669, ?x739) >> conf = 0.82 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0146pg award_nominee 02zft0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 139.000 64.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22653-06_wqk4 PRED entity: 06_wqk4 PRED relation: film_crew_role PRED expected values: 02_n3z 0ch6mp2 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.83 #109, 0.82 #41, 0.81 #211), 02r96rf (0.70 #377, 0.69 #105, 0.68 #37), 0dxtw (0.39 #1112, 0.36 #974, 0.36 #1286), 01vx2h (0.33 #114, 0.32 #182, 0.32 #975), 01pvkk (0.29 #1702, 0.28 #976, 0.27 #2464), 02ynfr (0.22 #409, 0.22 #118, 0.22 #50), 0d2b38 (0.22 #409, 0.17 #548, 0.15 #2207), 02_n3z (0.22 #409, 0.17 #548, 0.15 #2207), 04pyp5 (0.22 #409, 0.17 #548, 0.15 #2207), 02rh1dz (0.22 #409, 0.17 #548, 0.15 #2207) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 047svrl; >> query: (?x857, 0ch6mp2) <- production_companies(?x857, ?x1478), ?x1478 = 054lpb6, film_crew_role(?x857, ?x137), ?x137 = 09zzb8 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 06_wqk4 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 06_wqk4 film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 95.000 95.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22652-07g9f PRED entity: 07g9f PRED relation: nominated_for! PRED expected values: 03d_w3h => 89 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1209): 0f721s (0.68 #27899, 0.68 #30224, 0.63 #41849), 0bbxd3 (0.68 #27899, 0.68 #30224, 0.63 #41849), 02k76g (0.57 #20925, 0.57 #16275, 0.54 #11624), 0410cp (0.51 #34873, 0.46 #81376, 0.46 #60454), 02f_k_ (0.51 #34873, 0.46 #60454, 0.45 #37198), 0219q (0.51 #34873, 0.46 #60454, 0.45 #37198), 04wvhz (0.27 #11625, 0.25 #4859, 0.20 #4650), 01rzqj (0.27 #11625, 0.25 #5358, 0.20 #4650), 059j4x (0.27 #11625, 0.20 #4650, 0.11 #127884), 05p5nc (0.25 #3798, 0.20 #4650, 0.13 #102305) >> Best rule #27899 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 097h2; 02gl58; >> query: (?x10089, ?x1394) <- program(?x3571, ?x10089), program(?x1394, ?x10089), award(?x10089, ?x435), award(?x3571, ?x4921) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #118581 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 603 *> proper extension: 014lc_; 027qgy; 02vp1f_; 03rtz1; 047msdk; 09p0ct; 0cd2vh9; 031t2d; 0by1wkq; 09k56b7; ... *> query: (?x10089, ?x221) <- nominated_for(?x5662, ?x10089), actor(?x9350, ?x5662), award_nominee(?x5662, ?x221) *> conf = 0.10 ranks of expected_values: 58 EVAL 07g9f nominated_for! 03d_w3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 89.000 55.000 0.681 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22651-01gjw PRED entity: 01gjw PRED relation: artists PRED expected values: 02w4fkq 02k5sc => 54 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1016): 011z3g (0.69 #3826, 0.56 #4901, 0.43 #7055), 016376 (0.62 #5255, 0.62 #4180, 0.33 #954), 0bs1g5r (0.54 #3975, 0.44 #5050, 0.33 #749), 01wk7ql (0.54 #4123, 0.44 #5198, 0.33 #897), 024qwq (0.54 #4091, 0.44 #5166, 0.33 #865), 012z8_ (0.54 #3622, 0.44 #4697, 0.33 #396), 01vwyqp (0.50 #4575, 0.46 #3500, 0.43 #1348), 0136p1 (0.50 #4445, 0.46 #3370, 0.33 #144), 01vvycq (0.46 #3273, 0.44 #4348, 0.43 #6502), 01x1cn2 (0.46 #3422, 0.44 #4497, 0.33 #196) >> Best rule #3826 for best value: >> intensional similarity = 8 >> extensional distance = 11 >> proper extension: 03_d0; 02x8m; 06by7; 06j6l; 025sc50; 0gywn; 02k_kn; 026z9; 09nwwf; 02b71x; >> query: (?x10319, 011z3g) <- artists(?x10319, ?x8799), artists(?x10319, ?x6651), parent_genre(?x10319, ?x3108), ?x6651 = 019f9z, award_winner(?x1088, ?x8799), award_winner(?x3375, ?x8799), profession(?x8799, ?x131), artist(?x2931, ?x8799) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #708 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 064t9; *> query: (?x10319, 02k5sc) <- artists(?x10319, ?x10320), artists(?x10319, ?x8799), artists(?x10319, ?x6651), artists(?x10319, ?x2908), parent_genre(?x10319, ?x3108), ?x6651 = 019f9z, ?x8799 = 02f1c, ?x10320 = 02twdq, film(?x2908, ?x781), award(?x2908, ?x2322) *> conf = 0.33 ranks of expected_values: 116, 250 EVAL 01gjw artists 02k5sc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 54.000 17.000 0.692 http://example.org/music/genre/artists EVAL 01gjw artists 02w4fkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 54.000 17.000 0.692 http://example.org/music/genre/artists #22650-018ndc PRED entity: 018ndc PRED relation: group! PRED expected values: 0342h 018j2 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 73): 0342h (0.89 #1109, 0.89 #1535, 0.87 #1279), 02hnl (0.79 #710, 0.78 #625, 0.78 #200), 05148p4 (0.74 #1125, 0.69 #1295, 0.69 #1551), 018vs (0.67 #183, 0.62 #1118, 0.61 #608), 0l14md (0.59 #1282, 0.57 #1538, 0.56 #1112), 028tv0 (0.47 #692, 0.46 #607, 0.44 #182), 05r5c (0.35 #263, 0.34 #433, 0.32 #518), 03qjg (0.33 #217, 0.30 #727, 0.29 #642), 01vj9c (0.27 #1545, 0.26 #524, 0.26 #694), 0l14qv (0.23 #1536, 0.22 #1280, 0.21 #685) >> Best rule #1109 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 0m19t; 05563d; 05xq9; 01j59b0; 02mq_y; 013rfk; 01516r; 070b4; 07rnh; 01shhf; ... >> query: (?x3109, 0342h) <- group(?x75, ?x3109), artists(?x302, ?x3109), ?x302 = 016clz >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12 EVAL 018ndc group! 018j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 102.000 102.000 0.889 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018ndc group! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.889 http://example.org/music/performance_role/regular_performances./music/group_membership/group #22649-05yvfd PRED entity: 05yvfd PRED relation: type_of_union PRED expected values: 04ztj => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.84 #13, 0.75 #115, 0.75 #123), 01g63y (0.20 #437, 0.12 #148, 0.12 #182), 0jgjn (0.20 #437), 01bl8s (0.20 #437) >> Best rule #13 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 01wj5hp; 02jxsq; >> query: (?x9465, 04ztj) <- profession(?x9465, ?x1032), religion(?x9465, ?x8967), ?x1032 = 02hrh1q, location(?x9465, ?x7412), ?x8967 = 03j6c >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05yvfd type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.839 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22648-02n9k PRED entity: 02n9k PRED relation: gender PRED expected values: 02zsn => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #98, 0.88 #108, 0.88 #128), 02zsn (0.67 #6, 0.62 #8, 0.52 #61) >> Best rule #98 for best value: >> intensional similarity = 3 >> extensional distance = 228 >> proper extension: 033cw; 0bt23; >> query: (?x7893, 05zppz) <- influenced_by(?x12571, ?x7893), location(?x7893, ?x108), student(?x3439, ?x12571) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 05szp; 0lfbm; *> query: (?x7893, 02zsn) <- nationality(?x7893, ?x94), profession(?x7893, ?x9682), inductee(?x13697, ?x7893), spouse(?x12571, ?x7893) *> conf = 0.67 ranks of expected_values: 2 EVAL 02n9k gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.887 http://example.org/people/person/gender #22647-0dnkmq PRED entity: 0dnkmq PRED relation: language PRED expected values: 05zjd => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 37): 064_8sq (0.32 #191, 0.15 #1749, 0.15 #1632), 03_9r (0.24 #351, 0.17 #123, 0.07 #696), 04306rv (0.21 #175, 0.11 #461, 0.11 #518), 02bjrlw (0.21 #172, 0.09 #458, 0.09 #515), 0jzc (0.17 #132, 0.11 #189, 0.05 #303), 06b_j (0.14 #21, 0.09 #306, 0.09 #421), 0t_2 (0.14 #12, 0.03 #412, 0.02 #641), 03hkp (0.11 #70, 0.03 #758, 0.03 #1391), 01r2l (0.11 #194, 0.08 #137, 0.04 #480), 012w70 (0.11 #182, 0.05 #239, 0.05 #698) >> Best rule #191 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 08nvyr; 01kf5lf; 029v40; 042g97; >> query: (?x10515, 064_8sq) <- language(?x10515, ?x2502), film(?x96, ?x10515), currency(?x10515, ?x170), prequel(?x10515, ?x3938), ?x2502 = 06nm1 >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #366 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 015qy1; *> query: (?x10515, 05zjd) <- language(?x10515, ?x254), genre(?x10515, ?x225), country(?x10515, ?x94), film(?x4832, ?x10515) *> conf = 0.04 ranks of expected_values: 20 EVAL 0dnkmq language 05zjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 99.000 99.000 0.316 http://example.org/film/film/language #22646-0b90_r PRED entity: 0b90_r PRED relation: country! PRED expected values: 01hp22 0dwxr => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 30): 07jjt (0.68 #521, 0.65 #371, 0.61 #911), 01z27 (0.65 #578, 0.65 #668, 0.61 #908), 07bs0 (0.65 #577, 0.63 #907, 0.61 #667), 01hp22 (0.65 #574, 0.59 #484, 0.58 #664), 09_bl (0.64 #485, 0.51 #1501, 0.50 #365), 0dwxr (0.59 #494, 0.54 #584, 0.53 #314), 019w9j (0.59 #495, 0.51 #1501, 0.50 #375), 035d1m (0.58 #673, 0.55 #373, 0.53 #163), 0d1t3 (0.58 #317, 0.58 #587, 0.55 #377), 0d1tm (0.55 #361, 0.51 #1501, 0.50 #511) >> Best rule #521 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0bq0p9; 03b79; >> query: (?x151, 07jjt) <- combatants(?x789, ?x151), nationality(?x2167, ?x151), ?x789 = 0f8l9c >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #574 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 05r4w; 0jgd; 03_3d; 0d0vqn; 04gzd; 04v3q; 0h7x; 06mkj; 06t2t; 03h64; ... *> query: (?x151, 01hp22) <- film_release_region(?x3843, ?x151), country(?x150, ?x151), ?x3843 = 080nwsb *> conf = 0.65 ranks of expected_values: 4, 6 EVAL 0b90_r country! 0dwxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 189.000 189.000 0.682 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0b90_r country! 01hp22 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 189.000 189.000 0.682 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #22645-02l4rh PRED entity: 02l4rh PRED relation: student! PRED expected values: 02l9wl => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 56): 07tg4 (0.12 #86, 0.11 #613, 0.07 #2194), 015nl4 (0.12 #67, 0.06 #2175, 0.03 #31698), 0m4yg (0.12 #365, 0.01 #2473, 0.01 #25670), 02l9wl (0.11 #779, 0.02 #2360, 0.02 #3414), 0fr9jp (0.10 #1399, 0.05 #872, 0.01 #4034), 07tds (0.07 #1203), 07tgn (0.06 #2125, 0.02 #11086, 0.02 #14248), 0bwfn (0.06 #275, 0.05 #9236, 0.05 #802), 0gjv_ (0.06 #206, 0.01 #2314, 0.01 #1787), 0gk7z (0.06 #363, 0.01 #2471) >> Best rule #86 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02_hj4; >> query: (?x7045, 07tg4) <- award_nominee(?x3604, ?x7045), award_winner(?x3567, ?x7045), ?x3604 = 03v3xp >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #779 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 05slvm; 02vntj; 04znsy; 0525b; *> query: (?x7045, 02l9wl) <- award_winner(?x2880, ?x7045), award_nominee(?x7045, ?x374), ?x2880 = 02ppm4q *> conf = 0.11 ranks of expected_values: 4 EVAL 02l4rh student! 02l9wl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 95.000 95.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #22644-01l3mk3 PRED entity: 01l3mk3 PRED relation: award PRED expected values: 05q8pss => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 293): 01c427 (0.78 #1583, 0.75 #3559, 0.74 #17785), 0d085 (0.78 #1583, 0.75 #3559, 0.74 #17785), 02h3d1 (0.40 #966, 0.31 #570, 0.13 #39921), 01l29r (0.38 #556, 0.12 #952, 0.06 #3323), 0gr4k (0.38 #429, 0.08 #19399, 0.06 #22166), 0ck27z (0.31 #1672, 0.13 #21036, 0.13 #36059), 01l78d (0.31 #676, 0.04 #1072, 0.02 #18460), 05pcn59 (0.30 #1266, 0.28 #5218, 0.27 #2057), 05p09zm (0.29 #2096, 0.27 #1305, 0.26 #5257), 01by1l (0.28 #13940, 0.27 #6036, 0.25 #107) >> Best rule #1583 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 014zcr; 01q_ph; 05gml8; 01pcq3; 0151w_; 030hcs; 0bj9k; 01tfck; 01vs_v8; 0bbf1f; ... >> query: (?x7955, ?x1079) <- student(?x2909, ?x7955), award_winner(?x1079, ?x7955), participant(?x7955, ?x3235) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #205 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 01jllg1; *> query: (?x7955, 05q8pss) <- award_winner(?x10412, ?x7955), ?x10412 = 016jll, award_winner(?x1079, ?x7955) *> conf = 0.25 ranks of expected_values: 13 EVAL 01l3mk3 award 05q8pss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 124.000 124.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22643-01l2fn PRED entity: 01l2fn PRED relation: gender PRED expected values: 02zsn => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #57, 0.72 #73, 0.71 #71), 02zsn (0.52 #46, 0.52 #14, 0.51 #12) >> Best rule #57 for best value: >> intensional similarity = 2 >> extensional distance = 379 >> proper extension: 05g8ky; 0f1vrl; 01_k1z; 06qjgc; 03p01x; 01vzz1c; 01nrz4; 02y0dd; >> query: (?x1634, 05zppz) <- location(?x1634, ?x362), currency(?x1634, ?x170) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 142 *> proper extension: 01pw2f1; 02v60l; 02hhtj; 06tp4h; 06_bq1; 0mdyn; 01pgk0; *> query: (?x1634, 02zsn) <- film(?x1634, ?x908), vacationer(?x1025, ?x1634) *> conf = 0.52 ranks of expected_values: 2 EVAL 01l2fn gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.717 http://example.org/people/person/gender #22642-01vh3r PRED entity: 01vh3r PRED relation: gender PRED expected values: 05zppz => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #11, 0.88 #35, 0.87 #13), 02zsn (0.46 #22, 0.45 #64, 0.45 #2) >> Best rule #11 for best value: >> intensional similarity = 2 >> extensional distance = 89 >> proper extension: 02s2ft; 05d7rk; 0byfz; 016gr2; 028lc8; 0fsm8c; 015gw6; 01xsbh; 0171cm; 0b_dy; ... >> query: (?x11985, 05zppz) <- award(?x11985, ?x112), ?x112 = 027dtxw >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vh3r gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.912 http://example.org/people/person/gender #22641-0gtt5fb PRED entity: 0gtt5fb PRED relation: film_release_region PRED expected values: 06bnz => 102 concepts (48 used for prediction) PRED predicted values (max 10 best out of 145): 05qhw (0.88 #1056, 0.87 #1205, 0.86 #2695), 03spz (0.85 #1282, 0.79 #2027, 0.78 #1133), 0d060g (0.84 #2687, 0.82 #3134, 0.81 #3283), 06bnz (0.83 #1235, 0.81 #3321, 0.80 #3172), 05v8c (0.77 #1207, 0.70 #1654, 0.69 #3144), 03rj0 (0.72 #1248, 0.72 #1993, 0.71 #2589), 015qh (0.70 #1230, 0.68 #1081, 0.62 #1677), 01mjq (0.68 #1233, 0.68 #1084, 0.66 #1680), 06mzp (0.68 #1212, 0.62 #1063, 0.57 #2553), 06qd3 (0.68 #1077, 0.66 #1226, 0.62 #3610) >> Best rule #1056 for best value: >> intensional similarity = 8 >> extensional distance = 38 >> proper extension: 01fmys; >> query: (?x5588, 05qhw) <- film_release_region(?x5588, ?x789), film_release_region(?x5588, ?x429), currency(?x5588, ?x170), ?x429 = 03rt9, film(?x3101, ?x5588), nominated_for(?x3101, ?x638), written_by(?x5588, ?x9281), ?x789 = 0f8l9c >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1235 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 45 *> proper extension: 05p1tzf; 01vksx; 017gl1; 0bwfwpj; 08hmch; 01c22t; 0jjy0; 0c0nhgv; 0872p_c; 053rxgm; ... *> query: (?x5588, 06bnz) <- film_release_region(?x5588, ?x2629), film_release_region(?x5588, ?x2152), film_release_region(?x5588, ?x429), currency(?x5588, ?x170), ?x429 = 03rt9, film(?x1733, ?x5588), film_crew_role(?x5588, ?x137), ?x2152 = 06mkj, ?x2629 = 06f32 *> conf = 0.83 ranks of expected_values: 4 EVAL 0gtt5fb film_release_region 06bnz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 48.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22640-0c1pj PRED entity: 0c1pj PRED relation: participant! PRED expected values: 0pmhf => 134 concepts (101 used for prediction) PRED predicted values (max 10 best out of 219): 029q_y (0.08 #1115, 0.04 #8735, 0.04 #10005), 0pz91 (0.06 #4531, 0.05 #6436, 0.04 #8341), 0gx_p (0.05 #5498, 0.05 #6133, 0.04 #7403), 07r1h (0.05 #5489, 0.04 #7394, 0.04 #4854), 014zcr (0.05 #22880, 0.05 #24151, 0.04 #29236), 046zh (0.04 #7342, 0.04 #5437, 0.04 #13057), 0205dx (0.04 #27312, 0.03 #18417, 0.02 #13971), 0q5hw (0.04 #4645, 0.04 #835, 0.02 #8455), 0237fw (0.04 #802, 0.03 #24301, 0.03 #28750), 06mt91 (0.04 #1078, 0.03 #6793, 0.02 #15049) >> Best rule #1115 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 01hkhq; >> query: (?x556, 029q_y) <- nominated_for(?x556, ?x174), company(?x556, ?x10629), religion(?x556, ?x1985) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c1pj participant! 0pmhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 101.000 0.077 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #22639-01b1mj PRED entity: 01b1mj PRED relation: category PRED expected values: 08mbj5d => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.94 #4, 0.93 #35, 0.93 #30) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 02g839; 037s9x; 01jq34; 02bjhv; 027xx3; 02fgdx; 02183k; 0kw4j; 01ymvk; 07tds; ... >> query: (?x1087, 08mbj5d) <- colors(?x1087, ?x1101), currency(?x1087, ?x170), ?x1101 = 06fvc, ?x170 = 09nqf >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01b1mj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.939 http://example.org/common/topic/webpage./common/webpage/category #22638-0d9v9q PRED entity: 0d9v9q PRED relation: team PRED expected values: 02b15h => 91 concepts (82 used for prediction) PRED predicted values (max 10 best out of 832): 01tqfs (0.88 #4235, 0.87 #6351, 0.84 #4234), 0cnk2q (0.33 #354, 0.11 #6352, 0.10 #3882), 01rl_3 (0.25 #457, 0.11 #6352, 0.06 #3985), 015_z1 (0.17 #1531, 0.17 #1885, 0.13 #1179), 02b0_6 (0.17 #476, 0.14 #1534, 0.14 #1888), 02b0xq (0.17 #421, 0.12 #774, 0.11 #6352), 02b16p (0.17 #568, 0.11 #6352, 0.09 #1274), 0dwz3t (0.17 #544, 0.11 #6352, 0.08 #4072), 0182r9 (0.17 #385, 0.11 #6352, 0.08 #3913), 037css (0.17 #705, 0.11 #6352, 0.06 #1058) >> Best rule #4235 for best value: >> intensional similarity = 5 >> extensional distance = 46 >> proper extension: 05_6_y; 02vl_pz; 09l9xt; 02y9ln; 02v_4xv; 026n047; 08b0cj; 0dhrqx; 0c2rr7; 0gtgp6; ... >> query: (?x7212, ?x6503) <- team(?x7212, ?x6503), profession(?x7212, ?x7623), team(?x7212, ?x11518), gender(?x7212, ?x231), team(?x3031, ?x6503) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #6352 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 72 *> proper extension: 0fv6dr; 09r1j5; 0d3f83; 0dv1hh; 02zbjwr; 07zr66; 03m5111; *> query: (?x7212, ?x59) <- team(?x7212, ?x6503), team(?x7212, ?x11518), team(?x3031, ?x6503), team(?x63, ?x6503), team(?x3031, ?x59) *> conf = 0.11 ranks of expected_values: 71 EVAL 0d9v9q team 02b15h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 91.000 82.000 0.881 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #22637-0pd64 PRED entity: 0pd64 PRED relation: written_by PRED expected values: 03g62 => 107 concepts (67 used for prediction) PRED predicted values (max 10 best out of 131): 022_lg (0.14 #337, 0.14 #2696, 0.13 #4721), 0343h (0.09 #44, 0.06 #1053, 0.04 #3413), 02vyw (0.09 #104, 0.05 #2800, 0.05 #2463), 03_gd (0.09 #1030, 0.03 #3390, 0.03 #2042), 0bs8d (0.09 #171, 0.02 #2867, 0.02 #2530), 0p50v (0.09 #254, 0.02 #2613, 0.01 #11379), 06l6nj (0.09 #312, 0.02 #2671, 0.01 #4020), 06t8b (0.09 #238, 0.02 #2597, 0.01 #3946), 015wfg (0.08 #12474, 0.08 #13487, 0.08 #19546), 02q9kqf (0.08 #12474, 0.08 #13487, 0.08 #19546) >> Best rule #337 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0bx0l; 0221zw; 0k4fz; 067ghz; 0hv81; 0ccck7; >> query: (?x7711, ?x1431) <- award_winner(?x7711, ?x1431), award(?x7711, ?x591), list(?x7711, ?x3004), film_regional_debut_venue(?x7711, ?x5416) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pd64 written_by 03g62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 67.000 0.143 http://example.org/film/film/written_by #22636-0qmpd PRED entity: 0qmpd PRED relation: artists! PRED expected values: 05w3f => 78 concepts (41 used for prediction) PRED predicted values (max 10 best out of 241): 06by7 (0.88 #4722, 0.71 #5035, 0.71 #958), 05w3f (0.71 #662, 0.58 #5641, 0.57 #1249), 0xhtw (0.57 #954, 0.53 #5660, 0.53 #4718), 0cx7f (0.53 #10495, 0.43 #1075, 0.35 #7978), 059kh (0.46 #3807, 0.44 #1926, 0.38 #4436), 05bt6j (0.45 #3173, 0.44 #2233, 0.42 #3487), 03_d0 (0.43 #5025, 0.41 #7220, 0.38 #10040), 016clz (0.43 #941, 0.41 #8471, 0.40 #8155), 03lty (0.43 #1279, 0.33 #3472, 0.33 #29), 09nwwf (0.43 #1073, 0.24 #4837, 0.20 #5465) >> Best rule #4722 for best value: >> intensional similarity = 10 >> extensional distance = 15 >> proper extension: 0ftps; 01k47c; 0140t7; 023322; 020_4z; 01mxnvc; 01pny5; >> query: (?x9196, 06by7) <- category(?x9196, ?x134), artists(?x6210, ?x9196), artists(?x2808, ?x9196), artists(?x1380, ?x9196), ?x1380 = 0dl5d, ?x6210 = 01fh36, parent_genre(?x2809, ?x2808), ?x2809 = 05w3f, artists(?x2808, ?x7810), ?x7810 = 0187x8 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #662 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 04m2zj; *> query: (?x9196, 05w3f) <- category(?x9196, ?x134), artists(?x5379, ?x9196), artists(?x2808, ?x9196), artists(?x1380, ?x9196), ?x1380 = 0dl5d, ?x2808 = 0190_q, ?x134 = 08mbj5d, artists(?x5379, ?x8579), artists(?x5379, ?x4595), role(?x4595, ?x212), ?x8579 = 01vs4f3 *> conf = 0.71 ranks of expected_values: 2 EVAL 0qmpd artists! 05w3f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 41.000 0.882 http://example.org/music/genre/artists #22635-0404j37 PRED entity: 0404j37 PRED relation: titles! PRED expected values: 0c3351 => 108 concepts (69 used for prediction) PRED predicted values (max 10 best out of 88): 07s9rl0 (0.39 #1632, 0.39 #1530, 0.37 #407), 04xvlr (0.35 #308, 0.31 #1328, 0.26 #105), 082gq (0.21 #3264, 0.21 #1631, 0.20 #2651), 01z4y (0.20 #850, 0.19 #3300, 0.19 #951), 024qqx (0.20 #588, 0.19 #1302, 0.19 #691), 01hmnh (0.19 #841, 0.17 #1043, 0.16 #942), 07ssc (0.13 #2152, 0.12 #314, 0.11 #111), 017fp (0.12 #1552, 0.11 #124, 0.11 #1962), 04t36 (0.11 #109, 0.06 #3273, 0.06 #721), 07c52 (0.11 #4730, 0.11 #4833, 0.11 #4936) >> Best rule #1632 for best value: >> intensional similarity = 4 >> extensional distance = 162 >> proper extension: 04kzqz; 0yyn5; 0258dh; >> query: (?x6448, ?x53) <- award_winner(?x6448, ?x3036), genre(?x6448, ?x53), currency(?x3036, ?x170), ?x53 = 07s9rl0 >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #2193 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 241 *> proper extension: 0jwvf; 0p_tz; 0gndh; 0c5qvw; 03mr85; *> query: (?x6448, 0c3351) <- nominated_for(?x1313, ?x6448), nominated_for(?x72, ?x6448), ?x1313 = 0gs9p *> conf = 0.05 ranks of expected_values: 16 EVAL 0404j37 titles! 0c3351 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 108.000 69.000 0.390 http://example.org/media_common/netflix_genre/titles #22634-02mjmr PRED entity: 02mjmr PRED relation: politician! PRED expected values: 0d075m => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 20): 0d075m (0.58 #243, 0.56 #387, 0.47 #363), 07wbk (0.40 #145, 0.37 #433, 0.35 #481), 01c9x (0.25 #76, 0.20 #148, 0.20 #124), 07wf9 (0.22 #390, 0.21 #606, 0.09 #1062), 07wgm (0.14 #614, 0.11 #398, 0.04 #542), 02245 (0.11 #427, 0.10 #139, 0.08 #763), 07wdw (0.07 #607, 0.06 #391, 0.05 #1063), 07wpm (0.06 #712, 0.01 #1168), 0135dr (0.05 #1194, 0.03 #714, 0.01 #2446), 01fpdh (0.05 #455, 0.04 #623, 0.02 #1079) >> Best rule #243 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 03txms; >> query: (?x2669, 0d075m) <- legislative_sessions(?x2669, ?x845), ?x845 = 07p__7, nationality(?x2669, ?x94) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02mjmr politician! 0d075m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 190.000 190.000 0.583 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #22633-0cp08zg PRED entity: 0cp08zg PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 34): 0ch6mp2 (0.88 #204, 0.68 #2209, 0.68 #2170), 09zzb8 (0.67 #235, 0.64 #157, 0.61 #79), 02r96rf (0.67 #472, 0.63 #1255, 0.61 #1808), 09vw2b7 (0.61 #359, 0.59 #125, 0.55 #554), 01vx2h (0.44 #482, 0.35 #1068, 0.32 #170), 0dxtw (0.33 #286, 0.32 #169, 0.31 #1462), 01xy5l_ (0.33 #17, 0.29 #212, 0.25 #56), 01pvkk (0.33 #483, 0.28 #835, 0.28 #1030), 0215hd (0.33 #22, 0.25 #217, 0.25 #61), 02ynfr (0.33 #19, 0.25 #58, 0.24 #370) >> Best rule #204 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 05c5z8j; >> query: (?x7700, 0ch6mp2) <- country(?x7700, ?x94), titles(?x1014, ?x7700), film_distribution_medium(?x7700, ?x2099), language(?x7700, ?x254), film_festivals(?x7700, ?x7988) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cp08zg film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.875 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22632-0205dx PRED entity: 0205dx PRED relation: people! PRED expected values: 0x67 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 46): 041rx (0.19 #158, 0.14 #1236, 0.14 #389), 02w7gg (0.17 #79, 0.07 #387, 0.07 #310), 033tf_ (0.15 #931, 0.13 #777, 0.12 #161), 0x67 (0.13 #780, 0.11 #2321, 0.10 #934), 07hwkr (0.12 #166, 0.08 #551, 0.08 #705), 09vc4s (0.09 #240, 0.07 #394, 0.07 #317), 01qhm_ (0.08 #83, 0.06 #930, 0.06 #776), 048z7l (0.08 #117, 0.06 #194, 0.04 #733), 09kr66 (0.08 #120, 0.06 #197, 0.03 #351), 07bch9 (0.08 #100, 0.06 #639, 0.05 #562) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 06y9c2; >> query: (?x4767, 041rx) <- type_of_union(?x4767, ?x566), student(?x3440, ?x4767), participant(?x4767, ?x105) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #780 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 187 *> proper extension: 01vvycq; 02l840; 033wx9; 01ttg5; 03n93; 018n6m; 01s21dg; 01vvyc_; 03g5_y; 03f3yfj; ... *> query: (?x4767, 0x67) <- award_nominee(?x100, ?x4767), participant(?x105, ?x4767), profession(?x4767, ?x319) *> conf = 0.13 ranks of expected_values: 4 EVAL 0205dx people! 0x67 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 103.000 0.188 http://example.org/people/ethnicity/people #22631-0cqgl9 PRED entity: 0cqgl9 PRED relation: ceremony PRED expected values: 092t4b => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 131): 05c1t6z (0.48 #1060, 0.43 #405, 0.43 #143), 0gvstc3 (0.43 #162, 0.42 #1079, 0.29 #424), 03nnm4t (0.43 #199, 0.40 #1116, 0.29 #330), 0gx_st (0.43 #165, 0.37 #1082, 0.29 #427), 02q690_ (0.42 #1108, 0.29 #191, 0.23 #1501), 0gpjbt (0.34 #3171, 0.33 #3040, 0.33 #3302), 09n4nb (0.33 #3190, 0.33 #3321, 0.33 #3059), 0466p0j (0.33 #3215, 0.33 #3084, 0.32 #3346), 02rjjll (0.33 #3150, 0.32 #3281, 0.32 #3019), 056878 (0.32 #3174, 0.32 #3305, 0.32 #3043) >> Best rule #1060 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 054knh; 02py_sj; >> query: (?x3722, 05c1t6z) <- ceremony(?x3722, ?x873), nominated_for(?x3722, ?x4581), genre(?x4581, ?x53), actor(?x4581, ?x1538) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #704 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 099cng; 02ppm4q; *> query: (?x3722, 092t4b) <- ceremony(?x3722, ?x873), award(?x4103, ?x3722), award(?x2028, ?x3722), ?x4103 = 02jsgf, ?x2028 = 028knk *> conf = 0.22 ranks of expected_values: 36 EVAL 0cqgl9 ceremony 092t4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 46.000 46.000 0.484 http://example.org/award/award_category/winners./award/award_honor/ceremony #22630-025ndl PRED entity: 025ndl PRED relation: jurisdiction_of_office! PRED expected values: 04syw => 223 concepts (223 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.71 #1248, 0.70 #1340, 0.68 #834), 060bp (0.68 #1338, 0.67 #1545, 0.67 #1246), 04syw (0.60 #145, 0.52 #2892, 0.41 #2706), 0pqc5 (0.52 #1688, 0.49 #1991, 0.46 #2339), 02079p (0.41 #2706, 0.41 #3666, 0.41 #3642), 0f6c3 (0.38 #2970, 0.37 #2690, 0.20 #3438), 0fkvn (0.37 #2686, 0.33 #2966, 0.20 #1226), 09n5b9 (0.37 #2974, 0.35 #2694, 0.19 #3442), 0p5vf (0.33 #405, 0.30 #1707, 0.19 #1028), 01zq91 (0.25 #61, 0.22 #407, 0.20 #130) >> Best rule #1248 for best value: >> intensional similarity = 5 >> extensional distance = 49 >> proper extension: 0j5g9; >> query: (?x1611, 060c4) <- adjoins(?x1611, ?x9328), locations(?x9798, ?x9328), capital(?x1611, ?x14119), contains(?x5073, ?x1611), nationality(?x5249, ?x9328) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #145 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0d0vqn; *> query: (?x1611, 04syw) <- combatants(?x10176, ?x1611), contains(?x5073, ?x1611), ?x10176 = 01gqg3, adjoins(?x9328, ?x1611), entity_involved(?x6982, ?x9328) *> conf = 0.60 ranks of expected_values: 3 EVAL 025ndl jurisdiction_of_office! 04syw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 223.000 223.000 0.706 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #22629-02hvd PRED entity: 02hvd PRED relation: production_companies! PRED expected values: 02fqrf 0btpm6 => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 1149): 01hp5 (0.50 #2364, 0.47 #9154, 0.41 #6865), 01hr1 (0.50 #2316, 0.47 #9154, 0.41 #6865), 035xwd (0.50 #2371, 0.33 #1227, 0.06 #16103), 03cd0x (0.47 #9154, 0.41 #6865, 0.33 #1751), 012s1d (0.47 #9154, 0.41 #6865, 0.33 #28608), 01hq1 (0.47 #9154, 0.41 #6865, 0.26 #11443), 0fqt1ns (0.47 #9154, 0.41 #6865, 0.26 #11443), 0340hj (0.47 #9154, 0.41 #6865, 0.26 #11443), 02wgk1 (0.47 #9154, 0.41 #6865, 0.26 #11443), 02fqrf (0.47 #9154, 0.41 #6865, 0.26 #11443) >> Best rule #2364 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0338lq; >> query: (?x4585, 01hp5) <- production_companies(?x11333, ?x4585), production_companies(?x9169, ?x4585), production_companies(?x4331, ?x4585), ?x4331 = 01hqk, film_release_region(?x9169, ?x94), film(?x382, ?x11333), story_by(?x9169, ?x4238) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9154 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 099ks0; 04rcl7; *> query: (?x4585, ?x1511) <- company(?x7851, ?x4585), production_companies(?x7208, ?x4585), story_by(?x1511, ?x7851), genre(?x7208, ?x53), language(?x7208, ?x254) *> conf = 0.47 ranks of expected_values: 10, 89 EVAL 02hvd production_companies! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 184.000 184.000 0.500 http://example.org/film/film/production_companies EVAL 02hvd production_companies! 02fqrf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 184.000 184.000 0.500 http://example.org/film/film/production_companies #22628-01vsykc PRED entity: 01vsykc PRED relation: award PRED expected values: 01c427 02f705 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 267): 01c92g (0.77 #14619, 0.72 #47023, 0.71 #15410), 02f6ym (0.44 #1041, 0.31 #2226, 0.30 #3411), 02f5qb (0.40 #151, 0.31 #2521, 0.30 #4891), 05p09zm (0.37 #4071, 0.27 #4466, 0.26 #5651), 01ckcd (0.34 #5068, 0.33 #5463, 0.20 #3883), 01c99j (0.33 #1010, 0.27 #2195, 0.24 #1800), 09sb52 (0.33 #13472, 0.28 #12287, 0.26 #33227), 02f71y (0.31 #2548, 0.28 #968, 0.20 #1758), 02f705 (0.31 #2518, 0.20 #148, 0.19 #2123), 03c7tr1 (0.29 #4007, 0.21 #4402, 0.21 #5587) >> Best rule #14619 for best value: >> intensional similarity = 3 >> extensional distance = 235 >> proper extension: 01vrx3g; 01vt5c_; 017959; >> query: (?x3290, ?x1232) <- origin(?x3290, ?x362), contains(?x362, ?x639), award_winner(?x1232, ?x3290) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2518 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 03zz8b; *> query: (?x3290, 02f705) <- origin(?x3290, ?x362), award_winner(?x3290, ?x1206), participant(?x3290, ?x3291) *> conf = 0.31 ranks of expected_values: 9, 44 EVAL 01vsykc award 02f705 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 134.000 134.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vsykc award 01c427 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 134.000 134.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22627-012dtf PRED entity: 012dtf PRED relation: people! PRED expected values: 04p3w => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 41): 0gk4g (0.19 #1265, 0.18 #1199, 0.17 #1133), 04p3w (0.14 #11, 0.10 #1200, 0.10 #1266), 0m32h (0.14 #23, 0.05 #684, 0.04 #2202), 01psyx (0.14 #45, 0.04 #1960, 0.03 #640), 01l2m3 (0.14 #16, 0.03 #2261, 0.03 #3185), 01tf_6 (0.14 #31, 0.03 #163, 0.03 #229), 0j8hd (0.14 #47, 0.01 #1170, 0.01 #1236), 0dq9p (0.13 #678, 0.11 #1140, 0.11 #1206), 0qcr0 (0.12 #67, 0.08 #662, 0.07 #1982), 02k6hp (0.12 #103, 0.05 #1160, 0.05 #1226) >> Best rule #1265 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 01n44c; 015gy7; 015dcj; 029cpw; 016ynj; 015np0; 03dbww; 044bn; 04zn7g; >> query: (?x7028, 0gk4g) <- film(?x7028, ?x6218), place_of_death(?x7028, ?x739), profession(?x7028, ?x1032), gender(?x7028, ?x231) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 04nw9; 015076; *> query: (?x7028, 04p3w) <- film(?x7028, ?x6218), place_of_death(?x7028, ?x739), participant(?x7028, ?x1149), award(?x7028, ?x591) *> conf = 0.14 ranks of expected_values: 2 EVAL 012dtf people! 04p3w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.187 http://example.org/people/cause_of_death/people #22626-02wd48 PRED entity: 02wd48 PRED relation: film PRED expected values: 0gwgn1k => 104 concepts (63 used for prediction) PRED predicted values (max 10 best out of 376): 015bpl (0.33 #1392, 0.04 #3184, 0.02 #13937), 027r7k (0.33 #1725, 0.04 #3517, 0.01 #19646), 011yth (0.33 #300, 0.04 #2092, 0.01 #18221), 049mql (0.33 #685, 0.02 #2477, 0.02 #4269), 02rn00y (0.33 #561, 0.02 #2353, 0.01 #18482), 047myg9 (0.33 #1129, 0.02 #2921), 04pmnt (0.33 #1074, 0.02 #2866), 0dgq_kn (0.33 #1040, 0.02 #2832), 0b44shh (0.33 #882, 0.02 #2674), 0cq7tx (0.33 #738, 0.02 #2530) >> Best rule #1392 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01ycbq; >> query: (?x8510, 015bpl) <- student(?x4199, ?x8510), profession(?x8510, ?x987), award(?x8510, ?x2750), type_of_union(?x8510, ?x566), ?x4199 = 016ndm >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5135 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 55 *> proper extension: 01vv6_6; 0phx4; 0fpj4lx; 01d1yr; 02fybl; 018d6l; 0jsg0m; 017f4y; 07f7jp; *> query: (?x8510, 0gwgn1k) <- student(?x4199, ?x8510), profession(?x8510, ?x2348), profession(?x8510, ?x1032), ?x2348 = 0nbcg, ?x1032 = 02hrh1q, gender(?x8510, ?x231) *> conf = 0.02 ranks of expected_values: 189 EVAL 02wd48 film 0gwgn1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 104.000 63.000 0.333 http://example.org/film/actor/film./film/performance/film #22625-05vc35 PRED entity: 05vc35 PRED relation: language PRED expected values: 03_9r => 108 concepts (100 used for prediction) PRED predicted values (max 10 best out of 50): 03_9r (0.83 #996, 0.80 #1462, 0.80 #1229), 05zjd (0.45 #1104, 0.33 #25, 0.30 #2444), 02bjrlw (0.45 #1104, 0.30 #2444, 0.17 #291), 02hwhyv (0.45 #1104, 0.17 #377, 0.14 #610), 01r2l (0.45 #1104, 0.17 #372, 0.14 #605), 064_8sq (0.30 #2444, 0.25 #3090, 0.18 #2992), 06nm1 (0.30 #2444, 0.14 #2513, 0.13 #2571), 04306rv (0.30 #2444, 0.11 #1981, 0.11 #3095), 02bv9 (0.30 #2444, 0.03 #5703, 0.01 #2237), 0t_2 (0.25 #3090, 0.04 #4680, 0.04 #5287) >> Best rule #996 for best value: >> intensional similarity = 8 >> extensional distance = 10 >> proper extension: 0b60sq; >> query: (?x10642, 03_9r) <- genre(?x10642, ?x2540), actor(?x10642, ?x12353), language(?x10642, ?x254), category(?x12353, ?x134), genre(?x2223, ?x2540), film_release_distribution_medium(?x10642, ?x81), ?x2223 = 01_1pv, location(?x12353, ?x3634) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05vc35 language 03_9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 100.000 0.833 http://example.org/film/film/language #22624-090s_0 PRED entity: 090s_0 PRED relation: actor PRED expected values: 01xsc9 => 96 concepts (28 used for prediction) PRED predicted values (max 10 best out of 641): 030znt (0.65 #9238, 0.64 #10162, 0.61 #8314), 014g22 (0.65 #9238, 0.64 #10162, 0.61 #8314), 01n8_g (0.65 #9238, 0.64 #10162, 0.61 #8314), 02vkvcz (0.42 #18478, 0.40 #13857, 0.36 #24948), 01ggc9 (0.33 #762, 0.25 #1685, 0.11 #4456), 01x0sy (0.33 #714, 0.25 #1637, 0.11 #4408), 01vh18t (0.33 #707, 0.25 #1630, 0.11 #4401), 04qsdh (0.33 #620, 0.25 #1543, 0.11 #4314), 018fwv (0.33 #912, 0.25 #1835, 0.11 #4606), 049sb (0.33 #865, 0.25 #1788, 0.11 #4559) >> Best rule #9238 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 07ng9k; 02bg8v; 027pfb2; 0gbtbm; 02ppg1r; 01f39b; 0bbm7r; 02gd6x; 02z44tp; 03ffcz; ... >> query: (?x293, ?x1343) <- country_of_origin(?x293, ?x94), genre(?x293, ?x811), actor(?x293, ?x294), film(?x1343, ?x293) >> conf = 0.65 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 090s_0 actor 01xsc9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 28.000 0.648 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #22623-0gpx6 PRED entity: 0gpx6 PRED relation: category PRED expected values: 08mbj5d => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.36 #3, 0.30 #11, 0.30 #1) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 03g9xj; >> query: (?x7735, 08mbj5d) <- nominated_for(?x5959, ?x7735), titles(?x2502, ?x7735), major_field_of_study(?x481, ?x2502) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gpx6 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.359 http://example.org/common/topic/webpage./common/webpage/category #22622-03hj3b3 PRED entity: 03hj3b3 PRED relation: titles! PRED expected values: 07s9rl0 => 89 concepts (48 used for prediction) PRED predicted values (max 10 best out of 58): 07s9rl0 (0.44 #1134, 0.42 #925, 0.40 #1651), 04xvlr (0.27 #1137, 0.25 #1240, 0.24 #2270), 02l7c8 (0.21 #1546, 0.21 #1856, 0.20 #3406), 01z4y (0.20 #2717, 0.18 #2509, 0.17 #138), 09blyk (0.20 #47, 0.08 #868, 0.07 #1697), 07ssc (0.20 #1143, 0.18 #214, 0.14 #316), 06l3bl (0.19 #361, 0.11 #774, 0.09 #259), 017fp (0.18 #1260, 0.17 #845, 0.17 #1880), 01jfsb (0.17 #122, 0.13 #3323, 0.12 #1048), 0c3351 (0.17 #154, 0.06 #461, 0.05 #565) >> Best rule #1134 for best value: >> intensional similarity = 5 >> extensional distance = 89 >> proper extension: 0ds35l9; 0m313; 01jc6q; 011yph; 0dgst_d; 0kvgxk; 0bpx1k; 0b1y_2; 0gyfp9c; 0ctb4g; ... >> query: (?x1944, 07s9rl0) <- nominated_for(?x2880, ?x1944), nominated_for(?x591, ?x1944), language(?x1944, ?x254), ?x2880 = 02ppm4q, award(?x123, ?x591) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hj3b3 titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 48.000 0.440 http://example.org/media_common/netflix_genre/titles #22621-01n30p PRED entity: 01n30p PRED relation: award PRED expected values: 02x8n1n => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 204): 02n9nmz (0.33 #526, 0.09 #759, 0.03 #5197), 09d28z (0.33 #662, 0.07 #4866, 0.06 #3700), 02x8n1n (0.25 #7475, 0.25 #3038, 0.25 #9342), 0gr4k (0.25 #7475, 0.25 #3038, 0.25 #9342), 03hkv_r (0.25 #7475, 0.25 #3038, 0.25 #9342), 09sb52 (0.22 #17052, 0.11 #21020, 0.10 #12613), 094qd5 (0.22 #17052, 0.11 #21020, 0.10 #12613), 0bsjcw (0.22 #17052, 0.11 #21020, 0.10 #12613), 0ck27z (0.22 #17052, 0.11 #21020, 0.10 #9809), 0cqhb3 (0.22 #17052, 0.11 #21020, 0.10 #9809) >> Best rule #526 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03wy8t; >> query: (?x8158, 02n9nmz) <- produced_by(?x8158, ?x6279), country(?x8158, ?x94), film(?x10851, ?x8158), ?x10851 = 01pg1d >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7475 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 602 *> proper extension: 02nf2c; 04p5cr; 01fs__; 0m123; *> query: (?x8158, ?x384) <- nominated_for(?x384, ?x8158), titles(?x2753, ?x8158), award_winner(?x8158, ?x368), award_winner(?x560, ?x368) *> conf = 0.25 ranks of expected_values: 3 EVAL 01n30p award 02x8n1n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 110.000 0.333 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22620-0l6qt PRED entity: 0l6qt PRED relation: profession PRED expected values: 0kyk => 75 concepts (52 used for prediction) PRED predicted values (max 10 best out of 69): 01d_h8 (0.63 #1612, 0.49 #2344, 0.47 #5264), 02jknp (0.55 #1613, 0.38 #2345, 0.36 #5265), 03gjzk (0.49 #5271, 0.46 #2351, 0.38 #1619), 0kyk (0.47 #320, 0.35 #1050, 0.33 #612), 05z96 (0.29 #41, 0.23 #333, 0.20 #1063), 02krf9 (0.26 #7449, 0.20 #2363, 0.16 #1631), 05sxg2 (0.26 #7449, 0.12 #1, 0.05 #439), 09jwl (0.21 #2209, 0.19 #3085, 0.18 #5713), 018gz8 (0.19 #1183, 0.19 #161, 0.18 #2353), 025352 (0.18 #641, 0.17 #495, 0.16 #933) >> Best rule #1612 for best value: >> intensional similarity = 2 >> extensional distance = 334 >> proper extension: 0qf43; 05whq_9; 04b19t; 01f7v_; 058nh2; 03hy3g; 01wk51; 06b_0; 063_t; 030vmc; ... >> query: (?x164, 01d_h8) <- profession(?x164, ?x353), written_by(?x2111, ?x164) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #320 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 07ym0; 06hgj; 01vh096; 026ck; *> query: (?x164, 0kyk) <- profession(?x164, ?x6421), ?x6421 = 02hv44_, location(?x164, ?x4510) *> conf = 0.47 ranks of expected_values: 4 EVAL 0l6qt profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 52.000 0.634 http://example.org/people/person/profession #22619-026y23w PRED entity: 026y23w PRED relation: gender PRED expected values: 05zppz => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #45, 0.91 #47, 0.90 #51), 02zsn (0.46 #209, 0.45 #202, 0.45 #212) >> Best rule #45 for best value: >> intensional similarity = 5 >> extensional distance = 98 >> proper extension: 02qjj7; 019y64; 03n69x; 0frmb1; 01f492; 01gct2; 0cymln; 019g65; 02bf2s; 03l26m; ... >> query: (?x5763, 05zppz) <- nationality(?x5763, ?x512), team(?x5763, ?x9644), team(?x5763, ?x5764), sport(?x5764, ?x471), team(?x60, ?x9644) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026y23w gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.910 http://example.org/people/person/gender #22618-01m23s PRED entity: 01m23s PRED relation: category PRED expected values: 08mbj5d => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #1, 0.80 #11, 0.80 #13) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01m1_t; >> query: (?x13745, 08mbj5d) <- county(?x13745, ?x13940), contains(?x1755, ?x13745), adjoins(?x3164, ?x13940), ?x1755 = 01x73 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m23s category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.800 http://example.org/common/topic/webpage./common/webpage/category #22617-0k54q PRED entity: 0k54q PRED relation: film_distribution_medium PRED expected values: 0dq6p => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.59 #100, 0.15 #80, 0.14 #14), 02nxhr (0.40 #1, 0.27 #97, 0.14 #16), 0dq6p (0.29 #12, 0.21 #98, 0.17 #7), 07z4p (0.04 #26, 0.02 #101), 07c52 (0.04 #26) >> Best rule #100 for best value: >> intensional similarity = 6 >> extensional distance = 162 >> proper extension: 0cpllql; 0d_2fb; 03176f; 0243cq; 0dln8jk; 016ky6; 0bt4g; 01xvjb; 0ndsl1x; 0353tm; ... >> query: (?x5378, 0735l) <- film(?x4800, ?x5378), country(?x5378, ?x94), film(?x5202, ?x5378), genre(?x5378, ?x225), award_nominee(?x1039, ?x5202), film_distribution_medium(?x5378, ?x81) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 01f8gz; 045j3w; 01rxyb; *> query: (?x5378, 0dq6p) <- film(?x4800, ?x5378), language(?x5378, ?x2164), film(?x1382, ?x5378), ?x2164 = 03_9r, film_release_region(?x5378, ?x142) *> conf = 0.29 ranks of expected_values: 3 EVAL 0k54q film_distribution_medium 0dq6p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.591 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #22616-0qpsn PRED entity: 0qpsn PRED relation: locations! PRED expected values: 0b_6zk => 153 concepts (121 used for prediction) PRED predicted values (max 10 best out of 107): 0b_6zk (0.26 #1428, 0.25 #285, 0.24 #1555), 0b_6mr (0.26 #1485, 0.25 #342, 0.21 #1612), 0b_75k (0.25 #303, 0.23 #557, 0.21 #1573), 0b_6lb (0.25 #331, 0.22 #1474, 0.18 #1601), 0b_6q5 (0.25 #349, 0.18 #1619, 0.17 #1492), 0bzrsh (0.25 #333, 0.18 #1603, 0.17 #2239), 0b_6pv (0.25 #334, 0.18 #1604, 0.15 #2240), 0b_6qj (0.25 #321, 0.15 #1591, 0.15 #2227), 0b_6x2 (0.19 #2194, 0.12 #1558, 0.11 #5732), 0b_6rk (0.18 #1570, 0.17 #1443, 0.17 #2206) >> Best rule #1428 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0f04v; >> query: (?x12358, 0b_6zk) <- county(?x12358, ?x7409), state(?x12358, ?x938), locations(?x5258, ?x12358) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qpsn locations! 0b_6zk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 121.000 0.261 http://example.org/time/event/locations #22615-016tb7 PRED entity: 016tb7 PRED relation: award_winner! PRED expected values: 092_25 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 111): 092c5f (0.10 #14, 0.04 #1988, 0.04 #155), 03nnm4t (0.10 #74, 0.02 #1061, 0.02 #1907), 0gx_st (0.10 #37, 0.02 #3844, 0.02 #1870), 092t4b (0.06 #52, 0.04 #1885, 0.03 #3859), 0hr3c8y (0.06 #10, 0.03 #3817, 0.03 #2266), 0g55tzk (0.06 #137, 0.03 #2393, 0.03 #278), 01c6qp (0.06 #442, 0.03 #7351, 0.03 #10030), 013b2h (0.05 #503, 0.04 #7412, 0.04 #2900), 09qvms (0.05 #1846, 0.05 #1705, 0.04 #3820), 019bk0 (0.05 #439, 0.03 #1708, 0.03 #7348) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 01sxq9; 07z1_q; >> query: (?x3694, 092c5f) <- nominated_for(?x3694, ?x1295), award(?x3694, ?x2603), ?x2603 = 09qs08 >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #3879 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 931 *> proper extension: 012ljv; 0244r8; 0gsg7; 06k02; 0cjdk; 01dw9z; 094wz7q; 02tv80; 05gnf; 01hmk9; ... *> query: (?x3694, 092_25) <- nominated_for(?x3694, ?x1295), award_winner(?x3694, ?x513), award_winner(?x678, ?x3694) *> conf = 0.03 ranks of expected_values: 53 EVAL 016tb7 award_winner! 092_25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 108.000 108.000 0.097 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22614-0315q3 PRED entity: 0315q3 PRED relation: produced_by! PRED expected values: 047vnkj => 115 concepts (109 used for prediction) PRED predicted values (max 10 best out of 148): 04yc76 (0.15 #4737, 0.10 #49266, 0.10 #85280), 034qrh (0.15 #4737, 0.10 #85280, 0.09 #57795), 034qzw (0.05 #13262, 0.03 #12314, 0.03 #40734), 02rx2m5 (0.05 #13262, 0.03 #12314, 0.03 #40734), 05sns6 (0.05 #13262, 0.03 #12314, 0.03 #40734), 01shy7 (0.05 #13262, 0.03 #12314, 0.03 #40734), 02ntb8 (0.05 #13262, 0.03 #12314, 0.03 #40734), 0315rp (0.05 #13262, 0.03 #12314, 0.03 #40734), 01s7w3 (0.04 #1762, 0.03 #4604, 0.02 #2709), 033pf1 (0.04 #1703, 0.03 #4545, 0.02 #2650) >> Best rule #4737 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 079ws; 027zz; >> query: (?x4631, ?x437) <- award_winner(?x400, ?x4631), influenced_by(?x4631, ?x397), nominated_for(?x4631, ?x437) >> conf = 0.15 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0315q3 produced_by! 047vnkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 109.000 0.149 http://example.org/film/film/produced_by #22613-02bjrlw PRED entity: 02bjrlw PRED relation: official_language! PRED expected values: 06sff 012m_ => 71 concepts (46 used for prediction) PRED predicted values (max 10 best out of 315): 0jgd (0.55 #1846, 0.53 #2216, 0.50 #2954), 015fr (0.55 #1846, 0.53 #2216, 0.50 #2954), 0b90_r (0.55 #1846, 0.53 #2216, 0.50 #2954), 06t8v (0.55 #1846, 0.53 #2216, 0.50 #2954), 01pj7 (0.55 #1846, 0.53 #2216, 0.50 #2954), 07ytt (0.55 #1846, 0.53 #2216, 0.50 #2954), 06sff (0.55 #1846, 0.53 #2216, 0.50 #2954), 05r7t (0.50 #481, 0.40 #850, 0.33 #112), 0d060g (0.50 #192, 0.33 #8, 0.25 #377), 0366c (0.50 #360, 0.33 #176, 0.25 #545) >> Best rule #1846 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 0c_v2; >> query: (?x90, ?x142) <- language(?x1597, ?x90), service_language(?x555, ?x90), countries_spoken_in(?x90, ?x142), languages_spoken(?x3584, ?x90), nominated_for(?x618, ?x1597), ?x618 = 09qwmm >> conf = 0.55 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7, 118 EVAL 02bjrlw official_language! 012m_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 71.000 46.000 0.548 http://example.org/location/country/official_language EVAL 02bjrlw official_language! 06sff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 71.000 46.000 0.548 http://example.org/location/country/official_language #22612-0bc1yhb PRED entity: 0bc1yhb PRED relation: film! PRED expected values: 01chc7 => 55 concepts (38 used for prediction) PRED predicted values (max 10 best out of 873): 01l9p (0.15 #277, 0.03 #2353, 0.02 #4430), 0gnbw (0.10 #3340, 0.07 #5417, 0.05 #9569), 055c8 (0.10 #2615, 0.05 #4692, 0.05 #6768), 01wy5m (0.10 #2930, 0.05 #5007, 0.05 #7083), 01chc7 (0.10 #2632, 0.04 #15089, 0.04 #8861), 0jfx1 (0.10 #2478, 0.04 #62292, 0.04 #21163), 046_v (0.09 #35300, 0.09 #39453, 0.09 #29068), 04zd4m (0.09 #35300, 0.09 #39453, 0.09 #29068), 0f0kz (0.09 #4665, 0.08 #12969, 0.07 #6741), 0h5g_ (0.09 #4224, 0.07 #2147, 0.07 #12528) >> Best rule #277 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 04gknr; 011yqc; 09gq0x5; 03kg2v; 017z49; 06zn2v2; 0404j37; 0gfh84d; 02fwfb; 0466s8n; >> query: (?x5270, 01l9p) <- film_release_region(?x5270, ?x1536), film(?x72, ?x5270), ?x72 = 0184jc, film_release_region(?x3377, ?x1536), olympics(?x1536, ?x391), ?x3377 = 0gj8nq2 >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #2632 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 28 *> proper extension: 087wc7n; 0fpkhkz; 06wbm8q; 03z9585; *> query: (?x5270, 01chc7) <- film_release_region(?x5270, ?x1536), film_release_region(?x5270, ?x1471), film_release_region(?x5270, ?x1003), film(?x4969, ?x5270), ?x1536 = 06c1y, ?x1003 = 03gj2, ?x1471 = 07t21, nominated_for(?x4969, ?x146) *> conf = 0.10 ranks of expected_values: 5 EVAL 0bc1yhb film! 01chc7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 55.000 38.000 0.154 http://example.org/film/actor/film./film/performance/film #22611-0h95927 PRED entity: 0h95927 PRED relation: film_release_region PRED expected values: 0chghy 02vzc 03h64 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 145): 03h64 (0.89 #1615, 0.89 #1473, 0.88 #1899), 0b90_r (0.89 #713, 0.86 #1991, 0.85 #2275), 0chghy (0.89 #1996, 0.87 #2280, 0.86 #1854), 02vzc (0.82 #3026, 0.81 #2031, 0.80 #3594), 01ls2 (0.74 #720, 0.64 #1856, 0.63 #1430), 05v8c (0.71 #2285, 0.71 #2001, 0.68 #1575), 016wzw (0.65 #1616, 0.63 #1474, 0.63 #764), 06t8v (0.61 #207, 0.60 #1627, 0.59 #775), 06qd3 (0.54 #742, 0.53 #3015, 0.52 #316), 07f1x (0.54 #815, 0.50 #1951, 0.49 #1525) >> Best rule #1615 for best value: >> intensional similarity = 5 >> extensional distance = 80 >> proper extension: 08hmch; 0bh8yn3; 07x4qr; 0c3xw46; 05c26ss; 0ndsl1x; 0by17xn; >> query: (?x7651, 03h64) <- film_release_region(?x7651, ?x1917), film_release_region(?x7651, ?x1003), ?x1003 = 03gj2, ?x1917 = 01p1v, nominated_for(?x1445, ?x7651) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4 EVAL 0h95927 film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.890 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h95927 film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 69.000 0.890 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h95927 film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 69.000 0.890 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22610-08959 PRED entity: 08959 PRED relation: religion PRED expected values: 02rsw => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 36): 0631_ (0.45 #98, 0.33 #188, 0.24 #323), 0c8wxp (0.33 #6, 0.20 #456, 0.19 #546), 0n2g (0.33 #13, 0.11 #58, 0.09 #418), 01spm (0.33 #37, 0.11 #82, 0.07 #532), 02rsw (0.22 #69, 0.18 #159, 0.16 #429), 051kv (0.19 #365, 0.18 #140, 0.12 #275), 03_gx (0.16 #689, 0.12 #1095, 0.10 #1230), 019cr (0.16 #416, 0.13 #641, 0.12 #731), 01lp8 (0.12 #226, 0.11 #46, 0.09 #136), 07x21 (0.12 #308, 0.11 #398, 0.10 #533) >> Best rule #98 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 042fk; >> query: (?x13592, 0631_) <- profession(?x13592, ?x1359), basic_title(?x13592, ?x346), politician(?x8714, ?x13592), ?x346 = 060c4, ?x8714 = 0d075m >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #69 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 01pj3h; *> query: (?x13592, 02rsw) <- profession(?x13592, ?x8290), ?x8290 = 099md, nationality(?x13592, ?x94), location(?x13592, ?x2740), type_of_union(?x13592, ?x566) *> conf = 0.22 ranks of expected_values: 5 EVAL 08959 religion 02rsw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 92.000 0.455 http://example.org/people/person/religion #22609-0ksf29 PRED entity: 0ksf29 PRED relation: profession PRED expected values: 0dxtg => 163 concepts (107 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.71 #10000, 0.71 #1206, 0.70 #11639), 0dxtg (0.55 #4633, 0.55 #5080, 0.54 #4931), 02jknp (0.55 #5074, 0.54 #2242, 0.54 #2391), 03gjzk (0.52 #909, 0.49 #1058, 0.49 #2101), 09jwl (0.41 #6428, 0.40 #7472, 0.39 #7770), 016z4k (0.28 #7457, 0.28 #7755, 0.27 #6413), 0nbcg (0.28 #7783, 0.27 #8975, 0.27 #7485), 0dz3r (0.26 #7455, 0.26 #7753, 0.23 #6411), 0cbd2 (0.26 #304, 0.24 #453, 0.22 #6), 02krf9 (0.22 #27, 0.19 #1070, 0.17 #1964) >> Best rule #10000 for best value: >> intensional similarity = 4 >> extensional distance = 883 >> proper extension: 01sl1q; 044mz_; 07nznf; 012ljv; 02s2ft; 05bnp0; 04qvl7; 01k7d9; 0337vz; 06151l; ... >> query: (?x1714, 02hrh1q) <- award_winner(?x11230, ?x1714), profession(?x1714, ?x319), nominated_for(?x1714, ?x9996), place_of_birth(?x1714, ?x3448) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4633 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 236 *> proper extension: 058kqy; 052gzr; 05jm7; 0d05fv; 02dbp7; 01qbjg; 081l_; 02mc79; 030g9z; 01vhrz; ... *> query: (?x1714, 0dxtg) <- award_winner(?x11230, ?x1714), profession(?x1714, ?x319), produced_by(?x2598, ?x1714), nationality(?x1714, ?x94) *> conf = 0.55 ranks of expected_values: 2 EVAL 0ksf29 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 163.000 107.000 0.710 http://example.org/people/person/profession #22608-01gfhk PRED entity: 01gfhk PRED relation: time_zones PRED expected values: 02fqwt => 212 concepts (212 used for prediction) PRED predicted values (max 10 best out of 13): 02fqwt (0.81 #538, 0.74 #1080, 0.71 #1879), 02hcv8 (0.61 #212, 0.57 #68, 0.57 #55), 02llzg (0.41 #475, 0.30 #502, 0.30 #488), 02hczc (0.40 #28, 0.29 #54, 0.25 #15), 02lcqs (0.32 #1622, 0.30 #1438, 0.30 #701), 042g7t (0.29 #63, 0.25 #24, 0.14 #76), 02lcrv (0.25 #20, 0.14 #59, 0.13 #2609), 03plfd (0.17 #232, 0.16 #258, 0.14 #601), 05jphn (0.14 #78, 0.14 #65, 0.12 #104), 03bdv (0.13 #886, 0.12 #1545, 0.11 #1597) >> Best rule #538 for best value: >> intensional similarity = 5 >> extensional distance = 58 >> proper extension: 013ksx; 01lxw6; 017j7y; >> query: (?x13910, ?x1638) <- contains(?x151, ?x13910), category(?x13910, ?x134), adjoins(?x13910, ?x8181), time_zones(?x8181, ?x1638), place_of_birth(?x2442, ?x8181) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gfhk time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 212.000 0.806 http://example.org/location/location/time_zones #22607-02rb607 PRED entity: 02rb607 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 34): 0ch6mp2 (0.72 #1393, 0.71 #1842, 0.69 #1505), 02r96rf (0.62 #1389, 0.61 #1838, 0.58 #1501), 0dxtw (0.35 #1397, 0.34 #1846, 0.33 #760), 01pvkk (0.32 #163, 0.29 #88, 0.28 #762), 01vx2h (0.30 #1398, 0.29 #534, 0.29 #87), 0215hd (0.15 #95, 0.15 #355, 0.15 #429), 02rh1dz (0.15 #532, 0.12 #683, 0.11 #944), 02ynfr (0.14 #1403, 0.14 #1852, 0.14 #1515), 01xy5l_ (0.14 #90, 0.12 #165, 0.10 #15), 02_n3z (0.14 #76, 0.12 #151, 0.09 #113) >> Best rule #1393 for best value: >> intensional similarity = 4 >> extensional distance = 857 >> proper extension: 02_1sj; 09p35z; 0963mq; 05p3738; 035s95; 0pvms; 014nq4; 0c34mt; 0c57yj; 05_5rjx; ... >> query: (?x2403, 0ch6mp2) <- country(?x2403, ?x205), language(?x2403, ?x90), film_release_distribution_medium(?x2403, ?x81), film_crew_role(?x2403, ?x137) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rb607 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.719 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22606-01xv77 PRED entity: 01xv77 PRED relation: gender PRED expected values: 02zsn => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #9, 0.84 #51, 0.77 #39), 02zsn (0.52 #12, 0.51 #16, 0.51 #28) >> Best rule #9 for best value: >> intensional similarity = 2 >> extensional distance = 87 >> proper extension: 0f3zf_; 0gp9mp; 025tdwc; 04g865; 02rgz97; 07xr3w; 0bqytm; 0b9l3x; 018ty9; 09bxq9; ... >> query: (?x6236, 05zppz) <- profession(?x6236, ?x2265), ?x2265 = 0dgd_ >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 93 *> proper extension: 02wb6yq; *> query: (?x6236, 02zsn) <- participant(?x6236, ?x338), vacationer(?x4627, ?x6236), nominated_for(?x6236, ?x153) *> conf = 0.52 ranks of expected_values: 2 EVAL 01xv77 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.899 http://example.org/people/person/gender #22605-03_wvl PRED entity: 03_wvl PRED relation: gender PRED expected values: 05zppz => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #83, 0.71 #119, 0.71 #125), 02zsn (0.40 #4, 0.32 #12, 0.31 #16) >> Best rule #83 for best value: >> intensional similarity = 2 >> extensional distance = 1610 >> proper extension: 07c37; >> query: (?x5769, 05zppz) <- student(?x4955, ?x5769), citytown(?x4955, ?x1523) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_wvl gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.726 http://example.org/people/person/gender #22604-01c6l PRED entity: 01c6l PRED relation: nationality PRED expected values: 09c7w0 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 47): 09c7w0 (0.78 #6417, 0.76 #2407, 0.76 #2708), 0d05w3 (0.12 #150, 0.04 #1150, 0.04 #1452), 03rk0 (0.11 #846, 0.09 #4856, 0.08 #4956), 02jx1 (0.10 #1133, 0.10 #2940, 0.10 #6046), 07ssc (0.10 #4124, 0.10 #315, 0.09 #5527), 0d060g (0.05 #1709, 0.05 #607, 0.05 #6423), 0f8l9c (0.05 #222, 0.04 #6114, 0.04 #3308), 03rjj (0.05 #3613, 0.04 #6114, 0.02 #2110), 0chghy (0.04 #6114, 0.04 #3308, 0.04 #110), 03_3d (0.04 #6114, 0.04 #3308, 0.04 #106) >> Best rule #6417 for best value: >> intensional similarity = 2 >> extensional distance = 1546 >> proper extension: 0784v1; 05fh2; >> query: (?x5468, 09c7w0) <- place_of_birth(?x5468, ?x6253), time_zones(?x6253, ?x2674) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c6l nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.780 http://example.org/people/person/nationality #22603-01jft4 PRED entity: 01jft4 PRED relation: genre PRED expected values: 05p553 => 84 concepts (83 used for prediction) PRED predicted values (max 10 best out of 118): 02kdv5l (0.58 #4617, 0.53 #972, 0.35 #486), 05p553 (0.50 #246, 0.50 #125, 0.46 #367), 01jfsb (0.50 #618, 0.42 #983, 0.35 #4628), 03k9fj (0.49 #2076, 0.47 #4627, 0.40 #982), 02l7c8 (0.33 #259, 0.31 #1109, 0.31 #1718), 06n90 (0.27 #4629, 0.23 #498, 0.23 #984), 0lsxr (0.27 #493, 0.26 #735, 0.25 #857), 04xvlr (0.26 #1458, 0.25 #1580, 0.22 #1093), 0vgkd (0.25 #253, 0.23 #374, 0.12 #132), 01t_vv (0.25 #176, 0.17 #297, 0.15 #418) >> Best rule #4617 for best value: >> intensional similarity = 3 >> extensional distance = 784 >> proper extension: 06n90; >> query: (?x7248, 02kdv5l) <- genre(?x7248, ?x1510), genre(?x3276, ?x1510), ?x3276 = 0gjc4d3 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #246 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0q9b0; *> query: (?x7248, 05p553) <- film(?x2534, ?x7248), nominated_for(?x1336, ?x7248), ?x2534 = 0lx2l, language(?x7248, ?x254) *> conf = 0.50 ranks of expected_values: 2 EVAL 01jft4 genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 83.000 0.584 http://example.org/film/film/genre #22602-01515w PRED entity: 01515w PRED relation: profession PRED expected values: 01d_h8 => 114 concepts (107 used for prediction) PRED predicted values (max 10 best out of 63): 01d_h8 (0.85 #4563, 0.85 #4416, 0.84 #3240), 03gjzk (0.40 #3247, 0.40 #4570, 0.37 #4423), 0d1pc (0.28 #784, 0.21 #1225, 0.21 #1813), 09jwl (0.25 #164, 0.21 #4868, 0.21 #3104), 0nbcg (0.25 #177, 0.14 #3558, 0.14 #2235), 039v1 (0.25 #182, 0.06 #3563, 0.05 #5621), 0cbd2 (0.22 #7505, 0.14 #13975, 0.14 #12652), 018gz8 (0.17 #7513, 0.15 #4278, 0.13 #7954), 0np9r (0.16 #7958, 0.14 #11781, 0.14 #13693), 02krf9 (0.15 #7523, 0.13 #4582, 0.12 #613) >> Best rule #4563 for best value: >> intensional similarity = 2 >> extensional distance = 377 >> proper extension: 0gg9_5q; 0glyyw; 024t0y; 0g_rs_; >> query: (?x6157, 01d_h8) <- produced_by(?x4050, ?x6157), profession(?x6157, ?x524) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01515w profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 107.000 0.855 http://example.org/people/person/profession #22601-0sl2w PRED entity: 0sl2w PRED relation: category PRED expected values: 08mbj5d => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #55, 0.78 #7, 0.78 #62) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 347 >> proper extension: 013jz2; 0txhf; >> query: (?x13225, 08mbj5d) <- contains(?x94, ?x13225), ?x94 = 09c7w0, place(?x13225, ?x13225) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sl2w category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.782 http://example.org/common/topic/webpage./common/webpage/category #22600-0170z3 PRED entity: 0170z3 PRED relation: film_crew_role PRED expected values: 09zzb8 09vw2b7 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.73 #1011, 0.70 #1087, 0.70 #488), 02r96rf (0.73 #41, 0.72 #303, 0.70 #152), 09vw2b7 (0.62 #1094, 0.59 #495, 0.59 #532), 01vx2h (0.45 #160, 0.45 #49, 0.43 #311), 01pvkk (0.33 #13, 0.30 #1023, 0.29 #463), 02rh1dz (0.21 #48, 0.17 #535, 0.17 #498), 02ynfr (0.21 #467, 0.19 #504, 0.19 #541), 015h31 (0.18 #47, 0.17 #10, 0.14 #158), 089g0h (0.15 #58, 0.11 #471, 0.10 #169), 0215hd (0.14 #319, 0.14 #131, 0.13 #281) >> Best rule #1011 for best value: >> intensional similarity = 3 >> extensional distance = 688 >> proper extension: 01br2w; 0dckvs; 0djb3vw; 0fq27fp; 04dsnp; 0d6b7; 091z_p; 040rmy; 0crh5_f; 026njb5; ... >> query: (?x54, 09zzb8) <- film_crew_role(?x54, ?x1284), genre(?x54, ?x53), ?x53 = 07s9rl0 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0170z3 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 69.000 0.733 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0170z3 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.733 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22599-04yj5z PRED entity: 04yj5z PRED relation: award_nominee! PRED expected values: 05dbf => 109 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1137): 05dbf (0.81 #114289, 0.81 #109624, 0.81 #72304), 0315q3 (0.33 #3429, 0.25 #5761, 0.18 #8094), 04zqmj (0.33 #4565, 0.25 #6897, 0.18 #9230), 07cjqy (0.33 #3128, 0.25 #5460, 0.18 #7793), 02p65p (0.20 #26, 0.12 #4690, 0.09 #7023), 0hvb2 (0.20 #393, 0.12 #5057, 0.09 #7390), 05vsxz (0.20 #8, 0.12 #4672, 0.09 #7005), 01cj6y (0.20 #1014, 0.12 #5678, 0.09 #8011), 05yh_t (0.20 #1350, 0.12 #6014, 0.09 #8347), 0blbxk (0.20 #262, 0.12 #4926, 0.09 #7259) >> Best rule #114289 for best value: >> intensional similarity = 3 >> extensional distance = 1042 >> proper extension: 07_grx; >> query: (?x804, ?x2275) <- award_nominee(?x804, ?x2275), gender(?x804, ?x231), student(?x7545, ?x804) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04yj5z award_nominee! 05dbf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 51.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22598-03qd_ PRED entity: 03qd_ PRED relation: nationality PRED expected values: 09c7w0 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.85 #1503, 0.83 #602, 0.82 #401), 0ctw_b (0.29 #127, 0.12 #327, 0.07 #528), 02jx1 (0.20 #3042, 0.19 #1435, 0.19 #3242), 07ssc (0.18 #1016, 0.15 #916, 0.13 #1919), 03rjj (0.17 #5, 0.02 #2713, 0.02 #8140), 0d060g (0.09 #407, 0.09 #808, 0.08 #908), 03rk0 (0.07 #5770, 0.06 #8181, 0.05 #10987), 06q1r (0.06 #778, 0.04 #1078, 0.03 #1680), 0chghy (0.06 #711, 0.02 #911, 0.02 #1311), 030qb3t (0.05 #501, 0.04 #1603) >> Best rule #1503 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 023qfd; 02qnbs; 046_v; 02gnj2; >> query: (?x806, 09c7w0) <- place_of_birth(?x806, ?x1523), tv_program(?x806, ?x6884), genre(?x6884, ?x258) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qd_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.853 http://example.org/people/person/nationality #22597-0221zw PRED entity: 0221zw PRED relation: award PRED expected values: 02wkmx => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 184): 0p9sw (0.45 #488, 0.38 #720, 0.11 #2579), 0gs9p (0.36 #532, 0.31 #764, 0.18 #5183), 0k611 (0.36 #541, 0.31 #773, 0.12 #5192), 02ppm4q (0.33 #234, 0.33 #114, 0.27 #13953), 099t8j (0.33 #103, 0.06 #7314, 0.05 #14418), 09td7p (0.33 #92, 0.05 #7303, 0.04 #2418), 0gq9h (0.27 #530, 0.23 #762, 0.18 #5181), 0gq_v (0.27 #487, 0.23 #719, 0.10 #5138), 03qgjwc (0.27 #13953, 0.25 #2559, 0.25 #15116), 02x4x18 (0.27 #13953, 0.25 #2559, 0.25 #15116) >> Best rule #488 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0j_tw; >> query: (?x3500, 0p9sw) <- film(?x2657, ?x3500), film(?x166, ?x3500), film_festivals(?x3500, ?x13076), list(?x3500, ?x3004) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #480 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0j_tw; *> query: (?x3500, 02wkmx) <- film(?x2657, ?x3500), film(?x166, ?x3500), film_festivals(?x3500, ?x13076), list(?x3500, ?x3004) *> conf = 0.09 ranks of expected_values: 67 EVAL 0221zw award 02wkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 106.000 106.000 0.455 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22596-0fx80y PRED entity: 0fx80y PRED relation: family! PRED expected values: 03q5t => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 116): 02fsn (0.50 #340, 0.38 #1112, 0.33 #36), 01xqw (0.50 #355, 0.33 #51, 0.27 #300), 02hnl (0.50 #256, 0.29 #868, 0.27 #300), 011k_j (0.50 #280, 0.29 #892, 0.27 #300), 02dlh2 (0.50 #278, 0.29 #890, 0.27 #300), 01p970 (0.50 #279, 0.29 #891, 0.22 #1278), 026g73 (0.50 #282, 0.29 #894, 0.22 #1281), 014zz1 (0.33 #58, 0.31 #1304, 0.29 #1378), 0l14qv (0.33 #769, 0.27 #300, 0.25 #465), 02sgy (0.33 #153, 0.27 #300, 0.25 #299) >> Best rule #340 for best value: >> intensional similarity = 25 >> extensional distance = 2 >> proper extension: 01vj9c; >> query: (?x7256, 02fsn) <- family(?x1969, ?x7256), family(?x894, ?x7256), family(?x716, ?x7256), role(?x3161, ?x894), role(?x1267, ?x894), role(?x1225, ?x894), role(?x1166, ?x894), role(?x212, ?x894), role(?x1089, ?x894), ?x1267 = 07brj, role(?x894, ?x2157), role(?x316, ?x894), ?x212 = 026t6, ?x1225 = 01qbl, ?x716 = 018vs, role(?x1436, ?x1166), instrumentalists(?x1166, ?x130), group(?x1166, ?x10502), ?x10502 = 016vn3, performance_role(?x885, ?x1166), ?x3161 = 01v1d8, role(?x2157, ?x6938), role(?x248, ?x1166), role(?x366, ?x1969), ?x1436 = 0xzly >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #300 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 2 *> proper extension: 026t6; 0l14md; *> query: (?x7256, ?x1466) <- family(?x894, ?x7256), family(?x227, ?x7256), role(?x2725, ?x894), role(?x1267, ?x894), role(?x1166, ?x894), role(?x2690, ?x894), ?x1267 = 07brj, role(?x894, ?x1466), role(?x316, ?x894), ?x2725 = 0l1589, instrumentalists(?x894, ?x1231), group(?x227, ?x13145), group(?x227, ?x10145), group(?x227, ?x717), instrumentalists(?x227, ?x5048), location(?x2690, ?x362), ?x10145 = 0p76z, role(?x214, ?x227), ?x717 = 0150jk, ?x13145 = 0p8h0, type_of_union(?x2690, ?x566), instrumentalists(?x1166, ?x130), role(?x1292, ?x1166), ?x5048 = 015x1f *> conf = 0.27 ranks of expected_values: 37 EVAL 0fx80y family! 03q5t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 51.000 51.000 0.500 http://example.org/music/instrument/family #22595-0498y PRED entity: 0498y PRED relation: partially_contains PRED expected values: 0f2pf9 => 179 concepts (162 used for prediction) PRED predicted values (max 10 best out of 37): 02cgp8 (0.29 #97, 0.21 #134, 0.20 #170), 0k3nk (0.25 #49, 0.10 #305, 0.08 #1046), 06c6l (0.25 #64, 0.05 #320, 0.03 #1024), 026zt (0.14 #1203, 0.13 #1056, 0.12 #683), 04ykz (0.13 #470, 0.13 #506, 0.13 #543), 0fb18 (0.11 #1053, 0.02 #3750, 0.02 #4356), 0p2n (0.11 #1211, 0.08 #691, 0.07 #1359), 0lcd (0.10 #675, 0.10 #1048, 0.09 #1195), 0f8l9c (0.10 #297, 0.07 #1333, 0.02 #3125), 09glw (0.09 #1198, 0.06 #678, 0.05 #1346) >> Best rule #97 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0f8x_r; >> query: (?x4061, 02cgp8) <- adjoins(?x1426, ?x4061), adjoins(?x177, ?x4061), ?x1426 = 07z1m, first_level_division_of(?x177, ?x94) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #327 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 05g2v; 09b69; 03v9w; *> query: (?x4061, 0f2pf9) <- contains(?x4061, ?x5259), partially_contains(?x4061, ?x4540), locations(?x3797, ?x5259) *> conf = 0.02 ranks of expected_values: 25 EVAL 0498y partially_contains 0f2pf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 179.000 162.000 0.286 http://example.org/location/location/partially_contains #22594-09r8l PRED entity: 09r8l PRED relation: profession PRED expected values: 016z4k => 107 concepts (80 used for prediction) PRED predicted values (max 10 best out of 64): 0nbcg (0.61 #1771, 0.57 #2061, 0.52 #900), 016z4k (0.49 #3630, 0.49 #2905, 0.44 #3485), 01d_h8 (0.32 #1892, 0.31 #1457, 0.30 #9015), 0dxtg (0.30 #1900, 0.25 #11055, 0.25 #11492), 0n1h (0.29 #11333, 0.27 #882, 0.24 #3493), 03lgtv (0.29 #11333, 0.03 #2432, 0.03 #2577), 02jknp (0.23 #3344, 0.22 #1894, 0.20 #4794), 03gjzk (0.22 #11056, 0.22 #9024, 0.22 #11493), 0fnpj (0.21 #928, 0.15 #1218, 0.15 #2089), 0cbd2 (0.20 #3778, 0.19 #2763, 0.18 #2618) >> Best rule #1771 for best value: >> intensional similarity = 3 >> extensional distance = 141 >> proper extension: 07_3qd; 04mx7s; >> query: (?x3957, 0nbcg) <- artist(?x2241, ?x3957), instrumentalists(?x716, ?x3957), ?x716 = 018vs >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #3630 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 352 *> proper extension: 094xh; 02ht0ln; *> query: (?x3957, 016z4k) <- artist(?x2241, ?x3957), instrumentalists(?x227, ?x3957), award(?x3957, ?x2420) *> conf = 0.49 ranks of expected_values: 2 EVAL 09r8l profession 016z4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 80.000 0.608 http://example.org/people/person/profession #22593-01qszl PRED entity: 01qszl PRED relation: industry PRED expected values: 01mw1 03r8gp => 216 concepts (216 used for prediction) PRED predicted values (max 10 best out of 47): 01mw1 (0.71 #380, 0.62 #570, 0.58 #1230), 02vxn (0.62 #2136, 0.50 #1278, 0.47 #1613), 020mfr (0.57 #474, 0.57 #442, 0.57 #395), 02jjt (0.45 #2425, 0.36 #6198, 0.33 #197), 04rlf (0.40 #1955, 0.33 #108, 0.30 #2431), 0hz28 (0.36 #6198, 0.36 #787, 0.29 #1924), 0sydc (0.36 #6198, 0.27 #7377, 0.25 #6622), 05jnl (0.36 #6198, 0.27 #7377, 0.24 #7376), 01mf0 (0.36 #6198, 0.24 #7376, 0.23 #2370), 06xw2 (0.36 #6198, 0.24 #7376, 0.23 #2370) >> Best rule #380 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 01dycg; 01qckn; 0260p2; >> query: (?x14600, 01mw1) <- industry(?x14600, ?x2271), citytown(?x14600, ?x9559), ?x9559 = 07dfk, organization(?x4682, ?x14600), industry(?x13890, ?x2271), industry(?x12752, ?x2271), artist(?x12752, ?x646), ?x13890 = 02b07b >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 23 EVAL 01qszl industry 03r8gp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 216.000 216.000 0.714 http://example.org/business/business_operation/industry EVAL 01qszl industry 01mw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 216.000 216.000 0.714 http://example.org/business/business_operation/industry #22592-038w8 PRED entity: 038w8 PRED relation: basic_title PRED expected values: 0fkvn => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 18): 0fkvn (0.47 #190, 0.46 #105, 0.44 #208), 0dq3c (0.40 #53, 0.38 #104, 0.38 #2), 0789n (0.25 #77, 0.21 #231, 0.20 #333), 01t7n9 (0.24 #205, 0.19 #580, 0.08 #133), 0fkzq (0.24 #205, 0.19 #580, 0.06 #183), 0f6c3 (0.24 #205, 0.19 #580, 0.05 #229), 09n5b9 (0.24 #205, 0.19 #580), 01gkgk (0.22 #261, 0.17 #499, 0.17 #602), 060bp (0.15 #495, 0.13 #563, 0.13 #546), 0pqc5 (0.12 #191, 0.08 #123, 0.07 #498) >> Best rule #190 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 060c4; >> query: (?x11869, 0fkvn) <- jurisdiction_of_office(?x11869, ?x335), contains(?x335, ?x322), adjoins(?x1755, ?x335), vacationer(?x335, ?x794), jurisdiction_of_office(?x900, ?x335) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 038w8 basic_title 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.471 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #22591-0ctw_b PRED entity: 0ctw_b PRED relation: jurisdiction_of_office! PRED expected values: 02079p 0fj45 => 204 concepts (204 used for prediction) PRED predicted values (max 10 best out of 19): 060c4 (0.76 #1749, 0.75 #903, 0.75 #615), 0f6c3 (0.71 #1230, 0.66 #1176, 0.50 #1590), 09n5b9 (0.65 #1234, 0.61 #1180, 0.45 #1594), 0pqc5 (0.57 #1642, 0.53 #2579, 0.51 #2669), 0fj45 (0.50 #429, 0.45 #502, 0.27 #33), 0789n (0.25 #241, 0.22 #385, 0.17 #97), 0dq3c (0.25 #73, 0.22 #578, 0.20 #452), 01zq91 (0.23 #462, 0.20 #858, 0.20 #263), 0fkzq (0.21 #1184, 0.18 #1598, 0.16 #1238), 01t7n9 (0.21 #248, 0.19 #392, 0.12 #320) >> Best rule #1749 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 0169t; 02khs; 04j53; 0jdd; 07dvs; 0166v; 07dzf; 088vb; 07t_x; 0j4b; ... >> query: (?x1023, 060c4) <- participating_countries(?x784, ?x1023), adjoins(?x390, ?x1023), olympics(?x1023, ?x452) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #429 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 06s6l; 07fsv; 020p1; *> query: (?x1023, 0fj45) <- participating_countries(?x784, ?x1023), country(?x150, ?x1023), jurisdiction_of_office(?x3444, ?x1023) *> conf = 0.50 ranks of expected_values: 5, 12 EVAL 0ctw_b jurisdiction_of_office! 0fj45 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 204.000 204.000 0.755 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0ctw_b jurisdiction_of_office! 02079p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 204.000 204.000 0.755 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #22590-05p92jn PRED entity: 05p92jn PRED relation: gender PRED expected values: 02zsn => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #13, 0.79 #7, 0.73 #123), 02zsn (0.36 #12, 0.33 #16, 0.32 #40) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 478 >> proper extension: 07kb5; 0ct9_; >> query: (?x6622, 05zppz) <- profession(?x6622, ?x353), ?x353 = 0cbd2 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 397 *> proper extension: 01m65sp; 044mfr; 02rmxx; 01kmd4; 0163t3; 02_wxh; 04bbv7; 01tpl1p; 07bsj; 01j5sv; ... *> query: (?x6622, 02zsn) <- actor(?x1631, ?x6622), people(?x5540, ?x6622) *> conf = 0.36 ranks of expected_values: 2 EVAL 05p92jn gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 95.000 0.792 http://example.org/people/person/gender #22589-01pk8b PRED entity: 01pk8b PRED relation: category PRED expected values: 08mbj5d => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.74 #53, 0.73 #55, 0.73 #59) >> Best rule #53 for best value: >> intensional similarity = 6 >> extensional distance = 947 >> proper extension: 01j4rs; >> query: (?x14657, 08mbj5d) <- contains(?x1781, ?x14657), countries_within(?x6956, ?x1781), taxonomy(?x1781, ?x939), entity_involved(?x12976, ?x1781), contains(?x6304, ?x1781), participating_countries(?x1931, ?x1781) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pk8b category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.741 http://example.org/common/topic/webpage./common/webpage/category #22588-05s34b PRED entity: 05s34b PRED relation: place_founded PRED expected values: 02_286 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 24): 0f2wj (0.11 #529, 0.10 #659, 0.09 #204), 013yq (0.08 #1631, 0.05 #3592, 0.04 #782), 0r00l (0.06 #1624, 0.05 #2211, 0.04 #2407), 0k_q_ (0.06 #474, 0.05 #539, 0.04 #932), 04jpl (0.06 #459, 0.03 #1243, 0.03 #1308), 030qb3t (0.05 #2491, 0.05 #1904, 0.05 #533), 02_286 (0.05 #530, 0.05 #595, 0.05 #726), 01n7q (0.05 #597, 0.02 #1838, 0.01 #4193), 06q1r (0.05 #756, 0.02 #1605, 0.02 #1931), 0l2hf (0.03 #1005, 0.03 #1461, 0.02 #1787) >> Best rule #529 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 030_1m; 01_8w2; >> query: (?x12827, 0f2wj) <- award_winner(?x1762, ?x12827), company(?x5456, ?x12827), child(?x2276, ?x12827), profession(?x5456, ?x131) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #530 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 030_1m; 01_8w2; *> query: (?x12827, 02_286) <- award_winner(?x1762, ?x12827), company(?x5456, ?x12827), child(?x2276, ?x12827), profession(?x5456, ?x131) *> conf = 0.05 ranks of expected_values: 7 EVAL 05s34b place_founded 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 112.000 112.000 0.105 http://example.org/organization/organization/place_founded #22587-0165v PRED entity: 0165v PRED relation: film_release_region! PRED expected values: 017gl1 0dtfn 017gm7 => 109 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1352): 0bpm4yw (0.83 #9755, 0.78 #15020, 0.65 #1859), 043tvp3 (0.81 #10128, 0.79 #15393, 0.62 #916), 047vnkj (0.79 #9908, 0.78 #15173, 0.60 #21753), 03nm_fh (0.79 #9814, 0.76 #15079, 0.65 #1918), 0661ql3 (0.79 #9503, 0.73 #14768, 0.56 #5555), 04f52jw (0.78 #14806, 0.75 #9541, 0.62 #10857), 0gkz15s (0.75 #9299, 0.75 #14564, 0.60 #1403), 0gd0c7x (0.75 #9452, 0.73 #14717, 0.65 #1556), 05pdh86 (0.75 #9778, 0.72 #15043, 0.60 #1882), 017gm7 (0.75 #14636, 0.73 #9371, 0.62 #10687) >> Best rule #9755 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 07dfk; >> query: (?x9816, 0bpm4yw) <- film_release_region(?x1035, ?x9816), ?x1035 = 08hmch, administrative_parent(?x9816, ?x551) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #14636 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 65 *> proper extension: 06y57; 02kx3; *> query: (?x9816, 017gm7) <- film_release_region(?x1035, ?x9816), ?x1035 = 08hmch *> conf = 0.75 ranks of expected_values: 10, 21, 25 EVAL 0165v film_release_region! 017gm7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 109.000 97.000 0.827 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0165v film_release_region! 0dtfn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 109.000 97.000 0.827 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0165v film_release_region! 017gl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 109.000 97.000 0.827 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22586-0crc2cp PRED entity: 0crc2cp PRED relation: film_crew_role PRED expected values: 09zzb8 0dxtw => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 32): 0ch6mp2 (0.86 #80, 0.84 #1531, 0.84 #1496), 09zzb8 (0.83 #1490, 0.82 #1525, 0.80 #74), 0dxtw (0.50 #652, 0.46 #47, 0.45 #793), 02ynfr (0.27 #15, 0.25 #1504, 0.24 #656), 02rh1dz (0.24 #651, 0.24 #508, 0.23 #472), 089fss (0.17 #79, 0.15 #3451, 0.13 #1739), 015h31 (0.17 #366, 0.15 #864, 0.15 #1111), 0215hd (0.16 #2045, 0.15 #3451, 0.15 #410), 0d2b38 (0.15 #3451, 0.15 #61, 0.15 #382), 05smlt (0.15 #3451, 0.13 #1739, 0.12 #377) >> Best rule #80 for best value: >> intensional similarity = 6 >> extensional distance = 33 >> proper extension: 03t97y; 02z0f6l; >> query: (?x3191, 0ch6mp2) <- film_crew_role(?x3191, ?x1171), featured_film_locations(?x3191, ?x362), ?x1171 = 09vw2b7, film_release_distribution_medium(?x3191, ?x81), ?x362 = 04jpl, genre(?x3191, ?x225) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1490 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 263 *> proper extension: 0pvms; 0dpl44; 0cqr0q; 07tlfx; *> query: (?x3191, 09zzb8) <- film_crew_role(?x3191, ?x1171), featured_film_locations(?x3191, ?x362), ?x1171 = 09vw2b7, film_release_distribution_medium(?x3191, ?x81), contains(?x362, ?x639) *> conf = 0.83 ranks of expected_values: 2, 3 EVAL 0crc2cp film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 105.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0crc2cp film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 105.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22585-03pvt PRED entity: 03pvt PRED relation: nationality PRED expected values: 09c7w0 => 105 concepts (102 used for prediction) PRED predicted values (max 10 best out of 42): 09c7w0 (0.89 #5908, 0.89 #5506, 0.78 #1902), 07ssc (0.40 #8831, 0.32 #7417, 0.22 #1615), 03rt9 (0.40 #8831, 0.32 #7417, 0.05 #1413), 0jdx (0.40 #8831, 0.32 #7417), 02jx1 (0.29 #1133, 0.22 #733, 0.22 #2034), 03rk0 (0.20 #1846, 0.14 #2447, 0.14 #446), 0d060g (0.15 #607, 0.10 #1407, 0.08 #3009), 0f8l9c (0.15 #622, 0.05 #522, 0.03 #3024), 03_3d (0.09 #706, 0.05 #506, 0.03 #1806), 06q1r (0.08 #377, 0.03 #2078, 0.03 #5505) >> Best rule #5908 for best value: >> intensional similarity = 3 >> extensional distance = 1334 >> proper extension: 04cy8rb; 07m69t; >> query: (?x3710, 09c7w0) <- place_of_birth(?x3710, ?x1860), location(?x827, ?x1860), source(?x1860, ?x958) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03pvt nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 102.000 0.894 http://example.org/people/person/nationality #22584-04nlb94 PRED entity: 04nlb94 PRED relation: titles! PRED expected values: 07yjb => 126 concepts (87 used for prediction) PRED predicted values (max 10 best out of 211): 01jfsb (0.86 #3930, 0.76 #733, 0.29 #631), 07ssc (0.75 #3196, 0.69 #4432, 0.69 #3298), 01hmnh (0.62 #3807, 0.62 #3730, 0.62 #3702), 017fp (0.59 #2698, 0.12 #3210, 0.11 #3312), 0d0vqn (0.44 #3390, 0.14 #7186, 0.12 #1428), 07s9rl0 (0.44 #2675, 0.42 #6367, 0.41 #1531), 03npn (0.40 #8517, 0.36 #4011, 0.32 #5443), 02l7c8 (0.40 #8517, 0.36 #4011, 0.32 #5443), 04xvlr (0.37 #2678, 0.34 #6370, 0.33 #3190), 03q4nz (0.36 #4011, 0.35 #4829, 0.30 #2776) >> Best rule #3930 for best value: >> intensional similarity = 5 >> extensional distance = 167 >> proper extension: 0b2v79; 016fyc; 026p_bs; 04mzf8; 026n4h6; 05cj_j; 06rmdr; 0fq7dv_; 070fnm; 075cph; ... >> query: (?x12641, 01jfsb) <- titles(?x6820, ?x12641), film(?x609, ?x12641), genre(?x12641, ?x53), titles(?x6820, ?x1498), ?x1498 = 04jkpgv >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #683 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 03f7xg; 04fv5b; 085wqm; *> query: (?x12641, 07yjb) <- film_crew_role(?x12641, ?x2178), genre(?x12641, ?x571), nominated_for(?x6165, ?x12641), ?x2178 = 01pvkk, ?x571 = 03npn *> conf = 0.18 ranks of expected_values: 20 EVAL 04nlb94 titles! 07yjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 126.000 87.000 0.864 http://example.org/media_common/netflix_genre/titles #22583-04v89z PRED entity: 04v89z PRED relation: film! PRED expected values: 0cj8x => 68 concepts (47 used for prediction) PRED predicted values (max 10 best out of 784): 01b9ck (0.64 #45675, 0.64 #53979, 0.55 #4153), 05728w1 (0.55 #4153, 0.44 #91350, 0.43 #93426), 04vt98 (0.44 #91350, 0.43 #93426, 0.43 #70587), 016ggh (0.25 #1865, 0.10 #3941, 0.08 #6019), 0g_92 (0.25 #1549, 0.06 #33217, 0.05 #11928), 044qx (0.25 #733, 0.06 #11112, 0.06 #15265), 0chsq (0.25 #79, 0.06 #10458, 0.05 #14611), 04__f (0.25 #1378, 0.06 #11757, 0.05 #15910), 0jvtp (0.25 #1439, 0.02 #11818, 0.01 #15971), 0127m7 (0.20 #2483, 0.15 #4561, 0.12 #6636) >> Best rule #45675 for best value: >> intensional similarity = 3 >> extensional distance = 801 >> proper extension: 01f3p_; 07wqr6; 0cskb; 0123qq; >> query: (?x8217, ?x1300) <- nominated_for(?x1300, ?x8217), participant(?x509, ?x1300), nationality(?x1300, ?x94) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #33217 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 526 *> proper extension: 01cgz; *> query: (?x8217, ?x286) <- films(?x326, ?x8217), films(?x326, ?x3496), film(?x286, ?x3496) *> conf = 0.06 ranks of expected_values: 140 EVAL 04v89z film! 0cj8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 68.000 47.000 0.641 http://example.org/film/actor/film./film/performance/film #22582-019v9k PRED entity: 019v9k PRED relation: student PRED expected values: 03gkn5 01kb2j 06crk 01w_10 => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1454): 01zwy (0.50 #1753, 0.33 #147, 0.25 #3160), 014vk4 (0.40 #2198, 0.38 #3206, 0.38 #3005), 02v406 (0.40 #2080, 0.33 #2483, 0.33 #75), 03swmf (0.40 #2162, 0.33 #358, 0.25 #3170), 04pp9s (0.40 #2171, 0.33 #367, 0.25 #3179), 02r34n (0.40 #2028, 0.33 #224, 0.25 #3036), 01hbq0 (0.40 #2203, 0.33 #399, 0.25 #3211), 0d0l91 (0.40 #2192, 0.33 #388, 0.25 #3200), 04z0g (0.38 #3119, 0.38 #2918, 0.33 #508), 0b78hw (0.38 #3095, 0.38 #2894, 0.33 #484) >> Best rule #1753 for best value: >> intensional similarity = 25 >> extensional distance = 2 >> proper extension: 02_xgp2; >> query: (?x1771, 01zwy) <- institution(?x1771, ?x13101), institution(?x1771, ?x8706), institution(?x1771, ?x8287), institution(?x1771, ?x6814), institution(?x1771, ?x6637), institution(?x1771, ?x5280), institution(?x1771, ?x4889), institution(?x1771, ?x2327), institution(?x1771, ?x2079), major_field_of_study(?x1771, ?x90), ?x6637 = 07vjm, student(?x1771, ?x4265), ?x8287 = 02x9g_, school(?x580, ?x6814), ?x2327 = 07wjk, state_province_region(?x6814, ?x4776), ?x5280 = 07vhb, currency(?x13101, ?x170), ?x2079 = 01bvw5, country(?x13101, ?x94), ?x8706 = 0trv, contains(?x335, ?x4889), influenced_by(?x1029, ?x4265), profession(?x4265, ?x353), location(?x4265, ?x1591) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3070 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 6 *> proper extension: 022h5x; *> query: (?x1771, 03gkn5) <- institution(?x1771, ?x13101), institution(?x1771, ?x9912), institution(?x1771, ?x6814), institution(?x1771, ?x6787), institution(?x1771, ?x2821), institution(?x1771, ?x2621), institution(?x1771, ?x546), major_field_of_study(?x1771, ?x6760), major_field_of_study(?x1771, ?x5900), major_field_of_study(?x1771, ?x3995), currency(?x9912, ?x170), ?x546 = 01j_9c, student(?x2821, ?x672), student(?x1771, ?x744), school(?x580, ?x6814), major_field_of_study(?x2172, ?x3995), contains(?x94, ?x6787), school_type(?x13101, ?x1044), major_field_of_study(?x5900, ?x1154), category(?x6787, ?x134), disciplines_or_subjects(?x850, ?x6760), company(?x5510, ?x2621) *> conf = 0.25 ranks of expected_values: 147, 153, 214, 1353 EVAL 019v9k student 01w_10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 23.000 23.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 019v9k student 06crk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 23.000 23.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 019v9k student 01kb2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 23.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 019v9k student 03gkn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 23.000 23.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #22581-02snj9 PRED entity: 02snj9 PRED relation: group PRED expected values: 04qzm => 84 concepts (41 used for prediction) PRED predicted values (max 10 best out of 942): 02vnpv (0.77 #4628, 0.76 #6619, 0.75 #2830), 05563d (0.73 #3788, 0.71 #2531, 0.70 #3069), 0134wr (0.71 #2601, 0.71 #2422, 0.70 #3320), 0134tg (0.71 #2553, 0.64 #3810, 0.62 #2732), 017lb_ (0.71 #2606, 0.62 #2785, 0.60 #3144), 07mvp (0.70 #3292, 0.62 #4550, 0.61 #5633), 03c3yf (0.70 #3307, 0.60 #1154, 0.50 #4203), 0b_xm (0.69 #4568, 0.62 #2770, 0.62 #4385), 0163m1 (0.67 #1817, 0.60 #4872, 0.60 #3073), 0gr69 (0.62 #2762, 0.60 #3302, 0.57 #2583) >> Best rule #4628 for best value: >> intensional similarity = 15 >> extensional distance = 11 >> proper extension: 02sgy; 042v_gx; >> query: (?x3214, 02vnpv) <- performance_role(?x2876, ?x3214), role(?x3214, ?x2764), role(?x3214, ?x314), ?x2764 = 01s0ps, performance_role(?x212, ?x3214), group(?x3214, ?x498), instrumentalists(?x75, ?x2876), role(?x314, ?x1437), award(?x2876, ?x724), role(?x217, ?x314), profession(?x2876, ?x220), group(?x314, ?x442), role(?x565, ?x314), artist(?x3265, ?x2876), ?x1437 = 01vdm0 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1065 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 2 *> proper extension: 0l14qv; *> query: (?x3214, ?x379) <- performance_role(?x2876, ?x3214), role(?x3214, ?x2764), role(?x3214, ?x316), role(?x3214, ?x227), ?x2764 = 01s0ps, performance_role(?x212, ?x3214), group(?x3214, ?x1684), ?x2876 = 01vn35l, performance_role(?x3214, ?x10811), ?x10811 = 0d8lm, ?x1684 = 01wv9xn, ?x316 = 05r5c, instrumentalists(?x227, ?x8282), group(?x227, ?x11749), group(?x227, ?x379), role(?x227, ?x74), ?x11749 = 016t0h, ?x8282 = 01q_wyj *> conf = 0.46 ranks of expected_values: 159 EVAL 02snj9 group 04qzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 84.000 41.000 0.769 http://example.org/music/performance_role/regular_performances./music/group_membership/group #22580-01pbs9w PRED entity: 01pbs9w PRED relation: role PRED expected values: 05r5c => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 85): 05r5c (0.50 #115, 0.45 #647, 0.40 #221), 0342h (0.33 #1918, 0.33 #2025, 0.33 #2665), 01vdm0 (0.29 #140, 0.19 #2694, 0.18 #2907), 01s0ps (0.24 #2767, 0.08 #702, 0.08 #1021), 06ncr (0.23 #745, 0.23 #4052, 0.23 #3303), 02sgy (0.21 #113, 0.21 #1920, 0.20 #2027), 042v_gx (0.21 #116, 0.19 #1923, 0.19 #2030), 05842k (0.21 #187, 0.16 #2741, 0.11 #293), 018vs (0.21 #121, 0.16 #2675, 0.11 #3958), 026t6 (0.21 #109, 0.14 #2663, 0.11 #215) >> Best rule #115 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 05563d; >> query: (?x5757, 05r5c) <- artists(?x1572, ?x5757), artists(?x597, ?x5757), ?x1572 = 06by7, ?x597 = 0ggq0m >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pbs9w role 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #22579-0sg4x PRED entity: 0sg4x PRED relation: time_zones PRED expected values: 02fqwt => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.46 #81, 0.44 #237, 0.44 #172), 02fqwt (0.32 #40, 0.27 #66, 0.25 #275), 02hczc (0.25 #275, 0.24 #248, 0.17 #862), 02lcrv (0.25 #275, 0.24 #248, 0.17 #862), 042g7t (0.25 #275, 0.24 #248), 02lcqs (0.24 #109, 0.24 #83, 0.21 #135), 03bdv (0.06 #320, 0.03 #214, 0.03 #672), 02llzg (0.05 #722, 0.05 #748, 0.05 #761), 03plfd (0.01 #1093, 0.01 #1002, 0.01 #493) >> Best rule #81 for best value: >> intensional similarity = 5 >> extensional distance = 371 >> proper extension: 0rs6x; 0k049; 06_kh; 0fm9_; 02_286; 0r62v; 0yc84; 0cc56; 0fvxz; 0r1yc; ... >> query: (?x14549, 02hcv8) <- source(?x14549, ?x958), contains(?x3818, ?x14549), state(?x405, ?x3818), district_represented(?x176, ?x3818), jurisdiction_of_office(?x2669, ?x3818) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 01_d4; 0psxp; 0nvt9; 0nv2x; 0nvg4; 0ntxg; 0nv6n; 0nty_; 0nvvw; 0nv5y; *> query: (?x14549, 02fqwt) <- source(?x14549, ?x958), contains(?x3818, ?x14549), ?x3818 = 03v0t, ?x958 = 0jbk9 *> conf = 0.32 ranks of expected_values: 2 EVAL 0sg4x time_zones 02fqwt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.464 http://example.org/location/location/time_zones #22578-05l0j5 PRED entity: 05l0j5 PRED relation: profession PRED expected values: 02hrh1q => 74 concepts (46 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.91 #4604, 0.89 #5641, 0.89 #2827), 0dxtg (0.60 #458, 0.53 #754, 0.47 #902), 03gjzk (0.53 #460, 0.40 #756, 0.28 #5331), 0kyk (0.35 #918, 0.17 #474, 0.16 #1510), 01d_h8 (0.34 #451, 0.33 #1191, 0.31 #1339), 0np9r (0.31 #465, 0.28 #5331, 0.27 #761), 02krf9 (0.28 #5331, 0.26 #323, 0.26 #6664), 09jwl (0.28 #5331, 0.26 #6664, 0.20 #4312), 02jknp (0.28 #5331, 0.26 #6664, 0.20 #2228), 0nbcg (0.14 #4325, 0.11 #6102, 0.11 #6546) >> Best rule #4604 for best value: >> intensional similarity = 3 >> extensional distance = 1652 >> proper extension: 045931; >> query: (?x7752, 02hrh1q) <- profession(?x7752, ?x353), award(?x7752, ?x678), film(?x7752, ?x7878) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05l0j5 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 46.000 0.908 http://example.org/people/person/profession #22577-01f7j9 PRED entity: 01f7j9 PRED relation: award PRED expected values: 02vm9nd => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 277): 019f4v (0.72 #29651, 0.70 #19766, 0.70 #17395), 02qt02v (0.70 #19766, 0.70 #17395, 0.70 #16205), 0gq9h (0.61 #469, 0.55 #1656, 0.41 #74), 04dn09n (0.35 #832, 0.27 #1228, 0.25 #42), 0gr4k (0.32 #821, 0.24 #426, 0.24 #1217), 09sb52 (0.32 #10710, 0.31 #11105, 0.30 #9525), 03hkv_r (0.26 #804, 0.17 #14, 0.16 #1200), 0f_nbyh (0.23 #403, 0.20 #1985, 0.20 #1590), 02rdyk7 (0.21 #481, 0.21 #1272, 0.19 #1668), 02n9nmz (0.19 #857, 0.15 #67, 0.14 #1253) >> Best rule #29651 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x2182, ?x10747) <- award_winner(?x10747, ?x2182), award(?x4297, ?x10747) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #28464 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2199 *> proper extension: 0knjh; *> query: (?x2182, ?x198) <- award_nominee(?x2182, ?x5647), gender(?x5647, ?x231), award(?x5647, ?x198) *> conf = 0.13 ranks of expected_values: 22 EVAL 01f7j9 award 02vm9nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 90.000 90.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22576-0jt3qpk PRED entity: 0jt3qpk PRED relation: ceremony! PRED expected values: 027qq9b => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 243): 0gqy2 (0.50 #4613, 0.50 #4863, 0.42 #4115), 0257pw (0.50 #1991, 0.38 #2240, 0.31 #3993), 02hdky (0.50 #1960, 0.38 #2209, 0.31 #3962), 024_fw (0.50 #1915, 0.38 #2164, 0.31 #3917), 025mb9 (0.50 #1889, 0.38 #2138, 0.31 #3891), 02nbqh (0.50 #1834, 0.38 #2083, 0.31 #3836), 02v1m7 (0.50 #1831, 0.38 #2080, 0.31 #3833), 01by1l (0.50 #1830, 0.38 #2079, 0.31 #3832), 01c4_6 (0.50 #1814, 0.38 #2063, 0.31 #3816), 02wh75 (0.50 #1756, 0.38 #2005, 0.31 #3758) >> Best rule #4613 for best value: >> intensional similarity = 16 >> extensional distance = 119 >> proper extension: 073hkh; 0clfdj; 0bzk8w; 02yw5r; 0hr6lkl; 059x66; 073hmq; 0bzm81; 0dth6b; 02yv_b; ... >> query: (?x2751, 0gqy2) <- ceremony(?x588, ?x2751), award_winner(?x2751, ?x2442), award_winner(?x2751, ?x691), award_winner(?x2751, ?x439), honored_for(?x2751, ?x2829), award_nominee(?x439, ?x415), award_winner(?x439, ?x2476), nominated_for(?x4115, ?x2829), nominated_for(?x691, ?x6678), award_nominee(?x691, ?x3974), award_winner(?x10359, ?x439), award(?x2435, ?x588), profession(?x691, ?x4725), nominated_for(?x588, ?x416), award_winner(?x5277, ?x2442), gender(?x691, ?x514) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1249 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 073hd1; *> query: (?x2751, ?x4115) <- ceremony(?x588, ?x2751), award_winner(?x2751, ?x2442), award_winner(?x2751, ?x691), award_winner(?x2751, ?x439), honored_for(?x2751, ?x2829), award_nominee(?x439, ?x415), award_winner(?x439, ?x2476), nominated_for(?x4115, ?x2829), nominated_for(?x691, ?x6678), award_nominee(?x691, ?x6171), award_winner(?x10359, ?x439), award(?x2435, ?x588), profession(?x691, ?x4725), nominated_for(?x588, ?x416), award_winner(?x5277, ?x2442), gender(?x691, ?x514), ?x6171 = 020ffd *> conf = 0.46 ranks of expected_values: 65 EVAL 0jt3qpk ceremony! 027qq9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 23.000 23.000 0.504 http://example.org/award/award_category/winners./award/award_honor/ceremony #22575-02hwww PRED entity: 02hwww PRED relation: major_field_of_study PRED expected values: 03ytc => 138 concepts (132 used for prediction) PRED predicted values (max 10 best out of 122): 01mkq (0.62 #507, 0.56 #999, 0.56 #753), 03g3w (0.60 #519, 0.46 #1134, 0.39 #1011), 062z7 (0.55 #520, 0.43 #766, 0.42 #1012), 02j62 (0.52 #523, 0.50 #646, 0.49 #1015), 05qfh (0.45 #529, 0.23 #1021, 0.22 #1390), 04rjg (0.44 #635, 0.43 #512, 0.41 #1004), 05qjt (0.38 #500, 0.35 #623, 0.34 #746), 0fdys (0.38 #532, 0.29 #286, 0.25 #1024), 0g26h (0.36 #535, 0.30 #1027, 0.29 #658), 037mh8 (0.36 #561, 0.28 #1176, 0.28 #807) >> Best rule #507 for best value: >> intensional similarity = 6 >> extensional distance = 40 >> proper extension: 08815; 01jssp; 052nd; 065y4w7; 01j_cy; 07szy; 09kvv; 01s0_f; 07wjk; 07wlf; ... >> query: (?x11607, 01mkq) <- student(?x11607, ?x656), institution(?x2636, ?x11607), institution(?x1771, ?x11607), ?x1771 = 019v9k, ?x2636 = 027f2w, major_field_of_study(?x11607, ?x1154) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #4445 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 355 *> proper extension: 0146hc; 01cwdk; 02fs_d; 07x4c; 019pwv; 02ngbs; 029qzx; 01s7pm; 0225bv; *> query: (?x11607, ?x90) <- student(?x11607, ?x656), institution(?x1771, ?x11607), major_field_of_study(?x1771, ?x90), institution(?x1771, ?x4257), ?x4257 = 01q0kg *> conf = 0.08 ranks of expected_values: 71 EVAL 02hwww major_field_of_study 03ytc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 138.000 132.000 0.619 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #22574-07mvp PRED entity: 07mvp PRED relation: group! PRED expected values: 05r5c 02g9p4 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 106): 05r5c (0.71 #3199, 0.40 #360, 0.31 #502), 02g9p4 (0.71 #3199, 0.08 #1208, 0.07 #3271), 028tv0 (0.43 #1926, 0.40 #362, 0.38 #504), 01vj9c (0.37 #1144, 0.31 #3207, 0.30 #292), 07y_7 (0.30 #286, 0.26 #1138, 0.16 #641), 02fsn (0.30 #319, 0.08 #1208, 0.07 #3271), 0l14j_ (0.20 #1173, 0.20 #321, 0.13 #3734), 07brj (0.20 #298, 0.11 #1150, 0.09 #3213), 07c6l (0.20 #290, 0.10 #361, 0.09 #1142), 0g2dz (0.20 #304, 0.08 #1208, 0.07 #3271) >> Best rule #3199 for best value: >> intensional similarity = 5 >> extensional distance = 95 >> proper extension: 0ql36; >> query: (?x6475, ?x228) <- artists(?x378, ?x6475), group(?x4918, ?x6475), group(?x1291, ?x6475), role(?x1291, ?x228), profession(?x4918, ?x655) >> conf = 0.71 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2 EVAL 07mvp group! 02g9p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 07mvp group! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #22573-0csdzz PRED entity: 0csdzz PRED relation: music! PRED expected values: 03qnc6q => 123 concepts (82 used for prediction) PRED predicted values (max 10 best out of 840): 0g9lm2 (0.76 #21008, 0.70 #9007, 0.14 #36012), 0gkz3nz (0.76 #21008, 0.70 #9007, 0.14 #36012), 0fgpvf (0.76 #21008, 0.70 #9007, 0.14 #36012), 05zvzf3 (0.76 #21008, 0.70 #9007, 0.14 #36012), 026p4q7 (0.76 #21008, 0.70 #9007, 0.14 #36012), 01pv91 (0.12 #4257, 0.05 #8257, 0.04 #11258), 03h3x5 (0.12 #4262, 0.05 #8262, 0.04 #13263), 078mm1 (0.12 #4819, 0.05 #8819, 0.04 #13820), 01s7w3 (0.07 #18865, 0.06 #4864, 0.06 #22866), 09d3b7 (0.07 #14836, 0.04 #21837, 0.03 #19836) >> Best rule #21008 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 02rgz4; 01nqfh_; 01wl38s; 0p5mw; 07qy0b; 02bh9; 05_pkf; 01pr6q7; 08c9b0; 02z81h; ... >> query: (?x10634, ?x695) <- profession(?x10634, ?x563), music(?x3035, ?x10634), film_release_region(?x3035, ?x87), nominated_for(?x10634, ?x695) >> conf = 0.76 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 0csdzz music! 03qnc6q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 82.000 0.760 http://example.org/film/film/music #22572-027j9wd PRED entity: 027j9wd PRED relation: film! PRED expected values: 0g1rw => 66 concepts (54 used for prediction) PRED predicted values (max 10 best out of 50): 054g1r (0.27 #104, 0.22 #177, 0.20 #321), 086k8 (0.20 #507, 0.20 #1669, 0.19 #2), 05qd_ (0.20 #79, 0.17 #729, 0.17 #7), 017s11 (0.16 #1526, 0.15 #364, 0.14 #1235), 016tw3 (0.14 #2760, 0.14 #2832, 0.14 #442), 06jntd (0.14 #28, 0.07 #750, 0.05 #461), 04yj5z (0.14 #145, 0.06 #867, 0.05 #1085), 012x2b (0.14 #145, 0.06 #867, 0.05 #1085), 01gb54 (0.13 #98, 0.09 #1038, 0.09 #171), 04mkft (0.12 #33, 0.09 #755, 0.06 #538) >> Best rule #104 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 02q3fdr; >> query: (?x6000, 054g1r) <- genre(?x6000, ?x2540), film(?x574, ?x6000), ?x2540 = 0hcr, nominated_for(?x804, ?x6000) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #439 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 116 *> proper extension: 03t97y; 01k0vq; 0cqr0q; 0419kt; *> query: (?x6000, 0g1rw) <- genre(?x6000, ?x225), language(?x6000, ?x254), film_crew_role(?x6000, ?x281), prequel(?x6000, ?x3088) *> conf = 0.10 ranks of expected_values: 11 EVAL 027j9wd film! 0g1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 66.000 54.000 0.273 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #22571-01hjy5 PRED entity: 01hjy5 PRED relation: institution! PRED expected values: 014mlp 03bwzr4 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 17): 014mlp (0.82 #3, 0.75 #347, 0.74 #289), 03bwzr4 (0.80 #10, 0.69 #29, 0.68 #48), 019v9k (0.70 #6, 0.64 #25, 0.64 #44), 04zx3q1 (0.55 #39, 0.53 #20, 0.52 #1), 013zdg (0.40 #24, 0.39 #5, 0.38 #43), 01rr_d (0.30 #13, 0.20 #357, 0.18 #299), 03mkk4 (0.29 #27, 0.26 #46, 0.24 #103), 0bjrnt (0.25 #4, 0.19 #42, 0.19 #99), 022h5x (0.20 #35, 0.19 #54, 0.14 #111), 028dcg (0.16 #15, 0.16 #91, 0.13 #34) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 0jhjl; >> query: (?x8354, 014mlp) <- institution(?x1526, ?x8354), ?x1526 = 0bkj86, list(?x8354, ?x2197) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01hjy5 institution! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01hjy5 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22570-0jcx PRED entity: 0jcx PRED relation: profession PRED expected values: 06q2q 04s2z => 192 concepts (166 used for prediction) PRED predicted values (max 10 best out of 117): 02hrh1q (0.79 #8337, 0.73 #10820, 0.71 #7899), 01d_h8 (0.60 #298, 0.42 #1613, 0.41 #4533), 09jwl (0.60 #311, 0.39 #2356, 0.38 #2210), 0nbcg (0.60 #323, 0.31 #4558, 0.31 #2952), 0dxtg (0.50 #5562, 0.49 #5416, 0.43 #14181), 01c72t (0.43 #900, 0.40 #316, 0.28 #4551), 012t_z (0.42 #1619, 0.40 #304, 0.29 #888), 0fnpj (0.40 #352, 0.29 #936, 0.16 #4587), 016z4k (0.40 #296, 0.20 #8327, 0.19 #2925), 04f2zj (0.40 #386, 0.14 #970, 0.14 #824) >> Best rule #8337 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 01gvr1; 018db8; 032_jg; 015pkc; 0j1yf; 086qd; 0gdh5; 03bnv; 025ldg; 01wy5m; ... >> query: (?x3335, 02hrh1q) <- location(?x3335, ?x1264), diet(?x3335, ?x3130), award_winner(?x11301, ?x3335) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #44 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 01tdnyh; *> query: (?x3335, 06q2q) <- company(?x3335, ?x5281), award_winner(?x14509, ?x3335), ?x14509 = 03j2ts, people(?x1050, ?x3335) *> conf = 0.33 ranks of expected_values: 12, 13 EVAL 0jcx profession 04s2z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 192.000 166.000 0.788 http://example.org/people/person/profession EVAL 0jcx profession 06q2q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 192.000 166.000 0.788 http://example.org/people/person/profession #22569-0j1z8 PRED entity: 0j1z8 PRED relation: film_release_region! PRED expected values: 0gmcwlb 02fqrf 02bg55 027pfg 065ym0c => 110 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1654): 043tvp3 (0.89 #11159, 0.84 #891, 0.83 #23994), 01fmys (0.88 #6655, 0.84 #237, 0.78 #9222), 06wbm8q (0.86 #10573, 0.76 #6723, 0.72 #11856), 0gwjw0c (0.86 #11160, 0.72 #12443, 0.67 #7310), 05zlld0 (0.85 #6875, 0.84 #457, 0.78 #10725), 0dtfn (0.84 #152, 0.81 #10420, 0.79 #6570), 0bwfwpj (0.84 #112, 0.79 #6530, 0.73 #10380), 02vxq9m (0.84 #10285, 0.82 #6435, 0.81 #7719), 047vnkj (0.84 #10943, 0.80 #12226, 0.80 #23778), 0fpgp26 (0.84 #11376, 0.80 #12659, 0.80 #24211) >> Best rule #11159 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 047yc; >> query: (?x311, 043tvp3) <- film_release_region(?x634, ?x311), jurisdiction_of_office(?x182, ?x311), ?x634 = 0gx9rvq >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #10418 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 047yc; *> query: (?x311, 0gmcwlb) <- film_release_region(?x634, ?x311), jurisdiction_of_office(?x182, ?x311), ?x634 = 0gx9rvq *> conf = 0.81 ranks of expected_values: 33, 37, 52, 80, 299 EVAL 0j1z8 film_release_region! 065ym0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 110.000 58.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j1z8 film_release_region! 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 110.000 58.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j1z8 film_release_region! 02bg55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 110.000 58.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j1z8 film_release_region! 02fqrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 110.000 58.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j1z8 film_release_region! 0gmcwlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 110.000 58.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22568-015qy1 PRED entity: 015qy1 PRED relation: film_release_region PRED expected values: 09c7w0 => 94 concepts (81 used for prediction) PRED predicted values (max 10 best out of 188): 09c7w0 (0.93 #14112, 0.93 #10540, 0.93 #9293), 0d060g (0.76 #2692, 0.50 #723, 0.46 #1070), 06mkj (0.71 #2757, 0.50 #788, 0.27 #13290), 07ssc (0.67 #2705, 0.50 #736, 0.26 #13238), 05r4w (0.67 #2684, 0.50 #715, 0.25 #14111), 0d0vqn (0.62 #724, 0.62 #2693, 0.29 #13226), 02vzc (0.62 #2751, 0.50 #782, 0.26 #13104), 059j2 (0.62 #2726, 0.50 #757, 0.26 #14153), 03rjj (0.62 #2690, 0.50 #721, 0.25 #14117), 0345h (0.62 #2728, 0.50 #759, 0.24 #13261) >> Best rule #14112 for best value: >> intensional similarity = 10 >> extensional distance = 1299 >> proper extension: 03h4fq7; >> query: (?x9802, 09c7w0) <- film_release_region(?x9802, ?x252), genre(?x9802, ?x225), genre(?x5081, ?x225), language(?x5081, ?x254), country_of_origin(?x419, ?x252), film_release_region(?x2783, ?x252), film_release_region(?x1163, ?x252), ?x2783 = 0879bpq, ?x1163 = 0c0nhgv, country(?x150, ?x252) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015qy1 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 81.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22567-01xdf5 PRED entity: 01xdf5 PRED relation: award_winner! PRED expected values: 05pd94v => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 127): 0gvstc3 (0.20 #1006, 0.11 #589, 0.10 #16404), 027n06w (0.17 #1045, 0.08 #1601, 0.06 #2852), 05c1t6z (0.16 #987, 0.14 #431, 0.12 #14), 027hjff (0.15 #1307, 0.06 #56, 0.06 #3253), 09v0p2c (0.15 #1054, 0.08 #1610, 0.06 #81), 03gt46z (0.13 #1035, 0.07 #618, 0.05 #1591), 09qvms (0.13 #1263, 0.10 #1680, 0.07 #4738), 02q690_ (0.12 #64, 0.12 #1037, 0.10 #1593), 02wzl1d (0.12 #10, 0.07 #427, 0.06 #288), 0bq_mx (0.12 #1104, 0.06 #1660, 0.04 #2911) >> Best rule #1006 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 01r216; >> query: (?x236, 0gvstc3) <- tv_program(?x236, ?x3626), award_winner(?x762, ?x236), award_nominee(?x236, ?x1040) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #16404 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2018 *> proper extension: 02r3zy; 03g5jw; 0fb0v; 0dvqq; 09b3v; 0163m1; 01yzl2; 01dwrc; 07bzp; 01vw917; ... *> query: (?x236, ?x139) <- award_nominee(?x3625, ?x236), award_winner(?x139, ?x3625) *> conf = 0.10 ranks of expected_values: 23 EVAL 01xdf5 award_winner! 05pd94v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 143.000 143.000 0.195 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22566-01p970 PRED entity: 01p970 PRED relation: role! PRED expected values: 043c4j => 67 concepts (48 used for prediction) PRED predicted values (max 10 best out of 958): 050z2 (0.62 #5415, 0.56 #11614, 0.52 #16382), 05qhnq (0.62 #5541, 0.44 #11740, 0.38 #9353), 023l9y (0.54 #9252, 0.50 #4488, 0.50 #2586), 082brv (0.50 #4545, 0.50 #3118, 0.50 #2643), 0l12d (0.50 #2914, 0.50 #2439, 0.39 #13400), 0lzkm (0.50 #4447, 0.50 #2545, 0.38 #17315), 01wxdn3 (0.50 #4690, 0.50 #2788, 0.38 #5642), 01lvcs1 (0.50 #4441, 0.50 #2539, 0.33 #172), 01s21dg (0.50 #3070, 0.50 #2595, 0.33 #701), 01vsy7t (0.50 #3063, 0.50 #2588, 0.33 #694) >> Best rule #5415 for best value: >> intensional similarity = 17 >> extensional distance = 6 >> proper extension: 0l14md; >> query: (?x3967, 050z2) <- performance_role(?x3967, ?x1750), role(?x2785, ?x3967), role(?x1267, ?x3967), role(?x212, ?x3967), role(?x75, ?x3967), role(?x3967, ?x5676), ?x212 = 026t6, ?x5676 = 0151b0, role(?x2690, ?x1267), group(?x1267, ?x10745), role(?x2206, ?x1267), ?x2785 = 0jtg0, role(?x1148, ?x1267), ?x2206 = 07gql, ?x2690 = 0892sx, ?x1148 = 02qjv, origin(?x10745, ?x8771) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1289 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 1 *> proper extension: 02hnl; *> query: (?x3967, 043c4j) <- family(?x3967, ?x212), role(?x3967, ?x3991), role(?x3967, ?x3418), role(?x3967, ?x3112), role(?x3967, ?x1663), role(?x3967, ?x1225), ?x212 = 026t6, ?x1663 = 01w4dy, role(?x3112, ?x4769), role(?x3112, ?x3161), role(?x3112, ?x1574), ?x3991 = 05842k, role(?x3967, ?x75), ?x3161 = 01v1d8, role(?x3171, ?x3112), ?x1225 = 01qbl, group(?x3967, ?x5838), ?x4769 = 0dwt5, ?x1574 = 0l15bq, ?x3418 = 02w4b *> conf = 0.33 ranks of expected_values: 58 EVAL 01p970 role! 043c4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 67.000 48.000 0.625 http://example.org/music/artist/track_contributions./music/track_contribution/role #22565-016yzz PRED entity: 016yzz PRED relation: award_nominee! PRED expected values: 026g4l_ => 89 concepts (24 used for prediction) PRED predicted values (max 10 best out of 822): 026g4l_ (0.80 #56057), 07s8r0 (0.14 #340, 0.02 #54062), 024n3z (0.14 #600, 0.02 #54322), 0h1nt (0.14 #250, 0.02 #53972), 0g8st4 (0.14 #1534, 0.02 #55256), 05th8t (0.14 #572, 0.01 #54294), 02zfdp (0.14 #1970, 0.01 #55692), 0fthdk (0.14 #1996, 0.01 #55718), 053y4h (0.14 #1215, 0.01 #54937), 026l37 (0.14 #1087, 0.01 #54809) >> Best rule #56057 for best value: >> intensional similarity = 3 >> extensional distance = 821 >> proper extension: 01sl1q; 07nznf; 05vsxz; 01j5ts; 01qscs; 0p_pd; 03rs8y; 0z4s; 03w1v2; 027dtv3; ... >> query: (?x3980, ?x5714) <- film(?x3980, ?x1586), award_winner(?x5863, ?x3980), award_nominee(?x3980, ?x5714) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016yzz award_nominee! 026g4l_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 24.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22564-091rc5 PRED entity: 091rc5 PRED relation: film! PRED expected values: 02dth1 => 87 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1040): 01795t (0.46 #16636, 0.45 #16635, 0.44 #24954), 0bxtg (0.40 #2156, 0.07 #66566, 0.04 #49997), 0p8r1 (0.40 #2664, 0.06 #21380, 0.03 #10981), 01mylz (0.40 #4024, 0.03 #16500, 0.02 #22740), 013tjc (0.40 #3899, 0.02 #12216, 0.02 #14296), 01rs5p (0.40 #3870, 0.02 #12187, 0.02 #14267), 09y20 (0.20 #2327, 0.09 #4407, 0.07 #66566), 07r1h (0.20 #1087, 0.07 #66566, 0.06 #7325), 02661h (0.20 #3474, 0.07 #66566, 0.05 #5554), 0h96g (0.20 #2928, 0.07 #66566, 0.05 #5008) >> Best rule #16636 for best value: >> intensional similarity = 3 >> extensional distance = 117 >> proper extension: 014lc_; 02v63m; 031t2d; 03l6q0; 05_5_22; 031hcx; 0gvvf4j; 09rx7tx; >> query: (?x5012, ?x3853) <- nominated_for(?x3853, ?x5012), award(?x3853, ?x401), prequel(?x5012, ?x9193) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #722 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 051zy_b; 02bj22; *> query: (?x5012, 02dth1) <- films(?x5011, ?x5012), film(?x2564, ?x5012), ?x2564 = 02lf1j *> conf = 0.20 ranks of expected_values: 17 EVAL 091rc5 film! 02dth1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 87.000 48.000 0.457 http://example.org/film/actor/film./film/performance/film #22563-05xq9 PRED entity: 05xq9 PRED relation: artists! PRED expected values: 08jyyk => 92 concepts (44 used for prediction) PRED predicted values (max 10 best out of 267): 06by7 (0.67 #1867, 0.59 #2791, 0.59 #4944), 0xhtw (0.60 #1862, 0.60 #940, 0.49 #5860), 064t9 (0.60 #3705, 0.46 #3089, 0.45 #2782), 059kh (0.53 #6198, 0.33 #48, 0.24 #8308), 08jyyk (0.50 #372, 0.40 #679, 0.29 #5601), 03lty (0.40 #1873, 0.40 #951, 0.31 #5871), 0cx7f (0.40 #1059, 0.38 #2288, 0.33 #3828), 09jw2 (0.40 #1084, 0.24 #8308, 0.23 #6767), 05jg58 (0.40 #1963, 0.15 #922, 0.12 #1348), 05bt6j (0.36 #2811, 0.33 #3118, 0.33 #42) >> Best rule #1867 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 01pfr3; 01gf5h; 02r3zy; 0285c; 01wy61y; 01j59b0; 01vng3b; 0ycp3; 01wqflx; >> query: (?x4942, 06by7) <- origin(?x4942, ?x3052), artists(?x12974, ?x4942), artists(?x3642, ?x4942), artists(?x12974, ?x11929), ?x11929 = 07n3s, ?x3642 = 0dls3 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #372 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 07n3s; *> query: (?x4942, 08jyyk) <- origin(?x4942, ?x3052), artists(?x12974, ?x4942), artists(?x2996, ?x4942), ?x12974 = 01rthc, ?x2996 = 01243b, group(?x227, ?x4942) *> conf = 0.50 ranks of expected_values: 5 EVAL 05xq9 artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 44.000 0.667 http://example.org/music/genre/artists #22562-04t969 PRED entity: 04t969 PRED relation: film PRED expected values: 0299hs => 63 concepts (51 used for prediction) PRED predicted values (max 10 best out of 599): 0fhzwl (0.48 #32229, 0.48 #32230, 0.41 #28647), 034qzw (0.09 #2125, 0.03 #5706, 0.02 #7496), 011yxg (0.07 #3623), 01shy7 (0.07 #2215, 0.03 #16536, 0.02 #38025), 04x4vj (0.06 #775, 0.03 #4356, 0.01 #7937), 0fh694 (0.06 #142, 0.03 #53715, 0.02 #1933), 0gwjw0c (0.06 #1214, 0.03 #53715, 0.02 #3005), 0872p_c (0.06 #175, 0.03 #53715, 0.01 #3756), 0c9t0y (0.06 #1255, 0.03 #53715, 0.01 #4836), 0d99k_ (0.06 #1748, 0.03 #53715) >> Best rule #32229 for best value: >> intensional similarity = 4 >> extensional distance = 1230 >> proper extension: 049tjg; >> query: (?x7382, ?x8870) <- nominated_for(?x7382, ?x8870), nominated_for(?x7382, ?x7246), location(?x7382, ?x1719), nominated_for(?x1063, ?x7246) >> conf = 0.48 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04t969 film 0299hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 51.000 0.480 http://example.org/film/actor/film./film/performance/film #22561-01795t PRED entity: 01795t PRED relation: film PRED expected values: 02_fm2 0c3zjn7 02c7k4 => 117 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1630): 03nx8mj (0.69 #25951, 0.60 #36637, 0.59 #41218), 0kcn7 (0.58 #39690, 0.58 #39691, 0.44 #80902), 05zy2cy (0.58 #39690, 0.58 #39691, 0.44 #80902), 039zft (0.58 #39690, 0.58 #39691, 0.44 #80902), 05nlx4 (0.58 #39690, 0.58 #39691, 0.43 #80901), 047gn4y (0.58 #39690, 0.58 #39691, 0.43 #80901), 019kyn (0.58 #39690, 0.58 #39691, 0.43 #80901), 0g56t9t (0.58 #39690, 0.58 #39691, 0.43 #79372), 0g4pl7z (0.58 #39690, 0.44 #80902, 0.43 #80901), 0m63c (0.58 #27478, 0.37 #36636, 0.03 #131272) >> Best rule #25951 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0146mv; >> query: (?x2156, ?x1080) <- award(?x2156, ?x1105), nominated_for(?x2156, ?x1080), child(?x3920, ?x2156) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #11505 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 05qd_; *> query: (?x2156, 0c3zjn7) <- film(?x2156, ?x5936), film(?x2156, ?x5016), ?x5016 = 062zm5h, actor(?x5936, ?x489) *> conf = 0.50 ranks of expected_values: 15, 68, 73 EVAL 01795t film 02c7k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 117.000 102.000 0.694 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 01795t film 0c3zjn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 117.000 102.000 0.694 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 01795t film 02_fm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 117.000 102.000 0.694 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #22560-0157m PRED entity: 0157m PRED relation: religion PRED expected values: 01lp8 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 37): 0kpl (0.50 #514, 0.24 #1482, 0.21 #1860), 0c8wxp (0.46 #2825, 0.46 #2445, 0.44 #4089), 03_gx (0.29 #517, 0.21 #4352, 0.20 #4649), 0kq2 (0.29 #521, 0.14 #1489, 0.09 #436), 02rsw (0.20 #316, 0.20 #106, 0.18 #400), 07y1z (0.20 #124, 0.12 #166, 0.10 #334), 092bf5 (0.20 #98, 0.08 #1487, 0.07 #2454), 01hng3 (0.20 #120, 0.04 #1635, 0.02 #1761), 0631_ (0.19 #1059, 0.18 #385, 0.14 #1396), 03j6c (0.19 #566, 0.11 #1408, 0.09 #4656) >> Best rule #514 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02ck1; 052h3; 01n1gc; 0mj0c; 03rx9; >> query: (?x1620, 0kpl) <- religion(?x1620, ?x962), profession(?x1620, ?x8340), ?x8340 = 016fly >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0gcs9; 01tc9r; 044k8; 01k23t; 01j6mff; 0163kf; 02pbrn; *> query: (?x1620, 01lp8) <- award_winner(?x2186, ?x1620), religion(?x1620, ?x962), ?x2186 = 056878 *> conf = 0.09 ranks of expected_values: 15 EVAL 0157m religion 01lp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 159.000 159.000 0.500 http://example.org/people/person/religion #22559-0cn68 PRED entity: 0cn68 PRED relation: people PRED expected values: 0f3zsq => 48 concepts (38 used for prediction) PRED predicted values (max 10 best out of 2745): 03rx9 (0.60 #4818, 0.40 #3095, 0.33 #20317), 04z0g (0.60 #4270, 0.40 #2547, 0.33 #7715), 0lrh (0.60 #3829, 0.40 #2106, 0.33 #7274), 08swgx (0.50 #382, 0.20 #2105, 0.17 #7273), 0311wg (0.42 #15790, 0.40 #2012, 0.38 #10624), 01vrt_c (0.40 #3598, 0.40 #1875, 0.38 #10487), 01twdk (0.40 #4118, 0.40 #2395, 0.38 #11007), 052hl (0.40 #4382, 0.40 #2659, 0.33 #19881), 05xpv (0.40 #4686, 0.40 #2963, 0.33 #8131), 02z1yj (0.40 #4845, 0.40 #3122, 0.33 #8290) >> Best rule #4818 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 013b6_; >> query: (?x12136, 03rx9) <- languages_spoken(?x12136, ?x2164), people(?x12136, ?x7578), people(?x12136, ?x3395), student(?x3424, ?x7578), ?x3424 = 01w5m, participant(?x10963, ?x3395), artists(?x302, ?x7578), profession(?x3395, ?x1032), film(?x3395, ?x1702), location(?x7578, ?x739), ?x739 = 02_286 >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cn68 people 0f3zsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 38.000 0.600 http://example.org/people/ethnicity/people #22558-0lvng PRED entity: 0lvng PRED relation: institution! PRED expected values: 02_xgp2 => 244 concepts (244 used for prediction) PRED predicted values (max 10 best out of 23): 03bwzr4 (0.89 #231, 0.81 #280, 0.66 #714), 02h4rq6 (0.86 #268, 0.84 #219, 0.83 #875), 02_xgp2 (0.84 #229, 0.84 #712, 0.81 #278), 014mlp (0.84 #222, 0.81 #271, 0.75 #1094), 0bkj86 (0.84 #225, 0.71 #274, 0.59 #758), 016t_3 (0.84 #220, 0.68 #703, 0.67 #269), 019v9k (0.75 #783, 0.74 #443, 0.73 #759), 04zx3q1 (0.74 #218, 0.67 #267, 0.50 #701), 07s6fsf (0.58 #217, 0.50 #750, 0.48 #700), 013zdg (0.53 #224, 0.43 #273, 0.32 #707) >> Best rule #231 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 08815; 07tgn; 07tg4; 08qnnv; >> query: (?x7363, 03bwzr4) <- company(?x3335, ?x7363), major_field_of_study(?x7363, ?x1668), ?x1668 = 01mkq, company(?x4095, ?x7363) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #229 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 08815; 07tgn; 07tg4; 08qnnv; *> query: (?x7363, 02_xgp2) <- company(?x3335, ?x7363), major_field_of_study(?x7363, ?x1668), ?x1668 = 01mkq, company(?x4095, ?x7363) *> conf = 0.84 ranks of expected_values: 3 EVAL 0lvng institution! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 244.000 244.000 0.895 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22557-03nsm5x PRED entity: 03nsm5x PRED relation: film_release_region PRED expected values: 09c7w0 0b90_r 01mjq 06mkj 03rj0 016wzw => 119 concepts (112 used for prediction) PRED predicted values (max 10 best out of 181): 06mkj (0.92 #1443, 0.89 #1863, 0.89 #3126), 09c7w0 (0.92 #14728, 0.92 #14868, 0.92 #14448), 0b90_r (0.87 #2245, 0.87 #2105, 0.86 #1965), 03rj0 (0.76 #1027, 0.76 #887, 0.71 #607), 09pmkv (0.67 #579, 0.52 #2259, 0.52 #1979), 03rk0 (0.65 #2002, 0.64 #2282, 0.63 #2142), 015qh (0.62 #589, 0.62 #2129, 0.62 #2269), 01ls2 (0.62 #570, 0.62 #2110, 0.62 #1970), 01p1v (0.62 #598, 0.62 #2138, 0.62 #1998), 016wzw (0.60 #2293, 0.60 #1453, 0.59 #2013) >> Best rule #1443 for best value: >> intensional similarity = 6 >> extensional distance = 60 >> proper extension: 014lc_; 0ds35l9; 0djb3vw; 02x3lt7; 0c0nhgv; 0872p_c; 053rxgm; 0gj8t_b; 03bx2lk; 011yqc; ... >> query: (?x8025, 06mkj) <- film_release_region(?x8025, ?x1353), film_release_region(?x8025, ?x205), ?x1353 = 035qy, currency(?x8025, ?x170), produced_by(?x8025, ?x521), ?x205 = 03rjj >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 10, 11 EVAL 03nsm5x film_release_region 016wzw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 119.000 112.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03nsm5x film_release_region 03rj0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 112.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03nsm5x film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 112.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03nsm5x film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 119.000 112.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03nsm5x film_release_region 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 112.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03nsm5x film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 112.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22556-0fz2y7 PRED entity: 0fz2y7 PRED relation: award_winner PRED expected values: 0f2df 081nh 01jpmpv 01938t => 41 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1364): 076lxv (0.35 #4625, 0.33 #9252, 0.33 #1635), 072twv (0.35 #4625, 0.33 #9252, 0.33 #1890), 04__f (0.35 #4625, 0.33 #9252, 0.31 #13876), 0bdt8 (0.35 #4625, 0.33 #2523, 0.31 #13876), 0fmqp6 (0.35 #4625, 0.33 #9252, 0.31 #13876), 053vcrp (0.35 #4625, 0.33 #9252, 0.31 #13876), 0g1rw (0.35 #4625, 0.33 #9252, 0.31 #13876), 0bw87 (0.35 #4625, 0.31 #13876, 0.25 #10260), 044qx (0.35 #4625, 0.31 #13876, 0.24 #7708), 0gl88b (0.33 #3374, 0.33 #1832, 0.33 #290) >> Best rule #4625 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 0fk0xk; >> query: (?x4388, ?x788) <- ceremony(?x3617, ?x4388), ceremony(?x2222, ?x4388), ceremony(?x591, ?x4388), award_winner(?x4388, ?x3348), ?x2222 = 0gs96, cinematography(?x9611, ?x3348), cinematography(?x2721, ?x3348), ?x3617 = 0gvx_, honored_for(?x4388, ?x4300), award_winner(?x9611, ?x788), ?x591 = 0f4x7, place_of_death(?x3348, ?x1523), instance_of_recurring_event(?x4388, ?x3459), place_of_birth(?x3348, ?x7184), nominated_for(?x4300, ?x1745), language(?x2721, ?x254), film_release_distribution_medium(?x4300, ?x81) >> conf = 0.35 => this is the best rule for 9 predicted values *> Best rule #6510 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 4 *> proper extension: 0fv89q; *> query: (?x4388, 081nh) <- ceremony(?x3617, ?x4388), ceremony(?x2222, ?x4388), ceremony(?x591, ?x4388), award_winner(?x4388, ?x3348), ?x2222 = 0gs96, cinematography(?x9611, ?x3348), cinematography(?x2721, ?x3348), ?x3617 = 0gvx_, honored_for(?x4388, ?x4300), award_winner(?x9611, ?x788), ?x591 = 0f4x7, place_of_death(?x3348, ?x1523), film_art_direction_by(?x4300, ?x4251), genre(?x9611, ?x53), language(?x2721, ?x254), produced_by(?x2721, ?x8225), nominated_for(?x4240, ?x2721) *> conf = 0.33 ranks of expected_values: 12, 60, 629, 639 EVAL 0fz2y7 award_winner 01938t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 26.000 0.353 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0fz2y7 award_winner 01jpmpv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 41.000 26.000 0.353 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0fz2y7 award_winner 081nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 41.000 26.000 0.353 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0fz2y7 award_winner 0f2df CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 26.000 0.353 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22555-0f40w PRED entity: 0f40w PRED relation: production_companies PRED expected values: 05qd_ => 70 concepts (45 used for prediction) PRED predicted values (max 10 best out of 59): 01gb54 (0.29 #38, 0.12 #702, 0.09 #785), 05qd_ (0.14 #10, 0.14 #259, 0.11 #342), 0hpt3 (0.14 #21, 0.09 #104, 0.03 #1183), 02slt7 (0.14 #30, 0.05 #113, 0.02 #445), 05rrtf (0.14 #58, 0.04 #307, 0.03 #1553), 086k8 (0.13 #334, 0.10 #1497, 0.10 #749), 030_1_ (0.10 #681, 0.05 #1429, 0.05 #1179), 016tt2 (0.10 #1166, 0.09 #751, 0.08 #834), 016tw3 (0.10 #1673, 0.09 #759, 0.09 #510), 054lpb6 (0.09 #181, 0.08 #1676, 0.07 #2008) >> Best rule #38 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0872p_c; >> query: (?x2288, 01gb54) <- film_crew_role(?x2288, ?x137), nominated_for(?x2288, ?x2289), person(?x2288, ?x6008), titles(?x600, ?x2288) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0872p_c; *> query: (?x2288, 05qd_) <- film_crew_role(?x2288, ?x137), nominated_for(?x2288, ?x2289), person(?x2288, ?x6008), titles(?x600, ?x2288) *> conf = 0.14 ranks of expected_values: 2 EVAL 0f40w production_companies 05qd_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 45.000 0.286 http://example.org/film/film/production_companies #22554-03c6vl PRED entity: 03c6vl PRED relation: award_winner! PRED expected values: 07y_p6 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 120): 05c1t6z (0.23 #432, 0.20 #3754, 0.10 #571), 03nnm4t (0.23 #491, 0.09 #630, 0.08 #352), 0gvstc3 (0.21 #451, 0.05 #590, 0.05 #312), 07y9ts (0.20 #3754, 0.11 #485, 0.07 #624), 07z31v (0.20 #3754, 0.11 #448, 0.06 #309), 07y_p6 (0.20 #3754, 0.09 #515, 0.05 #376), 09qftb (0.20 #3754, 0.05 #113, 0.03 #669), 092t4b (0.20 #3754, 0.05 #469, 0.04 #1998), 0gx_st (0.15 #454, 0.09 #315, 0.08 #593), 02q690_ (0.14 #482, 0.10 #621, 0.10 #343) >> Best rule #432 for best value: >> intensional similarity = 3 >> extensional distance = 175 >> proper extension: 09xwz; >> query: (?x9214, 05c1t6z) <- award_winner(?x9450, ?x9214), ceremony(?x4386, ?x9450), ?x4386 = 0fc9js >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #3754 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1240 *> proper extension: 01nzs7; *> query: (?x9214, ?x1265) <- award_winner(?x337, ?x9214), nominated_for(?x9214, ?x6482), honored_for(?x1265, ?x6482) *> conf = 0.20 ranks of expected_values: 6 EVAL 03c6vl award_winner! 07y_p6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 81.000 0.232 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22553-02jp5r PRED entity: 02jp5r PRED relation: ceremony! PRED expected values: 0f4x7 0gqz2 => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 273): 0gqz2 (0.90 #5346, 0.87 #5104, 0.87 #4624), 0f4x7 (0.89 #7001, 0.88 #8684, 0.88 #4832), 054krc (0.40 #2459, 0.33 #294, 0.33 #54), 04dn09n (0.40 #2432, 0.33 #267, 0.33 #27), 054ks3 (0.40 #2495, 0.33 #330, 0.33 #90), 054knh (0.40 #2590, 0.33 #425, 0.33 #185), 0gqzz (0.40 #758, 0.26 #4853, 0.25 #519), 019f4v (0.33 #281, 0.33 #41, 0.30 #6785), 054ky1 (0.33 #307, 0.33 #67, 0.30 #2472), 027s4dn (0.33 #414, 0.30 #2579, 0.20 #6918) >> Best rule #5346 for best value: >> intensional similarity = 15 >> extensional distance = 38 >> proper extension: 0fk0xk; >> query: (?x5349, 0gqz2) <- ceremony(?x4573, ?x5349), award_winner(?x5349, ?x7815), award_winner(?x5349, ?x1933), award_winner(?x5349, ?x989), ceremony(?x4573, ?x8407), ceremony(?x4573, ?x3254), ceremony(?x4573, ?x3029), ?x3029 = 0fy6bh, award_winner(?x198, ?x989), ?x3254 = 073h9x, ?x8407 = 0n8_m93, crewmember(?x97, ?x1933), award(?x382, ?x4573), nominated_for(?x7815, ?x1496), gender(?x7815, ?x231) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02jp5r ceremony! 0gqz2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.900 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02jp5r ceremony! 0f4x7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.900 http://example.org/award/award_category/winners./award/award_honor/ceremony #22552-01w5m PRED entity: 01w5m PRED relation: school_type PRED expected values: 07tf8 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.56 #256, 0.52 #371, 0.52 #187), 01_9fk (0.48 #185, 0.20 #415, 0.19 #254), 07tf8 (0.33 #77, 0.30 #376, 0.28 #192), 01rs41 (0.33 #4, 0.28 #1062, 0.27 #1247), 0257h9 (0.33 #42, 0.03 #686, 0.02 #824), 01_srz (0.08 #163, 0.07 #577, 0.07 #301), 02dk5q (0.08 #167, 0.03 #673, 0.02 #811), 04399 (0.05 #588, 0.02 #933, 0.02 #1071), 01jlsn (0.05 #844, 0.04 #614, 0.03 #1143), 0m4mb (0.05 #838, 0.03 #769, 0.03 #1137) >> Best rule #256 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 01q460; 021q2j; >> query: (?x3424, 05jxkf) <- major_field_of_study(?x3424, ?x10417), ?x10417 = 01r4k, institution(?x620, ?x3424) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #77 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 06pwq; *> query: (?x3424, 07tf8) <- student(?x3424, ?x7759), student(?x3424, ?x6485), location(?x6485, ?x2713), ?x7759 = 0gt3p *> conf = 0.33 ranks of expected_values: 3 EVAL 01w5m school_type 07tf8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 81.000 81.000 0.556 http://example.org/education/educational_institution/school_type #22551-0bs5k8r PRED entity: 0bs5k8r PRED relation: film_release_region PRED expected values: 0d060g => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 8): 06mkj (0.04 #41, 0.01 #91), 0345h (0.04 #110, 0.03 #135, 0.02 #35), 0d060g (0.03 #104, 0.02 #79, 0.02 #154), 0chghy (0.03 #670, 0.03 #971, 0.03 #945), 07ssc (0.02 #32, 0.01 #58), 0jgd (0.02 #26, 0.01 #697), 01znc_ (0.01 #87), 06qd3 (0.01 #86) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 011yxg; 060v34; 04tc1g; 03s5lz; 09txzv; 01dyvs; 09gq0x5; 0fq7dv_; 01j8wk; 02c638; ... >> query: (?x4276, 06mkj) <- genre(?x4276, ?x53), titles(?x1316, ?x4276), country(?x4276, ?x390), ?x390 = 0chghy >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 123 *> proper extension: 047svrl; *> query: (?x4276, 0d060g) <- film(?x4809, ?x4276), film_crew_role(?x4276, ?x137), titles(?x53, ?x4276), film_festivals(?x4276, ?x11147) *> conf = 0.03 ranks of expected_values: 3 EVAL 0bs5k8r film_release_region 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 62.000 0.044 http://example.org/film/film/runtime./film/film_cut/film_release_region #22550-05q7cj PRED entity: 05q7cj PRED relation: ceremony! PRED expected values: 0gqxm => 33 concepts (30 used for prediction) PRED predicted values (max 10 best out of 353): 0gqxm (0.61 #1071, 0.56 #831, 0.50 #1552), 0gqzz (0.40 #515, 0.25 #1475, 0.24 #1714), 019f4v (0.34 #2392, 0.30 #1677, 0.28 #1196), 09qv_s (0.34 #2392, 0.30 #1677, 0.28 #1196), 09qwmm (0.34 #2392, 0.30 #1677, 0.28 #1196), 02w9sd7 (0.34 #2392, 0.30 #1677, 0.28 #1196), 09sb52 (0.34 #2392, 0.30 #1677, 0.28 #1196), 09sdmz (0.34 #2392, 0.30 #1677, 0.28 #1196), 02y_rq5 (0.34 #2392, 0.30 #1677, 0.28 #1196), 02x4w6g (0.34 #2392, 0.30 #1677, 0.28 #1196) >> Best rule #1071 for best value: >> intensional similarity = 13 >> extensional distance = 16 >> proper extension: 0bzm81; >> query: (?x6861, 0gqxm) <- ceremony(?x1313, ?x6861), honored_for(?x6861, ?x2215), instance_of_recurring_event(?x6861, ?x3459), ?x1313 = 0gs9p, award_winner(?x6861, ?x3210), award_winner(?x6861, ?x3056), award(?x3056, ?x401), nominated_for(?x3056, ?x83), people(?x1446, ?x3210), celebrity(?x376, ?x3056), award_winner(?x834, ?x3056), film_release_region(?x2215, ?x94), award(?x2215, ?x451) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05q7cj ceremony! 0gqxm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 30.000 0.611 http://example.org/award/award_category/winners./award/award_honor/ceremony #22549-0fhxv PRED entity: 0fhxv PRED relation: location PRED expected values: 0bdg5 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 231): 02_286 (0.33 #37, 0.19 #5658, 0.18 #15299), 010rvx (0.33 #758, 0.01 #8790, 0.01 #10396), 04jpl (0.32 #6441, 0.09 #1623, 0.08 #820), 030qb3t (0.28 #13738, 0.27 #12934, 0.25 #20163), 0cr3d (0.10 #9783, 0.09 #4160, 0.08 #948), 0ccvx (0.08 #1025, 0.05 #1828, 0.03 #2631), 094jv (0.08 #896, 0.04 #4911, 0.02 #12141), 0r0m6 (0.08 #1021, 0.04 #5839, 0.03 #13069), 0fr0t (0.08 #1011, 0.03 #2617, 0.02 #11452), 0vmt (0.08 #848, 0.03 #2454, 0.01 #8077) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01wd9lv; >> query: (?x4646, 02_286) <- participant(?x4646, ?x380), award_winner(?x5224, ?x4646), ?x5224 = 025mbn >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6889 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 0448r; 040dv; *> query: (?x4646, 0bdg5) <- languages(?x4646, ?x254), nationality(?x4646, ?x1310), ?x1310 = 02jx1 *> conf = 0.03 ranks of expected_values: 47 EVAL 0fhxv location 0bdg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 129.000 129.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #22548-082brv PRED entity: 082brv PRED relation: artists! PRED expected values: 0pm85 => 72 concepts (32 used for prediction) PRED predicted values (max 10 best out of 228): 016clz (0.79 #1260, 0.75 #947, 0.50 #633), 06by7 (0.66 #6927, 0.55 #1906, 0.50 #3470), 064t9 (0.46 #5033, 0.45 #6918, 0.44 #5345), 02yv6b (0.40 #101, 0.28 #729, 0.17 #1984), 06j6l (0.40 #49, 0.27 #3184, 0.27 #5380), 016jny (0.40 #107, 0.21 #1049, 0.16 #1362), 0155w (0.40 #109, 0.21 #7013, 0.17 #6064), 05w3f (0.40 #40, 0.17 #1923, 0.17 #668), 03ckfl9 (0.33 #477, 0.09 #1419, 0.09 #1731), 05bt6j (0.33 #6949, 0.22 #673, 0.21 #5064) >> Best rule #1260 for best value: >> intensional similarity = 2 >> extensional distance = 41 >> proper extension: 01k_yf; 016lmg; >> query: (?x6049, 016clz) <- artists(?x2996, ?x6049), ?x2996 = 01243b >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1101 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 01cv3n; 0259r0; 0kvnn; 03d9d6; 04mky3; 01qmy04; *> query: (?x6049, 0pm85) <- instrumentalists(?x227, ?x6049), artists(?x2996, ?x6049), ?x2996 = 01243b *> conf = 0.08 ranks of expected_values: 74 EVAL 082brv artists! 0pm85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 72.000 32.000 0.791 http://example.org/music/genre/artists #22547-049l7 PRED entity: 049l7 PRED relation: award_winner! PRED expected values: 02wwsh8 => 76 concepts (69 used for prediction) PRED predicted values (max 10 best out of 176): 02rdyk7 (0.40 #524, 0.09 #92, 0.06 #958), 02wkmx (0.33 #866, 0.33 #447, 0.32 #6921), 0789r6 (0.32 #6921, 0.31 #10811, 0.31 #12109), 0fq9zdv (0.32 #6921, 0.31 #10811, 0.31 #12109), 02pqp12 (0.31 #10811, 0.31 #12109, 0.31 #12975), 040njc (0.31 #10811, 0.31 #12109, 0.31 #12975), 03nqnk3 (0.31 #10811, 0.31 #12109, 0.31 #12975), 02wypbh (0.27 #782, 0.17 #1649, 0.17 #1216), 0gqng (0.20 #434, 0.09 #2, 0.09 #1301), 02w_6xj (0.20 #672, 0.06 #1106, 0.05 #3269) >> Best rule #524 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 04k25; 01f7v_; 041jlr; 026670; >> query: (?x11391, 02rdyk7) <- gender(?x11391, ?x231), award(?x11391, ?x372), ?x231 = 05zppz, ?x372 = 02wkmx >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #733 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 04k25; 01f7v_; 041jlr; 026670; *> query: (?x11391, 02wwsh8) <- gender(?x11391, ?x231), award(?x11391, ?x372), ?x231 = 05zppz, ?x372 = 02wkmx *> conf = 0.07 ranks of expected_values: 47 EVAL 049l7 award_winner! 02wwsh8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 76.000 69.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #22546-06pcz0 PRED entity: 06pcz0 PRED relation: profession PRED expected values: 0dxtg 018gz8 => 98 concepts (85 used for prediction) PRED predicted values (max 10 best out of 64): 0dxtg (0.84 #1898, 0.82 #2188, 0.82 #1172), 0cbd2 (0.30 #1892, 0.29 #2182, 0.29 #2037), 018gz8 (0.29 #1029, 0.25 #449, 0.25 #739), 09jwl (0.25 #3352, 0.25 #2627, 0.23 #2917), 0d1pc (0.22 #192, 0.17 #11751, 0.12 #1498), 0dz3r (0.21 #2613, 0.20 #3338, 0.20 #2903), 0nbcg (0.20 #1769, 0.19 #11053, 0.19 #3364), 0kyk (0.17 #11751, 0.16 #461, 0.13 #2202), 0np9r (0.17 #11751, 0.15 #1614, 0.15 #4370), 016z4k (0.17 #11751, 0.15 #2615, 0.14 #1745) >> Best rule #1898 for best value: >> intensional similarity = 3 >> extensional distance = 280 >> proper extension: 0q59y; 0gv2r; >> query: (?x11437, 0dxtg) <- written_by(?x4178, ?x11437), language(?x4178, ?x90), nominated_for(?x794, ?x4178) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 06pcz0 profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 85.000 0.840 http://example.org/people/person/profession EVAL 06pcz0 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 85.000 0.840 http://example.org/people/person/profession #22545-07c6l PRED entity: 07c6l PRED relation: role PRED expected values: 03bx0bm => 77 concepts (48 used for prediction) PRED predicted values (max 10 best out of 121): 03bx0bm (0.85 #2650, 0.84 #433, 0.83 #543), 0342h (0.85 #2650, 0.83 #543, 0.83 #4144), 05r5c (0.84 #433, 0.83 #543, 0.83 #3407), 04rzd (0.83 #543, 0.83 #1543, 0.83 #2647), 02pprs (0.83 #543, 0.83 #1543, 0.82 #5249), 02fsn (0.83 #543, 0.83 #1543, 0.82 #2535), 018vs (0.83 #4160, 0.81 #4049, 0.79 #3836), 07y_7 (0.81 #1105, 0.79 #2982, 0.79 #2876), 03qjg (0.81 #1105, 0.79 #3034, 0.78 #2162), 013y1f (0.81 #1105, 0.76 #2214, 0.74 #4516) >> Best rule #2650 for best value: >> intensional similarity = 23 >> extensional distance = 10 >> proper extension: 0151b0; >> query: (?x569, ?x3214) <- role(?x5417, ?x569), role(?x3214, ?x569), role(?x3214, ?x4769), role(?x3214, ?x3161), role(?x3214, ?x315), ?x315 = 0l14md, role(?x569, ?x2460), role(?x569, ?x1433), role(?x569, ?x212), group(?x3214, ?x498), group(?x569, ?x1751), ?x4769 = 0dwt5, role(?x8014, ?x569), ?x212 = 026t6, performance_role(?x8539, ?x3214), role(?x5417, ?x1886), ?x1886 = 02k84w, ?x3161 = 01v1d8, role(?x4595, ?x8014), ?x4595 = 023l9y, nationality(?x8539, ?x512), ?x1433 = 0239kh, instrumentalists(?x2460, ?x680) >> conf = 0.85 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 07c6l role 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 48.000 0.853 http://example.org/music/performance_role/regular_performances./music/group_membership/role #22544-04y79_n PRED entity: 04y79_n PRED relation: gender PRED expected values: 05zppz => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.71 #98, 0.71 #122, 0.71 #120), 02zsn (0.53 #91, 0.36 #2, 0.34 #4) >> Best rule #98 for best value: >> intensional similarity = 1 >> extensional distance = 2093 >> proper extension: 019y64; 01d494; 0j3v; 0dzkq; 099bk; 0cm03; 07c37; 01xyt7; 0frmb1; 0xnc3; ... >> query: (?x1405, 05zppz) <- student(?x2838, ?x1405) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04y79_n gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.714 http://example.org/people/person/gender #22543-0pgm3 PRED entity: 0pgm3 PRED relation: film PRED expected values: 011x_4 03wy8t => 78 concepts (38 used for prediction) PRED predicted values (max 10 best out of 287): 01shy7 (0.12 #421, 0.08 #2208, 0.03 #11143), 0b3n61 (0.12 #1357, 0.04 #3144, 0.02 #12079), 03lrht (0.12 #258, 0.04 #2045, 0.02 #10980), 05c26ss (0.12 #629, 0.04 #2416, 0.01 #11351), 05567m (0.12 #1545, 0.04 #3332), 01y9jr (0.12 #1160, 0.04 #2947), 01l_pn (0.12 #965, 0.04 #2752), 035s95 (0.12 #339, 0.04 #2126), 04gv3db (0.12 #751, 0.03 #11473, 0.01 #32918), 034qzw (0.12 #332, 0.03 #11054, 0.01 #30712) >> Best rule #421 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 03pmzt; >> query: (?x12710, 01shy7) <- award(?x12710, ?x3064), profession(?x12710, ?x319), film(?x12710, ?x1811), ?x1811 = 01pgp6 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #5158 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 149 *> proper extension: 0l6qt; 041h0; 06cv1; 019z7q; 012t1; 07s93v; 027cxsm; 01gzm2; 0261g5l; 01q415; ... *> query: (?x12710, 03wy8t) <- award(?x12710, ?x3064), profession(?x12710, ?x353), ?x353 = 0cbd2, nominated_for(?x12710, ?x1811) *> conf = 0.01 ranks of expected_values: 208 EVAL 0pgm3 film 03wy8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 78.000 38.000 0.125 http://example.org/film/actor/film./film/performance/film EVAL 0pgm3 film 011x_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 38.000 0.125 http://example.org/film/actor/film./film/performance/film #22542-09v3jyg PRED entity: 09v3jyg PRED relation: film! PRED expected values: 02_p5w => 66 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1176): 01vvb4m (0.35 #15097, 0.17 #2605, 0.07 #6769), 038rzr (0.33 #2552, 0.04 #15044, 0.01 #17126), 0f7hc (0.27 #4996, 0.06 #23734, 0.05 #17488), 01q_ph (0.24 #22959, 0.09 #4221, 0.03 #41700), 01v42g (0.21 #14778, 0.17 #2286, 0.01 #18942), 0jfx1 (0.20 #407, 0.11 #10817, 0.02 #19145), 040696 (0.20 #1266, 0.07 #7512, 0.05 #9594), 0f4vbz (0.20 #363, 0.04 #25348, 0.03 #17019), 0143wl (0.20 #1070, 0.03 #17726, 0.02 #19808), 01chc7 (0.20 #561, 0.03 #27629, 0.03 #10971) >> Best rule #15097 for best value: >> intensional similarity = 5 >> extensional distance = 55 >> proper extension: 01dvbd; >> query: (?x6931, 01vvb4m) <- film(?x9288, ?x6931), film(?x2156, ?x6931), country(?x6931, ?x94), film(?x9288, ?x4287), ?x4287 = 05f4_n0 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #11057 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 02bj22; *> query: (?x6931, 02_p5w) <- film(?x3785, ?x6931), film(?x2156, ?x6931), film_crew_role(?x6931, ?x468), ?x2156 = 01795t, ?x468 = 02r96rf *> conf = 0.11 ranks of expected_values: 49 EVAL 09v3jyg film! 02_p5w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 66.000 45.000 0.351 http://example.org/film/actor/film./film/performance/film #22541-06c1y PRED entity: 06c1y PRED relation: olympics PRED expected values: 0l998 => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 27): 018ctl (0.71 #435, 0.66 #1949, 0.66 #2031), 09n48 (0.71 #435, 0.66 #1949, 0.66 #2031), 0l998 (0.65 #276, 0.58 #303, 0.58 #384), 0lv1x (0.57 #281, 0.54 #308, 0.52 #335), 0nbjq (0.57 #284, 0.50 #311, 0.47 #419), 016r9z (0.54 #1381, 0.35 #286, 0.33 #394), 0blfl (0.54 #1381, 0.30 #290, 0.27 #317), 018qb4 (0.48 #292, 0.46 #319, 0.34 #346), 0blg2 (0.48 #283, 0.42 #310, 0.42 #581), 0lk8j (0.48 #282, 0.42 #309, 0.39 #580) >> Best rule #435 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03_3d; 0d060g; 0d0vqn; 04gzd; 0chghy; ... >> query: (?x1536, ?x391) <- film_release_region(?x6321, ?x1536), currency(?x1536, ?x170), olympics(?x1536, ?x391), ?x6321 = 0gg8z1f >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #276 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 03rjj; 015fr; *> query: (?x1536, 0l998) <- film_release_region(?x5315, ?x1536), film_release_region(?x4684, ?x1536), ?x5315 = 0glqh5_, combatants(?x756, ?x1536), ?x4684 = 03nm_fh *> conf = 0.65 ranks of expected_values: 3 EVAL 06c1y olympics 0l998 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 203.000 203.000 0.715 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #22540-01fx2g PRED entity: 01fx2g PRED relation: nominated_for PRED expected values: 0294mx => 120 concepts (74 used for prediction) PRED predicted values (max 10 best out of 633): 0294mx (0.40 #30818, 0.35 #17841, 0.35 #30817), 03kxj2 (0.35 #17841, 0.35 #30817, 0.34 #34064), 0fg04 (0.35 #17841, 0.35 #30817, 0.34 #34064), 0kvgxk (0.35 #17841, 0.35 #30817, 0.34 #34064), 033pf1 (0.35 #17841, 0.35 #30817, 0.34 #34064), 0b6f8pf (0.35 #17841, 0.35 #30817, 0.34 #34064), 01738w (0.35 #17841, 0.35 #30817, 0.34 #34064), 02z3r8t (0.35 #17841, 0.35 #30817, 0.34 #34063), 09v9mks (0.35 #17841, 0.35 #30817, 0.34 #34063), 04h41v (0.35 #17841, 0.35 #30817, 0.34 #34063) >> Best rule #30818 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 062dn7; >> query: (?x5240, ?x7283) <- film(?x5240, ?x7283), participant(?x5240, ?x2927), films(?x942, ?x7283) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fx2g nominated_for 0294mx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 74.000 0.401 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22539-01cl2y PRED entity: 01cl2y PRED relation: artist PRED expected values: 0m2l9 01fl3 0dvqq 01vvpjj 01n8gr 02qwg => 53 concepts (29 used for prediction) PRED predicted values (max 10 best out of 499): 01wx756 (0.43 #7840, 0.33 #6263, 0.25 #9414), 01v0sxx (0.40 #3836, 0.40 #3049, 0.17 #6201), 01vtj38 (0.40 #2860, 0.33 #6012, 0.20 #3647), 02_fj (0.40 #4726, 0.25 #2364, 0.20 #5515), 0gdh5 (0.40 #2525, 0.25 #1736, 0.20 #4887), 01vsy95 (0.40 #2577, 0.25 #8880, 0.20 #3364), 0gr69 (0.40 #2843, 0.20 #3630, 0.17 #5995), 01w7nwm (0.40 #2561, 0.20 #3348, 0.17 #5713), 0fq117k (0.40 #2853, 0.20 #3640, 0.17 #6005), 0p7h7 (0.40 #2668, 0.20 #3455, 0.17 #5820) >> Best rule #7840 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 056252; 05cl8y; >> query: (?x5634, 01wx756) <- artist(?x5634, ?x5635), artist(?x5634, ?x4850), artist(?x5634, ?x4790), ?x4790 = 01kph_c, profession(?x4850, ?x220), instrumentalists(?x716, ?x5635), award_winner(?x342, ?x4850), ?x716 = 018vs >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #131 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 02cjlk; *> query: (?x5634, 0dvqq) <- artist(?x5634, ?x7237), artist(?x5634, ?x5635), artist(?x5634, ?x3234), ?x5635 = 0kxbc, award_winner(?x724, ?x3234), artists(?x3061, ?x3234), role(?x7237, ?x227) *> conf = 0.33 ranks of expected_values: 37, 75, 171, 494, 497 EVAL 01cl2y artist 02qwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 53.000 29.000 0.429 http://example.org/music/record_label/artist EVAL 01cl2y artist 01n8gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 53.000 29.000 0.429 http://example.org/music/record_label/artist EVAL 01cl2y artist 01vvpjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 29.000 0.429 http://example.org/music/record_label/artist EVAL 01cl2y artist 0dvqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 53.000 29.000 0.429 http://example.org/music/record_label/artist EVAL 01cl2y artist 01fl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 29.000 0.429 http://example.org/music/record_label/artist EVAL 01cl2y artist 0m2l9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 29.000 0.429 http://example.org/music/record_label/artist #22538-0ddbjy4 PRED entity: 0ddbjy4 PRED relation: film_festivals PRED expected values: 0j63cyr => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 15): 0kfhjq0 (0.09 #5, 0.08 #26, 0.07 #47), 0j63cyr (0.09 #3, 0.08 #24, 0.05 #129), 0gg7gsl (0.06 #400, 0.05 #442, 0.04 #505), 0bmj62v (0.04 #411, 0.03 #348, 0.03 #516), 0hrcs29 (0.04 #141, 0.03 #267, 0.03 #288), 0fpkxfd (0.03 #405, 0.03 #447, 0.02 #510), 04_m9gk (0.03 #307, 0.03 #55, 0.02 #76), 03nn7l2 (0.03 #248, 0.02 #290, 0.02 #332), 03wf1p2 (0.03 #56, 0.02 #77, 0.01 #98), 0g57ws5 (0.02 #406, 0.02 #259, 0.02 #448) >> Best rule #5 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 03qnvdl; 0gj9tn5; 01jrbb; 0gj8nq2; 0gtxj2q; 05b6rdt; 0gmd3k7; 0ds1glg; 0fpgp26; >> query: (?x9652, 0kfhjq0) <- film_release_region(?x9652, ?x6691), film_release_region(?x9652, ?x1264), film_release_region(?x9652, ?x550), ?x1264 = 0345h, ?x550 = 05v8c, film(?x1914, ?x9652), ?x6691 = 02k8k, country(?x9652, ?x512) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 9 *> proper extension: 03qnvdl; 0gj9tn5; 01jrbb; 0gj8nq2; 0gtxj2q; 05b6rdt; 0gmd3k7; 0ds1glg; 0fpgp26; *> query: (?x9652, 0j63cyr) <- film_release_region(?x9652, ?x6691), film_release_region(?x9652, ?x1264), film_release_region(?x9652, ?x550), ?x1264 = 0345h, ?x550 = 05v8c, film(?x1914, ?x9652), ?x6691 = 02k8k, country(?x9652, ?x512) *> conf = 0.09 ranks of expected_values: 2 EVAL 0ddbjy4 film_festivals 0j63cyr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.091 http://example.org/film/film/film_festivals #22537-06yrj6 PRED entity: 06yrj6 PRED relation: award_nominee PRED expected values: 0dbc1s => 95 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1071): 0dbc1s (0.81 #95867, 0.81 #70147, 0.81 #28060), 0b7t3p (0.81 #95867, 0.81 #70147, 0.81 #28060), 0fxky3 (0.81 #95867, 0.81 #70147, 0.81 #28060), 06v_gh (0.76 #74825, 0.75 #67807, 0.75 #67809), 06yrj6 (0.47 #35073, 0.44 #14029, 0.38 #51441), 02773m2 (0.47 #35073, 0.44 #14029, 0.25 #46764), 0pyww (0.38 #51441, 0.36 #100545, 0.34 #42089), 01w0yrc (0.38 #51441, 0.36 #100545, 0.34 #42089), 0q9vf (0.38 #51441, 0.36 #100545, 0.34 #42089), 033jj1 (0.38 #51441, 0.36 #100545, 0.31 #60794) >> Best rule #95867 for best value: >> intensional similarity = 4 >> extensional distance = 980 >> proper extension: 04qzm; 016ppr; >> query: (?x8295, ?x201) <- award_nominee(?x10575, ?x8295), award_nominee(?x201, ?x8295), award_winner(?x10575, ?x5677), currency(?x10575, ?x170) >> conf = 0.81 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 06yrj6 award_nominee 0dbc1s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 51.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22536-058vfp4 PRED entity: 058vfp4 PRED relation: award_nominee PRED expected values: 03gyh_z => 94 concepts (45 used for prediction) PRED predicted values (max 10 best out of 923): 03gyh_z (0.81 #32751, 0.81 #30410, 0.80 #25732), 058vfp4 (0.33 #4410, 0.25 #88888, 0.22 #102919), 0j_c (0.25 #88888, 0.22 #102919, 0.22 #56141), 09qh1 (0.25 #88888, 0.22 #102919, 0.22 #56141), 03thw4 (0.25 #88888, 0.22 #102919, 0.22 #56141), 05218gr (0.24 #5170, 0.19 #9849, 0.15 #7509), 076psv (0.24 #5719, 0.19 #10398, 0.15 #8058), 0fqjks (0.24 #6366, 0.14 #11045, 0.12 #8705), 04vzv4 (0.22 #102919, 0.22 #56141, 0.03 #10427), 025cn2 (0.22 #102919, 0.22 #56141, 0.01 #13133) >> Best rule #32751 for best value: >> intensional similarity = 3 >> extensional distance = 636 >> proper extension: 06qgvf; 01vvydl; 05cljf; 05ty4m; 01r42_g; 0m2wm; 02zq43; 03rs8y; 07lmxq; 06jzh; ... >> query: (?x9825, ?x3548) <- award_nominee(?x3548, ?x9825), student(?x1771, ?x3548), institution(?x1771, ?x99) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 058vfp4 award_nominee 03gyh_z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 45.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22535-0d9kl PRED entity: 0d9kl PRED relation: film PRED expected values: 03h3x5 => 7 concepts (7 used for prediction) PRED predicted values (max 10 best out of 252): 04954r (0.06 #617, 0.06 #2409, 0.06 #4209), 01lbcqx (0.05 #10446, 0.05 #1452, 0.05 #3244), 01f39b (0.05 #980, 0.05 #2772, 0.05 #4572), 03rg2b (0.05 #1095, 0.05 #2887, 0.05 #4687), 0jvt9 (0.04 #540, 0.04 #2332, 0.04 #4132), 01wb95 (0.04 #623, 0.04 #2415, 0.04 #4215), 02sfnv (0.04 #4492, 0.03 #900, 0.03 #2692), 0bm2g (0.04 #8990, 0.03 #338, 0.03 #2130), 0pd57 (0.04 #8990, 0.02 #701, 0.02 #2493), 0qmfz (0.04 #8990, 0.02 #8991, 0.02 #8993) >> Best rule #617 for best value: >> intensional similarity = 2 >> extensional distance = 94 >> proper extension: 0q9kd; 09fb5; 0chsq; 033hqf; 02knnd; 0j582; 0f2df; 0157m; 015_30; 014dq7; ... >> query: (?x13013, 04954r) <- celebrities_impersonated(?x3649, ?x13013), ?x3649 = 03m6t5 >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #5389 for first EXPECTED value: *> intensional similarity = 54 *> extensional distance = 97 *> proper extension: 0187y5; 0gnbw; 01nr63; *> query: (?x13013, ?x1259) <- celebrities_impersonated(?x3649, ?x13013), profession(?x3649, ?x1383), profession(?x3649, ?x1032), celebrities_impersonated(?x3649, ?x13073), celebrities_impersonated(?x3649, ?x12037), celebrities_impersonated(?x3649, ?x11290), celebrities_impersonated(?x3649, ?x11088), celebrities_impersonated(?x3649, ?x10905), celebrities_impersonated(?x3649, ?x9355), celebrities_impersonated(?x3649, ?x8473), celebrities_impersonated(?x3649, ?x7958), celebrities_impersonated(?x3649, ?x7414), celebrities_impersonated(?x3649, ?x7391), celebrities_impersonated(?x3649, ?x6138), celebrities_impersonated(?x3649, ?x5442), celebrities_impersonated(?x3649, ?x2387), location(?x9355, ?x3670), nominated_for(?x9355, ?x1133), nationality(?x3649, ?x94), film(?x12037, ?x6218), film(?x3649, ?x6375), participant(?x9355, ?x5239), artist(?x3240, ?x7414), profession(?x7414, ?x319), award_winner(?x602, ?x7391), gender(?x9355, ?x231), place_of_death(?x6138, ?x1523), location(?x10905, ?x4253), award_winner(?x2915, ?x7391), film(?x7391, ?x2779), location(?x13073, ?x2850), film(?x10905, ?x1259), people(?x3591, ?x7414), artists(?x378, ?x5442), ?x2387 = 0tc7, participant(?x9355, ?x5079), artist(?x7089, ?x5442), participant(?x7958, ?x1149), artists(?x5905, ?x7414), religion(?x11290, ?x2769), award_winner(?x2071, ?x12037), ?x2915 = 027c95y, award_winner(?x7958, ?x1126), award_winner(?x5585, ?x10905), people(?x1446, ?x12037), participant(?x9355, ?x4926), location(?x5442, ?x5381), ?x1383 = 0np9r, people(?x4322, ?x8473), type_of_union(?x13073, ?x566), location(?x3649, ?x1196), award_winner(?x2822, ?x7958), celebrity(?x11088, ?x509), ?x1032 = 02hrh1q *> conf = 0.02 ranks of expected_values: 171 EVAL 0d9kl film 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 7.000 7.000 0.062 http://example.org/film/actor/film./film/performance/film #22534-01ps2h8 PRED entity: 01ps2h8 PRED relation: film PRED expected values: 0cp0t91 => 106 concepts (77 used for prediction) PRED predicted values (max 10 best out of 669): 017gm7 (0.59 #62311, 0.57 #12461, 0.48 #56970), 0q9sg (0.25 #764), 01771z (0.25 #440), 034fl9 (0.07 #33824), 027r9t (0.05 #4801, 0.04 #3021, 0.02 #11921), 02z3r8t (0.05 #1887, 0.03 #5447, 0.03 #108605), 01shy7 (0.05 #7542, 0.04 #5762, 0.04 #2202), 0fphf3v (0.04 #6695, 0.03 #8475, 0.01 #20936), 016z7s (0.04 #121067, 0.04 #2115), 08r4x3 (0.04 #121067, 0.03 #7272, 0.03 #5492) >> Best rule #62311 for best value: >> intensional similarity = 3 >> extensional distance = 1270 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 04bdxl; 02s2ft; 05vsxz; 06qgvf; 0grwj; ... >> query: (?x5283, ?x972) <- gender(?x5283, ?x231), nominated_for(?x5283, ?x972), film(?x5283, ?x306) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #10344 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 206 *> proper extension: 01pcql; 016kkx; 023jq1; 0gdqy; *> query: (?x5283, 0cp0t91) <- gender(?x5283, ?x231), languages(?x5283, ?x90), award_winner(?x628, ?x5283) *> conf = 0.02 ranks of expected_values: 172 EVAL 01ps2h8 film 0cp0t91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 106.000 77.000 0.593 http://example.org/film/actor/film./film/performance/film #22533-01fjfv PRED entity: 01fjfv PRED relation: artist PRED expected values: 0134s5 04qzm => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 1361): 06rgq (0.38 #164, 0.33 #255, 0.33 #225), 01wd9lv (0.38 #164, 0.33 #85, 0.28 #165), 0140t7 (0.38 #164, 0.33 #228, 0.28 #165), 0kr_t (0.38 #164, 0.33 #213, 0.28 #165), 0dw4g (0.38 #164, 0.33 #214, 0.28 #165), 01vrt_c (0.38 #164, 0.33 #181, 0.28 #165), 01vvyfh (0.38 #164, 0.33 #204, 0.28 #165), 0b_j2 (0.38 #164, 0.33 #218, 0.28 #165), 0lbj1 (0.38 #164, 0.33 #177, 0.28 #165), 016vn3 (0.38 #164, 0.33 #234, 0.28 #165) >> Best rule #164 for best value: >> intensional similarity = 97 >> extensional distance = 1 >> proper extension: 04y652m; >> query: (?x8738, ?x1136) <- artist(?x8738, ?x10565), artist(?x8738, ?x4960), artist(?x8738, ?x3875), artist(?x8738, ?x3494), artist(?x8738, ?x3390), artist(?x8738, ?x646), artist(?x8738, ?x140), group(?x1466, ?x3875), group(?x716, ?x3875), group(?x316, ?x3875), artists(?x1000, ?x10565), award(?x10565, ?x2877), award(?x10565, ?x2634), group(?x745, ?x10565), ?x1466 = 03bx0bm, award_winner(?x10565, ?x2181), ?x646 = 04rcr, award(?x3390, ?x1565), artist(?x3265, ?x3875), artists(?x9013, ?x3390), artists(?x7808, ?x3390), artists(?x5934, ?x3390), artists(?x505, ?x3390), award_winner(?x2877, ?x483), award(?x11749, ?x2877), award(?x7581, ?x2877), award(?x5493, ?x2877), award(?x2876, ?x2877), award(?x2040, ?x2877), award(?x1398, ?x2877), award(?x1388, ?x2877), artists(?x7124, ?x3875), artists(?x2722, ?x3875), ?x2634 = 02f72n, award(?x140, ?x2139), instrumentalists(?x228, ?x140), award_winner(?x342, ?x10565), origin(?x3390, ?x739), ?x716 = 018vs, participant(?x140, ?x1410), profession(?x140, ?x1032), ?x1388 = 05mt_q, ?x5934 = 05r6t, ?x228 = 0l14qv, parent_genre(?x119, ?x505), artists(?x505, ?x5623), ?x5623 = 01vsyg9, profession(?x4960, ?x6565), award(?x1410, ?x1007), ?x1398 = 01j4ls, award_nominee(?x100, ?x1410), location(?x140, ?x1523), artist(?x5666, ?x4960), award_nominee(?x1410, ?x489), artist(?x10426, ?x140), award(?x4960, ?x462), ?x2040 = 0dtd6, artist(?x3240, ?x3390), artists(?x2722, ?x2658), religion(?x3494, ?x492), award_winner(?x2139, ?x1136), ?x7124 = 01hcvm, industry(?x5666, ?x3368), artist(?x5666, ?x6228), ?x7581 = 01wf86y, ?x7808 = 0jmwg, artists(?x671, ?x4960), currency(?x4960, ?x170), ?x2876 = 01vn35l, nominated_for(?x3494, ?x1642), award_winner(?x2704, ?x4960), profession(?x4237, ?x6565), ?x11749 = 016t0h, ?x5493 = 0kr_t, ?x1032 = 02hrh1q, film(?x140, ?x8063), role(?x885, ?x316), role(?x780, ?x316), film(?x1410, ?x570), ?x780 = 01qzyz, ?x885 = 0dwtp, role(?x316, ?x569), ?x6228 = 01q99h, instrumentalists(?x316, ?x7701), instrumentalists(?x316, ?x7053), instrumentalists(?x316, ?x2782), nationality(?x3494, ?x94), ?x7701 = 02jxkw, ?x9013 = 09nwwf, ?x4237 = 01w524f, ?x7053 = 01p0vf, role(?x1574, ?x316), ceremony(?x247, ?x2704), ?x2658 = 01vv126, ?x1574 = 0l15bq, ?x2782 = 014q2g, role(?x1004, ?x316) >> conf = 0.38 => this is the best rule for 292 predicted values *> Best rule #283 for first EXPECTED value: *> intensional similarity = 115 *> extensional distance = 4 *> proper extension: 0jrv_; 04f73rc; *> query: (?x8738, 0134s5) <- artist(?x8738, ?x10565), artist(?x8738, ?x9868), artist(?x8738, ?x3875), artist(?x8738, ?x3390), group(?x1466, ?x3875), group(?x716, ?x3875), group(?x645, ?x3875), artists(?x2249, ?x10565), artists(?x1000, ?x10565), award(?x10565, ?x2180), group(?x745, ?x10565), ?x1466 = 03bx0bm, artists(?x9013, ?x3390), artists(?x1127, ?x3390), artists(?x505, ?x3390), artists(?x302, ?x3390), artist(?x3240, ?x3390), origin(?x10565, ?x12875), ?x9013 = 09nwwf, artists(?x1000, ?x12228), artists(?x1000, ?x11704), artists(?x1000, ?x11635), artists(?x1000, ?x11233), artists(?x1000, ?x10625), artists(?x1000, ?x10198), artists(?x1000, ?x9589), artists(?x1000, ?x8999), artists(?x1000, ?x8640), artists(?x1000, ?x8012), artists(?x1000, ?x7987), artists(?x1000, ?x7272), artists(?x1000, ?x6876), artists(?x1000, ?x6469), artists(?x1000, ?x5329), artists(?x1000, ?x5208), artists(?x1000, ?x3867), artists(?x1000, ?x3024), artists(?x1000, ?x2930), artists(?x1000, ?x2073), artists(?x1000, ?x379), artist(?x9224, ?x10565), artist(?x2299, ?x10565), ?x645 = 028tv0, ?x3867 = 0bkg4, parent_genre(?x13553, ?x1000), parent_genre(?x12618, ?x1000), ?x11635 = 01nrz4, ?x716 = 018vs, ?x5329 = 014_lq, ?x12618 = 04_sqm, ?x6876 = 0ycp3, artists(?x505, ?x10539), artists(?x505, ?x7683), artists(?x505, ?x5637), artists(?x505, ?x3399), artists(?x505, ?x3316), artists(?x505, ?x1732), ?x3316 = 0407f, ceremony(?x2180, ?x6869), ceremony(?x2180, ?x2054), ceremony(?x2180, ?x1480), parent_genre(?x119, ?x505), ?x6869 = 01xqqp, ?x7683 = 043c4j, ?x1732 = 03t9sp, ?x7272 = 01vsyjy, ?x11233 = 01vsn38, ?x7987 = 0j6cj, award(?x3390, ?x3391), ?x6469 = 04bgy, ?x8012 = 01wt4wc, ?x379 = 089tm, artist(?x3265, ?x9868), ?x1127 = 02x8m, ?x9224 = 0n85g, ?x10539 = 028qyn, award(?x9868, ?x247), artists(?x302, ?x9882), artists(?x302, ?x3962), artists(?x302, ?x1989), ?x13553 = 0b_6yv, ?x5208 = 01s7qqw, artists(?x10306, ?x9868), ?x11704 = 0560w, ?x9882 = 04vrxh, ?x1480 = 01c6qp, ?x2930 = 0pkyh, role(?x745, ?x9413), role(?x745, ?x8957), role(?x745, ?x1495), role(?x745, ?x885), ?x9413 = 07m2y, ?x5637 = 016890, ?x1989 = 04mn81, ?x3024 = 0gkg6, ?x885 = 0dwtp, ?x12228 = 016m5c, ?x2054 = 0gpjbt, ?x8957 = 03f5mt, role(?x745, ?x3161), role(?x211, ?x745), ?x2299 = 033hn8, ?x3962 = 01vrkdt, ?x3399 = 01gx5f, ?x2073 = 01czx, ?x1495 = 013y1f, ?x2249 = 03lty, ?x10198 = 01wqpnm, role(?x74, ?x745), ?x10625 = 01y_rz, parent_genre(?x10306, ?x3061), ?x8999 = 0bk1p, ?x9589 = 02cw1m, ?x3161 = 01v1d8, ?x8640 = 020hh3 *> conf = 0.33 ranks of expected_values: 293, 321 EVAL 01fjfv artist 04qzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 6.000 6.000 0.375 http://example.org/broadcast/content/artist EVAL 01fjfv artist 0134s5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 6.000 6.000 0.375 http://example.org/broadcast/content/artist #22532-0c_md_ PRED entity: 0c_md_ PRED relation: student! PRED expected values: 02j62 => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 59): 03qsdpk (0.57 #2173, 0.13 #4741, 0.12 #2601), 02822 (0.21 #4736, 0.20 #2596, 0.16 #4186), 03g3w (0.20 #82, 0.17 #143, 0.14 #1671), 0g26h (0.17 #153, 0.12 #702, 0.11 #886), 0w7c (0.16 #2607, 0.10 #4808, 0.09 #1996), 0fdys (0.15 #1983, 0.12 #1434, 0.09 #4734), 05qjt (0.14 #1655, 0.08 #1411, 0.07 #1777), 06ms6 (0.13 #561, 0.08 #439, 0.08 #378), 02vxn (0.13 #553, 0.06 #2570, 0.04 #3303), 04rjg (0.12 #1969, 0.04 #1297, 0.04 #1420) >> Best rule #2173 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0djywgn; >> query: (?x9684, 03qsdpk) <- student(?x2606, ?x9684), major_field_of_study(?x5581, ?x2606), ?x5581 = 037fqp, major_field_of_study(?x2606, ?x373) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1978 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 0kn4c; 01dvtx; *> query: (?x9684, 02j62) <- student(?x2606, ?x9684), major_field_of_study(?x122, ?x2606), major_field_of_study(?x734, ?x2606), place_of_death(?x9684, ?x13207) *> conf = 0.09 ranks of expected_values: 13 EVAL 0c_md_ student! 02j62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 196.000 196.000 0.568 http://example.org/education/field_of_study/students_majoring./education/education/student #22531-081yw PRED entity: 081yw PRED relation: contains PRED expected values: 0mlyw 0mmr1 0mm0p 0kf9p 0mlzk => 228 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2677): 010v8k (0.83 #114019, 0.81 #154951, 0.80 #96477), 0mlzk (0.66 #29232, 0.60 #204646, 0.60 #257263), 02frhbc (0.66 #29232, 0.60 #204646, 0.29 #4345), 0mx3k (0.66 #29232, 0.60 #204646, 0.14 #4673), 0mlyw (0.66 #29232, 0.60 #204646), 0mm0p (0.66 #29232, 0.05 #306971), 05kj_ (0.60 #257263, 0.45 #333279, 0.14 #2987), 015jr (0.60 #257263, 0.45 #333279, 0.14 #3952), 081yw (0.60 #257263, 0.45 #333279, 0.14 #3511), 041_3z (0.60 #257263, 0.45 #333279, 0.14 #5486) >> Best rule #114019 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 04p0c; 0d9rp; >> query: (?x4600, ?x7957) <- adjoins(?x4600, ?x726), contains(?x4600, ?x1087), country(?x4600, ?x94), state(?x7957, ?x4600) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #29232 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 017wh; *> query: (?x4600, ?x11062) <- adjoins(?x4600, ?x726), contains(?x4600, ?x10514), category(?x4600, ?x134), adjoins(?x11062, ?x10514) *> conf = 0.66 ranks of expected_values: 2, 5, 6, 22, 1148 EVAL 081yw contains 0mlzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 228.000 116.000 0.829 http://example.org/location/location/contains EVAL 081yw contains 0kf9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 228.000 116.000 0.829 http://example.org/location/location/contains EVAL 081yw contains 0mm0p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 228.000 116.000 0.829 http://example.org/location/location/contains EVAL 081yw contains 0mmr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 228.000 116.000 0.829 http://example.org/location/location/contains EVAL 081yw contains 0mlyw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 228.000 116.000 0.829 http://example.org/location/location/contains #22530-0478__m PRED entity: 0478__m PRED relation: profession PRED expected values: 0nbcg => 138 concepts (110 used for prediction) PRED predicted values (max 10 best out of 90): 03gjzk (0.84 #4865, 0.41 #2071, 0.37 #4276), 09jwl (0.78 #4721, 0.74 #2663, 0.71 #10325), 0dxtg (0.66 #4864, 0.58 #4275, 0.57 #2070), 01d_h8 (0.55 #2063, 0.48 #4857, 0.45 #1328), 0nbcg (0.52 #4734, 0.49 #912, 0.49 #10338), 016z4k (0.50 #444, 0.45 #3825, 0.43 #591), 0cbd2 (0.49 #6625, 0.48 #6037, 0.46 #6772), 018gz8 (0.42 #4278, 0.38 #2073, 0.24 #4425), 0kyk (0.33 #6059, 0.33 #6647, 0.31 #6794), 039v1 (0.32 #6914, 0.32 #4739, 0.30 #7653) >> Best rule #4865 for best value: >> intensional similarity = 3 >> extensional distance = 221 >> proper extension: 04n7njg; 02wr2r; 02c0mv; 023jq1; 0f1jhc; 07f7jp; 08f3yq; >> query: (?x4593, 03gjzk) <- profession(?x4593, ?x131), producer_type(?x4593, ?x632), gender(?x4593, ?x514) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #4734 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 183 *> proper extension: 0pmw9; 03m6pk; *> query: (?x4593, 0nbcg) <- profession(?x4593, ?x131), award(?x4593, ?x724), role(?x4593, ?x1466) *> conf = 0.52 ranks of expected_values: 5 EVAL 0478__m profession 0nbcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 110.000 0.839 http://example.org/people/person/profession #22529-043p28m PRED entity: 043p28m PRED relation: country_of_origin PRED expected values: 09c7w0 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.91 #184, 0.90 #172, 0.89 #161), 07ssc (0.12 #204, 0.12 #158, 0.12 #445), 03_3d (0.11 #680, 0.09 #862, 0.09 #908), 0d060g (0.09 #83, 0.07 #345, 0.06 #380), 03rt9 (0.03 #277, 0.02 #337, 0.02 #349), 03rjj (0.02 #366, 0.02 #438, 0.02 #508), 02jx1 (0.02 #375, 0.01 #781, 0.01 #591), 04jpl (0.01 #610, 0.01 #683), 05v8c (0.01 #951, 0.01 #698) >> Best rule #184 for best value: >> intensional similarity = 10 >> extensional distance = 19 >> proper extension: 01b66t; >> query: (?x11042, ?x94) <- genre(?x11042, ?x11043), actor(?x11042, ?x11630), religion(?x11630, ?x1985), profession(?x11630, ?x1032), actor(?x11629, ?x11630), ?x1032 = 02hrh1q, location(?x11630, ?x2623), student(?x6083, ?x11630), category(?x11630, ?x134), country_of_origin(?x11629, ?x94) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043p28m country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.909 http://example.org/tv/tv_program/country_of_origin #22528-08mhyd PRED entity: 08mhyd PRED relation: cinematography! PRED expected values: 0yzbg 03z9585 => 103 concepts (28 used for prediction) PRED predicted values (max 10 best out of 387): 02bqxb (0.33 #334, 0.02 #2031, 0.02 #2372), 01k0vq (0.33 #249, 0.02 #1946, 0.02 #2287), 0372j5 (0.33 #230, 0.02 #1927, 0.02 #2268), 04cppj (0.33 #219, 0.02 #1916, 0.02 #2257), 02krdz (0.33 #110, 0.02 #1807, 0.02 #2148), 03l6q0 (0.33 #107, 0.02 #1804, 0.02 #2145), 01pgp6 (0.33 #53, 0.02 #1750, 0.02 #2091), 0kbhf (0.11 #535, 0.04 #1893, 0.03 #2234), 05pbl56 (0.06 #385, 0.04 #1018, 0.04 #724), 084qpk (0.06 #361, 0.04 #700, 0.04 #1719) >> Best rule #334 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02rgz97; >> query: (?x7327, 02bqxb) <- cinematography(?x83, ?x7327), award(?x7327, ?x13042), award(?x7327, ?x1243), ?x13042 = 02qrbbx, award(?x197, ?x1243) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08mhyd cinematography! 03z9585 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 28.000 0.333 http://example.org/film/film/cinematography EVAL 08mhyd cinematography! 0yzbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 28.000 0.333 http://example.org/film/film/cinematography #22527-0cchk3 PRED entity: 0cchk3 PRED relation: category PRED expected values: 08mbj5d => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #24, 0.90 #6, 0.90 #37) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 02jyr8; 02zcz3; >> query: (?x2821, 08mbj5d) <- contains(?x4622, ?x2821), institution(?x865, ?x2821), school(?x2820, ?x2821), ?x865 = 02h4rq6 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cchk3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.904 http://example.org/common/topic/webpage./common/webpage/category #22526-01304j PRED entity: 01304j PRED relation: role PRED expected values: 0395lw => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 118): 05r5c (0.47 #968, 0.45 #1258, 0.44 #776), 018vs (0.34 #588, 0.33 #108, 0.27 #1456), 07y_7 (0.32 #3083, 0.32 #2985, 0.05 #2406), 0l15bq (0.26 #323, 0.16 #611, 0.14 #804), 06w7v (0.25 #82, 0.12 #1526, 0.09 #1430), 04rzd (0.25 #42, 0.11 #138, 0.11 #330), 026t6 (0.24 #1927, 0.24 #2312, 0.23 #1543), 0l14md (0.23 #4823, 0.23 #4048, 0.09 #1930), 02qjv (0.22 #117, 0.11 #597, 0.09 #405), 01vj9c (0.21 #1458, 0.21 #302, 0.20 #975) >> Best rule #968 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 0pmw9; >> query: (?x11186, 05r5c) <- role(?x11186, ?x3991), religion(?x11186, ?x109), role(?x75, ?x3991), location(?x11186, ?x8852) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 07s3vqk; 0kzy0; 01vsl3_; 01w02sy; 02qwg; 0fhxv; 03j24kf; 03f0fnk; 01vsy3q; 09889g; ... *> query: (?x11186, 0395lw) <- role(?x11186, ?x227), group(?x11186, ?x1751), category(?x11186, ?x134), languages(?x11186, ?x254) *> conf = 0.05 ranks of expected_values: 38 EVAL 01304j role 0395lw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 146.000 146.000 0.469 http://example.org/music/artist/track_contributions./music/track_contribution/role #22525-0b2km_ PRED entity: 0b2km_ PRED relation: film! PRED expected values: 01vvb4m 05dtwm => 122 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1034): 01wjrn (0.33 #231, 0.04 #12714, 0.02 #21038), 01f7dd (0.22 #1208, 0.06 #3289, 0.05 #5370), 025j1t (0.22 #1075, 0.05 #7318, 0.04 #13558), 0hvb2 (0.22 #298, 0.04 #12781, 0.04 #27349), 01kwsg (0.22 #838, 0.04 #13321, 0.03 #27889), 021npv (0.22 #1947, 0.03 #14430, 0.02 #22754), 01pllx (0.22 #1545, 0.03 #14028, 0.02 #22352), 05kfs (0.17 #85335, 0.17 #60360, 0.16 #14564), 016zp5 (0.12 #3057, 0.10 #5138, 0.02 #38437), 0170qf (0.11 #8689, 0.08 #10769, 0.07 #114472) >> Best rule #231 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0g22z; 059rc; 0sxmx; 09sr0; 02p86pb; >> query: (?x10024, 01wjrn) <- films(?x8435, ?x10024), language(?x10024, ?x5359), film(?x777, ?x10024), ?x777 = 05kfs, titles(?x53, ?x10024), countries_spoken_in(?x5359, ?x279) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #114472 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 488 *> proper extension: 01cgz; *> query: (?x10024, ?x879) <- films(?x8435, ?x10024), films(?x8435, ?x1255), language(?x1255, ?x254), film(?x879, ?x1255), nominated_for(?x767, ?x1255), award(?x1255, ?x372) *> conf = 0.07 ranks of expected_values: 108, 571 EVAL 0b2km_ film! 05dtwm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 122.000 76.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 0b2km_ film! 01vvb4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 122.000 76.000 0.333 http://example.org/film/actor/film./film/performance/film #22524-052p7 PRED entity: 052p7 PRED relation: location! PRED expected values: 01ycbq 07cn2c 084m3 => 276 concepts (172 used for prediction) PRED predicted values (max 10 best out of 2273): 063vn (0.58 #7510, 0.57 #15021, 0.52 #190255), 0c00lh (0.58 #7510, 0.57 #15021, 0.52 #190255), 03c_8t (0.58 #7510, 0.57 #15021, 0.46 #82610), 027y_ (0.33 #1761, 0.15 #19285, 0.12 #6767), 01s21dg (0.27 #20985, 0.12 #36006, 0.10 #10971), 0gd5z (0.25 #5460, 0.17 #12971, 0.17 #454), 0m2l9 (0.25 #5070, 0.17 #12581, 0.17 #64), 0bq2g (0.20 #20703, 0.20 #10689, 0.09 #30717), 0dn3n (0.20 #20610, 0.17 #15604, 0.11 #23114), 0pyww (0.20 #10988, 0.13 #21002, 0.13 #31016) >> Best rule #7510 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0t6sb; >> query: (?x2474, ?x1984) <- country(?x2474, ?x279), place_of_birth(?x1984, ?x2474), citytown(?x481, ?x2474), ?x279 = 0d060g >> conf = 0.58 => this is the best rule for 3 predicted values *> Best rule #364 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 080h2; 01r32; 015jr; *> query: (?x2474, 01ycbq) <- country(?x2474, ?x279), featured_film_locations(?x603, ?x2474), location(?x1410, ?x2474), ?x279 = 0d060g *> conf = 0.17 ranks of expected_values: 69, 465, 467 EVAL 052p7 location! 084m3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 276.000 172.000 0.583 http://example.org/people/person/places_lived./people/place_lived/location EVAL 052p7 location! 07cn2c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 276.000 172.000 0.583 http://example.org/people/person/places_lived./people/place_lived/location EVAL 052p7 location! 01ycbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 276.000 172.000 0.583 http://example.org/people/person/places_lived./people/place_lived/location #22523-01bl7g PRED entity: 01bl7g PRED relation: genre PRED expected values: 02kdv5l => 71 concepts (43 used for prediction) PRED predicted values (max 10 best out of 98): 07s9rl0 (0.93 #3606, 0.81 #4420, 0.65 #465), 02kdv5l (0.52 #700, 0.46 #235, 0.43 #3), 04xvlr (0.41 #350, 0.39 #466, 0.33 #583), 02l7c8 (0.40 #479, 0.37 #4434, 0.36 #363), 01hmnh (0.36 #249, 0.29 #714, 0.25 #365), 060__y (0.29 #480, 0.25 #597, 0.23 #364), 06n90 (0.25 #709, 0.23 #244, 0.17 #1988), 06cvj (0.21 #2097, 0.14 #4, 0.14 #120), 0lsxr (0.19 #706, 0.18 #1752, 0.18 #2335), 082gq (0.19 #494, 0.17 #611, 0.15 #378) >> Best rule #3606 for best value: >> intensional similarity = 5 >> extensional distance = 1075 >> proper extension: 0fq27fp; 0cnztc4; 04m1bm; 0d6b7; 05dy7p; 02rb607; 040rmy; 0crh5_f; 0bmc4cm; 0192hw; ... >> query: (?x5502, 07s9rl0) <- genre(?x5502, ?x4088), genre(?x5304, ?x4088), genre(?x522, ?x4088), ?x5304 = 0y_9q, ?x522 = 01h7bb >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #700 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 145 *> proper extension: 0gtsx8c; *> query: (?x5502, 02kdv5l) <- language(?x5502, ?x254), prequel(?x5502, ?x5313), film(?x147, ?x5502), film(?x7980, ?x5502) *> conf = 0.52 ranks of expected_values: 2 EVAL 01bl7g genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 43.000 0.934 http://example.org/film/film/genre #22522-026gb3v PRED entity: 026gb3v PRED relation: profession PRED expected values: 01d_h8 => 109 concepts (72 used for prediction) PRED predicted values (max 10 best out of 47): 01d_h8 (0.85 #598, 0.85 #746, 0.84 #302), 02hrh1q (0.73 #8602, 0.69 #10230, 0.69 #2087), 0dxtg (0.65 #901, 0.61 #1493, 0.60 #1197), 0cbd2 (0.29 #8743, 0.19 #1784, 0.17 #895), 0kyk (0.29 #4294, 0.28 #6960, 0.14 #8765), 02krf9 (0.23 #914, 0.22 #1210, 0.21 #1506), 09jwl (0.17 #5496, 0.17 #2831, 0.16 #5644), 018gz8 (0.13 #8752, 0.11 #8160, 0.11 #460), 0nbcg (0.12 #5509, 0.12 #3436, 0.11 #10395), 0dz3r (0.11 #5480, 0.11 #10366, 0.10 #5628) >> Best rule #598 for best value: >> intensional similarity = 3 >> extensional distance = 287 >> proper extension: 02xnjd; 0glyyw; 0gs5q; >> query: (?x12159, 01d_h8) <- produced_by(?x6079, ?x12159), award_winner(?x6079, ?x193), nominated_for(?x112, ?x6079) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026gb3v profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 72.000 0.851 http://example.org/people/person/profession #22521-0cbn7c PRED entity: 0cbn7c PRED relation: genre PRED expected values: 02n4kr 060__y => 81 concepts (49 used for prediction) PRED predicted values (max 10 best out of 95): 02kdv5l (0.63 #1267, 0.59 #4951, 0.44 #577), 02n4kr (0.53 #238, 0.16 #4957, 0.12 #1618), 03k9fj (0.44 #815, 0.41 #585, 0.37 #700), 04xvh5 (0.43 #32, 0.16 #1757, 0.11 #1527), 017fp (0.43 #13, 0.11 #4847, 0.09 #5078), 05p553 (0.43 #3916, 0.42 #4031, 0.38 #119), 060__y (0.40 #245, 0.25 #1740, 0.22 #1510), 02l7c8 (0.39 #1854, 0.35 #934, 0.33 #1049), 04t36 (0.38 #121, 0.28 #351, 0.15 #1846), 06n90 (0.37 #701, 0.36 #1276, 0.33 #816) >> Best rule #1267 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 047qxs; 014nq4; 038bh3; >> query: (?x7864, 02kdv5l) <- story_by(?x7864, ?x1030), genre(?x7864, ?x812), country(?x7864, ?x94), ?x812 = 01jfsb >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #238 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 016ks5; *> query: (?x7864, 02n4kr) <- film(?x6239, ?x7864), film(?x5348, ?x7864), genre(?x7864, ?x11108), ?x11108 = 02xh1 *> conf = 0.53 ranks of expected_values: 2, 7 EVAL 0cbn7c genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 49.000 0.628 http://example.org/film/film/genre EVAL 0cbn7c genre 02n4kr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 49.000 0.628 http://example.org/film/film/genre #22520-0f8l9c PRED entity: 0f8l9c PRED relation: location_of_ceremony! PRED expected values: 01gq0b => 225 concepts (225 used for prediction) PRED predicted values (max 10 best out of 200): 0dvld (0.14 #2406, 0.09 #4419, 0.08 #2910), 03m2fg (0.14 #2441, 0.08 #2945, 0.07 #5961), 01fkxr (0.14 #2465, 0.08 #2969, 0.05 #3975), 01w23w (0.14 #2419, 0.08 #2923, 0.05 #3929), 034np8 (0.14 #2299, 0.08 #2803, 0.05 #3809), 02yy8 (0.14 #2505, 0.08 #3009, 0.04 #4518), 03l26m (0.14 #2494, 0.08 #2998, 0.04 #4507), 0djywgn (0.14 #2452, 0.08 #2956, 0.04 #4465), 05cx7x (0.14 #2436, 0.08 #2940, 0.04 #4449), 01p4r3 (0.14 #2404, 0.08 #2908, 0.04 #4417) >> Best rule #2406 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 0281s1; >> query: (?x789, 0dvld) <- location(?x4536, ?x789), ?x4536 = 09yrh >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f8l9c location_of_ceremony! 01gq0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 225.000 225.000 0.143 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #22519-06n90 PRED entity: 06n90 PRED relation: genre! PRED expected values: 024rwx 06f0k => 72 concepts (52 used for prediction) PRED predicted values (max 10 best out of 476): 02py9yf (0.67 #7563, 0.50 #7817, 0.50 #2991), 0123qq (0.67 #7570, 0.50 #2998, 0.41 #9357), 0cskb (0.56 #7550, 0.50 #2978, 0.40 #7804), 0431v3 (0.56 #7456, 0.35 #9243, 0.33 #344), 04hs7d (0.50 #5583, 0.50 #5328, 0.50 #2527), 04f6hhm (0.50 #2940, 0.50 #1668, 0.44 #7512), 0hz55 (0.50 #2110, 0.50 #1602, 0.40 #7700), 06dfz1 (0.50 #2946, 0.50 #1674, 0.33 #7518), 045qmr (0.50 #5230, 0.50 #2175, 0.33 #5485), 0dr1c2 (0.50 #5203, 0.50 #2912, 0.33 #7484) >> Best rule #7563 for best value: >> intensional similarity = 10 >> extensional distance = 7 >> proper extension: 01z4y; >> query: (?x1013, 02py9yf) <- genre(?x12434, ?x1013), genre(?x10661, ?x1013), genre(?x9636, ?x1013), genre(?x4084, ?x1013), genre(?x4084, ?x13654), actor(?x4084, ?x3865), program_creator(?x10661, ?x1683), language(?x9636, ?x254), ?x13654 = 02vnz, tv_program(?x11404, ?x12434) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2891 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 01htzx; *> query: (?x1013, 024rwx) <- genre(?x13050, ?x1013), genre(?x10284, ?x1013), genre(?x10089, ?x1013), genre(?x7928, ?x1013), genre(?x4084, ?x1013), ?x4084 = 01rf57, ?x10284 = 02gl58, ?x13050 = 0gxr1c, actor(?x7928, ?x3210), nominated_for(?x435, ?x10089), nominated_for(?x2554, ?x10089) *> conf = 0.25 ranks of expected_values: 151, 154 EVAL 06n90 genre! 06f0k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 72.000 52.000 0.667 http://example.org/tv/tv_program/genre EVAL 06n90 genre! 024rwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 72.000 52.000 0.667 http://example.org/tv/tv_program/genre #22518-01wj18h PRED entity: 01wj18h PRED relation: award PRED expected values: 02f716 => 136 concepts (112 used for prediction) PRED predicted values (max 10 best out of 309): 02w7fs (0.78 #11796, 0.78 #11402, 0.77 #6685), 03m79j_ (0.78 #11796, 0.78 #11402, 0.77 #6685), 01by1l (0.50 #1685, 0.44 #7583, 0.40 #6403), 026rsl9 (0.50 #328, 0.23 #722, 0.20 #13371), 0gqz2 (0.45 #2833, 0.23 #1653, 0.14 #15416), 0c4z8 (0.42 #2824, 0.31 #1644, 0.27 #7542), 01bgqh (0.39 #2796, 0.36 #6334, 0.35 #1616), 025m8l (0.31 #1692, 0.29 #2872, 0.18 #23201), 01c99j (0.31 #1794, 0.18 #7692, 0.18 #23201), 09sb52 (0.29 #5939, 0.26 #9084, 0.25 #16949) >> Best rule #11796 for best value: >> intensional similarity = 4 >> extensional distance = 237 >> proper extension: 08wq0g; 0pz91; 07s6prs; 021bk; 0jfx1; 0p_47; 01vd7hn; 03k0yw; 0149xx; 03_0p; ... >> query: (?x3200, ?x884) <- award_nominee(?x3200, ?x4740), instrumentalists(?x315, ?x3200), award_winner(?x884, ?x3200), performance_role(?x228, ?x315) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #23201 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 519 *> proper extension: 01wbsdz; *> query: (?x3200, ?x724) <- artists(?x284, ?x3200), award_nominee(?x4593, ?x3200), award(?x4593, ?x724) *> conf = 0.18 ranks of expected_values: 29 EVAL 01wj18h award 02f716 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 136.000 112.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22517-0gd9k PRED entity: 0gd9k PRED relation: film PRED expected values: 048vhl => 97 concepts (79 used for prediction) PRED predicted values (max 10 best out of 621): 02qzh2 (0.19 #53603, 0.19 #53601, 0.19 #53602), 0dgrwqr (0.19 #53603, 0.19 #53601, 0.19 #53602), 02rmd_2 (0.16 #33952, 0.15 #33951, 0.14 #12516), 048vhl (0.14 #12518, 0.05 #1789, 0.01 #37231), 05tgks (0.14 #12518, 0.05 #1789), 01kff7 (0.14 #12518, 0.05 #1789), 016dj8 (0.10 #2903, 0.09 #4690, 0.09 #6477), 0k_9j (0.10 #3193, 0.09 #4980, 0.09 #6767), 0295sy (0.07 #958, 0.05 #2748, 0.05 #4535), 0fdv3 (0.07 #281, 0.05 #2071, 0.05 #3858) >> Best rule #53603 for best value: >> intensional similarity = 4 >> extensional distance = 295 >> proper extension: 01r216; >> query: (?x7984, ?x5024) <- written_by(?x5024, ?x7984), written_by(?x4160, ?x7984), film(?x989, ?x5024), film(?x166, ?x4160) >> conf = 0.19 => this is the best rule for 2 predicted values *> Best rule #12518 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 0p51w; *> query: (?x7984, ?x5365) <- film(?x7984, ?x4160), award_winner(?x2902, ?x7984), nominated_for(?x4160, ?x5365) *> conf = 0.14 ranks of expected_values: 4 EVAL 0gd9k film 048vhl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 79.000 0.192 http://example.org/film/actor/film./film/performance/film #22516-0bh72t PRED entity: 0bh72t PRED relation: language PRED expected values: 03_9r => 83 concepts (82 used for prediction) PRED predicted values (max 10 best out of 51): 06nm1 (0.48 #1942, 0.41 #1400, 0.38 #416), 02bjrlw (0.48 #1942, 0.41 #1400, 0.12 #349), 012w70 (0.48 #1942, 0.06 #1532, 0.06 #1592), 03_9r (0.48 #1951, 0.44 #705, 0.44 #531), 064_8sq (0.41 #1400, 0.17 #953, 0.15 #2255), 04306rv (0.41 #1400, 0.12 #352, 0.11 #2062), 05zjd (0.41 #1400, 0.12 #373, 0.10 #605), 02bv9 (0.41 #1400, 0.04 #3606, 0.04 #1761), 06b_j (0.21 #954, 0.19 #1305, 0.18 #1247), 04h9h (0.12 #390, 0.10 #622, 0.08 #680) >> Best rule #1942 for best value: >> intensional similarity = 13 >> extensional distance = 66 >> proper extension: 02_fm2; 02qhqz4; 02pb2bp; 02w86hz; 02_qt; 027s39y; 0g9yrw; 0k54q; 02r9p0c; 014bpd; ... >> query: (?x6649, ?x90) <- genre(?x6649, ?x6459), genre(?x6649, ?x1013), ?x1013 = 06n90, film(?x6678, ?x6649), genre(?x6620, ?x6459), genre(?x3759, ?x6459), genre(?x2339, ?x6459), ?x3759 = 023p7l, film_release_region(?x6620, ?x2984), film_crew_role(?x2339, ?x137), ?x2984 = 082fr, language(?x6620, ?x90), executive_produced_by(?x6620, ?x846) >> conf = 0.48 => this is the best rule for 3 predicted values *> Best rule #1951 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 67 *> proper extension: 05hd32; *> query: (?x6649, 03_9r) <- country(?x6649, ?x252), ?x252 = 03_3d *> conf = 0.48 ranks of expected_values: 4 EVAL 0bh72t language 03_9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 82.000 0.483 http://example.org/film/film/language #22515-0640m69 PRED entity: 0640m69 PRED relation: film! PRED expected values: 06x58 02dlfh => 76 concepts (37 used for prediction) PRED predicted values (max 10 best out of 511): 060j8b (0.20 #1101), 06rq2l (0.12 #14551, 0.12 #6236, 0.11 #10394), 05ty4m (0.10 #45, 0.05 #35335, 0.05 #39492), 026c1 (0.10 #355, 0.05 #49883, 0.03 #62355), 0d608 (0.10 #1301, 0.03 #9616, 0.02 #5458), 05p92jn (0.10 #1156, 0.03 #62355), 03hh89 (0.10 #961, 0.03 #62355), 08vr94 (0.10 #674, 0.03 #62355), 01r93l (0.10 #746, 0.03 #2824, 0.03 #6982), 02qgyv (0.10 #381, 0.02 #2459, 0.02 #6617) >> Best rule #1101 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06q8qh; >> query: (?x11980, 060j8b) <- film(?x4490, ?x11980), ?x4490 = 02k21g, film_crew_role(?x11980, ?x137) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #35335 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 797 *> proper extension: 02_1q9; 0358x_; 0ddd0gc; 02hct1; 01b66d; 01j7mr; 0gj50; 030cx; 01b66t; 0304nh; ... *> query: (?x11980, ?x364) <- nominated_for(?x102, ?x11980), nominated_for(?x1335, ?x11980), participant(?x1335, ?x364) *> conf = 0.05 ranks of expected_values: 63 EVAL 0640m69 film! 02dlfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 76.000 37.000 0.200 http://example.org/film/actor/film./film/performance/film EVAL 0640m69 film! 06x58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 37.000 0.200 http://example.org/film/actor/film./film/performance/film #22514-059rby PRED entity: 059rby PRED relation: location! PRED expected values: 016dsy 0hwbd 06nns1 01tzm9 04954 0232lm 0gd_s => 197 concepts (142 used for prediction) PRED predicted values (max 10 best out of 2575): 0146pg (0.47 #186858, 0.46 #76219, 0.45 #346667), 02rn_bj (0.47 #186858, 0.46 #76219, 0.45 #346667), 01d8yn (0.47 #186858, 0.46 #76219, 0.45 #346667), 02cx72 (0.47 #186858, 0.46 #76219, 0.45 #346667), 05zh9c (0.47 #186858, 0.46 #76219, 0.45 #346667), 04z0g (0.33 #10980, 0.33 #6064, 0.20 #8522), 02p5hf (0.33 #6968, 0.29 #14344, 0.17 #11884), 0prfz (0.33 #4963, 0.20 #7421, 0.18 #14797), 01rh0w (0.33 #5161, 0.18 #14995, 0.17 #12291), 05kfs (0.33 #5028, 0.18 #14862, 0.17 #9944) >> Best rule #186858 for best value: >> intensional similarity = 2 >> extensional distance = 94 >> proper extension: 0_3cs; 0n96z; >> query: (?x335, ?x669) <- adjoins(?x1755, ?x335), place_of_birth(?x669, ?x335) >> conf = 0.47 => this is the best rule for 5 predicted values *> Best rule #6673 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 02_286; *> query: (?x335, 0232lm) <- contains(?x335, ?x8538), contains(?x335, ?x1131), ?x1131 = 0cc56, ?x8538 = 026ssfj *> conf = 0.33 ranks of expected_values: 287, 473, 691, 1290, 2562 EVAL 059rby location! 0gd_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 197.000 142.000 0.471 http://example.org/people/person/places_lived./people/place_lived/location EVAL 059rby location! 0232lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 197.000 142.000 0.471 http://example.org/people/person/places_lived./people/place_lived/location EVAL 059rby location! 04954 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 197.000 142.000 0.471 http://example.org/people/person/places_lived./people/place_lived/location EVAL 059rby location! 01tzm9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 197.000 142.000 0.471 http://example.org/people/person/places_lived./people/place_lived/location EVAL 059rby location! 06nns1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 197.000 142.000 0.471 http://example.org/people/person/places_lived./people/place_lived/location EVAL 059rby location! 0hwbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 197.000 142.000 0.471 http://example.org/people/person/places_lived./people/place_lived/location EVAL 059rby location! 016dsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 197.000 142.000 0.471 http://example.org/people/person/places_lived./people/place_lived/location #22513-0kzy0 PRED entity: 0kzy0 PRED relation: artist! PRED expected values: 017l96 01cf93 => 126 concepts (91 used for prediction) PRED predicted values (max 10 best out of 127): 015_1q (0.24 #3395, 0.24 #9609, 0.21 #7313), 0g768 (0.21 #844, 0.18 #979, 0.17 #34), 01trtc (0.20 #338, 0.12 #2094, 0.09 #4797), 01t04r (0.20 #466, 0.06 #1682, 0.04 #9652), 017l96 (0.18 #1232, 0.17 #1638, 0.14 #1503), 0181dw (0.17 #39, 0.15 #1795, 0.14 #3416), 033hn8 (0.17 #13, 0.14 #9604, 0.12 #4742), 064r9cb (0.17 #99, 0.12 #234, 0.03 #774), 01w40h (0.17 #27, 0.12 #432, 0.10 #702), 0229rs (0.17 #16, 0.12 #421, 0.06 #1637) >> Best rule #3395 for best value: >> intensional similarity = 4 >> extensional distance = 189 >> proper extension: 01lcxbb; >> query: (?x654, 015_1q) <- artists(?x671, ?x654), award_winner(?x4912, ?x654), artist(?x2149, ?x654), role(?x654, ?x316) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #1232 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 43 *> proper extension: 06br6t; *> query: (?x654, 017l96) <- artists(?x5379, ?x654), artists(?x1748, ?x654), artists(?x1748, ?x1749), ?x1749 = 01fl3, ?x5379 = 08jyyk *> conf = 0.18 ranks of expected_values: 5, 22 EVAL 0kzy0 artist! 01cf93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 126.000 91.000 0.241 http://example.org/music/record_label/artist EVAL 0kzy0 artist! 017l96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 126.000 91.000 0.241 http://example.org/music/record_label/artist #22512-026lgs PRED entity: 026lgs PRED relation: currency PRED expected values: 09nqf => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.82 #29, 0.81 #15, 0.78 #50), 01nv4h (0.11 #2, 0.07 #9, 0.03 #114), 088n7 (0.07 #14), 02l6h (0.04 #46, 0.04 #39, 0.03 #60), 02gsvk (0.01 #27, 0.01 #69) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 029zqn; 04jplwp; 07tlfx; >> query: (?x5418, 09nqf) <- nominated_for(?x2393, ?x5418), nominated_for(?x3782, ?x5418), film(?x1700, ?x5418), ?x2393 = 02x258x >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026lgs currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.822 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #22511-01xvjb PRED entity: 01xvjb PRED relation: language PRED expected values: 02h40lc => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 31): 02h40lc (0.94 #179, 0.92 #238, 0.92 #594), 06nm1 (0.33 #11, 0.12 #722, 0.11 #960), 0653m (0.33 #12, 0.04 #189, 0.04 #1674), 012w70 (0.33 #13, 0.03 #1198, 0.03 #1376), 064_8sq (0.21 #81, 0.17 #140, 0.16 #912), 06b_j (0.11 #82, 0.09 #141, 0.07 #259), 04306rv (0.10 #300, 0.10 #360, 0.09 #420), 02bjrlw (0.07 #1245, 0.07 #712, 0.07 #178), 03_9r (0.06 #543, 0.06 #2680, 0.05 #69), 04h9h (0.05 #102, 0.04 #161, 0.03 #220) >> Best rule #179 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0g56t9t; 034qmv; 01hr1; 0czyxs; 01qb5d; 0340hj; 04n52p6; 05qbckf; 02vqhv0; 047qxs; ... >> query: (?x8965, 02h40lc) <- film(?x6236, ?x8965), story_by(?x8965, ?x1039), participant(?x2321, ?x6236) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xvjb language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.944 http://example.org/film/film/language #22510-01k31p PRED entity: 01k31p PRED relation: entity_involved! PRED expected values: 0j5ym => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 79): 07_nf (0.69 #1042, 0.55 #1301, 0.50 #1622), 01y998 (0.62 #1090, 0.50 #3874, 0.49 #4136), 0j5ym (0.62 #1090, 0.22 #4858, 0.22 #4856), 0d06vc (0.35 #1222, 0.25 #2061, 0.22 #1158), 086m1 (0.33 #20, 0.17 #276, 0.11 #533), 018w0j (0.33 #482, 0.15 #1252, 0.14 #1574), 0dl4z (0.31 #1868, 0.22 #4858, 0.22 #4856), 03jqfx (0.31 #2537, 0.29 #2407, 0.29 #2083), 0ql7q (0.29 #2395, 0.28 #2525, 0.25 #2591), 03gqgt3 (0.22 #502, 0.11 #2902, 0.10 #4334) >> Best rule #1042 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 0chghy; 0d05w3; 0d04z6; 0g8bw; 05b7q; 07f1x; 0g970; 0hw29; >> query: (?x13640, 07_nf) <- entity_involved(?x9351, ?x13640), films(?x9351, ?x2402), entity_involved(?x9351, ?x2629), entity_involved(?x9351, ?x1528), ?x2629 = 06f32, locations(?x9351, ?x2346), location(?x1528, ?x9259) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1090 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 14 *> proper extension: 0chghy; 0d05w3; 0d04z6; 0g8bw; 05b7q; 07f1x; 0g970; 0hw29; *> query: (?x13640, ?x8303) <- entity_involved(?x9351, ?x13640), films(?x9351, ?x2402), entity_involved(?x9351, ?x2629), entity_involved(?x9351, ?x1528), ?x2629 = 06f32, entity_involved(?x8303, ?x1528), locations(?x9351, ?x2346), location(?x1528, ?x9259) *> conf = 0.62 ranks of expected_values: 3 EVAL 01k31p entity_involved! 0j5ym CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 126.000 0.688 http://example.org/base/culturalevent/event/entity_involved #22509-059j1m PRED entity: 059j1m PRED relation: award PRED expected values: 05zr6wv => 72 concepts (50 used for prediction) PRED predicted values (max 10 best out of 220): 05zr6wv (0.38 #423, 0.12 #16648, 0.11 #1235), 09sb52 (0.32 #3695, 0.32 #4913, 0.32 #4507), 05p09zm (0.25 #531, 0.15 #18679, 0.12 #16648), 05zvj3m (0.25 #500, 0.15 #18679, 0.12 #16648), 01by1l (0.19 #1737, 0.11 #2549, 0.11 #4173), 01bgqh (0.16 #1667, 0.12 #449, 0.09 #2073), 0ck27z (0.15 #3747, 0.14 #4965, 0.14 #4559), 05pcn59 (0.15 #18679, 0.12 #16648, 0.11 #1300), 0cqhk0 (0.15 #18679, 0.12 #16648, 0.09 #3691), 03qbh5 (0.13 #1831, 0.12 #613, 0.07 #2237) >> Best rule #423 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 06b3g4; >> query: (?x8440, 05zr6wv) <- film(?x8440, ?x4820), nationality(?x8440, ?x94), ?x4820 = 033f8n >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059j1m award 05zr6wv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 50.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22508-01hlwv PRED entity: 01hlwv PRED relation: service_location PRED expected values: 05r7t => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 116): 0chghy (0.43 #10, 0.33 #584, 0.31 #201), 07ssc (0.37 #1162, 0.36 #588, 0.35 #205), 0345h (0.27 #599, 0.27 #216, 0.26 #1173), 0f8l9c (0.24 #1167, 0.21 #593, 0.19 #210), 05v8c (0.14 #15, 0.09 #589, 0.08 #1258), 03rt9 (0.14 #11, 0.08 #202, 0.06 #680), 059j2 (0.13 #1172, 0.12 #598, 0.12 #215), 06mkj (0.12 #609, 0.11 #1183, 0.10 #10258), 03h64 (0.12 #617, 0.11 #1191, 0.08 #1095), 03rjj (0.12 #579, 0.11 #1153, 0.08 #196) >> Best rule #10 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0hpt3; 0gvbw; 01n073; 0k8z; 045c7b; 01zpmq; 0dmtp; 01nn79; 04sv4; 0k9ts; ... >> query: (?x11727, 0chghy) <- service_location(?x11727, ?x279), list(?x11727, ?x5997), currency(?x11727, ?x170), company(?x265, ?x11727), ?x279 = 0d060g >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #724 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 02d6ph; *> query: (?x11727, 05r7t) <- list(?x11727, ?x5997), ?x5997 = 04k4rt, industry(?x11727, ?x12014), organization(?x4682, ?x11727) *> conf = 0.03 ranks of expected_values: 49 EVAL 01hlwv service_location 05r7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 166.000 166.000 0.429 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #22507-0gt3p PRED entity: 0gt3p PRED relation: award PRED expected values: 0bdwqv => 174 concepts (146 used for prediction) PRED predicted values (max 10 best out of 279): 09sb52 (0.33 #26301, 0.32 #23473, 0.30 #27111), 0bdwqv (0.31 #5020, 0.21 #6636, 0.14 #11080), 0gqy2 (0.28 #11072, 0.28 #11477, 0.25 #7436), 0f4x7 (0.25 #10938, 0.24 #7302, 0.23 #11343), 0gqwc (0.25 #478, 0.13 #1690, 0.12 #2094), 054ky1 (0.25 #513, 0.08 #8997, 0.07 #11017), 05b4l5x (0.25 #409, 0.06 #7681, 0.06 #25054), 03nqnk3 (0.25 #538, 0.06 #5386, 0.06 #2558), 0bb57s (0.25 #648, 0.05 #11557, 0.05 #7516), 07cbcy (0.22 #4926, 0.20 #6542, 0.10 #6946) >> Best rule #26301 for best value: >> intensional similarity = 3 >> extensional distance = 613 >> proper extension: 04smkr; 0g2mbn; 01fxck; 01xllf; >> query: (?x7759, 09sb52) <- film(?x7759, ?x9100), nominated_for(?x591, ?x9100), honored_for(?x9100, ?x8769) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5020 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 0gn30; *> query: (?x7759, 0bdwqv) <- film(?x7759, ?x5856), award(?x7759, ?x102), ?x102 = 04ljl_l, student(?x581, ?x7759) *> conf = 0.31 ranks of expected_values: 2 EVAL 0gt3p award 0bdwqv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 174.000 146.000 0.332 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22506-0ch3qr1 PRED entity: 0ch3qr1 PRED relation: award PRED expected values: 04ljl_l => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 171): 05b1610 (0.27 #14612, 0.27 #14841, 0.26 #1370), 04ljl_l (0.19 #459, 0.09 #5022, 0.09 #915), 0gq9h (0.13 #3027, 0.10 #4624, 0.09 #5767), 0gs9p (0.12 #3028, 0.09 #4625, 0.09 #5768), 0p9sw (0.12 #2986, 0.09 #3442, 0.09 #3214), 0f4x7 (0.12 #2991, 0.09 #5022, 0.08 #4588), 019f4v (0.11 #3018, 0.09 #3474, 0.09 #4615), 05zr6wv (0.10 #15528, 0.09 #5022, 0.05 #12098), 05pcn59 (0.10 #15528, 0.09 #5022, 0.05 #12098), 09qv3c (0.10 #15528, 0.05 #12098, 0.02 #4604) >> Best rule #14612 for best value: >> intensional similarity = 3 >> extensional distance = 989 >> proper extension: 02nf2c; 011yfd; 05_61y; 03j63k; 0m123; 097h2; 05y0cr; 02_1ky; 019g8j; 0147w8; ... >> query: (?x5672, ?x688) <- award(?x5672, ?x350), nominated_for(?x688, ?x5672), nominated_for(?x350, ?x103) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #459 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 0kv2hv; *> query: (?x5672, 04ljl_l) <- film(?x1335, ?x5672), award(?x5672, ?x154), award_winner(?x5672, ?x541), ?x154 = 05b4l5x *> conf = 0.19 ranks of expected_values: 2 EVAL 0ch3qr1 award 04ljl_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.269 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22505-03548 PRED entity: 03548 PRED relation: form_of_government PRED expected values: 01d9r3 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 5): 01d9r3 (0.48 #24, 0.40 #31, 0.40 #29), 01fpfn (0.43 #49, 0.39 #34, 0.38 #39), 018wl5 (0.34 #148, 0.33 #48, 0.31 #58), 01q20 (0.31 #150, 0.30 #50, 0.27 #60), 026wp (0.07 #42, 0.07 #67, 0.06 #137) >> Best rule #24 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 02wm6l; >> query: (?x6572, 01d9r3) <- form_of_government(?x6572, ?x48), ?x48 = 06cx9 >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03548 form_of_government 01d9r3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.479 http://example.org/location/country/form_of_government #22504-029sk PRED entity: 029sk PRED relation: notable_people_with_this_condition PRED expected values: 046lt 01bpnd 0gd_s => 15 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1794): 0484q (0.50 #165, 0.40 #372, 0.40 #270), 0134w7 (0.33 #8, 0.25 #111, 0.20 #318), 0j1yf (0.33 #13, 0.25 #116, 0.20 #323), 0170s4 (0.33 #18, 0.25 #121, 0.20 #328), 0147dk (0.33 #4, 0.25 #107, 0.20 #314), 049g_xj (0.33 #11, 0.25 #114, 0.20 #321), 03lt8g (0.33 #9, 0.25 #112, 0.20 #319), 05vk_d (0.33 #71, 0.25 #174, 0.20 #381), 0227vl (0.33 #73, 0.25 #176, 0.20 #383), 06tp4h (0.33 #58, 0.25 #161, 0.20 #368) >> Best rule #165 for best value: >> intensional similarity = 24 >> extensional distance = 2 >> proper extension: 01g2q; >> query: (?x1502, 0484q) <- notable_people_with_this_condition(?x1502, ?x11949), notable_people_with_this_condition(?x1502, ?x1880), notable_people_with_this_condition(?x1502, ?x1634), notable_people_with_this_condition(?x1502, ?x1424), nationality(?x11949, ?x512), profession(?x11949, ?x1032), award_nominee(?x5364, ?x1424), award_nominee(?x2728, ?x1424), award_nominee(?x989, ?x1424), religion(?x11949, ?x2694), film(?x1424, ?x508), friend(?x1424, ?x4005), nominated_for(?x2728, ?x1392), spouse(?x5364, ?x4123), award(?x1880, ?x154), participant(?x545, ?x989), award(?x989, ?x198), film(?x1634, ?x908), award_nominee(?x1634, ?x100), company(?x11949, ?x11950), award(?x1634, ?x941), award_winner(?x1880, ?x1630), location_of_ceremony(?x989, ?x8569), award_winner(?x989, ?x92) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #512 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 5 *> proper extension: 02vrr; *> query: (?x1502, ?x96) <- notable_people_with_this_condition(?x1502, ?x11949), notable_people_with_this_condition(?x1502, ?x1424), nationality(?x11949, ?x512), profession(?x11949, ?x1032), award_nominee(?x230, ?x1424), religion(?x11949, ?x2694), film(?x1424, ?x10515), film(?x1424, ?x4610), award_nominee(?x1424, ?x1846), film_release_region(?x5644, ?x512), film_release_region(?x1999, ?x512), film_release_region(?x1904, ?x512), country(?x1156, ?x512), contains(?x512, ?x362), country(?x136, ?x512), location(?x1424, ?x739), olympics(?x512, ?x358), film(?x96, ?x10515), region(?x54, ?x512), ?x1999 = 0gd0c7x, combatants(?x151, ?x512), ?x1904 = 09146g, combatants(?x512, ?x94), combatants(?x326, ?x512), ?x5644 = 0dll_t2, film_release_region(?x4610, ?x87) *> conf = 0.03 ranks of expected_values: 871, 904, 1221 EVAL 029sk notable_people_with_this_condition 0gd_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 15.000 14.000 0.500 http://example.org/medicine/disease/notable_people_with_this_condition EVAL 029sk notable_people_with_this_condition 01bpnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 15.000 14.000 0.500 http://example.org/medicine/disease/notable_people_with_this_condition EVAL 029sk notable_people_with_this_condition 046lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 15.000 14.000 0.500 http://example.org/medicine/disease/notable_people_with_this_condition #22503-03cp4cn PRED entity: 03cp4cn PRED relation: genre PRED expected values: 060__y => 120 concepts (70 used for prediction) PRED predicted values (max 10 best out of 143): 02l7c8 (0.73 #6057, 0.63 #476, 0.50 #12), 03g3w (0.50 #21, 0.23 #719, 0.11 #1299), 03k9fj (0.49 #2100, 0.48 #2216, 0.43 #822), 05p553 (0.46 #351, 0.37 #6048, 0.37 #1165), 02kdv5l (0.42 #1163, 0.42 #1744, 0.42 #1395), 03bxz7 (0.38 #167, 0.25 #51, 0.21 #515), 01hmnh (0.35 #2106, 0.35 #2222, 0.26 #6410), 06n90 (0.31 #2101, 0.31 #2217, 0.24 #2681), 060__y (0.31 #4077, 0.29 #2917, 0.25 #13), 0c3351 (0.30 #1079, 0.17 #2473, 0.12 #2705) >> Best rule #6057 for best value: >> intensional similarity = 4 >> extensional distance = 299 >> proper extension: 058kh7; 06y611; >> query: (?x6267, 02l7c8) <- genre(?x6267, ?x162), produced_by(?x6267, ?x595), titles(?x162, ?x144), ?x144 = 0m313 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #4077 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 156 *> proper extension: 0k2m6; 0267wwv; *> query: (?x6267, 060__y) <- genre(?x6267, ?x162), story_by(?x6267, ?x6071), titles(?x162, ?x4678), ?x4678 = 0prhz *> conf = 0.31 ranks of expected_values: 9 EVAL 03cp4cn genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 120.000 70.000 0.734 http://example.org/film/film/genre #22502-01vw37m PRED entity: 01vw37m PRED relation: artists! PRED expected values: 036jv => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 164): 064t9 (0.53 #634, 0.46 #324, 0.45 #2494), 025sc50 (0.50 #672, 0.40 #52, 0.27 #362), 0gywn (0.50 #60, 0.30 #680, 0.27 #370), 012yc (0.50 #151, 0.16 #771, 0.08 #461), 06by7 (0.42 #333, 0.36 #2503, 0.36 #9016), 06j6l (0.40 #50, 0.38 #670, 0.31 #360), 01fm07 (0.30 #127, 0.10 #747, 0.04 #437), 0ggx5q (0.25 #701, 0.19 #391, 0.14 #2561), 02lnbg (0.24 #681, 0.23 #371, 0.13 #2541), 05bt6j (0.23 #355, 0.20 #2525, 0.18 #4075) >> Best rule #634 for best value: >> intensional similarity = 2 >> extensional distance = 133 >> proper extension: 0m19t; >> query: (?x6264, 064t9) <- artists(?x2937, ?x6264), ?x2937 = 0glt670 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #814 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 133 *> proper extension: 0m19t; *> query: (?x6264, 036jv) <- artists(?x2937, ?x6264), ?x2937 = 0glt670 *> conf = 0.10 ranks of expected_values: 26 EVAL 01vw37m artists! 036jv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 87.000 87.000 0.533 http://example.org/music/genre/artists #22501-01k23t PRED entity: 01k23t PRED relation: award PRED expected values: 02f79n => 146 concepts (121 used for prediction) PRED predicted values (max 10 best out of 301): 054ks3 (0.79 #3592, 0.78 #12372, 0.77 #14767), 09sb52 (0.44 #30775, 0.27 #8023, 0.26 #8821), 01bgqh (0.44 #442, 0.40 #3235, 0.38 #2437), 01d38g (0.42 #2422, 0.36 #3220, 0.35 #4418), 03qbh5 (0.35 #2597, 0.30 #3395, 0.28 #602), 02v1m7 (0.32 #511, 0.14 #6098, 0.12 #12084), 01cky2 (0.30 #2586, 0.25 #3384, 0.24 #4582), 031b3h (0.29 #2593, 0.27 #3391, 0.27 #4589), 02f71y (0.28 #579, 0.18 #3372, 0.16 #2574), 02f73b (0.28 #681, 0.16 #6268, 0.13 #12254) >> Best rule #3592 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 046p9; 016376; >> query: (?x7794, ?x1323) <- artists(?x3928, ?x7794), ?x3928 = 0gywn, award_winner(?x1323, ?x7794) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #33131 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 962 *> proper extension: 0261g5l; 08qvhv; 059_gf; 0cdf37; 01tt43d; 0hz_1; 0306bt; 06y9bd; *> query: (?x7794, ?x198) <- award_winner(?x7794, ?x3910), award_winner(?x2186, ?x7794), award_nominee(?x7794, ?x3434), award(?x3434, ?x198) *> conf = 0.15 ranks of expected_values: 48 EVAL 01k23t award 02f79n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 146.000 121.000 0.795 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22500-03q91d PRED entity: 03q91d PRED relation: currency PRED expected values: 09nqf => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.26 #22, 0.25 #31, 0.25 #13) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 226 >> proper extension: 02j8nx; 0863x_; 0p_r5; >> query: (?x7745, 09nqf) <- film(?x7745, ?x3088), profession(?x7745, ?x1146), ?x1146 = 018gz8, nationality(?x7745, ?x94) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q91d currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.263 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #22499-081nh PRED entity: 081nh PRED relation: award_winner! PRED expected values: 0fz2y7 0fz0c2 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 132): 0jzphpx (0.21 #305, 0.06 #3098, 0.05 #3763), 09q_6t (0.21 #540, 0.15 #1604, 0.07 #1471), 01mhwk (0.14 #307, 0.08 #174, 0.06 #3100), 019bk0 (0.14 #282, 0.08 #1213, 0.05 #6400), 0gpjbt (0.14 #295, 0.06 #6413, 0.04 #7876), 01bx35 (0.14 #273, 0.04 #1337, 0.04 #1204), 01xqqp (0.14 #360, 0.03 #6478, 0.03 #7675), 0275n3y (0.12 #473, 0.10 #739, 0.05 #8985), 09pj68 (0.12 #502, 0.10 #768, 0.04 #11308), 0n8_m93 (0.12 #513, 0.10 #779, 0.04 #11308) >> Best rule #305 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 011zf2; >> query: (?x2426, 0jzphpx) <- award_winner(?x3846, ?x2426), award_winner(?x4445, ?x2426), ?x3846 = 05qck >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #902 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 08815; 06pwq; 01w3v; 07szy; 09kvv; 01w5m; 03ksy; 07tds; 02zd460; 01p5xy; ... *> query: (?x2426, 0fz0c2) <- organizations_founded(?x2426, ?x99), registering_agency(?x99, ?x1982), country(?x99, ?x94) *> conf = 0.05 ranks of expected_values: 74, 105 EVAL 081nh award_winner! 0fz0c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 160.000 160.000 0.214 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 081nh award_winner! 0fz2y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 160.000 160.000 0.214 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22498-02x8n1n PRED entity: 02x8n1n PRED relation: nominated_for PRED expected values: 0djlxb => 57 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1421): 0b44shh (0.69 #40639, 0.68 #37514, 0.65 #40638), 04b2qn (0.65 #40638, 0.65 #37513, 0.63 #15621), 0gmcwlb (0.65 #11110, 0.40 #7987, 0.33 #4863), 07w8fz (0.61 #11382, 0.45 #8259, 0.42 #5135), 09gq0x5 (0.57 #11182, 0.55 #8059, 0.33 #252), 011yl_ (0.57 #11452, 0.50 #3644, 0.40 #8329), 07s846j (0.57 #11526, 0.35 #8403, 0.25 #39672), 03hmt9b (0.57 #11517, 0.33 #2148, 0.26 #39663), 0_92w (0.57 #11081, 0.25 #7958, 0.25 #4834), 05hjnw (0.52 #11687, 0.50 #8564, 0.50 #3879) >> Best rule #40639 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 02vl9ln; >> query: (?x2252, ?x2251) <- award(?x2251, ?x2252), film_regional_debut_venue(?x2251, ?x6601), nominated_for(?x4662, ?x2251), award_winner(?x2252, ?x100) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #475 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 0gqyl; *> query: (?x2252, 0djlxb) <- nominated_for(?x2252, ?x10173), nominated_for(?x2252, ?x9701), award(?x123, ?x2252), ?x9701 = 0h1x5f, ?x10173 = 01kqq7 *> conf = 0.33 ranks of expected_values: 238 EVAL 02x8n1n nominated_for 0djlxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 32.000 0.687 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22497-058s44 PRED entity: 058s44 PRED relation: nominated_for PRED expected values: 057lbk => 89 concepts (42 used for prediction) PRED predicted values (max 10 best out of 193): 05t0_2v (0.32 #43804, 0.29 #30825, 0.28 #25957), 09sh8k (0.29 #30825, 0.28 #25957, 0.25 #64900), 091xrc (0.29 #30825, 0.28 #25957, 0.25 #64900), 0cc846d (0.29 #30825, 0.28 #25957, 0.25 #64900), 0b6l1st (0.29 #30825, 0.28 #25957, 0.25 #64900), 02z2mr7 (0.29 #30825, 0.28 #25957, 0.25 #64900), 01n30p (0.12 #48673), 051ys82 (0.12 #48673), 02qpt1w (0.12 #48673), 0vjr (0.12 #48673) >> Best rule #43804 for best value: >> intensional similarity = 3 >> extensional distance = 825 >> proper extension: 03j0br4; 03d9v8; 013bd1; >> query: (?x5788, ?x5016) <- film(?x5788, ?x5016), people(?x1446, ?x5788), award_winner(?x5016, ?x2373) >> conf = 0.32 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 058s44 nominated_for 057lbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 42.000 0.319 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22496-012ky3 PRED entity: 012ky3 PRED relation: music! PRED expected values: 026zlh9 02gqm3 => 125 concepts (86 used for prediction) PRED predicted values (max 10 best out of 513): 01k5y0 (0.25 #953, 0.17 #1966, 0.14 #2979), 0dnw1 (0.25 #620, 0.17 #1633, 0.14 #2646), 06krf3 (0.25 #92, 0.17 #1105, 0.14 #2118), 05dl1s (0.25 #969, 0.17 #1982, 0.02 #10086), 01fx4k (0.25 #918, 0.17 #1931, 0.02 #10035), 0cq806 (0.25 #850, 0.17 #1863, 0.02 #9967), 015qqg (0.25 #496, 0.17 #1509, 0.02 #9613), 0sxmx (0.25 #485, 0.17 #1498, 0.02 #9602), 01k60v (0.25 #440, 0.17 #1453, 0.02 #9557), 016kv6 (0.25 #344, 0.17 #1357, 0.02 #9461) >> Best rule #953 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 03h4mp; 01vttb9; >> query: (?x4139, 01k5y0) <- award_winner(?x7099, ?x4139), ?x7099 = 02x201b, artists(?x4910, ?x4139) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 012ky3 music! 02gqm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 86.000 0.250 http://example.org/film/film/music EVAL 012ky3 music! 026zlh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 86.000 0.250 http://example.org/film/film/music #22495-0892sx PRED entity: 0892sx PRED relation: origin PRED expected values: 059rby => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 118): 030qb3t (0.09 #9866, 0.09 #10803, 0.08 #968), 02dtg (0.08 #945, 0.06 #477, 0.05 #2350), 013yq (0.06 #43, 0.06 #511, 0.03 #2384), 02jx1 (0.06 #1873, 0.03 #7728, 0.02 #10771), 07ssc (0.06 #1873, 0.03 #7728, 0.02 #10771), 09c7w0 (0.06 #1639, 0.05 #937, 0.04 #10772), 03dm7 (0.05 #419, 0.02 #2058, 0.02 #2760), 0dclg (0.04 #2383, 0.04 #510, 0.03 #978), 01_d4 (0.04 #1208, 0.04 #1442, 0.03 #9872), 0f2tj (0.04 #1520, 0.03 #1286, 0.02 #1754) >> Best rule #9866 for best value: >> intensional similarity = 3 >> extensional distance = 420 >> proper extension: 02_5x9; 01qqwp9; 02t3ln; 02mq_y; 01v27pl; >> query: (?x2690, 030qb3t) <- category(?x2690, ?x134), origin(?x2690, ?x362), artists(?x302, ?x2690) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0892sx origin 059rby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 124.000 0.090 http://example.org/music/artist/origin #22494-0f87jy PRED entity: 0f87jy PRED relation: producer_type PRED expected values: 0ckd1 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.72 #6, 0.70 #18, 0.65 #21) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 02f9wb; 03p01x; 01lct6; 0b1s_q; >> query: (?x10593, 0ckd1) <- profession(?x10593, ?x987), ?x987 = 0dxtg, student(?x11185, ?x10593), program(?x10593, ?x1395) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f87jy producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.724 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #22493-045w_4 PRED entity: 045w_4 PRED relation: place_of_birth PRED expected values: 0d9jr => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 28): 02_286 (0.08 #2132, 0.08 #17626, 0.07 #38054), 0ftxw (0.08 #96, 0.06 #801, 0.03 #1505), 06wxw (0.08 #157, 0.06 #862, 0.03 #1566), 01_d4 (0.08 #66, 0.05 #2179, 0.04 #19082), 0kcw2 (0.08 #615, 0.03 #2024), 0_jq4 (0.08 #440), 0f2tj (0.08 #248), 01m7mv (0.06 #1336, 0.03 #2040), 05fkf (0.06 #725, 0.03 #1429), 030qb3t (0.04 #19070, 0.04 #38089, 0.04 #39498) >> Best rule #2132 for best value: >> intensional similarity = 3 >> extensional distance = 164 >> proper extension: 02k76g; 0p_r5; >> query: (?x4636, 02_286) <- nationality(?x4636, ?x94), profession(?x4636, ?x1032), tv_program(?x4636, ?x4011) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 045w_4 place_of_birth 0d9jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 74.000 0.078 http://example.org/people/person/place_of_birth #22492-06mmb PRED entity: 06mmb PRED relation: film PRED expected values: 020bv3 => 81 concepts (61 used for prediction) PRED predicted values (max 10 best out of 349): 020bv3 (0.80 #3892, 0.79 #11040, 0.67 #7466), 0c0zq (0.31 #10495), 01k0xy (0.31 #10215), 03wjm2 (0.27 #7118, 0.25 #8905, 0.16 #12479), 05vxdh (0.25 #774, 0.18 #6135, 0.17 #7922), 0cz_ym (0.25 #294, 0.10 #3868, 0.01 #43185), 0fh2v5 (0.25 #1602, 0.10 #5176), 047vnkj (0.25 #910, 0.10 #4484), 0260bz (0.25 #335, 0.10 #3909), 05pbl56 (0.25 #245, 0.10 #3819) >> Best rule #3892 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 05tk7y; 0djywgn; >> query: (?x2559, 020bv3) <- award_nominee(?x2559, ?x5743), award_nominee(?x2559, ?x2284), ?x5743 = 0175wg, ?x2284 = 07hbxm >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mmb film 020bv3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 61.000 0.800 http://example.org/film/actor/film./film/performance/film #22491-01fs__ PRED entity: 01fs__ PRED relation: genre PRED expected values: 01z4y => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 90): 01z4y (0.75 #491, 0.44 #570, 0.43 #886), 01z77k (0.47 #264, 0.47 #106, 0.42 #422), 06q7n (0.35 #357, 0.25 #41, 0.16 #594), 01hmnh (0.27 #94, 0.22 #173, 0.21 #410), 0vgkd (0.25 #9, 0.21 #483, 0.19 #878), 06nbt (0.25 #494, 0.16 #969, 0.16 #573), 02n4kr (0.25 #7, 0.13 #639, 0.12 #560), 06n90 (0.23 #2466, 0.21 #249, 0.21 #407), 0hcr (0.21 #1204, 0.19 #4612, 0.19 #4533), 01htzx (0.19 #2470, 0.16 #2390, 0.16 #727) >> Best rule #491 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 01h72l; >> query: (?x7365, 01z4y) <- honored_for(?x4760, ?x7365), nominated_for(?x1447, ?x7365), genre(?x7365, ?x8534), ?x8534 = 0c4xc >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fs__ genre 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.750 http://example.org/tv/tv_program/genre #22490-0bwhdbl PRED entity: 0bwhdbl PRED relation: film! PRED expected values: 016vg8 => 99 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1177): 02pzck (0.40 #7958, 0.40 #5881, 0.33 #10033), 0prfz (0.40 #6284, 0.40 #4207, 0.33 #8359), 01pk3z (0.33 #984, 0.20 #5135, 0.17 #9287), 01gy7r (0.33 #727, 0.05 #21483, 0.05 #11106), 02zyy4 (0.33 #269, 0.05 #10648, 0.04 #33482), 02yplc (0.33 #739, 0.05 #11118, 0.03 #19420), 0f6_x (0.33 #625, 0.05 #11004, 0.03 #19306), 0pgm3 (0.33 #1998, 0.05 #12377, 0.03 #22754), 0fb7c (0.33 #1090, 0.05 #11469, 0.03 #21846), 02l3_5 (0.33 #1406, 0.05 #11785, 0.02 #15935) >> Best rule #7958 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 02v5_g; >> query: (?x8130, 02pzck) <- film(?x4611, ?x8130), film(?x513, ?x8130), ?x4611 = 02v60l, genre(?x8130, ?x812), ?x513 = 01rr9f, ?x812 = 01jfsb >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #7057 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 02v5_g; *> query: (?x8130, 016vg8) <- film(?x4611, ?x8130), film(?x513, ?x8130), ?x4611 = 02v60l, genre(?x8130, ?x812), ?x513 = 01rr9f, ?x812 = 01jfsb *> conf = 0.20 ranks of expected_values: 32 EVAL 0bwhdbl film! 016vg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 99.000 55.000 0.400 http://example.org/film/actor/film./film/performance/film #22489-0215hd PRED entity: 0215hd PRED relation: film_crew_role! PRED expected values: 02v8kmz 0416y94 02vqhv0 033f8n 0642xf3 047bynf 09rvwmy 07ykkx5 => 26 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1558): 01gwk3 (0.75 #15477, 0.62 #14343, 0.60 #10941), 05p1qyh (0.75 #13863, 0.60 #10461, 0.57 #12730), 024l2y (0.75 #14915, 0.60 #10379, 0.50 #13781), 03whyr (0.75 #15745, 0.60 #11209, 0.50 #14611), 02d003 (0.75 #15541, 0.60 #11005, 0.50 #14407), 049xgc (0.75 #15383, 0.60 #10847, 0.50 #14249), 07z6xs (0.75 #15334, 0.60 #10798, 0.50 #14200), 0g3zrd (0.75 #13857, 0.60 #10455, 0.50 #14991), 047wh1 (0.75 #15336, 0.57 #13069, 0.57 #11933), 0fdv3 (0.75 #14932, 0.57 #11529, 0.50 #9263) >> Best rule #15477 for best value: >> intensional similarity = 19 >> extensional distance = 6 >> proper extension: 02ynfr; >> query: (?x4305, 01gwk3) <- film_crew_role(?x7081, ?x4305), film_crew_role(?x5113, ?x4305), film_crew_role(?x5074, ?x4305), film_crew_role(?x4287, ?x4305), film_crew_role(?x1721, ?x4305), film_crew_role(?x1487, ?x4305), ?x1487 = 05sxzwc, ?x4287 = 05f4_n0, film_release_distribution_medium(?x1721, ?x81), country(?x5074, ?x512), nominated_for(?x2532, ?x7081), film_crew_role(?x5074, ?x2154), ?x2154 = 01vx2h, film(?x237, ?x5113), country(?x7081, ?x94), award(?x276, ?x2532), film(?x561, ?x1721), production_companies(?x5113, ?x6413), music(?x5113, ?x7205) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #14872 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 6 *> proper extension: 02ynfr; *> query: (?x4305, 0416y94) <- film_crew_role(?x7081, ?x4305), film_crew_role(?x5113, ?x4305), film_crew_role(?x5074, ?x4305), film_crew_role(?x4287, ?x4305), film_crew_role(?x1721, ?x4305), film_crew_role(?x1487, ?x4305), ?x1487 = 05sxzwc, ?x4287 = 05f4_n0, film_release_distribution_medium(?x1721, ?x81), country(?x5074, ?x512), nominated_for(?x2532, ?x7081), film_crew_role(?x5074, ?x2154), ?x2154 = 01vx2h, film(?x237, ?x5113), country(?x7081, ?x94), award(?x276, ?x2532), film(?x561, ?x1721), production_companies(?x5113, ?x6413), music(?x5113, ?x7205) *> conf = 0.62 ranks of expected_values: 42, 70, 203, 224, 243, 251, 500, 874 EVAL 0215hd film_crew_role! 07ykkx5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 14.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0215hd film_crew_role! 09rvwmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 26.000 14.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0215hd film_crew_role! 047bynf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 14.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0215hd film_crew_role! 0642xf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 26.000 14.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0215hd film_crew_role! 033f8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 26.000 14.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0215hd film_crew_role! 02vqhv0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 26.000 14.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0215hd film_crew_role! 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 26.000 14.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0215hd film_crew_role! 02v8kmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 26.000 14.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22488-026mg3 PRED entity: 026mg3 PRED relation: ceremony PRED expected values: 01xqqp => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 125): 01xqqp (0.70 #206, 0.46 #1082, 0.44 #456), 0gx1673 (0.50 #230, 0.41 #751, 0.33 #105), 09pj68 (0.41 #751, 0.16 #2002, 0.04 #340), 05c1t6z (0.23 #261, 0.18 #386, 0.18 #636), 02q690_ (0.20 #304, 0.18 #429, 0.17 #1180), 0gvstc3 (0.19 #276, 0.17 #401, 0.17 #651), 03nnm4t (0.19 #313, 0.16 #438, 0.16 #939), 0gx_st (0.17 #279, 0.15 #654, 0.15 #404), 0bzm81 (0.16 #266, 0.14 #391, 0.14 #516), 0n8_m93 (0.16 #353, 0.14 #478, 0.14 #603) >> Best rule #206 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 0257yf; >> query: (?x341, 01xqqp) <- award(?x1413, ?x341), ceremony(?x341, ?x3121), ?x3121 = 09n4nb >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026mg3 ceremony 01xqqp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.702 http://example.org/award/award_category/winners./award/award_honor/ceremony #22487-0r0ss PRED entity: 0r0ss PRED relation: category PRED expected values: 08mbj5d => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #1, 0.77 #8, 0.76 #45) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0q_xk; 0r02m; >> query: (?x12250, 08mbj5d) <- contains(?x2949, ?x12250), source(?x12250, ?x958), ?x2949 = 0kpys >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r0ss category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.800 http://example.org/common/topic/webpage./common/webpage/category #22486-0j1yf PRED entity: 0j1yf PRED relation: artists! PRED expected values: 02qdgx 0gywn 02ny8t => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 219): 06by7 (0.48 #946, 0.41 #8033, 0.40 #16663), 05bt6j (0.40 #5282, 0.26 #1585, 0.24 #8055), 0gywn (0.34 #5295, 0.20 #12384, 0.17 #18857), 017_qw (0.33 #9306, 0.30 #12079, 0.29 #8381), 0xhtw (0.29 #324, 0.17 #941, 0.17 #16658), 03lty (0.29 #336, 0.13 #953, 0.11 #16670), 0y3_8 (0.27 #5286, 0.19 #1589, 0.17 #1897), 0155w (0.23 #8116, 0.22 #1029, 0.16 #16746), 01lyv (0.22 #4349, 0.17 #18835, 0.17 #8355), 02ny8t (0.22 #5370, 0.14 #1673, 0.13 #1981) >> Best rule #946 for best value: >> intensional similarity = 2 >> extensional distance = 21 >> proper extension: 01vs4f3; >> query: (?x1896, 06by7) <- notable_people_with_this_condition(?x8318, ?x1896), instrumentalists(?x227, ?x1896) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #5295 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 83 *> proper extension: 01l_vgt; 02twdq; *> query: (?x1896, 0gywn) <- artists(?x3996, ?x1896), ?x3996 = 02lnbg *> conf = 0.34 ranks of expected_values: 3, 10, 61 EVAL 0j1yf artists! 02ny8t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 149.000 149.000 0.478 http://example.org/music/genre/artists EVAL 0j1yf artists! 0gywn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 149.000 149.000 0.478 http://example.org/music/genre/artists EVAL 0j1yf artists! 02qdgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 149.000 149.000 0.478 http://example.org/music/genre/artists #22485-034qmv PRED entity: 034qmv PRED relation: film_crew_role PRED expected values: 09zzb8 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 19): 09zzb8 (0.71 #761, 0.69 #942, 0.69 #792), 0dxtw (0.35 #218, 0.35 #799, 0.35 #829), 02ynfr (0.16 #221, 0.15 #252, 0.15 #343), 02rh1dz (0.16 #217, 0.15 #339, 0.14 #248), 0215hd (0.12 #774, 0.12 #805, 0.12 #835), 089g0h (0.11 #775, 0.10 #806, 0.09 #836), 015h31 (0.10 #429, 0.10 #96, 0.09 #216), 02_n3z (0.08 #762, 0.07 #793, 0.07 #943), 094hwz (0.07 #10, 0.04 #433, 0.04 #100), 04pyp5 (0.06 #803, 0.06 #833, 0.06 #953) >> Best rule #761 for best value: >> intensional similarity = 4 >> extensional distance = 975 >> proper extension: 0g56t9t; 02y_lrp; 02_fm2; 02v8kmz; 02vp1f_; 047gn4y; 0ddfwj1; 0ds3t5x; 0g5qs2k; 0gkz15s; ... >> query: (?x148, 09zzb8) <- genre(?x148, ?x53), film_release_distribution_medium(?x148, ?x81), film(?x147, ?x148), film_crew_role(?x148, ?x468) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034qmv film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.705 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22484-0l5mz PRED entity: 0l5mz PRED relation: major_field_of_study! PRED expected values: 04zx3q1 01ysy9 => 63 concepts (45 used for prediction) PRED predicted values (max 10 best out of 14): 04zx3q1 (0.72 #217, 0.67 #201, 0.67 #186), 07s6fsf (0.59 #90, 0.57 #74, 0.55 #184), 01rr_d (0.59 #90, 0.57 #74, 0.55 #184), 013zdg (0.59 #90, 0.57 #74, 0.55 #184), 027f2w (0.59 #90, 0.57 #74, 0.55 #184), 02cq61 (0.59 #90, 0.57 #74, 0.55 #184), 022h5x (0.57 #74, 0.50 #56, 0.50 #15), 03mkk4 (0.57 #74, 0.50 #15, 0.41 #182), 02m4yg (0.51 #121, 0.50 #129, 0.41 #182), 02mjs7 (0.51 #121, 0.44 #309, 0.41 #182) >> Best rule #217 for best value: >> intensional similarity = 10 >> extensional distance = 16 >> proper extension: 0dc_v; >> query: (?x9079, 04zx3q1) <- major_field_of_study(?x1771, ?x9079), major_field_of_study(?x1368, ?x9079), ?x1368 = 014mlp, major_field_of_study(?x2327, ?x9079), ?x2327 = 07wjk, institution(?x1771, ?x3576), ?x3576 = 012fvq, major_field_of_study(?x1771, ?x5671), student(?x1771, ?x744), ?x5671 = 06b_j >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11 EVAL 0l5mz major_field_of_study! 01ysy9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 63.000 45.000 0.722 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 0l5mz major_field_of_study! 04zx3q1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 45.000 0.722 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #22483-04r1t PRED entity: 04r1t PRED relation: group! PRED expected values: 028tv0 => 71 concepts (69 used for prediction) PRED predicted values (max 10 best out of 105): 018vs (0.62 #1460, 0.60 #183, 0.38 #98), 0mkg (0.50 #95, 0.40 #180, 0.21 #265), 03qjg (0.40 #215, 0.38 #130, 0.26 #1492), 05r5c (0.38 #92, 0.30 #177, 0.24 #1454), 018j2 (0.38 #115, 0.30 #200, 0.20 #30), 028tv0 (0.35 #1459, 0.30 #182, 0.25 #97), 013y1f (0.30 #196, 0.25 #111, 0.10 #1473), 06w7v (0.30 #241, 0.25 #156, 0.08 #666), 0l14qv (0.30 #175, 0.23 #1452, 0.12 #90), 06ncr (0.30 #206, 0.13 #1483, 0.12 #121) >> Best rule #1460 for best value: >> intensional similarity = 6 >> extensional distance = 110 >> proper extension: 0150jk; 067mj; 01vsxdm; 01fl3; 0dtd6; 05563d; 04qmr; 01rm8b; 018gm9; 02cpp; ... >> query: (?x1929, 018vs) <- artists(?x2664, ?x1929), artists(?x2664, ?x9246), artists(?x2664, ?x6461), ?x6461 = 01t110, group(?x227, ?x1929), award_winner(?x1479, ?x9246) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1459 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 110 *> proper extension: 0150jk; 067mj; 01vsxdm; 01fl3; 0dtd6; 05563d; 04qmr; 01rm8b; 018gm9; 02cpp; ... *> query: (?x1929, 028tv0) <- artists(?x2664, ?x1929), artists(?x2664, ?x9246), artists(?x2664, ?x6461), ?x6461 = 01t110, group(?x227, ?x1929), award_winner(?x1479, ?x9246) *> conf = 0.35 ranks of expected_values: 6 EVAL 04r1t group! 028tv0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 71.000 69.000 0.625 http://example.org/music/performance_role/regular_performances./music/group_membership/group #22482-0f5xn PRED entity: 0f5xn PRED relation: award_nominee! PRED expected values: 0dvmd => 87 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1074): 0f5xn (0.22 #102675, 0.16 #95672, 0.04 #74666), 033tln (0.22 #102675, 0.16 #95672, 0.01 #46665), 0kjrx (0.22 #102675, 0.16 #95672), 0693l (0.22 #102675, 0.16 #95672), 054_mz (0.22 #102675, 0.16 #95672), 06y0xx (0.22 #102675), 0bgrsl (0.22 #102675), 026rm_y (0.16 #95672, 0.04 #74666, 0.01 #46665), 021yzs (0.16 #95672, 0.04 #74666, 0.01 #46665), 016fjj (0.16 #95672, 0.04 #74666, 0.01 #46665) >> Best rule #102675 for best value: >> intensional similarity = 3 >> extensional distance = 1522 >> proper extension: 0fvppk; >> query: (?x5462, ?x459) <- nominated_for(?x5462, ?x2177), currency(?x2177, ?x170), nominated_for(?x459, ?x2177) >> conf = 0.22 => this is the best rule for 7 predicted values *> Best rule #95672 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1480 *> proper extension: 024rbz; 03czrpj; 0kk9v; 0kcdl; *> query: (?x5462, ?x368) <- nominated_for(?x5462, ?x2177), film(?x368, ?x2177), award(?x2177, ?x68) *> conf = 0.16 ranks of expected_values: 12 EVAL 0f5xn award_nominee! 0dvmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 87.000 49.000 0.224 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22481-0dvqq PRED entity: 0dvqq PRED relation: group! PRED expected values: 011k_j => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 113): 018vs (0.67 #901, 0.65 #1550, 0.63 #1225), 028tv0 (0.42 #252, 0.38 #819, 0.38 #1062), 03qjg (0.33 #446, 0.30 #689, 0.28 #1013), 0l14qv (0.27 #896, 0.24 #1788, 0.24 #1545), 04rzd (0.21 #270, 0.15 #918, 0.14 #1405), 013y1f (0.20 #23, 0.19 #428, 0.15 #671), 0l14j_ (0.14 #450, 0.14 #1865, 0.14 #936), 018j2 (0.14 #433, 0.14 #1865, 0.09 #919), 026t6 (0.14 #1865, 0.13 #165, 0.07 #1297), 07y_7 (0.14 #1865, 0.12 #893, 0.12 #407) >> Best rule #901 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 01t_xp_; 01pfr3; 0m19t; 03t9sp; 02_5x9; 05k79; 0frsw; 016fmf; 01vrwfv; 01qqwp9; ... >> query: (?x2395, 018vs) <- category(?x2395, ?x134), origin(?x2395, ?x3976), group(?x1166, ?x2395), ?x1166 = 05148p4 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1865 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 183 *> proper extension: 05563d; 0123r4; 07rnh; 06br6t; *> query: (?x2395, ?x314) <- group(?x432, ?x2395), performance_role(?x314, ?x432), role(?x211, ?x432) *> conf = 0.14 ranks of expected_values: 21 EVAL 0dvqq group! 011k_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 103.000 103.000 0.670 http://example.org/music/performance_role/regular_performances./music/group_membership/group #22480-01znc_ PRED entity: 01znc_ PRED relation: combatants! PRED expected values: 03gqgt3 => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 78): 081pw (0.44 #2306, 0.44 #129, 0.43 #2050), 03gqgt3 (0.44 #183, 0.41 #696, 0.35 #952), 01cpp0 (0.44 #186, 0.15 #955, 0.14 #1275), 0cm2xh (0.36 #396, 0.33 #139, 0.31 #332), 01gjd0 (0.33 #131, 0.25 #67, 0.23 #324), 018w0j (0.33 #163, 0.25 #99, 0.21 #420), 02h2z_ (0.33 #179, 0.14 #436, 0.14 #692), 07j9n (0.29 #28, 0.25 #92, 0.22 #221), 0gfq9 (0.23 #263, 0.22 #134, 0.15 #327), 075k5 (0.23 #347, 0.21 #411, 0.15 #283) >> Best rule #2306 for best value: >> intensional similarity = 2 >> extensional distance = 52 >> proper extension: 0193qj; >> query: (?x1499, 081pw) <- combatants(?x2391, ?x1499), olympics(?x1499, ?x584) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #183 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0v74; *> query: (?x1499, 03gqgt3) <- combatants(?x9203, ?x1499), combatants(?x2391, ?x1499), ?x2391 = 0d06vc, entity_involved(?x9203, ?x279) *> conf = 0.44 ranks of expected_values: 2 EVAL 01znc_ combatants! 03gqgt3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 169.000 169.000 0.444 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #22479-03tps5 PRED entity: 03tps5 PRED relation: film! PRED expected values: 03xq0f => 74 concepts (48 used for prediction) PRED predicted values (max 10 best out of 49): 03xq0f (0.89 #82, 0.60 #383, 0.54 #157), 054g1r (0.50 #35, 0.08 #713, 0.07 #1323), 01gb54 (0.46 #1364, 0.44 #2273, 0.38 #303), 016tw3 (0.46 #1364, 0.44 #2273, 0.38 #303), 01795t (0.42 #18, 0.08 #696, 0.08 #245), 086k8 (0.20 #79, 0.18 #154, 0.18 #380), 05qd_ (0.17 #161, 0.17 #86, 0.15 #387), 016tt2 (0.15 #231, 0.13 #81, 0.13 #307), 017s11 (0.13 #757, 0.12 #832, 0.12 #1367), 0g1rw (0.11 #235, 0.09 #461, 0.08 #311) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 0522wp; >> query: (?x4409, 03xq0f) <- film_distribution_medium(?x4409, ?x2099), region(?x4409, ?x512), ?x2099 = 0735l >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03tps5 film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 48.000 0.886 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #22478-031x_3 PRED entity: 031x_3 PRED relation: student! PRED expected values: 07szy => 141 concepts (123 used for prediction) PRED predicted values (max 10 best out of 123): 07tg4 (0.12 #1665, 0.11 #612, 0.08 #3770), 03ksy (0.11 #2737, 0.11 #632, 0.08 #1685), 02183k (0.11 #3262, 0.10 #4840, 0.04 #7471), 0bwfn (0.10 #16585, 0.09 #23949, 0.09 #26580), 02l9wl (0.09 #1304, 0.08 #1830, 0.06 #3935), 07tds (0.09 #1202, 0.06 #2780, 0.05 #675), 07wrz (0.09 #62, 0.05 #588, 0.05 #1115), 02hp6p (0.09 #443, 0.05 #969, 0.05 #1496), 0187nd (0.09 #365, 0.05 #891, 0.05 #1418), 08815 (0.08 #2633, 0.08 #1581, 0.06 #6316) >> Best rule #1665 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 083pr; 0n00; 0b22w; 042q3; 0tfc; >> query: (?x8583, 07tg4) <- student(?x5306, ?x8583), organization(?x8583, ?x4542), people(?x2510, ?x8583) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #16351 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 321 *> proper extension: 044mz_; 079vf; 07fq1y; 02qgqt; 02p65p; 0197tq; 04t2l2; 0lbj1; 014zcr; 0h0jz; ... *> query: (?x8583, 07szy) <- award_winner(?x352, ?x8583), student(?x5306, ?x8583), people(?x2510, ?x8583) *> conf = 0.01 ranks of expected_values: 113 EVAL 031x_3 student! 07szy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 141.000 123.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #22477-01d5z PRED entity: 01d5z PRED relation: season PRED expected values: 027mvrc => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 2): 027mvrc (0.79 #68, 0.77 #64, 0.76 #62), 04n36qk (0.20 #10, 0.08 #57, 0.08 #51) >> Best rule #68 for best value: >> intensional similarity = 9 >> extensional distance = 27 >> proper extension: 01yhm; >> query: (?x1010, 027mvrc) <- season(?x1010, ?x701), draft(?x1010, ?x3334), draft(?x1632, ?x3334), school(?x3334, ?x581), school(?x1010, ?x6953), colors(?x1010, ?x663), institution(?x865, ?x6953), position(?x1010, ?x2010), ?x1632 = 0cqt41 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d5z season 027mvrc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.793 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #22476-018db8 PRED entity: 018db8 PRED relation: nominated_for PRED expected values: 046488 => 99 concepts (51 used for prediction) PRED predicted values (max 10 best out of 608): 017180 (0.82 #8099, 0.79 #4858, 0.79 #68060), 02q87z6 (0.30 #45367, 0.30 #48610, 0.26 #69684), 05jzt3 (0.30 #45367, 0.30 #48610, 0.26 #69684), 02cbhg (0.12 #1252, 0.03 #7731, 0.02 #4490), 06z8s_ (0.11 #1740, 0.04 #6600, 0.04 #8220), 04vr_f (0.11 #1777, 0.03 #3396, 0.03 #5016), 09xbpt (0.11 #1662, 0.03 #3281, 0.03 #4901), 0gwjw0c (0.11 #2704, 0.03 #7564, 0.02 #4323), 03hkch7 (0.11 #2089, 0.01 #29631, 0.01 #8569), 0cz_ym (0.11 #1892, 0.01 #68334, 0.01 #26192) >> Best rule #8099 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 028lc8; 01xsbh; 01g42; 015np0; >> query: (?x793, ?x1077) <- award_winner(?x1077, ?x793), award(?x793, ?x112), ?x112 = 027dtxw >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #4020 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 02t__3; 0bw87; 017yxq; 031sg0; *> query: (?x793, 046488) <- award_winner(?x1077, ?x793), award(?x793, ?x112), participant(?x793, ?x843) *> conf = 0.02 ranks of expected_values: 361 EVAL 018db8 nominated_for 046488 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 99.000 51.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22475-026n9h3 PRED entity: 026n9h3 PRED relation: type_of_union PRED expected values: 04ztj => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.69 #169, 0.69 #221, 0.68 #29), 01g63y (0.14 #30, 0.11 #170, 0.10 #222) >> Best rule #169 for best value: >> intensional similarity = 3 >> extensional distance = 1962 >> proper extension: 05d7rk; 01l1b90; 01vw87c; 01yznp; 0fp_v1x; 04rs03; 01cv3n; 042rnl; 03ds3; 0152cw; ... >> query: (?x6970, 04ztj) <- award(?x6970, ?x2720), profession(?x6970, ?x1032), ?x1032 = 02hrh1q >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026n9h3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.693 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22474-037hgm PRED entity: 037hgm PRED relation: profession PRED expected values: 039v1 => 137 concepts (83 used for prediction) PRED predicted values (max 10 best out of 69): 02hrh1q (0.84 #7089, 0.76 #7976, 0.74 #2664), 01d_h8 (0.53 #2655, 0.50 #3244, 0.47 #3980), 016z4k (0.52 #2359, 0.51 #6194, 0.50 #6047), 039v1 (0.49 #2391, 0.48 #329, 0.40 #4598), 03gjzk (0.45 #2665, 0.37 #3254, 0.34 #3696), 0dxtg (0.45 #2663, 0.32 #8714, 0.32 #8861), 09lbv (0.43 #754, 0.25 #19, 0.13 #2228), 01c72t (0.40 #1346, 0.38 #464, 0.36 #1789), 025352 (0.25 #58, 0.22 #205, 0.11 #1381), 0g0vx (0.25 #107, 0.11 #254, 0.08 #7372) >> Best rule #7089 for best value: >> intensional similarity = 4 >> extensional distance = 416 >> proper extension: 022769; 01wb8bs; 0grrq8; >> query: (?x4759, 02hrh1q) <- place_of_birth(?x4759, ?x3976), student(?x3416, ?x4759), award_nominee(?x4759, ?x7571), participant(?x7571, ?x5240) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2391 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 98 *> proper extension: 03d9d6; 09lwrt; 089pg7; *> query: (?x4759, 039v1) <- instrumentalists(?x227, ?x4759), artists(?x302, ?x4759), ?x227 = 0342h, ?x302 = 016clz *> conf = 0.49 ranks of expected_values: 4 EVAL 037hgm profession 039v1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 137.000 83.000 0.840 http://example.org/people/person/profession #22473-0jswp PRED entity: 0jswp PRED relation: currency PRED expected values: 09nqf => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.77 #29, 0.77 #78, 0.76 #57), 01nv4h (0.02 #65, 0.02 #156, 0.02 #51), 02l6h (0.01 #256) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 395 >> proper extension: 01br2w; 0dckvs; 0djb3vw; 04969y; 04dsnp; 091z_p; 02q3fdr; 0hv81; 012jfb; 064lsn; ... >> query: (?x3369, 09nqf) <- language(?x3369, ?x254), film(?x2800, ?x3369), nominated_for(?x601, ?x3369), produced_by(?x3369, ?x4075) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jswp currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.766 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #22472-07y2s PRED entity: 07y2s PRED relation: citytown PRED expected values: 01_d4 => 52 concepts (48 used for prediction) PRED predicted values (max 10 best out of 157): 0f2rq (0.25 #126, 0.20 #494, 0.17 #862), 0rh6k (0.25 #1, 0.20 #369, 0.17 #737), 02_286 (0.22 #7435, 0.22 #5576, 0.21 #8174), 0r6cx (0.20 #619, 0.17 #987, 0.11 #1356), 0bxbr (0.17 #870, 0.07 #2358, 0.06 #2731), 04jpl (0.14 #2977, 0.05 #12614, 0.04 #12982), 0d9jr (0.11 #1599, 0.11 #1223, 0.07 #2342), 0d6lp (0.11 #1550, 0.11 #1174, 0.07 #2293), 05qtj (0.11 #1584, 0.11 #1208, 0.07 #2327), 013yq (0.11 #1524, 0.11 #1148, 0.06 #4127) >> Best rule #126 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 02vk52z; 0cv9b; >> query: (?x1540, 0f2rq) <- organization(?x4682, ?x1540), service_location(?x1540, ?x455), service_location(?x1540, ?x151), ?x4682 = 0dq_5, ?x455 = 02j9z, service_language(?x1540, ?x254), film_release_region(?x5271, ?x151), film_release_region(?x3252, ?x151), country(?x150, ?x151), ?x5271 = 047vnkj, ?x3252 = 0gh8zks, combatants(?x151, ?x172), administrative_area_type(?x151, ?x2792) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #13752 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 167 *> proper extension: 02583l; 07lx1s; 01jtp7; 01k7xz; 02rff2; 01q2sk; 01ymvk; 017y26; 09krm_; 05njyy; ... *> query: (?x1540, 01_d4) <- organization(?x4682, ?x1540), category(?x1540, ?x134), organization(?x4682, ?x11693), organization(?x4682, ?x11504), organization(?x4682, ?x9806), organization(?x4682, ?x5956), company(?x4682, ?x555), ?x11693 = 02p8454, company(?x265, ?x11504), company(?x10482, ?x5956), currency(?x9806, ?x170), state_province_region(?x9806, ?x1227) *> conf = 0.02 ranks of expected_values: 83 EVAL 07y2s citytown 01_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 52.000 48.000 0.250 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #22471-0ggbfwf PRED entity: 0ggbfwf PRED relation: genre PRED expected values: 02l7c8 => 72 concepts (61 used for prediction) PRED predicted values (max 10 best out of 88): 02l7c8 (0.34 #2073, 0.34 #500, 0.32 #137), 01jfsb (0.33 #12, 0.31 #3160, 0.31 #3038), 02kdv5l (0.29 #2, 0.28 #1817, 0.28 #3150), 03k9fj (0.28 #253, 0.28 #11, 0.27 #737), 0lsxr (0.23 #1823, 0.21 #2186, 0.20 #2429), 06n90 (0.21 #255, 0.19 #739, 0.19 #376), 06cvj (0.20 #2060, 0.20 #1939, 0.14 #487), 01hmnh (0.17 #1712, 0.17 #139, 0.16 #1954), 01t_vv (0.16 #2112, 0.16 #539, 0.14 #1991), 04xvlr (0.15 #6429, 0.14 #3271, 0.14 #3149) >> Best rule #2073 for best value: >> intensional similarity = 4 >> extensional distance = 397 >> proper extension: 02y_lrp; 06wzvr; 011yxg; 0dnvn3; 03h_yy; 02_1sj; 02z3r8t; 09p35z; 02hxhz; 0963mq; ... >> query: (?x5827, 02l7c8) <- film_crew_role(?x5827, ?x137), film(?x3580, ?x5827), genre(?x5827, ?x258), ?x258 = 05p553 >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ggbfwf genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 61.000 0.336 http://example.org/film/film/genre #22470-016dgz PRED entity: 016dgz PRED relation: film PRED expected values: 0c_j9x => 132 concepts (96 used for prediction) PRED predicted values (max 10 best out of 819): 0275kr (0.24 #7162, 0.19 #16115, 0.16 #19697), 017kct (0.11 #5953, 0.09 #2373, 0.05 #582), 0jvt9 (0.10 #9491, 0.10 #13072, 0.09 #539), 03rg2b (0.10 #10046, 0.09 #1094, 0.07 #17209), 01s9vc (0.10 #48342, 0.01 #21347, 0.01 #28507), 01jr4j (0.10 #48342, 0.01 #20947, 0.01 #28107), 05css_ (0.10 #48342, 0.01 #20714, 0.01 #27874), 02r_pp (0.10 #48342, 0.01 #20574, 0.01 #27734), 02jr6k (0.10 #48342, 0.01 #20385, 0.01 #27545), 0k5g9 (0.10 #48342, 0.01 #20130, 0.01 #27290) >> Best rule #7162 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 07rzf; >> query: (?x10724, ?x10827) <- actor(?x10827, ?x10724), award(?x10724, ?x458), ?x458 = 0789_m >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #5744 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 07rzf; *> query: (?x10724, 0c_j9x) <- actor(?x10827, ?x10724), award(?x10724, ?x458), ?x458 = 0789_m *> conf = 0.03 ranks of expected_values: 366 EVAL 016dgz film 0c_j9x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 132.000 96.000 0.239 http://example.org/film/actor/film./film/performance/film #22469-0885n PRED entity: 0885n PRED relation: major_field_of_study PRED expected values: 0299ct => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 108): 02j62 (0.53 #2430, 0.41 #1590, 0.37 #1710), 02lp1 (0.52 #1692, 0.48 #1932, 0.48 #1572), 062z7 (0.43 #2427, 0.32 #1346, 0.30 #1587), 04rjg (0.41 #2420, 0.33 #1580, 0.29 #1940), 01lj9 (0.33 #1599, 0.30 #1719, 0.29 #1959), 0fdys (0.31 #2438, 0.30 #1718, 0.29 #1958), 06ms6 (0.30 #616, 0.21 #1336, 0.20 #2417), 01540 (0.26 #1740, 0.26 #1620, 0.26 #1980), 0g26h (0.26 #1721, 0.23 #1961, 0.22 #1601), 02ky346 (0.26 #1696, 0.23 #1936, 0.22 #1576) >> Best rule #2430 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 0kz2w; >> query: (?x7066, 02j62) <- institution(?x620, ?x7066), major_field_of_study(?x7066, ?x8221), ?x8221 = 037mh8 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #8286 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 388 *> proper extension: 019q50; 0ym4t; *> query: (?x7066, ?x742) <- contains(?x279, ?x7066), institution(?x2759, ?x7066), currency(?x7066, ?x2244), major_field_of_study(?x2759, ?x742) *> conf = 0.05 ranks of expected_values: 84 EVAL 0885n major_field_of_study 0299ct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 166.000 166.000 0.529 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #22468-01pbwwl PRED entity: 01pbwwl PRED relation: people! PRED expected values: 041rx => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 47): 041rx (0.20 #312, 0.18 #81, 0.15 #1467), 02w7gg (0.18 #926, 0.16 #2158, 0.07 #772), 063k3h (0.12 #262, 0.07 #724, 0.06 #647), 07bch9 (0.12 #716, 0.11 #639, 0.09 #254), 033tf_ (0.10 #777, 0.09 #854, 0.09 #2317), 0x67 (0.10 #395, 0.09 #3013, 0.09 #2936), 0xnvg (0.09 #244, 0.07 #552, 0.06 #2323), 013b6_ (0.09 #130, 0.04 #361, 0.03 #515), 013xrm (0.09 #97, 0.03 #1252, 0.03 #1714), 09vc4s (0.08 #163, 0.05 #548, 0.04 #856) >> Best rule #312 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 09r9m7; 0164y7; >> query: (?x10547, 041rx) <- nationality(?x10547, ?x512), award_winner(?x4951, ?x10547), music(?x7760, ?x10547), student(?x12936, ?x10547) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pbwwl people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.200 http://example.org/people/ethnicity/people #22467-0h9qh PRED entity: 0h9qh PRED relation: titles PRED expected values: 02dr9j => 60 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2003): 0gc_c_ (0.35 #3114, 0.33 #495, 0.33 #24911), 0hx4y (0.35 #3114, 0.33 #398, 0.33 #24911), 04fzfj (0.35 #3114, 0.33 #24911, 0.32 #20241), 02nx2k (0.35 #3114, 0.33 #24911, 0.32 #20241), 03t95n (0.33 #989, 0.25 #5662, 0.17 #7221), 015x74 (0.33 #241, 0.25 #4914, 0.17 #6473), 03y0pn (0.33 #1061, 0.25 #5734, 0.17 #7293), 04w7rn (0.33 #200, 0.25 #4873, 0.17 #6432), 09fqgj (0.33 #1425, 0.25 #6098, 0.17 #7657), 0d6_s (0.33 #1418, 0.25 #6091, 0.17 #7650) >> Best rule #3114 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 03npn; >> query: (?x6154, ?x3600) <- genre(?x3600, ?x6154), titles(?x6154, ?x4422), titles(?x6154, ?x2714), titles(?x6154, ?x1074), ?x4422 = 06zn2v2, ?x2714 = 0kv238, film(?x548, ?x1074), crewmember(?x1074, ?x6166), titles(?x2480, ?x3600) >> conf = 0.35 => this is the best rule for 4 predicted values *> Best rule #12460 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 20 *> proper extension: 0jgd; *> query: (?x6154, ?x54) <- titles(?x6154, ?x4422), titles(?x6154, ?x1074), film_crew_role(?x4422, ?x2095), film_release_region(?x4422, ?x1917), film_release_distribution_medium(?x1074, ?x81), ?x1917 = 01p1v, film_crew_role(?x13292, ?x2095), film_crew_role(?x54, ?x2095), ?x13292 = 076tw54 *> conf = 0.06 ranks of expected_values: 1061 EVAL 0h9qh titles 02dr9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 16.000 0.355 http://example.org/media_common/netflix_genre/titles #22466-05zrvfd PRED entity: 05zrvfd PRED relation: nominated_for PRED expected values: 07s3m4g 04y9mm8 => 47 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1842): 026p4q7 (0.83 #1945, 0.59 #3537, 0.57 #5129), 07024 (0.67 #2021, 0.64 #3613, 0.61 #5205), 017gl1 (0.67 #1722, 0.55 #3314, 0.52 #4906), 0ch26b_ (0.67 #1863, 0.48 #5047, 0.46 #6640), 0dr_4 (0.61 #4998, 0.59 #3406, 0.58 #6591), 0gwjw0c (0.60 #1064, 0.39 #5838, 0.38 #7431), 0gj9tn5 (0.60 #247, 0.14 #3429, 0.13 #5021), 03hmt9b (0.58 #2183, 0.57 #5367, 0.55 #3775), 09gq0x5 (0.58 #1846, 0.50 #3438, 0.48 #5030), 08nvyr (0.58 #2282, 0.50 #3874, 0.48 #5466) >> Best rule #1945 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 040njc; 0f_nbyh; 02hsq3m; 019f4v; 054krc; 0gr42; 02qvyrt; 02x258x; 0fhpv4; 02qyntr; >> query: (?x2115, 026p4q7) <- nominated_for(?x2115, ?x2394), nominated_for(?x2115, ?x2340), ?x2394 = 0661ql3, award(?x7804, ?x2115), location(?x7804, ?x2740), ?x2340 = 0fpv_3_ >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #28662 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 194 *> proper extension: 02sp_v; *> query: (?x2115, ?x66) <- nominated_for(?x2115, ?x2394), film_release_region(?x2394, ?x172), olympics(?x172, ?x391), film_release_region(?x66, ?x172) *> conf = 0.03 ranks of expected_values: 1477 EVAL 05zrvfd nominated_for 04y9mm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 18.000 0.833 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zrvfd nominated_for 07s3m4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 18.000 0.833 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22465-02l9wl PRED entity: 02l9wl PRED relation: student PRED expected values: 02l4rh => 52 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1855): 020_95 (0.30 #940, 0.04 #9240, 0.03 #5092), 03f4w4 (0.27 #4151, 0.21 #10374, 0.02 #10316), 0159h6 (0.20 #56, 0.09 #2133, 0.07 #4208), 03dpqd (0.20 #795, 0.09 #2872, 0.07 #4947), 07b2lv (0.20 #342, 0.09 #2419, 0.07 #4494), 073bb (0.20 #286, 0.09 #2363, 0.07 #4438), 01tdnyh (0.20 #883, 0.04 #2960, 0.04 #13331), 0l6qt (0.20 #16, 0.04 #2093, 0.04 #12464), 016xh5 (0.20 #1059, 0.03 #4152, 0.03 #10375), 02wcx8c (0.20 #232, 0.02 #8532, 0.02 #18901) >> Best rule #940 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 07tgn; 017z88; 07tg4; 017j69; 02hmw9; 01bm_; 01vmv_; 0ym20; >> query: (?x7021, 020_95) <- student(?x7021, ?x9236), award_nominee(?x1549, ?x9236), award_nominee(?x100, ?x9236), participant(?x2012, ?x9236), ?x1549 = 09y20, ?x100 = 05vsxz >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #64300 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 252 *> proper extension: 02h7qr; *> query: (?x7021, ?x381) <- student(?x7021, ?x9236), award_nominee(?x9236, ?x3034), award_nominee(?x9236, ?x1410), award_nominee(?x381, ?x1410), participant(?x3034, ?x445), film(?x9236, ?x2029) *> conf = 0.02 ranks of expected_values: 1396 EVAL 02l9wl student 02l4rh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 32.000 0.300 http://example.org/education/educational_institution/students_graduates./education/education/student #22464-019g8j PRED entity: 019g8j PRED relation: actor PRED expected values: 01mmslz 01tpl1p => 99 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1318): 02gf_l (0.50 #7051, 0.43 #6125, 0.29 #8903), 0725ny (0.50 #1567, 0.38 #8050, 0.33 #9902), 0582cf (0.40 #4403, 0.33 #9960, 0.33 #699), 05z775 (0.33 #826, 0.21 #39829, 0.20 #4530), 0404wqb (0.33 #801, 0.20 #4505, 0.12 #8210), 02bkdn (0.33 #143, 0.20 #3847, 0.12 #7552), 0gcdzz (0.33 #108, 0.20 #3812, 0.12 #7517), 04n7njg (0.33 #94, 0.20 #3798, 0.12 #7503), 02bwjv (0.33 #593, 0.20 #4297, 0.12 #8002), 02zq43 (0.33 #26, 0.20 #3730, 0.12 #7435) >> Best rule #7051 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 05nlzq; >> query: (?x11599, 02gf_l) <- program(?x11453, ?x11599), genre(?x11599, ?x10159), ?x10159 = 025s89p, languages(?x11599, ?x254), actor(?x11599, ?x12054), actor(?x3144, ?x12054), country_of_origin(?x11599, ?x94), ?x3144 = 015w8_ >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #39829 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 56 *> proper extension: 074j87; *> query: (?x11599, ?x478) <- program(?x11453, ?x11599), category(?x11599, ?x134), actor(?x11599, ?x10109), actor(?x5938, ?x10109), languages(?x11599, ?x254), actor(?x5938, ?x478), genre(?x5938, ?x258), country_of_origin(?x11599, ?x94) *> conf = 0.21 ranks of expected_values: 44, 1030 EVAL 019g8j actor 01tpl1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 99.000 63.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 019g8j actor 01mmslz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 99.000 63.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #22463-01rxyb PRED entity: 01rxyb PRED relation: genre PRED expected values: 02kdv5l => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 99): 02kdv5l (0.66 #366, 0.56 #123, 0.55 #245), 04t2t (0.52 #7398, 0.52 #9942, 0.50 #12129), 06n90 (0.50 #256, 0.38 #377, 0.30 #620), 03k9fj (0.44 #133, 0.42 #619, 0.41 #3526), 0lsxr (0.43 #9, 0.25 #1222, 0.24 #1343), 05p553 (0.42 #4729, 0.42 #4366, 0.37 #4971), 04pbhw (0.36 #300, 0.24 #421, 0.17 #1997), 02l7c8 (0.36 #1714, 0.32 #4378, 0.31 #8383), 01hmnh (0.29 #3532, 0.29 #18, 0.27 #261), 02n4kr (0.22 #129, 0.16 #10192, 0.15 #1099) >> Best rule #366 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 02v63m; >> query: (?x4375, 02kdv5l) <- executive_produced_by(?x4375, ?x4060), featured_film_locations(?x4375, ?x151), film(?x166, ?x4375), prequel(?x10446, ?x4375) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rxyb genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.655 http://example.org/film/film/genre #22462-04ddm4 PRED entity: 04ddm4 PRED relation: film_release_region PRED expected values: 0chghy 0f8l9c 059j2 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 164): 09c7w0 (0.93 #1884, 0.93 #6174, 0.93 #5143), 0f8l9c (0.91 #200, 0.90 #1056, 0.89 #885), 06mkj (0.82 #1611, 0.74 #1098, 0.74 #927), 059j2 (0.80 #1582, 0.57 #1069, 0.54 #898), 0chghy (0.79 #1554, 0.65 #185, 0.60 #1041), 0k6nt (0.78 #204, 0.78 #1573, 0.78 #889), 03rjj (0.78 #1547, 0.74 #178, 0.53 #1034), 03h64 (0.76 #1622, 0.57 #253, 0.43 #1109), 0jgd (0.75 #1544, 0.57 #860, 0.57 #1031), 07ssc (0.74 #1561, 0.52 #192, 0.52 #877) >> Best rule #1884 for best value: >> intensional similarity = 4 >> extensional distance = 251 >> proper extension: 0gtsx8c; 0gx1bnj; 0dtw1x; 04gknr; 05q96q6; 044g_k; 0cz8mkh; 02r8hh_; 03kg2v; 0crh5_f; ... >> query: (?x599, 09c7w0) <- film_crew_role(?x599, ?x2178), film_release_region(?x599, ?x87), country(?x599, ?x94), ?x2178 = 01pvkk >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #200 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 0gy4k; *> query: (?x599, 0f8l9c) <- written_by(?x599, ?x8961), film_release_region(?x599, ?x2984), film_release_distribution_medium(?x599, ?x81), ?x2984 = 082fr *> conf = 0.91 ranks of expected_values: 2, 4, 5 EVAL 04ddm4 film_release_region 059j2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 74.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04ddm4 film_release_region 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04ddm4 film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 74.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22461-04mjl PRED entity: 04mjl PRED relation: school PRED expected values: 01lnyf => 62 concepts (49 used for prediction) PRED predicted values (max 10 best out of 471): 01lnyf (0.50 #245, 0.40 #1705, 0.36 #2070), 01pl14 (0.50 #187, 0.36 #1830, 0.33 #5), 01j_06 (0.50 #382, 0.33 #746, 0.33 #16), 01jpqb (0.50 #514, 0.22 #1424, 0.14 #2519), 065y4w7 (0.45 #1834, 0.43 #921, 0.41 #3478), 01dzg0 (0.44 #1435, 0.43 #1071, 0.29 #2530), 012vwb (0.44 #1326, 0.38 #1144, 0.36 #2239), 07w0v (0.43 #924, 0.41 #2750, 0.40 #1654), 06pwq (0.40 #555, 0.38 #1101, 0.36 #2745), 012mzw (0.40 #671, 0.29 #1035, 0.18 #1948) >> Best rule #245 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 02d02; >> query: (?x7357, 01lnyf) <- team(?x2010, ?x7357), draft(?x7357, ?x3334), season(?x7357, ?x3431), season(?x7357, ?x701), school(?x7357, ?x9131), ?x2010 = 02lyr4, ?x3431 = 025ygqm, ?x701 = 05kcgsf, school(?x3334, ?x5907), sport(?x7357, ?x5063), teams(?x1523, ?x7357), ?x9131 = 02pptm, ?x5907 = 01jq4b >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mjl school 01lnyf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 49.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #22460-02y_j8g PRED entity: 02y_j8g PRED relation: award! PRED expected values: 0_b9f 02wk7b => 54 concepts (35 used for prediction) PRED predicted values (max 10 best out of 769): 0pv3x (0.50 #108, 0.25 #1126, 0.22 #35668), 0_b9f (0.25 #1495, 0.25 #477, 0.22 #35668), 0p_th (0.25 #1168, 0.25 #150, 0.22 #35668), 015qqg (0.25 #1506, 0.25 #488, 0.22 #35668), 0yxm1 (0.25 #1457, 0.25 #439, 0.20 #2475), 04j13sx (0.25 #1624, 0.25 #606, 0.20 #2642), 0286gm1 (0.25 #1662, 0.25 #644, 0.20 #2680), 083skw (0.25 #1266, 0.25 #248, 0.20 #2284), 097zcz (0.25 #1437, 0.25 #419, 0.20 #2455), 0sxns (0.25 #1649, 0.25 #631, 0.12 #34647) >> Best rule #108 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05zvq6g; 0gqyl; >> query: (?x7521, 0pv3x) <- nominated_for(?x7521, ?x1490), award(?x1009, ?x7521), award_winner(?x7521, ?x4254), ?x4254 = 0fbx6 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1495 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0bdwft; *> query: (?x7521, 0_b9f) <- nominated_for(?x7521, ?x1490), award(?x1009, ?x7521), award_winner(?x7521, ?x12287), ?x12287 = 039wsf *> conf = 0.25 ranks of expected_values: 2, 67 EVAL 02y_j8g award! 02wk7b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 54.000 35.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02y_j8g award! 0_b9f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 54.000 35.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22459-01wj5hp PRED entity: 01wj5hp PRED relation: profession PRED expected values: 016fly => 68 concepts (64 used for prediction) PRED predicted values (max 10 best out of 68): 01d_h8 (0.51 #6179, 0.32 #2527, 0.31 #3229), 01c72t (0.50 #161, 0.50 #20, 0.38 #302), 0nbcg (0.44 #1849, 0.43 #4235, 0.41 #1149), 016z4k (0.43 #845, 0.42 #1825, 0.34 #4211), 05vyk (0.38 #369, 0.38 #228, 0.33 #87), 01c8w0 (0.38 #148, 0.25 #289, 0.17 #7), 02jknp (0.35 #6180, 0.20 #3230, 0.19 #7720), 0n1h (0.29 #572, 0.24 #1132, 0.22 #852), 03gjzk (0.27 #6186, 0.23 #5626, 0.21 #994), 0gbbt (0.25 #290, 0.17 #8, 0.05 #1830) >> Best rule #6179 for best value: >> intensional similarity = 3 >> extensional distance = 1900 >> proper extension: 0q9kd; 0dbpyd; 06j0md; 06151l; 02rchht; 0qf43; 05g8ky; 0h5f5n; 03ckxdg; 050023; ... >> query: (?x8829, 01d_h8) <- profession(?x8829, ?x2225), profession(?x7264, ?x2225), ?x7264 = 03ftmg >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #349 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02ck1; 03f1zhf; *> query: (?x8829, 016fly) <- profession(?x8829, ?x7998), artists(?x2937, ?x8829), ?x7998 = 01d30f, people(?x2510, ?x8829) *> conf = 0.12 ranks of expected_values: 20 EVAL 01wj5hp profession 016fly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 68.000 64.000 0.513 http://example.org/people/person/profession #22458-070ltt PRED entity: 070ltt PRED relation: languages PRED expected values: 02h40lc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 52): 02h40lc (0.92 #671, 0.91 #577, 0.91 #716), 0t_2 (0.79 #726, 0.63 #609, 0.38 #564), 06nm1 (0.38 #564, 0.36 #633, 0.36 #646), 064_8sq (0.38 #564, 0.36 #633, 0.36 #646), 02bv9 (0.36 #633, 0.36 #646, 0.33 #837), 04306rv (0.36 #633, 0.36 #646, 0.33 #837), 02bjrlw (0.36 #633, 0.36 #646, 0.33 #837), 05zjd (0.36 #633, 0.36 #646, 0.33 #837), 03_9r (0.18 #692, 0.18 #1005, 0.12 #408), 01bkv (0.02 #904) >> Best rule #671 for best value: >> intensional similarity = 8 >> extensional distance = 119 >> proper extension: 02vjhf; >> query: (?x10551, 02h40lc) <- genre(?x10551, ?x53), program(?x6678, ?x10551), award_winner(?x6678, ?x8139), program(?x6678, ?x7511), titles(?x2008, ?x7511), citytown(?x6678, ?x739), award_winner(?x10271, ?x6678), award_nominee(?x8139, ?x829) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 070ltt languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.917 http://example.org/tv/tv_program/languages #22457-0grw_ PRED entity: 0grw_ PRED relation: award! PRED expected values: 02yl42 01x53m => 38 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2514): 019z7q (0.50 #13728, 0.50 #6971, 0.33 #3592), 07w21 (0.50 #16993, 0.42 #27129, 0.42 #23750), 0p8jf (0.50 #17722, 0.33 #27858, 0.33 #24479), 040_t (0.50 #8612, 0.33 #15369, 0.33 #1850), 0dvld (0.44 #22029, 0.06 #65957, 0.06 #62578), 01dzz7 (0.38 #17350, 0.33 #27486, 0.33 #24107), 014ps4 (0.38 #19170, 0.33 #29306, 0.33 #25927), 048_p (0.38 #18523, 0.33 #28659, 0.33 #25280), 01k56k (0.38 #20181, 0.33 #30317, 0.33 #26938), 0c3kw (0.38 #17340, 0.33 #27476, 0.33 #24097) >> Best rule #13728 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 040vk98; >> query: (?x8842, 019z7q) <- award(?x12382, ?x8842), award(?x8841, ?x8842), award(?x5335, ?x8842), nationality(?x12382, ?x94), location(?x12382, ?x4350), influenced_by(?x12382, ?x118), influenced_by(?x8841, ?x1946), ?x5335 = 013pp3, profession(?x8841, ?x987) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #17910 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 02664f; 0265vt; 01ppdy; *> query: (?x8842, 02yl42) <- award(?x12382, ?x8842), award(?x8841, ?x8842), nationality(?x12382, ?x94), location(?x12382, ?x4350), influenced_by(?x12382, ?x6457), influenced_by(?x8841, ?x1947), ?x6457 = 03_87, ?x1947 = 06dl_ *> conf = 0.38 ranks of expected_values: 20, 37 EVAL 0grw_ award! 01x53m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 38.000 20.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0grw_ award! 02yl42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 38.000 20.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22456-0p_tz PRED entity: 0p_tz PRED relation: language PRED expected values: 064_8sq => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 52): 04306rv (0.37 #574, 0.17 #3, 0.14 #517), 064_8sq (0.29 #591, 0.18 #306, 0.17 #477), 06nm1 (0.25 #66, 0.17 #580, 0.12 #295), 02bv9 (0.17 #26, 0.07 #540, 0.03 #5781), 04h9h (0.15 #612, 0.07 #212, 0.06 #897), 06b_j (0.14 #364, 0.14 #192, 0.13 #478), 0jzc (0.12 #75, 0.11 #132, 0.09 #475), 03hkp (0.12 #70, 0.04 #470, 0.03 #5781), 07zrf (0.12 #287, 0.07 #172, 0.05 #344), 03x42 (0.12 #276, 0.03 #5781, 0.01 #2056) >> Best rule #574 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 0gh6j94; >> query: (?x6740, 04306rv) <- films(?x6733, ?x6740), language(?x6740, ?x254), language(?x6740, ?x90), ?x254 = 02h40lc, ?x90 = 02bjrlw >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #591 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 0gh6j94; *> query: (?x6740, 064_8sq) <- films(?x6733, ?x6740), language(?x6740, ?x254), language(?x6740, ?x90), ?x254 = 02h40lc, ?x90 = 02bjrlw *> conf = 0.29 ranks of expected_values: 2 EVAL 0p_tz language 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.366 http://example.org/film/film/language #22455-015cxv PRED entity: 015cxv PRED relation: artists! PRED expected values: 01lyv 09n5t_ => 79 concepts (37 used for prediction) PRED predicted values (max 10 best out of 287): 0mhfr (0.84 #2793, 0.65 #3409, 0.61 #1562), 07sbbz2 (0.73 #3084, 0.43 #6471, 0.40 #622), 017_qw (0.73 #2218, 0.56 #1294, 0.46 #6835), 017371 (0.55 #3077, 0.55 #3693, 0.52 #1846), 01lyv (0.52 #2803, 0.49 #3419, 0.40 #649), 0xhtw (0.50 #632, 0.48 #3711, 0.45 #4941), 05bt6j (0.50 #44, 0.38 #3738, 0.35 #1582), 064t9 (0.46 #8938, 0.45 #8631, 0.44 #9245), 03_d0 (0.45 #1858, 0.39 #2473, 0.28 #1242), 016clz (0.43 #5236, 0.42 #3698, 0.40 #4928) >> Best rule #2793 for best value: >> intensional similarity = 6 >> extensional distance = 65 >> proper extension: 0zjpz; 01w8n89; 01wkmgb; >> query: (?x6635, 0mhfr) <- artists(?x114, ?x6635), artists(?x114, ?x2987), artists(?x114, ?x1654), ?x1654 = 01bpc9, parent_genre(?x114, ?x1928), ?x2987 = 01vw20_ >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2803 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 65 *> proper extension: 0zjpz; 01w8n89; 01wkmgb; *> query: (?x6635, 01lyv) <- artists(?x114, ?x6635), artists(?x114, ?x2987), artists(?x114, ?x1654), ?x1654 = 01bpc9, parent_genre(?x114, ?x1928), ?x2987 = 01vw20_ *> conf = 0.52 ranks of expected_values: 5, 13 EVAL 015cxv artists! 09n5t_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 79.000 37.000 0.836 http://example.org/music/genre/artists EVAL 015cxv artists! 01lyv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 37.000 0.836 http://example.org/music/genre/artists #22454-0f1sm PRED entity: 0f1sm PRED relation: location_of_ceremony! PRED expected values: 04ztj => 216 concepts (216 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.74 #21, 0.69 #189, 0.68 #193), 0jgjn (0.04 #28, 0.03 #192, 0.03 #196), 01g63y (0.04 #26, 0.03 #194, 0.03 #202), 01bl8s (0.02 #372, 0.02 #139, 0.01 #179) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 081m_; >> query: (?x9445, 04ztj) <- administrative_division(?x9445, ?x1755), contains(?x9445, ?x7271), teams(?x9445, ?x12541), contains(?x1755, ?x503) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f1sm location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 216.000 216.000 0.741 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #22453-02vyw PRED entity: 02vyw PRED relation: award PRED expected values: 04dn09n => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 336): 02pqp12 (0.78 #38247, 0.78 #23896, 0.77 #25889), 02g3ft (0.74 #41435, 0.73 #42632, 0.72 #33859), 027c924 (0.74 #41435, 0.73 #42632, 0.72 #33859), 054krc (0.52 #4064, 0.37 #10434, 0.22 #83), 0gqz2 (0.44 #76, 0.36 #4057, 0.28 #10427), 04dn09n (0.44 #1634, 0.33 #11589, 0.29 #2829), 02qyp19 (0.41 #1593, 0.20 #2788, 0.19 #11548), 0l8z1 (0.40 #4043, 0.29 #10413, 0.12 #41834), 02x4wr9 (0.38 #2916, 0.15 #1721, 0.12 #10879), 02x1dht (0.37 #1645, 0.16 #2840, 0.14 #11600) >> Best rule #38247 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 028q6; 07s3vqk; 0411q; 05cljf; 0hl3d; 01vrx3g; 0m2l9; 026ps1; 02mslq; 06cc_1; ... >> query: (?x3662, ?x1862) <- award_winner(?x1862, ?x3662), award(?x361, ?x1862), ceremony(?x1862, ?x78) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1634 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 0gv5c; 01ynzf; *> query: (?x3662, 04dn09n) <- award_winner(?x1862, ?x3662), ?x1862 = 0gr51, profession(?x3662, ?x524) *> conf = 0.44 ranks of expected_values: 6 EVAL 02vyw award 04dn09n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 128.000 128.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22452-0j3d9tn PRED entity: 0j3d9tn PRED relation: film_crew_role PRED expected values: 09zzb8 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.80 #1454, 0.79 #692, 0.77 #909), 09zzb8 (0.77 #1484, 0.77 #686, 0.74 #759), 01vx2h (0.38 #913, 0.36 #733, 0.36 #769), 01pvkk (0.31 #1495, 0.30 #589, 0.28 #1097), 02ynfr (0.19 #701, 0.18 #918, 0.18 #738), 089fss (0.17 #6, 0.15 #114, 0.13 #42), 02vs3x5 (0.17 #24, 0.15 #132, 0.13 #60), 0215hd (0.16 #199, 0.15 #415, 0.14 #343), 02rh1dz (0.13 #912, 0.12 #226, 0.12 #768), 089g0h (0.11 #416, 0.11 #705, 0.11 #922) >> Best rule #1454 for best value: >> intensional similarity = 5 >> extensional distance = 1122 >> proper extension: 03h_yy; 0170_p; 035xwd; 09p35z; 0b73_1d; 0963mq; 02qm_f; 0jyx6; 02rqwhl; 01pgp6; ... >> query: (?x5162, 0ch6mp2) <- film_crew_role(?x5162, ?x468), film_crew_role(?x4127, ?x468), film_crew_role(?x1318, ?x468), ?x1318 = 0416y94, ?x4127 = 049mql >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1484 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1141 *> proper extension: 0sxg4; 09z2b7; 04qw17; 03kxj2; 01242_; 04nnpw; 02psgq; 07q1m; 0k4p0; 03mnn0; ... *> query: (?x5162, 09zzb8) <- film_crew_role(?x5162, ?x468), film_crew_role(?x4422, ?x468), film_crew_role(?x1318, ?x468), ?x1318 = 0416y94, ?x4422 = 06zn2v2 *> conf = 0.77 ranks of expected_values: 2 EVAL 0j3d9tn film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 79.000 0.798 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22451-01b_5g PRED entity: 01b_5g PRED relation: risk_factors PRED expected values: 0c78m => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 99): 01hbgs (0.88 #3481, 0.75 #1540, 0.71 #897), 05zppz (0.83 #2984, 0.71 #860, 0.62 #1062), 0d19y2 (0.80 #307, 0.59 #436, 0.44 #870), 0217g (0.80 #307, 0.59 #436, 0.44 #870), 0dcp_ (0.80 #307, 0.59 #436, 0.44 #870), 025t3bg (0.80 #307, 0.59 #436, 0.44 #870), 0jpmt (0.79 #1804, 0.75 #1539, 0.68 #3413), 0x67 (0.71 #860, 0.62 #638, 0.46 #2306), 02zsn (0.65 #1120, 0.47 #2317, 0.45 #3035), 0432mrk (0.59 #436, 0.42 #629, 0.35 #573) >> Best rule #3481 for best value: >> intensional similarity = 27 >> extensional distance = 24 >> proper extension: 025hl8; 0h9dj; 09969; 06g7c; 0146bp; 0h3bn; >> query: (?x8676, 01hbgs) <- risk_factors(?x8676, ?x11678), risk_factors(?x8676, ?x8523), risk_factors(?x11126, ?x8523), risk_factors(?x11064, ?x8523), risk_factors(?x10613, ?x8523), risk_factors(?x5855, ?x8523), risk_factors(?x1158, ?x8523), people(?x11064, ?x7958), symptom_of(?x13487, ?x10613), symptom_of(?x10717, ?x10613), symptom_of(?x4905, ?x11064), people(?x5855, ?x4112), risk_factors(?x11064, ?x8023), ?x8023 = 0jpmt, ?x7958 = 04__f, people(?x10613, ?x10516), ?x1158 = 02y0js, ?x10717 = 0cjf0, risk_factors(?x13744, ?x11678), risk_factors(?x6720, ?x11678), ?x4112 = 014z8v, ?x6720 = 0m32h, ?x13487 = 01cdt5, ?x4905 = 01j6t0, ?x10516 = 0b22w, ?x13744 = 01qqwn, ?x11126 = 0hg45 >> conf = 0.88 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01b_5g risk_factors 0c78m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 59.000 0.885 http://example.org/medicine/disease/risk_factors #22450-0nc7s PRED entity: 0nc7s PRED relation: contains! PRED expected values: 0nlg4 => 83 concepts (71 used for prediction) PRED predicted values (max 10 best out of 240): 09c7w0 (0.81 #6261, 0.79 #7155, 0.70 #4473), 0dmy0 (0.25 #569, 0.17 #1463, 0.02 #4145), 01n7q (0.23 #9911, 0.20 #6335, 0.20 #5441), 0345h (0.22 #24226, 0.04 #31378, 0.04 #54637), 04jpl (0.18 #20590, 0.13 #3598, 0.10 #12539), 02qkt (0.18 #44166, 0.17 #41476, 0.17 #42373), 028n3 (0.17 #1267, 0.02 #3949, 0.02 #12890), 04_1l0v (0.11 #6707, 0.11 #7601, 0.10 #12071), 0dg3n1 (0.11 #43078, 0.11 #43974, 0.10 #41284), 0j0k (0.09 #43301, 0.09 #44197, 0.09 #41507) >> Best rule #6261 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 0qzhw; 0r3wm; >> query: (?x13899, 09c7w0) <- jurisdiction_of_office(?x1195, ?x13899), place_of_birth(?x1019, ?x13899), contains(?x512, ?x13899), origin(?x2854, ?x512) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #57242 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1874 *> proper extension: 0fqyc; 0h095; 0j11; 02_vs; 0flsf; *> query: (?x13899, ?x1156) <- contains(?x512, ?x13899), nationality(?x111, ?x512), country(?x1156, ?x512) *> conf = 0.01 ranks of expected_values: 134 EVAL 0nc7s contains! 0nlg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 83.000 71.000 0.810 http://example.org/location/location/contains #22449-0163r3 PRED entity: 0163r3 PRED relation: award_nominee! PRED expected values: 042xrr => 120 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1114): 0337vz (0.45 #2359, 0.17 #116614, 0.12 #16327), 01ksr1 (0.40 #3079, 0.17 #116614, 0.12 #16327), 0794g (0.40 #3080, 0.17 #116614, 0.12 #16327), 02cpb7 (0.40 #3443, 0.17 #116614, 0.12 #16327), 0227tr (0.40 #2892, 0.17 #116614, 0.12 #16327), 06t74h (0.35 #3269, 0.17 #116614, 0.12 #16327), 026r8q (0.35 #3987, 0.17 #116614, 0.12 #16327), 02p65p (0.25 #2358, 0.17 #116614, 0.12 #16327), 0163r3 (0.23 #153933, 0.17 #116614, 0.12 #16327), 05qd_ (0.23 #153933, 0.02 #46828, 0.02 #49160) >> Best rule #2359 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0337vz; 04lgymt; 01wmxfs; 047sxrj; 0227tr; 0794g; 03lq43; 02cpb7; 06g2d1; 026r8q; >> query: (?x6716, 0337vz) <- award_nominee(?x6716, ?x6264), award_winner(?x1323, ?x6716), ?x6264 = 01vw37m >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #116614 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 601 *> proper extension: 07sgfsl; 076df9; *> query: (?x6716, ?x100) <- award_nominee(?x6716, ?x1290), actor(?x9787, ?x6716), award_nominee(?x100, ?x1290) *> conf = 0.17 ranks of expected_values: 21 EVAL 0163r3 award_nominee! 042xrr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 120.000 72.000 0.450 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22448-0lyb_ PRED entity: 0lyb_ PRED relation: award_winner PRED expected values: 01m3x5p => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 0lyb_ award_winner 01m3x5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/award/award_category/winners./award/award_honor/award_winner #22447-01gf5h PRED entity: 01gf5h PRED relation: role PRED expected values: 01vdm0 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 119): 05r5c (0.39 #2017, 0.27 #429, 0.27 #528), 018vs (0.27 #528, 0.24 #1270, 0.24 #1269), 03qjg (0.27 #528, 0.24 #1270, 0.24 #1269), 04rzd (0.27 #528, 0.24 #1270, 0.24 #1269), 018j2 (0.27 #528, 0.24 #1270, 0.24 #1269), 01vdm0 (0.26 #2042, 0.20 #666, 0.17 #2148), 02hnl (0.26 #740, 0.02 #3276, 0.01 #676), 03bx0bm (0.26 #740, 0.02 #3276), 02sgy (0.23 #2015, 0.20 #639, 0.18 #427), 042v_gx (0.21 #2018, 0.16 #642, 0.15 #430) >> Best rule #2017 for best value: >> intensional similarity = 3 >> extensional distance = 383 >> proper extension: 053y0s; 01vvydl; 07s3vqk; 0197tq; 0411q; 0c9d9; 01lmj3q; 032nwy; 026ps1; 02rgz4; ... >> query: (?x1001, 05r5c) <- nationality(?x1001, ?x94), artists(?x302, ?x1001), role(?x1001, ?x227) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #2042 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 383 *> proper extension: 053y0s; 01vvydl; 07s3vqk; 0197tq; 0411q; 0c9d9; 01lmj3q; 032nwy; 026ps1; 02rgz4; ... *> query: (?x1001, 01vdm0) <- nationality(?x1001, ?x94), artists(?x302, ?x1001), role(?x1001, ?x227) *> conf = 0.26 ranks of expected_values: 6 EVAL 01gf5h role 01vdm0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 105.000 105.000 0.390 http://example.org/music/artist/track_contributions./music/track_contribution/role #22446-02qzmz6 PRED entity: 02qzmz6 PRED relation: genre PRED expected values: 02l7c8 => 60 concepts (59 used for prediction) PRED predicted values (max 10 best out of 81): 07s9rl0 (0.79 #3843, 0.65 #361, 0.65 #961), 02kdv5l (0.46 #123, 0.45 #243, 0.44 #723), 05p553 (0.40 #125, 0.39 #725, 0.38 #485), 03k9fj (0.33 #13, 0.31 #133, 0.29 #253), 0lsxr (0.33 #10, 0.19 #3852, 0.19 #1090), 04t36 (0.33 #7, 0.10 #367, 0.07 #3608), 04pbhw (0.33 #56, 0.06 #176, 0.05 #776), 02l7c8 (0.28 #857, 0.28 #977, 0.28 #1457), 01hmnh (0.20 #499, 0.18 #739, 0.17 #1579), 06n90 (0.20 #254, 0.19 #134, 0.18 #494) >> Best rule #3843 for best value: >> intensional similarity = 3 >> extensional distance = 1272 >> proper extension: 0fq27fp; >> query: (?x3820, 07s9rl0) <- genre(?x3820, ?x3250), titles(?x3250, ?x4772), ?x4772 = 06kl78 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #857 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 164 *> proper extension: 021gzd; *> query: (?x3820, 02l7c8) <- produced_by(?x3820, ?x8159), nominated_for(?x8445, ?x3820), cinematography(?x3820, ?x10704), award_winner(?x1007, ?x8445) *> conf = 0.28 ranks of expected_values: 8 EVAL 02qzmz6 genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 60.000 59.000 0.790 http://example.org/film/film/genre #22445-0n2bh PRED entity: 0n2bh PRED relation: nominated_for! PRED expected values: 0bp_b2 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 177): 0fbtbt (0.35 #645, 0.33 #1127, 0.28 #2814), 0bdx29 (0.33 #567, 0.21 #1049, 0.21 #2977), 05p1dby (0.33 #84, 0.19 #14468, 0.19 #13742), 07bdd_ (0.33 #54, 0.19 #15434, 0.19 #13017), 05f4m9q (0.33 #12, 0.09 #10375, 0.06 #11099), 05b1610 (0.33 #33, 0.08 #10396, 0.07 #11120), 05b4l5x (0.33 #6, 0.06 #10369, 0.05 #11093), 05p09zm (0.33 #96, 0.06 #10459, 0.05 #1301), 03c7tr1 (0.33 #48, 0.04 #1253, 0.03 #1494), 0fbvqf (0.29 #521, 0.25 #1003, 0.21 #2690) >> Best rule #645 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 070ltt; 04x4gj; >> query: (?x2137, 0fbtbt) <- genre(?x2137, ?x53), ?x53 = 07s9rl0, program_creator(?x2137, ?x2136) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #499 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 070ltt; 04x4gj; *> query: (?x2137, 0bp_b2) <- genre(?x2137, ?x53), ?x53 = 07s9rl0, program_creator(?x2137, ?x2136) *> conf = 0.22 ranks of expected_values: 19 EVAL 0n2bh nominated_for! 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 66.000 66.000 0.353 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22444-0347xl PRED entity: 0347xl PRED relation: gender PRED expected values: 02zsn => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #17, 0.72 #69, 0.72 #43), 02zsn (0.41 #8, 0.38 #6, 0.32 #14) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 516 >> proper extension: 01wj9y9; 03mv0b; >> query: (?x3289, 05zppz) <- profession(?x3289, ?x987), location(?x3289, ?x1755), ?x987 = 0dxtg >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 06jzh; *> query: (?x3289, 02zsn) <- award_nominee(?x6314, ?x3289), award_nominee(?x1486, ?x3289), ?x6314 = 0c3p7, type_of_union(?x1486, ?x566) *> conf = 0.41 ranks of expected_values: 2 EVAL 0347xl gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.830 http://example.org/people/person/gender #22443-02qr46y PRED entity: 02qr46y PRED relation: titles! PRED expected values: 07c52 => 74 concepts (50 used for prediction) PRED predicted values (max 10 best out of 64): 07c52 (0.84 #736, 0.81 #2048, 0.80 #2454), 07s9rl0 (0.77 #3948, 0.67 #4048, 0.50 #402), 01jfsb (0.50 #19, 0.12 #521, 0.11 #4777), 04xvlr (0.44 #4051, 0.36 #1219, 0.34 #3951), 03mdt (0.38 #546, 0.22 #445, 0.22 #144), 016ywr (0.36 #604, 0.05 #603, 0.01 #2224), 015w9s (0.33 #448, 0.33 #147, 0.25 #247), 01z4y (0.18 #4793, 0.15 #4894, 0.14 #4387), 017fp (0.17 #525, 0.16 #4070, 0.13 #3970), 03mqtr (0.17 #446, 0.14 #3992, 0.11 #145) >> Best rule #736 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 0jq2r; 06qxh; >> query: (?x11829, 07c52) <- genre(?x11829, ?x53), titles(?x512, ?x11829), ?x53 = 07s9rl0 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qr46y titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 50.000 0.837 http://example.org/media_common/netflix_genre/titles #22442-02h6_6p PRED entity: 02h6_6p PRED relation: location! PRED expected values: 0g7k2g => 268 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2363): 05whq_9 (0.55 #226252, 0.54 #188544, 0.53 #226251), 04xm_ (0.25 #45247, 0.23 #65359, 0.21 #80444), 0k_mt (0.25 #45247, 0.23 #65359, 0.21 #35190), 017r2 (0.25 #286, 0.18 #47762, 0.12 #5312), 08c7cz (0.23 #65359, 0.20 #65360, 0.18 #52791), 014g9y (0.22 #12193, 0.16 #34816, 0.13 #22247), 015v3r (0.22 #10651, 0.13 #20705, 0.12 #3112), 09h_q (0.22 #11676, 0.13 #21730, 0.12 #4137), 01_f_5 (0.22 #11323, 0.13 #21377, 0.12 #3784), 01vsqvs (0.22 #11909, 0.13 #21963, 0.12 #4370) >> Best rule #226252 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 01d88c; >> query: (?x2611, ?x2595) <- place_of_birth(?x2595, ?x2611), capital(?x1679, ?x2611), nationality(?x2595, ?x1264) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #11793 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 035hm; *> query: (?x2611, 0g7k2g) <- location_of_ceremony(?x10445, ?x2611), country(?x2611, ?x1264), locations(?x584, ?x2611) *> conf = 0.11 ranks of expected_values: 428 EVAL 02h6_6p location! 0g7k2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 268.000 162.000 0.545 http://example.org/people/person/places_lived./people/place_lived/location #22441-01s7zw PRED entity: 01s7zw PRED relation: film PRED expected values: 02ljhg => 79 concepts (44 used for prediction) PRED predicted values (max 10 best out of 403): 017jd9 (0.56 #6121, 0.12 #7902, 0.03 #39191), 017gm7 (0.44 #5554, 0.10 #7335, 0.03 #39191), 017gl1 (0.44 #5486, 0.09 #7267, 0.02 #12611), 08bytj (0.39 #33846, 0.37 #40974, 0.34 #71263), 09lxv9 (0.33 #1500, 0.17 #3282, 0.01 #24658), 06r2_ (0.33 #573, 0.17 #2355), 0ndwt2w (0.26 #6341, 0.04 #8122, 0.01 #13466), 02ljhg (0.17 #3127, 0.12 #4908, 0.04 #6689), 034qmv (0.17 #1797, 0.12 #3578, 0.04 #5359), 032016 (0.17 #2282, 0.04 #5844, 0.01 #23658) >> Best rule #6121 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 02bfmn; 0f0kz; >> query: (?x2557, 017jd9) <- award_nominee(?x5951, ?x2557), award_nominee(?x4999, ?x2557), ?x4999 = 015t7v, award_nominee(?x398, ?x5951) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #3127 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 02zhkz; 02d6n_; *> query: (?x2557, 02ljhg) <- film(?x2557, ?x9616), film(?x2557, ?x8370), ?x8370 = 07ghq, film_release_region(?x9616, ?x94) *> conf = 0.17 ranks of expected_values: 8 EVAL 01s7zw film 02ljhg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 79.000 44.000 0.556 http://example.org/film/actor/film./film/performance/film #22440-0372j5 PRED entity: 0372j5 PRED relation: music PRED expected values: 095x_ => 83 concepts (50 used for prediction) PRED predicted values (max 10 best out of 64): 02rgz4 (0.25 #5), 0146pg (0.18 #642, 0.12 #1486, 0.12 #1275), 02bh9 (0.13 #472, 0.08 #261, 0.06 #3421), 06fxnf (0.08 #279, 0.07 #490, 0.04 #913), 01tc9r (0.08 #275, 0.07 #486, 0.03 #2594), 01x6v6 (0.08 #333, 0.07 #544, 0.03 #3493), 0jn5l (0.08 #306, 0.07 #517, 0.01 #3256), 04zwjd (0.08 #242, 0.07 #453, 0.01 #876), 05gml8 (0.07 #843, 0.07 #1687, 0.07 #7593), 0lx2l (0.07 #843, 0.07 #1687, 0.07 #7593) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01gglm; >> query: (?x6751, 02rgz4) <- film(?x709, ?x6751), language(?x6751, ?x732), ?x709 = 05gml8, service_language(?x555, ?x732) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0372j5 music 095x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 50.000 0.250 http://example.org/film/film/music #22439-022q32 PRED entity: 022q32 PRED relation: profession PRED expected values: 02hrh1q => 150 concepts (132 used for prediction) PRED predicted values (max 10 best out of 79): 02hrh1q (0.90 #12602, 0.89 #11714, 0.88 #7421), 01d_h8 (0.56 #17924, 0.38 #4747, 0.38 #2228), 0dxtg (0.55 #17932, 0.47 #310, 0.38 #9937), 0cbd2 (0.47 #8893, 0.42 #9930, 0.19 #17925), 09jwl (0.42 #908, 0.38 #760, 0.36 #9627), 02jknp (0.38 #17926, 0.33 #304, 0.22 #9635), 0nbcg (0.36 #9627, 0.36 #920, 0.34 #16290), 039v1 (0.36 #9627, 0.33 #3556, 0.31 #2963), 0d1pc (0.34 #6074, 0.34 #16290, 0.33 #3556), 03gkb0 (0.34 #6074, 0.34 #16290, 0.33 #3556) >> Best rule #12602 for best value: >> intensional similarity = 3 >> extensional distance = 352 >> proper extension: 01wmgrf; 06c0j; >> query: (?x10777, 02hrh1q) <- participant(?x56, ?x10777), participant(?x10777, ?x1733), profession(?x10777, ?x2225) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022q32 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 132.000 0.895 http://example.org/people/person/profession #22438-038zh6 PRED entity: 038zh6 PRED relation: team! PRED expected values: 021q23 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 8): 021q23 (0.75 #97, 0.71 #81, 0.67 #57), 07y9k (0.54 #173, 0.50 #85, 0.36 #269), 0355pl (0.40 #36, 0.36 #300, 0.21 #276), 0356lc (0.25 #18, 0.17 #466, 0.17 #58), 0h69c (0.20 #223, 0.16 #383, 0.15 #439), 059yj (0.16 #542, 0.13 #462, 0.13 #254), 03zv9 (0.06 #580, 0.05 #764, 0.04 #772), 01ddbl (0.03 #553, 0.03 #577, 0.03 #256) >> Best rule #97 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 038_3y; >> query: (?x13932, 021q23) <- sport(?x13932, ?x12682), ?x12682 = 09xp_ >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 038zh6 team! 021q23 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.750 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #22437-0252fh PRED entity: 0252fh PRED relation: student! PRED expected values: 02607j => 123 concepts (74 used for prediction) PRED predicted values (max 10 best out of 104): 09f2j (0.22 #6470, 0.20 #158, 0.06 #19099), 06thjt (0.20 #397, 0.06 #1449, 0.03 #1975), 01d34b (0.20 #255, 0.02 #4463, 0.02 #5515), 01qqv5 (0.20 #334), 02607j (0.20 #102), 0bwfn (0.12 #19215, 0.11 #1326, 0.08 #25002), 065y4w7 (0.11 #540, 0.07 #18955, 0.06 #1066), 08815 (0.11 #528, 0.05 #18943, 0.04 #21048), 03ksy (0.08 #19046, 0.05 #2209, 0.04 #36407), 07w0v (0.08 #6332, 0.06 #1072, 0.02 #18961) >> Best rule #6470 for best value: >> intensional similarity = 3 >> extensional distance = 284 >> proper extension: 08f3b1; 0d0vj4; 01x66d; 019y64; 0137n0; 02r4qs; 03cvfg; 02_j7t; 01wwvt2; 03yf3z; ... >> query: (?x7780, 09f2j) <- student(?x1675, ?x7780), major_field_of_study(?x1675, ?x6756), ?x6756 = 0_jm >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #102 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0bj9k; 02vyw; 04__f; *> query: (?x7780, 02607j) <- nominated_for(?x7780, ?x7016), film(?x7780, ?x755), location(?x7780, ?x191), ?x7016 = 07g1sm *> conf = 0.20 ranks of expected_values: 5 EVAL 0252fh student! 02607j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 123.000 74.000 0.224 http://example.org/education/educational_institution/students_graduates./education/education/student #22436-02vzc PRED entity: 02vzc PRED relation: geographic_distribution! PRED expected values: 06mvq => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 39): 0d29z (0.50 #141, 0.44 #461, 0.42 #301), 071x0k (0.36 #123, 0.34 #443, 0.33 #283), 04mvp8 (0.29 #314, 0.21 #154, 0.20 #675), 01rv7x (0.21 #302, 0.14 #663, 0.14 #142), 06mvq (0.17 #58, 0.14 #138, 0.09 #458), 0g6ff (0.14 #130, 0.11 #611, 0.11 #170), 013b6_ (0.14 #147, 0.09 #668, 0.08 #708), 012f86 (0.14 #152, 0.08 #312, 0.08 #112), 0g48m4 (0.13 #1808, 0.10 #2256, 0.08 #2456), 01xhh5 (0.13 #220, 0.12 #340, 0.12 #300) >> Best rule #141 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 01d8l; >> query: (?x1892, 0d29z) <- olympics(?x1892, ?x5395), combatants(?x756, ?x1892), ?x5395 = 018qb4 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 05r4w; 09c7w0; 0jgd; 0d0vqn; 0f8l9c; 0k6nt; 03gj2; 059j2; 06mkj; 082fr; *> query: (?x1892, 06mvq) <- olympics(?x1892, ?x391), film_release_region(?x3425, ?x1892), ?x3425 = 0qm9n *> conf = 0.17 ranks of expected_values: 5 EVAL 02vzc geographic_distribution! 06mvq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 148.000 148.000 0.500 http://example.org/people/ethnicity/geographic_distribution #22435-017d93 PRED entity: 017d93 PRED relation: film_crew_role PRED expected values: 09zzb8 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.75 #866, 0.75 #904, 0.71 #1093), 02r96rf (0.67 #117, 0.67 #907, 0.66 #869), 0dxtw (0.40 #876, 0.40 #914, 0.35 #1103), 01vx2h (0.33 #125, 0.31 #1368, 0.31 #877), 01pvkk (0.28 #1708, 0.27 #2012, 0.27 #1633), 02ynfr (0.17 #882, 0.17 #920, 0.17 #93), 0215hd (0.13 #923, 0.13 #885, 0.13 #1376), 015h31 (0.12 #497, 0.09 #122, 0.08 #1365), 089g0h (0.11 #1377, 0.11 #1641, 0.10 #924), 0d2b38 (0.11 #1383, 0.10 #1647, 0.09 #892) >> Best rule #866 for best value: >> intensional similarity = 4 >> extensional distance = 654 >> proper extension: 0bq6ntw; 03z9585; 078mm1; >> query: (?x6298, 09zzb8) <- genre(?x6298, ?x307), film(?x3842, ?x6298), produced_by(?x6298, ?x1039), film_crew_role(?x6298, ?x1171) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017d93 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22434-01_rh4 PRED entity: 01_rh4 PRED relation: film PRED expected values: 015bpl => 220 concepts (161 used for prediction) PRED predicted values (max 10 best out of 1382): 07bzz7 (0.33 #888, 0.22 #9824, 0.17 #6249), 01f39b (0.33 #977, 0.11 #9913, 0.06 #36723), 02ph9tm (0.25 #10724, 0.23 #28598, 0.22 #7149), 0gwgn1k (0.25 #5121, 0.12 #8696, 0.04 #73041), 08phg9 (0.25 #2670, 0.11 #18757, 0.05 #49141), 06lpmt (0.25 #2471, 0.10 #11408, 0.04 #86478), 03p2xc (0.25 #4818, 0.08 #36990, 0.05 #69164), 03tbg6 (0.25 #5227, 0.05 #40973, 0.05 #19527), 09rvwmy (0.25 #5265, 0.05 #41011, 0.04 #71398), 026390q (0.25 #3762, 0.04 #53809, 0.04 #69895) >> Best rule #888 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01vrnsk; >> query: (?x3395, 07bzz7) <- category(?x3395, ?x134), actor(?x7488, ?x3395), type_of_appearance(?x3395, ?x3429), ?x134 = 08mbj5d >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3176 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01wc7p; *> query: (?x3395, 015bpl) <- type_of_union(?x3395, ?x566), film(?x3395, ?x3524), religion(?x3395, ?x7422), ?x3524 = 06r2_ *> conf = 0.25 ranks of expected_values: 13 EVAL 01_rh4 film 015bpl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 220.000 161.000 0.333 http://example.org/film/actor/film./film/performance/film #22433-062z7 PRED entity: 062z7 PRED relation: student PRED expected values: 012gx2 => 114 concepts (61 used for prediction) PRED predicted values (max 10 best out of 826): 012x2b (0.40 #2257, 0.29 #2947, 0.18 #4797), 0bg539 (0.40 #2095, 0.17 #5096, 0.14 #3015), 0kn4c (0.33 #3479, 0.25 #1175, 0.14 #3018), 01mqh5 (0.33 #675, 0.25 #1596, 0.11 #3902), 0br1w (0.33 #540, 0.11 #3534, 0.09 #9537), 06hgj (0.33 #625, 0.11 #3619, 0.08 #5239), 09l3p (0.33 #557, 0.11 #3551, 0.08 #5171), 01j7rd (0.33 #495, 0.11 #3489, 0.08 #5109), 01t6b4 (0.33 #481, 0.11 #3475, 0.08 #5095), 016kjs (0.33 #478, 0.11 #3472, 0.08 #5092) >> Best rule #2257 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 02vxn; >> query: (?x2606, 012x2b) <- major_field_of_study(?x10869, ?x2606), major_field_of_study(?x7596, ?x2606), major_field_of_study(?x5671, ?x2606), major_field_of_study(?x1695, ?x2606), ?x1695 = 06ms6, major_field_of_study(?x6315, ?x5671), service_location(?x7596, ?x551), institution(?x620, ?x7596), ?x10869 = 03qdm >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1277 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 02j62; *> query: (?x2606, 012gx2) <- major_field_of_study(?x12667, ?x2606), major_field_of_study(?x5671, ?x2606), major_field_of_study(?x1695, ?x2606), language(?x508, ?x5671), ?x12667 = 02pdhz, student(?x1695, ?x3806), major_field_of_study(?x388, ?x1695) *> conf = 0.25 ranks of expected_values: 26 EVAL 062z7 student 012gx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 114.000 61.000 0.400 http://example.org/education/field_of_study/students_majoring./education/education/student #22432-0flpy PRED entity: 0flpy PRED relation: award_nominee! PRED expected values: 01jgkj2 => 124 concepts (41 used for prediction) PRED predicted values (max 10 best out of 709): 01wwvc5 (0.81 #37371, 0.81 #44378, 0.80 #42042), 02l840 (0.20 #157, 0.11 #51545, 0.08 #65564), 04xrx (0.20 #570, 0.04 #19255, 0.03 #5240), 01lvcs1 (0.20 #787, 0.02 #19472, 0.02 #17136), 03j24kf (0.11 #3455, 0.04 #10460, 0.04 #8125), 01w7nwm (0.10 #713, 0.09 #5383, 0.05 #19398), 01vw20h (0.10 #1060, 0.07 #52448, 0.07 #29089), 01wgxtl (0.10 #603, 0.07 #28632, 0.05 #2938), 0288fyj (0.10 #495, 0.06 #5165, 0.05 #19180), 02x_h0 (0.10 #1288, 0.06 #5958, 0.04 #19973) >> Best rule #37371 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 02pp_q_; 03n93; 01d1yr; 02x0bdb; 01c1px; 0grmhb; 06zd1c; >> query: (?x6290, ?x2731) <- award_nominee(?x6290, ?x2731), type_of_union(?x6290, ?x566), profession(?x6290, ?x131), people(?x11563, ?x6290) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #53725 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 247 *> proper extension: 01vsxdm; 017mbb; 011xhx; *> query: (?x6290, ?x6151) <- award(?x6290, ?x1389), award(?x6151, ?x1389), award(?x1388, ?x1389), ?x1388 = 05mt_q, award_winner(?x4532, ?x6151) *> conf = 0.02 ranks of expected_values: 438 EVAL 0flpy award_nominee! 01jgkj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 124.000 41.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22431-0738b8 PRED entity: 0738b8 PRED relation: award_winner PRED expected values: 0mdqp => 115 concepts (50 used for prediction) PRED predicted values (max 10 best out of 597): 02m92h (0.82 #37181, 0.81 #61427, 0.80 #30714), 0mdqp (0.82 #37181, 0.81 #61427, 0.80 #30714), 01pcz9 (0.36 #79215, 0.34 #43645, 0.33 #19401), 02r_d4 (0.36 #79215, 0.34 #43645, 0.33 #19401), 032xhg (0.36 #79215, 0.34 #43645, 0.33 #19401), 03q3x5 (0.36 #79215, 0.28 #50110), 03q43g (0.34 #43645, 0.33 #19401, 0.30 #11315), 03q3sy (0.34 #43645, 0.33 #19401, 0.30 #11315), 02tr7d (0.07 #3486, 0.05 #29351, 0.04 #77852), 0bz60q (0.06 #54961, 0.04 #10847, 0.03 #18931) >> Best rule #37181 for best value: >> intensional similarity = 3 >> extensional distance = 237 >> proper extension: 03d0ns; >> query: (?x2437, ?x364) <- participant(?x2437, ?x545), award_winner(?x364, ?x2437), profession(?x2437, ?x319) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0738b8 award_winner 0mdqp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 50.000 0.816 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #22430-015rhv PRED entity: 015rhv PRED relation: award_winner! PRED expected values: 0bs0bh => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 215): 0bdwqv (0.37 #9921, 0.37 #9920, 0.37 #30629), 0bs0bh (0.37 #9921, 0.37 #9920, 0.37 #30629), 0f4x7 (0.14 #1324, 0.10 #3049, 0.08 #462), 02z1nbg (0.13 #194, 0.06 #1056, 0.04 #1487), 0ck27z (0.13 #14326, 0.11 #11738, 0.10 #8718), 09sb52 (0.11 #19019, 0.11 #19451, 0.10 #21176), 027c95y (0.10 #588, 0.10 #1450, 0.08 #3175), 054ky1 (0.10 #1402, 0.08 #2696, 0.08 #1834), 02py7pj (0.10 #1600, 0.07 #3325, 0.07 #307), 0cqhk0 (0.09 #14271, 0.07 #11683, 0.07 #2193) >> Best rule #9921 for best value: >> intensional similarity = 3 >> extensional distance = 419 >> proper extension: 05218gr; 025cn2; 01h4rj; 05683cn; >> query: (?x2378, ?x3247) <- place_of_death(?x2378, ?x8732), award(?x2378, ?x3247), ceremony(?x3247, ?x1265) >> conf = 0.37 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 015rhv award_winner! 0bs0bh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 93.000 0.375 http://example.org/award/award_category/winners./award/award_honor/award_winner #22429-05pt0l PRED entity: 05pt0l PRED relation: film_crew_role PRED expected values: 09zzb8 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.62 #39, 0.56 #306, 0.56 #115), 02r96rf (0.55 #348, 0.55 #42, 0.55 #309), 09vw2b7 (0.53 #313, 0.52 #352, 0.52 #122), 0dxtw (0.32 #317, 0.32 #356, 0.30 #510), 01vx2h (0.30 #357, 0.30 #318, 0.29 #588), 01pvkk (0.23 #1088, 0.22 #1165, 0.22 #128), 0215hd (0.20 #21, 0.12 #59, 0.12 #135), 02ynfr (0.15 #323, 0.15 #362, 0.14 #56), 01xy5l_ (0.14 #54, 0.09 #360, 0.09 #321), 0d2b38 (0.12 #66, 0.10 #142, 0.10 #372) >> Best rule #39 for best value: >> intensional similarity = 7 >> extensional distance = 40 >> proper extension: 0czyxs; 061681; 09p0ct; 03twd6; 05pbl56; 04n52p6; 0fq7dv_; 064n1pz; 07nt8p; 0f4m2z; ... >> query: (?x7481, 09zzb8) <- genre(?x7481, ?x812), genre(?x7481, ?x604), genre(?x7481, ?x600), film_release_region(?x7481, ?x94), ?x812 = 01jfsb, ?x604 = 0lsxr, ?x600 = 02n4kr >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05pt0l film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.619 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22428-026g4l_ PRED entity: 026g4l_ PRED relation: award PRED expected values: 01l29r => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 246): 040njc (0.53 #2023, 0.30 #4038, 0.29 #3232), 01l29r (0.44 #571, 0.24 #974, 0.19 #11286), 0gr4k (0.43 #1242, 0.24 #839, 0.23 #2048), 07bdd_ (0.41 #1678, 0.18 #3693, 0.18 #2887), 019f4v (0.36 #2082, 0.23 #4097, 0.23 #3291), 01lj_c (0.33 #702, 0.33 #299, 0.24 #1105), 0gs9p (0.33 #2094, 0.23 #4109, 0.23 #3303), 05p1dby (0.31 #1719, 0.17 #107, 0.14 #2928), 04dn09n (0.24 #1253, 0.24 #850, 0.22 #2059), 07kjk7c (0.24 #1503, 0.24 #1100, 0.19 #11286) >> Best rule #2023 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 09xx0m; >> query: (?x5714, 040njc) <- nationality(?x5714, ?x94), award(?x5714, ?x1307), ?x1307 = 0gq9h >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #571 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 054lpb6; *> query: (?x5714, 01l29r) <- award_nominee(?x5714, ?x7274), award(?x5714, ?x1307), ?x7274 = 0dbpwb *> conf = 0.44 ranks of expected_values: 2 EVAL 026g4l_ award 01l29r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.527 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22427-01gkmx PRED entity: 01gkmx PRED relation: special_performance_type PRED expected values: 01pb34 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 4): 02t8yb (0.22 #9, 0.18 #14), 01pb34 (0.14 #48, 0.12 #43, 0.09 #88), 09_gdc (0.03 #27, 0.02 #32, 0.02 #47), 01kyvx (0.01 #406, 0.01 #395) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02_p5w; >> query: (?x9257, 02t8yb) <- film(?x9257, ?x5243), ?x5243 = 01pvxl, location(?x9257, ?x5867) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 137 *> proper extension: 01q7cb_; 0285c; 047hpm; 01n7qlf; 04cr6qv; 02hhtj; 0143wl; 0gs6vr; 02fybl; 01pgk0; ... *> query: (?x9257, 01pb34) <- film(?x9257, ?x430), celebrity(?x3581, ?x9257) *> conf = 0.14 ranks of expected_values: 2 EVAL 01gkmx special_performance_type 01pb34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.222 http://example.org/film/actor/film./film/performance/special_performance_type #22426-0r80l PRED entity: 0r80l PRED relation: contains! PRED expected values: 09c7w0 01n7q => 138 concepts (58 used for prediction) PRED predicted values (max 10 best out of 381): 01n7q (0.85 #50196, 0.85 #11649, 0.81 #31369), 09c7w0 (0.80 #2689, 0.79 #50199, 0.75 #32267), 0kpys (0.43 #8242, 0.43 #7344, 0.33 #16312), 02xry (0.42 #34222, 0.35 #44086, 0.07 #27942), 02jx1 (0.31 #31457, 0.04 #10839, 0.04 #13527), 07ssc (0.29 #31402, 0.04 #14370, 0.04 #17059), 02_286 (0.26 #37688, 0.06 #5415, 0.05 #17070), 059rby (0.24 #37665, 0.12 #20631, 0.11 #7183), 06pvr (0.23 #8227, 0.22 #6433, 0.17 #16297), 05kj_ (0.14 #41, 0.13 #44860, 0.13 #48445) >> Best rule #50196 for best value: >> intensional similarity = 4 >> extensional distance = 165 >> proper extension: 01f62; 081m_; >> query: (?x6950, ?x1227) <- administrative_division(?x6950, ?x4181), time_zones(?x6950, ?x2950), contains(?x1227, ?x4181), jurisdiction_of_office(?x900, ?x1227) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0r80l contains! 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 58.000 0.852 http://example.org/location/location/contains EVAL 0r80l contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 58.000 0.852 http://example.org/location/location/contains #22425-02r771y PRED entity: 02r771y PRED relation: category_of! PRED expected values: 02r771y => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 24): 01ppdy (0.10 #617, 0.06 #940, 0.04 #1588), 058vy5 (0.10 #621, 0.06 #944, 0.03 #1917), 01tgwv (0.09 #788, 0.05 #1111, 0.04 #1435), 01b8bn (0.05 #1103, 0.05 #1265, 0.04 #1427), 05x2s (0.05 #1118, 0.04 #1442, 0.04 #1767), 04jhhng (0.05 #1288, 0.04 #1613, 0.04 #1775), 02tzwd (0.03 #1922, 0.03 #2084, 0.02 #2407), 0j6j8 (0.03 #1906, 0.03 #2068, 0.02 #2391), 02v1ws (0.03 #2103, 0.02 #2426, 0.02 #2589), 01cd7p (0.03 #2101, 0.02 #2424, 0.02 #2587) >> Best rule #617 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 05f4m9q; 0bqsk5; >> query: (?x14221, 01ppdy) <- award_winner(?x14221, ?x9597), influenced_by(?x9597, ?x5336), profession(?x9597, ?x319), ?x5336 = 02kz_, location(?x9597, ?x1906) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #1130 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 19 *> proper extension: 05x2s; *> query: (?x14221, ?x3337) <- disciplines_or_subjects(?x14221, ?x6060), disciplines_or_subjects(?x14221, ?x5864), disciplines_or_subjects(?x14213, ?x6060), disciplines_or_subjects(?x3337, ?x6060), disciplines_or_subjects(?x575, ?x6060), ?x575 = 040vk98, ?x14213 = 01bb1c, ?x5864 = 04g51 *> conf = 0.01 ranks of expected_values: 15 EVAL 02r771y category_of! 02r771y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 60.000 60.000 0.100 http://example.org/award/award_category/category_of #22424-0cqh57 PRED entity: 0cqh57 PRED relation: cinematography! PRED expected values: 01cssf => 79 concepts (36 used for prediction) PRED predicted values (max 10 best out of 340): 087pfc (0.25 #296, 0.20 #638, 0.12 #980), 026wlxw (0.25 #275, 0.20 #617, 0.12 #959), 0f2sx4 (0.25 #268, 0.20 #610, 0.12 #952), 072r5v (0.25 #264, 0.20 #606, 0.12 #948), 02z9rr (0.25 #261, 0.20 #603, 0.12 #945), 01jft4 (0.25 #241, 0.20 #583, 0.12 #925), 0g7pm1 (0.25 #234, 0.20 #576, 0.12 #918), 095z4q (0.25 #218, 0.20 #560, 0.12 #902), 01zfzb (0.25 #177, 0.20 #519, 0.12 #861), 0y_9q (0.25 #176, 0.20 #518, 0.12 #860) >> Best rule #296 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 027t8fw; 08mhyd; >> query: (?x7427, 087pfc) <- award(?x7427, ?x7291), ?x7291 = 0274v0r, cinematography(?x308, ?x7427), gender(?x7427, ?x231) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cqh57 cinematography! 01cssf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 36.000 0.250 http://example.org/film/film/cinematography #22423-0lcx PRED entity: 0lcx PRED relation: people! PRED expected values: 08q1tg => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 41): 02y0js (0.22 #200, 0.09 #728, 0.09 #662), 06z5s (0.20 #91, 0.17 #157, 0.11 #223), 0d19y2 (0.20 #121, 0.08 #319, 0.04 #847), 0cycc (0.20 #56, 0.04 #848, 0.04 #1112), 0gk4g (0.17 #142, 0.14 #406, 0.09 #1396), 01k9gb (0.17 #195, 0.04 #987, 0.02 #1515), 07jwr (0.13 #669, 0.07 #405, 0.07 #471), 02vrr (0.11 #212, 0.07 #476, 0.04 #674), 09d11 (0.08 #350, 0.07 #416, 0.04 #878), 01l2m3 (0.07 #478, 0.04 #2656, 0.04 #1072) >> Best rule #200 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 045m1_; >> query: (?x4028, 02y0js) <- influenced_by(?x10598, ?x4028), gender(?x4028, ?x231), nationality(?x4028, ?x789), ?x10598 = 0mb0 >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0lcx people! 08q1tg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 143.000 0.222 http://example.org/people/cause_of_death/people #22422-03nqnnk PRED entity: 03nqnnk PRED relation: produced_by PRED expected values: 024t0y => 86 concepts (57 used for prediction) PRED predicted values (max 10 best out of 185): 04t38b (0.33 #162, 0.07 #3263, 0.02 #10256), 02kxbwx (0.27 #1971, 0.02 #11676, 0.02 #19045), 02kxbx3 (0.27 #2059, 0.01 #17581, 0.01 #11764), 04wvhz (0.25 #423, 0.09 #2363, 0.08 #2750), 06chf (0.25 #486, 0.09 #2426, 0.06 #3589), 01t6b4 (0.20 #818, 0.02 #10137, 0.02 #16729), 01gzm2 (0.20 #837, 0.01 #9770), 0fvf9q (0.18 #1946, 0.11 #1559, 0.04 #12809), 05mvd62 (0.18 #2184, 0.05 #4511, 0.04 #5677), 0jw67 (0.17 #2834, 0.12 #3610, 0.09 #2447) >> Best rule #162 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 01fwzk; >> query: (?x5929, 04t38b) <- film(?x7617, ?x5929), film(?x1343, ?x5929), film(?x1126, ?x5929), ?x1126 = 0h1mt, gender(?x1343, ?x514), award_nominee(?x1343, ?x444) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3084 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 053tj7; *> query: (?x5929, 024t0y) <- person(?x5929, ?x6658), produced_by(?x5929, ?x7617), location_of_ceremony(?x6658, ?x362), award(?x6658, ?x1336) *> conf = 0.08 ranks of expected_values: 46 EVAL 03nqnnk produced_by 024t0y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 86.000 57.000 0.333 http://example.org/film/film/produced_by #22421-07j8kh PRED entity: 07j8kh PRED relation: artists! PRED expected values: 017_qw => 143 concepts (105 used for prediction) PRED predicted values (max 10 best out of 236): 017_qw (0.80 #7247, 0.60 #2563, 0.57 #8808), 06by7 (0.54 #1583, 0.43 #1271, 0.39 #15946), 064t9 (0.50 #639, 0.47 #11877, 0.46 #4698), 016clz (0.43 #1254, 0.25 #630, 0.21 #15929), 0ggq0m (0.40 #2511, 0.35 #8756, 0.25 #638), 03_d0 (0.38 #1573, 0.25 #324, 0.23 #2510), 08jyyk (0.38 #695, 0.24 #1319, 0.13 #1631), 0827d (0.38 #629, 0.10 #1565, 0.08 #1253), 06j6l (0.26 #11913, 0.25 #10977, 0.25 #4734), 05w3f (0.25 #664, 0.24 #1288, 0.12 #1600) >> Best rule #7247 for best value: >> intensional similarity = 5 >> extensional distance = 135 >> proper extension: 0dhqyw; >> query: (?x5556, 017_qw) <- artists(?x888, ?x5556), artists(?x888, ?x9593), artists(?x888, ?x9480), ?x9593 = 03f4k, music(?x4688, ?x9480) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07j8kh artists! 017_qw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 105.000 0.803 http://example.org/music/genre/artists #22420-01cssf PRED entity: 01cssf PRED relation: cinematography PRED expected values: 0cqh57 => 72 concepts (47 used for prediction) PRED predicted values (max 10 best out of 22): 04qvl7 (0.03 #256, 0.03 #129, 0.02 #774), 06r_by (0.03 #23, 0.02 #536, 0.02 #473), 0bqytm (0.03 #17, 0.02 #209, 0.02 #337), 05y7hc (0.03 #1159, 0.03 #1160, 0.03 #838), 0dvmd (0.03 #1159, 0.03 #1160, 0.03 #838), 01gq0b (0.03 #1159, 0.03 #1160, 0.03 #838), 094wz7q (0.03 #1159, 0.03 #1224, 0.02 #1288), 013tcv (0.03 #838, 0.02 #967, 0.02 #1674), 03cx282 (0.02 #144, 0.02 #208, 0.02 #16), 0cqh57 (0.02 #163, 0.02 #227, 0.02 #419) >> Best rule #256 for best value: >> intensional similarity = 3 >> extensional distance = 286 >> proper extension: 016ztl; >> query: (?x638, 04qvl7) <- film_release_distribution_medium(?x638, ?x81), music(?x638, ?x6910), written_by(?x638, ?x9281) >> conf = 0.03 => this is the best rule for 1 predicted values *> Best rule #163 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 219 *> proper extension: 03g90h; 0dnvn3; 03ckwzc; 04gknr; 0963mq; 08hmch; 03t97y; 07g_0c; 02847m9; 0c00zd0; ... *> query: (?x638, 0cqh57) <- film(?x804, ?x638), crewmember(?x638, ?x3574), music(?x638, ?x6910) *> conf = 0.02 ranks of expected_values: 10 EVAL 01cssf cinematography 0cqh57 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 72.000 47.000 0.028 http://example.org/film/film/cinematography #22419-057bxr PRED entity: 057bxr PRED relation: institution! PRED expected values: 03bwzr4 => 206 concepts (206 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.75 #443, 0.71 #606, 0.70 #790), 03bwzr4 (0.73 #291, 0.62 #452, 0.61 #799), 019v9k (0.72 #447, 0.71 #286, 0.63 #1001), 016t_3 (0.65 #280, 0.58 #441, 0.52 #788), 0bkj86 (0.56 #285, 0.51 #470, 0.48 #609), 07s6fsf (0.48 #278, 0.41 #439, 0.40 #463), 04zx3q1 (0.46 #279, 0.38 #464, 0.34 #787), 027f2w (0.38 #287, 0.33 #56, 0.30 #472), 013zdg (0.33 #53, 0.29 #284, 0.22 #469), 028dcg (0.33 #65, 0.13 #712, 0.12 #296) >> Best rule #443 for best value: >> intensional similarity = 5 >> extensional distance = 83 >> proper extension: 0l2tk; >> query: (?x5695, 014mlp) <- contains(?x205, ?x5695), currency(?x5695, ?x5696), major_field_of_study(?x5695, ?x1668), major_field_of_study(?x8937, ?x1668), ?x8937 = 02482c >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #291 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 08qnnv; *> query: (?x5695, 03bwzr4) <- major_field_of_study(?x5695, ?x1668), institution(?x865, ?x5695), ?x1668 = 01mkq, company(?x3131, ?x5695) *> conf = 0.73 ranks of expected_values: 2 EVAL 057bxr institution! 03bwzr4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 206.000 0.753 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22418-04x4s2 PRED entity: 04x4s2 PRED relation: award PRED expected values: 02xcb6n => 107 concepts (98 used for prediction) PRED predicted values (max 10 best out of 288): 02xcb6n (0.71 #20965, 0.71 #2822, 0.71 #12901), 04njml (0.55 #505, 0.04 #908, 0.04 #2117), 0gqz2 (0.39 #484, 0.07 #887, 0.06 #11772), 0cjyzs (0.36 #2525, 0.32 #6154, 0.29 #2929), 0c4z8 (0.33 #475, 0.11 #72, 0.09 #878), 09sb52 (0.26 #16975, 0.25 #24231, 0.24 #22618), 054ks3 (0.24 #546, 0.14 #949, 0.11 #143), 04mqgr (0.24 #559, 0.02 #4994, 0.01 #10637), 01bgqh (0.17 #849, 0.11 #43, 0.09 #25039), 03qbh5 (0.17 #1012, 0.11 #206, 0.07 #4238) >> Best rule #20965 for best value: >> intensional similarity = 3 >> extensional distance = 1536 >> proper extension: 018p5f; 04qzm; >> query: (?x3762, ?x8660) <- award(?x3762, ?x3467), award_winner(?x8660, ?x3762), award_nominee(?x722, ?x3762) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04x4s2 award 02xcb6n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 98.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22417-02ny6g PRED entity: 02ny6g PRED relation: featured_film_locations PRED expected values: 030qb3t => 84 concepts (71 used for prediction) PRED predicted values (max 10 best out of 45): 02_286 (0.21 #1222, 0.18 #3627, 0.17 #3868), 030qb3t (0.09 #2203, 0.08 #760, 0.08 #3165), 0qr8z (0.08 #150, 0.06 #390, 0.02 #871), 0dclg (0.08 #53, 0.06 #293, 0.02 #533), 0fsv2 (0.08 #226, 0.06 #466, 0.02 #706), 0rh6k (0.08 #5291, 0.07 #5531, 0.06 #1203), 04jpl (0.07 #5299, 0.06 #5539, 0.06 #8434), 080h2 (0.06 #1226, 0.03 #5554, 0.03 #5314), 0f2wj (0.06 #257), 02nd_ (0.05 #837, 0.04 #2761, 0.03 #2280) >> Best rule #1222 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 0czyxs; 0gtv7pk; 08720; 0872p_c; 01f7gh; 0340hj; 0cd2vh9; 01dyvs; 0fvr1; 05zy2cy; ... >> query: (?x3639, 02_286) <- production_companies(?x3639, ?x1186), nominated_for(?x154, ?x3639), genre(?x3639, ?x1013), ?x1013 = 06n90 >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2203 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 134 *> proper extension: 02xhpl; 0q9jk; *> query: (?x3639, 030qb3t) <- honored_for(?x2749, ?x3639), nominated_for(?x102, ?x2749), honored_for(?x3639, ?x5441), award_winner(?x5441, ?x5338) *> conf = 0.09 ranks of expected_values: 2 EVAL 02ny6g featured_film_locations 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 71.000 0.209 http://example.org/film/film/featured_film_locations #22416-01z452 PRED entity: 01z452 PRED relation: film_release_region PRED expected values: 0d060g => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 10): 09c7w0 (0.17 #2, 0.07 #483, 0.06 #684), 0d060g (0.05 #31, 0.03 #81, 0.03 #131), 0jgd (0.03 #78, 0.03 #128, 0.01 #584), 05v8c (0.03 #85, 0.03 #135), 02vzc (0.03 #141, 0.01 #217, 0.01 #268), 0345h (0.02 #744, 0.02 #796, 0.02 #821), 0chghy (0.02 #158, 0.01 #184), 01pj7 (0.01 #191, 0.01 #216, 0.01 #267), 01znc_ (0.01 #392, 0.01 #418, 0.01 #240), 03_3d (0.01 #231) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 040_lv; >> query: (?x9258, 09c7w0) <- nominated_for(?x3961, ?x9258), ?x3961 = 06m6z6, film_crew_role(?x9258, ?x137) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0bpbhm; *> query: (?x9258, 0d060g) <- nominated_for(?x9343, ?x9258), nominated_for(?x91, ?x9258), ?x9343 = 02xj3rw *> conf = 0.05 ranks of expected_values: 2 EVAL 01z452 film_release_region 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.167 http://example.org/film/film/runtime./film/film_cut/film_release_region #22415-04bbpm PRED entity: 04bbpm PRED relation: major_field_of_study PRED expected values: 01x3g => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 116): 02j62 (0.47 #1907, 0.39 #4157, 0.38 #156), 02lp1 (0.46 #1887, 0.45 #11, 0.45 #1136), 01mkq (0.44 #1891, 0.40 #1140, 0.31 #4517), 04rjg (0.41 #1896, 0.37 #1145, 0.29 #4522), 0g26h (0.38 #1169, 0.28 #1920, 0.23 #169), 062z7 (0.34 #1904, 0.29 #1153, 0.27 #4154), 03g3w (0.34 #1903, 0.28 #1152, 0.27 #4153), 02_7t (0.32 #1192, 0.23 #1943, 0.18 #567), 04x_3 (0.29 #1902, 0.27 #1151, 0.18 #26), 01540 (0.29 #1939, 0.26 #1188, 0.23 #188) >> Best rule #1907 for best value: >> intensional similarity = 5 >> extensional distance = 94 >> proper extension: 08qnnv; >> query: (?x8069, 02j62) <- institution(?x1771, ?x8069), institution(?x1200, ?x8069), ?x1771 = 019v9k, school_type(?x8069, ?x3092), ?x1200 = 016t_3 >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #7012 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 540 *> proper extension: 041sbd; *> query: (?x8069, ?x90) <- institution(?x1771, ?x8069), major_field_of_study(?x1771, ?x1682), major_field_of_study(?x1771, ?x90), ?x1682 = 02ky346 *> conf = 0.06 ranks of expected_values: 76 EVAL 04bbpm major_field_of_study 01x3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 106.000 106.000 0.469 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #22414-0dgst_d PRED entity: 0dgst_d PRED relation: genre PRED expected values: 07s9rl0 => 72 concepts (70 used for prediction) PRED predicted values (max 10 best out of 91): 07s9rl0 (0.93 #3156, 0.80 #485, 0.80 #5461), 04xvlr (0.61 #1820, 0.30 #486, 0.28 #123), 03k9fj (0.53 #13, 0.32 #376, 0.29 #5473), 02l7c8 (0.47 #502, 0.45 #139, 0.30 #3173), 02kdv5l (0.44 #3, 0.35 #2065, 0.34 #1943), 082gq (0.43 #1850, 0.12 #3187, 0.10 #2216), 01jfsb (0.38 #1225, 0.37 #1588, 0.36 #1954), 05p553 (0.35 #2673, 0.34 #6554, 0.33 #1701), 0lsxr (0.28 #252, 0.18 #616, 0.18 #1950), 060__y (0.28 #140, 0.24 #625, 0.22 #1837) >> Best rule #3156 for best value: >> intensional similarity = 4 >> extensional distance = 847 >> proper extension: 016ztl; >> query: (?x1263, 07s9rl0) <- genre(?x1263, ?x3312), film_release_distribution_medium(?x1263, ?x81), genre(?x6362, ?x3312), ?x6362 = 03_gz8 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dgst_d genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 70.000 0.928 http://example.org/film/film/genre #22413-0487c3 PRED entity: 0487c3 PRED relation: nationality PRED expected values: 035yg => 91 concepts (86 used for prediction) PRED predicted values (max 10 best out of 69): 09c7w0 (0.88 #5518, 0.81 #7781, 0.78 #886), 06qd3 (0.60 #132, 0.33 #34, 0.06 #721), 014tss (0.35 #3249, 0.03 #6797, 0.01 #2929), 035yg (0.17 #1575, 0.15 #1279, 0.15 #491), 0k5p1 (0.17 #1575, 0.15 #1279, 0.15 #491), 0b_yz (0.17 #1575, 0.15 #1279, 0.15 #491), 0gyvgw (0.17 #1575, 0.15 #491, 0.13 #1378), 095l0 (0.17 #1575, 0.15 #491, 0.13 #1378), 0619_ (0.17 #1575, 0.12 #2460, 0.12 #2855), 0chghy (0.12 #402, 0.10 #993, 0.09 #1190) >> Best rule #5518 for best value: >> intensional similarity = 5 >> extensional distance = 2011 >> proper extension: 03zqc1; 05qsxy; 0308kx; 025vldk; 0bl60p; 01l3j; >> query: (?x982, 09c7w0) <- nationality(?x982, ?x1310), type_of_union(?x982, ?x566), ?x566 = 04ztj, nationality(?x12213, ?x1310), ?x12213 = 0gpmp >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1575 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 69 *> proper extension: 0ct_yc; 0bhtzw; *> query: (?x982, ?x11072) <- team(?x982, ?x6153), teams(?x11072, ?x6153), position(?x6153, ?x60), team(?x6152, ?x6153), position(?x62, ?x60) *> conf = 0.17 ranks of expected_values: 4 EVAL 0487c3 nationality 035yg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 91.000 86.000 0.880 http://example.org/people/person/nationality #22412-01gc7 PRED entity: 01gc7 PRED relation: film! PRED expected values: 053xw6 => 94 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1109): 05dbf (0.40 #365, 0.04 #4527, 0.04 #6608), 02cyfz (0.34 #89508, 0.34 #97835, 0.33 #60359), 02yxwd (0.20 #745, 0.10 #2826, 0.04 #4907), 01l2fn (0.20 #262, 0.09 #4424, 0.07 #6505), 01nwwl (0.20 #503, 0.05 #8828, 0.04 #4665), 01r93l (0.20 #749, 0.05 #9074, 0.04 #19480), 02ck7w (0.20 #941, 0.05 #9266, 0.03 #13429), 03fbb6 (0.20 #980, 0.04 #5142, 0.04 #7223), 0ywqc (0.20 #1789, 0.04 #5951, 0.02 #8032), 01nms7 (0.20 #1415, 0.04 #5577, 0.02 #7658) >> Best rule #365 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 011yxg; >> query: (?x299, 05dbf) <- music(?x299, ?x2214), titles(?x162, ?x299), ?x162 = 04xvlr, region(?x299, ?x512) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1255 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 011yxg; *> query: (?x299, 053xw6) <- music(?x299, ?x2214), titles(?x162, ?x299), ?x162 = 04xvlr, region(?x299, ?x512) *> conf = 0.20 ranks of expected_values: 17 EVAL 01gc7 film! 053xw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 94.000 48.000 0.400 http://example.org/film/actor/film./film/performance/film #22411-05fjf PRED entity: 05fjf PRED relation: religion PRED expected values: 05w5d => 213 concepts (213 used for prediction) PRED predicted values (max 10 best out of 24): 05w5d (0.71 #589, 0.68 #262, 0.67 #664), 021_0p (0.56 #585, 0.54 #258, 0.53 #660), 01s5nb (0.40 #591, 0.38 #666, 0.35 #817), 092bf5 (0.33 #5, 0.31 #381, 0.30 #230), 03j6c (0.33 #9, 0.25 #59, 0.23 #2289), 0kpl (0.33 #2, 0.25 #52, 0.23 #2289), 07w8f (0.33 #19, 0.25 #69, 0.23 #2289), 02t7t (0.25 #588, 0.23 #2289, 0.23 #839), 072w0 (0.23 #2289, 0.21 #190, 0.21 #165), 0b06q (0.23 #2289, 0.06 #383, 0.04 #508) >> Best rule #589 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 05kkh; 059rby; 03v1s; 05kj_; 059f4; 05fkf; 0vmt; 0hjy; 03s0w; 05fhy; ... >> query: (?x6895, 05w5d) <- location(?x916, ?x6895), contains(?x6895, ?x1214), district_represented(?x176, ?x6895) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fjf religion 05w5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 213.000 213.000 0.712 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #22410-081yw PRED entity: 081yw PRED relation: state! PRED expected values: 0kf9p 0ckhc => 125 concepts (114 used for prediction) PRED predicted values (max 10 best out of 330): 0ckhc (0.18 #5472, 0.15 #8518, 0.12 #17335), 0mlw1 (0.18 #5472, 0.15 #8518, 0.12 #17335), 0d1xx (0.18 #5472, 0.15 #8518, 0.12 #17335), 0mmrd (0.18 #5472, 0.15 #8518, 0.12 #17335), 0mlyj (0.18 #5472, 0.15 #8518, 0.12 #17335), 0ml_m (0.18 #5472, 0.15 #8518, 0.12 #17335), 0mlvc (0.18 #5472, 0.15 #8518, 0.12 #17335), 010rvx (0.18 #5472, 0.15 #8518, 0.12 #17335), 010r6f (0.18 #5472, 0.15 #8518, 0.12 #17335), 0mmty (0.18 #5472, 0.15 #8518, 0.12 #17335) >> Best rule #5472 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 017v_; 0jt5zcn; 0fqyc; 05j49; 04p0c; 02ly_; 0125q1; 015jr; 0ht8h; 06jtd; ... >> query: (?x4600, ?x1087) <- contains(?x94, ?x4600), contains(?x4600, ?x1087), state(?x7957, ?x4600) >> conf = 0.18 => this is the best rule for 21 predicted values ranks of expected_values: 1 EVAL 081yw state! 0ckhc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 114.000 0.176 http://example.org/base/biblioness/bibs_location/state EVAL 081yw state! 0kf9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 114.000 0.176 http://example.org/base/biblioness/bibs_location/state #22409-0dwz3t PRED entity: 0dwz3t PRED relation: sport PRED expected values: 02vx4 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.90 #464, 0.89 #327, 0.89 #455), 0z74 (0.49 #389, 0.49 #499, 0.47 #653), 0jm_ (0.37 #165, 0.29 #238, 0.21 #229), 018w8 (0.18 #103, 0.14 #230, 0.13 #199), 03tmr (0.18 #82, 0.13 #199, 0.13 #236), 018jz (0.13 #199, 0.13 #485, 0.11 #540), 039yzs (0.13 #199, 0.11 #862, 0.09 #106), 09xp_ (0.13 #199, 0.11 #862, 0.06 #514) >> Best rule #464 for best value: >> intensional similarity = 9 >> extensional distance = 88 >> proper extension: 04nrcg; 03d8m4; 04255q; 040whs; 01cw24; 04h5_c; 044lbv; 03_9x6; 03zbws; 03lygq; ... >> query: (?x8678, 02vx4) <- position(?x8678, ?x530), position(?x8678, ?x63), teams(?x9969, ?x8678), ?x63 = 02sdk9v, ?x530 = 02_j1w, team(?x203, ?x8678), team(?x60, ?x8678), ?x203 = 0dgrmp, ?x60 = 02nzb8 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dwz3t sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.900 http://example.org/sports/sports_team/sport #22408-05t0_2v PRED entity: 05t0_2v PRED relation: film_crew_role PRED expected values: 09vw2b7 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 26): 09vw2b7 (0.75 #286, 0.70 #161, 0.69 #255), 0dxtw (0.50 #40, 0.37 #915, 0.37 #758), 01xy5l_ (0.47 #168, 0.46 #293, 0.33 #262), 01pvkk (0.28 #947, 0.28 #447, 0.27 #1289), 033smt (0.23 #273, 0.22 #304, 0.18 #179), 02ynfr (0.21 #170, 0.19 #763, 0.19 #795), 015h31 (0.20 #288, 0.19 #163, 0.17 #257), 02rh1dz (0.17 #39, 0.13 #757, 0.13 #789), 089fss (0.17 #35, 0.11 #285, 0.10 #254), 02vs3x5 (0.17 #51, 0.07 #332, 0.06 #457) >> Best rule #286 for best value: >> intensional similarity = 5 >> extensional distance = 103 >> proper extension: 047svrl; >> query: (?x5945, 09vw2b7) <- film_crew_role(?x5945, ?x4305), film_crew_role(?x5945, ?x468), film(?x1596, ?x5945), ?x468 = 02r96rf, ?x4305 = 0215hd >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05t0_2v film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.752 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22407-018q7 PRED entity: 018q7 PRED relation: type_of_union PRED expected values: 04ztj => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.83 #77, 0.83 #93, 0.83 #73), 01g63y (0.31 #139, 0.25 #414, 0.20 #337), 0jgjn (0.25 #414, 0.02 #130, 0.02 #167), 01bl8s (0.01 #95, 0.01 #112) >> Best rule #77 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 034rd; 079dy; >> query: (?x12361, 04ztj) <- entity_involved(?x13967, ?x12361), nationality(?x12361, ?x512), profession(?x12361, ?x10014), combatants(?x13967, ?x1023) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018q7 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.833 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22406-0jvt9 PRED entity: 0jvt9 PRED relation: nominated_for! PRED expected values: 02sj1x => 104 concepts (46 used for prediction) PRED predicted values (max 10 best out of 651): 01pp3p (0.76 #7006, 0.60 #107437, 0.59 #105100), 05bht9 (0.76 #7006, 0.60 #107437, 0.59 #105100), 0454s1 (0.76 #7006, 0.60 #107437, 0.59 #105100), 0k9j_ (0.46 #56054, 0.37 #23357, 0.35 #58390), 044qx (0.46 #56054, 0.37 #23357, 0.35 #58390), 01y8cr (0.46 #56054, 0.37 #23357, 0.35 #58390), 0cf2h (0.46 #56054, 0.37 #23357, 0.35 #58390), 0cj8x (0.46 #56054, 0.37 #23357, 0.35 #58390), 070bjw (0.44 #14011, 0.38 #39705, 0.37 #42041), 043gj (0.35 #58390, 0.35 #56053, 0.34 #7005) >> Best rule #7006 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 048rn; >> query: (?x3294, ?x4926) <- list(?x3294, ?x3004), film(?x2416, ?x3294), film(?x4926, ?x3294) >> conf = 0.76 => this is the best rule for 3 predicted values *> Best rule #3071 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 07bz5; *> query: (?x3294, 02sj1x) <- list(?x3294, ?x3004), nominated_for(?x4526, ?x3294), honored_for(?x5723, ?x3294) *> conf = 0.03 ranks of expected_values: 130 EVAL 0jvt9 nominated_for! 02sj1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 104.000 46.000 0.756 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22405-0163zw PRED entity: 0163zw PRED relation: parent_genre PRED expected values: 0mmp3 => 51 concepts (46 used for prediction) PRED predicted values (max 10 best out of 233): 06by7 (0.68 #2291, 0.53 #3104, 0.42 #990), 05r6t (0.41 #1189, 0.41 #1353, 0.37 #3140), 09jw2 (0.41 #1237, 0.41 #1401, 0.12 #750), 03_d0 (0.40 #1799, 0.10 #5711, 0.10 #5873), 059kh (0.38 #684, 0.38 #522, 0.38 #360), 0mmp3 (0.35 #1530, 0.33 #65, 0.17 #1039), 029h7y (0.33 #27, 0.29 #190, 0.25 #677), 0glt670 (0.30 #2140, 0.11 #3277, 0.11 #2790), 0gywn (0.29 #1667, 0.23 #1829, 0.14 #202), 01pfpt (0.26 #1300, 0.25 #1465, 0.25 #708) >> Best rule #2291 for best value: >> intensional similarity = 5 >> extensional distance = 78 >> proper extension: 05hs4r; 061fhg; 01756d; 01cbwl; 01ym9b; 025sc50; 02k_kn; 07ym47; 05jt_; 01fm07; ... >> query: (?x12407, 06by7) <- parent_genre(?x12407, ?x3243), artists(?x12407, ?x2945), parent_genre(?x3243, ?x671), artists(?x3243, ?x7407), ?x7407 = 01dq9q >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1530 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 32 *> proper extension: 012x7b; *> query: (?x12407, 0mmp3) <- parent_genre(?x12407, ?x3243), artists(?x3243, ?x7476), artists(?x3243, ?x3929), ?x7476 = 048xh, parent_genre(?x3243, ?x671), award(?x3929, ?x567) *> conf = 0.35 ranks of expected_values: 6 EVAL 0163zw parent_genre 0mmp3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 51.000 46.000 0.675 http://example.org/music/genre/parent_genre #22404-026f__m PRED entity: 026f__m PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 24): 0ch6mp2 (0.83 #1069, 0.79 #1586, 0.79 #1295), 0dxtw (0.44 #813, 0.43 #653, 0.42 #105), 02rh1dz (0.33 #8, 0.20 #40, 0.14 #1589), 05smlt (0.20 #49, 0.12 #81, 0.05 #338), 0215hd (0.17 #143, 0.16 #1596, 0.14 #336), 089g0h (0.17 #337, 0.16 #112, 0.16 #820), 0d2b38 (0.16 #118, 0.15 #150, 0.14 #858), 01xy5l_ (0.16 #332, 0.15 #139, 0.13 #1592), 015h31 (0.11 #328, 0.11 #135, 0.10 #1588), 02_n3z (0.11 #322, 0.10 #1227, 0.10 #1291) >> Best rule #1069 for best value: >> intensional similarity = 4 >> extensional distance = 214 >> proper extension: 03ckwzc; 0gj8t_b; 02847m9; 0bby9p5; 02prwdh; 02qyv3h; 05n6sq; 03t95n; 02wyzmv; 02d003; ... >> query: (?x7728, 0ch6mp2) <- film(?x3054, ?x7728), category(?x7728, ?x134), film_crew_role(?x7728, ?x137), ?x137 = 09zzb8 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026f__m film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22403-0dr3sl PRED entity: 0dr3sl PRED relation: nominated_for! PRED expected values: 040njc 05zr6wv => 82 concepts (75 used for prediction) PRED predicted values (max 10 best out of 201): 0gq9h (0.48 #3491, 0.45 #1430, 0.35 #6926), 0k611 (0.42 #3501, 0.35 #1440, 0.30 #4646), 04dn09n (0.41 #3466, 0.21 #1405, 0.20 #7588), 019f4v (0.40 #3484, 0.31 #1423, 0.29 #6919), 0gs9p (0.39 #3493, 0.30 #7615, 0.29 #7845), 040njc (0.37 #3441, 0.31 #1380, 0.24 #7563), 02pqp12 (0.36 #3487, 0.31 #1426, 0.18 #4632), 0l8z1 (0.36 #4627, 0.33 #6230, 0.32 #1421), 02g3v6 (0.35 #249, 0.33 #478, 0.30 #936), 02qyntr (0.34 #3606, 0.34 #1545, 0.22 #4751) >> Best rule #3491 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 0170xl; >> query: (?x2868, 0gq9h) <- nominated_for(?x4019, ?x2868), genre(?x2868, ?x258), nominated_for(?x1162, ?x2868), ?x1162 = 099c8n >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #3441 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 148 *> proper extension: 0170xl; *> query: (?x2868, 040njc) <- nominated_for(?x4019, ?x2868), genre(?x2868, ?x258), nominated_for(?x1162, ?x2868), ?x1162 = 099c8n *> conf = 0.37 ranks of expected_values: 6, 42 EVAL 0dr3sl nominated_for! 05zr6wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 82.000 75.000 0.480 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0dr3sl nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 82.000 75.000 0.480 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22402-01rly6 PRED entity: 01rly6 PRED relation: team! PRED expected values: 0355pl => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 8): 0355pl (0.40 #115, 0.40 #35, 0.37 #155), 03zv9 (0.38 #90, 0.28 #433, 0.20 #50), 07y9k (0.28 #433, 0.25 #28, 0.17 #348), 059yj (0.15 #438, 0.14 #285, 0.14 #478), 0h69c (0.14 #439, 0.13 #479, 0.10 #567), 0356lc (0.11 #345, 0.10 #385, 0.09 #546), 01ddbl (0.04 #808, 0.04 #816, 0.04 #856), 021q23 (0.02 #745, 0.01 #833, 0.01 #881) >> Best rule #115 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 03zbg0; >> query: (?x11139, 0355pl) <- position(?x11139, ?x60), team(?x11510, ?x11139), team(?x982, ?x11139), teams(?x12884, ?x11139), team(?x11510, ?x7798), ?x7798 = 01cwm1, type_of_union(?x982, ?x566), nationality(?x11510, ?x429) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rly6 team! 0355pl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.400 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #22401-04bbv7 PRED entity: 04bbv7 PRED relation: profession PRED expected values: 09jwl => 126 concepts (65 used for prediction) PRED predicted values (max 10 best out of 61): 09jwl (0.73 #7585, 0.70 #3129, 0.61 #7436), 0nbcg (0.61 #7150, 0.52 #3141, 0.50 #4032), 01d_h8 (0.50 #302, 0.28 #9055, 0.27 #8314), 0dz3r (0.47 #7419, 0.42 #4003, 0.41 #3112), 016z4k (0.47 #4005, 0.46 #3114, 0.46 #2077), 0dxtg (0.33 #458, 0.30 #3421, 0.26 #3273), 01c72t (0.33 #7291, 0.28 #6548, 0.28 #5658), 039v1 (0.32 #3146, 0.28 #2109, 0.23 #7602), 018gz8 (0.31 #3424, 0.28 #2831, 0.27 #3276), 03gjzk (0.27 #3422, 0.23 #2829, 0.23 #3274) >> Best rule #7585 for best value: >> intensional similarity = 4 >> extensional distance = 655 >> proper extension: 032t2z; 01zmpg; >> query: (?x9269, 09jwl) <- profession(?x9269, ?x1032), artists(?x302, ?x9269), profession(?x5130, ?x1032), ?x5130 = 03pp73 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bbv7 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 65.000 0.729 http://example.org/people/person/profession #22400-0cc97st PRED entity: 0cc97st PRED relation: film! PRED expected values: 02qgyv 010p3 04wf_b => 96 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1391): 054g1r (0.43 #85235, 0.38 #78998, 0.34 #126806), 02_p5w (0.38 #13116, 0.33 #6880, 0.26 #19352), 060pl5 (0.29 #16628, 0.18 #18707, 0.17 #51967), 019803 (0.29 #6092, 0.12 #14406, 0.11 #8170), 04mlh8 (0.29 #5429, 0.11 #19979, 0.06 #13743), 02gf_l (0.26 #19974, 0.25 #13738, 0.22 #7502), 01rcmg (0.25 #1470, 0.20 #3548, 0.11 #7705), 0djywgn (0.25 #1485, 0.14 #5642, 0.11 #9798), 0f6_x (0.25 #626, 0.14 #4783, 0.06 #13097), 0fb1q (0.25 #541, 0.14 #4698, 0.06 #13012) >> Best rule #85235 for best value: >> intensional similarity = 4 >> extensional distance = 283 >> proper extension: 02qm_f; 02v63m; 0260bz; 048htn; 01dvbd; 0g9yrw; 05c5z8j; 0cq7tx; 016y_f; 049xgc; ... >> query: (?x5713, ?x5636) <- production_companies(?x5713, ?x10685), executive_produced_by(?x5713, ?x6682), film(?x545, ?x5713), nominated_for(?x5636, ?x5713) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #8696 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 03g90h; 0kv2hv; 04q00lw; 01f8hf; 01cmp9; 043h78; 01xdxy; *> query: (?x5713, 02qgyv) <- story_by(?x5713, ?x10917), film_release_region(?x5713, ?x94), award_winner(?x3946, ?x10917), film_release_distribution_medium(?x5713, ?x81) *> conf = 0.11 ranks of expected_values: 106, 934 EVAL 0cc97st film! 04wf_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 96.000 63.000 0.430 http://example.org/film/actor/film./film/performance/film EVAL 0cc97st film! 010p3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 63.000 0.430 http://example.org/film/actor/film./film/performance/film EVAL 0cc97st film! 02qgyv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 96.000 63.000 0.430 http://example.org/film/actor/film./film/performance/film #22399-0cqnss PRED entity: 0cqnss PRED relation: nominated_for! PRED expected values: 0gq9h 0gs96 => 57 concepts (56 used for prediction) PRED predicted values (max 10 best out of 206): 0gq9h (0.64 #530, 0.62 #765, 0.57 #1000), 0gs9p (0.54 #532, 0.52 #767, 0.50 #1002), 040njc (0.44 #477, 0.43 #712, 0.41 #947), 0gr4k (0.38 #495, 0.36 #730, 0.32 #965), 02qvyrt (0.37 #1503, 0.19 #2443, 0.14 #1738), 0f4x7 (0.37 #494, 0.35 #729, 0.34 #964), 04dn09n (0.36 #1444, 0.30 #504, 0.29 #974), 0gqy2 (0.35 #589, 0.33 #824, 0.32 #1059), 0gqyl (0.34 #546, 0.32 #781, 0.32 #1016), 0gr0m (0.32 #1467, 0.32 #527, 0.31 #762) >> Best rule #530 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 04v8x9; 0jyx6; 0c5dd; 0jym0; 083skw; 012mrr; 0bmpm; 0gcrg; 0hfzr; 0cq7tx; ... >> query: (?x4970, 0gq9h) <- nominated_for(?x2109, ?x4970), nominated_for(?x484, ?x4970), list(?x4970, ?x3004) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 15 EVAL 0cqnss nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 57.000 56.000 0.644 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0cqnss nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 56.000 0.644 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22398-0gs6vr PRED entity: 0gs6vr PRED relation: artists! PRED expected values: 064t9 0y3_8 06j6l => 174 concepts (133 used for prediction) PRED predicted values (max 10 best out of 221): 064t9 (0.87 #11414, 0.86 #4329, 0.79 #1554), 025sc50 (0.73 #4366, 0.64 #1591, 0.64 #11451), 0glt670 (0.59 #4665, 0.46 #9286, 0.44 #7130), 06j6l (0.56 #1898, 0.46 #4364, 0.41 #4672), 0gywn (0.37 #3450, 0.30 #4374, 0.29 #11459), 0y3_8 (0.36 #1588, 0.31 #1897, 0.24 #4363), 0155w (0.30 #3498, 0.26 #6887, 0.22 #17668), 0xhtw (0.29 #27449, 0.28 #30845, 0.26 #21279), 0dn16 (0.25 #1866, 0.16 #4332, 0.09 #5873), 02yv6b (0.23 #5646, 0.19 #30926, 0.17 #6879) >> Best rule #11414 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 0415mzy; >> query: (?x6577, 064t9) <- artists(?x5876, ?x6577), gender(?x6577, ?x514), ?x5876 = 0ggx5q >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 6 EVAL 0gs6vr artists! 06j6l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 174.000 133.000 0.872 http://example.org/music/genre/artists EVAL 0gs6vr artists! 0y3_8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 174.000 133.000 0.872 http://example.org/music/genre/artists EVAL 0gs6vr artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 133.000 0.872 http://example.org/music/genre/artists #22397-064lsn PRED entity: 064lsn PRED relation: genre PRED expected values: 03bxz7 => 106 concepts (98 used for prediction) PRED predicted values (max 10 best out of 109): 04xvlr (0.61 #9780, 0.60 #2170, 0.60 #2049), 07ssc (0.59 #4832, 0.59 #2169, 0.57 #4831), 05p553 (0.39 #124, 0.37 #485, 0.36 #6043), 02l7c8 (0.39 #618, 0.38 #257, 0.38 #2065), 01jfsb (0.37 #2912, 0.34 #10636, 0.34 #3515), 02kdv5l (0.35 #483, 0.34 #10866, 0.34 #1207), 03k9fj (0.31 #493, 0.27 #1338, 0.26 #1578), 060__y (0.29 #2066, 0.25 #619, 0.22 #4728), 03bxz7 (0.29 #55, 0.18 #2103, 0.17 #656), 0lsxr (0.26 #129, 0.21 #9, 0.20 #3511) >> Best rule #9780 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 07s8z_l; 02xhwm; >> query: (?x6121, ?x53) <- titles(?x53, ?x6121), genre(?x273, ?x53) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0_b3d; 0_92w; 0gmcwlb; 07j8r; 03qnc6q; 05ldxl; 0gvt53w; *> query: (?x6121, 03bxz7) <- nominated_for(?x3209, ?x6121), film_release_region(?x6121, ?x1264), ?x1264 = 0345h, ?x3209 = 02w9sd7 *> conf = 0.29 ranks of expected_values: 9 EVAL 064lsn genre 03bxz7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 106.000 98.000 0.612 http://example.org/film/film/genre #22396-0bpm4yw PRED entity: 0bpm4yw PRED relation: nominated_for! PRED expected values: 05ztjjw => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 162): 0gq9h (0.43 #5965, 0.29 #1244, 0.26 #1952), 02g3v6 (0.38 #731, 0.17 #2855, 0.17 #3091), 0gs9p (0.33 #5967, 0.22 #5495, 0.22 #4551), 0gr4k (0.31 #5929, 0.17 #4513, 0.17 #5457), 0k611 (0.30 #5976, 0.21 #5504, 0.20 #4560), 019f4v (0.29 #5956, 0.25 #5484, 0.24 #4540), 099c8n (0.29 #1238, 0.23 #5959, 0.22 #1946), 0gq_v (0.26 #5922, 0.25 #729, 0.21 #4506), 0gr42 (0.25 #799, 0.25 #563, 0.25 #327), 018wdw (0.25 #886, 0.25 #414, 0.22 #2066) >> Best rule #5965 for best value: >> intensional similarity = 3 >> extensional distance = 658 >> proper extension: 011yfd; 05y0cr; >> query: (?x4336, 0gq9h) <- nominated_for(?x3019, ?x4336), nominated_for(?x3019, ?x1259), ?x1259 = 04hwbq >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #247 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0dzlbx; 089j8p; *> query: (?x4336, 05ztjjw) <- film_release_region(?x4336, ?x3855), film_release_region(?x4336, ?x1273), currency(?x3855, ?x170), ?x1273 = 04wgh *> conf = 0.25 ranks of expected_values: 17 EVAL 0bpm4yw nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 71.000 71.000 0.435 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22395-03gj2 PRED entity: 03gj2 PRED relation: film_release_region! PRED expected values: 0g56t9t 07gp9 0gx1bnj 0h1cdwq 0_92w 03bx2lk 047msdk 0gvrws1 08052t3 0kv238 0jwmp 0cp0ph6 0gh65c5 047fjjr 01rwpj 0bh8tgs 0bt3j9 0ddj0x 067ghz 0bq6ntw 064lsn 04cppj 07pd_j 0gwjw0c 0280061 035zr0 02825nf 0gh6j94 0gvvm6l 0cp0t91 0ddbjy4 02vzpb 0267wwv => 192 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1044): 0kv238 (0.88 #17738, 0.77 #13618, 0.72 #22890), 0bq6ntw (0.84 #18117, 0.81 #35629, 0.79 #28419), 08052t3 (0.84 #17725, 0.78 #22877, 0.73 #13605), 0g5qmbz (0.84 #18399, 0.77 #14279, 0.62 #23551), 0bh8tgs (0.82 #28303, 0.76 #18001, 0.74 #35513), 047msdk (0.80 #17628, 0.77 #13508, 0.72 #22780), 03bx2lk (0.80 #17618, 0.72 #27920, 0.69 #22770), 0gwjw0c (0.79 #28500, 0.77 #14078, 0.76 #18198), 0gvrws1 (0.79 #27984, 0.73 #26954, 0.72 #17682), 0h1cdwq (0.77 #13430, 0.76 #17550, 0.73 #26822) >> Best rule #17738 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 05r4w; 09c7w0; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 0chghy; 07ssc; 015fr; ... >> query: (?x1003, 0kv238) <- olympics(?x1003, ?x358), film_release_region(?x542, ?x1003), ?x542 = 0djb3vw >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17, 18, 19, 20, 21, 22, 23, 24, 36, 37, 40, 43, 44, 49, 52, 56, 76, 83, 104, 189 EVAL 03gj2 film_release_region! 0267wwv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 02vzpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0ddbjy4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0cp0t91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0gvvm6l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0gh6j94 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 035zr0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0280061 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0gwjw0c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 07pd_j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 04cppj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 064lsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0bq6ntw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 067ghz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0ddj0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0bt3j9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0bh8tgs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 01rwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 047fjjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0gh65c5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0cp0ph6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0jwmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0kv238 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 08052t3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0gvrws1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 047msdk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 03bx2lk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0_92w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0h1cdwq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0gx1bnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 07gp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03gj2 film_release_region! 0g56t9t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 192.000 106.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22394-05qhnq PRED entity: 05qhnq PRED relation: nationality PRED expected values: 0chghy => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 28): 09c7w0 (0.70 #9824, 0.70 #9423, 0.68 #9323), 0chghy (0.37 #6721, 0.34 #10124, 0.20 #110), 02jx1 (0.33 #233, 0.25 #533, 0.24 #2638), 07ssc (0.33 #215, 0.25 #815, 0.19 #1015), 06q1r (0.17 #277, 0.12 #577, 0.06 #877), 0f8l9c (0.12 #522, 0.12 #422, 0.08 #622), 0d060g (0.08 #1508, 0.06 #1307, 0.06 #3416), 03rt9 (0.07 #1213, 0.06 #813, 0.05 #1013), 0345h (0.07 #731, 0.06 #931, 0.03 #2836), 03rk0 (0.06 #10371, 0.06 #10571, 0.06 #10471) >> Best rule #9824 for best value: >> intensional similarity = 2 >> extensional distance = 2145 >> proper extension: 06151l; 023tp8; 09fqtq; 01kwld; 064nh4k; 034x61; 016khd; 01j5x6; 02gvwz; 01yb09; ... >> query: (?x7210, 09c7w0) <- award_nominee(?x7210, ?x565), profession(?x7210, ?x1032) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #6721 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 565 *> proper extension: 01sl1q; 0grwj; 0337vz; 0byfz; 05ty4m; 01qscs; 0l8v5; 03w1v2; 04wqr; 06cv1; ... *> query: (?x7210, ?x390) <- category(?x7210, ?x134), profession(?x7210, ?x1032), award_nominee(?x7210, ?x565), nationality(?x565, ?x390) *> conf = 0.37 ranks of expected_values: 2 EVAL 05qhnq nationality 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.697 http://example.org/people/person/nationality #22393-0280061 PRED entity: 0280061 PRED relation: film_release_region PRED expected values: 0d060g 03gj2 01mjq 06mkj => 85 concepts (72 used for prediction) PRED predicted values (max 10 best out of 184): 03gj2 (0.90 #183, 0.90 #341, 0.88 #25), 0jgd (0.88 #161, 0.84 #1752, 0.83 #2549), 03spz (0.88 #100, 0.87 #258, 0.71 #416), 03h64 (0.88 #69, 0.85 #227, 0.81 #1818), 06mkj (0.87 #1651, 0.86 #1809, 0.85 #1330), 059j2 (0.87 #3055, 0.85 #1624, 0.85 #1303), 05qhw (0.87 #172, 0.83 #330, 0.82 #14), 06bnz (0.82 #48, 0.77 #206, 0.73 #1797), 07ssc (0.82 #1607, 0.81 #3038, 0.81 #174), 0b90_r (0.81 #162, 0.76 #1753, 0.75 #3026) >> Best rule #183 for best value: >> intensional similarity = 8 >> extensional distance = 50 >> proper extension: 03mgx6z; >> query: (?x7204, 03gj2) <- film_release_region(?x7204, ?x1264), film_release_region(?x7204, ?x608), film_release_region(?x7204, ?x429), film_release_region(?x7204, ?x304), ?x1264 = 0345h, ?x429 = 03rt9, ?x304 = 0d0vqn, ?x608 = 02k54 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 11, 21 EVAL 0280061 film_release_region 06mkj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 72.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0280061 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 85.000 72.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0280061 film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 72.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0280061 film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 85.000 72.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22392-02hy9p PRED entity: 02hy9p PRED relation: profession PRED expected values: 01d_h8 => 151 concepts (133 used for prediction) PRED predicted values (max 10 best out of 73): 01d_h8 (0.91 #1932, 0.89 #1043, 0.87 #8596), 02jknp (0.52 #8301, 0.50 #1045, 0.50 #8), 03gjzk (0.50 #5198, 0.49 #6235, 0.49 #1643), 09jwl (0.30 #462, 0.25 #2685, 0.24 #5054), 0d1pc (0.29 #50, 0.22 #3457, 0.21 #5974), 018gz8 (0.27 #10531, 0.19 #1793, 0.18 #11123), 0np9r (0.23 #3575, 0.21 #908, 0.20 #6092), 0cbd2 (0.21 #11114, 0.19 #10522, 0.16 #7116), 0nbcg (0.18 #5363, 0.16 #4919, 0.16 #5955), 016z4k (0.17 #4596, 0.17 #3856, 0.16 #5928) >> Best rule #1932 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 043q6n_; >> query: (?x8159, 01d_h8) <- produced_by(?x2816, ?x8159), award_nominee(?x1850, ?x8159), film(?x1850, ?x327) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hy9p profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 133.000 0.906 http://example.org/people/person/profession #22391-03spz PRED entity: 03spz PRED relation: participating_countries! PRED expected values: 09n48 018ctl => 222 concepts (222 used for prediction) PRED predicted values (max 10 best out of 41): 018ctl (0.87 #1218, 0.83 #359, 0.82 #1414), 09x3r (0.73 #129, 0.69 #949, 0.67 #988), 09n48 (0.71 #393, 0.65 #940, 0.64 #666), 0sx8l (0.64 #131, 0.57 #248, 0.54 #951), 0blfl (0.55 #145, 0.52 #1004, 0.50 #223), 016r9z (0.50 #958, 0.50 #255, 0.48 #606), 0c_tl (0.45 #140, 0.43 #257, 0.38 #179), 06sks6 (0.43 #609, 0.39 #375, 0.38 #961), 0jdk_ (0.36 #143, 0.29 #260, 0.28 #2072), 0kbvb (0.28 #2072, 0.28 #2152, 0.23 #5917) >> Best rule #1218 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0jgd; 0d0vqn; 02k54; 06npd; 0h7x; 06t2t; >> query: (?x4743, 018ctl) <- film_release_region(?x9657, ?x4743), nationality(?x2724, ?x4743), ?x9657 = 07jqjx >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 03spz participating_countries! 018ctl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 222.000 222.000 0.867 http://example.org/olympics/olympic_games/participating_countries EVAL 03spz participating_countries! 09n48 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 222.000 222.000 0.867 http://example.org/olympics/olympic_games/participating_countries #22390-01_x6v PRED entity: 01_x6v PRED relation: profession PRED expected values: 0cbd2 09jwl => 138 concepts (137 used for prediction) PRED predicted values (max 10 best out of 76): 0cbd2 (0.71 #1546, 0.70 #1126, 0.64 #566), 09jwl (0.70 #8276, 0.69 #5616, 0.67 #8697), 0nbcg (0.52 #2824, 0.47 #8285, 0.47 #5625), 0dz3r (0.49 #2802, 0.42 #8263, 0.41 #5603), 0kyk (0.48 #582, 0.47 #1562, 0.43 #1142), 016z4k (0.38 #7425, 0.38 #5605, 0.37 #5745), 018gz8 (0.35 #2673, 0.33 #3233, 0.31 #3373), 039v1 (0.29 #5630, 0.27 #8290, 0.26 #8711), 05sxg2 (0.28 #13863, 0.25 #1, 0.09 #141), 0fnpj (0.25 #2852, 0.15 #5653, 0.15 #5793) >> Best rule #1546 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 04093; >> query: (?x2390, 0cbd2) <- influenced_by(?x2390, ?x12459), story_by(?x2349, ?x2390), profession(?x2390, ?x319) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01_x6v profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 137.000 0.714 http://example.org/people/person/profession EVAL 01_x6v profession 0cbd2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 137.000 0.714 http://example.org/people/person/profession #22389-09jm8 PRED entity: 09jm8 PRED relation: award PRED expected values: 01by1l 02f72n 02f73b => 110 concepts (90 used for prediction) PRED predicted values (max 10 best out of 294): 02f73b (0.69 #283, 0.50 #5059, 0.23 #1875), 01by1l (0.58 #20810, 0.56 #112, 0.50 #14043), 02f5qb (0.56 #155, 0.52 #4931, 0.35 #1747), 02f72n (0.56 #145, 0.38 #4921, 0.26 #1737), 01bgqh (0.47 #19547, 0.46 #20741, 0.44 #43), 02f79n (0.38 #336, 0.25 #5112, 0.19 #1132), 02wh75 (0.38 #9, 0.16 #1601, 0.13 #22301), 0c4z8 (0.36 #19576, 0.33 #22364, 0.33 #14003), 05q8pss (0.35 #9365, 0.12 #210, 0.12 #4986), 02f77l (0.35 #1843, 0.25 #251, 0.20 #6619) >> Best rule #283 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 01vvycq; 01vn35l; 02qwg; 01bczm; >> query: (?x10561, 02f73b) <- award_winner(?x1565, ?x10561), award(?x10561, ?x9828), award(?x10561, ?x3631), ?x3631 = 02f73p, ?x9828 = 01ckcd >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 09jm8 award 02f73b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 90.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09jm8 award 02f72n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 90.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09jm8 award 01by1l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 90.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22388-0h21v2 PRED entity: 0h21v2 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.82 #235, 0.81 #162, 0.81 #203), 0735l (0.19 #71), 02nxhr (0.09 #7, 0.08 #78, 0.07 #73), 07c52 (0.08 #63, 0.08 #129, 0.08 #89), 07z4p (0.07 #101, 0.07 #116, 0.07 #81) >> Best rule #235 for best value: >> intensional similarity = 4 >> extensional distance = 679 >> proper extension: 0199wf; 025twgt; >> query: (?x5735, 029j_) <- language(?x5735, ?x254), music(?x5735, ?x8374), currency(?x5735, ?x170), ?x170 = 09nqf >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h21v2 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #22387-0259r0 PRED entity: 0259r0 PRED relation: instrumentalists! PRED expected values: 07xzm => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 110): 05r5c (0.54 #610, 0.52 #8, 0.47 #4329), 018vs (0.36 #1045, 0.29 #13, 0.28 #615), 02hnl (0.23 #636, 0.23 #34, 0.19 #1066), 03qjg (0.18 #824, 0.17 #2206, 0.17 #652), 0l14md (0.16 #93, 0.14 #609, 0.13 #781), 026t6 (0.12 #777, 0.12 #1035, 0.12 #2331), 0l14qv (0.11 #779, 0.11 #1382, 0.10 #2333), 06w7v (0.10 #71, 0.09 #157, 0.06 #759), 06ncr (0.10 #43, 0.08 #2113, 0.08 #817), 0l14j_ (0.10 #53, 0.06 #569, 0.05 #225) >> Best rule #610 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 06y9c2; 0bkg4; 04f7c55; 018y81; 04bgy; 01ydzx; 01r0t_j; 0kj34; 02pt27; 0517bc; ... >> query: (?x2786, 05r5c) <- instrumentalists(?x227, ?x2786), artists(?x3061, ?x2786), ?x3061 = 05bt6j >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #193 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 01wmxfs; 02xs0q; 0p_47; 06fmdb; 0hz_1; 0ckcvk; *> query: (?x2786, 07xzm) <- award_winner(?x2786, ?x2824), award_winner(?x139, ?x2786), nationality(?x2786, ?x94), ?x139 = 05pd94v *> conf = 0.05 ranks of expected_values: 20 EVAL 0259r0 instrumentalists! 07xzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 132.000 132.000 0.538 http://example.org/music/instrument/instrumentalists #22386-01f2w0 PRED entity: 01f2w0 PRED relation: program PRED expected values: 0dl6fv => 65 concepts (28 used for prediction) PRED predicted values (max 10 best out of 900): 04glx0 (0.40 #815, 0.33 #2246, 0.33 #1291), 097h2 (0.40 #864, 0.33 #2295, 0.33 #1340), 043qqt5 (0.40 #911, 0.33 #1387, 0.33 #198), 01fs__ (0.40 #828, 0.33 #1304, 0.25 #2021), 0q9jk (0.40 #843, 0.33 #1319, 0.25 #2036), 0124k9 (0.33 #1447, 0.33 #19, 0.25 #494), 015g28 (0.33 #1481, 0.33 #53, 0.25 #528), 0bx_hnp (0.33 #1602, 0.33 #174, 0.25 #649), 017dcd (0.33 #1190, 0.25 #1907, 0.25 #476), 06f0k (0.33 #210, 0.25 #685, 0.20 #1161) >> Best rule #815 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 05gnf; >> query: (?x10344, 04glx0) <- program(?x10344, ?x10250), program(?x10344, ?x6597), program(?x10344, ?x2777), titles(?x512, ?x2777), program(?x13510, ?x10250), award_winner(?x8762, ?x10344), titles(?x512, ?x5152), titles(?x512, ?x3157), titles(?x512, ?x1493), ?x1493 = 05j82v, genre(?x6597, ?x53), actor(?x6597, ?x988), genre(?x2777, ?x258), ?x3157 = 0ywrc, ?x5152 = 08sfxj >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #6435 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 35 *> proper extension: 01l50r; *> query: (?x10344, ?x631) <- program(?x10344, ?x2777), titles(?x512, ?x2777), genre(?x2777, ?x2480), titles(?x2480, ?x86), genre(?x631, ?x2480), languages(?x2777, ?x254) *> conf = 0.04 ranks of expected_values: 371 EVAL 01f2w0 program 0dl6fv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 65.000 28.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #22385-0223xd PRED entity: 0223xd PRED relation: disciplines_or_subjects PRED expected values: 04g51 => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 203): 04g51 (0.91 #601, 0.88 #777, 0.86 #519), 02xlf (0.90 #521, 0.89 #483, 0.70 #689), 0w7c (0.90 #561, 0.19 #862, 0.17 #900), 01hmnh (0.67 #469, 0.65 #589, 0.63 #675), 06n90 (0.61 #466, 0.59 #672, 0.57 #586), 02vxn (0.57 #537, 0.50 #334, 0.42 #797), 03nfmq (0.50 #390, 0.32 #620, 0.05 #412), 02j62 (0.33 #223, 0.32 #620, 0.25 #308), 04rjg (0.33 #179, 0.32 #620, 0.20 #426), 01mkq (0.33 #11, 0.32 #620, 0.11 #163) >> Best rule #601 for best value: >> intensional similarity = 33 >> extensional distance = 21 >> proper extension: 047xyn; 0265vt; 058bzgm; 01bb1c; >> query: (?x14751, 04g51) <- disciplines_or_subjects(?x14751, ?x13905), disciplines_or_subjects(?x14751, ?x2605), genre(?x9188, ?x13905), disciplines_or_subjects(?x13904, ?x13905), major_field_of_study(?x6584, ?x2605), major_field_of_study(?x5167, ?x2605), major_field_of_study(?x4750, ?x2605), major_field_of_study(?x3485, ?x2605), award_winner(?x13904, ?x8508), ?x8508 = 01zwy, student(?x2605, ?x5804), student(?x2605, ?x1328), student(?x2605, ?x1159), student(?x2605, ?x879), student(?x1526, ?x1328), student(?x4750, ?x2774), major_field_of_study(?x4750, ?x8925), major_field_of_study(?x2605, ?x254), ?x8925 = 01zc2w, colors(?x4750, ?x663), profession(?x1159, ?x3342), ?x3485 = 01mpwj, contains(?x94, ?x4750), major_field_of_study(?x1200, ?x2605), currency(?x5167, ?x170), organization(?x4750, ?x5487), student(?x12026, ?x1159), ?x879 = 01yk13, institution(?x620, ?x5167), location(?x1159, ?x2410), category(?x6584, ?x134), place_of_birth(?x5804, ?x9544), ?x1200 = 016t_3 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0223xd disciplines_or_subjects 04g51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 23.000 0.913 http://example.org/award/award_category/disciplines_or_subjects #22384-025jbj PRED entity: 025jbj PRED relation: place_of_birth PRED expected values: 01_d4 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 95): 02_286 (0.25 #19, 0.09 #3541, 0.09 #25369), 0sbv7 (0.12 #622, 0.01 #4849), 0gkgp (0.12 #350, 0.01 #4577), 0k049 (0.06 #33806, 0.06 #35217, 0.06 #28169), 0cr3d (0.06 #1503, 0.05 #2912, 0.05 #3616), 01_d4 (0.06 #6405, 0.06 #2179, 0.05 #10631), 0dclg (0.06 #2191, 0.03 #782, 0.02 #8530), 030qb3t (0.05 #52878, 0.04 #17659, 0.04 #18363), 0cc56 (0.04 #4964, 0.04 #7780, 0.03 #2146), 01531 (0.04 #5740, 0.03 #809, 0.03 #9965) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 09r9m7; >> query: (?x8426, 02_286) <- nominated_for(?x8426, ?x4513), profession(?x8426, ?x524), ?x4513 = 05dmmc, gender(?x8426, ?x231) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #6405 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 136 *> proper extension: 0grmhb; 0gqrb; 01rw116; 07zhd7; 02rf51g; *> query: (?x8426, 01_d4) <- award_winner(?x11348, ?x8426), people(?x6260, ?x8426), type_of_union(?x8426, ?x566) *> conf = 0.06 ranks of expected_values: 6 EVAL 025jbj place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 130.000 130.000 0.250 http://example.org/people/person/place_of_birth #22383-06rpd PRED entity: 06rpd PRED relation: team! PRED expected values: 059yj => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 8): 059yj (0.88 #53, 0.84 #109, 0.83 #141), 0h69c (0.43 #6, 0.28 #190, 0.27 #174), 0355pl (0.17 #419, 0.14 #683, 0.14 #555), 021q23 (0.12 #40, 0.08 #24, 0.08 #160), 07y9k (0.11 #836, 0.09 #860, 0.08 #868), 03zv9 (0.10 #682, 0.08 #762, 0.08 #794), 0356lc (0.07 #833, 0.05 #857, 0.04 #865), 01ddbl (0.06 #39, 0.06 #63, 0.05 #703) >> Best rule #53 for best value: >> intensional similarity = 8 >> extensional distance = 15 >> proper extension: 084l5; 05g49; 01c_d; >> query: (?x9172, 059yj) <- position(?x9172, ?x2247), position(?x9172, ?x1717), position(?x9172, ?x1114), ?x1114 = 047g8h, position_s(?x179, ?x1717), position_s(?x706, ?x1717), ?x2247 = 01_9c1, school(?x9172, ?x466) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06rpd team! 059yj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.882 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #22382-0pgjm PRED entity: 0pgjm PRED relation: profession PRED expected values: 0dxtg 02hrh1q 0np9r => 90 concepts (86 used for prediction) PRED predicted values (max 10 best out of 65): 02hrh1q (0.89 #9017, 0.88 #7566, 0.88 #5098), 0dxtg (0.85 #2191, 0.83 #3792, 0.83 #3209), 01d_h8 (0.70 #3201, 0.66 #2183, 0.65 #3784), 0nbcg (0.64 #464, 0.59 #3079, 0.57 #2497), 016z4k (0.63 #1310, 0.60 #1020, 0.59 #1165), 02jknp (0.57 #3203, 0.56 #2185, 0.53 #3786), 0dz3r (0.54 #147, 0.51 #1018, 0.50 #437), 03gjzk (0.44 #3794, 0.44 #3211, 0.40 #2193), 0n1h (0.34 #1028, 0.33 #1173, 0.33 #1318), 0np9r (0.33 #20, 0.29 #6245, 0.27 #9293) >> Best rule #9017 for best value: >> intensional similarity = 2 >> extensional distance = 2012 >> proper extension: 079vf; 05d7rk; 04yywz; 06688p; 01l1b90; 05bp8g; 05m63c; 01vw87c; 02g8h; 0d_84; ... >> query: (?x1345, 02hrh1q) <- film(?x1345, ?x2329), profession(?x1345, ?x1146) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 10 EVAL 0pgjm profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 90.000 86.000 0.892 http://example.org/people/person/profession EVAL 0pgjm profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 86.000 0.892 http://example.org/people/person/profession EVAL 0pgjm profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 86.000 0.892 http://example.org/people/person/profession #22381-047bynf PRED entity: 047bynf PRED relation: language PRED expected values: 02h40lc => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 46): 02h40lc (0.91 #1372, 0.90 #3186, 0.90 #3547), 064_8sq (0.24 #615, 0.21 #495, 0.19 #556), 04306rv (0.18 #478, 0.14 #539, 0.13 #2221), 02bjrlw (0.14 #1188, 0.11 #653, 0.10 #1250), 03_9r (0.12 #10, 0.09 #69, 0.07 #128), 06nm1 (0.12 #781, 0.12 #2047, 0.11 #1503), 0jzc (0.09 #79, 0.07 #138, 0.07 #375), 0653m (0.09 #71, 0.07 #130, 0.07 #189), 0x82 (0.09 #114, 0.07 #173, 0.07 #232), 01r2l (0.09 #84, 0.07 #143, 0.07 #202) >> Best rule #1372 for best value: >> intensional similarity = 5 >> extensional distance = 175 >> proper extension: 02vxq9m; 0ddt_; 02qzh2; 07bx6; >> query: (?x6636, 02h40lc) <- nominated_for(?x112, ?x6636), film_crew_role(?x6636, ?x137), film(?x7621, ?x6636), country(?x6636, ?x94), featured_film_locations(?x6636, ?x362) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047bynf language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.910 http://example.org/film/film/language #22380-09gq0x5 PRED entity: 09gq0x5 PRED relation: titles! PRED expected values: 07s9rl0 => 92 concepts (35 used for prediction) PRED predicted values (max 10 best out of 73): 07s9rl0 (0.38 #299, 0.38 #100, 0.36 #1), 01z4y (0.23 #33, 0.20 #1531, 0.19 #827), 03bxz7 (0.19 #298, 0.16 #2704), 02p0szs (0.19 #298, 0.16 #2704), 03g3w (0.19 #298, 0.16 #2704), 01jfsb (0.18 #18, 0.15 #216, 0.12 #1417), 04t36 (0.17 #106, 0.14 #7, 0.13 #305), 07c52 (0.15 #2932, 0.15 #2530, 0.14 #1727), 03mqtr (0.14 #43, 0.09 #638, 0.08 #341), 02n4kr (0.14 #12, 0.07 #111, 0.06 #310) >> Best rule #299 for best value: >> intensional similarity = 5 >> extensional distance = 50 >> proper extension: 06z8s_; 0gkz3nz; 03cvvlg; >> query: (?x1813, 07s9rl0) <- nominated_for(?x995, ?x1813), nominated_for(?x591, ?x1813), ?x995 = 099tbz, award(?x123, ?x591), ceremony(?x591, ?x78) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09gq0x5 titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 35.000 0.385 http://example.org/media_common/netflix_genre/titles #22379-0d35y PRED entity: 0d35y PRED relation: location_of_ceremony! PRED expected values: 08ff1k 02m30v => 276 concepts (234 used for prediction) PRED predicted values (max 10 best out of 260): 02m30v (0.11 #2521, 0.07 #3786, 0.06 #5299), 03j24kf (0.11 #2381, 0.03 #3646, 0.03 #5159), 0bkmf (0.08 #4291), 0dvld (0.07 #1156, 0.07 #3682, 0.06 #5195), 02fn5 (0.07 #1111, 0.06 #1616, 0.06 #1363), 03m2fg (0.07 #1190, 0.05 #2451, 0.03 #3716), 02yy8 (0.07 #1254, 0.05 #2515, 0.03 #3780), 03l26m (0.07 #1243, 0.05 #2504, 0.03 #3769), 0djywgn (0.07 #1201, 0.05 #2462, 0.03 #3727), 05cx7x (0.07 #1185, 0.05 #2446, 0.03 #3711) >> Best rule #2521 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 0jcg8; 0yl27; 09bkv; >> query: (?x4419, 02m30v) <- contains(?x4419, ?x9331), location(?x2135, ?x9331), place_of_death(?x12622, ?x4419) >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d35y location_of_ceremony! 02m30v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 276.000 234.000 0.105 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony EVAL 0d35y location_of_ceremony! 08ff1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 276.000 234.000 0.105 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #22378-0bwh6 PRED entity: 0bwh6 PRED relation: award_winner! PRED expected values: 027986c => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 286): 0cjyzs (0.50 #102, 0.05 #8086, 0.04 #3464), 02grdc (0.43 #452, 0.05 #2133, 0.03 #5493), 09sb52 (0.41 #421, 0.39 #7143, 0.37 #29832), 054krc (0.41 #421, 0.39 #7143, 0.37 #29832), 040njc (0.41 #421, 0.39 #7143, 0.37 #29832), 02pqp12 (0.41 #421, 0.39 #7143, 0.37 #29832), 02wkmx (0.41 #421, 0.39 #7143, 0.37 #29832), 0f4x7 (0.41 #421, 0.39 #7143, 0.37 #29832), 099ck7 (0.41 #421, 0.39 #7143, 0.37 #29832), 0fhpv4 (0.41 #421, 0.39 #7143, 0.37 #29832) >> Best rule #102 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 04wvhz; 0g2lq; >> query: (?x1365, 0cjyzs) <- produced_by(?x2488, ?x1365), ?x2488 = 02qr69m, award(?x1365, ?x198) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8032 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 194 *> proper extension: 0fvf9q; 04t2l2; 06dv3; 014zcr; 05ty4m; 01qscs; 02lfcm; 0z4s; 0bxtg; 07f8wg; ... *> query: (?x1365, 027986c) <- produced_by(?x1118, ?x1365), award(?x1365, ?x198), award_winner(?x538, ?x1365) *> conf = 0.06 ranks of expected_values: 90 EVAL 0bwh6 award_winner! 027986c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 128.000 128.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #22377-060__7 PRED entity: 060__7 PRED relation: film! PRED expected values: 0k525 08x5c_ => 73 concepts (49 used for prediction) PRED predicted values (max 10 best out of 994): 02jxmr (0.48 #6244, 0.48 #68677, 0.45 #72841), 06r_by (0.48 #6244, 0.48 #68677, 0.45 #72841), 016tw3 (0.48 #6244, 0.48 #68677, 0.45 #72841), 06s1qy (0.19 #54110, 0.12 #27053, 0.11 #64514), 01tnxc (0.15 #5587, 0.02 #1424, 0.01 #26396), 030_3z (0.12 #27053, 0.11 #64514, 0.11 #62432), 02q_cc (0.12 #27053, 0.11 #64514, 0.11 #62432), 05p5nc (0.12 #5365, 0.03 #101974, 0.02 #6246), 02nwxc (0.08 #5174, 0.02 #3091, 0.02 #23902), 01q6bg (0.07 #4163, 0.06 #45785, 0.05 #68679) >> Best rule #6244 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 01gglm; >> query: (?x8557, ?x1104) <- nominated_for(?x1104, ?x8557), film(?x4681, ?x8557), award_nominee(?x4681, ?x2900), ?x2900 = 02j9lm >> conf = 0.48 => this is the best rule for 3 predicted values *> Best rule #6006 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 01gglm; *> query: (?x8557, 0k525) <- nominated_for(?x1104, ?x8557), film(?x4681, ?x8557), award_nominee(?x4681, ?x2900), ?x2900 = 02j9lm *> conf = 0.02 ranks of expected_values: 734 EVAL 060__7 film! 08x5c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 49.000 0.480 http://example.org/film/actor/film./film/performance/film EVAL 060__7 film! 0k525 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 49.000 0.480 http://example.org/film/actor/film./film/performance/film #22376-072vj PRED entity: 072vj PRED relation: produced_by! PRED expected values: 045j3w => 127 concepts (73 used for prediction) PRED predicted values (max 10 best out of 459): 0340hj (0.44 #12297, 0.44 #12298, 0.43 #5675), 04s1zr (0.44 #12297, 0.44 #12298, 0.36 #11351), 0fy34l (0.44 #12297, 0.44 #12298, 0.36 #11351), 012s1d (0.44 #12297, 0.44 #12298, 0.36 #11350), 02wgk1 (0.40 #26485, 0.39 #22701, 0.37 #7567), 0cc5mcj (0.06 #1157, 0.06 #212, 0.02 #6833), 03bzyn4 (0.06 #1778, 0.02 #22588, 0.02 #7454), 05h43ls (0.06 #1171, 0.02 #21981, 0.02 #6847), 0djb3vw (0.06 #47, 0.03 #992, 0.02 #5722), 0b6l1st (0.06 #679, 0.03 #1624, 0.02 #30953) >> Best rule #12297 for best value: >> intensional similarity = 5 >> extensional distance = 162 >> proper extension: 0py5b; >> query: (?x12894, ?x1511) <- profession(?x12894, ?x319), film(?x12894, ?x5305), film(?x12894, ?x1511), film(?x123, ?x5305), produced_by(?x8414, ?x12894) >> conf = 0.44 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 072vj produced_by! 045j3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 73.000 0.444 http://example.org/film/film/produced_by #22375-0d060g PRED entity: 0d060g PRED relation: combatants! PRED expected values: 0gjw_ 03gqgt3 => 243 concepts (243 used for prediction) PRED predicted values (max 10 best out of 60): 0gjw_ (0.67 #397, 0.38 #153, 0.18 #1010), 01fc7p (0.67 #367, 0.20 #858, 0.16 #3484), 08qz1l (0.67 #406, 0.16 #3523, 0.15 #897), 0dl4z (0.62 #2200, 0.61 #2384, 0.61 #2446), 048n7 (0.62 #2200, 0.61 #2384, 0.61 #2446), 0cm2xh (0.50 #133, 0.44 #377, 0.35 #868), 03gqgt3 (0.48 #1092, 0.44 #542, 0.42 #1519), 01gjd0 (0.38 #124, 0.33 #368, 0.25 #859), 0gfq9 (0.38 #128, 0.33 #372, 0.15 #863), 018w0j (0.38 #156, 0.23 #1013, 0.22 #400) >> Best rule #397 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 01llxp; >> query: (?x279, 0gjw_) <- entity_involved(?x3278, ?x279), ?x3278 = 0dl4z >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 0d060g combatants! 03gqgt3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 243.000 243.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 0d060g combatants! 0gjw_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 243.000 243.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #22374-01s73z PRED entity: 01s73z PRED relation: place_founded PRED expected values: 0wqwj => 179 concepts (136 used for prediction) PRED predicted values (max 10 best out of 51): 02_286 (0.20 #145, 0.12 #1194, 0.11 #1720), 02cl1 (0.20 #143, 0.11 #339, 0.07 #1126), 030qb3t (0.17 #1526, 0.17 #214, 0.13 #1923), 0f2wj (0.17 #210, 0.09 #1522, 0.04 #3451), 0dclg (0.14 #2241, 0.11 #4378, 0.11 #1053), 0y1rf (0.11 #382, 0.09 #776, 0.08 #907), 0f04c (0.11 #351, 0.08 #876, 0.07 #1138), 01sn3 (0.11 #360, 0.07 #1147, 0.04 #1607), 06_kh (0.10 #531, 0.10 #466, 0.06 #1187), 0r5wt (0.10 #495, 0.06 #1216, 0.03 #1875) >> Best rule #145 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 03phgz; 0dq23; 01frpd; >> query: (?x5108, 02_286) <- state_province_region(?x5108, ?x3670), company(?x265, ?x5108), list(?x5108, ?x5997), ?x265 = 0dq3c, child(?x5108, ?x1104) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01s73z place_founded 0wqwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 179.000 136.000 0.200 http://example.org/organization/organization/place_founded #22373-01gzm2 PRED entity: 01gzm2 PRED relation: profession PRED expected values: 02hv44_ 0d8qb => 114 concepts (87 used for prediction) PRED predicted values (max 10 best out of 68): 03gjzk (0.47 #1739, 0.44 #3035, 0.44 #3323), 02krf9 (0.26 #3767, 0.23 #887, 0.22 #1751), 02hv44_ (0.23 #197, 0.17 #485, 0.14 #773), 018gz8 (0.22 #1021, 0.18 #733, 0.17 #1741), 0d1pc (0.20 #1198, 0.17 #4510, 0.12 #4942), 09jwl (0.18 #7935, 0.18 #5199, 0.17 #7215), 0q04f (0.13 #239, 0.06 #383, 0.06 #527), 0nbcg (0.12 #7947, 0.11 #11837, 0.11 #10108), 016z4k (0.11 #4468, 0.11 #7924, 0.10 #6916), 0dz3r (0.11 #7922, 0.11 #7202, 0.11 #10083) >> Best rule #1739 for best value: >> intensional similarity = 3 >> extensional distance = 169 >> proper extension: 03ys2f; 03ysmg; >> query: (?x1774, 03gjzk) <- award_nominee(?x5019, ?x1774), written_by(?x1318, ?x1774), student(?x5941, ?x1774) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #197 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 0fx02; *> query: (?x1774, 02hv44_) <- written_by(?x1318, ?x1774), student(?x5941, ?x1774), place_of_death(?x1774, ?x739) *> conf = 0.23 ranks of expected_values: 3, 20 EVAL 01gzm2 profession 0d8qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 114.000 87.000 0.474 http://example.org/people/person/profession EVAL 01gzm2 profession 02hv44_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 87.000 0.474 http://example.org/people/person/profession #22372-0g2lq PRED entity: 0g2lq PRED relation: location PRED expected values: 0r00l => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 133): 02_286 (0.24 #56919, 0.17 #52112, 0.15 #40898), 030qb3t (0.18 #56965, 0.17 #24119, 0.16 #24921), 04jpl (0.09 #56900, 0.06 #24054, 0.05 #79331), 01b8jj (0.09 #590, 0.03 #5397), 0cr3d (0.06 #47414, 0.05 #15367, 0.05 #41006), 059rby (0.06 #16, 0.04 #4823, 0.04 #56899), 01cx_ (0.06 #963, 0.04 #1764, 0.03 #7373), 0dclg (0.06 #917, 0.02 #56999, 0.02 #2519), 0cc56 (0.05 #56939, 0.04 #857, 0.04 #20888), 01n7q (0.04 #22496, 0.04 #24901, 0.03 #62) >> Best rule #56919 for best value: >> intensional similarity = 2 >> extensional distance = 1479 >> proper extension: 01h2_6; >> query: (?x7837, 02_286) <- location(?x7837, ?x682), place_of_death(?x199, ?x682) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #603 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 03ysmg; *> query: (?x7837, 0r00l) <- award_nominee(?x7837, ?x2015), award_winner(?x496, ?x7837), sibling(?x7837, ?x12566) *> conf = 0.03 ranks of expected_values: 19 EVAL 0g2lq location 0r00l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 110.000 110.000 0.236 http://example.org/people/person/places_lived./people/place_lived/location #22371-07nx9j PRED entity: 07nx9j PRED relation: place_of_birth PRED expected values: 071cn => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 44): 04rrd (0.28 #19734, 0.28 #9162, 0.27 #11278), 02_286 (0.09 #2133, 0.08 #8476, 0.08 #12003), 030qb3t (0.07 #1464, 0.07 #2168, 0.04 #27483), 01_d4 (0.06 #771, 0.04 #27483, 0.04 #1476), 0cc56 (0.06 #738, 0.04 #27483, 0.04 #1443), 0f2nf (0.06 #1052, 0.04 #27483, 0.04 #1757), 01531 (0.05 #1515, 0.05 #2219, 0.04 #27483), 0rh6k (0.04 #2820, 0.04 #27483, 0.04 #4934), 0cr3d (0.04 #27483, 0.04 #8551, 0.04 #27577), 01cx_ (0.04 #27483, 0.03 #814, 0.02 #1519) >> Best rule #19734 for best value: >> intensional similarity = 3 >> extensional distance = 1745 >> proper extension: 076df9; 0qkj7; >> query: (?x7585, ?x1767) <- gender(?x7585, ?x231), ?x231 = 05zppz, location(?x7585, ?x1767) >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07nx9j place_of_birth 071cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.282 http://example.org/people/person/place_of_birth #22370-02_cx_ PRED entity: 02_cx_ PRED relation: organization! PRED expected values: 07xl34 => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 15): 07xl34 (0.40 #10, 0.38 #154, 0.29 #22), 0dq_5 (0.24 #1304, 0.20 #1268, 0.18 #1184), 05k17c (0.12 #222, 0.12 #618, 0.12 #678), 0hm4q (0.06 #1087, 0.06 #1099, 0.05 #1027), 05c0jwl (0.04 #868, 0.04 #1024, 0.04 #1084), 01t7n9 (0.02 #1597, 0.02 #1695, 0.02 #1708), 09n5b9 (0.02 #1597, 0.02 #1695, 0.02 #1708), 02079p (0.02 #1597, 0.02 #1695, 0.02 #1708), 0789n (0.02 #1597, 0.02 #1695, 0.02 #1708), 0f6c3 (0.02 #1597, 0.02 #1695, 0.02 #1708) >> Best rule #10 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 019_6d; >> query: (?x6280, 07xl34) <- school_type(?x6280, ?x3092), state_province_region(?x6280, ?x3778), ?x3778 = 07h34, currency(?x6280, ?x170), contains(?x94, ?x6280) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_cx_ organization! 07xl34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.400 http://example.org/organization/role/leaders./organization/leadership/organization #22369-0gd_s PRED entity: 0gd_s PRED relation: influenced_by PRED expected values: 0dzkq => 132 concepts (46 used for prediction) PRED predicted values (max 10 best out of 326): 032l1 (0.53 #3517, 0.33 #88, 0.25 #1802), 040db (0.50 #911, 0.33 #55, 0.25 #2627), 0g5ff (0.33 #2764, 0.32 #4479, 0.30 #2335), 02lt8 (0.33 #119, 0.29 #3548, 0.25 #975), 01tz6vs (0.33 #175, 0.29 #3604, 0.25 #1031), 081k8 (0.33 #155, 0.25 #1869, 0.25 #1011), 01v9724 (0.33 #176, 0.25 #1032, 0.24 #3605), 04xjp (0.33 #56, 0.25 #912, 0.18 #1286), 03f0324 (0.33 #151, 0.25 #1007, 0.18 #3580), 084w8 (0.33 #3, 0.25 #859, 0.14 #5578) >> Best rule #3517 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 014dq7; 034bs; 0zm1; 03f0324; 058vp; 03_87; 0ct9_; 01rgr; >> query: (?x9284, 032l1) <- influenced_by(?x1752, ?x9284), influenced_by(?x9284, ?x8232), influenced_by(?x9284, ?x2994), influenced_by(?x8232, ?x3712), ?x2994 = 0379s >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #93 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0683n; *> query: (?x9284, 0dzkq) <- influenced_by(?x1752, ?x9284), influenced_by(?x9284, ?x8232), ?x8232 = 043tg, award(?x9284, ?x575) *> conf = 0.33 ranks of expected_values: 19 EVAL 0gd_s influenced_by 0dzkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 132.000 46.000 0.529 http://example.org/influence/influence_node/influenced_by #22368-05k7sb PRED entity: 05k7sb PRED relation: state! PRED expected values: 0tzt_ => 166 concepts (131 used for prediction) PRED predicted values (max 10 best out of 383): 0d739 (0.27 #13279, 0.18 #11506, 0.16 #8253), 0tygl (0.27 #13279, 0.18 #11506, 0.16 #8253), 0t_07 (0.27 #13279, 0.18 #11506, 0.16 #8253), 03ksy (0.27 #13279, 0.18 #11506, 0.16 #8253), 0t_3w (0.27 #13279, 0.18 #11506, 0.16 #8253), 0k3j0 (0.18 #11506, 0.16 #8253, 0.16 #3243), 0tzt_ (0.18 #11506, 0.16 #8253, 0.15 #7663), 0hz35 (0.18 #11506, 0.16 #8253, 0.15 #7663), 0t_48 (0.18 #11506, 0.16 #8253, 0.15 #7663), 0tzls (0.18 #11506, 0.16 #8253, 0.15 #7663) >> Best rule #13279 for best value: >> intensional similarity = 3 >> extensional distance = 86 >> proper extension: 01w0v; 0g14f; >> query: (?x2020, ?x3764) <- contains(?x2020, ?x3764), location(?x1204, ?x3764), state_province_region(?x1520, ?x2020) >> conf = 0.27 => this is the best rule for 5 predicted values *> Best rule #11506 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 0d9rp; *> query: (?x2020, ?x1151) <- contains(?x2020, ?x1151), state(?x12697, ?x2020), contains(?x94, ?x2020) *> conf = 0.18 ranks of expected_values: 7 EVAL 05k7sb state! 0tzt_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 166.000 131.000 0.269 http://example.org/base/biblioness/bibs_location/state #22367-0tz1x PRED entity: 0tz1x PRED relation: time_zones PRED expected values: 02hcv8 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.87 #94, 0.85 #16, 0.82 #107), 02lcqs (0.26 #174, 0.21 #161, 0.18 #187), 02fqwt (0.20 #183, 0.18 #248, 0.17 #157), 02hczc (0.17 #963, 0.16 #1003, 0.09 #132), 02lcrv (0.17 #963, 0.16 #1003, 0.01 #137), 02llzg (0.05 #993, 0.05 #953, 0.05 #680), 03bdv (0.04 #318, 0.04 #344, 0.04 #357), 042g7t (0.04 #115, 0.02 #63, 0.02 #89), 03plfd (0.01 #999, 0.01 #959, 0.01 #1104) >> Best rule #94 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 01tlmw; 02cl1; 0mp3l; 01qh7; 0pc7r; 01cx_; 01m1_t; 0mzvm; 0mmzt; 0t_gg; ... >> query: (?x3115, 02hcv8) <- currency(?x3115, ?x170), ?x170 = 09nqf, contains(?x94, ?x3115), county(?x3115, ?x6905) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tz1x time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.873 http://example.org/location/location/time_zones #22366-01wg6y PRED entity: 01wg6y PRED relation: award PRED expected values: 02gdjb => 143 concepts (92 used for prediction) PRED predicted values (max 10 best out of 296): 01ckrr (0.38 #2663, 0.33 #3473, 0.29 #3878), 01by1l (0.37 #7403, 0.32 #2948, 0.29 #6998), 01bgqh (0.33 #853, 0.32 #7333, 0.32 #2878), 0c4z8 (0.33 #7362, 0.32 #2907, 0.29 #4122), 03qbh5 (0.32 #3042, 0.25 #7497, 0.24 #2232), 02gdjb (0.30 #627, 0.27 #1032, 0.25 #222), 054ks3 (0.29 #4193, 0.29 #2573, 0.25 #3383), 025m8y (0.29 #4150, 0.23 #2935, 0.20 #9820), 01c92g (0.27 #7388, 0.25 #98, 0.23 #2933), 02tj96 (0.27 #1183, 0.20 #778, 0.12 #3613) >> Best rule #2663 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 0pgjm; 021bk; 01vtqml; >> query: (?x8978, 01ckrr) <- profession(?x8978, ?x1614), profession(?x8978, ?x1183), role(?x8978, ?x228), award_winner(?x725, ?x8978), ?x1614 = 01c72t, ?x1183 = 09jwl >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #627 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 02mslq; *> query: (?x8978, 02gdjb) <- artists(?x13572, ?x8978), ?x13572 = 037n97, place_of_birth(?x8978, ?x2633), artist(?x2299, ?x8978) *> conf = 0.30 ranks of expected_values: 6 EVAL 01wg6y award 02gdjb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 143.000 92.000 0.381 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22365-04j53 PRED entity: 04j53 PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 20): 060bp (0.82 #243, 0.77 #199, 0.76 #375), 060c4 (0.78 #69, 0.76 #267, 0.74 #465), 0f6c3 (0.56 #403, 0.49 #601, 0.48 #557), 09n5b9 (0.56 #407, 0.49 #605, 0.48 #561), 0fkvn (0.46 #400, 0.41 #598, 0.40 #972), 0pqc5 (0.37 #1413, 0.36 #2954, 0.34 #1215), 0fj45 (0.33 #217, 0.28 #261, 0.21 #547), 0p5vf (0.21 #298, 0.20 #232, 0.16 #628), 01zq91 (0.20 #14, 0.14 #36, 0.14 #300), 0fkzq (0.19 #412, 0.16 #610, 0.16 #566) >> Best rule #243 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 049nq; >> query: (?x3040, 060bp) <- form_of_government(?x3040, ?x1926), ?x1926 = 018wl5, administrative_parent(?x3040, ?x551) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04j53 jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.825 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #22364-03n3gl PRED entity: 03n3gl PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 25): 0ch6mp2 (0.84 #339, 0.79 #505, 0.78 #875), 09zzb8 (0.83 #333, 0.81 #533, 0.79 #233), 02rh1dz (0.30 #341, 0.22 #507, 0.21 #641), 02ynfr (0.23 #345, 0.22 #511, 0.22 #645), 0215hd (0.20 #514, 0.18 #648, 0.17 #348), 0d2b38 (0.18 #521, 0.16 #655, 0.16 #355), 089g0h (0.18 #515, 0.15 #315, 0.15 #649), 01xy5l_ (0.17 #509, 0.15 #643, 0.15 #343), 02vs3x5 (0.12 #21, 0.12 #153, 0.09 #87), 02_n3z (0.11 #500, 0.11 #234, 0.10 #568) >> Best rule #339 for best value: >> intensional similarity = 5 >> extensional distance = 164 >> proper extension: 08c6k9; 0n_hp; 04hk0w; >> query: (?x6365, 0ch6mp2) <- language(?x6365, ?x5359), film_crew_role(?x6365, ?x2154), film_crew_role(?x6365, ?x2095), ?x2154 = 01vx2h, ?x2095 = 0dxtw >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03n3gl film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.843 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22363-0405l PRED entity: 0405l PRED relation: student! PRED expected values: 017j69 => 136 concepts (73 used for prediction) PRED predicted values (max 10 best out of 200): 08815 (0.10 #2, 0.07 #2094, 0.06 #1571), 05nrkb (0.10 #868, 0.05 #9761, 0.03 #2961), 09f2j (0.07 #158, 0.05 #1204, 0.05 #10097), 0fr9jp (0.07 #341, 0.03 #10280, 0.02 #18654), 026gvfj (0.07 #110, 0.03 #1156, 0.02 #3249), 01qd_r (0.07 #277, 0.03 #1323, 0.02 #4985), 02s62q (0.07 #52, 0.03 #1621, 0.01 #2144), 02fy0z (0.07 #93, 0.02 #3232, 0.02 #4278), 03ksy (0.06 #1674, 0.06 #9521, 0.05 #11614), 065y4w7 (0.06 #12569, 0.05 #1583, 0.05 #31410) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 03ftmg; 0239zv; >> query: (?x11079, 08815) <- gender(?x11079, ?x231), profession(?x11079, ?x524), ?x524 = 02jknp, student(?x5864, ?x11079) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #1190 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 01nbq4; *> query: (?x11079, 017j69) <- location(?x11079, ?x739), student(?x5864, ?x11079), languages(?x11079, ?x254) *> conf = 0.04 ranks of expected_values: 18 EVAL 0405l student! 017j69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 136.000 73.000 0.103 http://example.org/education/educational_institution/students_graduates./education/education/student #22362-013m4v PRED entity: 013m4v PRED relation: contains! PRED expected values: 07b_l => 168 concepts (125 used for prediction) PRED predicted values (max 10 best out of 260): 07b_l (0.76 #21524, 0.67 #24213, 0.60 #84239), 02qkt (0.50 #46054, 0.48 #42471, 0.42 #3933), 059g4 (0.50 #1357, 0.17 #4947, 0.14 #14811), 0ms1n (0.45 #111120, 0.41 #8074, 0.08 #7853), 0ms6_ (0.45 #111120, 0.18 #3586, 0.11 #1791), 04_1l0v (0.32 #32725, 0.30 #47055, 0.26 #56012), 01n7q (0.31 #9047, 0.29 #29665, 0.28 #26082), 06pvr (0.31 #7341, 0.28 #9135, 0.22 #23480), 0d060g (0.25 #907, 0.12 #10777, 0.10 #28704), 02j9z (0.24 #45735, 0.23 #34093, 0.23 #42152) >> Best rule #21524 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 01dbxr; 0qpsn; 05bkf; 013d_f; >> query: (?x12915, ?x3634) <- adjoins(?x12915, ?x5719), time_zones(?x12915, ?x1638), state(?x5719, ?x3634) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013m4v contains! 07b_l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 125.000 0.759 http://example.org/location/location/contains #22361-09qs08 PRED entity: 09qs08 PRED relation: nominated_for PRED expected values: 05zr0xl => 48 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1359): 05f4vxd (0.81 #14156, 0.77 #14155, 0.68 #26758), 01q_y0 (0.81 #14156, 0.77 #14155, 0.68 #26758), 01s81 (0.81 #14156, 0.77 #14155, 0.68 #26758), 01lv85 (0.81 #14156, 0.77 #14155, 0.68 #26758), 0l76z (0.81 #14156, 0.77 #14155, 0.68 #26758), 05zr0xl (0.81 #14156, 0.77 #14155, 0.68 #26758), 0kfpm (0.81 #14156, 0.77 #14155, 0.68 #26758), 01rp13 (0.43 #7280, 0.33 #4134, 0.20 #5706), 02czd5 (0.40 #5974, 0.33 #1258, 0.15 #9121), 02rcwq0 (0.37 #12581, 0.33 #2352, 0.19 #8643) >> Best rule #14156 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 0fqnzts; >> query: (?x2603, ?x7551) <- award(?x1057, ?x2603), award(?x7551, ?x2603), genre(?x7551, ?x258), ceremony(?x2603, ?x1265) >> conf = 0.81 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 09qs08 nominated_for 05zr0xl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 48.000 18.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22360-0bw6y PRED entity: 0bw6y PRED relation: award_winner! PRED expected values: 02py7pj => 141 concepts (117 used for prediction) PRED predicted values (max 10 best out of 310): 02z1nbg (0.50 #621, 0.27 #2770, 0.16 #3628), 0gqwc (0.44 #6015, 0.36 #49806, 0.36 #2651), 0f4x7 (0.41 #7763, 0.39 #1750, 0.38 #14200), 04kxsb (0.41 #7856, 0.22 #1843, 0.17 #14293), 027c95y (0.39 #1874, 0.33 #6171, 0.33 #7887), 094qd5 (0.32 #2622, 0.11 #2192, 0.07 #6918), 02y_j8g (0.32 #2858, 0.11 #2428, 0.07 #4146), 0cqgl9 (0.32 #2765, 0.08 #1476, 0.07 #4053), 027986c (0.28 #1767, 0.23 #7780, 0.20 #14217), 09cn0c (0.27 #2894, 0.11 #2464, 0.07 #4182) >> Best rule #621 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01gvr1; >> query: (?x6744, 02z1nbg) <- spouse(?x3525, ?x6744), award(?x6744, ?x1245), award(?x6744, ?x686), ?x686 = 0bdw1g, ?x1245 = 0gqwc >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #305 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09x8ms; *> query: (?x6744, 02py7pj) <- participant(?x6744, ?x6934), student(?x10621, ?x6744), profession(?x6744, ?x1032), ?x6934 = 0cgbf *> conf = 0.25 ranks of expected_values: 12 EVAL 0bw6y award_winner! 02py7pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 141.000 117.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #22359-02p76f9 PRED entity: 02p76f9 PRED relation: featured_film_locations PRED expected values: 0f2tj => 126 concepts (104 used for prediction) PRED predicted values (max 10 best out of 88): 02_286 (0.45 #13616, 0.33 #13138, 0.31 #15766), 030qb3t (0.20 #753, 0.20 #277, 0.14 #13157), 0h7h6 (0.20 #281, 0.05 #13639, 0.03 #13878), 0135g (0.20 #342, 0.03 #1770, 0.03 #2485), 04jpl (0.12 #15516, 0.12 #17189, 0.12 #16711), 03gh4 (0.10 #828, 0.05 #1780, 0.05 #2495), 0b90_r (0.10 #480, 0.04 #956, 0.03 #1194), 0dclg (0.10 #529, 0.04 #2196, 0.03 #1958), 095w_ (0.10 #512, 0.01 #13154, 0.01 #5995), 0djd3 (0.10 #835) >> Best rule #13616 for best value: >> intensional similarity = 3 >> extensional distance = 366 >> proper extension: 0413cff; >> query: (?x8284, 02_286) <- currency(?x8284, ?x170), featured_film_locations(?x8284, ?x14186), adjoins(?x14186, ?x1879) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #24599 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1313 *> proper extension: 099bhp; *> query: (?x8284, ?x191) <- film(?x6187, ?x8284), film(?x382, ?x8284), location(?x6187, ?x191), award_winner(?x2090, ?x6187) *> conf = 0.03 ranks of expected_values: 40 EVAL 02p76f9 featured_film_locations 0f2tj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 126.000 104.000 0.454 http://example.org/film/film/featured_film_locations #22358-0c2tf PRED entity: 0c2tf PRED relation: participant! PRED expected values: 0bdt8 => 182 concepts (150 used for prediction) PRED predicted values (max 10 best out of 387): 0bdt8 (0.81 #27385, 0.81 #44987, 0.81 #36510), 01dvms (0.81 #27385, 0.81 #44987, 0.81 #36510), 0gmtm (0.28 #20866, 0.27 #6520, 0.27 #11736), 012gbb (0.28 #20866, 0.27 #6520, 0.27 #11736), 022q4j (0.14 #2539, 0.03 #5800, 0.02 #27969), 02kz_ (0.10 #2607, 0.08 #29992, 0.08 #35857), 0m6x4 (0.10 #2522, 0.08 #1870, 0.03 #4478), 0cgbf (0.10 #2410, 0.08 #1758, 0.02 #3714), 02l0sf (0.10 #2402, 0.08 #1750, 0.02 #3706), 0bkmf (0.10 #2521, 0.08 #1869, 0.02 #3825) >> Best rule #27385 for best value: >> intensional similarity = 3 >> extensional distance = 269 >> proper extension: 044zvm; >> query: (?x7676, ?x4057) <- nationality(?x7676, ?x94), award_winner(?x3369, ?x7676), participant(?x7676, ?x4057) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0c2tf participant! 0bdt8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 150.000 0.815 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #22357-048q6x PRED entity: 048q6x PRED relation: award_nominee! PRED expected values: 09btt1 => 92 concepts (28 used for prediction) PRED predicted values (max 10 best out of 826): 09btt1 (0.81 #46527, 0.80 #39548, 0.76 #62810), 044lyq (0.81 #46527, 0.80 #39548, 0.76 #62810), 048q6x (0.64 #1196, 0.60 #3522, 0.38 #11632), 04kr63w (0.53 #3612, 0.45 #1286, 0.38 #11632), 05lb87 (0.47 #2602, 0.45 #276, 0.38 #11632), 04gnbv1 (0.34 #9305, 0.18 #34895, 0.16 #55833), 02f9wb (0.34 #9305), 02d6cy (0.34 #9305), 0151w_ (0.32 #4654, 0.27 #44201, 0.20 #53507), 0f6_dy (0.32 #4654, 0.27 #44201, 0.20 #53507) >> Best rule #46527 for best value: >> intensional similarity = 3 >> extensional distance = 1024 >> proper extension: 0l56b; 01wn718; 0b80__; 01m3b1t; 03d1y3; 01933d; 01dhpj; 07sbk; 0gyy0; 03y3dk; ... >> query: (?x5041, ?x3789) <- award_winner(?x458, ?x5041), award_nominee(?x5041, ?x3789), award_winner(?x9306, ?x5041) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 048q6x award_nominee! 09btt1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 28.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22356-01c7qd PRED entity: 01c7qd PRED relation: performance_role PRED expected values: 03bx0bm => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 13): 03bx0bm (0.07 #768, 0.06 #900, 0.06 #1252), 0l14md (0.05 #358, 0.05 #270, 0.05 #314), 026t6 (0.04 #576, 0.03 #1634, 0.03 #1944), 05r5c (0.04 #95, 0.02 #139, 0.02 #359), 0l14qv (0.03 #445, 0.03 #356, 0.02 #1636), 013y1f (0.03 #371, 0.02 #945, 0.02 #460), 02sgy (0.02 #225, 0.02 #269, 0.02 #313), 0342h (0.02 #267, 0.02 #311, 0.02 #355), 042v_gx (0.01 #184, 0.01 #934, 0.01 #228), 03gvt (0.01 #523) >> Best rule #768 for best value: >> intensional similarity = 3 >> extensional distance = 210 >> proper extension: 05drq5; >> query: (?x9834, 03bx0bm) <- award_winner(?x1869, ?x9834), award_nominee(?x3410, ?x9834), music(?x124, ?x3410) >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c7qd performance_role 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.066 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #22355-02tn0_ PRED entity: 02tn0_ PRED relation: people! PRED expected values: 02w7gg => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 36): 02w7gg (0.23 #3698, 0.11 #695, 0.10 #464), 041rx (0.23 #543, 0.20 #235, 0.18 #1082), 0x67 (0.20 #3321, 0.20 #164, 0.18 #4091), 02ctzb (0.13 #169, 0.07 #92, 0.04 #2402), 01qhm_ (0.13 #160, 0.04 #2393, 0.04 #622), 0dryh9k (0.13 #93, 0.04 #1017, 0.03 #4790), 033tf_ (0.12 #1008, 0.12 #469, 0.10 #392), 0xnvg (0.09 #706, 0.09 #2400, 0.07 #3247), 048z7l (0.07 #425, 0.07 #733, 0.06 #1118), 07bch9 (0.07 #485, 0.05 #4258, 0.05 #3565) >> Best rule #3698 for best value: >> intensional similarity = 2 >> extensional distance = 394 >> proper extension: 0784v1; 07m69t; >> query: (?x9785, 02w7gg) <- nationality(?x9785, ?x1310), ?x1310 = 02jx1 >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tn0_ people! 02w7gg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.235 http://example.org/people/ethnicity/people #22354-0443y3 PRED entity: 0443y3 PRED relation: type_of_union PRED expected values: 04ztj 01g63y => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #161, 0.73 #225, 0.73 #237), 01g63y (0.45 #417, 0.21 #22, 0.17 #70) >> Best rule #161 for best value: >> intensional similarity = 2 >> extensional distance = 556 >> proper extension: 026lj; 0mj0c; 03_hd; 07c37; 021r7r; 07t2k; 03_js; 0hr3g; 042f1; 085q5; ... >> query: (?x2129, 04ztj) <- student(?x2909, ?x2129), religion(?x2129, ?x1985) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0443y3 type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.738 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 0443y3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.738 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22353-04l5d0 PRED entity: 04l5d0 PRED relation: team! PRED expected values: 02qvdc => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 50): 02sdk9v (0.90 #2470, 0.88 #2669, 0.77 #3773), 02qvdc (0.88 #1440, 0.88 #1390, 0.85 #1041), 02qvkj (0.80 #2818, 0.66 #2518, 0.59 #1211), 02nzb8 (0.80 #2469, 0.79 #2668, 0.73 #3772), 02_j1w (0.79 #2673, 0.75 #2474, 0.74 #3777), 0dgrmp (0.72 #2671, 0.72 #2472, 0.64 #2422), 02g_7z (0.59 #1685, 0.43 #274, 0.36 #2843), 01r3hr (0.57 #1664, 0.36 #2822, 0.33 #3), 05b3ts (0.57 #1682, 0.33 #21, 0.29 #271), 047g8h (0.55 #1670, 0.33 #9, 0.32 #2828) >> Best rule #2470 for best value: >> intensional similarity = 15 >> extensional distance = 86 >> proper extension: 02b15h; 04b4yg; 03yl2t; 01l3vx; 044l47; 02rytm; 03_r_5; 03_9hm; 0212mp; 02s2lg; ... >> query: (?x9547, 02sdk9v) <- team(?x3299, ?x9547), team(?x2918, ?x9547), team(?x2918, ?x13326), team(?x2918, ?x8541), team(?x2918, ?x5233), team(?x13270, ?x9547), sport(?x13326, ?x453), team(?x11825, ?x8541), colors(?x5233, ?x332), colors(?x13326, ?x4557), ?x4557 = 019sc, ?x332 = 01l849, teams(?x3689, ?x5233), position(?x8899, ?x3299), category(?x13270, ?x134) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1440 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 32 *> proper extension: 0gvt8sz; *> query: (?x9547, 02qvdc) <- team(?x3724, ?x9547), team(?x3299, ?x9547), team(?x2918, ?x9547), ?x2918 = 02qvl7, team(?x3299, ?x11995), team(?x3299, ?x10950), team(?x3299, ?x10713), team(?x3299, ?x7174), team(?x3299, ?x5233), team(?x3299, ?x3298), ?x10713 = 0gx159f, ?x11995 = 048ldh, ?x10950 = 0jnr_, position(?x14015, ?x3299), position(?x5380, ?x3299), ?x5380 = 0b6p3qf, position(?x5234, ?x3299), ?x14015 = 0jnlm, team(?x3724, ?x11368), team(?x3724, ?x8892), colors(?x3298, ?x663), ?x7174 = 05pcr, ?x11368 = 032yps, ?x5233 = 0j5m6, ?x8892 = 02fp3, position(?x6640, ?x3724) *> conf = 0.88 ranks of expected_values: 2 EVAL 04l5d0 team! 02qvdc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 77.000 0.898 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #22352-07sgdw PRED entity: 07sgdw PRED relation: genre PRED expected values: 0lsxr => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 88): 02kdv5l (0.67 #125, 0.28 #369, 0.28 #613), 07s9rl0 (0.60 #3175, 0.60 #1953, 0.58 #4277), 09q17 (0.52 #4276, 0.51 #3298, 0.48 #3174), 03k9fj (0.36 #12, 0.25 #256, 0.21 #3799), 01jfsb (0.33 #135, 0.31 #989, 0.30 #1843), 02l7c8 (0.28 #3191, 0.28 #4170, 0.27 #2579), 06n90 (0.27 #14, 0.21 #258, 0.16 #990), 0gf28 (0.27 #66, 0.18 #310, 0.07 #1408), 0lsxr (0.25 #985, 0.25 #497, 0.24 #863), 04xvlr (0.18 #2, 0.17 #3176, 0.16 #2564) >> Best rule #125 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 08984j; 03clwtw; >> query: (?x4749, 02kdv5l) <- film(?x5338, ?x4749), film(?x9164, ?x4749), ?x5338 = 0gn30 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #985 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 144 *> proper extension: 0dnvn3; 03s6l2; 026p_bs; 0d_wms; 02vrgnr; 0bbw2z6; 0k7tq; 042fgh; 023g6w; 025twgf; ... *> query: (?x4749, 0lsxr) <- film(?x806, ?x4749), honored_for(?x4749, ?x188), language(?x4749, ?x90) *> conf = 0.25 ranks of expected_values: 9 EVAL 07sgdw genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 70.000 70.000 0.667 http://example.org/film/film/genre #22351-0jzw PRED entity: 0jzw PRED relation: nominated_for! PRED expected values: 0gq_v 0gs9p 0k611 02qvyrt => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 185): 02wkmx (0.68 #4541, 0.68 #10683, 0.67 #12276), 0gs9p (0.58 #4369, 0.55 #5278, 0.40 #2553), 099c8n (0.54 #51, 0.24 #5273, 0.22 #1186), 0gq_v (0.50 #4331, 0.47 #1607, 0.32 #5240), 0k611 (0.49 #4377, 0.47 #5286, 0.35 #2561), 04dn09n (0.39 #5253, 0.37 #4344, 0.31 #31), 09sdmz (0.38 #134, 0.19 #17959, 0.12 #361), 0f4x7 (0.37 #4336, 0.32 #5245, 0.31 #23), 0gs96 (0.37 #4394, 0.29 #1670, 0.24 #5303), 03hkv_r (0.31 #13, 0.17 #5235, 0.15 #4326) >> Best rule #4541 for best value: >> intensional similarity = 4 >> extensional distance = 220 >> proper extension: 0c5qvw; >> query: (?x810, ?x372) <- award(?x810, ?x1107), award(?x810, ?x372), nominated_for(?x1107, ?x6680), ?x6680 = 01k7b0 >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #4369 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 220 *> proper extension: 0c5qvw; *> query: (?x810, 0gs9p) <- award(?x810, ?x1107), nominated_for(?x1107, ?x6680), ?x6680 = 01k7b0 *> conf = 0.58 ranks of expected_values: 2, 4, 5, 15 EVAL 0jzw nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 89.000 89.000 0.682 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0jzw nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 89.000 0.682 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0jzw nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.682 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0jzw nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 89.000 0.682 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22350-05w6cw PRED entity: 05w6cw PRED relation: origin PRED expected values: 02_286 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 110): 02_286 (0.11 #488, 0.10 #1667, 0.09 #7795), 04jpl (0.09 #8492, 0.07 #11787, 0.07 #5429), 013yq (0.08 #516, 0.06 #1695, 0.04 #751), 02dtg (0.08 #2368, 0.05 #1189, 0.05 #1896), 0cr3d (0.06 #1706, 0.06 #1941, 0.05 #2648), 09c7w0 (0.05 #9663, 0.05 #9192, 0.04 #8015), 0f2tj (0.05 #2947, 0.05 #3183, 0.03 #2003), 0vzm (0.05 #538, 0.02 #1952, 0.02 #7610), 01_d4 (0.05 #1690, 0.03 #2397, 0.03 #275), 0dclg (0.05 #1694, 0.03 #2401, 0.03 #7587) >> Best rule #488 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 044mfr; >> query: (?x8365, 02_286) <- origin(?x8365, ?x1523), participant(?x8365, ?x1093), artists(?x671, ?x8365), profession(?x8365, ?x220) >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05w6cw origin 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.105 http://example.org/music/artist/origin #22349-05k7sb PRED entity: 05k7sb PRED relation: contains! PRED expected values: 04_1l0v => 162 concepts (103 used for prediction) PRED predicted values (max 10 best out of 206): 04_1l0v (0.83 #6716, 0.81 #12980, 0.80 #7610), 059g4 (0.63 #72541, 0.21 #90455, 0.20 #92250), 07c5l (0.33 #393, 0.25 #1289, 0.15 #65768), 02qkt (0.33 #55864, 0.31 #69302, 0.30 #65720), 0j0k (0.29 #4853, 0.15 #65751, 0.15 #55895), 05k7sb (0.25 #1925, 0.25 #1028, 0.21 #90455), 0k3hn (0.25 #2167, 0.21 #90455, 0.20 #92250), 059rby (0.25 #1812, 0.12 #2706, 0.09 #25979), 0cymp (0.25 #2081, 0.01 #26248), 0f8l9c (0.23 #34019, 0.10 #49292, 0.08 #17949) >> Best rule #6716 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 04llb; >> query: (?x2020, 04_1l0v) <- religion(?x2020, ?x109), category(?x2020, ?x134), contains(?x94, ?x2020) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05k7sb contains! 04_1l0v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 103.000 0.833 http://example.org/location/location/contains #22348-0c921 PRED entity: 0c921 PRED relation: profession PRED expected values: 0d8qb => 121 concepts (74 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.78 #5122, 0.74 #4830, 0.73 #3662), 09jwl (0.53 #454, 0.18 #5857, 0.17 #9508), 016z4k (0.53 #442, 0.10 #5261, 0.10 #7013), 0dz3r (0.50 #440, 0.11 #9494, 0.11 #9641), 03gjzk (0.47 #9797, 0.45 #1034, 0.45 #2932), 0nbcg (0.44 #467, 0.13 #5870, 0.13 #1197), 0cbd2 (0.35 #1320, 0.33 #6, 0.32 #152), 02krf9 (0.29 #1046, 0.26 #1776, 0.23 #3090), 039v1 (0.28 #472, 0.04 #7043, 0.04 #5291), 012t_z (0.28 #9639, 0.09 #2054, 0.09 #2346) >> Best rule #5122 for best value: >> intensional similarity = 3 >> extensional distance = 433 >> proper extension: 080knyg; >> query: (?x9320, 02hrh1q) <- award_winner(?x9320, ?x4405), nominated_for(?x9320, ?x2779), people(?x1050, ?x9320) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #77 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 012cph; 03qjlz; 0mb5x; 064177; 0c4y8; 026m0; 06kbb6; *> query: (?x9320, 0d8qb) <- award(?x9320, ?x601), ?x601 = 0gr4k, place_of_death(?x9320, ?x191) *> conf = 0.05 ranks of expected_values: 34 EVAL 0c921 profession 0d8qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 121.000 74.000 0.784 http://example.org/people/person/profession #22347-02vkvcz PRED entity: 02vkvcz PRED relation: nationality PRED expected values: 07ssc => 148 concepts (147 used for prediction) PRED predicted values (max 10 best out of 44): 09c7w0 (0.79 #7473, 0.78 #9466, 0.77 #3774), 07ssc (0.42 #907, 0.41 #808, 0.37 #6972), 0chghy (0.35 #9665, 0.12 #10, 0.09 #11164), 0345h (0.28 #12360, 0.09 #11164, 0.05 #1022), 06q1r (0.28 #12360, 0.03 #14548, 0.02 #968), 0d0vqn (0.28 #12360, 0.02 #901), 03rjj (0.16 #302, 0.15 #401, 0.12 #500), 0f8l9c (0.09 #11164, 0.08 #517, 0.07 #617), 0d060g (0.09 #11164, 0.07 #6378, 0.07 #6577), 03rt9 (0.09 #11164, 0.06 #211, 0.06 #1104) >> Best rule #7473 for best value: >> intensional similarity = 3 >> extensional distance = 1269 >> proper extension: 07lmxq; 03m8lq; 01v3s2_; 0162c8; 06jvj7; 07qy0b; 025t9b; 037hgm; 06n9lt; 05dtwm; ... >> query: (?x12364, 09c7w0) <- nationality(?x12364, ?x1310), place_of_birth(?x12364, ?x362), nominated_for(?x12364, ?x6616) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #907 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 03f70xs; 080r3; 0bw7ly; 08304; 0kj34; 0202p_; 0935jw; 03d9wk; *> query: (?x12364, 07ssc) <- nationality(?x12364, ?x1310), place_of_birth(?x12364, ?x362), ?x362 = 04jpl *> conf = 0.42 ranks of expected_values: 2 EVAL 02vkvcz nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 148.000 147.000 0.787 http://example.org/people/person/nationality #22346-0jt3tjf PRED entity: 0jt3tjf PRED relation: jurisdiction_of_office! PRED expected values: 060c4 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.80 #46, 0.78 #24, 0.73 #244), 0pqc5 (0.36 #885, 0.36 #907, 0.12 #775), 0dq3c (0.34 #617, 0.16 #243, 0.15 #1), 09d6p2 (0.34 #617, 0.05 #1432, 0.03 #470), 0fkvn (0.23 #730, 0.22 #752, 0.21 #774), 0f6c3 (0.23 #734, 0.21 #535, 0.20 #778), 09n5b9 (0.19 #738, 0.18 #539, 0.18 #782), 04syw (0.18 #358, 0.18 #292, 0.17 #336), 0p5vf (0.11 #34, 0.11 #12, 0.10 #166), 01zq91 (0.11 #36, 0.11 #14, 0.08 #168) >> Best rule #46 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 0jgx; 07f1x; >> query: (?x9455, 060c4) <- country(?x5396, ?x9455), ?x5396 = 0486tv, adjoins(?x404, ?x9455) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jt3tjf jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.800 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #22345-0hhjk PRED entity: 0hhjk PRED relation: citytown PRED expected values: 02_286 => 126 concepts (123 used for prediction) PRED predicted values (max 10 best out of 244): 02_286 (0.79 #3338, 0.78 #4076, 0.71 #2969), 0psxp (0.14 #501, 0.14 #131, 0.10 #870), 05jbn (0.14 #478, 0.14 #108, 0.10 #847), 0rh6k (0.10 #740, 0.05 #18815, 0.05 #15130), 071vr (0.10 #896, 0.01 #16391), 0f2rq (0.09 #10461, 0.09 #7139, 0.09 #1603), 02zp1t (0.09 #1800, 0.09 #1430, 0.08 #2169), 0f2s6 (0.09 #1695, 0.09 #1325, 0.08 #2064), 043yj (0.09 #1795, 0.08 #2164, 0.07 #2902), 0106dv (0.09 #1344, 0.07 #2821, 0.02 #10940) >> Best rule #3338 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 03mdt; >> query: (?x13185, 02_286) <- service_location(?x13185, ?x94), state_province_region(?x13185, ?x335), ?x94 = 09c7w0, category(?x13185, ?x134), contact_category(?x13185, ?x897), ?x335 = 059rby >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hhjk citytown 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 123.000 0.786 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #22344-05p553 PRED entity: 05p553 PRED relation: genre! PRED expected values: 0gtsx8c 05jf85 03rtz1 069q4f 07qg8v 07y9w5 02f6g5 0m491 0pvms 047svrl 01bb9r 047p7fr 05wp1p 032016 03l6q0 02rn00y 09gkx35 04954r 0gcrg 0g9yrw 07kb7vh 02qzh2 043t8t 0421ng 07bwr 03c_cxn 03h4fq7 0cks1m 02704ff 05pyrb 05t0_2v 01cmp9 02q8ms8 041td_ 089j8p 011ykb 034qbx 0gnjh 07pd_j 0372j5 06c0ns 01jft4 047vp1n 01k0xy 01k0vq 017kz7 0b3n61 0fphf3v 01n30p 0cvkv5 09rvcvl 0c0zq 0f8j13 01sbv9 06t2t2 042g97 => 61 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1380): 0mb8c (0.67 #31699, 0.60 #18751, 0.50 #12284), 0cpllql (0.67 #29822, 0.50 #24640, 0.50 #11699), 0jqd3 (0.67 #25377, 0.50 #27966, 0.50 #12436), 02n72k (0.67 #25406, 0.50 #31880, 0.50 #12465), 08gsvw (0.67 #24661, 0.50 #31135, 0.50 #11720), 06t2t2 (0.67 #29651, 0.50 #28355, 0.50 #11531), 011ykb (0.67 #29281, 0.50 #27985, 0.50 #11161), 091xrc (0.67 #31035, 0.50 #10324, 0.50 #9030), 07nxnw (0.67 #30625, 0.50 #9914, 0.50 #8620), 03qnvdl (0.67 #24751, 0.50 #11810, 0.50 #9222) >> Best rule #31699 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0556j8; >> query: (?x258, 0mb8c) <- genre(?x8112, ?x258), genre(?x5890, ?x258), genre(?x3600, ?x258), genre(?x1185, ?x258), genre(?x791, ?x258), nominated_for(?x102, ?x3600), award_winner(?x3600, ?x1104), film(?x71, ?x791), award(?x5890, ?x1033), film(?x2156, ?x8112), ?x1185 = 033g4d >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #29651 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 06cvj; *> query: (?x258, 06t2t2) <- genre(?x7501, ?x258), genre(?x5142, ?x258), genre(?x4581, ?x258), genre(?x12533, ?x258), genre(?x2078, ?x258), award(?x12533, ?x757), nominated_for(?x1871, ?x5142), ?x7501 = 0gd92, honored_for(?x1112, ?x2078), ?x4581 = 02ppg1r *> conf = 0.67 ranks of expected_values: 6, 7, 13, 22, 122, 142, 169, 197, 216, 222, 223, 224, 229, 236, 237, 239, 262, 328, 377, 442, 549, 585, 621, 646, 648, 657, 658, 724, 734, 737, 748, 786, 829, 904, 910, 927, 982, 1046, 1105, 1106, 1125, 1133, 1140, 1166, 1167, 1171, 1189, 1201, 1214, 1230, 1252 EVAL 05p553 genre! 042g97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 06t2t2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 01sbv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0f8j13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0c0zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 09rvcvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0cvkv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 01n30p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0fphf3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0b3n61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 017kz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 01k0vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 01k0xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 047vp1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 01jft4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 06c0ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0372j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 07pd_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0gnjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 034qbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 011ykb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 089j8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 041td_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 02q8ms8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 01cmp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 05t0_2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 05pyrb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 02704ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0cks1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 03h4fq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 03c_cxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 07bwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0421ng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 043t8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 02qzh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 07kb7vh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0g9yrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0gcrg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 04954r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 09gkx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 02rn00y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 03l6q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 032016 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 05wp1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 047p7fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 01bb9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 047svrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0pvms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0m491 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 02f6g5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 07y9w5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 07qg8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 069q4f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 03rtz1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 05jf85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 44.000 0.667 http://example.org/film/film/genre EVAL 05p553 genre! 0gtsx8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 44.000 0.667 http://example.org/film/film/genre #22343-05qb8vx PRED entity: 05qb8vx PRED relation: ceremony! PRED expected values: 0gs96 0gqy2 0gqxm => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 372): 0gs96 (0.80 #2732, 0.74 #9677, 0.71 #7819), 0gqxm (0.80 #2777, 0.74 #9677, 0.60 #1562), 0gqy2 (0.79 #7852, 0.77 #8093, 0.74 #9677), 0czp_ (0.74 #9677, 0.12 #7939, 0.12 #6973), 02x201b (0.74 #9677, 0.09 #240, 0.09 #10404), 02qyp19 (0.47 #1446, 0.46 #1691, 0.44 #1447), 054krc (0.47 #1446, 0.44 #1447, 0.43 #962), 0drtkx (0.47 #1446, 0.44 #1447, 0.43 #962), 0fhpv4 (0.47 #1446, 0.44 #1447, 0.43 #962), 02r22gf (0.47 #1446, 0.44 #1447, 0.43 #962) >> Best rule #2732 for best value: >> intensional similarity = 18 >> extensional distance = 8 >> proper extension: 0gmdkyy; 0bvfqq; 02pgky2; 0bvhz9; >> query: (?x4224, 0gs96) <- ceremony(?x601, ?x4224), honored_for(?x4224, ?x3455), honored_for(?x4224, ?x573), nominated_for(?x3911, ?x3455), nominated_for(?x68, ?x3455), nominated_for(?x3945, ?x3455), award_winner(?x4224, ?x3036), language(?x3455, ?x254), nominated_for(?x7862, ?x573), nominated_for(?x1107, ?x573), ?x3911 = 02x1z2s, film_crew_role(?x3455, ?x281), ?x254 = 02h40lc, ?x601 = 0gr4k, ?x1107 = 019f4v, award(?x800, ?x7862), award(?x164, ?x68), film_crew_role(?x573, ?x137) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 05qb8vx ceremony! 0gqxm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 05qb8vx ceremony! 0gqy2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 05qb8vx ceremony! 0gs96 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony #22342-04vs9 PRED entity: 04vs9 PRED relation: organization PRED expected values: 07t65 => 66 concepts (60 used for prediction) PRED predicted values (max 10 best out of 47): 07t65 (0.92 #250, 0.92 #211, 0.90 #39), 01rz1 (0.52 #40, 0.31 #785, 0.31 #212), 0_2v (0.47 #23, 0.40 #42, 0.31 #785), 04k4l (0.42 #43, 0.32 #254, 0.31 #785), 018cqq (0.40 #47, 0.31 #785, 0.24 #85), 02jxk (0.33 #41, 0.31 #785, 0.16 #213), 085h1 (0.31 #785, 0.17 #172, 0.06 #29), 059dn (0.31 #785, 0.12 #51, 0.05 #146), 034h1h (0.23 #754, 0.18 #773, 0.02 #970), 02_l9 (0.10 #739, 0.07 #777) >> Best rule #250 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 03f2w; >> query: (?x9072, 07t65) <- medal(?x9072, ?x422), organization(?x9072, ?x127), olympics(?x9072, ?x1931) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04vs9 organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 60.000 0.920 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #22341-0571m PRED entity: 0571m PRED relation: film_crew_role PRED expected values: 09vw2b7 0ch6mp2 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 30): 0ch6mp2 (0.74 #81, 0.71 #491, 0.71 #2068), 09vw2b7 (0.67 #80, 0.63 #490, 0.59 #2067), 0dxtw (0.51 #85, 0.42 #495, 0.40 #199), 01vx2h (0.38 #86, 0.37 #496, 0.29 #2073), 01pvkk (0.32 #201, 0.29 #497, 0.27 #2074), 02rh1dz (0.26 #84, 0.18 #494, 0.09 #2071), 02ynfr (0.18 #91, 0.17 #501, 0.16 #205), 0215hd (0.13 #94, 0.11 #2081, 0.11 #504), 089g0h (0.11 #505, 0.09 #2082, 0.09 #1594), 015h31 (0.11 #493, 0.10 #83, 0.07 #1582) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 03g90h; 03h_yy; 04ddm4; 0bwfwpj; 0jjy0; 07y9w5; 09g7vfw; 05_5rjx; 065dc4; 033srr; ... >> query: (?x3251, 0ch6mp2) <- genre(?x3251, ?x600), crewmember(?x3251, ?x4703), ?x600 = 02n4kr >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0571m film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.744 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0571m film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.744 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22340-0gt1k PRED entity: 0gt1k PRED relation: film_release_region PRED expected values: 0h7x 02vzc => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 133): 09c7w0 (0.93 #3305, 0.93 #6447, 0.93 #6115), 02vzc (0.86 #553, 0.82 #1048, 0.81 #3029), 0154j (0.82 #2645, 0.74 #2976, 0.69 #4628), 03gj2 (0.82 #2669, 0.79 #4652, 0.76 #5148), 05qhw (0.78 #2656, 0.71 #2987, 0.70 #4639), 015fr (0.77 #2660, 0.76 #2991, 0.70 #4808), 07ssc (0.77 #5137, 0.77 #4806, 0.76 #4641), 01znc_ (0.77 #2687, 0.72 #3018, 0.70 #4670), 0b90_r (0.77 #2644, 0.72 #2975, 0.66 #499), 0d060g (0.72 #2978, 0.70 #2647, 0.66 #4630) >> Best rule #3305 for best value: >> intensional similarity = 4 >> extensional distance = 193 >> proper extension: 0gx1bnj; 09xbpt; 026mfbr; 0gj8t_b; 03bx2lk; 0j_tw; 0661m4p; 04g9gd; 05fgt1; 065zlr; ... >> query: (?x5499, 09c7w0) <- produced_by(?x5499, ?x11305), genre(?x5499, ?x258), ?x258 = 05p553, film_release_region(?x5499, ?x87) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #553 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 0gy2y8r; 01xlqd; *> query: (?x5499, 02vzc) <- genre(?x5499, ?x239), film_release_region(?x5499, ?x142), ?x142 = 0jgd, costume_design_by(?x5499, ?x13187) *> conf = 0.86 ranks of expected_values: 2, 22 EVAL 0gt1k film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gt1k film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 84.000 84.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22339-02b25y PRED entity: 02b25y PRED relation: instrumentalists! PRED expected values: 06ncr 03qjg => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 109): 05r5c (0.47 #5287, 0.45 #5534, 0.45 #3498), 018vs (0.28 #5291, 0.26 #5538, 0.26 #5620), 0j862 (0.23 #488, 0.03 #5608), 03qjg (0.16 #452, 0.15 #3536, 0.14 #2804), 026t6 (0.11 #5529, 0.11 #5611, 0.11 #409), 0l14md (0.11 #3497, 0.11 #5286, 0.11 #5533), 0l14qv (0.09 #3495, 0.09 #5284, 0.09 #2763), 018j2 (0.08 #5560, 0.08 #5642, 0.08 #5313), 06ncr (0.08 #2797, 0.07 #3529, 0.07 #5318), 04rzd (0.08 #2791, 0.07 #5312, 0.07 #5559) >> Best rule #5287 for best value: >> intensional similarity = 2 >> extensional distance = 581 >> proper extension: 023l9y; 01wbsdz; 04m2zj; >> query: (?x2584, 05r5c) <- artists(?x597, ?x2584), instrumentalists(?x227, ?x2584) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #452 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 01wxdn3; *> query: (?x2584, 03qjg) <- artists(?x597, ?x2584), student(?x6784, ?x2584), performance_role(?x2584, ?x7772) *> conf = 0.16 ranks of expected_values: 4, 9 EVAL 02b25y instrumentalists! 03qjg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 164.000 164.000 0.468 http://example.org/music/instrument/instrumentalists EVAL 02b25y instrumentalists! 06ncr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 164.000 164.000 0.468 http://example.org/music/instrument/instrumentalists #22338-05pzdk PRED entity: 05pzdk PRED relation: profession PRED expected values: 0dxtg => 71 concepts (70 used for prediction) PRED predicted values (max 10 best out of 51): 0dxtg (0.84 #460, 0.83 #609, 0.82 #758), 02hrh1q (0.81 #2845, 0.80 #2547, 0.79 #2994), 03gjzk (0.72 #462, 0.66 #611, 0.65 #760), 018gz8 (0.57 #17, 0.47 #315, 0.29 #166), 01d_h8 (0.35 #304, 0.33 #1794, 0.33 #1049), 0kyk (0.31 #1222, 0.09 #8377, 0.08 #477), 02krf9 (0.29 #325, 0.27 #7900, 0.20 #474), 015cjr (0.29 #50, 0.27 #7900, 0.12 #348), 025352 (0.28 #4918, 0.28 #5366, 0.27 #7900), 0196pc (0.28 #4918, 0.28 #5366, 0.14 #223) >> Best rule #460 for best value: >> intensional similarity = 2 >> extensional distance = 114 >> proper extension: 02k76g; >> query: (?x5311, 0dxtg) <- tv_program(?x5311, ?x3626), student(?x3424, ?x5311) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05pzdk profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 70.000 0.845 http://example.org/people/person/profession #22337-01d259 PRED entity: 01d259 PRED relation: film_release_region PRED expected values: 07ssc 015fr 059j2 0345h 01znc_ 06t2t 03h64 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 174): 059j2 (0.90 #3494, 0.90 #2286, 0.89 #4547), 03h64 (0.89 #1571, 0.89 #1421, 0.84 #3530), 035qy (0.89 #4401, 0.89 #5456, 0.89 #4551), 015fr (0.87 #3481, 0.86 #1522, 0.85 #5439), 0345h (0.86 #3797, 0.85 #4098, 0.84 #6358), 06t2t (0.85 #1416, 0.82 #1566, 0.79 #3525), 0d060g (0.84 #3470, 0.82 #1511, 0.80 #4373), 07ssc (0.84 #2725, 0.83 #2271, 0.82 #1520), 01znc_ (0.80 #1547, 0.79 #1397, 0.79 #4409), 015qh (0.73 #1546, 0.70 #1396, 0.54 #3505) >> Best rule #3494 for best value: >> intensional similarity = 10 >> extensional distance = 100 >> proper extension: 0c40vxk; >> query: (?x5721, 059j2) <- film_release_region(?x5721, ?x1603), film_release_region(?x5721, ?x985), film_release_region(?x5721, ?x172), film_release_region(?x5721, ?x151), film_crew_role(?x5721, ?x137), ?x172 = 0154j, ?x985 = 0k6nt, genre(?x5721, ?x53), ?x151 = 0b90_r, ?x1603 = 06bnz >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5, 6, 8, 9 EVAL 01d259 film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01d259 film_release_region 06t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01d259 film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 119.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01d259 film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01d259 film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01d259 film_release_region 015fr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01d259 film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 119.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22336-0d6yv PRED entity: 0d6yv PRED relation: location! PRED expected values: 016ypb => 117 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1913): 0hnp7 (0.33 #1241, 0.06 #18875, 0.03 #49106), 0139q5 (0.33 #1994, 0.06 #22147, 0.04 #29705), 0prfz (0.33 #49, 0.05 #22722, 0.04 #50434), 0c6g1l (0.33 #453, 0.05 #23126, 0.03 #35721), 032r1 (0.33 #2316, 0.03 #19950, 0.02 #87976), 01tdnyh (0.33 #1044, 0.03 #18678, 0.01 #58987), 0klw (0.33 #997, 0.03 #18631, 0.01 #58940), 01ww_vs (0.20 #4816, 0.06 #9854, 0.03 #17412), 05y5kf (0.20 #3508, 0.02 #26181, 0.02 #31219), 01wqpnm (0.17 #37788, 0.11 #120933) >> Best rule #1241 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 07ssc; >> query: (?x10165, 0hnp7) <- contains(?x10165, ?x2196), contains(?x512, ?x10165), location_of_ceremony(?x566, ?x10165), ?x2196 = 07w4j >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #33311 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 01914; 0f2tj; *> query: (?x10165, 016ypb) <- citytown(?x2196, ?x10165), featured_film_locations(?x9209, ?x10165), country(?x10165, ?x1310), film_crew_role(?x9209, ?x137) *> conf = 0.06 ranks of expected_values: 135 EVAL 0d6yv location! 016ypb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 117.000 51.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #22335-0bmfnjs PRED entity: 0bmfnjs PRED relation: film_crew_role PRED expected values: 01xy5l_ 05smlt => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.86 #813, 0.86 #531, 0.79 #1202), 09zzb8 (0.83 #246, 0.82 #808, 0.79 #526), 0dxtw (0.46 #290, 0.45 #817, 0.40 #570), 01vx2h (0.43 #818, 0.40 #571, 0.38 #2015), 02ynfr (0.25 #2252, 0.20 #260, 0.19 #295), 01xy5l_ (0.25 #2252, 0.19 #820, 0.18 #293), 02rh1dz (0.25 #2252, 0.18 #219, 0.17 #2394), 0215hd (0.25 #2252, 0.17 #543, 0.17 #2394), 04pyp5 (0.25 #2252, 0.17 #2394, 0.17 #2075), 0d2b38 (0.25 #2252, 0.17 #2394, 0.17 #2075) >> Best rule #813 for best value: >> intensional similarity = 8 >> extensional distance = 186 >> proper extension: 03mh_tp; >> query: (?x8682, 0ch6mp2) <- film_crew_role(?x8682, ?x1171), film_crew_role(?x8682, ?x1078), film_crew_role(?x8682, ?x468), ?x468 = 02r96rf, country(?x8682, ?x94), executive_produced_by(?x8682, ?x6944), ?x1171 = 09vw2b7, profession(?x199, ?x1078) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2252 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 856 *> proper extension: 0dnvn3; 0ds33; 04fzfj; 0963mq; 0k2sk; 0c00zd0; 01pgp6; 03kxj2; 05h43ls; 0x25q; ... *> query: (?x8682, ?x137) <- titles(?x162, ?x8682), language(?x8682, ?x254), titles(?x162, ?x7161), film_crew_role(?x8682, ?x468), film_crew_role(?x7161, ?x137), costume_design_by(?x7161, ?x13091) *> conf = 0.25 ranks of expected_values: 6, 27 EVAL 0bmfnjs film_crew_role 05smlt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 110.000 110.000 0.862 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0bmfnjs film_crew_role 01xy5l_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 110.000 110.000 0.862 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22334-0hm0k PRED entity: 0hm0k PRED relation: award_winner! PRED expected values: 0m7yy => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 247): 0m7yy (0.80 #10116, 0.75 #4500, 0.67 #6228), 05p1dby (0.50 #3996, 0.38 #14365, 0.36 #14797), 07bdd_ (0.40 #9570, 0.38 #3954, 0.36 #7410), 02x1z2s (0.33 #14455, 0.32 #14887, 0.27 #7542), 0gq9h (0.25 #3966, 0.17 #14335, 0.16 #14767), 02pr67 (0.20 #2580, 0.14 #45371, 0.14 #47967), 040njc (0.20 #6488, 0.12 #15993, 0.09 #12969), 09sb52 (0.19 #42385, 0.19 #41087, 0.18 #42818), 0ck27z (0.18 #38112, 0.11 #40706, 0.10 #45896), 05b4l5x (0.16 #11239, 0.07 #20744, 0.06 #19016) >> Best rule #10116 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 01nzs7; 027_tg; 01j7pt; >> query: (?x6092, 0m7yy) <- program(?x6092, ?x2447), award_winner(?x6093, ?x6092), nominated_for(?x2448, ?x2447), titles(?x2008, ?x2447) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hm0k award_winner! 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.800 http://example.org/award/award_category/winners./award/award_honor/award_winner #22333-02dtg PRED entity: 02dtg PRED relation: place_of_death! PRED expected values: 01mvpv => 192 concepts (164 used for prediction) PRED predicted values (max 10 best out of 698): 0hnjt (0.08 #959, 0.06 #1714, 0.04 #3982), 0b_fw (0.08 #835, 0.06 #1590, 0.04 #3858), 07csf4 (0.08 #807, 0.05 #2318, 0.04 #3830), 02h0f3 (0.08 #1111, 0.05 #2622, 0.04 #3022), 0dck27 (0.08 #829, 0.05 #2340, 0.04 #3022), 0h326 (0.08 #1509, 0.05 #3020, 0.02 #8309), 05f0r8 (0.08 #1503, 0.05 #3014, 0.02 #8303), 01l3j (0.08 #1498, 0.05 #3009, 0.02 #8298), 067x44 (0.08 #1489, 0.05 #3000, 0.02 #8289), 058z1hb (0.08 #1485, 0.05 #2996, 0.02 #8285) >> Best rule #959 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0gp5l6; >> query: (?x479, 0hnjt) <- citytown(?x2228, ?x479), administrative_division(?x479, ?x7387), film_release_region(?x4024, ?x479) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02dtg place_of_death! 01mvpv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 192.000 164.000 0.077 http://example.org/people/deceased_person/place_of_death #22332-035dk PRED entity: 035dk PRED relation: olympics PRED expected values: 0l98s => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 40): 0kbws (0.66 #2809, 0.66 #2931, 0.62 #3025), 06sks6 (0.64 #2310, 0.63 #2391, 0.62 #2028), 0kbvb (0.63 #687, 0.62 #2293, 0.59 #2374), 0jhn7 (0.63 #707, 0.61 #2313, 0.59 #3038), 0jdk_ (0.61 #706, 0.59 #506, 0.56 #146), 0lgxj (0.56 #388, 0.50 #708, 0.42 #228), 0l6mp (0.54 #698, 0.49 #498, 0.47 #378), 0l998 (0.53 #366, 0.41 #686, 0.41 #486), 018ctl (0.52 #2045, 0.52 #1804, 0.52 #1803), 0l6ny (0.52 #689, 0.46 #489, 0.44 #369) >> Best rule #2809 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 027rn; 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0h3y; ... >> query: (?x2051, ?x1081) <- contains(?x2051, ?x12330), organization(?x2051, ?x127), olympics(?x2051, ?x1081) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #365 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 0h44w; *> query: (?x2051, 0l98s) <- capital(?x2051, ?x12331), countries_spoken_in(?x254, ?x2051), contains(?x12331, ?x12330) *> conf = 0.42 ranks of expected_values: 12 EVAL 035dk olympics 0l98s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 177.000 177.000 0.664 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #22331-05r5c PRED entity: 05r5c PRED relation: group PRED expected values: 0khth 047cx 0l8g0 07mvp 09lwrt 01lf293 0bk1p 09jm8 01s560x => 82 concepts (53 used for prediction) PRED predicted values (max 10 best out of 729): 07mvp (0.67 #780, 0.62 #2232, 0.50 #2085), 02cw1m (0.67 #822, 0.38 #3145, 0.37 #3434), 0mjn2 (0.67 #830, 0.37 #3442, 0.35 #3153), 01s560x (0.67 #840, 0.33 #115, 0.31 #3163), 014pg1 (0.59 #2398, 0.58 #1814, 0.58 #1669), 047cx (0.59 #2358, 0.58 #1774, 0.50 #2065), 0163m1 (0.58 #1621, 0.53 #2350, 0.50 #3075), 05crg7 (0.58 #1606, 0.53 #2335, 0.50 #1751), 048xh (0.50 #2095, 0.50 #1224, 0.42 #1659), 01q99h (0.50 #775, 0.42 #1789, 0.42 #1644) >> Best rule #780 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 042v_gx; 018vs; 03qjg; >> query: (?x316, 07mvp) <- role(?x7882, ?x316), role(?x74, ?x316), group(?x316, ?x9868), instrumentalists(?x316, ?x130), award_winner(?x2169, ?x7882), ?x9868 = 0134pk >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 6, 26, 27, 41, 46, 51, 54 EVAL 05r5c group 01s560x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05r5c group 09jm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 82.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05r5c group 0bk1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 82.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05r5c group 01lf293 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 82.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05r5c group 09lwrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 82.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05r5c group 07mvp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05r5c group 0l8g0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 82.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05r5c group 047cx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05r5c group 0khth CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 82.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group #22330-0jryt PRED entity: 0jryt PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 99 concepts (44 used for prediction) PRED predicted values (max 10 best out of 5): 09c7w0 (0.87 #45, 0.86 #280, 0.86 #209), 04_1l0v (0.24 #321), 02xry (0.17 #147, 0.12 #589, 0.12 #123), 03rt9 (0.03 #112, 0.02 #240, 0.02 #256), 02jx1 (0.01 #315) >> Best rule #45 for best value: >> intensional similarity = 5 >> extensional distance = 123 >> proper extension: 0nj1c; >> query: (?x13382, 09c7w0) <- source(?x13382, ?x958), time_zones(?x13382, ?x2674), ?x2674 = 02hcv8, adjoins(?x13382, ?x4143), currency(?x13382, ?x170) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jryt second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 44.000 0.872 http://example.org/location/country/second_level_divisions #22329-06qgjh PRED entity: 06qgjh PRED relation: profession PRED expected values: 01d_h8 => 102 concepts (54 used for prediction) PRED predicted values (max 10 best out of 65): 01d_h8 (0.71 #2819, 0.70 #2671, 0.66 #1486), 0dxtg (0.65 #2826, 0.62 #1493, 0.61 #2678), 018gz8 (0.45 #312, 0.21 #460, 0.20 #2385), 09jwl (0.38 #3719, 0.37 #6236, 0.37 #5051), 03gjzk (0.36 #310, 0.36 #2679, 0.36 #1790), 0np9r (0.36 #316, 0.16 #2240, 0.16 #2389), 019x4f (0.33 #114), 0nbcg (0.29 #3732, 0.27 #6249, 0.26 #3880), 02krf9 (0.26 #2691, 0.23 #2839, 0.17 #1506), 016z4k (0.26 #3705, 0.24 #4001, 0.24 #3853) >> Best rule #2819 for best value: >> intensional similarity = 3 >> extensional distance = 338 >> proper extension: 01g4zr; 0c_mvb; 09ftwr; 03m_k0; 01n9d9; 012rng; 0638kv; 037d35; 013t9y; 043hg; ... >> query: (?x8432, 01d_h8) <- profession(?x8432, ?x524), student(?x3948, ?x8432), ?x524 = 02jknp >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06qgjh profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 54.000 0.706 http://example.org/people/person/profession #22328-03cz9_ PRED entity: 03cz9_ PRED relation: film PRED expected values: 02z5x7l => 119 concepts (54 used for prediction) PRED predicted values (max 10 best out of 867): 02z5x7l (0.60 #1208, 0.13 #8368, 0.08 #13739), 02gs6r (0.40 #2705, 0.14 #4495, 0.12 #6285), 026q3s3 (0.27 #7363, 0.20 #1993, 0.16 #18106), 0dh8v4 (0.27 #8101, 0.20 #2731, 0.14 #4521), 02vw1w2 (0.20 #2002, 0.20 #212, 0.13 #7372), 07ghv5 (0.20 #1168, 0.16 #10118, 0.12 #11908), 0dd6bf (0.20 #3025, 0.11 #10185, 0.08 #13766), 0ckr7s (0.20 #38, 0.11 #8988, 0.08 #10778), 05pyrb (0.20 #993, 0.07 #8153, 0.03 #13524), 0cks1m (0.20 #968, 0.05 #9918, 0.04 #11708) >> Best rule #1208 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02h8hr; 01qvtwm; >> query: (?x12375, 02z5x7l) <- actor(?x8610, ?x12375), profession(?x12375, ?x1032), ?x8610 = 01lk02, gender(?x12375, ?x231) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cz9_ film 02z5x7l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 54.000 0.600 http://example.org/film/actor/film./film/performance/film #22327-0kbws PRED entity: 0kbws PRED relation: olympics! PRED expected values: 07jjt 0d1t3 0486tv => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 23): 07jjt (0.61 #793, 0.58 #817, 0.56 #886), 07_53 (0.51 #856, 0.51 #855, 0.44 #381), 035d1m (0.51 #856, 0.51 #855, 0.43 #389), 09w1n (0.43 #327, 0.42 #841, 0.33 #9), 06zgc (0.42 #845, 0.33 #13, 0.30 #979), 01z27 (0.40 #143, 0.33 #28, 0.29 #323), 0486tv (0.33 #380, 0.28 #892, 0.21 #846), 02_5h (0.29 #322, 0.25 #836, 0.21 #970), 09wz9 (0.29 #325, 0.25 #839, 0.20 #145), 0152n0 (0.29 #335, 0.20 #155, 0.19 #808) >> Best rule #793 for best value: >> intensional similarity = 19 >> extensional distance = 21 >> proper extension: 0l6vl; 0l98s; 0l998; 0l6m5; 0lk8j; 0lbbj; 0nbjq; 0sxrz; 0jdk_; 018qb4; ... >> query: (?x1931, 07jjt) <- participating_countries(?x1931, ?x8033), participating_countries(?x1931, ?x3432), participating_countries(?x1931, ?x1892), sports(?x1931, ?x3015), exported_to(?x8033, ?x8593), ?x3015 = 071t0, olympics(?x1892, ?x4424), film_release_region(?x7293, ?x1892), film_release_region(?x7009, ?x1892), film_release_region(?x324, ?x1892), contains(?x2467, ?x3432), ?x324 = 07gp9, ?x7293 = 027m67, ?x7009 = 0bs8s1p, country(?x453, ?x1892), film_release_region(?x1710, ?x1892), organization(?x3432, ?x127), ?x4424 = 0blfl, jurisdiction_of_office(?x182, ?x3432) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 13 EVAL 0kbws olympics! 0486tv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 59.000 59.000 0.609 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 0kbws olympics! 0d1t3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 59.000 59.000 0.609 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 0kbws olympics! 07jjt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.609 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #22326-01d494 PRED entity: 01d494 PRED relation: interests PRED expected values: 0gt_hv => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 15): 04s0m (0.60 #54, 0.45 #296, 0.41 #311), 05r79 (0.35 #260, 0.33 #108, 0.30 #48), 05qt0 (0.30 #37, 0.24 #226, 0.21 #202), 0x0w (0.24 #226, 0.24 #299, 0.22 #314), 09xq9d (0.24 #226, 0.20 #51, 0.15 #186), 0gt_hv (0.24 #226, 0.18 #210, 0.18 #272), 05qfh (0.24 #226, 0.13 #543, 0.10 #185), 04rjg (0.13 #109, 0.11 #154, 0.10 #49), 097df (0.10 #58, 0.07 #208, 0.07 #118), 04g7x (0.10 #40, 0.07 #145, 0.05 #175) >> Best rule #54 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 07kb5; 04411; 045bg; 026lj; 01bpn; 03sbs; 0ct9_; 015n8; >> query: (?x1737, 04s0m) <- interests(?x1737, ?x6978), interests(?x1737, ?x713), ?x6978 = 02jhc, influenced_by(?x1737, ?x920), ?x713 = 02jcc >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #226 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 26 *> proper extension: 01tz6vs; *> query: (?x1737, ?x713) <- influenced_by(?x1737, ?x9600), influenced_by(?x1737, ?x6015), gender(?x1737, ?x231), interests(?x9600, ?x713), ?x6015 = 05qmj *> conf = 0.24 ranks of expected_values: 6 EVAL 01d494 interests 0gt_hv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 153.000 153.000 0.600 http://example.org/user/alexander/philosophy/philosopher/interests #22325-0163v PRED entity: 0163v PRED relation: film_release_region! PRED expected values: 0btyf5z 0fpv_3_ 08052t3 0gjcrrw => 103 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1310): 08hmch (0.86 #1426, 0.84 #18456, 0.75 #6666), 017jd9 (0.81 #1894, 0.76 #18924, 0.75 #7134), 0bpm4yw (0.81 #1848, 0.75 #18878, 0.74 #7088), 043tvp3 (0.81 #2222, 0.71 #19252, 0.70 #7462), 047vnkj (0.81 #2001, 0.71 #19031, 0.68 #7241), 0661ql3 (0.81 #1599, 0.67 #18629, 0.62 #6839), 087wc7n (0.81 #1399, 0.61 #18429, 0.60 #6639), 0dlngsd (0.81 #1895, 0.61 #18925, 0.57 #7135), 0cmf0m0 (0.81 #2363, 0.57 #7603, 0.56 #19393), 04f52jw (0.79 #1635, 0.74 #6875, 0.71 #18665) >> Best rule #1426 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 01ly5m; >> query: (?x2188, 08hmch) <- film_release_region(?x2933, ?x2188), film_release_region(?x428, ?x2188), ?x428 = 0h1cdwq, genre(?x2933, ?x225) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1586 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 01ly5m; *> query: (?x2188, 0fpv_3_) <- film_release_region(?x2933, ?x2188), film_release_region(?x428, ?x2188), ?x428 = 0h1cdwq, genre(?x2933, ?x225) *> conf = 0.74 ranks of expected_values: 22, 31, 90, 323 EVAL 0163v film_release_region! 0gjcrrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 103.000 68.000 0.860 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0163v film_release_region! 08052t3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 103.000 68.000 0.860 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0163v film_release_region! 0fpv_3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 103.000 68.000 0.860 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0163v film_release_region! 0btyf5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 103.000 68.000 0.860 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22324-02mq_y PRED entity: 02mq_y PRED relation: artist! PRED expected values: 096ysw 01th4s => 96 concepts (85 used for prediction) PRED predicted values (max 10 best out of 134): 03rhqg (0.43 #869, 0.27 #1153, 0.24 #3286), 02y21l (0.33 #665, 0.08 #1376, 0.08 #4648), 01th4s (0.33 #40, 0.07 #1888, 0.04 #2456), 015_1q (0.25 #304, 0.24 #2294, 0.22 #4429), 017l96 (0.25 #161, 0.20 #1014, 0.20 #445), 02p11jq (0.25 #155, 0.20 #439, 0.17 #581), 0181hw (0.25 #194, 0.20 #478, 0.14 #4694), 0dd2f (0.25 #267, 0.20 #551, 0.10 #1120), 037h1k (0.25 #199, 0.20 #483, 0.10 #1052), 01cl0d (0.25 #340, 0.14 #909, 0.08 #1335) >> Best rule #869 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 016s_5; 0fq117k; >> query: (?x5303, 03rhqg) <- artists(?x10318, ?x5303), artists(?x7083, ?x5303), artists(?x2491, ?x5303), ?x7083 = 02yv6b, ?x10318 = 03jsvl, category(?x5303, ?x134), artists(?x2491, ?x9179), parent_genre(?x2491, ?x283), parent_genre(?x2542, ?x2491), languages(?x9179, ?x2502) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 05qhnq; *> query: (?x5303, 01th4s) <- origin(?x5303, ?x8602), artists(?x3167, ?x5303), artists(?x302, ?x5303), ?x302 = 016clz, artists(?x3167, ?x7086), artists(?x3167, ?x2492), ?x8602 = 0chgzm, category(?x5303, ?x134), ?x7086 = 07r1_, ?x2492 = 01tp5bj *> conf = 0.33 ranks of expected_values: 3, 53 EVAL 02mq_y artist! 01th4s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 85.000 0.429 http://example.org/music/record_label/artist EVAL 02mq_y artist! 096ysw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 96.000 85.000 0.429 http://example.org/music/record_label/artist #22323-0h3k3f PRED entity: 0h3k3f PRED relation: genre PRED expected values: 04t36 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 94): 02l7c8 (0.44 #17, 0.42 #627, 0.41 #993), 01g6gs (0.38 #144, 0.34 #510, 0.29 #388), 05p553 (0.36 #4640, 0.35 #6958, 0.35 #1102), 06cvj (0.33 #3, 0.16 #369, 0.13 #491), 01jfsb (0.32 #4161, 0.31 #3551, 0.30 #5503), 02kdv5l (0.30 #1222, 0.29 #1710, 0.29 #1832), 04xvlr (0.28 #2197, 0.22 #855, 0.19 #733), 03k9fj (0.24 #5380, 0.23 #5746, 0.23 #866), 0lsxr (0.23 #1961, 0.23 #1229, 0.23 #1717), 060__y (0.22 #18, 0.20 #2092, 0.19 #2580) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0k5g9; 0dnw1; 04wddl; 0k419; 015gm8; 0286hyp; >> query: (?x8735, 02l7c8) <- nominated_for(?x4896, ?x8735), nominated_for(?x484, ?x8735), ?x4896 = 07hhnl, genre(?x8735, ?x53) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #616 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 01kf4tt; 02q_4ph; *> query: (?x8735, 04t36) <- film_sets_designed(?x2716, ?x8735), nominated_for(?x484, ?x8735), film(?x902, ?x8735) *> conf = 0.18 ranks of expected_values: 13 EVAL 0h3k3f genre 04t36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 95.000 95.000 0.444 http://example.org/film/film/genre #22322-01l1sq PRED entity: 01l1sq PRED relation: profession PRED expected values: 02dsz => 83 concepts (68 used for prediction) PRED predicted values (max 10 best out of 62): 01c72t (0.41 #741, 0.41 #885, 0.35 #1030), 01d_h8 (0.34 #2022, 0.33 #3030, 0.32 #6636), 03gjzk (0.33 #1310, 0.25 #4771, 0.25 #4915), 0dxtg (0.29 #4770, 0.28 #4914, 0.28 #4338), 0n1h (0.24 #154, 0.22 #2748, 0.19 #1163), 018gz8 (0.22 #1312, 0.13 #2609, 0.12 #2897), 02jknp (0.21 #6638, 0.20 #8510, 0.20 #7934), 0fnpj (0.18 #2362, 0.14 #3226, 0.14 #3370), 01c8w0 (0.16 #871, 0.13 #1016, 0.12 #1592), 0cbd2 (0.15 #4619, 0.15 #2167, 0.12 #9086) >> Best rule #741 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 02nfjp; 02z81h; 01m7f5r; 01x1fq; >> query: (?x1652, 01c72t) <- nominated_for(?x1652, ?x1849), profession(?x1652, ?x131), role(?x1652, ?x314) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #2790 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 395 *> proper extension: 04rcr; 02r3zy; 07c0j; 03g5jw; 0dvqq; 03fbc; 0249kn; 018ndc; 017j6; 04qmr; ... *> query: (?x1652, 02dsz) <- award_nominee(?x1652, ?x368), award(?x1652, ?x1670), artist(?x2299, ?x1652) *> conf = 0.03 ranks of expected_values: 34 EVAL 01l1sq profession 02dsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 83.000 68.000 0.411 http://example.org/people/person/profession #22321-07twz PRED entity: 07twz PRED relation: film_release_region! PRED expected values: 01vksx 047msdk 0gd0c7x 0dyb1 0bpm4yw 09v71cj 017jd9 0gg5qcw 0hv8w 09v3jyg 07g1sm 032clf => 105 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1292): 02vr3gz (0.91 #433, 0.88 #1684, 0.79 #6688), 03qnc6q (0.91 #291, 0.85 #6546, 0.85 #1542), 0jjy0 (0.90 #6375, 0.87 #120, 0.86 #5124), 017jd9 (0.90 #6801, 0.86 #5550, 0.83 #546), 0fpv_3_ (0.89 #5258, 0.77 #6509, 0.74 #254), 0bpm4yw (0.87 #501, 0.86 #5505, 0.85 #6756), 06fcqw (0.87 #774, 0.85 #2025, 0.83 #5778), 01vksx (0.87 #96, 0.85 #1347, 0.83 #5100), 05zlld0 (0.87 #431, 0.85 #1682, 0.82 #6686), 0645k5 (0.87 #326, 0.85 #1577, 0.79 #6581) >> Best rule #433 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 05r4w; 09c7w0; 03rjj; 03_3d; 0d060g; 05qhw; 07ssc; 015fr; 06mzp; 0f8l9c; ... >> query: (?x4737, 02vr3gz) <- film_release_region(?x2094, ?x4737), film_release_region(?x559, ?x4737), ?x2094 = 05z7c, ?x559 = 05p1tzf >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #6801 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 06t2t; *> query: (?x4737, 017jd9) <- film_release_region(?x3482, ?x4737), ?x3482 = 017z49, organization(?x4737, ?x127) *> conf = 0.90 ranks of expected_values: 4, 6, 8, 17, 36, 39, 102, 124, 163, 221, 309, 332 EVAL 07twz film_release_region! 032clf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 07g1sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 09v3jyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 0hv8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 0gg5qcw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 017jd9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 09v71cj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 0bpm4yw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 0dyb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 0gd0c7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 047msdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07twz film_release_region! 01vksx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 105.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22320-0ql36 PRED entity: 0ql36 PRED relation: artist! PRED expected values: 03q58q => 163 concepts (158 used for prediction) PRED predicted values (max 10 best out of 123): 011k1h (0.55 #424, 0.44 #1114, 0.36 #3047), 015_1q (0.33 #10231, 0.27 #431, 0.24 #6367), 0181dw (0.24 #868, 0.20 #178, 0.18 #10254), 0g768 (0.22 #10249, 0.20 #173, 0.15 #4315), 0n85g (0.22 #750, 0.14 #10274, 0.13 #2130), 016ckq (0.22 #1007, 0.20 #179, 0.17 #317), 01cl2y (0.20 #2512, 0.18 #442, 0.14 #1408), 0mzkr (0.20 #161, 0.17 #299, 0.11 #1265), 04fc6c (0.20 #212, 0.17 #350, 0.11 #1316), 01cszh (0.20 #149, 0.14 #1253, 0.13 #977) >> Best rule #424 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 017g21; >> query: (?x12670, 011k1h) <- artist(?x3240, ?x12670), artists(?x2809, ?x12670), ?x3240 = 017l96, ?x2809 = 05w3f >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #779 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 01w20rx; *> query: (?x12670, 03q58q) <- artists(?x9013, ?x12670), profession(?x12670, ?x1032), ?x9013 = 09nwwf, location(?x12670, ?x2624) *> conf = 0.06 ranks of expected_values: 59 EVAL 0ql36 artist! 03q58q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 163.000 158.000 0.545 http://example.org/music/record_label/artist #22319-0223xd PRED entity: 0223xd PRED relation: category_of! PRED expected values: 0223xd => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 15): 07n52 (0.33 #964, 0.02 #2745), 05x2s (0.05 #2088, 0.05 #2254, 0.04 #2416), 01b8bn (0.05 #2073, 0.05 #2239, 0.04 #2401), 04jhhng (0.05 #2262, 0.04 #2424, 0.02 #2747), 01ppdy (0.04 #2399, 0.02 #2722), 02v1ws (0.02 #2750), 02r0d0 (0.02 #2749), 01cd7p (0.02 #2748), 02xzd9 (0.02 #2746), 04hddx (0.02 #2735) >> Best rule #964 for best value: >> intensional similarity = 47 >> extensional distance = 1 >> proper extension: 07n52; >> query: (?x14751, 07n52) <- disciplines_or_subjects(?x14751, ?x2605), major_field_of_study(?x12667, ?x2605), major_field_of_study(?x12293, ?x2605), major_field_of_study(?x11397, ?x2605), major_field_of_study(?x11215, ?x2605), major_field_of_study(?x10659, ?x2605), major_field_of_study(?x10373, ?x2605), major_field_of_study(?x7545, ?x2605), major_field_of_study(?x7178, ?x2605), major_field_of_study(?x6732, ?x2605), major_field_of_study(?x5750, ?x2605), major_field_of_study(?x5486, ?x2605), major_field_of_study(?x5280, ?x2605), major_field_of_study(?x4672, ?x2605), major_field_of_study(?x3821, ?x2605), major_field_of_study(?x1011, ?x2605), major_field_of_study(?x216, ?x2605), ?x10659 = 01l8t8, ?x3821 = 0kw4j, ?x1011 = 07w0v, student(?x11215, ?x3853), ?x5280 = 07vhb, fraternities_and_sororities(?x11215, ?x4348), major_field_of_study(?x2605, ?x4100), major_field_of_study(?x734, ?x2605), major_field_of_study(?x2014, ?x2605), ?x4672 = 07tds, student(?x216, ?x217), contains(?x94, ?x11215), ?x11397 = 02hp70, citytown(?x216, ?x12060), ?x7545 = 0bwfn, institution(?x4981, ?x5486), ?x5750 = 01nnsv, currency(?x6732, ?x170), school_type(?x10373, ?x3092), colors(?x12667, ?x1101), category(?x12293, ?x134), school_type(?x12667, ?x3205), major_field_of_study(?x4846, ?x4100), organization(?x346, ?x7178), contains(?x1310, ?x10373), ?x346 = 060c4, registering_agency(?x12667, ?x1982), ?x4846 = 037njl, student(?x6732, ?x3961), ?x4981 = 03bwzr4 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0223xd category_of! 0223xd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 21.000 0.333 http://example.org/award/award_category/category_of #22318-07f5x PRED entity: 07f5x PRED relation: organization PRED expected values: 0b6css => 101 concepts (96 used for prediction) PRED predicted values (max 10 best out of 48): 0b6css (0.65 #401, 0.58 #925, 0.56 #89), 0j7v_ (0.65 #401, 0.58 #925, 0.32 #1409), 01rz1 (0.61 #1, 0.56 #41, 0.32 #1409), 0_2v (0.46 #3, 0.44 #43, 0.32 #1409), 018cqq (0.44 #50, 0.43 #10, 0.32 #1409), 04k4l (0.33 #104, 0.32 #1409, 0.31 #324), 02jxk (0.32 #2, 0.32 #1409, 0.29 #42), 085h1 (0.32 #1409, 0.16 #1759, 0.04 #131), 059dn (0.32 #1409, 0.14 #14, 0.12 #54), 034h1h (0.22 #1276, 0.21 #1175, 0.18 #1396) >> Best rule #401 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 059z0; >> query: (?x8948, ?x127) <- official_language(?x8948, ?x5607), adjoins(?x6431, ?x8948), organization(?x6431, ?x127) >> conf = 0.65 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 07f5x organization 0b6css CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 96.000 0.649 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #22317-04znsy PRED entity: 04znsy PRED relation: award_nominee PRED expected values: 0161sp 0c33pl => 122 concepts (46 used for prediction) PRED predicted values (max 10 best out of 959): 04znsy (0.23 #49208, 0.02 #28119), 0161sp (0.23 #49208, 0.02 #19401, 0.01 #17057), 0c33pl (0.23 #49208), 03xsby (0.23 #49208), 02qgyv (0.10 #502, 0.05 #7531, 0.04 #9874), 02qgqt (0.10 #20, 0.05 #46884, 0.04 #51571), 02bkdn (0.10 #402, 0.04 #54296, 0.04 #40237), 0c6qh (0.10 #543, 0.03 #9915, 0.03 #54437), 0dvmd (0.10 #697, 0.03 #52248, 0.03 #68651), 014g22 (0.10 #965, 0.02 #28119, 0.02 #97035) >> Best rule #49208 for best value: >> intensional similarity = 3 >> extensional distance = 505 >> proper extension: 0d02km; >> query: (?x9211, ?x1914) <- film(?x9211, ?x2742), film_festivals(?x2742, ?x13076), nominated_for(?x1914, ?x2742) >> conf = 0.23 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3 EVAL 04znsy award_nominee 0c33pl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 46.000 0.226 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 04znsy award_nominee 0161sp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 46.000 0.226 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22316-01r2l PRED entity: 01r2l PRED relation: countries_spoken_in PRED expected values: 01crd5 => 81 concepts (71 used for prediction) PRED predicted values (max 10 best out of 262): 04thp (0.70 #178, 0.70 #4452, 0.69 #1783), 03h64 (0.70 #178, 0.70 #4452, 0.69 #1783), 07ytt (0.56 #1405, 0.50 #1583, 0.42 #1761), 0hzlz (0.42 #1629, 0.40 #1987, 0.38 #2166), 0697s (0.33 #1320, 0.33 #430, 0.31 #1855), 0162v (0.33 #1302, 0.33 #412, 0.30 #1480), 01ppq (0.33 #1400, 0.33 #510, 0.30 #1578), 03_3d (0.33 #367, 0.33 #188, 0.29 #1078), 034m8 (0.33 #517, 0.33 #159, 0.25 #1763), 02lx0 (0.33 #439, 0.33 #81, 0.25 #793) >> Best rule #178 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 05zjd; >> query: (?x5974, ?x2645) <- languages_spoken(?x7562, ?x5974), service_language(?x555, ?x5974), countries_spoken_in(?x5974, ?x2316), official_language(?x11816, ?x5974), official_language(?x2645, ?x5974), language(?x136, ?x5974), ?x11816 = 04thp, film_release_region(?x124, ?x2316), jurisdiction_of_office(?x182, ?x2316) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #5698 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 31 *> proper extension: 09bnf; *> query: (?x5974, ?x404) <- countries_spoken_in(?x5974, ?x2346), participating_countries(?x418, ?x2346), country(?x206, ?x2346), nationality(?x754, ?x2346), contains(?x2346, ?x7351), administrative_parent(?x2346, ?x551), adjoins(?x2346, ?x404), jurisdiction_of_office(?x265, ?x2346) *> conf = 0.16 ranks of expected_values: 98 EVAL 01r2l countries_spoken_in 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 81.000 71.000 0.700 http://example.org/language/human_language/countries_spoken_in #22315-0hwbd PRED entity: 0hwbd PRED relation: award_winner! PRED expected values: 09q_6t => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 132): 09qvms (0.14 #13, 0.09 #293, 0.08 #5753), 0bzk2h (0.12 #8961, 0.03 #13023, 0.03 #329), 0c4hgj (0.12 #8961, 0.03 #13023), 02rjjll (0.10 #145, 0.07 #425, 0.06 #285), 03gyp30 (0.10 #257, 0.06 #5857, 0.04 #3617), 0hndn2q (0.10 #180, 0.06 #320, 0.05 #460), 01s695 (0.10 #143, 0.05 #843, 0.05 #983), 02q690_ (0.10 #205, 0.04 #3285, 0.03 #11686), 0c4hnm (0.10 #268, 0.03 #13023, 0.03 #408), 073hkh (0.10 #141, 0.03 #13023, 0.02 #981) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 05wjnt; >> query: (?x5821, 09qvms) <- film(?x5821, ?x1451), award_winner(?x782, ?x5821), ?x1451 = 04zyhx >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #848 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 07ss8_; 01w7nwm; 01wyz92; 01w_10; *> query: (?x5821, 09q_6t) <- film(?x5821, ?x1451), celebrity(?x5821, ?x4397), award_winner(?x9301, ?x5821) *> conf = 0.03 ranks of expected_values: 40 EVAL 0hwbd award_winner! 09q_6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 145.000 145.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22314-0ljsz PRED entity: 0ljsz PRED relation: place_of_death! PRED expected values: 038w8 => 168 concepts (81 used for prediction) PRED predicted values (max 10 best out of 697): 01y8d4 (0.25 #1149, 0.01 #20036, 0.01 #23059), 01jqr_5 (0.10 #61217, 0.07 #40045, 0.04 #44581), 0jcx (0.09 #6799, 0.07 #18132, 0.06 #3778), 025xt8y (0.04 #44581, 0.04 #15111, 0.04 #26442), 01sxq9 (0.04 #44581, 0.04 #15111, 0.04 #26442), 0h326 (0.03 #3020, 0.02 #3776, 0.02 #4532), 05f0r8 (0.03 #3014, 0.02 #3770, 0.02 #4526), 01l3j (0.03 #3009, 0.02 #3765, 0.02 #4521), 067x44 (0.03 #3000, 0.02 #3756, 0.02 #4512), 058z1hb (0.03 #2996, 0.02 #3752, 0.02 #4508) >> Best rule #1149 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 09c7w0; >> query: (?x10988, 01y8d4) <- contains(?x10988, ?x10036), location(?x838, ?x10988), ?x10036 = 03t4nx, contains(?x6895, ?x10988) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ljsz place_of_death! 038w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 168.000 81.000 0.250 http://example.org/people/deceased_person/place_of_death #22313-03wh8pq PRED entity: 03wh8pq PRED relation: profession PRED expected values: 03gjzk => 80 concepts (65 used for prediction) PRED predicted values (max 10 best out of 37): 03gjzk (0.83 #607, 0.83 #1051, 0.82 #1199), 02hrh1q (0.69 #6529, 0.69 #7863, 0.68 #9492), 01d_h8 (0.51 #302, 0.51 #1930, 0.50 #598), 02jknp (0.50 #452, 0.43 #1932, 0.42 #2228), 0np9r (0.29 #169, 0.26 #9034, 0.13 #465), 018gz8 (0.26 #9034, 0.17 #2237, 0.17 #1941), 0cbd2 (0.22 #2227, 0.19 #1043, 0.19 #895), 09jwl (0.17 #4903, 0.17 #5051, 0.17 #4459), 0kyk (0.12 #2249, 0.09 #1953, 0.08 #1213), 0nbcg (0.12 #4915, 0.12 #4471, 0.12 #5952) >> Best rule #607 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 04n7njg; >> query: (?x9271, 03gjzk) <- award_winner(?x4517, ?x9271), producer_type(?x9271, ?x632), profession(?x9271, ?x987) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03wh8pq profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 65.000 0.830 http://example.org/people/person/profession #22312-027jbr PRED entity: 027jbr PRED relation: inductee PRED expected values: 0hsph => 9 concepts (7 used for prediction) PRED predicted values (max 10 best out of 484): 03h_fk5 (0.50 #343, 0.40 #499, 0.33 #30), 028qyn (0.50 #445, 0.40 #601, 0.33 #132), 01zlh5 (0.40 #573, 0.33 #104, 0.30 #1098), 0127xk (0.40 #607, 0.29 #764, 0.25 #1080), 02qwg (0.33 #38, 0.30 #1098, 0.25 #351), 01vsl3_ (0.33 #29, 0.30 #1098, 0.25 #342), 014kyy (0.33 #147, 0.30 #1098, 0.25 #460), 0pk41 (0.33 #118, 0.30 #1098, 0.25 #431), 0134wr (0.33 #102, 0.30 #1098, 0.25 #415), 016732 (0.33 #89, 0.30 #1098, 0.25 #402) >> Best rule #343 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 0qjfl; >> query: (?x14757, 03h_fk5) <- inductee(?x14757, ?x11446), award(?x11446, ?x1801), languages(?x11446, ?x254), profession(?x11446, ?x1032), category(?x11446, ?x134), artists(?x114, ?x11446), ?x1032 = 02hrh1q, artist(?x2241, ?x11446), award_nominee(?x11446, ?x2214) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027jbr inductee 0hsph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 9.000 7.000 0.500 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #22311-02f72n PRED entity: 02f72n PRED relation: award! PRED expected values: 0147dk 02r3zy 01r9fv 0840vq 0394y 0dw4g 02ndj5 09jm8 => 36 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2352): 0dw4g (0.77 #43254, 0.73 #49910, 0.72 #43253), 011z3g (0.77 #43254, 0.73 #49910, 0.72 #43253), 016t0h (0.77 #43254, 0.73 #49910, 0.72 #43253), 02z4b_8 (0.75 #8688, 0.57 #15341, 0.50 #18668), 0gdh5 (0.67 #10724, 0.67 #4071, 0.62 #7398), 01vrt_c (0.67 #3609, 0.50 #6936, 0.44 #10262), 043zg (0.67 #4877, 0.44 #11530, 0.38 #8204), 01xzb6 (0.62 #8174, 0.50 #14827, 0.44 #18154), 0frsw (0.62 #7311, 0.36 #13964, 0.33 #3984), 02r3zy (0.62 #6903, 0.36 #13556, 0.31 #16883) >> Best rule #43254 for best value: >> intensional similarity = 4 >> extensional distance = 202 >> proper extension: 0m7yy; 05qck; 02qkk9_; 0d085; 02kgb7; 058vy5; 0bqsk5; 02q3s; >> query: (?x2634, ?x5329) <- award_winner(?x2634, ?x5329), award_winner(?x2634, ?x4593), artists(?x302, ?x5329), award(?x4593, ?x724) >> conf = 0.77 => this is the best rule for 3 predicted values ranks of expected_values: 1, 10, 18, 29, 133, 196, 1280 EVAL 02f72n award! 09jm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 36.000 18.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72n award! 02ndj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 18.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72n award! 0dw4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 18.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72n award! 0394y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 36.000 18.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72n award! 0840vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 36.000 18.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72n award! 01r9fv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 36.000 18.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72n award! 02r3zy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 36.000 18.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72n award! 0147dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 36.000 18.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22310-026dx PRED entity: 026dx PRED relation: religion PRED expected values: 0631_ => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 18): 0kpl (0.19 #1630, 0.18 #1675, 0.18 #460), 03_gx (0.17 #149, 0.16 #104, 0.15 #59), 0c8wxp (0.16 #501, 0.14 #456, 0.13 #726), 092bf5 (0.07 #241, 0.05 #61, 0.04 #106), 0kq2 (0.06 #468, 0.06 #558, 0.06 #1683), 0n2g (0.04 #553, 0.04 #463, 0.04 #1633), 01lp8 (0.04 #316, 0.04 #136, 0.04 #1), 0flw86 (0.04 #227, 0.02 #182, 0.02 #2388), 051kv (0.02 #50, 0.02 #95, 0.02 #140), 06nzl (0.02 #510, 0.02 #1410, 0.01 #1500) >> Best rule #1630 for best value: >> intensional similarity = 3 >> extensional distance = 277 >> proper extension: 055yr; >> query: (?x4703, 0kpl) <- influenced_by(?x8841, ?x4703), influenced_by(?x4703, ?x4915), influenced_by(?x4915, ?x2240) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #278 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 06lxn; *> query: (?x4703, 0631_) <- influenced_by(?x8841, ?x4703), award_winner(?x372, ?x4703), award_winner(?x4703, ?x5521) *> conf = 0.01 ranks of expected_values: 15 EVAL 026dx religion 0631_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 113.000 113.000 0.190 http://example.org/people/person/religion #22309-01mr2g6 PRED entity: 01mr2g6 PRED relation: student! PRED expected values: 036hv => 136 concepts (120 used for prediction) PRED predicted values (max 10 best out of 86): 0mg1w (0.33 #44, 0.04 #596, 0.03 #2140), 02822 (0.20 #2126, 0.15 #1941, 0.15 #1692), 04rlf (0.18 #229, 0.17 #598, 0.17 #290), 0fdys (0.17 #89, 0.11 #888, 0.10 #1690), 04g51 (0.17 #99, 0.09 #221, 0.08 #282), 03qsdpk (0.12 #2131, 0.11 #1759, 0.11 #895), 0w7c (0.10 #1952, 0.09 #2137, 0.08 #2632), 03g3w (0.09 #880, 0.09 #203, 0.08 #264), 062z7 (0.09 #204, 0.09 #1189, 0.08 #573), 05qfh (0.09 #209, 0.08 #270, 0.07 #886) >> Best rule #44 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0qf3p; >> query: (?x8272, 0mg1w) <- instrumentalists(?x227, ?x8272), artist(?x6474, ?x8272), ?x6474 = 0g768, student(?x2314, ?x8272) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #868 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 099bk; *> query: (?x8272, 036hv) <- type_of_union(?x8272, ?x566), student(?x734, ?x8272), religion(?x8272, ?x2694), student(?x2314, ?x8272) *> conf = 0.04 ranks of expected_values: 29 EVAL 01mr2g6 student! 036hv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 136.000 120.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #22308-02qfh PRED entity: 02qfh PRED relation: producer_type PRED expected values: 0ckd1 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 28): 0ckd1 (0.73 #17, 0.71 #23, 0.67 #14), 014kbl (0.02 #28), 06qc5 (0.02 #28), 033smt (0.02 #28), 026sdt1 (0.02 #28), 0d2b38 (0.02 #28), 02vs3x5 (0.02 #28), 02zdwq (0.02 #28), 089g0h (0.02 #28), 0215hd (0.02 #28) >> Best rule #17 for best value: >> intensional similarity = 13 >> extensional distance = 20 >> proper extension: 0n2bh; 01b9w3; >> query: (?x8686, 0ckd1) <- genre(?x8686, ?x8805), ?x8805 = 06q7n, actor(?x8686, ?x2092), country_of_origin(?x8686, ?x362), contains(?x362, ?x639), location(?x10924, ?x362), location(?x8383, ?x362), place_founded(?x2776, ?x362), award_winner(?x2139, ?x10924), languages(?x8686, ?x254), place_of_death(?x587, ?x362), origin(?x1407, ?x362), diet(?x8383, ?x3130) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qfh producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.727 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #22307-01cv3n PRED entity: 01cv3n PRED relation: location_of_ceremony PRED expected values: 0r0m6 => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 26): 0cv3w (0.05 #273, 0.03 #987, 0.03 #1583), 030qb3t (0.05 #614, 0.03 #971, 0.02 #733), 0r62v (0.04 #493), 04jpl (0.03 #961, 0.02 #723, 0.02 #1557), 01x73 (0.03 #1072, 0.01 #2859), 0162v (0.03 #620, 0.01 #977, 0.01 #1216), 03gh4 (0.03 #658, 0.01 #1254), 059rby (0.02 #1318), 012wgb (0.02 #756, 0.01 #994, 0.01 #1471), 013kcv (0.02 #730, 0.01 #1207, 0.01 #1445) >> Best rule #273 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 02bfxb; 09px1w; >> query: (?x680, 0cv3w) <- student(?x1151, ?x680), profession(?x680, ?x6476), profession(?x680, ?x1183), ?x6476 = 025352, ?x1183 = 09jwl >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01cv3n location_of_ceremony 0r0m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 152.000 152.000 0.053 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #22306-01pb34 PRED entity: 01pb34 PRED relation: film PRED expected values: 013q07 03h3x5 074w86 07j94 02v570 => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1195): 014lc_ (0.73 #104, 0.33 #105, 0.25 #163), 056xkh (0.73 #104, 0.33 #151, 0.25 #209), 0gmd3k7 (0.73 #104, 0.33 #135, 0.25 #193), 0m491 (0.73 #104, 0.33 #116, 0.25 #174), 02x8fs (0.73 #104, 0.33 #129, 0.25 #187), 02825nf (0.73 #104, 0.33 #145, 0.25 #203), 03bzjpm (0.73 #104, 0.33 #144, 0.25 #202), 02_sr1 (0.73 #104, 0.33 #124, 0.25 #182), 0322yj (0.73 #104, 0.09 #157, 0.07 #161), 09v8clw (0.73 #104, 0.09 #157, 0.07 #161) >> Best rule #104 for best value: >> intensional similarity = 46 >> extensional distance = 1 >> proper extension: 01kyvx; >> query: (?x4832, ?x66) <- special_performance_type(?x11882, ?x4832), special_performance_type(?x9681, ?x4832), special_performance_type(?x7489, ?x4832), special_performance_type(?x7040, ?x4832), special_performance_type(?x4740, ?x4832), special_performance_type(?x3183, ?x4832), special_performance_type(?x3002, ?x4832), special_performance_type(?x2135, ?x4832), special_performance_type(?x1445, ?x4832), special_performance_type(?x1206, ?x4832), type_of_union(?x3002, ?x566), student(?x481, ?x7489), person(?x5201, ?x4740), film(?x4832, ?x365), artists(?x9630, ?x1206), film(?x7489, ?x592), nominated_for(?x9681, ?x1372), award(?x7489, ?x435), nominated_for(?x7489, ?x782), award_nominee(?x3002, ?x3651), profession(?x4740, ?x220), participant(?x3002, ?x4240), profession(?x9681, ?x319), nominated_for(?x1445, ?x7651), award_nominee(?x2657, ?x7489), film(?x237, ?x365), genre(?x365, ?x53), people(?x4195, ?x7040), parent_genre(?x12070, ?x9630), award_nominee(?x1445, ?x1554), film(?x1445, ?x2617), gender(?x9681, ?x231), film_crew_role(?x365, ?x468), people(?x1446, ?x3002), film(?x3183, ?x66), location(?x11882, ?x12335), participant(?x10410, ?x7489), participant(?x444, ?x7040), award_nominee(?x9681, ?x382), actor(?x7488, ?x7489), religion(?x11882, ?x8967), student(?x13396, ?x1445), nominated_for(?x435, ?x337), student(?x3416, ?x7040), religion(?x2135, ?x7131), award_nominee(?x846, ?x2135) >> conf = 0.73 => this is the best rule for 471 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 97, 128, 467, 892 EVAL 01pb34 film 02v570 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 5.000 5.000 0.733 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film EVAL 01pb34 film 07j94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.733 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film EVAL 01pb34 film 074w86 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.733 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film EVAL 01pb34 film 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.733 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film EVAL 01pb34 film 013q07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 5.000 5.000 0.733 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #22305-04wlh PRED entity: 04wlh PRED relation: country! PRED expected values: 0bynt 01gqfm => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 55): 0bynt (0.89 #1276, 0.86 #1111, 0.85 #451), 071t0 (0.78 #133, 0.78 #353, 0.72 #408), 06f41 (0.78 #125, 0.65 #290, 0.63 #455), 01lb14 (0.74 #126, 0.65 #346, 0.60 #291), 06wrt (0.74 #127, 0.62 #347, 0.60 #402), 07gyv (0.70 #117, 0.62 #282, 0.61 #447), 03hr1p (0.70 #134, 0.62 #354, 0.57 #629), 07jbh (0.70 #144, 0.56 #474, 0.55 #309), 0194d (0.63 #158, 0.60 #323, 0.59 #488), 02y8z (0.63 #130, 0.50 #295, 0.50 #75) >> Best rule #1276 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 04gzd; 019rg5; 03gj2; 01mjq; 04w4s; 0bjv6; 04w8f; >> query: (?x8742, 0bynt) <- medal(?x8742, ?x422), country(?x1967, ?x8742), ?x1967 = 01cgz >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 19 EVAL 04wlh country! 01gqfm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 134.000 134.000 0.894 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 04wlh country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.894 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #22304-016vn3 PRED entity: 016vn3 PRED relation: group! PRED expected values: 0342h 028tv0 => 97 concepts (80 used for prediction) PRED predicted values (max 10 best out of 119): 0342h (0.93 #1535, 0.90 #2387, 0.90 #1110), 028tv0 (0.47 #267, 0.44 #522, 0.44 #1542), 03qjg (0.40 #895, 0.40 #725, 0.38 #555), 05r5c (0.37 #857, 0.32 #687, 0.26 #1197), 013y1f (0.33 #876, 0.32 #706, 0.26 #961), 01vj9c (0.33 #268, 0.31 #523, 0.27 #2650), 0l14j_ (0.24 #729, 0.23 #899, 0.16 #984), 04rzd (0.24 #710, 0.23 #965, 0.20 #880), 07y_7 (0.23 #937, 0.16 #682, 0.13 #852), 042v_gx (0.20 #263, 0.16 #943, 0.11 #1538) >> Best rule #1535 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 018gm9; 03k3b; 01516r; 07rnh; >> query: (?x10502, 0342h) <- group(?x716, ?x10502), artist(?x2193, ?x10502), artists(?x302, ?x10502), ?x716 = 018vs >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 016vn3 group! 028tv0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 80.000 0.927 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 016vn3 group! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 80.000 0.927 http://example.org/music/performance_role/regular_performances./music/group_membership/group #22303-0gd_b_ PRED entity: 0gd_b_ PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 62): 09c7w0 (0.80 #301, 0.79 #401, 0.76 #2402), 02jx1 (0.30 #5606, 0.11 #3637, 0.10 #5839), 07ssc (0.30 #5606, 0.09 #815, 0.09 #1415), 0d060g (0.30 #5606, 0.06 #1807, 0.06 #107), 03rk0 (0.08 #4951, 0.06 #9955, 0.06 #5752), 03rjj (0.05 #1005, 0.03 #3103, 0.03 #1405), 0f8l9c (0.03 #1022, 0.03 #3103, 0.02 #4927), 0chghy (0.03 #810, 0.03 #710, 0.03 #2612), 03rt9 (0.03 #1013, 0.03 #3103, 0.02 #4918), 0345h (0.03 #3103, 0.02 #4936, 0.02 #7238) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 157 >> proper extension: 01pw2f1; 047hpm; 02jyhv; 03kxp7; 017m2y; >> query: (?x3051, 09c7w0) <- film(?x3051, ?x796), participant(?x3789, ?x3051), actor(?x4881, ?x3051) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gd_b_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.799 http://example.org/people/person/nationality #22302-01bfjy PRED entity: 01bfjy PRED relation: company! PRED expected values: 060c4 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 34): 0dq_5 (0.69 #1998, 0.68 #1863, 0.42 #2133), 0krdk (0.64 #1987, 0.61 #1852, 0.39 #2122), 060c4 (0.61 #2573, 0.61 #2118, 0.60 #2163), 05_wyz (0.44 #1864, 0.41 #1999, 0.27 #2179), 0dq3c (0.44 #1847, 0.38 #1982, 0.27 #2117), 02k13d (0.40 #1004, 0.38 #464, 0.31 #824), 01yc02 (0.31 #1989, 0.29 #1854, 0.25 #54), 01rk91 (0.28 #1306, 0.27 #631, 0.25 #451), 0fkvn (0.25 #95, 0.20 #275, 0.14 #365), 02211by (0.25 #49, 0.14 #1984, 0.13 #1849) >> Best rule #1998 for best value: >> intensional similarity = 6 >> extensional distance = 86 >> proper extension: 087c7; 04qhdf; 02bh8z; 045c7b; 01s73z; 09j_g; 02630g; 077w0b; 0z90c; 03y7ml; ... >> query: (?x14343, 0dq_5) <- citytown(?x14343, ?x8951), company(?x8314, ?x14343), company(?x8314, ?x6678), company(?x8314, ?x5260), ?x6678 = 05gnf, child(?x10957, ?x5260) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2573 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 214 *> proper extension: 09c7w0; 0f8l9c; 0l8sx; 05g76; 03rj0; 0f1nl; 01_8w2; 02hcxm; 034f0d; 04htfd; ... *> query: (?x14343, 060c4) <- company(?x8314, ?x14343), company(?x8314, ?x14218), company(?x8314, ?x3487), child(?x10957, ?x14218), award_nominee(?x2246, ?x3487), award_winner(?x3487, ?x3381) *> conf = 0.61 ranks of expected_values: 3 EVAL 01bfjy company! 060c4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 120.000 0.693 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #22301-02773nt PRED entity: 02773nt PRED relation: award_winner! PRED expected values: 03nnm4t => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 96): 03nnm4t (0.25 #71, 0.14 #207, 0.12 #343), 0gvstc3 (0.20 #441, 0.14 #849, 0.13 #713), 027n06w (0.17 #478, 0.13 #750, 0.13 #886), 09g90vz (0.12 #119, 0.10 #9251, 0.10 #9250), 03gyp30 (0.12 #112, 0.10 #9251, 0.10 #9250), 0bq_mx (0.12 #536, 0.10 #400, 0.10 #808), 02wzl1d (0.10 #9251, 0.10 #9250, 0.10 #3265), 058m5m4 (0.10 #9251, 0.10 #9250, 0.10 #3265), 0bx6zs (0.09 #258, 0.06 #122, 0.05 #1482), 0gx_st (0.09 #1396, 0.09 #308, 0.09 #444) >> Best rule #71 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 05ty4m; 02778qt; >> query: (?x829, 03nnm4t) <- award_nominee(?x829, ?x830), gender(?x829, ?x231), ?x830 = 02773m2 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02773nt award_winner! 03nnm4t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22300-05q9g1 PRED entity: 05q9g1 PRED relation: profession PRED expected values: 025352 => 88 concepts (42 used for prediction) PRED predicted values (max 10 best out of 64): 01d_h8 (0.74 #882, 0.71 #736, 0.54 #1612), 09jwl (0.40 #1039, 0.34 #2061, 0.25 #2792), 03gjzk (0.38 #1911, 0.36 #1619, 0.35 #305), 0nbcg (0.33 #1052, 0.28 #2074, 0.17 #2805), 018gz8 (0.30 #307, 0.27 #1913, 0.26 #1621), 0dz3r (0.28 #2046, 0.27 #1024, 0.17 #2777), 016z4k (0.27 #1026, 0.24 #2048, 0.14 #2779), 02krf9 (0.26 #755, 0.21 #901, 0.17 #1923), 01c72t (0.25 #1044, 0.14 #2066, 0.10 #2797), 0cbd2 (0.22 #299, 0.19 #1905, 0.19 #1613) >> Best rule #882 for best value: >> intensional similarity = 6 >> extensional distance = 245 >> proper extension: 05dxl_; >> query: (?x10076, 01d_h8) <- profession(?x10076, ?x1032), profession(?x10076, ?x987), profession(?x10076, ?x524), ?x1032 = 02hrh1q, ?x987 = 0dxtg, ?x524 = 02jknp >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1079 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 247 *> proper extension: 07_grx; *> query: (?x10076, 025352) <- award_nominee(?x10076, ?x3890), award(?x3890, ?x462), music(?x3742, ?x3890) *> conf = 0.12 ranks of expected_values: 16 EVAL 05q9g1 profession 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 88.000 42.000 0.737 http://example.org/people/person/profession #22299-08sfxj PRED entity: 08sfxj PRED relation: language PRED expected values: 02h40lc => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 46): 02h40lc (0.91 #180, 0.90 #2465, 0.90 #1805), 06nm1 (0.25 #129, 0.11 #613, 0.11 #1814), 04306rv (0.19 #123, 0.10 #304, 0.10 #548), 064_8sq (0.17 #321, 0.17 #81, 0.14 #1825), 02bjrlw (0.11 #240, 0.08 #179, 0.08 #1682), 03_9r (0.09 #10, 0.08 #69, 0.06 #612), 0jzc (0.09 #20, 0.08 #79, 0.06 #319), 04h9h (0.09 #43, 0.08 #102, 0.04 #586), 032f6 (0.09 #56, 0.08 #115, 0.03 #599), 0653m (0.06 #190, 0.06 #130, 0.05 #432) >> Best rule #180 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 0qmfk; >> query: (?x5152, 02h40lc) <- country(?x5152, ?x94), film_release_region(?x5152, ?x279), award_winner(?x5152, ?x3447), film(?x1739, ?x5152) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08sfxj language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.906 http://example.org/film/film/language #22298-073bb PRED entity: 073bb PRED relation: type_of_union PRED expected values: 04ztj => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.83 #29, 0.80 #41, 0.80 #21), 01g63y (0.19 #577, 0.18 #174, 0.18 #118), 01bl8s (0.19 #577, 0.08 #27, 0.02 #87), 0jgjn (0.19 #577) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0chsq; 0f2df; 081nh; 06wvj; 0lgm5; 01fs_4; 0q59y; 0kvnn; 043gj; 03d_zl4; ... >> query: (?x1900, 04ztj) <- profession(?x1900, ?x353), people(?x6821, ?x1900), student(?x2999, ?x1900), languages(?x1900, ?x254) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 073bb type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.833 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22297-0dq_5 PRED entity: 0dq_5 PRED relation: company PRED expected values: 0jbk9 04kqk 01frpd => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 576): 03phgz (0.67 #2181, 0.62 #1032, 0.62 #260), 0hkqn (0.62 #1032, 0.62 #260, 0.60 #1506), 02y7t7 (0.62 #1032, 0.62 #260, 0.60 #1343), 0mgkg (0.62 #1032, 0.62 #260, 0.58 #4890), 055z7 (0.62 #1032, 0.62 #260, 0.58 #4890), 01ym8l (0.62 #1032, 0.62 #260, 0.58 #4890), 03mdt (0.62 #1032, 0.62 #260, 0.58 #4890), 073tm9 (0.62 #1032, 0.62 #260, 0.58 #4890), 0206k5 (0.62 #1032, 0.62 #260, 0.58 #4890), 01yx7f (0.62 #1032, 0.62 #260, 0.58 #4890) >> Best rule #2181 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 09d6p2; >> query: (?x4682, 03phgz) <- organization(?x4682, ?x11199), company(?x4682, ?x13277), company(?x4682, ?x2548), company(?x4682, ?x1160), currency(?x11199, ?x7888), state_province_region(?x13277, ?x1274), award_winner(?x3406, ?x2548), nominated_for(?x2548, ?x570), category(?x1160, ?x134), production_companies(?x349, ?x2548) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3083 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 01kr6k; *> query: (?x4682, 01frpd) <- company(?x4682, ?x6016), company(?x4682, ?x3920), company(?x4682, ?x2021), company(?x4682, ?x266), ?x3920 = 09b3v, company(?x265, ?x6016), ?x2021 = 0hpt3, currency(?x266, ?x170), place_founded(?x6016, ?x11315), ?x265 = 0dq3c *> conf = 0.50 ranks of expected_values: 41, 116 EVAL 0dq_5 company 01frpd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 34.000 34.000 0.667 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0dq_5 company 04kqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.667 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0dq_5 company 0jbk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 34.000 34.000 0.667 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #22296-05t54s PRED entity: 05t54s PRED relation: film! PRED expected values: 05ml_s => 161 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1356): 0c1j_ (0.33 #3883, 0.12 #24708, 0.08 #33040), 046lt (0.33 #2587, 0.06 #23412, 0.04 #29661), 03knl (0.33 #2239, 0.06 #23064, 0.04 #31396), 01nm3s (0.33 #2771, 0.06 #23596, 0.04 #31928), 0bmh4 (0.33 #2500, 0.06 #23325, 0.04 #31657), 01x6jd (0.33 #4017, 0.06 #24842, 0.04 #33174), 048hf (0.33 #3450, 0.06 #24275, 0.04 #32607), 01y665 (0.33 #520, 0.05 #60908, 0.04 #65073), 0sz28 (0.33 #193, 0.04 #85559, 0.03 #39762), 05bnp0 (0.33 #13, 0.03 #79136, 0.03 #39582) >> Best rule #3883 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01bn3l; >> query: (?x6773, 0c1j_) <- film(?x574, ?x6773), currency(?x6773, ?x170), prequel(?x6773, ?x2498), person(?x6773, ?x12571), influenced_by(?x7893, ?x12571) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10537 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0401sg; *> query: (?x6773, 05ml_s) <- film(?x574, ?x6773), currency(?x6773, ?x170), music(?x6773, ?x3414), ?x3414 = 0b6yp2, country(?x6773, ?x94) *> conf = 0.11 ranks of expected_values: 111 EVAL 05t54s film! 05ml_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 161.000 112.000 0.333 http://example.org/film/actor/film./film/performance/film #22295-0151ns PRED entity: 0151ns PRED relation: category PRED expected values: 08mbj5d => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.39 #4, 0.34 #8, 0.32 #14) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 073749; 0cgfb; >> query: (?x558, 08mbj5d) <- film(?x558, ?x8234), film(?x558, ?x2644), country(?x8234, ?x94), ?x2644 = 01shy7 >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0151ns category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.394 http://example.org/common/topic/webpage./common/webpage/category #22294-015t7v PRED entity: 015t7v PRED relation: award_nominee PRED expected values: 0dvmd => 82 concepts (35 used for prediction) PRED predicted values (max 10 best out of 553): 01rh0w (0.80 #51028, 0.80 #76544, 0.80 #25513), 015t56 (0.80 #51028, 0.80 #76544, 0.80 #25513), 015t7v (0.73 #3496, 0.72 #5817, 0.27 #53351), 0dlglj (0.27 #53351, 0.27 #81185, 0.26 #55672), 01yfm8 (0.27 #53351, 0.27 #81185, 0.26 #55672), 0jfx1 (0.27 #53351, 0.27 #81185, 0.26 #55672), 01l2fn (0.27 #53351, 0.27 #81185, 0.26 #55672), 040696 (0.27 #53351, 0.27 #81185, 0.26 #55672), 03l3jy (0.27 #53351, 0.27 #81185, 0.26 #55672), 05qg6g (0.27 #53351, 0.27 #81185, 0.26 #55672) >> Best rule #51028 for best value: >> intensional similarity = 3 >> extensional distance = 1287 >> proper extension: 01r216; 04glx0; 04s04; 01w_10; >> query: (?x4999, ?x230) <- award_winner(?x4999, ?x2728), award_nominee(?x230, ?x4999), nominated_for(?x2728, ?x972) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #37110 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1100 *> proper extension: 02pbp9; *> query: (?x4999, ?x800) <- nominated_for(?x4999, ?x1597), award_winner(?x1193, ?x4999), nominated_for(?x800, ?x1597) *> conf = 0.23 ranks of expected_values: 23 EVAL 015t7v award_nominee 0dvmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 82.000 35.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22293-015q43 PRED entity: 015q43 PRED relation: type_of_union PRED expected values: 01g63y => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 2): 01g63y (0.49 #259, 0.32 #31, 0.29 #100), 0jgjn (0.02 #51, 0.01 #81, 0.01 #78) >> Best rule #259 for best value: >> intensional similarity = 3 >> extensional distance = 1192 >> proper extension: 02knnd; 062hgx; 0knjh; 076df9; 0bm9xk; 02__ww; >> query: (?x5043, ?x566) <- award_nominee(?x5043, ?x2444), location(?x5043, ?x10242), type_of_union(?x2444, ?x566) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015q43 type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.488 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22292-0gx_st PRED entity: 0gx_st PRED relation: honored_for PRED expected values: 0hz55 05lfwd 01kt_j => 32 concepts (23 used for prediction) PRED predicted values (max 10 best out of 713): 039cq4 (0.71 #6859, 0.60 #5096, 0.57 #5683), 07zhjj (0.71 #6945, 0.60 #5182, 0.57 #5769), 06mr2s (0.71 #6735, 0.60 #4972, 0.57 #5559), 0hz55 (0.60 #7042, 0.59 #6455, 0.55 #7041), 0557yqh (0.60 #7042, 0.59 #6455, 0.55 #7041), 06qxh (0.60 #7042, 0.59 #6455, 0.55 #7041), 01b7h8 (0.60 #5217, 0.57 #6980, 0.57 #5804), 04xbq3 (0.60 #5197, 0.57 #6960, 0.57 #5784), 01vnbh (0.60 #5006, 0.57 #6769, 0.57 #5593), 080dwhx (0.60 #7042, 0.55 #7041, 0.54 #5279) >> Best rule #6859 for best value: >> intensional similarity = 18 >> extensional distance = 5 >> proper extension: 0lp_cd3; 03nnm4t; >> query: (?x2292, 039cq4) <- award_winner(?x2292, ?x12138), award_winner(?x2292, ?x4411), award_winner(?x2292, ?x3366), award_winner(?x2292, ?x1630), ceremony(?x4225, ?x2292), ?x4225 = 09qvf4, tv_program(?x12138, ?x4932), program(?x12138, ?x8870), honored_for(?x2292, ?x3626), ?x3626 = 01j7mr, profession(?x12138, ?x987), nationality(?x3366, ?x279), film(?x3366, ?x430), participant(?x4411, ?x5925), gender(?x1630, ?x231), participant(?x9232, ?x4411), people(?x1446, ?x4411), award_nominee(?x12138, ?x5645) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #7042 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 5 *> proper extension: 0lp_cd3; 03nnm4t; *> query: (?x2292, ?x4932) <- award_winner(?x2292, ?x12138), award_winner(?x2292, ?x4411), award_winner(?x2292, ?x3366), award_winner(?x2292, ?x1630), ceremony(?x4225, ?x2292), ?x4225 = 09qvf4, tv_program(?x12138, ?x4932), program(?x12138, ?x8870), honored_for(?x2292, ?x3626), ?x3626 = 01j7mr, profession(?x12138, ?x987), nationality(?x3366, ?x279), film(?x3366, ?x430), participant(?x4411, ?x5925), gender(?x1630, ?x231), participant(?x9232, ?x4411), people(?x1446, ?x4411), award_nominee(?x12138, ?x5645) *> conf = 0.60 ranks of expected_values: 4, 27, 37 EVAL 0gx_st honored_for 01kt_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 32.000 23.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0gx_st honored_for 05lfwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 32.000 23.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0gx_st honored_for 0hz55 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 32.000 23.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #22291-03xh50 PRED entity: 03xh50 PRED relation: current_club PRED expected values: 06vlk0 => 110 concepts (85 used for prediction) PRED predicted values (max 10 best out of 737): 0xbm (0.50 #166, 0.43 #902, 0.42 #1786), 04ltf (0.43 #956, 0.40 #809, 0.31 #2135), 06l22 (0.43 #941, 0.33 #1825, 0.31 #2120), 01634x (0.40 #815, 0.29 #962, 0.25 #1846), 023fb (0.40 #788, 0.25 #199, 0.20 #641), 0y9j (0.33 #1818, 0.31 #2113, 0.29 #934), 03x6m (0.31 #3168, 0.29 #3611, 0.28 #4053), 0hvgt (0.29 #900, 0.25 #1784, 0.25 #164), 0y54 (0.29 #891, 0.25 #155, 0.20 #4280), 02gys2 (0.29 #889, 0.25 #153, 0.20 #742) >> Best rule #166 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 01l3vx; 02ltg3; >> query: (?x7294, 0xbm) <- current_club(?x7294, ?x1599), position(?x7294, ?x60), ?x60 = 02nzb8, colors(?x1599, ?x663), teams(?x252, ?x7294), origin(?x12753, ?x252), olympics(?x252, ?x418), film_release_region(?x66, ?x252) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5304 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 27 *> proper extension: 01_lhg; 03d8m4; 02w64f; *> query: (?x7294, ?x59) <- current_club(?x7294, ?x1599), position(?x7294, ?x60), position(?x13495, ?x60), position(?x13306, ?x60), position(?x11991, ?x60), position(?x9971, ?x60), ?x13495 = 03zb6t, ?x11991 = 01dwyd, ?x9971 = 02s2ys, ?x13306 = 06ylv0, team(?x60, ?x59) *> conf = 0.01 ranks of expected_values: 478 EVAL 03xh50 current_club 06vlk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 110.000 85.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #22290-0gffmn8 PRED entity: 0gffmn8 PRED relation: film_release_region PRED expected values: 0154j 03_3d 015fr 0k6nt 02vzc 03rj0 0bjv6 03ryn 0jgx => 75 concepts (74 used for prediction) PRED predicted values (max 10 best out of 173): 015fr (0.89 #1193, 0.88 #405, 0.85 #1062), 02vzc (0.88 #565, 0.87 #1880, 0.82 #1090), 03_3d (0.88 #399, 0.87 #924, 0.85 #1056), 0154j (0.83 #1580, 0.83 #922, 0.82 #1316), 0k6nt (0.81 #2651, 0.81 #2518, 0.80 #939), 03rj0 (0.75 #570, 0.69 #438, 0.69 #1226), 06mzp (0.72 #1857, 0.69 #410, 0.62 #1067), 06f32 (0.69 #574, 0.56 #442, 0.53 #2151), 09pmkv (0.65 #1073, 0.62 #548, 0.60 #1467), 07ylj (0.59 #1075, 0.57 #1469, 0.57 #1206) >> Best rule #1193 for best value: >> intensional similarity = 8 >> extensional distance = 33 >> proper extension: 02vxq9m; 0gkz15s; 017gl1; 0bwfwpj; 08hmch; 0h3xztt; 053rxgm; 04hwbq; 05qbckf; 02yvct; ... >> query: (?x3217, 015fr) <- film_release_region(?x3217, ?x4743), film_release_region(?x3217, ?x4737), film_release_region(?x3217, ?x1174), genre(?x3217, ?x225), film(?x2387, ?x3217), ?x4737 = 07twz, ?x1174 = 047yc, ?x4743 = 03spz >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 12, 18, 26 EVAL 0gffmn8 film_release_region 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 75.000 74.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gffmn8 film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 75.000 74.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gffmn8 film_release_region 0bjv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 75.000 74.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gffmn8 film_release_region 03rj0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 74.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gffmn8 film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 74.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gffmn8 film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 74.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gffmn8 film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 74.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gffmn8 film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 74.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gffmn8 film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 74.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22289-0j4b PRED entity: 0j4b PRED relation: adjoins! PRED expected values: 01rxw => 113 concepts (102 used for prediction) PRED predicted values (max 10 best out of 380): 06bnz (0.23 #3991, 0.11 #9459, 0.11 #10240), 0694j (0.22 #301, 0.18 #1081, 0.18 #77329), 0j4b (0.22 #74983, 0.22 #39813, 0.21 #76548), 07dzf (0.22 #74983, 0.22 #39813, 0.21 #76548), 088q4 (0.22 #74983, 0.22 #39813, 0.21 #76548), 0166v (0.22 #74983, 0.22 #39813, 0.21 #76548), 06tw8 (0.22 #74983, 0.22 #39813, 0.21 #76548), 0169t (0.22 #74983, 0.22 #39813, 0.21 #76548), 01nyl (0.22 #74983, 0.22 #39813, 0.21 #76548), 01rxw (0.22 #74983, 0.22 #39813, 0.21 #76548) >> Best rule #3991 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 0glb5; 0b2ds; 01c6yz; 0p0mx; 0d8h4; 035p3; >> query: (?x6428, 06bnz) <- adjoins(?x1144, ?x6428), place_of_burial(?x7718, ?x1144), adjoins(?x94, ?x1144) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #74983 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 713 *> proper extension: 0s3y5; 0_3cs; 0rjg8; 0mkqr; 0nv2x; 0xn7b; 0nj3m; 01cz_1; 04pry; 0kwmc; ... *> query: (?x6428, ?x728) <- adjoins(?x1144, ?x6428), adjoins(?x1144, ?x728), contains(?x2467, ?x6428) *> conf = 0.22 ranks of expected_values: 10 EVAL 0j4b adjoins! 01rxw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 113.000 102.000 0.229 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #22288-03_ly PRED entity: 03_ly PRED relation: month! PRED expected values: 02cft 071vr 0947l 03czqs => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1302): 0947l (0.89 #25, 0.89 #16, 0.89 #14), 02cft (0.89 #25, 0.89 #16, 0.89 #14), 071vr (0.89 #25, 0.89 #16, 0.89 #14), 03czqs (0.89 #25, 0.89 #16, 0.89 #14), 0f2wj (0.47 #66, 0.17 #12, 0.13 #22), 015zxh (0.47 #66, 0.17 #12, 0.09 #10), 0281y0 (0.47 #66, 0.17 #12, 0.05 #11), 0r3wm (0.47 #66, 0.05 #11), 01lxw6 (0.47 #66, 0.04 #60), 0r3tq (0.17 #12, 0.13 #22, 0.11 #21) >> Best rule #25 for best value: >> intensional similarity = 131 >> extensional distance = 1 >> proper extension: 04w_7; >> query: (?x4827, ?x8956) <- month(?x12674, ?x4827), month(?x11197, ?x4827), month(?x9559, ?x4827), month(?x8977, ?x4827), month(?x8174, ?x4827), month(?x6959, ?x4827), month(?x6703, ?x4827), month(?x6494, ?x4827), month(?x6458, ?x4827), month(?x6054, ?x4827), month(?x5036, ?x4827), month(?x4826, ?x4827), month(?x4271, ?x4827), month(?x3501, ?x4827), month(?x3373, ?x4827), month(?x3106, ?x4827), month(?x3052, ?x4827), month(?x3026, ?x4827), month(?x2985, ?x4827), month(?x2611, ?x4827), month(?x2474, ?x4827), month(?x2316, ?x4827), month(?x1458, ?x4827), month(?x1036, ?x4827), month(?x739, ?x4827), month(?x659, ?x4827), month(?x108, ?x4827), seasonal_months(?x7298, ?x4827), seasonal_months(?x4869, ?x4827), seasonal_months(?x3107, ?x4827), seasonal_months(?x1459, ?x4827), seasonal_months(?x4827, ?x6303), ?x1036 = 080h2, ?x3107 = 05lf_, ?x4869 = 02xx5, ?x3052 = 01cx_, ?x108 = 0rh6k, ?x5036 = 06y57, ?x6054 = 0fn2g, ?x6703 = 0f04v, ?x2611 = 02h6_6p, ?x739 = 02_286, ?x3501 = 0f2v0, ?x2474 = 052p7, ?x1458 = 05ywg, ?x6959 = 06c62, ?x3106 = 049d1, ?x3026 = 0cv3w, ?x6458 = 08966, ?x4271 = 06wjf, ?x12674 = 0g6xq, ?x6494 = 02sn34, ?x7298 = 04wzr, ?x8174 = 01lfy, month(?x8956, ?x1459), month(?x6960, ?x1459), month(?x6357, ?x1459), ?x6303 = 0lkm, ?x11197 = 05l64, film_release_region(?x11701, ?x2316), film_release_region(?x9859, ?x2316), film_release_region(?x8292, ?x2316), film_release_region(?x7554, ?x2316), film_release_region(?x6543, ?x2316), film_release_region(?x6520, ?x2316), film_release_region(?x5826, ?x2316), film_release_region(?x5400, ?x2316), film_release_region(?x5142, ?x2316), film_release_region(?x4607, ?x2316), film_release_region(?x4464, ?x2316), film_release_region(?x3981, ?x2316), film_release_region(?x3958, ?x2316), film_release_region(?x3854, ?x2316), film_release_region(?x3745, ?x2316), film_release_region(?x3606, ?x2316), film_release_region(?x3482, ?x2316), film_release_region(?x3453, ?x2316), film_release_region(?x3292, ?x2316), film_release_region(?x2788, ?x2316), film_release_region(?x2685, ?x2316), film_release_region(?x2093, ?x2316), film_release_region(?x1916, ?x2316), film_release_region(?x1392, ?x2316), film_release_region(?x1163, ?x2316), film_release_region(?x791, ?x2316), film_release_region(?x634, ?x2316), film_release_region(?x467, ?x2316), ?x2788 = 05q4y12, ?x6520 = 02bg55, ?x3958 = 0gyh2wm, ?x6543 = 0421v9q, ?x7554 = 01mgw, ?x3373 = 0ply0, service_location(?x555, ?x2316), ?x2685 = 0g5879y, ?x3606 = 0gh65c5, ?x5400 = 0bhwhj, ?x1163 = 0c0nhgv, ?x3482 = 017z49, ?x791 = 087wc7n, ?x8292 = 0cmf0m0, ?x634 = 0gx9rvq, ?x5826 = 0gl02yg, ?x4826 = 0177z, ?x1392 = 017gm7, ?x467 = 0dckvs, film_crew_role(?x2093, ?x1171), ?x1916 = 0ch26b_, ?x6960 = 071vr, ?x3745 = 03cw411, ?x3292 = 0gvs1kt, ?x3854 = 03q0r1, ?x11701 = 0gys2jp, ?x4607 = 0h03fhx, ?x3981 = 047tsx3, jurisdiction_of_office(?x182, ?x2316), ?x9559 = 07dfk, ?x8977 = 02z0j, film(?x1289, ?x2093), mode_of_transportation(?x2985, ?x6665), ?x659 = 02cl1, country(?x1121, ?x2316), member_states(?x7695, ?x2316), ?x1121 = 0bynt, genre(?x2093, ?x53), ?x5142 = 0bt3j9, ?x6357 = 02cft, film(?x3462, ?x3453), ?x9859 = 0g57wgv, ?x1171 = 09vw2b7, ?x4464 = 05pdh86 >> conf = 0.89 => this is the best rule for 4 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 03_ly month! 03czqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.895 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 03_ly month! 0947l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.895 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 03_ly month! 071vr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.895 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 03_ly month! 02cft CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.895 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #22287-04y0yc PRED entity: 04y0yc PRED relation: languages PRED expected values: 03k50 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 16): 03k50 (0.50 #43, 0.43 #277, 0.43 #199), 02h40lc (0.43 #119, 0.36 #314, 0.36 #353), 07c9s (0.29 #208, 0.20 #247, 0.19 #286), 0999q (0.14 #218, 0.13 #257, 0.10 #2653), 09s02 (0.14 #231, 0.10 #2653, 0.10 #309), 01c7y (0.14 #226, 0.10 #2653, 0.10 #304), 055qm (0.14 #219, 0.10 #2653, 0.10 #297), 064_8sq (0.10 #2653, 0.07 #210, 0.05 #3045), 02hxcvy (0.10 #299, 0.08 #416, 0.07 #455), 0121sr (0.07 #228, 0.05 #306, 0.03 #3319) >> Best rule #43 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 047jhq; >> query: (?x10155, 03k50) <- award(?x10155, ?x10156), location(?x10155, ?x7412), ?x7412 = 04vmp, ?x10156 = 03r8v_, profession(?x10155, ?x4725) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04y0yc languages 03k50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.500 http://example.org/people/person/languages #22286-0333wf PRED entity: 0333wf PRED relation: film PRED expected values: 0g3zrd => 79 concepts (69 used for prediction) PRED predicted values (max 10 best out of 747): 0n08r (0.50 #3490, 0.33 #1703, 0.02 #24934), 053rxgm (0.33 #176, 0.25 #1963, 0.05 #3750), 04tqtl (0.33 #510, 0.25 #2297, 0.03 #36250), 04g9gd (0.33 #387, 0.25 #2174, 0.01 #23618), 04t6fk (0.33 #430, 0.25 #2217, 0.01 #39744), 03hj5lq (0.33 #1053, 0.25 #2840), 02z3r8t (0.25 #1895, 0.02 #5469, 0.02 #25126), 035s95 (0.25 #2128, 0.02 #11063, 0.02 #23572), 0symg (0.25 #3488, 0.02 #12423, 0.01 #8849), 026lgs (0.25 #2726, 0.01 #11661, 0.01 #24170) >> Best rule #3490 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 015wnl; >> query: (?x5343, 0n08r) <- film(?x5343, ?x13292), ?x13292 = 076tw54, profession(?x5343, ?x1032) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5730 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 06c0j; *> query: (?x5343, 0g3zrd) <- people(?x9428, ?x5343), participant(?x3421, ?x5343), diet(?x3421, ?x3130) *> conf = 0.01 ranks of expected_values: 608 EVAL 0333wf film 0g3zrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 79.000 69.000 0.500 http://example.org/film/actor/film./film/performance/film #22285-04ztj PRED entity: 04ztj PRED relation: type_of_union! PRED expected values: 01j5ts 01lmj3q 01r42_g 0p_pd 01yznp 0z4s 06jzh 018dnt 01gvr1 0kzy0 025vry 01n5309 042rnl 012c6x 03ds3 0dky9n 0bz5v2 01pr_j6 01vrncs 039bp 0h1mt 01vrz41 05fg2 013cr 0crx5w 04nw9 01nczg 017r2 01bpc9 02zyy4 01gzm2 030h95 0p5mw 073bb 0gt_k 063vn 01wz3cx 0g51l1 01wxyx1 0dck27 0443y3 04xjp 01f7j9 0pyg6 02lq10 01zmpg 01ww2fs 01hw6wq 01n8_g 01wk7b7 0738b8 0171cm 0c01c 01trhmt 045zr 04k25 024n3z 0hskw 0m32_ 0c9c0 01kj0p 08swgx 0klh7 03r1pr 078jt5 0b_dy 055c8 0f4dx2 0d4jl 0347xl 01wz_ml 01_j71 02l4pj 016h4r 03np3w 085pr 01fdc0 01k5zk 057hz 0253b6 016fjj 0fpj4lx 027r8p 02g5h5 03f5vvx 02xv8m 0cqt90 06jnvs 01svw8n 07cn2c 01sb5r 01m3x5p 02pqgt8 02dth1 01kp66 0dzc16 06whf 046qq 0d5_f 01bpn 0f502 02kxwk 08yx9q 01_x6d 028k57 02dbp7 02v60l 02t_99 0f7hc 07yw6t 03j24kf 021yzs 03f0vvr 0pyww 0837ql 01d0fp 022p06 01z_g6 018fmr 0c5tl 042z_g 02lvtb 0149xx 015xp4 01n44c 0bjkpt 02c6pq 02bxjp 016yvw 098n_m 029_l 016zp5 06c97 014gf8 05yh_t 03x22w 09r9m7 01bpnd 0167km 0127s7 02wk4d 0p__8 02f9wb 02q42j_ 02t__3 095b70 01wrcxr 05gp3x 06g2d1 01jfrg 09cdxn 06c44 01vvyd8 015gy7 05gpy 036jp8 015dcj 01386_ 01rrd4 0b_j2 0436kgz 05x8n 0gv2r 01520h 0g69lg 07fzq3 02w5q6 01my4f 06z4wj 026n9h3 01sxd1 012v9y 02l_7y 044lyq 027t8fw 017g21 01yfm8 05cx7x 01q9b9 0d608 034ls 01wf86y 07nx9j 05mc99 0451j 03_wtr 0421st 0crvfq 030tjk 01l3mk3 0gd9k 09h_q 01933d 016ynj 06qgjh 027rfxc 0djywgn 01nkxvx 01g42 09p0q 015g_7 01jmv8 035sc2 05jjl 0fp_xp 04qr6d 015np0 02f1c 01wj5hp 02byfd 02tf1y 028pzq 0147jt 03c5bz 01d4cb 05nqq3 03d2k 01w5gg6 04n32 05yvfd 013bd1 03mv0b 02ct_k 01q3_2 02wr6r 03c5f7l 054fvj 03z0l6 033_1p 039xcr 08z39v 0cymln 0sw62 01xllf 0htcn 023322 0gdqy 02lyx4 01bmlb 02784z 0c1ps1 0h25 03crmd 04bdqk 0czhv7 065d1h 0f87jy 016nvh 0969fd 014g91 0223g8 04fkg4 044bn 02d6n_ 042kg 016z68 0lzcs 0b4rf3 02qhm3 03n0pv 01cqz5 04d2yp 03nyts 054c1 0bwgc_ 01kkx2 01my95 07f7jp 02g9z1 075npt 0h1q6 0hcs3 02h48 02js_6 018q7 02vkvcz 0d3mlc 090gk3 0736qr 04xhwn 02yy8 071jrc 02_nkp 06r3p2 026c0p 04kwbt 02t901 0ldd 0cct7p 0dszr0 02665kn 09ld6g 04v68c 044ptm 01b3bp 045n3p 02jm9c 02m30v 02qnhk1 => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 938): 01nxzv (0.33 #165, 0.25 #339, 0.20 #513), 07myb2 (0.33 #153, 0.25 #327, 0.20 #501), 044mvs (0.33 #146, 0.25 #320, 0.20 #494), 03cws8h (0.33 #140, 0.25 #314, 0.20 #488), 033jj1 (0.33 #138, 0.25 #312, 0.20 #486), 0263tn1 (0.33 #114, 0.25 #288, 0.20 #462), 03mszl (0.33 #109, 0.25 #283, 0.20 #457), 0h0yt (0.33 #107, 0.25 #281, 0.20 #455), 044lyq (0.33 #106, 0.25 #280, 0.20 #454), 02ply6j (0.33 #104, 0.25 #278, 0.20 #452) >> Best rule #165 for best value: >> intensional similarity = 23 >> extensional distance = 1 >> proper extension: 01g63y; >> query: (?x566, 01nxzv) <- type_of_union(?x9665, ?x566), type_of_union(?x9482, ?x566), type_of_union(?x5582, ?x566), type_of_union(?x4735, ?x566), type_of_union(?x4470, ?x566), type_of_union(?x2602, ?x566), type_of_union(?x1583, ?x566), type_of_union(?x722, ?x566), type_of_union(?x525, ?x566), location_of_ceremony(?x566, ?x4510), location_of_ceremony(?x566, ?x448), gender(?x722, ?x231), award_winner(?x274, ?x2602), award(?x4470, ?x102), ?x9665 = 01syr4, currency(?x5582, ?x170), teams(?x4510, ?x4511), ?x9482 = 01npcy7, ?x170 = 09nqf, student(?x1368, ?x4735), state_province_region(?x2522, ?x448), award_nominee(?x450, ?x525), profession(?x1583, ?x1032) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #106 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 1 *> proper extension: 01g63y; *> query: (?x566, 044lyq) <- type_of_union(?x9665, ?x566), type_of_union(?x9482, ?x566), type_of_union(?x5582, ?x566), type_of_union(?x4735, ?x566), type_of_union(?x4470, ?x566), type_of_union(?x2602, ?x566), type_of_union(?x1583, ?x566), type_of_union(?x722, ?x566), type_of_union(?x525, ?x566), location_of_ceremony(?x566, ?x4510), location_of_ceremony(?x566, ?x448), gender(?x722, ?x231), award_winner(?x274, ?x2602), award(?x4470, ?x102), ?x9665 = 01syr4, currency(?x5582, ?x170), teams(?x4510, ?x4511), ?x9482 = 01npcy7, ?x170 = 09nqf, student(?x1368, ?x4735), state_province_region(?x2522, ?x448), award_nominee(?x450, ?x525), profession(?x1583, ?x1032) *> conf = 0.33 ranks of expected_values: 9, 14, 22, 41, 69, 79, 83, 94, 103, 107, 122, 124, 133, 138, 154, 159, 164, 169, 192, 209, 211, 220, 225, 230, 243, 250, 251, 254, 260, 269, 286, 294, 295, 304, 308, 315, 319, 321, 326, 327, 340, 348, 350, 357, 361, 362, 366, 370, 385, 390, 391, 394, 395, 400, 402, 404, 407, 425, 428, 429, 430, 446, 448, 452, 458, 463, 478, 484, 488, 489, 490, 499, 506, 510, 511, 525, 534, 535, 560, 561, 563, 565, 584, 591, 597, 598, 608, 628, 632, 638, 644, 648, 652, 656, 659, 674, 675, 679, 687, 697, 700, 701, 709, 710, 712, 713, 716, 737, 742, 743, 751, 755, 756, 767, 768, 770, 778, 784, 785, 791, 796, 800, 802, 814, 815, 818, 819, 823, 824, 825, 832, 835, 843, 844, 845, 856, 860, 864, 880, 883, 886, 888, 895, 897, 901, 906, 912, 914, 915, 916, 918, 922, 923, 927, 931, 933, 934 EVAL 04ztj type_of_union! 02qnhk1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02m30v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02jm9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 045n3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01b3bp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 044ptm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04v68c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 09ld6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02665kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0dszr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0cct7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0ldd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02t901 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04kwbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 026c0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 06r3p2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02_nkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 071jrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02yy8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04xhwn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0736qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 090gk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0d3mlc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02vkvcz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 018q7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02js_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02h48 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0hcs3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0h1q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 075npt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02g9z1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 07f7jp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01my95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01kkx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0bwgc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 054c1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03nyts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04d2yp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01cqz5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03n0pv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02qhm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0b4rf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0lzcs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 016z68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 042kg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02d6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 044bn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04fkg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0223g8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 014g91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0969fd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 016nvh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0f87jy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 065d1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0czhv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04bdqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03crmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0h25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0c1ps1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02784z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01bmlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02lyx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0gdqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 023322 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0htcn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01xllf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0sw62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0cymln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 08z39v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 039xcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 033_1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03z0l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 054fvj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03c5f7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02wr6r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01q3_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02ct_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03mv0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 013bd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 05yvfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04n32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01w5gg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03d2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 05nqq3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01d4cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03c5bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0147jt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 028pzq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02tf1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02byfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01wj5hp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02f1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 015np0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04qr6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0fp_xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 05jjl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 035sc2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01jmv8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 015g_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 09p0q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01g42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01nkxvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0djywgn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 027rfxc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 06qgjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 016ynj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01933d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 09h_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0gd9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01l3mk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 030tjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0crvfq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0421st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03_wtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0451j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 05mc99 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 07nx9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01wf86y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 034ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0d608 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01q9b9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 05cx7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01yfm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 017g21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 027t8fw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 044lyq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02l_7y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 012v9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01sxd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 026n9h3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 06z4wj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01my4f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02w5q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 07fzq3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0g69lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01520h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0gv2r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 05x8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0436kgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0b_j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01rrd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01386_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 015dcj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 036jp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 05gpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 015gy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01vvyd8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 06c44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 09cdxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01jfrg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 06g2d1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 05gp3x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01wrcxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 095b70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02t__3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02q42j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02f9wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0p__8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02wk4d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0127s7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0167km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01bpnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 09r9m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03x22w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 05yh_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 014gf8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 06c97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 016zp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 029_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 098n_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 016yvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02bxjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02c6pq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0bjkpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01n44c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 015xp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0149xx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02lvtb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 042z_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0c5tl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 018fmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01z_g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 022p06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01d0fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0837ql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0pyww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03f0vvr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 021yzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03j24kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 07yw6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0f7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02t_99 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02v60l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02dbp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 028k57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01_x6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 08yx9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02kxwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0f502 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01bpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0d5_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 046qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 06whf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0dzc16 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01kp66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02dth1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02pqgt8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01m3x5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01sb5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 07cn2c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01svw8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 06jnvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0cqt90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02xv8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03f5vvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02g5h5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 027r8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0fpj4lx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 016fjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0253b6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 057hz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01k5zk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01fdc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 085pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03np3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 016h4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02l4pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01_j71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01wz_ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0347xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0d4jl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0f4dx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 055c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0b_dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 078jt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03r1pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0klh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 08swgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01kj0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0c9c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0m32_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0hskw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 024n3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04k25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 045zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01trhmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0c01c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0171cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0738b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01wk7b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01n8_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01hw6wq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01ww2fs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01zmpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02lq10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0pyg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01f7j9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04xjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0443y3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0dck27 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01wxyx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0g51l1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01wz3cx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 063vn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0gt_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 073bb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0p5mw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 030h95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01gzm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 02zyy4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01bpc9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 017r2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01nczg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 04nw9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0crx5w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 013cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 05fg2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0h1mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 039bp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01vrncs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01pr_j6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0bz5v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0dky9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 03ds3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 012c6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 042rnl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01n5309 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 025vry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0kzy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01gvr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 018dnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 06jzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0z4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01yznp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 0p_pd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01r42_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01lmj3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 04ztj type_of_union! 01j5ts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.333 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22284-076_74 PRED entity: 076_74 PRED relation: award_winner! PRED expected values: 0468g4r => 96 concepts (83 used for prediction) PRED predicted values (max 10 best out of 293): 040njc (0.37 #16429, 0.37 #17728, 0.36 #12537), 0gr4k (0.37 #16429, 0.37 #17728, 0.36 #12537), 03hkv_r (0.37 #16429, 0.37 #17728, 0.36 #12537), 02n9nmz (0.37 #16429, 0.37 #17728, 0.36 #12537), 0gq9h (0.37 #16429, 0.37 #17728, 0.36 #12537), 025m8l (0.37 #16429, 0.37 #17728, 0.36 #12537), 0f_nbyh (0.37 #16429, 0.37 #17728, 0.36 #12537), 02x4sn8 (0.37 #16429, 0.37 #17728, 0.36 #12537), 09sb52 (0.17 #9120, 0.13 #11713, 0.12 #11281), 019f4v (0.16 #20323, 0.15 #21191, 0.15 #21190) >> Best rule #16429 for best value: >> intensional similarity = 3 >> extensional distance = 1336 >> proper extension: 012x4t; 015882; 027l0b; 0bt4r4; 02vntj; 0cw67g; 0cj2w; 01lct6; >> query: (?x3862, ?x198) <- profession(?x3862, ?x319), award(?x3862, ?x198), award_winner(?x3862, ?x2086) >> conf = 0.37 => this is the best rule for 8 predicted values *> Best rule #20323 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1462 *> proper extension: 01nzs7; *> query: (?x3862, ?x2880) <- award_winner(?x2085, ?x3862), award(?x2085, ?x2880) *> conf = 0.16 ranks of expected_values: 34 EVAL 076_74 award_winner! 0468g4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 96.000 83.000 0.365 http://example.org/award/award_category/winners./award/award_honor/award_winner #22283-013y1f PRED entity: 013y1f PRED relation: role! PRED expected values: 016h9b => 64 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1076): 016h9b (0.60 #1487, 0.60 #1204, 0.57 #2055), 04mx7s (0.60 #1637, 0.60 #1354, 0.57 #2205), 01p95y0 (0.60 #1669, 0.57 #2237, 0.46 #5074), 01vng3b (0.60 #1298, 0.50 #1015, 0.50 #732), 03bxwtd (0.60 #1211, 0.50 #645, 0.43 #2062), 01mxnvc (0.60 #1395, 0.44 #2531, 0.43 #2246), 0473q (0.60 #1325, 0.44 #2461, 0.43 #2176), 01v_pj6 (0.60 #1171, 0.43 #2022, 0.40 #1738), 01vsnff (0.60 #1182, 0.43 #2033, 0.40 #1465), 01s7qqw (0.60 #1274, 0.43 #2125, 0.40 #1557) >> Best rule #1487 for best value: >> intensional similarity = 19 >> extensional distance = 3 >> proper extension: 04rzd; >> query: (?x1495, 016h9b) <- role(?x8957, ?x1495), role(?x3214, ?x1495), role(?x2944, ?x1495), role(?x316, ?x1495), role(?x75, ?x1495), ?x316 = 05r5c, role(?x1495, ?x1750), ?x8957 = 03f5mt, group(?x1495, ?x5838), group(?x1495, ?x3207), group(?x1092, ?x3207), ?x1750 = 02hnl, role(?x2309, ?x1495), role(?x130, ?x1495), ?x2944 = 0l14j_, ?x5838 = 02dw1_, performance_role(?x1260, ?x1495), ?x75 = 07y_7, ?x3214 = 02snj9 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013y1f role! 016h9b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 39.000 0.600 http://example.org/music/group_member/membership./music/group_membership/role #22282-06jnvs PRED entity: 06jnvs PRED relation: type_of_union PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.68 #105, 0.68 #193, 0.68 #101), 01g63y (0.11 #86, 0.10 #250, 0.10 #150), 01bl8s (0.01 #11) >> Best rule #105 for best value: >> intensional similarity = 2 >> extensional distance = 955 >> proper extension: 01wj9y9; 0dfjb8; 01_k1z; 0c8hct; 0f2c8g; 021r7r; 0454s1; 04093; 03mv0b; 085q5; ... >> query: (?x3895, 04ztj) <- profession(?x3895, ?x987), ?x987 = 0dxtg >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06jnvs type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.683 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22281-047gpsd PRED entity: 047gpsd PRED relation: nominated_for! PRED expected values: 0bksh => 127 concepts (49 used for prediction) PRED predicted values (max 10 best out of 521): 07r_dg (0.78 #95984, 0.77 #51500, 0.77 #63202), 03rqww (0.40 #18726, 0.40 #23408, 0.40 #58520), 02779r4 (0.37 #98326, 0.36 #110030, 0.36 #100667), 018ygt (0.29 #46816, 0.29 #84275, 0.25 #4681), 0djtky (0.29 #46816, 0.25 #4681, 0.23 #103007), 01n7qlf (0.29 #46816, 0.25 #4681, 0.23 #103007), 01fwpt (0.29 #46816, 0.25 #4681, 0.23 #103007), 051z6rz (0.22 #53841, 0.22 #42133, 0.21 #39793), 024rgt (0.19 #4680, 0.19 #2870, 0.14 #4679), 04t2l2 (0.15 #95985, 0.02 #84276, 0.01 #23441) >> Best rule #95984 for best value: >> intensional similarity = 3 >> extensional distance = 635 >> proper extension: 02nf2c; 04p5cr; 0m123; >> query: (?x6719, ?x10103) <- titles(?x53, ?x6719), award_winner(?x6719, ?x10103), award_winner(?x237, ?x10103) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3407 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 04yc76; 0320fn; 0315w4; 04pmnt; 02bg55; *> query: (?x6719, 0bksh) <- titles(?x53, ?x6719), film(?x2549, ?x6719), ?x2549 = 024rgt, film(?x3477, ?x6719) *> conf = 0.02 ranks of expected_values: 145 EVAL 047gpsd nominated_for! 0bksh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 127.000 49.000 0.783 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22280-01q415 PRED entity: 01q415 PRED relation: people! PRED expected values: 07hwkr => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 34): 041rx (0.18 #389, 0.16 #774, 0.16 #851), 033tf_ (0.12 #7, 0.08 #1470, 0.08 #1239), 02ctzb (0.12 #15, 0.02 #1555, 0.02 #3403), 0x67 (0.10 #1319, 0.09 #2320, 0.09 #2936), 07hwkr (0.09 #320, 0.06 #12, 0.04 #1860), 02w7gg (0.08 #156, 0.08 #79, 0.06 #233), 048z7l (0.07 #194, 0.06 #271, 0.06 #348), 01qhm_ (0.06 #6, 0.03 #160, 0.03 #1546), 07bch9 (0.06 #23, 0.03 #1255, 0.03 #3411), 013xrm (0.06 #20, 0.03 #1021, 0.02 #1945) >> Best rule #389 for best value: >> intensional similarity = 3 >> extensional distance = 171 >> proper extension: 03ft8; 013zyw; 032md; 01y8d4; 0dr5y; 03p01x; 09zw90; 013km; >> query: (?x2248, 041rx) <- location(?x2248, ?x3634), nationality(?x2248, ?x94), written_by(?x2057, ?x2248) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #320 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 117 *> proper extension: 05gpy; 04093; *> query: (?x2248, 07hwkr) <- location(?x2248, ?x3634), story_by(?x9222, ?x2248), country(?x9222, ?x94) *> conf = 0.09 ranks of expected_values: 5 EVAL 01q415 people! 07hwkr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 94.000 0.179 http://example.org/people/ethnicity/people #22279-0q9kd PRED entity: 0q9kd PRED relation: award_winner! PRED expected values: 02xhpl => 109 concepts (85 used for prediction) PRED predicted values (max 10 best out of 368): 01b195 (0.43 #17043, 0.42 #4548, 0.41 #72698), 0q9b0 (0.43 #17043, 0.42 #4548, 0.41 #72698), 011yqc (0.43 #17043, 0.42 #4548, 0.41 #72698), 0277j40 (0.43 #17043, 0.42 #4548, 0.41 #72698), 04lhc4 (0.43 #17043, 0.42 #4548, 0.41 #72698), 01hq1 (0.43 #17043, 0.42 #4548, 0.41 #72698), 030cx (0.43 #17043, 0.42 #4548, 0.41 #72698), 0296rz (0.34 #17042, 0.32 #19315, 0.32 #18179), 0gxsh4 (0.28 #31812, 0.27 #32948, 0.27 #27267), 016kv6 (0.25 #388, 0.12 #1525) >> Best rule #17043 for best value: >> intensional similarity = 2 >> extensional distance = 239 >> proper extension: 04b19t; >> query: (?x71, ?x1496) <- film(?x71, ?x10300), nominated_for(?x71, ?x1496) >> conf = 0.43 => this is the best rule for 7 predicted values No rule for expected values ranks of expected_values: EVAL 0q9kd award_winner! 02xhpl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 85.000 0.429 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #22278-0k20s PRED entity: 0k20s PRED relation: award PRED expected values: 02wwsh8 => 108 concepts (79 used for prediction) PRED predicted values (max 10 best out of 210): 027c924 (0.43 #1149, 0.11 #2289, 0.10 #3885), 0gs9p (0.40 #1203, 0.37 #1141, 0.27 #4106), 0gr4k (0.37 #1141, 0.33 #25, 0.27 #4106), 02qyntr (0.37 #1141, 0.33 #169, 0.27 #4106), 0gq9h (0.37 #1141, 0.27 #4106, 0.27 #1201), 02rdyk7 (0.37 #1141, 0.27 #4106, 0.27 #1209), 02r0csl (0.33 #232, 0.29 #460, 0.12 #1373), 027b9ly (0.33 #156, 0.13 #1297, 0.08 #840), 02w_6xj (0.33 #1296, 0.11 #4032, 0.09 #4490), 02pqp12 (0.33 #1197, 0.09 #11666, 0.09 #3933) >> Best rule #1149 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 03hkch7; 04b2qn; >> query: (?x11110, 027c924) <- language(?x11110, ?x254), honored_for(?x3332, ?x11110), award(?x11110, ?x8364), genre(?x11110, ?x53), ?x8364 = 09d28z >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #643 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02r8hh_; *> query: (?x11110, 02wwsh8) <- language(?x11110, ?x254), titles(?x789, ?x11110), award(?x11110, ?x77), ?x789 = 0f8l9c *> conf = 0.14 ranks of expected_values: 44 EVAL 0k20s award 02wwsh8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 108.000 79.000 0.433 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22277-09xp_ PRED entity: 09xp_ PRED relation: sport! PRED expected values: 038_0z => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 522): 038_0z (0.84 #2783, 0.81 #2319, 0.07 #5103), 02s2ys (0.80 #3247, 0.33 #5101, 0.33 #2141), 0cnk2q (0.80 #3247, 0.33 #1856, 0.33 #928), 02pp1 (0.80 #3247, 0.33 #2194, 0.33 #928), 035l_9 (0.80 #3247, 0.33 #2259, 0.33 #928), 02yjk8 (0.33 #1388, 0.25 #3707, 0.17 #5097), 0jm4b (0.33 #1057, 0.25 #3376, 0.17 #4766), 0jmk7 (0.33 #1312, 0.25 #3631, 0.17 #5021), 0jm9w (0.33 #1217, 0.25 #3536, 0.17 #4926), 0jm3b (0.33 #1211, 0.25 #3530, 0.17 #4920) >> Best rule #2783 for best value: >> intensional similarity = 37 >> extensional distance = 1 >> proper extension: 0jm_; >> query: (?x12682, ?x14520) <- sport(?x14238, ?x12682), sport(?x13752, ?x12682), colors(?x14238, ?x3189), athlete(?x12682, ?x10562), athlete(?x12682, ?x4895), team(?x13559, ?x13752), ?x3189 = 01g5v, influenced_by(?x10562, ?x4072), award_nominee(?x4895, ?x4353), student(?x4390, ?x10562), influenced_by(?x12888, ?x10562), influenced_by(?x8210, ?x10562), profession(?x4353, ?x319), award_winner(?x198, ?x4353), location(?x4895, ?x512), nominated_for(?x4353, ?x1547), award_winner(?x1375, ?x4895), influenced_by(?x7861, ?x8210), place_of_birth(?x10562, ?x6885), film(?x4895, ?x4699), award(?x4353, ?x350), profession(?x10562, ?x6630), profession(?x10562, ?x5805), place_of_death(?x8210, ?x7769), type_of_union(?x4353, ?x566), profession(?x10394, ?x6630), ?x10394 = 03dq9, ?x319 = 01d_h8, story_by(?x2345, ?x12888), profession(?x12258, ?x5805), profession(?x11492, ?x5805), team(?x13559, ?x14520), ?x12258 = 019fz, specialization_of(?x13369, ?x6630), people(?x4322, ?x8210), ?x11492 = 082xp, people(?x1050, ?x4353) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09xp_ sport! 038_0z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.839 http://example.org/sports/sports_team/sport #22276-02z9hqn PRED entity: 02z9hqn PRED relation: language PRED expected values: 04306rv 03_9r => 105 concepts (84 used for prediction) PRED predicted values (max 10 best out of 43): 03_9r (0.89 #2552, 0.87 #2073, 0.79 #602), 02h40lc (0.88 #2494, 0.88 #2616, 0.88 #4422), 05zjd (0.25 #144, 0.15 #4360, 0.14 #262), 064_8sq (0.19 #1265, 0.15 #4360, 0.14 #909), 03k50 (0.19 #1252, 0.06 #2441, 0.05 #896), 06b_j (0.17 #1029, 0.12 #319, 0.11 #1207), 02hxcvy (0.17 #1277, 0.05 #921, 0.04 #2466), 02bjrlw (0.15 #4360, 0.14 #237, 0.13 #1007), 06nm1 (0.15 #4360, 0.14 #1906, 0.14 #1965), 04306rv (0.15 #4360, 0.12 #301, 0.12 #1307) >> Best rule #2552 for best value: >> intensional similarity = 5 >> extensional distance = 295 >> proper extension: 04kkz8; 01738w; 09dv8h; 03z9585; >> query: (?x869, ?x2164) <- film(?x12375, ?x869), genre(?x869, ?x53), film_release_region(?x869, ?x94), language(?x12375, ?x2164), profession(?x12375, ?x1032) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 02z9hqn language 03_9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 84.000 0.893 http://example.org/film/film/language EVAL 02z9hqn language 04306rv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 105.000 84.000 0.893 http://example.org/film/film/language #22275-02vw1w2 PRED entity: 02vw1w2 PRED relation: country PRED expected values: 03_3d => 82 concepts (64 used for prediction) PRED predicted values (max 10 best out of 156): 03_3d (0.88 #1111, 0.87 #989, 0.85 #804), 09c7w0 (0.80 #2395, 0.80 #676, 0.79 #1167), 07ssc (0.37 #1611, 0.33 #1856, 0.33 #17), 0345h (0.33 #641, 0.31 #517, 0.31 #455), 0d060g (0.29 #253, 0.20 #683, 0.17 #1174), 0f8l9c (0.29 #264, 0.09 #1062, 0.08 #1185), 016wzw (0.14 #353, 0.12 #414, 0.05 #906), 015fr (0.14 #323, 0.12 #384, 0.05 #876), 0chghy (0.13 #626, 0.10 #1301, 0.08 #1178), 03spz (0.09 #3066, 0.08 #2085, 0.03 #1901) >> Best rule #1111 for best value: >> intensional similarity = 10 >> extensional distance = 22 >> proper extension: 05pyrb; >> query: (?x1419, 03_3d) <- actor(?x1419, ?x51), genre(?x1419, ?x571), genre(?x1419, ?x225), genre(?x4991, ?x571), genre(?x2094, ?x571), genre(?x5081, ?x225), genre(?x3413, ?x571), ?x4991 = 02xs6_, ?x5081 = 0642xf3, film_release_region(?x2094, ?x87) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vw1w2 country 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 64.000 0.875 http://example.org/film/film/country #22274-07cz2 PRED entity: 07cz2 PRED relation: executive_produced_by PRED expected values: 0glyyw => 74 concepts (42 used for prediction) PRED predicted values (max 10 best out of 67): 0glyyw (0.25 #189, 0.07 #441, 0.03 #1702), 06pj8 (0.17 #307, 0.11 #812, 0.07 #1568), 079vf (0.14 #759, 0.10 #254, 0.10 #1515), 02qzjj (0.10 #488, 0.05 #993, 0.04 #1497), 05hj_k (0.07 #2115, 0.07 #2368, 0.07 #3125), 0343h (0.07 #1051, 0.06 #1303, 0.04 #1555), 06q8hf (0.06 #2184, 0.06 #2437, 0.05 #3194), 02q_cc (0.03 #280, 0.03 #1541, 0.03 #1289), 02xnjd (0.03 #428, 0.03 #933, 0.03 #1185), 032v0v (0.03 #302, 0.03 #807, 0.02 #1563) >> Best rule #189 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01dyvs; 0bv8h2; >> query: (?x2770, 0glyyw) <- featured_film_locations(?x2770, ?x5232), genre(?x2770, ?x1013), ?x1013 = 06n90, ?x5232 = 0135g >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07cz2 executive_produced_by 0glyyw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 42.000 0.250 http://example.org/film/film/executive_produced_by #22273-0kh6b PRED entity: 0kh6b PRED relation: profession PRED expected values: 0dxtg 03gjzk => 170 concepts (87 used for prediction) PRED predicted values (max 10 best out of 110): 0kyk (0.62 #3387, 0.42 #2657, 0.37 #3825), 0dxtg (0.58 #10095, 0.48 #10679, 0.47 #11701), 01d_h8 (0.53 #5556, 0.50 #2489, 0.50 #1467), 03gjzk (0.50 #1912, 0.50 #1035, 0.46 #2788), 02jknp (0.34 #10090, 0.33 #1176, 0.33 #153), 0np9r (0.33 #19, 0.26 #6739, 0.22 #1626), 0d8qb (0.33 #953, 0.20 #2998, 0.20 #661), 012t_z (0.33 #1764, 0.17 #1033, 0.14 #5561), 018gz8 (0.31 #2790, 0.30 #6735, 0.28 #7611), 0fj9f (0.28 #3703, 0.27 #3849, 0.27 #5164) >> Best rule #3387 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 01xdf5; 014z8v; 01g6bk; >> query: (?x3796, 0kyk) <- company(?x3796, ?x2776), people(?x5042, ?x3796), location(?x3796, ?x362), profession(?x3796, ?x353), ?x353 = 0cbd2 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #10095 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 153 *> proper extension: 01yznp; 03wpmd; 0c9c0; 0693l; 0p3sf; 0f7hc; 03lgg; 01vvyd8; 02mz_6; 05rx__; ... *> query: (?x3796, 0dxtg) <- nationality(?x3796, ?x1310), profession(?x3796, ?x801), profession(?x3796, ?x353), ?x353 = 0cbd2, profession(?x3560, ?x801), ?x3560 = 0391jz *> conf = 0.58 ranks of expected_values: 2, 4 EVAL 0kh6b profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 170.000 87.000 0.625 http://example.org/people/person/profession EVAL 0kh6b profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 170.000 87.000 0.625 http://example.org/people/person/profession #22272-012t1 PRED entity: 012t1 PRED relation: award PRED expected values: 04dn09n => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 279): 01l29r (0.78 #30254, 0.78 #25411, 0.76 #25816), 019f4v (0.36 #67, 0.24 #1278, 0.20 #4100), 0gr4k (0.33 #4066, 0.28 #3663, 0.26 #4873), 02h3d1 (0.31 #584, 0.09 #181, 0.08 #989), 04dn09n (0.30 #4077, 0.26 #3674, 0.24 #4481), 0gs9p (0.27 #1290, 0.25 #4112, 0.19 #7741), 040njc (0.27 #7670, 0.27 #3235, 0.24 #6864), 04mqgr (0.27 #154, 0.15 #557, 0.08 #962), 09sb52 (0.24 #13753, 0.24 #12141, 0.24 #2865), 03hkv_r (0.24 #4049, 0.21 #3646, 0.20 #4453) >> Best rule #30254 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 01wv9xn; 0134s5; 02lbrd; 0khth; 0g_g2; 0134tg; 0b1zz; 0l8g0; 015cxv; 011z3g; ... >> query: (?x1047, ?x9766) <- award_winner(?x9766, ?x1047), award(?x595, ?x9766), ceremony(?x9766, ?x4141) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4077 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 204 *> proper extension: 02pp_q_; 05jm7; 0gv2r; 02mz_6; 030tjk; 081l_; 027d5g5; 0kft; 030g9z; 0k_mt; ... *> query: (?x1047, 04dn09n) <- gender(?x1047, ?x231), award_winner(?x3105, ?x1047), nationality(?x1047, ?x94), written_by(?x5134, ?x1047) *> conf = 0.30 ranks of expected_values: 5 EVAL 012t1 award 04dn09n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 111.000 111.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22271-04gp58p PRED entity: 04gp58p PRED relation: nominated_for! PRED expected values: 02x4x18 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 197): 02x4x18 (0.70 #333, 0.67 #99, 0.38 #567), 02z1nbg (0.66 #13350, 0.66 #16162, 0.66 #14288), 094qd5 (0.59 #1440, 0.45 #2610, 0.40 #1674), 0gq9h (0.58 #1699, 0.48 #3806, 0.47 #2635), 0gs9p (0.54 #1701, 0.42 #3808, 0.40 #2637), 0gr4k (0.51 #4709, 0.41 #1665, 0.37 #2601), 02x4sn8 (0.50 #1051, 0.44 #817, 0.41 #583), 0f4x7 (0.49 #1664, 0.29 #3771, 0.29 #2600), 0gqy2 (0.47 #1758, 0.30 #3865, 0.29 #4802), 02x4w6g (0.47 #553, 0.46 #787, 0.45 #1021) >> Best rule #333 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0j8f09z; >> query: (?x8283, 02x4x18) <- nominated_for(?x2532, ?x8283), nominated_for(?x1245, ?x8283), ?x2532 = 02x4wr9, film_crew_role(?x8283, ?x1284), ?x1245 = 0gqwc >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gp58p nominated_for! 02x4x18 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.700 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22270-095w_ PRED entity: 095w_ PRED relation: featured_film_locations! PRED expected values: 01qdmh => 205 concepts (132 used for prediction) PRED predicted values (max 10 best out of 911): 0m9p3 (0.29 #901, 0.08 #3096, 0.05 #11871), 061681 (0.25 #47, 0.20 #4436, 0.20 #3705), 03k8th (0.25 #699, 0.15 #3626, 0.10 #12401), 04180vy (0.25 #712, 0.14 #1444, 0.11 #2175), 07kdkfj (0.25 #563, 0.14 #1295, 0.08 #20308), 04dsnp (0.25 #66, 0.14 #51991, 0.13 #4455), 024l2y (0.25 #113, 0.13 #3771, 0.11 #1576), 0ds2n (0.25 #230, 0.13 #3888, 0.11 #1693), 033srr (0.25 #279, 0.13 #3937, 0.11 #1742), 035yn8 (0.25 #119, 0.13 #3777, 0.11 #1582) >> Best rule #901 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0flsf; 05g2b; >> query: (?x1374, 0m9p3) <- time_zones(?x1374, ?x2864), category(?x1374, ?x134), featured_film_locations(?x1685, ?x1374), ?x2864 = 02llzg >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #32141 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 06t2t; 0fs1v; *> query: (?x1374, 01qdmh) <- capital(?x9006, ?x1374), nationality(?x13735, ?x9006), official_language(?x9006, ?x403), film(?x13735, ?x5499) *> conf = 0.03 ranks of expected_values: 587 EVAL 095w_ featured_film_locations! 01qdmh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 205.000 132.000 0.286 http://example.org/film/film/featured_film_locations #22269-0jgd PRED entity: 0jgd PRED relation: administrative_parent PRED expected values: 02j71 => 188 concepts (136 used for prediction) PRED predicted values (max 10 best out of 35): 02j71 (0.84 #12480, 0.83 #13029, 0.82 #16736), 09c7w0 (0.42 #11100, 0.38 #9042, 0.36 #11237), 06n3y (0.38 #3971, 0.21 #3970, 0.18 #13704), 03rjj (0.31 #3837, 0.07 #11652, 0.06 #15907), 07c5l (0.21 #3970, 0.18 #13704, 0.18 #13703), 07ssc (0.06 #6169, 0.06 #7128, 0.06 #3433), 0d05w3 (0.06 #456, 0.05 #3880, 0.05 #728), 049nq (0.06 #368, 0.04 #1461, 0.04 #1733), 03rk0 (0.05 #3601, 0.04 #5381, 0.03 #8259), 0345h (0.05 #843, 0.05 #980, 0.04 #16887) >> Best rule #12480 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 01nty; >> query: (?x142, 02j71) <- country(?x1967, ?x142), participating_countries(?x418, ?x142), ?x1967 = 01cgz >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgd administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 136.000 0.838 http://example.org/base/aareas/schema/administrative_area/administrative_parent #22268-0yyg4 PRED entity: 0yyg4 PRED relation: award PRED expected values: 027c95y => 62 concepts (48 used for prediction) PRED predicted values (max 10 best out of 195): 0gq9h (0.27 #6051, 0.27 #233, 0.27 #6050), 019f4v (0.27 #6051, 0.27 #233, 0.27 #6050), 0gs9p (0.27 #6051, 0.27 #233, 0.27 #6050), 0p9sw (0.27 #6051, 0.27 #233, 0.27 #6050), 04dn09n (0.27 #6051, 0.27 #233, 0.27 #6050), 0k611 (0.27 #6051, 0.27 #233, 0.27 #6050), 0f4x7 (0.27 #6051, 0.27 #233, 0.27 #6050), 02pqp12 (0.27 #6051, 0.27 #233, 0.27 #6050), 02qvyrt (0.27 #6051, 0.27 #233, 0.27 #6050), 0gr0m (0.27 #6051, 0.27 #233, 0.27 #6050) >> Best rule #6051 for best value: >> intensional similarity = 3 >> extensional distance = 1000 >> proper extension: 085bd1; 0c1sgd3; 04qk12; 02_1ky; 02rq7nd; >> query: (?x288, ?x1313) <- award(?x288, ?x289), nominated_for(?x1313, ?x288), award(?x269, ?x1313) >> conf = 0.27 => this is the best rule for 12 predicted values *> Best rule #116 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 102 *> proper extension: 0m313; 083shs; 09m6kg; 01gc7; 07xtqq; 095zlp; 0209hj; 0hmr4; 0p_sc; 0b6tzs; ... *> query: (?x288, 027c95y) <- award(?x288, ?x289), nominated_for(?x1313, ?x288), nominated_for(?x746, ?x288), ?x1313 = 0gs9p, ?x746 = 04dn09n *> conf = 0.11 ranks of expected_values: 28 EVAL 0yyg4 award 027c95y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 62.000 48.000 0.271 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22267-09sr0 PRED entity: 09sr0 PRED relation: production_companies PRED expected values: 03sb38 => 108 concepts (69 used for prediction) PRED predicted values (max 10 best out of 60): 05qd_ (0.29 #91, 0.13 #743, 0.13 #1636), 01gb54 (0.29 #119, 0.07 #1990, 0.06 #1177), 017s11 (0.17 #2, 0.09 #3187, 0.09 #1223), 016tw3 (0.13 #337, 0.13 #256, 0.12 #826), 030_1_ (0.12 #180, 0.06 #423, 0.04 #1969), 030_1m (0.12 #179, 0.03 #1968, 0.03 #830), 02slt7 (0.12 #193, 0.03 #1169, 0.02 #1737), 019v67 (0.12 #242, 0.02 #404), 04f525m (0.12 #174, 0.01 #662, 0.01 #579), 016tt2 (0.11 #1224, 0.10 #1143, 0.10 #1549) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02qjv1p; >> query: (?x9056, 05qd_) <- award_winner(?x9056, ?x1585), genre(?x9056, ?x53), ?x1585 = 021yc7p, nominated_for(?x112, ?x9056) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #299 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 04m1bm; 05zvzf3; *> query: (?x9056, 03sb38) <- award(?x9056, ?x637), film_release_region(?x9056, ?x94), film(?x382, ?x9056), genre(?x9056, ?x53) *> conf = 0.06 ranks of expected_values: 13 EVAL 09sr0 production_companies 03sb38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 108.000 69.000 0.286 http://example.org/film/film/production_companies #22266-0738b8 PRED entity: 0738b8 PRED relation: film PRED expected values: 02mt51 02mc5v => 107 concepts (99 used for prediction) PRED predicted values (max 10 best out of 718): 02hct1 (0.37 #165747, 0.35 #151488, 0.35 #153271), 03bzjpm (0.12 #3090, 0.12 #1308, 0.02 #40512), 05hjnw (0.12 #2620, 0.12 #838, 0.01 #18658), 034qzw (0.12 #2112, 0.05 #3894, 0.04 #9240), 034qrh (0.12 #1844, 0.05 #3626, 0.03 #122966), 043t8t (0.12 #784, 0.05 #4348, 0.02 #9694), 0209xj (0.12 #1880, 0.05 #3662, 0.01 #19700), 0gbfn9 (0.12 #956, 0.05 #4520, 0.01 #40160), 05dptj (0.12 #1324, 0.05 #4888, 0.01 #8452), 03nfnx (0.12 #3178, 0.04 #10306, 0.03 #13870) >> Best rule #165747 for best value: >> intensional similarity = 2 >> extensional distance = 2153 >> proper extension: 02rgz4; 0f3zf_; 0lgsq; 0244r8; 0ft7sr; 01q415; 05dppk; 02dh86; 01f8ld; 0bytkq; ... >> query: (?x2437, ?x2436) <- nominated_for(?x2437, ?x2436), profession(?x2437, ?x319) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #3176 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 6 *> proper extension: 05w6cw; *> query: (?x2437, 02mc5v) <- film(?x2437, ?x5847), ?x5847 = 0640y35 *> conf = 0.12 ranks of expected_values: 46, 283 EVAL 0738b8 film 02mc5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 107.000 99.000 0.368 http://example.org/film/actor/film./film/performance/film EVAL 0738b8 film 02mt51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 107.000 99.000 0.368 http://example.org/film/actor/film./film/performance/film #22265-04yg13l PRED entity: 04yg13l PRED relation: language PRED expected values: 0t_2 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 46): 064_8sq (0.46 #5385, 0.22 #21, 0.19 #196), 06nm1 (0.46 #5385, 0.22 #10, 0.16 #419), 04306rv (0.46 #5385, 0.22 #4, 0.13 #413), 02bjrlw (0.46 #5385, 0.11 #1, 0.10 #291), 02bv9 (0.46 #5385, 0.04 #1695, 0.02 #376), 0653m (0.12 #129, 0.10 #420, 0.09 #186), 06b_j (0.11 #22, 0.09 #1544, 0.08 #371), 04h9h (0.11 #41, 0.08 #100, 0.05 #740), 071fb (0.11 #17, 0.05 #307, 0.04 #1695), 03_9r (0.08 #241, 0.08 #2875, 0.07 #1356) >> Best rule #5385 for best value: >> intensional similarity = 5 >> extensional distance = 1202 >> proper extension: 05r1_t; 0h95b81; 07s8z_l; 02xhwm; 03czz87; >> query: (?x5052, ?x254) <- titles(?x811, ?x5052), genre(?x5561, ?x811), award_winner(?x5561, ?x10491), honored_for(?x1265, ?x5561), languages(?x5561, ?x254) >> conf = 0.46 => this is the best rule for 5 predicted values *> Best rule #422 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 68 *> proper extension: 04nlb94; *> query: (?x5052, 0t_2) <- titles(?x811, ?x5052), film(?x382, ?x5052), film_release_distribution_medium(?x5052, ?x81), film_crew_role(?x5052, ?x2095), ?x2095 = 0dxtw, film_format(?x5052, ?x6392) *> conf = 0.04 ranks of expected_values: 17 EVAL 04yg13l language 0t_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 117.000 117.000 0.458 http://example.org/film/film/language #22264-0560w PRED entity: 0560w PRED relation: category PRED expected values: 08mbj5d => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #28, 0.85 #29, 0.85 #20) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 0lbj1; 06cc_1; 0150jk; 0152cw; 01vrt_c; 01vrz41; 0dtd6; 086qd; 0pyg6; 010hn; ... >> query: (?x11704, 08mbj5d) <- award(?x11704, ?x11068), artists(?x1000, ?x11704), artist(?x11912, ?x11704), artist(?x441, ?x11704) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0560w category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.859 http://example.org/common/topic/webpage./common/webpage/category #22263-036hv PRED entity: 036hv PRED relation: student PRED expected values: 01mr2g6 => 56 concepts (39 used for prediction) PRED predicted values (max 10 best out of 341): 0kn4c (0.60 #1207, 0.45 #3104, 0.43 #1680), 09b6zr (0.33 #800, 0.25 #1982, 0.20 #1273), 083q7 (0.33 #727, 0.25 #1909, 0.20 #1200), 0br1w (0.33 #317, 0.20 #1025, 0.17 #3396), 0q9zc (0.33 #873, 0.20 #1346, 0.14 #1819), 01yk13 (0.33 #722, 0.20 #1195, 0.14 #1668), 02z1yj (0.33 #899, 0.20 #1372, 0.14 #1845), 03gkn5 (0.33 #779, 0.20 #1252, 0.14 #1725), 015p37 (0.33 #922, 0.20 #1395, 0.14 #1868), 04m_kpx (0.33 #919, 0.20 #1392, 0.14 #1865) >> Best rule #1207 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 04rjg; 01lj9; >> query: (?x947, 0kn4c) <- major_field_of_study(?x3437, ?x947), ?x3437 = 02_xgp2, major_field_of_study(?x1682, ?x947), student(?x947, ?x1600), major_field_of_study(?x12157, ?x947), major_field_of_study(?x6973, ?x947), major_field_of_study(?x4955, ?x947), major_field_of_study(?x4390, ?x947), basic_title(?x1600, ?x5402), school_type(?x12157, ?x3205), ?x4390 = 0h6rm, ?x4955 = 09f2j, institution(?x620, ?x6973), school(?x465, ?x6973), ?x620 = 07s6fsf >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3477 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 10 *> proper extension: 0h5k; 0jjw; 037mh8; 02jfc; *> query: (?x947, 01mr2g6) <- major_field_of_study(?x3437, ?x947), major_field_of_study(?x1200, ?x947), ?x3437 = 02_xgp2, major_field_of_study(?x1682, ?x947), student(?x947, ?x1600), major_field_of_study(?x11963, ?x947), major_field_of_study(?x6271, ?x947), major_field_of_study(?x4955, ?x947), major_field_of_study(?x1772, ?x947), ?x1200 = 016t_3, ?x4955 = 09f2j, taxonomy(?x947, ?x939), institution(?x2759, ?x11963), contains(?x3068, ?x1772), location(?x1600, ?x3014), organization(?x6271, ?x5487) *> conf = 0.08 ranks of expected_values: 100 EVAL 036hv student 01mr2g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 56.000 39.000 0.600 http://example.org/education/field_of_study/students_majoring./education/education/student #22262-0fbvqf PRED entity: 0fbvqf PRED relation: ceremony PRED expected values: 0gvstc3 0hn821n => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 138): 02q690_ (0.58 #340, 0.50 #478, 0.46 #616), 0gx_st (0.50 #450, 0.42 #312, 0.41 #588), 0hn821n (0.50 #542, 0.42 #404, 0.29 #680), 0bx6zs (0.50 #400, 0.42 #538, 0.27 #262), 07y_p6 (0.50 #371, 0.42 #509, 0.27 #233), 0gpjbt (0.48 #994, 0.35 #2374, 0.32 #2650), 09n4nb (0.47 #1013, 0.34 #2393, 0.32 #2669), 0gvstc3 (0.46 #585, 0.45 #171, 0.42 #309), 0466p0j (0.46 #1040, 0.34 #2420, 0.31 #2696), 05pd94v (0.46 #968, 0.33 #2348, 0.31 #2624) >> Best rule #340 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0bp_b2; 0bdw1g; 0cqh6z; 0ck27z; 0bdx29; 0bdw6t; 0fbtbt; 09v7wsg; 0cqhb3; 0gkr9q; >> query: (?x783, 02q690_) <- award_winner(?x783, ?x190), nominated_for(?x783, ?x337), ceremony(?x783, ?x1265), ?x337 = 0g60z >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #542 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0m7yy; *> query: (?x783, 0hn821n) <- award_winner(?x783, ?x7048), award(?x1849, ?x783), award(?x7048, ?x704), ?x1849 = 0kfv9 *> conf = 0.50 ranks of expected_values: 3, 8 EVAL 0fbvqf ceremony 0hn821n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 43.000 43.000 0.583 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0fbvqf ceremony 0gvstc3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 43.000 43.000 0.583 http://example.org/award/award_category/winners./award/award_honor/ceremony #22261-0dqcs3 PRED entity: 0dqcs3 PRED relation: featured_film_locations PRED expected values: 080h2 => 54 concepts (39 used for prediction) PRED predicted values (max 10 best out of 29): 02_286 (0.36 #20, 0.16 #260, 0.15 #1950), 030qb3t (0.14 #39, 0.07 #2449, 0.07 #3175), 0d6lp (0.14 #72, 0.01 #553, 0.01 #3208), 04jpl (0.13 #1458, 0.08 #249, 0.08 #490), 0fhsz (0.07 #203), 01bkb (0.07 #189), 01sn3 (0.07 #88), 07srw (0.07 #54), 0rh6k (0.05 #482, 0.05 #724, 0.05 #241), 080h2 (0.03 #747, 0.03 #505, 0.02 #264) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 03bx2lk; 0340hj; 0btyf5z; 03hkch7; 0830vk; 07k8rt4; 02wgk1; 09gb_4p; 080lkt7; 012s1d; ... >> query: (?x4839, 02_286) <- genre(?x4839, ?x571), film(?x123, ?x4839), ?x123 = 05bnp0 >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #747 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 157 *> proper extension: 04svwx; *> query: (?x4839, 080h2) <- genre(?x4839, ?x600), ?x600 = 02n4kr, country(?x4839, ?x279), countries_spoken_in(?x393, ?x279) *> conf = 0.03 ranks of expected_values: 10 EVAL 0dqcs3 featured_film_locations 080h2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 54.000 39.000 0.357 http://example.org/film/film/featured_film_locations #22260-0160nk PRED entity: 0160nk PRED relation: school! PRED expected values: 03nt7j => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 18): 02qw1zx (0.30 #94, 0.22 #166, 0.21 #220), 0f4vx0 (0.29 #28, 0.26 #100, 0.26 #172), 05vsb7 (0.25 #91, 0.17 #163, 0.17 #19), 092j54 (0.24 #98, 0.17 #26, 0.16 #224), 09l0x9 (0.22 #101, 0.16 #173, 0.15 #227), 02rl201 (0.20 #3, 0.11 #21, 0.10 #165), 03nt7j (0.20 #96, 0.14 #168, 0.14 #222), 0g3zpp (0.18 #92, 0.13 #218, 0.12 #164), 025tn92 (0.17 #228, 0.17 #102, 0.15 #174), 06439y (0.14 #36, 0.13 #108, 0.13 #234) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 0fht9f; >> query: (?x10572, 02qw1zx) <- school(?x8902, ?x10572), position_s(?x8902, ?x180), position(?x8902, ?x2312) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #96 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 0fht9f; *> query: (?x10572, 03nt7j) <- school(?x8902, ?x10572), position_s(?x8902, ?x180), position(?x8902, ?x2312) *> conf = 0.20 ranks of expected_values: 7 EVAL 0160nk school! 03nt7j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 110.000 110.000 0.303 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #22259-014zws PRED entity: 014zws PRED relation: organization! PRED expected values: 05k17c => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 16): 060c4 (0.76 #731, 0.73 #1095, 0.72 #1173), 07xl34 (0.40 #102, 0.30 #233, 0.27 #115), 05k17c (0.36 #72, 0.32 #1419, 0.31 #268), 0dq_5 (0.29 #1089, 0.26 #595, 0.26 #699), 0hm4q (0.25 #21, 0.10 #399, 0.09 #112), 0789n (0.09 #222), 0dq3c (0.09 #222), 05c0jwl (0.07 #318, 0.06 #383, 0.05 #1254), 04n1q6 (0.06 #1980, 0.05 #97, 0.03 #162), 05_wyz (0.06 #1980, 0.04 #1498) >> Best rule #731 for best value: >> intensional similarity = 5 >> extensional distance = 207 >> proper extension: 02jztz; >> query: (?x9045, 060c4) <- currency(?x9045, ?x170), colors(?x9045, ?x7203), major_field_of_study(?x9045, ?x2605), contains(?x94, ?x9045), ?x94 = 09c7w0 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #72 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 031n8c; *> query: (?x9045, 05k17c) <- citytown(?x9045, ?x3007), state_province_region(?x9045, ?x2020), ?x2020 = 05k7sb, school_type(?x9045, ?x3205), contains(?x94, ?x9045) *> conf = 0.36 ranks of expected_values: 3 EVAL 014zws organization! 05k17c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 172.000 172.000 0.756 http://example.org/organization/role/leaders./organization/leadership/organization #22258-012vct PRED entity: 012vct PRED relation: nationality PRED expected values: 09c7w0 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.84 #4810, 0.83 #6114, 0.83 #6619), 059j2 (0.33 #7320, 0.03 #229), 01n7q (0.33 #10531), 02jx1 (0.16 #233, 0.12 #1735, 0.12 #634), 07ssc (0.11 #5412, 0.11 #2719, 0.11 #2819), 03rk0 (0.11 #5412, 0.07 #1048, 0.07 #4755), 0d060g (0.11 #5412, 0.06 #207, 0.05 #1809), 03rjj (0.11 #5412, 0.05 #706, 0.05 #3807), 0f8l9c (0.11 #5412, 0.05 #3807, 0.04 #422), 0chghy (0.11 #5412, 0.05 #3807, 0.04 #310) >> Best rule #4810 for best value: >> intensional similarity = 3 >> extensional distance = 531 >> proper extension: 0ct_yc; >> query: (?x7232, 09c7w0) <- place_of_birth(?x7232, ?x2552), time_zones(?x2552, ?x2950), adjoins(?x3976, ?x2552) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012vct nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.839 http://example.org/people/person/nationality #22257-095zlp PRED entity: 095zlp PRED relation: nominated_for! PRED expected values: 02r0csl 09sb52 0gs96 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 178): 027571b (0.68 #6427, 0.66 #6426, 0.66 #429), 0gq_v (0.34 #17, 0.27 #2586, 0.25 #3014), 0gr0m (0.31 #48, 0.22 #2617, 0.20 #2189), 0p9sw (0.31 #18, 0.22 #12215, 0.21 #2587), 04kxsb (0.26 #77, 0.17 #2218, 0.17 #2646), 0gs96 (0.23 #72, 0.22 #12215, 0.20 #2641), 0f4x7 (0.22 #23, 0.22 #2592, 0.22 #2164), 09sb52 (0.22 #12215, 0.19 #11357, 0.19 #12216), 0bdw1g (0.22 #12215, 0.19 #11357, 0.19 #12216), 0ck27z (0.22 #12215, 0.19 #11357, 0.19 #12216) >> Best rule #6427 for best value: >> intensional similarity = 3 >> extensional distance = 986 >> proper extension: 06mmr; >> query: (?x414, ?x384) <- award(?x414, ?x384), nominated_for(?x384, ?x195), award(?x164, ?x384) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #72 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 119 *> proper extension: 0c5qvw; *> query: (?x414, 0gs96) <- nominated_for(?x163, ?x414), titles(?x714, ?x414), nominated_for(?x1443, ?x414), ?x1443 = 054krc *> conf = 0.23 ranks of expected_values: 6, 8, 73 EVAL 095zlp nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 68.000 68.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 095zlp nominated_for! 09sb52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 68.000 68.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 095zlp nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 68.000 68.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22256-02h40lc PRED entity: 02h40lc PRED relation: major_field_of_study! PRED expected values: 017v71 015q1n 01dthg => 62 concepts (48 used for prediction) PRED predicted values (max 10 best out of 606): 07wrz (0.67 #3870, 0.45 #6596, 0.35 #10416), 07wjk (0.67 #3871, 0.45 #6597, 0.35 #10417), 07tds (0.67 #3961, 0.43 #10507, 0.36 #6687), 07tgn (0.67 #3827, 0.39 #10373, 0.36 #6553), 05mv4 (0.67 #3939, 0.36 #6665, 0.30 #10485), 015cz0 (0.67 #3981, 0.36 #6707, 0.26 #10527), 0bwfn (0.64 #6810, 0.50 #4084, 0.43 #19357), 09f2j (0.61 #10515, 0.45 #6695, 0.45 #19242), 06pwq (0.52 #10370, 0.51 #19097, 0.50 #3824), 07t90 (0.52 #10505, 0.45 #6685, 0.33 #3959) >> Best rule #3870 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 01mkq; >> query: (?x254, 07wrz) <- major_field_of_study(?x4780, ?x254), major_field_of_study(?x3485, ?x254), major_field_of_study(?x254, ?x2314), ?x4780 = 017cy9, ?x3485 = 01mpwj, major_field_of_study(?x865, ?x254) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4010 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 01mkq; *> query: (?x254, 017v71) <- major_field_of_study(?x4780, ?x254), major_field_of_study(?x3485, ?x254), major_field_of_study(?x254, ?x2314), ?x4780 = 017cy9, ?x3485 = 01mpwj, major_field_of_study(?x865, ?x254) *> conf = 0.33 ranks of expected_values: 55, 160, 190 EVAL 02h40lc major_field_of_study! 01dthg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 48.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02h40lc major_field_of_study! 015q1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 62.000 48.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02h40lc major_field_of_study! 017v71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 62.000 48.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #22255-0gvstc3 PRED entity: 0gvstc3 PRED relation: ceremony! PRED expected values: 0bdw1g 0fbvqf 0fbtbt => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 320): 0gqwc (0.89 #4482, 0.57 #4949, 0.52 #5652), 0gqy2 (0.85 #4541, 0.59 #5008, 0.54 #5711), 0gq_d (0.83 #4574, 0.57 #5041, 0.53 #5744), 0k611 (0.82 #4495, 0.57 #4962, 0.52 #5665), 0p9sw (0.82 #4445, 0.55 #4912, 0.50 #5615), 0gvx_ (0.80 #4554, 0.56 #5021, 0.51 #5724), 0gqyl (0.79 #4503, 0.55 #4970, 0.51 #5673), 018wng (0.79 #4459, 0.55 #4926, 0.50 #5629), 0f4x7 (0.79 #4450, 0.55 #4917, 0.50 #5620), 0gs9p (0.79 #4484, 0.53 #4951, 0.48 #5654) >> Best rule #4482 for best value: >> intensional similarity = 17 >> extensional distance = 64 >> proper extension: 073hkh; 0bzk8w; 02yw5r; 059x66; 073hmq; 0bzm81; 0dth6b; 02yv_b; 0ftlkg; 073h1t; ... >> query: (?x2213, 0gqwc) <- honored_for(?x2213, ?x493), award_winner(?x2213, ?x72), ceremony(?x3247, ?x2213), ceremony(?x1132, ?x2213), award(?x8307, ?x1132), award(?x4349, ?x1132), award(?x3760, ?x1132), award(?x2028, ?x1132), ?x4349 = 01dvms, award_winner(?x1132, ?x1126), ?x8307 = 015nhn, nominated_for(?x1132, ?x715), award(?x968, ?x3247), ?x968 = 015grj, gender(?x3760, ?x514), ?x2028 = 028knk, nominated_for(?x3247, ?x1763) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1657 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 3 *> proper extension: 02q690_; *> query: (?x2213, 0bdw1g) <- honored_for(?x2213, ?x10447), honored_for(?x2213, ?x2583), honored_for(?x2213, ?x1434), award_winner(?x2213, ?x72), program(?x1762, ?x2583), ?x10447 = 07s8z_l, program(?x133, ?x2583), ceremony(?x8660, ?x2213), ceremony(?x7510, ?x2213), ceremony(?x4386, ?x2213), ceremony(?x3906, ?x2213), ceremony(?x2016, ?x2213), ceremony(?x870, ?x2213), ?x3906 = 03ccq3s, ?x7510 = 027gs1_, award(?x236, ?x4386), nominated_for(?x931, ?x1434), ?x870 = 09qv3c, ?x2016 = 0cjyzs, ?x8660 = 02xcb6n, nominated_for(?x686, ?x1434) *> conf = 0.60 ranks of expected_values: 22, 24, 25 EVAL 0gvstc3 ceremony! 0fbtbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 31.000 31.000 0.894 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gvstc3 ceremony! 0fbvqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 31.000 31.000 0.894 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gvstc3 ceremony! 0bdw1g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 31.000 31.000 0.894 http://example.org/award/award_category/winners./award/award_honor/ceremony #22254-01z5tr PRED entity: 01z5tr PRED relation: location PRED expected values: 0cr3d => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 137): 0cr3d (0.70 #66598, 0.51 #36113, 0.50 #46542), 030qb3t (0.33 #82, 0.31 #4094, 0.28 #13730), 01n7q (0.14 #2468, 0.13 #3271, 0.12 #4074), 0k049 (0.14 #2414, 0.13 #3217, 0.12 #4020), 01jr6 (0.14 #2610, 0.13 #3413, 0.12 #5019), 0rd5k (0.13 #3389, 0.12 #4192, 0.12 #4995), 0cc56 (0.09 #16915, 0.07 #2462, 0.07 #3265), 059rby (0.09 #1620, 0.05 #15271, 0.05 #23291), 0f2v0 (0.09 #1785, 0.03 #35310, 0.03 #52961), 094jv (0.09 #1696, 0.03 #35310, 0.01 #26575) >> Best rule #66598 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 07m69t; >> query: (?x7963, ?x2850) <- place_of_birth(?x7963, ?x2850), location(?x7963, ?x739) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01z5tr location 0cr3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #22253-03676 PRED entity: 03676 PRED relation: country! PRED expected values: 0bynt => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 56): 0bynt (0.85 #740, 0.84 #291, 0.84 #572), 01cgz (0.61 #576, 0.60 #295, 0.60 #632), 071t0 (0.57 #585, 0.57 #753, 0.57 #641), 01lb14 (0.48 #297, 0.48 #185, 0.46 #578), 07gyv (0.45 #287, 0.44 #343, 0.43 #399), 06f41 (0.40 #296, 0.39 #745, 0.38 #577), 07jbh (0.40 #842, 0.38 #596, 0.38 #652), 0w0d (0.40 #842, 0.37 #561, 0.37 #742), 0486tv (0.40 #842, 0.37 #561, 0.36 #995), 03fyrh (0.40 #842, 0.37 #561, 0.36 #1067) >> Best rule #740 for best value: >> intensional similarity = 2 >> extensional distance = 129 >> proper extension: 02jxk; >> query: (?x7665, 0bynt) <- jurisdiction_of_office(?x182, ?x7665), member_states(?x7695, ?x7665) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03676 country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.847 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #22252-0fsw_7 PRED entity: 0fsw_7 PRED relation: music PRED expected values: 01cbt3 => 90 concepts (53 used for prediction) PRED predicted values (max 10 best out of 96): 0146pg (0.22 #853, 0.13 #642, 0.11 #3807), 01cbt3 (0.20 #3165, 0.18 #91, 0.16 #4008), 02g1jh (0.20 #3165, 0.16 #4008, 0.09 #128), 0drc1 (0.17 #359, 0.04 #1626, 0.03 #1837), 01l9v7n (0.16 #4008, 0.09 #47, 0.04 #3001), 02sj1x (0.13 #477, 0.08 #1322, 0.07 #1111), 01tc9r (0.10 #697, 0.03 #1965, 0.03 #7027), 015wc0 (0.09 #1231, 0.08 #386, 0.07 #1442), 0k7pf (0.09 #44, 0.02 #887), 04f9r2 (0.09 #190) >> Best rule #853 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 04fzfj; 02q56mk; 05zlld0; 0dp7wt; >> query: (?x5399, 0146pg) <- film(?x5869, ?x5399), film_release_distribution_medium(?x5399, ?x81), nominated_for(?x836, ?x5399), story_by(?x5399, ?x3686) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #3165 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 154 *> proper extension: 01bb9r; 01qvz8; 07_fj54; 0ptx_; 0g7pm1; 07kdkfj; 02mpyh; 0286hyp; *> query: (?x5399, ?x5251) <- nominated_for(?x5399, ?x836), music(?x836, ?x5251), genre(?x5399, ?x225), production_companies(?x836, ?x788) *> conf = 0.20 ranks of expected_values: 2 EVAL 0fsw_7 music 01cbt3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 53.000 0.224 http://example.org/film/film/music #22251-01wp8w7 PRED entity: 01wp8w7 PRED relation: instrumentalists! PRED expected values: 07xzm => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 121): 05r5c (0.48 #2411, 0.47 #5559, 0.47 #3537), 018vs (0.43 #892, 0.36 #2577, 0.32 #2978), 0l14md (0.39 #886, 0.17 #165, 0.15 #245), 03bx0bm (0.38 #2647, 0.04 #4263, 0.03 #5797), 03gvt (0.31 #3694, 0.30 #4345, 0.27 #481), 042v_gx (0.31 #3694, 0.30 #4345, 0.27 #481), 02sgy (0.31 #3694, 0.30 #4345, 0.27 #481), 018j2 (0.19 #113, 0.12 #433, 0.12 #914), 0l14j_ (0.17 #287, 0.06 #928, 0.05 #3014), 06w7v (0.12 #145, 0.11 #225, 0.10 #305) >> Best rule #2411 for best value: >> intensional similarity = 3 >> extensional distance = 251 >> proper extension: 01l03w2; >> query: (?x1521, 05r5c) <- type_of_union(?x1521, ?x566), instrumentalists(?x212, ?x1521), location(?x1521, ?x335) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #99 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 01kcms4; *> query: (?x1521, 07xzm) <- artists(?x7440, ?x1521), influenced_by(?x1521, ?x215), ?x7440 = 0155w *> conf = 0.06 ranks of expected_values: 27 EVAL 01wp8w7 instrumentalists! 07xzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 140.000 140.000 0.478 http://example.org/music/instrument/instrumentalists #22250-0jdd PRED entity: 0jdd PRED relation: taxonomy PRED expected values: 04n6k => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.81 #23, 0.78 #25, 0.77 #15) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 059f4; 05fkf; 03s0w; 05fhy; 059_c; 01x73; 05k7sb; 06btq; 03s5t; 0gyh; ... >> query: (?x3352, 04n6k) <- adjoins(?x2146, ?x3352), religion(?x3352, ?x13970), jurisdiction_of_office(?x346, ?x3352) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jdd taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.806 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #22249-08052t3 PRED entity: 08052t3 PRED relation: film_crew_role PRED expected values: 089fss 0ch6mp2 089g0h => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.84 #639, 0.81 #1213, 0.80 #186), 0d2b38 (0.68 #140, 0.33 #200, 0.33 #502), 0215hd (0.68 #133, 0.33 #193, 0.31 #858), 089g0h (0.53 #134, 0.29 #859, 0.27 #194), 033smt (0.53 #142, 0.26 #504, 0.23 #353), 01pvkk (0.37 #519, 0.36 #580, 0.31 #610), 02_n3z (0.32 #121, 0.22 #846, 0.18 #181), 02ynfr (0.27 #522, 0.25 #583, 0.19 #855), 05smlt (0.26 #135, 0.14 #45, 0.13 #845), 0263ycg (0.21 #132, 0.13 #845, 0.13 #102) >> Best rule #639 for best value: >> intensional similarity = 4 >> extensional distance = 209 >> proper extension: 05dy7p; >> query: (?x2471, 0ch6mp2) <- film_crew_role(?x2471, ?x1171), film_format(?x2471, ?x6392), ?x1171 = 09vw2b7, genre(?x2471, ?x225) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 11 EVAL 08052t3 film_crew_role 089g0h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 72.000 0.839 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 08052t3 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.839 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 08052t3 film_crew_role 089fss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 72.000 72.000 0.839 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22248-0rqyx PRED entity: 0rqyx PRED relation: location_of_ceremony! PRED expected values: 01rzxl => 102 concepts (100 used for prediction) PRED predicted values (max 10 best out of 114): 02h48 (0.33 #245, 0.05 #1007, 0.03 #1261), 02fn5 (0.08 #611, 0.02 #1881, 0.02 #2135), 01kgg9 (0.08 #726, 0.02 #1996, 0.02 #3012), 05_2h8 (0.08 #667, 0.02 #1937, 0.02 #2953), 048hf (0.03 #1200, 0.03 #1708, 0.02 #2724), 01pqy_ (0.03 #1144, 0.03 #1652, 0.02 #2668), 0f502 (0.03 #1121, 0.03 #1629, 0.02 #2645), 0d7hg4 (0.03 #1333, 0.02 #1841, 0.02 #2095), 0cqt90 (0.03 #1615, 0.02 #2631), 01p45_v (0.02 #1809, 0.02 #2063) >> Best rule #245 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0rj0z; >> query: (?x5284, 02h48) <- contains(?x2623, ?x5284), adjoins(?x5284, ?x6084), ?x2623 = 02xry, category(?x5284, ?x134) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0rqyx location_of_ceremony! 01rzxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 100.000 0.333 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #22247-06b4wb PRED entity: 06b4wb PRED relation: film PRED expected values: 0jnwx => 104 concepts (67 used for prediction) PRED predicted values (max 10 best out of 750): 04fzfj (0.22 #3683, 0.14 #1894, 0.03 #5471), 03m8y5 (0.20 #406, 0.14 #2195, 0.11 #3984), 01ry_x (0.20 #1705, 0.14 #3494, 0.11 #5283), 04sh80 (0.20 #1747, 0.14 #3536, 0.11 #5325), 05sw5b (0.20 #813, 0.14 #2602, 0.06 #7968), 0407yj_ (0.20 #482, 0.14 #2271, 0.03 #21951), 0cfhfz (0.20 #491, 0.14 #2280, 0.02 #9434), 0y_yw (0.20 #1059, 0.14 #2848, 0.02 #10002), 02ppg1r (0.20 #771, 0.14 #2560, 0.01 #9714), 07c72 (0.15 #105542, 0.09 #46520, 0.08 #32207) >> Best rule #3683 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01h910; >> query: (?x12001, 04fzfj) <- actor(?x3180, ?x12001), film(?x12001, ?x1076), ?x3180 = 07c72, profession(?x12001, ?x1032) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #11028 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 167 *> proper extension: 01kwh5j; 0dszr0; *> query: (?x12001, 0jnwx) <- actor(?x4339, ?x12001), profession(?x12001, ?x1383), ?x1383 = 0np9r, languages(?x4339, ?x254) *> conf = 0.02 ranks of expected_values: 246 EVAL 06b4wb film 0jnwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 104.000 67.000 0.222 http://example.org/film/actor/film./film/performance/film #22246-048svj PRED entity: 048svj PRED relation: nationality PRED expected values: 03rk0 => 99 concepts (36 used for prediction) PRED predicted values (max 10 best out of 17): 03rk0 (0.84 #401, 0.84 #346, 0.77 #3625), 09c7w0 (0.76 #2612, 0.67 #3318, 0.67 #2916), 0byh8j (0.47 #402, 0.33 #3521, 0.29 #3522), 02jx1 (0.12 #1641, 0.12 #1441, 0.11 #3048), 0d060g (0.11 #409, 0.10 #611, 0.10 #711), 07ssc (0.09 #1122, 0.09 #1924, 0.08 #1823), 0f8l9c (0.07 #424, 0.05 #626, 0.05 #827), 0chghy (0.05 #614, 0.05 #412, 0.05 #714), 03rjj (0.05 #407, 0.04 #609, 0.04 #709), 0h7x (0.04 #437, 0.03 #739, 0.03 #840) >> Best rule #401 for best value: >> intensional similarity = 4 >> extensional distance = 85 >> proper extension: 0292l3; 015q43; 087_wh; 09tqx3; 06kl0k; 05_zc7; 050llt; 040nwr; 075p0r; 04328m; >> query: (?x14431, ?x2146) <- location(?x14431, ?x13551), profession(?x14431, ?x1032), contains(?x2146, ?x13551), ?x2146 = 03rk0 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 048svj nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 36.000 0.839 http://example.org/people/person/nationality #22245-0glyyw PRED entity: 0glyyw PRED relation: executive_produced_by! PRED expected values: 06z8s_ 02c638 07cz2 01bb9r 026wlxw 03tbg6 => 119 concepts (92 used for prediction) PRED predicted values (max 10 best out of 605): 01pj_5 (0.19 #2279, 0.19 #1769, 0.13 #4317), 0mbql (0.15 #2911, 0.14 #3929, 0.12 #4948), 0bt4g (0.15 #2954, 0.14 #3972, 0.12 #4991), 01f7kl (0.15 #2677, 0.14 #3695, 0.12 #4714), 02ryz24 (0.14 #661, 0.01 #14416), 0fsd9t (0.13 #4525, 0.12 #2487, 0.12 #1977), 049xgc (0.12 #2348, 0.12 #1838, 0.11 #5096), 0gmcwlb (0.12 #2103, 0.12 #1593, 0.09 #2549), 0gwjw0c (0.12 #2408, 0.12 #1898, 0.09 #4446), 03clwtw (0.12 #2420, 0.12 #1910, 0.09 #4458) >> Best rule #2279 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 04b19t; >> query: (?x8503, 01pj_5) <- produced_by(?x857, ?x8503), gender(?x8503, ?x231), company(?x8503, ?x382), film(?x382, ?x83) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #5096 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 0fvf9q; 0b13g7; 0b80__; 02z6l5f; 02508x; 07r1h; 06q8hf; 01nbq4; *> query: (?x8503, ?x349) <- nationality(?x8503, ?x94), company(?x8503, ?x2548), production_companies(?x349, ?x2548), award_nominee(?x2182, ?x2548) *> conf = 0.11 ranks of expected_values: 81, 90, 121, 124, 128, 165 EVAL 0glyyw executive_produced_by! 03tbg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 119.000 92.000 0.188 http://example.org/film/film/executive_produced_by EVAL 0glyyw executive_produced_by! 026wlxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 119.000 92.000 0.188 http://example.org/film/film/executive_produced_by EVAL 0glyyw executive_produced_by! 01bb9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 119.000 92.000 0.188 http://example.org/film/film/executive_produced_by EVAL 0glyyw executive_produced_by! 07cz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 119.000 92.000 0.188 http://example.org/film/film/executive_produced_by EVAL 0glyyw executive_produced_by! 02c638 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 119.000 92.000 0.188 http://example.org/film/film/executive_produced_by EVAL 0glyyw executive_produced_by! 06z8s_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 119.000 92.000 0.188 http://example.org/film/film/executive_produced_by #22244-07tw_b PRED entity: 07tw_b PRED relation: film! PRED expected values: 0pgjm 02jm0n => 69 concepts (50 used for prediction) PRED predicted values (max 10 best out of 715): 01tsbmv (0.25 #1895, 0.20 #3972, 0.05 #8126), 01gbn6 (0.25 #1624, 0.20 #3701, 0.04 #5778), 01bbwp (0.25 #1652, 0.20 #3729, 0.04 #5806), 0btxr (0.25 #1594, 0.20 #3671, 0.04 #5748), 06rgq (0.25 #1476, 0.20 #3553, 0.04 #5630), 026spg (0.25 #832, 0.20 #2909, 0.04 #4986), 0gdh5 (0.25 #475, 0.20 #2552, 0.04 #4629), 09qr6 (0.25 #211, 0.20 #2288, 0.04 #4365), 09fqtq (0.25 #70, 0.20 #2147, 0.04 #4224), 06cgy (0.25 #249, 0.20 #2326, 0.03 #10635) >> Best rule #1895 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0b2v79; 03hfmm; >> query: (?x4110, 01tsbmv) <- film(?x194, ?x4110), ?x194 = 06688p, featured_film_locations(?x4110, ?x739), genre(?x4110, ?x258) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #10600 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 145 *> proper extension: 04dsnp; 02wk7b; *> query: (?x4110, 0pgjm) <- film_crew_role(?x4110, ?x137), genre(?x4110, ?x258), written_by(?x4110, ?x6771), ?x258 = 05p553 *> conf = 0.03 ranks of expected_values: 188, 712 EVAL 07tw_b film! 02jm0n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 69.000 50.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 07tw_b film! 0pgjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 69.000 50.000 0.250 http://example.org/film/actor/film./film/performance/film #22243-02vqpx8 PRED entity: 02vqpx8 PRED relation: award_winner! PRED expected values: 03gt46z => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 132): 027n06w (0.11 #353, 0.09 #773, 0.07 #1053), 0gvstc3 (0.11 #174, 0.10 #314, 0.07 #734), 05c1t6z (0.10 #155, 0.09 #295, 0.08 #1695), 0d__c3 (0.10 #685, 0.08 #1385, 0.08 #1245), 0c53zb (0.10 #621, 0.08 #1321, 0.06 #1181), 03nnm4t (0.09 #494, 0.08 #1754, 0.08 #774), 05hmp6 (0.09 #647, 0.07 #1347, 0.07 #1207), 02q690_ (0.09 #765, 0.09 #1745, 0.08 #1045), 09v0p2c (0.09 #363, 0.06 #223, 0.05 #783), 0gx_st (0.08 #457, 0.08 #737, 0.07 #1017) >> Best rule #353 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 0glmv; 02pbp9; >> query: (?x7043, 027n06w) <- award_winner(?x1653, ?x7043), nationality(?x7043, ?x94), tv_program(?x7043, ?x6482) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #343 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 0glmv; 02pbp9; *> query: (?x7043, 03gt46z) <- award_winner(?x1653, ?x7043), nationality(?x7043, ?x94), tv_program(?x7043, ?x6482) *> conf = 0.07 ranks of expected_values: 16 EVAL 02vqpx8 award_winner! 03gt46z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 110.000 110.000 0.114 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22242-0rydq PRED entity: 0rydq PRED relation: category PRED expected values: 08mbj5d => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #10, 0.76 #9, 0.76 #11) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 169 >> proper extension: 0f2wj; 0r62v; 0r7fy; 030qb3t; 015zxh; 0k_q_; 0dc95; 0r1jr; 0d6lp; 01jr6; ... >> query: (?x14277, 08mbj5d) <- location(?x4157, ?x14277), state(?x14277, ?x3038), contains(?x3038, ?x2277), district_represented(?x176, ?x3038) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rydq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.784 http://example.org/common/topic/webpage./common/webpage/category #22241-0lyjf PRED entity: 0lyjf PRED relation: school! PRED expected values: 02qw1zx => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 14): 0f4vx0 (0.40 #35, 0.33 #21, 0.31 #134), 047dpm0 (0.40 #41, 0.33 #27, 0.31 #70), 02qw1zx (0.33 #17, 0.32 #284, 0.31 #312), 025tn92 (0.33 #22, 0.28 #135, 0.20 #324), 02pq_rp (0.33 #19, 0.20 #324, 0.20 #33), 02r6gw6 (0.33 #51, 0.20 #324, 0.19 #57), 02rl201 (0.31 #59, 0.20 #324, 0.19 #57), 02x2khw (0.23 #58, 0.20 #324, 0.19 #57), 04f4z1k (0.20 #324, 0.20 #40, 0.19 #57), 038c0q (0.20 #324, 0.19 #57, 0.17 #131) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01jq0j; 027ybp; >> query: (?x4904, 0f4vx0) <- school(?x3674, ?x4904), student(?x4904, ?x1683), ?x3674 = 05tg3, colors(?x4904, ?x663) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 065y4w7; *> query: (?x4904, 02qw1zx) <- school(?x1115, ?x4904), student(?x4904, ?x1683), ?x1683 = 03ft8, team(?x180, ?x1115) *> conf = 0.33 ranks of expected_values: 3 EVAL 0lyjf school! 02qw1zx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 170.000 170.000 0.400 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #22240-032_wv PRED entity: 032_wv PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.73 #1316, 0.69 #155, 0.68 #418), 02r96rf (0.64 #1312, 0.64 #376, 0.63 #414), 09vw2b7 (0.62 #1315, 0.58 #379, 0.58 #154), 01pvkk (0.45 #13, 0.28 #1583, 0.25 #1322), 0dxtw (0.35 #1320, 0.33 #384, 0.33 #347), 01vx2h (0.30 #1321, 0.30 #385, 0.29 #423), 0215hd (0.18 #20, 0.12 #94, 0.12 #168), 01xy5l_ (0.18 #15, 0.11 #163, 0.10 #314), 02ynfr (0.16 #428, 0.15 #1326, 0.15 #390), 0d2b38 (0.11 #400, 0.11 #175, 0.11 #101) >> Best rule #1316 for best value: >> intensional similarity = 2 >> extensional distance = 833 >> proper extension: 03_wm6; >> query: (?x1298, 0ch6mp2) <- film_crew_role(?x1298, ?x137), production_companies(?x1298, ?x902) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 032_wv film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.729 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22239-02r1c18 PRED entity: 02r1c18 PRED relation: nominated_for! PRED expected values: 099c8n => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 213): 0gs9p (0.68 #7318, 0.46 #7786, 0.38 #9191), 019f4v (0.59 #7308, 0.46 #7776, 0.37 #9181), 0k611 (0.54 #7327, 0.37 #7795, 0.33 #306), 040njc (0.45 #7261, 0.35 #7729, 0.32 #11001), 0gr4k (0.44 #7281, 0.32 #11001, 0.30 #12642), 04dn09n (0.44 #7290, 0.30 #7758, 0.30 #2141), 0f4x7 (0.42 #2131, 0.41 #7280, 0.32 #3067), 0gq_v (0.40 #7274, 0.33 #7742, 0.31 #2125), 0gqy2 (0.38 #7374, 0.30 #2225, 0.28 #7842), 0gr0m (0.38 #7314, 0.30 #7782, 0.30 #12642) >> Best rule #7318 for best value: >> intensional similarity = 4 >> extensional distance = 239 >> proper extension: 0yyg4; 0gzy02; 04v8x9; 0209xj; 0pv2t; 0c5dd; 04mzf8; 0sxfd; 0p_th; 070fnm; ... >> query: (?x1535, 0gs9p) <- award_winner(?x1535, ?x185), language(?x1535, ?x254), nominated_for(?x1307, ?x1535), ?x1307 = 0gq9h >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #290 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 02vxq9m; 0dgst_d; 0661ql3; 05zlld0; 017jd9; 0dzlbx; 0233bn; 03nsm5x; 0ndsl1x; *> query: (?x1535, 099c8n) <- award_winner(?x1535, ?x185), film_release_region(?x1535, ?x1536), film_release_region(?x1535, ?x1353), ?x1353 = 035qy, ?x1536 = 06c1y *> conf = 0.33 ranks of expected_values: 14 EVAL 02r1c18 nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 134.000 134.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22238-018w0j PRED entity: 018w0j PRED relation: entity_involved PRED expected values: 079dy => 68 concepts (45 used for prediction) PRED predicted values (max 10 best out of 206): 07ssc (0.71 #630, 0.71 #629, 0.67 #157), 0chghy (0.71 #630, 0.71 #629, 0.67 #157), 01z215 (0.71 #630, 0.71 #629, 0.67 #157), 0f8l9c (0.71 #630, 0.71 #629, 0.67 #157), 09c7w0 (0.71 #630, 0.71 #629, 0.67 #157), 0d05q4 (0.71 #630, 0.71 #629, 0.67 #157), 0jgd (0.71 #630, 0.71 #629, 0.67 #157), 03l5m1 (0.33 #86, 0.26 #6331, 0.25 #558), 024pcx (0.33 #81, 0.26 #6331, 0.25 #553), 03gk2 (0.33 #18, 0.26 #6331, 0.25 #490) >> Best rule #630 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0jfgk; >> query: (?x11047, ?x94) <- locations(?x11047, ?x4743), ?x4743 = 03spz, combatants(?x11047, ?x94) >> conf = 0.71 => this is the best rule for 7 predicted values *> Best rule #6331 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 45 *> proper extension: 0chhs; *> query: (?x11047, ?x1778) <- locations(?x11047, ?x4743), locations(?x11047, ?x1781), combatants(?x11047, ?x94), locations(?x12992, ?x4743), contains(?x1781, ?x6581), entity_involved(?x11047, ?x966), entity_involved(?x12992, ?x1778) *> conf = 0.26 ranks of expected_values: 23 EVAL 018w0j entity_involved 079dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 68.000 45.000 0.714 http://example.org/base/culturalevent/event/entity_involved #22237-0274ck PRED entity: 0274ck PRED relation: artist! PRED expected values: 01w31x => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 119): 023rwm (0.33 #2, 0.25 #712, 0.25 #286), 01p2b_ (0.33 #83, 0.25 #367, 0.20 #509), 0181dw (0.33 #185, 0.12 #3735, 0.12 #1463), 011k1h (0.31 #1004, 0.23 #1998, 0.20 #436), 0n85g (0.31 #1058, 0.20 #490, 0.15 #2194), 09zcbg (0.25 #404, 0.20 #546, 0.06 #1256), 015_1q (0.23 #1440, 0.21 #2292, 0.19 #2008), 043g7l (0.20 #458, 0.12 #742, 0.08 #2020), 0f38nv (0.20 #541, 0.08 #967, 0.06 #1251), 017l96 (0.19 #1439, 0.15 #1013, 0.15 #2007) >> Best rule #2 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01wt4wc; >> query: (?x764, 023rwm) <- nationality(?x764, ?x279), instrumentalists(?x75, ?x764), profession(?x764, ?x131), artists(?x13087, ?x764), ?x13087 = 02yw26 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0274ck artist! 01w31x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 149.000 149.000 0.333 http://example.org/music/record_label/artist #22236-03hj5lq PRED entity: 03hj5lq PRED relation: nominated_for! PRED expected values: 099c8n => 81 concepts (71 used for prediction) PRED predicted values (max 10 best out of 306): 057xs89 (0.84 #1013, 0.83 #788, 0.25 #10136), 04kxsb (0.66 #7433, 0.66 #10363, 0.66 #10362), 0gq9h (0.35 #4114, 0.35 #4339, 0.33 #4565), 0gs9p (0.31 #4341, 0.30 #4116, 0.30 #4567), 019f4v (0.30 #4105, 0.30 #4330, 0.28 #4556), 02hsq3m (0.30 #703, 0.25 #928, 0.14 #2729), 05ztjjw (0.29 #910, 0.27 #685, 0.12 #10364), 0k611 (0.27 #4125, 0.26 #4350, 0.24 #4576), 0gq_v (0.26 #4296, 0.26 #4071, 0.25 #4522), 040njc (0.25 #4059, 0.25 #4284, 0.24 #4510) >> Best rule #1013 for best value: >> intensional similarity = 5 >> extensional distance = 81 >> proper extension: 034qrh; 051zy_b; 02ny6g; 016y_f; 01msrb; 013q0p; 0gwjw0c; 0m63c; 02vnmc9; 02p76f9; ... >> query: (?x6076, 057xs89) <- nominated_for(?x462, ?x6076), award(?x7909, ?x462), award(?x6289, ?x462), ?x6289 = 0x3n, artist(?x2299, ?x7909) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #731 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 014_x2; 0ds35l9; 02vxq9m; 07gp9; 0ds3t5x; 01k1k4; 0ds11z; 05p1tzf; 0bth54; 0fg04; ... *> query: (?x6076, 099c8n) <- nominated_for(?x462, ?x6076), award(?x6289, ?x462), ?x6289 = 0x3n, film_crew_role(?x6076, ?x137) *> conf = 0.19 ranks of expected_values: 54 EVAL 03hj5lq nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 81.000 71.000 0.843 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22235-06qd3 PRED entity: 06qd3 PRED relation: olympics PRED expected values: 0ldqf => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 25): 0kbvv (0.74 #476, 0.68 #363, 0.68 #2635), 09x3r (0.68 #353, 0.60 #428, 0.60 #128), 0lbd9 (0.65 #543, 0.62 #292, 0.60 #442), 0nbjq (0.65 #434, 0.63 #359, 0.62 #284), 0lv1x (0.65 #431, 0.63 #356, 0.60 #131), 0l6vl (0.61 #528, 0.53 #778, 0.51 #1204), 0ldqf (0.60 #446, 0.57 #522, 0.55 #672), 018qb4 (0.58 #166, 0.52 #517, 0.50 #141), 0lk8j (0.58 #357, 0.55 #432, 0.53 #382), 0blg2 (0.58 #383, 0.52 #534, 0.52 #509) >> Best rule #476 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 07t_x; >> query: (?x1453, ?x778) <- country(?x3885, ?x1453), contains(?x1453, ?x2079), olympics(?x1453, ?x778), ?x3885 = 019w9j >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #446 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 03rt9; 0hzlz; 0ctw_b; 06t8v; *> query: (?x1453, 0ldqf) <- film_release_region(?x5721, ?x1453), teams(?x1453, ?x8511), olympics(?x1453, ?x418), ?x5721 = 01d259 *> conf = 0.60 ranks of expected_values: 7 EVAL 06qd3 olympics 0ldqf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 189.000 189.000 0.743 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #22234-0dll_t2 PRED entity: 0dll_t2 PRED relation: film_release_region PRED expected values: 03rjj 03_3d 04gzd 015qh => 78 concepts (73 used for prediction) PRED predicted values (max 10 best out of 134): 03rjj (0.89 #669, 0.89 #803, 0.89 #1070), 06mkj (0.89 #310, 0.87 #977, 0.87 #843), 03_3d (0.82 #538, 0.82 #671, 0.82 #805), 03rj0 (0.79 #178, 0.76 #312, 0.74 #979), 04gzd (0.76 #275, 0.65 #674, 0.65 #141), 015qh (0.70 #300, 0.66 #566, 0.61 #699), 047yc (0.67 #288, 0.60 #554, 0.59 #821), 06mzp (0.62 #950, 0.62 #816, 0.62 #682), 06qd3 (0.59 #563, 0.58 #830, 0.57 #696), 07f1x (0.52 #364, 0.48 #630, 0.43 #897) >> Best rule #669 for best value: >> intensional similarity = 9 >> extensional distance = 82 >> proper extension: 014lc_; 0gx1bnj; 0g5qs2k; 0g5838s; 05c26ss; 03q0r1; 0198b6; 0bpm4yw; 03yvf2; 0gg5kmg; ... >> query: (?x5644, 03rjj) <- film_release_region(?x5644, ?x2629), film_release_region(?x5644, ?x1353), film_release_region(?x5644, ?x550), film_release_region(?x5644, ?x429), ?x2629 = 06f32, ?x550 = 05v8c, genre(?x5644, ?x225), ?x1353 = 035qy, administrative_parent(?x1789, ?x429) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 5, 6 EVAL 0dll_t2 film_release_region 015qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 73.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dll_t2 film_release_region 04gzd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 73.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dll_t2 film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 73.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dll_t2 film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 73.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22233-07_m2 PRED entity: 07_m2 PRED relation: gender PRED expected values: 05zppz => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #57, 0.90 #33, 0.90 #71), 02zsn (0.54 #141, 0.48 #156, 0.46 #295) >> Best rule #57 for best value: >> intensional similarity = 6 >> extensional distance = 115 >> proper extension: 0343h; 02ld6x; 085pr; >> query: (?x10923, 05zppz) <- nationality(?x10923, ?x10382), student(?x8052, ?x10923), influenced_by(?x7495, ?x10923), location(?x10923, ?x13356), religion(?x7495, ?x2694), contains(?x10382, ?x1229) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07_m2 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.915 http://example.org/people/person/gender #22232-02qwg PRED entity: 02qwg PRED relation: award PRED expected values: 025m8l => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 293): 054krc (0.48 #4704, 0.18 #8939, 0.15 #2394), 0l8z1 (0.39 #4681, 0.14 #8916, 0.08 #15076), 0gqz2 (0.38 #4697, 0.18 #40426, 0.17 #2387), 02qvyrt (0.38 #4741, 0.17 #2431, 0.16 #8976), 09sb52 (0.37 #5814, 0.36 #26989, 0.34 #32764), 05pcn59 (0.28 #5853, 0.23 #3158, 0.20 #12783), 02f6ym (0.27 #1784, 0.20 #5634, 0.14 #5249), 01c427 (0.27 #1621, 0.16 #5086, 0.16 #4316), 025m8y (0.26 #4715, 0.18 #40426, 0.18 #95), 01c99j (0.24 #1754, 0.20 #4449, 0.20 #5219) >> Best rule #4704 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 0b82vw; 01p7b6b; 0csdzz; 07v4dm; >> query: (?x3403, 054krc) <- award(?x3403, ?x247), award_winner(?x342, ?x3403), music(?x1185, ?x3403) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #43892 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2165 *> proper extension: 0181hw; *> query: (?x3403, ?x724) <- award_nominee(?x3403, ?x1751), award_winner(?x724, ?x1751) *> conf = 0.15 ranks of expected_values: 44 EVAL 02qwg award 025m8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 134.000 134.000 0.481 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22231-01771z PRED entity: 01771z PRED relation: honored_for PRED expected values: 069q4f => 112 concepts (75 used for prediction) PRED predicted values (max 10 best out of 131): 069q4f (0.84 #769, 0.84 #614, 0.83 #2614), 02scbv (0.75 #154, 0.74 #615, 0.67 #1078), 01771z (0.62 #211, 0.60 #1845, 0.58 #2923), 0dfw0 (0.08 #546, 0.06 #1009, 0.05 #855), 0bxxzb (0.08 #577, 0.06 #1040, 0.05 #1962), 0dtfn (0.08 #491, 0.06 #954, 0.05 #1876), 0ddt_ (0.08 #521, 0.06 #984, 0.04 #2214), 05pxnmb (0.06 #591, 0.06 #1054, 0.05 #1822), 0f3m1 (0.06 #596, 0.05 #1059, 0.03 #2289), 0cf08 (0.06 #7226, 0.04 #7225, 0.02 #8912) >> Best rule #769 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 019vhk; 0glbqt; >> query: (?x2749, ?x188) <- honored_for(?x188, ?x2749), nominated_for(?x102, ?x2749), award_winner(?x2749, ?x5338), produced_by(?x2749, ?x7036) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01771z honored_for 069q4f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 75.000 0.842 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #22230-02bh9 PRED entity: 02bh9 PRED relation: music! PRED expected values: 035_2h 043tvp3 0ptdz => 108 concepts (96 used for prediction) PRED predicted values (max 10 best out of 919): 050xxm (0.46 #31366, 0.32 #9803, 0.17 #7842), 03hkch7 (0.46 #31366, 0.32 #9803, 0.06 #54893), 04s1zr (0.17 #7842, 0.05 #10784), 01s7w3 (0.12 #3780, 0.06 #8681, 0.06 #1820), 02ht1k (0.10 #7219, 0.06 #8200, 0.06 #3299), 0pdp8 (0.09 #3160, 0.08 #7080, 0.06 #9041), 07bzz7 (0.08 #7371, 0.06 #3451, 0.04 #10313), 0888c3 (0.06 #5678, 0.05 #7638, 0.04 #2738), 0dgq_kn (0.06 #1566, 0.03 #3526, 0.03 #4506), 09d3b7 (0.05 #13555, 0.05 #7672, 0.04 #8653) >> Best rule #31366 for best value: >> intensional similarity = 3 >> extensional distance = 192 >> proper extension: 01w7nww; 04qmr; 025l5; 01vwbts; 0dw4g; 03d9d6; 01w9wwg; 092ggq; 04n32; >> query: (?x3410, ?x708) <- award(?x3410, ?x1079), artists(?x302, ?x3410), nominated_for(?x3410, ?x708) >> conf = 0.46 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02bh9 music! 0ptdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 96.000 0.463 http://example.org/film/film/music EVAL 02bh9 music! 043tvp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 96.000 0.463 http://example.org/film/film/music EVAL 02bh9 music! 035_2h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 96.000 0.463 http://example.org/film/film/music #22229-07s6tbm PRED entity: 07s6tbm PRED relation: nominated_for PRED expected values: 0330r => 86 concepts (54 used for prediction) PRED predicted values (max 10 best out of 249): 0828jw (0.31 #913, 0.10 #2533, 0.09 #4153), 05f4vxd (0.08 #798, 0.03 #29968, 0.03 #38071), 039c26 (0.08 #494, 0.02 #32904, 0.02 #2114), 028k2x (0.08 #1184, 0.02 #2804, 0.02 #4424), 043qqt5 (0.08 #1565, 0.02 #3185, 0.02 #4805), 0h3mh3q (0.08 #1410, 0.02 #3030, 0.02 #4650), 06x77g (0.08 #1373, 0.02 #2993, 0.02 #4613), 0f61tk (0.08 #1316, 0.02 #2936, 0.02 #4556), 02x6dqb (0.08 #490, 0.02 #2110, 0.02 #3730), 014l6_ (0.08 #483, 0.02 #2103, 0.02 #3723) >> Best rule #913 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 054187; >> query: (?x1341, 0828jw) <- place_of_birth(?x1341, ?x94), ?x94 = 09c7w0 >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #6274 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 149 *> proper extension: 07nznf; 0grwj; 0dbpyd; 06j0md; 01xdf5; 0c4f4; 0bxtg; 02lf0c; 0d4fqn; 0415svh; ... *> query: (?x1341, 0330r) <- award_nominee(?x1340, ?x1341), program(?x1341, ?x8775), award_winner(?x589, ?x1341) *> conf = 0.06 ranks of expected_values: 17 EVAL 07s6tbm nominated_for 0330r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 86.000 54.000 0.308 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22228-0900j5 PRED entity: 0900j5 PRED relation: film! PRED expected values: 024rbz => 70 concepts (39 used for prediction) PRED predicted values (max 10 best out of 59): 024rgt (0.67 #672, 0.52 #241, 0.48 #297), 086k8 (0.30 #522, 0.18 #1199, 0.17 #2250), 017s11 (0.29 #151, 0.22 #3, 0.21 #301), 016tw3 (0.16 #1207, 0.14 #1735, 0.14 #1432), 05qd_ (0.15 #82, 0.14 #306, 0.14 #1205), 032j_n (0.15 #131, 0.11 #57, 0.08 #503), 0jz9f (0.14 #149, 0.11 #1, 0.11 #299), 03rwz3 (0.11 #43, 0.11 #341, 0.10 #415), 046b0s (0.11 #596, 0.10 #1195, 0.02 #1196), 054g1r (0.10 #108, 0.08 #930, 0.07 #1004) >> Best rule #672 for best value: >> intensional similarity = 4 >> extensional distance = 244 >> proper extension: 04bp0l; >> query: (?x3588, ?x2549) <- nominated_for(?x2549, ?x3588), state_province_region(?x2549, ?x1227), film(?x2549, ?x9303), genre(?x9303, ?x258) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #683 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 272 *> proper extension: 047svrl; *> query: (?x3588, 024rbz) <- film_release_region(?x3588, ?x550), titles(?x571, ?x3588), film(?x1897, ?x3588), executive_produced_by(?x3588, ?x11374) *> conf = 0.04 ranks of expected_values: 31 EVAL 0900j5 film! 024rbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 70.000 39.000 0.665 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #22227-01_d4 PRED entity: 01_d4 PRED relation: featured_film_locations! PRED expected values: 0gjc4d3 08fn5b => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 853): 04dsnp (0.43 #775, 0.16 #7886, 0.15 #8597), 047csmy (0.29 #1095, 0.14 #11050, 0.13 #12472), 0ds2n (0.29 #933, 0.12 #3778, 0.12 #3067), 01lsl (0.29 #1324, 0.10 #11279, 0.10 #12701), 0hmr4 (0.29 #753, 0.08 #7153, 0.08 #20663), 0g_zyp (0.29 #1348, 0.08 #7748, 0.08 #8459), 07nnp_ (0.29 #1413, 0.08 #7813, 0.08 #8524), 01z452 (0.29 #1329, 0.08 #7729, 0.08 #8440), 06x43v (0.29 #1245, 0.08 #7645, 0.08 #8356), 012s1d (0.29 #1098, 0.08 #7498, 0.08 #8209) >> Best rule #775 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 0fpkxfd; 0g57ws5; >> query: (?x1860, 04dsnp) <- film_regional_debut_venue(?x2954, ?x1860), ?x2954 = 0crh5_f >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3783 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 03khn; *> query: (?x1860, 0gjc4d3) <- citytown(?x1924, ?x1860), adjoins(?x448, ?x1860), month(?x1860, ?x1459) *> conf = 0.06 ranks of expected_values: 379 EVAL 01_d4 featured_film_locations! 08fn5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 191.000 191.000 0.429 http://example.org/film/film/featured_film_locations EVAL 01_d4 featured_film_locations! 0gjc4d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 191.000 191.000 0.429 http://example.org/film/film/featured_film_locations #22226-01n78x PRED entity: 01n78x PRED relation: nutrient! PRED expected values: 061_f 01645p 0dj75 => 54 concepts (49 used for prediction) PRED predicted values (max 10 best out of 8): 061_f (0.89 #454, 0.89 #21, 0.88 #448), 01645p (0.89 #21, 0.88 #233, 0.88 #31), 0dj75 (0.89 #21, 0.88 #233, 0.88 #31), 06x4c (0.89 #21, 0.88 #233, 0.88 #31), 0dcfv (0.89 #21, 0.88 #233, 0.88 #31), 04k8n (0.04 #237, 0.02 #543), 05wvs (0.04 #237, 0.02 #543), 01sh2 (0.04 #237, 0.02 #543) >> Best rule #454 for best value: >> intensional similarity = 120 >> extensional distance = 25 >> proper extension: 014d7f; >> query: (?x12083, 061_f) <- nutrient(?x10612, ?x12083), nutrient(?x9005, ?x12083), nutrient(?x7057, ?x12083), nutrient(?x6191, ?x12083), nutrient(?x6032, ?x12083), nutrient(?x5373, ?x12083), nutrient(?x4068, ?x12083), nutrient(?x2701, ?x12083), nutrient(?x1257, ?x12083), ?x6191 = 014j1m, nutrient(?x10612, ?x14210), nutrient(?x10612, ?x13944), nutrient(?x10612, ?x13545), nutrient(?x10612, ?x13498), nutrient(?x10612, ?x12902), nutrient(?x10612, ?x12454), nutrient(?x10612, ?x11758), nutrient(?x10612, ?x11592), nutrient(?x10612, ?x11270), nutrient(?x10612, ?x10891), nutrient(?x10612, ?x10098), nutrient(?x10612, ?x9949), nutrient(?x10612, ?x9915), nutrient(?x10612, ?x9733), nutrient(?x10612, ?x9619), nutrient(?x10612, ?x9436), nutrient(?x10612, ?x9426), nutrient(?x10612, ?x9365), nutrient(?x10612, ?x8442), nutrient(?x10612, ?x8413), nutrient(?x10612, ?x7894), nutrient(?x10612, ?x7720), nutrient(?x10612, ?x7652), nutrient(?x10612, ?x7431), nutrient(?x10612, ?x7364), nutrient(?x10612, ?x7362), nutrient(?x10612, ?x7219), nutrient(?x10612, ?x7135), nutrient(?x10612, ?x6192), nutrient(?x10612, ?x6160), nutrient(?x10612, ?x6026), nutrient(?x10612, ?x5526), nutrient(?x10612, ?x5451), nutrient(?x10612, ?x5010), nutrient(?x10612, ?x3469), nutrient(?x10612, ?x3203), nutrient(?x10612, ?x2702), nutrient(?x10612, ?x1960), nutrient(?x10612, ?x1304), nutrient(?x10612, ?x1258), ?x7652 = 025s0s0, ?x10098 = 0h1_c, ?x2702 = 0838f, ?x9915 = 025tkqy, nutrient(?x9005, ?x11409), nutrient(?x9005, ?x10709), nutrient(?x9005, ?x6286), nutrient(?x9005, ?x5337), nutrient(?x9005, ?x4069), nutrient(?x9005, ?x2018), ?x1257 = 09728, ?x5451 = 05wvs, ?x13944 = 0f4kp, ?x4068 = 0fbw6, nutrient(?x2701, ?x12868), nutrient(?x2701, ?x12481), nutrient(?x2701, ?x11784), nutrient(?x2701, ?x10195), nutrient(?x2701, ?x9855), nutrient(?x2701, ?x3264), ?x14210 = 0f4k5, ?x7219 = 0h1vg, ?x8442 = 02kcv4x, ?x13498 = 07q0m, ?x6032 = 01nkt, ?x7431 = 09gwd, ?x7362 = 02kc5rj, ?x1304 = 08lb68, ?x9855 = 0d9t0, ?x6160 = 041r51, ?x7720 = 025s7x6, ?x9949 = 02kd0rh, ?x9733 = 0h1tz, ?x9619 = 0h1tg, ?x12481 = 027g6p7, ?x11758 = 0q01m, ?x5526 = 09pbb, ?x6192 = 06jry, ?x3264 = 0dcfv, ?x9436 = 025sqz8, ?x1258 = 0h1wg, ?x7057 = 0fbdb, ?x3469 = 0h1zw, ?x5010 = 0h1vz, ?x6286 = 02y_3rf, ?x5373 = 0971v, ?x6026 = 025sf8g, ?x11270 = 02kc008, ?x9426 = 0h1yy, nutrient(?x6285, ?x7894), ?x11409 = 0h1yf, ?x5337 = 06x4c, ?x12902 = 0fzjh, ?x7364 = 09gvd, ?x1960 = 07hnp, ?x11592 = 025sf0_, ?x10709 = 0h1sz, ?x3203 = 04kl74p, ?x8413 = 02kc4sf, ?x12868 = 03d49, ?x10195 = 0hkwr, ?x11784 = 07zqy, ?x6285 = 01645p, ?x12454 = 025rw19, ?x4069 = 0hqw8p_, ?x2018 = 01sh2, ?x10891 = 0g5gq, ?x7135 = 025rsfk, ?x9365 = 04k8n, ?x13545 = 01w_3 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 01n78x nutrient! 0dj75 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 49.000 0.889 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01n78x nutrient! 01645p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 49.000 0.889 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01n78x nutrient! 061_f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 49.000 0.889 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #22225-025m8l PRED entity: 025m8l PRED relation: ceremony PRED expected values: 05pd94v 01s695 0jzphpx => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 127): 05pd94v (0.65 #632, 0.50 #1642, 0.50 #1012), 01s695 (0.65 #633, 0.47 #1139, 0.46 #1013), 0jzphpx (0.55 #663, 0.45 #1010, 0.39 #1169), 09p30_ (0.45 #1010, 0.35 #883, 0.26 #2145), 09bymc (0.45 #1010, 0.21 #4038, 0.21 #3911), 073h1t (0.35 #883, 0.26 #2145, 0.25 #149), 0418154 (0.35 #883, 0.26 #2145, 0.21 #4038), 02yw5r (0.35 #883, 0.25 #136, 0.21 #4038), 02hn5v (0.35 #883, 0.25 #161, 0.21 #4038), 0bzn6_ (0.35 #883, 0.25 #174, 0.21 #4038) >> Best rule #632 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 03tk6z; 02fv3t; >> query: (?x2238, 05pd94v) <- ceremony(?x2238, ?x486), award(?x4701, ?x2238), award(?x2214, ?x2238), ?x4701 = 03j24kf, award_winner(?x724, ?x2214) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 025m8l ceremony 0jzphpx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.650 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 025m8l ceremony 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.650 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 025m8l ceremony 05pd94v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.650 http://example.org/award/award_category/winners./award/award_honor/ceremony #22224-047g6 PRED entity: 047g6 PRED relation: nationality PRED expected values: 07ssc => 167 concepts (113 used for prediction) PRED predicted values (max 10 best out of 58): 09c7w0 (0.92 #8971, 0.83 #2782, 0.83 #10663), 0345h (0.50 #1221, 0.39 #7866, 0.36 #8066), 02jx1 (0.45 #2018, 0.25 #726, 0.25 #5608), 01nhhz (0.43 #5675), 07ssc (0.42 #2000, 0.40 #114, 0.39 #7866), 06mzp (0.36 #8066, 0.35 #7867, 0.34 #8067), 06q1r (0.36 #8066, 0.34 #8067, 0.33 #8567), 084n_ (0.35 #7867, 0.34 #8067, 0.33 #8567), 035qy (0.24 #9668, 0.24 #10165, 0.23 #9768), 0f8l9c (0.23 #1609, 0.17 #1807, 0.15 #2305) >> Best rule #8971 for best value: >> intensional similarity = 6 >> extensional distance = 393 >> proper extension: 05dtsb; 014dm6; >> query: (?x12216, 09c7w0) <- student(?x2637, ?x12216), student(?x2637, ?x11500), student(?x2637, ?x4974), nationality(?x12216, ?x1355), cinematography(?x915, ?x4974), gender(?x11500, ?x231) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #2000 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 015njf; 04bgy; *> query: (?x12216, 07ssc) <- gender(?x12216, ?x231), place_of_death(?x12216, ?x362), ?x231 = 05zppz, ?x362 = 04jpl *> conf = 0.42 ranks of expected_values: 5 EVAL 047g6 nationality 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 167.000 113.000 0.916 http://example.org/people/person/nationality #22223-0l6mp PRED entity: 0l6mp PRED relation: sports PRED expected values: 03hr1p => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 52): 03hr1p (0.81 #717, 0.80 #455, 0.75 #634), 02bkg (0.80 #445, 0.80 #671, 0.79 #805), 0d1t3 (0.80 #671, 0.78 #282, 0.78 #705), 07jjt (0.80 #671, 0.78 #282, 0.78 #705), 0dwxr (0.80 #671, 0.78 #282, 0.78 #705), 03krj (0.80 #671, 0.78 #282, 0.78 #705), 064vjs (0.78 #282, 0.78 #705, 0.78 #475), 02y74 (0.69 #704, 0.68 #672, 0.66 #281), 01sgl (0.69 #704, 0.68 #672, 0.66 #281), 02_5h (0.52 #31, 0.48 #280, 0.43 #154) >> Best rule #717 for best value: >> intensional similarity = 51 >> extensional distance = 14 >> proper extension: 0c_tl; >> query: (?x2233, 03hr1p) <- olympics(?x3728, ?x2233), olympics(?x2346, ?x2233), olympics(?x1453, ?x2233), olympics(?x1355, ?x2233), olympics(?x910, ?x2233), olympics(?x359, ?x2233), combatants(?x94, ?x3728), sports(?x2233, ?x2885), country(?x206, ?x2346), entity_involved(?x7455, ?x2346), titles(?x2346, ?x2889), film_release_region(?x7009, ?x2346), film_release_region(?x4336, ?x2346), film_release_region(?x2394, ?x2346), film_release_region(?x951, ?x2346), film_release_region(?x324, ?x2346), administrative_parent(?x8090, ?x2346), film_release_region(?x8137, ?x1453), film_release_region(?x8025, ?x1453), film_release_region(?x7016, ?x1453), film_release_region(?x6556, ?x1453), film_release_region(?x3938, ?x1453), film_release_region(?x2714, ?x1453), film_release_region(?x428, ?x1453), administrative_area_type(?x1453, ?x2792), ?x8025 = 03nsm5x, ?x324 = 07gp9, ?x7009 = 0bs8s1p, ?x2885 = 07jjt, ?x2714 = 0kv238, participating_countries(?x1741, ?x2346), medal(?x2233, ?x1242), region(?x1315, ?x1453), ?x951 = 0cwy47, official_language(?x910, ?x254), ?x428 = 0h1cdwq, ?x2394 = 0661ql3, locations(?x3654, ?x2346), taxonomy(?x2346, ?x939), nationality(?x12529, ?x2346), ?x1355 = 0h7x, ?x3938 = 024mpp, ?x8137 = 0gtx63s, ?x4336 = 0bpm4yw, ?x6556 = 05dss7, exported_to(?x1453, ?x5457), ?x7016 = 07g1sm, organization(?x2346, ?x127), country(?x150, ?x1453), exported_to(?x2346, ?x291), award_winner(?x7215, ?x12529) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l6mp sports 03hr1p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.812 http://example.org/user/jg/default_domain/olympic_games/sports #22222-0d2by PRED entity: 0d2by PRED relation: people PRED expected values: 011zf2 => 33 concepts (27 used for prediction) PRED predicted values (max 10 best out of 3817): 01tpl1p (0.33 #1470, 0.20 #11802, 0.13 #22389), 02ts3h (0.33 #995, 0.20 #11327, 0.12 #13049), 0x3n (0.33 #888, 0.20 #11220, 0.12 #12942), 0g824 (0.33 #901, 0.19 #12955, 0.13 #18121), 0311wg (0.33 #292, 0.15 #31294, 0.13 #33015), 03rs8y (0.33 #51, 0.14 #12054, 0.13 #22389), 01wgcvn (0.33 #513, 0.14 #12054, 0.10 #10845), 048s0r (0.33 #997, 0.14 #12054, 0.10 #11329), 02l840 (0.33 #102, 0.14 #12054, 0.10 #10434), 03pmzt (0.33 #389, 0.13 #22389, 0.10 #10721) >> Best rule #1470 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 0x67; >> query: (?x7562, 01tpl1p) <- people(?x7562, ?x8253), languages_spoken(?x7562, ?x3271), languages_spoken(?x7562, ?x254), ?x254 = 02h40lc, ?x8253 = 06s7rd, titles(?x3271, ?x6788), language(?x148, ?x3271), countries_spoken_in(?x3271, ?x1122), geographic_distribution(?x7562, ?x3634), film_crew_role(?x6788, ?x468) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #37892 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 48 *> proper extension: 04mvp8; *> query: (?x7562, ?x398) <- people(?x7562, ?x2307), languages_spoken(?x7562, ?x254), languages(?x7147, ?x254), languages(?x5951, ?x254), languages(?x5283, ?x254), languages(?x4988, ?x254), languages(?x1554, ?x254), award_nominee(?x5951, ?x398), nominated_for(?x1554, ?x887), gender(?x7147, ?x231), award_winner(?x5283, ?x628), award_nominee(?x4297, ?x4988) *> conf = 0.01 ranks of expected_values: 3318 EVAL 0d2by people 011zf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 27.000 0.333 http://example.org/people/ethnicity/people #22221-02dlh2 PRED entity: 02dlh2 PRED relation: role! PRED expected values: 0bm02 => 79 concepts (59 used for prediction) PRED predicted values (max 10 best out of 97): 018vs (0.88 #192, 0.87 #666, 0.86 #661), 0342h (0.88 #192, 0.87 #666, 0.85 #759), 0dwsp (0.88 #192, 0.87 #666, 0.85 #759), 01vnt4 (0.88 #192, 0.87 #666, 0.85 #759), 07brj (0.88 #192, 0.87 #666, 0.85 #759), 01qbl (0.88 #192, 0.87 #666, 0.85 #759), 01w4c9 (0.88 #192, 0.87 #666, 0.85 #759), 0mbct (0.88 #192, 0.87 #666, 0.85 #759), 0dwr4 (0.88 #192, 0.87 #666, 0.85 #759), 01p970 (0.88 #192, 0.87 #666, 0.85 #759) >> Best rule #192 for best value: >> intensional similarity = 25 >> extensional distance = 1 >> proper extension: 0151b0; >> query: (?x3703, ?x227) <- role(?x4769, ?x3703), role(?x4471, ?x3703), role(?x4311, ?x3703), role(?x3991, ?x3703), role(?x1663, ?x3703), role(?x1574, ?x3703), role(?x1437, ?x3703), role(?x432, ?x3703), role(?x314, ?x3703), role(?x75, ?x3703), ?x4471 = 026g73, ?x314 = 02sgy, ?x1437 = 01vdm0, role(?x3703, ?x645), ?x3991 = 05842k, role(?x3703, ?x227), ?x432 = 042v_gx, performance_role(?x3214, ?x1574), role(?x4769, ?x1166), role(?x1399, ?x4311), instrumentalists(?x4311, ?x562), role(?x211, ?x1574), ?x75 = 07y_7, role(?x1482, ?x4311), ?x1663 = 01w4dy >> conf = 0.88 => this is the best rule for 10 predicted values *> Best rule #95 for first EXPECTED value: *> intensional similarity = 29 *> extensional distance = 1 *> proper extension: 05842k; *> query: (?x3703, ?x74) <- role(?x4471, ?x3703), role(?x3991, ?x3703), role(?x2460, ?x3703), role(?x1437, ?x3703), role(?x1436, ?x3703), role(?x885, ?x3703), role(?x432, ?x3703), role(?x315, ?x3703), role(?x314, ?x3703), ?x4471 = 026g73, ?x314 = 02sgy, ?x1437 = 01vdm0, role(?x3703, ?x645), role(?x3991, ?x3716), role(?x3991, ?x2785), role(?x3991, ?x1267), role(?x3991, ?x74), role(?x3409, ?x3991), role(?x8599, ?x3991), ?x3716 = 03gvt, ?x3409 = 0680x0, ?x885 = 0dwtp, ?x8599 = 01nkxvx, ?x1267 = 07brj, ?x432 = 042v_gx, ?x315 = 0l14md, ?x2785 = 0jtg0, ?x1436 = 0xzly, ?x2460 = 01wy6 *> conf = 0.79 ranks of expected_values: 52 EVAL 02dlh2 role! 0bm02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 79.000 59.000 0.885 http://example.org/music/performance_role/track_performances./music/track_contribution/role #22220-06c97 PRED entity: 06c97 PRED relation: organizations_founded PRED expected values: 05f4p => 173 concepts (171 used for prediction) PRED predicted values (max 10 best out of 49): 05f4p (0.20 #278, 0.17 #482, 0.12 #891), 07wbk (0.17 #427, 0.12 #836, 0.11 #1143), 0d6qjf (0.17 #489, 0.11 #3351, 0.10 #1614), 0jbk9 (0.12 #928, 0.10 #1644, 0.10 #1542), 015dvh (0.12 #912, 0.10 #1423, 0.08 #2138), 01v9b1 (0.12 #917, 0.08 #2143, 0.08 #2041), 03z19 (0.12 #838, 0.08 #2268, 0.07 #2574), 0_00 (0.12 #904, 0.06 #2846, 0.06 #2743), 082x5 (0.11 #1327, 0.10 #1838, 0.10 #1532), 082mc (0.11 #1324, 0.10 #1835, 0.10 #1529) >> Best rule #278 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01vrncs; >> query: (?x5572, 05f4p) <- people(?x4195, ?x5572), religion(?x5572, ?x3616), celebrities_impersonated(?x3649, ?x5572), films(?x5572, ?x2989) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06c97 organizations_founded 05f4p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 171.000 0.200 http://example.org/organization/organization_founder/organizations_founded #22219-03mp9s PRED entity: 03mp9s PRED relation: award_nominee! PRED expected values: 02624g => 96 concepts (36 used for prediction) PRED predicted values (max 10 best out of 935): 03zg2x (0.81 #62698, 0.81 #39473, 0.81 #62697), 02vntj (0.81 #62698, 0.81 #39473, 0.81 #62697), 02d4ct (0.81 #62698, 0.81 #39473, 0.81 #62697), 03mp9s (0.26 #55729, 0.18 #78957, 0.16 #71989), 015rkw (0.26 #55729, 0.16 #71989, 0.08 #363), 051wwp (0.26 #55729, 0.16 #71989, 0.08 #1160), 0l6px (0.26 #55729, 0.16 #71989, 0.06 #501), 06j8wx (0.26 #55729, 0.16 #71989, 0.06 #1264), 01sp81 (0.26 #55729, 0.16 #71989, 0.06 #185), 016gr2 (0.26 #55729, 0.16 #71989, 0.06 #245) >> Best rule #62698 for best value: >> intensional similarity = 3 >> extensional distance = 1090 >> proper extension: 07sgfsl; 0fwy0h; 02x0bdb; 013ybx; >> query: (?x6977, ?x2626) <- award_winner(?x91, ?x6977), award_nominee(?x6977, ?x2626), film(?x2626, ?x363) >> conf = 0.81 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 03mp9s award_nominee! 02624g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 36.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22218-01pj7 PRED entity: 01pj7 PRED relation: country! PRED expected values: 07jbh 018w8 => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 36): 01cgz (0.78 #1883, 0.74 #910, 0.69 #982), 0w0d (0.77 #909, 0.71 #9, 0.70 #1053), 0194d (0.71 #102, 0.69 #930, 0.65 #1110), 07jbh (0.69 #918, 0.68 #54, 0.68 #1098), 019tzd (0.67 #96, 0.60 #1104, 0.60 #924), 01sgl (0.67 #99, 0.57 #27, 0.57 #171), 03rbzn (0.64 #14, 0.63 #914, 0.57 #86), 01gqfm (0.64 #32, 0.60 #932, 0.57 #1112), 06z68 (0.64 #17, 0.52 #89, 0.52 #161), 07jjt (0.62 #84, 0.58 #48, 0.57 #1056) >> Best rule #1883 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 04w4s; 088q4; >> query: (?x1790, 01cgz) <- olympics(?x1790, ?x778), country(?x520, ?x1790), ?x778 = 0kbvb >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #918 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 0166b; *> query: (?x1790, 07jbh) <- film_release_region(?x11313, ?x1790), ?x11313 = 0by17xn, administrative_area_type(?x1790, ?x2792) *> conf = 0.69 ranks of expected_values: 4, 24 EVAL 01pj7 country! 018w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 186.000 186.000 0.783 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01pj7 country! 07jbh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 186.000 186.000 0.783 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #22217-0f502 PRED entity: 0f502 PRED relation: award_nominee PRED expected values: 042ly5 => 119 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1007): 01wgcvn (0.81 #102388, 0.77 #16289, 0.01 #65998), 01jz6x (0.81 #102388, 0.77 #16289, 0.01 #76604), 0gx_p (0.81 #102388, 0.77 #16289), 0dzf_ (0.17 #48867, 0.17 #46540, 0.15 #76790), 0432b (0.17 #48867, 0.17 #46540, 0.15 #76790), 01wc7p (0.17 #48867, 0.17 #46540, 0.15 #76790), 02y_2y (0.17 #48867, 0.17 #46540, 0.15 #76790), 09qh1 (0.17 #48867, 0.17 #46540, 0.15 #76790), 0bw6y (0.17 #48867, 0.17 #46540, 0.15 #76790), 07r1h (0.17 #46540, 0.15 #11635, 0.15 #20943) >> Best rule #102388 for best value: >> intensional similarity = 2 >> extensional distance = 674 >> proper extension: 0fqy4p; 01vw917; 0c41qv; 076df9; 026v1z; >> query: (?x4360, ?x71) <- award_nominee(?x71, ?x4360), category(?x4360, ?x134) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #71448 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 426 *> proper extension: 03qcq; 02yygk; 025hzx; 03k48_; *> query: (?x4360, 042ly5) <- profession(?x4360, ?x319), participant(?x4360, ?x2857), award_nominee(?x4360, ?x71) *> conf = 0.02 ranks of expected_values: 537 EVAL 0f502 award_nominee 042ly5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 119.000 51.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22216-012t_z PRED entity: 012t_z PRED relation: profession! PRED expected values: 05m63c 06y9c2 0343h 04cw0j 03kdl 0mm1q 01zlh5 03kcyd 0c1ps1 02vptk_ 0n839 => 55 concepts (28 used for prediction) PRED predicted values (max 10 best out of 4071): 0g824 (0.70 #16660, 0.60 #14538, 0.35 #64525), 063t3j (0.70 #16660, 0.60 #16412, 0.33 #8084), 03h_0_z (0.70 #16660, 0.47 #24991, 0.47 #29157), 01yhvv (0.70 #16660, 0.47 #24991, 0.47 #29157), 0127s7 (0.70 #16660, 0.47 #24991, 0.47 #29157), 01fxck (0.70 #16660, 0.40 #19161, 0.40 #14996), 03f3yfj (0.70 #16660, 0.40 #15035, 0.33 #6707), 0205dx (0.70 #16660, 0.40 #18166, 0.33 #5673), 03xl77 (0.70 #16660, 0.40 #13356, 0.33 #5028), 044mfr (0.70 #16660, 0.40 #14291, 0.33 #5963) >> Best rule #16660 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 016z4k; 09jwl; >> query: (?x967, ?x338) <- profession(?x8793, ?x967), profession(?x1896, ?x967), ?x1896 = 0j1yf, people(?x1423, ?x8793), nationality(?x8793, ?x94), participant(?x8793, ?x338) >> conf = 0.70 => this is the best rule for 23 predicted values *> Best rule #17027 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0g7nc; *> query: (?x967, 0343h) <- profession(?x11562, ?x967), profession(?x7025, ?x967), profession(?x1896, ?x967), nationality(?x7025, ?x94), ?x11562 = 0d0l91, gender(?x1896, ?x231) *> conf = 0.40 ranks of expected_values: 342, 1042, 1280, 1468, 1561, 1786, 2094, 2145, 2997, 3809 EVAL 012t_z profession! 0n839 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 28.000 0.697 http://example.org/people/person/profession EVAL 012t_z profession! 02vptk_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 28.000 0.697 http://example.org/people/person/profession EVAL 012t_z profession! 0c1ps1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 28.000 0.697 http://example.org/people/person/profession EVAL 012t_z profession! 03kcyd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 28.000 0.697 http://example.org/people/person/profession EVAL 012t_z profession! 01zlh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 28.000 0.697 http://example.org/people/person/profession EVAL 012t_z profession! 0mm1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 28.000 0.697 http://example.org/people/person/profession EVAL 012t_z profession! 03kdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 28.000 0.697 http://example.org/people/person/profession EVAL 012t_z profession! 04cw0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 28.000 0.697 http://example.org/people/person/profession EVAL 012t_z profession! 0343h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 28.000 0.697 http://example.org/people/person/profession EVAL 012t_z profession! 06y9c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 28.000 0.697 http://example.org/people/person/profession EVAL 012t_z profession! 05m63c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 28.000 0.697 http://example.org/people/person/profession #22215-04htfd PRED entity: 04htfd PRED relation: place_founded PRED expected values: 02_286 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 65): 06pwq (0.60 #195, 0.07 #1781, 0.07 #768), 04jpl (0.50 #255, 0.16 #1081, 0.09 #3811), 02_286 (0.47 #1087, 0.25 #135, 0.22 #2479), 07ssc (0.25 #130, 0.25 #67, 0.17 #256), 06q1r (0.25 #164, 0.05 #1116, 0.03 #2381), 05v8c (0.25 #68, 0.02 #3813, 0.01 #4639), 0qcrj (0.20 #251, 0.07 #824, 0.07 #760), 07dfk (0.19 #2710, 0.16 #3855, 0.16 #3280), 0d6lp (0.15 #398, 0.12 #845, 0.10 #1858), 0y1rf (0.13 #683, 0.12 #874, 0.10 #1317) >> Best rule #195 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 019rl6; 06py2; >> query: (?x6156, 06pwq) <- currency(?x6156, ?x170), place_founded(?x6156, ?x94), list(?x6156, ?x5997), company(?x1157, ?x94) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1087 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 099ks0; *> query: (?x6156, 02_286) <- industry(?x6156, ?x8239), place_founded(?x6156, ?x94), service_location(?x127, ?x94) *> conf = 0.47 ranks of expected_values: 3 EVAL 04htfd place_founded 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 116.000 0.600 http://example.org/organization/organization/place_founded #22214-06pyc2 PRED entity: 06pyc2 PRED relation: film! PRED expected values: 0g1rw => 81 concepts (61 used for prediction) PRED predicted values (max 10 best out of 47): 05qd_ (0.67 #156, 0.25 #231, 0.21 #305), 03xq0f (0.39 #1799, 0.14 #1354, 0.13 #1502), 016tt2 (0.33 #77, 0.25 #300, 0.23 #375), 017jv5 (0.25 #14, 0.09 #686, 0.09 #461), 0g1rw (0.20 #379, 0.16 #304, 0.16 #679), 016tw3 (0.19 #233, 0.17 #84, 0.13 #1508), 017s11 (0.13 #748, 0.12 #2096, 0.12 #1500), 024rbz (0.08 #159, 0.05 #1509, 0.05 #1361), 048t8y (0.08 #127, 0.01 #1328, 0.01 #1477), 054g1r (0.08 #1384, 0.07 #855, 0.07 #1082) >> Best rule #156 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 09qycb; >> query: (?x10931, 05qd_) <- nominated_for(?x1089, ?x10931), language(?x10931, ?x254), film_sets_designed(?x8401, ?x10931), ?x8401 = 057bc6m >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #379 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 0b9rdk; 0286gm1; *> query: (?x10931, 0g1rw) <- nominated_for(?x1089, ?x10931), language(?x10931, ?x254), film_sets_designed(?x8401, ?x10931), award_winner(?x2716, ?x8401) *> conf = 0.20 ranks of expected_values: 5 EVAL 06pyc2 film! 0g1rw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 81.000 61.000 0.667 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #22213-05ff6 PRED entity: 05ff6 PRED relation: adjoins PRED expected values: 06mtq => 123 concepts (32 used for prediction) PRED predicted values (max 10 best out of 341): 05fly (0.50 #2696, 0.50 #1925, 0.33 #1154), 06mtq (0.50 #2227, 0.33 #2998, 0.33 #686), 0chgr2 (0.33 #1208, 0.33 #438, 0.17 #2750), 0vh3 (0.33 #637, 0.04 #4491, 0.01 #8352), 0chghy (0.24 #14668, 0.13 #6962, 0.05 #13125), 05nrg (0.24 #14668), 05ff6 (0.22 #20851, 0.21 #21624, 0.21 #19307), 04ych (0.11 #7769, 0.07 #8541, 0.06 #9312), 07z5n (0.09 #7065, 0.08 #3976, 0.02 #13247), 01znc_ (0.08 #3937, 0.05 #11663, 0.04 #12436) >> Best rule #2696 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 07cfx; >> query: (?x12908, 05fly) <- contains(?x390, ?x12908), ?x390 = 0chghy, jurisdiction_of_office(?x10118, ?x12908), capital(?x12908, ?x14084), ?x10118 = 0p5vf >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2227 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0chgr2; 0g39h; 0847q; *> query: (?x12908, 06mtq) <- contains(?x390, ?x12908), ?x390 = 0chghy, adjoins(?x12125, ?x12908), contains(?x12125, ?x8823), jurisdiction_of_office(?x900, ?x12125) *> conf = 0.50 ranks of expected_values: 2 EVAL 05ff6 adjoins 06mtq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 123.000 32.000 0.500 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #22212-048tgl PRED entity: 048tgl PRED relation: location PRED expected values: 02xry => 116 concepts (97 used for prediction) PRED predicted values (max 10 best out of 180): 0ply0 (0.61 #6438, 0.53 #3218, 0.49 #11270), 02_286 (0.20 #59593, 0.19 #4059, 0.16 #60398), 0r02m (0.17 #714, 0.10 #2323, 0.05 #3127), 0h3lt (0.17 #295, 0.10 #1904, 0.05 #2708), 04jpl (0.11 #2430, 0.10 #4039, 0.05 #8066), 0z1vw (0.10 #2193, 0.05 #2997, 0.02 #7022), 08809 (0.10 #2177, 0.02 #7006, 0.01 #11838), 0wh3 (0.10 #1664, 0.01 #11325, 0.01 #25812), 030qb3t (0.09 #62859, 0.09 #5716, 0.09 #60444), 07b_l (0.07 #5015, 0.02 #15483, 0.02 #6625) >> Best rule #6438 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 0d608; >> query: (?x10091, ?x3373) <- place_of_birth(?x10091, ?x3373), profession(?x10091, ?x1359), group(?x10091, ?x13142), award(?x13142, ?x2877), ?x2877 = 02f5qb >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #72442 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1831 *> proper extension: 049tjg; *> query: (?x10091, ?x2623) <- place_of_birth(?x10091, ?x3373), gender(?x10091, ?x231), ?x231 = 05zppz, contains(?x2623, ?x3373) *> conf = 0.04 ranks of expected_values: 38 EVAL 048tgl location 02xry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 116.000 97.000 0.606 http://example.org/people/person/places_lived./people/place_lived/location #22211-02wb6yq PRED entity: 02wb6yq PRED relation: place_of_birth PRED expected values: 0djd3 => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 231): 0f2rq (0.38 #11979, 0.37 #27478, 0.34 #9864), 01n7q (0.28 #103553, 0.28 #32411, 0.27 #108485), 02_286 (0.11 #16224, 0.10 #20450, 0.10 #18337), 030qb3t (0.11 #16259, 0.09 #7803, 0.08 #14145), 0c_m3 (0.09 #901, 0.06 #1606, 0.05 #2311), 05jbn (0.09 #880, 0.05 #2290, 0.04 #3699), 0d6lp (0.09 #818, 0.05 #2228, 0.03 #13501), 0v1xg (0.09 #1023, 0.05 #2433, 0.03 #5251), 0pqz3 (0.09 #1377, 0.05 #2787, 0.03 #6309), 05ksh (0.09 #741, 0.04 #3560, 0.02 #7786) >> Best rule #11979 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0b82vw; >> query: (?x3244, ?x5719) <- profession(?x3244, ?x220), award_winner(?x5808, ?x3244), origin(?x3244, ?x5719) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #17153 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 06lht1; *> query: (?x3244, 0djd3) <- nominated_for(?x3244, ?x5808), currency(?x3244, ?x170), award_winner(?x5808, ?x848), program_creator(?x5808, ?x10160) *> conf = 0.01 ranks of expected_values: 197 EVAL 02wb6yq place_of_birth 0djd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 168.000 168.000 0.375 http://example.org/people/person/place_of_birth #22210-01n44c PRED entity: 01n44c PRED relation: award PRED expected values: 02lp0w => 133 concepts (124 used for prediction) PRED predicted values (max 10 best out of 303): 02qkk9_ (0.78 #18198, 0.78 #14963, 0.77 #11321), 01bgqh (0.35 #447, 0.32 #43, 0.31 #10959), 01by1l (0.35 #11029, 0.31 #12647, 0.30 #17906), 09sb52 (0.26 #35213, 0.22 #40472, 0.21 #31980), 054krc (0.25 #1302, 0.22 #1707, 0.13 #4132), 02f73b (0.24 #288, 0.16 #11204, 0.12 #18081), 03qbnj (0.24 #234, 0.13 #14792, 0.12 #12768), 0c4z8 (0.22 #1691, 0.21 #1286, 0.20 #14630), 0gqz2 (0.20 #1700, 0.19 #1295, 0.16 #890), 0gqy2 (0.20 #8657, 0.14 #11487, 0.13 #7848) >> Best rule #18198 for best value: >> intensional similarity = 3 >> extensional distance = 463 >> proper extension: 089tm; 01pfr3; 04rcr; 02r3zy; 07c0j; 01v0sx2; 01vsxdm; 03g5jw; 01wv9xn; 0dvqq; ... >> query: (?x5181, ?x4796) <- award_winner(?x4796, ?x5181), artist(?x7089, ?x5181), artists(?x505, ?x5181) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2275 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 080r3; *> query: (?x5181, 02lp0w) <- gender(?x5181, ?x514), place_of_birth(?x5181, ?x2850), nationality(?x5181, ?x94), notable_people_with_this_condition(?x12870, ?x5181) *> conf = 0.04 ranks of expected_values: 209 EVAL 01n44c award 02lp0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 133.000 124.000 0.783 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22209-01y8zd PRED entity: 01y8zd PRED relation: student PRED expected values: 01trf3 => 76 concepts (71 used for prediction) PRED predicted values (max 10 best out of 951): 0f7h2g (0.07 #3716, 0.07 #7898, 0.02 #24626), 08k1lz (0.07 #3827, 0.07 #8009, 0.02 #24737), 063vn (0.07 #2387, 0.07 #6569, 0.02 #31661), 0d3k14 (0.07 #8126, 0.04 #3944, 0.02 #49946), 073v6 (0.07 #6799, 0.04 #2617, 0.02 #23527), 077yk0 (0.06 #1134, 0.04 #3225, 0.03 #7407), 02wd48 (0.06 #1479, 0.04 #5661, 0.03 #9843), 0391jz (0.06 #567, 0.04 #4749, 0.03 #8931), 01ycbq (0.06 #304, 0.04 #4486, 0.03 #8668), 030xr_ (0.06 #1599, 0.03 #9963, 0.03 #12054) >> Best rule #3716 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 03zw80; >> query: (?x3091, 0f7h2g) <- service_language(?x3091, ?x254), ?x254 = 02h40lc, contains(?x279, ?x3091) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01y8zd student 01trf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 71.000 0.071 http://example.org/education/educational_institution/students_graduates./education/education/student #22208-05d8vw PRED entity: 05d8vw PRED relation: category PRED expected values: 08mbj5d => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #14, 0.91 #17, 0.91 #7) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 01l_vgt; >> query: (?x2055, 08mbj5d) <- artists(?x671, ?x2055), ?x671 = 064t9, origin(?x2055, ?x4733), nationality(?x2055, ?x94) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05d8vw category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.923 http://example.org/common/topic/webpage./common/webpage/category #22207-03h_9lg PRED entity: 03h_9lg PRED relation: vacationer! PRED expected values: 06ryl => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 35): 03gh4 (0.10 #329, 0.09 #204, 0.06 #1201), 05qtj (0.07 #195, 0.06 #943, 0.06 #819), 0cv3w (0.06 #180, 0.05 #305, 0.04 #56), 0b90_r (0.05 #127, 0.05 #252, 0.04 #1124), 0f2v0 (0.05 #311, 0.04 #186, 0.03 #2926), 04jpl (0.04 #133, 0.03 #258, 0.03 #1380), 06c62 (0.03 #335, 0.03 #210, 0.02 #834), 02_286 (0.03 #886, 0.03 #138, 0.03 #388), 0160w (0.03 #126, 0.03 #1123, 0.03 #1248), 078lk (0.02 #177, 0.02 #302, 0.02 #801) >> Best rule #329 for best value: >> intensional similarity = 3 >> extensional distance = 170 >> proper extension: 0456xp; 013cr; 01pw2f1; 01mqz0; 019g40; 0zjpz; 01wz3cx; 03rl84; 01z0rcq; 0p3r8; ... >> query: (?x844, 03gh4) <- participant(?x844, ?x2275), place_of_birth(?x844, ?x5036), participant(?x6187, ?x2275) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03h_9lg vacationer! 06ryl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 104.000 0.099 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #22206-04twmk PRED entity: 04twmk PRED relation: languages PRED expected values: 02h40lc => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 7): 02h40lc (0.22 #236, 0.22 #1016, 0.22 #470), 0t_2 (0.03 #9, 0.02 #48), 04306rv (0.03 #3, 0.02 #42), 064_8sq (0.03 #249, 0.03 #561, 0.02 #444), 03k50 (0.02 #2890, 0.02 #2032, 0.01 #2929), 02bjrlw (0.01 #1288, 0.01 #1327, 0.01 #430), 06nm1 (0.01 #84, 0.01 #123, 0.01 #162) >> Best rule #236 for best value: >> intensional similarity = 3 >> extensional distance = 790 >> proper extension: 04shbh; 0p51w; 015wfg; 012gbb; 0jvtp; 06p0s1; 02vkvcz; >> query: (?x9435, 02h40lc) <- award_winner(?x782, ?x9435), nationality(?x9435, ?x94), location(?x9435, ?x682) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04twmk languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.220 http://example.org/people/person/languages #22205-06rq2l PRED entity: 06rq2l PRED relation: award_nominee! PRED expected values: 017s11 => 128 concepts (43 used for prediction) PRED predicted values (max 10 best out of 949): 0pz91 (0.82 #58367, 0.81 #44358, 0.81 #98054), 05qd_ (0.11 #2515, 0.10 #180, 0.06 #35199), 01wd9lv (0.10 #1472, 0.09 #3807, 0.03 #6142), 017s11 (0.07 #106, 0.06 #2441, 0.05 #53804), 03qmx_f (0.07 #588, 0.06 #2923, 0.04 #19267), 04g3p5 (0.07 #1100, 0.06 #3435, 0.03 #24448), 02z2xdf (0.07 #1580, 0.06 #3915, 0.03 #20259), 0b7xl8 (0.07 #1901, 0.06 #4236, 0.01 #15910), 06pj8 (0.07 #450, 0.05 #33135, 0.05 #54148), 086k8 (0.07 #53760, 0.07 #63098, 0.06 #60763) >> Best rule #58367 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 011zf2; 03yf3z; >> query: (?x9204, ?x1335) <- student(?x7545, ?x9204), category(?x9204, ?x134), award_nominee(?x9204, ?x1335) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #106 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 04b19t; 01vsps; 03fw4y; *> query: (?x9204, 017s11) <- profession(?x9204, ?x319), company(?x9204, ?x1836), produced_by(?x821, ?x9204) *> conf = 0.07 ranks of expected_values: 4 EVAL 06rq2l award_nominee! 017s11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 128.000 43.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22204-05krk PRED entity: 05krk PRED relation: school! PRED expected values: 05tfm 05g3v 0jm4v => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 86): 0512p (0.28 #94, 0.26 #177, 0.25 #11), 07147 (0.28 #142, 0.26 #225, 0.12 #890), 01yjl (0.28 #109, 0.26 #192, 0.12 #831), 01y3v (0.28 #107, 0.26 #190, 0.12 #831), 01ypc (0.28 #84, 0.26 #167, 0.12 #831), 051vz (0.25 #19, 0.22 #102, 0.21 #185), 01yhm (0.22 #99, 0.21 #182, 0.17 #16), 07l8x (0.22 #141, 0.21 #224, 0.14 #889), 07l4z (0.22 #145, 0.21 #228, 0.13 #893), 04wmvz (0.22 #152, 0.21 #235, 0.12 #733) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0frm7n; >> query: (?x388, 0512p) <- school(?x6462, ?x388), ?x6462 = 09l0x9, school(?x387, ?x388) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #831 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 02zkz7; *> query: (?x388, ?x387) <- citytown(?x388, ?x6453), school(?x685, ?x388), draft(?x387, ?x685) *> conf = 0.12 ranks of expected_values: 32, 44, 73 EVAL 05krk school! 0jm4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 96.000 96.000 0.278 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 05krk school! 05g3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 96.000 96.000 0.278 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 05krk school! 05tfm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 96.000 96.000 0.278 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #22203-03xds PRED entity: 03xds PRED relation: student! PRED expected values: 03ksy 07tds => 125 concepts (80 used for prediction) PRED predicted values (max 10 best out of 144): 0bwfn (0.62 #6575, 0.26 #10253, 0.13 #12879), 03ksy (0.25 #105, 0.22 #1680, 0.18 #2205), 06kknt (0.25 #465, 0.11 #2040, 0.09 #2565), 08815 (0.25 #2, 0.06 #12607, 0.04 #18918), 0lbfv (0.25 #222), 025v3k (0.20 #644, 0.17 #1169, 0.03 #3795), 07x4c (0.20 #783, 0.17 #1308, 0.01 #8660), 065y4w7 (0.17 #1064, 0.07 #12619, 0.05 #34700), 017j69 (0.16 #6445, 0.09 #2244, 0.07 #10123), 04b_46 (0.13 #6527, 0.05 #10205, 0.04 #7578) >> Best rule #6575 for best value: >> intensional similarity = 4 >> extensional distance = 197 >> proper extension: 0f6_dy; 09dv0sz; 050_qx; >> query: (?x12580, 0bwfn) <- student(?x1665, ?x12580), currency(?x1665, ?x170), institution(?x620, ?x1665), list(?x1665, ?x2197) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #105 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 05fg2; *> query: (?x12580, 03ksy) <- award_winner(?x14353, ?x12580), ?x14353 = 0blst_, profession(?x12580, ?x8368), student(?x1665, ?x12580) *> conf = 0.25 ranks of expected_values: 2, 119 EVAL 03xds student! 07tds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 125.000 80.000 0.618 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 03xds student! 03ksy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 80.000 0.618 http://example.org/education/educational_institution/students_graduates./education/education/student #22202-0h0wc PRED entity: 0h0wc PRED relation: award_winner! PRED expected values: 0h_cssd => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 126): 05c1t6z (0.14 #401, 0.13 #530, 0.08 #788), 0g55tzk (0.12 #125, 0.07 #383, 0.06 #3480), 09q_6t (0.12 #8, 0.07 #524, 0.05 #395), 0g5b0q5 (0.12 #19, 0.05 #277, 0.05 #664), 09pnw5 (0.12 #92, 0.05 #479, 0.05 #608), 03nnm4t (0.10 #454, 0.10 #583, 0.07 #196), 02wzl1d (0.10 #140, 0.09 #269, 0.09 #398), 027hjff (0.09 #2890, 0.08 #3277, 0.07 #438), 09qvms (0.09 #2852, 0.08 #4787, 0.07 #142), 09g90vz (0.09 #370, 0.09 #499, 0.08 #628) >> Best rule #401 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 03_l8m; >> query: (?x2551, 05c1t6z) <- award_winner(?x1193, ?x2551), nominated_for(?x2551, ?x414), student(?x4268, ?x2551) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #156 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 031zkw; 0d05fv; *> query: (?x2551, 0h_cssd) <- student(?x1368, ?x2551), award_winner(?x618, ?x2551), participant(?x6314, ?x2551) *> conf = 0.03 ranks of expected_values: 72 EVAL 0h0wc award_winner! 0h_cssd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 136.000 136.000 0.138 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22201-072w0 PRED entity: 072w0 PRED relation: religion! PRED expected values: 05fkf 050l8 01n4w => 86 concepts (41 used for prediction) PRED predicted values (max 10 best out of 165): 05kkh (0.85 #887, 0.80 #689, 0.78 #1084), 050l8 (0.80 #722, 0.77 #920, 0.75 #525), 03v1s (0.80 #695, 0.75 #498, 0.71 #400), 03s0w (0.80 #703, 0.75 #506, 0.69 #901), 01n4w (0.78 #1125, 0.77 #928, 0.75 #533), 0vmt (0.77 #900, 0.75 #505, 0.72 #1097), 02xry (0.75 #530, 0.71 #432, 0.70 #727), 059_c (0.75 #510, 0.70 #707, 0.69 #905), 01x73 (0.75 #519, 0.70 #716, 0.69 #914), 05mph (0.75 #561, 0.70 #758, 0.67 #366) >> Best rule #887 for best value: >> intensional similarity = 7 >> extensional distance = 11 >> proper extension: 058x5; 01y0s9; >> query: (?x11552, 05kkh) <- religion(?x2768, ?x11552), religion(?x1906, ?x11552), ?x2768 = 03s5t, adjoins(?x279, ?x1906), time_zones(?x1906, ?x1638), film_release_region(?x66, ?x279), nationality(?x199, ?x279) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #722 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 8 *> proper extension: 01s5nb; *> query: (?x11552, 050l8) <- religion(?x5622, ?x11552), religion(?x3908, ?x11552), religion(?x2768, ?x11552), religion(?x1906, ?x11552), religion(?x726, ?x11552), ?x2768 = 03s5t, ?x1906 = 04rrx, adjoins(?x1144, ?x5622), state_province_region(?x466, ?x3908), time_zones(?x726, ?x2088) *> conf = 0.80 ranks of expected_values: 2, 5, 27 EVAL 072w0 religion! 01n4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 41.000 0.846 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 072w0 religion! 050l8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 41.000 0.846 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 072w0 religion! 05fkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 86.000 41.000 0.846 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #22200-01zhs3 PRED entity: 01zhs3 PRED relation: sport PRED expected values: 02vx4 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 9): 02vx4 (0.90 #438, 0.90 #192, 0.89 #420), 0z74 (0.48 #912, 0.48 #819, 0.47 #719), 0jm_ (0.27 #330, 0.25 #348, 0.24 #385), 018jz (0.21 #240, 0.17 #387, 0.14 #132), 03tmr (0.16 #636, 0.12 #392, 0.11 #355), 018w8 (0.11 #355, 0.11 #639, 0.11 #666), 09xp_ (0.11 #355, 0.10 #554, 0.06 #241), 039yzs (0.11 #355, 0.10 #554, 0.05 #461), 06f3l (0.10 #554) >> Best rule #438 for best value: >> intensional similarity = 7 >> extensional distance = 82 >> proper extension: 019lvv; 07s8qm7; 09cvbq; >> query: (?x7122, 02vx4) <- colors(?x7122, ?x3189), position(?x7122, ?x203), team(?x208, ?x7122), colors(?x4369, ?x3189), ?x203 = 0dgrmp, colors(?x331, ?x3189), team(?x3797, ?x4369) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01zhs3 sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.905 http://example.org/sports/sports_team/sport #22199-0dwxr PRED entity: 0dwxr PRED relation: country PRED expected values: 0b90_r 03_3d 047lj 015qh 04g5k => 41 concepts (36 used for prediction) PRED predicted values (max 10 best out of 386): 035qy (0.85 #4500, 0.83 #3372, 0.82 #182), 0d0vqn (0.83 #4298, 0.82 #182, 0.79 #1486), 03_3d (0.82 #3165, 0.82 #182, 0.80 #5990), 0b90_r (0.82 #182, 0.79 #1486, 0.77 #1868), 059j2 (0.82 #182, 0.79 #1486, 0.77 #1868), 06mzp (0.82 #182, 0.79 #1486, 0.77 #1868), 0154j (0.82 #182, 0.79 #1486, 0.77 #1868), 0ctw_b (0.82 #182, 0.79 #1486, 0.77 #1868), 0k6nt (0.82 #182, 0.79 #1486, 0.77 #1868), 06c1y (0.82 #182, 0.79 #1486, 0.77 #1868) >> Best rule #4500 for best value: >> intensional similarity = 45 >> extensional distance = 11 >> proper extension: 07jbh; >> query: (?x3659, 035qy) <- sports(?x6464, ?x3659), sports(?x3729, ?x3659), sports(?x1608, ?x3659), sports(?x778, ?x3659), olympics(?x94, ?x6464), medal(?x6464, ?x422), country(?x3659, ?x5147), country(?x3659, ?x429), sports(?x6464, ?x3015), sports(?x6464, ?x1121), sports(?x6464, ?x779), olympics(?x2978, ?x778), olympics(?x6974, ?x778), olympics(?x4302, ?x778), olympics(?x421, ?x778), ?x421 = 03_r3, ?x779 = 096f8, ?x2978 = 03_8r, sports(?x3971, ?x3659), participating_countries(?x1608, ?x550), ?x3015 = 071t0, adjustment_currency(?x4302, ?x170), ?x5147 = 0d04z6, official_language(?x6974, ?x254), contains(?x2467, ?x6974), sports(?x778, ?x668), adjoins(?x6974, ?x6863), film_release_region(?x9174, ?x429), film_release_region(?x5825, ?x429), film_release_region(?x3000, ?x429), film_release_region(?x1724, ?x429), film_release_region(?x1552, ?x429), ?x3729 = 0jdk_, ?x3971 = 0jhn7, ?x3000 = 045j3w, nationality(?x2108, ?x429), ?x1121 = 0bynt, ?x1552 = 0gj9qxr, contains(?x429, ?x1788), country_of_origin(?x2447, ?x429), nominated_for(?x2108, ?x1448), ?x1724 = 02r8hh_, ?x5825 = 067ghz, medal(?x778, ?x1242), ?x9174 = 087pfc >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #3165 for first EXPECTED value: *> intensional similarity = 50 *> extensional distance = 9 *> proper extension: 07_53; *> query: (?x3659, 03_3d) <- sports(?x6464, ?x3659), sports(?x778, ?x3659), sports(?x775, ?x3659), olympics(?x3635, ?x6464), olympics(?x1497, ?x6464), olympics(?x1355, ?x6464), olympics(?x390, ?x6464), olympics(?x142, ?x6464), medal(?x6464, ?x422), country(?x3659, ?x1917), country(?x3659, ?x1603), sports(?x6464, ?x4045), sports(?x6464, ?x2885), sports(?x6464, ?x779), sports(?x6464, ?x766), sports(?x6464, ?x471), sports(?x6464, ?x359), ?x778 = 0kbvb, ?x471 = 02vx4, ?x390 = 0chghy, ?x359 = 02bkg, ?x766 = 01hp22, ?x775 = 0l998, ?x1497 = 015qh, ?x1355 = 0h7x, ?x3635 = 019pcs, currency(?x1917, ?x170), olympics(?x1917, ?x784), ?x4045 = 06z6r, ?x779 = 096f8, film_release_region(?x3491, ?x1917), film_release_region(?x80, ?x1917), film_release_region(?x3958, ?x142), film_release_region(?x2598, ?x142), film_release_region(?x1859, ?x142), film_release_region(?x1170, ?x142), ?x1859 = 0m491, jurisdiction_of_office(?x265, ?x142), contains(?x7708, ?x1917), ?x1170 = 09gdm7q, ?x3958 = 0gyh2wm, ?x2885 = 07jjt, religion(?x142, ?x962), ?x80 = 0b76d_m, country(?x668, ?x142), ?x3491 = 0gtvpkw, ?x1603 = 06bnz, ?x7708 = 04pnx, ?x2598 = 07f_7h, contains(?x142, ?x7661) *> conf = 0.82 ranks of expected_values: 3, 4, 17, 30, 47 EVAL 0dwxr country 04g5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 41.000 36.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0dwxr country 015qh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 41.000 36.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0dwxr country 047lj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 41.000 36.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0dwxr country 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 36.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0dwxr country 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 36.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #22198-02664f PRED entity: 02664f PRED relation: award! PRED expected values: 03rx9 03hpr => 45 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1850): 0g5ff (0.67 #11857, 0.60 #8494, 0.56 #25311), 01g6bk (0.67 #26731, 0.60 #9914, 0.50 #23367), 05jm7 (0.67 #11154, 0.60 #7791, 0.50 #4428), 0jt86 (0.67 #26614, 0.50 #23250, 0.50 #19886), 04r68 (0.62 #18290, 0.50 #11564, 0.40 #8201), 0b0pf (0.56 #25099, 0.50 #15008, 0.40 #8282), 03rx9 (0.56 #26301, 0.50 #6121, 0.38 #22937), 07zl1 (0.50 #12995, 0.44 #26449, 0.40 #9632), 01zkxv (0.44 #23671, 0.40 #6854, 0.33 #10217), 03hpr (0.44 #26419, 0.33 #12965, 0.33 #2876) >> Best rule #11857 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0265wl; >> query: (?x4418, 0g5ff) <- award_winner(?x4418, ?x12614), award_winner(?x4418, ?x1727), award(?x6688, ?x4418), award(?x1727, ?x3337), ?x12614 = 01k56k, award_winner(?x10270, ?x6688), ?x3337 = 01yz0x >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #26301 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 040vk98; 01yz0x; 0262yt; *> query: (?x4418, 03rx9) <- award_winner(?x4418, ?x12614), award_winner(?x4418, ?x1727), award(?x4417, ?x4418), award(?x1727, ?x8909), profession(?x12614, ?x353), ?x8909 = 040_9s0, ?x4417 = 04mhl *> conf = 0.56 ranks of expected_values: 7, 10 EVAL 02664f award! 03hpr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 45.000 25.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02664f award! 03rx9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 45.000 25.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22197-0m0jc PRED entity: 0m0jc PRED relation: artists PRED expected values: 01w7nww 046p9 04mky3 => 69 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1010): 01vsxdm (0.78 #10347, 0.70 #12397, 0.60 #11373), 01vvycq (0.71 #6192, 0.62 #7217, 0.50 #4140), 01x1cn2 (0.71 #6333, 0.50 #9409, 0.50 #8384), 0136p1 (0.71 #6284, 0.50 #9360, 0.50 #8335), 024qwq (0.71 #6966, 0.50 #3890, 0.38 #10042), 0qf11 (0.71 #6508, 0.50 #3432, 0.38 #7533), 01vrt_c (0.62 #7246, 0.57 #6221, 0.50 #3145), 025ldg (0.62 #7523, 0.57 #6498, 0.50 #3422), 0bqsy (0.62 #7510, 0.50 #9561, 0.50 #8536), 01s7ns (0.62 #8096, 0.50 #5019, 0.50 #3995) >> Best rule #10347 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 03lty; 01_bkd; 02t8gf; 0xv2x; 09jw2; 0173b0; >> query: (?x474, 01vsxdm) <- artists(?x474, ?x5126), artists(?x474, ?x4701), artists(?x474, ?x1407), ?x5126 = 03h502k, role(?x1407, ?x228), nationality(?x1407, ?x1310), award_winner(?x4701, ?x2799), award(?x4701, ?x567) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #7876 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 025sc50; 02lnbg; *> query: (?x474, 046p9) <- artists(?x474, ?x6715), artists(?x474, ?x5126), person(?x6043, ?x5126), role(?x5126, ?x74), celebrity(?x950, ?x5126), ?x6715 = 011z3g *> conf = 0.50 ranks of expected_values: 55, 637, 682 EVAL 0m0jc artists 04mky3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 69.000 30.000 0.778 http://example.org/music/genre/artists EVAL 0m0jc artists 046p9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 69.000 30.000 0.778 http://example.org/music/genre/artists EVAL 0m0jc artists 01w7nww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 69.000 30.000 0.778 http://example.org/music/genre/artists #22196-027t8fw PRED entity: 027t8fw PRED relation: type_of_union PRED expected values: 04ztj => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.76 #97, 0.76 #105, 0.75 #101), 01g63y (0.15 #46, 0.14 #54, 0.14 #50) >> Best rule #97 for best value: >> intensional similarity = 3 >> extensional distance = 538 >> proper extension: 06gn7r; >> query: (?x7249, 04ztj) <- profession(?x7249, ?x524), ?x524 = 02jknp, award(?x7249, ?x1243) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027t8fw type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.763 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22195-01y3v PRED entity: 01y3v PRED relation: draft PRED expected values: 02qw1zx => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 16): 09l0x9 (0.87 #184, 0.86 #345, 0.85 #618), 02qw1zx (0.73 #179, 0.71 #225, 0.71 #51), 02pq_x5 (0.67 #173, 0.44 #125, 0.37 #238), 02x2khw (0.67 #161, 0.44 #113, 0.32 #950), 02r6gw6 (0.58 #170, 0.56 #122, 0.39 #218), 02pq_rp (0.58 #165, 0.44 #117, 0.34 #954), 02z6872 (0.58 #166, 0.44 #118, 0.34 #955), 047dpm0 (0.56 #127, 0.50 #175, 0.34 #964), 02rl201 (0.56 #114, 0.50 #162, 0.32 #951), 04f4z1k (0.50 #174, 0.44 #126, 0.34 #963) >> Best rule #184 for best value: >> intensional similarity = 12 >> extensional distance = 13 >> proper extension: 01ct6; 05g3b; 05gg4; 05g49; 04vn5; >> query: (?x2574, 09l0x9) <- position(?x2574, ?x180), position_s(?x2574, ?x2312), position_s(?x2574, ?x2147), ?x2147 = 04nfpk, draft(?x2574, ?x465), team(?x11323, ?x2574), ?x11323 = 059yj, school(?x2574, ?x3779), ?x2312 = 02qpbqj, team(?x935, ?x2574), ?x180 = 01r3hr, contains(?x94, ?x3779) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #179 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 13 *> proper extension: 01ct6; 05g3b; 05gg4; 05g49; 04vn5; *> query: (?x2574, 02qw1zx) <- position(?x2574, ?x180), position_s(?x2574, ?x2312), position_s(?x2574, ?x2147), ?x2147 = 04nfpk, draft(?x2574, ?x465), team(?x11323, ?x2574), ?x11323 = 059yj, school(?x2574, ?x3779), ?x2312 = 02qpbqj, team(?x935, ?x2574), ?x180 = 01r3hr, contains(?x94, ?x3779) *> conf = 0.73 ranks of expected_values: 2 EVAL 01y3v draft 02qw1zx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.867 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #22194-0bpx1k PRED entity: 0bpx1k PRED relation: country PRED expected values: 0f8l9c => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 137): 0f8l9c (0.82 #79, 0.30 #19, 0.15 #562), 07ssc (0.76 #664, 0.76 #619, 0.36 #76), 01z4y (0.29 #665, 0.09 #422, 0.08 #3623), 0345h (0.22 #148, 0.20 #1172, 0.19 #752), 03_3d (0.10 #7, 0.09 #67, 0.06 #128), 03rjj (0.10 #6, 0.05 #609, 0.05 #187), 03rk0 (0.10 #39, 0.03 #160, 0.02 #1206), 03rt9 (0.09 #74, 0.04 #617, 0.03 #255), 03spz (0.09 #114, 0.02 #1206), 0d060g (0.07 #1696, 0.05 #309, 0.05 #793) >> Best rule #79 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0ddcbd5; >> query: (?x2881, 0f8l9c) <- film_release_distribution_medium(?x2881, ?x81), film_crew_role(?x2881, ?x137), titles(?x512, ?x2881), produced_by(?x2881, ?x5973), ?x5973 = 02q42j_ >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bpx1k country 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.818 http://example.org/film/film/country #22193-01gst_ PRED entity: 01gst_ PRED relation: district_represented PRED expected values: 03v1s => 36 concepts (34 used for prediction) PRED predicted values (max 10 best out of 411): 03v1s (0.90 #1022, 0.85 #800, 0.84 #131), 03s0w (0.84 #131, 0.84 #754, 0.83 #486), 04rrx (0.84 #131, 0.84 #754, 0.83 #486), 0vbk (0.84 #131, 0.84 #754, 0.83 #486), 0824r (0.84 #131, 0.84 #754, 0.83 #486), 07b_l (0.84 #131, 0.84 #754, 0.83 #486), 02xry (0.84 #131, 0.84 #754, 0.83 #486), 081mh (0.66 #485, 0.63 #844, 0.60 #1332), 0f8x_r (0.66 #485, 0.63 #844, 0.60 #1332), 02_286 (0.66 #485, 0.63 #844, 0.44 #1522) >> Best rule #1022 for best value: >> intensional similarity = 34 >> extensional distance = 19 >> proper extension: 02gkzs; >> query: (?x2712, 03v1s) <- district_represented(?x2712, ?x7518), district_represented(?x2712, ?x4061), district_represented(?x2712, ?x2831), district_represented(?x2712, ?x2713), district_represented(?x2712, ?x1767), district_represented(?x2712, ?x177), legislative_sessions(?x2712, ?x759), ?x4061 = 0498y, state_province_region(?x13148, ?x177), district_represented(?x952, ?x2713), district_represented(?x653, ?x2713), contains(?x94, ?x7518), religion(?x177, ?x492), location(?x932, ?x177), contains(?x7518, ?x2832), category(?x177, ?x134), ?x952 = 06f0dc, jurisdiction_of_office(?x900, ?x177), administrative_division(?x8263, ?x2713), location(?x6880, ?x1767), location(?x6157, ?x1767), contains(?x2713, ?x2056), ?x653 = 070m6c, film(?x6157, ?x2757), contains(?x1767, ?x1396), ?x2757 = 0170th, jurisdiction_of_office(?x1157, ?x177), ?x2831 = 0gyh, featured_film_locations(?x2754, ?x1767), adjoins(?x177, ?x1905), religion(?x6880, ?x7422), school(?x2820, ?x13148), colors(?x13148, ?x1101), ?x492 = 0flw86 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gst_ district_represented 03v1s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 34.000 0.905 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #22192-086h6p PRED entity: 086h6p PRED relation: citytown PRED expected values: 04lh6 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 90): 071vr (0.53 #13354, 0.52 #7415, 0.50 #4816), 052p7 (0.53 #13354, 0.52 #7415, 0.50 #4816), 07dfk (0.40 #3545, 0.36 #9487, 0.33 #3702), 024bqj (0.33 #3702, 0.29 #3705, 0.29 #3704), 02_286 (0.33 #4461, 0.25 #14479, 0.25 #10769), 0y2dl (0.33 #70, 0.20 #3775, 0.10 #7486), 01_d4 (0.33 #781, 0.09 #9312, 0.09 #8194), 0d6lp (0.33 #1554, 0.06 #33439, 0.06 #32320), 030qb3t (0.30 #14862, 0.27 #8556, 0.27 #8184), 04jpl (0.30 #7050, 0.19 #12989, 0.18 #7793) >> Best rule #13354 for best value: >> intensional similarity = 11 >> extensional distance = 14 >> proper extension: 073tm9; >> query: (?x13919, ?x6960) <- child(?x11303, ?x13919), industry(?x13919, ?x10022), service_location(?x11303, ?x252), child(?x11303, ?x14310), administrative_parent(?x536, ?x252), taxonomy(?x252, ?x939), citytown(?x11303, ?x9310), service_language(?x11303, ?x2164), citytown(?x14310, ?x6960), contains(?x1453, ?x9310), location_of_ceremony(?x566, ?x9310) >> conf = 0.53 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 086h6p citytown 04lh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 91.000 0.525 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #22191-06msq2 PRED entity: 06msq2 PRED relation: award_winner! PRED expected values: 07y_p6 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 101): 05c1t6z (0.75 #289, 0.43 #152, 0.20 #15), 0lp_cd3 (0.75 #297, 0.43 #160, 0.20 #23), 0gvstc3 (0.50 #307, 0.43 #170, 0.25 #444), 03nnm4t (0.43 #207, 0.40 #70, 0.38 #344), 0hn821n (0.25 #538, 0.17 #8360, 0.17 #8361), 07y_p6 (0.20 #94, 0.17 #8360, 0.17 #8361), 03gwpw2 (0.20 #9, 0.17 #8360, 0.17 #8361), 056878 (0.20 #31, 0.14 #168, 0.12 #305), 0hndn2q (0.17 #8360, 0.17 #8361, 0.13 #2604), 0hr3c8y (0.17 #8360, 0.17 #8361, 0.13 #2604) >> Best rule #289 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0bz5v2; 01j7rd; 02xs0q; 05bnq3j; 05pzdk; 04crrxr; >> query: (?x4415, 05c1t6z) <- award_nominee(?x236, ?x4415), nominated_for(?x4415, ?x3626), ?x3626 = 01j7mr >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #94 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 01j7pt; *> query: (?x4415, 07y_p6) <- award_winner(?x4386, ?x4415), nominated_for(?x4415, ?x5698), ?x5698 = 05_z42 *> conf = 0.20 ranks of expected_values: 6 EVAL 06msq2 award_winner! 07y_p6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 90.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22190-0cpvcd PRED entity: 0cpvcd PRED relation: influenced_by! PRED expected values: 032r1 => 148 concepts (61 used for prediction) PRED predicted values (max 10 best out of 384): 03_hd (0.50 #180, 0.14 #1718, 0.08 #20522), 03sbs (0.50 #285, 0.08 #13341, 0.08 #20522), 048cl (0.33 #297, 0.11 #4400, 0.09 #1835), 0j3v (0.33 #80, 0.09 #1618, 0.08 #20522), 043s3 (0.33 #153, 0.08 #13341, 0.08 #20522), 0tfc (0.33 #485, 0.08 #13341, 0.08 #20522), 0372p (0.33 #149, 0.08 #20522, 0.07 #24117), 028p0 (0.33 #38, 0.07 #24117, 0.06 #20521), 0683n (0.27 #2388, 0.24 #1361, 0.12 #4440), 0dzkq (0.24 #1150, 0.18 #1664, 0.14 #2177) >> Best rule #180 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 07kb5; >> query: (?x11500, 03_hd) <- influenced_by(?x5266, ?x11500), influenced_by(?x3711, ?x11500), ?x3711 = 052h3, profession(?x11500, ?x10210), student(?x331, ?x5266) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2010 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 04411; 01d494; 0j3v; 0dzkq; 052h3; 099bk; 0372p; 01dvtx; 043s3; 0b78hw; ... *> query: (?x11500, 032r1) <- nationality(?x11500, ?x94), influenced_by(?x11500, ?x3712), student(?x2637, ?x11500), interests(?x11500, ?x713) *> conf = 0.18 ranks of expected_values: 18 EVAL 0cpvcd influenced_by! 032r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 148.000 61.000 0.500 http://example.org/influence/influence_node/influenced_by #22189-0ms6_ PRED entity: 0ms6_ PRED relation: source PRED expected values: 0jbk9 => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #58, 0.94 #57, 0.93 #47) >> Best rule #58 for best value: >> intensional similarity = 5 >> extensional distance = 205 >> proper extension: 0m2j5; >> query: (?x12793, ?x958) <- second_level_divisions(?x94, ?x12793), adjoins(?x12793, ?x12294), adjoins(?x12793, ?x11836), contains(?x12294, ?x9713), source(?x11836, ?x958) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ms6_ source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.937 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #22188-03_2y PRED entity: 03_2y PRED relation: produced_by! PRED expected values: 02r9p0c => 109 concepts (72 used for prediction) PRED predicted values (max 10 best out of 279): 0mb8c (0.37 #4738, 0.36 #8527, 0.35 #2842), 02b61v (0.37 #4738, 0.36 #8527, 0.35 #2842), 02_fz3 (0.37 #4738, 0.36 #8527, 0.35 #2842), 0df92l (0.20 #550, 0.01 #3392), 040b5k (0.20 #258, 0.01 #3100), 01f8f7 (0.20 #650), 065ym0c (0.06 #22730, 0.02 #47347), 0h03fhx (0.04 #2319, 0.03 #3267, 0.03 #4215), 0bwfwpj (0.03 #1037, 0.03 #1984, 0.02 #2932), 05ch98 (0.03 #1683, 0.02 #3578, 0.01 #5474) >> Best rule #4738 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 0gd9k; 036dyy; 05vtbl; >> query: (?x10186, ?x5871) <- film(?x10186, ?x5230), film(?x10186, ?x5871), profession(?x10186, ?x319) >> conf = 0.37 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 03_2y produced_by! 02r9p0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 72.000 0.373 http://example.org/film/film/produced_by #22187-03nkts PRED entity: 03nkts PRED relation: award_nominee! PRED expected values: 02t_tp => 81 concepts (22 used for prediction) PRED predicted values (max 10 best out of 614): 03xkps (0.81 #49011, 0.81 #18668, 0.81 #42006), 0187y5 (0.17 #23336, 0.02 #131, 0.01 #16464), 016zdd (0.17 #23336), 0p17j (0.17 #23336), 0522wp (0.17 #23336), 03nkts (0.17 #23336), 02t_tp (0.17 #23336), 05m883 (0.17 #23336), 0hvb2 (0.07 #392, 0.05 #16725, 0.04 #2725), 01g23m (0.07 #920, 0.04 #5586, 0.04 #7919) >> Best rule #49011 for best value: >> intensional similarity = 3 >> extensional distance = 1266 >> proper extension: 02vtnf; >> query: (?x6397, ?x286) <- profession(?x6397, ?x319), award_nominee(?x6397, ?x286), religion(?x286, ?x1985) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #23336 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 515 *> proper extension: 04n_g; 012gbb; *> query: (?x6397, ?x1179) <- film(?x6397, ?x3784), participant(?x6844, ?x6397), nominated_for(?x1179, ?x3784) *> conf = 0.17 ranks of expected_values: 7 EVAL 03nkts award_nominee! 02t_tp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 81.000 22.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22186-03b78r PRED entity: 03b78r PRED relation: people! PRED expected values: 0x67 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 32): 0x67 (0.19 #10, 0.18 #87, 0.16 #318), 041rx (0.18 #697, 0.18 #158, 0.17 #774), 0xnvg (0.12 #90, 0.11 #167, 0.11 #244), 033tf_ (0.09 #84, 0.08 #1855, 0.08 #2548), 02w7gg (0.07 #1850, 0.07 #2389, 0.06 #156), 013xrm (0.05 #1406, 0.05 #1175, 0.04 #944), 09vc4s (0.05 #625, 0.03 #1857, 0.02 #2396), 048z7l (0.05 #425, 0.05 #117, 0.05 #194), 01qhm_ (0.05 #6, 0.03 #2547, 0.03 #622), 038723 (0.05 #69, 0.03 #454, 0.02 #223) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 012v1t; >> query: (?x7395, 0x67) <- place_of_birth(?x7395, ?x1705), ?x1705 = 094jv, nationality(?x7395, ?x94) >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03b78r people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.190 http://example.org/people/ethnicity/people #22185-03s9v PRED entity: 03s9v PRED relation: profession PRED expected values: 0h9c 06q2q => 135 concepts (90 used for prediction) PRED predicted values (max 10 best out of 116): 0kyk (0.94 #11946, 0.41 #6648, 0.40 #5472), 02hrh1q (0.78 #5309, 0.75 #5603, 0.71 #6779), 0cbd2 (0.60 #448, 0.54 #8978, 0.53 #8094), 0dxtg (0.48 #4278, 0.37 #2807, 0.36 #8837), 06q2q (0.46 #12063, 0.29 #4412, 0.20 #634), 01d_h8 (0.36 #888, 0.35 #4270, 0.33 #1035), 09jwl (0.35 #1490, 0.32 #2225, 0.29 #6931), 02jknp (0.30 #449, 0.27 #4272, 0.26 #1331), 016fly (0.28 #9120, 0.27 #12800, 0.27 #8971), 01d30f (0.28 #9120, 0.27 #12800, 0.27 #8971) >> Best rule #11946 for best value: >> intensional similarity = 4 >> extensional distance = 341 >> proper extension: 019389; 0cgfb; >> query: (?x7251, 0kyk) <- profession(?x7251, ?x11056), profession(?x5131, ?x11056), ?x5131 = 01tdnyh, specialization_of(?x11056, ?x3802) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #12063 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 341 *> proper extension: 019389; 0cgfb; *> query: (?x7251, ?x3802) <- profession(?x7251, ?x11056), profession(?x5131, ?x11056), ?x5131 = 01tdnyh, specialization_of(?x11056, ?x3802) *> conf = 0.46 ranks of expected_values: 5, 68 EVAL 03s9v profession 06q2q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 135.000 90.000 0.942 http://example.org/people/person/profession EVAL 03s9v profession 0h9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 135.000 90.000 0.942 http://example.org/people/person/profession #22184-03y0pn PRED entity: 03y0pn PRED relation: nominated_for! PRED expected values: 0gr42 => 83 concepts (81 used for prediction) PRED predicted values (max 10 best out of 197): 019f4v (0.44 #2600, 0.37 #4224, 0.36 #4456), 0gs96 (0.43 #1245, 0.43 #1013, 0.38 #549), 099c8n (0.43 #1211, 0.38 #515, 0.36 #979), 0gq9h (0.43 #2609, 0.41 #4697, 0.40 #4233), 0k611 (0.43 #2620, 0.33 #300, 0.31 #1460), 0gr0m (0.40 #2606, 0.26 #4462, 0.25 #518), 02x2gy0 (0.38 #561, 0.36 #1257, 0.33 #329), 027dtxw (0.38 #468, 0.36 #1164, 0.29 #932), 0gqwc (0.38 #519, 0.29 #1215, 0.29 #983), 0gqxm (0.38 #590, 0.27 #1518, 0.21 #1286) >> Best rule #2600 for best value: >> intensional similarity = 3 >> extensional distance = 92 >> proper extension: 0c3xpwy; >> query: (?x7207, 019f4v) <- nominated_for(?x1983, ?x7207), honored_for(?x5592, ?x7207), crewmember(?x508, ?x1983) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1476 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 064n1pz; *> query: (?x7207, 0gr42) <- film_release_distribution_medium(?x7207, ?x81), region(?x7207, ?x512), honored_for(?x5592, ?x7207) *> conf = 0.31 ranks of expected_values: 18 EVAL 03y0pn nominated_for! 0gr42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 83.000 81.000 0.436 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #22183-01lw3kh PRED entity: 01lw3kh PRED relation: student! PRED expected values: 04sylm => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 110): 04sylm (0.17 #76, 0.06 #5873, 0.06 #3765), 017j69 (0.17 #145, 0.02 #5415, 0.01 #27550), 01j_9c (0.17 #10, 0.02 #5280), 07tgn (0.14 #1071, 0.04 #15300, 0.01 #38495), 015nl4 (0.11 #1121, 0.07 #15350, 0.05 #22728), 017z88 (0.08 #8514, 0.07 #10622, 0.06 #10095), 02l9wl (0.07 #1306, 0.03 #15535, 0.01 #22913), 09f2j (0.07 #8591, 0.05 #10172, 0.04 #10699), 07tg4 (0.06 #15369, 0.03 #2194, 0.02 #5356), 0bwfn (0.05 #8707, 0.05 #27680, 0.04 #28207) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02mslq; 01lz4tf; >> query: (?x6237, 04sylm) <- artist(?x1954, ?x6237), gender(?x6237, ?x231), award(?x1954, ?x3105), music(?x8677, ?x6237) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lw3kh student! 04sylm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #22182-0p7qm PRED entity: 0p7qm PRED relation: genre PRED expected values: 07s9rl0 => 90 concepts (73 used for prediction) PRED predicted values (max 10 best out of 82): 07s9rl0 (0.90 #2903, 0.84 #581, 0.79 #1626), 05p553 (0.42 #1745, 0.41 #1513, 0.36 #120), 01hmnh (0.33 #829, 0.18 #1177, 0.15 #3730), 01drsx (0.33 #39, 0.05 #155, 0.04 #271), 01jfsb (0.31 #6976, 0.30 #6512, 0.30 #7092), 04xvlr (0.31 #2207, 0.30 #582, 0.23 #698), 06cvj (0.24 #1744, 0.23 #1512, 0.10 #119), 01g6gs (0.21 #135, 0.16 #483, 0.16 #367), 06n90 (0.21 #826, 0.13 #5933, 0.13 #5468), 0hcr (0.20 #835, 0.06 #5942, 0.06 #5477) >> Best rule #2903 for best value: >> intensional similarity = 4 >> extensional distance = 769 >> proper extension: 0fq27fp; >> query: (?x2924, 07s9rl0) <- currency(?x2924, ?x170), genre(?x2924, ?x1509), genre(?x2057, ?x1509), ?x2057 = 0jym0 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p7qm genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 73.000 0.898 http://example.org/film/film/genre #22181-01vvb4m PRED entity: 01vvb4m PRED relation: participant! PRED expected values: 01pllx => 123 concepts (88 used for prediction) PRED predicted values (max 10 best out of 466): 01pllx (0.81 #12105, 0.80 #16568, 0.80 #11467), 023n39 (0.43 #1910, 0.35 #8283, 0.01 #20390), 03h_fqv (0.43 #1910, 0.35 #8283, 0.01 #20390), 01rzqj (0.33 #239, 0.17 #875), 0kjrx (0.10 #3184, 0.10 #4458, 0.10 #5732), 023tp8 (0.10 #3184, 0.10 #4458, 0.10 #5732), 01n7qlf (0.10 #3184, 0.10 #4458, 0.10 #5732), 014zcr (0.09 #2565, 0.07 #3839, 0.06 #1928), 01rr9f (0.09 #1306, 0.05 #7680, 0.04 #10863), 0227vl (0.09 #11466, 0.08 #1909, 0.08 #2547) >> Best rule #12105 for best value: >> intensional similarity = 3 >> extensional distance = 319 >> proper extension: 012rng; 012dr7; >> query: (?x3056, ?x2444) <- nominated_for(?x3056, ?x83), type_of_union(?x3056, ?x566), participant(?x3056, ?x2444) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vvb4m participant! 01pllx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 88.000 0.806 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #22180-012mzw PRED entity: 012mzw PRED relation: institution! PRED expected values: 019v9k => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 14): 019v9k (0.78 #183, 0.76 #118, 0.69 #251), 02_xgp2 (0.70 #122, 0.57 #187, 0.57 #89), 013zdg (0.57 #117, 0.30 #182, 0.24 #558), 027f2w (0.36 #86, 0.32 #69, 0.31 #184), 0bjrnt (0.30 #1309, 0.25 #83, 0.24 #66), 028dcg (0.30 #1309, 0.23 #29, 0.22 #127), 02m4yg (0.30 #1309, 0.21 #92, 0.20 #75), 01ysy9 (0.30 #1309, 0.20 #79, 0.18 #96), 071tyz (0.30 #1309, 0.11 #87, 0.08 #70), 01gkg3 (0.30 #1309, 0.05 #1805, 0.02 #322) >> Best rule #183 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 0j_sncb; 017j69; 017cy9; 02bqy; 05zl0; 01p79b; 02mp0g; 02zkdz; >> query: (?x7596, 019v9k) <- major_field_of_study(?x7596, ?x2606), school(?x1239, ?x7596), ?x2606 = 062z7 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012mzw institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22179-0c3xw46 PRED entity: 0c3xw46 PRED relation: film_release_region PRED expected values: 0d0vqn 059j2 06mkj 06t8v => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 105): 0d0vqn (0.92 #427, 0.91 #991, 0.89 #1132), 059j2 (0.85 #1011, 0.81 #447, 0.81 #1152), 06mkj (0.85 #467, 0.84 #1031, 0.82 #1172), 047yc (0.73 #20, 0.34 #1007, 0.32 #1148), 06t8v (0.65 #65, 0.35 #488, 0.34 #1052), 016wzw (0.58 #54, 0.34 #477, 0.33 #1041), 01ls2 (0.58 #8, 0.34 #995, 0.33 #431), 03rj0 (0.50 #48, 0.49 #1035, 0.48 #1176), 06qd3 (0.50 #30, 0.43 #1017, 0.43 #453), 07t21 (0.50 #32, 0.31 #6786, 0.31 #7916) >> Best rule #427 for best value: >> intensional similarity = 5 >> extensional distance = 185 >> proper extension: 0ds35l9; 0g56t9t; 02vxq9m; 028_yv; 0c3ybss; 02vp1f_; 01gc7; 011yrp; 0ds3t5x; 0m2kd; ... >> query: (?x3812, 0d0vqn) <- film_release_region(?x3812, ?x985), film_release_region(?x3812, ?x87), ?x985 = 0k6nt, ?x87 = 05r4w, nominated_for(?x4956, ?x3812) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5 EVAL 0c3xw46 film_release_region 06t8v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 58.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c3xw46 film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c3xw46 film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c3xw46 film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22178-033071 PRED entity: 033071 PRED relation: nationality PRED expected values: 09c7w0 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 29): 09c7w0 (0.89 #905, 0.89 #805, 0.89 #1105), 0d060g (0.40 #1807, 0.31 #4713, 0.14 #107), 07ssc (0.25 #317, 0.24 #9230, 0.23 #8528), 02jx1 (0.24 #9230, 0.23 #8528, 0.22 #1437), 05kyr (0.24 #9230, 0.23 #8528, 0.05 #570), 03rk0 (0.09 #3057, 0.07 #4358, 0.07 #3857), 03_3d (0.08 #1712, 0.06 #1913, 0.05 #2013), 03rt9 (0.05 #1417, 0.03 #1719, 0.03 #2020), 0f8l9c (0.03 #1426, 0.02 #3033, 0.02 #6844), 0d05w3 (0.03 #2860, 0.02 #3061, 0.02 #4362) >> Best rule #905 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 06j0md; 0265v21; 02l5rm; 0638kv; 01x6v6; 03kcyd; 0bq4j6; 03fqv5; >> query: (?x11972, 09c7w0) <- student(?x122, ?x11972), nominated_for(?x11972, ?x8837), profession(?x11972, ?x524), ?x122 = 08815 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033071 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.895 http://example.org/people/person/nationality #22177-0p07_ PRED entity: 0p07_ PRED relation: currency PRED expected values: 09nqf => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.88 #8, 0.88 #7, 0.86 #17) >> Best rule #8 for best value: >> intensional similarity = 6 >> extensional distance = 23 >> proper extension: 0jcgs; 0m28g; 0p03t; 0m27n; 0p01x; 0m2cb; 0jcjq; 0m2b5; 0n56v; 0n_ps; ... >> query: (?x12966, 09nqf) <- time_zones(?x12966, ?x2088), ?x2088 = 02hczc, source(?x12966, ?x958), ?x958 = 0jbk9, second_level_divisions(?x94, ?x12966), ?x94 = 09c7w0 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p07_ currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.880 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #22176-07m9cm PRED entity: 07m9cm PRED relation: award_winner! PRED expected values: 099tbz => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 246): 099tbz (0.67 #57, 0.17 #31899, 0.14 #21120), 0gqy2 (0.17 #31899, 0.17 #162, 0.07 #35348), 02x73k6 (0.17 #31899, 0.17 #60, 0.07 #35348), 02z13jg (0.17 #31899, 0.17 #49, 0.07 #35348), 099jhq (0.17 #31899, 0.17 #20, 0.07 #28018), 027b9j5 (0.17 #31899, 0.17 #228, 0.07 #28018), 09sdmz (0.17 #31899, 0.07 #35348, 0.07 #40090), 07bdd_ (0.17 #65, 0.14 #21120, 0.10 #22414), 05zr6wv (0.17 #18, 0.14 #21120, 0.10 #22414), 07cbcy (0.17 #78, 0.14 #21120, 0.10 #22414) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 026rm_y; 08qxx9; >> query: (?x4543, 099tbz) <- award_winner(?x4543, ?x815), award_nominee(?x8764, ?x4543), ?x8764 = 0336mc >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07m9cm award_winner! 099tbz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #22175-052hl PRED entity: 052hl PRED relation: story_by! PRED expected values: 026wlxw => 126 concepts (107 used for prediction) PRED predicted values (max 10 best out of 151): 065zlr (0.20 #82, 0.02 #1455, 0.01 #4892), 0bv8h2 (0.20 #121), 0291hr (0.11 #2747, 0.10 #15798, 0.09 #12366), 018f8 (0.11 #2747, 0.10 #15798, 0.09 #12366), 0qmd5 (0.10 #7904, 0.06 #6873, 0.06 #4466), 063hp4 (0.07 #919, 0.02 #2636), 01cmp9 (0.07 #900), 0291ck (0.06 #1373, 0.05 #5841, 0.05 #11336), 02czd5 (0.05 #5841, 0.04 #5154, 0.01 #21290), 0mbql (0.05 #574, 0.04 #1260, 0.02 #1604) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 09h_q; 0427y; 03rx9; >> query: (?x6771, 065zlr) <- people(?x9943, ?x6771), influenced_by(?x1145, ?x6771), ?x9943 = 09kr66 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 052hl story_by! 026wlxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 107.000 0.200 http://example.org/film/film/story_by #22174-07cz2 PRED entity: 07cz2 PRED relation: category PRED expected values: 08mbj5d => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.36 #2, 0.33 #4, 0.32 #11) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 061681; 013q0p; 016dj8; >> query: (?x2770, 08mbj5d) <- featured_film_locations(?x2770, ?x5036), film_release_region(?x2770, ?x94), prequel(?x3055, ?x2770), award(?x2770, ?x298) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07cz2 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.357 http://example.org/common/topic/webpage./common/webpage/category #22173-025cbm PRED entity: 025cbm PRED relation: role! PRED expected values: 03q5t => 71 concepts (53 used for prediction) PRED predicted values (max 10 best out of 110): 02sgy (0.88 #3187, 0.87 #3924, 0.85 #2439), 0dwt5 (0.85 #2401, 0.80 #1463, 0.79 #2721), 018j2 (0.84 #3644, 0.82 #3854, 0.80 #1463), 0342h (0.84 #4458, 0.83 #412, 0.83 #3709), 03q5t (0.83 #412, 0.83 #3709, 0.83 #3708), 07brj (0.83 #412, 0.83 #3709, 0.83 #3708), 0dwsp (0.82 #2118, 0.77 #2433, 0.77 #2333), 0dwtp (0.81 #3178, 0.81 #3088, 0.79 #2658), 04rzd (0.81 #3113, 0.78 #2043, 0.77 #4387), 01dnws (0.80 #1463, 0.79 #2687, 0.78 #2047) >> Best rule #3187 for best value: >> intensional similarity = 17 >> extensional distance = 14 >> proper extension: 02fsn; >> query: (?x433, 02sgy) <- role(?x3215, ?x433), role(?x614, ?x433), role(?x432, ?x433), role(?x212, ?x433), role(?x2765, ?x433), role(?x211, ?x433), award_nominee(?x2765, ?x1089), profession(?x2765, ?x220), ?x432 = 042v_gx, role(?x211, ?x2048), ?x614 = 0mkg, ?x2048 = 018j2, ?x212 = 026t6, profession(?x211, ?x131), category(?x2765, ?x134), role(?x74, ?x3215), role(?x217, ?x3215) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #412 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 2 *> proper extension: 0342h; *> query: (?x433, ?x74) <- role(?x3991, ?x433), role(?x3409, ?x433), role(?x2460, ?x433), role(?x6626, ?x433), role(?x3399, ?x433), role(?x1656, ?x433), role(?x211, ?x433), ?x3409 = 0680x0, role(?x433, ?x1267), role(?x433, ?x74), ?x1656 = 0l12d, ?x3991 = 05842k, profession(?x211, ?x131), ?x2460 = 01wy6, artists(?x671, ?x211), artists(?x597, ?x6626), artists(?x505, ?x6626), award_nominee(?x6626, ?x6207), instrumentalists(?x1166, ?x6626), ?x1267 = 07brj, ?x597 = 0ggq0m, ?x3399 = 01gx5f, ?x505 = 03_d0 *> conf = 0.83 ranks of expected_values: 5 EVAL 025cbm role! 03q5t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 71.000 53.000 0.875 http://example.org/music/performance_role/track_performances./music/track_contribution/role #22172-03cvvlg PRED entity: 03cvvlg PRED relation: film_crew_role PRED expected values: 02r96rf => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.79 #3, 0.67 #145, 0.66 #1208), 01vx2h (0.31 #1215, 0.31 #720, 0.31 #222), 01pvkk (0.28 #721, 0.27 #1216, 0.26 #403), 02ynfr (0.20 #157, 0.19 #725, 0.18 #407), 0215hd (0.18 #18, 0.15 #195, 0.14 #160), 089g0h (0.18 #19, 0.15 #161, 0.11 #196), 02rh1dz (0.16 #221, 0.16 #329, 0.13 #115), 0d2b38 (0.15 #25, 0.14 #167, 0.13 #202), 02_n3z (0.12 #143, 0.10 #72, 0.09 #178), 01xy5l_ (0.12 #190, 0.11 #155, 0.10 #405) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 01h7bb; 0b6tzs; 0fh694; 0dgst_d; 01719t; 0fy34l; 0260bz; 02c638; 011yd2; 02ryz24; ... >> query: (?x8438, 02r96rf) <- produced_by(?x8438, ?x163), nominated_for(?x1253, ?x8438), nominated_for(?x451, ?x8438), ?x451 = 099jhq >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cvvlg film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.788 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22171-0prh7 PRED entity: 0prh7 PRED relation: film_crew_role PRED expected values: 09zzb8 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 35): 09zzb8 (0.77 #796, 0.76 #1525, 0.75 #1), 0dxtw (0.40 #805, 0.39 #118, 0.38 #10), 01vx2h (0.38 #119, 0.35 #806, 0.31 #1136), 01pvkk (0.27 #1536, 0.27 #1899, 0.27 #1137), 02ynfr (0.18 #811, 0.15 #594, 0.15 #522), 02rh1dz (0.18 #117, 0.14 #81, 0.13 #804), 015h31 (0.15 #116, 0.13 #80, 0.12 #1924), 0215hd (0.15 #814, 0.12 #1543, 0.12 #1924), 089fss (0.12 #6, 0.12 #1924, 0.07 #801), 089g0h (0.12 #1924, 0.12 #815, 0.10 #1145) >> Best rule #796 for best value: >> intensional similarity = 3 >> extensional distance = 729 >> proper extension: 0fq27fp; 0gh6j94; >> query: (?x4874, 09zzb8) <- film_crew_role(?x4874, ?x1171), ?x1171 = 09vw2b7, genre(?x4874, ?x53) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0prh7 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.773 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22170-0436zq PRED entity: 0436zq PRED relation: profession PRED expected values: 02hrh1q => 115 concepts (114 used for prediction) PRED predicted values (max 10 best out of 76): 02hrh1q (0.89 #1365, 0.88 #8569, 0.88 #11120), 01d_h8 (0.38 #156, 0.37 #7060, 0.36 #606), 0dxtg (0.38 #164, 0.29 #7068, 0.29 #614), 0np9r (0.36 #622, 0.25 #22, 0.16 #9326), 02jknp (0.29 #758, 0.29 #608, 0.28 #7062), 03gjzk (0.29 #616, 0.19 #12321, 0.19 #9020), 018gz8 (0.29 #618, 0.15 #7972, 0.14 #9322), 02krf9 (0.25 #628, 0.25 #28, 0.11 #6604), 026sdt1 (0.25 #220, 0.11 #6604, 0.03 #3821), 09lbv (0.25 #21, 0.08 #15006, 0.02 #3922) >> Best rule #1365 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 014v1q; >> query: (?x11697, 02hrh1q) <- award(?x11697, ?x3066), gender(?x11697, ?x231), ?x3066 = 0gqy2, nationality(?x11697, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0436zq profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 114.000 0.887 http://example.org/people/person/profession #22169-07b_l PRED entity: 07b_l PRED relation: contains PRED expected values: 013m43 010bnr 013m4v 0mrf1 0kpw3 0104lr 0msck => 233 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2821): 0vzm (0.83 #144791, 0.82 #194036, 0.81 #188244), 03l2n (0.82 #194036, 0.81 #188244, 0.81 #167964), 02gr81 (0.72 #159273, 0.70 #211413, 0.68 #211412), 03zw80 (0.58 #14477, 0.53 #237479, 0.51 #86872), 051pnv (0.58 #14477, 0.53 #237479, 0.51 #86872), 01skcy (0.58 #14477, 0.53 #237479, 0.51 #86872), 0py9b (0.58 #14477, 0.53 #237479, 0.51 #86872), 08z129 (0.58 #14477, 0.53 #237479, 0.51 #86872), 0cv9b (0.58 #14477, 0.53 #237479, 0.51 #86872), 03_05 (0.58 #14477, 0.51 #86872, 0.48 #144790) >> Best rule #144791 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 07c98; 01hpnh; 0cxgc; 055vr; 086g2; >> query: (?x3634, ?x3269) <- state_province_region(?x216, ?x3634), location(?x56, ?x3634), contains(?x3634, ?x1569), capital(?x3634, ?x3269) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #170861 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 06t2t; *> query: (?x3634, ?x94) <- contains(?x3634, ?x8093), contains(?x94, ?x8093), geographic_distribution(?x1176, ?x3634), jurisdiction_of_office(?x900, ?x3634) *> conf = 0.58 ranks of expected_values: 14, 15, 814, 1472 EVAL 07b_l contains 0msck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 233.000 93.000 0.830 http://example.org/location/location/contains EVAL 07b_l contains 0104lr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 233.000 93.000 0.830 http://example.org/location/location/contains EVAL 07b_l contains 0kpw3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 233.000 93.000 0.830 http://example.org/location/location/contains EVAL 07b_l contains 0mrf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 233.000 93.000 0.830 http://example.org/location/location/contains EVAL 07b_l contains 013m4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 233.000 93.000 0.830 http://example.org/location/location/contains EVAL 07b_l contains 010bnr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 233.000 93.000 0.830 http://example.org/location/location/contains EVAL 07b_l contains 013m43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 233.000 93.000 0.830 http://example.org/location/location/contains #22168-017xm3 PRED entity: 017xm3 PRED relation: category PRED expected values: 08mbj5d => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #46, 0.84 #43, 0.83 #39) >> Best rule #46 for best value: >> intensional similarity = 3 >> extensional distance = 463 >> proper extension: 016qtt; 01vvydl; 07s3vqk; 0197tq; 0411q; 05cljf; 0lbj1; 01vw87c; 01vrx3g; 01lmj3q; ... >> query: (?x3426, 08mbj5d) <- artists(?x2664, ?x3426), award_winner(?x1088, ?x3426), artist(?x3265, ?x3426) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017xm3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.862 http://example.org/common/topic/webpage./common/webpage/category #22167-0g824 PRED entity: 0g824 PRED relation: student! PRED expected values: 01w5m => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 159): 04b_46 (0.11 #227, 0.09 #755, 0.05 #1810), 017z88 (0.11 #82, 0.04 #2193, 0.04 #13789), 01w5m (0.10 #4851, 0.09 #633, 0.04 #13284), 01cszh (0.10 #528, 0.06 #16870, 0.04 #13707), 07szy (0.09 #568, 0.07 #1096, 0.04 #3205), 0217m9 (0.09 #699, 0.05 #4917, 0.05 #1754), 05q2c (0.09 #842), 02d9nr (0.09 #816), 03ksy (0.07 #4852, 0.05 #13285, 0.04 #16448), 07tgn (0.07 #4763, 0.02 #7925, 0.01 #8979) >> Best rule #227 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 03q2t9; >> query: (?x6383, 04b_46) <- award(?x6383, ?x724), company(?x6383, ?x2190), role(?x6383, ?x316) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #4851 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 016hvl; 02g3w; *> query: (?x6383, 01w5m) <- profession(?x6383, ?x3746), award_winner(?x567, ?x6383), ?x3746 = 05z96 *> conf = 0.10 ranks of expected_values: 3 EVAL 0g824 student! 01w5m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 136.000 136.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #22166-0b_dy PRED entity: 0b_dy PRED relation: nationality PRED expected values: 02jx1 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 83): 09c7w0 (0.80 #301, 0.79 #201, 0.77 #101), 07ssc (0.33 #9008, 0.09 #4718, 0.09 #4618), 0kqb0 (0.33 #9008), 02jx1 (0.11 #533, 0.11 #1833, 0.11 #3735), 03rk0 (0.07 #2046, 0.06 #5749, 0.06 #6149), 0d060g (0.06 #107, 0.06 #207, 0.05 #7), 0chghy (0.03 #6704, 0.03 #610, 0.03 #410), 0345h (0.03 #6704, 0.02 #2331, 0.02 #5734), 03rt9 (0.03 #6704, 0.02 #113, 0.02 #213), 06mkj (0.03 #6704, 0.02 #147, 0.02 #247) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 06l9n8; >> query: (?x3139, 09c7w0) <- award_nominee(?x192, ?x3139), ?x192 = 02p65p, film(?x3139, ?x161) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #533 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 639 *> proper extension: 04gr35; *> query: (?x3139, 02jx1) <- award_nominee(?x156, ?x3139), film(?x3139, ?x161), people(?x743, ?x3139) *> conf = 0.11 ranks of expected_values: 4 EVAL 0b_dy nationality 02jx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 96.000 0.800 http://example.org/people/person/nationality #22165-0163t3 PRED entity: 0163t3 PRED relation: person! PRED expected values: 043q4d => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 8): 043q4d (0.61 #158, 0.50 #8, 0.42 #117), 026h21_ (0.17 #162, 0.12 #30, 0.11 #36), 02k13d (0.15 #138, 0.13 #124, 0.12 #27), 09jwl (0.03 #157, 0.02 #116, 0.02 #130), 05ll37 (0.03 #161), 029bkp (0.02 #160), 04_tv (0.01 #185), 0cbd2 (0.01 #185) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 016qtt; >> query: (?x8986, 043q4d) <- profession(?x8986, ?x319), people(?x1050, ?x8986), person(?x9277, ?x8986) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0163t3 person! 043q4d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.610 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #22164-04xhwn PRED entity: 04xhwn PRED relation: people! PRED expected values: 09vc4s 0dbxy => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 50): 041rx (0.29 #807, 0.25 #223, 0.25 #4020), 07bch9 (0.29 #166, 0.22 #531, 0.22 #385), 0x67 (0.27 #2855, 0.27 #2928, 0.26 #2636), 03bkbh (0.22 #394, 0.14 #175, 0.11 #978), 02w7gg (0.17 #2, 0.14 #148, 0.14 #1170), 06gbnc (0.17 #24, 0.08 #754, 0.07 #900), 0g8_vp (0.17 #19, 0.02 #1698, 0.02 #1844), 065b6q (0.14 #149, 0.11 #952, 0.11 #514), 07hwkr (0.14 #156, 0.11 #959, 0.11 #521), 013xrm (0.14 #163, 0.11 #528, 0.11 #382) >> Best rule #807 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01n4f8; 01lqf49; >> query: (?x12566, 041rx) <- film(?x12566, ?x2907), film(?x12566, ?x590), ?x590 = 02_1sj, film_crew_role(?x2907, ?x137) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 049qx; *> query: (?x12566, 09vc4s) <- film(?x12566, ?x6963), languages(?x12566, ?x254), sibling(?x7837, ?x12566), honored_for(?x6963, ?x188) *> conf = 0.14 ranks of expected_values: 11, 16 EVAL 04xhwn people! 0dbxy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 106.000 106.000 0.286 http://example.org/people/ethnicity/people EVAL 04xhwn people! 09vc4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 106.000 106.000 0.286 http://example.org/people/ethnicity/people #22163-01z645 PRED entity: 01z645 PRED relation: contains! PRED expected values: 07ssc => 49 concepts (34 used for prediction) PRED predicted values (max 10 best out of 118): 09c7w0 (0.63 #7180, 0.50 #19742, 0.50 #14359), 07ssc (0.60 #8074, 0.50 #2723, 0.49 #18842), 04jpl (0.45 #1816, 0.42 #2713, 0.33 #22), 02jx1 (0.36 #1881, 0.33 #2778, 0.33 #87), 04_1l0v (0.19 #7628, 0.12 #9421, 0.11 #11215), 03rk0 (0.10 #15389, 0.07 #16286, 0.06 #17183), 0kpys (0.10 #1077, 0.05 #7358, 0.05 #4666), 0d060g (0.10 #909, 0.05 #7190, 0.05 #4498), 0gx1l (0.10 #1501, 0.05 #5090, 0.05 #5988), 0694j (0.10 #1267, 0.05 #4856, 0.04 #6651) >> Best rule #7180 for best value: >> intensional similarity = 5 >> extensional distance = 55 >> proper extension: 03_3d; 01z28b; >> query: (?x14722, 09c7w0) <- location(?x5743, ?x14722), nationality(?x5743, ?x512), film(?x5743, ?x9996), film(?x6969, ?x9996), ?x6969 = 081bls >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #8074 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 55 *> proper extension: 03_3d; 01z28b; *> query: (?x14722, ?x512) <- location(?x5743, ?x14722), nationality(?x5743, ?x512), film(?x5743, ?x9996), film(?x6969, ?x9996), ?x6969 = 081bls *> conf = 0.60 ranks of expected_values: 2 EVAL 01z645 contains! 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 49.000 34.000 0.632 http://example.org/location/location/contains #22162-02sdx PRED entity: 02sdx PRED relation: location PRED expected values: 03rjj => 191 concepts (151 used for prediction) PRED predicted values (max 10 best out of 275): 09c7w0 (0.33 #8845, 0.33 #4021, 0.27 #4824), 02_286 (0.33 #4055, 0.23 #20964, 0.23 #32234), 064xp (0.33 #1526, 0.13 #42661, 0.09 #5543), 03rjj (0.33 #8845, 0.12 #10455, 0.11 #18508), 01cx_ (0.33 #1770, 0.07 #9812, 0.07 #11424), 0n1rj (0.33 #297, 0.05 #6727, 0.02 #15582), 0345h (0.25 #2479, 0.09 #13744, 0.04 #8108), 0k6nt (0.25 #2461, 0.04 #8894, 0.04 #10505), 0ljsz (0.25 #2956, 0.03 #12612, 0.02 #14221), 02h6_6p (0.25 #2543, 0.03 #12199, 0.02 #13808) >> Best rule #8845 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0gz_; 0m93; 04lg6; >> query: (?x11055, ?x94) <- gender(?x11055, ?x231), profession(?x11055, ?x3802), ?x3802 = 06q2q, nationality(?x11055, ?x94) >> conf = 0.33 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 02sdx location 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 191.000 151.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #22161-01frpd PRED entity: 01frpd PRED relation: company! PRED expected values: 0dq_5 => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 36): 0dq_5 (0.88 #603, 0.87 #1569, 0.84 #2115), 09d6p2 (0.48 #940, 0.44 #268, 0.44 #226), 01yc02 (0.45 #1560, 0.44 #1140, 0.44 #636), 02211by (0.30 #842, 0.24 #590, 0.23 #1472), 0142rn (0.24 #611, 0.22 #737, 0.20 #3823), 04192r (0.20 #373, 0.20 #3823, 0.20 #5464), 09lq2c (0.20 #867, 0.20 #3823, 0.20 #5464), 02y6fz (0.20 #3823, 0.20 #5464, 0.16 #819), 021q0l (0.20 #3823, 0.20 #5464, 0.11 #259), 06hpx2 (0.20 #3823, 0.20 #5464, 0.11 #6643) >> Best rule #603 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 0k8z; 0178g; 01hlwv; >> query: (?x14236, 0dq_5) <- list(?x14236, ?x5997), category(?x14236, ?x134), currency(?x14236, ?x170), company(?x265, ?x14236), place_founded(?x14236, ?x659) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01frpd company! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 194.000 0.882 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #22160-01f873 PRED entity: 01f873 PRED relation: award_winner! PRED expected values: 02z13jg => 103 concepts (72 used for prediction) PRED predicted values (max 10 best out of 194): 02x4w6g (0.46 #863, 0.36 #29327, 0.30 #30621), 09v51c2 (0.33 #751, 0.20 #320, 0.16 #17681), 09v92_x (0.17 #1139, 0.17 #707, 0.07 #14661), 09v4bym (0.17 #756, 0.16 #17681, 0.07 #14661), 07kfzsg (0.16 #17681, 0.07 #14661, 0.07 #17249), 0789r6 (0.16 #17681, 0.04 #1261, 0.01 #2123), 02z13jg (0.16 #17681, 0.03 #2206, 0.03 #5654), 09sb52 (0.16 #1766, 0.13 #4783, 0.13 #6938), 0ck27z (0.11 #6990, 0.11 #7853, 0.09 #7421), 09v1lrz (0.09 #20269, 0.09 #20268, 0.08 #16386) >> Best rule #863 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01t2h2; 069_0y; 02404v; 04jb97; >> query: (?x11657, ?x2183) <- nominated_for(?x11657, ?x7502), ?x7502 = 0233bn, nationality(?x11657, ?x2346), award(?x11657, ?x2183) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #17681 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1450 *> proper extension: 08xz51; 0grmhb; *> query: (?x11657, ?x9377) <- award_winner(?x6788, ?x11657), nominated_for(?x4169, ?x6788), award(?x6788, ?x9377) *> conf = 0.16 ranks of expected_values: 7 EVAL 01f873 award_winner! 02z13jg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 103.000 72.000 0.462 http://example.org/award/award_category/winners./award/award_honor/award_winner #22159-0gxfz PRED entity: 0gxfz PRED relation: award PRED expected values: 09cn0c => 100 concepts (99 used for prediction) PRED predicted values (max 10 best out of 192): 0gqwc (0.27 #1634, 0.27 #1633, 0.26 #5140), 0gq9h (0.27 #1634, 0.27 #1633, 0.26 #5140), 0gs9p (0.27 #1634, 0.27 #1633, 0.26 #5140), 0gqyl (0.27 #1634, 0.27 #1633, 0.26 #5140), 094qd5 (0.26 #12604, 0.18 #2835, 0.17 #3070), 09cn0c (0.26 #12604, 0.13 #19847, 0.12 #18911), 0bdwft (0.26 #12604, 0.13 #19847, 0.12 #18911), 02y_j8g (0.26 #12604, 0.13 #19847, 0.12 #18911), 02py7pj (0.26 #12604, 0.13 #19847, 0.12 #18911), 054ky1 (0.26 #12604, 0.12 #18911, 0.12 #18912) >> Best rule #1634 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 01_mdl; 0pd57; >> query: (?x2721, ?x1245) <- award_winner(?x2721, ?x4240), nominated_for(?x1245, ?x2721), film_art_direction_by(?x2721, ?x2449), award(?x241, ?x1245) >> conf = 0.27 => this is the best rule for 4 predicted values *> Best rule #12604 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 822 *> proper extension: 01b9w3; *> query: (?x2721, ?x2060) <- award_winner(?x2721, ?x4240), award(?x2721, ?x591), nominated_for(?x788, ?x2721), award_winner(?x2060, ?x4240) *> conf = 0.26 ranks of expected_values: 6 EVAL 0gxfz award 09cn0c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 100.000 99.000 0.270 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22158-02bqvs PRED entity: 02bqvs PRED relation: film_crew_role PRED expected values: 01pvkk => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 26): 09vw2b7 (0.68 #588, 0.67 #732, 0.65 #840), 0dxtw (0.38 #736, 0.38 #844, 0.37 #409), 01vx2h (0.38 #593, 0.33 #665, 0.33 #83), 01pvkk (0.36 #12, 0.28 #738, 0.28 #411), 02ynfr (0.19 #598, 0.18 #88, 0.17 #742), 0215hd (0.15 #673, 0.14 #853, 0.14 #745), 0d2b38 (0.14 #26, 0.13 #2213, 0.11 #680), 01xy5l_ (0.14 #14, 0.13 #2213, 0.11 #596), 015h31 (0.14 #8, 0.13 #2213, 0.10 #80), 089fss (0.14 #5, 0.13 #2213, 0.07 #731) >> Best rule #588 for best value: >> intensional similarity = 3 >> extensional distance = 749 >> proper extension: 03wh49y; 02wyzmv; 0h63q6t; >> query: (?x8790, 09vw2b7) <- film_crew_role(?x8790, ?x468), film(?x1093, ?x8790), ?x468 = 02r96rf >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 0dll_t2; 0ds5_72; 0ddf2bm; *> query: (?x8790, 01pvkk) <- music(?x8790, ?x13700), film(?x1093, ?x8790), ?x13700 = 03c_8t *> conf = 0.36 ranks of expected_values: 4 EVAL 02bqvs film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 69.000 0.683 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22157-043ljr PRED entity: 043ljr PRED relation: artist PRED expected values: 01kstn9 051m56 01vq3nl => 100 concepts (57 used for prediction) PRED predicted values (max 10 best out of 858): 08w4pm (0.50 #2252, 0.38 #4763, 0.25 #8945), 0565cz (0.38 #4371, 0.33 #1025, 0.25 #8553), 0249kn (0.38 #4363, 0.25 #8545, 0.25 #1852), 016m5c (0.38 #4984, 0.25 #9166, 0.25 #2473), 02f1c (0.38 #4824, 0.25 #9006, 0.21 #9844), 0167km (0.38 #4599, 0.25 #8781, 0.14 #9619), 03xhj6 (0.36 #9504, 0.33 #1138, 0.25 #5322), 02vr7 (0.36 #9811, 0.17 #8973, 0.12 #5629), 01wg25j (0.33 #1454, 0.29 #13163, 0.25 #4800), 07zft (0.33 #1485, 0.29 #9851, 0.25 #5669) >> Best rule #2252 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 02p11jq; 02p4jf0; >> query: (?x3006, 08w4pm) <- artist(?x3006, ?x4343), artist(?x3006, ?x3202), ?x3202 = 0bhvtc, award_nominee(?x7258, ?x4343), award_winner(?x139, ?x4343), ?x7258 = 05sq0m >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9445 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 03rhqg; 0229rs; 015_1q; 02bh8z; 0mzkr; 073tm9; 0181dw; 05cl8y; 03mp8k; 012b30; *> query: (?x3006, 01kstn9) <- state_province_region(?x3006, ?x2020), artist(?x3006, ?x367), contains(?x2020, ?x3439), student(?x3439, ?x562), institution(?x620, ?x3439) *> conf = 0.14 ranks of expected_values: 259, 483 EVAL 043ljr artist 01vq3nl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 57.000 0.500 http://example.org/music/record_label/artist EVAL 043ljr artist 051m56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 57.000 0.500 http://example.org/music/record_label/artist EVAL 043ljr artist 01kstn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 100.000 57.000 0.500 http://example.org/music/record_label/artist #22156-0167bx PRED entity: 0167bx PRED relation: award PRED expected values: 0dt49 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1): 0dt49 (0.14 #6805, 0.09 #14563, 0.08 #12213) >> Best rule #6805 for best value: >> intensional similarity = 31 >> extensional distance = 5 >> proper extension: 011zdm; 01qqwn; 087z2; >> query: (?x11739, 0dt49) <- symptom_of(?x10717, ?x11739), symptom_of(?x9509, ?x11739), symptom_of(?x4905, ?x11739), ?x9509 = 0gxb2, ?x10717 = 0cjf0, symptom_of(?x4905, ?x13560), symptom_of(?x4905, ?x13485), symptom_of(?x4905, ?x10199), symptom_of(?x4905, ?x9933), symptom_of(?x4905, ?x9898), symptom_of(?x4905, ?x8523), symptom_of(?x4905, ?x6656), symptom_of(?x4905, ?x4959), symptom_of(?x4905, ?x4322), people(?x8523, ?x2807), ?x10199 = 02k6hp, people(?x4959, ?x6639), ?x9898 = 09jg8, ?x6656 = 03p41, people(?x4322, ?x5370), people(?x4322, ?x3194), ?x13485 = 07s4l, ?x3194 = 0jrny, ?x13560 = 04nz3, ?x6639 = 0137hn, risk_factors(?x1158, ?x8523), ?x5370 = 016gkf, risk_factors(?x4959, ?x8524), notable_people_with_this_condition(?x9933, ?x1984), ?x8524 = 01hbgs, people(?x9933, ?x9020) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0167bx award 0dt49 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.143 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22155-02f9wb PRED entity: 02f9wb PRED relation: program PRED expected values: 0d66j2 => 84 concepts (60 used for prediction) PRED predicted values (max 10 best out of 83): 02rzdcp (0.34 #873, 0.33 #40, 0.29 #214), 0kfv9 (0.33 #19, 0.29 #193, 0.21 #2617), 080dwhx (0.17 #5, 0.14 #179, 0.03 #529), 01j67j (0.17 #31, 0.14 #205, 0.01 #555), 0d68qy (0.10 #377, 0.07 #553, 0.07 #1076), 08jgk1 (0.07 #541, 0.07 #715, 0.07 #1064), 0l76z (0.07 #582, 0.07 #931, 0.07 #1105), 0828jw (0.07 #602, 0.07 #1125, 0.06 #951), 072kp (0.05 #1577, 0.05 #1055, 0.04 #1751), 07g9f (0.04 #663, 0.04 #1708, 0.04 #1012) >> Best rule #873 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 04r7p; >> query: (?x5958, ?x3310) <- award_winner(?x8229, ?x5958), program_creator(?x3310, ?x8229), award_nominee(?x8229, ?x3763), award_winner(?x944, ?x8229) >> conf = 0.34 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02f9wb program 0d66j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 60.000 0.343 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/program #22154-015qh PRED entity: 015qh PRED relation: country! PRED expected values: 01dys 0dwxr 019w9j => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 36): 01lb14 (0.70 #406, 0.68 #262, 0.67 #1198), 07jbh (0.65 #416, 0.65 #380, 0.64 #128), 09w1n (0.64 #122, 0.64 #230, 0.54 #446), 01z27 (0.64 #227, 0.56 #263, 0.50 #371), 01sgl (0.57 #137, 0.55 #389, 0.53 #281), 035d1m (0.57 #123, 0.47 #411, 0.42 #375), 09wz9 (0.57 #121, 0.41 #265, 0.41 #193), 019tzd (0.55 #206, 0.53 #278, 0.53 #422), 03rbzn (0.53 #268, 0.50 #376, 0.50 #124), 01dys (0.50 #223, 0.50 #115, 0.41 #259) >> Best rule #406 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 05r4w; 0jgd; 03_3d; 0d0vqn; 03rt9; 05qx1; 01znc_; 01p1v; 06mkj; 03rj0; ... >> query: (?x1497, 01lb14) <- olympics(?x1497, ?x584), film_release_region(?x6520, ?x1497), ?x6520 = 02bg55 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #223 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 01g_k3; *> query: (?x1497, 01dys) <- teams(?x1497, ?x11532), contains(?x455, ?x1497), ?x455 = 02j9z *> conf = 0.50 ranks of expected_values: 10, 18, 20 EVAL 015qh country! 019w9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 170.000 170.000 0.700 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 015qh country! 0dwxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 170.000 170.000 0.700 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 015qh country! 01dys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 170.000 170.000 0.700 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #22153-0m31m PRED entity: 0m31m PRED relation: people! PRED expected values: 02w7gg => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 43): 02w7gg (0.21 #79, 0.17 #2, 0.07 #1547), 033tf_ (0.20 #238, 0.15 #161, 0.14 #854), 041rx (0.17 #4, 0.16 #1394, 0.16 #1316), 0x67 (0.10 #318, 0.10 #395, 0.10 #1555), 0xnvg (0.09 #860, 0.08 #244, 0.06 #1247), 07bch9 (0.08 #23, 0.07 #716, 0.06 #254), 0d7wh (0.08 #17, 0.06 #171, 0.05 #94), 02ctzb (0.06 #323, 0.05 #708, 0.04 #1327), 065b6q (0.06 #157, 0.05 #234, 0.04 #388), 013xrm (0.05 #97, 0.03 #1410, 0.03 #1332) >> Best rule #79 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 0184jc; 016gr2; 0kszw; 03hzl42; 03y_46; 05kwx2; 016xk5; 03yk8z; 016ggh; 02my3z; >> query: (?x2654, 02w7gg) <- award_nominee(?x1738, ?x2654), gender(?x2654, ?x231), ?x1738 = 0170pk >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m31m people! 02w7gg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.211 http://example.org/people/ethnicity/people #22152-0979zs PRED entity: 0979zs PRED relation: role! PRED expected values: 0342h 01679d => 62 concepts (52 used for prediction) PRED predicted values (max 10 best out of 102): 03gvt (0.91 #97, 0.88 #200, 0.88 #411), 01vdm0 (0.91 #97, 0.88 #411, 0.86 #199), 01679d (0.91 #97, 0.88 #411, 0.86 #199), 0342h (0.88 #200, 0.88 #411, 0.87 #3039), 018j2 (0.88 #2116, 0.78 #1388, 0.77 #2966), 04rzd (0.82 #1592, 0.79 #2721, 0.79 #2647), 05148p4 (0.81 #1355, 0.79 #2417, 0.75 #206), 01dnws (0.81 #1355, 0.73 #100, 0.73 #99), 0680x0 (0.81 #1355, 0.73 #100, 0.73 #99), 0979zs (0.81 #1355, 0.73 #100, 0.73 #99) >> Best rule #97 for best value: >> intensional similarity = 39 >> extensional distance = 1 >> proper extension: 01vdm0; >> query: (?x4425, ?x2253) <- role(?x7033, ?x4425), role(?x4769, ?x4425), role(?x3991, ?x4425), role(?x3418, ?x4425), role(?x3215, ?x4425), role(?x1495, ?x4425), role(?x1432, ?x4425), role(?x1332, ?x4425), role(?x894, ?x4425), role(?x885, ?x4425), role(?x314, ?x4425), role(?x228, ?x4425), role(?x212, ?x4425), role(?x74, ?x4425), ?x4769 = 0dwt5, ?x7033 = 0gkd1, ?x894 = 03m5k, ?x228 = 0l14qv, ?x314 = 02sgy, role(?x7112, ?x4425), role(?x4425, ?x3716), role(?x4425, ?x2253), role(?x4425, ?x227), ?x74 = 03q5t, ?x1432 = 0395lw, ?x1332 = 03qlv7, ?x227 = 0342h, ?x885 = 0dwtp, ?x1495 = 013y1f, ?x3716 = 03gvt, ?x3418 = 02w4b, ?x7112 = 0133x7, ?x3215 = 0bxl5, role(?x2253, ?x1436), role(?x2253, ?x2048), instrumentalists(?x2253, ?x6949), ?x2048 = 018j2, ?x3991 = 05842k, ?x212 = 026t6 >> conf = 0.91 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 4 EVAL 0979zs role! 01679d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 52.000 0.912 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 0979zs role! 0342h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 52.000 0.912 http://example.org/music/performance_role/track_performances./music/track_contribution/role #22151-0d58_ PRED entity: 0d58_ PRED relation: category PRED expected values: 08mbj5d => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.73 #22, 0.73 #19, 0.72 #18) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 0qlrh; >> query: (?x9112, 08mbj5d) <- time_zones(?x9112, ?x2864), place_of_death(?x11460, ?x9112), gender(?x11460, ?x231), nationality(?x11460, ?x1003) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d58_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.735 http://example.org/common/topic/webpage./common/webpage/category #22150-0cj2w PRED entity: 0cj2w PRED relation: nationality PRED expected values: 07ssc => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.80 #2302, 0.80 #701, 0.79 #3504), 02jx1 (0.40 #10525, 0.36 #8317, 0.23 #833), 07ssc (0.40 #10525, 0.33 #15, 0.17 #815), 0n58p (0.34 #6111), 05fjf (0.34 #6111), 0d060g (0.11 #10423, 0.07 #507, 0.07 #407), 0chghy (0.11 #10423, 0.07 #510, 0.03 #610), 0345h (0.11 #10423, 0.06 #1331, 0.06 #1031), 0f8l9c (0.11 #10423, 0.06 #1422, 0.06 #1122), 03rjj (0.11 #10423, 0.05 #1505, 0.04 #2506) >> Best rule #2302 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 07fzq3; >> query: (?x11322, 09c7w0) <- award_nominee(?x11322, ?x11698), award_winner(?x594, ?x11322), place_of_death(?x11322, ?x12314) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #10525 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2574 *> proper extension: 0784v1; 0c11mj; 01qx13; 0bhtzw; 04gtq43; *> query: (?x11322, ?x512) <- place_of_birth(?x11322, ?x9042), place_of_birth(?x3011, ?x9042), nationality(?x3011, ?x512) *> conf = 0.40 ranks of expected_values: 3 EVAL 0cj2w nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 117.000 0.800 http://example.org/people/person/nationality #22149-05v8c PRED entity: 05v8c PRED relation: film_release_region! PRED expected values: 0g56t9t 0gkz15s 0bq8tmw 02r8hh_ 0gvrws1 0ct5zc 0879bpq 0645k5 0bmhvpr 062zm5h 067ghz 0fphf3v 03z9585 05zvzf3 01xlqd 0g57wgv => 173 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1615): 0jjy0 (0.91 #10287, 0.89 #20467, 0.86 #22729), 0g9wdmc (0.90 #22793, 0.89 #20531, 0.83 #32972), 02bg55 (0.86 #10908, 0.79 #23350, 0.72 #33529), 0bwfwpj (0.86 #22718, 0.86 #4620, 0.85 #27242), 01vksx (0.86 #22705, 0.86 #20443, 0.85 #27229), 0gg8z1f (0.86 #23329, 0.86 #5231, 0.82 #21067), 0ds3t5x (0.86 #20390, 0.86 #4554, 0.83 #22652), 02r8hh_ (0.86 #20523, 0.86 #4687, 0.83 #22785), 0gwjw0c (0.86 #21125, 0.86 #5289, 0.82 #10945), 062zm5h (0.86 #20898, 0.83 #33339, 0.83 #23160) >> Best rule #10287 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0jgd; 0154j; 03rjj; 0chghy; 0f8l9c; 0k6nt; 0ctw_b; 059j2; 01znc_; 06bnz; ... >> query: (?x550, 0jjy0) <- film_release_region(?x6684, ?x550), film_release_region(?x6235, ?x550), ?x6235 = 05b6rdt, ?x6684 = 07pd_j >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #20523 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 047yc; *> query: (?x550, 02r8hh_) <- film_release_region(?x4441, ?x550), film_release_region(?x3830, ?x550), ?x4441 = 0125xq, ?x3830 = 0gjcrrw *> conf = 0.86 ranks of expected_values: 8, 10, 13, 18, 19, 21, 23, 25, 27, 28, 65, 75, 88, 106, 123, 198 EVAL 05v8c film_release_region! 0g57wgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 01xlqd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 03z9585 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 0fphf3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 067ghz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 062zm5h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 0bmhvpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 0645k5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 0879bpq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 0ct5zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 0gvrws1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 02r8hh_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 0bq8tmw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v8c film_release_region! 0g56t9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 173.000 153.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22148-0t_2 PRED entity: 0t_2 PRED relation: language! PRED expected values: 04yg13l => 35 concepts (7 used for prediction) PRED predicted values (max 10 best out of 1874): 020bv3 (0.70 #12057, 0.38 #7194, 0.33 #5471), 05k2xy (0.70 #12057, 0.38 #7237, 0.33 #5514), 011ysn (0.70 #12057, 0.33 #5707, 0.33 #540), 02r8hh_ (0.70 #12057, 0.33 #5418, 0.33 #1974), 0dt8xq (0.70 #12057, 0.33 #5997, 0.14 #11164), 0dgpwnk (0.70 #12057, 0.33 #5700, 0.14 #10867), 02bqvs (0.70 #12057, 0.33 #6591, 0.12 #8314), 0bwhdbl (0.70 #12057, 0.33 #6512, 0.12 #8235), 01cycq (0.70 #12057, 0.33 #6470, 0.12 #8193), 02c7k4 (0.70 #12057, 0.33 #6219, 0.12 #7942) >> Best rule #12057 for best value: >> intensional similarity = 9 >> extensional distance = 12 >> proper extension: 0x82; >> query: (?x3592, ?x3081) <- languages(?x8020, ?x3592), language(?x10158, ?x3592), language(?x1450, ?x3592), participant(?x2352, ?x8020), film(?x8020, ?x3081), genre(?x10158, ?x53), titles(?x812, ?x10158), film_crew_role(?x10158, ?x137), film(?x574, ?x1450) >> conf = 0.70 => this is the best rule for 18 predicted values *> Best rule #5993 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x3592, 04yg13l) <- languages_spoken(?x1446, ?x3592), language(?x8137, ?x3592), language(?x2555, ?x3592), ?x8137 = 0gtx63s, languages(?x3848, ?x3592), languages(?x1093, ?x3592), nominated_for(?x1765, ?x2555), ?x1446 = 033tf_, genre(?x2555, ?x258), program(?x2554, ?x2555), tv_program(?x1483, ?x2555), participant(?x1093, ?x6035), honored_for(?x1764, ?x2555) *> conf = 0.33 ranks of expected_values: 1061 EVAL 0t_2 language! 04yg13l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 7.000 0.700 http://example.org/film/film/language #22147-01pj7 PRED entity: 01pj7 PRED relation: adjoins! PRED expected values: 06t8v => 178 concepts (121 used for prediction) PRED predicted values (max 10 best out of 491): 06t8v (0.84 #75111, 0.83 #17208, 0.82 #64941), 0166b (0.84 #75111, 0.83 #17208, 0.82 #64941), 0345h (0.24 #5539, 0.19 #2408, 0.18 #3974), 0f8l9c (0.24 #821, 0.20 #7859, 0.20 #5514), 015qh (0.20 #72765, 0.18 #862, 0.15 #1643), 0h7x (0.20 #72765, 0.15 #1638, 0.14 #3985), 01pj7 (0.20 #72765, 0.15 #1662, 0.12 #881), 06c1y (0.20 #72765, 0.15 #1645, 0.12 #864), 07t21 (0.20 #72765, 0.14 #3988, 0.12 #860), 03rjj (0.20 #72765, 0.12 #5482, 0.10 #7827) >> Best rule #75111 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 05k7sb; 01ly8d; >> query: (?x1790, ?x1003) <- jurisdiction_of_office(?x182, ?x1790), adjoins(?x1790, ?x1003), film_release_region(?x66, ?x1003) >> conf = 0.84 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01pj7 adjoins! 06t8v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 121.000 0.841 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #22146-019mcm PRED entity: 019mcm PRED relation: teams! PRED expected values: 06gmr => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 146): 0947l (0.33 #182, 0.20 #452, 0.14 #1262), 03hrz (0.20 #629, 0.14 #1711, 0.14 #1441), 02m77 (0.20 #425, 0.14 #1235, 0.14 #965), 01n43d (0.20 #481, 0.14 #1291, 0.14 #1021), 01vx3m (0.20 #716, 0.14 #1798, 0.03 #2608), 0htqt (0.20 #759, 0.03 #2651, 0.02 #4546), 04swd (0.14 #1529, 0.11 #2069, 0.06 #2609), 0j7ng (0.14 #1853, 0.11 #2123, 0.03 #2663), 01vc3y (0.14 #1888, 0.11 #2158, 0.03 #2698), 03pbf (0.14 #1456, 0.11 #1996, 0.03 #2536) >> Best rule #182 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 03x6m; >> query: (?x13520, 0947l) <- team(?x203, ?x13520), team(?x63, ?x13520), team(?x2201, ?x13520), sport(?x13520, ?x471), position(?x13520, ?x530), position(?x13520, ?x60), ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x203 = 0dgrmp, ?x2201 = 0c11mj, ?x60 = 02nzb8, colors(?x13520, ?x4557), colors(?x13520, ?x663), ?x4557 = 019sc, ?x471 = 02vx4, colors(?x216, ?x663) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3008 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 32 *> proper extension: 0182r9; 01k2yr; 0j2pg; 01j95f; 0hvjr; 01fjz9; 011v3; 01x4wq; 0k_l4; 01kj5h; ... *> query: (?x13520, 06gmr) <- team(?x203, ?x13520), team(?x63, ?x13520), team(?x2201, ?x13520), sport(?x13520, ?x471), position(?x13520, ?x530), position(?x13520, ?x60), ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x203 = 0dgrmp, type_of_union(?x2201, ?x566), ?x60 = 02nzb8, team(?x2201, ?x10977), ?x471 = 02vx4, nationality(?x2201, ?x205), team(?x8360, ?x10977) *> conf = 0.03 ranks of expected_values: 33 EVAL 019mcm teams! 06gmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 111.000 111.000 0.333 http://example.org/sports/sports_team_location/teams #22145-047qxs PRED entity: 047qxs PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.89 #51, 0.86 #56, 0.86 #61), 02nxhr (0.25 #194, 0.25 #205, 0.23 #138), 07c52 (0.25 #194, 0.25 #205, 0.23 #138), 07z4p (0.25 #194, 0.25 #205, 0.23 #138), 0735l (0.22 #111, 0.21 #226, 0.21 #285) >> Best rule #51 for best value: >> intensional similarity = 12 >> extensional distance = 35 >> proper extension: 02qkwl; >> query: (?x2036, 029j_) <- genre(?x2036, ?x11523), genre(?x2036, ?x1013), genre(?x2036, ?x812), genre(?x2036, ?x811), ?x812 = 01jfsb, ?x1013 = 06n90, film_crew_role(?x2036, ?x137), genre(?x5946, ?x11523), ?x5946 = 063zky, music(?x2036, ?x562), genre(?x6334, ?x811), ?x6334 = 0kvbl6 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047qxs film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #22144-02p7_k PRED entity: 02p7_k PRED relation: film PRED expected values: 02ljhg => 64 concepts (39 used for prediction) PRED predicted values (max 10 best out of 359): 039c26 (0.59 #25046, 0.59 #26835, 0.47 #5365), 017jd9 (0.53 #779, 0.05 #2567, 0.03 #39356), 0cfhfz (0.48 #2280, 0.04 #69766, 0.03 #39356), 017gl1 (0.42 #143, 0.01 #12667, 0.01 #14455), 017gm7 (0.32 #211, 0.05 #1999, 0.04 #69766), 0ndwt2w (0.26 #999, 0.01 #9944), 0fg04 (0.11 #101, 0.05 #1889, 0.03 #39356), 0g56t9t (0.11 #10, 0.05 #1798, 0.03 #39356), 03wh49y (0.11 #950), 0qm9n (0.10 #2341, 0.05 #553, 0.03 #39356) >> Best rule #25046 for best value: >> intensional similarity = 3 >> extensional distance = 1315 >> proper extension: 02jm0n; 01wxyx1; 01wk7b7; 0m32_; 01v3vp; 018fmr; 01vzxmq; 03m6pk; 07nx9j; 0451j; ... >> query: (?x3660, ?x2336) <- nominated_for(?x3660, ?x2336), award(?x3660, ?x704), film(?x3660, ?x3093) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02p7_k film 02ljhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 39.000 0.588 http://example.org/film/actor/film./film/performance/film #22143-03z5xd PRED entity: 03z5xd PRED relation: legislative_sessions! PRED expected values: 021sv1 02hy5d => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 34): 021sv1 (0.82 #608, 0.82 #582, 0.80 #555), 02hy5d (0.82 #625, 0.80 #580, 0.80 #572), 0bymv (0.77 #635, 0.77 #76, 0.75 #636), 016lh0 (0.77 #635, 0.77 #76, 0.75 #636), 012v1t (0.77 #635, 0.77 #76, 0.75 #636), 0d3qd0 (0.77 #635, 0.77 #76, 0.75 #636), 03txms (0.77 #635, 0.77 #76, 0.75 #636), 01lct6 (0.77 #76, 0.75 #636, 0.72 #366), 0d06m5 (0.77 #76, 0.72 #366, 0.70 #393), 02mjmr (0.77 #76, 0.72 #366, 0.70 #393) >> Best rule #608 for best value: >> intensional similarity = 47 >> extensional distance = 9 >> proper extension: 02bp37; >> query: (?x1830, 021sv1) <- legislative_sessions(?x1830, ?x6728), legislative_sessions(?x1830, ?x4730), legislative_sessions(?x1830, ?x3765), legislative_sessions(?x1830, ?x2861), legislative_sessions(?x1830, ?x356), ?x2861 = 03tcbx, district_represented(?x1830, ?x2049), legislative_sessions(?x355, ?x1830), district_represented(?x6728, ?x13269), district_represented(?x6728, ?x7518), district_represented(?x6728, ?x6895), district_represented(?x6728, ?x6521), district_represented(?x6728, ?x4758), district_represented(?x6728, ?x4105), district_represented(?x6728, ?x3908), district_represented(?x6728, ?x3670), district_represented(?x6728, ?x1782), district_represented(?x6728, ?x1755), district_represented(?x6728, ?x1227), district_represented(?x6728, ?x760), ?x6895 = 05fjf, legislative_sessions(?x11605, ?x6728), legislative_sessions(?x11440, ?x6728), legislative_sessions(?x5932, ?x6728), legislative_sessions(?x4567, ?x6728), ?x1227 = 01n7q, ?x7518 = 026mj, ?x4730 = 02cg7g, ?x11440 = 01lct6, ?x1782 = 0488g, legislative_sessions(?x2860, ?x356), legislative_sessions(?x6742, ?x356), ?x3908 = 04ly1, ?x4758 = 0vbk, ?x3670 = 05tbn, ?x3765 = 04gp1d, ?x5932 = 012v1t, basic_title(?x4567, ?x5402), ?x6521 = 05mph, people(?x1446, ?x4567), ?x13269 = 0czr9_, ?x4105 = 0824r, ?x11605 = 024_vw, ?x2049 = 050l8, ?x1755 = 01x73, nationality(?x4567, ?x94), ?x760 = 05fkf >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03z5xd legislative_sessions! 02hy5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.818 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 03z5xd legislative_sessions! 021sv1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.818 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #22142-02jx_v PRED entity: 02jx_v PRED relation: institution! PRED expected values: 02h4rq6 019v9k => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 24): 02h4rq6 (0.69 #403, 0.68 #453, 0.67 #378), 019v9k (0.64 #185, 0.59 #235, 0.57 #110), 014mlp (0.62 #1884, 0.62 #2686, 0.61 #1558), 01rr_d (0.60 #94, 0.57 #144, 0.28 #294), 016t_3 (0.56 #154, 0.47 #429, 0.46 #329), 02_xgp2 (0.51 #464, 0.48 #364, 0.47 #414), 03bwzr4 (0.45 #366, 0.42 #716, 0.42 #466), 07s6fsf (0.41 #576, 0.40 #651, 0.40 #451), 0bkj86 (0.38 #334, 0.36 #359, 0.34 #734), 04zx3q1 (0.33 #27, 0.32 #352, 0.27 #402) >> Best rule #403 for best value: >> intensional similarity = 5 >> extensional distance = 43 >> proper extension: 036hnm; >> query: (?x13150, 02h4rq6) <- state_province_region(?x13150, ?x9494), organization(?x5510, ?x13150), colors(?x13150, ?x663), ?x5510 = 07xl34, school_type(?x13150, ?x3092) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02jx_v institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.689 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02jx_v institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.689 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22141-01jfr3y PRED entity: 01jfr3y PRED relation: profession PRED expected values: 0dz3r => 103 concepts (71 used for prediction) PRED predicted values (max 10 best out of 80): 09jwl (0.79 #1794, 0.72 #4163, 0.72 #1202), 0nbcg (0.55 #2104, 0.51 #4176, 0.50 #771), 0dz3r (0.53 #742, 0.51 #2075, 0.49 #1038), 01d_h8 (0.45 #2226, 0.42 #3854, 0.41 #4002), 0dxtg (0.33 #2234, 0.29 #3862, 0.29 #4010), 0d1pc (0.33 #346, 0.29 #642, 0.27 #198), 039v1 (0.32 #4181, 0.28 #1812, 0.28 #5664), 01c72t (0.32 #1651, 0.32 #5651, 0.31 #5948), 03gjzk (0.30 #4011, 0.29 #2235, 0.28 #4604), 0n1h (0.28 #751, 0.26 #1491, 0.24 #1195) >> Best rule #1794 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 02fybl; >> query: (?x5878, 09jwl) <- location(?x5878, ?x2850), profession(?x5878, ?x1032), ?x1032 = 02hrh1q, role(?x5878, ?x316) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #742 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 09qr6; 07ss8_; 047sxrj; 01x1cn2; 01vx5w7; 0dl567; 025ldg; 0dzc16; 012z8_; 0gs6vr; ... *> query: (?x5878, 0dz3r) <- artists(?x3996, ?x5878), ?x3996 = 02lnbg, currency(?x5878, ?x170) *> conf = 0.53 ranks of expected_values: 3 EVAL 01jfr3y profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 71.000 0.788 http://example.org/people/person/profession #22140-02h30z PRED entity: 02h30z PRED relation: colors PRED expected values: 03wkwg => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.30 #443, 0.29 #643, 0.26 #663), 01l849 (0.26 #541, 0.25 #661, 0.25 #641), 019sc (0.19 #667, 0.18 #647, 0.18 #787), 06fvc (0.17 #442, 0.16 #642, 0.15 #1023), 04mkbj (0.13 #110, 0.10 #70, 0.09 #290), 036k5h (0.12 #105, 0.11 #85, 0.10 #345), 03wkwg (0.11 #95, 0.07 #821, 0.07 #275), 01jnf1 (0.10 #71, 0.07 #821, 0.07 #1282), 038hg (0.09 #1033, 0.09 #672, 0.09 #652), 0jc_p (0.08 #284, 0.08 #324, 0.07 #504) >> Best rule #443 for best value: >> intensional similarity = 4 >> extensional distance = 273 >> proper extension: 02mg7n; >> query: (?x11854, 01g5v) <- contains(?x94, ?x11854), colors(?x11854, ?x663), citytown(?x11854, ?x3450), location(?x1222, ?x94) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #95 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 95 *> proper extension: 01nn7r; *> query: (?x11854, 03wkwg) <- contains(?x94, ?x11854), major_field_of_study(?x11854, ?x2014), ?x2014 = 04rjg, category(?x11854, ?x134) *> conf = 0.11 ranks of expected_values: 7 EVAL 02h30z colors 03wkwg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 145.000 145.000 0.302 http://example.org/education/educational_institution/colors #22139-0r02m PRED entity: 0r02m PRED relation: location! PRED expected values: 0cbgl => 163 concepts (78 used for prediction) PRED predicted values (max 10 best out of 2128): 09fb5 (0.23 #2568, 0.15 #15153, 0.13 #7602), 0q9kd (0.23 #2519, 0.15 #15104, 0.13 #7553), 014v6f (0.21 #6151, 0.11 #18736, 0.10 #16219), 0gl88b (0.17 #10438, 0.11 #370, 0.08 #2887), 0kvnn (0.17 #10943, 0.09 #21011, 0.07 #31080), 05ry0p (0.17 #12228, 0.08 #4677, 0.07 #32365), 023mdt (0.17 #11932, 0.08 #4381, 0.07 #32069), 022yb4 (0.17 #11777, 0.08 #4226, 0.07 #31914), 01s21dg (0.17 #11031, 0.08 #3480, 0.07 #31168), 0lkr7 (0.15 #3532, 0.11 #11083, 0.10 #16117) >> Best rule #2568 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 0q_xk; 0r03f; 0r066; 0r0ls; >> query: (?x13255, 09fb5) <- source(?x13255, ?x958), contains(?x2949, ?x13255), contains(?x94, ?x13255), ?x94 = 09c7w0, ?x2949 = 0kpys >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #45308 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 52 *> proper extension: 01pxqx; *> query: (?x13255, ?x14008) <- citytown(?x8525, ?x13255), citytown(?x8463, ?x13255), country(?x13255, ?x94), category(?x8463, ?x134), company(?x14008, ?x8525) *> conf = 0.11 ranks of expected_values: 198 EVAL 0r02m location! 0cbgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 163.000 78.000 0.231 http://example.org/people/person/places_lived./people/place_lived/location #22138-0164qt PRED entity: 0164qt PRED relation: story_by PRED expected values: 0fx02 => 110 concepts (60 used for prediction) PRED predicted values (max 10 best out of 67): 042xh (0.50 #433, 0.04 #2179, 0.02 #4360), 0fx02 (0.25 #60, 0.03 #4859, 0.03 #4641), 02lfwp (0.06 #5672, 0.06 #8077, 0.05 #5891), 03crcpt (0.05 #2838, 0.04 #653, 0.04 #1309), 0343h (0.05 #1108, 0.05 #2639, 0.05 #452), 01wd02c (0.05 #772, 0.01 #4047), 01y8d4 (0.05 #571, 0.02 #5156, 0.02 #4936), 011s9r (0.05 #632, 0.02 #5217, 0.02 #2601), 03j2gxx (0.05 #616, 0.02 #835, 0.01 #1272), 05qzv (0.05 #604, 0.02 #823, 0.01 #1260) >> Best rule #433 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 03hxsv; 031hcx; >> query: (?x835, 042xh) <- film(?x5332, ?x835), genre(?x835, ?x225), nominated_for(?x835, ?x836), ?x5332 = 06ltr >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #60 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 02sg5v; 02qrv7; *> query: (?x835, 0fx02) <- film(?x2805, ?x835), produced_by(?x835, ?x3692), nominated_for(?x835, ?x2160), ?x2160 = 014kq6 *> conf = 0.25 ranks of expected_values: 2 EVAL 0164qt story_by 0fx02 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 60.000 0.500 http://example.org/film/film/story_by #22137-02_286 PRED entity: 02_286 PRED relation: place_of_death! PRED expected values: 01vrx3g 03_0p 02wr6r 0py5b => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1130): 0l99s (0.25 #298, 0.10 #974, 0.06 #2328), 03n0q5 (0.25 #90, 0.10 #766, 0.06 #2120), 041xl (0.25 #297, 0.10 #973, 0.06 #2327), 016ghw (0.25 #663, 0.10 #1339, 0.06 #2693), 011zwl (0.25 #648, 0.10 #1324, 0.06 #2678), 01b0k1 (0.25 #620, 0.10 #1296, 0.06 #2650), 02vkvcz (0.25 #604, 0.10 #1280, 0.06 #2634), 047g6 (0.25 #598, 0.10 #1274, 0.06 #2628), 01tw31 (0.25 #510, 0.10 #1186, 0.06 #2540), 06myp (0.25 #508, 0.10 #1184, 0.06 #2538) >> Best rule #298 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 03t1s; >> query: (?x739, 0l99s) <- featured_film_locations(?x6636, ?x739), ?x6636 = 047bynf >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #20990 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 68 *> proper extension: 0136jw; 0r3w7; 019rvp; *> query: (?x739, ?x5019) <- place_of_death(?x1774, ?x739), award_nominee(?x1774, ?x5019) *> conf = 0.04 ranks of expected_values: 552 EVAL 02_286 place_of_death! 0py5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 153.000 0.250 http://example.org/people/deceased_person/place_of_death EVAL 02_286 place_of_death! 02wr6r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 153.000 0.250 http://example.org/people/deceased_person/place_of_death EVAL 02_286 place_of_death! 03_0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 153.000 153.000 0.250 http://example.org/people/deceased_person/place_of_death EVAL 02_286 place_of_death! 01vrx3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 153.000 0.250 http://example.org/people/deceased_person/place_of_death #22136-02zd460 PRED entity: 02zd460 PRED relation: major_field_of_study PRED expected values: 02lp1 01mkq 03g3w 06n6p 02j62 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 96): 01mkq (0.72 #1670, 0.71 #934, 0.70 #381), 03g3w (0.70 #388, 0.59 #941, 0.58 #572), 02j62 (0.65 #944, 0.60 #391, 0.54 #1680), 02lp1 (0.62 #1666, 0.58 #469, 0.55 #3507), 01540 (0.50 #502, 0.50 #410, 0.42 #594), 04sh3 (0.50 #420, 0.42 #604, 0.42 #512), 01zc2w (0.50 #417, 0.42 #601, 0.33 #509), 0l5mz (0.42 #510, 0.40 #418, 0.33 #602), 0db86 (0.40 #403, 0.33 #1692, 0.33 #587), 0h5k (0.40 #385, 0.33 #569, 0.31 #1674) >> Best rule #1670 for best value: >> intensional similarity = 2 >> extensional distance = 37 >> proper extension: 0d06m5; 0d05fv; >> query: (?x5288, 01mkq) <- list(?x5288, ?x2197), organization(?x5288, ?x5487) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 48 EVAL 02zd460 major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.718 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02zd460 major_field_of_study 06n6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 112.000 112.000 0.718 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02zd460 major_field_of_study 03g3w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.718 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02zd460 major_field_of_study 01mkq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.718 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02zd460 major_field_of_study 02lp1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.718 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #22135-02xj3rw PRED entity: 02xj3rw PRED relation: award! PRED expected values: 01cwcr 036dyy => 56 concepts (29 used for prediction) PRED predicted values (max 10 best out of 2187): 014zcr (0.50 #3427, 0.47 #16927, 0.38 #10176), 0c6qh (0.50 #4039, 0.38 #10788, 0.24 #17539), 055c8 (0.50 #4252, 0.38 #11001, 0.17 #7627), 0pmhf (0.50 #4071, 0.38 #10820, 0.10 #78325), 05bnp0 (0.50 #3390, 0.31 #10139, 0.17 #6765), 031k24 (0.50 #5716, 0.31 #12465, 0.17 #9091), 016k6x (0.50 #4832, 0.31 #11581, 0.10 #79086), 03ym1 (0.50 #5057, 0.31 #11806, 0.10 #79311), 0blq0z (0.50 #4093, 0.31 #10842, 0.10 #78347), 02m501 (0.50 #6178, 0.31 #12927, 0.09 #80432) >> Best rule #3427 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0f4x7; 0gr51; 09qv_s; 099ck7; >> query: (?x9343, 014zcr) <- award(?x8389, ?x9343), award(?x3124, ?x9343), ?x3124 = 03hkch7, profession(?x8389, ?x3746), ?x3746 = 05z96 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9154 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 099tbz; 02x8n1n; 03hl6lc; *> query: (?x9343, 036dyy) <- award(?x8389, ?x9343), award(?x9701, ?x9343), award(?x3124, ?x9343), ?x3124 = 03hkch7, profession(?x8389, ?x353), ?x9701 = 0h1x5f *> conf = 0.17 ranks of expected_values: 275, 1208 EVAL 02xj3rw award! 036dyy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 29.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02xj3rw award! 01cwcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 29.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22134-0gr36 PRED entity: 0gr36 PRED relation: languages PRED expected values: 02h40lc => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 13): 02h40lc (0.34 #119, 0.34 #236, 0.27 #275), 06nm1 (0.11 #45, 0.03 #123, 0.02 #240), 02bjrlw (0.11 #40, 0.03 #118, 0.02 #235), 03k50 (0.06 #1291, 0.05 #1564, 0.02 #4024), 0c_v2 (0.06 #1639, 0.06 #2304), 03_9r (0.06 #1639, 0.06 #2304), 07c9s (0.03 #1300, 0.03 #1573), 064_8sq (0.03 #1107, 0.03 #1185, 0.03 #405), 04306rv (0.02 #81), 0999q (0.02 #1310, 0.01 #1583) >> Best rule #119 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 04ns3gy; >> query: (?x2916, 02h40lc) <- award_winner(?x4598, ?x2916), company(?x2916, ?x8540), profession(?x2916, ?x1032) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gr36 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.338 http://example.org/people/person/languages #22133-01ppq PRED entity: 01ppq PRED relation: organization PRED expected values: 02vk52z 07t65 => 118 concepts (105 used for prediction) PRED predicted values (max 10 best out of 50): 07t65 (0.93 #289, 0.92 #399, 0.92 #377), 02vk52z (0.87 #1170, 0.85 #1082, 0.85 #950), 0_2v (0.50 #26, 0.46 #401, 0.46 #379), 018cqq (0.46 #408, 0.44 #386, 0.43 #298), 0b6css (0.43 #539, 0.40 #297, 0.38 #407), 04k4l (0.42 #380, 0.40 #292, 0.38 #402), 041288 (0.38 #1496, 0.37 #1386, 0.34 #1694), 02jxk (0.33 #290, 0.29 #378, 0.29 #400), 0gkjy (0.28 #536, 0.27 #1266, 0.26 #1487), 085h1 (0.26 #89, 0.20 #1192, 0.03 #541) >> Best rule #289 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 04j53; 07fj_; >> query: (?x8958, 07t65) <- organization(?x8958, ?x1062), country(?x1557, ?x8958), ?x1557 = 07bs0 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01ppq organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 105.000 0.929 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 01ppq organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 105.000 0.929 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #22132-02tz9z PRED entity: 02tz9z PRED relation: institution! PRED expected values: 016t_3 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 17): 014mlp (0.77 #81, 0.75 #101, 0.70 #178), 016t_3 (0.49 #196, 0.48 #79, 0.46 #332), 0bkj86 (0.48 #84, 0.43 #201, 0.40 #104), 04zx3q1 (0.34 #78, 0.27 #98, 0.24 #195), 013zdg (0.26 #200, 0.25 #123, 0.21 #356), 022h5x (0.23 #210, 0.20 #346, 0.17 #556), 0bjrnt (0.20 #82, 0.17 #102, 0.11 #199), 028dcg (0.18 #250, 0.16 #209, 0.15 #269), 03mkk4 (0.16 #86, 0.13 #106, 0.13 #359), 01rr_d (0.14 #90, 0.12 #110, 0.12 #920) >> Best rule #81 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 01rtm4; 01jssp; 052nd; 065y4w7; 07w0v; 01k2wn; 01j_cy; 07szy; 049dk; 0lfgr; ... >> query: (?x12127, 014mlp) <- category(?x12127, ?x134), contains(?x94, ?x12127), major_field_of_study(?x12127, ?x254), ?x254 = 02h40lc >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #196 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 86 *> proper extension: 0l2tk; 02y9bj; 01z3bz; *> query: (?x12127, 016t_3) <- contains(?x94, ?x12127), institution(?x865, ?x12127), institution(?x620, ?x12127), ?x620 = 07s6fsf, ?x865 = 02h4rq6 *> conf = 0.49 ranks of expected_values: 2 EVAL 02tz9z institution! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 127.000 0.773 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22131-026s90 PRED entity: 026s90 PRED relation: artist PRED expected values: 0178kd 017mbb 01304j 0889x => 89 concepts (86 used for prediction) PRED predicted values (max 10 best out of 838): 01wp8w7 (0.60 #3404, 0.33 #4237, 0.29 #5899), 07zft (0.57 #6468, 0.50 #4806, 0.25 #16446), 016t0h (0.43 #6609, 0.33 #4947, 0.20 #4114), 01vvybv (0.40 #4060, 0.33 #4893, 0.29 #6555), 02vr7 (0.40 #3933, 0.32 #23889, 0.20 #3101), 0mjn2 (0.40 #4051, 0.29 #6546, 0.21 #24007), 01w60_p (0.40 #2611, 0.28 #20073, 0.19 #30052), 0gbwp (0.40 #3598, 0.22 #20228, 0.20 #2766), 01vrz41 (0.40 #2553, 0.22 #20015, 0.20 #3385), 013rds (0.40 #3316, 0.22 #20778, 0.16 #24104) >> Best rule #3404 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 01w40h; 06x2ww; >> query: (?x6946, 01wp8w7) <- artist(?x6946, ?x7359), artist(?x6946, ?x4840), artist(?x6946, ?x317), artists(?x497, ?x317), role(?x317, ?x227), spouse(?x317, ?x11354), ?x7359 = 01k_n63, award_nominee(?x4840, ?x2806) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6267 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 02bh8z; *> query: (?x6946, 0178kd) <- artist(?x6946, ?x4840), artist(?x6946, ?x672), artist(?x6946, ?x317), ?x317 = 0c9d9, profession(?x672, ?x319), people(?x4322, ?x4840), ?x319 = 01d_h8, artists(?x378, ?x4840) *> conf = 0.29 ranks of expected_values: 64, 92, 105, 117 EVAL 026s90 artist 0889x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 89.000 86.000 0.600 http://example.org/music/record_label/artist EVAL 026s90 artist 01304j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 89.000 86.000 0.600 http://example.org/music/record_label/artist EVAL 026s90 artist 017mbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 89.000 86.000 0.600 http://example.org/music/record_label/artist EVAL 026s90 artist 0178kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 89.000 86.000 0.600 http://example.org/music/record_label/artist #22130-01vrx3g PRED entity: 01vrx3g PRED relation: place_of_death PRED expected values: 02_286 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 58): 030qb3t (0.18 #1574, 0.17 #1380, 0.16 #410), 02_286 (0.16 #401, 0.09 #5058, 0.09 #3894), 04jpl (0.10 #783, 0.07 #2723, 0.07 #2917), 0f2wj (0.08 #1564, 0.04 #4087, 0.04 #400), 0k049 (0.08 #4854, 0.07 #2331, 0.07 #3884), 05jbn (0.07 #847, 0.06 #1429, 0.04 #2787), 0c_m3 (0.05 #858, 0.04 #1440, 0.04 #276), 06_kh (0.04 #1363, 0.04 #5632, 0.04 #2721), 04swd (0.04 #2836, 0.04 #508, 0.04 #3030), 0qpqn (0.04 #518, 0.03 #712, 0.02 #906) >> Best rule #1574 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 0h1_w; 015wfg; >> query: (?x366, 030qb3t) <- people(?x268, ?x366), ?x268 = 0qcr0, award(?x366, ?x2420) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #401 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 01kws3; 016dgz; 0168dy; *> query: (?x366, 02_286) <- people(?x268, ?x366), profession(?x366, ?x1183), award_nominee(?x367, ?x366), ?x1183 = 09jwl *> conf = 0.16 ranks of expected_values: 2 EVAL 01vrx3g place_of_death 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.180 http://example.org/people/deceased_person/place_of_death #22129-07_pf PRED entity: 07_pf PRED relation: place_of_death! PRED expected values: 0c1jh => 175 concepts (88 used for prediction) PRED predicted values (max 10 best out of 575): 0gs7x (0.25 #6705, 0.25 #5192, 0.09 #14275), 02hh8j (0.25 #6557, 0.25 #5044, 0.09 #14127), 01rgr (0.25 #6547, 0.25 #5034, 0.09 #14117), 0knjh (0.25 #6495, 0.25 #4982, 0.09 #14065), 01vh096 (0.25 #6487, 0.25 #4974, 0.09 #14057), 0ct9_ (0.25 #6468, 0.25 #4955, 0.09 #14038), 07ym0 (0.25 #6463, 0.25 #4950, 0.09 #14033), 012gbb (0.25 #6451, 0.25 #4938, 0.09 #14021), 0399p (0.25 #6450, 0.25 #4937, 0.09 #14020), 043tg (0.25 #6449, 0.25 #4936, 0.09 #14019) >> Best rule #6705 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 03qhnx; >> query: (?x10496, 0gs7x) <- featured_film_locations(?x4786, ?x10496), ?x4786 = 0bbw2z6 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07_pf place_of_death! 0c1jh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 175.000 88.000 0.250 http://example.org/people/deceased_person/place_of_death #22128-04jpl PRED entity: 04jpl PRED relation: contains PRED expected values: 0nccd 015g1w 01_c4 01zzk4 => 285 concepts (175 used for prediction) PRED predicted values (max 10 best out of 2870): 01g4yw (0.67 #130215, 0.44 #298034, 0.39 #350120), 02237m (0.67 #130215, 0.44 #298034, 0.39 #350120), 041sbd (0.67 #130215, 0.44 #298034, 0.39 #350120), 0nlg4 (0.53 #506371, 0.50 #7748, 0.50 #367480), 012wyq (0.53 #506371, 0.50 #367480, 0.49 #457181), 036wy (0.53 #506371, 0.50 #367480, 0.49 #457181), 048kw (0.53 #506371, 0.50 #367480, 0.49 #457181), 02jx1 (0.53 #506371, 0.50 #367480, 0.49 #457181), 04jpl (0.53 #506371, 0.50 #367480, 0.49 #457181), 01_c4 (0.53 #506371, 0.50 #367480, 0.49 #457181) >> Best rule #130215 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 0jpkg; >> query: (?x362, ?x12066) <- citytown(?x12066, ?x362), institution(?x11690, ?x12066), mode_of_transportation(?x362, ?x4272) >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #506371 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 117 *> proper extension: 06mz5; 05j49; 04s7y; 07371; 048kw; 0glh3; 0g14f; 0kqb0; *> query: (?x362, ?x512) <- contains(?x362, ?x6132), state_province_region(?x3487, ?x362), contains(?x512, ?x6132) *> conf = 0.53 ranks of expected_values: 10, 15, 1772, 1781 EVAL 04jpl contains 01zzk4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 285.000 175.000 0.672 http://example.org/location/location/contains EVAL 04jpl contains 01_c4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 285.000 175.000 0.672 http://example.org/location/location/contains EVAL 04jpl contains 015g1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 285.000 175.000 0.672 http://example.org/location/location/contains EVAL 04jpl contains 0nccd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 285.000 175.000 0.672 http://example.org/location/location/contains #22127-06nzl PRED entity: 06nzl PRED relation: religion! PRED expected values: 01trhmt 02g0mx 0sw62 => 90 concepts (17 used for prediction) PRED predicted values (max 10 best out of 4163): 0mb5x (0.33 #1735, 0.21 #3839, 0.21 #2787), 0948xk (0.33 #1845, 0.14 #3949, 0.14 #2897), 0q9kd (0.33 #1052, 0.14 #3156, 0.14 #2104), 02j9lm (0.33 #1259, 0.14 #3363, 0.14 #2311), 015p37 (0.33 #1941, 0.14 #4045, 0.14 #2993), 0jmj (0.33 #1399, 0.14 #3503, 0.14 #2451), 019f2f (0.33 #1229, 0.14 #3333, 0.14 #2281), 0qf43 (0.33 #1063, 0.14 #3167, 0.14 #2115), 01chc7 (0.33 #1288, 0.14 #3392, 0.14 #2340), 04v7k2 (0.33 #2083, 0.14 #4187, 0.14 #3135) >> Best rule #1735 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0c8wxp; >> query: (?x7300, 0mb5x) <- religion(?x2857, ?x7300), religion(?x2784, ?x7300), award_winner(?x2322, ?x2784), ?x2857 = 0bbf1f, participant(?x2784, ?x3581), artists(?x302, ?x2784), type_of_union(?x2784, ?x566) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #11570 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 27 *> proper extension: 02rxj; 021_0p; 06yyp; 05w5d; 013b6_; 06pq6; *> query: (?x7300, ?x1674) <- religion(?x2857, ?x7300), religion(?x2784, ?x7300), award_winner(?x4892, ?x2784), film(?x2857, ?x557), award(?x2784, ?x1565), profession(?x2857, ?x1032), award_winner(?x4892, ?x1674) *> conf = 0.05 ranks of expected_values: 1332, 1892, 3462 EVAL 06nzl religion! 0sw62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 17.000 0.333 http://example.org/people/person/religion EVAL 06nzl religion! 02g0mx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 90.000 17.000 0.333 http://example.org/people/person/religion EVAL 06nzl religion! 01trhmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 90.000 17.000 0.333 http://example.org/people/person/religion #22126-029pnn PRED entity: 029pnn PRED relation: location_of_ceremony PRED expected values: 0lmgy => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 14): 030qb3t (0.06 #138, 0.06 #257, 0.03 #495), 0cv3w (0.06 #154, 0.04 #511, 0.03 #869), 03rjj (0.01 #839, 0.01 #481, 0.01 #958), 0b90_r (0.01 #479, 0.01 #956, 0.01 #1195), 0k049 (0.01 #480, 0.01 #1911), 0ggyr (0.01 #568), 0gkgp (0.01 #558), 07fr_ (0.01 #549), 0rsjf (0.01 #542), 03gh4 (0.01 #539) >> Best rule #138 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0q9kd; 012d40; 0mdqp; 0fb1q; 019vgs; 029_3; 0dpqk; 01h1b; 03b78r; 01pjr7; ... >> query: (?x8257, 030qb3t) <- profession(?x8257, ?x1146), special_performance_type(?x8257, ?x4832), ?x1146 = 018gz8, type_of_union(?x8257, ?x566) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 029pnn location_of_ceremony 0lmgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 75.000 0.059 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #22125-08jbxf PRED entity: 08jbxf PRED relation: gender PRED expected values: 05zppz => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #19, 0.91 #31, 0.90 #15), 02zsn (0.46 #89, 0.46 #98, 0.46 #97) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 02qjj7; 037gjc; 03n69x; 03l295; 0cv72h; 01f492; 01sg7_; 012xdf; 0f2zc; 0cg39k; ... >> query: (?x4246, 05zppz) <- team(?x4246, ?x11591), team(?x60, ?x11591), profession(?x4246, ?x7623) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08jbxf gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.911 http://example.org/people/person/gender #22124-07wlf PRED entity: 07wlf PRED relation: institution! PRED expected values: 03mkk4 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 14): 0bkj86 (0.55 #170, 0.44 #357, 0.44 #308), 03mkk4 (0.33 #81, 0.25 #96, 0.22 #51), 0bjrnt (0.33 #17, 0.22 #48, 0.19 #168), 01rr_d (0.33 #55, 0.20 #70, 0.20 #9), 028dcg (0.33 #57, 0.20 #72, 0.20 #11), 013zdg (0.26 #169, 0.25 #356, 0.25 #292), 02m4yg (0.22 #54, 0.17 #23, 0.07 #190), 022h5x (0.20 #365, 0.19 #178, 0.18 #301), 02mjs7 (0.13 #167, 0.11 #47, 0.10 #62), 02cq61 (0.13 #176, 0.11 #56, 0.09 #546) >> Best rule #170 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 07tgn; 01mpwj; 01_qgp; >> query: (?x2760, 0bkj86) <- major_field_of_study(?x2760, ?x6870), major_field_of_study(?x2760, ?x2981), ?x2981 = 02j62, ?x6870 = 01540 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #81 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 04jr87; *> query: (?x2760, 03mkk4) <- contains(?x94, ?x2760), institution(?x620, ?x2760), split_to(?x2760, ?x9165) *> conf = 0.33 ranks of expected_values: 2 EVAL 07wlf institution! 03mkk4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 115.000 0.548 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22123-01qxc7 PRED entity: 01qxc7 PRED relation: film! PRED expected values: 018ygt => 90 concepts (31 used for prediction) PRED predicted values (max 10 best out of 876): 0g9zcgx (0.52 #6239, 0.48 #8320, 0.46 #24959), 0170pk (0.33 #282, 0.06 #16918, 0.05 #19000), 0jfx1 (0.33 #406, 0.05 #19124, 0.04 #17042), 09wj5 (0.33 #101, 0.04 #16737, 0.04 #20800), 01l2fn (0.33 #263, 0.04 #21063, 0.04 #20800), 01tsbmv (0.33 #1896, 0.04 #20800, 0.03 #22696), 05qg6g (0.33 #735, 0.04 #20800, 0.02 #21535), 07rd7 (0.23 #8319, 0.11 #35365), 0c6qh (0.20 #2494, 0.10 #6653, 0.10 #8734), 015c4g (0.20 #2858, 0.07 #7017, 0.07 #13256) >> Best rule #6239 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 050xxm; 02krdz; 02fttd; 063_j5; >> query: (?x4489, ?x3025) <- award_winner(?x4489, ?x3025), film(?x382, ?x4489), genre(?x4489, ?x809), ?x809 = 0vgkd >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #5275 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 050xxm; 02krdz; 02fttd; 063_j5; *> query: (?x4489, 018ygt) <- award_winner(?x4489, ?x3025), film(?x382, ?x4489), genre(?x4489, ?x809), ?x809 = 0vgkd *> conf = 0.03 ranks of expected_values: 248 EVAL 01qxc7 film! 018ygt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 90.000 31.000 0.517 http://example.org/film/actor/film./film/performance/film #22122-058j2 PRED entity: 058j2 PRED relation: contact_category PRED expected values: 03w5xm => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 3): 03w5xm (0.76 #243, 0.75 #234, 0.75 #285), 02zdwq (0.30 #242, 0.27 #287, 0.27 #275), 014dgf (0.22 #398, 0.22 #307, 0.22 #328) >> Best rule #243 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 04htfd; >> query: (?x6972, 03w5xm) <- service_language(?x6972, ?x254), state_province_region(?x6972, ?x335), industry(?x6972, ?x12816), ?x254 = 02h40lc >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 058j2 contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 220.000 220.000 0.760 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #22121-03n0cd PRED entity: 03n0cd PRED relation: genre PRED expected values: 06n90 => 78 concepts (62 used for prediction) PRED predicted values (max 10 best out of 97): 01z4y (0.62 #6269, 0.62 #6512, 0.56 #3855), 07s9rl0 (0.61 #3735, 0.59 #6149, 0.59 #6392), 02l7c8 (0.60 #16, 0.50 #137, 0.37 #1101), 01jfsb (0.50 #1458, 0.38 #373, 0.36 #253), 03k9fj (0.39 #1457, 0.25 #2178, 0.24 #372), 06n90 (0.29 #1459, 0.19 #374, 0.16 #856), 06cvj (0.26 #604, 0.26 #484, 0.24 #1328), 0lsxr (0.23 #249, 0.21 #1454, 0.19 #851), 04xvlr (0.20 #3736, 0.17 #966, 0.17 #6150), 060__y (0.20 #378, 0.15 #860, 0.15 #3751) >> Best rule #6269 for best value: >> intensional similarity = 3 >> extensional distance = 1194 >> proper extension: 05jyb2; 0413cff; 02qjv1p; 09rfh9; 0k20s; 0c5qvw; >> query: (?x8788, ?x2480) <- genre(?x8788, ?x225), titles(?x2480, ?x8788), genre(?x631, ?x2480) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1459 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 389 *> proper extension: 076xkdz; *> query: (?x8788, 06n90) <- genre(?x8788, ?x225), film(?x541, ?x8788), ?x225 = 02kdv5l *> conf = 0.29 ranks of expected_values: 6 EVAL 03n0cd genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 78.000 62.000 0.623 http://example.org/film/film/genre #22120-01w31x PRED entity: 01w31x PRED relation: artist PRED expected values: 0274ck => 59 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1187): 01vv6_6 (0.50 #241, 0.43 #1081, 0.41 #5043), 04mx7s (0.45 #3135, 0.09 #19130, 0.08 #11548), 081wh1 (0.44 #4202, 0.26 #3361, 0.25 #12616), 01vw8mh (0.43 #4550, 0.38 #3709, 0.35 #6232), 01wz3cx (0.41 #5043, 0.30 #5884, 0.25 #12616), 01s1zk (0.41 #5043, 0.30 #5884, 0.25 #12616), 02pt7h_ (0.41 #5043, 0.30 #5884, 0.25 #12616), 01kph_c (0.41 #5043, 0.30 #5884, 0.25 #12616), 016sp_ (0.41 #5043, 0.30 #5884, 0.25 #12616), 01vsksr (0.41 #5043, 0.30 #5884, 0.25 #12616) >> Best rule #241 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0fb0v; 0181dw; 05cl8y; 01sqd7; >> query: (?x14652, 01vv6_6) <- artist(?x14652, ?x8560), category(?x14652, ?x134), ?x8560 = 02y7sr, ?x134 = 08mbj5d >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6725 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 15 *> proper extension: 03rhqg; *> query: (?x14652, ?x211) <- artist(?x14652, ?x8560), category(?x14652, ?x134), instrumentalists(?x7938, ?x8560), profession(?x8560, ?x655), nationality(?x8560, ?x94), role(?x8560, ?x716), artists(?x302, ?x8560), ?x7938 = 048j4l, role(?x716, ?x9219), ?x9219 = 01399x, instrumentalists(?x716, ?x211) *> conf = 0.06 ranks of expected_values: 698 EVAL 01w31x artist 0274ck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 59.000 23.000 0.500 http://example.org/music/record_label/artist #22119-01chpn PRED entity: 01chpn PRED relation: award PRED expected values: 02n9nmz => 89 concepts (80 used for prediction) PRED predicted values (max 10 best out of 190): 02x73k6 (0.33 #2099, 0.33 #1913, 0.29 #931), 09sdmz (0.31 #604, 0.29 #931, 0.27 #1165), 0gs9p (0.31 #528, 0.21 #2860, 0.18 #6514), 027dtxw (0.29 #931, 0.27 #1165, 0.26 #3030), 0gr4k (0.29 #931, 0.27 #1165, 0.26 #3030), 02ppm4q (0.29 #931, 0.27 #1165, 0.26 #3030), 0gqyl (0.29 #931, 0.27 #1165, 0.26 #3030), 019f4v (0.29 #931, 0.27 #1165, 0.26 #3030), 09qv_s (0.29 #931, 0.27 #1165, 0.26 #3030), 0f4x7 (0.29 #931, 0.27 #1165, 0.26 #3030) >> Best rule #2099 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 0b2v79; 0qm98; 09gq0x5; 047n8xt; 02stbw; 0m9p3; 015whm; 04j4tx; 015qqg; 0191n; ... >> query: (?x6288, ?x1033) <- nominated_for(?x1033, ?x6288), nominated_for(?x92, ?x6288), ?x1033 = 02x73k6, film(?x91, ?x6288) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6747 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 558 *> proper extension: 03czz87; *> query: (?x6288, ?x68) <- nominated_for(?x4871, ?x6288), honored_for(?x6238, ?x6288), award(?x4871, ?x68) *> conf = 0.09 ranks of expected_values: 70 EVAL 01chpn award 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 89.000 80.000 0.333 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22118-06hmd PRED entity: 06hmd PRED relation: profession PRED expected values: 0cbd2 => 82 concepts (74 used for prediction) PRED predicted values (max 10 best out of 76): 0cbd2 (0.80 #902, 0.59 #3286, 0.54 #4181), 02hrh1q (0.73 #9561, 0.61 #8665, 0.61 #8964), 05z96 (0.50 #938, 0.50 #341, 0.40 #490), 0dxtg (0.42 #3293, 0.40 #610, 0.38 #7322), 0d8qb (0.31 #6562, 0.29 #7607, 0.20 #527), 0fj9f (0.31 #6562, 0.29 #7607, 0.16 #1248), 016wtf (0.31 #6562, 0.29 #7607, 0.10 #1024), 05t4q (0.31 #6562, 0.29 #7607, 0.05 #1702), 01d_h8 (0.29 #9552, 0.26 #8507, 0.26 #9254), 03gjzk (0.28 #4025, 0.25 #6712, 0.25 #4473) >> Best rule #902 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 042v2; >> query: (?x5334, 0cbd2) <- influenced_by(?x5334, ?x6320), influenced_by(?x5334, ?x5040), ?x6320 = 05gpy, gender(?x5334, ?x231), peers(?x5040, ?x5988) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06hmd profession 0cbd2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 74.000 0.800 http://example.org/people/person/profession #22117-02c7lt PRED entity: 02c7lt PRED relation: profession PRED expected values: 02hrh1q => 118 concepts (47 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.94 #2660, 0.88 #3248, 0.88 #1483), 01d_h8 (0.77 #888, 0.77 #4271, 0.75 #4124), 03gjzk (0.70 #2955, 0.69 #2219, 0.67 #1337), 0np9r (0.33 #1343, 0.23 #2225, 0.23 #1784), 018gz8 (0.29 #2368, 0.23 #3987, 0.20 #163), 09jwl (0.27 #1635, 0.20 #6342, 0.19 #2812), 0cbd2 (0.25 #3684, 0.25 #3389, 0.25 #595), 0kyk (0.25 #616, 0.25 #469, 0.23 #1057), 0nbcg (0.25 #618, 0.23 #912, 0.22 #2824), 016z4k (0.25 #592, 0.19 #2798, 0.15 #886) >> Best rule #2660 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 09wj5; 01rh0w; 01pw2f1; 049g_xj; 01l2fn; 0j1yf; 03rl84; 0170s4; 01w02sy; 013knm; ... >> query: (?x11284, 02hrh1q) <- profession(?x11284, ?x987), participant(?x11284, ?x6073), notable_people_with_this_condition(?x13845, ?x11284), profession(?x2875, ?x987), ?x2875 = 02645b >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02c7lt profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 47.000 0.939 http://example.org/people/person/profession #22116-0bzrsh PRED entity: 0bzrsh PRED relation: team PRED expected values: 026xxv_ 02pzy52 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 29): 026xxv_ (0.82 #177, 0.78 #187, 0.75 #97), 02pzy52 (0.79 #169, 0.76 #179, 0.70 #139), 027yf83 (0.67 #184, 0.67 #54, 0.64 #144), 02pyyld (0.62 #110, 0.57 #90, 0.50 #80), 02pqcfz (0.61 #182, 0.59 #172, 0.57 #202), 04088s0 (0.56 #185, 0.50 #135, 0.50 #125), 02ptzz0 (0.50 #181, 0.48 #201, 0.43 #161), 03d5m8w (0.50 #58, 0.42 #158, 0.40 #48), 02r2qt7 (0.50 #56, 0.40 #116, 0.36 #146), 03d555l (0.45 #143, 0.44 #183, 0.43 #163) >> Best rule #177 for best value: >> intensional similarity = 9 >> extensional distance = 15 >> proper extension: 0bzrxn; 0b_6s7; >> query: (?x9956, 026xxv_) <- locations(?x9956, ?x2277), location(?x5405, ?x2277), origin(?x1206, ?x2277), team(?x9956, ?x2303), citytown(?x10217, ?x2277), profession(?x5405, ?x131), artists(?x671, ?x5405), contains(?x94, ?x10217), month(?x2277, ?x1459) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0bzrsh team 02pzy52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.824 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0bzrsh team 026xxv_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.824 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #22115-05qqm PRED entity: 05qqm PRED relation: service_language! PRED expected values: 018mxj => 43 concepts (39 used for prediction) PRED predicted values (max 10 best out of 162): 01c6k4 (0.67 #1019, 0.60 #1598, 0.60 #441), 064f29 (0.60 #499, 0.50 #353, 0.45 #1367), 0p4wb (0.60 #444, 0.50 #1167, 0.45 #1312), 069b85 (0.60 #571, 0.50 #425, 0.36 #1439), 04sv4 (0.60 #523, 0.33 #232, 0.33 #88), 05b5c (0.60 #570, 0.33 #279, 0.33 #135), 0gvbw (0.60 #460, 0.33 #169, 0.33 #25), 018mxj (0.50 #299, 0.40 #445, 0.36 #1313), 07zl6m (0.50 #429, 0.40 #575, 0.33 #1153), 05w3y (0.50 #355, 0.40 #501, 0.33 #210) >> Best rule #1019 for best value: >> intensional similarity = 16 >> extensional distance = 7 >> proper extension: 071fb; >> query: (?x10486, 01c6k4) <- language(?x7680, ?x10486), official_language(?x9006, ?x10486), contains(?x9006, ?x5127), film_release_region(?x7680, ?x4743), film_release_region(?x7680, ?x2513), film_release_region(?x7680, ?x2346), film_release_region(?x7680, ?x1353), film_release_region(?x7680, ?x985), film_release_region(?x7680, ?x94), ?x94 = 09c7w0, film_release_distribution_medium(?x7680, ?x81), ?x4743 = 03spz, ?x1353 = 035qy, ?x985 = 0k6nt, ?x2513 = 05b4w, ?x2346 = 0d05w3 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #299 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 06nm1; *> query: (?x10486, 018mxj) <- language(?x5098, ?x10486), language(?x4971, ?x10486), official_language(?x9006, ?x10486), contains(?x9006, ?x5127), combatants(?x3141, ?x9006), ?x5098 = 05znxx, entity_involved(?x612, ?x9006), nationality(?x4724, ?x9006), languages_spoken(?x11184, ?x10486), film(?x1850, ?x4971), films(?x326, ?x4971), combatants(?x612, ?x390), film(?x269, ?x4971) *> conf = 0.50 ranks of expected_values: 8 EVAL 05qqm service_language! 018mxj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 43.000 39.000 0.667 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #22114-02snj9 PRED entity: 02snj9 PRED relation: role! PRED expected values: 018vs 018j2 0cfdd => 76 concepts (60 used for prediction) PRED predicted values (max 10 best out of 103): 05r5c (0.90 #1593, 0.89 #2587, 0.89 #4677), 013y1f (0.90 #1593, 0.89 #4677, 0.88 #1694), 042v_gx (0.85 #1287, 0.84 #388, 0.84 #1086), 06ncr (0.85 #1287, 0.84 #388, 0.84 #1086), 01s0ps (0.85 #1287, 0.84 #388, 0.84 #1086), 07_l6 (0.85 #1287, 0.84 #388, 0.84 #1086), 07kc_ (0.85 #1287, 0.84 #388, 0.84 #1086), 04q7r (0.85 #1287, 0.84 #388, 0.84 #1086), 0cfdd (0.80 #1676, 0.79 #2571, 0.70 #1577), 03gvt (0.80 #1660, 0.69 #2848, 0.64 #2555) >> Best rule #1593 for best value: >> intensional similarity = 18 >> extensional distance = 8 >> proper extension: 02hnl; >> query: (?x3214, ?x1495) <- role(?x3214, ?x4769), role(?x3214, ?x2725), role(?x3214, ?x1495), ?x4769 = 0dwt5, group(?x3214, ?x498), performance_role(?x212, ?x3214), instrumentalists(?x1495, ?x2242), role(?x7987, ?x1495), role(?x2048, ?x1495), group(?x1495, ?x997), role(?x1495, ?x894), role(?x1407, ?x1495), ?x7987 = 0j6cj, performance_role(?x1260, ?x1495), ?x2048 = 018j2, ?x2725 = 0l1589, role(?x1495, ?x214), ?x2242 = 09prnq >> conf = 0.90 => this is the best rule for 2 predicted values *> Best rule #1676 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 8 *> proper extension: 0l14md; *> query: (?x3214, 0cfdd) <- role(?x3214, ?x4769), role(?x3214, ?x1969), role(?x3214, ?x1647), role(?x3214, ?x1166), role(?x5494, ?x4769), ?x1647 = 05ljv7, performance_role(?x764, ?x3214), ?x1166 = 05148p4, role(?x2944, ?x4769), ?x2944 = 0l14j_, role(?x922, ?x4769), ?x5494 = 018x3, role(?x366, ?x1969), role(?x1969, ?x894), role(?x367, ?x1969), instrumentalists(?x1969, ?x1001), role(?x642, ?x3214), group(?x4769, ?x3516), ?x3516 = 05563d *> conf = 0.80 ranks of expected_values: 9, 22, 23 EVAL 02snj9 role! 0cfdd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 76.000 60.000 0.900 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 02snj9 role! 018j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 76.000 60.000 0.900 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 02snj9 role! 018vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 76.000 60.000 0.900 http://example.org/music/performance_role/regular_performances./music/group_membership/role #22113-0lvng PRED entity: 0lvng PRED relation: colors PRED expected values: 083jv => 197 concepts (197 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.50 #62, 0.49 #2002, 0.39 #1002), 06fvc (0.40 #23, 0.25 #63, 0.20 #263), 01l849 (0.34 #241, 0.31 #2001, 0.30 #421), 019sc (0.33 #7, 0.25 #387, 0.20 #547), 038hg (0.33 #52, 0.18 #92, 0.14 #252), 03wkwg (0.14 #215, 0.11 #255, 0.11 #175), 04mkbj (0.12 #70, 0.11 #290, 0.09 #2030), 088fh (0.12 #66, 0.08 #626, 0.07 #3041), 036k5h (0.12 #505, 0.10 #1005, 0.09 #2185), 0jc_p (0.11 #644, 0.11 #504, 0.10 #624) >> Best rule #62 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0pz6q; >> query: (?x7363, 083jv) <- student(?x7363, ?x9046), organization(?x4095, ?x7363), colors(?x7363, ?x3189), ?x4095 = 0hm4q >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lvng colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 197.000 197.000 0.500 http://example.org/education/educational_institution/colors #22112-02pjvc PRED entity: 02pjvc PRED relation: nationality PRED expected values: 03rjj => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 31): 02jx1 (0.12 #230, 0.11 #1915, 0.11 #2610), 0d060g (0.12 #6, 0.05 #997, 0.05 #1196), 07ssc (0.10 #2592, 0.09 #3987, 0.09 #3688), 03rjj (0.10 #697, 0.03 #1094, 0.03 #202), 03rk0 (0.08 #3320, 0.06 #6109, 0.05 #4515), 03spz (0.04 #66, 0.01 #1057), 05bcl (0.04 #59, 0.01 #1149), 0345h (0.03 #129, 0.03 #2807, 0.02 #6193), 0f8l9c (0.03 #219, 0.02 #2202, 0.02 #1111), 02k1b (0.03 #281, 0.02 #380, 0.01 #578) >> Best rule #230 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 01wk7b7; 012_53; 047hpm; 0jrny; 01wc7p; 01qn8k; >> query: (?x5794, 02jx1) <- film(?x5794, ?x4939), friend(?x5794, ?x2435), actor(?x6482, ?x5794) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #697 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 01l9v7n; 0n6kf; 012v1t; 010p3; *> query: (?x5794, 03rjj) <- people(?x3591, ?x5794), location(?x5794, ?x1523), ?x3591 = 0xnvg *> conf = 0.10 ranks of expected_values: 4 EVAL 02pjvc nationality 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 102.000 0.121 http://example.org/people/person/nationality #22111-01mhwk PRED entity: 01mhwk PRED relation: ceremony! PRED expected values: 01c92g 01dpdh 02hgm4 03tcnt 02flpc 01ckrr 025mbn 02w7fs => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 224): 02hgm4 (0.86 #1772, 0.85 #1584, 0.80 #1396), 03tcnt (0.86 #1787, 0.80 #1411, 0.77 #4512), 02w7fs (0.80 #1491, 0.79 #1867, 0.77 #4512), 01ckrr (0.80 #1440, 0.79 #1816, 0.77 #4512), 02flpc (0.80 #1418, 0.77 #4512, 0.77 #3758), 0257yf (0.77 #4512, 0.77 #3758, 0.77 #3946), 025mbn (0.77 #4512, 0.77 #3758, 0.77 #3946), 03qpp9 (0.77 #4512, 0.77 #3758, 0.77 #3946), 02g3gj (0.77 #4512, 0.77 #3758, 0.77 #3946), 01c92g (0.77 #4512, 0.77 #3758, 0.77 #3946) >> Best rule #1772 for best value: >> intensional similarity = 19 >> extensional distance = 12 >> proper extension: 0gx1673; >> query: (?x2704, 02hgm4) <- ceremony(?x11048, ?x2704), ceremony(?x5765, ?x2704), ceremony(?x4958, ?x2704), ceremony(?x2238, ?x2704), ?x2238 = 025m8l, award_winner(?x2704, ?x13142), award_winner(?x2704, ?x4239), award_winner(?x2704, ?x3069), award_winner(?x2704, ?x2662), award(?x13142, ?x2877), profession(?x2662, ?x131), award_winner(?x3069, ?x1489), nominated_for(?x3069, ?x667), ?x4958 = 03qbnj, ?x5765 = 024_fw, award_winner(?x4239, ?x367), ceremony(?x11048, ?x342), ?x342 = 01s695, award_nominee(?x366, ?x4239) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 7, 10, 11 EVAL 01mhwk ceremony! 02w7fs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 32.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mhwk ceremony! 025mbn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 32.000 32.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mhwk ceremony! 01ckrr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 32.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mhwk ceremony! 02flpc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 32.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mhwk ceremony! 03tcnt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 32.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mhwk ceremony! 02hgm4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 32.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mhwk ceremony! 01dpdh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 32.000 32.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mhwk ceremony! 01c92g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 32.000 32.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony #22110-05zlld0 PRED entity: 05zlld0 PRED relation: produced_by PRED expected values: 05mvd62 => 73 concepts (51 used for prediction) PRED predicted values (max 10 best out of 160): 09pl3f (0.24 #10088, 0.01 #987), 09pl3s (0.24 #10088, 0.01 #852), 04w391 (0.10 #15913, 0.10 #17470, 0.10 #17081), 042xrr (0.10 #15913, 0.10 #17470, 0.10 #17081), 05vk_d (0.10 #15913, 0.10 #17470, 0.10 #17081), 0284n42 (0.10 #15913, 0.10 #17470, 0.10 #12032), 04qmr (0.10 #15913, 0.10 #17470, 0.10 #12032), 01t6b4 (0.06 #431, 0.05 #3530, 0.03 #818), 02xnjd (0.05 #1048, 0.04 #2210, 0.03 #1436), 06pj8 (0.05 #3554, 0.04 #3944, 0.04 #3167) >> Best rule #10088 for best value: >> intensional similarity = 2 >> extensional distance = 636 >> proper extension: 058kh7; 0199wf; 05vc35; 0gfzfj; >> query: (?x3748, ?x2442) <- film(?x250, ?x3748), written_by(?x3748, ?x2442) >> conf = 0.24 => this is the best rule for 2 predicted values *> Best rule #3731 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 187 *> proper extension: 0c3xpwy; *> query: (?x3748, 05mvd62) <- nominated_for(?x8638, ?x3748), nominated_for(?x666, ?x3748), crewmember(?x141, ?x666), gender(?x8638, ?x514) *> conf = 0.03 ranks of expected_values: 20 EVAL 05zlld0 produced_by 05mvd62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 73.000 51.000 0.241 http://example.org/film/film/produced_by #22109-0d4fqn PRED entity: 0d4fqn PRED relation: profession PRED expected values: 0dxtg => 82 concepts (58 used for prediction) PRED predicted values (max 10 best out of 42): 0dxtg (0.91 #754, 0.87 #902, 0.84 #1346), 01d_h8 (0.48 #2374, 0.47 #3854, 0.47 #2226), 02jknp (0.37 #3856, 0.28 #2228, 0.26 #1932), 02krf9 (0.33 #470, 0.33 #1654, 0.33 #1506), 0cbd2 (0.33 #599, 0.24 #1191, 0.23 #1339), 018gz8 (0.28 #1940, 0.19 #2384, 0.19 #2236), 0np9r (0.27 #612, 0.16 #1944, 0.13 #2388), 09jwl (0.22 #2090, 0.19 #2978, 0.18 #5790), 0nbcg (0.17 #2103, 0.13 #2991, 0.12 #4471), 01c72t (0.14 #2095, 0.10 #5795, 0.10 #2983) >> Best rule #754 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 011s9r; >> query: (?x636, 0dxtg) <- award_winner(?x4806, ?x636), tv_program(?x636, ?x3180), profession(?x636, ?x1032) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d4fqn profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 58.000 0.910 http://example.org/people/person/profession #22108-0b90_r PRED entity: 0b90_r PRED relation: medal PRED expected values: 02lpp7 => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.82 #29, 0.81 #23, 0.81 #22) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 015qh; 01pj7; 077qn; >> query: (?x151, 02lpp7) <- film_release_region(?x141, ?x151), currency(?x151, ?x170), ?x141 = 0gtsx8c >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b90_r medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.821 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #22107-05218gr PRED entity: 05218gr PRED relation: gender PRED expected values: 05zppz => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #19, 0.85 #15, 0.85 #23), 02zsn (0.25 #61, 0.23 #63, 0.23 #85) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 159 >> proper extension: 01k31p; >> query: (?x2304, 05zppz) <- place_of_death(?x2304, ?x1990), time_zones(?x1990, ?x2950), category(?x1990, ?x134) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05218gr gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.857 http://example.org/people/person/gender #22106-0175rc PRED entity: 0175rc PRED relation: company! PRED expected values: 02md_2 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 39): 02md_2 (0.71 #338, 0.43 #385, 0.20 #200), 0dq_5 (0.50 #63, 0.41 #3085, 0.41 #2711), 0krdk (0.45 #2654, 0.44 #2700, 0.37 #2418), 02y6fz (0.40 #531, 0.20 #162, 0.15 #2834), 0dq3c (0.35 #2414, 0.34 #2555, 0.31 #2742), 01yc02 (0.33 #8, 0.25 #2702, 0.25 #100), 07xl34 (0.29 #3857, 0.13 #3905, 0.11 #2787), 033smt (0.25 #122, 0.20 #214, 0.10 #537), 05_wyz (0.24 #2712, 0.24 #2666, 0.23 #2197), 09d6p2 (0.23 #2198, 0.17 #2572, 0.17 #2431) >> Best rule #338 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 01ync; >> query: (?x11507, 02md_2) <- team(?x203, ?x11507), colors(?x11507, ?x663), company(?x346, ?x11507), company(?x346, ?x7690), company(?x346, ?x4338), major_field_of_study(?x4338, ?x732), institution(?x620, ?x4338), state_province_region(?x7690, ?x1227) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0175rc company! 02md_2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.714 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #22105-0315w4 PRED entity: 0315w4 PRED relation: currency PRED expected values: 09nqf => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.85 #22, 0.84 #43, 0.83 #36), 01nv4h (0.25 #484, 0.03 #51, 0.02 #135), 02l6h (0.25 #484, 0.01 #221, 0.01 #193) >> Best rule #22 for best value: >> intensional similarity = 5 >> extensional distance = 77 >> proper extension: 013q0p; >> query: (?x4799, 09nqf) <- nominated_for(?x507, ?x4799), genre(?x4799, ?x812), genre(?x664, ?x812), ?x507 = 02g3v6, ?x664 = 0401sg >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0315w4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.848 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #22104-021lby PRED entity: 021lby PRED relation: profession PRED expected values: 02hrh1q => 94 concepts (67 used for prediction) PRED predicted values (max 10 best out of 56): 02hrh1q (0.73 #6979, 0.72 #7560, 0.67 #3351), 0dxtg (0.73 #1752, 0.73 #1172, 0.72 #1462), 0cbd2 (0.57 #296, 0.17 #1021, 0.17 #731), 0n1h (0.29 #300, 0.06 #5814, 0.06 #6976), 09jwl (0.19 #5821, 0.17 #6547, 0.16 #6983), 016z4k (0.12 #5808, 0.10 #6970, 0.09 #6534), 0dgd_ (0.12 #1043, 0.10 #898, 0.10 #1914), 0nbcg (0.11 #8591, 0.11 #8446, 0.11 #9606), 0dz3r (0.11 #5806, 0.11 #8564, 0.10 #8419), 018gz8 (0.11 #5964, 0.10 #6400, 0.09 #4804) >> Best rule #6979 for best value: >> intensional similarity = 3 >> extensional distance = 1254 >> proper extension: 03cd1q; >> query: (?x2464, 02hrh1q) <- award_nominee(?x2464, ?x11580), profession(?x11580, ?x319), location(?x2464, ?x3014) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021lby profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 67.000 0.735 http://example.org/people/person/profession #22103-018jz PRED entity: 018jz PRED relation: sport! PRED expected values: 01ypc 05m_8 01ync 01slc 07l4z 04wmvz 02h8p8 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 484): 0jm74 (0.50 #3489, 0.33 #156, 0.25 #2772), 0jm5b (0.50 #3489, 0.33 #335, 0.25 #2951), 0wsr (0.50 #3489, 0.25 #2323, 0.16 #6979), 01ct6 (0.50 #3489, 0.25 #2188, 0.16 #6979), 02896 (0.50 #3489, 0.25 #2183, 0.16 #6979), 06rny (0.50 #3489, 0.25 #2297, 0.14 #7969), 084l5 (0.50 #3489, 0.25 #2260, 0.14 #7932), 026xxv_ (0.50 #3489, 0.16 #6979, 0.14 #8061), 04wmvz (0.50 #3489, 0.16 #6979), 07l4z (0.50 #3489, 0.16 #6979) >> Best rule #3489 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 03tmr; >> query: (?x5063, ?x7136) <- athlete(?x5063, ?x11924), sport(?x7499, ?x5063), sport(?x2174, ?x5063), sports(?x778, ?x5063), team(?x11924, ?x7136), country(?x5063, ?x94), team(?x11844, ?x2174), team(?x2010, ?x7499) >> conf = 0.50 => this is the best rule for 14 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 9, 10, 11, 14, 441, 471 EVAL 018jz sport! 02h8p8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 87.000 0.496 http://example.org/sports/sports_team/sport EVAL 018jz sport! 04wmvz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 87.000 0.496 http://example.org/sports/sports_team/sport EVAL 018jz sport! 07l4z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 87.000 0.496 http://example.org/sports/sports_team/sport EVAL 018jz sport! 01slc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 87.000 0.496 http://example.org/sports/sports_team/sport EVAL 018jz sport! 01ync CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 87.000 0.496 http://example.org/sports/sports_team/sport EVAL 018jz sport! 05m_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 87.000 87.000 0.496 http://example.org/sports/sports_team/sport EVAL 018jz sport! 01ypc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 87.000 0.496 http://example.org/sports/sports_team/sport #22102-04gxp2 PRED entity: 04gxp2 PRED relation: institution! PRED expected values: 02mjs7 02cq61 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 23): 019v9k (0.75 #9, 0.60 #445, 0.59 #274), 014mlp (0.68 #271, 0.67 #442, 0.66 #468), 02h4rq6 (0.64 #439, 0.64 #268, 0.63 #465), 02_xgp2 (0.56 #38, 0.50 #13, 0.44 #86), 016t_3 (0.50 #4, 0.43 #149, 0.37 #221), 03bwzr4 (0.50 #15, 0.39 #40, 0.37 #160), 07s6fsf (0.50 #1, 0.33 #26, 0.30 #437), 0bkj86 (0.40 #225, 0.39 #33, 0.38 #153), 04zx3q1 (0.33 #27, 0.32 #147, 0.29 #1279), 027f2w (0.33 #35, 0.25 #10, 0.22 #155) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01j_cy; >> query: (?x13215, 019v9k) <- major_field_of_study(?x13215, ?x5179), category(?x13215, ?x134), contains(?x1906, ?x13215), ?x1906 = 04rrx >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1279 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 566 *> proper extension: 071_8; 01dbns; *> query: (?x13215, ?x734) <- major_field_of_study(?x13215, ?x5179), institution(?x1519, ?x13215), major_field_of_study(?x734, ?x5179) *> conf = 0.29 ranks of expected_values: 13, 15 EVAL 04gxp2 institution! 02cq61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 93.000 93.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 04gxp2 institution! 02mjs7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 93.000 93.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22101-04wgh PRED entity: 04wgh PRED relation: organization PRED expected values: 0b6css => 187 concepts (180 used for prediction) PRED predicted values (max 10 best out of 50): 0gkjy (0.82 #470, 0.73 #801, 0.56 #2899), 041288 (0.69 #479, 0.61 #810, 0.37 #2134), 0b6css (0.67 #473, 0.56 #804, 0.54 #253), 0_2v (0.50 #620, 0.48 #488, 0.47 #201), 018cqq (0.50 #209, 0.38 #33, 0.38 #254), 04k4l (0.46 #114, 0.43 #1304, 0.41 #1415), 01rz1 (0.44 #199, 0.41 #574, 0.39 #398), 0j7v_ (0.32 #3347, 0.31 #468, 0.29 #115), 02jxk (0.32 #3347, 0.29 #575, 0.26 #2853), 059dn (0.32 #3347, 0.26 #2853, 0.20 #15) >> Best rule #470 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 07p7g; >> query: (?x1273, 0gkjy) <- contains(?x2467, ?x1273), administrative_parent(?x1273, ?x551), ?x2467 = 0dg3n1 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #473 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 07p7g; *> query: (?x1273, 0b6css) <- contains(?x2467, ?x1273), administrative_parent(?x1273, ?x551), ?x2467 = 0dg3n1 *> conf = 0.67 ranks of expected_values: 3 EVAL 04wgh organization 0b6css CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 187.000 180.000 0.822 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #22100-065zr PRED entity: 065zr PRED relation: state_province_region! PRED expected values: 05ftw3 => 89 concepts (64 used for prediction) PRED predicted values (max 10 best out of 721): 01yf40 (0.17 #12012, 0.16 #19523, 0.16 #18772), 023vwt (0.17 #12012, 0.16 #19523, 0.16 #18772), 0xnt5 (0.17 #12012, 0.16 #19523, 0.16 #18772), 05xb7q (0.08 #1772, 0.01 #2522, 0.01 #3272), 077w0b (0.04 #2596, 0.04 #3346, 0.03 #4096), 05ftw3 (0.04 #22530), 0d2fd7 (0.03 #2698, 0.02 #3448, 0.02 #4198), 0178g (0.03 #2402, 0.02 #3152, 0.02 #3902), 0sxdg (0.03 #2687, 0.02 #3437, 0.02 #4937), 05njw (0.03 #2826, 0.02 #4326, 0.02 #5076) >> Best rule #12012 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 0djgt; 0ck1d; 0nr2v; 04sqj; 0lbl6; 0cx2r; 0p0mx; 01gh6z; 014ck4; 01tmtg; ... >> query: (?x2364, ?x4344) <- contains(?x2364, ?x4344), administrative_parent(?x2364, ?x2236), film_release_region(?x66, ?x2236) >> conf = 0.17 => this is the best rule for 3 predicted values *> Best rule #22530 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 275 *> proper extension: 06mx8; 02v3m7; *> query: (?x2364, ?x9150) <- contains(?x2364, ?x7593), contains(?x7593, ?x9150) *> conf = 0.04 ranks of expected_values: 6 EVAL 065zr state_province_region! 05ftw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 89.000 64.000 0.171 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #22099-01vxxb PRED entity: 01vxxb PRED relation: award PRED expected values: 057xs89 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 249): 0gqy2 (0.37 #570, 0.12 #13935, 0.11 #975), 05p09zm (0.37 #934, 0.25 #1744, 0.24 #1339), 05pcn59 (0.33 #891, 0.30 #3321, 0.29 #1296), 04kxsb (0.27 #126, 0.15 #531, 0.14 #936), 05b4l5x (0.22 #816, 0.21 #1221, 0.19 #1626), 0789_m (0.22 #425, 0.20 #20, 0.06 #830), 03c7tr1 (0.22 #868, 0.19 #1273, 0.18 #1678), 057xs89 (0.20 #161, 0.19 #566, 0.14 #2996), 09qv_s (0.20 #152, 0.13 #32408, 0.13 #34434), 05zvj3m (0.20 #93, 0.10 #903, 0.09 #2118) >> Best rule #570 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 01kt17; >> query: (?x4366, 0gqy2) <- award_nominee(?x3101, ?x4366), gender(?x4366, ?x231), ?x3101 = 0dvmd >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 03q5dr; *> query: (?x4366, 057xs89) <- award_nominee(?x5951, ?x4366), ?x5951 = 0dvld, profession(?x4366, ?x319) *> conf = 0.20 ranks of expected_values: 8 EVAL 01vxxb award 057xs89 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 107.000 107.000 0.370 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22098-01bczm PRED entity: 01bczm PRED relation: artist! PRED expected values: 03gfvsz => 120 concepts (103 used for prediction) PRED predicted values (max 10 best out of 4): 03gfvsz (0.38 #1, 0.36 #7, 0.25 #61), 01fjfv (0.27 #8, 0.25 #2, 0.14 #62), 04rqd (0.12 #5, 0.11 #71, 0.11 #65), 04y652m (0.02 #447, 0.02 #64, 0.02 #475) >> Best rule #1 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0kr_t; 0dw4g; 06mj4; >> query: (?x5550, 03gfvsz) <- award(?x5550, ?x9828), award(?x5550, ?x3631), ?x9828 = 01ckcd, award_nominee(?x5550, ?x248), ?x3631 = 02f73p >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bczm artist! 03gfvsz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 103.000 0.375 http://example.org/broadcast/content/artist #22097-04hqz PRED entity: 04hqz PRED relation: locations! PRED expected values: 018vbf => 142 concepts (124 used for prediction) PRED predicted values (max 10 best out of 121): 01w1sx (0.22 #603, 0.20 #860, 0.16 #219), 081pw (0.22 #513, 0.20 #770, 0.16 #129), 06k75 (0.19 #1721, 0.17 #696, 0.15 #1981), 03jqfx (0.19 #1746, 0.14 #2519, 0.14 #2006), 0k4y6 (0.14 #586, 0.13 #202, 0.12 #843), 0jnh (0.13 #222, 0.12 #863, 0.08 #606), 01gqg3 (0.13 #213, 0.11 #597, 0.10 #2909), 018w0j (0.13 #221, 0.11 #605, 0.10 #862), 0b_6jz (0.12 #5946, 0.11 #4915, 0.09 #9549), 07_nf (0.12 #828, 0.10 #187, 0.08 #571) >> Best rule #603 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 02qkt; >> query: (?x7413, 01w1sx) <- locations(?x13684, ?x7413), contains(?x7413, ?x461), films(?x13684, ?x7114) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04hqz locations! 018vbf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 124.000 0.222 http://example.org/time/event/locations #22096-0c5v2 PRED entity: 0c5v2 PRED relation: source PRED expected values: 0jbk9 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #26, 0.93 #25, 0.93 #5) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 198 >> proper extension: 0mn0v; 0qlrh; >> query: (?x13119, ?x958) <- county(?x13119, ?x9290), currency(?x9290, ?x170), source(?x9290, ?x958) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c5v2 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.930 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #22095-07jrjb PRED entity: 07jrjb PRED relation: producer_type PRED expected values: 0ckd1 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.68 #44, 0.68 #48, 0.67 #45) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 043q6n_; >> query: (?x8535, 0ckd1) <- program(?x8535, ?x5307), student(?x3439, ?x8535), award(?x8535, ?x2016) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07jrjb producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.680 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #22094-014zwb PRED entity: 014zwb PRED relation: currency PRED expected values: 09nqf => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.78 #106, 0.78 #113, 0.78 #148), 01nv4h (0.11 #492, 0.10 #358, 0.03 #9), 02l6h (0.11 #492, 0.10 #358, 0.01 #46), 088n7 (0.11 #492, 0.10 #358, 0.01 #91), 02gsvk (0.11 #492, 0.10 #358, 0.01 #174), 0kz1h (0.11 #492, 0.10 #358), 0ptk_ (0.11 #492, 0.10 #358) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 494 >> proper extension: 05dy7p; 02n9bh; 0gcrg; 027ct7c; 08j7lh; >> query: (?x3071, 09nqf) <- genre(?x3071, ?x239), film_crew_role(?x3071, ?x137), language(?x3071, ?x254), film(?x1774, ?x3071) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014zwb currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.784 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #22093-023n39 PRED entity: 023n39 PRED relation: location PRED expected values: 030qb3t => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 237): 030qb3t (0.39 #16128, 0.33 #4896, 0.29 #8104), 0f2rq (0.20 #279, 0.09 #1081, 0.02 #8301), 071vr (0.20 #335, 0.09 #1137, 0.01 #4347), 059rby (0.18 #818, 0.07 #15259, 0.07 #16062), 04jpl (0.12 #36122, 0.12 #38529, 0.10 #30504), 0cr3d (0.10 #6561, 0.09 #45878, 0.09 #2549), 01n7q (0.09 #864, 0.08 #1666, 0.07 #6480), 0d6lp (0.09 #968, 0.08 #1770, 0.05 #4980), 0r0m6 (0.09 #1018, 0.06 #8238, 0.04 #2622), 0ccvx (0.09 #1022, 0.04 #65796, 0.03 #9846) >> Best rule #16128 for best value: >> intensional similarity = 3 >> extensional distance = 303 >> proper extension: 045zr; 02kz_; 01pcvn; 07r4c; 0knjh; 01kgg9; >> query: (?x6849, 030qb3t) <- location(?x6849, ?x1558), participant(?x6849, ?x6850), film_release_region(?x124, ?x1558) >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023n39 location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.393 http://example.org/people/person/places_lived./people/place_lived/location #22092-027r9t PRED entity: 027r9t PRED relation: film_crew_role PRED expected values: 09vw2b7 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 27): 09vw2b7 (0.59 #1489, 0.57 #42, 0.55 #114), 0dxtw (0.50 #46, 0.34 #1493, 0.32 #118), 01vx2h (0.43 #47, 0.38 #119, 0.29 #1494), 01pvkk (0.27 #1495, 0.26 #266, 0.24 #193), 015h31 (0.23 #8, 0.14 #44, 0.11 #116), 0d2b38 (0.21 #62, 0.15 #26, 0.14 #134), 02ynfr (0.15 #16, 0.15 #342, 0.15 #1499), 0215hd (0.15 #19, 0.14 #236, 0.11 #200), 02rh1dz (0.15 #9, 0.14 #299, 0.09 #1492), 02_n3z (0.14 #37, 0.11 #109, 0.08 #399) >> Best rule #1489 for best value: >> intensional similarity = 3 >> extensional distance = 1211 >> proper extension: 02_fm2; 03g90h; 0gx1bnj; 0ddfwj1; 0dq626; 0czyxs; 0gtv7pk; 0h1cdwq; 02_1sj; 0c40vxk; ... >> query: (?x7141, 09vw2b7) <- genre(?x7141, ?x53), film(?x396, ?x7141), film_crew_role(?x7141, ?x137) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027r9t film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.585 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22091-01g42 PRED entity: 01g42 PRED relation: location_of_ceremony PRED expected values: 0qr8z => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 26): 0qxhc (0.12 #236), 0cv3w (0.03 #632, 0.03 #1466, 0.03 #1346), 0rsjf (0.02 #304, 0.02 #424, 0.02 #663), 0kc40 (0.02 #341, 0.02 #461, 0.01 #819), 013n2h (0.02 #310, 0.02 #430, 0.01 #1145), 059rby (0.02 #246, 0.02 #366, 0.01 #1081), 0dclg (0.02 #265, 0.02 #385), 0k_q_ (0.02 #387, 0.01 #745, 0.01 #1102), 030qb3t (0.02 #1330, 0.02 #616, 0.01 #1450), 0b90_r (0.02 #1314, 0.01 #1792) >> Best rule #236 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 08f3b1; 0157m; 0d06m5; 0315q3; 0svqs; 016z68; >> query: (?x8634, 0qxhc) <- religion(?x8634, ?x962), award(?x8634, ?x112), ?x962 = 05sfs >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01g42 location_of_ceremony 0qr8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 119.000 0.125 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #22090-0m0jc PRED entity: 0m0jc PRED relation: parent_genre PRED expected values: 03mb9 => 59 concepts (45 used for prediction) PRED predicted values (max 10 best out of 295): 05r6t (0.48 #4478, 0.45 #4641, 0.25 #1521), 0glt670 (0.33 #192, 0.30 #2477, 0.25 #843), 011j5x (0.33 #21, 0.25 #1489, 0.22 #2305), 03lty (0.33 #18, 0.22 #4114, 0.15 #7228), 016_rm (0.33 #296, 0.10 #2581, 0.09 #2745), 0mmp3 (0.30 #2516, 0.21 #3009, 0.15 #7373), 08cyft (0.30 #3802, 0.25 #2817, 0.21 #2981), 05bt6j (0.27 #3793, 0.25 #1497, 0.22 #1987), 01pfpt (0.25 #1527, 0.25 #549, 0.22 #2017), 02w4v (0.25 #1172, 0.25 #1010, 0.15 #7373) >> Best rule #4478 for best value: >> intensional similarity = 7 >> extensional distance = 83 >> proper extension: 028cl7; 088vmr; >> query: (?x474, 05r6t) <- parent_genre(?x474, ?x3915), artists(?x3915, ?x5760), artists(?x3915, ?x1004), ?x1004 = 01vv7sc, award(?x5760, ?x528), artists(?x3562, ?x5760), ?x3562 = 025sc50 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2517 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 06953q; *> query: (?x474, 03mb9) <- parent_genre(?x474, ?x3915), parent_genre(?x474, ?x1572), ?x3915 = 07gxw, artists(?x1572, ?x12506), artist(?x11715, ?x12506) *> conf = 0.20 ranks of expected_values: 29 EVAL 0m0jc parent_genre 03mb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 59.000 45.000 0.482 http://example.org/music/genre/parent_genre #22089-0k8z PRED entity: 0k8z PRED relation: list PRED expected values: 04k4rt => 262 concepts (262 used for prediction) PRED predicted values (max 10 best out of 4): 04k4rt (0.81 #1061, 0.81 #1055, 0.73 #611), 09g7thr (0.77 #366, 0.69 #246, 0.53 #843), 05glt (0.38 #1051, 0.38 #1057, 0.18 #607), 026cl_m (0.17 #518, 0.14 #608, 0.09 #1052) >> Best rule #1061 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 07bz5; >> query: (?x3793, ?x5997) <- list(?x3793, ?x7472), list(?x266, ?x7472), list(?x266, ?x5997) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k8z list 04k4rt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 262.000 262.000 0.814 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #22088-035s95 PRED entity: 035s95 PRED relation: film! PRED expected values: 032w8h 02l4pj 01d0fp 025j1t 01mqc_ => 81 concepts (40 used for prediction) PRED predicted values (max 10 best out of 949): 03dbds (0.21 #14497, 0.17 #6212, 0.03 #60056), 03ym1 (0.18 #1001, 0.04 #34138, 0.03 #5142), 02gvwz (0.18 #187, 0.04 #8469, 0.03 #27111), 0f0kz (0.18 #512, 0.03 #4653, 0.03 #33649), 0js9s (0.18 #1144, 0.03 #9426, 0.03 #13570), 015t56 (0.18 #466, 0.03 #33603, 0.02 #10819), 012d40 (0.18 #16, 0.03 #18656, 0.02 #45577), 0241jw (0.18 #294, 0.02 #33431, 0.02 #4435), 02ck7w (0.18 #928, 0.02 #34065, 0.02 #15425), 0svqs (0.18 #864, 0.02 #34001, 0.02 #9146) >> Best rule #14497 for best value: >> intensional similarity = 5 >> extensional distance = 147 >> proper extension: 0436yk; 08fbnx; 05vc35; >> query: (?x2128, ?x7621) <- genre(?x2128, ?x225), film(?x4536, ?x2128), ?x225 = 02kdv5l, profession(?x4536, ?x319), written_by(?x2128, ?x7621) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #7277 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 99 *> proper extension: 0cbl95; *> query: (?x2128, 025j1t) <- genre(?x2128, ?x8280), genre(?x2128, ?x225), ?x225 = 02kdv5l, production_companies(?x2128, ?x963), genre(?x7231, ?x8280), ?x7231 = 0k4bc *> conf = 0.05 ranks of expected_values: 98, 372, 518, 732, 840 EVAL 035s95 film! 01mqc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 81.000 40.000 0.205 http://example.org/film/actor/film./film/performance/film EVAL 035s95 film! 025j1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 81.000 40.000 0.205 http://example.org/film/actor/film./film/performance/film EVAL 035s95 film! 01d0fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 81.000 40.000 0.205 http://example.org/film/actor/film./film/performance/film EVAL 035s95 film! 02l4pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 81.000 40.000 0.205 http://example.org/film/actor/film./film/performance/film EVAL 035s95 film! 032w8h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 81.000 40.000 0.205 http://example.org/film/actor/film./film/performance/film #22087-0d1t3 PRED entity: 0d1t3 PRED relation: olympics PRED expected values: 0kbws => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 38): 0kbws (0.85 #828, 0.81 #1256, 0.80 #943), 0jhn7 (0.67 #756, 0.63 #79, 0.56 #595), 0sxrz (0.67 #234, 0.63 #79, 0.55 #253), 0l6ny (0.63 #79, 0.55 #253, 0.54 #83), 0l998 (0.63 #79, 0.55 #253, 0.54 #83), 0lbbj (0.63 #79, 0.55 #253, 0.54 #83), 0l98s (0.63 #79, 0.55 #253, 0.54 #83), 0lgxj (0.63 #79, 0.55 #253, 0.54 #83), 09x3r (0.63 #79, 0.55 #253, 0.54 #83), 018ljb (0.63 #79, 0.55 #253, 0.54 #83) >> Best rule #828 for best value: >> intensional similarity = 49 >> extensional distance = 11 >> proper extension: 01sgl; >> query: (?x4876, 0kbws) <- country(?x4876, ?x2513), country(?x4876, ?x1229), country(?x4876, ?x583), country(?x4876, ?x252), country(?x4876, ?x172), country(?x4876, ?x87), ?x2513 = 05b4w, ?x172 = 0154j, film_release_region(?x10860, ?x87), film_release_region(?x9859, ?x87), film_release_region(?x7887, ?x87), film_release_region(?x7864, ?x87), film_release_region(?x5109, ?x87), film_release_region(?x4448, ?x87), film_release_region(?x3491, ?x87), film_release_region(?x2788, ?x87), film_release_region(?x2340, ?x87), film_release_region(?x2093, ?x87), film_release_region(?x1535, ?x87), film_release_region(?x1228, ?x87), film_release_region(?x1150, ?x87), film_release_region(?x511, ?x87), film_release_region(?x428, ?x87), ?x583 = 015fr, ?x2340 = 0fpv_3_, jurisdiction_of_office(?x182, ?x87), ?x1535 = 02r1c18, ?x7887 = 04z_3pm, ?x2093 = 0gydcp7, ?x9859 = 0g57wgv, ?x511 = 0dscrwf, ?x428 = 0h1cdwq, ?x5109 = 0b44shh, ?x10860 = 049w1q, ?x2788 = 05q4y12, olympics(?x87, ?x778), ?x778 = 0kbvb, country(?x297, ?x252), film_release_region(?x1150, ?x2645), ?x1229 = 059j2, ?x7864 = 0cbn7c, ?x4448 = 01k60v, film_release_region(?x5013, ?x252), film_festivals(?x1228, ?x11147), ?x3491 = 0gtvpkw, ?x5013 = 011ycb, contains(?x87, ?x7809), ?x2645 = 03h64, administrative_parent(?x252, ?x551) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d1t3 olympics 0kbws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #22086-04hpck PRED entity: 04hpck PRED relation: award PRED expected values: 02x4w6g => 95 concepts (87 used for prediction) PRED predicted values (max 10 best out of 248): 0ck27z (0.80 #4537, 0.65 #1305, 0.60 #1709), 09qvc0 (0.67 #444, 0.35 #1252, 0.32 #1656), 0bdw6t (0.60 #1727, 0.57 #919, 0.57 #1323), 05p09zm (0.51 #3356, 0.14 #4164, 0.14 #6184), 08_vwq (0.50 #676, 0.14 #1080, 0.13 #1484), 0gqy2 (0.43 #5417, 0.33 #165, 0.17 #569), 0bp_b2 (0.37 #3250, 0.17 #422, 0.15 #5270), 0f4x7 (0.35 #5283, 0.33 #31, 0.14 #3263), 0bdwqv (0.33 #577, 0.33 #5425, 0.14 #3405), 09sb52 (0.33 #445, 0.30 #1253, 0.30 #5293) >> Best rule #4537 for best value: >> intensional similarity = 4 >> extensional distance = 197 >> proper extension: 01k7d9; 06151l; 02qflgv; 06b0d2; 02w9895; 01k8rb; 02tr7d; 02lf70; 01541z; 06lj1m; ... >> query: (?x1031, 0ck27z) <- award(?x1031, ?x8250), award(?x8431, ?x8250), type_of_union(?x1031, ?x566), ?x8431 = 022yb4 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #6465 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 260 *> proper extension: 07nznf; 01qscs; 0p_pd; 03rs8y; 06jzh; 01n5309; 01wmxfs; 02lnhv; 048lv; 05mt_q; ... *> query: (?x1031, ?x384) <- award(?x1031, ?x2192), currency(?x1031, ?x170), film(?x1031, ?x4602), nominated_for(?x384, ?x4602) *> conf = 0.11 ranks of expected_values: 40 EVAL 04hpck award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 95.000 87.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22085-03bkbh PRED entity: 03bkbh PRED relation: people PRED expected values: 09fb5 015rkw 04smkr 01l87db 022qw7 0dfrq 084nh => 30 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2861): 08f3b1 (0.33 #1775, 0.33 #87, 0.25 #3464), 03d9v8 (0.33 #2955, 0.33 #1267, 0.25 #4644), 0159h6 (0.33 #57, 0.30 #8498, 0.17 #1688), 0m2wm (0.33 #1729, 0.25 #3418, 0.20 #6795), 0320jz (0.33 #1917, 0.25 #3606, 0.20 #6983), 048cl (0.33 #2701, 0.25 #4390, 0.20 #7767), 0k4gf (0.33 #1838, 0.25 #3527, 0.20 #6904), 0h1mt (0.33 #1827, 0.25 #3516, 0.20 #6893), 0hskw (0.33 #2038, 0.25 #3727, 0.20 #7104), 026c1 (0.33 #1961, 0.25 #3650, 0.20 #7027) >> Best rule #1775 for best value: >> intensional similarity = 14 >> extensional distance = 1 >> proper extension: 013xrm; >> query: (?x7322, 08f3b1) <- people(?x7322, ?x10919), people(?x7322, ?x9797), people(?x7322, ?x5541), people(?x7322, ?x5301), people(?x7322, ?x4992), people(?x7322, ?x1554), people(?x7322, ?x875), ?x4992 = 0lkr7, ?x5541 = 01pk3z, ?x1554 = 06cgy, participant(?x2258, ?x5301), ?x875 = 032_jg, location(?x9797, ?x9895), award_nominee(?x820, ?x10919) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 02w7gg; *> query: (?x7322, 09fb5) <- people(?x7322, ?x4992), people(?x7322, ?x692), people(?x7322, ?x489), nominated_for(?x4992, ?x3496), film(?x489, ?x5425), ?x5425 = 02prwdh, award(?x4992, ?x451), award(?x489, ?x102), vacationer(?x1096, ?x692) *> conf = 0.33 ranks of expected_values: 96, 293, 1405, 2778 EVAL 03bkbh people 084nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 03bkbh people 0dfrq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 03bkbh people 022qw7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 03bkbh people 01l87db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 03bkbh people 04smkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 30.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 03bkbh people 015rkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 03bkbh people 09fb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 30.000 23.000 0.333 http://example.org/people/ethnicity/people #22084-0c921 PRED entity: 0c921 PRED relation: gender PRED expected values: 05zppz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #57, 0.88 #21, 0.87 #33), 02zsn (0.46 #223, 0.29 #92, 0.28 #28) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 263 >> proper extension: 03ft8; 0cm89v; 054187; 032md; 03p01x; >> query: (?x9320, 05zppz) <- nationality(?x9320, ?x94), written_by(?x5137, ?x9320), nominated_for(?x902, ?x5137) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c921 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.879 http://example.org/people/person/gender #22083-01rnxn PRED entity: 01rnxn PRED relation: award PRED expected values: 0bp_b2 => 108 concepts (80 used for prediction) PRED predicted values (max 10 best out of 227): 09sb52 (0.48 #1653, 0.38 #4881, 0.38 #4476), 0gqyl (0.33 #104, 0.13 #21770, 0.13 #507), 0gqwc (0.33 #74, 0.13 #21770, 0.12 #32254), 02ppm4q (0.33 #156, 0.13 #1768, 0.10 #2017), 094qd5 (0.33 #45, 0.10 #2017, 0.10 #1657), 03c7tr1 (0.33 #59, 0.08 #865, 0.08 #1268), 0bb57s (0.33 #243, 0.06 #1855, 0.05 #11128), 0bsjcw (0.33 #202, 0.03 #3428, 0.03 #4637), 0ck27z (0.29 #6543, 0.27 #6140, 0.26 #3720), 0f4x7 (0.18 #1643, 0.14 #2451, 0.14 #4466) >> Best rule #1653 for best value: >> intensional similarity = 4 >> extensional distance = 356 >> proper extension: 0q9kd; 0184jc; 02s2ft; 05vsxz; 05d7rk; 02qgqt; 02p65p; 0337vz; 04t2l2; 01j5ts; ... >> query: (?x2991, 09sb52) <- film(?x2991, ?x8827), award_winner(?x1033, ?x2991), nominated_for(?x112, ?x8827), ?x112 = 027dtxw >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #21770 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1535 *> proper extension: 0kcd5; *> query: (?x2991, ?x591) <- award_winner(?x1033, ?x2991), nominated_for(?x2991, ?x3505), nominated_for(?x591, ?x3505) *> conf = 0.13 ranks of expected_values: 24 EVAL 01rnxn award 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 108.000 80.000 0.483 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22082-0rsjf PRED entity: 0rsjf PRED relation: place! PRED expected values: 0rsjf => 104 concepts (70 used for prediction) PRED predicted values (max 10 best out of 211): 0rk71 (0.20 #282, 0.08 #1312, 0.05 #21669), 0rh7t (0.20 #145, 0.08 #1175, 0.05 #21669), 0ply0 (0.20 #73, 0.05 #21669, 0.03 #2133), 0r62v (0.09 #532, 0.06 #1562, 0.03 #2077), 030qb3t (0.09 #545, 0.06 #1575, 0.03 #2090), 0gkgp (0.09 #766, 0.06 #1796, 0.03 #2311), 0k_q_ (0.09 #562, 0.03 #2107, 0.03 #3139), 06kx2 (0.09 #909, 0.03 #2454, 0.03 #3486), 01cx_ (0.09 #579, 0.03 #2124, 0.03 #2640), 0b2ds (0.09 #706, 0.03 #2251, 0.03 #3283) >> Best rule #282 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0rh7t; >> query: (?x6495, 0rk71) <- country(?x6495, ?x94), county(?x6495, ?x13155), state(?x6495, ?x2623), ?x2623 = 02xry >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #21669 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 247 *> proper extension: 0hc8h; *> query: (?x6495, ?x2624) <- country(?x6495, ?x94), state(?x6495, ?x2623), state(?x2624, ?x2623), contains(?x2623, ?x95) *> conf = 0.05 ranks of expected_values: 30 EVAL 0rsjf place! 0rsjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 104.000 70.000 0.200 http://example.org/location/hud_county_place/place #22081-02s8qk PRED entity: 02s8qk PRED relation: contains! PRED expected values: 09c7w0 => 137 concepts (81 used for prediction) PRED predicted values (max 10 best out of 279): 09c7w0 (0.86 #9841, 0.82 #32199, 0.77 #16994), 0m7d0 (0.67 #69766, 0.60 #51871, 0.56 #9838), 02jx1 (0.55 #61800, 0.29 #47484, 0.27 #69853), 07ssc (0.49 #69798, 0.38 #61745, 0.21 #51903), 02_286 (0.26 #13458, 0.20 #22401, 0.14 #1832), 059rby (0.22 #1809, 0.21 #13435, 0.19 #10752), 01n7q (0.19 #22436, 0.16 #68948, 0.16 #13493), 0dclg (0.19 #144, 0.06 #13559, 0.04 #2828), 030qb3t (0.14 #13516, 0.11 #22459, 0.08 #5467), 04jpl (0.13 #61735, 0.09 #31324, 0.09 #18804) >> Best rule #9841 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 06xpp7; >> query: (?x6257, 09c7w0) <- citytown(?x6257, ?x4499), contains(?x3670, ?x6257), dog_breed(?x4499, ?x1706), teams(?x4499, ?x1576) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02s8qk contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 81.000 0.860 http://example.org/location/location/contains #22080-099bhp PRED entity: 099bhp PRED relation: film! PRED expected values: 03dn9v => 64 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1105): 0glmv (0.50 #8856, 0.33 #6780, 0.12 #21312), 02gf_l (0.33 #5419, 0.33 #3343, 0.22 #15799), 0725ny (0.33 #7673, 0.25 #9749, 0.11 #15977), 062hgx (0.33 #6994, 0.25 #9070, 0.10 #17374), 016ks_ (0.33 #7012, 0.25 #9088, 0.03 #38152), 03wy70 (0.33 #7514, 0.25 #9590, 0.03 #22046), 01mylz (0.33 #8170, 0.25 #10246, 0.03 #22702), 01h4rj (0.33 #7885, 0.25 #9961, 0.03 #22417), 02sb1w (0.33 #7351, 0.25 #9427, 0.03 #21883), 02g5h5 (0.33 #6882, 0.25 #8958, 0.03 #21414) >> Best rule #8856 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 050f0s; >> query: (?x10072, 0glmv) <- film(?x12054, ?x10072), language(?x10072, ?x254), ?x12054 = 0sw6y, country(?x10072, ?x94), genre(?x10072, ?x258), ?x94 = 09c7w0 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #32975 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 190 *> proper extension: 0gtsx8c; *> query: (?x10072, 03dn9v) <- film(?x12054, ?x10072), film(?x8513, ?x10072), language(?x10072, ?x254), ?x254 = 02h40lc, actor(?x5286, ?x12054), nationality(?x12054, ?x94), location(?x8513, ?x2850) *> conf = 0.02 ranks of expected_values: 563 EVAL 099bhp film! 03dn9v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 64.000 31.000 0.500 http://example.org/film/actor/film./film/performance/film #22079-04954r PRED entity: 04954r PRED relation: produced_by PRED expected values: 0638kv => 86 concepts (53 used for prediction) PRED predicted values (max 10 best out of 105): 01v5h (0.20 #302, 0.01 #12809), 024c1b (0.20 #387), 01pp3p (0.20 #182), 0fvf9q (0.17 #394, 0.12 #782, 0.03 #3496), 06mn7 (0.17 #539), 02w670 (0.10 #3101, 0.10 #14361, 0.10 #1552), 04kj2v (0.10 #3101, 0.10 #14361, 0.10 #1552), 06pj8 (0.04 #3557, 0.02 #4723, 0.02 #4336), 03ktjq (0.04 #5635, 0.02 #13398, 0.02 #9519), 02q_cc (0.03 #3523, 0.02 #9351, 0.02 #2746) >> Best rule #302 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0j80w; 0kb07; 029jt9; >> query: (?x3755, 01v5h) <- nominated_for(?x5206, ?x3755), nominated_for(?x484, ?x3755), ?x5206 = 02w670, genre(?x3755, ?x53) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04954r produced_by 0638kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 53.000 0.200 http://example.org/film/film/produced_by #22078-02xpy5 PRED entity: 02xpy5 PRED relation: category PRED expected values: 08mbj5d => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #34, 0.92 #33, 0.90 #30) >> Best rule #34 for best value: >> intensional similarity = 5 >> extensional distance = 187 >> proper extension: 02zd2b; 04p_hy; 01pdgp; 0l0wv; 03205_; 030w19; >> query: (?x6460, ?x134) <- currency(?x6460, ?x170), contains(?x13739, ?x6460), contains(?x94, ?x6460), ?x94 = 09c7w0, category(?x13739, ?x134) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xpy5 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.915 http://example.org/common/topic/webpage./common/webpage/category #22077-0697kh PRED entity: 0697kh PRED relation: award PRED expected values: 02xcb6n => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 253): 0gq9h (0.54 #885, 0.34 #2905, 0.32 #4521), 040njc (0.45 #816, 0.27 #2836, 0.24 #3644), 09sb52 (0.31 #4888, 0.25 #9736, 0.23 #16200), 0gr51 (0.28 #1312, 0.26 #100, 0.24 #2120), 019f4v (0.27 #874, 0.19 #2894, 0.17 #1278), 0gr4k (0.27 #32, 0.27 #2052, 0.27 #1244), 04dn09n (0.27 #43, 0.26 #1255, 0.25 #2063), 0gs9p (0.26 #887, 0.19 #2907, 0.18 #3311), 03hkv_r (0.23 #15, 0.22 #2035, 0.20 #1227), 03hl6lc (0.21 #1391, 0.20 #179, 0.18 #2199) >> Best rule #885 for best value: >> intensional similarity = 2 >> extensional distance = 180 >> proper extension: 024c1b; >> query: (?x8337, 0gq9h) <- produced_by(?x5128, ?x8337), honored_for(?x4224, ?x5128) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #19393 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1577 *> proper extension: 01nzs7; *> query: (?x8337, ?x435) <- award_winner(?x5810, ?x8337), nominated_for(?x435, ?x5810) *> conf = 0.14 ranks of expected_values: 23 EVAL 0697kh award 02xcb6n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 94.000 94.000 0.538 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22076-0f4x7 PRED entity: 0f4x7 PRED relation: ceremony PRED expected values: 073hmq 02jp5r 0bzknt 05hmp6 0bzkvd 0fy59t 073h5b => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 83): 02jp5r (0.85 #623, 0.33 #457, 0.17 #208), 073h5b (0.85 #659, 0.33 #493, 0.17 #244), 0bzkvd (0.80 #646, 0.33 #480, 0.17 #231), 073hmq (0.80 #598, 0.25 #432, 0.17 #183), 0bzknt (0.75 #631, 0.33 #465, 0.17 #216), 05hmp6 (0.75 #635, 0.25 #469, 0.17 #220), 0gpjbt (0.61 #850, 0.34 #2344, 0.34 #1929), 0fy59t (0.60 #648, 0.33 #482, 0.17 #233), 09n4nb (0.60 #863, 0.34 #2357, 0.33 #1942), 0466p0j (0.59 #878, 0.33 #2123, 0.33 #1957) >> Best rule #623 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x591, 02jp5r) <- award(?x858, ?x591), location(?x858, ?x859), ceremony(?x591, ?x5761), ?x5761 = 02ywhz >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 8 EVAL 0f4x7 ceremony 073h5b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.850 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0f4x7 ceremony 0fy59t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 46.000 0.850 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0f4x7 ceremony 0bzkvd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.850 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0f4x7 ceremony 05hmp6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.850 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0f4x7 ceremony 0bzknt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.850 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0f4x7 ceremony 02jp5r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.850 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0f4x7 ceremony 073hmq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.850 http://example.org/award/award_category/winners./award/award_honor/ceremony #22075-01htxr PRED entity: 01htxr PRED relation: award_winner! PRED expected values: 0gpjbt 02cg41 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 117): 01mhwk (0.19 #836, 0.18 #703, 0.17 #969), 013b2h (0.18 #3666, 0.13 #4730, 0.13 #4863), 01c6qp (0.17 #11307, 0.17 #11173, 0.17 #150), 02rjjll (0.17 #11307, 0.17 #11173, 0.13 #4792), 056878 (0.17 #11307, 0.17 #11173, 0.12 #4818), 0bzn6_ (0.17 #11307, 0.17 #11173, 0.06 #584), 01bx35 (0.17 #139, 0.10 #4927, 0.10 #3597), 0bz6sb (0.17 #193, 0.07 #459, 0.06 #1390), 0clfdj (0.17 #136, 0.07 #402, 0.06 #535), 059x66 (0.17 #149, 0.04 #2676, 0.03 #1346) >> Best rule #836 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 02whj; 0cg9y; 016z1t; 02jq1; 01vsy9_; >> query: (?x6207, 01mhwk) <- celebrities_impersonated(?x3649, ?x6207), artists(?x505, ?x6207), award_winner(?x537, ?x6207) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #650 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 019n7x; *> query: (?x6207, 02cg41) <- celebrities_impersonated(?x3649, ?x6207), participant(?x6207, ?x3884), category(?x6207, ?x134) *> conf = 0.12 ranks of expected_values: 14, 18 EVAL 01htxr award_winner! 02cg41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 134.000 134.000 0.190 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01htxr award_winner! 0gpjbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 134.000 134.000 0.190 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22074-01tzfz PRED entity: 01tzfz PRED relation: citytown PRED expected values: 0m0bj => 153 concepts (91 used for prediction) PRED predicted values (max 10 best out of 183): 04jpl (0.38 #1112, 0.23 #5537, 0.22 #4800), 02jx1 (0.29 #737, 0.27 #6638, 0.27 #6639), 0dbdy (0.29 #737, 0.27 #6638, 0.27 #6639), 0978r (0.22 #3391, 0.22 #4868, 0.21 #5605), 02_286 (0.20 #24363, 0.19 #25471, 0.18 #26953), 05l5n (0.20 #3353, 0.14 #4830, 0.13 #5567), 052bw (0.17 #560, 0.04 #6461, 0.03 #7570), 0m75g (0.17 #530, 0.03 #2372, 0.03 #2741), 0k33p (0.17 #224, 0.03 #2434, 0.03 #2803), 015zxh (0.17 #32, 0.02 #2980, 0.02 #5194) >> Best rule #1112 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 04f525m; >> query: (?x10373, 04jpl) <- state_province_region(?x10373, ?x1758), contains(?x1758, ?x14342), administrative_parent(?x1757, ?x1758), ?x14342 = 01hvzr >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01tzfz citytown 0m0bj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 91.000 0.375 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #22073-01xwqn PRED entity: 01xwqn PRED relation: location PRED expected values: 030qb3t => 146 concepts (103 used for prediction) PRED predicted values (max 10 best out of 258): 030qb3t (0.31 #885, 0.26 #36979, 0.26 #3292), 0cr3d (0.15 #947, 0.14 #144, 0.10 #4156), 05k7sb (0.15 #911, 0.10 #4120, 0.06 #4923), 01cx_ (0.15 #965, 0.06 #3372, 0.05 #20215), 0dclg (0.14 #116, 0.06 #2524, 0.03 #7337), 0rh6k (0.14 #1609, 0.06 #8829, 0.04 #28079), 0n1rj (0.14 #296, 0.02 #14735), 0947l (0.12 #2836, 0.01 #19679), 0cc56 (0.09 #22515, 0.09 #56208, 0.08 #859), 0156q (0.08 #5704, 0.01 #63457, 0.01 #26556) >> Best rule #885 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 05g8ky; 05ty4m; 016_mj; 01j7rd; 015pxr; 03q3sy; 0bqs56; 049fgvm; 02z3zp; 0q9zc; ... >> query: (?x10963, 030qb3t) <- influenced_by(?x10963, ?x986), location(?x10963, ?x739), profession(?x10963, ?x319), ?x986 = 081lh >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xwqn location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 103.000 0.308 http://example.org/people/person/places_lived./people/place_lived/location #22072-04rwx PRED entity: 04rwx PRED relation: institution! PRED expected values: 04zx3q1 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 16): 027f2w (0.59 #57, 0.46 #74, 0.44 #5), 04zx3q1 (0.53 #173, 0.52 #121, 0.51 #53), 013zdg (0.36 #56, 0.33 #4, 0.32 #73), 0bjrnt (0.31 #55, 0.21 #72, 0.21 #209), 03mkk4 (0.28 #59, 0.28 #76, 0.19 #179), 01rr_d (0.25 #183, 0.24 #131, 0.24 #166), 028dcg (0.22 #13, 0.18 #65, 0.15 #185), 02cq61 (0.22 #12, 0.13 #184, 0.13 #64), 02mjs7 (0.18 #54, 0.17 #105, 0.15 #174), 022h5x (0.16 #83, 0.15 #66, 0.12 #134) >> Best rule #57 for best value: >> intensional similarity = 2 >> extensional distance = 37 >> proper extension: 0d06m5; 0d05fv; >> query: (?x1665, 027f2w) <- list(?x1665, ?x2197), organization(?x1665, ?x5487) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #173 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 66 *> proper extension: 08815; 01jssp; 05krk; 052nd; 06pwq; 065y4w7; 07tgn; 0277jc; 07szy; 09kvv; ... *> query: (?x1665, 04zx3q1) <- list(?x1665, ?x2197), student(?x1665, ?x4463), ?x2197 = 09g7thr *> conf = 0.53 ranks of expected_values: 2 EVAL 04rwx institution! 04zx3q1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.590 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22071-0jdk0 PRED entity: 0jdk0 PRED relation: people PRED expected values: 010xjr => 35 concepts (18 used for prediction) PRED predicted values (max 10 best out of 737): 053yx (0.23 #96, 0.20 #1467, 0.17 #4212), 01938t (0.23 #282, 0.13 #2341, 0.13 #1653), 07pzc (0.21 #1105, 0.20 #2479, 0.18 #3850), 03rx9 (0.20 #1825, 0.17 #4570, 0.16 #6637), 0gyy0 (0.17 #4495, 0.16 #6562, 0.16 #5872), 02dth1 (0.17 #4260, 0.16 #5637, 0.16 #4948), 04__f (0.17 #4458, 0.16 #5835, 0.16 #5146), 0jrny (0.16 #6289, 0.16 #5599, 0.16 #4910), 0chsq (0.15 #13, 0.14 #6883, 0.12 #8259), 0b22w (0.15 #492, 0.13 #2551, 0.13 #1863) >> Best rule #96 for best value: >> intensional similarity = 8 >> extensional distance = 11 >> proper extension: 09969; >> query: (?x3538, 053yx) <- people(?x3538, ?x7342), people(?x3538, ?x3539), people(?x3538, ?x1872), award_winner(?x2139, ?x3539), award_nominee(?x3539, ?x1247), profession(?x7342, ?x319), award(?x1872, ?x1862), ?x1862 = 0gr51 >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jdk0 people 010xjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 35.000 18.000 0.231 http://example.org/people/cause_of_death/people #22070-05pbl56 PRED entity: 05pbl56 PRED relation: films! PRED expected values: 0d6qjf => 68 concepts (43 used for prediction) PRED predicted values (max 10 best out of 39): 0ddct (0.12 #88, 0.04 #400, 0.04 #244), 0cm2xh (0.12 #47, 0.04 #203), 01w1sx (0.11 #559, 0.10 #716, 0.07 #1030), 07jq_ (0.11 #550, 0.10 #707, 0.07 #1021), 02vnz (0.10 #749, 0.07 #592, 0.07 #1063), 081pw (0.07 #471, 0.07 #628, 0.05 #942), 0l8bg (0.07 #742, 0.05 #1056, 0.04 #585), 05489 (0.06 #1148, 0.03 #2242, 0.03 #834), 0fzyg (0.05 #836, 0.05 #993, 0.04 #366), 07s2s (0.04 #255, 0.02 #1352, 0.02 #1509) >> Best rule #88 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 04n52p6; 08052t3; 026p4q7; 08phg9; 05nlx4; 06znpjr; >> query: (?x1595, 0ddct) <- film_crew_role(?x1595, ?x1966), film_crew_role(?x1595, ?x1776), ?x1776 = 020xn5, ?x1966 = 015h31, production_companies(?x1595, ?x1478) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #727 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 0d1qmz; *> query: (?x1595, 0d6qjf) <- nominated_for(?x2258, ?x1595), genre(?x1595, ?x5104), ?x5104 = 0bkbm, language(?x1595, ?x254) *> conf = 0.03 ranks of expected_values: 16 EVAL 05pbl56 films! 0d6qjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 68.000 43.000 0.125 http://example.org/film/film_subject/films #22069-0bwfn PRED entity: 0bwfn PRED relation: major_field_of_study PRED expected values: 05qfh 01zc2w 06mq7 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 98): 05qjt (0.56 #203, 0.47 #1774, 0.41 #595), 0193x (0.56 #225, 0.24 #617, 0.19 #1796), 01lhy (0.44 #207, 0.33 #11, 0.18 #599), 04x_3 (0.41 #611, 0.32 #1790, 0.26 #2967), 05qfh (0.38 #1797, 0.33 #30, 0.31 #2974), 02h40lc (0.33 #200, 0.29 #592, 0.28 #1771), 0dc_v (0.33 #34, 0.22 #230, 0.17 #1801), 04g51 (0.33 #237, 0.13 #9328, 0.12 #3043), 07c52 (0.33 #115, 0.13 #9328, 0.12 #3043), 06ms6 (0.29 #603, 0.26 #2959, 0.25 #1782) >> Best rule #203 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 039cpd; >> query: (?x7545, 05qjt) <- child(?x7545, ?x8850), contains(?x205, ?x7545) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1797 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 019q50; *> query: (?x7545, 05qfh) <- list(?x7545, ?x2197), institution(?x1771, ?x7545), ?x1771 = 019v9k *> conf = 0.38 ranks of expected_values: 5, 12, 43 EVAL 0bwfn major_field_of_study 06mq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 114.000 114.000 0.556 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0bwfn major_field_of_study 01zc2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 114.000 114.000 0.556 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0bwfn major_field_of_study 05qfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 114.000 114.000 0.556 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #22068-0fbq2n PRED entity: 0fbq2n PRED relation: team! PRED expected values: 05b3ts => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 43): 02g_6j (0.83 #415, 0.81 #550, 0.80 #1186), 05b3ts (0.82 #330, 0.80 #285, 0.78 #240), 047g8h (0.79 #864, 0.75 #1047, 0.75 #729), 04nfpk (0.77 #1374, 0.75 #1054, 0.74 #1191), 05zm34 (0.77 #1188, 0.75 #1371, 0.75 #1051), 08ns5s (0.71 #2234, 0.71 #2233, 0.71 #1207), 0bgv8y (0.71 #2234, 0.71 #2233, 0.70 #2187), 0bgv4g (0.71 #2234, 0.71 #2233, 0.70 #2187), 03h42s4 (0.71 #2234, 0.71 #2233, 0.70 #2187), 02vkdwz (0.71 #2234, 0.71 #2233, 0.70 #2187) >> Best rule #415 for best value: >> intensional similarity = 9 >> extensional distance = 10 >> proper extension: 01jv_6; 01y49; >> query: (?x179, 02g_6j) <- team(?x7749, ?x179), position_s(?x179, ?x2312), position_s(?x179, ?x1717), position_s(?x179, ?x935), teams(?x13303, ?x179), ?x1717 = 02g_6x, colors(?x179, ?x3621), ?x935 = 06b1q, position(?x387, ?x2312) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #330 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 9 *> proper extension: 03lsq; *> query: (?x179, 05b3ts) <- team(?x7749, ?x179), position_s(?x179, ?x1717), position_s(?x179, ?x1240), position_s(?x179, ?x935), position_s(?x179, ?x180), teams(?x13303, ?x179), ?x1717 = 02g_6x, ?x1240 = 023wyl, ?x935 = 06b1q, position_s(?x180, ?x706) *> conf = 0.82 ranks of expected_values: 2 EVAL 0fbq2n team! 05b3ts CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.833 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #22067-0zqq8 PRED entity: 0zqq8 PRED relation: time_zones PRED expected values: 02hcv8 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.78 #159, 0.64 #540, 0.54 #67), 02fqwt (0.28 #68, 0.25 #14, 0.23 #212), 02lcqs (0.27 #98, 0.25 #111, 0.25 #359), 02hczc (0.25 #15, 0.14 #41, 0.11 #121), 042g7t (0.25 #24, 0.14 #50, 0.07 #64), 02lcrv (0.25 #20, 0.05 #33, 0.05 #46), 03bdv (0.18 #178, 0.13 #59, 0.09 #191), 02llzg (0.10 #176, 0.07 #202, 0.07 #399), 052vwh (0.05 #38, 0.01 #171, 0.01 #184), 05jphn (0.05 #52, 0.04 #185, 0.03 #66) >> Best rule #159 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 01mc11; 013yq; 01cx_; 03l2n; 0f2nf; 07l5z; 0l39b; 0fsv2; 031sn; 0nqph; >> query: (?x8241, ?x2674) <- contains(?x8241, ?x4750), county(?x8241, ?x12846), contains(?x3670, ?x8241), time_zones(?x12846, ?x2674) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0zqq8 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.776 http://example.org/location/location/time_zones #22066-05qtj PRED entity: 05qtj PRED relation: citytown! PRED expected values: 04mwxk3 => 250 concepts (159 used for prediction) PRED predicted values (max 10 best out of 720): 02cw8s (0.57 #77935, 0.49 #65883, 0.49 #118939), 0g_tv (0.57 #77935, 0.49 #65883, 0.49 #118939), 01j2_7 (0.57 #77935, 0.49 #65883, 0.49 #118939), 04gdr (0.57 #77935, 0.49 #65883, 0.49 #118939), 025txrl (0.47 #54635, 0.22 #15262, 0.21 #18477), 02_l39 (0.36 #15261, 0.07 #6173, 0.05 #10992), 0473m9 (0.33 #43, 0.07 #5665, 0.07 #7271), 04kqk (0.33 #751, 0.07 #6373, 0.07 #7979), 013807 (0.33 #548, 0.07 #6170, 0.07 #7776), 02jd_7 (0.33 #528, 0.07 #6150, 0.07 #7756) >> Best rule #77935 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 0fm2_; 0nqph; >> query: (?x4627, ?x2593) <- citytown(?x4619, ?x4627), teams(?x4627, ?x13580), contains(?x4627, ?x2593) >> conf = 0.57 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 05qtj citytown! 04mwxk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 250.000 159.000 0.570 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #22065-0gv10 PRED entity: 0gv10 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.90 #109, 0.88 #96, 0.88 #45), 01g63y (0.07 #10, 0.06 #74, 0.06 #38), 0jgjn (0.04 #76, 0.03 #125, 0.03 #94) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 0f8l9c; 06q1r; >> query: (?x4156, 04ztj) <- location_of_ceremony(?x4567, ?x4156), time_zones(?x4156, ?x2674), time_zones(?x94, ?x2674), nationality(?x51, ?x94) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gv10 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.897 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #22064-04gxf PRED entity: 04gxf PRED relation: source PRED expected values: 0jbk9 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.82 #9, 0.79 #14, 0.78 #30) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 02_286; 0lhql; 0fpzwf; >> query: (?x7996, 0jbk9) <- jurisdiction_of_office(?x1195, ?x7996), ?x1195 = 0pqc5, locations(?x5258, ?x7996), teams(?x7996, ?x6379) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gxf source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.824 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #22063-03gj2 PRED entity: 03gj2 PRED relation: organization PRED expected values: 04k4l => 200 concepts (178 used for prediction) PRED predicted values (max 10 best out of 16): 018cqq (0.73 #292, 0.71 #232, 0.67 #170), 0b6css (0.67 #231, 0.66 #1854, 0.58 #2463), 0_2v (0.66 #1854, 0.62 #284, 0.58 #2463), 04k4l (0.66 #1854, 0.58 #2463, 0.58 #2381), 0j7v_ (0.45 #1119, 0.26 #1877, 0.25 #2203), 041288 (0.40 #1130, 0.35 #2214, 0.35 #2254), 034h1h (0.40 #128, 0.22 #2792, 0.22 #2812), 059dn (0.33 #73, 0.33 #33, 0.27 #215), 0gkjy (0.28 #1408, 0.28 #1962, 0.27 #2084), 085h1 (0.24 #1465, 0.24 #1464, 0.20 #1896) >> Best rule #292 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 04gzd; 047lj; 03rt9; >> query: (?x1003, 018cqq) <- film_release_region(?x2318, ?x1003), film_release_region(?x86, ?x1003), ?x2318 = 06v9_x, ?x86 = 0ds35l9 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1854 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 130 *> proper extension: 04fh3; *> query: (?x1003, ?x127) <- currency(?x1003, ?x170), adjoins(?x1003, ?x1790), organization(?x1790, ?x127) *> conf = 0.66 ranks of expected_values: 4 EVAL 03gj2 organization 04k4l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 200.000 178.000 0.731 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #22062-04gv3db PRED entity: 04gv3db PRED relation: film! PRED expected values: 03xq0f => 64 concepts (34 used for prediction) PRED predicted values (max 10 best out of 44): 03xq0f (0.82 #226, 0.14 #300, 0.12 #818), 05qd_ (0.20 #230, 0.13 #304, 0.12 #8), 020h2v (0.18 #118, 0.17 #192, 0.04 #932), 086k8 (0.16 #1859, 0.15 #1186, 0.14 #1112), 016tw3 (0.15 #1867, 0.14 #1194, 0.14 #1419), 017s11 (0.13 #373, 0.12 #3, 0.12 #965), 01gb54 (0.12 #28, 0.12 #176, 0.12 #250), 01795t (0.11 #461, 0.09 #91, 0.08 #313), 0g1rw (0.10 #303, 0.06 #451, 0.06 #1492), 054g1r (0.09 #108, 0.07 #478, 0.06 #1144) >> Best rule #226 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0522wp; >> query: (?x4479, 03xq0f) <- region(?x4479, ?x512), category(?x4479, ?x134), film(?x574, ?x4479) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gv3db film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 34.000 0.824 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #22061-01grrf PRED entity: 01grrf PRED relation: district_represented PRED expected values: 07z1m 05k7sb 05tbn => 38 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1063): 05k7sb (0.91 #999, 0.90 #1000, 0.88 #1864), 05tbn (0.91 #999, 0.90 #1000, 0.88 #610), 07z1m (0.91 #999, 0.90 #1000, 0.88 #610), 0g0syc (0.90 #1000, 0.88 #610, 0.87 #333), 03v1s (0.80 #1622, 0.79 #1453, 0.79 #1281), 04ych (0.80 #1622, 0.79 #1453, 0.79 #1281), 04tgp (0.80 #1622, 0.79 #1453, 0.79 #1281), 0gyh (0.80 #1622, 0.79 #1453, 0.79 #1281), 050ks (0.80 #1622, 0.79 #1453, 0.79 #1281), 03v0t (0.80 #1622, 0.79 #1453, 0.79 #1281) >> Best rule #999 for best value: >> intensional similarity = 41 >> extensional distance = 9 >> proper extension: 01gssz; >> query: (?x7914, ?x2020) <- district_represented(?x7914, ?x7518), district_represented(?x7914, ?x4776), district_represented(?x7914, ?x2713), district_represented(?x7914, ?x760), district_represented(?x7914, ?x728), district_represented(?x7914, ?x177), legislative_sessions(?x11142, ?x7914), legislative_sessions(?x5256, ?x7914), legislative_sessions(?x7715, ?x11142), ?x2713 = 06btq, legislative_sessions(?x2860, ?x5256), ?x7518 = 026mj, district_represented(?x11142, ?x3670), district_represented(?x11142, ?x2020), district_represented(?x11142, ?x1426), ?x728 = 059f4, ?x177 = 05kkh, ?x3670 = 05tbn, contains(?x760, ?x13620), contains(?x760, ?x11318), contains(?x760, ?x5907), legislative_sessions(?x7914, ?x5005), ?x1426 = 07z1m, district_represented(?x6728, ?x760), district_represented(?x759, ?x760), district_represented(?x653, ?x760), contains(?x94, ?x760), contains(?x13620, ?x4117), school(?x8894, ?x5907), ?x8894 = 02d02, student(?x11318, ?x1290), ?x759 = 043djx, location(?x120, ?x760), ?x4776 = 06yxd, ?x7715 = 01grp0, ?x6728 = 070mff, contains(?x2020, ?x1151), state_province_region(?x1520, ?x2020), location(?x237, ?x2020), religion(?x760, ?x109), ?x653 = 070m6c >> conf = 0.91 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2, 3 EVAL 01grrf district_represented 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 36.000 0.914 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01grrf district_represented 05k7sb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 36.000 0.914 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01grrf district_represented 07z1m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 36.000 0.914 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #22060-078vc PRED entity: 078vc PRED relation: languages_spoken PRED expected values: 071fb => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 130): 0t_2 (0.34 #2374, 0.33 #2082, 0.32 #2276), 07c9s (0.33 #109, 0.29 #447, 0.25 #157), 0121sr (0.33 #136, 0.29 #474, 0.25 #184), 09s02 (0.33 #139, 0.29 #477, 0.25 #187), 01c7y (0.33 #132, 0.29 #470, 0.25 #180), 06b_j (0.33 #16, 0.25 #209, 0.21 #338), 0880p (0.33 #38, 0.25 #231, 0.13 #1964), 064_8sq (0.31 #1990, 0.25 #208, 0.22 #1892), 06nm1 (0.25 #200, 0.21 #338, 0.19 #1926), 03hkp (0.25 #204, 0.21 #338, 0.13 #1937) >> Best rule #2374 for best value: >> intensional similarity = 10 >> extensional distance = 65 >> proper extension: 07bch9; 063k3h; >> query: (?x10322, 0t_2) <- languages_spoken(?x10322, ?x9113), language(?x5001, ?x9113), language(?x4579, ?x9113), language(?x2381, ?x9113), country(?x5001, ?x94), film(?x157, ?x5001), production_companies(?x5001, ?x1104), written_by(?x5001, ?x3751), film_crew_role(?x2381, ?x137), nominated_for(?x2065, ?x4579) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #338 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 3 *> proper extension: 02sch9; *> query: (?x10322, ?x5121) <- languages_spoken(?x10322, ?x13468), languages_spoken(?x10322, ?x9113), languages_spoken(?x10322, ?x4605), languages_spoken(?x10322, ?x1882), ?x9113 = 02hxcvy, official_language(?x3352, ?x13468), countries_spoken_in(?x4605, ?x7747), languages(?x10074, ?x1882), languages(?x10027, ?x1882), countries_spoken_in(?x1882, ?x792), language(?x1745, ?x4605), languages(?x10027, ?x5121), sibling(?x12024, ?x10074) *> conf = 0.21 ranks of expected_values: 39 EVAL 078vc languages_spoken 071fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 52.000 52.000 0.343 http://example.org/people/ethnicity/languages_spoken #22059-029jt9 PRED entity: 029jt9 PRED relation: film_release_region PRED expected values: 09c7w0 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 123): 09c7w0 (0.69 #3587, 0.69 #4304, 0.69 #3946), 0d0vqn (0.40 #191, 0.33 #370, 0.23 #3596), 0f8l9c (0.40 #211, 0.33 #390, 0.22 #3616), 02vzc (0.40 #249, 0.33 #428, 0.21 #3475), 0k6nt (0.40 #215, 0.33 #394, 0.19 #3441), 05r4w (0.40 #181, 0.18 #3407, 0.18 #16312), 0h7x (0.40 #229, 0.17 #408, 0.15 #1304), 0345h (0.33 #7706, 0.30 #15772, 0.28 #11472), 03_3d (0.33 #368, 0.21 #3594, 0.20 #3415), 0chghy (0.33 #375, 0.21 #3422, 0.20 #196) >> Best rule #3587 for best value: >> intensional similarity = 3 >> extensional distance = 355 >> proper extension: 0d_2fb; 0gs973; >> query: (?x8941, 09c7w0) <- genre(?x8941, ?x811), film(?x9477, ?x8941), ?x811 = 03k9fj >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 029jt9 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.695 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22058-02_qt PRED entity: 02_qt PRED relation: genre PRED expected values: 01zhp => 64 concepts (32 used for prediction) PRED predicted values (max 10 best out of 93): 05p553 (0.55 #1651, 0.54 #1768, 0.45 #1885), 07s9rl0 (0.53 #3642, 0.53 #3170, 0.52 #3524), 01jfsb (0.52 #2361, 0.52 #2127, 0.51 #2478), 03q4nz (0.50 #486, 0.41 #1074, 0.38 #1191), 04xvlr (0.38 #706, 0.33 #236, 0.11 #3171), 02l7c8 (0.38 #484, 0.29 #1072, 0.26 #3656), 01zhp (0.33 #1721, 0.31 #1838, 0.26 #1955), 04rlf (0.33 #62, 0.17 #296, 0.08 #1709), 0bj8m2 (0.25 #515, 0.24 #1220, 0.19 #1456), 0219x_ (0.25 #141, 0.15 #728, 0.14 #375) >> Best rule #1651 for best value: >> intensional similarity = 6 >> extensional distance = 76 >> proper extension: 06w99h3; 0h1cdwq; 03mh94; 087wc7n; 0crfwmx; 02qm_f; 0k2sk; 01c22t; 04hwbq; 07y9w5; ... >> query: (?x3844, 05p553) <- genre(?x3844, ?x5937), language(?x3844, ?x254), film(?x4463, ?x3844), genre(?x14357, ?x5937), ?x14357 = 03q4hl, award_nominee(?x4463, ?x450) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1721 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 76 *> proper extension: 06w99h3; 0h1cdwq; 03mh94; 087wc7n; 0crfwmx; 02qm_f; 0k2sk; 01c22t; 04hwbq; 07y9w5; ... *> query: (?x3844, 01zhp) <- genre(?x3844, ?x5937), language(?x3844, ?x254), film(?x4463, ?x3844), genre(?x14357, ?x5937), ?x14357 = 03q4hl, award_nominee(?x4463, ?x450) *> conf = 0.33 ranks of expected_values: 7 EVAL 02_qt genre 01zhp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 64.000 32.000 0.551 http://example.org/film/film/genre #22057-0btpm6 PRED entity: 0btpm6 PRED relation: story_by PRED expected values: 02nygk => 103 concepts (58 used for prediction) PRED predicted values (max 10 best out of 106): 02nygk (0.20 #209, 0.11 #1068, 0.06 #854), 022wxh (0.20 #72, 0.01 #6933), 079vf (0.16 #861, 0.14 #1289, 0.12 #3002), 04zd4m (0.16 #876, 0.10 #1304, 0.07 #2160), 01wy5m (0.13 #217, 0.09 #215, 0.02 #7291), 0237fw (0.13 #217, 0.09 #215, 0.02 #7291), 03y1mlp (0.13 #217, 0.09 #215, 0.02 #7291), 02jxmr (0.13 #217, 0.02 #7291, 0.02 #10955), 0150t6 (0.13 #217, 0.02 #7291, 0.02 #10955), 041h0 (0.11 #864, 0.10 #1292, 0.06 #2576) >> Best rule #209 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0cqr0q; >> query: (?x7493, 02nygk) <- award_winner(?x7493, ?x4835), film(?x3186, ?x7493), ?x4835 = 01wy5m, nominated_for(?x3069, ?x7493) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0btpm6 story_by 02nygk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 58.000 0.200 http://example.org/film/film/story_by #22056-0jqn5 PRED entity: 0jqn5 PRED relation: genre PRED expected values: 03k9fj => 115 concepts (114 used for prediction) PRED predicted values (max 10 best out of 105): 02kdv5l (0.61 #4808, 0.60 #482, 0.42 #1443), 024qqx (0.60 #1201, 0.54 #12503, 0.52 #10219), 03k9fj (0.49 #1453, 0.48 #1933, 0.48 #4336), 01jfsb (0.45 #4819, 0.36 #493, 0.33 #3977), 05p553 (0.41 #844, 0.39 #2526, 0.37 #604), 0lsxr (0.40 #249, 0.19 #5176, 0.19 #2050), 02l7c8 (0.39 #1337, 0.33 #3619, 0.31 #736), 04xvlr (0.24 #1081, 0.23 #1802, 0.20 #4085), 082gq (0.23 #30, 0.19 #1110, 0.18 #1591), 060__y (0.22 #1097, 0.20 #1818, 0.20 #4703) >> Best rule #4808 for best value: >> intensional similarity = 2 >> extensional distance = 212 >> proper extension: 06n90; >> query: (?x1452, 02kdv5l) <- genre(?x1452, ?x1013), ?x1013 = 06n90 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1453 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 03qcfvw; 01gc7; 01k1k4; 0g5qs2k; 01r97z; 01vksx; 03cvwkr; 047msdk; 0dtfn; 0dr_4; ... *> query: (?x1452, 03k9fj) <- music(?x1452, ?x669), award(?x1452, ?x500), film_release_region(?x1452, ?x87), film_distribution_medium(?x1452, ?x81) *> conf = 0.49 ranks of expected_values: 3 EVAL 0jqn5 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 114.000 0.607 http://example.org/film/film/genre #22055-03_gd PRED entity: 03_gd PRED relation: profession PRED expected values: 02hrh1q => 121 concepts (73 used for prediction) PRED predicted values (max 10 best out of 77): 02hrh1q (0.88 #4875, 0.88 #2871, 0.87 #4302), 0cbd2 (0.73 #5300, 0.56 #1866, 0.54 #865), 0kyk (0.39 #884, 0.31 #1885, 0.31 #168), 09jwl (0.25 #4592, 0.25 #4163, 0.20 #6453), 06q2q (0.23 #183, 0.12 #40, 0.04 #5334), 02krf9 (0.22 #2311, 0.22 #4744, 0.19 #738), 0dz3r (0.21 #4151, 0.21 #4580, 0.14 #6441), 0nbcg (0.20 #4605, 0.19 #4176, 0.14 #6466), 018gz8 (0.18 #2587, 0.17 #2730, 0.16 #1443), 02hv44_ (0.17 #1770, 0.16 #912, 0.16 #2485) >> Best rule #4875 for best value: >> intensional similarity = 2 >> extensional distance = 538 >> proper extension: 02784z; >> query: (?x800, 02hrh1q) <- religion(?x800, ?x2694), film(?x800, ?x1597) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_gd profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 73.000 0.878 http://example.org/people/person/profession #22054-01ggbx PRED entity: 01ggbx PRED relation: religion PRED expected values: 03j6c => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 21): 03j6c (0.33 #111, 0.31 #883, 0.26 #201), 0c8wxp (0.21 #687, 0.20 #6, 0.18 #777), 03_gx (0.17 #284, 0.12 #421, 0.11 #603), 0flw86 (0.10 #182, 0.10 #227, 0.10 #864), 0kpl (0.10 #10, 0.10 #280, 0.08 #1602), 01lp8 (0.10 #1, 0.08 #46, 0.05 #181), 06yyp (0.10 #22, 0.08 #67, 0.05 #112), 019cr (0.08 #56, 0.02 #281, 0.02 #327), 06nzl (0.08 #60, 0.02 #285, 0.02 #331), 092bf5 (0.05 #286, 0.04 #423, 0.04 #469) >> Best rule #111 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0cvbb9q; 05b1062; >> query: (?x13441, 03j6c) <- profession(?x13441, ?x319), nationality(?x13441, ?x2146), award(?x13441, ?x4443), ?x4443 = 0b6k___ >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ggbx religion 03j6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.333 http://example.org/people/person/religion #22053-04cw0j PRED entity: 04cw0j PRED relation: profession PRED expected values: 012t_z => 84 concepts (65 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.84 #2382, 0.83 #753, 0.82 #2085), 0dxtg (0.48 #3565, 0.44 #12, 0.37 #308), 02jknp (0.45 #3559, 0.31 #2221, 0.30 #5034), 03gjzk (0.33 #14, 0.31 #3567, 0.31 #2221), 02hv44_ (0.31 #2221, 0.30 #5034, 0.29 #5923), 0cbd2 (0.31 #2221, 0.30 #5034, 0.29 #5923), 02krf9 (0.31 #2221, 0.30 #5034, 0.29 #5923), 0kyk (0.18 #1953, 0.18 #1805, 0.17 #2250), 09jwl (0.18 #2090, 0.18 #7275, 0.18 #7571), 018gz8 (0.14 #2681, 0.12 #460, 0.11 #16) >> Best rule #2382 for best value: >> intensional similarity = 3 >> extensional distance = 373 >> proper extension: 02n9k; 05vzql; >> query: (?x3170, 02hrh1q) <- place_of_birth(?x3170, ?x739), languages(?x3170, ?x254), profession(?x3170, ?x106) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0fvf9q; 02rchht; 017s11; 04r7jc; 0yfp; 0hskw; 0fb1q; 061dn_; 03kpvp; 07h07; ... *> query: (?x3170, 012t_z) <- award_nominee(?x3170, ?x6866), award_nominee(?x541, ?x3170), ?x6866 = 03m9c8 *> conf = 0.11 ranks of expected_values: 16 EVAL 04cw0j profession 012t_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 84.000 65.000 0.837 http://example.org/people/person/profession #22052-02lq67 PRED entity: 02lq67 PRED relation: medal! PRED expected values: 0160w 0jgd 03rjj 0h3y 06mzp 015qh 01pj7 03rk0 082fr 019pcs 01mk6 0jhd 07tp2 01p8s 01d8l => 3 concepts (3 used for prediction) PRED predicted values (max 10 best out of 126): 03rjj (0.87 #78, 0.81 #75, 0.68 #73), 01mk6 (0.87 #78, 0.81 #75, 0.58 #71), 06mzp (0.87 #78, 0.68 #73, 0.58 #71), 082fr (0.87 #78, 0.58 #71, 0.33 #49), 015qh (0.81 #75, 0.60 #76, 0.58 #71), 01pj7 (0.81 #75, 0.58 #71, 0.33 #45), 059z0 (0.81 #75), 0193qj (0.81 #75), 0bq0p9 (0.81 #75), 04g61 (0.68 #73, 0.58 #71, 0.53 #77) >> Best rule #78 for best value: >> intensional similarity = 286 >> extensional distance = 1 >> proper extension: 02lq5w; >> query: (?x422, ?x205) <- medal(?x11872, ?x422), medal(?x7287, ?x422), medal(?x6305, ?x422), medal(?x5482, ?x422), medal(?x5147, ?x422), medal(?x5114, ?x422), medal(?x5073, ?x422), medal(?x4569, ?x422), medal(?x3728, ?x422), medal(?x3357, ?x422), medal(?x3227, ?x422), medal(?x3016, ?x422), medal(?x2645, ?x422), medal(?x2346, ?x422), medal(?x2000, ?x422), medal(?x1453, ?x422), medal(?x1355, ?x422), medal(?x1203, ?x422), medal(?x985, ?x422), medal(?x910, ?x422), medal(?x792, ?x422), medal(?x756, ?x422), medal(?x421, ?x422), medal(?x304, ?x422), medal(?x94, ?x422), medal(?x47, ?x422), medal(?x7688, ?x422), medal(?x7051, ?x422), medal(?x3110, ?x422), medal(?x2748, ?x422), medal(?x2630, ?x422), medal(?x1931, ?x422), medal(?x1617, ?x422), medal(?x1608, ?x422), medal(?x784, ?x422), medal(?x391, ?x422), medal(?x358, ?x422), ?x784 = 018ctl, film_release_region(?x9902, ?x3227), film_release_region(?x9194, ?x3227), film_release_region(?x8176, ?x3227), film_release_region(?x6886, ?x3227), film_release_region(?x6168, ?x3227), film_release_region(?x5713, ?x3227), film_release_region(?x4336, ?x3227), film_release_region(?x1392, ?x3227), ?x8176 = 0gvvm6l, adjoins(?x1497, ?x3227), ?x11872 = 03f2w, adjoins(?x3227, ?x7833), countries_spoken_in(?x10580, ?x3227), country(?x3309, ?x3227), country(?x2631, ?x3227), country(?x1967, ?x3227), country(?x1121, ?x3227), ?x6305 = 07t_x, ?x304 = 0d0vqn, ?x5147 = 0d04z6, administrative_parent(?x3227, ?x551), ?x1203 = 07ylj, administrative_area_type(?x3227, ?x2792), member_states(?x7695, ?x3227), ?x9902 = 0j8f09z, ?x94 = 09c7w0, ?x421 = 03_r3, ?x2346 = 0d05w3, sports(?x1617, ?x8190), sports(?x1617, ?x2752), sports(?x1617, ?x520), sports(?x1617, ?x453), ?x5482 = 04g5k, ?x3357 = 04w8f, ?x1355 = 0h7x, currency(?x3227, ?x170), ?x1392 = 017gm7, contains(?x455, ?x985), organization(?x985, ?x5701), organization(?x985, ?x3750), country(?x3641, ?x985), country(?x2315, ?x985), country(?x2266, ?x985), film_release_region(?x11218, ?x985), film_release_region(?x9657, ?x985), film_release_region(?x9002, ?x985), film_release_region(?x8370, ?x985), film_release_region(?x8193, ?x985), film_release_region(?x7336, ?x985), film_release_region(?x7265, ?x985), film_release_region(?x7204, ?x985), film_release_region(?x6621, ?x985), film_release_region(?x6620, ?x985), film_release_region(?x6543, ?x985), film_release_region(?x6235, ?x985), film_release_region(?x6218, ?x985), film_release_region(?x6216, ?x985), film_release_region(?x6181, ?x985), film_release_region(?x6095, ?x985), film_release_region(?x5992, ?x985), film_release_region(?x5877, ?x985), film_release_region(?x5496, ?x985), film_release_region(?x5418, ?x985), film_release_region(?x5092, ?x985), film_release_region(?x5070, ?x985), film_release_region(?x4684, ?x985), film_release_region(?x4545, ?x985), film_release_region(?x4422, ?x985), film_release_region(?x4313, ?x985), film_release_region(?x4040, ?x985), film_release_region(?x4024, ?x985), film_release_region(?x3938, ?x985), film_release_region(?x3843, ?x985), film_release_region(?x3276, ?x985), film_release_region(?x3000, ?x985), film_release_region(?x2933, ?x985), film_release_region(?x2783, ?x985), film_release_region(?x2717, ?x985), film_release_region(?x2714, ?x985), film_release_region(?x2598, ?x985), film_release_region(?x2434, ?x985), film_release_region(?x1932, ?x985), film_release_region(?x1743, ?x985), film_release_region(?x1701, ?x985), film_release_region(?x1642, ?x985), film_release_region(?x1625, ?x985), film_release_region(?x1470, ?x985), film_release_region(?x1370, ?x985), film_release_region(?x1118, ?x985), film_release_region(?x1108, ?x985), film_release_region(?x984, ?x985), film_release_region(?x908, ?x985), film_release_region(?x903, ?x985), film_release_region(?x204, ?x985), film_release_region(?x186, ?x985), film_release_region(?x86, ?x985), film_release_region(?x66, ?x985), ?x6168 = 0gj96ln, ?x6620 = 0mbql, ?x3000 = 045j3w, ?x2783 = 0879bpq, ?x2434 = 085ccd, ?x3641 = 03fyrh, ?x984 = 0m_mm, ?x2752 = 09_94, ?x9002 = 0ndsl1x, ?x1608 = 09x3r, ?x391 = 0l6vl, ?x1121 = 0bynt, combatants(?x2391, ?x985), country(?x1009, ?x985), ?x186 = 02vxq9m, film(?x541, ?x1642), ?x6095 = 0bq6ntw, ?x1701 = 0bh8yn3, ?x204 = 028_yv, ?x7336 = 0bdjd, ?x1108 = 0jjy0, ?x453 = 03tmr, ?x66 = 014lc_, film_crew_role(?x4422, ?x137), music(?x4313, ?x3410), film_crew_role(?x4313, ?x281), ?x2714 = 0kv238, film_crew_role(?x6235, ?x1171), category(?x1642, ?x134), genre(?x1642, ?x225), ?x2315 = 06wrt, countries_spoken_in(?x9057, ?x3016), languages_spoken(?x3584, ?x10580), genre(?x6235, ?x571), combatants(?x1790, ?x1497), ?x1118 = 0_92w, ?x8193 = 03z9585, ?x2717 = 0k5g9, ?x2933 = 0407yj_, ?x7265 = 04tng0, ?x7695 = 085h1, ?x11218 = 0ccck7, olympics(?x985, ?x2233), ?x9657 = 07jqjx, ?x8190 = 09_9n, ?x792 = 0hzlz, film(?x4771, ?x6235), ?x6886 = 0gwjw0c, ?x3750 = 0_2v, ?x2000 = 0d0kn, ?x5701 = 0b6css, edited_by(?x1932, ?x323), film_release_region(?x6931, ?x1497), film_release_region(?x6882, ?x1497), film_release_region(?x3784, ?x1497), film_release_region(?x3498, ?x1497), olympics(?x205, ?x1617), ?x2631 = 01z27, ?x5114 = 05vz3zq, ?x1470 = 03twd6, ?x520 = 01dys, ?x6181 = 0hv27, ?x908 = 01vksx, ?x8370 = 07ghq, ?x3728 = 087vz, ?x7204 = 0280061, teams(?x985, ?x3587), ?x6882 = 043tvp3, language(?x4422, ?x3592), form_of_government(?x1497, ?x1926), ?x3784 = 0bmhvpr, country(?x8174, ?x985), medal(?x1497, ?x1242), language(?x148, ?x10580), ?x1931 = 0kbws, ?x2266 = 01lb14, taxonomy(?x5073, ?x939), contains(?x985, ?x9028), geographic_distribution(?x1571, ?x3016), ?x910 = 019rg5, ?x903 = 04969y, adjoins(?x7833, ?x2979), nationality(?x2259, ?x7833), ?x2630 = 0swff, film(?x4325, ?x1642), ?x281 = 02_n3z, ?x3938 = 024mpp, ?x4545 = 05p09dd, religion(?x7833, ?x492), ?x4336 = 0bpm4yw, film(?x123, ?x1932), ?x7688 = 0jkvj, film(?x1548, ?x6218), ?x1743 = 0c8tkt, ?x4040 = 02mt51, ?x9194 = 0fpgp26, country(?x13383, ?x3227), film(?x2156, ?x4313), ?x4325 = 0fby2t, ?x1967 = 01cgz, film(?x6444, ?x1009), ?x7287 = 05b7q, genre(?x5092, ?x53), ?x2748 = 0c_tl, ?x541 = 017s11, ?x6543 = 0421v9q, film_release_region(?x80, ?x2645), nominated_for(?x112, ?x5092), production_companies(?x6235, ?x1850), ?x3110 = 0kbvv, sports(?x7051, ?x2867), ?x358 = 018wrk, ?x756 = 06npd, film_release_distribution_medium(?x1625, ?x81), produced_by(?x6218, ?x3637), ?x1453 = 06qd3, genre(?x1009, ?x307), service_location(?x555, ?x985), ?x4024 = 0n04r, ?x4684 = 03nm_fh, service_location(?x1492, ?x2645), ?x3276 = 0gjc4d3, ?x6931 = 09v3jyg, ?x5713 = 0cc97st, countries_within(?x6956, ?x3016), film(?x8394, ?x5992), ?x86 = 0ds35l9, written_by(?x4422, ?x12856), film(?x7903, ?x6621), ?x3843 = 080nwsb, ?x5418 = 026lgs, film_festivals(?x5992, ?x10083), ?x5496 = 07l50vn, ?x5877 = 02qyv3h, written_by(?x5070, ?x2790), ?x3498 = 02fqrf, film_crew_role(?x1932, ?x1284), country(?x13852, ?x3016), ?x47 = 027rn, capital(?x7833, ?x14080), language(?x1625, ?x2164), ?x2598 = 07f_7h, ?x6216 = 06fcqw, ?x9057 = 07qv_, film_festivals(?x5070, ?x6557), ?x1370 = 0gmcwlb, location(?x4245, ?x2645), ?x4569 = 09lxtg, film(?x4129, ?x1009), ?x3309 = 09w1n, ?x2867 = 02y8z >> conf = 0.87 => this is the best rule for 4 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 11, 18, 20, 21, 95, 96, 97, 98, 109 EVAL 02lq67 medal! 01d8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 01p8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 07tp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 0jhd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 01mk6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 019pcs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 082fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 03rk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 01pj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 015qh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 06mzp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 0h3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 0jgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 0160w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 3.000 3.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #22051-0fq9zdn PRED entity: 0fq9zdn PRED relation: award! PRED expected values: 0462hhb => 49 concepts (25 used for prediction) PRED predicted values (max 10 best out of 881): 0ds3t5x (0.50 #30, 0.38 #1048, 0.20 #2066), 0pv3x (0.38 #109, 0.23 #1127, 0.10 #2145), 05c46y6 (0.31 #1284, 0.15 #2302, 0.12 #266), 0g9lm2 (0.25 #428, 0.24 #6110, 0.22 #14264), 0m313 (0.25 #6, 0.23 #1024, 0.22 #15284), 0c9k8 (0.25 #293, 0.23 #1311, 0.12 #2329), 09k56b7 (0.25 #190, 0.23 #1208, 0.12 #2226), 0jym0 (0.25 #198, 0.23 #1216, 0.12 #2234), 0294mx (0.25 #733, 0.23 #1751, 0.10 #2769), 02qpt1w (0.25 #580, 0.23 #1598, 0.07 #2616) >> Best rule #30 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 09sb52; 05zvq6g; 0gqwc; 099cng; 0gqyl; 02ppm4q; >> query: (?x941, 0ds3t5x) <- award(?x1415, ?x941), award(?x5951, ?x941), award(?x4872, ?x941), ?x4872 = 02d42t, ?x5951 = 0dvld >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6110 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 0dgshf6; 04zx08r; 0dgr5xp; 09v1lrz; *> query: (?x941, ?x695) <- award_winner(?x941, ?x940), nominated_for(?x941, ?x695), disciplines_or_subjects(?x941, ?x373), award(?x548, ?x941) *> conf = 0.24 ranks of expected_values: 28 EVAL 0fq9zdn award! 0462hhb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 49.000 25.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22050-03g5_y PRED entity: 03g5_y PRED relation: nominated_for PRED expected values: 09sh8k => 58 concepts (29 used for prediction) PRED predicted values (max 10 best out of 340): 039cq4 (0.11 #2709, 0.06 #5957, 0.06 #10826), 09sh8k (0.07 #47078, 0.07 #45453, 0.06 #45452), 0dfw0 (0.07 #47078, 0.04 #27591, 0.02 #37336), 02tjl3 (0.07 #47078, 0.04 #27591, 0.02 #37336), 0fdv3 (0.07 #47078, 0.04 #27591, 0.02 #37336), 057lbk (0.07 #45453, 0.06 #45452, 0.02 #37336), 04tqtl (0.07 #45453, 0.06 #45452, 0.01 #6962), 02q7yfq (0.07 #45453, 0.06 #45452), 051ys82 (0.07 #45453, 0.06 #45452), 02y_lrp (0.07 #45453, 0.06 #45452) >> Best rule #2709 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 07c0j; 03g5jw; >> query: (?x7872, 039cq4) <- award_nominee(?x9781, ?x7872), influenced_by(?x7872, ?x1145), participant(?x1117, ?x9781) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #47078 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1929 *> proper extension: 0jz9f; 086k8; 03zqc1; 017s11; 016tt2; 025jfl; 04rcr; 0g1rw; 0kx4m; 05qd_; ... *> query: (?x7872, ?x136) <- award_nominee(?x9781, ?x7872), nominated_for(?x9781, ?x136), award_winner(?x102, ?x9781) *> conf = 0.07 ranks of expected_values: 2 EVAL 03g5_y nominated_for 09sh8k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 29.000 0.111 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22049-04fhn_ PRED entity: 04fhn_ PRED relation: film PRED expected values: 0c3zjn7 => 104 concepts (54 used for prediction) PRED predicted values (max 10 best out of 470): 01hqhm (0.29 #329, 0.12 #33907, 0.01 #21742), 02b6n9 (0.29 #1567, 0.01 #22980), 058kh7 (0.20 #3357, 0.06 #5141, 0.01 #26555), 01hv3t (0.14 #1290, 0.10 #3074, 0.03 #4858), 02qzh2 (0.14 #691, 0.03 #4259, 0.02 #22104), 07bwr (0.14 #866, 0.03 #4434, 0.01 #22279), 020y73 (0.14 #366, 0.02 #5718, 0.02 #9286), 07vn_9 (0.14 #1678, 0.02 #7030, 0.01 #14166), 03k8th (0.14 #1715, 0.02 #7067, 0.01 #10635), 03twd6 (0.14 #225, 0.02 #5577, 0.01 #9145) >> Best rule #329 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 02qgqt; 04t7ts; 0169dl; 0bq2g; 01r93l; >> query: (?x3952, 01hqhm) <- nationality(?x3952, ?x94), film(?x3952, ?x8574), film(?x3952, ?x4920), ?x8574 = 02mpyh, film_crew_role(?x4920, ?x137) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04fhn_ film 0c3zjn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 54.000 0.286 http://example.org/film/actor/film./film/performance/film #22048-034ns PRED entity: 034ns PRED relation: taxonomy PRED expected values: 04n6k => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.73 #12, 0.72 #13, 0.71 #7) >> Best rule #12 for best value: >> intensional similarity = 12 >> extensional distance = 42 >> proper extension: 0mg1w; 02stgt; >> query: (?x10518, 04n6k) <- major_field_of_study(?x1771, ?x10518), major_field_of_study(?x865, ?x10518), major_field_of_study(?x6912, ?x10518), ?x1771 = 019v9k, ?x865 = 02h4rq6, major_field_of_study(?x6912, ?x1154), institution(?x620, ?x6912), ?x1154 = 02lp1, citytown(?x6912, ?x3052), fraternities_and_sororities(?x6912, ?x3697), school_type(?x6912, ?x1044), student(?x6912, ?x1564) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034ns taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.727 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #22047-044rvb PRED entity: 044rvb PRED relation: film PRED expected values: 04s1zr => 91 concepts (63 used for prediction) PRED predicted values (max 10 best out of 616): 02ryz24 (0.25 #2238, 0.10 #4013, 0.01 #41297), 03bx2lk (0.25 #1957, 0.02 #23260, 0.02 #33915), 0418wg (0.17 #397, 0.12 #2172, 0.03 #7497), 06z8s_ (0.17 #127, 0.12 #1902, 0.03 #7227), 09xbpt (0.17 #46, 0.12 #1821, 0.03 #7146), 04vr_f (0.17 #168, 0.12 #1943, 0.02 #7268), 02yxbc (0.17 #1287, 0.12 #3062), 0660b9b (0.17 #986, 0.12 #2761), 03z106 (0.17 #632, 0.12 #2407), 0298n7 (0.17 #1337, 0.10 #4887) >> Best rule #2238 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 032xhg; 06m6p7; >> query: (?x643, 02ryz24) <- film(?x643, ?x4086), profession(?x643, ?x1032), ?x4086 = 06_x996 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #35442 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 847 *> proper extension: 03zqc1; 04sx9_; 04shbh; 019_1h; 03f1zdw; 02k6rq; 0f6_dy; 01hkhq; 03q1vd; 02j9lm; ... *> query: (?x643, 04s1zr) <- film(?x643, ?x392), genre(?x392, ?x258), nominated_for(?x392, ?x5964) *> conf = 0.01 ranks of expected_values: 582 EVAL 044rvb film 04s1zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 91.000 63.000 0.250 http://example.org/film/actor/film./film/performance/film #22046-07024 PRED entity: 07024 PRED relation: film! PRED expected values: 05fnl9 => 77 concepts (40 used for prediction) PRED predicted values (max 10 best out of 810): 06pj8 (0.65 #29047, 0.61 #41500, 0.57 #62255), 04m064 (0.57 #62255, 0.52 #64331, 0.48 #68486), 02qjpv5 (0.57 #62255, 0.52 #64331, 0.48 #68486), 0b6mgp_ (0.48 #68486, 0.47 #4149, 0.45 #68485), 0c94fn (0.48 #68486, 0.47 #4149, 0.45 #68485), 01gb54 (0.48 #68486, 0.47 #4149, 0.45 #68485), 0146pg (0.48 #68486, 0.47 #4149, 0.45 #68485), 03q8ch (0.47 #4149, 0.45 #68485, 0.44 #66410), 05mvd62 (0.44 #37348, 0.42 #78861, 0.41 #6224), 02mjf2 (0.40 #772, 0.01 #44349, 0.01 #56802) >> Best rule #29047 for best value: >> intensional similarity = 3 >> extensional distance = 579 >> proper extension: 0gfzgl; 01b7h8; 0cskb; 06ys2; >> query: (?x2928, ?x2858) <- nominated_for(?x2858, ?x2928), award_winner(?x2858, ?x7569), participant(?x56, ?x2858) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07024 film! 05fnl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 40.000 0.654 http://example.org/film/actor/film./film/performance/film #22045-0kv2hv PRED entity: 0kv2hv PRED relation: genre PRED expected values: 0bbc17 => 95 concepts (93 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.61 #5840, 0.61 #3932, 0.61 #1192), 01z4y (0.61 #8464, 0.54 #8463, 0.54 #1191), 02kdv5l (0.42 #479, 0.36 #717, 0.36 #1790), 01jfsb (0.33 #488, 0.33 #12, 0.32 #726), 0lsxr (0.33 #8, 0.28 #365, 0.28 #603), 0gf28 (0.33 #63, 0.27 #1311, 0.12 #3518), 0556j8 (0.33 #42, 0.07 #3497, 0.07 #3854), 03k9fj (0.32 #487, 0.31 #963, 0.29 #1560), 06n90 (0.27 #1311, 0.22 #489, 0.20 #132), 0bkbm (0.27 #1311, 0.07 #1826, 0.07 #2778) >> Best rule #5840 for best value: >> intensional similarity = 3 >> extensional distance = 854 >> proper extension: 021gzd; >> query: (?x886, 07s9rl0) <- film(?x4657, ?x886), genre(?x886, ?x239), award(?x886, ?x102) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #337 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 033g4d; 05fcbk7; 02gd6x; 01k0vq; 043h78; *> query: (?x886, 0bbc17) <- film(?x4657, ?x886), production_companies(?x886, ?x3323), nominated_for(?x102, ?x886), film_release_region(?x886, ?x94) *> conf = 0.03 ranks of expected_values: 55 EVAL 0kv2hv genre 0bbc17 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 95.000 93.000 0.614 http://example.org/film/film/genre #22044-02r3cn PRED entity: 02r3cn PRED relation: artists! PRED expected values: 0781g => 189 concepts (133 used for prediction) PRED predicted values (max 10 best out of 259): 064t9 (0.89 #12912, 0.78 #1548, 0.72 #15677), 06by7 (0.66 #32274, 0.61 #16608, 0.60 #14457), 0glt670 (0.59 #3115, 0.45 #6186, 0.42 #2191), 025sc50 (0.56 #1585, 0.43 #12949, 0.42 #2199), 02lnbg (0.46 #2208, 0.45 #5281, 0.44 #1594), 0ggx5q (0.46 #2228, 0.44 #1614, 0.42 #1921), 06j6l (0.42 #2197, 0.39 #1583, 0.39 #5270), 0xhtw (0.39 #23671, 0.28 #20292, 0.27 #24592), 0gywn (0.33 #2207, 0.30 #5280, 0.28 #1593), 0m0jc (0.32 #3081, 0.26 #1850, 0.17 #19668) >> Best rule #12912 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 01p0vf; >> query: (?x6035, 064t9) <- participant(?x6035, ?x1093), artists(?x5934, ?x6035), parent_genre(?x2407, ?x5934), titles(?x5934, ?x6103) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #5099 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 03yf3z; *> query: (?x6035, 0781g) <- category(?x6035, ?x134), artists(?x302, ?x6035), spouse(?x10777, ?x6035), nationality(?x6035, ?x94) *> conf = 0.05 ranks of expected_values: 161 EVAL 02r3cn artists! 0781g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 189.000 133.000 0.890 http://example.org/music/genre/artists #22043-02rjz5 PRED entity: 02rjz5 PRED relation: colors PRED expected values: 01g5v => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 17): 03vtbc (0.56 #125, 0.13 #1524, 0.13 #1488), 01g5v (0.45 #257, 0.44 #274, 0.38 #104), 02rnmb (0.33 #11, 0.13 #1524, 0.13 #1488), 088fh (0.26 #872, 0.25 #107, 0.25 #324), 038hg (0.26 #872, 0.25 #324, 0.20 #796), 0jc_p (0.26 #872, 0.14 #857, 0.13 #1524), 01l849 (0.26 #872, 0.13 #1524, 0.13 #1488), 036k5h (0.26 #872, 0.13 #1524, 0.13 #1488), 09ggk (0.25 #324, 0.19 #1078, 0.17 #1079), 06kqt3 (0.13 #1524, 0.13 #1488, 0.13 #1648) >> Best rule #125 for best value: >> intensional similarity = 10 >> extensional distance = 7 >> proper extension: 02fbb5; >> query: (?x10066, 03vtbc) <- colors(?x10066, ?x4557), colors(?x10066, ?x663), ?x4557 = 019sc, ?x663 = 083jv, teams(?x13691, ?x10066), sport(?x10066, ?x471), team(?x5471, ?x10066), location_of_ceremony(?x566, ?x13691), contains(?x205, ?x13691), athlete(?x471, ?x208) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #257 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 18 *> proper extension: 0223bl; 0182r9; 03_qj1; 0199gx; 01zhs3; 0272vm; 0284h6; 0329gm; 014nzp; 0lmm3; ... *> query: (?x10066, 01g5v) <- position(?x10066, ?x203), position(?x10066, ?x63), position(?x10066, ?x530), position(?x10066, ?x60), team(?x5471, ?x10066), ?x203 = 0dgrmp, ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x60 = 02nzb8, colors(?x10066, ?x663) *> conf = 0.45 ranks of expected_values: 2 EVAL 02rjz5 colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.556 http://example.org/sports/sports_team/colors #22042-016h4r PRED entity: 016h4r PRED relation: location PRED expected values: 05jbn 0r5y9 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 204): 030qb3t (0.25 #34621, 0.22 #31409, 0.22 #32212), 02_286 (0.23 #24938, 0.20 #34575, 0.19 #9673), 013yq (0.11 #922, 0.08 #1725, 0.05 #3331), 01531 (0.11 #961, 0.08 #1764, 0.04 #5779), 0hptm (0.11 #1106, 0.08 #1909, 0.03 #17169), 0ply0 (0.11 #980, 0.08 #1783, 0.03 #2586), 0f2w0 (0.11 #897, 0.08 #1700, 0.03 #2503), 0f2tj (0.11 #1132, 0.08 #1935, 0.01 #4344), 058cm (0.11 #1533, 0.08 #2336, 0.01 #5548), 0rqf1 (0.11 #1368, 0.08 #2171, 0.01 #5383) >> Best rule #34621 for best value: >> intensional similarity = 3 >> extensional distance = 423 >> proper extension: 0ph2w; 02kz_; 0bw6y; 0261x8t; 02x2t07; 01j851; 01kgg9; 02c7lt; 01q8fxx; >> query: (?x3495, 030qb3t) <- participant(?x3495, ?x538), award(?x3495, ?x1232), location(?x3495, ?x10465) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4268 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 03f0fnk; *> query: (?x3495, 05jbn) <- artist(?x2931, ?x3495), award_winner(?x2576, ?x3495), languages(?x3495, ?x254) *> conf = 0.04 ranks of expected_values: 30 EVAL 016h4r location 0r5y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 131.000 0.247 http://example.org/people/person/places_lived./people/place_lived/location EVAL 016h4r location 05jbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 131.000 131.000 0.247 http://example.org/people/person/places_lived./people/place_lived/location #22041-0b1xl PRED entity: 0b1xl PRED relation: institution! PRED expected values: 02h4rq6 => 165 concepts (127 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.82 #739, 0.82 #718, 0.82 #634), 02_xgp2 (0.59 #297, 0.58 #385, 0.57 #361), 016t_3 (0.59 #104, 0.56 #864, 0.52 #549), 0bkj86 (0.55 #27, 0.50 #293, 0.50 #553), 04zx3q1 (0.35 #351, 0.35 #547, 0.34 #375), 013zdg (0.30 #6, 0.29 #87, 0.29 #67), 027f2w (0.28 #373, 0.27 #28, 0.27 #554), 03mkk4 (0.28 #373, 0.25 #193, 0.22 #556), 028dcg (0.28 #373, 0.21 #98, 0.20 #17), 01rr_d (0.28 #373, 0.20 #561, 0.19 #301) >> Best rule #739 for best value: >> intensional similarity = 6 >> extensional distance = 161 >> proper extension: 015fsv; >> query: (?x5145, 02h4rq6) <- major_field_of_study(?x5145, ?x742), institution(?x1771, ?x5145), institution(?x1771, ?x9181), ?x9181 = 012lzr, school(?x2820, ?x5145), major_field_of_study(?x1771, ?x90) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b1xl institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 127.000 0.822 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #22040-07c0j PRED entity: 07c0j PRED relation: group! PRED expected values: 03bnv 03j24kf => 133 concepts (80 used for prediction) PRED predicted values (max 10 best out of 74): 0136pk (0.20 #236, 0.08 #835, 0.02 #2642), 0473q (0.20 #331, 0.08 #930), 0dn44 (0.17 #792, 0.05 #1190, 0.01 #3598), 03dq9 (0.17 #782, 0.05 #1180, 0.01 #3588), 07h5d (0.17 #735, 0.05 #1133, 0.01 #3541), 0dpqk (0.17 #692, 0.05 #1090, 0.01 #3498), 04yt7 (0.17 #680, 0.05 #1078, 0.01 #3486), 048tgl (0.06 #4591, 0.04 #6593, 0.03 #7799), 01w724 (0.05 #1043, 0.03 #1848, 0.02 #2049), 0gkg6 (0.05 #1048, 0.03 #1853, 0.02 #4465) >> Best rule #236 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 03k3b; >> query: (?x1136, 0136pk) <- artist(?x10727, ?x1136), ?x10727 = 041p3y, group(?x227, ?x1136) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07c0j group! 03j24kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 80.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group EVAL 07c0j group! 03bnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 80.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group #22039-04b19t PRED entity: 04b19t PRED relation: profession PRED expected values: 0dxtg => 108 concepts (77 used for prediction) PRED predicted values (max 10 best out of 78): 0dxtg (0.87 #1048, 0.86 #3418, 0.85 #3566), 02hrh1q (0.81 #4308, 0.76 #1345, 0.69 #8309), 0cbd2 (0.54 #1782, 0.43 #746, 0.42 #2376), 03gjzk (0.50 #902, 0.48 #7718, 0.44 #1198), 0kyk (0.43 #1805, 0.43 #769, 0.25 #2843), 0nbcg (0.29 #8475, 0.20 #179, 0.11 #10991), 0np9r (0.26 #1056, 0.14 #760, 0.11 #464), 026sdt1 (0.25 #68, 0.20 #364, 0.08 #660), 09jwl (0.23 #8462, 0.16 #10978, 0.16 #11126), 02krf9 (0.23 #5210, 0.23 #4914, 0.22 #3284) >> Best rule #1048 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 052gzr; 081l_; >> query: (?x2618, 0dxtg) <- award_winner(?x4443, ?x2618), story_by(?x657, ?x2618), type_of_union(?x2618, ?x566), film(?x2618, ?x2617) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04b19t profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 77.000 0.871 http://example.org/people/person/profession #22038-0p_pd PRED entity: 0p_pd PRED relation: award_nominee! PRED expected values: 01y_px => 106 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1079): 0bq2g (0.83 #27854, 0.82 #27853, 0.82 #34819), 01q_ph (0.83 #27854, 0.82 #27853, 0.82 #34819), 01y_px (0.82 #27853, 0.82 #34819, 0.82 #6964), 0151w_ (0.47 #9485, 0.19 #143907, 0.06 #18770), 0171cm (0.47 #9834, 0.19 #143907, 0.03 #19119), 0dgskx (0.47 #10786, 0.19 #143907, 0.03 #20071), 01tspc6 (0.47 #9484, 0.03 #18769, 0.02 #16448), 030hcs (0.42 #5017, 0.19 #143907, 0.17 #374), 034np8 (0.42 #5016, 0.17 #373, 0.04 #30177), 0lpjn (0.41 #9905, 0.19 #143907, 0.08 #5263) >> Best rule #27854 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 01w5n51; >> query: (?x397, ?x4929) <- award_nominee(?x397, ?x4929), people(?x1050, ?x4929), influenced_by(?x397, ?x2283) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #27853 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 103 *> proper extension: 01w5n51; *> query: (?x397, ?x241) <- award_nominee(?x397, ?x4929), award_nominee(?x397, ?x241), people(?x1050, ?x4929), influenced_by(?x397, ?x2283) *> conf = 0.82 ranks of expected_values: 3 EVAL 0p_pd award_nominee! 01y_px CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 62.000 0.829 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22037-0g96wd PRED entity: 0g96wd PRED relation: languages_spoken PRED expected values: 02h40lc => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 54): 02h40lc (0.93 #758, 0.67 #164, 0.62 #218), 0t_2 (0.56 #498, 0.56 #552, 0.54 #390), 0880p (0.50 #151, 0.31 #421, 0.25 #529), 03hkp (0.33 #121, 0.25 #67, 0.23 #391), 0h407 (0.33 #210, 0.25 #264, 0.22 #318), 06b_j (0.33 #128, 0.23 #398, 0.22 #614), 064_8sq (0.23 #397, 0.19 #505, 0.19 #775), 06nm1 (0.17 #117, 0.15 #387, 0.13 #1522), 04306rv (0.17 #113, 0.15 #383, 0.13 #437), 032f6 (0.17 #157, 0.15 #427, 0.13 #481) >> Best rule #758 for best value: >> intensional similarity = 8 >> extensional distance = 25 >> proper extension: 071x0k; 078vc; 078ds; 0fk3s; 04czx7; 0c41n; >> query: (?x12950, 02h40lc) <- languages_spoken(?x12950, ?x12326), languages_spoken(?x12950, ?x9617), countries_spoken_in(?x9617, ?x512), ?x512 = 07ssc, languages_spoken(?x5042, ?x12326), language(?x1071, ?x12326), official_language(?x4221, ?x9617), ?x5042 = 0d7wh >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g96wd languages_spoken 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.926 http://example.org/people/ethnicity/languages_spoken #22036-014vm4 PRED entity: 014vm4 PRED relation: time_zones PRED expected values: 052vwh => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 12): 052vwh (0.67 #155, 0.50 #64, 0.33 #300), 02fqwt (0.33 #263, 0.31 #302, 0.28 #276), 02hcv8 (0.31 #683, 0.31 #697, 0.29 #710), 02lcqs (0.30 #200, 0.27 #227, 0.23 #241), 02hczc (0.20 #80, 0.20 #67, 0.17 #119), 02llzg (0.20 #82, 0.18 #750, 0.16 #815), 03bdv (0.09 #857, 0.08 #843, 0.07 #830), 02lcrv (0.03 #282, 0.03 #308, 0.03 #321), 03plfd (0.03 #769, 0.03 #534, 0.03 #795), 0gsrz4 (0.03 #767, 0.02 #793, 0.01 #873) >> Best rule #155 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 016v46; >> query: (?x10087, 052vwh) <- administrative_division(?x10087, ?x9259), contains(?x2346, ?x9259), ?x2346 = 0d05w3, contains(?x2346, ?x10087) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014vm4 time_zones 052vwh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.667 http://example.org/location/location/time_zones #22035-02mgp PRED entity: 02mgp PRED relation: major_field_of_study! PRED expected values: 02h4rq6 016t_3 019v9k => 57 concepts (46 used for prediction) PRED predicted values (max 10 best out of 22): 016t_3 (0.86 #92, 0.80 #277, 0.80 #115), 02h4rq6 (0.83 #299, 0.80 #322, 0.80 #346), 014mlp (0.82 #537, 0.80 #117, 0.80 #513), 019v9k (0.80 #306, 0.80 #121, 0.79 #98), 0bkj86 (0.73 #282, 0.73 #120, 0.72 #167), 03bwzr4 (0.66 #333, 0.64 #357, 0.64 #102), 04zx3q1 (0.59 #158, 0.57 #90, 0.56 #321), 0bjrnt (0.59 #158, 0.50 #29, 0.44 #111), 02m4yg (0.59 #158, 0.50 #60, 0.44 #822), 01ysy9 (0.59 #158, 0.44 #822, 0.36 #110) >> Best rule #92 for best value: >> intensional similarity = 12 >> extensional distance = 12 >> proper extension: 01mkq; 062z7; 06n6p; 02j62; 05qfh; 03nfmq; 0_jm; 01tbp; 01540; >> query: (?x12377, 016t_3) <- major_field_of_study(?x4390, ?x12377), major_field_of_study(?x3948, ?x12377), ?x3948 = 025v3k, taxonomy(?x12377, ?x939), ?x939 = 04n6k, major_field_of_study(?x4390, ?x947), company(?x4095, ?x4390), citytown(?x4390, ?x6885), student(?x4390, ?x6400), institution(?x865, ?x4390), nationality(?x6400, ?x512), ?x947 = 036hv >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 02mgp major_field_of_study! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 57.000 46.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 02mgp major_field_of_study! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 46.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 02mgp major_field_of_study! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 46.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #22034-0bqvs2 PRED entity: 0bqvs2 PRED relation: award PRED expected values: 02f76h => 99 concepts (82 used for prediction) PRED predicted values (max 10 best out of 247): 01by1l (0.73 #6577, 0.42 #1729, 0.40 #2133), 02f76h (0.47 #1795, 0.33 #179, 0.13 #30705), 01c9dd (0.39 #1930, 0.33 #314, 0.18 #2334), 03t5b6 (0.39 #1820, 0.09 #2628, 0.08 #2224), 01bgqh (0.39 #6507, 0.32 #2467, 0.26 #10143), 09sb52 (0.34 #22665, 0.32 #21857, 0.32 #23473), 02f75t (0.33 #260, 0.28 #1876, 0.10 #1068), 02f79n (0.33 #342, 0.17 #1958, 0.11 #2766), 03qbh5 (0.31 #2631, 0.26 #2227, 0.23 #3035), 02f5qb (0.28 #1773, 0.17 #2581, 0.16 #6621) >> Best rule #6577 for best value: >> intensional similarity = 3 >> extensional distance = 256 >> proper extension: 03cd1q; >> query: (?x7547, 01by1l) <- award(?x7547, ?x4837), award(?x4836, ?x4837), ?x4836 = 0837ql >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1795 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 03fbc; 017j6; 011z3g; 01f2q5; *> query: (?x7547, 02f76h) <- artist(?x6474, ?x7547), award(?x7547, ?x9295), ?x9295 = 023vrq *> conf = 0.47 ranks of expected_values: 2 EVAL 0bqvs2 award 02f76h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 82.000 0.733 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #22033-06jrhz PRED entity: 06jrhz PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #17, 0.84 #7, 0.84 #23), 02zsn (0.52 #130, 0.51 #176, 0.51 #153) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 01jbx1; 024swd; 03p01x; >> query: (?x5832, 05zppz) <- program_creator(?x11377, ?x5832), student(?x3439, ?x5832), program(?x11291, ?x11377) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06jrhz gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.857 http://example.org/people/person/gender #22032-0fkvn PRED entity: 0fkvn PRED relation: jurisdiction_of_office PRED expected values: 03v1s 01x73 0488g 06btq 0gyh 01n4w 0498y 0824r 018qpq 05r7t 07ytt 034m8 019fm7 0fnb4 026mx4 => 46 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2321): 09c7w0 (0.70 #7754, 0.69 #8137, 0.67 #3881), 0f8l9c (0.69 #8137, 0.57 #5459, 0.50 #6974), 0chghy (0.69 #8137, 0.57 #2322, 0.50 #6974), 06mkj (0.69 #8137, 0.57 #2322, 0.50 #6974), 0d05w3 (0.69 #8137, 0.50 #6974, 0.50 #3581), 015fr (0.69 #8137, 0.50 #6974, 0.33 #4295), 03ryn (0.69 #8137, 0.50 #6974, 0.33 #4410), 0jgd (0.69 #8137, 0.50 #6974, 0.33 #4272), 06bnz (0.69 #8137, 0.50 #6974, 0.33 #450), 07ssc (0.69 #8137, 0.50 #6974, 0.29 #5452) >> Best rule #7754 for best value: >> intensional similarity = 16 >> extensional distance = 8 >> proper extension: 0dq3c; 060c4; >> query: (?x900, 09c7w0) <- jurisdiction_of_office(?x900, ?x12632), jurisdiction_of_office(?x900, ?x1426), jurisdiction_of_office(?x900, ?x1227), contains(?x1227, ?x1358), contains(?x1227, ?x191), contains(?x1426, ?x347), religion(?x1426, ?x109), ?x191 = 0k049, location(?x2566, ?x1227), location(?x397, ?x1227), ?x1358 = 0284jb, country(?x12632, ?x205), profession(?x397, ?x987), artist(?x2193, ?x2566), award_winner(?x696, ?x397), nationality(?x397, ?x94) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1548 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 0789n; *> query: (?x900, ?x108) <- jurisdiction_of_office(?x900, ?x5147), jurisdiction_of_office(?x900, ?x1426), jurisdiction_of_office(?x900, ?x1227), ?x1227 = 01n7q, adjoins(?x108, ?x1426), location(?x1654, ?x1426), contains(?x1426, ?x11719), district_represented(?x1754, ?x1426), country(?x359, ?x5147), basic_title(?x744, ?x900), ?x1754 = 01grnp, nationality(?x1524, ?x5147), jurisdiction_of_office(?x1195, ?x11719), administrative_parent(?x5147, ?x551) *> conf = 0.61 ranks of expected_values: 14, 15, 22, 23, 24, 25, 26, 27, 80, 1393, 1444, 1453, 1574 EVAL 0fkvn jurisdiction_of_office 026mx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 0fnb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 019fm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 034m8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 07ytt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 05r7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 018qpq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 0824r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 0498y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 01n4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 0gyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 06btq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 0488g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 01x73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkvn jurisdiction_of_office 03v1s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 46.000 26.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #22031-016sp_ PRED entity: 016sp_ PRED relation: profession PRED expected values: 016z4k => 122 concepts (99 used for prediction) PRED predicted values (max 10 best out of 63): 0dz3r (0.54 #2177, 0.53 #727, 0.46 #872), 0nbcg (0.50 #4671, 0.49 #5251, 0.49 #2203), 016z4k (0.49 #4647, 0.45 #729, 0.44 #3341), 0dxtg (0.46 #6543, 0.38 #1172, 0.36 #1317), 03gjzk (0.37 #1318, 0.36 #1173, 0.34 #593), 039v1 (0.30 #5256, 0.27 #4676, 0.23 #6709), 0n1h (0.26 #880, 0.24 #735, 0.23 #4508), 021wpb (0.25 #194, 0.17 #339), 0d1pc (0.24 #482, 0.20 #627, 0.19 #1062), 018gz8 (0.21 #1175, 0.20 #3932, 0.16 #6255) >> Best rule #2177 for best value: >> intensional similarity = 3 >> extensional distance = 188 >> proper extension: 032t2z; 0fpj4lx; 0bkg4; 027dpx; 018y81; 01vsyjy; 021r7r; 012ycy; 015196; 0889x; >> query: (?x2518, 0dz3r) <- currency(?x2518, ?x170), profession(?x2518, ?x319), artists(?x2664, ?x2518) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #4647 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 352 *> proper extension: 02ht0ln; *> query: (?x2518, 016z4k) <- award(?x2518, ?x704), instrumentalists(?x227, ?x2518), artist(?x5634, ?x2518) *> conf = 0.49 ranks of expected_values: 3 EVAL 016sp_ profession 016z4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 122.000 99.000 0.542 http://example.org/people/person/profession #22030-01fsv9 PRED entity: 01fsv9 PRED relation: student PRED expected values: 055c8 => 212 concepts (160 used for prediction) PRED predicted values (max 10 best out of 1473): 0gs5q (0.33 #1522, 0.20 #5710, 0.18 #11992), 02t_w8 (0.25 #3015, 0.08 #13485, 0.04 #23957), 03ktjq (0.18 #7288, 0.10 #19853, 0.08 #26136), 084w8 (0.17 #10, 0.10 #4198, 0.09 #10480), 02r4qs (0.17 #228, 0.10 #4416, 0.09 #10698), 0168cl (0.17 #86, 0.08 #12650, 0.07 #16839), 0cv72h (0.17 #1227, 0.07 #17980, 0.04 #22168), 0fwy0h (0.12 #2936, 0.09 #9218, 0.09 #7124), 030hcs (0.12 #2368, 0.09 #6556, 0.08 #12838), 0cbgl (0.12 #4182, 0.09 #10464, 0.08 #14652) >> Best rule #1522 for best value: >> intensional similarity = 2 >> extensional distance = 4 >> proper extension: 055c8; >> query: (?x10899, 0gs5q) <- state_province_region(?x10899, ?x4622), ?x4622 = 04tgp >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01fsv9 student 055c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 212.000 160.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #22029-0fkbh PRED entity: 0fkbh PRED relation: country PRED expected values: 03rk0 => 158 concepts (61 used for prediction) PRED predicted values (max 10 best out of 45): 03rk0 (0.79 #658, 0.78 #1228, 0.77 #611), 0byh8j (0.50 #874, 0.50 #610, 0.46 #435), 09c7w0 (0.36 #3289, 0.30 #3378, 0.29 #3110), 0chghy (0.19 #1153, 0.17 #1419, 0.17 #1598), 055vr (0.09 #2473, 0.08 #2653, 0.03 #2925), 086g2 (0.09 #2473, 0.03 #2925, 0.03 #2923), 0yyh (0.09 #2473, 0.03 #2925, 0.03 #2923), 07c98 (0.09 #2473, 0.03 #2925, 0.03 #2923), 0d060g (0.07 #1415, 0.06 #1770, 0.05 #1860), 07ssc (0.07 #2947, 0.06 #3036, 0.05 #979) >> Best rule #658 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 09f07; >> query: (?x10903, 03rk0) <- service_location(?x10867, ?x10903), ?x10867 = 06_9lg, location_of_ceremony(?x566, ?x10903), ?x566 = 04ztj >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fkbh country 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 61.000 0.786 http://example.org/base/biblioness/bibs_location/country #22028-07x4qr PRED entity: 07x4qr PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 32): 0ch6mp2 (0.77 #932, 0.71 #1004, 0.71 #1224), 09zzb8 (0.73 #926, 0.70 #1073, 0.70 #1255), 09vw2b7 (0.68 #931, 0.60 #1003, 0.60 #1223), 0dxtw (0.44 #83, 0.37 #936, 0.36 #1008), 01pvkk (0.28 #1009, 0.28 #1414, 0.27 #343), 02rh1dz (0.21 #82, 0.15 #415, 0.14 #561), 02ynfr (0.19 #941, 0.18 #567, 0.16 #88), 0d2b38 (0.15 #98, 0.14 #146, 0.13 #431), 015h31 (0.14 #146, 0.14 #81, 0.14 #414), 0215hd (0.14 #146, 0.14 #944, 0.13 #91) >> Best rule #932 for best value: >> intensional similarity = 3 >> extensional distance = 749 >> proper extension: 07kb7vh; 01gglm; >> query: (?x2512, 0ch6mp2) <- film_crew_role(?x2512, ?x468), film(?x905, ?x2512), ?x468 = 02r96rf >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07x4qr film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.775 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #22027-018j2 PRED entity: 018j2 PRED relation: group PRED expected values: 018ndc 0p8h0 => 71 concepts (40 used for prediction) PRED predicted values (max 10 best out of 447): 07mvp (0.77 #3212, 0.62 #2157, 0.60 #5694), 07m4c (0.62 #2170, 0.56 #2520, 0.54 #3225), 05563d (0.60 #1592, 0.57 #4586, 0.57 #1942), 01wv9xn (0.60 #1571, 0.55 #5263, 0.40 #1220), 0cfgd (0.60 #1707, 0.55 #5263, 0.40 #1356), 06nv27 (0.60 #1609, 0.54 #3190, 0.50 #2312), 0gr69 (0.60 #1640, 0.50 #2343, 0.50 #2166), 0187x8 (0.60 #1651, 0.50 #1825, 0.44 #2527), 02r3zy (0.60 #1566, 0.50 #1740, 0.44 #2442), 0134pk (0.60 #1688, 0.50 #1862, 0.40 #1337) >> Best rule #3212 for best value: >> intensional similarity = 14 >> extensional distance = 11 >> proper extension: 0gghm; >> query: (?x2048, 07mvp) <- role(?x1886, ?x2048), role(?x314, ?x2048), instrumentalists(?x2048, ?x4635), instrumentalists(?x2048, ?x2799), nationality(?x4635, ?x94), award_nominee(?x1051, ?x4635), role(?x212, ?x2048), award_nominee(?x3321, ?x2799), spouse(?x10924, ?x2799), role(?x2048, ?x8957), group(?x2048, ?x997), award_nominee(?x286, ?x1051), role(?x217, ?x314), ?x1886 = 02k84w >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2430 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 6 *> proper extension: 026t6; 02hnl; 04rzd; 03qjg; *> query: (?x2048, 0p8h0) <- role(?x3156, ?x2048), instrumentalists(?x2048, ?x8799), instrumentalists(?x2048, ?x4635), nationality(?x4635, ?x94), award_nominee(?x1051, ?x4635), ?x1051 = 0jdhp, role(?x212, ?x2048), award_winner(?x2139, ?x4635), award_winner(?x5766, ?x4635), performance_role(?x3156, ?x10843), role(?x3156, ?x1332), profession(?x8799, ?x131) *> conf = 0.50 ranks of expected_values: 36, 51 EVAL 018j2 group 0p8h0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 71.000 40.000 0.769 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018j2 group 018ndc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 71.000 40.000 0.769 http://example.org/music/performance_role/regular_performances./music/group_membership/group #22026-06mnr PRED entity: 06mnr PRED relation: major_field_of_study! PRED expected values: 01zn4y => 67 concepts (36 used for prediction) PRED predicted values (max 10 best out of 638): 01w3v (0.71 #2350, 0.56 #7644, 0.50 #2934), 03ksy (0.68 #7746, 0.59 #13043, 0.58 #15393), 05zl0 (0.68 #7858, 0.50 #813, 0.44 #3148), 01w5m (0.67 #4205, 0.62 #3622, 0.62 #5388), 08815 (0.60 #7043, 0.60 #1754, 0.59 #9394), 09f2j (0.60 #1929, 0.56 #7806, 0.55 #17214), 0gl5_ (0.60 #2022, 0.50 #1438, 0.35 #6721), 02yxjs (0.60 #2071, 0.50 #1487, 0.33 #319), 0j_sncb (0.60 #1839, 0.38 #3006, 0.36 #7716), 07vyf (0.60 #1905, 0.32 #1752, 0.32 #5271) >> Best rule #2350 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 05qjt; 06ms6; 05qfh; 04g51; 037mh8; >> query: (?x7403, 01w3v) <- major_field_of_study(?x7403, ?x2014), ?x2014 = 04rjg, major_field_of_study(?x9861, ?x7403), major_field_of_study(?x6856, ?x7403), major_field_of_study(?x5288, ?x7403), major_field_of_study(?x3213, ?x7403), colors(?x9861, ?x663), ?x5288 = 02zd460, taxonomy(?x7403, ?x939), ?x939 = 04n6k, currency(?x6856, ?x170), major_field_of_study(?x620, ?x3213) >> conf = 0.71 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06mnr major_field_of_study! 01zn4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 36.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #22025-02m30v PRED entity: 02m30v PRED relation: gender PRED expected values: 02zsn => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #93, 0.83 #99, 0.83 #91), 02zsn (0.48 #62, 0.47 #46, 0.47 #52) >> Best rule #93 for best value: >> intensional similarity = 4 >> extensional distance = 593 >> proper extension: 016hvl; 012z8_; 03d_zl4; 0132k4; 01vz0g4; 04jvt; 0gzh; >> query: (?x14459, 05zppz) <- profession(?x14459, ?x1032), people(?x4322, ?x14459), people(?x4322, ?x10554), award_winner(?x1821, ?x10554) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #62 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 233 *> proper extension: 01wmgrf; 01mt1fy; *> query: (?x14459, 02zsn) <- profession(?x14459, ?x1032), ?x1032 = 02hrh1q, spouse(?x14459, ?x5438), nominated_for(?x5438, ?x951) *> conf = 0.48 ranks of expected_values: 2 EVAL 02m30v gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.829 http://example.org/people/person/gender #22024-026kq4q PRED entity: 026kq4q PRED relation: award_winner PRED expected values: 0q9kd 02qlkc3 => 34 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1817): 03knl (0.50 #3193, 0.12 #12378, 0.06 #9313), 02bkdn (0.50 #3323, 0.12 #12508, 0.03 #10714), 03h26tm (0.33 #4719, 0.33 #1656, 0.08 #16968), 01fwk3 (0.33 #4983, 0.33 #1920, 0.08 #8042), 0cgzj (0.33 #1348, 0.25 #4412, 0.04 #13597), 0g9zcgx (0.33 #2510, 0.17 #5573, 0.07 #14758), 04jspq (0.33 #2530, 0.17 #5593, 0.07 #14778), 027y151 (0.33 #2871, 0.17 #5934, 0.07 #15119), 0chw_ (0.33 #2804, 0.17 #5867, 0.04 #25779), 01gbn6 (0.33 #2853, 0.17 #5916, 0.04 #13569) >> Best rule #3193 for best value: >> intensional similarity = 17 >> extensional distance = 2 >> proper extension: 092t4b; 092_25; >> query: (?x3001, 03knl) <- award_winner(?x3001, ?x5559), ceremony(?x1443, ?x3001), honored_for(?x3001, ?x1588), nominated_for(?x1443, ?x10531), nominated_for(?x1443, ?x9452), nominated_for(?x1443, ?x5519), nominated_for(?x1443, ?x4541), award_winner(?x1443, ?x84), ?x9452 = 0c0zq, film_production_design_by(?x10531, ?x6514), ?x5559 = 02tkzn, ?x4541 = 08nvyr, award(?x308, ?x1443), award(?x460, ?x1443), language(?x5519, ?x254), produced_by(?x10531, ?x8480), award(?x5519, ?x289) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13781 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 24 *> proper extension: 092c5f; *> query: (?x3001, ?x647) <- award_winner(?x3001, ?x5559), ceremony(?x1443, ?x3001), honored_for(?x3001, ?x1588), nominated_for(?x1443, ?x11619), nominated_for(?x1443, ?x10531), nominated_for(?x1443, ?x9452), nominated_for(?x1443, ?x3430), award_winner(?x1443, ?x84), ?x9452 = 0c0zq, film_production_design_by(?x10531, ?x6514), nominated_for(?x647, ?x3430), nominated_for(?x1079, ?x3430), ?x1079 = 0l8z1, gender(?x5559, ?x231), award(?x460, ?x1443), ?x11619 = 07l50_1 *> conf = 0.02 ranks of expected_values: 927 EVAL 026kq4q award_winner 02qlkc3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 026kq4q award_winner 0q9kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #22023-02w4v PRED entity: 02w4v PRED relation: parent_genre! PRED expected values: 02fhtq => 51 concepts (35 used for prediction) PRED predicted values (max 10 best out of 318): 0y3_8 (0.60 #1342, 0.33 #1602, 0.33 #301), 05w3f (0.50 #1593, 0.50 #1073, 0.33 #292), 018ysx (0.50 #1246, 0.33 #1766, 0.33 #465), 0dl5d (0.50 #1058, 0.33 #1578, 0.33 #277), 015pdg (0.50 #1049, 0.33 #1569, 0.33 #268), 0dn16 (0.40 #1314, 0.33 #12, 0.25 #1835), 01b4p4 (0.40 #1464, 0.14 #2506, 0.10 #2766), 01_qp_ (0.40 #1474, 0.10 #9155, 0.10 #9158), 0133k0 (0.40 #1499, 0.09 #2541, 0.07 #2801), 01_sz1 (0.40 #1369, 0.06 #2411, 0.05 #2671) >> Best rule #1342 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0y3_8; 059kh; >> query: (?x3108, 0y3_8) <- artists(?x3108, ?x1292), artists(?x3108, ?x872), parent_genre(?x1572, ?x3108), ?x1292 = 03kwtb, award(?x872, ?x1389), profession(?x872, ?x220), instrumentalists(?x227, ?x872) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2868 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 40 *> proper extension: 016_nr; 01flzq; *> query: (?x3108, ?x302) <- artists(?x3108, ?x8490), artists(?x3108, ?x4550), parent_genre(?x1572, ?x3108), artists(?x302, ?x4550), award(?x8490, ?x724), friend(?x2669, ?x8490) *> conf = 0.07 ranks of expected_values: 162 EVAL 02w4v parent_genre! 02fhtq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 35.000 0.600 http://example.org/music/genre/parent_genre #22022-0164w8 PRED entity: 0164w8 PRED relation: nominated_for PRED expected values: 0p9tm => 113 concepts (71 used for prediction) PRED predicted values (max 10 best out of 326): 0p9tm (0.81 #93887, 0.80 #97124, 0.80 #101980), 0gwjw0c (0.07 #1086, 0.04 #5942, 0.03 #2705), 02yvct (0.07 #325, 0.04 #5181, 0.03 #1944), 07xtqq (0.06 #1670, 0.04 #4907, 0.04 #51), 0jvt9 (0.06 #3732, 0.04 #5350, 0.04 #494), 0bcndz (0.05 #6724, 0.03 #13199, 0.02 #14817), 0bj25 (0.04 #7810, 0.04 #1334, 0.03 #4572), 0k4kk (0.04 #6725, 0.03 #13200, 0.03 #14818), 0233bn (0.04 #6018), 029jt9 (0.04 #1349, 0.04 #7825, 0.03 #2968) >> Best rule #93887 for best value: >> intensional similarity = 3 >> extensional distance = 1422 >> proper extension: 06vqdf; >> query: (?x8288, ?x7846) <- nominated_for(?x8288, ?x4591), award_winner(?x7846, ?x8288), profession(?x8288, ?x2265) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0164w8 nominated_for 0p9tm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 71.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #22021-045931 PRED entity: 045931 PRED relation: nationality PRED expected values: 09c7w0 => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 69): 09c7w0 (0.78 #401, 0.75 #301, 0.70 #1805), 03rjj (0.30 #3109, 0.03 #1208, 0.03 #5013), 03h64 (0.30 #3109, 0.03 #5013, 0.03 #4812), 0d060g (0.25 #7, 0.14 #107, 0.12 #207), 02jx1 (0.18 #534, 0.14 #736, 0.14 #636), 03rk0 (0.14 #749, 0.11 #849, 0.07 #949), 07ssc (0.10 #1318, 0.10 #1618, 0.09 #1418), 0j5g9 (0.09 #563, 0.07 #765, 0.07 #665), 03_3d (0.05 #1109, 0.03 #5013, 0.03 #4812), 0f8l9c (0.04 #1804, 0.03 #2908, 0.03 #5013) >> Best rule #401 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0h0wc; 03_48k; 05nzw6; 0338g8; 03swmf; 01g969; 02__ww; >> query: (?x11741, 09c7w0) <- profession(?x11741, ?x1032), film(?x11741, ?x4448), film(?x11741, ?x1454), ?x4448 = 01k60v, films(?x3490, ?x1454) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045931 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.778 http://example.org/people/person/nationality #22020-015vql PRED entity: 015vql PRED relation: film PRED expected values: 0c8qq 0286hyp => 76 concepts (28 used for prediction) PRED predicted values (max 10 best out of 356): 05z43v (0.22 #1356, 0.03 #3148, 0.02 #4941), 031hcx (0.11 #1276, 0.06 #15614, 0.06 #17406), 03177r (0.11 #465, 0.05 #14803, 0.05 #16595), 031778 (0.11 #316, 0.05 #14654, 0.04 #16446), 01cz7r (0.11 #1326, 0.04 #3118, 0.04 #4911), 04jpg2p (0.11 #1465, 0.04 #15803, 0.03 #17595), 03176f (0.11 #708, 0.04 #15046, 0.03 #16838), 011ywj (0.11 #1438, 0.03 #17568, 0.03 #19360), 09fqgj (0.11 #1663, 0.03 #17793, 0.03 #19585), 020bv3 (0.11 #319, 0.03 #18241, 0.03 #16449) >> Best rule #1356 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 01vh3r; >> query: (?x12889, 05z43v) <- nationality(?x12889, ?x1310), nationality(?x12889, ?x512), ?x1310 = 02jx1, award(?x12889, ?x112), ?x512 = 07ssc, ?x112 = 027dtxw >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015vql film 0286hyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 28.000 0.222 http://example.org/film/actor/film./film/performance/film EVAL 015vql film 0c8qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 28.000 0.222 http://example.org/film/actor/film./film/performance/film #22019-02wgbb PRED entity: 02wgbb PRED relation: production_companies PRED expected values: 030_1_ => 83 concepts (57 used for prediction) PRED predicted values (max 10 best out of 61): 016tw3 (0.40 #2836, 0.11 #1763, 0.10 #1597), 086k8 (0.33 #919, 0.31 #4338, 0.31 #335), 017s11 (0.22 #170, 0.21 #671, 0.07 #1006), 05qd_ (0.22 #261, 0.18 #345, 0.17 #1013), 030_1_ (0.17 #100, 0.11 #268, 0.09 #352), 0jz9f (0.17 #84, 0.11 #252, 0.09 #336), 06rq1k (0.16 #686, 0.12 #520, 0.11 #603), 01795t (0.13 #441, 0.12 #524, 0.11 #607), 016tt2 (0.11 #255, 0.11 #171, 0.09 #339), 054lpb6 (0.11 #600, 0.10 #1018, 0.09 #766) >> Best rule #2836 for best value: >> intensional similarity = 4 >> extensional distance = 336 >> proper extension: 04bp0l; >> query: (?x7800, ?x1104) <- nominated_for(?x7605, ?x7800), nominated_for(?x1104, ?x7800), award_winner(?x8695, ?x7605), film(?x1104, ?x86) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #100 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 01bn3l; 09rx7tx; *> query: (?x7800, 030_1_) <- person(?x7800, ?x12065), prequel(?x7800, ?x7801), film(?x382, ?x7800), nominated_for(?x102, ?x7800) *> conf = 0.17 ranks of expected_values: 5 EVAL 02wgbb production_companies 030_1_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 83.000 57.000 0.401 http://example.org/film/film/production_companies #22018-02665kn PRED entity: 02665kn PRED relation: type_of_union PRED expected values: 04ztj => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.71 #154, 0.70 #162, 0.70 #41), 01g63y (0.23 #137, 0.19 #10, 0.13 #155), 01bl8s (0.23 #137, 0.01 #15), 0jgjn (0.23 #137) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 1265 >> proper extension: 02784z; >> query: (?x13346, 04ztj) <- location(?x13346, ?x3086), film(?x13346, ?x6288), nationality(?x13346, ?x94) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02665kn type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.706 http://example.org/people/person/spouse_s./people/marriage/type_of_union #22017-02mf7 PRED entity: 02mf7 PRED relation: location! PRED expected values: 01w02sy => 173 concepts (59 used for prediction) PRED predicted values (max 10 best out of 2012): 012xdf (0.10 #9397, 0.09 #6879, 0.08 #11915), 02lt8 (0.10 #8351, 0.08 #10869, 0.07 #13387), 023kzp (0.10 #8770, 0.08 #11288, 0.07 #13806), 099p5 (0.10 #4418, 0.08 #1900, 0.03 #11972), 01vsy3q (0.09 #6027, 0.09 #18618, 0.07 #8545), 01q_ph (0.09 #5086, 0.08 #10122, 0.07 #7604), 0gl88b (0.09 #5407, 0.07 #12961, 0.07 #7925), 01s21dg (0.09 #6000, 0.07 #13554, 0.07 #8518), 073749 (0.09 #5839, 0.07 #13393, 0.07 #15912), 01m4yn (0.09 #6414, 0.07 #8932, 0.05 #11450) >> Best rule #9397 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 02_286; 0ply0; >> query: (?x13303, 012xdf) <- teams(?x13303, ?x179), state(?x13303, ?x726), colors(?x179, ?x3621), source(?x13303, ?x958) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #13186 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 0619_; *> query: (?x13303, 01w02sy) <- teams(?x13303, ?x179), state(?x13303, ?x726), team(?x180, ?x179), team(?x7749, ?x179) *> conf = 0.05 ranks of expected_values: 138 EVAL 02mf7 location! 01w02sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 173.000 59.000 0.103 http://example.org/people/person/places_lived./people/place_lived/location #22016-019ltg PRED entity: 019ltg PRED relation: sport PRED expected values: 02vx4 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.88 #570, 0.88 #471, 0.88 #597), 0z74 (0.50 #670, 0.50 #623, 0.49 #689), 0jm_ (0.14 #93, 0.13 #526, 0.12 #499), 03tmr (0.13 #91, 0.11 #280, 0.10 #100), 018jz (0.11 #646, 0.10 #618, 0.10 #591), 018w8 (0.06 #94, 0.06 #500, 0.06 #527), 039yzs (0.03 #696, 0.03 #686, 0.03 #639), 09xp_ (0.02 #529, 0.02 #96, 0.01 #502) >> Best rule #570 for best value: >> intensional similarity = 14 >> extensional distance = 165 >> proper extension: 0371rb; 04112r; 0gxkm; 01kwhf; 0f5hyg; 01vqc7; 051n13; 011v3; 0690dn; 02_lt; ... >> query: (?x9247, 02vx4) <- position(?x9247, ?x530), position(?x9247, ?x203), colors(?x9247, ?x663), ?x203 = 0dgrmp, position(?x13674, ?x530), position(?x9511, ?x530), position(?x8703, ?x530), position(?x5655, ?x530), ?x9511 = 04knh6, ?x5655 = 03x6xl, ?x8703 = 019m9h, position(?x11645, ?x530), ?x13674 = 02psgvg, ?x11645 = 03tc5p >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019ltg sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.880 http://example.org/sports/sports_team/sport #22015-04ldyx1 PRED entity: 04ldyx1 PRED relation: ceremony PRED expected values: 0lp_cd3 => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 137): 0gx_st (0.58 #172, 0.47 #722, 0.44 #997), 0gpjbt (0.51 #1951, 0.51 #2501, 0.50 #2227), 09n4nb (0.51 #1969, 0.50 #2519, 0.48 #2245), 0466p0j (0.50 #1996, 0.49 #2546, 0.49 #2272), 03nnm4t (0.50 #208, 0.49 #758, 0.48 #1033), 05pd94v (0.49 #1925, 0.49 #2475, 0.48 #2201), 02rjjll (0.49 #1928, 0.48 #2478, 0.48 #2204), 056878 (0.49 #1954, 0.48 #2504, 0.47 #2230), 02cg41 (0.49 #2045, 0.48 #2595, 0.47 #2321), 01c6qp (0.48 #1941, 0.48 #2491, 0.47 #292) >> Best rule #172 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0cqhk0; 09qj50; 09qv3c; 0cjyzs; 047sgz4; 09qs08; 03ccq3s; 09qvf4; 027gs1_; 0cqhmg; >> query: (?x4728, 0gx_st) <- award(?x1434, ?x4728), award(?x84, ?x4728), nominated_for(?x4728, ?x2528), ?x2528 = 0d68qy, nominated_for(?x84, ?x83) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #825 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 47 *> proper extension: 0fqnzts; *> query: (?x4728, ?x1265) <- award(?x9188, ?x4728), award(?x84, ?x4728), honored_for(?x1265, ?x9188), genre(?x9188, ?x8681), genre(?x903, ?x8681) *> conf = 0.27 ranks of expected_values: 63 EVAL 04ldyx1 ceremony 0lp_cd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 51.000 51.000 0.583 http://example.org/award/award_category/winners./award/award_honor/ceremony #22014-01c59k PRED entity: 01c59k PRED relation: profession PRED expected values: 02krf9 => 57 concepts (51 used for prediction) PRED predicted values (max 10 best out of 88): 01d_h8 (0.93 #2212, 0.70 #1623, 0.67 #2802), 02hrh1q (0.91 #5604, 0.83 #5898, 0.82 #6045), 0dxtg (0.66 #2809, 0.65 #601, 0.65 #2219), 0kyk (0.62 #469, 0.33 #322, 0.33 #175), 0cbd2 (0.50 #448, 0.33 #301, 0.33 #154), 03gjzk (0.38 #456, 0.35 #4576, 0.28 #4429), 0dgd_ (0.33 #323, 0.33 #29, 0.12 #470), 0lgw7 (0.33 #340, 0.33 #46, 0.12 #487), 094hwz (0.33 #183, 0.17 #330, 0.12 #477), 0d2b38 (0.33 #212, 0.17 #359, 0.12 #506) >> Best rule #2212 for best value: >> intensional similarity = 4 >> extensional distance = 471 >> proper extension: 0byfz; 0qf43; 014zcr; 09fb5; 02qjj7; 0m2l9; 02nb2s; 02pp_q_; 01vvycq; 09byk; ... >> query: (?x1775, 01d_h8) <- profession(?x1775, ?x1776), profession(?x1775, ?x524), ?x524 = 02jknp, film_crew_role(?x148, ?x1776) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #614 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 50 *> proper extension: 012d40; 03_gd; 0l12d; 025tdwc; 04gcd1; 081nh; 01vsl3_; 02_fj; 021yw7; 02fn5; ... *> query: (?x1775, 02krf9) <- profession(?x1775, ?x1776), profession(?x1775, ?x524), ?x524 = 02jknp, film_crew_role(?x2649, ?x1776), film_release_region(?x2649, ?x94) *> conf = 0.25 ranks of expected_values: 14 EVAL 01c59k profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 57.000 51.000 0.932 http://example.org/people/person/profession #22013-03v52f PRED entity: 03v52f PRED relation: company! PRED expected values: 01yc02 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 33): 01yc02 (0.50 #259, 0.49 #469, 0.39 #637), 0dq3c (0.48 #632, 0.47 #590, 0.47 #464), 09d6p2 (0.35 #436, 0.33 #268, 0.33 #394), 01kr6k (0.29 #66, 0.28 #444, 0.26 #654), 02211by (0.20 #255, 0.20 #339, 0.17 #423), 0142rn (0.17 #359, 0.16 #317, 0.14 #2311), 021q1c (0.16 #1605, 0.14 #51, 0.12 #135), 02y6fz (0.16 #315, 0.14 #2311, 0.13 #273), 021q0l (0.14 #2311, 0.10 #3160, 0.09 #2950), 014l7h (0.14 #2311, 0.09 #2167, 0.06 #3557) >> Best rule #259 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 0537b; >> query: (?x7442, 01yc02) <- company(?x1491, ?x7442), company(?x346, ?x7442), ?x346 = 060c4, ?x1491 = 0krdk, currency(?x7442, ?x170) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03v52f company! 01yc02 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.500 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #22012-049nq PRED entity: 049nq PRED relation: partially_contains! PRED expected values: 0157g9 => 113 concepts (98 used for prediction) PRED predicted values (max 10 best out of 112): 0j0k (0.38 #1031, 0.27 #1127, 0.21 #1615), 05rgl (0.23 #997, 0.20 #1093, 0.20 #800), 059g4 (0.20 #841, 0.15 #1038, 0.13 #1134), 0f8l9c (0.20 #780, 0.12 #1561, 0.10 #2050), 02qkt (0.17 #2614), 05g2v (0.15 #1027, 0.13 #1123, 0.12 #1611), 04swx (0.15 #1055, 0.13 #1151, 0.10 #858), 06n3y (0.15 #1052, 0.13 #1148, 0.10 #855), 03v0t (0.14 #2089, 0.12 #2281, 0.11 #2571), 0j3b (0.13 #1079, 0.08 #1567, 0.08 #983) >> Best rule #1031 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0f8l9c; 06bnz; 0cdbq; 05vz3zq; >> query: (?x10382, 0j0k) <- contains(?x10382, ?x1229), nationality(?x10923, ?x10382), partially_contains(?x455, ?x10382) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1645 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 0lcd; 0lm0n; *> query: (?x10382, ?x7273) <- contains(?x10382, ?x10190), partially_contains(?x455, ?x10382), contains(?x7273, ?x10190) *> conf = 0.03 ranks of expected_values: 104 EVAL 049nq partially_contains! 0157g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 113.000 98.000 0.385 http://example.org/location/location/partially_contains #22011-02q52q PRED entity: 02q52q PRED relation: currency PRED expected values: 09nqf => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.81 #190, 0.79 #99, 0.78 #57), 01nv4h (0.03 #128, 0.02 #275, 0.02 #457), 02gsvk (0.02 #202, 0.01 #216, 0.01 #195), 088n7 (0.01 #91), 02l6h (0.01 #95, 0.01 #256, 0.01 #207) >> Best rule #190 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 09xbpt; 0bvn25; 01k1k4; 034qrh; 060v34; 02x3lt7; 0209xj; 061681; 09p35z; 04dsnp; ... >> query: (?x1804, 09nqf) <- titles(?x307, ?x1804), produced_by(?x1804, ?x10384), category(?x1804, ?x134) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q52q currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.812 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #22010-02bm1v PRED entity: 02bm1v PRED relation: industry PRED expected values: 01mw1 => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 45): 01mw1 (0.80 #3904, 0.77 #2964, 0.74 #1553), 02vxn (0.39 #2589, 0.33 #331, 0.28 #2166), 09t4t (0.33 #16, 0.10 #439, 0.08 #628), 01mf0 (0.31 #689, 0.18 #2070, 0.18 #5738), 029g_vk (0.21 #1516, 0.20 #1046, 0.18 #1281), 019z7b (0.18 #2070, 0.18 #5738, 0.17 #6778), 07c1v (0.17 #607, 0.12 #1312, 0.11 #1406), 02jjt (0.15 #714, 0.12 #2642, 0.12 #2030), 01mfj (0.15 #695, 0.12 #1165, 0.10 #1729), 08mh3kd (0.13 #1047, 0.12 #1282, 0.10 #2223) >> Best rule #3904 for best value: >> intensional similarity = 7 >> extensional distance = 73 >> proper extension: 01swdw; 01tlrp; 0dwcl; 01tkfj; 07733f; 02b07b; 070ny; >> query: (?x9806, 01mw1) <- industry(?x9806, ?x10022), industry(?x13222, ?x10022), industry(?x12074, ?x10022), industry(?x10419, ?x10022), ?x13222 = 021gk7, ?x12074 = 02rfft, ?x10419 = 08z84_ >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bm1v industry 01mw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.800 http://example.org/business/business_operation/industry #22009-0g48m4 PRED entity: 0g48m4 PRED relation: people PRED expected values: 01d_4t => 41 concepts (39 used for prediction) PRED predicted values (max 10 best out of 2166): 0bx_q (0.50 #11161, 0.25 #21516, 0.20 #9435), 046zh (0.33 #11099, 0.31 #24907, 0.31 #23180), 011zd3 (0.33 #2016, 0.25 #5468, 0.25 #3742), 01ypsj (0.33 #3107, 0.25 #6559, 0.25 #4833), 09h4b5 (0.33 #2838, 0.25 #6290, 0.25 #4564), 06s7rd (0.33 #2889, 0.25 #6341, 0.25 #4615), 07ftc0 (0.33 #2876, 0.25 #6328, 0.25 #4602), 03xds (0.33 #3390, 0.25 #6842, 0.25 #5116), 099d4 (0.33 #3353, 0.25 #6805, 0.25 #5079), 033m23 (0.33 #2809, 0.25 #6261, 0.25 #4535) >> Best rule #11161 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0g8_vp; >> query: (?x1176, 0bx_q) <- people(?x1176, ?x4631), people(?x1176, ?x4258), profession(?x4258, ?x1032), currency(?x4258, ?x170), ?x4631 = 0315q3, ?x1032 = 02hrh1q, award_winner(?x9220, ?x4258), ?x170 = 09nqf >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g48m4 people 01d_4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 39.000 0.500 http://example.org/people/ethnicity/people #22008-016fjj PRED entity: 016fjj PRED relation: award_nominee PRED expected values: 07ddz9 => 113 concepts (46 used for prediction) PRED predicted values (max 10 best out of 881): 02bkdn (0.81 #7009, 0.81 #100478, 0.81 #79447), 016fjj (0.58 #835, 0.15 #107488, 0.14 #102815), 07ddz9 (0.58 #2109, 0.15 #107488, 0.14 #102815), 02qgyv (0.29 #5174, 0.17 #502, 0.15 #107488), 0h0wc (0.23 #5228, 0.08 #556, 0.08 #2892), 03x3qv (0.23 #4723, 0.08 #51, 0.02 #21081), 05l4yg (0.23 #6224, 0.08 #1552, 0.01 #45950), 0335fp (0.23 #6447, 0.08 #1775, 0.01 #55520), 01kb2j (0.20 #5882, 0.12 #3546, 0.11 #8220), 017149 (0.20 #4773, 0.08 #101, 0.05 #16456) >> Best rule #7009 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 02f8lw; 05zjx; 01ggc9; >> query: (?x3701, ?x3756) <- award_winner(?x112, ?x3701), award_nominee(?x3756, ?x3701), award_nominee(?x1871, ?x3701), ?x1871 = 02bkdn >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #2109 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 07ddz9; *> query: (?x3701, 07ddz9) <- award_nominee(?x3701, ?x5058), film(?x3701, ?x708), ?x5058 = 06wm0z *> conf = 0.58 ranks of expected_values: 3 EVAL 016fjj award_nominee 07ddz9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 46.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22007-065d1h PRED entity: 065d1h PRED relation: award_winner! PRED expected values: 09v8db5 => 96 concepts (70 used for prediction) PRED predicted values (max 10 best out of 192): 09v8db5 (0.25 #252, 0.11 #23337, 0.11 #19879), 09v0wy2 (0.25 #234, 0.07 #11234, 0.05 #27226), 09v1lrz (0.25 #378, 0.03 #9073, 0.03 #29389), 09sb52 (0.11 #4361, 0.10 #13867, 0.10 #4793), 09v51c2 (0.11 #23337, 0.11 #19879, 0.11 #18582), 099tbz (0.08 #2218, 0.05 #4378, 0.05 #4810), 019bnn (0.07 #1132, 0.06 #1564, 0.03 #3292), 04fgkf_ (0.07 #1152, 0.06 #1584), 03rbj2 (0.07 #1086, 0.06 #1518), 0ck27z (0.06 #9166, 0.06 #7869, 0.06 #9598) >> Best rule #252 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 054k_8; >> query: (?x10573, 09v8db5) <- film(?x10573, ?x6219), nationality(?x10573, ?x2346), profession(?x10573, ?x319), ?x6219 = 05znbh7 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065d1h award_winner! 09v8db5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 70.000 0.250 http://example.org/award/award_category/winners./award/award_honor/award_winner #22006-0330r PRED entity: 0330r PRED relation: award PRED expected values: 0m7yy => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 177): 0m7yy (0.49 #357, 0.47 #1270, 0.42 #1042), 09qs08 (0.46 #458, 0.43 #5024, 0.41 #3881), 09qj50 (0.41 #35, 0.15 #1177, 0.14 #5253), 0fbtbt (0.22 #380, 0.14 #1293, 0.13 #1977), 0cqhmg (0.21 #207, 0.19 #4338, 0.18 #9592), 0bdw6t (0.19 #4338, 0.18 #9592, 0.14 #5253), 03ccq3s (0.19 #4338, 0.18 #9592, 0.14 #5253), 02y_rq5 (0.19 #4338, 0.18 #9592, 0.14 #5253), 09sb52 (0.19 #4338, 0.18 #9592, 0.14 #5253), 0bdwft (0.19 #4338, 0.18 #9592, 0.14 #5253) >> Best rule #357 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 03j63k; >> query: (?x9541, 0m7yy) <- nominated_for(?x678, ?x9541), award(?x9541, ?x7510), program_creator(?x9541, ?x7095) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0330r award 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.492 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #22005-07f_7h PRED entity: 07f_7h PRED relation: film_release_region PRED expected values: 03rt9 01znc_ 06t8v 07f1x => 78 concepts (70 used for prediction) PRED predicted values (max 10 best out of 161): 05b4w (0.90 #2786, 0.86 #2498, 0.84 #4505), 01znc_ (0.86 #3766, 0.85 #3480, 0.85 #2764), 03rt9 (0.83 #2741, 0.82 #3457, 0.81 #3743), 05v8c (0.83 #2455, 0.80 #2743, 0.80 #1307), 03rk0 (0.80 #1343, 0.69 #2491, 0.68 #2779), 09pmkv (0.80 #1317, 0.66 #2465, 0.61 #2753), 0ctw_b (0.75 #3467, 0.71 #4470, 0.70 #3753), 01p1v (0.71 #4494, 0.68 #2775, 0.67 #3491), 07f1x (0.69 #2550, 0.67 #1402, 0.61 #2838), 016wzw (0.69 #4507, 0.66 #2500, 0.64 #3790) >> Best rule #2786 for best value: >> intensional similarity = 14 >> extensional distance = 39 >> proper extension: 087wc7n; >> query: (?x2598, 05b4w) <- film_release_region(?x2598, ?x2645), film_release_region(?x2598, ?x1892), film_release_region(?x2598, ?x1475), film_release_region(?x2598, ?x410), film_release_region(?x2598, ?x172), film_release_region(?x2598, ?x151), film_release_region(?x2598, ?x94), ?x410 = 01ls2, ?x1892 = 02vzc, ?x2645 = 03h64, ?x172 = 0154j, ?x1475 = 05qx1, ?x151 = 0b90_r, nationality(?x51, ?x94) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #3766 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 62 *> proper extension: 05qbckf; 0cc5mcj; 0gtsxr4; 0bpm4yw; 0bt3j9; 0bq6ntw; 01f85k; *> query: (?x2598, 01znc_) <- film_release_region(?x2598, ?x2645), film_release_region(?x2598, ?x1892), film_release_region(?x2598, ?x1353), film_release_region(?x2598, ?x410), film_release_region(?x2598, ?x390), ?x410 = 01ls2, ?x1892 = 02vzc, ?x2645 = 03h64, film(?x541, ?x2598), ?x390 = 0chghy, ?x1353 = 035qy *> conf = 0.86 ranks of expected_values: 2, 3, 9, 15 EVAL 07f_7h film_release_region 07f1x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 78.000 70.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f_7h film_release_region 06t8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 78.000 70.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f_7h film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 70.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f_7h film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 70.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22004-0bhwhj PRED entity: 0bhwhj PRED relation: film_release_region PRED expected values: 06mkj => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 165): 035qy (0.90 #3613, 0.90 #2866, 0.89 #1373), 06mkj (0.90 #2887, 0.88 #498, 0.88 #3634), 05b4w (0.84 #2895, 0.81 #3642, 0.78 #1402), 06bnz (0.84 #3623, 0.82 #2876, 0.82 #487), 0d060g (0.84 #3587, 0.81 #2840, 0.76 #451), 05v8c (0.76 #460, 0.74 #1356, 0.68 #2849), 016wzw (0.76 #509, 0.67 #1405, 0.66 #2898), 04gzd (0.71 #3590, 0.68 #2843, 0.59 #454), 03rj0 (0.71 #502, 0.70 #1398, 0.65 #2891), 015qh (0.71 #482, 0.67 #1378, 0.66 #2871) >> Best rule #3613 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 0b76d_m; 0c3ybss; 0dscrwf; 0h3xztt; 0bq8tmw; 0gj9tn5; 0cc7hmk; 0fq7dv_; 0cp0ph6; 05c26ss; ... >> query: (?x5400, 035qy) <- nominated_for(?x459, ?x5400), film_release_region(?x5400, ?x1917), ?x1917 = 01p1v >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2887 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 66 *> proper extension: 0ds35l9; 0g56t9t; 02vxq9m; 0ds3t5x; 0g5qs2k; 02x3lt7; 0gkz15s; 02d44q; 0872p_c; 0dgst_d; ... *> query: (?x5400, 06mkj) <- nominated_for(?x459, ?x5400), film_release_region(?x5400, ?x1917), film_release_region(?x5400, ?x1892), ?x1917 = 01p1v, ?x1892 = 02vzc *> conf = 0.90 ranks of expected_values: 2 EVAL 0bhwhj film_release_region 06mkj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #22003-014v6f PRED entity: 014v6f PRED relation: award_nominee! PRED expected values: 04qsdh => 88 concepts (36 used for prediction) PRED predicted values (max 10 best out of 775): 027bs_2 (0.83 #9306, 0.83 #8624, 0.83 #9305), 030znt (0.83 #9305, 0.81 #41879, 0.81 #83775), 05vsxz (0.83 #9305, 0.81 #41879, 0.81 #83775), 0pmhf (0.83 #9305, 0.81 #41879, 0.81 #83775), 0785v8 (0.83 #9305, 0.81 #41879, 0.81 #83775), 06qgvf (0.83 #9305, 0.81 #41879, 0.81 #83775), 04qsdh (0.83 #9305, 0.81 #41879, 0.81 #83775), 014v6f (0.50 #8257, 0.18 #51192, 0.15 #83776), 01900g (0.28 #8024, 0.15 #83776, 0.15 #55848), 01_xtx (0.28 #7855, 0.15 #83776, 0.15 #55848) >> Best rule #9306 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01900g; >> query: (?x5461, ?x7313) <- award_nominee(?x5461, ?x7313), film(?x5461, ?x667), ?x7313 = 027bs_2 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #9305 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 01900g; *> query: (?x5461, ?x100) <- award_nominee(?x5461, ?x7313), award_nominee(?x5461, ?x100), film(?x5461, ?x667), ?x7313 = 027bs_2 *> conf = 0.83 ranks of expected_values: 7 EVAL 014v6f award_nominee! 04qsdh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 88.000 36.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #22002-024qqx PRED entity: 024qqx PRED relation: titles PRED expected values: 04v8x9 031778 0bm2g 04yc76 0ddt_ 01s7w3 => 23 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1910): 07z6xs (0.50 #3684, 0.38 #5168, 0.33 #6652), 0296rz (0.50 #4310, 0.38 #5794, 0.33 #1342), 09m6kg (0.50 #2990, 0.33 #1506, 0.33 #22), 08zrbl (0.50 #4078, 0.33 #2594, 0.33 #1110), 084302 (0.50 #3384, 0.33 #1900, 0.33 #416), 08c4yn (0.50 #4393, 0.33 #2909, 0.33 #1425), 03h_yy (0.50 #3026, 0.33 #58, 0.31 #7479), 0f4m2z (0.50 #3315, 0.33 #347, 0.31 #4799), 047fjjr (0.50 #3473, 0.33 #505, 0.31 #4957), 01qbg5 (0.50 #3996, 0.33 #1028, 0.25 #8449) >> Best rule #3684 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 04xvlr; >> query: (?x8581, 07z6xs) <- titles(?x8581, ?x7336), titles(?x8581, ?x2394), film_release_region(?x2394, ?x1892), film_release_region(?x2394, ?x1203), ?x7336 = 0bdjd, nominated_for(?x68, ?x2394), ?x1203 = 07ylj, ?x1892 = 02vzc >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #23752 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 74 *> proper extension: 01zcrv; 0146mv; *> query: (?x8581, ?x573) <- titles(?x8581, ?x6332), titles(?x8581, ?x5128), titles(?x8581, ?x2394), award(?x2394, ?x500), award_winner(?x5128, ?x4252), nominated_for(?x981, ?x6332), nominated_for(?x4252, ?x573) *> conf = 0.17 ranks of expected_values: 746, 808, 1040, 1292, 1688, 1738 EVAL 024qqx titles 01s7w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 17.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 024qqx titles 0ddt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 17.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 024qqx titles 04yc76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 17.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 024qqx titles 0bm2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 17.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 024qqx titles 031778 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 17.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 024qqx titles 04v8x9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 17.000 0.500 http://example.org/media_common/netflix_genre/titles #22001-05sbv3 PRED entity: 05sbv3 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #87, 0.82 #31, 0.82 #138), 07c52 (0.07 #18, 0.04 #394, 0.03 #374), 07z4p (0.06 #10, 0.03 #396, 0.03 #376), 02nxhr (0.04 #22, 0.03 #393, 0.03 #337) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 395 >> proper extension: 0gx1bnj; 07g_0c; 0436yk; 05q4y12; 0ddcbd5; 02qsqmq; 063zky; >> query: (?x11348, 029j_) <- production_companies(?x11348, ?x788), genre(?x11348, ?x258), ?x258 = 05p553 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05sbv3 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.834 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #22000-0h0yt PRED entity: 0h0yt PRED relation: languages PRED expected values: 02h40lc => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 14): 02h40lc (0.44 #510, 0.42 #392, 0.42 #353), 064_8sq (0.17 #54, 0.11 #171, 0.10 #15), 0295r (0.04 #294), 02bjrlw (0.03 #509, 0.03 #391, 0.02 #706), 03k50 (0.03 #1998, 0.02 #1686, 0.02 #1374), 0fdys (0.02 #430, 0.01 #785), 03_9r (0.02 #513, 0.01 #750, 0.01 #395), 0999q (0.02 #571), 07c9s (0.01 #1695, 0.01 #2007, 0.01 #1227), 0x82 (0.01 #388, 0.01 #467) >> Best rule #510 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 02wxvtv; 03f0324; 03f1zhf; 0c73z; 06c0j; >> query: (?x7746, 02h40lc) <- gender(?x7746, ?x231), student(?x3995, ?x7746), people(?x743, ?x7746) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h0yt languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.444 http://example.org/people/person/languages #21999-01jgpsh PRED entity: 01jgpsh PRED relation: location PRED expected values: 030qb3t => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 114): 0c9cw (0.50 #24911, 0.44 #44195, 0.43 #48213), 01cx_ (0.34 #4178, 0.06 #1769, 0.05 #2572), 0ccvx (0.33 #4237, 0.03 #3434, 0.03 #11472), 02_286 (0.23 #40213, 0.18 #841, 0.17 #2447), 0c_m3 (0.20 #270, 0.01 #14733), 0wqwj (0.20 #755), 049lr (0.20 #451), 030qb3t (0.18 #40258, 0.16 #2492, 0.15 #18564), 04jpl (0.16 #5641, 0.08 #40193, 0.06 #55471), 0dclg (0.09 #920, 0.08 #1723, 0.03 #2526) >> Best rule #24911 for best value: >> intensional similarity = 2 >> extensional distance = 1050 >> proper extension: 0f2c8g; 021r7r; 07h1q; 085q5; 045gzq; >> query: (?x6363, ?x14032) <- people(?x13372, ?x6363), place_of_birth(?x6363, ?x14032) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #40258 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1538 *> proper extension: 036hf4; *> query: (?x6363, 030qb3t) <- location(?x6363, ?x1310), teams(?x1310, ?x11309) *> conf = 0.18 ranks of expected_values: 8 EVAL 01jgpsh location 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 101.000 101.000 0.504 http://example.org/people/person/places_lived./people/place_lived/location #21998-045j3w PRED entity: 045j3w PRED relation: genre PRED expected values: 02n4kr => 95 concepts (57 used for prediction) PRED predicted values (max 10 best out of 120): 07s9rl0 (0.71 #5653, 0.71 #4930, 0.67 #841), 02kdv5l (0.65 #6015, 0.45 #5293, 0.45 #1445), 03k9fj (0.56 #372, 0.46 #732, 0.44 #5302), 05p553 (0.51 #6379, 0.43 #485, 0.38 #3972), 02n4kr (0.50 #248, 0.38 #2774, 0.29 #5178), 0jxy (0.45 #1488, 0.44 #1006, 0.26 #886), 0hcr (0.43 #1466, 0.38 #984, 0.28 #864), 01hmnh (0.41 #258, 0.36 #6030, 0.33 #378), 0lsxr (0.40 #5420, 0.32 #2775, 0.31 #5179), 02l7c8 (0.32 #2300, 0.31 #3022, 0.31 #1096) >> Best rule #5653 for best value: >> intensional similarity = 4 >> extensional distance = 616 >> proper extension: 03kq98; >> query: (?x3000, 07s9rl0) <- titles(?x571, ?x3000), genre(?x3413, ?x571), genre(?x4991, ?x571), ?x4991 = 02xs6_ >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #248 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 20 *> proper extension: 03t97y; 07j94; 04nnpw; 03t79f; 02tktw; 031786; 09gdh6k; 05ch98; 032xky; *> query: (?x3000, 02n4kr) <- film(?x2681, ?x3000), country(?x3000, ?x94), genre(?x3000, ?x6277), film_release_distribution_medium(?x3000, ?x81), ?x6277 = 0fdjb, language(?x3000, ?x254) *> conf = 0.50 ranks of expected_values: 5 EVAL 045j3w genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 95.000 57.000 0.710 http://example.org/film/film/genre #21997-06pj8 PRED entity: 06pj8 PRED relation: company PRED expected values: 016tw3 => 155 concepts (144 used for prediction) PRED predicted values (max 10 best out of 92): 04gmlt (0.53 #575, 0.50 #1341, 0.33 #2299), 056ws9 (0.53 #575, 0.50 #1341, 0.33 #2299), 09c7w0 (0.22 #192, 0.15 #1725, 0.10 #2683), 02jd_7 (0.14 #529, 0.09 #721, 0.08 #1104), 0kx4m (0.14 #399, 0.08 #1165, 0.05 #2123), 07wrz (0.14 #802, 0.04 #11719, 0.03 #3869), 03ksy (0.09 #816, 0.03 #9820, 0.02 #5414), 02zd460 (0.09 #850, 0.01 #3917), 032j_n (0.08 #1299, 0.07 #533, 0.05 #2257), 061dn_ (0.08 #1199, 0.07 #433, 0.05 #2157) >> Best rule #575 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02q_cc; 0pz91; 0343h; 02vyw; 05hj_k; 0b478; 03h304l; 0m593; 03y2kr; 059x0w; ... >> query: (?x2135, ?x1686) <- executive_produced_by(?x825, ?x2135), location(?x2135, ?x739), organizations_founded(?x2135, ?x1686) >> conf = 0.53 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 06pj8 company 016tw3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 144.000 0.529 http://example.org/people/person/employment_history./business/employment_tenure/company #21996-016wzw PRED entity: 016wzw PRED relation: country! PRED expected values: 01lb14 09w1n => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 46): 01lb14 (0.82 #13, 0.79 #289, 0.79 #1026), 03hr1p (0.82 #294, 0.79 #156, 0.77 #18), 0w0d (0.82 #11, 0.76 #287, 0.71 #195), 07jbh (0.73 #27, 0.68 #579, 0.66 #1040), 064vjs (0.68 #25, 0.68 #163, 0.67 #485), 07bs0 (0.68 #12, 0.64 #472, 0.64 #288), 01hp22 (0.68 #145, 0.67 #283, 0.65 #191), 02y8z (0.65 #567, 0.64 #15, 0.61 #199), 019tzd (0.64 #309, 0.64 #33, 0.61 #171), 07rlg (0.64 #1, 0.61 #139, 0.55 #553) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 05v8c; 06mzp; 06qd3; 06f32; 03spz; >> query: (?x2843, 01lb14) <- film_release_region(?x7680, ?x2843), film_release_region(?x2868, ?x2843), ?x2868 = 0dr3sl, ?x7680 = 0gh6j94 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 15 EVAL 016wzw country! 09w1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 105.000 105.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 016wzw country! 01lb14 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #21995-01ckcd PRED entity: 01ckcd PRED relation: award! PRED expected values: 0134s5 0178kd => 48 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2434): 01dq9q (0.81 #6646, 0.81 #9969, 0.80 #3323), 09889g (0.75 #1429, 0.62 #4752, 0.39 #8075), 01xzb6 (0.75 #1524, 0.50 #4847, 0.44 #8170), 01vs_v8 (0.69 #3896, 0.50 #7219, 0.50 #573), 0fhxv (0.69 #4653, 0.50 #7976, 0.50 #1330), 01vrz41 (0.62 #3609, 0.56 #6932, 0.50 #286), 01vw20h (0.62 #1268, 0.44 #4591, 0.39 #7914), 0lbj1 (0.56 #6690, 0.44 #3367, 0.38 #44), 01vsykc (0.50 #7536, 0.50 #4213, 0.50 #890), 01vsgrn (0.50 #4934, 0.50 #1611, 0.33 #8257) >> Best rule #6646 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 05zkcn5; 025m8l; 01ck6h; 054ks3; 02f72n; 02x17c2; 02f72_; 02f79n; >> query: (?x9828, ?x702) <- award_winner(?x9828, ?x702), award(?x8226, ?x9828), award(?x5550, ?x9828), award(?x2876, ?x9828), group(?x227, ?x8226), ?x5550 = 01bczm, category(?x2876, ?x134) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #5159 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 14 *> proper extension: 05zkcn5; 025m8l; 01ck6h; 054ks3; 02f72n; 02x17c2; 02f72_; 02f79n; *> query: (?x9828, 0178kd) <- award_winner(?x9828, ?x702), award(?x8226, ?x9828), award(?x5550, ?x9828), award(?x2876, ?x9828), group(?x227, ?x8226), ?x5550 = 01bczm, category(?x2876, ?x134) *> conf = 0.12 ranks of expected_values: 291, 1313 EVAL 01ckcd award! 0178kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 22.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01ckcd award! 0134s5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 22.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21994-0gg9_5q PRED entity: 0gg9_5q PRED relation: student! PRED expected values: 09f2j => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 134): 0gdm1 (0.33 #230), 065y4w7 (0.25 #541, 0.18 #1068, 0.15 #2122), 09f2j (0.12 #686, 0.12 #4904, 0.12 #1213), 06pwq (0.11 #2647, 0.05 #7922, 0.04 #7395), 0bwfn (0.09 #11348, 0.09 #7658, 0.08 #8185), 04b_46 (0.09 #1808, 0.08 #2335, 0.06 #3389), 0fr9jp (0.07 #2980, 0.03 #8782, 0.03 #9309), 02cttt (0.06 #546, 0.06 #1073, 0.05 #1600), 02607j (0.06 #630, 0.06 #1157, 0.05 #1684), 02mzg9 (0.06 #935, 0.06 #1462, 0.05 #1989) >> Best rule #230 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 05nn4k; >> query: (?x3744, 0gdm1) <- place_of_birth(?x3744, ?x1523), produced_by(?x11313, ?x3744), produced_by(?x4688, ?x3744), ?x4688 = 09jcj6, production_companies(?x11313, ?x1478) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #686 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 03y2kr; *> query: (?x3744, 09f2j) <- organizations_founded(?x3744, ?x1478), executive_produced_by(?x4967, ?x3744), produced_by(?x4967, ?x2332) *> conf = 0.12 ranks of expected_values: 3 EVAL 0gg9_5q student! 09f2j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 110.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #21993-0kz4w PRED entity: 0kz4w PRED relation: colors PRED expected values: 06fvc => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 17): 083jv (0.79 #287, 0.64 #803, 0.63 #249), 06fvc (0.41 #633, 0.40 #536, 0.39 #804), 019sc (0.30 #387, 0.28 #1079, 0.28 #1039), 088fh (0.24 #400, 0.20 #44, 0.15 #1052), 09ggk (0.24 #400, 0.16 #630, 0.15 #1052), 038hg (0.16 #630, 0.15 #1052, 0.14 #1248), 01l849 (0.15 #1052, 0.15 #381, 0.14 #1248), 0jc_p (0.15 #1052, 0.14 #1248, 0.14 #821), 036k5h (0.15 #1052, 0.14 #1248, 0.14 #821), 02rnmb (0.15 #1052, 0.14 #821, 0.10 #393) >> Best rule #287 for best value: >> intensional similarity = 6 >> extensional distance = 80 >> proper extension: 0175rc; 0mmd6; >> query: (?x11253, 083jv) <- position(?x11253, ?x60), position(?x11253, ?x63), team(?x9106, ?x11253), colors(?x11253, ?x3189), colors(?x6548, ?x3189), ?x6548 = 0yls9 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #633 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 175 *> proper extension: 05ls3r; 0mgcc; *> query: (?x11253, 06fvc) <- team(?x530, ?x11253), team(?x63, ?x11253), colors(?x11253, ?x3189), ?x63 = 02sdk9v, position(?x12029, ?x530), position(?x11855, ?x530), position(?x9695, ?x530), ?x12029 = 04r7f2, ?x9695 = 05z01, ?x11855 = 02b1l_ *> conf = 0.41 ranks of expected_values: 2 EVAL 0kz4w colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.793 http://example.org/sports/sports_team/colors #21992-0bvn25 PRED entity: 0bvn25 PRED relation: film! PRED expected values: 05bnp0 => 67 concepts (31 used for prediction) PRED predicted values (max 10 best out of 799): 05ty4m (0.65 #35177, 0.63 #39320, 0.63 #45531), 016tw3 (0.48 #31037, 0.45 #53811, 0.45 #12417), 032xhg (0.33 #6273, 0.13 #8342, 0.02 #26962), 01vvb4m (0.29 #4661, 0.11 #6729, 0.03 #29486), 07m77x (0.25 #1530, 0.22 #7740, 0.20 #3600), 072bb1 (0.25 #441, 0.20 #2511, 0.13 #8720), 0716t2 (0.25 #1896, 0.20 #3966, 0.07 #10175), 04bdxl (0.25 #6, 0.20 #2076, 0.07 #8285), 0h27vc (0.22 #7217, 0.13 #9286, 0.02 #11355), 03hh89 (0.22 #7168, 0.03 #43460, 0.02 #62089) >> Best rule #35177 for best value: >> intensional similarity = 3 >> extensional distance = 658 >> proper extension: 0gfzgl; 0cskb; >> query: (?x365, ?x364) <- nominated_for(?x364, ?x365), participant(?x364, ?x237), award_winner(?x364, ?x2437) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #6223 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0ds35l9; 03bx2lk; 065_cjc; 047p798; *> query: (?x365, 05bnp0) <- film(?x10371, ?x365), film(?x3927, ?x365), participant(?x2352, ?x10371), ?x3927 = 08vr94 *> conf = 0.11 ranks of expected_values: 60 EVAL 0bvn25 film! 05bnp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 67.000 31.000 0.648 http://example.org/film/actor/film./film/performance/film #21991-02xtxw PRED entity: 02xtxw PRED relation: genre PRED expected values: 01t_vv => 90 concepts (88 used for prediction) PRED predicted values (max 10 best out of 79): 07s9rl0 (0.61 #4617, 0.61 #1182, 0.60 #3196), 02kdv5l (0.33 #2130, 0.32 #2249, 0.31 #2486), 01jfsb (0.32 #2258, 0.31 #2495, 0.31 #2139), 02l7c8 (0.30 #1197, 0.27 #4632, 0.27 #3211), 03k9fj (0.24 #2138, 0.24 #1428, 0.24 #2494), 0lsxr (0.19 #1190, 0.17 #3676, 0.17 #4505), 06n90 (0.17 #249, 0.14 #3444, 0.13 #4154), 01hmnh (0.17 #2145, 0.16 #2501, 0.16 #3449), 04xvlr (0.15 #4618, 0.15 #3197, 0.14 #7221), 060__y (0.15 #253, 0.14 #2976, 0.13 #7709) >> Best rule #4617 for best value: >> intensional similarity = 4 >> extensional distance = 929 >> proper extension: 02d413; 016z5x; 0gjk1d; 069q4f; 0b76kw1; 0bm2g; 016z9n; 01771z; 0bmpm; 07sp4l; ... >> query: (?x3559, 07s9rl0) <- nominated_for(?x906, ?x3559), award_winner(?x3559, ?x2352), film(?x7795, ?x3559), genre(?x3559, ?x258) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #2419 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 510 *> proper extension: 0407yfx; 026zlh9; 03pc89; 016yxn; *> query: (?x3559, 01t_vv) <- nominated_for(?x906, ?x3559), award(?x3559, ?x401), production_companies(?x3559, ?x902) *> conf = 0.09 ranks of expected_values: 17 EVAL 02xtxw genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 90.000 88.000 0.613 http://example.org/film/film/genre #21990-01kgg9 PRED entity: 01kgg9 PRED relation: film PRED expected values: 01xlqd => 144 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1050): 03kxj2 (0.40 #2151, 0.20 #7528, 0.17 #5736), 02v8kmz (0.40 #1820, 0.05 #39454, 0.05 #23325), 0bm2x (0.33 #916, 0.06 #22421, 0.03 #31381), 02qr3k8 (0.20 #8460, 0.17 #6668, 0.09 #12044), 05h43ls (0.20 #2207, 0.10 #7584, 0.09 #11168), 03bzyn4 (0.20 #3362, 0.10 #8739, 0.09 #12323), 078sj4 (0.20 #2247, 0.10 #7624, 0.09 #11208), 042y1c (0.20 #2173, 0.10 #7550, 0.09 #11134), 0272_vz (0.20 #2550, 0.09 #11511, 0.05 #24055), 0320fn (0.20 #4247, 0.09 #9623, 0.04 #27543) >> Best rule #2151 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 01rs5p; >> query: (?x9777, 03kxj2) <- award(?x9777, ?x3989), award(?x9777, ?x2603), award(?x9777, ?x1972), ?x2603 = 09qs08, ?x3989 = 0bsjcw, nominated_for(?x1972, ?x86) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kgg9 film 01xlqd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 81.000 0.400 http://example.org/film/actor/film./film/performance/film #21989-0c53zb PRED entity: 0c53zb PRED relation: award_winner PRED expected values: 05v1sb 0fmqp6 053vcrp => 43 concepts (26 used for prediction) PRED predicted values (max 10 best out of 760): 01pp3p (0.60 #17641, 0.33 #5373, 0.33 #3840), 07zhd7 (0.50 #10701, 0.40 #16836, 0.33 #26036), 0h005 (0.40 #17583, 0.40 #14514, 0.38 #22182), 0gl88b (0.40 #18693, 0.33 #7958, 0.25 #12558), 02wb6d (0.40 #14838, 0.33 #8705, 0.25 #10237), 01vvdm (0.40 #18975, 0.33 #2106, 0.25 #9772), 0c2tf (0.40 #17992, 0.33 #5724, 0.12 #22591), 0c6g29 (0.40 #14098, 0.30 #15337, 0.25 #9497), 012vct (0.36 #16869, 0.33 #8736, 0.33 #3071), 04__f (0.36 #16869, 0.33 #2687, 0.28 #10734) >> Best rule #17641 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 0fz0c2; >> query: (?x4445, 01pp3p) <- honored_for(?x4445, ?x2112), award_winner(?x4445, ?x5611), award_winner(?x4445, ?x3519), award_winner(?x4445, ?x3017), award_winner(?x4445, ?x2426), award_winner(?x2109, ?x5611), ceremony(?x1313, ?x4445), profession(?x2426, ?x319), place_of_death(?x3017, ?x1523), award_winner(?x2060, ?x2426), nominated_for(?x3519, ?x10614), ?x10614 = 03bdkd, ?x1313 = 0gs9p, type_of_union(?x3017, ?x566), award_winner(?x1745, ?x3519), participant(?x10325, ?x3017) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #16869 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 3 *> proper extension: 0ftlxj; *> query: (?x4445, ?x4379) <- honored_for(?x4445, ?x9572), honored_for(?x4445, ?x7231), award_winner(?x4445, ?x5611), ?x5611 = 02cqbx, produced_by(?x7231, ?x7232), nominated_for(?x4379, ?x9572), nominated_for(?x198, ?x9572), ceremony(?x1862, ?x4445), genre(?x9572, ?x53), award(?x361, ?x1862), award(?x7243, ?x1862), nominated_for(?x1862, ?x69), ?x198 = 040njc, nominated_for(?x68, ?x7243) *> conf = 0.36 ranks of expected_values: 11, 13, 141 EVAL 0c53zb award_winner 053vcrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 43.000 26.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0c53zb award_winner 0fmqp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 43.000 26.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0c53zb award_winner 05v1sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 43.000 26.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21988-01dy7j PRED entity: 01dy7j PRED relation: award PRED expected values: 02x4x18 09qvf4 0cqhmg => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 269): 09sb52 (0.30 #14039, 0.27 #439, 0.27 #10839), 01by1l (0.28 #8908, 0.12 #4908, 0.11 #4108), 0bdx29 (0.27 #104, 0.15 #25601, 0.12 #38002), 01bgqh (0.20 #8841, 0.08 #4841, 0.08 #24041), 0fbvqf (0.18 #45, 0.15 #845, 0.15 #25601), 02x4x18 (0.18 #529, 0.09 #1329, 0.08 #2929), 02ppm4q (0.18 #553, 0.08 #4953, 0.08 #18553), 0c4z8 (0.16 #8868, 0.07 #21668, 0.07 #24068), 05zr6wv (0.15 #25601, 0.12 #4017, 0.12 #38002), 0gqyl (0.15 #25601, 0.12 #38002, 0.12 #40003) >> Best rule #14039 for best value: >> intensional similarity = 3 >> extensional distance = 631 >> proper extension: 01sl1q; 044mz_; 0184jc; 02s2ft; 05bnp0; 01vvydl; 02qgqt; 0fvf9q; 02p65p; 0337vz; ... >> query: (?x2965, 09sb52) <- award_nominee(?x9647, ?x2965), award_winner(?x5459, ?x2965), actor(?x1849, ?x9647) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #529 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 027dtv3; *> query: (?x2965, 02x4x18) <- award_nominee(?x7776, ?x2965), award_winner(?x1059, ?x2965), ?x7776 = 06dn58 *> conf = 0.18 ranks of expected_values: 6, 21, 98 EVAL 01dy7j award 0cqhmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 126.000 126.000 0.302 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01dy7j award 09qvf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 126.000 126.000 0.302 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01dy7j award 02x4x18 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 126.000 126.000 0.302 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21987-05jm7 PRED entity: 05jm7 PRED relation: award_winner! PRED expected values: 0j6j8 => 155 concepts (132 used for prediction) PRED predicted values (max 10 best out of 342): 0m7yy (0.78 #3167, 0.13 #37715, 0.11 #9991), 0262zm (0.46 #4267, 0.46 #1365, 0.45 #2988), 0265vt (0.46 #4267, 0.45 #4585, 0.45 #2988), 0208wk (0.46 #4267, 0.45 #2988, 0.38 #2135), 0j6j8 (0.46 #4267, 0.45 #2988, 0.38 #2135), 039yzf (0.46 #4267, 0.45 #2988, 0.38 #2135), 02tzwd (0.46 #4267, 0.45 #2988, 0.38 #2135), 027x4ws (0.46 #4267, 0.45 #2988, 0.38 #2135), 0gr51 (0.41 #1809, 0.27 #6502, 0.14 #16308), 040vk98 (0.41 #4297, 0.25 #7710, 0.23 #1311) >> Best rule #3167 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0gsg7; 0cjdk; 027_tg; 05gnf; >> query: (?x3858, 0m7yy) <- award_winner(?x11712, ?x3858), award_winner(?x14002, ?x3858), category(?x14002, ?x134), disciplines_or_subjects(?x14002, ?x5864) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4267 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 044k8; 01nz1q6; 016m5c; *> query: (?x3858, ?x1375) <- award_winner(?x11712, ?x3858), peers(?x3858, ?x6723), award_winner(?x3337, ?x3858), award(?x3858, ?x1375) *> conf = 0.46 ranks of expected_values: 5 EVAL 05jm7 award_winner! 0j6j8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 155.000 132.000 0.778 http://example.org/award/award_category/winners./award/award_honor/award_winner #21986-03rjj PRED entity: 03rjj PRED relation: partially_contains! PRED expected values: 04swx => 238 concepts (165 used for prediction) PRED predicted values (max 10 best out of 18): 05rgl (0.12 #1002, 0.11 #421, 0.11 #1195), 059g4 (0.12 #1043, 0.11 #462, 0.11 #1236), 04swx (0.12 #1060, 0.11 #1253, 0.10 #1449), 0j3b (0.12 #988, 0.10 #1377, 0.09 #1764), 02j9z (0.11 #1169, 0.10 #1365, 0.09 #3399), 0j0k (0.09 #3459, 0.08 #3848, 0.08 #2489), 05g2v (0.08 #740, 0.06 #3358, 0.06 #1032), 06n3y (0.08 #863, 0.06 #1057, 0.05 #1250), 03rz4 (0.08 #855, 0.06 #1049, 0.05 #1242), 03v9w (0.06 #1055, 0.05 #1444, 0.05 #1831) >> Best rule #1002 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 06jnv; >> query: (?x205, 05rgl) <- participating_countries(?x418, ?x205), location(?x6370, ?x205), influenced_by(?x118, ?x6370) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #1060 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 06jnv; *> query: (?x205, 04swx) <- participating_countries(?x418, ?x205), location(?x6370, ?x205), influenced_by(?x118, ?x6370) *> conf = 0.12 ranks of expected_values: 3 EVAL 03rjj partially_contains! 04swx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 238.000 165.000 0.118 http://example.org/location/location/partially_contains #21985-016nvh PRED entity: 016nvh PRED relation: artists! PRED expected values: 03mb9 => 94 concepts (42 used for prediction) PRED predicted values (max 10 best out of 242): 064t9 (0.61 #10813, 0.55 #1862, 0.49 #5257), 016clz (0.60 #4940, 0.50 #313, 0.35 #1854), 06by7 (0.60 #1562, 0.50 #2798, 0.49 #3107), 0hh2s (0.50 #443, 0.22 #751, 0.21 #11730), 0193f (0.50 #429, 0.22 #737, 0.10 #1970), 0glt670 (0.48 #5285, 0.25 #7137, 0.24 #8986), 0ggx5q (0.45 #1926, 0.33 #693, 0.25 #385), 06j6l (0.36 #5293, 0.31 #10849, 0.27 #8994), 0y3_8 (0.35 #1897, 0.22 #664, 0.21 #11730), 03_d0 (0.33 #5255, 0.26 #2169, 0.16 #11120) >> Best rule #10813 for best value: >> intensional similarity = 4 >> extensional distance = 574 >> proper extension: 01pfr3; 0m19t; 0150jk; 01v0sx2; 01fl3; 0dtd6; 016fmf; 01rm8b; 0fcsd; 03xhj6; ... >> query: (?x10624, 064t9) <- artists(?x3916, ?x10624), parent_genre(?x3243, ?x3916), artists(?x3916, ?x6835), ?x6835 = 06mt91 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1950 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 03t9sp; 05k79; 03fbc; 0dm5l; 01dwrc; 07sbk; 0jg77; *> query: (?x10624, 03mb9) <- artists(?x3916, ?x10624), ?x3916 = 08cyft, artist(?x9114, ?x10624) *> conf = 0.30 ranks of expected_values: 15 EVAL 016nvh artists! 03mb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 94.000 42.000 0.611 http://example.org/music/genre/artists #21984-0b78hw PRED entity: 0b78hw PRED relation: interests PRED expected values: 05qfh => 146 concepts (122 used for prediction) PRED predicted values (max 10 best out of 13): 02jcc (0.48 #157, 0.40 #40, 0.33 #14), 04s0m (0.40 #48, 0.36 #165, 0.22 #74), 05r79 (0.32 #160, 0.20 #43, 0.12 #121), 09xq9d (0.24 #163, 0.05 #639, 0.03 #397), 0x0w (0.19 #128, 0.12 #167, 0.09 #115), 04rjg (0.08 #161, 0.05 #639, 0.02 #434), 06ms6 (0.06 #120, 0.04 #159, 0.01 #432), 05qfh (0.05 #639, 0.04 #162, 0.02 #305), 097df (0.04 #168, 0.01 #441, 0.01 #467), 06mq7 (0.04 #169) >> Best rule #157 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 05qmj; >> query: (?x4308, 02jcc) <- gender(?x4308, ?x231), profession(?x4308, ?x353), interests(?x4308, ?x6364), influenced_by(?x2608, ?x4308) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #639 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 232 *> proper extension: 0459z; *> query: (?x4308, ?x3490) <- influenced_by(?x4308, ?x7296), influenced_by(?x7296, ?x2240), gender(?x4308, ?x231), interests(?x2240, ?x3490) *> conf = 0.05 ranks of expected_values: 8 EVAL 0b78hw interests 05qfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 146.000 122.000 0.480 http://example.org/user/alexander/philosophy/philosopher/interests #21983-01sb5r PRED entity: 01sb5r PRED relation: type_of_union PRED expected values: 04ztj => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #145, 0.87 #173, 0.84 #45), 01g63y (0.32 #6, 0.31 #138, 0.29 #2), 01bl8s (0.01 #83, 0.01 #87) >> Best rule #145 for best value: >> intensional similarity = 4 >> extensional distance = 144 >> proper extension: 014x77; 03m8lq; 04nw9; 01f7j9; 0bymv; 0d7hg4; 06w6_; 01fdc0; 05y5fw; 03n52j; ... >> query: (?x4140, 04ztj) <- gender(?x4140, ?x231), nationality(?x4140, ?x94), ?x94 = 09c7w0, location_of_ceremony(?x4140, ?x1523) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sb5r type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.870 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21982-0bt23 PRED entity: 0bt23 PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (87 used for prediction) PRED predicted values (max 10 best out of 39): 09c7w0 (0.86 #4911, 0.85 #5815, 0.83 #601), 01n7q (0.28 #8635, 0.25 #8738, 0.25 #8737), 0kpzy (0.25 #8738, 0.25 #8737, 0.23 #7729), 06pvr (0.25 #8738, 0.25 #8737, 0.23 #7729), 02jx1 (0.24 #3709, 0.22 #1801, 0.16 #1233), 0d060g (0.24 #3709, 0.22 #1801, 0.06 #6928), 0f8l9c (0.20 #122, 0.18 #322, 0.07 #1322), 07ssc (0.19 #1215, 0.16 #715, 0.15 #2017), 0345h (0.14 #31, 0.13 #1331, 0.12 #1131), 03rt9 (0.13 #513, 0.10 #113, 0.09 #313) >> Best rule #4911 for best value: >> intensional similarity = 3 >> extensional distance = 543 >> proper extension: 0gl88b; >> query: (?x11092, 09c7w0) <- student(?x5288, ?x11092), institution(?x3386, ?x5288), ?x3386 = 03mkk4 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bt23 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 87.000 0.862 http://example.org/people/person/nationality #21981-05z_kps PRED entity: 05z_kps PRED relation: production_companies PRED expected values: 05mgj0 => 97 concepts (86 used for prediction) PRED predicted values (max 10 best out of 59): 05d6q1 (0.44 #5369, 0.31 #6365, 0.31 #4788), 05mgj0 (0.25 #147, 0.20 #65, 0.15 #229), 086k8 (0.14 #1319, 0.11 #5288, 0.10 #989), 025jfl (0.13 #5, 0.11 #169, 0.06 #87), 05qd_ (0.12 #3637, 0.10 #5296, 0.10 #1574), 02j_j0 (0.12 #1365, 0.08 #2271, 0.07 #1941), 016tt2 (0.10 #3631, 0.09 #662, 0.09 #744), 016tw3 (0.09 #5298, 0.08 #835, 0.08 #1329), 017s11 (0.09 #250, 0.09 #3630, 0.08 #1567), 0c41qv (0.09 #303, 0.06 #467, 0.04 #549) >> Best rule #5369 for best value: >> intensional similarity = 4 >> extensional distance = 931 >> proper extension: 016ztl; >> query: (?x1228, ?x8394) <- language(?x1228, ?x254), genre(?x1228, ?x53), film(?x8394, ?x1228), production_companies(?x1228, ?x9518) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #147 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0fsd9t; 04jn6y7; *> query: (?x1228, 05mgj0) <- executive_produced_by(?x1228, ?x4857), language(?x1228, ?x254), film_crew_role(?x1228, ?x281), ?x4857 = 02z6l5f *> conf = 0.25 ranks of expected_values: 2 EVAL 05z_kps production_companies 05mgj0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 86.000 0.443 http://example.org/film/film/production_companies #21980-02j490 PRED entity: 02j490 PRED relation: film PRED expected values: 07sp4l => 106 concepts (80 used for prediction) PRED predicted values (max 10 best out of 628): 0180mw (0.62 #66052, 0.49 #69623, 0.48 #101761), 01xbxn (0.38 #3175, 0.23 #4960, 0.06 #6745), 033qdy (0.37 #41057, 0.01 #40444, 0.01 #33303), 06bc59 (0.37 #41057), 07cyl (0.37 #41057), 06cm5 (0.25 #2854, 0.15 #4639, 0.03 #8209), 0234j5 (0.25 #3205, 0.15 #4990, 0.01 #6775), 0jsf6 (0.12 #2872, 0.08 #4657, 0.06 #8227), 028_yv (0.12 #1809, 0.08 #3594, 0.04 #5379), 0fphf3v (0.12 #3143, 0.08 #4928, 0.04 #8498) >> Best rule #66052 for best value: >> intensional similarity = 3 >> extensional distance = 696 >> proper extension: 0m2wm; 02zq43; 08w7vj; 07ymr5; 02lq10; 05hdf; 06mmb; 02xbw2; 02wycg2; 0dh73w; ... >> query: (?x10897, ?x6482) <- people(?x3591, ?x10897), film(?x10897, ?x4093), nominated_for(?x10897, ?x6482) >> conf = 0.62 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02j490 film 07sp4l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 80.000 0.617 http://example.org/film/actor/film./film/performance/film #21979-0k9p4 PRED entity: 0k9p4 PRED relation: category PRED expected values: 08mbj5d => 243 concepts (243 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #56, 0.82 #46, 0.81 #140) >> Best rule #56 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 0r6rq; 0qyzb; 0r6c4; >> query: (?x9417, 08mbj5d) <- county(?x9417, ?x578), time_zones(?x9417, ?x2950), contains(?x1227, ?x9417), ?x1227 = 01n7q >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k9p4 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 243.000 243.000 0.857 http://example.org/common/topic/webpage./common/webpage/category #21978-04rzd PRED entity: 04rzd PRED relation: role! PRED expected values: 04bpm6 018gkb => 81 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1162): 01wxdn3 (0.75 #4892, 0.67 #3091, 0.62 #4440), 02s6sh (0.75 #4918, 0.60 #1768, 0.55 #8070), 0lzkm (0.75 #4664, 0.60 #2414, 0.55 #7816), 0l12d (0.75 #4565, 0.50 #966, 0.46 #11769), 02qtywd (0.67 #3126, 0.42 #9878, 0.40 #6277), 04bpm6 (0.64 #7721, 0.62 #4569, 0.62 #11773), 01vsl3_ (0.64 #7767, 0.60 #2365, 0.50 #4615), 0137g1 (0.62 #4613, 0.60 #2363, 0.57 #12718), 01vs4ff (0.62 #4789, 0.60 #2539, 0.56 #5688), 03h502k (0.62 #4723, 0.50 #1124, 0.40 #2473) >> Best rule #4892 for best value: >> intensional similarity = 13 >> extensional distance = 6 >> proper extension: 05842k; >> query: (?x1969, 01wxdn3) <- role(?x1969, ?x2206), role(?x1969, ?x645), group(?x1969, ?x1929), role(?x4595, ?x1969), role(?x2297, ?x1969), ?x2297 = 051hrr, ?x4595 = 023l9y, ?x645 = 028tv0, group(?x2206, ?x1751), instrumentalists(?x2206, ?x7240), instrumentalists(?x2206, ?x669), artists(?x505, ?x7240), music(?x670, ?x669) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #7721 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 9 *> proper extension: 0xzly; *> query: (?x1969, 04bpm6) <- role(?x1969, ?x74), group(?x1969, ?x10263), role(?x4701, ?x1969), role(?x1166, ?x1969), role(?x2865, ?x1969), ?x4701 = 03j24kf, instrumentalists(?x1166, ?x3890), ?x3890 = 01gg59, award(?x10263, ?x724), group(?x1166, ?x442) *> conf = 0.64 ranks of expected_values: 6, 73 EVAL 04rzd role! 018gkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 81.000 39.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 04rzd role! 04bpm6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 39.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role #21977-01qdmh PRED entity: 01qdmh PRED relation: featured_film_locations PRED expected values: 095w_ => 61 concepts (59 used for prediction) PRED predicted values (max 10 best out of 46): 02_286 (0.30 #3361, 0.14 #1452, 0.14 #3838), 030qb3t (0.12 #3379, 0.08 #1470, 0.08 #2424), 04jpl (0.12 #1203, 0.11 #3351, 0.06 #1442), 0rh6k (0.07 #1434, 0.07 #2388, 0.07 #2150), 01_d4 (0.04 #285, 0.04 #3387, 0.03 #523), 02jx1 (0.03 #717, 0.01 #750), 0h7h6 (0.03 #3383, 0.02 #281, 0.02 #1951), 0f2tj (0.02 #8612, 0.02 #10761, 0.01 #361), 01n7q (0.02 #8612, 0.02 #10761, 0.01 #506), 0l39b (0.02 #8612, 0.02 #10761) >> Best rule #3361 for best value: >> intensional similarity = 3 >> extensional distance = 710 >> proper extension: 0872p_c; 0gj8t_b; 031t2d; 09k56b7; 02vqhv0; 0jym0; 01hqhm; 0j_tw; 0ddjy; 07x4qr; ... >> query: (?x11148, 02_286) <- genre(?x11148, ?x225), film(?x2499, ?x11148), featured_film_locations(?x11148, ?x461) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01qdmh featured_film_locations 095w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 59.000 0.298 http://example.org/film/film/featured_film_locations #21976-06ztvyx PRED entity: 06ztvyx PRED relation: film_release_region PRED expected values: 0154j 0f8l9c 06f32 03h64 07f1x => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 76): 0f8l9c (0.91 #405, 0.90 #275, 0.87 #145), 03h64 (0.87 #435, 0.87 #175, 0.85 #305), 0154j (0.80 #263, 0.77 #1303, 0.74 #1563), 01mjq (0.65 #289, 0.61 #419, 0.60 #159), 06t8v (0.60 #186, 0.52 #446, 0.50 #316), 06f32 (0.53 #174, 0.48 #1344, 0.48 #434), 077qn (0.52 #455, 0.50 #325, 0.40 #195), 01pj7 (0.50 #293, 0.48 #423, 0.40 #163), 06mzp (0.47 #144, 0.46 #1314, 0.44 #1574), 0d0kn (0.47 #167, 0.40 #297, 0.35 #427) >> Best rule #405 for best value: >> intensional similarity = 8 >> extensional distance = 21 >> proper extension: 0h3xztt; 072hx4; >> query: (?x2709, 0f8l9c) <- film_release_region(?x2709, ?x3855), film_release_region(?x2709, ?x1471), film_release_region(?x2709, ?x429), film_release_region(?x2709, ?x390), ?x429 = 03rt9, ?x390 = 0chghy, ?x3855 = 0jgx, olympics(?x1471, ?x1277) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 6, 11 EVAL 06ztvyx film_release_region 07f1x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 66.000 66.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06ztvyx film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06ztvyx film_release_region 06f32 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 66.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06ztvyx film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06ztvyx film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21975-0gppg PRED entity: 0gppg PRED relation: student! PRED expected values: 01k7xz => 124 concepts (115 used for prediction) PRED predicted values (max 10 best out of 171): 01w5m (0.20 #105, 0.12 #3267, 0.12 #4848), 07wrz (0.20 #62, 0.12 #3224, 0.10 #1116), 07tgn (0.20 #1071, 0.12 #3179, 0.09 #10030), 02zd460 (0.20 #697, 0.10 #1224, 0.08 #2278), 01hjy5 (0.20 #306, 0.10 #1360, 0.06 #3468), 07tg4 (0.20 #613, 0.09 #1667, 0.07 #2721), 026m3y (0.20 #923, 0.03 #5666, 0.02 #8301), 013nky (0.20 #909, 0.02 #10395), 03ksy (0.18 #1687, 0.15 #2214, 0.10 #3795), 09f2j (0.10 #1213, 0.09 #1740, 0.08 #2267) >> Best rule #105 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 09dt7; 0klw; >> query: (?x9950, 01w5m) <- award(?x9950, ?x5050), award(?x9950, ?x3337), place_of_death(?x9950, ?x13331), ?x5050 = 0265wl, profession(?x9950, ?x353), ?x3337 = 01yz0x >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1647 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 0jt90f5; 0gd5z; 02yl42; 03772; 0b0pf; 056wb; 0fvt2; 01g6bk; *> query: (?x9950, 01k7xz) <- award(?x9950, ?x575), category(?x9950, ?x134), ?x134 = 08mbj5d, ?x575 = 040vk98, location(?x9950, ?x3007) *> conf = 0.09 ranks of expected_values: 14 EVAL 0gppg student! 01k7xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 124.000 115.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #21974-01mxt_ PRED entity: 01mxt_ PRED relation: influenced_by PRED expected values: 02whj => 142 concepts (61 used for prediction) PRED predicted values (max 10 best out of 353): 0j6cj (0.22 #5678, 0.21 #6115, 0.20 #6989), 03sbs (0.18 #5464, 0.17 #5027, 0.16 #5901), 0448r (0.17 #699, 0.10 #5067, 0.09 #5941), 032l1 (0.17 #4894, 0.16 #5331, 0.16 #6205), 01vsy3q (0.16 #13540, 0.15 #11353, 0.12 #1023), 03_87 (0.16 #5881, 0.15 #5007, 0.13 #6318), 081lh (0.15 #7009, 0.11 #8318, 0.09 #7445), 08433 (0.14 #9191, 0.12 #3078, 0.08 #7446), 026lj (0.13 #5723, 0.13 #481, 0.11 #5286), 0379s (0.13 #515, 0.10 #4883, 0.09 #5757) >> Best rule #5678 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 01h2_6; >> query: (?x5587, ?x7987) <- student(?x1151, ?x5587), peers(?x5587, ?x7987), influenced_by(?x5208, ?x5587) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01mxt_ influenced_by 02whj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 61.000 0.222 http://example.org/influence/influence_node/influenced_by #21973-0hd7j PRED entity: 0hd7j PRED relation: organization! PRED expected values: 07xl34 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 17): 060c4 (0.81 #80, 0.78 #158, 0.78 #197), 07xl34 (0.39 #76, 0.36 #102, 0.23 #284), 0dq_5 (0.17 #986, 0.17 #973, 0.16 #960), 05k17c (0.15 #912, 0.11 #872, 0.09 #696), 0hm4q (0.15 #912, 0.11 #872, 0.09 #216), 04n1q6 (0.15 #912, 0.11 #872, 0.02 #149), 08jcfy (0.15 #912, 0.11 #872, 0.02 #389), 05c0jwl (0.11 #872, 0.05 #226, 0.04 #213), 09d6p2 (0.11 #872), 01t7n9 (0.03 #1108, 0.02 #1213, 0.02 #1319) >> Best rule #80 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 02jztz; >> query: (?x4603, 060c4) <- contains(?x94, ?x4603), major_field_of_study(?x4603, ?x1154), school(?x465, ?x4603), school_type(?x4603, ?x1962), currency(?x4603, ?x170) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 65 *> proper extension: 08815; 05zjtn4; 065y4w7; 07tgn; 07w0v; 05f7s1; 01j_cy; 07szy; 09kvv; 0bx8pn; ... *> query: (?x4603, 07xl34) <- contains(?x94, ?x4603), institution(?x734, ?x4603), major_field_of_study(?x4603, ?x1154), ?x734 = 04zx3q1, student(?x4603, ?x118) *> conf = 0.39 ranks of expected_values: 2 EVAL 0hd7j organization! 07xl34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.806 http://example.org/organization/role/leaders./organization/leadership/organization #21972-021y7yw PRED entity: 021y7yw PRED relation: currency PRED expected values: 09nqf => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.79 #85, 0.77 #78, 0.76 #106), 01nv4h (0.03 #65, 0.03 #72, 0.03 #58), 02l6h (0.03 #4, 0.02 #67, 0.02 #74), 02gsvk (0.02 #34, 0.02 #48, 0.01 #41), 0ptk_ (0.01 #3) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 680 >> proper extension: 01gglm; >> query: (?x2458, 09nqf) <- nominated_for(?x163, ?x2458), film_release_distribution_medium(?x2458, ?x81), production_companies(?x2458, ?x617), language(?x2458, ?x254) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021y7yw currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.787 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21971-0d075m PRED entity: 0d075m PRED relation: category PRED expected values: 08mbj5d => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #134, 0.79 #85, 0.79 #84) >> Best rule #134 for best value: >> intensional similarity = 5 >> extensional distance = 628 >> proper extension: 0k__z; 01fy2s; 018sg9; 01lvrm; >> query: (?x8714, 08mbj5d) <- citytown(?x8714, ?x108), citytown(?x13177, ?x108), citytown(?x5750, ?x108), organization(?x4682, ?x13177), institution(?x620, ?x5750) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d075m category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.805 http://example.org/common/topic/webpage./common/webpage/category #21970-012x7b PRED entity: 012x7b PRED relation: parent_genre PRED expected values: 025tm81 => 45 concepts (31 used for prediction) PRED predicted values (max 10 best out of 219): 06by7 (0.81 #2928, 0.72 #1794, 0.71 #338), 03lty (0.74 #2121, 0.70 #2283, 0.39 #3421), 0xhtw (0.55 #1304, 0.43 #335, 0.33 #981), 01243b (0.55 #1969, 0.42 #2589, 0.42 #2454), 016clz (0.52 #1621, 0.38 #1946, 0.28 #2431), 064t9 (0.50 #172, 0.33 #11, 0.29 #333), 016jny (0.43 #1519, 0.10 #3959, 0.09 #1614), 05r6t (0.38 #1995, 0.33 #53, 0.31 #1831), 0dl5d (0.33 #498, 0.28 #1144, 0.27 #821), 03mb9 (0.33 #65, 0.25 #226, 0.20 #322) >> Best rule #2928 for best value: >> intensional similarity = 14 >> extensional distance = 71 >> proper extension: 0hdf8; >> query: (?x12831, 06by7) <- parent_genre(?x12831, ?x6107), parent_genre(?x12831, ?x2809), artists(?x2809, ?x10145), artists(?x2809, ?x7084), artists(?x2809, ?x6456), artists(?x2809, ?x1291), parent_genre(?x6107, ?x1572), artists(?x6107, ?x3166), ?x7084 = 01vs4ff, role(?x1291, ?x228), award_winner(?x1292, ?x1291), artist(?x2190, ?x6456), instrumentalists(?x1886, ?x6456), ?x10145 = 0p76z >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #216 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 0grjmv; *> query: (?x12831, 025tm81) <- parent_genre(?x12831, ?x2809), parent_genre(?x12831, ?x2439), ?x2809 = 05w3f, parent_genre(?x2439, ?x497), artists(?x2439, ?x8947), artists(?x2439, ?x8806), artists(?x2439, ?x4052), artists(?x2439, ?x1674), artists(?x2439, ?x317), spouse(?x5330, ?x317), nationality(?x317, ?x789), category(?x8806, ?x134), ?x4052 = 050z2, ?x8947 = 017b2p, origin(?x317, ?x9499), ?x1674 = 01v_pj6, profession(?x317, ?x131) *> conf = 0.25 ranks of expected_values: 15 EVAL 012x7b parent_genre 025tm81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 45.000 31.000 0.808 http://example.org/music/genre/parent_genre #21969-03hl6lc PRED entity: 03hl6lc PRED relation: award_winner PRED expected values: 081lh 07h07 => 54 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1236): 0151w_ (0.43 #10040, 0.33 #2653, 0.33 #19703), 081lh (0.33 #7576, 0.29 #10038, 0.18 #12500), 0693l (0.33 #8060, 0.29 #10522, 0.18 #12984), 0chw_ (0.33 #6846, 0.18 #14233, 0.06 #24088), 01hkhq (0.33 #2983, 0.17 #5445, 0.12 #7388), 0154qm (0.33 #5631, 0.17 #3169, 0.12 #7388), 02x7vq (0.33 #6163, 0.17 #3701, 0.12 #7388), 0p__8 (0.33 #3789, 0.17 #16100, 0.06 #30885), 050zr4 (0.33 #4260, 0.17 #6722, 0.06 #16571), 02hfp_ (0.33 #9127, 0.17 #4202, 0.03 #23906) >> Best rule #10040 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0f4x7; 04dn09n; 0gq9h; 0gr51; 0gqy2; >> query: (?x3435, 0151w_) <- nominated_for(?x3435, ?x5323), nominated_for(?x3435, ?x1118), award(?x299, ?x3435), award_winner(?x3435, ?x826), ?x5323 = 011yn5, ?x1118 = 0_92w >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #7576 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 02qyp19; 0fbtbt; *> query: (?x3435, 081lh) <- nominated_for(?x3435, ?x238), award(?x299, ?x3435), award_winner(?x3435, ?x3260), country(?x238, ?x94), ?x3260 = 05ldnp *> conf = 0.33 ranks of expected_values: 2, 105 EVAL 03hl6lc award_winner 07h07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 54.000 14.000 0.429 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 03hl6lc award_winner 081lh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 54.000 14.000 0.429 http://example.org/award/award_category/winners./award/award_honor/award_winner #21968-0cbn7c PRED entity: 0cbn7c PRED relation: music PRED expected values: 07zhd7 => 67 concepts (50 used for prediction) PRED predicted values (max 10 best out of 78): 01pr6q7 (0.25 #62, 0.05 #694, 0.03 #1537), 02bh9 (0.17 #262, 0.05 #894, 0.04 #3843), 086k8 (0.10 #6536, 0.08 #1686, 0.07 #4004), 03_bcg (0.08 #7596, 0.08 #1686, 0.07 #4004), 0c12h (0.08 #7596, 0.08 #1686, 0.07 #4004), 0146pg (0.07 #3802, 0.07 #4858, 0.07 #3170), 0csdzz (0.05 #1241, 0.05 #3136, 0.04 #2715), 01tc9r (0.05 #908, 0.03 #1963, 0.03 #1752), 015wc0 (0.05 #1651, 0.05 #808, 0.02 #4603), 06fxnf (0.05 #2807, 0.04 #1967, 0.04 #2387) >> Best rule #62 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02q_ncg; >> query: (?x7864, 01pr6q7) <- genre(?x7864, ?x1805), nominated_for(?x6239, ?x7864), ?x6239 = 0c12h, ?x1805 = 01g6gs >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1678 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 07bz5; *> query: (?x7864, 07zhd7) <- nominated_for(?x382, ?x7864), list(?x7864, ?x3004), award_winner(?x1135, ?x382) *> conf = 0.02 ranks of expected_values: 48 EVAL 0cbn7c music 07zhd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 67.000 50.000 0.250 http://example.org/film/film/music #21967-02mzg9 PRED entity: 02mzg9 PRED relation: student PRED expected values: 05jjl => 130 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1577): 0ff3y (0.09 #2069, 0.04 #8346, 0.04 #10438), 05bnp0 (0.06 #11, 0.04 #8380, 0.03 #27211), 05kfs (0.06 #98, 0.04 #8467, 0.02 #18930), 02zft0 (0.06 #1050, 0.03 #3142, 0.02 #13605), 025j1t (0.06 #1062, 0.03 #15709, 0.02 #26170), 02cyfz (0.06 #334, 0.03 #17073, 0.02 #23350), 030hcs (0.06 #274, 0.03 #6551, 0.02 #8643), 018ygt (0.06 #1101, 0.03 #7378, 0.02 #9470), 012t1 (0.06 #145, 0.03 #6422, 0.02 #23161), 02t_w8 (0.06 #920, 0.03 #7197, 0.02 #13475) >> Best rule #2069 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 0b5hj5; >> query: (?x10861, 0ff3y) <- currency(?x10861, ?x170), institution(?x3437, ?x10861), institution(?x1519, ?x10861), ?x3437 = 02_xgp2, ?x1519 = 013zdg >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #1510 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 0b5hj5; *> query: (?x10861, 05jjl) <- currency(?x10861, ?x170), institution(?x3437, ?x10861), institution(?x1519, ?x10861), ?x3437 = 02_xgp2, ?x1519 = 013zdg *> conf = 0.03 ranks of expected_values: 311 EVAL 02mzg9 student 05jjl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 130.000 74.000 0.088 http://example.org/education/educational_institution/students_graduates./education/education/student #21966-03hmt9b PRED entity: 03hmt9b PRED relation: honored_for! PRED expected values: 04110lv => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 116): 09p2r9 (0.14 #196, 0.12 #315, 0.05 #434), 09bymc (0.14 #221, 0.12 #340, 0.04 #578), 050yyb (0.14 #150, 0.12 #269, 0.02 #2382), 09pj68 (0.14 #88, 0.10 #445, 0.07 #207), 0bvfqq (0.14 #26, 0.10 #383, 0.07 #145), 09p30_ (0.14 #70, 0.10 #427, 0.07 #189), 092c5f (0.14 #10, 0.07 #129, 0.06 #248), 02glmx (0.14 #66, 0.07 #185, 0.06 #304), 0hr6lkl (0.14 #12, 0.06 #250, 0.05 #607), 0hndn2q (0.14 #32, 0.05 #389, 0.04 #627) >> Best rule #196 for best value: >> intensional similarity = 7 >> extensional distance = 12 >> proper extension: 0298n7; >> query: (?x4007, 09p2r9) <- nominated_for(?x4091, ?x4007), nominated_for(?x2880, ?x4007), nominated_for(?x2341, ?x4007), ?x2341 = 02x17s4, ?x4091 = 09sdmz, titles(?x53, ?x4007), award(?x156, ?x2880) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2382 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 471 *> proper extension: 07bz5; *> query: (?x4007, ?x78) <- award(?x4007, ?x1703), ceremony(?x1703, ?x78), honored_for(?x2988, ?x4007), award(?x707, ?x1703) *> conf = 0.02 ranks of expected_values: 54 EVAL 03hmt9b honored_for! 04110lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 77.000 77.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #21965-0l2mg PRED entity: 0l2mg PRED relation: contains! PRED expected values: 01n7q => 86 concepts (41 used for prediction) PRED predicted values (max 10 best out of 93): 059_c (0.76 #17984, 0.69 #25179, 0.68 #27883), 01n7q (0.68 #27883, 0.67 #24279, 0.65 #1875), 09c7w0 (0.48 #35993, 0.47 #17988, 0.47 #9893), 05kj_ (0.22 #2738, 0.22 #4531, 0.21 #3635), 06pvr (0.20 #5553, 0.17 #6453, 0.16 #1963), 04_1l0v (0.17 #26532, 0.07 #31485, 0.04 #6738), 081yw (0.15 #4768, 0.13 #3872, 0.12 #2975), 059rby (0.14 #23400, 0.14 #24300, 0.13 #22502), 05tbn (0.11 #17309, 0.11 #11010, 0.11 #14610), 03v0t (0.10 #6520, 0.03 #11019, 0.03 #26314) >> Best rule #17984 for best value: >> intensional similarity = 6 >> extensional distance = 175 >> proper extension: 0rh6k; 0nm9y; >> query: (?x12341, ?x1138) <- adjoins(?x10702, ?x12341), source(?x10702, ?x958), time_zones(?x12341, ?x2950), ?x958 = 0jbk9, county_seat(?x10702, ?x12655), contains(?x1138, ?x10702) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #27883 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 273 *> proper extension: 0nlc7; 01_c4; 025r_t; 0nlg4; 0n96z; *> query: (?x12341, ?x1138) <- adjoins(?x10702, ?x12341), adjoins(?x7697, ?x10702), second_level_divisions(?x94, ?x12341), contains(?x1138, ?x10702), adjoins(?x1138, ?x726), state_province_region(?x3367, ?x1138) *> conf = 0.68 ranks of expected_values: 2 EVAL 0l2mg contains! 01n7q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 41.000 0.762 http://example.org/location/location/contains #21964-030_1_ PRED entity: 030_1_ PRED relation: award_winner! PRED expected values: 0180mw => 114 concepts (109 used for prediction) PRED predicted values (max 10 best out of 351): 01r97z (0.25 #3482, 0.20 #2346, 0.08 #9160), 01qvz8 (0.25 #3931, 0.14 #10744, 0.12 #14150), 072kp (0.20 #2330, 0.12 #3466, 0.07 #12550), 0dl6fv (0.20 #3207, 0.12 #4343, 0.03 #28192), 04mcw4 (0.17 #8446, 0.15 #9582, 0.14 #10717), 047csmy (0.17 #8546, 0.11 #24444, 0.10 #26716), 0dr_4 (0.15 #9254, 0.14 #10389, 0.13 #11525), 06bd5j (0.15 #9713, 0.14 #10848, 0.12 #14254), 02rb84n (0.12 #3604, 0.08 #9282, 0.07 #10417), 01l_pn (0.12 #4034, 0.08 #9712, 0.07 #10847) >> Best rule #3482 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 05xbx; >> query: (?x1686, 01r97z) <- award_winner(?x6678, ?x1686), country(?x1686, ?x94), award_winner(?x3486, ?x1686) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #44293 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 0p7tb; *> query: (?x1686, ?x531) <- company(?x2135, ?x1686), location(?x2135, ?x739), nominated_for(?x2135, ?x531) *> conf = 0.03 ranks of expected_values: 232 EVAL 030_1_ award_winner! 0180mw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 114.000 109.000 0.250 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #21963-0jzc PRED entity: 0jzc PRED relation: language! PRED expected values: 035yn8 0bz3jx 01_0f7 0g9z_32 02q0k7v 0fh2v5 => 84 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1823): 0f4_2k (0.71 #14530, 0.67 #12834, 0.62 #17923), 034xyf (0.71 #14926, 0.67 #13230, 0.50 #18319), 0dr_4 (0.67 #12105, 0.67 #10409, 0.62 #17194), 041td_ (0.67 #12909, 0.62 #16301, 0.57 #14605), 047vnkj (0.67 #11027, 0.62 #17812, 0.54 #21204), 01ffx4 (0.67 #12358, 0.57 #14054, 0.50 #17447), 03z9585 (0.67 #11504, 0.57 #14896, 0.50 #18289), 03twd6 (0.67 #12083, 0.57 #13779, 0.50 #17172), 0c_j9x (0.67 #12218, 0.57 #13914, 0.50 #17307), 02yvct (0.67 #12200, 0.57 #13896, 0.50 #17289) >> Best rule #14530 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 06nm1; >> query: (?x5359, 0f4_2k) <- language(?x4998, ?x5359), language(?x1074, ?x5359), film_crew_role(?x4998, ?x137), film_release_region(?x4998, ?x4737), film_release_region(?x4998, ?x1475), film_release_region(?x4998, ?x456), ?x1475 = 05qx1, film_distribution_medium(?x4998, ?x2099), major_field_of_study(?x6364, ?x5359), ?x456 = 05qhw, ?x4737 = 07twz, film(?x548, ?x1074), official_language(?x291, ?x5359) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #14650 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 5 *> proper extension: 06nm1; *> query: (?x5359, 01_0f7) <- language(?x4998, ?x5359), language(?x1074, ?x5359), film_crew_role(?x4998, ?x137), film_release_region(?x4998, ?x4737), film_release_region(?x4998, ?x1475), film_release_region(?x4998, ?x456), ?x1475 = 05qx1, film_distribution_medium(?x4998, ?x2099), major_field_of_study(?x6364, ?x5359), ?x456 = 05qhw, ?x4737 = 07twz, film(?x548, ?x1074), official_language(?x291, ?x5359) *> conf = 0.43 ranks of expected_values: 122, 148, 422, 807, 1185, 1612 EVAL 0jzc language! 0fh2v5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 24.000 0.714 http://example.org/film/film/language EVAL 0jzc language! 02q0k7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 24.000 0.714 http://example.org/film/film/language EVAL 0jzc language! 0g9z_32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 84.000 24.000 0.714 http://example.org/film/film/language EVAL 0jzc language! 01_0f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 84.000 24.000 0.714 http://example.org/film/film/language EVAL 0jzc language! 0bz3jx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 24.000 0.714 http://example.org/film/film/language EVAL 0jzc language! 035yn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 84.000 24.000 0.714 http://example.org/film/film/language #21962-0sxlb PRED entity: 0sxlb PRED relation: award PRED expected values: 09d28z => 84 concepts (73 used for prediction) PRED predicted values (max 10 best out of 173): 0f4x7 (0.25 #9596, 0.25 #11657, 0.25 #686), 0gqyl (0.25 #9596, 0.25 #11657, 0.25 #686), 0gq9h (0.25 #9596, 0.25 #11657, 0.25 #686), 0gs9p (0.25 #9596, 0.25 #11657, 0.25 #686), 0gr4k (0.25 #9596, 0.25 #11657, 0.25 #686), 04dn09n (0.25 #9596, 0.25 #11657, 0.25 #686), 0k611 (0.25 #9596, 0.25 #11657, 0.25 #686), 0gqy2 (0.25 #9596, 0.25 #11657, 0.25 #686), 02ppm4q (0.25 #9596, 0.25 #11657, 0.25 #686), 027c95y (0.21 #2741, 0.20 #4797, 0.18 #1144) >> Best rule #9596 for best value: >> intensional similarity = 3 >> extensional distance = 881 >> proper extension: 091z_p; 026njb5; 04lqvlr; 04lqvly; 011yfd; 02hfk5; 07l50vn; 0g9zljd; 05_61y; 0k2m6; ... >> query: (?x9761, ?x384) <- genre(?x9761, ?x53), nominated_for(?x384, ?x9761), award(?x9761, ?x1770) >> conf = 0.25 => this is the best rule for 9 predicted values *> Best rule #15089 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1510 *> proper extension: 0h95b81; *> query: (?x9761, ?x112) <- nominated_for(?x406, ?x9761), award_winner(?x112, ?x406) *> conf = 0.12 ranks of expected_values: 30 EVAL 0sxlb award 09d28z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 84.000 73.000 0.254 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #21961-02bm8 PRED entity: 02bm8 PRED relation: contains! PRED expected values: 0chghy => 96 concepts (34 used for prediction) PRED predicted values (max 10 best out of 341): 0chghy (0.83 #23368, 0.83 #10786, 0.82 #8991), 02jx1 (0.65 #18965, 0.45 #21660, 0.41 #13573), 0ctw_b (0.64 #1857, 0.62 #2758, 0.07 #14446), 09c7w0 (0.55 #28766, 0.46 #29662, 0.46 #26966), 07ssc (0.54 #19808, 0.46 #13518, 0.44 #18910), 0f8l9c (0.50 #7240, 0.30 #10833, 0.25 #12631), 0g39h (0.50 #1436, 0.24 #4134, 0.17 #5032), 03rt9 (0.50 #4520, 0.18 #9016, 0.14 #10811), 05nrg (0.48 #14383, 0.33 #567, 0.23 #9888), 0345h (0.38 #11768, 0.30 #9971, 0.25 #16263) >> Best rule #23368 for best value: >> intensional similarity = 6 >> extensional distance = 519 >> proper extension: 03qzj4; >> query: (?x14084, ?x390) <- contains(?x12908, ?x14084), contains(?x390, ?x12908), vacationer(?x390, ?x2275), adjoins(?x390, ?x1023), jurisdiction_of_office(?x3959, ?x390), location(?x4468, ?x390) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bm8 contains! 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 34.000 0.833 http://example.org/location/location/contains #21960-02d42t PRED entity: 02d42t PRED relation: award_winner! PRED expected values: 02wzl1d => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 117): 0clfdj (0.12 #4, 0.08 #424, 0.06 #284), 092t4b (0.12 #51, 0.08 #471, 0.05 #2571), 0275n3y (0.12 #74, 0.06 #354, 0.04 #1894), 0drtv8 (0.12 #65, 0.02 #6365, 0.02 #625), 09qvms (0.07 #1832, 0.06 #2532, 0.05 #3092), 09qftb (0.07 #252, 0.03 #392, 0.03 #532), 03nnm4t (0.07 #213, 0.03 #6373, 0.03 #8753), 02rjjll (0.07 #2245, 0.06 #2805, 0.06 #3785), 013b2h (0.06 #2319, 0.06 #3859, 0.06 #2879), 01s695 (0.06 #3, 0.06 #3783, 0.06 #2243) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 03d_w3h; 04205z; 016nff; 02tk74; >> query: (?x4872, 0clfdj) <- award(?x4872, ?x941), location(?x4872, ?x362), ?x941 = 0fq9zdn >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #6310 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1177 *> proper extension: 04f525m; 022_lg; 01vyp_; 01q4qv; 0g_g2; 024rdh; 07h76; 051z6rz; 0164w8; 0134pk; ... *> query: (?x4872, 02wzl1d) <- award(?x4872, ?x375), award(?x715, ?x375), award_winner(?x762, ?x4872) *> conf = 0.02 ranks of expected_values: 67 EVAL 02d42t award_winner! 02wzl1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 111.000 111.000 0.125 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21959-04l5b4 PRED entity: 04l5b4 PRED relation: colors PRED expected values: 01l849 019sc 09ggk => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 20): 019sc (0.57 #102, 0.55 #211, 0.55 #197), 06fvc (0.46 #928, 0.45 #192, 0.43 #97), 01l849 (0.39 #210, 0.29 #212, 0.28 #669), 0680m7 (0.39 #210, 0.29 #212, 0.25 #672), 03vtbc (0.39 #210, 0.29 #212, 0.23 #409), 088fh (0.39 #210, 0.29 #212, 0.23 #409), 01g5v (0.39 #968, 0.30 #988, 0.29 #212), 02rnmb (0.29 #212, 0.25 #343, 0.23 #675), 038hg (0.29 #212, 0.23 #409, 0.23 #675), 06kqt3 (0.22 #251, 0.20 #864, 0.17 #432) >> Best rule #102 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 02fp3; 0jnkr; 0hm2b; >> query: (?x13629, 019sc) <- team(?x5234, ?x13629), team(?x3724, ?x13629), ?x5234 = 02qvdc, colors(?x13629, ?x663), ?x3724 = 02qvzf, position(?x13629, ?x2918), ?x663 = 083jv, ?x2918 = 02qvl7, position(?x13629, ?x5234) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 14 EVAL 04l5b4 colors 09ggk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 51.000 51.000 0.571 http://example.org/sports/sports_team/colors EVAL 04l5b4 colors 019sc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.571 http://example.org/sports/sports_team/colors EVAL 04l5b4 colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 51.000 51.000 0.571 http://example.org/sports/sports_team/colors #21958-0dplh PRED entity: 0dplh PRED relation: institution! PRED expected values: 019v9k => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 17): 019v9k (0.63 #535, 0.62 #330, 0.60 #410), 016t_3 (0.42 #326, 0.41 #246, 0.40 #406), 013zdg (0.36 #126, 0.19 #249, 0.18 #209), 07s6fsf (0.34 #324, 0.32 #404, 0.29 #344), 04zx3q1 (0.31 #2, 0.27 #205, 0.26 #245), 0bjrnt (0.29 #5, 0.16 #125, 0.13 #248), 01rr_d (0.26 #134, 0.23 #14, 0.18 #177), 027f2w (0.20 #191, 0.19 #251, 0.19 #231), 02mjs7 (0.19 #124, 0.17 #167, 0.14 #147), 071tyz (0.17 #9, 0.10 #69, 0.09 #129) >> Best rule #535 for best value: >> intensional similarity = 3 >> extensional distance = 451 >> proper extension: 015zyd; 01rtm4; 014b4h; 02cttt; 01k2wn; 0ym8f; 0277jc; 01bzw5; 01ngz1; 05f7s1; ... >> query: (?x2142, 019v9k) <- institution(?x865, ?x2142), institution(?x865, ?x13753), ?x13753 = 02zkdz >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dplh institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.631 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21957-02rkkn1 PRED entity: 02rkkn1 PRED relation: nominated_for! PRED expected values: 0cjyzs => 78 concepts (76 used for prediction) PRED predicted values (max 10 best out of 189): 0cjyzs (0.41 #1988, 0.40 #2464, 0.30 #2226), 0fbtbt (0.34 #2305, 0.30 #2781, 0.27 #4209), 09qs08 (0.33 #2492, 0.31 #2016, 0.22 #2254), 02x8n1n (0.29 #1905, 0.21 #13571, 0.20 #16670), 025m8l (0.29 #1905, 0.21 #13571, 0.20 #16670), 0gqz2 (0.29 #1905, 0.21 #13571, 0.20 #16670), 02x17c2 (0.29 #1905, 0.21 #13571, 0.20 #16670), 09qv3c (0.29 #2423, 0.27 #1947, 0.21 #2185), 02xcb6n (0.29 #439, 0.22 #677, 0.18 #915), 054krc (0.29 #3642, 0.19 #12449, 0.19 #3166) >> Best rule #1988 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 02nf2c; 01cjhz; 0jq2r; 06f0k; >> query: (?x12535, 0cjyzs) <- genre(?x12535, ?x258), titles(?x2008, ?x12535), ?x258 = 05p553 >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rkkn1 nominated_for! 0cjyzs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 76.000 0.405 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21956-01y3c PRED entity: 01y3c PRED relation: draft PRED expected values: 02qw1zx => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 15): 02qw1zx (0.62 #335, 0.57 #472, 0.56 #229), 038c0q (0.60 #79, 0.38 #348, 0.38 #242), 038981 (0.60 #86, 0.38 #348, 0.38 #242), 09th87 (0.60 #85, 0.37 #1051, 0.33 #10), 0f4vx0 (0.40 #82, 0.38 #348, 0.38 #242), 06439y (0.40 #90, 0.38 #242, 0.37 #881), 025tn92 (0.40 #83, 0.37 #1051, 0.33 #8), 02z6872 (0.38 #348, 0.38 #242, 0.37 #881), 02pq_x5 (0.38 #242, 0.37 #1051, 0.36 #196), 02pq_rp (0.38 #580, 0.34 #489, 0.32 #610) >> Best rule #335 for best value: >> intensional similarity = 8 >> extensional distance = 22 >> proper extension: 05g3b; >> query: (?x1115, 02qw1zx) <- school(?x1115, ?x388), team(?x180, ?x1115), school(?x685, ?x388), major_field_of_study(?x388, ?x10046), ?x10046 = 041y2, student(?x388, ?x643), colors(?x388, ?x3364), ?x180 = 01r3hr >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y3c draft 02qw1zx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.625 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #21955-07ssc PRED entity: 07ssc PRED relation: film_release_region! PRED expected values: 0gtv7pk 0c0nhgv 0gj8t_b 03twd6 011yqc 0d6b7 0_7w6 047svrl 045j3w 0gtsxr4 0gj8nq2 0198b6 02dpl9 043sct5 0dr_9t7 011ycb 026lgs 01d259 064lsn 0gg5kmg 06fcqw 04cppj 0h63gl9 0ds1glg 0233bn 0fphf3v 0ds5_72 0hhggmy 0gwlfnb 065ym0c 04fjzv 02wtp6 => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 1145): 02vxq9m (0.83 #49962, 0.79 #33312, 0.77 #45064), 06fcqw (0.78 #8431, 0.76 #50546, 0.75 #38794), 04w7rn (0.78 #7960, 0.75 #16778, 0.73 #33425), 0gj8t_b (0.78 #7933, 0.70 #33398, 0.70 #50048), 047svrl (0.78 #8053, 0.67 #50168, 0.62 #16871), 0_7w6 (0.78 #7994, 0.64 #33459, 0.62 #16812), 026lgs (0.78 #8337, 0.63 #50452, 0.61 #38700), 0fphf3v (0.78 #8571, 0.62 #17389, 0.56 #9552), 035zr0 (0.78 #8543, 0.57 #50658, 0.56 #17361), 02prwdh (0.78 #8338, 0.55 #8815, 0.55 #33803) >> Best rule #49962 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 04v3q; >> query: (?x512, 02vxq9m) <- country(?x124, ?x512), member_states(?x2106, ?x512), film_release_region(?x66, ?x512) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #8431 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0d0vqn; *> query: (?x512, 06fcqw) <- country(?x124, ?x512), country_of_origin(?x2447, ?x512), film_release_region(?x66, ?x512) *> conf = 0.78 ranks of expected_values: 2, 4, 5, 6, 7, 8, 11, 13, 17, 18, 19, 22, 23, 24, 26, 27, 28, 29, 30, 32, 35, 37, 39, 40, 42, 45, 49, 52, 57, 416, 437, 502 EVAL 07ssc film_release_region! 02wtp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 04fjzv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 065ym0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0gwlfnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0hhggmy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0ds5_72 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0fphf3v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0233bn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0ds1glg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0h63gl9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 04cppj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 06fcqw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0gg5kmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 01d259 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 026lgs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 011ycb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0dr_9t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 043sct5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 02dpl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0198b6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0gj8nq2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0gtsxr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 045j3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 047svrl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0_7w6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0d6b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 011yqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 03twd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0gj8t_b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0c0nhgv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ssc film_release_region! 0gtv7pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 191.000 191.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21954-05r3qc PRED entity: 05r3qc PRED relation: film_crew_role PRED expected values: 02r96rf 0dxtw => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 25): 02r96rf (0.82 #343, 0.79 #481, 0.78 #551), 0ch6mp2 (0.76 #765, 0.76 #485, 0.75 #1113), 0dxtw (0.52 #350, 0.47 #488, 0.47 #558), 02ynfr (0.25 #354, 0.21 #492, 0.21 #562), 0d2b38 (0.15 #502, 0.15 #572, 0.14 #364), 01xy5l_ (0.14 #352, 0.14 #216, 0.13 #182), 015h31 (0.14 #732, 0.13 #487, 0.12 #349), 0215hd (0.14 #1123, 0.13 #495, 0.12 #565), 089g0h (0.12 #358, 0.12 #1124, 0.10 #1330), 094hwz (0.11 #353, 0.09 #183, 0.07 #217) >> Best rule #343 for best value: >> intensional similarity = 5 >> extensional distance = 95 >> proper extension: 0gj9qxr; 0h95zbp; 03_wm6; >> query: (?x6167, 02r96rf) <- genre(?x6167, ?x1013), genre(?x6167, ?x225), film_crew_role(?x6167, ?x137), ?x225 = 02kdv5l, ?x1013 = 06n90 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 05r3qc film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.825 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05r3qc film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.825 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21953-01nm8w PRED entity: 01nm8w PRED relation: institution! PRED expected values: 019v9k => 146 concepts (80 used for prediction) PRED predicted values (max 10 best out of 21): 03bwzr4 (0.84 #474, 0.80 #239, 0.76 #375), 02h4rq6 (0.82 #364, 0.81 #746, 0.80 #388), 019v9k (0.82 #493, 0.81 #1429, 0.80 #394), 014mlp (0.76 #489, 0.75 #341, 0.73 #609), 0bkj86 (0.69 #344, 0.68 #564, 0.65 #369), 03mkk4 (0.67 #191, 0.40 #237, 0.38 #348), 027f2w (0.60 #235, 0.59 #371, 0.52 #470), 013zdg (0.53 #368, 0.40 #232, 0.39 #563), 02m4yg (0.52 #362, 0.38 #1395, 0.38 #1617), 01ysy9 (0.52 #362, 0.38 #1395, 0.38 #1617) >> Best rule #474 for best value: >> intensional similarity = 9 >> extensional distance = 29 >> proper extension: 03ksy; 0lk0l; >> query: (?x9658, 03bwzr4) <- institution(?x3437, ?x9658), institution(?x1200, ?x9658), institution(?x734, ?x9658), contains(?x14475, ?x9658), ?x3437 = 02_xgp2, ?x734 = 04zx3q1, school_type(?x9658, ?x3092), ?x1200 = 016t_3, major_field_of_study(?x9658, ?x1668) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #493 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 32 *> proper extension: 07w0v; 02dq8f; *> query: (?x9658, 019v9k) <- institution(?x3437, ?x9658), institution(?x1200, ?x9658), contains(?x14475, ?x9658), currency(?x9658, ?x5696), major_field_of_study(?x9658, ?x2014), ?x1200 = 016t_3, ?x2014 = 04rjg, institution(?x3437, ?x4099), ?x4099 = 01f1r4, major_field_of_study(?x3437, ?x254) *> conf = 0.82 ranks of expected_values: 3 EVAL 01nm8w institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 146.000 80.000 0.839 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21952-05mlqj PRED entity: 05mlqj PRED relation: film PRED expected values: 02825nf => 77 concepts (54 used for prediction) PRED predicted values (max 10 best out of 393): 0h95927 (0.50 #4880, 0.06 #8441, 0.04 #64093), 0fhzwl (0.48 #26704, 0.45 #37385, 0.41 #42727), 02b6n9 (0.25 #1564, 0.05 #10465, 0.02 #14025), 01hqhm (0.25 #328, 0.03 #9229, 0.01 #12789), 02ht1k (0.25 #2406, 0.03 #9527, 0.01 #13087), 0888c3 (0.25 #3188, 0.03 #10309, 0.01 #24551), 02fj8n (0.25 #3071, 0.03 #10192), 07vn_9 (0.25 #1675, 0.01 #14136, 0.01 #19478), 0by17xn (0.20 #5273), 0234j5 (0.20 #4977) >> Best rule #4880 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 069ld1; >> query: (?x9384, 0h95927) <- film(?x9384, ?x1173), award_nominee(?x9384, ?x3866), ?x3866 = 02jtjz >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05mlqj film 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 54.000 0.500 http://example.org/film/actor/film./film/performance/film #21951-04pf4r PRED entity: 04pf4r PRED relation: place_of_birth PRED expected values: 01z8f0 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 122): 02jx1 (0.39 #19025, 0.38 #14797, 0.36 #4932), 0g284 (0.33 #76), 04jpl (0.21 #2827, 0.11 #31716, 0.10 #23260), 02_286 (0.10 #11293, 0.10 #8476, 0.10 #13407), 01b8w_ (0.08 #3153, 0.03 #5266, 0.02 #7381), 030qb3t (0.08 #3577, 0.06 #7806, 0.06 #6397), 0cr3d (0.06 #5731, 0.05 #6437, 0.05 #7141), 03b12 (0.03 #5339, 0.03 #1816, 0.02 #7454), 0f2tj (0.03 #5180, 0.02 #5885, 0.02 #3771), 07ssc (0.03 #15502, 0.03 #2831) >> Best rule #19025 for best value: >> intensional similarity = 3 >> extensional distance = 235 >> proper extension: 03c7ln; 0c9d9; 01pr_j6; 012zng; 01tp5bj; 0lgm5; 0p3sf; 01lcxbb; 01vv6_6; 0phx4; ... >> query: (?x4019, ?x1310) <- origin(?x4019, ?x1310), instrumentalists(?x315, ?x4019), role(?x315, ?x74) >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04pf4r place_of_birth 01z8f0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 132.000 0.391 http://example.org/people/person/place_of_birth #21950-052hl PRED entity: 052hl PRED relation: influenced_by PRED expected values: 015cbq => 108 concepts (41 used for prediction) PRED predicted values (max 10 best out of 299): 01hmk9 (0.14 #5450, 0.08 #2836, 0.08 #3271), 0p_47 (0.12 #5336, 0.07 #980, 0.06 #2722), 014z8v (0.11 #5350, 0.09 #994, 0.08 #4914), 014zfs (0.11 #5253, 0.07 #897, 0.07 #16565), 081lh (0.10 #5248, 0.08 #2634, 0.07 #892), 081k8 (0.10 #6694, 0.10 #9746, 0.09 #8872), 03_87 (0.10 #8919, 0.09 #8483, 0.09 #6305), 01k9lpl (0.09 #1182, 0.08 #5538, 0.08 #5102), 032l1 (0.09 #10987, 0.08 #13164, 0.08 #9679), 03f0324 (0.08 #8432, 0.07 #9306, 0.07 #10177) >> Best rule #5450 for best value: >> intensional similarity = 2 >> extensional distance = 132 >> proper extension: 0lhn5; 01d5g; >> query: (?x6771, 01hmk9) <- influenced_by(?x6771, ?x8286), award_winner(?x2465, ?x8286) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #16565 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 436 *> proper extension: 0d6b7; 07scx; *> query: (?x6771, ?x397) <- influenced_by(?x6008, ?x6771), influenced_by(?x6008, ?x397) *> conf = 0.07 ranks of expected_values: 29 EVAL 052hl influenced_by 015cbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 108.000 41.000 0.142 http://example.org/influence/influence_node/influenced_by #21949-0gjcrrw PRED entity: 0gjcrrw PRED relation: film_release_region PRED expected values: 0jgd 0154j 04gzd 0hzlz 015qh 0163v => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 155): 09c7w0 (0.92 #7461, 0.92 #7174, 0.92 #7604), 03rk0 (0.89 #1055, 0.69 #2202, 0.62 #3497), 0154j (0.84 #3454, 0.84 #2159, 0.81 #3740), 0jgd (0.82 #2158, 0.81 #3453, 0.81 #3739), 015qh (0.79 #1042, 0.76 #2189, 0.66 #3484), 016wzw (0.79 #1064, 0.73 #2211, 0.63 #3506), 06f32 (0.79 #1063, 0.55 #2210, 0.50 #3505), 04gzd (0.74 #3458, 0.73 #2163, 0.69 #3744), 01ls2 (0.68 #1018, 0.57 #2165, 0.55 #3460), 03rj0 (0.67 #2206, 0.66 #3501, 0.66 #3787) >> Best rule #7461 for best value: >> intensional similarity = 5 >> extensional distance = 1322 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; 018js4; ... >> query: (?x3830, 09c7w0) <- film_release_region(?x3830, ?x304), film_release_region(?x2878, ?x304), ?x2878 = 0hx4y, olympics(?x304, ?x418), country(?x150, ?x304) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #3454 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 115 *> proper extension: 014lc_; 02vxq9m; 0h1cdwq; 02x3lt7; 087wc7n; 017gl1; 0bwfwpj; 0jjy0; 04hwbq; 017gm7; ... *> query: (?x3830, 0154j) <- film_release_region(?x3830, ?x1174), film_release_region(?x3830, ?x456), film_release_region(?x3830, ?x304), ?x304 = 0d0vqn, ?x1174 = 047yc, ?x456 = 05qhw *> conf = 0.84 ranks of expected_values: 3, 4, 5, 8, 21, 63 EVAL 0gjcrrw film_release_region 0163v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 74.000 74.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gjcrrw film_release_region 015qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 74.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gjcrrw film_release_region 0hzlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 74.000 74.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gjcrrw film_release_region 04gzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 74.000 74.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gjcrrw film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 74.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gjcrrw film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 74.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21948-06cgy PRED entity: 06cgy PRED relation: award_nominee! PRED expected values: 01_xtx 01v80y => 131 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1346): 01csvq (0.82 #81173, 0.81 #44066, 0.81 #78854), 01v80y (0.82 #81173, 0.81 #44066, 0.81 #78854), 018ygt (0.80 #4638, 0.77 #44065, 0.77 #150735), 030hcs (0.80 #4638, 0.77 #44065, 0.77 #150735), 0f4vbz (0.80 #4638, 0.77 #44065, 0.77 #150735), 0169dl (0.80 #4638, 0.77 #44065, 0.77 #150735), 01pj5q (0.80 #4638, 0.77 #44065, 0.77 #150735), 01swck (0.80 #4638, 0.77 #44065, 0.77 #150735), 0h10vt (0.80 #4638, 0.77 #44065, 0.77 #150735), 0fvf9q (0.80 #4638, 0.77 #44065, 0.77 #150735) >> Best rule #81173 for best value: >> intensional similarity = 3 >> extensional distance = 436 >> proper extension: 035sc2; 0dbb3; >> query: (?x1554, ?x719) <- award_nominee(?x400, ?x1554), religion(?x1554, ?x1985), award_nominee(?x1554, ?x719) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 135 EVAL 06cgy award_nominee! 01v80y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 69.000 0.819 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 06cgy award_nominee! 01_xtx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 131.000 69.000 0.819 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21947-03csqj4 PRED entity: 03csqj4 PRED relation: nominated_for PRED expected values: 015qqg => 109 concepts (36 used for prediction) PRED predicted values (max 10 best out of 451): 0qmjd (0.55 #2707, 0.18 #42111, 0.16 #45351), 015qqg (0.42 #4860, 0.02 #18579, 0.01 #33157), 02vnmc9 (0.42 #4860, 0.01 #33595, 0.01 #28735), 0pd4f (0.20 #672, 0.09 #2291, 0.02 #12008), 02md2d (0.20 #643, 0.09 #2262, 0.01 #13599), 07gbf (0.20 #1420, 0.09 #3039), 0330r (0.20 #1412, 0.09 #3031), 0n04r (0.20 #603, 0.09 #2222), 0jyb4 (0.18 #2607, 0.04 #12324, 0.02 #17183), 0ptxj (0.18 #42111, 0.16 #45351, 0.16 #40491) >> Best rule #2707 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0jvtp; >> query: (?x12010, 0qmjd) <- nominated_for(?x12010, ?x1822), nominated_for(?x5212, ?x1822), ?x5212 = 0ptxj >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #4860 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 04gmp_z; *> query: (?x12010, ?x4870) <- nominated_for(?x12010, ?x3638), film_production_design_by(?x4870, ?x12010), film_release_region(?x3638, ?x87) *> conf = 0.42 ranks of expected_values: 2 EVAL 03csqj4 nominated_for 015qqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 36.000 0.545 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #21946-0flpy PRED entity: 0flpy PRED relation: artist! PRED expected values: 01w40h => 141 concepts (100 used for prediction) PRED predicted values (max 10 best out of 99): 033hn8 (0.29 #287, 0.12 #1384, 0.12 #698), 015_1q (0.22 #6463, 0.20 #7149, 0.20 #9891), 03rhqg (0.21 #700, 0.16 #289, 0.16 #1523), 03mp8k (0.19 #338, 0.15 #749, 0.10 #1435), 017l96 (0.19 #292, 0.11 #6188, 0.10 #2348), 016ckq (0.16 #727, 0.16 #316, 0.09 #1413), 011k1h (0.16 #284, 0.14 #147, 0.11 #11530), 0g768 (0.15 #1408, 0.13 #722, 0.13 #1682), 0k_kr (0.14 #180, 0.06 #317, 0.04 #591), 0181dw (0.14 #3606, 0.13 #726, 0.13 #1412) >> Best rule #287 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0163m1; 01fmz6; 016890; 015srx; 011z3g; 0134wr; 046p9; 017lb_; 016376; >> query: (?x6290, 033hn8) <- award(?x6290, ?x567), artists(?x5792, ?x6290), artist(?x2241, ?x6290), ?x5792 = 026z9 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #713 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 017yfz; 01ydzx; 03f3_p3; 01wg25j; 0gps0z; 020_4z; 0ql36; *> query: (?x6290, 01w40h) <- profession(?x6290, ?x131), artists(?x3928, ?x6290), instrumentalists(?x316, ?x6290), ?x3928 = 0gywn *> conf = 0.13 ranks of expected_values: 11 EVAL 0flpy artist! 01w40h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 141.000 100.000 0.290 http://example.org/music/record_label/artist #21945-02_286 PRED entity: 02_286 PRED relation: citytown! PRED expected values: 0l8sx 0fb0v 05d6kv 04d5v9 01d34b 026036 0kctd 06nvzg 0hhjk => 190 concepts (176 used for prediction) PRED predicted values (max 10 best out of 855): 01t0dy (0.76 #60825, 0.66 #72278, 0.52 #92322), 021q2j (0.76 #60825, 0.66 #72278, 0.52 #92322), 03_fmr (0.76 #60825, 0.66 #72278, 0.52 #92322), 01vg13 (0.76 #60825, 0.66 #72278, 0.52 #92322), 03bmmc (0.76 #60825, 0.66 #72278, 0.52 #92322), 04ftdq (0.76 #60825, 0.66 #72278, 0.52 #92322), 027kp3 (0.76 #60825, 0.66 #72278, 0.52 #92322), 01jzyx (0.76 #60825, 0.66 #72278, 0.52 #92322), 05bjp6 (0.76 #60825, 0.66 #72278, 0.52 #92322), 02sdwt (0.76 #60825, 0.66 #72278, 0.52 #92322) >> Best rule #60825 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 0t015; 013hxv; 0j8p6; 01z1c; 0sl2w; 013d_f; 031sn; >> query: (?x739, ?x1005) <- citytown(?x166, ?x739), state(?x739, ?x335), contains(?x739, ?x1005) >> conf = 0.76 => this is the best rule for 20 predicted values *> Best rule #79436 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 167 *> proper extension: 016tw3; 02301; 0l2l_; 07tds; 017cy9; 027kp3; 01n6r0; 02zd460; 02nd_; 0d331; ... *> query: (?x739, ?x574) <- featured_film_locations(?x1688, ?x739), production_companies(?x1688, ?x574) *> conf = 0.02 ranks of expected_values: 504, 587, 760, 762, 764, 766, 771, 817 EVAL 02_286 citytown! 0hhjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 190.000 176.000 0.757 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 02_286 citytown! 06nvzg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 190.000 176.000 0.757 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 02_286 citytown! 0kctd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 176.000 0.757 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 02_286 citytown! 026036 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 190.000 176.000 0.757 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 02_286 citytown! 01d34b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 190.000 176.000 0.757 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 02_286 citytown! 04d5v9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 190.000 176.000 0.757 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 02_286 citytown! 05d6kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 190.000 176.000 0.757 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 02_286 citytown! 0fb0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 190.000 176.000 0.757 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 02_286 citytown! 0l8sx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 190.000 176.000 0.757 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #21944-042fgh PRED entity: 042fgh PRED relation: film! PRED expected values: 04mlmx => 136 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1383): 04mlmx (0.50 #3513, 0.31 #4157, 0.25 #74813), 021lby (0.39 #29090, 0.20 #97674, 0.18 #105987), 0j_c (0.35 #27419, 0.27 #10796, 0.22 #50278), 04__f (0.33 #3456, 0.31 #4157, 0.25 #74813), 048lv (0.31 #4157, 0.25 #74813, 0.24 #45714), 042ly5 (0.31 #4157, 0.25 #74813, 0.24 #45714), 03mg35 (0.31 #4157, 0.25 #74813, 0.24 #45714), 02qx69 (0.31 #4157, 0.25 #74813, 0.24 #45714), 03l3ln (0.31 #4157, 0.25 #74813, 0.24 #45714), 0n839 (0.31 #4157, 0.25 #74813, 0.24 #45714) >> Best rule #3513 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 044g_k; >> query: (?x7425, 04mlmx) <- nominated_for(?x3672, ?x7425), nominated_for(?x1385, ?x7425), film(?x1384, ?x1385), film(?x382, ?x1385), language(?x7425, ?x254), produced_by(?x1385, ?x65), ?x3672 = 024mxd >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042fgh film! 04mlmx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 59.000 0.500 http://example.org/film/actor/film./film/performance/film #21943-0h_9252 PRED entity: 0h_9252 PRED relation: ceremony! PRED expected values: 09lvl1 => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 301): 0gqy2 (0.95 #1831, 0.79 #1587, 0.75 #2565), 0k611 (0.88 #1783, 0.77 #1539, 0.69 #2517), 0gq_d (0.88 #1867, 0.77 #1623, 0.69 #2601), 0gqwc (0.88 #1770, 0.77 #1526, 0.69 #2504), 0gvx_ (0.86 #1845, 0.79 #1601, 0.68 #2579), 018wng (0.84 #1746, 0.79 #1502, 0.67 #2480), 0f4x7 (0.84 #1736, 0.77 #1492, 0.67 #2470), 0p9sw (0.84 #1731, 0.77 #1487, 0.67 #2465), 0gqyl (0.84 #1789, 0.74 #1545, 0.67 #2523), 0gq9h (0.84 #1527, 0.83 #1771, 0.66 #2017) >> Best rule #1831 for best value: >> intensional similarity = 22 >> extensional distance = 62 >> proper extension: 073hkh; 0bzk8w; 02yw5r; 059x66; 073hmq; 0bzm81; 0dth6b; 02yv_b; 0ftlkg; 073h1t; ... >> query: (?x4141, 0gqy2) <- ceremony(?x7606, ?x4141), ceremony(?x458, ?x4141), award(?x9854, ?x7606), award(?x7561, ?x7606), award(?x7137, ?x7606), award(?x4666, ?x7606), profession(?x9854, ?x353), ?x7561 = 0164r9, award_winner(?x4141, ?x1894), film(?x9854, ?x1444), award_winner(?x3435, ?x4666), nationality(?x7137, ?x94), location(?x7137, ?x2850), award_winner(?x7606, ?x9610), award(?x9156, ?x458), award(?x2167, ?x458), award(?x1894, ?x1232), film(?x7137, ?x3218), ?x2167 = 0b_fw, student(?x546, ?x9610), profession(?x1894, ?x131), student(?x2502, ?x9156) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #188 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 1 *> proper extension: 0ds460j; *> query: (?x4141, 09lvl1) <- ceremony(?x8096, ?x4141), ceremony(?x7606, ?x4141), ceremony(?x7285, ?x4141), ceremony(?x6878, ?x4141), ceremony(?x5455, ?x4141), ceremony(?x3105, ?x4141), ?x7606 = 01l78d, ?x7285 = 01lk0l, award_winner(?x4141, ?x3692), award_winner(?x4141, ?x1894), award_winner(?x4141, ?x844), award_winner(?x4141, ?x541), award_winner(?x4141, ?x163), ?x5455 = 0bb57s, ?x6878 = 08_vwq, ?x3105 = 01l29r, ?x8096 = 01lj_c, executive_produced_by(?x1262, ?x3692), profession(?x1894, ?x131), award_winner(?x8500, ?x1894), nominated_for(?x1894, ?x188), award_winner(?x1894, ?x3732), ?x163 = 0fvf9q, produced_by(?x835, ?x3692), award(?x1894, ?x1232), award_nominee(?x844, ?x262), ?x8500 = 0gx1673, award_winner(?x519, ?x541) *> conf = 0.33 ranks of expected_values: 92 EVAL 0h_9252 ceremony! 09lvl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 17.000 17.000 0.953 http://example.org/award/award_category/winners./award/award_honor/ceremony #21942-0g72r PRED entity: 0g72r PRED relation: nationality PRED expected values: 09c7w0 => 137 concepts (132 used for prediction) PRED predicted values (max 10 best out of 53): 09c7w0 (0.79 #9545, 0.78 #7255, 0.78 #5764), 030qb3t (0.31 #8251, 0.31 #8552, 0.25 #10341), 01n7q (0.31 #8251, 0.31 #8552, 0.25 #10341), 0k_s5 (0.31 #8251, 0.31 #8552), 0kpys (0.31 #8251, 0.31 #8552), 02jx1 (0.25 #428, 0.25 #32, 0.25 #7056), 07ssc (0.25 #411, 0.25 #15, 0.17 #3292), 03rjj (0.25 #5, 0.25 #7056, 0.12 #401), 0f8l9c (0.25 #7056, 0.14 #319, 0.12 #7752), 06bnz (0.25 #7056, 0.12 #7752, 0.11 #634) >> Best rule #9545 for best value: >> intensional similarity = 4 >> extensional distance = 1305 >> proper extension: 01wb8bs; 0bn3jg; >> query: (?x12841, 09c7w0) <- student(?x12063, ?x12841), institution(?x1368, ?x12063), nationality(?x12841, ?x1264), ?x1368 = 014mlp >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g72r nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 132.000 0.792 http://example.org/people/person/nationality #21941-0f14q PRED entity: 0f14q PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (87 used for prediction) PRED predicted values (max 10 best out of 51): 09c7w0 (0.88 #5116, 0.85 #1602, 0.85 #1002), 05kkh (0.40 #8334, 0.34 #7123), 0d060g (0.20 #107, 0.12 #307, 0.08 #808), 02jx1 (0.19 #734, 0.16 #3339, 0.11 #1334), 07ssc (0.11 #3321, 0.11 #416, 0.08 #4929), 03rk0 (0.10 #3352, 0.07 #1447, 0.07 #6968), 0345h (0.07 #231, 0.04 #432, 0.03 #4612), 03_3d (0.07 #2310, 0.03 #4612, 0.03 #3112), 0chghy (0.06 #310, 0.03 #4612, 0.02 #7133), 06qd3 (0.06 #336, 0.03 #4612) >> Best rule #5116 for best value: >> intensional similarity = 4 >> extensional distance = 590 >> proper extension: 05218gr; >> query: (?x9957, 09c7w0) <- place_of_birth(?x9957, ?x6453), dog_breed(?x6453, ?x1706), place_founded(?x9675, ?x6453), teams(?x6453, ?x1759) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f14q nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 87.000 0.878 http://example.org/people/person/nationality #21940-0ky1 PRED entity: 0ky1 PRED relation: people! PRED expected values: 02g7sp => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 47): 0x67 (0.32 #1165, 0.32 #1396, 0.25 #318), 03lmx1 (0.31 #553, 0.06 #938, 0.05 #1092), 07bch9 (0.29 #177, 0.27 #254, 0.18 #793), 041rx (0.24 #466, 0.23 #81, 0.22 #697), 02ctzb (0.23 #92, 0.19 #169, 0.18 #246), 063k3h (0.14 #185, 0.14 #262, 0.12 #1340), 07hwkr (0.12 #628, 0.10 #782, 0.08 #2245), 033tf_ (0.12 #315, 0.09 #6015, 0.09 #3857), 02w7gg (0.11 #1542, 0.11 #6862, 0.07 #387), 048z7l (0.11 #425, 0.08 #117, 0.07 #502) >> Best rule #1165 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 0f1pyf; 069d71; >> query: (?x10562, 0x67) <- location(?x10562, ?x6885), nationality(?x10562, ?x512), gender(?x10562, ?x231), athlete(?x12682, ?x10562) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #480 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 04jzj; 0jcx; 0n00; 01dvtx; 06whf; 03f0324; 013pp3; 040_t; 0j6cj; 04xfb; ... *> query: (?x10562, 02g7sp) <- influenced_by(?x2993, ?x10562), profession(?x10562, ?x353), student(?x9111, ?x10562) *> conf = 0.03 ranks of expected_values: 28 EVAL 0ky1 people! 02g7sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 141.000 141.000 0.323 http://example.org/people/ethnicity/people #21939-027km64 PRED entity: 027km64 PRED relation: award_nominee PRED expected values: 066yfh => 86 concepts (24 used for prediction) PRED predicted values (max 10 best out of 843): 04wvhz (0.81 #53815, 0.81 #49135, 0.81 #21054), 066yfh (0.43 #4640, 0.25 #32756, 0.24 #28075), 01vhrz (0.29 #4348, 0.25 #32756, 0.24 #28075), 07rd7 (0.29 #3343, 0.25 #32756, 0.24 #28075), 01pw9v (0.29 #4371, 0.25 #32756, 0.24 #28075), 0bjkpt (0.25 #32756, 0.24 #28075, 0.16 #25735), 027km64 (0.25 #32756, 0.24 #28075, 0.16 #25735), 01xndd (0.25 #32756, 0.24 #28075, 0.16 #25735), 04m_zp (0.25 #32756, 0.24 #28075, 0.16 #25735), 02q42j_ (0.25 #32756, 0.24 #28075, 0.16 #25735) >> Best rule #53815 for best value: >> intensional similarity = 4 >> extensional distance = 1079 >> proper extension: 01sl1q; 044mz_; 07nznf; 0184jc; 06qgvf; 0grwj; 01vvydl; 07fq1y; 0337vz; 07s3vqk; ... >> query: (?x5202, ?x1039) <- film(?x5202, ?x5378), profession(?x5202, ?x1032), award_nominee(?x1039, ?x5202), production_companies(?x5378, ?x574) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #4640 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 07rd7; 062cg6; 0bjkpt; 01pw9v; 066yfh; *> query: (?x5202, 066yfh) <- award_nominee(?x5202, ?x2691), ?x2691 = 067pl7, profession(?x5202, ?x1032) *> conf = 0.43 ranks of expected_values: 2 EVAL 027km64 award_nominee 066yfh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 24.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21938-07q68q PRED entity: 07q68q PRED relation: gender PRED expected values: 05zppz => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #23, 0.79 #21, 0.79 #19), 02zsn (0.46 #100, 0.46 #113, 0.46 #112) >> Best rule #23 for best value: >> intensional similarity = 10 >> extensional distance = 1100 >> proper extension: 07nznf; 05bnp0; 0dbpyd; 0l6qt; 06j0md; 01xdf5; 02rchht; 083chw; 014zcr; 01vw87c; ... >> query: (?x14531, 05zppz) <- nationality(?x14531, ?x279), profession(?x14531, ?x1383), profession(?x9202, ?x1383), profession(?x8196, ?x1383), profession(?x8160, ?x1383), profession(?x6707, ?x1383), ?x8196 = 010p3, ?x9202 = 0bn8fw, ?x6707 = 03d_zl4, ?x8160 = 02dlfh >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07q68q gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.793 http://example.org/people/person/gender #21937-0prpt PRED entity: 0prpt PRED relation: film_regional_debut_venue! PRED expected values: 08720 0f4_l 05c26ss 0sxkh => 35 concepts (13 used for prediction) PRED predicted values (max 10 best out of 465): 0g5qmbz (0.33 #276, 0.33 #131, 0.22 #566), 0cp08zg (0.33 #262, 0.33 #117, 0.22 #552), 027m67 (0.33 #254, 0.33 #109, 0.22 #544), 0bh8x1y (0.33 #210, 0.33 #65, 0.22 #500), 0192hw (0.33 #194, 0.33 #49, 0.22 #484), 0j6b5 (0.33 #179, 0.33 #34, 0.22 #469), 02vz6dn (0.33 #256, 0.14 #691, 0.13 #984), 0gh8zks (0.33 #193, 0.14 #628, 0.13 #921), 0bmc4cm (0.33 #190, 0.14 #625, 0.13 #918), 0h2zvzr (0.33 #271, 0.14 #706, 0.13 #999) >> Best rule #276 for best value: >> intensional similarity = 18 >> extensional distance = 1 >> proper extension: 018cvf; >> query: (?x12806, 0g5qmbz) <- film_regional_debut_venue(?x10800, ?x12806), film_regional_debut_venue(?x3453, ?x12806), film_regional_debut_venue(?x3287, ?x12806), film_regional_debut_venue(?x2501, ?x12806), ?x2501 = 040rmy, film_release_region(?x3287, ?x1003), film_release_region(?x3287, ?x429), film_release_region(?x3287, ?x279), ?x279 = 0d060g, film_festivals(?x10800, ?x9189), film_release_region(?x3453, ?x142), music(?x3453, ?x1656), award(?x3287, ?x941), ?x1003 = 03gj2, instance_of_recurring_event(?x9932, ?x12806), ?x429 = 03rt9, executive_produced_by(?x3287, ?x4857), nominated_for(?x2902, ?x10800) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #872 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 13 *> proper extension: 03wf1p2; *> query: (?x12806, ?x80) <- film_regional_debut_venue(?x6209, ?x12806), film_regional_debut_venue(?x2501, ?x12806), film_release_region(?x2501, ?x512), film_release_region(?x2501, ?x252), film_release_region(?x2501, ?x142), titles(?x812, ?x2501), ?x252 = 03_3d, nominated_for(?x4695, ?x2501), nominated_for(?x9891, ?x6209), ?x512 = 07ssc, country(?x471, ?x142), film_release_region(?x1868, ?x142), film_release_region(?x80, ?x142), film_format(?x2501, ?x6392), ?x1868 = 0cc7hmk *> conf = 0.02 ranks of expected_values: 297, 313, 379, 441 EVAL 0prpt film_regional_debut_venue! 0sxkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 35.000 13.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 0prpt film_regional_debut_venue! 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 35.000 13.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 0prpt film_regional_debut_venue! 0f4_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 35.000 13.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 0prpt film_regional_debut_venue! 08720 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 35.000 13.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #21936-07wjk PRED entity: 07wjk PRED relation: student PRED expected values: 01y665 => 191 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1620): 0391jz (0.17 #565, 0.09 #4725, 0.08 #6805), 0p8jf (0.14 #2556, 0.12 #12957, 0.06 #8796), 0306ds (0.14 #2486, 0.10 #17047, 0.07 #21208), 015v3r (0.14 #2577, 0.07 #17138, 0.06 #12978), 02cx72 (0.14 #2680, 0.07 #17241, 0.06 #13081), 03h40_7 (0.14 #3882, 0.06 #10122, 0.06 #14283), 03xx9l (0.14 #3395, 0.06 #9635, 0.06 #11715), 01mqh5 (0.14 #3953, 0.06 #10193, 0.06 #12273), 036jb (0.14 #2844, 0.06 #9084, 0.06 #13245), 06pwf6 (0.14 #2541, 0.06 #8781, 0.04 #15022) >> Best rule #565 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01t3h6; >> query: (?x2327, 0391jz) <- contains(?x1658, ?x2327), contains(?x279, ?x2327), ?x1658 = 0h7h6, ?x279 = 0d060g >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07wjk student 01y665 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 191.000 94.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #21935-09g_31 PRED entity: 09g_31 PRED relation: actor PRED expected values: 0ccqd7 => 88 concepts (66 used for prediction) PRED predicted values (max 10 best out of 833): 0gcdzz (0.33 #108, 0.05 #19495, 0.03 #31498), 0404wqb (0.33 #800, 0.05 #20187, 0.03 #35884), 02bkdn (0.33 #143, 0.03 #35227, 0.03 #38780), 02bwjv (0.33 #593, 0.03 #38780, 0.03 #16286), 04n7njg (0.33 #94, 0.03 #38780, 0.03 #15787), 02zq43 (0.33 #26, 0.03 #38780, 0.03 #15719), 0154d7 (0.22 #2513, 0.14 #1590, 0.09 #8052), 0347db (0.22 #2407, 0.09 #7946, 0.09 #19387), 0sw6y (0.18 #15623, 0.18 #16546, 0.17 #18392), 03cz9_ (0.16 #5485, 0.14 #4562, 0.11 #10100) >> Best rule #108 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 0fkwzs; >> query: (?x8628, 0gcdzz) <- actor(?x8628, ?x12244), actor(?x8628, ?x11435), actor(?x8628, ?x8273), country_of_origin(?x8628, ?x94), ?x11435 = 05z775, type_of_union(?x12244, ?x566), film(?x8273, ?x626), place_of_birth(?x8273, ?x3014) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09g_31 actor 0ccqd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 66.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21934-070b4 PRED entity: 070b4 PRED relation: artists! PRED expected values: 05fx6 => 118 concepts (56 used for prediction) PRED predicted values (max 10 best out of 262): 06by7 (0.68 #5787, 0.68 #14608, 0.63 #15519), 0dl5d (0.61 #4874, 0.61 #3659, 0.56 #3355), 064t9 (0.50 #2439, 0.48 #4563, 0.42 #7296), 0xhtw (0.46 #3049, 0.46 #7300, 0.44 #3656), 05w3f (0.44 #3373, 0.42 #2464, 0.39 #3677), 06j6l (0.40 #957, 0.34 #4598, 0.29 #9460), 0155w (0.40 #1014, 0.32 #9517, 0.31 #9822), 0gywn (0.40 #966, 0.28 #4607, 0.25 #360), 05bt6j (0.39 #3682, 0.35 #4897, 0.33 #2469), 03w94xt (0.36 #2013, 0.33 #2316, 0.23 #13368) >> Best rule #5787 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 02mq_y; >> query: (?x8864, 06by7) <- group(?x3735, ?x8864), artists(?x302, ?x8864), ?x302 = 016clz, group(?x227, ?x8864) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #13062 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 03bxh; 03_f0; *> query: (?x8864, ?x301) <- artists(?x10933, ?x8864), influenced_by(?x5935, ?x8864), parent_genre(?x301, ?x10933) *> conf = 0.06 ranks of expected_values: 151 EVAL 070b4 artists! 05fx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 118.000 56.000 0.684 http://example.org/music/genre/artists #21933-06mnbn PRED entity: 06mnbn PRED relation: film PRED expected values: 02qsqmq 0ddbjy4 => 67 concepts (26 used for prediction) PRED predicted values (max 10 best out of 433): 0524b41 (0.59 #28597, 0.42 #25022, 0.42 #41110), 017jd9 (0.47 #2566, 0.12 #779, 0.08 #6140), 017gl1 (0.42 #1930, 0.12 #143, 0.08 #5504), 017gm7 (0.42 #1998, 0.12 #211, 0.06 #5572), 0ndwt2w (0.32 #2787, 0.04 #6361, 0.02 #8149), 01vw8k (0.12 #652, 0.11 #2439, 0.03 #6013), 04f6df0 (0.12 #1393, 0.11 #3180), 01_0f7 (0.12 #1155, 0.05 #2942, 0.01 #6516), 03cwwl (0.12 #1609, 0.05 #3396), 06_sc3 (0.12 #1418, 0.05 #3205) >> Best rule #28597 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 01pnn3; 0n8bn; 04mlh8; 012x2b; 065d1h; 01mylz; >> query: (?x4015, ?x7119) <- nominated_for(?x4015, ?x7119), profession(?x4015, ?x1032), film(?x4015, ?x2734) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #3364 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 02fgm7; *> query: (?x4015, 0ddbjy4) <- award_nominee(?x4015, ?x1194), film(?x4015, ?x2734), ?x1194 = 02gvwz *> conf = 0.05 ranks of expected_values: 68 EVAL 06mnbn film 0ddbjy4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 67.000 26.000 0.585 http://example.org/film/actor/film./film/performance/film EVAL 06mnbn film 02qsqmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 26.000 0.585 http://example.org/film/actor/film./film/performance/film #21932-0dls3 PRED entity: 0dls3 PRED relation: artists PRED expected values: 01516r => 77 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1052): 048tgl (0.57 #8359, 0.50 #1964, 0.43 #4095), 014_lq (0.56 #3198, 0.50 #1539, 0.43 #7934), 011_vz (0.56 #3198, 0.50 #1897, 0.43 #4028), 07r1_ (0.56 #3198, 0.50 #1689, 0.43 #3820), 0fpj4lx (0.54 #6716, 0.50 #1387, 0.43 #7782), 011z3g (0.54 #6988, 0.37 #19791, 0.35 #16589), 02z4b_8 (0.54 #7021, 0.33 #625, 0.30 #16622), 01vvycq (0.54 #6441, 0.33 #45, 0.28 #1067), 01shhf (0.50 #8319, 0.50 #1924, 0.43 #4055), 01vsxdm (0.50 #7561, 0.50 #1166, 0.43 #3297) >> Best rule #8359 for best value: >> intensional similarity = 8 >> extensional distance = 12 >> proper extension: 04b675; >> query: (?x3642, 048tgl) <- artists(?x3642, ?x7874), artists(?x3642, ?x5227), artists(?x3642, ?x3118), parent_genre(?x3642, ?x1572), nominated_for(?x3118, ?x2757), artist(?x1693, ?x7874), ?x5227 = 01j59b0, artists(?x1572, ?x115) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #737 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 016clz; *> query: (?x3642, 01516r) <- artists(?x3642, ?x6876), artists(?x3642, ?x5935), artists(?x3642, ?x3118), parent_genre(?x3642, ?x5934), ?x3118 = 01w02sy, ?x5935 = 0b1zz, ?x6876 = 0ycp3, artists(?x5934, ?x9241), ?x9241 = 01w5gg6 *> conf = 0.33 ranks of expected_values: 128 EVAL 0dls3 artists 01516r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 77.000 38.000 0.571 http://example.org/music/genre/artists #21931-0b_6qj PRED entity: 0b_6qj PRED relation: locations PRED expected values: 0ftxw => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 261): 071cn (0.50 #940, 0.50 #766, 0.50 #247), 0f__1 (0.50 #927, 0.50 #753, 0.50 #234), 0f2r6 (0.50 #884, 0.50 #191, 0.41 #3488), 0fsb8 (0.50 #303, 0.38 #2385, 0.35 #4342), 029cr (0.50 #573, 0.36 #1789, 0.36 #1963), 04f_d (0.50 #1258, 0.36 #1955, 0.35 #4342), 03l2n (0.50 #606, 0.35 #4342, 0.33 #1473), 0vzm (0.50 #412, 0.35 #4342, 0.32 #5385), 030qb3t (0.43 #2950, 0.36 #3677, 0.35 #4342), 02_286 (0.43 #2950, 0.10 #10826, 0.10 #9581) >> Best rule #940 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 0b_6q5; >> query: (?x9146, 071cn) <- team(?x9146, ?x10846), team(?x9146, ?x9983), team(?x9146, ?x9147), ?x9983 = 02q4ntp, ?x9147 = 0263cyj, locations(?x9146, ?x5719), ?x10846 = 02pzy52, month(?x5719, ?x4869), ?x4869 = 02xx5 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4342 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 22 *> proper extension: 0jnh; *> query: (?x9146, ?x674) <- locations(?x9146, ?x5719), locations(?x9146, ?x4978), vacationer(?x4978, ?x3421), locations(?x4368, ?x4978), contains(?x3778, ?x4978), adjoins(?x5719, ?x3300), jurisdiction_of_office(?x1195, ?x5719), locations(?x4368, ?x674), time_zones(?x5719, ?x1638) *> conf = 0.35 ranks of expected_values: 13 EVAL 0b_6qj locations 0ftxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 70.000 70.000 0.500 http://example.org/time/event/locations #21930-04vmp PRED entity: 04vmp PRED relation: place_of_birth! PRED expected values: 03m2fg 03hfxx 05wdgq 0cm19f => 185 concepts (124 used for prediction) PRED predicted values (max 10 best out of 2073): 047jhq (0.37 #144701, 0.36 #144702, 0.33 #284262), 0tj9 (0.37 #144701, 0.36 #144702, 0.33 #284262), 01zh29 (0.37 #144701, 0.36 #144702, 0.33 #284262), 03m3nzf (0.37 #144701, 0.36 #144702, 0.33 #284262), 03f02ct (0.37 #144701, 0.36 #144702, 0.33 #284262), 05nw9m (0.37 #144701, 0.36 #144702, 0.33 #284262), 02xgdv (0.37 #144701, 0.36 #144702, 0.33 #284262), 08d6bd (0.37 #144701, 0.36 #144702, 0.33 #284262), 01wttr1 (0.37 #144701, 0.36 #144702, 0.33 #284262), 08bqy9 (0.37 #144701, 0.36 #144702, 0.33 #284262) >> Best rule #144701 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 0fv_t; 0dqyw; 0dp90; >> query: (?x7412, ?x1806) <- location(?x2065, ?x7412), location(?x1806, ?x7412), capital(?x10782, ?x7412), profession(?x2065, ?x319) >> conf = 0.37 => this is the best rule for 19 predicted values No rule for expected values ranks of expected_values: EVAL 04vmp place_of_birth! 0cm19f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 185.000 124.000 0.369 http://example.org/people/person/place_of_birth EVAL 04vmp place_of_birth! 05wdgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 185.000 124.000 0.369 http://example.org/people/person/place_of_birth EVAL 04vmp place_of_birth! 03hfxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 185.000 124.000 0.369 http://example.org/people/person/place_of_birth EVAL 04vmp place_of_birth! 03m2fg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 185.000 124.000 0.369 http://example.org/people/person/place_of_birth #21929-0gj8nq2 PRED entity: 0gj8nq2 PRED relation: titles! PRED expected values: 01hmnh => 95 concepts (87 used for prediction) PRED predicted values (max 10 best out of 124): 03npn (0.33 #1148, 0.33 #1055, 0.32 #2614), 024qqx (0.33 #185, 0.25 #81, 0.17 #2485), 07s9rl0 (0.31 #6536, 0.27 #7701, 0.25 #4628), 01jfsb (0.28 #7173, 0.28 #3986, 0.27 #7089), 03k9fj (0.25 #19, 0.19 #1380, 0.14 #332), 0h9qh (0.25 #66, 0.13 #1110, 0.06 #1427), 07yjb (0.22 #493, 0.12 #2060, 0.11 #3532), 02kdv5l (0.20 #8331, 0.18 #7803, 0.18 #4517), 09blyk (0.20 #3292, 0.19 #4461, 0.18 #570), 01hmnh (0.19 #2222, 0.19 #1910, 0.19 #2957) >> Best rule #1148 for best value: >> intensional similarity = 8 >> extensional distance = 13 >> proper extension: 04sh80; >> query: (?x3377, ?x571) <- genre(?x3377, ?x571), ?x571 = 03npn, language(?x3377, ?x254), film(?x574, ?x3377), produced_by(?x3377, ?x4314), currency(?x3377, ?x170), film(?x4314, ?x485), film_release_distribution_medium(?x3377, ?x81) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2222 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 24 *> proper extension: 0fq7dv_; *> query: (?x3377, 01hmnh) <- film_release_region(?x3377, ?x3683), film_release_region(?x3377, ?x1499), film_release_region(?x3377, ?x1229), film_release_region(?x3377, ?x151), ?x3683 = 0161c, ?x1229 = 059j2, ?x1499 = 01znc_, film(?x6772, ?x3377), film_release_region(?x1602, ?x151), film_release_region(?x633, ?x151), ?x1602 = 0gxtknx, ?x633 = 0c40vxk *> conf = 0.19 ranks of expected_values: 10 EVAL 0gj8nq2 titles! 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 95.000 87.000 0.333 http://example.org/media_common/netflix_genre/titles #21928-02frhbc PRED entity: 02frhbc PRED relation: mode_of_transportation PRED expected values: 07jdr 025t3bg => 208 concepts (208 used for prediction) PRED predicted values (max 10 best out of 4): 07jdr (0.84 #45, 0.82 #33, 0.79 #65), 025t3bg (0.83 #98, 0.80 #110, 0.79 #118), 06d_3 (0.04 #176, 0.03 #60, 0.03 #84), 0k4j (0.04 #175, 0.03 #59, 0.02 #139) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0fn2g; >> query: (?x9605, 07jdr) <- country(?x9605, ?x94), place_of_birth(?x1400, ?x9605), location_of_ceremony(?x566, ?x9605), month(?x9605, ?x1459) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02frhbc mode_of_transportation 025t3bg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 208.000 208.000 0.844 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 02frhbc mode_of_transportation 07jdr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 208.000 208.000 0.844 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #21927-0bmhvpr PRED entity: 0bmhvpr PRED relation: nominated_for PRED expected values: 0gg5qcw => 78 concepts (32 used for prediction) PRED predicted values (max 10 best out of 69): 0fpmrm3 (0.11 #835, 0.04 #1339, 0.02 #3107), 0ddf2bm (0.11 #999, 0.04 #1503, 0.02 #2008), 03qnc6q (0.11 #836, 0.04 #1340, 0.02 #1845), 0g9lm2 (0.04 #1892, 0.01 #2145, 0.01 #2650), 0h1x5f (0.04 #2002, 0.01 #2255, 0.01 #2760), 01s9vc (0.02 #1754, 0.02 #5037), 05css_ (0.02 #1673, 0.02 #4956), 05cj_j (0.02 #1559, 0.02 #4842), 05dptj (0.02 #1726, 0.02 #1978), 0bpbhm (0.02 #1632, 0.02 #1884) >> Best rule #835 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0dgst_d; >> query: (?x3784, 0fpmrm3) <- film_release_region(?x3784, ?x2267), film_release_region(?x3784, ?x1061), film_release_region(?x3784, ?x344), ?x344 = 04gzd, ?x1061 = 04v3q, ?x2267 = 03rj0 >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bmhvpr nominated_for 0gg5qcw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 32.000 0.111 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #21926-0gh4g0 PRED entity: 0gh4g0 PRED relation: artist PRED expected values: 07r1_ => 63 concepts (35 used for prediction) PRED predicted values (max 10 best out of 924): 01w60_p (0.67 #3446, 0.50 #949, 0.44 #5111), 016szr (0.67 #3672, 0.50 #1175, 0.44 #5337), 0277c3 (0.50 #3761, 0.50 #1264, 0.33 #5426), 0153nq (0.50 #4160, 0.50 #1663, 0.33 #5825), 033s6 (0.50 #1514, 0.44 #5676, 0.36 #6509), 01wg25j (0.50 #1450, 0.33 #5612, 0.33 #3947), 0qf3p (0.50 #984, 0.33 #3481, 0.33 #152), 06gcn (0.50 #1383, 0.33 #3880, 0.33 #551), 01kph_c (0.50 #1169, 0.33 #3666, 0.33 #337), 01t110 (0.50 #1290, 0.33 #3787, 0.33 #458) >> Best rule #3446 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 01dtcb; 01trtc; >> query: (?x1693, 01w60_p) <- artist(?x1693, ?x7874), artist(?x1693, ?x7125), nominated_for(?x7874, ?x4331), artists(?x3753, ?x7874), artist(?x8738, ?x7125), instrumentalists(?x316, ?x7874), ?x3753 = 01_bkd, ?x316 = 05r5c >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2165 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 033hn8; 0n85g; *> query: (?x1693, 07r1_) <- artist(?x1693, ?x7874), artist(?x1693, ?x7125), nominated_for(?x7874, ?x4331), ?x7125 = 01jcxwp, award(?x7874, ?x247), artists(?x302, ?x7874), ?x247 = 02wh75, instrumentalists(?x316, ?x7874) *> conf = 0.25 ranks of expected_values: 137 EVAL 0gh4g0 artist 07r1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 63.000 35.000 0.667 http://example.org/music/record_label/artist #21925-02mg7n PRED entity: 02mg7n PRED relation: contains! PRED expected values: 02jx1 => 191 concepts (151 used for prediction) PRED predicted values (max 10 best out of 323): 09c7w0 (0.86 #128051, 0.86 #51041, 0.85 #7163), 02jx1 (0.78 #1876, 0.74 #5456, 0.69 #15303), 07ssc (0.75 #31339, 0.74 #77925, 0.57 #34922), 059rby (0.44 #60905, 0.35 #80600, 0.31 #97614), 01n7q (0.42 #94984, 0.27 #120957, 0.25 #121853), 05tbn (0.26 #60212, 0.15 #30666, 0.12 #7383), 0hzlz (0.21 #22427, 0.12 #129839, 0.03 #20636), 02_286 (0.19 #37651, 0.18 #39442, 0.17 #20631), 0978r (0.19 #15423, 0.16 #11842, 0.15 #13632), 0d060g (0.18 #8964, 0.14 #25080, 0.13 #26873) >> Best rule #128051 for best value: >> intensional similarity = 5 >> extensional distance = 786 >> proper extension: 0rs6x; 0rh6k; 05kkh; 0k049; 01fq7; 06_kh; 059rby; 0s3y5; 03v1s; 0plyy; ... >> query: (?x11306, 09c7w0) <- category(?x11306, ?x134), ?x134 = 08mbj5d, contains(?x362, ?x11306), place_of_death(?x587, ?x362), place_of_birth(?x361, ?x362) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1876 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0vkl2; 01g4yw; *> query: (?x11306, 02jx1) <- category(?x11306, ?x134), currency(?x11306, ?x1099), citytown(?x11306, ?x362), ?x1099 = 01nv4h, ?x362 = 04jpl *> conf = 0.78 ranks of expected_values: 2 EVAL 02mg7n contains! 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 191.000 151.000 0.862 http://example.org/location/location/contains #21924-01gwk3 PRED entity: 01gwk3 PRED relation: film! PRED expected values: 086k8 => 159 concepts (141 used for prediction) PRED predicted values (max 10 best out of 118): 086k8 (0.40 #150, 0.33 #2, 0.27 #1927), 03xq0f (0.33 #448, 0.33 #78, 0.28 #1781), 016tt2 (0.33 #965, 0.27 #299, 0.23 #595), 04mkft (0.33 #35, 0.20 #183, 0.12 #405), 05qd_ (0.29 #1192, 0.29 #1340, 0.26 #822), 03rwz3 (0.28 #5188, 0.22 #4446, 0.11 #931), 032dg7 (0.28 #5188, 0.22 #4446, 0.07 #5189), 06jntd (0.22 #474, 0.07 #2474, 0.07 #326), 030_1m (0.20 #235, 0.19 #383, 0.08 #753), 016tw3 (0.20 #158, 0.19 #1787, 0.18 #2305) >> Best rule #150 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 02xs6_; >> query: (?x6429, 086k8) <- genre(?x6429, ?x225), country(?x6429, ?x94), produced_by(?x6429, ?x519), film_crew_role(?x6429, ?x1078), ?x1078 = 089fss, prequel(?x6429, ?x324) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gwk3 film! 086k8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 141.000 0.400 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #21923-01j59b0 PRED entity: 01j59b0 PRED relation: group! PRED expected values: 02hnl => 78 concepts (70 used for prediction) PRED predicted values (max 10 best out of 122): 02hnl (0.77 #1628, 0.77 #2815, 0.77 #2899), 018vs (0.71 #1277, 0.68 #853, 0.68 #1529), 02snj9 (0.50 #223, 0.29 #559, 0.17 #475), 03qjg (0.48 #971, 0.31 #1563, 0.28 #1647), 05r5c (0.36 #597, 0.33 #428, 0.27 #849), 07y_7 (0.30 #929, 0.25 #86, 0.17 #1605), 06ncr (0.29 #541, 0.25 #119, 0.22 #962), 0l14j_ (0.25 #132, 0.22 #975, 0.18 #1651), 013y1f (0.25 #107, 0.17 #950, 0.14 #529), 0jtg0 (0.25 #213, 0.17 #465, 0.10 #2704) >> Best rule #1628 for best value: >> intensional similarity = 9 >> extensional distance = 81 >> proper extension: 01fmz6; 01k_yf; 0123r4; 0838y; 014pg1; 0qmpd; 0pqp3; 014_xj; >> query: (?x5227, 02hnl) <- group(?x1166, ?x5227), group(?x315, ?x5227), artists(?x9831, ?x5227), artists(?x7220, ?x5227), artists(?x9831, ?x5126), ?x1166 = 05148p4, parent_genre(?x2439, ?x7220), ?x315 = 0l14md, religion(?x5126, ?x2694) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j59b0 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 70.000 0.771 http://example.org/music/performance_role/regular_performances./music/group_membership/group #21922-02p8v8 PRED entity: 02p8v8 PRED relation: award_nominee PRED expected values: 016kft => 84 concepts (40 used for prediction) PRED predicted values (max 10 best out of 642): 016kft (0.40 #9032, 0.27 #13716, 0.03 #27768), 01tfck (0.40 #7494, 0.18 #12178, 0.04 #26230), 0mz73 (0.25 #1745, 0.20 #6429, 0.17 #11113), 055c8 (0.25 #716, 0.20 #5400, 0.17 #10084), 03f2_rc (0.25 #104, 0.20 #4788, 0.17 #9472), 0253b6 (0.25 #821, 0.20 #5505, 0.17 #10189), 014g22 (0.25 #3307, 0.16 #26727, 0.07 #15017), 02bkdn (0.25 #2743, 0.11 #26163, 0.04 #28505), 0h1nt (0.25 #2596, 0.03 #26016, 0.02 #58805), 043kzcr (0.25 #2886, 0.03 #26306, 0.01 #59095) >> Best rule #9032 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 016kft; >> query: (?x9686, 016kft) <- award_nominee(?x9686, ?x5460), award_nominee(?x9686, ?x1204), ?x1204 = 02sjf5, ?x5460 = 046m59 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02p8v8 award_nominee 016kft CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 40.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21921-01jbx1 PRED entity: 01jbx1 PRED relation: profession PRED expected values: 0d2ww => 130 concepts (82 used for prediction) PRED predicted values (max 10 best out of 79): 0dxtg (0.85 #4880, 0.84 #5595, 0.83 #3164), 01d_h8 (0.62 #149, 0.56 #3729, 0.54 #1293), 0fj9f (0.43 #479, 0.36 #908, 0.31 #336), 0np9r (0.41 #1593, 0.29 #6602, 0.22 #1020), 02krf9 (0.41 #1312, 0.33 #6751, 0.30 #4320), 0cbd2 (0.38 #293, 0.27 #4731, 0.26 #579), 09jwl (0.35 #10731, 0.35 #6171, 0.34 #7172), 0d2ww (0.35 #10731, 0.02 #2089, 0.01 #2520), 02jknp (0.32 #1295, 0.31 #3731, 0.31 #151), 018gz8 (0.32 #1302, 0.29 #3738, 0.28 #3881) >> Best rule #4880 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 0cj2k3; >> query: (?x3291, 0dxtg) <- profession(?x3291, ?x967), tv_program(?x3291, ?x11033), award(?x3291, ?x4260) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #10731 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 567 *> proper extension: 07c0j; 04qmr; 02qw2xb; 0cbm64; *> query: (?x3291, ?x1032) <- participant(?x9585, ?x3291), location(?x9585, ?x3976), profession(?x9585, ?x1032) *> conf = 0.35 ranks of expected_values: 8 EVAL 01jbx1 profession 0d2ww CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 130.000 82.000 0.848 http://example.org/people/person/profession #21920-0rh6k PRED entity: 0rh6k PRED relation: teams PRED expected values: 0jnl5 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 250): 038zh6 (0.25 #1775, 0.04 #3560, 0.03 #4989), 09glnr (0.20 #1056, 0.12 #1413, 0.11 #2484), 049dzz (0.20 #952, 0.12 #1309, 0.11 #2380), 0264v8r (0.20 #763, 0.12 #1120, 0.11 #2191), 05hyn5 (0.20 #1032, 0.11 #2460), 03b04g (0.20 #975, 0.11 #2403), 02rh_0 (0.20 #950, 0.11 #2378), 0cqt41 (0.20 #386, 0.04 #3600, 0.03 #4314), 0hmtk (0.20 #671, 0.04 #3885, 0.03 #4599), 05g76 (0.20 #391, 0.04 #3605, 0.03 #4319) >> Best rule #1775 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 04jpl; 02jx1; 0g284; 0j5g9; 0694j; 02m77; >> query: (?x108, 038zh6) <- featured_film_locations(?x103, ?x108), teams(?x108, ?x662), state_province_region(?x3228, ?x108) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0rh6k teams 0jnl5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 160.000 0.250 http://example.org/sports/sports_team_location/teams #21919-058kqy PRED entity: 058kqy PRED relation: currency PRED expected values: 09nqf => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.31 #7, 0.30 #10, 0.26 #13) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 03ysmg; 07h5d; 0dbb3; 02drd3; >> query: (?x815, 09nqf) <- location(?x815, ?x1523), award_winner(?x2499, ?x815), written_by(?x814, ?x815) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 058kqy currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.312 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #21918-02rq7nd PRED entity: 02rq7nd PRED relation: country_of_origin PRED expected values: 0chghy => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 60): 09c7w0 (0.92 #196, 0.92 #540, 0.92 #373), 01b8jj (0.85 #82, 0.84 #106, 0.82 #94), 062qg (0.85 #82, 0.84 #106, 0.82 #94), 0chgzm (0.85 #82, 0.84 #106, 0.82 #94), 06y57 (0.85 #82, 0.84 #106, 0.82 #94), 0mgp (0.85 #82, 0.84 #106, 0.82 #94), 07ssc (0.13 #852, 0.13 #481, 0.12 #270), 03_3d (0.13 #852, 0.11 #664, 0.09 #798), 0d060g (0.13 #852, 0.07 #122, 0.05 #610), 02jx1 (0.13 #852, 0.05 #908, 0.04 #933) >> Best rule #196 for best value: >> intensional similarity = 5 >> extensional distance = 63 >> proper extension: 0cwrr; >> query: (?x14197, 09c7w0) <- producer_type(?x14197, ?x632), award(?x14197, ?x14350), honored_for(?x13189, ?x14197), genre(?x14197, ?x53), ceremony(?x3245, ?x13189) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #740 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 212 *> proper extension: 027g8gr; *> query: (?x14197, ?x512) <- languages(?x14197, ?x254), genre(?x14197, ?x53), genre(?x5152, ?x53), genre(?x3430, ?x53), film(?x1739, ?x5152), country(?x3430, ?x512) *> conf = 0.02 ranks of expected_values: 54 EVAL 02rq7nd country_of_origin 0chghy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 105.000 105.000 0.923 http://example.org/tv/tv_program/country_of_origin #21917-039g82 PRED entity: 039g82 PRED relation: religion PRED expected values: 0c8wxp => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 13): 0c8wxp (0.16 #231, 0.16 #6, 0.15 #51), 03_gx (0.05 #2264, 0.05 #1229, 0.05 #284), 0kpl (0.05 #235, 0.05 #2260, 0.05 #2890), 03j6c (0.02 #1596, 0.02 #3441, 0.02 #4341), 092bf5 (0.02 #556, 0.02 #1006, 0.02 #331), 0kq2 (0.02 #2268, 0.02 #783, 0.02 #648), 01lp8 (0.02 #1171, 0.02 #811, 0.01 #2701), 0n2g (0.02 #2263, 0.01 #2893, 0.01 #3883), 06nzl (0.02 #15, 0.01 #1005, 0.01 #780), 0flw86 (0.01 #3647, 0.01 #4187, 0.01 #4322) >> Best rule #231 for best value: >> intensional similarity = 3 >> extensional distance = 477 >> proper extension: 02wrhj; 02lq10; 03pmzt; 0k8y7; 04205z; 01vzxmq; 01j5sd; 0427y; 0428bc; 02j4sk; ... >> query: (?x1784, 0c8wxp) <- location(?x1784, ?x1629), award_winner(?x873, ?x1784), film(?x1784, ?x1769) >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039g82 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.163 http://example.org/people/person/religion #21916-05q54f5 PRED entity: 05q54f5 PRED relation: film_crew_role PRED expected values: 01vx2h => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 35): 09zzb8 (0.76 #223, 0.75 #974, 0.74 #1579), 09vw2b7 (0.75 #229, 0.71 #416, 0.68 #1396), 01vx2h (0.55 #234, 0.39 #421, 0.38 #1401), 0dxtw (0.50 #233, 0.44 #420, 0.40 #984), 01pvkk (0.31 #13, 0.31 #235, 0.29 #422), 02ynfr (0.23 #239, 0.20 #426, 0.18 #1406), 02rh1dz (0.22 #232, 0.14 #419, 0.13 #1399), 0d2b38 (0.16 #249, 0.12 #27, 0.11 #1416), 0215hd (0.15 #20, 0.14 #1598, 0.14 #131), 02vs3x5 (0.15 #25, 0.10 #136, 0.10 #2755) >> Best rule #223 for best value: >> intensional similarity = 4 >> extensional distance = 252 >> proper extension: 03_wm6; >> query: (?x2892, 09zzb8) <- genre(?x2892, ?x225), film_crew_role(?x2892, ?x468), ?x468 = 02r96rf, ?x225 = 02kdv5l >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #234 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 252 *> proper extension: 03_wm6; *> query: (?x2892, 01vx2h) <- genre(?x2892, ?x225), film_crew_role(?x2892, ?x468), ?x468 = 02r96rf, ?x225 = 02kdv5l *> conf = 0.55 ranks of expected_values: 3 EVAL 05q54f5 film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 92.000 0.760 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21915-09th87 PRED entity: 09th87 PRED relation: school PRED expected values: 01j_cy 01jt2w 01yqqv => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 629): 065y4w7 (0.70 #1201, 0.60 #742, 0.57 #964), 0lyjf (0.50 #1304, 0.50 #1243, 0.38 #1123), 05krk (0.50 #1196, 0.38 #835, 0.38 #1076), 01vs5c (0.50 #1251, 0.38 #1131, 0.29 #1014), 06pwq (0.47 #1318, 0.38 #835, 0.33 #840), 0j_sncb (0.43 #983, 0.40 #1220, 0.40 #877), 015q1n (0.43 #1021, 0.40 #799, 0.38 #835), 0pspl (0.43 #989, 0.40 #767, 0.38 #835), 07vyf (0.40 #1356, 0.40 #777, 0.38 #835), 05x_5 (0.40 #805, 0.38 #835, 0.36 #1299) >> Best rule #1201 for best value: >> intensional similarity = 41 >> extensional distance = 8 >> proper extension: 02qw1zx; 09l0x9; >> query: (?x8542, 065y4w7) <- school(?x8542, ?x6856), school(?x8542, ?x4955), draft(?x2820, ?x8542), school(?x2820, ?x12732), school(?x2820, ?x12485), school(?x2820, ?x8706), school(?x2820, ?x4904), school(?x2820, ?x4209), school(?x2820, ?x3696), school(?x2820, ?x1681), major_field_of_study(?x6856, ?x4100), major_field_of_study(?x6856, ?x2606), ?x12485 = 0225bv, ?x2606 = 062z7, fraternities_and_sororities(?x6856, ?x3697), currency(?x12732, ?x170), student(?x1681, ?x1580), category(?x12732, ?x134), institution(?x620, ?x4955), ?x620 = 07s6fsf, major_field_of_study(?x1681, ?x1682), major_field_of_study(?x4955, ?x373), ?x1682 = 02ky346, student(?x4955, ?x123), school(?x1632, ?x6856), contains(?x760, ?x6856), citytown(?x8706, ?x4419), school(?x465, ?x1681), company(?x3131, ?x4955), ?x4904 = 0lyjf, contains(?x1106, ?x1681), organization(?x4955, ?x5487), institution(?x4321, ?x4209), school_type(?x4209, ?x3092), colors(?x3696, ?x663), team(?x1579, ?x2820), organization(?x5510, ?x4955), contains(?x5525, ?x3696), ?x4100 = 01lj9, disciplines_or_subjects(?x277, ?x373), list(?x4955, ?x2197) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1188 for first EXPECTED value: *> intensional similarity = 52 *> extensional distance = 6 *> proper extension: 0g3zpp; *> query: (?x8542, ?x6973) <- school(?x8542, ?x6856), school(?x8542, ?x4955), draft(?x2820, ?x8542), school(?x2820, ?x13141), school(?x2820, ?x13101), school(?x2820, ?x12732), school(?x2820, ?x12485), school(?x2820, ?x8706), school(?x2820, ?x6973), school(?x2820, ?x2760), school(?x2820, ?x1681), school(?x2820, ?x581), major_field_of_study(?x6856, ?x2606), ?x12485 = 0225bv, ?x2606 = 062z7, contains(?x94, ?x13101), fraternities_and_sororities(?x6856, ?x3697), currency(?x12732, ?x170), student(?x1681, ?x1580), category(?x12732, ?x134), institution(?x3437, ?x4955), institution(?x620, ?x4955), ?x620 = 07s6fsf, major_field_of_study(?x1681, ?x2601), major_field_of_study(?x1681, ?x1682), major_field_of_study(?x4955, ?x5614), ?x1682 = 02ky346, student(?x4955, ?x8439), student(?x4955, ?x7437), school(?x1632, ?x6856), contains(?x760, ?x6856), citytown(?x8706, ?x4419), ?x2601 = 04x_3, ?x3437 = 02_xgp2, organization(?x346, ?x13141), school(?x465, ?x1681), school(?x4487, ?x581), list(?x581, ?x2197), origin(?x7437, ?x1860), colors(?x6973, ?x4557), state_province_region(?x2760, ?x2256), major_field_of_study(?x581, ?x9111), profession(?x7437, ?x131), ?x4487 = 01ync, ?x9111 = 04sh3, company(?x3484, ?x6973), artist(?x8721, ?x7437), award_winner(?x3446, ?x8439), profession(?x8439, ?x1032), student(?x5614, ?x396), colors(?x6856, ?x663), films(?x5614, ?x308) *> conf = 0.29 ranks of expected_values: 66, 69, 94 EVAL 09th87 school 01yqqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 14.000 14.000 0.700 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 09th87 school 01jt2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 14.000 14.000 0.700 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 09th87 school 01j_cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 14.000 14.000 0.700 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #21914-0bdt8 PRED entity: 0bdt8 PRED relation: place_of_birth PRED expected values: 06mxs => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 107): 01531 (0.12 #105, 0.03 #12783, 0.02 #18417), 018x0q (0.12 #609), 0nqv1 (0.12 #399), 01_d4 (0.12 #2884, 0.08 #4998, 0.07 #6406), 02_286 (0.11 #4246, 0.11 #5655, 0.10 #6359), 04jpl (0.11 #712, 0.08 #2818, 0.06 #45777), 05qtj (0.11 #871, 0.03 #2280, 0.02 #10028), 030qb3t (0.07 #9915, 0.06 #21183, 0.06 #35971), 0cr3d (0.07 #2912, 0.05 #2207, 0.05 #4321), 018djs (0.05 #1369, 0.02 #4187) >> Best rule #105 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02lq10; >> query: (?x6440, 01531) <- film(?x6440, ?x2345), ?x2345 = 0c_j9x, award_winner(?x3029, ?x6440) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bdt8 place_of_birth 06mxs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 134.000 0.125 http://example.org/people/person/place_of_birth #21913-0_92w PRED entity: 0_92w PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 24): 0ch6mp2 (0.70 #1382, 0.56 #199, 0.52 #351), 09zzb8 (0.69 #1374, 0.58 #343, 0.55 #191), 02r96rf (0.61 #194, 0.61 #1377, 0.46 #346), 09vw2b7 (0.57 #1381, 0.42 #655, 0.42 #1650), 0dxtw (0.34 #1386, 0.26 #660, 0.26 #355), 01vx2h (0.29 #1387, 0.24 #661, 0.21 #1580), 02ynfr (0.14 #1391, 0.11 #208, 0.10 #1660), 0215hd (0.11 #1394, 0.09 #668, 0.09 #1587), 02rh1dz (0.10 #202, 0.09 #659, 0.09 #1385), 089g0h (0.09 #1395, 0.08 #821, 0.07 #1588) >> Best rule #1382 for best value: >> intensional similarity = 3 >> extensional distance = 1170 >> proper extension: 03_wm6; >> query: (?x1118, 0ch6mp2) <- genre(?x1118, ?x53), language(?x1118, ?x254), film_crew_role(?x1118, ?x2178) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_92w film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.703 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21912-0k6nt PRED entity: 0k6nt PRED relation: time_zones PRED expected values: 02llzg => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 12): 02llzg (0.63 #2305, 0.58 #2173, 0.55 #147), 02hcv8 (0.37 #913, 0.35 #2294, 0.30 #1070), 02fqwt (0.23 #1198, 0.21 #911, 0.20 #1640), 03plfd (0.23 #153, 0.20 #62, 0.17 #335), 042g7t (0.17 #89, 0.16 #2488, 0.13 #50), 03bdv (0.16 #2488, 0.12 #318, 0.11 #656), 052vwh (0.16 #2488, 0.06 #363, 0.05 #389), 02lcqs (0.15 #2296, 0.14 #1644, 0.14 #1371), 02hczc (0.12 #1160, 0.11 #1199, 0.10 #1641), 0gsrz4 (0.12 #580, 0.07 #541, 0.07 #1140) >> Best rule #2305 for best value: >> intensional similarity = 2 >> extensional distance = 579 >> proper extension: 0l2mg; 0mvxt; >> query: (?x985, ?x2864) <- adjoins(?x1264, ?x985), time_zones(?x1264, ?x2864) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k6nt time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.630 http://example.org/location/location/time_zones #21911-01kyln PRED entity: 01kyln PRED relation: category PRED expected values: 08mbj5d => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.76 #1, 0.76 #2, 0.76 #4) >> Best rule #1 for best value: >> intensional similarity = 2 >> extensional distance = 891 >> proper extension: 0rs6x; 015zyd; 0rh6k; 08815; 05kkh; 0k049; 05zjtn4; 01fq7; 06_kh; 01rtm4; ... >> query: (?x14242, 08mbj5d) <- contains(?x94, ?x14242), ?x94 = 09c7w0 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kyln category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.760 http://example.org/common/topic/webpage./common/webpage/category #21910-09fn1w PRED entity: 09fn1w PRED relation: genre PRED expected values: 02kdv5l 03k9fj 02l7c8 => 69 concepts (53 used for prediction) PRED predicted values (max 10 best out of 85): 02kdv5l (0.75 #2472, 0.34 #4707, 0.29 #354), 02l7c8 (0.67 #16, 0.50 #133, 0.35 #838), 03rk0 (0.58 #587, 0.54 #352, 0.54 #2823), 05p553 (0.50 #121, 0.50 #4, 0.35 #3299), 01jfsb (0.43 #4717, 0.39 #2482, 0.30 #4011), 04xvlr (0.39 #235, 0.38 #470, 0.34 #353), 03k9fj (0.39 #363, 0.38 #2481, 0.33 #245), 06l3bl (0.36 #270, 0.29 #388, 0.09 #505), 017fp (0.36 #484, 0.15 #249, 0.12 #367), 082gq (0.33 #263, 0.29 #381, 0.14 #851) >> Best rule #2472 for best value: >> intensional similarity = 3 >> extensional distance = 607 >> proper extension: 04svwx; >> query: (?x4444, 02kdv5l) <- genre(?x4444, ?x1626), genre(?x8465, ?x1626), ?x8465 = 05dfy_ >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 7 EVAL 09fn1w genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 53.000 0.754 http://example.org/film/film/genre EVAL 09fn1w genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 69.000 53.000 0.754 http://example.org/film/film/genre EVAL 09fn1w genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 53.000 0.754 http://example.org/film/film/genre #21909-03tw2s PRED entity: 03tw2s PRED relation: major_field_of_study PRED expected values: 01mkq 02_7t => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 116): 01mkq (0.70 #604, 0.62 #3674, 0.56 #722), 03g3w (0.63 #4987, 0.43 #616, 0.38 #2859), 04rjg (0.61 #609, 0.48 #727, 0.43 #3679), 01tbp (0.48 #647, 0.37 #765, 0.35 #411), 06ms6 (0.44 #4268, 0.35 #606, 0.25 #16), 041y2 (0.41 #784, 0.39 #666, 0.34 #1020), 0g4gr (0.41 #737, 0.29 #383, 0.26 #973), 05qjt (0.39 #598, 0.38 #2132, 0.33 #3786), 01540 (0.39 #648, 0.31 #3718, 0.28 #3836), 02_7t (0.36 #1478, 0.35 #416, 0.35 #652) >> Best rule #604 for best value: >> intensional similarity = 6 >> extensional distance = 21 >> proper extension: 01f1r4; 02bqy; 02hwww; >> query: (?x6814, 01mkq) <- major_field_of_study(?x6814, ?x4100), major_field_of_study(?x6814, ?x1154), category(?x6814, ?x134), citytown(?x6814, ?x8157), ?x1154 = 02lp1, ?x4100 = 01lj9 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 03tw2s major_field_of_study 02_7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 199.000 199.000 0.696 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03tw2s major_field_of_study 01mkq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.696 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #21908-03q45x PRED entity: 03q45x PRED relation: award PRED expected values: 0c422z4 09qs08 03ccq3s 0cqhmg => 92 concepts (84 used for prediction) PRED predicted values (max 10 best out of 257): 0c422z4 (0.72 #28622, 0.69 #7658, 0.69 #22574), 0cqhmg (0.55 #1167, 0.12 #27412, 0.11 #2376), 0cqhk0 (0.45 #843, 0.36 #2858, 0.24 #6485), 0fbtbt (0.39 #2246, 0.34 #9501, 0.32 #9098), 03ccq3s (0.34 #5839, 0.33 #3421, 0.32 #3018), 0cjcbg (0.33 #364, 0.17 #767, 0.08 #1976), 09sb52 (0.32 #10521, 0.28 #14954, 0.25 #15357), 0ck27z (0.32 #15408, 0.30 #15005, 0.25 #13796), 09qrn4 (0.25 #3059, 0.14 #4268, 0.11 #3462), 0gqz2 (0.24 #12978, 0.06 #2095, 0.05 #4110) >> Best rule #28622 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x7795, ?x2597) <- award_winner(?x2597, ?x7795), award(?x123, ?x2597) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 28 EVAL 03q45x award 0cqhmg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 84.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03q45x award 03ccq3s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 84.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03q45x award 09qs08 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 92.000 84.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03q45x award 0c422z4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 84.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21907-0b60sq PRED entity: 0b60sq PRED relation: film! PRED expected values: 07k2x => 100 concepts (92 used for prediction) PRED predicted values (max 10 best out of 59): 09b3v (0.50 #4902, 0.43 #1608, 0.41 #2122), 07k2x (0.38 #625, 0.33 #41, 0.29 #918), 017s11 (0.31 #733, 0.25 #806, 0.21 #953), 05qd_ (0.26 #959, 0.20 #1032, 0.19 #739), 016tw3 (0.25 #814, 0.25 #741, 0.17 #4839), 086k8 (0.22 #2050, 0.20 #1171, 0.20 #1098), 016tt2 (0.19 #1319, 0.15 #2564, 0.14 #2052), 03xq0f (0.18 #1101, 0.17 #1466, 0.16 #955), 054g1r (0.16 #1130, 0.14 #691, 0.14 #326), 0g1rw (0.15 #592, 0.14 #300, 0.12 #885) >> Best rule #4902 for best value: >> intensional similarity = 4 >> extensional distance = 695 >> proper extension: 07kb7vh; >> query: (?x596, ?x3920) <- currency(?x596, ?x12281), production_companies(?x596, ?x3920), country(?x596, ?x252), film(?x2156, ?x596) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #625 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 026q3s3; 05pyrb; 07ghv5; 0bh72t; 0dd6bf; 05dfy_; 05t0zfv; 0564x; *> query: (?x596, 07k2x) <- film_release_region(?x596, ?x94), actor(?x596, ?x1607), film(?x296, ?x596), film(?x2156, ?x596), genre(?x596, ?x225) *> conf = 0.38 ranks of expected_values: 2 EVAL 0b60sq film! 07k2x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 92.000 0.497 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #21906-0sxgv PRED entity: 0sxgv PRED relation: films! PRED expected values: 0fzyg => 70 concepts (19 used for prediction) PRED predicted values (max 10 best out of 44): 01cgz (0.43 #19, 0.03 #639, 0.02 #1419), 05489 (0.14 #52, 0.05 #826, 0.04 #1296), 081pw (0.08 #777, 0.08 #1558, 0.07 #158), 0fx2s (0.07 #228, 0.07 #847, 0.05 #1317), 0bxg3 (0.07 #235, 0.03 #390, 0.01 #1635), 06d4h (0.07 #663, 0.07 #1287, 0.06 #1443), 0fzyg (0.05 #674, 0.05 #2076, 0.05 #1609), 01vq3 (0.04 #1596, 0.04 #661, 0.04 #1285), 07s2s (0.04 #1653, 0.04 #718, 0.04 #2120), 0bq3x (0.04 #1585, 0.04 #2052, 0.04 #650) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0140g4; 0h6r5; 0q9sg; 06cm5; 0cf08; >> query: (?x6030, 01cgz) <- film(?x366, ?x6030), nominated_for(?x4495, ?x6030), ?x4495 = 04t38b, country(?x6030, ?x94) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #674 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 260 *> proper extension: 05f67hw; *> query: (?x6030, 0fzyg) <- produced_by(?x6030, ?x4495), films(?x11089, ?x6030), language(?x6030, ?x254) *> conf = 0.05 ranks of expected_values: 7 EVAL 0sxgv films! 0fzyg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 70.000 19.000 0.429 http://example.org/film/film_subject/films #21905-09s1f PRED entity: 09s1f PRED relation: major_field_of_study! PRED expected values: 07s6fsf 016t_3 => 53 concepts (44 used for prediction) PRED predicted values (max 10 best out of 17): 016t_3 (0.86 #153, 0.83 #120, 0.76 #188), 04zx3q1 (0.72 #119, 0.64 #152, 0.53 #136), 0bkj86 (0.67 #124, 0.64 #157, 0.64 #107), 0bjrnt (0.50 #238, 0.45 #220, 0.42 #139), 013zdg (0.50 #238, 0.45 #220, 0.38 #49), 027f2w (0.50 #238, 0.45 #220, 0.38 #49), 022h5x (0.50 #238, 0.45 #220, 0.38 #49), 01ysy9 (0.50 #238, 0.38 #49, 0.36 #134), 01rr_d (0.50 #238, 0.38 #49, 0.36 #134), 07s6fsf (0.45 #220, 0.38 #49, 0.36 #134) >> Best rule #153 for best value: >> intensional similarity = 9 >> extensional distance = 20 >> proper extension: 02h40lc; 05qjt; 02ky346; 06ms6; 04rjg; 01jzxy; 04x_3; 062z7; 05qfh; 01lj9; ... >> query: (?x12158, 016t_3) <- major_field_of_study(?x12157, ?x12158), major_field_of_study(?x5737, ?x12158), major_field_of_study(?x5288, ?x12158), student(?x12158, ?x8375), institution(?x620, ?x5737), ?x5288 = 02zd460, major_field_of_study(?x254, ?x12158), major_field_of_study(?x865, ?x12158), currency(?x12157, ?x170) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 09s1f major_field_of_study! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 44.000 0.864 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 09s1f major_field_of_study! 07s6fsf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 53.000 44.000 0.864 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #21904-02r6c_ PRED entity: 02r6c_ PRED relation: award PRED expected values: 04dn09n 02x1dht => 123 concepts (100 used for prediction) PRED predicted values (max 10 best out of 317): 02wkmx (0.70 #17125, 0.69 #27886, 0.69 #30278), 02w_6xj (0.70 #17125, 0.69 #27886, 0.69 #30278), 04dn09n (0.46 #2432, 0.42 #2033, 0.37 #4823), 0gr4k (0.38 #2421, 0.36 #9988, 0.33 #2022), 0gq9h (0.36 #2065, 0.36 #1268, 0.36 #2464), 0f_nbyh (0.33 #8, 0.14 #406, 0.14 #1202), 02x4wr9 (0.32 #1324, 0.22 #130, 0.16 #2121), 09sb52 (0.31 #17164, 0.26 #14775, 0.25 #17562), 03hkv_r (0.26 #9971, 0.24 #2404, 0.22 #14), 0f4x7 (0.24 #13571, 0.18 #15163, 0.11 #14765) >> Best rule #17125 for best value: >> intensional similarity = 4 >> extensional distance = 829 >> proper extension: 03zqc1; 03f1zdw; 027pdrh; 02j9lm; 028qdb; 01z7_f; 01l03w2; 02nwxc; 025vldk; 08mhyd; ... >> query: (?x8812, ?x372) <- award(?x8812, ?x68), type_of_union(?x8812, ?x566), award_winner(?x372, ?x8812), award_winner(?x1998, ?x8812) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #2432 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 05183k; 085pr; 05jjl; 06s1qy; *> query: (?x8812, 04dn09n) <- award(?x8812, ?x1862), ?x1862 = 0gr51, award_winner(?x372, ?x8812), written_by(?x2121, ?x8812) *> conf = 0.46 ranks of expected_values: 3, 11 EVAL 02r6c_ award 02x1dht CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 123.000 100.000 0.702 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02r6c_ award 04dn09n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 100.000 0.702 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21903-0c9c0 PRED entity: 0c9c0 PRED relation: type_of_union PRED expected values: 04ztj => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.81 #25, 0.78 #41, 0.78 #21), 01g63y (0.17 #94, 0.17 #194, 0.16 #114) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 136 >> proper extension: 016qtt; 0jf1b; 012t1; 03jldb; 01gzm2; 04y8r; 081nh; 0184dt; 04g865; 0p51w; ... >> query: (?x2790, 04ztj) <- people(?x743, ?x2790), award(?x2790, ?x601), produced_by(?x7311, ?x2790) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c9c0 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.812 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21902-0cc5mcj PRED entity: 0cc5mcj PRED relation: production_companies PRED expected values: 04cygb3 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 62): 016tw3 (0.35 #1045, 0.35 #1846, 0.33 #2409), 03xq0f (0.35 #1045, 0.35 #1846, 0.33 #2409), 086k8 (0.13 #2492, 0.12 #484, 0.12 #2), 016tt2 (0.12 #486, 0.12 #808, 0.12 #1209), 0c_j5d (0.12 #166, 0.08 #86, 0.08 #488), 0kx4m (0.12 #572, 0.06 #1855, 0.05 #2499), 05qd_ (0.12 #733, 0.12 #2500, 0.11 #5721), 030_1_ (0.10 #579, 0.09 #16, 0.09 #2666), 0c41qv (0.10 #213, 0.05 #1258, 0.04 #616), 01795t (0.10 #2511, 0.07 #905, 0.07 #3232) >> Best rule #1045 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 0c57yj; 04pmnt; 02tktw; 08984j; >> query: (?x2441, ?x609) <- crewmember(?x2441, ?x1983), written_by(?x2441, ?x2442), film(?x4731, ?x2441), film(?x609, ?x2441) >> conf = 0.35 => this is the best rule for 2 predicted values *> Best rule #45 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 0b2qtl; *> query: (?x2441, 04cygb3) <- genre(?x2441, ?x3515), executive_produced_by(?x2441, ?x2135), film(?x1018, ?x2441), ?x3515 = 082gq *> conf = 0.03 ranks of expected_values: 41 EVAL 0cc5mcj production_companies 04cygb3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 97.000 97.000 0.351 http://example.org/film/film/production_companies #21901-03ynwqj PRED entity: 03ynwqj PRED relation: nominated_for! PRED expected values: 06pcz0 => 73 concepts (26 used for prediction) PRED predicted values (max 10 best out of 199): 01gb54 (0.40 #2340, 0.10 #18716, 0.10 #21056), 06q5t7 (0.31 #58500, 0.31 #23397, 0.30 #42119), 07ymr5 (0.31 #58500, 0.31 #23397, 0.30 #42119), 03c5bz (0.31 #58500, 0.31 #23397, 0.29 #25740), 01twdk (0.30 #42119, 0.28 #39778, 0.27 #37436), 078jnn (0.30 #42119, 0.28 #39778, 0.27 #37436), 03f0r5w (0.29 #25740, 0.25 #53820, 0.24 #42121), 0j1yf (0.25 #381, 0.04 #23399, 0.04 #58501), 017s11 (0.25 #100, 0.03 #7121, 0.02 #14137), 0147dk (0.25 #2431, 0.02 #60841) >> Best rule #2340 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 07s846j; 0ds5_72; >> query: (?x8625, ?x4564) <- film(?x8146, ?x8625), film(?x4564, ?x8625), ?x8146 = 078jnn, nominated_for(?x1336, ?x8625) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #53820 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1133 *> proper extension: 0gc_c_; 09r94m; 0280061; *> query: (?x8625, ?x1736) <- film(?x1736, ?x8625), film(?x4564, ?x8625), film_release_distribution_medium(?x8625, ?x81), award_nominee(?x237, ?x1736) *> conf = 0.25 ranks of expected_values: 22 EVAL 03ynwqj nominated_for! 06pcz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 73.000 26.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #21900-013tjc PRED entity: 013tjc PRED relation: people! PRED expected values: 041rx => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 49): 041rx (0.30 #390, 0.28 #1083, 0.26 #852), 02ctzb (0.26 #478, 0.09 #1017, 0.08 #1171), 033tf_ (0.23 #778, 0.20 #7, 0.17 #84), 0x67 (0.22 #319, 0.17 #3245, 0.17 #3784), 07bch9 (0.22 #486, 0.13 #309, 0.12 #1795), 063k3h (0.22 #494, 0.11 #725, 0.07 #1033), 01qhm_ (0.20 #6, 0.17 #83, 0.12 #237), 0d7wh (0.17 #94, 0.07 #788, 0.04 #480), 07hwkr (0.13 #309, 0.11 #321, 0.08 #1322), 013xrm (0.13 #309, 0.11 #329, 0.06 #2639) >> Best rule #390 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01lc5; >> query: (?x10905, 041rx) <- influenced_by(?x4066, ?x10905), ?x4066 = 0ph2w, type_of_union(?x10905, ?x566), location(?x10905, ?x4253) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013tjc people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.300 http://example.org/people/ethnicity/people #21899-01vl17 PRED entity: 01vl17 PRED relation: profession PRED expected values: 015h31 => 102 concepts (46 used for prediction) PRED predicted values (max 10 best out of 73): 02hrh1q (0.71 #6206, 0.70 #2459, 0.65 #587), 0cbd2 (0.54 #1590, 0.54 #438, 0.52 #294), 09jwl (0.48 #1168, 0.47 #1312, 0.13 #6211), 03gjzk (0.45 #732, 0.44 #876, 0.41 #1020), 0nbcg (0.43 #1181, 0.41 #1325, 0.12 #1901), 016z4k (0.42 #1156, 0.40 #1300, 0.05 #2740), 0dz3r (0.33 #1298, 0.33 #1154, 0.06 #1874), 0kyk (0.30 #1611, 0.23 #315, 0.21 #459), 039v1 (0.25 #1186, 0.23 #1330, 0.03 #2770), 02krf9 (0.20 #4057, 0.16 #2472, 0.15 #744) >> Best rule #6206 for best value: >> intensional similarity = 4 >> extensional distance = 513 >> proper extension: 01j5ts; 01qscs; 054_mz; 027f7dj; 09pjnd; 02zyy4; 01n4f8; 016sp_; 02ld6x; 03jqw5; ... >> query: (?x8309, 02hrh1q) <- profession(?x8309, ?x319), gender(?x8309, ?x231), ?x319 = 01d_h8, location(?x8309, ?x14602) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #889 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 0f7hc; *> query: (?x8309, 015h31) <- profession(?x8309, ?x319), story_by(?x2153, ?x8309), ?x319 = 01d_h8, nationality(?x8309, ?x252) *> conf = 0.10 ranks of expected_values: 17 EVAL 01vl17 profession 015h31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 102.000 46.000 0.713 http://example.org/people/person/profession #21898-051cc PRED entity: 051cc PRED relation: award_winner! PRED expected values: 05qck => 155 concepts (132 used for prediction) PRED predicted values (max 10 best out of 343): 02sp_v (0.33 #160, 0.25 #590, 0.12 #1450), 02f777 (0.33 #306, 0.25 #736, 0.03 #5036), 05q8pss (0.33 #211, 0.25 #641, 0.02 #6661), 02f75t (0.33 #258, 0.25 #688, 0.01 #29929), 01by1l (0.25 #1402, 0.15 #29783, 0.14 #31933), 025m8y (0.25 #1389, 0.09 #29770, 0.08 #31920), 047byns (0.25 #912, 0.04 #6932, 0.03 #3922), 099vwn (0.25 #1504, 0.03 #4084, 0.03 #4514), 019bnn (0.21 #4137, 0.18 #4567, 0.06 #2417), 0ddd9 (0.14 #4785, 0.10 #6505, 0.08 #7365) >> Best rule #160 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01wgfp6; >> query: (?x8494, 02sp_v) <- profession(?x8494, ?x5786), ?x5786 = 067nv, award_winner(?x594, ?x8494), place_of_birth(?x8494, ?x2277) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6212 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 0157m; 034ls; 0466k4; *> query: (?x8494, 05qck) <- profession(?x8494, ?x353), person(?x2742, ?x8494), profession(?x12920, ?x353), ?x12920 = 079dy *> conf = 0.10 ranks of expected_values: 36 EVAL 051cc award_winner! 05qck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 155.000 132.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #21897-04rzd PRED entity: 04rzd PRED relation: role! PRED expected values: 01vsnff 021bk => 90 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1429): 0bg539 (0.71 #3115, 0.50 #586, 0.44 #5646), 018phr (0.60 #1960, 0.60 #1889, 0.31 #10679), 01mwsnc (0.60 #1249, 0.50 #688, 0.46 #8836), 02jg92 (0.57 #4259, 0.56 #5669, 0.50 #2293), 01kx_81 (0.57 #2831, 0.50 #7047, 0.47 #9577), 05qhnq (0.57 #4395, 0.50 #2429, 0.44 #5805), 0473q (0.57 #3277, 0.50 #748, 0.44 #4963), 08n__5 (0.57 #3242, 0.43 #4363, 0.40 #1556), 01vsnff (0.56 #4822, 0.50 #607, 0.43 #3697), 017g21 (0.50 #750, 0.44 #5810, 0.44 #5249) >> Best rule #3115 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 0l14qv; 02hnl; >> query: (?x1969, 0bg539) <- role(?x922, ?x1969), role(?x615, ?x1969), role(?x315, ?x1969), role(?x1969, ?x1436), ?x922 = 050rj, role(?x367, ?x1969), ?x315 = 0l14md, group(?x1969, ?x1929), instrumentalists(?x1969, ?x1001), performance_role(?x212, ?x1969), ?x615 = 0dwsp, ?x1436 = 0xzly, artist(?x2299, ?x367) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4822 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 7 *> proper extension: 05148p4; *> query: (?x1969, 01vsnff) <- role(?x2923, ?x1969), role(?x2377, ?x1969), role(?x1969, ?x228), instrumentalists(?x1969, ?x4140), instrumentalists(?x1969, ?x3740), group(?x1969, ?x1929), role(?x1969, ?x212), role(?x5543, ?x1969), ?x2377 = 01bns_, profession(?x3740, ?x220), award_winner(?x5543, ?x1089), ?x2923 = 02k856, ?x4140 = 01sb5r *> conf = 0.56 ranks of expected_values: 9, 74 EVAL 04rzd role! 021bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 90.000 55.000 0.714 http://example.org/music/group_member/membership./music/group_membership/role EVAL 04rzd role! 01vsnff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 90.000 55.000 0.714 http://example.org/music/group_member/membership./music/group_membership/role #21896-053tj7 PRED entity: 053tj7 PRED relation: production_companies PRED expected values: 08wjc1 => 145 concepts (135 used for prediction) PRED predicted values (max 10 best out of 128): 03mdt (0.42 #3149, 0.39 #3475, 0.36 #2659), 027jw0c (0.36 #2659, 0.34 #3802, 0.33 #2333), 025tlyv (0.36 #2659, 0.34 #3802, 0.33 #2333), 086k8 (0.33 #405, 0.30 #1852, 0.29 #1449), 046b0s (0.33 #425, 0.25 #987, 0.25 #505), 056ws9 (0.33 #124, 0.14 #1491, 0.12 #1571), 054lpb6 (0.32 #3572, 0.26 #3978, 0.20 #2592), 016tt2 (0.25 #647, 0.20 #1854, 0.18 #2176), 025jfl (0.25 #890, 0.20 #1291, 0.14 #1452), 0381pn (0.25 #643, 0.20 #1206, 0.11 #1850) >> Best rule #3149 for best value: >> intensional similarity = 9 >> extensional distance = 18 >> proper extension: 0bwfwpj; 08hmch; 0872p_c; 0btyf5z; 07x4qr; 06ztvyx; 0407yj_; 0cp0ph6; 05zlld0; 0dzlbx; ... >> query: (?x1315, ?x1104) <- genre(?x1315, ?x1014), film_release_region(?x1315, ?x2316), titles(?x1014, ?x424), film(?x1104, ?x1315), ?x2316 = 06t2t, award_winner(?x1307, ?x1104), award_nominee(?x1104, ?x1039), region(?x1315, ?x252), child(?x5108, ?x1104) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #2278 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 10 *> proper extension: 09m6kg; 01q7h2; *> query: (?x1315, 08wjc1) <- genre(?x1315, ?x1014), ?x1014 = 0jtdp, film(?x1104, ?x1315), film_release_region(?x1315, ?x2316), film_release_region(?x1315, ?x94), produced_by(?x1315, ?x1039), countries_within(?x6956, ?x2316), country(?x766, ?x2316), contains(?x6304, ?x2316), ?x94 = 09c7w0 *> conf = 0.08 ranks of expected_values: 33 EVAL 053tj7 production_companies 08wjc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 145.000 135.000 0.417 http://example.org/film/film/production_companies #21895-05qgd9 PRED entity: 05qgd9 PRED relation: educational_institution! PRED expected values: 05qgd9 => 195 concepts (108 used for prediction) PRED predicted values (max 10 best out of 216): 0g8rj (0.17 #163, 0.16 #3776, 0.16 #53425), 07wrz (0.17 #57, 0.12 #1674, 0.11 #2213), 05qgd9 (0.16 #3776, 0.16 #53425, 0.15 #39388), 07x4c (0.16 #3776, 0.14 #1315, 0.12 #8093), 07wf9 (0.16 #3776, 0.12 #8093, 0.06 #3236), 0d075m (0.16 #3776, 0.12 #8093, 0.06 #3236), 07wbk (0.16 #3776, 0.12 #8093, 0.06 #3236), 09kvv (0.16 #53425, 0.15 #39388, 0.04 #11365), 03ksy (0.16 #53425, 0.15 #39388, 0.04 #10887), 01vc5m (0.16 #53425, 0.15 #39388, 0.04 #10874) >> Best rule #163 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01w5gp; >> query: (?x12026, 0g8rj) <- organization(?x3484, ?x12026), organizations_founded(?x5254, ?x12026), citytown(?x12026, ?x2298), county_seat(?x9460, ?x2298) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #3776 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0g1rw; *> query: (?x12026, ?x1912) <- organization(?x3484, ?x12026), organizations_founded(?x5254, ?x12026), organizations_founded(?x5254, ?x1912), people(?x6260, ?x5254) *> conf = 0.16 ranks of expected_values: 3 EVAL 05qgd9 educational_institution! 05qgd9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 195.000 108.000 0.167 http://example.org/education/educational_institution_campus/educational_institution #21894-01mb87 PRED entity: 01mb87 PRED relation: contains! PRED expected values: 09c7w0 => 103 concepts (71 used for prediction) PRED predicted values (max 10 best out of 353): 09c7w0 (0.73 #6268, 0.63 #48349, 0.63 #15220), 02_286 (0.30 #6307, 0.21 #12572, 0.19 #42), 02jx1 (0.29 #57393, 0.17 #9036, 0.13 #14407), 07ssc (0.27 #8981, 0.20 #57338, 0.19 #14352), 01n7q (0.21 #9027, 0.15 #1867, 0.15 #2762), 04_1l0v (0.16 #7610, 0.16 #8505, 0.12 #33576), 03rk0 (0.13 #9086, 0.06 #25202, 0.05 #18935), 04jpl (0.12 #8971, 0.11 #47474, 0.10 #51056), 05fjf (0.11 #15590, 0.11 #22752, 0.11 #23647), 0cymp (0.11 #289, 0.05 #4764, 0.03 #6554) >> Best rule #6268 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 02cttt; 02kth6; 04sylm; 078bz; 017z88; 02q636; 01hb1t; 0ybkj; 02ccqg; 02607j; ... >> query: (?x12160, 09c7w0) <- category(?x12160, ?x134), ?x134 = 08mbj5d, contains(?x335, ?x12160), ?x335 = 059rby >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mb87 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 71.000 0.734 http://example.org/location/location/contains #21893-02dtg PRED entity: 02dtg PRED relation: featured_film_locations! PRED expected values: 03mnn0 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 715): 047csmy (0.09 #2585, 0.06 #13555, 0.06 #6241), 09fc83 (0.08 #6226, 0.06 #13540, 0.06 #5495), 0g3zrd (0.08 #5280, 0.06 #6011, 0.05 #893), 0bl1_ (0.08 #5457, 0.04 #6188, 0.04 #6919), 0dnkmq (0.07 #10922, 0.07 #7995, 0.06 #2877), 061681 (0.07 #10285, 0.06 #2240, 0.06 #13210), 04gv3db (0.06 #8361, 0.06 #2511, 0.06 #9093), 0btpm6 (0.06 #2737, 0.06 #3468, 0.06 #5662), 072x7s (0.06 #2304, 0.06 #3035, 0.05 #14736), 01lsl (0.06 #2822, 0.06 #6478, 0.06 #5747) >> Best rule #2585 for best value: >> intensional similarity = 2 >> extensional distance = 45 >> proper extension: 0t6hk; 079yb; >> query: (?x479, 047csmy) <- teams(?x479, ?x7643), place_of_death(?x1855, ?x479) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02dtg featured_film_locations! 03mnn0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 145.000 145.000 0.085 http://example.org/film/film/featured_film_locations #21892-08z956 PRED entity: 08z956 PRED relation: profession! PRED expected values: 02508x 01vw37m => 36 concepts (17 used for prediction) PRED predicted values (max 10 best out of 4182): 0jmj (0.72 #8444, 0.60 #22477, 0.50 #30923), 047c9l (0.72 #8444, 0.50 #5869, 0.42 #21109), 022yb4 (0.72 #8444, 0.42 #21109, 0.40 #16887), 0hvb2 (0.72 #8444, 0.42 #21109, 0.40 #16887), 02bkdn (0.72 #8444, 0.42 #21109, 0.40 #16887), 04bd8y (0.72 #8444, 0.42 #21109, 0.40 #16887), 04bcb1 (0.72 #8444, 0.42 #21109, 0.40 #16887), 0335fp (0.72 #8444, 0.42 #21109, 0.40 #16887), 05l4yg (0.72 #8444, 0.42 #21109, 0.40 #16887), 04yqlk (0.72 #8444, 0.42 #21109, 0.40 #16887) >> Best rule #8444 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0kyk; >> query: (?x8709, ?x820) <- profession(?x10919, ?x8709), profession(?x10645, ?x8709), award_nominee(?x1871, ?x10919), award_nominee(?x820, ?x10919), people(?x3591, ?x10919), ?x1871 = 02bkdn, ?x10645 = 0sx5w >> conf = 0.72 => this is the best rule for 10 predicted values *> Best rule #14708 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 09jwl; *> query: (?x8709, 01vw37m) <- profession(?x10919, ?x8709), profession(?x9156, ?x8709), award_nominee(?x1871, ?x10919), people(?x7322, ?x10919), ?x1871 = 02bkdn, ?x7322 = 03bkbh, award(?x9156, ?x435) *> conf = 0.50 ranks of expected_values: 365, 2741 EVAL 08z956 profession! 01vw37m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 36.000 17.000 0.720 http://example.org/people/person/profession EVAL 08z956 profession! 02508x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 36.000 17.000 0.720 http://example.org/people/person/profession #21891-01fh9 PRED entity: 01fh9 PRED relation: profession PRED expected values: 02hrh1q => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 61): 02hrh1q (0.91 #743, 0.91 #11986, 0.91 #305), 01d_h8 (0.77 #152, 0.66 #2635, 0.66 #1174), 03gjzk (0.45 #2643, 0.38 #1182, 0.38 #3519), 0cbd2 (0.30 #153, 0.28 #2928, 0.27 #3512), 018gz8 (0.26 #1184, 0.25 #308, 0.23 #4251), 0kyk (0.21 #465, 0.19 #611, 0.14 #2948), 02krf9 (0.20 #2653, 0.18 #1192, 0.17 #3529), 02hv44_ (0.17 #201, 0.13 #493, 0.12 #639), 01c72t (0.16 #459, 0.14 #605, 0.11 #21), 0nbcg (0.15 #29, 0.13 #1343, 0.13 #6892) >> Best rule #743 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 03h2d4; 062hgx; 03k48_; >> query: (?x1979, 02hrh1q) <- award_nominee(?x2518, ?x1979), film(?x1979, ?x508), language(?x1979, ?x254) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fh9 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.911 http://example.org/people/person/profession #21890-024_vw PRED entity: 024_vw PRED relation: legislative_sessions PRED expected values: 0495ys => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 29): 04h1rz (0.71 #105, 0.60 #47, 0.60 #18), 05l2z4 (0.71 #90, 0.60 #32, 0.60 #3), 0495ys (0.71 #89, 0.60 #31, 0.60 #2), 060ny2 (0.57 #104, 0.40 #46, 0.40 #17), 06r713 (0.43 #102, 0.40 #44, 0.40 #15), 01gtc0 (0.13 #186, 0.12 #244, 0.12 #273), 01h7xx (0.09 #196, 0.08 #254, 0.08 #283), 043djx (0.09 #178, 0.08 #236, 0.08 #265), 01gtcc (0.09 #182, 0.08 #240, 0.08 #269), 01gtbb (0.09 #180, 0.08 #238, 0.08 #267) >> Best rule #105 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 06bss; >> query: (?x11605, 04h1rz) <- legislative_sessions(?x11605, ?x4821), legislative_sessions(?x11605, ?x2861), legislative_sessions(?x11605, ?x1028), ?x4821 = 02bqm0, ?x1028 = 032ft5, district_represented(?x2861, ?x335) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #89 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 06bss; *> query: (?x11605, 0495ys) <- legislative_sessions(?x11605, ?x4821), legislative_sessions(?x11605, ?x2861), legislative_sessions(?x11605, ?x1028), ?x4821 = 02bqm0, ?x1028 = 032ft5, district_represented(?x2861, ?x335) *> conf = 0.71 ranks of expected_values: 3 EVAL 024_vw legislative_sessions 0495ys CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.714 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #21889-033w9g PRED entity: 033w9g PRED relation: film PRED expected values: 0443v1 => 99 concepts (51 used for prediction) PRED predicted values (max 10 best out of 339): 0828jw (0.57 #41132, 0.51 #5366, 0.36 #42922), 017jd9 (0.06 #55442, 0.06 #781, 0.03 #9724), 01b66d (0.06 #55442, 0.03 #60808), 017gm7 (0.06 #211, 0.03 #60808, 0.02 #3788), 08hmch (0.06 #152, 0.03 #60808), 05sy_5 (0.06 #1057, 0.02 #10000, 0.01 #24306), 03twd6 (0.06 #226, 0.01 #9169), 01cssf (0.06 #89, 0.01 #25126, 0.01 #1877), 02847m9 (0.06 #249, 0.01 #5615), 072192 (0.06 #1525) >> Best rule #41132 for best value: >> intensional similarity = 2 >> extensional distance = 939 >> proper extension: 06r3p2; >> query: (?x4527, ?x5810) <- film(?x4527, ?x8615), award_winner(?x5810, ?x4527) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 033w9g film 0443v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 51.000 0.574 http://example.org/film/actor/film./film/performance/film #21888-043tvp3 PRED entity: 043tvp3 PRED relation: language PRED expected values: 02bjrlw => 61 concepts (57 used for prediction) PRED predicted values (max 10 best out of 39): 06nm1 (0.16 #302, 0.15 #244, 0.15 #361), 064_8sq (0.16 #1198, 0.15 #138, 0.15 #901), 04306rv (0.13 #238, 0.11 #766, 0.11 #1181), 02bjrlw (0.10 #118, 0.08 #940, 0.07 #1295), 06b_j (0.09 #902, 0.09 #314, 0.09 #256), 03_9r (0.08 #948, 0.08 #1484, 0.07 #889), 0jzc (0.05 #781, 0.04 #1673, 0.03 #1494), 0653m (0.05 #950, 0.05 #1486, 0.05 #891), 03k50 (0.05 #125, 0.02 #1302, 0.02 #888), 012w70 (0.05 #951, 0.04 #304, 0.03 #1487) >> Best rule #302 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 0dnqr; 016y_f; 0yyn5; 025s1wg; >> query: (?x6882, 06nm1) <- executive_produced_by(?x6882, ?x519), genre(?x6882, ?x53), country(?x6882, ?x205), film(?x2531, ?x6882), edited_by(?x6882, ?x323) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #118 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 37 *> proper extension: 02vxq9m; 05p1tzf; 0401sg; 0gkz15s; 017gl1; 08hmch; 0h3xztt; 04hwbq; 0dtfn; 017gm7; ... *> query: (?x6882, 02bjrlw) <- film_release_region(?x6882, ?x4737), film_release_region(?x6882, ?x1023), film_release_region(?x6882, ?x456), ?x456 = 05qhw, ?x1023 = 0ctw_b, film(?x2531, ?x6882), ?x4737 = 07twz *> conf = 0.10 ranks of expected_values: 4 EVAL 043tvp3 language 02bjrlw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 61.000 57.000 0.161 http://example.org/film/film/language #21887-03k9fj PRED entity: 03k9fj PRED relation: genre! PRED expected values: 0524b41 05h95s 088tp3 0cskb 02gl58 => 65 concepts (55 used for prediction) PRED predicted values (max 10 best out of 669): 05hd32 (0.62 #4492, 0.50 #3703, 0.33 #1622), 01rf57 (0.60 #3186, 0.50 #2667, 0.43 #3973), 07gbf (0.57 #4091, 0.40 #3044, 0.37 #5662), 01kt_j (0.57 #4111, 0.40 #3064, 0.33 #721), 088tp3 (0.50 #4601, 0.50 #3812, 0.33 #1731), 05f7w84 (0.50 #4536, 0.44 #5323, 0.33 #3747), 06dfz1 (0.50 #2759, 0.43 #4065, 0.40 #3018), 02v5xg (0.50 #4595, 0.38 #4856, 0.33 #5119), 01f3p_ (0.50 #2655, 0.38 #4487, 0.33 #1356), 02rhwjr (0.50 #4685, 0.33 #3896, 0.33 #1815) >> Best rule #4492 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0jxy; >> query: (?x811, 05hd32) <- genre(?x9872, ?x811), genre(?x3909, ?x811), genre(?x1628, ?x811), film(?x338, ?x9872), genre(?x50, ?x811), film(?x541, ?x9872), ?x1628 = 0436yk, nominated_for(?x198, ?x3909) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #4601 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0jxy; *> query: (?x811, 088tp3) <- genre(?x9872, ?x811), genre(?x3909, ?x811), genre(?x1628, ?x811), film(?x338, ?x9872), genre(?x50, ?x811), film(?x541, ?x9872), ?x1628 = 0436yk, nominated_for(?x198, ?x3909) *> conf = 0.50 ranks of expected_values: 5, 12, 13, 14, 29 EVAL 03k9fj genre! 02gl58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 65.000 55.000 0.625 http://example.org/tv/tv_program/genre EVAL 03k9fj genre! 0cskb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 65.000 55.000 0.625 http://example.org/tv/tv_program/genre EVAL 03k9fj genre! 088tp3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 65.000 55.000 0.625 http://example.org/tv/tv_program/genre EVAL 03k9fj genre! 05h95s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 65.000 55.000 0.625 http://example.org/tv/tv_program/genre EVAL 03k9fj genre! 0524b41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 65.000 55.000 0.625 http://example.org/tv/tv_program/genre #21886-035s95 PRED entity: 035s95 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 34): 09vw2b7 (0.73 #182, 0.70 #682, 0.69 #218), 0dxtw (0.51 #186, 0.44 #222, 0.43 #686), 01vx2h (0.48 #223, 0.40 #187, 0.38 #401), 02ynfr (0.28 #227, 0.25 #50, 0.21 #262), 02rh1dz (0.27 #185, 0.17 #221, 0.17 #9), 089fss (0.25 #40, 0.16 #497, 0.12 #2041), 04pyp5 (0.20 #121, 0.16 #497, 0.13 #192), 02_n3z (0.19 #71, 0.16 #497, 0.14 #141), 0d2b38 (0.18 #201, 0.16 #497, 0.14 #165), 015h31 (0.18 #148, 0.17 #8, 0.16 #497) >> Best rule #182 for best value: >> intensional similarity = 5 >> extensional distance = 75 >> proper extension: 07gp9; 047gn4y; 0ds33; 0bth54; 0c40vxk; 0pc62; 0fg04; 01r97z; 0164qt; 06_wqk4; ... >> query: (?x2128, 09vw2b7) <- film(?x382, ?x2128), film_crew_role(?x2128, ?x137), genre(?x2128, ?x53), ?x137 = 09zzb8, edited_by(?x2128, ?x5971) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035s95 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.727 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21885-03s6l2 PRED entity: 03s6l2 PRED relation: film! PRED expected values: 09byk 026c1 0169dl => 82 concepts (39 used for prediction) PRED predicted values (max 10 best out of 646): 02mt4k (0.60 #41564, 0.60 #20782, 0.48 #47802), 06t8b (0.15 #14546, 0.13 #24938, 0.12 #6233), 06q8hf (0.15 #14546, 0.13 #24938, 0.12 #6233), 05hj_k (0.15 #14546, 0.13 #24938, 0.12 #6233), 0lpjn (0.06 #6710, 0.03 #8788, 0.02 #17102), 03kcyd (0.06 #58194, 0.04 #45723, 0.03 #33250), 04qz6n (0.06 #58194, 0.04 #45723, 0.03 #33250), 0350l7 (0.06 #58194, 0.04 #45723, 0.03 #33250), 01t6xz (0.06 #58194, 0.04 #45723, 0.03 #33250), 0210hf (0.06 #58194, 0.04 #45723, 0.03 #33250) >> Best rule #41564 for best value: >> intensional similarity = 4 >> extensional distance = 502 >> proper extension: 016z9n; 0kb57; 07tw_b; 0jsqk; 0sxns; 01mszz; 02825kb; 0y_pg; 09lxv9; 06zsk51; ... >> query: (?x603, ?x5492) <- nominated_for(?x5492, ?x603), featured_film_locations(?x603, ?x151), location(?x5492, ?x739), genre(?x603, ?x53) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2478 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 147 *> proper extension: 04tng0; *> query: (?x603, 0169dl) <- nominated_for(?x6288, ?x603), film_crew_role(?x603, ?x137), genre(?x603, ?x53) *> conf = 0.04 ranks of expected_values: 74, 210 EVAL 03s6l2 film! 0169dl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 82.000 39.000 0.597 http://example.org/film/actor/film./film/performance/film EVAL 03s6l2 film! 026c1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 82.000 39.000 0.597 http://example.org/film/actor/film./film/performance/film EVAL 03s6l2 film! 09byk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 39.000 0.597 http://example.org/film/actor/film./film/performance/film #21884-0244r8 PRED entity: 0244r8 PRED relation: artists! PRED expected values: 0xjl2 => 116 concepts (55 used for prediction) PRED predicted values (max 10 best out of 252): 03_d0 (0.58 #10018, 0.24 #4077, 0.22 #8767), 06by7 (0.58 #15667, 0.53 #16604, 0.46 #14414), 064t9 (0.50 #14405, 0.45 #11272, 0.41 #13153), 08jyyk (0.47 #382, 0.38 #694, 0.20 #69), 0dl5d (0.40 #20, 0.29 #645, 0.27 #333), 02v2lh (0.40 #226, 0.14 #851, 0.13 #539), 016clz (0.38 #630, 0.33 #318, 0.24 #9387), 0m0jc (0.38 #634, 0.33 #322, 0.09 #4075), 059kh (0.34 #9433, 0.20 #364, 0.14 #676), 0cx7f (0.29 #765, 0.27 #453, 0.15 #4206) >> Best rule #10018 for best value: >> intensional similarity = 3 >> extensional distance = 235 >> proper extension: 02lbrd; 0h08p; >> query: (?x1489, 03_d0) <- artists(?x4910, ?x1489), artists(?x4910, ?x4013), ?x4013 = 037lyl >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #9429 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 197 *> proper extension: 03t9sp; 05k79; 0frsw; 016fmf; 0dm5l; 01l_vgt; 01rm8b; 03xhj6; 018gm9; 047cx; ... *> query: (?x1489, 0xjl2) <- artists(?x4910, ?x1489), artists(?x4910, ?x5508), ?x5508 = 0jn5l *> conf = 0.06 ranks of expected_values: 102 EVAL 0244r8 artists! 0xjl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 116.000 55.000 0.582 http://example.org/music/genre/artists #21883-0h1wg PRED entity: 0h1wg PRED relation: nutrient! PRED expected values: 0hkxq 033cnk 037ls6 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 12): 0hkxq (0.92 #665, 0.90 #362, 0.90 #353), 033cnk (0.90 #87, 0.89 #85, 0.89 #527), 037ls6 (0.90 #87, 0.89 #85, 0.88 #219), 06x4c (0.90 #87, 0.89 #85, 0.88 #219), 0dcfv (0.90 #87, 0.89 #85, 0.88 #219), 01sh2 (0.03 #615, 0.02 #180, 0.01 #222), 04k8n (0.03 #615), 05wvs (0.03 #615), 025rw19 (0.02 #180, 0.01 #222, 0.01 #270), 025tkqy (0.02 #180, 0.01 #222, 0.01 #270) >> Best rule #665 for best value: >> intensional similarity = 115 >> extensional distance = 46 >> proper extension: 02y_3rt; >> query: (?x1258, 0hkxq) <- nutrient(?x7719, ?x1258), nutrient(?x7057, ?x1258), nutrient(?x6191, ?x1258), nutrient(?x5009, ?x1258), nutrient(?x4068, ?x1258), nutrient(?x3468, ?x1258), nutrient(?x1303, ?x1258), nutrient(?x3468, ?x13126), nutrient(?x3468, ?x12902), nutrient(?x3468, ?x12083), nutrient(?x3468, ?x11784), nutrient(?x3468, ?x11758), nutrient(?x3468, ?x11409), nutrient(?x3468, ?x11270), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10453), nutrient(?x3468, ?x10195), nutrient(?x3468, ?x10098), nutrient(?x3468, ?x9949), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x9840), nutrient(?x3468, ?x9733), nutrient(?x3468, ?x9490), nutrient(?x3468, ?x9436), nutrient(?x3468, ?x9426), nutrient(?x3468, ?x9365), nutrient(?x3468, ?x8442), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x7894), nutrient(?x3468, ?x7720), nutrient(?x3468, ?x7652), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7364), nutrient(?x3468, ?x7219), nutrient(?x3468, ?x7135), nutrient(?x3468, ?x6586), nutrient(?x3468, ?x6286), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x6160), nutrient(?x3468, ?x6033), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5526), nutrient(?x3468, ?x5451), nutrient(?x3468, ?x5010), nutrient(?x3468, ?x4069), nutrient(?x3468, ?x3469), nutrient(?x3468, ?x2702), nutrient(?x3468, ?x2018), nutrient(?x7719, ?x13498), nutrient(?x7719, ?x12868), nutrient(?x7719, ?x11592), nutrient(?x7719, ?x9855), nutrient(?x7719, ?x8487), nutrient(?x7719, ?x8243), nutrient(?x7719, ?x5374), nutrient(?x7719, ?x3264), ?x5549 = 025s7j4, ?x3469 = 0h1zw, ?x6192 = 06jry, ?x9365 = 04k8n, nutrient(?x6191, ?x12481), ?x10098 = 0h1_c, ?x11758 = 0q01m, ?x12902 = 0fzjh, nutrient(?x8298, ?x10453), nutrient(?x6159, ?x10453), ?x8298 = 037ls6, ?x9915 = 025tkqy, ?x9490 = 0h1sg, ?x6160 = 041r51, ?x5009 = 0fjfh, ?x8243 = 014d7f, ?x9840 = 02p0tjr, ?x8487 = 014yzm, ?x4068 = 0fbw6, ?x10891 = 0g5gq, ?x6586 = 05gh50, ?x7431 = 09gwd, ?x9426 = 0h1yy, ?x1303 = 0fj52s, ?x5526 = 09pbb, ?x9733 = 0h1tz, ?x12083 = 01n78x, ?x8413 = 02kc4sf, ?x8442 = 02kcv4x, ?x7720 = 025s7x6, ?x11409 = 0h1yf, ?x4069 = 0hqw8p_, ?x3264 = 0dcfv, ?x5374 = 025s0zp, ?x10195 = 0hkwr, ?x7652 = 025s0s0, ?x5451 = 05wvs, ?x11592 = 025sf0_, ?x6033 = 04zjxcz, ?x6286 = 02y_3rf, ?x13498 = 07q0m, ?x5010 = 0h1vz, ?x2702 = 0838f, ?x7135 = 025rsfk, ?x7894 = 0f4hc, ?x11784 = 07zqy, nutrient(?x7057, ?x3901), ?x12868 = 03d49, ?x7364 = 09gvd, ?x9855 = 0d9t0, ?x2018 = 01sh2, ?x7219 = 0h1vg, ?x12481 = 027g6p7, ?x9949 = 02kd0rh, ?x6159 = 033cnk, ?x3901 = 0466p20, ?x11270 = 02kc008, ?x9436 = 025sqz8, ?x13126 = 02kc_w5 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0h1wg nutrient! 037ls6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.917 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0h1wg nutrient! 033cnk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.917 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0h1wg nutrient! 0hkxq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.917 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #21882-0m3gy PRED entity: 0m3gy PRED relation: cinematography PRED expected values: 0f3zf_ => 58 concepts (56 used for prediction) PRED predicted values (max 10 best out of 43): 0854hr (0.12 #19, 0.03 #399, 0.02 #335), 06r_by (0.08 #149, 0.03 #850, 0.03 #978), 09bxq9 (0.06 #40, 0.02 #230, 0.02 #1248), 08z39v (0.06 #48), 02rgz97 (0.06 #10), 079hvk (0.06 #258, 0.06 #68, 0.04 #450), 071jrc (0.06 #124, 0.04 #314, 0.02 #506), 070bjw (0.06 #99, 0.03 #481, 0.02 #289), 0f3zf_ (0.06 #66, 0.02 #765, 0.02 #256), 06vdh8 (0.06 #107, 0.02 #297) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 04t9c0; >> query: (?x9294, 0854hr) <- film(?x5884, ?x9294), film(?x9707, ?x9294), film_release_distribution_medium(?x9294, ?x81), artists(?x4910, ?x9707) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #66 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0_92w; 0jymd; 0hv8w; 0gt1k; 0cbn7c; *> query: (?x9294, 0f3zf_) <- film(?x5884, ?x9294), film_release_region(?x9294, ?x390), ?x390 = 0chghy, list(?x9294, ?x3004) *> conf = 0.06 ranks of expected_values: 9 EVAL 0m3gy cinematography 0f3zf_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 58.000 56.000 0.125 http://example.org/film/film/cinematography #21881-06mzp PRED entity: 06mzp PRED relation: country! PRED expected values: 07rlg 02_5h 07bs0 06f41 03_8r 064vjs 01gqfm => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 36): 06f41 (0.80 #116, 0.76 #224, 0.74 #251), 03_8r (0.79 #442, 0.78 #307, 0.76 #388), 01cgz (0.75 #115, 0.71 #250, 0.68 #331), 07jbh (0.75 #123, 0.67 #717, 0.67 #231), 064vjs (0.75 #122, 0.67 #230, 0.62 #257), 07jjt (0.70 #117, 0.59 #252, 0.53 #90), 01gqfm (0.70 #133, 0.58 #106, 0.55 #241), 03rbzn (0.70 #119, 0.56 #254, 0.53 #92), 07gyv (0.65 #111, 0.61 #138, 0.59 #705), 07bs0 (0.65 #114, 0.57 #168, 0.55 #708) >> Best rule #116 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0chghy; >> query: (?x774, 06f41) <- film_release_region(?x1625, ?x774), country(?x359, ?x774), ?x1625 = 01f8gz >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 7, 10, 11, 16 EVAL 06mzp country! 01gqfm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 120.000 120.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06mzp country! 064vjs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 120.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06mzp country! 03_8r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06mzp country! 06f41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06mzp country! 07bs0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 120.000 120.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06mzp country! 02_5h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 120.000 120.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06mzp country! 07rlg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 120.000 120.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #21880-03shpq PRED entity: 03shpq PRED relation: film_crew_role PRED expected values: 02r96rf => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 24): 02r96rf (0.69 #147, 0.69 #220, 0.67 #838), 0dxtw (0.44 #154, 0.38 #227, 0.36 #845), 01vx2h (0.38 #155, 0.31 #846, 0.31 #662), 01pvkk (0.30 #156, 0.28 #517, 0.28 #847), 02rh1dz (0.19 #153, 0.10 #226, 0.10 #117), 02ynfr (0.18 #233, 0.18 #160, 0.17 #16), 0215hd (0.15 #91, 0.15 #236, 0.13 #854), 089g0h (0.13 #237, 0.12 #525, 0.12 #164), 01xy5l_ (0.13 #231, 0.10 #849, 0.10 #519), 0d2b38 (0.12 #243, 0.11 #170, 0.11 #677) >> Best rule #147 for best value: >> intensional similarity = 3 >> extensional distance = 306 >> proper extension: 0fq27fp; >> query: (?x8446, 02r96rf) <- film_crew_role(?x8446, ?x137), crewmember(?x8446, ?x6232), genre(?x8446, ?x53) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03shpq film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.695 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21879-03rs8y PRED entity: 03rs8y PRED relation: profession PRED expected values: 02krf9 => 96 concepts (74 used for prediction) PRED predicted values (max 10 best out of 55): 02jknp (0.44 #1477, 0.33 #7, 0.23 #154), 03gjzk (0.38 #1483, 0.36 #895, 0.35 #13), 09jwl (0.26 #311, 0.24 #752, 0.24 #458), 02krf9 (0.25 #7205, 0.16 #1495, 0.16 #25), 0dz3r (0.22 #296, 0.20 #737, 0.18 #443), 0np9r (0.21 #1636, 0.16 #19, 0.15 #166), 0cbd2 (0.20 #1476, 0.14 #3094, 0.14 #6181), 0nbcg (0.20 #765, 0.18 #324, 0.17 #471), 018gz8 (0.17 #1485, 0.17 #162, 0.17 #15), 016z4k (0.14 #298, 0.13 #739, 0.13 #445) >> Best rule #1477 for best value: >> intensional similarity = 3 >> extensional distance = 756 >> proper extension: 01q4qv; 01wj5hp; >> query: (?x427, 02jknp) <- profession(?x427, ?x987), award(?x427, ?x783), ?x987 = 0dxtg >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #7205 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1670 *> proper extension: 03xsby; *> query: (?x427, ?x1032) <- nominated_for(?x427, ?x6482), award_nominee(?x427, ?x5065), profession(?x5065, ?x1032) *> conf = 0.25 ranks of expected_values: 4 EVAL 03rs8y profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 74.000 0.435 http://example.org/people/person/profession #21878-012ycy PRED entity: 012ycy PRED relation: artists! PRED expected values: 08jyyk 0jmwg => 128 concepts (69 used for prediction) PRED predicted values (max 10 best out of 269): 064t9 (0.91 #19491, 0.45 #4949, 0.44 #4332), 06by7 (0.74 #8978, 0.71 #19810, 0.62 #7742), 05bt6j (0.50 #3127, 0.33 #3436, 0.33 #44), 02t8gf (0.44 #2302, 0.43 #1069, 0.13 #5697), 0mmp3 (0.44 #2260, 0.29 #1027, 0.22 #20097), 01_bkd (0.44 #2214, 0.29 #981, 0.11 #5609), 0xhtw (0.41 #2483, 0.37 #3719, 0.37 #7427), 03lty (0.38 #2187, 0.36 #5582, 0.29 #954), 06j6l (0.35 #2824, 0.30 #19527, 0.29 #2515), 011j5x (0.33 #342, 0.33 #33, 0.30 #1575) >> Best rule #19491 for best value: >> intensional similarity = 5 >> extensional distance = 384 >> proper extension: 0123r4; >> query: (?x9603, 064t9) <- artists(?x2996, ?x9603), artists(?x2996, ?x7865), parent_genre(?x8386, ?x2996), ?x7865 = 02k5sc, ?x8386 = 016ybr >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #378 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 070b4; *> query: (?x9603, 08jyyk) <- artists(?x10969, ?x9603), artists(?x5934, ?x9603), artists(?x2996, ?x9603), ?x2996 = 01243b, artist(?x9121, ?x9603), ?x10969 = 029fbr, ?x5934 = 05r6t *> conf = 0.33 ranks of expected_values: 13, 24 EVAL 012ycy artists! 0jmwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 128.000 69.000 0.912 http://example.org/music/genre/artists EVAL 012ycy artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 128.000 69.000 0.912 http://example.org/music/genre/artists #21877-028dcg PRED entity: 028dcg PRED relation: student PRED expected values: 048hf => 24 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1026): 0157m (0.40 #1313, 0.33 #672, 0.25 #1097), 014vk4 (0.33 #2345, 0.33 #1697, 0.33 #629), 01zwy (0.33 #1651, 0.33 #795, 0.33 #158), 024jwt (0.33 #1682, 0.33 #402, 0.33 #189), 04z0g (0.33 #542, 0.33 #117, 0.29 #1822), 0b78hw (0.33 #515, 0.33 #90, 0.29 #1795), 06y7d (0.33 #627, 0.33 #202, 0.29 #1907), 0969fd (0.33 #613, 0.33 #188, 0.29 #1893), 01tdnyh (0.33 #527, 0.33 #102, 0.29 #1807), 02v406 (0.33 #1576, 0.33 #720, 0.25 #1145) >> Best rule #1313 for best value: >> intensional similarity = 26 >> extensional distance = 3 >> proper extension: 013zdg; >> query: (?x8398, 0157m) <- student(?x8398, ?x9085), institution(?x8398, ?x7066), institution(?x8398, ?x6936), institution(?x8398, ?x5149), institution(?x8398, ?x4410), institution(?x8398, ?x3387), institution(?x8398, ?x2909), institution(?x8398, ?x2775), institution(?x8398, ?x388), ?x388 = 05krk, colors(?x7066, ?x663), major_field_of_study(?x8398, ?x373), state_province_region(?x7066, ?x1905), country(?x6936, ?x94), ?x3387 = 02fgdx, category(?x6936, ?x134), major_field_of_study(?x7066, ?x742), school_type(?x7066, ?x3092), student(?x2909, ?x2516), award_winner(?x624, ?x2516), award_nominee(?x2516, ?x3842), ?x4410 = 017j69, location(?x9085, ?x479), ?x3842 = 0cjsxp, ?x5149 = 02mj7c, ?x2775 = 078bz >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 028dcg student 048hf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 24.000 23.000 0.400 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #21876-0f4_l PRED entity: 0f4_l PRED relation: language PRED expected values: 06nm1 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 34): 04306rv (0.14 #4, 0.12 #119, 0.10 #1265), 02bjrlw (0.13 #59, 0.12 #173, 0.09 #459), 06nm1 (0.13 #297, 0.12 #125, 0.11 #1328), 0jzc (0.09 #191, 0.07 #77, 0.04 #934), 06b_j (0.07 #79, 0.06 #136, 0.06 #2034), 0653m (0.07 #69, 0.06 #126, 0.05 #526), 04h9h (0.07 #99, 0.05 #614, 0.04 #671), 02hwyss (0.07 #98, 0.03 #212, 0.02 #269), 06mp7 (0.06 #130, 0.03 #416, 0.02 #244), 02bv9 (0.06 #141) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 04zyhx; 02bg55; 03bzjpm; >> query: (?x2177, 04306rv) <- film_release_region(?x2177, ?x94), film(?x4438, ?x2177), ?x4438 = 033tln, film_crew_role(?x2177, ?x1171) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #297 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 03mz5b; 04h41v; 0h63gl9; *> query: (?x2177, 06nm1) <- award(?x2177, ?x1587), nominated_for(?x1587, ?x2251), film(?x368, ?x2177), ?x2251 = 01qncf *> conf = 0.13 ranks of expected_values: 3 EVAL 0f4_l language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.143 http://example.org/film/film/language #21875-01mmslz PRED entity: 01mmslz PRED relation: actor! PRED expected values: 02gs6r => 116 concepts (115 used for prediction) PRED predicted values (max 10 best out of 25): 016ztl (0.19 #314, 0.11 #447, 0.10 #248), 05t0zfv (0.12 #259, 0.03 #425, 0.02 #325), 02q3fdr (0.11 #313, 0.07 #446, 0.05 #413), 02gs6r (0.10 #243, 0.09 #309, 0.08 #442), 05pyrb (0.10 #246, 0.04 #312, 0.03 #445), 0dd6bf (0.10 #253, 0.04 #419, 0.02 #319), 0b60sq (0.09 #101, 0.05 #399, 0.05 #432), 031f_m (0.08 #423, 0.05 #257, 0.04 #323), 02vw1w2 (0.07 #238, 0.04 #304, 0.03 #437), 02z5x7l (0.07 #251, 0.04 #317, 0.01 #417) >> Best rule #314 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 066l3y; 09fp45; 04bz7q; >> query: (?x2416, 016ztl) <- actor(?x5529, ?x2416), language(?x2416, ?x254), place_of_birth(?x2416, ?x8093) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #243 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 07gkgp; 091n7z; *> query: (?x2416, 02gs6r) <- category(?x2416, ?x134), profession(?x2416, ?x1032), language(?x2416, ?x254) *> conf = 0.10 ranks of expected_values: 4 EVAL 01mmslz actor! 02gs6r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 115.000 0.191 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #21874-06pjs PRED entity: 06pjs PRED relation: nationality PRED expected values: 09c7w0 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 37): 09c7w0 (0.79 #1202, 0.78 #401, 0.78 #1303), 02jx1 (0.17 #933, 0.13 #1737, 0.13 #733), 07ssc (0.14 #915, 0.13 #2621, 0.13 #1719), 0d060g (0.12 #107, 0.06 #1107, 0.05 #5927), 0345h (0.10 #1937, 0.09 #1735, 0.08 #2738), 03rk0 (0.07 #346, 0.07 #6672, 0.06 #4156), 0f8l9c (0.05 #1928, 0.05 #1726, 0.04 #2729), 0h7x (0.05 #1941, 0.05 #1739, 0.04 #1638), 03rt9 (0.05 #513, 0.03 #913, 0.03 #1717), 03rjj (0.04 #605, 0.03 #905, 0.02 #1307) >> Best rule #1202 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 03ym1; >> query: (?x9153, 09c7w0) <- currency(?x9153, ?x170), award_winner(?x2551, ?x9153), place_of_birth(?x9153, ?x2277) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06pjs nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.793 http://example.org/people/person/nationality #21873-06w6_ PRED entity: 06w6_ PRED relation: award PRED expected values: 057xs89 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 259): 09sb52 (0.34 #7679, 0.33 #13307, 0.30 #5267), 0ck27z (0.30 #3710, 0.21 #14564, 0.19 #13760), 05p09zm (0.26 #123, 0.15 #4545, 0.14 #4143), 0cqhk0 (0.22 #3655, 0.16 #5263, 0.15 #2449), 05zr6wv (0.20 #17, 0.17 #4439, 0.15 #8459), 05ztrmj (0.17 #182, 0.12 #4604, 0.10 #584), 01bgqh (0.17 #43, 0.10 #4063, 0.10 #4465), 0f4x7 (0.15 #31, 0.15 #1237, 0.12 #4453), 07cbcy (0.15 #79, 0.08 #4501, 0.08 #481), 0cjyzs (0.15 #9754, 0.09 #5332, 0.08 #5734) >> Best rule #7679 for best value: >> intensional similarity = 3 >> extensional distance = 402 >> proper extension: 080knyg; >> query: (?x2681, 09sb52) <- award_winner(?x11317, ?x2681), film(?x2681, ?x3000), people(?x1050, ?x2681) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #158 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 82 *> proper extension: 01438g; 02qw2xb; *> query: (?x2681, 057xs89) <- award_winner(?x11317, ?x2681), friend(?x2681, ?x2582), award(?x2681, ?x1336) *> conf = 0.12 ranks of expected_values: 20 EVAL 06w6_ award 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 104.000 104.000 0.337 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21872-097ns PRED entity: 097ns PRED relation: people PRED expected values: 014g91 => 91 concepts (52 used for prediction) PRED predicted values (max 10 best out of 671): 0432b (0.33 #13280, 0.33 #1627, 0.25 #7796), 0b22w (0.33 #13520, 0.33 #3924, 0.22 #20381), 02hg53 (0.33 #13629, 0.33 #4033, 0.22 #20490), 03lpd0 (0.33 #13613, 0.33 #4017, 0.22 #20474), 01rw116 (0.33 #13548, 0.33 #3952, 0.22 #20409), 0bdlj (0.33 #13337, 0.33 #3741, 0.22 #20198), 09ld6g (0.33 #2041, 0.29 #15751, 0.25 #8210), 053yx (0.33 #3526, 0.25 #15867, 0.25 #6953), 016z51 (0.33 #12560, 0.25 #6390, 0.22 #20793), 028bs1p (0.33 #2046, 0.25 #8215, 0.20 #11641) >> Best rule #13280 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 04p3w; >> query: (?x7007, 0432b) <- risk_factors(?x7007, ?x514), people(?x7007, ?x10559), people(?x7007, ?x6993), symptom_of(?x4905, ?x7007), place_of_death(?x6993, ?x1523), profession(?x6993, ?x524), award(?x6993, ?x198), location(?x10559, ?x1705), artists(?x12513, ?x10559), ?x12513 = 015y_n >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3944 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 0dq9p; *> query: (?x7007, 014g91) <- risk_factors(?x7007, ?x11160), people(?x7007, ?x2208), risk_factors(?x9119, ?x7007), symptom_of(?x4905, ?x7007), symptom_of(?x10717, ?x9119), ?x11160 = 012jc, symptom_of(?x7007, ?x9898), ?x10717 = 0cjf0 *> conf = 0.33 ranks of expected_values: 32 EVAL 097ns people 014g91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 91.000 52.000 0.333 http://example.org/people/cause_of_death/people #21871-01r97z PRED entity: 01r97z PRED relation: film! PRED expected values: 04mg6l 04vq3h => 80 concepts (30 used for prediction) PRED predicted values (max 10 best out of 913): 019pm_ (0.65 #60411, 0.44 #37491, 0.43 #49992), 031rq5 (0.44 #37491, 0.43 #49992, 0.40 #43739), 017s11 (0.44 #37491, 0.43 #49992, 0.40 #43739), 0f0kz (0.09 #516, 0.09 #6767, 0.06 #10932), 01wbg84 (0.08 #10463, 0.05 #4213, 0.05 #47), 023kzp (0.08 #35407, 0.07 #35408, 0.06 #45824), 02qgyv (0.08 #35407, 0.07 #35408, 0.06 #45824), 01kb2j (0.08 #35407, 0.07 #35408, 0.06 #45824), 02qgqt (0.08 #35407, 0.07 #35408, 0.06 #45824), 0gy6z9 (0.08 #35407, 0.07 #35408, 0.06 #45824) >> Best rule #60411 for best value: >> intensional similarity = 3 >> extensional distance = 763 >> proper extension: 03y3bp7; 01f3p_; 03nymk; 07wqr6; 0123qq; 0clpml; >> query: (?x770, ?x2763) <- nominated_for(?x2763, ?x770), location(?x2763, ?x1523), participant(?x2763, ?x1733) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01r97z film! 04vq3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 30.000 0.647 http://example.org/film/actor/film./film/performance/film EVAL 01r97z film! 04mg6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 30.000 0.647 http://example.org/film/actor/film./film/performance/film #21870-03rjj PRED entity: 03rjj PRED relation: country! PRED expected values: 0f4_2k 011ywj => 211 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1797): 0dscrwf (0.35 #8372, 0.19 #29977, 0.19 #9969), 0gs973 (0.33 #16619, 0.29 #16618, 0.18 #9149), 08gsvw (0.33 #16619, 0.29 #16618, 0.12 #8414), 0pv3x (0.33 #16619, 0.29 #16618, 0.07 #6815), 03k8th (0.33 #16619, 0.29 #16618, 0.06 #9897), 0cc846d (0.33 #16619, 0.29 #16618, 0.06 #8713), 02mpyh (0.33 #16619, 0.29 #16618, 0.05 #41543), 01ffx4 (0.33 #16619, 0.29 #16618, 0.05 #41543), 023g6w (0.32 #14657, 0.24 #9671, 0.20 #19644), 04z4j2 (0.29 #9814, 0.18 #14800, 0.16 #33080) >> Best rule #8372 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 05r4w; 09c7w0; 0b90_r; 0chghy; 03rt9; 07ssc; 015fr; 0f8l9c; 0ctw_b; 059j2; ... >> query: (?x205, 0dscrwf) <- film_release_region(?x1744, ?x205), exported_to(?x205, ?x94), ?x1744 = 035yn8 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #9628 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 05r4w; 09c7w0; 0b90_r; 0chghy; 03rt9; 07ssc; 015fr; 0f8l9c; 0ctw_b; 059j2; ... *> query: (?x205, 011ywj) <- film_release_region(?x1744, ?x205), exported_to(?x205, ?x94), ?x1744 = 035yn8 *> conf = 0.06 ranks of expected_values: 988, 1088 EVAL 03rjj country! 011ywj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 211.000 60.000 0.353 http://example.org/film/film/country EVAL 03rjj country! 0f4_2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 211.000 60.000 0.353 http://example.org/film/film/country #21869-0k525 PRED entity: 0k525 PRED relation: actor! PRED expected values: 027tbrc => 118 concepts (54 used for prediction) PRED predicted values (max 10 best out of 35): 035yn8 (0.14 #6119, 0.12 #2392, 0.11 #11708), 0gvs1kt (0.11 #11708, 0.10 #5586, 0.10 #7453), 090s_0 (0.05 #3, 0.04 #268, 0.03 #798), 05f4vxd (0.03 #1415, 0.02 #1948, 0.01 #5941), 034vds (0.02 #248, 0.02 #513, 0.02 #778), 039cq4 (0.02 #129, 0.02 #659, 0.02 #924), 072kp (0.02 #10, 0.02 #540, 0.02 #805), 026bfsh (0.02 #1956, 0.02 #3821, 0.02 #8350), 02py4c8 (0.02 #1338, 0.01 #1871), 02_1q9 (0.02 #1331, 0.01 #4259, 0.01 #2664) >> Best rule #6119 for best value: >> intensional similarity = 4 >> extensional distance = 895 >> proper extension: 03bx_5q; >> query: (?x11155, ?x1744) <- profession(?x11155, ?x1032), nominated_for(?x11155, ?x1744), award_winner(?x1744, ?x1431), category(?x1744, ?x134) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k525 actor! 027tbrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 54.000 0.145 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21868-01xlqd PRED entity: 01xlqd PRED relation: genre PRED expected values: 04t36 02b5_l => 73 concepts (54 used for prediction) PRED predicted values (max 10 best out of 108): 07s9rl0 (0.85 #5607, 0.78 #3696, 0.69 #120), 04t36 (0.73 #4173, 0.72 #3815, 0.72 #4413), 03k9fj (0.40 #486, 0.38 #129, 0.36 #843), 0lsxr (0.38 #127, 0.18 #4540, 0.18 #5255), 02kdv5l (0.37 #479, 0.35 #1911, 0.34 #598), 01hmnh (0.37 #492, 0.25 #2757, 0.25 #1924), 01jfsb (0.35 #487, 0.31 #5857, 0.31 #1919), 04xvlr (0.33 #3697, 0.22 #4055, 0.21 #716), 06n90 (0.26 #369, 0.25 #607, 0.22 #845), 01t_vv (0.23 #767, 0.22 #1005, 0.21 #1124) >> Best rule #5607 for best value: >> intensional similarity = 5 >> extensional distance = 1175 >> proper extension: 027pfb2; 03cffvv; >> query: (?x9832, 07s9rl0) <- genre(?x9832, ?x239), genre(?x7563, ?x239), genre(?x6229, ?x239), ?x7563 = 03bzjpm, ?x6229 = 02q8ms8 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #4173 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 816 *> proper extension: 06cs95; 039c26; *> query: (?x9832, ?x307) <- nominated_for(?x4360, ?x9832), titles(?x307, ?x9832), genre(?x197, ?x307), genre(?x802, ?x307) *> conf = 0.73 ranks of expected_values: 2, 23 EVAL 01xlqd genre 02b5_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 73.000 54.000 0.855 http://example.org/film/film/genre EVAL 01xlqd genre 04t36 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 54.000 0.855 http://example.org/film/film/genre #21867-01w5gg6 PRED entity: 01w5gg6 PRED relation: type_of_union PRED expected values: 04ztj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.78 #37, 0.78 #45, 0.75 #29), 01g63y (0.15 #34, 0.14 #54, 0.13 #66), 0jgjn (0.03 #36, 0.02 #40, 0.02 #64) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 07_3qd; 0zjpz; >> query: (?x9241, 04ztj) <- artist(?x4868, ?x9241), instrumentalists(?x227, ?x9241), ?x4868 = 01w40h, artists(?x283, ?x9241) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w5gg6 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.780 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21866-05b49tt PRED entity: 05b49tt PRED relation: gender PRED expected values: 05zppz => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #50, 0.77 #17, 0.77 #15), 02zsn (0.26 #34, 0.25 #40, 0.25 #38) >> Best rule #50 for best value: >> intensional similarity = 2 >> extensional distance = 1273 >> proper extension: 017yfz; 023l9y; 0dfjb8; 01_k1z; 0c8hct; 01d5vk; 021r7r; 01wxdn3; 0f14q; 04m_kpx; ... >> query: (?x8814, 05zppz) <- profession(?x8814, ?x1078), film_crew_role(?x148, ?x1078) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05b49tt gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.784 http://example.org/people/person/gender #21865-01ptt7 PRED entity: 01ptt7 PRED relation: major_field_of_study PRED expected values: 01mkq 04x_3 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 118): 02lp1 (0.62 #12, 0.57 #504, 0.52 #2599), 0g26h (0.62 #43, 0.46 #535, 0.41 #1891), 01mkq (0.57 #508, 0.56 #16, 0.54 #2603), 02j62 (0.48 #1509, 0.46 #3727, 0.44 #2617), 062z7 (0.47 #519, 0.44 #27, 0.42 #1506), 01540 (0.38 #62, 0.37 #554, 0.32 #2279), 02_7t (0.38 #66, 0.35 #558, 0.31 #1421), 01tbp (0.35 #553, 0.31 #2278, 0.31 #1416), 05qfh (0.35 #528, 0.31 #36, 0.30 #2623), 0_jm (0.34 #1290, 0.34 #1414, 0.31 #675) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 049dk; >> query: (?x2175, 02lp1) <- major_field_of_study(?x2175, ?x2014), school_type(?x2175, ?x1507), school(?x4171, ?x2175), ?x4171 = 092j54 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #508 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 77 *> proper extension: 027xx3; 037fqp; 05zl0; 026vcc; 01bm_; 0bwfn; 01jt2w; 01n_g9; 0c5x_; 021996; ... *> query: (?x2175, 01mkq) <- student(?x2175, ?x6221), institution(?x4981, ?x2175), ?x4981 = 03bwzr4, school(?x580, ?x2175) *> conf = 0.57 ranks of expected_values: 3, 13 EVAL 01ptt7 major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 126.000 126.000 0.625 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01ptt7 major_field_of_study 01mkq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 126.000 0.625 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #21864-03g5jw PRED entity: 03g5jw PRED relation: origin PRED expected values: 07h34 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 107): 0d9jr (0.18 #334, 0.17 #570, 0.12 #1278), 04jpl (0.14 #950, 0.14 #6, 0.12 #7795), 04lh6 (0.14 #150, 0.09 #386, 0.08 #622), 030qb3t (0.11 #4046, 0.09 #7823, 0.09 #12544), 02_286 (0.10 #5901, 0.10 #1904, 0.09 #2140), 0d6lp (0.10 #5901, 0.10 #1953, 0.05 #12811), 09c7w0 (0.10 #5901, 0.09 #237, 0.08 #473), 05fjf (0.10 #5901, 0.08 #828, 0.04 #2480), 02dtg (0.10 #5901, 0.06 #3078, 0.03 #3786), 02cft (0.10 #5901, 0.06 #1290, 0.05 #1998) >> Best rule #334 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 07c0j; 0d193h; 014_lq; 07r1_; 01w5n51; 0bk1p; 07hgm; 09jm8; 0b1hw; >> query: (?x1573, 0d9jr) <- influenced_by(?x1573, ?x1089), award(?x1573, ?x724), group(?x227, ?x1573), award_winner(?x1088, ?x1089) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03g5jw origin 07h34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 115.000 0.182 http://example.org/music/artist/origin #21863-012fvq PRED entity: 012fvq PRED relation: campuses PRED expected values: 012fvq => 185 concepts (120 used for prediction) PRED predicted values (max 10 best out of 369): 0qlnr (0.20 #318, 0.05 #864, 0.05 #1410), 01g7_r (0.20 #244, 0.05 #790, 0.03 #2429), 0217m9 (0.05 #713, 0.03 #2898, 0.02 #3445), 01bk1y (0.05 #815, 0.03 #3000, 0.02 #3547), 01_s9q (0.05 #732, 0.03 #2917, 0.02 #3464), 01yqqv (0.05 #891, 0.03 #3076, 0.02 #3623), 02l424 (0.05 #897, 0.02 #3629, 0.02 #4721), 07wrz (0.05 #602, 0.02 #4426, 0.02 #3880), 0bwfn (0.05 #809, 0.02 #4087, 0.02 #5179), 0kw4j (0.05 #649, 0.02 #5566, 0.02 #7205) >> Best rule #318 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 061v5m; >> query: (?x3576, 0qlnr) <- state_province_region(?x3576, ?x3670), ?x3670 = 05tbn, currency(?x3576, ?x170), company(?x346, ?x3576) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #62309 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 373 *> proper extension: 01zzy3; *> query: (?x3576, ?x331) <- student(?x3576, ?x3497), state_province_region(?x3576, ?x3670), state_province_region(?x331, ?x3670), contains(?x3670, ?x854) *> conf = 0.02 ranks of expected_values: 89 EVAL 012fvq campuses 012fvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 185.000 120.000 0.200 http://example.org/education/educational_institution/campuses #21862-042f1 PRED entity: 042f1 PRED relation: jurisdiction_of_office PRED expected values: 09c7w0 => 217 concepts (169 used for prediction) PRED predicted values (max 10 best out of 255): 09c7w0 (0.77 #1883, 0.75 #1036, 0.74 #987), 059rby (0.20 #401, 0.17 #154, 0.15 #1437), 07z1m (0.20 #415, 0.15 #711, 0.10 #1105), 05kkh (0.20 #298, 0.15 #1038, 0.12 #890), 02_286 (0.17 #159, 0.05 #997, 0.05 #1046), 07ssc (0.11 #2484, 0.09 #2585, 0.08 #2287), 05fjf (0.10 #436, 0.10 #386, 0.08 #732), 02xry (0.10 #370, 0.09 #569, 0.09 #469), 0d0x8 (0.10 #422, 0.08 #718, 0.04 #1309), 0d04z6 (0.10 #330, 0.07 #2030, 0.06 #922) >> Best rule #1883 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 08f3b1; 083p7; 0bwh6; 0157m; 083pr; 0bymv; 0d06m5; 09b6zr; 0d05fv; 0d3qd0; ... >> query: (?x9765, 09c7w0) <- politician(?x8714, ?x9765), student(?x1884, ?x9765), type_of_union(?x9765, ?x566), profession(?x9765, ?x3342), jurisdiction_of_office(?x9765, ?x3778) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042f1 jurisdiction_of_office 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 217.000 169.000 0.769 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #21861-02qcr PRED entity: 02qcr PRED relation: film! PRED expected values: 098n_m 029m83 => 88 concepts (45 used for prediction) PRED predicted values (max 10 best out of 711): 06mn7 (0.52 #33302, 0.51 #79103, 0.47 #77020), 03gt0c5 (0.47 #77020, 0.42 #77019, 0.42 #83269), 073w14 (0.19 #759, 0.03 #4921, 0.03 #7002), 05sq84 (0.12 #236, 0.05 #4398, 0.04 #6479), 0f0kz (0.12 #516, 0.04 #13003, 0.03 #19248), 024bbl (0.12 #838, 0.04 #5000, 0.03 #15407), 0f4vbz (0.12 #363, 0.03 #4525, 0.03 #27419), 04t7ts (0.12 #211, 0.03 #70773, 0.02 #27267), 0dvmd (0.12 #528, 0.02 #4690, 0.02 #6771), 035rnz (0.12 #695, 0.02 #4857, 0.02 #6938) >> Best rule #33302 for best value: >> intensional similarity = 3 >> extensional distance = 313 >> proper extension: 0gfzgl; 0cskb; >> query: (?x9037, ?x4353) <- nominated_for(?x4353, ?x9037), category(?x9037, ?x134), film(?x4353, ?x9838) >> conf = 0.52 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02qcr film! 029m83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 45.000 0.522 http://example.org/film/actor/film./film/performance/film EVAL 02qcr film! 098n_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 45.000 0.522 http://example.org/film/actor/film./film/performance/film #21860-0ck27z PRED entity: 0ck27z PRED relation: award_winner PRED expected values: 044lyq => 35 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1667): 02mqc4 (0.35 #33173, 0.34 #18953, 0.33 #3225), 014zcr (0.35 #33173, 0.34 #18953, 0.33 #37), 02x7vq (0.35 #33173, 0.34 #18953, 0.33 #3542), 01rzqj (0.35 #33173, 0.34 #18953, 0.33 #5417), 0k2mxq (0.35 #33173, 0.34 #18953, 0.33 #40284), 044lyq (0.35 #33173, 0.34 #18953, 0.33 #40284), 02p65p (0.35 #33173, 0.34 #18953, 0.33 #40284), 065ydwb (0.35 #33173, 0.34 #18953, 0.33 #40284), 0kryqm (0.35 #33173, 0.34 #18953, 0.33 #40284), 08s_lw (0.35 #33173, 0.34 #18953, 0.33 #40284) >> Best rule #33173 for best value: >> intensional similarity = 4 >> extensional distance = 231 >> proper extension: 0m57f; >> query: (?x1670, ?x7959) <- award(?x7959, ?x1670), award_winner(?x1670, ?x5662), participant(?x5662, ?x722), award_winner(?x873, ?x7959) >> conf = 0.35 => this is the best rule for 48 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 0ck27z award_winner 044lyq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 35.000 17.000 0.350 http://example.org/award/award_category/winners./award/award_honor/award_winner #21859-0f2tj PRED entity: 0f2tj PRED relation: place_of_birth! PRED expected values: 04mz10g 02d4ct 01m3x5p => 159 concepts (146 used for prediction) PRED predicted values (max 10 best out of 2169): 01k5t_3 (0.38 #231210, 0.32 #135088, 0.29 #226014), 01z9_x (0.38 #231210, 0.32 #135088, 0.29 #226014), 016ksk (0.38 #231210, 0.29 #226014, 0.28 #231209), 01nm3s (0.36 #353286, 0.33 #376671, 0.33 #324716), 0c6qh (0.36 #353286, 0.33 #376671, 0.33 #324716), 04mz10g (0.36 #353286, 0.33 #376671, 0.33 #324716), 073749 (0.36 #353286, 0.33 #376671, 0.33 #324716), 02_p5w (0.36 #353286, 0.33 #376671, 0.33 #324716), 01m3b1t (0.32 #135088, 0.29 #226014, 0.28 #231209), 06lgq8 (0.25 #373, 0.04 #10763, 0.02 #26352) >> Best rule #231210 for best value: >> intensional similarity = 2 >> extensional distance = 185 >> proper extension: 0r3tb; 0n5d1; 02qjb7z; >> query: (?x6769, ?x1247) <- origin(?x1247, ?x6769), nationality(?x1247, ?x94) >> conf = 0.38 => this is the best rule for 3 predicted values *> Best rule #353286 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 419 *> proper extension: 0b_cr; *> query: (?x6769, ?x1404) <- place_of_birth(?x11573, ?x6769), location(?x1404, ?x6769), location(?x11573, ?x3892) *> conf = 0.36 ranks of expected_values: 6 EVAL 0f2tj place_of_birth! 01m3x5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 146.000 0.382 http://example.org/people/person/place_of_birth EVAL 0f2tj place_of_birth! 02d4ct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 146.000 0.382 http://example.org/people/person/place_of_birth EVAL 0f2tj place_of_birth! 04mz10g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 159.000 146.000 0.382 http://example.org/people/person/place_of_birth #21858-02qhm3 PRED entity: 02qhm3 PRED relation: religion PRED expected values: 0c8wxp => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 21): 0c8wxp (0.27 #276, 0.25 #999, 0.25 #1319), 0631_ (0.17 #8, 0.07 #143, 0.04 #3213), 03_gx (0.13 #826, 0.13 #149, 0.13 #780), 0kpl (0.12 #190, 0.10 #280, 0.09 #1506), 051kv (0.11 #50, 0.03 #230, 0.02 #726), 03j6c (0.04 #3213, 0.03 #787, 0.03 #1472), 0n2g (0.04 #3213, 0.03 #193, 0.03 #1554), 0kq2 (0.04 #3213, 0.03 #198, 0.03 #2012), 06nzl (0.04 #3213, 0.03 #195, 0.02 #556), 07x21 (0.04 #3213, 0.03 #218, 0.02 #398) >> Best rule #276 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 03n93; 044kwr; >> query: (?x11612, 0c8wxp) <- gender(?x11612, ?x514), participant(?x11612, ?x9355), people(?x10199, ?x11612) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qhm3 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.269 http://example.org/people/person/religion #21857-0680x0 PRED entity: 0680x0 PRED relation: role PRED expected values: 03gvt => 72 concepts (42 used for prediction) PRED predicted values (max 10 best out of 95): 05r5c (0.84 #2341, 0.84 #278, 0.83 #1459), 02qjv (0.84 #2341, 0.84 #278, 0.83 #1459), 01v1d8 (0.84 #2341, 0.84 #278, 0.83 #1459), 018j2 (0.84 #2341, 0.84 #278, 0.83 #1459), 01s0ps (0.84 #278, 0.83 #1459, 0.82 #3634), 03gvt (0.84 #278, 0.83 #1459, 0.82 #3634), 0dwtp (0.84 #278, 0.83 #1459, 0.82 #3634), 01kcd (0.84 #278, 0.83 #1459, 0.82 #3634), 0l15bq (0.82 #2369, 0.80 #3562, 0.80 #1882), 0l14md (0.82 #2347, 0.79 #2844, 0.78 #1762) >> Best rule #2341 for best value: >> intensional similarity = 21 >> extensional distance = 9 >> proper extension: 018j2; 01679d; >> query: (?x3409, ?x1148) <- role(?x3409, ?x9413), role(?x3409, ?x4311), role(?x3409, ?x1212), role(?x3409, ?x433), role(?x3409, ?x432), role(?x1148, ?x3409), ?x432 = 042v_gx, role(?x569, ?x9413), role(?x433, ?x74), role(?x9408, ?x3409), role(?x1148, ?x4917), role(?x1148, ?x315), ?x1212 = 07xzm, ?x4917 = 06w7v, ?x315 = 0l14md, role(?x9413, ?x214), ?x4311 = 01xqw, role(?x211, ?x433), instrumentalists(?x9413, ?x2945), award(?x9408, ?x1232), ?x1232 = 0c4z8 >> conf = 0.84 => this is the best rule for 4 predicted values *> Best rule #278 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 2 *> proper extension: 026t6; *> query: (?x3409, ?x316) <- role(?x3409, ?x9413), role(?x3409, ?x3296), role(?x3409, ?x2059), role(?x3409, ?x1495), role(?x3409, ?x614), role(?x3409, ?x228), ?x9413 = 07m2y, role(?x2048, ?x3409), role(?x316, ?x3409), ?x1495 = 013y1f, ?x614 = 0mkg, instrumentalists(?x2048, ?x9298), instrumentalists(?x2048, ?x8799), instrumentalists(?x2048, ?x1970), ?x9298 = 016j2t, role(?x6449, ?x2048), group(?x2048, ?x4909), ?x4909 = 01cblr, role(?x211, ?x2048), ?x228 = 0l14qv, ?x8799 = 02f1c, instrumentalists(?x3296, ?x1399), group(?x3296, ?x3109), ?x6449 = 014zz1, performance_role(?x1694, ?x2059), ?x1970 = 0zjpz *> conf = 0.84 ranks of expected_values: 6 EVAL 0680x0 role 03gvt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 72.000 42.000 0.845 http://example.org/music/performance_role/track_performances./music/track_contribution/role #21856-09rx7tx PRED entity: 09rx7tx PRED relation: production_companies PRED expected values: 04rqd => 98 concepts (76 used for prediction) PRED predicted values (max 10 best out of 62): 04rqd (0.33 #76, 0.10 #240, 0.08 #404), 030_1m (0.33 #987, 0.33 #986, 0.32 #3137), 054lpb6 (0.25 #260, 0.17 #96, 0.12 #342), 016tw3 (0.25 #257, 0.13 #2154, 0.12 #998), 024rgt (0.17 #106, 0.10 #188, 0.05 #1177), 06rq1k (0.12 #345, 0.07 #592, 0.07 #510), 086k8 (0.12 #2641, 0.12 #3306, 0.11 #2145), 016tt2 (0.10 #168, 0.08 #2643, 0.08 #3308), 030_1_ (0.10 #180, 0.06 #1003, 0.06 #1086), 0jz9f (0.10 #165, 0.04 #411, 0.03 #658) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 04cf_l; >> query: (?x10840, 04rqd) <- genre(?x10840, ?x225), person(?x10840, ?x11208), produced_by(?x10840, ?x976), ?x11208 = 03h8_g >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09rx7tx production_companies 04rqd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 76.000 0.333 http://example.org/film/film/production_companies #21855-019pm_ PRED entity: 019pm_ PRED relation: award_nominee PRED expected values: 016vg8 => 104 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1190): 02js9p (0.82 #73923, 0.81 #53129, 0.80 #41577), 09dv0sz (0.82 #73923, 0.81 #53129, 0.80 #41577), 016vg8 (0.82 #73923, 0.81 #53129, 0.80 #41577), 02bj6k (0.75 #66995, 0.73 #55441, 0.12 #99331), 019pm_ (0.71 #5225, 0.62 #9843, 0.59 #7534), 02qgyv (0.29 #494, 0.19 #12042, 0.14 #5115), 01gbn6 (0.19 #13549, 0.01 #66685), 0bksh (0.18 #66994, 0.16 #64684, 0.15 #94711), 011zd3 (0.18 #66994, 0.16 #64684, 0.15 #94711), 01g23m (0.18 #66994, 0.16 #64684, 0.15 #94711) >> Best rule #73923 for best value: >> intensional similarity = 3 >> extensional distance = 327 >> proper extension: 01wg982; 01c6l; 037d35; 08t7nz; 0htcn; 0hqly; >> query: (?x2763, ?x221) <- profession(?x2763, ?x524), award_nominee(?x221, ?x2763), ?x524 = 02jknp >> conf = 0.82 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 019pm_ award_nominee 016vg8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 53.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21854-01wb95 PRED entity: 01wb95 PRED relation: film_sets_designed! PRED expected values: 057bc6m => 88 concepts (67 used for prediction) PRED predicted values (max 10 best out of 20): 0520r2x (0.13 #199, 0.13 #122, 0.13 #148), 076psv (0.06 #103, 0.05 #129, 0.05 #180), 0cb77r (0.06 #123, 0.05 #150, 0.04 #149), 0c0tzp (0.06 #123, 0.05 #150, 0.04 #149), 05218gr (0.06 #123, 0.05 #150, 0.04 #149), 0f7h2g (0.06 #123, 0.04 #149, 0.03 #200), 057bc6m (0.05 #108, 0.05 #134, 0.04 #185), 07h1tr (0.05 #101, 0.05 #127, 0.04 #178), 076lxv (0.04 #99, 0.04 #125, 0.04 #176), 0579tg2 (0.04 #20, 0.03 #200, 0.02 #117) >> Best rule #199 for best value: >> intensional similarity = 4 >> extensional distance = 202 >> proper extension: 02qjv1p; >> query: (?x3783, ?x199) <- nominated_for(?x199, ?x3783), genre(?x3783, ?x53), place_of_death(?x199, ?x682), award_nominee(?x200, ?x199) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #108 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 166 *> proper extension: 05z43v; *> query: (?x3783, 057bc6m) <- nominated_for(?x199, ?x3783), genre(?x3783, ?x53), place_of_death(?x199, ?x682), award_winner(?x199, ?x200) *> conf = 0.05 ranks of expected_values: 7 EVAL 01wb95 film_sets_designed! 057bc6m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 88.000 67.000 0.133 http://example.org/film/film_set_designer/film_sets_designed #21853-01t6b4 PRED entity: 01t6b4 PRED relation: produced_by! PRED expected values: 09wnnb => 204 concepts (196 used for prediction) PRED predicted values (max 10 best out of 639): 07s8z_l (0.41 #72334, 0.24 #11129, 0.12 #51006), 01kt_j (0.40 #81610, 0.15 #16692, 0.14 #23183), 0g22z (0.40 #1862, 0.07 #7427, 0.06 #20408), 060__7 (0.40 #2616, 0.07 #8181, 0.04 #21162), 02q3fdr (0.40 #2405, 0.07 #7970, 0.04 #20951), 026p4q7 (0.40 #2064, 0.07 #7629, 0.04 #20610), 03bzyn4 (0.33 #5452, 0.31 #6380, 0.29 #8236), 05h43ls (0.33 #4854, 0.31 #5782, 0.29 #7638), 0435vm (0.25 #1263, 0.08 #4971, 0.08 #5899), 0900j5 (0.25 #1235, 0.08 #4943, 0.08 #5871) >> Best rule #72334 for best value: >> intensional similarity = 3 >> extensional distance = 242 >> proper extension: 01hrqc; >> query: (?x1285, ?x10447) <- produced_by(?x155, ?x1285), nationality(?x1285, ?x94), award_winner(?x10447, ?x1285) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #6491 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 05xbx; *> query: (?x1285, ?x148) <- award_nominee(?x1285, ?x3920), titles(?x3920, ?x218), production_companies(?x148, ?x3920) *> conf = 0.04 ranks of expected_values: 137 EVAL 01t6b4 produced_by! 09wnnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 204.000 196.000 0.410 http://example.org/film/film/produced_by #21852-04kxsb PRED entity: 04kxsb PRED relation: award! PRED expected values: 05bnp0 014zcr 01qscs 09fb5 0241jw 0b_dy 01ps2h8 01f6zc 016xh5 05kwx2 016kft => 52 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2411): 0170pk (0.78 #39476, 0.66 #49349, 0.62 #16865), 09fb5 (0.78 #39476, 0.66 #49349, 0.51 #29603), 016z2j (0.78 #39476, 0.66 #49349, 0.51 #29603), 039bp (0.78 #39476, 0.66 #49349, 0.51 #29603), 0170s4 (0.60 #10473, 0.54 #17051, 0.33 #3897), 05mkhs (0.60 #10882, 0.23 #17460, 0.13 #42765), 014zcr (0.54 #16492, 0.52 #19782, 0.40 #9914), 026r8q (0.54 #18513, 0.40 #11935, 0.19 #21803), 015grj (0.54 #16656, 0.33 #3502, 0.33 #213), 0pmhf (0.50 #13815, 0.40 #10526, 0.38 #17104) >> Best rule #39476 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 03r8v_; >> query: (?x2375, ?x406) <- award(?x397, ?x2375), nominated_for(?x2375, ?x89), sibling(?x12975, ?x397), award_winner(?x2375, ?x406) >> conf = 0.78 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 7, 17, 26, 28, 66, 77, 173, 291, 449, 620 EVAL 04kxsb award! 016kft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04kxsb award! 05kwx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04kxsb award! 016xh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04kxsb award! 01f6zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04kxsb award! 01ps2h8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04kxsb award! 0b_dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04kxsb award! 0241jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04kxsb award! 09fb5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04kxsb award! 01qscs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04kxsb award! 014zcr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04kxsb award! 05bnp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 52.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21851-0blg2 PRED entity: 0blg2 PRED relation: olympics! PRED expected values: 05b4w => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 193): 0b90_r (0.82 #1324, 0.80 #1213, 0.80 #1104), 06qd3 (0.82 #1345, 0.80 #1125, 0.78 #1015), 015fr (0.80 #1113, 0.78 #1003, 0.75 #1776), 03_3d (0.80 #1214, 0.75 #1768, 0.74 #2546), 06c1y (0.80 #1237, 0.75 #1791, 0.73 #1348), 015qh (0.80 #1236, 0.71 #686, 0.67 #1790), 0ctw_b (0.71 #675, 0.70 #1225, 0.67 #1779), 0d04z6 (0.71 #733, 0.67 #1837, 0.64 #1394), 019rg5 (0.67 #235, 0.64 #1335, 0.60 #1224), 05b4w (0.67 #266, 0.62 #3813, 0.61 #4033) >> Best rule #1324 for best value: >> intensional similarity = 11 >> extensional distance = 9 >> proper extension: 0lbbj; >> query: (?x2134, 0b90_r) <- olympics(?x3730, ?x2134), sports(?x2134, ?x3015), sports(?x2134, ?x779), ?x3730 = 03shp, ?x779 = 096f8, country(?x3015, ?x7833), country(?x3015, ?x7413), sports(?x391, ?x3015), ?x7413 = 04hqz, ?x7833 = 0jdx, sports(?x2134, ?x3659) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #266 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 06sks6; *> query: (?x2134, 05b4w) <- olympics(?x6559, ?x2134), olympics(?x4737, ?x2134), olympics(?x1892, ?x2134), olympics(?x1229, ?x2134), olympics(?x421, ?x2134), olympics(?x390, ?x2134), ?x1892 = 02vzc, ?x1229 = 059j2, ?x6559 = 05r7t, ?x390 = 0chghy, olympics(?x151, ?x2134), ?x421 = 03_r3, film_release_region(?x86, ?x4737), countries_spoken_in(?x2502, ?x4737) *> conf = 0.67 ranks of expected_values: 10 EVAL 0blg2 olympics! 05b4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 56.000 56.000 0.818 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #21850-01f85k PRED entity: 01f85k PRED relation: film_release_region PRED expected values: 05r4w 09c7w0 0jgd 0d0vqn 0345h => 144 concepts (140 used for prediction) PRED predicted values (max 10 best out of 143): 09c7w0 (0.92 #6332, 0.92 #4123, 0.92 #17698), 0d0vqn (0.91 #3539, 0.90 #1330, 0.90 #2507), 0345h (0.90 #1352, 0.88 #1058, 0.87 #1499), 05r4w (0.88 #1031, 0.84 #4563, 0.83 #1325), 0jgd (0.87 #1475, 0.85 #887, 0.82 #5890), 06t2t (0.85 #1085, 0.80 #1379, 0.77 #350), 047yc (0.85 #1053, 0.80 #1347, 0.64 #171), 04gzd (0.77 #1039, 0.77 #1333, 0.55 #3542), 05b4w (0.77 #941, 0.76 #3591, 0.75 #4620), 03rk0 (0.73 #1374, 0.73 #1080, 0.64 #198) >> Best rule #6332 for best value: >> intensional similarity = 4 >> extensional distance = 221 >> proper extension: 049mql; 03176f; 0298n7; >> query: (?x6376, 09c7w0) <- film_format(?x6376, ?x6392), film_release_region(?x6376, ?x252), nominated_for(?x4169, ?x6376), olympics(?x252, ?x418) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 01f85k film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 140.000 0.924 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01f85k film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 140.000 0.924 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01f85k film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 140.000 0.924 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01f85k film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 140.000 0.924 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01f85k film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 140.000 0.924 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21849-01qgr3 PRED entity: 01qgr3 PRED relation: category PRED expected values: 08mbj5d => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #33, 0.90 #12, 0.90 #34) >> Best rule #33 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 02zc7f; >> query: (?x7338, 08mbj5d) <- student(?x7338, ?x4976), school(?x729, ?x7338), colors(?x7338, ?x663), contains(?x3778, ?x7338) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qgr3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.908 http://example.org/common/topic/webpage./common/webpage/category #21848-0fq9zdv PRED entity: 0fq9zdv PRED relation: nominated_for PRED expected values: 021y7yw 01dvbd 035zr0 => 53 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1313): 05hjnw (0.75 #8607, 0.61 #13316, 0.50 #2328), 092vkg (0.69 #7995, 0.40 #3285, 0.33 #12704), 026p4q7 (0.64 #12912, 0.50 #8203, 0.33 #354), 011yqc (0.64 #12764, 0.38 #8055, 0.33 #206), 0b6tzs (0.64 #12688, 0.33 #130, 0.31 #7979), 017gl1 (0.64 #12691, 0.33 #133, 0.29 #4842), 0gmgwnv (0.62 #8802, 0.61 #13511, 0.33 #953), 02c638 (0.62 #8153, 0.42 #12862, 0.33 #304), 06_x996 (0.62 #8454, 0.39 #13163, 0.16 #33579), 04b2qn (0.62 #9035, 0.36 #13744, 0.33 #1186) >> Best rule #8607 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 03hkv_r; 0f4x7; 0gr4k; 099c8n; 0gqyl; 09td7p; 02x17s4; 09qv_s; 02ppm4q; 02x4sn8; ... >> query: (?x5886, 05hjnw) <- nominated_for(?x5886, ?x11213), nominated_for(?x5886, ?x3287), ?x11213 = 0170xl, film_release_region(?x3287, ?x87), executive_produced_by(?x3287, ?x4857), country(?x3287, ?x6401) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6628 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 09qwmm; 094qd5; 0fq9zdn; 05zvq6g; 099cng; *> query: (?x5886, 021y7yw) <- nominated_for(?x5886, ?x11213), nominated_for(?x5886, ?x7009), nominated_for(?x5886, ?x4329), nominated_for(?x5886, ?x4007), production_companies(?x11213, ?x1914), film_crew_role(?x4329, ?x281), ?x7009 = 0bs8s1p, award_winner(?x4007, ?x276) *> conf = 0.44 ranks of expected_values: 45, 610, 1096 EVAL 0fq9zdv nominated_for 035zr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 23.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fq9zdv nominated_for 01dvbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 23.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fq9zdv nominated_for 021y7yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 53.000 23.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21847-0fpxp PRED entity: 0fpxp PRED relation: actor PRED expected values: 01nczg => 75 concepts (41 used for prediction) PRED predicted values (max 10 best out of 553): 024jwt (0.38 #10190, 0.37 #8336, 0.36 #12043), 0n8bn (0.33 #545, 0.03 #3323, 0.02 #7954), 018dnt (0.33 #45, 0.03 #2823, 0.02 #3749), 01541z (0.33 #161, 0.03 #2939, 0.02 #3865), 0lzb8 (0.33 #46, 0.03 #2824, 0.02 #3750), 0l_dv (0.33 #910, 0.03 #3688, 0.02 #4614), 0jbp0 (0.33 #773, 0.03 #3551, 0.02 #4477), 0m32_ (0.33 #222, 0.03 #3000, 0.02 #3926), 01jbx1 (0.14 #1188, 0.10 #2114, 0.05 #3040), 0163t3 (0.14 #1609, 0.10 #2535, 0.05 #3461) >> Best rule #10190 for best value: >> intensional similarity = 4 >> extensional distance = 137 >> proper extension: 0hr41p6; >> query: (?x7904, ?x10694) <- genre(?x7904, ?x258), nominated_for(?x10694, ?x7904), nominated_for(?x2750, ?x7904), genre(?x86, ?x258) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #3828 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 06xkst; 0gxr1c; *> query: (?x7904, 01nczg) <- actor(?x7904, ?x3183), genre(?x7904, ?x258), film(?x3183, ?x66), special_performance_type(?x3183, ?x4832) *> conf = 0.02 ranks of expected_values: 488 EVAL 0fpxp actor 01nczg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 75.000 41.000 0.383 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21846-0252fh PRED entity: 0252fh PRED relation: award PRED expected values: 0bdwqv => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 247): 09sb52 (0.31 #1661, 0.30 #5306, 0.27 #41), 027dtxw (0.29 #1624, 0.13 #17823, 0.13 #19039), 02x73k6 (0.28 #1681, 0.27 #61, 0.13 #17823), 09sdmz (0.28 #1827, 0.18 #207, 0.07 #5472), 02w9sd7 (0.27 #171, 0.18 #1791, 0.13 #17823), 05p09zm (0.27 #125, 0.12 #1340, 0.10 #530), 0bdwqv (0.27 #1793, 0.09 #173, 0.09 #1388), 0f4x7 (0.26 #1651, 0.18 #31, 0.13 #1246), 0bfvd4 (0.22 #1736, 0.08 #1331, 0.06 #5381), 04kxsb (0.21 #1747, 0.18 #127, 0.13 #17823) >> Best rule #1661 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 0hwd8; >> query: (?x7780, 09sb52) <- nationality(?x7780, ?x94), award(?x7780, ?x3066), ?x3066 = 0gqy2 >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #1793 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 163 *> proper extension: 0hwd8; *> query: (?x7780, 0bdwqv) <- nationality(?x7780, ?x94), award(?x7780, ?x3066), ?x3066 = 0gqy2 *> conf = 0.27 ranks of expected_values: 7 EVAL 0252fh award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 79.000 79.000 0.309 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21845-03bxz7 PRED entity: 03bxz7 PRED relation: genre! PRED expected values: 0cnztc4 02r8hh_ 05dy7p 0gmblvq 0hfzr 0bs5k8r 0194zl 02lxrv 064lsn 041td_ 048xyn 03wj4r8 01gvpz 0k5px => 59 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1753): 01242_ (0.71 #23571, 0.62 #25333, 0.50 #11245), 0286vp (0.67 #20557, 0.50 #11753, 0.43 #24079), 047myg9 (0.62 #25744, 0.60 #13417, 0.57 #23982), 0fgpvf (0.62 #24753, 0.60 #12426, 0.50 #10665), 083skw (0.62 #25062, 0.60 #12735, 0.50 #10974), 03bxp5 (0.62 #25704, 0.60 #13377, 0.50 #11616), 0fsd9t (0.62 #26103, 0.60 #13776, 0.50 #12015), 0_9l_ (0.62 #26353, 0.60 #14026, 0.50 #12265), 08lr6s (0.62 #24701, 0.60 #12374, 0.50 #10613), 03kg2v (0.62 #25122, 0.60 #12795, 0.33 #21598) >> Best rule #23571 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 02p0szs; >> query: (?x6887, 01242_) <- genre(?x9981, ?x6887), genre(?x7283, ?x6887), genre(?x6362, ?x6887), genre(?x4359, ?x6887), genre(?x2932, ?x6887), titles(?x53, ?x7283), film_release_distribution_medium(?x2932, ?x81), currency(?x2932, ?x170), ?x6362 = 03_gz8, produced_by(?x4359, ?x2689), honored_for(?x3624, ?x4359), nominated_for(?x828, ?x9981) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #13015 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 02l7c8; 060__y; *> query: (?x6887, 0bs5k8r) <- genre(?x6352, ?x6887), genre(?x6178, ?x6887), genre(?x5520, ?x6887), genre(?x3514, ?x6887), genre(?x2932, ?x6887), ?x2932 = 0gyy53, nominated_for(?x1033, ?x5520), nominated_for(?x9781, ?x5520), film(?x914, ?x6352), film_release_region(?x3514, ?x87), ?x914 = 0htlr, titles(?x53, ?x6178) *> conf = 0.60 ranks of expected_values: 12, 48, 66, 80, 81, 206, 271, 408, 414, 445, 447, 626, 628, 909 EVAL 03bxz7 genre! 0k5px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 01gvpz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 03wj4r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 048xyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 041td_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 02lxrv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 0194zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 0bs5k8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 0hfzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 0gmblvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 05dy7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 02r8hh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 59.000 34.000 0.714 http://example.org/film/film/genre EVAL 03bxz7 genre! 0cnztc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 59.000 34.000 0.714 http://example.org/film/film/genre #21844-0r3w7 PRED entity: 0r3w7 PRED relation: source PRED expected values: 0jbk9 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #32, 0.91 #59, 0.84 #40) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 0f04v; >> query: (?x13207, 0jbk9) <- time_zones(?x13207, ?x2950), ?x2950 = 02lcqs, place(?x13207, ?x13207) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r3w7 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.944 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #21843-0rlz PRED entity: 0rlz PRED relation: notable_people_with_this_condition! PRED expected values: 02bft => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 20): 0h99n (0.25 #851, 0.07 #220, 0.02 #1385), 0g02vk (0.20 #222, 0.08 #348, 0.08 #411), 029sk (0.15 #842, 0.02 #1376, 0.02 #1418), 0m32h (0.14 #70, 0.13 #217, 0.08 #133), 01g2q (0.13 #219, 0.08 #850, 0.03 #451), 068p_ (0.10 #860, 0.07 #229, 0.04 #418), 07jwr (0.09 #107, 0.07 #212, 0.06 #254), 0542n (0.09 #126, 0.07 #231, 0.06 #273), 03p41 (0.08 #847, 0.01 #1144, 0.01 #1252), 0dcsx (0.07 #214, 0.04 #382, 0.04 #403) >> Best rule #851 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 02mslq; >> query: (?x5742, 0h99n) <- notable_people_with_this_condition(?x12882, ?x5742), nationality(?x5742, ?x94), ?x94 = 09c7w0 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0rlz notable_people_with_this_condition! 02bft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 128.000 0.250 http://example.org/medicine/disease/notable_people_with_this_condition #21842-0p5vf PRED entity: 0p5vf PRED relation: jurisdiction_of_office PRED expected values: 06t2t 024pcx => 30 concepts (25 used for prediction) PRED predicted values (max 10 best out of 691): 059j2 (0.60 #4011, 0.60 #1335, 0.56 #4469), 05qhw (0.60 #1335, 0.50 #915, 0.33 #1808), 03rjj (0.60 #1335, 0.50 #903, 0.33 #1796), 015qh (0.60 #1335, 0.50 #955, 0.33 #1848), 088q4 (0.60 #1335, 0.50 #1040, 0.33 #1933), 06mkj (0.60 #1335, 0.50 #986, 0.33 #1879), 04wlh (0.60 #1335, 0.50 #1200, 0.33 #2093), 0jdx (0.60 #1335, 0.50 #1179, 0.33 #2072), 01znc_ (0.60 #1335, 0.50 #956, 0.33 #1849), 0d05q4 (0.60 #1335, 0.50 #1070, 0.33 #1963) >> Best rule #4011 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 07xl34; >> query: (?x10118, ?x1229) <- company(?x10118, ?x1229), adjoins(?x1229, ?x172), organization(?x1229, ?x127), time_zones(?x1229, ?x2864) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #993 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 2 *> proper extension: 060c4; *> query: (?x10118, 06t2t) <- jurisdiction_of_office(?x10118, ?x12908), jurisdiction_of_office(?x10118, ?x8506), jurisdiction_of_office(?x10118, ?x3730), jurisdiction_of_office(?x10118, ?x2267), ?x2267 = 03rj0, basic_title(?x11492, ?x10118), basic_title(?x9680, ?x10118), student(?x9741, ?x11492), award_winner(?x921, ?x11492), ?x3730 = 03shp, contains(?x8506, ?x5036), featured_film_locations(?x308, ?x5036), month(?x5036, ?x1459), adjoins(?x12125, ?x12908), religion(?x9680, ?x1985) *> conf = 0.50 ranks of expected_values: 59, 299 EVAL 0p5vf jurisdiction_of_office 024pcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 30.000 25.000 0.600 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0p5vf jurisdiction_of_office 06t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 30.000 25.000 0.600 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #21841-01wxdn3 PRED entity: 01wxdn3 PRED relation: nationality PRED expected values: 07ssc => 119 concepts (95 used for prediction) PRED predicted values (max 10 best out of 29): 09c7w0 (0.85 #5807, 0.73 #8821, 0.72 #7612), 02jx1 (0.80 #9427, 0.80 #9531, 0.63 #6208), 07ssc (0.80 #9427, 0.80 #9531, 0.63 #6208), 0hl24 (0.41 #6209, 0.39 #9532, 0.39 #9428), 03rk0 (0.17 #6152, 0.06 #7858, 0.05 #8564), 06q1r (0.12 #377, 0.09 #477, 0.08 #677), 0d060g (0.09 #407, 0.07 #707, 0.06 #1507), 03rt9 (0.08 #613, 0.06 #1213, 0.05 #1313), 0d0vqn (0.08 #509, 0.02 #1409, 0.02 #6115), 0f8l9c (0.05 #6128, 0.02 #4728, 0.02 #3126) >> Best rule #5807 for best value: >> intensional similarity = 3 >> extensional distance = 461 >> proper extension: 03zqc1; >> query: (?x9735, 09c7w0) <- student(?x1103, ?x9735), organization(?x346, ?x1103), currency(?x1103, ?x170) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #9427 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1907 *> proper extension: 04bs3j; 01kwld; 064nh4k; 034x61; 016khd; 01yb09; 0277470; 01t6b4; 01g257; 0c3kw; ... *> query: (?x9735, ?x1310) <- profession(?x9735, ?x319), place_of_birth(?x9735, ?x9736), contains(?x1310, ?x9736), nationality(?x57, ?x1310), country(?x1552, ?x1310) *> conf = 0.80 ranks of expected_values: 3 EVAL 01wxdn3 nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 95.000 0.853 http://example.org/people/person/nationality #21840-02gnmp PRED entity: 02gnmp PRED relation: school! PRED expected values: 02d02 => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 93): 0jmj7 (0.69 #2355, 0.66 #2727, 0.65 #3192), 04wmvz (0.27 #79, 0.12 #637, 0.11 #823), 05m_8 (0.19 #1864, 0.14 #282, 0.12 #2329), 02d02 (0.18 #69, 0.11 #1930, 0.09 #627), 05tfm (0.18 #16, 0.06 #574, 0.06 #760), 01y49 (0.18 #22, 0.06 #580, 0.06 #766), 05tg3 (0.18 #33, 0.06 #591, 0.06 #777), 01slc (0.14 #1920, 0.09 #338, 0.08 #3222), 0713r (0.14 #316, 0.13 #1898, 0.09 #2735), 07l4z (0.13 #1931, 0.10 #2396, 0.08 #3233) >> Best rule #2355 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 02zkz7; >> query: (?x11244, 0jmj7) <- organization(?x346, ?x11244), school(?x7357, ?x11244), currency(?x11244, ?x170) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #69 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0ym17; *> query: (?x11244, 02d02) <- contains(?x94, ?x11244), time_zones(?x11244, ?x2950), institution(?x1526, ?x11244), ?x1526 = 0bkj86 *> conf = 0.18 ranks of expected_values: 4 EVAL 02gnmp school! 02d02 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 168.000 168.000 0.692 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #21839-01vh08 PRED entity: 01vh08 PRED relation: languages PRED expected values: 02h40lc => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 14): 02h40lc (0.36 #158, 0.30 #197, 0.30 #41), 064_8sq (0.09 #171, 0.03 #444, 0.03 #717), 02bjrlw (0.09 #157, 0.02 #196, 0.01 #430), 03_9r (0.09 #161), 02ztjwg (0.05 #181), 02hwhyv (0.05 #178), 03hkp (0.05 #166), 012w70 (0.05 #164), 0653m (0.05 #163), 04306rv (0.05 #159) >> Best rule #158 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 02d9k; 03f1zhf; >> query: (?x9036, 02h40lc) <- nationality(?x9036, ?x94), gender(?x9036, ?x231), location(?x9036, ?x191), ?x191 = 0k049 >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vh08 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.364 http://example.org/people/person/languages #21838-01vzxld PRED entity: 01vzxld PRED relation: gender PRED expected values: 02zsn => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #153, 0.72 #173, 0.71 #208), 02zsn (0.51 #175, 0.39 #40, 0.37 #48) >> Best rule #153 for best value: >> intensional similarity = 3 >> extensional distance = 1681 >> proper extension: 0j3v; 02ln1; 02x8mt; 02vptk_; 047g6; 01h2_6; 011zwl; >> query: (?x10181, 05zppz) <- student(?x13639, ?x10181), nationality(?x10181, ?x94), category(?x13639, ?x134) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #175 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2081 *> proper extension: 07c37; *> query: (?x10181, ?x231) <- student(?x13639, ?x10181), student(?x13639, ?x3295), gender(?x3295, ?x231) *> conf = 0.51 ranks of expected_values: 2 EVAL 01vzxld gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.719 http://example.org/people/person/gender #21837-0bh8tgs PRED entity: 0bh8tgs PRED relation: film_crew_role PRED expected values: 09vw2b7 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 31): 09vw2b7 (0.75 #6, 0.71 #751, 0.71 #70), 01pvkk (0.30 #432, 0.28 #334, 0.28 #1534), 02ynfr (0.22 #434, 0.19 #757, 0.17 #789), 02rh1dz (0.20 #431, 0.19 #105, 0.17 #300), 0215hd (0.18 #79, 0.17 #15, 0.14 #111), 0d2b38 (0.17 #118, 0.17 #22, 0.15 #86), 089g0h (0.15 #176, 0.11 #112, 0.11 #793), 015h31 (0.14 #104, 0.14 #397, 0.13 #364), 02_n3z (0.12 #129, 0.10 #1492, 0.09 #65), 033smt (0.10 #1492, 0.09 #184, 0.09 #2697) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 02y_lrp; 0cz_ym; 02vjp3; 03wj4r8; 09lxv9; 0408m53; >> query: (?x5089, 09vw2b7) <- film_crew_role(?x5089, ?x1284), nominated_for(?x489, ?x5089), person(?x5089, ?x2669), ?x1284 = 0ch6mp2 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bh8tgs film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21836-0d060g PRED entity: 0d060g PRED relation: featured_film_locations! PRED expected values: 075wx7_ => 222 concepts (195 used for prediction) PRED predicted values (max 10 best out of 712): 04dsnp (0.18 #23275, 0.10 #28433, 0.08 #22613), 02dwj (0.18 #23275, 0.08 #10569, 0.05 #9842), 0dnkmq (0.18 #23275, 0.08 #12316, 0.07 #46501), 05f4_n0 (0.18 #23275, 0.08 #11941, 0.06 #22852), 050gkf (0.18 #23275, 0.08 #11772, 0.05 #28503), 0d90m (0.18 #23275, 0.08 #11640, 0.05 #28371), 08hmch (0.18 #23275, 0.06 #45888, 0.05 #28434), 05sy_5 (0.18 #23275, 0.05 #28813, 0.05 #32449), 01cmp9 (0.18 #23275, 0.05 #28808, 0.04 #12077), 027qgy (0.18 #23275, 0.05 #28383, 0.04 #11652) >> Best rule #23275 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 01zv_; >> query: (?x279, ?x97) <- contains(?x279, ?x10683), featured_film_locations(?x1064, ?x279), featured_film_locations(?x97, ?x10683) >> conf = 0.18 => this is the best rule for 36 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 30 EVAL 0d060g featured_film_locations! 075wx7_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 222.000 195.000 0.177 http://example.org/film/film/featured_film_locations #21835-02fgdx PRED entity: 02fgdx PRED relation: service_language PRED expected values: 02h40lc => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 21): 02h40lc (0.91 #428, 0.90 #576, 0.89 #320), 06nm1 (0.16 #69, 0.14 #155, 0.13 #580), 03_9r (0.16 #68, 0.06 #26, 0.05 #474), 064_8sq (0.12 #563, 0.12 #584, 0.12 #73), 04306rv (0.09 #429, 0.09 #577, 0.08 #66), 01r2l (0.08 #75, 0.06 #33, 0.05 #481), 06b_j (0.08 #74, 0.06 #32, 0.04 #160), 05zjd (0.08 #76, 0.04 #162, 0.04 #566), 02hwhyv (0.06 #37, 0.04 #79, 0.04 #165), 01jb8r (0.06 #42, 0.04 #212, 0.03 #233) >> Best rule #428 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 02vk52z; 0p4wb; 018mxj; 07y2s; 0xbm; 0196bp; 0hm0k; 02qdyj; 0j47s; 064f29; ... >> query: (?x3387, 02h40lc) <- contact_category(?x3387, ?x897), organization(?x346, ?x3387), company(?x346, ?x94) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fgdx service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.912 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #21834-0h3k3f PRED entity: 0h3k3f PRED relation: language PRED expected values: 02h40lc => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 40): 02h40lc (0.95 #592, 0.94 #948, 0.93 #415), 064_8sq (0.19 #494, 0.18 #2450, 0.18 #81), 06b_j (0.15 #495, 0.10 #377, 0.09 #318), 04h9h (0.14 #161, 0.09 #515, 0.07 #397), 02bjrlw (0.13 #473, 0.12 #355, 0.09 #828), 04306rv (0.13 #1188, 0.11 #5, 0.11 #1129), 03_9r (0.11 #10, 0.05 #2795, 0.05 #2617), 0c_v2 (0.11 #17, 0.05 #135, 0.05 #76), 06nm1 (0.11 #2202, 0.11 #2499, 0.11 #2379), 0jzc (0.07 #374, 0.06 #492, 0.06 #551) >> Best rule #592 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 08cfr1; 0jqb8; >> query: (?x8735, 02h40lc) <- country(?x8735, ?x94), film(?x7091, ?x8735), film_art_direction_by(?x8735, ?x4896), production_companies(?x8735, ?x902) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h3k3f language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.948 http://example.org/film/film/language #21833-0347xz PRED entity: 0347xz PRED relation: film PRED expected values: 04tz52 => 78 concepts (62 used for prediction) PRED predicted values (max 10 best out of 190): 015ppk (0.08 #19697, 0.08 #17906, 0.08 #12534), 01cvtf (0.08 #19697, 0.08 #17906, 0.08 #12534), 02qkq0 (0.07 #23279, 0.06 #37603, 0.06 #48349), 0fhzwl (0.04 #44767, 0.03 #51931, 0.03 #57305), 0g60z (0.04 #44767, 0.03 #51931, 0.03 #57305), 03bx2lk (0.03 #5556, 0.02 #1974, 0.02 #14508), 0fphf3v (0.03 #1362, 0.02 #4944, 0.02 #10315), 017jd9 (0.02 #25849, 0.02 #13313, 0.02 #22267), 011ywj (0.02 #26506, 0.02 #6808, 0.02 #22924), 09cr8 (0.02 #25353, 0.02 #21771, 0.02 #36095) >> Best rule #19697 for best value: >> intensional similarity = 3 >> extensional distance = 613 >> proper extension: 049tjg; >> query: (?x10051, ?x7116) <- actor(?x7116, ?x10051), type_of_union(?x10051, ?x566), ?x566 = 04ztj >> conf = 0.08 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0347xz film 04tz52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 62.000 0.081 http://example.org/film/actor/film./film/performance/film #21832-0bxbb PRED entity: 0bxbb PRED relation: category PRED expected values: 08mbj5d => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #31, 0.83 #29, 0.82 #33) >> Best rule #31 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0fw4v; >> query: (?x6454, 08mbj5d) <- county_seat(?x9889, ?x6454), time_zones(?x6454, ?x2674), source(?x6454, ?x958), place_of_birth(?x976, ?x6454) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bxbb category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.841 http://example.org/common/topic/webpage./common/webpage/category #21831-0hhggmy PRED entity: 0hhggmy PRED relation: genre PRED expected values: 02kdv5l => 86 concepts (84 used for prediction) PRED predicted values (max 10 best out of 144): 07s9rl0 (0.80 #3515, 0.77 #8610, 0.76 #8364), 02kdv5l (0.72 #1578, 0.70 #1940, 0.64 #7638), 05p553 (0.72 #8003, 0.67 #8125, 0.47 #3761), 03k9fj (0.70 #1940, 0.34 #1345, 0.31 #1587), 09blyk (0.70 #1940, 0.16 #395, 0.14 #4030), 04t2t (0.70 #1940, 0.16 #422, 0.09 #664), 0c3351 (0.70 #1940, 0.11 #6338, 0.11 #6459), 03q4nz (0.50 #19, 0.15 #2928, 0.08 #7149), 06n90 (0.38 #255, 0.30 #1588, 0.28 #1467), 02n4kr (0.32 #7035, 0.29 #2434, 0.29 #2192) >> Best rule #3515 for best value: >> intensional similarity = 8 >> extensional distance = 232 >> proper extension: 050r1z; 0bscw; 01719t; 029zqn; 04jwly; 07yvsn; 016kv6; 02qhlwd; 014kkm; 02tktw; ... >> query: (?x8580, 07s9rl0) <- language(?x8580, ?x254), genre(?x8580, ?x604), produced_by(?x8580, ?x4685), country(?x8580, ?x789), profession(?x4685, ?x1041), ?x1041 = 03gjzk, genre(?x3287, ?x604), ?x3287 = 026njb5 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1578 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 59 *> proper extension: 025s1wg; *> query: (?x8580, 02kdv5l) <- language(?x8580, ?x254), genre(?x8580, ?x812), produced_by(?x8580, ?x4685), prequel(?x8580, ?x5791), film_release_distribution_medium(?x8580, ?x81), genre(?x2009, ?x812), genre(?x5871, ?x812), ?x5871 = 02b61v *> conf = 0.72 ranks of expected_values: 2 EVAL 0hhggmy genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 84.000 0.799 http://example.org/film/film/genre #21830-03bzyn4 PRED entity: 03bzyn4 PRED relation: genre PRED expected values: 06cvj => 115 concepts (107 used for prediction) PRED predicted values (max 10 best out of 101): 01z4y (0.61 #10125, 0.56 #4696, 0.53 #4937), 06cvj (0.52 #1203, 0.33 #3, 0.23 #963), 03k9fj (0.40 #131, 0.40 #611, 0.35 #371), 02kdv5l (0.38 #122, 0.38 #602, 0.37 #482), 01jfsb (0.35 #492, 0.35 #1092, 0.35 #4949), 01t_vv (0.34 #1014, 0.21 #1254, 0.10 #4628), 0gsy3b (0.33 #95, 0.05 #1295, 0.04 #1055), 01hmnh (0.29 #617, 0.27 #497, 0.24 #137), 04xvlr (0.26 #241, 0.21 #4575, 0.18 #6025), 082gq (0.25 #270, 0.13 #4604, 0.12 #6054) >> Best rule #10125 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 03y317; >> query: (?x9496, ?x2480) <- titles(?x2480, ?x9496), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1203 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 194 *> proper extension: 04svwx; *> query: (?x9496, 06cvj) <- genre(?x9496, ?x1403), genre(?x9496, ?x258), ?x258 = 05p553, ?x1403 = 02l7c8 *> conf = 0.52 ranks of expected_values: 2 EVAL 03bzyn4 genre 06cvj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 107.000 0.612 http://example.org/film/film/genre #21829-080lkt7 PRED entity: 080lkt7 PRED relation: film_release_region PRED expected values: 0d060g 0345h 035qy => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 223): 09c7w0 (0.93 #10632, 0.93 #11959, 0.93 #13289), 0345h (0.89 #3524, 0.85 #3690, 0.84 #5684), 03_3d (0.87 #2330, 0.81 #5654, 0.78 #4489), 0chghy (0.85 #5660, 0.82 #4495, 0.80 #7820), 035qy (0.83 #5686, 0.80 #2362, 0.79 #4521), 03h64 (0.81 #5721, 0.81 #4556, 0.76 #2397), 0jgd (0.80 #3490, 0.78 #5650, 0.78 #3656), 0154j (0.80 #5652, 0.77 #3658, 0.73 #4487), 015fr (0.79 #1180, 0.78 #5667, 0.76 #2343), 05b4w (0.77 #4553, 0.77 #5718, 0.75 #2394) >> Best rule #10632 for best value: >> intensional similarity = 5 >> extensional distance = 705 >> proper extension: 0gc_c_; >> query: (?x4643, 09c7w0) <- genre(?x4643, ?x53), film_release_region(?x4643, ?x87), film(?x123, ?x4643), place_of_birth(?x123, ?x2935), participant(?x1017, ?x123) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #3524 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 94 *> proper extension: 0b76d_m; 0ds35l9; 028_yv; 011yrp; 0ddfwj1; 0djb3vw; 0c40vxk; 01vksx; 0c0nhgv; 05z_kps; ... *> query: (?x4643, 0345h) <- genre(?x4643, ?x53), film_release_region(?x4643, ?x1003), film_regional_debut_venue(?x4643, ?x3288), film_crew_role(?x4643, ?x1284), ?x1003 = 03gj2 *> conf = 0.89 ranks of expected_values: 2, 5, 11 EVAL 080lkt7 film_release_region 035qy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 99.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 080lkt7 film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 080lkt7 film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 99.000 99.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21828-03q43g PRED entity: 03q43g PRED relation: place_of_birth PRED expected values: 0h7h6 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 146): 0h7h6 (0.12 #9210, 0.12 #7802, 0.01 #4986), 0qkcb (0.10 #292, 0.03 #8740, 0.02 #6628), 0v9qg (0.10 #146, 0.01 #3666, 0.01 #5074), 010v8k (0.10 #284), 02_n7 (0.10 #228), 0d7k1z (0.10 #208), 02_286 (0.08 #30300, 0.07 #31005, 0.07 #26779), 0281s1 (0.08 #1696, 0.08 #992, 0.04 #2400), 0r00l (0.08 #1895, 0.08 #1191, 0.03 #3303), 0cr3d (0.08 #798, 0.06 #2910, 0.04 #2206) >> Best rule #9210 for best value: >> intensional similarity = 2 >> extensional distance = 148 >> proper extension: 0dky9n; >> query: (?x6569, 0h7h6) <- nationality(?x6569, ?x279), ?x279 = 0d060g >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q43g place_of_birth 0h7h6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.120 http://example.org/people/person/place_of_birth #21827-060j8b PRED entity: 060j8b PRED relation: student! PRED expected values: 0fdys => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 25): 02822 (0.09 #777, 0.09 #1025, 0.05 #93), 03qsdpk (0.07 #782, 0.07 #1030, 0.02 #2891), 0fdys (0.06 #775, 0.06 #1023, 0.02 #215), 03g3w (0.05 #1015, 0.04 #767, 0.02 #145), 0w7c (0.03 #788, 0.03 #1036, 0.01 #2525), 062z7 (0.03 #768, 0.03 #1016), 01zc2w (0.03 #794, 0.03 #1042, 0.01 #856), 05qjt (0.02 #751, 0.02 #999), 05qfh (0.02 #1021, 0.02 #773, 0.02 #649), 02h40lc (0.02 #997, 0.02 #749, 0.01 #1308) >> Best rule #777 for best value: >> intensional similarity = 2 >> extensional distance = 202 >> proper extension: 01d494; 0frmb1; >> query: (?x6262, 02822) <- student(?x1368, ?x6262), student(?x6925, ?x6262) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #775 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 202 *> proper extension: 01d494; 0frmb1; *> query: (?x6262, 0fdys) <- student(?x1368, ?x6262), student(?x6925, ?x6262) *> conf = 0.06 ranks of expected_values: 3 EVAL 060j8b student! 0fdys CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 124.000 124.000 0.088 http://example.org/education/field_of_study/students_majoring./education/education/student #21826-01kv4mb PRED entity: 01kv4mb PRED relation: artists! PRED expected values: 02w4v => 121 concepts (51 used for prediction) PRED predicted values (max 10 best out of 197): 064t9 (0.51 #13628, 0.47 #14246, 0.44 #13), 05w3f (0.44 #37, 0.22 #964, 0.16 #3748), 0xhtw (0.41 #944, 0.35 #635, 0.29 #12085), 016clz (0.39 #623, 0.31 #13620, 0.29 #3716), 02w4v (0.35 #662, 0.30 #971, 0.14 #3136), 05bt6j (0.33 #43, 0.32 #12111, 0.30 #970), 08jyyk (0.33 #67, 0.16 #685, 0.13 #994), 0mhfr (0.32 #642, 0.26 #951, 0.10 #1878), 017_qw (0.29 #2226, 0.13 #4701, 0.12 #15533), 0155w (0.29 #723, 0.20 #1032, 0.17 #7220) >> Best rule #13628 for best value: >> intensional similarity = 3 >> extensional distance = 683 >> proper extension: 05xq9; 07rnh; >> query: (?x2124, 064t9) <- artists(?x1572, ?x2124), artists(?x1572, ?x11749), ?x11749 = 016t0h >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #662 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 0134tg; 013w8y; 0mjn2; *> query: (?x2124, 02w4v) <- award_winner(?x5766, ?x2124), artists(?x7329, ?x2124), ?x7329 = 016jny *> conf = 0.35 ranks of expected_values: 5 EVAL 01kv4mb artists! 02w4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 121.000 51.000 0.514 http://example.org/music/genre/artists #21825-015dqj PRED entity: 015dqj PRED relation: film PRED expected values: 0m9p3 => 103 concepts (53 used for prediction) PRED predicted values (max 10 best out of 445): 04954r (0.08 #616, 0.06 #2404, 0.05 #4192), 0ft18 (0.08 #1406, 0.06 #3194, 0.05 #4982), 0cbn7c (0.08 #1368, 0.06 #3156, 0.05 #4944), 0hv27 (0.08 #1080, 0.06 #2868, 0.05 #4656), 0cq7kw (0.08 #759, 0.06 #2547, 0.05 #4335), 03g90h (0.08 #35, 0.06 #1823, 0.05 #3611), 02qr3k8 (0.06 #6651, 0.02 #56716, 0.02 #88902), 07k8rt4 (0.06 #2528, 0.02 #6104), 02yvct (0.06 #2139, 0.02 #16443, 0.02 #12867), 0c1sgd3 (0.06 #2596, 0.01 #9748) >> Best rule #616 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0hnlx; 04k15; 019r_1; 04hcw; 082db; 032md; 0h326; >> query: (?x10007, 04954r) <- location(?x10007, ?x863), profession(?x10007, ?x319), ?x863 = 0fhp9, nationality(?x10007, ?x1355) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #5751 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 133 *> proper extension: 0cj2w; *> query: (?x10007, 0m9p3) <- profession(?x10007, ?x319), award(?x10007, ?x591), ?x591 = 0f4x7 *> conf = 0.04 ranks of expected_values: 25 EVAL 015dqj film 0m9p3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 103.000 53.000 0.077 http://example.org/film/actor/film./film/performance/film #21824-015076 PRED entity: 015076 PRED relation: gender PRED expected values: 05zppz => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #61, 0.87 #63, 0.85 #151), 02zsn (0.49 #72, 0.49 #82, 0.48 #54) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 017yfz; >> query: (?x11259, 05zppz) <- profession(?x11259, ?x1183), ?x1183 = 09jwl, place_of_death(?x11259, ?x1523) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015076 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.877 http://example.org/people/person/gender #21823-030qb3t PRED entity: 030qb3t PRED relation: featured_film_locations! PRED expected values: 0bvn25 03bx2lk 0340hj 024l2y 0571m 02ny6g 0435vm 062zjtt 0dln8jk 02vjp3 0ct2tf5 0ckt6 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1242): 0jqd3 (0.29 #1711, 0.08 #3008, 0.07 #4953), 061681 (0.14 #4579, 0.14 #1337, 0.12 #5228), 072x7s (0.14 #1391, 0.12 #2040, 0.12 #2688), 09sh8k (0.14 #1301, 0.12 #1950, 0.12 #2598), 024l2y (0.14 #1392, 0.12 #2041, 0.11 #4634), 0cc846d (0.14 #1468, 0.12 #2117, 0.08 #3413), 02vz6dn (0.14 #1768, 0.12 #2417, 0.08 #3065), 05f4_n0 (0.14 #1566, 0.12 #2215, 0.08 #2863), 01bl7g (0.14 #1656, 0.12 #2305, 0.08 #2953), 05c26ss (0.14 #1529, 0.12 #2178, 0.08 #2826) >> Best rule #1711 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 0fpkxfd; 0g57ws5; >> query: (?x1523, 0jqd3) <- film_regional_debut_venue(?x2954, ?x1523), ?x2954 = 0crh5_f >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1392 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 0fpkxfd; 0g57ws5; *> query: (?x1523, 024l2y) <- film_regional_debut_venue(?x2954, ?x1523), ?x2954 = 0crh5_f *> conf = 0.14 ranks of expected_values: 5, 45, 107, 712, 780, 831, 1009, 1073 EVAL 030qb3t featured_film_locations! 0ckt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 0ct2tf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 02vjp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 0dln8jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 062zjtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 0435vm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 02ny6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 0571m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 024l2y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 0340hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 03bx2lk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations EVAL 030qb3t featured_film_locations! 0bvn25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 153.000 153.000 0.286 http://example.org/film/film/featured_film_locations #21822-06t2t PRED entity: 06t2t PRED relation: country! PRED expected values: 06f41 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 48): 03_8r (0.87 #644, 0.86 #596, 0.83 #1124), 06f41 (0.83 #638, 0.83 #590, 0.75 #494), 03hr1p (0.83 #69, 0.80 #645, 0.79 #501), 01lb14 (0.83 #591, 0.78 #1071, 0.75 #495), 01cgz (0.79 #541, 0.73 #1021, 0.70 #973), 07gyv (0.77 #631, 0.72 #583, 0.68 #535), 0w0d (0.75 #491, 0.67 #635, 0.66 #587), 07bs0 (0.69 #588, 0.63 #636, 0.59 #1116), 019tzd (0.67 #83, 0.61 #515, 0.60 #1091), 07rlg (0.67 #49, 0.57 #1057, 0.57 #865) >> Best rule #644 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 06bnz; 05b4w; >> query: (?x2316, 03_8r) <- film_release_region(?x8292, ?x2316), film_release_region(?x6520, ?x2316), ?x6520 = 02bg55, ?x8292 = 0cmf0m0 >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #638 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 06bnz; 05b4w; *> query: (?x2316, 06f41) <- film_release_region(?x8292, ?x2316), film_release_region(?x6520, ?x2316), ?x6520 = 02bg55, ?x8292 = 0cmf0m0 *> conf = 0.83 ranks of expected_values: 2 EVAL 06t2t country! 06f41 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #21821-078jt5 PRED entity: 078jt5 PRED relation: profession PRED expected values: 02jknp => 116 concepts (105 used for prediction) PRED predicted values (max 10 best out of 45): 02jknp (0.89 #600, 0.88 #896, 0.86 #1784), 01d_h8 (0.78 #894, 0.78 #598, 0.78 #1782), 0dxtg (0.71 #162, 0.70 #902, 0.70 #1642), 02hrh1q (0.69 #9932, 0.68 #3715, 0.68 #10524), 012t_z (0.28 #6217, 0.20 #13, 0.06 #161), 02hv44_ (0.20 #57, 0.03 #11750, 0.03 #13082), 0cbd2 (0.20 #1931, 0.19 #2375, 0.19 #599), 018gz8 (0.19 #3421, 0.14 #757, 0.12 #2681), 09jwl (0.19 #5199, 0.18 #4755, 0.18 #7420), 0np9r (0.13 #3425, 0.11 #1945, 0.11 #2389) >> Best rule #600 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 0162c8; >> query: (?x3018, 02jknp) <- award_nominee(?x3018, ?x6913), film(?x3018, ?x13027), nominated_for(?x6913, ?x782) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 078jt5 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 105.000 0.890 http://example.org/people/person/profession #21820-01wwnh2 PRED entity: 01wwnh2 PRED relation: role PRED expected values: 05r5c 042v_gx 018vs => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 111): 05r5c (0.45 #312, 0.39 #2251, 0.39 #2354), 05842k (0.39 #383, 0.17 #689, 0.17 #2425), 042v_gx (0.38 #619, 0.24 #1231, 0.23 #1639), 01vj9c (0.35 #320, 0.20 #626, 0.16 #1238), 018vs (0.34 #318, 0.22 #624, 0.16 #2360), 05148p4 (0.33 #124, 0.18 #328, 0.17 #22), 0l14md (0.20 #311, 0.05 #2353, 0.05 #2250), 013y1f (0.17 #341, 0.16 #647, 0.14 #2280), 0l14qv (0.17 #310, 0.16 #2352, 0.15 #1228), 07brj (0.15 #331, 0.07 #229, 0.05 #2373) >> Best rule #312 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 06br6t; >> query: (?x10326, 05r5c) <- artists(?x671, ?x10326), role(?x10326, ?x212), ?x212 = 026t6 >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 5 EVAL 01wwnh2 role 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.451 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01wwnh2 role 042v_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.451 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01wwnh2 role 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.451 http://example.org/music/artist/track_contributions./music/track_contribution/role #21819-02lp0w PRED entity: 02lp0w PRED relation: award! PRED expected values: 015882 01n44c => 42 concepts (11 used for prediction) PRED predicted values (max 10 best out of 2549): 01ccr8 (0.82 #3368, 0.79 #20211, 0.78 #3369), 01sxq9 (0.82 #3368, 0.79 #20211, 0.78 #3369), 01mqz0 (0.82 #3368, 0.79 #20211, 0.78 #30318), 01gq0b (0.50 #485, 0.18 #23580, 0.14 #30320), 07s8r0 (0.50 #413, 0.14 #30320, 0.14 #30319), 02kxwk (0.50 #1239, 0.10 #11345, 0.08 #10106), 01z_g6 (0.50 #1484, 0.08 #10106, 0.02 #18327), 03zyvw (0.50 #1017, 0.05 #4386, 0.02 #17860), 02lf70 (0.50 #513, 0.03 #10619, 0.02 #17356), 08pth9 (0.50 #1293, 0.02 #24873, 0.02 #18136) >> Best rule #3368 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0bdw1g; 09sb52; 0cqh6z; 0ck27z; 0bdx29; 0bb57s; >> query: (?x5841, ?x1607) <- award_winner(?x5841, ?x1607), award_winner(?x5841, ?x1057), award(?x1871, ?x5841), ?x1871 = 02bkdn, gender(?x1057, ?x514), people(?x1050, ?x1057) >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #17308 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 218 *> proper extension: 02gx2k; 0274v0r; 03q27t; *> query: (?x5841, 015882) <- award_winner(?x5841, ?x1057), award(?x1871, ?x5841), gender(?x1871, ?x514), nominated_for(?x1871, ?x337), ceremony(?x5841, ?x4141) *> conf = 0.05 ranks of expected_values: 1025, 2245 EVAL 02lp0w award! 01n44c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 11.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02lp0w award! 015882 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 11.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21818-09v51c2 PRED entity: 09v51c2 PRED relation: nominated_for PRED expected values: 01f8f7 => 44 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1799): 0gmcwlb (0.38 #4926, 0.27 #12837, 0.26 #14419), 017gl1 (0.38 #3294, 0.29 #4875, 0.26 #12786), 0fg04 (0.38 #3257, 0.07 #12749, 0.06 #14331), 029zqn (0.38 #3401, 0.06 #12893, 0.06 #14475), 01sxdy (0.38 #3711, 0.05 #13203, 0.05 #14785), 026qnh6 (0.38 #3907, 0.05 #13399, 0.05 #14981), 026p4q7 (0.35 #5101, 0.29 #13012, 0.28 #14594), 0gmgwnv (0.35 #5702, 0.28 #13613, 0.27 #15195), 0pv3x (0.33 #4906, 0.23 #12817, 0.22 #14399), 09gq0x5 (0.32 #4998, 0.30 #12909, 0.27 #14491) >> Best rule #4926 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 02wwsh8; 03ybrwc; >> query: (?x9217, 0gmcwlb) <- award(?x5826, ?x9217), film_crew_role(?x5826, ?x137), executive_produced_by(?x5826, ?x1864), film_regional_debut_venue(?x5826, ?x2686) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #25312 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 163 *> proper extension: 03tk6z; *> query: (?x9217, ?x9216) <- award(?x8262, ?x9217), film(?x8262, ?x9216), gender(?x8262, ?x231), film_release_region(?x9216, ?x142) *> conf = 0.24 ranks of expected_values: 50 EVAL 09v51c2 nominated_for 01f8f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 44.000 18.000 0.377 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21817-0h3y PRED entity: 0h3y PRED relation: olympics PRED expected values: 0kbvb => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 37): 0kbvb (0.61 #154, 0.56 #526, 0.56 #191), 0jdk_ (0.50 #208, 0.42 #1596, 0.42 #394), 0swbd (0.50 #194, 0.41 #343, 0.40 #825), 0kbvv (0.48 #356, 0.48 #318, 0.48 #244), 09n48 (0.44 #522, 0.44 #818, 0.43 #1560), 0jhn7 (0.42 #1596, 0.40 #334, 0.40 #2377), 0l6m5 (0.42 #1596, 0.40 #334, 0.40 #2377), 0l6ny (0.42 #1596, 0.40 #334, 0.40 #2377), 0l6mp (0.39 #200, 0.33 #163, 0.28 #237), 0lgxj (0.39 #210, 0.29 #396, 0.28 #173) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0n3g; >> query: (?x291, 0kbvb) <- exported_to(?x1499, ?x291), vacationer(?x291, ?x3585), film_release_region(?x86, ?x1499) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h3y olympics 0kbvb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.611 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #21816-018f8 PRED entity: 018f8 PRED relation: cinematography PRED expected values: 087yty => 87 concepts (79 used for prediction) PRED predicted values (max 10 best out of 43): 079hvk (0.08 #69, 0.06 #133, 0.05 #452), 052hl (0.06 #128, 0.05 #447, 0.05 #639), 01hmk9 (0.06 #128, 0.05 #447, 0.05 #639), 02ch1w (0.06 #128, 0.05 #447, 0.05 #639), 06g60w (0.06 #78, 0.06 #142, 0.04 #269), 070bjw (0.06 #100, 0.06 #164, 0.03 #227), 071jrc (0.05 #61, 0.03 #252, 0.03 #316), 05br10 (0.04 #118, 0.04 #182, 0.03 #309), 0854hr (0.04 #147, 0.03 #19, 0.02 #83), 09cdxn (0.03 #215, 0.03 #24, 0.02 #88) >> Best rule #69 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 0bm2g; >> query: (?x1210, 079hvk) <- list(?x1210, ?x3004), nominated_for(?x5840, ?x1210), genre(?x1210, ?x258), featured_film_locations(?x1210, ?x1523) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #213 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 0j_tw; *> query: (?x1210, 087yty) <- list(?x1210, ?x3004), film(?x6771, ?x1210), film(?x2794, ?x1210), film_release_region(?x1210, ?x94) *> conf = 0.02 ranks of expected_values: 30 EVAL 018f8 cinematography 087yty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 87.000 79.000 0.083 http://example.org/film/film/cinematography #21815-03h26tm PRED entity: 03h26tm PRED relation: crewmember! PRED expected values: 06r2_ => 88 concepts (58 used for prediction) PRED predicted values (max 10 best out of 318): 0bdjd (0.35 #1270, 0.32 #318, 0.02 #5088), 0bt4g (0.35 #1270, 0.32 #318, 0.02 #5088), 0dtfn (0.17 #367, 0.16 #1001, 0.11 #684), 024mpp (0.17 #133, 0.11 #451, 0.11 #1085), 0hx4y (0.17 #101, 0.08 #419, 0.08 #1053), 011xg5 (0.17 #274, 0.06 #1905, 0.06 #2860), 01hq1 (0.17 #265, 0.06 #2860, 0.06 #583), 043tvp3 (0.17 #242, 0.06 #2860, 0.03 #560), 0jqn5 (0.11 #372, 0.11 #1006, 0.08 #54), 01kff7 (0.11 #1000, 0.08 #48, 0.08 #683) >> Best rule #1270 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 04cy8rb; 06cv1; 0284n42; 076lxv; 027rwmr; 021yc7p; 09rp4r_; 09pjnd; 0c94fn; 04ktcgn; ... >> query: (?x930, ?x7336) <- award_nominee(?x929, ?x930), crewmember(?x1076, ?x930), nominated_for(?x930, ?x7336) >> conf = 0.35 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 03h26tm crewmember! 06r2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 58.000 0.350 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #21814-056wb PRED entity: 056wb PRED relation: influenced_by PRED expected values: 034bs => 131 concepts (76 used for prediction) PRED predicted values (max 10 best out of 298): 042q3 (0.24 #8198, 0.07 #18632, 0.06 #22109), 03sbs (0.23 #8057, 0.08 #18491, 0.08 #21968), 032l1 (0.18 #7924, 0.12 #18358, 0.10 #21835), 0j3v (0.17 #7895, 0.07 #18329, 0.06 #21806), 03_87 (0.16 #8037, 0.09 #18471, 0.08 #21948), 02wh0 (0.16 #8216, 0.08 #22127, 0.07 #18650), 05qmj (0.16 #8027, 0.06 #24986, 0.06 #21938), 0klw (0.14 #150, 0.06 #22180, 0.05 #24793), 081nh (0.14 #65, 0.03 #1801, 0.02 #18333), 015n8 (0.14 #8244, 0.04 #18678, 0.04 #24768) >> Best rule #8198 for best value: >> intensional similarity = 2 >> extensional distance = 94 >> proper extension: 03sbs; 02ln1; >> query: (?x6045, 042q3) <- influenced_by(?x6045, ?x3969), organization(?x3969, ?x8603) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #7951 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 94 *> proper extension: 03sbs; 02ln1; *> query: (?x6045, 034bs) <- influenced_by(?x6045, ?x3969), organization(?x3969, ?x8603) *> conf = 0.05 ranks of expected_values: 78 EVAL 056wb influenced_by 034bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 131.000 76.000 0.240 http://example.org/influence/influence_node/influenced_by #21813-01w4c9 PRED entity: 01w4c9 PRED relation: role PRED expected values: 0l15bq 05842k => 67 concepts (46 used for prediction) PRED predicted values (max 10 best out of 110): 02dlh2 (0.87 #2240, 0.87 #1905, 0.82 #2472), 018vs (0.85 #4163, 0.84 #3931, 0.83 #3601), 05842k (0.85 #4260, 0.84 #3697, 0.83 #1759), 01p970 (0.85 #4260, 0.84 #3697, 0.82 #1790), 0859_ (0.83 #4141, 0.82 #2472, 0.82 #1790), 04rzd (0.83 #1499, 0.80 #2918, 0.80 #2843), 042v_gx (0.83 #1691, 0.75 #916, 0.74 #4035), 0g2dz (0.82 #1604, 0.75 #1377, 0.72 #1713), 02fsn (0.82 #1790, 0.81 #2805, 0.81 #1242), 0l15bq (0.76 #1607, 0.76 #2837, 0.75 #1380) >> Best rule #2240 for best value: >> intensional similarity = 22 >> extensional distance = 19 >> proper extension: 03gvt; >> query: (?x5480, ?x3703) <- role(?x3703, ?x5480), role(?x315, ?x5480), role(?x228, ?x5480), role(?x227, ?x5480), instrumentalists(?x5480, ?x1992), role(?x5480, ?x1466), ?x1466 = 03bx0bm, ?x228 = 0l14qv, role(?x5480, ?x4471), role(?x4471, ?x7449), role(?x4471, ?x2620), ?x227 = 0342h, instrumentalists(?x4471, ?x1073), role(?x4471, ?x214), ?x7449 = 01vnt4, role(?x6947, ?x4471), ?x2620 = 01kcd, role(?x8114, ?x3703), ?x8114 = 02mx98, performance_role(?x3703, ?x1574), role(?x3703, ?x645), instrumentalists(?x315, ?x226) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #4260 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 39 *> proper extension: 025cbm; *> query: (?x5480, ?x1268) <- role(?x4975, ?x5480), role(?x1268, ?x5480), role(?x316, ?x5480), role(?x228, ?x5480), role(?x227, ?x5480), role(?x212, ?x5480), role(?x5480, ?x615), ?x228 = 0l14qv, ?x316 = 05r5c, role(?x2747, ?x4975), ?x212 = 026t6, role(?x8172, ?x1268), ?x8172 = 06rvn, group(?x227, ?x4783), ?x4783 = 047cx, instrumentalists(?x227, ?x2782), instrumentalists(?x227, ?x317), role(?x2297, ?x227), role(?x1292, ?x227), ?x2297 = 051hrr, ?x317 = 0c9d9, ?x2782 = 014q2g *> conf = 0.85 ranks of expected_values: 3, 10 EVAL 01w4c9 role 05842k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 67.000 46.000 0.869 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 01w4c9 role 0l15bq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 67.000 46.000 0.869 http://example.org/music/performance_role/track_performances./music/track_contribution/role #21812-0c7xjb PRED entity: 0c7xjb PRED relation: place_of_birth PRED expected values: 0f2rq => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 166): 013m_x (0.33 #81700, 0.27 #61272, 0.27 #83815), 03b12 (0.16 #1111, 0.11 #407, 0.03 #13783), 02_286 (0.11 #19, 0.10 #13395, 0.10 #1427), 030qb3t (0.09 #9910, 0.09 #23993, 0.07 #24697), 02dtg (0.06 #10, 0.05 #714, 0.05 #3530), 0106dv (0.06 #395, 0.05 #1099, 0.03 #2507), 0chgzm (0.06 #310, 0.05 #1014, 0.02 #9462), 0tygl (0.06 #226, 0.05 #930), 09b8m (0.06 #116, 0.05 #820), 0cr3d (0.06 #5022, 0.05 #7134, 0.05 #17695) >> Best rule #81700 for best value: >> intensional similarity = 2 >> extensional distance = 2264 >> proper extension: 07c37; >> query: (?x4819, ?x5658) <- location(?x4819, ?x5658), place_of_birth(?x3018, ?x5658) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #909 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 01zmpg; 013v5j; 018pj3; 016h9b; 01m3x5p; 04cr6qv; 0137hn; 024dw0; 01kp_1t; 01mskc3; ... *> query: (?x4819, 0f2rq) <- artist(?x2149, ?x4819), sibling(?x2697, ?x4819), artists(?x671, ?x4819) *> conf = 0.05 ranks of expected_values: 12 EVAL 0c7xjb place_of_birth 0f2rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 134.000 134.000 0.331 http://example.org/people/person/place_of_birth #21811-03bzyn4 PRED entity: 03bzyn4 PRED relation: currency PRED expected values: 09nqf => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.90 #36, 0.88 #57, 0.86 #134), 01nv4h (0.03 #86, 0.03 #212, 0.03 #282), 02l6h (0.03 #144, 0.02 #221, 0.01 #564), 02gsvk (0.03 #202, 0.01 #419, 0.01 #272), 088n7 (0.02 #140, 0.02 #154, 0.02 #161) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0mbql; 072r5v; >> query: (?x9496, 09nqf) <- written_by(?x9496, ?x4589), film_distribution_medium(?x9496, ?x81), film_crew_role(?x9496, ?x468), ?x468 = 02r96rf >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bzyn4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.903 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21810-0427y PRED entity: 0427y PRED relation: award PRED expected values: 027dtxw => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 326): 09qvc0 (0.40 #40, 0.33 #850, 0.20 #445), 024dzn (0.40 #329, 0.05 #3164, 0.04 #47392), 0gr51 (0.37 #4150, 0.19 #10631, 0.19 #1720), 0gr4k (0.35 #4083, 0.20 #438, 0.20 #33), 04dn09n (0.32 #4094, 0.20 #449, 0.20 #44), 09sb52 (0.29 #21107, 0.29 #4496, 0.29 #30017), 05pcn59 (0.27 #4536, 0.18 #5751, 0.13 #5346), 0ck27z (0.24 #3332, 0.13 #21968, 0.13 #30068), 03hkv_r (0.24 #4066, 0.12 #13368, 0.11 #10547), 05zr6wv (0.23 #4472, 0.22 #1232, 0.18 #5687) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 014zfs; 01vb403; 052hl; >> query: (?x9596, 09qvc0) <- influenced_by(?x2534, ?x9596), people(?x1050, ?x9596), ?x2534 = 0lx2l >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #4459 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 01vh3r; *> query: (?x9596, 027dtxw) <- people(?x1050, ?x9596), film(?x9596, ?x4828), executive_produced_by(?x10274, ?x9596) *> conf = 0.12 ranks of expected_values: 69 EVAL 0427y award 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 125.000 125.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21809-02pqp12 PRED entity: 02pqp12 PRED relation: nominated_for PRED expected values: 028_yv 0bth54 092vkg 0ch26b_ 0fpv_3_ 0yyts 09p7fh 05cvgl 012mrr 02vqsll 0ggbhy7 011yr9 0sxmx 0_b9f 04lhc4 027pfg 05ldxl 0c0zq => 54 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1391): 02c638 (0.78 #6093, 0.60 #1723, 0.56 #11919), 0sxlb (0.78 #7098, 0.53 #10010, 0.47 #11466), 07xtqq (0.67 #2955, 0.65 #29137, 0.64 #29136), 0gmgwnv (0.67 #6691, 0.60 #2321, 0.56 #12517), 04b2qn (0.67 #6910, 0.60 #9822, 0.44 #5454), 011ycb (0.67 #6524, 0.47 #10892, 0.47 #9436), 09p3_s (0.67 #6600, 0.47 #9512, 0.40 #10968), 0mcl0 (0.67 #6345, 0.47 #9257, 0.40 #10713), 0llcx (0.67 #4002, 0.44 #5459, 0.40 #9827), 0sxmx (0.65 #29137, 0.64 #29136, 0.63 #29135) >> Best rule #6093 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 03hkv_r; 0gr4k; 019f4v; 02n9nmz; 0gq9h; 02rdyk7; >> query: (?x1198, 02c638) <- nominated_for(?x1198, ?x6149), nominated_for(?x1198, ?x3471), award(?x777, ?x1198), award(?x6149, ?x834), ?x777 = 05kfs, ?x3471 = 07cyl >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #29137 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 163 *> proper extension: 0m7yy; 02wwsh8; 03ybrwc; 02vl9ln; 0468g4r; *> query: (?x1198, ?x6121) <- award(?x6121, ?x1198), award(?x4007, ?x1198), award_winner(?x1198, ?x698), film_release_region(?x6121, ?x87), genre(?x4007, ?x53) *> conf = 0.65 ranks of expected_values: 10, 16, 29, 30, 31, 36, 39, 42, 48, 60, 67, 78, 108, 109, 116, 176, 383, 665 EVAL 02pqp12 nominated_for 0c0zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 05ldxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 04lhc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 0_b9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 0sxmx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 011yr9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 0ggbhy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 02vqsll CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 012mrr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 05cvgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 09p7fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 0yyts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 0fpv_3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 0ch26b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 092vkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 0bth54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pqp12 nominated_for 028_yv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 27.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21808-02fn5r PRED entity: 02fn5r PRED relation: profession PRED expected values: 09jwl => 137 concepts (136 used for prediction) PRED predicted values (max 10 best out of 65): 09jwl (0.84 #17, 0.83 #2509, 0.82 #3095), 0nbcg (0.63 #30, 0.60 #3108, 0.59 #2522), 0dz3r (0.54 #879, 0.50 #2494, 0.49 #1466), 01d_h8 (0.38 #1764, 0.31 #15512, 0.30 #10399), 01c72t (0.36 #2807, 0.34 #4707, 0.34 #3393), 03gjzk (0.27 #8639, 0.25 #15361, 0.22 #13475), 0kyk (0.27 #8639, 0.25 #15361, 0.17 #759), 04f2zj (0.27 #8639, 0.25 #15361, 0.08 #679), 012t_z (0.27 #8639, 0.25 #15361, 0.05 #2064), 028kk_ (0.27 #8639, 0.25 #15361, 0.02 #2712) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 06y9c2; 0zjpz; 03rl84; 07g2v; 02r3cn; 04kjrv; 01lz4tf; 0484q; 0167v4; >> query: (?x2638, 09jwl) <- role(?x2638, ?x227), spouse(?x2409, ?x2638), profession(?x2638, ?x220) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fn5r profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 136.000 0.842 http://example.org/people/person/profession #21807-07_fj54 PRED entity: 07_fj54 PRED relation: film_crew_role PRED expected values: 09vw2b7 02rh1dz => 80 concepts (78 used for prediction) PRED predicted values (max 10 best out of 23): 09vw2b7 (0.69 #1066, 0.69 #840, 0.67 #808), 05smlt (0.33 #49, 0.07 #81, 0.05 #113), 01pvkk (0.33 #169, 0.30 #330, 0.30 #1554), 02rh1dz (0.17 #40, 0.15 #104, 0.14 #136), 01xy5l_ (0.17 #43, 0.15 #107, 0.14 #139), 0d2b38 (0.17 #54, 0.15 #118, 0.14 #150), 02vs3x5 (0.17 #20, 0.13 #84, 0.10 #180), 020xn5 (0.17 #38, 0.10 #102, 0.10 #134), 0ckd1 (0.17 #35, 0.05 #99, 0.05 #131), 0215hd (0.15 #207, 0.14 #594, 0.14 #722) >> Best rule #1066 for best value: >> intensional similarity = 4 >> extensional distance = 730 >> proper extension: 0g5qs2k; 0c40vxk; 0gkz15s; 0bq8tmw; 03h0byn; >> query: (?x4953, 09vw2b7) <- film(?x2588, ?x4953), film_crew_role(?x4953, ?x468), ?x468 = 02r96rf, location(?x2588, ?x1523) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 07_fj54 film_crew_role 02rh1dz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 80.000 78.000 0.691 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 07_fj54 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 78.000 0.691 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21806-0p50v PRED entity: 0p50v PRED relation: award_winner! PRED expected values: 0dth6b => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 137): 0dth6b (0.18 #13582, 0.04 #17083, 0.02 #17364), 0dthsy (0.18 #13582, 0.01 #1047, 0.01 #8048), 0gvstc3 (0.10 #174, 0.03 #314, 0.03 #8015), 02wzl1d (0.09 #291, 0.07 #11, 0.06 #571), 0drtv8 (0.09 #346, 0.07 #66, 0.06 #626), 0bq_mx (0.08 #692, 0.06 #412, 0.04 #1672), 092t4b (0.08 #1032, 0.04 #3833, 0.04 #5793), 073hkh (0.07 #1, 0.06 #281, 0.04 #17083), 0bvfqq (0.07 #33, 0.04 #17083, 0.03 #1013), 09q_6t (0.07 #8, 0.04 #17083, 0.03 #568) >> Best rule #13582 for best value: >> intensional similarity = 4 >> extensional distance = 1399 >> proper extension: 02qflgv; 049dyj; 0mj1l; 06lj1m; 036c_0; 01mmslz; 01wk7b7; 026zvx7; 059t6d; 027xbpw; ... >> query: (?x8268, ?x1793) <- award(?x8268, ?x601), nominated_for(?x8268, ?x8773), gender(?x8268, ?x231), honored_for(?x1793, ?x8773) >> conf = 0.18 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0p50v award_winner! 0dth6b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.181 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21805-014_x2 PRED entity: 014_x2 PRED relation: production_companies PRED expected values: 0c41qv => 87 concepts (80 used for prediction) PRED predicted values (max 10 best out of 51): 086k8 (0.30 #2851, 0.30 #3527, 0.30 #3612), 05qd_ (0.10 #93, 0.08 #343, 0.08 #1852), 016tw3 (0.09 #1097, 0.08 #928, 0.08 #4130), 016tt2 (0.08 #254, 0.07 #504, 0.07 #587), 017s11 (0.07 #670, 0.07 #3, 0.07 #836), 030_1_ (0.07 #17, 0.04 #1102, 0.04 #267), 046b0s (0.07 #24, 0.04 #524, 0.04 #774), 024rgt (0.07 #25, 0.04 #1444, 0.03 #2204), 06rq1k (0.07 #18, 0.01 #1270, 0.01 #184), 054lpb6 (0.06 #931, 0.06 #3458, 0.05 #4133) >> Best rule #2851 for best value: >> intensional similarity = 4 >> extensional distance = 1023 >> proper extension: 0dq626; 0czyxs; 0gtv7pk; 0dtw1x; 0gx9rvq; 09p35z; 05q96q6; 0jjy0; 07sc6nw; 0gj8t_b; ... >> query: (?x83, ?x382) <- genre(?x83, ?x53), language(?x83, ?x254), film_crew_role(?x83, ?x137), film(?x382, ?x83) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #889 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 314 *> proper extension: 021gzd; *> query: (?x83, 0c41qv) <- genre(?x83, ?x53), language(?x83, ?x254), film(?x965, ?x83), cinematography(?x83, ?x7327) *> conf = 0.03 ranks of expected_values: 27 EVAL 014_x2 production_companies 0c41qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 87.000 80.000 0.299 http://example.org/film/film/production_companies #21804-027kmrb PRED entity: 027kmrb PRED relation: produced_by! PRED expected values: 07g_0c => 113 concepts (88 used for prediction) PRED predicted values (max 10 best out of 588): 0258dh (0.10 #689, 0.02 #9167, 0.02 #11052), 08mg_b (0.10 #612, 0.02 #9090, 0.02 #10975), 0yzbg (0.10 #680, 0.02 #11043), 0dqcs3 (0.10 #447, 0.02 #21180, 0.02 #15522), 0gzlb9 (0.10 #775, 0.01 #60311, 0.01 #59368), 0h03fhx (0.07 #8902, 0.03 #10787, 0.01 #22100), 0g54xkt (0.07 #8767, 0.03 #10652, 0.01 #21965), 02qr69m (0.07 #8694, 0.03 #10579, 0.01 #21892), 05fgt1 (0.07 #8693, 0.03 #10578, 0.01 #21891), 0b73_1d (0.07 #8551, 0.03 #10436, 0.01 #21749) >> Best rule #689 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 01c6l; >> query: (?x5647, 0258dh) <- produced_by(?x4361, ?x5647), award_nominee(?x382, ?x5647), ?x382 = 086k8 >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027kmrb produced_by! 07g_0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 88.000 0.100 http://example.org/film/film/produced_by #21803-02cbhg PRED entity: 02cbhg PRED relation: nominated_for! PRED expected values: 02n9nmz 099cng 02x17s4 026mmy => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 227): 0gqyl (0.70 #2259, 0.68 #11391, 0.66 #20817), 09qv_s (0.60 #317, 0.39 #2288, 0.36 #974), 0gq9h (0.59 #3557, 0.56 #4871, 0.55 #12320), 0gs9p (0.55 #3559, 0.53 #4873, 0.46 #12322), 02w9sd7 (0.47 #327, 0.25 #13582, 0.21 #20597), 09sb52 (0.46 #2220, 0.34 #906, 0.25 #13582), 02z0dfh (0.44 #2242, 0.43 #928, 0.17 #52), 0p9sw (0.39 #1113, 0.31 #3522, 0.30 #4836), 02hsq3m (0.37 #1120, 0.25 #3310, 0.25 #4186), 02x17s4 (0.36 #959, 0.33 #83, 0.28 #2273) >> Best rule #2259 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 0b73_1d; 08rr3p; 0gyfp9c; 011yg9; 046f3p; 02chhq; 016mhd; >> query: (?x8084, 0gqyl) <- nominated_for(?x2257, ?x8084), award_winner(?x8084, ?x248), ?x2257 = 09td7p >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #959 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 055td_; 04jm_hq; 05rfst; 0gvt53w; *> query: (?x8084, 02x17s4) <- nominated_for(?x2577, ?x8084), award_winner(?x8084, ?x248), genre(?x8084, ?x53), ?x2577 = 099t8j *> conf = 0.36 ranks of expected_values: 10, 15, 102 EVAL 02cbhg nominated_for! 026mmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 131.000 0.702 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02cbhg nominated_for! 02x17s4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 131.000 131.000 0.702 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02cbhg nominated_for! 099cng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 131.000 131.000 0.702 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02cbhg nominated_for! 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 131.000 131.000 0.702 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21802-05vtw PRED entity: 05vtw PRED relation: films PRED expected values: 07xtqq => 68 concepts (27 used for prediction) PRED predicted values (max 10 best out of 989): 0qm98 (0.33 #63, 0.17 #1644, 0.06 #2696), 011ypx (0.33 #285, 0.17 #1866, 0.06 #2918), 03fts (0.33 #1646, 0.08 #4279, 0.07 #4807), 011x_4 (0.33 #1967, 0.06 #6711, 0.04 #9346), 0hfzr (0.19 #2838, 0.08 #3893, 0.07 #4947), 08xvpn (0.18 #3626, 0.12 #3100, 0.10 #5736), 025rvx0 (0.18 #3440, 0.10 #5550, 0.09 #6078), 015g28 (0.18 #3351, 0.10 #5461, 0.09 #5989), 091rc5 (0.17 #1829, 0.08 #3936, 0.06 #2881), 07w8fz (0.17 #1734, 0.08 #4367, 0.07 #4895) >> Best rule #63 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 06796; >> query: (?x8278, 0qm98) <- films(?x8278, ?x3498), films(?x8278, ?x1295), ?x1295 = 03s5lz, film_release_region(?x3498, ?x279), genre(?x3498, ?x53), film_crew_role(?x3498, ?x137), country_of_origin(?x2447, ?x279), nationality(?x199, ?x279), country(?x1036, ?x279) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4763 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 25 *> proper extension: 051_y; *> query: (?x8278, 07xtqq) <- films(?x8278, ?x3111), films(?x8278, ?x1295), genre(?x1295, ?x258), executive_produced_by(?x1295, ?x6883), production_companies(?x1295, ?x382), genre(?x3111, ?x600), genre(?x3111, ?x571), ?x600 = 02n4kr, film(?x1554, ?x1295), titles(?x571, ?x249) *> conf = 0.07 ranks of expected_values: 104 EVAL 05vtw films 07xtqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 68.000 27.000 0.333 http://example.org/film/film_subject/films #21801-023kzp PRED entity: 023kzp PRED relation: nationality PRED expected values: 09c7w0 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 64): 09c7w0 (0.82 #4111, 0.80 #201, 0.78 #6220), 07ssc (0.33 #8228, 0.11 #15, 0.11 #2720), 0j5g9 (0.33 #8228, 0.06 #162, 0.01 #1363), 0b90_r (0.33 #8228, 0.06 #103), 0d060g (0.33 #8228, 0.05 #207, 0.05 #1708), 03rt9 (0.33 #8228, 0.02 #813, 0.02 #2718), 02jx1 (0.12 #3038, 0.11 #1134, 0.10 #1834), 03rk0 (0.08 #2047, 0.08 #3352, 0.08 #3755), 0345h (0.03 #2836, 0.02 #3740, 0.02 #2736), 0chghy (0.03 #1711, 0.02 #3115, 0.02 #1211) >> Best rule #4111 for best value: >> intensional similarity = 2 >> extensional distance = 1187 >> proper extension: 0c7ct; 01c59k; 071pf2; 07nv3_; 0fpj4lx; 0gg9_5q; 0372p; 03hbzj; 0hgqq; 0641g8; ... >> query: (?x5925, 09c7w0) <- place_of_birth(?x5925, ?x3501), state(?x3501, ?x2623) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023kzp nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.822 http://example.org/people/person/nationality #21800-0h5j77 PRED entity: 0h5j77 PRED relation: profession PRED expected values: 02jknp => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 41): 02hrh1q (0.73 #1337, 0.69 #5894, 0.69 #8540), 03gjzk (0.35 #603, 0.33 #1485, 0.29 #897), 0dxtg (0.32 #601, 0.31 #895, 0.30 #1483), 02jknp (0.24 #2065, 0.24 #889, 0.21 #5446), 09jwl (0.20 #1636, 0.18 #3106, 0.17 #2812), 0nbcg (0.13 #1647, 0.12 #3117, 0.12 #2823), 0dz3r (0.13 #1619, 0.11 #2795, 0.11 #3089), 0cbd2 (0.12 #9708, 0.12 #7209, 0.11 #10738), 018gz8 (0.12 #1487, 0.10 #605, 0.09 #1340), 016z4k (0.11 #1033, 0.11 #1621, 0.10 #4708) >> Best rule #1337 for best value: >> intensional similarity = 3 >> extensional distance = 915 >> proper extension: 09d5h; >> query: (?x7507, 02hrh1q) <- award_winner(?x496, ?x7507), nominated_for(?x496, ?x69), film(?x496, ?x392) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2065 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1106 *> proper extension: 024rbz; 099ks0; *> query: (?x7507, 02jknp) <- award_winner(?x4037, ?x7507), film(?x3381, ?x4037) *> conf = 0.24 ranks of expected_values: 4 EVAL 0h5j77 profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 75.000 0.726 http://example.org/people/person/profession #21799-0fsw_7 PRED entity: 0fsw_7 PRED relation: genre PRED expected values: 01jfsb => 101 concepts (87 used for prediction) PRED predicted values (max 10 best out of 97): 01jfsb (0.79 #134, 0.55 #6805, 0.50 #984), 07s9rl0 (0.76 #243, 0.74 #3640, 0.73 #3518), 07f1x (0.53 #8492, 0.52 #7765, 0.52 #1214), 02l7c8 (0.46 #259, 0.38 #380, 0.36 #623), 05p553 (0.45 #8253, 0.35 #7160, 0.33 #7769), 03k9fj (0.44 #983, 0.43 #1226, 0.43 #1347), 06n90 (0.32 #2790, 0.32 #1578, 0.28 #3895), 0lsxr (0.32 #1578, 0.25 #6801, 0.23 #1101), 082gq (0.32 #1578, 0.19 #638, 0.15 #1246), 0d2rhq (0.32 #1578, 0.03 #200, 0.01 #928) >> Best rule #134 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0c40vxk; 013q0p; 02gqm3; >> query: (?x5399, 01jfsb) <- currency(?x5399, ?x170), genre(?x5399, ?x5104), ?x5104 = 0bkbm, film(?x5869, ?x5399) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fsw_7 genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 87.000 0.788 http://example.org/film/film/genre #21798-07cbcy PRED entity: 07cbcy PRED relation: nominated_for PRED expected values: 0bv8h2 0b9rdk 0258dh 0dc7hc 09wnnb 085wqm => 46 concepts (11 used for prediction) PRED predicted values (max 10 best out of 1730): 02krdz (0.67 #10606, 0.66 #12122, 0.64 #12121), 0kvbl6 (0.67 #10606, 0.66 #12122, 0.64 #12121), 02y_lrp (0.67 #10606, 0.66 #12122, 0.64 #12121), 04cbbz (0.67 #10606, 0.66 #12122, 0.64 #12121), 069q4f (0.67 #10606, 0.66 #12122, 0.64 #12121), 05m_jsg (0.67 #10606, 0.66 #12122, 0.64 #12121), 01hqk (0.39 #2139, 0.12 #625, 0.05 #6685), 033f8n (0.33 #2227, 0.25 #713, 0.05 #5257), 0dfw0 (0.33 #2241, 0.12 #727, 0.06 #5271), 047csmy (0.33 #2303, 0.12 #789, 0.06 #9880) >> Best rule #10606 for best value: >> intensional similarity = 3 >> extensional distance = 170 >> proper extension: 0m7yy; 02wwsh8; 03ybrwc; 0468g4r; >> query: (?x1312, ?x8562) <- award(?x8562, ?x1312), currency(?x8562, ?x170), film(?x4360, ?x8562) >> conf = 0.67 => this is the best rule for 6 predicted values *> Best rule #1075 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 05zr6wv; 027986c; 07bdd_; 027c95y; 0hnf5vm; 027b9j5; *> query: (?x1312, 0258dh) <- nominated_for(?x1312, ?x188), award_winner(?x1312, ?x4360), award(?x294, ?x1312), ?x4360 = 0f502 *> conf = 0.25 ranks of expected_values: 26, 223, 554, 1213, 1456 EVAL 07cbcy nominated_for 085wqm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 11.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07cbcy nominated_for 09wnnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 11.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07cbcy nominated_for 0dc7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 11.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07cbcy nominated_for 0258dh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 46.000 11.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07cbcy nominated_for 0b9rdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 11.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07cbcy nominated_for 0bv8h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 11.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21797-03hkv_r PRED entity: 03hkv_r PRED relation: award! PRED expected values: 0c9c0 0499lc 0js9s 013t9y 02hfp_ 020x5r => 60 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2878): 081lh (0.92 #16621, 0.87 #9971, 0.87 #13297), 01ts_3 (0.62 #31940, 0.50 #5344, 0.29 #25291), 02hfp_ (0.60 #12260, 0.54 #32208, 0.50 #15586), 06b_0 (0.60 #12161, 0.54 #32109, 0.50 #8837), 0499lc (0.60 #11298, 0.33 #14624, 0.33 #1327), 0mb5x (0.57 #25658, 0.40 #12359, 0.33 #15685), 014zcr (0.56 #26644, 0.54 #29969, 0.50 #13347), 0js9s (0.54 #31799, 0.50 #15177, 0.50 #5203), 0dbbz (0.54 #32613, 0.50 #9341, 0.40 #12665), 01q4qv (0.54 #30772, 0.50 #7500, 0.40 #10824) >> Best rule #16621 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0gr4k; 02n9nmz; 063y_ky; >> query: (?x384, ?x488) <- nominated_for(?x384, ?x4864), award_winner(?x384, ?x488), award(?x989, ?x384), ?x4864 = 0qf2t, award_winner(?x969, ?x989) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #12260 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 04dn09n; *> query: (?x384, 02hfp_) <- nominated_for(?x384, ?x4864), award_winner(?x384, ?x488), award(?x8656, ?x384), ?x8656 = 042v2, nominated_for(?x166, ?x4864) *> conf = 0.60 ranks of expected_values: 3, 5, 8, 19, 36, 135 EVAL 03hkv_r award! 020x5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 60.000 25.000 0.918 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hkv_r award! 02hfp_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 25.000 0.918 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hkv_r award! 013t9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 60.000 25.000 0.918 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hkv_r award! 0js9s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 60.000 25.000 0.918 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hkv_r award! 0499lc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 60.000 25.000 0.918 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hkv_r award! 0c9c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 60.000 25.000 0.918 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21796-026wp PRED entity: 026wp PRED relation: form_of_government! PRED expected values: 02lx0 => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 545): 07ssc (0.50 #954, 0.50 #949, 0.50 #788), 06mkj (0.50 #958, 0.50 #821, 0.50 #183), 0f8l9c (0.50 #954, 0.50 #949, 0.50 #183), 0154j (0.50 #949, 0.50 #777, 0.50 #183), 0ctw_b (0.50 #949, 0.50 #795, 0.50 #183), 0k6nt (0.50 #949, 0.50 #794, 0.38 #770), 09pmkv (0.50 #967, 0.50 #796, 0.38 #770), 05qkp (0.50 #833, 0.50 #183, 0.38 #770), 0hg5 (0.50 #828, 0.50 #183, 0.38 #770), 01n8qg (0.50 #922, 0.50 #183, 0.38 #770) >> Best rule #954 for best value: >> intensional similarity = 179 >> extensional distance = 2 >> proper extension: 01q20; >> query: (?x6441, ?x789) <- form_of_government(?x8378, ?x6441), form_of_government(?x6559, ?x6441), form_of_government(?x456, ?x6441), ?x8378 = 07fb6, jurisdiction_of_office(?x3959, ?x6559), film_release_region(?x9174, ?x6559), film_release_region(?x4610, ?x6559), film_release_region(?x2050, ?x6559), ?x2050 = 01fmys, origin(?x7547, ?x6559), film_release_region(?x9941, ?x456), film_release_region(?x8373, ?x456), film_release_region(?x8025, ?x456), film_release_region(?x7897, ?x456), film_release_region(?x7700, ?x456), film_release_region(?x7680, ?x456), film_release_region(?x7538, ?x456), film_release_region(?x7009, ?x456), film_release_region(?x6932, ?x456), film_release_region(?x6882, ?x456), film_release_region(?x6492, ?x456), film_release_region(?x6422, ?x456), film_release_region(?x5721, ?x456), film_release_region(?x5644, ?x456), film_release_region(?x5271, ?x456), film_release_region(?x5067, ?x456), film_release_region(?x4607, ?x456), film_release_region(?x4372, ?x456), film_release_region(?x4290, ?x456), film_release_region(?x3843, ?x456), film_release_region(?x3423, ?x456), film_release_region(?x3287, ?x456), film_release_region(?x2961, ?x456), film_release_region(?x2695, ?x456), film_release_region(?x2598, ?x456), film_release_region(?x2471, ?x456), film_release_region(?x2093, ?x456), film_release_region(?x1999, ?x456), film_release_region(?x1956, ?x456), film_release_region(?x1803, ?x456), film_release_region(?x1724, ?x456), film_release_region(?x1701, ?x456), film_release_region(?x1496, ?x456), film_release_region(?x1456, ?x456), film_release_region(?x1386, ?x456), film_release_region(?x781, ?x456), film_release_region(?x622, ?x456), film_release_region(?x299, ?x456), ?x1999 = 0gd0c7x, ?x7897 = 03np63f, ?x1701 = 0bh8yn3, ?x2695 = 047svrl, ?x7538 = 035zr0, administrative_area_type(?x456, ?x2792), adjoins(?x456, ?x344), participating_countries(?x418, ?x456), ?x5721 = 01d259, country(?x4673, ?x456), country(?x3554, ?x456), country(?x2044, ?x456), country(?x1352, ?x456), country(?x150, ?x456), combatants(?x1353, ?x456), combatants(?x985, ?x456), combatants(?x789, ?x456), combatants(?x172, ?x456), ?x3423 = 09g7vfw, official_language(?x6559, ?x254), olympics(?x456, ?x391), ?x8373 = 0bs8hvm, ?x299 = 01gc7, ?x4290 = 0gtxj2q, ?x5644 = 0dll_t2, film(?x609, ?x7700), ?x9941 = 024lt6, ?x2598 = 07f_7h, ?x4673 = 07jbh, country(?x3554, ?x2152), country(?x3554, ?x774), country(?x3554, ?x429), country(?x3554, ?x142), film(?x1289, ?x2093), country(?x2044, ?x2146), country(?x2044, ?x1917), country(?x2044, ?x291), location_of_ceremony(?x566, ?x6559), contains(?x456, ?x6265), country(?x1283, ?x172), ?x774 = 06mzp, ?x1956 = 05qbckf, currency(?x456, ?x170), countries_spoken_in(?x10486, ?x456), ?x7680 = 0gh6j94, film(?x3175, ?x6882), ?x9174 = 087pfc, ?x142 = 0jgd, ?x291 = 0h3y, ?x5271 = 047vnkj, olympics(?x985, ?x2496), olympics(?x985, ?x1081), ?x2496 = 0sxrz, featured_film_locations(?x6882, ?x1523), countries_within(?x455, ?x1353), film_release_region(?x1456, ?x4743), film_release_region(?x1456, ?x3699), film_release_region(?x1456, ?x1122), film_release_region(?x11839, ?x172), film_release_region(?x3981, ?x172), film_release_region(?x3603, ?x172), film_release_region(?x3252, ?x172), film_release_region(?x2168, ?x172), film_release_region(?x1498, ?x172), language(?x7700, ?x732), nationality(?x2671, ?x985), ?x3603 = 09gkx35, medal(?x1353, ?x422), film_release_region(?x2889, ?x1353), ?x3252 = 0gh8zks, ?x1081 = 0l6m5, ?x2471 = 08052t3, ?x1386 = 0dtfn, ?x4372 = 02rmd_2, ?x6492 = 0ds6bmk, film_release_region(?x641, ?x985), ?x641 = 08720, genre(?x2093, ?x53), ?x2152 = 06mkj, ?x4743 = 03spz, ?x732 = 04306rv, ?x6932 = 027pfg, ?x2168 = 0bx0l, participating_countries(?x2553, ?x1353), ?x7009 = 0bs8s1p, combatants(?x2391, ?x456), ?x1803 = 0g9wdmc, ?x6422 = 02qk3fk, film(?x382, ?x6882), ?x4610 = 017jd9, ?x11839 = 072hx4, ?x1496 = 011yqc, ?x3981 = 047tsx3, capital(?x6559, ?x8428), production_companies(?x7700, ?x14079), ?x1122 = 09pmkv, ?x1724 = 02r8hh_, country(?x2547, ?x456), ?x429 = 03rt9, ?x2391 = 0d06vc, contains(?x789, ?x790), genre(?x781, ?x225), contains(?x985, ?x8174), nominated_for(?x298, ?x781), country(?x6423, ?x789), ?x1917 = 01p1v, ?x150 = 07rlg, film_release_region(?x4604, ?x789), ?x4604 = 0432_5, member_states(?x7695, ?x789), country(?x359, ?x789), ?x3843 = 080nwsb, titles(?x789, ?x2380), ?x5067 = 01rwpj, ?x3699 = 012wgb, ?x2146 = 03rk0, ?x3287 = 026njb5, ?x8025 = 03nsm5x, taxonomy(?x456, ?x939), country(?x518, ?x789), ?x622 = 0fq27fp, crewmember(?x781, ?x6546), country(?x12943, ?x985), organization(?x985, ?x127), ?x2889 = 040b5k, adjoins(?x3912, ?x789), ?x2961 = 047p7fr, ?x1352 = 0w0d, nationality(?x317, ?x789), ?x1498 = 04jkpgv, award_winner(?x4607, ?x286) >> conf = 0.50 => this is the best rule for 2 predicted values *> Best rule #183 for first EXPECTED value: *> intensional similarity = 181 *> extensional distance = 1 *> proper extension: 018wl5; *> query: (?x6441, ?x3912) <- form_of_government(?x8378, ?x6441), form_of_government(?x6559, ?x6441), form_of_government(?x792, ?x6441), form_of_government(?x456, ?x6441), form_of_government(?x47, ?x6441), ?x8378 = 07fb6, jurisdiction_of_office(?x3959, ?x6559), film_release_region(?x9174, ?x6559), film_release_region(?x4610, ?x6559), film_release_region(?x2050, ?x6559), ?x2050 = 01fmys, origin(?x7547, ?x6559), film_release_region(?x10535, ?x456), film_release_region(?x9941, ?x456), film_release_region(?x8373, ?x456), film_release_region(?x7897, ?x456), film_release_region(?x7700, ?x456), film_release_region(?x7680, ?x456), film_release_region(?x7538, ?x456), film_release_region(?x7009, ?x456), film_release_region(?x6932, ?x456), film_release_region(?x6882, ?x456), film_release_region(?x6492, ?x456), film_release_region(?x6422, ?x456), film_release_region(?x5721, ?x456), film_release_region(?x5644, ?x456), film_release_region(?x5271, ?x456), film_release_region(?x4668, ?x456), film_release_region(?x4372, ?x456), film_release_region(?x4290, ?x456), film_release_region(?x3565, ?x456), film_release_region(?x3423, ?x456), film_release_region(?x2695, ?x456), film_release_region(?x2676, ?x456), film_release_region(?x2598, ?x456), film_release_region(?x2512, ?x456), film_release_region(?x2471, ?x456), film_release_region(?x2093, ?x456), film_release_region(?x1999, ?x456), film_release_region(?x1956, ?x456), film_release_region(?x1803, ?x456), film_release_region(?x1724, ?x456), film_release_region(?x1701, ?x456), film_release_region(?x1496, ?x456), film_release_region(?x1456, ?x456), film_release_region(?x1386, ?x456), film_release_region(?x781, ?x456), film_release_region(?x299, ?x456), film_release_region(?x86, ?x456), ?x1999 = 0gd0c7x, ?x7897 = 03np63f, ?x1701 = 0bh8yn3, ?x2695 = 047svrl, ?x7538 = 035zr0, administrative_area_type(?x456, ?x2792), adjoins(?x456, ?x344), participating_countries(?x418, ?x456), ?x5721 = 01d259, country(?x4673, ?x456), country(?x3554, ?x456), country(?x2044, ?x456), country(?x150, ?x456), combatants(?x1353, ?x456), combatants(?x985, ?x456), combatants(?x789, ?x456), combatants(?x172, ?x456), ?x3423 = 09g7vfw, official_language(?x6559, ?x254), olympics(?x456, ?x391), ?x8373 = 0bs8hvm, ?x299 = 01gc7, ?x4290 = 0gtxj2q, ?x5644 = 0dll_t2, film(?x609, ?x7700), ?x9941 = 024lt6, ?x2598 = 07f_7h, ?x4673 = 07jbh, country(?x3554, ?x2152), country(?x3554, ?x774), country(?x3554, ?x429), country(?x3554, ?x142), film(?x1289, ?x2093), country(?x2044, ?x1917), country(?x2044, ?x291), location_of_ceremony(?x566, ?x6559), contains(?x456, ?x6265), country(?x1283, ?x172), ?x774 = 06mzp, ?x1956 = 05qbckf, film(?x541, ?x2512), currency(?x456, ?x170), countries_spoken_in(?x10486, ?x456), ?x7680 = 0gh6j94, film(?x3175, ?x6882), ?x9174 = 087pfc, ?x142 = 0jgd, ?x291 = 0h3y, ?x5271 = 047vnkj, olympics(?x985, ?x2496), olympics(?x985, ?x1081), ?x2496 = 0sxrz, featured_film_locations(?x6882, ?x1523), countries_within(?x455, ?x1353), film_release_region(?x1456, ?x4743), film_release_region(?x1456, ?x1122), film_release_region(?x11839, ?x172), film_release_region(?x3981, ?x172), film_release_region(?x3603, ?x172), film_release_region(?x3252, ?x172), film_release_region(?x2168, ?x172), language(?x7700, ?x732), nationality(?x2671, ?x985), ?x3603 = 09gkx35, medal(?x1353, ?x422), film_release_region(?x1069, ?x1353), ?x3252 = 0gh8zks, ?x1081 = 0l6m5, ?x2471 = 08052t3, ?x1386 = 0dtfn, ?x4372 = 02rmd_2, ?x6492 = 0ds6bmk, film_release_region(?x8370, ?x985), film_release_region(?x3599, ?x985), film_release_region(?x641, ?x985), ?x641 = 08720, genre(?x2093, ?x53), ?x2152 = 06mkj, film(?x8061, ?x2512), ?x4743 = 03spz, ?x732 = 04306rv, ?x6932 = 027pfg, ?x2168 = 0bx0l, participating_countries(?x2553, ?x1353), ?x7009 = 0bs8s1p, combatants(?x2391, ?x456), ?x1803 = 0g9wdmc, ?x6422 = 02qk3fk, film(?x382, ?x6882), ?x4610 = 017jd9, ?x11839 = 072hx4, ?x1496 = 011yqc, ?x3981 = 047tsx3, capital(?x6559, ?x8428), production_companies(?x7700, ?x14079), ?x1122 = 09pmkv, ?x1724 = 02r8hh_, country(?x2547, ?x456), ?x429 = 03rt9, ?x2391 = 0d06vc, contains(?x47, ?x13532), contains(?x789, ?x790), genre(?x781, ?x225), contains(?x985, ?x8174), nominated_for(?x1336, ?x781), country(?x6423, ?x789), ?x1917 = 01p1v, ?x150 = 07rlg, film_release_region(?x915, ?x789), ?x792 = 0hzlz, ?x8370 = 07ghq, ?x86 = 0ds35l9, organization(?x789, ?x127), ?x3565 = 0cp0ph6, exported_to(?x4164, ?x985), nationality(?x317, ?x789), contains(?x7273, ?x47), film(?x1914, ?x4668), ?x10535 = 09v42sf, produced_by(?x4668, ?x1532), adjoins(?x3912, ?x789), ?x2676 = 0f4m2z, country(?x359, ?x789), ?x3599 = 0kxf1, titles(?x812, ?x1456), ?x1336 = 05pcn59, entity_involved(?x9939, ?x789), film_regional_debut_venue(?x4668, ?x5416), country(?x518, ?x789), combatants(?x1140, ?x789), location(?x4536, ?x1353), ?x8061 = 0sw6g *> conf = 0.50 ranks of expected_values: 56 EVAL 026wp form_of_government! 02lx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 6.000 6.000 0.500 http://example.org/location/country/form_of_government #21795-0h5g_ PRED entity: 0h5g_ PRED relation: film PRED expected values: 0bw20 => 113 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1206): 08k40m (0.25 #480, 0.12 #5781, 0.06 #7548), 02t_h3 (0.25 #1738, 0.12 #7039, 0.06 #8806), 06fqlk (0.25 #1125, 0.07 #4659, 0.05 #9960), 084qpk (0.25 #121, 0.07 #3655, 0.03 #10723), 02704ff (0.25 #965, 0.04 #16868, 0.02 #15101), 04hk0w (0.25 #1760, 0.03 #12362), 027j9wd (0.25 #1017, 0.02 #20454), 01chpn (0.25 #1092, 0.02 #16995, 0.02 #39966), 0fh694 (0.25 #142, 0.02 #16045, 0.01 #17812), 08phg9 (0.25 #872, 0.01 #64485, 0.01 #85692) >> Best rule #480 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 012x2b; >> query: (?x489, 08k40m) <- film(?x489, ?x6826), ?x6826 = 0642ykh >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #24201 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 139 *> proper extension: 03zqc1; 01kwld; 01vsykc; 0gyx4; 023s8; 02vtnf; 044zvm; *> query: (?x489, 0bw20) <- award_nominee(?x100, ?x489), participant(?x489, ?x538), spouse(?x489, ?x9807) *> conf = 0.01 ranks of expected_values: 930 EVAL 0h5g_ film 0bw20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 113.000 71.000 0.250 http://example.org/film/actor/film./film/performance/film #21794-03_0p PRED entity: 03_0p PRED relation: profession PRED expected values: 01c8w0 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 87): 02hrh1q (0.76 #11875, 0.75 #11575, 0.75 #10675), 09jwl (0.71 #2270, 0.71 #7226, 0.71 #6326), 0nbcg (0.59 #483, 0.55 #1533, 0.53 #2133), 01c8w0 (0.50 #9, 0.30 #3902, 0.30 #10059), 01c72t (0.49 #625, 0.41 #475, 0.40 #775), 016z4k (0.47 #7360, 0.47 #2404, 0.47 #4659), 0dz3r (0.43 #7208, 0.42 #7959, 0.42 #3603), 01d_h8 (0.38 #906, 0.32 #2706, 0.31 #12317), 0dxtg (0.32 #914, 0.31 #1214, 0.31 #6770), 039v1 (0.31 #6344, 0.29 #7244, 0.28 #1538) >> Best rule #11875 for best value: >> intensional similarity = 3 >> extensional distance = 874 >> proper extension: 03m8lq; 01j5x6; 01v3s2_; 0162c8; 02wycg2; 0884fm; 0b478; 01pctb; 0g2mbn; 02pk6x; ... >> query: (?x5150, 02hrh1q) <- people(?x1050, ?x5150), award_nominee(?x5151, ?x5150), profession(?x5151, ?x563) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 01dhpj; *> query: (?x5150, 01c8w0) <- instrumentalists(?x75, ?x5150), award_winner(?x5151, ?x5150), award_winner(?x5132, ?x5150), ?x5151 = 016k62, award_winner(?x6869, ?x5132) *> conf = 0.50 ranks of expected_values: 4 EVAL 03_0p profession 01c8w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 143.000 143.000 0.759 http://example.org/people/person/profession #21793-0dgskx PRED entity: 0dgskx PRED relation: student! PRED expected values: 07w4j => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 47): 015nl4 (0.14 #67, 0.08 #1121, 0.06 #594), 0m4yg (0.14 #365, 0.06 #892, 0.04 #1419), 07tg4 (0.08 #1140, 0.07 #86, 0.06 #613), 02l9wl (0.07 #252, 0.04 #1306, 0.01 #1833), 07tgn (0.07 #17, 0.04 #1071, 0.01 #28487), 02hmw9 (0.07 #237, 0.01 #1291), 0ym20 (0.07 #524), 08tyb_ (0.07 #498), 01d650 (0.07 #374), 01bcwk (0.07 #161) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 044mz_; 0159h6; 01yhvv; 0170pk; 0170qf; 0171cm; 0m31m; 0fbx6; 016xh5; 01f7dd; ... >> query: (?x6612, 015nl4) <- place_of_birth(?x6612, ?x10786), award_nominee(?x1222, ?x6612), ?x1222 = 03f1zdw >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dgskx student! 07w4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 85.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #21792-04jpl PRED entity: 04jpl PRED relation: location! PRED expected values: 01pcrw 0bq2g 025t9b 03_hd 0282x 01v9724 0627sn 01wgjj5 0lfbm 019gz 03k545 => 205 concepts (156 used for prediction) PRED predicted values (max 10 best out of 2538): 05y5kf (0.52 #315742, 0.48 #334883, 0.46 #291821), 044lyq (0.52 #315742, 0.48 #334883, 0.46 #291821), 03hzl42 (0.52 #315742, 0.48 #334883, 0.46 #291821), 01qrbf (0.52 #315742, 0.48 #334883, 0.46 #291821), 0884fm (0.52 #315742, 0.48 #334883, 0.46 #291821), 03f4w4 (0.52 #315742, 0.48 #334883, 0.46 #291821), 0202p_ (0.52 #315742, 0.48 #334883, 0.46 #291821), 06t61y (0.52 #315742, 0.48 #334883, 0.46 #291821), 01fwf1 (0.52 #315742, 0.48 #334883, 0.46 #291821), 013bd1 (0.52 #315742, 0.48 #334883, 0.46 #234409) >> Best rule #315742 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 01km6_; 0fdpd; >> query: (?x362, ?x4468) <- place_of_birth(?x4468, ?x362), film(?x4468, ?x370), citytown(?x752, ?x362) >> conf = 0.52 => this is the best rule for 11 predicted values *> Best rule #652 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 02_286; *> query: (?x362, 0bq2g) <- featured_film_locations(?x414, ?x362), location(?x361, ?x362), ?x414 = 095zlp *> conf = 0.33 ranks of expected_values: 50, 547, 861, 1223, 1275, 1280, 1559, 1759 EVAL 04jpl location! 03k545 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04jpl location! 019gz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04jpl location! 0lfbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04jpl location! 01wgjj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04jpl location! 0627sn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04jpl location! 01v9724 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04jpl location! 0282x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04jpl location! 03_hd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04jpl location! 025t9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04jpl location! 0bq2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04jpl location! 01pcrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 205.000 156.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location #21791-0mb8c PRED entity: 0mb8c PRED relation: film_format PRED expected values: 0cj16 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 3): 0cj16 (0.36 #8, 0.30 #24, 0.27 #13), 07fb8_ (0.21 #52, 0.18 #47, 0.18 #93), 017fx5 (0.07 #71, 0.04 #30, 0.04 #35) >> Best rule #8 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0hmm7; 0fpmrm3; 0645k5; 0cmc26r; 0bh8x1y; 07l50vn; 0k7tq; 0bs8s1p; 0g4vmj8; 0btpm6; ... >> query: (?x5230, 0cj16) <- genre(?x5230, ?x812), titles(?x2645, ?x5230), ?x812 = 01jfsb, film_regional_debut_venue(?x5230, ?x5416), award(?x5230, ?x9217) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mb8c film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.357 http://example.org/film/film/film_format #21790-0h25 PRED entity: 0h25 PRED relation: influenced_by PRED expected values: 07kb5 0gz_ => 174 concepts (83 used for prediction) PRED predicted values (max 10 best out of 406): 02lt8 (0.51 #6990, 0.47 #8279, 0.40 #8711), 05qmj (0.38 #3626, 0.38 #3196, 0.21 #10935), 0j3v (0.38 #2636, 0.37 #1778, 0.36 #919), 048cl (0.36 #1090, 0.28 #4527, 0.17 #2807), 042q3 (0.35 #3797, 0.35 #3367, 0.25 #12824), 081k8 (0.32 #1872, 0.24 #12617, 0.22 #8747), 0379s (0.32 #1796, 0.16 #12972, 0.14 #937), 015n8 (0.31 #3841, 0.31 #3411, 0.21 #12868), 03_87 (0.28 #13094, 0.26 #1918, 0.25 #12663), 039n1 (0.27 #3758, 0.27 #3328, 0.21 #4618) >> Best rule #6990 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 01q9b9; 0ldd; >> query: (?x10500, 02lt8) <- influenced_by(?x10500, ?x11097), influenced_by(?x10000, ?x11097), ?x10000 = 03j0d, people(?x5540, ?x11097) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #3538 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 045bg; 03j43; 039n1; *> query: (?x10500, 0gz_) <- influenced_by(?x117, ?x10500), influenced_by(?x10500, ?x7250), ?x7250 = 03sbs, nationality(?x10500, ?x94) *> conf = 0.27 ranks of expected_values: 11, 54 EVAL 0h25 influenced_by 0gz_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 174.000 83.000 0.509 http://example.org/influence/influence_node/influenced_by EVAL 0h25 influenced_by 07kb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 174.000 83.000 0.509 http://example.org/influence/influence_node/influenced_by #21789-0315w4 PRED entity: 0315w4 PRED relation: film_release_region PRED expected values: 09c7w0 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 197): 09c7w0 (0.76 #540, 0.73 #1439, 0.72 #1977), 0345h (0.47 #7558, 0.47 #6837, 0.46 #6475), 0chghy (0.35 #554, 0.25 #734, 0.23 #10095), 0f8l9c (0.32 #569, 0.27 #10110, 0.27 #8849), 0d0vqn (0.32 #549, 0.27 #10090, 0.27 #1448), 02vzc (0.32 #607, 0.24 #10148, 0.24 #1506), 05r4w (0.32 #539, 0.24 #719, 0.24 #10080), 059j2 (0.30 #582, 0.24 #10123, 0.24 #8862), 06mkj (0.27 #613, 0.25 #10154, 0.25 #8893), 07ssc (0.27 #561, 0.25 #1460, 0.24 #1998) >> Best rule #540 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 026njb5; >> query: (?x4799, 09c7w0) <- titles(?x3613, ?x4799), ?x3613 = 09blyk, film_crew_role(?x4799, ?x137), nominated_for(?x507, ?x4799) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0315w4 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.757 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21788-0fvvg PRED entity: 0fvvg PRED relation: source PRED expected values: 0jbk9 => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #94, 0.90 #76, 0.86 #8) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x12662, 0jbk9) <- category(?x12662, ?x134), ?x134 = 08mbj5d, place(?x12662, ?x12662) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fvvg source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #21787-070c93 PRED entity: 070c93 PRED relation: religion PRED expected values: 01spm => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 12): 03j6c (0.34 #66, 0.32 #111, 0.29 #156), 0c8wxp (0.12 #456, 0.11 #1086, 0.11 #996), 0flw86 (0.10 #47, 0.10 #137, 0.10 #92), 03_gx (0.07 #329, 0.07 #239, 0.07 #14), 0kpl (0.07 #415, 0.04 #2035, 0.04 #2080), 06yyp (0.03 #67, 0.03 #112, 0.02 #157), 0n2g (0.02 #418), 0kq2 (0.02 #423, 0.01 #1368, 0.01 #1638), 01lp8 (0.02 #406, 0.01 #856, 0.01 #1036), 092bf5 (0.01 #466, 0.01 #1006, 0.01 #1096) >> Best rule #66 for best value: >> intensional similarity = 5 >> extensional distance = 137 >> proper extension: 05d7rk; 04rs03; 067jsf; 0292l3; 040wdl; 015npr; 02vmzp; 025tdwc; 06pwf6; 04cbtrw; ... >> query: (?x12230, 03j6c) <- type_of_union(?x12230, ?x566), ?x566 = 04ztj, nationality(?x12230, ?x2146), profession(?x12230, ?x1032), ?x2146 = 03rk0 >> conf = 0.34 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 070c93 religion 01spm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.338 http://example.org/people/person/religion #21786-02zd460 PRED entity: 02zd460 PRED relation: school! PRED expected values: 02pq_rp => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 20): 0f4vx0 (0.23 #492, 0.22 #532, 0.19 #592), 02qw1zx (0.16 #486, 0.15 #526, 0.13 #586), 025tn92 (0.14 #113, 0.10 #854, 0.09 #494), 09l0x9 (0.11 #493, 0.11 #533, 0.09 #593), 05vsb7 (0.10 #842, 0.08 #81, 0.08 #982), 02pq_x5 (0.10 #498, 0.10 #538, 0.09 #858), 092j54 (0.10 #850, 0.08 #89, 0.08 #982), 03nt7j (0.08 #87, 0.08 #982, 0.08 #848), 02pq_rp (0.08 #88, 0.08 #982, 0.07 #489), 038981 (0.08 #96, 0.08 #982, 0.07 #497) >> Best rule #492 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 01mmslz; 0n00; 043gj; 01tdnyh; 0bdlj; 0l99s; 06rgq; 05yjhm; 018_lb; 0716t2; >> query: (?x5288, 0f4vx0) <- category(?x5288, ?x134), ?x134 = 08mbj5d, organization(?x5288, ?x5487) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #88 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 10 *> proper extension: 06y3r; 023p29; 0n839; *> query: (?x5288, 02pq_rp) <- organizations_founded(?x5288, ?x5487), list(?x5288, ?x2197) *> conf = 0.08 ranks of expected_values: 9 EVAL 02zd460 school! 02pq_rp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 112.000 112.000 0.229 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #21785-035qy PRED entity: 035qy PRED relation: film_release_region! PRED expected values: 0g56t9t 0gkz15s 0crfwmx 02c6d 04hwbq 0gtvrv3 03qnvdl 0gj9qxr 0j6b5 040rmy 0gjc4d3 09g7vfw 0gyh2wm 02rmd_2 080lkt7 04zl8 02ylg6 02qk3fk 02825cv 032clf 0gvvm6l 05zvzf3 0bmfnjs 0crs0b8 08j7lh 072hx4 => 224 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1546): 0gj8nq2 (0.88 #8700, 0.85 #22350, 0.83 #18150), 0gtvrv3 (0.88 #17973, 0.75 #5372, 0.70 #22173), 0125xq (0.85 #22462, 0.75 #18262, 0.69 #30862), 0bc1yhb (0.85 #22577, 0.71 #18377, 0.69 #8927), 040rmy (0.83 #18072, 0.81 #8622, 0.78 #30672), 03qnvdl (0.83 #17978, 0.81 #8528, 0.78 #22178), 0gkz15s (0.83 #17916, 0.79 #44168, 0.78 #63068), 04hwbq (0.82 #63110, 0.79 #17958, 0.79 #44210), 0hgnl3t (0.81 #22476, 0.79 #18276, 0.75 #8826), 0gwgn1k (0.81 #22951, 0.71 #18751, 0.69 #9301) >> Best rule #8700 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 03rjj; 015fr; 01p1v; 06mkj; 07f1x; >> query: (?x1353, 0gj8nq2) <- film_release_region(?x2933, ?x1353), vacationer(?x1353, ?x2237), ?x2933 = 0407yj_ >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #17973 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 059j2; 03spz; *> query: (?x1353, 0gtvrv3) <- film_release_region(?x6247, ?x1353), film_release_region(?x385, ?x1353), ?x385 = 0ds3t5x, ?x6247 = 09v9mks *> conf = 0.88 ranks of expected_values: 2, 5, 6, 7, 8, 12, 13, 14, 15, 20, 22, 24, 27, 28, 29, 30, 32, 34, 42, 50, 87, 88, 95, 102, 121, 171 EVAL 035qy film_release_region! 072hx4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 08j7lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0crs0b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0bmfnjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0gvvm6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 032clf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 02825cv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 02qk3fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 02ylg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 04zl8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 080lkt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 02rmd_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0gyh2wm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 09g7vfw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0gjc4d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 040rmy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0j6b5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0gj9qxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 03qnvdl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0gtvrv3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 04hwbq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 02c6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0crfwmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035qy film_release_region! 0g56t9t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 224.000 124.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21784-084w8 PRED entity: 084w8 PRED relation: written_by! PRED expected values: 0cy__l => 159 concepts (101 used for prediction) PRED predicted values (max 10 best out of 125): 0jdr0 (0.17 #3222, 0.01 #14459, 0.01 #15120), 03cvwkr (0.14 #4017, 0.12 #5339, 0.10 #6661), 03whyr (0.12 #5876, 0.10 #7198, 0.01 #15130), 026zlh9 (0.12 #5040, 0.02 #13633, 0.01 #14294), 09p7fh (0.12 #4788, 0.02 #13381, 0.01 #14042), 01s7w3 (0.10 #7840, 0.01 #14450, 0.01 #15111), 03tn80 (0.10 #7608, 0.01 #14218, 0.01 #14879), 0hx4y (0.10 #7452, 0.01 #14062, 0.01 #14723), 015gm8 (0.10 #7264), 05hjnw (0.10 #6943) >> Best rule #3222 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0hcvy; >> query: (?x118, 0jdr0) <- influenced_by(?x118, ?x3969), influenced_by(?x118, ?x2162), organization(?x3969, ?x8603), ?x2162 = 04xjp, location(?x3969, ?x1025) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 084w8 written_by! 0cy__l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 101.000 0.167 http://example.org/film/film/written_by #21783-030qb3t PRED entity: 030qb3t PRED relation: location! PRED expected values: 04bdxl 0chsq 09wj5 01cv3n 01rh0w 039g82 05dbf 09yhzs 01ksr1 053y4h 0333wf 06vsbt 01jgpsh 0gs1_ 023n39 01kmd4 01z9_x 06hgym 03hhd3 05vk_d 0jpdn 01qqtr 09nhvw 023zsh 02t_y3 01xwqn 0m68w => 202 concepts (161 used for prediction) PRED predicted values (max 10 best out of 2700): 02qfhb (0.53 #305681, 0.50 #310177, 0.48 #319169), 044zvm (0.53 #305681, 0.50 #310177, 0.48 #319169), 0f4vbz (0.53 #305681, 0.50 #310177, 0.48 #319169), 02g0mx (0.53 #305681, 0.50 #310177, 0.48 #319169), 0flw6 (0.53 #305681, 0.50 #310177, 0.48 #319169), 0p17j (0.53 #305681, 0.50 #310177, 0.48 #319169), 0d_84 (0.53 #305681, 0.50 #310177, 0.48 #319169), 0chw_ (0.53 #305681, 0.50 #310177, 0.48 #319169), 015g_7 (0.53 #305681, 0.50 #310177, 0.48 #319169), 01cpqk (0.53 #305681, 0.50 #310177, 0.48 #319169) >> Best rule #305681 for best value: >> intensional similarity = 2 >> extensional distance = 204 >> proper extension: 01ykl0; 01423b; 01z56h; 01z26v; 0yz30; 0ncy4; >> query: (?x1523, ?x12743) <- place_of_birth(?x12743, ?x1523), participant(?x12743, ?x5216) >> conf = 0.53 => this is the best rule for 20 predicted values *> Best rule #269712 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 168 *> proper extension: 0_ytw; 0_lr1; 0ttxp; 0f0z_; 0mnk7; 09b83; 0dzs0; 018_7x; *> query: (?x1523, ?x71) <- contains(?x1523, ?x682), citytown(?x8056, ?x1523), student(?x8056, ?x71) *> conf = 0.13 ranks of expected_values: 345, 420, 421, 550, 556, 586, 622, 643, 659, 678, 734, 961, 1002, 1017, 1062, 1166, 1375, 1378, 1424, 1448, 1513, 1934, 2433 EVAL 030qb3t location! 0m68w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 01xwqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 02t_y3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 023zsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 09nhvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 01qqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 0jpdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 05vk_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 03hhd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 06hgym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 01z9_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 01kmd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 023n39 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 0gs1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 01jgpsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 06vsbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 0333wf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 053y4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 01ksr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 09yhzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 05dbf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 039g82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 01rh0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 01cv3n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 09wj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 0chsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location EVAL 030qb3t location! 04bdxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 161.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location #21782-02w_6xj PRED entity: 02w_6xj PRED relation: award_winner PRED expected values: 02pv_d => 59 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2155): 081lh (0.44 #7535, 0.40 #5086, 0.21 #12435), 0bzyh (0.40 #5756, 0.33 #8205, 0.33 #857), 01g1lp (0.40 #6588, 0.33 #1689, 0.08 #11487), 01t07j (0.40 #5283, 0.22 #7732, 0.06 #17532), 0kvqv (0.40 #5852, 0.06 #18101, 0.04 #25453), 01kp66 (0.38 #10718, 0.15 #17147, 0.09 #20519), 0159h6 (0.38 #9874, 0.11 #12324, 0.08 #19675), 01jw4r (0.38 #11625, 0.09 #21426, 0.06 #36126), 0jgwf (0.33 #9179, 0.33 #1831, 0.20 #6730), 0kr5_ (0.33 #7466, 0.33 #118, 0.20 #5017) >> Best rule #7535 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 054ky1; >> query: (?x5398, 081lh) <- award_winner(?x5398, ?x7310), award_winner(?x5398, ?x6643), award_winner(?x5398, ?x5898), award_winner(?x5398, ?x5591), ?x7310 = 04sry, participant(?x5283, ?x5898), languages(?x5591, ?x254), award(?x6643, ?x384) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1736 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 09d28z; *> query: (?x5398, 02pv_d) <- award_winner(?x5398, ?x7815), award_winner(?x5398, ?x2967), award(?x1402, ?x5398), ?x1402 = 0sxfd, ?x7815 = 0184jw, ?x2967 = 02l5rm *> conf = 0.33 ranks of expected_values: 22 EVAL 02w_6xj award_winner 02pv_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 59.000 16.000 0.444 http://example.org/award/award_category/winners./award/award_honor/award_winner #21781-016ky6 PRED entity: 016ky6 PRED relation: featured_film_locations PRED expected values: 02_286 => 107 concepts (90 used for prediction) PRED predicted values (max 10 best out of 70): 030qb3t (0.23 #279, 0.18 #1485, 0.16 #521), 02_286 (0.22 #985, 0.19 #1949, 0.16 #2434), 080h2 (0.13 #1711, 0.10 #264, 0.09 #748), 06y57 (0.10 #343, 0.05 #1549, 0.04 #585), 04jpl (0.07 #1455, 0.07 #6770, 0.07 #2180), 01_d4 (0.07 #287, 0.06 #529, 0.06 #771), 03gh4 (0.06 #597, 0.05 #1561, 0.05 #1802), 0d6lp (0.05 #1277, 0.03 #2001, 0.02 #3692), 0rh6k (0.04 #2897, 0.04 #2172, 0.04 #483), 052p7 (0.04 #540, 0.04 #1504, 0.03 #298) >> Best rule #279 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 0f4_2k; >> query: (?x5812, 030qb3t) <- film(?x609, ?x5812), produced_by(?x5812, ?x4385), region(?x5812, ?x512), written_by(?x5812, ?x10064) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #985 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 0198b6; 02754c9; 027m67; *> query: (?x5812, 02_286) <- award(?x5812, ?x834), film_production_design_by(?x5812, ?x12092), written_by(?x5812, ?x10064) *> conf = 0.22 ranks of expected_values: 2 EVAL 016ky6 featured_film_locations 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 90.000 0.233 http://example.org/film/film/featured_film_locations #21780-027kmrb PRED entity: 027kmrb PRED relation: nationality PRED expected values: 09c7w0 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 18): 09c7w0 (0.75 #301, 0.74 #2505, 0.72 #3307), 02jx1 (0.25 #133, 0.25 #33, 0.11 #6550), 07ssc (0.09 #3421, 0.09 #2619, 0.08 #2519), 03rk0 (0.05 #11663, 0.05 #11763, 0.05 #11963), 0d060g (0.04 #8124, 0.04 #3113, 0.04 #7624), 0345h (0.03 #2035, 0.03 #1935, 0.02 #11948), 03gj2 (0.03 #2030, 0.03 #1930), 03rjj (0.03 #2509, 0.02 #5619, 0.02 #2709), 0chghy (0.02 #2714, 0.02 #9027, 0.02 #6427), 03spz (0.02 #3173, 0.02 #2973, 0.02 #4073) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02633g; >> query: (?x5647, 09c7w0) <- award_nominee(?x5647, ?x521), gender(?x5647, ?x231), ?x521 = 0147dk >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027kmrb nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.750 http://example.org/people/person/nationality #21779-01tp5bj PRED entity: 01tp5bj PRED relation: role PRED expected values: 0l14qv => 154 concepts (105 used for prediction) PRED predicted values (max 10 best out of 123): 0l14qv (0.50 #4, 0.43 #204, 0.40 #601), 0bxl5 (0.50 #66, 0.20 #663, 0.14 #266), 0680x0 (0.50 #70, 0.20 #667, 0.14 #270), 05148p4 (0.48 #997, 0.48 #996, 0.36 #1297), 013y1f (0.45 #1228, 0.40 #730, 0.33 #929), 05842k (0.42 #970, 0.40 #771, 0.29 #274), 026t6 (0.40 #699, 0.26 #1098, 0.22 #3484), 03bx0bm (0.33 #101, 0.09 #4682, 0.09 #4481), 03gvt (0.33 #7277, 0.33 #6480, 0.32 #4480), 07gql (0.33 #7277, 0.33 #6480, 0.32 #4480) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 06k02; 09hnb; >> query: (?x2492, 0l14qv) <- instrumentalists(?x1437, ?x2492), artists(?x302, ?x2492), ?x1437 = 01vdm0, performance_role(?x2492, ?x1466) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tp5bj role 0l14qv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 105.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #21778-0gv07g PRED entity: 0gv07g PRED relation: music! PRED expected values: 03ynwqj => 114 concepts (12 used for prediction) PRED predicted values (max 10 best out of 788): 02ntb8 (0.69 #6058, 0.69 #5048, 0.67 #6057), 02ryz24 (0.69 #6058, 0.69 #5048, 0.67 #6057), 0ndsl1x (0.69 #6058, 0.69 #5048, 0.67 #6057), 08rr3p (0.08 #3300, 0.02 #4310, 0.02 #5320), 0jzw (0.08 #3097, 0.02 #4107, 0.02 #5117), 01s7w3 (0.05 #6926, 0.04 #9950, 0.04 #3896), 0n04r (0.05 #2412), 02rrfzf (0.04 #3354, 0.04 #7392, 0.03 #8400), 02ht1k (0.04 #3397, 0.03 #4407, 0.03 #5417), 09d3b7 (0.04 #3867, 0.03 #6897, 0.03 #7905) >> Best rule #6058 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 01p7b6b; >> query: (?x7205, ?x4888) <- award_winner(?x4888, ?x7205), music(?x4041, ?x7205), film(?x436, ?x4888), genre(?x4888, ?x225) >> conf = 0.69 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0gv07g music! 03ynwqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 12.000 0.692 http://example.org/film/film/music #21777-0h95zbp PRED entity: 0h95zbp PRED relation: film_release_region PRED expected values: 05r4w 0ctw_b 0345h 03ryn => 89 concepts (75 used for prediction) PRED predicted values (max 10 best out of 303): 05r4w (0.94 #3872, 0.93 #830, 0.92 #4568), 0345h (0.89 #3896, 0.88 #1959, 0.87 #4452), 0ctw_b (0.83 #1953, 0.82 #2508, 0.81 #2646), 03rj0 (0.82 #463, 0.80 #49, 0.79 #1153), 03_3d (0.79 #1247, 0.79 #3737, 0.78 #3460), 01ls2 (0.78 #699, 0.77 #423, 0.74 #1251), 01mjq (0.73 #450, 0.67 #312, 0.66 #2662), 05qx1 (0.70 #33, 0.65 #1275, 0.59 #447), 015qh (0.69 #2660, 0.68 #448, 0.68 #3904), 09pmkv (0.68 #1263, 0.60 #21, 0.52 #711) >> Best rule #3872 for best value: >> intensional similarity = 11 >> extensional distance = 95 >> proper extension: 08hmch; 03bx2lk; 0cz8mkh; 0gd0c7x; 0c3xw46; 07s846j; 0h03fhx; 0dlngsd; 0gg5qcw; 0bc1yhb; ... >> query: (?x5704, 05r4w) <- film_release_region(?x5704, ?x1917), film_release_region(?x5704, ?x1229), film_release_region(?x5704, ?x344), ?x1917 = 01p1v, film_release_region(?x5400, ?x1229), country(?x3407, ?x1229), contains(?x1229, ?x2351), ?x344 = 04gzd, olympics(?x1229, ?x391), combatants(?x1229, ?x613), ?x5400 = 0bhwhj >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 15 EVAL 0h95zbp film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 89.000 75.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h95zbp film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 75.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h95zbp film_release_region 0ctw_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 75.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h95zbp film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 75.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21776-031hxk PRED entity: 031hxk PRED relation: institution! PRED expected values: 014mlp => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 17): 014mlp (0.64 #101, 0.64 #333, 0.62 #468), 07s6fsf (0.64 #97, 0.62 #39, 0.33 #1), 016t_3 (0.62 #41, 0.56 #99, 0.44 #178), 019v9k (0.59 #104, 0.53 #471, 0.52 #183), 04zx3q1 (0.33 #2, 0.28 #717, 0.27 #177), 027f2w (0.33 #9, 0.28 #717, 0.22 #28), 013zdg (0.33 #7, 0.28 #717, 0.22 #103), 01rr_d (0.33 #34, 0.28 #717, 0.21 #133), 02cq61 (0.33 #16, 0.07 #112, 0.07 #191), 022h5x (0.28 #717, 0.22 #113, 0.10 #345) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 01jssp; 01pl14; 01j_9c; 065y4w7; 07w0v; 01bzw5; 07szy; 0bx8pn; 07w3r; 02bjhv; ... >> query: (?x9861, 014mlp) <- contains(?x792, ?x9861), major_field_of_study(?x9861, ?x7134), ?x7134 = 02_7t >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031hxk institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.644 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21775-07gknc PRED entity: 07gknc PRED relation: profession PRED expected values: 02hrh1q => 87 concepts (51 used for prediction) PRED predicted values (max 10 best out of 56): 02hrh1q (0.96 #6574, 0.95 #6276, 0.89 #6425), 0dxtg (0.78 #4784, 0.65 #5082, 0.55 #5381), 02jknp (0.45 #5076, 0.37 #5375, 0.35 #4778), 01d_h8 (0.43 #5074, 0.40 #4776, 0.37 #5373), 03gjzk (0.32 #165, 0.31 #4786, 0.30 #2252), 018gz8 (0.31 #1359, 0.31 #2254, 0.30 #2552), 09jwl (0.22 #6728, 0.19 #4492, 0.18 #765), 0cbd2 (0.18 #4777, 0.16 #6864, 0.16 #5075), 01c72t (0.17 #5391, 0.08 #5689, 0.08 #5838), 02krf9 (0.15 #5394, 0.14 #5095, 0.13 #4797) >> Best rule #6574 for best value: >> intensional similarity = 7 >> extensional distance = 2599 >> proper extension: 01vvydl; 0lbj1; 05m63c; 01vrx3g; 023tp8; 09fqtq; 033hqf; 04bs3j; 0lzb8; 01kwld; ... >> query: (?x12000, 02hrh1q) <- profession(?x12000, ?x1383), profession(?x12351, ?x1383), profession(?x11234, ?x1383), profession(?x9655, ?x1383), ?x12351 = 014v1q, ?x11234 = 027r0_f, ?x9655 = 02ct_k >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07gknc profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 51.000 0.960 http://example.org/people/person/profession #21774-01386_ PRED entity: 01386_ PRED relation: type_of_union PRED expected values: 04ztj => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.70 #409, 0.70 #45, 0.70 #474), 01g63y (0.24 #10, 0.20 #50, 0.19 #642), 01bl8s (0.19 #642, 0.18 #457, 0.04 #31), 0jgjn (0.19 #642, 0.01 #164) >> Best rule #409 for best value: >> intensional similarity = 4 >> extensional distance = 910 >> proper extension: 07c37; >> query: (?x6406, 04ztj) <- gender(?x6406, ?x231), student(?x8363, ?x6406), location(?x6406, ?x461), ?x231 = 05zppz >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01386_ type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.702 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21773-01ycfv PRED entity: 01ycfv PRED relation: music! PRED expected values: 0d90m 0pk1p => 134 concepts (82 used for prediction) PRED predicted values (max 10 best out of 882): 033g4d (0.14 #29146, 0.07 #62322, 0.07 #58299), 01msrb (0.14 #29146, 0.07 #62322, 0.06 #31157), 02rrfzf (0.13 #324, 0.04 #3339, 0.04 #11379), 0401sg (0.09 #51, 0.02 #21156, 0.02 #27186), 06929s (0.09 #419, 0.01 #6449, 0.01 #17504), 07bzz7 (0.08 #1528, 0.06 #2533, 0.04 #9568), 01s7w3 (0.07 #6894, 0.04 #9909, 0.04 #3879), 035s95 (0.04 #207, 0.03 #6237, 0.02 #3222), 01hp5 (0.04 #60, 0.03 #6090, 0.02 #12120), 04tqtl (0.04 #308, 0.03 #14378, 0.02 #3323) >> Best rule #29146 for best value: >> intensional similarity = 3 >> extensional distance = 207 >> proper extension: 05d6q1; >> query: (?x9408, ?x1185) <- nominated_for(?x9408, ?x1185), award_winner(?x725, ?x9408), category(?x9408, ?x134) >> conf = 0.14 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01ycfv music! 0pk1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 82.000 0.139 http://example.org/film/film/music EVAL 01ycfv music! 0d90m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 82.000 0.139 http://example.org/film/film/music #21772-026wmz6 PRED entity: 026wmz6 PRED relation: place_founded PRED expected values: 06c62 => 192 concepts (150 used for prediction) PRED predicted values (max 10 best out of 97): 0947l (0.56 #1884, 0.56 #515, 0.53 #2014), 07dfk (0.44 #497, 0.43 #757, 0.33 #889), 02_286 (0.35 #1178, 0.22 #1893, 0.21 #1632), 030qb3t (0.25 #1181, 0.16 #1896, 0.15 #2158), 07ssc (0.19 #2794, 0.03 #1692, 0.03 #2214), 01llj3 (0.19 #2794), 012wyq (0.19 #2794), 0cxgc (0.19 #2794), 049kw (0.19 #2794), 02jx1 (0.19 #2794) >> Best rule #1884 for best value: >> intensional similarity = 7 >> extensional distance = 29 >> proper extension: 0g8rj; >> query: (?x14118, ?x8956) <- category(?x14118, ?x134), ?x134 = 08mbj5d, place_founded(?x14118, ?x362), citytown(?x14118, ?x8956), location(?x361, ?x362), place_of_death(?x587, ?x362), citytown(?x752, ?x362) >> conf = 0.56 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026wmz6 place_founded 06c62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 192.000 150.000 0.559 http://example.org/organization/organization/place_founded #21771-029zqn PRED entity: 029zqn PRED relation: nominated_for! PRED expected values: 0l8z1 => 100 concepts (94 used for prediction) PRED predicted values (max 10 best out of 194): 0gq9h (0.37 #4488, 0.36 #2857, 0.31 #12414), 05ztjjw (0.33 #10, 0.11 #2341, 0.11 #943), 019f4v (0.33 #4480, 0.28 #518, 0.27 #2383), 0gs9p (0.32 #4490, 0.29 #2393, 0.27 #2859), 0k611 (0.30 #4499, 0.25 #2635, 0.24 #12425), 054krc (0.28 #4495, 0.17 #67, 0.16 #767), 0gr0m (0.28 #757, 0.27 #4485, 0.20 #12411), 040njc (0.26 #4435, 0.22 #2338, 0.21 #12361), 0gqy2 (0.26 #4545, 0.25 #2914, 0.24 #583), 0l8z1 (0.25 #4478, 0.19 #750, 0.16 #12404) >> Best rule #4488 for best value: >> intensional similarity = 3 >> extensional distance = 521 >> proper extension: 04z_x4v; >> query: (?x1734, 0gq9h) <- nominated_for(?x2222, ?x1734), award(?x308, ?x2222), ?x308 = 011yxg >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #4478 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 521 *> proper extension: 04z_x4v; *> query: (?x1734, 0l8z1) <- nominated_for(?x2222, ?x1734), award(?x308, ?x2222), ?x308 = 011yxg *> conf = 0.25 ranks of expected_values: 10 EVAL 029zqn nominated_for! 0l8z1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 100.000 94.000 0.373 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21770-0cqh57 PRED entity: 0cqh57 PRED relation: award PRED expected values: 0gr0m => 86 concepts (51 used for prediction) PRED predicted values (max 10 best out of 285): 0gr0m (0.76 #2903, 0.75 #3711, 0.75 #3307), 09sb52 (0.41 #4890, 0.34 #8127, 0.30 #5296), 0gqz2 (0.21 #4122, 0.06 #890, 0.06 #1294), 054ks3 (0.20 #4183, 0.18 #142, 0.06 #951), 025m8y (0.20 #4141, 0.09 #100, 0.04 #5355), 0l8z1 (0.20 #4105, 0.06 #873, 0.06 #6938), 0c4z8 (0.20 #4113, 0.06 #18677, 0.06 #1285), 054krc (0.18 #4129, 0.17 #12939, 0.17 #11725), 01by1l (0.18 #4154, 0.09 #18718, 0.06 #14672), 02qvyrt (0.17 #12939, 0.17 #11725, 0.16 #4169) >> Best rule #2903 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 0280mv7; 04cw0n4; 026sb55; >> query: (?x7427, 0gr0m) <- cinematography(?x308, ?x7427), film(?x804, ?x308), honored_for(?x472, ?x308) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cqh57 award 0gr0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 51.000 0.760 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21769-0l2sr PRED entity: 0l2sr PRED relation: currency PRED expected values: 09nqf => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.87 #41, 0.87 #40, 0.85 #76) >> Best rule #41 for best value: >> intensional similarity = 5 >> extensional distance = 171 >> proper extension: 0nv5y; >> query: (?x9582, ?x170) <- adjoins(?x12858, ?x9582), adjoins(?x5892, ?x9582), currency(?x12858, ?x170), source(?x12858, ?x958), administrative_division(?x5893, ?x5892) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l2sr currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.867 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #21768-01b66t PRED entity: 01b66t PRED relation: award_winner PRED expected values: 026dg51 => 72 concepts (61 used for prediction) PRED predicted values (max 10 best out of 563): 03cl8lb (0.62 #40892, 0.52 #86690, 0.52 #8179), 02g87m (0.62 #40892, 0.52 #86690, 0.52 #35983), 026n6cs (0.62 #40892, 0.52 #86690, 0.52 #35983), 026dg51 (0.52 #8179, 0.46 #6542, 0.43 #17996), 070w7s (0.52 #8179, 0.46 #6542, 0.43 #17996), 057d89 (0.52 #8179, 0.46 #6542, 0.43 #17996), 02_2v2 (0.52 #8179, 0.46 #6542, 0.43 #11452), 025vwmy (0.52 #35983, 0.50 #1635, 0.50 #42528), 045w_4 (0.52 #35983, 0.50 #1635, 0.50 #42528), 02kmx6 (0.43 #11451, 0.42 #35984, 0.41 #16358) >> Best rule #40892 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 04kzqz; 0n2bh; 02kk_c; 0c3xpwy; 05sy0cv; 06w7mlh; >> query: (?x4721, ?x3809) <- nominated_for(?x3809, ?x4721), program(?x4720, ?x4721), award_winner(?x2829, ?x3809) >> conf = 0.62 => this is the best rule for 3 predicted values *> Best rule #8179 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 09kn9; 099pks; 06qxh; *> query: (?x4721, ?x415) <- program(?x2062, ?x4721), tv_program(?x415, ?x4721), award_winner(?x2476, ?x415) *> conf = 0.52 ranks of expected_values: 4 EVAL 01b66t award_winner 026dg51 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 61.000 0.619 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #21767-01dq9q PRED entity: 01dq9q PRED relation: artist! PRED expected values: 0181dw => 80 concepts (46 used for prediction) PRED predicted values (max 10 best out of 119): 03rhqg (0.42 #15, 0.38 #156, 0.24 #438), 0229rs (0.25 #158, 0.25 #17, 0.11 #863), 015_1q (0.24 #865, 0.21 #301, 0.20 #1994), 033hn8 (0.19 #154, 0.17 #859, 0.17 #295), 0n85g (0.18 #627, 0.17 #345, 0.14 #1050), 01trtc (0.17 #1060, 0.16 #1201, 0.15 #496), 01clyr (0.17 #315, 0.17 #33, 0.13 #1302), 0g768 (0.17 #37, 0.16 #1588, 0.15 #1306), 086k8 (0.17 #1, 0.12 #142, 0.06 #847), 011k11 (0.17 #35, 0.12 #176, 0.06 #881) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 012vm6; >> query: (?x7407, 03rhqg) <- group(?x1166, ?x7407), artists(?x3319, ?x7407), ?x3319 = 06j6l, ?x1166 = 05148p4 >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1311 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 99 *> proper extension: 02wb6yq; 06tp4h; 01ydzx; 0232lm; 0kj34; 09nhvw; *> query: (?x7407, 0181dw) <- artists(?x3061, ?x7407), artist(?x2190, ?x7407), origin(?x7407, ?x362), ?x3061 = 05bt6j *> conf = 0.13 ranks of expected_values: 14 EVAL 01dq9q artist! 0181dw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 80.000 46.000 0.417 http://example.org/music/record_label/artist #21766-09lcsj PRED entity: 09lcsj PRED relation: genre PRED expected values: 07s9rl0 => 94 concepts (49 used for prediction) PRED predicted values (max 10 best out of 102): 07s9rl0 (0.97 #1631, 0.79 #2447, 0.77 #931), 03k9fj (0.71 #11, 0.50 #825, 0.43 #1523), 02l7c8 (0.68 #5027, 0.35 #712, 0.34 #2461), 05p553 (0.45 #5247, 0.43 #3, 0.38 #119), 06n90 (0.43 #12, 0.35 #1174, 0.35 #826), 02n4kr (0.37 #588, 0.24 #4437, 0.24 #3854), 0lsxr (0.35 #3971, 0.34 #2918, 0.34 #4438), 01hmnh (0.33 #831, 0.32 #1179, 0.32 #1529), 060__y (0.29 #2462, 0.25 #132, 0.23 #713), 0219x_ (0.29 #491, 0.13 #1072, 0.13 #1421) >> Best rule #1631 for best value: >> intensional similarity = 7 >> extensional distance = 121 >> proper extension: 0cbl95; >> query: (?x3537, 07s9rl0) <- genre(?x3537, ?x3613), genre(?x3537, ?x3515), ?x3515 = 082gq, titles(?x3613, ?x7947), titles(?x3613, ?x4454), ?x4454 = 016y_f, film(?x879, ?x7947) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09lcsj genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 49.000 0.967 http://example.org/film/film/genre #21765-07_pf PRED entity: 07_pf PRED relation: featured_film_locations! PRED expected values: 03r0g9 => 168 concepts (119 used for prediction) PRED predicted values (max 10 best out of 749): 04dsnp (0.33 #4482, 0.11 #20676, 0.10 #30984), 09fc83 (0.33 #4796, 0.06 #15837, 0.06 #20990), 01rxyb (0.33 #314, 0.06 #8411, 0.05 #9147), 092vkg (0.33 #69, 0.04 #30987, 0.03 #16998), 042zrm (0.33 #594, 0.04 #31512, 0.03 #17523), 01svry (0.33 #499, 0.03 #17428, 0.03 #18901), 04cppj (0.33 #487, 0.03 #17416, 0.03 #18889), 02nczh (0.33 #478, 0.03 #17407, 0.03 #18880), 02j69w (0.33 #342, 0.03 #17271, 0.03 #18744), 04tqtl (0.33 #224, 0.03 #17153, 0.03 #18626) >> Best rule #4482 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 035qy; 0c82s; >> query: (?x10496, 04dsnp) <- vacationer(?x10496, ?x2258), vacationer(?x10496, ?x1634), award_nominee(?x1634, ?x100), film(?x1634, ?x908), ?x2258 = 0f4vbz >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8361 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0160w; 0b90_r; 03rjj; 04jpl; 0chghy; 0ctw_b; 01n7q; 0345h; 02jx1; 07srw; ... *> query: (?x10496, 03r0g9) <- vacationer(?x10496, ?x1634), featured_film_locations(?x4786, ?x10496), taxonomy(?x10496, ?x939), location(?x2373, ?x10496) *> conf = 0.11 ranks of expected_values: 262 EVAL 07_pf featured_film_locations! 03r0g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 168.000 119.000 0.333 http://example.org/film/film/featured_film_locations #21764-01846t PRED entity: 01846t PRED relation: student! PRED expected values: 01b_d4 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 52): 04rkkv (0.25 #306, 0.17 #1358, 0.13 #832), 0m4yg (0.08 #364, 0.07 #890, 0.06 #1416), 09f2j (0.08 #158, 0.07 #684, 0.06 #1210), 031ns1 (0.08 #517, 0.07 #1043, 0.06 #1569), 01d650 (0.08 #373, 0.07 #899, 0.06 #1425), 02q253 (0.08 #504, 0.07 #1030, 0.06 #1556), 01_qgp (0.08 #275, 0.06 #1327, 0.02 #12102), 0bwfn (0.08 #12902, 0.07 #3956, 0.07 #10797), 02mw6c (0.07 #955, 0.06 #1481), 01q7q2 (0.07 #818, 0.06 #1344) >> Best rule #306 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 02gvwz; 0154qm; >> query: (?x3181, 04rkkv) <- award_nominee(?x3181, ?x3028), award_nominee(?x3181, ?x1424), ?x1424 = 01rh0w, ?x3028 = 0f0kz >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01846t student! 01b_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 80.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #21763-07ssc PRED entity: 07ssc PRED relation: organization PRED expected values: 04k4l => 206 concepts (206 used for prediction) PRED predicted values (max 10 best out of 41): 04k4l (0.50 #1047, 0.46 #656, 0.45 #765), 034h1h (0.48 #583, 0.22 #538, 0.18 #2644), 041288 (0.36 #2282, 0.35 #2221, 0.32 #2327), 0gkjy (0.25 #2215, 0.24 #2276, 0.23 #2107), 085h1 (0.19 #2087, 0.19 #2086, 0.06 #1220), 02_l9 (0.08 #586, 0.07 #2647, 0.05 #1910), 01r3kd (0.04 #2641), 02hcxm (0.02 #2643), 01prf3 (0.02 #2650), 020g9r (0.01 #322) >> Best rule #1047 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 01rdm0; >> query: (?x512, 04k4l) <- combatants(?x512, ?x94), combatants(?x326, ?x512), organization(?x512, ?x127) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ssc organization 04k4l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 206.000 206.000 0.500 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #21762-02d02 PRED entity: 02d02 PRED relation: school PRED expected values: 01j_cy 02gnmp => 80 concepts (57 used for prediction) PRED predicted values (max 10 best out of 669): 06pwq (0.70 #1820, 0.63 #7096, 0.60 #1828), 05krk (0.70 #1820, 0.58 #4004, 0.55 #902), 07w0v (0.70 #1820, 0.55 #902, 0.53 #904), 01pq4w (0.70 #1820, 0.55 #902, 0.53 #904), 01q0kg (0.70 #1820, 0.55 #902, 0.53 #904), 09f2j (0.70 #1820, 0.55 #902, 0.53 #904), 01dzg0 (0.70 #1820, 0.55 #902, 0.53 #904), 03tw2s (0.70 #1820, 0.55 #902, 0.53 #904), 01qgr3 (0.70 #1820, 0.55 #902, 0.53 #904), 012vwb (0.70 #1820, 0.55 #902, 0.53 #904) >> Best rule #1820 for best value: >> intensional similarity = 19 >> extensional distance = 3 >> proper extension: 05g3b; >> query: (?x8894, ?x4161) <- team(?x4244, ?x8894), team(?x4244, ?x10279), team(?x4244, ?x1823), school(?x8894, ?x9131), school(?x8894, ?x4556), school(?x8894, ?x3021), school(?x8894, ?x1681), ?x1681 = 07szy, ?x9131 = 02pptm, school(?x1823, ?x4296), school(?x10279, ?x8706), school(?x10279, ?x4161), ?x8706 = 0trv, draft(?x1823, ?x1161), ?x4296 = 07vyf, institution(?x4981, ?x4556), currency(?x4556, ?x170), sport(?x1823, ?x5063), contains(?x94, ?x3021) >> conf = 0.70 => this is the best rule for 48 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 25, 63 EVAL 02d02 school 02gnmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 80.000 57.000 0.701 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 02d02 school 01j_cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 80.000 57.000 0.701 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #21761-0bx8pn PRED entity: 0bx8pn PRED relation: school! PRED expected values: 0jm5b => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 77): 0jmm4 (0.21 #369, 0.10 #600, 0.10 #523), 05g49 (0.18 #348, 0.17 #40, 0.08 #579), 0jmnl (0.18 #384, 0.11 #461, 0.10 #615), 07l8x (0.17 #56, 0.14 #441, 0.12 #1365), 06wpc (0.17 #54, 0.14 #439, 0.11 #362), 07147 (0.17 #57, 0.12 #1366, 0.11 #981), 01y3c (0.17 #9, 0.11 #317, 0.06 #933), 0ws7 (0.17 #50, 0.07 #358, 0.06 #127), 01ync (0.17 #34, 0.05 #1343, 0.04 #958), 0cqt41 (0.14 #400, 0.14 #323, 0.09 #939) >> Best rule #369 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 06mkj; 0d05w3; >> query: (?x1884, 0jmm4) <- contains(?x94, ?x1884), school(?x1883, ?x1884), organization(?x1884, ?x5487) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #150 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0ygbf; 0jfqp; 0fsb8; 0p7vt; 0fvyg; 025rst1; 0yjvm; 0yfvf; *> query: (?x1884, 0jm5b) <- contains(?x760, ?x1884), category(?x1884, ?x134), ?x760 = 05fkf *> conf = 0.06 ranks of expected_values: 52 EVAL 0bx8pn school! 0jm5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 112.000 112.000 0.214 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #21760-04135 PRED entity: 04135 PRED relation: profession PRED expected values: 02jknp => 103 concepts (70 used for prediction) PRED predicted values (max 10 best out of 73): 0kyk (0.73 #4640, 0.51 #604, 0.46 #1900), 0cbd2 (0.68 #1879, 0.57 #871, 0.57 #295), 01d_h8 (0.53 #2599, 0.48 #1302, 0.48 #1734), 09jwl (0.52 #6789, 0.38 #2754, 0.30 #449), 02jknp (0.39 #6348, 0.38 #6492, 0.31 #1016), 03gjzk (0.35 #13, 0.35 #4769, 0.33 #6497), 05sxg2 (0.35 #1, 0.12 #145, 0.04 #2594), 0nbcg (0.35 #6802, 0.31 #2767, 0.26 #462), 016z4k (0.30 #2741, 0.26 #6776, 0.21 #436), 015btn (0.26 #2161, 0.07 #386, 0.06 #1970) >> Best rule #4640 for best value: >> intensional similarity = 5 >> extensional distance = 440 >> proper extension: 0hnlx; 063vn; 02lq10; 0453t; 013v5j; 01hb6v; 0gkg6; 0jcx; 032l1; 01_rh4; ... >> query: (?x9673, 0kyk) <- profession(?x9673, ?x3746), profession(?x3279, ?x3746), profession(?x1278, ?x3746), ?x3279 = 0d4jl, ?x1278 = 016hvl >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #6348 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1042 *> proper extension: 0379s; 014nvr; 082mw; 06hgj; 0h336; 03fnyk; 0f3nn; *> query: (?x9673, 02jknp) <- profession(?x9673, ?x3746), profession(?x12888, ?x3746), profession(?x3279, ?x3746), ?x3279 = 0d4jl, ?x12888 = 0ldd *> conf = 0.39 ranks of expected_values: 5 EVAL 04135 profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 103.000 70.000 0.731 http://example.org/people/person/profession #21759-05842k PRED entity: 05842k PRED relation: role! PRED expected values: 05ljv7 01rhl 03ndd 01w4c9 => 87 concepts (77 used for prediction) PRED predicted values (max 10 best out of 41): 0l15bq (0.89 #1652, 0.89 #1628, 0.87 #97), 02dlh2 (0.87 #97, 0.86 #549, 0.85 #419), 05148p4 (0.87 #97, 0.86 #549, 0.85 #419), 06w7v (0.87 #97, 0.86 #549, 0.85 #419), 03q5t (0.87 #97, 0.86 #549, 0.85 #419), 03m5k (0.87 #97, 0.86 #549, 0.85 #419), 0239kh (0.87 #97, 0.86 #549, 0.85 #419), 0dwvl (0.87 #97, 0.86 #549, 0.85 #419), 01rhl (0.87 #97, 0.86 #549, 0.85 #419), 01bns_ (0.87 #97, 0.86 #549, 0.85 #419) >> Best rule #1652 for best value: >> intensional similarity = 11 >> extensional distance = 17 >> proper extension: 02bxd; >> query: (?x3991, ?x1574) <- role(?x211, ?x3991), role(?x1969, ?x3991), role(?x569, ?x3991), role(?x314, ?x3991), ?x314 = 02sgy, role(?x8957, ?x1969), role(?x3991, ?x1574), ?x8957 = 03f5mt, ?x1574 = 0l15bq, role(?x366, ?x1969), ?x569 = 07c6l >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 01vj9c; *> query: (?x3991, ?x74) <- role(?x9735, ?x3991), role(?x5301, ?x3991), role(?x2690, ?x3991), role(?x2566, ?x3991), role(?x1267, ?x3991), ?x9735 = 01wxdn3, ?x1267 = 07brj, ?x2690 = 0892sx, role(?x3991, ?x74), group(?x5301, ?x5547), people(?x7322, ?x5301), place_of_birth(?x5301, ?x6357), award(?x2566, ?x1389), award_nominee(?x5301, ?x954) *> conf = 0.87 ranks of expected_values: 9, 13, 14, 22 EVAL 05842k role! 01w4c9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 87.000 77.000 0.895 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 05842k role! 03ndd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 87.000 77.000 0.895 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 05842k role! 01rhl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 77.000 0.895 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 05842k role! 05ljv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 87.000 77.000 0.895 http://example.org/music/performance_role/track_performances./music/track_contribution/role #21758-05wp1p PRED entity: 05wp1p PRED relation: genre PRED expected values: 05p553 => 92 concepts (61 used for prediction) PRED predicted values (max 10 best out of 93): 03k9fj (0.74 #3620, 0.73 #374, 0.67 #2659), 07s9rl0 (0.71 #3127, 0.71 #2767, 0.68 #1683), 05p553 (0.62 #6619, 0.60 #726, 0.59 #246), 01jfsb (0.51 #6748, 0.36 #1816, 0.32 #2660), 02kdv5l (0.43 #1805, 0.42 #3610, 0.40 #2649), 06n90 (0.35 #135, 0.29 #15, 0.25 #3622), 01zhp (0.35 #799, 0.32 #319, 0.30 #439), 02l7c8 (0.32 #1700, 0.32 #2784, 0.31 #3144), 03npn (0.24 #128, 0.21 #8, 0.21 #2654), 04t36 (0.23 #248, 0.18 #728, 0.17 #608) >> Best rule #3620 for best value: >> intensional similarity = 5 >> extensional distance = 493 >> proper extension: 02vw1w2; 0199wf; >> query: (?x3008, 03k9fj) <- genre(?x3008, ?x1510), genre(?x2539, ?x1510), genre(?x1812, ?x1510), ?x2539 = 01pv91, ?x1812 = 0fdv3 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #6619 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 940 *> proper extension: 0gh6j94; 03z9585; 09v42sf; *> query: (?x3008, 05p553) <- genre(?x3008, ?x1510), genre(?x2539, ?x1510), genre(?x1812, ?x1510), ?x2539 = 01pv91, film(?x1387, ?x1812) *> conf = 0.62 ranks of expected_values: 3 EVAL 05wp1p genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 61.000 0.743 http://example.org/film/film/genre #21757-0hd7j PRED entity: 0hd7j PRED relation: school! PRED expected values: 03nt7j => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 19): 02qw1zx (0.30 #99, 0.28 #118, 0.22 #137), 0f4vx0 (0.28 #124, 0.26 #143, 0.26 #105), 092j54 (0.24 #103, 0.16 #141, 0.15 #46), 09l0x9 (0.22 #106, 0.20 #49, 0.17 #125), 03nt7j (0.20 #44, 0.20 #101, 0.15 #139), 025tn92 (0.18 #126, 0.17 #107, 0.16 #145), 0g3zpp (0.18 #96, 0.15 #39, 0.14 #134), 038c0q (0.17 #5, 0.16 #119, 0.14 #138), 02x2khw (0.17 #2, 0.13 #116, 0.12 #135), 04f4z1k (0.17 #17, 0.09 #150, 0.09 #131) >> Best rule #99 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 0fht9f; 0frm7n; >> query: (?x4603, 02qw1zx) <- school(?x684, ?x4603), team(?x180, ?x684), position_s(?x684, ?x1517) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #44 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 0kw4j; 037q2p; *> query: (?x4603, 03nt7j) <- major_field_of_study(?x4603, ?x7134), institution(?x1519, ?x4603), ?x7134 = 02_7t, ?x1519 = 013zdg *> conf = 0.20 ranks of expected_values: 5 EVAL 0hd7j school! 03nt7j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 98.000 0.303 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #21756-0cv3w PRED entity: 0cv3w PRED relation: citytown! PRED expected values: 0797c7 => 216 concepts (181 used for prediction) PRED predicted values (max 10 best out of 688): 064f29 (0.18 #1925, 0.14 #6762, 0.14 #313), 01nds (0.18 #1381, 0.14 #7024, 0.12 #3800), 01dtcb (0.18 #1190, 0.13 #2802, 0.12 #3609), 0146mv (0.18 #1390, 0.13 #3002, 0.12 #3809), 06182p (0.18 #1200, 0.13 #2812, 0.12 #3619), 0338lq (0.18 #832, 0.12 #3251, 0.10 #6475), 0ky6d (0.14 #768, 0.13 #3186, 0.11 #4799), 049ql1 (0.14 #586, 0.10 #7035, 0.09 #1392), 022fj_ (0.14 #434, 0.07 #2852, 0.06 #5271), 03d6fyn (0.14 #194, 0.05 #6643, 0.04 #33247) >> Best rule #1925 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0l35f; >> query: (?x3026, 064f29) <- time_zones(?x3026, ?x2950), ?x2950 = 02lcqs, mode_of_transportation(?x3026, ?x6665) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cv3w citytown! 0797c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 216.000 181.000 0.182 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #21755-04mn81 PRED entity: 04mn81 PRED relation: artists! PRED expected values: 06by7 06j6l => 122 concepts (109 used for prediction) PRED predicted values (max 10 best out of 252): 06by7 (0.66 #15388, 0.57 #1554, 0.52 #633), 01flzq (0.41 #1343, 0.35 #1036, 0.21 #12297), 016_nr (0.41 #1298, 0.23 #991, 0.21 #12297), 0xhtw (0.36 #2780, 0.29 #15384, 0.28 #5855), 036jv (0.33 #1417, 0.27 #1110, 0.21 #12297), 05bt6j (0.32 #1575, 0.27 #2805, 0.25 #6495), 06j6l (0.31 #966, 0.30 #1273, 0.29 #6193), 0gywn (0.30 #5587, 0.30 #1897, 0.28 #6202), 02lnbg (0.25 #1898, 0.21 #12297, 0.21 #9223), 0ggx5q (0.25 #1918, 0.21 #12297, 0.21 #9223) >> Best rule #15388 for best value: >> intensional similarity = 3 >> extensional distance = 590 >> proper extension: 01qqwp9; 02t3ln; 0qmpd; >> query: (?x1989, 06by7) <- artists(?x302, ?x1989), artists(?x302, ?x6225), ?x6225 = 01vng3b >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 04mn81 artists! 06j6l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 109.000 0.657 http://example.org/music/genre/artists EVAL 04mn81 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 109.000 0.657 http://example.org/music/genre/artists #21754-0356lc PRED entity: 0356lc PRED relation: team PRED expected values: 044lbv => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 798): 02ryyk (0.33 #152, 0.25 #937, 0.20 #1094), 03ym73 (0.33 #150, 0.25 #935, 0.20 #1092), 02w64f (0.33 #146, 0.25 #931, 0.20 #1088), 03z1c5 (0.33 #144, 0.25 #929, 0.20 #1086), 033g54 (0.33 #141, 0.25 #926, 0.20 #1083), 03ytp3 (0.33 #137, 0.25 #922, 0.20 #1079), 039_ym (0.33 #132, 0.25 #917, 0.20 #1074), 033g0y (0.33 #131, 0.25 #916, 0.20 #1073), 03yvln (0.33 #130, 0.25 #915, 0.20 #1072), 03z2rz (0.33 #127, 0.25 #912, 0.20 #1069) >> Best rule #152 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 07y9k; >> query: (?x1142, 02ryyk) <- team(?x1142, ?x10493), team(?x1142, ?x6754), team(?x1142, ?x6180), team(?x1142, ?x3436), position(?x10493, ?x203), colors(?x6180, ?x663), current_club(?x6180, ?x6340), ?x203 = 0dgrmp, team(?x9231, ?x6180), sport(?x3436, ?x471), team(?x9672, ?x6754) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1574 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 5 *> proper extension: 059yj; 0h69c; *> query: (?x1142, ?x59) <- team(?x1142, ?x13273), team(?x1142, ?x10493), team(?x1142, ?x6180), team(?x1142, ?x3436), team(?x203, ?x10493), sport(?x3436, ?x471), team(?x203, ?x59), team(?x9231, ?x6180), teams(?x608, ?x13273) *> conf = 0.06 ranks of expected_values: 469 EVAL 0356lc team 044lbv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 151.000 151.000 0.333 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #21753-0dyb1 PRED entity: 0dyb1 PRED relation: genre PRED expected values: 0556j8 => 57 concepts (55 used for prediction) PRED predicted values (max 10 best out of 86): 07s9rl0 (0.61 #2984, 0.59 #3343, 0.58 #2149), 09b3v (0.52 #3462, 0.52 #3342, 0.52 #3103), 01jfsb (0.34 #1921, 0.31 #371, 0.30 #848), 02l7c8 (0.33 #2403, 0.30 #255, 0.29 #2999), 02kdv5l (0.32 #1912, 0.31 #362, 0.30 #839), 060__y (0.22 #256, 0.22 #614, 0.15 #3000), 06cvj (0.21 #2391, 0.09 #1078, 0.09 #363), 04t36 (0.20 #6, 0.11 #245, 0.10 #603), 0lsxr (0.20 #368, 0.18 #845, 0.17 #1918), 04xvlr (0.17 #2985, 0.17 #3344, 0.17 #2150) >> Best rule #2984 for best value: >> intensional similarity = 3 >> extensional distance = 954 >> proper extension: 011yfd; 05y0cr; 03xj05; 04nlb94; >> query: (?x3053, 07s9rl0) <- titles(?x1510, ?x3053), nominated_for(?x401, ?x3053), film(?x2156, ?x3053) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #2428 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 564 *> proper extension: 02q56mk; 03mh_tp; 02754c9; 01jnc_; 099bhp; 01bjbk; 0gfzfj; *> query: (?x3053, 0556j8) <- film(?x425, ?x3053), genre(?x3053, ?x258), ?x258 = 05p553 *> conf = 0.06 ranks of expected_values: 29 EVAL 0dyb1 genre 0556j8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 57.000 55.000 0.606 http://example.org/film/film/genre #21752-07jnt PRED entity: 07jnt PRED relation: film_crew_role PRED expected values: 09zzb8 01pvkk => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.64 #39, 0.58 #115, 0.57 #421), 02r96rf (0.64 #80, 0.61 #194, 0.59 #156), 09vw2b7 (0.46 #1343, 0.46 #160, 0.46 #198), 01vx2h (0.37 #203, 0.37 #165, 0.30 #127), 0dxtw (0.32 #50, 0.31 #355, 0.31 #202), 01pvkk (0.26 #128, 0.26 #434, 0.25 #472), 02rh1dz (0.20 #87, 0.13 #354, 0.12 #201), 02ynfr (0.17 #132, 0.14 #361, 0.12 #552), 0d2b38 (0.14 #104, 0.13 #142, 0.11 #218), 0215hd (0.12 #173, 0.12 #211, 0.11 #59) >> Best rule #39 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 0c40vxk; >> query: (?x6782, 09zzb8) <- film_release_region(?x6782, ?x512), film_release_region(?x6782, ?x87), ?x512 = 07ssc, cinematography(?x6782, ?x185), ?x87 = 05r4w >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 07jnt film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 71.000 71.000 0.643 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 07jnt film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.643 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21751-01xbxn PRED entity: 01xbxn PRED relation: genre PRED expected values: 0bj8m2 => 99 concepts (56 used for prediction) PRED predicted values (max 10 best out of 97): 03k9fj (0.74 #492, 0.69 #732, 0.67 #372), 07s9rl0 (0.69 #3726, 0.67 #4087, 0.66 #3966), 01jfsb (0.58 #5663, 0.43 #3617, 0.42 #2296), 02kdv5l (0.53 #5653, 0.47 #2286, 0.41 #723), 01hmnh (0.45 #619, 0.43 #1099, 0.42 #499), 02l7c8 (0.44 #6028, 0.34 #4583, 0.33 #5908), 06n90 (0.30 #254, 0.24 #5664, 0.23 #2297), 0lsxr (0.24 #5659, 0.23 #1330, 0.20 #4937), 01t_vv (0.21 #4621, 0.16 #1978, 0.13 #5946), 06cvj (0.21 #1927, 0.19 #4570, 0.18 #5895) >> Best rule #492 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 04hwbq; 02rb84n; 09146g; 02qhqz4; 07x4qr; 06ztvyx; 04f52jw; 06w839_; 03x7hd; 02lk60; ... >> query: (?x8028, 03k9fj) <- film_release_distribution_medium(?x8028, ?x81), genre(?x8028, ?x10185), ?x10185 = 01zhp, film_crew_role(?x8028, ?x468), film(?x446, ?x8028) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #410 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 087wc7n; *> query: (?x8028, 0bj8m2) <- film_release_distribution_medium(?x8028, ?x81), genre(?x8028, ?x10185), ?x10185 = 01zhp, film(?x5906, ?x8028), friend(?x5906, ?x3481) *> conf = 0.17 ranks of expected_values: 17 EVAL 01xbxn genre 0bj8m2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 99.000 56.000 0.737 http://example.org/film/film/genre #21750-015882 PRED entity: 015882 PRED relation: nationality PRED expected values: 09c7w0 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 77): 09c7w0 (0.86 #6112, 0.84 #7120, 0.84 #1501), 02jx1 (0.40 #433, 0.31 #833, 0.27 #1233), 0vmt (0.34 #8832, 0.34 #7927, 0.34 #7825), 0m2by (0.34 #8832, 0.34 #7927, 0.34 #7825), 07ssc (0.13 #815, 0.13 #1215, 0.12 #415), 0345h (0.10 #131, 0.04 #1431, 0.03 #1331), 0d060g (0.10 #507, 0.09 #707, 0.08 #407), 01ls2 (0.07 #211, 0.06 #311, 0.03 #611), 06q1r (0.07 #877, 0.03 #1277, 0.02 #5583), 03rk0 (0.06 #10987, 0.06 #8978, 0.06 #11187) >> Best rule #6112 for best value: >> intensional similarity = 3 >> extensional distance = 988 >> proper extension: 0frmb1; >> query: (?x1817, 09c7w0) <- student(?x8706, ?x1817), major_field_of_study(?x8706, ?x947), school(?x580, ?x8706) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015882 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.859 http://example.org/people/person/nationality #21749-093dqjy PRED entity: 093dqjy PRED relation: nominated_for! PRED expected values: 02rdxsh => 69 concepts (64 used for prediction) PRED predicted values (max 10 best out of 184): 0gs9p (0.66 #2321, 0.58 #739, 0.58 #1869), 019f4v (0.60 #2313, 0.60 #731, 0.51 #1861), 0k611 (0.55 #2329, 0.45 #1877, 0.44 #747), 094qd5 (0.54 #487, 0.24 #5878, 0.24 #35), 040njc (0.49 #2267, 0.41 #1815, 0.39 #685), 04dn09n (0.43 #2294, 0.40 #712, 0.39 #1842), 0gr51 (0.42 #3239, 0.29 #300, 0.27 #2334), 0f4x7 (0.42 #704, 0.41 #1834, 0.39 #2286), 0gq_v (0.39 #2280, 0.36 #698, 0.30 #2506), 0gqyl (0.39 #754, 0.35 #1884, 0.34 #2562) >> Best rule #2321 for best value: >> intensional similarity = 4 >> extensional distance = 191 >> proper extension: 083shs; 01jc6q; 0yyg4; 01gc7; 011yxg; 0gzy02; 095zlp; 04v8x9; 0bth54; 011yph; ... >> query: (?x3714, 0gs9p) <- nominated_for(?x1307, ?x3714), language(?x3714, ?x254), currency(?x3714, ?x170), ?x1307 = 0gq9h >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #2310 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 191 *> proper extension: 083shs; 01jc6q; 0yyg4; 01gc7; 011yxg; 0gzy02; 095zlp; 04v8x9; 0bth54; 011yph; ... *> query: (?x3714, 02rdxsh) <- nominated_for(?x1307, ?x3714), language(?x3714, ?x254), currency(?x3714, ?x170), ?x1307 = 0gq9h *> conf = 0.18 ranks of expected_values: 44 EVAL 093dqjy nominated_for! 02rdxsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 69.000 64.000 0.658 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21748-09bkv PRED entity: 09bkv PRED relation: citytown! PRED expected values: 01w92 02m97n => 166 concepts (112 used for prediction) PRED predicted values (max 10 best out of 806): 0nbrp (0.55 #46916, 0.48 #10511, 0.47 #57443), 01nds (0.10 #3810, 0.09 #8661, 0.09 #14320), 01dtcb (0.09 #8470, 0.09 #14129, 0.08 #14938), 064f29 (0.08 #1122, 0.08 #16485, 0.08 #20531), 049ql1 (0.08 #1395, 0.06 #8672, 0.06 #11906), 03d6fyn (0.08 #1003, 0.06 #8280, 0.06 #13939), 027lf1 (0.08 #1381, 0.06 #14317, 0.06 #15126), 016tt2 (0.08 #826, 0.06 #13762, 0.06 #14571), 0lk0l (0.08 #1507, 0.05 #3124, 0.05 #3933), 041pnt (0.08 #1460, 0.05 #3077, 0.05 #3886) >> Best rule #46916 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 099ty; 0yx74; >> query: (?x10042, ?x12461) <- place_of_birth(?x4214, ?x10042), citytown(?x8294, ?x10042), contains(?x10042, ?x12461), actor(?x4881, ?x4214) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2566 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 09bjv; 0fhp9; *> query: (?x10042, 01w92) <- place_of_birth(?x548, ?x10042), capital(?x9328, ?x10042), contains(?x10042, ?x12461), locations(?x9798, ?x9328) *> conf = 0.05 ranks of expected_values: 94, 400 EVAL 09bkv citytown! 02m97n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 166.000 112.000 0.552 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 09bkv citytown! 01w92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 166.000 112.000 0.552 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #21747-01gn36 PRED entity: 01gn36 PRED relation: influenced_by! PRED expected values: 0p_pd 014zfs => 84 concepts (57 used for prediction) PRED predicted values (max 10 best out of 638): 0bqs56 (0.12 #7159, 0.11 #8184, 0.11 #5625), 01s7qqw (0.12 #7159, 0.11 #8184, 0.11 #5625), 01wp_jm (0.12 #7159, 0.11 #8184, 0.11 #5625), 014zfs (0.12 #7159, 0.11 #8184, 0.11 #5625), 046lt (0.12 #7159, 0.11 #8184, 0.11 #5625), 049fgvm (0.12 #7159, 0.11 #8184, 0.11 #5625), 016_mj (0.12 #7159, 0.11 #8184, 0.11 #5625), 01xwqn (0.12 #7159, 0.11 #8184, 0.11 #5625), 01xwv7 (0.12 #7159, 0.11 #8184, 0.11 #5625), 0q5hw (0.12 #7159, 0.11 #8184, 0.11 #5625) >> Best rule #7159 for best value: >> intensional similarity = 3 >> extensional distance = 297 >> proper extension: 07kb5; >> query: (?x4554, ?x318) <- influenced_by(?x7183, ?x4554), influenced_by(?x318, ?x7183), location(?x7183, ?x3964) >> conf = 0.12 => this is the best rule for 29 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 21 EVAL 01gn36 influenced_by! 014zfs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 84.000 57.000 0.116 http://example.org/influence/influence_node/influenced_by EVAL 01gn36 influenced_by! 0p_pd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 84.000 57.000 0.116 http://example.org/influence/influence_node/influenced_by #21746-0fxky3 PRED entity: 0fxky3 PRED relation: award_nominee! PRED expected values: 0603qp => 84 concepts (43 used for prediction) PRED predicted values (max 10 best out of 852): 0q9zc (0.81 #23319, 0.81 #23318, 0.81 #25652), 06yrj6 (0.81 #23319, 0.81 #23318, 0.81 #25652), 0603qp (0.81 #23319, 0.81 #23318, 0.81 #25652), 04pz5c (0.81 #23319, 0.81 #23318, 0.81 #25652), 0fxky3 (0.60 #2075, 0.44 #6740, 0.42 #9073), 06j0md (0.38 #2363, 0.22 #23320, 0.20 #30), 0b7t3p (0.33 #6144, 0.25 #8477, 0.25 #3812), 01w0yrc (0.30 #95607, 0.30 #51303, 0.29 #72284), 0q9vf (0.30 #95607, 0.30 #51303, 0.29 #72284), 0pyww (0.30 #95607, 0.30 #51303, 0.29 #72284) >> Best rule #23319 for best value: >> intensional similarity = 3 >> extensional distance = 177 >> proper extension: 07nznf; 0grwj; 0dbpyd; 06j0md; 01xdf5; 0187y5; 0415svh; 04yj5z; 02l840; 02773nt; ... >> query: (?x9845, ?x5643) <- award_nominee(?x9845, ?x5643), producer_type(?x9845, ?x632), award_winner(?x5643, ?x1712) >> conf = 0.81 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0fxky3 award_nominee! 0603qp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 43.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21745-02tfl8 PRED entity: 02tfl8 PRED relation: symptom_of PRED expected values: 0167bx 07s4l 0h1wz => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 85): 0167bx (0.62 #512, 0.60 #604, 0.60 #371), 02k6hp (0.62 #550, 0.50 #410, 0.50 #167), 0gk4g (0.60 #346, 0.50 #149, 0.50 #138), 074m2 (0.50 #500, 0.50 #454, 0.50 #138), 07s4l (0.50 #515, 0.50 #420, 0.50 #226), 011zdm (0.50 #256, 0.50 #138, 0.50 #44), 07jwr (0.50 #148, 0.50 #138, 0.50 #44), 0h1wz (0.50 #274, 0.50 #138, 0.50 #44), 02psvcf (0.50 #208, 0.50 #138, 0.50 #44), 0hg11 (0.50 #151, 0.50 #138, 0.50 #44) >> Best rule #512 for best value: >> intensional similarity = 30 >> extensional distance = 6 >> proper extension: 0j5fv; 0cjf0; >> query: (?x3679, 0167bx) <- symptom_of(?x3679, ?x13131), symptom_of(?x3679, ?x11064), symptom_of(?x3679, ?x10480), symptom_of(?x3679, ?x7260), symptom_of(?x3679, ?x5118), symptom_of(?x3679, ?x3680), ?x10480 = 0h1n9, people(?x7260, ?x11011), people(?x7260, ?x7261), people(?x7260, ?x5440), symptom_of(?x13373, ?x3680), people(?x5118, ?x5119), gender(?x11011, ?x231), ?x13373 = 0f3kl, nationality(?x11011, ?x512), location(?x11011, ?x9026), profession(?x7261, ?x1032), ?x512 = 07ssc, place_of_death(?x11011, ?x739), ?x13131 = 0d19y2, award(?x7261, ?x591), type_of_union(?x7261, ?x566), people(?x3680, ?x5840), people(?x11064, ?x3542), award(?x3542, ?x921), influenced_by(?x3542, ?x6015), risk_factors(?x5118, ?x4195), award(?x11011, ?x458), influenced_by(?x3541, ?x3542), film(?x5440, ?x2924) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 8 EVAL 02tfl8 symptom_of 0h1wz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 21.000 21.000 0.625 http://example.org/medicine/symptom/symptom_of EVAL 02tfl8 symptom_of 07s4l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 21.000 21.000 0.625 http://example.org/medicine/symptom/symptom_of EVAL 02tfl8 symptom_of 0167bx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 21.000 21.000 0.625 http://example.org/medicine/symptom/symptom_of #21744-0f7hc PRED entity: 0f7hc PRED relation: influenced_by! PRED expected values: 02xfj0 => 132 concepts (77 used for prediction) PRED predicted values (max 10 best out of 380): 03g5jw (0.15 #2087, 0.05 #5150, 0.04 #9236), 0bqs56 (0.13 #1781, 0.10 #6886, 0.09 #3313), 0167xy (0.12 #2472, 0.06 #5535, 0.03 #1962), 05rx__ (0.10 #4392, 0.08 #3371, 0.06 #1839), 05ty4m (0.09 #3072, 0.09 #4093, 0.07 #6645), 05jm7 (0.09 #9332, 0.05 #20059, 0.05 #12395), 02kz_ (0.09 #6860, 0.04 #20141, 0.04 #10947), 0ph2w (0.08 #6794, 0.05 #3221, 0.05 #4242), 0lrh (0.08 #6742, 0.03 #20023, 0.03 #12359), 02yl42 (0.08 #9326, 0.05 #20053, 0.05 #10859) >> Best rule #2087 for best value: >> intensional similarity = 2 >> extensional distance = 73 >> proper extension: 04r1t; 07yg2; 05xq9; 07m4c; 0qmny; >> query: (?x4657, 03g5jw) <- influenced_by(?x1835, ?x4657), artist(?x3265, ?x4657) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #27581 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 515 *> proper extension: 02pb2bp; 02m4t; 0jrg; 01d5g; *> query: (?x4657, ?x318) <- influenced_by(?x4657, ?x7183), influenced_by(?x318, ?x7183) *> conf = 0.05 ranks of expected_values: 63 EVAL 0f7hc influenced_by! 02xfj0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 132.000 77.000 0.147 http://example.org/influence/influence_node/influenced_by #21743-055qm PRED entity: 055qm PRED relation: languages! PRED expected values: 01wttr1 => 29 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2878): 09r_wb (0.67 #2396, 0.50 #4337, 0.50 #3043), 046rfv (0.67 #2380, 0.50 #4321, 0.50 #3027), 03x31g (0.67 #2535, 0.50 #4476, 0.50 #3182), 01x2tm8 (0.50 #4391, 0.50 #3097, 0.50 #2450), 06kl0k (0.50 #4426, 0.50 #3132, 0.50 #2485), 0dfjb8 (0.50 #2239, 0.40 #4180, 0.38 #2886), 0738y5 (0.50 #2461, 0.38 #3108, 0.33 #518), 0kst7v (0.50 #2482, 0.38 #3129, 0.33 #539), 08s0m7 (0.50 #2570, 0.38 #3217, 0.30 #4511), 05vzql (0.44 #3804, 0.33 #2510, 0.33 #1214) >> Best rule #2396 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 07c9s; 0999q; 09s02; >> query: (?x8531, 09r_wb) <- languages(?x8917, ?x8531), languages(?x7082, ?x8531), location(?x7082, ?x12040), film(?x7082, ?x697), languages_spoken(?x12951, ?x8531), nationality(?x7082, ?x2146), ?x2146 = 03rk0, gender(?x7082, ?x231), people(?x5025, ?x7082), ?x12951 = 04gfy7, place_of_death(?x8917, ?x8918) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #645 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 03k50; *> query: (?x8531, 01wttr1) <- languages(?x13459, ?x8531), languages(?x12675, ?x8531), languages(?x10714, ?x8531), languages(?x8917, ?x8531), languages(?x7082, ?x8531), languages(?x3129, ?x8531), ?x10714 = 0flj39, ?x8917 = 0kt64b, ?x13459 = 03z_g7, languages_spoken(?x12078, ?x8531), ?x12675 = 040nwr, gender(?x7082, ?x231), award(?x7082, ?x4687), type_of_union(?x7082, ?x566), ?x3129 = 0241wg *> conf = 0.33 ranks of expected_values: 564 EVAL 055qm languages! 01wttr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 15.000 0.667 http://example.org/people/person/languages #21742-09gkdln PRED entity: 09gkdln PRED relation: honored_for PRED expected values: 0416y94 02pqs8l => 25 concepts (9 used for prediction) PRED predicted values (max 10 best out of 1523): 0b6tzs (0.60 #1785, 0.25 #1205, 0.14 #2944), 02q6gfp (0.60 #1873, 0.13 #1154, 0.11 #2311), 0ctb4g (0.60 #1928, 0.09 #2506, 0.07 #3087), 03x7hd (0.60 #1932, 0.09 #2510, 0.07 #3091), 02pqs8l (0.50 #1371, 0.24 #1153, 0.15 #3470), 0431v3 (0.50 #1479, 0.07 #3218, 0.07 #4959), 02rv_dz (0.40 #1819, 0.13 #1154, 0.11 #2311), 08zrbl (0.40 #2188, 0.13 #1154, 0.11 #2311), 061681 (0.40 #1775, 0.09 #2353, 0.07 #2934), 02rcdc2 (0.40 #1901, 0.09 #2479, 0.07 #3060) >> Best rule #1785 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 03gwpw2; 02wzl1d; 02pgky2; >> query: (?x8964, 0b6tzs) <- award_winner(?x8964, ?x1116), award_winner(?x8964, ?x748), ceremony(?x2393, ?x8964), honored_for(?x8964, ?x11619), honored_for(?x8964, ?x2612), nominated_for(?x1443, ?x11619), film_release_region(?x11619, ?x94), genre(?x2612, ?x53), ?x748 = 07lt7b, category(?x2612, ?x134), award(?x2612, ?x484), award_nominee(?x1116, ?x444), award_winner(?x2612, ?x4251), nominated_for(?x1116, ?x2078), nominated_for(?x788, ?x2612) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1371 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 05c1t6z; *> query: (?x8964, 02pqs8l) <- award_winner(?x8964, ?x2246), ceremony(?x2393, ?x8964), honored_for(?x8964, ?x11619), honored_for(?x8964, ?x2612), honored_for(?x8964, ?x2009), nominated_for(?x1443, ?x11619), genre(?x2612, ?x53), child(?x1908, ?x2246), ?x2009 = 03d34x8, award(?x2612, ?x484), film(?x1222, ?x11619), nominated_for(?x2246, ?x493), nominated_for(?x788, ?x2612), award(?x84, ?x1443) *> conf = 0.50 ranks of expected_values: 5, 145 EVAL 09gkdln honored_for 02pqs8l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 25.000 9.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 09gkdln honored_for 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 25.000 9.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #21741-080v2 PRED entity: 080v2 PRED relation: category PRED expected values: 08mbj5d => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14815, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 080v2 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #21740-07s2s PRED entity: 07s2s PRED relation: genre! PRED expected values: 07gp9 013q0p => 46 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1857): 014nq4 (0.69 #18612, 0.25 #2402, 0.25 #544), 0btyf5z (0.69 #18612, 0.25 #2175, 0.25 #317), 01f7kl (0.69 #18612, 0.25 #2265, 0.25 #407), 013q07 (0.69 #18612, 0.25 #2226, 0.05 #17121), 03_wm6 (0.50 #3049, 0.50 #1191, 0.15 #17944), 0436yk (0.50 #2120, 0.50 #262, 0.15 #17015), 0fqt1ns (0.50 #2676, 0.50 #818, 0.13 #17571), 0cd2vh9 (0.50 #2121, 0.50 #263, 0.13 #17016), 0340hj (0.50 #2104, 0.50 #246, 0.13 #16999), 0g9yrw (0.50 #2548, 0.50 #690, 0.13 #17443) >> Best rule #18612 for best value: >> intensional similarity = 8 >> extensional distance = 59 >> proper extension: 0ltv; >> query: (?x11523, ?x2218) <- genre(?x10902, ?x11523), genre(?x7854, ?x11523), genre(?x408, ?x11523), nominated_for(?x507, ?x10902), currency(?x7854, ?x170), prequel(?x2218, ?x408), nominated_for(?x2237, ?x408), film_crew_role(?x408, ?x137) >> conf = 0.69 => this is the best rule for 4 predicted values *> Best rule #2690 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 02kdv5l; *> query: (?x11523, 013q0p) <- genre(?x10902, ?x11523), genre(?x9017, ?x11523), written_by(?x10902, ?x8692), ?x9017 = 06r2h, language(?x10902, ?x254), country(?x10902, ?x94), film(?x851, ?x10902), nominated_for(?x2549, ?x10902) *> conf = 0.25 ranks of expected_values: 561, 676 EVAL 07s2s genre! 013q0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 15.000 0.692 http://example.org/film/film/genre EVAL 07s2s genre! 07gp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 15.000 0.692 http://example.org/film/film/genre #21739-0l2xl PRED entity: 0l2xl PRED relation: county! PRED expected values: 0mhdz => 130 concepts (70 used for prediction) PRED predicted values (max 10 best out of 183): 0r6ff (0.63 #6353, 0.59 #9989, 0.56 #10898), 01zqy6t (0.63 #6353, 0.59 #9989, 0.56 #10898), 0qcrj (0.06 #881, 0.05 #1184, 0.05 #8170), 0r5y9 (0.06 #708, 0.05 #1011, 0.05 #8170), 0r5wt (0.06 #670, 0.05 #973, 0.05 #8170), 0l0mk (0.06 #663, 0.05 #966, 0.05 #8170), 0qyzb (0.06 #849, 0.05 #1152, 0.04 #1456), 0qymv (0.06 #794, 0.05 #1097, 0.04 #1401), 0gdk0 (0.06 #724, 0.05 #1027, 0.04 #1331), 0r111 (0.06 #846, 0.05 #8170, 0.04 #1754) >> Best rule #6353 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 0fhp9; 0cc56; 0drsm; 0h7h6; 0d6lp; 0l2hf; 0l380; 0ccvx; 0mwsh; 0bxqq; ... >> query: (?x7964, ?x3794) <- time_zones(?x7964, ?x2950), second_level_divisions(?x94, ?x7964), adjoins(?x7964, ?x4577), contains(?x7964, ?x3794) >> conf = 0.63 => this is the best rule for 2 predicted values *> Best rule #8170 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 147 *> proper extension: 0mp3l; 0mmzt; 0mndw; 0mn8t; 0dzt9; 0fxwx; 0fwc0; 0nn83; 0mm_4; 0msyb; ... *> query: (?x7964, ?x581) <- time_zones(?x7964, ?x2950), county(?x2935, ?x7964), contains(?x1227, ?x7964), state(?x581, ?x1227) *> conf = 0.05 ranks of expected_values: 61 EVAL 0l2xl county! 0mhdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 130.000 70.000 0.628 http://example.org/location/hud_county_place/county #21738-013yq PRED entity: 013yq PRED relation: source PRED expected values: 0jbk9 => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #125, 0.88 #62, 0.88 #49) >> Best rule #125 for best value: >> intensional similarity = 2 >> extensional distance = 259 >> proper extension: 0qlrh; >> query: (?x2277, 0jbk9) <- county(?x2277, ?x13275), adjoins(?x13275, ?x9053) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013yq source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.916 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #21737-01m15br PRED entity: 01m15br PRED relation: award PRED expected values: 026mg3 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 246): 01by1l (0.33 #4960, 0.33 #3748, 0.33 #2132), 01bgqh (0.29 #2062, 0.28 #10142, 0.28 #9334), 03qbh5 (0.27 #2226, 0.24 #3842, 0.24 #9498), 0c4z8 (0.27 #2899, 0.27 #71, 0.24 #4111), 09sb52 (0.22 #34381, 0.20 #33573, 0.20 #35593), 054ks3 (0.19 #9030, 0.19 #3778, 0.19 #2162), 026mfs (0.18 #28685, 0.16 #2149, 0.15 #129), 026mg3 (0.18 #28685, 0.13 #39190, 0.08 #11), 025m98 (0.18 #28685, 0.13 #39190, 0.06 #238), 026m9w (0.18 #28685, 0.13 #39190, 0.04 #4737) >> Best rule #4960 for best value: >> intensional similarity = 3 >> extensional distance = 274 >> proper extension: 04k05; >> query: (?x4044, 01by1l) <- award_winner(?x506, ?x4044), award(?x4044, ?x159), artist(?x9671, ?x4044) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #28685 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1381 *> proper extension: 0g51l1; 0c_mvb; 0c01c; 0lzkm; 0280mv7; 02f9wb; 06_bq1; 0gdhhy; 015zql; 08xz51; ... *> query: (?x4044, ?x159) <- profession(?x4044, ?x220), award_winner(?x1399, ?x4044), award_winner(?x159, ?x1399) *> conf = 0.18 ranks of expected_values: 8 EVAL 01m15br award 026mg3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 114.000 114.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21736-086nl7 PRED entity: 086nl7 PRED relation: profession PRED expected values: 0dxtg => 109 concepts (108 used for prediction) PRED predicted values (max 10 best out of 75): 0dxtg (0.67 #1754, 0.64 #2914, 0.64 #2769), 01d_h8 (0.50 #1747, 0.41 #2037, 0.40 #441), 02krf9 (0.40 #458, 0.35 #1451, 0.34 #6529), 02jknp (0.40 #442, 0.34 #6529, 0.33 #7), 09jwl (0.35 #1451, 0.33 #1902, 0.29 #9140), 09lbv (0.35 #1451, 0.11 #887, 0.08 #1322), 025352 (0.34 #6529, 0.30 #5077, 0.29 #10447), 015h31 (0.34 #6529, 0.29 #10447, 0.29 #9140), 01c72t (0.34 #6529, 0.29 #10447, 0.29 #9140), 0d1pc (0.33 #47, 0.17 #1933, 0.17 #2223) >> Best rule #1754 for best value: >> intensional similarity = 2 >> extensional distance = 135 >> proper extension: 04cbtrw; >> query: (?x4465, 0dxtg) <- nominated_for(?x4465, ?x6884), influenced_by(?x4465, ?x6771) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 086nl7 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 108.000 0.672 http://example.org/people/person/profession #21735-07rlg PRED entity: 07rlg PRED relation: country PRED expected values: 0jgd 06mzp 0hzlz 03gj2 01mjq 01p1v 01p8s => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 601): 035qy (0.83 #2162, 0.83 #2722, 0.80 #2367), 01mjq (0.83 #2162, 0.80 #3979, 0.80 #2372), 0jgd (0.83 #2162, 0.80 #3953, 0.78 #1980), 05b4w (0.83 #2162, 0.71 #2157, 0.68 #538), 01znc_ (0.83 #2162, 0.67 #2190, 0.67 #2004), 03rk0 (0.83 #2162, 0.61 #2163, 0.50 #1841), 01p1v (0.80 #2380, 0.78 #2200, 0.78 #2014), 02vzc (0.80 #2379, 0.78 #2199, 0.71 #3450), 0345_ (0.80 #2434, 0.78 #2254, 0.71 #1703), 01pj7 (0.78 #2198, 0.71 #1647, 0.71 #1286) >> Best rule #2162 for best value: >> intensional similarity = 41 >> extensional distance = 7 >> proper extension: 06z6r; >> query: (?x150, ?x2146) <- country(?x150, ?x1471), country(?x150, ?x1023), country(?x150, ?x410), country(?x150, ?x390), country(?x150, ?x205), ?x390 = 0chghy, ?x1471 = 07t21, ?x205 = 03rjj, olympics(?x150, ?x2966), ?x410 = 01ls2, olympics(?x2146, ?x2966), olympics(?x6974, ?x2966), olympics(?x2804, ?x2966), olympics(?x183, ?x2966), sports(?x2966, ?x6150), sports(?x2966, ?x3659), sports(?x2966, ?x3641), sports(?x2966, ?x2885), sports(?x2966, ?x2867), sports(?x2966, ?x779), sports(?x2966, ?x471), ?x2885 = 07jjt, ?x471 = 02vx4, ?x3659 = 0dwxr, ?x2867 = 02y8z, organization(?x183, ?x127), ?x1023 = 0ctw_b, administrative_parent(?x14187, ?x2804), ?x6150 = 07_53, ?x779 = 096f8, country(?x2446, ?x2146), country(?x257, ?x2146), nationality(?x111, ?x2146), film_release_region(?x5644, ?x2146), film_release_region(?x428, ?x2146), ?x5644 = 0dll_t2, contains(?x2146, ?x1391), olympics(?x2146, ?x418), ?x428 = 0h1cdwq, ?x3641 = 03fyrh, currency(?x6974, ?x170) >> conf = 0.83 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3, 7, 15, 18, 30, 51 EVAL 07rlg country 01p8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 35.000 35.000 0.835 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07rlg country 01p1v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 35.000 35.000 0.835 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07rlg country 01mjq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 35.000 35.000 0.835 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07rlg country 03gj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 35.000 35.000 0.835 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07rlg country 0hzlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 35.000 35.000 0.835 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07rlg country 06mzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 35.000 35.000 0.835 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07rlg country 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 35.000 35.000 0.835 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #21734-03n3gl PRED entity: 03n3gl PRED relation: genre PRED expected values: 02kdv5l 01q03 => 85 concepts (55 used for prediction) PRED predicted values (max 10 best out of 109): 07s9rl0 (0.69 #4973, 0.66 #2424, 0.60 #231), 01z4y (0.61 #5551, 0.61 #3348, 0.53 #2076), 02kdv5l (0.45 #1385, 0.36 #923, 0.31 #2194), 02l7c8 (0.39 #1512, 0.38 #2090, 0.37 #704), 01jfsb (0.39 #1392, 0.34 #930, 0.32 #1047), 06cvj (0.27 #694, 0.26 #464, 0.23 #1733), 01hmnh (0.27 #936, 0.26 #1398, 0.19 #2207), 06n90 (0.24 #1393, 0.18 #2540, 0.18 #931), 0lsxr (0.22 #8, 0.20 #123, 0.20 #583), 01q03 (0.22 #5, 0.20 #120, 0.18 #2540) >> Best rule #4973 for best value: >> intensional similarity = 4 >> extensional distance = 1018 >> proper extension: 027qgy; 047q2k1; 0ckr7s; 087wc7n; 02z9hqn; 0147sh; 053tj7; 026n4h6; 07h9gp; 0283_zv; ... >> query: (?x6365, 07s9rl0) <- film_release_region(?x6365, ?x94), genre(?x6365, ?x2700), genre(?x1330, ?x2700), ?x1330 = 03m4mj >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1385 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 350 *> proper extension: 03tbg6; 0h63q6t; *> query: (?x6365, 02kdv5l) <- film(?x1765, ?x6365), film_crew_role(?x6365, ?x2154), ?x2154 = 01vx2h, genre(?x6365, ?x258) *> conf = 0.45 ranks of expected_values: 3, 10 EVAL 03n3gl genre 01q03 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 85.000 55.000 0.689 http://example.org/film/film/genre EVAL 03n3gl genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 55.000 0.689 http://example.org/film/film/genre #21733-06q8hf PRED entity: 06q8hf PRED relation: producer_type PRED expected values: 0ckd1 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.33 #2, 0.32 #40, 0.24 #18) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 05hj_k; >> query: (?x7324, 0ckd1) <- award_nominee(?x105, ?x7324), nominated_for(?x7324, ?x144), ?x105 = 0grwj >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06q8hf producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.333 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #21732-0c41qv PRED entity: 0c41qv PRED relation: state_province_region PRED expected values: 01n7q => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 49): 01n7q (0.68 #1621, 0.59 #4579, 0.55 #1127), 059rby (0.30 #990, 0.28 #4935, 0.28 #5058), 02jx1 (0.11 #883, 0.08 #2363, 0.08 #2733), 05k7sb (0.07 #6685, 0.06 #6562, 0.05 #8658), 03v0t (0.05 #5230, 0.03 #10406, 0.03 #10530), 05fjf (0.05 #5250, 0.02 #8577, 0.02 #8823), 015jr (0.05 #1806, 0.05 #1189, 0.04 #1929), 081yw (0.05 #1293, 0.04 #3020, 0.03 #3512), 07z1m (0.04 #5445, 0.04 #3104, 0.04 #5815), 06btq (0.04 #1888, 0.03 #3737, 0.03 #4477) >> Best rule #1621 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 01bzw5; 05cwl_; 06nzl; 06182p; 05q2c; 06kknt; 06b7s9; 03b8c4; >> query: (?x7339, 01n7q) <- category(?x7339, ?x134), citytown(?x7339, ?x1523), ?x1523 = 030qb3t >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c41qv state_province_region 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.682 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #21731-01dvbd PRED entity: 01dvbd PRED relation: genre PRED expected values: 0lsxr => 97 concepts (47 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.75 #721, 0.75 #4468, 0.68 #601), 02kdv5l (0.70 #4350, 0.55 #4954, 0.53 #1084), 05p553 (0.65 #125, 0.44 #485, 0.38 #1569), 03k9fj (0.56 #4360, 0.27 #373, 0.27 #854), 07ssc (0.53 #4227, 0.52 #3742, 0.51 #2411), 02l7c8 (0.38 #737, 0.38 #617, 0.35 #978), 0lsxr (0.35 #4961, 0.35 #130, 0.34 #1091), 06n90 (0.34 #2665, 0.24 #4361, 0.24 #1095), 0219x_ (0.33 #28, 0.21 #508, 0.17 #268), 04xvlr (0.25 #602, 0.22 #722, 0.20 #963) >> Best rule #721 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 05p3738; 0ddcbd5; 02qsqmq; >> query: (?x3048, 07s9rl0) <- film_crew_role(?x3048, ?x137), titles(?x512, ?x3048), language(?x3048, ?x254), ?x512 = 07ssc >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #4961 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 765 *> proper extension: 0413cff; 015qy1; *> query: (?x3048, 0lsxr) <- language(?x3048, ?x254), genre(?x3048, ?x13420), genre(?x4361, ?x13420), ?x4361 = 03wbqc4 *> conf = 0.35 ranks of expected_values: 7 EVAL 01dvbd genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 47.000 0.750 http://example.org/film/film/genre #21730-02zsn PRED entity: 02zsn PRED relation: gender! PRED expected values: 05m63c 023tp8 03zqc1 04bs3j 064nh4k 01wbgdv 0clvcx 01g257 01mqz0 01l2fn 07s8r0 058s57 015882 06t61y 0gl88b 0pyg6 07hbxm 01csrl 043kzcr 0cj2t3 01w02sy 0241wg 030x48 0154qm 0347xl 015f7 02bfxb 08b8vd 01y9xg 0gbwp 05slvm 014g22 02vntj 02yplc 0k8y7 0c7xjb 01l4g5 01gw4f 023v4_ 01fwf1 04r68 0fn5bx 02778yp 03hh89 03rwng 0m66w 01tnbn 020ffd 01xv77 0gx_p 0bdt8 05p92jn 04r7p 02z4b_8 025vldk 039x1k 03q45x 02n9k 01nms7 01w_10 024y6w 031x_3 01nkxvx 0cbkc 05zdk2 019l3m 017b2p 01qqtr 01kwh5j 01qn8k 0633p0 01kp_1t 06wvfq 01kgg9 03q5dr 06y9bd 01vzxld 033p3_ 023s8 04bdqk 05vzql 014g9y 03fwln 01r4bps 01cwkq 01hkck 0ck91 067sqt 01x6jd 044zvm 012g92 070c93 015010 06yj20 047jhq 040nwr 01g5kv 03gt0c5 0dszr0 01vs8ng 09jd9 042xh 02m30v => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 3384): 016tbr (0.67 #3228, 0.66 #3223, 0.63 #3220), 07r1h (0.67 #3228, 0.66 #3223, 0.63 #3220), 0f502 (0.67 #3228, 0.66 #3223, 0.63 #3220), 01r93l (0.67 #3228, 0.66 #3223, 0.63 #3220), 03v3xp (0.67 #3228, 0.66 #3223, 0.63 #3220), 01ksr1 (0.67 #3228, 0.66 #3223, 0.63 #3220), 0dvmd (0.67 #3228, 0.66 #3223, 0.63 #3220), 0lx2l (0.67 #3228, 0.66 #3223, 0.63 #3220), 0c6qh (0.67 #3228, 0.66 #3223, 0.63 #3220), 016z2j (0.67 #3228, 0.66 #3223, 0.63 #3220) >> Best rule #3228 for best value: >> intensional similarity = 27 >> extensional distance = 1 >> proper extension: 05zppz; >> query: (?x514, ?x1538) <- gender(?x10929, ?x514), gender(?x8716, ?x514), gender(?x6433, ?x514), gender(?x5853, ?x514), gender(?x5490, ?x514), gender(?x4490, ?x514), gender(?x3280, ?x514), gender(?x2440, ?x514), gender(?x1126, ?x514), profession(?x10929, ?x353), award_nominee(?x8716, ?x5788), award_winner(?x2075, ?x8716), award_nominee(?x1538, ?x5853), nationality(?x2440, ?x429), participant(?x3293, ?x8716), film(?x4490, ?x86), people(?x7322, ?x2440), participant(?x8716, ?x5881), award_winner(?x6869, ?x2440), award_winner(?x4360, ?x1126), award_nominee(?x4490, ?x4702), ?x6869 = 01xqqp, nominated_for(?x4490, ?x6884), artists(?x2936, ?x3280), award(?x5490, ?x1670), participant(?x4240, ?x6433), nominated_for(?x10929, ?x2223) >> conf = 0.67 => this is the best rule for 958 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 18, 20, 22, 37, 46, 126, 127, 133, 322, 327, 329, 333, 336, 338, 339, 343, 485, 486, 491, 495, 499, 502, 515, 517, 537, 538, 551, 552, 553, 626, 627, 793, 795, 796, 798, 803, 804, 810, 813, 814, 815, 1016, 1017, 1090, 1091, 1092, 1098, 1100, 1101, 1102, 1122 EVAL 02zsn gender! 02m30v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 042xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 09jd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01vs8ng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0dszr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 03gt0c5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01g5kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 040nwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 047jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 06yj20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 015010 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 070c93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 012g92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 044zvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01x6jd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 067sqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0ck91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01hkck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01cwkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01r4bps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 03fwln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 014g9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 05vzql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 04bdqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 023s8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 033p3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01vzxld CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 06y9bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 03q5dr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01kgg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 06wvfq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01kp_1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0633p0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01qn8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01kwh5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01qqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 017b2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 019l3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 05zdk2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0cbkc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01nkxvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 031x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 024y6w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01w_10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01nms7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 02n9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 03q45x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 039x1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 025vldk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 02z4b_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 04r7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 05p92jn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0bdt8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0gx_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01xv77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 020ffd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01tnbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0m66w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 03rwng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 03hh89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 02778yp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0fn5bx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 04r68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01fwf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 023v4_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01gw4f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01l4g5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0c7xjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0k8y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 02yplc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 02vntj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 014g22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 05slvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0gbwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01y9xg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 08b8vd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 02bfxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 015f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0347xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0154qm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 030x48 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0241wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01w02sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0cj2t3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 043kzcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01csrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 07hbxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0pyg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0gl88b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 06t61y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 015882 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 058s57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 07s8r0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01l2fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01mqz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01g257 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 0clvcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 01wbgdv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 064nh4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 04bs3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 03zqc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 023tp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender EVAL 02zsn gender! 05m63c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.666 http://example.org/people/person/gender #21729-07xpm PRED entity: 07xpm PRED relation: major_field_of_study PRED expected values: 02j62 => 198 concepts (167 used for prediction) PRED predicted values (max 10 best out of 117): 037mh8 (0.71 #787, 0.50 #307, 0.44 #67), 05qjt (0.70 #248, 0.56 #128, 0.56 #8), 01lj9 (0.70 #278, 0.56 #158, 0.56 #38), 05qfh (0.70 #274, 0.56 #34, 0.53 #754), 04x_3 (0.70 #265, 0.56 #25, 0.47 #1586), 0fdys (0.67 #157, 0.60 #277, 0.53 #757), 02j62 (0.67 #149, 0.55 #1470, 0.53 #749), 06ms6 (0.60 #257, 0.56 #17, 0.44 #137), 0g26h (0.60 #281, 0.56 #41, 0.39 #1602), 03nfmq (0.60 #276, 0.41 #756, 0.33 #156) >> Best rule #787 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 07tgn; 018sg9; >> query: (?x2396, 037mh8) <- institution(?x1390, ?x2396), company(?x12216, ?x2396), major_field_of_study(?x2396, ?x1668), ?x1390 = 0bjrnt >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 01_qgp; 0373qt; *> query: (?x2396, 02j62) <- institution(?x1305, ?x2396), major_field_of_study(?x2396, ?x2605), major_field_of_study(?x2396, ?x1668), ?x1305 = 02mjs7, ?x2605 = 03g3w, ?x1668 = 01mkq *> conf = 0.67 ranks of expected_values: 7 EVAL 07xpm major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 198.000 167.000 0.706 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #21728-02twdq PRED entity: 02twdq PRED relation: origin PRED expected values: 07dfk => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 43): 09c7w0 (0.33 #1, 0.03 #3781, 0.02 #4017), 030qb3t (0.15 #743, 0.10 #979, 0.07 #1215), 02_286 (0.11 #489, 0.04 #4742, 0.04 #4978), 07dfk (0.11 #632), 0gqkd (0.11 #552), 0f2rq (0.08 #812, 0.03 #1048, 0.02 #1284), 0mn0v (0.08 #762, 0.03 #998, 0.02 #1234), 0162v (0.08 #750, 0.03 #986, 0.02 #1222), 01ls2 (0.08 #717, 0.03 #953, 0.02 #1189), 0fm2_ (0.08 #735, 0.03 #971) >> Best rule #1 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 0161sp; >> query: (?x10320, 09c7w0) <- artists(?x10319, ?x10320), artists(?x3061, ?x10320), artists(?x671, ?x10320), ?x3061 = 05bt6j, ?x10319 = 01gjw, artists(?x671, ?x13145), artists(?x671, ?x8708), artists(?x671, ?x8035), ?x13145 = 0p8h0, ?x8035 = 095x_, instrumentalists(?x227, ?x8708) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #632 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 7 *> proper extension: 06k02; 01k3qj; *> query: (?x10320, 07dfk) <- artists(?x9401, ?x10320), artists(?x3061, ?x10320), artists(?x3061, ?x9087), artists(?x3061, ?x7682), artists(?x3061, ?x115), ?x7682 = 01323p, ?x9087 = 0kj34, parent_genre(?x1748, ?x3061), ?x9401 = 025g__, profession(?x115, ?x1183) *> conf = 0.11 ranks of expected_values: 4 EVAL 02twdq origin 07dfk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 48.000 48.000 0.333 http://example.org/music/artist/origin #21727-09n4nb PRED entity: 09n4nb PRED relation: ceremony! PRED expected values: 03tk6z 02gdjb 019bnn 056jm_ 03nc9d 01d38t 03r00m => 32 concepts (26 used for prediction) PRED predicted values (max 10 best out of 228): 01d38t (0.83 #2465, 0.80 #2108, 0.78 #3019), 03tk6z (0.80 #2059, 0.78 #3019, 0.78 #2661), 02gdjb (0.78 #3019, 0.78 #2661, 0.78 #3728), 01ck6h (0.78 #3019, 0.78 #2661, 0.78 #3728), 031b91 (0.78 #3019, 0.78 #2661, 0.78 #3728), 03nc9d (0.78 #3019, 0.78 #2661, 0.78 #3728), 03q_g6 (0.78 #3019, 0.78 #2661, 0.78 #3728), 01ckbq (0.78 #3019, 0.78 #2661, 0.78 #3728), 056jm_ (0.78 #3019, 0.78 #2661, 0.78 #3728), 02tj96 (0.78 #3019, 0.78 #2661, 0.78 #3728) >> Best rule #2465 for best value: >> intensional similarity = 25 >> extensional distance = 10 >> proper extension: 0gpjbt; >> query: (?x3121, 01d38t) <- ceremony(?x12819, ?x3121), ceremony(?x12701, ?x3121), ceremony(?x12458, ?x3121), ceremony(?x11068, ?x3121), ceremony(?x8705, ?x3121), ceremony(?x7005, ?x3121), ceremony(?x2139, ?x3121), ceremony(?x247, ?x3121), award_winner(?x3121, ?x6792), award_winner(?x3121, ?x4620), ?x8705 = 01c9dd, ?x12701 = 024fxq, type_of_union(?x6792, ?x566), artists(?x671, ?x4620), ?x12458 = 024_dt, award(?x115, ?x7005), religion(?x4620, ?x2694), nationality(?x4620, ?x512), ?x247 = 02wh75, role(?x4620, ?x227), category_of(?x12819, ?x2421), ?x11068 = 02x4wb, award_nominee(?x4620, ?x1291), award_winner(?x4620, ?x1136), ?x2139 = 01by1l >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 6, 9, 11, 12 EVAL 09n4nb ceremony! 03r00m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 32.000 26.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09n4nb ceremony! 01d38t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 26.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09n4nb ceremony! 03nc9d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 32.000 26.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09n4nb ceremony! 056jm_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 32.000 26.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09n4nb ceremony! 019bnn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 32.000 26.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09n4nb ceremony! 02gdjb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 26.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09n4nb ceremony! 03tk6z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 26.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony #21726-0f8l9c PRED entity: 0f8l9c PRED relation: country! PRED expected values: 0k6bt => 263 concepts (191 used for prediction) PRED predicted values (max 10 best out of 668): 0cp6w (0.61 #18234, 0.58 #9718, 0.57 #20670), 0d8r8 (0.61 #18234, 0.58 #9718, 0.57 #20670), 080g3 (0.61 #18234, 0.58 #9718, 0.57 #20670), 01b85 (0.61 #18234, 0.58 #9718, 0.57 #20670), 0fwdr (0.61 #18234, 0.58 #9718, 0.57 #20670), 09hzc (0.61 #18234, 0.58 #9718, 0.57 #20670), 0jq27 (0.61 #18234, 0.58 #9718, 0.57 #20670), 0cx2r (0.55 #11544, 0.54 #15192, 0.53 #17624), 04vg8 (0.55 #11544, 0.54 #15192, 0.53 #17624), 0hqzr (0.55 #11544, 0.54 #15192, 0.34 #61404) >> Best rule #18234 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 0cx2r; >> query: (?x789, ?x13806) <- contains(?x789, ?x13805), jurisdiction_of_office(?x182, ?x789), capital(?x13805, ?x13806) >> conf = 0.61 => this is the best rule for 7 predicted values No rule for expected values ranks of expected_values: EVAL 0f8l9c country! 0k6bt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 263.000 191.000 0.610 http://example.org/base/biblioness/bibs_location/country #21725-0fbx6 PRED entity: 0fbx6 PRED relation: award_nominee PRED expected values: 0170qf => 97 concepts (42 used for prediction) PRED predicted values (max 10 best out of 645): 0170qf (0.81 #58313, 0.81 #86309, 0.80 #37319), 015rkw (0.60 #372, 0.16 #83976, 0.12 #5036), 0154qm (0.40 #734, 0.16 #83976, 0.03 #35720), 01q_ph (0.40 #68, 0.16 #83976, 0.02 #63047), 0fbx6 (0.31 #95640, 0.30 #982, 0.16 #83976), 02hfp_ (0.31 #95640), 02lp3c (0.31 #95640), 09rp4r_ (0.31 #95640), 012ljv (0.31 #95640), 016gr2 (0.30 #250, 0.16 #83976, 0.12 #4914) >> Best rule #58313 for best value: >> intensional similarity = 3 >> extensional distance = 1190 >> proper extension: 0cnl80; 03x3qv; 05ty4m; 0m2wm; 02zq43; 012cj0; 069ld1; 0bg539; 01vs14j; 04t7ts; ... >> query: (?x4254, ?x57) <- profession(?x4254, ?x955), film(?x4254, ?x1463), award_nominee(?x57, ?x4254) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fbx6 award_nominee 0170qf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 42.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21724-0ds2n PRED entity: 0ds2n PRED relation: language PRED expected values: 02h40lc => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.93 #297, 0.91 #2566, 0.91 #1310), 064_8sq (0.18 #914, 0.15 #1033, 0.15 #973), 06nm1 (0.17 #306, 0.17 #129, 0.11 #962), 04306rv (0.15 #597, 0.13 #659, 0.11 #1195), 03hkp (0.13 #192, 0.11 #15, 0.02 #1205), 02bjrlw (0.13 #593, 0.12 #655, 0.09 #60), 03_9r (0.11 #10, 0.07 #305, 0.05 #4182), 0c_v2 (0.11 #17, 0.04 #194, 0.01 #791), 012w70 (0.10 #308, 0.04 #846, 0.04 #1143), 07zrf (0.09 #62, 0.04 #180, 0.04 #239) >> Best rule #297 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0gmcwlb; 0p3_y; 0c1sgd3; 017n9; >> query: (?x3218, 02h40lc) <- award(?x3218, ?x1336), film(?x434, ?x3218), featured_film_locations(?x3218, ?x1523), ?x1523 = 030qb3t >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ds2n language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.927 http://example.org/film/film/language #21723-01xk7r PRED entity: 01xk7r PRED relation: citytown PRED expected values: 0d6lp => 194 concepts (136 used for prediction) PRED predicted values (max 10 best out of 346): 02_286 (0.28 #29116, 0.17 #33173, 0.17 #33543), 030qb3t (0.23 #4447, 0.21 #4815, 0.17 #21390), 07bcn (0.16 #20626, 0.04 #37215, 0.03 #5656), 01snm (0.15 #149, 0.09 #2359, 0.04 #18561), 0r02m (0.11 #1438, 0.08 #3648, 0.07 #702), 01_d4 (0.10 #1880, 0.08 #2984, 0.06 #775), 0r00l (0.09 #26063, 0.07 #21643, 0.03 #11327), 0rh6k (0.09 #6997, 0.07 #6261, 0.06 #14362), 0d6lp (0.09 #5593, 0.08 #3384, 0.07 #21431), 0f04v (0.09 #5676, 0.05 #11935, 0.04 #17458) >> Best rule #29116 for best value: >> intensional similarity = 5 >> extensional distance = 152 >> proper extension: 01w92; >> query: (?x6936, 02_286) <- organization(?x346, ?x6936), state_province_region(?x6936, ?x1227), contains(?x1227, ?x12299), location_of_ceremony(?x1814, ?x1227), place_of_death(?x1021, ?x12299) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #5593 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 06py2; *> query: (?x6936, 0d6lp) <- currency(?x6936, ?x170), ?x170 = 09nqf, state_province_region(?x6936, ?x1227), ?x1227 = 01n7q *> conf = 0.09 ranks of expected_values: 9 EVAL 01xk7r citytown 0d6lp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 194.000 136.000 0.279 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #21722-0b_770 PRED entity: 0b_770 PRED relation: team PRED expected values: 02qk2d5 02pzy52 => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 339): 02qk2d5 (0.82 #187, 0.81 #254, 0.81 #241), 02pzy52 (0.74 #303, 0.74 #298, 0.71 #113), 02plv57 (0.73 #209, 0.71 #92, 0.68 #38), 026w398 (0.68 #38, 0.66 #90, 0.66 #76), 02pjzvh (0.68 #38, 0.66 #90, 0.66 #76), 03d5m8w (0.68 #38, 0.66 #90, 0.66 #76), 026dqjm (0.68 #38, 0.66 #90, 0.66 #76), 0263cyj (0.68 #38, 0.66 #90, 0.66 #76), 04088s0 (0.68 #38, 0.66 #90, 0.66 #76), 02r2qt7 (0.68 #38, 0.66 #90, 0.66 #76) >> Best rule #187 for best value: >> intensional similarity = 35 >> extensional distance = 9 >> proper extension: 0b_71r; >> query: (?x12798, 02qk2d5) <- team(?x12798, ?x9983), team(?x12798, ?x9909), team(?x12798, ?x8728), team(?x12798, ?x6003), team(?x12798, ?x4938), team(?x12798, ?x3798), instance_of_recurring_event(?x12798, ?x10863), ?x9983 = 02q4ntp, ?x4938 = 027yf83, ?x10863 = 02jp2w, team(?x1348, ?x6003), team(?x12451, ?x6003), team(?x12162, ?x6003), team(?x10673, ?x6003), team(?x10594, ?x6003), team(?x7042, ?x6003), team(?x5897, ?x6003), ?x5897 = 0b_6rk, ?x12451 = 0b_6xf, ?x10673 = 0b_6mr, ?x12162 = 0b_6_l, teams(?x5837, ?x3798), colors(?x6003, ?x3189), ?x8728 = 026xxv_, position(?x3798, ?x1579), position(?x11789, ?x1348), position(?x10837, ?x1348), position(?x5756, ?x1348), ?x7042 = 0b_72t, ?x10594 = 0b_756, ?x9909 = 026wlnm, ?x5756 = 0jm4b, ?x1579 = 0ctt4z, ?x11789 = 02pyyld, ?x10837 = 0jm7n >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0b_770 team 02pzy52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.818 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_770 team 02qk2d5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.818 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #21721-01k5zk PRED entity: 01k5zk PRED relation: vacationer! PRED expected values: 05fjy => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 115): 0b90_r (0.26 #366, 0.23 #609, 0.16 #1335), 05qtj (0.25 #795, 0.24 #1400, 0.18 #2370), 03gh4 (0.24 #2622, 0.23 #2258, 0.22 #2015), 0cv3w (0.17 #416, 0.15 #659, 0.15 #1385), 06c62 (0.15 #689, 0.11 #810, 0.08 #2264), 0160w (0.15 #608, 0.08 #729, 0.08 #2547), 0f2v0 (0.14 #786, 0.13 #1391, 0.10 #2361), 04jpl (0.13 #1340, 0.10 #8, 0.10 #735), 02_286 (0.10 #741, 0.08 #620, 0.08 #1346), 0261m (0.10 #825, 0.08 #1430, 0.06 #2036) >> Best rule #366 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 01vv126; 01pcvn; 01xyt7; 02r3cn; 0dq9wx; >> query: (?x3585, 0b90_r) <- vacationer(?x1273, ?x3585), participant(?x3585, ?x2035), olympics(?x1273, ?x778) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #1165 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 02qfhb; 01p47r; 0fqjhm; *> query: (?x3585, 05fjy) <- participant(?x2035, ?x3585), nominated_for(?x3585, ?x1402), film(?x3585, ?x5929) *> conf = 0.01 ranks of expected_values: 100 EVAL 01k5zk vacationer! 05fjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 111.000 111.000 0.259 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #21720-03h_0_z PRED entity: 03h_0_z PRED relation: artist! PRED expected values: 01f_3w => 134 concepts (103 used for prediction) PRED predicted values (max 10 best out of 104): 0181dw (0.33 #43, 0.14 #184, 0.13 #1030), 043g7l (0.33 #32, 0.14 #173, 0.12 #455), 015_1q (0.24 #443, 0.21 #1289, 0.20 #584), 033hn8 (0.23 #155, 0.14 #296, 0.13 #1001), 01f_3w (0.22 #35, 0.09 #2009, 0.07 #317), 01q940 (0.22 #53, 0.06 #476, 0.04 #2450), 01cszh (0.18 #152, 0.12 #434, 0.11 #575), 01trtc (0.17 #1201, 0.17 #778, 0.17 #1060), 03rhqg (0.17 #580, 0.16 #1285, 0.15 #8908), 0g768 (0.15 #743, 0.15 #461, 0.15 #1307) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02qzjj; >> query: (?x6144, 0181dw) <- award_nominee(?x827, ?x6144), profession(?x6144, ?x6759), ?x6759 = 064xm0 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #35 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 02qzjj; *> query: (?x6144, 01f_3w) <- award_nominee(?x827, ?x6144), profession(?x6144, ?x6759), ?x6759 = 064xm0 *> conf = 0.22 ranks of expected_values: 5 EVAL 03h_0_z artist! 01f_3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 134.000 103.000 0.333 http://example.org/music/record_label/artist #21719-0qdwr PRED entity: 0qdwr PRED relation: place_of_death PRED expected values: 030qb3t => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 31): 0f2wj (0.18 #595, 0.10 #401, 0.10 #1955), 0k049 (0.12 #3, 0.06 #2528, 0.05 #2140), 04jpl (0.12 #7, 0.05 #5446, 0.04 #6222), 0r2gj (0.12 #104), 0rj4g (0.12 #1943, 0.01 #4469), 030qb3t (0.10 #411, 0.08 #993, 0.05 #5461), 06_kh (0.10 #394, 0.08 #976, 0.05 #588), 02_286 (0.05 #207, 0.04 #1178, 0.04 #5452), 0xms9 (0.05 #357, 0.03 #1910, 0.02 #2106), 0r00l (0.05 #1909, 0.05 #551, 0.05 #2105) >> Best rule #595 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 016z1c; >> query: (?x9837, 0f2wj) <- people(?x8649, ?x9837), organizations_founded(?x9837, ?x902), award(?x9837, ?x1307) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #411 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 0gg9_5q; 03fw4y; 0m76b; 044kwr; 01hdht; 026ck; 04rfq; *> query: (?x9837, 030qb3t) <- profession(?x9837, ?x319), organizations_founded(?x9837, ?x902), film(?x902, ?x103) *> conf = 0.10 ranks of expected_values: 6 EVAL 0qdwr place_of_death 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 91.000 91.000 0.182 http://example.org/people/deceased_person/place_of_death #21718-02778yp PRED entity: 02778yp PRED relation: gender PRED expected values: 02zsn => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #15, 0.78 #19, 0.78 #17), 02zsn (0.43 #26, 0.42 #24, 0.41 #6) >> Best rule #15 for best value: >> intensional similarity = 2 >> extensional distance = 249 >> proper extension: 0bbxd3; 03p01x; 07lz9l; 02k76g; >> query: (?x5264, 05zppz) <- profession(?x5264, ?x987), program(?x5264, ?x2528) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 377 *> proper extension: 02v60l; *> query: (?x5264, 02zsn) <- spouse(?x9711, ?x5264), profession(?x5264, ?x987) *> conf = 0.43 ranks of expected_values: 2 EVAL 02778yp gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.801 http://example.org/people/person/gender #21717-0cp0t91 PRED entity: 0cp0t91 PRED relation: country PRED expected values: 015fr => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 115): 0345h (0.24 #562, 0.17 #1403, 0.17 #2116), 0154j (0.15 #3396, 0.15 #4218, 0.11 #3219), 015fr (0.15 #3396, 0.15 #4218, 0.11 #3219), 059j2 (0.15 #3396, 0.15 #4218, 0.11 #3219), 0k6nt (0.15 #3396, 0.15 #4218, 0.11 #3219), 0chghy (0.15 #3396, 0.11 #3219, 0.09 #3635), 02vzc (0.15 #3396, 0.11 #3219, 0.09 #3635), 0d0vqn (0.15 #3396, 0.11 #3219, 0.09 #3635), 03h64 (0.15 #3396, 0.11 #3219, 0.08 #2865), 03rjj (0.15 #4218, 0.13 #123, 0.11 #3219) >> Best rule #562 for best value: >> intensional similarity = 5 >> extensional distance = 61 >> proper extension: 02rq8k8; 015g28; 0g5ptf; >> query: (?x8471, 0345h) <- featured_film_locations(?x8471, ?x279), film(?x1414, ?x8471), country(?x8471, ?x512), music(?x8471, ?x4940), ?x512 = 07ssc >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #3396 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 852 *> proper extension: 0dtw1x; 04m1bm; 0d6b7; 064n1pz; 0crh5_f; 02phtzk; 0hv81; 03q8xj; 0gpx6; 0dmn0x; ... *> query: (?x8471, ?x583) <- film_crew_role(?x8471, ?x137), film(?x1414, ?x8471), film_release_region(?x8471, ?x583), film_release_region(?x5220, ?x583), ?x5220 = 0kbf1 *> conf = 0.15 ranks of expected_values: 3 EVAL 0cp0t91 country 015fr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 93.000 0.238 http://example.org/film/film/country #21716-015fs3 PRED entity: 015fs3 PRED relation: student PRED expected values: 063g7l => 92 concepts (73 used for prediction) PRED predicted values (max 10 best out of 985): 0m32_ (0.18 #440, 0.01 #4622, 0.01 #8804), 07f7jp (0.18 #1979, 0.01 #16616), 0prfz (0.18 #43, 0.01 #14680), 015wnl (0.18 #614), 05bnp0 (0.09 #11, 0.03 #2102, 0.02 #6284), 096lf_ (0.09 #1715, 0.03 #5897, 0.02 #10079), 036jb (0.09 #769, 0.03 #4951, 0.02 #9133), 0gs7x (0.09 #1940, 0.02 #16577, 0.01 #43761), 06l6nj (0.09 #1840, 0.02 #16477, 0.01 #8113), 0gd5z (0.09 #382, 0.02 #15019) >> Best rule #440 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 015nl4; >> query: (?x11215, 0m32_) <- major_field_of_study(?x11215, ?x7070), institution(?x865, ?x11215), ?x7070 = 0mg1w >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015fs3 student 063g7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 73.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student #21715-0dgpwnk PRED entity: 0dgpwnk PRED relation: genre PRED expected values: 07s9rl0 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.73 #1099, 0.70 #1466, 0.68 #733), 01jfsb (0.35 #623, 0.33 #2944, 0.33 #3066), 02kdv5l (0.34 #613, 0.30 #369, 0.30 #3056), 02l7c8 (0.33 #2703, 0.29 #749, 0.29 #139), 03k9fj (0.30 #500, 0.25 #256, 0.24 #2820), 06cvj (0.21 #2690, 0.14 #2568, 0.10 #3180), 01hmnh (0.20 #19, 0.19 #507, 0.18 #263), 06n90 (0.20 #14, 0.17 #502, 0.14 #2090), 060__y (0.20 #18, 0.14 #384, 0.14 #628), 082gq (0.20 #32, 0.10 #4797, 0.10 #3696) >> Best rule #1099 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 01kqq7; >> query: (?x3453, 07s9rl0) <- language(?x3453, ?x254), nominated_for(?x1656, ?x3453), film(?x396, ?x3453), film_regional_debut_venue(?x3453, ?x3288) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dgpwnk genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.726 http://example.org/film/film/genre #21714-0fb0v PRED entity: 0fb0v PRED relation: citytown PRED expected values: 02_286 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 80): 0r5wt (0.55 #10673, 0.12 #2677, 0.08 #3781), 0k_q_ (0.55 #10673, 0.12 #2624, 0.08 #3728), 0rj4g (0.33 #1331, 0.05 #6115, 0.03 #8691), 02_286 (0.26 #5535, 0.20 #2223, 0.18 #3327), 030qb3t (0.20 #4444, 0.12 #2604, 0.09 #7388), 0r04p (0.20 #2313, 0.10 #3049, 0.08 #3785), 0f2w0 (0.20 #2243, 0.10 #2979, 0.07 #4819), 0r00l (0.17 #6905, 0.15 #6169, 0.14 #7273), 04jpl (0.13 #4055, 0.10 #5895, 0.07 #4791), 013yq (0.12 #2619, 0.07 #4459, 0.03 #7403) >> Best rule #10673 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 07wrz; 06jplb; 0gy1_; >> query: (?x1954, ?x2495) <- organizations_founded(?x9373, ?x1954), organizations_founded(?x9373, ?x5970), profession(?x9373, ?x106), citytown(?x5970, ?x2495) >> conf = 0.55 => this is the best rule for 2 predicted values *> Best rule #5535 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 05cl8y; 05clg8; 064r9cb; 01fb6d; 08pn_9; *> query: (?x1954, 02_286) <- artist(?x1954, ?x4062), artist(?x1954, ?x2440), award(?x2440, ?x1323), artists(?x12611, ?x4062), ?x12611 = 0233qs *> conf = 0.26 ranks of expected_values: 4 EVAL 0fb0v citytown 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 71.000 71.000 0.551 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #21713-05zvzf3 PRED entity: 05zvzf3 PRED relation: film_release_region PRED expected values: 0b90_r 03_3d 05v8c 06mzp 035qy 06bnz 05b4w 03h64 07f1x => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 184): 035qy (0.93 #780, 0.84 #480, 0.84 #1080), 03h64 (0.91 #4721, 0.86 #812, 0.84 #1412), 03_3d (0.86 #756, 0.86 #2107, 0.86 #155), 05b4w (0.84 #1110, 0.84 #960, 0.81 #1410), 0b90_r (0.83 #1054, 0.81 #904, 0.80 #1354), 06t2t (0.80 #1108, 0.80 #1408, 0.78 #958), 06bnz (0.79 #1091, 0.77 #941, 0.77 #1391), 01znc_ (0.78 #787, 0.74 #1387, 0.73 #1087), 0ctw_b (0.77 #921, 0.71 #1371, 0.68 #1071), 05v8c (0.76 #763, 0.66 #1063, 0.64 #1363) >> Best rule #780 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 0djb3vw; 05p1tzf; 02x3lt7; 0401sg; 0gkz15s; 01vksx; 017gl1; 08hmch; 01c22t; 0jjy0; ... >> query: (?x8646, 035qy) <- currency(?x8646, ?x170), genre(?x8646, ?x53), film_release_region(?x8646, ?x608), ?x608 = 02k54 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 7, 10, 12, 28 EVAL 05zvzf3 film_release_region 07f1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 102.000 102.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zvzf3 film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zvzf3 film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zvzf3 film_release_region 06bnz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zvzf3 film_release_region 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zvzf3 film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 102.000 102.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zvzf3 film_release_region 05v8c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 102.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zvzf3 film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zvzf3 film_release_region 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21712-01846t PRED entity: 01846t PRED relation: type_of_union PRED expected values: 01g63y => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1): 01g63y (0.14 #10, 0.14 #55, 0.14 #7) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 780 >> proper extension: 058s57; 0136pk; 07z1_q; 02fx3c; 062ftr; 01wqmm8; 03dbds; 02m3sd; 03h8_g; >> query: (?x3181, 01g63y) <- award_nominee(?x3181, ?x230), film(?x3181, ?x3535), category(?x3535, ?x134) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01846t type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.145 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21711-02g3w PRED entity: 02g3w PRED relation: student! PRED expected values: 0ym1n => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 273): 013nky (0.20 #380, 0.03 #3005, 0.01 #10356), 01lhdt (0.20 #258, 0.02 #6558, 0.02 #3933), 0c5x_ (0.20 #301, 0.01 #2926, 0.01 #5026), 0ymf1 (0.13 #1048, 0.02 #56207, 0.01 #3148), 01w5m (0.11 #2729, 0.09 #3254, 0.09 #17433), 03ksy (0.09 #17434, 0.08 #4830, 0.08 #12181), 0bwfn (0.09 #21279, 0.07 #42820, 0.07 #13399), 0ylvj (0.09 #724, 0.03 #2824, 0.02 #56207), 0yls9 (0.09 #748, 0.02 #6523, 0.02 #56207), 09vzz (0.09 #1007) >> Best rule #380 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 087qxp; >> query: (?x11413, 013nky) <- profession(?x11413, ?x353), student(?x5638, ?x11413), ?x353 = 0cbd2, ?x5638 = 02bqy >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #56207 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1627 *> proper extension: 0854hr; 02x8kk; 076df9; 06p0s1; 02vkvcz; 069d71; *> query: (?x11413, ?x6034) <- location(?x11413, ?x1841), citytown(?x6034, ?x1841), major_field_of_study(?x6034, ?x2014) *> conf = 0.02 ranks of expected_values: 128 EVAL 02g3w student! 0ym1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 150.000 150.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #21710-02yplc PRED entity: 02yplc PRED relation: student! PRED expected values: 02822 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 42): 02822 (0.16 #653, 0.09 #967, 0.09 #1155), 03qsdpk (0.10 #658, 0.08 #1035, 0.08 #972), 01zc2w (0.09 #421, 0.06 #545, 0.04 #483), 02vxn (0.09 #377, 0.03 #626, 0.02 #1065), 0w7c (0.08 #291, 0.06 #353, 0.05 #789), 0fdys (0.07 #776, 0.06 #1090, 0.05 #965), 03g3w (0.05 #1020, 0.05 #957, 0.05 #768), 062z7 (0.05 #895, 0.04 #1021, 0.04 #1083), 04g51 (0.05 #412, 0.02 #536, 0.02 #912), 05qfh (0.04 #774, 0.03 #900, 0.03 #1026) >> Best rule #653 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 01p7yb; 0prfz; 02r_d4; 05ml_s; 01yk13; 0pz7h; 0yfp; 049dyj; 03lt8g; 02r34n; ... >> query: (?x4263, 02822) <- film(?x4263, ?x1295), type_of_union(?x4263, ?x566), location(?x4263, ?x4090), student(?x8398, ?x4263) >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02yplc student! 02822 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.165 http://example.org/education/field_of_study/students_majoring./education/education/student #21709-0gj4fx PRED entity: 0gj4fx PRED relation: contains PRED expected values: 0n5_g => 132 concepts (59 used for prediction) PRED predicted values (max 10 best out of 2700): 01m94f (0.82 #38278, 0.75 #79498, 0.42 #153117), 0n5_t (0.63 #29448, 0.62 #94222, 0.62 #97167), 0mpfn (0.63 #29448, 0.62 #94222, 0.62 #97167), 0n5y4 (0.63 #29448, 0.62 #94222, 0.62 #97167), 0xhj2 (0.60 #7896, 0.43 #13786, 0.33 #10842), 059f4 (0.48 #79500, 0.47 #58887, 0.42 #97168), 0n5yh (0.48 #79500, 0.46 #108947, 0.42 #97168), 0gj4fx (0.48 #79500, 0.42 #97168, 0.40 #108950), 09c7w0 (0.48 #79500, 0.42 #97168, 0.40 #108950), 0n5xb (0.46 #108947, 0.33 #2366, 0.20 #8254) >> Best rule #38278 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 059rby; 03v1s; 05kj_; 0vmt; 03s0w; 04ych; 059_c; 01n7q; 01x73; 04rrd; ... >> query: (?x12828, ?x7564) <- contains(?x12828, ?x7565), district_represented(?x4821, ?x12828), district_represented(?x845, ?x12828), ?x4821 = 02bqm0, ?x845 = 07p__7, contains(?x7565, ?x7564) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #173731 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 249 *> proper extension: 06srk; 02j7k; 06s_2; *> query: (?x12828, ?x94) <- contains(?x12828, ?x10545), location(?x5566, ?x10545), time_zones(?x10545, ?x2674), time_zones(?x94, ?x2674) *> conf = 0.03 ranks of expected_values: 2195 EVAL 0gj4fx contains 0n5_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 59.000 0.824 http://example.org/location/location/contains #21708-015fr PRED entity: 015fr PRED relation: combatants PRED expected values: 059j2 01mk6 => 195 concepts (144 used for prediction) PRED predicted values (max 10 best out of 287): 0chghy (0.85 #540, 0.84 #6344, 0.83 #926), 07ssc (0.85 #540, 0.84 #6344, 0.83 #926), 05b4w (0.85 #540, 0.84 #6344, 0.83 #926), 05qhw (0.85 #540, 0.84 #6344, 0.83 #926), 06bnz (0.85 #540, 0.84 #6344, 0.83 #926), 06f32 (0.85 #540, 0.84 #6344, 0.83 #926), 015qh (0.85 #540, 0.83 #926, 0.83 #3781), 015fr (0.50 #856, 0.50 #470, 0.43 #1782), 059j2 (0.50 #477, 0.46 #863, 0.43 #1789), 01mk6 (0.50 #900, 0.44 #514, 0.39 #1902) >> Best rule #540 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 09c7w0; 0b90_r; 0154j; 03rjj; 0chghy; 0f8l9c; 0k6nt; 03gj2; 0ctw_b; 059j2; ... >> query: (?x583, ?x94) <- film_release_region(?x409, ?x583), ?x409 = 0gtv7pk, combatants(?x94, ?x583) >> conf = 0.85 => this is the best rule for 7 predicted values *> Best rule #477 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 09c7w0; 0b90_r; 0154j; 03rjj; 0chghy; 0f8l9c; 0k6nt; 03gj2; 0ctw_b; 059j2; ... *> query: (?x583, 059j2) <- film_release_region(?x409, ?x583), ?x409 = 0gtv7pk, combatants(?x94, ?x583) *> conf = 0.50 ranks of expected_values: 9, 10 EVAL 015fr combatants 01mk6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 195.000 144.000 0.849 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants EVAL 015fr combatants 059j2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 195.000 144.000 0.849 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #21707-0bs5f0b PRED entity: 0bs5f0b PRED relation: film_crew_role PRED expected values: 02r96rf 0d2b38 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 23): 02r96rf (0.82 #243, 0.70 #346, 0.66 #37), 0dxtw (0.49 #250, 0.41 #44, 0.36 #353), 0215hd (0.33 #18, 0.18 #258, 0.15 #361), 01pvkk (0.32 #251, 0.30 #527, 0.30 #597), 02ynfr (0.22 #255, 0.17 #15, 0.15 #1016), 02rh1dz (0.21 #249, 0.12 #43, 0.12 #352), 015h31 (0.18 #248, 0.17 #8, 0.11 #42), 0d2b38 (0.17 #24, 0.17 #264, 0.13 #367), 01xy5l_ (0.17 #13, 0.16 #253, 0.12 #356), 033smt (0.17 #26, 0.10 #266, 0.04 #369) >> Best rule #243 for best value: >> intensional similarity = 4 >> extensional distance = 325 >> proper extension: 01gglm; >> query: (?x10105, 02r96rf) <- film_crew_role(?x10105, ?x2154), ?x2154 = 01vx2h, language(?x10105, ?x254), film(?x4508, ?x10105) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 0bs5f0b film_crew_role 0d2b38 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 75.000 75.000 0.817 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0bs5f0b film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.817 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21706-067nsm PRED entity: 067nsm PRED relation: artists! PRED expected values: 012yc => 77 concepts (75 used for prediction) PRED predicted values (max 10 best out of 185): 06by7 (0.48 #3749, 0.47 #952, 0.46 #2505), 05bt6j (0.31 #2527, 0.27 #3771, 0.26 #974), 0gywn (0.31 #367, 0.27 #677, 0.25 #3784), 012yc (0.25 #149, 0.20 #769, 0.17 #5899), 09096d (0.25 #282, 0.08 #592, 0.07 #902), 0ggx5q (0.24 #2561, 0.20 #698, 0.18 #3805), 016_nr (0.23 #383, 0.17 #5899, 0.17 #5588), 02lnbg (0.22 #2541, 0.17 #3785, 0.17 #5899), 03_d0 (0.22 #1252, 0.19 #942, 0.18 #2805), 016clz (0.20 #2798, 0.19 #7768, 0.19 #6526) >> Best rule #3749 for best value: >> intensional similarity = 3 >> extensional distance = 487 >> proper extension: 07_3qd; 04r1t; 01l_vgt; 03xhj6; 06nv27; 0123r4; 02vgh; 01kcms4; 08w4pm; 012vm6; ... >> query: (?x6573, 06by7) <- artists(?x3562, ?x6573), artists(?x3562, ?x5405), ?x5405 = 01vvlyt >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 04lgymt; *> query: (?x6573, 012yc) <- award_nominee(?x5536, ?x6573), award_nominee(?x4740, ?x6573), award(?x6573, ?x1232), ?x4740 = 03y82t6, ?x5536 = 01vsgrn *> conf = 0.25 ranks of expected_values: 4 EVAL 067nsm artists! 012yc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 75.000 0.479 http://example.org/music/genre/artists #21705-0yldt PRED entity: 0yldt PRED relation: school_type PRED expected values: 07tf8 => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 21): 05jxkf (0.56 #124, 0.55 #1252, 0.54 #196), 05pcjw (0.50 #169, 0.50 #73, 0.44 #337), 07tf8 (0.33 #9, 0.31 #225, 0.31 #201), 01rs41 (0.33 #341, 0.31 #821, 0.25 #1421), 01_9fk (0.17 #1226, 0.14 #1805, 0.12 #1250), 01jlsn (0.12 #113, 0.09 #929, 0.08 #593), 02p0qmm (0.11 #1066, 0.11 #802, 0.11 #130), 01y64 (0.11 #588, 0.06 #1092, 0.04 #1622), 04399 (0.09 #158, 0.06 #230, 0.02 #2567), 01_srz (0.07 #1251, 0.06 #1035, 0.06 #339) >> Best rule #124 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 035qv8; >> query: (?x13424, 05jxkf) <- student(?x13424, ?x11018), peers(?x12147, ?x11018), influenced_by(?x5345, ?x11018), religion(?x11018, ?x2694), major_field_of_study(?x13424, ?x5179), ?x5179 = 04gb7 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 07tgn; *> query: (?x13424, 07tf8) <- student(?x13424, ?x11271), institution(?x3437, ?x13424), institution(?x734, ?x13424), category(?x13424, ?x134), ?x11271 = 0hcvy, ?x3437 = 02_xgp2, ?x734 = 04zx3q1 *> conf = 0.33 ranks of expected_values: 3 EVAL 0yldt school_type 07tf8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 152.000 152.000 0.556 http://example.org/education/educational_institution/school_type #21704-05txrz PRED entity: 05txrz PRED relation: award PRED expected values: 05zvj3m 05ztrmj => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 292): 0gq9h (0.33 #10581, 0.32 #7349, 0.27 #3713), 09sb52 (0.30 #27108, 0.29 #21856, 0.29 #25896), 05p09zm (0.28 #1336, 0.14 #6184, 0.14 #8204), 04dn09n (0.27 #2871, 0.27 #4487, 0.26 #7719), 0gr4k (0.27 #4476, 0.27 #7708, 0.23 #2860), 0gr51 (0.27 #7776, 0.26 #4544, 0.24 #2928), 040njc (0.26 #10512, 0.25 #7280, 0.24 #3644), 05ztrmj (0.25 #992, 0.22 #588, 0.13 #42827), 057xs89 (0.25 #160, 0.17 #968, 0.11 #1372), 03hkv_r (0.23 #4460, 0.22 #2844, 0.18 #7692) >> Best rule #10581 for best value: >> intensional similarity = 2 >> extensional distance = 334 >> proper extension: 024c1b; >> query: (?x4371, 0gq9h) <- produced_by(?x6394, ?x4371), film_release_region(?x6394, ?x87) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #992 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0bl2g; 01vsn38; *> query: (?x4371, 05ztrmj) <- film(?x4371, ?x2709), ?x2709 = 06ztvyx, award_nominee(?x123, ?x4371) *> conf = 0.25 ranks of expected_values: 8, 22 EVAL 05txrz award 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 124.000 124.000 0.330 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05txrz award 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 124.000 124.000 0.330 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21703-0yx_w PRED entity: 0yx_w PRED relation: film! PRED expected values: 07r1h => 99 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1180): 02779r4 (0.44 #37508, 0.43 #93774, 0.43 #52097), 06s1qy (0.44 #37508, 0.43 #68771, 0.43 #64601), 0150t6 (0.44 #37508, 0.43 #68771, 0.43 #64601), 01v80y (0.43 #93774, 0.43 #52097, 0.41 #43761), 04yywz (0.17 #19, 0.07 #6269, 0.07 #4186), 012q4n (0.17 #1138, 0.07 #5305, 0.04 #11554), 07mz77 (0.17 #1419, 0.07 #5586, 0.04 #9752), 0gn30 (0.17 #949, 0.07 #5116, 0.02 #69720), 06bzwt (0.17 #1620, 0.07 #5787, 0.02 #12036), 0cbkc (0.17 #1542, 0.07 #5709, 0.02 #11958) >> Best rule #37508 for best value: >> intensional similarity = 4 >> extensional distance = 256 >> proper extension: 047svrl; >> query: (?x9456, ?x398) <- nominated_for(?x398, ?x9456), titles(?x53, ?x9456), executive_produced_by(?x9456, ?x10522), currency(?x9456, ?x170) >> conf = 0.44 => this is the best rule for 3 predicted values *> Best rule #66687 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 534 *> proper extension: 05sy0cv; *> query: (?x9456, ?x399) <- nominated_for(?x398, ?x9456), award(?x9456, ?x591), participant(?x399, ?x398) *> conf = 0.04 ranks of expected_values: 184 EVAL 0yx_w film! 07r1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 99.000 55.000 0.444 http://example.org/film/actor/film./film/performance/film #21702-024lff PRED entity: 024lff PRED relation: currency PRED expected values: 09nqf => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.89 #29, 0.87 #106, 0.86 #113), 01nv4h (0.04 #9, 0.03 #142, 0.02 #324), 02l6h (0.02 #67, 0.02 #151, 0.02 #74) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 01hr1; 0c_j9x; 0dnqr; 03l6q0; 02rrfzf; 011yr9; 02v5_g; 08ct6; 0sxmx; 02scbv; ... >> query: (?x3700, 09nqf) <- language(?x3700, ?x254), prequel(?x8979, ?x3700), ?x254 = 02h40lc, award_winner(?x3700, ?x7027) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024lff currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.890 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21701-082db PRED entity: 082db PRED relation: artists! PRED expected values: 0ggq0m => 156 concepts (128 used for prediction) PRED predicted values (max 10 best out of 238): 0ggq0m (0.80 #1258, 0.80 #13090, 0.56 #6236), 064t9 (0.45 #18070, 0.41 #27094, 0.40 #21804), 06by7 (0.44 #27102, 0.43 #34588, 0.42 #6556), 017_qw (0.43 #13452, 0.42 #17498, 0.40 #17187), 09xw2 (0.33 #616, 0.14 #928, 0.12 #1239), 015y_n (0.33 #533, 0.14 #845, 0.07 #11741), 0m40d (0.33 #461, 0.12 #1084, 0.05 #18206), 01wqlc (0.30 #1321, 0.25 #6299, 0.25 #2876), 0155w (0.28 #5087, 0.27 #9446, 0.26 #6955), 03_d0 (0.28 #4990, 0.27 #11531, 0.24 #13089) >> Best rule #1258 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0383f; >> query: (?x7386, 0ggq0m) <- artists(?x11193, ?x7386), ?x11193 = 06q6jz, people(?x9332, ?x7386) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 082db artists! 0ggq0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 128.000 0.800 http://example.org/music/genre/artists #21700-01qf54 PRED entity: 01qf54 PRED relation: category PRED expected values: 08mbj5d => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #129, 0.84 #128, 0.79 #122) >> Best rule #129 for best value: >> intensional similarity = 5 >> extensional distance = 604 >> proper extension: 06klyh; 01fy2s; >> query: (?x9626, ?x134) <- citytown(?x9626, ?x191), citytown(?x9318, ?x191), school_type(?x9318, ?x4017), category(?x9318, ?x134), ?x134 = 08mbj5d >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qf54 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.837 http://example.org/common/topic/webpage./common/webpage/category #21699-0226k3 PRED entity: 0226k3 PRED relation: service_location PRED expected values: 0ctw_b => 126 concepts (101 used for prediction) PRED predicted values (max 10 best out of 480): 09c7w0 (0.94 #4486, 0.89 #4779, 0.89 #3900), 0d060g (0.70 #594, 0.69 #982, 0.68 #1570), 03h64 (0.60 #241, 0.30 #631, 0.22 #1116), 07ssc (0.52 #1380, 0.50 #1577, 0.48 #1282), 0345h (0.44 #517, 0.33 #1294, 0.33 #1098), 05v8c (0.40 #212, 0.30 #602, 0.22 #506), 0f8l9c (0.33 #511, 0.27 #898, 0.25 #802), 02j71 (0.30 #3429, 0.30 #1676, 0.28 #3235), 06bnz (0.30 #617, 0.27 #908, 0.22 #521), 03rjj (0.27 #690, 0.22 #1077, 0.21 #1175) >> Best rule #4486 for best value: >> intensional similarity = 9 >> extensional distance = 88 >> proper extension: 05krk; 016tt2; 04rwx; 011k1h; 01xdn1; 0cchk3; 02607j; 03ksy; 0178g; 0221g_; ... >> query: (?x14288, 09c7w0) <- citytown(?x14288, ?x5036), service_location(?x14288, ?x390), film_release_region(?x6175, ?x390), film_release_region(?x4448, ?x390), film_release_region(?x1919, ?x390), contains(?x390, ?x901), ?x4448 = 01k60v, ?x6175 = 0gg5kmg, ?x1919 = 0_7w6 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #219 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 0cv9b; 01nn79; 069b85; *> query: (?x14288, 0ctw_b) <- citytown(?x14288, ?x5036), service_location(?x14288, ?x11117), service_location(?x14288, ?x390), contact_category(?x14288, ?x897), administrative_parent(?x11117, ?x1976), ?x390 = 0chghy, country(?x11117, ?x512), teams(?x11117, ?x8338) *> conf = 0.20 ranks of expected_values: 19 EVAL 0226k3 service_location 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 126.000 101.000 0.944 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #21698-07tf8 PRED entity: 07tf8 PRED relation: school_type! PRED expected values: 07tg4 01w5m 017cy9 0trv 0vkl2 0k2h6 029qzx 04_j5s 01g4yw 0yldt => 22 concepts (19 used for prediction) PRED predicted values (max 10 best out of 765): 02x9g_ (0.50 #1819, 0.38 #2849, 0.33 #3882), 0gy3w (0.50 #1801, 0.38 #2831, 0.33 #3864), 04hgpt (0.50 #1685, 0.33 #2200, 0.33 #655), 02hp70 (0.50 #1953, 0.33 #2468, 0.33 #923), 0jkhr (0.50 #1771, 0.33 #2286, 0.33 #741), 01ky7c (0.50 #1756, 0.33 #2271, 0.33 #726), 015q1n (0.50 #1746, 0.33 #2261, 0.33 #716), 07vht (0.50 #1608, 0.33 #2123, 0.33 #578), 01jq34 (0.50 #1597, 0.33 #2112, 0.33 #567), 05x_5 (0.50 #1779, 0.33 #2294, 0.33 #749) >> Best rule #1819 for best value: >> intensional similarity = 21 >> extensional distance = 2 >> proper extension: 01_9fk; >> query: (?x4994, 02x9g_) <- school_type(?x12368, ?x4994), school_type(?x8120, ?x4994), school_type(?x7991, ?x4994), school_type(?x7716, ?x4994), school_type(?x4096, ?x4994), school_type(?x2196, ?x4994), school_type(?x466, ?x4994), institution(?x865, ?x2196), colors(?x2196, ?x663), school(?x1883, ?x7991), student(?x4096, ?x7400), major_field_of_study(?x12368, ?x9111), major_field_of_study(?x7991, ?x3490), ?x8120 = 01rc6f, colors(?x4096, ?x332), contains(?x1229, ?x12368), ?x466 = 01pl14, school(?x4856, ?x7991), contains(?x94, ?x7716), contains(?x512, ?x2196), category(?x7716, ?x134) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #803 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 1 *> proper extension: 05jxkf; *> query: (?x4994, 0trv) <- school_type(?x11318, ?x4994), school_type(?x10627, ?x4994), school_type(?x7991, ?x4994), school_type(?x6177, ?x4994), school_type(?x2196, ?x4994), school_type(?x581, ?x4994), ?x7991 = 0lwyk, major_field_of_study(?x2196, ?x8221), major_field_of_study(?x581, ?x3440), ?x3440 = 0jjw, contains(?x94, ?x6177), school(?x580, ?x581), organization(?x2361, ?x10627), school(?x1161, ?x581), institution(?x1526, ?x10627), ?x8221 = 037mh8, institution(?x620, ?x581), colors(?x581, ?x663), ?x11318 = 02ldkf, student(?x581, ?x1299) *> conf = 0.33 ranks of expected_values: 123, 136, 158, 313, 319, 363, 375, 493, 628, 705 EVAL 07tf8 school_type! 0yldt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 19.000 0.500 http://example.org/education/educational_institution/school_type EVAL 07tf8 school_type! 01g4yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 22.000 19.000 0.500 http://example.org/education/educational_institution/school_type EVAL 07tf8 school_type! 04_j5s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 19.000 0.500 http://example.org/education/educational_institution/school_type EVAL 07tf8 school_type! 029qzx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 19.000 0.500 http://example.org/education/educational_institution/school_type EVAL 07tf8 school_type! 0k2h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 19.000 0.500 http://example.org/education/educational_institution/school_type EVAL 07tf8 school_type! 0vkl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 19.000 0.500 http://example.org/education/educational_institution/school_type EVAL 07tf8 school_type! 0trv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 22.000 19.000 0.500 http://example.org/education/educational_institution/school_type EVAL 07tf8 school_type! 017cy9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 19.000 0.500 http://example.org/education/educational_institution/school_type EVAL 07tf8 school_type! 01w5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 19.000 0.500 http://example.org/education/educational_institution/school_type EVAL 07tf8 school_type! 07tg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 22.000 19.000 0.500 http://example.org/education/educational_institution/school_type #21697-06x68 PRED entity: 06x68 PRED relation: school PRED expected values: 02183k 01jq4b 0trv 01yqqv => 93 concepts (69 used for prediction) PRED predicted values (max 10 best out of 670): 065y4w7 (0.86 #7162, 0.50 #2504, 0.50 #1614), 06pwq (0.83 #5721, 0.67 #3036, 0.67 #1255), 0bx8pn (0.67 #1271, 0.50 #5737, 0.43 #1984), 03tw2s (0.60 #817, 0.50 #1709, 0.50 #638), 0trv (0.60 #845, 0.44 #3161, 0.33 #5846), 01dzg0 (0.57 #2473, 0.50 #2651, 0.50 #1761), 012vwb (0.57 #1833, 0.45 #3618, 0.44 #3079), 01pq4w (0.57 #4868, 0.42 #5943, 0.38 #6664), 07w0v (0.50 #4651, 0.50 #1439, 0.46 #4115), 02rv1w (0.50 #2825, 0.44 #3359, 0.43 #2291) >> Best rule #7162 for best value: >> intensional similarity = 17 >> extensional distance = 27 >> proper extension: 0jmgb; >> query: (?x700, 065y4w7) <- school(?x700, ?x12761), school(?x700, ?x6333), school(?x700, ?x2711), currency(?x2711, ?x170), major_field_of_study(?x2711, ?x4321), fraternities_and_sororities(?x2711, ?x3697), institution(?x1771, ?x2711), ?x4321 = 0g26h, colors(?x6333, ?x7179), ?x1771 = 019v9k, student(?x6333, ?x5350), major_field_of_study(?x6333, ?x12158), major_field_of_study(?x8479, ?x12158), ?x8479 = 01hx2t, school_type(?x12761, ?x3205), time_zones(?x2711, ?x2674), category(?x12761, ?x134) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #845 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 3 *> proper extension: 04wmvz; *> query: (?x700, 0trv) <- school(?x700, ?x6953), school(?x700, ?x4846), school(?x700, ?x4296), season(?x700, ?x11501), ?x11501 = 027mvrc, team(?x4244, ?x700), contains(?x1024, ?x4846), ?x4244 = 028c_8, school_type(?x4846, ?x1044), institution(?x865, ?x4846), major_field_of_study(?x4296, ?x5614), colors(?x700, ?x663), ?x5614 = 03qsdpk, contains(?x94, ?x4296), ?x6953 = 01jq0j *> conf = 0.60 ranks of expected_values: 5, 97, 99, 166 EVAL 06x68 school 01yqqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 93.000 69.000 0.862 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 06x68 school 0trv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 69.000 0.862 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 06x68 school 01jq4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 93.000 69.000 0.862 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 06x68 school 02183k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 93.000 69.000 0.862 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #21696-03_1pg PRED entity: 03_1pg PRED relation: award_nominee! PRED expected values: 01rzqj => 79 concepts (39 used for prediction) PRED predicted values (max 10 best out of 745): 04pz5c (0.81 #23318, 0.81 #18653, 0.81 #20986), 01rzqj (0.81 #23318, 0.81 #18653, 0.81 #20986), 0gls4q_ (0.38 #1673, 0.16 #90921, 0.15 #74603), 0fxky3 (0.38 #2073, 0.16 #90921), 0dbc1s (0.38 #1588, 0.16 #90921), 06w58f (0.38 #2159), 0q5hw (0.38 #631), 05dtsb (0.28 #55952, 0.16 #88587, 0.16 #90921), 03_1pg (0.28 #55952, 0.16 #88587, 0.16 #90921), 02g5h5 (0.28 #55952, 0.02 #40505, 0.02 #42836) >> Best rule #23318 for best value: >> intensional similarity = 4 >> extensional distance = 412 >> proper extension: 02l840; >> query: (?x4933, ?x1410) <- participant(?x2437, ?x4933), award_nominee(?x4933, ?x5642), award_nominee(?x4933, ?x1410), award(?x5642, ?x678) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 03_1pg award_nominee! 01rzqj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 39.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21695-01d6g PRED entity: 01d6g PRED relation: season PRED expected values: 025ygqm => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 5): 025ygqm (0.81 #66, 0.80 #96, 0.80 #86), 02h7s73 (0.50 #68, 0.45 #98, 0.45 #88), 03c6s24 (0.38 #69, 0.35 #99, 0.35 #89), 03c74_8 (0.35 #97, 0.32 #82, 0.31 #67), 04n36qk (0.12 #30, 0.07 #135, 0.07 #130) >> Best rule #66 for best value: >> intensional similarity = 11 >> extensional distance = 14 >> proper extension: 01ync; 02__x; 07l8f; >> query: (?x8995, 025ygqm) <- season(?x8995, ?x11501), season(?x8995, ?x9498), position(?x8995, ?x4244), ?x4244 = 028c_8, draft(?x8995, ?x8499), draft(?x8995, ?x1633), ?x11501 = 027mvrc, season(?x1010, ?x9498), ?x8499 = 02r6gw6, ?x1633 = 02rl201, ?x1010 = 01d5z >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d6g season 025ygqm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.812 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #21694-0gg7gsl PRED entity: 0gg7gsl PRED relation: film_festivals! PRED expected values: 0bh8x1y => 70 concepts (26 used for prediction) PRED predicted values (max 10 best out of 431): 0g4vmj8 (0.82 #1995, 0.82 #663, 0.80 #662), 0gh6j94 (0.82 #1995, 0.80 #662, 0.80 #1108), 0gyfp9c (0.40 #287, 0.38 #508, 0.25 #954), 0h03fhx (0.40 #320, 0.25 #541, 0.18 #764), 0gvvm6l (0.40 #397, 0.25 #618, 0.18 #841), 0g5838s (0.40 #282, 0.25 #503, 0.18 #726), 0ddfwj1 (0.33 #6, 0.25 #894, 0.25 #448), 09gkx35 (0.33 #75, 0.25 #517, 0.18 #740), 0cnztc4 (0.33 #17, 0.25 #459, 0.18 #682), 0crh5_f (0.33 #59, 0.25 #501, 0.18 #724) >> Best rule #1995 for best value: >> intensional similarity = 9 >> extensional distance = 13 >> proper extension: 0cmd3zy; >> query: (?x2686, ?x1370) <- film_regional_debut_venue(?x5092, ?x2686), film_regional_debut_venue(?x1370, ?x2686), film_festivals(?x2685, ?x2686), film(?x157, ?x5092), film_release_region(?x5092, ?x87), film_release_region(?x1185, ?x87), genre(?x2685, ?x53), film_release_region(?x9652, ?x87), ?x9652 = 0ddbjy4 >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #543 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 0hr30wt; *> query: (?x2686, 0bh8x1y) <- film_regional_debut_venue(?x5092, ?x2686), film_festivals(?x2685, ?x2686), film(?x5595, ?x5092), film_release_region(?x5092, ?x1003), crewmember(?x2685, ?x2887), ?x1003 = 03gj2, people(?x2510, ?x5595) *> conf = 0.12 ranks of expected_values: 91 EVAL 0gg7gsl film_festivals! 0bh8x1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 70.000 26.000 0.821 http://example.org/film/film/film_festivals #21693-07ylj PRED entity: 07ylj PRED relation: medal PRED expected values: 02lpp7 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.78 #10, 0.78 #4, 0.73 #9) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0ctw_b; >> query: (?x1203, 02lpp7) <- film_release_region(?x7554, ?x1203), film_release_region(?x7493, ?x1203), currency(?x7493, ?x170), ?x7554 = 01mgw >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ylj medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.784 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #21692-02jztz PRED entity: 02jztz PRED relation: institution! PRED expected values: 019v9k 02m4yg => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 24): 02h4rq6 (0.81 #28, 0.80 #129, 0.80 #155), 019v9k (0.71 #60, 0.65 #162, 0.64 #136), 014mlp (0.68 #158, 0.67 #361, 0.67 #107), 03bwzr4 (0.65 #66, 0.54 #168, 0.54 #142), 02_xgp2 (0.58 #64, 0.47 #140, 0.47 #166), 016t_3 (0.55 #130, 0.54 #54, 0.53 #156), 07s6fsf (0.50 #26, 0.48 #51, 0.45 #153), 0bkj86 (0.48 #59, 0.38 #135, 0.38 #161), 013zdg (0.28 #134, 0.26 #160, 0.25 #186), 04zx3q1 (0.27 #52, 0.25 #27, 0.23 #128) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 01b1mj; 01t8sr; 02jyr8; 02t4yc; 016sd3; >> query: (?x11713, 02h4rq6) <- colors(?x11713, ?x663), school(?x12852, ?x11713), ?x663 = 083jv, school_type(?x11713, ?x1507) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #60 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 0jksm; *> query: (?x11713, 019v9k) <- colors(?x11713, ?x663), major_field_of_study(?x11713, ?x2601), ?x2601 = 04x_3, organization(?x346, ?x11713) *> conf = 0.71 ranks of expected_values: 2, 16 EVAL 02jztz institution! 02m4yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 115.000 115.000 0.812 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02jztz institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 115.000 0.812 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21691-05nzw6 PRED entity: 05nzw6 PRED relation: gender PRED expected values: 05zppz => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #33, 0.80 #27, 0.80 #25), 02zsn (0.43 #4, 0.35 #30, 0.34 #38) >> Best rule #33 for best value: >> intensional similarity = 5 >> extensional distance = 323 >> proper extension: 01wl38s; 0hnlx; 01pr_j6; 0244r8; 01wp8w7; 01w923; 01zmpg; 01vvpjj; 01271h; 09b0xs; ... >> query: (?x6777, 05zppz) <- profession(?x6777, ?x11127), profession(?x7955, ?x11127), profession(?x7386, ?x11127), ?x7955 = 01l3mk3, ?x7386 = 082db >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05nzw6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.840 http://example.org/people/person/gender #21690-095z4q PRED entity: 095z4q PRED relation: genre PRED expected values: 07s9rl0 => 61 concepts (58 used for prediction) PRED predicted values (max 10 best out of 85): 07s9rl0 (0.79 #591, 0.72 #473, 0.69 #1661), 01z4y (0.61 #4151, 0.50 #5571, 0.48 #3914), 02kdv5l (0.40 #711, 0.33 #947, 0.33 #3), 01jfsb (0.39 #719, 0.34 #955, 0.34 #1074), 02l7c8 (0.36 #605, 0.33 #1912, 0.33 #1794), 06cvj (0.33 #122, 0.21 #1783, 0.21 #1901), 0219x_ (0.27 #616, 0.17 #26, 0.10 #498), 03k9fj (0.26 #718, 0.24 #836, 0.24 #954), 04xvlr (0.24 #592, 0.20 #1662, 0.20 #1185), 03bxz7 (0.24 #526, 0.21 #644, 0.09 #1714) >> Best rule #591 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0d8w2n; >> query: (?x6507, 07s9rl0) <- genre(?x6507, ?x714), films(?x1083, ?x6507), ?x714 = 0hn10 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 095z4q genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 58.000 0.788 http://example.org/film/film/genre #21689-02jm9c PRED entity: 02jm9c PRED relation: type_of_union PRED expected values: 04ztj => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.71 #45, 0.71 #81, 0.70 #49), 01g63y (0.16 #2, 0.12 #34, 0.12 #54) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 1082 >> proper extension: 02vwckw; 017lqp; 0fs9jn; 03d8njj; 03f22dp; 02qx5h; >> query: (?x14245, 04ztj) <- profession(?x14245, ?x1032), student(?x4599, ?x14245), ?x1032 = 02hrh1q, institution(?x865, ?x4599) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jm9c type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.708 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21688-0crx5w PRED entity: 0crx5w PRED relation: type_of_union PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.78 #37, 0.77 #41, 0.76 #57), 01g63y (0.47 #113, 0.47 #226, 0.11 #115) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 01d494; >> query: (?x1541, 04ztj) <- gender(?x1541, ?x231), student(?x1368, ?x1541), award_winner(?x2016, ?x1541) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0crx5w type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.780 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21687-07_grx PRED entity: 07_grx PRED relation: profession PRED expected values: 01c8w0 => 119 concepts (105 used for prediction) PRED predicted values (max 10 best out of 78): 02hrh1q (0.75 #6919, 0.75 #7069, 0.73 #10669), 0dxtg (0.50 #164, 0.42 #314, 0.38 #1664), 01c72t (0.50 #175, 0.33 #25, 0.28 #3926), 02hv44_ (0.50 #209, 0.33 #59, 0.09 #809), 01d_h8 (0.42 #306, 0.38 #1656, 0.34 #3456), 09jwl (0.40 #3921, 0.36 #4221, 0.17 #11574), 0nbcg (0.33 #33, 0.32 #3934, 0.32 #4234), 025352 (0.33 #61, 0.25 #211, 0.14 #3962), 02jknp (0.32 #308, 0.24 #2408, 0.23 #3458), 03gjzk (0.28 #1666, 0.26 #5418, 0.25 #2266) >> Best rule #6919 for best value: >> intensional similarity = 4 >> extensional distance = 682 >> proper extension: 0q9kd; 0grwj; 07fq1y; 02qgqt; 0l6qt; 014zcr; 0h0jz; 05ty4m; 05cj4r; 01qscs; ... >> query: (?x4323, 02hrh1q) <- award_nominee(?x6519, ?x4323), location(?x4323, ?x739), student(?x3424, ?x4323), award(?x6519, ?x1079) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6904 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 673 *> proper extension: 032t2z; 0dky9n; 08b8vd; 079dy; 02zfg3; *> query: (?x4323, ?x987) <- people(?x4322, ?x4323), people(?x4322, ?x12480), award(?x12480, ?x1921), profession(?x12480, ?x987) *> conf = 0.09 ranks of expected_values: 21 EVAL 07_grx profession 01c8w0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 119.000 105.000 0.747 http://example.org/people/person/profession #21686-0hcvy PRED entity: 0hcvy PRED relation: nationality PRED expected values: 07ssc 02jx1 => 145 concepts (142 used for prediction) PRED predicted values (max 10 best out of 175): 09c7w0 (0.81 #1401, 0.81 #5903, 0.81 #9706), 07ssc (0.36 #1115, 0.31 #915, 0.28 #7203), 02jx1 (0.31 #933, 0.29 #1133, 0.28 #7203), 0f8l9c (0.28 #7203, 0.14 #1222, 0.06 #4123), 06q1r (0.28 #7203, 0.05 #2077, 0.03 #2678), 06mkj (0.28 #7203, 0.03 #2147, 0.01 #14214), 05qhw (0.28 #7203, 0.01 #14214, 0.01 #10307), 05l5n (0.25 #11510, 0.01 #12812), 06bnz (0.25 #41, 0.14 #1241, 0.11 #641), 0jgd (0.17 #102, 0.11 #602, 0.07 #1202) >> Best rule #1401 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 028qyn; >> query: (?x11271, 09c7w0) <- award(?x11271, ?x7606), profession(?x11271, ?x353), ?x7606 = 01l78d, profession(?x3563, ?x353), ?x3563 = 09bg4l >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1115 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 082xp; *> query: (?x11271, 07ssc) <- award(?x11271, ?x11388), award(?x11271, ?x921), student(?x892, ?x11271), award(?x5335, ?x11388), ?x921 = 0ddd9, influenced_by(?x5335, ?x118) *> conf = 0.36 ranks of expected_values: 2, 3 EVAL 0hcvy nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 142.000 0.812 http://example.org/people/person/nationality EVAL 0hcvy nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 142.000 0.812 http://example.org/people/person/nationality #21685-02kxg_ PRED entity: 02kxg_ PRED relation: locations PRED expected values: 02j9z => 40 concepts (34 used for prediction) PRED predicted values (max 10 best out of 199): 048fz (0.33 #527, 0.25 #719, 0.11 #1102), 0f8l9c (0.33 #21, 0.20 #2296, 0.20 #1934), 05vz3zq (0.33 #285, 0.10 #948, 0.07 #2680), 09b69 (0.33 #353, 0.06 #2676, 0.03 #1524), 0d05q4 (0.27 #2186, 0.23 #1806, 0.18 #2764), 02j9z (0.25 #1157, 0.22 #772, 0.21 #5159), 02k54 (0.25 #584, 0.13 #1930, 0.11 #967), 04gqr (0.25 #650, 0.13 #1996, 0.11 #1033), 07fj_ (0.25 #656, 0.11 #1039, 0.08 #1426), 04wgh (0.25 #596, 0.11 #979, 0.08 #1366) >> Best rule #527 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 048n7; >> query: (?x10764, 048fz) <- combatants(?x10764, ?x512), entity_involved(?x10764, ?x10218), entity_involved(?x10764, ?x3141), ?x512 = 07ssc, people(?x5590, ?x10218), combatants(?x7430, ?x3141), gender(?x10218, ?x231), combatants(?x3141, ?x3142), ?x7430 = 01mk6, place_of_death(?x10218, ?x8745) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1157 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 10 *> proper extension: 0cm2xh; 0py8j; 06k75; 01_3rn; 0gjw_; 0c3mz; 0cwt70; 03jv8d; *> query: (?x10764, 02j9z) <- combatants(?x10764, ?x512), entity_involved(?x10764, ?x10218), entity_involved(?x10764, ?x3141), ?x512 = 07ssc, people(?x5590, ?x10218), combatants(?x7430, ?x3141), gender(?x10218, ?x231), combatants(?x3141, ?x3142), combatants(?x326, ?x7430), adjoins(?x2517, ?x7430) *> conf = 0.25 ranks of expected_values: 6 EVAL 02kxg_ locations 02j9z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 40.000 34.000 0.333 http://example.org/time/event/locations #21684-0lfgr PRED entity: 0lfgr PRED relation: major_field_of_study PRED expected values: 03g3w => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 108): 01lj9 (0.42 #149, 0.41 #377, 0.30 #1175), 0g26h (0.39 #1177, 0.31 #2090, 0.31 #2205), 04rjg (0.38 #132, 0.35 #360, 0.33 #2071), 03g3w (0.35 #138, 0.35 #366, 0.32 #480), 05qjt (0.35 #349, 0.33 #121, 0.32 #2060), 0fdys (0.33 #148, 0.29 #376, 0.22 #490), 02_7t (0.29 #1198, 0.21 #2111, 0.20 #2226), 01tbp (0.28 #1194, 0.24 #2222, 0.22 #2107), 0h5k (0.28 #135, 0.24 #363, 0.14 #477), 04_tv (0.27 #13, 0.13 #1153, 0.12 #127) >> Best rule #149 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 02bh8z; >> query: (?x1809, 01lj9) <- list(?x1809, ?x2197), company(?x8841, ?x1809), citytown(?x1809, ?x10995) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #138 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 02bh8z; *> query: (?x1809, 03g3w) <- list(?x1809, ?x2197), company(?x8841, ?x1809), citytown(?x1809, ?x10995) *> conf = 0.35 ranks of expected_values: 4 EVAL 0lfgr major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 86.000 0.425 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #21683-0j_sncb PRED entity: 0j_sncb PRED relation: institution! PRED expected values: 02h4rq6 013zdg => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 13): 02h4rq6 (0.93 #63, 0.86 #1, 0.85 #261), 013zdg (0.43 #65, 0.32 #36, 0.32 #21), 028dcg (0.40 #17, 0.29 #28, 0.26 #87), 022h5x (0.40 #17, 0.23 #44, 0.23 #145), 0bjrnt (0.40 #17, 0.22 #64, 0.21 #108), 02m4yg (0.40 #17, 0.14 #7, 0.09 #55), 071tyz (0.40 #17, 0.11 #4, 0.06 #110), 01ysy9 (0.40 #17, 0.06 #489, 0.06 #13), 01gkg3 (0.40 #17, 0.05 #1446, 0.01 #440), 01rr_d (0.20 #85, 0.17 #8, 0.16 #26) >> Best rule #63 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 01nkcn; 01rc6f; 02l424; >> query: (?x2948, 02h4rq6) <- school(?x799, ?x2948), institution(?x620, ?x2948), major_field_of_study(?x2948, ?x2014), ?x2014 = 04rjg >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0j_sncb institution! 013zdg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.935 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0j_sncb institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.935 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21682-02n4lw PRED entity: 02n4lw PRED relation: genre! PRED expected values: 01f8gz => 25 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1901): 0sxns (0.75 #14243, 0.67 #12365, 0.65 #5616), 0gd92 (0.75 #14476, 0.67 #12598, 0.60 #8845), 02x0fs9 (0.75 #14849, 0.67 #12971, 0.60 #9218), 0209xj (0.75 #13232, 0.67 #11354, 0.60 #7601), 0sxfd (0.67 #11472, 0.65 #5616, 0.62 #13350), 04wddl (0.67 #12840, 0.65 #5616, 0.62 #14718), 0m313 (0.67 #11262, 0.65 #5616, 0.60 #7509), 0gbfn9 (0.67 #12240, 0.62 #14118, 0.60 #8487), 047msdk (0.67 #11465, 0.62 #13343, 0.60 #7712), 020bv3 (0.67 #11582, 0.62 #13460, 0.60 #7829) >> Best rule #14243 for best value: >> intensional similarity = 23 >> extensional distance = 6 >> proper extension: 06cvj; 0219x_; >> query: (?x11775, 0sxns) <- genre(?x3881, ?x11775), genre(?x3863, ?x11775), genre(?x2882, ?x11775), ?x2882 = 03rz2b, genre(?x3863, ?x812), country(?x3863, ?x94), nominated_for(?x7965, ?x3863), film_crew_role(?x3863, ?x468), nominated_for(?x1414, ?x3863), ?x7965 = 054knh, genre(?x10535, ?x812), genre(?x7541, ?x812), genre(?x6806, ?x812), genre(?x4615, ?x812), genre(?x1192, ?x812), ?x7541 = 02gpkt, ?x4615 = 0dlngsd, ?x6806 = 02q7yfq, genre(?x2009, ?x812), ?x10535 = 09v42sf, category(?x3881, ?x134), titles(?x90, ?x3881), ?x1192 = 07sc6nw >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #4007 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 2 *> proper extension: 02l7c8; *> query: (?x11775, 01f8gz) <- genre(?x9805, ?x11775), genre(?x7978, ?x11775), genre(?x3863, ?x11775), genre(?x2882, ?x11775), ?x2882 = 03rz2b, ?x3863 = 0dx8gj, ?x9805 = 07vfy4, country(?x7978, ?x252), written_by(?x7978, ?x9149), language(?x7978, ?x2164), film(?x7030, ?x7978), currency(?x7978, ?x170), nominated_for(?x1063, ?x7978), award(?x7978, ?x5516), film_release_distribution_medium(?x7978, ?x81), ?x1063 = 02rdxsh, ?x2164 = 03_9r, award_winner(?x5516, ?x826) *> conf = 0.50 ranks of expected_values: 262 EVAL 02n4lw genre! 01f8gz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 25.000 15.000 0.750 http://example.org/film/film/genre #21681-09rvcvl PRED entity: 09rvcvl PRED relation: executive_produced_by PRED expected values: 02z2xdf => 89 concepts (69 used for prediction) PRED predicted values (max 10 best out of 35): 015vq_ (0.20 #254, 0.20 #100, 0.08 #4559), 06pj8 (0.20 #55, 0.05 #561, 0.04 #3094), 02q_cc (0.20 #28, 0.02 #3067, 0.01 #1545), 0d0xs5 (0.07 #253, 0.02 #4051, 0.02 #9111), 0525b (0.07 #253, 0.02 #4051, 0.02 #9111), 024rdh (0.07 #253, 0.02 #4051, 0.02 #9111), 02z2xdf (0.05 #1170, 0.04 #917, 0.03 #1423), 02z6l5f (0.05 #1130, 0.04 #877, 0.03 #1383), 06q8hf (0.04 #2444, 0.03 #2699, 0.03 #1684), 05hj_k (0.04 #2630, 0.03 #2375, 0.03 #9209) >> Best rule #254 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 016ks5; >> query: (?x8723, ?x4128) <- language(?x8723, ?x254), film(?x902, ?x8723), nominated_for(?x4128, ?x8723), ?x4128 = 015vq_ >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1170 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 130 *> proper extension: 0sxfd; 0gxfz; 0f42nz; 02gd6x; *> query: (?x8723, 02z2xdf) <- language(?x8723, ?x254), nominated_for(?x4128, ?x8723), film_festivals(?x8723, ?x10083), nominated_for(?x143, ?x8723) *> conf = 0.05 ranks of expected_values: 7 EVAL 09rvcvl executive_produced_by 02z2xdf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 89.000 69.000 0.200 http://example.org/film/film/executive_produced_by #21680-077g7n PRED entity: 077g7n PRED relation: district_represented PRED expected values: 05kkh 03s0w 05fjy => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 33): 05fjy (0.83 #157, 0.82 #141, 0.81 #84), 03s0w (0.82 #137, 0.81 #84, 0.75 #153), 05kkh (0.81 #84, 0.63 #291, 0.61 #290), 05kr_ (0.61 #290, 0.59 #67, 0.51 #307), 04kdn (0.61 #290, 0.59 #67, 0.51 #307), 04kbn (0.61 #290, 0.59 #67, 0.51 #307), 04kcn (0.61 #290, 0.59 #67, 0.51 #307), 0d060g (0.61 #290, 0.59 #67, 0.51 #307), 05rgl (0.61 #290, 0.59 #67, 0.51 #307), 06nrt (0.61 #290, 0.59 #67, 0.51 #307) >> Best rule #157 for best value: >> intensional similarity = 17 >> extensional distance = 10 >> proper extension: 02glc4; >> query: (?x605, 05fjy) <- legislative_sessions(?x605, ?x3463), legislative_sessions(?x605, ?x653), legislative_sessions(?x11440, ?x605), profession(?x11440, ?x5805), profession(?x11440, ?x1041), ?x653 = 070m6c, district_represented(?x605, ?x2256), district_represented(?x605, ?x1782), legislative_sessions(?x1137, ?x605), ?x2256 = 07srw, award_winner(?x594, ?x11440), profession(?x2715, ?x1041), ?x2715 = 01fwk3, ?x5805 = 0fj9f, legislative_sessions(?x7961, ?x3463), ?x7961 = 03txms, jurisdiction_of_office(?x3959, ?x1782) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 077g7n district_represented 05fjy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.833 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 077g7n district_represented 03s0w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.833 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 077g7n district_represented 05kkh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.833 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #21679-0hv8w PRED entity: 0hv8w PRED relation: film_release_region PRED expected values: 0jgd 03rjj 0d0vqn 06mzp 0345h 07twz => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 139): 0d0vqn (0.91 #5492, 0.90 #5326, 0.90 #4330), 059j2 (0.90 #1035, 0.89 #4359, 0.88 #5521), 03rjj (0.87 #4327, 0.86 #3995, 0.86 #3497), 05r4w (0.85 #5318, 0.84 #4322, 0.84 #3990), 07ssc (0.84 #4009, 0.84 #3511, 0.83 #4175), 0345h (0.83 #1037, 0.83 #5357, 0.83 #4361), 0jgd (0.83 #1000, 0.80 #5320, 0.80 #4158), 035qy (0.83 #3533, 0.80 #4363, 0.80 #5359), 03gj2 (0.81 #3521, 0.81 #1027, 0.81 #4351), 05qhw (0.81 #3509, 0.78 #5335, 0.78 #4339) >> Best rule #5492 for best value: >> intensional similarity = 4 >> extensional distance = 212 >> proper extension: 0gj9qxr; 0g5q34q; 07s3m4g; >> query: (?x5473, 0d0vqn) <- film_release_region(?x5473, ?x1892), film_release_region(?x5473, ?x583), ?x583 = 015fr, ?x1892 = 02vzc >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 6, 7, 21, 40 EVAL 0hv8w film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 102.000 102.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hv8w film_release_region 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 102.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hv8w film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 102.000 102.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hv8w film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hv8w film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hv8w film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 102.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21678-03f1d47 PRED entity: 03f1d47 PRED relation: artist! PRED expected values: 0mzkr => 88 concepts (55 used for prediction) PRED predicted values (max 10 best out of 111): 015_1q (0.22 #991, 0.18 #435, 0.18 #1826), 011k1h (0.21 #9, 0.16 #426, 0.14 #148), 0n85g (0.21 #200, 0.18 #478, 0.17 #61), 03rhqg (0.18 #431, 0.17 #14, 0.15 #1544), 01trtc (0.17 #71, 0.16 #488, 0.14 #210), 0g768 (0.12 #36, 0.12 #1844, 0.11 #1705), 0181dw (0.11 #1014, 0.10 #1849, 0.09 #1571), 043g7l (0.11 #447, 0.10 #169, 0.07 #1838), 017l96 (0.11 #434, 0.10 #1547, 0.10 #1686), 01cl0d (0.10 #192, 0.08 #53, 0.08 #2366) >> Best rule #991 for best value: >> intensional similarity = 3 >> extensional distance = 260 >> proper extension: 04l19_; >> query: (?x4983, 015_1q) <- gender(?x4983, ?x514), people(?x2510, ?x4983), artist(?x1954, ?x4983) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1554 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 539 *> proper extension: 04r1t; 02r1tx7; 05563d; 07yg2; 07m4c; 08w4pm; 06br6t; *> query: (?x4983, 0mzkr) <- artists(?x9013, ?x4983), artists(?x9013, ?x4873), ?x4873 = 01vsy3q *> conf = 0.07 ranks of expected_values: 24 EVAL 03f1d47 artist! 0mzkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 88.000 55.000 0.218 http://example.org/music/record_label/artist #21677-016kjs PRED entity: 016kjs PRED relation: people! PRED expected values: 0x67 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 54): 0x67 (0.50 #87, 0.40 #10, 0.29 #934), 0xnvg (0.20 #13, 0.10 #1938, 0.10 #167), 041rx (0.18 #312, 0.17 #389, 0.16 #235), 033tf_ (0.14 #1470, 0.14 #1932, 0.14 #1701), 07hwkr (0.11 #243, 0.08 #89, 0.08 #1398), 07bch9 (0.09 #254, 0.08 #1178, 0.07 #562), 063k3h (0.09 #262, 0.06 #6857, 0.06 #7012), 02ctzb (0.08 #554, 0.07 #1170, 0.05 #2017), 02w7gg (0.07 #387, 0.06 #2235, 0.06 #2389), 048z7l (0.07 #1041, 0.06 #348, 0.06 #6857) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 026yqrr; >> query: (?x1125, 0x67) <- award_nominee(?x4475, ?x1125), ?x4475 = 01ws9n6, award_nominee(?x1125, ?x3737) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016kjs people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.500 http://example.org/people/ethnicity/people #21676-03x22w PRED entity: 03x22w PRED relation: nationality PRED expected values: 0chghy => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.82 #201, 0.76 #1602, 0.74 #801), 07ssc (0.42 #1101, 0.33 #7106, 0.32 #3402), 02jx1 (0.33 #7106, 0.32 #3402, 0.11 #33), 0345h (0.33 #7106, 0.32 #3402, 0.08 #131), 0d060g (0.33 #7106, 0.32 #3402, 0.05 #1208), 06qd3 (0.33 #7106, 0.32 #3402, 0.04 #236), 0d04z6 (0.11 #71, 0.08 #171, 0.04 #271), 03rk0 (0.06 #7552, 0.06 #5750, 0.05 #7752), 03_3d (0.03 #1407, 0.02 #1207, 0.01 #1307), 0chghy (0.02 #910, 0.02 #610, 0.02 #1010) >> Best rule #201 for best value: >> intensional similarity = 2 >> extensional distance = 26 >> proper extension: 04ls53; >> query: (?x5748, 09c7w0) <- nominated_for(?x5748, ?x5810), ?x5810 = 0828jw >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #910 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 727 *> proper extension: 01vvydl; 07s3vqk; 0cnl80; 05ty4m; 03rs8y; 02lfcm; 05gml8; 03m8lq; 012cj0; 03qd_; ... *> query: (?x5748, 0chghy) <- award_winner(?x57, ?x5748), award_nominee(?x5748, ?x56), film(?x5748, ?x2847) *> conf = 0.02 ranks of expected_values: 10 EVAL 03x22w nationality 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 80.000 80.000 0.821 http://example.org/people/person/nationality #21675-02lfcm PRED entity: 02lfcm PRED relation: award_winner! PRED expected values: 059gkk => 115 concepts (58 used for prediction) PRED predicted values (max 10 best out of 706): 059gkk (0.82 #63833, 0.82 #90970, 0.82 #41486), 02lfcm (0.67 #4839, 0.62 #3244, 0.62 #1648), 01wbg84 (0.52 #3192, 0.50 #51062, 0.47 #76604), 02lg3y (0.52 #3192, 0.50 #51062, 0.47 #76604), 01b9z4 (0.52 #3192, 0.50 #51062, 0.42 #73411), 0bl60p (0.52 #3192, 0.50 #51062, 0.42 #62237), 0c1ps1 (0.50 #51062, 0.42 #62237, 0.36 #71812), 02tr7d (0.15 #55854, 0.12 #3434, 0.12 #1838), 0cjsxp (0.15 #55854, 0.12 #3817, 0.12 #2221), 07s8hms (0.15 #55854, 0.12 #3816, 0.12 #2220) >> Best rule #63833 for best value: >> intensional similarity = 3 >> extensional distance = 949 >> proper extension: 08849; >> query: (?x447, ?x3284) <- award_winner(?x369, ?x447), award_winner(?x447, ?x3284), type_of_union(?x447, ?x566) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lfcm award_winner! 059gkk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 58.000 0.820 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #21674-0p8r1 PRED entity: 0p8r1 PRED relation: category PRED expected values: 08mbj5d => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.60 #5, 0.54 #11, 0.52 #8) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 01yznp; 09k2t1; 016ksk; 0pmw9; 03f1r6t; 02w5q6; 0261x8t; 0mdyn; 010p3; 02_wxh; ... >> query: (?x3417, 08mbj5d) <- profession(?x3417, ?x1032), ?x1032 = 02hrh1q, program(?x3417, ?x2583), religion(?x3417, ?x1985) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p8r1 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.600 http://example.org/common/topic/webpage./common/webpage/category #21673-09yhzs PRED entity: 09yhzs PRED relation: film PRED expected values: 07_fj54 => 126 concepts (84 used for prediction) PRED predicted values (max 10 best out of 295): 09sr0 (0.12 #1517, 0.03 #28562, 0.01 #6872), 0340hj (0.12 #236, 0.03 #28562, 0.01 #34154), 06gb1w (0.12 #731, 0.02 #2516, 0.01 #15011), 08r4x3 (0.12 #153, 0.02 #34071, 0.02 #18003), 01738w (0.12 #1126, 0.02 #35044), 02wgk1 (0.12 #755, 0.02 #34673), 0d90m (0.12 #8, 0.01 #1793, 0.01 #33926), 027m5wv (0.12 #1053, 0.01 #2838), 0n1s0 (0.12 #1030, 0.01 #2815), 062zm5h (0.12 #854, 0.01 #6209, 0.01 #34772) >> Best rule #1517 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 03m6pk; >> query: (?x3027, 09sr0) <- nationality(?x3027, ?x94), film(?x3027, ?x4664), ?x4664 = 0fqt1ns >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #7981 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 547 *> proper extension: 06jvj7; 013ybx; *> query: (?x3027, 07_fj54) <- award_winner(?x624, ?x3027), award_nominee(?x3027, ?x7156), people(?x913, ?x3027) *> conf = 0.01 ranks of expected_values: 268 EVAL 09yhzs film 07_fj54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 126.000 84.000 0.125 http://example.org/film/actor/film./film/performance/film #21672-01hp5 PRED entity: 01hp5 PRED relation: prequel! PRED expected values: 01hq1 => 100 concepts (83 used for prediction) PRED predicted values (max 10 best out of 17): 04gcyg (0.08 #135, 0.01 #495, 0.01 #675), 01hqk (0.08 #73, 0.01 #433, 0.01 #613), 01hr1 (0.08 #8, 0.01 #368, 0.01 #548), 056xkh (0.01 #519, 0.01 #699), 0315rp (0.01 #503, 0.01 #683), 031786 (0.01 #485, 0.01 #665), 043tvp3 (0.01 #478, 0.01 #658), 0jsf6 (0.01 #467, 0.01 #647), 02wgk1 (0.01 #439, 0.01 #619), 0bpm4yw (0.01 #434, 0.01 #614) >> Best rule #135 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01f39b; >> query: (?x751, 04gcyg) <- film(?x7718, ?x751), story_by(?x751, ?x13339), produced_by(?x751, ?x10522), place_of_burial(?x7718, ?x1144) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01hp5 prequel! 01hq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 83.000 0.077 http://example.org/film/film/prequel #21671-0837ql PRED entity: 0837ql PRED relation: profession PRED expected values: 0dz3r => 120 concepts (118 used for prediction) PRED predicted values (max 10 best out of 124): 09jwl (0.71 #906, 0.71 #4615, 0.69 #6397), 0dz3r (0.62 #298, 0.54 #1334, 0.42 #2521), 0nbcg (0.54 #1067, 0.49 #4628, 0.49 #1363), 01d_h8 (0.42 #3415, 0.41 #1931, 0.40 #3118), 09lbv (0.36 #1055, 0.06 #3577, 0.05 #6398), 0n1h (0.31 #307, 0.26 #10685, 0.25 #14090), 03gjzk (0.31 #1940, 0.30 #3424, 0.29 #3127), 01c72t (0.30 #1059, 0.29 #6849, 0.29 #6402), 039v1 (0.29 #2258, 0.29 #4633, 0.29 #924), 0dxtg (0.28 #3126, 0.28 #1939, 0.26 #3423) >> Best rule #906 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0157m; 021bk; >> query: (?x4836, 09jwl) <- award_nominee(?x827, ?x4836), instrumentalists(?x212, ?x4836), religion(?x4836, ?x492) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #298 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 01wn718; 01vvyd8; *> query: (?x4836, 0dz3r) <- award(?x4836, ?x4837), ?x4837 = 03t5kl, location(?x4836, ?x2624) *> conf = 0.62 ranks of expected_values: 2 EVAL 0837ql profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 118.000 0.712 http://example.org/people/person/profession #21670-06s26c PRED entity: 06s26c PRED relation: executive_produced_by! PRED expected values: 01hq1 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 320): 0bt4g (0.12 #422, 0.06 #3070, 0.04 #951), 0mbql (0.12 #378, 0.06 #3026, 0.04 #907), 01f7kl (0.12 #135, 0.06 #2783, 0.04 #664), 09d3b7 (0.11 #13239, 0.10 #8475, 0.10 #11121), 01kff7 (0.11 #13239, 0.10 #8475, 0.10 #11121), 01bn3l (0.08 #429, 0.04 #3077, 0.04 #958), 09gdh6k (0.08 #410, 0.04 #3058, 0.04 #939), 01f7jt (0.08 #512, 0.04 #3160, 0.04 #1041), 016y_f (0.08 #249, 0.04 #2897, 0.04 #778), 0k2sk (0.08 #49, 0.04 #2697, 0.04 #578) >> Best rule #422 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 03ft8; >> query: (?x10522, 0bt4g) <- spouse(?x10521, ?x10522), nationality(?x10522, ?x94), executive_produced_by(?x3385, ?x10522) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06s26c executive_produced_by! 01hq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 150.000 0.125 http://example.org/film/film/executive_produced_by #21669-0k7pf PRED entity: 0k7pf PRED relation: award PRED expected values: 01by1l => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 291): 054krc (0.44 #892, 0.42 #2098, 0.41 #3706), 0gqz2 (0.38 #1689, 0.36 #885, 0.31 #3699), 02qvyrt (0.35 #1736, 0.33 #932, 0.33 #3746), 09sb52 (0.34 #20543, 0.23 #17327, 0.23 #16121), 01by1l (0.33 #6545, 0.32 #6947, 0.30 #10967), 0l8z1 (0.33 #868, 0.32 #3682, 0.32 #1672), 054ks3 (0.31 #1751, 0.27 #947, 0.23 #3761), 01bgqh (0.30 #445, 0.27 #6475, 0.25 #10897), 03qbh5 (0.24 #6637, 0.22 #607, 0.21 #11059), 025m8y (0.24 #1708, 0.21 #2110, 0.20 #3718) >> Best rule #892 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 02mslq; 0b6yp2; 03h610; 01l79yc; 0fpjyd; 01m5m5b; 03c_8t; >> query: (?x3030, 054krc) <- nationality(?x3030, ?x512), student(?x9844, ?x3030), music(?x5139, ?x3030) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #6545 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 247 *> proper extension: 03f2_rc; 01pgzn_; 01dw9z; 01vx5w7; 01svw8n; 043zg; 01vsgrn; 03h_0_z; 023p29; *> query: (?x3030, 01by1l) <- profession(?x3030, ?x131), award_winner(?x3030, ?x1136), artist(?x2149, ?x3030) *> conf = 0.33 ranks of expected_values: 5 EVAL 0k7pf award 01by1l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 110.000 110.000 0.440 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21668-0244r8 PRED entity: 0244r8 PRED relation: nationality PRED expected values: 0chghy => 109 concepts (106 used for prediction) PRED predicted values (max 10 best out of 32): 09c7w0 (0.73 #3401, 0.71 #2401, 0.71 #3201), 02jx1 (0.25 #333, 0.24 #433, 0.22 #133), 0d060g (0.12 #7, 0.12 #407, 0.11 #9607), 03rjj (0.12 #5, 0.11 #9607, 0.08 #205), 0chghy (0.12 #10, 0.11 #110, 0.08 #210), 0ctw_b (0.12 #27, 0.11 #127, 0.08 #227), 07ssc (0.11 #9607, 0.11 #115, 0.11 #3015), 0345h (0.11 #9607, 0.11 #131, 0.08 #231), 0f8l9c (0.11 #9607, 0.06 #422, 0.03 #1722), 03rk0 (0.11 #9607, 0.05 #10453, 0.05 #9653) >> Best rule #3401 for best value: >> intensional similarity = 3 >> extensional distance = 504 >> proper extension: 0564mx; >> query: (?x1489, 09c7w0) <- gender(?x1489, ?x514), ?x514 = 02zsn, award_nominee(?x3069, ?x1489) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01ycbq; 04y8r; 09v6tz; *> query: (?x1489, 0chghy) <- nominated_for(?x1489, ?x1910), award(?x1489, ?x1443), gender(?x1489, ?x514), ?x1910 = 011yth *> conf = 0.12 ranks of expected_values: 5 EVAL 0244r8 nationality 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 109.000 106.000 0.727 http://example.org/people/person/nationality #21667-0ch6mp2 PRED entity: 0ch6mp2 PRED relation: profession! PRED expected values: 092ys_y => 22 concepts (14 used for prediction) PRED predicted values (max 10 best out of 48): 03r1pr (0.33 #17845, 0.25 #26338, 0.17 #39082), 027rwmr (0.33 #8732, 0.20 #29967, 0.17 #38465), 071dcs (0.33 #13424, 0.14 #43160), 02lp3c (0.33 #2034, 0.12 #48759, 0.06 #57261), 03wdsbz (0.33 #16812), 013km (0.33 #16753), 07s9tsr (0.33 #16709), 026xt5c (0.33 #16442), 04_1nk (0.33 #14549), 07hhnl (0.33 #14318) >> Best rule #17845 for best value: >> intensional similarity = 21 >> extensional distance = 1 >> proper extension: 09zzb8; >> query: (?x1284, 03r1pr) <- film_crew_role(?x7199, ?x1284), film_crew_role(?x6018, ?x1284), film_crew_role(?x5721, ?x1284), film_crew_role(?x5070, ?x1284), film_crew_role(?x4690, ?x1284), film_crew_role(?x3201, ?x1284), film_crew_role(?x1797, ?x1284), film_crew_role(?x835, ?x1284), film_crew_role(?x787, ?x1284), film_crew_role(?x770, ?x1284), ?x770 = 01r97z, ?x6018 = 04k9y6, film(?x926, ?x1797), ?x787 = 08gsvw, ?x5721 = 01d259, ?x835 = 0164qt, ?x5070 = 0dt8xq, ?x4690 = 0gkz3nz, ?x3201 = 01ffx4, ?x7199 = 05nlx4, genre(?x1797, ?x239) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ch6mp2 profession! 092ys_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 22.000 14.000 0.333 http://example.org/people/person/profession #21666-03t9sp PRED entity: 03t9sp PRED relation: group! PRED expected values: 0342h => 105 concepts (77 used for prediction) PRED predicted values (max 10 best out of 113): 0342h (0.90 #2259, 0.88 #1445, 0.87 #2619), 02hnl (0.76 #2645, 0.74 #1471, 0.74 #2285), 018vs (0.67 #1455, 0.67 #2269, 0.65 #2629), 0l14md (0.60 #1448, 0.60 #2262, 0.60 #2622), 01vj9c (0.50 #106, 0.33 #196, 0.33 #16), 0l14qv (0.50 #366, 0.24 #2620, 0.23 #2260), 028tv0 (0.37 #1454, 0.37 #2628, 0.36 #2448), 02snj9 (0.33 #59, 0.25 #149, 0.17 #419), 03qjg (0.26 #2484, 0.22 #2664, 0.21 #1490), 06ncr (0.25 #131, 0.17 #221, 0.16 #1121) >> Best rule #2259 for best value: >> intensional similarity = 5 >> extensional distance = 103 >> proper extension: 0mgcr; 03j_hq; >> query: (?x1732, 0342h) <- artists(?x3916, ?x1732), category(?x1732, ?x134), group(?x1166, ?x1732), artists(?x3916, ?x1004), ?x1004 = 01vv7sc >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t9sp group! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 77.000 0.895 http://example.org/music/performance_role/regular_performances./music/group_membership/group #21665-0nm9h PRED entity: 0nm9h PRED relation: adjoins! PRED expected values: 0nm3n => 144 concepts (66 used for prediction) PRED predicted values (max 10 best out of 470): 0n5xb (0.33 #709, 0.26 #45552, 0.25 #1492), 0n5yv (0.26 #45552, 0.25 #1132, 0.18 #50269), 0nm3n (0.26 #8633, 0.25 #28278, 0.25 #28277), 0n5_t (0.26 #8633, 0.25 #28278, 0.25 #28277), 0nm9h (0.26 #8633, 0.25 #28278, 0.24 #33771), 0f8l9c (0.15 #3177, 0.05 #11816, 0.04 #5533), 0nm42 (0.14 #1904, 0.12 #2689, 0.05 #13348), 0nm8n (0.14 #2130, 0.12 #2915, 0.05 #13348), 0nm9y (0.14 #2326, 0.12 #3111, 0.03 #32203), 0d060g (0.14 #1579, 0.06 #3148, 0.05 #5504) >> Best rule #709 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0nm3n; >> query: (?x12290, 0n5xb) <- adjoins(?x7954, ?x12290), ?x7954 = 0nm6z, time_zones(?x12290, ?x2674), ?x2674 = 02hcv8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8633 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 144 *> proper extension: 0235l; *> query: (?x12290, ?x7330) <- adjoins(?x12290, ?x7954), contains(?x7058, ?x12290), adjoins(?x7954, ?x7330), county(?x12289, ?x12290) *> conf = 0.26 ranks of expected_values: 3 EVAL 0nm9h adjoins! 0nm3n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 144.000 66.000 0.333 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #21664-0bqxw PRED entity: 0bqxw PRED relation: colors PRED expected values: 01l849 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.36 #1142, 0.35 #1062, 0.32 #202), 01l849 (0.25 #1061, 0.24 #1141, 0.21 #941), 019sc (0.18 #1067, 0.18 #167, 0.18 #1147), 03wkwg (0.18 #75, 0.15 #115, 0.14 #55), 036k5h (0.15 #105, 0.15 #125, 0.12 #245), 06fvc (0.15 #1143, 0.15 #1063, 0.13 #743), 0jc_p (0.14 #24, 0.09 #124, 0.08 #164), 067z2v (0.10 #49, 0.10 #29, 0.08 #169), 04mkbj (0.10 #50, 0.09 #130, 0.08 #1150), 038hg (0.09 #1072, 0.09 #1152, 0.08 #1012) >> Best rule #1142 for best value: >> intensional similarity = 2 >> extensional distance = 441 >> proper extension: 01w_sh; 01xk7r; 0ylzs; >> query: (?x4338, 083jv) <- institution(?x620, ?x4338), colors(?x4338, ?x3189) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1061 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 404 *> proper extension: 0ym8f; 024y8p; 01b1pf; 02gr81; 017j69; 071_8; 0b1xl; 027mdh; 01nnsv; 0gl5_; ... *> query: (?x4338, 01l849) <- institution(?x620, ?x4338), colors(?x4338, ?x3189), major_field_of_study(?x4338, ?x732) *> conf = 0.25 ranks of expected_values: 2 EVAL 0bqxw colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.359 http://example.org/education/educational_institution/colors #21663-01smm PRED entity: 01smm PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 20): 0pqc5 (0.80 #443, 0.76 #374, 0.68 #190), 060c4 (0.67 #26, 0.50 #3, 0.43 #95), 060bp (0.56 #24, 0.36 #93, 0.33 #1), 0f6c3 (0.33 #8, 0.19 #1136, 0.17 #1688), 0dq3c (0.33 #2, 0.14 #94, 0.11 #25), 09n5b9 (0.17 #1140, 0.15 #1692, 0.14 #1669), 0789n (0.17 #10, 0.14 #102, 0.07 #218), 01gkgk (0.17 #6, 0.14 #98, 0.07 #214), 01t7n9 (0.17 #19, 0.07 #111, 0.05 #3294), 02079p (0.17 #11, 0.07 #103, 0.05 #3294) >> Best rule #443 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 0f2s6; >> query: (?x6453, 0pqc5) <- location(?x2518, ?x6453), dog_breed(?x6453, ?x11363), ?x11363 = 01k3tq >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01smm jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.795 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #21662-04znsy PRED entity: 04znsy PRED relation: award_nominee! PRED expected values: 0161sp 0c33pl => 119 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1005): 02qgqt (0.14 #2353, 0.06 #19, 0.03 #93382), 02bkdn (0.10 #2728, 0.04 #82086, 0.04 #56412), 0dvmd (0.10 #3031, 0.03 #56715, 0.03 #82389), 03hzl42 (0.10 #3388, 0.02 #57072, 0.02 #31397), 02qgyv (0.08 #2831, 0.06 #497, 0.05 #5165), 0h0wc (0.08 #2887, 0.04 #100366, 0.03 #82245), 0lpjn (0.08 #2959, 0.04 #100366, 0.02 #82317), 0c6qh (0.08 #2874, 0.03 #9876, 0.02 #49555), 015rkw (0.08 #2700, 0.03 #82058, 0.03 #49381), 051wwp (0.08 #3501, 0.03 #82859, 0.03 #57185) >> Best rule #2353 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 06jzh; >> query: (?x9211, 02qgqt) <- award_nominee(?x2614, ?x9211), award(?x9211, ?x2257), ?x2257 = 09td7p >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #5321 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 188 *> proper extension: 024rbz; 0hm0k; *> query: (?x9211, 0161sp) <- award_winner(?x2742, ?x9211), category(?x9211, ?x134), film_crew_role(?x2742, ?x468) *> conf = 0.02 ranks of expected_values: 671 EVAL 04znsy award_nominee! 0c33pl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 51.000 0.140 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 04znsy award_nominee! 0161sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 119.000 51.000 0.140 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21661-03f0fnk PRED entity: 03f0fnk PRED relation: artist! PRED expected values: 06gst 01wdtv => 149 concepts (116 used for prediction) PRED predicted values (max 10 best out of 117): 015_1q (0.43 #156, 0.33 #19, 0.30 #430), 033hn8 (0.24 #1247, 0.17 #3165, 0.11 #11935), 011k1h (0.22 #1654, 0.17 #2065, 0.12 #2476), 01cf93 (0.20 #1289, 0.09 #3207, 0.09 #2111), 08pn_9 (0.18 #813, 0.12 #1224, 0.08 #1361), 017l96 (0.17 #2073, 0.17 #18, 0.16 #1662), 01sqd7 (0.17 #58, 0.04 #1839, 0.01 #2250), 02p11jq (0.16 #1794, 0.08 #8370, 0.08 #2479), 01clyr (0.16 #1676, 0.14 #2087, 0.11 #2772), 01t04r (0.16 #1296, 0.12 #3214, 0.07 #1844) >> Best rule #156 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 05qw5; >> query: (?x4712, 015_1q) <- nationality(?x4712, ?x94), artists(?x9853, ?x4712), ?x9853 = 02qm5j, artist(?x2931, ?x4712) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1879 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 53 *> proper extension: 05k79; 01k98nm; 05563d; 0khth; 02cpp; 07r1_; 06gcn; 08w4pm; 011_vz; 0p76z; ... *> query: (?x4712, 06gst) <- artist(?x5634, ?x4712), artists(?x1000, ?x4712), ?x5634 = 01cl2y *> conf = 0.04 ranks of expected_values: 60, 67 EVAL 03f0fnk artist! 01wdtv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 149.000 116.000 0.429 http://example.org/music/record_label/artist EVAL 03f0fnk artist! 06gst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 149.000 116.000 0.429 http://example.org/music/record_label/artist #21660-0hgnl3t PRED entity: 0hgnl3t PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.83 #46, 0.82 #86, 0.81 #66), 02nxhr (0.09 #17, 0.09 #7, 0.08 #12), 07c52 (0.06 #43, 0.04 #18, 0.04 #13), 07z4p (0.06 #45, 0.03 #155, 0.03 #160), 0735l (0.01 #9) >> Best rule #46 for best value: >> intensional similarity = 4 >> extensional distance = 405 >> proper extension: 02v63m; 01q2nx; >> query: (?x4518, 029j_) <- film_crew_role(?x4518, ?x1171), language(?x4518, ?x254), ?x1171 = 09vw2b7, produced_by(?x4518, ?x1533) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hgnl3t film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.826 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #21659-0234_c PRED entity: 0234_c PRED relation: student PRED expected values: 01dy7j 03yj_0n 02lymt => 98 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1664): 022411 (0.25 #1682, 0.06 #7943, 0.05 #3769), 024y6w (0.25 #1448, 0.06 #7709, 0.05 #3535), 01x53m (0.25 #1578, 0.03 #5752, 0.03 #7839), 059fjj (0.25 #1403, 0.03 #5577, 0.02 #11838), 01c6l (0.25 #946, 0.03 #7207, 0.03 #19730), 01vhrz (0.25 #1610, 0.03 #9958, 0.02 #12045), 09r9dp (0.25 #612, 0.02 #11047, 0.02 #13135), 02vg0 (0.25 #1286, 0.02 #11721, 0.02 #13809), 02sb1w (0.25 #1105, 0.02 #11540, 0.02 #13628), 0pksh (0.25 #2013, 0.02 #12448, 0.02 #14536) >> Best rule #1682 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01rtm4; 07w0v; >> query: (?x11036, 022411) <- institution(?x8398, ?x11036), student(?x11036, ?x3258), film(?x3258, ?x4694), ?x4694 = 02j69w >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #127330 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 384 *> proper extension: 01dnnt; *> query: (?x11036, ?x91) <- student(?x11036, ?x9437), award_winner(?x2478, ?x9437), award(?x91, ?x2478) *> conf = 0.01 ranks of expected_values: 1506, 1528 EVAL 0234_c student 02lymt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 61.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0234_c student 03yj_0n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 98.000 61.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0234_c student 01dy7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 98.000 61.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #21658-01nrq5 PRED entity: 01nrq5 PRED relation: nationality PRED expected values: 09c7w0 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 32): 09c7w0 (0.90 #1609, 0.89 #704, 0.88 #402), 081mh (0.33 #12658, 0.33 #4419, 0.33 #12860), 0gx1l (0.32 #5427), 0kpys (0.32 #5427), 02jx1 (0.24 #333, 0.13 #1841, 0.13 #2645), 07ssc (0.16 #315, 0.15 #115, 0.15 #15), 0345h (0.10 #2040, 0.10 #1839, 0.06 #4450), 0f8l9c (0.08 #1830, 0.07 #2031, 0.05 #3134), 03rk0 (0.06 #13207, 0.05 #13407, 0.05 #4060), 0d060g (0.06 #1112, 0.05 #6842, 0.05 #7945) >> Best rule #1609 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 0584j4n; 02s6sh; 011k4g; >> query: (?x3261, 09c7w0) <- place_of_death(?x3261, ?x1523), ?x1523 = 030qb3t, gender(?x3261, ?x231) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nrq5 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.897 http://example.org/people/person/nationality #21657-03mdt PRED entity: 03mdt PRED relation: award_winner PRED expected values: 0g5lhl7 => 147 concepts (114 used for prediction) PRED predicted values (max 10 best out of 963): 0hm0k (0.88 #14547, 0.87 #27479, 0.86 #11311), 0g5lhl7 (0.88 #14547, 0.87 #27479, 0.86 #11311), 01jq34 (0.88 #14547, 0.87 #27479, 0.86 #11311), 03jvmp (0.88 #14547, 0.87 #27479, 0.86 #11311), 026g4l_ (0.73 #11312, 0.52 #174575, 0.52 #176194), 0fvf9q (0.73 #11312, 0.52 #174575, 0.52 #176194), 0m66w (0.73 #11312, 0.52 #174575, 0.52 #176194), 024rgt (0.73 #11312, 0.52 #174575, 0.52 #176194), 07ym6ss (0.73 #11312, 0.52 #174575, 0.52 #176194), 070j61 (0.73 #11312, 0.52 #174575, 0.52 #176194) >> Best rule #14547 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 04cw0j; 03m9c8; 0dbpwb; >> query: (?x3381, ?x2171) <- award_winner(?x2171, ?x3381), award_nominee(?x3381, ?x5714), ?x5714 = 026g4l_ >> conf = 0.88 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 03mdt award_winner 0g5lhl7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 114.000 0.878 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #21656-04psf PRED entity: 04psf PRED relation: people PRED expected values: 0f14q 01gw8b => 61 concepts (28 used for prediction) PRED predicted values (max 10 best out of 3968): 014z8v (0.50 #1496, 0.22 #10323, 0.22 #9645), 07pzc (0.43 #7204, 0.22 #11278, 0.14 #7884), 01vz0g4 (0.43 #7137, 0.22 #11211), 0chsq (0.40 #3402, 0.33 #4083, 0.29 #7479), 0b22w (0.40 #3878, 0.33 #4559, 0.25 #16097), 06c0j (0.40 #4003, 0.18 #14189, 0.17 #4749), 018ty9 (0.40 #3680, 0.18 #13866, 0.17 #4361), 03fvqg (0.40 #3451, 0.18 #13637, 0.17 #4132), 05xpv (0.40 #3780, 0.18 #13966, 0.17 #4461), 0436zq (0.33 #4649, 0.29 #8045, 0.29 #6686) >> Best rule #1496 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 02k6hp; >> query: (?x3799, 014z8v) <- people(?x3799, ?x3800), people(?x3799, ?x487), symptom_of(?x4905, ?x3799), award_winner(?x5766, ?x487), ?x5766 = 013b2h, category(?x487, ?x134), place_of_death(?x3800, ?x242), award_winner(?x2212, ?x487), nationality(?x487, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5429 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 5 *> proper extension: 02vrr; *> query: (?x3799, ?x65) <- people(?x3799, ?x11271), people(?x3799, ?x3960), type_of_union(?x3960, ?x566), influenced_by(?x11271, ?x2162), ?x2162 = 04xjp, profession(?x11271, ?x319), profession(?x1678, ?x319), profession(?x65, ?x319), ?x1678 = 02zyy4 *> conf = 0.01 ranks of expected_values: 1422, 2393 EVAL 04psf people 01gw8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 28.000 0.500 http://example.org/people/cause_of_death/people EVAL 04psf people 0f14q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 28.000 0.500 http://example.org/people/cause_of_death/people #21655-0m1xv PRED entity: 0m1xv PRED relation: genre! PRED expected values: 02zv4b 05_z42 => 38 concepts (37 used for prediction) PRED predicted values (max 10 best out of 307): 0584r4 (0.50 #1800, 0.44 #2391, 0.44 #2097), 025ljp (0.46 #1388, 0.44 #1978, 0.42 #1181), 039cq4 (0.46 #1311, 0.42 #1181, 0.40 #1606), 05p9_ql (0.43 #728, 0.42 #1181, 0.37 #3088), 01fs__ (0.43 #730, 0.42 #1181, 0.32 #3090), 01j67j (0.43 #634, 0.42 #1181, 0.26 #2994), 02md2d (0.43 #665, 0.26 #3025, 0.21 #3320), 01fx1l (0.43 #693, 0.25 #2067, 0.17 #2758), 05lfwd (0.43 #697, 0.21 #3057, 0.17 #3352), 05397h (0.43 #873, 0.17 #2938, 0.16 #3233) >> Best rule #1800 for best value: >> intensional similarity = 11 >> extensional distance = 14 >> proper extension: 06qln; >> query: (?x14160, 0584r4) <- genre(?x11726, ?x14160), genre(?x3075, ?x14160), nominated_for(?x6171, ?x3075), nominated_for(?x691, ?x3075), program(?x4259, ?x3075), producer_type(?x3075, ?x632), nationality(?x691, ?x94), religion(?x6171, ?x1985), award_winner(?x11726, ?x4065), film(?x6171, ?x6588), place_of_birth(?x691, ?x12929) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1181 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 10 *> proper extension: 066wd; *> query: (?x14160, ?x802) <- genre(?x11726, ?x14160), genre(?x3075, ?x14160), genre(?x3075, ?x10647), program(?x4259, ?x3075), program(?x3150, ?x3075), tv_program(?x4065, ?x11726), company(?x4259, ?x3776), genre(?x802, ?x10647), program(?x4566, ?x11726), currency(?x3150, ?x170), location(?x3150, ?x739), profession(?x4065, ?x1032), location(?x4065, ?x1523) *> conf = 0.42 ranks of expected_values: 18 EVAL 0m1xv genre! 05_z42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 38.000 37.000 0.500 http://example.org/tv/tv_program/genre EVAL 0m1xv genre! 02zv4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 38.000 37.000 0.500 http://example.org/tv/tv_program/genre #21654-037s5h PRED entity: 037s5h PRED relation: student! PRED expected values: 027ydt => 178 concepts (104 used for prediction) PRED predicted values (max 10 best out of 292): 03ksy (0.89 #28514, 0.25 #1158, 0.17 #2736), 01mpwj (0.25 #1159, 0.17 #2737, 0.13 #28515), 011xy1 (0.25 #1369, 0.17 #2947, 0.12 #3473), 020yvh (0.25 #1471, 0.17 #3049, 0.12 #3575), 09r4xx (0.25 #649, 0.07 #5909, 0.06 #7487), 023znp (0.25 #645, 0.05 #10639, 0.03 #15374), 01qrb2 (0.22 #4557, 0.02 #28757, 0.01 #23495), 0bwfn (0.19 #25526, 0.17 #12899, 0.13 #11847), 08815 (0.17 #2632, 0.14 #24728, 0.13 #11575), 0fr9jp (0.17 #2974, 0.12 #3500, 0.08 #12443) >> Best rule #28514 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 01nqfh_; 01cv3n; 03qd_; 02773nt; 045bs6; 06x4l_; 03xpf_7; 02xs0q; 0fpj4lx; 06jnvs; ... >> query: (?x9574, 03ksy) <- student(?x6315, ?x9574), service_location(?x6315, ?x551), major_field_of_study(?x6315, ?x5671), language(?x508, ?x5671) >> conf = 0.89 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 037s5h student! 027ydt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 178.000 104.000 0.892 http://example.org/education/educational_institution/students_graduates./education/education/student #21653-0bsnm PRED entity: 0bsnm PRED relation: category PRED expected values: 08mbj5d => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.94 #4, 0.90 #39, 0.90 #35) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 017j69; >> query: (?x8191, 08mbj5d) <- currency(?x8191, ?x2244), institution(?x8398, ?x8191), colors(?x8191, ?x332), ?x8398 = 028dcg >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bsnm category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 173.000 0.935 http://example.org/common/topic/webpage./common/webpage/category #21652-01c333 PRED entity: 01c333 PRED relation: student PRED expected values: 05nn4k => 140 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1502): 03ktjq (0.15 #5192, 0.03 #15658, 0.02 #21937), 06hx2 (0.12 #1071, 0.11 #3164, 0.03 #9443), 0jt90f5 (0.12 #355, 0.11 #2448, 0.02 #8727), 054_mz (0.12 #51, 0.11 #2144, 0.02 #8423), 0164nb (0.12 #621, 0.11 #2714, 0.01 #15273), 0ff3y (0.08 #10442, 0.08 #6256, 0.03 #29280), 021bk (0.08 #4539, 0.06 #6632, 0.03 #15005), 049gc (0.08 #5114, 0.05 #9300, 0.02 #21859), 013pp3 (0.08 #5111, 0.05 #9297, 0.02 #21856), 01_xtx (0.08 #4816, 0.04 #15282, 0.03 #42491) >> Best rule #5192 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0pspl; 0kw4j; 02rg_4; 0bwfn; 016w7b; >> query: (?x3044, 03ktjq) <- currency(?x3044, ?x170), major_field_of_study(?x3044, ?x1668), ?x1668 = 01mkq, organization(?x346, ?x3044) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01c333 student 05nn4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 76.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #21651-05ty4m PRED entity: 05ty4m PRED relation: languages PRED expected values: 02h40lc => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.31 #860, 0.29 #470, 0.29 #1797), 064_8sq (0.03 #2473, 0.02 #2590, 0.02 #1459), 0t_2 (0.02 #87, 0.02 #867, 0.01 #204), 03_9r (0.02 #83, 0.02 #161, 0.01 #200), 06nm1 (0.02 #123, 0.01 #943, 0.01 #1372), 07c9s (0.02 #130, 0.01 #2120, 0.01 #2471), 0999q (0.02 #140), 03k50 (0.02 #2462, 0.02 #2813, 0.01 #3710), 02bjrlw (0.01 #2459, 0.01 #1913, 0.01 #2537), 04306rv (0.01 #1603, 0.01 #1759, 0.01 #1447) >> Best rule #860 for best value: >> intensional similarity = 2 >> extensional distance = 295 >> proper extension: 044mfr; >> query: (?x364, 02h40lc) <- participant(?x1736, ?x364), languages(?x1736, ?x254) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05ty4m languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.313 http://example.org/people/person/languages #21650-02kgb7 PRED entity: 02kgb7 PRED relation: award_winner PRED expected values: 04wqr => 23 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1402): 06pj8 (0.20 #436, 0.05 #17757, 0.04 #7859), 0p__8 (0.16 #1333, 0.03 #18654, 0.03 #3808), 0k8y7 (0.16 #24745, 0.13 #12372, 0.11 #2475), 0127m7 (0.15 #512, 0.04 #10409, 0.03 #15359), 0js9s (0.15 #1456, 0.03 #21251, 0.03 #23726), 01vs_v8 (0.13 #2937, 0.12 #5411, 0.11 #7885), 0pz91 (0.13 #262, 0.06 #2737, 0.05 #5211), 0bwh6 (0.13 #267, 0.04 #7690, 0.04 #10164), 0h1p (0.13 #429, 0.04 #17750, 0.04 #22699), 0151w_ (0.13 #191, 0.04 #10088, 0.04 #7614) >> Best rule #436 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 02x1z2s; >> query: (?x9628, 06pj8) <- award_winner(?x9628, ?x8371), award_winner(?x9628, ?x4638), award(?x4638, ?x1105), ?x1105 = 07bdd_, award_nominee(?x8371, ?x4285) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02kgb7 award_winner 04wqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 23.000 10.000 0.200 http://example.org/award/award_category/winners./award/award_honor/award_winner #21649-011yr9 PRED entity: 011yr9 PRED relation: nominated_for! PRED expected values: 02pqp12 => 100 concepts (83 used for prediction) PRED predicted values (max 10 best out of 220): 02r0csl (0.77 #8264, 0.68 #12731, 0.67 #3126), 099cng (0.77 #8264, 0.68 #12731, 0.67 #3126), 0gs9p (0.69 #2062, 0.67 #1616, 0.64 #2733), 027b9k6 (0.68 #12731, 0.67 #3126, 0.67 #8263), 027c924 (0.68 #12731, 0.67 #3126, 0.67 #8263), 0k611 (0.65 #2071, 0.60 #509, 0.58 #1625), 02pqp12 (0.58 #1613, 0.51 #497, 0.50 #720), 04dn09n (0.54 #1593, 0.50 #477, 0.50 #254), 0gqy2 (0.49 #2117, 0.39 #2788, 0.37 #332), 09sb52 (0.49 #476, 0.47 #699, 0.44 #253) >> Best rule #8264 for best value: >> intensional similarity = 3 >> extensional distance = 662 >> proper extension: 07bz5; >> query: (?x4159, ?x3458) <- award(?x4159, ?x3458), nominated_for(?x1738, ?x4159), ceremony(?x3458, ?x78) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #1613 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 157 *> proper extension: 0sxg4; 01jc6q; 0yyg4; 01gc7; 0209hj; 0p_sc; 0jzw; 0b73_1d; 09q5w2; 0_92w; ... *> query: (?x4159, 02pqp12) <- nominated_for(?x1307, ?x4159), nominated_for(?x1162, ?x4159), ?x1307 = 0gq9h, nominated_for(?x1162, ?x7275), ?x7275 = 0g4vmj8 *> conf = 0.58 ranks of expected_values: 7 EVAL 011yr9 nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 100.000 83.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21648-049fgvm PRED entity: 049fgvm PRED relation: gender PRED expected values: 05zppz => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #37, 0.89 #43, 0.87 #67), 02zsn (0.32 #40, 0.32 #50, 0.31 #88) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 0399p; >> query: (?x6693, 05zppz) <- location(?x6693, ?x2020), religion(?x6693, ?x1985), influenced_by(?x6693, ?x986) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049fgvm gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.889 http://example.org/people/person/gender #21647-05b4l5x PRED entity: 05b4l5x PRED relation: award! PRED expected values: 01rr9f 036gdw 01skmp 01vtj38 => 54 concepts (30 used for prediction) PRED predicted values (max 10 best out of 2683): 01xv77 (0.78 #46314, 0.77 #59557, 0.77 #29769), 0bx_q (0.77 #59557, 0.77 #29769, 0.73 #46313), 0227vl (0.77 #29769, 0.74 #52933, 0.73 #43003), 0j5q3 (0.77 #29769, 0.73 #46313, 0.71 #59554), 0gn30 (0.71 #21370, 0.56 #24677, 0.50 #11447), 0jrqq (0.60 #17586, 0.60 #14279, 0.57 #20895), 030g9z (0.60 #15812, 0.50 #12505, 0.44 #25735), 0343h (0.60 #13561, 0.50 #10254, 0.44 #23484), 032v0v (0.60 #13649, 0.50 #10342, 0.44 #23572), 02fcs2 (0.60 #13829, 0.50 #10522, 0.44 #23752) >> Best rule #46314 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 0d085; >> query: (?x154, ?x3034) <- award_winner(?x154, ?x3034), participant(?x3034, ?x1970), award_nominee(?x100, ?x3034) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6720 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 05p09zm; *> query: (?x154, 01rr9f) <- nominated_for(?x154, ?x4331), award(?x8445, ?x154), ?x4331 = 01hqk, ?x8445 = 0btpx *> conf = 0.33 ranks of expected_values: 96, 399, 404, 1062 EVAL 05b4l5x award! 01vtj38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 30.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05b4l5x award! 01skmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 30.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05b4l5x award! 036gdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 30.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05b4l5x award! 01rr9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 54.000 30.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21646-02h40lc PRED entity: 02h40lc PRED relation: language! PRED expected values: 01fwj8 02wrhj 01gq0b 0154qm 017vkx 03d_zl4 0dxmyh 0jbp0 03k545 07gknc 02hblj 0678gl => 72 concepts (55 used for prediction) PRED predicted values (max 10 best out of 4): 03cz9_ (0.33 #4, 0.11 #84, 0.10 #90), 01nsyf (0.33 #3, 0.11 #83, 0.10 #89), 02v92l (0.33 #2, 0.11 #82, 0.10 #88), 01zh29 (0.20 #59, 0.20 #55, 0.14 #77) >> Best rule #4 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 03_9r; >> query: (?x254, 03cz9_) <- language(?x6899, ?x254), language(?x1045, ?x254), nominated_for(?x71, ?x6899), languages(?x50, ?x254), language(?x51, ?x254), official_language(?x183, ?x254), currency(?x1045, ?x170) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02h40lc language! 0678gl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 02hblj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 07gknc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 03k545 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 0jbp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 0dxmyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 03d_zl4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 017vkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 0154qm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 01gq0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 02wrhj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language EVAL 02h40lc language! 01fwj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.333 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #21645-0bhwhj PRED entity: 0bhwhj PRED relation: currency PRED expected values: 09nqf => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.85 #155, 0.85 #113, 0.83 #204), 01nv4h (0.09 #44, 0.03 #254, 0.03 #72), 02l6h (0.07 #88, 0.04 #172, 0.03 #74), 088n7 (0.02 #91), 02gsvk (0.01 #104, 0.01 #419, 0.01 #433) >> Best rule #155 for best value: >> intensional similarity = 4 >> extensional distance = 141 >> proper extension: 03t97y; 047gpsd; >> query: (?x5400, 09nqf) <- genre(?x5400, ?x1014), crewmember(?x5400, ?x7675), award_winner(?x5400, ?x1152), film_release_distribution_medium(?x5400, ?x81) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bhwhj currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.853 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21644-02qzmz6 PRED entity: 02qzmz6 PRED relation: film! PRED expected values: 01mqnr => 98 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1041): 02hy9p (0.75 #70823, 0.58 #93739, 0.48 #70822), 05qd_ (0.48 #70822, 0.44 #93738, 0.43 #89571), 01f7dd (0.33 #1212, 0.03 #11623, 0.03 #5377), 07r1h (0.17 #1092, 0.06 #5257, 0.03 #3174), 0gnbw (0.17 #1273, 0.06 #5438, 0.03 #11684), 025j1t (0.17 #1079, 0.06 #13573, 0.03 #11490), 0dvmd (0.17 #529, 0.03 #2611, 0.02 #21356), 04gc65 (0.17 #1975, 0.03 #24886, 0.02 #20718), 063g7l (0.17 #1897, 0.03 #6062, 0.03 #14391), 0jfx1 (0.17 #407, 0.03 #6654, 0.02 #58731) >> Best rule #70823 for best value: >> intensional similarity = 4 >> extensional distance = 578 >> proper extension: 0dscrwf; 0jyx6; 0dgst_d; 0bq8tmw; 0by1wkq; 03hj3b3; 0gvrws1; 07b1gq; 0gcrg; 0dr_9t7; ... >> query: (?x3820, ?x8159) <- film_release_distribution_medium(?x3820, ?x81), nominated_for(?x8159, ?x3820), nominated_for(?x102, ?x3820), participant(?x3870, ?x8159) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #47265 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 425 *> proper extension: 03s6l2; 02z3r8t; 03ckwzc; 03t97y; 07g_0c; 03twd6; 02847m9; 0c8tkt; 028cg00; 085ccd; ... *> query: (?x3820, 01mqnr) <- titles(?x3613, ?x3820), film(?x8445, ?x3820), featured_film_locations(?x3820, ?x739), genre(?x253, ?x3613) *> conf = 0.01 ranks of expected_values: 1007 EVAL 02qzmz6 film! 01mqnr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 98.000 51.000 0.746 http://example.org/film/actor/film./film/performance/film #21643-04f2zj PRED entity: 04f2zj PRED relation: profession! PRED expected values: 03c7ln 012x4t 04bpm6 04mn81 01vsnff 03bxwtd 01sb5r 015x1f => 42 concepts (24 used for prediction) PRED predicted values (max 10 best out of 4103): 02fybl (0.67 #23401, 0.67 #14969, 0.60 #10753), 01vsy7t (0.67 #22549, 0.67 #18333, 0.60 #9901), 03f1zhf (0.67 #24283, 0.67 #15851, 0.60 #11635), 017g21 (0.67 #23433, 0.67 #15001, 0.60 #10785), 0144l1 (0.67 #21684, 0.60 #9036, 0.57 #25900), 0ddkf (0.67 #23298, 0.60 #10650, 0.57 #27514), 01tp5bj (0.67 #21792, 0.60 #9144, 0.57 #26008), 02cx90 (0.67 #22440, 0.60 #9792, 0.57 #26656), 01vsnff (0.67 #21691, 0.60 #9043, 0.57 #25907), 0161c2 (0.67 #21997, 0.60 #9349, 0.57 #26213) >> Best rule #23401 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 039v1; >> query: (?x11254, 02fybl) <- profession(?x7237, ?x11254), profession(?x2575, ?x11254), profession(?x1955, ?x11254), ?x1955 = 0285c, type_of_union(?x2575, ?x566), ?x7237 = 0473q, instrumentalists(?x2048, ?x2575), ?x2048 = 018j2 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #21691 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 039v1; *> query: (?x11254, 01vsnff) <- profession(?x7237, ?x11254), profession(?x2575, ?x11254), profession(?x1955, ?x11254), ?x1955 = 0285c, type_of_union(?x2575, ?x566), ?x7237 = 0473q, instrumentalists(?x2048, ?x2575), ?x2048 = 018j2 *> conf = 0.67 ranks of expected_values: 9, 174, 196, 207, 258, 262, 625, 775 EVAL 04f2zj profession! 015x1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 24.000 0.667 http://example.org/people/person/profession EVAL 04f2zj profession! 01sb5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 24.000 0.667 http://example.org/people/person/profession EVAL 04f2zj profession! 03bxwtd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 24.000 0.667 http://example.org/people/person/profession EVAL 04f2zj profession! 01vsnff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 42.000 24.000 0.667 http://example.org/people/person/profession EVAL 04f2zj profession! 04mn81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 42.000 24.000 0.667 http://example.org/people/person/profession EVAL 04f2zj profession! 04bpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 24.000 0.667 http://example.org/people/person/profession EVAL 04f2zj profession! 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 24.000 0.667 http://example.org/people/person/profession EVAL 04f2zj profession! 03c7ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 24.000 0.667 http://example.org/people/person/profession #21642-013l6l PRED entity: 013l6l PRED relation: place! PRED expected values: 013l6l => 132 concepts (114 used for prediction) PRED predicted values (max 10 best out of 119): 030qb3t (0.20 #24223, 0.19 #14425, 0.18 #12363), 02_286 (0.20 #24223, 0.19 #14425, 0.18 #12363), 0dyl9 (0.02 #669, 0.02 #1184, 0.01 #1699), 0f2rq (0.02 #654, 0.02 #1169, 0.01 #1684), 0c_m3 (0.02 #647, 0.02 #1162, 0.01 #1677), 03l2n (0.02 #626, 0.02 #1141, 0.01 #1656), 02j3w (0.02 #616, 0.02 #1131, 0.01 #1646), 0vzm (0.02 #586, 0.02 #1101, 0.01 #1616), 0fw2y (0.02 #569, 0.02 #1084, 0.01 #1599), 0f2w0 (0.02 #552, 0.02 #1067, 0.01 #1582) >> Best rule #24223 for best value: >> intensional similarity = 4 >> extensional distance = 240 >> proper extension: 0tz1x; 0zygc; 0t_gg; 07bcn; 0rn0z; 0s987; 0ycht; 0b_cr; >> query: (?x11163, ?x739) <- place_of_birth(?x5216, ?x11163), gender(?x5216, ?x514), location(?x5216, ?x739), source(?x11163, ?x958) >> conf = 0.20 => this is the best rule for 2 predicted values *> Best rule #5666 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 140 *> proper extension: 017cjb; 0978r; *> query: (?x11163, ?x1350) <- time_zones(?x11163, ?x1638), state(?x11163, ?x1351), place_of_birth(?x5216, ?x11163), contains(?x1351, ?x1350) *> conf = 0.01 ranks of expected_values: 65 EVAL 013l6l place! 013l6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 132.000 114.000 0.200 http://example.org/location/hud_county_place/place #21641-0dsx3f PRED entity: 0dsx3f PRED relation: nominated_for! PRED expected values: 0fbtbt => 120 concepts (107 used for prediction) PRED predicted values (max 10 best out of 216): 0fbtbt (0.71 #633, 0.33 #1813, 0.33 #4410), 0bdx29 (0.67 #556, 0.24 #3861, 0.23 #4333), 0fbvqf (0.62 #510, 0.25 #4287, 0.24 #3815), 0gkts9 (0.50 #596, 0.19 #3901, 0.17 #6261), 0bp_b2 (0.42 #489, 0.22 #21246, 0.20 #3794), 0cqh6z (0.42 #527, 0.16 #1707, 0.15 #2887), 02y_rq5 (0.41 #6920, 0.07 #17778, 0.07 #17070), 0gq9h (0.38 #15878, 0.38 #16586, 0.37 #17294), 0ck27z (0.38 #544, 0.25 #1724, 0.24 #22663), 09v7wsg (0.38 #649, 0.24 #1829, 0.24 #413) >> Best rule #633 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 07gbf; >> query: (?x6322, 0fbtbt) <- languages(?x6322, ?x254), nominated_for(?x686, ?x6322), award_winner(?x6322, ?x5030), ?x686 = 0bdw1g >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dsx3f nominated_for! 0fbtbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 107.000 0.708 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21640-0gjc4d3 PRED entity: 0gjc4d3 PRED relation: featured_film_locations PRED expected values: 01_d4 => 73 concepts (49 used for prediction) PRED predicted values (max 10 best out of 86): 02_286 (0.29 #4822, 0.29 #6504, 0.22 #737), 07b_l (0.25 #76, 0.11 #793, 0.03 #1513), 030qb3t (0.17 #277, 0.14 #994, 0.14 #4840), 0f2tj (0.17 #361, 0.12 #600, 0.03 #1800), 0nbwf (0.17 #386, 0.02 #1825, 0.01 #4709), 04lyk (0.17 #420), 0k049 (0.17 #244), 02dtg (0.12 #490, 0.02 #1208, 0.02 #968), 04jpl (0.12 #6493, 0.09 #3850, 0.08 #4811), 0h7h6 (0.11 #759, 0.03 #6526, 0.02 #4604) >> Best rule #4822 for best value: >> intensional similarity = 5 >> extensional distance = 272 >> proper extension: 047msdk; 02pjc1h; 01fmys; 085ccd; 0k4fz; 0jwvf; 0dnw1; 07vfy4; 0gt14; 0ckt6; >> query: (?x3276, 02_286) <- film(?x262, ?x3276), film_release_region(?x3276, ?x87), language(?x3276, ?x254), music(?x3276, ?x3069), featured_film_locations(?x3276, ?x1036) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1002 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 42 *> proper extension: 0bq8tmw; 047svrl; *> query: (?x3276, 01_d4) <- film(?x262, ?x3276), film_release_region(?x3276, ?x3277), film_release_region(?x3276, ?x985), produced_by(?x3276, ?x2533), ?x3277 = 06t8v, ?x985 = 0k6nt *> conf = 0.07 ranks of expected_values: 15 EVAL 0gjc4d3 featured_film_locations 01_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 73.000 49.000 0.292 http://example.org/film/film/featured_film_locations #21639-02y9ln PRED entity: 02y9ln PRED relation: athlete! PRED expected values: 02vx4 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 5): 02vx4 (0.90 #162, 0.90 #153, 0.89 #206), 0jm_ (0.26 #93, 0.25 #113, 0.24 #123), 018w8 (0.21 #96, 0.21 #126, 0.20 #116), 018jz (0.07 #117, 0.07 #127, 0.06 #137), 03tmr (0.04 #81, 0.02 #225, 0.02 #236) >> Best rule #162 for best value: >> intensional similarity = 7 >> extensional distance = 61 >> proper extension: 07nv3_; 0dhrqx; >> query: (?x6152, ?x471) <- team(?x6152, ?x8511), team(?x530, ?x8511), team(?x203, ?x8511), ?x203 = 0dgrmp, ?x530 = 02_j1w, gender(?x6152, ?x231), sport(?x8511, ?x471) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y9ln athlete! 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.905 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #21638-05vz3zq PRED entity: 05vz3zq PRED relation: combatants! PRED expected values: 02kxg_ => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 85): 0c3mz (0.67 #146, 0.33 #89, 0.27 #659), 0gjw_ (0.67 #141, 0.20 #654, 0.20 #27), 0cm2xh (0.50 #123, 0.40 #636, 0.40 #9), 018w0j (0.50 #143, 0.40 #29, 0.33 #86), 06k75 (0.50 #70, 0.33 #127, 0.30 #469), 02h2z_ (0.50 #159, 0.27 #672, 0.20 #45), 01fc7p (0.50 #115, 0.20 #628, 0.17 #970), 08qz1l (0.50 #150, 0.15 #4087, 0.15 #4109), 03gqgt3 (0.40 #505, 0.38 #1247, 0.38 #1018), 01h6pn (0.40 #466, 0.33 #124, 0.33 #67) >> Best rule #146 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0bq0p9; >> query: (?x5114, 0c3mz) <- combatants(?x5114, ?x94), ?x94 = 09c7w0, combatants(?x1140, ?x5114), ?x1140 = 01gjd0 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #313 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 07_m9_; *> query: (?x5114, 02kxg_) <- entity_involved(?x5352, ?x5114), ?x5352 = 05nqz *> conf = 0.29 ranks of expected_values: 21 EVAL 05vz3zq combatants! 02kxg_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 143.000 143.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #21637-0r04p PRED entity: 0r04p PRED relation: category PRED expected values: 08mbj5d => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #28, 0.83 #34, 0.83 #24) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 0r1yc; 0mp3l; 0l0mk; 0dq16; 0pzmf; 0fvwg; 0qpn9; 0b2ds; 0r3tb; 0rqf1; ... >> query: (?x4801, 08mbj5d) <- county(?x4801, ?x2949), time_zones(?x4801, ?x2950), contains(?x94, ?x4801), place_of_death(?x10164, ?x4801) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r04p category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.840 http://example.org/common/topic/webpage./common/webpage/category #21636-016j68 PRED entity: 016j68 PRED relation: nominated_for PRED expected values: 0f4yh => 112 concepts (55 used for prediction) PRED predicted values (max 10 best out of 415): 0f4yh (0.38 #12973, 0.34 #53515, 0.32 #77851), 0dnqr (0.38 #12973, 0.34 #53515, 0.32 #77851), 0m9p3 (0.38 #12973, 0.32 #77851, 0.30 #61628), 0p7pw (0.38 #12973, 0.32 #77851, 0.30 #61628), 017gm7 (0.25 #192, 0.04 #47218, 0.04 #11542), 017jd9 (0.25 #714, 0.04 #7200, 0.03 #47740), 017gl1 (0.25 #133, 0.04 #6619, 0.03 #9861), 0524b41 (0.25 #1110), 0k_9j (0.17 #48650, 0.12 #60005, 0.03 #37300), 030p35 (0.17 #2343, 0.11 #3964, 0.08 #5585) >> Best rule #12973 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 01l2fn; 06x58; 03rl84; 026zvx7; 016dsy; 06tp4h; 02h3tp; 03yrkt; 05vk_d; 013sg6; ... >> query: (?x6585, ?x9383) <- film(?x6585, ?x9383), notable_people_with_this_condition(?x6656, ?x6585), award(?x9383, ?x3209) >> conf = 0.38 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 016j68 nominated_for 0f4yh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 55.000 0.382 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #21635-0bl2g PRED entity: 0bl2g PRED relation: film PRED expected values: 04k9y6 048xyn => 93 concepts (74 used for prediction) PRED predicted values (max 10 best out of 799): 09fb5 (0.67 #19477, 0.66 #23019, 0.65 #56651), 01hv3t (0.20 #1278, 0.03 #4820, 0.01 #6590), 0gj8t_b (0.20 #180, 0.02 #9035, 0.01 #5492), 01hvjx (0.20 #371, 0.01 #12766, 0.01 #5683), 0dln8jk (0.20 #820, 0.01 #4362), 0q9b0 (0.20 #1259, 0.01 #15425, 0.01 #6571), 07024 (0.20 #477, 0.01 #5789, 0.01 #7560), 05sxr_ (0.20 #1653, 0.01 #6965, 0.01 #8736), 05dptj (0.20 #1315, 0.01 #6627, 0.01 #8398), 01npcx (0.20 #954, 0.01 #6266, 0.01 #8037) >> Best rule #19477 for best value: >> intensional similarity = 3 >> extensional distance = 376 >> proper extension: 01sl1q; 07nznf; 0q9kd; 0184jc; 04bdxl; 0grwj; 05bnp0; 0337vz; 01xdf5; 04t2l2; ... >> query: (?x398, ?x406) <- nominated_for(?x398, ?x406), film(?x398, ?x796), participant(?x398, ?x399) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2801 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 01nvmd_; 01vb403; 028d4v; 03q1vd; 02kxwk; 0252fh; 02zfg3; *> query: (?x398, 04k9y6) <- nominated_for(?x398, ?x406), film(?x398, ?x5294), ?x5294 = 035_2h *> conf = 0.09 ranks of expected_values: 43 EVAL 0bl2g film 048xyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 74.000 0.669 http://example.org/film/actor/film./film/performance/film EVAL 0bl2g film 04k9y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 93.000 74.000 0.669 http://example.org/film/actor/film./film/performance/film #21634-0p_sc PRED entity: 0p_sc PRED relation: nominated_for! PRED expected values: 040njc => 103 concepts (94 used for prediction) PRED predicted values (max 10 best out of 198): 040njc (0.57 #234, 0.43 #1146, 0.30 #3426), 0f4x7 (0.54 #253, 0.39 #1165, 0.27 #1849), 0gq_v (0.39 #5493, 0.36 #475, 0.33 #1843), 02qvyrt (0.39 #544, 0.35 #316, 0.24 #1228), 0gr0m (0.35 #1195, 0.35 #511, 0.32 #5529), 099c8n (0.34 #1193, 0.30 #281, 0.26 #509), 0gr51 (0.33 #1210, 0.26 #298, 0.24 #1894), 0gqyl (0.32 #1212, 0.31 #300, 0.25 #1896), 02n9nmz (0.32 #282, 0.24 #510, 0.23 #11861), 02w9sd7 (0.31 #344, 0.19 #1256, 0.14 #4561) >> Best rule #234 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 05y0cr; >> query: (?x776, 040njc) <- nominated_for(?x2375, ?x776), country(?x776, ?x94), genre(?x776, ?x53), ?x2375 = 04kxsb >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p_sc nominated_for! 040njc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 94.000 0.567 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21633-0bz5v2 PRED entity: 0bz5v2 PRED relation: award PRED expected values: 047byns => 108 concepts (97 used for prediction) PRED predicted values (max 10 best out of 264): 09sb52 (0.36 #19938, 0.35 #8975, 0.33 #11411), 0ck27z (0.33 #8621, 0.32 #9839, 0.25 #11869), 01by1l (0.24 #4987, 0.20 #7017, 0.20 #7829), 0cqhk0 (0.24 #2068, 0.18 #8565, 0.18 #9783), 0cjyzs (0.23 #2544, 0.13 #10259, 0.11 #6605), 01bgqh (0.21 #4917, 0.18 #7759, 0.17 #855), 0fc9js (0.19 #4874, 0.18 #32083, 0.15 #28427), 019bnn (0.19 #4874, 0.18 #32083, 0.15 #28427), 02grdc (0.19 #4874, 0.18 #32083, 0.15 #28427), 0gkvb7 (0.19 #4874, 0.15 #28427, 0.14 #24772) >> Best rule #19938 for best value: >> intensional similarity = 3 >> extensional distance = 1045 >> proper extension: 0c12h; 02_0d2; 04gr35; >> query: (?x1040, 09sb52) <- nominated_for(?x1040, ?x3626), award_nominee(?x236, ?x1040), film(?x1040, ?x2512) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #4874 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 332 *> proper extension: 016bx2; 01dq9q; 07d3x; *> query: (?x1040, ?x537) <- award_nominee(?x1040, ?x2127), award(?x2127, ?x537), influenced_by(?x237, ?x2127) *> conf = 0.19 ranks of expected_values: 11 EVAL 0bz5v2 award 047byns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 108.000 97.000 0.361 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21632-02c8d7 PRED entity: 02c8d7 PRED relation: artists PRED expected values: 01vx5w7 09z1lg => 43 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1051): 01dwrc (0.78 #1590, 0.45 #2665, 0.42 #3740), 0bqsy (0.64 #2499, 0.33 #351, 0.25 #3574), 0136p1 (0.58 #3365, 0.33 #142, 0.27 #2290), 03t9sp (0.56 #1196, 0.45 #2271, 0.42 #3346), 01vvycq (0.50 #3269, 0.45 #2194, 0.33 #1119), 024qwq (0.50 #4085, 0.36 #3010, 0.33 #862), 03f5spx (0.45 #2205, 0.44 #1130, 0.33 #3280), 01dq9q (0.45 #2809, 0.42 #3884, 0.33 #661), 01vtj38 (0.45 #2803, 0.33 #655, 0.25 #3878), 02z4b_8 (0.45 #2778, 0.33 #630, 0.25 #3853) >> Best rule #1590 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 07lnk; 0glt670; 0y3_8; 08cyft; 03mb9; 01d_s5; >> query: (?x2030, 01dwrc) <- parent_genre(?x9789, ?x2030), artists(?x2030, ?x3187), artists(?x2030, ?x2731), ?x3187 = 0840vq, award_nominee(?x827, ?x2731), award_winner(?x2431, ?x2731), award_nominee(?x2731, ?x1125) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1307 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 07lnk; 0glt670; 0y3_8; 08cyft; 03mb9; 01d_s5; *> query: (?x2030, 01vx5w7) <- parent_genre(?x9789, ?x2030), artists(?x2030, ?x3187), artists(?x2030, ?x2731), ?x3187 = 0840vq, award_nominee(?x827, ?x2731), award_winner(?x2431, ?x2731), award_nominee(?x2731, ?x1125) *> conf = 0.33 ranks of expected_values: 63, 347 EVAL 02c8d7 artists 09z1lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 14.000 0.778 http://example.org/music/genre/artists EVAL 02c8d7 artists 01vx5w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 43.000 14.000 0.778 http://example.org/music/genre/artists #21631-05m63c PRED entity: 05m63c PRED relation: gender PRED expected values: 02zsn => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #115, 0.72 #200, 0.72 #208), 02zsn (0.65 #34, 0.60 #10, 0.56 #18) >> Best rule #115 for best value: >> intensional similarity = 3 >> extensional distance = 739 >> proper extension: 043q6n_; 09bx1k; >> query: (?x287, 05zppz) <- student(?x3149, ?x287), major_field_of_study(?x3149, ?x3995), ?x3995 = 0fdys >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 09k2t1; *> query: (?x287, 02zsn) <- location(?x287, ?x3269), profession(?x287, ?x4773), ?x4773 = 0d1pc *> conf = 0.65 ranks of expected_values: 2 EVAL 05m63c gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.742 http://example.org/people/person/gender #21630-06s27s PRED entity: 06s27s PRED relation: gender PRED expected values: 05zppz => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.93 #39, 0.92 #37, 0.92 #75), 02zsn (0.46 #189, 0.21 #137, 0.21 #139) >> Best rule #39 for best value: >> intensional similarity = 8 >> extensional distance = 25 >> proper extension: 0d1swh; 0841zn; 08gwzt; 0czmk1; 02bf2s; 014g_s; >> query: (?x12826, 05zppz) <- team(?x12826, ?x10939), team(?x12826, ?x6348), category(?x10939, ?x134), team(?x2010, ?x10939), athlete(?x5063, ?x12826), nationality(?x12826, ?x1229), colors(?x6348, ?x663), ?x134 = 08mbj5d >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06s27s gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.926 http://example.org/people/person/gender #21629-0935jw PRED entity: 0935jw PRED relation: actor! PRED expected values: 0ctzf1 => 68 concepts (54 used for prediction) PRED predicted values (max 10 best out of 81): 07vqnc (0.22 #755), 02qfh (0.14 #168, 0.11 #433), 0fpxp (0.11 #679, 0.06 #944, 0.02 #1740), 08cx5g (0.11 #860, 0.05 #1126, 0.03 #1391), 08y2fn (0.11 #933, 0.05 #1199), 05f7w84 (0.11 #637, 0.04 #2493, 0.03 #3023), 024rwx (0.11 #636, 0.03 #2492, 0.03 #3022), 0124k9 (0.11 #551, 0.02 #1612, 0.02 #1877), 01hvv0 (0.11 #682, 0.02 #3068, 0.02 #2538), 0d68qy (0.11 #567) >> Best rule #755 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02r_d4; 02114t; 01lly5; 0164nb; 067hq2; >> query: (?x12649, 07vqnc) <- nationality(?x12649, ?x94), profession(?x12649, ?x1383), film(?x12649, ?x10327), ?x10327 = 03vfr_ >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #2522 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 260 *> proper extension: 02qjj7; 081nh; 059xvg; 0chrwb; 03v40v; 01kws3; 0bz60q; 03mstc; 01tpl1p; 0c408_; ... *> query: (?x12649, 0ctzf1) <- nationality(?x12649, ?x94), profession(?x12649, ?x1383), ?x94 = 09c7w0, ?x1383 = 0np9r *> conf = 0.03 ranks of expected_values: 32 EVAL 0935jw actor! 0ctzf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 68.000 54.000 0.222 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21628-014hr0 PRED entity: 014hr0 PRED relation: origin PRED expected values: 04jpl => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 51): 02_286 (0.15 #724, 0.09 #252, 0.07 #488), 04jpl (0.09 #242, 0.06 #950, 0.05 #5435), 0n95v (0.09 #427, 0.04 #663, 0.03 #899), 0rng (0.09 #380, 0.04 #616), 0281y0 (0.09 #364, 0.04 #600), 05ksh (0.09 #261), 0vzm (0.07 #539, 0.03 #775, 0.02 #4787), 030qb3t (0.07 #1686, 0.06 #2158, 0.06 #4046), 04lh6 (0.04 #622, 0.03 #1330, 0.01 #2038), 0c_m3 (0.04 #573, 0.02 #1281, 0.01 #3169) >> Best rule #724 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 0pmw9; 02ryx0; >> query: (?x2897, 02_286) <- award(?x2897, ?x4488), award(?x2897, ?x2139), award_winner(?x2897, ?x6311), ceremony(?x2139, ?x139), ?x4488 = 02gdjb >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #242 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 03cd1q; *> query: (?x2897, 04jpl) <- award(?x2897, ?x3045), award(?x2897, ?x2139), award_winner(?x2897, ?x6311), ?x2139 = 01by1l, ?x3045 = 02sp_v *> conf = 0.09 ranks of expected_values: 2 EVAL 014hr0 origin 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.152 http://example.org/music/artist/origin #21627-04vq3h PRED entity: 04vq3h PRED relation: film PRED expected values: 01r97z 0296rz => 98 concepts (80 used for prediction) PRED predicted values (max 10 best out of 125): 01kt_j (0.58 #71509, 0.57 #28606, 0.47 #12514), 017jd9 (0.03 #6143, 0.03 #7930, 0.03 #11505), 011ywj (0.03 #6798, 0.03 #8585, 0.02 #12160), 09cr8 (0.02 #5648, 0.02 #7435, 0.02 #11010), 02qr3k8 (0.02 #28105, 0.02 #104977, 0.02 #71008), 03bx2lk (0.02 #27003, 0.02 #1973, 0.02 #10911), 017gl1 (0.02 #5507, 0.02 #10869, 0.02 #7294), 08r4x3 (0.02 #10880, 0.02 #44849, 0.02 #46636), 017gm7 (0.02 #10937, 0.02 #5575, 0.02 #7362), 01shy7 (0.02 #45118, 0.02 #50481, 0.02 #41543) >> Best rule #71509 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 021yzs; 04bdqk; 016z68; 0h1q6; 04kwbt; >> query: (?x9998, ?x10595) <- film(?x9998, ?x1496), nominated_for(?x9998, ?x10595) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04vq3h film 0296rz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 80.000 0.585 http://example.org/film/actor/film./film/performance/film EVAL 04vq3h film 01r97z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 80.000 0.585 http://example.org/film/actor/film./film/performance/film #21626-05c1t6z PRED entity: 05c1t6z PRED relation: award_winner PRED expected values: 02778pf 0glmv 087qxp 05bnx3j => 41 concepts (31 used for prediction) PRED predicted values (max 10 best out of 2280): 0f4vbz (0.57 #19934, 0.33 #4835, 0.20 #13893), 02cm2m (0.50 #12665, 0.45 #12074, 0.40 #15684), 05bpg3 (0.50 #12898, 0.40 #15917, 0.33 #17429), 02p21g (0.50 #12284, 0.40 #15303, 0.33 #16815), 02wrhj (0.50 #7777, 0.33 #1742, 0.14 #21368), 0fvf9q (0.45 #12074, 0.36 #9050, 0.33 #16615), 0gz5hs (0.45 #12074, 0.26 #43797, 0.22 #39259), 01mh8zn (0.45 #12074, 0.20 #37745, 0.20 #46819), 01z7_f (0.43 #20274, 0.33 #649, 0.25 #9702), 01pcq3 (0.40 #13690, 0.33 #4632, 0.26 #43797) >> Best rule #19934 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 09q_6t; 092_25; 09pnw5; 09qftb; >> query: (?x1265, 0f4vbz) <- award_winner(?x1265, ?x9500), award_winner(?x1265, ?x8596), award_winner(?x1265, ?x2589), award_winner(?x1265, ?x2390), ?x8596 = 0hz_1, ceremony(?x435, ?x1265), award(?x9156, ?x435), award_winner(?x435, ?x1677), nominated_for(?x435, ?x337), honored_for(?x1265, ?x1631), type_of_union(?x9156, ?x566), profession(?x9500, ?x353), film(?x2589, ?x1744), languages(?x2390, ?x254) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #9050 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 0lp_cd3; *> query: (?x1265, ?x163) <- award_winner(?x1265, ?x5311), award_winner(?x1265, ?x4466), honored_for(?x1265, ?x3822), influenced_by(?x4466, ?x12459), genre(?x3822, ?x6277), award_winner(?x163, ?x4466), ceremony(?x3247, ?x1265), award_winner(?x1869, ?x4466), ?x5311 = 05pzdk, genre(?x485, ?x6277), award_winner(?x3247, ?x269), award(?x192, ?x3247) *> conf = 0.36 ranks of expected_values: 15, 17, 195, 1097 EVAL 05c1t6z award_winner 05bnx3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 31.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05c1t6z award_winner 087qxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 41.000 31.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05c1t6z award_winner 0glmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 41.000 31.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05c1t6z award_winner 02778pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 41.000 31.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21625-07m77x PRED entity: 07m77x PRED relation: award_winner! PRED expected values: 09g90vz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 102): 05zksls (0.17 #9453, 0.12 #34, 0.07 #451), 09g90vz (0.17 #9453, 0.09 #400, 0.08 #539), 02q690_ (0.17 #9453, 0.09 #342, 0.04 #620), 05c1t6z (0.17 #9453, 0.07 #710, 0.07 #571), 0gvstc3 (0.17 #9453, 0.07 #450, 0.07 #589), 0hn821n (0.17 #9453, 0.03 #546, 0.02 #3187), 0lp_cd3 (0.17 #9453, 0.03 #578, 0.02 #717), 0hhtgcw (0.17 #9453, 0.02 #2448, 0.01 #641), 058m5m4 (0.12 #193, 0.12 #54, 0.09 #332), 0g55tzk (0.12 #274, 0.12 #135, 0.07 #552) >> Best rule #9453 for best value: >> intensional similarity = 2 >> extensional distance = 1699 >> proper extension: 0dky9n; 024rbz; 01nzs7; 027_tg; 09mfvx; 01j7pt; 0kcdl; 0kctd; 0kc9f; >> query: (?x8896, ?x1265) <- nominated_for(?x8896, ?x5236), honored_for(?x1265, ?x5236) >> conf = 0.17 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07m77x award_winner! 09g90vz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.174 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21624-030155 PRED entity: 030155 PRED relation: artists! PRED expected values: 02vjzr => 110 concepts (37 used for prediction) PRED predicted values (max 10 best out of 218): 064t9 (0.73 #1254, 0.71 #3734, 0.70 #944), 06by7 (0.61 #6537, 0.51 #1883, 0.51 #3743), 025sc50 (0.54 #1292, 0.37 #362, 0.34 #52), 01lyv (0.38 #4996, 0.29 #36, 0.23 #656), 0glt670 (0.37 #1283, 0.27 #4073, 0.26 #3143), 05bt6j (0.37 #3766, 0.30 #976, 0.29 #46), 02lnbg (0.33 #1300, 0.26 #60, 0.25 #370), 0dl5d (0.31 #4981, 0.16 #1881, 0.11 #6535), 0ggx5q (0.29 #80, 0.28 #1320, 0.27 #390), 0xhtw (0.28 #4978, 0.27 #6532, 0.25 #1878) >> Best rule #1254 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 012vm6; 01v27pl; >> query: (?x3320, 064t9) <- artists(?x3319, ?x3320), origin(?x3320, ?x479), ?x3319 = 06j6l, category(?x3320, ?x134) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1067 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 07s3vqk; 0197tq; 0m2l9; 03f2_rc; 01w61th; 0b68vs; 0137n0; 012x4t; 015_30; 015882; ... *> query: (?x3320, 02vjzr) <- award(?x3320, ?x724), location(?x3320, ?x11058), artist(?x8721, ?x3320), ?x724 = 01bgqh *> conf = 0.24 ranks of expected_values: 15 EVAL 030155 artists! 02vjzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 110.000 37.000 0.727 http://example.org/music/genre/artists #21623-0dq630k PRED entity: 0dq630k PRED relation: role! PRED expected values: 0136pk => 57 concepts (37 used for prediction) PRED predicted values (max 10 best out of 871): 01nhkxp (0.71 #2755, 0.43 #3222, 0.41 #6045), 0137g1 (0.67 #1524, 0.60 #3400, 0.57 #2933), 0161sp (0.67 #1535, 0.60 #1066, 0.50 #3411), 023l9y (0.65 #5849, 0.50 #3493, 0.50 #1617), 02s6sh (0.65 #6076, 0.50 #1844, 0.43 #2786), 050z2 (0.60 #3469, 0.59 #5825, 0.57 #3002), 05qhnq (0.60 #1248, 0.50 #3593, 0.50 #2193), 01vs4ff (0.60 #1241, 0.50 #3586, 0.50 #2186), 01vsnff (0.60 #1028, 0.50 #1973, 0.50 #1497), 0770cd (0.60 #3358, 0.47 #4297, 0.46 #3826) >> Best rule #2755 for best value: >> intensional similarity = 25 >> extensional distance = 5 >> proper extension: 0342h; >> query: (?x2205, 01nhkxp) <- role(?x2205, ?x1750), role(?x2205, ?x316), role(?x2205, ?x315), role(?x2205, ?x314), role(?x3716, ?x2205), role(?x1147, ?x2205), role(?x2205, ?x3991), role(?x1165, ?x2205), ?x314 = 02sgy, ?x3991 = 05842k, ?x1750 = 02hnl, ?x315 = 0l14md, performance_role(?x1225, ?x3716), ?x1147 = 07kc_, ?x316 = 05r5c, role(?x3716, ?x4769), role(?x3716, ?x2888), instrumentalists(?x3716, ?x8272), ?x4769 = 0dwt5, role(?x3716, ?x2459), profession(?x1165, ?x220), role(?x2944, ?x3716), ?x8272 = 01mr2g6, participant(?x1165, ?x5246), ?x2888 = 02fsn >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1504 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 4 *> proper extension: 02sgy; 042v_gx; 0jtg0; *> query: (?x2205, 0136pk) <- role(?x2205, ?x314), role(?x212, ?x2205), role(?x2205, ?x780), role(?x1165, ?x2205), role(?x11182, ?x314), role(?x9321, ?x314), role(?x5126, ?x314), role(?x4162, ?x314), role(?x3632, ?x314), role(?x3160, ?x314), ?x3632 = 01309x, ?x9321 = 0140t7, ?x4162 = 01wy61y, role(?x4583, ?x314), role(?x645, ?x314), ?x4583 = 0bmnm, ?x1165 = 018y2s, award_winner(?x884, ?x11182), group(?x645, ?x646), role(?x8323, ?x645), ?x3160 = 01w806h, role(?x314, ?x214), group(?x314, ?x442), ?x5126 = 03h502k, ?x8323 = 01r0t_j *> conf = 0.33 ranks of expected_values: 130 EVAL 0dq630k role! 0136pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 57.000 37.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #21622-04x1_w PRED entity: 04x1_w PRED relation: film PRED expected values: 0cc5mcj => 91 concepts (46 used for prediction) PRED predicted values (max 10 best out of 248): 04p5cr (0.56 #7162, 0.53 #69825, 0.46 #28648), 011yn5 (0.09 #926, 0.06 #2717, 0.01 #6297), 08g_jw (0.09 #1690, 0.03 #3481, 0.02 #41182), 026hxwx (0.09 #1147, 0.03 #2938, 0.02 #41182), 044g_k (0.09 #208, 0.03 #1999, 0.02 #41182), 0g_zyp (0.09 #1592, 0.03 #3383), 026lgs (0.09 #938, 0.03 #2729), 04x4vj (0.09 #773, 0.03 #2564), 0879bpq (0.09 #448, 0.02 #41182, 0.01 #5819), 011yg9 (0.09 #1028, 0.02 #41182) >> Best rule #7162 for best value: >> intensional similarity = 3 >> extensional distance = 304 >> proper extension: 012d40; 0fvf9q; 0l6qt; 01j5ts; 0h0jz; 01wbg84; 01r42_g; 0m2wm; 02zq43; 01p7yb; ... >> query: (?x7402, ?x2649) <- languages(?x7402, ?x254), location(?x7402, ?x108), nominated_for(?x7402, ?x2649) >> conf = 0.56 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04x1_w film 0cc5mcj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 46.000 0.556 http://example.org/film/actor/film./film/performance/film #21621-03lfd_ PRED entity: 03lfd_ PRED relation: currency PRED expected values: 09nqf => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.82 #15, 0.76 #162, 0.75 #190), 01nv4h (0.04 #2, 0.04 #23, 0.03 #44), 02l6h (0.04 #25, 0.03 #46, 0.03 #60), 02gsvk (0.02 #69, 0.02 #167, 0.02 #76), 0ptk_ (0.01 #3), 0kz1h (0.01 #33, 0.01 #61) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 047qxs; 014nq4; 01q2nx; >> query: (?x8867, 09nqf) <- genre(?x8867, ?x258), film_crew_role(?x8867, ?x4305), film(?x902, ?x8867), ?x902 = 05qd_ >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03lfd_ currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.821 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21620-027jw0c PRED entity: 027jw0c PRED relation: production_companies! PRED expected values: 09sh8k => 151 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1853): 053tj7 (0.38 #13722, 0.35 #14867, 0.09 #11573), 085ccd (0.38 #13722, 0.35 #14867, 0.07 #7123), 049mql (0.38 #13722, 0.35 #14867, 0.07 #7317), 0bw20 (0.38 #13722, 0.35 #14867, 0.06 #9942), 01m13b (0.38 #13722, 0.35 #14867, 0.06 #9252), 03xj05 (0.38 #13722, 0.35 #14867, 0.02 #14866), 08g_jw (0.20 #3371, 0.14 #4516, 0.06 #9090), 09yxcz (0.20 #3365, 0.14 #4510, 0.06 #9084), 02pxmgz (0.20 #2417, 0.14 #3562, 0.06 #8136), 04g73n (0.20 #7765, 0.14 #11194, 0.13 #12338) >> Best rule #13722 for best value: >> intensional similarity = 8 >> extensional distance = 22 >> proper extension: 07k2x; >> query: (?x9997, ?x1009) <- production_companies(?x5725, ?x9997), production_companies(?x3191, ?x9997), film(?x9997, ?x1009), genre(?x3191, ?x225), film_release_region(?x3191, ?x87), film(?x2589, ?x5725), film(?x541, ?x5725), ?x541 = 017s11 >> conf = 0.38 => this is the best rule for 6 predicted values *> Best rule #6871 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 13 *> proper extension: 0hpt3; 046b0s; 09b3v; 02hvd; 056ws9; 04rcl7; *> query: (?x9997, 09sh8k) <- production_companies(?x409, ?x9997), film_release_region(?x409, ?x8593), film_release_region(?x409, ?x1917), film_release_region(?x409, ?x789), film_release_region(?x409, ?x205), ?x8593 = 01crd5, citytown(?x9997, ?x2611), ?x205 = 03rjj, ?x789 = 0f8l9c, ?x1917 = 01p1v *> conf = 0.07 ranks of expected_values: 419 EVAL 027jw0c production_companies! 09sh8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 151.000 13.000 0.378 http://example.org/film/film/production_companies #21619-01rv7x PRED entity: 01rv7x PRED relation: people PRED expected values: 0dfjb8 046rfv 01x2tm8 => 51 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2424): 0hwqz (0.40 #11174, 0.29 #14619, 0.22 #21502), 0lkr7 (0.40 #9322, 0.22 #21372, 0.20 #11044), 0g824 (0.38 #18120, 0.36 #26728, 0.33 #21562), 01rrd4 (0.38 #18134, 0.27 #25020, 0.27 #23298), 0gcs9 (0.38 #17616, 0.27 #24502, 0.27 #22780), 0311wg (0.36 #26119, 0.27 #24397, 0.27 #34728), 06cgy (0.36 #22580, 0.27 #34633, 0.25 #17416), 0b66qd (0.36 #10332), 01vwllw (0.33 #21099, 0.27 #26265, 0.27 #24543), 0807ml (0.33 #12953, 0.25 #18117, 0.25 #7788) >> Best rule #11174 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 0x67; 07hwkr; 013b6_; >> query: (?x9347, 0hwqz) <- languages_spoken(?x9347, ?x5121), people(?x9347, ?x3890), award_winner(?x1079, ?x3890), award_nominee(?x3890, ?x4693), award(?x3890, ?x462), instrumentalists(?x75, ?x3890), profession(?x3890, ?x131), music(?x3742, ?x3890), artists(?x284, ?x3890), geographic_distribution(?x9347, ?x279) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #727 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 0dryh9k; *> query: (?x9347, 0dfjb8) <- languages_spoken(?x9347, ?x5121), people(?x9347, ?x7295), people(?x9347, ?x3890), award_winner(?x1079, ?x3890), award_nominee(?x3890, ?x4693), award(?x3890, ?x462), award_winner(?x139, ?x3890), profession(?x3890, ?x2348), ?x7295 = 02n1p5, profession(?x2987, ?x2348), profession(?x2461, ?x2348), ?x2461 = 01cwhp, ?x2987 = 01vw20_ *> conf = 0.33 ranks of expected_values: 91, 1304 EVAL 01rv7x people 01x2tm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 23.000 0.400 http://example.org/people/ethnicity/people EVAL 01rv7x people 046rfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 23.000 0.400 http://example.org/people/ethnicity/people EVAL 01rv7x people 0dfjb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 51.000 23.000 0.400 http://example.org/people/ethnicity/people #21618-07f1x PRED entity: 07f1x PRED relation: service_location! PRED expected values: 03_c8p => 158 concepts (144 used for prediction) PRED predicted values (max 10 best out of 136): 01c6k4 (0.45 #6, 0.41 #965, 0.40 #143), 018mxj (0.40 #147, 0.38 #832, 0.36 #969), 069b85 (0.36 #129, 0.28 #540, 0.27 #1088), 0cv9b (0.36 #11, 0.27 #2340, 0.22 #422), 0p4wb (0.33 #420, 0.33 #146, 0.32 #968), 07zl6m (0.33 #270, 0.29 #955, 0.28 #544), 05b5c (0.33 #265, 0.28 #539, 0.27 #1087), 0k9ts (0.33 #229, 0.24 #914, 0.21 #2832), 064f29 (0.28 #471, 0.27 #1019, 0.27 #197), 01zpmq (0.28 #461, 0.27 #50, 0.27 #187) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0g8bw; >> query: (?x7747, 01c6k4) <- combatants(?x7747, ?x550), ?x550 = 05v8c, combatants(?x326, ?x7747) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #242 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0jgd; 03_3d; 0d0vqn; 03rt9; 06mkj; *> query: (?x7747, 03_c8p) <- film_release_region(?x5052, ?x7747), film_release_region(?x1724, ?x7747), ?x1724 = 02r8hh_, ?x5052 = 04yg13l *> conf = 0.13 ranks of expected_values: 27 EVAL 07f1x service_location! 03_c8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 158.000 144.000 0.455 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #21617-02r_pp PRED entity: 02r_pp PRED relation: film_art_direction_by PRED expected values: 07hhnl => 102 concepts (73 used for prediction) PRED predicted values (max 10 best out of 26): 07hhnl (0.22 #9, 0.14 #90, 0.12 #119), 0dh73w (0.22 #6, 0.14 #87, 0.12 #116), 072twv (0.17 #197, 0.10 #395, 0.10 #424), 05v1sb (0.10 #286, 0.10 #173, 0.09 #34), 0fqjks (0.10 #182, 0.09 #43, 0.08 #70), 05683cn (0.09 #480, 0.07 #337, 0.06 #473), 0c4qzm (0.09 #480, 0.07 #337, 0.03 #472), 057bc6m (0.09 #480, 0.07 #337), 0584j4n (0.09 #480, 0.07 #337), 076lxv (0.09 #480, 0.07 #337) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 05css_; >> query: (?x5095, 07hhnl) <- film(?x2465, ?x5095), nominated_for(?x5095, ?x5134), ?x5134 = 0k0rf, titles(?x600, ?x5095) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r_pp film_art_direction_by 07hhnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 73.000 0.222 http://example.org/film/film/film_art_direction_by #21616-015fs3 PRED entity: 015fs3 PRED relation: institution! PRED expected values: 014mlp => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 20): 014mlp (0.80 #90, 0.75 #386, 0.73 #238), 02_xgp2 (0.73 #96, 0.64 #118, 0.57 #160), 03bwzr4 (0.67 #98, 0.62 #162, 0.58 #120), 016t_3 (0.61 #152, 0.57 #236, 0.56 #258), 0bkj86 (0.53 #93, 0.47 #115, 0.41 #241), 04zx3q1 (0.53 #87, 0.39 #109, 0.31 #1707), 07s6fsf (0.46 #234, 0.45 #150, 0.44 #256), 027f2w (0.40 #94, 0.33 #116, 0.27 #158), 01rr_d (0.40 #16, 0.17 #418, 0.17 #101), 013zdg (0.33 #92, 0.31 #114, 0.28 #262) >> Best rule #90 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 01hhvg; 01rc6f; 02x9g_; >> query: (?x11215, 014mlp) <- institution(?x3386, ?x11215), institution(?x1771, ?x11215), major_field_of_study(?x11215, ?x1527), ?x1771 = 019v9k, ?x3386 = 03mkk4 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015fs3 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21615-01337_ PRED entity: 01337_ PRED relation: award_winner! PRED expected values: 02ywhz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 126): 027hjff (0.17 #57, 0.02 #6825, 0.02 #6966), 0drtv8 (0.17 #66, 0.02 #5706, 0.01 #8667), 013b2h (0.14 #221, 0.12 #362, 0.03 #10797), 0bzkvd (0.07 #255, 0.06 #396, 0.04 #10859), 0fz0c2 (0.07 #247, 0.06 #388, 0.04 #10859), 0bzknt (0.07 #223, 0.06 #364, 0.04 #10859), 05pd94v (0.07 #143, 0.06 #284, 0.03 #10719), 01c6qp (0.07 #160, 0.06 #301, 0.03 #10736), 0gx_st (0.07 #178, 0.06 #319, 0.02 #5113), 0bxs_d (0.07 #256, 0.06 #397, 0.01 #5191) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0f7hc; >> query: (?x9641, 027hjff) <- film(?x9641, ?x10274), film(?x9641, ?x1080), ?x10274 = 0d87hc, nominated_for(?x1933, ?x1080) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #10859 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1897 *> proper extension: 06lxn; *> query: (?x9641, ?x78) <- award_winner(?x3066, ?x9641), award(?x92, ?x3066), ceremony(?x3066, ?x78) *> conf = 0.04 ranks of expected_values: 55 EVAL 01337_ award_winner! 02ywhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 112.000 112.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21614-01mk6 PRED entity: 01mk6 PRED relation: olympics PRED expected values: 0lbd9 019n8z => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 27): 0kbws (0.77 #822, 0.71 #850, 0.69 #606), 0l98s (0.75 #595, 0.69 #1573, 0.67 #1819), 0sx8l (0.75 #595, 0.69 #1573, 0.67 #1819), 01f1kd (0.75 #595, 0.69 #1573, 0.67 #1819), 0kbvb (0.74 #816, 0.74 #329, 0.72 #383), 0jhn7 (0.74 #614, 0.72 #397, 0.71 #586), 06sks6 (0.72 #827, 0.71 #583, 0.69 #611), 0jdk_ (0.70 #342, 0.69 #613, 0.69 #585), 0l6m5 (0.63 #224, 0.63 #332, 0.62 #603), 0l6ny (0.63 #223, 0.63 #331, 0.55 #602) >> Best rule #822 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 05r4w; 0jgd; 03_3d; 0d0vqn; 01ls2; 01znc_; 06mkj; 05sb1; 03shp; 03__y; ... >> query: (?x7430, 0kbws) <- film_release_region(?x324, ?x7430), adjoins(?x7430, ?x2517), participating_countries(?x1608, ?x7430), combatants(?x326, ?x7430) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #346 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 25 *> proper extension: 02jx1; *> query: (?x7430, 0lbd9) <- olympics(?x7430, ?x8189), olympics(?x7430, ?x2134), olympics(?x7430, ?x1608), ?x1608 = 09x3r, locations(?x2134, ?x362), sports(?x8189, ?x453) *> conf = 0.63 ranks of expected_values: 11, 17 EVAL 01mk6 olympics 019n8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 165.000 165.000 0.766 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 01mk6 olympics 0lbd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 165.000 165.000 0.766 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #21613-045j3w PRED entity: 045j3w PRED relation: film_release_region PRED expected values: 07ssc 0ctw_b 06t2t => 69 concepts (53 used for prediction) PRED predicted values (max 10 best out of 117): 07ssc (0.86 #1958, 0.85 #845, 0.84 #705), 06t2t (0.83 #1995, 0.81 #1299, 0.80 #1160), 06bnz (0.83 #1144, 0.82 #1979, 0.81 #1283), 0d060g (0.82 #1952, 0.73 #2369, 0.71 #2091), 01p1v (0.77 #1151, 0.77 #316, 0.76 #1290), 06f32 (0.69 #1164, 0.68 #1303, 0.65 #886), 016wzw (0.68 #1304, 0.66 #1165, 0.65 #887), 03rk0 (0.68 #1294, 0.66 #1155, 0.65 #877), 0ctw_b (0.65 #851, 0.65 #1268, 0.64 #433), 09pmkv (0.57 #435, 0.55 #853, 0.54 #1131) >> Best rule #1958 for best value: >> intensional similarity = 6 >> extensional distance = 145 >> proper extension: 0hgnl3t; >> query: (?x3000, 07ssc) <- film_release_region(?x3000, ?x3683), film_release_region(?x3000, ?x1174), film_release_region(?x4352, ?x3683), ?x4352 = 09v71cj, ?x1174 = 047yc, currency(?x3683, ?x170) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 9 EVAL 045j3w film_release_region 06t2t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 53.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 045j3w film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 69.000 53.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 045j3w film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 53.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21612-01pvxl PRED entity: 01pvxl PRED relation: genre PRED expected values: 0jdm8 => 59 concepts (52 used for prediction) PRED predicted values (max 10 best out of 85): 02kdv5l (0.48 #1713, 0.48 #1371, 0.48 #1599), 02l7c8 (0.36 #357, 0.36 #243, 0.29 #2866), 06n90 (0.33 #11, 0.25 #1380, 0.25 #1722), 04xvlr (0.32 #572, 0.19 #1028, 0.16 #344), 017fp (0.30 #584, 0.17 #1040, 0.12 #5708), 01jfsb (0.29 #809, 0.29 #3204, 0.28 #695), 02xlf (0.29 #162, 0.07 #1075, 0.06 #505), 09kqc (0.29 #228, 0.01 #1141), 01zhp (0.24 #984, 0.14 #528, 0.13 #1098), 0jxy (0.22 #954, 0.05 #5251, 0.03 #1410) >> Best rule #1713 for best value: >> intensional similarity = 4 >> extensional distance = 353 >> proper extension: 019kyn; >> query: (?x5243, 02kdv5l) <- film(?x8412, ?x5243), genre(?x5243, ?x811), ?x811 = 03k9fj, gender(?x8412, ?x514) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1331 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 319 *> proper extension: 080dwhx; 0124k9; 0464pz; 0kfv9; 0l76z; 03nt59; 0524b41; 08bytj; 01g03q; 01ft14; ... *> query: (?x5243, 0jdm8) <- nominated_for(?x574, ?x5243), nominated_for(?x3911, ?x5243), award(?x382, ?x3911), production_companies(?x136, ?x574) *> conf = 0.02 ranks of expected_values: 71 EVAL 01pvxl genre 0jdm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 59.000 52.000 0.482 http://example.org/film/film/genre #21611-04t_mf PRED entity: 04t_mf PRED relation: religion! PRED expected values: 03czqs => 33 concepts (30 used for prediction) PRED predicted values (max 10 best out of 356): 09c7w0 (0.64 #439, 0.56 #332, 0.50 #873), 03v0t (0.62 #284, 0.50 #173, 0.47 #1261), 0rh6k (0.53 #1528, 0.50 #1636, 0.50 #548), 01n4w (0.53 #1251, 0.50 #2669, 0.50 #598), 02xry (0.53 #1245, 0.50 #592, 0.50 #268), 04rrx (0.53 #1237, 0.47 #1564, 0.46 #2655), 05fjf (0.53 #1177, 0.47 #1611, 0.44 #1719), 04rrd (0.53 #1234, 0.47 #1454, 0.43 #2652), 05kr_ (0.50 #583, 0.50 #259, 0.50 #148), 05kkh (0.50 #2621, 0.50 #226, 0.47 #1096) >> Best rule #439 for best value: >> intensional similarity = 14 >> extensional distance = 9 >> proper extension: 01lp8; 0c8wxp; 0kpl; 03_gx; 03j6c; 02t7t; 07w8f; >> query: (?x12643, 09c7w0) <- religion(?x7747, ?x12643), film_release_region(?x10080, ?x7747), film_release_region(?x8292, ?x7747), film_release_region(?x6270, ?x7747), film_release_region(?x3000, ?x7747), film_release_region(?x2656, ?x7747), ?x6270 = 0g9zljd, combatants(?x94, ?x7747), administrative_area_type(?x7747, ?x2792), ?x3000 = 045j3w, ?x2656 = 03qnc6q, ?x8292 = 0cmf0m0, adjustment_currency(?x7747, ?x170), ?x10080 = 065ym0c >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #865 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 12 *> proper extension: 06yyp; 042s9; *> query: (?x12643, 03czqs) <- religion(?x7747, ?x12643), film_release_region(?x6270, ?x7747), film_release_region(?x6095, ?x7747), film_release_region(?x1518, ?x7747), film_release_region(?x6270, ?x774), film_release_region(?x6270, ?x87), member_states(?x7695, ?x7747), ?x1518 = 04w7rn, ?x774 = 06mzp, contains(?x6304, ?x7747), ?x6095 = 0bq6ntw, country(?x171, ?x7747), ?x87 = 05r4w *> conf = 0.14 ranks of expected_values: 97 EVAL 04t_mf religion! 03czqs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 33.000 30.000 0.636 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #21610-0gyfp9c PRED entity: 0gyfp9c PRED relation: nominated_for! PRED expected values: 0gqyl => 65 concepts (50 used for prediction) PRED predicted values (max 10 best out of 236): 02x4w6g (0.67 #8670, 0.67 #8906, 0.66 #8669), 099c8n (0.56 #1229, 0.29 #292, 0.25 #527), 0gr4k (0.48 #1198, 0.24 #5882, 0.23 #5414), 0gq9h (0.44 #1235, 0.36 #5451, 0.36 #5919), 02n9nmz (0.44 #1230, 0.16 #528, 0.16 #293), 03hkv_r (0.35 #1186, 0.16 #249, 0.14 #15), 0k611 (0.33 #1246, 0.31 #309, 0.27 #5462), 04dn09n (0.33 #1207, 0.25 #5423, 0.24 #5891), 0gs9p (0.32 #5921, 0.32 #1237, 0.31 #5453), 019f4v (0.32 #1226, 0.31 #5442, 0.30 #5910) >> Best rule #8670 for best value: >> intensional similarity = 4 >> extensional distance = 972 >> proper extension: 06w7mlh; 06mmr; >> query: (?x3226, ?x2183) <- award(?x3226, ?x2183), award_winner(?x2183, ?x123), award(?x92, ?x2183), nominated_for(?x2183, ?x696) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1253 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 64 *> proper extension: 07jxpf; *> query: (?x3226, 0gqyl) <- genre(?x3226, ?x53), ?x53 = 07s9rl0, nominated_for(?x2341, ?x3226), ?x2341 = 02x17s4 *> conf = 0.30 ranks of expected_values: 12 EVAL 0gyfp9c nominated_for! 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 65.000 50.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21609-02lq10 PRED entity: 02lq10 PRED relation: award PRED expected values: 09qvc0 => 102 concepts (83 used for prediction) PRED predicted values (max 10 best out of 287): 09sb52 (0.41 #7349, 0.39 #3695, 0.38 #4913), 094qd5 (0.33 #451, 0.18 #2481, 0.13 #2887), 02lp0w (0.33 #658, 0.11 #32896, 0.09 #33709), 0gkvb7 (0.33 #433, 0.11 #32896, 0.08 #1245), 05b4l5x (0.33 #412, 0.10 #1630, 0.10 #3254), 03c7tr1 (0.33 #465, 0.08 #4931, 0.07 #7367), 02y_rq5 (0.33 #502, 0.06 #2532, 0.06 #11873), 024fz9 (0.33 #211, 0.02 #3459, 0.02 #8737), 03tk6z (0.33 #622, 0.02 #3464, 0.02 #13211), 0ck27z (0.29 #2529, 0.28 #2935, 0.20 #15930) >> Best rule #7349 for best value: >> intensional similarity = 4 >> extensional distance = 187 >> proper extension: 019f2f; 0flw6; 017gxw; 03n52j; 027bs_2; 0gm34; 01g969; 057_yx; 04v7kt; >> query: (?x2217, 09sb52) <- film(?x2217, ?x2218), nominated_for(?x2217, ?x8837), person(?x2218, ?x8986), film_crew_role(?x2218, ?x137) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #7348 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 187 *> proper extension: 019f2f; 0flw6; 017gxw; 03n52j; 027bs_2; 0gm34; 01g969; 057_yx; 04v7kt; *> query: (?x2217, 09qvc0) <- film(?x2217, ?x2218), nominated_for(?x2217, ?x8837), person(?x2218, ?x8986), film_crew_role(?x2218, ?x137) *> conf = 0.06 ranks of expected_values: 110 EVAL 02lq10 award 09qvc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 102.000 83.000 0.413 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21608-02yvct PRED entity: 02yvct PRED relation: nominated_for! PRED expected values: 057xs89 => 110 concepts (108 used for prediction) PRED predicted values (max 10 best out of 230): 099jhq (0.68 #14005, 0.67 #9332, 0.66 #14004), 027b9j5 (0.68 #14005, 0.67 #9332, 0.66 #14004), 040njc (0.60 #5, 0.50 #429, 0.50 #217), 02ppm4q (0.60 #96, 0.44 #520, 0.43 #308), 09qv_s (0.60 #94, 0.25 #518, 0.24 #17620), 03hkv_r (0.50 #435, 0.43 #223, 0.40 #11), 0gq_v (0.50 #8287, 0.46 #2560, 0.46 #3620), 02qvyrt (0.43 #289, 0.40 #77, 0.38 #501), 02r22gf (0.43 #235, 0.38 #447, 0.25 #3627), 0gr4k (0.43 #8504, 0.32 #2565, 0.31 #445) >> Best rule #14005 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 06mmr; >> query: (?x2189, ?x704) <- award(?x2189, ?x704), award_winner(?x2189, ?x815), award(?x57, ?x704) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #311 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 0m313; 02vxq9m; 0b6tzs; 017gl1; 0260bz; 04jwly; 03hmt9b; 04x4vj; 05hjnw; 011yhm; ... *> query: (?x2189, 057xs89) <- nominated_for(?x4091, ?x2189), nominated_for(?x1243, ?x2189), ?x1243 = 0gr0m, ?x4091 = 09sdmz, language(?x2189, ?x90) *> conf = 0.29 ranks of expected_values: 25 EVAL 02yvct nominated_for! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 110.000 108.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21607-04mky3 PRED entity: 04mky3 PRED relation: artists! PRED expected values: 0m0jc 0cx7f 03ckfl9 02rp117 => 116 concepts (39 used for prediction) PRED predicted values (max 10 best out of 283): 06by7 (0.65 #4015, 0.63 #4940, 0.58 #5555), 064t9 (0.57 #4007, 0.48 #8320, 0.46 #7094), 02rp117 (0.50 #507, 0.25 #200, 0.20 #1737), 0xhtw (0.40 #937, 0.33 #4318, 0.26 #2168), 03_d0 (0.40 #931, 0.26 #3697, 0.25 #5853), 02x8m (0.40 #939, 0.25 #632, 0.25 #19), 0m0jc (0.40 #928, 0.25 #621, 0.25 #8), 06j6l (0.39 #2505, 0.28 #8354, 0.25 #1275), 0gywn (0.39 #2514, 0.22 #7137, 0.19 #11432), 02w4v (0.37 #4344, 0.20 #10149, 0.20 #4037) >> Best rule #4015 for best value: >> intensional similarity = 5 >> extensional distance = 47 >> proper extension: 02whj; 01vs14j; 023l9y; 024dw0; 01whg97; 02vr7; >> query: (?x11947, 06by7) <- artists(?x7329, ?x11947), instrumentalists(?x1750, ?x11947), ?x1750 = 02hnl, artists(?x7329, ?x9179), ?x9179 = 01vsqvs >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #507 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 03rl84; *> query: (?x11947, 02rp117) <- artists(?x9750, ?x11947), artists(?x2542, ?x11947), ?x2542 = 03xnwz, ?x9750 = 016zgj, origin(?x11947, ?x479) *> conf = 0.50 ranks of expected_values: 3, 7, 21, 23 EVAL 04mky3 artists! 02rp117 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 39.000 0.653 http://example.org/music/genre/artists EVAL 04mky3 artists! 03ckfl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 116.000 39.000 0.653 http://example.org/music/genre/artists EVAL 04mky3 artists! 0cx7f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 116.000 39.000 0.653 http://example.org/music/genre/artists EVAL 04mky3 artists! 0m0jc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 116.000 39.000 0.653 http://example.org/music/genre/artists #21606-07kb5 PRED entity: 07kb5 PRED relation: influenced_by PRED expected values: 0gz_ => 109 concepts (44 used for prediction) PRED predicted values (max 10 best out of 278): 0gz_ (0.67 #1412, 0.54 #2721, 0.50 #2283), 03sbs (0.54 #2840, 0.50 #5458, 0.50 #4584), 043s3 (0.47 #3605, 0.42 #2296, 0.30 #5671), 015n8 (0.43 #2153, 0.27 #4772, 0.27 #5646), 039n1 (0.38 #2943, 0.33 #3814, 0.33 #2616), 026lj (0.33 #5280, 0.33 #3533, 0.33 #2224), 0tfc (0.33 #2595, 0.33 #1724, 0.19 #3925), 07c37 (0.33 #2367, 0.23 #2805, 0.20 #5423), 01rgr (0.33 #761, 0.14 #3923, 0.09 #4684), 04k15 (0.33 #544, 0.03 #5778, 0.03 #6652) >> Best rule #1412 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0nk72; >> query: (?x712, 0gz_) <- interests(?x712, ?x1858), interests(?x712, ?x713), gender(?x712, ?x231), ?x1858 = 05r79, ?x713 = 02jcc, influenced_by(?x712, ?x6015), ?x6015 = 05qmj >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07kb5 influenced_by 0gz_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 44.000 0.667 http://example.org/influence/influence_node/influenced_by #21605-02rk45 PRED entity: 02rk45 PRED relation: award PRED expected values: 0gr4k => 133 concepts (99 used for prediction) PRED predicted values (max 10 best out of 275): 09sb52 (0.50 #439, 0.36 #840, 0.34 #11669), 0gq9h (0.45 #8095, 0.21 #3610, 0.19 #4485), 0gr4k (0.33 #31, 0.32 #8053, 0.32 #4443), 0f4x7 (0.33 #29, 0.25 #430, 0.21 #3610), 0gqy2 (0.33 #161, 0.25 #562, 0.21 #3610), 099jhq (0.33 #17, 0.25 #418, 0.21 #3610), 09sdmz (0.33 #203, 0.25 #604, 0.21 #3610), 02x1dht (0.33 #52, 0.25 #453, 0.16 #34100), 09qv_s (0.33 #148, 0.25 #549, 0.16 #34100), 02x73k6 (0.33 #58, 0.25 #459, 0.16 #34100) >> Best rule #439 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 02kxwk; >> query: (?x9030, 09sb52) <- award_winner(?x5976, ?x9030), ?x5976 = 02q7fl9, award(?x9030, ?x350) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 01fh9; *> query: (?x9030, 0gr4k) <- award_winner(?x5976, ?x9030), ?x5976 = 02q7fl9, type_of_union(?x9030, ?x566) *> conf = 0.33 ranks of expected_values: 3 EVAL 02rk45 award 0gr4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 99.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21604-09yrh PRED entity: 09yrh PRED relation: student! PRED expected values: 01d34b => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 127): 0bwfn (0.15 #275, 0.08 #10815, 0.07 #20301), 01rtm4 (0.08 #4, 0.02 #2639, 0.02 #10017), 0gl5_ (0.08 #244, 0.02 #3406, 0.02 #9730), 07w0v (0.08 #20, 0.02 #3182, 0.02 #6871), 0cwx_ (0.08 #241, 0.01 #11308, 0.01 #17105), 01vmv_ (0.08 #434, 0.01 #3069), 04rwx (0.08 #38), 05krk (0.08 #7), 065y4w7 (0.05 #10554, 0.05 #541, 0.04 #10027), 05nrkb (0.05 #876, 0.05 #1403, 0.03 #2457) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01g257; 01dw9z; 015v3r; 014g22; 01z_g6; >> query: (?x4536, 0bwfn) <- award_nominee(?x513, ?x4536), film(?x4536, ?x2128), ?x2128 = 035s95 >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #1837 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 90 *> proper extension: 0522wp; 017yxq; *> query: (?x4536, 01d34b) <- award(?x4536, ?x2325), ?x2325 = 05p09zm *> conf = 0.03 ranks of expected_values: 18 EVAL 09yrh student! 01d34b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 118.000 118.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #21603-02lf70 PRED entity: 02lf70 PRED relation: award PRED expected values: 03c7tr1 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 261): 09sb52 (0.42 #441, 0.37 #4050, 0.36 #10466), 0gqwc (0.33 #474, 0.13 #4083, 0.13 #5286), 0fbvqf (0.25 #1250, 0.19 #24864, 0.18 #27675), 0cqhk0 (0.25 #37, 0.16 #2443, 0.13 #26470), 09qj50 (0.25 #45, 0.13 #26470, 0.08 #2852), 0bsjcw (0.25 #200, 0.13 #26470, 0.08 #2606), 05pcn59 (0.20 #4090, 0.19 #3689, 0.17 #4491), 0cqhb3 (0.19 #24864, 0.18 #27675, 0.18 #27273), 02ppm4q (0.17 #554, 0.12 #2559, 0.08 #14589), 094qd5 (0.17 #445, 0.11 #4054, 0.11 #3252) >> Best rule #441 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 02wgln; 02b25y; 01qq_lp; 015q43; 01ps2h8; 02f2p7; 0bdt8; 0btpx; 0cbkc; 02zl4d; >> query: (?x1991, 09sb52) <- award_nominee(?x1991, ?x368), languages(?x1991, ?x90), ?x90 = 02bjrlw >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #3266 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 149 *> proper extension: 03yf3z; *> query: (?x1991, 03c7tr1) <- award_nominee(?x1991, ?x368), spouse(?x2849, ?x1991), award_winner(?x1991, ?x1485) *> conf = 0.13 ranks of expected_values: 33 EVAL 02lf70 award 03c7tr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 111.000 111.000 0.417 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21602-01y20v PRED entity: 01y20v PRED relation: institution! PRED expected values: 014mlp 019v9k => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 22): 014mlp (0.74 #125, 0.73 #197, 0.69 #101), 02h4rq6 (0.67 #51, 0.63 #1205, 0.63 #1830), 019v9k (0.67 #57, 0.58 #1307, 0.56 #1619), 03bwzr4 (0.58 #63, 0.44 #15, 0.40 #39), 07s6fsf (0.50 #49, 0.33 #1, 0.30 #25), 02_xgp2 (0.42 #2375, 0.37 #1865, 0.36 #1383), 013zdg (0.33 #7, 0.30 #31, 0.22 #344), 0bkj86 (0.32 #2370, 0.32 #1860, 0.32 #1787), 04zx3q1 (0.29 #2923, 0.22 #2, 0.20 #26), 0bjrnt (0.29 #2923, 0.10 #1376, 0.10 #1328) >> Best rule #125 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 02d9nr; >> query: (?x6846, 014mlp) <- state_province_region(?x6846, ?x3818), currency(?x6846, ?x170), student(?x6846, ?x5785), colors(?x6846, ?x3189) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01y20v institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 165.000 165.000 0.738 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01y20v institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.738 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21601-01304j PRED entity: 01304j PRED relation: location PRED expected values: 0d6lp => 147 concepts (139 used for prediction) PRED predicted values (max 10 best out of 208): 02_286 (0.37 #57799, 0.37 #71438, 0.28 #82673), 030qb3t (0.23 #35380, 0.22 #82719, 0.22 #45005), 0_xdd (0.20 #1051, 0.02 #1853, 0.02 #3457), 01n7q (0.19 #24128, 0.06 #3271, 0.06 #57825), 04jpl (0.10 #82653, 0.08 #66605, 0.08 #78642), 0cr3d (0.09 #78770, 0.06 #13781, 0.06 #43463), 0cc56 (0.08 #57819, 0.07 #71458, 0.06 #66645), 02xry (0.07 #24198, 0.02 #57895, 0.02 #1737), 07z1m (0.07 #24144, 0.02 #71480, 0.02 #57841), 059rby (0.07 #71417, 0.06 #57778, 0.06 #66604) >> Best rule #57799 for best value: >> intensional similarity = 3 >> extensional distance = 691 >> proper extension: 033071; 0466k4; >> query: (?x11186, 02_286) <- location(?x11186, ?x8852), administrative_parent(?x8852, ?x151), type_of_union(?x11186, ?x566) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #82804 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1239 *> proper extension: 0c8hct; *> query: (?x11186, 0d6lp) <- location(?x11186, ?x11561), location_of_ceremony(?x2415, ?x11561) *> conf = 0.03 ranks of expected_values: 42 EVAL 01304j location 0d6lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 147.000 139.000 0.372 http://example.org/people/person/places_lived./people/place_lived/location #21600-04l19_ PRED entity: 04l19_ PRED relation: profession PRED expected values: 0dxtg 02hrh1q => 138 concepts (94 used for prediction) PRED predicted values (max 10 best out of 84): 02hrh1q (0.90 #5388, 0.90 #13517, 0.90 #1901), 0dxtg (0.71 #738, 0.68 #1318, 0.62 #3642), 09jwl (0.64 #7133, 0.63 #7859, 0.53 #3065), 0kyk (0.64 #461, 0.58 #606, 0.41 #1743), 0n1h (0.49 #1452, 0.40 #3485, 0.40 #4649), 0nbcg (0.47 #7871, 0.46 #7145, 0.41 #3077), 016z4k (0.42 #7847, 0.41 #7121, 0.39 #3053), 015cjr (0.41 #1743, 0.40 #3485, 0.40 #4649), 02hv44_ (0.41 #1743, 0.40 #3485, 0.40 #4649), 02jknp (0.40 #3485, 0.40 #4649, 0.35 #4795) >> Best rule #5388 for best value: >> intensional similarity = 4 >> extensional distance = 248 >> proper extension: 01hkhq; >> query: (?x6692, 02hrh1q) <- place_of_birth(?x6692, ?x7689), film(?x6692, ?x8068), languages(?x6692, ?x254), production_companies(?x8068, ?x738) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04l19_ profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 94.000 0.904 http://example.org/people/person/profession EVAL 04l19_ profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 94.000 0.904 http://example.org/people/person/profession #21599-0gkd1 PRED entity: 0gkd1 PRED relation: role! PRED expected values: 0197tq => 88 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1203): 050z2 (0.80 #8431, 0.71 #5682, 0.70 #15764), 0197tq (0.71 #4125, 0.67 #2297, 0.60 #1382), 0137g1 (0.67 #2410, 0.67 #1952, 0.62 #6528), 01w806h (0.67 #2432, 0.60 #1517, 0.57 #4718), 01vs4ff (0.67 #2593, 0.60 #1678, 0.57 #5339), 06x4l_ (0.67 #2417, 0.60 #1502, 0.57 #4703), 0m_v0 (0.67 #2455, 0.60 #1540, 0.57 #4283), 016ntp (0.67 #2434, 0.60 #1519, 0.57 #4262), 0j6cj (0.67 #3548, 0.50 #2633, 0.43 #5836), 023l9y (0.62 #7081, 0.62 #6622, 0.62 #6164) >> Best rule #8431 for best value: >> intensional similarity = 17 >> extensional distance = 8 >> proper extension: 07brj; >> query: (?x7033, 050z2) <- performance_role(?x7033, ?x227), role(?x7033, ?x4425), role(?x7033, ?x1437), role(?x7033, ?x1212), role(?x7033, ?x894), group(?x7033, ?x4715), role(?x211, ?x7033), role(?x7033, ?x3296), role(?x7033, ?x2798), ?x894 = 03m5k, ?x1437 = 01vdm0, ?x2798 = 03qjg, instrumentalists(?x3296, ?x1399), ?x1212 = 07xzm, role(?x5417, ?x3296), role(?x4425, ?x74), ?x5417 = 02w3w >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #4125 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 5 *> proper extension: 01s0ps; *> query: (?x7033, 0197tq) <- performance_role(?x7033, ?x227), role(?x7033, ?x4425), role(?x7033, ?x1432), role(?x7033, ?x1147), group(?x7033, ?x4715), ?x4425 = 0979zs, ?x1432 = 0395lw, role(?x211, ?x7033), ?x1147 = 07kc_, role(?x7033, ?x2460), role(?x1715, ?x7033), instrumentalists(?x2460, ?x680), role(?x2460, ?x214), instrumentalists(?x7033, ?x642) *> conf = 0.71 ranks of expected_values: 2 EVAL 0gkd1 role! 0197tq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 37.000 0.800 http://example.org/music/artist/track_contributions./music/track_contribution/role #21598-08vd2q PRED entity: 08vd2q PRED relation: award PRED expected values: 02pqp12 => 70 concepts (60 used for prediction) PRED predicted values (max 10 best out of 178): 09cm54 (0.35 #312, 0.10 #1944, 0.10 #1710), 0gqwc (0.31 #235, 0.31 #60, 0.27 #234), 0f4x7 (0.28 #260, 0.27 #234, 0.26 #233), 094qd5 (0.27 #234, 0.26 #233, 0.26 #2101), 02pqp12 (0.27 #234, 0.26 #233, 0.26 #2101), 0gs9p (0.27 #234, 0.26 #233, 0.26 #2101), 0gr51 (0.27 #234, 0.26 #233, 0.26 #2101), 02qyntr (0.27 #234, 0.26 #233, 0.26 #2101), 027986c (0.26 #274, 0.07 #1906, 0.06 #1672), 027c95y (0.26 #5355, 0.22 #702, 0.22 #352) >> Best rule #312 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 0yyts; 03hkch7; 05hjnw; 0404j37; 0pd64; 04b2qn; 02r858_; 0yx_w; 0170xl; >> query: (?x3803, 09cm54) <- film(?x5363, ?x3803), award_winner(?x3803, ?x7825), nominated_for(?x3209, ?x3803), ?x3209 = 02w9sd7 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #234 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 62 *> proper extension: 0j8f09z; *> query: (?x3803, ?x749) <- nominated_for(?x1313, ?x3803), nominated_for(?x1245, ?x3803), nominated_for(?x749, ?x3803), ?x1313 = 0gs9p, ?x1245 = 0gqwc, award_winner(?x749, ?x488) *> conf = 0.27 ranks of expected_values: 5 EVAL 08vd2q award 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 70.000 60.000 0.351 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #21597-02_06s PRED entity: 02_06s PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #21, 0.81 #115, 0.81 #230), 02nxhr (0.10 #7, 0.03 #111, 0.03 #198), 07c52 (0.03 #137, 0.03 #167, 0.03 #53), 07z4p (0.02 #55, 0.02 #223, 0.02 #169) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 05sxzwc; 05pbl56; 05_5_22; 035bcl; 03cp4cn; 01svry; 0b6l1st; 07kdkfj; 0g0x9c; 0466s8n; ... >> query: (?x7129, 029j_) <- film(?x722, ?x7129), film_crew_role(?x7129, ?x4305), ?x4305 = 0215hd, nominated_for(?x722, ?x641) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_06s film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.855 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #21596-05zrvfd PRED entity: 05zrvfd PRED relation: award! PRED expected values: 049g_xj => 47 concepts (12 used for prediction) PRED predicted values (max 10 best out of 2046): 028knk (0.67 #526, 0.30 #10657, 0.21 #3902), 0154qm (0.67 #903, 0.28 #11034, 0.21 #4279), 0lpjn (0.67 #766, 0.28 #10897, 0.21 #4142), 07lt7b (0.67 #157, 0.21 #3533, 0.20 #10288), 0mz73 (0.67 #2273, 0.21 #5649, 0.20 #12404), 0n6f8 (0.67 #317, 0.21 #3693, 0.19 #7070), 01tspc6 (0.67 #234, 0.21 #3610, 0.19 #6987), 01xcfy (0.67 #795, 0.21 #4171, 0.19 #7548), 01skmp (0.67 #1953, 0.21 #5329, 0.19 #8706), 02kxbx3 (0.53 #4363, 0.48 #7740, 0.17 #14493) >> Best rule #526 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 09qwmm; 0gqwc; 099cng; 02y_rq5; >> query: (?x2115, 028knk) <- nominated_for(?x2115, ?x3035), ?x3035 = 0j43swk, award(?x9084, ?x2115), award(?x2516, ?x2115), ?x2516 = 043kzcr, award_nominee(?x9084, ?x399) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #378 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 09qwmm; 0gqwc; 099cng; 02y_rq5; *> query: (?x2115, 049g_xj) <- nominated_for(?x2115, ?x3035), ?x3035 = 0j43swk, award(?x9084, ?x2115), award(?x2516, ?x2115), ?x2516 = 043kzcr, award_nominee(?x9084, ?x399) *> conf = 0.50 ranks of expected_values: 36 EVAL 05zrvfd award! 049g_xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 47.000 12.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21595-06jplb PRED entity: 06jplb PRED relation: child! PRED expected values: 0sxdg => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 37): 09b3v (0.11 #529, 0.11 #194, 0.11 #111), 0kx4m (0.11 #174, 0.11 #91, 0.08 #258), 01dtcb (0.10 #462, 0.04 #1128, 0.04 #1211), 03xsby (0.08 #264, 0.07 #349, 0.02 #515), 0l8sx (0.07 #348, 0.06 #597, 0.06 #680), 03rwz3 (0.07 #377, 0.05 #543, 0.04 #626), 086k8 (0.07 #337, 0.03 #503, 0.03 #586), 03phgz (0.07 #375, 0.02 #541, 0.01 #624), 01gb54 (0.07 #447, 0.02 #946, 0.02 #863), 02_l39 (0.05 #814, 0.05 #564, 0.04 #647) >> Best rule #529 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 054lpb6; 049ql1; 0674l0; 0kc9f; >> query: (?x13299, 09b3v) <- film(?x13299, ?x7307), language(?x7307, ?x4442), languages_spoken(?x3584, ?x4442), ?x3584 = 07hwkr >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #633 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 0jz9f; 086k8; 017s11; 016tt2; 03xq0f; 025jfl; 0338lq; 0g1rw; 05qd_; 04f525m; ... *> query: (?x13299, 0sxdg) <- film(?x13299, ?x7307), award(?x7307, ?x289), nominated_for(?x1063, ?x7307), genre(?x7307, ?x53) *> conf = 0.03 ranks of expected_values: 18 EVAL 06jplb child! 0sxdg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 19.000 19.000 0.115 http://example.org/organization/organization/child./organization/organization_relationship/child #21594-0dll_t2 PRED entity: 0dll_t2 PRED relation: film! PRED expected values: 02jt1k => 82 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1135): 0147dk (0.40 #6245, 0.36 #43715, 0.33 #35387), 07swvb (0.12 #697, 0.10 #2778, 0.05 #6943), 01wy5m (0.12 #858, 0.10 #2939, 0.05 #7104), 05fnl9 (0.08 #269, 0.07 #2350, 0.05 #6515), 055c8 (0.08 #542, 0.07 #2623, 0.05 #6788), 0kszw (0.08 #418, 0.07 #2499, 0.04 #14989), 062dn7 (0.08 #661, 0.07 #2742, 0.04 #6907), 07lt7b (0.08 #114, 0.07 #2195, 0.04 #6360), 071ywj (0.08 #509, 0.07 #2590, 0.04 #6755), 01f6zc (0.08 #943, 0.07 #3024, 0.04 #7189) >> Best rule #6245 for best value: >> intensional similarity = 5 >> extensional distance = 52 >> proper extension: 033pf1; >> query: (?x5644, ?x1299) <- genre(?x5644, ?x225), produced_by(?x5644, ?x1299), language(?x5644, ?x254), film(?x574, ?x5644), friend(?x4536, ?x1299) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #10681 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 87 *> proper extension: 0gtsx8c; 02d44q; 0hgnl3t; *> query: (?x5644, 02jt1k) <- film_crew_role(?x5644, ?x1171), ?x1171 = 09vw2b7, film_release_region(?x5644, ?x985), film_release_region(?x5644, ?x279), ?x279 = 0d060g, ?x985 = 0k6nt *> conf = 0.02 ranks of expected_values: 419 EVAL 0dll_t2 film! 02jt1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 82.000 53.000 0.400 http://example.org/film/actor/film./film/performance/film #21593-0739z6 PRED entity: 0739z6 PRED relation: profession PRED expected values: 016z4k 02hrh1q => 107 concepts (106 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.93 #1215, 0.91 #1065, 0.91 #915), 0dxtg (0.39 #314, 0.32 #1364, 0.30 #5115), 01d_h8 (0.39 #1506, 0.37 #2106, 0.37 #1956), 09jwl (0.39 #3020, 0.38 #2570, 0.37 #6771), 03gjzk (0.33 #5867, 0.32 #5117, 0.25 #13355), 0kyk (0.32 #331, 0.19 #1381, 0.15 #3631), 02jknp (0.29 #3608, 0.25 #13355, 0.25 #8), 0nbcg (0.28 #3033, 0.28 #2583, 0.26 #3934), 016z4k (0.28 #1804, 0.28 #2554, 0.27 #3004), 0dz3r (0.27 #3002, 0.25 #2552, 0.23 #3903) >> Best rule #1215 for best value: >> intensional similarity = 5 >> extensional distance = 105 >> proper extension: 04bdqk; >> query: (?x11918, 02hrh1q) <- award(?x11918, ?x3722), award(?x11918, ?x1972), nominated_for(?x11918, ?x3326), ?x1972 = 0gqyl, ceremony(?x3722, ?x873) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 0739z6 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 106.000 0.925 http://example.org/people/person/profession EVAL 0739z6 profession 016z4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 107.000 106.000 0.925 http://example.org/people/person/profession #21592-0sx8l PRED entity: 0sx8l PRED relation: participating_countries PRED expected values: 06c1y 05vz3zq => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 306): 0f8l9c (0.82 #2176, 0.74 #1389, 0.64 #616), 03rjj (0.74 #1389, 0.67 #1395, 0.64 #616), 06mzp (0.74 #1389, 0.64 #616, 0.64 #770), 03_3d (0.74 #1389, 0.64 #616, 0.64 #770), 05vz3zq (0.74 #1389, 0.64 #616, 0.64 #770), 0345h (0.67 #1416, 0.64 #2184, 0.60 #1262), 0k6nt (0.67 #1411, 0.56 #2026, 0.55 #2179), 03rk0 (0.67 #1434, 0.44 #2049, 0.40 #1126), 01p1v (0.64 #2197, 0.60 #1275, 0.50 #1429), 015fr (0.60 #1387, 0.60 #1250, 0.55 #2172) >> Best rule #2176 for best value: >> intensional similarity = 43 >> extensional distance = 9 >> proper extension: 06sks6; >> query: (?x1741, 0f8l9c) <- participating_countries(?x1741, ?x2984), participating_countries(?x1741, ?x456), olympics(?x205, ?x1741), film_release_region(?x9294, ?x2984), film_release_region(?x7628, ?x2984), film_release_region(?x5873, ?x2984), film_release_region(?x4841, ?x2984), film_release_region(?x3998, ?x2984), film_release_region(?x3599, ?x2984), first_level_division_of(?x6265, ?x456), film_release_region(?x4690, ?x456), film_release_region(?x4047, ?x456), film_release_region(?x3619, ?x456), film_release_region(?x3377, ?x456), film_release_region(?x1999, ?x456), film_release_region(?x1701, ?x456), film_release_region(?x1463, ?x456), film_release_region(?x66, ?x456), ?x5873 = 0cq86w, ?x4690 = 0gkz3nz, olympics(?x2984, ?x6893), olympics(?x2984, ?x5176), country(?x150, ?x456), member_states(?x2106, ?x456), ?x1999 = 0gd0c7x, ?x7628 = 0bcp9b, currency(?x456, ?x170), ?x5176 = 0sx92, ?x66 = 014lc_, ?x4841 = 0k4fz, combatants(?x326, ?x456), ?x3599 = 0kxf1, ?x1701 = 0bh8yn3, ?x6893 = 019n8z, ?x4047 = 07s846j, country(?x8990, ?x456), ?x3998 = 0184tc, ?x1463 = 0gtvrv3, ?x3619 = 0fphgb, medal(?x456, ?x422), ?x9294 = 0m3gy, geographic_distribution(?x12168, ?x456), ?x3377 = 0gj8nq2 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1389 for first EXPECTED value: *> intensional similarity = 45 *> extensional distance = 3 *> proper extension: 018ctl; *> query: (?x1741, ?x205) <- participating_countries(?x1741, ?x3728), participating_countries(?x1741, ?x2984), participating_countries(?x1741, ?x1497), participating_countries(?x1741, ?x1353), participating_countries(?x1741, ?x456), participating_countries(?x1741, ?x304), participating_countries(?x1741, ?x94), olympics(?x205, ?x1741), film_release_region(?x10208, ?x2984), film_release_region(?x7628, ?x2984), film_release_region(?x5849, ?x2984), film_release_region(?x5473, ?x2984), film_release_region(?x5139, ?x2984), film_release_region(?x3998, ?x2984), film_release_region(?x3599, ?x2984), film_release_region(?x2676, ?x2984), film_release_region(?x2434, ?x2984), film_release_region(?x204, ?x2984), ?x456 = 05qhw, contains(?x2984, ?x2985), ?x2676 = 0f4m2z, nationality(?x12564, ?x2984), ?x5849 = 02h22, medal(?x3728, ?x422), ?x304 = 0d0vqn, ?x3998 = 0184tc, olympics(?x2984, ?x867), olympics(?x3728, ?x6464), olympics(?x3728, ?x2134), ?x94 = 09c7w0, ?x10208 = 09rfpk, ?x6464 = 0lbd9, ?x7628 = 0bcp9b, ?x2134 = 0blg2, ?x1353 = 035qy, ?x1497 = 015qh, combatants(?x583, ?x3728), ?x5473 = 0hv8w, ?x5139 = 07bzz7, ?x583 = 015fr, gender(?x12564, ?x231), ?x204 = 028_yv, ?x867 = 0l6ny, ?x3599 = 0kxf1, ?x2434 = 085ccd *> conf = 0.74 ranks of expected_values: 5, 24 EVAL 0sx8l participating_countries 05vz3zq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 21.000 21.000 0.818 http://example.org/olympics/olympic_games/participating_countries EVAL 0sx8l participating_countries 06c1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 21.000 21.000 0.818 http://example.org/olympics/olympic_games/participating_countries #21591-0mx0f PRED entity: 0mx0f PRED relation: adjoins! PRED expected values: 0mx2h => 162 concepts (54 used for prediction) PRED predicted values (max 10 best out of 478): 0mx2h (0.81 #25922, 0.80 #40856, 0.80 #39282), 0mxhc (0.81 #25922, 0.80 #40856, 0.80 #39282), 0mx0f (0.31 #1238, 0.27 #1570, 0.26 #39283), 0mx3k (0.27 #1570, 0.26 #39283, 0.15 #1339), 0mx4_ (0.27 #1570, 0.08 #819, 0.07 #3962), 0mx6c (0.23 #894, 0.14 #4037, 0.09 #9537), 0mx5p (0.23 #1448, 0.11 #4591, 0.06 #784), 0k3ll (0.17 #447, 0.07 #2019, 0.06 #2805), 0n5yh (0.17 #245, 0.07 #1817, 0.06 #2603), 0k3j0 (0.17 #706, 0.07 #2278, 0.06 #3064) >> Best rule #25922 for best value: >> intensional similarity = 6 >> extensional distance = 104 >> proper extension: 02m4d; >> query: (?x9568, ?x3067) <- adjoins(?x9568, ?x9962), adjoins(?x9568, ?x3067), county_seat(?x9962, ?x6995), contains(?x726, ?x3067), source(?x9962, ?x958), featured_film_locations(?x407, ?x726) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0mx0f adjoins! 0mx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 54.000 0.809 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #21590-01vsksr PRED entity: 01vsksr PRED relation: group PRED expected values: 07mvp => 130 concepts (39 used for prediction) PRED predicted values (max 10 best out of 71): 01v0sx2 (0.08 #872, 0.06 #330, 0.06 #1089), 07mvp (0.07 #263, 0.06 #371, 0.06 #913), 01wv9xn (0.07 #225, 0.05 #442, 0.05 #550), 02_5x9 (0.07 #228, 0.05 #445, 0.05 #553), 01qqwp9 (0.05 #1537, 0.05 #888, 0.05 #1970), 081wh1 (0.04 #1785, 0.03 #486, 0.03 #2870), 02r1tx7 (0.04 #666, 0.04 #233, 0.03 #3268), 06mj4 (0.04 #282, 0.03 #1690, 0.03 #1798), 0123r4 (0.04 #261, 0.03 #1669, 0.03 #3079), 0qmny (0.04 #289, 0.03 #397, 0.03 #506) >> Best rule #872 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 01k5t_3; 0qdyf; 02z4b_8; >> query: (?x6351, 01v0sx2) <- artists(?x7440, ?x6351), nationality(?x6351, ?x1310), ?x7440 = 0155w, role(?x6351, ?x716) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #263 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 26 *> proper extension: 04_jsg; *> query: (?x6351, 07mvp) <- role(?x6351, ?x716), ?x716 = 018vs, profession(?x6351, ?x131), category(?x6351, ?x134), artist(?x8336, ?x6351) *> conf = 0.07 ranks of expected_values: 2 EVAL 01vsksr group 07mvp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 39.000 0.079 http://example.org/music/group_member/membership./music/group_membership/group #21589-0h1nt PRED entity: 0h1nt PRED relation: location PRED expected values: 0mp3l => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 46): 030qb3t (0.15 #48269, 0.14 #4900, 0.13 #7310), 0cc56 (0.09 #859, 0.04 #48243, 0.03 #56), 0cr3d (0.07 #48331, 0.06 #57970, 0.05 #61182), 04jpl (0.07 #48204, 0.06 #1623, 0.06 #57843), 059rby (0.04 #1622, 0.04 #8047, 0.04 #15274), 01531 (0.03 #960, 0.03 #48344, 0.02 #57983), 01_d4 (0.02 #50598, 0.02 #48288, 0.02 #1707), 013yq (0.02 #50598, 0.02 #5740, 0.01 #9755), 0rd6b (0.02 #50598), 0sjqm (0.02 #50598) >> Best rule #48269 for best value: >> intensional similarity = 2 >> extensional distance = 1858 >> proper extension: 042fk; >> query: (?x1244, 030qb3t) <- location(?x1244, ?x739), citytown(?x166, ?x739) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0h1nt location 0mp3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 87.000 0.146 http://example.org/people/person/places_lived./people/place_lived/location #21588-05k7sb PRED entity: 05k7sb PRED relation: contains PRED expected values: 0qkcb 0k3ll 0tz1j => 130 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2732): 01lxw6 (0.80 #8639, 0.61 #155526, 0.60 #181448), 04rwx (0.72 #123846, 0.55 #123847, 0.53 #17281), 01kvrz (0.72 #123846, 0.55 #123847, 0.53 #17281), 043q2z (0.72 #123846, 0.55 #123847, 0.53 #17281), 07lx1s (0.72 #123846, 0.55 #123847, 0.53 #17281), 014zws (0.72 #123846, 0.53 #17281, 0.49 #118082), 02kj7g (0.72 #123846, 0.53 #17281, 0.49 #118082), 017hnw (0.72 #123846, 0.53 #17281, 0.49 #118082), 01_f90 (0.72 #123846, 0.53 #17281, 0.49 #118082), 0gl5_ (0.72 #123846, 0.53 #17281, 0.49 #118082) >> Best rule #8639 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 09hzw; >> query: (?x2020, ?x3007) <- state(?x3007, ?x2020), contains(?x94, ?x2020), administrative_parent(?x12957, ?x2020) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #155526 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 121 *> proper extension: 05rgl; 0jcg8; 0jxgx; 017wh; 0clzr; *> query: (?x2020, ?x9504) <- contains(?x2020, ?x4990), contains(?x2020, ?x3115), category(?x3115, ?x134), adjoins(?x4990, ?x9504) *> conf = 0.61 ranks of expected_values: 12, 31, 33 EVAL 05k7sb contains 0tz1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 130.000 94.000 0.797 http://example.org/location/location/contains EVAL 05k7sb contains 0k3ll CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 130.000 94.000 0.797 http://example.org/location/location/contains EVAL 05k7sb contains 0qkcb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 130.000 94.000 0.797 http://example.org/location/location/contains #21587-04v8x9 PRED entity: 04v8x9 PRED relation: titles! PRED expected values: 024qqx => 73 concepts (38 used for prediction) PRED predicted values (max 10 best out of 67): 07ssc (0.83 #831, 0.12 #932, 0.11 #10), 04xvlr (0.38 #825, 0.27 #926, 0.23 #2375), 07s9rl0 (0.36 #923, 0.35 #1954, 0.35 #1849), 03k9fj (0.34 #2163, 0.22 #1024, 0.22 #1953), 02l7c8 (0.34 #2163, 0.22 #1024, 0.22 #1953), 01z4y (0.27 #36, 0.18 #3335, 0.17 #3540), 03g3w (0.22 #1024, 0.22 #1953, 0.19 #2888), 02kdv5l (0.22 #1024, 0.22 #1953, 0.19 #2888), 024qqx (0.17 #696, 0.14 #1309, 0.10 #184), 017fp (0.16 #435, 0.14 #128, 0.11 #946) >> Best rule #831 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x499, 07ssc) <- titles(?x4757, ?x499), titles(?x4757, ?x3514), ?x3514 = 04vh83 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #696 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 123 *> proper extension: 02gs6r; *> query: (?x499, 024qqx) <- award_winner(?x499, ?x877), genre(?x499, ?x811), currency(?x499, ?x170), ?x811 = 03k9fj *> conf = 0.17 ranks of expected_values: 9 EVAL 04v8x9 titles! 024qqx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 73.000 38.000 0.833 http://example.org/media_common/netflix_genre/titles #21586-02__7n PRED entity: 02__7n PRED relation: profession PRED expected values: 03gjzk => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 47): 01d_h8 (0.33 #2374, 0.32 #5334, 0.32 #1486), 0dxtg (0.29 #5934, 0.27 #12893, 0.27 #7118), 0nbcg (0.29 #179, 0.14 #623, 0.13 #8765), 03gjzk (0.25 #15, 0.24 #7119, 0.23 #1495), 02jknp (0.22 #5336, 0.21 #2376, 0.21 #8593), 0np9r (0.20 #3424, 0.20 #2980, 0.18 #1056), 0dz3r (0.14 #150, 0.12 #8736, 0.12 #2222), 0cbd2 (0.14 #10962, 0.12 #14218, 0.12 #13034), 018gz8 (0.13 #2977, 0.13 #905, 0.13 #6085), 01c72t (0.12 #23, 0.09 #8016, 0.09 #8757) >> Best rule #2374 for best value: >> intensional similarity = 3 >> extensional distance = 689 >> proper extension: 0q9kd; 0grwj; 012d40; 01j5ts; 042l3v; 0h5f5n; 01p7yb; 0p_pd; 041h0; 0159h6; ... >> query: (?x7268, 01d_h8) <- location(?x7268, ?x3689), award_winner(?x704, ?x7268), award_winner(?x3404, ?x7268) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 01507p; *> query: (?x7268, 03gjzk) <- location(?x7268, ?x3689), ?x3689 = 019fh, nominated_for(?x7268, ?x3404) *> conf = 0.25 ranks of expected_values: 4 EVAL 02__7n profession 03gjzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 112.000 112.000 0.333 http://example.org/people/person/profession #21585-02qd04y PRED entity: 02qd04y PRED relation: nominated_for! PRED expected values: 01t2h2 => 99 concepts (24 used for prediction) PRED predicted values (max 10 best out of 462): 024rdh (0.33 #1315, 0.20 #13003, 0.06 #8327), 017s11 (0.33 #100, 0.06 #7112, 0.05 #9451), 069_0y (0.24 #8664, 0.12 #13340, 0.10 #11003), 02rr_z4 (0.19 #14028), 01f873 (0.18 #9236, 0.16 #13912, 0.05 #11575), 01f7v_ (0.18 #7909, 0.12 #12585, 0.04 #9350), 02404v (0.18 #8665, 0.12 #13341, 0.02 #34391), 03cp7b3 (0.18 #8905, 0.08 #13581, 0.01 #20597), 0bytkq (0.14 #2994, 0.12 #5331, 0.05 #10008), 02mxbd (0.14 #3589, 0.12 #5926, 0.04 #19956) >> Best rule #1315 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 05g8pg; >> query: (?x9175, 024rdh) <- nominated_for(?x8117, ?x9175), ?x8117 = 0dgr5xp, film_crew_role(?x9175, ?x137), genre(?x9175, ?x162), country(?x9175, ?x2346) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5044 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 09sh8k; 0btbyn; 026qnh6; *> query: (?x9175, 01t2h2) <- nominated_for(?x8117, ?x9175), award(?x147, ?x8117), language(?x9175, ?x5974), ?x5974 = 01r2l, film_crew_role(?x9175, ?x137) *> conf = 0.12 ranks of expected_values: 33 EVAL 02qd04y nominated_for! 01t2h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 99.000 24.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #21584-0bt4r4 PRED entity: 0bt4r4 PRED relation: profession PRED expected values: 0dxtg => 92 concepts (91 used for prediction) PRED predicted values (max 10 best out of 56): 0dxtg (0.75 #307, 0.75 #13, 0.66 #602), 02krf9 (0.50 #319, 0.50 #25, 0.39 #442), 01d_h8 (0.42 #300, 0.42 #595, 0.40 #890), 018gz8 (0.39 #442, 0.39 #1473, 0.38 #737), 0kyk (0.39 #442, 0.39 #1473, 0.38 #737), 02jknp (0.39 #442, 0.39 #1473, 0.38 #737), 0np9r (0.39 #442, 0.39 #1473, 0.38 #737), 015cjr (0.39 #442, 0.39 #1473, 0.38 #737), 08z956 (0.39 #442, 0.39 #1473, 0.38 #737), 09jwl (0.39 #442, 0.38 #737, 0.30 #7356) >> Best rule #307 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0cj2nl; >> query: (?x2912, 0dxtg) <- award_winner(?x2819, ?x2912), award_winner(?x2602, ?x2912), ?x2819 = 0bczgm, profession(?x2602, ?x319) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bt4r4 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 91.000 0.750 http://example.org/people/person/profession #21583-038nv6 PRED entity: 038nv6 PRED relation: nationality PRED expected values: 09c7w0 => 79 concepts (75 used for prediction) PRED predicted values (max 10 best out of 79): 09c7w0 (0.90 #201, 0.89 #101, 0.76 #802), 07ssc (0.27 #2707, 0.27 #3711, 0.10 #716), 0345h (0.27 #2707, 0.27 #3711, 0.05 #431), 0d060g (0.12 #7, 0.06 #407, 0.05 #1711), 02jx1 (0.11 #3342, 0.11 #2740, 0.10 #3241), 03rk0 (0.11 #1649, 0.10 #1248, 0.10 #2252), 0h7x (0.03 #435, 0.02 #1939, 0.02 #736), 03rt9 (0.03 #313, 0.03 #714, 0.02 #1917), 03_3d (0.03 #1810, 0.03 #2112, 0.02 #2011), 0f8l9c (0.02 #1224, 0.02 #1926, 0.02 #1023) >> Best rule #201 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 05m63c; 02yplc; 04cr6qv; 0p_r5; >> query: (?x14340, 09c7w0) <- profession(?x14340, ?x1032), film(?x14340, ?x1454), people(?x1446, ?x14340), ?x1446 = 033tf_ >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 038nv6 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 75.000 0.900 http://example.org/people/person/nationality #21582-02r34n PRED entity: 02r34n PRED relation: profession PRED expected values: 02jknp => 122 concepts (33 used for prediction) PRED predicted values (max 10 best out of 82): 0cbd2 (0.53 #1761, 0.47 #2785, 0.46 #3223), 01c72t (0.50 #4553, 0.12 #606, 0.12 #4115), 09jwl (0.44 #1040, 0.38 #4111, 0.30 #2650), 03gjzk (0.38 #598, 0.24 #1914, 0.23 #1328), 01d_h8 (0.36 #1906, 0.33 #882, 0.32 #3368), 018gz8 (0.29 #600, 0.28 #1330, 0.22 #1916), 016z4k (0.27 #4097, 0.20 #1026, 0.17 #2636), 0nbcg (0.27 #4122, 0.26 #1051, 0.21 #2661), 0d2ww (0.25 #87), 0dz3r (0.24 #4095, 0.17 #1024, 0.17 #4533) >> Best rule #1761 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 032l1; 040_9; 0hgqq; 01v9724; 0424m; 030dr; >> query: (?x1188, 0cbd2) <- profession(?x1188, ?x2225), ?x2225 = 0kyk, religion(?x1188, ?x7422) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #2932 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 301 *> proper extension: 0cwtm; *> query: (?x1188, 02jknp) <- film(?x1188, ?x1452), student(?x10175, ?x1188), film_release_region(?x1452, ?x87), film_regional_debut_venue(?x1452, ?x12806) *> conf = 0.19 ranks of expected_values: 12 EVAL 02r34n profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 122.000 33.000 0.535 http://example.org/people/person/profession #21581-06mzp PRED entity: 06mzp PRED relation: olympics PRED expected values: 0jhn7 0sx92 0lbd9 => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 8): 0jhn7 (0.78 #65, 0.77 #209, 0.76 #49), 0ldqf (0.68 #47, 0.62 #55, 0.58 #39), 0124ld (0.61 #633, 0.60 #747, 0.60 #730), 018qb4 (0.56 #27, 0.53 #43, 0.48 #51), 0lbd9 (0.53 #44, 0.52 #52, 0.48 #68), 018ljb (0.47 #45, 0.44 #29, 0.38 #53), 01f1jf (0.44 #32, 0.32 #48, 0.29 #56), 0sx92 (0.37 #42, 0.33 #26, 0.32 #34) >> Best rule #65 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 02j71; >> query: (?x774, 0jhn7) <- currency(?x774, ?x170), administrative_parent(?x5291, ?x774), service_location(?x896, ?x774) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 8 EVAL 06mzp olympics 0lbd9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 182.000 182.000 0.783 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 06mzp olympics 0sx92 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 182.000 182.000 0.783 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 06mzp olympics 0jhn7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 182.000 0.783 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #21580-01wbl_r PRED entity: 01wbl_r PRED relation: artists! PRED expected values: 0glt670 0233qs => 96 concepts (86 used for prediction) PRED predicted values (max 10 best out of 190): 06by7 (0.60 #12736, 0.59 #13046, 0.56 #13356), 025sc50 (0.55 #670, 0.52 #980, 0.40 #5010), 0gywn (0.43 #988, 0.29 #5018, 0.25 #678), 02lnbg (0.40 #679, 0.38 #989, 0.25 #5019), 0ggx5q (0.35 #698, 0.29 #1008, 0.25 #5038), 05bt6j (0.33 #9344, 0.31 #5004, 0.30 #664), 016clz (0.30 #13339, 0.28 #13959, 0.25 #13029), 0glt670 (0.30 #10893, 0.29 #5001, 0.29 #4071), 01lyv (0.22 #7474, 0.22 #8714, 0.22 #5924), 0y3_8 (0.20 #668, 0.12 #9348, 0.10 #10900) >> Best rule #12736 for best value: >> intensional similarity = 3 >> extensional distance = 646 >> proper extension: 053y0s; 0c9d9; 089tm; 01t_xp_; 0m19t; 02rgz4; 01nqfh_; 01cv3n; 0150jk; 0274ck; ... >> query: (?x2031, 06by7) <- artists(?x671, ?x2031), artists(?x671, ?x7018), ?x7018 = 01sxd1 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #10893 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 448 *> proper extension: 024zq; 03_gx; *> query: (?x2031, 0glt670) <- artists(?x671, ?x2031), artists(?x671, ?x5901), ?x5901 = 01wgfp6 *> conf = 0.30 ranks of expected_values: 8, 41 EVAL 01wbl_r artists! 0233qs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 96.000 86.000 0.600 http://example.org/music/genre/artists EVAL 01wbl_r artists! 0glt670 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 96.000 86.000 0.600 http://example.org/music/genre/artists #21579-021yzs PRED entity: 021yzs PRED relation: gender PRED expected values: 05zppz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #11, 0.88 #3, 0.88 #7), 02zsn (0.30 #124, 0.29 #112, 0.29 #114) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 0hwd8; >> query: (?x4764, 05zppz) <- nationality(?x4764, ?x1310), award(?x4764, ?x3066), ?x3066 = 0gqy2 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021yzs gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.903 http://example.org/people/person/gender #21578-011yd2 PRED entity: 011yd2 PRED relation: film_crew_role PRED expected values: 02r96rf => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 25): 02r96rf (0.78 #183, 0.72 #1918, 0.71 #363), 0dxtw (0.39 #190, 0.37 #1307, 0.36 #370), 01vx2h (0.37 #191, 0.33 #371, 0.32 #1926), 01pvkk (0.29 #1092, 0.28 #1237, 0.28 #1744), 02ynfr (0.21 #196, 0.18 #1313, 0.17 #376), 0215hd (0.15 #199, 0.14 #1099, 0.13 #1171), 089g0h (0.14 #200, 0.13 #1100, 0.12 #1245), 0d2b38 (0.14 #206, 0.11 #1178, 0.11 #386), 01xy5l_ (0.12 #194, 0.12 #1094, 0.11 #374), 02rh1dz (0.11 #189, 0.10 #369, 0.10 #153) >> Best rule #183 for best value: >> intensional similarity = 3 >> extensional distance = 247 >> proper extension: 047qxs; 014nq4; 07p12s; >> query: (?x2215, 02r96rf) <- film_crew_role(?x2215, ?x1284), film_format(?x2215, ?x909), ?x1284 = 0ch6mp2 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011yd2 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.775 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21577-0flw6 PRED entity: 0flw6 PRED relation: award PRED expected values: 02w9sd7 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 277): 02g2wv (0.74 #13501, 0.71 #23832, 0.70 #31382), 027986c (0.74 #13501, 0.71 #23832, 0.70 #31382), 05pcn59 (0.40 #477, 0.33 #874, 0.22 #2859), 0gqyl (0.40 #501, 0.33 #898, 0.16 #2883), 0ck27z (0.37 #10811, 0.17 #885, 0.14 #22333), 0bfvd4 (0.33 #113, 0.20 #510, 0.17 #907), 02x73k6 (0.33 #59, 0.20 #456, 0.17 #853), 0cqh46 (0.33 #50, 0.20 #447, 0.17 #844), 0bp_b2 (0.33 #18, 0.20 #415, 0.17 #812), 02grdc (0.33 #31, 0.20 #428, 0.17 #825) >> Best rule #13501 for best value: >> intensional similarity = 2 >> extensional distance = 745 >> proper extension: 0kc6x; 065y4w7; 01y67v; 01jq34; 03yxwq; 0gsgr; 0kc8y; 02p10m; 05s34b; 01fkr_; ... >> query: (?x4324, ?x591) <- award_winner(?x591, ?x4324), category(?x4324, ?x134) >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #20253 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1070 *> proper extension: 03czrpj; 05d6q1; *> query: (?x4324, ?x384) <- award_winner(?x4224, ?x4324), nominated_for(?x4324, ?x7635), nominated_for(?x384, ?x7635) *> conf = 0.13 ranks of expected_values: 33 EVAL 0flw6 award 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 120.000 120.000 0.739 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21576-073tm9 PRED entity: 073tm9 PRED relation: industry PRED expected values: 04rlf => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 44): 020mfr (0.62 #2913, 0.56 #2637, 0.50 #567), 02vxn (0.57 #1519, 0.57 #3727, 0.55 #1381), 04rlf (0.33 #12, 0.32 #1898, 0.31 #2036), 03qh03g (0.26 #3730, 0.23 #1016, 0.21 #2028), 0hz28 (0.18 #5982, 0.15 #1500, 0.12 #1960), 029g_vk (0.18 #5982, 0.13 #1803, 0.12 #561), 01mf0 (0.18 #5982, 0.12 #1179, 0.12 #581), 01mfj (0.18 #5982, 0.12 #1185, 0.09 #2197), 011s0 (0.18 #5982, 0.12 #606, 0.09 #1618), 06mbny (0.18 #5982, 0.12 #625, 0.08 #1039) >> Best rule #2913 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 01t7jy; 01qf54; >> query: (?x6230, 020mfr) <- industry(?x6230, ?x245), citytown(?x6230, ?x739), industry(?x5961, ?x245), ?x5961 = 0123j6 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 02bh8z; *> query: (?x6230, 04rlf) <- artist(?x6230, ?x827), company(?x265, ?x6230), award_nominee(?x4836, ?x827), ?x4836 = 0837ql *> conf = 0.33 ranks of expected_values: 3 EVAL 073tm9 industry 04rlf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 161.000 161.000 0.625 http://example.org/business/business_operation/industry #21575-0crjn65 PRED entity: 0crjn65 PRED relation: place_of_birth! PRED expected values: 0133sq => 86 concepts (44 used for prediction) PRED predicted values (max 10 best out of 54): 01tp5bj (0.33 #465, 0.11 #3078, 0.10 #8304), 0dx97 (0.11 #3688, 0.10 #8914, 0.10 #6301), 0fvt2 (0.10 #7505, 0.05 #23185, 0.04 #28413), 02465 (0.10 #7500, 0.05 #23180, 0.04 #28408), 07rhpg (0.10 #6875, 0.05 #22555, 0.04 #27783), 03bnv (0.10 #5864, 0.05 #21544, 0.04 #26772), 01vrnsk (0.10 #6662, 0.05 #22342, 0.04 #27570), 025t9b (0.10 #5987, 0.05 #21667, 0.04 #26895), 098sx (0.08 #12628, 0.07 #17854, 0.06 #20468), 066yfh (0.08 #15505) >> Best rule #465 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 01hvzr; >> query: (?x1757, 01tp5bj) <- contains(?x1310, ?x1757), contains(?x512, ?x1757), ?x1310 = 02jx1, administrative_parent(?x1757, ?x1758), ?x1758 = 0dbdy, ?x512 = 07ssc >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0crjn65 place_of_birth! 0133sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 44.000 0.333 http://example.org/people/person/place_of_birth #21574-0fv4v PRED entity: 0fv4v PRED relation: adjoins! PRED expected values: 035dk => 96 concepts (86 used for prediction) PRED predicted values (max 10 best out of 365): 035dk (0.82 #61734, 0.82 #62516, 0.82 #64866), 06srk (0.25 #378, 0.23 #63299, 0.23 #62517), 088xp (0.24 #3263, 0.24 #2482, 0.21 #4826), 05cc1 (0.23 #63299, 0.23 #62517, 0.23 #60951), 06s_2 (0.23 #63299, 0.23 #62517, 0.23 #60951), 0fv4v (0.23 #63299, 0.23 #62517, 0.23 #60951), 0164v (0.23 #63299, 0.23 #62517, 0.23 #60951), 0h3y (0.23 #63299, 0.23 #62517, 0.23 #60951), 036b_ (0.23 #63299, 0.23 #62517, 0.23 #60951), 07f5x (0.23 #63299, 0.23 #62517, 0.23 #64083) >> Best rule #61734 for best value: >> intensional similarity = 3 >> extensional distance = 731 >> proper extension: 0glb5; 0135k2; 0mhdz; >> query: (?x7360, ?x2051) <- adjoins(?x6431, ?x7360), adjoins(?x7360, ?x2051), adjoins(?x6827, ?x6431) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fv4v adjoins! 035dk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 86.000 0.824 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #21573-0ftps PRED entity: 0ftps PRED relation: group PRED expected values: 0b_xm => 182 concepts (41 used for prediction) PRED predicted values (max 10 best out of 92): 02r1tx7 (0.11 #340, 0.04 #3048, 0.04 #3921), 0b_xm (0.10 #60, 0.04 #2009, 0.04 #1900), 081wh1 (0.10 #52, 0.03 #1783, 0.03 #3520), 06mj4 (0.10 #65, 0.02 #4295, 0.02 #2556), 02ndj5 (0.10 #83, 0.01 #1814, 0.01 #1923), 01v0sx2 (0.10 #437, 0.06 #3255, 0.06 #2279), 01qqwp9 (0.08 #1210, 0.07 #1643, 0.06 #777), 0123r4 (0.07 #1666, 0.07 #260, 0.07 #1775), 09jm8 (0.07 #304, 0.03 #736, 0.03 #952), 07c0j (0.06 #328, 0.04 #1193, 0.03 #1626) >> Best rule #340 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 01vn0t_; >> query: (?x1407, 02r1tx7) <- instrumentalists(?x3716, ?x1407), role(?x1407, ?x316), artists(?x1572, ?x1407), ?x1572 = 06by7, ?x3716 = 03gvt >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #60 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 02vgh; *> query: (?x1407, 0b_xm) <- artists(?x1380, ?x1407), artists(?x474, ?x1407), ?x1380 = 0dl5d, category(?x1407, ?x134), ?x474 = 0m0jc *> conf = 0.10 ranks of expected_values: 2 EVAL 0ftps group 0b_xm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 182.000 41.000 0.111 http://example.org/music/group_member/membership./music/group_membership/group #21572-0c0yh4 PRED entity: 0c0yh4 PRED relation: language PRED expected values: 064_8sq => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 51): 04h9h (0.33 #42, 0.20 #100, 0.09 #159), 06mp7 (0.20 #73, 0.04 #4422, 0.02 #309), 064_8sq (0.15 #432, 0.15 #1202, 0.14 #1084), 04306rv (0.14 #298, 0.12 #357, 0.11 #415), 06nm1 (0.11 #244, 0.10 #1014, 0.09 #1191), 06b_j (0.11 #197, 0.10 #256, 0.08 #316), 0653m (0.09 #128, 0.04 #4422, 0.04 #305), 0jzc (0.09 #194, 0.08 #253, 0.07 #313), 02bjrlw (0.08 #945, 0.08 #472, 0.07 #354), 03_9r (0.05 #3778, 0.04 #4962, 0.04 #4422) >> Best rule #42 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 05dl1s; >> query: (?x278, 04h9h) <- country(?x278, ?x279), nominated_for(?x9170, ?x278), nominated_for(?x3828, ?x278), ?x3828 = 0fqyzz, place_of_death(?x9170, ?x1523) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #432 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 263 *> proper extension: 061681; 04w7rn; 091z_p; 02ctc6; 017kct; 03cw411; 0sxkh; 06nr2h; 0j80w; 03mz5b; ... *> query: (?x278, 064_8sq) <- country(?x278, ?x279), nominated_for(?x548, ?x278), films(?x14144, ?x278), award(?x278, ?x2599) *> conf = 0.15 ranks of expected_values: 3 EVAL 0c0yh4 language 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.333 http://example.org/film/film/language #21571-0ds3t5x PRED entity: 0ds3t5x PRED relation: language PRED expected values: 02h40lc => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 40): 02h40lc (0.91 #1319, 0.89 #1140, 0.89 #482), 06nm1 (0.23 #190, 0.13 #310, 0.12 #791), 064_8sq (0.19 #1339, 0.18 #321, 0.15 #1518), 04306rv (0.11 #65, 0.11 #1322, 0.09 #1501), 04h9h (0.11 #103, 0.09 #342, 0.08 #222), 03_9r (0.11 #70, 0.05 #1087, 0.05 #5038), 0t_2 (0.08 #193, 0.03 #971, 0.02 #675), 02bjrlw (0.07 #300, 0.07 #1497, 0.06 #1618), 06b_j (0.06 #1579, 0.06 #1459, 0.06 #2237), 0jzc (0.05 #319, 0.05 #259, 0.04 #1576) >> Best rule #1319 for best value: >> intensional similarity = 3 >> extensional distance = 257 >> proper extension: 011yfd; >> query: (?x385, 02h40lc) <- award(?x385, ?x618), nominated_for(?x1307, ?x385), ?x1307 = 0gq9h >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ds3t5x language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.911 http://example.org/film/film/language #21570-04nw9 PRED entity: 04nw9 PRED relation: type_of_union PRED expected values: 04ztj => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.90 #89, 0.87 #214, 0.87 #117), 01g63y (0.26 #102, 0.26 #142, 0.26 #110), 01bl8s (0.06 #15), 0jgjn (0.05 #28, 0.02 #64, 0.02 #76) >> Best rule #89 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 012d40; 0byfz; 014zcr; 04wqr; 03m8lq; 01pcq3; 03pmty; 0151w_; 0lk90; 03lt8g; ... >> query: (?x1545, 04ztj) <- location_of_ceremony(?x1545, ?x1755), film(?x1545, ?x6604), award_winner(?x8924, ?x1545) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04nw9 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.897 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21569-0127gn PRED entity: 0127gn PRED relation: award_winner! PRED expected values: 03x3wf => 148 concepts (135 used for prediction) PRED predicted values (max 10 best out of 297): 024vjd (0.50 #621, 0.20 #193, 0.15 #43660), 024_41 (0.33 #723, 0.20 #295, 0.15 #43660), 024_fw (0.20 #244, 0.17 #672, 0.15 #43660), 024_dt (0.20 #379, 0.17 #807, 0.15 #43660), 0257__ (0.20 #388, 0.17 #816, 0.15 #43660), 03x3wf (0.20 #65, 0.15 #43660, 0.15 #45373), 024dzn (0.20 #322, 0.15 #43660, 0.15 #45373), 02qkk9_ (0.20 #234, 0.15 #43660, 0.15 #45373), 02grdc (0.20 #32, 0.15 #43660, 0.15 #45373), 01by1l (0.19 #1825, 0.19 #12098, 0.15 #15094) >> Best rule #621 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01dhpj; >> query: (?x5132, 024vjd) <- award(?x5132, ?x2703), award_nominee(?x5132, ?x5150), ?x2703 = 0257w4 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #65 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 03_0p; 016k62; 0bvzp; *> query: (?x5132, 03x3wf) <- award(?x5132, ?x2703), award_nominee(?x5125, ?x5132), ?x5125 = 0149xx, award_winner(?x2324, ?x5132) *> conf = 0.20 ranks of expected_values: 6 EVAL 0127gn award_winner! 03x3wf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 148.000 135.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #21568-03_dj PRED entity: 03_dj PRED relation: languages PRED expected values: 02h40lc => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 16): 02h40lc (0.32 #782, 0.26 #860, 0.24 #899), 06nm1 (0.12 #84, 0.10 #162, 0.06 #396), 064_8sq (0.12 #93, 0.10 #171, 0.05 #483), 02bjrlw (0.12 #79, 0.10 #157, 0.05 #469), 04306rv (0.12 #81, 0.10 #159, 0.05 #471), 0349s (0.12 #110, 0.10 #188, 0.05 #500), 04h9h (0.12 #108, 0.10 #186, 0.05 #498), 03hkp (0.12 #88, 0.10 #166, 0.05 #478), 03x42 (0.06 #814, 0.06 #463, 0.05 #892), 03k50 (0.05 #940, 0.04 #2075, 0.03 #4730) >> Best rule #782 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0h0jz; 02g8h; 0h5g_; 018dnt; 01vs14j; 015rkw; 0hvb2; 01vvpjj; 059t6d; 01vswwx; ... >> query: (?x12345, 02h40lc) <- profession(?x12345, ?x353), nationality(?x12345, ?x429), ?x429 = 03rt9, location(?x12345, ?x1591) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_dj languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.323 http://example.org/people/person/languages #21567-014g9y PRED entity: 014g9y PRED relation: gender PRED expected values: 02zsn => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #13, 0.87 #7, 0.87 #3), 02zsn (0.51 #123, 0.50 #44, 0.45 #12) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 05drq5; >> query: (?x10675, 05zppz) <- award(?x10675, ?x601), award(?x10675, ?x384), ?x601 = 0gr4k, nominated_for(?x384, ?x195) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1497 *> proper extension: 0c9d9; 06y9c2; 04n7njg; 05cv94; 01d494; 045bs6; 0453t; 01gp_x; 06rnl9; 06pwf6; ... *> query: (?x10675, ?x231) <- type_of_union(?x10675, ?x1873), student(?x6611, ?x10675), student(?x6611, ?x9354), gender(?x9354, ?x231) *> conf = 0.51 ranks of expected_values: 2 EVAL 014g9y gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.873 http://example.org/people/person/gender #21566-09byk PRED entity: 09byk PRED relation: film PRED expected values: 03s6l2 => 139 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1106): 03m8y5 (0.25 #408, 0.14 #5769, 0.08 #11130), 01qb5d (0.25 #138, 0.14 #5499, 0.08 #10860), 06gb1w (0.25 #733, 0.14 #6094, 0.08 #11455), 0d90m (0.25 #8, 0.14 #5369, 0.08 #10730), 01npcx (0.25 #964, 0.14 #6325, 0.08 #11686), 01svry (0.25 #1192, 0.14 #6553, 0.08 #11914), 0hhggmy (0.25 #1465, 0.14 #6826, 0.08 #12187), 0bxxzb (0.25 #1177, 0.14 #6538, 0.08 #11899), 03mgx6z (0.25 #1002, 0.14 #6363, 0.08 #11724), 01j8wk (0.25 #327, 0.14 #5688, 0.08 #11049) >> Best rule #408 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 015n8; >> query: (?x731, 03m8y5) <- gender(?x731, ?x231), nationality(?x731, ?x1229), ?x1229 = 059j2, location(?x731, ?x8252) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #26892 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 83 *> proper extension: 086qd; *> query: (?x731, 03s6l2) <- actor(?x8062, ?x731), profession(?x731, ?x319), location(?x731, ?x8252), people(?x5269, ?x731), ?x319 = 01d_h8 *> conf = 0.01 ranks of expected_values: 1046 EVAL 09byk film 03s6l2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 139.000 42.000 0.250 http://example.org/film/actor/film./film/performance/film #21565-04zjxcz PRED entity: 04zjxcz PRED relation: nutrient! PRED expected values: 0dj75 0frq6 => 56 concepts (52 used for prediction) PRED predicted values (max 10 best out of 12): 0frq6 (0.89 #427, 0.89 #30, 0.88 #54), 0dj75 (0.89 #30, 0.88 #54, 0.88 #22), 06x4c (0.89 #30, 0.88 #54, 0.88 #22), 0dcfv (0.89 #30, 0.88 #54, 0.88 #22), 01sh2 (0.04 #509, 0.02 #178, 0.02 #487), 04k8n (0.04 #509, 0.02 #178, 0.02 #487), 05wvs (0.04 #509, 0.02 #178, 0.02 #487), 025rw19 (0.02 #499), 025tkqy (0.02 #499), 014d7f (0.02 #499) >> Best rule #427 for best value: >> intensional similarity = 108 >> extensional distance = 26 >> proper extension: 0f4k5; >> query: (?x6033, 0frq6) <- nutrient(?x9732, ?x6033), nutrient(?x9005, ?x6033), nutrient(?x6285, ?x6033), nutrient(?x6191, ?x6033), nutrient(?x6159, ?x6033), nutrient(?x6032, ?x6033), nutrient(?x5373, ?x6033), nutrient(?x3468, ?x6033), nutrient(?x1959, ?x6033), nutrient(?x1303, ?x6033), nutrient(?x1257, ?x6033), ?x9005 = 04zpv, ?x5373 = 0971v, nutrient(?x1257, ?x14698), nutrient(?x1257, ?x13944), nutrient(?x1257, ?x13498), nutrient(?x1257, ?x12454), nutrient(?x1257, ?x12083), nutrient(?x1257, ?x11758), nutrient(?x1257, ?x11592), nutrient(?x1257, ?x11409), nutrient(?x1257, ?x11270), nutrient(?x1257, ?x10891), nutrient(?x1257, ?x9915), nutrient(?x1257, ?x9733), nutrient(?x1257, ?x9490), nutrient(?x1257, ?x9436), nutrient(?x1257, ?x9426), nutrient(?x1257, ?x9365), nutrient(?x1257, ?x8442), nutrient(?x1257, ?x8413), nutrient(?x1257, ?x7894), nutrient(?x1257, ?x7720), nutrient(?x1257, ?x7652), nutrient(?x1257, ?x7364), nutrient(?x1257, ?x7362), nutrient(?x1257, ?x7219), nutrient(?x1257, ?x7135), nutrient(?x1257, ?x6586), nutrient(?x1257, ?x5526), nutrient(?x1257, ?x5451), nutrient(?x1257, ?x5374), nutrient(?x1257, ?x5337), nutrient(?x1257, ?x2702), nutrient(?x1257, ?x2018), nutrient(?x1257, ?x1960), nutrient(?x1257, ?x1258), ?x14698 = 02kb_jm, ?x9426 = 0h1yy, ?x6586 = 05gh50, ?x8442 = 02kcv4x, nutrient(?x6191, ?x12902), nutrient(?x6191, ?x10709), nutrient(?x6191, ?x9949), nutrient(?x6191, ?x9708), nutrient(?x6191, ?x6160), nutrient(?x6191, ?x5010), ?x9708 = 061xhr, ?x1258 = 0h1wg, ?x12902 = 0fzjh, ?x6032 = 01nkt, ?x1960 = 07hnp, ?x13944 = 0f4kp, ?x11409 = 0h1yf, ?x6159 = 033cnk, ?x7219 = 0h1vg, ?x2018 = 01sh2, ?x9436 = 025sqz8, taxonomy(?x5337, ?x939), ?x6160 = 041r51, ?x2702 = 0838f, nutrient(?x1303, ?x1304), ?x939 = 04n6k, ?x11758 = 0q01m, ?x10891 = 0g5gq, ?x7364 = 09gvd, ?x7720 = 025s7x6, nutrient(?x6285, ?x3901), ?x9732 = 05z55, ?x7652 = 025s0s0, ?x13498 = 07q0m, ?x1304 = 08lb68, ?x5451 = 05wvs, ?x11592 = 025sf0_, ?x5526 = 09pbb, ?x11270 = 02kc008, ?x9733 = 0h1tz, ?x10709 = 0h1sz, ?x8413 = 02kc4sf, ?x9490 = 0h1sg, ?x7894 = 0f4hc, ?x7362 = 02kc5rj, ?x3901 = 0466p20, ?x9365 = 04k8n, ?x5374 = 025s0zp, ?x12454 = 025rw19, nutrient(?x1959, ?x13545), nutrient(?x1959, ?x12336), nutrient(?x1959, ?x6517), ?x7135 = 025rsfk, ?x12336 = 0f4l5, ?x6517 = 02kd8zw, ?x12083 = 01n78x, ?x9949 = 02kd0rh, ?x3468 = 0cxn2, ?x13545 = 01w_3, ?x9915 = 025tkqy, ?x5010 = 0h1vz >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04zjxcz nutrient! 0frq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 52.000 0.893 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 04zjxcz nutrient! 0dj75 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 52.000 0.893 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #21564-0286hyp PRED entity: 0286hyp PRED relation: nominated_for! PRED expected values: 040njc => 67 concepts (58 used for prediction) PRED predicted values (max 10 best out of 185): 0gq9h (0.43 #1500, 0.29 #3173, 0.27 #4847), 0gs9p (0.33 #1502, 0.26 #3175, 0.24 #5088), 040njc (0.29 #7, 0.27 #1199, 0.27 #1198), 019f4v (0.28 #1491, 0.27 #1199, 0.27 #1198), 0k611 (0.27 #1511, 0.22 #3184, 0.21 #4858), 0gqy2 (0.27 #1199, 0.27 #1198, 0.25 #5982), 02w9sd7 (0.27 #1199, 0.27 #1198, 0.25 #5982), 04dn09n (0.27 #1199, 0.27 #1198, 0.24 #957), 0gqyl (0.27 #1199, 0.27 #1198, 0.24 #957), 0gqwc (0.27 #1199, 0.27 #1198, 0.24 #957) >> Best rule #1500 for best value: >> intensional similarity = 3 >> extensional distance = 327 >> proper extension: 0sw0q; 023ny6; 07bz5; 0123qq; 015pnb; >> query: (?x14075, 0gq9h) <- nominated_for(?x4896, ?x14075), place_of_death(?x4896, ?x1523), profession(?x4896, ?x3197) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 134 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x14075, 040njc) <- titles(?x512, ?x14075), ?x512 = 07ssc *> conf = 0.29 ranks of expected_values: 3 EVAL 0286hyp nominated_for! 040njc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 67.000 58.000 0.429 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21563-01mkn_d PRED entity: 01mkn_d PRED relation: nominated_for PRED expected values: 03t95n => 92 concepts (7 used for prediction) PRED predicted values (max 10 best out of 297): 03t95n (0.77 #1618), 085wqm (0.37 #8090, 0.32 #8089, 0.31 #9710), 0fphf3v (0.37 #8090, 0.32 #8089, 0.31 #9710), 026f__m (0.37 #8090, 0.32 #8089, 0.31 #9710), 02x3lt7 (0.37 #8090, 0.32 #8089, 0.31 #9710), 03nx8mj (0.37 #8090, 0.32 #8089, 0.31 #9710), 06_wqk4 (0.37 #8090, 0.32 #8089, 0.31 #9710), 01cmp9 (0.14 #952, 0.12 #2570, 0.10 #4187), 09lxv9 (0.14 #1346, 0.12 #2964, 0.10 #4581), 04jpg2p (0.14 #1305, 0.12 #2923, 0.10 #4540) >> Best rule #1618 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 06k02; >> query: (?x6664, ?x3344) <- artists(?x7052, ?x6664), ?x7052 = 0l14gg, award_winner(?x3344, ?x6664) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mkn_d nominated_for 03t95n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 7.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #21562-0j2pg PRED entity: 0j2pg PRED relation: position PRED expected values: 0dgrmp => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 5): 02sdk9v (0.87 #452, 0.87 #446, 0.86 #288), 0dgrmp (0.81 #587, 0.81 #629, 0.81 #628), 03f0fp (0.69 #204, 0.29 #610, 0.02 #175), 02md_2 (0.29 #610, 0.02 #167, 0.01 #222), 02qvgy (0.29 #610) >> Best rule #452 for best value: >> intensional similarity = 11 >> extensional distance = 150 >> proper extension: 0d2psv; 04d817; >> query: (?x2074, ?x63) <- team(?x530, ?x2074), team(?x203, ?x2074), team(?x63, ?x2074), team(?x60, ?x2074), ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x60 = 02nzb8, ?x203 = 0dgrmp, position(?x2074, ?x60), position(?x2074, ?x63), position(?x2074, ?x60) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #587 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 320 *> proper extension: 05xzcz; *> query: (?x2074, ?x530) <- position(?x2074, ?x530), position(?x2074, ?x203), position(?x2074, ?x63), position(?x2074, ?x60), ?x63 = 02sdk9v, ?x60 = 02nzb8, ?x203 = 0dgrmp, position(?x13211, ?x530), position(?x10568, ?x530), ?x10568 = 0dt_q_, ?x13211 = 0j2jr *> conf = 0.81 ranks of expected_values: 2 EVAL 0j2pg position 0dgrmp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 104.000 0.868 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #21561-03y82t6 PRED entity: 03y82t6 PRED relation: award PRED expected values: 02f73p => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 302): 09sb52 (0.39 #7115, 0.34 #22835, 0.34 #10259), 05pcn59 (0.31 #4404, 0.31 #867, 0.23 #6369), 05p09zm (0.27 #4839, 0.27 #909, 0.26 #4446), 01d38g (0.27 #2386, 0.12 #15355, 0.12 #8281), 03c7tr1 (0.26 #4774, 0.23 #844, 0.19 #4381), 04kxsb (0.25 #4448, 0.17 #7199, 0.17 #6413), 0ck27z (0.23 #17777, 0.15 #22886, 0.15 #20135), 054ks3 (0.23 #534, 0.23 #2106, 0.20 #8394), 026mfs (0.23 #521, 0.16 #2486, 0.12 #6023), 099vwn (0.22 #211, 0.15 #604, 0.13 #33013) >> Best rule #7115 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 04sx9_; 0157m; 02f8lw; 03lq43; 0k8y7; 04r7p; 015gjr; 01933d; 01d6jf; 01vh18t; ... >> query: (?x4740, 09sb52) <- spouse(?x7527, ?x4740), award_winner(?x827, ?x4740), film(?x4740, ?x2512) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #33013 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2131 *> proper extension: 0gfmc_; *> query: (?x4740, ?x2180) <- award_nominee(?x4740, ?x4593), award(?x4740, ?x724), award(?x4593, ?x2180) *> conf = 0.13 ranks of expected_values: 34 EVAL 03y82t6 award 02f73p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 103.000 103.000 0.391 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21560-03_1pg PRED entity: 03_1pg PRED relation: film PRED expected values: 0416y94 0h1x5f => 72 concepts (50 used for prediction) PRED predicted values (max 10 best out of 364): 0hz55 (0.34 #78824, 0.34 #77032, 0.33 #69866), 051zy_b (0.33 #580, 0.03 #42995, 0.02 #4162), 04hwbq (0.33 #1983, 0.03 #42995, 0.02 #7356), 025ts_z (0.33 #1494, 0.03 #42995, 0.01 #6867), 07_fj54 (0.33 #846, 0.03 #42995, 0.01 #8010), 0407yj_ (0.33 #2275, 0.03 #42995, 0.01 #7648), 01k1k4 (0.33 #1849, 0.03 #42995, 0.01 #9013), 027s39y (0.33 #2442, 0.03 #42995), 0gtsxr4 (0.33 #2308, 0.03 #42995), 01kjr0 (0.33 #1089, 0.03 #42995) >> Best rule #78824 for best value: >> intensional similarity = 2 >> extensional distance = 2132 >> proper extension: 06x77g; >> query: (?x4933, ?x4932) <- award(?x4933, ?x1670), nominated_for(?x4933, ?x4932) >> conf = 0.34 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_1pg film 0h1x5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 50.000 0.345 http://example.org/film/actor/film./film/performance/film EVAL 03_1pg film 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 50.000 0.345 http://example.org/film/actor/film./film/performance/film #21559-029ghl PRED entity: 029ghl PRED relation: people! PRED expected values: 033tf_ 06v41q => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 48): 07bch9 (0.40 #98, 0.23 #478, 0.21 #706), 01rv7x (0.33 #38, 0.04 #342), 041rx (0.26 #2361, 0.23 #3959, 0.22 #2970), 0x67 (0.23 #390, 0.21 #2595, 0.21 #2367), 02ctzb (0.20 #471, 0.20 #91, 0.18 #699), 033tf_ (0.20 #83, 0.19 #235, 0.17 #919), 063k3h (0.20 #106, 0.12 #182, 0.12 #714), 07hwkr (0.20 #88, 0.08 #2521, 0.07 #2445), 0dryh9k (0.18 #168, 0.09 #472, 0.08 #700), 02w7gg (0.11 #2587, 0.10 #2968, 0.09 #4567) >> Best rule #98 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0bwh6; 0157m; 06c0j; >> query: (?x9301, 07bch9) <- profession(?x9301, ?x319), award_winner(?x1105, ?x9301), politician(?x1912, ?x9301), spouse(?x2841, ?x9301) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #83 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0bwh6; 0157m; 06c0j; *> query: (?x9301, 033tf_) <- profession(?x9301, ?x319), award_winner(?x1105, ?x9301), politician(?x1912, ?x9301), spouse(?x2841, ?x9301) *> conf = 0.20 ranks of expected_values: 6, 18 EVAL 029ghl people! 06v41q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 128.000 128.000 0.400 http://example.org/people/ethnicity/people EVAL 029ghl people! 033tf_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 128.000 128.000 0.400 http://example.org/people/ethnicity/people #21558-01fkv0 PRED entity: 01fkv0 PRED relation: film PRED expected values: 01_mdl 026wlxw => 101 concepts (62 used for prediction) PRED predicted values (max 10 best out of 516): 01_mdl (0.62 #1943, 0.11 #48146, 0.07 #39229), 042g97 (0.38 #3543, 0.11 #48146, 0.07 #39229), 024mxd (0.38 #2386, 0.11 #48146, 0.07 #39229), 09cr8 (0.20 #284, 0.03 #37729, 0.02 #14548), 01gglm (0.20 #1399, 0.02 #6748, 0.01 #15663), 016z9n (0.20 #369, 0.02 #37814, 0.02 #59214), 02q56mk (0.20 #417, 0.01 #5766, 0.01 #37862), 03h_yy (0.20 #73, 0.01 #5422, 0.01 #37518), 02fqxm (0.20 #1772), 0ptdz (0.20 #1751) >> Best rule #1943 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 012c6x; 02mxw0; 0jrny; >> query: (?x1019, 01_mdl) <- film(?x1019, ?x3847), nationality(?x1019, ?x1310), ?x3847 = 0d_wms >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 459 EVAL 01fkv0 film 026wlxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 101.000 62.000 0.625 http://example.org/film/actor/film./film/performance/film EVAL 01fkv0 film 01_mdl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 62.000 0.625 http://example.org/film/actor/film./film/performance/film #21557-047svrl PRED entity: 047svrl PRED relation: film! PRED expected values: 07cjqy => 51 concepts (37 used for prediction) PRED predicted values (max 10 best out of 858): 01wbg84 (0.30 #47, 0.06 #6289, 0.05 #4208), 01fyzy (0.22 #1059, 0.08 #7301, 0.05 #43691), 0p8r1 (0.14 #4747, 0.05 #15148, 0.04 #2666), 01nm3s (0.13 #690, 0.08 #2770, 0.04 #15252), 03xb2w (0.13 #878, 0.05 #43691, 0.05 #56177), 06rq2l (0.13 #1575, 0.05 #43691, 0.05 #56177), 0d608 (0.13 #1303, 0.05 #7545, 0.01 #40831), 0p_pd (0.12 #6296, 0.03 #12536, 0.01 #18777), 05ty4m (0.12 #41610, 0.11 #27047, 0.05 #43691), 0f0kz (0.09 #4677, 0.05 #10918, 0.05 #15078) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 080dwhx; 0kfv9; 0d68qy; 039cq4; >> query: (?x2695, 01wbg84) <- nominated_for(?x1335, ?x2695), titles(?x2480, ?x2695), film(?x1335, ?x7967), ?x7967 = 0f2sx4 >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #13085 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 103 *> proper extension: 08s6mr; *> query: (?x2695, 07cjqy) <- film(?x1335, ?x2695), film_crew_role(?x2695, ?x5136), film(?x541, ?x2695), ?x5136 = 089g0h *> conf = 0.02 ranks of expected_values: 546 EVAL 047svrl film! 07cjqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 37.000 0.304 http://example.org/film/actor/film./film/performance/film #21556-0dp7wt PRED entity: 0dp7wt PRED relation: nominated_for! PRED expected values: 07bdd_ => 73 concepts (70 used for prediction) PRED predicted values (max 10 best out of 179): 07bdd_ (0.82 #528, 0.80 #290, 0.20 #13583), 04ljl_l (0.68 #7386, 0.66 #7147, 0.66 #4526), 0gq9h (0.55 #62, 0.42 #1966, 0.36 #2921), 05b4l5x (0.53 #244, 0.45 #482, 0.15 #1434), 05p1dby (0.53 #320, 0.35 #558, 0.20 #13583), 07cbcy (0.47 #301, 0.37 #539, 0.22 #1491), 0gqy2 (0.45 #122, 0.29 #2026, 0.22 #3695), 019f4v (0.36 #53, 0.34 #1957, 0.26 #1243), 0gr0m (0.36 #59, 0.31 #1963, 0.25 #3632), 0gq_v (0.36 #19, 0.31 #1923, 0.30 #2878) >> Best rule #528 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 06wzvr; 09g8vhw; 0ddt_; 05fm6m; 09y6pb; 0n08r; >> query: (?x7822, 07bdd_) <- film(?x6850, ?x7822), nominated_for(?x688, ?x7822), film_crew_role(?x7822, ?x137), ?x688 = 05b1610 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dp7wt nominated_for! 07bdd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 70.000 0.816 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21555-0g3zrd PRED entity: 0g3zrd PRED relation: film! PRED expected values: 08w7vj 030hcs 01wy5m 0333wf 03fbb6 03x22w => 79 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1079): 07j8kh (0.47 #29093, 0.27 #24934, 0.04 #31172), 09bxq9 (0.47 #29093, 0.27 #24934, 0.02 #24935), 01l2fn (0.29 #2340, 0.22 #4417, 0.03 #21039), 015pkc (0.29 #2356, 0.22 #4433, 0.02 #39484), 01tsbmv (0.22 #8127, 0.02 #22672, 0.02 #39484), 04y8r (0.20 #27013, 0.19 #35329, 0.17 #41562), 08qxx9 (0.20 #1517, 0.11 #7749, 0.11 #5672), 0294fd (0.20 #717, 0.11 #6949, 0.05 #10388), 0kjgl (0.20 #1377, 0.11 #7609, 0.04 #15920), 0k269 (0.20 #610, 0.11 #6842, 0.03 #13076) >> Best rule #29093 for best value: >> intensional similarity = 4 >> extensional distance = 184 >> proper extension: 0g22z; 0yyg4; 05jf85; 08lr6s; 0jzw; 084qpk; 026n4h6; 01dyvs; 04kzqz; 01qncf; ... >> query: (?x2331, ?x5556) <- film(?x5282, ?x2331), award_nominee(?x2762, ?x5282), ?x2762 = 015t56, nominated_for(?x5556, ?x2331) >> conf = 0.47 => this is the best rule for 2 predicted values *> Best rule #12758 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 96 *> proper extension: 03g90h; 05sxzwc; 05qbckf; 09g8vhw; 05zy2cy; 0b1y_2; 047p7fr; 04ydr95; 093dqjy; 047d21r; ... *> query: (?x2331, 030hcs) <- film_crew_role(?x2331, ?x5136), film_crew_role(?x2331, ?x1284), film(?x748, ?x2331), ?x5136 = 089g0h, ?x1284 = 0ch6mp2 *> conf = 0.03 ranks of expected_values: 130, 238, 363, 397, 416, 626 EVAL 0g3zrd film! 03x22w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 79.000 33.000 0.475 http://example.org/film/actor/film./film/performance/film EVAL 0g3zrd film! 03fbb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 79.000 33.000 0.475 http://example.org/film/actor/film./film/performance/film EVAL 0g3zrd film! 0333wf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 79.000 33.000 0.475 http://example.org/film/actor/film./film/performance/film EVAL 0g3zrd film! 01wy5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 79.000 33.000 0.475 http://example.org/film/actor/film./film/performance/film EVAL 0g3zrd film! 030hcs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 79.000 33.000 0.475 http://example.org/film/actor/film./film/performance/film EVAL 0g3zrd film! 08w7vj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 79.000 33.000 0.475 http://example.org/film/actor/film./film/performance/film #21554-047q2k1 PRED entity: 047q2k1 PRED relation: film! PRED expected values: 049m19 => 57 concepts (25 used for prediction) PRED predicted values (max 10 best out of 571): 015npr (0.41 #31239, 0.40 #35404, 0.37 #29156), 02_p5w (0.09 #647, 0.06 #2729, 0.02 #13142), 08y7b9 (0.09 #1942, 0.03 #4024, 0.02 #6106), 03f2_rc (0.09 #86, 0.03 #2168, 0.01 #4250), 02gf_l (0.07 #1269, 0.05 #3351, 0.02 #13764), 01zh29 (0.07 #1411, 0.03 #3493, 0.01 #5575), 0241wg (0.07 #534, 0.03 #2616, 0.01 #4698), 01v3vp (0.07 #711, 0.03 #2793), 0h0wc (0.06 #4589, 0.02 #10838, 0.02 #29581), 0jfx1 (0.05 #4571, 0.02 #27478, 0.02 #33728) >> Best rule #31239 for best value: >> intensional similarity = 4 >> extensional distance = 637 >> proper extension: 03p2xc; >> query: (?x257, ?x2065) <- genre(?x257, ?x53), ?x53 = 07s9rl0, film_release_distribution_medium(?x257, ?x81), nominated_for(?x2065, ?x257) >> conf = 0.41 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 047q2k1 film! 049m19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 25.000 0.406 http://example.org/film/actor/film./film/performance/film #21553-02vrr PRED entity: 02vrr PRED relation: notable_people_with_this_condition PRED expected values: 016ypb 04jvt => 57 concepts (32 used for prediction) PRED predicted values (max 10 best out of 122): 0484q (0.33 #532, 0.33 #186, 0.29 #880), 0n839 (0.33 #227, 0.29 #804, 0.22 #1388), 06x58 (0.33 #134, 0.29 #711, 0.22 #1295), 01pw2f1 (0.33 #130, 0.29 #707, 0.22 #1291), 0227vl (0.33 #200, 0.20 #315, 0.17 #546), 05vk_d (0.33 #197, 0.20 #312, 0.17 #543), 03yrkt (0.33 #192, 0.20 #307, 0.17 #538), 06tp4h (0.33 #182, 0.20 #297, 0.17 #528), 044mfr (0.33 #174, 0.20 #289, 0.17 #520), 01z0rcq (0.33 #152, 0.20 #267, 0.17 #498) >> Best rule #532 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 0dcsx; 01g2q; 0g02vk; >> query: (?x5784, 0484q) <- notable_people_with_this_condition(?x5784, ?x7201), notable_people_with_this_condition(?x5784, ?x702), role(?x7201, ?x227), award(?x702, ?x1232), ?x227 = 0342h, location(?x702, ?x5771), artists(?x302, ?x702), artists(?x5934, ?x7201), ceremony(?x1232, ?x139) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02vrr notable_people_with_this_condition 04jvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 32.000 0.333 http://example.org/medicine/disease/notable_people_with_this_condition EVAL 02vrr notable_people_with_this_condition 016ypb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 32.000 0.333 http://example.org/medicine/disease/notable_people_with_this_condition #21552-0nbzp PRED entity: 0nbzp PRED relation: jurisdiction_of_office! PRED expected values: 01q24l => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 20): 01q24l (0.33 #189, 0.30 #255, 0.28 #233), 060c4 (0.32 #1457, 0.31 #1391, 0.30 #1303), 060bp (0.27 #1455, 0.26 #1389, 0.22 #1301), 0fkvn (0.26 #730, 0.25 #996, 0.24 #686), 0f6c3 (0.22 #821, 0.22 #689, 0.21 #667), 09n5b9 (0.20 #825, 0.19 #737, 0.18 #1003), 0fkzq (0.15 #969, 0.07 #676, 0.07 #698), 04syw (0.15 #969, 0.07 #1460, 0.07 #1394), 0dq3c (0.15 #969, 0.06 #1390, 0.06 #1302), 0789n (0.15 #969, 0.05 #713, 0.05 #669) >> Best rule #189 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 07bcn; 0qzhw; >> query: (?x14091, 01q24l) <- jurisdiction_of_office(?x1195, ?x14091), contains(?x13066, ?x14091), adjoins(?x13066, ?x5088), source(?x13066, ?x958) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nbzp jurisdiction_of_office! 01q24l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.327 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #21551-0kfv9 PRED entity: 0kfv9 PRED relation: nominated_for! PRED expected values: 06sn8m 025y9fn => 70 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1149): 0c7t58 (0.80 #23142, 0.78 #115716, 0.78 #48601), 0b1f49 (0.80 #23142, 0.78 #115716, 0.78 #48601), 02lg9w (0.80 #23142, 0.78 #115716, 0.78 #48601), 02lgfh (0.58 #13886, 0.58 #9257, 0.57 #18515), 02d4ct (0.24 #2788, 0.02 #5103, 0.01 #7417), 03mp9s (0.19 #3799), 086k8 (0.19 #69494, 0.05 #106516, 0.02 #111145), 02tr7d (0.17 #4629, 0.15 #111089, 0.10 #106460), 07s8hms (0.17 #4629, 0.15 #111089, 0.10 #106460), 02l6dy (0.17 #4629, 0.15 #111089, 0.10 #106460) >> Best rule #23142 for best value: >> intensional similarity = 2 >> extensional distance = 103 >> proper extension: 07s8z_l; >> query: (?x1849, ?x446) <- award_winner(?x1849, ?x446), producer_type(?x1849, ?x632) >> conf = 0.80 => this is the best rule for 3 predicted values *> Best rule #6662 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 0cwrr; 015g28; 03ffcz; 021gzd; 05h95s; 05fgr_; 05sy0cv; *> query: (?x1849, 025y9fn) <- actor(?x1849, ?x369), award(?x1849, ?x3486), ?x3486 = 0m7yy *> conf = 0.05 ranks of expected_values: 75, 888 EVAL 0kfv9 nominated_for! 025y9fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 70.000 53.000 0.796 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0kfv9 nominated_for! 06sn8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 70.000 53.000 0.796 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #21550-07l450 PRED entity: 07l450 PRED relation: language PRED expected values: 071fb => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 28): 06nm1 (0.12 #121, 0.11 #234, 0.11 #177), 02bjrlw (0.08 #1, 0.08 #283, 0.08 #226), 06b_j (0.07 #132, 0.06 #302, 0.06 #245), 03_9r (0.06 #120, 0.06 #8, 0.05 #1540), 04h9h (0.05 #40, 0.03 #265, 0.03 #96), 0jzc (0.04 #130, 0.03 #1550, 0.03 #2232), 0653m (0.04 #10, 0.04 #748, 0.04 #1485), 012w70 (0.03 #11, 0.03 #1658, 0.03 #749), 03hkp (0.02 #125, 0.02 #465, 0.02 #1202), 03k50 (0.02 #1654, 0.02 #1311, 0.02 #7) >> Best rule #121 for best value: >> intensional similarity = 4 >> extensional distance = 167 >> proper extension: 06mmr; >> query: (?x9599, 06nm1) <- award(?x9599, ?x2375), award(?x157, ?x2375), nominated_for(?x2375, ?x586), ?x586 = 050r1z >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 143 *> proper extension: 035xwd; 05p3738; 035s95; 0pvms; 0gyy53; 0415ggl; 02z2mr7; 0bw20; 0fsd9t; *> query: (?x9599, 071fb) <- genre(?x9599, ?x162), film(?x940, ?x9599), film_crew_role(?x9599, ?x137), ?x162 = 04xvlr *> conf = 0.01 ranks of expected_values: 24 EVAL 07l450 language 071fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 95.000 95.000 0.124 http://example.org/film/film/language #21549-032nl2 PRED entity: 032nl2 PRED relation: artist! PRED expected values: 05clg8 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 105): 015_1q (0.19 #3772, 0.18 #714, 0.18 #5579), 03rhqg (0.17 #849, 0.15 #1266, 0.15 #2934), 0181dw (0.17 #320, 0.13 #876, 0.13 #1015), 01trtc (0.13 #1461, 0.12 #1878, 0.11 #210), 017l96 (0.13 #852, 0.12 #991, 0.12 #296), 0n85g (0.13 #756, 0.12 #1173, 0.11 #478), 0fb0v (0.12 #6, 0.11 #701, 0.10 #1257), 0g768 (0.12 #5597, 0.12 #1983, 0.12 #6153), 01w40h (0.12 #445, 0.12 #723, 0.09 #2947), 011k1h (0.11 #1955, 0.10 #6125, 0.10 #5152) >> Best rule #3772 for best value: >> intensional similarity = 3 >> extensional distance = 483 >> proper extension: 0163m1; 02pt7h_; 0gr69; 0134wr; 012x1l; >> query: (?x8053, 015_1q) <- category(?x8053, ?x134), artist(?x1124, ?x8053), gender(?x8053, ?x231) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1481 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 143 *> proper extension: 07c0j; 0cbm64; *> query: (?x8053, 05clg8) <- participant(?x8053, ?x5662), artist(?x1124, ?x8053), artists(?x302, ?x8053) *> conf = 0.03 ranks of expected_values: 66 EVAL 032nl2 artist! 05clg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 88.000 88.000 0.194 http://example.org/music/record_label/artist #21548-02ctyy PRED entity: 02ctyy PRED relation: people! PRED expected values: 0dryh9k => 90 concepts (83 used for prediction) PRED predicted values (max 10 best out of 48): 0dryh9k (0.46 #16, 0.40 #93, 0.40 #940), 033tf_ (0.20 #315, 0.17 #546, 0.14 #392), 041rx (0.18 #620, 0.16 #1544, 0.15 #774), 0x67 (0.16 #395, 0.16 #1011, 0.16 #1165), 0bpjh3 (0.13 #102, 0.11 #256, 0.06 #179), 07hwkr (0.09 #397, 0.06 #1167, 0.06 #1321), 01rv7x (0.08 #39, 0.07 #116, 0.05 #270), 06j2v (0.08 #70, 0.07 #147, 0.05 #301), 0fqp6zk (0.08 #77, 0.07 #154, 0.05 #308), 02sch9 (0.08 #35, 0.07 #959, 0.06 #189) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 07f0tw; 01wttr1; >> query: (?x6313, 0dryh9k) <- award(?x6313, ?x10156), ?x10156 = 03r8v_, location(?x6313, ?x7412), type_of_union(?x6313, ?x566) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ctyy people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 83.000 0.462 http://example.org/people/ethnicity/people #21547-04z257 PRED entity: 04z257 PRED relation: genre PRED expected values: 0vgkd => 147 concepts (64 used for prediction) PRED predicted values (max 10 best out of 99): 07s9rl0 (0.80 #465, 0.76 #581, 0.67 #1047), 02kdv5l (0.54 #2799, 0.53 #5247, 0.51 #1398), 01hmnh (0.53 #3379, 0.37 #1412, 0.37 #3279), 060__y (0.50 #16, 0.25 #946, 0.22 #1062), 02l7c8 (0.43 #945, 0.36 #1294, 0.35 #595), 0lsxr (0.41 #4438, 0.37 #2805, 0.36 #5836), 06n90 (0.34 #1407, 0.24 #2808, 0.24 #5256), 02xlf (0.33 #50, 0.21 #7459, 0.10 #746), 06l3bl (0.33 #36, 0.21 #7459, 0.09 #966), 04xvlr (0.30 #350, 0.30 #932, 0.27 #1747) >> Best rule #465 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 0pv3x; 02yvct; 02s4l6; 01cmp9; 02cbhg; 03ntbmw; >> query: (?x3612, 07s9rl0) <- executive_produced_by(?x3612, ?x4060), country(?x3612, ?x94), ?x4060 = 05hj_k, films(?x9516, ?x3612), genre(?x3612, ?x258), language(?x3612, ?x90) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #7459 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 371 *> proper extension: 0cp0ph6; 07b1gq; 043mk4y; *> query: (?x3612, ?x53) <- executive_produced_by(?x3612, ?x4060), country(?x3612, ?x94), executive_produced_by(?x2525, ?x4060), executive_produced_by(?x603, ?x4060), award_nominee(?x105, ?x4060), genre(?x2525, ?x53), film_release_distribution_medium(?x603, ?x81) *> conf = 0.21 ranks of expected_values: 20 EVAL 04z257 genre 0vgkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 147.000 64.000 0.800 http://example.org/film/film/genre #21546-03fbc PRED entity: 03fbc PRED relation: award_winner! PRED expected values: 09n4nb => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 85): 01bx35 (0.25 #430, 0.17 #853, 0.16 #1135), 02rjjll (0.21 #428, 0.17 #146, 0.16 #1557), 02cg41 (0.21 #549, 0.12 #1678, 0.12 #1537), 019bk0 (0.20 #1427, 0.16 #1568, 0.14 #439), 01mhwk (0.18 #464, 0.13 #1310, 0.12 #1169), 09n4nb (0.17 #189, 0.12 #1459, 0.11 #2728), 01s695 (0.16 #1414, 0.14 #426, 0.09 #1272), 05pd94v (0.14 #425, 0.12 #1554, 0.12 #2682), 01c6qp (0.13 #1430, 0.11 #1571, 0.11 #442), 01mh_q (0.13 #1500, 0.09 #2064, 0.09 #1641) >> Best rule #430 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 03cd1q; >> query: (?x2635, 01bx35) <- award(?x2635, ?x4018), award(?x2635, ?x3045), ?x3045 = 02sp_v, award(?x4343, ?x4018), ?x4343 = 02cx90 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #189 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 01k3qj; *> query: (?x2635, 09n4nb) <- category(?x2635, ?x134), artists(?x5909, ?x2635), ?x5909 = 041738 *> conf = 0.17 ranks of expected_values: 6 EVAL 03fbc award_winner! 09n4nb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 80.000 80.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21545-041_y PRED entity: 041_y PRED relation: people! PRED expected values: 041rx => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 44): 041rx (0.73 #620, 0.26 #1468, 0.26 #2931), 0dryh9k (0.38 #555, 0.35 #863, 0.32 #324), 013b6_ (0.18 #669, 0.10 #515, 0.09 #130), 033tf_ (0.15 #1239, 0.15 #392, 0.12 #4398), 0xnvg (0.15 #398, 0.08 #706, 0.08 #783), 013xrm (0.14 #636, 0.09 #1021, 0.08 #174), 0bpjh3 (0.12 #333, 0.07 #564, 0.07 #872), 0x67 (0.12 #703, 0.10 #6408, 0.10 #6254), 048z7l (0.11 #271, 0.10 #502, 0.10 #40), 02sch9 (0.10 #574, 0.08 #882) >> Best rule #620 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 045m1_; >> query: (?x7039, 041rx) <- influenced_by(?x1900, ?x7039), nationality(?x7039, ?x94), religion(?x7039, ?x7131), ?x7131 = 03_gx >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 041_y people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.727 http://example.org/people/ethnicity/people #21544-081nh PRED entity: 081nh PRED relation: location PRED expected values: 0f8l9c => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 302): 030qb3t (0.25 #1689, 0.21 #42631, 0.18 #20956), 02_286 (0.23 #19305, 0.20 #20910, 0.19 #8867), 0r00l (0.19 #23283, 0.12 #52178, 0.10 #71441), 09c7w0 (0.12 #14450, 0.04 #13650, 0.04 #44153), 0cr3d (0.11 #143, 0.10 #16200, 0.10 #15396), 0k_q_ (0.11 #126, 0.04 #2534, 0.04 #3338), 01m1_d (0.11 #674, 0.04 #2280, 0.04 #5492), 03s5t (0.10 #15253, 0.10 #21676, 0.08 #28902), 0cc56 (0.08 #1663, 0.08 #860, 0.05 #10492), 0k049 (0.08 #1614, 0.04 #14458, 0.04 #4826) >> Best rule #1689 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 01qq_lp; 0cgzj; >> query: (?x2426, 030qb3t) <- award(?x2426, ?x2060), award_winner(?x4445, ?x2426), ?x2060 = 054ky1 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #42588 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 246 *> proper extension: 050llt; *> query: (?x2426, 0f8l9c) <- languages(?x2426, ?x254), award_winner(?x720, ?x2426), nominated_for(?x2426, ?x2425) *> conf = 0.01 ranks of expected_values: 266 EVAL 081nh location 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 151.000 151.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #21543-05crg7 PRED entity: 05crg7 PRED relation: artists! PRED expected values: 06by7 => 108 concepts (60 used for prediction) PRED predicted values (max 10 best out of 253): 064t9 (0.70 #4942, 0.69 #4635, 0.63 #8636), 06by7 (0.68 #5873, 0.65 #3718, 0.65 #8028), 016clz (0.50 #2780, 0.50 #313, 0.48 #2160), 05r6t (0.50 #390, 0.29 #2237, 0.26 #3779), 03lty (0.46 #5571, 0.24 #7725, 0.20 #644), 03_d0 (0.44 #3095, 0.44 #4326, 0.41 #5247), 0glt670 (0.43 #963, 0.34 #9583, 0.33 #9276), 0dl5d (0.42 #1866, 0.40 #635, 0.39 #4333), 0p9xd (0.40 #773, 0.09 #5392, 0.08 #3240), 06j6l (0.37 #8669, 0.35 #4975, 0.34 #4668) >> Best rule #4942 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 02wb6yq; >> query: (?x1751, 064t9) <- award_winner(?x342, ?x1751), artists(?x3061, ?x1751), ?x3061 = 05bt6j >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #5873 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 85 *> proper extension: 089tm; 01pfr3; 01vsxdm; 01wv9xn; 0frsw; 01vrwfv; 014_lq; 02jqjm; 0178kd; 0143q0; ... *> query: (?x1751, 06by7) <- group(?x75, ?x1751), artists(?x1000, ?x1751), award_winner(?x4018, ?x1751), award(?x215, ?x4018) *> conf = 0.68 ranks of expected_values: 2 EVAL 05crg7 artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 60.000 0.696 http://example.org/music/genre/artists #21542-04fyhv PRED entity: 04fyhv PRED relation: organizations_founded PRED expected values: 04f525m => 108 concepts (63 used for prediction) PRED predicted values (max 10 best out of 20): 030_1_ (0.06 #221, 0.04 #323, 0.02 #425), 09xwz (0.03 #181, 0.03 #591, 0.01 #1829), 07y2b (0.03 #192, 0.03 #296, 0.02 #398), 02jd_7 (0.03 #271, 0.02 #373, 0.02 #681), 04kqk (0.03 #300, 0.02 #402, 0.01 #504), 04rtpt (0.03 #251, 0.02 #353, 0.01 #455), 0kx4m (0.03 #214, 0.02 #316, 0.01 #418), 03rwz3 (0.02 #617, 0.01 #824), 017s11 (0.02 #617, 0.01 #824), 032dg7 (0.02 #617) >> Best rule #221 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 081bls; >> query: (?x8208, 030_1_) <- award_winner(?x2022, ?x8208), ?x2022 = 05p1dby, nominated_for(?x8208, ?x351) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04fyhv organizations_founded 04f525m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 63.000 0.065 http://example.org/organization/organization_founder/organizations_founded #21541-04kwbt PRED entity: 04kwbt PRED relation: gender PRED expected values: 05zppz => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #15, 0.81 #33, 0.80 #51), 02zsn (0.30 #40, 0.29 #38, 0.28 #48) >> Best rule #15 for best value: >> intensional similarity = 6 >> extensional distance = 284 >> proper extension: 01vvycq; 03mv0b; 0gry51; >> query: (?x12741, 05zppz) <- profession(?x12741, ?x1032), profession(?x12741, ?x524), profession(?x12741, ?x319), ?x319 = 01d_h8, ?x1032 = 02hrh1q, ?x524 = 02jknp >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04kwbt gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.829 http://example.org/people/person/gender #21540-07bs0 PRED entity: 07bs0 PRED relation: athlete PRED expected values: 069d68 020hh3 => 41 concepts (40 used for prediction) PRED predicted values (max 10 best out of 188): 02m501 (0.33 #631, 0.25 #1039, 0.25 #903), 0f2zc (0.33 #214, 0.20 #1575, 0.14 #2123), 01jqr_5 (0.33 #151, 0.20 #1512, 0.14 #2060), 03n69x (0.33 #166, 0.14 #2075, 0.12 #2347), 019tzd (0.33 #444, 0.02 #540), 04bdpf (0.33 #245), 063g7l (0.33 #243), 0444x (0.33 #240), 03vrv9 (0.33 #238), 014g_s (0.33 #236) >> Best rule #631 for best value: >> intensional similarity = 40 >> extensional distance = 1 >> proper extension: 01cgz; >> query: (?x1557, 02m501) <- country(?x1557, ?x8958), country(?x1557, ?x4059), country(?x1557, ?x1023), country(?x1557, ?x985), country(?x1557, ?x512), country(?x1557, ?x404), sports(?x2432, ?x1557), sports(?x2233, ?x1557), olympics(?x1557, ?x778), film_release_region(?x9432, ?x4059), film_release_region(?x7832, ?x4059), film_release_region(?x7493, ?x4059), film_release_region(?x6527, ?x4059), film_release_region(?x5016, ?x4059), film_release_region(?x4336, ?x4059), film_release_region(?x2656, ?x4059), film_release_region(?x1071, ?x4059), athlete(?x1557, ?x2992), ?x985 = 0k6nt, ?x1023 = 0ctw_b, countries_spoken_in(?x8650, ?x4059), ?x9432 = 0gvt53w, ?x2432 = 0nbjq, gender(?x2992, ?x231), sports(?x3729, ?x1557), ?x7832 = 0fphf3v, ?x404 = 047lj, ?x5016 = 062zm5h, film_release_region(?x280, ?x8958), administrative_area_type(?x4059, ?x2792), ?x512 = 07ssc, ?x2233 = 0l6mp, location_of_ceremony(?x566, ?x8958), ?x6527 = 0gfh84d, ?x280 = 03g90h, ?x2656 = 03qnc6q, ?x1071 = 02d44q, contains(?x455, ?x4059), ?x7493 = 0btpm6, ?x4336 = 0bpm4yw >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07bs0 athlete 020hh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 40.000 0.333 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete EVAL 07bs0 athlete 069d68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 40.000 0.333 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #21539-03w9sgh PRED entity: 03w9sgh PRED relation: profession PRED expected values: 03gjzk 02krf9 => 102 concepts (71 used for prediction) PRED predicted values (max 10 best out of 40): 03gjzk (0.89 #310, 0.87 #606, 0.85 #162), 02hrh1q (0.73 #7858, 0.69 #8746, 0.69 #5637), 01d_h8 (0.67 #2226, 0.55 #1782, 0.51 #2078), 0cbd2 (0.42 #7, 0.38 #155, 0.28 #303), 02krf9 (0.39 #322, 0.38 #174, 0.33 #26), 018gz8 (0.23 #164, 0.20 #1792, 0.17 #2088), 09jwl (0.19 #5346, 0.18 #4754, 0.18 #5790), 0np9r (0.15 #168, 0.14 #1796, 0.12 #1056), 0kyk (0.14 #1805, 0.09 #2101, 0.09 #1657), 0nbcg (0.13 #5359, 0.12 #5803, 0.12 #4767) >> Best rule #310 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0cj2t3; 062ftr; >> query: (?x9011, 03gjzk) <- award_nominee(?x9011, ?x1630), ?x1630 = 027cxsm, award(?x9011, ?x2016) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 03w9sgh profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 71.000 0.889 http://example.org/people/person/profession EVAL 03w9sgh profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 71.000 0.889 http://example.org/people/person/profession #21538-0k3k1 PRED entity: 0k3k1 PRED relation: contains PRED expected values: 01m2n1 => 181 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2762): 0t_3w (0.82 #167545, 0.81 #155789, 0.78 #111704), 0t_4_ (0.82 #167545, 0.81 #155789, 0.78 #111704), 01cx_ (0.63 #88194, 0.61 #126399, 0.57 #182241), 04rwx (0.55 #252788, 0.55 #223395, 0.55 #97009), 03ksy (0.55 #252788, 0.55 #223395, 0.55 #97009), 01k7xz (0.55 #252788, 0.55 #223395, 0.55 #226334), 014zws (0.55 #252788, 0.55 #223395, 0.55 #226334), 01_f90 (0.55 #252788, 0.55 #223395, 0.55 #226334), 01hr11 (0.55 #252788, 0.50 #4160, 0.36 #252789), 05k7sb (0.50 #182242, 0.50 #120520, 0.47 #258665) >> Best rule #167545 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 0n5j_; 0jcgs; 0mw89; 0mwh1; 0kpys; 0d22f; 0m2gk; 0m7d0; 0m2gz; 0jxgx; ... >> query: (?x9065, ?x3007) <- adjoins(?x9065, ?x4990), county(?x3007, ?x9065), second_level_divisions(?x94, ?x9065), contains(?x9065, ?x11331) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #5714 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 05k7sb; *> query: (?x9065, 01m2n1) <- adjoins(?x9065, ?x4990), contains(?x9065, ?x13089), contains(?x9065, ?x3007), school_type(?x13089, ?x1044), ?x3007 = 01qh7 *> conf = 0.50 ranks of expected_values: 43 EVAL 0k3k1 contains 01m2n1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 181.000 94.000 0.818 http://example.org/location/location/contains #21537-03115z PRED entity: 03115z PRED relation: languages! PRED expected values: 0139q5 => 40 concepts (29 used for prediction) PRED predicted values (max 10 best out of 823): 040nwr (0.62 #5858, 0.50 #6511, 0.43 #4553), 01x2tm8 (0.62 #5733, 0.42 #6386, 0.40 #3773), 09r_wb (0.62 #5678, 0.40 #3718, 0.33 #6331), 046rfv (0.62 #5662, 0.40 #3702, 0.33 #6315), 03x31g (0.62 #5819, 0.40 #3859, 0.33 #6472), 012d40 (0.60 #2614, 0.50 #1310, 0.43 #4575), 02wk4d (0.60 #2949, 0.50 #1645, 0.43 #4910), 05vzql (0.50 #5793, 0.40 #3833, 0.33 #6446), 06kl0k (0.50 #5768, 0.40 #3808, 0.33 #6421), 0dfjb8 (0.50 #5521, 0.25 #6174, 0.25 #1604) >> Best rule #5858 for best value: >> intensional similarity = 14 >> extensional distance = 6 >> proper extension: 09s02; >> query: (?x10296, 040nwr) <- languages(?x8801, ?x10296), languages(?x7835, ?x10296), countries_spoken_in(?x10296, ?x2629), actor(?x4721, ?x7835), people(?x3591, ?x8801), location(?x8801, ?x739), languages_spoken(?x9979, ?x10296), location_of_ceremony(?x8801, ?x957), nationality(?x8801, ?x94), program(?x2062, ?x4721), gender(?x7835, ?x514), place_of_death(?x2109, ?x957), award(?x7835, ?x1670), contains(?x1227, ?x957) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #5107 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 5 *> proper extension: 0t_2; *> query: (?x10296, 0139q5) <- language(?x6014, ?x10296), language(?x4427, ?x10296), language(?x1625, ?x10296), language(?x1185, ?x10296), produced_by(?x6014, ?x7739), film_release_region(?x6014, ?x142), genre(?x6014, ?x1626), film_release_region(?x1625, ?x550), genre(?x1625, ?x600), ?x142 = 0jgd, nominated_for(?x7215, ?x1625), ?x1626 = 03q4nz, currency(?x1185, ?x170), film_crew_role(?x4427, ?x137), ?x550 = 05v8c *> conf = 0.29 ranks of expected_values: 43 EVAL 03115z languages! 0139q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 40.000 29.000 0.625 http://example.org/people/person/languages #21536-0c8qq PRED entity: 0c8qq PRED relation: film_release_region PRED expected values: 05r4w 09c7w0 0d0vqn 0f8l9c => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 161): 09c7w0 (0.92 #12407, 0.92 #12751, 0.92 #12579), 0d0vqn (0.90 #1731, 0.89 #2591, 0.89 #2247), 0f8l9c (0.89 #2266, 0.89 #1922, 0.89 #718), 06mkj (0.87 #1103, 0.86 #1275, 0.86 #1619), 059j2 (0.86 #2277, 0.83 #2793, 0.83 #1933), 03rjj (0.85 #1212, 0.83 #1040, 0.83 #1556), 05r4w (0.82 #2239, 0.82 #1207, 0.81 #1895), 0chghy (0.82 #1564, 0.82 #1220, 0.82 #2252), 0345h (0.82 #2279, 0.77 #2795, 0.77 #1419), 05qhw (0.77 #2257, 0.74 #1225, 0.73 #1569) >> Best rule #12407 for best value: >> intensional similarity = 4 >> extensional distance = 1317 >> proper extension: 014lc_; 02d413; 0g22z; 018js4; 0b2v79; 01jc6q; 027qgy; 047q2k1; 0ckr7s; 08lr6s; ... >> query: (?x3311, 09c7w0) <- film_release_region(?x3311, ?x142), film_release_region(?x9652, ?x142), ?x9652 = 0ddbjy4, countries_spoken_in(?x90, ?x142) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 7 EVAL 0c8qq film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c8qq film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c8qq film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c8qq film_release_region 05r4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 92.000 92.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21535-07r1_ PRED entity: 07r1_ PRED relation: artist! PRED expected values: 0gh4g0 => 89 concepts (42 used for prediction) PRED predicted values (max 10 best out of 112): 015_1q (0.28 #160, 0.27 #861, 0.26 #441), 03rhqg (0.28 #156, 0.23 #1840, 0.22 #2120), 03x9yr (0.25 #136, 0.02 #2801, 0.02 #2941), 043g7l (0.18 #452, 0.12 #592, 0.11 #171), 0g768 (0.17 #1861, 0.17 #177, 0.16 #2141), 01clyr (0.17 #33, 0.16 #594, 0.11 #173), 0fb0v (0.17 #7, 0.14 #287, 0.11 #848), 02y21l (0.17 #95, 0.10 #516, 0.06 #1919), 01cf93 (0.17 #198, 0.09 #2863, 0.08 #3003), 01trtc (0.16 #1195, 0.13 #2737, 0.12 #1476) >> Best rule #160 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 05crg7; >> query: (?x7086, 015_1q) <- award(?x7086, ?x724), artists(?x2809, ?x7086), ?x2809 = 05w3f, group(?x227, ?x7086) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 01wxdn3; 01ww_vs; *> query: (?x7086, 0gh4g0) <- artist(?x5634, ?x7086), artists(?x3167, ?x7086), ?x3167 = 0xjl2, category(?x7086, ?x134) *> conf = 0.08 ranks of expected_values: 28 EVAL 07r1_ artist! 0gh4g0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 89.000 42.000 0.278 http://example.org/music/record_label/artist #21534-05myd2 PRED entity: 05myd2 PRED relation: film PRED expected values: 0fphgb => 110 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1123): 011ysn (0.15 #567, 0.06 #13105, 0.03 #14896), 07kdkfj (0.15 #1342, 0.06 #13880, 0.02 #33584), 05p1qyh (0.15 #377, 0.04 #12915, 0.01 #32619), 02b6n9 (0.15 #1574, 0.03 #6947, 0.03 #14112), 09cxm4 (0.15 #1432, 0.03 #13970), 06dfz1 (0.09 #69858, 0.09 #19704, 0.08 #51945), 03hfmm (0.08 #3270, 0.07 #5061, 0.04 #10434), 03p2xc (0.08 #1246, 0.06 #13784, 0.03 #17366), 0g22z (0.08 #16, 0.04 #16136, 0.03 #12554), 08r4x3 (0.08 #154, 0.04 #12692, 0.03 #5527) >> Best rule #567 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01713c; >> query: (?x9512, 011ysn) <- award(?x9512, ?x2183), languages(?x9512, ?x254), film(?x9512, ?x5353), ?x2183 = 02x4w6g >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #13138 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 02s2ft; 01wmxfs; 01fkv0; 0flw6; 06lht1; 016k6x; 01w23w; 02624g; 026r8q; 02bj6k; ... *> query: (?x9512, 0fphgb) <- award(?x9512, ?x2183), ?x2183 = 02x4w6g, film(?x9512, ?x5353), currency(?x5353, ?x170) *> conf = 0.01 ranks of expected_values: 800 EVAL 05myd2 film 0fphgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 110.000 62.000 0.154 http://example.org/film/actor/film./film/performance/film #21533-07fq1y PRED entity: 07fq1y PRED relation: award PRED expected values: 0gqyl => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 239): 09sb52 (0.62 #845, 0.45 #443, 0.38 #4865), 0gqy2 (0.40 #164, 0.16 #18495, 0.15 #23322), 05pcn59 (0.27 #483, 0.13 #4905, 0.13 #3297), 09td7p (0.20 #121, 0.19 #925, 0.16 #18495), 09sdmz (0.20 #205, 0.18 #21712, 0.18 #607), 027dtxw (0.20 #4, 0.16 #18495, 0.15 #23322), 02z0dfh (0.20 #75, 0.16 #18495, 0.15 #23322), 0bs0bh (0.20 #103, 0.16 #18495, 0.15 #23322), 0gr4k (0.20 #33, 0.16 #18495, 0.15 #23322), 0bfvd4 (0.19 #919, 0.16 #18495, 0.15 #23322) >> Best rule #845 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02qgqt; 02p65p; 01g257; 0dlglj; 030h95; 01tfck; 015v3r; 03vgp7; 013knm; 01zg98; ... >> query: (?x156, 09sb52) <- award_winner(?x3139, ?x156), award(?x156, ?x1132), ?x3139 = 0b_dy >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #909 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 02qgqt; 02p65p; 01g257; 0dlglj; 030h95; 01tfck; 015v3r; 03vgp7; 013knm; 01zg98; ... *> query: (?x156, 0gqyl) <- award_winner(?x3139, ?x156), award(?x156, ?x1132), ?x3139 = 0b_dy *> conf = 0.19 ranks of expected_values: 11 EVAL 07fq1y award 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 105.000 105.000 0.625 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21532-09xrxq PRED entity: 09xrxq PRED relation: profession PRED expected values: 02jknp => 100 concepts (77 used for prediction) PRED predicted values (max 10 best out of 45): 03gjzk (0.61 #161, 0.58 #14, 0.38 #308), 02jknp (0.52 #8, 0.52 #155, 0.44 #302), 01d_h8 (0.52 #300, 0.52 #6, 0.50 #888), 0cbd2 (0.21 #301, 0.20 #889, 0.16 #595), 09jwl (0.20 #1341, 0.20 #1047, 0.19 #4282), 018gz8 (0.20 #16, 0.17 #898, 0.17 #163), 0np9r (0.18 #20, 0.16 #167, 0.11 #314), 0dz3r (0.14 #1325, 0.12 #4266, 0.12 #2502), 0nbcg (0.13 #1353, 0.13 #4294, 0.13 #3559), 016z4k (0.13 #1033, 0.12 #4268, 0.11 #3533) >> Best rule #161 for best value: >> intensional similarity = 3 >> extensional distance = 238 >> proper extension: 06v8s0; >> query: (?x10464, 03gjzk) <- gender(?x10464, ?x231), profession(?x10464, ?x1943), ?x1943 = 02krf9 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 130 *> proper extension: 0htlr; 031zkw; 01wyzyl; 01_vfy; 012_53; 0m32_; 0p8r1; 01v3vp; 012rng; 01mt1fy; ... *> query: (?x10464, 02jknp) <- location(?x10464, ?x4030), profession(?x10464, ?x1943), ?x1943 = 02krf9 *> conf = 0.52 ranks of expected_values: 2 EVAL 09xrxq profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 77.000 0.613 http://example.org/people/person/profession #21531-06tpmy PRED entity: 06tpmy PRED relation: music PRED expected values: 01nc3rh => 79 concepts (56 used for prediction) PRED predicted values (max 10 best out of 76): 03f4xvm (0.14 #1683, 0.07 #6540, 0.07 #6752), 0csdzz (0.09 #187, 0.06 #817, 0.05 #1027), 01tc9r (0.09 #65, 0.04 #485, 0.03 #4488), 016szr (0.08 #1553, 0.01 #6410, 0.01 #6622), 06fxnf (0.06 #489, 0.05 #909, 0.05 #1331), 0150t6 (0.06 #46, 0.05 #886, 0.04 #1097), 02jxmr (0.06 #74, 0.05 #284, 0.04 #494), 023361 (0.06 #150, 0.05 #360, 0.04 #780), 0146pg (0.06 #1905, 0.06 #4011, 0.05 #2747), 02bh9 (0.05 #2156, 0.05 #261, 0.04 #4898) >> Best rule #1683 for best value: >> intensional similarity = 4 >> extensional distance = 119 >> proper extension: 080dwhx; 03ln8b; 05f4vxd; 03nt59; 039cq4; 016tvq; 07zhjj; 01b7h8; 0cs134; 0266s9; >> query: (?x4514, ?x4548) <- nominated_for(?x4548, ?x4514), people(?x2510, ?x4548), award_nominee(?x4548, ?x2635), artist(?x1954, ?x4548) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2077 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 221 *> proper extension: 04v8x9; 0dj0m5; 04tc1g; 04kzqz; 02tqm5; 02ny6g; 015whm; 0sxgv; 059lwy; 02r9p0c; ... *> query: (?x4514, 01nc3rh) <- film(?x574, ?x4514), film(?x382, ?x4514), country(?x4514, ?x94), ?x382 = 086k8, award_nominee(?x541, ?x574) *> conf = 0.01 ranks of expected_values: 56 EVAL 06tpmy music 01nc3rh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 79.000 56.000 0.145 http://example.org/film/film/music #21530-029jpy PRED entity: 029jpy PRED relation: contains PRED expected values: 06btq 07_f2 => 134 concepts (72 used for prediction) PRED predicted values (max 10 best out of 2844): 07_f2 (0.68 #97043, 0.64 #99984, 0.64 #111746), 06btq (0.68 #97043, 0.64 #99984, 0.64 #111746), 059rby (0.68 #97043, 0.64 #99984, 0.64 #111746), 0n5_g (0.64 #99984, 0.64 #111746, 0.63 #14702), 0d060g (0.64 #99984, 0.64 #111746, 0.62 #85280), 0694j (0.64 #99984, 0.64 #111746, 0.62 #85280), 059s8 (0.64 #99984, 0.64 #111746, 0.62 #85280), 0j3b (0.64 #99984, 0.64 #111746, 0.62 #85280), 0n5xb (0.64 #99984, 0.64 #111746, 0.62 #135272), 0n5y4 (0.64 #99984, 0.64 #111746, 0.62 #135272) >> Best rule #97043 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 027rqbx; 08xpv_; >> query: (?x3448, ?x335) <- contains(?x3448, ?x1755), adjoins(?x1755, ?x335), contains(?x1755, ?x4074), featured_film_locations(?x2788, ?x4074) >> conf = 0.68 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2 EVAL 029jpy contains 07_f2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 72.000 0.682 http://example.org/location/location/contains EVAL 029jpy contains 06btq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 72.000 0.682 http://example.org/location/location/contains #21529-085pr PRED entity: 085pr PRED relation: profession PRED expected values: 0cbd2 => 97 concepts (96 used for prediction) PRED predicted values (max 10 best out of 111): 02hrh1q (0.85 #12366, 0.78 #7072, 0.77 #5896), 01d_h8 (0.73 #447, 0.67 #2064, 0.66 #3093), 02jknp (0.71 #449, 0.61 #2066, 0.57 #3095), 0cbd2 (0.51 #1771, 0.51 #2800, 0.48 #3388), 018gz8 (0.47 #752, 0.38 #1193, 0.31 #1928), 03gjzk (0.42 #750, 0.40 #3102, 0.37 #3837), 05z96 (0.32 #4999, 0.32 #5147, 0.16 #2835), 09jwl (0.26 #901, 0.21 #607, 0.20 #1048), 0np9r (0.20 #756, 0.19 #1197, 0.16 #1932), 02krf9 (0.18 #468, 0.17 #3114, 0.17 #3849) >> Best rule #12366 for best value: >> intensional similarity = 3 >> extensional distance = 2950 >> proper extension: 02zq43; 01ty7ll; 01j5x6; 01n7qlf; 0dfjb8; 0c8hct; 01wbsdz; 0lh0c; 0f2c8g; 05qhnq; ... >> query: (?x3527, 02hrh1q) <- profession(?x3527, ?x6421), profession(?x7209, ?x6421), ?x7209 = 01v90t >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1771 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 183 *> proper extension: 05ty4m; 017r2; 0126rp; 01vs_v8; 053yx; 0p8jf; 019vgs; 0n6kf; 0hky; 04l19_; ... *> query: (?x3527, 0cbd2) <- influenced_by(?x3527, ?x6163), award(?x3527, ?x384), student(?x3424, ?x3527) *> conf = 0.51 ranks of expected_values: 4 EVAL 085pr profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 96.000 0.846 http://example.org/people/person/profession #21528-03d_w3h PRED entity: 03d_w3h PRED relation: award_winner! PRED expected values: 07g9f => 70 concepts (61 used for prediction) PRED predicted values (max 10 best out of 89): 03np63f (0.41 #34082, 0.40 #34081, 0.38 #3408), 08y2fn (0.41 #34082, 0.40 #34081, 0.38 #39762), 0gydcp7 (0.40 #34081, 0.38 #3408, 0.37 #39761), 07l450 (0.11 #28398, 0.10 #48849, 0.09 #52259), 0ddcbd5 (0.11 #28398, 0.10 #48849, 0.09 #52259), 07nxvj (0.08 #455, 0.02 #2726, 0.01 #8406), 01cmp9 (0.08 #678, 0.02 #22262, 0.01 #2949), 08phg9 (0.08 #580, 0.01 #22164), 02b6n9 (0.08 #1001), 02p76f9 (0.08 #897) >> Best rule #34082 for best value: >> intensional similarity = 3 >> extensional distance = 1403 >> proper extension: 01zcrv; >> query: (?x940, ?x7897) <- nominated_for(?x940, ?x7897), award_winner(?x686, ?x940), titles(?x162, ?x7897) >> conf = 0.41 => this is the best rule for 2 predicted values *> Best rule #49985 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1744 *> proper extension: 0kc6x; 065y4w7; 01y67v; 01jq34; 0l2tk; 01_8w2; 01p5yn; 01gl9g; 03yxwq; 02y9bj; ... *> query: (?x940, ?x337) <- award_winner(?x686, ?x940), award(?x337, ?x686) *> conf = 0.02 ranks of expected_values: 43 EVAL 03d_w3h award_winner! 07g9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 70.000 61.000 0.411 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #21527-09qvf4 PRED entity: 09qvf4 PRED relation: category_of PRED expected values: 0gcf2r => 52 concepts (49 used for prediction) PRED predicted values (max 10 best out of 3): 0gcf2r (0.87 #65, 0.64 #44, 0.55 #107), 0c4ys (0.37 #598, 0.36 #511, 0.36 #641), 0g_w (0.17 #192, 0.16 #213, 0.12 #87) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 09v7wsg; >> query: (?x4225, 0gcf2r) <- award_winner(?x4225, ?x6363), award_nominee(?x6363, ?x636), ceremony(?x4225, ?x1265), ?x1265 = 05c1t6z >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09qvf4 category_of 0gcf2r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 49.000 0.867 http://example.org/award/award_category/category_of #21526-0k6nt PRED entity: 0k6nt PRED relation: film_release_region! PRED expected values: 0dtw1x 0gkz15s 0_b3d 03bx2lk 02r1c18 04n52p6 0407yfx 06v9_x 0661ql3 07x4qr 0fpmrm3 0645k5 06w839_ 0crc2cp 0gffmn8 0jwmp 0gtvpkw 02fqrf 06r2_ 0bmhvpr 05c26ss 0jymd 0184tc 01k60v 0h03fhx 0dzlbx 062zm5h 0bc1yhb 04pk1f 0gj96ln 02bg55 0k7tq 05ft32 07jnt 043tvp3 09v3jyg 01cm8w 0fphf3v 078mm1 0fpgp26 0g5qmbz 047p798 049w1q 0by17xn 02wtp6 => 183 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1029): 043tvp3 (0.93 #20566, 0.91 #16586, 0.90 #22556), 062zm5h (0.91 #16390, 0.85 #20370, 0.83 #30320), 0407yfx (0.91 #16094, 0.85 #20074, 0.83 #9129), 0fpgp26 (0.90 #33678, 0.90 #22733, 0.89 #20743), 04n52p6 (0.89 #9086, 0.83 #16051, 0.81 #20031), 0gj96ln (0.87 #16516, 0.85 #20496, 0.82 #26466), 0661ql3 (0.86 #30051, 0.85 #34031, 0.84 #31046), 0h03fhx (0.85 #26287, 0.83 #22307, 0.78 #9372), 0bmhvpr (0.85 #20231, 0.83 #9286, 0.83 #16251), 0fpv_3_ (0.85 #33025, 0.83 #39990, 0.83 #16110) >> Best rule #20566 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 01ls2; 015qh; >> query: (?x985, 043tvp3) <- film_release_region(?x6283, ?x985), film_release_region(?x634, ?x985), ?x6283 = 0gmd3k7, ?x634 = 0gx9rvq, contains(?x455, ?x985) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27, 29, 30, 31, 33, 34, 36, 37, 42, 45, 47, 49, 58, 62, 63, 66, 71, 75, 79, 81, 97, 110 EVAL 0k6nt film_release_region! 02wtp6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0by17xn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 049w1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 047p798 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0g5qmbz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0fpgp26 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 078mm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0fphf3v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 01cm8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 09v3jyg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 043tvp3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 07jnt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 05ft32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0k7tq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 02bg55 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0gj96ln CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 04pk1f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0bc1yhb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 062zm5h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0dzlbx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0h03fhx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 01k60v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0184tc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0jymd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 05c26ss CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0bmhvpr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 06r2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 02fqrf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0gtvpkw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0jwmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0gffmn8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0crc2cp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 06w839_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0645k5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0fpmrm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 07x4qr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0661ql3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 06v9_x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0407yfx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 04n52p6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 02r1c18 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 03bx2lk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0_b3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k6nt film_release_region! 0dtw1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 183.000 124.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21525-015fr PRED entity: 015fr PRED relation: country! PRED expected values: 0cp0t91 => 169 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1840): 01m13b (0.50 #8662, 0.47 #10366, 0.46 #3551), 023g6w (0.44 #9914, 0.32 #11618, 0.31 #4803), 01f8f7 (0.33 #7955, 0.16 #11361, 0.15 #28391), 0bmch_x (0.31 #9301, 0.29 #5895, 0.27 #7599), 0401sg (0.31 #8608, 0.29 #5202, 0.23 #6813), 0cp08zg (0.31 #9780, 0.29 #6374, 0.23 #6813), 049mql (0.31 #9156, 0.27 #7454, 0.26 #21080), 04lqvly (0.31 #9126, 0.22 #17644, 0.18 #29562), 0fjyzt (0.29 #5997, 0.25 #9403, 0.22 #17921), 06_sc3 (0.29 #6452, 0.25 #9858, 0.22 #18376) >> Best rule #8662 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 09c7w0; 0154j; 0d060g; 05qhw; 07ssc; 0f8l9c; 0k6nt; 03gj2; 059j2; 0345h; ... >> query: (?x583, 01m13b) <- film_release_region(?x370, ?x583), combatants(?x94, ?x583), ?x370 = 0ddfwj1 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6813 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 04v3q; *> query: (?x583, ?x66) <- film_release_region(?x5644, ?x583), film_release_region(?x2558, ?x583), film_release_region(?x66, ?x583), ?x5644 = 0dll_t2, ?x2558 = 0bby9p5 *> conf = 0.23 ranks of expected_values: 41 EVAL 015fr country! 0cp0t91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 169.000 98.000 0.500 http://example.org/film/film/country #21524-01n4w PRED entity: 01n4w PRED relation: time_zones PRED expected values: 02hczc => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 13): 02hczc (0.78 #1419, 0.69 #1288, 0.64 #2203), 02fqwt (0.69 #1288, 0.64 #2203, 0.59 #1759), 02hcv8 (0.42 #120, 0.42 #1840, 0.41 #1277), 02lcqs (0.21 #1149, 0.21 #174, 0.20 #1253), 03bdv (0.20 #695, 0.17 #357, 0.12 #669), 02llzg (0.17 #69, 0.12 #160, 0.12 #706), 03plfd (0.06 #1363, 0.06 #1337, 0.05 #1389), 042g7t (0.06 #76, 0.05 #453, 0.04 #167), 0gsrz4 (0.05 #866, 0.05 #957, 0.04 #970), 052vwh (0.05 #1208, 0.04 #38, 0.04 #1104) >> Best rule #1419 for best value: >> intensional similarity = 4 >> extensional distance = 217 >> proper extension: 0mtdx; 0nh0f; 0nrqh; 0msyb; 0n3ll; 0n474; 0nm87; 0mkv3; 0mkp7; 0njj0; ... >> query: (?x2982, ?x2088) <- contains(?x2982, ?x14151), contains(?x2982, ?x11245), source(?x14151, ?x958), time_zones(?x11245, ?x2088) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n4w time_zones 02hczc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.777 http://example.org/location/location/time_zones #21523-01pgzn_ PRED entity: 01pgzn_ PRED relation: award_nominee! PRED expected values: 0h0wc 01gbn6 => 138 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1309): 0h0wc (0.82 #46131, 0.82 #43823, 0.82 #140690), 0hvb2 (0.82 #46131, 0.82 #43823, 0.82 #140690), 01pgzn_ (0.67 #9720, 0.64 #7413, 0.57 #2798), 01kb2j (0.22 #5803, 0.16 #159146, 0.14 #154534), 02bkdn (0.22 #5003, 0.14 #154534, 0.13 #168371), 017149 (0.22 #4709, 0.14 #154534, 0.13 #168371), 05dbf (0.22 #5087, 0.14 #154534, 0.13 #168371), 01gq0b (0.22 #5007, 0.14 #154534, 0.13 #168371), 0372kf (0.22 #5815, 0.14 #154534, 0.13 #168371), 01kt17 (0.22 #6572, 0.14 #154534, 0.13 #168371) >> Best rule #46131 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 04nw9; 01n4f8; 03_6y; 0bqs56; 01934k; 019n7x; >> query: (?x2352, ?x221) <- award_nominee(?x2352, ?x221), participant(?x1338, ?x2352), religion(?x2352, ?x1985) >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1, 138 EVAL 01pgzn_ award_nominee! 01gbn6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 138.000 84.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 01pgzn_ award_nominee! 0h0wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 84.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21522-0ntwb PRED entity: 0ntwb PRED relation: contains! PRED expected values: 03v0t => 96 concepts (32 used for prediction) PRED predicted values (max 10 best out of 79): 03v0t (0.71 #23400, 0.68 #18894, 0.63 #10792), 09c7w0 (0.53 #18897, 0.49 #11697, 0.48 #24304), 04_1l0v (0.41 #12145, 0.34 #13946, 0.31 #14847), 07b_l (0.26 #10113, 0.24 #11014, 0.22 #12816), 01n7q (0.22 #22577, 0.17 #18071, 0.15 #15376), 059rby (0.15 #22519, 0.14 #27018, 0.12 #27918), 0d060g (0.14 #25199, 0.02 #18907, 0.02 #12607), 0164b (0.14 #25199), 03h2c (0.14 #25199), 03s0w (0.12 #5453, 0.10 #6353, 0.09 #7252) >> Best rule #23400 for best value: >> intensional similarity = 7 >> extensional distance = 185 >> proper extension: 05rgl; 0843m; >> query: (?x9368, ?x3818) <- adjoins(?x9368, ?x13667), contains(?x3818, ?x13667), time_zones(?x13667, ?x1638), source(?x13667, ?x958), adjoins(?x13596, ?x13667), contains(?x13596, ?x13681), jurisdiction_of_office(?x2669, ?x3818) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ntwb contains! 03v0t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 32.000 0.707 http://example.org/location/location/contains #21521-07vfz PRED entity: 07vfz PRED relation: contains! PRED expected values: 09c7w0 01n7q => 157 concepts (81 used for prediction) PRED predicted values (max 10 best out of 253): 09c7w0 (0.84 #9848, 0.83 #41205, 0.70 #42995), 059rby (0.60 #34052, 0.14 #17028, 0.12 #9865), 02jx1 (0.50 #3667, 0.45 #4562, 0.43 #1877), 01n7q (0.43 #2763, 0.35 #29549, 0.33 #78), 0978r (0.36 #1996, 0.25 #4681, 0.22 #3786), 07ssc (0.22 #3612, 0.21 #1822, 0.20 #5402), 05l5n (0.22 #3702, 0.20 #4597, 0.16 #5492), 06pvr (0.22 #67181, 0.17 #17008, 0.07 #2851), 03rk0 (0.21 #52983, 0.04 #15354, 0.03 #14459), 04jpl (0.17 #52868, 0.11 #6287, 0.07 #15239) >> Best rule #9848 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 0194_r; >> query: (?x8281, 09c7w0) <- company(?x2998, ?x8281), currency(?x8281, ?x170), contains(?x3125, ?x8281), ?x170 = 09nqf >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 07vfz contains! 01n7q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 157.000 81.000 0.836 http://example.org/location/location/contains EVAL 07vfz contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 81.000 0.836 http://example.org/location/location/contains #21520-0flbm PRED entity: 0flbm PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 162 concepts (58 used for prediction) PRED predicted values (max 10 best out of 5): 09c7w0 (0.89 #215, 0.89 #253, 0.89 #241), 01n4w (0.24 #309, 0.11 #526, 0.11 #484), 03rt9 (0.02 #683, 0.02 #476, 0.02 #490), 0f8l9c (0.01 #158, 0.01 #303), 02jx1 (0.01 #770) >> Best rule #215 for best value: >> intensional similarity = 5 >> extensional distance = 226 >> proper extension: 0nv99; >> query: (?x14360, 09c7w0) <- contains(?x2982, ?x14360), adjoins(?x7010, ?x14360), source(?x14360, ?x958), currency(?x14360, ?x170), ?x170 = 09nqf >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0flbm second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 58.000 0.886 http://example.org/location/country/second_level_divisions #21519-02_fj PRED entity: 02_fj PRED relation: award_winner! PRED expected values: 0gqy2 02py7pj => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 348): 0f4x7 (0.57 #1282, 0.39 #20501, 0.39 #46983), 01c92g (0.57 #1282, 0.39 #20501, 0.39 #46983), 03qbh5 (0.57 #1282, 0.39 #20501, 0.39 #46983), 02nhxf (0.21 #5222, 0.07 #1379, 0.05 #7784), 027c95y (0.20 #1436, 0.15 #3144, 0.12 #5706), 02py7pj (0.20 #1585, 0.11 #12688, 0.07 #3293), 0gq9h (0.18 #930, 0.09 #5628, 0.08 #14596), 01c99j (0.18 #5344, 0.16 #7052, 0.09 #7906), 0c4z8 (0.16 #7757, 0.15 #5195, 0.11 #6903), 025mb9 (0.15 #5324, 0.05 #8740, 0.03 #50832) >> Best rule #1282 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 04411; 081nh; 072twv; 022p06; 0m593; 03fw4y; 01v5h; 016z1c; 016jll; >> query: (?x3017, ?x591) <- place_of_death(?x3017, ?x1523), organizations_founded(?x3017, ?x5634), award(?x3017, ?x591) >> conf = 0.57 => this is the best rule for 3 predicted values *> Best rule #1585 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0hwd8; *> query: (?x3017, 02py7pj) <- place_of_death(?x3017, ?x1523), award_winner(?x724, ?x3017), special_performance_type(?x3017, ?x4832) *> conf = 0.20 ranks of expected_values: 6, 84 EVAL 02_fj award_winner! 02py7pj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 153.000 153.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02_fj award_winner! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 153.000 153.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner #21518-0l98s PRED entity: 0l98s PRED relation: sports PRED expected values: 064vjs => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 35): 018w8 (0.86 #67, 0.86 #66, 0.85 #425), 0w0d (0.86 #67, 0.86 #66, 0.85 #425), 064vjs (0.80 #406, 0.60 #147, 0.53 #639), 01sgl (0.70 #658, 0.32 #624, 0.29 #1302), 07bs0 (0.40 #140, 0.32 #624, 0.29 #632), 06z68 (0.40 #148, 0.32 #624, 0.29 #640), 019w9j (0.40 #146, 0.32 #624, 0.29 #1302), 09_9n (0.37 #1295, 0.33 #1263, 0.32 #624), 02_5h (0.34 #1274, 0.32 #624, 0.30 #1242), 01z27 (0.34 #1277, 0.32 #624, 0.29 #1302) >> Best rule #67 for best value: >> intensional similarity = 17 >> extensional distance = 2 >> proper extension: 0l998; >> query: (?x584, ?x1967) <- sports(?x584, ?x6150), sports(?x584, ?x5182), sports(?x584, ?x3127), sports(?x584, ?x1967), sports(?x584, ?x779), sports(?x584, ?x766), ?x766 = 01hp22, ?x5182 = 0crlz, ?x779 = 096f8, ?x3127 = 03hr1p, olympics(?x456, ?x584), olympics(?x151, ?x584), country(?x1967, ?x7037), ?x6150 = 07_53, ?x456 = 05qhw, ?x151 = 0b90_r, form_of_government(?x7037, ?x48) >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 8 *> proper extension: 0lbbj; 0jkvj; *> query: (?x584, 064vjs) <- sports(?x584, ?x766), olympics(?x7413, ?x584), olympics(?x5114, ?x584), olympics(?x1003, ?x584), ?x1003 = 03gj2, form_of_government(?x7413, ?x48), film_release_region(?x7832, ?x7413), country(?x766, ?x1603), ?x1603 = 06bnz, olympics(?x766, ?x1931), ?x5114 = 05vz3zq, ?x1931 = 0kbws, ?x7832 = 0fphf3v *> conf = 0.80 ranks of expected_values: 3 EVAL 0l98s sports 064vjs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 65.000 0.860 http://example.org/olympics/olympic_games/sports #21517-0kyk PRED entity: 0kyk PRED relation: split_to! PRED expected values: 0kyk => 32 concepts (25 used for prediction) PRED predicted values (max 10 best out of 24): 02hrh1q (0.33 #209, 0.25 #504, 0.25 #405), 03gjzk (0.10 #1195, 0.02 #1693, 0.01 #1797), 09lbv (0.02 #1595, 0.02 #1698, 0.01 #1802), 0mzj_ (0.02 #1768, 0.01 #1872, 0.01 #1974), 04gb7 (0.02 #1729, 0.01 #1833, 0.01 #1935), 0g0vx (0.01 #1282), 025syph (0.01 #1282), 012qdp (0.01 #1282), 0d8qb (0.01 #1282), 08z956 (0.01 #1282) >> Best rule #209 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 02hrh1q; >> query: (?x2225, 02hrh1q) <- profession(?x10275, ?x2225), profession(?x8938, ?x2225), profession(?x6914, ?x2225), profession(?x4407, ?x2225), ?x6914 = 02b29, ?x4407 = 039crh, ?x10275 = 03hpr, gender(?x8938, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1282 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 03gjzk; 02hv44_; *> query: (?x2225, ?x5805) <- profession(?x11290, ?x2225), profession(?x6914, ?x2225), profession(?x3426, ?x2225), ?x6914 = 02b29, celebrities_impersonated(?x3649, ?x11290), profession(?x11290, ?x5805), artists(?x2664, ?x3426) *> conf = 0.01 ranks of expected_values: 17 EVAL 0kyk split_to! 0kyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 32.000 25.000 0.333 http://example.org/dataworld/gardening_hint/split_to #21516-01hkck PRED entity: 01hkck PRED relation: place_of_birth PRED expected values: 0b_cr => 146 concepts (120 used for prediction) PRED predicted values (max 10 best out of 175): 0b_cr (0.42 #25380, 0.33 #1410, 0.33 #1351), 0qymv (0.33 #410), 01_d4 (0.20 #2181, 0.20 #1476, 0.10 #2886), 01531 (0.20 #1515, 0.10 #5040, 0.06 #24780), 0f2w0 (0.20 #2177, 0.07 #4291, 0.05 #5702), 02_286 (0.14 #15528, 0.12 #19759, 0.12 #13414), 0l2k7 (0.11 #25381, 0.02 #9870, 0.02 #62722), 0135p7 (0.10 #3390, 0.08 #4094, 0.07 #4799), 062qg (0.08 #3846, 0.04 #6667), 030qb3t (0.07 #40883, 0.07 #42293, 0.07 #4283) >> Best rule #25380 for best value: >> intensional similarity = 3 >> extensional distance = 185 >> proper extension: 01r42_g; 066m4g; 06b0d2; 01541z; 086qd; 01dy7j; 01_j71; 018n6m; 01wgfp6; 09wlpl; ... >> query: (?x11311, ?x13692) <- location(?x11311, ?x13692), actor(?x5529, ?x11311), county_seat(?x14029, ?x13692) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hkck place_of_birth 0b_cr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 120.000 0.419 http://example.org/people/person/place_of_birth #21515-0ks67 PRED entity: 0ks67 PRED relation: school! PRED expected values: 02z6872 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 19): 0f4vx0 (0.58 #106, 0.28 #125, 0.24 #353), 02qw1zx (0.35 #100, 0.22 #347, 0.20 #233), 09l0x9 (0.26 #107, 0.17 #126, 0.16 #354), 02pq_x5 (0.23 #111, 0.20 #244, 0.16 #358), 05vsb7 (0.21 #229, 0.17 #343, 0.16 #96), 025tn92 (0.19 #108, 0.18 #355, 0.16 #241), 092j54 (0.19 #104, 0.16 #351, 0.16 #237), 03nt7j (0.17 #235, 0.16 #102, 0.15 #349), 06439y (0.16 #114, 0.13 #247, 0.12 #361), 02pq_rp (0.16 #103, 0.11 #350, 0.09 #236) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 06mkj; 0d05w3; >> query: (?x5807, 0f4vx0) <- organization(?x5807, ?x5487), school(?x8542, ?x5807), organization(?x581, ?x5487), place_founded(?x5072, ?x581) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #238 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 016sd3; *> query: (?x5807, 02z6872) <- school_type(?x5807, ?x1507), school(?x8542, ?x5807), currency(?x5807, ?x170), school(?x5419, ?x5807) *> conf = 0.13 ranks of expected_values: 14 EVAL 0ks67 school! 02z6872 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 108.000 108.000 0.581 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #21514-034qg PRED entity: 034qg PRED relation: people PRED expected values: 083p7 07_m2 => 34 concepts (26 used for prediction) PRED predicted values (max 10 best out of 677): 01vsl3_ (0.50 #2139, 0.33 #1459, 0.25 #14295), 04jvt (0.33 #1821, 0.33 #458, 0.25 #14295), 0gzh (0.33 #2026, 0.25 #14295, 0.25 #2706), 0132k4 (0.33 #1661, 0.25 #14295, 0.25 #2341), 016hvl (0.33 #1404, 0.25 #14295, 0.25 #2084), 09889g (0.33 #872, 0.25 #14295, 0.20 #14976), 041wm (0.33 #532, 0.25 #14295, 0.20 #14976), 0d3k14 (0.33 #526, 0.25 #14295, 0.20 #14976), 083p7 (0.33 #34, 0.25 #14295, 0.20 #14976), 0chsq (0.27 #5459, 0.27 #4779, 0.23 #6139) >> Best rule #2139 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 032s66; >> query: (?x9888, 01vsl3_) <- people(?x9888, ?x8383), people(?x9888, ?x8169), people(?x9888, ?x6707), people(?x9888, ?x4576), profession(?x6707, ?x1146), nationality(?x6707, ?x94), place_of_birth(?x6707, ?x13959), ?x8169 = 01vz0g4, profession(?x8510, ?x1146), influenced_by(?x8383, ?x5434), ?x8510 = 02wd48, award(?x4576, ?x2563) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 012hw; *> query: (?x9888, 083p7) <- people(?x9888, ?x10328), people(?x9888, ?x6707), profession(?x6707, ?x987), nationality(?x6707, ?x94), place_of_birth(?x6707, ?x13959), ?x10328 = 05hks, profession(?x2934, ?x987), profession(?x2819, ?x987), profession(?x2357, ?x987), ?x2934 = 04cbtrw, ?x2819 = 0bczgm, ?x2357 = 0bymv *> conf = 0.33 ranks of expected_values: 9, 393 EVAL 034qg people 07_m2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 26.000 0.500 http://example.org/people/cause_of_death/people EVAL 034qg people 083p7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 34.000 26.000 0.500 http://example.org/people/cause_of_death/people #21513-04xfb PRED entity: 04xfb PRED relation: profession PRED expected values: 0fj9f => 141 concepts (90 used for prediction) PRED predicted values (max 10 best out of 108): 02hrh1q (0.81 #8164, 0.80 #3717, 0.80 #6091), 0fj9f (0.80 #1685, 0.75 #3905, 0.74 #3461), 0dxtg (0.52 #9944, 0.51 #10388, 0.41 #10980), 0kyk (0.50 #326, 0.40 #178, 0.39 #1809), 01d_h8 (0.44 #3709, 0.39 #2081, 0.38 #6083), 02jknp (0.43 #9938, 0.33 #3710, 0.27 #7121), 09jwl (0.41 #2242, 0.32 #4019, 0.32 #2390), 05z96 (0.40 #42, 0.33 #338, 0.30 #12894), 0nbcg (0.38 #2255, 0.29 #2403, 0.26 #3291), 02hv44_ (0.30 #12894, 0.30 #12893, 0.28 #1038) >> Best rule #8164 for best value: >> intensional similarity = 3 >> extensional distance = 194 >> proper extension: 05zdk2; 06wvfq; 0bxy67; 05vzql; 03fwln; 01vzz1c; 07jmnh; 070c93; 040nwr; 03z_g7; >> query: (?x8383, 02hrh1q) <- profession(?x8383, ?x353), nationality(?x8383, ?x2146), ?x2146 = 03rk0 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1685 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0157m; *> query: (?x8383, 0fj9f) <- profession(?x8383, ?x3342), politician(?x13990, ?x8383), type_of_union(?x8383, ?x566), ?x3342 = 04gc2 *> conf = 0.80 ranks of expected_values: 2 EVAL 04xfb profession 0fj9f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 90.000 0.811 http://example.org/people/person/profession #21512-0b68vs PRED entity: 0b68vs PRED relation: origin PRED expected values: 034cm => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 89): 04p3c (0.32 #3073, 0.31 #2364, 0.18 #4492), 02_286 (0.11 #16, 0.09 #726, 0.06 #1198), 03b12 (0.11 #171, 0.03 #1589, 0.03 #1825), 01531 (0.11 #61, 0.02 #3134, 0.02 #1243), 018d5b (0.11 #220, 0.01 #456), 04jpl (0.08 #478, 0.04 #1896, 0.04 #952), 030qb3t (0.06 #744, 0.06 #1452, 0.05 #980), 02dtg (0.06 #1664, 0.05 #1428, 0.04 #956), 0dclg (0.05 #990, 0.04 #1462, 0.04 #1698), 09c7w0 (0.04 #947, 0.04 #711, 0.03 #1419) >> Best rule #3073 for best value: >> intensional similarity = 3 >> extensional distance = 212 >> proper extension: 01w61th; 01kwlwp; 058s57; 01vyp_; 0840vq; 024dgj; 0bqsy; 049qx; 01817f; 03h610; ... >> query: (?x1181, ?x4510) <- award_winner(?x486, ?x1181), place_of_birth(?x1181, ?x4510), artists(?x671, ?x1181) >> conf = 0.32 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0b68vs origin 034cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 130.000 0.318 http://example.org/music/artist/origin #21511-01qgry PRED entity: 01qgry PRED relation: artists! PRED expected values: 0gywn => 138 concepts (62 used for prediction) PRED predicted values (max 10 best out of 243): 06by7 (0.74 #16559, 0.53 #958, 0.52 #5017), 064t9 (0.74 #11554, 0.63 #5321, 0.62 #6568), 0xhtw (0.68 #954, 0.32 #1265, 0.23 #4076), 01fh36 (0.58 #1024, 0.26 #1335, 0.24 #712), 0gywn (0.51 #2242, 0.46 #5366, 0.43 #6613), 0dl5d (0.47 #956, 0.41 #644, 0.33 #331), 025sc50 (0.42 #5358, 0.37 #6605, 0.30 #11591), 016clz (0.38 #17475, 0.32 #5000, 0.27 #6870), 03_d0 (0.38 #2195, 0.27 #323, 0.26 #948), 0155w (0.35 #1355, 0.26 #1044, 0.25 #108) >> Best rule #16559 for best value: >> intensional similarity = 5 >> extensional distance = 524 >> proper extension: 02mq_y; >> query: (?x5141, 06by7) <- artists(?x1127, ?x5141), artists(?x1127, ?x12593), artists(?x1127, ?x5364), ?x12593 = 012x03, award_nominee(?x5364, ?x286) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #2242 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 01kx_81; 09qr6; 01zmpg; 03j0br4; 01w724; 016h9b; 01vsykc; 0407f; 01w8n89; 01vrkdt; ... *> query: (?x5141, 0gywn) <- artist(?x4081, ?x5141), instrumentalists(?x227, ?x5141), artists(?x1127, ?x5141), ?x1127 = 02x8m *> conf = 0.51 ranks of expected_values: 5 EVAL 01qgry artists! 0gywn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 62.000 0.740 http://example.org/music/genre/artists #21510-04wp63 PRED entity: 04wp63 PRED relation: crewmember! PRED expected values: 04mcw4 => 84 concepts (69 used for prediction) PRED predicted values (max 10 best out of 316): 0ddt_ (0.34 #2496, 0.34 #2184, 0.04 #1037), 0f3m1 (0.34 #2496, 0.34 #2184, 0.04 #624), 0dnqr (0.34 #2496, 0.34 #2184, 0.04 #624), 01hw5kk (0.34 #2496, 0.34 #2184, 0.04 #624), 033dbw (0.12 #1243, 0.08 #2179, 0.07 #2803), 024mpp (0.11 #2000, 0.09 #2624, 0.09 #2312), 07nxnw (0.11 #2107, 0.09 #2731, 0.09 #2419), 031t2d (0.11 #1934, 0.09 #2558, 0.09 #2246), 01kff7 (0.11 #1920, 0.09 #2544, 0.09 #2232), 0hx4y (0.09 #409, 0.08 #1969, 0.07 #2593) >> Best rule #2496 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 02vxyl5; >> query: (?x10262, ?x1012) <- nominated_for(?x10262, ?x1012), crewmember(?x3455, ?x10262), nominated_for(?x68, ?x3455) >> conf = 0.34 => this is the best rule for 4 predicted values *> Best rule #2643 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 01yznp; *> query: (?x10262, 04mcw4) <- crewmember(?x4902, ?x10262), film(?x574, ?x4902), titles(?x8581, ?x4902) *> conf = 0.02 ranks of expected_values: 289 EVAL 04wp63 crewmember! 04mcw4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 69.000 0.345 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #21509-017r13 PRED entity: 017r13 PRED relation: film PRED expected values: 0d61px => 105 concepts (60 used for prediction) PRED predicted values (max 10 best out of 832): 02rv_dz (0.72 #7108, 0.67 #37308, 0.66 #31979), 0g57wgv (0.72 #7108, 0.67 #37308, 0.66 #31979), 01n30p (0.40 #1777, 0.17 #3555, 0.11 #30202), 02qr3k8 (0.05 #10162, 0.05 #8386, 0.05 #6609), 011ysn (0.05 #563, 0.03 #5894, 0.02 #7671), 013q07 (0.05 #355, 0.02 #2133, 0.02 #11015), 040_lv (0.05 #1039, 0.01 #6370, 0.01 #15251), 02ny6g (0.05 #598, 0.01 #23692, 0.01 #11258), 01shy7 (0.04 #21738, 0.04 #27069, 0.04 #25293), 03bx2lk (0.04 #3739, 0.03 #184, 0.02 #14396) >> Best rule #7108 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 02t__l; 01vsps; 0121rx; >> query: (?x6279, ?x1531) <- award(?x6279, ?x3066), nominated_for(?x6279, ?x1531), ?x3066 = 0gqy2 >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #4248 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 113 *> proper extension: 01xyt7; *> query: (?x6279, 0d61px) <- participant(?x6278, ?x6279), student(?x1440, ?x6279), religion(?x6279, ?x2694) *> conf = 0.03 ranks of expected_values: 111 EVAL 017r13 film 0d61px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 105.000 60.000 0.717 http://example.org/film/actor/film./film/performance/film #21508-0f2sx4 PRED entity: 0f2sx4 PRED relation: produced_by PRED expected values: 02r251z => 66 concepts (42 used for prediction) PRED predicted values (max 10 best out of 172): 02pv_d (0.39 #6200, 0.27 #1935, 0.26 #4651), 02r251z (0.36 #241, 0.10 #1015, 0.04 #1402), 05m883 (0.27 #1935, 0.26 #4651, 0.03 #6201), 0320jz (0.16 #774, 0.12 #6199, 0.12 #1161), 016tw3 (0.16 #774, 0.12 #6199, 0.12 #1161), 0b13g7 (0.15 #504, 0.03 #8254, 0.03 #8640), 02q42j_ (0.15 #596, 0.03 #8346, 0.02 #8732), 04wvhz (0.10 #423, 0.06 #810, 0.03 #5074), 058frd (0.10 #600, 0.04 #1374, 0.01 #987), 06rq2l (0.09 #307, 0.09 #387, 0.04 #1081) >> Best rule #6200 for best value: >> intensional similarity = 4 >> extensional distance = 574 >> proper extension: 05fgr_; 06dfz1; >> query: (?x7967, ?x1335) <- nominated_for(?x1335, ?x7967), titles(?x2480, ?x7967), produced_by(?x821, ?x1335), award_nominee(?x1335, ?x541) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 02ph9tm; *> query: (?x7967, 02r251z) <- film(?x5975, ?x7967), ?x5975 = 01fyzy, film(?x1104, ?x7967) *> conf = 0.36 ranks of expected_values: 2 EVAL 0f2sx4 produced_by 02r251z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 42.000 0.393 http://example.org/film/film/produced_by #21507-0c4hx0 PRED entity: 0c4hx0 PRED relation: honored_for PRED expected values: 0cq7kw => 43 concepts (41 used for prediction) PRED predicted values (max 10 best out of 955): 097zcz (0.33 #854, 0.33 #596, 0.15 #12548), 0286gm1 (0.33 #596, 0.25 #2169, 0.25 #1574), 0h0wd9 (0.33 #596, 0.25 #1745, 0.15 #12548), 0d68qy (0.33 #152, 0.22 #15696, 0.20 #19889), 04p5cr (0.33 #395, 0.15 #14742, 0.15 #15939), 05lfwd (0.33 #348, 0.12 #15892, 0.12 #14695), 08jgk1 (0.33 #92, 0.12 #15636, 0.11 #17433), 072192 (0.33 #1106, 0.10 #5882, 0.06 #10666), 0hv27 (0.33 #974, 0.10 #5750, 0.06 #10534), 05dmmc (0.33 #865, 0.10 #5641, 0.06 #10425) >> Best rule #854 for best value: >> intensional similarity = 20 >> extensional distance = 1 >> proper extension: 0d__c3; >> query: (?x9667, 097zcz) <- award_winner(?x9667, ?x12378), award_winner(?x9667, ?x11729), award_winner(?x9667, ?x8635), ?x12378 = 0c0tzp, instance_of_recurring_event(?x9667, ?x3459), profession(?x8635, ?x967), ceremony(?x3066, ?x9667), ceremony(?x1862, ?x9667), ?x967 = 012t_z, ?x3066 = 0gqy2, ?x3459 = 0g_w, honored_for(?x9667, ?x7434), honored_for(?x9667, ?x5870), student(?x9879, ?x11729), film_release_region(?x5870, ?x94), ?x1862 = 0gr51, award(?x11729, ?x2585), profession(?x11729, ?x1614), ?x2585 = 054ks3, award_winner(?x7434, ?x294) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8362 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 11 *> proper extension: 0c53vt; *> query: (?x9667, ?x6243) <- award_winner(?x9667, ?x12378), award_winner(?x9667, ?x10741), award_winner(?x9667, ?x8423), film_sets_designed(?x12378, ?x4280), ceremony(?x5409, ?x9667), gender(?x10741, ?x231), award_winner(?x5409, ?x10464), ceremony(?x5409, ?x8150), ceremony(?x5409, ?x5723), ceremony(?x5409, ?x5703), nationality(?x10741, ?x94), award(?x5816, ?x5409), award_nominee(?x2068, ?x12378), cinematography(?x6243, ?x10741), ?x5816 = 03_80b, ?x8150 = 0bzkvd, ?x5703 = 02yvhx, ?x5723 = 0fk0xk, award(?x8423, ?x1245), award_nominee(?x10464, ?x4328) *> conf = 0.10 ranks of expected_values: 88 EVAL 0c4hx0 honored_for 0cq7kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 43.000 41.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #21506-03q0r1 PRED entity: 03q0r1 PRED relation: film_release_region PRED expected values: 0jgd 01ls2 03rt9 0345h 06qd3 05qx1 03rj0 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 101): 0345h (0.85 #967, 0.84 #1777, 0.84 #1507), 0jgd (0.85 #948, 0.79 #1488, 0.79 #1758), 03rt9 (0.75 #956, 0.71 #1496, 0.69 #1766), 03rj0 (0.63 #992, 0.62 #1532, 0.61 #1802), 06qd3 (0.62 #1782, 0.61 #1512, 0.54 #972), 01p1v (0.57 #985, 0.51 #1525, 0.51 #1795), 01ls2 (0.49 #954, 0.46 #1494, 0.46 #1764), 09pmkv (0.48 #153, 0.43 #1503, 0.42 #1773), 05qx1 (0.47 #1515, 0.45 #1785, 0.43 #975), 01pj7 (0.46 #982, 0.37 #1522, 0.36 #1792) >> Best rule #967 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 07l50vn; >> query: (?x3854, 0345h) <- nominated_for(?x1053, ?x3854), film_release_region(?x3854, ?x3277), ?x3277 = 06t8v >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 7, 9 EVAL 03q0r1 film_release_region 03rj0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.849 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03q0r1 film_release_region 05qx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 88.000 88.000 0.849 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03q0r1 film_release_region 06qd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.849 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03q0r1 film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.849 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03q0r1 film_release_region 03rt9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.849 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03q0r1 film_release_region 01ls2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.849 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03q0r1 film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.849 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21505-0391jz PRED entity: 0391jz PRED relation: special_performance_type PRED expected values: 01pb34 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.09 #13, 0.07 #53, 0.06 #68), 09_gdc (0.04 #12, 0.02 #2, 0.02 #7), 01kyvx (0.02 #1, 0.02 #6, 0.02 #92) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 98 >> proper extension: 02jg92; 01lz4tf; >> query: (?x3560, 01pb34) <- location(?x3560, ?x1658), participant(?x3560, ?x7156) >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0391jz special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.090 http://example.org/film/actor/film./film/performance/special_performance_type #21504-014w_8 PRED entity: 014w_8 PRED relation: risk_factors PRED expected values: 01hbgs => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 88): 0fltx (0.70 #1767, 0.67 #242, 0.60 #137), 05zppz (0.70 #1767, 0.67 #694, 0.56 #805), 0jpmt (0.70 #1767, 0.63 #2859, 0.56 #502), 01hbgs (0.70 #1767, 0.60 #127, 0.56 #1927), 012jc (0.70 #1767, 0.52 #1926, 0.51 #2192), 0k95h (0.70 #1767, 0.52 #1926, 0.51 #2192), 0d19y2 (0.50 #197, 0.33 #575, 0.31 #2073), 02zsn (0.40 #1387, 0.26 #1068, 0.22 #859), 0432mrk (0.36 #260, 0.33 #745, 0.20 #1226), 0fk1z (0.36 #260, 0.33 #745, 0.20 #1226) >> Best rule #1767 for best value: >> intensional similarity = 11 >> extensional distance = 13 >> proper extension: 01mtqf; >> query: (?x10613, ?x231) <- risk_factors(?x10613, ?x6260), people(?x10613, ?x3194), people(?x4322, ?x3194), profession(?x3194, ?x2225), risk_factors(?x4322, ?x231), film(?x3194, ?x3847), genre(?x3847, ?x225), profession(?x2614, ?x2225), profession(?x806, ?x2225), ?x2614 = 04xrx, ?x806 = 03qd_ >> conf = 0.70 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 014w_8 risk_factors 01hbgs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 67.000 67.000 0.702 http://example.org/medicine/disease/risk_factors #21503-035wq7 PRED entity: 035wq7 PRED relation: student! PRED expected values: 017z88 09f2j => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 95): 0bwfn (0.08 #16058, 0.07 #4482, 0.06 #5008), 01w5m (0.06 #4313, 0.05 #13256, 0.05 #13783), 0gl5_ (0.05 #1295, 0.04 #2347, 0.04 #3399), 015nl4 (0.05 #4801, 0.05 #15325, 0.04 #6379), 09f2j (0.05 #4892, 0.04 #15942, 0.03 #5418), 03ksy (0.05 #13257, 0.05 #13784, 0.04 #11679), 017z88 (0.05 #4816, 0.03 #5342, 0.03 #15866), 065y4w7 (0.04 #15798, 0.04 #20533, 0.04 #1066), 06182p (0.04 #3979, 0.02 #15555, 0.02 #16081), 0g8rj (0.04 #1227, 0.03 #2805, 0.03 #2279) >> Best rule #16058 for best value: >> intensional similarity = 3 >> extensional distance = 1110 >> proper extension: 09bx1k; >> query: (?x11885, 0bwfn) <- gender(?x11885, ?x231), student(?x4889, ?x11885), nominated_for(?x11885, ?x11422) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #4892 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 456 *> proper extension: 0clvcx; *> query: (?x11885, 09f2j) <- nationality(?x11885, ?x94), actor(?x12739, ?x11885), student(?x4889, ?x11885) *> conf = 0.05 ranks of expected_values: 5, 7 EVAL 035wq7 student! 09f2j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 95.000 95.000 0.080 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 035wq7 student! 017z88 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 95.000 95.000 0.080 http://example.org/education/educational_institution/students_graduates./education/education/student #21502-0gcs9 PRED entity: 0gcs9 PRED relation: award PRED expected values: 03qbh5 02f72_ => 165 concepts (146 used for prediction) PRED predicted values (max 10 best out of 313): 02wh75 (0.82 #8150, 0.81 #11645, 0.76 #38038), 01ckrr (0.82 #8150, 0.81 #11645, 0.76 #38038), 01ck6v (0.82 #8150, 0.81 #11645, 0.76 #38038), 02sp_v (0.82 #8150, 0.81 #11645, 0.76 #38038), 02f6xy (0.82 #8150, 0.81 #11645, 0.76 #11256), 09sb52 (0.28 #31089, 0.26 #34194, 0.26 #29925), 02f72n (0.26 #1302, 0.26 #914, 0.25 #1691), 03qbh5 (0.26 #971, 0.25 #1748, 0.24 #5628), 02v1m7 (0.25 #1661, 0.23 #1272, 0.23 #884), 01c9jp (0.25 #5612, 0.19 #955, 0.16 #1343) >> Best rule #8150 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 0pgjm; 021bk; 018pj3; 03bxwtd; 01n8gr; 024dgj; 01tc9r; 01vvyfh; 01wwvd2; 06m61; ... >> query: (?x2963, ?x247) <- role(?x2963, ?x227), profession(?x2963, ?x220), award_winner(?x247, ?x2963) >> conf = 0.82 => this is the best rule for 5 predicted values *> Best rule #971 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 03c3yf; 033s6; *> query: (?x2963, 03qbh5) <- origin(?x2963, ?x6895), influenced_by(?x1573, ?x2963), award(?x2963, ?x462) *> conf = 0.26 ranks of expected_values: 8, 22 EVAL 0gcs9 award 02f72_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 165.000 146.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gcs9 award 03qbh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 165.000 146.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21501-01r97z PRED entity: 01r97z PRED relation: currency PRED expected values: 09nqf => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.83 #99, 0.82 #162, 0.81 #169), 02l6h (0.03 #32, 0.02 #53, 0.02 #18), 01nv4h (0.02 #297, 0.02 #290, 0.02 #206), 02gsvk (0.02 #76, 0.01 #146, 0.01 #34) >> Best rule #99 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 0cp08zg; >> query: (?x770, 09nqf) <- film(?x541, ?x770), nominated_for(?x703, ?x770), film_distribution_medium(?x770, ?x81) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r97z currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.834 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21500-0gghm PRED entity: 0gghm PRED relation: role! PRED expected values: 028tv0 => 89 concepts (51 used for prediction) PRED predicted values (max 10 best out of 110): 02sgy (0.89 #3700, 0.86 #2608, 0.85 #2392), 0342h (0.85 #4781, 0.84 #1181, 0.83 #1072), 0l14md (0.84 #1181, 0.83 #1072, 0.83 #1398), 018vs (0.84 #1181, 0.83 #1072, 0.83 #1398), 028tv0 (0.84 #1181, 0.83 #1072, 0.83 #1398), 07y_7 (0.84 #1181, 0.83 #1072, 0.83 #1398), 07kc_ (0.84 #1181, 0.83 #1072, 0.83 #1398), 05148p4 (0.83 #4576, 0.82 #1393, 0.81 #5336), 013y1f (0.82 #4555, 0.81 #5239, 0.78 #3834), 0bxl5 (0.82 #1793, 0.79 #2561, 0.75 #1571) >> Best rule #3700 for best value: >> intensional similarity = 22 >> extensional distance = 16 >> proper extension: 07y_7; >> query: (?x2310, 02sgy) <- role(?x4583, ?x2310), role(?x745, ?x2310), role(?x614, ?x2310), role(?x432, ?x2310), role(?x2310, ?x212), ?x614 = 0mkg, role(?x75, ?x4583), role(?x1260, ?x2310), role(?x4583, ?x4975), role(?x4583, ?x780), ?x4975 = 0859_, ?x780 = 01qzyz, role(?x7987, ?x432), role(?x1652, ?x432), ?x7987 = 0j6cj, role(?x1433, ?x432), group(?x432, ?x442), performance_role(?x9987, ?x432), ?x1433 = 0239kh, ?x745 = 01vj9c, ?x1652 = 01l1sq, role(?x1291, ?x432) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1181 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 5 *> proper extension: 03f5mt; *> query: (?x2310, ?x75) <- role(?x5417, ?x2310), role(?x4917, ?x2310), role(?x2944, ?x2310), role(?x2923, ?x2310), role(?x1969, ?x2310), role(?x1466, ?x2310), instrumentalists(?x2310, ?x2575), ?x1969 = 04rzd, role(?x2310, ?x75), ?x4917 = 06w7v, ?x1466 = 03bx0bm, role(?x314, ?x5417), ?x2923 = 02k856, role(?x367, ?x5417), role(?x1332, ?x2944), role(?x120, ?x2944), role(?x2944, ?x74), ?x74 = 03q5t, ?x1332 = 03qlv7 *> conf = 0.84 ranks of expected_values: 5 EVAL 0gghm role! 028tv0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 89.000 51.000 0.889 http://example.org/music/performance_role/regular_performances./music/group_membership/role #21499-06t2t PRED entity: 06t2t PRED relation: jurisdiction_of_office! PRED expected values: 0p5vf => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 27): 0pqc5 (0.84 #235, 0.43 #655, 0.42 #529), 0p5vf (0.32 #264, 0.26 #137, 0.25 #432), 0f6c3 (0.31 #1942, 0.22 #2237, 0.22 #1016), 0fkvn (0.29 #1938, 0.24 #1033, 0.24 #886), 09n5b9 (0.28 #1946, 0.18 #2241, 0.17 #2283), 04syw (0.28 #973, 0.25 #300, 0.17 #426), 01zq91 (0.26 #139, 0.18 #581, 0.17 #434), 0dq3c (0.21 #169, 0.19 #674, 0.19 #380), 01q24l (0.19 #244, 0.10 #223, 0.09 #33), 0377k9 (0.19 #140, 0.17 #98, 0.14 #77) >> Best rule #235 for best value: >> intensional similarity = 2 >> extensional distance = 29 >> proper extension: 0mbf4; 0jpkg; >> query: (?x2316, 0pqc5) <- jurisdiction_of_office(?x182, ?x2316), mode_of_transportation(?x2316, ?x4272) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #264 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 0h3y; *> query: (?x2316, 0p5vf) <- exported_to(?x87, ?x2316), contains(?x2316, ?x3354), country(?x343, ?x2316) *> conf = 0.32 ranks of expected_values: 2 EVAL 06t2t jurisdiction_of_office! 0p5vf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 137.000 0.839 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #21498-0d193h PRED entity: 0d193h PRED relation: group! PRED expected values: 018vs => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 116): 05148p4 (0.74 #1111, 0.74 #1195, 0.73 #439), 0l14md (0.65 #1856, 0.62 #343, 0.61 #427), 018vs (0.64 #1104, 0.62 #1188, 0.61 #2365), 05r5c (0.48 #428, 0.30 #1857, 0.29 #1184), 01vj9c (0.42 #433, 0.31 #1862, 0.29 #1021), 0l14qv (0.36 #425, 0.25 #1854, 0.25 #1013), 013y1f (0.36 #446, 0.17 #1875, 0.15 #1707), 04rzd (0.30 #450, 0.16 #1711, 0.13 #1879), 042v_gx (0.18 #429, 0.14 #1690, 0.11 #1858), 018j2 (0.18 #451, 0.09 #1712, 0.06 #1681) >> Best rule #1111 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 07rnh; >> query: (?x4261, 05148p4) <- artists(?x302, ?x4261), group(?x227, ?x4261), ?x302 = 016clz, artist(?x2299, ?x4261) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 07rnh; *> query: (?x4261, 018vs) <- artists(?x302, ?x4261), group(?x227, ?x4261), ?x302 = 016clz, artist(?x2299, ?x4261) *> conf = 0.64 ranks of expected_values: 3 EVAL 0d193h group! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 106.000 0.743 http://example.org/music/performance_role/regular_performances./music/group_membership/group #21497-02z1yj PRED entity: 02z1yj PRED relation: film PRED expected values: 09xbpt => 165 concepts (137 used for prediction) PRED predicted values (max 10 best out of 1163): 0jqkh (0.20 #1331, 0.03 #6701, 0.03 #8491), 0cz_ym (0.20 #294, 0.03 #7454, 0.02 #14614), 0ds2n (0.20 #524, 0.01 #38114, 0.01 #41694), 02qhqz4 (0.13 #5713, 0.05 #7503, 0.04 #16453), 02qr3k8 (0.11 #3080, 0.03 #31720, 0.03 #72890), 02704ff (0.11 #4563, 0.06 #17093, 0.03 #87711), 03tbg6 (0.11 #5236, 0.05 #8816, 0.05 #10606), 01xbxn (0.11 #4975, 0.05 #8555, 0.04 #15715), 026390q (0.11 #3767, 0.04 #10927, 0.04 #14507), 05sns6 (0.11 #4290, 0.04 #11450, 0.04 #15030) >> Best rule #1331 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0pj9t; >> query: (?x9944, 0jqkh) <- student(?x10869, ?x9944), award_winner(?x10337, ?x9944), ?x10869 = 03qdm >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3627 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 01jfrg; *> query: (?x9944, 09xbpt) <- spouse(?x8269, ?x9944), award_winner(?x1132, ?x9944), student(?x2605, ?x9944) *> conf = 0.05 ranks of expected_values: 122 EVAL 02z1yj film 09xbpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 165.000 137.000 0.200 http://example.org/film/actor/film./film/performance/film #21496-07cjqy PRED entity: 07cjqy PRED relation: gender PRED expected values: 05zppz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #31, 0.73 #134, 0.71 #174), 02zsn (0.50 #28, 0.46 #22, 0.45 #62) >> Best rule #31 for best value: >> intensional similarity = 2 >> extensional distance = 317 >> proper extension: 05g8ky; 0f1vrl; 0fpj4lx; 03lh3v; 01_k1z; 018y81; 01386_; 0cv72h; 021r7r; 06y3r; ... >> query: (?x3536, 05zppz) <- place_of_birth(?x3536, ?x3450), currency(?x3536, ?x170) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07cjqy gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.727 http://example.org/people/person/gender #21495-03tn9w PRED entity: 03tn9w PRED relation: honored_for PRED expected values: 015qqg => 37 concepts (21 used for prediction) PRED predicted values (max 10 best out of 680): 0jqn5 (0.25 #80, 0.23 #7168, 0.22 #4175), 027ct7c (0.25 #332, 0.23 #7168, 0.22 #4175), 0jym0 (0.25 #119, 0.23 #7168, 0.22 #4175), 0pd6l (0.25 #232, 0.23 #7168, 0.22 #4175), 01_1pv (0.25 #135, 0.23 #7168, 0.22 #4175), 0209hj (0.25 #36, 0.23 #7168, 0.22 #4175), 0qmhk (0.25 #330, 0.23 #7168, 0.22 #4175), 0bcp9b (0.25 #447, 0.23 #7168, 0.22 #4175), 0k4p0 (0.25 #343, 0.23 #7168, 0.22 #4175), 01k5y0 (0.25 #560, 0.23 #7168, 0.22 #4175) >> Best rule #80 for best value: >> intensional similarity = 14 >> extensional distance = 2 >> proper extension: 0bzknt; 0bzmt8; >> query: (?x6686, 0jqn5) <- ceremony(?x3617, ?x6686), ceremony(?x1972, ?x6686), ?x3617 = 0gvx_, award_winner(?x6686, ?x4398), award_winner(?x6686, ?x669), ?x669 = 0146pg, ?x1972 = 0gqyl, nominated_for(?x4398, ?x3430), location(?x4398, ?x362), student(?x7021, ?x4398), film(?x4398, ?x80), gender(?x4398, ?x514), actor(?x5529, ?x4398), story_by(?x3430, ?x2609) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #10758 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 57 *> proper extension: 02pgky2; *> query: (?x6686, ?x1386) <- ceremony(?x3617, ?x6686), ceremony(?x1323, ?x6686), ceremony(?x1313, ?x6686), ?x3617 = 0gvx_, award_winner(?x6686, ?x669), ceremony(?x1323, ?x6344), award_winner(?x1386, ?x669), nominated_for(?x669, ?x670), ?x6344 = 0bzm__, profession(?x669, ?x563), award_winner(?x1323, ?x538), award(?x269, ?x1313), nominated_for(?x1313, ?x144) *> conf = 0.17 ranks of expected_values: 74 EVAL 03tn9w honored_for 015qqg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 37.000 21.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #21494-01jkqfz PRED entity: 01jkqfz PRED relation: award_winner! PRED expected values: 0jzphpx 01mhwk => 104 concepts (101 used for prediction) PRED predicted values (max 10 best out of 127): 013b2h (0.54 #912, 0.42 #1051, 0.23 #7230), 01c6qp (0.23 #7230, 0.22 #2781, 0.15 #851), 0466p0j (0.23 #7230, 0.22 #2781, 0.13 #1186), 02rjjll (0.23 #7230, 0.22 #2781, 0.13 #1950), 02cg41 (0.23 #7230, 0.22 #2781, 0.11 #2765), 09n4nb (0.23 #7230, 0.22 #2781, 0.11 #1158), 0gpjbt (0.23 #7230, 0.22 #2781, 0.10 #3364), 0jzphpx (0.23 #7230, 0.22 #2781, 0.09 #1983), 01xqqp (0.23 #7230, 0.22 #2781, 0.08 #2457), 01mh_q (0.23 #7230, 0.22 #2781, 0.08 #2728) >> Best rule #912 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 035_2h; >> query: (?x8393, 013b2h) <- award_winner(?x2638, ?x8393), nominated_for(?x341, ?x8393), award_nominee(?x1795, ?x2638) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #7230 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 953 *> proper extension: 0gsg7; 0l56b; 0lzkm; 01wn718; 0h32q; 0pmw9; 044k8; 0khth; 09r9m7; 0hwbd; ... *> query: (?x8393, ?x1480) <- award_winner(?x3419, ?x8393), award_winner(?x342, ?x8393), award_winner(?x1480, ?x3419) *> conf = 0.23 ranks of expected_values: 8, 11 EVAL 01jkqfz award_winner! 01mhwk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 104.000 101.000 0.538 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01jkqfz award_winner! 0jzphpx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 104.000 101.000 0.538 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21493-028pzq PRED entity: 028pzq PRED relation: type_of_union PRED expected values: 04ztj => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.95 #352, 0.95 #337, 0.95 #295), 0jgjn (0.40 #103, 0.36 #242, 0.19 #407), 01bl8s (0.19 #407) >> Best rule #352 for best value: >> intensional similarity = 3 >> extensional distance = 3024 >> proper extension: 028qdb; >> query: (?x9099, 04ztj) <- type_of_union(?x9099, ?x1873), type_of_union(?x9244, ?x1873), ?x9244 = 037w7r >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028pzq type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.946 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21492-0fj45 PRED entity: 0fj45 PRED relation: jurisdiction_of_office PRED expected values: 0bq0p9 0ctw_b 0l3h => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 807): 0f8l9c (0.62 #1853, 0.56 #2309, 0.50 #4583), 09c7w0 (0.58 #3641, 0.51 #3184, 0.50 #3185), 0ctw_b (0.51 #3184, 0.50 #3185, 0.50 #499), 0b90_r (0.51 #3184, 0.50 #3185, 0.36 #907), 03ryn (0.51 #3184, 0.50 #3185, 0.26 #2271), 0162b (0.51 #3184, 0.50 #3185, 0.26 #2271), 0d05w3 (0.51 #3184, 0.50 #3185, 0.26 #2271), 05sb1 (0.51 #3184, 0.50 #3185, 0.26 #2271), 06m_5 (0.51 #3184, 0.50 #3185, 0.26 #2271), 0345_ (0.51 #3184, 0.50 #3185, 0.26 #2271) >> Best rule #1853 for best value: >> intensional similarity = 26 >> extensional distance = 6 >> proper extension: 060c4; 01gkgk; 0789n; 0p5vf; >> query: (?x12773, 0f8l9c) <- jurisdiction_of_office(?x12773, ?x3120), jurisdiction_of_office(?x12773, ?x421), countries_within(?x8483, ?x421), organization(?x421, ?x4403), location_of_ceremony(?x566, ?x421), film_release_region(?x186, ?x421), country(?x1967, ?x421), olympics(?x421, ?x2134), olympics(?x421, ?x867), olympics(?x421, ?x778), jurisdiction_of_office(?x3444, ?x421), adjustment_currency(?x3120, ?x170), ?x778 = 0kbvb, form_of_government(?x421, ?x1926), ?x867 = 0l6ny, ?x1967 = 01cgz, basic_title(?x1913, ?x12773), vacationer(?x421, ?x5665), ?x2134 = 0blg2, organization(?x11052, ?x4403), organization(?x5360, ?x4403), organization(?x4164, ?x4403), nationality(?x2538, ?x421), ?x11052 = 04ty8, ?x4164 = 047t_, ?x5360 = 07dzf >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3184 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 8 *> proper extension: 0fkzq; *> query: (?x12773, ?x4954) <- jurisdiction_of_office(?x12773, ?x10183), jurisdiction_of_office(?x12773, ?x7096), jurisdiction_of_office(?x12773, ?x421), jurisdiction_of_office(?x12773, ?x126), country(?x171, ?x421), vacationer(?x126, ?x10754), vacationer(?x126, ?x2352), vacationer(?x126, ?x2221), taxonomy(?x126, ?x939), currency(?x10754, ?x170), official_language(?x7096, ?x254), adjoins(?x10183, ?x4954), award_nominee(?x4259, ?x10754), friend(?x4625, ?x10754), film(?x10754, ?x7800), participant(?x1126, ?x10754), award_nominee(?x2352, ?x221), participant(?x2221, ?x3101), award_winner(?x2221, ?x541), ?x939 = 04n6k, participant(?x400, ?x2352), film_release_region(?x972, ?x4954), nominated_for(?x2221, ?x1810), profession(?x2221, ?x319), award_winner(?x1562, ?x2352) *> conf = 0.51 ranks of expected_values: 3, 36, 63 EVAL 0fj45 jurisdiction_of_office 0l3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 18.000 18.000 0.625 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fj45 jurisdiction_of_office 0ctw_b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 18.000 18.000 0.625 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fj45 jurisdiction_of_office 0bq0p9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 18.000 18.000 0.625 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #21491-0h6rm PRED entity: 0h6rm PRED relation: list PRED expected values: 09g7thr => 233 concepts (233 used for prediction) PRED predicted values (max 10 best out of 4): 09g7thr (0.50 #99, 0.48 #281, 0.47 #190), 01ptsx (0.33 #502, 0.30 #656, 0.27 #677), 04k4rt (0.22 #655, 0.21 #529, 0.21 #501), 01pd60 (0.21 #657, 0.21 #503, 0.20 #531) >> Best rule #99 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 07tgn; 07szy; 0bx8pn; 07tg4; 09f2j; 0dzst; >> query: (?x4390, 09g7thr) <- student(?x4390, ?x5171), company(?x4095, ?x4390), profession(?x5171, ?x5805), type_of_union(?x5171, ?x566), ?x5805 = 0fj9f >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h6rm list 09g7thr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 233.000 233.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #21490-01fx2g PRED entity: 01fx2g PRED relation: award PRED expected values: 02x4x18 0gkts9 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 252): 0ck27z (0.47 #902, 0.25 #20747, 0.21 #23582), 09sb52 (0.42 #7736, 0.40 #446, 0.38 #8141), 05p09zm (0.34 #1744, 0.32 #2554, 0.31 #2149), 05zr6wv (0.29 #1637, 0.26 #2447, 0.24 #2852), 0gqy2 (0.27 #570, 0.10 #14745, 0.10 #25680), 03c7tr1 (0.26 #1679, 0.24 #2489, 0.19 #2894), 019f4v (0.25 #67, 0.07 #3307, 0.06 #17482), 02pqp12 (0.25 #71, 0.07 #3311, 0.05 #17486), 0cc8l6d (0.25 #174, 0.02 #5844, 0.02 #5439), 02qyxs5 (0.25 #148, 0.02 #5818, 0.02 #5413) >> Best rule #902 for best value: >> intensional similarity = 2 >> extensional distance = 17 >> proper extension: 0gsg7; >> query: (?x5240, 0ck27z) <- nominated_for(?x5240, ?x5808), ?x5808 = 05lfwd >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1753 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 0q5hw; 02wb6yq; *> query: (?x5240, 02x4x18) <- friend(?x5240, ?x2444), celebrity(?x2927, ?x5240), nominated_for(?x5240, ?x5808) *> conf = 0.17 ranks of expected_values: 16, 38 EVAL 01fx2g award 0gkts9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 130.000 130.000 0.474 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01fx2g award 02x4x18 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 130.000 130.000 0.474 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21489-05dss7 PRED entity: 05dss7 PRED relation: film_release_region PRED expected values: 03rjj 0d060g 07dfk => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 227): 03rjj (0.90 #618, 0.90 #2153, 0.88 #2000), 015fr (0.90 #2014, 0.87 #2474, 0.86 #1400), 03_3d (0.87 #1081, 0.86 #2155, 0.81 #1388), 03spz (0.84 #1169, 0.79 #1476, 0.75 #2243), 035qy (0.84 #3258, 0.82 #4027, 0.80 #2182), 0d060g (0.79 #3232, 0.77 #1543, 0.76 #4001), 0b90_r (0.78 #1999, 0.77 #1385, 0.76 #3997), 06bnz (0.78 #3270, 0.76 #4039, 0.74 #1427), 03rt9 (0.77 #1244, 0.74 #2011, 0.74 #2471), 05v8c (0.72 #2166, 0.71 #631, 0.67 #1399) >> Best rule #618 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 014lc_; 024mpp; 047vnkj; >> query: (?x6556, 03rjj) <- language(?x6556, ?x254), production_companies(?x6556, ?x752), film_release_region(?x6556, ?x1023), ?x1023 = 0ctw_b, written_by(?x6556, ?x8235) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 77 EVAL 05dss7 film_release_region 07dfk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 124.000 124.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05dss7 film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 124.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05dss7 film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21488-0154j PRED entity: 0154j PRED relation: combatants PRED expected values: 07ssc => 184 concepts (87 used for prediction) PRED predicted values (max 10 best out of 295): 07ssc (0.84 #588, 0.83 #4502, 0.83 #394), 0154j (0.71 #330, 0.67 #524, 0.67 #265), 0bq0p9 (0.40 #139, 0.36 #336, 0.33 #271), 07f1x (0.36 #371, 0.33 #306, 0.28 #565), 05v8c (0.33 #269, 0.29 #334, 0.28 #1245), 06qd3 (0.33 #276, 0.29 #341, 0.26 #2933), 0g8bw (0.33 #301, 0.29 #366, 0.26 #3912), 03rjj (0.30 #134, 0.26 #2933, 0.26 #653), 0c4b8 (0.26 #2933, 0.26 #653, 0.26 #3912), 05b7q (0.26 #2933, 0.26 #653, 0.26 #3912) >> Best rule #588 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 03bxbql; >> query: (?x172, ?x94) <- combatants(?x7430, ?x172), combatants(?x94, ?x172), ?x7430 = 01mk6, combatants(?x5530, ?x172) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0154j combatants 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 87.000 0.839 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #21487-03ccq3s PRED entity: 03ccq3s PRED relation: nominated_for PRED expected values: 02r1ysd 0330r => 44 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1435): 08jgk1 (0.75 #1574, 0.67 #36244, 0.65 #28366), 0330r (0.33 #1376, 0.22 #31521, 0.19 #14179), 02czd5 (0.33 #1257, 0.22 #31521, 0.19 #14179), 0kfpm (0.33 #101, 0.22 #31521, 0.19 #14179), 01ft14 (0.33 #1432, 0.22 #31521, 0.19 #14179), 01lv85 (0.33 #1133, 0.11 #2707, 0.07 #4281), 05p9_ql (0.33 #1110, 0.11 #4258, 0.10 #5833), 0cs134 (0.33 #1468, 0.08 #3042, 0.07 #4616), 023ny6 (0.33 #1408, 0.08 #2982, 0.07 #4556), 01b9w3 (0.33 #656, 0.08 #2230, 0.07 #3804) >> Best rule #1574 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0cjyzs; >> query: (?x3906, ?x1631) <- award(?x3895, ?x3906), ?x3895 = 06jnvs, award(?x1631, ?x3906), nominated_for(?x3906, ?x631) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1376 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0cjyzs; *> query: (?x3906, 0330r) <- award(?x3895, ?x3906), ?x3895 = 06jnvs, award(?x1631, ?x3906), nominated_for(?x3906, ?x631) *> conf = 0.33 ranks of expected_values: 2, 992 EVAL 03ccq3s nominated_for 0330r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 23.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03ccq3s nominated_for 02r1ysd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 23.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21486-03ntbmw PRED entity: 03ntbmw PRED relation: production_companies PRED expected values: 059x3p => 116 concepts (111 used for prediction) PRED predicted values (max 10 best out of 64): 0jz9f (0.32 #6556, 0.32 #5372, 0.30 #7570), 01gb54 (0.18 #38, 0.13 #121, 0.09 #1291), 030_1_ (0.18 #17, 0.13 #100, 0.06 #1270), 086k8 (0.16 #251, 0.13 #750, 0.12 #1339), 03sb38 (0.16 #1141, 0.06 #470, 0.03 #1727), 02j_j0 (0.15 #1134, 0.04 #2137, 0.04 #1971), 016tt2 (0.13 #87, 0.11 #503, 0.10 #586), 05qd_ (0.13 #93, 0.09 #926, 0.09 #10), 02slt7 (0.12 #1116, 0.07 #113, 0.01 #2543), 016tw3 (0.12 #261, 0.11 #1013, 0.09 #1098) >> Best rule #6556 for best value: >> intensional similarity = 4 >> extensional distance = 803 >> proper extension: 02rb607; 0h95zbp; 0j8f09z; >> query: (?x12403, ?x166) <- genre(?x12403, ?x53), film(?x166, ?x12403), ?x53 = 07s9rl0, nominated_for(?x166, ?x144) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 01cgz; *> query: (?x12403, 059x3p) <- films(?x5069, ?x12403), films(?x5069, ?x1488), ?x1488 = 01719t *> conf = 0.02 ranks of expected_values: 45 EVAL 03ntbmw production_companies 059x3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 116.000 111.000 0.323 http://example.org/film/film/production_companies #21485-0jbp0 PRED entity: 0jbp0 PRED relation: actor! PRED expected values: 03nymk => 136 concepts (109 used for prediction) PRED predicted values (max 10 best out of 136): 06r2h (0.21 #15067, 0.21 #16654, 0.08 #27243), 02py4c8 (0.14 #276, 0.09 #805, 0.02 #7682), 026bfsh (0.14 #361, 0.05 #7767, 0.05 #8295), 07c72 (0.14 #312, 0.04 #1370, 0.03 #2164), 02md2d (0.14 #335, 0.01 #2187, 0.01 #3511), 0g60z (0.12 #1326, 0.03 #8730, 0.02 #7674), 0127ps (0.10 #15332, 0.09 #17447, 0.09 #19561), 0cs134 (0.10 #741, 0.01 #2328, 0.01 #2592), 0266s9 (0.09 #1026), 02r1ysd (0.09 #919) >> Best rule #15067 for best value: >> intensional similarity = 4 >> extensional distance = 809 >> proper extension: 0162c8; 0b4rf3; >> query: (?x10398, ?x9017) <- nominated_for(?x10398, ?x10661), nominated_for(?x10398, ?x9017), profession(?x10398, ?x1032), languages(?x10661, ?x254) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jbp0 actor! 03nymk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 109.000 0.210 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21484-0427y PRED entity: 0427y PRED relation: influenced_by! PRED expected values: 0f7hc => 137 concepts (57 used for prediction) PRED predicted values (max 10 best out of 372): 046lt (0.12 #16933, 0.12 #1135, 0.11 #24120), 01xwv7 (0.12 #16933, 0.12 #8119, 0.11 #24120), 01xwqn (0.12 #16933, 0.11 #24120, 0.11 #11217), 016_mj (0.12 #16933, 0.11 #24120, 0.10 #568), 02633g (0.12 #16933, 0.11 #24120, 0.10 #831), 049fgvm (0.12 #16933, 0.11 #24120, 0.10 #777), 01xdf5 (0.12 #16933, 0.11 #24120, 0.10 #516), 01hmk9 (0.12 #16933, 0.11 #24120, 0.10 #796), 0q5hw (0.12 #16933, 0.11 #24120, 0.10 #615), 03g5_y (0.12 #16933, 0.11 #24120, 0.10 #825) >> Best rule #16933 for best value: >> intensional similarity = 3 >> extensional distance = 161 >> proper extension: 01h2_6; >> query: (?x9596, ?x236) <- influenced_by(?x3917, ?x9596), people(?x1050, ?x9596), influenced_by(?x236, ?x3917) >> conf = 0.12 => this is the best rule for 32 predicted values *> Best rule #696 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 03f3yfj; 03f1zhf; *> query: (?x9596, 0f7hc) <- category(?x9596, ?x134), person(?x9723, ?x9596), profession(?x9596, ?x319), ?x319 = 01d_h8 *> conf = 0.10 ranks of expected_values: 37 EVAL 0427y influenced_by! 0f7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 137.000 57.000 0.122 http://example.org/influence/influence_node/influenced_by #21483-06n90 PRED entity: 06n90 PRED relation: genre! PRED expected values: 014lc_ 07gp9 03mh94 0cpllql 08hmch 02pxmgz 0bscw 0gvrws1 0661ql3 01f7kl 0hx4y 0x25q 024mxd 0315w4 0dfw0 01svry 02mmwk 011xg5 015ynm 02v5xg 03n0cd 076xkps 06r2h 01f7jt 04svwx => 73 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1699): 024mxd (0.74 #44930, 0.50 #28836, 0.40 #23840), 0dfw0 (0.74 #44930, 0.50 #15734, 0.40 #45697), 0x25q (0.74 #44930, 0.50 #15423, 0.40 #23748), 07gp9 (0.74 #44930, 0.40 #23331, 0.40 #21667), 0g5pv3 (0.74 #44930, 0.40 #23473, 0.40 #21809), 031hcx (0.74 #44930, 0.33 #34454, 0.33 #32791), 08hmch (0.74 #44930, 0.33 #28431, 0.33 #8457), 01f7jt (0.74 #44930, 0.33 #31527, 0.33 #13214), 0g5pvv (0.74 #44930, 0.33 #9282, 0.33 #2630), 014lc_ (0.74 #44930, 0.33 #8318, 0.33 #1666) >> Best rule #44930 for best value: >> intensional similarity = 13 >> extensional distance = 8 >> proper extension: 0jtdp; 0hcr; >> query: (?x1013, ?x3672) <- genre(?x12214, ?x1013), genre(?x11615, ?x1013), genre(?x4565, ?x1013), genre(?x3433, ?x1013), genre(?x723, ?x1013), genre(?x4063, ?x1013), nominated_for(?x507, ?x4565), disciplines_or_subjects(?x575, ?x1013), titles(?x600, ?x11615), prequel(?x3672, ?x12214), film_release_region(?x3433, ?x94), film(?x722, ?x723), actor(?x4063, ?x194) >> conf = 0.74 => this is the best rule for 12 predicted values ranks of expected_values: 1, 2, 3, 4, 7, 8, 10, 13, 19, 21, 74, 99, 125, 150, 159, 168, 298, 346, 373, 377, 506, 595, 705, 892, 922 EVAL 06n90 genre! 04svwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 01f7jt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 06r2h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 076xkps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 03n0cd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 02v5xg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 015ynm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 011xg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 02mmwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 01svry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 0dfw0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 0315w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 024mxd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 0x25q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 0hx4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 01f7kl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 0gvrws1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 0bscw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 02pxmgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 08hmch CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 0cpllql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 03mh94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 07gp9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 47.000 0.736 http://example.org/film/film/genre EVAL 06n90 genre! 014lc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 73.000 47.000 0.736 http://example.org/film/film/genre #21482-06v9sf PRED entity: 06v9sf PRED relation: combatants! PRED expected values: 02h2z_ => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 79): 03gqgt3 (0.71 #538, 0.50 #194, 0.38 #811), 01h6pn (0.69 #1637, 0.67 #1847, 0.67 #1429), 0chhs (0.69 #1637, 0.67 #1847, 0.67 #1429), 02kxg_ (0.69 #1637, 0.67 #1847, 0.67 #1429), 06k75 (0.57 #1177, 0.45 #563, 0.40 #288), 081pw (0.55 #548, 0.52 #1228, 0.50 #756), 0cm2xh (0.50 #150, 0.43 #494, 0.40 #284), 048n7 (0.45 #570, 0.43 #505, 0.33 #639), 02h2z_ (0.45 #599, 0.33 #52, 0.32 #1213), 01gjd0 (0.45 #550, 0.33 #619, 0.29 #1164) >> Best rule #538 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 0345h; >> query: (?x3057, 03gqgt3) <- combatants(?x3057, ?x613), combatants(?x3057, ?x390), combatants(?x3057, ?x94), combatants(?x8303, ?x3057), combatants(?x456, ?x3057), ?x390 = 0chghy, ?x94 = 09c7w0, ?x613 = 0bq0p9, film_release_region(?x5496, ?x456), ?x5496 = 07l50vn >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #599 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 9 *> proper extension: 09c7w0; 0d060g; 0chghy; 07ssc; 0bq0p9; 05vz3zq; 04g61; 059z0; *> query: (?x3057, 02h2z_) <- entity_involved(?x5530, ?x3057), combatants(?x1229, ?x3057), combatants(?x5530, ?x279), ?x1229 = 059j2, participating_countries(?x784, ?x279), country(?x150, ?x279), film_release_region(?x66, ?x279) *> conf = 0.45 ranks of expected_values: 9 EVAL 06v9sf combatants! 02h2z_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 65.000 65.000 0.714 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #21481-047vnkj PRED entity: 047vnkj PRED relation: produced_by PRED expected values: 0gg9_5q 0315q3 => 85 concepts (52 used for prediction) PRED predicted values (max 10 best out of 165): 032v0v (0.34 #11180, 0.26 #2695, 0.03 #1153), 0205dx (0.23 #17739, 0.17 #13107, 0.09 #17738), 094tsh6 (0.17 #13107, 0.11 #12338, 0.11 #13495), 0170s4 (0.11 #12338, 0.11 #13495, 0.11 #19666), 01t6b4 (0.07 #8088, 0.04 #2737, 0.03 #1195), 02xnjd (0.07 #8088, 0.03 #1039, 0.03 #6424), 05mvd62 (0.07 #8088, 0.03 #1395, 0.03 #1781), 02kxbwx (0.07 #8088, 0.03 #1926, 0.02 #799), 02kxbx3 (0.07 #8088, 0.03 #1926, 0.01 #12840), 01vb6z (0.07 #8088, 0.02 #1382, 0.01 #1768) >> Best rule #11180 for best value: >> intensional similarity = 4 >> extensional distance = 532 >> proper extension: 018js4; 01br2w; 0m2kd; 0djb3vw; 0b60sq; 026p_bs; 026mfbr; 01hp5; 084qpk; 06z8s_; ... >> query: (?x5271, ?x1689) <- language(?x5271, ?x90), genre(?x5271, ?x53), film_release_region(?x5271, ?x142), film(?x1689, ?x5271) >> conf = 0.34 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 047vnkj produced_by 0315q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 52.000 0.341 http://example.org/film/film/produced_by EVAL 047vnkj produced_by 0gg9_5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 52.000 0.341 http://example.org/film/film/produced_by #21480-0178kd PRED entity: 0178kd PRED relation: group! PRED expected values: 018vs 06ncr => 92 concepts (59 used for prediction) PRED predicted values (max 10 best out of 122): 018vs (0.83 #530, 0.67 #185, 0.65 #1046), 0l14md (0.74 #524, 0.61 #1040, 0.58 #1903), 06ncr (0.42 #210, 0.20 #1071, 0.20 #297), 0l14qv (0.33 #264, 0.33 #177, 0.31 #522), 0mkg (0.33 #183, 0.25 #11, 0.11 #528), 05r5c (0.33 #180, 0.24 #955, 0.24 #1904), 03qjg (0.29 #564, 0.27 #1080, 0.25 #47), 01vj9c (0.28 #1910, 0.28 #1649, 0.27 #961), 018j2 (0.25 #204, 0.25 #32, 0.11 #549), 04rzd (0.25 #203, 0.17 #548, 0.16 #1064) >> Best rule #530 for best value: >> intensional similarity = 6 >> extensional distance = 33 >> proper extension: 01qqwp9; >> query: (?x6368, 018vs) <- group(?x1166, ?x6368), group(?x645, ?x6368), origin(?x6368, ?x5036), artists(?x302, ?x6368), ?x645 = 028tv0, ?x1166 = 05148p4 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0178kd group! 06ncr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 59.000 0.829 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0178kd group! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 59.000 0.829 http://example.org/music/performance_role/regular_performances./music/group_membership/group #21479-03pvt PRED entity: 03pvt PRED relation: student! PRED expected values: 0bjqh => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 183): 065y4w7 (0.33 #1066, 0.14 #2118, 0.09 #2644), 021w0_ (0.33 #1375, 0.14 #2427, 0.09 #2953), 0g8rj (0.25 #1753, 0.02 #12273), 02fy0z (0.25 #1671, 0.01 #13769), 08815 (0.13 #3684, 0.09 #2632, 0.09 #7892), 0bwfn (0.12 #5534, 0.11 #6586, 0.09 #7112), 02g839 (0.09 #2655, 0.07 #3181, 0.07 #3707), 01pcj4 (0.09 #2998, 0.07 #4050, 0.06 #5628), 06rjp (0.09 #3066, 0.07 #4118, 0.06 #5696), 03ksy (0.08 #12204, 0.06 #4314, 0.06 #5366) >> Best rule #1066 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 06pj8; >> query: (?x3710, 065y4w7) <- special_performance_type(?x3710, ?x4832), type_of_appearance(?x3710, ?x3429), award(?x3710, ?x102), currency(?x3710, ?x170) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03pvt student! 0bjqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 127.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #21478-02g8h PRED entity: 02g8h PRED relation: people! PRED expected values: 033tf_ => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 46): 041rx (0.20 #4, 0.17 #1159, 0.16 #851), 0xnvg (0.20 #13, 0.07 #475, 0.07 #552), 07bch9 (0.20 #23, 0.05 #408, 0.05 #947), 0x67 (0.17 #318, 0.16 #1396, 0.14 #3321), 065b6q (0.12 #80, 0.03 #2775, 0.03 #1466), 06gbnc (0.12 #104, 0.02 #412), 033tf_ (0.12 #2779, 0.09 #238, 0.09 #315), 02ctzb (0.10 #169, 0.09 #323, 0.05 #477), 02w7gg (0.10 #156, 0.08 #541, 0.07 #618), 07hwkr (0.10 #166, 0.06 #782, 0.06 #320) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 073749; >> query: (?x318, 041rx) <- category(?x318, ?x134), student(?x3922, ?x318), ?x3922 = 023znp >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2779 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 474 *> proper extension: 014zcr; 09fb5; 0h5g_; 03zqc1; 06jzh; 05gml8; 01csvq; 018db8; 04yj5z; 0pz7h; ... *> query: (?x318, 033tf_) <- film(?x318, ?x3088), award(?x318, ?x2192), participant(?x318, ?x2647) *> conf = 0.12 ranks of expected_values: 7 EVAL 02g8h people! 033tf_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 111.000 111.000 0.200 http://example.org/people/ethnicity/people #21477-0cdw6 PRED entity: 0cdw6 PRED relation: citytown! PRED expected values: 02dj3 => 80 concepts (15 used for prediction) PRED predicted values (max 10 best out of 70): 0ym69 (0.02 #1596, 0.02 #2406, 0.02 #3216), 0ymf1 (0.02 #1589, 0.02 #2399, 0.02 #3209), 0ym20 (0.02 #1587, 0.02 #2397, 0.02 #3207), 0ym4t (0.02 #1576, 0.02 #2386, 0.02 #3196), 0ym1n (0.02 #1571, 0.02 #2381, 0.02 #3191), 0f11p (0.02 #1550, 0.02 #2360, 0.02 #3170), 0yldt (0.02 #1546, 0.02 #2356, 0.02 #3166), 0yl_3 (0.02 #1475, 0.02 #2285, 0.02 #3095), 0yl_j (0.02 #1458, 0.02 #2268, 0.02 #3078), 0yl_w (0.02 #1444, 0.02 #2254, 0.02 #3064) >> Best rule #1596 for best value: >> intensional similarity = 5 >> extensional distance = 51 >> proper extension: 0ymbl; 0dhdp; 0fm2_; 02jx1; 05l5n; 09tlh; 0978r; 0hyxv; 05bcl; 0j5g9; ... >> query: (?x14505, 0ym69) <- time_zones(?x14505, ?x5327), contains(?x12381, ?x14505), administrative_parent(?x4510, ?x12381), ?x5327 = 03bdv, contains(?x512, ?x12381) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cdw6 citytown! 02dj3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 15.000 0.019 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #21476-02r2j8 PRED entity: 02r2j8 PRED relation: country_of_origin PRED expected values: 09c7w0 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 67): 09c7w0 (0.84 #191, 0.81 #442, 0.81 #476), 0d060g (0.62 #52, 0.59 #140, 0.58 #88), 03rjj (0.62 #52, 0.59 #140, 0.58 #88), 0345h (0.62 #52, 0.59 #140, 0.58 #88), 07ssc (0.51 #213, 0.28 #163, 0.27 #124), 02jx1 (0.51 #213, 0.21 #64, 0.01 #1169), 03_3d (0.38 #43, 0.28 #216, 0.27 #227), 0d0vqn (0.05 #108, 0.05 #120, 0.04 #132), 05v8c (0.02 #464, 0.02 #245, 0.01 #429), 03rt9 (0.02 #464, 0.01 #267, 0.01 #336) >> Best rule #191 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 02zv4b; 026bfsh; 01f39b; >> query: (?x7928, 09c7w0) <- actor(?x7928, ?x3210), nominated_for(?x3210, ?x1230), participant(?x3210, ?x1208), participant(?x3210, ?x1301) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r2j8 country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.838 http://example.org/tv/tv_program/country_of_origin #21475-07bwr PRED entity: 07bwr PRED relation: film! PRED expected values: 01kb2j => 95 concepts (50 used for prediction) PRED predicted values (max 10 best out of 906): 01kb2j (0.48 #83013, 0.45 #89237, 0.44 #80937), 0flw6 (0.45 #89237, 0.44 #80937, 0.44 #24903), 02dztn (0.27 #3410, 0.02 #7559, 0.01 #32467), 057_yx (0.20 #1834, 0.03 #5983, 0.02 #10133), 0pz91 (0.20 #210, 0.03 #35491, 0.02 #37566), 083wr9 (0.20 #2048, 0.02 #10347, 0.01 #14499), 016ypb (0.20 #497, 0.02 #23325, 0.02 #48228), 04w391 (0.20 #686, 0.02 #8985, 0.02 #6909), 04hxyv (0.20 #2033, 0.02 #10332), 03dn9v (0.20 #1832, 0.02 #10131) >> Best rule #83013 for best value: >> intensional similarity = 4 >> extensional distance = 882 >> proper extension: 07wqr6; 0123qq; >> query: (?x5066, ?x4004) <- nominated_for(?x4004, ?x5066), nationality(?x4004, ?x94), ?x94 = 09c7w0, people(?x3584, ?x4004) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07bwr film! 01kb2j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 50.000 0.483 http://example.org/film/actor/film./film/performance/film #21474-032zq6 PRED entity: 032zq6 PRED relation: genre PRED expected values: 01hmnh => 155 concepts (95 used for prediction) PRED predicted values (max 10 best out of 108): 01hmnh (0.67 #1557, 0.61 #8976, 0.60 #5023), 02kdv5l (0.56 #481, 0.50 #362, 0.46 #1678), 03k9fj (0.53 #372, 0.42 #1210, 0.40 #851), 01jfsb (0.49 #1689, 0.45 #2047, 0.44 #492), 05p553 (0.48 #2276, 0.42 #6464, 0.35 #3472), 06n90 (0.47 #493, 0.40 #14, 0.37 #853), 04xvlr (0.35 #8857, 0.34 #11131, 0.30 #10770), 06cvj (0.31 #2275, 0.25 #6463, 0.21 #3829), 0lsxr (0.29 #607, 0.26 #1087, 0.26 #1445), 04pbhw (0.25 #534, 0.20 #55, 0.18 #894) >> Best rule #1557 for best value: >> intensional similarity = 5 >> extensional distance = 91 >> proper extension: 011yqc; 08052t3; 048htn; 07f_7h; 017z49; 0jymd; 043tz0c; 07z6xs; 02q_ncg; >> query: (?x4152, ?x1510) <- language(?x4152, ?x254), featured_film_locations(?x4152, ?x2552), film_format(?x4152, ?x909), titles(?x1510, ?x4152), genre(?x97, ?x1510) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 032zq6 genre 01hmnh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 95.000 0.667 http://example.org/film/film/genre #21473-017d77 PRED entity: 017d77 PRED relation: major_field_of_study PRED expected values: 01z4y => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 115): 03g3w (0.51 #1766, 0.49 #2511, 0.47 #2138), 02j62 (0.51 #1769, 0.49 #2514, 0.47 #651), 04rjg (0.51 #2131, 0.47 #2504, 0.43 #517), 01mkq (0.51 #2126, 0.44 #2499, 0.43 #1754), 05qjt (0.51 #2118, 0.44 #2491, 0.40 #256), 06ms6 (0.50 #266, 0.33 #18, 0.27 #2501), 037mh8 (0.42 #2553, 0.41 #2180, 0.40 #1062), 01lj9 (0.40 #289, 0.39 #2151, 0.38 #2524), 01540 (0.39 #2173, 0.38 #2546, 0.33 #63), 0fdys (0.38 #1778, 0.37 #2150, 0.36 #2523) >> Best rule #1766 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 02g839; 02zc7f; 015wy_; 02htv6; >> query: (?x1513, 03g3w) <- student(?x1513, ?x6907), school_type(?x1513, ?x1044), currency(?x1513, ?x170), music(?x675, ?x6907) >> conf = 0.51 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 017d77 major_field_of_study 01z4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 142.000 0.514 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #21472-04rwx PRED entity: 04rwx PRED relation: major_field_of_study PRED expected values: 02j62 04g51 01540 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 105): 02j62 (0.65 #1755, 0.62 #673, 0.58 #1539), 05qfh (0.64 #569, 0.56 #677, 0.56 #1109), 0g26h (0.60 #249, 0.55 #1656, 0.48 #1114), 0fdys (0.57 #571, 0.56 #679, 0.50 #246), 03g3w (0.57 #562, 0.54 #1536, 0.52 #1102), 02h40lc (0.57 #545, 0.50 #653, 0.36 #1085), 06ms6 (0.57 #554, 0.46 #446, 0.40 #229), 01540 (0.48 #1131, 0.43 #591, 0.38 #1565), 04sh3 (0.44 #711, 0.40 #278, 0.38 #1577), 0h5k (0.43 #559, 0.36 #1099, 0.31 #1533) >> Best rule #1755 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 059j2; 03rj0; 04hzj; 05c74; >> query: (?x1665, 02j62) <- organization(?x1665, ?x5487), company(?x346, ?x1665), contains(?x94, ?x1665) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8, 36 EVAL 04rwx major_field_of_study 01540 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 147.000 147.000 0.645 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rwx major_field_of_study 04g51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 147.000 147.000 0.645 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rwx major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.645 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #21471-05ys0xf PRED entity: 05ys0xf PRED relation: film_festivals! PRED expected values: 0d6b7 0k2cb => 75 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1352): 03cw411 (0.25 #326, 0.25 #87, 0.20 #563), 02rb607 (0.25 #290, 0.20 #766, 0.20 #527), 047vp1n (0.25 #416, 0.20 #892, 0.20 #653), 0462hhb (0.25 #353, 0.20 #590, 0.17 #5134), 09tqkv2 (0.25 #281, 0.20 #518, 0.17 #8606), 051ys82 (0.25 #387, 0.20 #624, 0.17 #8606), 080lkt7 (0.25 #349, 0.20 #586, 0.17 #8606), 0b76t12 (0.25 #277, 0.20 #514, 0.17 #8606), 07l4zhn (0.25 #373, 0.20 #610, 0.11 #5154), 05zvzf3 (0.25 #439, 0.20 #676, 0.11 #5220) >> Best rule #326 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 059_y8d; 0cmd3zy; >> query: (?x9457, 03cw411) <- film_festivals(?x288, ?x9457), titles(?x53, ?x288), currency(?x288, ?x170), award(?x288, ?x289), nominated_for(?x1307, ?x288), genre(?x288, ?x600), films(?x5069, ?x288), ?x1307 = 0gq9h, country(?x288, ?x94), ?x170 = 09nqf >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #8606 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 21 *> proper extension: 09n48; *> query: (?x9457, ?x80) <- locations(?x9457, ?x1646), featured_film_locations(?x2189, ?x1646), location(?x8740, ?x1646), locations(?x11852, ?x1646), film_festivals(?x80, ?x11852), student(?x3564, ?x8740), award_winner(?x112, ?x8740), nominated_for(?x459, ?x2189), nominated_for(?x68, ?x2189) *> conf = 0.17 ranks of expected_values: 45, 391 EVAL 05ys0xf film_festivals! 0k2cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 75.000 37.000 0.250 http://example.org/film/film/film_festivals EVAL 05ys0xf film_festivals! 0d6b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 75.000 37.000 0.250 http://example.org/film/film/film_festivals #21470-01vsgrn PRED entity: 01vsgrn PRED relation: award_winner! PRED expected values: 023vrq => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 257): 02f73p (0.38 #37823, 0.37 #41608, 0.37 #21014), 023vrq (0.38 #37823, 0.37 #41608, 0.37 #21014), 02f73b (0.38 #37823, 0.37 #41608, 0.37 #21014), 03t5n3 (0.38 #37823, 0.37 #41608, 0.37 #21014), 03t5kl (0.38 #37823, 0.37 #41608, 0.37 #21014), 02f72n (0.38 #37823, 0.37 #41608, 0.37 #21014), 01by1l (0.38 #37823, 0.37 #41608, 0.37 #21014), 01bgqh (0.38 #37823, 0.37 #41608, 0.37 #21014), 025m8l (0.38 #37823, 0.37 #41608, 0.37 #21014), 054ks3 (0.38 #37823, 0.37 #41608, 0.37 #21014) >> Best rule #37823 for best value: >> intensional similarity = 3 >> extensional distance = 1840 >> proper extension: 012gbb; 0p9gg; >> query: (?x5536, ?x724) <- award_winner(?x3978, ?x5536), award(?x5536, ?x724), ceremony(?x3978, ?x342) >> conf = 0.38 => this is the best rule for 12 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01vsgrn award_winner! 023vrq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 115.000 0.384 http://example.org/award/award_category/winners./award/award_honor/award_winner #21469-0p_pd PRED entity: 0p_pd PRED relation: influenced_by PRED expected values: 01gn36 => 103 concepts (54 used for prediction) PRED predicted values (max 10 best out of 315): 01hmk9 (0.15 #3702, 0.10 #5005, 0.07 #12182), 01k9lpl (0.13 #3791, 0.07 #12182, 0.06 #5094), 081k8 (0.11 #9292, 0.10 #11031, 0.08 #12337), 081lh (0.10 #3502, 0.09 #4805, 0.07 #12182), 0p_47 (0.10 #3589, 0.07 #12182, 0.06 #4892), 032l1 (0.10 #10965, 0.10 #9226, 0.08 #12271), 05rx__ (0.10 #243, 0.03 #678, 0.03 #1113), 03_87 (0.09 #9339, 0.09 #11078, 0.08 #8469), 05qmj (0.09 #9329, 0.07 #11068, 0.06 #11937), 014zfs (0.09 #3507, 0.09 #4810, 0.08 #7423) >> Best rule #3702 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 0kzy0; 02p21g; 0126rp; 0jt90f5; 01x1cn2; 046lt; 0bv7t; 0j6cj; 01x4r3; 01xwv7; ... >> query: (?x397, 01hmk9) <- award(?x397, ?x591), influenced_by(?x397, ?x2283), currency(?x397, ?x170) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #5791 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 079ws; *> query: (?x397, 01gn36) <- nominated_for(?x397, ?x696), profession(?x397, ?x987), influenced_by(?x1814, ?x397) *> conf = 0.03 ranks of expected_values: 101 EVAL 0p_pd influenced_by 01gn36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 103.000 54.000 0.147 http://example.org/influence/influence_node/influenced_by #21468-02r6c_ PRED entity: 02r6c_ PRED relation: participant! PRED expected values: 05dbf => 119 concepts (60 used for prediction) PRED predicted values (max 10 best out of 58): 0gx_p (0.08 #1059, 0.02 #9356, 0.02 #9994), 01xwqn (0.05 #610), 0c6qh (0.04 #811, 0.02 #9746, 0.02 #9108), 023v4_ (0.04 #986, 0.02 #4814, 0.02 #6090), 046zh (0.04 #998, 0.02 #9295, 0.02 #10571), 07r1h (0.04 #1050, 0.01 #12538, 0.01 #13814), 02mjmr (0.04 #826, 0.01 #7207, 0.01 #8485), 07g2v (0.04 #884, 0.01 #1522, 0.01 #2798), 0210hf (0.04 #978, 0.01 #9275, 0.01 #9913), 014g_s (0.04 #1241) >> Best rule #1059 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 012x2b; >> query: (?x8812, 0gx_p) <- student(?x865, ?x8812), student(?x5778, ?x8812), written_by(?x2121, ?x8812), profession(?x8812, ?x524) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #9728 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 197 *> proper extension: 04b19t; *> query: (?x8812, 05dbf) <- award_winner(?x372, ?x8812), profession(?x8812, ?x524), film(?x8812, ?x2121), award(?x810, ?x372) *> conf = 0.01 ranks of expected_values: 57 EVAL 02r6c_ participant! 05dbf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 119.000 60.000 0.080 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #21467-05hyf PRED entity: 05hyf PRED relation: films PRED expected values: 02yvct => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 05hyf films 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/film/film_subject/films #21466-0184jw PRED entity: 0184jw PRED relation: profession PRED expected values: 01d_h8 02jknp => 110 concepts (108 used for prediction) PRED predicted values (max 10 best out of 49): 02jknp (0.90 #604, 0.88 #455, 0.87 #1945), 01d_h8 (0.87 #2241, 0.86 #3583, 0.86 #1049), 02hrh1q (0.86 #1653, 0.81 #4336, 0.81 #4634), 03gjzk (0.48 #3890, 0.45 #2250, 0.43 #1058), 02krf9 (0.34 #176, 0.27 #623, 0.26 #921), 02hv44_ (0.24 #356, 0.07 #5274, 0.06 #3933), 0cbd2 (0.22 #5223, 0.19 #3882, 0.18 #305), 0dgd_ (0.21 #478, 0.11 #31, 0.10 #180), 09jwl (0.19 #5086, 0.19 #7024, 0.19 #4788), 018gz8 (0.17 #5233, 0.15 #3892, 0.10 #4339) >> Best rule #604 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 0cm89v; 013zyw; 032md; >> query: (?x7815, 02jknp) <- film(?x7815, ?x1496), place_of_birth(?x7815, ?x12655), type_of_union(?x7815, ?x1873) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0184jw profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 108.000 0.898 http://example.org/people/person/profession EVAL 0184jw profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 108.000 0.898 http://example.org/people/person/profession #21465-01b39j PRED entity: 01b39j PRED relation: state_province_region PRED expected values: 04ykg => 139 concepts (128 used for prediction) PRED predicted values (max 10 best out of 171): 059rby (0.50 #3345, 0.36 #5326, 0.36 #6191), 01n7q (0.36 #18, 0.31 #389, 0.29 #1378), 04ykg (0.28 #5446, 0.27 #14123, 0.24 #11885), 09c7w0 (0.28 #5446, 0.27 #14123, 0.24 #11885), 0nhmw (0.28 #11886, 0.25 #1608, 0.22 #9783), 081yw (0.25 #3341, 0.18 #184, 0.12 #1174), 05kkh (0.25 #3341, 0.10 #13005, 0.09 #868), 0vbk (0.25 #3341, 0.10 #13005, 0.03 #12879), 07b_l (0.18 #173, 0.18 #50, 0.17 #916), 0d0x8 (0.16 #1528, 0.06 #5860, 0.06 #6231) >> Best rule #3345 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 0181dw; 01w5gp; 03x8cz; 0146mv; 02975m; 02wbnv; >> query: (?x8934, 059rby) <- organization(?x4682, ?x8934), ?x4682 = 0dq_5, citytown(?x8934, ?x5771), county(?x5771, ?x10567), teams(?x5771, ?x1438) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5446 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 70 *> proper extension: 03rhqg; *> query: (?x8934, ?x94) <- organization(?x4682, ?x8934), ?x4682 = 0dq_5, citytown(?x8934, ?x5771), county(?x5771, ?x10567), contains(?x94, ?x5771) *> conf = 0.28 ranks of expected_values: 3 EVAL 01b39j state_province_region 04ykg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 139.000 128.000 0.500 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #21464-0dtzkt PRED entity: 0dtzkt PRED relation: genre PRED expected values: 017fp => 156 concepts (141 used for prediction) PRED predicted values (max 10 best out of 141): 07s9rl0 (0.98 #16388, 0.89 #14232, 0.88 #14591), 0hcr (0.78 #8397, 0.77 #8758, 0.63 #6961), 05p553 (0.74 #15556, 0.52 #2876, 0.49 #6702), 016z4k (0.70 #240, 0.65 #6936, 0.61 #16148), 03g3w (0.50 #8159, 0.47 #8639, 0.46 #7201), 01jfsb (0.48 #7309, 0.41 #5873, 0.41 #3125), 03k9fj (0.47 #5154, 0.45 #8385, 0.44 #8746), 02l7c8 (0.46 #2768, 0.34 #7792, 0.33 #5757), 04t36 (0.43 #3357, 0.38 #2998, 0.33 #2638), 01hmnh (0.41 #6955, 0.40 #8391, 0.40 #8752) >> Best rule #16388 for best value: >> intensional similarity = 7 >> extensional distance = 1021 >> proper extension: 0ckr7s; 0fq27fp; 0gj8t_b; 0283_zv; 02rb607; 03m8y5; 0g5838s; 0gh65c5; 0gbtbm; 0gkz3nz; ... >> query: (?x10796, 07s9rl0) <- genre(?x10796, ?x8681), genre(?x9527, ?x8681), genre(?x4650, ?x8681), genre(?x3201, ?x8681), ?x3201 = 01ffx4, ?x4650 = 0fgrm, ?x9527 = 01rnly >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #8149 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 150 *> proper extension: 04v8x9; 0dj0m5; 065z3_x; 03z106; 03lv4x; 026qnh6; 046488; 043n0v_; 064lsn; 048xyn; ... *> query: (?x10796, 017fp) <- genre(?x10796, ?x8681), titles(?x220, ?x10796), major_field_of_study(?x865, ?x8681), student(?x8681, ?x1795) *> conf = 0.20 ranks of expected_values: 21 EVAL 0dtzkt genre 017fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 156.000 141.000 0.983 http://example.org/film/film/genre #21463-0nzw2 PRED entity: 0nzw2 PRED relation: adjoins! PRED expected values: 0nzlp => 130 concepts (40 used for prediction) PRED predicted values (max 10 best out of 390): 0nzlp (0.80 #8631, 0.79 #14916, 0.78 #29063), 0nzw2 (0.26 #7845, 0.25 #26705, 0.25 #1456), 0nz_b (0.26 #7845, 0.25 #26705, 0.23 #29849), 07h34 (0.08 #3326, 0.07 #4111, 0.05 #16680), 0vbk (0.08 #3373, 0.07 #4158, 0.03 #16727), 04tgp (0.08 #3369, 0.07 #4154, 0.02 #13577), 05mph (0.08 #3429, 0.05 #4214, 0.03 #16783), 05kr_ (0.06 #11093, 0.04 #26812, 0.03 #24455), 0d060g (0.06 #10999, 0.04 #26718, 0.03 #28290), 04ych (0.06 #3190, 0.05 #3975, 0.04 #14185) >> Best rule #8631 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 0ky0b; >> query: (?x12545, ?x10821) <- second_level_divisions(?x94, ?x12545), adjoins(?x12545, ?x10821), administrative_division(?x6966, ?x12545), country(?x54, ?x94) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nzw2 adjoins! 0nzlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 40.000 0.797 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #21462-03c7twt PRED entity: 03c7twt PRED relation: film_release_region PRED expected values: 0jgd => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 15): 0345h (0.18 #203, 0.15 #155, 0.07 #107), 07ssc (0.10 #56, 0.05 #152, 0.05 #200), 03h64 (0.10 #69, 0.04 #165, 0.03 #213), 0jgd (0.08 #194, 0.07 #146, 0.05 #50), 0d060g (0.08 #149, 0.07 #197, 0.06 #77), 01znc_ (0.06 #157, 0.06 #205, 0.05 #61), 06mkj (0.06 #209, 0.05 #65, 0.04 #161), 02vzc (0.05 #63, 0.04 #159, 0.04 #207), 05v8c (0.05 #57, 0.04 #201, 0.03 #153), 05r4w (0.04 #193, 0.03 #145) >> Best rule #203 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 04tz52; 045j3w; 03nqnnk; 042fgh; 048tv9; 0fpgp26; 0m3gy; >> query: (?x10697, 0345h) <- film_release_distribution_medium(?x10697, ?x81), genre(?x10697, ?x53), film_release_region(?x10697, ?x94) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #194 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 04tz52; 045j3w; 03nqnnk; 042fgh; 048tv9; 0fpgp26; 0m3gy; *> query: (?x10697, 0jgd) <- film_release_distribution_medium(?x10697, ?x81), genre(?x10697, ?x53), film_release_region(?x10697, ?x94) *> conf = 0.08 ranks of expected_values: 4 EVAL 03c7twt film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 89.000 89.000 0.183 http://example.org/film/film/runtime./film/film_cut/film_release_region #21461-07qy0b PRED entity: 07qy0b PRED relation: music! PRED expected values: 0d87hc 0d99k_ => 103 concepts (51 used for prediction) PRED predicted values (max 10 best out of 887): 01_1pv (0.22 #1220, 0.05 #7244, 0.04 #9252), 02rrfzf (0.21 #2331, 0.10 #4339, 0.06 #5343), 05t54s (0.14 #2693, 0.03 #4701, 0.03 #5705), 0jzw (0.11 #1074, 0.04 #3082, 0.03 #4086), 03s9kp (0.11 #1993, 0.04 #4001, 0.03 #5005), 03h0byn (0.11 #1963, 0.04 #3971, 0.03 #4975), 0c0zq (0.11 #1884, 0.04 #3892, 0.03 #4896), 0bnzd (0.11 #1711, 0.04 #3719, 0.03 #4723), 0_9wr (0.11 #1707, 0.04 #3715, 0.03 #4719), 04lhc4 (0.11 #1699, 0.04 #3707, 0.03 #4711) >> Best rule #1220 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 09889g; 03f4k; >> query: (?x3371, 01_1pv) <- profession(?x3371, ?x563), music(?x886, ?x3371), sibling(?x3371, ?x6783) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07qy0b music! 0d99k_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 51.000 0.222 http://example.org/film/film/music EVAL 07qy0b music! 0d87hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 51.000 0.222 http://example.org/film/film/music #21460-0g9wdmc PRED entity: 0g9wdmc PRED relation: film_crew_role PRED expected values: 02r96rf => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 27): 02r96rf (0.67 #1050, 0.64 #905, 0.62 #1523), 0dxtw (0.38 #407, 0.38 #1530, 0.38 #1057), 01vx2h (0.32 #1058, 0.30 #913, 0.29 #1531), 01pvkk (0.27 #1350, 0.27 #1532, 0.27 #1059), 02ynfr (0.18 #413, 0.18 #1063, 0.17 #16), 0215hd (0.14 #1066, 0.14 #308, 0.12 #1539), 02rh1dz (0.12 #81, 0.11 #911, 0.10 #262), 089g0h (0.11 #1067, 0.11 #20, 0.10 #309), 01xy5l_ (0.11 #14, 0.11 #1061, 0.09 #1534), 02_n3z (0.11 #1, 0.08 #1048, 0.07 #1521) >> Best rule #1050 for best value: >> intensional similarity = 4 >> extensional distance = 859 >> proper extension: 02v63m; 014zwb; >> query: (?x1803, 02r96rf) <- film_crew_role(?x1803, ?x1284), genre(?x1803, ?x53), ?x1284 = 0ch6mp2, film(?x2938, ?x1803) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g9wdmc film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.669 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21459-03qjg PRED entity: 03qjg PRED relation: role! PRED expected values: 01hrqc => 76 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1110): 0bg539 (0.67 #2962, 0.60 #2428, 0.38 #5371), 05qhnq (0.60 #2051, 0.56 #4457, 0.50 #4726), 08n__5 (0.60 #2552, 0.50 #3086, 0.33 #414), 01mxnvc (0.57 #3450, 0.50 #4255, 0.50 #1311), 037hgm (0.50 #1724, 0.50 #1457, 0.33 #387), 0473q (0.50 #3121, 0.43 #3656, 0.43 #3389), 04f7c55 (0.50 #3886, 0.40 #2283, 0.40 #2015), 04mn81 (0.50 #3783, 0.40 #2180, 0.40 #1912), 0fpj9pm (0.50 #3120, 0.40 #2586, 0.33 #448), 04s5_s (0.50 #4004, 0.33 #3200, 0.33 #1065) >> Best rule #2962 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 0dwr4; >> query: (?x2798, 0bg539) <- instrumentalists(?x2798, ?x3241), instrumentalists(?x2798, ?x3200), instrumentalists(?x2798, ?x2799), role(?x2798, ?x1332), award_nominee(?x3200, ?x4740), role(?x565, ?x2798), role(?x1332, ?x6039), artists(?x284, ?x3200), award(?x3200, ?x884), ?x6039 = 05kms, ?x3241 = 0pj9t, gender(?x2799, ?x231) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #192 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x2798, 01hrqc) <- instrumentalists(?x2798, ?x8308), instrumentalists(?x2798, ?x3200), instrumentalists(?x2798, ?x2807), role(?x2798, ?x2157), ?x3200 = 01wj18h, role(?x314, ?x2798), ?x2807 = 03h_fk5, role(?x3991, ?x2798), ?x8308 = 04mx7s, ?x3991 = 05842k, group(?x2798, ?x997), ?x2157 = 011_6p *> conf = 0.33 ranks of expected_values: 136 EVAL 03qjg role! 01hrqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 76.000 40.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role #21458-06nbt PRED entity: 06nbt PRED relation: genre! PRED expected values: 05jf85 0963mq 0299hs => 64 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1861): 04z_3pm (0.67 #19744, 0.50 #10566, 0.50 #8732), 050xxm (0.67 #18634, 0.50 #7622, 0.33 #3951), 0k2sk (0.62 #29532, 0.50 #18520, 0.50 #7508), 02ntb8 (0.62 #30212, 0.33 #4517, 0.29 #24704), 01y9jr (0.62 #30539, 0.33 #4844, 0.29 #23196), 0c5dd (0.60 #31373, 0.33 #3844, 0.25 #27704), 02pjc1h (0.57 #24080, 0.50 #29588, 0.50 #25916), 0b7l4x (0.57 #24908, 0.50 #26744, 0.50 #10226), 0bxsk (0.57 #25079, 0.50 #26915, 0.25 #30587), 0992d9 (0.57 #24856, 0.50 #26692, 0.25 #30364) >> Best rule #19744 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 06qln; >> query: (?x2700, 04z_3pm) <- genre(?x9787, ?x2700), genre(?x6884, ?x2700), genre(?x1876, ?x2700), ?x1876 = 0584r4, actor(?x9787, ?x2390), tv_program(?x236, ?x6884), titles(?x2008, ?x9787), genre(?x240, ?x2700), honored_for(?x1265, ?x6884) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3813 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 05p553; *> query: (?x2700, 0963mq) <- genre(?x11454, ?x2700), genre(?x9787, ?x2700), genre(?x5698, ?x2700), genre(?x3180, ?x2700), ?x9787 = 06y_n, genre(?x1330, ?x2700), ?x3180 = 07c72, program(?x11453, ?x11454), ?x5698 = 05_z42, ?x1330 = 03m4mj *> conf = 0.33 ranks of expected_values: 520, 1295, 1337 EVAL 06nbt genre! 0299hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 21.000 0.667 http://example.org/film/film/genre EVAL 06nbt genre! 0963mq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 64.000 21.000 0.667 http://example.org/film/film/genre EVAL 06nbt genre! 05jf85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 21.000 0.667 http://example.org/film/film/genre #21457-01d5z PRED entity: 01d5z PRED relation: team! PRED expected values: 02lyr4 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 51): 02lyr4 (0.88 #1452, 0.87 #1702, 0.85 #1600), 02wszf (0.84 #1718, 0.83 #1418, 0.82 #1666), 02g_6x (0.81 #99, 0.49 #2787, 0.40 #509), 06b1q (0.81 #99, 0.49 #2781, 0.35 #3034), 01r3hr (0.81 #99, 0.47 #2776, 0.41 #1837), 02g_7z (0.81 #99, 0.47 #2799, 0.41 #2548), 04nfpk (0.81 #99, 0.45 #2791, 0.40 #513), 02g_6j (0.81 #99, 0.45 #2785, 0.40 #507), 01_9c1 (0.81 #99, 0.42 #2792, 0.41 #1853), 047g8h (0.81 #99, 0.42 #2782, 0.40 #504) >> Best rule #1452 for best value: >> intensional similarity = 12 >> extensional distance = 22 >> proper extension: 049n7; 0512p; 0x2p; 0713r; 01ync; 02__x; 07l8f; 06wpc; 07l4z; 01d6g; ... >> query: (?x1010, 02lyr4) <- school(?x1010, ?x6953), season(?x1010, ?x8517), team(?x4244, ?x1010), draft(?x1010, ?x3334), position(?x1010, ?x2010), ?x3334 = 02pq_rp, school_type(?x6953, ?x3092), major_field_of_study(?x6953, ?x3213), citytown(?x6953, ?x2624), student(?x6953, ?x117), ?x8517 = 0285r5d, school(?x465, ?x6953) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d5z team! 02lyr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.875 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #21456-01r4hry PRED entity: 01r4hry PRED relation: gender PRED expected values: 05zppz => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #15, 0.90 #11, 0.90 #5), 02zsn (0.26 #46, 0.26 #50, 0.26 #36) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 01vsgrn; 020jqv; >> query: (?x7856, 05zppz) <- music(?x146, ?x7856), nominated_for(?x7856, ?x9193), titles(?x1510, ?x9193) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r4hry gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.912 http://example.org/people/person/gender #21455-0ds33 PRED entity: 0ds33 PRED relation: film_distribution_medium PRED expected values: 0dq6p => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.24 #7, 0.17 #37, 0.17 #13), 0735l (0.20 #41, 0.19 #47, 0.17 #29), 0dq6p (0.15 #15, 0.10 #45, 0.10 #39), 02nxhr (0.11 #44, 0.11 #38, 0.11 #26), 07z4p (0.03 #12, 0.02 #54, 0.01 #42) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 016fyc; 0jzw; 0c_j9x; 0dnqr; 0f4yh; 0h6r5; 0y_yw; 0286gm1; 0f3m1; >> query: (?x508, 029j_) <- nominated_for(?x4525, ?x508), edited_by(?x508, ?x707), film(?x368, ?x508) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 02vnmc9; *> query: (?x508, 0dq6p) <- nominated_for(?x4525, ?x508), nominated_for(?x154, ?x508), crewmember(?x508, ?x666) *> conf = 0.15 ranks of expected_values: 3 EVAL 0ds33 film_distribution_medium 0dq6p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.242 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #21454-09cpb PRED entity: 09cpb PRED relation: origin! PRED expected values: 016ntp => 113 concepts (68 used for prediction) PRED predicted values (max 10 best out of 208): 06nv27 (0.17 #735, 0.14 #3317, 0.08 #1252), 0cfgd (0.09 #478, 0.08 #1511, 0.06 #2544), 03fbc (0.09 #92, 0.05 #3190, 0.04 #4738), 02lfp4 (0.09 #3307, 0.02 #15697, 0.02 #16214), 0dm5l (0.09 #3206, 0.02 #15596, 0.02 #16113), 047cx (0.08 #1233, 0.08 #716, 0.05 #3298), 0fcsd (0.08 #1212, 0.08 #695, 0.05 #3277), 04pf4r (0.08 #1197, 0.08 #680, 0.05 #3262), 0167_s (0.08 #1108, 0.08 #591, 0.05 #3173), 01w923 (0.08 #1081, 0.08 #564, 0.05 #3146) >> Best rule #735 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 02j71; >> query: (?x10887, 06nv27) <- administrative_parent(?x11472, ?x10887), location_of_ceremony(?x566, ?x11472), contains(?x512, ?x11472), location_of_ceremony(?x3869, ?x11472) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09cpb origin! 016ntp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 68.000 0.167 http://example.org/music/artist/origin #21453-01xbxn PRED entity: 01xbxn PRED relation: film! PRED expected values: 0147dk => 97 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1263): 0147dk (0.65 #31130, 0.64 #33207, 0.57 #74723), 05txrz (0.40 #764, 0.16 #2839, 0.15 #4914), 0738b8 (0.40 #401, 0.16 #2476, 0.15 #4551), 0bl2g (0.40 #55, 0.11 #2130, 0.10 #4205), 012d40 (0.40 #16, 0.11 #2091, 0.10 #4166), 0mdqp (0.27 #10493, 0.16 #2192, 0.15 #4267), 02g8h (0.20 #42, 0.11 #2117, 0.10 #4192), 02wycg2 (0.20 #703, 0.11 #2778, 0.10 #4853), 02w29z (0.20 #1410, 0.07 #7636, 0.05 #3485), 02lkcc (0.20 #241, 0.06 #64344, 0.05 #2316) >> Best rule #31130 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 05sy0cv; 07bz5; 025x1t; >> query: (?x8028, ?x521) <- award_winner(?x8028, ?x521), friend(?x521, ?x6187), type_of_union(?x521, ?x566) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xbxn film! 0147dk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 48.000 0.652 http://example.org/film/actor/film./film/performance/film #21452-02x1dht PRED entity: 02x1dht PRED relation: award! PRED expected values: 05_k56 022_q8 02r6c_ => 53 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2722): 0169dl (0.69 #63755, 0.68 #67114, 0.67 #67116), 0136g9 (0.67 #3682, 0.23 #10390, 0.20 #13746), 043hg (0.66 #67113, 0.66 #63754, 0.59 #26839), 05_k56 (0.62 #246, 0.25 #3601, 0.15 #10309), 07s93v (0.58 #3757, 0.25 #402, 0.23 #10465), 0499lc (0.58 #4695, 0.25 #1340, 0.23 #11403), 013t9y (0.50 #1906, 0.33 #5261, 0.24 #22037), 05kfs (0.50 #3517, 0.29 #20293, 0.25 #162), 03hy3g (0.50 #5195, 0.29 #21971, 0.25 #1840), 02bfxb (0.50 #4291, 0.25 #936, 0.24 #21067) >> Best rule #63755 for best value: >> intensional similarity = 5 >> extensional distance = 174 >> proper extension: 05f3q; >> query: (?x899, ?x989) <- award_winner(?x899, ?x2590), award_winner(?x899, ?x989), produced_by(?x3133, ?x2590), award_winner(?x969, ?x989), award_winner(?x989, ?x92) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #246 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 0gqzz; 02qyxs5; 0drtkx; 0h3vhfb; *> query: (?x899, 05_k56) <- award(?x6682, ?x899), award(?x361, ?x899), nominated_for(?x899, ?x54), written_by(?x2932, ?x361), ?x6682 = 04jspq *> conf = 0.62 ranks of expected_values: 4, 79, 80 EVAL 02x1dht award! 02r6c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 53.000 24.000 0.686 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x1dht award! 022_q8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 53.000 24.000 0.686 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x1dht award! 05_k56 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 53.000 24.000 0.686 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21451-0nqph PRED entity: 0nqph PRED relation: source PRED expected values: 0jbk9 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #29, 0.92 #26, 0.91 #47) >> Best rule #29 for best value: >> intensional similarity = 2 >> extensional distance = 251 >> proper extension: 0mn0v; 0mmzt; 0d7k1z; 0h3lt; 0g_wn2; 0mndw; 0r2gj; 0mn8t; 0_kq3; 0r2kh; ... >> query: (?x13949, 0jbk9) <- time_zones(?x13949, ?x1638), county(?x13949, ?x9712) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nqph source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.917 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #21450-05h95s PRED entity: 05h95s PRED relation: languages PRED expected values: 02h40lc => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 9): 02h40lc (0.98 #706, 0.98 #646, 0.98 #302), 03_9r (0.16 #204, 0.13 #735, 0.13 #114), 064_8sq (0.13 #735, 0.08 #36, 0.06 #66), 04306rv (0.13 #735, 0.08 #33, 0.06 #63), 02bv9 (0.13 #735, 0.08 #38, 0.06 #68), 02bjrlw (0.13 #735, 0.08 #31, 0.06 #61), 0t_2 (0.13 #735, 0.07 #55, 0.04 #185), 05zjd (0.13 #735, 0.03 #117, 0.02 #167), 07qv_ (0.13 #735, 0.01 #299) >> Best rule #706 for best value: >> intensional similarity = 5 >> extensional distance = 249 >> proper extension: 0bx_hnp; >> query: (?x7566, 02h40lc) <- languages(?x7566, ?x2502), countries_spoken_in(?x2502, ?x47), language(?x1298, ?x2502), languages(?x804, ?x2502), ?x1298 = 032_wv >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05h95s languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.984 http://example.org/tv/tv_program/languages #21449-019n7x PRED entity: 019n7x PRED relation: profession PRED expected values: 012wxt => 145 concepts (102 used for prediction) PRED predicted values (max 10 best out of 83): 03gjzk (0.53 #309, 0.50 #15, 0.45 #603), 0dxtg (0.53 #308, 0.40 #1631, 0.40 #602), 09jwl (0.42 #14721, 0.41 #3695, 0.41 #2960), 01d_h8 (0.40 #1770, 0.38 #2064, 0.38 #153), 018gz8 (0.38 #1046, 0.35 #605, 0.33 #1634), 0np9r (0.37 #315, 0.30 #609, 0.22 #2373), 0dz3r (0.34 #3678, 0.32 #2943, 0.26 #2502), 016z4k (0.33 #2945, 0.31 #3680, 0.28 #6032), 0nbcg (0.31 #2973, 0.31 #3708, 0.30 #4737), 0kyk (0.29 #30, 0.28 #1353, 0.26 #471) >> Best rule #309 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 03xmy1; 04mhbh; 03h8_g; >> query: (?x10915, 03gjzk) <- profession(?x10915, ?x4725), profession(?x10915, ?x1032), ?x4725 = 015cjr, participant(?x2697, ?x10915), ?x1032 = 02hrh1q >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1858 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 06crng; *> query: (?x10915, 012wxt) <- profession(?x10915, ?x1032), category(?x10915, ?x134), student(?x1276, ?x10915), participant(?x2697, ?x10915) *> conf = 0.03 ranks of expected_values: 56 EVAL 019n7x profession 012wxt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 145.000 102.000 0.526 http://example.org/people/person/profession #21448-01vvycq PRED entity: 01vvycq PRED relation: artist! PRED expected values: 04rqd => 130 concepts (104 used for prediction) PRED predicted values (max 10 best out of 3): 03gfvsz (0.18 #66, 0.10 #168, 0.10 #26), 04rqd (0.10 #69, 0.05 #171, 0.05 #177), 04y652m (0.03 #48, 0.02 #358, 0.01 #74) >> Best rule #66 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 089tm; 01pfr3; 05mt_q; 01j4ls; 01vsxdm; 01r9fv; 0dtd6; 033wx9; 01vvyfh; 03f1d47; ... >> query: (?x702, 03gfvsz) <- artists(?x302, ?x702), award(?x702, ?x3631), ?x3631 = 02f73p >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #69 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 089tm; 01pfr3; 05mt_q; 01j4ls; 01vsxdm; 01r9fv; 0dtd6; 033wx9; 01vvyfh; 03f1d47; ... *> query: (?x702, 04rqd) <- artists(?x302, ?x702), award(?x702, ?x3631), ?x3631 = 02f73p *> conf = 0.10 ranks of expected_values: 2 EVAL 01vvycq artist! 04rqd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 104.000 0.179 http://example.org/broadcast/content/artist #21447-029bkp PRED entity: 029bkp PRED relation: profession! PRED expected values: 0f0y8 053yx 024qwq => 57 concepts (25 used for prediction) PRED predicted values (max 10 best out of 4149): 02fybl (0.67 #27614, 0.67 #23398, 0.64 #40261), 03kts (0.67 #36294, 0.67 #27862, 0.50 #11000), 01vsy7t (0.67 #26758, 0.57 #39405, 0.56 #35190), 014q2g (0.67 #26105, 0.57 #38752, 0.56 #34537), 01ydzx (0.67 #27480, 0.57 #40127, 0.56 #35912), 01k_n63 (0.67 #27681, 0.57 #40328, 0.56 #36113), 01wp8w7 (0.67 #25697, 0.57 #38344, 0.50 #21481), 0144l1 (0.67 #25900, 0.56 #34332, 0.50 #38547), 01vtqml (0.67 #26493, 0.56 #34925, 0.50 #9631), 0136p1 (0.67 #25842, 0.56 #34274, 0.50 #8980) >> Best rule #27614 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02hrh1q; 025352; >> query: (?x4654, 02fybl) <- profession(?x4184, ?x4654), profession(?x2747, ?x4654), instrumentalists(?x1332, ?x2747), ?x1332 = 03qlv7, ?x4184 = 01m3x5p, award(?x2747, ?x2561) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #25294 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 0dz3r; 0gbbt; *> query: (?x4654, ?x120) <- profession(?x6351, ?x4654), profession(?x2747, ?x4654), instrumentalists(?x1831, ?x2747), instrumentalists(?x1332, ?x2747), ?x1332 = 03qlv7, role(?x120, ?x1831), role(?x1831, ?x212), ?x6351 = 01vsksr *> conf = 0.39 ranks of expected_values: 502, 506, 939 EVAL 029bkp profession! 024qwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 25.000 0.667 http://example.org/people/person/profession EVAL 029bkp profession! 053yx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 25.000 0.667 http://example.org/people/person/profession EVAL 029bkp profession! 0f0y8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 25.000 0.667 http://example.org/people/person/profession #21446-03sww PRED entity: 03sww PRED relation: profession PRED expected values: 01d_h8 03gjzk 09jwl => 130 concepts (72 used for prediction) PRED predicted values (max 10 best out of 121): 018gz8 (0.77 #2920, 0.29 #1903, 0.26 #1320), 0cbd2 (0.73 #8590, 0.65 #8735, 0.54 #1894), 09jwl (0.70 #1032, 0.70 #3797, 0.65 #1177), 03gjzk (0.61 #9033, 0.43 #2918, 0.36 #1318), 01d_h8 (0.50 #3201, 0.44 #4223, 0.42 #4078), 016z4k (0.47 #583, 0.45 #3783, 0.44 #5092), 01c72t (0.43 #312, 0.30 #9311, 0.29 #7146), 02jknp (0.39 #6258, 0.26 #6548, 0.24 #1312), 0np9r (0.33 #19, 0.29 #2924, 0.21 #9913), 0n1h (0.33 #1754, 0.32 #881, 0.31 #1171) >> Best rule #2920 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 01rrwf6; 02mhfy; 03pmzt; 01nrq5; 01lly5; 04s430; 03xn3s2; 01v6480; 02y0yt; 030wkp; ... >> query: (?x4877, 018gz8) <- profession(?x4877, ?x2225), profession(?x12255, ?x2225), actor(?x3326, ?x4877), ?x12255 = 0m68w >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1032 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 62 *> proper extension: 0yxl; 01r4zfk; 02cj_f; 04x56; 03hmr_; 029k55; *> query: (?x4877, 09jwl) <- profession(?x4877, ?x2348), profession(?x4877, ?x2225), ?x2225 = 0kyk, profession(?x3767, ?x2348), ?x3767 = 01wbz9 *> conf = 0.70 ranks of expected_values: 3, 4, 5 EVAL 03sww profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 72.000 0.769 http://example.org/people/person/profession EVAL 03sww profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 72.000 0.769 http://example.org/people/person/profession EVAL 03sww profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 72.000 0.769 http://example.org/people/person/profession #21445-0d6lp PRED entity: 0d6lp PRED relation: place_founded! PRED expected values: 01yx7f => 210 concepts (204 used for prediction) PRED predicted values (max 10 best out of 113): 064f29 (0.15 #8882, 0.11 #5519, 0.11 #5518), 04kqk (0.15 #8882, 0.11 #5519, 0.11 #5518), 02jd_7 (0.15 #8882, 0.11 #5519, 0.11 #5518), 03xsby (0.14 #871, 0.06 #2713, 0.05 #3578), 04htfd (0.12 #252, 0.10 #468, 0.08 #1768), 0k8z (0.12 #343, 0.04 #1535, 0.04 #1315), 013807 (0.11 #5519, 0.11 #5518, 0.09 #8883), 07vfz (0.11 #5519, 0.11 #5518, 0.09 #8883), 0473m9 (0.11 #5519, 0.11 #5518, 0.09 #8883), 02jmst (0.11 #5519, 0.11 #5518, 0.09 #8883) >> Best rule #8882 for best value: >> intensional similarity = 2 >> extensional distance = 86 >> proper extension: 01xhb_; >> query: (?x3125, ?x13750) <- citytown(?x13750, ?x3125), industry(?x13750, ?x245) >> conf = 0.15 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0d6lp place_founded! 01yx7f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 210.000 204.000 0.153 http://example.org/organization/organization/place_founded #21444-0mbhr PRED entity: 0mbhr PRED relation: place_of_birth PRED expected values: 01q_22 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 164): 030qb3t (0.22 #2168, 0.06 #4985, 0.05 #10622), 01cx_ (0.17 #1519, 0.11 #2223, 0.10 #2928), 02d6c (0.17 #1821, 0.06 #5342, 0.06 #4638), 04vmp (0.11 #2382, 0.06 #11540, 0.05 #10131), 0dlv0 (0.11 #2468, 0.05 #45833, 0.05 #35249), 0c8tk (0.11 #2269, 0.03 #5791, 0.03 #11427), 01sv6k (0.11 #2709, 0.01 #11867, 0.01 #13278), 01_d4 (0.10 #2885, 0.06 #4293, 0.06 #3589), 06_kh (0.10 #2824, 0.06 #4232, 0.06 #3528), 01jr6 (0.10 #2962, 0.06 #4370, 0.06 #3666) >> Best rule #2168 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 05vzql; >> query: (?x11081, 030qb3t) <- special_performance_type(?x11081, ?x3558), people(?x743, ?x11081), nationality(?x11081, ?x512), profession(?x11081, ?x4773), ?x4773 = 0d1pc >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0mbhr place_of_birth 01q_22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 124.000 0.222 http://example.org/people/person/place_of_birth #21443-01l7qw PRED entity: 01l7qw PRED relation: instrumentalists! PRED expected values: 018j2 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 54): 0342h (0.44 #3833, 0.43 #361, 0.41 #4101), 05r5c (0.32 #4105, 0.31 #3837, 0.29 #4284), 05148p4 (0.23 #3850, 0.22 #378, 0.21 #4118), 018vs (0.18 #3842, 0.16 #5001, 0.16 #4110), 03qjg (0.13 #409, 0.12 #3881, 0.10 #4149), 02hnl (0.12 #3864, 0.11 #4132, 0.09 #5023), 0l14md (0.08 #364, 0.08 #3836, 0.07 #275), 0l14j_ (0.07 #234, 0.03 #3884, 0.03 #4331), 026t6 (0.07 #270, 0.07 #4099, 0.07 #3831), 04rzd (0.07 #306, 0.07 #395, 0.05 #573) >> Best rule #3833 for best value: >> intensional similarity = 3 >> extensional distance = 424 >> proper extension: 028qdb; >> query: (?x11835, 0342h) <- type_of_union(?x11835, ?x566), artists(?x2480, ?x11835), award(?x11835, ?x2375) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #4136 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 461 *> proper extension: 011zf2; 031x_3; 014g91; *> query: (?x11835, 018j2) <- location(?x11835, ?x4030), nationality(?x11835, ?x512), artists(?x2480, ?x11835) *> conf = 0.06 ranks of expected_values: 12 EVAL 01l7qw instrumentalists! 018j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 120.000 120.000 0.437 http://example.org/music/instrument/instrumentalists #21442-01c8w0 PRED entity: 01c8w0 PRED relation: profession! PRED expected values: 01sbf2 04zwjd 02sj1x 012ky3 07_grx 03_0p 03hzkq => 37 concepts (16 used for prediction) PRED predicted values (max 10 best out of 4192): 0473q (0.62 #19006, 0.50 #10666, 0.44 #27351), 03j24kf (0.60 #13997, 0.50 #26512, 0.50 #18167), 06k02 (0.60 #13150, 0.38 #17320, 0.33 #4810), 01vrz41 (0.60 #12823, 0.33 #25338, 0.33 #4483), 02fybl (0.50 #18981, 0.50 #10641, 0.44 #27326), 014q2g (0.50 #17484, 0.50 #9144, 0.44 #25829), 09889g (0.50 #18259, 0.50 #9919, 0.40 #14089), 01vsy7t (0.50 #18134, 0.50 #9794, 0.40 #13964), 01ydzx (0.50 #18847, 0.50 #10507, 0.40 #14677), 0phx4 (0.50 #17770, 0.50 #9430, 0.40 #13600) >> Best rule #19006 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0dz3r; 09jwl; 028kk_; 04f2zj; >> query: (?x563, 0473q) <- profession(?x7955, ?x563), profession(?x3890, ?x563), award(?x7955, ?x1079), music(?x1454, ?x7955), place_of_birth(?x7955, ?x1131), award_winner(?x7955, ?x6011), ?x3890 = 01gg59 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #13773 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 05vyk; *> query: (?x563, 012ky3) <- profession(?x10682, ?x563), profession(?x7955, ?x563), profession(?x4109, ?x563), ?x7955 = 01l3mk3, award(?x4109, ?x2192), film(?x4109, ?x3988), religion(?x10682, ?x2694), film_release_region(?x3988, ?x87) *> conf = 0.40 ranks of expected_values: 174, 175, 227, 310, 312, 707 EVAL 01c8w0 profession! 03hzkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.625 http://example.org/people/person/profession EVAL 01c8w0 profession! 03_0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 16.000 0.625 http://example.org/people/person/profession EVAL 01c8w0 profession! 07_grx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 16.000 0.625 http://example.org/people/person/profession EVAL 01c8w0 profession! 012ky3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 37.000 16.000 0.625 http://example.org/people/person/profession EVAL 01c8w0 profession! 02sj1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 16.000 0.625 http://example.org/people/person/profession EVAL 01c8w0 profession! 04zwjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 37.000 16.000 0.625 http://example.org/people/person/profession EVAL 01c8w0 profession! 01sbf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 16.000 0.625 http://example.org/people/person/profession #21441-0dvmd PRED entity: 0dvmd PRED relation: award_nominee! PRED expected values: 015t7v 0f5xn => 118 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1113): 018ygt (0.81 #89938, 0.81 #41508, 0.81 #29978), 02kxwk (0.81 #89938, 0.81 #41508, 0.81 #29978), 06cgy (0.81 #89938, 0.81 #41508, 0.81 #29978), 0dvld (0.39 #3675, 0.23 #133746, 0.14 #89939), 015t7v (0.39 #3474, 0.23 #133746, 0.14 #89939), 02bkdn (0.33 #4997, 0.23 #133746, 0.14 #89939), 05dbf (0.33 #5080, 0.23 #133746, 0.14 #89939), 017149 (0.29 #4709, 0.23 #133746, 0.14 #89939), 0g8st4 (0.29 #6119, 0.23 #133746, 0.14 #89939), 01kb2j (0.29 #5795, 0.23 #133746, 0.14 #89939) >> Best rule #89938 for best value: >> intensional similarity = 3 >> extensional distance = 645 >> proper extension: 0kvrb; 0565cz; 0phx4; 0137hn; 05qhnq; 01r4hry; 01zlh5; 076df9; >> query: (?x3101, ?x2551) <- award_nominee(?x3101, ?x2551), award_nominee(?x2551, ?x92), category(?x3101, ?x134) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #3474 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 16 *> proper extension: 0bxfmk; *> query: (?x3101, 015t7v) <- nominated_for(?x3101, ?x1597), ?x1597 = 0dr_4 *> conf = 0.39 ranks of expected_values: 5, 296 EVAL 0dvmd award_nominee! 0f5xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 118.000 61.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 0dvmd award_nominee! 015t7v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 118.000 61.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21440-0c35b1 PRED entity: 0c35b1 PRED relation: award PRED expected values: 09sb52 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 222): 09sb52 (0.44 #446, 0.39 #7737, 0.38 #5306), 0bb57s (0.33 #245, 0.05 #2270, 0.04 #7536), 0bfvd4 (0.22 #521, 0.18 #21874, 0.13 #32814), 0cqh46 (0.22 #457, 0.13 #32814, 0.05 #1672), 0ck27z (0.19 #7789, 0.18 #21874, 0.17 #93), 02x73k6 (0.18 #21874, 0.13 #32814, 0.11 #466), 0789_m (0.18 #21874, 0.13 #32814, 0.11 #425), 027dtxw (0.18 #21874, 0.08 #5269, 0.08 #1624), 0cqhk0 (0.17 #37, 0.12 #7733, 0.10 #1657), 02ppm4q (0.17 #157, 0.07 #2182, 0.07 #7448) >> Best rule #446 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 04f525m; >> query: (?x7779, 09sb52) <- award_winner(?x6448, ?x7779), ?x6448 = 0404j37, award(?x7779, ?x2252) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c35b1 award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.444 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21439-0lx2l PRED entity: 0lx2l PRED relation: profession PRED expected values: 018gz8 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 74): 02jknp (0.52 #1771, 0.50 #4270, 0.48 #5005), 018gz8 (0.47 #1191, 0.41 #1485, 0.31 #1338), 0cbd2 (0.46 #6180, 0.45 #6033, 0.44 #3534), 03gjzk (0.43 #2512, 0.40 #1189, 0.40 #5011), 0kyk (0.32 #1939, 0.31 #6202, 0.31 #3556), 0np9r (0.30 #7057, 0.24 #1195, 0.20 #1489), 09jwl (0.30 #7057, 0.21 #17, 0.21 #8985), 01c72t (0.30 #7057, 0.09 #1345, 0.09 #2080), 05sxg2 (0.30 #7057, 0.07 #1, 0.07 #148), 025352 (0.30 #7057, 0.04 #1234, 0.03 #58) >> Best rule #1771 for best value: >> intensional similarity = 2 >> extensional distance = 213 >> proper extension: 03p01x; >> query: (?x2534, 02jknp) <- produced_by(?x7248, ?x2534), location(?x2534, ?x12456) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1191 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 140 *> proper extension: 02_j7t; 04h07s; 03b78r; 0djywgn; 030wkp; 04j_gs; 05g7q; 0btj0; *> query: (?x2534, 018gz8) <- film(?x2534, ?x339), influenced_by(?x2534, ?x1145) *> conf = 0.47 ranks of expected_values: 2 EVAL 0lx2l profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 128.000 0.516 http://example.org/people/person/profession #21438-01vrx3g PRED entity: 01vrx3g PRED relation: nationality PRED expected values: 09c7w0 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 37): 09c7w0 (0.86 #101, 0.71 #6206, 0.70 #8412), 02jx1 (0.27 #1835, 0.24 #1134, 0.23 #734), 07ssc (0.16 #1116, 0.15 #1517, 0.14 #1417), 0d060g (0.06 #2410, 0.06 #2510, 0.06 #2209), 03rk0 (0.06 #10557, 0.05 #10457, 0.05 #10757), 0vbk (0.05 #4704, 0.04 #2303, 0.04 #301), 06q1r (0.04 #1178, 0.04 #1378, 0.02 #1479), 0ctw_b (0.04 #428, 0.01 #1929, 0.01 #2229), 0345h (0.04 #231, 0.04 #1332, 0.04 #732), 0f8l9c (0.04 #8210, 0.03 #8211, 0.03 #6027) >> Best rule #101 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 051m56; >> query: (?x366, 09c7w0) <- award_nominee(?x366, ?x4239), ?x4239 = 0x3b7, award_winner(?x139, ?x366), profession(?x366, ?x131) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrx3g nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.864 http://example.org/people/person/nationality #21437-0gd9k PRED entity: 0gd9k PRED relation: politician! PRED expected values: 07wbk => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 6): 0d075m (0.04 #1467, 0.03 #411, 0.02 #1731), 07wbk (0.03 #1465, 0.03 #913, 0.02 #1657), 01f53 (0.02 #166, 0.01 #214, 0.01 #238), 07w42 (0.01 #253), 07wf9 (0.01 #1470), 02245 (0.01 #883) >> Best rule #1467 for best value: >> intensional similarity = 2 >> extensional distance = 556 >> proper extension: 079vf; 01cv3n; 08f3b1; 01g4zr; 083p7; 02r34n; 02c4s; 083pr; 01wz3cx; 01wyzyl; ... >> query: (?x7984, 0d075m) <- religion(?x7984, ?x1985), student(?x10478, ?x7984) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #1465 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 556 *> proper extension: 079vf; 01cv3n; 08f3b1; 01g4zr; 083p7; 02r34n; 02c4s; 083pr; 01wz3cx; 01wyzyl; ... *> query: (?x7984, 07wbk) <- religion(?x7984, ?x1985), student(?x10478, ?x7984) *> conf = 0.03 ranks of expected_values: 2 EVAL 0gd9k politician! 07wbk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 122.000 0.036 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #21436-09889g PRED entity: 09889g PRED relation: influenced_by! PRED expected values: 0478__m => 161 concepts (117 used for prediction) PRED predicted values (max 10 best out of 437): 019z7q (0.25 #25, 0.13 #1054, 0.09 #6196), 0n6kf (0.25 #189, 0.13 #1218, 0.09 #6360), 013pp3 (0.25 #220, 0.13 #1249, 0.06 #6391), 01vdrw (0.25 #443, 0.13 #1472, 0.06 #6614), 07h07 (0.25 #149, 0.07 #1178, 0.06 #6320), 0p8jf (0.25 #110, 0.07 #1139, 0.05 #29405), 04hcw (0.25 #287, 0.07 #1316, 0.05 #19304), 0mb0 (0.25 #427, 0.07 #1456, 0.05 #9684), 0jt90f5 (0.25 #80, 0.07 #1109, 0.04 #29375), 0c1jh (0.25 #386, 0.07 #1415, 0.04 #3986) >> Best rule #25 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 0mj0c; >> query: (?x4960, 019z7q) <- sibling(?x4960, ?x2274), peers(?x4960, ?x702) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1723 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 15 *> proper extension: 054c1; *> query: (?x4960, 0478__m) <- participant(?x4960, ?x5625), inductee(?x1091, ?x4960) *> conf = 0.06 ranks of expected_values: 116 EVAL 09889g influenced_by! 0478__m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 161.000 117.000 0.250 http://example.org/influence/influence_node/influenced_by #21435-03d_w3h PRED entity: 03d_w3h PRED relation: place_of_birth PRED expected values: 01_d4 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 142): 030qb3t (0.28 #84536, 0.27 #85946, 0.27 #85945), 04jpl (0.28 #84536, 0.27 #85946, 0.27 #85945), 0v9qg (0.28 #84536, 0.27 #85946, 0.27 #85945), 059rby (0.28 #84536, 0.27 #85946, 0.27 #85945), 02_286 (0.10 #7765, 0.09 #11995, 0.09 #6356), 02dtg (0.10 #2123, 0.04 #9166, 0.04 #10576), 0f2rq (0.05 #909, 0.03 #6542), 04lh6 (0.05 #1037, 0.02 #10193, 0.01 #7374), 0f2wj (0.05 #4947, 0.03 #1426, 0.01 #21152), 04f_d (0.05 #2186, 0.02 #4298, 0.02 #9229) >> Best rule #84536 for best value: >> intensional similarity = 2 >> extensional distance = 2301 >> proper extension: 04hqbbz; 05yvfd; 0kbn5; 029rk; >> query: (?x940, ?x335) <- gender(?x940, ?x514), location(?x940, ?x335) >> conf = 0.28 => this is the best rule for 4 predicted values *> Best rule #4291 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 58 *> proper extension: 01mt1fy; 0143wl; 0154d7; 01x0sy; 0ccqd7; 029k55; 08p1gp; 0sw6y; 075npt; 022s1m; *> query: (?x940, 01_d4) <- language(?x940, ?x254), student(?x741, ?x940) *> conf = 0.03 ranks of expected_values: 14 EVAL 03d_w3h place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 134.000 134.000 0.275 http://example.org/people/person/place_of_birth #21434-03_r3 PRED entity: 03_r3 PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.73 #1243, 0.72 #865, 0.72 #1222), 060bp (0.68 #569, 0.67 #590, 0.65 #989), 0f6c3 (0.51 #827, 0.48 #470, 0.41 #112), 0fkvn (0.51 #467, 0.45 #109, 0.45 #824), 0pqc5 (0.47 #1497, 0.42 #1014, 0.36 #1980), 09n5b9 (0.46 #831, 0.42 #474, 0.35 #1083), 0p5vf (0.26 #202, 0.25 #75, 0.21 #160), 0dq3c (0.19 #171, 0.15 #1305, 0.14 #234), 0789n (0.17 #114, 0.15 #472, 0.14 #51), 01t7n9 (0.17 #123, 0.10 #481, 0.08 #838) >> Best rule #1243 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 077qn; 05r7t; 0165b; >> query: (?x421, 060c4) <- country(?x171, ?x421), jurisdiction_of_office(?x3119, ?x421), olympics(?x421, ?x391) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #569 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 04v3q; 04gqr; 0166b; 02k8k; 0fv4v; *> query: (?x421, 060bp) <- organization(?x421, ?x127), countries_within(?x8483, ?x421), contains(?x421, ?x3248) *> conf = 0.68 ranks of expected_values: 2 EVAL 03_r3 jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.729 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #21433-01w1kyf PRED entity: 01w1kyf PRED relation: actor! PRED expected values: 0ddd0gc => 95 concepts (53 used for prediction) PRED predicted values (max 10 best out of 59): 0sxfd (0.12 #4779, 0.10 #8241, 0.09 #8507), 0b_5d (0.12 #4779, 0.10 #8241, 0.09 #8507), 06x77g (0.09 #7975, 0.09 #5046, 0.08 #7709), 02sfnv (0.09 #7975, 0.09 #5046, 0.08 #7709), 015whm (0.09 #7975, 0.09 #5046, 0.08 #7709), 0dr_9t7 (0.09 #7975, 0.09 #5046, 0.08 #7709), 026bfsh (0.05 #97, 0.04 #1159, 0.03 #363), 02py4c8 (0.03 #278, 0.03 #543, 0.03 #12), 0kfpm (0.03 #279), 034fl9 (0.03 #709, 0.02 #444) >> Best rule #4779 for best value: >> intensional similarity = 3 >> extensional distance = 902 >> proper extension: 01wz01; 0d02km; 044zvm; >> query: (?x5094, ?x1402) <- award_winner(?x1402, ?x5094), profession(?x5094, ?x319), film(?x5094, ?x857) >> conf = 0.12 => this is the best rule for 2 predicted values *> Best rule #286 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 04bdqk; *> query: (?x5094, 0ddd0gc) <- award(?x5094, ?x1132), location(?x5094, ?x10428), ?x1132 = 0bdwft *> conf = 0.02 ranks of expected_values: 22 EVAL 01w1kyf actor! 0ddd0gc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 95.000 53.000 0.120 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21432-01jszm PRED entity: 01jszm PRED relation: school! PRED expected values: 0f4vx0 => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 20): 025tn92 (0.22 #53, 0.19 #283, 0.16 #364), 02rl201 (0.22 #44, 0.19 #283, 0.16 #364), 0f4vx0 (0.21 #131, 0.19 #151, 0.19 #283), 02pq_x5 (0.19 #283, 0.16 #364, 0.16 #117), 02x2khw (0.19 #283, 0.16 #364, 0.15 #262), 038981 (0.19 #283, 0.16 #364, 0.15 #262), 02z6872 (0.19 #283, 0.16 #364, 0.15 #262), 02pq_rp (0.19 #283, 0.16 #364, 0.15 #262), 06439y (0.19 #283, 0.16 #364, 0.15 #262), 038c0q (0.19 #283, 0.16 #364, 0.15 #262) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0f102; 01stj9; >> query: (?x5324, 025tn92) <- school_type(?x5324, ?x3205), school(?x8995, ?x5324), colors(?x5324, ?x5325), ?x5325 = 03vtbc >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #131 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 93 *> proper extension: 08815; 01jswq; 01f1r4; 0m9_5; 02zd460; *> query: (?x5324, 0f4vx0) <- school_type(?x5324, ?x3205), currency(?x5324, ?x170), school(?x8995, ?x5324), student(?x5324, ?x4806) *> conf = 0.21 ranks of expected_values: 3 EVAL 01jszm school! 0f4vx0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 155.000 155.000 0.222 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #21431-09gb_4p PRED entity: 09gb_4p PRED relation: genre PRED expected values: 01jfsb => 71 concepts (69 used for prediction) PRED predicted values (max 10 best out of 97): 0c3351 (0.53 #2525, 0.51 #2526, 0.51 #3372), 01jfsb (0.53 #2416, 0.47 #1334, 0.43 #2659), 02kdv5l (0.44 #1324, 0.42 #122, 0.36 #963), 082gq (0.38 #31, 0.26 #151, 0.17 #511), 02p0szs (0.38 #29, 0.16 #149, 0.12 #750), 03mqtr (0.37 #1472, 0.14 #270, 0.12 #751), 04xvlr (0.37 #121, 0.35 #722, 0.33 #1443), 05p553 (0.36 #2048, 0.35 #2530, 0.33 #6753), 017fp (0.35 #736, 0.32 #135, 0.29 #1457), 060__y (0.34 #257, 0.19 #497, 0.18 #617) >> Best rule #2525 for best value: >> intensional similarity = 4 >> extensional distance = 335 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x4602, ?x53) <- titles(?x4205, ?x4602), titles(?x53, ?x4602), titles(?x4205, ?x4136), ?x4136 = 02jr6k >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #2416 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 335 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x4602, 01jfsb) <- titles(?x4205, ?x4602), titles(?x4205, ?x4136), ?x4136 = 02jr6k *> conf = 0.53 ranks of expected_values: 2 EVAL 09gb_4p genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 69.000 0.531 http://example.org/film/film/genre #21430-017jv5 PRED entity: 017jv5 PRED relation: film PRED expected values: 02qrv7 0d1qmz 01wb95 0hv27 025twgt => 121 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1545): 08720 (0.69 #19984, 0.63 #36900, 0.63 #13835), 043n1r5 (0.69 #19984, 0.63 #36900, 0.63 #13835), 0q9sg (0.69 #19984, 0.63 #36900, 0.63 #13835), 01v1ln (0.69 #19984, 0.63 #36900, 0.63 #13835), 02qhlwd (0.69 #19984, 0.63 #36900, 0.63 #13835), 0140g4 (0.69 #19984, 0.63 #36900, 0.63 #13835), 08gsvw (0.69 #19984, 0.63 #36900, 0.63 #13835), 03r0g9 (0.69 #19984, 0.63 #36900, 0.63 #13835), 03mh_tp (0.69 #19984, 0.63 #13835, 0.63 #12297), 05b6rdt (0.69 #19984, 0.63 #13835, 0.63 #12297) >> Best rule #19984 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 0kk9v; >> query: (?x1850, ?x188) <- award_winner(?x4993, ?x1850), production_companies(?x188, ?x1850), award_nominee(?x1850, ?x269) >> conf = 0.69 => this is the best rule for 11 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0g1rw; *> query: (?x1850, 02qrv7) <- film(?x1850, ?x4175), film(?x1850, ?x407), ?x4175 = 0d61px, award(?x407, ?x112) *> conf = 0.33 ranks of expected_values: 99 EVAL 017jv5 film 025twgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 99.000 0.694 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017jv5 film 0hv27 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 99.000 0.694 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017jv5 film 01wb95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 99.000 0.694 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017jv5 film 0d1qmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 99.000 0.694 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017jv5 film 02qrv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 121.000 99.000 0.694 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #21429-01w61th PRED entity: 01w61th PRED relation: artists! PRED expected values: 06cqb => 132 concepts (80 used for prediction) PRED predicted values (max 10 best out of 222): 0ggx5q (0.77 #691, 0.44 #1919, 0.42 #1305), 06by7 (0.65 #1556, 0.62 #5548, 0.61 #4627), 06j6l (0.62 #662, 0.61 #1276, 0.44 #1583), 0glt670 (0.46 #655, 0.33 #1269, 0.31 #1883), 0gywn (0.38 #671, 0.38 #1592, 0.33 #1285), 02vjzr (0.36 #1362, 0.21 #1055, 0.16 #4740), 02ny8t (0.34 #1054, 0.15 #1361, 0.10 #4739), 02k_kn (0.30 #1292, 0.24 #4670, 0.24 #1599), 01gjw (0.29 #165, 0.22 #472, 0.06 #2007), 016clz (0.28 #5531, 0.28 #4610, 0.26 #1539) >> Best rule #691 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0j1yf; >> query: (?x883, 0ggx5q) <- award_winner(?x6416, ?x883), languages(?x883, ?x254), award(?x883, ?x3488), ?x3488 = 02f71y >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #4609 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 163 *> proper extension: 01pbxb; 0150jk; 01fl3; 0dtd6; 01vv126; 01rm8b; 0bkg4; 018gm9; 02r3cn; 018y81; ... *> query: (?x883, 06cqb) <- artists(?x3061, ?x883), ?x3061 = 05bt6j, category(?x883, ?x134) *> conf = 0.04 ranks of expected_values: 82 EVAL 01w61th artists! 06cqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 132.000 80.000 0.769 http://example.org/music/genre/artists #21428-0vbk PRED entity: 0vbk PRED relation: religion PRED expected values: 05sfs 051kv 01y0s9 => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 25): 051kv (0.87 #149, 0.87 #120, 0.81 #675), 05sfs (0.87 #118, 0.81 #147, 0.76 #265), 01y0s9 (0.63 #122, 0.61 #151, 0.60 #269), 058x5 (0.48 #148, 0.47 #119, 0.36 #674), 03_gx (0.45 #155, 0.44 #418, 0.43 #126), 01s5nb (0.43 #134, 0.42 #689, 0.40 #281), 0flw86 (0.40 #2567, 0.39 #1550, 0.38 #1579), 072w0 (0.40 #2567, 0.26 #164, 0.25 #77), 02t7t (0.33 #132, 0.32 #161, 0.29 #279), 092bf5 (0.27 #127, 0.26 #156, 0.25 #11) >> Best rule #149 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 0488g; 026mj; >> query: (?x4758, 051kv) <- religion(?x4758, ?x8249), religion(?x4758, ?x2591), ?x8249 = 021_0p, adjoins(?x1025, ?x4758), ?x2591 = 0631_ >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0vbk religion 01y0s9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.871 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 0vbk religion 051kv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.871 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 0vbk religion 05sfs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.871 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #21427-0c5x_ PRED entity: 0c5x_ PRED relation: student PRED expected values: 08k881 => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1050): 0ff3y (0.12 #4157, 0.12 #6247, 0.03 #12517), 01n1gc (0.12 #2700, 0.12 #4790, 0.02 #13150), 02m7r (0.12 #2455, 0.12 #4545, 0.02 #21267), 0432cd (0.12 #3408, 0.12 #5498, 0.02 #11768), 01tdnyh (0.12 #2977, 0.12 #5067, 0.01 #19698), 0l6qt (0.12 #2106, 0.12 #4196, 0.01 #18827), 030dr (0.12 #3964, 0.12 #6054, 0.01 #22776), 0kn3g (0.12 #3755, 0.12 #5845, 0.01 #22567), 0xnc3 (0.12 #3531, 0.12 #5621, 0.01 #22343), 04hcw (0.12 #3352, 0.12 #5442, 0.01 #22164) >> Best rule #4157 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0277jc; 0yldt; 0ym20; >> query: (?x8220, 0ff3y) <- institution(?x2759, ?x8220), student(?x8220, ?x1787), ?x2759 = 071tyz >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c5x_ student 08k881 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 55.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #21426-0bdx29 PRED entity: 0bdx29 PRED relation: award! PRED expected values: 03z509 01_njt => 50 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2823): 02rmfm (0.80 #40155, 0.79 #46850, 0.79 #50198), 02x7vq (0.80 #40155, 0.79 #46850, 0.79 #50198), 015nhn (0.80 #40155, 0.79 #46850, 0.79 #50198), 01jw4r (0.64 #22533, 0.43 #15842, 0.34 #16729), 01gvr1 (0.57 #13517, 0.50 #20208, 0.33 #3479), 02kxwk (0.57 #21298, 0.43 #14607, 0.33 #4569), 01jmv8 (0.57 #22549, 0.43 #15858, 0.33 #5820), 0hsn_ (0.57 #22565, 0.43 #15874, 0.33 #5836), 01gq0b (0.57 #13864, 0.29 #20555, 0.20 #10517), 028knk (0.57 #20593, 0.29 #13902, 0.20 #23941) >> Best rule #40155 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 02r9qt; >> query: (?x2041, ?x4919) <- award_winner(?x2041, ?x4919), category_of(?x2041, ?x2758), film(?x4919, ?x1330) >> conf = 0.80 => this is the best rule for 3 predicted values *> Best rule #16729 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0cqh6z; 0bdwft; 0cqgl9; *> query: (?x2041, ?x1676) <- nominated_for(?x2041, ?x2009), award(?x2372, ?x2041), ?x2372 = 0l6px, actor(?x2009, ?x1676) *> conf = 0.34 ranks of expected_values: 87, 251 EVAL 0bdx29 award! 01_njt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 50.000 20.000 0.796 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdx29 award! 03z509 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 50.000 20.000 0.796 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21425-081yw PRED entity: 081yw PRED relation: location! PRED expected values: 017xm3 => 205 concepts (152 used for prediction) PRED predicted values (max 10 best out of 2059): 0c01c (0.53 #269015, 0.49 #344442, 0.47 #138277), 03nb5v (0.21 #8863, 0.19 #11378, 0.17 #6349), 0cgbf (0.21 #8934, 0.17 #3906, 0.12 #11449), 094xh (0.17 #6105, 0.17 #3591, 0.09 #13648), 09yrh (0.17 #5940, 0.17 #3426, 0.07 #21025), 01lbp (0.17 #5177, 0.17 #2663, 0.07 #7691), 02ghq (0.17 #4703, 0.12 #12246, 0.08 #7217), 01wp8w7 (0.17 #2774, 0.08 #5288, 0.08 #17859), 0p_pd (0.17 #2562, 0.08 #5076, 0.07 #20161), 0c6qh (0.17 #2974, 0.08 #5488, 0.07 #20573) >> Best rule #269015 for best value: >> intensional similarity = 3 >> extensional distance = 202 >> proper extension: 06gmr; >> query: (?x4600, ?x2560) <- place_of_birth(?x2560, ?x4600), contains(?x94, ?x4600), participant(?x1986, ?x2560) >> conf = 0.53 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 081yw location! 017xm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 205.000 152.000 0.533 http://example.org/people/person/places_lived./people/place_lived/location #21424-01738f PRED entity: 01738f PRED relation: parent_genre PRED expected values: 016clz 09nwwf => 60 concepts (39 used for prediction) PRED predicted values (max 10 best out of 274): 06by7 (0.73 #3058, 0.67 #3218, 0.62 #4020), 0gywn (0.43 #516, 0.25 #197, 0.22 #479), 0jmwg (0.43 #1188, 0.17 #869, 0.16 #1510), 0jrv_ (0.40 #423, 0.20 #1380, 0.14 #5450), 01243b (0.37 #2588, 0.32 #1464, 0.30 #2109), 09jw2 (0.32 #1697, 0.21 #2019, 0.20 #1536), 016clz (0.27 #2087, 0.25 #2566, 0.24 #1442), 0xhtw (0.27 #1289, 0.25 #810, 0.22 #479), 0283d (0.25 #225, 0.14 #544, 0.12 #4966), 011j5x (0.24 #1458, 0.21 #1619, 0.17 #1941) >> Best rule #3058 for best value: >> intensional similarity = 6 >> extensional distance = 61 >> proper extension: 01756d; 01g888; 06cp5; 0pm85; 07s7gk6; >> query: (?x8031, 06by7) <- artists(?x8031, ?x8032), parent_genre(?x8031, ?x3753), award_nominee(?x976, ?x8032), profession(?x8032, ?x131), artists(?x3753, ?x1060), ?x1060 = 02r3zy >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2087 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 42 *> proper extension: 0175zz; *> query: (?x8031, 016clz) <- artists(?x8031, ?x8032), artists(?x8031, ?x7125), parent_genre(?x8031, ?x2937), category(?x8032, ?x134), profession(?x8032, ?x131), artists(?x2937, ?x7201), ?x7201 = 04n65n, group(?x227, ?x7125) *> conf = 0.27 ranks of expected_values: 7, 19 EVAL 01738f parent_genre 09nwwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 60.000 39.000 0.730 http://example.org/music/genre/parent_genre EVAL 01738f parent_genre 016clz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 60.000 39.000 0.730 http://example.org/music/genre/parent_genre #21423-0crd8q6 PRED entity: 0crd8q6 PRED relation: genre PRED expected values: 0lsxr => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.65 #2565, 0.64 #3178, 0.62 #2811), 01z4y (0.61 #8077, 0.52 #7585, 0.52 #5504), 03k9fj (0.39 #622, 0.37 #378, 0.36 #256), 02kdv5l (0.35 #369, 0.34 #613, 0.34 #247), 01jfsb (0.35 #4415, 0.33 #3801, 0.32 #1723), 02l7c8 (0.32 #3194, 0.32 #505, 0.31 #2581), 01hmnh (0.30 #629, 0.28 #385, 0.26 #751), 06n90 (0.23 #380, 0.22 #624, 0.20 #258), 0lsxr (0.23 #9, 0.23 #497, 0.22 #1352), 02n4kr (0.23 #8, 0.16 #1351, 0.13 #2084) >> Best rule #2565 for best value: >> intensional similarity = 3 >> extensional distance = 450 >> proper extension: 03xj05; >> query: (?x10191, 07s9rl0) <- award(?x10191, ?x3019), titles(?x2480, ?x10191), film_crew_role(?x10191, ?x137) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 02_qt; 027r7k; *> query: (?x10191, 0lsxr) <- film(?x10626, ?x10191), language(?x10191, ?x254), ?x10626 = 0ywqc *> conf = 0.23 ranks of expected_values: 9 EVAL 0crd8q6 genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 93.000 93.000 0.650 http://example.org/film/film/genre #21422-02wrhj PRED entity: 02wrhj PRED relation: gender PRED expected values: 05zppz => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #43, 0.74 #19, 0.73 #81), 02zsn (0.50 #219, 0.50 #286, 0.46 #313) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 246 >> proper extension: 0hhqw; 026kmvf; >> query: (?x1765, 05zppz) <- place_of_birth(?x1765, ?x1658), featured_film_locations(?x10515, ?x1658), teams(?x1658, ?x6179), ?x10515 = 0dnkmq >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wrhj gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.742 http://example.org/people/person/gender #21421-01mskc3 PRED entity: 01mskc3 PRED relation: award_winner! PRED expected values: 013b2h => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 117): 01bx35 (0.25 #147, 0.10 #14703, 0.08 #7287), 0hhtgcw (0.25 #225, 0.10 #14703, 0.05 #2325), 019bk0 (0.17 #296, 0.13 #996, 0.10 #2116), 013b2h (0.17 #2179, 0.11 #1059, 0.11 #4419), 02rjjll (0.16 #1125, 0.15 #2105, 0.14 #425), 05pd94v (0.14 #422, 0.13 #2102, 0.11 #1402), 01mhwk (0.14 #601, 0.11 #2141, 0.07 #2561), 0gx1673 (0.14 #539, 0.10 #14703, 0.05 #2359), 01s695 (0.14 #423, 0.10 #2103, 0.08 #7283), 09bymc (0.14 #540, 0.03 #820, 0.02 #12581) >> Best rule #147 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0288fyj; >> query: (?x11953, 01bx35) <- nationality(?x11953, ?x421), place_of_birth(?x11953, ?x3249), award_nominee(?x8722, ?x11953), ?x8722 = 01w5jwb >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2179 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 190 *> proper extension: 05cljf; 0m2l9; 026ps1; 0137n0; 01l4zqz; 04dqdk; 011zf2; 0l12d; 03gr7w; 01w60_p; ... *> query: (?x11953, 013b2h) <- nationality(?x11953, ?x421), artists(?x283, ?x11953), location(?x11953, ?x3249), award_winner(?x3121, ?x11953) *> conf = 0.17 ranks of expected_values: 4 EVAL 01mskc3 award_winner! 013b2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 142.000 142.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21420-0mnsf PRED entity: 0mnsf PRED relation: place PRED expected values: 0mnsf => 128 concepts (82 used for prediction) PRED predicted values (max 10 best out of 186): 0dclg (0.36 #1547, 0.25 #43, 0.14 #38662), 013yq (0.36 #1547, 0.25 #45, 0.14 #38662), 0mnsf (0.36 #1547, 0.25 #13910, 0.19 #18549), 07z1m (0.25 #13910, 0.19 #18549, 0.19 #23703), 09c7w0 (0.25 #13910, 0.19 #18549, 0.19 #23703), 094jv (0.14 #551, 0.05 #1583, 0.04 #2098), 02_286 (0.12 #1045, 0.04 #2591, 0.03 #3621), 0vrmb (0.12 #1433), 01vsl (0.12 #1227), 0f2w0 (0.05 #1584, 0.04 #2614, 0.02 #4674) >> Best rule #1547 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02_286; 01vsl; 0vrmb; >> query: (?x7478, ?x2254) <- location(?x8996, ?x7478), student(?x3513, ?x8996), ?x3513 = 0pspl, location(?x8996, ?x2254) >> conf = 0.36 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0mnsf place 0mnsf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 128.000 82.000 0.357 http://example.org/location/hud_county_place/place #21419-0m3gy PRED entity: 0m3gy PRED relation: film_release_region PRED expected values: 0d0vqn 03h64 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 134): 09c7w0 (0.94 #5409, 0.94 #5573, 0.94 #6063), 0d0vqn (0.91 #1975, 0.89 #3446, 0.89 #2466), 06mkj (0.90 #2029, 0.86 #2520, 0.83 #226), 0345h (0.78 #2003, 0.77 #2494, 0.76 #3474), 03h64 (0.76 #2531, 0.75 #3511, 0.74 #3675), 015fr (0.72 #3457, 0.69 #1986, 0.69 #3621), 0154j (0.70 #2463, 0.68 #3607, 0.68 #3443), 0b90_r (0.66 #3442, 0.64 #1971, 0.63 #2462), 0d060g (0.64 #2465, 0.64 #3445, 0.64 #1974), 03spz (0.63 #2070, 0.55 #3705, 0.55 #3541) >> Best rule #5409 for best value: >> intensional similarity = 3 >> extensional distance = 740 >> proper extension: 01vksx; 03fts; 05sxzwc; 05pbl56; 0kv238; 025n07; 01ffx4; 093dqjy; 0btbyn; 02ll45; ... >> query: (?x9294, 09c7w0) <- currency(?x9294, ?x170), titles(?x571, ?x9294), film_release_region(?x9294, ?x87) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #1975 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 79 *> proper extension: 0gtvrv3; 047svrl; 07k2mq; 0372j5; *> query: (?x9294, 0d0vqn) <- film(?x9707, ?x9294), currency(?x9294, ?x170), film_release_region(?x9294, ?x1003), ?x1003 = 03gj2 *> conf = 0.91 ranks of expected_values: 2, 5 EVAL 0m3gy film_release_region 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 61.000 61.000 0.945 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0m3gy film_release_region 0d0vqn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 61.000 61.000 0.945 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21418-0b005 PRED entity: 0b005 PRED relation: genre PRED expected values: 0c031k6 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 67): 07s9rl0 (0.51 #1901, 0.50 #317, 0.49 #634), 0hcr (0.33 #18, 0.21 #1918, 0.19 #1601), 0c4xc (0.30 #42, 0.25 #200, 0.25 #121), 06nbt (0.22 #20, 0.11 #494, 0.10 #890), 01htzx (0.19 #17, 0.17 #887, 0.17 #491), 095bb (0.19 #37, 0.07 #511, 0.06 #1304), 01t_vv (0.17 #903, 0.17 #270, 0.16 #112), 06q7n (0.17 #281, 0.16 #439, 0.16 #123), 06n90 (0.16 #1833, 0.16 #1438, 0.16 #1754), 03k9fj (0.15 #485, 0.15 #11, 0.15 #1436) >> Best rule #1901 for best value: >> intensional similarity = 3 >> extensional distance = 256 >> proper extension: 07qht4; 05397h; 049rl0; >> query: (?x6694, 07s9rl0) <- genre(?x6694, ?x9669), genre(?x3905, ?x9669), program(?x3817, ?x3905) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #764 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 126 *> proper extension: 07s8z_l; *> query: (?x6694, 0c031k6) <- genre(?x6694, ?x258), award_winner(?x6694, ?x5562), type_of_union(?x5562, ?x566) *> conf = 0.04 ranks of expected_values: 32 EVAL 0b005 genre 0c031k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 57.000 57.000 0.508 http://example.org/tv/tv_program/genre #21417-059rby PRED entity: 059rby PRED relation: state_province_region! PRED expected values: 0xwj 09glbnt 05cl8y 02sdwt 0kcdl => 181 concepts (143 used for prediction) PRED predicted values (max 10 best out of 718): 027kp3 (0.62 #39245, 0.57 #32283, 0.33 #25943), 02kbtf (0.62 #39245, 0.57 #32283, 0.33 #25943), 01jzyx (0.62 #39245, 0.57 #32283, 0.33 #25943), 02kth6 (0.62 #39245, 0.57 #32283, 0.33 #25943), 04b_46 (0.62 #39245, 0.57 #32283, 0.33 #25943), 0l0wv (0.62 #39245, 0.33 #25943, 0.29 #43677), 0hpv3 (0.62 #39245, 0.33 #25943, 0.29 #43677), 04d5v9 (0.62 #39245, 0.33 #25943, 0.29 #43677), 03205_ (0.62 #39245, 0.28 #48746, 0.25 #60781), 0n6dc (0.25 #60781, 0.22 #18982, 0.22 #20247) >> Best rule #39245 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 0glh3; 0ftkx; >> query: (?x335, ?x4794) <- contains(?x335, ?x4794), state_province_region(?x166, ?x335), institution(?x620, ?x4794) >> conf = 0.62 => this is the best rule for 9 predicted values *> Best rule #4775 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 03_r3; 02wt0; *> query: (?x335, 05cl8y) <- contains(?x335, ?x322), jurisdiction_of_office(?x7891, ?x335), vacationer(?x335, ?x794) *> conf = 0.06 ranks of expected_values: 250 EVAL 059rby state_province_region! 0kcdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 143.000 0.624 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 059rby state_province_region! 02sdwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 143.000 0.624 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 059rby state_province_region! 05cl8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 181.000 143.000 0.624 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 059rby state_province_region! 09glbnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 143.000 0.624 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 059rby state_province_region! 0xwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 143.000 0.624 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #21416-02j71 PRED entity: 02j71 PRED relation: administrative_parent! PRED expected values: 0jgd 027nb 0d060g 06npd 04v3q 06qd3 06c1y 01mjq 02khs 06mkj 06f32 0161c 03ryn 077qn 06ryl 06tw8 06v36 06dfg 02kcz 06nnj 05bmq => 15 concepts (15 used for prediction) PRED predicted values (max 10 best out of 348): 059j2 (0.47 #2990, 0.20 #18, 0.05 #1299), 070zc (0.47 #2990, 0.14 #627, 0.07 #1054), 01n7q (0.47 #2990, 0.14 #444, 0.07 #871), 06k5_ (0.47 #2990, 0.14 #649, 0.04 #1930), 0f1_p (0.47 #2990, 0.14 #483, 0.04 #1764), 078lk (0.47 #2990, 0.07 #898, 0.03 #2179), 0fhnf (0.47 #2990, 0.05 #1455, 0.03 #2309), 052gtg (0.47 #2990, 0.02 #2763), 075mb (0.47 #2990, 0.02 #2602), 0p_x (0.47 #2990) >> Best rule #2990 for best value: >> intensional similarity = 2 >> extensional distance = 40 >> proper extension: 04rrx; >> query: (?x551, ?x1144) <- administrative_parent(?x5622, ?x551), adjoins(?x5622, ?x1144) >> conf = 0.47 => this is the best rule for 41 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 18, 19, 21, 22, 23, 24, 26, 27, 28, 30, 32, 33, 34, 38, 40 EVAL 02j71 administrative_parent! 05bmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 06nnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 02kcz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 06dfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 06v36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 06tw8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 06ryl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 0161c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 06f32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 06mkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 02khs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 06c1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 06qd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 04v3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 06npd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 0d060g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 027nb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent EVAL 02j71 administrative_parent! 0jgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 15.000 15.000 0.466 http://example.org/base/aareas/schema/administrative_area/administrative_parent #21415-0170th PRED entity: 0170th PRED relation: award PRED expected values: 05h5nb8 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 167): 02x73k6 (0.33 #1395, 0.33 #1208, 0.26 #8830), 0gs9p (0.26 #8830, 0.26 #9991, 0.24 #1394), 0f4x7 (0.26 #8830, 0.26 #9991, 0.24 #1394), 099c8n (0.26 #8830, 0.26 #9991, 0.24 #1394), 0gqy2 (0.23 #1280, 0.07 #4183, 0.07 #4184), 027dtxw (0.15 #1165, 0.12 #4, 0.06 #21840), 09d28z (0.15 #1352, 0.05 #5071, 0.05 #13938), 0gq_v (0.14 #2110, 0.11 #948, 0.08 #4899), 02w_6xj (0.14 #1318, 0.05 #13938, 0.05 #4341), 0gr4k (0.12 #26, 0.12 #6506, 0.09 #1187) >> Best rule #1395 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 0gmgwnv; >> query: (?x2757, ?x1033) <- nominated_for(?x777, ?x2757), nominated_for(?x1033, ?x2757), ?x1033 = 02x73k6 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13938 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x2757, ?x198) <- award(?x2757, ?x746), award(?x4359, ?x746), award(?x4359, ?x198) *> conf = 0.05 ranks of expected_values: 97 EVAL 0170th award 05h5nb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 105.000 105.000 0.333 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #21414-05kj_ PRED entity: 05kj_ PRED relation: jurisdiction_of_office! PRED expected values: 09n5b9 => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 18): 09n5b9 (0.85 #251, 0.85 #411, 0.84 #130), 0pqc5 (0.54 #1786, 0.52 #726, 0.51 #2027), 060c4 (0.47 #1565, 0.45 #2326, 0.39 #1745), 060bp (0.38 #1743, 0.38 #2324, 0.37 #1563), 0fkzq (0.38 #1883, 0.28 #94, 0.25 #416), 01t7n9 (0.38 #1883, 0.20 #96, 0.15 #258), 01gkgk (0.38 #1883, 0.12 #25, 0.10 #367), 0fkx3 (0.10 #119, 0.10 #600, 0.06 #1180), 0p5vf (0.10 #632, 0.10 #612, 0.10 #592), 01q24l (0.09 #2334, 0.09 #733, 0.09 #313) >> Best rule #251 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 0n3g; >> query: (?x726, 09n5b9) <- religion(?x726, ?x1624), religion(?x726, ?x962), ?x962 = 05sfs, ?x1624 = 051kv >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05kj_ jurisdiction_of_office! 09n5b9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.850 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #21413-0dln8jk PRED entity: 0dln8jk PRED relation: language PRED expected values: 02h40lc 0c_v2 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 29): 02h40lc (0.89 #2638, 0.88 #361, 0.88 #3055), 064_8sq (0.16 #381, 0.14 #262, 0.14 #500), 06nm1 (0.11 #370, 0.11 #489, 0.10 #131), 04306rv (0.10 #364, 0.10 #245, 0.09 #483), 02bjrlw (0.07 #241, 0.06 #360, 0.05 #778), 06b_j (0.05 #501, 0.05 #1099, 0.05 #979), 03_9r (0.04 #130, 0.04 #3364, 0.04 #3182), 0jzc (0.04 #737, 0.04 #140, 0.03 #976), 0653m (0.04 #132, 0.04 #908, 0.04 #848), 06mp7 (0.03 #256, 0.02 #375, 0.02 #197) >> Best rule #2638 for best value: >> intensional similarity = 3 >> extensional distance = 1558 >> proper extension: 04xbq3; >> query: (?x4847, 02h40lc) <- film(?x6980, ?x4847), award_nominee(?x8066, ?x6980), award_winner(?x451, ?x6980) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dln8jk language 0c_v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 58.000 0.886 http://example.org/film/film/language EVAL 0dln8jk language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.886 http://example.org/film/film/language #21412-0p_sc PRED entity: 0p_sc PRED relation: currency PRED expected values: 09nqf => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.78 #15, 0.77 #1, 0.77 #36), 01nv4h (0.07 #58, 0.07 #51, 0.03 #128), 02l6h (0.02 #130, 0.01 #214, 0.01 #228), 02gsvk (0.01 #132, 0.01 #69, 0.01 #139), 088n7 (0.01 #49) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 146 >> proper extension: 02qr69m; 08rr3p; 02rn00y; 023p7l; 05hjnw; 04tng0; 0m63c; 09cxm4; 02zk08; 02fqxm; >> query: (?x776, 09nqf) <- nominated_for(?x1443, ?x776), nominated_for(?x1107, ?x776), award(?x299, ?x1107), ?x1443 = 054krc >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p_sc currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.784 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21411-0zcbl PRED entity: 0zcbl PRED relation: location PRED expected values: 0d9jr => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 94): 0f2nf (0.49 #10444, 0.47 #8033, 0.47 #4016), 059rby (0.17 #16, 0.05 #4032, 0.04 #819), 030qb3t (0.16 #4099, 0.15 #20971, 0.15 #20168), 02_286 (0.15 #69120, 0.15 #13694, 0.15 #20122), 01cx_ (0.08 #162, 0.03 #3374, 0.03 #4178), 0f2wj (0.08 #34, 0.02 #6459, 0.02 #13691), 02cft (0.08 #306, 0.02 #1912, 0.01 #23603), 04jt2 (0.08 #632, 0.02 #2238), 042tq (0.08 #424, 0.02 #2030), 0f2tj (0.08 #328, 0.02 #1934) >> Best rule #10444 for best value: >> intensional similarity = 2 >> extensional distance = 602 >> proper extension: 07h1q; 01cqz5; >> query: (?x6980, ?x9336) <- religion(?x6980, ?x2694), place_of_birth(?x6980, ?x9336) >> conf = 0.49 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0zcbl location 0d9jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 105.000 0.487 http://example.org/people/person/places_lived./people/place_lived/location #21410-0488g PRED entity: 0488g PRED relation: time_zones PRED expected values: 02fqwt => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 12): 02fqwt (0.98 #914, 0.54 #565, 0.36 #85), 02hcv8 (0.46 #831, 0.45 #1276, 0.45 #1108), 02lcqs (0.25 #833, 0.19 #1254, 0.19 #1242), 03bdv (0.23 #113, 0.12 #449, 0.12 #485), 02llzg (0.22 #231, 0.22 #447, 0.21 #459), 03plfd (0.10 #465, 0.08 #453, 0.06 #598), 052vwh (0.09 #119, 0.07 #239, 0.04 #588), 0gsrz4 (0.08 #451, 0.05 #776, 0.05 #379), 042g7t (0.06 #454, 0.04 #46, 0.04 #370), 0d2t4g (0.04 #116, 0.02 #585, 0.01 #416) >> Best rule #914 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 0fczy; 02v3m7; 0n474; 05g56; 0mlm_; 0mw2m; >> query: (?x1782, ?x1638) <- contains(?x1782, ?x2986), time_zones(?x1782, ?x2088), time_zones(?x2986, ?x1638) >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0488g time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.978 http://example.org/location/location/time_zones #21409-051wwp PRED entity: 051wwp PRED relation: film PRED expected values: 09qycb => 79 concepts (54 used for prediction) PRED predicted values (max 10 best out of 651): 092vkg (0.58 #56873, 0.35 #85315, 0.34 #79982), 0cc7hmk (0.37 #5625, 0.30 #7402, 0.08 #2071), 0pdp8 (0.33 #368, 0.08 #2145, 0.05 #35543), 01k0xy (0.33 #1275, 0.06 #4829, 0.03 #92428), 01738w (0.33 #1123, 0.03 #71094, 0.03 #92428), 08phg9 (0.31 #4433, 0.05 #6210, 0.05 #35543), 02b6n9 (0.27 #10446, 0.20 #12223, 0.06 #5115), 0c0zq (0.26 #6882, 0.22 #8659, 0.12 #5105), 011yqc (0.26 #7342, 0.16 #5565, 0.03 #71094), 0djlxb (0.25 #4088, 0.05 #35543, 0.04 #47987) >> Best rule #56873 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 04yywz; 049tjg; 02g8h; 0d_84; 0h1_w; 02nb2s; 04bs3j; 014x77; 0151ns; 0lzb8; ... >> query: (?x4928, ?x1064) <- film(?x4928, ?x2384), nominated_for(?x4928, ?x1064) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #3411 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 10 *> proper extension: 044ntk; *> query: (?x4928, 09qycb) <- film(?x4928, ?x4130), ?x4130 = 06lpmt *> conf = 0.08 ranks of expected_values: 108 EVAL 051wwp film 09qycb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 79.000 54.000 0.584 http://example.org/film/actor/film./film/performance/film #21408-02wvf2s PRED entity: 02wvf2s PRED relation: category PRED expected values: 08mbj5d => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.67 #11, 0.67 #9, 0.57 #37) >> Best rule #11 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 0fbq2n; >> query: (?x13320, 08mbj5d) <- team(?x2247, ?x13320), team(?x1717, ?x13320), team(?x1240, ?x13320), team(?x935, ?x13320), team(?x180, ?x13320), ?x2247 = 01_9c1, ?x180 = 01r3hr, ?x935 = 06b1q, ?x1717 = 02g_6x, ?x1240 = 023wyl, position_s(?x13320, ?x2247), position_s(?x13320, ?x935), position_s(?x13320, ?x1717), position_s(?x13320, ?x1240) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wvf2s category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.667 http://example.org/common/topic/webpage./common/webpage/category #21407-0py9b PRED entity: 0py9b PRED relation: company! PRED expected values: 0142rn => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 37): 05_wyz (0.54 #605, 0.48 #1369, 0.47 #1957), 0dq3c (0.54 #2112, 0.54 #3345, 0.52 #1356), 09d6p2 (0.47 #737, 0.44 #1958, 0.44 #2934), 02211by (0.38 #297, 0.33 #45, 0.27 #3258), 01kr6k (0.33 #1336, 0.32 #1378, 0.32 #2134), 09lq2c (0.33 #70, 0.25 #322, 0.25 #154), 02y6fz (0.27 #3258, 0.25 #147, 0.23 #611), 01rk91 (0.27 #3258, 0.20 #3807, 0.19 #5076), 021q0l (0.25 #301, 0.25 #133, 0.20 #3807), 0142rn (0.21 #657, 0.20 #3807, 0.17 #1039) >> Best rule #605 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 02fgdx; 029d_; >> query: (?x7970, 05_wyz) <- contact_category(?x7970, ?x6046), contact_category(?x7970, ?x897), currency(?x7970, ?x170), ?x897 = 03w5xm, category(?x7970, ?x134), ?x6046 = 02zdwq >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #657 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0558_1; *> query: (?x7970, 0142rn) <- contact_category(?x7970, ?x3231), currency(?x7970, ?x170), ?x3231 = 014dgf, state_province_region(?x7970, ?x3634) *> conf = 0.21 ranks of expected_values: 10 EVAL 0py9b company! 0142rn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 171.000 171.000 0.538 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #21406-020bv3 PRED entity: 020bv3 PRED relation: nominated_for! PRED expected values: 099tbz => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 187): 03hkv_r (0.44 #966, 0.20 #12141, 0.19 #13809), 0gq9h (0.33 #1014, 0.33 #4823, 0.33 #5537), 040njc (0.33 #958, 0.23 #5481, 0.21 #6909), 0gr4k (0.33 #978, 0.22 #4787, 0.21 #5501), 02n9nmz (0.33 #1009, 0.20 #12141, 0.19 #13809), 094qd5 (0.33 #987, 0.20 #12141, 0.19 #13809), 0bdwqv (0.33 #366, 0.20 #12141, 0.19 #13809), 0bfvd4 (0.33 #326, 0.20 #12141, 0.19 #13809), 07kjk7c (0.33 #429, 0.20 #12141, 0.19 #13809), 09v82c0 (0.33 #424, 0.01 #6375, 0.01 #7803) >> Best rule #966 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 05cvgl; 0bpx1k; 0gh65c5; 05vxdh; 011yg9; 01cz7r; >> query: (?x2029, 03hkv_r) <- film(?x8566, ?x2029), film(?x6122, ?x2029), award_nominee(?x8566, ?x1222), ?x6122 = 016xh5 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1949 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 246 *> proper extension: 0g5qmbz; *> query: (?x2029, 099tbz) <- award_winner(?x2029, ?x488), film_crew_role(?x2029, ?x137), written_by(?x2029, ?x1367) *> conf = 0.09 ranks of expected_values: 84 EVAL 020bv3 nominated_for! 099tbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 70.000 70.000 0.444 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21405-01vvyd8 PRED entity: 01vvyd8 PRED relation: artists! PRED expected values: 016_nr 01flzq => 117 concepts (90 used for prediction) PRED predicted values (max 10 best out of 235): 064t9 (0.56 #3154, 0.53 #3782, 0.52 #6294), 025sc50 (0.50 #3192, 0.34 #1622, 0.34 #2878), 06by7 (0.49 #6617, 0.48 #9758, 0.45 #11957), 036jv (0.45 #196, 0.11 #3336, 0.10 #1766), 06j6l (0.42 #3190, 0.30 #6330, 0.30 #2876), 01flzq (0.36 #121, 0.14 #3261, 0.13 #749), 0gywn (0.33 #3200, 0.23 #2886, 0.22 #1630), 05bt6j (0.30 #359, 0.26 #6639, 0.25 #5383), 0ggx5q (0.28 #3221, 0.23 #6361, 0.23 #5419), 016_nr (0.27 #76, 0.12 #3216, 0.08 #2274) >> Best rule #3154 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 0m19t; >> query: (?x6231, 064t9) <- artist(?x6230, ?x6231), category(?x6231, ?x134), artists(?x2937, ?x6231), ?x2937 = 0glt670 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #121 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 016ksk; 01wgcvn; 03sww; *> query: (?x6231, 01flzq) <- artist(?x6230, ?x6231), profession(?x6231, ?x131), award(?x6231, ?x6287), ?x6287 = 02f75t *> conf = 0.36 ranks of expected_values: 6, 10 EVAL 01vvyd8 artists! 01flzq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 117.000 90.000 0.561 http://example.org/music/genre/artists EVAL 01vvyd8 artists! 016_nr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 117.000 90.000 0.561 http://example.org/music/genre/artists #21404-0217m9 PRED entity: 0217m9 PRED relation: major_field_of_study PRED expected values: 03nfmq => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 115): 01mkq (0.75 #483, 0.49 #2707, 0.39 #1771), 02lp1 (0.75 #479, 0.39 #2703, 0.35 #6917), 02_7t (0.50 #528, 0.29 #177, 0.22 #4976), 04rjg (0.44 #488, 0.42 #2712, 0.40 #1190), 01tbp (0.44 #524, 0.22 #4972, 0.22 #2748), 02ky346 (0.44 #484, 0.20 #1186, 0.19 #2708), 0fdys (0.40 #1206, 0.26 #1792, 0.25 #4952), 05qfh (0.38 #501, 0.24 #2725, 0.23 #1203), 04x_3 (0.38 #494, 0.22 #4707, 0.22 #4942), 01540 (0.38 #525, 0.21 #4973, 0.20 #7783) >> Best rule #483 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 065y4w7; 07szy; 0f1nl; 0j_sncb; 07vyf; 04hgpt; 07ccs; 0gl5_; 01qd_r; 0g8fs; ... >> query: (?x5306, 01mkq) <- student(?x5306, ?x1290), institution(?x734, ?x5306), major_field_of_study(?x5306, ?x11820), ?x11820 = 0w7s >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #503 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 065y4w7; 07szy; 0f1nl; 0j_sncb; 07vyf; 04hgpt; 07ccs; 0gl5_; 01qd_r; 0g8fs; ... *> query: (?x5306, 03nfmq) <- student(?x5306, ?x1290), institution(?x734, ?x5306), major_field_of_study(?x5306, ?x11820), ?x11820 = 0w7s *> conf = 0.19 ranks of expected_values: 25 EVAL 0217m9 major_field_of_study 03nfmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 172.000 172.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #21403-0j8f09z PRED entity: 0j8f09z PRED relation: film_release_region PRED expected values: 0154j 03rjj 05qhw 0hzlz 0h7x 06f32 077qn => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 129): 05qhw (0.93 #711, 0.90 #1413, 0.86 #1973), 0154j (0.88 #703, 0.86 #1545, 0.85 #1405), 03rjj (0.88 #2386, 0.87 #1125, 0.86 #2246), 04gzd (0.82 #1408, 0.61 #1688, 0.60 #1968), 05v8c (0.76 #1415, 0.66 #1975, 0.65 #2255), 01p1v (0.70 #1444, 0.57 #2004, 0.55 #1584), 015qh (0.65 #1436, 0.61 #1716, 0.61 #1576), 01ls2 (0.64 #1411, 0.56 #709, 0.51 #1691), 016wzw (0.62 #1456, 0.57 #2016, 0.56 #754), 06qd3 (0.57 #2133, 0.57 #1152, 0.56 #1573) >> Best rule #711 for best value: >> intensional similarity = 8 >> extensional distance = 39 >> proper extension: 0bq8tmw; >> query: (?x9902, 05qhw) <- film_crew_role(?x9902, ?x137), film_release_region(?x9902, ?x1023), film_release_region(?x9902, ?x1003), film_release_region(?x9902, ?x390), ?x390 = 0chghy, ?x1003 = 03gj2, ?x1023 = 0ctw_b, award(?x9902, ?x11466) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 11, 19, 20, 23 EVAL 0j8f09z film_release_region 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 89.000 89.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j8f09z film_release_region 06f32 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 89.000 89.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j8f09z film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 89.000 89.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j8f09z film_release_region 0hzlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 89.000 89.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j8f09z film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j8f09z film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j8f09z film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21402-01t6b4 PRED entity: 01t6b4 PRED relation: profession PRED expected values: 03gjzk => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 95): 02hrh1q (0.86 #8370, 0.84 #9115, 0.81 #8817), 0dxtg (0.86 #2553, 0.85 #1506, 0.78 #3150), 03gjzk (0.85 #4791, 0.83 #1807, 0.83 #5388), 02jknp (0.56 #4038, 0.53 #6574, 0.53 #6723), 09jwl (0.34 #617, 0.34 #1214, 0.30 #915), 0nbcg (0.34 #630, 0.28 #1227, 0.28 #1376), 02krf9 (0.33 #2567, 0.32 #4803, 0.31 #1819), 018gz8 (0.33 #2408, 0.24 #2856, 0.23 #3452), 0cbd2 (0.28 #2695, 0.25 #2247, 0.21 #1648), 0kyk (0.25 #2121, 0.23 #2719, 0.19 #2271) >> Best rule #8370 for best value: >> intensional similarity = 2 >> extensional distance = 385 >> proper extension: 01l1b90; 01vw87c; 03ds3; 031zkw; 01nczg; 019g40; 0285c; 02_j7t; 047hpm; 015z4j; ... >> query: (?x1285, 02hrh1q) <- people(?x1050, ?x1285), participant(?x1285, ?x2444) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #4791 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 162 *> proper extension: 0f721s; *> query: (?x1285, 03gjzk) <- award_winner(?x10447, ?x1285), program(?x1285, ?x10595) *> conf = 0.85 ranks of expected_values: 3 EVAL 01t6b4 profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 144.000 144.000 0.863 http://example.org/people/person/profession #21401-049xgc PRED entity: 049xgc PRED relation: country PRED expected values: 0345h => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 112): 03rjj (0.40 #5379, 0.33 #6, 0.10 #66), 07ssc (0.40 #5379, 0.33 #136, 0.24 #196), 0d060g (0.40 #5379, 0.05 #607, 0.04 #368), 02jx1 (0.40 #5379, 0.01 #566), 0h3y (0.40 #5379), 0345h (0.22 #26, 0.11 #147, 0.11 #2719), 0f8l9c (0.13 #139, 0.11 #319, 0.10 #2711), 06c1y (0.11 #33, 0.03 #93, 0.02 #4906), 017fp (0.08 #301, 0.07 #361, 0.07 #241), 04xvlr (0.08 #301, 0.07 #361, 0.07 #241) >> Best rule #5379 for best value: >> intensional similarity = 2 >> extensional distance = 1542 >> proper extension: 0123qq; >> query: (?x5648, ?x94) <- nominated_for(?x6062, ?x5648), nationality(?x6062, ?x94) >> conf = 0.40 => this is the best rule for 5 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 05dy7p; *> query: (?x5648, 0345h) <- titles(?x53, ?x5648), nominated_for(?x3080, ?x5648), nominated_for(?x68, ?x5648), ?x3080 = 0bytkq *> conf = 0.22 ranks of expected_values: 6 EVAL 049xgc country 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 103.000 103.000 0.404 http://example.org/film/film/country #21400-04x4gj PRED entity: 04x4gj PRED relation: actor PRED expected values: 0h7pj => 97 concepts (66 used for prediction) PRED predicted values (max 10 best out of 940): 044mvs (0.50 #7302, 0.40 #5435, 0.29 #9168), 05gml8 (0.33 #987, 0.20 #3785, 0.17 #7517), 018z_c (0.33 #1289, 0.20 #4087, 0.17 #7819), 02p65p (0.33 #943, 0.20 #3741, 0.17 #7473), 035kl6 (0.33 #1736, 0.20 #4534, 0.17 #8266), 050t68 (0.33 #1250, 0.20 #4048, 0.17 #7780), 07s8r0 (0.33 #1059, 0.20 #3857, 0.17 #7589), 0f14q (0.22 #11945, 0.17 #12878, 0.11 #11013), 0d_rw (0.22 #42916, 0.17 #8397, 0.17 #6531), 02gf_l (0.20 #5234, 0.17 #8033, 0.17 #7101) >> Best rule #7302 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 05sy2k_; >> query: (?x12434, 044mvs) <- program(?x2062, ?x12434), genre(?x12434, ?x9083), genre(?x12434, ?x1510), genre(?x9082, ?x9083), ?x9082 = 06w7mlh, languages(?x12434, ?x254), program_creator(?x12434, ?x13221), category(?x2062, ?x134), ?x1510 = 01hmnh >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04x4gj actor 0h7pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 66.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21399-012z8_ PRED entity: 012z8_ PRED relation: instrumentalists! PRED expected values: 05148p4 02hnl => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 113): 05148p4 (0.80 #107, 0.79 #1064, 0.79 #2112), 05r5c (0.80 #94, 0.69 #268, 0.62 #7), 0342h (0.74 #1398, 0.68 #1660, 0.66 #1748), 03gvt (0.50 #152, 0.38 #326, 0.31 #3029), 02hnl (0.45 #208, 0.25 #2213, 0.23 #1253), 013y1f (0.40 #118, 0.14 #2995, 0.13 #2123), 018vs (0.38 #1056, 0.32 #2104, 0.31 #2976), 0mkg (0.30 #97, 0.18 #184, 0.08 #271), 03qjg (0.27 #225, 0.24 #1182, 0.24 #1532), 026t6 (0.27 #177, 0.23 #264, 0.20 #90) >> Best rule #107 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 032t2z; 01w724; 0p3sf; >> query: (?x4576, 05148p4) <- profession(?x4576, ?x6565), ?x6565 = 0fnpj, place_of_death(?x4576, ?x1523), artists(?x505, ?x4576) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 012z8_ instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 123.000 123.000 0.800 http://example.org/music/instrument/instrumentalists EVAL 012z8_ instrumentalists! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.800 http://example.org/music/instrument/instrumentalists #21398-0fjyzt PRED entity: 0fjyzt PRED relation: executive_produced_by PRED expected values: 029m83 => 81 concepts (39 used for prediction) PRED predicted values (max 10 best out of 65): 06q8hf (0.38 #167, 0.33 #1428, 0.30 #1176), 05hj_k (0.38 #98, 0.29 #1107, 0.26 #1359), 079vf (0.12 #756, 0.04 #5041, 0.04 #6046), 029m83 (0.09 #680, 0.06 #177, 0.05 #429), 0fvf9q (0.06 #6, 0.02 #1015, 0.02 #1267), 0glyyw (0.05 #5227, 0.05 #440, 0.05 #3714), 06s26c (0.05 #472), 0bs1yy (0.05 #328), 0h5f5n (0.05 #262), 06pj8 (0.05 #6099, 0.05 #5094, 0.04 #3581) >> Best rule #167 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 03m8y5; >> query: (?x5465, 06q8hf) <- genre(?x5465, ?x53), film(?x166, ?x5465), ?x166 = 0jz9f, cinematography(?x5465, ?x7740) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #680 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 072192; *> query: (?x5465, 029m83) <- genre(?x5465, ?x1509), award(?x5465, ?x289), ?x1509 = 060__y, honored_for(?x5465, ?x2852) *> conf = 0.09 ranks of expected_values: 4 EVAL 0fjyzt executive_produced_by 029m83 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 39.000 0.375 http://example.org/film/film/executive_produced_by #21397-0hzlz PRED entity: 0hzlz PRED relation: location! PRED expected values: 04xfb => 267 concepts (190 used for prediction) PRED predicted values (max 10 best out of 2022): 02_jkc (0.50 #5035, 0.47 #292025, 0.45 #407818), 01vh3r (0.33 #7374, 0.09 #115623, 0.09 #113104), 044mvs (0.33 #7099, 0.07 #27240, 0.07 #115348), 0139q5 (0.29 #4509, 0.18 #9546, 0.07 #24650), 0prfz (0.29 #2566, 0.13 #10121, 0.11 #22707), 01_f_5 (0.22 #6307, 0.14 #3789, 0.07 #23930), 01wp8w7 (0.22 #5294, 0.11 #22917, 0.10 #30470), 0dx97 (0.22 #6099, 0.11 #26240, 0.07 #111829), 01yzhn (0.22 #7166, 0.08 #77655, 0.07 #24789), 0p_pd (0.22 #5083, 0.07 #22706, 0.07 #113332) >> Best rule #5035 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02_286; >> query: (?x792, ?x5298) <- film_release_region(?x8682, ?x792), place_of_birth(?x5298, ?x792), ?x8682 = 0bmfnjs >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6736 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 01c1nm; *> query: (?x792, 04xfb) <- taxonomy(?x792, ?x939), place_of_birth(?x5298, ?x792), location_of_ceremony(?x8180, ?x792) *> conf = 0.11 ranks of expected_values: 539 EVAL 0hzlz location! 04xfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 267.000 190.000 0.496 http://example.org/people/person/places_lived./people/place_lived/location #21396-07r_dg PRED entity: 07r_dg PRED relation: film PRED expected values: 06w99h3 => 99 concepts (59 used for prediction) PRED predicted values (max 10 best out of 534): 026wlxw (0.25 #3201, 0.17 #1415, 0.04 #71451), 09qycb (0.17 #1642, 0.12 #3428, 0.04 #71451), 02qr3k8 (0.17 #1287, 0.12 #3073, 0.03 #8431), 01s3vk (0.17 #902, 0.12 #2688, 0.03 #64306), 0ndsl1x (0.17 #1512, 0.12 #3298, 0.03 #64306), 0f61tk (0.17 #1468, 0.12 #3254, 0.03 #64306), 0f4k49 (0.17 #823, 0.12 #2609, 0.03 #64306), 03rtz1 (0.17 #167, 0.12 #1953, 0.03 #64306), 09lxv9 (0.17 #1503, 0.08 #5075, 0.03 #8647), 02ctc6 (0.17 #521, 0.08 #4093, 0.03 #5879) >> Best rule #3201 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 04t2l2; 015grj; 05fnl9; 0fsm8c; 01_p6t; 02t_st; >> query: (?x10103, 026wlxw) <- award_winner(?x10103, ?x237), film(?x10103, ?x9701), ?x9701 = 0h1x5f >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07r_dg film 06w99h3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 59.000 0.250 http://example.org/film/actor/film./film/performance/film #21395-043qqt5 PRED entity: 043qqt5 PRED relation: producer_type PRED expected values: 0ckd1 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.78 #15, 0.75 #26, 0.72 #29) >> Best rule #15 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 0g60z; 080dwhx; 019nnl; 0124k9; 03ln8b; 01q_y0; 01h72l; 01b64v; 01b66d; 01j7mr; ... >> query: (?x11477, 0ckd1) <- program(?x2776, ?x11477), award_winner(?x11477, ?x11484), actor(?x11477, ?x7001), category(?x11477, ?x134), program(?x9339, ?x11477) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043qqt5 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.778 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #21394-0bsnm PRED entity: 0bsnm PRED relation: student PRED expected values: 02_t2t => 169 concepts (78 used for prediction) PRED predicted values (max 10 best out of 1691): 0ff3y (0.06 #6256, 0.04 #14633, 0.04 #16727), 0gs1_ (0.05 #3228, 0.03 #9512, 0.02 #36726), 03q45x (0.05 #31405, 0.04 #14657, 0.03 #16751), 02rrsz (0.05 #31405, 0.04 #14657, 0.03 #16751), 0157m (0.05 #14906, 0.04 #29560, 0.03 #4435), 03ft8 (0.05 #4443, 0.04 #12820, 0.04 #14914), 0203v (0.05 #4432, 0.04 #12809, 0.03 #19091), 083chw (0.05 #4212, 0.04 #14683, 0.03 #12589), 07ymr5 (0.05 #4478, 0.04 #14949, 0.03 #12855), 0306ds (0.05 #4594, 0.04 #15065, 0.03 #12971) >> Best rule #6256 for best value: >> intensional similarity = 5 >> extensional distance = 61 >> proper extension: 08815; 07tgn; 017d77; 017z88; 07tg4; 02rff2; 02bq1j; 04b_46; 01_qgp; 02ldmw; ... >> query: (?x8191, 0ff3y) <- school_type(?x8191, ?x3092), institution(?x620, ?x8191), student(?x8191, ?x6569), award_nominee(?x436, ?x6569), spouse(?x7795, ?x6569) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #1445 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 53 *> proper extension: 02jx_v; *> query: (?x8191, 02_t2t) <- school_type(?x8191, ?x3092), ?x3092 = 05jxkf, colors(?x8191, ?x663), ?x663 = 083jv, currency(?x8191, ?x2244) *> conf = 0.02 ranks of expected_values: 350 EVAL 0bsnm student 02_t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 169.000 78.000 0.063 http://example.org/education/educational_institution/students_graduates./education/education/student #21393-02ctc6 PRED entity: 02ctc6 PRED relation: language PRED expected values: 03hkp => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 29): 02bjrlw (0.18 #1, 0.10 #410, 0.08 #823), 0653m (0.18 #11, 0.04 #950, 0.04 #245), 064_8sq (0.14 #430, 0.13 #1136, 0.13 #1546), 04306rv (0.13 #413, 0.11 #767, 0.11 #826), 06nm1 (0.12 #185, 0.11 #302, 0.11 #244), 06b_j (0.09 #22, 0.07 #431, 0.07 #785), 04h9h (0.09 #42, 0.04 #451, 0.03 #392), 03k50 (0.09 #8, 0.02 #1474, 0.02 #2001), 05zjd (0.09 #25, 0.02 #1491, 0.02 #259), 0459q4 (0.09 #36, 0.01 #975, 0.01 #386) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0fq7dv_; >> query: (?x3211, 02bjrlw) <- language(?x3211, ?x254), film(?x4767, ?x3211), ?x4767 = 0205dx >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #423 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 329 *> proper extension: 05f67hw; *> query: (?x3211, 03hkp) <- language(?x3211, ?x254), film_release_region(?x3211, ?x94), films(?x14329, ?x3211) *> conf = 0.02 ranks of expected_values: 19 EVAL 02ctc6 language 03hkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 66.000 66.000 0.182 http://example.org/film/film/language #21392-026_w57 PRED entity: 026_w57 PRED relation: student! PRED expected values: 026gvfj => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 97): 02l9wl (0.17 #251, 0.08 #777, 0.07 #1303), 0bwfn (0.08 #800, 0.07 #1326, 0.07 #20800), 06182p (0.08 #823, 0.07 #1349, 0.02 #6614), 09f2j (0.08 #684, 0.05 #1736, 0.04 #4370), 017j69 (0.08 #671, 0.03 #4357, 0.02 #5410), 01ky7c (0.08 #749, 0.01 #2328), 0cwx_ (0.08 #766, 0.01 #19712, 0.01 #20239), 01bzw5 (0.08 #555), 017z88 (0.07 #2632, 0.06 #3686, 0.06 #3159), 01w5m (0.07 #1157, 0.04 #19577, 0.04 #20104) >> Best rule #251 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 030znt; 05slvm; 086sj; 01bh6y; >> query: (?x3687, 02l9wl) <- location(?x3687, ?x5093), award_nominee(?x10792, ?x3687), ?x10792 = 05ry0p >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #4323 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 334 *> proper extension: 06sn8m; *> query: (?x3687, 026gvfj) <- location(?x3687, ?x5093), actor(?x3822, ?x3687), student(?x4599, ?x3687) *> conf = 0.01 ranks of expected_values: 61 EVAL 026_w57 student! 026gvfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 99.000 99.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #21391-01p0vf PRED entity: 01p0vf PRED relation: profession PRED expected values: 09jwl 039v1 => 110 concepts (88 used for prediction) PRED predicted values (max 10 best out of 59): 02hrh1q (0.90 #8857, 0.89 #12093, 0.87 #7972), 09jwl (0.81 #2818, 0.80 #1196, 0.79 #3999), 016z4k (0.57 #151, 0.46 #1180, 0.44 #6193), 0dz3r (0.46 #1178, 0.46 #3981, 0.43 #4572), 039v1 (0.39 #1212, 0.37 #4015, 0.37 #2834), 01d_h8 (0.38 #7669, 0.36 #3690, 0.36 #8257), 01c8w0 (0.35 #891, 0.25 #303, 0.24 #1774), 0dxtg (0.33 #8265, 0.32 #455, 0.27 #9738), 0n1h (0.32 #5748, 0.21 #3105, 0.20 #600), 02jknp (0.30 #449, 0.26 #7671, 0.26 #8259) >> Best rule #8857 for best value: >> intensional similarity = 3 >> extensional distance = 1190 >> proper extension: 0d02km; >> query: (?x7053, 02hrh1q) <- film(?x7053, ?x7305), profession(?x7053, ?x1614), award_nominee(?x12422, ?x7053) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2818 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 198 *> proper extension: 01wwvd2; 05683p; 02ldv0; 04954; 01r4zfk; 03cs_xw; *> query: (?x7053, 09jwl) <- type_of_union(?x7053, ?x566), profession(?x7053, ?x1614), role(?x7053, ?x645) *> conf = 0.81 ranks of expected_values: 2, 5 EVAL 01p0vf profession 039v1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 88.000 0.900 http://example.org/people/person/profession EVAL 01p0vf profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 88.000 0.900 http://example.org/people/person/profession #21390-0g5lhl7 PRED entity: 0g5lhl7 PRED relation: program PRED expected values: 027tbrc => 156 concepts (123 used for prediction) PRED predicted values (max 10 best out of 236): 01cjhz (0.62 #2767, 0.55 #2305, 0.55 #2304), 03j63k (0.62 #2767, 0.55 #2305, 0.55 #2304), 03ctqqf (0.36 #7144, 0.35 #7605, 0.33 #431), 02rkkn1 (0.33 #209, 0.20 #2051, 0.17 #2282), 01ft14 (0.33 #173, 0.20 #2015, 0.17 #2246), 02qjv1p (0.33 #139, 0.20 #1981, 0.17 #2212), 08bytj (0.33 #121, 0.20 #1963, 0.17 #2194), 0524b41 (0.33 #109, 0.20 #1951, 0.17 #2182), 03nt59 (0.33 #91, 0.20 #1933, 0.17 #2164), 064r97z (0.33 #84, 0.20 #1926, 0.17 #2157) >> Best rule #2767 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0kctd; 0ljc_; >> query: (?x2776, ?x2777) <- program(?x2776, ?x4037), titles(?x2776, ?x2777), actor(?x4037, ?x3295) >> conf = 0.62 => this is the best rule for 2 predicted values *> Best rule #952 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 01w92; 0hm0k; *> query: (?x2776, 027tbrc) <- program(?x2776, ?x1542), award_winner(?x5007, ?x2776), ?x5007 = 05xbx *> conf = 0.25 ranks of expected_values: 25 EVAL 0g5lhl7 program 027tbrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 156.000 123.000 0.622 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #21389-06z9yh PRED entity: 06z9yh PRED relation: profession PRED expected values: 0dxtg => 73 concepts (41 used for prediction) PRED predicted values (max 10 best out of 69): 02hrh1q (0.82 #4833, 0.72 #13, 0.70 #159), 0dxtg (0.78 #3225, 0.77 #3663, 0.70 #1472), 09jwl (0.25 #4399, 0.18 #1331, 0.17 #2354), 0cbd2 (0.24 #3365, 0.19 #3657, 0.18 #3219), 018gz8 (0.21 #1767, 0.20 #1475, 0.18 #453), 0dz3r (0.20 #4384, 0.09 #4968, 0.08 #2046), 016z4k (0.19 #4386, 0.09 #1610, 0.09 #2341), 0nbcg (0.16 #4411, 0.11 #1635, 0.11 #4995), 0np9r (0.15 #457, 0.15 #165, 0.14 #1771), 0kyk (0.12 #3386, 0.11 #3678, 0.10 #3240) >> Best rule #4833 for best value: >> intensional similarity = 4 >> extensional distance = 1829 >> proper extension: 0436f4; 01rr9f; 01gvr1; 04bd8y; 066m4g; 01j5x6; 0bz5v2; 0blbxk; 02tr7d; 02jt1k; ... >> query: (?x13392, 02hrh1q) <- place_of_birth(?x13392, ?x13293), profession(?x13392, ?x524), profession(?x4685, ?x524), ?x4685 = 0b478 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #3225 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1133 *> proper extension: 05v1sb; 03mdw3c; *> query: (?x13392, 0dxtg) <- profession(?x13392, ?x524), gender(?x13392, ?x231), profession(?x1742, ?x524), ?x1742 = 0b_c7 *> conf = 0.78 ranks of expected_values: 2 EVAL 06z9yh profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 41.000 0.815 http://example.org/people/person/profession #21388-02qcr PRED entity: 02qcr PRED relation: film_crew_role PRED expected values: 09zzb8 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.70 #282, 0.66 #243, 0.65 #165), 09zzb8 (0.69 #274, 0.56 #549, 0.55 #828), 02r96rf (0.63 #277, 0.55 #160, 0.55 #238), 09vw2b7 (0.62 #281, 0.53 #556, 0.51 #242), 01vx2h (0.35 #287, 0.27 #562, 0.24 #53), 0dxtw (0.33 #286, 0.31 #561, 0.25 #840), 01pvkk (0.21 #54, 0.21 #1313, 0.21 #93), 01xy5l_ (0.20 #17, 0.11 #290, 0.10 #2088), 089g0h (0.20 #23, 0.10 #2088, 0.10 #296), 02_n3z (0.20 #2, 0.10 #2088, 0.10 #158) >> Best rule #282 for best value: >> intensional similarity = 4 >> extensional distance = 264 >> proper extension: 02d413; 02y_lrp; 047gn4y; 0g5qs2k; 09q5w2; 0bq8tmw; 0bh8yn3; 07w8fz; 0ds2n; 0c38gj; ... >> query: (?x9037, 0ch6mp2) <- nominated_for(?x2275, ?x9037), film(?x6187, ?x9037), film_release_distribution_medium(?x9037, ?x81), vacationer(?x390, ?x2275) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #274 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 264 *> proper extension: 02d413; 02y_lrp; 047gn4y; 0g5qs2k; 09q5w2; 0bq8tmw; 0bh8yn3; 07w8fz; 0ds2n; 0c38gj; ... *> query: (?x9037, 09zzb8) <- nominated_for(?x2275, ?x9037), film(?x6187, ?x9037), film_release_distribution_medium(?x9037, ?x81), vacationer(?x390, ?x2275) *> conf = 0.69 ranks of expected_values: 2 EVAL 02qcr film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.695 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21387-02hn5v PRED entity: 02hn5v PRED relation: honored_for PRED expected values: 01chpn => 35 concepts (27 used for prediction) PRED predicted values (max 10 best out of 931): 07xtqq (0.33 #20, 0.25 #2977, 0.20 #4158), 0j_t1 (0.33 #157, 0.25 #3114, 0.20 #4295), 0ptx_ (0.33 #369, 0.25 #3326, 0.20 #4507), 01c9d (0.33 #568, 0.25 #3525, 0.20 #4706), 0b1y_2 (0.33 #1946, 0.25 #3722, 0.16 #7102), 05650n (0.33 #2121, 0.25 #3897, 0.09 #2362), 04vr_f (0.33 #1833, 0.25 #3609, 0.05 #14875), 0g9lm2 (0.33 #2029, 0.25 #3805, 0.04 #15665), 07l450 (0.33 #2295, 0.25 #4071, 0.04 #15931), 0gy0l_ (0.33 #2277, 0.25 #4053, 0.03 #6421) >> Best rule #20 for best value: >> intensional similarity = 14 >> extensional distance = 1 >> proper extension: 0bzjvm; >> query: (?x2707, 07xtqq) <- award_winner(?x2707, ?x3708), award_winner(?x2707, ?x1585), ceremony(?x1313, ?x2707), honored_for(?x2707, ?x2163), ?x1313 = 0gs9p, award_winner(?x3139, ?x3708), award(?x3708, ?x704), ?x3139 = 0b_dy, award_nominee(?x286, ?x3708), award(?x2163, ?x1723), nominated_for(?x3708, ?x1820), film_release_region(?x2163, ?x87), ?x1820 = 09cr8, award_nominee(?x1585, ?x6232) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2745 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 09p3h7; *> query: (?x2707, 01chpn) <- award_winner(?x2707, ?x2275), ceremony(?x1313, ?x2707), honored_for(?x2707, ?x414), award(?x269, ?x1313), nominated_for(?x1313, ?x9100), nominated_for(?x1313, ?x6607), nominated_for(?x1313, ?x4007), nominated_for(?x1313, ?x3255), nominated_for(?x1313, ?x161), ?x161 = 0sxg4, ?x6607 = 05qm9f, ?x3255 = 0_816, ?x2275 = 05dbf, genre(?x9100, ?x53), film_release_region(?x9100, ?x94), ?x4007 = 03hmt9b *> conf = 0.25 ranks of expected_values: 34 EVAL 02hn5v honored_for 01chpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 35.000 27.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #21386-04bpm6 PRED entity: 04bpm6 PRED relation: artists! PRED expected values: 064t9 => 107 concepts (105 used for prediction) PRED predicted values (max 10 best out of 233): 064t9 (0.51 #12211, 0.49 #9084, 0.44 #9708), 03_d0 (0.38 #325, 0.30 #12, 0.23 #637), 05bt6j (0.32 #9114, 0.30 #9738, 0.27 #12241), 016clz (0.30 #1881, 0.30 #5, 0.30 #5007), 08jyyk (0.30 #68, 0.15 #693, 0.15 #1319), 0xhtw (0.28 #1894, 0.28 #8775, 0.27 #9712), 06j6l (0.27 #674, 0.26 #12246, 0.25 #11310), 0gywn (0.23 #372, 0.18 #11008, 0.18 #11320), 0ggq0m (0.23 #326, 0.17 #1577, 0.14 #4079), 025sc50 (0.21 #10688, 0.21 #11000, 0.21 #11624) >> Best rule #12211 for best value: >> intensional similarity = 3 >> extensional distance = 689 >> proper extension: 01l1b90; 0c7ct; 0152cw; 01v0sx2; 01wv9xn; 01fl3; 01czx; 0167_s; 013v5j; 0cg9y; ... >> query: (?x1715, 064t9) <- artists(?x1572, ?x1715), artists(?x1572, ?x8100), ?x8100 = 02p68d >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bpm6 artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 105.000 0.509 http://example.org/music/genre/artists #21385-09zf_q PRED entity: 09zf_q PRED relation: story_by PRED expected values: 03hnd => 143 concepts (88 used for prediction) PRED predicted values (max 10 best out of 89): 02nygk (0.17 #211, 0.04 #2591, 0.04 #3456), 056wb (0.12 #322, 0.10 #539, 0.09 #972), 046mxj (0.12 #313, 0.04 #2477, 0.02 #3558), 04511f (0.12 #287, 0.04 #2451, 0.02 #3532), 01rlxt (0.12 #312, 0.02 #2476, 0.02 #5072), 03_gd (0.12 #225, 0.02 #5853, 0.01 #3254), 09zw90 (0.12 #406, 0.01 #3435, 0.01 #3867), 01y8d4 (0.11 #1219, 0.11 #1435, 0.09 #1003), 011s9r (0.11 #1280, 0.11 #1496, 0.09 #1064), 0343h (0.11 #2398, 0.09 #884, 0.09 #668) >> Best rule #211 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 091xrc; >> query: (?x5054, 02nygk) <- genre(?x5054, ?x225), music(?x5054, ?x10295), ?x225 = 02kdv5l, film_release_distribution_medium(?x5054, ?x81), ?x10295 = 01nc3rh >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #707 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 04gcyg; 025s1wg; *> query: (?x5054, 03hnd) <- genre(?x5054, ?x1013), cinematography(?x5054, ?x7427), ?x1013 = 06n90, film(?x4564, ?x5054), organization(?x4682, ?x4564) *> conf = 0.09 ranks of expected_values: 13 EVAL 09zf_q story_by 03hnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 143.000 88.000 0.167 http://example.org/film/film/story_by #21384-01xr6x PRED entity: 01xr6x PRED relation: time_zones PRED expected values: 03bdv => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 12): 03bdv (0.62 #84, 0.50 #188, 0.50 #19), 02hcv8 (0.42 #133, 0.39 #264, 0.38 #1174), 02llzg (0.18 #239, 0.14 #265, 0.14 #525), 02fqwt (0.18 #509, 0.17 #795, 0.17 #782), 02lcqs (0.17 #669, 0.15 #682, 0.14 #513), 02hczc (0.13 #536, 0.11 #562, 0.11 #432), 052vwh (0.07 #312, 0.05 #168, 0.03 #403), 03plfd (0.04 #205, 0.03 #1077, 0.03 #231), 042g7t (0.03 #246, 0.02 #363, 0.02 #402), 02lcrv (0.02 #268, 0.01 #476, 0.01 #658) >> Best rule #84 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 05bcl; >> query: (?x11143, 03bdv) <- first_level_division_of(?x11143, ?x4221), nationality(?x5086, ?x4221), ?x5086 = 0j0pf, contains(?x4221, ?x4220) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xr6x time_zones 03bdv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.625 http://example.org/location/location/time_zones #21383-09qc1 PRED entity: 09qc1 PRED relation: award PRED expected values: 040njc => 107 concepts (85 used for prediction) PRED predicted values (max 10 best out of 336): 0789r6 (0.71 #25817, 0.71 #25413, 0.71 #25412), 03ybrwc (0.71 #25817, 0.71 #25413, 0.71 #25412), 02wwsh8 (0.71 #25817, 0.71 #25413, 0.71 #25412), 09sb52 (0.30 #5282, 0.30 #19800, 0.27 #12944), 0gqwc (0.20 #881, 0.20 #478, 0.08 #9750), 0gq9h (0.20 #5722, 0.16 #11772, 0.15 #10561), 040njc (0.19 #5652, 0.17 #8473, 0.16 #10491), 019f4v (0.19 #5711, 0.16 #8532, 0.13 #13777), 05zr6wv (0.16 #5258, 0.09 #11711, 0.09 #14130), 094qd5 (0.16 #851, 0.16 #448, 0.07 #9720) >> Best rule #25817 for best value: >> intensional similarity = 3 >> extensional distance = 1536 >> proper extension: 030_1_; 024rdh; 018p5f; 032dg7; 04qzm; 09jm8; 0c9l1; >> query: (?x4732, ?x8313) <- award_nominee(?x8572, ?x4732), award_winner(?x8313, ?x4732), award(?x4732, ?x1313) >> conf = 0.71 => this is the best rule for 3 predicted values *> Best rule #5652 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 267 *> proper extension: 04h6mm; 03v40v; 02m92h; 04rvy8; 034hck; 02tn0_; 04h68j; 042kbj; 06y0xx; *> query: (?x4732, 040njc) <- award_nominee(?x4732, ?x8572), type_of_union(?x4732, ?x566), profession(?x4732, ?x524), ?x524 = 02jknp *> conf = 0.19 ranks of expected_values: 7 EVAL 09qc1 award 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 107.000 85.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21382-03mdt PRED entity: 03mdt PRED relation: titles PRED expected values: 043mk4y => 189 concepts (163 used for prediction) PRED predicted values (max 10 best out of 1546): 0524b41 (0.60 #9257, 0.33 #1044, 0.30 #7714), 080dwhx (0.60 #9257, 0.33 #56, 0.30 #7714), 03cv_gy (0.60 #9257, 0.30 #7714, 0.25 #13885), 02qjv1p (0.60 #9257, 0.30 #7714, 0.25 #13885), 02kk_c (0.60 #9257, 0.30 #7714, 0.25 #13885), 0bx_hnp (0.60 #9257, 0.30 #7714, 0.25 #13885), 045r_9 (0.50 #2875, 0.15 #47625, 0.04 #109344), 02wyzmv (0.50 #2540, 0.15 #47290, 0.04 #109009), 043mk4y (0.50 #2687, 0.12 #47437, 0.03 #109156), 02q_x_l (0.50 #2835, 0.08 #47585, 0.02 #109304) >> Best rule #9257 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01zcrv; 0kctd; >> query: (?x3381, ?x493) <- category(?x3381, ?x134), titles(?x3381, ?x715), program(?x3381, ?x493) >> conf = 0.60 => this is the best rule for 6 predicted values *> Best rule #2687 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2 *> proper extension: 07s9rl0; 015w9s; *> query: (?x3381, 043mk4y) <- titles(?x3381, ?x6375), ?x6375 = 0b6m5fy *> conf = 0.50 ranks of expected_values: 9 EVAL 03mdt titles 043mk4y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 189.000 163.000 0.600 http://example.org/media_common/netflix_genre/titles #21381-04rwx PRED entity: 04rwx PRED relation: company! PRED expected values: 04n1q6 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 44): 0dq_5 (0.64 #543, 0.57 #411, 0.57 #631), 0krdk (0.62 #534, 0.57 #622, 0.54 #402), 0dq3c (0.43 #530, 0.38 #618, 0.34 #354), 05_wyz (0.38 #632, 0.31 #544, 0.30 #368), 01yc02 (0.31 #536, 0.28 #624, 0.28 #448), 09d6p2 (0.23 #633, 0.22 #545, 0.21 #369), 01kr6k (0.17 #553, 0.16 #641, 0.16 #465), 02211by (0.16 #443, 0.15 #399, 0.13 #619), 07t3gd (0.14 #20, 0.14 #944, 0.13 #1913), 02y6fz (0.14 #550, 0.12 #462, 0.11 #374) >> Best rule #543 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 02zs4; 087c7; 0cv9b; 0l8sx; 0hpt3; 09d5h; 01xdn1; 0gvbw; 01n073; 02r5dz; ... >> query: (?x1665, 0dq_5) <- state_province_region(?x1665, ?x2020), service_language(?x1665, ?x254), list(?x1665, ?x2197) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #54 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 04jr87; *> query: (?x1665, 04n1q6) <- school_type(?x1665, ?x4994), ?x4994 = 07tf8, company(?x4308, ?x1665) *> conf = 0.12 ranks of expected_values: 13 EVAL 04rwx company! 04n1q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 139.000 139.000 0.638 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #21380-01242_ PRED entity: 01242_ PRED relation: nominated_for! PRED expected values: 04kxsb => 71 concepts (51 used for prediction) PRED predicted values (max 10 best out of 225): 0gqy2 (0.69 #576, 0.33 #2646, 0.25 #2416), 099c8n (0.58 #512, 0.50 #282, 0.44 #52), 03hl6lc (0.56 #354, 0.20 #124, 0.20 #584), 0gr51 (0.53 #302, 0.22 #532, 0.20 #72), 099cng (0.51 #63, 0.20 #523, 0.12 #7364), 099jhq (0.43 #475, 0.10 #2545, 0.08 #6442), 027dtxw (0.42 #463, 0.22 #233, 0.21 #693), 0gq_v (0.41 #707, 0.38 #937, 0.37 #1167), 0gq9h (0.40 #2357, 0.38 #2587, 0.36 #287), 019f4v (0.39 #739, 0.36 #2349, 0.31 #2579) >> Best rule #576 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 09hy79; >> query: (?x4197, 0gqy2) <- genre(?x4197, ?x53), film_release_distribution_medium(?x4197, ?x81), nominated_for(?x4091, ?x4197), ?x4091 = 09sdmz >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2620 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 563 *> proper extension: 0fvr1; 05y0cr; *> query: (?x4197, 04kxsb) <- genre(?x4197, ?x53), nominated_for(?x4091, ?x4197), award(?x4969, ?x4091), ?x4969 = 016k6x *> conf = 0.23 ranks of expected_values: 33 EVAL 01242_ nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 71.000 51.000 0.692 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21379-04f_d PRED entity: 04f_d PRED relation: dog_breed PRED expected values: 0km3f 01k3tq => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 2): 0km3f (0.63 #15, 0.57 #13, 0.53 #25), 01k3tq (0.63 #16, 0.57 #14, 0.53 #26) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0t6hk; >> query: (?x2017, 0km3f) <- citytown(?x7177, ?x2017), source(?x2017, ?x958), teams(?x2017, ?x1160) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04f_d dog_breed 01k3tq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.630 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 04f_d dog_breed 0km3f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.630 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #21378-01n4w_ PRED entity: 01n4w_ PRED relation: category PRED expected values: 08mbj5d => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.95 #7, 0.91 #37, 0.91 #56) >> Best rule #7 for best value: >> intensional similarity = 6 >> extensional distance = 35 >> proper extension: 015zyd; >> query: (?x11185, 08mbj5d) <- student(?x11185, ?x10593), institution(?x1771, ?x11185), institution(?x1519, ?x11185), currency(?x11185, ?x170), ?x1771 = 019v9k, student(?x1519, ?x1620) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n4w_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.946 http://example.org/common/topic/webpage./common/webpage/category #21377-04qw17 PRED entity: 04qw17 PRED relation: award PRED expected values: 027b9k6 027571b => 86 concepts (81 used for prediction) PRED predicted values (max 10 best out of 249): 02qyp19 (0.27 #11652, 0.27 #10965, 0.27 #10964), 02qyntr (0.27 #11652, 0.27 #10965, 0.27 #10964), 040njc (0.27 #11652, 0.27 #10965, 0.27 #10964), 0gs9p (0.27 #11652, 0.27 #10965, 0.27 #10964), 0gr51 (0.27 #11652, 0.27 #10965, 0.27 #10964), 02ppm4q (0.27 #11652, 0.27 #10965, 0.27 #10964), 0gqwc (0.27 #11652, 0.27 #10965, 0.27 #10964), 09qwmm (0.27 #11652, 0.27 #10965, 0.27 #10964), 099cng (0.27 #11652, 0.27 #10965, 0.27 #10964), 02y_rq5 (0.27 #11652, 0.27 #10965, 0.27 #10964) >> Best rule #11652 for best value: >> intensional similarity = 3 >> extensional distance = 1000 >> proper extension: 02nf2c; 03j63k; 0m123; 097h2; 02gl58; 02_1ky; 019g8j; 0147w8; 0300ml; 02rq7nd; >> query: (?x1863, ?x1245) <- award(?x1863, ?x749), nominated_for(?x1245, ?x1863), award(?x241, ?x1245) >> conf = 0.27 => this is the best rule for 11 predicted values *> Best rule #11422 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 979 *> proper extension: 0c3xpwy; 0275kr; *> query: (?x1863, ?x704) <- nominated_for(?x2549, ?x1863), award_winner(?x1863, ?x988), award_winner(?x704, ?x988), profession(?x988, ?x1032) *> conf = 0.21 ranks of expected_values: 13, 14 EVAL 04qw17 award 027571b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 86.000 81.000 0.271 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 04qw17 award 027b9k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 86.000 81.000 0.271 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #21376-01jfrg PRED entity: 01jfrg PRED relation: film PRED expected values: 0pc62 0fb7sd 05zpghd => 107 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1144): 01rf57 (0.44 #12503, 0.41 #32152, 0.35 #71447), 048vhl (0.44 #12503, 0.41 #32152, 0.35 #71447), 043tz0c (0.18 #753), 03z20c (0.12 #2261, 0.06 #4047, 0.05 #18336), 0n6ds (0.12 #3411, 0.05 #15914, 0.03 #24845), 0277j40 (0.12 #3008, 0.04 #19083, 0.02 #31587), 0cfhfz (0.11 #5849, 0.09 #11207, 0.08 #12994), 0fphf3v (0.10 #8504, 0.09 #10290, 0.06 #17435), 02v5_g (0.09 #9720, 0.07 #7934, 0.06 #2576), 01shy7 (0.09 #422, 0.08 #21856, 0.06 #16497) >> Best rule #12503 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 01dy7j; 030b93; 027ht3n; >> query: (?x6113, ?x4084) <- award(?x6113, ?x686), ?x686 = 0bdw1g, nominated_for(?x6113, ?x4084) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #21528 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 02qjj7; 03n69x; 01xyt7; 01f492; 026_dq6; 01g0jn; *> query: (?x6113, 0pc62) <- student(?x11452, ?x6113), vacationer(?x3052, ?x6113), category(?x11452, ?x134) *> conf = 0.05 ranks of expected_values: 217, 483, 577 EVAL 01jfrg film 05zpghd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 107.000 41.000 0.437 http://example.org/film/actor/film./film/performance/film EVAL 01jfrg film 0fb7sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 107.000 41.000 0.437 http://example.org/film/actor/film./film/performance/film EVAL 01jfrg film 0pc62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 107.000 41.000 0.437 http://example.org/film/actor/film./film/performance/film #21375-047cqr PRED entity: 047cqr PRED relation: influenced_by PRED expected values: 0yxl => 112 concepts (33 used for prediction) PRED predicted values (max 10 best out of 295): 014z8v (0.18 #2738, 0.12 #4047, 0.09 #3174), 0p_47 (0.16 #980, 0.11 #2724, 0.07 #4033), 014zfs (0.16 #897, 0.10 #3077, 0.09 #2641), 02lt8 (0.16 #993, 0.07 #7104, 0.07 #11358), 01hmk9 (0.13 #2837, 0.09 #4146, 0.09 #3273), 02mpb (0.12 #11359, 0.12 #10920, 0.11 #13547), 0ky1 (0.12 #11359, 0.12 #10920, 0.11 #13547), 03hpr (0.12 #11359, 0.11 #13547, 0.11 #13546), 0jt90f5 (0.12 #11359, 0.11 #13547, 0.11 #13546), 07d3x (0.12 #11359, 0.11 #13547, 0.11 #13546) >> Best rule #2738 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 01yznp; 02xfj0; 010p3; 02_wxh; >> query: (?x10667, 014z8v) <- influenced_by(?x10667, ?x7180), profession(?x10667, ?x1041), ?x1041 = 03gjzk >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #11358 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 431 *> proper extension: 014_lq; 0716b6; *> query: (?x10667, ?x118) <- influenced_by(?x10667, ?x7180), influenced_by(?x7180, ?x2343), influenced_by(?x2343, ?x118) *> conf = 0.07 ranks of expected_values: 52 EVAL 047cqr influenced_by 0yxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 112.000 33.000 0.183 http://example.org/influence/influence_node/influenced_by #21374-02tgz4 PRED entity: 02tgz4 PRED relation: film! PRED expected values: 044lyq => 60 concepts (45 used for prediction) PRED predicted values (max 10 best out of 688): 017s11 (0.45 #27035, 0.45 #62388, 0.44 #35355), 024rgt (0.45 #27035, 0.45 #62388, 0.44 #35355), 03xb2w (0.29 #2957, 0.20 #5036, 0.11 #7115), 01nm3s (0.20 #4847, 0.17 #689, 0.16 #6926), 01pg1d (0.17 #1813, 0.11 #8050, 0.07 #3892), 03qmj9 (0.17 #250, 0.11 #6487, 0.07 #2329), 01wk51 (0.17 #1327, 0.11 #7564, 0.07 #5485), 09l3p (0.17 #748, 0.05 #6985, 0.03 #9064), 0lkr7 (0.17 #894, 0.05 #7131, 0.02 #9210), 02661h (0.14 #3475, 0.11 #7633, 0.08 #1396) >> Best rule #27035 for best value: >> intensional similarity = 3 >> extensional distance = 766 >> proper extension: 09rfpk; >> query: (?x8987, ?x541) <- film_crew_role(?x8987, ?x137), titles(?x2480, ?x8987), nominated_for(?x541, ?x8987) >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #1263 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0bz3jx; *> query: (?x8987, 044lyq) <- film(?x9204, ?x8987), film(?x368, ?x8987), ?x368 = 01wbg84, language(?x8987, ?x254), produced_by(?x821, ?x9204) *> conf = 0.08 ranks of expected_values: 52 EVAL 02tgz4 film! 044lyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 60.000 45.000 0.449 http://example.org/film/actor/film./film/performance/film #21373-03gyl PRED entity: 03gyl PRED relation: contains! PRED expected values: 04pnx => 121 concepts (118 used for prediction) PRED predicted values (max 10 best out of 183): 059g4 (0.78 #34028, 0.73 #49253, 0.72 #48356), 02j71 (0.64 #97641, 0.59 #98538, 0.57 #87771), 02qkt (0.56 #20943, 0.54 #29895, 0.54 #37955), 0dg3n1 (0.53 #18961, 0.39 #15381, 0.35 #33287), 09c7w0 (0.52 #102130, 0.46 #103027, 0.43 #103925), 07ssc (0.42 #98571, 0.41 #99469, 0.21 #76155), 06n3y (0.31 #9679, 0.18 #7889, 0.11 #41019), 02j9z (0.29 #44800, 0.29 #7192, 0.27 #37636), 04pnx (0.29 #9378, 0.20 #7588, 0.19 #1320), 04_1l0v (0.27 #50600, 0.26 #67614, 0.23 #74779) >> Best rule #34028 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 04fh3; >> query: (?x4714, ?x8483) <- jurisdiction_of_office(?x346, ?x4714), ?x346 = 060c4, countries_within(?x8483, ?x4714) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #9378 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 01dtq1; *> query: (?x4714, 04pnx) <- contains(?x7273, ?x4714), contains(?x7273, ?x142), ?x142 = 0jgd *> conf = 0.29 ranks of expected_values: 9 EVAL 03gyl contains! 04pnx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 121.000 118.000 0.780 http://example.org/location/location/contains #21372-070j61 PRED entity: 070j61 PRED relation: profession PRED expected values: 02jknp 03gjzk => 119 concepts (118 used for prediction) PRED predicted values (max 10 best out of 62): 03gjzk (0.91 #909, 0.85 #1058, 0.85 #4485), 02jknp (0.89 #1945, 0.88 #1647, 0.88 #4329), 01d_h8 (0.88 #3433, 0.85 #5221, 0.85 #5072), 02hrh1q (0.83 #5974, 0.79 #6570, 0.78 #7017), 02krf9 (0.40 #921, 0.38 #1070, 0.31 #4050), 018gz8 (0.39 #166, 0.28 #1209, 0.23 #1805), 0cbd2 (0.31 #1199, 0.30 #3285, 0.27 #156), 0d1pc (0.28 #9835, 0.09 #6011, 0.08 #5564), 0np9r (0.24 #1213, 0.18 #170, 0.18 #1809), 09jwl (0.21 #5681, 0.20 #6873, 0.18 #7916) >> Best rule #909 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 04l3_z; 0jt90f5; 06t8b; 0gd9k; >> query: (?x7611, 03gjzk) <- award(?x7611, ?x688), producer_type(?x7611, ?x632), written_by(?x2586, ?x7611) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 070j61 profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 118.000 0.906 http://example.org/people/person/profession EVAL 070j61 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 118.000 0.906 http://example.org/people/person/profession #21371-07s6fsf PRED entity: 07s6fsf PRED relation: major_field_of_study PRED expected values: 0_jm 09s1f => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 139): 02j62 (0.79 #1855, 0.78 #1514, 0.77 #1626), 05qjt (0.78 #1491, 0.69 #1718, 0.68 #793), 062z7 (0.78 #1512, 0.69 #1739, 0.68 #793), 01mkq (0.78 #1498, 0.68 #793, 0.67 #1383), 036hv (0.78 #1494, 0.68 #793, 0.66 #679), 05qfh (0.78 #1519, 0.66 #679, 0.64 #1860), 04rjg (0.78 #1503, 0.66 #679, 0.62 #1272), 06ms6 (0.78 #1500, 0.66 #679, 0.61 #452), 0_jm (0.68 #793, 0.67 #1075, 0.66 #679), 02lp1 (0.68 #793, 0.67 #1495, 0.66 #679) >> Best rule #1855 for best value: >> intensional similarity = 22 >> extensional distance = 12 >> proper extension: 03mkk4; >> query: (?x620, 02j62) <- institution(?x620, ?x11975), institution(?x620, ?x11467), institution(?x620, ?x7338), institution(?x620, ?x3779), institution(?x620, ?x2999), major_field_of_study(?x620, ?x5954), major_field_of_study(?x2999, ?x5031), colors(?x7338, ?x663), school(?x8901, ?x7338), school(?x7725, ?x7338), category(?x11467, ?x134), registering_agency(?x3779, ?x1982), currency(?x3779, ?x170), list(?x2999, ?x2197), student(?x2999, ?x164), ?x8901 = 07l4z, contains(?x3778, ?x7338), films(?x5954, ?x667), ?x5031 = 0dc_v, position(?x7725, ?x261), school_type(?x11975, ?x3092), school(?x1883, ?x7338) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #793 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 3 *> proper extension: 0bjrnt; *> query: (?x620, ?x742) <- institution(?x620, ?x12127), institution(?x620, ?x7920), institution(?x620, ?x7338), institution(?x620, ?x6973), institution(?x620, ?x6912), institution(?x620, ?x4410), institution(?x620, ?x2388), ?x7338 = 01qgr3, school(?x2820, ?x2388), major_field_of_study(?x620, ?x1527), colors(?x6912, ?x663), student(?x4410, ?x4397), student(?x4410, ?x510), film(?x510, ?x499), company(?x9738, ?x6912), ?x7920 = 01p79b, student(?x6912, ?x1564), major_field_of_study(?x6973, ?x2502), major_field_of_study(?x6973, ?x742), ?x2502 = 06nm1, celebrity(?x1126, ?x4397), participant(?x4397, ?x1896), school_type(?x12127, ?x1044) *> conf = 0.68 ranks of expected_values: 9, 32 EVAL 07s6fsf major_field_of_study 09s1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 25.000 25.000 0.786 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 07s6fsf major_field_of_study 0_jm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 25.000 25.000 0.786 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #21370-03mb9 PRED entity: 03mb9 PRED relation: parent_genre! PRED expected values: 0m0jc 01d_s5 => 60 concepts (34 used for prediction) PRED predicted values (max 10 best out of 273): 0y3_8 (0.60 #1341, 0.50 #1602, 0.50 #822), 0dn16 (0.40 #1313, 0.33 #1574, 0.33 #12), 0133k0 (0.40 #1499, 0.33 #1760, 0.33 #198), 01_sz1 (0.40 #1368, 0.33 #67, 0.25 #588), 01b4p4 (0.40 #1465, 0.33 #164, 0.25 #685), 01_qp_ (0.40 #1475, 0.33 #174, 0.25 #695), 03xnwz (0.40 #1328, 0.25 #1068, 0.25 #809), 07gxw (0.33 #1609, 0.33 #47, 0.25 #829), 03mb9 (0.33 #1645, 0.33 #83, 0.25 #865), 0163zw (0.33 #1741, 0.33 #179, 0.25 #961) >> Best rule #1341 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 059kh; >> query: (?x7267, 0y3_8) <- artists(?x7267, ?x3187), artists(?x7267, ?x2854), ?x2854 = 0dm5l, parent_genre(?x7267, ?x3916), category(?x3187, ?x134), parent_genre(?x2439, ?x7267), origin(?x3187, ?x4627), artists(?x3916, ?x1407) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #8628 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 163 *> proper extension: 02p4l6s; *> query: (?x7267, ?x1000) <- parent_genre(?x9881, ?x7267), parent_genre(?x9881, ?x1000) *> conf = 0.10 ranks of expected_values: 52, 56 EVAL 03mb9 parent_genre! 01d_s5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 60.000 34.000 0.600 http://example.org/music/genre/parent_genre EVAL 03mb9 parent_genre! 0m0jc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 60.000 34.000 0.600 http://example.org/music/genre/parent_genre #21369-0c94fn PRED entity: 0c94fn PRED relation: student! PRED expected values: 065y4w7 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 47): 015zyd (0.12 #528, 0.01 #5798, 0.01 #6325), 065y4w7 (0.06 #4757, 0.06 #1595, 0.05 #2122), 02sdwt (0.06 #929), 026vcc (0.06 #747), 03bmmc (0.06 #723), 02fgdx (0.06 #629), 0bjqh (0.06 #573), 0bwfn (0.05 #8180, 0.05 #11343, 0.05 #6599), 09kvv (0.04 #1095, 0.03 #1622, 0.03 #2149), 052nd (0.04 #1063, 0.03 #1590, 0.02 #2644) >> Best rule #528 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 074qgb; >> query: (?x1933, 015zyd) <- award_nominee(?x3782, ?x1933), award(?x1933, ?x4573), ?x4573 = 0gq_d >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #4757 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 035_2h; *> query: (?x1933, 065y4w7) <- award_winner(?x1933, ?x4393), award_winner(?x324, ?x4393), crewmember(?x1386, ?x4393) *> conf = 0.06 ranks of expected_values: 2 EVAL 0c94fn student! 065y4w7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #21368-02vxq9m PRED entity: 02vxq9m PRED relation: film_release_region PRED expected values: 0chghy 01ls2 06npd 05cgv 0h7x 06qd3 01pj7 0697s 06t8v 05l8y 07f1x => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 100): 07ssc (0.87 #10, 0.82 #478, 0.81 #595), 0chghy (0.85 #474, 0.84 #6, 0.83 #591), 01ls2 (0.62 #8, 0.58 #476, 0.56 #593), 06qd3 (0.60 #22, 0.51 #490, 0.51 #607), 0h7x (0.58 #19, 0.39 #604, 0.34 #487), 06t8v (0.56 #520, 0.53 #637, 0.51 #52), 05qx1 (0.53 #25, 0.52 #493, 0.48 #610), 07f1x (0.51 #85, 0.43 #553, 0.41 #670), 047lj (0.45 #475, 0.44 #592, 0.36 #7), 01pj7 (0.44 #499, 0.42 #31, 0.40 #616) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 0g5qs2k; 02x3lt7; 0gkz15s; 02d44q; 01c22t; 0h3xztt; 0g9wdmc; 0cc7hmk; 0gd0c7x; 01fmys; ... >> query: (?x186, 07ssc) <- nominated_for(?x112, ?x186), film_release_region(?x186, ?x1203), ?x1203 = 07ylj >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #474 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 86 *> proper extension: 0c40vxk; 087wc7n; 08hmch; 0gj8t_b; 03bx2lk; 04zyhx; 0661m4p; 05q4y12; 0gffmn8; 0gjc4d3; ... *> query: (?x186, 0chghy) <- film_release_region(?x186, ?x2513), film_release_region(?x186, ?x1536), ?x1536 = 06c1y, ?x2513 = 05b4w *> conf = 0.85 ranks of expected_values: 2, 3, 4, 5, 6, 8, 10, 12, 39, 42 EVAL 02vxq9m film_release_region 07f1x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vxq9m film_release_region 05l8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vxq9m film_release_region 06t8v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vxq9m film_release_region 0697s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vxq9m film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vxq9m film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vxq9m film_release_region 0h7x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vxq9m film_release_region 05cgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vxq9m film_release_region 06npd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vxq9m film_release_region 01ls2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vxq9m film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21367-051wwp PRED entity: 051wwp PRED relation: people! PRED expected values: 041rx => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 24): 041rx (0.14 #4, 0.13 #2394, 0.13 #1162), 0x67 (0.11 #87, 0.10 #627, 0.10 #1168), 033tf_ (0.08 #392, 0.08 #624, 0.07 #701), 02w7gg (0.07 #387, 0.07 #1776, 0.07 #542), 0xnvg (0.05 #927, 0.05 #90, 0.04 #398), 07hwkr (0.05 #927, 0.03 #1863, 0.03 #2248), 06v41q (0.05 #927, 0.02 #646, 0.02 #723), 03lmx1 (0.05 #927, 0.01 #1788, 0.01 #2250), 0d7wh (0.05 #927, 0.01 #4409, 0.01 #4563), 0g8_vp (0.05 #927, 0.01 #99) >> Best rule #4 for best value: >> intensional similarity = 2 >> extensional distance = 348 >> proper extension: 024c1b; >> query: (?x4928, 041rx) <- produced_by(?x8367, ?x4928), nominated_for(?x68, ?x8367) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051wwp people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.143 http://example.org/people/ethnicity/people #21366-027b9k6 PRED entity: 027b9k6 PRED relation: award! PRED expected values: 01vfqh 02q5g1z 04qw17 0c0zq => 43 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1195): 0g9lm2 (0.42 #1436, 0.40 #424, 0.38 #2448), 0_9l_ (0.40 #976, 0.33 #1988, 0.31 #3000), 095zlp (0.40 #33, 0.25 #1045, 0.23 #2057), 0gvvm6l (0.40 #806, 0.25 #1818, 0.23 #2830), 04qw17 (0.40 #177, 0.17 #1189, 0.15 #2201), 02wk7b (0.40 #809, 0.17 #1821, 0.15 #2833), 0fpkhkz (0.27 #15196, 0.23 #2024, 0.23 #3036), 02rqwhl (0.27 #15196, 0.23 #2024, 0.23 #3036), 0sxfd (0.25 #1138, 0.23 #2150, 0.20 #126), 0bcp9b (0.25 #1768, 0.23 #2780, 0.20 #756) >> Best rule #1436 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 02r0csl; 0gqwc; 099cng; 02y_rq5; 0gs96; 02qvyrt; 0gqxm; >> query: (?x4226, 0g9lm2) <- nominated_for(?x4226, ?x2380), ?x2380 = 02q6gfp, award_winner(?x4226, ?x988), profession(?x988, ?x1032) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #177 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 094qd5; 05zvq6g; 027571b; *> query: (?x4226, 04qw17) <- nominated_for(?x4226, ?x2380), ?x2380 = 02q6gfp, award_winner(?x4226, ?x988), ?x988 = 01tspc6 *> conf = 0.40 ranks of expected_values: 5, 75, 248, 576 EVAL 027b9k6 award! 0c0zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 43.000 20.000 0.417 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 027b9k6 award! 04qw17 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 43.000 20.000 0.417 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 027b9k6 award! 02q5g1z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 20.000 0.417 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 027b9k6 award! 01vfqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 20.000 0.417 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #21365-0r6c4 PRED entity: 0r6c4 PRED relation: place_of_birth! PRED expected values: 05233hy => 138 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1566): 0cg39k (0.23 #47045, 0.06 #4613, 0.06 #7226), 030h95 (0.06 #313, 0.06 #2926, 0.06 #5539), 01dw4q (0.06 #53, 0.06 #2666, 0.06 #5279), 05bnp0 (0.06 #11, 0.06 #2624, 0.06 #5237), 06dl_ (0.06 #344, 0.06 #2957, 0.03 #10796), 02mv9b (0.06 #2595, 0.06 #5208, 0.03 #13047), 018qql (0.06 #2584, 0.06 #5197, 0.03 #13036), 09xvf7 (0.06 #2531, 0.06 #5144, 0.03 #12983), 0p_r5 (0.06 #2526, 0.06 #5139, 0.03 #12978), 06w38l (0.06 #2513, 0.06 #5126, 0.03 #12965) >> Best rule #47045 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 01pt5w; >> query: (?x12691, ?x9586) <- contains(?x94, ?x12691), citytown(?x6016, ?x12691), place_founded(?x6016, ?x11315), place_of_birth(?x9586, ?x11315) >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r6c4 place_of_birth! 05233hy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 103.000 0.232 http://example.org/people/person/place_of_birth #21364-049rl0 PRED entity: 049rl0 PRED relation: award_winner! PRED expected values: 05xbx => 57 concepts (31 used for prediction) PRED predicted values (max 10 best out of 817): 09d5h (0.33 #316, 0.08 #9989, 0.07 #11599), 05gnf (0.10 #10780, 0.10 #12390, 0.09 #14005), 05qd_ (0.07 #24318, 0.06 #50018, 0.06 #48404), 0gsg7 (0.06 #9941, 0.06 #50018, 0.06 #48404), 0hm0k (0.06 #10702, 0.06 #12312, 0.05 #13927), 0g5lhl7 (0.06 #50018, 0.06 #48404, 0.05 #10122), 05xbx (0.06 #50018, 0.06 #48404, 0.04 #25806), 03jvmp (0.06 #50018, 0.06 #48404, 0.04 #25806), 061dn_ (0.06 #50018, 0.06 #48404, 0.04 #25806), 03mdt (0.06 #50018, 0.06 #48404, 0.04 #25806) >> Best rule #316 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 04glx0; >> query: (?x14380, 09d5h) <- genre(?x14380, ?x10961), genre(?x5698, ?x10961), award_winner(?x3486, ?x14380), program(?x236, ?x5698), languages(?x5698, ?x254) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #50018 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 633 *> proper extension: 0lbj1; 03f2_rc; 04yj5z; 03h_9lg; 021vwt; 015882; 0gz5hs; 0126rp; 086qd; 02t_zq; ... *> query: (?x14380, ?x105) <- award_winner(?x3486, ?x14380), award(?x4898, ?x3486), award(?x3180, ?x3486), award_winner(?x3486, ?x105), actor(?x4898, ?x1204), nominated_for(?x686, ?x4898), program(?x2285, ?x3180) *> conf = 0.06 ranks of expected_values: 7 EVAL 049rl0 award_winner! 05xbx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 57.000 31.000 0.333 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #21363-03q8xj PRED entity: 03q8xj PRED relation: country PRED expected values: 0f8l9c => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 60): 0f8l9c (0.23 #340, 0.21 #880, 0.16 #826), 03_3d (0.18 #330, 0.06 #2545, 0.05 #816), 03rjj (0.11 #329, 0.07 #869, 0.06 #815), 0chghy (0.11 #821, 0.10 #335, 0.09 #173), 0d060g (0.10 #817, 0.09 #169, 0.07 #115), 0d05w3 (0.10 #362, 0.05 #848, 0.04 #902), 0ctw_b (0.10 #344, 0.02 #560, 0.02 #614), 03h64 (0.09 #365, 0.06 #851, 0.06 #311), 03rk0 (0.09 #358, 0.05 #898, 0.02 #466), 06mkj (0.07 #359, 0.05 #899, 0.04 #845) >> Best rule #340 for best value: >> intensional similarity = 2 >> extensional distance = 89 >> proper extension: 0dr1c2; >> query: (?x7081, 0f8l9c) <- genre(?x7081, ?x1626), ?x1626 = 03q4nz >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q8xj country 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.231 http://example.org/film/film/country #21362-07d2d PRED entity: 07d2d PRED relation: parent_genre PRED expected values: 0m0jc => 56 concepts (41 used for prediction) PRED predicted values (max 10 best out of 210): 06by7 (0.68 #2454, 0.58 #826, 0.51 #2779), 016clz (0.62 #1301, 0.43 #166, 0.33 #4), 05r6t (0.58 #863, 0.57 #215, 0.46 #1350), 01243b (0.42 #1324, 0.29 #189, 0.20 #1978), 018ysx (0.33 #134, 0.14 #296, 0.12 #459), 03lty (0.32 #2782, 0.23 #1154, 0.20 #992), 064t9 (0.29 #173, 0.25 #821, 0.24 #1635), 016jny (0.24 #1692, 0.10 #555, 0.09 #716), 0xhtw (0.20 #986, 0.14 #175, 0.14 #1148), 08cyft (0.20 #1989, 0.09 #1825, 0.09 #1496) >> Best rule #2454 for best value: >> intensional similarity = 7 >> extensional distance = 85 >> proper extension: 018ysx; 028cl7; 017ht; >> query: (?x6714, 06by7) <- parent_genre(?x6714, ?x2491), artists(?x2491, ?x7682), artists(?x2491, ?x2005), ?x7682 = 01323p, artist(?x2931, ?x2005), group(?x227, ?x2005), ?x227 = 0342h >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1957 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 54 *> proper extension: 0133k0; 06953q; 01vw77; *> query: (?x6714, 0m0jc) <- parent_genre(?x6714, ?x2491), artists(?x2491, ?x7972), artists(?x2491, ?x2005), ?x2005 = 05k79, role(?x7972, ?x228), ?x228 = 0l14qv *> conf = 0.12 ranks of expected_values: 25 EVAL 07d2d parent_genre 0m0jc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 56.000 41.000 0.678 http://example.org/music/genre/parent_genre #21361-0cj2nl PRED entity: 0cj2nl PRED relation: gender PRED expected values: 05zppz => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #3, 0.88 #43, 0.87 #31), 02zsn (0.28 #52, 0.26 #62, 0.26 #60) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 05whq_9; >> query: (?x3896, 05zppz) <- written_by(?x3565, ?x3896), film_release_region(?x3565, ?x87), film_distribution_medium(?x3565, ?x2099) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cj2nl gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.893 http://example.org/people/person/gender #21360-07ymr5 PRED entity: 07ymr5 PRED relation: cast_members! PRED expected values: 02k21g => 99 concepts (71 used for prediction) PRED predicted values (max 10 best out of 9): 02k21g (0.69 #12, 0.62 #3, 0.56 #7), 04s430 (0.56 #8, 0.54 #13, 0.50 #4), 07ymr5 (0.50 #2, 0.46 #11, 0.44 #6), 0pz7h (0.38 #10, 0.25 #1, 0.22 #5), 09px1w (0.04 #9), 092ggq (0.04 #9), 091yn0 (0.04 #9), 01vwllw (0.04 #9), 0j1yf (0.04 #9) >> Best rule #12 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 04s430; >> query: (?x1942, 02k21g) <- profession(?x1942, ?x319), cast_members(?x1942, ?x3927) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ymr5 cast_members! 02k21g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 71.000 0.692 http://example.org/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members #21359-0cymln PRED entity: 0cymln PRED relation: gender PRED expected values: 05zppz => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.93 #5, 0.92 #15, 0.88 #7), 02zsn (0.39 #30, 0.29 #36, 0.27 #52) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 01xyt7; >> query: (?x10097, 05zppz) <- people(?x2510, ?x10097), team(?x10097, ?x4571), draft(?x4571, ?x2569) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cymln gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.929 http://example.org/people/person/gender #21358-0l3h PRED entity: 0l3h PRED relation: jurisdiction_of_office! PRED expected values: 0fj45 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 21): 0f6c3 (0.90 #367, 0.62 #703, 0.51 #808), 09n5b9 (0.88 #371, 0.59 #707, 0.46 #812), 0fkvn (0.76 #364, 0.53 #700, 0.50 #151), 060c4 (0.71 #258, 0.71 #405, 0.71 #129), 0pqc5 (0.49 #1856, 0.38 #1352, 0.36 #2193), 0fj45 (0.45 #123, 0.44 #187, 0.22 #484), 0fkzq (0.26 #376, 0.14 #817, 0.14 #712), 0789n (0.25 #156, 0.18 #242, 0.16 #2126), 0dq3c (0.23 #572, 0.22 #214, 0.21 #128), 01zq91 (0.21 #140, 0.16 #2126, 0.12 #182) >> Best rule #367 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0rh6k; 05kkh; 059rby; 03v1s; 059f4; 05fkf; 05fhy; 01n7q; 04ykg; 06mz5; ... >> query: (?x5622, 0f6c3) <- currency(?x5622, ?x170), religion(?x5622, ?x2769), ?x2769 = 019cr >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 0160w; *> query: (?x5622, 0fj45) <- currency(?x5622, ?x170), organization(?x5622, ?x127), jurisdiction_of_office(?x3444, ?x5622) *> conf = 0.45 ranks of expected_values: 6 EVAL 0l3h jurisdiction_of_office! 0fj45 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 119.000 119.000 0.905 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #21357-018009 PRED entity: 018009 PRED relation: film PRED expected values: 01jrbb 01vw8k => 76 concepts (58 used for prediction) PRED predicted values (max 10 best out of 399): 01vw8k (0.42 #66058, 0.41 #89277, 0.41 #80346), 05fcbk7 (0.10 #459), 04z4j2 (0.08 #1623), 017jd9 (0.06 #776, 0.06 #48201, 0.05 #98203), 02nx2k (0.06 #1211), 05p09dd (0.06 #764), 011yxg (0.06 #42), 0djlxb (0.06 #55342, 0.06 #48201, 0.05 #98203), 0cc7hmk (0.06 #55342, 0.06 #48201, 0.05 #98203), 048scx (0.06 #55342, 0.06 #48201, 0.05 #98203) >> Best rule #66058 for best value: >> intensional similarity = 2 >> extensional distance = 1480 >> proper extension: 049tjg; 01nzs7; 02x2097; >> query: (?x4318, ?x1685) <- nominated_for(?x4318, ?x1685), film_release_region(?x1685, ?x94) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1, 184 EVAL 018009 film 01vw8k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 58.000 0.421 http://example.org/film/actor/film./film/performance/film EVAL 018009 film 01jrbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.421 http://example.org/film/actor/film./film/performance/film #21356-044mvs PRED entity: 044mvs PRED relation: location PRED expected values: 04ly1 0947l => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 119): 02_286 (0.30 #20061, 0.18 #44094, 0.17 #55310), 030qb3t (0.22 #4086, 0.22 #20107, 0.16 #26515), 01n7q (0.13 #12077, 0.11 #8072, 0.11 #4066), 0z1vw (0.12 #2183, 0.01 #5387), 0cvw9 (0.10 #2798, 0.01 #5201), 0cr3d (0.08 #4148, 0.07 #44202, 0.07 #48208), 05tbn (0.08 #3390, 0.05 #8197, 0.04 #12202), 07b_l (0.08 #3389, 0.04 #12201, 0.04 #13803), 05r7t (0.08 #3522, 0.03 #4323), 0xn7q (0.08 #3805) >> Best rule #20061 for best value: >> intensional similarity = 3 >> extensional distance = 670 >> proper extension: 09dt7; 015npr; 049qx; 01jrvr6; 08bqy9; 0f5zj6; 02ctyy; 08d6bd; 01zh29; 02dlfh; ... >> query: (?x10188, 02_286) <- award_winner(?x1670, ?x10188), location(?x10188, ?x335), location_of_ceremony(?x1652, ?x335) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #8212 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 283 *> proper extension: 081hvm; *> query: (?x10188, 04ly1) <- award_winner(?x1670, ?x10188), location(?x10188, ?x335), religion(?x335, ?x492) *> conf = 0.03 ranks of expected_values: 35 EVAL 044mvs location 0947l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 119.000 0.301 http://example.org/people/person/places_lived./people/place_lived/location EVAL 044mvs location 04ly1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 119.000 119.000 0.301 http://example.org/people/person/places_lived./people/place_lived/location #21355-011ypx PRED entity: 011ypx PRED relation: language PRED expected values: 02h40lc => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.92 #593, 0.90 #297, 0.90 #830), 064_8sq (0.38 #1301, 0.23 #140, 0.19 #317), 04306rv (0.38 #1301, 0.13 #773, 0.13 #182), 06nm1 (0.38 #1301, 0.12 #720, 0.12 #1134), 01wgr (0.38 #1301, 0.03 #217, 0.03 #749), 02bjrlw (0.25 #1, 0.14 #60, 0.12 #592), 0349s (0.25 #45, 0.14 #104, 0.03 #340), 0653m (0.15 #130, 0.06 #248, 0.04 #2268), 032f6 (0.14 #115, 0.08 #174, 0.06 #292), 06b_j (0.08 #614, 0.06 #2636, 0.06 #1861) >> Best rule #593 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 042fgh; >> query: (?x5927, 02h40lc) <- genre(?x5927, ?x53), honored_for(?x5927, ?x2107), featured_film_locations(?x5927, ?x3052) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011ypx language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.918 http://example.org/film/film/language #21354-01g969 PRED entity: 01g969 PRED relation: film PRED expected values: 084qpk => 79 concepts (52 used for prediction) PRED predicted values (max 10 best out of 492): 03wy8t (0.25 #12285, 0.05 #3365, 0.04 #5149), 011wtv (0.20 #769, 0.04 #4337, 0.04 #12489), 013q07 (0.20 #357, 0.02 #12846, 0.02 #19982), 08052t3 (0.20 #394), 067ghz (0.11 #2790, 0.08 #8142, 0.08 #6358), 06z8s_ (0.11 #1914, 0.06 #10834, 0.06 #7266), 0pd6l (0.11 #2442, 0.06 #7794, 0.06 #6010), 017kct (0.11 #2366, 0.06 #7718, 0.06 #5934), 0830vk (0.10 #593, 0.05 #2377, 0.04 #4161), 01pgp6 (0.10 #282, 0.04 #3850, 0.04 #12489) >> Best rule #12285 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 0cf_h9; >> query: (?x9783, 03wy8t) <- film(?x9783, ?x7415), film(?x7570, ?x7415), ?x7570 = 01dw_f >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #12610 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 213 *> proper extension: 02jg92; 01rc4p; 01lz4tf; 0163t3; 01hdht; *> query: (?x9783, 084qpk) <- gender(?x9783, ?x231), place_of_birth(?x9783, ?x3764), spouse(?x9783, ?x9782) *> conf = 0.02 ranks of expected_values: 362 EVAL 01g969 film 084qpk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 79.000 52.000 0.245 http://example.org/film/actor/film./film/performance/film #21353-016bx2 PRED entity: 016bx2 PRED relation: influenced_by! PRED expected values: 04sry => 111 concepts (48 used for prediction) PRED predicted values (max 10 best out of 424): 040db (0.12 #5739, 0.07 #21698, 0.07 #22214), 0683n (0.11 #6001, 0.09 #853, 0.08 #10635), 05jm7 (0.11 #5803, 0.08 #1683, 0.08 #13528), 0343h (0.11 #22137, 0.03 #5704, 0.02 #3126), 0p8jf (0.09 #5775, 0.06 #627, 0.05 #1655), 013pp3 (0.09 #737, 0.08 #1765, 0.07 #5885), 03vrp (0.09 #711, 0.05 #5859, 0.03 #1739), 04cbtrw (0.09 #623, 0.04 #10405, 0.03 #5771), 0ph2w (0.08 #2727, 0.07 #2213, 0.05 #8909), 03_87 (0.08 #5923, 0.05 #10557, 0.04 #21368) >> Best rule #5739 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 03qcq; 0hnlx; 028p0; 04xjp; 01wj9y9; 03f70xs; 0379s; 032l1; 040_9; 02lt8; ... >> query: (?x5669, 040db) <- profession(?x5669, ?x524), influenced_by(?x3662, ?x5669), written_by(?x810, ?x3662) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #9783 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 181 *> proper extension: 06lxn; *> query: (?x5669, ?x698) <- influenced_by(?x3662, ?x5669), award_winner(?x2523, ?x5669), award(?x698, ?x2523) *> conf = 0.01 ranks of expected_values: 398 EVAL 016bx2 influenced_by! 04sry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 111.000 48.000 0.122 http://example.org/influence/influence_node/influenced_by #21352-01p726 PRED entity: 01p726 PRED relation: category PRED expected values: 08mbj5d => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.79 #3, 0.79 #82, 0.78 #7) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 01fpvz; 01jsn5; 01m1_t; 01zmqw; 09krm_; 02bq1j; 0136jw; 02j04_; 01rgn3; 03kmyy; ... >> query: (?x12912, 08mbj5d) <- contains(?x1755, ?x12912), contains(?x94, ?x12912), ?x94 = 09c7w0, ?x1755 = 01x73 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p726 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.789 http://example.org/common/topic/webpage./common/webpage/category #21351-03qcq PRED entity: 03qcq PRED relation: influenced_by PRED expected values: 041mt => 123 concepts (55 used for prediction) PRED predicted values (max 10 best out of 361): 0p_47 (0.30 #962, 0.12 #1391, 0.11 #2251), 032l1 (0.22 #87, 0.15 #4806, 0.11 #3092), 0465_ (0.22 #194, 0.07 #1052, 0.06 #1481), 0c1jh (0.22 #315, 0.07 #1173, 0.06 #1602), 081lh (0.19 #877, 0.12 #1306, 0.08 #2166), 052hl (0.19 #1061, 0.08 #1920, 0.07 #2350), 014z8v (0.18 #1404, 0.12 #2264, 0.11 #975), 01hmk9 (0.15 #1073, 0.15 #1502, 0.11 #2362), 01svq8 (0.15 #1276, 0.07 #2565, 0.06 #1705), 013tjc (0.15 #1656, 0.08 #2516, 0.08 #2945) >> Best rule #962 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 04bs3j; >> query: (?x117, 0p_47) <- participant(?x117, ?x2444), influenced_by(?x117, ?x10871), story_by(?x6030, ?x10871) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #57 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0j_c; *> query: (?x117, 041mt) <- participant(?x117, ?x2444), influenced_by(?x117, ?x118), people(?x6821, ?x117) *> conf = 0.11 ranks of expected_values: 20 EVAL 03qcq influenced_by 041mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 123.000 55.000 0.296 http://example.org/influence/influence_node/influenced_by #21350-0jqp3 PRED entity: 0jqp3 PRED relation: film! PRED expected values: 01wmcbg => 70 concepts (46 used for prediction) PRED predicted values (max 10 best out of 730): 02fn5 (0.43 #12487, 0.42 #77017, 0.34 #87426), 09ftwr (0.11 #31221), 0hvb2 (0.07 #62442, 0.05 #91589, 0.03 #95752), 0dvmd (0.07 #62442, 0.05 #91589, 0.03 #95752), 018ygt (0.07 #62442, 0.05 #91589, 0.03 #95752), 02yxwd (0.07 #62442, 0.05 #91589, 0.02 #15310), 0gy6z9 (0.07 #62442, 0.05 #91589, 0.01 #15133), 0p8r1 (0.06 #583, 0.06 #2664, 0.05 #6826), 015p3p (0.05 #62441, 0.05 #68689, 0.02 #13580), 09k0f (0.05 #62441, 0.05 #68689) >> Best rule #12487 for best value: >> intensional similarity = 3 >> extensional distance = 235 >> proper extension: 02s4l6; 02qhlwd; 0315w4; 0dpl44; 0gldyz; >> query: (?x1069, ?x406) <- featured_film_locations(?x1069, ?x3832), time_zones(?x3832, ?x2088), award_winner(?x1069, ?x406) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jqp3 film! 01wmcbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 46.000 0.426 http://example.org/film/actor/film./film/performance/film #21349-043sct5 PRED entity: 043sct5 PRED relation: film_release_region PRED expected values: 07ssc 059j2 01mjq 06mkj 06t2t 01crd5 => 107 concepts (88 used for prediction) PRED predicted values (max 10 best out of 249): 03gj2 (0.94 #5138, 0.90 #5774, 0.90 #5456), 06bnz (0.90 #5794, 0.89 #6113, 0.89 #5476), 05r4w (0.90 #6069, 0.89 #7029, 0.88 #1758), 06mkj (0.89 #6125, 0.88 #7085, 0.87 #5170), 06t2t (0.89 #5494, 0.86 #5812, 0.86 #6131), 059j2 (0.88 #7060, 0.85 #9459, 0.85 #4985), 0b90_r (0.87 #5434, 0.86 #6071, 0.85 #5752), 01znc_ (0.85 #5472, 0.85 #5790, 0.85 #6109), 0154j (0.85 #5435, 0.85 #2721, 0.85 #1281), 07ssc (0.83 #7043, 0.83 #4968, 0.82 #2732) >> Best rule #5138 for best value: >> intensional similarity = 13 >> extensional distance = 51 >> proper extension: 087wc7n; 01fmys; 0407yj_; 03mgx6z; 07s3m4g; 0fpgp26; >> query: (?x4430, 03gj2) <- film_release_region(?x4430, ?x1536), film_release_region(?x4430, ?x1264), film_release_region(?x4430, ?x583), film_release_region(?x4430, ?x456), film_release_region(?x4430, ?x279), film_release_region(?x4430, ?x142), ?x456 = 05qhw, ?x1264 = 0345h, ?x279 = 0d060g, ?x583 = 015fr, ?x1536 = 06c1y, ?x142 = 0jgd, genre(?x4430, ?x162) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #6125 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 89 *> proper extension: 03bx2lk; 0j43swk; *> query: (?x4430, 06mkj) <- film_release_region(?x4430, ?x1497), film_release_region(?x4430, ?x1264), film_release_region(?x4430, ?x583), film_release_region(?x4430, ?x456), film_release_region(?x4430, ?x279), ?x456 = 05qhw, ?x1264 = 0345h, ?x279 = 0d060g, film_release_region(?x5735, ?x583), countries_spoken_in(?x90, ?x583), combatants(?x583, ?x151), ?x5735 = 0h21v2, ?x1497 = 015qh *> conf = 0.89 ranks of expected_values: 4, 5, 6, 10, 23, 75 EVAL 043sct5 film_release_region 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 107.000 88.000 0.943 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043sct5 film_release_region 06t2t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 88.000 0.943 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043sct5 film_release_region 06mkj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 88.000 0.943 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043sct5 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 107.000 88.000 0.943 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043sct5 film_release_region 059j2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 88.000 0.943 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043sct5 film_release_region 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 107.000 88.000 0.943 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21348-0cjyzs PRED entity: 0cjyzs PRED relation: award! PRED expected values: 05zr0xl => 53 concepts (29 used for prediction) PRED predicted values (max 10 best out of 837): 03ln8b (0.43 #2216, 0.33 #1207, 0.08 #4234), 0vjr (0.39 #5047, 0.35 #2018, 0.33 #1551), 05f4vxd (0.39 #5047, 0.35 #2018, 0.33 #1513), 02czd5 (0.39 #5047, 0.35 #2018, 0.31 #3027), 0cs134 (0.39 #5047, 0.35 #2018, 0.31 #3027), 01ft14 (0.39 #5047, 0.35 #2018, 0.31 #3027), 03nt59 (0.39 #5047, 0.35 #2018, 0.31 #3027), 01b9w3 (0.39 #5047, 0.35 #2018, 0.31 #3027), 01vnbh (0.39 #5047, 0.35 #2018, 0.31 #3027), 0124k9 (0.39 #5047, 0.35 #2018, 0.31 #3027) >> Best rule #2216 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 09qj50; 03c7tr1; 05p1dby; 0cqhmg; >> query: (?x2016, 03ln8b) <- award_winner(?x2016, ?x5889), nominated_for(?x2016, ?x758), award(?x3739, ?x2016), ?x5889 = 0m66w, profession(?x3739, ?x353) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1837 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 0cqhk0; *> query: (?x2016, 05zr0xl) <- award_winner(?x2016, ?x5889), nominated_for(?x2016, ?x758), award(?x3739, ?x2016), ?x5889 = 0m66w, ?x3739 = 01y0y6 *> conf = 0.33 ranks of expected_values: 22 EVAL 0cjyzs award! 05zr0xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 53.000 29.000 0.429 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #21347-0l8z1 PRED entity: 0l8z1 PRED relation: award! PRED expected values: 01mkn_d 0163r3 => 60 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2690): 02fgpf (0.80 #63162, 0.79 #9969, 0.79 #43215), 01cbt3 (0.80 #63162, 0.79 #9969, 0.79 #43215), 012201 (0.80 #63162, 0.79 #9969, 0.79 #43215), 0l12d (0.80 #63162, 0.79 #9969, 0.79 #43215), 02g40r (0.80 #63162, 0.79 #9969, 0.79 #43215), 095p3z (0.80 #63162, 0.79 #9969, 0.79 #43215), 012ljv (0.80 #63162, 0.79 #9969, 0.79 #43215), 03n0pv (0.80 #63162, 0.79 #9969, 0.79 #43215), 03n0q5 (0.80 #63162, 0.79 #9969, 0.79 #43215), 07zft (0.80 #63162, 0.79 #9969, 0.79 #43215) >> Best rule #63162 for best value: >> intensional similarity = 6 >> extensional distance = 150 >> proper extension: 02f72n; 02f705; 02f5qb; 02f716; 02f71y; 02f73p; 02f6xy; 02f764; 02f72_; 02f77l; ... >> query: (?x1079, ?x84) <- award(?x6519, ?x1079), award(?x5772, ?x1079), gender(?x6519, ?x231), award_winner(?x1079, ?x84), award_winner(?x6519, ?x6518), music(?x5873, ?x5772) >> conf = 0.80 => this is the best rule for 12 predicted values *> Best rule #31834 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 04njml; 04mqgr; 01l29r; 08_vwq; 024dzn; 0bm70b; 031b91; *> query: (?x1079, 0163r3) <- ceremony(?x1079, ?x78), award(?x5206, ?x1079), music(?x3755, ?x5206), people(?x10900, ?x5206) *> conf = 0.18 ranks of expected_values: 460, 796 EVAL 0l8z1 award! 0163r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 60.000 24.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0l8z1 award! 01mkn_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 24.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21346-0257w4 PRED entity: 0257w4 PRED relation: ceremony PRED expected values: 05pd94v => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 121): 05pd94v (0.83 #502, 0.82 #627, 0.82 #252), 0gx1673 (0.49 #730, 0.48 #605, 0.47 #230), 05c1t6z (0.19 #1637, 0.18 #1387, 0.12 #2012), 02q690_ (0.18 #1680, 0.17 #1430, 0.12 #2055), 0gvstc3 (0.17 #1652, 0.16 #1402, 0.10 #2027), 03nnm4t (0.16 #1689, 0.15 #1439, 0.11 #814), 0bzm81 (0.16 #1142, 0.15 #1642, 0.14 #1392), 0n8_m93 (0.16 #1228, 0.15 #1728, 0.14 #1478), 0gx_st (0.15 #1655, 0.15 #1405, 0.11 #780), 02yxh9 (0.15 #1211, 0.14 #1711, 0.13 #1461) >> Best rule #502 for best value: >> intensional similarity = 7 >> extensional distance = 73 >> proper extension: 0257yf; >> query: (?x2703, 05pd94v) <- ceremony(?x2703, ?x6869), ceremony(?x2703, ?x5766), ?x5766 = 013b2h, ceremony(?x10102, ?x6869), ceremony(?x724, ?x6869), ?x724 = 01bgqh, ?x10102 = 031b91 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0257w4 ceremony 05pd94v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.827 http://example.org/award/award_category/winners./award/award_honor/ceremony #21345-07v4dm PRED entity: 07v4dm PRED relation: music! PRED expected values: 06__m6 => 135 concepts (77 used for prediction) PRED predicted values (max 10 best out of 783): 0fhzwl (0.14 #29207, 0.14 #29208, 0.12 #33237), 0n1s0 (0.12 #601, 0.02 #2616, 0.01 #4631), 07bzz7 (0.07 #2540, 0.04 #6569, 0.02 #22682), 01s7w3 (0.06 #10937, 0.05 #9930, 0.05 #16979), 03h3x5 (0.05 #2274, 0.03 #4289, 0.03 #6303), 08rr3p (0.05 #2286, 0.02 #4301, 0.02 #6315), 078mm1 (0.05 #2835, 0.02 #4850, 0.02 #6864), 05qm9f (0.05 #2689, 0.02 #4704, 0.02 #6718), 03_gz8 (0.05 #2665, 0.02 #6694, 0.02 #8708), 09cxm4 (0.05 #2822, 0.02 #6851, 0.02 #9872) >> Best rule #29207 for best value: >> intensional similarity = 5 >> extensional distance = 197 >> proper extension: 0gsg7; 0cjdk; 0kk9v; 05xbx; 05gnf; 05d6q1; >> query: (?x11105, ?x7858) <- nominated_for(?x11105, ?x8870), nominated_for(?x11105, ?x7858), award_winner(?x8964, ?x11105), category(?x11105, ?x134), nominated_for(?x435, ?x8870) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07v4dm music! 06__m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 77.000 0.140 http://example.org/film/film/music #21344-0581vn8 PRED entity: 0581vn8 PRED relation: films! PRED expected values: 0hzlz => 64 concepts (18 used for prediction) PRED predicted values (max 10 best out of 50): 081pw (0.08 #1566, 0.04 #316, 0.04 #2037), 0fx2s (0.06 #228, 0.05 #1635, 0.05 #541), 0bq3x (0.06 #186, 0.04 #1593, 0.04 #1277), 018h2 (0.06 #178, 0.04 #1585, 0.02 #647), 05qtj (0.06 #193, 0.02 #506), 0fzyg (0.05 #989, 0.05 #1616, 0.02 #522), 05489 (0.05 #1143, 0.04 #1614, 0.03 #1298), 0chghy (0.05 #473, 0.03 #629), 0g284 (0.05 #313), 07s2s (0.04 #1661, 0.01 #1818, 0.01 #2289) >> Best rule #1566 for best value: >> intensional similarity = 4 >> extensional distance = 500 >> proper extension: 01cgz; >> query: (?x9250, 081pw) <- films(?x5069, ?x9250), films(?x5069, ?x54), genre(?x54, ?x53), award_winner(?x54, ?x6157) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0581vn8 films! 0hzlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 18.000 0.078 http://example.org/film/film_subject/films #21343-05c5z8j PRED entity: 05c5z8j PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.85 #6, 0.82 #44, 0.82 #79), 07c52 (0.21 #299, 0.06 #30, 0.05 #3), 02nxhr (0.21 #299, 0.05 #12, 0.04 #55), 07z4p (0.21 #299, 0.05 #32, 0.04 #43), 0735l (0.20 #16, 0.20 #22, 0.02 #4) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 0gxfz; 02gd6x; >> query: (?x4329, 029j_) <- titles(?x512, ?x4329), award_winner(?x4329, ?x4328), film_festivals(?x4329, ?x13076) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05c5z8j film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #21342-09wnnb PRED entity: 09wnnb PRED relation: nominated_for! PRED expected values: 07cbcy => 80 concepts (75 used for prediction) PRED predicted values (max 10 best out of 193): 0f4x7 (0.50 #24, 0.22 #11959, 0.20 #3109), 019f4v (0.33 #53, 0.24 #1009, 0.22 #7706), 0gqy2 (0.33 #123, 0.22 #1079, 0.22 #11959), 0gs9p (0.33 #64, 0.21 #7717, 0.20 #7239), 0gqyl (0.33 #80, 0.20 #319, 0.19 #558), 0k611 (0.33 #73, 0.20 #7726, 0.18 #2703), 04dn09n (0.33 #34, 0.19 #7687, 0.17 #2664), 0gr42 (0.27 #3349, 0.24 #1763, 0.22 #1674), 07cbcy (0.27 #3349, 0.23 #2932, 0.22 #1674), 063y_ky (0.27 #3349, 0.22 #1674, 0.20 #339) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01719t; >> query: (?x10130, 0f4x7) <- film(?x3056, ?x10130), nominated_for(?x4277, ?x10130), ?x4277 = 046qq, award_winner(?x4360, ?x3056) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3349 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 216 *> proper extension: 01p9hgt; 0pmw9; *> query: (?x10130, ?x1312) <- nominated_for(?x102, ?x10130), nominated_for(?x10130, ?x7975), nominated_for(?x1312, ?x7975) *> conf = 0.27 ranks of expected_values: 9 EVAL 09wnnb nominated_for! 07cbcy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 80.000 75.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21341-01t94_1 PRED entity: 01t94_1 PRED relation: religion PRED expected values: 03_gx => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 31): 0c8wxp (0.33 #96, 0.27 #142, 0.23 #952), 0kpl (0.29 #55, 0.27 #146, 0.27 #236), 0kq2 (0.27 #154, 0.27 #244, 0.16 #559), 03_gx (0.25 #420, 0.22 #1140, 0.21 #510), 01lp8 (0.17 #1, 0.07 #677, 0.06 #407), 0g5llry (0.14 #73, 0.06 #389), 0n2g (0.12 #554, 0.11 #103, 0.09 #149), 0631_ (0.08 #594, 0.07 #189, 0.06 #414), 04pk9 (0.07 #336, 0.02 #1146, 0.02 #651), 01spm (0.07 #308, 0.01 #1299) >> Best rule #96 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0cl_m; >> query: (?x8830, 0c8wxp) <- place_of_burial(?x8830, ?x1227), state_province_region(?x99, ?x1227), location(?x397, ?x1227), contains(?x1227, ?x191) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #420 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 02whj; 081nh; 0bmh4; 053yx; 044qx; 0k8y7; 03n6r; 0l786; 03bw6; 0gyy0; ... *> query: (?x8830, 03_gx) <- place_of_burial(?x8830, ?x1227), award(?x8830, ?x537), award_winner(?x7940, ?x8830), people(?x1050, ?x8830) *> conf = 0.25 ranks of expected_values: 4 EVAL 01t94_1 religion 03_gx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 131.000 131.000 0.333 http://example.org/people/person/religion #21340-027c95y PRED entity: 027c95y PRED relation: award_winner PRED expected values: 06dv3 0h0jz 046qq 015np0 01385g => 37 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1254): 0170pk (0.57 #5196, 0.22 #7624, 0.07 #10051), 015vq_ (0.57 #5734, 0.11 #8162, 0.09 #58284), 06dv3 (0.57 #4890, 0.11 #33999, 0.04 #9745), 016yvw (0.57 #6042, 0.06 #10897, 0.05 #13326), 0zcbl (0.50 #3939, 0.44 #8794, 0.33 #1512), 01nr36 (0.50 #4217, 0.33 #9072, 0.14 #6644), 01ycbq (0.50 #2829, 0.33 #7684, 0.11 #33999), 014v6f (0.50 #3633, 0.22 #8488, 0.11 #33999), 0f7hc (0.50 #3456, 0.22 #8311, 0.09 #58284), 01wy5m (0.50 #3498, 0.22 #8353, 0.02 #10780) >> Best rule #5196 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0f4x7; 027986c; 04kxsb; 027b9j5; >> query: (?x2915, 0170pk) <- award_winner(?x2915, ?x1582), award_winner(?x2915, ?x851), ?x851 = 016khd, film(?x1582, ?x186), participant(?x1582, ?x4295) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4890 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0f4x7; 027986c; 04kxsb; 027b9j5; *> query: (?x2915, 06dv3) <- award_winner(?x2915, ?x1582), award_winner(?x2915, ?x851), ?x851 = 016khd, film(?x1582, ?x186), participant(?x1582, ?x4295) *> conf = 0.57 ranks of expected_values: 3, 12, 56, 170, 1090 EVAL 027c95y award_winner 01385g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 37.000 24.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027c95y award_winner 015np0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 37.000 24.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027c95y award_winner 046qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 37.000 24.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027c95y award_winner 0h0jz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 24.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027c95y award_winner 06dv3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 37.000 24.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner #21339-06w87 PRED entity: 06w87 PRED relation: role! PRED expected values: 03qjg => 71 concepts (40 used for prediction) PRED predicted values (max 10 best out of 121): 05148p4 (0.89 #3519, 0.83 #4489, 0.82 #1323), 05r5c (0.86 #3995, 0.79 #3746, 0.75 #3504), 03bx0bm (0.83 #2816, 0.82 #1720, 0.80 #1601), 0gghm (0.82 #1323, 0.82 #722, 0.82 #2412), 0j862 (0.82 #722, 0.82 #2412, 0.82 #3134), 0g2dz (0.82 #1722, 0.80 #1603, 0.80 #1480), 0l14md (0.80 #3869, 0.80 #3142, 0.79 #3024), 018j2 (0.80 #1492, 0.75 #1253, 0.73 #1734), 0g33q (0.80 #1533, 0.70 #1656, 0.67 #577), 0342h (0.79 #3744, 0.79 #3502, 0.78 #2789) >> Best rule #3519 for best value: >> intensional similarity = 18 >> extensional distance = 26 >> proper extension: 0j871; >> query: (?x736, 05148p4) <- role(?x2923, ?x736), role(?x645, ?x736), role(?x314, ?x736), role(?x75, ?x2923), role(?x4769, ?x2923), role(?x1472, ?x2923), role(?x1432, ?x2923), role(?x1212, ?x2923), ?x1432 = 0395lw, instrumentalists(?x2923, ?x6225), ?x1212 = 07xzm, role(?x2964, ?x2923), ?x314 = 02sgy, ?x4769 = 0dwt5, ?x1472 = 0319l, ?x645 = 028tv0, profession(?x6225, ?x131), role(?x2923, ?x316) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1803 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 9 *> proper extension: 07gql; *> query: (?x736, ?x316) <- role(?x736, ?x212), role(?x6049, ?x736), role(?x2784, ?x736), nationality(?x6049, ?x94), role(?x736, ?x7772), artists(?x302, ?x2784), award(?x2784, ?x3926), ?x7772 = 0j862, gender(?x2784, ?x231), profession(?x6049, ?x1614), award(?x1338, ?x3926), ?x1338 = 09qr6, role(?x2784, ?x316), profession(?x2784, ?x131) *> conf = 0.62 ranks of expected_values: 48 EVAL 06w87 role! 03qjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 71.000 40.000 0.893 http://example.org/music/performance_role/regular_performances./music/group_membership/role #21338-0b44shh PRED entity: 0b44shh PRED relation: film_release_region PRED expected values: 09c7w0 0b90_r => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 131): 09c7w0 (0.93 #3575, 0.92 #7458, 0.92 #9321), 0chghy (0.86 #1722, 0.83 #2342, 0.83 #2032), 03h64 (0.81 #2088, 0.76 #1778, 0.76 #2398), 07ssc (0.80 #1727, 0.79 #2037, 0.79 #2347), 02vzc (0.79 #2073, 0.77 #1763, 0.76 #2383), 01znc_ (0.73 #2063, 0.72 #1753, 0.71 #2373), 0b90_r (0.73 #2026, 0.70 #1716, 0.69 #2336), 03rj0 (0.63 #1772, 0.62 #2392, 0.62 #2082), 05v8c (0.58 #2038, 0.57 #1728, 0.56 #2348), 0ctw_b (0.58 #2047, 0.56 #1737, 0.53 #2357) >> Best rule #3575 for best value: >> intensional similarity = 4 >> extensional distance = 324 >> proper extension: 0j43swk; 0pd57; 0gmgwnv; 0h3k3f; >> query: (?x5109, 09c7w0) <- nominated_for(?x112, ?x5109), film(?x2033, ?x5109), honored_for(?x873, ?x5109), film_release_region(?x5109, ?x87) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 0b44shh film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 90.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b44shh film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21337-0300cp PRED entity: 0300cp PRED relation: company! PRED expected values: 028fjr => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 33): 01kr6k (0.33 #566, 0.33 #527, 0.31 #683), 014l7h (0.25 #21, 0.12 #3046, 0.11 #3671), 02211by (0.24 #470, 0.23 #626, 0.23 #587), 02y6fz (0.24 #485, 0.20 #407, 0.19 #641), 021q0l (0.16 #2269, 0.11 #3671, 0.10 #3946), 0142rn (0.14 #838, 0.13 #877, 0.13 #253), 09lq2c (0.13 #258, 0.12 #492, 0.11 #3671), 04192r (0.12 #502, 0.11 #541, 0.11 #3671), 01rk91 (0.12 #3046, 0.11 #3671, 0.10 #3946), 028fjr (0.12 #3046, 0.11 #3671, 0.10 #3946) >> Best rule #566 for best value: >> intensional similarity = 6 >> extensional distance = 28 >> proper extension: 0l8sx; 03sc8; 0py9b; 07xyn1; 01pf21; 060ppp; 0lwkh; 0vlf; 01npw8; >> query: (?x2270, 01kr6k) <- company(?x4682, ?x2270), company(?x346, ?x2270), ?x346 = 060c4, list(?x2270, ?x7472), ?x4682 = 0dq_5, industry(?x2270, ?x2271) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3046 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 206 *> proper extension: 049mr; 0ky6d; *> query: (?x2270, ?x346) <- industry(?x2270, ?x12816), industry(?x9077, ?x12816), industry(?x4585, ?x12816), company(?x7851, ?x4585), company(?x346, ?x9077) *> conf = 0.12 ranks of expected_values: 10 EVAL 0300cp company! 028fjr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 131.000 131.000 0.333 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #21336-0kft PRED entity: 0kft PRED relation: profession PRED expected values: 02jknp => 119 concepts (48 used for prediction) PRED predicted values (max 10 best out of 76): 02jknp (0.92 #303, 0.91 #155, 0.88 #1635), 02hrh1q (0.77 #6528, 0.73 #2677, 0.69 #3863), 03gjzk (0.49 #5344, 0.47 #2678, 0.46 #3568), 0cbd2 (0.38 #746, 0.36 #1042, 0.32 #1486), 018gz8 (0.27 #2680, 0.26 #3866, 0.19 #5346), 0kyk (0.26 #769, 0.19 #1065, 0.17 #3879), 02krf9 (0.23 #2098, 0.21 #322, 0.21 #1654), 02hv44_ (0.20 #1537, 0.20 #1389, 0.19 #797), 0dgd_ (0.17 #178, 0.17 #326, 0.15 #474), 09jwl (0.15 #4460, 0.15 #6533, 0.13 #2386) >> Best rule #303 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0kr5_; 0jf1b; 05kfs; 02kxbwx; 081lh; 0151w_; 0prjs; 0h1p; 06pj8; 0hskw; ... >> query: (?x9149, 02jknp) <- award_winner(?x1198, ?x9149), nationality(?x9149, ?x252), ?x1198 = 02pqp12, award(?x9149, ?x198) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kft profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 48.000 0.917 http://example.org/people/person/profession #21335-0svqs PRED entity: 0svqs PRED relation: category PRED expected values: 08mbj5d => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.48 #3, 0.45 #2, 0.45 #5) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 03f7jfh; >> query: (?x4923, 08mbj5d) <- award_nominee(?x230, ?x4923), diet(?x4923, ?x3130), award_winner(?x4923, ?x1846) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0svqs category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.480 http://example.org/common/topic/webpage./common/webpage/category #21334-033wx9 PRED entity: 033wx9 PRED relation: award PRED expected values: 02f77y => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 302): 01by1l (0.53 #15464, 0.41 #2940, 0.36 #3344), 01bgqh (0.46 #3274, 0.36 #4890, 0.33 #2870), 02f716 (0.39 #3409, 0.24 #5025, 0.23 #2601), 05p09zm (0.37 #8608, 0.33 #5780, 0.32 #10628), 05pcn59 (0.37 #8565, 0.31 #16241, 0.30 #10585), 09sb52 (0.36 #23876, 0.35 #8524, 0.35 #26300), 0c4z8 (0.36 #3303, 0.27 #4919, 0.24 #18251), 02f73b (0.36 #3519, 0.23 #4731, 0.22 #7559), 02f77y (0.33 #261, 0.23 #2685, 0.21 #4301), 054ks3 (0.33 #950, 0.21 #3374, 0.20 #4990) >> Best rule #15464 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 01h5f8; >> query: (?x2697, 01by1l) <- award_nominee(?x4819, ?x2697), company(?x4819, ?x3265), origin(?x4819, ?x5658) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #261 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 04smkr; *> query: (?x2697, 02f77y) <- participant(?x2697, ?x5881), award_nominee(?x3547, ?x2697), ?x5881 = 02hhtj *> conf = 0.33 ranks of expected_values: 9 EVAL 033wx9 award 02f77y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 173.000 173.000 0.532 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21333-030p35 PRED entity: 030p35 PRED relation: genre PRED expected values: 01t_vv => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 78): 01z4y (0.64 #518, 0.63 #601, 0.39 #768), 0c4xc (0.45 #626, 0.42 #543, 0.27 #1879), 01t_vv (0.31 #534, 0.28 #617, 0.24 #367), 0hcr (0.24 #519, 0.23 #3022, 0.23 #2938), 06n90 (0.23 #1765, 0.21 #1014, 0.20 #2932), 01htzx (0.23 #182, 0.20 #433, 0.20 #1018), 06nbt (0.19 #521, 0.17 #604, 0.14 #437), 03k9fj (0.19 #427, 0.17 #2930, 0.17 #1763), 06q7n (0.18 #44, 0.17 #127, 0.16 #1046), 0lsxr (0.18 #174, 0.17 #91, 0.15 #342) >> Best rule #518 for best value: >> intensional similarity = 3 >> extensional distance = 86 >> proper extension: 017dtf; 04svwx; 095sx6; >> query: (?x4639, 01z4y) <- genre(?x4639, ?x258), actor(?x4639, ?x192), ?x258 = 05p553 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #534 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 86 *> proper extension: 017dtf; 04svwx; 095sx6; *> query: (?x4639, 01t_vv) <- genre(?x4639, ?x258), actor(?x4639, ?x192), ?x258 = 05p553 *> conf = 0.31 ranks of expected_values: 3 EVAL 030p35 genre 01t_vv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 95.000 0.636 http://example.org/tv/tv_program/genre #21332-051vz PRED entity: 051vz PRED relation: school PRED expected values: 0lyjf => 94 concepts (65 used for prediction) PRED predicted values (max 10 best out of 660): 022lly (0.60 #1274, 0.50 #2347, 0.50 #1096), 01pl14 (0.60 #1612, 0.40 #1968, 0.38 #3932), 07w0v (0.55 #7894, 0.50 #2863, 0.50 #2685), 01dzg0 (0.50 #2474, 0.40 #2117, 0.40 #1401), 01pq4w (0.50 #1116, 0.40 #1832, 0.40 #1294), 01vs5c (0.45 #3472, 0.40 #1689, 0.38 #3114), 06fq2 (0.40 #2088, 0.40 #1732, 0.33 #2266), 01fsv9 (0.40 #2120, 0.38 #2833, 0.33 #3367), 03tw2s (0.40 #2066, 0.33 #2423, 0.33 #282), 09f2j (0.40 #2032, 0.33 #2389, 0.33 #248) >> Best rule #1274 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 01d6g; >> query: (?x2174, 022lly) <- school(?x2174, ?x6333), school(?x2174, ?x3777), school(?x2174, ?x2175), ?x3777 = 012vwb, position(?x2174, ?x2010), colors(?x2174, ?x332), contact_category(?x6333, ?x897), sport(?x2174, ?x5063), organization(?x346, ?x2175), season(?x2174, ?x2406), student(?x2175, ?x6221), service_location(?x6333, ?x94) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2565 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 0bjkk9; *> query: (?x2174, 0lyjf) <- team(?x2010, ?x2174), category(?x2174, ?x134), teams(?x6088, ?x2174), ?x134 = 08mbj5d, contains(?x94, ?x6088), locations(?x9908, ?x6088), locations(?x8527, ?x6088), ?x9908 = 0b_6lb, location(?x105, ?x6088), locations(?x8527, ?x13387), place_of_birth(?x2794, ?x6088), ?x13387 = 0kcw2 *> conf = 0.33 ranks of expected_values: 27 EVAL 051vz school 0lyjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 94.000 65.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #21331-06jrhz PRED entity: 06jrhz PRED relation: award_winner! PRED expected values: 0bq_mx => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 104): 0bxs_d (0.25 #115, 0.11 #397, 0.11 #256), 0bx6zs (0.25 #127, 0.06 #409, 0.06 #268), 07y_p6 (0.25 #98, 0.06 #380, 0.06 #239), 02q690_ (0.23 #488, 0.19 #911, 0.18 #1052), 0hn821n (0.17 #413, 0.17 #272, 0.06 #836), 03nnm4t (0.16 #638, 0.14 #497, 0.11 #1202), 05c1t6z (0.14 #2271, 0.14 #438, 0.13 #579), 03gyp30 (0.14 #2373, 0.04 #2937, 0.03 #3219), 0gx_st (0.14 #460, 0.11 #883, 0.11 #1024), 027n06w (0.14 #496, 0.11 #919, 0.11 #1060) >> Best rule #115 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 09b0xs; >> query: (?x5832, 0bxs_d) <- award_nominee(?x2135, ?x5832), ?x2135 = 06pj8, tv_program(?x5832, ?x3144) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2107 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 04bs3j; 02lk1s; 02jm0n; 0721cy; 0277990; 03xpf_7; 0d9_96; 04snp2; 04fcx7; 05y5fw; ... *> query: (?x5832, 0bq_mx) <- student(?x3439, ?x5832), nominated_for(?x5832, ?x3144), tv_program(?x5832, ?x7657) *> conf = 0.08 ranks of expected_values: 19 EVAL 06jrhz award_winner! 0bq_mx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 110.000 110.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21330-01swck PRED entity: 01swck PRED relation: award_winner! PRED expected values: 030hcs => 105 concepts (28 used for prediction) PRED predicted values (max 10 best out of 730): 0h10vt (0.82 #33656, 0.82 #36865, 0.82 #38470), 01wz01 (0.82 #33656, 0.82 #36865, 0.82 #38470), 030hcs (0.82 #33656, 0.82 #36865, 0.82 #38470), 01swck (0.35 #28846, 0.07 #35261, 0.05 #2372), 0gy6z9 (0.16 #36866, 0.16 #38471, 0.10 #2144), 042xrr (0.16 #36866, 0.16 #38471, 0.08 #2394), 02yxwd (0.16 #36866, 0.16 #38471, 0.08 #2324), 09fb5 (0.16 #36866, 0.16 #38471, 0.07 #35261), 01p7yb (0.16 #36866, 0.16 #38471, 0.07 #35261), 0c6qh (0.16 #36866, 0.16 #38471, 0.05 #1994) >> Best rule #33656 for best value: >> intensional similarity = 3 >> extensional distance = 738 >> proper extension: 01y8d4; 011s9r; 08f3yq; >> query: (?x4520, ?x4173) <- award_winner(?x4520, ?x4173), place_of_birth(?x4520, ?x2850), location(?x4173, ?x1131) >> conf = 0.82 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 01swck award_winner! 030hcs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 28.000 0.823 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #21329-02rx2m5 PRED entity: 02rx2m5 PRED relation: nominated_for! PRED expected values: 03hkv_r 099jhq 09sb52 => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 169): 0gqyl (0.57 #311, 0.19 #9052, 0.19 #10447), 0gq9h (0.43 #295, 0.24 #1919, 0.24 #1455), 04dn09n (0.43 #268, 0.16 #1892, 0.16 #2124), 040njc (0.43 #239, 0.15 #3256, 0.15 #1863), 0gr4k (0.43 #259, 0.15 #4669, 0.15 #1651), 0l8z1 (0.43 #285, 0.13 #4695, 0.12 #3302), 019f4v (0.29 #287, 0.21 #1911, 0.21 #1447), 0gs9p (0.29 #297, 0.21 #1457, 0.21 #2153), 04kxsb (0.29 #326, 0.19 #9052, 0.19 #10447), 05pcn59 (0.29 #67, 0.19 #9052, 0.19 #10447) >> Best rule #311 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 016017; >> query: (?x1866, 0gqyl) <- film(?x1865, ?x1866), ?x1865 = 03k7bd, language(?x1866, ?x254) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #10447 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1588 *> proper extension: 06g60w; *> query: (?x1866, ?x247) <- nominated_for(?x8556, ?x1866), award(?x8556, ?x247) *> conf = 0.19 ranks of expected_values: 29, 33, 54 EVAL 02rx2m5 nominated_for! 09sb52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 54.000 54.000 0.571 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02rx2m5 nominated_for! 099jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 54.000 54.000 0.571 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02rx2m5 nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 54.000 54.000 0.571 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21328-01vrz41 PRED entity: 01vrz41 PRED relation: currency PRED expected values: 09nqf => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.44 #22, 0.40 #1, 0.40 #19), 01nv4h (0.05 #14, 0.05 #32, 0.03 #23) >> Best rule #22 for best value: >> intensional similarity = 2 >> extensional distance = 151 >> proper extension: 03xnq9_; >> query: (?x1231, 09nqf) <- artists(?x671, ?x1231), participant(?x2647, ?x1231) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrz41 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.438 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #21327-05pd94v PRED entity: 05pd94v PRED relation: award_winner PRED expected values: 028q6 01xdf5 03g5jw 0412f5y 09889g 02rxbmt 01vvyc_ 05q9g1 => 36 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1459): 032nwy (0.62 #17960, 0.60 #13478, 0.57 #14970), 02fn5r (0.62 #18270, 0.58 #22748, 0.57 #15280), 01htxr (0.60 #12845, 0.50 #11354, 0.40 #14336), 09hnb (0.60 #13800, 0.50 #10818, 0.40 #12309), 01vvyvk (0.57 #15584, 0.50 #18574, 0.50 #6634), 01lmj3q (0.57 #14950, 0.50 #17940, 0.50 #7492), 05pdbs (0.50 #22545, 0.50 #18067, 0.50 #10603), 02r3zy (0.50 #6103, 0.50 #4609, 0.43 #15053), 01w60_p (0.50 #22678, 0.50 #10736, 0.40 #13718), 0x3b7 (0.50 #17027, 0.44 #20017, 0.40 #21508) >> Best rule #17960 for best value: >> intensional similarity = 20 >> extensional distance = 6 >> proper extension: 013b2h; >> query: (?x139, 032nwy) <- ceremony(?x11010, ?x139), ceremony(?x10556, ?x139), ceremony(?x9034, ?x139), ceremony(?x8076, ?x139), ceremony(?x6090, ?x139), ceremony(?x3835, ?x139), ?x3835 = 01cky2, award_winner(?x139, ?x1556), award_winner(?x139, ?x828), ?x11010 = 02w7fs, ?x9034 = 03nc9d, film(?x1556, ?x365), artist(?x2190, ?x1556), award(?x828, ?x880), ?x6090 = 03q_g6, ?x10556 = 02flq1, category_of(?x8076, ?x2421), award_nominee(?x193, ?x828), artists(?x1555, ?x1556), ceremony(?x880, ?x873) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #7981 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 2 *> proper extension: 0466p0j; *> query: (?x139, 0412f5y) <- ceremony(?x11010, ?x139), ceremony(?x10881, ?x139), ceremony(?x9034, ?x139), ceremony(?x8409, ?x139), ceremony(?x8076, ?x139), ceremony(?x3835, ?x139), ceremony(?x3467, ?x139), ceremony(?x2962, ?x139), ceremony(?x1584, ?x139), ?x3835 = 01cky2, award_winner(?x139, ?x3890), award_winner(?x139, ?x1556), ?x11010 = 02w7fs, ?x9034 = 03nc9d, ?x8076 = 026mml, ?x3467 = 02h3d1, ?x2962 = 02ddqh, ?x8409 = 03ncb2, ?x10881 = 026mmy, award_winner(?x1079, ?x3890), category(?x1556, ?x134), award(?x1322, ?x1584), profession(?x3890, ?x131) *> conf = 0.25 ranks of expected_values: 194, 199, 256, 288, 289, 290, 292, 311 EVAL 05pd94v award_winner 05q9g1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 36.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05pd94v award_winner 01vvyc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 36.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05pd94v award_winner 02rxbmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 36.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05pd94v award_winner 09889g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 36.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05pd94v award_winner 0412f5y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 36.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05pd94v award_winner 03g5jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 36.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05pd94v award_winner 01xdf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 36.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05pd94v award_winner 028q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 36.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21326-019f9z PRED entity: 019f9z PRED relation: nationality PRED expected values: 09c7w0 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 47): 09c7w0 (0.89 #7418, 0.83 #5811, 0.74 #5710), 0d060g (0.40 #10127, 0.06 #11128, 0.06 #507), 02jx1 (0.16 #1633, 0.14 #633, 0.12 #3936), 07ssc (0.09 #1615, 0.09 #4622, 0.08 #5222), 03rk0 (0.07 #1546, 0.07 #6860, 0.06 #9771), 01ls2 (0.06 #11128, 0.05 #111, 0.02 #611), 0chghy (0.06 #11128, 0.03 #310, 0.03 #410), 0162v (0.06 #11128, 0.03 #345, 0.02 #545), 0345h (0.06 #11128, 0.03 #1531, 0.02 #2833), 03rt9 (0.06 #11128, 0.01 #1913, 0.01 #2414) >> Best rule #7418 for best value: >> intensional similarity = 2 >> extensional distance = 1420 >> proper extension: 07m69t; >> query: (?x6651, 09c7w0) <- place_of_birth(?x6651, ?x2254), source(?x2254, ?x958) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019f9z nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.891 http://example.org/people/person/nationality #21325-0df92l PRED entity: 0df92l PRED relation: nominated_for! PRED expected values: 09v8db5 => 84 concepts (71 used for prediction) PRED predicted values (max 10 best out of 195): 02g3v6 (0.68 #9324, 0.66 #9090, 0.66 #10490), 0gq_v (0.49 #2816, 0.33 #1884, 0.24 #2117), 0gq9h (0.43 #2860, 0.35 #3326, 0.31 #1928), 0gr0m (0.39 #2857, 0.25 #1925, 0.24 #2158), 0p9sw (0.38 #1885, 0.25 #2817, 0.24 #2118), 0gs9p (0.35 #2862, 0.32 #3328, 0.26 #6590), 019f4v (0.34 #2851, 0.31 #3317, 0.27 #3550), 0k611 (0.33 #2871, 0.27 #1939, 0.27 #3337), 099c8n (0.32 #2854, 0.20 #1689, 0.19 #2155), 02qvyrt (0.28 #1729, 0.22 #2195, 0.17 #2894) >> Best rule #9324 for best value: >> intensional similarity = 4 >> extensional distance = 950 >> proper extension: 06w7mlh; >> query: (?x5782, ?x507) <- nominated_for(?x3574, ?x5782), award(?x5782, ?x507), nominated_for(?x507, ?x1734), nominated_for(?x2549, ?x1734) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #12823 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1226 *> proper extension: 06dfz1; 03_b1g; *> query: (?x5782, ?x500) <- nominated_for(?x3574, ?x5782), titles(?x2645, ?x5782), award(?x3574, ?x500) *> conf = 0.19 ranks of expected_values: 33 EVAL 0df92l nominated_for! 09v8db5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 84.000 71.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21324-0ds35l9 PRED entity: 0ds35l9 PRED relation: film_release_region PRED expected values: 0d060g 06c1y 06t2t 03spz => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 109): 0d060g (0.88 #288, 0.77 #1282, 0.76 #1424), 06t2t (0.77 #334, 0.74 #1470, 0.71 #1328), 03spz (0.76 #367, 0.75 #1361, 0.72 #1503), 047yc (0.65 #305, 0.53 #1441, 0.51 #447), 0ctw_b (0.64 #303, 0.62 #1439, 0.56 #1297), 03rk0 (0.64 #329, 0.59 #471, 0.48 #1465), 016wzw (0.61 #481, 0.61 #339, 0.48 #1333), 015qh (0.61 #315, 0.55 #1451, 0.50 #1025), 01mjq (0.60 #1454, 0.57 #460, 0.57 #1028), 06mzp (0.56 #1293, 0.49 #441, 0.48 #1435) >> Best rule #288 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 0g56t9t; 0ds3t5x; 0g5qs2k; 0bq8tmw; 0cmc26r; 01mgw; 02825nf; >> query: (?x86, 0d060g) <- film_release_region(?x86, ?x344), produced_by(?x86, ?x364), film(?x3927, ?x86), ?x344 = 04gzd >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 14 EVAL 0ds35l9 film_release_region 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds35l9 film_release_region 06t2t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds35l9 film_release_region 06c1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 82.000 82.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds35l9 film_release_region 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21323-01gp_x PRED entity: 01gp_x PRED relation: award PRED expected values: 0fbtbt => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 264): 0fbtbt (0.38 #640, 0.31 #1046, 0.27 #3888), 0gq9h (0.33 #4544, 0.32 #2920, 0.32 #5762), 0cjyzs (0.32 #3761, 0.27 #513, 0.25 #919), 040njc (0.26 #2850, 0.24 #5692, 0.24 #5286), 09sb52 (0.23 #8161, 0.23 #8567, 0.21 #7349), 02q1tc5 (0.21 #1774, 0.18 #2180, 0.16 #2586), 07bdd_ (0.19 #472, 0.18 #4532, 0.15 #5750), 019f4v (0.19 #2909, 0.17 #5345, 0.17 #5751), 05zvj3m (0.17 #1312, 0.08 #94, 0.06 #4154), 05b1610 (0.17 #445, 0.11 #851, 0.09 #2881) >> Best rule #640 for best value: >> intensional similarity = 2 >> extensional distance = 61 >> proper extension: 07nznf; 0grwj; 01t6b4; 0162c8; 0hskw; 03xpf_7; 0d9_96; 04fcx7; 01rlxt; 0dbc1s; ... >> query: (?x2643, 0fbtbt) <- produced_by(?x11615, ?x2643), producer_type(?x2643, ?x632) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gp_x award 0fbtbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.381 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21322-07ccs PRED entity: 07ccs PRED relation: institution! PRED expected values: 02_xgp2 => 165 concepts (140 used for prediction) PRED predicted values (max 10 best out of 18): 02_xgp2 (0.79 #240, 0.70 #530, 0.66 #125), 0bkj86 (0.66 #236, 0.57 #889, 0.55 #179), 07s6fsf (0.55 #116, 0.54 #307, 0.53 #423), 04zx3q1 (0.55 #232, 0.45 #1639, 0.43 #1446), 027f2w (0.47 #237, 0.44 #7, 0.43 #46), 02m4yg (0.45 #1639, 0.43 #1446, 0.28 #883), 01ysy9 (0.45 #1639, 0.43 #1446, 0.28 #883), 01gkg3 (0.45 #1639, 0.43 #1446, 0.01 #1516), 013zdg (0.38 #235, 0.34 #120, 0.32 #178), 03mkk4 (0.33 #9, 0.29 #48, 0.28 #883) >> Best rule #240 for best value: >> intensional similarity = 2 >> extensional distance = 51 >> proper extension: 01prf3; >> query: (?x6333, 02_xgp2) <- citytown(?x6333, ?x1569), organization(?x6333, ?x5487) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ccs institution! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 140.000 0.792 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21321-0blq0z PRED entity: 0blq0z PRED relation: film PRED expected values: 062zjtt 05n6sq => 112 concepts (60 used for prediction) PRED predicted values (max 10 best out of 797): 0h03fhx (0.29 #778, 0.04 #6139, 0.03 #44678), 016fyc (0.14 #56, 0.04 #3630, 0.03 #44678), 0pc62 (0.14 #94, 0.04 #3668, 0.03 #44678), 0m313 (0.14 #13, 0.03 #5374, 0.03 #3587), 02vqsll (0.14 #493, 0.03 #78637, 0.03 #2280), 0b76t12 (0.14 #290, 0.03 #78637), 0260bz (0.14 #335, 0.03 #3909, 0.03 #2122), 027m5wv (0.14 #1055, 0.03 #4629, 0.03 #44678), 05szq8z (0.14 #942, 0.03 #4516, 0.03 #44678), 0prhz (0.14 #795, 0.03 #4369, 0.03 #44678) >> Best rule #778 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0151w_; 018swb; 0f6_dy; 09r9dp; 0ksrf8; >> query: (?x2670, 0h03fhx) <- award_winner(?x7046, ?x2670), award(?x2670, ?x451), ?x7046 = 06_bq1 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2905 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 05bnp0; 0flw6; 06lht1; *> query: (?x2670, 05n6sq) <- award_nominee(?x72, ?x2670), award(?x2670, ?x6729), ?x6729 = 099ck7 *> conf = 0.03 ranks of expected_values: 276, 687 EVAL 0blq0z film 05n6sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 112.000 60.000 0.286 http://example.org/film/actor/film./film/performance/film EVAL 0blq0z film 062zjtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 112.000 60.000 0.286 http://example.org/film/actor/film./film/performance/film #21320-02fttd PRED entity: 02fttd PRED relation: film_release_region PRED expected values: 082fr => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 151): 07ssc (0.82 #1496, 0.76 #3300, 0.76 #1988), 03h64 (0.79 #2207, 0.73 #3355, 0.72 #1879), 06qd3 (0.78 #42, 0.59 #1520, 0.56 #1848), 0jgd (0.77 #2137, 0.77 #2793, 0.75 #3285), 0345h (0.76 #3319, 0.73 #2171, 0.71 #1515), 03gj2 (0.74 #2162, 0.73 #3310, 0.71 #1506), 035qy (0.72 #1517, 0.69 #2829, 0.69 #2009), 015fr (0.70 #1498, 0.68 #2154, 0.67 #3302), 05qhw (0.70 #1494, 0.67 #3298, 0.65 #2150), 0154j (0.69 #1975, 0.69 #1483, 0.69 #3287) >> Best rule #1496 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 0m2kd; 05p1tzf; 02x3lt7; 01vksx; 053rxgm; 0gmcwlb; 0dtfn; 0gtvrv3; 0168ls; 0fq7dv_; ... >> query: (?x4828, 07ssc) <- film_release_region(?x4828, ?x1355), nominated_for(?x1554, ?x4828), currency(?x4828, ?x170), ?x1355 = 0h7x >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1883 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 86 *> proper extension: 0401sg; 053tj7; 03twd6; 0j_tw; 0crh5_f; 0crc2cp; 080nwsb; 024mpp; 0gbfn9; 03mgx6z; ... *> query: (?x4828, 082fr) <- production_companies(?x4828, ?x574), film_release_region(?x4828, ?x1355), ?x1355 = 0h7x *> conf = 0.26 ranks of expected_values: 43 EVAL 02fttd film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 98.000 98.000 0.825 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21319-0pmhf PRED entity: 0pmhf PRED relation: profession PRED expected values: 02hrh1q => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 84): 02hrh1q (0.90 #4935, 0.89 #7469, 0.89 #4786), 0dxtg (0.70 #1653, 0.67 #1951, 0.62 #610), 02jknp (0.53 #3289, 0.47 #1051, 0.45 #9251), 03gjzk (0.52 #910, 0.49 #1059, 0.47 #761), 0cbd2 (0.44 #6418, 0.43 #7014, 0.43 #3139), 018gz8 (0.41 #1210, 0.36 #614, 0.34 #1955), 02krf9 (0.40 #176, 0.33 #5220, 0.27 #2535), 09jwl (0.36 #1808, 0.33 #5220, 0.30 #2835), 0dz3r (0.33 #5220, 0.30 #2835, 0.27 #2535), 016z4k (0.33 #5220, 0.30 #2835, 0.27 #2535) >> Best rule #4935 for best value: >> intensional similarity = 3 >> extensional distance = 356 >> proper extension: 04bdxl; 0m2wm; 032xhg; 01rr9f; 03m8lq; 01j5x6; 0lk90; 04cf09; 01k8rb; 01yhvv; ... >> query: (?x2596, 02hrh1q) <- participant(?x2596, ?x2908), award_nominee(?x2596, ?x100), film(?x2596, ?x675) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pmhf profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.899 http://example.org/people/person/profession #21318-01wwvt2 PRED entity: 01wwvt2 PRED relation: artist! PRED expected values: 03rhqg => 135 concepts (119 used for prediction) PRED predicted values (max 10 best out of 110): 015_1q (0.23 #3544, 0.22 #5100, 0.21 #4393), 0181dw (0.18 #465, 0.14 #324, 0.12 #1170), 03rhqg (0.15 #3540, 0.15 #1425, 0.15 #1566), 0g768 (0.15 #742, 0.12 #4411, 0.12 #7946), 033hn8 (0.15 #436, 0.13 #2974, 0.12 #718), 0181hw (0.14 #51, 0.02 #1320, 0.01 #756), 01w56k (0.14 #115, 0.01 #2089, 0.01 #5055), 013x0b (0.14 #3), 01clyr (0.13 #456, 0.13 #1443, 0.12 #315), 017l96 (0.13 #441, 0.12 #3543, 0.10 #1428) >> Best rule #3544 for best value: >> intensional similarity = 3 >> extensional distance = 249 >> proper extension: 0m0hw; >> query: (?x2392, 015_1q) <- type_of_union(?x2392, ?x566), award_winner(?x247, ?x2392), artist(?x2149, ?x2392) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #3540 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 249 *> proper extension: 0m0hw; *> query: (?x2392, 03rhqg) <- type_of_union(?x2392, ?x566), award_winner(?x247, ?x2392), artist(?x2149, ?x2392) *> conf = 0.15 ranks of expected_values: 3 EVAL 01wwvt2 artist! 03rhqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 135.000 119.000 0.227 http://example.org/music/record_label/artist #21317-0gvx_ PRED entity: 0gvx_ PRED relation: award! PRED expected values: 081l_ => 64 concepts (36 used for prediction) PRED predicted values (max 10 best out of 3033): 04v048 (0.81 #13489, 0.79 #104545, 0.79 #91052), 03s2y9 (0.81 #13489, 0.79 #104545, 0.79 #91052), 014l4w (0.81 #13489, 0.79 #104545, 0.79 #91052), 05qd_ (0.67 #10320, 0.57 #13692, 0.33 #27178), 0g1rw (0.67 #10282, 0.57 #13654, 0.33 #27140), 086k8 (0.67 #10185, 0.57 #13557, 0.33 #27043), 0k9ctht (0.50 #11748, 0.43 #15120, 0.33 #1631), 0gyx4 (0.36 #21483, 0.33 #18112, 0.33 #1251), 052gzr (0.33 #10589, 0.33 #472, 0.29 #13961), 03_80b (0.33 #11826, 0.33 #1709, 0.29 #15198) >> Best rule #13489 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x3617, ?x6091) <- award(?x2426, ?x3617), ceremony(?x3617, ?x6594), ?x2426 = 081nh, ?x6594 = 02pgky2, award_winner(?x3617, ?x6091) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #84305 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 141 *> proper extension: 04ljl_l; 05b4l5x; 0f_nbyh; 05f4m9q; 02wkmx; 05zr6wv; 02g3v6; 05b1610; 027986c; 02z13jg; ... *> query: (?x3617, ?x3186) <- award(?x574, ?x3617), nominated_for(?x3617, ?x11839), category(?x11839, ?x134), film_release_region(?x11839, ?x87), award_winner(?x11839, ?x3186) *> conf = 0.14 ranks of expected_values: 354 EVAL 0gvx_ award! 081l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 36.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21316-07bxqz PRED entity: 07bxqz PRED relation: language PRED expected values: 02h40lc => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 24): 02h40lc (0.91 #297, 0.90 #534, 0.90 #595), 012w70 (0.25 #72, 0.09 #131, 0.08 #190), 05zjd (0.25 #85, 0.09 #144, 0.08 #203), 064_8sq (0.16 #258, 0.13 #615, 0.12 #554), 06nm1 (0.13 #247, 0.09 #306, 0.09 #604), 04306rv (0.13 #241, 0.09 #300, 0.08 #182), 02bjrlw (0.11 #237, 0.09 #296, 0.07 #594), 0t_2 (0.09 #132, 0.08 #191), 06mp7 (0.08 #193, 0.03 #252, 0.01 #370), 06b_j (0.08 #259, 0.06 #318, 0.05 #555) >> Best rule #297 for best value: >> intensional similarity = 3 >> extensional distance = 136 >> proper extension: 05dy7p; 027ct7c; 0hv81; 0gy0l_; >> query: (?x11417, 02h40lc) <- film_production_design_by(?x11417, ?x12092), nominated_for(?x68, ?x11417), nominated_for(?x986, ?x11417) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07bxqz language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.906 http://example.org/film/film/language #21315-029h45 PRED entity: 029h45 PRED relation: people! PRED expected values: 0gk4g => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 37): 0gk4g (0.74 #1049, 0.49 #1180, 0.39 #1377), 04p3w (0.22 #75, 0.09 #1639, 0.08 #140), 0dq9p (0.19 #1384, 0.14 #796, 0.13 #1645), 0qcr0 (0.14 #781, 0.13 #716, 0.13 #911), 02k6hp (0.13 #1207, 0.10 #1404, 0.09 #751), 01psyx (0.11 #109, 0.04 #174, 0.04 #824), 01mtqf (0.11 #68, 0.04 #133, 0.04 #198), 0d19y2 (0.11 #119, 0.04 #704, 0.04 #249), 01l2m3 (0.10 #1186, 0.06 #535, 0.05 #1775), 02knxx (0.08 #681, 0.07 #616, 0.06 #551) >> Best rule #1049 for best value: >> intensional similarity = 4 >> extensional distance = 189 >> proper extension: 017r2; 02h48; >> query: (?x5650, 0gk4g) <- people(?x1158, ?x5650), risk_factors(?x1158, ?x231), people(?x1158, ?x9407), ?x9407 = 024qwq >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 029h45 people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.743 http://example.org/people/cause_of_death/people #21314-0jgd PRED entity: 0jgd PRED relation: medal PRED expected values: 02lq67 => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.92 #12, 0.87 #9, 0.86 #15) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0b90_r; 047lj; 09pmkv; 047yc; 06t8v; 03ryn; >> query: (?x142, 02lq67) <- film_release_region(?x8193, ?x142), film_release_region(?x6536, ?x142), ?x8193 = 03z9585, ?x6536 = 09gmmt6 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgd medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.923 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #21313-01p45_v PRED entity: 01p45_v PRED relation: location PRED expected values: 0f2rq => 159 concepts (150 used for prediction) PRED predicted values (max 10 best out of 251): 030qb3t (0.48 #69174, 0.29 #79618, 0.28 #80422), 02_286 (0.38 #79572, 0.36 #80376, 0.30 #83589), 0mrhq (0.27 #73911, 0.01 #33744), 04lh6 (0.20 #2844, 0.17 #7664, 0.12 #5253), 0fpzwf (0.20 #2690, 0.12 #5099, 0.04 #10722), 01_d4 (0.20 #905, 0.12 #6526, 0.06 #21787), 04f_d (0.20 #911, 0.06 #6532, 0.03 #11352), 06_kh (0.20 #814, 0.05 #8043, 0.04 #10452), 0cc56 (0.17 #3269, 0.14 #4072, 0.11 #73164), 0vbk (0.17 #3457, 0.14 #4260, 0.06 #6669) >> Best rule #69174 for best value: >> intensional similarity = 4 >> extensional distance = 563 >> proper extension: 06jzh; 0785v8; 02wrhj; 022769; 02j9lm; 04n_g; 05lb30; 03xyp_; 05mlqj; 03k545; ... >> query: (?x1534, 030qb3t) <- location(?x1534, ?x4733), place_of_birth(?x1093, ?x4733), teams(?x4733, ?x2398), administrative_division(?x4733, ?x10820) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #16342 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 54 *> proper extension: 01pny5; *> query: (?x1534, 0f2rq) <- profession(?x1534, ?x2659), place_of_birth(?x1534, ?x4733), artists(?x671, ?x1534), contains(?x4733, ?x8202), ?x2659 = 039v1 *> conf = 0.02 ranks of expected_values: 169 EVAL 01p45_v location 0f2rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 159.000 150.000 0.481 http://example.org/people/person/places_lived./people/place_lived/location #21312-01518s PRED entity: 01518s PRED relation: artists! PRED expected values: 02yv6b 0172rj => 63 concepts (41 used for prediction) PRED predicted values (max 10 best out of 279): 016clz (0.71 #1235, 0.71 #10829, 0.67 #927), 0dl5d (0.54 #8055, 0.33 #942, 0.32 #4030), 0155w (0.51 #9071, 0.37 #10619, 0.33 #10309), 05r6t (0.47 #7186, 0.43 #8737, 0.43 #1927), 05w3f (0.46 #4046, 0.23 #9310, 0.23 #7451), 0xv2x (0.43 #1380, 0.41 #3543, 0.40 #766), 064t9 (0.41 #6503, 0.40 #12077, 0.36 #10528), 05jt_ (0.40 #433, 0.36 #2585, 0.36 #2277), 09jw2 (0.40 #470, 0.33 #1082, 0.29 #2314), 05bt6j (0.34 #9626, 0.31 #10246, 0.30 #5600) >> Best rule #1235 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 048tgl; >> query: (?x12506, 016clz) <- artists(?x9248, ?x12506), artists(?x2407, ?x12506), artists(?x2249, ?x12506), artists(?x1572, ?x12506), artists(?x1000, ?x12506), ?x2249 = 03lty, ?x9248 = 02t8gf, ?x1000 = 0xhtw, ?x1572 = 06by7, category(?x12506, ?x134), ?x134 = 08mbj5d, parent_genre(?x2407, ?x5934), parent_genre(?x12785, ?x2407) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #7412 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 172 *> proper extension: 0lbj1; 01vv7sc; 02r3zy; 01vrt_c; 09mq4m; 012zng; 0gt_k; 05qw5; 01wwvt2; 0dvqq; ... *> query: (?x12506, ?x7083) <- artists(?x2249, ?x12506), artists(?x2249, ?x3740), artists(?x2249, ?x2250), parent_genre(?x13553, ?x2249), parent_genre(?x7200, ?x2249), ?x7200 = 02srgf, parent_genre(?x2249, ?x7083), ?x3740 = 0fpj4lx, artists(?x13553, ?x1412), origin(?x2250, ?x1310) *> conf = 0.32 ranks of expected_values: 14, 32 EVAL 01518s artists! 0172rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 63.000 41.000 0.714 http://example.org/music/genre/artists EVAL 01518s artists! 02yv6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 63.000 41.000 0.714 http://example.org/music/genre/artists #21311-04p5cr PRED entity: 04p5cr PRED relation: genre PRED expected values: 07s9rl0 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 86): 07s9rl0 (0.92 #547, 0.82 #469, 0.60 #1), 06n90 (0.34 #3138, 0.23 #2044, 0.19 #5172), 0hcr (0.32 #328, 0.29 #3143, 0.24 #94), 0c4xc (0.31 #3165, 0.29 #116, 0.27 #1209), 06nbt (0.29 #96, 0.24 #330, 0.19 #1189), 0lsxr (0.24 #86, 0.21 #476, 0.19 #554), 01jfsb (0.24 #88, 0.21 #478, 0.16 #1415), 01hmnh (0.22 #3141, 0.17 #1498, 0.17 #2047), 01htzx (0.20 #327, 0.19 #2048, 0.19 #3378), 06q7n (0.20 #40, 0.19 #1211, 0.17 #664) >> Best rule #547 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 047m_w; >> query: (?x6439, 07s9rl0) <- category(?x6439, ?x134), genre(?x6439, ?x9446), genre(?x3306, ?x9446), ?x3306 = 03f7xg >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04p5cr genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.919 http://example.org/tv/tv_program/genre #21310-047qxs PRED entity: 047qxs PRED relation: film! PRED expected values: 03w4sh 01kgxf => 75 concepts (25 used for prediction) PRED predicted values (max 10 best out of 807): 056wb (0.24 #10408, 0.21 #27062), 01tsbmv (0.22 #3981, 0.20 #6062, 0.15 #8143), 01q_ph (0.22 #2139, 0.20 #4220, 0.15 #6301), 012d40 (0.22 #2098, 0.20 #4179, 0.15 #6260), 023mdt (0.22 #1579, 0.05 #39051, 0.02 #43217), 021lby (0.21 #43720, 0.14 #31225), 0bq2g (0.20 #4769, 0.15 #6850, 0.12 #11014), 015wnl (0.20 #8975, 0.10 #25629, 0.03 #48535), 079vf (0.20 #8333, 0.10 #24987, 0.02 #41646), 01vh3r (0.19 #12341, 0.15 #8177, 0.12 #18585) >> Best rule #10408 for best value: >> intensional similarity = 13 >> extensional distance = 13 >> proper extension: 0dcz8_; >> query: (?x2036, ?x6045) <- genre(?x2036, ?x4088), genre(?x2036, ?x1013), genre(?x2036, ?x812), genre(?x6184, ?x4088), genre(?x1763, ?x4088), genre(?x144, ?x4088), ?x144 = 0m313, story_by(?x2036, ?x6045), film(?x1561, ?x6184), music(?x6184, ?x2214), ?x1013 = 06n90, ?x812 = 01jfsb, titles(?x53, ?x1763) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #9472 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 13 *> proper extension: 0dcz8_; *> query: (?x2036, 03w4sh) <- genre(?x2036, ?x4088), genre(?x2036, ?x1013), genre(?x2036, ?x812), genre(?x6184, ?x4088), genre(?x1763, ?x4088), genre(?x144, ?x4088), ?x144 = 0m313, story_by(?x2036, ?x6045), film(?x1561, ?x6184), music(?x6184, ?x2214), ?x1013 = 06n90, ?x812 = 01jfsb, titles(?x53, ?x1763) *> conf = 0.07 ranks of expected_values: 195 EVAL 047qxs film! 01kgxf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 25.000 0.238 http://example.org/film/actor/film./film/performance/film EVAL 047qxs film! 03w4sh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 75.000 25.000 0.238 http://example.org/film/actor/film./film/performance/film #21309-025rxjq PRED entity: 025rxjq PRED relation: film! PRED expected values: 01fwpt => 113 concepts (46 used for prediction) PRED predicted values (max 10 best out of 975): 01vsn38 (0.55 #20796, 0.54 #70687, 0.45 #60292), 0gcs9 (0.48 #14556, 0.45 #60292, 0.45 #20797), 01g257 (0.20 #254, 0.05 #4412, 0.04 #2334), 01q_ph (0.12 #2137, 0.10 #57, 0.06 #16692), 01wbg84 (0.12 #2127, 0.03 #6285, 0.02 #22926), 0b13g7 (0.11 #93558, 0.10 #74849, 0.10 #76928), 01r93l (0.10 #749, 0.07 #4907, 0.03 #9067), 019vgs (0.10 #661, 0.05 #6899, 0.01 #19377), 0863x_ (0.10 #842, 0.05 #5000, 0.03 #7080), 0bxtg (0.10 #77, 0.04 #2157, 0.03 #31268) >> Best rule #20796 for best value: >> intensional similarity = 5 >> extensional distance = 305 >> proper extension: 01j7mr; 0304nh; 01fx1l; 05_z42; 0gxsh4; >> query: (?x7819, ?x11233) <- nominated_for(?x11233, ?x7819), nominated_for(?x2963, ?x7819), influenced_by(?x2963, ?x1089), award_winner(?x247, ?x2963), film(?x11233, ?x1692) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #10991 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 246 *> proper extension: 02ppg1r; *> query: (?x7819, 01fwpt) <- genre(?x7819, ?x53), film(?x3708, ?x7819), titles(?x2480, ?x7819), ?x2480 = 01z4y *> conf = 0.02 ranks of expected_values: 549 EVAL 025rxjq film! 01fwpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 113.000 46.000 0.553 http://example.org/film/actor/film./film/performance/film #21308-0fnc_ PRED entity: 0fnc_ PRED relation: location_of_ceremony! PRED expected values: 08b0cj => 60 concepts (36 used for prediction) PRED predicted values (max 10 best out of 85): 02m30v (0.11 #1778, 0.04 #3557, 0.03 #3049), 01rwcgb (0.06 #1752, 0.03 #3023, 0.03 #3277), 014v1q (0.06 #1770, 0.03 #3041, 0.03 #3295), 0436kgz (0.06 #1686, 0.03 #2957, 0.03 #3211), 02g0rb (0.06 #1681, 0.03 #2952, 0.03 #3206), 03j24kf (0.06 #1636, 0.03 #2907, 0.03 #3161), 01vsy7t (0.06 #1634, 0.03 #2905, 0.03 #3159), 02_j7t (0.06 #1571, 0.03 #2842, 0.03 #3096), 01hbq0 (0.06 #1774, 0.02 #3553), 01nglk (0.06 #1765, 0.02 #3544) >> Best rule #1778 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01vskn; >> query: (?x14378, 02m30v) <- location(?x11481, ?x14378), team(?x11481, ?x1143), team(?x11481, ?x2333) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fnc_ location_of_ceremony! 08b0cj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 36.000 0.111 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #21307-01vvyfh PRED entity: 01vvyfh PRED relation: award PRED expected values: 01c92g 02v1m7 02f72_ => 122 concepts (112 used for prediction) PRED predicted values (max 10 best out of 308): 01bgqh (0.50 #832, 0.34 #7548, 0.32 #4782), 01ckcd (0.50 #327, 0.26 #5067, 0.18 #4672), 02f72n (0.50 #145, 0.18 #4885, 0.12 #2910), 03qbh5 (0.33 #990, 0.28 #7706, 0.24 #4940), 01ck6h (0.33 #516, 0.16 #16595, 0.15 #18177), 03qbnj (0.28 #1016, 0.22 #4966, 0.18 #7732), 031b3h (0.27 #7702, 0.25 #196, 0.17 #986), 02x17c2 (0.27 #607, 0.16 #16595, 0.15 #18177), 0c4z8 (0.26 #4811, 0.25 #71, 0.24 #9157), 0f4x7 (0.26 #2005, 0.26 #7141, 0.25 #8721) >> Best rule #832 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0167xy; >> query: (?x3929, 01bgqh) <- artist(?x2299, ?x3929), influenced_by(?x3929, ?x4576), currency(?x4576, ?x170) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 01vvycq; 016vn3; *> query: (?x3929, 01c92g) <- artist(?x6672, ?x3929), artists(?x3243, ?x3929), ?x3243 = 0y3_8 *> conf = 0.25 ranks of expected_values: 14, 18, 58 EVAL 01vvyfh award 02f72_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 122.000 112.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vvyfh award 02v1m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 122.000 112.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vvyfh award 01c92g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 122.000 112.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21306-06cl2w PRED entity: 06cl2w PRED relation: film PRED expected values: 0j43swk => 86 concepts (48 used for prediction) PRED predicted values (max 10 best out of 312): 0qm8b (0.20 #2029, 0.20 #244, 0.03 #3815), 017jd9 (0.20 #2563, 0.20 #778, 0.03 #4349), 017gl1 (0.20 #1928, 0.20 #143, 0.03 #3714), 03y0pn (0.20 #3039, 0.20 #1254, 0.03 #4825), 01vksx (0.20 #1920, 0.20 #135, 0.03 #3706), 0ndwt2w (0.20 #2782, 0.20 #997, 0.02 #15281), 02z3r8t (0.20 #1893, 0.20 #108, 0.01 #14392), 01vw8k (0.20 #2437, 0.20 #652, 0.01 #14936), 0gy0n (0.20 #3519, 0.20 #1734), 05nlx4 (0.20 #3037, 0.20 #1252) >> Best rule #2029 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 02j9lm; >> query: (?x11152, 0qm8b) <- film(?x11152, ?x10147), film(?x11152, ?x4902), ?x10147 = 04z4j2, crewmember(?x4902, ?x9391), nominated_for(?x9391, ?x155) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06cl2w film 0j43swk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 48.000 0.200 http://example.org/film/actor/film./film/performance/film #21305-05wm88 PRED entity: 05wm88 PRED relation: award PRED expected values: 05zvj3m 05p09zm => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 288): 05p09zm (0.41 #928, 0.23 #7376, 0.13 #3346), 05zr6wv (0.41 #822, 0.12 #4852, 0.11 #3643), 01bgqh (0.37 #7296, 0.32 #3266, 0.13 #4072), 040njc (0.36 #8, 0.18 #3635, 0.12 #2023), 0gr51 (0.36 #98, 0.16 #2113, 0.12 #12188), 0gs9p (0.36 #77, 0.12 #2092, 0.10 #12167), 01c99j (0.35 #3449, 0.16 #7479, 0.10 #1837), 054ks3 (0.35 #543, 0.14 #3364, 0.14 #4573), 0gqz2 (0.35 #481, 0.11 #4511, 0.08 #1690), 03qbnj (0.34 #3456, 0.15 #7486, 0.08 #1844) >> Best rule #928 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0tc7; 025n3p; 0479b; 084m3; 06crng; 01vxqyl; 04gr35; >> query: (?x11838, 05p09zm) <- award(?x11838, ?x1312), profession(?x11838, ?x319), currency(?x11838, ?x170), ?x1312 = 07cbcy >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1, 20 EVAL 05wm88 award 05p09zm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.414 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05wm88 award 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 82.000 82.000 0.414 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21304-01n073 PRED entity: 01n073 PRED relation: child PRED expected values: 05925 => 163 concepts (152 used for prediction) PRED predicted values (max 10 best out of 295): 031rq5 (0.40 #52, 0.20 #221, 0.17 #728), 06nfl (0.20 #502, 0.20 #333, 0.20 #164), 017s11 (0.20 #172, 0.20 #3, 0.17 #679), 0c41qv (0.20 #239, 0.20 #70, 0.15 #2947), 030_1m (0.20 #182, 0.20 #13, 0.15 #2890), 024rdh (0.20 #222, 0.20 #53, 0.08 #2591), 01jx9 (0.20 #1906, 0.14 #1060, 0.12 #4232), 01qszl (0.20 #503, 0.14 #1179, 0.12 #4227), 0dwcl (0.20 #2001, 0.14 #1155, 0.12 #4203), 01scmq (0.20 #490, 0.12 #4214, 0.11 #1674) >> Best rule #52 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 086k8; >> query: (?x3253, 031rq5) <- state_province_region(?x3253, ?x1227), child(?x3253, ?x13222), ?x1227 = 01n7q, place_founded(?x13222, ?x3269), month(?x3269, ?x1459) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #9994 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 0338lq; *> query: (?x3253, ?x244) <- child(?x3253, ?x14222), child(?x3253, ?x13222), industry(?x13222, ?x245), category(?x14222, ?x134), industry(?x244, ?x245) *> conf = 0.03 ranks of expected_values: 162 EVAL 01n073 child 05925 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 163.000 152.000 0.400 http://example.org/organization/organization/child./organization/organization_relationship/child #21303-01fscv PRED entity: 01fscv PRED relation: time_zones PRED expected values: 02fqwt => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 10): 02fqwt (0.83 #79, 0.83 #53, 0.75 #14), 02hcv8 (0.44 #1121, 0.44 #1134, 0.43 #1160), 02lcqs (0.23 #1019, 0.19 #499, 0.19 #408), 02llzg (0.20 #69, 0.14 #394, 0.13 #251), 03bdv (0.18 #136, 0.10 #188, 0.10 #201), 02hczc (0.08 #41, 0.07 #93, 0.07 #158), 03plfd (0.05 #374, 0.05 #400, 0.04 #257), 042g7t (0.03 #115, 0.02 #297, 0.02 #531), 0gsrz4 (0.02 #138), 02lcrv (0.01 #150) >> Best rule #79 for best value: >> intensional similarity = 2 >> extensional distance = 16 >> proper extension: 0mkdm; 0mkv3; 0mkp7; 0mkc3; >> query: (?x12567, 02fqwt) <- contains(?x4105, ?x12567), ?x4105 = 0824r >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fscv time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.833 http://example.org/location/location/time_zones #21302-01p0vf PRED entity: 01p0vf PRED relation: nationality PRED expected values: 02jx1 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.70 #1101, 0.70 #6305, 0.70 #7507), 02jx1 (0.46 #133, 0.25 #933, 0.24 #733), 07ssc (0.33 #11014, 0.23 #115, 0.16 #3417), 0jt5zcn (0.33 #11014), 06q1r (0.15 #177, 0.05 #477, 0.03 #577), 0d060g (0.13 #207, 0.07 #607, 0.05 #5010), 03rk0 (0.09 #46, 0.07 #246, 0.06 #11961), 0jgd (0.09 #2, 0.03 #502, 0.03 #302), 03_3d (0.09 #6, 0.03 #806, 0.03 #4408), 0345h (0.09 #331, 0.07 #231, 0.06 #1431) >> Best rule #1101 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 06w2sn5; 019g40; 04mn81; 01wz3cx; 0p3r8; 01wn718; 03f7m4h; 01s7ns; >> query: (?x7053, 09c7w0) <- artists(?x302, ?x7053), place_of_birth(?x7053, ?x1841), participant(?x7053, ?x12422) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #133 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 062dn7; *> query: (?x7053, 02jx1) <- film(?x7053, ?x7305), ?x7305 = 031786, nominated_for(?x7053, ?x7911) *> conf = 0.46 ranks of expected_values: 2 EVAL 01p0vf nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 129.000 0.703 http://example.org/people/person/nationality #21301-01v1ln PRED entity: 01v1ln PRED relation: country PRED expected values: 07ssc => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 26): 07ssc (0.80 #196, 0.78 #136, 0.73 #256), 0d05w3 (0.43 #403, 0.03 #523, 0.02 #1185), 03rt9 (0.42 #4528, 0.35 #1805, 0.01 #3452), 02jx1 (0.42 #4528, 0.35 #1805), 06q1r (0.35 #1805), 09pmkv (0.35 #1805), 02k54 (0.35 #1805), 03h64 (0.33 #406, 0.04 #526, 0.03 #586), 0345h (0.29 #87, 0.20 #27, 0.14 #387), 01mjq (0.20 #35, 0.14 #95, 0.11 #155) >> Best rule #196 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 08gsvw; 02qrv7; 01kf3_9; 02n72k; 0g5ptf; >> query: (?x6994, 07ssc) <- language(?x6994, ?x254), film(?x3692, ?x6994), ?x3692 = 03kpvp, prequel(?x835, ?x6994) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v1ln country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.800 http://example.org/film/film/country #21300-01dvbd PRED entity: 01dvbd PRED relation: film! PRED expected values: 0lbj1 => 102 concepts (58 used for prediction) PRED predicted values (max 10 best out of 702): 0mm1q (0.69 #29187, 0.63 #112583, 0.63 #114671), 01lqnff (0.49 #10422, 0.44 #18761, 0.44 #37527), 027kmrb (0.30 #6253, 0.12 #22930, 0.12 #27101), 01wbg84 (0.20 #47, 0.14 #4215, 0.03 #20892), 012d40 (0.19 #4184, 0.03 #8353, 0.02 #16692), 016ggh (0.14 #3955, 0.10 #1871, 0.06 #12294), 02tr7d (0.14 #2351, 0.10 #267, 0.01 #10690), 01sp81 (0.14 #2233, 0.02 #6402, 0.01 #10572), 02js_6 (0.14 #6144, 0.02 #8229, 0.01 #33248), 0147dk (0.14 #4250, 0.02 #6335, 0.01 #31354) >> Best rule #29187 for best value: >> intensional similarity = 3 >> extensional distance = 327 >> proper extension: 0clpml; >> query: (?x3048, ?x5565) <- nominated_for(?x5565, ?x3048), type_of_union(?x5565, ?x566), celebrity(?x5565, ?x2237) >> conf = 0.69 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01dvbd film! 0lbj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 58.000 0.689 http://example.org/film/actor/film./film/performance/film #21299-0cymln PRED entity: 0cymln PRED relation: currency PRED expected values: 09nqf => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.78 #7, 0.73 #16, 0.67 #10), 01nv4h (0.03 #56), 02l6h (0.01 #66, 0.01 #69, 0.01 #72) >> Best rule #7 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01jz6d; 02lm0t; >> query: (?x10097, 09nqf) <- profession(?x10097, ?x1581), team(?x10097, ?x4571), ?x1581 = 01445t, location(?x10097, ?x1523), nationality(?x10097, ?x94) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cymln currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.778 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #21298-01hp22 PRED entity: 01hp22 PRED relation: country PRED expected values: 0b90_r 0154j 0d060g 03rt9 03gj2 0345h 06t8v 03spz 01crd5 => 36 concepts (34 used for prediction) PRED predicted values (max 10 best out of 465): 0345h (0.92 #5496, 0.89 #4664, 0.87 #4499), 0d060g (0.90 #5481, 0.89 #5151, 0.89 #4649), 0b90_r (0.85 #3814, 0.85 #2829, 0.82 #3486), 0154j (0.82 #1325, 0.81 #3154, 0.81 #3156), 0ctw_b (0.82 #1325, 0.81 #3156, 0.78 #5145), 03gj2 (0.82 #1325, 0.75 #165, 0.73 #170), 0k6nt (0.82 #1325, 0.75 #165, 0.73 #170), 016wzw (0.82 #1325, 0.75 #165, 0.73 #170), 04v3q (0.82 #1325, 0.73 #170, 0.65 #2819), 05b4w (0.81 #3154, 0.81 #3156, 0.80 #5144) >> Best rule #5496 for best value: >> intensional similarity = 52 >> extensional distance = 49 >> proper extension: 0d1tm; 02vx4; 096f8; 07bs0; 09_94; 09wz9; 09w1n; 0d1t3; 07_53; 0152n0; ... >> query: (?x766, 0345h) <- country(?x766, ?x2346), country(?x766, ?x1353), film_release_region(?x11313, ?x1353), film_release_region(?x8025, ?x1353), film_release_region(?x7887, ?x1353), film_release_region(?x7864, ?x1353), film_release_region(?x6931, ?x1353), film_release_region(?x6516, ?x1353), film_release_region(?x6270, ?x1353), film_release_region(?x6247, ?x1353), film_release_region(?x6235, ?x1353), film_release_region(?x5688, ?x1353), film_release_region(?x5255, ?x1353), film_release_region(?x3566, ?x1353), film_release_region(?x3482, ?x1353), film_release_region(?x2783, ?x1353), film_release_region(?x2676, ?x1353), film_release_region(?x2628, ?x1353), film_release_region(?x1547, ?x1353), film_release_region(?x1421, ?x1353), ?x5688 = 0dr89x, ?x6931 = 09v3jyg, ?x1547 = 0168ls, combatants(?x172, ?x1353), combatants(?x151, ?x1353), ?x6247 = 09v9mks, ?x6270 = 0g9zljd, ?x2628 = 06wbm8q, ?x151 = 0b90_r, combatants(?x326, ?x1353), country(?x1352, ?x1353), ?x8025 = 03nsm5x, ?x7887 = 04z_3pm, olympics(?x1353, ?x358), contains(?x2346, ?x1885), country(?x206, ?x2346), ?x6235 = 05b6rdt, ?x11313 = 0by17xn, combatants(?x2346, ?x13876), ?x2783 = 0879bpq, jurisdiction_of_office(?x265, ?x2346), ?x3482 = 017z49, ?x2676 = 0f4m2z, currency(?x2346, ?x170), story_by(?x7864, ?x1030), genre(?x3566, ?x53), country(?x1352, ?x4073), ?x6516 = 04cppj, ?x4073 = 07dvs, ?x5255 = 01sby_, ?x1421 = 07qg8v, ?x172 = 0154j >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 20, 25, 29, 62 EVAL 01hp22 country 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 36.000 34.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01hp22 country 03spz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 36.000 34.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01hp22 country 06t8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 36.000 34.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01hp22 country 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 34.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01hp22 country 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 36.000 34.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01hp22 country 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 36.000 34.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01hp22 country 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 34.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01hp22 country 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 34.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01hp22 country 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 34.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #21297-02l101 PRED entity: 02l101 PRED relation: place_of_death PRED expected values: 06_kh => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 33): 030qb3t (0.22 #1579, 0.21 #410, 0.17 #2163), 0k049 (0.15 #391, 0.12 #586, 0.12 #1170), 06c62 (0.11 #101, 0.03 #10330), 0qpqn (0.10 #324), 06_kh (0.09 #393, 0.06 #2146, 0.06 #1951), 0f2wj (0.08 #1958, 0.08 #2348, 0.08 #2153), 04jpl (0.07 #2148, 0.07 #1953, 0.06 #2343), 02_286 (0.06 #1959, 0.06 #2349, 0.05 #596), 0r3tq (0.05 #3115, 0.03 #537, 0.03 #4287), 0r0m6 (0.05 #643, 0.04 #1227, 0.04 #1032) >> Best rule #1579 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 02m7r; 0c8br; >> query: (?x9423, 030qb3t) <- gender(?x9423, ?x231), award_winner(?x458, ?x9423), place_of_burial(?x9423, ?x8044) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #393 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 012c6x; 04nw9; 01bmlb; 01kkx2; 04zn7g; *> query: (?x9423, 06_kh) <- film(?x9423, ?x3220), place_of_burial(?x9423, ?x8044), gender(?x9423, ?x231), place_of_birth(?x9423, ?x1860) *> conf = 0.09 ranks of expected_values: 5 EVAL 02l101 place_of_death 06_kh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 113.000 113.000 0.216 http://example.org/people/deceased_person/place_of_death #21296-07t90 PRED entity: 07t90 PRED relation: featured_film_locations! PRED expected values: 08720 => 130 concepts (112 used for prediction) PRED predicted values (max 10 best out of 13): 024mpp (0.12 #1749, 0.06 #4697, 0.03 #7645), 07cdz (0.12 #1725, 0.06 #4673, 0.03 #7621), 04kkz8 (0.12 #1534, 0.06 #4482, 0.03 #7430), 0j_tw (0.04 #6782, 0.02 #11204, 0.01 #11941), 02dwj (0.03 #7763), 08phg9 (0.03 #9227, 0.01 #15860), 047csmy (0.02 #9977, 0.02 #11451, 0.01 #12188), 08hmch (0.02 #10385), 0413cff (0.02 #39433, 0.02 #40907, 0.01 #42381), 0p_tz (0.02 #11556, 0.01 #12293) >> Best rule #1749 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 07wjk; >> query: (?x4599, 024mpp) <- major_field_of_study(?x4599, ?x6859), major_field_of_study(?x4599, ?x4268), ?x6859 = 01tbp, ?x4268 = 02822 >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07t90 featured_film_locations! 08720 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 112.000 0.125 http://example.org/film/film/featured_film_locations #21295-0l5yl PRED entity: 0l5yl PRED relation: profession PRED expected values: 02hrh1q => 185 concepts (184 used for prediction) PRED predicted values (max 10 best out of 105): 02hrh1q (0.87 #14033, 0.87 #10604, 0.87 #8964), 0dxtg (0.56 #8218, 0.53 #12392, 0.48 #6427), 0cbd2 (0.52 #6420, 0.44 #11192, 0.42 #13726), 01d_h8 (0.51 #2692, 0.50 #1647, 0.48 #4780), 03gjzk (0.50 #8220, 0.42 #315, 0.39 #12394), 09jwl (0.44 #3153, 0.39 #2556, 0.35 #6134), 0kyk (0.35 #6443, 0.33 #329, 0.29 #11215), 02jknp (0.33 #307, 0.29 #4782, 0.29 #1649), 02krf9 (0.33 #326, 0.25 #22066, 0.20 #176), 0nbcg (0.32 #3166, 0.30 #1077, 0.29 #2569) >> Best rule #14033 for best value: >> intensional similarity = 2 >> extensional distance = 383 >> proper extension: 01l1b90; 0m2wm; 0320jz; 02_j7t; 01wxyx1; 0tc7; 05hdf; 01wgxtl; 01vw20_; 02wb6yq; ... >> query: (?x8286, 02hrh1q) <- people(?x1050, ?x8286), participant(?x1545, ?x8286) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l5yl profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 185.000 184.000 0.873 http://example.org/people/person/profession #21294-063_t PRED entity: 063_t PRED relation: profession PRED expected values: 0dxtg 02hrh1q => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 102): 02hrh1q (0.90 #11641, 0.89 #9256, 0.89 #3294), 0dxtg (0.84 #6571, 0.84 #6720, 0.82 #5379), 01d_h8 (0.71 #5371, 0.66 #6712, 0.65 #6563), 02jknp (0.65 #5373, 0.58 #3436, 0.56 #6714), 03gjzk (0.45 #1506, 0.44 #1655, 0.43 #2550), 0cbd2 (0.45 #7905, 0.44 #2988, 0.43 #8502), 09jwl (0.38 #2702, 0.37 #2851, 0.36 #2403), 0kyk (0.37 #924, 0.35 #3011, 0.33 #179), 01p5_g (0.33 #240, 0.04 #2028, 0.03 #1581), 0d1pc (0.30 #1988, 0.22 #3330, 0.20 #6161) >> Best rule #11641 for best value: >> intensional similarity = 3 >> extensional distance = 902 >> proper extension: 0glmv; 01wz01; 01qrbf; 03b78r; 06l9n8; 03fnyk; >> query: (?x8460, 02hrh1q) <- award_winner(?x3943, ?x8460), profession(?x8460, ?x1146), film(?x8460, ?x5228) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 063_t profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.905 http://example.org/people/person/profession EVAL 063_t profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.905 http://example.org/people/person/profession #21293-09p3h7 PRED entity: 09p3h7 PRED relation: ceremony! PRED expected values: 019f4v => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 331): 0cqh6z (0.60 #2093, 0.57 #2604, 0.50 #2858), 09qv_s (0.60 #2155, 0.57 #2666, 0.50 #2920), 0cqhb3 (0.60 #2252, 0.57 #2763, 0.50 #3017), 09td7p (0.60 #2132, 0.57 #2643, 0.50 #2897), 09qwmm (0.60 #2069, 0.57 #2580, 0.50 #2834), 02py7pj (0.60 #2256, 0.57 #2767, 0.50 #3021), 0cqgl9 (0.60 #2180, 0.57 #2691, 0.50 #2945), 0cqhk0 (0.60 #2072, 0.57 #2583, 0.50 #2837), 0gqy2 (0.59 #6502, 0.57 #6759, 0.56 #7016), 0k611 (0.57 #6453, 0.55 #6710, 0.55 #6967) >> Best rule #2093 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 09g90vz; >> query: (?x5392, 0cqh6z) <- award_winner(?x5392, ?x2900), award_winner(?x5392, ?x262), honored_for(?x5392, ?x4932), ?x4932 = 0hz55, award_nominee(?x222, ?x2900), nominated_for(?x262, ?x1077), ?x222 = 06gp3f, award_nominee(?x262, ?x2414), award_nominee(?x262, ?x100), film(?x262, ?x1038), nominated_for(?x2414, ?x945), award_nominee(?x100, ?x380), award_nominee(?x2033, ?x262), award_winner(?x2252, ?x100), award(?x262, ?x591) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4389 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 26 *> proper extension: 0hr6lkl; 073h1t; 0fy6bh; 0bzk2h; 02ywhz; *> query: (?x5392, 019f4v) <- award_winner(?x5392, ?x9281), award_winner(?x5392, ?x3960), award_winner(?x5392, ?x3058), award_winner(?x5392, ?x2900), honored_for(?x5392, ?x9350), honored_for(?x5392, ?x8367), honored_for(?x5392, ?x7087), award(?x9350, ?x1111), currency(?x7087, ?x170), award(?x3960, ?x1307), gender(?x2900, ?x514), award_winner(?x9452, ?x3058), program_creator(?x3822, ?x3058), ?x1307 = 0gq9h, award_winner(?x9350, ?x221), written_by(?x638, ?x9281), nominated_for(?x926, ?x8367) *> conf = 0.43 ranks of expected_values: 35 EVAL 09p3h7 ceremony! 019f4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 30.000 30.000 0.600 http://example.org/award/award_category/winners./award/award_honor/ceremony #21292-06nfl PRED entity: 06nfl PRED relation: citytown PRED expected values: 0d6lp => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 257): 030qb3t (0.51 #23547, 0.35 #61427, 0.34 #58482), 04jpl (0.49 #27945, 0.42 #12121, 0.42 #16537), 02_286 (0.46 #20964, 0.44 #17288, 0.44 #40828), 024bqj (0.36 #13794, 0.35 #61427, 0.34 #58482), 0h7h6 (0.35 #61427, 0.34 #58482, 0.34 #58481), 03hrz (0.35 #61427, 0.34 #58482, 0.34 #58481), 0dqyw (0.35 #61427, 0.34 #58482, 0.34 #58481), 018dk_ (0.35 #61427, 0.34 #58482, 0.34 #58481), 0r5y9 (0.35 #61427, 0.34 #58482, 0.34 #58481), 0r6cx (0.35 #61427, 0.34 #58482, 0.34 #58481) >> Best rule #23547 for best value: >> intensional similarity = 6 >> extensional distance = 63 >> proper extension: 05t7c1; >> query: (?x14538, 030qb3t) <- citytown(?x14538, ?x9559), citytown(?x11304, ?x9559), film_release_region(?x903, ?x9559), place_of_birth(?x256, ?x9559), ?x11304 = 01nds, jurisdiction_of_office(?x900, ?x9559) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #3007 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 04kqk; *> query: (?x14538, 0d6lp) <- industry(?x14538, ?x245), ?x245 = 01mw1, category(?x14538, ?x134), child(?x11427, ?x14538), ?x134 = 08mbj5d, citytown(?x14538, ?x9559), organization(?x4682, ?x11427) *> conf = 0.14 ranks of expected_values: 26 EVAL 06nfl citytown 0d6lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 194.000 194.000 0.508 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #21291-03r0g9 PRED entity: 03r0g9 PRED relation: award_winner PRED expected values: 01gf5h => 118 concepts (88 used for prediction) PRED predicted values (max 10 best out of 600): 0lpjn (0.49 #24666, 0.45 #24665, 0.40 #41113), 03kpvp (0.49 #24666, 0.45 #24665, 0.40 #41113), 03y1mlp (0.49 #24666, 0.45 #24665, 0.40 #41113), 04_1nk (0.49 #24666, 0.45 #24665, 0.40 #41113), 05ldnp (0.30 #37824, 0.29 #93754), 03_gd (0.20 #1760, 0.06 #11625, 0.04 #23137), 05qg6g (0.20 #2345, 0.04 #10566, 0.02 #22077), 059_gf (0.14 #110202, 0.13 #32890, 0.13 #77303), 0n839 (0.13 #32890, 0.13 #77303, 0.12 #105268), 06lvlf (0.12 #4260, 0.05 #7548) >> Best rule #24666 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 09p7fh; >> query: (?x3693, ?x3692) <- nominated_for(?x3692, ?x3693), nominated_for(?x637, ?x3693), award_winner(?x4141, ?x3692), ?x637 = 02r22gf >> conf = 0.49 => this is the best rule for 4 predicted values *> Best rule #115134 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 857 *> proper extension: 07s8z_l; 06mmr; *> query: (?x3693, ?x1001) <- award_winner(?x3693, ?x7027), nationality(?x7027, ?x1310), award_nominee(?x7027, ?x1001) *> conf = 0.08 ranks of expected_values: 38 EVAL 03r0g9 award_winner 01gf5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 118.000 88.000 0.489 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #21290-01gvyp PRED entity: 01gvyp PRED relation: award PRED expected values: 0gqwc => 109 concepts (97 used for prediction) PRED predicted values (max 10 best out of 230): 0bfvw2 (0.42 #14, 0.09 #2432, 0.09 #2029), 0gqyl (0.38 #104, 0.14 #2522, 0.14 #2119), 0cqgl9 (0.30 #191, 0.07 #2609, 0.07 #594), 03qgjwc (0.30 #182, 0.06 #585, 0.06 #2600), 09sb52 (0.28 #40, 0.27 #2861, 0.26 #6488), 0gqwc (0.28 #73, 0.15 #2491, 0.15 #2088), 0bdwft (0.28 #67, 0.11 #2485, 0.10 #2082), 0bb57s (0.28 #243, 0.07 #2661, 0.06 #2258), 02z0dfh (0.26 #74, 0.09 #2492, 0.08 #2089), 09qvf4 (0.20 #209, 0.06 #2627, 0.06 #2224) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 02nwxc; 01bh6y; 0739z6; >> query: (?x6951, 0bfvw2) <- award(?x6951, ?x3184), film(?x6951, ?x407), ?x3184 = 0gkts9 >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #73 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 02nwxc; 01bh6y; 0739z6; *> query: (?x6951, 0gqwc) <- award(?x6951, ?x3184), film(?x6951, ?x407), ?x3184 = 0gkts9 *> conf = 0.28 ranks of expected_values: 6 EVAL 01gvyp award 0gqwc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 109.000 97.000 0.420 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21289-01tv3x2 PRED entity: 01tv3x2 PRED relation: artists! PRED expected values: 06rqw => 122 concepts (47 used for prediction) PRED predicted values (max 10 best out of 248): 016clz (0.66 #3705, 0.56 #929, 0.54 #1546), 06by7 (0.54 #2488, 0.52 #1255, 0.51 #4342), 04n7jdv (0.50 #599, 0.07 #907, 0.07 #1523), 064t9 (0.43 #11778, 0.42 #7444, 0.41 #7752), 0glt670 (0.40 #350, 0.30 #658, 0.27 #2816), 01lyv (0.38 #35, 0.20 #7774, 0.19 #7466), 02yv6b (0.38 #98, 0.17 #6594, 0.16 #7217), 02x8m (0.38 #19, 0.13 #1561, 0.11 #2794), 03lty (0.33 #954, 0.30 #338, 0.27 #1262), 0xhtw (0.31 #942, 0.30 #6513, 0.30 #326) >> Best rule #3705 for best value: >> intensional similarity = 4 >> extensional distance = 133 >> proper extension: 04bbv7; >> query: (?x6609, 016clz) <- type_of_union(?x6609, ?x566), artists(?x5934, ?x6609), artists(?x5934, ?x8864), ?x8864 = 070b4 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #393 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01q7cb_; 01vw20_; 01vw26l; 03sww; 01vxqyl; *> query: (?x6609, 06rqw) <- type_of_union(?x6609, ?x566), artists(?x6513, ?x6609), nationality(?x6609, ?x94), ?x6513 = 06cp5 *> conf = 0.10 ranks of expected_values: 66 EVAL 01tv3x2 artists! 06rqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 122.000 47.000 0.659 http://example.org/music/genre/artists #21288-0gthm PRED entity: 0gthm PRED relation: influenced_by! PRED expected values: 01hb6v => 138 concepts (49 used for prediction) PRED predicted values (max 10 best out of 473): 0j0pf (0.36 #6900, 0.06 #22865, 0.05 #19771), 01hc9_ (0.33 #362, 0.19 #5152, 0.10 #2424), 067xw (0.33 #283, 0.10 #2345, 0.09 #6978), 02ghq (0.33 #445, 0.10 #2507, 0.09 #7140), 042xh (0.33 #509, 0.10 #2571, 0.09 #3087), 06d6y (0.33 #367, 0.10 #2429, 0.09 #2945), 0ph2w (0.29 #4792, 0.09 #24719, 0.07 #4277), 05jm7 (0.27 #6834, 0.09 #22799, 0.07 #24343), 01x4r3 (0.25 #897, 0.18 #5018, 0.14 #4503), 01xwqn (0.24 #5080, 0.14 #4565, 0.07 #14342) >> Best rule #6900 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0g5ff; >> query: (?x9854, 0j0pf) <- influenced_by(?x1725, ?x9854), award(?x9854, ?x9285), ?x9285 = 0265vt >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #5758 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 07hyk; 0gzh; *> query: (?x9854, 01hb6v) <- influenced_by(?x1946, ?x9854), politician(?x8714, ?x9854), location(?x1946, ?x739) *> conf = 0.11 ranks of expected_values: 50 EVAL 0gthm influenced_by! 01hb6v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 138.000 49.000 0.364 http://example.org/influence/influence_node/influenced_by #21287-07tw_b PRED entity: 07tw_b PRED relation: nominated_for! PRED expected values: 054ks3 => 70 concepts (62 used for prediction) PRED predicted values (max 10 best out of 289): 0gq9h (0.37 #1020, 0.26 #2694, 0.24 #9389), 02r22gf (0.33 #29, 0.24 #985, 0.18 #1941), 0l8z1 (0.33 #53, 0.16 #1965, 0.14 #9378), 054krc (0.33 #71, 0.14 #1983, 0.13 #9396), 02qvyrt (0.33 #98, 0.12 #2010, 0.12 #9423), 057xs89 (0.33 #121, 0.08 #4664, 0.08 #2033), 0g_w (0.33 #133, 0.04 #14829, 0.04 #13631), 0gr0m (0.32 #1017, 0.23 #539, 0.16 #9386), 019f4v (0.29 #1011, 0.21 #9380, 0.20 #2685), 040njc (0.29 #963, 0.19 #2637, 0.18 #1202) >> Best rule #1020 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 02754c9; >> query: (?x4110, 0gq9h) <- category(?x4110, ?x134), film(?x194, ?x4110), nominated_for(?x2238, ?x4110), cinematography(?x4110, ?x4974) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #12913 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1343 *> proper extension: 04xbq3; *> query: (?x4110, ?x401) <- nominated_for(?x2238, ?x4110), film(?x2101, ?x4110), award(?x2101, ?x401), nominated_for(?x2101, ?x2102) *> conf = 0.10 ranks of expected_values: 79 EVAL 07tw_b nominated_for! 054ks3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 70.000 62.000 0.368 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21286-07bcn PRED entity: 07bcn PRED relation: category PRED expected values: 08mbj5d => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #7, 0.83 #34, 0.81 #57) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01x96; 0235l; 0fvzz; >> query: (?x5893, 08mbj5d) <- county_seat(?x5892, ?x5893), capital(?x1227, ?x5893), religion(?x1227, ?x109) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07bcn category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.857 http://example.org/common/topic/webpage./common/webpage/category #21285-096ysw PRED entity: 096ysw PRED relation: artist PRED expected values: 02mq_y => 51 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1185): 0163r3 (0.33 #476, 0.25 #1311, 0.12 #2147), 01vtj38 (0.29 #2197, 0.24 #6379, 0.24 #3869), 048xh (0.29 #2207, 0.24 #3879, 0.18 #6389), 01w524f (0.29 #1965, 0.24 #3637, 0.18 #6147), 033s6 (0.29 #2354, 0.24 #4026, 0.17 #3189), 03f0fnk (0.29 #2003, 0.24 #3675, 0.15 #6185), 03qmj9 (0.28 #2588, 0.17 #4261, 0.13 #5098), 03j1p2n (0.28 #3071, 0.06 #12286, 0.06 #2236), 0qf3p (0.27 #4332, 0.23 #7683, 0.22 #2659), 01vvybv (0.25 #1572, 0.17 #7428, 0.17 #3243) >> Best rule #476 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 01w1w9; >> query: (?x4081, 0163r3) <- artist(?x4081, ?x8272), artist(?x4081, ?x5391), artist(?x4081, ?x5141), ?x5141 = 01qgry, artists(?x302, ?x8272), artists(?x1572, ?x5391), role(?x5391, ?x1267), profession(?x8272, ?x131), role(?x1267, ?x5480), performance_role(?x227, ?x1267), role(?x315, ?x1267), ?x5480 = 01w4c9 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 096ysw artist 02mq_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 16.000 0.333 http://example.org/music/record_label/artist #21284-03hzkq PRED entity: 03hzkq PRED relation: profession PRED expected values: 02jknp 01c8w0 02hv44_ => 54 concepts (43 used for prediction) PRED predicted values (max 10 best out of 47): 09jwl (0.72 #304, 0.19 #4336, 0.18 #4913), 02jknp (0.55 #1590, 0.46 #726, 0.45 #1446), 03gjzk (0.48 #1740, 0.39 #2028, 0.32 #1020), 0nbcg (0.38 #316, 0.13 #4348, 0.11 #5934), 0dz3r (0.36 #290, 0.10 #4322, 0.09 #2738), 016z4k (0.21 #292, 0.09 #4324, 0.08 #2596), 01c72t (0.18 #309, 0.09 #2757, 0.08 #1893), 0kyk (0.15 #314, 0.10 #2042, 0.09 #4923), 018gz8 (0.13 #2030, 0.12 #1742, 0.11 #3758), 039v1 (0.12 #321, 0.05 #4353, 0.05 #4930) >> Best rule #304 for best value: >> intensional similarity = 5 >> extensional distance = 128 >> proper extension: 021r7r; 01wxdn3; >> query: (?x10890, 09jwl) <- profession(?x10890, ?x7998), profession(?x10890, ?x319), ?x319 = 01d_h8, profession(?x6996, ?x7998), ?x6996 = 0132k4 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1590 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1195 *> proper extension: 027rwmr; 07vc_9; 01c59k; 0c94fn; 0bs1yy; 05h72z; 04w391; 01n9d9; 01vy_v8; 03nqbvz; ... *> query: (?x10890, 02jknp) <- profession(?x10890, ?x319), profession(?x8348, ?x319), profession(?x6589, ?x319), profession(?x1416, ?x319), ?x1416 = 0162c8, ?x6589 = 0js9s, ?x8348 = 02_j8x *> conf = 0.55 ranks of expected_values: 2, 22, 28 EVAL 03hzkq profession 02hv44_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 54.000 43.000 0.723 http://example.org/people/person/profession EVAL 03hzkq profession 01c8w0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 54.000 43.000 0.723 http://example.org/people/person/profession EVAL 03hzkq profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 54.000 43.000 0.723 http://example.org/people/person/profession #21283-026zvx7 PRED entity: 026zvx7 PRED relation: award_nominee PRED expected values: 05lb65 => 63 concepts (38 used for prediction) PRED predicted values (max 10 best out of 715): 05dxl5 (0.81 #30254, 0.81 #55853, 0.81 #13963), 05lb65 (0.68 #8534, 0.65 #6207, 0.56 #10861), 06b0d2 (0.65 #4878, 0.59 #7205, 0.56 #9532), 01rs5p (0.55 #9146, 0.50 #2165, 0.44 #11473), 026zvx7 (0.53 #5211, 0.50 #7538, 0.50 #557), 030znt (0.50 #278, 0.47 #4932, 0.41 #7259), 0308kx (0.50 #7936, 0.41 #10263, 0.35 #5609), 01wb8bs (0.45 #7877, 0.41 #10204, 0.35 #5550), 05lb87 (0.45 #7258, 0.38 #9585, 0.29 #4931), 058ncz (0.31 #9403, 0.27 #7076, 0.24 #60509) >> Best rule #30254 for best value: >> intensional similarity = 3 >> extensional distance = 540 >> proper extension: 03sww; >> query: (?x2579, ?x444) <- gender(?x2579, ?x514), award_nominee(?x444, ?x2579), actor(?x2078, ?x2579) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #8534 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 01dw4q; 058ncz; 035gjq; 0443y3; 038g2x; 0gd_b_; 07z1_q; 04psyp; 04vmqg; *> query: (?x2579, 05lb65) <- gender(?x2579, ?x514), award_nominee(?x2579, ?x6632), ?x6632 = 05lb30 *> conf = 0.68 ranks of expected_values: 2 EVAL 026zvx7 award_nominee 05lb65 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 38.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21282-0cq7tx PRED entity: 0cq7tx PRED relation: honored_for! PRED expected values: 0glbqt => 93 concepts (55 used for prediction) PRED predicted values (max 10 best out of 97): 027rpym (0.37 #2007, 0.34 #3177, 0.31 #1672), 02q_4ph (0.37 #2007, 0.34 #3177, 0.31 #1672), 0bcndz (0.37 #2007, 0.34 #3177, 0.31 #1672), 0dfw0 (0.09 #758, 0.04 #1593, 0.04 #1928), 0cqnss (0.08 #258, 0.02 #1094, 0.02 #1261), 0fdv3 (0.06 #707, 0.04 #1542, 0.04 #1877), 0dtfn (0.06 #697, 0.03 #1867, 0.03 #1700), 069q4f (0.06 #1528, 0.05 #3033, 0.05 #3368), 0cwfgz (0.06 #1621, 0.04 #3126, 0.04 #3461), 037xlx (0.06 #1604, 0.04 #3109, 0.04 #3444) >> Best rule #2007 for best value: >> intensional similarity = 3 >> extensional distance = 136 >> proper extension: 044g_k; 09g8vhw; 02q_4ph; 059lwy; >> query: (?x4404, ?x1745) <- award(?x4404, ?x484), genre(?x4404, ?x53), nominated_for(?x1745, ?x4404) >> conf = 0.37 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0cq7tx honored_for! 0glbqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 55.000 0.373 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #21281-033g4d PRED entity: 033g4d PRED relation: language PRED expected values: 012w70 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 32): 02bjrlw (0.25 #1, 0.09 #662, 0.08 #276), 03_9r (0.25 #64, 0.08 #505, 0.08 #284), 012w70 (0.25 #66, 0.06 #507, 0.05 #397), 064_8sq (0.19 #240, 0.19 #185, 0.16 #406), 06nm1 (0.12 #65, 0.11 #671, 0.11 #727), 04306rv (0.12 #721, 0.11 #665, 0.09 #555), 0jzc (0.11 #238, 0.09 #183, 0.08 #293), 06b_j (0.08 #738, 0.07 #682, 0.07 #1236), 05zjd (0.05 #354, 0.03 #189, 0.03 #244), 03hkp (0.04 #564, 0.03 #509, 0.03 #619) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0h6r5; 06cm5; >> query: (?x1185, 02bjrlw) <- produced_by(?x1185, ?x2464), nominated_for(?x7733, ?x1185), titles(?x7160, ?x1185), ?x7733 = 01pj5q >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #66 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 043sct5; *> query: (?x1185, 012w70) <- language(?x1185, ?x10296), country(?x1185, ?x94), ?x10296 = 03115z *> conf = 0.25 ranks of expected_values: 3 EVAL 033g4d language 012w70 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 117.000 0.250 http://example.org/film/film/language #21280-04gycf PRED entity: 04gycf PRED relation: currency PRED expected values: 09nqf => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.45 #7, 0.45 #67, 0.44 #70), 01nv4h (0.09 #41, 0.08 #59, 0.08 #29) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0pyg6; >> query: (?x3546, 09nqf) <- award_nominee(?x2841, ?x3546), person(?x3480, ?x3546), participant(?x1690, ?x3546) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gycf currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.455 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #21279-0bsnm PRED entity: 0bsnm PRED relation: institution! PRED expected values: 04zx3q1 016t_3 => 173 concepts (170 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.80 #536, 0.79 #124, 0.78 #145), 02_xgp2 (0.74 #1525, 0.65 #153, 0.61 #544), 03bwzr4 (0.71 #546, 0.67 #155, 0.66 #134), 016t_3 (0.68 #1619, 0.59 #146, 0.58 #125), 027f2w (0.41 #150, 0.34 #129, 0.32 #541), 04zx3q1 (0.39 #123, 0.37 #144, 0.35 #1450), 013zdg (0.39 #107, 0.34 #128, 0.29 #211), 02m4yg (0.35 #1450, 0.31 #2023, 0.30 #2150), 01ysy9 (0.35 #1450, 0.31 #2023, 0.30 #2150), 01gkg3 (0.35 #1450, 0.31 #2023, 0.30 #2150) >> Best rule #536 for best value: >> intensional similarity = 5 >> extensional distance = 115 >> proper extension: 0373qg; 01hr11; 01csqg; 04jhp; >> query: (?x8191, 02h4rq6) <- institution(?x1526, ?x8191), major_field_of_study(?x8191, ?x1154), ?x1154 = 02lp1, major_field_of_study(?x1526, ?x3400), ?x3400 = 0pf2 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1619 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 288 *> proper extension: 07b2yw; *> query: (?x8191, 016t_3) <- institution(?x1526, ?x8191), institution(?x1526, ?x11975), institution(?x1526, ?x6056), institution(?x1526, ?x5887), ?x6056 = 05zl0, ?x11975 = 050xpd, ?x5887 = 025rcc *> conf = 0.68 ranks of expected_values: 4, 6 EVAL 0bsnm institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 173.000 170.000 0.803 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bsnm institution! 04zx3q1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 173.000 170.000 0.803 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21278-0jt3qpk PRED entity: 0jt3qpk PRED relation: honored_for PRED expected values: 01b65l => 27 concepts (19 used for prediction) PRED predicted values (max 10 best out of 728): 0ds3t5x (0.57 #2973, 0.25 #2382, 0.06 #5349), 0b44shh (0.57 #3262, 0.06 #5638, 0.04 #10986), 0d68qy (0.47 #4887, 0.27 #4292, 0.26 #7853), 0ch26b_ (0.43 #3065, 0.25 #2474, 0.04 #10789), 0gmcwlb (0.43 #3026, 0.04 #10750, 0.04 #8964), 0g9wdmc (0.43 #3053, 0.03 #5429, 0.03 #10777), 04p5cr (0.40 #4532, 0.21 #5127, 0.17 #5721), 08jgk1 (0.33 #4232, 0.33 #681, 0.21 #4827), 0cs134 (0.33 #1146, 0.27 #4697, 0.16 #5292), 01b65l (0.33 #1424, 0.25 #2607, 0.25 #2014) >> Best rule #2973 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 0h_cssd; 0gmdkyy; >> query: (?x2751, 0ds3t5x) <- ceremony(?x588, ?x2751), award_winner(?x2751, ?x4185), award_winner(?x2751, ?x691), honored_for(?x2751, ?x8976), award_nominee(?x691, ?x5574), nominated_for(?x691, ?x6678), award_winner(?x912, ?x4185), student(?x3416, ?x5574), category(?x8976, ?x134), award_winner(?x2062, ?x6678), award_nominee(?x2477, ?x4185), ?x2062 = 09d5h, location(?x5574, ?x108), type_of_union(?x691, ?x566) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1424 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 1 *> proper extension: 0gkxgfq; *> query: (?x2751, 01b65l) <- ceremony(?x6853, ?x2751), award_winner(?x2751, ?x9038), award_winner(?x2751, ?x4377), award_winner(?x2751, ?x3150), award_winner(?x2751, ?x3074), award_winner(?x2751, ?x438), honored_for(?x2751, ?x8976), honored_for(?x2751, ?x2829), ?x8976 = 04mx8h4, ?x4377 = 02pt6k_, ?x6853 = 02p_04b, award_nominee(?x6580, ?x438), award_nominee(?x4497, ?x438), ?x3074 = 06jvj7, award_winner(?x415, ?x438), ?x6580 = 03cl8lb, ?x3150 = 049_zz, award_winner(?x2477, ?x4497), ?x2829 = 01b64v, ?x9038 = 05yjhm *> conf = 0.33 ranks of expected_values: 10 EVAL 0jt3qpk honored_for 01b65l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 27.000 19.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #21277-0by17xn PRED entity: 0by17xn PRED relation: film_crew_role PRED expected values: 02r96rf => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 33): 02r96rf (0.75 #618, 0.75 #208, 0.75 #584), 0dxtw (0.46 #625, 0.45 #727, 0.44 #693), 01pvkk (0.36 #1380, 0.36 #1345, 0.30 #1001), 02rh1dz (0.19 #384, 0.16 #282, 0.15 #316), 0215hd (0.16 #598, 0.15 #734, 0.15 #530), 089g0h (0.14 #599, 0.13 #531, 0.13 #633), 0d2b38 (0.13 #605, 0.12 #741, 0.12 #878), 01xy5l_ (0.13 #526, 0.13 #491, 0.13 #730), 015h31 (0.11 #827, 0.11 #383, 0.09 #76), 089fss (0.10 #347, 0.09 #2815, 0.08 #416) >> Best rule #618 for best value: >> intensional similarity = 6 >> extensional distance = 427 >> proper extension: 0416y94; 0pvms; 0bpbhm; 0bs5k8r; 02chhq; >> query: (?x11313, 02r96rf) <- film_crew_role(?x11313, ?x1171), film_crew_role(?x11313, ?x137), ?x137 = 09zzb8, film_release_distribution_medium(?x11313, ?x81), ?x1171 = 09vw2b7, language(?x11313, ?x90) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0by17xn film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.751 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21276-01pq5j7 PRED entity: 01pq5j7 PRED relation: artists! PRED expected values: 02x8m => 134 concepts (126 used for prediction) PRED predicted values (max 10 best out of 246): 025sc50 (0.42 #5543, 0.30 #11949, 0.29 #12864), 0ggx5q (0.42 #5571, 0.25 #77, 0.24 #12892), 05bt6j (0.41 #1264, 0.37 #1570, 0.35 #3400), 02lnbg (0.40 #5551, 0.27 #12872, 0.25 #57), 0glt670 (0.35 #5534, 0.32 #12855, 0.31 #14382), 0xhtw (0.31 #3373, 0.31 #8866, 0.30 #3983), 0cx6f (0.31 #1096, 0.17 #4453, 0.17 #4148), 05w3f (0.29 #2479, 0.27 #4004, 0.23 #4309), 0m0jc (0.29 #314, 0.17 #5503, 0.15 #619), 016clz (0.28 #16789, 0.28 #5499, 0.27 #8245) >> Best rule #5543 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0152cw; 0161c2; 07g2v; 024dgj; 049qx; 01_ztw; 01wvxw1; >> query: (?x5225, 025sc50) <- award(?x5225, ?x724), vacationer(?x1957, ?x5225), artists(?x505, ?x5225) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #18 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0lrh; *> query: (?x5225, 02x8m) <- peers(?x5225, ?x4713), place_of_death(?x5225, ?x5226), participant(?x3397, ?x5225) *> conf = 0.25 ranks of expected_values: 11 EVAL 01pq5j7 artists! 02x8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 134.000 126.000 0.425 http://example.org/music/genre/artists #21275-02xry PRED entity: 02xry PRED relation: time_zones PRED expected values: 02hcv8 => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.84 #254, 0.84 #650, 0.73 #62), 02lcqs (0.25 #796, 0.24 #112, 0.23 #748), 02llzg (0.25 #879, 0.20 #507, 0.19 #351), 02hczc (0.25 #25, 0.25 #13, 0.23 #301), 02lcrv (0.25 #30, 0.25 #18, 0.10 #42), 042g7t (0.25 #34, 0.25 #22, 0.10 #142), 03bdv (0.13 #881, 0.12 #689, 0.11 #581), 03plfd (0.11 #93, 0.10 #885, 0.08 #1138), 0gsrz4 (0.05 #1413, 0.05 #775, 0.05 #811), 052vwh (0.05 #1177, 0.04 #1067, 0.03 #143) >> Best rule #254 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 01tlmw; 02cl1; 0rd5k; 0mzvm; 01zmqw; 0mmzt; 0tygl; 0mndw; 0167q3; 01m9f1; ... >> query: (?x2623, 02hcv8) <- category(?x2623, ?x134), location(?x91, ?x2623), currency(?x2623, ?x170) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xry time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 201.000 201.000 0.841 http://example.org/location/location/time_zones #21274-0mwx6 PRED entity: 0mwx6 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 109 concepts (62 used for prediction) PRED predicted values (max 10 best out of 8): 09c7w0 (0.88 #314, 0.86 #352, 0.86 #340), 0mwx6 (0.13 #572, 0.10 #148, 0.09 #275), 05tbn (0.09 #393, 0.09 #101, 0.08 #490), 08xpv_ (0.08 #665, 0.05 #407, 0.05 #422), 02jx1 (0.07 #512, 0.06 #792, 0.06 #581), 03rt9 (0.03 #152, 0.02 #482, 0.01 #627), 03rjj (0.01 #574, 0.01 #612), 07ssc (0.01 #457) >> Best rule #314 for best value: >> intensional similarity = 4 >> extensional distance = 240 >> proper extension: 0l2mg; >> query: (?x9948, 09c7w0) <- source(?x9948, ?x958), adjoins(?x990, ?x9948), currency(?x9948, ?x170), ?x170 = 09nqf >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwx6 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 62.000 0.880 http://example.org/location/country/second_level_divisions #21273-0cjyzs PRED entity: 0cjyzs PRED relation: award! PRED expected values: 02pp_q_ 07s6tbm => 54 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2927): 0pz7h (0.80 #9869, 0.79 #32905, 0.78 #23033), 0277470 (0.80 #9869, 0.79 #32905, 0.78 #23033), 0bt4r4 (0.80 #9869, 0.79 #32905, 0.78 #23033), 02qlkc3 (0.80 #9869, 0.79 #32905, 0.78 #23033), 03xp8d5 (0.80 #9869, 0.79 #32905, 0.78 #23033), 03cws8h (0.80 #9869, 0.79 #32905, 0.78 #23033), 09ftwr (0.80 #9869, 0.79 #32905, 0.78 #23033), 049fgvm (0.50 #1888, 0.40 #5177, 0.08 #8468), 0jmj (0.25 #1196, 0.23 #7776, 0.20 #4485), 05m9f9 (0.25 #1467, 0.20 #4756, 0.16 #52647) >> Best rule #9869 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 09v7wsg; >> query: (?x2016, ?x201) <- award_winner(?x2016, ?x201), ceremony(?x2016, ?x5585), nominated_for(?x2016, ?x758), ?x5585 = 03nnm4t >> conf = 0.80 => this is the best rule for 7 predicted values *> Best rule #59230 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 210 *> proper extension: 02qwdhq; 03hj5vf; 0g_w; 02pzxlw; 02p_04b; 02_3zj; 0fm3kw; 0fqpg6b; *> query: (?x2016, ?x2912) <- nominated_for(?x2016, ?x758), award(?x2143, ?x2016), award_nominee(?x2143, ?x2912) *> conf = 0.12 ranks of expected_values: 315, 316 EVAL 0cjyzs award! 07s6tbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 19.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cjyzs award! 02pp_q_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 19.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21272-01r7pq PRED entity: 01r7pq PRED relation: actor! PRED expected values: 034vds => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 64): 02_1q9 (0.17 #268, 0.03 #4743, 0.02 #10529), 026bfsh (0.10 #623, 0.07 #1412, 0.06 #4835), 0d68qy (0.08 #300, 0.02 #4775, 0.02 #5564), 0vjr (0.08 #358, 0.01 #4833, 0.01 #5622), 05jyb2 (0.08 #321, 0.01 #4796, 0.01 #5848), 02r2j8 (0.08 #413), 02_1kl (0.08 #395), 02zk08 (0.08 #21594, 0.08 #19488, 0.07 #21067), 05631 (0.05 #781), 06y_n (0.04 #985) >> Best rule #268 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 04fhn_; >> query: (?x7536, 02_1q9) <- nationality(?x7536, ?x94), actor(?x9029, ?x7536), ?x9029 = 034fl9 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #6037 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 556 *> proper extension: 018dnt; 09byk; 01wjrn; 01pw2f1; 02wrhj; 02_j7t; 012_53; 01tszq; 047hpm; 02tqkf; ... *> query: (?x7536, 034vds) <- nationality(?x7536, ?x94), actor(?x9029, ?x7536), location(?x7536, ?x3014) *> conf = 0.01 ranks of expected_values: 61 EVAL 01r7pq actor! 034vds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 114.000 114.000 0.167 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21271-03bxwtd PRED entity: 03bxwtd PRED relation: profession PRED expected values: 04f2zj => 131 concepts (76 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.71 #14, 0.69 #8721, 0.68 #10633), 016z4k (0.54 #3, 0.44 #2807, 0.44 #2070), 039v1 (0.43 #771, 0.38 #1361, 0.37 #919), 0d1pc (0.38 #49, 0.07 #1819, 0.07 #1228), 01c72t (0.35 #1055, 0.33 #170, 0.30 #317), 0fnpj (0.33 #206, 0.30 #353, 0.18 #795), 01d_h8 (0.31 #7533, 0.30 #10624, 0.30 #7827), 0dxtg (0.30 #8720, 0.30 #6213, 0.29 #10338), 03gjzk (0.25 #8722, 0.24 #6215, 0.22 #10634), 0n1h (0.23 #2815, 0.22 #3109, 0.21 #11) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 02twdq; >> query: (?x3062, 02hrh1q) <- artist(?x3265, ?x3062), artists(?x8878, ?x3062), ?x8878 = 02ny8t >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #242 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 028qdb; *> query: (?x3062, 04f2zj) <- role(?x3062, ?x1166), ?x1166 = 05148p4, award(?x3062, ?x724) *> conf = 0.08 ranks of expected_values: 22 EVAL 03bxwtd profession 04f2zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 131.000 76.000 0.708 http://example.org/people/person/profession #21270-03wh49y PRED entity: 03wh49y PRED relation: film_crew_role PRED expected values: 02vs3x5 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 36): 0ch6mp2 (0.77 #2620, 0.75 #1160, 0.74 #2916), 01pvkk (0.38 #984, 0.36 #1093, 0.31 #1457), 01vx2h (0.37 #2625, 0.37 #2221, 0.33 #11), 0dxtw (0.37 #2624, 0.35 #2698, 0.35 #2920), 0215hd (0.21 #1173, 0.19 #1318, 0.17 #1464), 02ynfr (0.19 #2630, 0.15 #2926, 0.15 #2704), 06qc5 (0.17 #353, 0.08 #3968, 0.08 #3931), 0d2b38 (0.15 #2236, 0.10 #1180, 0.10 #2640), 01xy5l_ (0.13 #1168, 0.11 #1313, 0.10 #2628), 02rh1dz (0.13 #2219, 0.13 #2623, 0.10 #2919) >> Best rule #2620 for best value: >> intensional similarity = 4 >> extensional distance = 695 >> proper extension: 0cvkv5; >> query: (?x5517, 0ch6mp2) <- film_crew_role(?x5517, ?x468), genre(?x5517, ?x258), ?x468 = 02r96rf, country(?x5517, ?x512) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #996 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 27 *> proper extension: 02h22; 0g5q34q; 064lsn; *> query: (?x5517, 02vs3x5) <- language(?x5517, ?x254), film_release_region(?x5517, ?x512), film_release_region(?x5517, ?x1264), ?x1264 = 0345h, film_crew_role(?x5517, ?x137), ?x254 = 02h40lc *> conf = 0.10 ranks of expected_values: 14 EVAL 03wh49y film_crew_role 02vs3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 115.000 115.000 0.770 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21269-0gvrws1 PRED entity: 0gvrws1 PRED relation: genre PRED expected values: 01jfsb 06n90 => 74 concepts (72 used for prediction) PRED predicted values (max 10 best out of 83): 07s9rl0 (0.58 #5288, 0.56 #5167, 0.56 #5528), 01hmnh (0.51 #2898, 0.26 #1338, 0.25 #2418), 05p553 (0.44 #2524, 0.43 #844, 0.40 #2644), 01jfsb (0.36 #492, 0.34 #732, 0.31 #1332), 02l7c8 (0.28 #2656, 0.27 #3256, 0.27 #5543), 06n90 (0.25 #1333, 0.25 #2893, 0.21 #1933), 0lsxr (0.25 #9, 0.19 #2289, 0.19 #2529), 04xvlr (0.25 #2, 0.14 #5289, 0.14 #5529), 03g3w (0.25 #25, 0.05 #5312, 0.05 #4347), 0hfjk (0.25 #64, 0.04 #2944, 0.03 #3184) >> Best rule #5288 for best value: >> intensional similarity = 4 >> extensional distance = 1141 >> proper extension: 01fs__; >> query: (?x2037, 07s9rl0) <- language(?x2037, ?x254), ?x254 = 02h40lc, nominated_for(?x154, ?x2037), nominated_for(?x1897, ?x2037) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #492 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 115 *> proper extension: 01s3vk; *> query: (?x2037, 01jfsb) <- film(?x2108, ?x2037), story_by(?x2037, ?x9982), participant(?x2035, ?x2108), vacationer(?x205, ?x2108) *> conf = 0.36 ranks of expected_values: 4, 6 EVAL 0gvrws1 genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 74.000 72.000 0.576 http://example.org/film/film/genre EVAL 0gvrws1 genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 72.000 0.576 http://example.org/film/film/genre #21268-02bxd PRED entity: 02bxd PRED relation: family PRED expected values: 01kcd => 74 concepts (55 used for prediction) PRED predicted values (max 10 best out of 130): 0fx80y (0.33 #567, 0.33 #57, 0.28 #953), 0342h (0.33 #27, 0.33 #2, 0.25 #112), 05148p4 (0.33 #271, 0.25 #815, 0.25 #784), 0l14md (0.21 #389, 0.20 #181, 0.20 #152), 06ncr (0.21 #749, 0.08 #1224, 0.03 #1591), 0d8lm (0.21 #1036, 0.20 #208, 0.19 #880), 02qjv (0.20 #230, 0.18 #441, 0.17 #515), 0l14_3 (0.20 #419, 0.18 #494, 0.15 #647), 01vj9c (0.20 #190, 0.14 #743, 0.12 #862), 026t6 (0.15 #1614, 0.12 #807, 0.12 #776) >> Best rule #567 for best value: >> intensional similarity = 29 >> extensional distance = 10 >> proper extension: 042v_gx; 04rzd; >> query: (?x1662, 0fx80y) <- role(?x4975, ?x1662), role(?x2798, ?x1662), role(?x1574, ?x1662), role(?x1432, ?x1662), role(?x569, ?x1662), ?x1432 = 0395lw, role(?x569, ?x3418), role(?x569, ?x2944), role(?x569, ?x1831), role(?x569, ?x1166), instrumentalists(?x569, ?x669), role(?x569, ?x2059), group(?x569, ?x4995), ?x1574 = 0l15bq, performance_role(?x10811, ?x4975), ?x1166 = 05148p4, family(?x569, ?x2620), ?x1831 = 03t22m, ?x4995 = 01fmz6, role(?x1332, ?x569), role(?x6801, ?x2059), ?x2798 = 03qjg, instrumentalists(?x2944, ?x120), role(?x2205, ?x3418), ?x6801 = 01c3q, role(?x2059, ?x1212), role(?x2944, ?x432), music(?x670, ?x669), role(?x1466, ?x4975) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1494 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 29 *> proper extension: 01qzyz; *> query: (?x1662, ?x2620) <- role(?x4975, ?x1662), role(?x1574, ?x1662), role(?x1432, ?x1662), role(?x569, ?x1662), role(?x75, ?x1662), ?x1432 = 0395lw, role(?x569, ?x2048), role(?x569, ?x1831), role(?x569, ?x1166), role(?x569, ?x885), instrumentalists(?x569, ?x2584), role(?x569, ?x2460), role(?x569, ?x1148), group(?x569, ?x1751), ?x1574 = 0l15bq, performance_role(?x10811, ?x4975), ?x1166 = 05148p4, family(?x569, ?x2620), role(?x1466, ?x569), ?x1148 = 02qjv, ?x2460 = 01wy6, ?x2048 = 018j2, special_performance_type(?x2584, ?x9609), ?x885 = 0dwtp, role(?x120, ?x1831), role(?x1831, ?x8014), role(?x4975, ?x922), instrumentalists(?x1831, ?x1832), ?x8014 = 0214km, role(?x1887, ?x75) *> conf = 0.10 ranks of expected_values: 17 EVAL 02bxd family 01kcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 74.000 55.000 0.333 http://example.org/music/instrument/family #21267-0ccqd7 PRED entity: 0ccqd7 PRED relation: gender PRED expected values: 05zppz => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.75 #7, 0.73 #23, 0.73 #19), 02zsn (0.51 #103, 0.46 #140, 0.36 #16) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0fpjyd; 0bn3jg; >> query: (?x9894, 05zppz) <- student(?x4955, ?x9894), ?x4955 = 09f2j, place_of_birth(?x9894, ?x739) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ccqd7 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.745 http://example.org/people/person/gender #21266-04f525m PRED entity: 04f525m PRED relation: organization! PRED expected values: 0dq_5 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 10): 0dq_5 (0.61 #699, 0.60 #35, 0.53 #686), 060c4 (0.55 #666, 0.44 #991, 0.42 #1043), 07xl34 (0.19 #219, 0.17 #375, 0.14 #63), 05k17c (0.07 #879, 0.07 #736, 0.07 #905), 01yc02 (0.06 #82, 0.05 #134, 0.04 #160), 0krdk (0.06 #81, 0.05 #133, 0.04 #159), 05c0jwl (0.05 #825, 0.04 #838, 0.03 #929), 0hm4q (0.05 #1023, 0.04 #1101, 0.04 #971), 04n1q6 (0.04 #214, 0.03 #370, 0.01 #826), 08jcfy (0.03 #376, 0.02 #832, 0.02 #949) >> Best rule #699 for best value: >> intensional similarity = 2 >> extensional distance = 120 >> proper extension: 0l8sx; 01n073; 02y7t7; 02vyh; 03sc8; 05925; 04htfd; 061v5m; 0537b; 0py9b; ... >> query: (?x963, 0dq_5) <- industry(?x963, ?x373), state_province_region(?x963, ?x1310) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04f525m organization! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.607 http://example.org/organization/role/leaders./organization/leadership/organization #21265-015xp4 PRED entity: 015xp4 PRED relation: location PRED expected values: 0d6lp => 151 concepts (114 used for prediction) PRED predicted values (max 10 best out of 277): 0r3wm (0.33 #16070, 0.30 #7231, 0.29 #13660), 02_286 (0.27 #4858, 0.25 #6464, 0.25 #1643), 04lh6 (0.14 #3649, 0.13 #5256, 0.07 #4452), 01_d4 (0.14 #4118, 0.11 #101, 0.10 #6528), 04jpl (0.13 #14480, 0.11 #17, 0.07 #53039), 0c_m3 (0.11 #270, 0.10 #1073, 0.10 #7501), 0rw2x (0.11 #716, 0.07 #4733, 0.05 #7143), 0wqwj (0.11 #755, 0.05 #7182, 0.05 #8789), 05jbn (0.11 #252, 0.05 #7483, 0.04 #18732), 06yxd (0.11 #246, 0.05 #8280, 0.03 #13906) >> Best rule #16070 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0459z; >> query: (?x5140, ?x10400) <- instrumentalists(?x227, ?x5140), place_of_death(?x5140, ?x10400), location(?x5140, ?x1523), nationality(?x5140, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2577 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 018ty9; 0168ql; *> query: (?x5140, 0d6lp) <- nationality(?x5140, ?x94), people(?x5801, ?x5140), ?x5801 = 0dcsx *> conf = 0.08 ranks of expected_values: 31 EVAL 015xp4 location 0d6lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 151.000 114.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #21264-018t8f PRED entity: 018t8f PRED relation: campuses PRED expected values: 018t8f => 134 concepts (79 used for prediction) PRED predicted values (max 10 best out of 144): 018t8f (0.06 #27322, 0.06 #22947, 0.06 #18030), 0k9wp (0.06 #27322, 0.06 #22947, 0.06 #18030), 02h659 (0.03 #359, 0.01 #1451), 03zj9 (0.03 #180, 0.01 #1272), 0gdm1 (0.03 #222, 0.01 #1860), 01mpwj (0.03 #95, 0.01 #1733), 01k2wn (0.03 #19, 0.01 #1657), 016w7b (0.03 #519), 08htt0 (0.03 #505), 02hp6p (0.03 #443) >> Best rule #27322 for best value: >> intensional similarity = 4 >> extensional distance = 398 >> proper extension: 07vj4v; >> query: (?x9237, ?x5983) <- citytown(?x9237, ?x7770), contains(?x94, ?x7770), contains(?x7770, ?x5983), time_zones(?x7770, ?x2088) >> conf = 0.06 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 018t8f campuses 018t8f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 79.000 0.065 http://example.org/education/educational_institution/campuses #21263-0745k7 PRED entity: 0745k7 PRED relation: actor! PRED expected values: 05nlzq => 93 concepts (66 used for prediction) PRED predicted values (max 10 best out of 102): 05nlzq (0.43 #185, 0.03 #449, 0.03 #1505), 015w8_ (0.43 #46, 0.02 #1366, 0.02 #1632), 063zky (0.29 #108, 0.02 #1428, 0.02 #2488), 0ctzf1 (0.14 #136, 0.03 #400, 0.02 #1456), 01h72l (0.14 #38, 0.02 #1624, 0.02 #302), 025x1t (0.14 #223, 0.01 #487, 0.01 #1543), 026bfsh (0.05 #4327, 0.04 #3535, 0.04 #5120), 05f7w84 (0.04 #635, 0.03 #1693, 0.03 #2487), 024rwx (0.03 #370, 0.03 #1426, 0.03 #1958), 02_1q9 (0.03 #3971, 0.03 #2913, 0.02 #3443) >> Best rule #185 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 029cpw; 02gf_l; 04hxyv; >> query: (?x13493, 05nlzq) <- profession(?x13493, ?x1383), actor(?x11477, ?x13493), ?x11477 = 043qqt5, ?x1383 = 0np9r >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0745k7 actor! 05nlzq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 66.000 0.429 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21262-04rrd PRED entity: 04rrd PRED relation: contains! PRED expected values: 04_1l0v => 234 concepts (156 used for prediction) PRED predicted values (max 10 best out of 277): 04_1l0v (0.86 #8509, 0.85 #4928, 0.83 #12987), 02qkt (0.34 #89061, 0.32 #106091, 0.32 #68448), 04rrd (0.33 #135315, 0.29 #12537, 0.24 #118293), 0dn8b (0.33 #135315, 0.29 #12537, 0.24 #118293), 0cv0r (0.33 #135315, 0.24 #118293, 0.22 #78863), 027rqbx (0.33 #135315, 0.24 #118293, 0.22 #78863), 0cc07 (0.33 #135315, 0.24 #118293, 0.22 #78863), 0cv1h (0.33 #135315, 0.24 #118293, 0.22 #78863), 0cc1v (0.33 #135315, 0.24 #118293, 0.22 #78863), 0bx9y (0.33 #135315, 0.24 #118293, 0.22 #78863) >> Best rule #8509 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 03s0w; >> query: (?x1767, 04_1l0v) <- location(?x820, ?x1767), district_represented(?x845, ?x1767), ?x845 = 07p__7 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04rrd contains! 04_1l0v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 234.000 156.000 0.857 http://example.org/location/location/contains #21261-02z0f6l PRED entity: 02z0f6l PRED relation: featured_film_locations PRED expected values: 0bvqq => 90 concepts (50 used for prediction) PRED predicted values (max 10 best out of 98): 02_286 (0.16 #5730, 0.15 #7157, 0.14 #2162), 030qb3t (0.10 #993, 0.09 #2181, 0.08 #3845), 0rh6k (0.05 #2144, 0.03 #6663, 0.03 #6426), 0cv3w (0.05 #69, 0.03 #1024, 0.02 #546), 0h7h6 (0.05 #42, 0.02 #519, 0.02 #2422), 01yj2 (0.04 #389, 0.03 #1342, 0.01 #2530), 01_d4 (0.03 #1001, 0.03 #2189, 0.03 #1714), 080h2 (0.03 #6448, 0.03 #2166, 0.02 #6685), 0qr8z (0.02 #388, 0.02 #1341, 0.01 #1817), 0vzm (0.02 #550, 0.02 #73, 0.02 #789) >> Best rule #5730 for best value: >> intensional similarity = 4 >> extensional distance = 538 >> proper extension: 0d7vtk; >> query: (?x6900, 02_286) <- produced_by(?x6900, ?x3528), nominated_for(?x143, ?x6900), language(?x6900, ?x254), titles(?x53, ?x6900) >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02z0f6l featured_film_locations 0bvqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 50.000 0.156 http://example.org/film/film/featured_film_locations #21260-01k5y0 PRED entity: 01k5y0 PRED relation: language PRED expected values: 02h40lc 064_8sq => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 33): 02h40lc (0.92 #63, 0.90 #1483, 0.90 #240), 064_8sq (0.18 #142, 0.17 #319, 0.17 #260), 06nm1 (0.12 #11, 0.12 #604, 0.10 #1373), 04306rv (0.10 #480, 0.09 #421, 0.09 #1367), 02bjrlw (0.07 #121, 0.07 #594, 0.06 #890), 03_9r (0.07 #130, 0.06 #307, 0.06 #248), 06b_j (0.06 #912, 0.06 #498, 0.06 #676), 012w70 (0.04 #606, 0.03 #1375, 0.02 #1731), 0jzc (0.04 #140, 0.04 #317, 0.04 #258), 0653m (0.04 #132, 0.04 #605, 0.04 #1374) >> Best rule #63 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 014zwb; >> query: (?x10752, 02h40lc) <- film_release_distribution_medium(?x10752, ?x81), genre(?x10752, ?x239), nominated_for(?x4951, ?x10752), ?x239 = 06cvj >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01k5y0 language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.918 http://example.org/film/film/language EVAL 01k5y0 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.918 http://example.org/film/film/language #21259-0_816 PRED entity: 0_816 PRED relation: film! PRED expected values: 026l37 => 70 concepts (49 used for prediction) PRED predicted values (max 10 best out of 456): 02w0dc0 (0.44 #58243, 0.43 #35363, 0.43 #2080), 02779r4 (0.43 #2080, 0.42 #93611, 0.42 #76965), 01v80y (0.43 #2080, 0.42 #93611, 0.42 #76965), 02sjp (0.43 #2080, 0.42 #93611, 0.42 #76965), 0c6qh (0.05 #10813, 0.03 #6653, 0.03 #413), 0bxtg (0.05 #10477, 0.03 #77, 0.02 #22961), 02lf70 (0.05 #8321), 01vvb4m (0.05 #10920, 0.02 #40041, 0.02 #44201), 014zcr (0.04 #10437, 0.02 #14598, 0.02 #22921), 0j_c (0.04 #4569, 0.03 #409, 0.02 #2489) >> Best rule #58243 for best value: >> intensional similarity = 3 >> extensional distance = 923 >> proper extension: 01h72l; 0g5qmbz; >> query: (?x3255, ?x2849) <- award_winner(?x3255, ?x2849), gender(?x2849, ?x231), genre(?x3255, ?x53) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0_816 film! 026l37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 49.000 0.438 http://example.org/film/actor/film./film/performance/film #21258-01wy6 PRED entity: 01wy6 PRED relation: role PRED expected values: 042v_gx => 84 concepts (57 used for prediction) PRED predicted values (max 10 best out of 85): 01vj9c (0.88 #1591, 0.88 #3950, 0.86 #1765), 042v_gx (0.88 #1591, 0.86 #3591, 0.85 #1761), 0l14j_ (0.86 #1765, 0.85 #1761, 0.85 #2459), 018vs (0.86 #1765, 0.85 #1761, 0.84 #1760), 01xqw (0.86 #1765, 0.85 #1761, 0.84 #1760), 0g2dz (0.85 #1761, 0.84 #1760, 0.83 #1762), 03qmg1 (0.85 #1761, 0.84 #1760, 0.83 #1762), 05kms (0.85 #1761, 0.84 #1760, 0.83 #1762), 0bmnm (0.84 #1760, 0.83 #1762, 0.83 #3146), 0l15f_ (0.84 #1760, 0.83 #3146, 0.83 #3144) >> Best rule #1591 for best value: >> intensional similarity = 21 >> extensional distance = 4 >> proper extension: 0l14qv; >> query: (?x2460, ?x1495) <- role(?x4311, ?x2460), role(?x3716, ?x2460), role(?x1969, ?x2460), role(?x1495, ?x2460), ?x3716 = 03gvt, instrumentalists(?x2460, ?x3492), instrumentalists(?x2460, ?x2120), profession(?x2120, ?x220), ?x1969 = 04rzd, performance_role(?x228, ?x1495), role(?x1495, ?x8957), role(?x1495, ?x2798), role(?x1495, ?x2205), ?x2205 = 0dq630k, role(?x3767, ?x2460), role(?x130, ?x1495), gender(?x2120, ?x514), ?x3492 = 01lvcs1, ?x8957 = 03f5mt, group(?x4311, ?x1945), ?x2798 = 03qjg >> conf = 0.88 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01wy6 role 042v_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 57.000 0.885 http://example.org/music/performance_role/track_performances./music/track_contribution/role #21257-04q24zv PRED entity: 04q24zv PRED relation: film_format PRED expected values: 0cj16 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 5): 0cj16 (0.30 #19, 0.28 #36, 0.28 #31), 07fb8_ (0.19 #77, 0.19 #12, 0.18 #1), 017fx5 (0.09 #90, 0.08 #150, 0.07 #144), 0hcr (0.02 #23), 03g3w (0.02 #28) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 02rmd_2; 07l50vn; >> query: (?x2797, 0cj16) <- category(?x2797, ?x134), film_crew_role(?x2797, ?x137), titles(?x53, ?x2797), film_festivals(?x2797, ?x11147) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04q24zv film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.295 http://example.org/film/film/film_format #21256-04bfg PRED entity: 04bfg PRED relation: student PRED expected values: 04511f => 140 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1610): 0c9xjl (0.11 #948, 0.05 #3037, 0.04 #5126), 044rvb (0.11 #80, 0.05 #2169, 0.04 #4258), 03hpr (0.11 #1742, 0.05 #3831, 0.04 #5920), 04r68 (0.11 #875, 0.05 #2964, 0.04 #5053), 02_n5d (0.11 #541, 0.05 #2630, 0.04 #4719), 016sp_ (0.11 #387, 0.05 #2476, 0.04 #4565), 01z0lb (0.11 #1802, 0.05 #3891, 0.04 #5980), 04n2vgk (0.11 #1592, 0.05 #3681, 0.01 #18305), 04t7ts (0.11 #194, 0.05 #2283, 0.01 #16907), 0157m (0.09 #6516, 0.04 #21140, 0.03 #12783) >> Best rule #948 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02_2kg; >> query: (?x6602, 0c9xjl) <- currency(?x6602, ?x170), state_province_region(?x6602, ?x177), ?x177 = 05kkh, school_type(?x6602, ?x3092) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #13250 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 0288zy; 037s9x; 01hb1t; 01pcj4; 0ymff; 06thjt; 02m0b0; *> query: (?x6602, 04511f) <- currency(?x6602, ?x170), student(?x6602, ?x5263), school_type(?x6602, ?x3092), spouse(?x5263, ?x906) *> conf = 0.01 ranks of expected_values: 1287 EVAL 04bfg student 04511f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 140.000 104.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #21255-0581vn8 PRED entity: 0581vn8 PRED relation: honored_for! PRED expected values: 09k5jh7 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 108): 0n8_m93 (0.18 #103, 0.06 #347, 0.04 #835), 04n2r9h (0.14 #36, 0.06 #158, 0.05 #280), 0hr6lkl (0.14 #12, 0.04 #256, 0.03 #134), 09k5jh7 (0.09 #71, 0.07 #437, 0.06 #193), 05zksls (0.09 #28, 0.07 #394, 0.03 #516), 09pj68 (0.09 #90, 0.06 #212, 0.05 #334), 0bvfqq (0.09 #26, 0.06 #148, 0.05 #270), 09gkdln (0.09 #106, 0.06 #472, 0.03 #228), 0bvhz9 (0.09 #114, 0.04 #1090, 0.03 #724), 0275n3y (0.09 #64, 0.04 #308, 0.03 #186) >> Best rule #103 for best value: >> intensional similarity = 6 >> extensional distance = 20 >> proper extension: 0fpv_3_; 0cc5qkt; 0h03fhx; 0gmgwnv; >> query: (?x9250, 0n8_m93) <- nominated_for(?x2341, ?x9250), nominated_for(?x277, ?x9250), language(?x9250, ?x254), ?x2341 = 02x17s4, ?x277 = 0f_nbyh, nominated_for(?x6589, ?x9250) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #71 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 20 *> proper extension: 0fpv_3_; 0cc5qkt; 0h03fhx; 0gmgwnv; *> query: (?x9250, 09k5jh7) <- nominated_for(?x2341, ?x9250), nominated_for(?x277, ?x9250), language(?x9250, ?x254), ?x2341 = 02x17s4, ?x277 = 0f_nbyh, nominated_for(?x6589, ?x9250) *> conf = 0.09 ranks of expected_values: 4 EVAL 0581vn8 honored_for! 09k5jh7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.182 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #21254-01wqg8 PRED entity: 01wqg8 PRED relation: major_field_of_study PRED expected values: 04sh3 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 111): 02lp1 (0.48 #1146, 0.36 #264, 0.29 #1273), 01mkq (0.45 #1150, 0.30 #1277, 0.29 #646), 02j62 (0.43 #284, 0.41 #662, 0.35 #1166), 04rjg (0.39 #651, 0.33 #1155, 0.32 #1282), 03g3w (0.37 #658, 0.33 #1289, 0.33 #1036), 0g26h (0.36 #297, 0.33 #1179, 0.22 #1433), 05qjt (0.34 #638, 0.29 #1142, 0.26 #1016), 062z7 (0.31 #1163, 0.29 #281, 0.25 #1417), 01lj9 (0.29 #672, 0.22 #1176, 0.21 #420), 037mh8 (0.29 #323, 0.27 #701, 0.20 #1079) >> Best rule #1146 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 027mdh; >> query: (?x8903, 02lp1) <- category(?x8903, ?x134), institution(?x4981, ?x8903), ?x4981 = 03bwzr4 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1087 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 135 *> proper extension: 020yvh; *> query: (?x8903, 04sh3) <- school_type(?x8903, ?x3205), student(?x8903, ?x3941), influenced_by(?x920, ?x3941) *> conf = 0.16 ranks of expected_values: 25 EVAL 01wqg8 major_field_of_study 04sh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 114.000 114.000 0.481 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #21253-01vrt_c PRED entity: 01vrt_c PRED relation: award_winner! PRED expected values: 02f77y => 89 concepts (81 used for prediction) PRED predicted values (max 10 best out of 289): 02f716 (0.39 #2736, 0.35 #2991, 0.34 #14101), 03qbh5 (0.35 #2991, 0.34 #14101, 0.32 #11534), 01by1l (0.35 #2991, 0.34 #14101, 0.32 #11534), 02f73b (0.35 #2991, 0.34 #14101, 0.32 #11534), 02f705 (0.35 #2991, 0.34 #14101, 0.32 #11534), 03qbnj (0.35 #2991, 0.34 #14101, 0.32 #11534), 02f71y (0.35 #2991, 0.34 #14101, 0.32 #11534), 01c99j (0.35 #2991, 0.34 #14101, 0.32 #11534), 02x17c2 (0.35 #2991, 0.34 #14101, 0.32 #11534), 02f72_ (0.30 #2789, 0.20 #4926, 0.12 #18373) >> Best rule #2736 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 01v0sx2; 0frsw; 0137g1; 0gcs9; 0d193h; 011z3g; 07r1_; 01wf86y; 017959; 016l09; >> query: (?x1206, 02f716) <- award(?x1206, ?x2877), award(?x1206, ?x2139), ?x2877 = 02f5qb, ?x2139 = 01by1l >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #258 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0p3r8; *> query: (?x1206, 02f77y) <- artists(?x2995, ?x1206), artists(?x671, ?x1206), ?x2995 = 01cbwl, ?x671 = 064t9 *> conf = 0.14 ranks of expected_values: 36 EVAL 01vrt_c award_winner! 02f77y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 89.000 81.000 0.394 http://example.org/award/award_category/winners./award/award_honor/award_winner #21252-01v42g PRED entity: 01v42g PRED relation: award_nominee PRED expected values: 073w14 => 93 concepts (41 used for prediction) PRED predicted values (max 10 best out of 602): 044lyq (0.81 #58487, 0.81 #42109, 0.81 #49127), 03mg35 (0.25 #412, 0.01 #26146, 0.01 #28485), 0ksrf8 (0.17 #1318, 0.02 #3658), 01pkhw (0.17 #933), 071ywj (0.17 #671), 0kszw (0.10 #60827, 0.10 #58486, 0.08 #545), 06cgy (0.10 #60827, 0.10 #58486, 0.08 #327), 04954 (0.10 #60827, 0.10 #58486, 0.08 #1687), 02xv8m (0.10 #60827, 0.10 #58486, 0.08 #879), 016gr2 (0.10 #60827, 0.10 #58486, 0.07 #4931) >> Best rule #58487 for best value: >> intensional similarity = 2 >> extensional distance = 1236 >> proper extension: 054_mz; 01mvth; 02lfl4; 0yfp; 03lt8g; 01vrz41; 044mm6; 02lg9w; 0gt_k; 06lgq8; ... >> query: (?x1289, ?x1290) <- film(?x1289, ?x1640), award_nominee(?x1290, ?x1289) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #60827 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1239 *> proper extension: 06449; *> query: (?x1289, ?x395) <- film(?x1289, ?x9329), award_nominee(?x1289, ?x1290), film(?x395, ?x9329) *> conf = 0.10 ranks of expected_values: 27 EVAL 01v42g award_nominee 073w14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 93.000 41.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21251-015fr PRED entity: 015fr PRED relation: nationality! PRED expected values: 05tk7y => 219 concepts (98 used for prediction) PRED predicted values (max 10 best out of 4150): 03h8_g (0.26 #48770, 0.23 #113805, 0.22 #134127), 020hyj (0.18 #11332, 0.13 #31652, 0.12 #19460), 054k_8 (0.18 #9834, 0.12 #17962, 0.10 #62669), 02h761 (0.18 #13347, 0.12 #17411, 0.09 #25539), 04kj2v (0.18 #12878, 0.12 #16942, 0.09 #25070), 0b_fw (0.18 #8704, 0.12 #16832, 0.09 #24960), 01304j (0.18 #11617, 0.12 #19745, 0.09 #27873), 051x52f (0.18 #10582, 0.12 #18710, 0.09 #26838), 049fgvm (0.18 #10198, 0.12 #18326, 0.09 #26454), 03v40v (0.18 #9761, 0.12 #17889, 0.09 #26017) >> Best rule #48770 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 01nhhz; >> query: (?x583, ?x11208) <- combatants(?x326, ?x583), nationality(?x6390, ?x583), location(?x11208, ?x583) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #97544 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0j1z8; 06mzp; 09pmkv; 04wgh; 07t21; 05sb1; 02wt0; 06t2t; 03shp; 03__y; ... *> query: (?x583, ?x1365) <- film_release_region(?x1118, ?x583), exported_to(?x94, ?x583), film(?x1365, ?x1118) *> conf = 0.06 ranks of expected_values: 3582 EVAL 015fr nationality! 05tk7y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 98.000 0.257 http://example.org/people/person/nationality #21250-08c9b0 PRED entity: 08c9b0 PRED relation: people! PRED expected values: 03lmx1 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 41): 0x67 (0.26 #1627, 0.25 #1704, 0.24 #1858), 02w7gg (0.21 #2312, 0.08 #2697, 0.07 #3468), 041rx (0.18 #697, 0.15 #235, 0.14 #466), 0d7wh (0.14 #94, 0.13 #17, 0.12 #171), 033tf_ (0.07 #3396, 0.07 #2702, 0.07 #4782), 0xnvg (0.06 #1707, 0.06 #1630, 0.06 #244), 013b6_ (0.05 #746, 0.04 #284, 0.03 #1362), 09vc4s (0.05 #933, 0.04 #779, 0.03 #1626), 013xrm (0.04 #1098, 0.04 #1021, 0.04 #482), 07bch9 (0.04 #2718, 0.04 #793, 0.03 #2872) >> Best rule #1627 for best value: >> intensional similarity = 4 >> extensional distance = 202 >> proper extension: 01vw917; >> query: (?x4911, 0x67) <- artists(?x4910, ?x4911), film(?x4911, ?x2155), nationality(?x4911, ?x6401), genre(?x2155, ?x225) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 01nqfh_; *> query: (?x4911, 03lmx1) <- music(?x4273, ?x4911), music(?x2155, ?x4911), region(?x4273, ?x512), film_release_region(?x2155, ?x87), film_crew_role(?x4273, ?x137) *> conf = 0.03 ranks of expected_values: 12 EVAL 08c9b0 people! 03lmx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 112.000 112.000 0.265 http://example.org/people/ethnicity/people #21249-0p_qr PRED entity: 0p_qr PRED relation: language PRED expected values: 02h40lc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.91 #2, 0.88 #357, 0.88 #2152), 064_8sq (0.24 #140, 0.23 #81, 0.18 #258), 04306rv (0.19 #123, 0.19 #64, 0.17 #241), 0jzc (0.13 #138, 0.12 #256, 0.07 #79), 06nm1 (0.10 #306, 0.10 #847, 0.09 #1025), 06b_j (0.10 #141, 0.09 #82, 0.07 #259), 02bjrlw (0.10 #656, 0.09 #356, 0.09 #595), 03_9r (0.09 #10, 0.07 #69, 0.07 #246), 04h9h (0.06 #43, 0.06 #220, 0.04 #338), 0653m (0.05 #71, 0.05 #426, 0.04 #130) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0dqytn; 0209xj; 0pv2t; 092vkg; 0_92w; 05j82v; 0p_th; 011yth; 0yzvw; 0f4_l; ... >> query: (?x3505, 02h40lc) <- award(?x3505, ?x834), nominated_for(?x746, ?x3505), genre(?x3505, ?x53), ?x834 = 027986c >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p_qr language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.909 http://example.org/film/film/language #21248-0187nd PRED entity: 0187nd PRED relation: institution! PRED expected values: 019v9k => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 18): 019v9k (0.79 #6, 0.69 #159, 0.69 #83), 016t_3 (0.71 #2, 0.67 #79, 0.66 #117), 0bkj86 (0.71 #82, 0.70 #44, 0.70 #63), 03bwzr4 (0.71 #88, 0.68 #11, 0.67 #50), 04zx3q1 (0.52 #59, 0.51 #40, 0.50 #78), 027f2w (0.51 #46, 0.50 #84, 0.48 #65), 022h5x (0.39 #194, 0.29 #17, 0.20 #170), 0bjrnt (0.39 #194, 0.23 #43, 0.21 #119), 01rr_d (0.39 #194, 0.17 #478, 0.15 #72), 071tyz (0.39 #194, 0.09 #47, 0.08 #472) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 06mkj; 0d05w3; >> query: (?x9847, 019v9k) <- organization(?x9847, ?x5487), contains(?x94, ?x9847), school(?x3334, ?x9847) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0187nd institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.786 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21247-04n2r9h PRED entity: 04n2r9h PRED relation: honored_for PRED expected values: 02w9k1c => 44 concepts (23 used for prediction) PRED predicted values (max 10 best out of 709): 0kfv9 (0.60 #2411, 0.18 #10498, 0.13 #11077), 030cx (0.60 #2571, 0.07 #10658, 0.07 #11237), 017jd9 (0.50 #1423, 0.33 #3731, 0.10 #6040), 02c638 (0.50 #1279, 0.33 #3587, 0.10 #5896), 02cbhg (0.50 #1613, 0.33 #3921, 0.10 #6230), 0294mx (0.50 #1572, 0.33 #3880, 0.10 #6189), 01ft14 (0.40 #2839, 0.07 #10926, 0.07 #11505), 0d68qy (0.33 #3609, 0.28 #11121, 0.27 #11698), 0hz55 (0.33 #3749, 0.25 #1441, 0.15 #11261), 0ds3t5x (0.33 #4057, 0.19 #5789, 0.12 #4635) >> Best rule #2411 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 07y_p6; >> query: (?x2988, 0kfv9) <- ceremony(?x899, ?x2988), honored_for(?x2988, ?x6097), award_winner(?x2988, ?x6383), languages(?x6383, ?x254), award(?x6383, ?x724), location(?x6383, ?x335), award_winner(?x6383, ?x827), people(?x1446, ?x6383), nominated_for(?x3064, ?x6097), ?x3064 = 05q5t0b >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7842 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 33 *> proper extension: 0gpjbt; 026kq4q; 0bz6sb; 026kqs9; 0d__c3; *> query: (?x2988, 02w9k1c) <- ceremony(?x899, ?x2988), honored_for(?x2988, ?x6097), honored_for(?x2988, ?x4998), award_winner(?x2988, ?x6383), film(?x719, ?x6097), nominated_for(?x5389, ?x6097), written_by(?x6097, ?x3662), artist(?x2190, ?x6383), gender(?x6383, ?x514), genre(?x6097, ?x604), film_release_region(?x4998, ?x87), genre(?x2955, ?x604), ?x2955 = 01bb9r, people(?x1446, ?x6383) *> conf = 0.03 ranks of expected_values: 429 EVAL 04n2r9h honored_for 02w9k1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #21246-01mqc_ PRED entity: 01mqc_ PRED relation: award_nominee! PRED expected values: 015t56 03_6y => 87 concepts (31 used for prediction) PRED predicted values (max 10 best out of 944): 08swgx (0.82 #13882, 0.82 #13881, 0.81 #43965), 01mqc_ (0.65 #8599, 0.14 #62477, 0.14 #43966), 015t56 (0.65 #7542, 0.14 #62477, 0.14 #43966), 03_6y (0.50 #7713, 0.14 #62477, 0.14 #43966), 03zz8b (0.27 #55534, 0.21 #32397, 0.14 #62477), 02k4b2 (0.27 #55534, 0.21 #32397, 0.14 #62477), 086k8 (0.27 #55534, 0.21 #32397, 0.14 #62477), 04zwtdy (0.27 #55534, 0.21 #32397, 0.14 #62477), 01vvb4m (0.27 #55534, 0.21 #32397, 0.14 #62477), 0h1mt (0.27 #55534, 0.21 #32397, 0.14 #62477) >> Best rule #13882 for best value: >> intensional similarity = 3 >> extensional distance = 117 >> proper extension: 02hy9p; >> query: (?x7525, ?x5022) <- award_nominee(?x7525, ?x5022), award_nominee(?x5022, ?x72), celebrity(?x4126, ?x7525) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #7542 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0184jc; 06151l; 0z4s; 0c4f4; 0hvb2; 03_wj_; 01pgzn_; 015t56; 019pm_; 08swgx; ... *> query: (?x7525, 015t56) <- award_nominee(?x7525, ?x5022), ?x5022 = 0278x6s, film(?x7525, ?x1045) *> conf = 0.65 ranks of expected_values: 3, 4 EVAL 01mqc_ award_nominee! 03_6y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 31.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 01mqc_ award_nominee! 015t56 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 31.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21245-0854hr PRED entity: 0854hr PRED relation: nationality PRED expected values: 09c7w0 => 82 concepts (71 used for prediction) PRED predicted values (max 10 best out of 93): 09c7w0 (0.88 #802, 0.86 #501, 0.84 #3416), 03rjj (0.29 #105, 0.10 #305, 0.06 #405), 02jx1 (0.25 #233, 0.13 #633, 0.12 #733), 07ssc (0.12 #215, 0.11 #1216, 0.09 #3530), 0chghy (0.07 #710, 0.06 #1011, 0.06 #1111), 0d060g (0.07 #1208, 0.05 #5332, 0.04 #6234), 03rt9 (0.06 #413, 0.02 #1517, 0.02 #1618), 0f8l9c (0.05 #3616, 0.04 #3819, 0.04 #3818), 059j2 (0.05 #3616, 0.04 #3819, 0.04 #3818), 03gj2 (0.05 #3616, 0.04 #3819, 0.04 #3818) >> Best rule #802 for best value: >> intensional similarity = 2 >> extensional distance = 41 >> proper extension: 0191h5; >> query: (?x5389, 09c7w0) <- location(?x5389, ?x4253), ?x4253 = 0ccvx >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0854hr nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 71.000 0.884 http://example.org/people/person/nationality #21244-0b3n61 PRED entity: 0b3n61 PRED relation: currency PRED expected values: 09nqf => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.88 #43, 0.87 #36, 0.86 #22), 01nv4h (0.25 #365, 0.03 #9, 0.02 #58), 088n7 (0.25 #365, 0.02 #63), 02l6h (0.01 #123, 0.01 #137) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 0d1qmz; 025twgt; >> query: (?x7806, 09nqf) <- film_release_distribution_medium(?x7806, ?x81), prequel(?x3839, ?x7806), film(?x8619, ?x7806), people(?x2510, ?x8619) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b3n61 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.879 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21243-016yvw PRED entity: 016yvw PRED relation: type_of_union PRED expected values: 04ztj 01g63y => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #13, 0.86 #9, 0.84 #17), 0jgjn (0.58 #317), 01g63y (0.35 #6, 0.32 #18, 0.29 #10) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 253 >> proper extension: 0ff2k; 06c0j; >> query: (?x5363, 04ztj) <- award_winner(?x591, ?x5363), spouse(?x11354, ?x5363) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 016yvw type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.875 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 016yvw type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.875 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21242-01l78d PRED entity: 01l78d PRED relation: award! PRED expected values: 017r2 016yzz 01gw4f => 47 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2436): 0mb5x (0.80 #16724, 0.78 #26758, 0.77 #30103), 06mr6 (0.80 #16724, 0.78 #26758, 0.77 #30103), 0c4y8 (0.80 #16724, 0.78 #26758, 0.77 #30103), 0dbpwb (0.80 #16724, 0.78 #26758, 0.77 #30103), 0l6qt (0.80 #16724, 0.78 #26758, 0.77 #30103), 0g1rw (0.67 #23575, 0.58 #20229, 0.53 #26920), 086k8 (0.58 #23479, 0.58 #20133, 0.53 #26824), 016tt2 (0.58 #20193, 0.47 #26884, 0.44 #30229), 05hj_k (0.50 #4476, 0.25 #24546, 0.22 #14512), 02kxbx3 (0.50 #11009, 0.22 #14355, 0.16 #6689) >> Best rule #16724 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 019f4v; 02pqp12; 0gq9h; 01l29r; 0fbtbt; 0bm70b; >> query: (?x7606, ?x164) <- award_winner(?x7606, ?x164), award(?x12392, ?x7606), award(?x4008, ?x7606), award(?x2733, ?x7606), ?x2733 = 0hskw, gender(?x12392, ?x231), award_winner(?x747, ?x4008) >> conf = 0.80 => this is the best rule for 5 predicted values *> Best rule #6689 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 01lj_c; *> query: (?x7606, ?x488) <- award_winner(?x7606, ?x3170), award(?x12392, ?x7606), award(?x2733, ?x7606), award(?x902, ?x7606), ?x902 = 05qd_, ?x3170 = 04cw0j, award_nominee(?x488, ?x2733), profession(?x12392, ?x319) *> conf = 0.16 ranks of expected_values: 418, 460, 1418 EVAL 01l78d award! 01gw4f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 20.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01l78d award! 016yzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 20.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01l78d award! 017r2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 20.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21241-0353tm PRED entity: 0353tm PRED relation: genre PRED expected values: 01jfsb => 117 concepts (69 used for prediction) PRED predicted values (max 10 best out of 119): 01jfsb (0.97 #3979, 0.75 #974, 0.71 #734), 02l7c8 (0.82 #2181, 0.36 #1339, 0.34 #2421), 03k9fj (0.74 #1936, 0.71 #2056, 0.62 #2897), 07s9rl0 (0.73 #5168, 0.70 #7457, 0.68 #3126), 02kdv5l (0.53 #3969, 0.51 #2888, 0.50 #2768), 05p553 (0.50 #3370, 0.47 #2650, 0.47 #3250), 01hmnh (0.50 #1221, 0.47 #1582, 0.43 #2063), 02n4kr (0.50 #849, 0.27 #3974, 0.25 #1691), 060__y (0.40 #1220, 0.23 #2302, 0.20 #3504), 01j1n2 (0.36 #1502, 0.27 #1382, 0.20 #541) >> Best rule #3979 for best value: >> intensional similarity = 7 >> extensional distance = 58 >> proper extension: 0dnvn3; 09p0ct; 072x7s; 03lrht; 020y73; 0g3zrd; 0cc5mcj; 0k5g9; 078sj4; 033srr; ... >> query: (?x9213, 01jfsb) <- genre(?x9213, ?x571), film(?x2549, ?x9213), music(?x9213, ?x12768), film(?x2141, ?x9213), film_format(?x9213, ?x909), genre(?x7415, ?x571), ?x7415 = 02qr3k8 >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0353tm genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 69.000 0.967 http://example.org/film/film/genre #21240-04f4z1k PRED entity: 04f4z1k PRED relation: school PRED expected values: 033x5p 09f2j 016sd3 => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 805): 0lyjf (0.64 #1025, 0.60 #1373, 0.60 #1314), 065y4w7 (0.64 #1025, 0.60 #1150, 0.57 #1259), 01vs5c (0.64 #1025, 0.60 #1322, 0.57 #1259), 07vyf (0.64 #1025, 0.60 #1257, 0.57 #1259), 07w0v (0.64 #1025, 0.60 #1270, 0.57 #1259), 0f1nl (0.64 #1025, 0.57 #1259, 0.50 #1615), 012vwb (0.64 #1025, 0.57 #1259, 0.50 #1261), 01hx2t (0.64 #1025, 0.57 #1259, 0.45 #1384), 01tx9m (0.64 #1025, 0.57 #1259, 0.45 #1384), 025v3k (0.64 #1025, 0.57 #1259, 0.44 #1378) >> Best rule #1025 for best value: >> intensional similarity = 61 >> extensional distance = 2 >> proper extension: 06439y; >> query: (?x10600, ?x735) <- school(?x10600, ?x8363), school(?x10600, ?x6953), school(?x10600, ?x4916), school(?x10600, ?x3779), draft(?x12042, ?x10600), draft(?x7060, ?x10600), draft(?x2067, ?x10600), draft(?x1010, ?x10600), major_field_of_study(?x3779, ?x1668), school(?x7060, ?x8479), school(?x7060, ?x5651), institution(?x3437, ?x3779), institution(?x1771, ?x3779), institution(?x1368, ?x3779), institution(?x1200, ?x3779), school(?x1010, ?x3948), school(?x1010, ?x2497), ?x5651 = 027mdh, category(?x4916, ?x134), contains(?x3778, ?x3779), currency(?x3779, ?x170), ?x3948 = 025v3k, student(?x4916, ?x9232), team(?x4244, ?x1010), school(?x12042, ?x735), ?x1771 = 019v9k, ?x3437 = 02_xgp2, school(?x2067, ?x1276), colors(?x12042, ?x1101), team(?x2010, ?x12042), team(?x12323, ?x1010), citytown(?x4916, ?x5775), fraternities_and_sororities(?x3779, ?x3697), ?x1101 = 06fvc, major_field_of_study(?x4916, ?x1682), company(?x1907, ?x2067), colors(?x4916, ?x5845), ?x1200 = 016t_3, ?x2497 = 0f1nl, state_province_region(?x8479, ?x726), teams(?x2254, ?x12042), ?x1668 = 01mkq, student(?x8363, ?x2046), school(?x1239, ?x6953), ?x170 = 09nqf, major_field_of_study(?x8479, ?x1154), institution(?x4981, ?x8363), major_field_of_study(?x8363, ?x3213), student(?x6953, ?x117), position_s(?x1239, ?x180), organization(?x346, ?x8363), district_represented(?x176, ?x3778), fraternities_and_sororities(?x4916, ?x4348), state(?x4350, ?x3778), ?x134 = 08mbj5d, location(?x396, ?x3778), adjoins(?x1025, ?x3778), ?x1368 = 014mlp, ?x3213 = 0g4gr, team(?x1114, ?x1239), list(?x3779, ?x2197) >> conf = 0.64 => this is the best rule for 25 predicted values *> Best rule #1384 for first EXPECTED value: *> intensional similarity = 66 *> extensional distance = 3 *> proper extension: 03nt7j; *> query: (?x10600, ?x621) <- school(?x10600, ?x3779), school(?x10600, ?x466), draft(?x8111, ?x10600), draft(?x7060, ?x10600), draft(?x6074, ?x10600), draft(?x1632, ?x10600), draft(?x1010, ?x10600), major_field_of_study(?x3779, ?x6859), major_field_of_study(?x3779, ?x4321), school(?x7060, ?x8851), school(?x7060, ?x8479), school(?x7060, ?x7596), school(?x7060, ?x5651), school(?x7060, ?x2830), school(?x7060, ?x621), institution(?x3437, ?x3779), institution(?x1200, ?x3779), school(?x1010, ?x6814), school(?x6074, ?x7439), school(?x6074, ?x4904), ?x1200 = 016t_3, major_field_of_study(?x5651, ?x742), currency(?x5651, ?x170), ?x742 = 05qjt, category(?x6074, ?x134), school(?x8111, ?x8120), teams(?x1860, ?x7060), ?x4904 = 0lyjf, ?x7596 = 012mzw, contains(?x94, ?x3779), school(?x1823, ?x3779), ?x6859 = 01tbp, ?x3437 = 02_xgp2, sport(?x1632, ?x5063), ?x4321 = 0g26h, team(?x12323, ?x1010), list(?x3779, ?x2197), ?x2197 = 09g7thr, colors(?x7060, ?x4557), team(?x5412, ?x1632), ?x5412 = 03n69x, team(?x11844, ?x6074), major_field_of_study(?x8851, ?x2601), ?x466 = 01pl14, contains(?x2831, ?x2830), organization(?x3779, ?x5487), organization(?x346, ?x8851), student(?x8851, ?x2135), school(?x1632, ?x6856), school(?x1632, ?x5486), student(?x3779, ?x2409), fraternities_and_sororities(?x7439, ?x3697), major_field_of_study(?x6856, ?x2014), student(?x6856, ?x6700), major_field_of_study(?x2830, ?x1154), colors(?x3779, ?x5845), ?x8120 = 01rc6f, contains(?x1426, ?x5486), major_field_of_study(?x5486, ?x254), ?x6814 = 03tw2s, school_type(?x8479, ?x3092), colors(?x2830, ?x8271), team(?x2066, ?x7060), contains(?x1025, ?x7439), institution(?x865, ?x6856), school(?x3089, ?x6856) *> conf = 0.45 ranks of expected_values: 60, 66, 81 EVAL 04f4z1k school 016sd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 18.000 18.000 0.636 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 04f4z1k school 09f2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 18.000 18.000 0.636 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 04f4z1k school 033x5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 18.000 18.000 0.636 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #21239-016xk5 PRED entity: 016xk5 PRED relation: student! PRED expected values: 01vmv_ => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 61): 015nl4 (0.15 #594, 0.14 #67, 0.07 #2175), 01722w (0.10 #305, 0.01 #2413), 08815 (0.07 #2, 0.02 #7382, 0.02 #1583), 07tg4 (0.06 #613, 0.06 #2194, 0.03 #86), 07tgn (0.06 #544, 0.04 #2125, 0.02 #6869), 02l9wl (0.06 #779, 0.03 #252, 0.03 #2360), 0m4yg (0.06 #892, 0.03 #2473, 0.01 #1946), 02cw8s (0.06 #597), 0bwfn (0.05 #4492, 0.05 #6600, 0.05 #8182), 09k23 (0.03 #488, 0.03 #1015, 0.01 #2596) >> Best rule #594 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 07hbxm; 04rsd2; 0175wg; 02cgb8; 01qrbf; 0djywgn; >> query: (?x7077, 015nl4) <- award_nominee(?x7077, ?x1222), profession(?x7077, ?x353), ?x1222 = 03f1zdw >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #961 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 07hbxm; 04rsd2; 0175wg; 02cgb8; 01qrbf; 0djywgn; *> query: (?x7077, 01vmv_) <- award_nominee(?x7077, ?x1222), profession(?x7077, ?x353), ?x1222 = 03f1zdw *> conf = 0.03 ranks of expected_values: 28 EVAL 016xk5 student! 01vmv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 77.000 77.000 0.147 http://example.org/education/educational_institution/students_graduates./education/education/student #21238-05dss7 PRED entity: 05dss7 PRED relation: currency PRED expected values: 09nqf => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.87 #183, 0.83 #218, 0.82 #316), 01nv4h (0.09 #9, 0.05 #163, 0.05 #100), 02l6h (0.06 #32, 0.05 #109, 0.04 #123), 02gsvk (0.01 #349, 0.01 #377, 0.01 #405) >> Best rule #183 for best value: >> intensional similarity = 7 >> extensional distance = 89 >> proper extension: 020fcn; 0340hj; 0fdv3; 050gkf; 05_5rjx; 0415ggl; 0yxf4; 01y9r2; 01gglm; 03m5y9p; ... >> query: (?x6556, 09nqf) <- production_companies(?x6556, ?x752), film_crew_role(?x6556, ?x2095), film_crew_role(?x6556, ?x1284), ?x2095 = 0dxtw, ?x1284 = 0ch6mp2, country(?x6556, ?x94), film(?x8235, ?x6556) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05dss7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.868 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21237-0m19t PRED entity: 0m19t PRED relation: artists! PRED expected values: 08jyyk 01pfpt 0163zw 01g_bs => 87 concepts (37 used for prediction) PRED predicted values (max 10 best out of 276): 064t9 (0.78 #2369, 0.62 #3843, 0.50 #601), 06by7 (0.78 #2378, 0.61 #7100, 0.57 #8878), 05r6t (0.75 #7749, 0.62 #8640, 0.40 #2732), 0ggx5q (0.67 #2433, 0.44 #3907, 0.25 #1257), 08jyyk (0.60 #1540, 0.45 #6255, 0.41 #5959), 025sc50 (0.56 #3878, 0.56 #2404, 0.14 #3539), 02lnbg (0.56 #2413, 0.38 #3887, 0.09 #10384), 03_d0 (0.51 #7976, 0.50 #8272, 0.25 #895), 03lty (0.47 #4154, 0.42 #9473, 0.25 #913), 02t8gf (0.47 #4259, 0.25 #3377, 0.17 #6027) >> Best rule #2369 for best value: >> intensional similarity = 10 >> extensional distance = 7 >> proper extension: 01vs73g; >> query: (?x498, 064t9) <- category(?x498, ?x134), ?x134 = 08mbj5d, artists(?x7220, ?x498), artists(?x2936, ?x498), artists(?x7220, ?x5227), artists(?x7220, ?x5126), ?x2936 = 029h7y, ?x5126 = 03h502k, ?x5227 = 01j59b0, parent_genre(?x2439, ?x7220) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1540 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 03fbc; 016ntp; *> query: (?x498, 08jyyk) <- category(?x498, ?x134), artist(?x4483, ?x498), artists(?x6210, ?x498), artists(?x2996, ?x498), artists(?x474, ?x498), ?x2996 = 01243b, ?x474 = 0m0jc, artists(?x6210, ?x1521), ?x1521 = 01wp8w7 *> conf = 0.60 ranks of expected_values: 5, 42, 51, 160 EVAL 0m19t artists! 01g_bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 87.000 37.000 0.778 http://example.org/music/genre/artists EVAL 0m19t artists! 0163zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 87.000 37.000 0.778 http://example.org/music/genre/artists EVAL 0m19t artists! 01pfpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 87.000 37.000 0.778 http://example.org/music/genre/artists EVAL 0m19t artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 37.000 0.778 http://example.org/music/genre/artists #21236-0154qm PRED entity: 0154qm PRED relation: nominated_for PRED expected values: 02mpyh => 97 concepts (38 used for prediction) PRED predicted values (max 10 best out of 429): 049xgc (0.79 #29010, 0.77 #30623, 0.77 #38685), 027m5wv (0.79 #29010, 0.77 #30623, 0.77 #38685), 0ndwt2w (0.28 #29011, 0.28 #4835, 0.25 #22565), 026p4q7 (0.28 #29011, 0.28 #4835, 0.25 #22565), 04s1zr (0.28 #29011, 0.28 #4835, 0.25 #22565), 04ynx7 (0.28 #29011, 0.28 #4835, 0.25 #22565), 04mcw4 (0.28 #29011, 0.28 #4835, 0.25 #22565), 08k40m (0.28 #29011, 0.28 #4835, 0.25 #22565), 05q96q6 (0.28 #29011, 0.28 #4835, 0.25 #22565), 0dr_4 (0.27 #5061, 0.23 #3449, 0.12 #32237) >> Best rule #29010 for best value: >> intensional similarity = 2 >> extensional distance = 939 >> proper extension: 03kxp7; 01rw116; >> query: (?x3281, ?x972) <- award_winner(?x972, ?x3281), film(?x3281, ?x1038) >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #32237 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 985 *> proper extension: 01sl1q; 044mz_; 0184jc; 02s2ft; 01vvydl; 02qgqt; 0fvf9q; 0jz9f; 02p65p; 0337vz; ... *> query: (?x3281, ?x4610) <- award_nominee(?x3281, ?x4999), award_winner(?x747, ?x3281), award_winner(?x4610, ?x4999) *> conf = 0.12 ranks of expected_values: 27 EVAL 0154qm nominated_for 02mpyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 97.000 38.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #21235-059y0 PRED entity: 059y0 PRED relation: profession PRED expected values: 06q2q => 190 concepts (127 used for prediction) PRED predicted values (max 10 best out of 108): 0cbd2 (0.75 #1646, 0.73 #1348, 0.64 #7011), 02hrh1q (0.68 #18056, 0.65 #8511, 0.65 #8660), 06q2q (0.50 #493, 0.37 #11032, 0.33 #195), 0dxtg (0.48 #12984, 0.48 #10002, 0.47 #9853), 0kyk (0.47 #1819, 0.41 #3608, 0.41 #9422), 01pxg (0.37 #11032, 0.31 #16847, 0.31 #17147), 01d_h8 (0.34 #9994, 0.33 #11783, 0.33 #9845), 036n1 (0.33 #277, 0.25 #575, 0.04 #2512), 07lqg0 (0.33 #267, 0.25 #565, 0.04 #2502), 05z96 (0.33 #342, 0.24 #4217, 0.24 #3621) >> Best rule #1646 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 03f0324; 040_t; 04xfb; 0ky1; 0c73z; >> query: (?x10913, 0cbd2) <- influenced_by(?x10913, ?x2397), people(?x5855, ?x10913), student(?x742, ?x10913), profession(?x10913, ?x11056) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #493 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01t_z; *> query: (?x10913, 06q2q) <- influenced_by(?x10913, ?x2397), award_winner(?x12587, ?x10913), place_of_birth(?x10913, ?x8174), ?x12587 = 020qjg *> conf = 0.50 ranks of expected_values: 3 EVAL 059y0 profession 06q2q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 127.000 0.750 http://example.org/people/person/profession #21234-01pb34 PRED entity: 01pb34 PRED relation: special_performance_type! PRED expected values: 01q_ph 06w2sn5 02p21g 02vmzp 0309jm 0bq2g 0391jz 01j5sd 01vtg4q 01gkmx 0gthm 04qp06 01l3j => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 2128): 015p37 (0.50 #182, 0.33 #104, 0.25 #256), 0151w_ (0.33 #70, 0.25 #222, 0.25 #148), 015pkc (0.33 #74, 0.25 #226, 0.25 #152), 01y665 (0.33 #77, 0.25 #229, 0.25 #155), 01k8rb (0.33 #72, 0.25 #224, 0.25 #150), 03q1vd (0.33 #76, 0.25 #228, 0.25 #154), 01pjr7 (0.33 #90, 0.25 #242, 0.25 #168), 01xllf (0.33 #101, 0.25 #253, 0.25 #179), 0kjrx (0.33 #93, 0.25 #245, 0.25 #171), 01qr1_ (0.33 #78, 0.25 #230, 0.25 #156) >> Best rule #182 for best value: >> intensional similarity = 67 >> extensional distance = 2 >> proper extension: 01kyvx; >> query: (?x4832, 015p37) <- film(?x4832, ?x5016), film(?x4832, ?x787), special_performance_type(?x12652, ?x4832), special_performance_type(?x6433, ?x4832), special_performance_type(?x6187, ?x4832), special_performance_type(?x5338, ?x4832), special_performance_type(?x4782, ?x4832), special_performance_type(?x4065, ?x4832), special_performance_type(?x2805, ?x4832), special_performance_type(?x2444, ?x4832), currency(?x5016, ?x170), award_nominee(?x4782, ?x91), film_release_region(?x5016, ?x2000), film_release_region(?x5016, ?x87), nominated_for(?x1018, ?x787), location(?x6433, ?x739), participant(?x6187, ?x2443), film(?x6187, ?x634), country(?x1967, ?x2000), film_release_region(?x6931, ?x87), film_release_region(?x6078, ?x87), film_release_region(?x5400, ?x87), film_release_region(?x4355, ?x87), film_release_region(?x4041, ?x87), film_release_region(?x3276, ?x87), film_release_region(?x2550, ?x87), film_release_region(?x2441, ?x87), film_release_region(?x791, ?x87), gender(?x12652, ?x514), nominated_for(?x5338, ?x1178), ?x3276 = 0gjc4d3, olympics(?x87, ?x778), participant(?x5665, ?x2444), award_nominee(?x748, ?x2805), ?x2441 = 0cc5mcj, participant(?x4065, ?x1145), religion(?x87, ?x1985), participant(?x4782, ?x1896), award_nominee(?x382, ?x5338), country(?x150, ?x87), participating_countries(?x1608, ?x87), profession(?x12652, ?x1032), award_winner(?x3275, ?x2443), ?x1967 = 01cgz, nominated_for(?x637, ?x787), ?x4355 = 08tq4x, award(?x4782, ?x1007), profession(?x5338, ?x319), ?x4041 = 0gy2y8r, film(?x2444, ?x485), award_nominee(?x2443, ?x2626), ?x6078 = 04pk1f, nominated_for(?x2670, ?x5016), people(?x1446, ?x6187), ?x2550 = 07j8r, award(?x5338, ?x102), participant(?x2443, ?x262), ?x791 = 087wc7n, ?x5400 = 0bhwhj, participant(?x4065, ?x2046), ?x6931 = 09v3jyg, film(?x2805, ?x144), genre(?x787, ?x225), participant(?x2444, ?x117), award(?x2805, ?x618), student(?x7021, ?x2805), film(?x4782, ?x1811) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #216 for first EXPECTED value: *> intensional similarity = 67 *> extensional distance = 2 *> proper extension: 01kyvx; *> query: (?x4832, ?x2443) <- film(?x4832, ?x5016), film(?x4832, ?x787), special_performance_type(?x12652, ?x4832), special_performance_type(?x6433, ?x4832), special_performance_type(?x6187, ?x4832), special_performance_type(?x5338, ?x4832), special_performance_type(?x4782, ?x4832), special_performance_type(?x4065, ?x4832), special_performance_type(?x2805, ?x4832), special_performance_type(?x2444, ?x4832), currency(?x5016, ?x170), award_nominee(?x4782, ?x91), film_release_region(?x5016, ?x2000), film_release_region(?x5016, ?x87), nominated_for(?x1018, ?x787), location(?x6433, ?x739), participant(?x6187, ?x2443), film(?x6187, ?x634), country(?x1967, ?x2000), film_release_region(?x6931, ?x87), film_release_region(?x6078, ?x87), film_release_region(?x5400, ?x87), film_release_region(?x4355, ?x87), film_release_region(?x4041, ?x87), film_release_region(?x3276, ?x87), film_release_region(?x2550, ?x87), film_release_region(?x2441, ?x87), film_release_region(?x791, ?x87), gender(?x12652, ?x514), nominated_for(?x5338, ?x1178), ?x3276 = 0gjc4d3, olympics(?x87, ?x778), participant(?x5665, ?x2444), award_nominee(?x748, ?x2805), ?x2441 = 0cc5mcj, participant(?x4065, ?x1145), religion(?x87, ?x1985), participant(?x4782, ?x1896), award_nominee(?x382, ?x5338), country(?x150, ?x87), participating_countries(?x1608, ?x87), profession(?x12652, ?x1032), award_winner(?x3275, ?x2443), ?x1967 = 01cgz, nominated_for(?x637, ?x787), ?x4355 = 08tq4x, award(?x4782, ?x1007), profession(?x5338, ?x319), ?x4041 = 0gy2y8r, film(?x2444, ?x485), award_nominee(?x2443, ?x2626), ?x6078 = 04pk1f, nominated_for(?x2670, ?x5016), people(?x1446, ?x6187), ?x2550 = 07j8r, award(?x5338, ?x102), participant(?x2443, ?x262), ?x791 = 087wc7n, ?x5400 = 0bhwhj, participant(?x4065, ?x2046), ?x6931 = 09v3jyg, film(?x2805, ?x144), genre(?x787, ?x225), participant(?x2444, ?x117), award(?x2805, ?x618), student(?x7021, ?x2805), film(?x4782, ?x1811) *> conf = 0.15 ranks of expected_values: 61, 128, 231, 436, 515, 516, 550, 1787 EVAL 01pb34 special_performance_type! 01l3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 04qp06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 0gthm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 01gkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 01vtg4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 01j5sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 0391jz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 0bq2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 0309jm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 02vmzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 02p21g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 06w2sn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 01pb34 special_performance_type! 01q_ph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 5.000 5.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type #21233-0d060g PRED entity: 0d060g PRED relation: contains PRED expected values: 080h2 02583l 0pmp2 01vqq1 02qjb7z 0778_3 0fnx1 => 200 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2846): 05kr_ (0.87 #22913, 0.84 #105972, 0.82 #177577), 05j49 (0.87 #22913, 0.75 #203358, 0.75 #226278), 080h2 (0.80 #289291, 0.75 #203358, 0.75 #226278), 0t6sb (0.80 #289291, 0.75 #203358, 0.75 #226278), 052p7 (0.80 #289291, 0.75 #203358, 0.75 #226278), 02_286 (0.80 #289291, 0.57 #183307, 0.09 #34438), 0pmp2 (0.80 #289291, 0.03 #80478, 0.02 #169269), 0kf14 (0.75 #203358, 0.75 #226278), 02dtg (0.63 #217684, 0.57 #183307, 0.06 #17230), 029jpy (0.63 #217684, 0.57 #183307, 0.06 #17609) >> Best rule #22913 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 049nq; >> query: (?x279, ?x1905) <- nationality(?x199, ?x279), contains(?x279, ?x481), first_level_division_of(?x1905, ?x279) >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #289291 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 113 *> proper extension: 0j0k; *> query: (?x279, ?x2453) <- contains(?x279, ?x12016), contains(?x279, ?x1658), citytown(?x1306, ?x1658), citytown(?x12016, ?x2453) *> conf = 0.80 ranks of expected_values: 3, 7, 11, 36, 2369, 2743 EVAL 0d060g contains 0fnx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 200.000 129.000 0.871 http://example.org/location/location/contains EVAL 0d060g contains 0778_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 200.000 129.000 0.871 http://example.org/location/location/contains EVAL 0d060g contains 02qjb7z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 200.000 129.000 0.871 http://example.org/location/location/contains EVAL 0d060g contains 01vqq1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 200.000 129.000 0.871 http://example.org/location/location/contains EVAL 0d060g contains 0pmp2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 200.000 129.000 0.871 http://example.org/location/location/contains EVAL 0d060g contains 02583l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 200.000 129.000 0.871 http://example.org/location/location/contains EVAL 0d060g contains 080h2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 200.000 129.000 0.871 http://example.org/location/location/contains #21232-01p95y0 PRED entity: 01p95y0 PRED relation: artists! PRED expected values: 0827d 06by7 => 91 concepts (42 used for prediction) PRED predicted values (max 10 best out of 235): 06by7 (0.55 #3108, 0.51 #3416, 0.46 #8978), 0xhtw (0.51 #3103, 0.48 #635, 0.41 #945), 016jny (0.35 #722, 0.30 #1032, 0.19 #3190), 0dl5d (0.35 #3106, 0.27 #3414, 0.17 #1258), 05bt6j (0.33 #3438, 0.20 #1282, 0.19 #8689), 016clz (0.32 #5558, 0.31 #5, 0.29 #4324), 01lyv (0.30 #653, 0.26 #963, 0.15 #12090), 08jyyk (0.28 #3154, 0.23 #67, 0.15 #996), 0cx7f (0.28 #3223, 0.18 #3531, 0.15 #1065), 0glt670 (0.28 #7760, 0.27 #8377, 0.25 #9309) >> Best rule #3108 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 067mj; 01fl3; 05563d; 07yg2; 0394y; 047cx; 06nv27; 0l8g0; 07bzp; 07mvp; ... >> query: (?x10239, 06by7) <- artists(?x2809, ?x10239), ?x2809 = 05w3f, artist(?x5891, ?x10239) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1, 39 EVAL 01p95y0 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 42.000 0.551 http://example.org/music/genre/artists EVAL 01p95y0 artists! 0827d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 91.000 42.000 0.551 http://example.org/music/genre/artists #21231-03xn3s2 PRED entity: 03xn3s2 PRED relation: nationality PRED expected values: 09c7w0 => 86 concepts (80 used for prediction) PRED predicted values (max 10 best out of 16): 09c7w0 (0.78 #601, 0.76 #401, 0.74 #101), 02jx1 (0.12 #2234, 0.11 #2934, 0.11 #3034), 07ssc (0.09 #2216, 0.08 #515, 0.08 #2816), 0d060g (0.08 #1408, 0.08 #507, 0.07 #1508), 03rk0 (0.07 #4749, 0.06 #6150, 0.06 #5150), 03_3d (0.07 #1206, 0.06 #1106, 0.03 #1607), 0345h (0.02 #1332, 0.02 #731, 0.02 #1632), 0chghy (0.02 #2211, 0.02 #4113, 0.02 #4213), 0ctw_b (0.02 #427, 0.01 #1828), 0f8l9c (0.02 #2423, 0.02 #6526, 0.02 #2523) >> Best rule #601 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 0d02km; 0q1lp; >> query: (?x6825, 09c7w0) <- category(?x6825, ?x134), actor(?x3180, ?x6825), nominated_for(?x806, ?x3180), program(?x1394, ?x3180) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xn3s2 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 80.000 0.782 http://example.org/people/person/nationality #21230-02bgmr PRED entity: 02bgmr PRED relation: gender PRED expected values: 05zppz => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #35, 0.87 #47, 0.86 #21), 02zsn (0.45 #111, 0.28 #68, 0.27 #139) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 236 >> proper extension: 0m0hw; 06z4wj; 0gv07g; 082_p; 02cj_f; 01p7b6b; 01z0lb; 01rw116; 0127xk; 06lk0_; ... >> query: (?x5768, 05zppz) <- profession(?x5768, ?x1614), ?x1614 = 01c72t, nationality(?x5768, ?x142), film_release_region(?x80, ?x142) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bgmr gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.887 http://example.org/people/person/gender #21229-06b19 PRED entity: 06b19 PRED relation: institution! PRED expected values: 03bwzr4 => 197 concepts (197 used for prediction) PRED predicted values (max 10 best out of 18): 02_xgp2 (0.62 #8, 0.60 #66, 0.57 #27), 03bwzr4 (0.59 #126, 0.54 #704, 0.51 #550), 01rr_d (0.54 #13, 0.45 #208, 0.44 #91), 04zx3q1 (0.50 #39, 0.36 #117, 0.32 #541), 0bkj86 (0.46 #544, 0.44 #698, 0.43 #640), 013zdg (0.36 #41, 0.35 #158, 0.33 #198), 027f2w (0.27 #121, 0.24 #431, 0.24 #102), 0bjrnt (0.27 #118, 0.19 #177, 0.19 #157), 028dcg (0.27 #131, 0.18 #2064, 0.14 #2316), 02m4yg (0.25 #90, 0.18 #2064, 0.17 #1057) >> Best rule #8 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 01y9st; >> query: (?x7912, 02_xgp2) <- contains(?x279, ?x7912), colors(?x7912, ?x332), currency(?x7912, ?x2244), ?x279 = 0d060g, category(?x7912, ?x134) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #126 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 01zh3_; *> query: (?x7912, 03bwzr4) <- major_field_of_study(?x7912, ?x1695), institution(?x1305, ?x7912), colors(?x7912, ?x332), ?x1305 = 02mjs7 *> conf = 0.59 ranks of expected_values: 2 EVAL 06b19 institution! 03bwzr4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 197.000 197.000 0.615 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21228-0dqmt0 PRED entity: 0dqmt0 PRED relation: executive_produced_by! PRED expected values: 09rvwmy => 97 concepts (21 used for prediction) PRED predicted values (max 10 best out of 274): 0dqcs3 (0.10 #10111, 0.03 #1596, 0.03 #6385), 01msrb (0.05 #263, 0.05 #795, 0.02 #2923), 018nnz (0.05 #94, 0.02 #626, 0.02 #1158), 07y9w5 (0.05 #74, 0.02 #606, 0.02 #1138), 05pdd86 (0.05 #350, 0.02 #882, 0.02 #2477), 0h1v19 (0.05 #149, 0.02 #6386, 0.02 #5470), 0ft18 (0.05 #448, 0.02 #6386, 0.01 #3108), 0cy__l (0.05 #315, 0.02 #6386, 0.01 #2975), 0yx_w (0.05 #489, 0.01 #3149, 0.01 #3682), 034hwx (0.05 #486, 0.01 #3146, 0.01 #3679) >> Best rule #10111 for best value: >> intensional similarity = 4 >> extensional distance = 184 >> proper extension: 02qggqc; >> query: (?x7146, ?x4839) <- award(?x7146, ?x1105), executive_produced_by(?x7107, ?x7146), nationality(?x7146, ?x4743), nominated_for(?x7146, ?x4839) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dqmt0 executive_produced_by! 09rvwmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 21.000 0.097 http://example.org/film/film/executive_produced_by #21227-023rwm PRED entity: 023rwm PRED relation: category PRED expected values: 08mbj5d => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #21, 0.83 #29, 0.83 #28) >> Best rule #21 for best value: >> intensional similarity = 8 >> extensional distance = 20 >> proper extension: 05w3y; >> query: (?x441, 08mbj5d) <- artist(?x441, ?x10106), artist(?x441, ?x4484), artists(?x474, ?x4484), group(?x227, ?x10106), ?x227 = 0342h, artist(?x382, ?x4484), ?x382 = 086k8, ?x474 = 0m0jc >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023rwm category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.864 http://example.org/common/topic/webpage./common/webpage/category #21226-02dpl9 PRED entity: 02dpl9 PRED relation: genre PRED expected values: 03k9fj => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 108): 064_8sq (0.55 #1581, 0.54 #3162, 0.52 #4871), 0f8l9c (0.55 #1581, 0.54 #3162, 0.52 #4871), 0lsxr (0.48 #9, 0.22 #6085, 0.20 #6693), 01jfsb (0.43 #13, 0.32 #1959, 0.32 #2080), 02kdv5l (0.39 #2, 0.29 #1827, 0.28 #1948), 02l7c8 (0.37 #138, 0.36 #625, 0.33 #989), 05p553 (0.35 #2680, 0.35 #2558, 0.32 #7550), 03k9fj (0.26 #1715, 0.26 #2200, 0.24 #2322), 04xvlr (0.24 #123, 0.24 #610, 0.22 #6085), 03q4nz (0.22 #141, 0.22 #6085, 0.22 #19) >> Best rule #1581 for best value: >> intensional similarity = 3 >> extensional distance = 315 >> proper extension: 019nnl; 07c72; 0828jw; 0fhzwl; 07gbf; 053x8hr; 0266s9; >> query: (?x3897, ?x789) <- nominated_for(?x9891, ?x3897), titles(?x789, ?x3897), music(?x915, ?x9891) >> conf = 0.55 => this is the best rule for 2 predicted values *> Best rule #1715 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 415 *> proper extension: 0qm8b; 0888c3; *> query: (?x3897, 03k9fj) <- country(?x3897, ?x789), film_crew_role(?x3897, ?x137), music(?x3897, ?x9891), award_winner(?x4488, ?x9891) *> conf = 0.26 ranks of expected_values: 8 EVAL 02dpl9 genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 67.000 67.000 0.545 http://example.org/film/film/genre #21225-076tw54 PRED entity: 076tw54 PRED relation: film! PRED expected values: 0fs9jn => 109 concepts (61 used for prediction) PRED predicted values (max 10 best out of 965): 02pjvc (0.14 #3110, 0.04 #7274, 0.02 #9356), 02mjf2 (0.12 #4940, 0.07 #2858, 0.04 #7022), 09l3p (0.12 #4914, 0.04 #6996, 0.02 #9078), 016ks_ (0.12 #4950, 0.02 #9114, 0.01 #77826), 015v3r (0.12 #4699, 0.01 #25520, 0.01 #83822), 0252fh (0.12 #5519, 0.01 #15929, 0.01 #18012), 0gg9_5q (0.12 #81205, 0.11 #74958, 0.10 #58301), 06m6p7 (0.11 #7616, 0.08 #1370, 0.05 #9698), 032xhg (0.08 #64, 0.07 #6310, 0.07 #2146), 05bnp0 (0.08 #13, 0.07 #6259, 0.07 #2095) >> Best rule #3110 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 09fc83; >> query: (?x13292, 02pjvc) <- film_release_region(?x13292, ?x94), currency(?x13292, ?x170), ?x94 = 09c7w0, genre(?x13292, ?x811), ?x811 = 03k9fj >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #12135 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 58 *> proper extension: 0bm2g; 0p_qr; *> query: (?x13292, 0fs9jn) <- titles(?x1510, ?x13292), film_release_region(?x13292, ?x94), genre(?x13292, ?x3515), film_release_distribution_medium(?x13292, ?x81), ?x3515 = 082gq *> conf = 0.02 ranks of expected_values: 543 EVAL 076tw54 film! 0fs9jn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 109.000 61.000 0.143 http://example.org/film/actor/film./film/performance/film #21224-02mq_y PRED entity: 02mq_y PRED relation: group! PRED expected values: 013y1f 02hnl 04rzd 01dnws 07_l6 06w7v => 86 concepts (55 used for prediction) PRED predicted values (max 10 best out of 116): 0342h (0.93 #2295, 0.93 #558, 0.91 #2132), 02hnl (0.81 #497, 0.80 #180, 0.79 #894), 018vs (0.66 #2219, 0.63 #2300, 0.62 #2460), 03bx0bm (0.63 #415, 0.59 #2147, 0.58 #2470), 013y1f (0.47 #178, 0.33 #21, 0.31 #495), 0l14qv (0.40 #162, 0.33 #5, 0.30 #401), 028tv0 (0.36 #2299, 0.36 #2540, 0.36 #2459), 06ncr (0.33 #32, 0.20 #586, 0.20 #189), 07c6l (0.33 #6, 0.19 #402, 0.14 #79), 07gql (0.33 #30, 0.19 #426, 0.14 #79) >> Best rule #2295 for best value: >> intensional similarity = 7 >> extensional distance = 178 >> proper extension: 089tm; 01pfr3; 04rcr; 0150jk; 067mj; 01vsxdm; 03g5jw; 01wv9xn; 05crg7; 05k79; ... >> query: (?x5303, 0342h) <- group(?x745, ?x5303), role(?x654, ?x745), role(?x745, ?x1482), role(?x745, ?x5676), role(?x214, ?x745), ?x1482 = 02g9p4, ?x5676 = 0151b0 >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #497 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 30 *> proper extension: 07c0j; 01fl3; 02_5x9; 0dvqq; 01qqwp9; 07yg2; 015srx; 07r1_; 0b_xm; 0qmny; ... *> query: (?x5303, 02hnl) <- group(?x2798, ?x5303), group(?x745, ?x5303), ?x745 = 01vj9c, group(?x565, ?x5303), role(?x8957, ?x2798), ?x8957 = 03f5mt, instrumentalists(?x2798, ?x211), role(?x2798, ?x212) *> conf = 0.81 ranks of expected_values: 2, 5, 14, 15, 60, 75 EVAL 02mq_y group! 06w7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 86.000 55.000 0.928 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02mq_y group! 07_l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 86.000 55.000 0.928 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02mq_y group! 01dnws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 86.000 55.000 0.928 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02mq_y group! 04rzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 86.000 55.000 0.928 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02mq_y group! 02hnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 55.000 0.928 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02mq_y group! 013y1f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 55.000 0.928 http://example.org/music/performance_role/regular_performances./music/group_membership/group #21223-0q1lp PRED entity: 0q1lp PRED relation: profession PRED expected values: 02hrh1q => 141 concepts (43 used for prediction) PRED predicted values (max 10 best out of 82): 02hrh1q (0.92 #2786, 0.90 #1910, 0.90 #5855), 03gjzk (0.61 #743, 0.54 #451, 0.49 #2933), 018gz8 (0.54 #453, 0.44 #2935, 0.43 #1037), 02krf9 (0.38 #316, 0.27 #754, 0.20 #2944), 015h31 (0.35 #317, 0.18 #755, 0.14 #2945), 09jwl (0.34 #1477, 0.29 #5129, 0.28 #4837), 0cbd2 (0.30 #3072, 0.27 #2634, 0.25 #5410), 0kyk (0.26 #173, 0.25 #1487, 0.20 #2655), 0nbcg (0.24 #1489, 0.21 #5141, 0.21 #4849), 016z4k (0.18 #1464, 0.16 #5116, 0.16 #4824) >> Best rule #2786 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 01p7yb; 0prfz; 02r_d4; 02r34n; 01wyzyl; 0jt90f5; 0h0wc; 02tqkf; 0391jz; 01ft2l; ... >> query: (?x9650, 02hrh1q) <- film(?x9650, ?x3919), student(?x1368, ?x9650), profession(?x9650, ?x319), nationality(?x9650, ?x94) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q1lp profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 43.000 0.916 http://example.org/people/person/profession #21222-02k1b PRED entity: 02k1b PRED relation: country! PRED expected values: 01lb14 03_8r => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 47): 03_8r (0.78 #20, 0.76 #819, 0.75 #913), 01hp22 (0.78 #6, 0.75 #100, 0.71 #194), 07gyv (0.78 #5, 0.64 #193, 0.58 #99), 01lb14 (0.70 #812, 0.69 #906, 0.57 #1423), 06wrt (0.68 #813, 0.67 #907, 0.67 #14), 06f41 (0.68 #811, 0.67 #905, 0.67 #12), 07jbh (0.67 #27, 0.64 #826, 0.63 #920), 0w0d (0.67 #10, 0.60 #809, 0.59 #903), 02y8z (0.67 #17, 0.60 #816, 0.59 #910), 0194d (0.62 #839, 0.61 #933, 0.56 #40) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05r4w; 0f8l9c; >> query: (?x8449, 03_8r) <- teams(?x8449, ?x11489), film_release_region(?x186, ?x8449), partially_contains(?x1879, ?x8449) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 02k1b country! 03_8r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.778 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02k1b country! 01lb14 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 139.000 139.000 0.778 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #21221-0j0pf PRED entity: 0j0pf PRED relation: profession PRED expected values: 0h9c => 135 concepts (116 used for prediction) PRED predicted values (max 10 best out of 110): 02hrh1q (0.89 #16728, 0.73 #15234, 0.70 #15832), 0dxtg (0.70 #13892, 0.58 #2698, 0.57 #2549), 0kyk (0.67 #1073, 0.67 #477, 0.64 #924), 01d_h8 (0.51 #4330, 0.49 #4629, 0.49 #2542), 018gz8 (0.50 #17, 0.48 #2702, 0.40 #2553), 02jknp (0.50 #7, 0.45 #14482, 0.31 #13886), 03gjzk (0.42 #14490, 0.40 #2700, 0.38 #2551), 03jgz (0.41 #6712, 0.39 #7608, 0.37 #6562), 016fly (0.41 #6712, 0.39 #7608, 0.37 #6562), 03sbb (0.41 #6712, 0.39 #7608, 0.37 #6562) >> Best rule #16728 for best value: >> intensional similarity = 5 >> extensional distance = 2800 >> proper extension: 06v8s0; 05bp8g; 01r42_g; 0m2wm; 02zq43; 01rrwf6; 04wqr; 07lmxq; 01ty7ll; 0f830f; ... >> query: (?x5086, 02hrh1q) <- profession(?x5086, ?x353), profession(?x3927, ?x353), profession(?x916, ?x353), ?x916 = 019z7q, ?x3927 = 08vr94 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #17311 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3051 *> proper extension: 04qvl7; 03ckxdg; 050023; 026dcvf; 042rnl; 026dg51; 027rwmr; 04l3_z; 0f3zf_; 04wtx1; ... *> query: (?x5086, ?x2225) <- profession(?x5086, ?x353), award(?x5086, ?x8880), award(?x5506, ?x8880), profession(?x5506, ?x2225) *> conf = 0.09 ranks of expected_values: 42 EVAL 0j0pf profession 0h9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 135.000 116.000 0.892 http://example.org/people/person/profession #21220-09d5d5 PRED entity: 09d5d5 PRED relation: profession PRED expected values: 01d_h8 => 123 concepts (108 used for prediction) PRED predicted values (max 10 best out of 61): 01d_h8 (0.87 #456, 0.86 #306, 0.85 #1507), 02hrh1q (0.78 #3617, 0.77 #4218, 0.76 #4068), 02jknp (0.64 #158, 0.54 #1208, 0.54 #2860), 0dxtg (0.57 #314, 0.55 #1214, 0.54 #764), 03gjzk (0.49 #1366, 0.47 #766, 0.47 #916), 0dgd_ (0.34 #4053, 0.34 #1501, 0.31 #11557), 02krf9 (0.34 #4053, 0.34 #1501, 0.31 #11557), 0cbd2 (0.34 #4053, 0.34 #1501, 0.30 #7206), 09jwl (0.21 #3772, 0.20 #6323, 0.20 #5423), 0nbcg (0.14 #3785, 0.13 #6336, 0.12 #9039) >> Best rule #456 for best value: >> intensional similarity = 3 >> extensional distance = 140 >> proper extension: 079vf; 076_74; 09pl3f; 0184jw; 0gdhhy; 029ghl; 037q1z; 016z1c; >> query: (?x8652, 01d_h8) <- award_winner(?x3828, ?x8652), produced_by(?x3157, ?x8652), place_of_birth(?x8652, ?x362) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09d5d5 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 108.000 0.866 http://example.org/people/person/profession #21219-0gyy0 PRED entity: 0gyy0 PRED relation: award_winner! PRED expected values: 04kxsb => 154 concepts (137 used for prediction) PRED predicted values (max 10 best out of 270): 0f4x7 (0.40 #14163, 0.39 #8584, 0.39 #39914), 04kxsb (0.40 #14163, 0.39 #8584, 0.39 #39914), 03x3wf (0.33 #64, 0.11 #1351, 0.06 #3496), 01c92g (0.33 #97, 0.03 #6964, 0.03 #1384), 027c95y (0.29 #6592, 0.15 #42062, 0.14 #4446), 09sb52 (0.25 #469, 0.17 #18065, 0.17 #25361), 027986c (0.24 #6484, 0.07 #906, 0.05 #4338), 05qck (0.22 #1477, 0.11 #7057, 0.09 #4909), 02w9sd7 (0.22 #6601, 0.08 #4455, 0.07 #1023), 09cm54 (0.20 #6532, 0.07 #1812, 0.06 #4386) >> Best rule #14163 for best value: >> intensional similarity = 3 >> extensional distance = 291 >> proper extension: 04135; >> query: (?x8473, ?x591) <- award_winner(?x458, ?x8473), award(?x8473, ?x591), people(?x4322, ?x8473) >> conf = 0.40 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0gyy0 award_winner! 04kxsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 154.000 137.000 0.399 http://example.org/award/award_category/winners./award/award_honor/award_winner #21218-016ndm PRED entity: 016ndm PRED relation: institution! PRED expected values: 027f2w => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 16): 03bwzr4 (0.68 #181, 0.61 #60, 0.51 #252), 016t_3 (0.55 #174, 0.55 #53, 0.49 #279), 04zx3q1 (0.48 #52, 0.40 #70, 0.33 #244), 027f2w (0.39 #56, 0.34 #74, 0.30 #177), 013zdg (0.33 #55, 0.23 #281, 0.23 #73), 03mkk4 (0.24 #58, 0.19 #76, 0.17 #1569), 02mjs7 (0.21 #54, 0.19 #107, 0.17 #1569), 022h5x (0.20 #291, 0.17 #1569, 0.15 #208), 02m4yg (0.17 #1569, 0.15 #62, 0.15 #208), 028dcg (0.17 #1569, 0.15 #208, 0.14 #290) >> Best rule #181 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 01jswq; 0373qg; 02bb47; 01n1pp; 01f1r4; 015cz0; 02bqy; 01hr11; 01csqg; 0ym17; ... >> query: (?x4199, 03bwzr4) <- major_field_of_study(?x4199, ?x1154), contains(?x279, ?x4199), institution(?x620, ?x4199), ?x1154 = 02lp1 >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 31 *> proper extension: 06pwq; 01w5m; 09f2j; *> query: (?x4199, 027f2w) <- major_field_of_study(?x4199, ?x1154), institution(?x1390, ?x4199), institution(?x865, ?x4199), ?x1390 = 0bjrnt, ?x865 = 02h4rq6 *> conf = 0.39 ranks of expected_values: 4 EVAL 016ndm institution! 027f2w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 148.000 148.000 0.683 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21217-023vcd PRED entity: 023vcd PRED relation: film_release_region PRED expected values: 0d0vqn 0chghy => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 188): 0d0vqn (0.90 #1530, 0.89 #1023, 0.88 #2544), 02vzc (0.87 #1075, 0.81 #1582, 0.81 #2089), 03_3d (0.86 #683, 0.86 #1021, 0.80 #1359), 03h64 (0.85 #754, 0.77 #1768, 0.76 #1937), 035qy (0.85 #716, 0.76 #1392, 0.75 #2068), 05r4w (0.83 #2030, 0.81 #1523, 0.81 #2537), 015fr (0.83 #698, 0.76 #2050, 0.76 #1036), 0154j (0.83 #681, 0.72 #2033, 0.71 #1695), 0chghy (0.83 #2042, 0.82 #690, 0.81 #1197), 0k6nt (0.81 #1551, 0.80 #1213, 0.78 #1044) >> Best rule #1530 for best value: >> intensional similarity = 5 >> extensional distance = 165 >> proper extension: 0j8f09z; >> query: (?x10246, 0d0vqn) <- nominated_for(?x1312, ?x10246), film_release_region(?x10246, ?x1003), film_release_region(?x10246, ?x94), ?x1003 = 03gj2, ?x94 = 09c7w0 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 023vcd film_release_region 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 98.000 98.000 0.898 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 023vcd film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.898 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21216-02r_d4 PRED entity: 02r_d4 PRED relation: actor! PRED expected values: 01cvtf => 92 concepts (78 used for prediction) PRED predicted values (max 10 best out of 96): 0vjr (0.07 #4719, 0.01 #617, 0.01 #12332), 034vds (0.07 #4719, 0.01 #3915), 02rcwq0 (0.06 #610, 0.04 #872, 0.03 #1134), 01fx1l (0.05 #360, 0.03 #622, 0.02 #884), 07g9f (0.05 #460, 0.03 #722, 0.02 #984), 0hz55 (0.05 #347, 0.01 #3755, 0.01 #5067), 02_1q9 (0.03 #3675, 0.02 #4987, 0.01 #529), 0180mw (0.03 #642, 0.03 #904, 0.03 #3788), 01kt_j (0.03 #730, 0.03 #992, 0.03 #1254), 039cq4 (0.03 #651, 0.02 #3797, 0.02 #913) >> Best rule #4719 for best value: >> intensional similarity = 3 >> extensional distance = 674 >> proper extension: 03mz9r; >> query: (?x665, ?x2436) <- award_nominee(?x5899, ?x665), student(?x10910, ?x665), actor(?x2436, ?x5899) >> conf = 0.07 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02r_d4 actor! 01cvtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 78.000 0.068 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21215-0k2sk PRED entity: 0k2sk PRED relation: film_crew_role PRED expected values: 02r96rf => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.75 #1041, 0.71 #697, 0.67 #239), 09zzb8 (0.74 #1033, 0.71 #689, 0.68 #39), 02r96rf (0.69 #692, 0.69 #1036, 0.66 #156), 09vw2b7 (0.66 #696, 0.66 #1040, 0.65 #46), 0dxtw (0.43 #701, 0.42 #51, 0.35 #1045), 01vx2h (0.40 #52, 0.38 #702, 0.35 #1046), 01pvkk (0.30 #53, 0.28 #703, 0.28 #167), 0215hd (0.23 #173, 0.15 #1053, 0.14 #251), 02ynfr (0.20 #56, 0.20 #706, 0.16 #1050), 015h31 (0.20 #49, 0.13 #699, 0.13 #241) >> Best rule #1041 for best value: >> intensional similarity = 4 >> extensional distance = 414 >> proper extension: 0gtsx8c; >> query: (?x1076, 0ch6mp2) <- film(?x2089, ?x1076), executive_produced_by(?x1076, ?x846), film_crew_role(?x1076, ?x2848), award_nominee(?x221, ?x2089) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #692 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 237 *> proper extension: 02vqhv0; 034qbx; *> query: (?x1076, 02r96rf) <- film(?x1802, ?x1076), currency(?x1076, ?x170), film(?x609, ?x1076), crewmember(?x1076, ?x930) *> conf = 0.69 ranks of expected_values: 3 EVAL 0k2sk film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 92.000 0.755 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21214-01grrf PRED entity: 01grrf PRED relation: legislative_sessions! PRED expected values: 0b3wk 07t58 => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 7): 07t58 (0.93 #231, 0.90 #209, 0.90 #224), 0b3wk (0.89 #193, 0.89 #251, 0.89 #155), 0x2sv (0.07 #254, 0.02 #246), 0h6dy (0.05 #255, 0.02 #247), 0l_j_ (0.03 #256, 0.02 #248), 0162kb (0.02 #249), 030p4s (0.02 #258) >> Best rule #231 for best value: >> intensional similarity = 33 >> extensional distance = 42 >> proper extension: 03tcbx; 03rtmz; 02gkzs; 02glc4; >> query: (?x7914, 07t58) <- district_represented(?x7914, ?x2713), district_represented(?x7914, ?x1767), district_represented(?x7914, ?x1755), legislative_sessions(?x3973, ?x7914), ?x1755 = 01x73, country(?x1767, ?x94), jurisdiction_of_office(?x900, ?x1767), district_represented(?x1829, ?x1767), district_represented(?x605, ?x1767), contains(?x1767, ?x6454), religion(?x2713, ?x10107), religion(?x2713, ?x2769), religion(?x2713, ?x109), contains(?x2713, ?x6925), state_province_region(?x2021, ?x2713), ?x1829 = 02bp37, ?x109 = 01lp8, religion(?x1767, ?x1363), ?x2769 = 019cr, district_represented(?x3973, ?x1426), location(?x6880, ?x1767), ?x605 = 077g7n, ?x900 = 0fkvn, location(?x4806, ?x2713), adjoins(?x1767, ?x108), profession(?x6880, ?x524), major_field_of_study(?x6925, ?x254), institution(?x734, ?x6925), ?x10107 = 05w5d, category(?x6925, ?x134), citytown(?x5961, ?x6454), list(?x6925, ?x2197), ?x254 = 02h40lc >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01grrf legislative_sessions! 07t58 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.932 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions EVAL 01grrf legislative_sessions! 0b3wk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.932 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #21213-07jq_ PRED entity: 07jq_ PRED relation: films PRED expected values: 09r94m 01jmyj 0b2km_ => 51 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1810): 065dc4 (0.44 #5185, 0.02 #518, 0.02 #519), 015g28 (0.43 #4341, 0.40 #2780, 0.17 #3300), 0m9p3 (0.40 #2705, 0.29 #4266, 0.17 #3225), 07xtqq (0.33 #1575, 0.25 #2092, 0.15 #5725), 025rvx0 (0.33 #3386, 0.20 #5463, 0.20 #2866), 0pc62 (0.33 #3137, 0.20 #5214, 0.20 #2617), 0bl5c (0.33 #3380, 0.20 #5457, 0.20 #2860), 09qycb (0.33 #3580, 0.20 #5657, 0.20 #3060), 01jwxx (0.33 #3350, 0.20 #5427, 0.20 #2830), 091rc5 (0.33 #245, 0.20 #2833, 0.14 #4394) >> Best rule #5185 for best value: >> intensional similarity = 14 >> extensional distance = 7 >> proper extension: 05489; 05vtw; 0fx2s; 0ddct; 0kbq; 06ys2; >> query: (?x9677, ?x3953) <- films(?x9677, ?x7444), films(?x9677, ?x5960), films(?x9677, ?x4529), film_crew_role(?x4529, ?x2178), language(?x4529, ?x254), ?x2178 = 01pvkk, featured_film_locations(?x4529, ?x1658), film(?x9388, ?x4529), film(?x510, ?x7444), film(?x574, ?x7444), film_production_design_by(?x7444, ?x5532), genre(?x5960, ?x600), film_release_region(?x4529, ?x94), prequel(?x5960, ?x3953) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2533 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 0d063v; *> query: (?x9677, 0b2km_) <- films(?x9677, ?x7462), films(?x9677, ?x7444), films(?x9677, ?x4529), films(?x9677, ?x1002), film_crew_role(?x4529, ?x2178), language(?x4529, ?x5359), ?x2178 = 01pvkk, featured_film_locations(?x4529, ?x1658), film(?x9388, ?x4529), film(?x510, ?x7444), film(?x574, ?x7444), nominated_for(?x384, ?x1002), currency(?x1002, ?x170), featured_film_locations(?x1002, ?x362), ?x5359 = 0jzc, genre(?x7462, ?x53), film_release_region(?x1002, ?x87) *> conf = 0.25 ranks of expected_values: 70, 109, 113 EVAL 07jq_ films 0b2km_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 51.000 21.000 0.444 http://example.org/film/film_subject/films EVAL 07jq_ films 01jmyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 51.000 21.000 0.444 http://example.org/film/film_subject/films EVAL 07jq_ films 09r94m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 51.000 21.000 0.444 http://example.org/film/film_subject/films #21212-051zy_b PRED entity: 051zy_b PRED relation: nominated_for! PRED expected values: 0gq9h 0gqy2 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 222): 0gq9h (0.38 #1705, 0.33 #60, 0.29 #5465), 0gr0m (0.33 #57, 0.30 #1702, 0.17 #5462), 0f4x7 (0.33 #24, 0.26 #1669, 0.22 #12933), 09sb52 (0.33 #33, 0.24 #8697, 0.24 #8698), 040njc (0.33 #7, 0.24 #1652, 0.22 #12933), 04kxsb (0.33 #93, 0.22 #12933, 0.21 #8461), 02w9sd7 (0.33 #122, 0.22 #12933, 0.21 #8461), 03hkv_r (0.33 #14, 0.22 #12933, 0.21 #8461), 02n9nmz (0.33 #55, 0.22 #12933, 0.21 #8461), 09qv_s (0.33 #112, 0.22 #12933, 0.21 #8461) >> Best rule #1705 for best value: >> intensional similarity = 3 >> extensional distance = 225 >> proper extension: 027ct7c; 0cq8nx; 0bx_hnp; 0267wwv; >> query: (?x3534, 0gq9h) <- award_winner(?x3534, ?x2763), nominated_for(?x406, ?x3534), cinematography(?x3534, ?x6062) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1, 35 EVAL 051zy_b nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 66.000 66.000 0.383 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 051zy_b nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.383 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21211-083skw PRED entity: 083skw PRED relation: featured_film_locations PRED expected values: 0f2tj => 125 concepts (98 used for prediction) PRED predicted values (max 10 best out of 75): 02_286 (0.29 #6972, 0.28 #8654, 0.21 #1217), 030qb3t (0.14 #8673, 0.12 #6991, 0.08 #1714), 04jpl (0.10 #6961, 0.10 #8643, 0.06 #2641), 02nd_ (0.08 #1073, 0.06 #1791, 0.05 #594), 01_d4 (0.07 #47, 0.06 #1004, 0.05 #525), 0345h (0.07 #33, 0.04 #272, 0.02 #990), 05qtj (0.07 #96, 0.02 #2968, 0.02 #1053), 0rh6k (0.06 #8635, 0.05 #6953, 0.05 #718), 0fr0t (0.04 #1041, 0.03 #562, 0.02 #801), 01jr6 (0.04 #322, 0.03 #561, 0.02 #2955) >> Best rule #6972 for best value: >> intensional similarity = 3 >> extensional distance = 299 >> proper extension: 02y_lrp; 0cw3yd; 01zfzb; 05nlx4; 0gzlb9; 0dl6fv; >> query: (?x2612, 02_286) <- award_winner(?x2612, ?x4251), award(?x2612, ?x484), featured_film_locations(?x2612, ?x10534) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1080 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 06cgf; *> query: (?x2612, 0f2tj) <- list(?x2612, ?x3004), genre(?x2612, ?x53), ?x3004 = 05glt, films(?x11988, ?x2612) *> conf = 0.02 ranks of expected_values: 36 EVAL 083skw featured_film_locations 0f2tj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 125.000 98.000 0.292 http://example.org/film/film/featured_film_locations #21210-01tx9m PRED entity: 01tx9m PRED relation: campuses PRED expected values: 01tx9m => 154 concepts (99 used for prediction) PRED predicted values (max 10 best out of 240): 0bwfn (0.20 #809, 0.07 #1901, 0.05 #4085), 01tntf (0.20 #922, 0.05 #4198, 0.05 #3652), 02g839 (0.11 #1112, 0.05 #4388, 0.01 #13126), 01rtm4 (0.11 #1097, 0.05 #4373, 0.01 #14749), 03qdm (0.11 #1497), 02sdwt (0.11 #1488), 0778p (0.11 #1190), 01b1mj (0.11 #1110), 03zw80 (0.07 #2296, 0.06 #2842, 0.02 #6664), 05krk (0.07 #1645, 0.05 #3829, 0.05 #3283) >> Best rule #809 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 017j69; 0bwfn; 01tntf; >> query: (?x6177, 0bwfn) <- major_field_of_study(?x6177, ?x3995), ?x3995 = 0fdys, school(?x580, ?x6177), currency(?x6177, ?x170) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #23485 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 113 *> proper extension: 0frm7n; *> query: (?x6177, ?x581) <- school(?x7060, ?x6177), season(?x7060, ?x701), school(?x7060, ?x581), draft(?x7060, ?x1161) *> conf = 0.03 ranks of expected_values: 70 EVAL 01tx9m campuses 01tx9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 154.000 99.000 0.200 http://example.org/education/educational_institution/campuses #21209-0b6tzs PRED entity: 0b6tzs PRED relation: language PRED expected values: 06nm1 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 44): 064_8sq (0.15 #486, 0.15 #893, 0.15 #79), 06nm1 (0.14 #10, 0.13 #68, 0.12 #126), 04306rv (0.10 #178, 0.10 #352, 0.09 #62), 02bjrlw (0.08 #698, 0.07 #1106, 0.07 #989), 06b_j (0.08 #80, 0.07 #370, 0.06 #196), 03_9r (0.05 #532, 0.05 #125, 0.05 #2691), 0653m (0.05 #11, 0.04 #1234, 0.04 #883), 0jzc (0.05 #77, 0.04 #367, 0.03 #4329), 04h9h (0.05 #42, 0.03 #158, 0.03 #4329), 012w70 (0.03 #12, 0.03 #4329, 0.03 #651) >> Best rule #486 for best value: >> intensional similarity = 4 >> extensional distance = 353 >> proper extension: 064q5v; >> query: (?x945, 064_8sq) <- award_winner(?x945, ?x163), film_crew_role(?x945, ?x468), award(?x945, ?x112), award_winner(?x163, ?x164) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 0m63c; *> query: (?x945, 06nm1) <- award_winner(?x945, ?x1582), nominated_for(?x3019, ?x945), ?x3019 = 057xs89, nominated_for(?x1582, ?x186) *> conf = 0.14 ranks of expected_values: 2 EVAL 0b6tzs language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.149 http://example.org/film/film/language #21208-021sv1 PRED entity: 021sv1 PRED relation: politician! PRED expected values: 0d075m => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 20): 0d075m (0.64 #147, 0.50 #315, 0.50 #99), 07wbk (0.42 #169, 0.38 #121, 0.37 #409), 07wf9 (0.24 #270, 0.23 #294, 0.19 #318), 07wgm (0.16 #278, 0.15 #326, 0.15 #302), 0_00 (0.12 #132, 0.04 #468, 0.04 #252), 049tb (0.12 #128, 0.04 #464, 0.04 #536), 07wdw (0.10 #367, 0.08 #271, 0.08 #319), 01c9x (0.08 #244, 0.08 #172, 0.08 #340), 07wpm (0.08 #256, 0.08 #352, 0.04 #448), 07w42 (0.05 #205, 0.05 #229, 0.04 #253) >> Best rule #147 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0d06m5; 0d3qd0; 03txms; >> query: (?x652, 0d075m) <- legislative_sessions(?x652, ?x3540), legislative_sessions(?x652, ?x1028), ?x3540 = 024tcq, district_represented(?x1028, ?x335) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021sv1 politician! 0d075m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.636 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #21207-01j5sd PRED entity: 01j5sd PRED relation: profession PRED expected values: 0cbd2 0kyk => 132 concepts (83 used for prediction) PRED predicted values (max 10 best out of 74): 03gjzk (0.55 #304, 0.53 #2056, 0.52 #2202), 02krf9 (0.27 #316, 0.26 #462, 0.23 #608), 0cbd2 (0.23 #1028, 0.18 #6868, 0.16 #2926), 018gz8 (0.22 #744, 0.18 #2350, 0.15 #890), 09jwl (0.21 #7608, 0.20 #1038, 0.18 #11992), 0d1pc (0.20 #2822, 0.15 #6764, 0.15 #4866), 0kyk (0.17 #1049, 0.13 #9372, 0.13 #10102), 02hv44_ (0.17 #1077, 0.05 #931, 0.04 #2975), 05z96 (0.15 #1062, 0.04 #9385, 0.03 #10115), 0np9r (0.15 #1624, 0.14 #11555, 0.13 #10240) >> Best rule #304 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 014zcr; 0c1pj; 05kfs; 06cgy; 06chf; 025b5y; 07g7h2; 0m593; 06q8hf; 06t8b; ... >> query: (?x8269, 03gjzk) <- executive_produced_by(?x1002, ?x8269), religion(?x8269, ?x1985), type_of_union(?x8269, ?x566), award_winner(?x7767, ?x8269) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1028 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 0f1pyf; *> query: (?x8269, 0cbd2) <- nationality(?x8269, ?x429), gender(?x8269, ?x231), ?x429 = 03rt9 *> conf = 0.23 ranks of expected_values: 3, 7 EVAL 01j5sd profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 132.000 83.000 0.545 http://example.org/people/person/profession EVAL 01j5sd profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 132.000 83.000 0.545 http://example.org/people/person/profession #21206-02gnh0 PRED entity: 02gnh0 PRED relation: institution! PRED expected values: 014mlp => 108 concepts (65 used for prediction) PRED predicted values (max 10 best out of 22): 014mlp (0.72 #74, 0.72 #51, 0.68 #311), 03bwzr4 (0.70 #154, 0.69 #130, 0.43 #602), 02_xgp2 (0.60 #152, 0.58 #128, 0.44 #600), 016t_3 (0.59 #119, 0.55 #143, 0.40 #591), 07s6fsf (0.44 #117, 0.44 #141, 0.31 #469), 0bkj86 (0.41 #124, 0.40 #148, 0.37 #268), 027f2w (0.34 #125, 0.32 #149, 0.19 #1421), 04zx3q1 (0.30 #142, 0.29 #118, 0.23 #262), 013zdg (0.21 #173, 0.19 #1421, 0.19 #290), 03mkk4 (0.19 #1421, 0.17 #271, 0.13 #127) >> Best rule #74 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 015wy_; >> query: (?x8046, 014mlp) <- colors(?x8046, ?x332), major_field_of_study(?x8046, ?x1154), currency(?x8046, ?x170), ?x332 = 01l849 >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02gnh0 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 65.000 0.718 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21205-02s8qk PRED entity: 02s8qk PRED relation: colors PRED expected values: 06fvc => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.44 #42, 0.39 #62, 0.39 #22), 036k5h (0.33 #5, 0.21 #305, 0.19 #345), 03wkwg (0.33 #15, 0.12 #55, 0.09 #35), 01l849 (0.33 #141, 0.29 #541, 0.29 #401), 019sc (0.18 #167, 0.18 #407, 0.18 #387), 06fvc (0.17 #23, 0.16 #143, 0.16 #303), 04mkbj (0.16 #50, 0.14 #70, 0.13 #30), 0jc_p (0.11 #224, 0.11 #884, 0.10 #984), 038hg (0.11 #252, 0.10 #712, 0.10 #152), 04d18d (0.10 #199, 0.09 #259, 0.08 #319) >> Best rule #42 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 01j_06; 07w3r; 02bjhv; 02fgdx; 037njl; 01bm_; 012mzw; 06fq2; 01q8hj; 0dzst; ... >> query: (?x6257, 083jv) <- school(?x2820, ?x6257), currency(?x6257, ?x170), school_type(?x6257, ?x1044), ?x1044 = 05pcjw, colors(?x6257, ?x3189) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #23 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 02c9dj; *> query: (?x6257, 06fvc) <- school(?x2820, ?x6257), currency(?x6257, ?x170), school_type(?x6257, ?x1044), ?x1044 = 05pcjw, ?x2820 = 0jmj7 *> conf = 0.17 ranks of expected_values: 6 EVAL 02s8qk colors 06fvc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 199.000 199.000 0.440 http://example.org/education/educational_institution/colors #21204-07rd7 PRED entity: 07rd7 PRED relation: student! PRED expected values: 015zyd => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 197): 065y4w7 (0.19 #2649, 0.15 #1068, 0.12 #5284), 0bwfn (0.12 #7126, 0.11 #6072, 0.10 #18720), 015zyd (0.11 #3690, 0.08 #5271, 0.05 #6852), 09f2j (0.11 #1740, 0.08 #2794, 0.08 #1213), 07tgn (0.09 #8976, 0.04 #20570, 0.04 #21624), 04b_46 (0.08 #1281, 0.06 #6551, 0.05 #6024), 01w5m (0.08 #9064, 0.06 #22239, 0.06 #20658), 01qd_r (0.08 #1335, 0.06 #3970, 0.05 #1862), 02g839 (0.08 #1079, 0.06 #3714, 0.05 #1606), 021w0_ (0.08 #1378, 0.05 #1905, 0.05 #2432) >> Best rule #2649 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 019z7q; 0343h; 01t07j; 03s9b; 0dr5y; 0k_mt; >> query: (?x4314, 065y4w7) <- influenced_by(?x4314, ?x3028), film(?x4314, ?x485), type_of_union(?x4314, ?x566) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #3690 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 33 *> proper extension: 01c59k; 019r_1; 01p8r8; 03k1vm; 0b57p6; 0jnb0; 01c5d5; 05h7tk; *> query: (?x4314, 015zyd) <- profession(?x4314, ?x1966), ?x1966 = 015h31 *> conf = 0.11 ranks of expected_values: 3 EVAL 07rd7 student! 015zyd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 141.000 141.000 0.192 http://example.org/education/educational_institution/students_graduates./education/education/student #21203-03pmty PRED entity: 03pmty PRED relation: people! PRED expected values: 033tf_ => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 40): 033tf_ (0.40 #7, 0.25 #161, 0.18 #392), 07bch9 (0.25 #177, 0.20 #23, 0.08 #254), 041rx (0.22 #389, 0.20 #4, 0.19 #466), 01qhm_ (0.20 #6, 0.08 #237, 0.07 #391), 02w7gg (0.14 #79, 0.08 #1543, 0.07 #618), 0d2by (0.14 #110), 07hwkr (0.12 #166, 0.08 #243, 0.07 #551), 04gfy7 (0.12 #219), 0x67 (0.11 #1551, 0.10 #1320, 0.10 #4169), 0xnvg (0.09 #552, 0.08 #244, 0.07 #1939) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 014zcr; 016z2j; 02d4ct; >> query: (?x969, 033tf_) <- award_nominee(?x8783, ?x969), ?x8783 = 0gpprt, participant(?x3308, ?x969) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03pmty people! 033tf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.400 http://example.org/people/ethnicity/people #21202-0g4pl7z PRED entity: 0g4pl7z PRED relation: film_release_region PRED expected values: 012wgb => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 258): 03h64 (0.87 #1181, 0.86 #1500, 0.85 #1340), 05qhw (0.86 #1125, 0.85 #1444, 0.80 #1284), 0k6nt (0.80 #1614, 0.79 #2570, 0.78 #1295), 02vzc (0.80 #1642, 0.78 #2917, 0.78 #2598), 03spz (0.76 #1531, 0.76 #1212, 0.70 #1371), 05b4w (0.76 #1178, 0.75 #1497, 0.70 #1337), 06t2t (0.75 #1494, 0.75 #1175, 0.70 #1334), 03rt9 (0.72 #1124, 0.70 #1443, 0.66 #1283), 05v8c (0.69 #1127, 0.65 #1286, 0.62 #1446), 04gzd (0.66 #1119, 0.60 #1438, 0.58 #1278) >> Best rule #1181 for best value: >> intensional similarity = 5 >> extensional distance = 109 >> proper extension: 0ds3t5x; 0c40vxk; 0bq8tmw; 0bh8yn3; 0407yfx; 0fpmrm3; 01jrbb; 0cc97st; 0ndsl1x; >> query: (?x8955, 03h64) <- film_release_region(?x8955, ?x1603), film_release_region(?x8955, ?x583), ?x1603 = 06bnz, production_companies(?x8955, ?x2156), ?x583 = 015fr >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1197 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 109 *> proper extension: 0ds3t5x; 0c40vxk; 0bq8tmw; 0bh8yn3; 0407yfx; 0fpmrm3; 01jrbb; 0cc97st; 0ndsl1x; *> query: (?x8955, 012wgb) <- film_release_region(?x8955, ?x1603), film_release_region(?x8955, ?x583), ?x1603 = 06bnz, production_companies(?x8955, ?x2156), ?x583 = 015fr *> conf = 0.09 ranks of expected_values: 95 EVAL 0g4pl7z film_release_region 012wgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 73.000 73.000 0.874 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21201-07y2s PRED entity: 07y2s PRED relation: contact_category PRED expected values: 014dgf => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 2): 014dgf (0.50 #3, 0.33 #1, 0.26 #17), 02zdwq (0.44 #6, 0.38 #8, 0.37 #10) >> Best rule #3 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 077w0b; >> query: (?x1540, 014dgf) <- service_location(?x1540, ?x279), service_location(?x1540, ?x151), service_location(?x1540, ?x94), ?x94 = 09c7w0, service_language(?x1540, ?x254), ?x151 = 0b90_r, film_release_region(?x3854, ?x279), film_release_region(?x1518, ?x279), nationality(?x199, ?x279), ?x1518 = 04w7rn, ?x3854 = 03q0r1, film_release_region(?x1064, ?x279) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07y2s contact_category 014dgf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #21200-04vrxh PRED entity: 04vrxh PRED relation: role PRED expected values: 06ncr => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 127): 018vs (0.43 #641, 0.43 #547, 0.27 #1494), 0342h (0.43 #537, 0.38 #1926, 0.37 #2350), 01vj9c (0.41 #549, 0.17 #978, 0.14 #872), 05r5c (0.40 #1182, 0.31 #1611, 0.30 #757), 01vdm0 (0.33 #140, 0.32 #1207, 0.25 #782), 06ncr (0.29 #639, 0.28 #2561, 0.28 #2669), 05148p4 (0.29 #639, 0.28 #2561, 0.28 #2669), 028tv0 (0.27 #1494, 0.25 #1709, 0.25 #2452), 02sgy (0.26 #755, 0.26 #968, 0.25 #862), 026t6 (0.24 #535, 0.20 #215, 0.16 #1924) >> Best rule #641 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0bg539; 01qvgl; 037hgm; 01s7qqw; 02mx98; 03f1zhf; 023slg; >> query: (?x9882, ?x716) <- instrumentalists(?x227, ?x9882), role(?x9882, ?x716), ?x716 = 018vs >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #639 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 0bg539; 01qvgl; 037hgm; 01s7qqw; 02mx98; 03f1zhf; 023slg; *> query: (?x9882, ?x227) <- instrumentalists(?x227, ?x9882), role(?x9882, ?x716), ?x716 = 018vs *> conf = 0.29 ranks of expected_values: 6 EVAL 04vrxh role 06ncr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 100.000 100.000 0.435 http://example.org/music/artist/track_contributions./music/track_contribution/role #21199-06bss PRED entity: 06bss PRED relation: legislative_sessions PRED expected values: 07p__7 02bqn1 02bp37 03tcbx 06r713 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 38): 07p__7 (0.78 #97, 0.77 #128, 0.67 #66), 070mff (0.69 #144, 0.67 #113, 0.67 #82), 02bqn1 (0.67 #98, 0.67 #67, 0.62 #129), 02bp37 (0.67 #100, 0.62 #131, 0.61 #157), 06r713 (0.61 #157, 0.61 #156, 0.50 #49), 03tcbx (0.61 #157, 0.61 #156, 0.44 #102), 077g7n (0.61 #157, 0.61 #156, 0.42 #220), 01gtc0 (0.16 #252, 0.12 #203, 0.12 #235), 01grr2 (0.16 #252, 0.08 #209, 0.08 #241), 01h7xx (0.16 #252, 0.08 #212, 0.08 #244) >> Best rule #97 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 0bymv; 0d3qd0; 016lh0; 02hy5d; 024_vw; >> query: (?x6742, 07p__7) <- legislative_sessions(?x6742, ?x6933), legislative_sessions(?x6742, ?x6139), legislative_sessions(?x6742, ?x1027), student(?x3821, ?x6742), ?x1027 = 02bn_p, ?x6933 = 024tkd, district_represented(?x6139, ?x335) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 5, 6 EVAL 06bss legislative_sessions 06r713 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.778 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 06bss legislative_sessions 03tcbx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.778 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 06bss legislative_sessions 02bp37 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.778 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 06bss legislative_sessions 02bqn1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.778 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 06bss legislative_sessions 07p__7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.778 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #21198-01c65z PRED entity: 01c65z PRED relation: film PRED expected values: 0gmcwlb => 112 concepts (75 used for prediction) PRED predicted values (max 10 best out of 570): 034qrh (0.33 #63, 0.02 #1849, 0.01 #3635), 08952r (0.33 #717, 0.01 #15005, 0.01 #22149), 04s1zr (0.33 #1721), 0d7vtk (0.09 #55367, 0.08 #57154, 0.07 #64301), 01y9jr (0.05 #2947, 0.03 #26165, 0.03 #29737), 03bzjpm (0.05 #4886, 0.03 #12030, 0.03 #17388), 0f42nz (0.03 #66997, 0.03 #20555, 0.03 #24127), 02qr3k8 (0.03 #4860, 0.03 #20934, 0.02 #12004), 013q07 (0.03 #3929, 0.03 #2143, 0.02 #21789), 03nfnx (0.03 #4974, 0.02 #12118, 0.02 #17476) >> Best rule #63 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0378zn; >> query: (?x12448, 034qrh) <- profession(?x12448, ?x319), film(?x12448, ?x6628), actor(?x9636, ?x12448), ?x6628 = 0bxxzb >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01c65z film 0gmcwlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 75.000 0.333 http://example.org/film/actor/film./film/performance/film #21197-066wd PRED entity: 066wd PRED relation: genre! PRED expected values: 05b6s5j 074j87 => 29 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1361): 02py9yf (0.95 #1186, 0.50 #1419, 0.50 #824), 099pks (0.95 #1186, 0.50 #1290, 0.50 #695), 05p9_ql (0.95 #1186, 0.50 #1326, 0.50 #731), 0123qq (0.95 #1186, 0.50 #1426, 0.50 #831), 0266s9 (0.95 #1186, 0.50 #1441, 0.50 #846), 0cskb (0.95 #1186, 0.50 #1401, 0.50 #806), 02r1ysd (0.95 #1186, 0.50 #1317, 0.50 #722), 0431v3 (0.95 #1186, 0.50 #1289, 0.50 #694), 03ln8b (0.95 #1186, 0.50 #1220, 0.50 #625), 0cs134 (0.95 #1186, 0.50 #1420, 0.50 #825) >> Best rule #1186 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0jtdp; 03g3w; >> query: (?x11043, ?x273) <- genre(?x10551, ?x11043), genre(?x8818, ?x11043), program(?x11630, ?x10551), program(?x13117, ?x8818), genre(?x10551, ?x53), child(?x7326, ?x13117), genre(?x54, ?x53), titles(?x53, ?x253), genre(?x4881, ?x53), genre(?x273, ?x53), ?x4881 = 02kk_c >> conf = 0.95 => this is the best rule for 129 predicted values *> Best rule #297 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 07s9rl0; *> query: (?x11043, ?x11629) <- genre(?x13068, ?x11043), genre(?x11042, ?x11043), genre(?x10551, ?x11043), genre(?x8818, ?x11043), ?x10551 = 070ltt, ?x8818 = 01yb1y, actor(?x11042, ?x11630), country_of_origin(?x13068, ?x94), actor(?x11629, ?x11630), type_of_union(?x11630, ?x566), student(?x6083, ?x11630), religion(?x11630, ?x1985) *> conf = 0.72 ranks of expected_values: 130, 133 EVAL 066wd genre! 074j87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 29.000 25.000 0.952 http://example.org/tv/tv_program/genre EVAL 066wd genre! 05b6s5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 29.000 25.000 0.952 http://example.org/tv/tv_program/genre #21196-07z5n PRED entity: 07z5n PRED relation: country! PRED expected values: 0bynt => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 55): 0bynt (0.85 #891, 0.85 #1221, 0.84 #2763), 06z6r (0.84 #253, 0.82 #198, 0.78 #1133), 071t0 (0.71 #739, 0.65 #244, 0.62 #189), 01cgz (0.63 #785, 0.63 #235, 0.62 #1500), 01lb14 (0.51 #732, 0.49 #237, 0.47 #1117), 06f41 (0.51 #236, 0.46 #731, 0.44 #181), 07gyv (0.49 #227, 0.46 #172, 0.46 #722), 064vjs (0.47 #254, 0.39 #749, 0.38 #199), 0486tv (0.46 #206, 0.44 #261, 0.39 #756), 0w0d (0.46 #728, 0.41 #398, 0.40 #1333) >> Best rule #891 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 027nb; 04gzd; 06npd; 019rg5; 03gj2; 04v3q; 05cgv; 01znc_; 01mjq; 02vzc; ... >> query: (?x2291, 0bynt) <- countries_spoken_in(?x254, ?x2291), administrative_area_type(?x2291, ?x2792), currency(?x2291, ?x170) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07z5n country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.847 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #21195-02k54 PRED entity: 02k54 PRED relation: film_release_region! PRED expected values: 0g5qs2k 0fq7dv_ 0dr3sl 047tsx3 06tpmy 0bt3j9 03yvf2 043tvp3 => 205 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1258): 043tvp3 (0.83 #48674, 0.79 #62514, 0.76 #27285), 0fpgp26 (0.81 #48886, 0.79 #19949, 0.78 #66500), 0gj8nq2 (0.81 #48190, 0.73 #26801, 0.72 #65804), 06wbm8q (0.79 #48095, 0.73 #26706, 0.71 #19158), 062zm5h (0.79 #48417, 0.72 #62257, 0.71 #66031), 0872p_c (0.79 #47930, 0.67 #26541, 0.65 #61770), 024mpp (0.77 #48263, 0.61 #62103, 0.61 #26874), 0661m4p (0.76 #26678, 0.75 #48067, 0.68 #61907), 087wc7n (0.76 #26502, 0.73 #47891, 0.64 #18954), 017gm7 (0.75 #47952, 0.73 #26563, 0.72 #65566) >> Best rule #48674 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 01ls2; 06mzp; 0k6nt; 04v3q; 047yc; 0h7x; 05qx1; 01znc_; 03rj0; 0161c; ... >> query: (?x608, 043tvp3) <- country(?x471, ?x608), film_release_region(?x908, ?x608), ?x908 = 01vksx >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 32, 52, 70, 100, 109, 118, 180 EVAL 02k54 film_release_region! 043tvp3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 205.000 117.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02k54 film_release_region! 03yvf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 205.000 117.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02k54 film_release_region! 0bt3j9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 205.000 117.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02k54 film_release_region! 06tpmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 205.000 117.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02k54 film_release_region! 047tsx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 205.000 117.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02k54 film_release_region! 0dr3sl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 205.000 117.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02k54 film_release_region! 0fq7dv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 205.000 117.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02k54 film_release_region! 0g5qs2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 205.000 117.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21194-0c8wxp PRED entity: 0c8wxp PRED relation: religion! PRED expected values: 059rby 0chghy 0h7x 081mh 0846v 04g61 07_f2 => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 318): 0846v (0.75 #413, 0.75 #325, 0.75 #148), 081mh (0.75 #147, 0.67 #412, 0.67 #324), 059rby (0.67 #401, 0.62 #136, 0.58 #313), 09c7w0 (0.67 #223, 0.46 #489, 0.40 #577), 07_f2 (0.50 #425, 0.50 #204, 0.50 #160), 03rk0 (0.44 #235, 0.31 #501, 0.29 #1080), 03rt9 (0.33 #227, 0.33 #5, 0.23 #493), 0ctw_b (0.33 #230, 0.23 #496, 0.20 #584), 0chghy (0.33 #226, 0.23 #492, 0.20 #580), 05kyr (0.33 #243, 0.23 #509, 0.20 #597) >> Best rule #413 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 01s5nb; >> query: (?x1985, 0846v) <- religion(?x1274, ?x1985), religion(?x1227, ?x1985), ?x1274 = 04ykg, state(?x581, ?x1227), location(?x397, ?x1227), contains(?x1227, ?x191) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 9, 29 EVAL 0c8wxp religion! 07_f2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 40.000 40.000 0.750 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 0c8wxp religion! 04g61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 40.000 40.000 0.750 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 0c8wxp religion! 0846v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.750 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 0c8wxp religion! 081mh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.750 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 0c8wxp religion! 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 40.000 40.000 0.750 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 0c8wxp religion! 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 40.000 40.000 0.750 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 0c8wxp religion! 059rby CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.750 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #21193-0blbxk PRED entity: 0blbxk PRED relation: type_of_union PRED expected values: 01g63y => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.79 #17, 0.73 #105, 0.73 #129), 01g63y (0.17 #46, 0.16 #26, 0.15 #50) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 0bw6y; 01j851; >> query: (?x1290, 04ztj) <- location(?x1290, ?x108), award(?x1290, ?x1132), ?x1132 = 0bdwft >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 639 *> proper extension: 0m2wm; 02zq43; 01j5x6; 01v3s2_; 025t9b; 02wycg2; 01pctb; 02qfhb; 0g2mbn; 02pk6x; ... *> query: (?x1290, 01g63y) <- film(?x1290, ?x1702), award_nominee(?x100, ?x1290), people(?x2510, ?x1290) *> conf = 0.17 ranks of expected_values: 2 EVAL 0blbxk type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.793 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21192-027pfb2 PRED entity: 027pfb2 PRED relation: actor PRED expected values: 02gvwz => 79 concepts (36 used for prediction) PRED predicted values (max 10 best out of 461): 0410cp (0.65 #10170, 0.64 #8321, 0.63 #11095), 0h0jz (0.33 #19, 0.12 #3716, 0.12 #1868), 01ft2l (0.33 #287, 0.12 #2136, 0.06 #3984), 02mxw0 (0.33 #220, 0.12 #2069, 0.06 #3917), 037w7r (0.33 #699, 0.12 #2548, 0.06 #4396), 03jg5t (0.33 #595, 0.12 #2444, 0.06 #4292), 02l4rh (0.33 #552, 0.12 #2401, 0.06 #4249), 01xsbh (0.33 #193, 0.12 #2042, 0.06 #3890), 019_1h (0.33 #81, 0.12 #1930, 0.06 #3778), 01tspc6 (0.25 #1929, 0.08 #2853, 0.07 #9325) >> Best rule #10170 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 070g7; 0b6m5fy; >> query: (?x4138, ?x4137) <- actor(?x4138, ?x9437), actor(?x4138, ?x3836), film(?x4137, ?x4138), nominated_for(?x3836, ?x3326), profession(?x9437, ?x1032), student(?x4410, ?x3836) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027pfb2 actor 02gvwz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 36.000 0.651 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21191-0r5y9 PRED entity: 0r5y9 PRED relation: location! PRED expected values: 016h4r => 119 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1612): 04wf_b (0.55 #45330, 0.52 #141032, 0.50 #118366), 02v2jy (0.52 #141032, 0.50 #118366, 0.49 #118365), 01xyt7 (0.50 #12594, 0.50 #118366, 0.49 #118365), 0c6g1l (0.25 #2971, 0.20 #5490, 0.05 #13047), 096lf_ (0.17 #2043, 0.03 #24709, 0.02 #44853), 032xhg (0.17 #55, 0.01 #93182, 0.01 #22721), 01934k (0.17 #1732, 0.01 #24398), 073x6y (0.17 #1368, 0.01 #24034), 02__94 (0.17 #1197, 0.01 #23863), 0b82vw (0.17 #342, 0.01 #23008) >> Best rule #45330 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 0f04v; 0f2tj; 0x335; >> query: (?x6598, ?x9218) <- state(?x6598, ?x1227), place_of_birth(?x9218, ?x6598), film(?x9218, ?x2709), award_nominee(?x9218, ?x1410) >> conf = 0.55 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r5y9 location! 016h4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 67.000 0.547 http://example.org/people/person/places_lived./people/place_lived/location #21190-045n3p PRED entity: 045n3p PRED relation: place_of_birth PRED expected values: 0hj6h => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 131): 0hj6h (0.20 #486, 0.04 #6831, 0.03 #6126), 03rk0 (0.14 #780, 0.08 #2189, 0.06 #3525), 09c17 (0.14 #1922, 0.03 #4742, 0.02 #6152), 01sv6k (0.14 #2005), 04vmp (0.12 #6613, 0.12 #5639, 0.12 #8459), 0cvw9 (0.12 #3118, 0.06 #3824, 0.05 #5233), 02_286 (0.10 #17646, 0.08 #14121, 0.08 #23990), 05qtj (0.10 #5101, 0.08 #7921, 0.08 #9334), 055vr (0.08 #5640, 0.07 #8460, 0.07 #9167), 01c1nm (0.08 #5640, 0.07 #8460, 0.07 #9167) >> Best rule #486 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0b5x23; >> query: (?x14156, 0hj6h) <- place_of_death(?x14156, ?x7412), ?x7412 = 04vmp, people(?x5025, ?x14156), languages(?x14156, ?x1882), ?x5025 = 0dryh9k >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045n3p place_of_birth 0hj6h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.200 http://example.org/people/person/place_of_birth #21189-02h4rq6 PRED entity: 02h4rq6 PRED relation: student PRED expected values: 01mr2g6 => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2089): 014vk4 (0.40 #2338, 0.33 #4273, 0.33 #1486), 04z0g (0.40 #2248, 0.33 #1396, 0.33 #1184), 0b78hw (0.40 #2220, 0.33 #1368, 0.33 #1156), 06y7d (0.40 #2336, 0.33 #1484, 0.33 #846), 01tdnyh (0.40 #2232, 0.33 #1380, 0.33 #742), 0969fd (0.40 #2321, 0.33 #1469, 0.33 #831), 0jsw9l (0.40 #2532, 0.33 #1466, 0.29 #3390), 02mjmr (0.40 #2609, 0.33 #1327, 0.25 #1539), 06w38l (0.40 #2985, 0.25 #3845, 0.25 #3626), 0d06m5 (0.40 #2618, 0.25 #3474, 0.25 #1548) >> Best rule #2338 for best value: >> intensional similarity = 23 >> extensional distance = 3 >> proper extension: 01ysy9; >> query: (?x865, 014vk4) <- institution(?x865, ?x12726), institution(?x865, ?x10572), institution(?x865, ?x9847), institution(?x865, ?x8427), institution(?x865, ?x8046), institution(?x865, ?x3777), institution(?x865, ?x2150), institution(?x865, ?x892), ?x12726 = 09vzz, major_field_of_study(?x865, ?x4268), ?x9847 = 0187nd, ?x892 = 07tgn, institution(?x4981, ?x8427), state_province_region(?x2150, ?x6521), category(?x8427, ?x134), colors(?x8427, ?x332), institution(?x734, ?x10572), student(?x8046, ?x4470), school(?x260, ?x3777), ?x734 = 04zx3q1, major_field_of_study(?x2909, ?x4268), student(?x4268, ?x906), ?x4981 = 03bwzr4 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1431 for first EXPECTED value: *> intensional similarity = 32 *> extensional distance = 1 *> proper extension: 014mlp; *> query: (?x865, 01mr2g6) <- institution(?x865, ?x12293), institution(?x865, ?x11975), institution(?x865, ?x10910), institution(?x865, ?x10666), institution(?x865, ?x9409), institution(?x865, ?x8715), institution(?x865, ?x8427), institution(?x865, ?x8220), institution(?x865, ?x6763), institution(?x865, ?x6434), institution(?x865, ?x4531), institution(?x865, ?x4211), institution(?x865, ?x946), ?x8427 = 021996, major_field_of_study(?x865, ?x11378), major_field_of_study(?x865, ?x3995), major_field_of_study(?x865, ?x2606), ?x2606 = 062z7, ?x946 = 01hhvg, ?x4211 = 0221g_, ?x6763 = 01j_5k, ?x8715 = 01wv24, ?x8220 = 0c5x_, ?x4531 = 071_8, ?x11975 = 050xpd, ?x11378 = 01lhf, ?x6434 = 01vg13, ?x12293 = 01pj48, ?x10666 = 01dzg0, ?x3995 = 0fdys, ?x10910 = 013807, category(?x9409, ?x134) *> conf = 0.33 ranks of expected_values: 18 EVAL 02h4rq6 student 01mr2g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 25.000 25.000 0.400 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #21188-02_06s PRED entity: 02_06s PRED relation: nominated_for! PRED expected values: 02x4x18 => 75 concepts (59 used for prediction) PRED predicted values (max 10 best out of 200): 02qyp19 (0.73 #464, 0.73 #696, 0.50 #1), 02x1dht (0.70 #7187, 0.69 #695, 0.68 #8117), 03ybrwc (0.69 #695, 0.67 #7186, 0.66 #7650), 0gq9h (0.68 #1681, 0.60 #523, 0.59 #755), 0gs9p (0.67 #1683, 0.64 #757, 0.60 #525), 040njc (0.59 #702, 0.53 #470, 0.46 #1628), 02pqp12 (0.55 #752, 0.53 #520, 0.50 #57), 019f4v (0.54 #1674, 0.49 #2137, 0.43 #1442), 02qyntr (0.50 #868, 0.50 #173, 0.40 #636), 0f4x7 (0.49 #1415, 0.40 #1647, 0.32 #2110) >> Best rule #464 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0m313; 01gc7; 02rv_dz; 0661ql3; 07j8r; 03hkch7; 02mt51; 0_b9f; 05sy_5; 011ywj; ... >> query: (?x7129, 02qyp19) <- award(?x7129, ?x3435), ?x3435 = 03hl6lc, film_crew_role(?x7129, ?x281) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #13671 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1494 *> proper extension: 0c3xpwy; *> query: (?x7129, ?x618) <- nominated_for(?x5454, ?x7129), award_winner(?x1132, ?x5454), award(?x5454, ?x618) *> conf = 0.19 ranks of expected_values: 54 EVAL 02_06s nominated_for! 02x4x18 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 75.000 59.000 0.733 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21187-02wbnv PRED entity: 02wbnv PRED relation: state_province_region PRED expected values: 01n7q => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 53): 01n7q (0.57 #14035, 0.54 #4729, 0.53 #3491), 059rby (0.45 #3600, 0.42 #3724, 0.41 #3973), 09c7w0 (0.30 #14036, 0.28 #3720, 0.25 #14161), 07b_l (0.20 #543, 0.09 #1908, 0.08 #4637), 05k7sb (0.13 #6727, 0.11 #4618, 0.07 #8588), 081yw (0.12 #1421, 0.10 #554, 0.09 #3534), 05kr_ (0.10 #2726, 0.05 #6973, 0.04 #7222), 07dfk (0.10 #2726, 0.02 #12663, 0.02 #12788), 048fz (0.10 #2726), 06qd3 (0.10 #2726) >> Best rule #14035 for best value: >> intensional similarity = 3 >> extensional distance = 590 >> proper extension: 026v1z; 09k0h5; >> query: (?x14310, ?x1227) <- citytown(?x14310, ?x6960), contains(?x1227, ?x6960), state_province_region(?x99, ?x1227) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wbnv state_province_region 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.567 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #21186-01hc9_ PRED entity: 01hc9_ PRED relation: influenced_by PRED expected values: 081k8 042v2 => 145 concepts (49 used for prediction) PRED predicted values (max 10 best out of 354): 03_87 (0.50 #618, 0.29 #2314, 0.19 #1890), 02lt8 (0.48 #1812, 0.18 #2236, 0.12 #16134), 081k8 (0.29 #2269, 0.19 #1845, 0.17 #573), 0j3v (0.26 #2178, 0.17 #482, 0.11 #19532), 01tz6vs (0.24 #2289, 0.17 #593, 0.12 #14433), 0379s (0.24 #2195, 0.17 #499, 0.12 #14433), 014zfs (0.22 #4267, 0.10 #7241, 0.09 #9363), 01v9724 (0.21 #2290, 0.19 #1866, 0.11 #19532), 084w8 (0.21 #2123, 0.17 #427, 0.13 #1699), 014z8v (0.20 #4359, 0.12 #7333, 0.10 #9455) >> Best rule #618 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 073bb; 02ld6x; 013pp3; 040_t; >> query: (?x8841, 03_87) <- award(?x8841, ?x8842), nationality(?x8841, ?x252), influenced_by(?x8841, ?x7039), ?x7039 = 041_y >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2269 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 02m4t; *> query: (?x8841, 081k8) <- influenced_by(?x8841, ?x5346), influenced_by(?x8841, ?x3336), ?x3336 = 032l1, type_of_union(?x5346, ?x566) *> conf = 0.29 ranks of expected_values: 3, 121 EVAL 01hc9_ influenced_by 042v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 145.000 49.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 01hc9_ influenced_by 081k8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 49.000 0.500 http://example.org/influence/influence_node/influenced_by #21185-03xq0f PRED entity: 03xq0f PRED relation: film PRED expected values: 047msdk 02f6g5 0btyf5z 0hx4y 04z257 03tps5 04gv3db 02gpkt 01xbxn 0ndsl1x 06y611 01sbv9 => 27 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1706): 02rb84n (0.50 #7555, 0.33 #6090, 0.33 #1694), 04f52jw (0.50 #7677, 0.33 #6212, 0.17 #9142), 02yxbc (0.50 #8378, 0.33 #6913, 0.15 #11311), 02mmwk (0.50 #8349, 0.33 #6884, 0.09 #10260), 09hy79 (0.50 #8325, 0.33 #6860, 0.09 #10260), 03f7nt (0.50 #8001, 0.33 #6536, 0.09 #10260), 0gldyz (0.50 #8683, 0.33 #7218, 0.09 #10260), 03cp4cn (0.50 #8222, 0.33 #6757, 0.09 #10260), 01sbv9 (0.50 #8665, 0.33 #7200, 0.09 #10260), 034qbx (0.50 #8269, 0.33 #6804, 0.09 #10260) >> Best rule #7555 for best value: >> intensional similarity = 16 >> extensional distance = 2 >> proper extension: 01gb54; >> query: (?x609, 02rb84n) <- film(?x609, ?x9752), film(?x609, ?x3565), film(?x609, ?x3498), film(?x609, ?x2933), film(?x609, ?x2928), film(?x609, ?x1283), film_release_region(?x2933, ?x1497), ?x2928 = 07024, ?x1497 = 015qh, film(?x1538, ?x2933), film(?x274, ?x3565), film_crew_role(?x3565, ?x137), film_release_region(?x3498, ?x608), genre(?x9752, ?x271), film_regional_debut_venue(?x1283, ?x1658), nominated_for(?x1500, ?x3498) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8665 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 01gb54; *> query: (?x609, 01sbv9) <- film(?x609, ?x9752), film(?x609, ?x3565), film(?x609, ?x3498), film(?x609, ?x2933), film(?x609, ?x2928), film(?x609, ?x1283), film_release_region(?x2933, ?x1497), ?x2928 = 07024, ?x1497 = 015qh, film(?x1538, ?x2933), film(?x274, ?x3565), film_crew_role(?x3565, ?x137), film_release_region(?x3498, ?x608), genre(?x9752, ?x271), film_regional_debut_venue(?x1283, ?x1658), nominated_for(?x1500, ?x3498) *> conf = 0.50 ranks of expected_values: 9, 233, 349, 397, 427, 606, 628, 724, 883, 1022, 1098, 1208 EVAL 03xq0f film 01sbv9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 06y611 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 0ndsl1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 01xbxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 02gpkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 04gv3db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 03tps5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 04z257 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 0hx4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 0btyf5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 02f6g5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xq0f film 047msdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 15.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #21184-0345_ PRED entity: 0345_ PRED relation: country! PRED expected values: 06z6r 01sgl 0194d => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 49): 0bynt (0.88 #303, 0.87 #352, 0.87 #1185), 06z6r (0.88 #760, 0.85 #368, 0.84 #809), 06f41 (0.73 #357, 0.68 #308, 0.58 #161), 01cgz (0.69 #454, 0.68 #356, 0.68 #797), 07jbh (0.65 #371, 0.58 #322, 0.57 #175), 064vjs (0.65 #369, 0.53 #320, 0.49 #173), 0w0d (0.63 #354, 0.57 #305, 0.52 #795), 03fyrh (0.58 #366, 0.47 #170, 0.47 #807), 02y8z (0.56 #360, 0.52 #311, 0.49 #164), 0194d (0.55 #385, 0.53 #336, 0.47 #826) >> Best rule #303 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 027rn; 05r4w; 0154j; 047lj; 01ls2; 015fr; 047yc; 035qy; 06qd3; 07t21; ... >> query: (?x4954, 0bynt) <- film_release_region(?x5016, ?x4954), olympics(?x4954, ?x1931), ?x5016 = 062zm5h >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #760 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 09c7w0; 0jgd; 0b90_r; 03rjj; 03_3d; 0d060g; 0d0vqn; 04gzd; 0chghy; 03_r3; ... *> query: (?x4954, 06z6r) <- film_release_region(?x972, ?x4954), olympics(?x4954, ?x1931), capital(?x4954, ?x10174) *> conf = 0.88 ranks of expected_values: 2, 10, 19 EVAL 0345_ country! 0194d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 110.000 110.000 0.883 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0345_ country! 01sgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 110.000 110.000 0.883 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0345_ country! 06z6r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.883 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #21183-025vldk PRED entity: 025vldk PRED relation: gender PRED expected values: 02zsn => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.76 #3, 0.76 #7, 0.75 #5), 02zsn (0.33 #2, 0.31 #12, 0.25 #16) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 137 >> proper extension: 0jt90f5; 03d_zl4; 023qfd; >> query: (?x7229, 05zppz) <- award(?x7229, ?x2720), nationality(?x7229, ?x94), tv_program(?x7229, ?x6080) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 050023; 057d89; 04gtdnh; 026b7bz; *> query: (?x7229, 02zsn) <- award_winner(?x3544, ?x7229), award_winner(?x7229, ?x415), ?x3544 = 0phrl *> conf = 0.33 ranks of expected_values: 2 EVAL 025vldk gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.763 http://example.org/people/person/gender #21182-03q5t PRED entity: 03q5t PRED relation: role! PRED expected values: 042v_gx 01679d => 72 concepts (49 used for prediction) PRED predicted values (max 10 best out of 98): 01vj9c (0.92 #4087, 0.90 #3684, 0.86 #2673), 0l14qv (0.84 #198, 0.84 #895, 0.84 #894), 042v_gx (0.84 #198, 0.84 #895, 0.84 #894), 0gkd1 (0.84 #198, 0.84 #895, 0.84 #894), 01s0ps (0.84 #198, 0.84 #895, 0.84 #894), 0bxl5 (0.84 #198, 0.84 #895, 0.84 #894), 018j2 (0.84 #198, 0.84 #895, 0.84 #894), 01679d (0.84 #198, 0.84 #895, 0.84 #894), 0151b0 (0.84 #198, 0.84 #895, 0.84 #894), 0xzly (0.81 #1180, 0.78 #590, 0.76 #1084) >> Best rule #4087 for best value: >> intensional similarity = 21 >> extensional distance = 23 >> proper extension: 07c6l; >> query: (?x74, 01vj9c) <- role(?x74, ?x2157), role(?x74, ?x2048), role(?x74, ?x1436), role(?x74, ?x1166), role(?x2157, ?x6938), role(?x2157, ?x4583), role(?x2157, ?x1574), role(?x2157, ?x432), ?x1166 = 05148p4, role(?x74, ?x2253), role(?x1818, ?x74), group(?x74, ?x4715), ?x4583 = 0bmnm, ?x6938 = 023r2x, ?x1574 = 0l15bq, role(?x1436, ?x3703), ?x432 = 042v_gx, role(?x433, ?x74), ?x2048 = 018j2, role(?x1997, ?x2253), instrumentalists(?x74, ?x642) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #198 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 1 *> proper extension: 07brj; *> query: (?x74, ?x228) <- role(?x74, ?x2798), role(?x74, ?x2157), role(?x74, ?x1436), role(?x74, ?x614), role(?x74, ?x316), ?x2157 = 011_6p, role(?x74, ?x2764), role(?x74, ?x432), role(?x74, ?x314), role(?x74, ?x228), role(?x6208, ?x74), ?x2798 = 03qjg, instrumentalists(?x1436, ?x8311), profession(?x6208, ?x131), ?x314 = 02sgy, ?x2764 = 01s0ps, ?x316 = 05r5c, ?x432 = 042v_gx, role(?x1534, ?x1436), role(?x614, ?x4917), role(?x1436, ?x1433), group(?x74, ?x4715), ?x4917 = 06w7v *> conf = 0.84 ranks of expected_values: 3, 8 EVAL 03q5t role! 01679d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 72.000 49.000 0.920 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 03q5t role! 042v_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 49.000 0.920 http://example.org/music/performance_role/track_performances./music/track_contribution/role #21181-02fj8n PRED entity: 02fj8n PRED relation: genre PRED expected values: 04pbhw => 158 concepts (63 used for prediction) PRED predicted values (max 10 best out of 101): 04pbhw (0.83 #527, 0.50 #173, 0.38 #1000), 03k9fj (0.73 #6638, 0.55 #1666, 0.48 #1310), 06n90 (0.67 #484, 0.50 #130, 0.43 #957), 07yjb (0.65 #945, 0.62 #2365, 0.56 #7100), 07s9rl0 (0.65 #3904, 0.56 #6035, 0.54 #4259), 05p553 (0.43 #594, 0.36 #5443, 0.35 #4144), 0lsxr (0.34 #6161, 0.32 #1426, 0.29 #2609), 09blyk (0.30 #266, 0.23 #1093, 0.20 #2276), 02n4kr (0.29 #1425, 0.27 #3792, 0.25 #2253), 02l7c8 (0.29 #605, 0.27 #6286, 0.26 #4981) >> Best rule #527 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 0czyxs; 0cd2vh9; >> query: (?x7463, 04pbhw) <- film_crew_role(?x7463, ?x137), ?x137 = 09zzb8, genre(?x7463, ?x11401), genre(?x7463, ?x225), ?x225 = 02kdv5l, ?x11401 = 0btmb, production_companies(?x7463, ?x738) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fj8n genre 04pbhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 63.000 0.833 http://example.org/film/film/genre #21180-03np63f PRED entity: 03np63f PRED relation: titles! PRED expected values: 07s9rl0 => 69 concepts (30 used for prediction) PRED predicted values (max 10 best out of 55): 07s9rl0 (0.32 #1635, 0.32 #1735, 0.32 #1432), 01z4y (0.21 #2677, 0.20 #1869, 0.18 #2273), 01jfsb (0.16 #18, 0.15 #424, 0.13 #2661), 07c52 (0.13 #1459, 0.10 #1762, 0.10 #1662), 0f8l9c (0.12 #508, 0.08 #507, 0.08 #509), 03mqtr (0.11 #450, 0.06 #2283, 0.06 #2687), 01hmnh (0.09 #2668, 0.09 #1860, 0.08 #2264), 09c7w0 (0.08 #507), 017fp (0.08 #1656, 0.08 #1756, 0.08 #428), 024qqx (0.08 #2722, 0.07 #588, 0.07 #1914) >> Best rule #1635 for best value: >> intensional similarity = 3 >> extensional distance = 785 >> proper extension: 0g60z; 080dwhx; 03kq98; 072kp; 039fgy; 02k_4g; 02nf2c; 0124k9; 08jgk1; 0464pz; ... >> query: (?x7897, 07s9rl0) <- award(?x7897, ?x941), nominated_for(?x1063, ?x7897), titles(?x162, ?x7897) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03np63f titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 30.000 0.323 http://example.org/media_common/netflix_genre/titles #21179-07l50vn PRED entity: 07l50vn PRED relation: film_release_region PRED expected values: 027rn => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 211): 06t2t (0.87 #2427, 0.86 #2149, 0.85 #3123), 0jgd (0.87 #3213, 0.86 #3772, 0.86 #3074), 03rj0 (0.73 #2425, 0.72 #2147, 0.72 #3260), 04gzd (0.71 #2382, 0.70 #3217, 0.70 #2104), 047yc (0.67 #2398, 0.67 #3233, 0.66 #2120), 03rk0 (0.65 #3256, 0.65 #3117, 0.64 #3815), 01ls2 (0.64 #3081, 0.63 #3779, 0.61 #3220), 015qh (0.64 #2409, 0.63 #1153, 0.63 #3244), 01mjq (0.63 #1155, 0.61 #2133, 0.60 #2411), 06mzp (0.60 #1136, 0.55 #3786, 0.54 #3227) >> Best rule #2427 for best value: >> intensional similarity = 7 >> extensional distance = 83 >> proper extension: 08hmch; 04hwbq; 0gd0c7x; 01jrbb; 067ghz; >> query: (?x5496, 06t2t) <- film_release_region(?x5496, ?x4743), film_release_region(?x5496, ?x2843), film_release_region(?x5496, ?x1892), ?x4743 = 03spz, ?x2843 = 016wzw, film_crew_role(?x5496, ?x137), ?x1892 = 02vzc >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #8233 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 270 *> proper extension: 01gglm; *> query: (?x5496, ?x87) <- film_release_distribution_medium(?x5496, ?x81), film_format(?x5496, ?x6392), ?x81 = 029j_, film_crew_role(?x5496, ?x137), film_format(?x3287, ?x6392), film_release_region(?x3287, ?x87) *> conf = 0.09 ranks of expected_values: 85 EVAL 07l50vn film_release_region 027rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 125.000 125.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21178-03q95r PRED entity: 03q95r PRED relation: profession PRED expected values: 02hrh1q => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 66): 02hrh1q (0.89 #2565, 0.88 #3015, 0.88 #9916), 01d_h8 (0.38 #906, 0.35 #1356, 0.34 #3456), 02jknp (0.36 #8, 0.26 #10652, 0.25 #11854), 0dxtg (0.35 #914, 0.35 #3464, 0.32 #1514), 03gjzk (0.27 #3466, 0.27 #1516, 0.26 #10652), 09jwl (0.27 #1370, 0.26 #10652, 0.25 #11854), 018gz8 (0.26 #10652, 0.25 #11854, 0.25 #1518), 0np9r (0.26 #10652, 0.25 #11854, 0.25 #11553), 02krf9 (0.26 #10652, 0.25 #11854, 0.25 #11553), 021wpb (0.26 #10652, 0.25 #11854, 0.25 #11553) >> Best rule #2565 for best value: >> intensional similarity = 3 >> extensional distance = 334 >> proper extension: 0dszr0; >> query: (?x4482, 02hrh1q) <- location(?x4482, ?x1274), student(?x3948, ?x4482), actor(?x5594, ?x4482) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q95r profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.887 http://example.org/people/person/profession #21177-0j8hd PRED entity: 0j8hd PRED relation: notable_people_with_this_condition PRED expected values: 0lgsq 01817f => 59 concepts (42 used for prediction) PRED predicted values (max 10 best out of 120): 01fdc0 (0.43 #939, 0.43 #938, 0.18 #1769), 0bdt8 (0.43 #939, 0.43 #938, 0.18 #1769), 0h1m9 (0.43 #939, 0.43 #938, 0.18 #1769), 03f3_p3 (0.43 #938, 0.11 #1530, 0.11 #2006), 0n839 (0.33 #112, 0.25 #345, 0.17 #2475), 06x58 (0.33 #20, 0.25 #253, 0.17 #2383), 01pw2f1 (0.33 #16, 0.25 #249, 0.17 #2379), 0484q (0.33 #73, 0.17 #2436, 0.10 #3748), 0227vl (0.33 #87, 0.08 #2450, 0.05 #3762), 05vk_d (0.33 #84, 0.08 #2447, 0.05 #3759) >> Best rule #939 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 0dcqh; 04nz3; >> query: (?x11990, ?x3533) <- people(?x11990, ?x3533), notable_people_with_this_condition(?x11990, ?x4394), notable_people_with_this_condition(?x11990, ?x4082), award(?x3533, ?x704), nationality(?x4394, ?x390), award(?x4082, ?x1008), location(?x4082, ?x4627), artists(?x474, ?x4394), origin(?x4394, ?x8602) >> conf = 0.43 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0j8hd notable_people_with_this_condition 01817f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 42.000 0.429 http://example.org/medicine/disease/notable_people_with_this_condition EVAL 0j8hd notable_people_with_this_condition 0lgsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 42.000 0.429 http://example.org/medicine/disease/notable_people_with_this_condition #21176-0cv0r PRED entity: 0cv0r PRED relation: adjoins PRED expected values: 0dn8b => 104 concepts (70 used for prediction) PRED predicted values (max 10 best out of 455): 05rgl (0.33 #2417, 0.33 #102, 0.20 #1644), 0j3b (0.33 #60, 0.20 #1602, 0.17 #2375), 0d060g (0.33 #10, 0.20 #1552, 0.17 #2325), 0cc07 (0.25 #1403, 0.24 #49472, 0.23 #40206), 0bx9y (0.25 #1235, 0.24 #49472, 0.23 #40206), 0cc1v (0.25 #1283, 0.24 #49472, 0.23 #40206), 0dn8b (0.25 #1404, 0.24 #49472, 0.23 #40206), 09dfcj (0.24 #49472, 0.23 #40206, 0.02 #4440), 0mwsh (0.24 #49472, 0.23 #40206, 0.02 #4075), 0l3n4 (0.24 #49472, 0.23 #40206, 0.01 #18185) >> Best rule #2417 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01n7q; 0l2lk; >> query: (?x14070, 05rgl) <- contains(?x14070, ?x1705), contains(?x1767, ?x14070), location(?x1092, ?x1705), ?x1092 = 02whj >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1404 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0dn8b; 0cv1w; *> query: (?x14070, 0dn8b) <- contains(?x14070, ?x1705), second_level_divisions(?x94, ?x14070), contains(?x6845, ?x14070), ?x6845 = 027rqbx *> conf = 0.25 ranks of expected_values: 7 EVAL 0cv0r adjoins 0dn8b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 104.000 70.000 0.333 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #21175-06znpjr PRED entity: 06znpjr PRED relation: produced_by PRED expected values: 05nn4k => 78 concepts (55 used for prediction) PRED predicted values (max 10 best out of 168): 03_dj (0.19 #3090, 0.10 #3478), 0pz91 (0.18 #1977, 0.04 #4687, 0.04 #3913), 02r251z (0.13 #2173, 0.04 #4109, 0.03 #4883), 05ty4m (0.13 #1943, 0.03 #4653, 0.02 #5039), 0127m7 (0.10 #2009, 0.03 #78, 0.03 #464), 03kpvp (0.09 #125, 0.09 #511, 0.07 #898), 06rq2l (0.06 #2239, 0.02 #4175, 0.01 #4949), 047q2wc (0.06 #2069, 0.01 #4779), 07rd7 (0.06 #149, 0.06 #535, 0.05 #922), 02pq9yv (0.06 #117, 0.06 #503, 0.05 #890) >> Best rule #3090 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 01q7h2; >> query: (?x7878, ?x12345) <- film_release_distribution_medium(?x7878, ?x81), produced_by(?x7878, ?x11233), story_by(?x7878, ?x12345), film(?x710, ?x7878) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #2102 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 60 *> proper extension: 0bhwhj; 012jfb; 04cf_l; *> query: (?x7878, 05nn4k) <- country(?x7878, ?x94), produced_by(?x7878, ?x11233), nominated_for(?x1312, ?x7878), category(?x11233, ?x134), film(?x11233, ?x1692) *> conf = 0.03 ranks of expected_values: 31 EVAL 06znpjr produced_by 05nn4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 78.000 55.000 0.186 http://example.org/film/film/produced_by #21174-016_v3 PRED entity: 016_v3 PRED relation: parent_genre PRED expected values: 0glt670 => 41 concepts (28 used for prediction) PRED predicted values (max 10 best out of 183): 06j6l (0.40 #364, 0.25 #858, 0.14 #694), 0glt670 (0.35 #852, 0.33 #522, 0.22 #1016), 016_nr (0.33 #542, 0.33 #48, 0.25 #213), 06by7 (0.25 #1168, 0.23 #1834, 0.22 #3164), 0gywn (0.25 #206, 0.17 #535, 0.14 #701), 02x8m (0.17 #508, 0.15 #838, 0.08 #1002), 016_rm (0.17 #627, 0.15 #957, 0.08 #1121), 05r6t (0.17 #549, 0.10 #3203, 0.10 #4200), 03lty (0.17 #513, 0.09 #4164, 0.07 #3832), 0xhtw (0.17 #507, 0.06 #1831, 0.04 #1497) >> Best rule #364 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 025sc50; 0gywn; >> query: (?x8184, 06j6l) <- artists(?x8184, ?x11371), artists(?x8184, ?x6268), artists(?x8184, ?x3756), award_nominee(?x6268, ?x5536), award_nominee(?x6268, ?x4594), profession(?x11371, ?x131), ?x5536 = 01vsgrn, ?x3756 = 01wgcvn, award_nominee(?x4594, ?x1989), award(?x4594, ?x528) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #852 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 18 *> proper extension: 02lnbg; 016_nr; 01flzq; 012yc; 036jv; 016_rm; 0190zg; 06czq; 09096d; 01t_1p; *> query: (?x8184, 0glt670) <- artists(?x8184, ?x11371), artists(?x8184, ?x6268), award_nominee(?x6268, ?x6835), award_nominee(?x6268, ?x5536), profession(?x11371, ?x131), ?x5536 = 01vsgrn, ?x131 = 0dz3r, ?x6835 = 06mt91 *> conf = 0.35 ranks of expected_values: 2 EVAL 016_v3 parent_genre 0glt670 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 28.000 0.400 http://example.org/music/genre/parent_genre #21173-0c4z8 PRED entity: 0c4z8 PRED relation: ceremony PRED expected values: 02rjjll 019bk0 0jzphpx 013b2h 02cg41 => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 129): 02rjjll (0.65 #643, 0.58 #1027, 0.48 #1155), 02cg41 (0.63 #753, 0.58 #1137, 0.57 #497), 019bk0 (0.60 #653, 0.54 #1037, 0.45 #1677), 013b2h (0.57 #711, 0.57 #455, 0.53 #1095), 0jzphpx (0.57 #417, 0.50 #673, 0.50 #545), 09pnw5 (0.50 #219, 0.37 #3329, 0.27 #5253), 026kqs9 (0.50 #208, 0.37 #3329, 0.27 #5253), 09p30_ (0.50 #204, 0.37 #3329, 0.27 #5253), 09p3h7 (0.50 #191, 0.37 #3329, 0.27 #5253), 0418154 (0.50 #224, 0.37 #3329, 0.27 #5253) >> Best rule #643 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 02qkk9_; >> query: (?x1232, 02rjjll) <- award_winner(?x1232, ?x2963), ceremony(?x1232, ?x139), role(?x2963, ?x227), profession(?x2963, ?x220) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 0c4z8 ceremony 02cg41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.652 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c4z8 ceremony 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.652 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c4z8 ceremony 0jzphpx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.652 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c4z8 ceremony 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.652 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c4z8 ceremony 02rjjll CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.652 http://example.org/award/award_category/winners./award/award_honor/ceremony #21172-01tf_6 PRED entity: 01tf_6 PRED relation: people PRED expected values: 04bgy 0g10g 0p9gg => 30 concepts (19 used for prediction) PRED predicted values (max 10 best out of 735): 03rx9 (0.33 #452, 0.25 #1133, 0.18 #1814), 0pj8m (0.33 #345, 0.25 #1026, 0.18 #1707), 014z8v (0.33 #142, 0.25 #823, 0.18 #1504), 021j72 (0.33 #505, 0.25 #1186, 0.18 #1867), 042d1 (0.33 #488, 0.25 #1169, 0.09 #1850), 045n3p (0.33 #676, 0.25 #1357, 0.09 #2038), 0131kb (0.33 #630, 0.25 #1311, 0.09 #1992), 03f22dp (0.33 #605, 0.25 #1286, 0.09 #1967), 07zhd7 (0.33 #604, 0.25 #1285, 0.09 #1966), 01lwx (0.33 #602, 0.25 #1283, 0.09 #1964) >> Best rule #452 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 01l2m3; >> query: (?x9023, 03rx9) <- people(?x9023, ?x11259), people(?x9023, ?x9477), people(?x9023, ?x9024), people(?x9023, ?x8004), ?x8004 = 01w9ph_, ?x11259 = 015076, influenced_by(?x7183, ?x9024), influenced_by(?x2127, ?x9024), ?x7183 = 01hmk9, profession(?x9477, ?x220), ?x2127 = 01j7rd >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3368 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 13 *> proper extension: 0j8hd; 032s66; *> query: (?x9023, 0p9gg) <- people(?x9023, ?x11259), people(?x9023, ?x9024), people(?x9023, ?x8004), profession(?x8004, ?x524), peers(?x8004, ?x4608), type_of_union(?x9024, ?x566), people(?x5741, ?x8004), place_of_death(?x9024, ?x1523), participant(?x10792, ?x11259) *> conf = 0.07 ranks of expected_values: 562 EVAL 01tf_6 people 0p9gg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 19.000 0.333 http://example.org/people/cause_of_death/people EVAL 01tf_6 people 0g10g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 19.000 0.333 http://example.org/people/cause_of_death/people EVAL 01tf_6 people 04bgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 19.000 0.333 http://example.org/people/cause_of_death/people #21171-05hjmd PRED entity: 05hjmd PRED relation: award_nominee! PRED expected values: 086k8 => 129 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1074): 016tt2 (0.77 #184452, 0.77 #175112, 0.77 #147091), 01b9ck (0.33 #266, 0.28 #186788, 0.27 #158765), 05qd_ (0.28 #186788, 0.27 #158765, 0.20 #35204), 03v1w7 (0.28 #186788, 0.27 #158765, 0.17 #1469), 04flrx (0.28 #186788, 0.27 #158765, 0.12 #58377), 05hjmd (0.28 #186788, 0.27 #158765, 0.12 #58377), 017jv5 (0.28 #186788, 0.27 #158765, 0.05 #7392), 017s11 (0.21 #44468, 0.20 #28126, 0.19 #51475), 0g1rw (0.20 #2477, 0.10 #28162, 0.09 #9481), 03rwz3 (0.18 #6350, 0.10 #18022, 0.04 #53049) >> Best rule #184452 for best value: >> intensional similarity = 3 >> extensional distance = 1282 >> proper extension: 0lbj1; 03zqc1; 02gvwz; 04y79_n; 01713c; 01l2fn; 0241jw; 016ywr; 0c3ns; 02xb2bt; ... >> query: (?x11030, ?x574) <- nationality(?x11030, ?x94), award_nominee(?x6488, ?x11030), award_winner(?x11030, ?x574) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #62 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 02vyh; *> query: (?x11030, 086k8) <- award_winner(?x11030, ?x574), award_nominee(?x6488, ?x11030), ?x6488 = 03_bcg *> conf = 0.17 ranks of expected_values: 11 EVAL 05hjmd award_nominee! 086k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 129.000 80.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21170-0h21v2 PRED entity: 0h21v2 PRED relation: films! PRED expected values: 07c52 => 128 concepts (70 used for prediction) PRED predicted values (max 10 best out of 70): 081pw (0.08 #475, 0.05 #631, 0.04 #788), 0ddct (0.08 #244, 0.04 #402, 0.04 #1500), 02w1b8 (0.08 #308, 0.04 #466, 0.01 #1093), 03dhbp (0.08 #288, 0.01 #1073, 0.01 #1387), 0bq3x (0.08 #1598, 0.03 #1285, 0.03 #1442), 0fx2s (0.06 #545, 0.04 #1171, 0.03 #3220), 018h2 (0.06 #494, 0.02 #1590, 0.02 #7124), 07_nf (0.04 #1165, 0.04 #381, 0.03 #852), 07s2s (0.04 #1980, 0.04 #413, 0.02 #1040), 06d4h (0.04 #357, 0.03 #6826, 0.03 #5243) >> Best rule #475 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 0g5pv3; >> query: (?x5735, 081pw) <- film(?x5408, ?x5735), music(?x5735, ?x8374), film(?x1850, ?x5735), ?x1850 = 017jv5 >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #492 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0g5pv3; *> query: (?x5735, 07c52) <- film(?x5408, ?x5735), music(?x5735, ?x8374), film(?x1850, ?x5735), ?x1850 = 017jv5 *> conf = 0.03 ranks of expected_values: 24 EVAL 0h21v2 films! 07c52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 128.000 70.000 0.083 http://example.org/film/film_subject/films #21169-07bsj PRED entity: 07bsj PRED relation: place_of_birth PRED expected values: 0nbwf => 153 concepts (147 used for prediction) PRED predicted values (max 10 best out of 143): 0978r (0.33 #119, 0.25 #1527, 0.07 #3639), 01xr6x (0.20 #2549, 0.07 #3957, 0.05 #5365), 0ck6r (0.20 #2481, 0.07 #3889, 0.05 #5297), 0d9jr (0.20 #2306, 0.05 #5122), 02_286 (0.15 #5651, 0.14 #2835, 0.12 #6356), 030qb3t (0.12 #9207, 0.10 #10615, 0.08 #7095), 0cr3d (0.07 #2910, 0.07 #8543, 0.06 #6431), 0c_m3 (0.07 #3013, 0.06 #7942, 0.05 #8646), 0k049 (0.07 #2819, 0.03 #6340, 0.02 #12677), 071vr (0.07 #3074, 0.03 #6595, 0.02 #18564) >> Best rule #119 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0dzlk; >> query: (?x10805, 0978r) <- people(?x6736, ?x10805), people(?x5540, ?x10805), ?x6736 = 06gbnc, participant(?x10805, ?x8571), ?x5540 = 013xrm >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8051 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0grwj; 014zcr; 01dw4q; 01pw2f1; 0j1yf; 0bj9k; 05dbf; 01vhb0; 01pgzn_; 016z2j; ... *> query: (?x10805, 0nbwf) <- actor(?x2137, ?x10805), type_of_union(?x10805, ?x566), participant(?x8571, ?x10805), participant(?x10805, ?x11200) *> conf = 0.03 ranks of expected_values: 54 EVAL 07bsj place_of_birth 0nbwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 153.000 147.000 0.333 http://example.org/people/person/place_of_birth #21168-05ztrmj PRED entity: 05ztrmj PRED relation: nominated_for PRED expected values: 08hmch 034qzw 0ddt_ 047d21r 02_sr1 01v1ln 0dcz8_ => 49 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1609): 07cz2 (0.68 #23254, 0.65 #24807, 0.28 #3482), 02_sr1 (0.68 #23254, 0.65 #24807, 0.26 #6194), 023vcd (0.68 #23254, 0.65 #24807, 0.17 #10851), 09gq0x5 (0.56 #3337, 0.34 #4886, 0.22 #11093), 026p4q7 (0.52 #3439, 0.35 #4988, 0.21 #11195), 03hmt9b (0.52 #3675, 0.33 #579, 0.30 #5224), 0b6tzs (0.52 #3220, 0.27 #4769, 0.16 #14077), 017jd9 (0.52 #3784, 0.27 #5333, 0.15 #14641), 0dr_4 (0.48 #3312, 0.33 #216, 0.33 #4861), 0m313 (0.48 #3108, 0.33 #12, 0.31 #4657) >> Best rule #23254 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 06196; >> query: (?x3508, ?x1721) <- award_winner(?x3508, ?x147), award(?x4835, ?x3508), award(?x1721, ?x3508), film(?x4835, ?x2287) >> conf = 0.68 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 96, 124, 135, 264, 653, 891 EVAL 05ztrmj nominated_for 0dcz8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztrmj nominated_for 01v1ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 17.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztrmj nominated_for 02_sr1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 49.000 17.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztrmj nominated_for 047d21r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 49.000 17.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztrmj nominated_for 0ddt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 17.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztrmj nominated_for 034qzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 17.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztrmj nominated_for 08hmch CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 17.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21167-03q0r1 PRED entity: 03q0r1 PRED relation: film_crew_role PRED expected values: 089g0h => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 24): 0ch6mp2 (0.74 #838, 0.72 #802, 0.71 #910), 02r96rf (0.71 #40, 0.69 #76, 0.69 #256), 09zzb8 (0.71 #830, 0.70 #938, 0.70 #902), 09vw2b7 (0.63 #837, 0.59 #801, 0.58 #909), 0dxtw (0.36 #841, 0.33 #805, 0.32 #1093), 01pvkk (0.26 #121, 0.26 #85, 0.26 #1130), 02ynfr (0.18 #17, 0.16 #846, 0.14 #954), 02rh1dz (0.17 #47, 0.16 #11, 0.15 #119), 0d2b38 (0.14 #27, 0.13 #63, 0.09 #964), 01xy5l_ (0.12 #51, 0.10 #15, 0.09 #87) >> Best rule #838 for best value: >> intensional similarity = 3 >> extensional distance = 676 >> proper extension: 03_wm6; >> query: (?x3854, 0ch6mp2) <- production_companies(?x3854, ?x2156), film_crew_role(?x3854, ?x1966), film_release_distribution_medium(?x3854, ?x81) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #814 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 650 *> proper extension: 05dy7p; 027ct7c; *> query: (?x3854, 089g0h) <- nominated_for(?x4850, ?x3854), film_crew_role(?x3854, ?x1966), award_winner(?x669, ?x4850), award_winner(?x342, ?x4850) *> conf = 0.10 ranks of expected_values: 13 EVAL 03q0r1 film_crew_role 089g0h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 71.000 71.000 0.737 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21166-0cwx_ PRED entity: 0cwx_ PRED relation: fraternities_and_sororities PRED expected values: 04m8fy => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 2): 0325pb (0.34 #19, 0.31 #21, 0.30 #31), 04m8fy (0.07 #8, 0.06 #20, 0.04 #38) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0d06m5; 0d05fv; >> query: (?x6894, 0325pb) <- organization(?x6894, ?x5487), category(?x6894, ?x134), list(?x6894, ?x2197) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 061v5m; *> query: (?x6894, 04m8fy) <- state_province_region(?x6894, ?x3670), ?x3670 = 05tbn, currency(?x6894, ?x170) *> conf = 0.07 ranks of expected_values: 2 EVAL 0cwx_ fraternities_and_sororities 04m8fy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.343 http://example.org/education/university/fraternities_and_sororities #21165-04rcr PRED entity: 04rcr PRED relation: artists! PRED expected values: 06by7 => 94 concepts (68 used for prediction) PRED predicted values (max 10 best out of 272): 06by7 (0.70 #2823, 0.63 #5939, 0.61 #4070), 064t9 (0.61 #2504, 0.52 #13415, 0.49 #7490), 0dl5d (0.50 #953, 0.29 #331, 0.23 #5937), 015pdg (0.50 #2490, 0.38 #6230, 0.13 #3123), 016clz (0.47 #1871, 0.45 #2806, 0.44 #1249), 06j6l (0.44 #2539, 0.28 #10336, 0.28 #11270), 05bt6j (0.44 #5337, 0.43 #4092, 0.30 #7831), 01_bkd (0.43 #678, 0.36 #1611, 0.20 #1922), 05r6t (0.38 #6230, 0.23 #3508, 0.22 #1328), 0hdf8 (0.38 #6230, 0.21 #1627, 0.06 #20561) >> Best rule #2823 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 089tm; 0frsw; 0hvbj; 01dq9q; 01323p; 046p9; 017959; >> query: (?x646, 06by7) <- award(?x646, ?x7535), artist(?x2931, ?x646), group(?x227, ?x646), ?x7535 = 02f73b >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04rcr artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 68.000 0.700 http://example.org/music/genre/artists #21164-02lf0c PRED entity: 02lf0c PRED relation: award PRED expected values: 040njc 0gq9h => 133 concepts (115 used for prediction) PRED predicted values (max 10 best out of 291): 0gq9h (0.48 #6911, 0.44 #12942, 0.38 #14550), 0gs9p (0.41 #9728, 0.37 #13748, 0.35 #8119), 019f4v (0.41 #9715, 0.34 #8106, 0.34 #4890), 040njc (0.41 #6842, 0.38 #9657, 0.34 #8048), 0fbtbt (0.39 #232, 0.35 #9076, 0.34 #4654), 0cjyzs (0.39 #4528, 0.37 #2116, 0.36 #8950), 09sb52 (0.31 #20946, 0.24 #30596, 0.24 #22554), 02pqp12 (0.27 #8110, 0.24 #9719, 0.22 #4894), 07bdd_ (0.27 #65, 0.23 #3683, 0.20 #12930), 0gr51 (0.26 #8140, 0.23 #9749, 0.23 #4924) >> Best rule #6911 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 01b0k1; >> query: (?x595, 0gq9h) <- produced_by(?x2386, ?x595), titles(?x53, ?x2386), ?x53 = 07s9rl0 >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 02lf0c award 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 115.000 0.475 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02lf0c award 040njc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 115.000 0.475 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21163-04bs3j PRED entity: 04bs3j PRED relation: award PRED expected values: 09qvf4 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 317): 0cjyzs (0.60 #509, 0.15 #21010, 0.15 #29599), 0cqhk0 (0.40 #440, 0.23 #1248, 0.15 #21010), 03ccq3s (0.40 #602, 0.15 #21010, 0.11 #1814), 0ck27z (0.33 #91, 0.31 #1303, 0.21 #26757), 0gkvb7 (0.32 #2452, 0.28 #1643, 0.24 #4472), 09sb52 (0.31 #1252, 0.28 #32362, 0.27 #35594), 05pcn59 (0.31 #888, 0.21 #18261, 0.20 #13413), 019bnn (0.28 #1884, 0.21 #2693, 0.18 #3501), 05zr6wv (0.23 #12138, 0.20 #7290, 0.19 #7694), 05p09zm (0.22 #4972, 0.17 #5780, 0.16 #13456) >> Best rule #509 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 015pxr; 0q5hw; 04pz5c; >> query: (?x545, 0cjyzs) <- influenced_by(?x545, ?x8375), people(?x1050, ?x545), ?x8375 = 0q9zc >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1422 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 03x3qv; 02__7n; 02wr6r; 01rs5p; *> query: (?x545, 09qvf4) <- award(?x545, ?x594), film(?x545, ?x240), ?x240 = 02v8kmz *> conf = 0.15 ranks of expected_values: 35 EVAL 04bs3j award 09qvf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 128.000 128.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21162-03rjj PRED entity: 03rjj PRED relation: nationality! PRED expected values: 07kb5 047hpm 04gycf 04ls53 02pjvc => 228 concepts (86 used for prediction) PRED predicted values (max 10 best out of 4104): 07vc_9 (0.59 #220195, 0.33 #312273, 0.29 #340297), 0ngg (0.59 #220195, 0.33 #312273, 0.29 #340297), 01pcrw (0.59 #220195, 0.29 #340297, 0.06 #120967), 03bxh (0.59 #220195, 0.29 #340297, 0.05 #41756), 06c44 (0.59 #220195, 0.06 #25913, 0.05 #37925), 0kryqm (0.59 #220195, 0.05 #30124, 0.05 #34128), 031296 (0.59 #220195, 0.05 #33079, 0.04 #188167), 016z2j (0.59 #220195, 0.05 #32656, 0.04 #188167), 07lt7b (0.36 #124107, 0.25 #16182, 0.05 #28193), 06dv3 (0.36 #124107, 0.11 #24072, 0.10 #32080) >> Best rule #220195 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 05k7sb; 06n3y; >> query: (?x205, ?x1278) <- contains(?x205, ?x5481), geographic_distribution(?x1571, ?x205), location(?x1278, ?x5481) >> conf = 0.59 => this is the best rule for 8 predicted values *> Best rule #33806 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 01k6y1; *> query: (?x205, 02pjvc) <- nationality(?x101, ?x205), combatants(?x94, ?x205), location(?x4587, ?x205) *> conf = 0.05 ranks of expected_values: 1398, 2959, 3261 EVAL 03rjj nationality! 02pjvc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 228.000 86.000 0.585 http://example.org/people/person/nationality EVAL 03rjj nationality! 04ls53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 86.000 0.585 http://example.org/people/person/nationality EVAL 03rjj nationality! 04gycf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 86.000 0.585 http://example.org/people/person/nationality EVAL 03rjj nationality! 047hpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 86.000 0.585 http://example.org/people/person/nationality EVAL 03rjj nationality! 07kb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 86.000 0.585 http://example.org/people/person/nationality #21161-011xg5 PRED entity: 011xg5 PRED relation: nominated_for! PRED expected values: 02hsq3m => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 211): 02g3gw (0.70 #1655, 0.67 #6144, 0.66 #12048), 0gq9h (0.57 #1480, 0.44 #62, 0.40 #1717), 0k611 (0.47 #1490, 0.40 #1727, 0.33 #72), 0gq_v (0.46 #1438, 0.37 #1675, 0.34 #2619), 0gr0m (0.46 #1477, 0.33 #1714, 0.26 #2658), 0gs9p (0.45 #1482, 0.36 #1719, 0.33 #64), 0p9sw (0.43 #1439, 0.32 #257, 0.31 #2620), 02qvyrt (0.40 #1513, 0.38 #1750, 0.27 #331), 040njc (0.39 #1425, 0.33 #7, 0.33 #1662), 02qyntr (0.37 #1596, 0.33 #178, 0.32 #414) >> Best rule #1655 for best value: >> intensional similarity = 4 >> extensional distance = 125 >> proper extension: 083shs; 011yrp; 07xtqq; 0pv3x; 0gmcwlb; 0dtfn; 09p0ct; 0ch26b_; 016z9n; 0c_j9x; ... >> query: (?x8349, ?x7862) <- nominated_for(?x669, ?x8349), nominated_for(?x1079, ?x8349), award(?x8349, ?x7862), ?x1079 = 0l8z1 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1181 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 02fn5r; *> query: (?x8349, ?x112) <- nominated_for(?x8349, ?x4235), category(?x8349, ?x134), nominated_for(?x112, ?x4235) *> conf = 0.22 ranks of expected_values: 40 EVAL 011xg5 nominated_for! 02hsq3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 92.000 92.000 0.695 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21160-06zmg7m PRED entity: 06zmg7m PRED relation: people! PRED expected values: 0dryh9k => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 51): 0dryh9k (0.71 #472, 0.71 #396, 0.33 #548), 041rx (0.50 #156, 0.29 #4, 0.27 #612), 0x67 (0.37 #1988, 0.29 #1683, 0.17 #2444), 013xrm (0.36 #780, 0.14 #20, 0.07 #1998), 03ts0c (0.17 #786, 0.03 #2004, 0.03 #1699), 048z7l (0.17 #191, 0.08 #267, 0.04 #647), 09kr66 (0.17 #194, 0.08 #270, 0.02 #1791), 0xnvg (0.14 #13, 0.14 #1686, 0.13 #1991), 07bch9 (0.14 #23, 0.12 #1696, 0.10 #2001), 0g6ff (0.14 #21, 0.02 #1999, 0.02 #1694) >> Best rule #472 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 0cfywh; >> query: (?x9540, 0dryh9k) <- people(?x9347, ?x9540), nationality(?x9540, ?x2146), ?x2146 = 03rk0, type_of_union(?x9540, ?x566), ?x566 = 04ztj >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06zmg7m people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.712 http://example.org/people/ethnicity/people #21159-086qd PRED entity: 086qd PRED relation: award_nominee PRED expected values: 01kp_1t => 90 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1061): 012vd6 (0.81 #107716, 0.81 #9368, 0.79 #39807), 04xrx (0.81 #107716, 0.81 #9368, 0.79 #39807), 01mxqyk (0.79 #84300, 0.75 #133460, 0.72 #93669), 02qwg (0.12 #7792, 0.09 #12477, 0.08 #14818), 03knl (0.12 #4683, 0.11 #7025), 047q2wc (0.12 #39808, 0.11 #131119, 0.02 #92238), 02fcs2 (0.12 #39808, 0.11 #131119), 02l840 (0.12 #39808, 0.11 #30601, 0.04 #91488), 01yzl2 (0.12 #39808, 0.03 #31730, 0.03 #3630), 0770cd (0.12 #39808, 0.03 #30831, 0.03 #7415) >> Best rule #107716 for best value: >> intensional similarity = 2 >> extensional distance = 594 >> proper extension: 02zq43; 01j5x6; 01v3s2_; 04cf09; 07hbxm; 04rsd2; 01pctb; 0g2mbn; 07y8l9; 05dtwm; ... >> query: (?x2138, ?x2451) <- actor(?x5529, ?x2138), award_nominee(?x2451, ?x2138) >> conf = 0.81 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 086qd award_nominee 01kp_1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 58.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21158-0cv3w PRED entity: 0cv3w PRED relation: location_of_ceremony! PRED expected values: 04ztj => 195 concepts (195 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.93 #174, 0.93 #85, 0.93 #77), 01g63y (0.78 #274, 0.76 #125, 0.31 #660), 0jgjn (0.12 #12, 0.08 #20, 0.08 #24), 01bl8s (0.03 #27, 0.03 #35, 0.03 #63) >> Best rule #174 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 0xmqf; >> query: (?x3026, 04ztj) <- location(?x1773, ?x3026), location_of_ceremony(?x286, ?x3026), time_zones(?x3026, ?x2950) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cv3w location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 195.000 0.930 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #21157-02773m2 PRED entity: 02773m2 PRED relation: profession PRED expected values: 01d_h8 02krf9 => 81 concepts (68 used for prediction) PRED predicted values (max 10 best out of 45): 01d_h8 (0.49 #2946, 0.48 #1182, 0.48 #1035), 02krf9 (0.33 #760, 0.33 #1936, 0.33 #1348), 02jknp (0.28 #1037, 0.27 #2948, 0.26 #3095), 0cbd2 (0.28 #5587, 0.28 #7058, 0.25 #10002), 018gz8 (0.28 #5587, 0.28 #7058, 0.25 #10002), 0np9r (0.28 #5587, 0.28 #7058, 0.25 #10002), 0196pc (0.25 #10002, 0.25 #10001, 0.07 #219), 09jwl (0.20 #4868, 0.20 #3251, 0.20 #3398), 0dz3r (0.14 #3383, 0.14 #3236, 0.14 #4265), 0nbcg (0.13 #3264, 0.13 #3411, 0.13 #4881) >> Best rule #2946 for best value: >> intensional similarity = 3 >> extensional distance = 455 >> proper extension: 03ywyk; 024jwt; >> query: (?x830, 01d_h8) <- award_nominee(?x830, ?x832), profession(?x830, ?x1041), ?x1041 = 03gjzk >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02773m2 profession 02krf9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 68.000 0.492 http://example.org/people/person/profession EVAL 02773m2 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 68.000 0.492 http://example.org/people/person/profession #21156-0k4bc PRED entity: 0k4bc PRED relation: list PRED expected values: 05glt => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.41 #9, 0.41 #16, 0.40 #2) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 02gqm3; >> query: (?x7231, 05glt) <- genre(?x7231, ?x53), film_release_distribution_medium(?x7231, ?x81), film_art_direction_by(?x7231, ?x4896), film(?x1357, ?x7231) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k4bc list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.411 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #21155-01cgz PRED entity: 01cgz PRED relation: sports! PRED expected values: 0lk8j => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 28): 0l6m5 (0.78 #26, 0.77 #79, 0.75 #817), 0sxrz (0.78 #26, 0.77 #79, 0.75 #817), 0ldqf (0.78 #26, 0.77 #79, 0.75 #817), 06sks6 (0.78 #26, 0.77 #79, 0.75 #817), 0kbws (0.78 #26, 0.77 #79, 0.74 #260), 0l6ny (0.75 #817, 0.67 #238, 0.67 #212), 0lgxj (0.75 #817, 0.50 #249, 0.45 #78), 0lk8j (0.75 #817, 0.45 #78, 0.40 #191), 0l998 (0.75 #817, 0.45 #78, 0.36 #768), 016r9z (0.50 #245, 0.45 #78, 0.40 #193) >> Best rule #26 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 0bynt; >> query: (?x1967, ?x391) <- country(?x1967, ?x8857), country(?x1967, ?x6974), country(?x1967, ?x1957), olympics(?x1967, ?x1081), olympics(?x1967, ?x391), ?x6974 = 01nln, ?x1957 = 0162v, ?x8857 = 0164v, ?x1081 = 0l6m5 >> conf = 0.78 => this is the best rule for 5 predicted values *> Best rule #817 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 49 *> proper extension: 04lgq; *> query: (?x1967, ?x775) <- sports(?x2233, ?x1967), sports(?x775, ?x1967), olympics(?x1536, ?x2233), film_release_region(?x3958, ?x1536), ?x3958 = 0gyh2wm *> conf = 0.75 ranks of expected_values: 8 EVAL 01cgz sports! 0lk8j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 62.000 62.000 0.783 http://example.org/user/jg/default_domain/olympic_games/sports #21154-01gvr1 PRED entity: 01gvr1 PRED relation: nationality PRED expected values: 09c7w0 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.83 #201, 0.77 #2908, 0.76 #3612), 02jx1 (0.16 #533, 0.15 #834, 0.15 #33), 07ssc (0.13 #816, 0.11 #315, 0.08 #4129), 03rk0 (0.08 #446, 0.06 #9981, 0.06 #747), 03_3d (0.07 #807, 0.05 #6, 0.03 #4722), 0345h (0.06 #631, 0.04 #431, 0.04 #732), 0d060g (0.06 #407, 0.05 #4322, 0.05 #7), 0h3y (0.05 #8, 0.02 #308), 05b4w (0.05 #51), 0f8l9c (0.05 #322, 0.03 #923, 0.03 #1425) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 01pw2f1; 0l_dv; >> query: (?x624, 09c7w0) <- actor(?x623, ?x624), location(?x624, ?x2277), diet(?x624, ?x3130) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gvr1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.829 http://example.org/people/person/nationality #21153-0dfrq PRED entity: 0dfrq PRED relation: influenced_by PRED expected values: 084nh => 134 concepts (46 used for prediction) PRED predicted values (max 10 best out of 390): 03_87 (0.40 #199, 0.38 #1064, 0.33 #2794), 0379s (0.40 #77, 0.25 #942, 0.10 #6569), 02wh0 (0.38 #1245, 0.20 #380, 0.17 #813), 040_9 (0.38 #962, 0.20 #97, 0.16 #2595), 032l1 (0.33 #2683, 0.33 #2249, 0.26 #3116), 03f70xs (0.33 #501, 0.25 #1365, 0.25 #933), 0448r (0.27 #4585, 0.16 #2595, 0.15 #1988), 0420y (0.26 #3429, 0.12 #2996, 0.12 #2562), 03jxw (0.25 #1202, 0.23 #2066, 0.20 #337), 05qmj (0.25 #1055, 0.20 #190, 0.19 #11009) >> Best rule #199 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0p8jf; 0gd_s; >> query: (?x9278, 03_87) <- student(?x11459, ?x9278), award_winner(?x921, ?x9278), influenced_by(?x9278, ?x6370), ?x6370 = 0465_ >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2595 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 0j3v; *> query: (?x9278, ?x3541) <- student(?x11459, ?x9278), influenced_by(?x9278, ?x5004), influenced_by(?x9278, ?x587), ?x5004 = 081k8, influenced_by(?x587, ?x3541) *> conf = 0.16 ranks of expected_values: 48 EVAL 0dfrq influenced_by 084nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 134.000 46.000 0.400 http://example.org/influence/influence_node/influenced_by #21152-05567m PRED entity: 05567m PRED relation: film_distribution_medium PRED expected values: 0735l => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 2): 0735l (0.94 #5, 0.92 #26, 0.83 #17), 07z4p (0.02 #55, 0.01 #9, 0.01 #12) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 02bj22; >> query: (?x9303, 0735l) <- region(?x9303, ?x512), genre(?x9303, ?x258), award_winner(?x9303, ?x2534), film_distribution_medium(?x9303, ?x81) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05567m film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.938 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #21151-09ps01 PRED entity: 09ps01 PRED relation: award PRED expected values: 05zx7xk => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 176): 05zx7xk (0.22 #12936, 0.22 #15759, 0.22 #6113), 0gr51 (0.22 #9172, 0.13 #15995, 0.06 #16939), 02x4wr9 (0.22 #9172, 0.13 #15995, 0.06 #16939), 05b4l5x (0.22 #9172, 0.13 #15995, 0.06 #16939), 05q5t0b (0.22 #9172, 0.13 #15995, 0.06 #16939), 04dn09n (0.22 #9172, 0.13 #15995, 0.06 #2385), 03hl6lc (0.22 #9172, 0.13 #15995, 0.05 #1070), 0789r6 (0.22 #9172, 0.13 #15995, 0.02 #16231), 05pcn59 (0.14 #67, 0.13 #15995, 0.06 #16939), 02y_j8g (0.14 #182, 0.13 #15995, 0.06 #16939) >> Best rule #12936 for best value: >> intensional similarity = 3 >> extensional distance = 1261 >> proper extension: 03kq98; 039fgy; 0kfpm; 0ddd0gc; 0kfv9; 03d34x8; 02xhpl; 01j67j; 01bv8b; 039c26; ... >> query: (?x4778, ?x13311) <- nominated_for(?x4106, ?x4778), film(?x4106, ?x1490), nominated_for(?x13311, ?x4778) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09ps01 award 05zx7xk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.224 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #21150-0l6m5 PRED entity: 0l6m5 PRED relation: olympics! PRED expected values: 01sgl => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 43): 01sgl (0.88 #1716, 0.85 #2015, 0.80 #1206), 07jjt (0.84 #396, 0.77 #222, 0.68 #176), 01hp22 (0.84 #396, 0.77 #222, 0.68 #176), 06f41 (0.84 #396, 0.77 #222, 0.68 #176), 02bkg (0.84 #396, 0.77 #222, 0.68 #176), 06wrt (0.84 #396, 0.77 #222, 0.68 #176), 0d1tm (0.84 #396, 0.77 #222, 0.68 #176), 0486tv (0.84 #396, 0.68 #176, 0.68 #175), 071t0 (0.77 #222, 0.68 #176, 0.68 #175), 0w0d (0.77 #222, 0.68 #176, 0.68 #175) >> Best rule #1716 for best value: >> intensional similarity = 16 >> extensional distance = 14 >> proper extension: 0lv1x; >> query: (?x1081, 01sgl) <- sports(?x1081, ?x3015), sports(?x1081, ?x1121), olympics(?x3635, ?x1081), sports(?x1081, ?x1967), adjoins(?x3635, ?x5457), medal(?x1081, ?x422), sports(?x391, ?x3015), olympics(?x3635, ?x784), country(?x3015, ?x7833), country(?x3015, ?x7413), ?x5457 = 06tw8, jurisdiction_of_office(?x182, ?x3635), ?x7413 = 04hqz, ?x1121 = 0bynt, film_release_region(?x186, ?x3635), ?x7833 = 0jdx >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l6m5 olympics! 01sgl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.875 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #21149-07th_ PRED entity: 07th_ PRED relation: institution! PRED expected values: 016t_3 => 231 concepts (142 used for prediction) PRED predicted values (max 10 best out of 22): 014mlp (0.81 #2212, 0.78 #2358, 0.67 #3356), 019v9k (0.77 #58, 0.63 #929, 0.59 #711), 02h4rq6 (0.73 #800, 0.71 #704, 0.71 #922), 0bkj86 (0.54 #57, 0.48 #710, 0.46 #806), 03bwzr4 (0.54 #716, 0.52 #812, 0.52 #1008), 016t_3 (0.49 #1024, 0.48 #801, 0.47 #997), 07s6fsf (0.39 #798, 0.37 #1021, 0.37 #871), 04zx3q1 (0.34 #703, 0.30 #995, 0.30 #1022), 027f2w (0.31 #1004, 0.31 #59, 0.30 #712), 013zdg (0.31 #275, 0.30 #469, 0.29 #517) >> Best rule #2212 for best value: >> intensional similarity = 5 >> extensional distance = 359 >> proper extension: 024y8p; 071_8; >> query: (?x13206, 014mlp) <- institution(?x3437, ?x13206), organization(?x4095, ?x13206), major_field_of_study(?x13206, ?x2014), institution(?x3437, ?x11583), ?x11583 = 014d4v >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1024 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 170 *> proper extension: 0mbwf; *> query: (?x13206, 016t_3) <- institution(?x3437, ?x13206), organization(?x4095, ?x13206), major_field_of_study(?x13206, ?x2014), ?x3437 = 02_xgp2 *> conf = 0.49 ranks of expected_values: 6 EVAL 07th_ institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 231.000 142.000 0.809 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21148-03rt9 PRED entity: 03rt9 PRED relation: contains! PRED expected values: 02j9z => 180 concepts (121 used for prediction) PRED predicted values (max 10 best out of 170): 04_1l0v (0.70 #17454, 0.52 #30878, 0.51 #60412), 02j9z (0.69 #11635, 0.38 #22402, 0.36 #24191), 09c7w0 (0.64 #75185, 0.63 #17008, 0.54 #62653), 02j71 (0.61 #81449, 0.59 #65335, 0.08 #87715), 0j0k (0.54 #73768, 0.28 #33489, 0.28 #45124), 07ssc (0.52 #76974, 0.36 #41197, 0.33 #36723), 03rk0 (0.43 #20720, 0.29 #67263, 0.22 #7298), 03rt9 (0.40 #88612, 0.33 #25, 0.30 #3606), 0jtf1 (0.40 #88612, 0.25 #895, 0.08 #100246), 0hkq4 (0.40 #88612, 0.25 #895, 0.08 #100246) >> Best rule #17454 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 07ytt; 04llb; 03czqs; >> query: (?x429, 04_1l0v) <- religion(?x429, ?x109), country(?x429, ?x3699), location_of_ceremony(?x566, ?x429) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #11635 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 057xkj_; 04n7ps6; *> query: (?x429, ?x455) <- split_to(?x3699, ?x429), contains(?x455, ?x3699), service_location(?x127, ?x455) *> conf = 0.69 ranks of expected_values: 2 EVAL 03rt9 contains! 02j9z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 180.000 121.000 0.700 http://example.org/location/location/contains #21147-0flry PRED entity: 0flry PRED relation: combatants PRED expected values: 0d0vqn 01d8l => 69 concepts (66 used for prediction) PRED predicted values (max 10 best out of 293): 024pcx (0.71 #1270, 0.65 #1271, 0.62 #3045), 0ck1d (0.71 #1270, 0.56 #376, 0.50 #1269), 07ssc (0.62 #4073, 0.49 #4456, 0.48 #4712), 02p4pt3 (0.56 #376, 0.50 #1269, 0.46 #1140), 014tss (0.50 #443, 0.44 #1078, 0.40 #570), 043870 (0.50 #488, 0.44 #1123, 0.33 #237), 03x1x (0.50 #470, 0.22 #1105, 0.22 #977), 01m3dv (0.50 #464, 0.22 #1099, 0.22 #971), 09c7w0 (0.47 #2163, 0.44 #886, 0.44 #1910), 0chghy (0.47 #2171, 0.38 #1918, 0.31 #4070) >> Best rule #1270 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 01fc7p; >> query: (?x11183, ?x7101) <- combatants(?x11183, ?x9602), entity_involved(?x11183, ?x9328), entity_involved(?x11183, ?x7101), films(?x11183, ?x6273), combatants(?x9602, ?x1679), contains(?x1679, ?x1680), official_language(?x9328, ?x254), contains(?x789, ?x7101) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #631 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 0ql7q; 01hwkn; *> query: (?x11183, ?x1679) <- combatants(?x11183, ?x13063), combatants(?x11183, ?x9602), combatants(?x11183, ?x1778), entity_involved(?x11183, ?x7101), locations(?x11183, ?x789), ?x1778 = 03gk2, ?x9602 = 0285m87, combatants(?x8949, ?x13063), combatants(?x1679, ?x13063), ?x8949 = 0dv0z *> conf = 0.33 ranks of expected_values: 20, 26 EVAL 0flry combatants 01d8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 69.000 66.000 0.706 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 0flry combatants 0d0vqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 69.000 66.000 0.706 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #21146-05bmq PRED entity: 05bmq PRED relation: participating_countries! PRED expected values: 0kbws => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 40): 0kbws (0.80 #2193, 0.76 #298, 0.75 #948), 018ctl (0.67 #129, 0.66 #47, 0.47 #453), 0lgxj (0.55 #68, 0.45 #150, 0.41 #392), 09x3r (0.48 #51, 0.47 #133, 0.34 #375), 016r9z (0.36 #61, 0.22 #143, 0.18 #385), 0sx8l (0.32 #53, 0.29 #135, 0.23 #459), 0blfl (0.30 #69, 0.25 #151, 0.21 #596), 06sks6 (0.27 #406, 0.26 #851, 0.20 #64), 0c_tl (0.20 #63, 0.16 #145, 0.13 #510), 0l6m5 (0.20 #487, 0.20 #405, 0.19 #163) >> Best rule #2193 for best value: >> intensional similarity = 2 >> extensional distance = 181 >> proper extension: 0cdbq; >> query: (?x9458, 0kbws) <- participating_countries(?x418, ?x9458), olympics(?x453, ?x418) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05bmq participating_countries! 0kbws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.798 http://example.org/olympics/olympic_games/participating_countries #21145-03n6r PRED entity: 03n6r PRED relation: location_of_ceremony PRED expected values: 0f1sm => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 49): 06y57 (0.17 #295, 0.03 #892, 0.02 #1252), 027rn (0.17 #239, 0.03 #836, 0.02 #1196), 0f2w0 (0.06 #380, 0.02 #977, 0.02 #1577), 013n2h (0.06 #430, 0.02 #1146, 0.02 #1747), 0k_q_ (0.06 #387, 0.01 #1942, 0.01 #3137), 0kc40 (0.05 #580, 0.03 #818, 0.03 #938), 04jpl (0.05 #486, 0.02 #964, 0.02 #2998), 012wgb (0.05 #519, 0.02 #997, 0.02 #1477), 03s5t (0.05 #629, 0.03 #868, 0.02 #1348), 0cv3w (0.04 #4222, 0.04 #1230, 0.04 #1590) >> Best rule #295 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 06crk; 02nygk; >> query: (?x5348, 06y57) <- place_of_death(?x5348, ?x1523), ?x1523 = 030qb3t, company(?x5348, ?x13773) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03n6r location_of_ceremony 0f1sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 166.000 166.000 0.167 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #21144-06w7v PRED entity: 06w7v PRED relation: role! PRED expected values: 0565cz => 88 concepts (51 used for prediction) PRED predicted values (max 10 best out of 990): 050z2 (0.75 #15114, 0.71 #5148, 0.70 #6050), 03ryks (0.62 #9782, 0.57 #12048, 0.50 #1649), 05qhnq (0.60 #6172, 0.50 #7529, 0.50 #3915), 01vsyg9 (0.60 #2962, 0.50 #2057, 0.40 #7023), 0326tc (0.57 #5307, 0.54 #10736, 0.50 #15273), 02s6sh (0.57 #5390, 0.50 #16261, 0.50 #15356), 0j6cj (0.57 #5308, 0.50 #3953, 0.50 #2149), 0133x7 (0.57 #5264, 0.50 #3909, 0.50 #2105), 082brv (0.56 #16097, 0.55 #19268, 0.50 #15192), 02vr7 (0.50 #6223, 0.50 #3966, 0.50 #2162) >> Best rule #15114 for best value: >> intensional similarity = 18 >> extensional distance = 14 >> proper extension: 011k_j; >> query: (?x4917, 050z2) <- role(?x4917, ?x745), role(?x4917, ?x614), instrumentalists(?x4917, ?x4140), ?x614 = 0mkg, role(?x227, ?x4917), role(?x211, ?x4917), role(?x75, ?x4917), role(?x745, ?x3967), role(?x745, ?x1647), role(?x745, ?x1472), ?x1647 = 05ljv7, role(?x8048, ?x745), ?x4140 = 01sb5r, ?x1472 = 0319l, ?x8048 = 0dw3l, role(?x745, ?x2725), group(?x745, ?x498), ?x3967 = 01p970 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #8711 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 10 *> proper extension: 051hrr; *> query: (?x4917, 0565cz) <- role(?x4917, ?x3215), role(?x4917, ?x1482), role(?x2888, ?x4917), ?x1482 = 02g9p4, role(?x1291, ?x4917), group(?x4917, ?x3207), group(?x6947, ?x3207), group(?x3206, ?x3207), profession(?x3206, ?x131), ?x131 = 0dz3r, role(?x211, ?x4917), role(?x2888, ?x9413), role(?x2888, ?x3214), ?x3214 = 02snj9, ?x9413 = 07m2y, role(?x6947, ?x1433), role(?x217, ?x3215) *> conf = 0.33 ranks of expected_values: 102 EVAL 06w7v role! 0565cz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 88.000 51.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role #21143-01jswq PRED entity: 01jswq PRED relation: school_type PRED expected values: 01_9fk => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 18): 07tf8 (0.44 #77, 0.26 #192, 0.23 #376), 01rs41 (0.37 #1591, 0.35 #1660, 0.33 #947), 05pcjw (0.32 #1588, 0.32 #1358, 0.31 #2117), 01_9fk (0.31 #439, 0.31 #255, 0.31 #485), 01_srz (0.11 #49, 0.10 #2991, 0.10 #463), 04399 (0.10 #2991, 0.08 #473, 0.07 #542), 06cs1 (0.10 #2991, 0.07 #258, 0.05 #396), 02p0qmm (0.10 #2991, 0.04 #193, 0.04 #2194), 04qbv (0.10 #2991, 0.04 #199, 0.03 #314), 01jlsn (0.10 #2991, 0.03 #1557, 0.02 #2500) >> Best rule #77 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01s0_f; 07vjm; 0cwx_; 01q7q2; >> query: (?x2711, 07tf8) <- currency(?x2711, ?x170), student(?x2711, ?x5200), fraternities_and_sororities(?x2711, ?x3697), major_field_of_study(?x2711, ?x1154), time_zones(?x2711, ?x2674) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #439 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 56 *> proper extension: 02zc7f; 03wv2g; *> query: (?x2711, 01_9fk) <- school(?x700, ?x2711), organization(?x346, ?x2711), ?x346 = 060c4, fraternities_and_sororities(?x2711, ?x3697), contains(?x94, ?x2711) *> conf = 0.31 ranks of expected_values: 4 EVAL 01jswq school_type 01_9fk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 174.000 174.000 0.444 http://example.org/education/educational_institution/school_type #21142-0mkp7 PRED entity: 0mkp7 PRED relation: source PRED expected values: 0jbk9 => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #75, 0.91 #79, 0.91 #20) >> Best rule #75 for best value: >> intensional similarity = 3 >> extensional distance = 315 >> proper extension: 0n5_g; >> query: (?x12678, 0jbk9) <- second_level_divisions(?x94, ?x12678), ?x94 = 09c7w0, time_zones(?x12678, ?x1638) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mkp7 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.915 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #21141-05qzv PRED entity: 05qzv PRED relation: influenced_by! PRED expected values: 07h5d => 134 concepts (68 used for prediction) PRED predicted values (max 10 best out of 744): 0p8jf (0.31 #1125, 0.20 #1633, 0.16 #13800), 0683n (0.31 #1348, 0.17 #14023, 0.12 #18083), 027y_ (0.31 #1367, 0.09 #5933, 0.05 #2889), 07lp1 (0.29 #410, 0.12 #14101, 0.09 #18258), 04cbtrw (0.27 #1629, 0.09 #615, 0.07 #19273), 0n6kf (0.23 #1204, 0.14 #188, 0.11 #13879), 019z7q (0.23 #1040, 0.11 #8648, 0.07 #19273), 0gthm (0.23 #1410, 0.06 #5976, 0.03 #9018), 05jm7 (0.23 #4196, 0.19 #3184, 0.18 #647), 01zkxv (0.23 #4074, 0.16 #6106, 0.14 #3568) >> Best rule #1125 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0bqch; >> query: (?x9982, 0p8jf) <- influenced_by(?x8841, ?x9982), place_of_death(?x9982, ?x13451), influenced_by(?x8841, ?x1946), ?x1946 = 014dq7 >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #34499 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 311 *> proper extension: 07yg2; 0qmny; *> query: (?x9982, ?x576) <- influenced_by(?x1752, ?x9982), award_winner(?x1375, ?x1752), award(?x1752, ?x11263), award(?x576, ?x11263) *> conf = 0.01 ranks of expected_values: 574 EVAL 05qzv influenced_by! 07h5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 134.000 68.000 0.308 http://example.org/influence/influence_node/influenced_by #21140-0dzf_ PRED entity: 0dzf_ PRED relation: film PRED expected values: 0bmssv => 134 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1040): 024hbv (0.63 #63866, 0.59 #115326, 0.38 #131294), 01b_lz (0.35 #106451, 0.33 #134844, 0.33 #145491), 0jnwx (0.35 #106451, 0.33 #134844, 0.33 #145491), 0ndwt2w (0.29 #990, 0.02 #16956, 0.02 #22278), 05fm6m (0.14 #3080, 0.14 #1306, 0.08 #6628), 0bshwmp (0.14 #1930, 0.14 #156, 0.05 #3704), 098s2w (0.14 #2906, 0.14 #1132, 0.03 #10002), 03lvwp (0.14 #2805, 0.14 #1031, 0.01 #13449), 04w7rn (0.14 #2010, 0.14 #236, 0.01 #12654), 03l6q0 (0.14 #2314, 0.14 #540, 0.01 #66182) >> Best rule #63866 for best value: >> intensional similarity = 2 >> extensional distance = 420 >> proper extension: 02wb6yq; >> query: (?x4563, ?x463) <- nominated_for(?x4563, ?x463), participant(?x4563, ?x4360) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #18431 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 07ymr5; 03b78r; 0djywgn; 04j_gs; *> query: (?x4563, 0bmssv) <- film(?x4563, ?x463), influenced_by(?x4563, ?x4353), award_nominee(?x4563, ?x989) *> conf = 0.03 ranks of expected_values: 238 EVAL 0dzf_ film 0bmssv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 134.000 82.000 0.627 http://example.org/film/actor/film./film/performance/film #21139-02630g PRED entity: 02630g PRED relation: industry PRED expected values: 03qh03g => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 45): 09t4t (0.33 #16, 0.16 #448, 0.12 #64), 01mw1 (0.32 #1105, 0.31 #241, 0.28 #2209), 020mfr (0.31 #257, 0.27 #2609, 0.25 #65), 02jjt (0.22 #104, 0.19 #632, 0.17 #680), 02vxn (0.22 #98, 0.16 #1490, 0.14 #1778), 01mf0 (0.20 #511, 0.17 #367, 0.16 #463), 0191_7 (0.18 #232, 0.11 #376, 0.09 #760), 05jnl (0.15 #310, 0.12 #70, 0.11 #454), 03qh03g (0.15 #293, 0.11 #101, 0.10 #485), 0hz28 (0.15 #318, 0.06 #2046, 0.06 #2478) >> Best rule #16 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 087c7; >> query: (?x6638, 09t4t) <- place_founded(?x6638, ?x11086), organization(?x1491, ?x6638), currency(?x6638, ?x170), ?x11086 = 0y1rf, company(?x265, ?x6638) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #293 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 11 *> proper extension: 0300cp; *> query: (?x6638, 03qh03g) <- company(?x4792, ?x6638), company(?x4682, ?x6638), company(?x346, ?x6638), company(?x265, ?x6638), ?x265 = 0dq3c, ?x4682 = 0dq_5, ?x346 = 060c4, ?x4792 = 05_wyz *> conf = 0.15 ranks of expected_values: 9 EVAL 02630g industry 03qh03g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 126.000 126.000 0.333 http://example.org/business/business_operation/industry #21138-04n36qk PRED entity: 04n36qk PRED relation: season! PRED expected values: 0713r => 3 concepts (3 used for prediction) PRED predicted values (max 10 best out of 318): 01slc (0.87 #46, 0.50 #17, 0.06 #88), 01yjl (0.87 #41, 0.50 #12, 0.06 #88), 02d02 (0.80 #51, 0.71 #59, 0.50 #22), 06x68 (0.80 #32, 0.50 #3, 0.17 #94), 01d5z (0.80 #33, 0.50 #4, 0.06 #88), 01ypc (0.73 #89, 0.73 #30, 0.50 #1), 01yhm (0.73 #37, 0.50 #8, 0.17 #94), 07l4z (0.72 #60, 0.71 #59, 0.53 #98), 0487_ (0.72 #60, 0.32 #61, 0.17 #72), 0cqt41 (0.71 #59, 0.67 #36, 0.50 #7) >> Best rule #46 for best value: >> intensional similarity = 386 >> extensional distance = 13 >> proper extension: 05kcgsf; 025ygqm; 0285r5d; 025ygws; 03c74_8; 04110b0; 027pwzc; 026fmqm; 027mvrc; 02h7s73; ... >> query: (?x13879, 01slc) <- season(?x4208, ?x13879), season(?x662, ?x13879), ?x4208 = 061xq, team(?x11844, ?x662), draft(?x662, ?x8786), draft(?x662, ?x3334), position(?x662, ?x13623), position(?x662, ?x8520), position(?x662, ?x5727), position(?x662, ?x4244), position(?x662, ?x2010), ?x5727 = 02wszf, ?x4244 = 028c_8, teams(?x108, ?x662), ?x8786 = 02pq_x5, ?x3334 = 02pq_rp, ?x2010 = 02lyr4, season(?x662, ?x9267), season(?x662, ?x2406), ?x8520 = 01z9v6, colors(?x662, ?x8271), colors(?x662, ?x1101), colors(?x662, ?x663), colors(?x662, ?x332), ?x11844 = 0h69c, sport(?x662, ?x5063), ?x332 = 01l849, colors(?x14015, ?x1101), colors(?x13947, ?x1101), colors(?x13795, ?x1101), colors(?x13480, ?x1101), colors(?x13326, ?x1101), colors(?x12706, ?x1101), colors(?x12414, ?x1101), colors(?x12072, ?x1101), colors(?x12043, ?x1101), colors(?x11320, ?x1101), colors(?x11195, ?x1101), colors(?x10990, ?x1101), colors(?x10463, ?x1101), colors(?x10142, ?x1101), colors(?x10066, ?x1101), colors(?x9835, ?x1101), colors(?x9543, ?x1101), colors(?x9473, ?x1101), colors(?x8912, ?x1101), colors(?x8826, ?x1101), colors(?x8678, ?x1101), colors(?x8186, ?x1101), colors(?x7136, ?x1101), colors(?x6645, ?x1101), colors(?x6179, ?x1101), colors(?x5918, ?x1101), colors(?x5914, ?x1101), colors(?x5175, ?x1101), colors(?x4907, ?x1101), colors(?x4802, ?x1101), colors(?x2919, ?x1101), colors(?x2677, ?x1101), colors(?x2398, ?x1101), colors(?x2303, ?x1101), colors(?x2011, ?x1101), colors(?x1639, ?x1101), colors(?x1100, ?x1101), colors(?x260, ?x1101), ?x2398 = 0jmfb, ?x12706 = 03j0ss, colors(?x13707, ?x1101), colors(?x13491, ?x1101), colors(?x13148, ?x1101), colors(?x12761, ?x1101), colors(?x12667, ?x1101), colors(?x12485, ?x1101), colors(?x12356, ?x1101), colors(?x11963, ?x1101), colors(?x11632, ?x1101), colors(?x11559, ?x1101), colors(?x10217, ?x1101), colors(?x9724, ?x1101), colors(?x9344, ?x1101), colors(?x8463, ?x1101), colors(?x8363, ?x1101), colors(?x6417, ?x1101), colors(?x6223, ?x1101), colors(?x6038, ?x1101), colors(?x5920, ?x1101), colors(?x3416, ?x1101), colors(?x2171, ?x1101), colors(?x1981, ?x1101), ?x10990 = 0329gm, team(?x13623, ?x6074), team(?x13623, ?x4243), team(?x13623, ?x1160), ?x7136 = 0jm74, ?x2677 = 0g701n, ?x13326 = 0hm2b, ?x13480 = 07sqbl, ?x13947 = 03yfh3, ?x5175 = 051n13, ?x13148 = 03hvk2, ?x2011 = 04913k, ?x12043 = 03jb2n, ?x6038 = 01y9qr, ?x8463 = 04cnp4, ?x13707 = 024cg8, school(?x662, ?x7439), school(?x662, ?x5486), ?x9473 = 02b1ng, ?x12667 = 02pdhz, ?x1160 = 049n7, ?x9724 = 02vnp2, ?x5063 = 018jz, ?x3416 = 02183k, ?x10463 = 032498, ?x14015 = 0jnlm, colors(?x11919, ?x8271), colors(?x9995, ?x8271), colors(?x8079, ?x8271), colors(?x4986, ?x8271), colors(?x2174, ?x8271), colors(?x10045, ?x8271), colors(?x9691, ?x8271), colors(?x6637, ?x8271), colors(?x5581, ?x8271), ?x1639 = 07l24, ?x10066 = 02rjz5, ?x5920 = 01xrlm, ?x4243 = 0713r, ?x13795 = 044p4_, ?x9344 = 02nq10, ?x8079 = 04cxw5b, ?x8363 = 0k__z, ?x6074 = 02__x, ?x12761 = 0225v9, ?x9543 = 07s8qm7, ?x9691 = 0g8fs, ?x13491 = 0f11p, ?x5918 = 01xn5th, ?x11320 = 02vpvk, colors(?x14124, ?x663), colors(?x14073, ?x663), colors(?x13704, ?x663), colors(?x13520, ?x663), colors(?x12977, ?x663), colors(?x12792, ?x663), colors(?x11673, ?x663), colors(?x11390, ?x663), colors(?x11368, ?x663), colors(?x11153, ?x663), colors(?x10908, ?x663), colors(?x10085, ?x663), colors(?x10034, ?x663), colors(?x9931, ?x663), colors(?x9760, ?x663), colors(?x9247, ?x663), colors(?x8901, ?x663), colors(?x8899, ?x663), colors(?x8697, ?x663), colors(?x8606, ?x663), colors(?x8387, ?x663), colors(?x8361, ?x663), colors(?x8338, ?x663), colors(?x8326, ?x663), colors(?x8051, ?x663), colors(?x7643, ?x663), colors(?x7499, ?x663), colors(?x7078, ?x663), colors(?x5982, ?x663), colors(?x5428, ?x663), colors(?x4487, ?x663), colors(?x3791, ?x663), colors(?x3674, ?x663), colors(?x2198, ?x663), colors(?x2114, ?x663), colors(?x1576, ?x663), colors(?x1115, ?x663), colors(?x684, ?x663), colors(?x13150, ?x663), colors(?x12132, ?x663), colors(?x10627, ?x663), colors(?x10071, ?x663), colors(?x9912, ?x663), colors(?x9880, ?x663), colors(?x9879, ?x663), colors(?x8191, ?x663), colors(?x7918, ?x663), colors(?x5324, ?x663), colors(?x3044, ?x663), colors(?x2838, ?x663), colors(?x2351, ?x663), colors(?x1520, ?x663), colors(?x1153, ?x663), ?x2406 = 03c6sl9, ?x3674 = 05tg3, ?x9835 = 02hqt6, ?x4986 = 04ls81, ?x13704 = 0mgcc, ?x8606 = 02wwr5n, ?x9995 = 0jm9w, ?x10627 = 0138t4, ?x1576 = 05tfm, ?x12792 = 03x726, ?x12485 = 0225bv, ?x1981 = 037s9x, ?x5914 = 011v3, ?x8361 = 049bp4, ?x10045 = 01_k7f, ?x8826 = 03x6w8, ?x4907 = 01vqc7, ?x8678 = 0dwz3t, ?x684 = 01ct6, ?x8051 = 04mp75, ?x13150 = 02jx_v, ?x10217 = 03818y, ?x2198 = 05g3v, ?x7078 = 0ws7, ?x1153 = 0ym8f, ?x5428 = 019lxm, ?x11390 = 0fvly, ?x3044 = 01c333, ?x260 = 01ypc, ?x9760 = 0bwjj, ?x8191 = 0bsnm, ?x11368 = 032yps, ?x14124 = 04l590, ?x2303 = 02plv57, ?x11919 = 04b5l3, major_field_of_study(?x5486, ?x10391), major_field_of_study(?x5486, ?x9079), major_field_of_study(?x5486, ?x7134), major_field_of_study(?x5486, ?x6756), major_field_of_study(?x5486, ?x5031), major_field_of_study(?x5486, ?x4268), major_field_of_study(?x5486, ?x4100), major_field_of_study(?x5486, ?x3490), major_field_of_study(?x5486, ?x2605), major_field_of_study(?x5486, ?x2314), major_field_of_study(?x5486, ?x2014), ?x13520 = 019mcm, ?x9079 = 0l5mz, ?x11632 = 0mbwf, ?x11963 = 01bzs9, ?x10085 = 02fbb5, featured_film_locations(?x103, ?x108), location(?x236, ?x108), ?x6417 = 01t0dy, contains(?x94, ?x108), ?x94 = 09c7w0, organization(?x346, ?x5486), institution(?x4981, ?x5486), institution(?x3437, ?x5486), institution(?x2636, ?x5486), institution(?x1771, ?x5486), institution(?x1526, ?x5486), institution(?x1519, ?x5486), institution(?x1368, ?x5486), institution(?x1200, ?x5486), institution(?x865, ?x5486), institution(?x734, ?x5486), institution(?x620, ?x5486), student(?x5486, ?x118), citytown(?x3513, ?x108), ?x3791 = 02mplj, dog_breed(?x108, ?x11363), dog_breed(?x108, ?x6596), dog_breed(?x108, ?x5194), dog_breed(?x108, ?x1706), ?x2351 = 0q19t, ?x2605 = 03g3w, ?x1520 = 07lx1s, ?x1771 = 019v9k, ?x6645 = 0wsr, ?x620 = 07s6fsf, ?x6637 = 07vjm, ?x8901 = 07l4z, state_province_region(?x5486, ?x1426), currency(?x5486, ?x170), school_type(?x5486, ?x3092), ?x4268 = 02822, school(?x465, ?x5486), ?x1706 = 0km5c, ?x12414 = 035tjy, ?x10034 = 0jnq8, ?x2314 = 0h5k, ?x10071 = 0gl6x, ?x1519 = 013zdg, jurisdiction_of_office(?x1195, ?x108), locations(?x6583, ?x108), ?x2838 = 065r8g, ?x9267 = 0dx84s, ?x7134 = 02_7t, ?x5194 = 01t032, ?x10391 = 02jfc, ?x2171 = 01jq34, ?x2636 = 027f2w, origin(?x6124, ?x108), ?x8326 = 045xx, category(?x108, ?x134), ?x2174 = 051vz, ?x10908 = 03915c, ?x1368 = 014mlp, ?x11153 = 080_y, ?x3437 = 02_xgp2, source(?x108, ?x958), ?x4981 = 03bwzr4, ?x9247 = 019ltg, ?x7499 = 0132_h, ?x12132 = 027b43, ?x4100 = 01lj9, citytown(?x5486, ?x2298), ?x6596 = 0km3f, ?x10142 = 02r7lqg, ?x8899 = 0175tv, ?x6756 = 0_jm, ?x134 = 08mbj5d, ?x6179 = 0cgwt8, ?x1526 = 0bkj86, ?x8912 = 01lpx8, ?x11673 = 02gtm4, ?x5982 = 03c0vy, ?x9931 = 0jm3b, ?x2114 = 01y49, ?x8338 = 0cj_v7, ?x958 = 0jbk9, ?x9880 = 0jpkw, ?x5324 = 01jszm, ?x1115 = 01y3c, ?x2919 = 0c41y70, ?x734 = 04zx3q1, ?x12977 = 0jnkr, ?x865 = 02h4rq6, ?x3490 = 05qfh, ?x9879 = 01pcj4, ?x5581 = 037fqp, ?x12072 = 0346qt, list(?x5486, ?x2197), ?x8186 = 0jnm_, ?x7643 = 02c_4, ?x6223 = 05d9y_, ?x4802 = 019lty, ?x3092 = 05jxkf, ?x2014 = 04rjg, ?x9912 = 01p896, ?x1100 = 03qx63, school(?x8499, ?x7439), ?x11363 = 01k3tq, ?x5031 = 0dc_v, contains(?x1025, ?x7439), ?x2197 = 09g7thr, student(?x7439, ?x4004), ?x170 = 09nqf, fraternities_and_sororities(?x7439, ?x3697), ?x7918 = 0gl6f, ?x8387 = 019lvv, ?x1200 = 016t_3, ?x8499 = 02r6gw6, ?x4487 = 01ync, colors(?x5486, ?x3315), ?x11195 = 0kwv2, ?x8697 = 01wx_y, ?x1195 = 0pqc5, ?x3697 = 0325pb, time_zones(?x108, ?x2674), ?x12356 = 07wkd, ?x14073 = 0h3c3g, ?x2674 = 02hcv8, colors(?x11559, ?x8271), colors(?x10142, ?x663), currency(?x5486, ?x170), colors(?x9835, ?x663), team(?x5727, ?x662), colors(?x2677, ?x663), colors(?x1100, ?x663), country(?x108, ?x94), colors(?x9543, ?x663), category(?x5486, ?x134), colors(?x8826, ?x663), contains(?x108, ?x3513), colors(?x7439, ?x663), colors(?x9344, ?x663), colors(?x2919, ?x663), colors(?x13326, ?x663), state_province_region(?x7439, ?x1025), place_of_birth(?x236, ?x108), colors(?x9724, ?x663), colors(?x8912, ?x663) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #101 for first EXPECTED value: *> intensional similarity = 386 *> extensional distance = 13 *> proper extension: 05kcgsf; 025ygqm; 0285r5d; 025ygws; 03c74_8; 04110b0; 027pwzc; 026fmqm; 027mvrc; 02h7s73; ... *> query: (?x13879, ?x4243) <- season(?x4208, ?x13879), season(?x662, ?x13879), ?x4208 = 061xq, team(?x11844, ?x662), draft(?x662, ?x8786), draft(?x662, ?x3334), position(?x662, ?x13623), position(?x662, ?x8520), position(?x662, ?x5727), position(?x662, ?x4244), position(?x662, ?x2010), ?x5727 = 02wszf, ?x4244 = 028c_8, teams(?x108, ?x662), ?x8786 = 02pq_x5, ?x3334 = 02pq_rp, ?x2010 = 02lyr4, season(?x662, ?x9267), season(?x662, ?x2406), ?x8520 = 01z9v6, colors(?x662, ?x8271), colors(?x662, ?x1101), colors(?x662, ?x663), colors(?x662, ?x332), ?x11844 = 0h69c, sport(?x662, ?x5063), ?x332 = 01l849, colors(?x14015, ?x1101), colors(?x13947, ?x1101), colors(?x13795, ?x1101), colors(?x13480, ?x1101), colors(?x13326, ?x1101), colors(?x12706, ?x1101), colors(?x12414, ?x1101), colors(?x12072, ?x1101), colors(?x12043, ?x1101), colors(?x11320, ?x1101), colors(?x11195, ?x1101), colors(?x10990, ?x1101), colors(?x10463, ?x1101), colors(?x10142, ?x1101), colors(?x10066, ?x1101), colors(?x9835, ?x1101), colors(?x9543, ?x1101), colors(?x9473, ?x1101), colors(?x8912, ?x1101), colors(?x8826, ?x1101), colors(?x8678, ?x1101), colors(?x8186, ?x1101), colors(?x7136, ?x1101), colors(?x6645, ?x1101), colors(?x6179, ?x1101), colors(?x5918, ?x1101), colors(?x5914, ?x1101), colors(?x5175, ?x1101), colors(?x4907, ?x1101), colors(?x4802, ?x1101), colors(?x2919, ?x1101), colors(?x2677, ?x1101), colors(?x2398, ?x1101), colors(?x2303, ?x1101), colors(?x2011, ?x1101), colors(?x1639, ?x1101), colors(?x1100, ?x1101), colors(?x260, ?x1101), ?x2398 = 0jmfb, ?x12706 = 03j0ss, colors(?x13707, ?x1101), colors(?x13491, ?x1101), colors(?x13148, ?x1101), colors(?x12761, ?x1101), colors(?x12667, ?x1101), colors(?x12485, ?x1101), colors(?x12356, ?x1101), colors(?x11963, ?x1101), colors(?x11632, ?x1101), colors(?x11559, ?x1101), colors(?x10217, ?x1101), colors(?x9724, ?x1101), colors(?x9344, ?x1101), colors(?x8463, ?x1101), colors(?x8363, ?x1101), colors(?x6417, ?x1101), colors(?x6223, ?x1101), colors(?x6038, ?x1101), colors(?x5920, ?x1101), colors(?x3416, ?x1101), colors(?x2171, ?x1101), colors(?x1981, ?x1101), ?x10990 = 0329gm, team(?x13623, ?x6074), team(?x13623, ?x4243), team(?x13623, ?x1160), ?x7136 = 0jm74, ?x2677 = 0g701n, ?x13326 = 0hm2b, ?x13480 = 07sqbl, ?x13947 = 03yfh3, ?x5175 = 051n13, ?x13148 = 03hvk2, ?x2011 = 04913k, ?x12043 = 03jb2n, ?x6038 = 01y9qr, ?x8463 = 04cnp4, ?x13707 = 024cg8, school(?x662, ?x7439), school(?x662, ?x5486), ?x9473 = 02b1ng, ?x12667 = 02pdhz, ?x1160 = 049n7, ?x9724 = 02vnp2, ?x5063 = 018jz, ?x3416 = 02183k, ?x10463 = 032498, ?x14015 = 0jnlm, colors(?x11919, ?x8271), colors(?x9995, ?x8271), colors(?x8079, ?x8271), colors(?x4986, ?x8271), colors(?x2174, ?x8271), colors(?x10045, ?x8271), colors(?x9691, ?x8271), colors(?x6637, ?x8271), colors(?x5581, ?x8271), ?x1639 = 07l24, ?x10066 = 02rjz5, ?x5920 = 01xrlm, ?x4243 = 0713r, ?x13795 = 044p4_, ?x9344 = 02nq10, ?x8079 = 04cxw5b, ?x8363 = 0k__z, ?x6074 = 02__x, ?x12761 = 0225v9, ?x9543 = 07s8qm7, ?x9691 = 0g8fs, ?x13491 = 0f11p, ?x5918 = 01xn5th, ?x11320 = 02vpvk, colors(?x14124, ?x663), colors(?x14073, ?x663), colors(?x13704, ?x663), colors(?x13520, ?x663), colors(?x12977, ?x663), colors(?x12792, ?x663), colors(?x11673, ?x663), colors(?x11390, ?x663), colors(?x11368, ?x663), colors(?x11153, ?x663), colors(?x10908, ?x663), colors(?x10085, ?x663), colors(?x10034, ?x663), colors(?x9931, ?x663), colors(?x9760, ?x663), colors(?x9247, ?x663), colors(?x8901, ?x663), colors(?x8899, ?x663), colors(?x8697, ?x663), colors(?x8606, ?x663), colors(?x8387, ?x663), colors(?x8361, ?x663), colors(?x8338, ?x663), colors(?x8326, ?x663), colors(?x8051, ?x663), colors(?x7643, ?x663), colors(?x7499, ?x663), colors(?x7078, ?x663), colors(?x5982, ?x663), colors(?x5428, ?x663), colors(?x4487, ?x663), colors(?x3791, ?x663), colors(?x3674, ?x663), colors(?x2198, ?x663), colors(?x2114, ?x663), colors(?x1576, ?x663), colors(?x1115, ?x663), colors(?x684, ?x663), colors(?x13150, ?x663), colors(?x12132, ?x663), colors(?x10627, ?x663), colors(?x10071, ?x663), colors(?x9912, ?x663), colors(?x9880, ?x663), colors(?x9879, ?x663), colors(?x8191, ?x663), colors(?x7918, ?x663), colors(?x5324, ?x663), colors(?x3044, ?x663), colors(?x2838, ?x663), colors(?x2351, ?x663), colors(?x1520, ?x663), colors(?x1153, ?x663), ?x2406 = 03c6sl9, ?x3674 = 05tg3, ?x9835 = 02hqt6, ?x4986 = 04ls81, ?x13704 = 0mgcc, ?x8606 = 02wwr5n, ?x9995 = 0jm9w, ?x10627 = 0138t4, ?x1576 = 05tfm, ?x12792 = 03x726, ?x12485 = 0225bv, ?x1981 = 037s9x, ?x5914 = 011v3, ?x8361 = 049bp4, ?x10045 = 01_k7f, ?x8826 = 03x6w8, ?x4907 = 01vqc7, ?x8678 = 0dwz3t, ?x684 = 01ct6, ?x8051 = 04mp75, ?x13150 = 02jx_v, ?x10217 = 03818y, ?x2198 = 05g3v, ?x7078 = 0ws7, ?x1153 = 0ym8f, ?x5428 = 019lxm, ?x11390 = 0fvly, ?x3044 = 01c333, ?x260 = 01ypc, ?x9760 = 0bwjj, ?x8191 = 0bsnm, ?x11368 = 032yps, ?x14124 = 04l590, ?x2303 = 02plv57, ?x11919 = 04b5l3, major_field_of_study(?x5486, ?x10391), major_field_of_study(?x5486, ?x9079), major_field_of_study(?x5486, ?x7134), major_field_of_study(?x5486, ?x6756), major_field_of_study(?x5486, ?x5031), major_field_of_study(?x5486, ?x4268), major_field_of_study(?x5486, ?x4100), major_field_of_study(?x5486, ?x3490), major_field_of_study(?x5486, ?x2605), major_field_of_study(?x5486, ?x2314), major_field_of_study(?x5486, ?x2014), ?x13520 = 019mcm, ?x9079 = 0l5mz, ?x11632 = 0mbwf, ?x11963 = 01bzs9, ?x10085 = 02fbb5, featured_film_locations(?x103, ?x108), location(?x236, ?x108), ?x6417 = 01t0dy, contains(?x94, ?x108), ?x94 = 09c7w0, organization(?x346, ?x5486), institution(?x4981, ?x5486), institution(?x3437, ?x5486), institution(?x2636, ?x5486), institution(?x1771, ?x5486), institution(?x1526, ?x5486), institution(?x1519, ?x5486), institution(?x1368, ?x5486), institution(?x1200, ?x5486), institution(?x865, ?x5486), institution(?x734, ?x5486), institution(?x620, ?x5486), student(?x5486, ?x118), citytown(?x3513, ?x108), ?x3791 = 02mplj, dog_breed(?x108, ?x11363), dog_breed(?x108, ?x6596), dog_breed(?x108, ?x5194), dog_breed(?x108, ?x1706), ?x2351 = 0q19t, ?x2605 = 03g3w, ?x1520 = 07lx1s, ?x1771 = 019v9k, ?x6645 = 0wsr, ?x620 = 07s6fsf, ?x6637 = 07vjm, ?x8901 = 07l4z, state_province_region(?x5486, ?x1426), currency(?x5486, ?x170), school_type(?x5486, ?x3092), ?x4268 = 02822, school(?x465, ?x5486), ?x1706 = 0km5c, ?x12414 = 035tjy, ?x10034 = 0jnq8, ?x2314 = 0h5k, ?x10071 = 0gl6x, ?x1519 = 013zdg, jurisdiction_of_office(?x1195, ?x108), locations(?x6583, ?x108), ?x2838 = 065r8g, ?x9267 = 0dx84s, ?x7134 = 02_7t, ?x5194 = 01t032, ?x10391 = 02jfc, ?x2171 = 01jq34, ?x2636 = 027f2w, origin(?x6124, ?x108), ?x8326 = 045xx, category(?x108, ?x134), ?x2174 = 051vz, ?x10908 = 03915c, ?x1368 = 014mlp, ?x11153 = 080_y, ?x3437 = 02_xgp2, source(?x108, ?x958), ?x4981 = 03bwzr4, ?x9247 = 019ltg, ?x7499 = 0132_h, ?x12132 = 027b43, ?x4100 = 01lj9, citytown(?x5486, ?x2298), ?x6596 = 0km3f, ?x10142 = 02r7lqg, ?x8899 = 0175tv, ?x6756 = 0_jm, ?x134 = 08mbj5d, ?x6179 = 0cgwt8, ?x1526 = 0bkj86, ?x8912 = 01lpx8, ?x11673 = 02gtm4, ?x5982 = 03c0vy, ?x9931 = 0jm3b, ?x2114 = 01y49, ?x8338 = 0cj_v7, ?x958 = 0jbk9, ?x9880 = 0jpkw, ?x5324 = 01jszm, ?x1115 = 01y3c, ?x2919 = 0c41y70, ?x734 = 04zx3q1, ?x12977 = 0jnkr, ?x865 = 02h4rq6, ?x3490 = 05qfh, ?x9879 = 01pcj4, ?x5581 = 037fqp, ?x12072 = 0346qt, list(?x5486, ?x2197), ?x8186 = 0jnm_, ?x7643 = 02c_4, ?x6223 = 05d9y_, ?x4802 = 019lty, ?x3092 = 05jxkf, ?x2014 = 04rjg, ?x9912 = 01p896, ?x1100 = 03qx63, school(?x8499, ?x7439), ?x11363 = 01k3tq, ?x5031 = 0dc_v, contains(?x1025, ?x7439), ?x2197 = 09g7thr, student(?x7439, ?x4004), ?x170 = 09nqf, fraternities_and_sororities(?x7439, ?x3697), ?x7918 = 0gl6f, ?x8387 = 019lvv, ?x1200 = 016t_3, ?x8499 = 02r6gw6, ?x4487 = 01ync, colors(?x5486, ?x3315), ?x11195 = 0kwv2, ?x8697 = 01wx_y, ?x1195 = 0pqc5, ?x3697 = 0325pb, time_zones(?x108, ?x2674), ?x12356 = 07wkd, ?x14073 = 0h3c3g, ?x2674 = 02hcv8, colors(?x11559, ?x8271), colors(?x10142, ?x663), currency(?x5486, ?x170), colors(?x9835, ?x663), team(?x5727, ?x662), colors(?x2677, ?x663), colors(?x1100, ?x663), country(?x108, ?x94), colors(?x9543, ?x663), category(?x5486, ?x134), colors(?x8826, ?x663), contains(?x108, ?x3513), colors(?x7439, ?x663), colors(?x9344, ?x663), colors(?x2919, ?x663), colors(?x13326, ?x663), state_province_region(?x7439, ?x1025), place_of_birth(?x236, ?x108), colors(?x9724, ?x663), colors(?x8912, ?x663) *> conf = 0.53 ranks of expected_values: 16 EVAL 04n36qk season! 0713r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 3.000 3.000 0.867 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #21137-03j24kf PRED entity: 03j24kf PRED relation: award_winner! PRED expected values: 02rjv2w => 141 concepts (118 used for prediction) PRED predicted values (max 10 best out of 368): 02rjv2w (0.22 #295, 0.15 #109080, 0.07 #10519), 07bzz7 (0.14 #23857, 0.10 #134092, 0.02 #42035), 03ln8b (0.09 #1363, 0.05 #46806, 0.02 #49080), 0ywrc (0.09 #2616, 0.04 #17384, 0.04 #6024), 0dgpwnk (0.09 #2648, 0.04 #6056, 0.03 #7192), 0cs134 (0.09 #2192, 0.02 #23776, 0.02 #35137), 04vr_f (0.09 #1253, 0.02 #22837, 0.02 #83066), 01bb9r (0.09 #1464, 0.02 #23048, 0.01 #34409), 03l6q0 (0.09 #1500, 0.02 #18540), 01jw67 (0.09 #1848, 0.01 #23432) >> Best rule #295 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0pj8m; >> query: (?x4701, 02rjv2w) <- award_winner(?x3321, ?x4701), award(?x4701, ?x567), ?x3321 = 03bnv >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03j24kf award_winner! 02rjv2w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 118.000 0.222 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #21136-01gr00 PRED entity: 01gr00 PRED relation: contains! PRED expected values: 01cx_ => 44 concepts (11 used for prediction) PRED predicted values (max 10 best out of 116): 01cx_ (0.40 #1980, 0.33 #195, 0.25 #1087), 07ssc (0.19 #4494, 0.15 #5386, 0.15 #6278), 0k3k1 (0.17 #3172, 0.02 #4958, 0.01 #3571), 02jx1 (0.12 #4549, 0.11 #6333, 0.11 #9009), 0k3hn (0.11 #3051, 0.01 #3571), 01n7q (0.09 #6324, 0.09 #5432, 0.08 #7216), 059rby (0.07 #4482, 0.06 #3590, 0.05 #8050), 0k3jc (0.06 #3471, 0.01 #3571), 0k3jq (0.06 #3339, 0.01 #3571), 0k3gj (0.06 #3021, 0.01 #3571) >> Best rule #1980 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01l9vr; >> query: (?x14449, 01cx_) <- contains(?x7309, ?x14449), contains(?x2020, ?x14449), ?x7309 = 0k3l5, ?x2020 = 05k7sb >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gr00 contains! 01cx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 11.000 0.400 http://example.org/location/location/contains #21135-0fz20l PRED entity: 0fz20l PRED relation: award_winner PRED expected values: 095p3z => 40 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1234): 072twv (0.50 #9591, 0.50 #8051, 0.42 #12672), 076lxv (0.40 #6255, 0.38 #9336, 0.33 #12417), 081nh (0.40 #6505, 0.38 #9586, 0.33 #15748), 0b_fw (0.40 #6468, 0.33 #8009, 0.33 #1844), 0fx0j2 (0.40 #7661, 0.33 #9202, 0.25 #10742), 01l1rw (0.39 #15407, 0.36 #12325, 0.24 #6161), 01pp3p (0.33 #8478, 0.33 #773, 0.25 #13099), 076psv (0.33 #2225, 0.27 #16092, 0.25 #13011), 0579tg2 (0.33 #3052, 0.25 #10757, 0.20 #7676), 026m0 (0.33 #2981, 0.25 #10686, 0.20 #7605) >> Best rule #9591 for best value: >> intensional similarity = 20 >> extensional distance = 6 >> proper extension: 0c53zb; 0c4hgj; >> query: (?x3518, 072twv) <- award_winner(?x3518, ?x3519), award_winner(?x3518, ?x200), ceremony(?x1972, ?x3518), ceremony(?x1243, ?x3518), ?x1972 = 0gqyl, honored_for(?x3518, ?x5220), ?x3519 = 02sj1x, nominated_for(?x1243, ?x3992), nominated_for(?x1243, ?x3498), nominated_for(?x1243, ?x2833), ceremony(?x1243, ?x9899), ceremony(?x1243, ?x6344), ?x3992 = 0pd6l, award(?x185, ?x1243), ?x6344 = 0bzm__, ?x9899 = 0c4hnm, film_release_region(?x3498, ?x142), award_nominee(?x200, ?x199), profession(?x200, ?x1078), ?x2833 = 04jwly >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5912 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 2 *> proper extension: 0ftlxj; *> query: (?x3518, 095p3z) <- award_winner(?x3518, ?x6766), award_winner(?x3518, ?x3519), award_winner(?x3518, ?x200), ceremony(?x1972, ?x3518), ceremony(?x1323, ?x3518), ?x1972 = 0gqyl, honored_for(?x3518, ?x5220), place_of_death(?x3519, ?x682), award_winner(?x199, ?x200), nominated_for(?x3519, ?x4179), ceremony(?x1323, ?x7940), award_winner(?x1323, ?x538), award_nominee(?x200, ?x4251), award(?x115, ?x1323), type_of_union(?x200, ?x566), ?x7940 = 0bzjvm, artists(?x114, ?x115), ?x6766 = 07fzq3 *> conf = 0.25 ranks of expected_values: 54 EVAL 0fz20l award_winner 095p3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 40.000 29.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21134-0hfml PRED entity: 0hfml PRED relation: company PRED expected values: 05f4p => 128 concepts (64 used for prediction) PRED predicted values (max 10 best out of 97): 07wh1 (0.27 #949, 0.20 #757, 0.12 #4031), 0gsg7 (0.17 #26, 0.10 #603, 0.03 #1564), 09d5h (0.17 #33, 0.10 #610, 0.03 #1571), 0gsgr (0.17 #114, 0.03 #1652, 0.03 #1845), 09c7w0 (0.13 #5581, 0.11 #2119, 0.10 #578), 01skqzw (0.12 #4221, 0.11 #4989, 0.09 #4029), 01cl0d (0.10 #705, 0.09 #897, 0.03 #3979), 07vsl (0.10 #763, 0.09 #955, 0.03 #2304), 01wsj0 (0.10 #743, 0.09 #935, 0.02 #2669), 043g7l (0.10 #664, 0.09 #856, 0.02 #2782) >> Best rule #949 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 036jp8; 01q9b9; >> query: (?x7474, 07wh1) <- inductee(?x11145, ?x7474), gender(?x7474, ?x231), company(?x7474, ?x10370), profession(?x7474, ?x353) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hfml company 05f4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 64.000 0.273 http://example.org/people/person/employment_history./business/employment_tenure/company #21133-01wxdn3 PRED entity: 01wxdn3 PRED relation: role PRED expected values: 011_6p => 164 concepts (91 used for prediction) PRED predicted values (max 10 best out of 115): 013y1f (0.46 #2812, 0.44 #3273, 0.44 #1658), 02snj9 (0.44 #3273, 0.42 #2811, 0.41 #1723), 026t6 (0.41 #1000, 0.39 #1092, 0.33 #2449), 0dwt5 (0.25 #798, 0.16 #888, 0.15 #997), 03gvt (0.22 #337, 0.21 #1698, 0.18 #1063), 04rzd (0.22 #305, 0.18 #1031, 0.18 #578), 018j2 (0.22 #306, 0.18 #1032, 0.15 #997), 0dwtp (0.22 #283, 0.15 #997, 0.13 #1101), 0l15bq (0.22 #298, 0.15 #997, 0.12 #2815), 01v1d8 (0.22 #329, 0.15 #997, 0.12 #2815) >> Best rule #2812 for best value: >> intensional similarity = 5 >> extensional distance = 63 >> proper extension: 04mky3; >> query: (?x9735, ?x315) <- performance_role(?x9735, ?x315), artists(?x302, ?x9735), performance_role(?x227, ?x315), role(?x315, ?x74), family(?x1268, ?x315) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1724 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 37 *> proper extension: 02ryx0; *> query: (?x9735, ?x74) <- performance_role(?x9735, ?x315), role(?x9735, ?x716), role(?x9735, ?x316), profession(?x9735, ?x319), ?x316 = 05r5c, role(?x716, ?x74) *> conf = 0.07 ranks of expected_values: 98 EVAL 01wxdn3 role 011_6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 164.000 91.000 0.463 http://example.org/music/artist/track_contributions./music/track_contribution/role #21132-072x7s PRED entity: 072x7s PRED relation: nominated_for! PRED expected values: 0gr4k 019f4v 0gq9h => 115 concepts (108 used for prediction) PRED predicted values (max 10 best out of 202): 019f4v (0.64 #1932, 0.53 #992, 0.39 #2402), 0gq9h (0.62 #1940, 0.48 #2410, 0.45 #60), 040njc (0.47 #947, 0.43 #1887, 0.35 #2357), 054krc (0.47 #1006, 0.27 #1946, 0.27 #2416), 02qvyrt (0.47 #1033, 0.26 #2443, 0.25 #563), 02hsq3m (0.45 #30, 0.25 #265, 0.20 #500), 02qyntr (0.44 #2057, 0.42 #1117, 0.34 #2527), 02pqp12 (0.43 #1936, 0.42 #996, 0.35 #2406), 0gr0m (0.42 #997, 0.40 #1937, 0.36 #57), 0gqy2 (0.39 #1999, 0.31 #2469, 0.30 #1059) >> Best rule #1932 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 02v8kmz; 07w8fz; >> query: (?x1685, 019f4v) <- nominated_for(?x746, ?x1685), produced_by(?x1685, ?x846), genre(?x1685, ?x53), ?x746 = 04dn09n >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 22 EVAL 072x7s nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 108.000 0.642 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 072x7s nominated_for! 019f4v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 108.000 0.642 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 072x7s nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 115.000 108.000 0.642 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21131-030k94 PRED entity: 030k94 PRED relation: nominated_for! PRED expected values: 0cqhk0 0ck27z => 88 concepts (83 used for prediction) PRED predicted values (max 10 best out of 184): 0bp_b2 (0.71 #469, 0.70 #1877, 0.70 #2814), 0m7yy (0.71 #469, 0.70 #1877, 0.70 #2814), 027gs1_ (0.71 #889, 0.28 #1828, 0.27 #3000), 0cjyzs (0.58 #785, 0.32 #551, 0.29 #1959), 09qs08 (0.47 #811, 0.22 #15473, 0.21 #577), 03ccq3s (0.45 #845, 0.23 #1784, 0.22 #15473), 09qj50 (0.42 #741, 0.22 #15473, 0.21 #507), 09qrn4 (0.42 #866, 0.22 #15473, 0.21 #15943), 09qv3c (0.42 #744, 0.22 #15473, 0.21 #15943), 0cqhk0 (0.39 #734, 0.22 #15473, 0.21 #15943) >> Best rule #469 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 06r2_; >> query: (?x3169, ?x435) <- nominated_for(?x8596, ?x3169), nominated_for(?x7489, ?x3169), award_winner(?x139, ?x8596), ?x7489 = 084m3, award(?x3169, ?x435) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #734 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 072kp; 0kfpm; 0124k9; 08jgk1; 0584r4; 03ln8b; 01q_y0; 02hct1; 0d68qy; 07c72; ... *> query: (?x3169, 0cqhk0) <- nominated_for(?x1762, ?x3169), genre(?x3169, ?x2480), award(?x3169, ?x435), ?x2480 = 01z4y *> conf = 0.39 ranks of expected_values: 10, 24 EVAL 030k94 nominated_for! 0ck27z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 88.000 83.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 030k94 nominated_for! 0cqhk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 88.000 83.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21130-016t00 PRED entity: 016t00 PRED relation: artist! PRED expected values: 015_1q => 121 concepts (68 used for prediction) PRED predicted values (max 10 best out of 117): 015_1q (0.31 #992, 0.28 #1409, 0.25 #436), 03rhqg (0.27 #988, 0.21 #154, 0.21 #849), 0g768 (0.20 #1147, 0.17 #1008, 0.15 #2676), 0181dw (0.18 #1569, 0.17 #1708, 0.14 #1986), 011k1h (0.16 #1122, 0.15 #1400, 0.12 #1678), 017l96 (0.16 #1130, 0.14 #1269, 0.14 #852), 01clyr (0.15 #1004, 0.14 #170, 0.14 #865), 033hn8 (0.15 #2654, 0.13 #986, 0.12 #1681), 03mp8k (0.14 #204, 0.14 #1594, 0.13 #1733), 0k_kr (0.14 #181, 0.12 #459, 0.10 #876) >> Best rule #992 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 015srx; 01v0sxx; 04k05; 0jltp; >> query: (?x11446, 015_1q) <- award(?x11446, ?x1801), artists(?x1572, ?x11446), ?x1572 = 06by7, inductee(?x1091, ?x11446) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016t00 artist! 015_1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 68.000 0.308 http://example.org/music/record_label/artist #21129-01k98nm PRED entity: 01k98nm PRED relation: artist! PRED expected values: 03rhqg => 139 concepts (65 used for prediction) PRED predicted values (max 10 best out of 92): 017l96 (0.62 #984, 0.11 #5141, 0.10 #3060), 03rhqg (0.58 #15, 0.17 #5138, 0.16 #1395), 015_1q (0.33 #295, 0.29 #433, 0.24 #847), 02p11jq (0.32 #12, 0.11 #2500, 0.10 #1392), 02zn1b (0.26 #8, 0.05 #146, 0.04 #1112), 011k1h (0.26 #975, 0.15 #699, 0.11 #5132), 04fcjt (0.19 #995, 0.05 #29, 0.04 #1409), 01dtcb (0.19 #184, 0.16 #46, 0.09 #874), 043g7l (0.19 #306, 0.17 #444, 0.15 #582), 01clyr (0.18 #722, 0.09 #5155, 0.08 #5293) >> Best rule #984 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 04r1t; 07m4c; 08w4pm; >> query: (?x3234, 017l96) <- artist(?x12719, ?x3234), artist(?x12719, ?x2737), ?x2737 = 0126y2 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 019389; *> query: (?x3234, 03rhqg) <- award_nominee(?x3234, ?x3235), artist(?x5634, ?x3234), ?x5634 = 01cl2y *> conf = 0.58 ranks of expected_values: 2 EVAL 01k98nm artist! 03rhqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 139.000 65.000 0.624 http://example.org/music/record_label/artist #21128-03ncb2 PRED entity: 03ncb2 PRED relation: award! PRED expected values: 01vsy95 02qtywd => 41 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2596): 01vsy95 (0.80 #33833, 0.79 #30448, 0.78 #50751), 02mslq (0.80 #33833, 0.79 #30448, 0.78 #50751), 01lvcs1 (0.78 #50751, 0.78 #50750, 0.77 #16914), 01vs_v8 (0.31 #10732, 0.27 #27650, 0.26 #31035), 0gbwp (0.24 #11262, 0.21 #28180, 0.21 #31565), 0fhxv (0.24 #11496, 0.20 #28414, 0.20 #31799), 09889g (0.24 #11598, 0.19 #28516, 0.19 #31901), 01vrz41 (0.24 #10441, 0.19 #27359, 0.19 #30744), 01vvycq (0.23 #10295, 0.21 #27213, 0.21 #30598), 02qwg (0.21 #11082, 0.19 #28000, 0.19 #31385) >> Best rule #33833 for best value: >> intensional similarity = 5 >> extensional distance = 108 >> proper extension: 05qck; >> query: (?x8409, ?x3374) <- award_winner(?x8409, ?x3374), role(?x3374, ?x212), origin(?x3374, ?x2017), award_winner(?x342, ?x3374), award(?x3374, ?x341) >> conf = 0.80 => this is the best rule for 2 predicted values ranks of expected_values: 1, 24 EVAL 03ncb2 award! 02qtywd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 41.000 16.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03ncb2 award! 01vsy95 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 16.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21127-0k345 PRED entity: 0k345 PRED relation: artists PRED expected values: 02whj 06br6t => 58 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1035): 067mj (0.60 #7620, 0.50 #8694, 0.50 #5469), 03t9sp (0.50 #8717, 0.50 #5492, 0.40 #7643), 01vvycq (0.50 #8641, 0.50 #5416, 0.40 #7567), 014pg1 (0.50 #9327, 0.50 #6102, 0.40 #8253), 07r4c (0.50 #12375, 0.50 #5926, 0.40 #8077), 05563d (0.50 #5677, 0.40 #7828, 0.38 #12126), 011z3g (0.50 #5970, 0.40 #8121, 0.33 #9195), 013w8y (0.50 #6187, 0.40 #8338, 0.33 #9412), 02z4b_8 (0.50 #6004, 0.40 #8155, 0.33 #9229), 0197tq (0.50 #5381, 0.40 #7532, 0.33 #8606) >> Best rule #7620 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 03w94xt; >> query: (?x10307, 067mj) <- artists(?x10307, ?x8539), artists(?x10307, ?x7951), artists(?x10307, ?x3657), artists(?x10307, ?x3399), ?x8539 = 01w9mnm, ?x3657 = 01w8n89, award(?x7951, ?x724), ?x3399 = 01gx5f, profession(?x7951, ?x131), nationality(?x7951, ?x1310) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #17002 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 0dls3; 03p7rp; *> query: (?x10307, 06br6t) <- artists(?x10307, ?x8864), artists(?x10307, ?x8539), nationality(?x8539, ?x512), role(?x8539, ?x1166), ?x1166 = 05148p4, category(?x8539, ?x134), ?x8864 = 070b4, country(?x124, ?x512) *> conf = 0.40 ranks of expected_values: 19, 21 EVAL 0k345 artists 06br6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 58.000 26.000 0.600 http://example.org/music/genre/artists EVAL 0k345 artists 02whj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 58.000 26.000 0.600 http://example.org/music/genre/artists #21126-0f7hc PRED entity: 0f7hc PRED relation: location_of_ceremony PRED expected values: 02_286 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 31): 0cv3w (0.08 #154, 0.04 #392, 0.04 #273), 0r62v (0.04 #374), 03_3d (0.04 #364), 03s5t (0.04 #509, 0.01 #1580), 04jpl (0.02 #1199, 0.02 #604, 0.02 #1318), 06y57 (0.02 #652, 0.01 #1247, 0.01 #1366), 012wgb (0.02 #637, 0.01 #1232, 0.01 #1351), 027rn (0.02 #596, 0.01 #1191, 0.01 #1310), 0fw3f (0.02 #708, 0.01 #1303), 0f2wj (0.02 #607, 0.01 #1202) >> Best rule #154 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 01k9lpl; 01wp_jm; 0c5vh; >> query: (?x4657, 0cv3w) <- influenced_by(?x10560, ?x4657), ?x10560 = 01xwv7 >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #1560 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 91 *> proper extension: 079ws; 0p_jc; *> query: (?x4657, 02_286) <- influenced_by(?x1835, ?x4657), nominated_for(?x4657, ?x886) *> conf = 0.02 ranks of expected_values: 15 EVAL 0f7hc location_of_ceremony 02_286 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 138.000 138.000 0.077 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #21125-04hwbq PRED entity: 04hwbq PRED relation: film! PRED expected values: 0bxtg => 102 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1217): 04rcl7 (0.45 #14503, 0.36 #60081, 0.30 #62153), 05_k56 (0.27 #70440, 0.10 #174, 0.05 #4316), 0f0kz (0.12 #2586, 0.08 #19160, 0.07 #4657), 01nm3s (0.10 #685, 0.07 #4827, 0.06 #2756), 0bxtg (0.10 #77, 0.07 #4219, 0.06 #12507), 01h1b (0.10 #1201, 0.07 #5343, 0.03 #3272), 01vy_v8 (0.10 #729, 0.07 #9015, 0.05 #6943), 03qd_ (0.10 #123, 0.06 #2194, 0.05 #4265), 01pcbg (0.10 #581, 0.06 #2652, 0.05 #4723), 0jbp0 (0.10 #1752, 0.05 #5894, 0.03 #26612) >> Best rule #14503 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 02y_lrp; 0dnvn3; 0ds33; 04fzfj; 0dsvzh; 0kv2hv; 04tc1g; 0416y94; 01kff7; 0260bz; ... >> query: (?x1259, ?x10685) <- music(?x1259, ?x4850), nominated_for(?x3053, ?x1259), titles(?x1510, ?x1259), award_winner(?x1259, ?x10685) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #77 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0jyx6; *> query: (?x1259, 0bxtg) <- music(?x1259, ?x4850), film(?x2156, ?x1259), film(?x10121, ?x1259), ?x10121 = 085q5 *> conf = 0.10 ranks of expected_values: 5 EVAL 04hwbq film! 0bxtg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 102.000 41.000 0.445 http://example.org/film/actor/film./film/performance/film #21124-01pd60 PRED entity: 01pd60 PRED relation: list! PRED expected values: 02zs4 03mnk 0py9b 04fv0k 03bnb 03qbm 05njw 0841v => 7 concepts (7 used for prediction) PRED predicted values (max 10 best out of 1060): 05njw (0.91 #1049, 0.87 #1567, 0.87 #1566), 07_dn (0.91 #1049, 0.87 #1567, 0.87 #1566), 0sxdg (0.91 #1049, 0.87 #1567, 0.87 #1566), 0mgkg (0.91 #1049, 0.87 #1567, 0.87 #1566), 0py9b (0.91 #1049, 0.87 #1567, 0.87 #1566), 01s73z (0.91 #1049, 0.87 #1567, 0.87 #1566), 02zs4 (0.91 #1049, 0.87 #1567, 0.87 #1566), 0vlf (0.91 #1049, 0.87 #1567, 0.87 #1566), 060ppp (0.91 #1049, 0.87 #1567, 0.87 #1566), 0z90c (0.91 #1049, 0.87 #1567, 0.87 #1566) >> Best rule #1049 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 05glt; >> query: (?x8915, ?x266) <- list(?x9873, ?x8915), list(?x3920, ?x8915), list(?x3795, ?x8915), list(?x1762, ?x8915), list(?x9873, ?x7472), category(?x3795, ?x134), award(?x3920, ?x1105), nominated_for(?x1762, ?x5808), award_winner(?x1105, ?x382), nominated_for(?x1105, ?x103), nominated_for(?x435, ?x5808), list(?x266, ?x7472), award(?x270, ?x1105), ?x134 = 08mbj5d >> conf = 0.91 => this is the best rule for 36 predicted values ranks of expected_values: 1, 5, 7, 23, 25, 31, 679 EVAL 01pd60 list! 0841v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 7.000 7.000 0.911 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01pd60 list! 05njw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 7.000 7.000 0.911 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01pd60 list! 03qbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 7.000 7.000 0.911 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01pd60 list! 03bnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 7.000 7.000 0.911 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01pd60 list! 04fv0k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 7.000 7.000 0.911 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01pd60 list! 0py9b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 7.000 7.000 0.911 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01pd60 list! 03mnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 7.000 7.000 0.911 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01pd60 list! 02zs4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 7.000 7.000 0.911 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #21123-023w9s PRED entity: 023w9s PRED relation: profession PRED expected values: 02jknp => 195 concepts (117 used for prediction) PRED predicted values (max 10 best out of 90): 02hrh1q (0.93 #9337, 0.89 #16000, 0.87 #15112), 02jknp (0.91 #7851, 0.90 #11849, 0.89 #10516), 03gjzk (0.56 #754, 0.50 #606, 0.48 #3270), 09jwl (0.53 #1942, 0.47 #3126, 0.41 #5346), 0nbcg (0.50 #1215, 0.44 #919, 0.33 #5359), 01c72t (0.44 #911, 0.40 #1207, 0.25 #171), 0cbd2 (0.42 #13772, 0.41 #9774, 0.41 #8442), 02krf9 (0.38 #618, 0.27 #2098, 0.26 #10535), 012t_z (0.33 #900, 0.30 #1196, 0.25 #160), 0n1h (0.33 #899, 0.30 #1195, 0.25 #159) >> Best rule #9337 for best value: >> intensional similarity = 4 >> extensional distance = 189 >> proper extension: 024y6w; 02m30v; >> query: (?x9235, 02hrh1q) <- location_of_ceremony(?x9235, ?x12655), profession(?x9235, ?x319), profession(?x2557, ?x319), ?x2557 = 01s7zw >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #7851 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 134 *> proper extension: 07nznf; 0162c8; 032v0v; 01gzm2; 03wpmd; 0bgrsl; 02ld6x; 04g865; 022wxh; 098n_m; ... *> query: (?x9235, 02jknp) <- profession(?x9235, ?x319), film(?x9235, ?x9234), ?x319 = 01d_h8, place_of_birth(?x9235, ?x1860) *> conf = 0.91 ranks of expected_values: 2 EVAL 023w9s profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 195.000 117.000 0.927 http://example.org/people/person/profession #21122-06pk8 PRED entity: 06pk8 PRED relation: profession PRED expected values: 0dxtg => 125 concepts (124 used for prediction) PRED predicted values (max 10 best out of 75): 0dxtg (0.85 #1482, 0.85 #2070, 0.83 #3099), 03gjzk (0.49 #307, 0.43 #1483, 0.42 #4276), 0dz3r (0.40 #737, 0.28 #5882, 0.25 #6471), 09jwl (0.38 #5897, 0.32 #6486, 0.30 #1193), 0nbcg (0.32 #765, 0.29 #5910, 0.26 #6499), 018gz8 (0.30 #1338, 0.15 #456, 0.15 #1485), 0cbd2 (0.29 #4710, 0.28 #3534, 0.26 #300), 02krf9 (0.23 #2965, 0.23 #3847, 0.22 #1495), 016z4k (0.23 #5884, 0.22 #739, 0.22 #592), 0d1pc (0.22 #931, 0.20 #1960, 0.19 #1078) >> Best rule #1482 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 0884hk; >> query: (?x976, 0dxtg) <- nominated_for(?x976, ?x2955), written_by(?x11192, ?x976), award_winner(?x976, ?x1674) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06pk8 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 124.000 0.853 http://example.org/people/person/profession #21121-01vsy95 PRED entity: 01vsy95 PRED relation: award PRED expected values: 03ncb2 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 308): 03ncb2 (0.77 #30571, 0.76 #13272, 0.75 #15685), 01by1l (0.36 #2525, 0.33 #10971, 0.32 #11775), 01bgqh (0.33 #1650, 0.29 #444, 0.29 #2053), 01ckrr (0.32 #3046, 0.20 #1035, 0.18 #3850), 01ck6h (0.31 #1730, 0.29 #926, 0.22 #3339), 054ks3 (0.25 #4162, 0.22 #2152, 0.20 #5770), 03qbh5 (0.25 #1813, 0.22 #5834, 0.20 #15890), 025m8y (0.24 #501, 0.10 #6934, 0.09 #13773), 01c9jp (0.23 #3004, 0.08 #5818, 0.08 #6622), 0c4z8 (0.23 #875, 0.22 #1679, 0.21 #10930) >> Best rule #30571 for best value: >> intensional similarity = 3 >> extensional distance = 1371 >> proper extension: 0kk9v; >> query: (?x3374, ?x5123) <- award_winner(?x5123, ?x3374), award_nominee(?x3374, ?x487), ceremony(?x5123, ?x342) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vsy95 award 03ncb2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21120-024mpp PRED entity: 024mpp PRED relation: produced_by PRED expected values: 09zw90 => 139 concepts (116 used for prediction) PRED predicted values (max 10 best out of 243): 0h1p (0.51 #24688, 0.43 #28558, 0.41 #23144), 01xndd (0.33 #523, 0.25 #910, 0.17 #2067), 0697kh (0.33 #670, 0.25 #1057, 0.17 #2214), 02bfxb (0.29 #2428, 0.22 #2814, 0.13 #4355), 01r2c7 (0.25 #1085, 0.17 #2242, 0.04 #15736), 0d6484 (0.25 #1096, 0.17 #2253, 0.04 #10735), 04pqqb (0.18 #3647, 0.14 #18689, 0.12 #20234), 030_3z (0.17 #2091, 0.10 #20219, 0.07 #24077), 0b13g7 (0.15 #10144, 0.10 #24033, 0.10 #3203), 02q42j_ (0.15 #10235, 0.10 #3294, 0.09 #24124) >> Best rule #24688 for best value: >> intensional similarity = 6 >> extensional distance = 180 >> proper extension: 0jqn5; 02r1c18; 0bpx1k; 0dlngsd; 0gtt5fb; 05650n; 011yhm; 02bqxb; >> query: (?x3938, ?x2086) <- film_release_region(?x3938, ?x94), produced_by(?x3938, ?x3862), language(?x3938, ?x254), production_companies(?x3938, ?x1104), award_winner(?x1039, ?x1104), film(?x2086, ?x3938) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #3828 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 048yqf; *> query: (?x3938, 09zw90) <- produced_by(?x3938, ?x7976), ?x7976 = 02xnjd, genre(?x3938, ?x225), film_crew_role(?x3938, ?x468), production_companies(?x3938, ?x738), ?x468 = 02r96rf *> conf = 0.09 ranks of expected_values: 35 EVAL 024mpp produced_by 09zw90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 139.000 116.000 0.505 http://example.org/film/film/produced_by #21119-04vrxh PRED entity: 04vrxh PRED relation: award_winner! PRED expected values: 03qbnj => 110 concepts (108 used for prediction) PRED predicted values (max 10 best out of 218): 03t5n3 (0.39 #3026, 0.35 #3892, 0.32 #15559), 02x17c2 (0.39 #3026, 0.35 #3892, 0.32 #15559), 0gqz2 (0.33 #2674, 0.24 #3540, 0.09 #7431), 054ks3 (0.33 #2735, 0.23 #3601, 0.12 #3169), 025m8l (0.30 #2713, 0.21 #3579, 0.06 #3147), 01by1l (0.25 #113, 0.23 #7031, 0.22 #4438), 03qbnj (0.25 #232, 0.15 #32842, 0.15 #33275), 0l8z1 (0.25 #64, 0.14 #1361, 0.12 #1793), 025m98 (0.25 #236, 0.14 #1533, 0.12 #1965), 02f73b (0.25 #285, 0.12 #6486, 0.09 #2878) >> Best rule #3026 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0pgjm; 05q9g1; >> query: (?x9882, ?x5799) <- award(?x9882, ?x5799), award(?x9882, ?x4481), ?x4481 = 02x17c2, award_winner(?x9882, ?x1896) >> conf = 0.39 => this is the best rule for 2 predicted values *> Best rule #232 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09hnb; *> query: (?x9882, 03qbnj) <- artists(?x2936, ?x9882), award_winner(?x9882, ?x1896), role(?x9882, ?x316), ?x2936 = 029h7y *> conf = 0.25 ranks of expected_values: 7 EVAL 04vrxh award_winner! 03qbnj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 110.000 108.000 0.392 http://example.org/award/award_category/winners./award/award_honor/award_winner #21118-01jv_6 PRED entity: 01jv_6 PRED relation: colors PRED expected values: 019sc => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.90 #861, 0.38 #954, 0.32 #936), 019sc (0.89 #846, 0.64 #187, 0.60 #169), 038hg (0.65 #557, 0.23 #109, 0.22 #473), 06fvc (0.36 #898, 0.34 #842, 0.32 #935), 03vtbc (0.31 #261, 0.29 #116, 0.27 #188), 01l849 (0.30 #529, 0.29 #474, 0.27 #585), 067z2v (0.29 #117, 0.25 #44, 0.18 #189), 02rnmb (0.23 #109, 0.22 #473, 0.21 #492), 09ggk (0.23 #109, 0.22 #473, 0.21 #492), 04d18d (0.23 #109, 0.22 #473, 0.21 #492) >> Best rule #861 for best value: >> intensional similarity = 12 >> extensional distance = 116 >> proper extension: 03k2hn; >> query: (?x934, 01g5v) <- colors(?x934, ?x3315), colors(?x13736, ?x3315), colors(?x13181, ?x3315), colors(?x4363, ?x3315), colors(?x3314, ?x3315), colors(?x2948, ?x3315), ?x13181 = 016w7b, ?x2948 = 0j_sncb, school_type(?x3314, ?x1507), category(?x4363, ?x134), institution(?x865, ?x4363), school(?x2820, ?x13736) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #846 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 111 *> proper extension: 026w398; *> query: (?x934, 019sc) <- colors(?x934, ?x3315), colors(?x8995, ?x3315), colors(?x7499, ?x3315), colors(?x13181, ?x3315), colors(?x4904, ?x3315), ?x7499 = 0132_h, ?x8995 = 01d6g, currency(?x13181, ?x170), company(?x346, ?x4904), school(?x465, ?x4904), category(?x13181, ?x134) *> conf = 0.89 ranks of expected_values: 2 EVAL 01jv_6 colors 019sc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.898 http://example.org/sports/sports_team/colors #21117-084z0w PRED entity: 084z0w PRED relation: religion PRED expected values: 03j6c => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 22): 03j6c (0.31 #876, 0.28 #1011, 0.11 #246), 0flw86 (0.25 #2, 0.10 #992, 0.09 #857), 0c8wxp (0.20 #816, 0.18 #771, 0.18 #1131), 03_gx (0.18 #59, 0.16 #239, 0.11 #329), 0kpl (0.11 #595, 0.10 #100, 0.09 #55), 092bf5 (0.06 #151, 0.05 #826, 0.03 #466), 01lp8 (0.06 #406, 0.06 #721, 0.05 #271), 06nzl (0.04 #240, 0.03 #150, 0.02 #420), 019cr (0.03 #101, 0.03 #146, 0.02 #596), 04pk9 (0.03 #110, 0.03 #200, 0.02 #290) >> Best rule #876 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 0cfywh; >> query: (?x4645, 03j6c) <- type_of_union(?x4645, ?x566), nationality(?x4645, ?x2146), ?x2146 = 03rk0 >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 084z0w religion 03j6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.311 http://example.org/people/person/religion #21116-04y8r PRED entity: 04y8r PRED relation: profession PRED expected values: 0dxtg => 113 concepts (111 used for prediction) PRED predicted values (max 10 best out of 61): 02hrh1q (0.92 #9718, 0.91 #11629, 0.90 #8835), 0dxtg (0.87 #747, 0.85 #2364, 0.84 #3394), 02krf9 (0.33 #319, 0.32 #1348, 0.29 #2524), 0cbd2 (0.32 #1035, 0.31 #1917, 0.29 #2358), 018gz8 (0.30 #1191, 0.21 #1485, 0.20 #1044), 09jwl (0.23 #5016, 0.17 #4722, 0.17 #7369), 0nbcg (0.19 #5029, 0.13 #4735, 0.11 #11352), 0dz3r (0.18 #5001, 0.12 #4707, 0.11 #7354), 0np9r (0.15 #1489, 0.15 #4724, 0.15 #12076), 0kyk (0.15 #1057, 0.14 #1939, 0.13 #4880) >> Best rule #9718 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 033071; >> query: (?x2332, 02hrh1q) <- film(?x2332, ?x4621), profession(?x2332, ?x319), nominated_for(?x2332, ?x1910) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #747 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 106 *> proper extension: 0l6qt; 0h5f5n; 01q_ph; 0159h6; 04r7jc; 02lk1s; 081lh; 05_k56; 0343h; 018grr; ... *> query: (?x2332, 0dxtg) <- award_nominee(?x2332, ?x3528), written_by(?x6855, ?x2332), executive_produced_by(?x6855, ?x4854) *> conf = 0.87 ranks of expected_values: 2 EVAL 04y8r profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 111.000 0.916 http://example.org/people/person/profession #21115-0l2tk PRED entity: 0l2tk PRED relation: category PRED expected values: 08mbj5d => 198 concepts (198 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #59, 0.91 #18, 0.91 #60) >> Best rule #59 for best value: >> intensional similarity = 5 >> extensional distance = 140 >> proper extension: 04wlz2; 01hhvg; 01bzw5; 07w3r; 02bjhv; 01jsn5; 022lly; 01wdj_; 01swxv; 01r3y2; ... >> query: (?x2895, 08mbj5d) <- institution(?x4981, ?x2895), school(?x1823, ?x2895), currency(?x2895, ?x170), major_field_of_study(?x2895, ?x254), major_field_of_study(?x4981, ?x742) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l2tk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 198.000 0.915 http://example.org/common/topic/webpage./common/webpage/category #21114-0gvbw PRED entity: 0gvbw PRED relation: service_location PRED expected values: 07ssc => 202 concepts (202 used for prediction) PRED predicted values (max 10 best out of 243): 07ssc (0.57 #201, 0.36 #3323, 0.36 #484), 0f8l9c (0.57 #207, 0.25 #301, 0.18 #490), 02j71 (0.45 #3325, 0.28 #770, 0.28 #1622), 06mkj (0.29 #222, 0.08 #5244, 0.08 #5530), 03rjj (0.29 #193, 0.08 #1424, 0.08 #3315), 059j2 (0.17 #306, 0.14 #495, 0.14 #212), 03h64 (0.17 #324, 0.14 #230, 0.09 #987), 06bnz (0.17 #310, 0.14 #216, 0.07 #1729), 05v8c (0.17 #296, 0.08 #1149, 0.08 #1244), 06qd3 (0.14 #215, 0.08 #309, 0.06 #8633) >> Best rule #201 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 07zl6m; >> query: (?x2975, 07ssc) <- industry(?x2975, ?x13047), service_location(?x2975, ?x1264), service_location(?x2975, ?x94), ?x94 = 09c7w0, category(?x2975, ?x134), ?x1264 = 0345h >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gvbw service_location 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.571 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #21113-0q9vf PRED entity: 0q9vf PRED relation: profession PRED expected values: 0dxtg => 114 concepts (65 used for prediction) PRED predicted values (max 10 best out of 72): 0dxtg (0.65 #8838, 0.63 #14, 0.56 #3397), 01d_h8 (0.52 #594, 0.51 #2212, 0.50 #447), 02jknp (0.32 #8832, 0.31 #2214, 0.28 #596), 0np9r (0.31 #19, 0.25 #8843, 0.18 #2666), 0cbd2 (0.30 #7942, 0.29 #7059, 0.28 #9562), 02krf9 (0.28 #2231, 0.26 #2378, 0.26 #3114), 09jwl (0.26 #1635, 0.25 #899, 0.25 #2958), 0dz3r (0.23 #1914, 0.23 #1767, 0.22 #737), 0nbcg (0.20 #1942, 0.20 #765, 0.20 #2971), 016z4k (0.17 #1622, 0.16 #886, 0.15 #1769) >> Best rule #8838 for best value: >> intensional similarity = 3 >> extensional distance = 1475 >> proper extension: 06v8s0; 01yh3y; 03fghg; 02_4fn; 02nfjp; 098n_m; 04cr6qv; 066l3y; 09fp45; 01kymm; ... >> query: (?x6600, 0dxtg) <- profession(?x6600, ?x1041), profession(?x5832, ?x1041), ?x5832 = 06jrhz >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q9vf profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 65.000 0.647 http://example.org/people/person/profession #21112-01jq34 PRED entity: 01jq34 PRED relation: institution! PRED expected values: 019v9k => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 17): 03bwzr4 (0.69 #207, 0.68 #243, 0.59 #280), 019v9k (0.65 #587, 0.64 #203, 0.64 #349), 07s6fsf (0.53 #199, 0.49 #235, 0.43 #272), 013zdg (0.40 #202, 0.38 #238, 0.26 #348), 03mkk4 (0.29 #205, 0.26 #241, 0.24 #278), 01rr_d (0.26 #356, 0.23 #430, 0.20 #503), 022h5x (0.20 #213, 0.19 #249, 0.18 #597), 028dcg (0.20 #14, 0.18 #1745, 0.13 #212), 0bjrnt (0.19 #237, 0.19 #274, 0.19 #347), 02mjs7 (0.18 #1745, 0.14 #346, 0.13 #236) >> Best rule #207 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 02z_b; >> query: (?x2171, 03bwzr4) <- citytown(?x2171, ?x8980), organization(?x2171, ?x5487), state_province_region(?x2171, ?x1767) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #587 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 161 *> proper extension: 01pl14; 065y4w7; 07w0v; 0j_sncb; 027xx3; 01r3y2; 03ksy; 0hd7j; 07tds; 019dwp; ... *> query: (?x2171, 019v9k) <- colors(?x2171, ?x332), major_field_of_study(?x2171, ?x742), school(?x580, ?x2171) *> conf = 0.65 ranks of expected_values: 2 EVAL 01jq34 institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.689 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #21111-0jt3tjf PRED entity: 0jt3tjf PRED relation: participating_countries! PRED expected values: 09n48 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 40): 018ctl (0.47 #368, 0.47 #531, 0.47 #409), 09n48 (0.47 #123, 0.45 #404, 0.45 #526), 0lgxj (0.42 #551, 0.41 #510, 0.41 #429), 09x3r (0.34 #535, 0.33 #494, 0.33 #372), 06sks6 (0.26 #564, 0.26 #523, 0.25 #645), 0sx8l (0.23 #496, 0.23 #415, 0.22 #537), 0blfl (0.21 #149, 0.20 #552, 0.20 #511), 0jdk_ (0.18 #401, 0.18 #846, 0.13 #1732), 016r9z (0.18 #422, 0.17 #141, 0.16 #381), 0l6ny (0.13 #1732, 0.09 #1733, 0.02 #329) >> Best rule #368 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 02khs; 0d05q4; 04wlh; 034m8; >> query: (?x9455, 018ctl) <- adjoins(?x9455, ?x404), olympics(?x9455, ?x1931), country(?x668, ?x9455) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 07ytt; *> query: (?x9455, 09n48) <- contains(?x6304, ?x9455), administrative_area_type(?x9455, ?x2792), ?x6304 = 02qkt *> conf = 0.47 ranks of expected_values: 2 EVAL 0jt3tjf participating_countries! 09n48 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 73.000 0.473 http://example.org/olympics/olympic_games/participating_countries #21110-02qnyr7 PRED entity: 02qnyr7 PRED relation: languages PRED expected values: 09s02 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 15): 03k50 (0.34 #82, 0.28 #121, 0.26 #277), 09s02 (0.29 #36, 0.25 #75, 0.09 #114), 02h40lc (0.27 #353, 0.26 #80, 0.25 #41), 07c9s (0.21 #91, 0.17 #130, 0.14 #13), 0999q (0.14 #23, 0.12 #62, 0.07 #101), 02hxcvy (0.07 #104, 0.05 #143, 0.04 #299), 064_8sq (0.04 #171, 0.03 #93, 0.03 #405), 09bnf (0.04 #351, 0.04 #156, 0.03 #234), 055qm (0.04 #297, 0.03 #336, 0.03 #102), 01c7y (0.04 #148, 0.02 #343, 0.02 #226) >> Best rule #82 for best value: >> intensional similarity = 5 >> extensional distance = 66 >> proper extension: 06wvfq; 05vzql; 03fwln; 01vzz1c; 040nwr; >> query: (?x14317, 03k50) <- location(?x14317, ?x5384), nationality(?x14317, ?x2146), profession(?x14317, ?x1032), ?x2146 = 03rk0, ?x1032 = 02hrh1q >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #36 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 01gj8_; 03x31g; 071wvh; 047s_cr; 02rzmzk; *> query: (?x14317, 09s02) <- location(?x14317, ?x5384), nationality(?x14317, ?x2146), gender(?x14317, ?x231), ?x2146 = 03rk0, ?x5384 = 09c6w *> conf = 0.29 ranks of expected_values: 2 EVAL 02qnyr7 languages 09s02 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.338 http://example.org/people/person/languages #21109-0kvnn PRED entity: 0kvnn PRED relation: type_of_union PRED expected values: 01g63y => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.81 #17, 0.78 #41, 0.78 #279), 01g63y (0.26 #278, 0.15 #102, 0.14 #70), 0jgjn (0.26 #278, 0.01 #136), 01bl8s (0.04 #23, 0.02 #51, 0.02 #95) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0c_drn; >> query: (?x4387, 04ztj) <- music(?x5927, ?x4387), people(?x6821, ?x4387), place_of_birth(?x4387, ?x8451), genre(?x5927, ?x53) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #278 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 599 *> proper extension: 0cj2w; *> query: (?x4387, ?x566) <- profession(?x4387, ?x220), instrumentalists(?x2798, ?x4387), instrumentalists(?x2798, ?x1338), type_of_union(?x1338, ?x566) *> conf = 0.26 ranks of expected_values: 2 EVAL 0kvnn type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 138.000 0.815 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21108-0k3jq PRED entity: 0k3jq PRED relation: adjoins PRED expected values: 0k3j0 => 113 concepts (42 used for prediction) PRED predicted values (max 10 best out of 433): 0k3j0 (0.84 #3860, 0.81 #27826, 0.81 #6949), 0k3jc (0.84 #3860, 0.81 #27826, 0.81 #6949), 0m2gz (0.40 #1715, 0.24 #31697, 0.24 #27827), 0m2fr (0.40 #1932, 0.05 #3476, 0.03 #14298), 0n5yh (0.33 #237, 0.24 #31697, 0.24 #27827), 0k3kg (0.27 #3322, 0.24 #31697, 0.24 #27827), 059rby (0.25 #789, 0.20 #2331, 0.06 #10834), 0d060g (0.25 #782, 0.20 #2324, 0.05 #7735), 05rgl (0.25 #874, 0.20 #2416, 0.04 #12466), 059f4 (0.25 #806, 0.20 #2348, 0.03 #22446) >> Best rule #3860 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0k3kv; 0nm6k; 0k3ll; 0k3gw; 0cv1w; >> query: (?x11677, ?x9504) <- contains(?x11677, ?x13065), adjoins(?x9504, ?x11677), adjoins(?x6296, ?x11677), second_level_divisions(?x94, ?x6296), currency(?x13065, ?x170) >> conf = 0.84 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0k3jq adjoins 0k3j0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 42.000 0.838 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #21107-01qmy04 PRED entity: 01qmy04 PRED relation: artists! PRED expected values: 064t9 06by7 => 111 concepts (48 used for prediction) PRED predicted values (max 10 best out of 215): 06by7 (0.79 #2462, 0.74 #3073, 0.60 #1852), 03lty (0.60 #639, 0.12 #13165, 0.11 #14388), 064t9 (0.50 #2453, 0.50 #2148, 0.49 #4590), 0dl5d (0.50 #1545, 0.44 #2155, 0.40 #1850), 05bt6j (0.50 #1569, 0.40 #1874, 0.38 #2179), 0fd3y (0.40 #925, 0.25 #1535, 0.20 #1840), 0m0jc (0.40 #923, 0.17 #1533, 0.10 #2448), 0jmwg (0.40 #721, 0.03 #13247, 0.03 #14470), 0jrv_ (0.40 #785, 0.02 #13311, 0.02 #14534), 025tm81 (0.40 #1002) >> Best rule #2462 for best value: >> intensional similarity = 4 >> extensional distance = 182 >> proper extension: 07s3vqk; 0197tq; 0lbj1; 01vrx3g; 0m2l9; 032nwy; 02mslq; 01vvycq; 01w61th; 03f5spx; ... >> query: (?x12121, 06by7) <- award_winner(?x12139, ?x12121), artists(?x9063, ?x12121), artists(?x9063, ?x4461), ?x4461 = 0fcsd >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01qmy04 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 48.000 0.793 http://example.org/music/genre/artists EVAL 01qmy04 artists! 064t9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 48.000 0.793 http://example.org/music/genre/artists #21106-01trf3 PRED entity: 01trf3 PRED relation: student! PRED expected values: 01y8zd => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 181): 065y4w7 (0.11 #14, 0.05 #7394, 0.05 #3177), 01ljpm (0.11 #222, 0.02 #2858, 0.01 #8656), 026gvfj (0.11 #111, 0.01 #10126, 0.01 #3801), 0778_3 (0.11 #1024), 02m0b0 (0.11 #926), 08815 (0.10 #7382, 0.08 #4219, 0.07 #6328), 03ksy (0.09 #3269, 0.08 #1160, 0.07 #1687), 0bwfn (0.08 #1329, 0.08 #2383, 0.06 #6073), 0kqj1 (0.08 #1189, 0.03 #1716, 0.03 #2243), 078bz (0.08 #1131, 0.03 #6403, 0.02 #4294) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01h4rj; >> query: (?x4233, 065y4w7) <- film(?x4233, ?x3859), film(?x4233, ?x124), ?x3859 = 0c57yj, genre(?x124, ?x258) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #4834 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 0drdv; *> query: (?x4233, 01y8zd) <- profession(?x4233, ?x1146), ?x1146 = 018gz8, religion(?x4233, ?x1985) *> conf = 0.01 ranks of expected_values: 122 EVAL 01trf3 student! 01y8zd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 119.000 119.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #21105-02x258x PRED entity: 02x258x PRED relation: nominated_for PRED expected values: 09cr8 019vhk 06gb1w 017jd9 07z6xs 049xgc => 42 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1440): 0b6tzs (0.80 #4701, 0.77 #3174, 0.67 #1648), 0260bz (0.80 #6105, 0.80 #4578, 0.80 #7631), 09q5w2 (0.80 #6105, 0.80 #4578, 0.80 #7631), 0pv3x (0.80 #6105, 0.80 #4578, 0.80 #7631), 09p0ct (0.80 #6105, 0.80 #4578, 0.80 #7631), 040b5k (0.80 #6105, 0.80 #4578, 0.80 #7631), 03hmt9b (0.77 #3614, 0.73 #5141, 0.50 #6667), 04q827 (0.73 #6007, 0.69 #4480, 0.50 #7533), 04vr_f (0.69 #3201, 0.67 #4728, 0.57 #6254), 04b2qn (0.67 #5726, 0.67 #2673, 0.64 #7252) >> Best rule #4701 for best value: >> intensional similarity = 7 >> extensional distance = 13 >> proper extension: 02rdxsh; 099c8n; >> query: (?x2393, 0b6tzs) <- nominated_for(?x2393, ?x6018), nominated_for(?x2393, ?x2116), nominated_for(?x2393, ?x972), ?x2116 = 02c638, award(?x1077, ?x2393), ?x972 = 017gl1, genre(?x6018, ?x53) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3723 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 03hkv_r; 0gr4k; 09sb52; 019f4v; 02n9nmz; 0gq9h; 0gs9p; 02rdyk7; 04kxsb; 0gqy2; ... *> query: (?x2393, 017jd9) <- nominated_for(?x2393, ?x2116), nominated_for(?x2393, ?x972), ?x2116 = 02c638, award(?x1077, ?x2393), ?x972 = 017gl1, award(?x523, ?x2393) *> conf = 0.62 ranks of expected_values: 25, 78, 89, 128, 860, 982 EVAL 02x258x nominated_for 049xgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 42.000 21.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x258x nominated_for 07z6xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 21.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x258x nominated_for 017jd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 42.000 21.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x258x nominated_for 06gb1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 21.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x258x nominated_for 019vhk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 42.000 21.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x258x nominated_for 09cr8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 42.000 21.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21104-02mg5r PRED entity: 02mg5r PRED relation: educational_institution! PRED expected values: 02mg5r => 156 concepts (89 used for prediction) PRED predicted values (max 10 best out of 190): 015wy_ (0.17 #994, 0.17 #455, 0.14 #2072), 01314k (0.17 #204, 0.14 #1821, 0.14 #1282), 0gl6x (0.17 #371, 0.14 #1988, 0.10 #27525), 02mg7n (0.17 #958, 0.10 #27525, 0.09 #35093), 014b4h (0.14 #1629, 0.14 #1090, 0.12 #2168), 026m3y (0.14 #1468, 0.10 #27525, 0.09 #35093), 02kzfw (0.12 #2352, 0.10 #27525, 0.09 #35093), 014xf6 (0.11 #2981, 0.10 #27525, 0.09 #35093), 015ln1 (0.11 #2879, 0.10 #27525, 0.09 #35093), 01g4yw (0.11 #3201, 0.08 #4818, 0.06 #27524) >> Best rule #994 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 02mg7n; >> query: (?x12605, 015wy_) <- citytown(?x12605, ?x362), ?x362 = 04jpl, currency(?x12605, ?x1099), category(?x12605, ?x134), ?x134 = 08mbj5d >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #27525 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 252 *> proper extension: 05hf_5; *> query: (?x12605, ?x639) <- citytown(?x12605, ?x362), contains(?x362, ?x639), school_type(?x12605, ?x3092), major_field_of_study(?x12605, ?x2601) *> conf = 0.10 ranks of expected_values: 16 EVAL 02mg5r educational_institution! 02mg5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 156.000 89.000 0.167 http://example.org/education/educational_institution_campus/educational_institution #21103-0dzst PRED entity: 0dzst PRED relation: fraternities_and_sororities PRED expected values: 0325pb => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 3): 035tlh (0.48 #2, 0.43 #14, 0.43 #17), 0325pb (0.45 #1, 0.38 #26, 0.34 #4), 04m8fy (0.05 #12, 0.04 #15, 0.04 #25) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 06mkj; 0d05w3; >> query: (?x9200, 035tlh) <- organization(?x9200, ?x5487), school(?x8542, ?x9200), draft(?x799, ?x8542) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 06mkj; 0d05w3; *> query: (?x9200, 0325pb) <- organization(?x9200, ?x5487), school(?x8542, ?x9200), draft(?x799, ?x8542) *> conf = 0.45 ranks of expected_values: 2 EVAL 0dzst fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.484 http://example.org/education/university/fraternities_and_sororities #21102-05nlx4 PRED entity: 05nlx4 PRED relation: language PRED expected values: 02h40lc => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 36): 02h40lc (0.93 #477, 0.92 #537, 0.91 #1497), 064_8sq (0.21 #22, 0.20 #81, 0.15 #141), 06nm1 (0.13 #189, 0.12 #546, 0.12 #486), 04306rv (0.12 #124, 0.12 #480, 0.11 #836), 02bjrlw (0.09 #832, 0.08 #714, 0.08 #1435), 06b_j (0.07 #975, 0.07 #736, 0.06 #1698), 03_9r (0.05 #664, 0.05 #10, 0.05 #1083), 0653m (0.05 #487, 0.05 #547, 0.04 #964), 0jzc (0.05 #436, 0.05 #972, 0.04 #733), 04h9h (0.03 #874, 0.03 #1055, 0.03 #281) >> Best rule #477 for best value: >> intensional similarity = 3 >> extensional distance = 177 >> proper extension: 0gj8t_b; 0j_tw; 09lcsj; 05c26ss; 0456zg; 0353tm; 03whyr; 0dnkmq; 091xrc; >> query: (?x7199, 02h40lc) <- featured_film_locations(?x7199, ?x6226), film(?x629, ?x7199), category(?x7199, ?x134) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05nlx4 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.933 http://example.org/film/film/language #21101-080nwsb PRED entity: 080nwsb PRED relation: film_release_region PRED expected values: 0154j 03rt9 03rj0 => 77 concepts (75 used for prediction) PRED predicted values (max 10 best out of 221): 09c7w0 (0.95 #8293, 0.92 #8752, 0.91 #6449), 035qy (0.91 #2023, 0.89 #1717, 0.87 #1104), 06bnz (0.88 #1577, 0.88 #1730, 0.84 #2189), 03rjj (0.88 #2303, 0.87 #2149, 0.86 #2917), 03spz (0.83 #2085, 0.82 #1166, 0.82 #3006), 01znc_ (0.82 #1572, 0.78 #2952, 0.78 #1725), 0154j (0.82 #2148, 0.80 #2302, 0.80 #2916), 05v8c (0.79 #1087, 0.78 #2313, 0.78 #2159), 0ctw_b (0.77 #1096, 0.71 #2322, 0.70 #2168), 03rt9 (0.77 #2311, 0.75 #2157, 0.74 #1085) >> Best rule #8293 for best value: >> intensional similarity = 7 >> extensional distance = 1285 >> proper extension: 01jc6q; 0jzw; 053tj7; 03fts; 0yyts; 04tz52; 0c9k8; 0gffmn8; 0jvt9; 051zy_b; ... >> query: (?x3843, 09c7w0) <- film_release_region(?x3843, ?x252), film_release_region(?x5929, ?x252), film_release_region(?x1370, ?x252), country(?x10405, ?x252), ?x5929 = 03nqnnk, ?x10405 = 0d6_s, ?x1370 = 0gmcwlb >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #2148 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 74 *> proper extension: 0ds35l9; 0g56t9t; 0ds3t5x; 0gtv7pk; 0h1cdwq; 0dscrwf; 05p1tzf; 02x3lt7; 017gl1; 08hmch; ... *> query: (?x3843, 0154j) <- film_release_region(?x3843, ?x2513), film_release_region(?x3843, ?x344), country(?x3843, ?x94), ?x2513 = 05b4w, film_release_distribution_medium(?x3843, ?x81), ?x344 = 04gzd, film_crew_role(?x3843, ?x137) *> conf = 0.82 ranks of expected_values: 7, 10, 11 EVAL 080nwsb film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 77.000 75.000 0.948 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 080nwsb film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 77.000 75.000 0.948 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 080nwsb film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 75.000 0.948 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21100-03548 PRED entity: 03548 PRED relation: member_states! PRED expected values: 085h1 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 13): 085h1 (0.70 #55, 0.69 #31, 0.68 #59), 018cqq (0.20 #54, 0.18 #30, 0.16 #80), 02jxk (0.17 #53, 0.16 #33, 0.15 #29), 059dn (0.17 #36, 0.16 #56, 0.15 #20), 0gkjy (0.07 #246, 0.06 #62, 0.06 #61), 07t65 (0.07 #246, 0.06 #62, 0.06 #61), 02vk52z (0.07 #246, 0.06 #62, 0.06 #61), 041288 (0.07 #246, 0.06 #62, 0.06 #61), 0b6css (0.07 #246, 0.06 #62, 0.06 #61), 0j7v_ (0.07 #246) >> Best rule #55 for best value: >> intensional similarity = 2 >> extensional distance = 121 >> proper extension: 01d8l; >> query: (?x6572, 085h1) <- participating_countries(?x784, ?x6572), medal(?x6572, ?x1242) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03548 member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.699 http://example.org/user/ktrueman/default_domain/international_organization/member_states #21099-03x23q PRED entity: 03x23q PRED relation: campuses! PRED expected values: 03x23q => 97 concepts (66 used for prediction) PRED predicted values (max 10 best out of 71): 019tfm (0.07 #546, 0.01 #34974, 0.01 #35522), 02vkzcx (0.07 #543, 0.01 #34974, 0.01 #35522), 03x33n (0.07 #117, 0.01 #34974, 0.01 #35522), 01pl14 (0.07 #8, 0.01 #34974, 0.01 #35522), 035ktt (0.07 #175), 03hvk2 (0.03 #1064, 0.02 #1610, 0.02 #2156), 02pdhz (0.03 #1038, 0.02 #1584, 0.02 #2130), 02gnmp (0.03 #965, 0.02 #1511, 0.02 #2057), 05q2c (0.03 #850, 0.02 #1396, 0.02 #1942), 02yxjs (0.03 #830, 0.02 #1376, 0.02 #1922) >> Best rule #546 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0fvvz; 0tln7; 013d7t; 0tk02; 0tn9j; 013gz; >> query: (?x12732, 019tfm) <- contains(?x3908, ?x12732), contains(?x94, ?x12732), ?x94 = 09c7w0, category(?x12732, ?x134), ?x3908 = 04ly1 >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #34974 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 742 *> proper extension: 01nn79; *> query: (?x12732, ?x3172) <- state_province_region(?x12732, ?x3908), state_province_region(?x3172, ?x3908), category(?x3172, ?x134) *> conf = 0.01 ranks of expected_values: 66 EVAL 03x23q campuses! 03x23q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 97.000 66.000 0.071 http://example.org/education/educational_institution/campuses #21098-0bx6zs PRED entity: 0bx6zs PRED relation: award_winner PRED expected values: 06j0md 05gp3x => 29 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1994): 01j7rd (0.64 #12604, 0.60 #15680, 0.60 #4910), 02xs0q (0.55 #12853, 0.47 #15929, 0.40 #5159), 04ns3gy (0.45 #13632, 0.33 #16708, 0.20 #5938), 01dy7j (0.44 #11211, 0.38 #9669, 0.29 #14287), 02661h (0.40 #5773, 0.38 #10387, 0.33 #11929), 06msq2 (0.40 #5295, 0.36 #12989, 0.27 #16065), 0c7t58 (0.40 #5183, 0.27 #12877, 0.25 #9797), 04wvhz (0.40 #4748, 0.25 #9362, 0.25 #3213), 0h0wc (0.38 #17286, 0.21 #18824, 0.20 #20365), 05bnq3j (0.36 #13033, 0.33 #16109, 0.25 #726) >> Best rule #12604 for best value: >> intensional similarity = 12 >> extensional distance = 9 >> proper extension: 07y9ts; >> query: (?x9450, 01j7rd) <- honored_for(?x9450, ?x1849), award_winner(?x9450, ?x1871), ceremony(?x7510, ?x9450), nominated_for(?x5557, ?x1849), languages(?x1849, ?x254), award_winner(?x1871, ?x820), award_nominee(?x1871, ?x92), award_winner(?x624, ?x1871), genre(?x1849, ?x53), award_winner(?x5557, ?x3763), nominated_for(?x783, ?x1849), ?x7510 = 027gs1_ >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #27687 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 100 *> proper extension: 0fzrtf; 05hmp6; 0n8_m93; *> query: (?x9450, ?x369) <- honored_for(?x9450, ?x1849), honored_for(?x9450, ?x337), award_winner(?x9450, ?x9815), award_winner(?x9450, ?x1871), ceremony(?x2041, ?x9450), nominated_for(?x5557, ?x1849), nominated_for(?x369, ?x1849), nominated_for(?x368, ?x1849), nominated_for(?x783, ?x337), nominated_for(?x2041, ?x4932), award_winner(?x624, ?x1871), film(?x368, ?x508), award_winner(?x4760, ?x5557), genre(?x4932, ?x225), award_winner(?x9815, ?x4411), actor(?x4932, ?x3366), award_nominee(?x336, ?x1871), award(?x9815, ?x678) *> conf = 0.18 ranks of expected_values: 217, 270 EVAL 0bx6zs award_winner 05gp3x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 29.000 19.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bx6zs award_winner 06j0md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 29.000 19.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21097-0fphf3v PRED entity: 0fphf3v PRED relation: film! PRED expected values: 07m9cm => 79 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1090): 01vsn38 (0.42 #5981, 0.21 #8050, 0.11 #16327), 015grj (0.40 #153, 0.08 #2070, 0.02 #14638), 02ndbd (0.40 #24831, 0.01 #14619, 0.01 #6342), 086k8 (0.40 #24831), 02t_st (0.20 #1279, 0.12 #41388, 0.04 #3349), 019vgs (0.20 #656, 0.08 #2726, 0.02 #23417), 04t2l2 (0.20 #28, 0.08 #2070, 0.03 #4167), 01_p6t (0.20 #1016, 0.08 #2070, 0.01 #23777), 09yhzs (0.20 #510, 0.04 #2580, 0.01 #10857), 0170s4 (0.20 #393, 0.03 #4532, 0.03 #8670) >> Best rule #5981 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 02ny6g; 0kbwb; 0symg; >> query: (?x7832, 01vsn38) <- film(?x300, ?x7832), nominated_for(?x350, ?x7832), artists(?x2249, ?x300), ?x2249 = 03lty >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #25631 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 199 *> proper extension: 0dsvzh; 03t97y; 020fcn; 0fdv3; 03m8y5; 032zq6; 01hqk; 01qxc7; 05tgks; 08sk8l; ... *> query: (?x7832, 07m9cm) <- film_crew_role(?x7832, ?x2178), film_crew_role(?x7832, ?x468), film(?x300, ?x7832), ?x468 = 02r96rf, ?x2178 = 01pvkk *> conf = 0.02 ranks of expected_values: 580 EVAL 0fphf3v film! 07m9cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 79.000 22.000 0.424 http://example.org/film/actor/film./film/performance/film #21096-0d6d2 PRED entity: 0d6d2 PRED relation: profession PRED expected values: 02hrh1q => 133 concepts (132 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.89 #6527, 0.88 #161, 0.88 #7563), 0dxtg (0.48 #1642, 0.45 #754, 0.38 #1938), 0cbd2 (0.42 #2820, 0.41 #3264, 0.41 #1932), 018gz8 (0.34 #758, 0.30 #1646, 0.19 #1942), 0kyk (0.28 #1955, 0.27 #3139, 0.26 #2843), 03gjzk (0.28 #459, 0.26 #2236, 0.26 #904), 09jwl (0.20 #6088, 0.19 #760, 0.19 #18), 0np9r (0.20 #20, 0.16 #10826, 0.14 #14674), 0d1pc (0.15 #50, 0.11 #2716, 0.11 #2124), 05z96 (0.14 #1672, 0.13 #2856, 0.13 #3152) >> Best rule #6527 for best value: >> intensional similarity = 3 >> extensional distance = 931 >> proper extension: 01sl1q; 0184jc; 016qtt; 012d40; 01k7d9; 0337vz; 06151l; 01xdf5; 01j5ts; 06dv3; ... >> query: (?x8151, 02hrh1q) <- film(?x8151, ?x650), award_winner(?x591, ?x8151), nominated_for(?x8151, ?x7846) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d6d2 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 132.000 0.889 http://example.org/people/person/profession #21095-01242_ PRED entity: 01242_ PRED relation: genre PRED expected values: 0219x_ => 71 concepts (45 used for prediction) PRED predicted values (max 10 best out of 118): 07ssc (0.70 #1277, 0.55 #4777, 0.54 #4894), 03bxz7 (0.40 #51, 0.37 #1211, 0.36 #747), 01jfsb (0.39 #1869, 0.33 #2104, 0.31 #3155), 05p553 (0.38 #931, 0.36 #2095, 0.34 #3847), 02kdv5l (0.36 #1858, 0.29 #2093, 0.26 #3144), 03k9fj (0.29 #2921, 0.23 #1868, 0.21 #2103), 060__y (0.25 #1408, 0.24 #1640, 0.23 #1292), 0lsxr (0.20 #1865, 0.18 #1517, 0.17 #2100), 082gq (0.20 #27, 0.19 #1304, 0.17 #1652), 04t36 (0.20 #5, 0.16 #585, 0.15 #817) >> Best rule #1277 for best value: >> intensional similarity = 6 >> extensional distance = 99 >> proper extension: 0413cff; >> query: (?x4197, ?x512) <- genre(?x4197, ?x3312), genre(?x4197, ?x1316), ?x1316 = 017fp, language(?x4197, ?x254), titles(?x512, ?x4197), genre(?x1763, ?x3312) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #952 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 84 *> proper extension: 0gxsh4; *> query: (?x4197, 0219x_) <- nominated_for(?x11835, ?x4197), award(?x11835, ?x2375), influenced_by(?x2125, ?x11835), religion(?x11835, ?x2694) *> conf = 0.15 ranks of expected_values: 14 EVAL 01242_ genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 71.000 45.000 0.698 http://example.org/film/film/genre #21094-02jfc PRED entity: 02jfc PRED relation: major_field_of_study! PRED expected values: 0bkj86 => 80 concepts (74 used for prediction) PRED predicted values (max 10 best out of 15): 0bkj86 (0.74 #304, 0.72 #318, 0.71 #218), 071tyz (0.52 #504, 0.45 #940, 0.35 #220), 022h5x (0.52 #504, 0.45 #940, 0.32 #773), 07s6fsf (0.52 #504, 0.45 #940, 0.32 #773), 01gkg3 (0.52 #504, 0.45 #940, 0.31 #206), 02m4yg (0.38 #179, 0.38 #107, 0.32 #773), 028dcg (0.37 #254, 0.32 #773, 0.32 #214), 01rr_d (0.32 #773, 0.32 #214, 0.30 #862), 013zdg (0.32 #773, 0.32 #214, 0.30 #862), 03mkk4 (0.32 #773, 0.32 #214, 0.30 #862) >> Best rule #304 for best value: >> intensional similarity = 13 >> extensional distance = 21 >> proper extension: 0_jm; 01540; 041y2; >> query: (?x10391, 0bkj86) <- major_field_of_study(?x10391, ?x1695), major_field_of_study(?x4981, ?x10391), major_field_of_study(?x3437, ?x10391), major_field_of_study(?x1368, ?x10391), major_field_of_study(?x1200, ?x10391), ?x1368 = 014mlp, ?x1200 = 016t_3, ?x4981 = 03bwzr4, institution(?x3437, ?x13316), institution(?x3437, ?x2171), ?x13316 = 01stzp, ?x2171 = 01jq34, student(?x1695, ?x3806) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jfc major_field_of_study! 0bkj86 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 74.000 0.739 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #21093-014q2g PRED entity: 014q2g PRED relation: artists! PRED expected values: 02yv6b => 96 concepts (76 used for prediction) PRED predicted values (max 10 best out of 219): 064t9 (0.59 #1864, 0.57 #12674, 0.42 #321), 0glt670 (0.41 #1890, 0.21 #965, 0.16 #11770), 02yv6b (0.38 #1333, 0.21 #97, 0.19 #2565), 016clz (0.38 #4, 0.36 #3399, 0.29 #4634), 025sc50 (0.37 #1899, 0.20 #974, 0.18 #12709), 02lnbg (0.32 #1908, 0.17 #983, 0.13 #12718), 0xhtw (0.29 #1252, 0.28 #2484, 0.28 #4646), 0gywn (0.29 #1291, 0.24 #1907, 0.18 #5614), 0ggx5q (0.28 #1928, 0.17 #1003, 0.13 #12738), 05bt6j (0.27 #12703, 0.23 #1893, 0.23 #659) >> Best rule #1864 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 07c0j; 04qmr; >> query: (?x2782, 064t9) <- participant(?x1424, ?x2782), award_nominee(?x2782, ?x1291), artist(?x5891, ?x2782) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #1333 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 04r1t; 0134tg; 07mvp; 011z3g; 01kcms4; 033s6; *> query: (?x2782, 02yv6b) <- artist(?x5891, ?x2782), artists(?x7440, ?x2782), ?x7440 = 0155w *> conf = 0.38 ranks of expected_values: 3 EVAL 014q2g artists! 02yv6b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 76.000 0.592 http://example.org/music/genre/artists #21092-0342h PRED entity: 0342h PRED relation: role! PRED expected values: 0197tq 018x3 023slg => 68 concepts (59 used for prediction) PRED predicted values (max 10 best out of 997): 018x3 (0.60 #2896, 0.50 #2293, 0.33 #781), 0140t7 (0.60 #2989, 0.38 #7232, 0.33 #571), 01xzb6 (0.60 #2887, 0.38 #302, 0.33 #469), 032t2z (0.60 #2740, 0.33 #301, 0.31 #2117), 028qdb (0.60 #2855, 0.33 #134, 0.25 #2252), 01wg6y (0.60 #2980, 0.33 #259, 0.25 #2377), 07q1v4 (0.60 #2746, 0.25 #2143, 0.25 #1538), 0f0qfz (0.60 #2854, 0.25 #2251, 0.25 #1646), 0137g1 (0.50 #1899, 0.46 #7047, 0.40 #2804), 01wl38s (0.50 #1832, 0.40 #2737, 0.38 #302) >> Best rule #2896 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 01vdm0; >> query: (?x227, 018x3) <- instrumentalists(?x227, ?x6939), instrumentalists(?x227, ?x4840), instrumentalists(?x227, ?x3241), award(?x4840, ?x594), role(?x7859, ?x227), ?x7859 = 03j1p2n, nationality(?x6939, ?x94), award_nominee(?x4397, ?x3241) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 52, 111 EVAL 0342h role! 023slg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 68.000 59.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0342h role! 018x3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 59.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0342h role! 0197tq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 68.000 59.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role #21091-09txzv PRED entity: 09txzv PRED relation: film! PRED expected values: 04bdxl => 75 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1018): 03h610 (0.50 #78994, 0.50 #4158, 0.49 #87311), 06pk8 (0.18 #6237, 0.13 #47815, 0.12 #8317), 02gf_l (0.17 #1264, 0.02 #7501, 0.02 #13738), 0bxtg (0.13 #56131, 0.12 #56130, 0.11 #51972), 0415svh (0.13 #56131, 0.12 #56130, 0.11 #51972), 01pw9v (0.13 #56131, 0.12 #56130, 0.11 #51972), 016ypb (0.11 #2576, 0.03 #6734, 0.03 #23363), 0f0kz (0.10 #4672, 0.06 #10909, 0.06 #12988), 0glyyw (0.10 #35341), 014gf8 (0.09 #3083, 0.02 #7241, 0.02 #21791) >> Best rule #78994 for best value: >> intensional similarity = 4 >> extensional distance = 1089 >> proper extension: 06r1k; 06ys2; >> query: (?x1644, ?x4644) <- nominated_for(?x4644, ?x1644), location(?x4644, ?x739), award_winner(?x4684, ?x4644), award_nominee(?x3442, ?x4644) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6243 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 88 *> proper extension: 02d44q; *> query: (?x1644, 04bdxl) <- produced_by(?x1644, ?x496), category(?x1644, ?x134), film_crew_role(?x1644, ?x281), film(?x976, ?x1644) *> conf = 0.03 ranks of expected_values: 159 EVAL 09txzv film! 04bdxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 75.000 52.000 0.501 http://example.org/film/actor/film./film/performance/film #21090-026t6 PRED entity: 026t6 PRED relation: role PRED expected values: 0dwsp 01s0ps => 73 concepts (65 used for prediction) PRED predicted values (max 10 best out of 47): 04rzd (0.85 #1278, 0.82 #686, 0.82 #1810), 06ncr (0.84 #1347, 0.83 #687, 0.83 #2110), 07brj (0.82 #686, 0.82 #1810, 0.82 #282), 011k_j (0.82 #686, 0.82 #1810, 0.82 #282), 06w7v (0.82 #686, 0.82 #1810, 0.82 #282), 07_l6 (0.82 #686, 0.82 #1810, 0.82 #282), 07kc_ (0.82 #686, 0.82 #1810, 0.82 #282), 01qbl (0.82 #686, 0.82 #1810, 0.82 #282), 01rhl (0.82 #686, 0.82 #1810, 0.82 #282), 0g33q (0.82 #686, 0.82 #1810, 0.82 #282) >> Best rule #1278 for best value: >> intensional similarity = 7 >> extensional distance = 18 >> proper extension: 0myk8; >> query: (?x212, 04rzd) <- instrumentalists(?x212, ?x12422), instrumentalists(?x212, ?x3657), role(?x2957, ?x212), ?x2957 = 01v8y9, role(?x212, ?x228), artists(?x302, ?x3657), profession(?x12422, ?x955) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #582 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 0395lw; *> query: (?x212, 01s0ps) <- instrumentalists(?x212, ?x4960), role(?x7033, ?x212), role(?x1432, ?x212), role(?x565, ?x212), ?x7033 = 0gkd1, role(?x315, ?x1432), instrumentalists(?x1432, ?x8311), ?x565 = 01wl38s, place_of_birth(?x4960, ?x10584) *> conf = 0.75 ranks of expected_values: 17, 20 EVAL 026t6 role 01s0ps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 73.000 65.000 0.850 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 026t6 role 0dwsp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 73.000 65.000 0.850 http://example.org/music/performance_role/track_performances./music/track_contribution/role #21089-04jwp PRED entity: 04jwp PRED relation: student! PRED expected values: 019vsw => 124 concepts (107 used for prediction) PRED predicted values (max 10 best out of 217): 03ksy (0.30 #28981, 0.16 #13755, 0.15 #14805), 01w5m (0.25 #28980, 0.16 #4829, 0.13 #3779), 0138t4 (0.25 #402, 0.12 #927, 0.11 #1977), 01v2xl (0.25 #439, 0.11 #2014, 0.01 #8839), 09f2j (0.23 #29034, 0.07 #2783, 0.07 #49513), 07tgn (0.22 #1067, 0.17 #4217, 0.12 #4742), 013nky (0.21 #2481, 0.03 #14031, 0.03 #15081), 07tl0 (0.14 #2130, 0.03 #8430, 0.02 #5805), 0bwfn (0.14 #49629, 0.08 #49103, 0.08 #50154), 0h6rm (0.12 #4343, 0.12 #4868, 0.11 #5393) >> Best rule #28981 for best value: >> intensional similarity = 4 >> extensional distance = 273 >> proper extension: 043q6n_; 0308kx; 0443c; >> query: (?x5912, 03ksy) <- student(?x11614, ?x5912), category(?x11614, ?x134), major_field_of_study(?x11614, ?x11206), ?x11206 = 05b6c >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #8758 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 06p0s1; *> query: (?x5912, 019vsw) <- location(?x5912, ?x362), ?x362 = 04jpl, gender(?x5912, ?x231), ?x231 = 05zppz *> conf = 0.01 ranks of expected_values: 174 EVAL 04jwp student! 019vsw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 124.000 107.000 0.302 http://example.org/education/educational_institution/students_graduates./education/education/student #21088-08hmch PRED entity: 08hmch PRED relation: executive_produced_by PRED expected values: 079vf => 85 concepts (76 used for prediction) PRED predicted values (max 10 best out of 76): 05hj_k (0.19 #97, 0.13 #600, 0.10 #851), 04jspq (0.13 #653, 0.12 #150, 0.07 #904), 04pqqb (0.13 #619, 0.12 #116, 0.04 #2130), 06q8hf (0.12 #166, 0.09 #7734, 0.09 #5710), 079vf (0.11 #253, 0.06 #2, 0.05 #5546), 05y5fw (0.10 #503, 0.09 #1761, 0.08 #7568), 06pj8 (0.09 #1817, 0.07 #1564, 0.07 #4086), 01twdk (0.09 #615, 0.07 #866, 0.06 #112), 0343h (0.08 #1804, 0.07 #1551, 0.06 #293), 02qzjj (0.06 #235, 0.06 #486, 0.04 #738) >> Best rule #97 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 017gl1; 01c22t; 017gm7; 05qbckf; 01jrbb; 05zlld0; 07s846j; 017jd9; 047vnkj; 03yvf2; ... >> query: (?x1035, 05hj_k) <- film_release_region(?x1035, ?x1023), film_release_region(?x1035, ?x756), ?x756 = 06npd, executive_produced_by(?x1035, ?x2464), ?x1023 = 0ctw_b >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 08984j; *> query: (?x1035, 079vf) <- written_by(?x1035, ?x5033), film(?x574, ?x1035), crewmember(?x1035, ?x6166), prequel(?x1035, ?x4392) *> conf = 0.11 ranks of expected_values: 5 EVAL 08hmch executive_produced_by 079vf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 85.000 76.000 0.188 http://example.org/film/film/executive_produced_by #21087-0d608 PRED entity: 0d608 PRED relation: gender PRED expected values: 05zppz => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #83, 0.87 #97, 0.87 #87), 02zsn (0.46 #233, 0.45 #126, 0.44 #30) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 07y_r; 014v1q; >> query: (?x7522, 05zppz) <- award(?x7522, ?x3066), nationality(?x7522, ?x279), ?x3066 = 0gqy2 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d608 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.903 http://example.org/people/person/gender #21086-02r8hh_ PRED entity: 02r8hh_ PRED relation: executive_produced_by PRED expected values: 02q_cc => 74 concepts (34 used for prediction) PRED predicted values (max 10 best out of 69): 05hj_k (0.14 #351, 0.07 #1611, 0.05 #2872), 06q8hf (0.10 #420, 0.09 #1680, 0.06 #2941), 06pj8 (0.06 #560, 0.06 #1064, 0.05 #812), 04jspq (0.05 #908, 0.04 #1412, 0.04 #1160), 01twdk (0.05 #1122, 0.05 #366, 0.05 #618), 063b4k (0.05 #502, 0.03 #754, 0.03 #1006), 079vf (0.05 #255, 0.02 #1011, 0.02 #1263), 0343h (0.05 #295, 0.02 #3825, 0.02 #5086), 07f8wg (0.05 #271, 0.01 #2540, 0.01 #1027), 04fyhv (0.05 #435, 0.01 #1191, 0.01 #1443) >> Best rule #351 for best value: >> intensional similarity = 7 >> extensional distance = 19 >> proper extension: 03bx2lk; >> query: (?x1724, 05hj_k) <- film(?x5959, ?x1724), film_release_region(?x1724, ?x7413), film_release_region(?x1724, ?x1892), film_release_region(?x1724, ?x410), ?x1892 = 02vzc, ?x410 = 01ls2, ?x7413 = 04hqz >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #5579 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 382 *> proper extension: 0gzlb9; *> query: (?x1724, 02q_cc) <- film(?x5959, ?x1724), award(?x1724, ?x8313), currency(?x1724, ?x170), film_crew_role(?x1724, ?x281), film(?x1208, ?x1724) *> conf = 0.02 ranks of expected_values: 26 EVAL 02r8hh_ executive_produced_by 02q_cc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 74.000 34.000 0.143 http://example.org/film/film/executive_produced_by #21085-05zx7xk PRED entity: 05zx7xk PRED relation: award! PRED expected values: 09ps01 => 51 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1111): 0c0zq (0.33 #906, 0.16 #2954, 0.14 #5002), 05hjnw (0.33 #500, 0.16 #2548, 0.14 #6647), 0hfzr (0.33 #416, 0.15 #2464, 0.13 #7171), 0f4_l (0.33 #219, 0.14 #6366, 0.13 #4315), 09cr8 (0.33 #176, 0.13 #2224, 0.13 #3248), 07s846j (0.33 #401, 0.13 #2449, 0.11 #14750), 0m313 (0.33 #6, 0.13 #3078, 0.11 #2054), 09m6kg (0.33 #16, 0.11 #3088, 0.11 #2064), 011yqc (0.33 #144, 0.09 #6291, 0.09 #14493), 0y_9q (0.33 #541, 0.08 #2589, 0.07 #6688) >> Best rule #906 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 02qyp19; 03hkv_r; 0gr4k; 04dn09n; 0gr51; 02x17s4; 03hl6lc; >> query: (?x13311, 0c0zq) <- award(?x5565, ?x13311), award(?x1616, ?x13311), nominated_for(?x13311, ?x616), ?x1616 = 07s93v, participant(?x5565, ?x286), film(?x5565, ?x1707) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #12297 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 144 *> proper extension: 0gqng; 04ljl_l; 027dtxw; 05b4l5x; 040njc; 0f_nbyh; 027c924; 05f4m9q; 02wkmx; 0bfvw2; ... *> query: (?x13311, ?x616) <- award(?x1616, ?x13311), nominated_for(?x13311, ?x616), award(?x3048, ?x13311), written_by(?x1163, ?x1616) *> conf = 0.22 ranks of expected_values: 54 EVAL 05zx7xk award! 09ps01 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 51.000 17.000 0.333 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #21084-01v1ln PRED entity: 01v1ln PRED relation: film_crew_role PRED expected values: 0dxtw => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 25): 0dxtw (0.50 #9, 0.36 #491, 0.35 #560), 01vx2h (0.35 #44, 0.33 #183, 0.31 #492), 01pvkk (0.29 #562, 0.28 #493, 0.27 #286), 02rh1dz (0.16 #181, 0.15 #42, 0.14 #215), 015h31 (0.14 #7, 0.12 #180, 0.11 #214), 0215hd (0.14 #499, 0.13 #292, 0.11 #568), 089g0h (0.11 #500, 0.10 #293, 0.10 #569), 01xy5l_ (0.11 #495, 0.11 #47, 0.10 #288), 0d2b38 (0.11 #197, 0.10 #506, 0.10 #163), 02_n3z (0.09 #276, 0.09 #483, 0.08 #552) >> Best rule #9 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 05nlx4; >> query: (?x6994, 0dxtw) <- genre(?x6994, ?x225), film(?x11684, ?x6994), film(?x444, ?x6994), ?x11684 = 01tsbmv, award_nominee(?x444, ?x516) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v1ln film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.500 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21083-020fgy PRED entity: 020fgy PRED relation: award PRED expected values: 054ks3 => 146 concepts (119 used for prediction) PRED predicted values (max 10 best out of 296): 02qvyrt (0.77 #16448, 0.72 #40531, 0.71 #4012), 054ks3 (0.58 #2545, 0.54 #940, 0.37 #1743), 01bgqh (0.44 #8467, 0.39 #2450, 0.24 #16089), 01by1l (0.43 #8532, 0.39 #2515, 0.29 #16154), 025m8l (0.39 #2522, 0.21 #4930, 0.20 #3725), 02x201b (0.38 #1074, 0.33 #272, 0.27 #673), 05q8pss (0.35 #2617, 0.31 #1012, 0.17 #210), 02gdjb (0.33 #217, 0.31 #1019, 0.18 #618), 09sb52 (0.31 #24111, 0.30 #30533, 0.26 #17291), 025mbn (0.31 #1037, 0.09 #3043, 0.07 #4648) >> Best rule #16448 for best value: >> intensional similarity = 3 >> extensional distance = 393 >> proper extension: 0kzy0; 01bpc9; 01vvpjj; 016fmf; 0137g1; 02lbrd; 0d193h; 0khth; 0g_g2; 01qgry; ... >> query: (?x9170, ?x1443) <- award_winner(?x3618, ?x9170), award_winner(?x1443, ?x9170), artists(?x4910, ?x9170) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2545 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 024yxd; *> query: (?x9170, 054ks3) <- award_winner(?x2168, ?x9170), award(?x9170, ?x1232), ?x1232 = 0c4z8 *> conf = 0.58 ranks of expected_values: 2 EVAL 020fgy award 054ks3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 119.000 0.774 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21082-01whg97 PRED entity: 01whg97 PRED relation: location PRED expected values: 0vfs8 => 141 concepts (108 used for prediction) PRED predicted values (max 10 best out of 295): 0vfs8 (0.70 #85169, 0.53 #20884, 0.51 #20079), 02_286 (0.24 #3249, 0.19 #60294, 0.17 #9674), 030qb3t (0.22 #46679, 0.14 #62750, 0.13 #17752), 059rby (0.17 #1622, 0.07 #8849, 0.04 #17685), 01cx_ (0.17 #163, 0.03 #14620, 0.03 #22653), 0tz14 (0.17 #591), 0z1vw (0.17 #583), 0cr3d (0.09 #28262, 0.09 #29868, 0.08 #19420), 02dtg (0.08 #1630, 0.06 #3236, 0.04 #4842), 02jx1 (0.08 #1677, 0.03 #6495, 0.03 #3283) >> Best rule #85169 for best value: >> intensional similarity = 4 >> extensional distance = 1278 >> proper extension: 04sx9_; 04shbh; 019_1h; 030znt; 022769; 01hkhq; 0p51w; 01qx13; 01438g; 04n_g; ... >> query: (?x8149, ?x8115) <- gender(?x8149, ?x231), place_of_birth(?x8149, ?x8115), nationality(?x8149, ?x94), location(?x8149, ?x11359) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01whg97 location 0vfs8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 108.000 0.703 http://example.org/people/person/places_lived./people/place_lived/location #21081-049n3s PRED entity: 049n3s PRED relation: position PRED expected values: 0dgrmp => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 45): 0dgrmp (0.81 #469, 0.81 #485, 0.81 #480), 03f0fp (0.50 #503, 0.44 #410, 0.38 #431), 02md_2 (0.50 #503, 0.32 #510, 0.31 #517), 02qvgy (0.36 #411, 0.24 #318, 0.09 #27), 05b3ts (0.30 #425, 0.25 #448, 0.17 #181), 04nfpk (0.30 #425, 0.25 #448, 0.17 #181), 02g_6x (0.30 #425, 0.25 #448, 0.17 #181), 01r3hr (0.30 #425, 0.25 #448, 0.17 #181), 02qvkj (0.07 #62, 0.07 #278, 0.04 #409), 02sddg (0.07 #62, 0.07 #278, 0.04 #409) >> Best rule #469 for best value: >> intensional similarity = 20 >> extensional distance = 572 >> proper extension: 04jbyg; 047kn_; 05cws2; 049f88; 05zj6x; 03mp4f; 04vn_k; 06rk8r; 037ts6; 06pqy_; ... >> query: (?x12032, ?x60) <- position(?x12032, ?x530), position(?x12032, ?x203), position(?x12032, ?x60), ?x530 = 02_j1w, position(?x13445, ?x203), position(?x12780, ?x203), position(?x12526, ?x203), position(?x10996, ?x203), position(?x8515, ?x203), position(?x6040, ?x203), position(?x5209, ?x203), ?x6040 = 03fnnn, position(?x8826, ?x203), ?x8515 = 096cw_, ?x13445 = 042l8n, ?x10996 = 06zpgb2, ?x8826 = 03x6w8, ?x12780 = 019mdt, ?x5209 = 0gd70t, ?x12526 = 0bg4f9 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049n3s position 0dgrmp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.808 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #21080-03q95r PRED entity: 03q95r PRED relation: student! PRED expected values: 01dq5z => 109 concepts (76 used for prediction) PRED predicted values (max 10 best out of 158): 03ksy (0.15 #2210, 0.08 #6418, 0.08 #19571), 015zyd (0.14 #1579, 0.08 #527, 0.05 #1053), 065y4w7 (0.13 #10008, 0.07 #19479, 0.05 #16848), 0m4yg (0.12 #364, 0.02 #17198, 0.02 #18250), 016w7b (0.12 #506), 0dzbl (0.12 #500), 0bwfn (0.12 #19739, 0.09 #28158, 0.09 #17634), 01mpwj (0.12 #2211, 0.05 #2737, 0.03 #6419), 09f2j (0.11 #10152, 0.06 #19623, 0.05 #9626), 01w5m (0.08 #6417, 0.08 #6943, 0.08 #631) >> Best rule #2210 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 07_m9_; 06c97; 011xjd; 079dy; >> query: (?x4482, 03ksy) <- nationality(?x4482, ?x94), person(?x3847, ?x4482), people(?x4659, ?x4482) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03q95r student! 01dq5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 76.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #21079-074w86 PRED entity: 074w86 PRED relation: nominated_for! PRED expected values: 0f2sx4 => 85 concepts (25 used for prediction) PRED predicted values (max 10 best out of 193): 0f2sx4 (0.82 #6356, 0.82 #5847, 0.82 #1522), 02ntb8 (0.12 #900, 0.03 #1408, 0.03 #1662), 0ddt_ (0.09 #592, 0.03 #1862, 0.03 #2370), 0dtfn (0.09 #544, 0.03 #4103, 0.03 #4612), 0fdv3 (0.09 #556, 0.03 #1826, 0.02 #4115), 0f3m1 (0.09 #731, 0.02 #4290, 0.02 #2001), 074w86 (0.08 #874, 0.06 #367, 0.03 #1128), 051ys82 (0.08 #924, 0.03 #1178, 0.02 #2703), 034qzw (0.08 #820, 0.02 #1328, 0.02 #1582), 075cph (0.08 #1087, 0.07 #1341, 0.06 #1595) >> Best rule #6356 for best value: >> intensional similarity = 4 >> extensional distance = 245 >> proper extension: 02fn5r; >> query: (?x4054, ?x2907) <- nominated_for(?x4054, ?x7967), nominated_for(?x4054, ?x2907), nominated_for(?x2325, ?x7967), award(?x286, ?x2325) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 074w86 nominated_for! 0f2sx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 25.000 0.820 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #21078-03bnb PRED entity: 03bnb PRED relation: list PRED expected values: 01pd60 => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 4): 01pd60 (0.81 #869, 0.81 #863, 0.78 #836), 09g7thr (0.56 #156, 0.49 #790, 0.47 #748), 05glt (0.38 #859, 0.38 #865, 0.09 #776), 026cl_m (0.15 #833, 0.09 #860, 0.09 #866) >> Best rule #869 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 07bz5; >> query: (?x10808, ?x8915) <- list(?x10808, ?x7472), list(?x3795, ?x7472), list(?x3795, ?x8915) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bnb list 01pd60 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 193.000 0.814 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #21077-0db86 PRED entity: 0db86 PRED relation: major_field_of_study! PRED expected values: 021996 => 77 concepts (61 used for prediction) PRED predicted values (max 10 best out of 625): 03ksy (0.88 #13769, 0.73 #7510, 0.71 #4095), 07wjk (0.73 #7460, 0.71 #4045, 0.67 #9169), 07wrz (0.72 #12014, 0.64 #7459, 0.60 #2335), 01j_cy (0.71 #4022, 0.71 #3454, 0.60 #2313), 017cy9 (0.71 #4146, 0.71 #3578, 0.60 #2437), 0bwfn (0.71 #4269, 0.70 #6545, 0.64 #8254), 02bqy (0.71 #4179, 0.64 #7594, 0.60 #2470), 0lfgr (0.71 #4026, 0.60 #2317, 0.60 #1748), 07vyf (0.67 #5269, 0.57 #3562, 0.50 #4700), 0j_sncb (0.67 #5205, 0.50 #4636, 0.50 #650) >> Best rule #13769 for best value: >> intensional similarity = 11 >> extensional distance = 40 >> proper extension: 07c1v; >> query: (?x5900, 03ksy) <- major_field_of_study(?x10859, ?x5900), major_field_of_study(?x4599, ?x5900), major_field_of_study(?x3424, ?x5900), major_field_of_study(?x1768, ?x5900), currency(?x10859, ?x1099), student(?x1768, ?x11777), institution(?x620, ?x3424), student(?x3424, ?x117), company(?x2663, ?x3424), ?x11777 = 0gs7x, colors(?x4599, ?x332) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #4309 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 5 *> proper extension: 037mh8; *> query: (?x5900, 021996) <- major_field_of_study(?x10859, ?x5900), major_field_of_study(?x6856, ?x5900), major_field_of_study(?x5844, ?x5900), major_field_of_study(?x1768, ?x5900), ?x1768 = 09kvv, major_field_of_study(?x865, ?x5900), contains(?x760, ?x6856), school(?x700, ?x5844), ?x865 = 02h4rq6, ?x700 = 06x68, student(?x10859, ?x587), adjoins(?x7392, ?x10859) *> conf = 0.43 ranks of expected_values: 72 EVAL 0db86 major_field_of_study! 021996 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 77.000 61.000 0.881 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #21076-0tct_ PRED entity: 0tct_ PRED relation: source PRED expected values: 0jbk9 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #19, 0.90 #9, 0.75 #20) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x4765, 0jbk9) <- category(?x4765, ?x134), ?x134 = 08mbj5d, place(?x4765, ?x4765) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tct_ source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #21075-02pqs8l PRED entity: 02pqs8l PRED relation: actor PRED expected values: 03_vx9 => 71 concepts (53 used for prediction) PRED predicted values (max 10 best out of 756): 030znt (0.72 #17527, 0.36 #20297, 0.36 #10142), 06w2yp9 (0.46 #18450, 0.46 #19374, 0.46 #7377), 04myfb7 (0.46 #18450, 0.46 #19374, 0.46 #7377), 01pcq3 (0.46 #7377, 0.38 #11986, 0.38 #9220), 01541z (0.38 #11986, 0.38 #9220, 0.36 #11985), 03m_k0 (0.36 #20297, 0.36 #10142, 0.36 #17526), 048hf (0.13 #606, 0.03 #1533, 0.02 #2454), 01k8rb (0.13 #110, 0.02 #1958, 0.02 #10252), 02mxw0 (0.13 #217, 0.01 #14051, 0.01 #13125), 01dw4q (0.11 #11987, 0.07 #13834, 0.07 #13832) >> Best rule #17527 for best value: >> intensional similarity = 3 >> extensional distance = 158 >> proper extension: 01f3p_; 07wqr6; 03g9xj; 0123qq; >> query: (?x3822, ?x6889) <- nominated_for(?x6889, ?x3822), genre(?x3822, ?x53), actor(?x4581, ?x6889) >> conf = 0.72 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02pqs8l actor 03_vx9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 53.000 0.716 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21074-07lmxq PRED entity: 07lmxq PRED relation: profession PRED expected values: 02krf9 => 56 concepts (55 used for prediction) PRED predicted values (max 10 best out of 44): 01d_h8 (0.32 #1497, 0.31 #1795, 0.30 #1050), 0dxtg (0.28 #3293, 0.26 #2399, 0.26 #7171), 03gjzk (0.26 #3876, 0.26 #5815, 0.25 #4622), 0np9r (0.26 #3876, 0.26 #5815, 0.25 #4622), 02krf9 (0.26 #3876, 0.26 #5815, 0.25 #4622), 0d8qb (0.26 #3876, 0.26 #5815, 0.25 #4622), 02jknp (0.22 #1499, 0.21 #1797, 0.20 #3287), 09jwl (0.16 #7176, 0.16 #7027, 0.16 #5386), 0cbd2 (0.15 #306, 0.13 #7164, 0.12 #2243), 018gz8 (0.12 #2700, 0.12 #2998, 0.12 #3445) >> Best rule #1497 for best value: >> intensional similarity = 3 >> extensional distance = 1542 >> proper extension: 0854hr; >> query: (?x539, 01d_h8) <- gender(?x539, ?x231), nominated_for(?x539, ?x2973), film(?x540, ?x2973) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #3876 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1989 *> proper extension: 01pbxb; 03qcq; 04qvl7; 042l3v; 06cv1; 02lf0c; 01kwlwp; 03cs_z7; 07s6tbm; 05cv94; ... *> query: (?x539, ?x1032) <- award_nominee(?x539, ?x4043), nominated_for(?x4043, ?x2973), profession(?x4043, ?x1032) *> conf = 0.26 ranks of expected_values: 5 EVAL 07lmxq profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 56.000 55.000 0.319 http://example.org/people/person/profession #21073-0mzww PRED entity: 0mzww PRED relation: location! PRED expected values: 0c0tzp => 191 concepts (133 used for prediction) PRED predicted values (max 10 best out of 2249): 01jw4r (0.57 #55301, 0.47 #110598, 0.47 #72896), 023361 (0.47 #110598, 0.47 #72896, 0.47 #173438), 05_2h8 (0.47 #110598, 0.47 #72896, 0.47 #173438), 05ry0p (0.40 #2157, 0.05 #9697, 0.05 #17241), 023mdt (0.40 #1861, 0.05 #9401, 0.05 #16945), 022yb4 (0.40 #1707, 0.05 #9247, 0.05 #16791), 03l26m (0.40 #2287, 0.05 #9827, 0.05 #17371), 04z0g (0.40 #1178, 0.04 #48938, 0.03 #16262), 01jz6x (0.40 #2110, 0.03 #17194, 0.03 #22220), 09nhvw (0.40 #1901, 0.03 #16985, 0.03 #22011) >> Best rule #55301 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 01z26v; >> query: (?x6987, ?x8612) <- state(?x6987, ?x1227), place_of_birth(?x8612, ?x6987), participant(?x9782, ?x8612) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0mzww location! 0c0tzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 191.000 133.000 0.565 http://example.org/people/person/places_lived./people/place_lived/location #21072-0k049 PRED entity: 0k049 PRED relation: jurisdiction_of_office! PRED expected values: 01q24l => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.41 #861, 0.40 #817, 0.32 #1917), 060bp (0.39 #859, 0.38 #815, 0.27 #1915), 0f6c3 (0.25 #7, 0.22 #1041, 0.20 #1063), 09n5b9 (0.25 #11, 0.17 #33, 0.16 #1045), 0fkvn (0.24 #1038, 0.23 #1060, 0.17 #1852), 01q24l (0.22 #915, 0.20 #761, 0.19 #739), 04syw (0.09 #292, 0.07 #864, 0.07 #1920), 01zq91 (0.08 #872, 0.06 #498, 0.05 #828), 0fkzq (0.07 #1050, 0.07 #1072, 0.06 #82), 0p5vf (0.07 #1068, 0.06 #56, 0.06 #496) >> Best rule #861 for best value: >> intensional similarity = 2 >> extensional distance = 101 >> proper extension: 05vz3zq; >> query: (?x191, 060c4) <- locations(?x1553, ?x191), jurisdiction_of_office(?x1195, ?x191) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #915 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 108 *> proper extension: 0xy28; 0r679; 013hxv; 0ys4f; 0qpjt; 0r4wn; 0r4z7; 0msyb; 0mp36; 0r2bv; ... *> query: (?x191, 01q24l) <- jurisdiction_of_office(?x1195, ?x191), source(?x191, ?x958), contains(?x94, ?x191) *> conf = 0.22 ranks of expected_values: 6 EVAL 0k049 jurisdiction_of_office! 01q24l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 103.000 103.000 0.408 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #21071-0fgpvf PRED entity: 0fgpvf PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.80 #953, 0.75 #1097, 0.74 #43), 02r96rf (0.73 #949, 0.65 #1093, 0.60 #1057), 0dxtw (0.36 #1101, 0.36 #83, 0.35 #957), 01vx2h (0.35 #958, 0.30 #1102, 0.28 #593), 02ynfr (0.21 #52, 0.20 #16, 0.18 #88), 0d2b38 (0.20 #26, 0.13 #1932, 0.12 #135), 01xy5l_ (0.20 #14, 0.13 #1932, 0.12 #123), 089g0h (0.20 #20, 0.13 #1932, 0.11 #165), 089fss (0.20 #6, 0.13 #1932, 0.06 #1096), 020xn5 (0.20 #8, 0.13 #1932, 0.05 #44) >> Best rule #953 for best value: >> intensional similarity = 6 >> extensional distance = 312 >> proper extension: 0gtvrv3; >> query: (?x695, 0ch6mp2) <- film_crew_role(?x695, ?x137), category(?x695, ?x134), film_crew_role(?x1199, ?x137), film_crew_role(?x86, ?x137), ?x86 = 0ds35l9, ?x1199 = 0pv3x >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fgpvf film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.796 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21070-01jnc_ PRED entity: 01jnc_ PRED relation: titles! PRED expected values: 01z4y => 64 concepts (34 used for prediction) PRED predicted values (max 10 best out of 66): 01z4y (0.44 #36, 0.33 #1175, 0.31 #140), 07s9rl0 (0.24 #312, 0.24 #3014, 0.23 #3222), 01hmnh (0.22 #27, 0.13 #857, 0.12 #2101), 04t36 (0.20 #1036, 0.17 #2073, 0.17 #2072), 04228s (0.20 #1036, 0.17 #2073, 0.17 #2072), 0556j8 (0.20 #1036, 0.17 #2073, 0.17 #2072), 05p553 (0.20 #1036, 0.17 #2073, 0.17 #2072), 02kdv5l (0.20 #1036, 0.17 #2073, 0.17 #2072), 04xvlr (0.17 #4, 0.16 #1973, 0.15 #3225), 024qqx (0.16 #911, 0.11 #81, 0.09 #601) >> Best rule #36 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 016z5x; 0pc62; 04t6fk; 02x8fs; 03np63f; 0k_9j; 033pf1; 03n0cd; 058kh7; 01d2v1; ... >> query: (?x9507, 01z4y) <- film(?x8305, ?x9507), film(?x7522, ?x9507), profession(?x8305, ?x220), genre(?x9507, ?x225), category(?x8305, ?x134), ?x7522 = 0d608 >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jnc_ titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 34.000 0.444 http://example.org/media_common/netflix_genre/titles #21069-01qscs PRED entity: 01qscs PRED relation: award PRED expected values: 04kxsb 09sdmz => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 288): 0gqy2 (0.72 #42838, 0.71 #28427, 0.70 #4003), 02x8n1n (0.72 #42838, 0.71 #28427, 0.70 #4003), 0gq9h (0.33 #7679, 0.32 #4877, 0.30 #1674), 0gs9p (0.28 #1676, 0.20 #476, 0.17 #7681), 040njc (0.26 #7612, 0.25 #4810, 0.24 #1607), 0f4x7 (0.25 #29, 0.18 #14815, 0.16 #16817), 09sdmz (0.25 #203, 0.18 #14815, 0.16 #16817), 02x4w6g (0.25 #111, 0.18 #14815, 0.16 #16817), 099ck7 (0.25 #264, 0.18 #14815, 0.16 #16817), 02w9sd7 (0.25 #167, 0.18 #14815, 0.16 #16817) >> Best rule #42838 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 099ks0; >> query: (?x395, ?x704) <- award_winner(?x704, ?x395), award(?x57, ?x704) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #203 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 060_7; *> query: (?x395, 09sdmz) <- religion(?x395, ?x1985), people(?x1575, ?x395), ?x1575 = 03ttfc *> conf = 0.25 ranks of expected_values: 7, 11 EVAL 01qscs award 09sdmz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 125.000 125.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01qscs award 04kxsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 125.000 125.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21068-027kwc PRED entity: 027kwc PRED relation: group! PRED expected values: 01vj9c 0319l => 92 concepts (61 used for prediction) PRED predicted values (max 10 best out of 118): 03qjg (0.48 #453, 0.33 #207, 0.30 #125), 013y1f (0.37 #434, 0.25 #188, 0.17 #270), 05r5c (0.33 #416, 0.25 #170, 0.24 #1896), 01vj9c (0.30 #421, 0.28 #2644, 0.27 #2313), 07y_7 (0.30 #412, 0.15 #576, 0.13 #1645), 02k84w (0.29 #27, 0.20 #109, 0.11 #273), 04rzd (0.22 #438, 0.18 #602, 0.15 #1176), 018j2 (0.20 #111, 0.15 #439, 0.14 #29), 042v_gx (0.19 #417, 0.17 #171, 0.14 #7), 06w7v (0.17 #232, 0.11 #478, 0.11 #314) >> Best rule #453 for best value: >> intensional similarity = 8 >> extensional distance = 25 >> proper extension: 05563d; >> query: (?x12810, 03qjg) <- group(?x315, ?x12810), group(?x228, ?x12810), group(?x227, ?x12810), ?x227 = 0342h, artists(?x378, ?x12810), ?x315 = 0l14md, artist(?x3887, ?x12810), ?x228 = 0l14qv >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 25 *> proper extension: 05563d; *> query: (?x12810, 01vj9c) <- group(?x315, ?x12810), group(?x228, ?x12810), group(?x227, ?x12810), ?x227 = 0342h, artists(?x378, ?x12810), ?x315 = 0l14md, artist(?x3887, ?x12810), ?x228 = 0l14qv *> conf = 0.30 ranks of expected_values: 4, 49 EVAL 027kwc group! 0319l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 92.000 61.000 0.481 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 027kwc group! 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 92.000 61.000 0.481 http://example.org/music/performance_role/regular_performances./music/group_membership/group #21067-0df2zx PRED entity: 0df2zx PRED relation: titles! PRED expected values: 01z4y => 124 concepts (93 used for prediction) PRED predicted values (max 10 best out of 70): 01z4y (0.46 #2810, 0.42 #3847, 0.40 #1989), 024qqx (0.40 #184, 0.33 #390, 0.30 #288), 07s9rl0 (0.37 #8876, 0.35 #1644, 0.35 #4121), 01hmnh (0.25 #337, 0.23 #1465, 0.20 #1980), 04xvlr (0.24 #8879, 0.19 #4124, 0.18 #5776), 0jtdp (0.19 #1357, 0.18 #1745, 0.18 #7532), 05p553 (0.18 #1745, 0.18 #7532, 0.18 #6911), 01jfsb (0.16 #8895, 0.13 #4140, 0.13 #7758), 07c52 (0.13 #5283, 0.13 #5385, 0.02 #5078), 03k9fj (0.11 #1457, 0.08 #329, 0.07 #2281) >> Best rule #2810 for best value: >> intensional similarity = 5 >> extensional distance = 118 >> proper extension: 0dtw1x; 0gj9qxr; 0g5q34q; 076xkdz; >> query: (?x11192, 01z4y) <- category(?x11192, ?x134), film(?x609, ?x11192), country(?x11192, ?x94), genre(?x11192, ?x258), ?x258 = 05p553 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0df2zx titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 93.000 0.458 http://example.org/media_common/netflix_genre/titles #21066-0kv238 PRED entity: 0kv238 PRED relation: genre PRED expected values: 07s9rl0 => 98 concepts (96 used for prediction) PRED predicted values (max 10 best out of 98): 03npn (0.81 #1842, 0.76 #124, 0.76 #8), 07s9rl0 (0.79 #736, 0.78 #9443, 0.74 #1351), 0h9qh (0.73 #123, 0.72 #5515, 0.72 #5883), 0852z (0.73 #123, 0.68 #1841, 0.62 #982), 02kdv5l (0.59 #616, 0.45 #2577, 0.44 #2089), 03k9fj (0.51 #626, 0.45 #504, 0.45 #382), 05p553 (0.42 #252, 0.38 #3801, 0.36 #2823), 02l7c8 (0.38 #752, 0.38 #1367, 0.35 #876), 02n4kr (0.38 #1727, 0.29 #9, 0.27 #133), 01hmnh (0.36 #266, 0.25 #2105, 0.23 #632) >> Best rule #1842 for best value: >> intensional similarity = 4 >> extensional distance = 201 >> proper extension: 015qsq; 03h_yy; 0dj0m5; 0dsvzh; 020fcn; 04mzf8; 06rmdr; 09cr8; 01j8wk; 06g77c; ... >> query: (?x2714, ?x571) <- nominated_for(?x10416, ?x2714), titles(?x571, ?x2714), genre(?x1419, ?x571), ?x1419 = 02vw1w2 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #736 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 79 *> proper extension: 0ds11z; 0416y94; 09z2b7; 0c00zd0; 0g3zrd; 05dy7p; 04jwly; 019vhk; 0gyy53; 07yvsn; ... *> query: (?x2714, 07s9rl0) <- nominated_for(?x10416, ?x2714), titles(?x571, ?x2714), costume_design_by(?x2714, ?x4190), film_crew_role(?x2714, ?x468) *> conf = 0.79 ranks of expected_values: 2 EVAL 0kv238 genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 96.000 0.814 http://example.org/film/film/genre #21065-030p35 PRED entity: 030p35 PRED relation: award_winner PRED expected values: 015qq1 => 79 concepts (59 used for prediction) PRED predicted values (max 10 best out of 778): 0f6_x (0.54 #26293, 0.51 #52584, 0.50 #90373), 01my4f (0.54 #26293, 0.50 #90373, 0.50 #92018), 05gml8 (0.54 #26293, 0.50 #90373, 0.50 #92018), 02p65p (0.54 #26293, 0.50 #90373, 0.50 #92018), 018z_c (0.49 #64090, 0.48 #95304, 0.47 #41081), 01d0b1 (0.49 #64090, 0.48 #95304, 0.47 #41081), 029q_y (0.48 #95304, 0.47 #41081, 0.47 #96946), 015qq1 (0.48 #95304, 0.47 #41081, 0.47 #96946), 02ct_k (0.33 #1430, 0.05 #54229, 0.01 #16221), 03wh8pq (0.33 #1391, 0.04 #7961, 0.03 #9605) >> Best rule #26293 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 0dsx3f; 01fs__; 0ph24; >> query: (?x4639, ?x3651) <- award(?x4639, ?x435), nominated_for(?x3651, ?x4639), award_winner(?x3002, ?x3651), country_of_origin(?x4639, ?x94) >> conf = 0.54 => this is the best rule for 4 predicted values *> Best rule #95304 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 855 *> proper extension: 03rg2b; *> query: (?x4639, ?x190) <- nominated_for(?x190, ?x4639), award_winner(?x4639, ?x1641), film(?x1641, ?x167) *> conf = 0.48 ranks of expected_values: 8 EVAL 030p35 award_winner 015qq1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 79.000 59.000 0.544 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #21064-0bxtg PRED entity: 0bxtg PRED relation: award_nominee! PRED expected values: 015p3p => 120 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1339): 0g2lq (0.81 #133699, 0.81 #103730, 0.81 #165970), 04m_zp (0.81 #133699, 0.81 #103730, 0.81 #165970), 04wg38 (0.81 #133699, 0.81 #103730, 0.81 #165970), 0z05l (0.81 #133699, 0.81 #103730, 0.81 #165970), 02lkcc (0.81 #133699, 0.81 #103730, 0.81 #165969), 0dn3n (0.81 #133699, 0.81 #103730, 0.81 #165969), 0h5j77 (0.77 #136005, 0.77 #89899, 0.77 #124477), 0bxtg (0.24 #55326, 0.22 #165971, 0.20 #124478), 06brp0 (0.24 #55326, 0.22 #165971, 0.20 #124478), 029m83 (0.24 #55326, 0.22 #165971, 0.20 #124478) >> Best rule #133699 for best value: >> intensional similarity = 3 >> extensional distance = 1112 >> proper extension: 04bdxl; 05vsxz; 0dbpyd; 012d40; 07fq1y; 0l6qt; 06j0md; 02rchht; 0byfz; 0h0jz; ... >> query: (?x496, ?x495) <- award_winner(?x525, ?x496), award_nominee(?x496, ?x495), nominated_for(?x496, ?x69) >> conf = 0.81 => this is the best rule for 6 predicted values No rule for expected values ranks of expected_values: EVAL 0bxtg award_nominee! 015p3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 72.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #21063-01c6k4 PRED entity: 01c6k4 PRED relation: state_province_region PRED expected values: 05k7sb => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 90): 01n7q (0.50 #1750, 0.50 #1134, 0.40 #1011), 07b_l (0.33 #50, 0.20 #1166, 0.20 #547), 059rby (0.30 #2849, 0.26 #5085, 0.25 #4575), 01vsb_ (0.25 #476, 0.12 #723, 0.10 #846), 081yw (0.20 #931, 0.18 #1300, 0.13 #1546), 03v0t (0.12 #2403, 0.10 #923, 0.09 #1292), 059_c (0.10 #1875, 0.10 #1010, 0.06 #2244), 05tbn (0.10 #1167, 0.08 #7866, 0.08 #11947), 07z1m (0.10 #892, 0.08 #1384, 0.06 #1631), 0488g (0.10 #898, 0.06 #1637, 0.03 #11403) >> Best rule #1750 for best value: >> intensional similarity = 7 >> extensional distance = 16 >> proper extension: 0vlf; >> query: (?x555, 01n7q) <- service_location(?x555, ?x2467), contact_category(?x555, ?x6046), company(?x4682, ?x555), ?x4682 = 0dq_5, ?x6046 = 02zdwq, contains(?x2467, ?x291), location(?x3410, ?x2467) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3617 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 55 *> proper extension: 0288zy; 031n8c; 086xm; 0pspl; 012fvq; 0kw4j; 01_s9q; 01ljpm; 03v52f; 01rgn3; ... *> query: (?x555, 05k7sb) <- category(?x555, ?x134), currency(?x555, ?x170), ?x134 = 08mbj5d, company(?x265, ?x555), jurisdiction_of_office(?x265, ?x94), company(?x265, ?x5108), ?x5108 = 01s73z *> conf = 0.05 ranks of expected_values: 21 EVAL 01c6k4 state_province_region 05k7sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 118.000 118.000 0.500 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #21062-0cgfb PRED entity: 0cgfb PRED relation: award PRED expected values: 05q8pss => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 288): 05p09zm (0.43 #1343, 0.33 #125, 0.21 #7840), 05pcn59 (0.43 #1300, 0.21 #8609, 0.19 #17136), 09sb52 (0.40 #447, 0.33 #41, 0.26 #15877), 02f6xy (0.40 #608, 0.25 #1826, 0.20 #2232), 03t5kl (0.40 #635, 0.25 #1853, 0.17 #9162), 02f76h (0.40 #585, 0.25 #1803, 0.15 #2615), 05zr6wv (0.33 #17, 0.29 #1235, 0.20 #423), 03c7tr1 (0.33 #59, 0.20 #465, 0.17 #5337), 02f77y (0.33 #263, 0.20 #669, 0.10 #10414), 0cqhk0 (0.33 #849, 0.15 #13806, 0.08 #18715) >> Best rule #1343 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0f502; >> query: (?x11098, 05p09zm) <- profession(?x11098, ?x967), participant(?x11098, ?x2857), artists(?x671, ?x11098), ?x2857 = 0bbf1f >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1433 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0f502; *> query: (?x11098, 05q8pss) <- profession(?x11098, ?x967), participant(?x11098, ?x2857), artists(?x671, ?x11098), ?x2857 = 0bbf1f *> conf = 0.14 ranks of expected_values: 85 EVAL 0cgfb award 05q8pss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 136.000 136.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21061-026wlxw PRED entity: 026wlxw PRED relation: executive_produced_by PRED expected values: 0glyyw => 76 concepts (49 used for prediction) PRED predicted values (max 10 best out of 55): 02q4mt (0.10 #2027, 0.07 #1521), 03h304l (0.09 #377, 0.08 #630, 0.01 #3916), 027z0pl (0.09 #473, 0.04 #726, 0.01 #4012), 05txrz (0.09 #357, 0.04 #610), 04l3_z (0.09 #284, 0.04 #537), 0415svh (0.09 #280, 0.04 #533), 0bxtg (0.09 #270, 0.04 #523), 0gg9_5q (0.09 #90, 0.02 #1106, 0.01 #7432), 0fz27v (0.09 #218, 0.01 #4770, 0.01 #4010), 015c4g (0.09 #105) >> Best rule #2027 for best value: >> intensional similarity = 3 >> extensional distance = 235 >> proper extension: 0267wwv; 09rfpk; >> query: (?x8214, ?x11873) <- story_by(?x8214, ?x11873), film_crew_role(?x8214, ?x137), genre(?x8214, ?x225) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #1205 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 206 *> proper extension: 0dtw1x; 04dsnp; 053tj7; 0gj9qxr; 091z_p; 0crh5_f; 02phtzk; 02h22; 0cp08zg; 076xkdz; ... *> query: (?x8214, 0glyyw) <- category(?x8214, ?x134), film_release_region(?x8214, ?x94), production_companies(?x8214, ?x382) *> conf = 0.04 ranks of expected_values: 16 EVAL 026wlxw executive_produced_by 0glyyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 76.000 49.000 0.100 http://example.org/film/film/executive_produced_by #21060-031ldd PRED entity: 031ldd PRED relation: film_crew_role PRED expected values: 02r96rf => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 27): 02r96rf (0.78 #811, 0.77 #148, 0.72 #700), 09vw2b7 (0.71 #815, 0.70 #152, 0.63 #704), 01vx2h (0.50 #819, 0.48 #156, 0.42 #782), 01pvkk (0.36 #636, 0.30 #820, 0.29 #1699), 02rh1dz (0.22 #155, 0.20 #818, 0.18 #266), 02ynfr (0.22 #824, 0.19 #713, 0.16 #750), 0215hd (0.16 #91, 0.14 #716, 0.14 #679), 02_n3z (0.16 #73, 0.09 #146, 0.09 #478), 0d2b38 (0.15 #797, 0.14 #834, 0.14 #686), 015h31 (0.14 #154, 0.14 #486, 0.12 #265) >> Best rule #811 for best value: >> intensional similarity = 4 >> extensional distance = 290 >> proper extension: 0ds33; 02qm_f; 03t97y; 01kff7; 0fdv3; 035s95; 04t6fk; 02_sr1; 01hqk; 02ntb8; ... >> query: (?x6014, 02r96rf) <- genre(?x6014, ?x225), ?x225 = 02kdv5l, country(?x6014, ?x2346), film_crew_role(?x6014, ?x137) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031ldd film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.777 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #21059-02qmsr PRED entity: 02qmsr PRED relation: film! PRED expected values: 0gy6z9 => 81 concepts (51 used for prediction) PRED predicted values (max 10 best out of 625): 03pmzt (0.17 #494, 0.04 #2573, 0.03 #4652), 06z8gn (0.17 #1512, 0.04 #3591, 0.03 #5670), 02fybl (0.17 #1256, 0.04 #3335, 0.03 #5414), 02_l96 (0.17 #904, 0.04 #2983, 0.03 #5062), 0fgg4 (0.17 #881, 0.04 #2960, 0.03 #5039), 01vvb4m (0.17 #519, 0.04 #27553, 0.03 #35871), 0716t2 (0.17 #1907, 0.03 #10225, 0.02 #20624), 05bnp0 (0.17 #13, 0.03 #8331, 0.02 #18730), 05nzw6 (0.17 #1190, 0.02 #17828, 0.02 #32384), 05p5nc (0.17 #1202, 0.02 #9520, 0.02 #11600) >> Best rule #494 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0dqcs3; >> query: (?x2525, 03pmzt) <- nominated_for(?x793, ?x2525), film(?x2589, ?x2525), ?x2589 = 019f2f, country(?x2525, ?x94) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #19281 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 253 *> proper extension: 01vksx; 0284b56; 02nt3d; 0b3n61; 02bqvs; 03c7twt; *> query: (?x2525, 0gy6z9) <- nominated_for(?x793, ?x2525), film_crew_role(?x2525, ?x137), nominated_for(?x372, ?x2525), vacationer(?x1523, ?x793) *> conf = 0.02 ranks of expected_values: 346 EVAL 02qmsr film! 0gy6z9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 81.000 51.000 0.167 http://example.org/film/actor/film./film/performance/film #21058-02v63m PRED entity: 02v63m PRED relation: executive_produced_by PRED expected values: 05hj_k => 58 concepts (48 used for prediction) PRED predicted values (max 10 best out of 50): 03p01x (0.33 #218, 0.02 #5298, 0.02 #2773), 0gg9_5q (0.11 #341, 0.05 #1349, 0.02 #1854), 02qjpv5 (0.11 #459, 0.02 #1467), 059x0w (0.11 #454), 021lby (0.11 #315), 05hj_k (0.09 #1357, 0.07 #853, 0.06 #1608), 0glyyw (0.06 #1447, 0.04 #943, 0.02 #4732), 079vf (0.06 #1261, 0.03 #757, 0.02 #1766), 06pj8 (0.05 #1314, 0.03 #1565, 0.02 #2576), 03c9pqt (0.04 #1505, 0.02 #5041, 0.02 #748) >> Best rule #218 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02v5_g; >> query: (?x1184, 03p01x) <- film(?x10212, ?x1184), film(?x399, ?x1184), ?x399 = 0prfz, genre(?x1184, ?x258), ?x10212 = 02pzck >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1357 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 208 *> proper extension: 026njb5; 03q8xj; *> query: (?x1184, 05hj_k) <- featured_film_locations(?x1184, ?x9405), film_crew_role(?x1184, ?x137), executive_produced_by(?x1184, ?x7324) *> conf = 0.09 ranks of expected_values: 6 EVAL 02v63m executive_produced_by 05hj_k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 58.000 48.000 0.333 http://example.org/film/film/executive_produced_by #21057-0s987 PRED entity: 0s987 PRED relation: category PRED expected values: 08mbj5d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #6, 0.81 #5, 0.80 #12) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 0f04c; 0v1xg; 06kx2; 0r0ls; >> query: (?x11979, 08mbj5d) <- location_of_ceremony(?x566, ?x11979), source(?x11979, ?x958), place_of_birth(?x3800, ?x11979), contains(?x94, ?x11979) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0s987 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.821 http://example.org/common/topic/webpage./common/webpage/category #21056-02pw_n PRED entity: 02pw_n PRED relation: currency PRED expected values: 09nqf => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.84 #57, 0.83 #8, 0.79 #64), 01nv4h (0.03 #65, 0.02 #86, 0.02 #93), 02gsvk (0.01 #34, 0.01 #41, 0.01 #202), 02l6h (0.01 #242, 0.01 #200, 0.01 #284) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 332 >> proper extension: 0g22z; 02vxq9m; 027qgy; 09m6kg; 011yrp; 0ds3t5x; 01k1k4; 034qrh; 0ds11z; 0ds33; ... >> query: (?x6619, 09nqf) <- nominated_for(?x1336, ?x6619), award(?x2763, ?x1336), ?x2763 = 019pm_ >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pw_n currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.841 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21055-06c1y PRED entity: 06c1y PRED relation: olympics PRED expected values: 0kbvv => 234 concepts (234 used for prediction) PRED predicted values (max 10 best out of 33): 0kbws (0.83 #444, 0.80 #382, 0.75 #630), 0kbvv (0.74 #454, 0.68 #640, 0.65 #547), 0swbd (0.61 #441, 0.60 #317, 0.58 #348), 0jhn7 (0.60 #393, 0.48 #455, 0.45 #145), 09x3r (0.51 #747, 0.46 #1370, 0.43 #1558), 016r9z (0.51 #747, 0.46 #1370, 0.42 #966), 0blfl (0.51 #747, 0.46 #1370, 0.42 #966), 0l6m5 (0.45 #130, 0.45 #378, 0.43 #1558), 0l6mp (0.45 #138, 0.43 #1558, 0.43 #1557), 0swff (0.43 #452, 0.43 #47, 0.40 #390) >> Best rule #444 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 03_r3; 06qd3; >> query: (?x1536, 0kbws) <- film_release_region(?x1724, ?x1536), country(?x2884, ?x1536), ?x2884 = 09wz9, nominated_for(?x1053, ?x1724) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #454 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 03_r3; 06qd3; *> query: (?x1536, 0kbvv) <- film_release_region(?x1724, ?x1536), country(?x2884, ?x1536), ?x2884 = 09wz9, nominated_for(?x1053, ?x1724) *> conf = 0.74 ranks of expected_values: 2 EVAL 06c1y olympics 0kbvv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 234.000 234.000 0.826 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #21054-06v36 PRED entity: 06v36 PRED relation: organization PRED expected values: 0j7v_ 0b6css => 135 concepts (122 used for prediction) PRED predicted values (max 10 best out of 17): 0b6css (0.65 #313, 0.56 #283, 0.56 #538), 0j7v_ (0.56 #283, 0.50 #529, 0.50 #528), 0_2v (0.44 #264, 0.42 #450, 0.42 #43), 01rz1 (0.42 #448, 0.33 #387, 0.33 #508), 018cqq (0.38 #251, 0.33 #396, 0.33 #70), 04k4l (0.32 #1979, 0.32 #2002, 0.32 #2000), 02jxk (0.32 #1979, 0.32 #2002, 0.32 #2000), 059dn (0.32 #1979, 0.32 #2002, 0.32 #2000), 085h1 (0.32 #1979, 0.32 #2002, 0.32 #2000), 034h1h (0.21 #1966, 0.18 #1987, 0.03 #2364) >> Best rule #313 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 05g2v; >> query: (?x6437, 0b6css) <- taxonomy(?x6437, ?x939), contains(?x2467, ?x6437), ?x939 = 04n6k, ?x2467 = 0dg3n1 >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 06v36 organization 0b6css CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 122.000 0.649 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 06v36 organization 0j7v_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 122.000 0.649 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #21053-03lrht PRED entity: 03lrht PRED relation: film! PRED expected values: 015p37 => 83 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1048): 016ggh (0.25 #3938, 0.01 #51678, 0.01 #68282), 0b1f49 (0.13 #20758, 0.12 #22834, 0.11 #33212), 0fz27v (0.13 #20758, 0.12 #22834, 0.11 #33212), 02qfhb (0.12 #2948, 0.02 #9177, 0.01 #25783), 03f2_rc (0.11 #2160), 0bksh (0.09 #851, 0.05 #51891, 0.03 #5001), 0dvld (0.09 #1057, 0.05 #51891, 0.02 #5207), 0372kf (0.09 #918, 0.05 #51891, 0.01 #13373), 04fzk (0.09 #703, 0.05 #2778, 0.03 #6931), 05gml8 (0.09 #107, 0.04 #37364, 0.04 #78871) >> Best rule #3938 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 02z2mr7; 03mnn0; 01jnc_; >> query: (?x1692, 016ggh) <- film(?x6328, ?x1692), type_of_union(?x6328, ?x566), profession(?x6328, ?x319), performance_role(?x6328, ?x1466) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #78871 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 985 *> proper extension: 0c_j9x; 09p7fh; 0c9k8; 0cfhfz; 023p7l; 0m_q0; 02ppg1r; 03b1sb; 0gyv0b4; *> query: (?x1692, ?x3694) <- film(?x1593, ?x1692), nominated_for(?x3019, ?x1692), participant(?x3694, ?x1593), award(?x71, ?x3019) *> conf = 0.04 ranks of expected_values: 85 EVAL 03lrht film! 015p37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 83.000 42.000 0.250 http://example.org/film/actor/film./film/performance/film #21052-04z0g PRED entity: 04z0g PRED relation: religion PRED expected values: 0kq2 => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 29): 0c8wxp (0.37 #4665, 0.35 #1332, 0.34 #4440), 03_gx (0.36 #145, 0.36 #852, 0.33 #1428), 0kq2 (0.21 #193, 0.18 #149, 0.12 #705), 0631_ (0.19 #404, 0.07 #184, 0.05 #1200), 03j6c (0.14 #64, 0.08 #4679, 0.07 #4136), 092bf5 (0.14 #59, 0.06 #279, 0.05 #675), 07mfk (0.14 #75, 0.03 #427, 0.01 #1003), 019cr (0.11 #406, 0.06 #450, 0.04 #1425), 05sfs (0.09 #135, 0.05 #399, 0.04 #443), 02t7t (0.09 #154, 0.01 #1881) >> Best rule #4665 for best value: >> intensional similarity = 3 >> extensional distance = 968 >> proper extension: 01w3v; 0mcf4; >> query: (?x5790, 0c8wxp) <- religion(?x5790, ?x2694), religion(?x11949, ?x2694), organizations_founded(?x11949, ?x1693) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #193 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 03gkn5; *> query: (?x5790, 0kq2) <- profession(?x5790, ?x353), company(?x5790, ?x2313), student(?x3437, ?x5790), ?x3437 = 02_xgp2 *> conf = 0.21 ranks of expected_values: 3 EVAL 04z0g religion 0kq2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 179.000 179.000 0.372 http://example.org/people/person/religion #21051-0sxfd PRED entity: 0sxfd PRED relation: nominated_for! PRED expected values: 0gr4k => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 196): 09cn0c (0.68 #5938, 0.67 #5937, 0.66 #7084), 02z1nbg (0.68 #5938, 0.67 #5937, 0.66 #7084), 027571b (0.68 #5938, 0.67 #5937, 0.66 #7084), 09d28z (0.68 #5938, 0.67 #5937, 0.66 #7084), 02w_6xj (0.68 #5938, 0.67 #5937, 0.66 #7084), 027c924 (0.68 #5938, 0.67 #5937, 0.66 #7084), 0gr4k (0.45 #2308, 0.25 #4818, 0.25 #5504), 040njc (0.43 #2290, 0.28 #692, 0.27 #4800), 0f4x7 (0.40 #2307, 0.23 #5045, 0.23 #4817), 0gs96 (0.40 #765, 0.24 #2363, 0.19 #5101) >> Best rule #5938 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 06mmr; >> query: (?x1402, ?x3066) <- award(?x1402, ?x3066), award_winner(?x1402, ?x3585), award(?x92, ?x3066) >> conf = 0.68 => this is the best rule for 6 predicted values *> Best rule #2308 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 274 *> proper extension: 0sxg4; 083shs; 01jc6q; 0yyg4; 01gc7; 011yrp; 011yxg; 0gzy02; 0ds3t5x; 095zlp; ... *> query: (?x1402, 0gr4k) <- genre(?x1402, ?x53), nominated_for(?x1307, ?x1402), film(?x71, ?x1402), ?x1307 = 0gq9h *> conf = 0.45 ranks of expected_values: 7 EVAL 0sxfd nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 67.000 67.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21050-0btyl PRED entity: 0btyl PRED relation: place_of_death PRED expected values: 09bkv => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 55): 030qb3t (0.16 #1382, 0.15 #11876, 0.15 #410), 02_286 (0.15 #983, 0.12 #207, 0.09 #11867), 0k049 (0.11 #585, 0.10 #391, 0.09 #1363), 06_kh (0.09 #587, 0.08 #199, 0.07 #1559), 05qtj (0.08 #258, 0.07 #64, 0.04 #646), 0f2wj (0.07 #400, 0.07 #12, 0.04 #206), 0r3w7 (0.07 #177, 0.05 #565, 0.04 #759), 01j2_7 (0.07 #186, 0.02 #768, 0.02 #1156), 04vmp (0.05 #3799, 0.04 #2439, 0.04 #1078), 04jpl (0.05 #2338, 0.05 #3698, 0.04 #977) >> Best rule #1382 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 01l3j; >> query: (?x3870, 030qb3t) <- religion(?x3870, ?x2769), people(?x6260, ?x3870), film(?x3870, ?x8477) >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0btyl place_of_death 09bkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 128.000 0.164 http://example.org/people/deceased_person/place_of_death #21049-06t8b PRED entity: 06t8b PRED relation: gender PRED expected values: 05zppz => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #37, 0.91 #19, 0.91 #39), 02zsn (0.37 #128, 0.37 #126, 0.36 #136) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 06y3r; >> query: (?x7903, 05zppz) <- executive_produced_by(?x603, ?x7903), profession(?x7903, ?x319), religion(?x7903, ?x2694) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06t8b gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.917 http://example.org/people/person/gender #21048-01ckrr PRED entity: 01ckrr PRED relation: award! PRED expected values: 01vv6_6 => 56 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2835): 0gcs9 (0.80 #40029, 0.80 #40028, 0.80 #73391), 02qsjt (0.80 #40029, 0.80 #40028, 0.80 #73391), 04rcr (0.80 #40029, 0.80 #40028, 0.78 #83398), 01wv9xn (0.80 #40029, 0.80 #40028, 0.78 #83398), 0kr_t (0.52 #11603, 0.25 #4933, 0.25 #31618), 0gdh5 (0.50 #4080, 0.40 #7415, 0.35 #10750), 016fmf (0.50 #4054, 0.40 #7389, 0.29 #10724), 06rgq (0.50 #5763, 0.40 #9098, 0.26 #12433), 02vr7 (0.50 #5719, 0.40 #9054, 0.23 #12389), 01vw20_ (0.50 #4142, 0.40 #7477, 0.19 #50035) >> Best rule #40029 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 02x8n1n; >> query: (?x4912, ?x2963) <- award(?x5623, ?x4912), award_winner(?x4912, ?x2963), instrumentalists(?x228, ?x5623), ?x228 = 0l14qv >> conf = 0.80 => this is the best rule for 4 predicted values *> Best rule #33356 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 05p09zm; 02f705; 02f76h; 02f71y; 05q8pss; 02f764; 02f77y; 02f777; *> query: (?x4912, ?x379) <- award(?x1412, ?x4912), group(?x227, ?x1412), artists(?x7083, ?x1412), artists(?x7083, ?x379) *> conf = 0.05 ranks of expected_values: 1166 EVAL 01ckrr award! 01vv6_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 26.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21047-08720 PRED entity: 08720 PRED relation: film_regional_debut_venue PRED expected values: 0prpt => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 14): 018cvf (0.12 #189, 0.11 #396, 0.10 #569), 0prpt (0.08 #408, 0.08 #581, 0.07 #443), 015hr (0.07 #394, 0.06 #118, 0.05 #187), 0j63cyr (0.05 #255, 0.04 #393, 0.04 #566), 02_286 (0.05 #3, 0.02 #555, 0.02 #244), 04jpl (0.05 #1, 0.01 #380, 0.01 #35), 01ly5m (0.05 #9, 0.01 #43), 0kfhjq0 (0.04 #395, 0.04 #568, 0.03 #188), 07zmj (0.04 #273, 0.03 #584, 0.03 #446), 07751 (0.03 #424, 0.03 #562, 0.03 #389) >> Best rule #189 for best value: >> intensional similarity = 4 >> extensional distance = 206 >> proper extension: 0c40vxk; 06v9_x; 0jqzt; >> query: (?x641, 018cvf) <- film(?x722, ?x641), film_crew_role(?x641, ?x137), film_release_region(?x641, ?x151), ?x151 = 0b90_r >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #408 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 269 *> proper extension: 053tj7; *> query: (?x641, 0prpt) <- film_release_region(?x641, ?x2645), ?x2645 = 03h64, language(?x641, ?x254) *> conf = 0.08 ranks of expected_values: 2 EVAL 08720 film_regional_debut_venue 0prpt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.120 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #21046-047sxrj PRED entity: 047sxrj PRED relation: artist! PRED expected values: 03d96s => 107 concepts (62 used for prediction) PRED predicted values (max 10 best out of 95): 01trtc (0.27 #214, 0.26 #919, 0.13 #355), 073tm9 (0.27 #177, 0.22 #882, 0.17 #600), 01dtcb (0.22 #611, 0.20 #329, 0.17 #47), 015_1q (0.20 #1993, 0.18 #1429, 0.18 #1711), 01f_3w (0.20 #739, 0.10 #1726, 0.10 #1021), 03mp8k (0.18 #1759, 0.15 #1477, 0.13 #349), 043g7l (0.18 #1723, 0.10 #1441, 0.09 #2005), 04fc6c (0.18 #218, 0.07 #1487, 0.07 #359), 0g768 (0.17 #2011, 0.15 #883, 0.14 #1729), 033hn8 (0.17 #1705, 0.16 #718, 0.13 #1423) >> Best rule #214 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 05mt_q; 03bxwtd; 01vw20h; 03y82t6; 01vsgrn; 026yqrr; 06mt91; 02wwwv5; 01wwnh2; >> query: (?x2334, 01trtc) <- award_nominee(?x3607, ?x2334), award(?x2334, ?x1389), artists(?x671, ?x2334), ?x3607 = 0412f5y >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0288fyj; *> query: (?x2334, 03d96s) <- award_nominee(?x5536, ?x2334), award_nominee(?x527, ?x2334), award(?x2334, ?x1389), ?x5536 = 01vsgrn, ?x527 = 04lgymt *> conf = 0.17 ranks of expected_values: 12 EVAL 047sxrj artist! 03d96s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 107.000 62.000 0.273 http://example.org/music/record_label/artist #21045-0223bl PRED entity: 0223bl PRED relation: colors PRED expected values: 01g5v => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 15): 083jv (0.87 #1826, 0.84 #1883, 0.74 #1921), 01g5v (0.55 #173, 0.52 #330, 0.46 #503), 04mkbj (0.55 #173, 0.44 #153, 0.33 #500), 019sc (0.46 #622, 0.36 #391, 0.35 #372), 01l849 (0.30 #712, 0.27 #558, 0.22 #423), 088fh (0.18 #1113, 0.18 #1112, 0.14 #82), 038hg (0.18 #1113, 0.18 #1112, 0.14 #1448), 02rnmb (0.18 #1113, 0.18 #1112, 0.12 #127), 036k5h (0.18 #1113, 0.18 #1112, 0.12 #138), 0jc_p (0.18 #1113, 0.18 #1112, 0.11 #1668) >> Best rule #1826 for best value: >> intensional similarity = 14 >> extensional distance = 254 >> proper extension: 02fbb5; 03dkx; >> query: (?x470, 083jv) <- colors(?x470, ?x1101), colors(?x11312, ?x1101), colors(?x10142, ?x1101), colors(?x6348, ?x1101), colors(?x5828, ?x1101), colors(?x3719, ?x1101), ?x11312 = 03w7kx, colors(?x3387, ?x1101), ?x6348 = 021f30, ?x10142 = 02r7lqg, ?x3719 = 044crp, ?x5828 = 01rl_3, school(?x2820, ?x3387), student(?x3387, ?x2136) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #173 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 6 *> proper extension: 025txtg; *> query: (?x470, ?x663) <- team(?x6502, ?x470), position(?x470, ?x530), position(?x470, ?x63), position(?x470, ?x60), ?x60 = 02nzb8, ?x530 = 02_j1w, colors(?x470, ?x1101), ?x63 = 02sdk9v, team(?x6502, ?x9434), team(?x6502, ?x6537), colors(?x6537, ?x663), ?x9434 = 02qhlm, sport(?x470, ?x471), ?x471 = 02vx4 *> conf = 0.55 ranks of expected_values: 2 EVAL 0223bl colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 120.000 0.867 http://example.org/sports/sports_team/colors #21044-059xnf PRED entity: 059xnf PRED relation: award PRED expected values: 0ck27z => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 247): 04kxsb (0.50 #125, 0.40 #528, 0.14 #24995), 0ck27z (0.33 #2913, 0.31 #2510, 0.29 #1704), 0bfvd4 (0.25 #114, 0.20 #517, 0.14 #24995), 0gqy2 (0.25 #164, 0.20 #567, 0.11 #970), 0f4x7 (0.25 #30, 0.20 #433, 0.10 #4464), 09sdmz (0.25 #205, 0.20 #608, 0.10 #1011), 0bdwqv (0.25 #172, 0.20 #575, 0.08 #1785), 0cqh46 (0.25 #50, 0.20 #453, 0.06 #856), 02w9sd7 (0.25 #170, 0.20 #573, 0.06 #976), 099jhq (0.25 #18, 0.20 #421, 0.05 #824) >> Best rule #125 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0171cm; 01wk3c; >> query: (?x7047, 04kxsb) <- film(?x7047, ?x616), ?x616 = 011yph, award_winner(?x704, ?x7047) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2913 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 574 *> proper extension: 076df9; *> query: (?x7047, 0ck27z) <- actor(?x7119, ?x7047), award_nominee(?x10886, ?x7047), location(?x10886, ?x4030) *> conf = 0.33 ranks of expected_values: 2 EVAL 059xnf award 0ck27z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21043-0ldqf PRED entity: 0ldqf PRED relation: sports PRED expected values: 06wrt 06z6r => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 44): 0crlz (0.86 #586, 0.85 #549, 0.82 #474), 06wrt (0.80 #646, 0.79 #572, 0.77 #535), 01cgz (0.79 #375, 0.79 #224, 0.77 #487), 096f8 (0.79 #375, 0.79 #224, 0.77 #487), 0d1t3 (0.79 #375, 0.79 #224, 0.77 #487), 064vjs (0.79 #375, 0.79 #224, 0.77 #487), 06z6r (0.69 #543, 0.64 #580, 0.64 #468), 01sgl (0.69 #225, 0.08 #1826), 0w0d (0.67 #643, 0.64 #569, 0.62 #532), 07_53 (0.62 #551, 0.60 #662, 0.57 #588) >> Best rule #586 for best value: >> intensional similarity = 12 >> extensional distance = 12 >> proper extension: 0sxrz; >> query: (?x7441, 0crlz) <- participating_countries(?x7441, ?x512), sports(?x7441, ?x171), olympics(?x583, ?x7441), ?x583 = 015fr, country(?x124, ?x512), film_release_region(?x3076, ?x512), film_release_region(?x2501, ?x512), nationality(?x111, ?x512), contains(?x512, ?x362), ?x3076 = 0g5838s, ?x2501 = 040rmy, jurisdiction_of_office(?x5402, ?x512) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #646 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 13 *> proper extension: 0kbvb; *> query: (?x7441, 06wrt) <- participating_countries(?x7441, ?x512), sports(?x7441, ?x171), olympics(?x583, ?x7441), ?x583 = 015fr, country(?x5555, ?x512), film_release_region(?x3076, ?x512), nationality(?x9321, ?x512), contains(?x512, ?x362), ?x3076 = 0g5838s, artists(?x505, ?x9321), genre(?x5555, ?x53) *> conf = 0.80 ranks of expected_values: 2, 7 EVAL 0ldqf sports 06z6r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 55.000 55.000 0.857 http://example.org/user/jg/default_domain/olympic_games/sports EVAL 0ldqf sports 06wrt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 55.000 55.000 0.857 http://example.org/user/jg/default_domain/olympic_games/sports #21042-03nfnx PRED entity: 03nfnx PRED relation: language PRED expected values: 02h40lc => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.92 #950, 0.91 #1487, 0.91 #1309), 064_8sq (0.22 #554, 0.18 #1209, 0.17 #1448), 06nm1 (0.18 #366, 0.17 #189, 0.17 #248), 04306rv (0.17 #183, 0.17 #242, 0.16 #360), 03_9r (0.12 #1137, 0.09 #2204, 0.08 #2086), 0653m (0.12 #71, 0.08 #1259, 0.07 #1378), 02hwyss (0.12 #101, 0.03 #812, 0.03 #1468), 05f_3 (0.12 #86), 02bjrlw (0.11 #179, 0.11 #238, 0.10 #356), 06b_j (0.08 #142, 0.07 #1567, 0.07 #201) >> Best rule #950 for best value: >> intensional similarity = 6 >> extensional distance = 74 >> proper extension: 0gy0l_; >> query: (?x8075, 02h40lc) <- genre(?x8075, ?x258), production_companies(?x8075, ?x902), currency(?x8075, ?x170), country(?x8075, ?x94), prequel(?x8075, ?x2868), film(?x609, ?x8075) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03nfnx language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.921 http://example.org/film/film/language #21041-06b_j PRED entity: 06b_j PRED relation: countries_spoken_in PRED expected values: 07t21 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 315): 07dvs (0.65 #699, 0.59 #2628, 0.59 #2980), 05vz3zq (0.65 #699, 0.59 #2628, 0.59 #2980), 0jt3tjf (0.65 #699, 0.59 #2628, 0.59 #2980), 07ytt (0.50 #1204, 0.50 #678, 0.44 #1728), 0162v (0.50 #575, 0.33 #1451, 0.33 #226), 034m8 (0.50 #680, 0.33 #331, 0.22 #1556), 05r7t (0.50 #641, 0.33 #292, 0.12 #2214), 03h2c (0.50 #603, 0.33 #254, 0.12 #2176), 06s0l (0.50 #651, 0.33 #302, 0.12 #2224), 01ppq (0.38 #1374, 0.38 #1199, 0.33 #1723) >> Best rule #699 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 06nm1; >> query: (?x5671, ?x404) <- language(?x6099, ?x5671), language(?x6030, ?x5671), official_language(?x2188, ?x5671), official_language(?x404, ?x5671), ?x6099 = 0473rc, form_of_government(?x2188, ?x48), nominated_for(?x6860, ?x6030), contains(?x455, ?x2188), film_release_region(?x249, ?x2188), ?x6860 = 018wdw >> conf = 0.65 => this is the best rule for 3 predicted values *> Best rule #6185 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 33 *> proper extension: 0swlx; *> query: (?x5671, 07t21) <- countries_spoken_in(?x5671, ?x279), official_language(?x4073, ?x5671), languages_spoken(?x9428, ?x5671), people(?x9428, ?x4259), people(?x9428, ?x538), award_winner(?x538, ?x772), award_nominee(?x538, ?x1136), profession(?x538, ?x220), award_winner(?x3183, ?x4259) *> conf = 0.11 ranks of expected_values: 143 EVAL 06b_j countries_spoken_in 07t21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 79.000 79.000 0.652 http://example.org/language/human_language/countries_spoken_in #21040-0x3b7 PRED entity: 0x3b7 PRED relation: award_winner! PRED expected values: 026m9w => 136 concepts (128 used for prediction) PRED predicted values (max 10 best out of 275): 026mfs (0.43 #1416, 0.38 #6868, 0.37 #37345), 03x3wf (0.40 #65, 0.14 #26185, 0.09 #1781), 024fz9 (0.40 #206, 0.09 #47654, 0.03 #1922), 01c9f2 (0.38 #6868, 0.37 #37345, 0.37 #36915), 026mg3 (0.38 #6868, 0.37 #37345, 0.37 #36915), 054ks3 (0.38 #6868, 0.37 #37345, 0.37 #36915), 099vwn (0.38 #6868, 0.37 #37345, 0.37 #36915), 01c99j (0.38 #6868, 0.37 #37345, 0.37 #36915), 02x17c2 (0.38 #6868, 0.37 #37345, 0.37 #36915), 02581q (0.30 #1294, 0.14 #26185, 0.12 #6008) >> Best rule #1416 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 01x15dc; >> query: (?x4239, 026mfs) <- award_winner(?x4239, ?x4343), award_winner(?x4239, ?x367), ?x4343 = 02cx90, role(?x367, ?x432) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #287 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 01hgwkr; *> query: (?x4239, 026m9w) <- award_winner(?x4239, ?x4840), role(?x4239, ?x227), ?x4840 = 06m61 *> conf = 0.20 ranks of expected_values: 13 EVAL 0x3b7 award_winner! 026m9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 136.000 128.000 0.435 http://example.org/award/award_category/winners./award/award_honor/award_winner #21039-0gs9p PRED entity: 0gs9p PRED relation: award! PRED expected values: 0bs4r 0c0zq => 64 concepts (21 used for prediction) PRED predicted values (max 10 best out of 698): 0bs4r (0.60 #4462, 0.60 #3491, 0.57 #7373), 0h03fhx (0.60 #4313, 0.60 #3342, 0.57 #7224), 0bdjd (0.60 #4583, 0.56 #9436, 0.43 #8466), 0c0zq (0.60 #3768, 0.47 #15423, 0.46 #13480), 0bmpm (0.60 #4159, 0.43 #8042, 0.43 #7070), 0bm2x (0.60 #4385, 0.43 #8268, 0.43 #7296), 08zrbl (0.60 #5608, 0.30 #12406, 0.17 #6581), 01mgw (0.56 #10426, 0.29 #7513, 0.27 #5825), 0m313 (0.50 #10685, 0.47 #14573, 0.43 #6800), 083skw (0.50 #230, 0.44 #8966, 0.40 #2170) >> Best rule #4462 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0gr4k; 019f4v; 0gq9h; >> query: (?x1313, 0bs4r) <- nominated_for(?x1313, ?x8773), nominated_for(?x1313, ?x4559), nominated_for(?x1313, ?x1916), ?x4559 = 0ccd3x, ?x1916 = 0ch26b_, ?x8773 = 0cq806, award_winner(?x1313, ?x276) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0gs9p award! 0c0zq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 21.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0gs9p award! 0bs4r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 21.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #21038-05hjnw PRED entity: 05hjnw PRED relation: titles! PRED expected values: 0d63kt => 111 concepts (67 used for prediction) PRED predicted values (max 10 best out of 71): 04xvlr (0.45 #709, 0.36 #305, 0.36 #810), 018h2 (0.29 #603, 0.05 #534, 0.04 #2026), 02l7c8 (0.23 #302, 0.20 #23, 0.19 #3140), 0d63kt (0.23 #302, 0.19 #3140, 0.19 #5477), 01z4y (0.21 #6623, 0.20 #536, 0.20 #4703), 017fp (0.21 #324, 0.17 #1132, 0.15 #524), 01hmnh (0.20 #226, 0.14 #125, 0.10 #4694), 024qqx (0.20 #79, 0.11 #481, 0.10 #280), 0ltv (0.20 #83), 07ssc (0.18 #311, 0.15 #1728, 0.12 #816) >> Best rule #709 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 02h22; >> query: (?x4939, 04xvlr) <- nominated_for(?x1243, ?x4939), ?x1243 = 0gr0m, films(?x2286, ?x4939) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #302 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 05n6sq; *> query: (?x4939, ?x53) <- film(?x2443, ?x4939), ?x2443 = 0237fw, film(?x902, ?x4939), genre(?x4939, ?x53) *> conf = 0.23 ranks of expected_values: 4 EVAL 05hjnw titles! 0d63kt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 67.000 0.448 http://example.org/media_common/netflix_genre/titles #21037-06wvj PRED entity: 06wvj PRED relation: profession PRED expected values: 01c72t => 194 concepts (123 used for prediction) PRED predicted values (max 10 best out of 107): 01c72t (0.91 #10713, 0.86 #3138, 0.80 #1656), 02hrh1q (0.85 #4167, 0.85 #16333, 0.85 #5501), 09jwl (0.72 #14857, 0.69 #15598, 0.69 #17375), 0nbcg (0.53 #1814, 0.53 #6410, 0.53 #11760), 016z4k (0.47 #7871, 0.45 #4453, 0.44 #7572), 0kyk (0.43 #1366, 0.33 #1218, 0.33 #180), 02jknp (0.40 #752, 0.27 #4901, 0.24 #3717), 0dz3r (0.40 #14840, 0.39 #10990, 0.39 #11730), 01d_h8 (0.38 #8023, 0.34 #13363, 0.33 #6), 025352 (0.34 #3322, 0.18 #3174, 0.16 #10749) >> Best rule #10713 for best value: >> intensional similarity = 4 >> extensional distance = 150 >> proper extension: 0c8hct; >> query: (?x2536, 01c72t) <- location(?x2536, ?x3125), profession(?x2536, ?x563), profession(?x8476, ?x563), ?x8476 = 012201 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06wvj profession 01c72t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 123.000 0.914 http://example.org/people/person/profession #21036-04_tv PRED entity: 04_tv PRED relation: specialization_of! PRED expected values: 02y5kn 022qqh 047vnfs => 70 concepts (64 used for prediction) PRED predicted values (max 10 best out of 133): 0d8qb (0.33 #256, 0.11 #2014, 0.11 #2015), 02hv44_ (0.33 #242, 0.11 #2014, 0.11 #2015), 05z96 (0.33 #231, 0.11 #2014, 0.11 #2015), 0kyk (0.33 #223, 0.11 #2014, 0.11 #2015), 0dxtg (0.33 #216, 0.11 #2014, 0.11 #2015), 02xlf (0.33 #244, 0.08 #979, 0.08 #1086), 02pjxr (0.25 #435, 0.16 #1050, 0.11 #2014), 01nxfc (0.25 #495, 0.11 #2014, 0.11 #2015), 0196pc (0.25 #462, 0.11 #2014, 0.11 #2015), 09jwl (0.25 #426, 0.11 #2014, 0.11 #2015) >> Best rule #256 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 0cbd2; >> query: (?x1527, 0d8qb) <- specialization_of(?x12763, ?x1527), specialization_of(?x3197, ?x1527), profession(?x12444, ?x3197), profession(?x4896, ?x3197), award_nominee(?x2716, ?x4896), nominated_for(?x4896, ?x6094), award(?x12444, ?x8843), ?x6094 = 0dnw1, disciplines_or_subjects(?x5039, ?x12763), nominated_for(?x12444, ?x4444), place_of_birth(?x4896, ?x1860) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04_tv specialization_of! 047vnfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 64.000 0.333 http://example.org/people/profession/specialization_of EVAL 04_tv specialization_of! 022qqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 64.000 0.333 http://example.org/people/profession/specialization_of EVAL 04_tv specialization_of! 02y5kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 64.000 0.333 http://example.org/people/profession/specialization_of #21035-0bs8hvm PRED entity: 0bs8hvm PRED relation: film_release_region PRED expected values: 02vzc => 137 concepts (128 used for prediction) PRED predicted values (max 10 best out of 229): 0d0vqn (0.92 #5263, 0.91 #9498, 0.91 #8990), 06mkj (0.91 #5999, 0.90 #3794, 0.89 #2776), 03gj2 (0.89 #3587, 0.86 #4776, 0.85 #5793), 02vzc (0.89 #4466, 0.89 #5484, 0.88 #6500), 035qy (0.88 #4277, 0.84 #9530, 0.84 #9022), 0345h (0.87 #8004, 0.87 #2241, 0.86 #3596), 0jgd (0.85 #5766, 0.81 #11523, 0.81 #6612), 0chghy (0.85 #5946, 0.83 #9503, 0.83 #8995), 03h64 (0.84 #4146, 0.83 #3977, 0.83 #10414), 05v8c (0.83 #1374, 0.70 #3918, 0.68 #4087) >> Best rule #5263 for best value: >> intensional similarity = 9 >> extensional distance = 51 >> proper extension: 0c0nhgv; 01jrbb; >> query: (?x8373, 0d0vqn) <- film_crew_role(?x8373, ?x137), film_release_region(?x8373, ?x1603), film_release_region(?x8373, ?x1229), film_release_region(?x8373, ?x985), ?x985 = 0k6nt, written_by(?x8373, ?x10245), ?x1603 = 06bnz, film_release_region(?x633, ?x1229), ?x633 = 0c40vxk >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #4466 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 35 *> proper extension: 011yrp; 0cwy47; 0jqn5; 0168ls; 0n04r; 0k4fz; 089j8p; *> query: (?x8373, 02vzc) <- film_crew_role(?x8373, ?x137), film_release_region(?x8373, ?x985), film_release_region(?x8373, ?x94), film(?x10245, ?x8373), ?x985 = 0k6nt, titles(?x732, ?x8373), film_regional_debut_venue(?x8373, ?x5416), nationality(?x51, ?x94), taxonomy(?x94, ?x939) *> conf = 0.89 ranks of expected_values: 4 EVAL 0bs8hvm film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 137.000 128.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21034-01pfpt PRED entity: 01pfpt PRED relation: artists PRED expected values: 0m19t 048xh 02ndj5 => 69 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1091): 0dm5l (0.61 #3257, 0.59 #3256, 0.56 #3255), 03t9sp (0.61 #3257, 0.59 #3256, 0.52 #1086), 02ndj5 (0.61 #3257, 0.59 #3256, 0.52 #1086), 03fbc (0.61 #3257, 0.59 #3256, 0.52 #1086), 0m19t (0.61 #3257, 0.59 #3256, 0.52 #1086), 0jg77 (0.61 #3257, 0.59 #3256, 0.52 #1086), 02hzz (0.61 #3257, 0.59 #3256, 0.50 #5095), 03xhj6 (0.61 #3257, 0.59 #3256, 0.50 #4740), 016vn3 (0.61 #3257, 0.59 #3256, 0.42 #6378), 016t0h (0.61 #3257, 0.59 #3256, 0.33 #6454) >> Best rule #3257 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 0xv2x; >> query: (?x6479, ?x2538) <- parent_genre(?x6479, ?x12498), parent_genre(?x6479, ?x1572), ?x1572 = 06by7, parent_genre(?x11737, ?x6479), artists(?x11737, ?x7682), artists(?x11737, ?x5883), artists(?x11737, ?x2538), role(?x5883, ?x75), group(?x1166, ?x7682), artist(?x2149, ?x7682), ?x12498 = 05c6073 >> conf = 0.61 => this is the best rule for 126 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 5, 26 EVAL 01pfpt artists 02ndj5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 33.000 0.607 http://example.org/music/genre/artists EVAL 01pfpt artists 048xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 69.000 33.000 0.607 http://example.org/music/genre/artists EVAL 01pfpt artists 0m19t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 33.000 0.607 http://example.org/music/genre/artists #21033-02t_tp PRED entity: 02t_tp PRED relation: award PRED expected values: 0gqy2 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 205): 0ck27z (0.21 #92, 0.20 #902, 0.19 #4142), 0gqy2 (0.15 #20253, 0.13 #21874, 0.13 #22280), 0gq9h (0.15 #20253, 0.13 #21874, 0.13 #22280), 040njc (0.15 #20253, 0.13 #21874, 0.13 #22280), 0f_nbyh (0.15 #20253, 0.13 #21874, 0.13 #22280), 099ck7 (0.15 #20253, 0.13 #21874, 0.13 #22280), 063y_ky (0.15 #20253, 0.13 #21874, 0.13 #22280), 02x1dht (0.15 #20253, 0.13 #21874, 0.13 #22280), 02x73k6 (0.15 #20253, 0.13 #22280, 0.11 #6481), 02grdc (0.15 #20253, 0.13 #22280, 0.11 #6481) >> Best rule #92 for best value: >> intensional similarity = 2 >> extensional distance = 577 >> proper extension: 084x96; >> query: (?x2587, 0ck27z) <- actor(?x9636, ?x2587), place_of_birth(?x2587, ?x11086) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #20253 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2035 *> proper extension: 0l56b; *> query: (?x2587, ?x594) <- award_nominee(?x2587, ?x3808), award_nominee(?x2587, ?x286), award_winner(?x594, ?x3808), award_winner(?x286, ?x426) *> conf = 0.15 ranks of expected_values: 2 EVAL 02t_tp award 0gqy2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 81.000 0.209 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21032-0272_vz PRED entity: 0272_vz PRED relation: genre PRED expected values: 06cvj => 97 concepts (95 used for prediction) PRED predicted values (max 10 best out of 112): 07s9rl0 (0.66 #4503, 0.66 #486, 0.64 #4992), 04t36 (0.63 #8412, 0.62 #8658, 0.61 #8535), 064t9 (0.60 #122, 0.56 #849, 0.56 #1216), 01jfsb (0.54 #135, 0.42 #740, 0.37 #1107), 02kdv5l (0.38 #125, 0.36 #246, 0.36 #4141), 03k9fj (0.28 #134, 0.24 #2687, 0.23 #1712), 06cvj (0.25 #4, 0.20 #975, 0.09 #489), 01t_vv (0.25 #55, 0.19 #1026, 0.09 #4557), 04xvlr (0.22 #487, 0.20 #4504, 0.19 #1702), 0lsxr (0.21 #252, 0.20 #1103, 0.20 #2074) >> Best rule #4503 for best value: >> intensional similarity = 3 >> extensional distance = 601 >> proper extension: 0jvt9; >> query: (?x4501, 07s9rl0) <- titles(?x307, ?x4501), genre(?x802, ?x307), award(?x4501, ?x102) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0pvms; *> query: (?x4501, 06cvj) <- country(?x4501, ?x94), produced_by(?x4501, ?x798), ?x798 = 0415svh, titles(?x307, ?x4501) *> conf = 0.25 ranks of expected_values: 7 EVAL 0272_vz genre 06cvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 95.000 0.662 http://example.org/film/film/genre #21031-05fnl9 PRED entity: 05fnl9 PRED relation: award_winner! PRED expected values: 04n2r9h => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 110): 0bq_mx (0.13 #814, 0.02 #1636, 0.01 #3006), 058m5m4 (0.12 #190, 0.06 #1286, 0.06 #1149), 0bxs_d (0.10 #112, 0.07 #797, 0.05 #934), 0418154 (0.10 #105, 0.06 #242, 0.04 #379), 09p3h7 (0.10 #69, 0.05 #754, 0.03 #1165), 0hn821n (0.10 #127, 0.05 #812, 0.03 #949), 09q_6t (0.10 #8, 0.05 #693, 0.02 #1104), 059x66 (0.10 #17, 0.02 #839, 0.01 #2894), 073h1t (0.10 #26, 0.01 #2492, 0.01 #2903), 02yv_b (0.10 #24) >> Best rule #814 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 03m_k0; 04snp2; 023qfd; >> query: (?x1676, 0bq_mx) <- award(?x1676, ?x4921), profession(?x1676, ?x353), ?x4921 = 0fbtbt >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #317 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 03dbww; *> query: (?x1676, 04n2r9h) <- award(?x1676, ?x704), award_nominee(?x1676, ?x968), language(?x1676, ?x254) *> conf = 0.02 ranks of expected_values: 66 EVAL 05fnl9 award_winner! 04n2r9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 91.000 91.000 0.131 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #21030-0mnzd PRED entity: 0mnzd PRED relation: place_of_death! PRED expected values: 06bss => 136 concepts (39 used for prediction) PRED predicted values (max 10 best out of 586): 02zfg3 (0.11 #725, 0.10 #2240, 0.07 #4508), 011w20 (0.11 #670, 0.10 #2185, 0.07 #4453), 03v1xb (0.11 #461, 0.10 #1976, 0.07 #4244), 05d1y (0.11 #404, 0.10 #1919, 0.07 #4187), 0bc71w (0.11 #331, 0.10 #1846, 0.07 #4114), 02z81h (0.11 #279, 0.10 #1794, 0.07 #4062), 02_jkc (0.11 #237, 0.10 #1752, 0.07 #4020), 0237fw (0.11 #92, 0.10 #1607, 0.07 #3875), 012t1 (0.11 #29, 0.10 #1544, 0.07 #3812), 07cbs (0.10 #1750, 0.09 #3262, 0.04 #4776) >> Best rule #725 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02cl1; 0cc56; 0mmzt; 0mndw; 0dzt9; 013h9; 0mnyn; >> query: (?x1427, 02zfg3) <- county_seat(?x12702, ?x1427), place_of_birth(?x9711, ?x1427), category(?x1427, ?x134), second_level_divisions(?x94, ?x1427) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0mnzd place_of_death! 06bss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 39.000 0.111 http://example.org/people/deceased_person/place_of_death #21029-0cmc26r PRED entity: 0cmc26r PRED relation: language PRED expected values: 02h40lc => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 45): 02h40lc (0.91 #421, 0.91 #603, 0.91 #1085), 04306rv (0.33 #5, 0.11 #1872, 0.11 #244), 064_8sq (0.17 #22, 0.15 #501, 0.14 #926), 06nm1 (0.17 #11, 0.12 #612, 0.12 #915), 02bjrlw (0.17 #1, 0.11 #61, 0.09 #240), 06b_j (0.17 #23, 0.11 #83, 0.08 #1528), 03_9r (0.07 #489, 0.06 #611, 0.06 #1636), 04h9h (0.06 #282, 0.05 #402, 0.05 #163), 0653m (0.06 #1277, 0.04 #855, 0.04 #916), 0jzc (0.05 #140, 0.05 #743, 0.04 #1103) >> Best rule #421 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 0k2m6; 0267wwv; >> query: (?x4111, 02h40lc) <- film_release_region(?x4111, ?x87), story_by(?x4111, ?x647), film(?x5898, ?x4111), film_crew_role(?x4111, ?x137) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cmc26r language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.915 http://example.org/film/film/language #21028-09gb_4p PRED entity: 09gb_4p PRED relation: nominated_for! PRED expected values: 0f_nbyh 0f4x7 02x4wr9 => 105 concepts (91 used for prediction) PRED predicted values (max 10 best out of 206): 019f4v (0.69 #1819, 0.55 #50, 0.54 #4030), 0gs9p (0.67 #1826, 0.60 #4037, 0.55 #57), 040njc (0.65 #1776, 0.65 #7, 0.43 #3987), 0f4x7 (0.65 #25, 0.38 #2678, 0.36 #2899), 09sb52 (0.60 #32, 0.25 #1801, 0.22 #7739), 0p9sw (0.46 #8644, 0.46 #1789, 0.43 #4000), 0gr0m (0.46 #1822, 0.42 #4033, 0.34 #5581), 0gs96 (0.45 #80, 0.24 #4060, 0.24 #1849), 0gqy2 (0.40 #107, 0.38 #2760, 0.36 #2981), 0f_nbyh (0.40 #8, 0.27 #10393, 0.22 #7739) >> Best rule #1819 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 0qm8b; 0fpv_3_; 011ydl; 0yx7h; 0ddj0x; 0hv4t; 03pc89; 01fwzk; >> query: (?x4602, 019f4v) <- nominated_for(?x6909, ?x4602), nominated_for(?x1703, ?x4602), ?x1703 = 0k611, ?x6909 = 02qyntr >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #25 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 18 *> proper extension: 02rx2m5; 0c38gj; *> query: (?x4602, 0f4x7) <- nominated_for(?x6729, ?x4602), nominated_for(?x2853, ?x4602), nominated_for(?x1703, ?x4602), ?x1703 = 0k611, ?x6729 = 099ck7, ?x2853 = 09qv_s *> conf = 0.65 ranks of expected_values: 4, 10, 26 EVAL 09gb_4p nominated_for! 02x4wr9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 105.000 91.000 0.687 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09gb_4p nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 91.000 0.687 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09gb_4p nominated_for! 0f_nbyh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 105.000 91.000 0.687 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21027-0cq7tx PRED entity: 0cq7tx PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.89 #111, 0.87 #121, 0.86 #146), 07c52 (0.08 #3, 0.07 #18, 0.06 #58), 02nxhr (0.08 #2, 0.07 #7, 0.05 #92), 07z4p (0.08 #5, 0.05 #60, 0.04 #140), 0735l (0.01 #64) >> Best rule #111 for best value: >> intensional similarity = 4 >> extensional distance = 142 >> proper extension: 02qrv7; 018nnz; 0g5pvv; 08984j; 042fgh; 025twgf; 025twgt; >> query: (?x4404, 029j_) <- country(?x4404, ?x94), film(?x2033, ?x4404), nominated_for(?x4404, ?x1745), category(?x2033, ?x134) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cq7tx film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #21026-02_j8x PRED entity: 02_j8x PRED relation: profession PRED expected values: 0dxtg => 87 concepts (85 used for prediction) PRED predicted values (max 10 best out of 56): 03gjzk (0.84 #13, 0.25 #748, 0.24 #895), 0dxtg (0.66 #12, 0.28 #4720, 0.28 #1777), 0cbd2 (0.27 #6621, 0.20 #153, 0.16 #6), 018gz8 (0.27 #6621, 0.14 #15, 0.14 #750), 02krf9 (0.25 #25, 0.09 #1643, 0.09 #4733), 09jwl (0.21 #1046, 0.21 #899, 0.20 #1193), 0kyk (0.15 #175, 0.10 #4001, 0.10 #7825), 0np9r (0.15 #7963, 0.14 #6197, 0.14 #4433), 016z4k (0.13 #886, 0.13 #1180, 0.12 #1033), 0nbcg (0.13 #912, 0.13 #1206, 0.13 #471) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 157 >> proper extension: 08f3yq; >> query: (?x8348, 03gjzk) <- place_of_birth(?x8348, ?x12461), producer_type(?x8348, ?x632) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 157 *> proper extension: 08f3yq; *> query: (?x8348, 0dxtg) <- place_of_birth(?x8348, ?x12461), producer_type(?x8348, ?x632) *> conf = 0.66 ranks of expected_values: 2 EVAL 02_j8x profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 85.000 0.836 http://example.org/people/person/profession #21025-01hr1 PRED entity: 01hr1 PRED relation: nominated_for! PRED expected values: 057xs89 018wdw => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 207): 0k611 (0.60 #544, 0.33 #72, 0.31 #5264), 02w9sd7 (0.60 #597, 0.33 #125, 0.13 #4845), 02hsq3m (0.50 #736, 0.44 #1208, 0.31 #1916), 0gq9h (0.40 #533, 0.36 #5253, 0.35 #4781), 019f4v (0.40 #525, 0.33 #53, 0.33 #5245), 0gs9p (0.40 #535, 0.33 #63, 0.33 #5255), 04dn09n (0.40 #506, 0.33 #34, 0.25 #5226), 0gr4k (0.40 #497, 0.33 #25, 0.23 #5217), 099c8n (0.40 #528, 0.33 #56, 0.22 #2416), 04kxsb (0.40 #567, 0.33 #95, 0.22 #21486) >> Best rule #544 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0h6r5; 03f7nt; >> query: (?x339, 0k611) <- language(?x339, ?x254), film(?x1909, ?x339), ?x1909 = 0mj1l, nominated_for(?x500, ?x339) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #827 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01hp5; 02fqrf; 01hqk; 0bpm4yw; 03cd0x; 01hq1; *> query: (?x339, 057xs89) <- language(?x339, ?x254), story_by(?x339, ?x13339), ?x13339 = 02nygk, country(?x339, ?x94) *> conf = 0.38 ranks of expected_values: 16, 106 EVAL 01hr1 nominated_for! 018wdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 105.000 105.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01hr1 nominated_for! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 105.000 105.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21024-0g02vk PRED entity: 0g02vk PRED relation: notable_people_with_this_condition PRED expected values: 015vq_ => 64 concepts (31 used for prediction) PRED predicted values (max 10 best out of 633): 034rd (0.50 #517, 0.33 #174, 0.23 #2251), 01tdnyh (0.33 #54, 0.25 #397, 0.20 #744), 0gzh (0.33 #227, 0.20 #1264, 0.08 #2304), 0rlz (0.33 #175, 0.20 #1212, 0.08 #2252), 095nx (0.29 #1494, 0.25 #342, 0.22 #1961), 01vn0t_ (0.29 #1464, 0.25 #312, 0.22 #1931), 0g_92 (0.29 #1380, 0.18 #1495, 0.17 #1962), 01385g (0.29 #1380, 0.18 #1495, 0.17 #1962), 07d3x (0.25 #436, 0.25 #321, 0.20 #1244), 06c0j (0.25 #454, 0.25 #339, 0.20 #1262) >> Best rule #517 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 07jwr; >> query: (?x9933, 034rd) <- people(?x9933, ?x9020), notable_people_with_this_condition(?x9933, ?x10154), people(?x6393, ?x10154), nationality(?x10154, ?x2346), profession(?x10154, ?x5805), entity_involved(?x9203, ?x10154), ?x5805 = 0fj9f >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g02vk notable_people_with_this_condition 015vq_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 31.000 0.500 http://example.org/medicine/disease/notable_people_with_this_condition #21023-015fr PRED entity: 015fr PRED relation: contains PRED expected values: 09wwlj => 175 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2796): 01gh6z (0.87 #23538, 0.84 #61789, 0.83 #158891), 01l_9d (0.87 #23538, 0.84 #61789, 0.83 #158891), 02f8zw (0.12 #9892, 0.06 #6950, 0.04 #27548), 0bwfn (0.12 #12816, 0.09 #3989, 0.09 #18700), 01lhdt (0.09 #3916, 0.09 #18627, 0.08 #21569), 0d34_ (0.09 #5498, 0.09 #20209, 0.08 #23151), 09f8q (0.09 #5236, 0.09 #19947, 0.08 #22889), 05bkf (0.09 #5163, 0.09 #19874, 0.08 #22816), 0d58_ (0.09 #4269, 0.09 #18980, 0.08 #21922), 02h6_6p (0.09 #3249, 0.09 #17960, 0.08 #20902) >> Best rule #23538 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 09krp; >> query: (?x583, ?x12465) <- combatants(?x94, ?x583), contains(?x583, ?x1167), administrative_parent(?x12465, ?x583) >> conf = 0.87 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 015fr contains 09wwlj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 175.000 99.000 0.871 http://example.org/location/location/contains #21022-027s39y PRED entity: 027s39y PRED relation: nominated_for! PRED expected values: 05bm4sm => 112 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1159): 060pl5 (0.79 #51424, 0.78 #35064, 0.78 #88822), 01x6v6 (0.44 #53763, 0.38 #60774, 0.14 #2338), 04wp63 (0.37 #25715, 0.37 #46750, 0.35 #79471), 0h96g (0.37 #25715, 0.37 #46750, 0.35 #79471), 01pcbg (0.37 #25715, 0.37 #46750, 0.35 #79471), 04pz5c (0.37 #25715, 0.37 #46750, 0.35 #79471), 0p8r1 (0.37 #25715, 0.37 #46750, 0.35 #79471), 06rnl9 (0.34 #35065, 0.23 #44413, 0.08 #9963), 086k8 (0.19 #39796, 0.04 #21099, 0.04 #30447), 01795t (0.15 #25716, 0.15 #9351, 0.15 #7450) >> Best rule #51424 for best value: >> intensional similarity = 4 >> extensional distance = 258 >> proper extension: 02y_lrp; 083shs; 01sxly; 0170_p; 0209xj; 0kv2hv; 0jyx6; 02prw4h; 0416y94; 02rqwhl; ... >> query: (?x3946, ?x1052) <- nominated_for(?x500, ?x3946), award_winner(?x3946, ?x1052), genre(?x3946, ?x239), executive_produced_by(?x3946, ?x6682) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #19964 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 032zq6; 0b2qtl; 0kt_4; *> query: (?x3946, 05bm4sm) <- nominated_for(?x500, ?x3946), honored_for(?x2988, ?x3946), film(?x3402, ?x3946), story_by(?x3946, ?x3456) *> conf = 0.06 ranks of expected_values: 37 EVAL 027s39y nominated_for! 05bm4sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 112.000 40.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #21021-06ybb1 PRED entity: 06ybb1 PRED relation: film_release_region PRED expected values: 09c7w0 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 116): 09c7w0 (0.70 #1078, 0.69 #4839, 0.68 #3049), 0f8l9c (0.21 #9528, 0.21 #22248, 0.21 #21711), 0d0vqn (0.21 #21511, 0.21 #22228, 0.21 #9508), 06mkj (0.20 #9572, 0.19 #21575, 0.19 #21755), 02vzc (0.19 #2578, 0.19 #9566, 0.18 #21569), 03rjj (0.19 #9504, 0.18 #8068, 0.18 #21507), 0k6nt (0.19 #9532, 0.18 #21535, 0.18 #2544), 07ssc (0.19 #21523, 0.19 #21703, 0.19 #919), 059j2 (0.19 #21544, 0.18 #22799, 0.18 #22978), 03_3d (0.18 #2518, 0.17 #21509, 0.17 #22764) >> Best rule #1078 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0m313; 0140g4; 011yxg; 0ds33; 0hmr4; 0jzw; 0pv2t; 0kv2hv; 017gl1; 09q5w2; ... >> query: (?x2165, 09c7w0) <- film_release_distribution_medium(?x2165, ?x81), honored_for(?x2165, ?x1311), written_by(?x2165, ?x5338), nominated_for(?x102, ?x2165) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06ybb1 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.698 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #21020-0flw86 PRED entity: 0flw86 PRED relation: religion! PRED expected values: 0162b 01bkb => 35 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1582): 01n4w (0.64 #1406, 0.50 #1622, 0.50 #248), 0vmt (0.59 #1382, 0.50 #224, 0.50 #81), 02xry (0.55 #1400, 0.50 #242, 0.50 #99), 01n7q (0.55 #1388, 0.45 #1313, 0.42 #951), 059_c (0.55 #1387, 0.42 #1676, 0.42 #1603), 03v1s (0.50 #1375, 0.50 #217, 0.50 #74), 04rrx (0.50 #1395, 0.50 #1320, 0.42 #958), 05kj_ (0.50 #1377, 0.50 #76, 0.38 #1593), 0gyh (0.50 #1404, 0.45 #1329, 0.42 #1693), 0824r (0.50 #113, 0.45 #1414, 0.42 #1630) >> Best rule #1406 for best value: >> intensional similarity = 10 >> extensional distance = 20 >> proper extension: 078vc; >> query: (?x492, 01n4w) <- religion(?x1264, ?x492), religion(?x279, ?x492), religion(?x3977, ?x492), religion(?x1020, ?x492), service_location(?x555, ?x1264), profession(?x3977, ?x131), contains(?x7273, ?x279), award(?x3977, ?x3978), award(?x1020, ?x3066), type_of_union(?x1020, ?x566) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #498 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 11 *> proper extension: 01fgks; 01gr6h; *> query: (?x492, ?x1353) <- religion(?x8558, ?x492), religion(?x7833, ?x492), religion(?x2000, ?x492), religion(?x335, ?x492), contains(?x6956, ?x2000), contains(?x455, ?x2000), ?x455 = 02j9z, adjoins(?x7833, ?x1353), adjoins(?x2000, ?x3855), countries_within(?x6956, ?x252), organization(?x8558, ?x127), adjoins(?x2020, ?x335) *> conf = 0.34 ranks of expected_values: 37, 129 EVAL 0flw86 religion! 01bkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 35.000 25.000 0.636 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 0flw86 religion! 0162b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 35.000 25.000 0.636 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #21019-06rvn PRED entity: 06rvn PRED relation: role PRED expected values: 03qjg => 80 concepts (61 used for prediction) PRED predicted values (max 10 best out of 124): 0l14md (0.88 #3895, 0.85 #4419, 0.85 #4302), 03qjg (0.85 #6255, 0.85 #6194, 0.82 #5861), 0dwtp (0.85 #4287, 0.85 #4184, 0.79 #2063), 0l14qv (0.82 #3652, 0.79 #2063, 0.77 #4171), 05148p4 (0.81 #5494, 0.77 #5756, 0.77 #4190), 04rzd (0.81 #6177, 0.79 #2063, 0.77 #4676), 02hnl (0.81 #1928, 0.79 #4549, 0.78 #1927), 02dlh2 (0.81 #1928, 0.79 #4549, 0.78 #1927), 026t6 (0.81 #1928, 0.78 #1927, 0.71 #3774), 0395lw (0.81 #1928, 0.78 #1927, 0.71 #3774) >> Best rule #3895 for best value: >> intensional similarity = 27 >> extensional distance = 9 >> proper extension: 0g2dz; >> query: (?x8172, ?x315) <- role(?x8172, ?x4769), role(?x8172, ?x2059), role(?x8172, ?x716), role(?x8172, ?x315), role(?x8172, ?x228), role(?x8172, ?x212), ?x4769 = 0dwt5, ?x2059 = 0dwr4, performance_role(?x8172, ?x1750), ?x212 = 026t6, ?x716 = 018vs, role(?x315, ?x10811), role(?x315, ?x894), role(?x315, ?x75), instrumentalists(?x315, ?x6877), ?x228 = 0l14qv, role(?x1282, ?x315), group(?x315, ?x8156), ?x894 = 03m5k, performance_role(?x5718, ?x315), performance_role(?x5543, ?x315), ?x10811 = 0d8lm, ?x6877 = 0ddkf, award(?x8156, ?x2634), ?x75 = 07y_7, profession(?x5543, ?x220), artist(?x3265, ?x5718) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #6255 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 24 *> proper extension: 03f5mt; *> query: (?x8172, ?x2798) <- role(?x8172, ?x2157), role(?x8172, ?x716), role(?x2798, ?x8172), ?x716 = 018vs, ?x2798 = 03qjg, role(?x2059, ?x2157), role(?x1831, ?x2157), role(?x2956, ?x2157), role(?x2459, ?x2157), ?x2956 = 0myk8, performance_role(?x6328, ?x2157), ?x1831 = 03t22m, role(?x11443, ?x2157), ?x2459 = 021bmf, ?x2059 = 0dwr4, origin(?x11443, ?x1523), profession(?x6328, ?x319), role(?x2157, ?x314), profession(?x11443, ?x131), place_of_birth(?x11443, ?x11444), nationality(?x11443, ?x94) *> conf = 0.85 ranks of expected_values: 2 EVAL 06rvn role 03qjg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 61.000 0.882 http://example.org/music/performance_role/regular_performances./music/group_membership/role #21018-0209xj PRED entity: 0209xj PRED relation: nominated_for! PRED expected values: 0gs9p 02qvyrt => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 195): 04dn09n (0.68 #7419, 0.67 #7418, 0.66 #5672), 094qd5 (0.68 #7419, 0.67 #7418, 0.66 #5672), 027986c (0.68 #7419, 0.67 #7418, 0.66 #5672), 0gs9p (0.64 #2237, 0.64 #1365, 0.32 #3546), 0k611 (0.58 #1373, 0.52 #2245, 0.28 #719), 0gr42 (0.50 #78, 0.40 #296, 0.33 #514), 057xs89 (0.50 #103, 0.40 #321, 0.33 #539), 09sb52 (0.47 #1340, 0.18 #904, 0.16 #686), 0gr4k (0.45 #2204, 0.43 #1332, 0.24 #678), 0f_nbyh (0.42 #1314, 0.16 #878, 0.13 #2186) >> Best rule #7419 for best value: >> intensional similarity = 3 >> extensional distance = 938 >> proper extension: 07bz5; >> query: (?x696, ?x899) <- nominated_for(?x397, ?x696), award(?x696, ?x899), award(?x286, ?x899) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #2237 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 286 *> proper extension: 011yfd; *> query: (?x696, 0gs9p) <- nominated_for(?x1307, ?x696), ?x1307 = 0gq9h *> conf = 0.64 ranks of expected_values: 4, 21 EVAL 0209xj nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 74.000 74.000 0.677 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0209xj nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 74.000 0.677 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21017-0bz5v2 PRED entity: 0bz5v2 PRED relation: actor! PRED expected values: 05pbsry => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 96): 02hct1 (0.09 #827, 0.01 #1091), 0124k9 (0.08 #21, 0.04 #813, 0.01 #1077), 063ykwt (0.08 #58, 0.01 #2963, 0.01 #4549), 025x1t (0.08 #222), 0vhm (0.08 #90), 01h72l (0.08 #38), 039cq4 (0.05 #920, 0.04 #1184, 0.02 #4619), 08jgk1 (0.05 #286, 0.02 #550, 0.02 #2663), 0304nh (0.05 #348, 0.02 #612, 0.01 #1140), 016tvq (0.05 #424, 0.02 #688) >> Best rule #827 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 01j7pt; >> query: (?x1040, 02hct1) <- nominated_for(?x1040, ?x3626), program(?x236, ?x3626), tv_program(?x3625, ?x3626) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #1049 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 01j7pt; *> query: (?x1040, 05pbsry) <- nominated_for(?x1040, ?x3626), program(?x236, ?x3626), tv_program(?x3625, ?x3626) *> conf = 0.02 ranks of expected_values: 46 EVAL 0bz5v2 actor! 05pbsry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 85.000 85.000 0.088 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #21016-0pyww PRED entity: 0pyww PRED relation: type_of_union PRED expected values: 04ztj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.84 #9, 0.80 #5, 0.76 #13), 01g63y (0.17 #22, 0.17 #34, 0.16 #86) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 013ybx; >> query: (?x4816, 04ztj) <- award_nominee(?x4816, ?x6600), award_nominee(?x2817, ?x4816), special_performance_type(?x4816, ?x4832) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pyww type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.844 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21015-02xb2bt PRED entity: 02xb2bt PRED relation: gender PRED expected values: 05zppz => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #5, 0.71 #153, 0.71 #159), 02zsn (0.53 #109, 0.51 #116, 0.38 #2) >> Best rule #5 for best value: >> intensional similarity = 2 >> extensional distance = 40 >> proper extension: 06whf; 0f1pyf; 01l87db; 03_87; 0739y; 01tw31; 019gz; 0g9zjp; 02y0dd; 03_dj; >> query: (?x2371, 05zppz) <- nationality(?x2371, ?x429), ?x429 = 03rt9 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xb2bt gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.881 http://example.org/people/person/gender #21014-0sxfd PRED entity: 0sxfd PRED relation: titles! PRED expected values: 07s9rl0 => 77 concepts (36 used for prediction) PRED predicted values (max 10 best out of 70): 01z4y (0.51 #548, 0.50 #651, 0.41 #1686), 07s9rl0 (0.49 #822, 0.45 #1238, 0.43 #410), 04xvlr (0.39 #1029, 0.27 #1757, 0.26 #1241), 07c52 (0.38 #1159, 0.08 #3666, 0.08 #3037), 07ssc (0.27 #831, 0.25 #317, 0.17 #419), 02l7c8 (0.24 #1340, 0.23 #1128, 0.22 #2694), 05p553 (0.24 #1340, 0.23 #1128, 0.22 #2694), 06cvj (0.24 #1340, 0.23 #1128, 0.22 #2694), 01t_vv (0.24 #1340, 0.23 #1128, 0.22 #2694), 0q9mp (0.24 #1340, 0.23 #1128, 0.22 #2694) >> Best rule #548 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 050xxm; 05dptj; 02x0fs9; >> query: (?x1402, 01z4y) <- genre(?x1402, ?x239), nominated_for(?x484, ?x1402), award_winner(?x1402, ?x3585), ?x239 = 06cvj >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #822 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 92 *> proper extension: 01jc6q; 07xtqq; 095zlp; 01cssf; 0209xj; 0209hj; 0hmr4; 0p_sc; 0jzw; 0b6tzs; ... *> query: (?x1402, 07s9rl0) <- award(?x1402, ?x9130), nominated_for(?x9130, ?x1490), award(?x1863, ?x9130), ?x1863 = 04qw17, award(?x2493, ?x9130) *> conf = 0.49 ranks of expected_values: 2 EVAL 0sxfd titles! 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 36.000 0.515 http://example.org/media_common/netflix_genre/titles #21013-01q3_2 PRED entity: 01q3_2 PRED relation: type_of_union PRED expected values: 04ztj => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.72 #41, 0.70 #173, 0.70 #49), 01g63y (0.12 #126, 0.12 #130, 0.12 #122), 0jgjn (0.08 #12, 0.06 #16, 0.05 #20) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 0146pg; 01vrncs; 01vrz41; 0pgjm; 02lz1s; 02fgpf; 02cyfz; 03n0q5; 02bfxb; 0m_v0; ... >> query: (?x9731, 04ztj) <- award(?x9731, ?x1323), award_nominee(?x9731, ?x568), ?x1323 = 0gqz2, nominated_for(?x9731, ?x8677) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q3_2 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.717 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21012-01c9jp PRED entity: 01c9jp PRED relation: award! PRED expected values: 01fl3 0g_g2 019f9z => 48 concepts (31 used for prediction) PRED predicted values (max 10 best out of 2318): 03j24kf (0.79 #13317, 0.79 #13316, 0.78 #56594), 0163kf (0.79 #13317, 0.79 #13316, 0.78 #56594), 01wwvc5 (0.73 #24029, 0.70 #17373, 0.64 #20701), 0dvqq (0.67 #3952, 0.43 #7281, 0.26 #9987), 0d193h (0.67 #4518, 0.29 #11176, 0.29 #7847), 01pfr3 (0.67 #3423, 0.29 #6752, 0.20 #94), 06mj4 (0.67 #5631, 0.20 #2302, 0.14 #12289), 02z4b_8 (0.57 #12029, 0.57 #8700, 0.30 #18688), 017959 (0.57 #9366, 0.50 #6037, 0.20 #19354), 01vs_v8 (0.57 #10563, 0.43 #7234, 0.36 #23878) >> Best rule #13317 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 02q3s; >> query: (?x3647, ?x5059) <- award_winner(?x3647, ?x5059), award_winner(?x3647, ?x2461), type_of_union(?x5059, ?x566), award(?x5059, ?x567), ?x2461 = 01cwhp >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #9987 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 01c427; 02f716; *> query: (?x3647, ?x521) <- award(?x5512, ?x3647), award(?x1660, ?x3647), award_nominee(?x1660, ?x521), award_winner(?x1801, ?x1660), ?x5512 = 02jqjm *> conf = 0.26 ranks of expected_values: 214, 276, 2237 EVAL 01c9jp award! 019f9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 48.000 31.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c9jp award! 0g_g2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 48.000 31.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c9jp award! 01fl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 31.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21011-0fz3b1 PRED entity: 0fz3b1 PRED relation: nominated_for! PRED expected values: 05zvj3m 063y_ky => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 179): 0gq9h (0.28 #1497, 0.25 #1019, 0.21 #12013), 019f4v (0.25 #1488, 0.24 #1010, 0.18 #3400), 0k611 (0.25 #1508, 0.21 #1030, 0.16 #12024), 0p9sw (0.23 #1454, 0.15 #3366, 0.15 #5039), 0gs9p (0.23 #1021, 0.23 #1499, 0.18 #12015), 099c8n (0.23 #1491, 0.21 #1252, 0.19 #774), 04dn09n (0.23 #991, 0.20 #1469, 0.15 #4098), 027dtxw (0.20 #14580, 0.19 #15777, 0.19 #15776), 05ztrmj (0.20 #14580, 0.19 #15777, 0.19 #15776), 09sdmz (0.20 #14580, 0.19 #15777, 0.19 #15776) >> Best rule #1497 for best value: >> intensional similarity = 3 >> extensional distance = 216 >> proper extension: 0d7vtk; >> query: (?x4326, 0gq9h) <- nominated_for(?x4325, ?x4326), country(?x4326, ?x94), story_by(?x1642, ?x4325) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #14580 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1511 *> proper extension: 0c3xpwy; *> query: (?x4326, ?x112) <- nominated_for(?x4325, ?x4326), gender(?x4325, ?x231), award(?x4325, ?x112) *> conf = 0.20 ranks of expected_values: 11, 35 EVAL 0fz3b1 nominated_for! 063y_ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 74.000 74.000 0.275 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fz3b1 nominated_for! 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 74.000 74.000 0.275 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #21010-01gvts PRED entity: 01gvts PRED relation: currency PRED expected values: 09nqf => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.79 #43, 0.77 #134, 0.77 #176), 01nv4h (0.05 #37, 0.05 #9, 0.03 #23), 02gsvk (0.02 #69, 0.01 #132), 0ptk_ (0.02 #17), 02l6h (0.01 #277, 0.01 #32, 0.01 #102) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 227 >> proper extension: 02_1sj; 03ckwzc; 0963mq; 02vqhv0; 0j_tw; 01b195; 04z257; 0d1qmz; 03wbqc4; 02rmd_2; ... >> query: (?x7194, 09nqf) <- films(?x11683, ?x7194), film_release_distribution_medium(?x7194, ?x81), film(?x7310, ?x7194) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gvts currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.786 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #21009-01vswwx PRED entity: 01vswwx PRED relation: currency PRED expected values: 09nqf => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.58 #7, 0.47 #52, 0.45 #70), 01nv4h (0.11 #11, 0.11 #17, 0.09 #23) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 01cwhp; 01ttg5; 01d1st; >> query: (?x5301, 09nqf) <- award_nominee(?x954, ?x5301), artists(?x302, ?x5301), vacationer(?x3288, ?x5301) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vswwx currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.576 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #21008-069q4f PRED entity: 069q4f PRED relation: language PRED expected values: 02h40lc => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.89 #2928, 0.89 #1490, 0.89 #3640), 06b_j (0.46 #952, 0.42 #1249, 0.18 #202), 06nm1 (0.46 #952, 0.42 #1249, 0.13 #249), 04306rv (0.46 #952, 0.42 #1249, 0.09 #184), 07zrf (0.46 #952, 0.42 #1249, 0.09 #182), 02bjrlw (0.46 #952, 0.09 #180, 0.09 #1251), 064_8sq (0.16 #1569, 0.15 #1929, 0.14 #558), 03_9r (0.06 #3223, 0.05 #4303, 0.05 #1617), 02ztjwg (0.06 #3223, 0.04 #687, 0.03 #627), 0653m (0.06 #3223, 0.04 #548, 0.03 #2160) >> Best rule #2928 for best value: >> intensional similarity = 3 >> extensional distance = 744 >> proper extension: 05f67hw; >> query: (?x1311, 02h40lc) <- country(?x1311, ?x94), ?x94 = 09c7w0, produced_by(?x1311, ?x595) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 069q4f language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.890 http://example.org/film/film/language #21007-017l96 PRED entity: 017l96 PRED relation: artist PRED expected values: 0kzy0 0b68vs 01fl3 01w724 01vsl3_ 01m65sp 01nn6c 01vrnsk 01p0vf 01lz4tf => 95 concepts (44 used for prediction) PRED predicted values (max 10 best out of 776): 0qf3p (0.43 #2431, 0.33 #139, 0.20 #1667), 01vn0t_ (0.43 #2873, 0.33 #581, 0.20 #2109), 01vrnsk (0.43 #2739, 0.20 #1211, 0.12 #764), 0dtd6 (0.40 #1629, 0.40 #865, 0.33 #101), 0kj34 (0.40 #2135, 0.40 #1371, 0.14 #2899), 0cg9y (0.40 #1651, 0.33 #123, 0.20 #887), 0167xy (0.40 #2209, 0.33 #681, 0.20 #1445), 0144l1 (0.40 #871, 0.29 #2399, 0.20 #1635), 016376 (0.40 #1447, 0.25 #4503, 0.15 #6029), 01bczm (0.40 #1889, 0.20 #1125, 0.14 #2653) >> Best rule #2431 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 011k1h; 01clyr; 0k_kr; 03qy3l; 041p3y; >> query: (?x3240, 0qf3p) <- artist(?x3240, ?x12670), artist(?x3240, ?x2835), artist(?x3240, ?x2737), artist(?x3240, ?x1136), ?x1136 = 07c0j, category(?x12670, ?x134), currency(?x2737, ?x170), award_winner(?x2431, ?x2835) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2739 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 011k1h; 01clyr; 0k_kr; 03qy3l; 041p3y; *> query: (?x3240, 01vrnsk) <- artist(?x3240, ?x12670), artist(?x3240, ?x2835), artist(?x3240, ?x2737), artist(?x3240, ?x1136), ?x1136 = 07c0j, category(?x12670, ?x134), currency(?x2737, ?x170), award_winner(?x2431, ?x2835) *> conf = 0.43 ranks of expected_values: 3, 41, 65, 201, 213, 228, 263, 511, 547 EVAL 017l96 artist 01lz4tf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 95.000 44.000 0.429 http://example.org/music/record_label/artist EVAL 017l96 artist 01p0vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 95.000 44.000 0.429 http://example.org/music/record_label/artist EVAL 017l96 artist 01vrnsk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 44.000 0.429 http://example.org/music/record_label/artist EVAL 017l96 artist 01nn6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 95.000 44.000 0.429 http://example.org/music/record_label/artist EVAL 017l96 artist 01m65sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 44.000 0.429 http://example.org/music/record_label/artist EVAL 017l96 artist 01vsl3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 95.000 44.000 0.429 http://example.org/music/record_label/artist EVAL 017l96 artist 01w724 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 95.000 44.000 0.429 http://example.org/music/record_label/artist EVAL 017l96 artist 01fl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 95.000 44.000 0.429 http://example.org/music/record_label/artist EVAL 017l96 artist 0b68vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 95.000 44.000 0.429 http://example.org/music/record_label/artist EVAL 017l96 artist 0kzy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 95.000 44.000 0.429 http://example.org/music/record_label/artist #21006-0llcx PRED entity: 0llcx PRED relation: film! PRED expected values: 0205dx => 97 concepts (55 used for prediction) PRED predicted values (max 10 best out of 874): 0c0k1 (0.64 #95912, 0.43 #10423, 0.43 #27105), 020fgy (0.44 #27104, 0.44 #50044, 0.43 #10423), 0c3ns (0.43 #10423, 0.43 #27105, 0.42 #114680), 08mhyd (0.43 #10423, 0.43 #27105, 0.42 #114680), 0j_c (0.08 #2495, 0.03 #40026, 0.02 #37940), 0bj9k (0.08 #329, 0.04 #8667, 0.03 #10752), 0zcbl (0.06 #1223, 0.02 #11646, 0.02 #7476), 015c4g (0.05 #9120, 0.04 #782, 0.02 #11205), 016yvw (0.05 #9290, 0.02 #15546, 0.02 #952), 02xs5v (0.05 #3492, 0.04 #5577, 0.02 #55622) >> Best rule #95912 for best value: >> intensional similarity = 3 >> extensional distance = 847 >> proper extension: 0123qq; >> query: (?x7883, ?x8704) <- nominated_for(?x8704, ?x7883), participant(?x8704, ?x1397), profession(?x8704, ?x319) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #11276 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 141 *> proper extension: 0b2v79; 01_mdl; 01719t; 02rx2m5; 0ch26b_; 0bx0l; 0gh65c5; 07s846j; 043tz0c; 04v8h1; ... *> query: (?x7883, 0205dx) <- film(?x5283, ?x7883), nominated_for(?x1443, ?x7883), ?x1443 = 054krc *> conf = 0.01 ranks of expected_values: 556 EVAL 0llcx film! 0205dx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 97.000 55.000 0.638 http://example.org/film/actor/film./film/performance/film #21005-040nwr PRED entity: 040nwr PRED relation: gender PRED expected values: 02zsn => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #69, 0.73 #61, 0.72 #257), 02zsn (0.67 #14, 0.64 #24, 0.64 #44) >> Best rule #69 for best value: >> intensional similarity = 2 >> extensional distance = 205 >> proper extension: 0frpd5; 02qfk4j; >> query: (?x12675, 05zppz) <- nationality(?x12675, ?x2146), ?x2146 = 03rk0 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 090gpr; *> query: (?x12675, 02zsn) <- award(?x12675, ?x10156), ?x10156 = 03r8v_, profession(?x12675, ?x1032) *> conf = 0.67 ranks of expected_values: 2 EVAL 040nwr gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 152.000 152.000 0.739 http://example.org/people/person/gender #21004-03n_7k PRED entity: 03n_7k PRED relation: award PRED expected values: 099jhq => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 225): 09sb52 (0.70 #24132, 0.70 #8445, 0.68 #5227), 0ck27z (0.21 #2102, 0.18 #12870, 0.15 #12065), 05pcn59 (0.18 #12870, 0.17 #885, 0.15 #12065), 05zr6wv (0.18 #12870, 0.15 #12065, 0.14 #12064), 04ljl_l (0.18 #12870, 0.15 #12065, 0.14 #12064), 0bdwqv (0.18 #12870, 0.15 #12065, 0.14 #12064), 0bfvw2 (0.18 #12870, 0.15 #12065, 0.14 #12064), 02x8n1n (0.18 #12870, 0.15 #12065, 0.14 #12064), 09qv3c (0.18 #12870, 0.15 #12065, 0.14 #12064), 0f4x7 (0.15 #12065, 0.14 #12064, 0.13 #24535) >> Best rule #24132 for best value: >> intensional similarity = 2 >> extensional distance = 2276 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01vsxdm; 01wv9xn; 0frsw; 016fmf; 01vrwfv; 0134s5; 02lbrd; ... >> query: (?x2414, ?x704) <- award(?x2414, ?x1033), award_winner(?x704, ?x2414) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #12065 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1487 *> proper extension: 03yxwq; 03lpbx; *> query: (?x2414, ?x102) <- award_winner(?x2414, ?x2415), award(?x2415, ?x870), award(?x2415, ?x102), nominated_for(?x870, ?x758) *> conf = 0.15 ranks of expected_values: 14 EVAL 03n_7k award 099jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 80.000 80.000 0.701 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #21003-0mmpz PRED entity: 0mmpz PRED relation: adjoins PRED expected values: 0mlzk => 140 concepts (77 used for prediction) PRED predicted values (max 10 best out of 466): 0mlzk (0.82 #57213, 0.81 #25513, 0.81 #8503), 0mmpz (0.33 #581, 0.26 #30932, 0.25 #38664), 0mlw1 (0.33 #2069, 0.25 #40980, 0.23 #47162), 03s5t (0.33 #910, 0.08 #6320, 0.07 #7093), 05kj_ (0.33 #806, 0.06 #6216, 0.06 #6989), 015jr (0.33 #1109, 0.02 #8840, 0.02 #21978), 0mlyw (0.26 #30932, 0.25 #38664, 0.25 #40981), 0mm0p (0.25 #40980, 0.23 #47162, 0.23 #54894), 0ml_m (0.25 #40980, 0.08 #4459, 0.01 #9873), 0mlvc (0.25 #40980, 0.04 #4442, 0.01 #29190) >> Best rule #57213 for best value: >> intensional similarity = 4 >> extensional distance = 365 >> proper extension: 0xn7b; >> query: (?x11525, ?x11569) <- adjoins(?x11525, ?x12383), adjoins(?x11569, ?x11525), contains(?x4600, ?x12383), state(?x5267, ?x4600) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mmpz adjoins 0mlzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 77.000 0.815 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #21002-07ssc PRED entity: 07ssc PRED relation: nationality! PRED expected values: 0bz5v2 09mq4m 0126rp 027pdrh 02f2dn 0m31m 01nn6c 0k269 0kh6b 01vvyfh 015q43 08_83x 0627sn 07c37 0dvld 0hky 0bk4s 018d6l 0151zx 017lqp 01bbwp 01wxdn3 020_4z 06g4_ 02465 0fvt2 0hcvy 0cj2w 01vzz1c 06p0s1 047g6 02vkvcz 0kbg6 => 219 concepts (114 used for prediction) PRED predicted values (max 10 best out of 3909): 0kn4c (0.71 #115800, 0.52 #175570, 0.52 #194248), 09wj5 (0.71 #115800, 0.52 #175569, 0.47 #388505), 09xrxq (0.71 #115800, 0.52 #175569, 0.47 #388505), 0h10vt (0.71 #115800, 0.52 #175569, 0.47 #388505), 07rhpg (0.71 #115800, 0.52 #175569, 0.47 #388505), 0g8st4 (0.71 #115800, 0.52 #175569, 0.47 #388505), 0184dt (0.71 #115800, 0.52 #175569, 0.47 #388505), 038rzr (0.71 #115800, 0.52 #175569, 0.47 #388505), 044mz_ (0.71 #115800, 0.52 #175569, 0.47 #388505), 0dpqk (0.71 #115800, 0.52 #175569, 0.47 #388505) >> Best rule #115800 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 070zc; >> query: (?x512, ?x57) <- contains(?x512, ?x9844), combatants(?x326, ?x512), student(?x9844, ?x57) >> conf = 0.71 => this is the best rule for 68 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 13, 14, 28, 38, 50, 62, 69, 71, 84, 92, 109, 115, 117, 137, 185, 187, 238, 239, 262, 286, 297, 987, 994, 3623, 3839, 3898 EVAL 07ssc nationality! 0kbg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 02vkvcz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 047g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 06p0s1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 01vzz1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0cj2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0hcvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0fvt2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 02465 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 06g4_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 020_4z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 01wxdn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 01bbwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 017lqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0151zx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 018d6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0bk4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0hky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0dvld CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 07c37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0627sn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 08_83x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 015q43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 01vvyfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0kh6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0k269 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 01nn6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0m31m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 02f2dn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 027pdrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0126rp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 09mq4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 114.000 0.712 http://example.org/people/person/nationality EVAL 07ssc nationality! 0bz5v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 219.000 114.000 0.712 http://example.org/people/person/nationality #21001-01f7j9 PRED entity: 01f7j9 PRED relation: type_of_union PRED expected values: 04ztj => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.89 #29, 0.87 #33, 0.87 #89), 01g63y (0.19 #18, 0.12 #246, 0.12 #34) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0kr5_; 022_lg; 01t07j; 0h1p; 01_vfy; 0p51w; 02645b; 03tf_h; 01f8ld; 01q4qv; ... >> query: (?x2182, 04ztj) <- award(?x2182, ?x198), film(?x2182, ?x1076), ?x198 = 040njc >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f7j9 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.889 http://example.org/people/person/spouse_s./people/marriage/type_of_union #21000-01lv85 PRED entity: 01lv85 PRED relation: category PRED expected values: 08mbj5d => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.41 #4, 0.38 #1, 0.38 #6) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 06r1k; 0gxsh4; 03czz87; >> query: (?x7511, 08mbj5d) <- actor(?x7511, ?x879), nominated_for(?x201, ?x7511), genre(?x7511, ?x258), tv_program(?x4491, ?x7511) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lv85 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.406 http://example.org/common/topic/webpage./common/webpage/category #20999-01mr2g6 PRED entity: 01mr2g6 PRED relation: origin PRED expected values: 030qb3t => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 69): 0118d3 (0.31 #5209, 0.07 #8997, 0.06 #17525), 02dtg (0.09 #721, 0.02 #8534, 0.02 #9007), 0vzm (0.09 #778, 0.01 #5276), 030qb3t (0.07 #1928, 0.05 #7610, 0.05 #7373), 0d6lp (0.06 #1959, 0.04 #776), 04jpl (0.05 #1900, 0.03 #12077, 0.03 #6398), 02_286 (0.05 #253, 0.04 #490, 0.04 #7355), 03dm7 (0.04 #898, 0.03 #1845, 0.03 #2081), 0lphb (0.04 #595, 0.02 #1068, 0.01 #2251), 01_d4 (0.04 #751, 0.02 #5249, 0.02 #1462) >> Best rule #5209 for best value: >> intensional similarity = 3 >> extensional distance = 226 >> proper extension: 03qd_; 01qvgl; 01vs_v8; 0jfx1; 09hnb; 01wwvc5; 053yx; 01w02sy; 01wmgrf; 03bnv; ... >> query: (?x8272, ?x12721) <- type_of_union(?x8272, ?x566), place_of_birth(?x8272, ?x12721), instrumentalists(?x227, ?x8272) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #1928 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 78 *> proper extension: 0m19t; 067mj; 0249kn; 07yg2; 0394y; 02t3ln; 0134tg; 01q99h; 01kcms4; 08w4pm; ... *> query: (?x8272, 030qb3t) <- artists(?x7329, ?x8272), ?x7329 = 016jny *> conf = 0.07 ranks of expected_values: 4 EVAL 01mr2g6 origin 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 102.000 0.311 http://example.org/music/artist/origin #20998-02h758 PRED entity: 02h758 PRED relation: program PRED expected values: 0ckh4k => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 301): 04glx0 (0.50 #1085, 0.50 #595, 0.48 #491), 097h2 (0.48 #491, 0.39 #4196, 0.38 #1138), 08jgk1 (0.48 #491, 0.39 #4196, 0.33 #756), 02sqkh (0.48 #491, 0.39 #4196, 0.33 #68), 01fs__ (0.48 #491, 0.39 #4196, 0.33 #119), 039cq4 (0.48 #491, 0.39 #4196, 0.33 #112), 02czd5 (0.48 #491, 0.39 #4196, 0.33 #140), 072kp (0.48 #491, 0.39 #4196, 0.33 #10), 0dsx3f (0.48 #491, 0.39 #4196, 0.33 #97), 017f3m (0.48 #491, 0.39 #4196, 0.33 #74) >> Best rule #1085 for best value: >> intensional similarity = 9 >> extensional distance = 6 >> proper extension: 0cjdk; >> query: (?x12690, 04glx0) <- program(?x12690, ?x11477), program(?x14590, ?x11477), program(?x12505, ?x11477), program(?x6678, ?x11477), ?x6678 = 05gnf, genre(?x11477, ?x258), category(?x14590, ?x134), company(?x8314, ?x12690), award_winner(?x12505, ?x1686) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5047 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 25 *> proper extension: 01y81r; 03jl0_; 01bfjy; *> query: (?x12690, 0ckh4k) <- program(?x12690, ?x11477), program(?x6678, ?x11477), award_winner(?x631, ?x6678), genre(?x11477, ?x258), award_winner(?x6678, ?x1686), ?x258 = 05p553 *> conf = 0.04 ranks of expected_values: 273 EVAL 02h758 program 0ckh4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 65.000 65.000 0.500 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #20997-05strv PRED entity: 05strv PRED relation: profession PRED expected values: 02jknp => 90 concepts (45 used for prediction) PRED predicted values (max 10 best out of 38): 02jknp (0.55 #1023, 0.52 #878, 0.48 #297), 0cbd2 (0.28 #1022, 0.18 #6, 0.16 #1312), 0dz3r (0.28 #6530, 0.28 #6529, 0.11 #3338), 012t_z (0.28 #6530, 0.28 #6529, 0.09 #157), 018gz8 (0.18 #1320, 0.16 #885, 0.13 #1030), 09jwl (0.17 #3497, 0.17 #3787, 0.17 #3352), 0np9r (0.15 #889, 0.12 #1324, 0.09 #18), 0kyk (0.13 #1042, 0.09 #5247, 0.08 #5538), 0nbcg (0.12 #4814, 0.12 #2639, 0.12 #3364), 016z4k (0.09 #3485, 0.09 #3340, 0.09 #3775) >> Best rule #1023 for best value: >> intensional similarity = 3 >> extensional distance = 303 >> proper extension: 0fx02; 012x2b; 0dr5y; 0k_mt; >> query: (?x10151, 02jknp) <- nationality(?x10151, ?x94), written_by(?x522, ?x10151), profession(?x10151, ?x319) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05strv profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 45.000 0.548 http://example.org/people/person/profession #20996-0235l PRED entity: 0235l PRED relation: time_zones PRED expected values: 02lcqs => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 11): 02lcqs (0.67 #31, 0.64 #66, 0.33 #5), 02hczc (0.64 #66, 0.20 #171, 0.17 #146), 02hcv8 (0.52 #382, 0.52 #226, 0.51 #447), 02llzg (0.36 #43, 0.15 #109, 0.11 #604), 02fqwt (0.22 #119, 0.22 #67, 0.21 #80), 02lcrv (0.20 #171, 0.09 #978, 0.03 #59), 042g7t (0.20 #171, 0.09 #978, 0.02 #142), 03bdv (0.04 #242, 0.04 #255, 0.04 #606), 03plfd (0.03 #141, 0.02 #584, 0.02 #519), 0gsrz4 (0.02 #582, 0.02 #699, 0.01 #712) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0n6rv; >> query: (?x7697, 02lcqs) <- contains(?x1138, ?x7697), source(?x7697, ?x958), ?x1138 = 059_c >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0235l time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.667 http://example.org/location/location/time_zones #20995-01r3y2 PRED entity: 01r3y2 PRED relation: organization! PRED expected values: 060c4 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 18): 060c4 (0.85 #41, 0.82 #2, 0.79 #288), 07xl34 (0.26 #89, 0.24 #24, 0.23 #414), 0dq_5 (0.16 #880, 0.16 #893, 0.16 #711), 05k17c (0.12 #475, 0.07 #852, 0.07 #865), 0hm4q (0.05 #593, 0.05 #723, 0.05 #853), 05c0jwl (0.05 #512, 0.04 #538, 0.04 #577), 04n1q6 (0.04 #84, 0.01 #578), 01t7n9 (0.02 #1405), 0fkzq (0.02 #1405), 02079p (0.02 #1405) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 01q0kg; 017cy9; 01n6r0; 027mdh; 017v3q; 012mzw; 0dzst; >> query: (?x3090, 060c4) <- major_field_of_study(?x3090, ?x742), institution(?x865, ?x3090), school(?x3089, ?x3090), ?x742 = 05qjt >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r3y2 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.850 http://example.org/organization/role/leaders./organization/leadership/organization #20994-0416y94 PRED entity: 0416y94 PRED relation: nominated_for! PRED expected values: 03hkv_r 09qwmm 094qd5 0gqwc => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 208): 09cn0c (0.69 #1430, 0.68 #7385, 0.67 #10005), 0gq9h (0.44 #2208, 0.39 #9830, 0.37 #302), 0gs9p (0.42 #2210, 0.35 #9832, 0.33 #9117), 019f4v (0.37 #2199, 0.36 #4818, 0.34 #9821), 0k611 (0.34 #2218, 0.31 #312, 0.29 #9840), 0gq_v (0.33 #258, 0.28 #2164, 0.27 #735), 0gr0m (0.31 #299, 0.28 #2205, 0.26 #538), 040njc (0.31 #2151, 0.28 #9773, 0.26 #245), 04dn09n (0.30 #2180, 0.26 #4799, 0.25 #9802), 0gs96 (0.30 #328, 0.22 #1043, 0.22 #3662) >> Best rule #1430 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 015qsq; 0m313; 0209xj; 092vkg; 0bshwmp; 04jkpgv; 0fdv3; 09cr8; 0283_zv; 06ybb1; ... >> query: (?x1318, ?x1441) <- currency(?x1318, ?x170), film_release_distribution_medium(?x1318, ?x81), nominated_for(?x1318, ?x7590), award(?x1318, ?x1441) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #300 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 02_kd; 04k9y6; *> query: (?x1318, 0gqwc) <- currency(?x1318, ?x170), music(?x1318, ?x10634), costume_design_by(?x1318, ?x3685), award(?x1318, ?x1441) *> conf = 0.22 ranks of expected_values: 31, 36, 42, 44 EVAL 0416y94 nominated_for! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 119.000 119.000 0.687 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0416y94 nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 119.000 119.000 0.687 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0416y94 nominated_for! 09qwmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 119.000 119.000 0.687 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0416y94 nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 119.000 119.000 0.687 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20993-049n3s PRED entity: 049n3s PRED relation: colors PRED expected values: 083jv => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.85 #575, 0.75 #613, 0.64 #60), 01g5v (0.52 #461, 0.42 #62, 0.41 #176), 06fvc (0.43 #614, 0.43 #422, 0.41 #22), 01l849 (0.31 #268, 0.18 #58, 0.17 #754), 067z2v (0.26 #691, 0.18 #58, 0.17 #754), 04d18d (0.26 #691, 0.18 #58, 0.16 #815), 038hg (0.18 #58, 0.17 #754, 0.16 #815), 088fh (0.18 #58, 0.17 #754, 0.16 #815), 0jc_p (0.18 #58, 0.17 #754, 0.16 #815), 06kqt3 (0.18 #58, 0.17 #754, 0.16 #815) >> Best rule #575 for best value: >> intensional similarity = 9 >> extensional distance = 213 >> proper extension: 04088s0; 026xxv_; 0263cyj; 026wlnm; >> query: (?x12032, 083jv) <- colors(?x12032, ?x4557), sport(?x12032, ?x471), sport(?x12952, ?x471), team(?x5191, ?x12952), team(?x60, ?x12952), colors(?x9724, ?x4557), ?x9724 = 02vnp2, colors(?x11673, ?x4557), ?x11673 = 02gtm4 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049n3s colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.847 http://example.org/sports/sports_team/colors #20992-02x2gy0 PRED entity: 02x2gy0 PRED relation: nominated_for PRED expected values: 09q5w2 0gmcwlb 0260bz 07yvsn 0c34mt 0gh65c5 07vf5c 0prh7 04jplwp 02jxrw => 45 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1887): 011yxg (0.66 #16959, 0.64 #16958, 0.57 #4661), 02r79_h (0.66 #16959, 0.64 #16958, 0.50 #196), 07vf5c (0.66 #16959, 0.64 #16958, 0.43 #5246), 0_9l_ (0.62 #7647, 0.60 #3024, 0.53 #9189), 0462hhb (0.62 #6881, 0.60 #8423, 0.40 #2258), 011yg9 (0.62 #7051, 0.53 #8593, 0.50 #887), 0gmgwnv (0.60 #4014, 0.60 #2472, 0.57 #5555), 0pv3x (0.60 #3239, 0.60 #1697, 0.54 #6320), 09q5w2 (0.60 #3227, 0.57 #4768, 0.56 #9394), 0ptxj (0.60 #3873, 0.57 #5414, 0.08 #6954) >> Best rule #16959 for best value: >> intensional similarity = 4 >> extensional distance = 169 >> proper extension: 0m7yy; 02wwsh8; 03ybrwc; 02vl9ln; 0468g4r; >> query: (?x2489, ?x6362) <- award(?x6362, ?x2489), nominated_for(?x143, ?x6362), film_crew_role(?x6362, ?x137), language(?x6362, ?x254) >> conf = 0.66 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 9, 49, 54, 56, 57, 254, 262, 862, 1731 EVAL 02x2gy0 nominated_for 02jxrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 45.000 12.000 0.660 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x2gy0 nominated_for 04jplwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 45.000 12.000 0.660 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x2gy0 nominated_for 0prh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 45.000 12.000 0.660 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x2gy0 nominated_for 07vf5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 45.000 12.000 0.660 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x2gy0 nominated_for 0gh65c5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 45.000 12.000 0.660 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x2gy0 nominated_for 0c34mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 45.000 12.000 0.660 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x2gy0 nominated_for 07yvsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 45.000 12.000 0.660 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x2gy0 nominated_for 0260bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 45.000 12.000 0.660 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x2gy0 nominated_for 0gmcwlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 45.000 12.000 0.660 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x2gy0 nominated_for 09q5w2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 45.000 12.000 0.660 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20991-0k6nt PRED entity: 0k6nt PRED relation: combatants! PRED expected values: 03gqgt3 01cpp0 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 63): 081pw (0.60 #193, 0.55 #1986, 0.55 #1410), 07_nf (0.60 #209, 0.27 #1234, 0.24 #1298), 03gqgt3 (0.45 #375, 0.44 #183, 0.40 #1080), 0cm2xh (0.45 #331, 0.44 #75, 0.36 #395), 048n7 (0.44 #151, 0.39 #471, 0.36 #343), 01cpp0 (0.44 #186, 0.30 #250, 0.27 #378), 018w0j (0.36 #356, 0.33 #164, 0.33 #100), 01gjd0 (0.36 #323, 0.33 #131, 0.33 #67), 02h2z_ (0.33 #179, 0.30 #243, 0.29 #819), 01h6pn (0.27 #268, 0.24 #1421, 0.24 #1293) >> Best rule #193 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0g8bw; >> query: (?x985, 081pw) <- combatants(?x985, ?x550), combatants(?x985, ?x390), ?x390 = 0chghy, ?x550 = 05v8c >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #375 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 02j9z; 0dg3n1; *> query: (?x985, 03gqgt3) <- service_location(?x555, ?x985), ?x555 = 01c6k4, location(?x5283, ?x985) *> conf = 0.45 ranks of expected_values: 3, 6 EVAL 0k6nt combatants! 01cpp0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 147.000 147.000 0.600 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 0k6nt combatants! 03gqgt3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 147.000 147.000 0.600 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #20990-0161c PRED entity: 0161c PRED relation: medal PRED expected values: 02lpp7 => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 2): 02lpp7 (0.88 #18, 0.85 #30, 0.83 #16), 02lq5w (0.80 #57, 0.79 #17, 0.79 #15) >> Best rule #18 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 05r4w; 0jgd; 0154j; 03rjj; 0d0vqn; 03rt9; 07ssc; 015fr; 0f8l9c; 03gj2; ... >> query: (?x3683, 02lpp7) <- official_language(?x3683, ?x5359), film_release_region(?x6520, ?x3683), film_release_region(?x1915, ?x3683), ?x1915 = 0fq7dv_, ?x6520 = 02bg55 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0161c medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.875 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #20989-0kctd PRED entity: 0kctd PRED relation: citytown PRED expected values: 02_286 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 47): 02_286 (0.35 #1855, 0.32 #2591, 0.31 #4431), 030qb3t (0.27 #764, 0.14 #4076, 0.12 #4444), 0cc56 (0.25 #19, 0.06 #1491, 0.04 #2595), 013yq (0.12 #1515, 0.09 #779, 0.08 #2619), 04jpl (0.11 #2215, 0.10 #4055, 0.09 #743), 024bqj (0.06 #4615, 0.05 #5719, 0.05 #6087), 07dfk (0.06 #1685, 0.05 #5733, 0.05 #6101), 0rj4g (0.06 #2067, 0.04 #3907, 0.03 #4275), 052p7 (0.06 #1887, 0.03 #4095, 0.03 #7041), 0rh6k (0.06 #1841, 0.03 #4049, 0.02 #10676) >> Best rule #1855 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 01_8w2; 018_q8; 0c0sl; >> query: (?x11493, 02_286) <- award_winner(?x3486, ?x11493), organization(?x4682, ?x11493), ?x4682 = 0dq_5, ?x3486 = 0m7yy >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kctd citytown 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.353 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #20988-02cbvn PRED entity: 02cbvn PRED relation: institution! PRED expected values: 02h4rq6 => 143 concepts (87 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.87 #986, 0.86 #1035, 0.81 #222), 014mlp (0.72 #442, 0.70 #394, 0.69 #693), 019v9k (0.72 #374, 0.71 #253, 0.71 #545), 02_xgp2 (0.68 #450, 0.64 #402, 0.64 #549), 0bkj86 (0.67 #105, 0.57 #445, 0.56 #373), 016t_3 (0.58 #440, 0.56 #392, 0.56 #368), 01rr_d (0.50 #66, 0.45 #187, 0.44 #162), 04zx3q1 (0.50 #50, 0.43 #366, 0.42 #562), 0bjrnt (0.50 #55, 0.33 #151, 0.33 #7), 07s6fsf (0.44 #462, 0.43 #437, 0.43 #365) >> Best rule #986 for best value: >> intensional similarity = 7 >> extensional distance = 371 >> proper extension: 0kz2w; 01r3w7; 0trv; 0373qt; 01p896; 01d650; 02v992; 013719; 07wkd; 012gyf; >> query: (?x4289, 02h4rq6) <- institution(?x4981, ?x4289), institution(?x4981, ?x10104), institution(?x4981, ?x6419), student(?x4981, ?x118), ?x6419 = 0ny75, ?x10104 = 0177sq, major_field_of_study(?x4981, ?x254) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cbvn institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 87.000 0.871 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20987-0cv5l PRED entity: 0cv5l PRED relation: contains PRED expected values: 0hc8h => 152 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2539): 0hc8h (0.84 #97205, 0.83 #117823, 0.83 #103096), 0cv5l (0.29 #138443, 0.20 #44184, 0.05 #100150), 02jx1 (0.29 #138443, 0.20 #44184, 0.05 #100150), 07ssc (0.29 #138443, 0.20 #44184, 0.05 #100150), 04ftdq (0.25 #1246, 0.11 #42485, 0.03 #48375), 05cwl_ (0.25 #739, 0.11 #41978, 0.03 #56706), 01bzw5 (0.25 #134, 0.11 #41373, 0.03 #56101), 03b8c4 (0.25 #2243, 0.11 #43482, 0.03 #58210), 06b7s9 (0.25 #2113, 0.11 #43352, 0.03 #58080), 06kknt (0.25 #2045, 0.11 #43284, 0.03 #58012) >> Best rule #97205 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 05fly; >> query: (?x13888, ?x12767) <- country(?x13888, ?x1310), state(?x12767, ?x13888), location(?x1975, ?x13888) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cv5l contains 0hc8h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 92.000 0.837 http://example.org/location/location/contains #20986-03nyts PRED entity: 03nyts PRED relation: type_of_union PRED expected values: 04ztj => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.79 #215, 0.79 #408, 0.79 #348), 01g63y (0.41 #464, 0.41 #260, 0.40 #339), 01bl8s (0.41 #464, 0.41 #260, 0.40 #339) >> Best rule #215 for best value: >> intensional similarity = 5 >> extensional distance = 293 >> proper extension: 01wj9y9; 06nz46; 024zq; 04jwp; 07hyk; 0bt23; 014zn0; 014kg4; >> query: (?x11874, 04ztj) <- gender(?x11874, ?x231), ?x231 = 05zppz, location(?x11874, ?x11211), people(?x5855, ?x11874), profession(?x11874, ?x13971) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03nyts type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 170.000 0.790 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20985-0jg77 PRED entity: 0jg77 PRED relation: artists! PRED expected values: 016clz 02t8gf => 72 concepts (39 used for prediction) PRED predicted values (max 10 best out of 270): 0m0jc (0.82 #1865, 0.68 #1555, 0.67 #1246), 017_qw (0.60 #3152, 0.28 #5621, 0.17 #991), 016clz (0.57 #5, 0.45 #313, 0.45 #1861), 0y3_8 (0.57 #49, 0.45 #357, 0.39 #977), 03mb9 (0.57 #102, 0.31 #1339, 0.31 #1030), 064t9 (0.56 #1250, 0.56 #941, 0.55 #1559), 06by7 (0.56 #2495, 0.47 #4963, 0.47 #2187), 0ggx5q (0.45 #1625, 0.44 #1007, 0.44 #1316), 059kh (0.43 #51, 0.27 #359, 0.23 #4066), 0glt670 (0.43 #2823, 0.24 #1898, 0.24 #2207) >> Best rule #1865 for best value: >> intensional similarity = 6 >> extensional distance = 74 >> proper extension: 01pfr3; 03f5spx; 04mn81; 0gdh5; 01271h; 016ntp; 0phx4; 01wy61y; 03xhj6; 01vwbts; ... >> query: (?x13142, 0m0jc) <- artists(?x3916, ?x13142), artists(?x3916, ?x4593), artists(?x3916, ?x2306), ?x2306 = 06k02, parent_genre(?x2439, ?x3916), ?x4593 = 0478__m >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #5 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 03t9sp; 05k79; 03fbc; 0dm5l; 016lmg; *> query: (?x13142, 016clz) <- artists(?x3916, ?x13142), ?x3916 = 08cyft, award(?x13142, ?x2322), artist(?x3888, ?x13142), group(?x228, ?x13142) *> conf = 0.57 ranks of expected_values: 3, 25 EVAL 0jg77 artists! 02t8gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 72.000 39.000 0.816 http://example.org/music/genre/artists EVAL 0jg77 artists! 016clz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 39.000 0.816 http://example.org/music/genre/artists #20984-02vxq9m PRED entity: 02vxq9m PRED relation: story_by PRED expected values: 0fx02 => 65 concepts (38 used for prediction) PRED predicted values (max 10 best out of 32): 05183k (0.06 #2813, 0.02 #2814, 0.02 #2812), 03kpvp (0.06 #2813, 0.02 #2814, 0.02 #2812), 01j2xj (0.05 #4330, 0.02 #4986, 0.02 #2812), 02nygk (0.03 #643, 0.01 #3891, 0.01 #2156), 0fx02 (0.03 #2005, 0.02 #3740, 0.02 #492), 0343h (0.02 #18, 0.01 #3698), 0kb3n (0.02 #143, 0.01 #575), 05jcn8 (0.02 #54), 04qvl7 (0.02 #2814, 0.02 #4986, 0.02 #2812), 01713c (0.02 #2814, 0.02 #4986, 0.02 #2812) >> Best rule #2813 for best value: >> intensional similarity = 4 >> extensional distance = 533 >> proper extension: 02kk_c; 03d17dg; 025x1t; >> query: (?x186, ?x1532) <- nominated_for(?x1532, ?x186), award_winner(?x186, ?x6783), written_by(?x66, ?x1532), profession(?x1532, ?x319) >> conf = 0.06 => this is the best rule for 2 predicted values *> Best rule #2005 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 487 *> proper extension: 02vl9ln; *> query: (?x186, 0fx02) <- country(?x186, ?x512), country(?x1481, ?x512), ?x1481 = 02r79_h *> conf = 0.03 ranks of expected_values: 5 EVAL 02vxq9m story_by 0fx02 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 65.000 38.000 0.062 http://example.org/film/film/story_by #20983-015zyd PRED entity: 015zyd PRED relation: student PRED expected values: 07rd7 01mkn_d => 149 concepts (109 used for prediction) PRED predicted values (max 10 best out of 1487): 06hx2 (0.17 #9382, 0.14 #21863, 0.07 #34343), 0194xc (0.17 #9950, 0.14 #22431, 0.07 #34911), 02lt8 (0.17 #8989, 0.14 #21470, 0.07 #33950), 084w8 (0.17 #8330, 0.07 #20811, 0.06 #27050), 02779r4 (0.17 #9472, 0.07 #21953, 0.06 #28192), 03r1pr (0.17 #8776, 0.07 #21257, 0.06 #27496), 08chdb (0.17 #10067, 0.07 #22548, 0.06 #28787), 01w_10 (0.17 #9718, 0.07 #22199, 0.06 #28438), 0pz7h (0.17 #8436, 0.07 #20917, 0.06 #27156), 0h0wc (0.17 #8707, 0.07 #21188, 0.06 #25348) >> Best rule #9382 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 054lpb6; >> query: (?x99, 06hx2) <- organization(?x346, ?x99), category(?x99, ?x134), ?x346 = 060c4, organizations_founded(?x2426, ?x99) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015zyd student 01mkn_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 149.000 109.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 015zyd student 07rd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 149.000 109.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #20982-048s0r PRED entity: 048s0r PRED relation: award_winner! PRED expected values: 099tbz => 89 concepts (87 used for prediction) PRED predicted values (max 10 best out of 183): 099tbz (0.56 #57, 0.33 #488, 0.15 #18538), 09td7p (0.17 #552, 0.15 #18538, 0.15 #18106), 0cqhk0 (0.15 #18538, 0.15 #18106, 0.15 #12932), 0gqyl (0.15 #18538, 0.15 #18106, 0.15 #12932), 02ppm4q (0.15 #18538, 0.15 #18106, 0.15 #12932), 099t8j (0.15 #18538, 0.15 #18106, 0.15 #12932), 057xs89 (0.15 #18538, 0.15 #18106, 0.15 #12932), 02lp0w (0.15 #18538, 0.15 #18106, 0.15 #12932), 0cqgl9 (0.15 #18538, 0.15 #18106, 0.15 #12932), 09qs08 (0.15 #18538, 0.15 #18106, 0.15 #12932) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 011zd3; 01438g; 0686zv; 013knm; 01wgcvn; 02__7n; 030xr_; >> query: (?x7157, 099tbz) <- award_winner(?x7157, ?x2353), gender(?x7157, ?x231), ?x2353 = 02qgyv >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 048s0r award_winner! 099tbz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 87.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner #20981-015pkc PRED entity: 015pkc PRED relation: film PRED expected values: 0f8j13 => 146 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1184): 06ys2 (0.72 #42699, 0.65 #81839, 0.64 #94294), 0c0zq (0.33 #1554, 0.04 #19571, 0.03 #142334), 013q07 (0.17 #2132, 0.08 #353, 0.07 #14585), 03bzjpm (0.17 #3086, 0.08 #10202, 0.07 #11981), 0660b9b (0.17 #989, 0.07 #4547, 0.03 #142334), 02qzh2 (0.17 #2466, 0.06 #11361, 0.04 #14919), 01qvz8 (0.17 #2578, 0.06 #11473, 0.04 #18590), 0f40w (0.17 #2138, 0.05 #9254, 0.05 #5696), 011wtv (0.17 #2543, 0.05 #9659, 0.05 #6101), 08052t3 (0.17 #2170, 0.05 #9286, 0.05 #5728) >> Best rule #42699 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 0jlv5; 0g476; >> query: (?x1733, ?x167) <- award_winner(?x1336, ?x1733), participant(?x1733, ?x2763), nominated_for(?x1733, ?x167) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #44255 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 0h1m9; 02lnhv; 0f2df; 0c2ry; 0gmtm; 01wc7p; 0c2tf; 01wvxw1; 012gbb; 0chw_; ... *> query: (?x1733, 0f8j13) <- award_winner(?x1336, ?x1733), participant(?x1733, ?x2763), participant(?x1733, ?x4106) *> conf = 0.02 ranks of expected_values: 759 EVAL 015pkc film 0f8j13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 146.000 88.000 0.721 http://example.org/film/actor/film./film/performance/film #20980-02gm9n PRED entity: 02gm9n PRED relation: ceremony PRED expected values: 02rjjll 0466p0j 013b2h 02cg41 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 128): 02cg41 (0.86 #747, 0.86 #620, 0.86 #493), 013b2h (0.85 #705, 0.85 #578, 0.84 #451), 02rjjll (0.85 #257, 0.84 #638, 0.84 #1019), 0466p0j (0.84 #1082, 0.83 #320, 0.83 #447), 092c5f (0.21 #6231, 0.21 #5975, 0.21 #6487), 02wzl1d (0.21 #6231, 0.21 #5975, 0.21 #6487), 0hhtgcw (0.21 #6231, 0.21 #5975, 0.21 #6487), 0bzm81 (0.21 #906, 0.19 #1160, 0.16 #2304), 03gyp30 (0.20 #104, 0.11 #5720, 0.10 #6996), 0hr3c8y (0.20 #7, 0.11 #5720, 0.10 #6996) >> Best rule #747 for best value: >> intensional similarity = 5 >> extensional distance = 72 >> proper extension: 02grdc; 01c9f2; 01c427; 02581c; 026mfs; 01cw51; 01c9jp; 03t5b6; 03nc9d; >> query: (?x12940, 02cg41) <- award(?x2444, ?x12940), ceremony(?x12940, ?x2704), ceremony(?x12940, ?x725), ?x2704 = 01mhwk, award_winner(?x725, ?x248) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 02gm9n ceremony 02cg41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.865 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02gm9n ceremony 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.865 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02gm9n ceremony 0466p0j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.865 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02gm9n ceremony 02rjjll CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.865 http://example.org/award/award_category/winners./award/award_honor/ceremony #20979-0bjv6 PRED entity: 0bjv6 PRED relation: countries_spoken_in! PRED expected values: 0k0sb => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 53): 02h40lc (0.33 #488, 0.33 #920, 0.30 #2271), 02ztjwg (0.21 #29, 0.18 #137, 0.18 #83), 06nm1 (0.19 #1467, 0.19 #1142, 0.19 #1413), 04306rv (0.18 #113, 0.18 #5, 0.15 #167), 064_8sq (0.17 #2233, 0.17 #2125, 0.17 #936), 0jzc (0.17 #502, 0.16 #934, 0.16 #880), 0cjk9 (0.15 #4, 0.13 #112, 0.13 #58), 02bjrlw (0.15 #1, 0.10 #109, 0.10 #55), 06b_j (0.13 #127, 0.13 #73, 0.12 #19), 0k0sb (0.13 #103, 0.12 #211, 0.12 #49) >> Best rule #488 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 049nq; >> query: (?x3227, 02h40lc) <- capital(?x3227, ?x13383), administrative_parent(?x3227, ?x551), countries_spoken_in(?x10580, ?x3227) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #103 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 05r4w; 0154j; 03rjj; 0d0vqn; 04gzd; 03rt9; 05qhw; 07ssc; 06npd; 06mzp; ... *> query: (?x3227, 0k0sb) <- organization(?x3227, ?x1062), ?x1062 = 01rz1, adjoins(?x3227, ?x1497) *> conf = 0.13 ranks of expected_values: 10 EVAL 0bjv6 countries_spoken_in! 0k0sb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 78.000 78.000 0.333 http://example.org/language/human_language/countries_spoken_in #20978-0gnbw PRED entity: 0gnbw PRED relation: award_winner! PRED expected values: 019vhk => 81 concepts (55 used for prediction) PRED predicted values (max 10 best out of 74): 02c6d (0.40 #37347, 0.38 #43007, 0.37 #46403), 017180 (0.40 #37347, 0.38 #43007, 0.37 #46403), 05fm6m (0.16 #12448, 0.15 #11316, 0.14 #14711), 0btpm6 (0.16 #12448, 0.15 #11316, 0.14 #14711), 013q07 (0.16 #12448, 0.15 #11316, 0.14 #14711), 02fqrf (0.16 #12448, 0.15 #11316, 0.14 #14711), 0m9p3 (0.16 #12448, 0.15 #11316, 0.14 #14711), 0661ql3 (0.16 #12448, 0.15 #11316, 0.14 #14711), 0407yj_ (0.16 #12448, 0.15 #11316, 0.14 #14711), 016ks5 (0.16 #12448, 0.15 #11316, 0.14 #14711) >> Best rule #37347 for best value: >> intensional similarity = 2 >> extensional distance = 1558 >> proper extension: 0dky9n; 0gsg7; 0cjdk; 027_tg; 05gnf; 01j7pt; 01zcrv; 0kctd; 0kcd5; 0kc9f; >> query: (?x7269, ?x167) <- nominated_for(?x7269, ?x167), award_winner(?x2375, ?x7269) >> conf = 0.40 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0gnbw award_winner! 019vhk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 55.000 0.405 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #20977-07ymr5 PRED entity: 07ymr5 PRED relation: award_winner! PRED expected values: 05gnf => 110 concepts (80 used for prediction) PRED predicted values (max 10 best out of 749): 092ggq (0.53 #118919, 0.46 #41781, 0.40 #17674), 01vwllw (0.53 #118919, 0.46 #41781, 0.40 #17674), 07ymr5 (0.50 #3511, 0.27 #128557, 0.18 #6725), 05gnf (0.35 #10747, 0.33 #4319, 0.27 #128557), 0c_mvb (0.33 #393, 0.17 #3607, 0.06 #10035), 09d5h (0.33 #315, 0.17 #3529, 0.06 #9957), 0gsg7 (0.33 #268, 0.17 #3482, 0.06 #9910), 014hdb (0.33 #1485, 0.17 #4699, 0.03 #11127), 015cbq (0.33 #1433, 0.17 #4647, 0.03 #11075), 031rq5 (0.33 #1001, 0.17 #4215, 0.03 #10643) >> Best rule #118919 for best value: >> intensional similarity = 3 >> extensional distance = 1352 >> proper extension: 0fvf9q; 0520r2x; 0cb77r; 06gp3f; 01lmj3q; 01r42_g; 03ckxdg; 050023; 026dcvf; 07f8wg; ... >> query: (?x1942, ?x1896) <- award_nominee(?x1942, ?x1896), profession(?x1942, ?x319), award_winner(?x1942, ?x6884) >> conf = 0.53 => this is the best rule for 2 predicted values *> Best rule #10747 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 32 *> proper extension: 030_1_; *> query: (?x1942, 05gnf) <- award_winner(?x6884, ?x1942), nominated_for(?x829, ?x6884) *> conf = 0.35 ranks of expected_values: 4 EVAL 07ymr5 award_winner! 05gnf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 80.000 0.525 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #20976-02qggqc PRED entity: 02qggqc PRED relation: place_of_birth PRED expected values: 0r5wt => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 64): 071vr (0.33 #258, 0.04 #2370, 0.04 #3075), 04n3l (0.20 #827), 02h6_6p (0.20 #791), 02_286 (0.12 #1427, 0.10 #4244, 0.10 #4948), 030qb3t (0.07 #4279, 0.06 #4983, 0.06 #13443), 0c630 (0.06 #1796, 0.04 #2500, 0.04 #3205), 0tgcy (0.06 #1792, 0.04 #2496, 0.04 #3201), 0rtv (0.06 #1414, 0.04 #2118, 0.04 #2823), 0cr3d (0.05 #5728, 0.05 #7842, 0.05 #6432), 01snm (0.04 #2351, 0.04 #3056, 0.03 #4464) >> Best rule #258 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0bs1yy; >> query: (?x707, 071vr) <- edited_by(?x5890, ?x707), edited_by(?x5135, ?x707), edited_by(?x3084, ?x707), ?x3084 = 03mh_tp, nominated_for(?x510, ?x5135), genre(?x5890, ?x53) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02qggqc place_of_birth 0r5wt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 57.000 0.333 http://example.org/people/person/place_of_birth #20975-03qjg PRED entity: 03qjg PRED relation: role! PRED expected values: 0kzy0 09swkk 05qhnq => 72 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1160): 050z2 (0.67 #4216, 0.67 #3765, 0.62 #6467), 082brv (0.67 #3844, 0.60 #3394, 0.60 #2945), 02s6sh (0.67 #4454, 0.57 #5803, 0.50 #2209), 045zr (0.67 #4141, 0.50 #6836, 0.50 #1449), 04bpm6 (0.60 #3201, 0.60 #2752, 0.53 #9481), 02qtywd (0.60 #3562, 0.57 #5361, 0.50 #7158), 0326tc (0.60 #3470, 0.50 #7066, 0.50 #6622), 01w272y (0.60 #3283, 0.50 #1939, 0.43 #5082), 024dw0 (0.57 #5698, 0.50 #4349, 0.50 #1657), 03ryks (0.55 #11499, 0.52 #16425, 0.50 #6577) >> Best rule #4216 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0l14md; 05148p4; >> query: (?x2798, 050z2) <- role(?x1332, ?x2798), role(?x1147, ?x2798), role(?x7522, ?x2798), instrumentalists(?x2798, ?x6461), ?x1332 = 03qlv7, ?x6461 = 01t110, group(?x2798, ?x997), ?x1147 = 07kc_, role(?x2798, ?x74), performance_role(?x8323, ?x2798), award_winner(?x102, ?x7522) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #8375 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 10 *> proper extension: 01w4dy; *> query: (?x2798, 05qhnq) <- role(?x6039, ?x2798), role(?x3991, ?x2798), role(?x3296, ?x2798), role(?x1225, ?x2798), role(?x1166, ?x2798), role(?x74, ?x2798), ?x3296 = 07_l6, role(?x2798, ?x2157), ?x3991 = 05842k, role(?x6039, ?x75), ?x1225 = 01qbl, instrumentalists(?x1166, ?x130), role(?x248, ?x1166) *> conf = 0.50 ranks of expected_values: 12, 265, 423 EVAL 03qjg role! 05qhnq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 72.000 42.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 03qjg role! 09swkk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 42.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 03qjg role! 0kzy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 72.000 42.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #20974-01hq1 PRED entity: 01hq1 PRED relation: prequel PRED expected values: 01hp5 => 105 concepts (59 used for prediction) PRED predicted values (max 10 best out of 53): 01hq1 (0.10 #147, 0.02 #509, 0.01 #1052), 01hr1 (0.10 #6, 0.02 #368, 0.01 #911), 02bj22 (0.07 #342), 0bt4g (0.02 #504, 0.01 #866), 0g5pv3 (0.02 #386, 0.01 #929), 07g1sm (0.02 #486), 013q0p (0.02 #453), 02wgk1 (0.02 #445), 0dnqr (0.02 #412), 0p9lw (0.02 #380) >> Best rule #147 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 07bwr; >> query: (?x7881, 01hq1) <- production_companies(?x7881, ?x752), films(?x5011, ?x7881), genre(?x7881, ?x225), ?x752 = 0338lq >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01hq1 prequel 01hp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 59.000 0.100 http://example.org/film/film/prequel #20973-01fwzk PRED entity: 01fwzk PRED relation: nominated_for! PRED expected values: 02y_rq5 => 60 concepts (54 used for prediction) PRED predicted values (max 10 best out of 199): 0gqy2 (0.77 #4573, 0.76 #2287, 0.67 #4572), 0gq9h (0.70 #58, 0.50 #2115, 0.49 #286), 0p9sw (0.44 #19, 0.33 #247, 0.32 #475), 04dn09n (0.43 #33, 0.31 #2090, 0.31 #717), 040njc (0.42 #6, 0.34 #2063, 0.30 #2979), 0gr0m (0.41 #56, 0.37 #512, 0.36 #284), 0f4x7 (0.37 #2997, 0.31 #24, 0.31 #2081), 02pqp12 (0.35 #55, 0.29 #913, 0.29 #739), 0l8z1 (0.32 #49, 0.31 #277, 0.30 #505), 099c8n (0.31 #53, 0.23 #1653, 0.23 #3026) >> Best rule #4573 for best value: >> intensional similarity = 4 >> extensional distance = 645 >> proper extension: 07bz5; >> query: (?x8827, ?x3066) <- award(?x8827, ?x3066), nominated_for(?x4897, ?x8827), ceremony(?x3066, ?x78), award_winner(?x3066, ?x92) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #7090 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x8827, ?x384) <- award(?x8827, ?x2880), award(?x253, ?x2880), award(?x253, ?x384) *> conf = 0.12 ranks of expected_values: 53 EVAL 01fwzk nominated_for! 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 60.000 54.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20972-09kn9 PRED entity: 09kn9 PRED relation: titles! PRED expected values: 0kctd => 85 concepts (46 used for prediction) PRED predicted values (max 10 best out of 44): 0215n (0.33 #496, 0.17 #1121, 0.16 #2996), 01nzs7 (0.29 #833, 0.21 #4068, 0.20 #3856), 0hfjk (0.25 #79, 0.04 #1565, 0.03 #2897), 0kctd (0.20 #196, 0.18 #820, 0.06 #2072), 01z77k (0.20 #167, 0.11 #2878, 0.10 #4445), 01hmnh (0.20 #134, 0.06 #3964, 0.05 #864), 06n90 (0.15 #210, 0.15 #209, 0.14 #105), 03k9fj (0.15 #210, 0.15 #209, 0.14 #105), 01htzx (0.15 #210, 0.15 #209, 0.14 #105), 07s9rl0 (0.14 #2401, 0.09 #3861, 0.09 #3967) >> Best rule #496 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 09g_31; 06qv_; 06r1k; >> query: (?x1843, 0215n) <- genre(?x1843, ?x1013), genre(?x1843, ?x811), actor(?x1843, ?x3808), ?x1013 = 06n90, titles(?x2008, ?x1843), ?x811 = 03k9fj, languages(?x1843, ?x254) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #196 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 06w7mlh; *> query: (?x1843, 0kctd) <- program(?x1648, ?x1843), genre(?x1843, ?x811), titles(?x2008, ?x1843), ?x1648 = 01nzs7, languages(?x1843, ?x254) *> conf = 0.20 ranks of expected_values: 4 EVAL 09kn9 titles! 0kctd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 46.000 0.333 http://example.org/media_common/netflix_genre/titles #20971-03h304l PRED entity: 03h304l PRED relation: executive_produced_by! PRED expected values: 07yk1xz 0298n7 => 122 concepts (116 used for prediction) PRED predicted values (max 10 best out of 296): 01q2nx (0.25 #298, 0.11 #823, 0.05 #1875), 09q5w2 (0.25 #48, 0.11 #573, 0.02 #6878), 01s7w3 (0.25 #476, 0.11 #1001), 011xg5 (0.25 #448, 0.11 #973), 01y9r2 (0.25 #419, 0.11 #944), 01738w (0.25 #359, 0.11 #884), 0260bz (0.25 #110, 0.11 #635), 0298n7 (0.25 #421, 0.11 #2103, 0.03 #3575), 07yk1xz (0.25 #117, 0.11 #2103, 0.03 #3271), 01msrb (0.25 #260, 0.03 #5515, 0.03 #3414) >> Best rule #298 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 027z0pl; >> query: (?x4946, 01q2nx) <- award_nominee(?x9743, ?x4946), ?x9743 = 0d6484, executive_produced_by(?x103, ?x4946) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 027z0pl; *> query: (?x4946, 0298n7) <- award_nominee(?x9743, ?x4946), ?x9743 = 0d6484, executive_produced_by(?x103, ?x4946) *> conf = 0.25 ranks of expected_values: 8, 9 EVAL 03h304l executive_produced_by! 0298n7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 122.000 116.000 0.250 http://example.org/film/film/executive_produced_by EVAL 03h304l executive_produced_by! 07yk1xz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 122.000 116.000 0.250 http://example.org/film/film/executive_produced_by #20970-0fw2d3 PRED entity: 0fw2d3 PRED relation: team PRED expected values: 05169r => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 536): 0cxbth (0.88 #1758, 0.88 #1757, 0.86 #4229), 043t1s (0.88 #1758, 0.88 #1757, 0.86 #4229), 0dwz3t (0.40 #1595, 0.33 #541, 0.14 #7401), 037mp6 (0.40 #3876, 0.07 #4935, 0.02 #8106), 0j2pg (0.33 #387, 0.20 #1441, 0.14 #2148), 027pwl (0.33 #403, 0.20 #1457, 0.14 #7401), 01tqfs (0.33 #495, 0.20 #1549, 0.14 #7401), 04ltf (0.33 #539, 0.20 #1593, 0.14 #7401), 02s2ys (0.33 #588, 0.20 #1642, 0.14 #7401), 02b17f (0.33 #591, 0.20 #1645, 0.14 #7401) >> Best rule #1758 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 083qy7; >> query: (?x7703, ?x5708) <- team(?x7703, ?x10996), ?x10996 = 06zpgb2, team(?x7703, ?x5708), profession(?x7703, ?x7623), position(?x5708, ?x60), nationality(?x7703, ?x1499) >> conf = 0.88 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0fw2d3 team 05169r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 97.000 0.880 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #20969-04grkmd PRED entity: 04grkmd PRED relation: language PRED expected values: 02h40lc => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 34): 02h40lc (0.92 #834, 0.89 #1313, 0.89 #1787), 02bjrlw (0.23 #119, 0.15 #358, 0.14 #239), 06nm1 (0.20 #189, 0.12 #843, 0.12 #962), 04306rv (0.15 #123, 0.12 #362, 0.10 #421), 064_8sq (0.15 #795, 0.14 #260, 0.13 #1092), 03_9r (0.10 #902, 0.08 #426, 0.08 #128), 06b_j (0.08 #796, 0.07 #557, 0.06 #677), 0jzc (0.06 #852, 0.06 #495, 0.05 #733), 0653m (0.05 #1082, 0.05 #725, 0.05 #844), 01jb8r (0.05 #232, 0.03 #351, 0.02 #470) >> Best rule #834 for best value: >> intensional similarity = 5 >> extensional distance = 175 >> proper extension: 02d44q; >> query: (?x3512, 02h40lc) <- film_crew_role(?x3512, ?x2095), ?x2095 = 0dxtw, produced_by(?x3512, ?x2086), film(?x1104, ?x3512), film_release_region(?x3512, ?x94) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04grkmd language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.915 http://example.org/film/film/language #20968-07t21 PRED entity: 07t21 PRED relation: organization PRED expected values: 02vk52z => 158 concepts (155 used for prediction) PRED predicted values (max 10 best out of 50): 02vk52z (0.93 #1261, 0.87 #1417, 0.86 #661), 0_2v (0.59 #223, 0.51 #245, 0.49 #267), 018cqq (0.58 #1970, 0.58 #1903, 0.51 #275), 02jxk (0.58 #1970, 0.58 #1903, 0.31 #266), 059dn (0.58 #1970, 0.58 #1903, 0.14 #279), 04k4l (0.49 #246, 0.48 #576, 0.44 #4), 0b6css (0.44 #230, 0.44 #208, 0.44 #98), 041288 (0.38 #1565, 0.38 #1698, 0.36 #1254), 0j7v_ (0.28 #1130, 0.27 #1243, 0.26 #1687), 0gkjy (0.26 #1689, 0.26 #1556, 0.25 #1267) >> Best rule #1261 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 07z5n; 088xp; 07bxhl; 04hzj; 04vjh; >> query: (?x1471, 02vk52z) <- country(?x150, ?x1471), adjoins(?x1471, ?x456), currency(?x1471, ?x170) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07t21 organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 155.000 0.928 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #20967-01y3v PRED entity: 01y3v PRED relation: school PRED expected values: 05x_5 => 91 concepts (74 used for prediction) PRED predicted values (max 10 best out of 309): 065y4w7 (0.42 #5018, 0.42 #2228, 0.42 #1857), 06pwq (0.40 #1844, 0.40 #1666, 0.40 #561), 0trv (0.40 #690, 0.30 #1795, 0.16 #1845), 025v3k (0.40 #605, 0.20 #1710, 0.14 #4315), 05krk (0.33 #3525, 0.25 #188, 0.23 #6507), 0g8rj (0.33 #82, 0.17 #3603, 0.17 #2301), 01hx2t (0.33 #134, 0.16 #1845, 0.12 #11352), 01jq0j (0.31 #2515, 0.26 #4746, 0.25 #4190), 01pl14 (0.30 #1664, 0.25 #2967, 0.25 #2224), 0bx8pn (0.30 #1682, 0.23 #4287, 0.21 #11938) >> Best rule #5018 for best value: >> intensional similarity = 11 >> extensional distance = 22 >> proper extension: 01lpx8; >> query: (?x2574, 065y4w7) <- position(?x2574, ?x1517), colors(?x2574, ?x8271), position_s(?x2574, ?x2247), colors(?x8228, ?x8271), team(?x2247, ?x5491), team(?x2247, ?x1239), position_s(?x10253, ?x1517), position(?x5491, ?x935), ?x8228 = 0jmcv, ?x10253 = 0bs09lb, draft(?x1239, ?x465) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #297 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 086hg9; *> query: (?x2574, 05x_5) <- position(?x2574, ?x3346), position(?x2574, ?x2247), position(?x2574, ?x2147), position(?x2574, ?x1240), position(?x2574, ?x935), ?x935 = 06b1q, position_s(?x2574, ?x1517), ?x1240 = 023wyl, teams(?x1860, ?x2574), ?x3346 = 02g_7z, ?x2247 = 01_9c1, contains(?x1860, ?x2313), category(?x2574, ?x134), ?x2147 = 04nfpk *> conf = 0.25 ranks of expected_values: 19 EVAL 01y3v school 05x_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 91.000 74.000 0.417 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #20966-03j1p2n PRED entity: 03j1p2n PRED relation: category PRED expected values: 08mbj5d => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #15, 0.85 #7, 0.85 #16) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 334 >> proper extension: 02wb6yq; >> query: (?x7859, 08mbj5d) <- artist(?x2931, ?x7859), award_winner(?x342, ?x7859), artists(?x497, ?x7859) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03j1p2n category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.851 http://example.org/common/topic/webpage./common/webpage/category #20965-06__m6 PRED entity: 06__m6 PRED relation: film! PRED expected values: 01tvz5j 0bbf1f => 78 concepts (54 used for prediction) PRED predicted values (max 10 best out of 769): 0c00lh (0.44 #87305, 0.44 #39496, 0.43 #93540), 0716t2 (0.25 #1906, 0.03 #37245, 0.02 #33088), 015pkc (0.25 #278, 0.03 #8593, 0.03 #14831), 04fzk (0.25 #708, 0.02 #9023, 0.02 #15261), 01h8f (0.25 #930, 0.01 #7165, 0.01 #9245), 01f7dd (0.19 #1209, 0.02 #5366, 0.02 #49019), 05bnp0 (0.19 #13, 0.02 #35352, 0.02 #22881), 014zcr (0.12 #37, 0.04 #27062, 0.03 #2115), 032w8h (0.12 #280, 0.02 #31462, 0.02 #52247), 079vf (0.12 #8, 0.02 #35347, 0.02 #74841) >> Best rule #87305 for best value: >> intensional similarity = 4 >> extensional distance = 960 >> proper extension: 0bx_hnp; >> query: (?x5991, ?x5351) <- genre(?x5991, ?x53), film_crew_role(?x5991, ?x137), nominated_for(?x5351, ?x5991), genre(?x273, ?x53) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #4647 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 133 *> proper extension: 053tj7; 05_61y; *> query: (?x5991, 0bbf1f) <- genre(?x5991, ?x2753), genre(?x5991, ?x2700), ?x2753 = 0219x_, genre(?x6588, ?x2700), film_crew_role(?x6588, ?x137) *> conf = 0.01 ranks of expected_values: 487 EVAL 06__m6 film! 0bbf1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 78.000 54.000 0.439 http://example.org/film/actor/film./film/performance/film EVAL 06__m6 film! 01tvz5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 54.000 0.439 http://example.org/film/actor/film./film/performance/film #20964-02zfdp PRED entity: 02zfdp PRED relation: actor! PRED expected values: 0pc_l => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 148): 026bfsh (0.21 #360, 0.05 #1154, 0.04 #4053), 0cfhfz (0.21 #5801, 0.17 #264, 0.10 #8969), 08bytj (0.19 #146, 0.01 #3576, 0.01 #4366), 02rcwq0 (0.06 #87, 0.02 #11869, 0.02 #13455), 030p35 (0.06 #80, 0.02 #11869, 0.02 #13455), 02ppg1r (0.06 #77, 0.01 #3507, 0.01 #12398), 01xr2s (0.06 #30, 0.01 #12398, 0.01 #13719), 02k_4g (0.06 #14, 0.01 #12398, 0.01 #15037), 03_b1g (0.06 #247), 06dfz1 (0.06 #166) >> Best rule #360 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0f8grf; >> query: (?x9152, 026bfsh) <- film(?x9152, ?x504), artists(?x8138, ?x9152), actor(?x3303, ?x9152) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02zfdp actor! 0pc_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 81.000 0.209 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #20963-065mm1 PRED entity: 065mm1 PRED relation: nationality PRED expected values: 09c7w0 => 98 concepts (70 used for prediction) PRED predicted values (max 10 best out of 56): 09c7w0 (0.89 #4113, 0.89 #6432, 0.88 #6431), 07ssc (0.17 #1020, 0.12 #3326, 0.11 #3025), 02jx1 (0.12 #3244, 0.12 #3344, 0.11 #5757), 0d060g (0.12 #2116, 0.08 #2216, 0.07 #3418), 03_3d (0.10 #2215, 0.06 #3417, 0.06 #3116), 03rt9 (0.10 #918, 0.05 #1922, 0.05 #717), 03rk0 (0.05 #3958, 0.05 #6780, 0.04 #6982), 0345h (0.04 #1036, 0.03 #1438, 0.03 #1539), 0j5g9 (0.04 #1067, 0.03 #1469, 0.03 #1570), 0chghy (0.04 #1117, 0.03 #1106, 0.03 #1317) >> Best rule #4113 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 021yc7p; 05218gr; 03q8ch; 012v1t; 02q9kqf; 0c_drn; 0646qh; 053j4w4; 040j2_; 095zvfg; ... >> query: (?x10653, 09c7w0) <- place_of_birth(?x10653, ?x2017), teams(?x2017, ?x1160), source(?x2017, ?x958) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065mm1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 70.000 0.892 http://example.org/people/person/nationality #20962-04xn_ PRED entity: 04xn_ PRED relation: organization PRED expected values: 0_2v => 132 concepts (131 used for prediction) PRED predicted values (max 10 best out of 50): 0_2v (0.56 #25, 0.56 #1576, 0.55 #1089), 0b6css (0.56 #1576, 0.55 #1089, 0.46 #32), 04k4l (0.56 #1576, 0.55 #1089, 0.42 #114), 0j7v_ (0.56 #1576, 0.55 #1089, 0.32 #2068), 01rz1 (0.40 #1, 0.37 #244, 0.36 #111), 018cqq (0.39 #33, 0.38 #121, 0.36 #209), 041288 (0.34 #1283, 0.33 #1637, 0.33 #1547), 0gkjy (0.32 #2068, 0.28 #426, 0.27 #784), 02jxk (0.32 #2068, 0.26 #112, 0.25 #200), 085h1 (0.32 #2068, 0.24 #642, 0.22 #575) >> Best rule #25 for best value: >> intensional similarity = 2 >> extensional distance = 39 >> proper extension: 012wgb; >> query: (?x6307, 0_2v) <- country(?x9050, ?x6307), geographic_distribution(?x9148, ?x6307) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04xn_ organization 0_2v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 131.000 0.561 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #20961-0963mq PRED entity: 0963mq PRED relation: film! PRED expected values: 0c35b1 => 88 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1103): 06pjs (0.20 #20753, 0.17 #45663, 0.16 #49814), 04t7ts (0.13 #209, 0.04 #8510, 0.03 #20962), 01wbg84 (0.09 #46, 0.08 #6271, 0.05 #4196), 01j5ts (0.09 #28, 0.05 #8329, 0.01 #45691), 014zcr (0.09 #36, 0.05 #18713, 0.04 #8337), 02qgqt (0.09 #17, 0.05 #4167, 0.04 #8318), 01q_ph (0.09 #56, 0.04 #8357, 0.03 #45719), 0lx2l (0.09 #418, 0.04 #10794, 0.03 #21171), 0738b8 (0.09 #402, 0.03 #25307, 0.03 #8703), 0lpjn (0.07 #2552, 0.02 #12928, 0.02 #15004) >> Best rule #20753 for best value: >> intensional similarity = 5 >> extensional distance = 171 >> proper extension: 06zn1c; >> query: (?x943, ?x9153) <- titles(?x942, ?x943), film(?x9153, ?x943), film_release_distribution_medium(?x943, ?x81), genre(?x943, ?x258), ?x258 = 05p553 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #24180 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 192 *> proper extension: 0bhwhj; *> query: (?x943, 0c35b1) <- titles(?x942, ?x943), country(?x943, ?x94), crewmember(?x943, ?x9151), film(?x5959, ?x943), language(?x943, ?x254) *> conf = 0.01 ranks of expected_values: 1089 EVAL 0963mq film! 0c35b1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 88.000 57.000 0.196 http://example.org/film/actor/film./film/performance/film #20960-047cx PRED entity: 047cx PRED relation: artists! PRED expected values: 0xhtw 0dl5d 08jyyk => 96 concepts (47 used for prediction) PRED predicted values (max 10 best out of 286): 0xhtw (0.89 #6195, 0.75 #4956, 0.71 #5575), 016clz (0.84 #13302, 0.57 #4326, 0.50 #2168), 06by7 (0.77 #4961, 0.69 #5269, 0.65 #6509), 064t9 (0.51 #7735, 0.48 #14238, 0.46 #5880), 05bt6j (0.50 #352, 0.42 #5910, 0.40 #1280), 08jyyk (0.47 #3155, 0.34 #4697, 0.29 #4079), 0dl5d (0.47 #4650, 0.46 #4032, 0.40 #4959), 03_d0 (0.40 #3099, 0.40 #1867, 0.40 #939), 0k345 (0.40 #1091, 0.33 #164, 0.27 #3251), 05r6t (0.40 #1627, 0.33 #5949, 0.26 #13606) >> Best rule #6195 for best value: >> intensional similarity = 6 >> extensional distance = 73 >> proper extension: 01s7qqw; 018y81; 020hh3; 03f1zhf; 048tgl; 01y_rz; 015196; >> query: (?x4783, 0xhtw) <- artists(?x9063, ?x4783), artists(?x2249, ?x4783), category(?x4783, ?x134), ?x2249 = 03lty, artists(?x9063, ?x9693), ?x9693 = 02pt27 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 7 EVAL 047cx artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 96.000 47.000 0.893 http://example.org/music/genre/artists EVAL 047cx artists! 0dl5d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 96.000 47.000 0.893 http://example.org/music/genre/artists EVAL 047cx artists! 0xhtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 47.000 0.893 http://example.org/music/genre/artists #20959-05563d PRED entity: 05563d PRED relation: group! PRED expected values: 0dwr4 => 58 concepts (53 used for prediction) PRED predicted values (max 10 best out of 102): 018vs (0.74 #771, 0.73 #1032, 0.72 #1096), 02snj9 (0.33 #38, 0.17 #417, 0.15 #547), 03t22m (0.33 #17, 0.15 #829, 0.10 #444), 0mkg (0.33 #5, 0.10 #444, 0.10 #764), 0jtg0 (0.33 #31, 0.10 #444, 0.10 #764), 04rzd (0.17 #398, 0.15 #829, 0.13 #1499), 0gkd1 (0.17 #437, 0.15 #829, 0.10 #444), 01s0ps (0.17 #409, 0.15 #829, 0.10 #444), 0cfdd (0.17 #432, 0.10 #444, 0.10 #764), 03q5t (0.17 #380, 0.10 #444, 0.10 #764) >> Best rule #771 for best value: >> intensional similarity = 12 >> extensional distance = 44 >> proper extension: 04qmr; 0mgcr; 0163m1; 0g_g2; 01fmz6; 01j59b0; 06nv27; 03d9d6; 015srx; 013w2r; ... >> query: (?x3516, 018vs) <- group(?x1495, ?x3516), group(?x645, ?x3516), group(?x315, ?x3516), group(?x214, ?x3516), ?x645 = 028tv0, ?x315 = 0l14md, instrumentalists(?x214, ?x215), role(?x214, ?x614), role(?x214, ?x716), role(?x74, ?x1495), instrumentalists(?x1495, ?x211), performance_role(?x1147, ?x1495) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #829 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 44 *> proper extension: 04qmr; 0mgcr; 0163m1; 0g_g2; 01fmz6; 01j59b0; 06nv27; 03d9d6; 015srx; 013w2r; ... *> query: (?x3516, ?x1147) <- group(?x1495, ?x3516), group(?x645, ?x3516), group(?x315, ?x3516), group(?x214, ?x3516), ?x645 = 028tv0, ?x315 = 0l14md, instrumentalists(?x214, ?x215), role(?x214, ?x614), role(?x214, ?x716), role(?x74, ?x1495), instrumentalists(?x1495, ?x211), performance_role(?x1147, ?x1495) *> conf = 0.15 ranks of expected_values: 42 EVAL 05563d group! 0dwr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 58.000 53.000 0.739 http://example.org/music/performance_role/regular_performances./music/group_membership/group #20958-0344gc PRED entity: 0344gc PRED relation: film_format PRED expected values: 0cj16 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 4): 0cj16 (0.43 #88, 0.42 #84, 0.41 #32), 017fx5 (0.11 #85, 0.09 #93, 0.08 #89), 01dc60 (0.04 #4, 0.03 #8, 0.02 #12), 0hcr (0.01 #31) >> Best rule #88 for best value: >> intensional similarity = 4 >> extensional distance = 221 >> proper extension: 0340hj; 0cn_b8; 06pyc2; >> query: (?x898, 0cj16) <- nominated_for(?x618, ?x898), country(?x898, ?x94), film_format(?x898, ?x909), film_release_distribution_medium(?x898, ?x81) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0344gc film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.426 http://example.org/film/film/film_format #20957-06sn8m PRED entity: 06sn8m PRED relation: gender PRED expected values: 05zppz => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #45, 0.83 #37, 0.78 #5), 02zsn (0.45 #85, 0.41 #26, 0.39 #16) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 0d4jl; 05x8n; 01k56k; >> query: (?x6962, 05zppz) <- profession(?x6962, ?x1032), award(?x6962, ?x435), award(?x9132, ?x435), ?x9132 = 022qw7 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06sn8m gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.842 http://example.org/people/person/gender #20956-05qkp PRED entity: 05qkp PRED relation: organization PRED expected values: 07t65 0_2v => 82 concepts (76 used for prediction) PRED predicted values (max 10 best out of 49): 07t65 (0.90 #527, 0.90 #654, 0.90 #590), 0_2v (0.55 #653, 0.42 #25, 0.41 #4), 04k4l (0.55 #653, 0.32 #1181, 0.27 #362), 018cqq (0.55 #653, 0.32 #1181, 0.24 #11), 0b6css (0.53 #73, 0.40 #178, 0.36 #367), 0gkjy (0.43 #70, 0.32 #1181, 0.31 #175), 01rz1 (0.32 #1181, 0.29 #2, 0.29 #296), 02jxk (0.32 #1181, 0.18 #3, 0.15 #297), 059dn (0.32 #1181, 0.07 #57, 0.06 #15), 085h1 (0.32 #1181, 0.03 #75, 0.02 #327) >> Best rule #527 for best value: >> intensional similarity = 3 >> extensional distance = 151 >> proper extension: 07ww5; 077qn; >> query: (?x3120, 07t65) <- country(?x1121, ?x3120), adjoins(?x390, ?x3120), jurisdiction_of_office(?x182, ?x3120) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05qkp organization 0_2v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 76.000 0.902 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 05qkp organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 76.000 0.902 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #20955-015pxr PRED entity: 015pxr PRED relation: type_of_union PRED expected values: 01g63y => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.80 #45, 0.76 #105, 0.75 #101), 01g63y (0.19 #22, 0.18 #26, 0.17 #30) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 0qf43; 0jf1b; 05kfs; 022_lg; 04gcd1; 021lby; 0184dt; 0hskw; 0b_7k; 0p51w; ... >> query: (?x2143, 04ztj) <- award(?x2143, ?x2016), film(?x2143, ?x5534), award_winner(?x944, ?x2143) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #22 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 016hvl; 01p45_v; 01h8f; 01bbwp; 0kc6; 029k55; 01pgk0; *> query: (?x2143, 01g63y) <- profession(?x2143, ?x2225), profession(?x2143, ?x524), ?x524 = 02jknp, ?x2225 = 0kyk *> conf = 0.19 ranks of expected_values: 2 EVAL 015pxr type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 124.000 0.800 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20954-0h6sv PRED entity: 0h6sv PRED relation: award_winner! PRED expected values: 024_dt => 135 concepts (100 used for prediction) PRED predicted values (max 10 best out of 293): 025m8y (0.58 #7411, 0.39 #3541, 0.38 #5261), 0257w4 (0.57 #1006, 0.22 #1866, 0.05 #7026), 024_41 (0.44 #2018, 0.14 #1158, 0.09 #2448), 024vjd (0.43 #1056, 0.33 #1916, 0.07 #4066), 0l8z1 (0.40 #5225, 0.36 #3505, 0.25 #1355), 024_dt (0.39 #12476, 0.38 #12475, 0.38 #12907), 02h3d1 (0.33 #7489, 0.22 #1899, 0.12 #1469), 024_fw (0.33 #1967, 0.14 #1107, 0.05 #7987), 054krc (0.33 #5249, 0.29 #3529, 0.20 #7399), 0257wh (0.29 #1200, 0.11 #2060, 0.04 #43028) >> Best rule #7411 for best value: >> intensional similarity = 5 >> extensional distance = 79 >> proper extension: 01r6jt2; 02qmncd; 0csdzz; >> query: (?x13167, 025m8y) <- award_winner(?x2324, ?x13167), award(?x5172, ?x2324), award(?x5151, ?x2324), artists(?x505, ?x5151), ?x5172 = 06fmdb >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #12476 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 222 *> proper extension: 02mslq; 011zf2; 03yf3z; 03h610; 09bx1k; 094xh; 01l79yc; 0fpjyd; 031x_3; 01m5m5b; *> query: (?x13167, ?x12458) <- student(?x9239, ?x13167), artists(?x888, ?x13167), award(?x13167, ?x12458), ceremony(?x12458, ?x342) *> conf = 0.39 ranks of expected_values: 6 EVAL 0h6sv award_winner! 024_dt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 135.000 100.000 0.580 http://example.org/award/award_category/winners./award/award_honor/award_winner #20953-0q9nj PRED entity: 0q9nj PRED relation: country_of_origin PRED expected values: 09c7w0 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 12): 09c7w0 (0.93 #194, 0.93 #250, 0.92 #239), 07ssc (0.50 #122, 0.28 #155, 0.26 #167), 0d060g (0.47 #686, 0.46 #851, 0.43 #113), 03_3d (0.13 #771, 0.09 #877, 0.09 #899), 0d0vqn (0.08 #82, 0.01 #426), 03rjj (0.06 #115, 0.03 #148, 0.03 #160), 02jx1 (0.06 #124, 0.03 #157, 0.03 #169), 04jpl (0.06 #119, 0.03 #152, 0.03 #164), 0wq3z (0.05 #445, 0.04 #238), 02_286 (0.03 #170, 0.01 #468) >> Best rule #194 for best value: >> intensional similarity = 8 >> extensional distance = 39 >> proper extension: 03y3bp7; >> query: (?x9135, 09c7w0) <- program_creator(?x9135, ?x4720), program(?x4720, ?x4721), award(?x4720, ?x1105), award_winner(?x4721, ?x415), tv_program(?x912, ?x4721), program(?x2062, ?x4721), producer_type(?x4720, ?x632), award_nominee(?x4720, ?x2549) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q9nj country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.927 http://example.org/tv/tv_program/country_of_origin #20952-01jq0j PRED entity: 01jq0j PRED relation: school! PRED expected values: 0g3zpp 02rl201 09l0x9 => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 16): 03nt7j (0.41 #54, 0.40 #22, 0.19 #134), 02qw1zx (0.41 #52, 0.30 #20, 0.29 #148), 09l0x9 (0.35 #58, 0.30 #26, 0.21 #138), 02z6872 (0.32 #89, 0.16 #137, 0.14 #450), 092j54 (0.30 #24, 0.26 #88, 0.25 #8), 025tn92 (0.25 #11, 0.21 #91, 0.20 #155), 02r6gw6 (0.25 #12, 0.14 #450, 0.14 #433), 02pq_x5 (0.24 #63, 0.23 #143, 0.20 #31), 0g3zpp (0.24 #49, 0.20 #33, 0.16 #161), 09th87 (0.24 #61, 0.15 #189, 0.14 #450) >> Best rule #54 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 02jyr8; 03tw2s; 01rc6f; 02pptm; >> query: (?x6953, 03nt7j) <- school_type(?x6953, ?x3092), institution(?x865, ?x6953), school(?x465, ?x6953), colors(?x6953, ?x332), ?x465 = 05vsb7 >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 02jyr8; 03tw2s; 01rc6f; 02pptm; *> query: (?x6953, 09l0x9) <- school_type(?x6953, ?x3092), institution(?x865, ?x6953), school(?x465, ?x6953), colors(?x6953, ?x332), ?x465 = 05vsb7 *> conf = 0.35 ranks of expected_values: 3, 9, 15 EVAL 01jq0j school! 09l0x9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 174.000 174.000 0.412 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 01jq0j school! 02rl201 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 174.000 174.000 0.412 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 01jq0j school! 0g3zpp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 174.000 174.000 0.412 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #20951-01sn04 PRED entity: 01sn04 PRED relation: contains PRED expected values: 01jzyx => 108 concepts (52 used for prediction) PRED predicted values (max 10 best out of 150): 04ftdq (0.17 #1246, 0.12 #4194, 0.03 #7142), 027kp3 (0.17 #615, 0.12 #3563, 0.03 #6511), 04d5v9 (0.17 #455, 0.12 #3403, 0.03 #6351), 02lwv5 (0.17 #1745, 0.12 #4693, 0.03 #7641), 01sn04 (0.17 #126, 0.12 #3074, 0.03 #6022), 03qdm (0.17 #1711, 0.12 #4659, 0.03 #7607), 01f38z (0.17 #2742, 0.12 #5690, 0.03 #8638), 03dm7 (0.17 #1882, 0.12 #4830, 0.03 #7778), 021q2j (0.12 #4210, 0.03 #7158, 0.03 #10106), 03bmmc (0.12 #3726, 0.03 #6674, 0.03 #9622) >> Best rule #1246 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0f94t; 0cc56; 01531; 0ccvx; >> query: (?x1234, 04ftdq) <- place_of_birth(?x1233, ?x1234), contains(?x4253, ?x1234), contains(?x739, ?x1234), time_zones(?x4253, ?x2674), ?x739 = 02_286, citytown(?x5426, ?x4253) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #109066 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 463 *> proper extension: 017cjb; *> query: (?x1234, ?x4794) <- place_of_birth(?x1233, ?x1234), contains(?x335, ?x1234), state_province_region(?x166, ?x335), contains(?x335, ?x4794), institution(?x620, ?x4794) *> conf = 0.02 ranks of expected_values: 61 EVAL 01sn04 contains 01jzyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 108.000 52.000 0.167 http://example.org/location/location/contains #20950-0308kx PRED entity: 0308kx PRED relation: gender PRED expected values: 05zppz => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #51, 0.72 #73, 0.72 #170), 02zsn (0.52 #87, 0.50 #6, 0.42 #2) >> Best rule #51 for best value: >> intensional similarity = 3 >> extensional distance = 921 >> proper extension: 0n6f8; 01qvgl; 09ftwr; 0p8jf; 03pvt; 017yfz; 0n6kf; 01pqy_; 03cn92; 07j8kh; ... >> query: (?x4149, 05zppz) <- place_of_birth(?x4149, ?x1659), student(?x1043, ?x4149), type_of_union(?x4149, ?x566) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0308kx gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.730 http://example.org/people/person/gender #20949-04ykg PRED entity: 04ykg PRED relation: teams PRED expected values: 0512p => 208 concepts (208 used for prediction) PRED predicted values (max 10 best out of 306): 038zh6 (0.05 #1785, 0.02 #20100, 0.02 #4658), 023zd7 (0.04 #1239, 0.03 #1598, 0.03 #2316), 0cqt41 (0.04 #1107, 0.03 #1466, 0.02 #10084), 0hmtk (0.04 #1393, 0.03 #1752, 0.02 #10370), 05g76 (0.04 #1112, 0.03 #1471, 0.02 #10089), 0jm3v (0.04 #1090, 0.03 #1449, 0.02 #10067), 01l3vx (0.04 #1121, 0.03 #2198, 0.02 #4353), 02bh_v (0.04 #1292, 0.03 #2369, 0.02 #4524), 01j48s (0.03 #1744, 0.03 #2462, 0.02 #3539), 0jm5b (0.03 #1723, 0.03 #2441, 0.02 #3518) >> Best rule #1785 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 0pfd9; >> query: (?x1274, 038zh6) <- administrative_parent(?x1274, ?x94), teams(?x1274, ?x10690), contains(?x94, ?x95) >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #10080 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 0727_; *> query: (?x1274, 0512p) <- jurisdiction_of_office(?x900, ?x1274), teams(?x1274, ?x10690), place_of_birth(?x2650, ?x1274) *> conf = 0.02 ranks of expected_values: 184 EVAL 04ykg teams 0512p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 208.000 208.000 0.054 http://example.org/sports/sports_team_location/teams #20948-0plxn PRED entity: 0plxn PRED relation: place PRED expected values: 0plxn => 122 concepts (52 used for prediction) PRED predicted values (max 10 best out of 18): 0q8s4 (0.14 #110, 0.12 #1140, 0.12 #625), 0q6lr (0.14 #361, 0.12 #1391, 0.12 #876), 0q8jl (0.14 #292, 0.12 #1322, 0.12 #807), 0qc7l (0.12 #993, 0.08 #2538, 0.08 #3570), 0lphb (0.12 #1205, 0.08 #2235, 0.08 #3783), 0fttg (0.09 #1923, 0.08 #3986, 0.08 #3470), 0q48z (0.09 #1861, 0.08 #3924, 0.08 #3408), 0q8sw (0.02 #6184, 0.02 #17584, 0.01 #17063), 0q8p8 (0.02 #10330, 0.01 #17063), 058cm (0.02 #14475, 0.01 #17063) >> Best rule #110 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0q8s4; 0q8jl; 0q8p8; 0q6lr; 058cm; >> query: (?x13051, 0q8s4) <- contains(?x2831, ?x13051), ?x2831 = 0gyh, time_zones(?x13051, ?x1638), source(?x13051, ?x958), category(?x13051, ?x134) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #17063 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 733 *> proper extension: 0kygv; 0cp6w; *> query: (?x13051, ?x1201) <- contains(?x2831, ?x13051), capital(?x2831, ?x12384), adjoins(?x2831, ?x2623), contains(?x2831, ?x1201), vacationer(?x2623, ?x5625) *> conf = 0.01 ranks of expected_values: 11 EVAL 0plxn place 0plxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 122.000 52.000 0.143 http://example.org/location/hud_county_place/place #20947-0p3sf PRED entity: 0p3sf PRED relation: artists! PRED expected values: 01lxd4 => 97 concepts (52 used for prediction) PRED predicted values (max 10 best out of 217): 0m40d (0.60 #769, 0.50 #150, 0.10 #460), 03_d0 (0.50 #12, 0.46 #631, 0.41 #322), 06by7 (0.45 #3127, 0.44 #8397, 0.43 #9639), 064t9 (0.44 #10558, 0.43 #9940, 0.43 #13972), 0h08p (0.38 #215, 0.20 #834, 0.14 #525), 0155w (0.31 #109, 0.24 #419, 0.23 #728), 06j6l (0.28 #3155, 0.27 #7498, 0.26 #9977), 0gywn (0.28 #371, 0.26 #6581, 0.23 #7817), 016clz (0.26 #6213, 0.23 #3109, 0.23 #15827), 0glt670 (0.26 #6563, 0.24 #7799, 0.24 #7490) >> Best rule #769 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 01fl3; >> query: (?x3171, 0m40d) <- artists(?x12513, ?x3171), artists(?x12513, ?x7803), ?x7803 = 03h_yfh >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #312 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 02_fj; 017yfz; 01ws9n6; 044k8; 015xp4; 01n44c; 02jq1; 013qvn; 01vz0g4; 07pzc; ... *> query: (?x3171, 01lxd4) <- artists(?x5355, ?x3171), place_of_death(?x3171, ?x6952), people(?x2510, ?x3171), artist(?x8489, ?x3171) *> conf = 0.07 ranks of expected_values: 58 EVAL 0p3sf artists! 01lxd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 97.000 52.000 0.600 http://example.org/music/genre/artists #20946-03z8bw PRED entity: 03z8bw PRED relation: colors PRED expected values: 083jv => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 17): 083jv (0.81 #1202, 0.78 #593, 0.76 #612), 06fvc (0.57 #22, 0.50 #3, 0.44 #689), 019sc (0.39 #522, 0.28 #1365, 0.27 #1226), 01l849 (0.30 #782, 0.15 #1339, 0.15 #1338), 038hg (0.17 #12, 0.15 #1339, 0.15 #1338), 088fh (0.16 #331, 0.15 #1339, 0.15 #1338), 036k5h (0.15 #1339, 0.15 #1338, 0.15 #1337), 02rnmb (0.15 #1339, 0.15 #1338, 0.15 #1337), 0jc_p (0.15 #1339, 0.15 #1338, 0.15 #1337), 04d18d (0.15 #1339, 0.15 #1338, 0.15 #1337) >> Best rule #1202 for best value: >> intensional similarity = 11 >> extensional distance = 271 >> proper extension: 01lpx8; >> query: (?x9799, 083jv) <- colors(?x9799, ?x3189), colors(?x10935, ?x3189), colors(?x10636, ?x3189), colors(?x5233, ?x3189), ?x10636 = 04h54p, colors(?x12742, ?x3189), colors(?x7777, ?x3189), ?x12742 = 032r4n, ?x5233 = 0j5m6, ?x10935 = 0122wc, ?x7777 = 057wlm >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03z8bw colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.813 http://example.org/sports/sports_team/colors #20945-09gdh6k PRED entity: 09gdh6k PRED relation: genre PRED expected values: 01hmnh => 92 concepts (91 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.74 #1681, 0.73 #361, 0.72 #1441), 02kdv5l (0.55 #963, 0.53 #1083, 0.53 #723), 03k9fj (0.49 #732, 0.48 #1092, 0.48 #972), 03npn (0.45 #248, 0.15 #1809, 0.15 #4818), 02l7c8 (0.43 #376, 0.35 #1216, 0.33 #1336), 05p553 (0.39 #3371, 0.39 #845, 0.36 #1205), 01hmnh (0.36 #258, 0.29 #978, 0.27 #1098), 0lsxr (0.36 #1810, 0.34 #1569, 0.34 #4819), 0vgkd (0.33 #11, 0.14 #2643, 0.10 #371), 06n90 (0.32 #973, 0.32 #1093, 0.31 #733) >> Best rule #1681 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 0cnztc4; 0crh5_f; 043sct5; 0cp08zg; >> query: (?x7532, 07s9rl0) <- film(?x382, ?x7532), film_festivals(?x7532, ?x10083), language(?x7532, ?x254) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #258 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 04nnpw; 01kjr0; *> query: (?x7532, 01hmnh) <- executive_produced_by(?x7532, ?x361), genre(?x7532, ?x6277), language(?x7532, ?x254), ?x6277 = 0fdjb *> conf = 0.36 ranks of expected_values: 7 EVAL 09gdh6k genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 92.000 91.000 0.744 http://example.org/film/film/genre #20944-0jnrk PRED entity: 0jnrk PRED relation: colors PRED expected values: 0jc_p => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 17): 083jv (0.85 #1136, 0.83 #1098, 0.71 #177), 06fvc (0.65 #852, 0.44 #99, 0.43 #178), 01l849 (0.31 #727, 0.22 #97, 0.20 #528), 01g5v (0.30 #1414, 0.30 #1452, 0.30 #1433), 03vtbc (0.22 #104, 0.20 #528, 0.20 #570), 088fh (0.21 #694, 0.20 #528, 0.20 #570), 038hg (0.20 #528, 0.20 #570, 0.18 #77), 06kqt3 (0.20 #528, 0.20 #570, 0.18 #77), 0jc_p (0.20 #528, 0.20 #570, 0.18 #77), 02rnmb (0.19 #306, 0.18 #77, 0.18 #431) >> Best rule #1136 for best value: >> intensional similarity = 15 >> extensional distance = 260 >> proper extension: 02896; 0lhp1; 03lpp_; 06x68; 01jv_6; 03y_f8; 075q_; 03mqj_; 01d5z; 02gys2; ... >> query: (?x8037, 083jv) <- colors(?x8037, ?x4557), colors(?x14124, ?x4557), colors(?x11368, ?x4557), colors(?x8995, ?x4557), colors(?x684, ?x4557), ?x14124 = 04l590, colors(?x12132, ?x4557), colors(?x7918, ?x4557), colors(?x1520, ?x4557), position(?x8995, ?x2010), ?x7918 = 0gl6f, ?x11368 = 032yps, ?x684 = 01ct6, ?x12132 = 027b43, ?x1520 = 07lx1s >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #528 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 72 *> proper extension: 01y3c; 05g3v; 0ws7; 0mgcc; *> query: (?x8037, ?x1101) <- colors(?x8037, ?x4557), ?x4557 = 019sc, team(?x3724, ?x8037), team(?x3724, ?x9835), team(?x3724, ?x3298), sport(?x8037, ?x453), colors(?x3298, ?x1101), colors(?x3298, ?x663), team(?x13270, ?x9835), ?x663 = 083jv *> conf = 0.20 ranks of expected_values: 9 EVAL 0jnrk colors 0jc_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 78.000 78.000 0.847 http://example.org/sports/sports_team/colors #20943-0322yj PRED entity: 0322yj PRED relation: film! PRED expected values: 013knm => 110 concepts (81 used for prediction) PRED predicted values (max 10 best out of 959): 0146pg (0.47 #31234, 0.45 #124913, 0.45 #47889), 0b25vg (0.20 #3853, 0.20 #1772, 0.12 #8016), 0dvmd (0.20 #2609, 0.20 #528, 0.07 #13020), 0hvb2 (0.20 #2380, 0.20 #299, 0.06 #6543), 016fjj (0.20 #2716, 0.20 #635, 0.06 #6879), 0716t2 (0.20 #3990, 0.20 #1909, 0.06 #8153), 01jfrg (0.20 #3161, 0.20 #1080, 0.06 #7324), 05np4c (0.20 #2658, 0.20 #577, 0.06 #6821), 019vgs (0.20 #2742, 0.20 #661, 0.03 #54134), 0p8r1 (0.20 #2667, 0.20 #586, 0.02 #40147) >> Best rule #31234 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 012jfb; >> query: (?x12437, ?x669) <- award_winner(?x12437, ?x669), country(?x12437, ?x94), currency(?x12437, ?x170), category(?x12437, ?x134) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #13130 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 84 *> proper extension: 0g5qs2k; 0b73_1d; 0b6tzs; 03bx2lk; 01vfqh; 019vhk; 0kvgtf; 03tn80; 02pg45; 03t79f; ... *> query: (?x12437, 013knm) <- edited_by(?x12437, ?x4215), genre(?x12437, ?x53), produced_by(?x12437, ?x1533), film_release_distribution_medium(?x12437, ?x81) *> conf = 0.02 ranks of expected_values: 470 EVAL 0322yj film! 013knm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 110.000 81.000 0.466 http://example.org/film/actor/film./film/performance/film #20942-01bkb PRED entity: 01bkb PRED relation: religion PRED expected values: 0flw86 => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 28): 0c8wxp (0.85 #368, 0.81 #269, 0.77 #203), 0631_ (0.85 #370, 0.77 #205, 0.75 #139), 051kv (0.85 #367, 0.77 #202, 0.75 #136), 019cr (0.83 #373, 0.75 #142, 0.67 #43), 04pk9 (0.80 #380, 0.77 #215, 0.67 #50), 05w5d (0.80 #383, 0.77 #218, 0.67 #53), 05sfs (0.80 #365, 0.69 #200, 0.67 #35), 01y0s9 (0.67 #41, 0.61 #371, 0.42 #173), 021_0p (0.63 #379, 0.54 #214, 0.50 #49), 03_gx (0.62 #211, 0.51 #376, 0.50 #145) >> Best rule #368 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 05kkh; 05fkf; 0vmt; 03s0w; 05fhy; 059_c; 07z1m; 0488g; 050l8; 07z5n; ... >> query: (?x11382, 0c8wxp) <- religion(?x11382, ?x7422), country(?x11382, ?x3749), religion(?x248, ?x7422) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #331 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 07f1x; 016zwt; 0jbs5; 011hq1; *> query: (?x11382, 0flw86) <- religion(?x11382, ?x7422), religion(?x11382, ?x109), ?x7422 = 092bf5, religion(?x521, ?x109) *> conf = 0.44 ranks of expected_values: 11 EVAL 01bkb religion 0flw86 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 53.000 53.000 0.854 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #20941-0cmdwwg PRED entity: 0cmdwwg PRED relation: language PRED expected values: 02h40lc => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.96 #2513, 0.94 #1342, 0.93 #1459), 02bjrlw (0.21 #291, 0.14 #59, 0.06 #1458), 064_8sq (0.14 #311, 0.14 #79, 0.13 #1128), 06nm1 (0.14 #359, 0.09 #1351, 0.09 #2639), 04306rv (0.14 #63, 0.08 #2516, 0.07 #2633), 05zjd (0.14 #83, 0.07 #315, 0.05 #2046), 07qv_ (0.14 #90, 0.07 #322, 0.05 #2046), 01jb8r (0.14 #111, 0.07 #343), 03_9r (0.11 #184, 0.05 #3338, 0.05 #2638), 0295r (0.07 #318) >> Best rule #2513 for best value: >> intensional similarity = 3 >> extensional distance = 1514 >> proper extension: 03lv4x; 0f42nz; 0bl5c; 0kbhf; 05znbh7; 052_mn; 02q_x_l; 0k5px; >> query: (?x6394, 02h40lc) <- film(?x241, ?x6394), award_winner(?x670, ?x241), language(?x6394, ?x3592) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cmdwwg language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.955 http://example.org/film/film/language #20940-092t4b PRED entity: 092t4b PRED relation: ceremony! PRED expected values: 0cqhk0 0cqgl9 => 15 concepts (15 used for prediction) PRED predicted values (max 10 best out of 312): 0gqy2 (0.50 #3372, 0.46 #2620, 0.46 #2369), 0k611 (0.48 #3323, 0.46 #2571, 0.45 #2320), 0gq_d (0.48 #3409, 0.45 #2657, 0.44 #1653), 0gqwc (0.48 #3311, 0.45 #2559, 0.44 #2308), 0gvx_ (0.48 #3387, 0.45 #2635, 0.44 #2384), 018wng (0.47 #3287, 0.46 #2535, 0.45 #2284), 0gqyl (0.47 #3331, 0.45 #2579, 0.43 #2328), 0f4x7 (0.47 #3279, 0.44 #2527, 0.43 #2276), 0p9sw (0.47 #3274, 0.43 #2522, 0.41 #1266), 0gq9h (0.46 #3312, 0.42 #2560, 0.41 #2309) >> Best rule #3372 for best value: >> intensional similarity = 15 >> extensional distance = 120 >> proper extension: 073h9x; 0fz2y7; 0fzrtf; 0c4hnm; >> query: (?x3460, 0gqy2) <- award_winner(?x3460, ?x3461), award_winner(?x3460, ?x1870), award_winner(?x3460, ?x1739), ceremony(?x618, ?x3460), award_winner(?x1870, ?x192), award(?x1870, ?x693), award_nominee(?x1335, ?x3461), honored_for(?x3460, ?x337), award_nominee(?x495, ?x1870), nominated_for(?x1870, ?x810), nationality(?x3461, ?x512), award_winner(?x401, ?x1335), award_winner(?x3226, ?x495), award_winner(?x72, ?x1739), award(?x3461, ?x749) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #131 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 0g55tzk; *> query: (?x3460, 0cqgl9) <- award_winner(?x3460, ?x3461), award_winner(?x3460, ?x3452), award_winner(?x3460, ?x1870), award_winner(?x3460, ?x1739), ceremony(?x618, ?x3460), award_winner(?x820, ?x3452), ?x1739 = 015rkw, award_nominee(?x100, ?x1870), award_nominee(?x5422, ?x3461), award_winner(?x7974, ?x3461), award_winner(?x337, ?x1870), ?x5422 = 06j8wx, nominated_for(?x618, ?x144), award(?x5043, ?x618), award(?x516, ?x618), ?x5043 = 015q43, award_winner(?x678, ?x516), nationality(?x1870, ?x94) *> conf = 0.33 ranks of expected_values: 31, 32 EVAL 092t4b ceremony! 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 15.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 092t4b ceremony! 0cqhk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 15.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/ceremony #20939-024030 PRED entity: 024030 PRED relation: award_winner PRED expected values: 0674cw => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 024030 award_winner 0674cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/award/award_category/winners./award/award_honor/award_winner #20938-01mkq PRED entity: 01mkq PRED relation: student PRED expected values: 01n5309 => 68 concepts (60 used for prediction) PRED predicted values (max 10 best out of 237): 0kn4c (0.50 #983, 0.33 #3375, 0.33 #744), 083q7 (0.40 #3129, 0.33 #19, 0.25 #976), 04z0g (0.33 #847, 0.25 #2521, 0.25 #2043), 03j2gxx (0.33 #937, 0.25 #2611, 0.25 #2133), 0dx97 (0.33 #837, 0.25 #2511, 0.25 #2033), 0n00 (0.33 #788, 0.25 #2462, 0.25 #1984), 0djywgn (0.33 #414, 0.25 #2806, 0.17 #3763), 0d06m5 (0.33 #68, 0.20 #3178, 0.09 #5090), 014vk4 (0.33 #234, 0.20 #3344, 0.09 #5256), 0c_md_ (0.33 #191, 0.20 #3301, 0.09 #5213) >> Best rule #983 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 03g3w; >> query: (?x1668, 0kn4c) <- major_field_of_study(?x8930, ?x1668), major_field_of_study(?x4889, ?x1668), major_field_of_study(?x2497, ?x1668), major_field_of_study(?x581, ?x1668), ?x581 = 06pwq, major_field_of_study(?x1771, ?x1668), school(?x2568, ?x2497), major_field_of_study(?x1668, ?x742), ?x1771 = 019v9k, contains(?x94, ?x4889), ?x8930 = 0373qt, school(?x685, ?x2497), ?x2568 = 0jmcb >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01mkq student 01n5309 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 60.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/student #20937-01s1zk PRED entity: 01s1zk PRED relation: profession PRED expected values: 09jwl => 110 concepts (90 used for prediction) PRED predicted values (max 10 best out of 54): 09jwl (0.76 #1736, 0.73 #5612, 0.72 #5756), 01d_h8 (0.40 #1437, 0.38 #719, 0.37 #3159), 0dxtg (0.36 #726, 0.27 #3597, 0.25 #9338), 01c72t (0.33 #6048, 0.30 #1168, 0.29 #4470), 03gjzk (0.28 #727, 0.26 #2881, 0.25 #3167), 0d1pc (0.26 #11187, 0.15 #618, 0.13 #1479), 012t_z (0.26 #11187, 0.10 #296, 0.07 #725), 047rgpy (0.26 #11187, 0.02 #962, 0.02 #676), 02jknp (0.20 #9905, 0.19 #9762, 0.18 #11622), 0fnpj (0.16 #1345, 0.15 #1775, 0.15 #913) >> Best rule #1736 for best value: >> intensional similarity = 4 >> extensional distance = 276 >> proper extension: 017yfz; 01wy61y; 023l9y; 01l4g5; 018d6l; 021r7r; 017f4y; 023slg; >> query: (?x7614, 09jwl) <- artists(?x2937, ?x7614), profession(?x7614, ?x2348), ?x2348 = 0nbcg, instrumentalists(?x227, ?x7614) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01s1zk profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 90.000 0.759 http://example.org/people/person/profession #20936-05dxl5 PRED entity: 05dxl5 PRED relation: award_nominee PRED expected values: 03w4sh => 77 concepts (27 used for prediction) PRED predicted values (max 10 best out of 626): 05683p (0.80 #23234, 0.80 #48806, 0.78 #9293), 05lb65 (0.80 #23234, 0.80 #48806, 0.75 #41829), 038g2x (0.78 #9293, 0.76 #51132, 0.76 #48804), 030znt (0.78 #9293, 0.76 #51132, 0.76 #48804), 05dxl5 (0.71 #3216, 0.57 #893, 0.39 #7862), 03w4sh (0.64 #3811, 0.64 #1488, 0.32 #8457), 09r9dp (0.36 #7817, 0.31 #53459, 0.18 #51133), 048q6x (0.36 #8159, 0.16 #34854), 0bbvr84 (0.32 #9057, 0.31 #53459, 0.18 #51133), 03v1jf (0.32 #8193, 0.31 #53459, 0.18 #51133) >> Best rule #23234 for best value: >> intensional similarity = 3 >> extensional distance = 860 >> proper extension: 012ljv; 0134w7; 015rmq; 01sbf2; 02_hj4; 010hn; 076_74; 0k8y7; 033jkj; 01lvzbl; ... >> query: (?x3956, ?x1117) <- award_winner(?x3956, ?x444), award_nominee(?x1117, ?x3956), place_of_birth(?x3956, ?x6453) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #3811 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 026zvx7; 048hf; 03x16f; 04vmqg; *> query: (?x3956, 03w4sh) <- award_nominee(?x3956, ?x8256), award_nominee(?x3956, ?x3051), ?x3051 = 0gd_b_, ?x8256 = 02s_qz *> conf = 0.64 ranks of expected_values: 6 EVAL 05dxl5 award_nominee 03w4sh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 27.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20935-04ljl_l PRED entity: 04ljl_l PRED relation: nominated_for PRED expected values: 01r97z 02hxhz 03rtz1 07sp4l 05pdh86 02sfnv 0ch3qr1 037xlx 0bz6sq 06zn1c 042g97 => 56 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1546): 01vksx (0.71 #6225, 0.62 #9279, 0.27 #21381), 05qbbfb (0.63 #22911, 0.33 #13107, 0.31 #14635), 0dp7wt (0.63 #22911, 0.19 #14885, 0.13 #13357), 0fdv3 (0.63 #22911, 0.17 #3293, 0.14 #4819), 04vr_f (0.62 #7780, 0.38 #10835, 0.27 #21381), 05567m (0.57 #7417, 0.50 #10471, 0.50 #1309), 017jd9 (0.57 #6788, 0.50 #9842, 0.23 #11369), 05hjnw (0.54 #11421, 0.50 #3786, 0.38 #8366), 02c638 (0.54 #10982, 0.50 #7927, 0.31 #14035), 0btpm6 (0.50 #10255, 0.50 #8727, 0.43 #7201) >> Best rule #6225 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 05ztjjw; >> query: (?x102, 01vksx) <- nominated_for(?x102, ?x7199), nominated_for(?x102, ?x4953), ?x7199 = 05nlx4, film(?x400, ?x4953), award_winner(?x102, ?x133) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #13048 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 01cdjp; *> query: (?x102, 0ch3qr1) <- award(?x3873, ?x102), category(?x102, ?x134), award_winner(?x902, ?x3873) *> conf = 0.47 ranks of expected_values: 23, 88, 99, 233, 237, 242, 246, 264, 265, 484, 952 EVAL 04ljl_l nominated_for 042g97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04ljl_l nominated_for 06zn1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04ljl_l nominated_for 0bz6sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04ljl_l nominated_for 037xlx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04ljl_l nominated_for 0ch3qr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04ljl_l nominated_for 02sfnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04ljl_l nominated_for 05pdh86 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04ljl_l nominated_for 07sp4l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04ljl_l nominated_for 03rtz1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04ljl_l nominated_for 02hxhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04ljl_l nominated_for 01r97z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 22.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20934-0lkr7 PRED entity: 0lkr7 PRED relation: student! PRED expected values: 02gn8s 0trv => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 75): 0bwfn (0.06 #10818, 0.06 #12927, 0.05 #2383), 015nl4 (0.05 #1648, 0.03 #25370, 0.03 #19045), 017z88 (0.03 #3244, 0.03 #5879, 0.03 #6933), 07tg4 (0.03 #2194, 0.03 #1667, 0.02 #3775), 01w5m (0.03 #105, 0.03 #22772, 0.03 #632), 09f2j (0.03 #4902, 0.03 #3321, 0.03 #2794), 065y4w7 (0.03 #17938, 0.03 #16357, 0.03 #16884), 03ksy (0.03 #15922, 0.03 #22773, 0.03 #11704), 08815 (0.03 #3164, 0.02 #8435, 0.02 #12127), 026gvfj (0.03 #638, 0.02 #1165, 0.02 #111) >> Best rule #10818 for best value: >> intensional similarity = 3 >> extensional distance = 823 >> proper extension: 030pr; 0b6yp2; 01l79yc; 03bw6; 01njxvw; 0bn3jg; >> query: (?x4992, 0bwfn) <- nominated_for(?x4992, ?x5561), people(?x3584, ?x4992), award_winner(?x5561, ?x10491) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #2427 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 180 *> proper extension: 0bz5v2; 07ymr5; 01nrq5; 02j8nx; 073749; 0g2mbn; 04crrxr; 02cgb8; 02zrv7; 0djywgn; ... *> query: (?x4992, 0trv) <- nominated_for(?x4992, ?x3496), profession(?x4992, ?x1146), ?x1146 = 018gz8 *> conf = 0.01 ranks of expected_values: 65 EVAL 0lkr7 student! 0trv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 114.000 114.000 0.057 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0lkr7 student! 02gn8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 114.000 0.057 http://example.org/education/educational_institution/students_graduates./education/education/student #20933-0jsf6 PRED entity: 0jsf6 PRED relation: film! PRED expected values: 01csvq => 138 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1352): 02vyw (0.55 #89287, 0.49 #60211, 0.44 #95520), 0kb3n (0.55 #89287, 0.49 #60211, 0.44 #95520), 05yzt_ (0.55 #89287, 0.49 #60211, 0.44 #95520), 012201 (0.55 #89287, 0.49 #60211, 0.44 #95520), 0854hr (0.55 #89287, 0.49 #60211, 0.44 #95520), 0cdf37 (0.55 #89287, 0.49 #60211, 0.44 #95520), 053j4w4 (0.49 #60211, 0.41 #120439, 0.41 #141199), 04__f (0.33 #1377, 0.14 #3454, 0.12 #5530), 0c0k1 (0.33 #1504, 0.14 #3581, 0.12 #5657), 014gf8 (0.33 #1006, 0.07 #7236, 0.05 #9312) >> Best rule #89287 for best value: >> intensional similarity = 4 >> extensional distance = 201 >> proper extension: 05n6sq; 032clf; 09rfpk; >> query: (?x6213, ?x6096) <- nominated_for(?x6096, ?x6213), titles(?x162, ?x6213), ?x162 = 04xvlr, location(?x6096, ?x1523) >> conf = 0.55 => this is the best rule for 6 predicted values *> Best rule #2187 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0cq86w; *> query: (?x6213, 01csvq) <- honored_for(?x6213, ?x2047), nominated_for(?x1313, ?x6213), story_by(?x6213, ?x8408), ?x1313 = 0gs9p *> conf = 0.14 ranks of expected_values: 28 EVAL 0jsf6 film! 01csvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 138.000 68.000 0.546 http://example.org/film/actor/film./film/performance/film #20932-0f7hc PRED entity: 0f7hc PRED relation: languages PRED expected values: 02h40lc => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 12): 02h40lc (0.40 #236, 0.39 #119, 0.34 #977), 064_8sq (0.05 #912, 0.04 #990, 0.04 #1107), 0t_2 (0.04 #126, 0.03 #594, 0.02 #1101), 06nm1 (0.02 #981, 0.01 #435, 0.01 #2580), 06mp7 (0.02 #284), 04306rv (0.02 #1797, 0.02 #1953, 0.02 #1719), 03_9r (0.02 #317, 0.02 #395, 0.01 #434), 03k50 (0.02 #4299, 0.02 #4377, 0.02 #3476), 02bjrlw (0.02 #3082, 0.02 #1561, 0.01 #976), 04h9h (0.01 #459) >> Best rule #236 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 016qtt; 03f2_rc; 03kwtb; 012x4t; 0pyg6; 01dw9z; 01vsl3_; 06449; 0gcs9; 02_fj; ... >> query: (?x4657, 02h40lc) <- award_winner(?x886, ?x4657), artist(?x3265, ?x4657), people(?x2510, ?x4657) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f7hc languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.405 http://example.org/people/person/languages #20931-030vmc PRED entity: 030vmc PRED relation: film PRED expected values: 0f7hw => 95 concepts (65 used for prediction) PRED predicted values (max 10 best out of 359): 0c8tkt (0.50 #1653), 02z3r8t (0.07 #872, 0.01 #12438, 0.01 #1699), 04tqtl (0.04 #1083, 0.01 #5214, 0.01 #6040), 02pg45 (0.04 #1287, 0.01 #5418, 0.01 #6244), 084qpk (0.04 #878, 0.01 #5009, 0.01 #5835), 0277j40 (0.03 #597, 0.02 #1423), 016dj8 (0.03 #554, 0.02 #1380), 03kxj2 (0.03 #185, 0.02 #1011), 031t2d (0.03 #128, 0.02 #954), 01kff7 (0.03 #100, 0.02 #926) >> Best rule #1653 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 042rnl; 0m32_; 0gd9k; >> query: (?x9164, ?x1743) <- film(?x9164, ?x9507), film(?x2169, ?x9507), prequel(?x9507, ?x1743) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 030vmc film 0f7hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 65.000 0.500 http://example.org/film/director/film #20930-06wzvr PRED entity: 06wzvr PRED relation: nominated_for! PRED expected values: 05q8pss => 85 concepts (77 used for prediction) PRED predicted values (max 10 best out of 199): 07bdd_ (0.69 #4250, 0.68 #11100, 0.68 #11337), 0gq_v (0.57 #727, 0.45 #1671, 0.43 #1435), 0gq9h (0.45 #1713, 0.43 #1241, 0.32 #3129), 019f4v (0.43 #760, 0.28 #1704, 0.27 #996), 0gqyl (0.43 #313, 0.21 #2674, 0.21 #786), 094qd5 (0.43 #270, 0.19 #3103, 0.17 #2159), 0gs9p (0.34 #1714, 0.29 #770, 0.29 #297), 0gs96 (0.29 #1268, 0.29 #796, 0.28 #1740), 0p9sw (0.29 #1200, 0.26 #1672, 0.25 #1436), 0f4x7 (0.29 #1677, 0.25 #1205, 0.20 #5218) >> Best rule #4250 for best value: >> intensional similarity = 5 >> extensional distance = 219 >> proper extension: 02q56mk; 0gyfp9c; 0gyh2wm; 0dr_9t7; 0cc97st; 05650n; 0cmdwwg; 0h63gl9; 0p9tm; 076xkdz; ... >> query: (?x270, ?x1105) <- nominated_for(?x154, ?x270), genre(?x270, ?x258), ?x258 = 05p553, country(?x270, ?x94), award(?x270, ?x1105) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #148 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0164qt; 014kq6; 02x6dqb; 02fqrf; *> query: (?x270, 05q8pss) <- nominated_for(?x154, ?x270), genre(?x270, ?x258), costume_design_by(?x270, ?x12521), film_crew_role(?x270, ?x12763), ?x154 = 05b4l5x *> conf = 0.17 ranks of expected_values: 33 EVAL 06wzvr nominated_for! 05q8pss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 85.000 77.000 0.693 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20929-07nznf PRED entity: 07nznf PRED relation: award PRED expected values: 040njc => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 269): 02g3ft (0.72 #32814, 0.70 #19443, 0.70 #22685), 0gs9p (0.43 #485, 0.40 #6965, 0.38 #7370), 0gq9h (0.43 #483, 0.34 #8988, 0.33 #10203), 0cjyzs (0.38 #5777, 0.31 #6587, 0.29 #2942), 0gr51 (0.37 #1316, 0.26 #3341, 0.22 #6986), 019f4v (0.37 #6952, 0.35 #7357, 0.35 #3307), 040njc (0.34 #6893, 0.34 #3248, 0.33 #7298), 09sb52 (0.29 #17457, 0.27 #18268, 0.25 #13406), 05b1610 (0.29 #444, 0.17 #2064, 0.16 #1254), 02pqp12 (0.23 #6956, 0.23 #3311, 0.22 #7361) >> Best rule #32814 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x65, ?x1429) <- award_winner(?x1429, ?x65), award(?x276, ?x1429) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #6893 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 243 *> proper extension: 022_lg; 0171lb; 05cgy8; 0gv2r; 0flddp; 0dh1n_; 04rvy8; *> query: (?x65, 040njc) <- profession(?x65, ?x319), film(?x65, ?x4188), nominated_for(?x3042, ?x4188) *> conf = 0.34 ranks of expected_values: 7 EVAL 07nznf award 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 104.000 104.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20928-01v42g PRED entity: 01v42g PRED relation: film PRED expected values: 01ffx4 0bz6sq => 86 concepts (68 used for prediction) PRED predicted values (max 10 best out of 717): 0418wg (0.18 #2178, 0.08 #399, 0.03 #7515), 09xbpt (0.18 #1826, 0.05 #80061, 0.03 #72942), 02yvct (0.18 #2129, 0.05 #80061, 0.03 #72942), 06z8s_ (0.18 #1909, 0.03 #72942, 0.02 #7246), 0gj8t_b (0.14 #1960, 0.01 #5518), 0fh694 (0.09 #1921, 0.08 #142, 0.05 #80061), 02704ff (0.09 #2753, 0.08 #974, 0.03 #76501), 051ys82 (0.09 #2808, 0.08 #1029, 0.02 #8145), 011ysn (0.09 #2341, 0.08 #562, 0.02 #7678), 049xgc (0.09 #2743, 0.05 #80061, 0.05 #78281) >> Best rule #2178 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 02pq9yv; 0b1f49; 0kvqv; 06r_by; >> query: (?x1289, 0418wg) <- nationality(?x1289, ?x1310), award_nominee(?x1289, ?x2499), ?x2499 = 0c6qh >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #13960 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 190 *> proper extension: 044mz_; 0184jc; 05vsxz; 07fq1y; 0lbj1; 0byfz; 0h0jz; 0159h6; 027dtv3; 09wj5; ... *> query: (?x1289, 0bz6sq) <- nationality(?x1289, ?x1310), film(?x1289, ?x1640), ?x1310 = 02jx1 *> conf = 0.02 ranks of expected_values: 317 EVAL 01v42g film 0bz6sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 86.000 68.000 0.182 http://example.org/film/actor/film./film/performance/film EVAL 01v42g film 01ffx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 68.000 0.182 http://example.org/film/actor/film./film/performance/film #20927-09btt1 PRED entity: 09btt1 PRED relation: location PRED expected values: 0mnzd => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 67): 0_vn7 (0.70 #26512, 0.45 #19283, 0.42 #49001), 030qb3t (0.23 #83, 0.21 #887, 0.13 #7314), 02_286 (0.16 #25745, 0.15 #37, 0.14 #841), 04ykg (0.14 #872, 0.03 #1676), 07l5z (0.07 #1352, 0.02 #2156), 0fpzwf (0.07 #1086, 0.02 #1890), 0cr3d (0.06 #25853, 0.06 #18624, 0.05 #45130), 04jpl (0.05 #47411, 0.05 #45002, 0.05 #29741), 0cc56 (0.03 #25765, 0.03 #1665, 0.03 #29781), 059rby (0.03 #7247, 0.03 #12872, 0.03 #24921) >> Best rule #26512 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 01qx13; 03lh3v; 0f1pyf; 012v1t; 040j2_; 0399p; 07m69t; 03xyp_; 02x8kk; 02x8mt; ... >> query: (?x4508, ?x4350) <- location(?x4508, ?x9605), place_of_birth(?x4508, ?x4350) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09btt1 location 0mnzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 71.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #20926-0d9xq PRED entity: 0d9xq PRED relation: nationality PRED expected values: 09c7w0 => 151 concepts (148 used for prediction) PRED predicted values (max 10 best out of 81): 09c7w0 (0.89 #4012, 0.85 #4517, 0.82 #1904), 07z1m (0.33 #11075, 0.32 #12183, 0.30 #10973), 059rby (0.29 #10158, 0.29 #10566, 0.26 #10362), 0cymp (0.29 #10158, 0.29 #10566, 0.26 #10362), 02jx1 (0.16 #2336, 0.15 #4550, 0.14 #233), 07ssc (0.14 #1517, 0.11 #2318, 0.10 #1116), 03rk0 (0.09 #5774, 0.09 #8087, 0.09 #5472), 0d060g (0.09 #4422, 0.08 #707, 0.07 #2910), 0f8l9c (0.08 #4437, 0.06 #322, 0.04 #9551), 03_3d (0.07 #206, 0.06 #406, 0.04 #9551) >> Best rule #4012 for best value: >> intensional similarity = 4 >> extensional distance = 268 >> proper extension: 02gf_l; 011w20; >> query: (?x5101, 09c7w0) <- place_of_birth(?x5101, ?x6695), gender(?x5101, ?x514), currency(?x6695, ?x170), contains(?x1426, ?x6695) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d9xq nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 148.000 0.885 http://example.org/people/person/nationality #20925-0105y2 PRED entity: 0105y2 PRED relation: time_zones PRED expected values: 02fqwt => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 10): 02fqwt (0.80 #40, 0.79 #66, 0.75 #14), 02hcv8 (0.54 #875, 0.43 #1031, 0.43 #1005), 02hczc (0.50 #1094, 0.17 #2, 0.16 #1368), 02lcqs (0.42 #96, 0.36 #109, 0.32 #122), 02llzg (0.10 #355, 0.09 #498, 0.09 #550), 03bdv (0.08 #266, 0.06 #357, 0.05 #1073), 03plfd (0.05 #504, 0.04 #647, 0.03 #700), 0gsrz4 (0.03 #554, 0.03 #632, 0.03 #645), 042g7t (0.02 #648, 0.02 #453, 0.02 #701), 02lcrv (0.01 #293) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0ms6_; >> query: (?x9767, 02fqwt) <- contains(?x3634, ?x9767), ?x3634 = 07b_l, contains(?x9767, ?x7716), category(?x7716, ?x134) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0105y2 time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.800 http://example.org/location/location/time_zones #20924-016mhd PRED entity: 016mhd PRED relation: nominated_for! PRED expected values: 02qvyrt => 82 concepts (60 used for prediction) PRED predicted values (max 10 best out of 200): 0gqy2 (0.72 #4514, 0.30 #7157, 0.29 #1428), 0k611 (0.71 #1384, 0.57 #2705, 0.57 #2265), 02qt02v (0.71 #3744, 0.70 #3966, 0.70 #3965), 02qvyrt (0.59 #1404, 0.46 #2725, 0.45 #2285), 0gq9h (0.53 #4460, 0.52 #1374, 0.44 #7323), 019f4v (0.50 #1367, 0.42 #4453, 0.38 #2248), 0p9sw (0.47 #2219, 0.47 #2659, 0.43 #1338), 0gq_v (0.43 #1337, 0.42 #2218, 0.41 #2658), 0gr0m (0.43 #1371, 0.40 #2692, 0.40 #2252), 04dn09n (0.41 #1351, 0.32 #4437, 0.31 #1571) >> Best rule #4514 for best value: >> intensional similarity = 3 >> extensional distance = 260 >> proper extension: 0h3mh3q; 053x8hr; 02gjrc; 02qr46y; 026y3cf; >> query: (?x7982, 0gqy2) <- nominated_for(?x704, ?x7982), award(?x9256, ?x704), ?x9256 = 01kt17 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1404 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 54 *> proper extension: 0m313; 0yyg4; 011yxg; 011yph; 01cssf; 0209hj; 0jzw; 0jyx6; 0pv3x; 0gmcwlb; ... *> query: (?x7982, 02qvyrt) <- film(?x1104, ?x7982), nominated_for(?x6909, ?x7982), nominated_for(?x637, ?x7982), ?x637 = 02r22gf, nominated_for(?x4857, ?x7982), ?x6909 = 02qyntr *> conf = 0.59 ranks of expected_values: 4 EVAL 016mhd nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 60.000 0.721 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20923-0_2v PRED entity: 0_2v PRED relation: organization! PRED expected values: 0d060g 059j2 06t2t 05qkp 04xn_ 07fb6 => 78 concepts (19 used for prediction) PRED predicted values (max 10 best out of 368): 059j2 (0.73 #1630, 0.68 #1062, 0.62 #2971), 0d060g (0.68 #1062, 0.60 #803, 0.53 #3469), 0chghy (0.68 #1062, 0.53 #3469, 0.52 #2128), 05qhw (0.68 #1062, 0.53 #3469, 0.52 #2128), 06t8v (0.68 #1062, 0.53 #3469, 0.52 #2128), 01mjq (0.68 #1062, 0.53 #3469, 0.52 #2128), 06bnz (0.68 #1062, 0.53 #3469, 0.52 #2128), 04xn_ (0.68 #1062, 0.53 #3469, 0.52 #2128), 07dvs (0.68 #1062, 0.53 #3469, 0.52 #2128), 0hg5 (0.68 #1062, 0.53 #3469, 0.52 #2128) >> Best rule #1630 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 01rz1; 02jxk; 04k4l; 0j7v_; 018cqq; 059dn; >> query: (?x3750, 059j2) <- organization(?x2346, ?x3750), locations(?x3654, ?x2346), country(?x150, ?x2346), nationality(?x754, ?x2346), olympics(?x2346, ?x418), administrative_parent(?x206, ?x2346), countries_spoken_in(?x3271, ?x2346), film_release_region(?x7789, ?x2346) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 8, 108, 149, 154 EVAL 0_2v organization! 07fb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 78.000 19.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0_2v organization! 04xn_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 78.000 19.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0_2v organization! 05qkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 78.000 19.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0_2v organization! 06t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 78.000 19.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0_2v organization! 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 19.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0_2v organization! 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 19.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #20922-02c_4 PRED entity: 02c_4 PRED relation: school PRED expected values: 01jsk6 => 64 concepts (62 used for prediction) PRED predicted values (max 10 best out of 190): 0lyjf (0.48 #3068, 0.47 #1752, 0.47 #1566), 01vs5c (0.33 #645, 0.29 #1394, 0.29 #1020), 05krk (0.29 #1312, 0.28 #2249, 0.28 #2061), 0g8rj (0.29 #269, 0.25 #455, 0.21 #1017), 01jsk6 (0.29 #353, 0.25 #539, 0.12 #1848), 01j_9c (0.25 #378, 0.14 #192, 0.12 #3756), 0gy3w (0.25 #122, 0.11 #7727, 0.11 #9438), 027ybp (0.25 #152, 0.09 #897, 0.09 #8488), 01wdj_ (0.22 #2091, 0.18 #1715, 0.18 #1342), 01pl14 (0.21 #939, 0.20 #2625, 0.20 #6583) >> Best rule #3068 for best value: >> intensional similarity = 12 >> extensional distance = 19 >> proper extension: 070xg; >> query: (?x7643, 0lyjf) <- position(?x7643, ?x1517), position(?x7643, ?x1114), draft(?x7643, ?x3089), ?x1114 = 047g8h, team(?x2247, ?x7643), ?x3089 = 03nt7j, ?x1517 = 02g_6j, position(?x6292, ?x2247), position(?x5491, ?x2247), ?x6292 = 02pd1q9, ?x5491 = 086hg9, position_s(?x3114, ?x2247) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #353 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 5 *> proper extension: 05tfm; 051q5; 05gg4; 05g49; 0wsr; *> query: (?x7643, 01jsk6) <- position(?x7643, ?x2573), position(?x7643, ?x2147), position(?x7643, ?x1517), position(?x7643, ?x1114), draft(?x7643, ?x3089), ?x1114 = 047g8h, team(?x2247, ?x7643), ?x3089 = 03nt7j, ?x1517 = 02g_6j, ?x2247 = 01_9c1, school(?x7643, ?x735), ?x2147 = 04nfpk, ?x735 = 065y4w7, ?x2573 = 05b3ts *> conf = 0.29 ranks of expected_values: 5 EVAL 02c_4 school 01jsk6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 62.000 0.476 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #20921-03h3x5 PRED entity: 03h3x5 PRED relation: production_companies PRED expected values: 03yxwq => 76 concepts (66 used for prediction) PRED predicted values (max 10 best out of 116): 030_1_ (0.39 #98, 0.38 #262, 0.29 #16), 0kx4m (0.36 #8, 0.15 #254, 0.14 #90), 03xq0f (0.32 #3537, 0.32 #2139, 0.31 #3784), 01gb54 (0.22 #119, 0.21 #283, 0.13 #201), 016tw3 (0.18 #750, 0.15 #257, 0.14 #93), 016tt2 (0.12 #742, 0.09 #906, 0.08 #495), 05qd_ (0.11 #666, 0.11 #748, 0.10 #2969), 054lpb6 (0.08 #1328, 0.06 #3633, 0.06 #4208), 017s11 (0.07 #577, 0.07 #659, 0.07 #1565), 024rgt (0.07 #763, 0.07 #927, 0.06 #434) >> Best rule #98 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 01_mdl; 012mrr; 0d_wms; 027ct7c; 0gmgwnv; 042fgh; 0315rp; 02k1pr; 02p86pb; 0199wf; >> query: (?x2642, 030_1_) <- music(?x2642, ?x669), ?x669 = 0146pg, film(?x2135, ?x2642), language(?x2642, ?x254) >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03h3x5 production_companies 03yxwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 66.000 0.389 http://example.org/film/film/production_companies #20920-02zq43 PRED entity: 02zq43 PRED relation: award_nominee! PRED expected values: 0175wg 02fz3w => 81 concepts (39 used for prediction) PRED predicted values (max 10 best out of 651): 02fz3w (0.86 #6612, 0.84 #4291, 0.30 #81218), 016xh5 (0.85 #4641, 0.84 #6962, 0.81 #69614), 05tk7y (0.85 #4641, 0.84 #6962, 0.81 #69614), 01yhvv (0.85 #4641, 0.84 #6962, 0.81 #69614), 0175wg (0.84 #3660, 0.81 #5981, 0.30 #81218), 02zq43 (0.74 #2379, 0.67 #4700, 0.30 #81218), 0171cm (0.30 #81218, 0.15 #74256, 0.10 #5185), 0l6px (0.30 #81218, 0.15 #74256, 0.07 #7458), 017gxw (0.30 #81218, 0.15 #74256, 0.05 #3524), 02ply6j (0.30 #81218, 0.15 #74256, 0.05 #3922) >> Best rule #6612 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 05vsxz; 0m2wm; 0159h6; 0h5g_; 03f1zdw; 01yhvv; 09y20; 05tk7y; 01l2fn; 07hbxm; ... >> query: (?x381, 02fz3w) <- award_nominee(?x2487, ?x381), award_nominee(?x381, ?x1410), ?x2487 = 04rsd2 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 02zq43 award_nominee! 02fz3w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 39.000 0.857 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 02zq43 award_nominee! 0175wg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 39.000 0.857 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20919-02lg3y PRED entity: 02lg3y PRED relation: award_nominee! PRED expected values: 01l1sq => 110 concepts (51 used for prediction) PRED predicted values (max 10 best out of 626): 0436f4 (0.85 #2323, 0.81 #97538, 0.81 #95214), 01l1sq (0.75 #336, 0.28 #20900, 0.28 #2659), 02lgj6 (0.69 #301, 0.32 #2624, 0.17 #97539), 0c1ps1 (0.62 #2137, 0.32 #4460, 0.28 #20900), 059gkk (0.56 #734, 0.32 #3057, 0.28 #20900), 02lg3y (0.56 #1025, 0.28 #20900, 0.28 #3348), 02tr7d (0.56 #2663, 0.17 #97539, 0.17 #92891), 03yj_0n (0.56 #3130, 0.17 #97539, 0.17 #92891), 0bx0lc (0.52 #3683, 0.17 #97539, 0.17 #92891), 08w7vj (0.52 #2492, 0.17 #97539, 0.17 #92891) >> Best rule #2323 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01wbg84; 02lfcm; 021_rm; 02lfns; 01l1sq; 02lf70; 01dy7j; 059gkk; 03zyvw; 01z7_f; ... >> query: (?x4401, ?x368) <- location(?x4401, ?x4253), award_nominee(?x4401, ?x369), award_nominee(?x4401, ?x368), ?x369 = 01r42_g >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #336 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 01wbg84; 02lfcm; 021_rm; 02lfns; 01l1sq; 02lf70; 01dy7j; 059gkk; 03zyvw; 01z7_f; ... *> query: (?x4401, 01l1sq) <- location(?x4401, ?x4253), award_nominee(?x4401, ?x369), ?x369 = 01r42_g *> conf = 0.75 ranks of expected_values: 2 EVAL 02lg3y award_nominee! 01l1sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 51.000 0.851 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20918-0gvt53w PRED entity: 0gvt53w PRED relation: film_release_region PRED expected values: 0154j 05qhw 0f8l9c 01znc_ 01p1v 06t2t 03spz => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 83): 09c7w0 (0.93 #1827, 0.93 #1067, 0.93 #2892), 0f8l9c (0.89 #173, 0.86 #781, 0.83 #629), 0154j (0.83 #156, 0.68 #612, 0.63 #764), 03gj2 (0.77 #177, 0.76 #633, 0.69 #785), 05qhw (0.77 #165, 0.69 #621, 0.63 #773), 01znc_ (0.72 #194, 0.65 #650, 0.62 #802), 06t2t (0.71 #213, 0.58 #669, 0.49 #821), 03spz (0.71 #246, 0.55 #702, 0.51 #854), 05v8c (0.60 #167, 0.50 #623, 0.44 #775), 04gzd (0.57 #160, 0.43 #616, 0.35 #768) >> Best rule #1827 for best value: >> intensional similarity = 3 >> extensional distance = 533 >> proper extension: 0czyxs; 05q96q6; 03qnvdl; 0gd0c7x; 02725hs; 0fphgb; 024mxd; 02vr3gz; 03q5db; 0fb7sd; ... >> query: (?x9432, 09c7w0) <- produced_by(?x9432, ?x9754), nominated_for(?x68, ?x9432), film_release_region(?x9432, ?x87) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #173 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 053tj7; *> query: (?x9432, 0f8l9c) <- produced_by(?x9432, ?x9754), film_release_region(?x9432, ?x1603), ?x1603 = 06bnz *> conf = 0.89 ranks of expected_values: 2, 3, 5, 6, 7, 8, 11 EVAL 0gvt53w film_release_region 03spz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 76.000 76.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvt53w film_release_region 06t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 76.000 76.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvt53w film_release_region 01p1v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 76.000 76.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvt53w film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 76.000 76.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvt53w film_release_region 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvt53w film_release_region 05qhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 76.000 76.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvt53w film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20917-0fn2g PRED entity: 0fn2g PRED relation: month PRED expected values: 040fb => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 1): 040fb (0.91 #6, 0.91 #27, 0.90 #23) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0rh6k; 01914; 04jpl; 080h2; 05ywg; 052p7; 0cv3w; 01cx_; 049d1; 0d6lp; ... >> query: (?x6054, 040fb) <- country(?x6054, ?x7747), category(?x6054, ?x134), month(?x6054, ?x9905), ?x9905 = 028kb >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fn2g month 040fb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.913 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #20916-0kz2w PRED entity: 0kz2w PRED relation: major_field_of_study PRED expected values: 02j62 05qfh 0mg1w => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 115): 02lp1 (0.58 #1287, 0.54 #823, 0.51 #1519), 04rjg (0.56 #133, 0.52 #597, 0.51 #829), 02j62 (0.53 #1650, 0.51 #838, 0.51 #1302), 0193x (0.44 #147, 0.25 #1075, 0.25 #379), 04x_3 (0.43 #603, 0.42 #371, 0.41 #835), 0g26h (0.43 #39, 0.40 #1083, 0.35 #735), 05qfh (0.42 #1308, 0.36 #1656, 0.35 #1076), 0fdys (0.42 #383, 0.34 #847, 0.33 #1311), 01lj9 (0.39 #848, 0.38 #616, 0.35 #1312), 01tbp (0.39 #1331, 0.34 #1563, 0.33 #171) >> Best rule #1287 for best value: >> intensional similarity = 2 >> extensional distance = 55 >> proper extension: 03_c8p; 0cv_2; 02z_b; >> query: (?x1043, 02lp1) <- organization(?x1043, ?x5487), organization(?x346, ?x1043) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1650 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 01y17m; 0jhjl; *> query: (?x1043, 02j62) <- major_field_of_study(?x1043, ?x8221), institution(?x865, ?x1043), ?x8221 = 037mh8 *> conf = 0.53 ranks of expected_values: 3, 7, 75 EVAL 0kz2w major_field_of_study 0mg1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 93.000 93.000 0.579 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0kz2w major_field_of_study 05qfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 93.000 93.000 0.579 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0kz2w major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 93.000 0.579 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20915-02qtywd PRED entity: 02qtywd PRED relation: role PRED expected values: 01vj9c => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 112): 05r5c (0.57 #205, 0.57 #7, 0.51 #305), 02sgy (0.53 #105, 0.43 #6, 0.40 #204), 0l14qv (0.53 #104, 0.30 #203, 0.27 #303), 042v_gx (0.47 #107, 0.34 #206, 0.30 #306), 026t6 (0.41 #201, 0.37 #301, 0.33 #102), 01vj9c (0.40 #113, 0.33 #212, 0.29 #312), 0bxl5 (0.29 #67, 0.17 #596, 0.13 #265), 01s0ps (0.29 #58, 0.17 #596, 0.10 #256), 0dwt5 (0.27 #180, 0.11 #279, 0.10 #379), 02fsn (0.25 #995, 0.25 #1095, 0.24 #796) >> Best rule #205 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 03c7ln; 0285c; 03xl77; 01nn6c; 01gx5f; 023l9y; 01wgjj5; 018y81; 043c4j; 01w9ph_; ... >> query: (?x11533, 05r5c) <- role(?x11533, ?x3991), role(?x11533, ?x1166), ?x3991 = 05842k, family(?x228, ?x1166) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #113 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 082brv; 024dw0; 01wxdn3; 02s6sh; *> query: (?x11533, 01vj9c) <- role(?x11533, ?x3991), role(?x11533, ?x1166), ?x3991 = 05842k, ?x1166 = 05148p4 *> conf = 0.40 ranks of expected_values: 6 EVAL 02qtywd role 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 98.000 98.000 0.571 http://example.org/music/artist/track_contributions./music/track_contribution/role #20914-0353tm PRED entity: 0353tm PRED relation: film! PRED expected values: 059xvg => 129 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1308): 09l3p (0.33 #2831, 0.17 #17406, 0.09 #13241), 02qgqt (0.33 #2100, 0.10 #8346, 0.09 #12510), 01yhvv (0.33 #229, 0.10 #8557, 0.09 #10639), 068g3p (0.33 #5758, 0.10 #9922, 0.08 #18251), 02yxwd (0.33 #2826, 0.09 #13236, 0.07 #23649), 05dbf (0.33 #2447, 0.09 #12857, 0.07 #23270), 053xw6 (0.33 #3336, 0.09 #13746, 0.06 #19994), 0ywqc (0.33 #3873, 0.09 #14283, 0.06 #20531), 02t1cp (0.33 #4889, 0.09 #13217, 0.06 #19465), 01r93l (0.33 #2830, 0.09 #13240, 0.04 #23653) >> Best rule #2831 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 02cbhg; >> query: (?x9213, 09l3p) <- featured_film_locations(?x9213, ?x1036), genre(?x9213, ?x571), film_distribution_medium(?x9213, ?x81), country(?x9213, ?x205), ?x205 = 03rjj, music(?x9213, ?x12768) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0353tm film! 059xvg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 77.000 0.333 http://example.org/film/actor/film./film/performance/film #20913-03rl84 PRED entity: 03rl84 PRED relation: film PRED expected values: 047msdk => 142 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1167): 047msdk (0.61 #67836, 0.59 #158884, 0.55 #30348), 011yr9 (0.14 #691, 0.10 #2476, 0.07 #4261), 01jnc_ (0.09 #6921, 0.05 #40839, 0.05 #44409), 03bzyn4 (0.09 #8705, 0.01 #74756, 0.01 #76542), 0431v3 (0.08 #124961, 0.07 #135673, 0.07 #76763), 07c72 (0.08 #124961, 0.07 #135673, 0.07 #76763), 01shy7 (0.06 #45051, 0.06 #12918, 0.06 #30771), 01l_pn (0.05 #15246, 0.05 #72371, 0.04 #77728), 03bzjpm (0.05 #15594, 0.04 #47726, 0.04 #8453), 0418wg (0.05 #14682, 0.04 #34319, 0.03 #46814) >> Best rule #67836 for best value: >> intensional similarity = 3 >> extensional distance = 140 >> proper extension: 0d7hg4; 02rybfn; >> query: (?x2012, ?x1364) <- award(?x2012, ?x757), location_of_ceremony(?x2012, ?x5267), nominated_for(?x2012, ?x1364) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rl84 film 047msdk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 95.000 0.612 http://example.org/film/actor/film./film/performance/film #20912-02dwpf PRED entity: 02dwpf PRED relation: team PRED expected values: 01slc => 32 concepts (28 used for prediction) PRED predicted values (max 10 best out of 978): 0x2p (0.85 #7752, 0.84 #7754, 0.83 #14542), 01slc (0.85 #7752, 0.84 #7754, 0.83 #14542), 049n7 (0.83 #2901, 0.83 #2898, 0.81 #3872), 05xvj (0.83 #2901, 0.83 #2898, 0.81 #3872), 07l8f (0.83 #2901, 0.83 #2898, 0.81 #3872), 01d6g (0.83 #2901, 0.83 #2898, 0.81 #3872), 06x68 (0.83 #2901, 0.83 #2898, 0.81 #3872), 04mjl (0.83 #2901, 0.83 #2898, 0.81 #3872), 02__x (0.83 #2901, 0.83 #2898, 0.81 #3872), 07147 (0.83 #2901, 0.83 #2898, 0.81 #3872) >> Best rule #7752 for best value: >> intensional similarity = 34 >> extensional distance = 6 >> proper extension: 01yvvn; >> query: (?x12238, ?x2405) <- position(?x2405, ?x12238), position(?x1438, ?x12238), season(?x1438, ?x10017), season(?x1438, ?x3431), season(?x1438, ?x701), ?x3431 = 025ygqm, school(?x1438, ?x5621), school(?x1438, ?x466), team(?x5727, ?x1438), team(?x2010, ?x1438), ?x701 = 05kcgsf, draft(?x1438, ?x11905), draft(?x1438, ?x10600), draft(?x1438, ?x1633), draft(?x1438, ?x1161), teams(?x5771, ?x1438), ?x10600 = 04f4z1k, colors(?x1438, ?x663), ?x1633 = 02rl201, ?x10017 = 026fmqm, student(?x5621, ?x525), institution(?x1368, ?x5621), ?x11905 = 047dpm0, school(?x3333, ?x5621), ?x5727 = 02wszf, fraternities_and_sororities(?x5621, ?x4348), major_field_of_study(?x5621, ?x254), ?x1161 = 02x2khw, ?x3333 = 01yjl, ?x1368 = 014mlp, school(?x2820, ?x466), teams(?x2277, ?x2405), ?x2820 = 0jmj7, ?x2010 = 02lyr4 >> conf = 0.85 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02dwpf team 01slc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 32.000 28.000 0.854 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #20911-02gvwz PRED entity: 02gvwz PRED relation: actor! PRED expected values: 027pfb2 => 84 concepts (48 used for prediction) PRED predicted values (max 10 best out of 94): 02gjrc (0.12 #490, 0.07 #5029, 0.06 #755), 0ddd0gc (0.12 #284, 0.07 #5029, 0.02 #2399), 0464pz (0.12 #287, 0.07 #5029, 0.01 #3726), 03wh49y (0.10 #889, 0.07 #5029, 0.06 #625), 017jd9 (0.09 #4764, 0.09 #8739, 0.09 #8209), 017gl1 (0.09 #8739, 0.09 #8209, 0.07 #5560), 06cs95 (0.07 #5029, 0.06 #536, 0.05 #800), 02ppg1r (0.07 #5029, 0.06 #607, 0.05 #871), 02648p (0.07 #5029), 01_2n (0.06 #724, 0.05 #988) >> Best rule #490 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 059t6d; 06mnbn; 026g801; 02624g; 0356dp; >> query: (?x1194, 02gjrc) <- award_nominee(?x1194, ?x10577), film(?x1194, ?x972), ?x10577 = 03rgvr >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02gvwz actor! 027pfb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 48.000 0.125 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #20910-08cg36 PRED entity: 08cg36 PRED relation: parent_genre PRED expected values: 0133_p => 60 concepts (45 used for prediction) PRED predicted values (max 10 best out of 293): 0133_p (0.60 #572, 0.33 #252, 0.25 #413), 03lty (0.50 #1137, 0.40 #3062, 0.33 #18), 05bt6j (0.50 #349, 0.14 #1789, 0.12 #669), 05w3f (0.39 #1303, 0.38 #824, 0.33 #984), 0glt670 (0.33 #27, 0.24 #3552, 0.20 #4032), 011j5x (0.33 #180, 0.20 #500, 0.18 #4486), 017371 (0.33 #261, 0.20 #581, 0.12 #742), 0jrv_ (0.33 #104, 0.20 #1223, 0.12 #744), 0jmwg (0.33 #233, 0.20 #553, 0.12 #640), 01750n (0.33 #313, 0.20 #633, 0.08 #953) >> Best rule #572 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 0621cs; 08s6r6; >> query: (?x14354, 0133_p) <- artists(?x14354, ?x8149), parent_genre(?x14354, ?x9853), artist(?x2190, ?x8149), artists(?x2249, ?x8149), ?x9853 = 02qm5j, artists(?x2249, ?x12791), artists(?x2249, ?x10671), ?x12791 = 01pny5, ?x10671 = 04k05 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08cg36 parent_genre 0133_p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 45.000 0.600 http://example.org/music/genre/parent_genre #20909-011j5x PRED entity: 011j5x PRED relation: parent_genre! PRED expected values: 088vmr => 69 concepts (38 used for prediction) PRED predicted values (max 10 best out of 300): 0bt7w (0.50 #852, 0.43 #2647, 0.40 #1876), 0xv2x (0.50 #889, 0.43 #2684, 0.40 #1913), 06cp5 (0.50 #839, 0.43 #2634, 0.40 #1608), 0dls3 (0.50 #808, 0.43 #2603, 0.40 #1577), 018ysx (0.50 #968, 0.43 #2763, 0.38 #3274), 0pm85 (0.50 #893, 0.33 #125, 0.29 #2688), 059kh (0.50 #806, 0.33 #38, 0.29 #2601), 02qm5j (0.50 #890, 0.33 #122, 0.29 #2685), 08cg36 (0.50 #1001, 0.33 #233, 0.29 #2796), 0621cs (0.50 #900, 0.33 #132, 0.29 #2695) >> Best rule #852 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 05r6t; >> query: (?x2491, 0bt7w) <- artists(?x2491, ?x8311), artists(?x2491, ?x4595), role(?x4595, ?x2725), ?x8311 = 02vr7, parent_genre(?x3167, ?x2491), role(?x2725, ?x74), ?x3167 = 0xjl2, parent_genre(?x2491, ?x283) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1505 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 0xjl2; *> query: (?x2491, 088vmr) <- artists(?x2491, ?x9735), artists(?x2491, ?x5303), artists(?x2491, ?x1838), ?x5303 = 02mq_y, category(?x1838, ?x134), parent_genre(?x302, ?x2491), profession(?x1838, ?x655), ?x9735 = 01wxdn3 *> conf = 0.25 ranks of expected_values: 65 EVAL 011j5x parent_genre! 088vmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 69.000 38.000 0.500 http://example.org/music/genre/parent_genre #20908-0vjr PRED entity: 0vjr PRED relation: actor PRED expected values: 042ly5 => 73 concepts (57 used for prediction) PRED predicted values (max 10 best out of 766): 01nzz8 (0.72 #10152, 0.36 #10151, 0.36 #16612), 011zd3 (0.40 #4615, 0.38 #6461, 0.37 #13843), 01jgpsh (0.40 #4615, 0.38 #6461, 0.37 #13843), 01my4f (0.40 #4615, 0.38 #6461, 0.37 #13843), 0cjdk (0.40 #4615, 0.38 #6461, 0.37 #13843), 0gcdzz (0.10 #17535, 0.10 #5538, 0.10 #21225), 0pz7h (0.10 #17535, 0.10 #5538, 0.10 #21225), 018ygt (0.10 #17535, 0.10 #5538, 0.10 #21225), 0863x_ (0.10 #17535, 0.10 #5538, 0.10 #21225), 0210hf (0.10 #17535, 0.10 #5538, 0.10 #21225) >> Best rule #10152 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 01b7h8; >> query: (?x5386, ?x3815) <- producer_type(?x5386, ?x632), nominated_for(?x3815, ?x5386), actor(?x4932, ?x3815) >> conf = 0.72 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0vjr actor 042ly5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 57.000 0.722 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #20907-02plv57 PRED entity: 02plv57 PRED relation: team! PRED expected values: 0br1xn 0bzrxn 05g_nr => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 10): 0bqthy (0.75 #245, 0.73 #195, 0.71 #90), 0bzrxn (0.75 #94, 0.67 #239, 0.67 #64), 0b_6pv (0.69 #284, 0.67 #67, 0.65 #442), 0b_75k (0.67 #63, 0.64 #188, 0.62 #93), 05g_nr (0.62 #105, 0.58 #240, 0.58 #387), 0b_6s7 (0.62 #96, 0.58 #241, 0.57 #441), 0b_6mr (0.58 #243, 0.55 #193, 0.50 #163), 0b_770 (0.55 #194, 0.50 #244, 0.50 #38), 0b_734 (0.39 #446, 0.37 #393, 0.36 #196), 0br1xn (0.29 #82, 0.29 #72, 0.27 #197) >> Best rule #245 for best value: >> intensional similarity = 10 >> extensional distance = 10 >> proper extension: 02pjzvh; >> query: (?x2303, 0bqthy) <- position(?x2303, ?x1348), team(?x10441, ?x2303), team(?x9146, ?x2303), ?x10441 = 0b_71r, locations(?x9146, ?x3983), team(?x9146, ?x9576), team(?x9146, ?x6003), ?x9576 = 02qk2d5, ?x6003 = 02py8_w, dog_breed(?x3983, ?x1706) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #94 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 6 *> proper extension: 026xxv_; *> query: (?x2303, 0bzrxn) <- team(?x9146, ?x2303), team(?x5897, ?x2303), team(?x4803, ?x2303), team(?x9146, ?x9833), ?x4803 = 0b_6jz, ?x5897 = 0b_6rk, ?x9833 = 03y9p40, locations(?x9146, ?x4978), location(?x105, ?x4978), ?x105 = 0grwj *> conf = 0.75 ranks of expected_values: 2, 5, 10 EVAL 02plv57 team! 05g_nr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 104.000 104.000 0.750 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02plv57 team! 0bzrxn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 104.000 0.750 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02plv57 team! 0br1xn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 104.000 104.000 0.750 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #20906-0294mx PRED entity: 0294mx PRED relation: nominated_for! PRED expected values: 01fx2g => 72 concepts (22 used for prediction) PRED predicted values (max 10 best out of 630): 01rnxn (0.38 #2338, 0.37 #7014, 0.34 #25726), 03hh89 (0.38 #2338, 0.37 #7014, 0.34 #25726), 0gm8_p (0.38 #2338, 0.37 #7014, 0.34 #25726), 01fx2g (0.38 #2338, 0.37 #7014, 0.34 #25726), 0dvmd (0.20 #660, 0.03 #28726, 0.02 #14691), 021yc7p (0.20 #315, 0.02 #19025, 0.02 #30719), 02x2t07 (0.20 #1887, 0.02 #4226, 0.02 #6563), 029m83 (0.20 #1713, 0.02 #20423, 0.01 #25101), 06r_by (0.20 #1332, 0.02 #15363, 0.02 #22381), 0693l (0.20 #661, 0.01 #28727) >> Best rule #2338 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0p_qr; 0gwjw0c; 01fwzk; >> query: (?x7283, ?x5240) <- genre(?x7283, ?x162), award(?x7283, ?x618), film(?x5240, ?x7283), film(?x2991, ?x7283), ?x2991 = 01rnxn >> conf = 0.38 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 0294mx nominated_for! 01fx2g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 22.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20905-06w839_ PRED entity: 06w839_ PRED relation: film_distribution_medium PRED expected values: 0735l => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.80 #89, 0.77 #83, 0.27 #53), 029j_ (0.17 #79, 0.16 #85, 0.09 #145), 02nxhr (0.12 #86, 0.10 #80, 0.10 #56), 0dq6p (0.09 #87, 0.07 #81, 0.06 #159) >> Best rule #89 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 07kb7vh; >> query: (?x3088, 0735l) <- film(?x8092, ?x3088), nominated_for(?x8092, ?x437), nationality(?x8092, ?x94), region(?x3088, ?x512) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06w839_ film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.798 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #20904-0436kgz PRED entity: 0436kgz PRED relation: type_of_union PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.90 #29, 0.90 #17, 0.90 #25), 01g63y (0.29 #106, 0.28 #110, 0.28 #126), 0jgjn (0.01 #64, 0.01 #80, 0.01 #84) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 01l_vgt; 03l295; 01xyt7; 01vv6xv; >> query: (?x6658, 04ztj) <- location_of_ceremony(?x6658, ?x362), gender(?x6658, ?x231), participant(?x6658, ?x6470) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0436kgz type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.903 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20903-012xdf PRED entity: 012xdf PRED relation: profession PRED expected values: 02hrh1q => 167 concepts (151 used for prediction) PRED predicted values (max 10 best out of 92): 02hrh1q (0.91 #3590, 0.90 #8068, 0.90 #6277), 09jwl (0.63 #2105, 0.58 #1211, 0.57 #7774), 0dz3r (0.54 #5518, 0.53 #1194, 0.50 #2684), 0nbcg (0.53 #1224, 0.47 #7041, 0.46 #5548), 0gl2ny2 (0.48 #3495, 0.46 #3346, 0.41 #3793), 0dxtg (0.48 #13886, 0.37 #6425, 0.37 #7172), 016z4k (0.46 #2686, 0.44 #2090, 0.41 #3878), 02jknp (0.45 #13880, 0.27 #5971, 0.24 #6419), 01445t (0.44 #619, 0.44 #470, 0.36 #917), 03gjzk (0.37 #5979, 0.34 #6427, 0.34 #7174) >> Best rule #3590 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 0f2c8g; >> query: (?x9070, 02hrh1q) <- film(?x9070, ?x1728), gender(?x9070, ?x231), student(?x620, ?x9070) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012xdf profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 151.000 0.907 http://example.org/people/person/profession #20902-0kvt9 PRED entity: 0kvt9 PRED relation: currency PRED expected values: 09nqf => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.84 #7, 0.82 #27, 0.81 #4) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0mn9x; >> query: (?x9887, 09nqf) <- county(?x6834, ?x9887), place_of_birth(?x1119, ?x6834), contains(?x94, ?x9887), film_release_region(?x54, ?x94) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kvt9 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.844 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #20901-037n97 PRED entity: 037n97 PRED relation: parent_genre PRED expected values: 06j6l => 54 concepts (50 used for prediction) PRED predicted values (max 10 best out of 229): 06by7 (0.83 #2451, 0.66 #2287, 0.35 #3756), 0glt670 (0.80 #1163, 0.36 #1487, 0.33 #675), 016jny (0.42 #718, 0.11 #2668, 0.05 #2342), 05r9t (0.33 #63, 0.20 #225, 0.07 #550), 0gywn (0.29 #364, 0.21 #688, 0.20 #201), 01243b (0.25 #2626, 0.14 #2300, 0.11 #3769), 06j6l (0.25 #1493, 0.17 #681, 0.16 #1656), 05r6t (0.24 #3795, 0.24 #2490, 0.22 #2326), 016_rm (0.23 #1268, 0.12 #780, 0.11 #1592), 0y3_8 (0.22 #1492, 0.12 #1005, 0.12 #842) >> Best rule #2451 for best value: >> intensional similarity = 7 >> extensional distance = 69 >> proper extension: 028cl7; 017ht; >> query: (?x13572, 06by7) <- parent_genre(?x13572, ?x1127), artists(?x1127, ?x8362), artists(?x1127, ?x3399), artists(?x1127, ?x3160), role(?x3160, ?x212), ?x3399 = 01gx5f, ?x8362 = 01wg25j >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1493 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 34 *> proper extension: 01h0kx; *> query: (?x13572, 06j6l) <- parent_genre(?x13572, ?x1127), parent_genre(?x1731, ?x13572), artists(?x1127, ?x6151), artists(?x1127, ?x2273), ?x6151 = 013w7j, role(?x2273, ?x745) *> conf = 0.25 ranks of expected_values: 7 EVAL 037n97 parent_genre 06j6l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 54.000 50.000 0.831 http://example.org/music/genre/parent_genre #20900-01z215 PRED entity: 01z215 PRED relation: adjoins PRED expected values: 0697s => 126 concepts (102 used for prediction) PRED predicted values (max 10 best out of 409): 0d05q4 (0.83 #60737, 0.83 #63810, 0.82 #60736), 0697s (0.83 #60737, 0.83 #63810, 0.82 #60736), 01z88t (0.82 #60736, 0.82 #66114, 0.81 #63809), 01z215 (0.36 #1631, 0.29 #3167, 0.20 #2399), 03spz (0.36 #1758, 0.29 #3294, 0.20 #2526), 06vbd (0.25 #208, 0.21 #1744, 0.20 #2512), 02k54 (0.25 #29, 0.08 #796, 0.07 #5380), 01znc_ (0.21 #1617, 0.20 #2385, 0.18 #3153), 0d05w3 (0.20 #14726, 0.18 #11647, 0.15 #18571), 0jdd (0.16 #14770, 0.10 #11691, 0.09 #18615) >> Best rule #60737 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 0154gx; >> query: (?x1781, ?x3683) <- contains(?x6304, ?x1781), adjoins(?x3683, ?x1781), film_release_region(?x186, ?x3683) >> conf = 0.83 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01z215 adjoins 0697s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 102.000 0.830 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #20899-06tp4h PRED entity: 06tp4h PRED relation: artists! PRED expected values: 064t9 => 162 concepts (94 used for prediction) PRED predicted values (max 10 best out of 214): 064t9 (0.78 #28335, 0.73 #8414, 0.71 #3747), 06by7 (0.67 #3756, 0.46 #14959, 0.45 #26477), 025sc50 (0.54 #8451, 0.41 #2851, 0.41 #4095), 0ggx5q (0.47 #1012, 0.44 #1323, 0.44 #2879), 0gywn (0.42 #8459, 0.33 #2859, 0.31 #3481), 0glt670 (0.41 #6575, 0.37 #8443, 0.37 #9688), 016clz (0.33 #5, 0.31 #3427, 0.29 #9023), 02vjzr (0.29 #9023, 0.28 #1379, 0.26 #3868), 0y3_8 (0.29 #9023, 0.25 #360, 0.24 #982), 06924p (0.29 #9023, 0.25 #488, 0.23 #12757) >> Best rule #28335 for best value: >> intensional similarity = 5 >> extensional distance = 449 >> proper extension: 0123r4; >> query: (?x6613, 064t9) <- artists(?x3996, ?x6613), artists(?x3996, ?x10148), artists(?x3996, ?x7553), ?x10148 = 02h9_l, ?x7553 = 01wqmm8 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06tp4h artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 94.000 0.780 http://example.org/music/genre/artists #20898-0154j PRED entity: 0154j PRED relation: combatants! PRED expected values: 081pw => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 64): 081pw (0.75 #190, 0.64 #821, 0.58 #2018), 0cm2xh (0.31 #201, 0.28 #832, 0.27 #895), 02h2z_ (0.31 #240, 0.24 #871, 0.19 #366), 07_nf (0.27 #900, 0.25 #206, 0.23 #2286), 0d06vc (0.26 #572, 0.25 #194, 0.23 #1140), 018w0j (0.25 #224, 0.19 #918, 0.17 #602), 01gjd0 (0.25 #192, 0.19 #886, 0.16 #823), 0c3mz (0.25 #227, 0.19 #353, 0.16 #858), 01cpp0 (0.25 #246, 0.16 #877, 0.15 #2452), 0bqtx (0.25 #232, 0.15 #926, 0.13 #4037) >> Best rule #190 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 059z0; >> query: (?x172, 081pw) <- adjoins(?x172, ?x789), combatants(?x172, ?x7430), ?x7430 = 01mk6 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0154j combatants! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 181.000 0.750 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #20897-018grr PRED entity: 018grr PRED relation: category PRED expected values: 08mbj5d => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.74 #14, 0.52 #16, 0.51 #15) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 032t2z; >> query: (?x2101, 08mbj5d) <- currency(?x2101, ?x170), profession(?x2101, ?x2348), ?x2348 = 0nbcg >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018grr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.735 http://example.org/common/topic/webpage./common/webpage/category #20896-0gkxgfq PRED entity: 0gkxgfq PRED relation: honored_for PRED expected values: 01b66t => 19 concepts (14 used for prediction) PRED predicted values (max 10 best out of 605): 01j7mr (0.57 #4956, 0.56 #4363, 0.53 #5550), 039cq4 (0.44 #4559, 0.43 #5152, 0.33 #5746), 07zhjj (0.44 #4645, 0.38 #4052, 0.36 #5238), 06mr2s (0.44 #4432, 0.36 #5025, 0.33 #5619), 06hwzy (0.44 #4304, 0.36 #4897, 0.33 #5491), 01b66t (0.42 #2372, 0.33 #868, 0.20 #2650), 01b7h8 (0.38 #4088, 0.33 #4681, 0.29 #5274), 0d68qy (0.38 #3708, 0.28 #6083, 0.22 #4301), 05lfwd (0.38 #3900, 0.15 #6275, 0.13 #5680), 07s8z_l (0.36 #5295, 0.33 #5889, 0.33 #4702) >> Best rule #4956 for best value: >> intensional similarity = 18 >> extensional distance = 12 >> proper extension: 07z31v; 02q690_; 09p2r9; 07y_p6; 0bx6zs; 0hn821n; >> query: (?x7721, 01j7mr) <- honored_for(?x7721, ?x4891), honored_for(?x7721, ?x2829), ceremony(?x2773, ?x7721), ceremony(?x588, ?x7721), award_winner(?x7721, ?x6170), award_winner(?x7721, ?x2894), award_winner(?x2773, ?x2308), award(?x3104, ?x2773), award(?x540, ?x2773), award_nominee(?x4817, ?x6170), award_winner(?x2829, ?x6970), nominated_for(?x1056, ?x2829), program(?x3183, ?x4891), program(?x2062, ?x2829), nominated_for(?x588, ?x416), producer_type(?x2894, ?x632), gender(?x6170, ?x514), actor(?x416, ?x1594) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2372 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 2 *> proper extension: 09v0p2c; *> query: (?x7721, ?x4721) <- honored_for(?x7721, ?x4891), honored_for(?x7721, ?x2829), ceremony(?x6853, ?x7721), ceremony(?x2773, ?x7721), award_winner(?x7721, ?x7824), award_winner(?x7721, ?x5574), award_winner(?x7721, ?x4377), award_winner(?x7721, ?x3809), award_winner(?x7721, ?x3150), award_nominee(?x4433, ?x5574), ?x2829 = 01b64v, award_winner(?x4721, ?x3809), award_winner(?x2828, ?x3809), nominated_for(?x3183, ?x4891), award(?x589, ?x6853), award_winner(?x4377, ?x4817), student(?x3439, ?x7824), award_nominee(?x3809, ?x912), student(?x6575, ?x3150), program(?x1762, ?x4891), location(?x5574, ?x108), type_of_union(?x3150, ?x566), nominated_for(?x2773, ?x416), ?x2828 = 026n6cs *> conf = 0.42 ranks of expected_values: 6 EVAL 0gkxgfq honored_for 01b66t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 19.000 14.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #20895-0r02m PRED entity: 0r02m PRED relation: place_of_death! PRED expected values: 0cbgl => 98 concepts (70 used for prediction) PRED predicted values (max 10 best out of 313): 0b82vw (0.12 #64, 0.11 #820, 0.10 #2333), 09xvf7 (0.12 #710, 0.11 #1466, 0.10 #2979), 06lk0_ (0.12 #700, 0.11 #1456, 0.10 #2969), 01200d (0.12 #668, 0.11 #1424, 0.10 #2937), 02_01w (0.12 #660, 0.11 #1416, 0.10 #2929), 0blpnz (0.12 #659, 0.11 #1415, 0.10 #2928), 0jnb0 (0.12 #645, 0.11 #1401, 0.10 #2914), 012c6j (0.12 #644, 0.11 #1400, 0.10 #2913), 03zrp (0.12 #590, 0.11 #1346, 0.10 #2859), 05hjmd (0.12 #589, 0.11 #1345, 0.10 #2858) >> Best rule #64 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0k049; 0k_q_; 0r0m6; 0nbwf; 0r15k; 0r00l; >> query: (?x13255, 0b82vw) <- contains(?x2949, ?x13255), ?x2949 = 0kpys, citytown(?x7178, ?x13255), category(?x13255, ?x134) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r02m place_of_death! 0cbgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 70.000 0.125 http://example.org/people/deceased_person/place_of_death #20894-02qgqt PRED entity: 02qgqt PRED relation: award_nominee! PRED expected values: 01g23m 0bqdvt => 79 concepts (34 used for prediction) PRED predicted values (max 10 best out of 749): 07d3z7 (0.81 #45983, 0.81 #73572, 0.81 #25289), 04bdxl (0.81 #45983, 0.81 #73572, 0.81 #25289), 01g257 (0.81 #45983, 0.81 #73572, 0.81 #25289), 0h0wc (0.81 #45983, 0.81 #73572, 0.81 #25289), 0bqdvt (0.81 #45983, 0.81 #73572, 0.81 #25289), 02x7vq (0.77 #29889, 0.76 #75871, 0.76 #73571), 02qgqt (0.29 #66675, 0.28 #36790, 0.19 #29890), 0169dl (0.29 #66675, 0.08 #2800, 0.03 #9695), 014zcr (0.29 #66675, 0.06 #2341, 0.02 #23032), 0bxtg (0.29 #66675, 0.06 #86, 0.02 #23075) >> Best rule #45983 for best value: >> intensional similarity = 2 >> extensional distance = 1239 >> proper extension: 011hdn; 03b78r; >> query: (?x157, ?x91) <- award_nominee(?x157, ?x91), film(?x157, ?x974) >> conf = 0.81 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 699 EVAL 02qgqt award_nominee! 0bqdvt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 34.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 02qgqt award_nominee! 01g23m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 79.000 34.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20893-048_p PRED entity: 048_p PRED relation: award PRED expected values: 0262zm => 97 concepts (64 used for prediction) PRED predicted values (max 10 best out of 277): 01bb1c (0.76 #20869, 0.75 #2364, 0.72 #2363), 0262zm (0.68 #2446, 0.64 #870, 0.53 #2051), 0208wk (0.50 #734, 0.27 #1128, 0.20 #1916), 040_9s0 (0.45 #1100, 0.41 #2676, 0.40 #1888), 0fbtbt (0.42 #2987, 0.17 #624, 0.07 #1806), 09qvc0 (0.20 #39, 0.15 #1222, 0.03 #9099), 08_vwq (0.20 #267, 0.08 #1450, 0.04 #23231), 0bfvd4 (0.20 #114, 0.06 #12716, 0.05 #12321), 0bdw6t (0.20 #109, 0.03 #9169, 0.03 #17832), 09qrn4 (0.20 #236, 0.03 #9296, 0.02 #3387) >> Best rule #20869 for best value: >> intensional similarity = 3 >> extensional distance = 1874 >> proper extension: 0kk9v; 08_83x; 02fgm7; >> query: (?x5506, ?x5050) <- award_winner(?x5050, ?x5506), award(?x10232, ?x5050), influenced_by(?x2993, ?x10232) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #2446 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 0j0pf; *> query: (?x5506, 0262zm) <- student(?x6637, ?x5506), award(?x5506, ?x12769), award(?x5506, ?x8880), ?x8880 = 0262x6, disciplines_or_subjects(?x12769, ?x1013) *> conf = 0.68 ranks of expected_values: 2 EVAL 048_p award 0262zm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 64.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20892-040db PRED entity: 040db PRED relation: profession PRED expected values: 01l5t6 => 199 concepts (110 used for prediction) PRED predicted values (max 10 best out of 110): 02hrh1q (0.69 #10022, 0.67 #9877, 0.67 #1898), 03gjzk (0.57 #2769, 0.39 #12780, 0.38 #1754), 01d_h8 (0.57 #12772, 0.50 #2761, 0.42 #4938), 02jknp (0.46 #12773, 0.44 #4939, 0.30 #2762), 02hv44_ (0.35 #2375, 0.34 #11606, 0.34 #11170), 0d8qb (0.34 #11606, 0.34 #11170, 0.33 #77), 03jgz (0.34 #11606, 0.34 #11170, 0.27 #13347), 04s2z (0.34 #11606, 0.34 #11170, 0.27 #13347), 05t4q (0.34 #11606, 0.34 #11170, 0.27 #13347), 0lgw7 (0.34 #11606, 0.34 #11170, 0.27 #13347) >> Best rule #10022 for best value: >> intensional similarity = 4 >> extensional distance = 212 >> proper extension: 0785v8; 03ym1; 05lb30; 031k24; 09d5d5; 026rm_y; >> query: (?x2161, 02hrh1q) <- award_winner(?x4879, ?x2161), location(?x2161, ?x2911), category(?x2911, ?x134), film_release_region(?x428, ?x2911) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #11606 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 240 *> proper extension: 05txrz; *> query: (?x2161, ?x353) <- influenced_by(?x2161, ?x5040), type_of_union(?x2161, ?x566), location(?x2161, ?x1649), profession(?x5040, ?x353) *> conf = 0.34 ranks of expected_values: 19 EVAL 040db profession 01l5t6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 199.000 110.000 0.692 http://example.org/people/person/profession #20891-04kj2v PRED entity: 04kj2v PRED relation: religion PRED expected values: 03_gx => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 12): 03_gx (0.29 #509, 0.08 #3173, 0.07 #737), 0c8wxp (0.17 #821, 0.15 #729, 0.14 #1318), 0kpl (0.11 #415, 0.10 #460, 0.09 #370), 03j6c (0.07 #1152, 0.02 #1558, 0.02 #3180), 0kq2 (0.04 #423, 0.03 #468, 0.02 #513), 092bf5 (0.04 #376, 0.04 #421, 0.02 #511), 0n2g (0.04 #463, 0.03 #418, 0.01 #1144), 0flw86 (0.03 #1133, 0.02 #3161, 0.02 #679), 01spm (0.02 #487, 0.01 #442), 01lp8 (0.02 #3160, 0.02 #1538, 0.02 #678) >> Best rule #509 for best value: >> intensional similarity = 2 >> extensional distance = 352 >> proper extension: 015c1b; >> query: (?x2507, 03_gx) <- people(?x1050, ?x2507), ?x1050 = 041rx >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04kj2v religion 03_gx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.294 http://example.org/people/person/religion #20890-035rnz PRED entity: 035rnz PRED relation: nationality PRED expected values: 09c7w0 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 136): 09c7w0 (0.74 #2210, 0.73 #2109, 0.71 #605), 0824r (0.33 #6127, 0.01 #1006), 02jx1 (0.12 #337, 0.12 #437, 0.11 #537), 07ssc (0.10 #419, 0.09 #920, 0.09 #319), 03rk0 (0.08 #951, 0.08 #1052, 0.07 #249), 0d060g (0.05 #210, 0.04 #2417, 0.04 #1413), 03rjj (0.04 #2712, 0.03 #4217, 0.03 #409), 0chghy (0.04 #2712, 0.03 #4217, 0.03 #715), 0345h (0.04 #2712, 0.03 #4217, 0.02 #936), 0f8l9c (0.04 #2712, 0.03 #4217, 0.02 #927) >> Best rule #2210 for best value: >> intensional similarity = 2 >> extensional distance = 1362 >> proper extension: 0l56b; >> query: (?x4039, 09c7w0) <- place_of_birth(?x4039, ?x14503), award_nominee(?x4039, ?x2028) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035rnz nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.737 http://example.org/people/person/nationality #20889-01mxqyk PRED entity: 01mxqyk PRED relation: award_winner! PRED expected values: 01wwvd2 => 116 concepts (54 used for prediction) PRED predicted values (max 10 best out of 486): 01wwvc5 (0.82 #54735, 0.82 #85319, 0.82 #43464), 02dbp7 (0.82 #54735, 0.82 #85319, 0.82 #43464), 01kp_1t (0.82 #54735, 0.82 #85319, 0.82 #43464), 04xrx (0.50 #46684, 0.49 #43463, 0.46 #48294), 03q2t9 (0.50 #46684, 0.49 #43463, 0.46 #48294), 02cx90 (0.09 #18451, 0.06 #32940, 0.06 #42595), 0x3b7 (0.08 #18426, 0.06 #32915, 0.05 #42570), 016h9b (0.07 #476, 0.03 #6916, 0.02 #10135), 0137hn (0.07 #1102, 0.02 #10761, 0.01 #7542), 016jfw (0.07 #1031, 0.02 #10690, 0.01 #7471) >> Best rule #54735 for best value: >> intensional similarity = 3 >> extensional distance = 444 >> proper extension: 0gsg7; 01jq34; 0cjdk; 014hr0; 0khth; 0hm0k; 07mvp; 05gnf; 03yxwq; 0gsgr; ... >> query: (?x11621, ?x2138) <- category(?x11621, ?x134), award_winner(?x11621, ?x2138), ?x134 = 08mbj5d >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #12034 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 123 *> proper extension: 015srx; 0qmny; *> query: (?x11621, 01wwvd2) <- artists(?x3928, ?x11621), ?x3928 = 0gywn *> conf = 0.02 ranks of expected_values: 337 EVAL 01mxqyk award_winner! 01wwvd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 116.000 54.000 0.818 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #20888-06z8s_ PRED entity: 06z8s_ PRED relation: currency PRED expected values: 09nqf => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.84 #127, 0.82 #92, 0.81 #64), 01nv4h (0.04 #23, 0.03 #65, 0.02 #30), 02l6h (0.02 #18, 0.02 #74, 0.02 #102), 02gsvk (0.01 #181, 0.01 #223, 0.01 #230) >> Best rule #127 for best value: >> intensional similarity = 4 >> extensional distance = 349 >> proper extension: 0djb3vw; 05dy7p; 064lsn; 072r5v; 0267wwv; 02yy9r; >> query: (?x876, 09nqf) <- film_crew_role(?x876, ?x137), production_companies(?x876, ?x382), film(?x7903, ?x876), genre(?x876, ?x225) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06z8s_ currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.838 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #20887-01jb26 PRED entity: 01jb26 PRED relation: participant PRED expected values: 026r8q => 125 concepts (55 used for prediction) PRED predicted values (max 10 best out of 237): 026r8q (0.84 #29257, 0.84 #32516, 0.83 #4549), 01wgjj5 (0.33 #396, 0.01 #9492), 02lf1j (0.33 #176), 046zh (0.25 #1659, 0.20 #2959, 0.10 #4259), 01pnn3 (0.25 #1480, 0.20 #2780, 0.10 #4080), 01hxs4 (0.25 #1358, 0.20 #2658, 0.10 #3958), 0gz5hs (0.25 #1427, 0.20 #2727, 0.10 #4027), 06dv3 (0.25 #1305, 0.20 #2605, 0.05 #3905), 01gkmx (0.25 #1862, 0.20 #3162, 0.05 #4462), 0169dl (0.25 #1461, 0.20 #2761, 0.05 #4061) >> Best rule #29257 for best value: >> intensional similarity = 4 >> extensional distance = 484 >> proper extension: 01l_vgt; >> query: (?x5268, ?x7346) <- nationality(?x5268, ?x94), participant(?x5268, ?x3673), gender(?x5268, ?x514), participant(?x7346, ?x5268) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jb26 participant 026r8q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 55.000 0.843 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #20886-03t8v3 PRED entity: 03t8v3 PRED relation: award_winner! PRED expected values: 03rbj2 => 93 concepts (64 used for prediction) PRED predicted values (max 10 best out of 190): 03rbj2 (0.32 #1518, 0.25 #222, 0.20 #2382), 03r8tl (0.25 #105, 0.16 #1401, 0.12 #2265), 03r8v_ (0.22 #1205, 0.12 #2501, 0.07 #4662), 0f4x7 (0.09 #3920, 0.08 #6081, 0.08 #6513), 027c95y (0.08 #6208, 0.08 #4047, 0.07 #6640), 09sb52 (0.08 #1337, 0.06 #18622, 0.06 #17758), 027986c (0.08 #3938, 0.06 #6099, 0.06 #6531), 04fgkf_ (0.08 #2880, 0.02 #2016, 0.01 #8498), 07cbcy (0.07 #3968, 0.04 #6561, 0.04 #9586), 0b6k___ (0.07 #3242, 0.06 #4970, 0.05 #4538) >> Best rule #1518 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 0265z9l; 081hvm; >> query: (?x13784, 03rbj2) <- gender(?x13784, ?x231), nationality(?x13784, ?x2146), ?x2146 = 03rk0, ?x231 = 05zppz, film(?x13784, ?x5247) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t8v3 award_winner! 03rbj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 64.000 0.324 http://example.org/award/award_category/winners./award/award_honor/award_winner #20885-024n3z PRED entity: 024n3z PRED relation: type_of_union PRED expected values: 04ztj => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #29, 0.70 #149, 0.70 #173), 01g63y (0.17 #14, 0.14 #26, 0.14 #6) >> Best rule #29 for best value: >> intensional similarity = 2 >> extensional distance = 1101 >> proper extension: 02jt1k; 0126rp; 0m32_; 01jbx1; 01v3vp; 01mt1fy; 03cn92; 04l19_; 023n39; 01gvyp; ... >> query: (?x2727, 04ztj) <- film(?x2727, ?x1392), student(?x5778, ?x2727) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024n3z type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.721 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20884-03h8_g PRED entity: 03h8_g PRED relation: location PRED expected values: 07tp2 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 197): 0ncy4 (0.50 #47953, 0.49 #36760, 0.49 #39958), 02_286 (0.28 #52785, 0.28 #57583, 0.22 #54383), 030qb3t (0.26 #24851, 0.25 #25650, 0.24 #22454), 01n7q (0.14 #60, 0.08 #8850, 0.07 #1658), 0cr3d (0.08 #54489, 0.08 #141, 0.08 #63284), 05k7sb (0.08 #105, 0.05 #7296, 0.05 #6497), 059rby (0.08 #4011, 0.06 #3212, 0.06 #5609), 0k049 (0.07 #1606, 0.06 #5601, 0.05 #12793), 0cc56 (0.06 #13639, 0.06 #5648, 0.05 #52805), 0ccvx (0.06 #3413, 0.05 #217, 0.05 #5011) >> Best rule #47953 for best value: >> intensional similarity = 2 >> extensional distance = 1050 >> proper extension: 0dj5q; 07h1q; 0cfywh; >> query: (?x11208, ?x14413) <- people(?x3584, ?x11208), place_of_birth(?x11208, ?x14413) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03h8_g location 07tp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 140.000 0.504 http://example.org/people/person/places_lived./people/place_lived/location #20883-01wv9xn PRED entity: 01wv9xn PRED relation: origin PRED expected values: 02jx1 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 106): 04jpl (0.27 #476, 0.25 #1181, 0.22 #946), 030qb3t (0.18 #9669, 0.14 #10846, 0.13 #5439), 01_d4 (0.13 #2155, 0.11 #3330, 0.10 #3095), 02_286 (0.11 #1896, 0.09 #4011, 0.09 #10358), 01cx_ (0.10 #64, 0.09 #534, 0.06 #2414), 0r3tb (0.10 #138, 0.08 #1783, 0.02 #4368), 0n90z (0.10 #231, 0.06 #1171, 0.04 #1406), 0cr3d (0.10 #56, 0.05 #10868, 0.04 #1701), 0nbwf (0.10 #140, 0.04 #1785, 0.03 #2255), 01hvzr (0.10 #467, 0.04 #1877, 0.01 #4697) >> Best rule #476 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0dw4g; 07mvp; 0bk1p; 033s6; 07hgm; 0134pk; 0mjn2; 0ycfj; 012x1l; >> query: (?x1684, 04jpl) <- award(?x1684, ?x9828), group(?x227, ?x1684), ?x9828 = 01ckcd, artists(?x1000, ?x1684), inductee(?x1091, ?x1684) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #737 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 01vsqvs; *> query: (?x1684, 02jx1) <- artists(?x11746, ?x1684), artists(?x1000, ?x1684), ?x11746 = 03w94xt, artists(?x1000, ?x9589), ?x9589 = 02cw1m *> conf = 0.08 ranks of expected_values: 16 EVAL 01wv9xn origin 02jx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 100.000 100.000 0.273 http://example.org/music/artist/origin #20882-02xp18 PRED entity: 02xp18 PRED relation: profession PRED expected values: 01d_h8 => 95 concepts (54 used for prediction) PRED predicted values (max 10 best out of 48): 01d_h8 (0.88 #3631, 0.76 #5226, 0.64 #296), 0dxtg (0.73 #739, 0.71 #1029, 0.71 #884), 02jknp (0.38 #5663, 0.37 #5228, 0.36 #3633), 018gz8 (0.28 #3206, 0.26 #2916, 0.23 #3351), 015h31 (0.28 #315, 0.26 #460, 0.20 #170), 0kyk (0.20 #172, 0.12 #317, 0.11 #462), 0cbd2 (0.20 #1892, 0.19 #2327, 0.19 #442), 0196pc (0.19 #506, 0.16 #361, 0.13 #216), 09jwl (0.16 #6689, 0.16 #6108, 0.16 #3643), 0n1h (0.14 #5232, 0.13 #157, 0.11 #3637) >> Best rule #3631 for best value: >> intensional similarity = 3 >> extensional distance = 558 >> proper extension: 01w02sy; 02vntj; 051wwp; >> query: (?x3396, 01d_h8) <- film(?x3396, ?x3998), profession(?x3396, ?x13327), film_crew_role(?x1372, ?x13327) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xp18 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 54.000 0.877 http://example.org/people/person/profession #20881-01w0yrc PRED entity: 01w0yrc PRED relation: profession PRED expected values: 02krf9 => 105 concepts (87 used for prediction) PRED predicted values (max 10 best out of 68): 0dxtg (0.55 #2955, 0.51 #1338, 0.40 #308), 01d_h8 (0.49 #1036, 0.48 #2947, 0.46 #1183), 02krf9 (0.33 #25, 0.26 #2966, 0.16 #1937), 0np9r (0.30 #1343, 0.19 #166, 0.18 #2078), 02jknp (0.26 #2949, 0.24 #302, 0.24 #1185), 0cbd2 (0.26 #6911, 0.22 #448, 0.17 #743), 09jwl (0.25 #900, 0.24 #753, 0.23 #1047), 0dz3r (0.21 #1032, 0.12 #1473, 0.11 #7354), 0nbcg (0.18 #1060, 0.15 #1501, 0.14 #766), 0kyk (0.18 #469, 0.16 #322, 0.15 #764) >> Best rule #2955 for best value: >> intensional similarity = 2 >> extensional distance = 629 >> proper extension: 016qtt; 0d4fqn; 05fnl9; 09gffmz; 0993r; 02778qt; 0b05xm; 027kmrb; 02qlkc3; 01d5vk; ... >> query: (?x10153, 0dxtg) <- profession(?x10153, ?x1041), ?x1041 = 03gjzk >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #25 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 06v_gh; 0q9vf; 0gls4q_; 0q9zc; *> query: (?x10153, 02krf9) <- location(?x10153, ?x6253), award_nominee(?x10153, ?x2817), ?x2817 = 0q5hw *> conf = 0.33 ranks of expected_values: 3 EVAL 01w0yrc profession 02krf9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 87.000 0.552 http://example.org/people/person/profession #20880-09c7w0 PRED entity: 09c7w0 PRED relation: teams PRED expected values: 02s9vc => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 173): 0cqt41 (0.20 #1470, 0.06 #5070, 0.02 #11192), 0hmtk (0.20 #1757, 0.06 #5357, 0.02 #11479), 05g76 (0.20 #1475, 0.06 #5075, 0.02 #11197), 0jm3v (0.20 #1453, 0.06 #5053, 0.02 #11175), 0jm3b (0.20 #1661, 0.01 #23627), 0bwjj (0.17 #2737, 0.01 #22543, 0.01 #23623), 0j2zj (0.17 #2731, 0.01 #22537, 0.01 #23617), 02wvfxl (0.17 #2621, 0.01 #22427, 0.01 #23507), 01d5z (0.17 #2538, 0.01 #22344, 0.01 #23424), 020wyp (0.10 #3213, 0.08 #3933, 0.07 #4293) >> Best rule #1470 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 0cr3d; >> query: (?x94, 0cqt41) <- contains(?x94, ?x5981), ?x5981 = 03bmmc >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09c7w0 teams 02s9vc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 135.000 0.200 http://example.org/sports/sports_team_location/teams #20879-0d1qmz PRED entity: 0d1qmz PRED relation: country PRED expected values: 07ssc => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 39): 07ssc (0.83 #262, 0.83 #201, 0.80 #140), 09c7w0 (0.83 #1046, 0.81 #861, 0.81 #985), 0345h (0.45 #1662, 0.43 #2153, 0.42 #2215), 02jx1 (0.45 #1662, 0.43 #2153, 0.42 #2215), 0f8l9c (0.23 #1354, 0.22 #1291, 0.15 #326), 03_3d (0.23 #1354, 0.22 #1291, 0.07 #497), 03spz (0.23 #1354, 0.22 #1291, 0.04 #544), 03rjj (0.23 #1354, 0.22 #1291, 0.03 #1298), 03rk0 (0.23 #1354, 0.22 #1291, 0.01 #1764), 0d060g (0.08 #315, 0.04 #1917, 0.04 #2781) >> Best rule #262 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 025twgt; >> query: (?x3643, 07ssc) <- genre(?x3643, ?x225), nominated_for(?x11362, ?x3643), nominated_for(?x6533, ?x3643), ?x6533 = 02n72k, language(?x11362, ?x5671) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d1qmz country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.833 http://example.org/film/film/country #20878-0hm4q PRED entity: 0hm4q PRED relation: organization PRED expected values: 01nrnm 09b_0m 02_jjm => 36 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1125): 09vzz (0.62 #3742, 0.58 #7481, 0.57 #5236), 018sg9 (0.62 #3742, 0.58 #7481, 0.57 #5236), 0h6rm (0.62 #3742, 0.58 #7481, 0.57 #5236), 011kn2 (0.33 #4448, 0.33 #3700, 0.33 #1456), 017cy9 (0.33 #3952, 0.33 #3204, 0.33 #960), 0mbwf (0.33 #4337, 0.33 #3589, 0.33 #1345), 0hpv3 (0.33 #4211, 0.33 #3463, 0.33 #1219), 03x33n (0.33 #3922, 0.33 #3174, 0.33 #930), 022jr5 (0.33 #3991, 0.33 #3243, 0.33 #999), 01y9pk (0.33 #3830, 0.33 #3082, 0.33 #838) >> Best rule #3742 for best value: >> intensional similarity = 15 >> extensional distance = 4 >> proper extension: 04n1q6; >> query: (?x4095, ?x12726) <- organization(?x4095, ?x13206), organization(?x4095, ?x9658), organization(?x4095, ?x6784), currency(?x9658, ?x5696), institution(?x734, ?x9658), major_field_of_study(?x13206, ?x2314), major_field_of_study(?x9658, ?x1668), colors(?x9658, ?x3189), school_type(?x6784, ?x3092), company(?x4095, ?x12726), contains(?x2513, ?x13206), major_field_of_study(?x9093, ?x2314), major_field_of_study(?x12726, ?x742), contains(?x512, ?x12726), ?x9093 = 040p_q >> conf = 0.62 => this is the best rule for 3 predicted values *> Best rule #3740 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 4 *> proper extension: 04n1q6; *> query: (?x4095, ?x481) <- organization(?x4095, ?x13206), organization(?x4095, ?x9658), organization(?x4095, ?x6784), currency(?x9658, ?x5696), institution(?x734, ?x9658), major_field_of_study(?x13206, ?x2314), major_field_of_study(?x9658, ?x1668), colors(?x9658, ?x3189), school_type(?x6784, ?x3092), company(?x4095, ?x12726), contains(?x2513, ?x13206), major_field_of_study(?x9093, ?x2314), major_field_of_study(?x481, ?x2314), major_field_of_study(?x12726, ?x742), contains(?x512, ?x12726), ?x9093 = 040p_q *> conf = 0.20 ranks of expected_values: 481, 617, 631 EVAL 0hm4q organization 02_jjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 22.000 0.622 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 0hm4q organization 09b_0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 22.000 0.622 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 0hm4q organization 01nrnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 22.000 0.622 http://example.org/organization/role/leaders./organization/leadership/organization #20877-01jr6 PRED entity: 01jr6 PRED relation: place_of_birth! PRED expected values: 07ftc0 => 144 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2011): 01ty4 (0.33 #5001, 0.02 #18019, 0.02 #20623), 06b4wb (0.33 #4986, 0.02 #18004, 0.02 #20608), 0gppg (0.33 #4663, 0.02 #17681, 0.02 #20285), 016kft (0.33 #4543, 0.02 #17561, 0.02 #20165), 0q9t7 (0.33 #4351, 0.02 #17369, 0.02 #19973), 03g5_y (0.33 #4235, 0.02 #17253, 0.02 #19857), 0h3mrc (0.33 #3373, 0.02 #16391, 0.02 #18995), 02q_cc (0.32 #208320, 0.31 #208319, 0.29 #80722), 0c6g1l (0.32 #208320, 0.31 #208319, 0.29 #80722), 07ymr5 (0.32 #208320, 0.31 #208319, 0.29 #80722) >> Best rule #5001 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01qh7; >> query: (?x3976, 01ty4) <- location(?x989, ?x3976), location(?x846, ?x3976), ?x989 = 0151w_, student(?x4257, ?x846) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jr6 place_of_birth! 07ftc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 89.000 0.333 http://example.org/people/person/place_of_birth #20876-0h5m7 PRED entity: 0h5m7 PRED relation: capital! PRED expected values: 03f4n1 => 146 concepts (56 used for prediction) PRED predicted values (max 10 best out of 25): 03f4n1 (0.20 #130, 0.10 #538, 0.07 #674), 06mkj (0.14 #408, 0.14 #318, 0.10 #544), 01s47p (0.14 #406, 0.10 #542, 0.07 #678), 0285m87 (0.07 #661, 0.06 #798, 0.02 #1074), 0cdbq (0.03 #1029, 0.02 #1575, 0.01 #1986), 06q1r (0.02 #1047, 0.01 #1319, 0.01 #1458), 014tss (0.02 #1046, 0.01 #1318, 0.01 #1457), 02jx1 (0.02 #989, 0.01 #1261, 0.01 #1400), 07ssc (0.02 #972, 0.01 #1244, 0.01 #1383), 0jgd (0.02 #960, 0.01 #1232, 0.01 #1371) >> Best rule #130 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 01f62; >> query: (?x14083, 03f4n1) <- contains(?x2152, ?x14083), ?x2152 = 06mkj, teams(?x14083, ?x529), position(?x529, ?x63), current_club(?x978, ?x529), position(?x529, ?x60) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h5m7 capital! 03f4n1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 56.000 0.200 http://example.org/location/country/capital #20875-0cp0t91 PRED entity: 0cp0t91 PRED relation: film! PRED expected values: 04fzk 01ps2h8 => 91 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1265): 041mt (0.14 #18725, 0.12 #6241, 0.09 #33290), 0154qm (0.13 #562, 0.06 #21368, 0.04 #17205), 03fbb6 (0.13 #978, 0.04 #9299, 0.02 #32187), 02vyw (0.12 #18726, 0.09 #14563, 0.02 #2710), 0f0kz (0.11 #4676, 0.06 #31725, 0.05 #12998), 016z2j (0.09 #4549, 0.06 #17032, 0.04 #10790), 06cgy (0.08 #58505, 0.03 #60585, 0.03 #16893), 079vf (0.08 #4168, 0.07 #16651, 0.07 #10409), 0151ns (0.07 #14656, 0.05 #25060, 0.02 #4253), 0c6qh (0.07 #58669, 0.05 #21220, 0.04 #12896) >> Best rule #18725 for best value: >> intensional similarity = 5 >> extensional distance = 117 >> proper extension: 09fc83; >> query: (?x8471, ?x2208) <- film(?x4507, ?x8471), story_by(?x8471, ?x2208), executive_produced_by(?x8471, ?x3662), award_winner(?x1342, ?x4507), language(?x8471, ?x254) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #707 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 0bhwhj; *> query: (?x8471, 04fzk) <- film(?x4533, ?x8471), film(?x1414, ?x8471), ?x4533 = 0fqy4p, film_release_region(?x8471, ?x94), nominated_for(?x1414, ?x696) *> conf = 0.07 ranks of expected_values: 16, 117 EVAL 0cp0t91 film! 01ps2h8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 91.000 51.000 0.136 http://example.org/film/actor/film./film/performance/film EVAL 0cp0t91 film! 04fzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 91.000 51.000 0.136 http://example.org/film/actor/film./film/performance/film #20874-01ry0f PRED entity: 01ry0f PRED relation: profession PRED expected values: 02hrh1q => 103 concepts (94 used for prediction) PRED predicted values (max 10 best out of 44): 02hrh1q (0.91 #3618, 0.90 #4969, 0.89 #4519), 01d_h8 (0.50 #606, 0.31 #3759, 0.31 #3309), 0dxtg (0.50 #164, 0.31 #3767, 0.30 #7971), 02jknp (0.40 #8, 0.21 #3311, 0.21 #3761), 018gz8 (0.33 #168, 0.17 #11416, 0.15 #1068), 03gjzk (0.23 #4670, 0.21 #766, 0.21 #7973), 0np9r (0.21 #3175, 0.20 #2123, 0.20 #322), 02krf9 (0.20 #28, 0.18 #478, 0.17 #11416), 0kyk (0.20 #331, 0.17 #181, 0.15 #631), 0cbd2 (0.18 #7213, 0.17 #6313, 0.17 #7964) >> Best rule #3618 for best value: >> intensional similarity = 3 >> extensional distance = 573 >> proper extension: 02_340; >> query: (?x4748, 02hrh1q) <- film(?x4748, ?x7243), nominated_for(?x591, ?x7243), ?x591 = 0f4x7 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ry0f profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 94.000 0.908 http://example.org/people/person/profession #20873-05ff6 PRED entity: 05ff6 PRED relation: adjoins! PRED expected values: 06mtq => 170 concepts (57 used for prediction) PRED predicted values (max 10 best out of 415): 0g39h (0.86 #3922, 0.83 #19641, 0.82 #26717), 05fly (0.43 #1962, 0.33 #392, 0.27 #2746), 0chghy (0.38 #3159, 0.25 #4732, 0.10 #21213), 0chgr2 (0.33 #1232, 0.33 #447, 0.14 #2017), 06mtq (0.33 #1482, 0.29 #2267, 0.25 #21212), 0vh3 (0.33 #1435, 0.06 #4574, 0.04 #6146), 07z5n (0.31 #3261, 0.21 #4834, 0.11 #4047), 05ff6 (0.29 #2271, 0.25 #21212, 0.24 #33013), 01n8qg (0.25 #3597, 0.17 #5170, 0.06 #4383), 0f8l9c (0.17 #3964, 0.12 #5536, 0.08 #22827) >> Best rule #3922 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 07z5n; 05qkp; 01n8qg; 03188; >> query: (?x12908, ?x9725) <- adjoins(?x12908, ?x9725), contains(?x390, ?x12908), contains(?x390, ?x10889), contains(?x390, ?x8823), ?x8823 = 062qg, institution(?x865, ?x10889) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1482 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 05fly; *> query: (?x12908, 06mtq) <- adjoins(?x12908, ?x9725), contains(?x12908, ?x14084), jurisdiction_of_office(?x10118, ?x12908), ?x10118 = 0p5vf, ?x9725 = 0g39h *> conf = 0.33 ranks of expected_values: 5 EVAL 05ff6 adjoins! 06mtq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 170.000 57.000 0.857 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #20872-02hv44_ PRED entity: 02hv44_ PRED relation: profession! PRED expected values: 07g2b 0207wx 01gzm2 098n5 0hnjt 01_k0d 0d0mbj 0dt645q 03hzkq 01vdrw => 55 concepts (24 used for prediction) PRED predicted values (max 10 best out of 4166): 0dpqk (0.67 #30505, 0.60 #18108, 0.56 #38769), 015pxr (0.67 #29515, 0.60 #17118, 0.50 #37779), 06m6z6 (0.67 #30122, 0.60 #17725, 0.50 #13594), 05mcjs (0.67 #31049, 0.60 #18652, 0.50 #14521), 01gzm2 (0.67 #29397, 0.60 #17000, 0.50 #12869), 01vsl3_ (0.67 #29734, 0.50 #37998, 0.50 #13206), 0q9t7 (0.67 #31627, 0.50 #15099, 0.50 #20659), 0k_mt (0.67 #32232, 0.50 #15704, 0.50 #20659), 049fgvm (0.67 #31055, 0.50 #14527, 0.50 #20659), 03g5_y (0.67 #31445, 0.50 #14917, 0.50 #20659) >> Best rule #30505 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02jknp; >> query: (?x6421, 0dpqk) <- profession(?x5626, ?x6421), profession(?x4477, ?x6421), award_nominee(?x6935, ?x5626), actor(?x10595, ?x5626), ?x4477 = 0gv5c, ?x6935 = 01d1st >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #29397 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 02jknp; *> query: (?x6421, 01gzm2) <- profession(?x5626, ?x6421), profession(?x4477, ?x6421), award_nominee(?x6935, ?x5626), actor(?x10595, ?x5626), ?x4477 = 0gv5c, ?x6935 = 01d1st *> conf = 0.67 ranks of expected_values: 5, 324, 883, 910, 920, 938, 1009, 1438, 2028, 3261 EVAL 02hv44_ profession! 01vdrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 24.000 0.667 http://example.org/people/person/profession EVAL 02hv44_ profession! 03hzkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 24.000 0.667 http://example.org/people/person/profession EVAL 02hv44_ profession! 0dt645q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 24.000 0.667 http://example.org/people/person/profession EVAL 02hv44_ profession! 0d0mbj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 24.000 0.667 http://example.org/people/person/profession EVAL 02hv44_ profession! 01_k0d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 24.000 0.667 http://example.org/people/person/profession EVAL 02hv44_ profession! 0hnjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 24.000 0.667 http://example.org/people/person/profession EVAL 02hv44_ profession! 098n5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 24.000 0.667 http://example.org/people/person/profession EVAL 02hv44_ profession! 01gzm2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 55.000 24.000 0.667 http://example.org/people/person/profession EVAL 02hv44_ profession! 0207wx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 24.000 0.667 http://example.org/people/person/profession EVAL 02hv44_ profession! 07g2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 24.000 0.667 http://example.org/people/person/profession #20871-09kzxt PRED entity: 09kzxt PRED relation: position PRED expected values: 0dgrmp => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 4): 0dgrmp (0.73 #134, 0.72 #76, 0.72 #106), 03f0fp (0.51 #133, 0.50 #152, 0.35 #164), 02md_2 (0.51 #133, 0.33 #156, 0.32 #160), 02qvgy (0.50 #152) >> Best rule #134 for best value: >> intensional similarity = 24 >> extensional distance = 679 >> proper extension: 056xx8; 03qx63; 01k2yr; 0371rb; 03l7rh; 04112r; 0gxkm; 03fhm5; 02mplj; 04mnts; ... >> query: (?x11896, 0dgrmp) <- team(?x60, ?x11896), team(?x60, ?x12981), team(?x60, ?x11494), team(?x60, ?x11445), team(?x60, ?x10664), team(?x60, ?x10196), team(?x60, ?x9511), team(?x60, ?x3158), team(?x60, ?x2883), team(?x60, ?x1085), position(?x12091, ?x60), position(?x6785, ?x60), position(?x2971, ?x60), ?x1085 = 02gys2, ?x12091 = 035s37, ?x11445 = 0j13b, ?x2883 = 09pgj2, ?x12981 = 01kkfp, ?x9511 = 04knh6, ?x11494 = 04mrgz, ?x3158 = 0xbm, ?x10664 = 04994l, ?x6785 = 03z0dt, ?x10196 = 02rh_0 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09kzxt position 0dgrmp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.725 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #20870-0bxc4 PRED entity: 0bxc4 PRED relation: place! PRED expected values: 0bxc4 => 113 concepts (60 used for prediction) PRED predicted values (max 10 best out of 64): 0bxc4 (0.38 #11351, 0.29 #18066, 0.28 #9289), 0xszy (0.38 #11351, 0.29 #18066, 0.28 #9289), 0bxbb (0.25 #162, 0.05 #16516), 0d8jf (0.25 #135, 0.05 #16516), 01q1j (0.13 #15998, 0.02 #4647), 094jv (0.13 #15998, 0.02 #4647), 030qb3t (0.13 #15998, 0.02 #4647), 04jpl (0.13 #15998, 0.02 #4647), 0bx9y (0.06 #1031, 0.06 #8773, 0.05 #13414), 01qh7 (0.03 #576, 0.03 #1093, 0.02 #1609) >> Best rule #11351 for best value: >> intensional similarity = 3 >> extensional distance = 249 >> proper extension: 07sb1; >> query: (?x13690, ?x10059) <- citytown(?x10166, ?x13690), citytown(?x10166, ?x10059), time_zones(?x10059, ?x2674) >> conf = 0.38 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0bxc4 place! 0bxc4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 60.000 0.376 http://example.org/location/hud_county_place/place #20869-03j24kf PRED entity: 03j24kf PRED relation: profession PRED expected values: 0dz3r => 133 concepts (119 used for prediction) PRED predicted values (max 10 best out of 90): 0dxtg (0.62 #144, 0.44 #6232, 0.41 #7858), 0dz3r (0.57 #2299, 0.50 #5816, 0.50 #5952), 0cbd2 (0.45 #7854, 0.45 #6228, 0.45 #9206), 0kyk (0.38 #157, 0.34 #6245, 0.31 #7871), 07s467s (0.33 #8), 03gjzk (0.32 #1766, 0.32 #3660, 0.28 #3118), 02jknp (0.25 #141, 0.23 #1222, 0.22 #817), 018gz8 (0.24 #5014, 0.23 #3932, 0.21 #6235), 0d1pc (0.15 #1796, 0.15 #6534, 0.15 #2472), 0np9r (0.14 #14493, 0.13 #15843, 0.13 #12872) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0dzkq; >> query: (?x4701, 0dxtg) <- location(?x4701, ?x9026), type_of_appearance(?x4701, ?x3429), influenced_by(?x3929, ?x4701) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2299 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 01wwvd2; 03m6pk; *> query: (?x4701, 0dz3r) <- profession(?x4701, ?x220), role(?x4701, ?x227), currency(?x4701, ?x1099) *> conf = 0.57 ranks of expected_values: 2 EVAL 03j24kf profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 119.000 0.625 http://example.org/people/person/profession #20868-05qjt PRED entity: 05qjt PRED relation: major_field_of_study! PRED expected values: 052nd 0f1nl 0j_sncb 027xx3 01c333 09f2j 01h8rk 0g8rj 05zl0 02bbyw 01dbns 06fq2 0trv 017ztv 02hrb2 0g8fs 0g2jl 02185j 07wtc 01hc1j 0lk0l 01trxd 03hvk2 => 69 concepts (51 used for prediction) PRED predicted values (max 10 best out of 594): 0bwfn (0.75 #7707, 0.71 #3224, 0.69 #10198), 017j69 (0.71 #3119, 0.67 #7602, 0.62 #10093), 0bx8pn (0.71 #3028, 0.50 #11997, 0.50 #7511), 09f2j (0.69 #12599, 0.67 #8112, 0.64 #7114), 07w0v (0.67 #7984, 0.64 #6986, 0.64 #6488), 01j_cy (0.64 #6506, 0.55 #6008, 0.50 #1528), 065y4w7 (0.62 #12466, 0.60 #5484, 0.58 #7979), 0cwx_ (0.57 #3206, 0.55 #6691, 0.55 #6193), 0373qt (0.57 #3279, 0.50 #1786, 0.42 #8260), 01bzs9 (0.57 #3411, 0.42 #8392, 0.40 #5897) >> Best rule #7707 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 04x_3; 02822; 040p_q; >> query: (?x742, 0bwfn) <- major_field_of_study(?x892, ?x742), major_field_of_study(?x546, ?x742), ?x546 = 01j_9c, student(?x892, ?x13298), institution(?x620, ?x892), student(?x742, ?x3335), ?x13298 = 0ff3y >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #12599 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 14 *> proper extension: 02vxn; 03qsdpk; *> query: (?x742, 09f2j) <- major_field_of_study(?x11252, ?x742), major_field_of_study(?x9522, ?x742), major_field_of_study(?x9166, ?x742), major_field_of_study(?x4099, ?x742), major_field_of_study(?x1681, ?x742), ?x4099 = 01f1r4, registering_agency(?x9522, ?x1982), major_field_of_study(?x734, ?x742), currency(?x9166, ?x170), student(?x1681, ?x1580), category(?x11252, ?x134) *> conf = 0.69 ranks of expected_values: 4, 12, 14, 21, 24, 26, 31, 35, 68, 70, 93, 101, 166, 177, 178, 210, 232, 261, 380, 383, 505, 533, 551 EVAL 05qjt major_field_of_study! 03hvk2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 01trxd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 0lk0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 01hc1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 07wtc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 02185j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 0g2jl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 0g8fs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 02hrb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 017ztv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 0trv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 06fq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 01dbns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 02bbyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 05zl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 0g8rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 01h8rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 09f2j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 01c333 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 027xx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 0j_sncb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 0f1nl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qjt major_field_of_study! 052nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 69.000 51.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20867-043tg PRED entity: 043tg PRED relation: influenced_by PRED expected values: 05qmj 039n1 => 219 concepts (77 used for prediction) PRED predicted values (max 10 best out of 406): 0399p (0.58 #6456, 0.50 #3872, 0.50 #2583), 060_7 (0.58 #6456, 0.50 #3872, 0.50 #2583), 02wh0 (0.55 #9415, 0.50 #3821, 0.42 #6835), 05qmj (0.54 #10519, 0.53 #11810, 0.50 #4924), 03sbs (0.50 #10548, 0.50 #4953, 0.50 #2373), 039n1 (0.50 #5056, 0.40 #2046, 0.33 #6780), 02ln1 (0.50 #277, 0.40 #5009, 0.28 #7746), 01lwx (0.50 #834, 0.33 #2556, 0.28 #7746), 01h2_6 (0.50 #413, 0.20 #5145, 0.17 #2565), 03f0324 (0.40 #5314, 0.30 #4453, 0.28 #7746) >> Best rule #6456 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 01bpn; 04hcw; 032r1; 0tfc; 01h2_6; >> query: (?x8232, ?x7495) <- interests(?x8232, ?x3561), influenced_by(?x8232, ?x3712), influenced_by(?x3428, ?x8232), peers(?x8232, ?x7495), nationality(?x8232, ?x789) >> conf = 0.58 => this is the best rule for 2 predicted values *> Best rule #10519 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 045bg; 052h3; 03_hd; 07c37; 02ln1; 039n1; 02wh0; 0cpvcd; 015n8; *> query: (?x8232, 05qmj) <- interests(?x8232, ?x3561), influenced_by(?x8232, ?x3712), influenced_by(?x3428, ?x8232), place_of_death(?x8232, ?x4627) *> conf = 0.54 ranks of expected_values: 4, 6 EVAL 043tg influenced_by 039n1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 219.000 77.000 0.583 http://example.org/influence/influence_node/influenced_by EVAL 043tg influenced_by 05qmj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 219.000 77.000 0.583 http://example.org/influence/influence_node/influenced_by #20866-09s1f PRED entity: 09s1f PRED relation: major_field_of_study! PRED expected values: 025v3k 0b1xl 01hr11 023zl => 54 concepts (27 used for prediction) PRED predicted values (max 10 best out of 619): 07szy (0.75 #6213, 0.75 #5088, 0.67 #2844), 0j_sncb (0.75 #5132, 0.69 #6257, 0.63 #8503), 03ksy (0.69 #6286, 0.67 #5161, 0.60 #4039), 01w5m (0.67 #2916, 0.67 #2353, 0.64 #4600), 07w0v (0.67 #2260, 0.58 #5067, 0.55 #4507), 04rwx (0.67 #2279, 0.50 #2842, 0.42 #5086), 01bk1y (0.67 #2529, 0.40 #1969, 0.33 #5336), 07tds (0.64 #4649, 0.56 #6334, 0.50 #5772), 025v3k (0.60 #3494, 0.55 #4618, 0.50 #5741), 0trv (0.60 #4259, 0.40 #3697, 0.36 #4821) >> Best rule #6213 for best value: >> intensional similarity = 12 >> extensional distance = 14 >> proper extension: 0fdys; 01zc2w; >> query: (?x12158, 07szy) <- major_field_of_study(?x10297, ?x12158), major_field_of_study(?x6912, ?x12158), major_field_of_study(?x581, ?x12158), major_field_of_study(?x4981, ?x12158), contains(?x94, ?x10297), institution(?x620, ?x6912), student(?x6912, ?x5821), student(?x10297, ?x2451), ?x5821 = 0hwbd, ?x581 = 06pwq, ?x94 = 09c7w0, student(?x4981, ?x118) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3494 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 06274w; *> query: (?x12158, 025v3k) <- major_field_of_study(?x6434, ?x12158), major_field_of_study(?x4980, ?x12158), major_field_of_study(?x331, ?x12158), ?x4980 = 01n6r0, institution(?x865, ?x6434), colors(?x6434, ?x332), school(?x685, ?x331) *> conf = 0.60 ranks of expected_values: 9, 168, 251, 330 EVAL 09s1f major_field_of_study! 023zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 27.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 09s1f major_field_of_study! 01hr11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 54.000 27.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 09s1f major_field_of_study! 0b1xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 54.000 27.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 09s1f major_field_of_study! 025v3k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 54.000 27.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20865-05jzt3 PRED entity: 05jzt3 PRED relation: nominated_for! PRED expected values: 0f_nbyh 04kxsb => 110 concepts (76 used for prediction) PRED predicted values (max 10 best out of 205): 099cng (0.69 #8642, 0.69 #7957, 0.68 #456), 09cn0c (0.69 #8642, 0.69 #7957, 0.68 #456), 0gq9h (0.66 #1877, 0.61 #1650, 0.45 #4601), 0gs9p (0.60 #1879, 0.55 #1652, 0.40 #4603), 019f4v (0.55 #1870, 0.50 #1643, 0.40 #3913), 04dn09n (0.44 #1852, 0.40 #1625, 0.29 #4576), 040njc (0.43 #1827, 0.40 #1600, 0.32 #4551), 04kxsb (0.43 #1680, 0.29 #1907, 0.22 #4631), 0gr0m (0.42 #1875, 0.26 #3918, 0.26 #4599), 02qyntr (0.40 #1989, 0.29 #2443, 0.27 #1762) >> Best rule #8642 for best value: >> intensional similarity = 4 >> extensional distance = 507 >> proper extension: 06mmr; >> query: (?x861, ?x618) <- award(?x861, ?x618), honored_for(?x1112, ?x861), nominated_for(?x618, ?x144), award(?x396, ?x618) >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #1680 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 148 *> proper extension: 016y_f; *> query: (?x861, 04kxsb) <- titles(?x53, ?x861), nominated_for(?x591, ?x861), ?x591 = 0f4x7 *> conf = 0.43 ranks of expected_values: 8, 67 EVAL 05jzt3 nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 110.000 76.000 0.686 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05jzt3 nominated_for! 0f_nbyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 110.000 76.000 0.686 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20864-03fts PRED entity: 03fts PRED relation: genre PRED expected values: 02kdv5l => 99 concepts (95 used for prediction) PRED predicted values (max 10 best out of 95): 07s9rl0 (0.80 #243, 0.76 #121, 0.72 #1450), 01z4y (0.59 #1087, 0.55 #4467, 0.55 #5791), 02l7c8 (0.45 #7367, 0.41 #379, 0.37 #740), 01jfsb (0.33 #1822, 0.31 #3754, 0.31 #3274), 02kdv5l (0.30 #1813, 0.30 #3745, 0.30 #3265), 01hmnh (0.25 #7369, 0.19 #3279, 0.18 #4605), 0hn10 (0.24 #130, 0.22 #252, 0.07 #976), 04xvlr (0.22 #1451, 0.22 #968, 0.22 #244), 0lsxr (0.21 #854, 0.20 #1939, 0.20 #975), 06cvj (0.21 #4229, 0.13 #7356, 0.12 #124) >> Best rule #243 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 0g5q34q; 0d8w2n; >> query: (?x1474, 07s9rl0) <- films(?x2008, ?x1474), genre(?x1474, ?x258), titles(?x2008, ?x273) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1813 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 389 *> proper extension: 014lc_; 0g56t9t; 09sh8k; 02y_lrp; 018js4; 047gn4y; 0ds3t5x; 0dnvn3; 016fyc; 03mh94; ... *> query: (?x1474, 02kdv5l) <- music(?x1474, ?x3371), film(?x275, ?x1474), production_companies(?x1474, ?x4564), produced_by(?x1474, ?x6718) *> conf = 0.30 ranks of expected_values: 5 EVAL 03fts genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 99.000 95.000 0.797 http://example.org/film/film/genre #20863-0hnp7 PRED entity: 0hnp7 PRED relation: people! PRED expected values: 033tf_ => 190 concepts (188 used for prediction) PRED predicted values (max 10 best out of 58): 041rx (0.47 #1752, 0.43 #1676, 0.42 #1904), 0bbz66j (0.44 #275, 0.09 #2023, 0.06 #351), 063k3h (0.40 #638, 0.19 #2386, 0.10 #562), 033tf_ (0.25 #2363, 0.21 #463, 0.20 #539), 0x67 (0.19 #10351, 0.18 #10503, 0.18 #11267), 02w7gg (0.18 #1902, 0.09 #12021, 0.08 #11565), 0dryh9k (0.12 #3360, 0.10 #10585, 0.06 #7542), 0xnvg (0.12 #4194, 0.12 #6703, 0.12 #6551), 02ctzb (0.11 #167, 0.10 #547, 0.10 #2143), 013xrm (0.11 #248, 0.10 #2756, 0.07 #6254) >> Best rule #1752 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0c921; >> query: (?x6073, 041rx) <- people(?x5741, ?x6073), place_of_burial(?x6073, ?x1227), nationality(?x6073, ?x390), place_of_death(?x6073, ?x1036) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #2363 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 62 *> proper extension: 0bymv; 02x7vq; 0q9t7; 01r4zfk; *> query: (?x6073, 033tf_) <- people(?x5741, ?x6073), gender(?x6073, ?x231), nationality(?x6073, ?x390), ?x5741 = 07bch9 *> conf = 0.25 ranks of expected_values: 4 EVAL 0hnp7 people! 033tf_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 190.000 188.000 0.474 http://example.org/people/ethnicity/people #20862-07rzf PRED entity: 07rzf PRED relation: student! PRED expected values: 02822 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 41): 02822 (0.44 #396, 0.25 #947, 0.24 #823), 03g3w (0.33 #81, 0.17 #203, 0.08 #1244), 04g51 (0.33 #99, 0.17 #221, 0.06 #404), 064_8sq (0.13 #339, 0.01 #1258, 0.01 #1319), 0w7c (0.12 #407, 0.11 #834, 0.10 #958), 03qsdpk (0.12 #952, 0.11 #828, 0.09 #1443), 02vxn (0.12 #369, 0.06 #920, 0.04 #430), 04rlf (0.12 #473, 0.07 #351, 0.05 #839), 041y2 (0.12 #477, 0.04 #1274, 0.03 #1335), 05qdh (0.09 #287, 0.07 #348, 0.06 #409) >> Best rule #396 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 012vf6; >> query: (?x11465, 02822) <- location(?x11465, ?x1296), student(?x254, ?x11465), film(?x11465, ?x10873), list(?x10873, ?x3004) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07rzf student! 02822 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.438 http://example.org/education/field_of_study/students_majoring./education/education/student #20861-06mmb PRED entity: 06mmb PRED relation: nominated_for PRED expected values: 020bv3 => 88 concepts (41 used for prediction) PRED predicted values (max 10 best out of 295): 020bv3 (0.70 #3539, 0.68 #1917, 0.02 #5161), 06hwzy (0.36 #9734, 0.36 #8111, 0.31 #6489), 01k0vq (0.30 #4866, 0.30 #3244, 0.28 #19467), 01pj_5 (0.30 #4866, 0.30 #3244, 0.28 #19467), 0prrm (0.30 #4866, 0.30 #3244, 0.28 #19467), 0gtsx8c (0.30 #4866, 0.30 #3244, 0.28 #19467), 05gnf (0.20 #7556, 0.17 #9178, 0.09 #12423), 01h1bf (0.18 #6953, 0.15 #8575, 0.08 #11820), 07zhjj (0.17 #1338, 0.02 #6204, 0.02 #7827), 03y0pn (0.11 #2742, 0.10 #4364, 0.02 #9733) >> Best rule #3539 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0m2wm; 0175wg; >> query: (?x2559, 020bv3) <- film(?x2559, ?x141), award_nominee(?x9236, ?x2559), ?x9236 = 02fz3w >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mmb nominated_for 020bv3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 41.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20860-0hfzr PRED entity: 0hfzr PRED relation: genre PRED expected values: 03bxz7 => 73 concepts (71 used for prediction) PRED predicted values (max 10 best out of 89): 01jfsb (0.73 #2804, 0.32 #245, 0.31 #5825), 02kdv5l (0.41 #2793, 0.28 #1745, 0.27 #5231), 02l7c8 (0.38 #596, 0.31 #131, 0.31 #3039), 03k9fj (0.37 #244, 0.26 #824, 0.24 #1871), 05p553 (0.36 #1863, 0.34 #1747, 0.33 #5000), 0lsxr (0.30 #8, 0.26 #2800, 0.25 #124), 01hmnh (0.26 #249, 0.25 #132, 0.18 #2444), 02n4kr (0.21 #240, 0.19 #123, 0.18 #2444), 09blyk (0.20 #27, 0.18 #2444, 0.09 #2819), 03bxz7 (0.18 #400, 0.18 #2444, 0.16 #748) >> Best rule #2804 for best value: >> intensional similarity = 3 >> extensional distance = 658 >> proper extension: 0192hw; 0c0wvx; >> query: (?x4216, 01jfsb) <- genre(?x4216, ?x1805), genre(?x11125, ?x1805), ?x11125 = 0gy4k >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #400 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 02vxq9m; 01sxly; 0p_sc; 0jqp3; 04m1bm; 0g9wdmc; 0c9k8; 0f4yh; 017kct; 0h03fhx; ... *> query: (?x4216, 03bxz7) <- nominated_for(?x112, ?x4216), award(?x4216, ?x484), ?x112 = 027dtxw *> conf = 0.18 ranks of expected_values: 10 EVAL 0hfzr genre 03bxz7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 73.000 71.000 0.732 http://example.org/film/film/genre #20859-02ctzb PRED entity: 02ctzb PRED relation: people PRED expected values: 0d0vj4 083q7 0282x 05l0j5 042f1 03f4w4 => 59 concepts (29 used for prediction) PRED predicted values (max 10 best out of 4195): 0g824 (0.40 #14179, 0.33 #2533, 0.31 #27490), 0311wg (0.40 #13592, 0.33 #1946, 0.29 #36889), 02qhm3 (0.40 #14838, 0.33 #3192, 0.27 #21495), 042f1 (0.40 #14641, 0.33 #2995, 0.18 #21298), 0bdxs5 (0.40 #14496, 0.33 #2850, 0.18 #21153), 07t2k (0.40 #14321, 0.33 #2675, 0.18 #20978), 0rlz (0.40 #14103, 0.33 #2457, 0.18 #20760), 02jq1 (0.40 #14055, 0.33 #2409, 0.18 #20712), 043gj (0.40 #13945, 0.33 #2299, 0.18 #20602), 02lt8 (0.40 #13861, 0.33 #2215, 0.18 #20518) >> Best rule #14179 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 063k3h; >> query: (?x4195, 0g824) <- people(?x4195, ?x12186), people(?x4195, ?x9046), people(?x4195, ?x6514), award_winner(?x5053, ?x6514), award_winner(?x2168, ?x6514), place_of_death(?x9046, ?x108), basic_title(?x9046, ?x346), award_nominee(?x12186, ?x9659), student(?x3439, ?x9046), legislative_sessions(?x9046, ?x4437) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #14641 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 063k3h; *> query: (?x4195, 042f1) <- people(?x4195, ?x12186), people(?x4195, ?x9046), people(?x4195, ?x6514), award_winner(?x5053, ?x6514), award_winner(?x2168, ?x6514), place_of_death(?x9046, ?x108), basic_title(?x9046, ?x346), award_nominee(?x12186, ?x9659), student(?x3439, ?x9046), legislative_sessions(?x9046, ?x4437) *> conf = 0.40 ranks of expected_values: 4, 64, 1893, 2785, 3260, 4075 EVAL 02ctzb people 03f4w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 59.000 29.000 0.400 http://example.org/people/ethnicity/people EVAL 02ctzb people 042f1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 59.000 29.000 0.400 http://example.org/people/ethnicity/people EVAL 02ctzb people 05l0j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 29.000 0.400 http://example.org/people/ethnicity/people EVAL 02ctzb people 0282x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 29.000 0.400 http://example.org/people/ethnicity/people EVAL 02ctzb people 083q7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 59.000 29.000 0.400 http://example.org/people/ethnicity/people EVAL 02ctzb people 0d0vj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 29.000 0.400 http://example.org/people/ethnicity/people #20858-0p_pd PRED entity: 0p_pd PRED relation: award PRED expected values: 099ck7 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 313): 02x8n1n (0.75 #6321, 0.70 #43471, 0.69 #38725), 027986c (0.75 #6321, 0.70 #43471, 0.69 #38725), 09sb52 (0.71 #2410, 0.37 #9523, 0.36 #2015), 05pcn59 (0.57 #474, 0.36 #2054, 0.22 #9562), 05zr6wv (0.43 #412, 0.36 #1992, 0.25 #1202), 02n9nmz (0.33 #67, 0.14 #31608, 0.12 #43075), 02x17s4 (0.33 #120, 0.12 #1305, 0.07 #2885), 04jly7r (0.33 #366), 0gkvb7 (0.30 #1607, 0.15 #5557, 0.14 #7534), 0bp_b2 (0.30 #1598, 0.14 #413, 0.09 #1993) >> Best rule #6321 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 03l295; 01xyt7; >> query: (?x397, ?x834) <- location_of_ceremony(?x397, ?x3026), award_winner(?x834, ?x397), currency(?x397, ?x170) >> conf = 0.75 => this is the best rule for 2 predicted values *> Best rule #31608 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1230 *> proper extension: 02wb6yq; *> query: (?x397, ?x68) <- nominated_for(?x397, ?x4690), nominated_for(?x68, ?x4690), location(?x397, ?x335) *> conf = 0.14 ranks of expected_values: 93 EVAL 0p_pd award 099ck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 128.000 128.000 0.753 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20857-0g3zrd PRED entity: 0g3zrd PRED relation: film_crew_role PRED expected values: 09zzb8 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.79 #314, 0.72 #1430, 0.71 #536), 0215hd (0.70 #171, 0.70 #140, 0.63 #266), 01vx2h (0.54 #8, 0.51 #226, 0.50 #70), 0d2b38 (0.46 #334, 0.42 #272, 0.40 #303), 01pvkk (0.39 #227, 0.31 #9, 0.30 #134), 033smt (0.28 #661, 0.28 #179, 0.28 #148), 02rh1dz (0.28 #661, 0.24 #225, 0.23 #7), 02ynfr (0.28 #661, 0.23 #12, 0.21 #230), 0ckd1 (0.28 #661, 0.20 #128, 0.19 #159), 015h31 (0.28 #661, 0.19 #257, 0.17 #288) >> Best rule #314 for best value: >> intensional similarity = 5 >> extensional distance = 110 >> proper extension: 034qmv; 09xbpt; 047gn4y; 06_wqk4; 04dsnp; 053rxgm; 09p0ct; 05sxzwc; 05pbl56; 024l2y; ... >> query: (?x2331, 09zzb8) <- film_crew_role(?x2331, ?x2472), film_crew_role(?x2331, ?x281), ?x2472 = 01xy5l_, film_crew_role(?x10722, ?x281), ?x10722 = 07p12s >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g3zrd film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.786 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20856-059rby PRED entity: 059rby PRED relation: religion PRED expected values: 01lp8 0c8wxp => 202 concepts (202 used for prediction) PRED predicted values (max 10 best out of 24): 0c8wxp (0.88 #804, 0.85 #528, 0.80 #980), 01lp8 (0.80 #526, 0.78 #802, 0.78 #978), 021_0p (0.65 #535, 0.57 #987, 0.56 #811), 01s5nb (0.43 #414, 0.41 #991, 0.40 #916), 058x5 (0.37 #527, 0.37 #3063, 0.36 #402), 072w0 (0.37 #3063, 0.24 #540, 0.22 #992), 03j6c (0.20 #36, 0.18 #261, 0.16 #211), 0kpl (0.20 #30, 0.06 #205, 0.06 #255), 07w8f (0.20 #44, 0.03 #219, 0.03 #269), 01spm (0.06 #122, 0.03 #247, 0.03 #222) >> Best rule #804 for best value: >> intensional similarity = 2 >> extensional distance = 48 >> proper extension: 03czqs; >> query: (?x335, 0c8wxp) <- religion(?x335, ?x492), country(?x335, ?x94) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 059rby religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.880 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 059rby religion 01lp8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.880 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #20855-0885n PRED entity: 0885n PRED relation: institution! PRED expected values: 014mlp 0bkj86 => 164 concepts (157 used for prediction) PRED predicted values (max 10 best out of 20): 014mlp (0.75 #1016, 0.72 #124, 0.69 #1036), 019v9k (0.62 #1020, 0.59 #898, 0.58 #980), 03bwzr4 (0.52 #394, 0.47 #312, 0.44 #761), 016t_3 (0.47 #384, 0.47 #751, 0.46 #302), 0bkj86 (0.46 #389, 0.42 #307, 0.37 #327), 04zx3q1 (0.32 #1155, 0.30 #383, 0.29 #241), 0bjrnt (0.32 #1155, 0.28 #1853, 0.16 #1806), 02m4yg (0.32 #1155, 0.28 #1853, 0.16 #1806), 01ysy9 (0.32 #1155, 0.28 #1853, 0.16 #1806), 01gkg3 (0.32 #1155, 0.28 #1853, 0.16 #1806) >> Best rule #1016 for best value: >> intensional similarity = 4 >> extensional distance = 370 >> proper extension: 01v3ht; 026m3y; 0ylzs; 0yl_w; 032r4n; >> query: (?x7066, 014mlp) <- institution(?x620, ?x7066), colors(?x7066, ?x663), institution(?x620, ?x9066), ?x9066 = 03l78j >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 0885n institution! 0bkj86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 164.000 157.000 0.747 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0885n institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 157.000 0.747 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20854-06tp4h PRED entity: 06tp4h PRED relation: profession PRED expected values: 0np9r => 168 concepts (140 used for prediction) PRED predicted values (max 10 best out of 95): 0np9r (0.73 #3842, 0.71 #8259, 0.69 #3254), 09jwl (0.67 #8846, 0.63 #18123, 0.61 #11054), 016z4k (0.67 #1327, 0.67 #886, 0.63 #2062), 0dz3r (0.57 #11038, 0.56 #4119, 0.53 #6031), 0nbcg (0.56 #2383, 0.50 #9889, 0.50 #8859), 0dxtg (0.49 #12815, 0.48 #20481, 0.46 #18414), 02jknp (0.47 #18408, 0.45 #20475, 0.39 #17228), 03gjzk (0.45 #12816, 0.43 #161, 0.38 #1043), 012t_z (0.43 #159, 0.20 #747, 0.14 #2217), 0d1pc (0.42 #932, 0.40 #1520, 0.37 #2108) >> Best rule #3842 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 091n7z; >> query: (?x6613, 0np9r) <- language(?x6613, ?x254), category(?x6613, ?x134), profession(?x6613, ?x319) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06tp4h profession 0np9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 140.000 0.732 http://example.org/people/person/profession #20853-0d6lp PRED entity: 0d6lp PRED relation: location! PRED expected values: 015xp4 01304j 0dq9wx => 216 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2346): 0bt23 (0.54 #250547, 0.53 #265440, 0.50 #12402), 0g5ff (0.54 #250547, 0.50 #12402, 0.49 #128986), 0k1bs (0.53 #265440, 0.50 #12402, 0.49 #128986), 0kp2_ (0.53 #265440, 0.50 #12402, 0.49 #128986), 08yx9q (0.50 #12402, 0.49 #128986, 0.48 #69452), 09rp4r_ (0.50 #12402, 0.49 #128986, 0.48 #69452), 05zrx3v (0.50 #12402, 0.49 #128986, 0.48 #69452), 0f_zkz (0.50 #12402, 0.49 #128986, 0.48 #69452), 06y3r (0.50 #12402, 0.49 #128986, 0.48 #69452), 064177 (0.50 #12402, 0.49 #128986, 0.48 #69452) >> Best rule #250547 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 058wp; 0tcj6; 0kc40; 0yz30; >> query: (?x3125, ?x11092) <- contains(?x94, ?x3125), place_of_birth(?x11092, ?x3125), influenced_by(?x2208, ?x11092) >> conf = 0.54 => this is the best rule for 2 predicted values *> Best rule #22159 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0gp5l6; *> query: (?x3125, 0dq9wx) <- place_founded(?x5956, ?x3125), citytown(?x1168, ?x3125), film_release_region(?x1861, ?x3125) *> conf = 0.08 ranks of expected_values: 762, 819 EVAL 0d6lp location! 0dq9wx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 216.000 162.000 0.545 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0d6lp location! 01304j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 216.000 162.000 0.545 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0d6lp location! 015xp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 216.000 162.000 0.545 http://example.org/people/person/places_lived./people/place_lived/location #20852-02qrbbx PRED entity: 02qrbbx PRED relation: award! PRED expected values: 03f7xg => 51 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1019): 02r858_ (0.53 #1023, 0.50 #2051, 0.50 #2048), 0dl9_4 (0.53 #1023, 0.50 #2048, 0.50 #1554), 06kl78 (0.53 #1023, 0.50 #2048, 0.48 #2049), 0qmfz (0.53 #1023, 0.50 #2048, 0.40 #3074), 027r7k (0.53 #1023, 0.50 #2048, 0.40 #3074), 02vp1f_ (0.50 #1040, 0.43 #2066, 0.33 #15), 0cmc26r (0.50 #1436, 0.43 #2462, 0.33 #411), 07s846j (0.50 #3478, 0.36 #5528, 0.36 #4503), 0p_th (0.50 #3229, 0.36 #5279, 0.36 #4254), 014bpd (0.50 #1827, 0.33 #802, 0.29 #2853) >> Best rule #1023 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 02qwdhq; >> query: (?x13042, ?x3201) <- nominated_for(?x13042, ?x8277), nominated_for(?x13042, ?x4772), nominated_for(?x13042, ?x3201), ?x8277 = 02r858_, award(?x7327, ?x13042), ?x4772 = 06kl78, award_winner(?x7144, ?x7327), award(?x7246, ?x13042) >> conf = 0.53 => this is the best rule for 5 predicted values *> Best rule #2376 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 02qwzkm; *> query: (?x13042, 03f7xg) <- nominated_for(?x13042, ?x8277), nominated_for(?x13042, ?x4772), nominated_for(?x13042, ?x3201), ?x8277 = 02r858_, award(?x7246, ?x13042), ?x3201 = 01ffx4, genre(?x4772, ?x53), film_crew_role(?x4772, ?x137) *> conf = 0.29 ranks of expected_values: 17 EVAL 02qrbbx award! 03f7xg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 51.000 17.000 0.533 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #20851-047cqr PRED entity: 047cqr PRED relation: award_nominee PRED expected values: 01xndd => 88 concepts (46 used for prediction) PRED predicted values (max 10 best out of 931): 01xndd (0.81 #7012, 0.79 #14026, 0.50 #927), 0brkwj (0.60 #1803, 0.22 #16364, 0.18 #105195), 09hd6f (0.50 #2130, 0.46 #9350, 0.26 #23377), 047cqr (0.50 #2176, 0.46 #9350, 0.22 #16364), 09_99w (0.46 #9350, 0.40 #1916, 0.26 #23377), 0h53p1 (0.46 #9350, 0.40 #626, 0.26 #23377), 04snp2 (0.46 #9350, 0.01 #77148, 0.01 #35068), 031ydm (0.29 #70136, 0.26 #23377, 0.22 #16364), 07f3xb (0.29 #70136, 0.26 #23377, 0.22 #16364), 03_wj_ (0.29 #70136, 0.26 #23377, 0.22 #16364) >> Best rule #7012 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 01lct6; >> query: (?x10667, ?x4023) <- program(?x10667, ?x5810), category(?x10667, ?x134), award_nominee(?x4023, ?x10667) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047cqr award_nominee 01xndd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 46.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20850-05myd2 PRED entity: 05myd2 PRED relation: gender PRED expected values: 05zppz => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #19, 0.87 #7, 0.86 #9), 02zsn (0.54 #131, 0.48 #106, 0.48 #79) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 210 >> proper extension: 02pb53; 017g2y; 0gthm; 01pbwwl; >> query: (?x9512, 05zppz) <- award(?x9512, ?x2183), film(?x9512, ?x5353), award(?x7137, ?x2183), ?x7137 = 01gbb4 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05myd2 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.901 http://example.org/people/person/gender #20849-07hyk PRED entity: 07hyk PRED relation: people! PRED expected values: 063k3h => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 47): 041rx (0.35 #4000, 0.34 #3482, 0.29 #4740), 033tf_ (0.29 #969, 0.21 #1043, 0.21 #1413), 063k3h (0.27 #1286, 0.25 #842, 0.25 #768), 07hwkr (0.20 #1122, 0.18 #1344, 0.17 #2084), 09vc4s (0.20 #9, 0.14 #601, 0.14 #305), 06v41q (0.20 #174, 0.07 #692, 0.06 #988), 0x67 (0.18 #6744, 0.18 #6596, 0.16 #7780), 0xnvg (0.18 #975, 0.07 #2381, 0.07 #679), 013xrm (0.12 #980, 0.12 #3496, 0.09 #3940), 01qhm_ (0.12 #894, 0.10 #1782, 0.09 #1856) >> Best rule #4000 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 030pr; 0443c; >> query: (?x10888, 041rx) <- people(?x10199, ?x10888), people(?x4195, ?x10888), student(?x3439, ?x10888) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1286 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 0d0vj4; *> query: (?x10888, 063k3h) <- basic_title(?x10888, ?x265), company(?x10888, ?x94), student(?x3439, ?x10888) *> conf = 0.27 ranks of expected_values: 3 EVAL 07hyk people! 063k3h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 130.000 0.346 http://example.org/people/ethnicity/people #20848-01z7dr PRED entity: 01z7dr PRED relation: artists PRED expected values: 01nqfh_ => 80 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1208): 025ldg (0.73 #2539, 0.33 #372, 0.18 #12306), 01vtj38 (0.64 #2831, 0.33 #664, 0.25 #12598), 01vrz41 (0.55 #2247, 0.37 #1083, 0.33 #80), 011z3g (0.55 #2772, 0.33 #605, 0.25 #12539), 02z4b_8 (0.55 #2806, 0.33 #639, 0.20 #12573), 01wj18h (0.55 #2436, 0.33 #269, 0.18 #12203), 016jfw (0.55 #2720, 0.33 #553, 0.15 #12487), 0134wr (0.55 #2909, 0.33 #742, 0.15 #12676), 02p68d (0.55 #2911, 0.33 #744, 0.13 #12678), 06mt91 (0.55 #2780, 0.20 #12547, 0.17 #7591) >> Best rule #2539 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 02lnbg; >> query: (?x11342, 025ldg) <- artists(?x11342, ?x10181), artists(?x11342, ?x10039), ?x10181 = 01vzxld, type_of_union(?x10039, ?x566), category(?x10039, ?x134), artist(?x7793, ?x10039) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1117 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 017_qw; *> query: (?x11342, 01nqfh_) <- artists(?x11342, ?x10181), artists(?x11342, ?x10039), artists(?x11342, ?x3069), instrumentalists(?x75, ?x10039), nationality(?x10181, ?x94), award_winner(?x10180, ?x10181), ?x3069 = 0150t6 *> conf = 0.33 ranks of expected_values: 109 EVAL 01z7dr artists 01nqfh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 80.000 30.000 0.727 http://example.org/music/genre/artists #20847-0gjk1d PRED entity: 0gjk1d PRED relation: executive_produced_by PRED expected values: 0b13g7 => 80 concepts (54 used for prediction) PRED predicted values (max 10 best out of 49): 0gg9_5q (0.20 #90, 0.03 #1356, 0.02 #3631), 029m83 (0.20 #176, 0.01 #1442, 0.01 #1696), 02hfp_ (0.20 #177), 01nr36 (0.19 #1520, 0.08 #759, 0.06 #6315), 06q8hf (0.12 #1432, 0.06 #670, 0.06 #3707), 05hj_k (0.12 #1364, 0.06 #3639, 0.04 #6159), 06pj8 (0.04 #1321, 0.03 #559, 0.03 #1068), 030_3z (0.04 #1374, 0.02 #867, 0.02 #1121), 04jspq (0.04 #654, 0.03 #1416, 0.02 #4447), 02z6l5f (0.03 #370, 0.03 #2396, 0.02 #1384) >> Best rule #90 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 04cv9m; >> query: (?x1209, 0gg9_5q) <- film(?x1208, ?x1209), production_companies(?x1209, ?x752), country(?x1209, ?x94), ?x1208 = 0sz28 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1352 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 206 *> proper extension: 0gffmn8; 01svry; 04sh80; *> query: (?x1209, 0b13g7) <- film(?x1208, ?x1209), written_by(?x1209, ?x8491), executive_produced_by(?x1209, ?x5973), award(?x1208, ?x384) *> conf = 0.02 ranks of expected_values: 16 EVAL 0gjk1d executive_produced_by 0b13g7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 80.000 54.000 0.200 http://example.org/film/film/executive_produced_by #20846-06v36 PRED entity: 06v36 PRED relation: administrative_parent PRED expected values: 02j71 => 138 concepts (103 used for prediction) PRED predicted values (max 10 best out of 27): 02j71 (0.86 #1112, 0.86 #3587, 0.86 #5100), 09c7w0 (0.47 #3438, 0.41 #3712, 0.39 #4402), 0dg3n1 (0.24 #4539, 0.23 #3848, 0.22 #13660), 03rk0 (0.05 #1278, 0.03 #11200, 0.03 #7889), 07ssc (0.05 #11, 0.04 #1521, 0.03 #562), 0b90_r (0.05 #3, 0.01 #4680), 0d060g (0.05 #9096, 0.04 #9512, 0.03 #4407), 059rby (0.04 #13387, 0.03 #13527, 0.03 #558), 049nq (0.03 #510, 0.03 #1332, 0.02 #2016), 0345h (0.03 #577, 0.03 #3184, 0.03 #3463) >> Best rule #1112 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 05g2v; >> query: (?x6437, 02j71) <- contains(?x2467, ?x6437), ?x2467 = 0dg3n1, taxonomy(?x6437, ?x939) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06v36 administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 103.000 0.865 http://example.org/base/aareas/schema/administrative_area/administrative_parent #20845-02lq5w PRED entity: 02lq5w PRED relation: medal! PRED expected values: 0sx7r 01f1jy 0sx8l 0nbjq 0lbd9 018ljb 0124ld => 3 concepts (3 used for prediction) PRED predicted values (max 10 best out of 7): 0lbd9 (0.89 #8, 0.85 #10, 0.33 #18), 0124ld (0.89 #8, 0.85 #10, 0.33 #17), 0nbjq (0.89 #8, 0.85 #10, 0.33 #14), 01f1jy (0.89 #8, 0.33 #2), 0sx7r (0.85 #10, 0.74 #9, 0.33 #11), 0sx8l (0.85 #10, 0.74 #9, 0.33 #13), 018ljb (0.85 #10, 0.33 #6) >> Best rule #8 for best value: >> intensional similarity = 426 >> extensional distance = 1 >> proper extension: 02lq67; >> query: (?x1242, ?x2432) <- medal(?x10801, ?x1242), medal(?x9455, ?x1242), medal(?x9251, ?x1242), medal(?x8197, ?x1242), medal(?x7413, ?x1242), medal(?x7360, ?x1242), medal(?x5453, ?x1242), medal(?x5360, ?x1242), medal(?x5274, ?x1242), medal(?x5147, ?x1242), medal(?x5114, ?x1242), medal(?x4714, ?x1242), medal(?x4059, ?x1242), medal(?x3855, ?x1242), medal(?x3730, ?x1242), medal(?x3728, ?x1242), medal(?x3357, ?x1242), medal(?x2984, ?x1242), medal(?x1790, ?x1242), medal(?x1499, ?x1242), medal(?x1471, ?x1242), medal(?x1355, ?x1242), medal(?x1353, ?x1242), medal(?x1241, ?x1242), medal(?x1003, ?x1242), medal(?x792, ?x1242), medal(?x608, ?x1242), medal(?x550, ?x1242), medal(?x429, ?x1242), medal(?x172, ?x1242), medal(?x151, ?x1242), medal(?x142, ?x1242), medal(?x94, ?x1242), medal(?x87, ?x1242), ?x3728 = 087vz, medal(?x8584, ?x1242), medal(?x8189, ?x1242), medal(?x7441, ?x1242), medal(?x5176, ?x1242), medal(?x4424, ?x1242), medal(?x3971, ?x1242), medal(?x3729, ?x1242), medal(?x2966, ?x1242), medal(?x2496, ?x1242), medal(?x2134, ?x1242), medal(?x2043, ?x1242), medal(?x1608, ?x1242), medal(?x784, ?x1242), medal(?x584, ?x1242), ?x2496 = 0sxrz, ?x7441 = 0ldqf, ?x2966 = 06sks6, ?x584 = 0l98s, ?x2134 = 0blg2, ?x172 = 0154j, ?x1471 = 07t21, ?x4424 = 0blfl, teams(?x4714, ?x10006), ?x3971 = 0jhn7, ?x5147 = 0d04z6, ?x5114 = 05vz3zq, country(?x1121, ?x4714), ?x4059 = 077qn, ?x550 = 05v8c, ?x3730 = 03shp, sports(?x8189, ?x11927), sports(?x8189, ?x8190), sports(?x8189, ?x3309), country(?x1352, ?x5274), combatants(?x11183, ?x5274), entity_involved(?x9939, ?x10801), ?x5453 = 088vb, jurisdiction_of_office(?x182, ?x4714), ?x8584 = 01f1jf, adjoins(?x9251, ?x2804), ?x3855 = 0jgx, ?x1608 = 09x3r, combatants(?x10801, ?x9328), combatants(?x10801, ?x1611), country(?x13378, ?x9251), organization(?x9251, ?x5701), organization(?x9251, ?x312), sports(?x8189, ?x2884), film_release_region(?x11839, ?x142), film_release_region(?x9902, ?x142), film_release_region(?x9657, ?x142), film_release_region(?x9529, ?x142), film_release_region(?x9501, ?x142), film_release_region(?x9216, ?x142), film_release_region(?x9194, ?x142), film_release_region(?x9174, ?x142), film_release_region(?x8955, ?x142), film_release_region(?x8770, ?x142), film_release_region(?x8646, ?x142), film_release_region(?x8370, ?x142), film_release_region(?x8258, ?x142), film_release_region(?x8176, ?x142), film_release_region(?x7651, ?x142), film_release_region(?x7524, ?x142), film_release_region(?x7393, ?x142), film_release_region(?x7016, ?x142), film_release_region(?x6782, ?x142), film_release_region(?x6621, ?x142), film_release_region(?x6603, ?x142), film_release_region(?x6247, ?x142), film_release_region(?x6235, ?x142), film_release_region(?x6215, ?x142), film_release_region(?x6181, ?x142), film_release_region(?x6168, ?x142), film_release_region(?x6121, ?x142), film_release_region(?x6078, ?x142), film_release_region(?x5992, ?x142), film_release_region(?x5849, ?x142), film_release_region(?x5791, ?x142), film_release_region(?x5721, ?x142), film_release_region(?x5704, ?x142), film_release_region(?x5576, ?x142), film_release_region(?x5347, ?x142), film_release_region(?x5092, ?x142), film_release_region(?x5089, ?x142), film_release_region(?x5013, ?x142), film_release_region(?x4950, ?x142), film_release_region(?x4684, ?x142), film_release_region(?x4604, ?x142), film_release_region(?x4545, ?x142), film_release_region(?x4446, ?x142), film_release_region(?x4355, ?x142), film_release_region(?x4352, ?x142), film_release_region(?x3998, ?x142), film_release_region(?x3958, ?x142), film_release_region(?x3938, ?x142), film_release_region(?x3886, ?x142), film_release_region(?x3812, ?x142), film_release_region(?x3603, ?x142), film_release_region(?x3565, ?x142), film_release_region(?x3377, ?x142), film_release_region(?x3292, ?x142), film_release_region(?x3268, ?x142), film_release_region(?x3226, ?x142), film_release_region(?x3201, ?x142), film_release_region(?x3081, ?x142), film_release_region(?x3076, ?x142), film_release_region(?x3053, ?x142), film_release_region(?x3000, ?x142), film_release_region(?x2783, ?x142), film_release_region(?x2676, ?x142), film_release_region(?x2644, ?x142), film_release_region(?x2628, ?x142), film_release_region(?x2598, ?x142), film_release_region(?x2501, ?x142), film_release_region(?x2394, ?x142), film_release_region(?x2350, ?x142), film_release_region(?x2342, ?x142), film_release_region(?x2340, ?x142), film_release_region(?x2318, ?x142), film_release_region(?x2189, ?x142), film_release_region(?x2155, ?x142), film_release_region(?x2050, ?x142), film_release_region(?x2037, ?x142), film_release_region(?x1525, ?x142), film_release_region(?x1470, ?x142), film_release_region(?x1451, ?x142), film_release_region(?x1370, ?x142), film_release_region(?x1315, ?x142), film_release_region(?x1293, ?x142), film_release_region(?x1259, ?x142), film_release_region(?x1178, ?x142), film_release_region(?x1173, ?x142), film_release_region(?x1108, ?x142), film_release_region(?x1012, ?x142), film_release_region(?x972, ?x142), film_release_region(?x791, ?x142), film_release_region(?x785, ?x142), film_release_region(?x781, ?x142), film_release_region(?x664, ?x142), film_release_region(?x504, ?x142), film_release_region(?x430, ?x142), film_release_region(?x428, ?x142), film_release_region(?x251, ?x142), film_release_region(?x141, ?x142), film_release_region(?x80, ?x142), administrative_parent(?x9251, ?x551), official_language(?x4714, ?x5607), ?x6621 = 0h63gl9, adjustment_currency(?x9251, ?x170), ?x1121 = 0bynt, ?x1108 = 0jjy0, ?x9529 = 0gwf191, ?x3958 = 0gyh2wm, adjoins(?x1592, ?x142), locations(?x4908, ?x142), ?x3309 = 09w1n, ?x4604 = 0432_5, ?x87 = 05r4w, ?x4355 = 08tq4x, ?x9455 = 0jt3tjf, film_crew_role(?x5089, ?x2095), film_crew_role(?x5089, ?x137), nominated_for(?x489, ?x5089), film(?x7156, ?x3081), ?x428 = 0h1cdwq, ?x2783 = 0879bpq, ?x4352 = 09v71cj, country(?x5989, ?x142), country(?x3641, ?x142), country(?x3598, ?x142), country(?x3554, ?x142), country(?x2315, ?x142), country(?x766, ?x142), adjoins(?x7360, ?x2051), ?x785 = 03hjv97, contains(?x455, ?x5274), time_zones(?x7360, ?x5327), ?x1241 = 05cgv, film_release_region(?x5782, ?x142), film_release_region(?x1797, ?x142), ?x9657 = 07jqjx, ?x3226 = 0gyfp9c, produced_by(?x2644, ?x163), language(?x9216, ?x254), film_release_region(?x3081, ?x774), ?x3565 = 0cp0ph6, ?x254 = 02h40lc, ?x6603 = 094g2z, capital(?x9328, ?x10042), ?x5989 = 019tzd, film_festivals(?x2644, ?x6557), taxonomy(?x5274, ?x939), ?x1470 = 03twd6, ?x4950 = 07k2mq, ?x1797 = 050xxm, ?x1293 = 07g_0c, ?x429 = 03rt9, ?x3603 = 09gkx35, ?x5849 = 02h22, ?x2394 = 0661ql3, ?x7524 = 01cm8w, ?x2501 = 040rmy, genre(?x5791, ?x812), ?x7651 = 0h95927, ?x312 = 07t65, contains(?x142, ?x7661), ?x504 = 0g5qs2k, ?x5782 = 0df92l, ?x1370 = 0gmcwlb, ?x7016 = 07g1sm, adjoins(?x1577, ?x5360), ?x9902 = 0j8f09z, film(?x380, ?x2644), cinematography(?x3081, ?x4997), ?x1611 = 025ndl, ?x1315 = 053tj7, ?x1353 = 035qy, form_of_government(?x5360, ?x6377), combatants(?x1790, ?x8687), ?x3998 = 0184tc, ?x2095 = 0dxtw, ?x1355 = 0h7x, ?x5092 = 0gg5qcw, film_release_region(?x791, ?x1475), ?x2318 = 06v9_x, ?x3000 = 045j3w, ?x4446 = 0db94w, ?x1178 = 053rxgm, combatants(?x9328, ?x13430), member_states(?x7695, ?x5360), ?x1475 = 05qx1, nominated_for(?x185, ?x6782), ?x972 = 017gl1, ?x5701 = 0b6css, ?x3886 = 0198b6, countries_within(?x2467, ?x8197), ?x9194 = 0fpgp26, adjoins(?x8197, ?x5700), ?x2340 = 0fpv_3_, nominated_for(?x11115, ?x9216), ?x1012 = 0bwfwpj, ?x1525 = 03qnvdl, ?x4684 = 03nm_fh, member_states(?x8868, ?x5274), ?x94 = 09c7w0, language(?x5791, ?x5359), ?x1451 = 04zyhx, ?x5704 = 0h95zbp, ?x8955 = 0g4pl7z, location_of_ceremony(?x566, ?x142), ?x13430 = 040vgd, contains(?x1790, ?x1791), ?x2189 = 02yvct, ?x11839 = 072hx4, ?x3357 = 04w8f, ?x2037 = 0gvrws1, ?x1259 = 04hwbq, ?x1003 = 03gj2, ?x2043 = 0lv1x, ?x781 = 0gkz15s, olympics(?x10801, ?x7051), ?x766 = 01hp22, ?x2315 = 06wrt, contains(?x9251, ?x3010), ?x5013 = 011ycb, ?x3292 = 0gvs1kt, award(?x6782, ?x3233), ?x1173 = 0872p_c, production_companies(?x5089, ?x1104), ?x6181 = 0hv27, jurisdiction_of_office(?x265, ?x142), ?x3377 = 0gj8nq2, film_distribution_medium(?x5791, ?x2099), film(?x5542, ?x6782), film(?x5058, ?x791), ?x6215 = 0jyb4, film_release_region(?x7141, ?x1790), ?x2467 = 0dg3n1, ?x6168 = 0gj96ln, ?x2050 = 01fmys, contains(?x7273, ?x4714), ?x2155 = 0407yfx, ?x664 = 0401sg, ?x792 = 0hzlz, country(?x5182, ?x1790), partially_contains(?x142, ?x12972), film_release_distribution_medium(?x791, ?x81), genre(?x5089, ?x1013), ?x8868 = 059dn, ?x784 = 018ctl, film_release_region(?x11209, ?x2984), film_release_region(?x9616, ?x2984), film_release_region(?x4971, ?x2984), film_release_region(?x204, ?x2984), titles(?x2480, ?x2644), ?x812 = 01jfsb, participating_countries(?x1741, ?x142), ?x3201 = 01ffx4, ?x5721 = 01d259, institution(?x1368, ?x7661), citytown(?x7661, ?x2911), capital(?x2984, ?x10334), film_crew_role(?x2644, ?x1078), ?x5058 = 06wm0z, olympics(?x142, ?x2432), ?x1499 = 01znc_, ?x3812 = 0c3xw46, combatants(?x1777, ?x9328), produced_by(?x3081, ?x1285), administrative_area_type(?x1577, ?x2792), ?x3076 = 0g5838s, ?x204 = 028_yv, major_field_of_study(?x7661, ?x3878), ?x251 = 02vp1f_, ?x430 = 0m2kd, ?x5176 = 0sx92, ?x137 = 09zzb8, ?x8370 = 07ghq, ?x3554 = 035d1m, ?x1777 = 0845v, ?x3598 = 03rbzn, ?x11115 = 09v478h, ?x3729 = 0jdk_, written_by(?x5791, ?x4685), film_release_region(?x3850, ?x1790), genre(?x791, ?x258), nationality(?x1940, ?x142), ?x2676 = 0f4m2z, ?x4545 = 05p09dd, ?x8687 = 059z0, ?x8770 = 025ts_z, ?x3641 = 03fyrh, ?x1352 = 0w0d, nominated_for(?x3053, ?x9487), ?x9501 = 0g5qmbz, ?x6247 = 09v9mks, ?x3268 = 02x6dqb, ?x2342 = 0ct5zc, ?x5576 = 0gbfn9, films(?x4450, ?x6782), films(?x11183, ?x6273), genre(?x3081, ?x53), written_by(?x3053, ?x1052), ?x7393 = 02vz6dn, countries_spoken_in(?x5003, ?x9251), ?x151 = 0b90_r, music(?x3081, ?x2363), ?x3850 = 047fjjr, ?x80 = 0b76d_m, ?x2350 = 0661m4p, production_companies(?x6235, ?x1850), nominated_for(?x1716, ?x8176), ?x6121 = 064lsn, ?x4971 = 01jwxx, ?x6377 = 01d9r3, film_format(?x6235, ?x6392), contains(?x5274, ?x1464), ?x2628 = 06wbm8q, ?x939 = 04n6k, country(?x1009, ?x142), countries_within(?x8483, ?x4714), ?x608 = 02k54, nominated_for(?x3456, ?x3053), contains(?x4714, ?x11656), ?x1716 = 02y_rq5, ?x5992 = 0g5q34q, ?x7413 = 04hqz, ?x3938 = 024mpp, ?x9939 = 03jqfx, location(?x8299, ?x1790), titles(?x1510, ?x3053), ?x9174 = 087pfc, combatants(?x11047, ?x142), music(?x8176, ?x3774), ?x141 = 0gtsx8c, ?x2598 = 07f_7h, ?x8646 = 05zvzf3, film(?x4771, ?x6235), ?x11209 = 04fjzv, ?x9616 = 045r_9, ?x1368 = 014mlp, ?x6078 = 04pk1f, official_language(?x5274, ?x732), ?x5347 = 02ylg6, ?x8258 = 05ldxl, ?x11927 = 09f6b, ?x774 = 06mzp, ?x8190 = 09_9n, official_language(?x1790, ?x5814), film_release_region(?x6100, ?x2984) >> conf = 0.89 => this is the best rule for 4 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7 EVAL 02lq5w medal! 0124ld CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.886 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 018ljb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.886 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 0lbd9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.886 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 0nbjq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.886 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 0sx8l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.886 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 01f1jy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.886 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lq5w medal! 0sx7r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.886 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #20844-04d817 PRED entity: 04d817 PRED relation: position PRED expected values: 02_j1w => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 3): 02_j1w (0.82 #214, 0.82 #235, 0.82 #209), 03f0fp (0.51 #222, 0.51 #245), 02md_2 (0.51 #222, 0.51 #245) >> Best rule #214 for best value: >> intensional similarity = 26 >> extensional distance = 552 >> proper extension: 0223bl; 04b4yg; 08pgl8; 03fn8k; 024tsn; 02b1mc; 04jbyg; 02_cq0; 017_1x; 02v4vl; ... >> query: (?x9389, ?x203) <- position(?x9389, ?x60), ?x60 = 02nzb8, team(?x203, ?x9389), position(?x13980, ?x203), position(?x13726, ?x203), position(?x13604, ?x203), position(?x13521, ?x203), position(?x11489, ?x203), position(?x8512, ?x203), position(?x8265, ?x203), position(?x5686, ?x203), position(?x3529, ?x203), position(?x983, ?x203), position(?x202, ?x203), ?x11489 = 03zj_3, ?x3529 = 0212mp, ?x13726 = 026n13j, ?x202 = 01453, ?x8265 = 02nt75, ?x13604 = 088lls, ?x983 = 01bdxz, ?x13521 = 02b149, ?x13980 = 03mck3c, position(?x993, ?x203), ?x5686 = 085v7, ?x8512 = 0420td >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04d817 position 02_j1w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.819 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #20843-05bt6j PRED entity: 05bt6j PRED relation: artists PRED expected values: 0167_s 01vrwfv 0p3r8 0bqsy 049qx 01s21dg 04cr6qv 01bczm 044mfr 03xnq9_ 02bgmr 0167km 0677ng 0b_xm 01k23t 05w6cw 01nkxvx 01vn0t_ 0cbm64 0gps0z 01m7pwq 01wk7ql => 62 concepts (17 used for prediction) PRED predicted values (max 10 best out of 942): 048tgl (0.62 #9731, 0.55 #2693, 0.40 #6141), 06cc_1 (0.60 #4523, 0.55 #2693, 0.50 #6318), 0zjpz (0.60 #5505, 0.55 #2693, 0.50 #9993), 01k23t (0.60 #5069, 0.55 #2693, 0.50 #6864), 016fnb (0.60 #4828, 0.55 #2693, 0.50 #2132), 044mfr (0.60 #4914, 0.55 #2693, 0.50 #2218), 01wk7ql (0.60 #5232, 0.50 #7027, 0.50 #4334), 01309x (0.60 #4751, 0.50 #3853, 0.50 #2055), 019f9z (0.60 #4983, 0.50 #4085, 0.50 #2287), 01x1cn2 (0.60 #4649, 0.50 #1953, 0.45 #11830) >> Best rule #9731 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 02t8gf; >> query: (?x3061, 048tgl) <- artists(?x3061, ?x8873), artists(?x3061, ?x8344), artists(?x3061, ?x4237), award(?x8344, ?x1232), artists(?x2491, ?x4237), ?x2491 = 011j5x, ?x8873 = 0232lm >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #5069 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 06j6l; *> query: (?x3061, 01k23t) <- artists(?x3061, ?x10181), artists(?x3061, ?x8344), artists(?x3061, ?x6854), award(?x8344, ?x1232), ?x6854 = 0178_w, ?x10181 = 01vzxld, parent_genre(?x996, ?x3061) *> conf = 0.60 ranks of expected_values: 4, 6, 7, 19, 20, 42, 66, 67, 90, 92, 95, 125, 133, 212, 233, 283, 319, 326, 342, 429, 482, 819 EVAL 05bt6j artists 01wk7ql CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 01m7pwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 0gps0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 0cbm64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 01vn0t_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 01nkxvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 05w6cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 01k23t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 0b_xm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 0677ng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 0167km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 02bgmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 03xnq9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 044mfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 01bczm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 04cr6qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 01s21dg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 049qx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 0bqsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 0p3r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 01vrwfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 17.000 0.625 http://example.org/music/genre/artists EVAL 05bt6j artists 0167_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 17.000 0.625 http://example.org/music/genre/artists #20842-013zyw PRED entity: 013zyw PRED relation: film PRED expected values: 02v63m => 103 concepts (100 used for prediction) PRED predicted values (max 10 best out of 450): 0353tm (0.25 #6620, 0.20 #4965, 0.12 #9929), 02z3r8t (0.14 #873, 0.03 #3355, 0.02 #6666), 08g_jw (0.14 #1618, 0.03 #4100), 04qk12 (0.14 #1526, 0.03 #4008), 0415ggl (0.14 #1316, 0.03 #3798), 03rz2b (0.14 #1066, 0.03 #3548), 0prrm (0.13 #4964, 0.02 #42189, 0.01 #23989), 04tqtl (0.08 #1912, 0.06 #4394, 0.02 #5223), 02pg45 (0.08 #2116, 0.03 #4598, 0.02 #5427), 084qpk (0.08 #1706, 0.03 #4188, 0.02 #5017) >> Best rule #6620 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 05183k; 03kpvp; 0fby2t; 0p__8; 0237jb; >> query: (?x5785, ?x9213) <- place_of_birth(?x5785, ?x4090), profession(?x5785, ?x319), story_by(?x9213, ?x5785), ?x319 = 01d_h8 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 013zyw film 02v63m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 100.000 0.252 http://example.org/film/director/film #20841-02cpb7 PRED entity: 02cpb7 PRED relation: film PRED expected values: 047vnkj => 115 concepts (81 used for prediction) PRED predicted values (max 10 best out of 876): 02b61v (0.48 #67872, 0.44 #117894, 0.39 #117893), 03xf_m (0.25 #2893, 0.05 #94670, 0.03 #48225), 06t6dz (0.25 #2607, 0.05 #94670, 0.03 #48225), 01shy7 (0.20 #423, 0.06 #5781, 0.04 #23641), 08r4x3 (0.20 #153, 0.04 #26943, 0.04 #18013), 03bx2lk (0.20 #184, 0.04 #14472, 0.04 #12686), 0blpg (0.20 #656, 0.04 #6014, 0.03 #7800), 0dfw0 (0.20 #840, 0.03 #15128, 0.03 #4412), 0ddt_ (0.20 #474, 0.03 #14762, 0.02 #45126), 0bxsk (0.20 #1208, 0.03 #48225, 0.03 #92883) >> Best rule #67872 for best value: >> intensional similarity = 2 >> extensional distance = 886 >> proper extension: 01m7f5r; >> query: (?x4670, ?x5871) <- nominated_for(?x4670, ?x5871), people(?x743, ?x4670) >> conf = 0.48 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02cpb7 film 047vnkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 81.000 0.476 http://example.org/film/actor/film./film/performance/film #20840-0f87jy PRED entity: 0f87jy PRED relation: nationality PRED expected values: 09c7w0 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.90 #1104, 0.85 #3106, 0.84 #603), 020d5 (0.46 #501, 0.44 #602), 0mnm2 (0.25 #6718, 0.24 #5512), 07ssc (0.20 #516, 0.08 #4924, 0.08 #7036), 02jx1 (0.18 #534, 0.09 #735, 0.09 #9158), 0f8l9c (0.16 #422, 0.04 #523, 0.02 #5333), 03rk0 (0.08 #3251, 0.06 #8470, 0.06 #8570), 03rjj (0.07 #405, 0.02 #506, 0.02 #3210), 0345h (0.06 #431, 0.04 #532, 0.02 #8655), 0d060g (0.06 #909, 0.05 #709, 0.05 #207) >> Best rule #1104 for best value: >> intensional similarity = 3 >> extensional distance = 204 >> proper extension: 04cy8rb; >> query: (?x10593, 09c7w0) <- place_of_birth(?x10593, ?x9846), contains(?x9846, ?x10104), currency(?x9846, ?x170) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f87jy nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.903 http://example.org/people/person/nationality #20839-0dls3 PRED entity: 0dls3 PRED relation: parent_genre PRED expected values: 016clz 03lty => 73 concepts (55 used for prediction) PRED predicted values (max 10 best out of 303): 03lty (0.82 #2561, 0.81 #2402, 0.79 #3036), 0dl5d (0.38 #1923, 0.12 #3351, 0.11 #4312), 016clz (0.33 #798, 0.33 #481, 0.29 #639), 011j5x (0.33 #814, 0.33 #497, 0.29 #655), 017371 (0.33 #102, 0.20 #420, 0.17 #579), 05w3f (0.31 #1933, 0.30 #1136, 0.25 #184), 03_d0 (0.28 #5574, 0.23 #6052, 0.21 #5258), 02yv6b (0.25 #222, 0.20 #1174, 0.20 #380), 0pm85 (0.25 #254, 0.20 #412, 0.14 #3977), 0190_q (0.25 #183, 0.14 #3977, 0.10 #1135) >> Best rule #2561 for best value: >> intensional similarity = 10 >> extensional distance = 38 >> proper extension: 02yw0y; >> query: (?x3642, 03lty) <- artists(?x3642, ?x475), parent_genre(?x3642, ?x1000), artists(?x1000, ?x10907), artists(?x1000, ?x10198), artists(?x1000, ?x5208), artists(?x1000, ?x2901), profession(?x10907, ?x131), ?x5208 = 01s7qqw, category(?x2901, ?x134), ?x10198 = 01wqpnm >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0dls3 parent_genre 03lty CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 55.000 0.825 http://example.org/music/genre/parent_genre EVAL 0dls3 parent_genre 016clz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 55.000 0.825 http://example.org/music/genre/parent_genre #20838-01fl3 PRED entity: 01fl3 PRED relation: inductee! PRED expected values: 0g2c8 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 5): 0g2c8 (0.22 #64, 0.20 #1, 0.19 #19), 04dm2n (0.08 #17, 0.06 #26, 0.05 #35), 04045y (0.06 #24, 0.05 #33, 0.05 #42), 06szd3 (0.02 #200, 0.02 #83, 0.01 #362), 0qjfl (0.02 #84, 0.01 #120) >> Best rule #64 for best value: >> intensional similarity = 8 >> extensional distance = 34 >> proper extension: 01p95y0; >> query: (?x1749, 0g2c8) <- artists(?x9427, ?x1749), artists(?x2809, ?x1749), artists(?x9427, ?x9367), artists(?x9427, ?x1338), ?x1338 = 09qr6, people(?x4322, ?x9367), ?x2809 = 05w3f, gender(?x9367, ?x231) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fl3 inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.222 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #20837-02gf_l PRED entity: 02gf_l PRED relation: actor! PRED expected values: 0vhm 05f7w84 => 116 concepts (57 used for prediction) PRED predicted values (max 10 best out of 160): 07c72 (0.25 #304, 0.07 #820, 0.06 #1339), 0d68qy (0.25 #37, 0.02 #1588, 0.02 #4432), 01q_y0 (0.25 #34, 0.02 #2103, 0.01 #3913), 05f7w84 (0.20 #1656, 0.15 #2692, 0.13 #2950), 09g_31 (0.18 #1196, 0.17 #678, 0.11 #2749), 04mx8h4 (0.17 #689, 0.07 #947, 0.06 #1207), 02rcwq0 (0.17 #602, 0.06 #1120, 0.02 #1637), 030cx (0.17 #591, 0.03 #4470, 0.01 #5761), 015g28 (0.17 #578, 0.01 #10137, 0.01 #10395), 04sskp (0.17 #665) >> Best rule #304 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02xfj0; >> query: (?x7266, 07c72) <- film(?x7266, ?x3839), film(?x7266, ?x2512), ?x2512 = 07x4qr, film_release_region(?x3839, ?x94), gender(?x7266, ?x231) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1656 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 06sn8m; *> query: (?x7266, 05f7w84) <- actor(?x3144, ?x7266), language(?x7266, ?x254), genre(?x3144, ?x258), program(?x2135, ?x3144) *> conf = 0.20 ranks of expected_values: 4, 23 EVAL 02gf_l actor! 05f7w84 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 57.000 0.250 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 02gf_l actor! 0vhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 116.000 57.000 0.250 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #20836-064q5v PRED entity: 064q5v PRED relation: person PRED expected values: 028rk => 124 concepts (76 used for prediction) PRED predicted values (max 10 best out of 186): 04qb6g (0.44 #721, 0.21 #1986, 0.07 #2889), 0hm0k (0.44 #721, 0.21 #1986, 0.07 #2889), 01n4f8 (0.33 #29, 0.25 #2557, 0.10 #6002), 02yy_j (0.33 #150, 0.07 #2136, 0.06 #2678), 03pvt (0.33 #72, 0.07 #2058, 0.06 #2600), 02l840 (0.33 #195, 0.07 #2181, 0.05 #3448), 019z7q (0.33 #16, 0.06 #2544, 0.05 #5989), 02fn5 (0.33 #77, 0.06 #2605, 0.03 #6050), 0jw67 (0.29 #609, 0.19 #2597, 0.12 #4593), 06c97 (0.19 #2627, 0.14 #2085, 0.12 #2447) >> Best rule #721 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 012jfb; >> query: (?x6093, ?x6092) <- genre(?x6093, ?x2753), person(?x6093, ?x966), award_winner(?x6093, ?x6092), ?x2753 = 0219x_, country(?x6093, ?x279) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #4576 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 24 *> proper extension: 05_61y; *> query: (?x6093, 028rk) <- genre(?x6093, ?x1014), person(?x6093, ?x1620), film(?x1620, ?x1619), religion(?x1620, ?x962), award_winner(?x594, ?x1620) *> conf = 0.08 ranks of expected_values: 43 EVAL 064q5v person 028rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 124.000 76.000 0.444 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #20835-071jrc PRED entity: 071jrc PRED relation: type_of_union PRED expected values: 04ztj => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.80 #45, 0.75 #77, 0.72 #69), 01g63y (0.33 #2, 0.25 #6, 0.12 #122) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 209 >> proper extension: 021bk; 0c2ry; 012dr7; 01_k71; 0523v5y; 0cyhq; 03zrp; 01hkck; >> query: (?x12576, 04ztj) <- profession(?x12576, ?x2265), nominated_for(?x12576, ?x4841), film_release_region(?x4841, ?x87), list(?x4841, ?x3004) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 071jrc type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.796 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20834-060__7 PRED entity: 060__7 PRED relation: currency PRED expected values: 09nqf => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.82 #1, 0.78 #50, 0.78 #22), 01nv4h (0.03 #114, 0.03 #107, 0.03 #23), 02l6h (0.01 #109, 0.01 #172, 0.01 #32), 088n7 (0.01 #56), 02gsvk (0.01 #111, 0.01 #167) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 163 >> proper extension: 0gtsx8c; 07k2mq; >> query: (?x8557, 09nqf) <- film_crew_role(?x8557, ?x468), ?x468 = 02r96rf, production_companies(?x8557, ?x1104), crewmember(?x8557, ?x9391) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 060__7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.818 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #20833-0f3zsq PRED entity: 0f3zsq PRED relation: award PRED expected values: 02x258x => 80 concepts (66 used for prediction) PRED predicted values (max 10 best out of 391): 0gr0m (0.80 #886, 0.73 #1292, 0.69 #2916), 02x258x (0.32 #941, 0.21 #2971, 0.20 #3783), 09sb52 (0.30 #4913, 0.29 #8163, 0.27 #8569), 0ck27z (0.26 #7809, 0.23 #10246, 0.13 #13494), 0fc9js (0.18 #1841, 0.15 #2653, 0.12 #623), 02grdc (0.18 #1656, 0.15 #2468, 0.12 #438), 05zr6wv (0.17 #6109, 0.17 #6515, 0.16 #6921), 0bfvd4 (0.17 #116, 0.16 #15026, 0.13 #24373), 02rdyk7 (0.17 #92, 0.16 #4558, 0.13 #24373), 05pcn59 (0.17 #82, 0.16 #6174, 0.15 #6580) >> Best rule #886 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 04qvl7; 0f3zf_; 079hvk; 05dppk; 04g865; 07mb57; 03cx282; 0bqytm; 0854hr; 06r_by; ... >> query: (?x10542, 0gr0m) <- nominated_for(?x10542, ?x3471), gender(?x10542, ?x231), cinematography(?x1108, ?x10542), crewmember(?x1108, ?x5653) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #941 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 04qvl7; 0f3zf_; 079hvk; 05dppk; 04g865; 07mb57; 03cx282; 0bqytm; 0854hr; 06r_by; ... *> query: (?x10542, 02x258x) <- nominated_for(?x10542, ?x3471), gender(?x10542, ?x231), cinematography(?x1108, ?x10542), crewmember(?x1108, ?x5653) *> conf = 0.32 ranks of expected_values: 2 EVAL 0f3zsq award 02x258x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 66.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20832-01s73z PRED entity: 01s73z PRED relation: service_location PRED expected values: 09c7w0 => 178 concepts (167 used for prediction) PRED predicted values (max 10 best out of 152): 09c7w0 (0.90 #3464, 0.85 #5344, 0.84 #3562), 02j71 (0.52 #2985, 0.50 #116, 0.30 #3480), 0d060g (0.31 #4459, 0.28 #6442, 0.27 #6941), 0345h (0.22 #720, 0.21 #1610, 0.13 #3490), 0chghy (0.22 #704, 0.20 #3474, 0.16 #3572), 0f8l9c (0.22 #714, 0.14 #1604, 0.10 #3484), 07ssc (0.22 #6351, 0.21 #1598, 0.18 #6949), 03h64 (0.19 #11710, 0.07 #1628, 0.06 #5046), 059j2 (0.15 #1312, 0.11 #719, 0.11 #2302), 06t2t (0.14 #1621, 0.11 #731, 0.08 #1424) >> Best rule #3464 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 0gztl; 01qygl; 01b39j; >> query: (?x5108, 09c7w0) <- company(?x1491, ?x5108), ?x1491 = 0krdk, currency(?x5108, ?x170), service_language(?x5108, ?x254), contact_category(?x5108, ?x897) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01s73z service_location 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 167.000 0.900 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #20831-02cl1 PRED entity: 02cl1 PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 21): 0pqc5 (0.82 #534, 0.74 #143, 0.66 #856), 060c4 (0.30 #1084, 0.27 #1705, 0.26 #2027), 060bp (0.28 #1082, 0.26 #1703, 0.22 #1887), 0f6c3 (0.19 #1457, 0.12 #2147, 0.09 #2745), 09n5b9 (0.17 #1461, 0.09 #2151, 0.08 #2749), 0fkvn (0.15 #1453, 0.12 #2143, 0.10 #1545), 01q24l (0.12 #129, 0.12 #267, 0.11 #566), 0fkzq (0.07 #1466, 0.04 #2156, 0.03 #408), 0p5vf (0.07 #1117, 0.07 #1094, 0.07 #1416), 01zq91 (0.07 #1096, 0.06 #1119, 0.06 #1326) >> Best rule #534 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0fsb8; >> query: (?x659, 0pqc5) <- dog_breed(?x659, ?x5194), location(?x2390, ?x659), location_of_ceremony(?x566, ?x659) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cl1 jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.816 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #20830-02q6cv4 PRED entity: 02q6cv4 PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.87 #1604, 0.86 #1001, 0.85 #2007), 0gx1l (0.33 #9647), 0kpys (0.33 #9647), 02jx1 (0.11 #933, 0.10 #633, 0.10 #8672), 05v8c (0.10 #316, 0.01 #1619, 0.01 #1518), 07ssc (0.08 #8654, 0.08 #8153, 0.08 #9762), 03rk0 (0.06 #3554, 0.06 #10094, 0.06 #10394), 030qb3t (0.05 #1806, 0.05 #2107, 0.04 #1704), 0ctw_b (0.05 #627, 0.04 #727, 0.03 #827), 0d060g (0.05 #907, 0.05 #2013, 0.04 #5431) >> Best rule #1604 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 0bz5v2; 03mz9r; 054187; 02qnbs; 0564mx; 02gnj2; >> query: (?x8713, 09c7w0) <- place_of_birth(?x8713, ?x1523), tv_program(?x8713, ?x1395), honored_for(?x2213, ?x1395), country_of_origin(?x1395, ?x94) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q6cv4 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.867 http://example.org/people/person/nationality #20829-016h4r PRED entity: 016h4r PRED relation: company PRED expected values: 07wh1 => 124 concepts (108 used for prediction) PRED predicted values (max 10 best out of 43): 07wh1 (0.05 #760, 0.03 #953, 0.03 #1920), 01trtc (0.05 #726, 0.03 #919, 0.01 #1693), 07tgn (0.05 #596, 0.02 #1352, 0.01 #1756), 03d96s (0.05 #699, 0.01 #1859), 07wj1 (0.03 #918, 0.03 #1498, 0.01 #1885), 07y0n (0.03 #940, 0.02 #4807, 0.02 #2874), 0c0cs (0.03 #964, 0.01 #1931), 0d6qjf (0.03 #936, 0.01 #1903), 053mhx (0.03 #894, 0.01 #1861), 017v3q (0.03 #876, 0.01 #1843) >> Best rule #760 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 06pwf6; 06g4_; 042xh; >> query: (?x3495, 07wh1) <- languages(?x3495, ?x254), student(?x3995, ?x3495), category(?x3495, ?x134) >> conf = 0.05 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016h4r company 07wh1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 108.000 0.045 http://example.org/people/person/employment_history./business/employment_tenure/company #20828-05cv8 PRED entity: 05cv8 PRED relation: influenced_by PRED expected values: 07lp1 => 147 concepts (51 used for prediction) PRED predicted values (max 10 best out of 377): 09dt7 (0.33 #32, 0.20 #1333, 0.20 #3066), 06bng (0.33 #282, 0.20 #1583, 0.17 #3316), 03rx9 (0.33 #329, 0.12 #15174, 0.10 #1630), 05cv8 (0.33 #361, 0.09 #434, 0.05 #22113), 0g5ff (0.27 #3229, 0.27 #1496, 0.10 #8427), 03_87 (0.21 #1070, 0.16 #6270, 0.15 #9302), 03hnd (0.20 #1401, 0.15 #3134, 0.12 #9199), 014z8v (0.18 #6622, 0.16 #6189, 0.13 #9655), 0j3v (0.17 #928, 0.12 #2662, 0.10 #2228), 03f0324 (0.16 #8385, 0.15 #6220, 0.15 #9252) >> Best rule #32 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01zkxv; >> query: (?x10578, 09dt7) <- award_winner(?x11263, ?x10578), influenced_by(?x10578, ?x5087), award(?x576, ?x11263), ?x5087 = 018fq >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2601 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 01dvtx; *> query: (?x10578, ?x2161) <- student(?x6912, ?x10578), student(?x10518, ?x10578), influenced_by(?x10578, ?x9982), influenced_by(?x9982, ?x2161) *> conf = 0.11 ranks of expected_values: 37 EVAL 05cv8 influenced_by 07lp1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 147.000 51.000 0.333 http://example.org/influence/influence_node/influenced_by #20827-019l3m PRED entity: 019l3m PRED relation: award PRED expected values: 0bdwft => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 229): 094qd5 (0.54 #45, 0.07 #1257, 0.07 #2065), 02y_rq5 (0.41 #95, 0.06 #27878, 0.05 #2115), 09sb52 (0.37 #41, 0.28 #849, 0.28 #1253), 09qwmm (0.37 #34, 0.06 #27878, 0.05 #438), 02ppm4q (0.33 #156, 0.07 #2176, 0.06 #1368), 0bdwft (0.32 #69, 0.06 #473, 0.06 #1685), 02z0dfh (0.28 #75, 0.06 #27878, 0.05 #2095), 02x4x18 (0.27 #132, 0.07 #536, 0.06 #27878), 0cqgl9 (0.27 #192, 0.06 #27878, 0.04 #596), 099cng (0.26 #86, 0.06 #27878, 0.03 #894) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 01l2fn; 018fmr; >> query: (?x8946, 094qd5) <- profession(?x8946, ?x1032), film(?x8946, ?x1746), award(?x8946, ?x1245), ?x1245 = 0gqwc >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #69 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 105 *> proper extension: 01l2fn; 018fmr; *> query: (?x8946, 0bdwft) <- profession(?x8946, ?x1032), film(?x8946, ?x1746), award(?x8946, ?x1245), ?x1245 = 0gqwc *> conf = 0.32 ranks of expected_values: 6 EVAL 019l3m award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 99.000 99.000 0.542 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20826-09qc1 PRED entity: 09qc1 PRED relation: profession PRED expected values: 0dxtg => 126 concepts (85 used for prediction) PRED predicted values (max 10 best out of 87): 0dxtg (0.84 #893, 0.83 #305, 0.80 #1040), 02hrh1q (0.76 #7071, 0.76 #4864, 0.75 #6335), 0kyk (0.52 #5322, 0.50 #5469, 0.37 #1939), 03gjzk (0.41 #5748, 0.34 #4277, 0.33 #5160), 05z96 (0.30 #2500, 0.28 #5000, 0.28 #11324), 02krf9 (0.26 #1054, 0.21 #6936, 0.17 #907), 09jwl (0.18 #11341, 0.18 #6340, 0.18 #11488), 02hv44_ (0.17 #350, 0.17 #56, 0.15 #644), 02p0s5r (0.17 #125, 0.02 #1595, 0.01 #2330), 018gz8 (0.16 #7809, 0.16 #8691, 0.15 #1926) >> Best rule #893 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 03m_k0; 06s1qy; >> query: (?x4732, 0dxtg) <- award(?x4732, ?x1862), ?x1862 = 0gr51, award_winner(?x13075, ?x4732), nominated_for(?x13075, ?x534) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09qc1 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 85.000 0.843 http://example.org/people/person/profession #20825-01323p PRED entity: 01323p PRED relation: award PRED expected values: 02f716 => 79 concepts (48 used for prediction) PRED predicted values (max 10 best out of 211): 01bgqh (0.55 #6878, 0.50 #2455, 0.45 #847), 01by1l (0.49 #8556, 0.44 #10165, 0.43 #6948), 02f72n (0.45 #950, 0.31 #2558, 0.24 #2960), 01ckcd (0.33 #2344, 0.27 #1138, 0.22 #2746), 02f6ym (0.33 #257, 0.20 #7092, 0.20 #659), 05b4l5x (0.33 #6, 0.20 #408, 0.07 #9248), 03qbh5 (0.31 #2616, 0.26 #7039, 0.24 #3018), 02gdjb (0.30 #6250, 0.14 #2631, 0.12 #3435), 01ckrr (0.28 #6261, 0.10 #3044, 0.10 #2240), 0c4z8 (0.28 #10124, 0.26 #6907, 0.22 #8917) >> Best rule #6878 for best value: >> intensional similarity = 5 >> extensional distance = 233 >> proper extension: 01pfkw; 020jqv; >> query: (?x7682, 01bgqh) <- artist(?x2149, ?x7682), award(?x7682, ?x7535), award_winner(?x7535, ?x1674), award(?x7331, ?x7535), ?x7331 = 01vtj38 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2588 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 34 *> proper extension: 052hl; 03cd1q; *> query: (?x7682, 02f716) <- award(?x7682, ?x7535), award(?x7682, ?x3045), ?x3045 = 02sp_v, award(?x8693, ?x7535), award(?x4646, ?x7535), ?x4646 = 0fhxv, award_winner(?x607, ?x8693) *> conf = 0.25 ranks of expected_values: 13 EVAL 01323p award 02f716 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 79.000 48.000 0.549 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20824-07z1m PRED entity: 07z1m PRED relation: state_province_region! PRED expected values: 0plw => 162 concepts (128 used for prediction) PRED predicted values (max 10 best out of 781): 02bpy_ (0.60 #24912, 0.54 #37373, 0.29 #40304), 0dzt9 (0.29 #40304, 0.23 #13916, 0.22 #14649), 0k1jg (0.29 #40304, 0.23 #13916, 0.22 #14649), 013h9 (0.29 #40304, 0.23 #13916, 0.22 #14649), 0fwc0 (0.29 #40304, 0.23 #13916, 0.22 #14649), 0mnwd (0.29 #40304, 0.23 #13916, 0.22 #14649), 0mp36 (0.29 #40304, 0.23 #13916, 0.22 #14649), 0mp08 (0.29 #40304, 0.23 #13916, 0.22 #14649), 0mnk7 (0.29 #40304, 0.23 #13916, 0.22 #14649), 0mm_4 (0.29 #40304, 0.23 #13916, 0.22 #14649) >> Best rule #24912 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 02fvv; >> query: (?x1426, ?x6919) <- contains(?x1426, ?x6919), country(?x1426, ?x94), colors(?x6919, ?x332) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1379 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0162v; 0hptm; *> query: (?x1426, 0plw) <- location(?x5200, ?x1426), jurisdiction_of_office(?x5254, ?x1426), award_winner(?x822, ?x5200) *> conf = 0.04 ranks of expected_values: 234 EVAL 07z1m state_province_region! 0plw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 162.000 128.000 0.600 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #20823-06gb1w PRED entity: 06gb1w PRED relation: nominated_for! PRED expected values: 02x258x => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 215): 0p9sw (0.52 #2181, 0.36 #2901, 0.34 #3381), 019f4v (0.44 #2214, 0.31 #3654, 0.30 #3894), 0gq9h (0.43 #2223, 0.36 #3663, 0.35 #3903), 0gq_v (0.43 #2180, 0.33 #3380, 0.33 #2900), 0k611 (0.43 #2234, 0.33 #554, 0.30 #3434), 0gr0m (0.40 #2220, 0.23 #2940, 0.23 #3420), 02r22gf (0.38 #2188, 0.27 #2908, 0.25 #4561), 02hsq3m (0.37 #2189, 0.33 #29, 0.27 #2909), 0gs9p (0.36 #2225, 0.31 #4385, 0.31 #3905), 040njc (0.35 #2167, 0.26 #3847, 0.26 #3607) >> Best rule #2181 for best value: >> intensional similarity = 5 >> extensional distance = 92 >> proper extension: 080dwhx; 03d34x8; 0c3xpwy; >> query: (?x4392, 0p9sw) <- nominated_for(?x9391, ?x4392), honored_for(?x5592, ?x4392), nominated_for(?x9391, ?x9642), genre(?x9642, ?x53), crewmember(?x392, ?x9391) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #2259 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 92 *> proper extension: 080dwhx; 03d34x8; 0c3xpwy; *> query: (?x4392, 02x258x) <- nominated_for(?x9391, ?x4392), honored_for(?x5592, ?x4392), nominated_for(?x9391, ?x9642), genre(?x9642, ?x53), crewmember(?x392, ?x9391) *> conf = 0.18 ranks of expected_values: 34 EVAL 06gb1w nominated_for! 02x258x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 49.000 49.000 0.521 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20822-02gl58 PRED entity: 02gl58 PRED relation: genre PRED expected values: 03k9fj => 76 concepts (74 used for prediction) PRED predicted values (max 10 best out of 177): 03k9fj (0.60 #89, 0.25 #1735, 0.24 #324), 05p553 (0.51 #789, 0.47 #630, 0.46 #709), 01z4y (0.37 #956, 0.35 #1034, 0.35 #878), 01jfsb (0.30 #90, 0.29 #247, 0.21 #481), 01z77k (0.30 #180, 0.19 #337, 0.16 #4242), 01t_vv (0.30 #420, 0.28 #576, 0.25 #1048), 0c4xc (0.28 #823, 0.25 #743, 0.23 #1057), 0hcr (0.26 #329, 0.22 #3624, 0.20 #3073), 0jxy (0.26 #340, 0.09 #3635, 0.09 #2143), 06q7n (0.23 #1920, 0.19 #745, 0.16 #1217) >> Best rule #89 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 053x8hr; 02py9yf; >> query: (?x10284, 03k9fj) <- languages(?x10284, ?x254), genre(?x10284, ?x1844), genre(?x10284, ?x53), ?x53 = 07s9rl0, honored_for(?x1265, ?x10284), ?x1844 = 01htzx, actor(?x10284, ?x4327) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02gl58 genre 03k9fj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 74.000 0.600 http://example.org/tv/tv_program/genre #20821-01r3y2 PRED entity: 01r3y2 PRED relation: colors PRED expected values: 06fvc => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 20): 01g5v (0.40 #63, 0.32 #203, 0.31 #23), 03wkwg (0.33 #15, 0.13 #55, 0.09 #255), 01l849 (0.32 #101, 0.28 #501, 0.28 #621), 019sc (0.23 #107, 0.20 #487, 0.19 #587), 06fvc (0.20 #82, 0.15 #842, 0.15 #262), 0jc_p (0.15 #324, 0.14 #284, 0.13 #344), 038hg (0.14 #912, 0.14 #112, 0.11 #192), 04mkbj (0.14 #190, 0.09 #950, 0.09 #1090), 036k5h (0.11 #225, 0.11 #185, 0.10 #85), 04d18d (0.11 #199, 0.09 #359, 0.08 #39) >> Best rule #63 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0dplh; 01dq5z; 0217m9; 0hsb3; 01jvxb; 0gl6f; >> query: (?x3090, 01g5v) <- student(?x3090, ?x3547), currency(?x3547, ?x170), artist(?x5021, ?x3547), participant(?x1503, ?x3547) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #82 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 18 *> proper extension: 0g2c8; *> query: (?x3090, 06fvc) <- state_province_region(?x3090, ?x177), ?x177 = 05kkh *> conf = 0.20 ranks of expected_values: 5 EVAL 01r3y2 colors 06fvc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 120.000 120.000 0.400 http://example.org/education/educational_institution/colors #20820-0bvhz9 PRED entity: 0bvhz9 PRED relation: ceremony! PRED expected values: 0l8z1 0gr51 => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 362): 0gr51 (0.90 #4596, 0.89 #5790, 0.86 #6028), 0l8z1 (0.85 #3144, 0.83 #2668, 0.83 #4335), 0czp_ (0.74 #7159, 0.74 #716, 0.73 #8354), 02x201b (0.74 #7159, 0.74 #716, 0.73 #8354), 04dn09n (0.60 #743, 0.50 #1940, 0.43 #1461), 054knh (0.60 #901, 0.50 #2098, 0.43 #1619), 054krc (0.60 #769, 0.50 #1966, 0.43 #1487), 019f4v (0.60 #757, 0.50 #1954, 0.43 #1475), 054ks3 (0.60 #806, 0.50 #2003, 0.43 #1524), 0drtkx (0.60 #902, 0.43 #1620, 0.38 #2099) >> Best rule #4596 for best value: >> intensional similarity = 22 >> extensional distance = 39 >> proper extension: 0bzk8w; 0bzkgg; 0bzk2h; 0fz20l; 05qb8vx; >> query: (?x9921, 0gr51) <- ceremony(?x2222, ?x9921), ceremony(?x1243, ?x9921), ceremony(?x720, ?x9921), ceremony(?x601, ?x9921), ?x720 = 018wng, ?x1243 = 0gr0m, nominated_for(?x2222, ?x5795), nominated_for(?x2222, ?x4216), nominated_for(?x2222, ?x2852), nominated_for(?x2222, ?x2640), ?x5795 = 025rvx0, ?x4216 = 0hfzr, ceremony(?x2222, ?x6594), ?x6594 = 02pgky2, award(?x1255, ?x2222), award_winner(?x2222, ?x771), nominated_for(?x1616, ?x2852), production_companies(?x2640, ?x2156), ?x601 = 0gr4k, genre(?x2852, ?x53), film_crew_role(?x2852, ?x137), film_release_distribution_medium(?x2640, ?x81) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0bvhz9 ceremony! 0gr51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.902 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bvhz9 ceremony! 0l8z1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.902 http://example.org/award/award_category/winners./award/award_honor/ceremony #20819-0_75d PRED entity: 0_75d PRED relation: contains! PRED expected values: 09c7w0 => 135 concepts (84 used for prediction) PRED predicted values (max 10 best out of 328): 0fxyd (0.88 #30464, 0.86 #55568, 0.82 #7172), 09c7w0 (0.74 #17030, 0.73 #38531, 0.73 #5382), 01n7q (0.30 #46676, 0.23 #9938, 0.20 #61023), 02jx1 (0.27 #62825, 0.14 #67308, 0.12 #13529), 07ssc (0.26 #62770, 0.20 #11683, 0.17 #13474), 0mwl2 (0.25 #50, 0.17 #947, 0.11 #3636), 0mwh1 (0.25 #172, 0.10 #51083, 0.10 #50184), 0mwzv (0.17 #1175, 0.11 #3864, 0.11 #2967), 0mw7h (0.17 #1163, 0.11 #2955, 0.09 #6542), 0mwm6 (0.17 #1475, 0.11 #4164, 0.06 #7751) >> Best rule #30464 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 017_4z; 08966; 0gp5l6; >> query: (?x4201, ?x4202) <- contains(?x3670, ?x4201), administrative_division(?x4201, ?x4202), contains(?x4202, ?x12415), citytown(?x7092, ?x12415) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #17030 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 45 *> proper extension: 0f__1; 0mn8t; *> query: (?x4201, 09c7w0) <- county(?x4201, ?x4202), contains(?x3670, ?x4201), origin(?x6996, ?x4201), artist(?x2299, ?x6996), place_of_birth(?x426, ?x4201) *> conf = 0.74 ranks of expected_values: 2 EVAL 0_75d contains! 09c7w0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 84.000 0.880 http://example.org/location/location/contains #20818-08j7lh PRED entity: 08j7lh PRED relation: nominated_for! PRED expected values: 09v4bym => 103 concepts (98 used for prediction) PRED predicted values (max 10 best out of 194): 019f4v (0.42 #10722, 0.28 #8113, 0.27 #6691), 02hsq3m (0.37 #504, 0.30 #741, 0.29 #978), 0gq9h (0.35 #10731, 0.31 #8122, 0.30 #6700), 0gs9p (0.34 #10733, 0.32 #8124, 0.31 #6702), 0p9sw (0.33 #258, 0.20 #495, 0.19 #2154), 0k611 (0.30 #312, 0.27 #10742, 0.24 #8133), 0gq_v (0.27 #257, 0.23 #10687, 0.21 #12583), 099c8n (0.27 #295, 0.23 #3376, 0.22 #4798), 0l8z1 (0.27 #290, 0.18 #2186, 0.18 #10720), 054krc (0.27 #10738, 0.21 #308, 0.17 #6707) >> Best rule #10722 for best value: >> intensional similarity = 5 >> extensional distance = 552 >> proper extension: 01tspc6; >> query: (?x9216, 019f4v) <- nominated_for(?x11115, ?x9216), nominated_for(?x5923, ?x9216), award(?x754, ?x5923), disciplines_or_subjects(?x11115, ?x373), award(?x3376, ?x5923) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1867 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 55 *> proper extension: 0m_h6; *> query: (?x9216, 09v4bym) <- nominated_for(?x5923, ?x9216), language(?x9216, ?x9980), languages_spoken(?x7562, ?x9980), genre(?x9216, ?x53), language(?x6014, ?x9980), ?x6014 = 031ldd *> conf = 0.25 ranks of expected_values: 15 EVAL 08j7lh nominated_for! 09v4bym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 103.000 98.000 0.415 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20817-04bpm6 PRED entity: 04bpm6 PRED relation: role PRED expected values: 0dwtp 04rzd => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 98): 0l14md (0.33 #593, 0.32 #592, 0.29 #341), 0l14qv (0.31 #88, 0.27 #340, 0.19 #510), 0bxl5 (0.23 #141, 0.11 #393, 0.07 #421), 07brj (0.19 #100, 0.19 #352, 0.09 #522), 0l15bq (0.19 #110, 0.13 #362, 0.07 #421), 01s0ps (0.19 #132, 0.09 #384, 0.07 #2159), 03qjg (0.15 #134, 0.13 #386, 0.08 #556), 0395lw (0.15 #103, 0.09 #355, 0.07 #421), 025cbm (0.15 #90, 0.08 #342, 0.07 #421), 04rzd (0.12 #117, 0.08 #369, 0.07 #421) >> Best rule #593 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 01vzz1c; >> query: (?x1715, ?x315) <- artists(?x1572, ?x1715), performance_role(?x1715, ?x315), group(?x315, ?x379) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #117 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 03c7ln; 032t2z; 050z2; 01vs4ff; 04kjrv; 0326tc; 01r0t_j; 01nn3m; *> query: (?x1715, 04rzd) <- artists(?x1572, ?x1715), role(?x1715, ?x3716), ?x3716 = 03gvt *> conf = 0.12 ranks of expected_values: 10, 11 EVAL 04bpm6 role 04rzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 100.000 100.000 0.330 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 04bpm6 role 0dwtp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 100.000 100.000 0.330 http://example.org/music/artist/track_contributions./music/track_contribution/role #20816-01bb9r PRED entity: 01bb9r PRED relation: executive_produced_by PRED expected values: 0glyyw => 81 concepts (50 used for prediction) PRED predicted values (max 10 best out of 63): 06q8hf (0.06 #422, 0.05 #2190, 0.05 #2699), 05hj_k (0.06 #353, 0.05 #6685, 0.04 #606), 06m6z6 (0.06 #6333, 0.04 #7858, 0.03 #6587), 0glyyw (0.05 #4238, 0.03 #4744, 0.02 #3985), 014zcr (0.05 #254, 0.04 #7858, 0.03 #1014), 06pk8 (0.05 #254, 0.04 #7858, 0.03 #1014), 0gy6z9 (0.04 #7858, 0.02 #6080, 0.02 #10398), 0343h (0.03 #2319, 0.03 #2574, 0.03 #802), 04fyhv (0.03 #182, 0.02 #942, 0.01 #2205), 02hy9p (0.03 #181, 0.02 #941, 0.01 #2204) >> Best rule #422 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 03clwtw; >> query: (?x2955, 06q8hf) <- genre(?x2955, ?x3515), country(?x2955, ?x94), ?x3515 = 082gq, film(?x3961, ?x2955) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #4238 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 351 *> proper extension: 0522wp; *> query: (?x2955, 0glyyw) <- film(?x382, ?x2955), film(?x382, ?x3498), ?x3498 = 02fqrf *> conf = 0.05 ranks of expected_values: 4 EVAL 01bb9r executive_produced_by 0glyyw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 50.000 0.064 http://example.org/film/film/executive_produced_by #20815-03177r PRED entity: 03177r PRED relation: nominated_for! PRED expected values: 02g3v6 => 99 concepts (82 used for prediction) PRED predicted values (max 10 best out of 191): 02g3v6 (0.36 #258, 0.29 #495, 0.28 #1920), 0gq_v (0.36 #256, 0.25 #3796, 0.21 #493), 0gs96 (0.36 #325, 0.25 #3796, 0.14 #5307), 057xs89 (0.29 #592, 0.27 #1423, 0.25 #3796), 05ztrmj (0.29 #607, 0.20 #844, 0.13 #2032), 0gr0m (0.27 #1423, 0.25 #3796, 0.25 #1899), 05ztjjw (0.27 #1423, 0.25 #3796, 0.25 #1899), 02x1z2s (0.27 #1423, 0.25 #3796, 0.25 #1899), 03m73lj (0.27 #822, 0.14 #585, 0.12 #111), 027dtxw (0.25 #3796, 0.21 #241, 0.17 #5223) >> Best rule #258 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 09tkzy; >> query: (?x2869, 02g3v6) <- film(?x2372, ?x2869), nominated_for(?x143, ?x2869), ?x2372 = 0l6px >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03177r nominated_for! 02g3v6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 82.000 0.357 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20814-04jt2 PRED entity: 04jt2 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.65 #46, 0.61 #62, 0.59 #42), 01bl8s (0.03 #44, 0.03 #56, 0.02 #60), 01g63y (0.02 #80, 0.02 #114, 0.02 #122), 0jgjn (0.02 #82, 0.01 #254, 0.01 #137) >> Best rule #46 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 03rk0; >> query: (?x12195, 04ztj) <- administrative_parent(?x12195, ?x13284), place_of_birth(?x2938, ?x12195), contains(?x1310, ?x12195), profession(?x2938, ?x987), ?x987 = 0dxtg >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04jt2 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.647 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #20813-04_j5s PRED entity: 04_j5s PRED relation: school_type PRED expected values: 07tf8 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 20): 01rs41 (0.63 #441, 0.47 #142, 0.43 #165), 05jxkf (0.61 #49, 0.57 #187, 0.55 #118), 07tf8 (0.14 #792, 0.14 #768, 0.13 #353), 01_9fk (0.13 #369, 0.11 #415, 0.11 #461), 01_srz (0.10 #301, 0.08 #439, 0.08 #1502), 01y64 (0.10 #34, 0.06 #172, 0.03 #218), 02dk5q (0.08 #167, 0.05 #29, 0.03 #328), 0bwd5 (0.08 #1502, 0.07 #156, 0.02 #179), 02p0qmm (0.08 #1502, 0.06 #78, 0.05 #101), 04399 (0.08 #1502, 0.04 #312, 0.03 #450) >> Best rule #441 for best value: >> intensional similarity = 3 >> extensional distance = 218 >> proper extension: 020yvh; >> query: (?x11711, 01rs41) <- school_type(?x11711, ?x1044), school_type(?x3182, ?x1044), ?x3182 = 02ccqg >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #792 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 372 *> proper extension: 031q3w; *> query: (?x11711, 07tf8) <- citytown(?x11711, ?x739), school_type(?x11711, ?x1044), school_type(?x6925, ?x1044), colors(?x6925, ?x663) *> conf = 0.14 ranks of expected_values: 3 EVAL 04_j5s school_type 07tf8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 111.000 0.627 http://example.org/education/educational_institution/school_type #20812-01fpvz PRED entity: 01fpvz PRED relation: major_field_of_study PRED expected values: 03g3w => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 118): 02j62 (0.57 #32, 0.46 #782, 0.40 #1157), 01mkq (0.41 #766, 0.32 #1141, 0.30 #891), 03g3w (0.38 #1153, 0.38 #778, 0.33 #903), 062z7 (0.38 #779, 0.35 #1154, 0.30 #904), 04rjg (0.32 #1146, 0.30 #771, 0.30 #896), 02lp1 (0.29 #1137, 0.28 #887, 0.26 #3640), 01lj9 (0.29 #1167, 0.24 #792, 0.24 #917), 02h40lc (0.29 #4, 0.22 #754, 0.16 #1129), 0h5k (0.29 #24, 0.19 #774, 0.16 #1149), 0fdys (0.27 #791, 0.24 #1166, 0.22 #916) >> Best rule #32 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 01k2wn; 01jsn5; 02bq1j; 02j04_; 01rgn3; >> query: (?x579, 02j62) <- contains(?x1755, ?x579), major_field_of_study(?x579, ?x8221), student(?x579, ?x1913), institution(?x865, ?x579), ?x1755 = 01x73 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1153 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 61 *> proper extension: 01dnnt; *> query: (?x579, 03g3w) <- student(?x579, ?x5401), student(?x579, ?x1913), basic_title(?x5401, ?x265), people(?x5741, ?x1913), politician(?x8714, ?x5401) *> conf = 0.38 ranks of expected_values: 3 EVAL 01fpvz major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 124.000 124.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20811-07sc6nw PRED entity: 07sc6nw PRED relation: film! PRED expected values: 04mkft => 107 concepts (87 used for prediction) PRED predicted values (max 10 best out of 56): 03xq0f (0.84 #1114, 0.64 #818, 0.60 #1040), 0jz9f (0.43 #1, 0.08 #741, 0.08 #371), 017s11 (0.29 #2, 0.14 #2521, 0.14 #1927), 032j_n (0.29 #57, 0.10 #427, 0.07 #797), 06jntd (0.20 #474, 0.14 #178, 0.11 #548), 04mkft (0.18 #183, 0.16 #479, 0.11 #553), 05qd_ (0.17 #1266, 0.16 #230, 0.16 #1044), 01795t (0.17 #905, 0.14 #17, 0.14 #165), 025tlyv (0.16 #502, 0.15 #576, 0.14 #206), 016tt2 (0.16 #891, 0.15 #1632, 0.13 #2300) >> Best rule #1114 for best value: >> intensional similarity = 4 >> extensional distance = 141 >> proper extension: 0522wp; >> query: (?x1192, 03xq0f) <- film(?x382, ?x1192), film_distribution_medium(?x1192, ?x81), film(?x382, ?x6422), ?x6422 = 02qk3fk >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #183 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 0372j5; *> query: (?x1192, 04mkft) <- titles(?x3613, ?x1192), language(?x1192, ?x254), film_distribution_medium(?x1192, ?x627), film_crew_role(?x1192, ?x137), ?x627 = 02nxhr *> conf = 0.18 ranks of expected_values: 6 EVAL 07sc6nw film! 04mkft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 107.000 87.000 0.839 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #20810-0l56b PRED entity: 0l56b PRED relation: gender PRED expected values: 05zppz => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #35, 0.90 #41, 0.89 #49), 02zsn (0.55 #296, 0.46 #323, 0.46 #340) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 04411; 04wvhz; 083q7; 05cv94; 0343h; 06pj8; 01k165; 0jcx; 02vyw; 0gg9_5q; ... >> query: (?x2181, 05zppz) <- place_of_birth(?x2181, ?x1411), organizations_founded(?x2181, ?x14451), profession(?x2181, ?x353), nationality(?x2181, ?x94) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l56b gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.919 http://example.org/people/person/gender #20809-02j8nx PRED entity: 02j8nx PRED relation: gender PRED expected values: 05zppz => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #35, 0.85 #9, 0.80 #19), 02zsn (0.46 #147, 0.29 #76, 0.29 #68) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 206 >> proper extension: 045bg; 017r2; 014dq7; 04g865; 0lrh; 08n9ng; 0d5_f; 027y_; 02yy_j; 0113sg; ... >> query: (?x3282, 05zppz) <- profession(?x3282, ?x987), profession(?x3282, ?x353), ?x353 = 0cbd2, ?x987 = 0dxtg >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02j8nx gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.865 http://example.org/people/person/gender #20808-0fbzp PRED entity: 0fbzp PRED relation: time_zones PRED expected values: 02hcv8 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.86 #79, 0.83 #340, 0.82 #407), 02lcqs (0.24 #44, 0.21 #70, 0.20 #516), 02fqwt (0.17 #816, 0.16 #789, 0.16 #763), 02hczc (0.10 #133, 0.10 #172, 0.09 #185), 03bdv (0.07 #45, 0.05 #847, 0.05 #808), 02llzg (0.06 #646, 0.06 #673, 0.06 #988), 03plfd (0.02 #638, 0.02 #693, 0.02 #652), 0gsrz4 (0.02 #636, 0.02 #623, 0.02 #650), 042g7t (0.01 #639, 0.01 #653, 0.01 #680) >> Best rule #79 for best value: >> intensional similarity = 4 >> extensional distance = 162 >> proper extension: 0nv5y; >> query: (?x13350, ?x2674) <- currency(?x13350, ?x170), adjoins(?x8192, ?x13350), county_seat(?x8192, ?x14270), time_zones(?x8192, ?x2674) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fbzp time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.861 http://example.org/location/location/time_zones #20807-01bczm PRED entity: 01bczm PRED relation: award PRED expected values: 02wh75 0gqz2 03qbh5 01ck6v => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 274): 03qbh5 (0.60 #196, 0.34 #6436, 0.32 #1756), 02f5qb (0.55 #3659, 0.20 #149, 0.19 #6389), 01by1l (0.53 #6349, 0.50 #1669, 0.43 #499), 01d38g (0.40 #27, 0.29 #417, 0.25 #1587), 01cw51 (0.40 #134, 0.29 #524, 0.18 #1694), 0gkvb7 (0.40 #26, 0.14 #416, 0.12 #30811), 02v1m7 (0.35 #3620, 0.29 #500, 0.20 #110), 054krc (0.34 #9055, 0.23 #2815, 0.20 #3205), 09sb52 (0.32 #9400, 0.31 #7840, 0.24 #30460), 02qvyrt (0.29 #9092, 0.23 #3242, 0.21 #2852) >> Best rule #196 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03f2_rc; 01k_mc; >> query: (?x5550, 03qbh5) <- artist(?x5744, ?x5550), music(?x4623, ?x5550), artists(?x9007, ?x5550), ?x9007 = 02vjzr >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12, 16, 20 EVAL 01bczm award 01ck6v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 112.000 112.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bczm award 03qbh5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bczm award 0gqz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 112.000 112.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bczm award 02wh75 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 112.000 112.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20806-01pq4w PRED entity: 01pq4w PRED relation: institution! PRED expected values: 02h4rq6 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 16): 02h4rq6 (0.88 #224, 0.82 #327, 0.82 #1026), 03bwzr4 (0.76 #197, 0.75 #231, 0.74 #112), 04zx3q1 (0.56 #189, 0.51 #104, 0.43 #343), 013zdg (0.36 #193, 0.31 #108, 0.28 #330), 03mkk4 (0.33 #195, 0.29 #110, 0.29 #41), 0bjrnt (0.31 #107, 0.24 #192, 0.21 #38), 022h5x (0.30 #99, 0.29 #14, 0.24 #254), 028dcg (0.26 #98, 0.20 #116, 0.17 #508), 01rr_d (0.23 #114, 0.19 #96, 0.18 #233), 02mjs7 (0.20 #106, 0.15 #191, 0.12 #225) >> Best rule #224 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 07vht; 02dj3; 023zl; 012gyf; >> query: (?x3779, 02h4rq6) <- major_field_of_study(?x3779, ?x6859), institution(?x620, ?x3779), ?x6859 = 01tbp >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pq4w institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.877 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20805-01x5fb PRED entity: 01x5fb PRED relation: institution! PRED expected values: 014mlp => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 23): 02_xgp2 (0.89 #14, 0.79 #64, 0.79 #39), 02h4rq6 (0.78 #53, 0.76 #3, 0.76 #28), 03bwzr4 (0.75 #66, 0.72 #16, 0.70 #92), 019v9k (0.72 #10, 0.71 #60, 0.66 #86), 014mlp (0.67 #6, 0.66 #31, 0.65 #458), 016t_3 (0.67 #4, 0.60 #29, 0.60 #54), 0bkj86 (0.63 #9, 0.59 #59, 0.56 #85), 04zx3q1 (0.54 #2, 0.53 #52, 0.50 #27), 07s6fsf (0.54 #1, 0.47 #51, 0.44 #77), 027f2w (0.48 #11, 0.43 #61, 0.41 #36) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 019q50; >> query: (?x13827, 02_xgp2) <- list(?x13827, ?x2197), currency(?x13827, ?x1099), state_province_region(?x13827, ?x11432) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 019q50; *> query: (?x13827, 014mlp) <- list(?x13827, ?x2197), currency(?x13827, ?x1099), state_province_region(?x13827, ?x11432) *> conf = 0.67 ranks of expected_values: 5 EVAL 01x5fb institution! 014mlp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 46.000 46.000 0.891 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20804-081pw PRED entity: 081pw PRED relation: locations PRED expected values: 02j9z 06bnz 06mx8 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 243): 02j9z (0.53 #1798, 0.43 #2334, 0.42 #2513), 059g4 (0.36 #1021, 0.26 #2448, 0.25 #2627), 02vzc (0.33 #179, 0.33 #43, 0.17 #580), 05qhw (0.33 #14, 0.17 #551, 0.17 #178), 07t21 (0.33 #32, 0.17 #569, 0.05 #1638), 01mzwp (0.33 #169, 0.17 #706, 0.05 #1775), 0jhd (0.33 #128, 0.17 #665, 0.05 #1734), 0cdbq (0.33 #84, 0.17 #621, 0.05 #1690), 0jgx (0.33 #75, 0.17 #612, 0.05 #1681), 03shp (0.33 #73, 0.17 #610, 0.05 #1679) >> Best rule #1798 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 05t2fh4; >> query: (?x326, 02j9z) <- locations(?x326, ?x6304), contains(?x6304, ?x8420), contains(?x6304, ?x1499), partially_contains(?x455, ?x1499), featured_film_locations(?x9805, ?x8420), film_release_region(?x86, ?x1499) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1, 85, 231 EVAL 081pw locations 06mx8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 58.000 58.000 0.526 http://example.org/time/event/locations EVAL 081pw locations 06bnz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 58.000 58.000 0.526 http://example.org/time/event/locations EVAL 081pw locations 02j9z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.526 http://example.org/time/event/locations #20803-09v6gc9 PRED entity: 09v6gc9 PRED relation: award PRED expected values: 03ccq3s => 92 concepts (90 used for prediction) PRED predicted values (max 10 best out of 277): 0fbtbt (0.46 #2665, 0.39 #3880, 0.39 #4690), 09sb52 (0.31 #10573, 0.26 #19890, 0.24 #12193), 0ck27z (0.23 #9410, 0.19 #1713, 0.13 #12245), 027gs1_ (0.18 #21876, 0.15 #21065, 0.15 #22688), 02q1tc5 (0.17 #1364, 0.07 #4201, 0.05 #3391), 0gr4k (0.16 #4895, 0.07 #18261, 0.06 #7325), 03ccq3s (0.15 #21065, 0.15 #22688, 0.15 #22689), 0gkr9q (0.15 #21065, 0.15 #22688, 0.15 #22689), 026mmy (0.15 #21065, 0.15 #22688, 0.15 #22689), 02_3zj (0.15 #21065, 0.15 #22689, 0.12 #28768) >> Best rule #2665 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 01r216; 09hd6f; >> query: (?x5061, 0fbtbt) <- award_winner(?x1553, ?x5061), program(?x5061, ?x5060), place_of_birth(?x5061, ?x94) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #21065 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1482 *> proper extension: 09d5h; 07mvp; 039cq4; 06lxn; *> query: (?x5061, ?x2016) <- award_winner(?x6539, ?x5061), gender(?x6539, ?x231), award(?x6539, ?x2016) *> conf = 0.15 ranks of expected_values: 7 EVAL 09v6gc9 award 03ccq3s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 92.000 90.000 0.461 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20802-01vx5w7 PRED entity: 01vx5w7 PRED relation: participant PRED expected values: 015z4j => 122 concepts (59 used for prediction) PRED predicted values (max 10 best out of 267): 015z4j (0.81 #18017, 0.81 #21879, 0.80 #9652), 014zcr (0.11 #661, 0.05 #7095, 0.04 #9026), 0c6qh (0.11 #809, 0.03 #7243, 0.03 #2738), 0227vl (0.11 #542, 0.03 #2471, 0.02 #5689), 049qx (0.11 #303, 0.02 #5450, 0.02 #3519), 03y82t6 (0.11 #332, 0.02 #5479, 0.01 #7409), 01vw20h (0.11 #314, 0.02 #3530, 0.02 #4175), 06mt91 (0.11 #451, 0.02 #3667, 0.01 #6241), 05m7zg (0.11 #640), 0dzlk (0.11 #621) >> Best rule #18017 for best value: >> intensional similarity = 3 >> extensional distance = 436 >> proper extension: 03ds3; 031zkw; 0j582; 0157m; 01wxyx1; 01vhb0; 01wk7b7; 01gbbz; 05r5w; 09qh1; ... >> query: (?x2925, ?x3020) <- participant(?x3020, ?x2925), film(?x2925, ?x9213), award(?x2925, ?x528) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vx5w7 participant 015z4j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 59.000 0.810 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #20801-01gwk3 PRED entity: 01gwk3 PRED relation: production_companies PRED expected values: 086k8 => 171 concepts (166 used for prediction) PRED predicted values (max 10 best out of 72): 017s11 (0.34 #3912, 0.34 #2579, 0.34 #2498), 016tw3 (0.19 #678, 0.16 #1261, 0.13 #1593), 054lpb6 (0.18 #2510, 0.14 #4511, 0.12 #681), 016tt2 (0.17 #1419, 0.13 #3165, 0.12 #4), 05qd_ (0.16 #1923, 0.15 #177, 0.12 #5007), 0c_j5d (0.16 #1171, 0.12 #256, 0.10 #588), 0g1rw (0.15 #507, 0.12 #8, 0.12 #258), 086k8 (0.13 #1915, 0.12 #4999, 0.12 #668), 024rgt (0.12 #109, 0.09 #1523, 0.09 #774), 032dg7 (0.12 #63, 0.02 #11087, 0.02 #11171) >> Best rule #3912 for best value: >> intensional similarity = 5 >> extensional distance = 155 >> proper extension: 04969y; 0gwjw0c; >> query: (?x6429, ?x541) <- film_release_region(?x6429, ?x94), produced_by(?x6429, ?x519), film(?x541, ?x6429), language(?x6429, ?x254), executive_produced_by(?x6429, ?x11526) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #1915 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 66 *> proper extension: 0ds11z; 0ds33; 01kff7; 065dc4; 05r3qc; 02vjp3; 09rvwmy; *> query: (?x6429, 086k8) <- produced_by(?x6429, ?x8345), film_crew_role(?x6429, ?x2091), ?x2091 = 02rh1dz, film(?x2387, ?x6429), award(?x8345, ?x2022) *> conf = 0.13 ranks of expected_values: 8 EVAL 01gwk3 production_companies 086k8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 171.000 166.000 0.338 http://example.org/film/film/production_companies #20800-02pk6x PRED entity: 02pk6x PRED relation: award PRED expected values: 05ztrmj => 130 concepts (112 used for prediction) PRED predicted values (max 10 best out of 283): 09sb52 (0.34 #18717, 0.33 #12627, 0.31 #23589), 0ck27z (0.25 #93, 0.19 #18769, 0.19 #15521), 05ztrmj (0.25 #186, 0.18 #29640, 0.14 #25985), 04ljl_l (0.25 #3, 0.18 #29640, 0.13 #38574), 0f4x7 (0.25 #31, 0.14 #4091, 0.12 #1655), 04kxsb (0.25 #127, 0.14 #4187, 0.11 #3781), 0789_m (0.25 #20, 0.08 #832, 0.06 #22756), 03c7tr1 (0.24 #4931, 0.21 #5337, 0.12 #6149), 05pcn59 (0.24 #3736, 0.22 #4954, 0.20 #9420), 05zr6wv (0.23 #829, 0.15 #2453, 0.14 #6107) >> Best rule #18717 for best value: >> intensional similarity = 3 >> extensional distance = 824 >> proper extension: 08_83x; 025vl4m; 02m92h; 03cws8h; >> query: (?x5597, 09sb52) <- award_nominee(?x11879, ?x5597), type_of_union(?x5597, ?x566), actor(?x4275, ?x11879) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #186 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 05dtsb; *> query: (?x5597, 05ztrmj) <- film(?x5597, ?x5791), film(?x5597, ?x5598), ?x5791 = 03mgx6z, genre(?x5598, ?x225) *> conf = 0.25 ranks of expected_values: 3 EVAL 02pk6x award 05ztrmj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 112.000 0.337 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20799-016dgz PRED entity: 016dgz PRED relation: student! PRED expected values: 01w5m => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 79): 0bwfn (0.10 #1327, 0.08 #12373, 0.08 #18688), 01w5m (0.08 #105, 0.06 #1157, 0.04 #4313), 08815 (0.08 #2, 0.04 #1054, 0.03 #18415), 01d34b (0.08 #256, 0.04 #1308, 0.02 #782), 017j69 (0.08 #145, 0.03 #5405, 0.02 #12243), 06kknt (0.08 #466), 02m0b0 (0.08 #398), 02vnp2 (0.08 #358), 02bqy (0.08 #182), 0b1xl (0.08 #164) >> Best rule #1327 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 02lk1s; 02_hj4; 01pgzn_; 04cw0j; 03kpvp; 037lyl; 0gn30; 01l1rw; 02q9kqf; 02hy9p; ... >> query: (?x10724, 0bwfn) <- place_of_birth(?x10724, ?x739), award_nominee(?x8151, ?x10724), ?x739 = 02_286 >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #105 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 046qq; *> query: (?x10724, 01w5m) <- film(?x10724, ?x10349), award(?x10724, ?x458), ?x10349 = 09qycb *> conf = 0.08 ranks of expected_values: 2 EVAL 016dgz student! 01w5m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 106.000 0.096 http://example.org/education/educational_institution/students_graduates./education/education/student #20798-0sw0q PRED entity: 0sw0q PRED relation: genre PRED expected values: 01z4y 0c4xc => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 98): 01z4y (0.75 #102, 0.72 #18, 0.37 #354), 07s9rl0 (0.66 #2615, 0.57 #1182, 0.55 #2699), 0c4xc (0.64 #127, 0.64 #43, 0.29 #379), 0hcr (0.35 #1369, 0.21 #187, 0.19 #608), 01t_vv (0.29 #118, 0.24 #286, 0.23 #455), 01htzx (0.27 #185, 0.22 #775, 0.19 #1956), 06nbt (0.21 #189, 0.16 #21, 0.16 #357), 06n90 (0.19 #181, 0.19 #1363, 0.18 #771), 03k9fj (0.19 #179, 0.17 #1950, 0.17 #600), 01hmnh (0.16 #184, 0.15 #1366, 0.14 #2630) >> Best rule #102 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 02xhpl; >> query: (?x9098, 01z4y) <- country_of_origin(?x9098, ?x94), nominated_for(?x2603, ?x9098), ?x94 = 09c7w0, ?x2603 = 09qs08 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0sw0q genre 0c4xc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.750 http://example.org/tv/tv_program/genre EVAL 0sw0q genre 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.750 http://example.org/tv/tv_program/genre #20797-0275_pj PRED entity: 0275_pj PRED relation: award PRED expected values: 02q1tc5 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 219): 02q1tc5 (0.80 #962, 0.75 #1774, 0.73 #1368), 0ck27z (0.28 #4153, 0.27 #3747, 0.25 #4559), 09sb52 (0.26 #6538, 0.24 #9790, 0.24 #16291), 0cjyzs (0.23 #3355, 0.19 #2543, 0.19 #2137), 0fbtbt (0.20 #2264, 0.20 #2670, 0.17 #3076), 027qq9b (0.17 #615, 0.17 #209, 0.16 #14220), 02pzz3p (0.16 #14220, 0.12 #16657, 0.12 #24377), 02py_sj (0.16 #14220, 0.12 #16657, 0.12 #24377), 02p_04b (0.16 #14220, 0.12 #16657, 0.12 #24377), 02pzxlw (0.16 #14220, 0.12 #16657, 0.12 #24377) >> Best rule #962 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 050023; 026dcvf; 03ckvj9; >> query: (?x2476, 02q1tc5) <- award_nominee(?x10892, ?x2476), award_nominee(?x6970, ?x2476), ?x10892 = 025vwmy, award_nominee(?x439, ?x6970) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0275_pj award 02q1tc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20796-03vyw8 PRED entity: 03vyw8 PRED relation: film! PRED expected values: 02nwxc 0301yj => 81 concepts (46 used for prediction) PRED predicted values (max 10 best out of 606): 0bzyh (0.41 #89259, 0.41 #35290, 0.38 #31136), 09yrh (0.40 #2876), 01b9z4 (0.25 #1642, 0.20 #3717), 02nwxc (0.25 #1011, 0.03 #7237, 0.03 #93410), 070yzk (0.25 #1482), 0mdyn (0.25 #1364), 0333wf (0.25 #946), 03n_7k (0.25 #397), 02lfcm (0.25 #67), 01p4vl (0.20 #3436, 0.09 #5512, 0.03 #35288) >> Best rule #89259 for best value: >> intensional similarity = 3 >> extensional distance = 1076 >> proper extension: 0cp08zg; 0bx_hnp; >> query: (?x6058, ?x3195) <- country(?x6058, ?x94), nominated_for(?x3195, ?x6058), ?x94 = 09c7w0 >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1011 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0286vp; *> query: (?x6058, 02nwxc) <- film(?x12148, ?x6058), music(?x6058, ?x925), genre(?x6058, ?x53), ?x12148 = 0mbs8 *> conf = 0.25 ranks of expected_values: 4 EVAL 03vyw8 film! 0301yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 46.000 0.414 http://example.org/film/actor/film./film/performance/film EVAL 03vyw8 film! 02nwxc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 46.000 0.414 http://example.org/film/actor/film./film/performance/film #20795-05cj4r PRED entity: 05cj4r PRED relation: nationality PRED expected values: 02jx1 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 16): 09c7w0 (0.74 #802, 0.73 #1803, 0.73 #301), 02jx1 (0.48 #133, 0.33 #233, 0.31 #33), 07ssc (0.38 #15, 0.22 #215, 0.22 #115), 0chghy (0.09 #110, 0.02 #3417, 0.02 #1612), 03rk0 (0.06 #8055, 0.06 #2650, 0.05 #8355), 0f8l9c (0.06 #22, 0.04 #122, 0.04 #222), 04xn_ (0.06 #74, 0.04 #174, 0.04 #274), 03rt9 (0.06 #13, 0.04 #213, 0.02 #814), 0d060g (0.05 #1709, 0.05 #1809, 0.04 #1408), 03rjj (0.02 #2609, 0.02 #1607, 0.02 #3011) >> Best rule #802 for best value: >> intensional similarity = 3 >> extensional distance = 559 >> proper extension: 02tf1y; 01gw8b; >> query: (?x374, 09c7w0) <- award_nominee(?x374, ?x473), award(?x374, ?x375), actor(?x7254, ?x374) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #133 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0184jc; 01q_ph; 03f1zdw; 0kszw; 0154qm; *> query: (?x374, 02jx1) <- award_nominee(?x374, ?x1739), award_winner(?x374, ?x1669), ?x1739 = 015rkw *> conf = 0.48 ranks of expected_values: 2 EVAL 05cj4r nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.736 http://example.org/people/person/nationality #20794-01zn4y PRED entity: 01zn4y PRED relation: major_field_of_study PRED expected values: 06mnr => 130 concepts (126 used for prediction) PRED predicted values (max 10 best out of 112): 01mkq (0.46 #1150, 0.40 #142, 0.37 #1780), 04rjg (0.43 #1155, 0.38 #2037, 0.37 #1785), 02lp1 (0.39 #1146, 0.34 #1776, 0.32 #2028), 02j62 (0.36 #1796, 0.35 #1166, 0.33 #2048), 05qjt (0.35 #1142, 0.33 #134, 0.32 #2024), 03g3w (0.35 #1162, 0.31 #1792, 0.29 #2044), 062z7 (0.33 #1163, 0.22 #1793, 0.22 #2801), 0g26h (0.31 #423, 0.25 #2061, 0.24 #1179), 0fdys (0.28 #1175, 0.24 #1805, 0.23 #2057), 02_7t (0.28 #1202, 0.21 #1832, 0.19 #446) >> Best rule #1150 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 06pwq; 01w5m; 09f2j; 0ymcz; >> query: (?x6836, 01mkq) <- citytown(?x6836, ?x1156), company(?x5652, ?x6836), currency(?x6836, ?x1099), category(?x6836, ?x134) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1203 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 06pwq; 01w5m; 09f2j; 0ymcz; *> query: (?x6836, 06mnr) <- citytown(?x6836, ?x1156), company(?x5652, ?x6836), currency(?x6836, ?x1099), category(?x6836, ?x134) *> conf = 0.07 ranks of expected_values: 66 EVAL 01zn4y major_field_of_study 06mnr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 130.000 126.000 0.457 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20793-0dd6bf PRED entity: 0dd6bf PRED relation: film! PRED expected values: 04j5fx => 103 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1019): 02t1dv (0.33 #12444, 0.33 #4117, 0.31 #16607), 0dt645q (0.33 #3848, 0.31 #16338, 0.29 #37156), 01wphh2 (0.33 #3434, 0.12 #24251, 0.12 #26332), 03q64h (0.25 #20772, 0.25 #6200, 0.22 #29097), 04j5fx (0.25 #8091, 0.25 #6009, 0.17 #30988), 02h8hr (0.25 #7129, 0.23 #15455, 0.23 #13373), 01qvtwm (0.25 #6150, 0.23 #16558, 0.23 #14476), 05bp8g (0.25 #4196, 0.20 #8360, 0.17 #29175), 03cz9_ (0.25 #6140, 0.15 #16548, 0.15 #14466), 03d29b (0.25 #6224, 0.08 #12469, 0.08 #16632) >> Best rule #12444 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0dr1c2; >> query: (?x7029, 02t1dv) <- genre(?x7029, ?x5937), genre(?x7029, ?x1510), film(?x1418, ?x7029), ?x1510 = 01hmnh, ?x5937 = 0jxy >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8091 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 02vw1w2; *> query: (?x7029, 04j5fx) <- actor(?x7029, ?x51), film(?x12484, ?x7029), film(?x12321, ?x7029), ?x12484 = 04f62k, genre(?x7029, ?x1510), ?x51 = 06v8s0, film(?x296, ?x7029), ?x12321 = 03cz4j *> conf = 0.25 ranks of expected_values: 5 EVAL 0dd6bf film! 04j5fx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 103.000 75.000 0.333 http://example.org/film/actor/film./film/performance/film #20792-0dg3jz PRED entity: 0dg3jz PRED relation: award_winner! PRED expected values: 0dznvw => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 133): 0d__c3 (0.18 #124, 0.10 #6023, 0.10 #5181), 0dznvw (0.10 #6023, 0.10 #5181, 0.10 #5322), 0c53vt (0.10 #6023, 0.10 #5181, 0.10 #5322), 0c4hx0 (0.10 #6023, 0.10 #5181, 0.10 #5322), 0c53zb (0.10 #6023, 0.10 #5181, 0.10 #5322), 0ftlxj (0.10 #6023, 0.10 #5181, 0.10 #5322), 0fzrhn (0.10 #6023, 0.10 #5181, 0.10 #5322), 013b2h (0.06 #1900, 0.05 #2040, 0.04 #3020), 0fy6bh (0.06 #47, 0.04 #6724, 0.03 #327), 0bvfqq (0.06 #33, 0.04 #6724, 0.02 #10085) >> Best rule #124 for best value: >> intensional similarity = 2 >> extensional distance = 32 >> proper extension: 025_nbr; >> query: (?x10132, 0d__c3) <- profession(?x10132, ?x7630), ?x7630 = 026sdt1 >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #6023 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1879 *> proper extension: 04lgymt; 04rcr; 02r3zy; 011zf2; 0ggl02; 03g5jw; 05crg7; 0fb0v; 0288fyj; 0dvqq; ... *> query: (?x10132, ?x4445) <- award_nominee(?x5611, ?x10132), award(?x10132, ?x2222), award_winner(?x4445, ?x5611) *> conf = 0.10 ranks of expected_values: 2 EVAL 0dg3jz award_winner! 0dznvw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 79.000 0.176 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20791-06bw5 PRED entity: 06bw5 PRED relation: institution! PRED expected values: 02h4rq6 => 216 concepts (209 used for prediction) PRED predicted values (max 10 best out of 20): 02h4rq6 (0.88 #588, 0.75 #922, 0.75 #789), 03bwzr4 (0.76 #598, 0.65 #553, 0.63 #264), 02_xgp2 (0.66 #262, 0.60 #11, 0.59 #596), 04zx3q1 (0.60 #2, 0.43 #1744, 0.40 #164), 0bkj86 (0.52 #592, 0.50 #75, 0.50 #52), 07s6fsf (0.44 #139, 0.43 #586, 0.35 #429), 0bjrnt (0.43 #1744, 0.40 #5, 0.31 #2961), 02m4yg (0.43 #1744, 0.31 #2961, 0.30 #3297), 071tyz (0.43 #1744, 0.31 #2961, 0.30 #3297), 01ysy9 (0.43 #1744, 0.31 #2961, 0.30 #3297) >> Best rule #588 for best value: >> intensional similarity = 5 >> extensional distance = 56 >> proper extension: 01r3y2; 08qnnv; >> query: (?x5777, 02h4rq6) <- major_field_of_study(?x5777, ?x2314), major_field_of_study(?x5777, ?x742), ?x742 = 05qjt, major_field_of_study(?x1783, ?x2314), ?x1783 = 049dk >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06bw5 institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 216.000 209.000 0.879 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20790-03_3d PRED entity: 03_3d PRED relation: country_of_origin! PRED expected values: 045nc5 => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 312): 02gl58 (0.20 #1927, 0.18 #2422, 0.16 #2918), 06k176 (0.13 #1952, 0.12 #2447, 0.11 #2943), 027tbrc (0.13 #1762, 0.12 #2257, 0.11 #2753), 04sskp (0.13 #1877, 0.12 #2372, 0.11 #2868), 05z43v (0.13 #1873, 0.12 #2368, 0.11 #2864), 0b005 (0.13 #1850, 0.12 #2345, 0.11 #2841), 01hn_t (0.13 #1798, 0.12 #2293, 0.11 #2789), 090s_0 (0.13 #1732, 0.12 #2227, 0.11 #2723), 03cf9ly (0.13 #1954, 0.12 #2449, 0.11 #2945), 07g9f (0.13 #1922, 0.12 #2417, 0.11 #2913) >> Best rule #1927 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 016zwt; >> query: (?x252, 02gl58) <- region(?x1315, ?x252), adjoins(?x252, ?x2346), exported_to(?x94, ?x252) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_3d country_of_origin! 045nc5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 172.000 0.200 http://example.org/tv/tv_program/country_of_origin #20789-01nms7 PRED entity: 01nms7 PRED relation: gender PRED expected values: 02zsn => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.76 #13, 0.71 #79, 0.71 #123), 02zsn (0.47 #4, 0.45 #6, 0.40 #10) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 342 >> proper extension: 018d6l; >> query: (?x8099, 05zppz) <- profession(?x8099, ?x1183), profession(?x8099, ?x1032), ?x1032 = 02hrh1q, ?x1183 = 09jwl >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0h5g_; 0169dl; 01j5ws; 011_3s; 0154qm; 04fzk; 062hgx; 01wy5m; 01x_d8; 01h1b; ... *> query: (?x8099, 02zsn) <- award_nominee(?x8099, ?x91), film(?x8099, ?x1866), actor(?x596, ?x8099) *> conf = 0.47 ranks of expected_values: 2 EVAL 01nms7 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.765 http://example.org/people/person/gender #20788-03l6q0 PRED entity: 03l6q0 PRED relation: story_by PRED expected values: 030g9z => 65 concepts (40 used for prediction) PRED predicted values (max 10 best out of 21): 01c7qd (0.33 #168, 0.20 #599, 0.14 #815), 0jf1b (0.33 #7, 0.20 #438, 0.14 #654), 01pfkw (0.03 #1727, 0.02 #2160, 0.02 #4765), 0fx02 (0.02 #3307, 0.01 #5907, 0.01 #6991), 04jspq (0.02 #2276), 05jcn8 (0.02 #2215), 025b3k (0.02 #1026, 0.01 #1242, 0.01 #2541), 0py5b (0.02 #1065, 0.01 #1281), 02q4mt (0.02 #1059, 0.01 #1275), 04wvhz (0.02 #877, 0.01 #1093) >> Best rule #168 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01cmp9; >> query: (?x3317, 01c7qd) <- film(?x3756, ?x3317), ?x3756 = 01wgcvn, executive_produced_by(?x3317, ?x4060), film(?x166, ?x3317) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03l6q0 story_by 030g9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 40.000 0.333 http://example.org/film/film/story_by #20787-03y82t6 PRED entity: 03y82t6 PRED relation: languages PRED expected values: 02h40lc => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.47 #41, 0.34 #275, 0.34 #470), 064_8sq (0.04 #873, 0.04 #795, 0.03 #483), 02bjrlw (0.04 #274, 0.03 #859, 0.02 #781), 03k50 (0.03 #1135, 0.03 #1213, 0.02 #1408), 06nm1 (0.03 #279, 0.03 #1600, 0.02 #357), 05zjd (0.03 #1600, 0.02 #135, 0.01 #213), 0t_2 (0.03 #1600, 0.02 #477, 0.02 #633), 03_9r (0.03 #1600), 07c9s (0.02 #1222, 0.01 #1417), 04306rv (0.01 #783, 0.01 #861) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 081nh; 02fn5; >> query: (?x4740, 02h40lc) <- profession(?x4740, ?x955), ?x955 = 0n1h, location_of_ceremony(?x4740, ?x2146) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03y82t6 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.467 http://example.org/people/person/languages #20786-0jrv_ PRED entity: 0jrv_ PRED relation: parent_genre! PRED expected values: 02yw1c => 88 concepts (41 used for prediction) PRED predicted values (max 10 best out of 279): 01738f (0.60 #1666, 0.50 #1143, 0.33 #355), 06cp5 (0.50 #1124, 0.50 #597, 0.40 #3224), 02z7f3 (0.50 #1191, 0.46 #1834, 0.40 #1714), 03p7rp (0.50 #1198, 0.43 #2773, 0.40 #1721), 04_sqm (0.50 #708, 0.40 #1496, 0.33 #2020), 0173b0 (0.50 #675, 0.40 #1463, 0.33 #1987), 0b_6yv (0.50 #734, 0.40 #1522, 0.33 #2046), 01_bkd (0.50 #1097, 0.40 #1620, 0.33 #309), 02srgf (0.50 #1130, 0.40 #1653, 0.33 #342), 028cl7 (0.50 #1270, 0.40 #1793, 0.33 #482) >> Best rule #1666 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 01_bkd; >> query: (?x10930, 01738f) <- parent_genre(?x13938, ?x10930), parent_genre(?x12808, ?x10930), ?x13938 = 04f73rc, parent_genre(?x10930, ?x2249), artists(?x10930, ?x8640), artists(?x10930, ?x2408), artists(?x12808, ?x764), person(?x903, ?x8640), profession(?x2408, ?x319), parent_genre(?x13095, ?x12808) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #360 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 03lty; *> query: (?x10930, 02yw1c) <- parent_genre(?x13938, ?x10930), parent_genre(?x12808, ?x10930), ?x13938 = 04f73rc, parent_genre(?x10930, ?x2249), artists(?x10930, ?x3657), artists(?x10930, ?x2408), artists(?x10930, ?x646), ?x12808 = 03fpx, ?x2408 = 01wg982, ?x646 = 04rcr, ?x3657 = 01w8n89 *> conf = 0.33 ranks of expected_values: 39 EVAL 0jrv_ parent_genre! 02yw1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 88.000 41.000 0.600 http://example.org/music/genre/parent_genre #20785-01vsyg9 PRED entity: 01vsyg9 PRED relation: award PRED expected values: 02f79n => 125 concepts (105 used for prediction) PRED predicted values (max 10 best out of 305): 01bgqh (0.50 #43, 0.32 #1659, 0.29 #2467), 01by1l (0.38 #9809, 0.36 #1729, 0.32 #2537), 09sb52 (0.38 #5293, 0.24 #10141, 0.24 #24282), 0c4z8 (0.32 #1688, 0.28 #9768, 0.24 #10980), 03qbh5 (0.25 #207, 0.24 #9903, 0.24 #2631), 054ks3 (0.25 #143, 0.24 #1355, 0.23 #1759), 02ddq4 (0.25 #345, 0.18 #35963, 0.18 #35962), 026mfs (0.25 #130, 0.18 #2554, 0.15 #35153), 01c9f2 (0.25 #83, 0.12 #2507, 0.05 #25454), 026mg3 (0.25 #12, 0.09 #2436, 0.07 #10920) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01x15dc; >> query: (?x5623, 01bgqh) <- award_nominee(?x5623, ?x2930), gender(?x5623, ?x231), ?x2930 = 0pkyh >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1554 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 01hgwkr; *> query: (?x5623, 02f79n) <- role(?x5623, ?x432), currency(?x5623, ?x170), ?x432 = 042v_gx *> conf = 0.18 ranks of expected_values: 33 EVAL 01vsyg9 award 02f79n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 125.000 105.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20784-0181hw PRED entity: 0181hw PRED relation: child! PRED expected values: 0fb0v => 75 concepts (66 used for prediction) PRED predicted values (max 10 best out of 70): 09b3v (0.29 #444, 0.25 #528, 0.22 #1116), 02bh8z (0.25 #109, 0.12 #1285, 0.10 #610), 03rhqg (0.25 #101, 0.04 #1277, 0.03 #4972), 01bfjy (0.25 #247, 0.03 #4972, 0.02 #2430), 01dtcb (0.16 #1303, 0.14 #1808, 0.13 #3918), 0l8sx (0.14 #850, 0.12 #513, 0.10 #597), 02_l39 (0.14 #479, 0.12 #563, 0.09 #731), 049ql1 (0.14 #485, 0.12 #569, 0.09 #737), 01s73z (0.14 #447, 0.12 #531, 0.09 #699), 061dn_ (0.14 #441, 0.12 #525, 0.09 #693) >> Best rule #444 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 017s11; 016tw3; 01795t; 020h2v; 032j_n; >> query: (?x8170, 09b3v) <- award_nominee(?x8170, ?x1104), place_founded(?x8170, ?x1860), industry(?x8170, ?x373), ?x373 = 02vxn >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #668 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 086k8; 043ljr; 043g7l; 011k11; 0g768; 01dycg; 075znj; 05b0f7; *> query: (?x8170, ?x2241) <- artist(?x8170, ?x7477), place_founded(?x8170, ?x1860), artist(?x2241, ?x7477), profession(?x7477, ?x131) *> conf = 0.01 ranks of expected_values: 55 EVAL 0181hw child! 0fb0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 75.000 66.000 0.286 http://example.org/organization/organization/child./organization/organization_relationship/child #20783-0gvvm6l PRED entity: 0gvvm6l PRED relation: award PRED expected values: 0gqng => 78 concepts (65 used for prediction) PRED predicted values (max 10 best out of 203): 09cn0c (0.43 #190, 0.11 #1342, 0.08 #2946), 0gr51 (0.35 #692, 0.35 #533, 0.25 #922), 027571b (0.29 #170, 0.14 #1322, 0.10 #2926), 02z1nbg (0.29 #134, 0.13 #1286, 0.11 #2890), 0gqwc (0.25 #2813, 0.24 #1151, 0.23 #4587), 02qyp19 (0.24 #1151, 0.23 #4587, 0.23 #921), 0gq9h (0.24 #1151, 0.23 #4587, 0.23 #921), 0gs9p (0.24 #1151, 0.23 #4587, 0.23 #921), 02pqp12 (0.24 #1151, 0.23 #4587, 0.23 #921), 0gqng (0.24 #1151, 0.23 #4587, 0.23 #921) >> Best rule #190 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 04qw17; >> query: (?x8176, 09cn0c) <- nominated_for(?x1862, ?x8176), nominated_for(?x1008, ?x8176), ?x1862 = 0gr51, ?x1008 = 05zvq6g, nominated_for(?x8767, ?x8176) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1151 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 111 *> proper extension: 0gfsq9; 0ct2tf5; *> query: (?x8176, ?x68) <- nominated_for(?x1862, ?x8176), nominated_for(?x68, ?x8176), nominated_for(?x1862, ?x69), ?x69 = 02d413, film_format(?x8176, ?x6392) *> conf = 0.24 ranks of expected_values: 10 EVAL 0gvvm6l award 0gqng CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 78.000 65.000 0.429 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #20782-02mt51 PRED entity: 02mt51 PRED relation: category PRED expected values: 08mbj5d => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.40 #1, 0.31 #9, 0.30 #38) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 03mh_tp; >> query: (?x4040, 08mbj5d) <- film_crew_role(?x4040, ?x468), film(?x4039, ?x4040), ?x4039 = 035rnz, ?x468 = 02r96rf >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02mt51 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.400 http://example.org/common/topic/webpage./common/webpage/category #20781-06cv1 PRED entity: 06cv1 PRED relation: participant! PRED expected values: 0693l => 159 concepts (89 used for prediction) PRED predicted values (max 10 best out of 387): 0gx_p (0.17 #420, 0.09 #1692, 0.07 #2328), 07r1h (0.17 #411, 0.09 #1683, 0.07 #2319), 01m4yn (0.15 #17189, 0.11 #10184, 0.09 #20373), 0151w_ (0.09 #1336, 0.04 #16616, 0.04 #5154), 014zcr (0.08 #10840, 0.08 #16570, 0.06 #13388), 05dbf (0.08 #10976, 0.03 #19890, 0.03 #21163), 09889g (0.07 #6074, 0.06 #3529, 0.05 #4166), 046zh (0.07 #11181, 0.03 #41746, 0.02 #49390), 01rr9f (0.06 #3215, 0.05 #3852, 0.04 #16586), 01pcrw (0.06 #3401, 0.05 #4038, 0.04 #5946) >> Best rule #420 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 08h79x; >> query: (?x523, 0gx_p) <- spouse(?x6844, ?x523), award_winner(?x4481, ?x523), edited_by(?x814, ?x523) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #9769 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 06y9c2; 0167v4; *> query: (?x523, 0693l) <- spouse(?x6844, ?x523), profession(?x523, ?x1183), ?x1183 = 09jwl *> conf = 0.04 ranks of expected_values: 57 EVAL 06cv1 participant! 0693l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 159.000 89.000 0.167 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #20780-04f73rc PRED entity: 04f73rc PRED relation: parent_genre! PRED expected values: 03gt7s => 68 concepts (35 used for prediction) PRED predicted values (max 10 best out of 271): 05jt_ (0.50 #1942, 0.43 #2996, 0.42 #3787), 06cp5 (0.50 #1654, 0.42 #3761, 0.40 #1129), 03p7rp (0.50 #1727, 0.40 #1202, 0.33 #1989), 01738f (0.50 #1673, 0.40 #1148, 0.29 #2989), 04f73rc (0.43 #3118, 0.40 #1277, 0.33 #2064), 08z0wx (0.40 #1388, 0.33 #2177, 0.33 #72), 028cl7 (0.40 #1274, 0.33 #2061, 0.33 #1799), 02srgf (0.40 #1134, 0.33 #1659, 0.25 #3948), 01_bkd (0.40 #1100, 0.33 #1625, 0.18 #3204), 064lqk (0.40 #1306, 0.17 #1831, 0.16 #2105) >> Best rule #1942 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 06by7; >> query: (?x13938, 05jt_) <- parent_genre(?x12808, ?x13938), parent_genre(?x13938, ?x2249), artists(?x13938, ?x12246), artists(?x13938, ?x7125), ?x12808 = 03fpx, group(?x227, ?x7125), artist(?x4483, ?x12246) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1814 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 02x8m; 03lty; 05r6t; *> query: (?x13938, 03gt7s) <- parent_genre(?x7124, ?x13938), parent_genre(?x13938, ?x2249), artists(?x13938, ?x7125), ?x7125 = 01jcxwp, artists(?x7124, ?x8215), ?x8215 = 04_jsg *> conf = 0.33 ranks of expected_values: 14 EVAL 04f73rc parent_genre! 03gt7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 68.000 35.000 0.500 http://example.org/music/genre/parent_genre #20779-0grrq8 PRED entity: 0grrq8 PRED relation: profession PRED expected values: 01d_h8 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 79): 01d_h8 (0.86 #2256, 0.84 #156, 0.84 #4806), 02hrh1q (0.76 #4065, 0.73 #5115, 0.72 #3315), 02jknp (0.68 #1058, 0.64 #608, 0.60 #3608), 0dxtg (0.58 #14, 0.58 #914, 0.56 #3014), 03gjzk (0.47 #1666, 0.47 #1966, 0.46 #2116), 09jwl (0.28 #320, 0.25 #470, 0.20 #7070), 0cbd2 (0.28 #13055, 0.26 #16956, 0.17 #1807), 018gz8 (0.28 #13055, 0.26 #16956, 0.14 #18457), 0kyk (0.28 #13055, 0.26 #16956, 0.14 #18457), 02hv44_ (0.28 #13055, 0.26 #16956, 0.14 #18457) >> Best rule #2256 for best value: >> intensional similarity = 3 >> extensional distance = 189 >> proper extension: 037q1z; >> query: (?x4562, 01d_h8) <- award_winner(?x306, ?x4562), place_of_birth(?x4562, ?x739), produced_by(?x1330, ?x4562) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0grrq8 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.859 http://example.org/people/person/profession #20778-0l3h PRED entity: 0l3h PRED relation: countries_spoken_in! PRED expected values: 02h40lc => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 56): 02h40lc (0.71 #1460, 0.69 #2529, 0.69 #2472), 06nm1 (0.65 #456, 0.56 #624, 0.56 #736), 02bv9 (0.25 #24, 0.09 #584, 0.08 #1089), 064_8sq (0.23 #1421, 0.21 #1140, 0.19 #2096), 0cjk9 (0.20 #116, 0.11 #508, 0.08 #396), 0jzc (0.17 #1194, 0.16 #800, 0.16 #968), 02bjrlw (0.17 #169, 0.12 #449, 0.09 #617), 071fb (0.16 #1136, 0.07 #854, 0.07 #2261), 04306rv (0.13 #117, 0.11 #509, 0.11 #1183), 02hxc3j (0.13 #118, 0.11 #510, 0.09 #62) >> Best rule #1460 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 04hvw; >> query: (?x5622, ?x254) <- official_language(?x5622, ?x254), adjustment_currency(?x5622, ?x170), countries_within(?x8483, ?x5622) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l3h countries_spoken_in! 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.714 http://example.org/language/human_language/countries_spoken_in #20777-02y_lrp PRED entity: 02y_lrp PRED relation: category PRED expected values: 08mbj5d => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.37 #1, 0.34 #6, 0.32 #14) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0413cff; >> query: (?x146, 08mbj5d) <- genre(?x146, ?x258), person(?x146, ?x827), titles(?x2480, ?x146) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y_lrp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.367 http://example.org/common/topic/webpage./common/webpage/category #20776-02qfv5d PRED entity: 02qfv5d PRED relation: genre! PRED expected values: 048scx 02x2jl_ => 55 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1848): 03shpq (0.80 #3713, 0.80 #3711, 0.79 #12990), 08720 (0.80 #3713, 0.80 #3711, 0.79 #12990), 02rrh1w (0.80 #3713, 0.80 #3711, 0.79 #12990), 09gdm7q (0.80 #3713, 0.80 #3711, 0.79 #12990), 01jmyj (0.80 #3713, 0.80 #3711, 0.79 #12990), 078sj4 (0.80 #3713, 0.80 #3711, 0.79 #12990), 0b4lkx (0.80 #3713, 0.80 #3711, 0.79 #12990), 01j5ql (0.80 #3713, 0.80 #3711, 0.79 #12990), 026p_bs (0.80 #3711, 0.79 #12990, 0.79 #3712), 016kv6 (0.80 #3711, 0.79 #12990, 0.79 #1855) >> Best rule #3713 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 07s9rl0; >> query: (?x11405, ?x7792) <- genre(?x9893, ?x11405), genre(?x5074, ?x11405), ?x5074 = 05mrf_p, titles(?x11405, ?x7792), titles(?x11405, ?x650), ?x9893 = 0dmn0x, film_crew_role(?x7792, ?x137), film_release_region(?x650, ?x94), language(?x650, ?x254) >> conf = 0.80 => this is the best rule for 8 predicted values *> Best rule #13154 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 04xvlr; 02n4kr; 03mqtr; *> query: (?x11405, 048scx) <- genre(?x10300, ?x11405), genre(?x5074, ?x11405), genre(?x5044, ?x11405), titles(?x512, ?x5074), titles(?x11405, ?x641), nominated_for(?x11879, ?x5074), featured_film_locations(?x5044, ?x362), film(?x11879, ?x2755), ?x10300 = 0296rz *> conf = 0.50 ranks of expected_values: 124, 177 EVAL 02qfv5d genre! 02x2jl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 22.000 0.801 http://example.org/film/film/genre EVAL 02qfv5d genre! 048scx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 55.000 22.000 0.801 http://example.org/film/film/genre #20775-0170yd PRED entity: 0170yd PRED relation: nominated_for! PRED expected values: 02x4sn8 => 74 concepts (58 used for prediction) PRED predicted values (max 10 best out of 198): 09cm54 (0.68 #4411, 0.67 #4410, 0.67 #4644), 0gq9h (0.66 #3310, 0.64 #526, 0.62 #2846), 04kxsb (0.59 #558, 0.52 #1254, 0.50 #790), 099c8n (0.59 #520, 0.50 #752, 0.40 #56), 0gs9p (0.57 #3312, 0.56 #2848, 0.56 #1224), 019f4v (0.55 #749, 0.50 #53, 0.49 #2837), 040njc (0.50 #703, 0.50 #471, 0.50 #7), 099ck7 (0.50 #635, 0.42 #867, 0.40 #171), 0gr51 (0.50 #78, 0.29 #1238, 0.28 #4722), 02x4sn8 (0.50 #114, 0.24 #7433, 0.23 #9061) >> Best rule #4411 for best value: >> intensional similarity = 4 >> extensional distance = 506 >> proper extension: 06mmr; >> query: (?x8410, ?x3066) <- award(?x8410, ?x3066), honored_for(?x5761, ?x8410), nominated_for(?x3066, ?x144), award_winner(?x3066, ?x92) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #114 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0gmcwlb; *> query: (?x8410, 02x4sn8) <- nominated_for(?x2532, ?x8410), nominated_for(?x591, ?x8410), ?x591 = 0f4x7, ?x2532 = 02x4wr9 *> conf = 0.50 ranks of expected_values: 10 EVAL 0170yd nominated_for! 02x4sn8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 74.000 58.000 0.677 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20774-03k545 PRED entity: 03k545 PRED relation: profession PRED expected values: 02hrh1q => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 81): 02hrh1q (0.90 #4368, 0.90 #7218, 0.89 #3167), 0np9r (0.67 #772, 0.55 #622, 0.38 #322), 01d_h8 (0.35 #1956, 0.35 #5709, 0.34 #6609), 018gz8 (0.31 #318, 0.24 #468, 0.16 #1968), 0dxtg (0.31 #6467, 0.30 #7967, 0.30 #9167), 04gf49c (0.25 #124), 0cbd2 (0.25 #907, 0.19 #2858, 0.18 #3459), 02jknp (0.24 #6761, 0.24 #6611, 0.22 #6161), 03gjzk (0.24 #6169, 0.23 #6469, 0.23 #7969), 0kyk (0.23 #931, 0.17 #2882, 0.16 #3483) >> Best rule #4368 for best value: >> intensional similarity = 5 >> extensional distance = 620 >> proper extension: 01wbsdz; >> query: (?x11470, 02hrh1q) <- film(?x11470, ?x4089), genre(?x4089, ?x225), genre(?x4089, ?x53), ?x53 = 07s9rl0, ?x225 = 02kdv5l >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03k545 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.904 http://example.org/people/person/profession #20773-02n9k PRED entity: 02n9k PRED relation: people! PRED expected values: 07jwr => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 40): 0dq9p (0.25 #347, 0.25 #281, 0.20 #17), 02k6hp (0.20 #37, 0.14 #763, 0.12 #301), 08g5q7 (0.20 #42, 0.14 #240, 0.12 #372), 0gk4g (0.20 #76, 0.12 #2387, 0.10 #3641), 0dcsx (0.14 #213, 0.08 #609, 0.05 #939), 01l2m3 (0.14 #742, 0.04 #3119, 0.03 #3779), 012hw (0.14 #1240, 0.12 #382, 0.07 #1901), 051_y (0.12 #312, 0.08 #642, 0.05 #1038), 0kh3 (0.12 #282, 0.05 #1206, 0.02 #1867), 019dmc (0.10 #512, 0.07 #842, 0.06 #908) >> Best rule #347 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 09bg4l; 03f5vvx; 016lh0; 0d3k14; >> query: (?x7893, 0dq9p) <- people(?x5269, ?x7893), basic_title(?x7893, ?x14269), influenced_by(?x7893, ?x12571) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1990 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 46 *> proper extension: 0bymv; 06c97; 012bk; 042d1; 0b22w; 081t6; *> query: (?x7893, 07jwr) <- people(?x5269, ?x7893), basic_title(?x7893, ?x14269) *> conf = 0.06 ranks of expected_values: 19 EVAL 02n9k people! 07jwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 162.000 162.000 0.250 http://example.org/people/cause_of_death/people #20772-07zl6m PRED entity: 07zl6m PRED relation: service_location PRED expected values: 059j2 => 55 concepts (43 used for prediction) PRED predicted values (max 10 best out of 399): 0chghy (0.50 #278, 0.43 #290, 0.40 #565), 059j2 (0.50 #278, 0.30 #836, 0.20 #578), 02vzc (0.50 #278, 0.16 #1022, 0.09 #403), 05v8c (0.50 #278, 0.16 #1022, 0.09 #1771), 03_3d (0.50 #278, 0.16 #1022, 0.08 #3672), 03rt9 (0.50 #278, 0.13 #1772, 0.12 #566), 06t2t (0.50 #278, 0.09 #406, 0.08 #3672), 01n7q (0.50 #278, 0.08 #3672, 0.07 #3380), 09pmkv (0.50 #278, 0.08 #3672, 0.07 #3380), 03rjj (0.36 #378, 0.33 #4, 0.30 #836) >> Best rule #278 for best value: >> intensional similarity = 18 >> extensional distance = 3 >> proper extension: 04fv0k; >> query: (?x13954, ?x252) <- service_location(?x13954, ?x1264), service_location(?x13954, ?x512), service_location(?x13954, ?x279), service_language(?x13954, ?x254), industry(?x13954, ?x10022), ?x1264 = 0345h, ?x279 = 0d060g, industry(?x13872, ?x10022), industry(?x13460, ?x10022), industry(?x11303, ?x10022), industry(?x9309, ?x10022), ?x512 = 07ssc, category(?x13872, ?x134), citytown(?x9309, ?x1036), company(?x346, ?x11303), service_location(?x11303, ?x252), state_province_region(?x13460, ?x3634), place_founded(?x13872, ?x739) >> conf = 0.50 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07zl6m service_location 059j2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 55.000 43.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #20771-017180 PRED entity: 017180 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 28): 0ch6mp2 (0.84 #42, 0.81 #1208, 0.80 #1869), 0dxtw (0.40 #1212, 0.36 #1175, 0.35 #1469), 01vx2h (0.38 #1176, 0.35 #1213, 0.31 #1874), 01pvkk (0.28 #1729, 0.28 #1471, 0.28 #1875), 02ynfr (0.19 #1218, 0.18 #1181, 0.16 #1879), 0215hd (0.16 #55, 0.15 #1184, 0.14 #1221), 089g0h (0.13 #56, 0.12 #1185, 0.11 #1222), 02rh1dz (0.13 #1211, 0.13 #1174, 0.10 #1872), 0d2b38 (0.12 #1191, 0.12 #62, 0.11 #1228), 01xy5l_ (0.12 #50, 0.12 #1179, 0.11 #1216) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 0hgnl3t; >> query: (?x6721, 0ch6mp2) <- nominated_for(?x2853, ?x6721), film_crew_role(?x6721, ?x137), ?x2853 = 09qv_s, nominated_for(?x617, ?x6721) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017180 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.836 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20770-0gqyl PRED entity: 0gqyl PRED relation: award! PRED expected values: 07fq1y 01p7yb 0blbxk 01csrl 019f2f 011_3s 01gv_f 01tj34 01gw4f 020_95 030b93 01bh6y 0b25vg => 44 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2593): 01x209s (0.80 #19629, 0.79 #42542, 0.79 #26175), 0lpjn (0.80 #19629, 0.79 #42542, 0.79 #26175), 019l68 (0.80 #19629, 0.79 #42542, 0.79 #26175), 022411 (0.80 #19629, 0.79 #42542, 0.79 #26175), 01phtd (0.80 #19629, 0.79 #42542, 0.79 #26175), 0fgg4 (0.80 #19629, 0.79 #42542, 0.79 #26175), 01tl50z (0.80 #19629, 0.79 #42542, 0.79 #26175), 027f7dj (0.80 #19629, 0.79 #42542, 0.79 #26175), 01g257 (0.80 #19629, 0.79 #42542, 0.79 #26175), 03_gd (0.43 #6708, 0.26 #13250, 0.24 #9979) >> Best rule #19629 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x1972, ?x1559) <- ceremony(?x1972, ?x3173), award(?x91, ?x1972), ?x3173 = 0bzk2h, award_winner(?x1972, ?x1559) >> conf = 0.80 => this is the best rule for 9 predicted values *> Best rule #6114 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 03t5b6; 03tk6z; *> query: (?x1972, 0b25vg) <- ceremony(?x1972, ?x78), award(?x3756, ?x1972), award_winner(?x1972, ?x1559), ?x3756 = 01wgcvn *> conf = 0.36 ranks of expected_values: 16, 17, 57, 60, 181, 220, 257, 508, 744, 883, 1031, 1033, 1303 EVAL 0gqyl award! 0b25vg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 01bh6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 030b93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 020_95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 01gw4f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 01tj34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 01gv_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 011_3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 019f2f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 01csrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 0blbxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 01p7yb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqyl award! 07fq1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 17.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20769-0g2jl PRED entity: 0g2jl PRED relation: major_field_of_study PRED expected values: 05qjt => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 96): 0fdys (0.50 #35, 0.30 #151, 0.22 #615), 03g3w (0.47 #24, 0.34 #140, 0.33 #836), 01lj9 (0.47 #36, 0.32 #152, 0.24 #384), 05qjt (0.47 #8, 0.28 #820, 0.23 #356), 05qfh (0.44 #32, 0.30 #148, 0.27 #380), 037mh8 (0.44 #62, 0.25 #178, 0.22 #294), 06ms6 (0.38 #15, 0.20 #131, 0.17 #827), 04x_3 (0.34 #23, 0.25 #139, 0.24 #371), 04sh3 (0.34 #70, 0.23 #186, 0.20 #302), 0h5k (0.34 #20, 0.16 #136, 0.14 #252) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 045c7b; >> query: (?x10576, 0fdys) <- citytown(?x10576, ?x4356), list(?x10576, ?x2197), organization(?x10576, ?x5487) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 045c7b; *> query: (?x10576, 05qjt) <- citytown(?x10576, ?x4356), list(?x10576, ?x2197), organization(?x10576, ?x5487) *> conf = 0.47 ranks of expected_values: 4 EVAL 0g2jl major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 66.000 66.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20768-06mnps PRED entity: 06mnps PRED relation: award PRED expected values: 027dtxw => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 259): 09sb52 (0.61 #4485, 0.29 #6505, 0.29 #6101), 0ck27z (0.32 #3324, 0.27 #2112, 0.14 #13021), 0f4x7 (0.21 #4475, 0.09 #31, 0.09 #4879), 01by1l (0.20 #3748, 0.17 #2536, 0.15 #6980), 0cqhk0 (0.18 #3269, 0.13 #2057, 0.09 #12966), 01bgqh (0.17 #3679, 0.14 #2467, 0.13 #6911), 0gqy2 (0.15 #4608, 0.12 #25456, 0.12 #17375), 0gq9h (0.14 #15354, 0.13 #10101, 0.12 #25456), 094qd5 (0.14 #15354, 0.13 #10101, 0.12 #25456), 040njc (0.14 #15354, 0.13 #10101, 0.12 #25456) >> Best rule #4485 for best value: >> intensional similarity = 3 >> extensional distance = 661 >> proper extension: 0150jk; 018ndc; 01cblr; >> query: (?x3295, 09sb52) <- award(?x3295, ?x1336), award(?x6085, ?x1336), ?x6085 = 06g2d1 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #25456 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2257 *> proper extension: 04qw17; 02xb2bt; 0ccd3x; *> query: (?x3295, ?x500) <- nominated_for(?x3295, ?x6119), nominated_for(?x500, ?x6119) *> conf = 0.12 ranks of expected_values: 39 EVAL 06mnps award 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 86.000 86.000 0.606 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20767-06g7c PRED entity: 06g7c PRED relation: risk_factors PRED expected values: 0jpmt => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 90): 05zppz (0.79 #700, 0.75 #281, 0.69 #345), 0c58k (0.71 #782, 0.50 #252, 0.49 #691), 0jpmt (0.52 #1415, 0.50 #186, 0.42 #303), 0fltx (0.50 #262, 0.44 #1003, 0.38 #378), 0k95h (0.49 #691, 0.44 #1003, 0.35 #521), 02ctzb (0.49 #691, 0.35 #521, 0.34 #219), 0x67 (0.44 #1003, 0.35 #521, 0.33 #4), 012jc (0.35 #521, 0.34 #219, 0.33 #1298), 098s1 (0.35 #521, 0.19 #1191, 0.17 #1645), 059_w (0.34 #219, 0.27 #1572, 0.27 #1982) >> Best rule #700 for best value: >> intensional similarity = 27 >> extensional distance = 17 >> proper extension: 0h99n; 0gwj; >> query: (?x13632, 05zppz) <- risk_factors(?x13632, ?x8524), risk_factors(?x13632, ?x514), risk_factors(?x14098, ?x8524), risk_factors(?x14096, ?x8524), risk_factors(?x10199, ?x8524), risk_factors(?x6720, ?x8524), risk_factors(?x6484, ?x8524), risk_factors(?x4959, ?x8524), risk_factors(?x1158, ?x8524), ?x14098 = 01k9gb, symptom_of(?x9509, ?x14096), ?x9509 = 0gxb2, ?x6484 = 017s1k, ?x6720 = 0m32h, ?x1158 = 02y0js, ?x10199 = 02k6hp, gender(?x9276, ?x514), gender(?x6844, ?x514), gender(?x5521, ?x514), gender(?x3931, ?x514), gender(?x156, ?x514), people(?x4959, ?x598), participant(?x3422, ?x9276), film(?x156, ?x4502), award_winner(?x3931, ?x1620), location(?x5521, ?x1523), participant(?x6844, ?x523) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1415 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 31 *> proper extension: 0qcr0; 01mtqf; 04p3w; 0k95h; 0h1n9; 0j8hd; 0h1wz; *> query: (?x13632, 0jpmt) <- risk_factors(?x13632, ?x8524), risk_factors(?x14098, ?x8524), risk_factors(?x14096, ?x8524), risk_factors(?x13231, ?x8524), risk_factors(?x10199, ?x8524), risk_factors(?x8523, ?x8524), risk_factors(?x6655, ?x8524), risk_factors(?x6484, ?x8524), risk_factors(?x4959, ?x8524), risk_factors(?x1158, ?x8524), ?x14098 = 01k9gb, symptom_of(?x13373, ?x14096), symptom_of(?x9509, ?x14096), ?x9509 = 0gxb2, ?x4959 = 01dcqj, symptom_of(?x6780, ?x13231), risk_factors(?x6484, ?x231), ?x1158 = 02y0js, ?x8523 = 0c58k, ?x13373 = 0f3kl, ?x10199 = 02k6hp, ?x231 = 05zppz, people(?x6655, ?x6975), ?x6780 = 0j5fv *> conf = 0.52 ranks of expected_values: 3 EVAL 06g7c risk_factors 0jpmt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 35.000 0.789 http://example.org/medicine/disease/risk_factors #20766-0jk_8 PRED entity: 0jk_8 PRED relation: adjoins! PRED expected values: 014ck4 => 153 concepts (75 used for prediction) PRED predicted values (max 10 best out of 472): 014ck4 (0.87 #25800, 0.86 #43808, 0.83 #34410), 0d05w3 (0.75 #6375, 0.25 #10280, 0.13 #19667), 0jk_8 (0.71 #3898, 0.57 #3116, 0.50 #5460), 03h64 (0.33 #133, 0.25 #1696, 0.12 #5604), 03rk0 (0.31 #6366, 0.15 #10271, 0.13 #14179), 0qb62 (0.25 #2265, 0.10 #36756, 0.10 #39888), 0d6lp (0.14 #8757, 0.08 #11100, 0.07 #11881), 0f04v (0.14 #8893, 0.07 #12017, 0.06 #12799), 059rby (0.12 #10957, 0.11 #11738, 0.09 #12520), 05sb1 (0.12 #6369, 0.08 #15747, 0.07 #10274) >> Best rule #25800 for best value: >> intensional similarity = 5 >> extensional distance = 120 >> proper extension: 0ky0b; >> query: (?x14204, ?x12531) <- adjoins(?x14204, ?x12531), adjoins(?x14204, ?x206), contains(?x2346, ?x14204), administrative_parent(?x11508, ?x12531), location_of_ceremony(?x566, ?x206) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jk_8 adjoins! 014ck4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 75.000 0.867 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #20765-02v0ff PRED entity: 02v0ff PRED relation: film PRED expected values: 03mh94 => 79 concepts (72 used for prediction) PRED predicted values (max 10 best out of 510): 05gnf (0.48 #41175, 0.35 #98479, 0.35 #100270), 01h1bf (0.48 #41175, 0.35 #98479, 0.35 #100270), 05c26ss (0.44 #4212), 01sbv9 (0.31 #5214), 02nt3d (0.12 #4665, 0.01 #26149), 08952r (0.12 #4298, 0.01 #15041), 02_qt (0.12 #4214), 0f42nz (0.09 #9863, 0.02 #22394, 0.02 #27764), 02qydsh (0.09 #6869, 0.07 #1499, 0.07 #3289), 0bvn25 (0.09 #5420, 0.03 #10793, 0.02 #14373) >> Best rule #41175 for best value: >> intensional similarity = 2 >> extensional distance = 886 >> proper extension: 0p51w; 01l79yc; 03bw6; 0bn3jg; >> query: (?x3975, ?x3075) <- nominated_for(?x3975, ?x3075), people(?x2510, ?x3975) >> conf = 0.48 => this is the best rule for 2 predicted values *> Best rule #28708 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 639 *> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 04bdxl; 06qgvf; 0grwj; 016qtt; 01vvydl; 07fq1y; ... *> query: (?x3975, 03mh94) <- film(?x3975, ?x6306), award_nominee(?x691, ?x3975), people(?x2510, ?x3975) *> conf = 0.02 ranks of expected_values: 283 EVAL 02v0ff film 03mh94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 79.000 72.000 0.476 http://example.org/film/actor/film./film/performance/film #20764-01pq4w PRED entity: 01pq4w PRED relation: student PRED expected values: 029b9k => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 1493): 03yf3z (0.20 #410, 0.02 #31781, 0.02 #38054), 03gr7w (0.20 #270, 0.02 #31641, 0.02 #37914), 0grwj (0.20 #7, 0.02 #43925, 0.01 #192396), 073v6 (0.17 #8891, 0.12 #2617, 0.10 #4708), 02t_w8 (0.15 #11376, 0.05 #15559, 0.04 #23924), 07nx9j (0.14 #13861, 0.09 #7588, 0.07 #28501), 01963w (0.14 #12751, 0.04 #27391, 0.02 #71305), 01d494 (0.12 #2355, 0.08 #8629, 0.07 #12811), 04411 (0.12 #2215, 0.08 #8489, 0.07 #12671), 028r4y (0.12 #3039, 0.08 #9313, 0.05 #15587) >> Best rule #410 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 019_6d; >> query: (?x3779, 03yf3z) <- institution(?x620, ?x3779), citytown(?x3779, ?x4978), ?x4978 = 05jbn >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pq4w student 029b9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 131.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #20763-0cdbq PRED entity: 0cdbq PRED relation: jurisdiction_of_office! PRED expected values: 05hks => 138 concepts (31 used for prediction) PRED predicted values (max 10 best out of 103): 02c4s (0.25 #8, 0.20 #237, 0.14 #620), 0kn4c (0.25 #6, 0.20 #235, 0.14 #618), 0lzcs (0.25 #61, 0.20 #290, 0.14 #673), 0948xk (0.25 #48, 0.20 #277, 0.14 #660), 03f77 (0.25 #26, 0.20 #255, 0.14 #638), 03f5vvx (0.25 #21, 0.20 #250, 0.14 #633), 05wh0sh (0.20 #323, 0.09 #860, 0.08 #1167), 03_lf (0.20 #359, 0.09 #896, 0.08 #1203), 0d1_f (0.14 #1244, 0.12 #2242, 0.12 #2321), 083pr (0.13 #1312, 0.12 #2158, 0.12 #1542) >> Best rule #8 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 07ssc; 05kyr; >> query: (?x4492, 02c4s) <- combatants(?x10008, ?x4492), ?x10008 = 0cbvg, nationality(?x1221, ?x4492), capital(?x4492, ?x7184), combatants(?x4492, ?x4493) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #842 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 0g78xc; 0dv0z; 0285m87; 040vgd; 01s47p; *> query: (?x4492, ?x13265) <- combatants(?x13264, ?x4492), combatants(?x10008, ?x4492), entity_involved(?x10008, ?x6830), combatants(?x4492, ?x4493), ?x6830 = 0j5b8, entity_involved(?x13264, ?x13265) *> conf = 0.05 ranks of expected_values: 87 EVAL 0cdbq jurisdiction_of_office! 05hks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 138.000 31.000 0.250 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #20762-0nppc PRED entity: 0nppc PRED relation: time_zones PRED expected values: 02fqwt => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 9): 02fqwt (0.87 #53, 0.85 #158, 0.71 #1), 02hcv8 (0.65 #29, 0.62 #330, 0.59 #82), 02hczc (0.41 #1177, 0.33 #1137, 0.29 #680), 02lcqs (0.23 #254, 0.21 #501, 0.21 #423), 02llzg (0.07 #461, 0.07 #448, 0.06 #697), 03bdv (0.03 #346, 0.03 #385, 0.03 #515), 03plfd (0.03 #467, 0.03 #454, 0.02 #742), 0gsrz4 (0.02 #714, 0.02 #740, 0.02 #858), 042g7t (0.01 #743) >> Best rule #53 for best value: >> intensional similarity = 5 >> extensional distance = 204 >> proper extension: 0ntwb; >> query: (?x14338, ?x1638) <- adjoins(?x14338, ?x7165), source(?x14338, ?x958), ?x958 = 0jbk9, county(?x7166, ?x7165), time_zones(?x7165, ?x1638) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nppc time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.870 http://example.org/location/location/time_zones #20761-0bwfwpj PRED entity: 0bwfwpj PRED relation: genre PRED expected values: 07s9rl0 => 85 concepts (35 used for prediction) PRED predicted values (max 10 best out of 90): 07s9rl0 (0.64 #3223, 0.61 #1789, 0.59 #3343), 03k9fj (0.57 #11, 0.53 #368, 0.50 #249), 0lsxr (0.39 #1557, 0.38 #2034, 0.37 #1080), 05p553 (0.37 #1434, 0.35 #480, 0.35 #718), 01hmnh (0.30 #1327, 0.27 #968, 0.25 #373), 09blyk (0.26 #1102, 0.25 #1579, 0.21 #2056), 02l7c8 (0.24 #3117, 0.24 #728, 0.23 #1802), 060__y (0.21 #610, 0.19 #1087, 0.18 #1564), 04xvlr (0.20 #1790, 0.16 #1909, 0.13 #2745), 03npn (0.19 #1079, 0.19 #1556, 0.16 #2033) >> Best rule #3223 for best value: >> intensional similarity = 5 >> extensional distance = 388 >> proper extension: 014lc_; 0m313; 0g22z; 01br2w; 0b2v79; 02v8kmz; 011yrp; 07gp9; 01k1k4; 01h7bb; ... >> query: (?x1012, 07s9rl0) <- genre(?x1012, ?x1013), film_release_region(?x1012, ?x87), written_by(?x1012, ?x4035), genre(?x4664, ?x1013), ?x4664 = 0fqt1ns >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bwfwpj genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 35.000 0.641 http://example.org/film/film/genre #20760-050gkf PRED entity: 050gkf PRED relation: film_crew_role PRED expected values: 02r96rf => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 27): 02r96rf (0.81 #1129, 0.78 #754, 0.73 #1265), 01pvkk (0.34 #761, 0.33 #1136, 0.32 #1034), 02ynfr (0.26 #1140, 0.24 #765, 0.22 #1481), 02rh1dz (0.24 #1135, 0.19 #760, 0.15 #486), 089g0h (0.19 #769, 0.15 #1144, 0.15 #222), 0215hd (0.18 #221, 0.17 #768, 0.17 #1143), 0d2b38 (0.18 #775, 0.16 #1150, 0.14 #126), 015h31 (0.16 #657, 0.14 #1134, 0.13 #759), 01xy5l_ (0.15 #763, 0.15 #1138, 0.13 #216), 089fss (0.13 #74, 0.11 #142, 0.09 #1132) >> Best rule #1129 for best value: >> intensional similarity = 5 >> extensional distance = 241 >> proper extension: 0h63q6t; >> query: (?x1968, 02r96rf) <- film_crew_role(?x1968, ?x2154), film_crew_role(?x1968, ?x137), ?x137 = 09zzb8, ?x2154 = 01vx2h, country(?x1968, ?x94) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050gkf film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.811 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20759-04nlb94 PRED entity: 04nlb94 PRED relation: film_release_region PRED expected values: 09c7w0 => 170 concepts (162 used for prediction) PRED predicted values (max 10 best out of 177): 09c7w0 (0.81 #4862, 0.81 #4320, 0.79 #5762), 0d0vqn (0.52 #8456, 0.51 #8996, 0.50 #4678), 07ssc (0.40 #897, 0.40 #741, 0.33 #2336), 0f8l9c (0.40 #749, 0.33 #8128, 0.33 #5432), 03_3d (0.40 #727, 0.33 #1087, 0.33 #10), 0jgd (0.40 #722, 0.33 #1082, 0.33 #5), 015fr (0.40 #743, 0.33 #1103, 0.33 #26), 05r4w (0.40 #719, 0.33 #2, 0.31 #2158), 0345h (0.40 #764, 0.33 #47, 0.31 #5447), 03rjj (0.40 #725, 0.33 #8, 0.30 #8104) >> Best rule #4862 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 07p62k; 026njb5; 04lqvlr; 08j7lh; >> query: (?x12641, 09c7w0) <- film_crew_role(?x12641, ?x2178), film_format(?x12641, ?x6392), genre(?x12641, ?x53), titles(?x4442, ?x12641), ?x2178 = 01pvkk >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04nlb94 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 162.000 0.814 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20758-03g90h PRED entity: 03g90h PRED relation: genre PRED expected values: 0j5nm => 175 concepts (105 used for prediction) PRED predicted values (max 10 best out of 157): 05p553 (0.99 #11004, 0.43 #2840, 0.40 #8874), 01hmnh (0.98 #5216, 0.58 #8886, 0.56 #2262), 02kdv5l (0.84 #6265, 0.75 #3664, 0.68 #7926), 01jfsb (0.65 #2729, 0.65 #6038, 0.63 #7579), 03bxz7 (0.50 #53, 0.09 #11999, 0.08 #11644), 0lsxr (0.39 #6035, 0.38 #1073, 0.26 #1545), 060__y (0.38 #1080, 0.27 #844, 0.25 #10542), 02l7c8 (0.35 #8293, 0.33 #3912, 0.33 #11014), 04pbhw (0.34 #2300, 0.31 #4070, 0.30 #2772), 082gq (0.33 #383, 0.25 #29, 0.22 #501) >> Best rule #11004 for best value: >> intensional similarity = 6 >> extensional distance = 228 >> proper extension: 0kbwb; >> query: (?x280, 05p553) <- produced_by(?x280, ?x3806), genre(?x280, ?x3652), country(?x280, ?x94), film_release_distribution_medium(?x280, ?x81), genre(?x6375, ?x3652), ?x6375 = 0b6m5fy >> conf = 0.99 => this is the best rule for 1 predicted values *> Best rule #1180 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 14 *> proper extension: 032xky; *> query: (?x280, 0j5nm) <- story_by(?x280, ?x3806), genre(?x280, ?x600), genre(?x280, ?x53), language(?x280, ?x254), ?x600 = 02n4kr, film(?x4655, ?x280), ?x53 = 07s9rl0 *> conf = 0.06 ranks of expected_values: 61 EVAL 03g90h genre 0j5nm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 175.000 105.000 0.991 http://example.org/film/film/genre #20757-01w3v PRED entity: 01w3v PRED relation: student PRED expected values: 02qdymm => 167 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1732): 0d3k14 (0.25 #12201, 0.18 #20494, 0.14 #5983), 0683n (0.17 #11808, 0.14 #5590, 0.13 #13881), 0hnjt (0.17 #11180, 0.14 #4962, 0.13 #13253), 03fykz (0.17 #11114, 0.14 #4896, 0.13 #13187), 01n1gc (0.17 #10969, 0.14 #4751, 0.13 #13042), 0432cd (0.17 #11670, 0.14 #5452, 0.13 #13743), 0blt6 (0.17 #10931, 0.14 #4713, 0.13 #13004), 02hsgn (0.17 #11179, 0.14 #4961, 0.13 #13252), 0gt3p (0.17 #11690, 0.14 #5472, 0.12 #19983), 02cqbx (0.17 #11338, 0.14 #5120, 0.12 #19631) >> Best rule #12201 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 059j2; >> query: (?x741, 0d3k14) <- organizations_founded(?x741, ?x5487), company(?x265, ?x741) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w3v student 02qdymm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 96.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #20756-06cgy PRED entity: 06cgy PRED relation: award_nominee PRED expected values: 01_xtx => 106 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1378): 027bs_2 (0.81 #116431, 0.81 #32602, 0.80 #95473), 03yj_0n (0.81 #116431, 0.81 #32602, 0.80 #95473), 0dvmd (0.81 #116431, 0.81 #32602, 0.80 #95473), 01pj5q (0.77 #67530, 0.76 #48903, 0.76 #46574), 018ygt (0.77 #67530, 0.76 #48903, 0.76 #46574), 0169dl (0.77 #67530, 0.76 #48903, 0.76 #46574), 01swck (0.77 #67530, 0.76 #48903, 0.76 #46574), 030hcs (0.77 #67530, 0.76 #48903, 0.76 #46574), 0f4vbz (0.77 #67530, 0.76 #48903, 0.76 #46574), 0fvf9q (0.77 #67530, 0.76 #48903, 0.76 #46574) >> Best rule #116431 for best value: >> intensional similarity = 3 >> extensional distance = 1190 >> proper extension: 01wbsdz; >> query: (?x1554, ?x400) <- film(?x1554, ?x195), award_nominee(?x400, ?x1554), profession(?x1554, ?x319) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #114102 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1165 *> proper extension: 044zvm; *> query: (?x1554, ?x2967) <- film(?x1554, ?x5129), award_nominee(?x1554, ?x400), nominated_for(?x2967, ?x5129) *> conf = 0.21 ranks of expected_values: 30 EVAL 06cgy award_nominee 01_xtx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 106.000 53.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20755-0gmgwnv PRED entity: 0gmgwnv PRED relation: nominated_for! PRED expected values: 099jhq 02ppm4q => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 182): 0gq_v (0.77 #7429, 0.68 #8255, 0.68 #8463), 09sdmz (0.77 #7429, 0.68 #8255, 0.68 #8463), 02qyntr (0.70 #560, 0.55 #2002, 0.54 #2208), 09cm54 (0.68 #8255, 0.68 #8463, 0.67 #4535), 02pqp12 (0.67 #456, 0.56 #1898, 0.54 #2104), 02r22gf (0.43 #433, 0.26 #1669, 0.26 #1875), 0gr51 (0.37 #470, 0.30 #2118, 0.30 #1706), 02hsq3m (0.37 #434, 0.17 #1464, 0.16 #846), 099jhq (0.36 #219, 0.28 #10535, 0.24 #6188), 02x4wr9 (0.36 #282, 0.12 #9914, 0.08 #1518) >> Best rule #7429 for best value: >> intensional similarity = 4 >> extensional distance = 680 >> proper extension: 06mmr; >> query: (?x6176, ?x2853) <- award(?x6176, ?x2853), ceremony(?x2853, ?x873), award(?x123, ?x2853), nominated_for(?x2853, ?x144) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #219 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0gmcwlb; 0gh8zks; 0bmhvpr; 07s846j; 0hgnl3t; 0g4vmj8; 0h95927; 0gvt53w; *> query: (?x6176, 099jhq) <- nominated_for(?x6729, ?x6176), film_release_region(?x6176, ?x304), ?x304 = 0d0vqn, ?x6729 = 099ck7 *> conf = 0.36 ranks of expected_values: 9, 14 EVAL 0gmgwnv nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 81.000 81.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gmgwnv nominated_for! 099jhq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 81.000 81.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20754-05zm34 PRED entity: 05zm34 PRED relation: position! PRED expected values: 04vn5 => 33 concepts (29 used for prediction) PRED predicted values (max 10 best out of 443): 070xg (0.89 #264, 0.89 #116, 0.84 #382), 05gg4 (0.89 #264, 0.89 #116, 0.84 #382), 05tg3 (0.89 #264, 0.86 #479, 0.86 #259), 05l71 (0.89 #116, 0.86 #479, 0.86 #259), 0289q (0.89 #116, 0.84 #382, 0.84 #232), 07l2m (0.89 #116, 0.81 #389, 0.80 #506), 01y49 (0.86 #479, 0.86 #259, 0.85 #477), 06x76 (0.84 #382, 0.84 #114, 0.84 #113), 0g0z58 (0.84 #114, 0.84 #113, 0.84 #170), 026bt_h (0.84 #114, 0.84 #113, 0.84 #170) >> Best rule #264 for best value: >> intensional similarity = 24 >> extensional distance = 3 >> proper extension: 047g8h; >> query: (?x1792, ?x8902) <- position(?x8902, ?x1792), position(?x7019, ?x1792), position(?x6645, ?x1792), position(?x4986, ?x1792), position(?x3674, ?x1792), position(?x3114, ?x1792), position(?x1639, ?x1792), team(?x1792, ?x6379), team(?x1792, ?x1115), position(?x8902, ?x180), ?x1115 = 01y3c, ?x3674 = 05tg3, ?x7019 = 026ldz7, team(?x3113, ?x8902), sport(?x8902, ?x1083), ?x3114 = 070xg, position_s(?x8329, ?x1792), school(?x1639, ?x2830), ?x6645 = 0wsr, teams(?x6084, ?x1639), ?x4986 = 04ls81, team(?x10287, ?x1639), ?x6379 = 0bjkk9, ?x3113 = 0b13yt >> conf = 0.89 => this is the best rule for 3 predicted values *> Best rule #513 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 12 *> proper extension: 03h42s4; *> query: (?x1792, ?x684) <- team(?x1792, ?x11061), team(?x1792, ?x6645), team(?x1792, ?x6379), team(?x1792, ?x1639), position(?x1792, ?x3346), ?x6379 = 0bjkk9, draft(?x1639, ?x465), position(?x6645, ?x2573), ?x2573 = 05b3ts, position_s(?x1639, ?x3113), colors(?x1639, ?x4557), team(?x10287, ?x1639), position_s(?x387, ?x1792), position_s(?x6022, ?x3346), ?x4557 = 019sc, school(?x6645, ?x735), position(?x684, ?x3346), position_s(?x179, ?x3346), position_s(?x11061, ?x706), school(?x11061, ?x3777) *> conf = 0.67 ranks of expected_values: 15 EVAL 05zm34 position! 04vn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 33.000 29.000 0.891 http://example.org/sports/sports_team/roster./american_football/football_roster_position/position #20753-0bth54 PRED entity: 0bth54 PRED relation: film! PRED expected values: 04zqmj => 102 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1243): 03_gd (0.48 #31225, 0.47 #20816, 0.47 #60371), 02cyfz (0.47 #20816, 0.47 #60371, 0.46 #18734), 0b6mgp_ (0.47 #20816, 0.47 #60371, 0.46 #18734), 04ktcgn (0.47 #20816, 0.47 #60371, 0.46 #18734), 01s7zw (0.15 #426, 0.03 #27487, 0.02 #52469), 016dmx (0.12 #6245, 0.10 #70782, 0.09 #8327), 0z4s (0.12 #67, 0.03 #33374, 0.03 #27128), 04xhwn (0.12 #1990, 0.03 #4071, 0.02 #29051), 0f0kz (0.11 #2597, 0.08 #21332, 0.05 #42150), 0f5xn (0.11 #3050, 0.06 #34276, 0.06 #15540) >> Best rule #31225 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 09k56b7; 0b1y_2; 03hkch7; 02rn00y; 093dqjy; 05hjnw; 011yn5; 01hv3t; 0h95927; 04b2qn; ... >> query: (?x573, ?x800) <- award_winner(?x573, ?x800), film(?x574, ?x573), nominated_for(?x1162, ?x573), ?x1162 = 099c8n >> conf = 0.48 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bth54 film! 04zqmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 51.000 0.481 http://example.org/film/actor/film./film/performance/film #20752-01pfkw PRED entity: 01pfkw PRED relation: profession PRED expected values: 015btn => 126 concepts (124 used for prediction) PRED predicted values (max 10 best out of 88): 02hrh1q (0.92 #12338, 0.92 #6247, 0.91 #1027), 01d_h8 (0.77 #2615, 0.68 #1020, 0.64 #1890), 09jwl (0.63 #11907, 0.62 #12197, 0.60 #4076), 0nbcg (0.60 #29, 0.53 #2929, 0.50 #2494), 0d1pc (0.60 #47, 0.29 #3962, 0.28 #1207), 016z4k (0.50 #293, 0.49 #4208, 0.47 #6818), 02jknp (0.50 #1022, 0.48 #2617, 0.38 #297), 0n1h (0.48 #1171, 0.40 #11, 0.38 #301), 018gz8 (0.43 #159, 0.35 #4944, 0.32 #2334), 02krf9 (0.41 #2344, 0.36 #2054, 0.34 #2634) >> Best rule #12338 for best value: >> intensional similarity = 3 >> extensional distance = 618 >> proper extension: 02s2ft; 06qgvf; 01k7d9; 0byfz; 03x3qv; 05cj4r; 025h4z; 017149; 02nb2s; 0lzb8; ... >> query: (?x4420, 02hrh1q) <- nominated_for(?x4420, ?x3317), profession(?x4420, ?x131), actor(?x7433, ?x4420) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #244 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 01s7z0; *> query: (?x4420, 015btn) <- program(?x4420, ?x7433), program_creator(?x3905, ?x4420), nationality(?x4420, ?x512) *> conf = 0.14 ranks of expected_values: 25 EVAL 01pfkw profession 015btn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 126.000 124.000 0.924 http://example.org/people/person/profession #20751-07147 PRED entity: 07147 PRED relation: season PRED expected values: 0285r5d => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 7): 0285r5d (0.87 #201, 0.85 #179, 0.85 #108), 05kcgsf (0.69 #149, 0.64 #106, 0.62 #107), 02h7s73 (0.64 #106, 0.57 #33, 0.56 #153), 04110b0 (0.64 #106, 0.52 #177, 0.50 #152), 03c6s24 (0.64 #106, 0.52 #177, 0.49 #199), 03c74_8 (0.64 #106, 0.52 #177, 0.49 #199), 04n36qk (0.52 #177, 0.49 #199, 0.46 #235) >> Best rule #201 for best value: >> intensional similarity = 15 >> extensional distance = 29 >> proper extension: 07l8f; >> query: (?x8111, 0285r5d) <- position(?x8111, ?x5727), school(?x8111, ?x4955), category(?x4955, ?x134), student(?x4955, ?x4552), student(?x4955, ?x3101), student(?x4955, ?x806), award_nominee(?x1345, ?x806), award_winner(?x806, ?x2328), participant(?x3101, ?x1733), award(?x806, ?x6739), award(?x3101, ?x401), award_nominee(?x3101, ?x406), team(?x2010, ?x8111), produced_by(?x153, ?x4552), gender(?x3101, ?x231) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07147 season 0285r5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.871 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #20750-067xw PRED entity: 067xw PRED relation: gender PRED expected values: 05zppz => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #47, 0.88 #51, 0.88 #55), 02zsn (0.52 #10, 0.47 #111, 0.46 #172) >> Best rule #47 for best value: >> intensional similarity = 6 >> extensional distance = 190 >> proper extension: 01xdf5; 04t2l2; 02g8h; 05g8ky; 05ty4m; 0p_pd; 01q_ph; 04bs3j; 0mdqp; 0pz7h; ... >> query: (?x7180, 05zppz) <- profession(?x7180, ?x8310), influenced_by(?x7180, ?x2343), profession(?x13504, ?x8310), profession(?x12948, ?x8310), ?x12948 = 02gnj2, ?x13504 = 02gn9g >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 067xw gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.885 http://example.org/people/person/gender #20749-011k1h PRED entity: 011k1h PRED relation: artist PRED expected values: 0c9d9 01r9fv 01w60_p 01vsl3_ 016ksk 03j1p2n 06mj4 02cw1m 01wqpnm 0167xy 0cfgd => 141 concepts (131 used for prediction) PRED predicted values (max 10 best out of 1465): 01vsy7t (0.50 #2551, 0.40 #3306, 0.16 #32752), 01whg97 (0.40 #3552, 0.33 #531, 0.25 #2797), 03xhj6 (0.40 #3293, 0.27 #9333, 0.25 #2538), 0565cz (0.40 #3198, 0.25 #2443, 0.18 #9238), 01k23t (0.40 #3523, 0.25 #2768, 0.16 #32969), 0g824 (0.40 #3425, 0.25 #2670, 0.13 #32871), 015xp4 (0.40 #3346, 0.25 #2591, 0.11 #33548), 019x62 (0.40 #3467, 0.25 #2712, 0.07 #2266), 0qf11 (0.40 #3286, 0.20 #14611, 0.18 #9326), 02ktrs (0.40 #3723, 0.04 #33925, 0.04 #50535) >> Best rule #2551 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 01q940; 0n85g; >> query: (?x2149, 01vsy7t) <- artist(?x2149, ?x10437), artist(?x2149, ?x9493), artist(?x2149, ?x1068), ?x1068 = 01x66d, profession(?x10437, ?x220), gender(?x9493, ?x231), nationality(?x10437, ?x1310) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9170 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 9 *> proper extension: 02bh8z; 01x7jb; 03q58q; *> query: (?x2149, 01w60_p) <- artist(?x2149, ?x8579), artist(?x2149, ?x7331), artist(?x2149, ?x1068), award(?x1068, ?x8929), artists(?x2809, ?x8579), ?x7331 = 01vtj38, instrumentalists(?x212, ?x8579) *> conf = 0.27 ranks of expected_values: 48, 99, 134, 191, 242, 288, 344, 388, 534, 747, 764 EVAL 011k1h artist 0cfgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 141.000 131.000 0.500 http://example.org/music/record_label/artist EVAL 011k1h artist 0167xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 141.000 131.000 0.500 http://example.org/music/record_label/artist EVAL 011k1h artist 01wqpnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 141.000 131.000 0.500 http://example.org/music/record_label/artist EVAL 011k1h artist 02cw1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 141.000 131.000 0.500 http://example.org/music/record_label/artist EVAL 011k1h artist 06mj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 141.000 131.000 0.500 http://example.org/music/record_label/artist EVAL 011k1h artist 03j1p2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 141.000 131.000 0.500 http://example.org/music/record_label/artist EVAL 011k1h artist 016ksk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 141.000 131.000 0.500 http://example.org/music/record_label/artist EVAL 011k1h artist 01vsl3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 141.000 131.000 0.500 http://example.org/music/record_label/artist EVAL 011k1h artist 01w60_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 141.000 131.000 0.500 http://example.org/music/record_label/artist EVAL 011k1h artist 01r9fv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 141.000 131.000 0.500 http://example.org/music/record_label/artist EVAL 011k1h artist 0c9d9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 141.000 131.000 0.500 http://example.org/music/record_label/artist #20748-0l14j_ PRED entity: 0l14j_ PRED relation: group PRED expected values: 02dw1_ 0p76z => 81 concepts (34 used for prediction) PRED predicted values (max 10 best out of 317): 02vnpv (0.77 #2143, 0.75 #972, 0.73 #1809), 02dw1_ (0.67 #1226, 0.67 #559, 0.62 #891), 0134wr (0.62 #926, 0.56 #1261, 0.50 #760), 0gr69 (0.62 #911, 0.56 #1246, 0.50 #745), 07mvp (0.62 #902, 0.55 #1739, 0.54 #2073), 0khth (0.62 #873, 0.50 #707, 0.50 #541), 0p8h0 (0.56 #1329, 0.50 #994, 0.50 #828), 02t3ln (0.56 #1209, 0.50 #542, 0.38 #874), 06nv27 (0.56 #1216, 0.46 #2052, 0.38 #881), 07m4c (0.56 #1248, 0.41 #2587, 0.38 #913) >> Best rule #2143 for best value: >> intensional similarity = 8 >> extensional distance = 11 >> proper extension: 0l14md; >> query: (?x2944, 02vnpv) <- role(?x2944, ?x1482), role(?x2944, ?x212), role(?x2944, ?x74), group(?x2944, ?x5303), role(?x120, ?x2944), role(?x1482, ?x2297), role(?x211, ?x212), ?x5303 = 02mq_y >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1226 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 07xzm; *> query: (?x2944, 02dw1_) <- role(?x2944, ?x212), role(?x2944, ?x1655), instrumentalists(?x2944, ?x120), ?x212 = 026t6, family(?x2944, ?x3156), role(?x1583, ?x2944), performance_role(?x2944, ?x1432), ?x1655 = 01hww_ *> conf = 0.67 ranks of expected_values: 2, 127 EVAL 0l14j_ group 0p76z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 81.000 34.000 0.769 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14j_ group 02dw1_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 34.000 0.769 http://example.org/music/performance_role/regular_performances./music/group_membership/group #20747-03bxsw PRED entity: 03bxsw PRED relation: award PRED expected values: 09qwmm => 85 concepts (65 used for prediction) PRED predicted values (max 10 best out of 238): 027571b (0.70 #13048, 0.70 #11861, 0.69 #9484), 02y_j8g (0.70 #13048, 0.70 #11861, 0.69 #9484), 0bdwft (0.41 #1647, 0.33 #1252, 0.20 #65), 02x4x18 (0.40 #126, 0.32 #1708, 0.13 #1313), 02z0dfh (0.40 #71, 0.29 #1653, 0.24 #1258), 05b4l5x (0.40 #6, 0.22 #1193, 0.08 #5538), 09qwmm (0.39 #1615, 0.30 #33, 0.14 #15419), 0cqgl9 (0.32 #1765, 0.30 #183, 0.27 #1370), 03c7tr1 (0.30 #55, 0.14 #1637, 0.11 #450), 03qgjwc (0.29 #1361, 0.18 #1756, 0.10 #174) >> Best rule #13048 for best value: >> intensional similarity = 3 >> extensional distance = 1453 >> proper extension: 0l56b; 024y6w; >> query: (?x3327, ?x749) <- award_winner(?x749, ?x3327), award_nominee(?x3327, ?x2938), profession(?x3327, ?x1032) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #1615 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 0n6f8; 01dw9z; 0h32q; 0hwbd; 02g0rb; 01skmp; 0lfbm; 02jr26; 0421st; 015nhn; ... *> query: (?x3327, 09qwmm) <- nominated_for(?x3327, ?x3326), award(?x3327, ?x1716), ?x1716 = 02y_rq5 *> conf = 0.39 ranks of expected_values: 7 EVAL 03bxsw award 09qwmm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 65.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20746-02_j1w PRED entity: 02_j1w PRED relation: team PRED expected values: 058dm9 05p8bf9 => 12 concepts (10 used for prediction) PRED predicted values (max 10 best out of 336): 02w64f (0.88 #1940, 0.87 #772, 0.84 #771), 01x4wq (0.88 #1940, 0.87 #772, 0.84 #771), 03j6_5 (0.88 #1940, 0.87 #772, 0.84 #771), 019mdt (0.88 #1940, 0.87 #772, 0.84 #771), 05glrg (0.88 #1940, 0.87 #772, 0.84 #771), 03y_f8 (0.88 #1940, 0.87 #772, 0.84 #771), 037mp6 (0.88 #1940, 0.87 #772, 0.84 #771), 056zf9 (0.88 #1940, 0.87 #772, 0.84 #771), 0d9qmn (0.88 #1940, 0.87 #772, 0.84 #771), 049n3s (0.88 #1940, 0.87 #772, 0.84 #771) >> Best rule #1940 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 02md_2; >> query: (?x530, ?x7616) <- team(?x530, ?x11645), team(?x530, ?x5292), team(?x530, ?x1100), position(?x7616, ?x530), team(?x60, ?x7616), sport(?x11645, ?x471), colors(?x1100, ?x663), ?x5292 = 04zw9hs, ?x60 = 02nzb8 >> conf = 0.88 => this is the best rule for 77 predicted values *> Best rule #771 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 02sdk9v; *> query: (?x530, ?x62) <- position(?x12526, ?x530), position(?x9068, ?x530), position(?x8960, ?x530), position(?x8454, ?x530), position(?x6831, ?x530), position(?x6326, ?x530), position(?x5403, ?x530), position(?x62, ?x530), ?x8454 = 03zrc_, position(?x14107, ?x530), position(?x9799, ?x530), position(?x4511, ?x530), ?x9068 = 0ljbg, colors(?x12526, ?x663), ?x6831 = 0199gx, ?x4511 = 01xn7x1, ?x9799 = 03z8bw, ?x5403 = 02b0y3, ?x6326 = 08r98b, ?x8960 = 03zbg0, ?x14107 = 06k90b *> conf = 0.84 ranks of expected_values: 111, 155 EVAL 02_j1w team 05p8bf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 12.000 10.000 0.880 http://example.org/sports/sports_position/players./sports/sports_team_roster/team EVAL 02_j1w team 058dm9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 12.000 10.000 0.880 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #20745-0d0x8 PRED entity: 0d0x8 PRED relation: film_release_region! PRED expected values: 0g5qs2k => 174 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1327): 0bpm4yw (0.60 #68225, 0.56 #17799, 0.52 #66898), 08hmch (0.58 #67796, 0.52 #66469, 0.51 #17370), 02vxq9m (0.56 #67694, 0.54 #66367, 0.49 #17268), 0fpgp26 (0.56 #68824, 0.53 #18398, 0.50 #92710), 04f52jw (0.56 #68010, 0.49 #66683, 0.47 #17584), 017jd9 (0.55 #68272, 0.49 #17846, 0.49 #66945), 0gd0c7x (0.55 #67920, 0.49 #17494, 0.49 #66593), 047vnkj (0.54 #68379, 0.53 #17953, 0.49 #67052), 043tvp3 (0.54 #68602, 0.47 #18176, 0.45 #67275), 017gm7 (0.54 #67839, 0.46 #66512, 0.45 #91725) >> Best rule #68225 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 047yc; 05qx1; 0697s; 04g61; >> query: (?x3038, 0bpm4yw) <- contains(?x3038, ?x2277), film_release_region(?x2394, ?x3038), currency(?x3038, ?x170) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #17304 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 056vv; 0bjv6; *> query: (?x3038, 0g5qs2k) <- currency(?x3038, ?x170), time_zones(?x3038, ?x2674), film_release_region(?x2394, ?x3038) *> conf = 0.44 ranks of expected_values: 39 EVAL 0d0x8 film_release_region! 0g5qs2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 174.000 102.000 0.595 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20744-01k3qj PRED entity: 01k3qj PRED relation: artist! PRED expected values: 01w40h => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 133): 0181dw (0.40 #2918, 0.38 #7302, 0.17 #2233), 017l96 (0.34 #3306, 0.15 #7279, 0.15 #2895), 081g_l (0.29 #297, 0.25 #434, 0.22 #571), 015_1q (0.27 #1526, 0.26 #1800, 0.24 #4403), 01w40h (0.21 #849, 0.13 #7288, 0.11 #3178), 03rhqg (0.21 #4399, 0.21 #1796, 0.18 #2481), 0n85g (0.20 #1979, 0.17 #3349, 0.16 #2938), 02bh8z (0.20 #158, 0.16 #1939, 0.14 #295), 01cl0d (0.20 #190, 0.14 #327, 0.12 #464), 07gqbk (0.20 #232, 0.14 #369, 0.12 #506) >> Best rule #2918 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 01wmxfs; >> query: (?x7578, 0181dw) <- artist(?x4483, ?x7578), artist(?x4483, ?x3324), ?x3324 = 014488, people(?x12136, ?x7578) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #849 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 01vw87c; 01vrncs; 0lgsq; 01vrz41; 0137n0; 01vn35l; 016ntp; 01wbz9; 01vswx5; 01pq5j7; ... *> query: (?x7578, 01w40h) <- artists(?x302, ?x7578), role(?x7578, ?x1437), nationality(?x7578, ?x94), artist(?x4483, ?x7578), ?x4483 = 0mzkr *> conf = 0.21 ranks of expected_values: 5 EVAL 01k3qj artist! 01w40h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 179.000 179.000 0.400 http://example.org/music/record_label/artist #20743-04ktcgn PRED entity: 04ktcgn PRED relation: student! PRED expected values: 013807 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 42): 02g839 (0.25 #25, 0.01 #8459, 0.01 #21112), 015nl4 (0.15 #594, 0.03 #4810, 0.03 #4283), 04rkkv (0.15 #834), 0bwfn (0.05 #9763, 0.05 #8182, 0.05 #10818), 065y4w7 (0.05 #1595, 0.05 #2649, 0.05 #2122), 031ns1 (0.05 #1045), 09k23 (0.05 #1015), 01tzfz (0.05 #919), 01_qgp (0.05 #803), 080z7 (0.05 #715) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01795t; >> query: (?x1983, 02g839) <- nominated_for(?x1983, ?x8359), ?x8359 = 015ynm, award(?x1983, ?x500) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04ktcgn student! 013807 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 94.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #20742-0bj8m2 PRED entity: 0bj8m2 PRED relation: genre! PRED expected values: 039zft 02q3fdr 01xbxn 016017 => 46 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1854): 03cyslc (0.67 #21611, 0.40 #16053, 0.25 #10496), 02fwfb (0.67 #21677, 0.25 #10562, 0.25 #6856), 0bmpm (0.67 #20887, 0.25 #9772, 0.25 #4211), 03q8xj (0.60 #14234, 0.50 #19793, 0.50 #10530), 03x7hd (0.60 #17250, 0.50 #11692, 0.50 #4279), 02xbyr (0.60 #17497, 0.50 #11939, 0.50 #4526), 04f52jw (0.60 #17126, 0.50 #11568, 0.50 #4155), 02qydsh (0.60 #18212, 0.50 #12654, 0.50 #5241), 04hwbq (0.60 #16873, 0.50 #11315, 0.50 #3902), 06w839_ (0.60 #17196, 0.50 #11638, 0.50 #4225) >> Best rule #21611 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 04t36; 04rlf; >> query: (?x6459, 03cyslc) <- genre(?x7723, ?x6459), genre(?x1628, ?x6459), genre(?x148, ?x6459), genre(?x148, ?x258), genre(?x148, ?x225), ?x7723 = 03kx49, featured_film_locations(?x148, ?x362), ?x258 = 05p553, film(?x296, ?x1628), film(?x147, ?x148), music(?x148, ?x1292), ?x225 = 02kdv5l, film(?x2156, ?x148) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #23278 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 5 *> proper extension: 0jxy; *> query: (?x6459, 02q3fdr) <- genre(?x7723, ?x6459), genre(?x5286, ?x6459), genre(?x3268, ?x6459), ?x5286 = 02gs6r, film(?x1677, ?x3268), country(?x3268, ?x94), nominated_for(?x484, ?x3268), award_winner(?x484, ?x786), language(?x3268, ?x254), award(?x199, ?x484), film_release_region(?x3268, ?x142), nominated_for(?x10527, ?x7723), award(?x197, ?x484) *> conf = 0.57 ranks of expected_values: 16, 34, 36, 126 EVAL 0bj8m2 genre! 016017 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 46.000 13.000 0.667 http://example.org/film/film/genre EVAL 0bj8m2 genre! 01xbxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 46.000 13.000 0.667 http://example.org/film/film/genre EVAL 0bj8m2 genre! 02q3fdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 46.000 13.000 0.667 http://example.org/film/film/genre EVAL 0bj8m2 genre! 039zft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 46.000 13.000 0.667 http://example.org/film/film/genre #20741-01vyv9 PRED entity: 01vyv9 PRED relation: award_nominee! PRED expected values: 02t_vx 057_yx => 81 concepts (43 used for prediction) PRED predicted values (max 10 best out of 843): 01vyv9 (0.82 #8044, 0.82 #1072, 0.16 #99930), 015vq_ (0.82 #7925, 0.81 #58089, 0.81 #58088), 014g22 (0.81 #58089, 0.81 #58088, 0.81 #55763), 02t_vx (0.81 #58089, 0.81 #58088, 0.81 #55763), 02p7_k (0.81 #58089, 0.81 #58088, 0.81 #55763), 0z4s (0.81 #58089, 0.81 #58088, 0.81 #55763), 057_yx (0.59 #9174, 0.55 #2202, 0.16 #99930), 02bkdn (0.16 #99930, 0.14 #16268, 0.12 #7364), 01kb2j (0.16 #99930, 0.14 #16268, 0.12 #8172), 023kzp (0.16 #99930, 0.14 #16268, 0.09 #1381) >> Best rule #8044 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 02p7_k; 02jsgf; 015vq_; 02t_vx; 02ct_k; 057_yx; >> query: (?x4553, 01vyv9) <- award_nominee(?x4553, ?x5840), nominated_for(?x4553, ?x2336), ?x5840 = 02ch1w >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #58089 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1044 *> proper extension: 01v3bn; 0bqs56; 049fgvm; *> query: (?x4553, ?x7923) <- award_nominee(?x4553, ?x7923), location(?x4553, ?x1227), film(?x7923, ?x349) *> conf = 0.81 ranks of expected_values: 4, 7 EVAL 01vyv9 award_nominee! 057_yx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 43.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 01vyv9 award_nominee! 02t_vx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 43.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20740-04cppj PRED entity: 04cppj PRED relation: film! PRED expected values: 02zrv7 => 99 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1022): 017yxq (0.58 #70742, 0.54 #87391, 0.49 #10401), 02zrv7 (0.49 #10401, 0.46 #91553, 0.44 #64500), 04gc65 (0.33 #1973, 0.02 #35257, 0.02 #43583), 01y_px (0.33 #364, 0.02 #8684, 0.02 #33648), 03qd_ (0.33 #123, 0.02 #25085, 0.01 #77109), 017149 (0.33 #83, 0.02 #68743, 0.01 #50018), 02lf1j (0.33 #430, 0.01 #33714, 0.01 #35795), 01tnxc (0.33 #1424, 0.01 #34708, 0.01 #18065), 03lvyj (0.33 #1495, 0.01 #18136), 0gm8_p (0.33 #1359, 0.01 #40887) >> Best rule #70742 for best value: >> intensional similarity = 4 >> extensional distance = 517 >> proper extension: 02_1q9; 02_1rq; 02xhpl; 030cx; 0304nh; 01fx1l; 05lfwd; 01jkqfz; 03ctqqf; >> query: (?x6516, ?x8399) <- nominated_for(?x8399, ?x6516), nominated_for(?x1691, ?x6516), location(?x8399, ?x1196), spouse(?x2221, ?x8399) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #10401 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 02vxq9m; 011yrp; 07gp9; 05p1tzf; 02x3lt7; 0bwfwpj; 08hmch; 02d44q; 0c0nhgv; 047msdk; ... *> query: (?x6516, ?x6328) <- featured_film_locations(?x6516, ?x3269), film_release_region(?x6516, ?x583), ?x583 = 015fr, nominated_for(?x6328, ?x6516) *> conf = 0.49 ranks of expected_values: 2 EVAL 04cppj film! 02zrv7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 60.000 0.584 http://example.org/film/actor/film./film/performance/film #20739-065zlr PRED entity: 065zlr PRED relation: cinematography PRED expected values: 027t8fw => 83 concepts (61 used for prediction) PRED predicted values (max 10 best out of 35): 04qvl7 (0.04 #888, 0.03 #1600, 0.02 #1340), 0854hr (0.04 #208, 0.02 #652), 09cdxn (0.04 #213), 0f7hc (0.03 #2750, 0.03 #3711, 0.03 #2176), 06r_by (0.03 #338, 0.03 #1041, 0.02 #1491), 03cx282 (0.02 #458, 0.02 #205, 0.02 #1743), 0cqh57 (0.02 #477), 09bxq9 (0.02 #927, 0.02 #1639, 0.01 #355), 08mhyd (0.02 #411, 0.02 #856, 0.01 #601), 027t8fw (0.02 #1114, 0.02 #220, 0.01 #2271) >> Best rule #888 for best value: >> intensional similarity = 4 >> extensional distance = 326 >> proper extension: 01br2w; 0d6b7; 0gcrg; 027ct7c; >> query: (?x2494, 04qvl7) <- film_crew_role(?x2494, ?x137), genre(?x2494, ?x239), film(?x975, ?x2494), music(?x2494, ?x3371) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #1114 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 363 *> proper extension: 064n1pz; *> query: (?x2494, 027t8fw) <- film_crew_role(?x2494, ?x2154), genre(?x2494, ?x239), ?x2154 = 01vx2h *> conf = 0.02 ranks of expected_values: 10 EVAL 065zlr cinematography 027t8fw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 83.000 61.000 0.037 http://example.org/film/film/cinematography #20738-060c4 PRED entity: 060c4 PRED relation: basic_title! PRED expected values: 0157m 028rk 09bg4l 03kdl 09b6zr 0rlz 07hyk 0835q => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 95): 0dq2k (0.40 #422, 0.33 #687, 0.33 #23), 07hyk (0.40 #480, 0.33 #37, 0.29 #926), 012gx2 (0.33 #25, 0.20 #557, 0.20 #468), 09bg4l (0.33 #16, 0.20 #548, 0.20 #459), 0d05fv (0.33 #19, 0.20 #551, 0.20 #462), 0d0vj4 (0.33 #3, 0.20 #535, 0.20 #446), 082xp (0.33 #262, 0.20 #438, 0.17 #703), 0948xk (0.33 #258, 0.20 #434, 0.17 #699), 03f77 (0.33 #244, 0.20 #420, 0.17 #685), 0kn4c (0.33 #227, 0.20 #403, 0.17 #668) >> Best rule #422 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0789n; 0p5vf; >> query: (?x346, 0dq2k) <- company(?x346, ?x94), jurisdiction_of_office(?x346, ?x1790), basic_title(?x2669, ?x346), film_release_region(?x3287, ?x1790), ?x3287 = 026njb5, entity_involved(?x2391, ?x2669), film_release_region(?x7141, ?x1790) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #480 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 0fkvn; *> query: (?x346, 07hyk) <- company(?x346, ?x9879), company(?x346, ?x9522), company(?x346, ?x1665), jurisdiction_of_office(?x346, ?x4743), basic_title(?x1157, ?x346), institution(?x620, ?x1665), student(?x9879, ?x2328), major_field_of_study(?x9522, ?x742), combatants(?x7419, ?x4743) *> conf = 0.40 ranks of expected_values: 2, 4, 27, 28, 29, 30, 35, 40 EVAL 060c4 basic_title! 0835q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 60.000 60.000 0.400 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title EVAL 060c4 basic_title! 07hyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 60.000 60.000 0.400 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title EVAL 060c4 basic_title! 0rlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 60.000 60.000 0.400 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title EVAL 060c4 basic_title! 09b6zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 60.000 60.000 0.400 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title EVAL 060c4 basic_title! 03kdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 60.000 60.000 0.400 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title EVAL 060c4 basic_title! 09bg4l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 60.000 0.400 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title EVAL 060c4 basic_title! 028rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 60.000 60.000 0.400 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title EVAL 060c4 basic_title! 0157m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 60.000 60.000 0.400 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #20737-0fvd03 PRED entity: 0fvd03 PRED relation: student PRED expected values: 032t2z 017g21 => 231 concepts (121 used for prediction) PRED predicted values (max 10 best out of 1780): 0cqt90 (0.29 #6918, 0.18 #11105, 0.09 #29944), 0641g8 (0.29 #7139, 0.18 #11326, 0.08 #17606), 03h2d4 (0.20 #2805, 0.08 #17460, 0.05 #27926), 01wsl7c (0.20 #2392, 0.08 #17047, 0.05 #27513), 016wvy (0.18 #14291, 0.17 #16384, 0.10 #26850), 03hnd (0.18 #13124, 0.17 #15217, 0.10 #25683), 0bkg4 (0.18 #13193, 0.17 #15286, 0.10 #25752), 0hpz8 (0.18 #14423, 0.17 #16516, 0.08 #20703), 07f3xb (0.18 #12792, 0.17 #14885, 0.08 #19072), 0kvrb (0.18 #12920, 0.17 #15013, 0.08 #19200) >> Best rule #6918 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0k__z; >> query: (?x12958, 0cqt90) <- institution(?x1200, ?x12958), category(?x12958, ?x134), contains(?x362, ?x12958), featured_film_locations(?x3283, ?x12958), location_of_ceremony(?x2092, ?x362) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #115134 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 124 *> proper extension: 02kj7g; *> query: (?x12958, ?x587) <- school_type(?x12958, ?x3092), citytown(?x12958, ?x362), location(?x361, ?x362), origin(?x1407, ?x362), place_of_death(?x587, ?x362) *> conf = 0.01 ranks of expected_values: 1657 EVAL 0fvd03 student 017g21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 231.000 121.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0fvd03 student 032t2z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 231.000 121.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #20736-02lyx4 PRED entity: 02lyx4 PRED relation: type_of_union PRED expected values: 04ztj => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.87 #13, 0.86 #37, 0.86 #25), 01g63y (0.37 #58, 0.36 #66, 0.31 #115) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 05szp; 04kjrv; 044pqn; >> query: (?x10410, 04ztj) <- spouse(?x10410, ?x6025), gender(?x10410, ?x514), religion(?x10410, ?x1985) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lyx4 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.874 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20735-02js6_ PRED entity: 02js6_ PRED relation: award PRED expected values: 09sdmz => 122 concepts (107 used for prediction) PRED predicted values (max 10 best out of 282): 05ztrmj (0.31 #180, 0.14 #30403, 0.13 #42809), 05zr6wv (0.29 #416, 0.26 #2016, 0.17 #7617), 04kxsb (0.29 #522, 0.15 #122, 0.14 #30403), 05p09zm (0.24 #4520, 0.24 #1320, 0.22 #2120), 03c7tr1 (0.24 #1256, 0.20 #4456, 0.16 #4056), 094qd5 (0.24 #1242, 0.15 #42, 0.14 #442), 01by1l (0.24 #16510, 0.20 #7310, 0.19 #22911), 0f4x7 (0.23 #29, 0.15 #8430, 0.14 #7630), 099ck7 (0.23 #263, 0.14 #663, 0.14 #30403), 09qv_s (0.23 #148, 0.09 #7749, 0.09 #8549) >> Best rule #180 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01pk3z; >> query: (?x2626, 05ztrmj) <- award_nominee(?x2626, ?x2443), award_nominee(?x4247, ?x2626), ?x4247 = 02vntj >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #602 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 023mdt; *> query: (?x2626, 09sdmz) <- participant(?x2626, ?x2373), award_winner(?x4939, ?x2626), sibling(?x2626, ?x9207) *> conf = 0.14 ranks of expected_values: 28 EVAL 02js6_ award 09sdmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 122.000 107.000 0.308 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20734-0k95h PRED entity: 0k95h PRED relation: symptom_of! PRED expected values: 0j5fv => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 77): 01j6t0 (0.67 #632, 0.67 #539, 0.64 #1274), 0cjf0 (0.50 #550, 0.37 #1273, 0.33 #143), 01cdt5 (0.40 #395, 0.40 #388, 0.37 #1273), 012qjw (0.40 #471, 0.38 #885, 0.37 #1273), 0brgy (0.40 #470, 0.37 #1273, 0.33 #906), 0gxb2 (0.37 #1273, 0.36 #1257, 0.33 #249), 01pf6 (0.37 #1273, 0.33 #143, 0.33 #51), 0hgxh (0.37 #1273, 0.33 #641, 0.33 #96), 0hg45 (0.37 #1273, 0.33 #57, 0.33 #48), 02tfl8 (0.37 #1273, 0.33 #906, 0.32 #1741) >> Best rule #632 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 0167bx; >> query: (?x5802, 01j6t0) <- risk_factors(?x5802, ?x11160), risk_factors(?x10199, ?x5802), risk_factors(?x6655, ?x11160), people(?x10199, ?x10500), people(?x10199, ?x5440), symptom_of(?x4905, ?x10199), profession(?x10500, ?x987), influenced_by(?x117, ?x10500), nationality(?x5440, ?x94), symptom_of(?x9438, ?x6655), profession(?x5440, ?x524), influenced_by(?x10500, ?x3336), place_of_birth(?x10500, ?x7184) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #575 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 4 *> proper extension: 09969; *> query: (?x5802, 0j5fv) <- risk_factors(?x5802, ?x8023), risk_factors(?x14024, ?x8023), risk_factors(?x6197, ?x8023), risk_factors(?x5118, ?x8023), risk_factors(?x4959, ?x8023), risk_factors(?x268, ?x8023), ?x4959 = 01dcqj, people(?x268, ?x8579), risk_factors(?x6483, ?x268), ?x8579 = 01vs4f3, symptom_of(?x5802, ?x6781), people(?x5118, ?x5119), ?x6197 = 05mdx, symptom_of(?x10717, ?x14024), symptom_of(?x3679, ?x5118), ?x10717 = 0cjf0, ?x3679 = 02tfl8 *> conf = 0.33 ranks of expected_values: 12 EVAL 0k95h symptom_of! 0j5fv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 73.000 73.000 0.667 http://example.org/medicine/symptom/symptom_of #20733-07ssc PRED entity: 07ssc PRED relation: country! PRED expected values: 0gzy02 02s4l6 06gjk9 0d1qmz 0pd6l 0bmssv 0prhz 09jcj6 0194zl 0413cff 07bwr 0dl9_4 03lvwp 01_0f7 01v1ln 0bl3nn 072zl1 0bh8drv 05dptj 011ywj 0bmfnjs 04xg2f 09fqgj 0170xl 08c4yn => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 1413): 020bv3 (0.82 #18853, 0.75 #13466, 0.53 #28278), 09z2b7 (0.82 #18853, 0.75 #13466, 0.53 #28278), 09bw4_ (0.82 #18853, 0.75 #13466, 0.53 #28278), 0c_j9x (0.82 #18853, 0.75 #13466, 0.53 #28278), 026zlh9 (0.82 #18853, 0.75 #13466, 0.33 #798), 03ffcz (0.82 #18853, 0.75 #13466, 0.33 #871), 0ddcbd5 (0.82 #18853, 0.75 #13466, 0.30 #149442), 08sfxj (0.82 #18853, 0.75 #13466, 0.30 #149442), 0qmd5 (0.82 #18853, 0.75 #13466, 0.30 #149442), 01sxly (0.82 #18853, 0.75 #13466, 0.30 #149442) >> Best rule #18853 for best value: >> intensional similarity = 2 >> extensional distance = 13 >> proper extension: 07f1x; >> query: (?x512, ?x144) <- film_release_region(?x66, ?x512), titles(?x512, ?x144) >> conf = 0.82 => this is the best rule for 35 predicted values *> Best rule #28278 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 0j5g9; *> query: (?x512, ?x2345) <- contains(?x512, ?x362), nationality(?x6131, ?x512), music(?x2345, ?x6131) *> conf = 0.53 ranks of expected_values: 92, 108, 110, 112, 117, 120, 126, 147, 155, 156, 306, 507, 521, 771, 820, 858, 931, 992, 1057, 1285, 1336, 1393 EVAL 07ssc country! 08c4yn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0170xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 09fqgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 04xg2f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0bmfnjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 011ywj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 05dptj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0bh8drv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 072zl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0bl3nn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 01v1ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 01_0f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 03lvwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0dl9_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 07bwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0413cff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0194zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 09jcj6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0prhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0bmssv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0pd6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0d1qmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 06gjk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 02s4l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 192.000 192.000 0.819 http://example.org/film/film/country EVAL 07ssc country! 0gzy02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 192.000 192.000 0.819 http://example.org/film/film/country #20732-01xbgx PRED entity: 01xbgx PRED relation: country! PRED expected values: 01cgz 0486tv => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 54): 071t0 (0.82 #454, 0.79 #1264, 0.79 #2236), 01cgz (0.79 #445, 0.70 #553, 0.69 #1849), 01lb14 (0.77 #339, 0.71 #447, 0.67 #69), 06f41 (0.75 #446, 0.73 #338, 0.72 #68), 03hr1p (0.69 #347, 0.64 #455, 0.63 #401), 0194d (0.69 #371, 0.63 #587, 0.61 #479), 064vjs (0.67 #85, 0.63 #409, 0.62 #355), 06wrt (0.67 #394, 0.61 #70, 0.58 #340), 09w1n (0.67 #78, 0.59 #402, 0.57 #456), 07jjt (0.67 #75, 0.58 #345, 0.56 #291) >> Best rule #454 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 017v_; >> query: (?x7748, 071t0) <- administrative_parent(?x13886, ?x7748), combatants(?x1140, ?x7748), adjoins(?x7747, ?x7748) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #445 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 017v_; *> query: (?x7748, 01cgz) <- administrative_parent(?x13886, ?x7748), combatants(?x1140, ?x7748), adjoins(?x7747, ?x7748) *> conf = 0.79 ranks of expected_values: 2, 19 EVAL 01xbgx country! 0486tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 132.000 132.000 0.821 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01xbgx country! 01cgz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 132.000 0.821 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #20731-0hv7l PRED entity: 0hv7l PRED relation: location! PRED expected values: 044mrh => 173 concepts (110 used for prediction) PRED predicted values (max 10 best out of 1686): 03l3jy (0.25 #10955, 0.25 #8436, 0.25 #5917), 099d4 (0.25 #4886, 0.25 #2366, 0.17 #25039), 073749 (0.25 #15918, 0.25 #3323, 0.14 #25995), 0139q5 (0.25 #4514, 0.25 #1994, 0.12 #17109), 0jlv5 (0.25 #3874, 0.25 #1354, 0.12 #16469), 02mz_6 (0.25 #3977, 0.25 #1457, 0.12 #16572), 0pksh (0.25 #4926, 0.25 #2406, 0.12 #17521), 033m23 (0.25 #4092, 0.25 #1572, 0.12 #16687), 06101p (0.25 #4971, 0.25 #2451, 0.12 #17566), 07ftc0 (0.25 #4176, 0.25 #1656, 0.12 #16771) >> Best rule #10955 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01bvw5; >> query: (?x14401, 03l3jy) <- contains(?x1453, ?x14401), category(?x14401, ?x134), ?x1453 = 06qd3, ?x134 = 08mbj5d >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hv7l location! 044mrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 110.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #20730-06qgjh PRED entity: 06qgjh PRED relation: type_of_union PRED expected values: 04ztj => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.78 #53, 0.76 #45, 0.75 #93), 01g63y (0.27 #10, 0.15 #22, 0.14 #42) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 424 >> proper extension: 06w33f8; 0gv5c; 05dxl_; 04dz_y7; 0gry51; >> query: (?x8432, 04ztj) <- profession(?x8432, ?x1032), profession(?x8432, ?x524), ?x524 = 02jknp, ?x1032 = 02hrh1q >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06qgjh type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.784 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20729-019v67 PRED entity: 019v67 PRED relation: industry PRED expected values: 02vxn => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 33): 02vxn (0.65 #489, 0.65 #293, 0.63 #974), 01mw1 (0.35 #2382, 0.21 #1360, 0.17 #682), 020mfr (0.25 #2398, 0.25 #212, 0.10 #4804), 02jjt (0.17 #1563, 0.15 #835, 0.14 #1709), 029g_vk (0.12 #206, 0.04 #935, 0.04 #1032), 03qh03g (0.10 #248, 0.09 #1364, 0.07 #2386), 0g4gr (0.10 #250, 0.06 #347, 0.06 #396), 03r8gp (0.08 #1526, 0.06 #360, 0.05 #1575), 04rlf (0.06 #1373, 0.04 #2347, 0.04 #2972), 0hcr (0.05 #639, 0.05 #591, 0.04 #930) >> Best rule #489 for best value: >> intensional similarity = 8 >> extensional distance = 18 >> proper extension: 04mwxk3; >> query: (?x13217, 02vxn) <- production_companies(?x6345, ?x13217), film(?x13217, ?x3048), film_release_region(?x6345, ?x304), genre(?x6345, ?x53), nominated_for(?x77, ?x6345), nominated_for(?x5565, ?x3048), genre(?x3048, ?x812), country(?x6345, ?x789) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019v67 industry 02vxn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.650 http://example.org/business/business_operation/industry #20728-018qpb PRED entity: 018qpb PRED relation: place_of_death PRED expected values: 0k049 => 94 concepts (69 used for prediction) PRED predicted values (max 10 best out of 20): 030qb3t (0.30 #22, 0.24 #412, 0.17 #1582), 0k049 (0.16 #393, 0.12 #1563, 0.11 #1759), 06_kh (0.11 #395, 0.10 #5, 0.10 #199), 01j2_7 (0.10 #186, 0.03 #576, 0.01 #967), 04jpl (0.10 #201, 0.07 #1567, 0.07 #1763), 01m23s (0.09 #585, 0.06 #389, 0.03 #7796), 02_286 (0.08 #403, 0.05 #207, 0.04 #1573), 0f2wj (0.08 #1572, 0.07 #1768, 0.05 #402), 0r0m6 (0.05 #450, 0.05 #254, 0.02 #1620), 0r00l (0.05 #356, 0.03 #552, 0.01 #1138) >> Best rule #22 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0h1m9; 0c2ry; 08gyz_; >> query: (?x12582, 030qb3t) <- gender(?x12582, ?x514), ?x514 = 02zsn, award(?x12582, ?x1245), profession(?x12582, ?x987), place_of_burial(?x12582, ?x11327) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #393 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 0k8y7; 0l5yl; *> query: (?x12582, 0k049) <- award(?x12582, ?x1245), location(?x12582, ?x13745), nationality(?x12582, ?x94), place_of_burial(?x12582, ?x11327), ?x94 = 09c7w0 *> conf = 0.16 ranks of expected_values: 2 EVAL 018qpb place_of_death 0k049 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 69.000 0.300 http://example.org/people/deceased_person/place_of_death #20727-05kj_ PRED entity: 05kj_ PRED relation: featured_film_locations! PRED expected values: 0m2kd 04t6fk 0466s8n => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 712): 08hmch (0.20 #2253, 0.03 #60575, 0.02 #30684), 08phg9 (0.20 #2565, 0.02 #8397, 0.02 #13500), 0j_tw (0.20 #2335), 0413cff (0.15 #7657, 0.09 #22966, 0.08 #13489), 061681 (0.12 #2962, 0.12 #7336, 0.08 #13168), 04gv3db (0.12 #3234, 0.07 #3963, 0.06 #22917), 0872p_c (0.12 #2993, 0.07 #3722, 0.06 #13199), 033srr (0.12 #3192, 0.07 #3921, 0.06 #13398), 0443v1 (0.12 #3623, 0.07 #4352, 0.05 #7997), 09cr8 (0.12 #3040, 0.07 #3769, 0.05 #7414) >> Best rule #2253 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 017cy9; 01n6r0; 0k__z; >> query: (?x726, 08hmch) <- featured_film_locations(?x407, ?x726), contains(?x94, ?x726), school(?x6462, ?x726) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05kj_ featured_film_locations! 0466s8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 180.000 0.200 http://example.org/film/film/featured_film_locations EVAL 05kj_ featured_film_locations! 04t6fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 180.000 0.200 http://example.org/film/film/featured_film_locations EVAL 05kj_ featured_film_locations! 0m2kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 180.000 0.200 http://example.org/film/film/featured_film_locations #20726-03cprft PRED entity: 03cprft PRED relation: people! PRED expected values: 0dryh9k => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 33): 0dryh9k (0.40 #16, 0.38 #633, 0.37 #402), 041rx (0.15 #1546, 0.14 #1083, 0.14 #1160), 0x67 (0.09 #2938, 0.08 #3015, 0.08 #4402), 02w7gg (0.08 #2699, 0.04 #4086, 0.04 #3700), 033tf_ (0.06 #3628, 0.06 #2935, 0.06 #3012), 01rv7x (0.06 #425, 0.06 #502, 0.05 #579), 02sch9 (0.06 #421, 0.05 #344, 0.04 #652), 04mvp8 (0.05 #376, 0.05 #453, 0.04 #530), 03w9bjf (0.05 #363, 0.03 #1234, 0.02 #517), 04gfy7 (0.05 #374, 0.03 #1234) >> Best rule #16 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 02vmzp; 0674cw; 0cvbb9q; 02xgdv; 0k0q8q; 03fw4y; 0b5x23; 01k6nm; 0cfz_z; 0cct7p; ... >> query: (?x13646, 0dryh9k) <- gender(?x13646, ?x231), place_of_death(?x13646, ?x7412), ?x7412 = 04vmp, ?x231 = 05zppz, nationality(?x13646, ?x2146) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cprft people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.400 http://example.org/people/ethnicity/people #20725-016xh5 PRED entity: 016xh5 PRED relation: award PRED expected values: 04kxsb => 99 concepts (83 used for prediction) PRED predicted values (max 10 best out of 257): 05pcn59 (0.27 #889, 0.27 #2101, 0.27 #2505), 05p09zm (0.27 #932, 0.25 #2144, 0.23 #1336), 01by1l (0.26 #9001, 0.23 #8193, 0.22 #9405), 05b4l5x (0.23 #814, 0.18 #2026, 0.18 #2834), 05zr6wv (0.21 #1229, 0.19 #825, 0.15 #2037), 01bgqh (0.19 #8931, 0.17 #8123, 0.17 #9335), 03c7tr1 (0.19 #866, 0.16 #2482, 0.16 #2078), 057xs89 (0.18 #564, 0.13 #33546, 0.12 #1372), 0gqwc (0.17 #74, 0.15 #32331, 0.15 #32330), 027dtxw (0.17 #4, 0.15 #32331, 0.15 #32330) >> Best rule #889 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 01wxyx1; >> query: (?x6122, 05pcn59) <- nominated_for(?x6122, ?x2029), participant(?x2647, ?x6122), participant(?x4884, ?x6122) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #32331 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2165 *> proper extension: 028q6; 0jz9f; 0520r2x; 0cb77r; 06j0md; 0hl3d; 03ckxdg; 050023; 026dcvf; 032nwy; ... *> query: (?x6122, ?x384) <- award_nominee(?x6122, ?x488), award_winner(?x384, ?x488) *> conf = 0.15 ranks of expected_values: 20 EVAL 016xh5 award 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 99.000 83.000 0.274 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20724-0bksh PRED entity: 0bksh PRED relation: nominated_for PRED expected values: 047gpsd => 99 concepts (62 used for prediction) PRED predicted values (max 10 best out of 443): 01gglm (0.79 #100027, 0.78 #77437, 0.78 #100026), 0cfhfz (0.63 #2059, 0.60 #447, 0.01 #87566), 011wtv (0.36 #8062, 0.32 #33872, 0.30 #43557), 08052t3 (0.36 #8062, 0.32 #33872, 0.30 #43557), 05dss7 (0.36 #8062, 0.32 #33872, 0.30 #43557), 07k8rt4 (0.36 #8062, 0.32 #33872, 0.30 #43557), 03nfnx (0.36 #8062, 0.32 #33872, 0.30 #43557), 02lk60 (0.36 #8062, 0.32 #33872, 0.30 #43557), 035s95 (0.36 #8062, 0.32 #33872, 0.30 #43557), 0fpmrm3 (0.11 #2001, 0.10 #389) >> Best rule #100027 for best value: >> intensional similarity = 2 >> extensional distance = 1624 >> proper extension: 0f721s; 06jntd; 03lpbx; >> query: (?x4782, ?x8089) <- award_winner(?x8089, ?x4782), nominated_for(?x828, ?x8089) >> conf = 0.79 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bksh nominated_for 047gpsd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 62.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20723-0gyy53 PRED entity: 0gyy53 PRED relation: production_companies PRED expected values: 054lpb6 => 74 concepts (65 used for prediction) PRED predicted values (max 10 best out of 52): 017s11 (0.35 #2455, 0.34 #326, 0.31 #979), 024rgt (0.17 #24, 0.06 #1004, 0.05 #1494), 02j_j0 (0.17 #47, 0.05 #1027, 0.05 #209), 05mgj0 (0.17 #64, 0.05 #553, 0.04 #879), 0338lq (0.17 #7, 0.02 #903, 0.02 #2131), 05qd_ (0.14 #254, 0.13 #336, 0.11 #2300), 086k8 (0.13 #164, 0.12 #1636, 0.12 #2045), 016tw3 (0.11 #1482, 0.11 #2055, 0.10 #3129), 054lpb6 (0.10 #994, 0.09 #1484, 0.09 #1238), 016tt2 (0.09 #1638, 0.08 #1966, 0.08 #2294) >> Best rule #2455 for best value: >> intensional similarity = 4 >> extensional distance = 928 >> proper extension: 04969y; 02zk08; 0g4pl7z; 06zn1c; >> query: (?x2932, ?x1414) <- titles(?x53, ?x2932), film(?x1414, ?x2932), nominated_for(?x4318, ?x2932), nominated_for(?x1414, ?x696) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #994 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 362 *> proper extension: 063zky; *> query: (?x2932, 054lpb6) <- film(?x3705, ?x2932), genre(?x2932, ?x53), production_companies(?x2932, ?x1414), executive_produced_by(?x2932, ?x163) *> conf = 0.10 ranks of expected_values: 9 EVAL 0gyy53 production_companies 054lpb6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 74.000 65.000 0.351 http://example.org/film/film/production_companies #20722-04swx PRED entity: 04swx PRED relation: contains PRED expected values: 01xyy => 169 concepts (79 used for prediction) PRED predicted values (max 10 best out of 2886): 0jdx (0.69 #5879, 0.65 #26457, 0.65 #197003), 07fj_ (0.69 #5879, 0.65 #26457, 0.65 #197003), 09pxc (0.57 #61734, 0.50 #197001, 0.37 #147013), 03rjj (0.54 #73496, 0.53 #197002, 0.52 #199942), 02j9z (0.54 #73496, 0.52 #199942, 0.50 #55855), 04swx (0.54 #73496, 0.52 #199942, 0.50 #55855), 0j0k (0.54 #73496, 0.52 #199942, 0.50 #55855), 02qkt (0.54 #73496, 0.52 #199942, 0.50 #55855), 0f8l9c (0.54 #73496, 0.52 #199942, 0.50 #55855), 02j71 (0.52 #117604, 0.40 #179359, 0.35 #211699) >> Best rule #5879 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 02qkt; >> query: (?x12727, ?x7833) <- contains(?x12727, ?x14424), contains(?x12727, ?x8958), contains(?x12727, ?x1061), adjoins(?x7833, ?x14424), ?x1061 = 04v3q, ?x8958 = 01ppq >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #28253 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 03rz4; *> query: (?x12727, 01xyy) <- locations(?x326, ?x12727), adjoins(?x3912, ?x12727), vacationer(?x12727, ?x2444), country(?x150, ?x3912) *> conf = 0.07 ranks of expected_values: 1769 EVAL 04swx contains 01xyy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 169.000 79.000 0.686 http://example.org/location/location/contains #20721-049qx PRED entity: 049qx PRED relation: artists! PRED expected values: 05bt6j => 159 concepts (93 used for prediction) PRED predicted values (max 10 best out of 256): 02x8m (0.57 #922, 0.38 #1524, 0.33 #1826), 05bt6j (0.55 #3960, 0.43 #947, 0.40 #3057), 0gywn (0.50 #11797, 0.39 #3971, 0.31 #12099), 02k_kn (0.43 #3977, 0.43 #964, 0.19 #1566), 02qdgx (0.43 #942, 0.25 #1544, 0.22 #3955), 0glt670 (0.37 #6365, 0.35 #8472, 0.34 #9676), 016clz (0.33 #3320, 0.27 #10845, 0.23 #27113), 03mb9 (0.31 #3410, 0.19 #2205, 0.14 #1300), 03_d0 (0.25 #11754, 0.21 #7239, 0.21 #10550), 01lyv (0.24 #4251, 0.22 #3950, 0.20 #334) >> Best rule #922 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 016h9b; 0gbwp; 016jfw; 0137hn; >> query: (?x4394, 02x8m) <- award(?x4394, ?x528), sibling(?x649, ?x4394), artists(?x1572, ?x4394), ?x1572 = 06by7 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #3960 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 0134wr; *> query: (?x4394, 05bt6j) <- award(?x4394, ?x528), artists(?x9007, ?x4394), artist(?x648, ?x4394), ?x9007 = 02vjzr *> conf = 0.55 ranks of expected_values: 2 EVAL 049qx artists! 05bt6j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 159.000 93.000 0.571 http://example.org/music/genre/artists #20720-027986c PRED entity: 027986c PRED relation: award_winner PRED expected values: 0bwh6 016kb7 => 51 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1624): 01wmxfs (0.55 #7482, 0.36 #5035, 0.25 #2588), 03f1zdw (0.50 #2676, 0.45 #5123, 0.36 #7570), 039bp (0.45 #7545, 0.27 #5098, 0.12 #34266), 0d6d2 (0.36 #9091, 0.18 #6644, 0.06 #13986), 016ynj (0.36 #9115, 0.18 #6668, 0.04 #14010), 026rm_y (0.36 #6735, 0.09 #9182, 0.07 #14077), 0bq2g (0.33 #751, 0.12 #34266, 0.10 #44057), 0pz91 (0.33 #252, 0.12 #34266, 0.10 #44057), 018grr (0.33 #416, 0.12 #34266, 0.10 #44057), 01q_ph (0.33 #58, 0.12 #34266, 0.10 #44057) >> Best rule #7482 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 02n9nmz; 09cm54; 027c95y; 02w9sd7; >> query: (?x834, 01wmxfs) <- award(?x2596, ?x834), award_winner(?x834, ?x1738), award(?x9599, ?x834), award_nominee(?x1738, ?x72), ?x9599 = 07l450 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #51403 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 210 *> proper extension: 02p_7cr; 02pzxlw; 02p_04b; 02_3zj; *> query: (?x834, ?x1616) <- award(?x2596, ?x834), award(?x2852, ?x834), nominated_for(?x68, ?x2852), nominated_for(?x1616, ?x2852) *> conf = 0.11 ranks of expected_values: 138, 139 EVAL 027986c award_winner 016kb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 51.000 25.000 0.545 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027986c award_winner 0bwh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 51.000 25.000 0.545 http://example.org/award/award_category/winners./award/award_honor/award_winner #20719-0vgkd PRED entity: 0vgkd PRED relation: genre! PRED expected values: 03kxj2 05fgt1 04z257 05sns6 02lxrv 011yhm 0b6f8pf 09v42sf => 55 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1917): 09jcj6 (0.67 #18885, 0.50 #11652, 0.40 #15268), 0ptdz (0.67 #19854, 0.50 #21662, 0.38 #30709), 06zn1c (0.67 #21578, 0.50 #19770, 0.33 #1685), 01hw5kk (0.67 #20577, 0.50 #18769, 0.33 #684), 0ckr7s (0.67 #19931, 0.50 #18123, 0.33 #38), 01pvxl (0.67 #20799, 0.43 #24419, 0.33 #22608), 01_1pv (0.67 #20252, 0.43 #23872, 0.33 #22061), 027pfg (0.67 #21113, 0.38 #30160, 0.33 #19305), 0kvgtf (0.67 #18709, 0.33 #20517, 0.33 #624), 03z20c (0.67 #18562, 0.33 #20370, 0.33 #477) >> Best rule #18885 for best value: >> intensional similarity = 15 >> extensional distance = 4 >> proper extension: 06cvj; 02l7c8; 01hmnh; >> query: (?x809, 09jcj6) <- genre(?x808, ?x809), genre(?x6507, ?x809), genre(?x6099, ?x809), genre(?x2349, ?x809), genre(?x1708, ?x809), genre(?x1535, ?x809), nominated_for(?x4405, ?x1708), nominated_for(?x2094, ?x1708), ?x6099 = 0473rc, titles(?x307, ?x2349), nominated_for(?x68, ?x1535), nominated_for(?x102, ?x6507), film_release_region(?x1535, ?x87), produced_by(?x6507, ?x7090), film_crew_role(?x2349, ?x468) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #16137 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 3 *> proper extension: 01jfsb; *> query: (?x809, 09v42sf) <- genre(?x808, ?x809), genre(?x10425, ?x809), genre(?x8859, ?x809), genre(?x6588, ?x809), genre(?x4489, ?x809), genre(?x4287, ?x809), genre(?x1708, ?x809), ?x1708 = 05cj_j, nominated_for(?x788, ?x10425), titles(?x2480, ?x6588), film(?x2383, ?x4489), currency(?x4287, ?x170), production_companies(?x4287, ?x1914), ?x8859 = 063_j5, featured_film_locations(?x10425, ?x279), nominated_for(?x899, ?x10425) *> conf = 0.60 ranks of expected_values: 18, 71, 74, 97, 143, 303, 494, 667 EVAL 0vgkd genre! 09v42sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 55.000 26.000 0.667 http://example.org/film/film/genre EVAL 0vgkd genre! 0b6f8pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 26.000 0.667 http://example.org/film/film/genre EVAL 0vgkd genre! 011yhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 55.000 26.000 0.667 http://example.org/film/film/genre EVAL 0vgkd genre! 02lxrv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 55.000 26.000 0.667 http://example.org/film/film/genre EVAL 0vgkd genre! 05sns6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 55.000 26.000 0.667 http://example.org/film/film/genre EVAL 0vgkd genre! 04z257 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 26.000 0.667 http://example.org/film/film/genre EVAL 0vgkd genre! 05fgt1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 26.000 0.667 http://example.org/film/film/genre EVAL 0vgkd genre! 03kxj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 26.000 0.667 http://example.org/film/film/genre #20718-0b_6jz PRED entity: 0b_6jz PRED relation: team PRED expected values: 02py8_w => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 26): 026wlnm (0.80 #251, 0.77 #240, 0.75 #217), 091tgz (0.80 #249, 0.75 #215, 0.74 #204), 02qk2d5 (0.79 #205, 0.74 #194, 0.73 #239), 02py8_w (0.73 #236, 0.68 #202, 0.68 #191), 027yf83 (0.71 #112, 0.60 #211, 0.59 #234), 026dqjm (0.60 #110, 0.58 #198, 0.53 #154), 04088s0 (0.57 #113, 0.53 #168, 0.50 #235), 02ptzz0 (0.50 #122, 0.45 #210, 0.44 #244), 03d5m8w (0.50 #75, 0.43 #119, 0.40 #130), 02pyyld (0.43 #120, 0.40 #253, 0.40 #109) >> Best rule #251 for best value: >> intensional similarity = 14 >> extensional distance = 23 >> proper extension: 0f9rw9; >> query: (?x4803, 026wlnm) <- team(?x4803, ?x9833), team(?x4803, ?x8728), team(?x4803, ?x6803), team(?x12798, ?x8728), team(?x10673, ?x8728), team(?x10594, ?x8728), team(?x4368, ?x8728), ?x10594 = 0b_756, ?x12798 = 0b_770, team(?x1348, ?x9833), sport(?x9833, ?x12913), ?x10673 = 0b_6mr, ?x4368 = 0b_6x2, ?x6803 = 03by7wc >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #236 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 20 *> proper extension: 05g_nr; 0b_734; *> query: (?x4803, 02py8_w) <- team(?x4803, ?x9833), team(?x4803, ?x8728), team(?x4803, ?x4804), ?x9833 = 03y9p40, team(?x12451, ?x8728), team(?x9974, ?x8728), team(?x9908, ?x8728), ?x12451 = 0b_6xf, colors(?x8728, ?x663), colors(?x4804, ?x332), ?x9974 = 0b_6pv, ?x9908 = 0b_6lb, teams(?x6088, ?x4804) *> conf = 0.73 ranks of expected_values: 4 EVAL 0b_6jz team 02py8_w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 68.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #20717-04f62k PRED entity: 04f62k PRED relation: nationality PRED expected values: 03_3d => 88 concepts (31 used for prediction) PRED predicted values (max 10 best out of 30): 03_3d (0.88 #710, 0.85 #510, 0.79 #610), 09c7w0 (0.83 #2419, 0.79 #2010, 0.76 #2621), 0g3bw (0.48 #1207, 0.46 #3132, 0.46 #3131), 03rk0 (0.18 #1553, 0.16 #1654, 0.14 #2156), 0d060g (0.12 #1414, 0.08 #2320, 0.08 #1916), 02jx1 (0.07 #2756, 0.07 #2857, 0.07 #1942), 0f8l9c (0.05 #3049), 07ssc (0.05 #2535, 0.05 #2839, 0.05 #1322), 03rt9 (0.03 #1119, 0.02 #3025, 0.01 #2223), 06q1r (0.02 #1384, 0.02 #3025) >> Best rule #710 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 01kwh5j; >> query: (?x12484, 03_3d) <- special_performance_type(?x12484, ?x296), ?x296 = 01kyvx, profession(?x12484, ?x1383), ?x1383 = 0np9r, actor(?x13050, ?x12484) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04f62k nationality 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 31.000 0.880 http://example.org/people/person/nationality #20716-02b15h PRED entity: 02b15h PRED relation: team! PRED expected values: 0d9v9q => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 429): 0d3f83 (0.57 #114, 0.41 #1302, 0.33 #739), 04v68c (0.57 #114, 0.40 #560, 0.38 #1120), 0784v1 (0.57 #114, 0.38 #1018, 0.33 #682), 080dyk (0.57 #114, 0.33 #568, 0.33 #120), 0bn9sc (0.57 #114, 0.33 #565, 0.33 #117), 0135nb (0.57 #114, 0.33 #136, 0.30 #2251), 07h1h5 (0.57 #114, 0.33 #241, 0.25 #353), 0dv1hh (0.57 #114, 0.33 #741, 0.25 #853), 02y0dd (0.57 #114, 0.33 #317, 0.24 #115), 0dhrqx (0.57 #114, 0.33 #166, 0.24 #115) >> Best rule #114 for best value: >> intensional similarity = 13 >> extensional distance = 1 >> proper extension: 0272vm; >> query: (?x209, ?x10129) <- position(?x209, ?x60), team(?x208, ?x209), team(?x8194, ?x209), team(?x8194, ?x12081), team(?x8194, ?x8338), team(?x8194, ?x6874), team(?x203, ?x6874), team(?x927, ?x6874), teams(?x11117, ?x8338), colors(?x12081, ?x1101), team(?x10129, ?x12081), sport(?x8338, ?x471), ?x208 = 05_6_y >> conf = 0.57 => this is the best rule for 40 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 24 EVAL 02b15h team! 0d9v9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 76.000 76.000 0.571 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #20715-0170s4 PRED entity: 0170s4 PRED relation: gender PRED expected values: 05zppz => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #33, 0.74 #7, 0.73 #13), 02zsn (0.48 #28, 0.46 #46, 0.45 #52) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 299 >> proper extension: 0bkg4; 016lh0; 01vsyjy; 08gwzt; 012ycy; >> query: (?x2415, 05zppz) <- type_of_union(?x2415, ?x566), nationality(?x2415, ?x94), currency(?x2415, ?x170) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0170s4 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.738 http://example.org/people/person/gender #20714-0fthdk PRED entity: 0fthdk PRED relation: award_nominee PRED expected values: 07s8r0 0306ds => 118 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1079): 07s8r0 (0.81 #153828, 0.81 #170148, 0.81 #158491), 06jzh (0.81 #153828, 0.81 #170148, 0.81 #158491), 0306ds (0.81 #153828, 0.81 #170148, 0.81 #158491), 07lmxq (0.81 #153828, 0.81 #170148, 0.81 #158491), 0fthdk (0.65 #4315, 0.16 #125861, 0.15 #170149), 02bkdn (0.16 #125861, 0.15 #170149, 0.12 #2730), 02p65p (0.16 #125861, 0.15 #170149, 0.12 #2357), 0cjsxp (0.16 #125861, 0.15 #170149, 0.12 #3198), 03yj_0n (0.16 #125861, 0.15 #170149, 0.12 #3141), 043kzcr (0.16 #125861, 0.15 #170149, 0.12 #2873) >> Best rule #153828 for best value: >> intensional similarity = 4 >> extensional distance = 1439 >> proper extension: 0275_pj; 03kpvp; 03fykz; 01d1yr; 03ys2f; 03ysmg; 01mkn_d; 08qmfm; 055sjw; 025hzx; ... >> query: (?x9314, ?x539) <- award_nominee(?x6314, ?x9314), award_nominee(?x539, ?x9314), nominated_for(?x9314, ?x2973), location(?x6314, ?x1131) >> conf = 0.81 => this is the best rule for 4 predicted values ranks of expected_values: 1, 3 EVAL 0fthdk award_nominee 0306ds CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 73.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 0fthdk award_nominee 07s8r0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 73.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20713-011zf2 PRED entity: 011zf2 PRED relation: artist! PRED expected values: 025t8bv => 115 concepts (84 used for prediction) PRED predicted values (max 10 best out of 104): 0g768 (0.34 #3000, 0.13 #462, 0.12 #179), 015_1q (0.24 #2277, 0.24 #2841, 0.23 #2559), 03rhqg (0.22 #157, 0.16 #4533, 0.16 #3401), 01clyr (0.20 #2996, 0.08 #3419, 0.08 #6251), 01w40h (0.20 #2991, 0.15 #453, 0.11 #1722), 041bnw (0.18 #69, 0.05 #2185, 0.05 #2326), 0bfp0l (0.18 #106, 0.04 #8629, 0.03 #1799), 01cszh (0.15 #576, 0.15 #858, 0.15 #435), 01f_3w (0.14 #600, 0.12 #882, 0.11 #459), 043g7l (0.14 #879, 0.13 #456, 0.11 #597) >> Best rule #3000 for best value: >> intensional similarity = 3 >> extensional distance = 293 >> proper extension: 0m19t; 04r1t; 0167_s; 02r1tx7; 07yg2; 01j59b0; 02vgh; 01kcms4; 06gcn; 03k3b; ... >> query: (?x1399, 0g768) <- artist(?x7089, ?x1399), artist(?x7089, ?x6947), ?x6947 = 01vrnsk >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #8629 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 840 *> proper extension: 01shhf; 02twdq; *> query: (?x1399, ?x3888) <- artist(?x7089, ?x1399), artist(?x7089, ?x6947), artist(?x3888, ?x6947) *> conf = 0.04 ranks of expected_values: 62 EVAL 011zf2 artist! 025t8bv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 115.000 84.000 0.342 http://example.org/music/record_label/artist #20712-05p553 PRED entity: 05p553 PRED relation: genre! PRED expected values: 07hpv3 01p4wv 09g_31 04mx8h4 => 56 concepts (31 used for prediction) PRED predicted values (max 10 best out of 737): 0gxr1c (0.71 #5738, 0.43 #5530, 0.40 #3051), 03g9xj (0.71 #5689, 0.33 #5071, 0.33 #4028), 02pqs8l (0.67 #4137, 0.40 #3110, 0.33 #451), 07ng9k (0.57 #5566, 0.40 #2879, 0.33 #4948), 06r1k (0.57 #5706, 0.40 #3019, 0.33 #361), 02gl58 (0.57 #5696, 0.33 #5078, 0.33 #351), 06qw_ (0.57 #5740, 0.33 #395, 0.29 #5532), 06qv_ (0.57 #5702, 0.33 #357, 0.29 #5494), 06qxh (0.57 #5692, 0.33 #347, 0.29 #5484), 06r4f (0.57 #5681, 0.33 #336, 0.29 #5473) >> Best rule #5738 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 01htzx; >> query: (?x258, 0gxr1c) <- genre(?x9633, ?x258), genre(?x6726, ?x258), genre(?x419, ?x258), ?x419 = 020qr4, genre(?x9633, ?x812), nominated_for(?x5105, ?x6726), ?x812 = 01jfsb, nominated_for(?x686, ?x9633) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2469 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0c4xc; *> query: (?x258, 07hpv3) <- genre(?x13443, ?x258), genre(?x9633, ?x258), genre(?x1542, ?x258), ?x13443 = 05pbsry, country_of_origin(?x9633, ?x94), actor(?x9633, ?x1397), ?x1542 = 0124k9 *> conf = 0.50 ranks of expected_values: 17, 20, 39, 125 EVAL 05p553 genre! 04mx8h4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 56.000 31.000 0.714 http://example.org/tv/tv_program/genre EVAL 05p553 genre! 09g_31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 56.000 31.000 0.714 http://example.org/tv/tv_program/genre EVAL 05p553 genre! 01p4wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 56.000 31.000 0.714 http://example.org/tv/tv_program/genre EVAL 05p553 genre! 07hpv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 56.000 31.000 0.714 http://example.org/tv/tv_program/genre #20711-0cv0r PRED entity: 0cv0r PRED relation: adjoins! PRED expected values: 0dn8b => 97 concepts (44 used for prediction) PRED predicted values (max 10 best out of 407): 0mw7h (0.33 #786, 0.33 #785, 0.33 #222), 0cv1w (0.33 #785, 0.27 #2256, 0.27 #1575), 0mwvq (0.33 #450, 0.09 #2026, 0.04 #3598), 0cc1v (0.27 #2097, 0.18 #1308, 0.11 #3669), 0bx9y (0.18 #2050, 0.18 #1261, 0.11 #2836), 0cv1h (0.18 #2219, 0.18 #1430, 0.11 #3005), 0cc07 (0.18 #2223, 0.18 #1434, 0.11 #3009), 0mnlq (0.18 #2244, 0.18 #1455, 0.11 #3030), 0mwq7 (0.18 #2301, 0.18 #1512, 0.11 #3087), 0k3ll (0.15 #5171, 0.11 #5958, 0.05 #9107) >> Best rule #786 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0l3n4; >> query: (?x14070, ?x4357) <- contains(?x14070, ?x1705), adjoins(?x14070, ?x4357), contains(?x1767, ?x14070), ?x4357 = 0mw7h >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2224 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0fr59; 0fr61; *> query: (?x14070, 0dn8b) <- contains(?x1767, ?x14070), ?x1767 = 04rrd, currency(?x14070, ?x170) *> conf = 0.09 ranks of expected_values: 19 EVAL 0cv0r adjoins! 0dn8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 97.000 44.000 0.333 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #20710-016yr0 PRED entity: 016yr0 PRED relation: student! PRED expected values: 02gnmp => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 94): 0bwfn (0.12 #275, 0.06 #2910, 0.06 #3964), 017z88 (0.12 #82, 0.06 #2717, 0.03 #7988), 053mhx (0.12 #295, 0.02 #2930, 0.02 #7147), 01w3v (0.12 #15, 0.02 #1596, 0.02 #2123), 02zy1z (0.12 #496, 0.01 #1550), 01vg13 (0.12 #219, 0.01 #2854), 01mpwj (0.12 #107), 015nl4 (0.06 #1121, 0.03 #8500, 0.03 #9027), 04b_46 (0.04 #1808, 0.03 #754, 0.03 #2335), 09f2j (0.04 #3321, 0.03 #7538, 0.03 #8592) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0kctd; >> query: (?x4327, 0bwfn) <- nominated_for(?x4327, ?x8870), award_winner(?x783, ?x4327), ?x8870 = 0fhzwl >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016yr0 student! 02gnmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 84.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #20709-09c7w0 PRED entity: 09c7w0 PRED relation: second_level_divisions PRED expected values: 0njvn 0mwl2 0nvrd 0p0cw 0k3kg 0nvt9 0n1xp 0nf3h 0k3l5 0mn8t 0nm6z 0fczy 0mm0p 0lmgy 0ntwb 0kv4k 0mwx6 0mqs0 0nt6b 0drr3 0nt4s 0k3jq 0mp08 0ms1n 0cyn3 0m2kw 0n5dt 0nm9h 0mwk9 0d_kd 0n2vl 0d1y7 0nhr5 0mwq7 0n5by 0jryt 0mrf1 0myfz 0jrhl 0nvvw 0mtl5 0nm9y 0mnrb 0flbm => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 1263): 0rh6k (0.17 #8286, 0.16 #7381, 0.12 #1165), 05fjf (0.17 #8286, 0.16 #7381, 0.12 #1165), 059rby (0.17 #8286, 0.16 #7381, 0.12 #1165), 0498y (0.17 #8286, 0.16 #7381, 0.12 #1165), 02xry (0.17 #8286, 0.16 #7381, 0.12 #1165), 05fkf (0.17 #8286, 0.16 #7381, 0.12 #1165), 01x73 (0.17 #8286, 0.16 #7381, 0.12 #1165), 05mph (0.17 #8286, 0.16 #7381, 0.12 #1165), 081yw (0.17 #8286, 0.16 #7381, 0.12 #1165), 04ych (0.17 #8286, 0.16 #7381, 0.12 #1165) >> Best rule #8286 for best value: >> intensional similarity = 2 >> extensional distance = 47 >> proper extension: 02w9s; >> query: (?x94, ?x108) <- country(?x108, ?x94), adjustment_currency(?x94, ?x170) >> conf = 0.17 => this is the best rule for 47 predicted values *> Best rule #12045 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 80 *> proper extension: 03gk2; 06cmp; 020d5; *> query: (?x94, ?x4776) <- nationality(?x10491, ?x94), location(?x10491, ?x4776) *> conf = 0.03 ranks of expected_values: 1155, 1227, 1228, 1229, 1230, 1232, 1233, 1234, 1235, 1238, 1242, 1243, 1244, 1245, 1246, 1252, 1257 EVAL 09c7w0 second_level_divisions 0flbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mnrb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0nm9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mtl5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0nvvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0jrhl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0myfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mrf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0jryt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0n5by CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mwq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0nhr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0d1y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0n2vl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0d_kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mwk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0nm9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0n5dt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0m2kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0cyn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0ms1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mp08 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0k3jq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0nt4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0drr3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0nt6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mqs0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mwx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0kv4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0ntwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0lmgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mm0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0fczy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0nm6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mn8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0k3l5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0nf3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0n1xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0nvt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0k3kg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0p0cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0nvrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0mwl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions EVAL 09c7w0 second_level_divisions 0njvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.171 http://example.org/location/country/second_level_divisions #20708-02dj3 PRED entity: 02dj3 PRED relation: institution! PRED expected values: 07s6fsf 02h4rq6 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 18): 02h4rq6 (0.87 #208, 0.77 #509, 0.72 #489), 019v9k (0.83 #640, 0.77 #214, 0.68 #130), 03bwzr4 (0.77 #218, 0.55 #519, 0.53 #499), 016t_3 (0.74 #209, 0.50 #125, 0.49 #531), 07s6fsf (0.55 #206, 0.46 #165, 0.38 #612), 0bkj86 (0.54 #619, 0.50 #129, 0.48 #150), 04zx3q1 (0.46 #165, 0.36 #207, 0.36 #611), 022h5x (0.46 #165, 0.36 #611, 0.30 #1408), 01ysy9 (0.46 #165, 0.36 #611, 0.30 #1408), 01kxxq (0.46 #165, 0.36 #611, 0.30 #1408) >> Best rule #208 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 01w3v; 017j69; 09f2j; 0373qt; 0g2jl; >> query: (?x5085, 02h4rq6) <- major_field_of_study(?x5085, ?x6859), state_province_region(?x5085, ?x9370), institution(?x1368, ?x5085), ?x6859 = 01tbp >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 02dj3 institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.872 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02dj3 institution! 07s6fsf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 158.000 158.000 0.872 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20707-0hwqg PRED entity: 0hwqg PRED relation: people! PRED expected values: 0gg4h => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 42): 0gk4g (0.19 #1066, 0.19 #1264, 0.18 #604), 0qcr0 (0.13 #595, 0.08 #397, 0.08 #133), 0dq9p (0.12 #1271, 0.11 #413, 0.11 #149), 04p3w (0.11 #1067, 0.10 #1265, 0.08 #143), 02k6hp (0.08 #169, 0.07 #433, 0.06 #631), 0gg4h (0.08 #168, 0.03 #102, 0.03 #432), 02y0js (0.08 #200, 0.06 #662, 0.06 #794), 019dmc (0.08 #248, 0.06 #50, 0.04 #710), 01ddth (0.06 #59, 0.01 #389), 01dcqj (0.05 #144, 0.05 #210, 0.03 #804) >> Best rule #1066 for best value: >> intensional similarity = 3 >> extensional distance = 151 >> proper extension: 069z_5; 02zfg3; >> query: (?x10795, 0gk4g) <- film(?x10795, ?x1547), place_of_death(?x10795, ?x12935), type_of_union(?x10795, ?x566) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #168 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 015gw6; 0cj8x; 0dh73w; 015dcj; 0bdt8; 015gjr; 01t9qj_; 0d6d2; 01v5h; 01vh18t; ... *> query: (?x10795, 0gg4h) <- film(?x10795, ?x1547), place_of_death(?x10795, ?x12935), award_winner(?x6331, ?x10795) *> conf = 0.08 ranks of expected_values: 6 EVAL 0hwqg people! 0gg4h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 132.000 132.000 0.190 http://example.org/people/cause_of_death/people #20706-0bqch PRED entity: 0bqch PRED relation: languages PRED expected values: 064_8sq => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 9): 02h40lc (0.30 #41, 0.30 #314, 0.28 #509), 064_8sq (0.14 #132, 0.07 #678, 0.06 #171), 02bjrlw (0.11 #118, 0.06 #157, 0.03 #664), 04306rv (0.07 #120, 0.03 #666, 0.02 #159), 01c7y (0.04 #109, 0.01 #304, 0.01 #343), 03k50 (0.02 #1447, 0.02 #589, 0.02 #1330), 06nm1 (0.02 #435, 0.02 #669, 0.01 #1917), 06b_j (0.02 #484, 0.01 #211, 0.01 #250), 07c9s (0.01 #1534) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 053yx; >> query: (?x11335, 02h40lc) <- location(?x11335, ?x12144), profession(?x11335, ?x955), place_of_death(?x11335, ?x13699), ?x955 = 0n1h >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #132 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 019_1h; *> query: (?x11335, 064_8sq) <- location(?x11335, ?x12144), type_of_union(?x11335, ?x566), nationality(?x11335, ?x789), ?x789 = 0f8l9c *> conf = 0.14 ranks of expected_values: 2 EVAL 0bqch languages 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.300 http://example.org/people/person/languages #20705-07xyn1 PRED entity: 07xyn1 PRED relation: service_language PRED expected values: 02h40lc => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 21): 02h40lc (0.90 #1115, 0.90 #821, 0.89 #1073), 06nm1 (0.26 #363, 0.19 #699, 0.19 #510), 064_8sq (0.21 #514, 0.20 #367, 0.16 #703), 04306rv (0.12 #108, 0.11 #360, 0.10 #822), 01r2l (0.12 #117, 0.09 #369, 0.09 #327), 05zjd (0.12 #118, 0.07 #475, 0.06 #496), 03_9r (0.09 #362, 0.08 #509, 0.08 #698), 02bjrlw (0.06 #505, 0.06 #358, 0.05 #694), 06b_j (0.06 #326, 0.05 #473, 0.04 #494), 02hwhyv (0.06 #331, 0.05 #478, 0.04 #499) >> Best rule #1115 for best value: >> intensional similarity = 7 >> extensional distance = 114 >> proper extension: 084l5; 0196bp; >> query: (?x8237, 02h40lc) <- contact_category(?x8237, ?x897), contact_category(?x11070, ?x897), contact_category(?x8931, ?x897), contact_category(?x8121, ?x897), company(?x265, ?x8931), industry(?x8121, ?x5078), service_location(?x11070, ?x94) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07xyn1 service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.897 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #20704-02v0ff PRED entity: 02v0ff PRED relation: profession PRED expected values: 02hrh1q => 94 concepts (68 used for prediction) PRED predicted values (max 10 best out of 61): 02hrh1q (0.92 #5877, 0.89 #3446, 0.88 #3875), 0dxtg (0.70 #6019, 0.70 #6162, 0.68 #1013), 02jknp (0.54 #1007, 0.33 #6729, 0.32 #6156), 09jwl (0.52 #5166, 0.39 #1590, 0.37 #3736), 018gz8 (0.33 #587, 0.25 #730, 0.19 #1302), 0nbcg (0.28 #1601, 0.28 #9584, 0.26 #4033), 01d30f (0.28 #9584, 0.06 #66, 0.05 #209), 05t4q (0.28 #9584, 0.06 #57, 0.05 #200), 0dz3r (0.25 #1575, 0.22 #3721, 0.21 #4007), 016z4k (0.25 #1577, 0.23 #3723, 0.22 #3580) >> Best rule #5877 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 04yywz; 02g8h; 0d_84; 02nb2s; 04bs3j; 014x77; 0151ns; 0lzb8; 025p38; 0kr5_; ... >> query: (?x3975, 02hrh1q) <- nominated_for(?x3975, ?x3075), film(?x3975, ?x6306), profession(?x3975, ?x319) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v0ff profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 68.000 0.916 http://example.org/people/person/profession #20703-0841v PRED entity: 0841v PRED relation: company! PRED expected values: 060c4 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 39): 060c4 (0.75 #3919, 0.74 #3052, 0.73 #374), 05_wyz (0.45 #674, 0.44 #1495, 0.44 #2443), 07xl34 (0.36 #2679, 0.05 #5793, 0.05 #5876), 09d6p2 (0.36 #2444, 0.33 #1372, 0.30 #1496), 02211by (0.33 #334, 0.28 #498, 0.25 #4000), 01kr6k (0.30 #846, 0.27 #2451, 0.26 #1297), 01rk91 (0.29 #290, 0.17 #249, 0.17 #1), 05k17c (0.22 #176, 0.22 #94, 0.17 #258), 014l7h (0.21 #313, 0.15 #2594, 0.15 #2721), 021q1c (0.17 #1323, 0.17 #256, 0.15 #2594) >> Best rule #3919 for best value: >> intensional similarity = 5 >> extensional distance = 104 >> proper extension: 017s11; 01j_9c; 01w3v; 0288zy; 07w0v; 0gsg7; 09kvv; 049dk; 031n8c; 01cyd5; ... >> query: (?x13100, 060c4) <- company(?x1491, ?x13100), state_province_region(?x13100, ?x4758), company(?x1491, ?x7008), category(?x13100, ?x134), ?x7008 = 03phgz >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0841v company! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.745 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #20702-01dhmw PRED entity: 01dhmw PRED relation: location PRED expected values: 05mph => 143 concepts (124 used for prediction) PRED predicted values (max 10 best out of 301): 02_286 (0.43 #8059, 0.37 #53794, 0.35 #14479), 0jgg3 (0.27 #57768), 0mxsm (0.27 #57768), 030qb3t (0.27 #53840, 0.19 #14525, 0.16 #42602), 01n7q (0.18 #1667, 0.14 #865, 0.13 #4073), 0cr3d (0.18 #1749, 0.14 #947, 0.09 #7364), 0h7h6 (0.14 #892, 0.09 #1694, 0.06 #2496), 0vzm (0.14 #974, 0.09 #1776, 0.05 #8194), 07ssc (0.14 #828, 0.09 #1630, 0.03 #7245), 07b_l (0.14 #988, 0.09 #1790, 0.02 #3394) >> Best rule #8059 for best value: >> intensional similarity = 4 >> extensional distance = 94 >> proper extension: 09fqd3; >> query: (?x3338, 02_286) <- profession(?x3338, ?x353), ?x353 = 0cbd2, location(?x3338, ?x2941), dog_breed(?x2941, ?x1706) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #43322 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 430 *> proper extension: 07m69t; *> query: (?x3338, ?x6521) <- place_of_birth(?x3338, ?x2941), location(?x3338, ?x5865), time_zones(?x2941, ?x1638), state(?x2941, ?x6521) *> conf = 0.12 ranks of expected_values: 13 EVAL 01dhmw location 05mph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 143.000 124.000 0.427 http://example.org/people/person/places_lived./people/place_lived/location #20701-0l14qv PRED entity: 0l14qv PRED relation: role PRED expected values: 03f5mt => 89 concepts (78 used for prediction) PRED predicted values (max 10 best out of 75): 0342h (0.86 #1958, 0.84 #1321, 0.83 #3071), 018vs (0.85 #4608, 0.84 #1321, 0.83 #4187), 07brj (0.84 #1321, 0.83 #1323, 0.82 #1322), 07c6l (0.84 #1321, 0.83 #1323, 0.82 #1322), 037c9s (0.84 #1321, 0.83 #1323, 0.82 #1322), 0dwr4 (0.84 #1321, 0.83 #1323, 0.82 #1739), 0395lw (0.84 #1321, 0.82 #1739, 0.82 #3139), 0dq630k (0.84 #1321, 0.82 #1739, 0.82 #3139), 0l15bq (0.84 #1321, 0.82 #1739, 0.82 #3139), 021bmf (0.83 #1770, 0.64 #276, 0.60 #1424) >> Best rule #1958 for best value: >> intensional similarity = 9 >> extensional distance = 12 >> proper extension: 01dnws; 06w7v; >> query: (?x228, 0342h) <- role(?x614, ?x228), instrumentalists(?x228, ?x4576), group(?x228, ?x1573), role(?x228, ?x2957), role(?x2306, ?x228), ?x614 = 0mkg, artist(?x3265, ?x4576), ?x2957 = 01v8y9, artists(?x284, ?x2306) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1733 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 10 *> proper extension: 07gql; *> query: (?x228, 03f5mt) <- role(?x1147, ?x228), instrumentalists(?x228, ?x3867), group(?x228, ?x7810), role(?x228, ?x214), performance_role(?x2620, ?x228), ?x1147 = 07kc_, role(?x130, ?x228), artists(?x1000, ?x3867), award(?x7810, ?x2634) *> conf = 0.75 ranks of expected_values: 22 EVAL 0l14qv role 03f5mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 89.000 78.000 0.857 http://example.org/music/performance_role/regular_performances./music/group_membership/role #20700-04nlb94 PRED entity: 04nlb94 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 129 concepts (128 used for prediction) PRED predicted values (max 10 best out of 35): 09zzb8 (0.91 #2504, 0.91 #2726, 0.91 #3206), 0ch6mp2 (0.83 #445, 0.82 #887, 0.79 #556), 09vw2b7 (0.82 #2991, 0.74 #1327, 0.71 #444), 01vx2h (0.64 #300, 0.62 #375, 0.58 #337), 02ynfr (0.38 #16, 0.24 #1042, 0.24 #1336), 02rh1dz (0.31 #155, 0.20 #1111, 0.19 #2181), 015h31 (0.28 #447, 0.25 #1441, 0.24 #1036), 01xy5l_ (0.23 #158, 0.21 #266, 0.18 #50), 0215hd (0.23 #163, 0.18 #55, 0.17 #1045), 0d2b38 (0.21 #1052, 0.21 #278, 0.18 #351) >> Best rule #2504 for best value: >> intensional similarity = 8 >> extensional distance = 581 >> proper extension: 0b76d_m; 014_x2; 0ds35l9; 0m313; 0sxg4; 083shs; 01br2w; 028_yv; 02v8kmz; 09m6kg; ... >> query: (?x12641, 09zzb8) <- film_crew_role(?x12641, ?x2178), genre(?x12641, ?x53), titles(?x4442, ?x12641), film_crew_role(?x5873, ?x2178), film_crew_role(?x414, ?x2178), ?x5873 = 0cq86w, ?x414 = 095zlp, nominated_for(?x6165, ?x12641) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #2991 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 885 *> proper extension: 02z9rr; *> query: (?x12641, 09vw2b7) <- film_crew_role(?x12641, ?x2178), genre(?x12641, ?x53), film_crew_role(?x8682, ?x2178), film_crew_role(?x6140, ?x2178), film_crew_role(?x5871, ?x2178), film_crew_role(?x3693, ?x2178), ?x3693 = 03r0g9, ?x5871 = 02b61v, ?x6140 = 0241y7, ?x8682 = 0bmfnjs *> conf = 0.82 ranks of expected_values: 3 EVAL 04nlb94 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 128.000 0.909 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20699-02xcb6n PRED entity: 02xcb6n PRED relation: award_winner PRED expected values: 0cp9f9 => 52 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1793): 01z_g6 (0.33 #1150, 0.09 #14825, 0.07 #29655), 05mcjs (0.31 #12352, 0.30 #22238, 0.29 #19766), 046mxj (0.31 #12352, 0.30 #22238, 0.29 #19766), 0cp9f9 (0.31 #12352, 0.30 #22238, 0.29 #19766), 04gnbv1 (0.31 #12352, 0.30 #22238, 0.29 #19766), 04glr5h (0.31 #12352, 0.30 #22238, 0.29 #19766), 06y9bd (0.31 #12352, 0.30 #22238, 0.29 #19766), 05gp3x (0.31 #12352, 0.30 #22238, 0.29 #19766), 03m_k0 (0.31 #12352, 0.29 #19766, 0.29 #19767), 01gq0b (0.17 #390, 0.10 #27182, 0.10 #37066) >> Best rule #1150 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0bdw1g; 0fbvqf; 0bdx29; 0fbtbt; >> query: (?x8660, 01z_g6) <- award(?x687, ?x8660), award(?x3058, ?x8660), ceremony(?x8660, ?x1265), nominated_for(?x8660, ?x1434), ?x687 = 039fgy >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #12352 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 54 *> proper extension: 0gq_v; 0gkvb7; 02p_7cr; 0cqhk0; 0cqh46; 047byns; 0cqh6z; 0gr0m; 0ck27z; 0gs96; ... *> query: (?x8660, ?x3058) <- award(?x687, ?x8660), award(?x3058, ?x8660), ceremony(?x8660, ?x1265), nominated_for(?x8660, ?x1434), genre(?x687, ?x53) *> conf = 0.31 ranks of expected_values: 4 EVAL 02xcb6n award_winner 0cp9f9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 52.000 19.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #20698-01wyz92 PRED entity: 01wyz92 PRED relation: people! PRED expected values: 059_w => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 51): 041rx (0.25 #1372, 0.23 #4869, 0.21 #7529), 033tf_ (0.19 #995, 0.18 #1071, 0.17 #843), 02ctzb (0.15 #90, 0.11 #166, 0.10 #926), 0xnvg (0.14 #468, 0.14 #164, 0.14 #1076), 01qhm_ (0.12 #462, 0.11 #158, 0.09 #1070), 09vc4s (0.11 #161, 0.10 #237, 0.09 #1073), 02w7gg (0.10 #4867, 0.09 #7679, 0.08 #7527), 07bch9 (0.08 #478, 0.08 #1542, 0.06 #1010), 048z7l (0.08 #115, 0.07 #419, 0.06 #1407), 07hwkr (0.07 #239, 0.07 #695, 0.07 #391) >> Best rule #1372 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 03nbbv; 03gvpk; 02qnbs; 06hgj; 030dx5; 0kbn5; 08jtv5; 045g4l; 03l26m; 035wq7; ... >> query: (?x3481, 041rx) <- profession(?x3481, ?x1032), location(?x3481, ?x2850), ?x2850 = 0cr3d >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #105 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 02ts3h; *> query: (?x3481, 059_w) <- award(?x3481, ?x2877), program(?x3481, ?x2583), award_winner(?x1206, ?x3481) *> conf = 0.04 ranks of expected_values: 21 EVAL 01wyz92 people! 059_w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 133.000 133.000 0.254 http://example.org/people/ethnicity/people #20697-0prhz PRED entity: 0prhz PRED relation: cinematography PRED expected values: 08z39v => 59 concepts (43 used for prediction) PRED predicted values (max 10 best out of 25): 0f3zf_ (0.14 #3, 0.02 #447, 0.02 #129), 03rqww (0.14 #42), 0bqytm (0.12 #80, 0.03 #143, 0.02 #270), 04qvl7 (0.03 #191, 0.02 #767, 0.02 #381), 0b_c7 (0.03 #1676, 0.03 #444, 0.03 #1221), 015gw6 (0.03 #1676, 0.03 #444, 0.03 #1221), 02vx4c2 (0.02 #350, 0.02 #735, 0.01 #670), 06r_by (0.02 #467, 0.02 #213, 0.02 #789), 08mhyd (0.02 #158, 0.02 #605, 0.01 #542), 027t8fw (0.02 #157, 0.01 #732, 0.01 #1120) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 033pf1; >> query: (?x4678, 0f3zf_) <- film(?x5495, ?x4678), film(?x556, ?x4678), music(?x4678, ?x9163), ?x556 = 0c1pj, award_winner(?x628, ?x5495) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #238 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 262 *> proper extension: 01br2w; 0hv81; 064lsn; 02qjv1p; 09rfpk; *> query: (?x4678, 08z39v) <- titles(?x162, ?x4678), genre(?x4678, ?x1403), ?x162 = 04xvlr *> conf = 0.01 ranks of expected_values: 18 EVAL 0prhz cinematography 08z39v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 59.000 43.000 0.143 http://example.org/film/film/cinematography #20696-021pqy PRED entity: 021pqy PRED relation: language PRED expected values: 02h40lc => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.96 #1281, 0.96 #1397, 0.95 #1809), 01chg (0.21 #115, 0.07 #345, 0.06 #696), 03mqtr (0.21 #115, 0.07 #345, 0.06 #696), 03rk0 (0.21 #115, 0.07 #345, 0.06 #696), 064_8sq (0.20 #834, 0.18 #308, 0.16 #659), 0688f (0.18 #94, 0.04 #152, 0.01 #209), 06nm1 (0.11 #240, 0.11 #882, 0.11 #1289), 04306rv (0.10 #292, 0.10 #351, 0.09 #3031), 0121sr (0.09 #103), 02bjrlw (0.08 #288, 0.08 #814, 0.07 #639) >> Best rule #1281 for best value: >> intensional similarity = 5 >> extensional distance = 415 >> proper extension: 01gglm; >> query: (?x4579, 02h40lc) <- film(?x2065, ?x4579), titles(?x1882, ?x4579), currency(?x4579, ?x10674), language(?x4579, ?x9113), produced_by(?x10774, ?x2065) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021pqy language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.962 http://example.org/film/film/language #20695-055c8 PRED entity: 055c8 PRED relation: award_winner! PRED expected values: 050yyb => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 132): 073hgx (0.33 #234, 0.02 #1346, 0.01 #3709), 0275n3y (0.21 #908, 0.11 #769, 0.10 #1047), 02wzl1d (0.17 #150, 0.03 #2791, 0.02 #4459), 09qvms (0.11 #430, 0.09 #1820, 0.08 #2793), 0418154 (0.11 #523, 0.07 #1357, 0.02 #3720), 058m5m4 (0.11 #471, 0.06 #1861, 0.05 #2000), 0g55tzk (0.11 #552, 0.05 #1942, 0.05 #2081), 0g5b0q5 (0.11 #437, 0.05 #1271, 0.03 #3634), 0fqpc7d (0.11 #453, 0.04 #731, 0.03 #870), 09q_6t (0.11 #425, 0.03 #1259, 0.03 #2788) >> Best rule #234 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0d608; >> query: (?x3186, 073hgx) <- award_winner(?x1033, ?x3186), nominated_for(?x3186, ?x2370), ?x2370 = 0yyts >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1567 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 142 *> proper extension: 03ft8; 03y2kr; 03p01x; 09zw90; 01g04k; *> query: (?x3186, 050yyb) <- location(?x3186, ?x5381), executive_produced_by(?x2203, ?x3186) *> conf = 0.03 ranks of expected_values: 67 EVAL 055c8 award_winner! 050yyb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 104.000 104.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20694-02ktrs PRED entity: 02ktrs PRED relation: participant! PRED expected values: 01wmgrf => 114 concepts (53 used for prediction) PRED predicted values (max 10 best out of 155): 01wmgrf (0.80 #17880, 0.80 #14046, 0.80 #14685), 0bbf1f (0.08 #206, 0.02 #7866, 0.02 #2758), 06tp4h (0.08 #434, 0.02 #8094, 0.02 #2986), 03xnq9_ (0.08 #382, 0.02 #4850, 0.02 #2934), 03lt8g (0.08 #70, 0.02 #17311, 0.02 #13477), 01mqh5 (0.08 #616), 062dn7 (0.08 #272), 013cr (0.08 #92), 0pz7h (0.08 #57), 01trhmt (0.05 #2731, 0.04 #1455, 0.02 #7839) >> Best rule #17880 for best value: >> intensional similarity = 3 >> extensional distance = 438 >> proper extension: 03zqc1; 02nwxc; 02qw2xb; 036hf4; >> query: (?x11519, ?x3122) <- award(?x11519, ?x567), participant(?x11519, ?x3122), film(?x11519, ?x2920) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ktrs participant! 01wmgrf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 53.000 0.804 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #20693-01ckbq PRED entity: 01ckbq PRED relation: award! PRED expected values: 0gbwp => 42 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2294): 01vs_v8 (0.94 #17401, 0.87 #14037, 0.43 #7309), 0478__m (0.73 #14777, 0.53 #18141, 0.50 #11413), 0gbwp (0.67 #14566, 0.59 #17930, 0.43 #7838), 03j24kf (0.62 #11452, 0.57 #8088, 0.43 #4723), 02z4b_8 (0.60 #15520, 0.44 #18884, 0.43 #5427), 03y82t6 (0.60 #14831, 0.43 #8103, 0.43 #4738), 01cwhp (0.57 #4015, 0.43 #7380, 0.38 #10744), 010hn (0.57 #3995, 0.20 #14088, 0.14 #7360), 015_30 (0.57 #3823, 0.14 #7188, 0.12 #17280), 06mt91 (0.53 #15432, 0.47 #18796, 0.33 #1975) >> Best rule #17401 for best value: >> intensional similarity = 7 >> extensional distance = 30 >> proper extension: 05b4l5x; 05zkcn5; 02g3gj; 03c7tr1; 02v1m7; 025m8l; 05p09zm; 054ks3; 02f72n; 02sp_v; ... >> query: (?x1479, 01vs_v8) <- award(?x8049, ?x1479), award(?x7112, ?x1479), award(?x1206, ?x1479), award_winner(?x342, ?x7112), award_winner(?x8049, ?x5297), award_nominee(?x1206, ?x7859), ?x7859 = 03j1p2n >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #14566 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 13 *> proper extension: 01by1l; 02f705; 02f5qb; 02f716; 02f71y; 02x17c2; 02f6ym; 02f73b; 02f777; 02f79n; *> query: (?x1479, 0gbwp) <- award(?x7112, ?x1479), award(?x1206, ?x1479), award_winner(?x342, ?x7112), ?x1206 = 01vrt_c, role(?x7112, ?x227), profession(?x7112, ?x220) *> conf = 0.67 ranks of expected_values: 3 EVAL 01ckbq award! 0gbwp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 42.000 17.000 0.938 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20692-026hh0m PRED entity: 026hh0m PRED relation: film! PRED expected values: 02k6rq => 105 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1112): 046qq (0.33 #742, 0.20 #4907, 0.14 #2825), 03cglm (0.33 #1046, 0.20 #5211, 0.14 #3129), 05nzw6 (0.20 #5357, 0.17 #1192, 0.14 #3275), 0prfz (0.17 #56, 0.14 #2139, 0.10 #4221), 0c35b1 (0.17 #1353, 0.14 #3436, 0.10 #5518), 0h0wc (0.17 #423, 0.14 #2506, 0.10 #4588), 051wwp (0.17 #876, 0.14 #2959, 0.10 #5041), 03kbb8 (0.17 #1246, 0.14 #3329, 0.10 #5411), 044lyq (0.17 #1264, 0.14 #3347, 0.10 #5429), 06151l (0.17 #25, 0.14 #2108, 0.10 #4190) >> Best rule #742 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01719t; 0gg5qcw; >> query: (?x10158, 046qq) <- language(?x10158, ?x254), produced_by(?x10158, ?x5781), film(?x5595, ?x10158), titles(?x812, ?x10158), ?x5595 = 059_gf >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #14905 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 86 *> proper extension: 026p_bs; 0b6tzs; 04mzf8; 0cc7hmk; 0f4_l; 0f40w; 07kh6f3; 02qzmz6; 0dx8gj; 065dc4; ... *> query: (?x10158, 02k6rq) <- language(?x10158, ?x254), produced_by(?x10158, ?x5781), film(?x1018, ?x10158), titles(?x812, ?x10158), ?x812 = 01jfsb *> conf = 0.01 ranks of expected_values: 1003 EVAL 026hh0m film! 02k6rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 105.000 56.000 0.333 http://example.org/film/actor/film./film/performance/film #20691-03d8njj PRED entity: 03d8njj PRED relation: nationality PRED expected values: 03rk0 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 71): 03rk0 (0.87 #846, 0.87 #346, 0.84 #646), 09c7w0 (0.77 #4103, 0.74 #4304, 0.74 #2201), 0dlv0 (0.25 #5308, 0.25 #5709, 0.25 #4604), 015y2q (0.25 #5709, 0.25 #4604, 0.24 #5007), 07ssc (0.11 #1815, 0.10 #1615, 0.10 #1415), 02jx1 (0.10 #3935, 0.10 #4236, 0.10 #2633), 0162b (0.07 #398, 0.02 #6511, 0.01 #3802), 0d060g (0.06 #707, 0.05 #1507, 0.04 #3507), 03rjj (0.05 #705, 0.04 #1005, 0.04 #905), 0f8l9c (0.04 #922, 0.03 #1022, 0.03 #2822) >> Best rule #846 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0338lq; 084z0w; 07yw6t; 02ctyy; 05zdk2; 04y0yc; 0bxy67; 02x2097; 03fwln; 01zt10; ... >> query: (?x11604, 03rk0) <- award(?x11604, ?x4443), nominated_for(?x4443, ?x2617), award_winner(?x4443, ?x2065), ?x2617 = 01p3ty >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03d8njj nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.868 http://example.org/people/person/nationality #20690-0jwvf PRED entity: 0jwvf PRED relation: list PRED expected values: 05glt => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.47 #100, 0.44 #107, 0.39 #142) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 02v8kmz; 0k4kk; 02q52q; 0gxfz; 0k4f3; 016kz1; 024lff; 0gcpc; 097zcz; 0cqnss; ... >> query: (?x5856, 05glt) <- film_sets_designed(?x2716, ?x5856), music(?x5856, ?x6971), film_release_region(?x5856, ?x94), nominated_for(?x601, ?x5856) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jwvf list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.474 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #20689-01yhvv PRED entity: 01yhvv PRED relation: award_nominee PRED expected values: 016xh5 => 98 concepts (39 used for prediction) PRED predicted values (max 10 best out of 854): 02cllz (0.85 #9309, 0.84 #2327, 0.83 #11636), 0993r (0.85 #9309, 0.84 #2327, 0.83 #11636), 02zq43 (0.85 #9309, 0.84 #2327, 0.83 #11636), 016xh5 (0.85 #9309, 0.84 #2327, 0.83 #11636), 04wf_b (0.85 #9309, 0.84 #2327, 0.83 #11636), 04rsd2 (0.85 #9309, 0.84 #2327, 0.83 #11636), 07hbxm (0.85 #9309, 0.84 #2327, 0.83 #11636), 01541z (0.85 #9309, 0.84 #2327, 0.83 #11636), 05mkhs (0.85 #9309, 0.84 #2327, 0.83 #11636), 01yhvv (0.73 #7277, 0.45 #295, 0.42 #9604) >> Best rule #9309 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 05tk7y; 0djywgn; >> query: (?x1410, ?x380) <- award_nominee(?x9236, ?x1410), award_nominee(?x380, ?x1410), award_nominee(?x100, ?x1410), ?x100 = 05vsxz, ?x9236 = 02fz3w >> conf = 0.85 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 01yhvv award_nominee 016xh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 39.000 0.845 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20688-0d810y PRED entity: 0d810y PRED relation: location PRED expected values: 04ly1 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 37): 0f2tj (0.42 #14478, 0.42 #46647, 0.42 #12065), 02_286 (0.18 #842, 0.17 #37, 0.11 #22557), 030qb3t (0.09 #25015, 0.09 #9735, 0.09 #27429), 0cr3d (0.04 #13818, 0.04 #50010, 0.04 #11405), 04jpl (0.03 #822, 0.03 #16909, 0.03 #9669), 0cc56 (0.03 #11317, 0.03 #862, 0.02 #57), 01_d4 (0.02 #102, 0.02 #907, 0.01 #13775), 0dclg (0.02 #117, 0.02 #922, 0.01 #17009), 0rh6k (0.02 #4, 0.02 #3222, 0.01 #11264), 059rby (0.02 #24948, 0.02 #26557, 0.02 #32188) >> Best rule #14478 for best value: >> intensional similarity = 2 >> extensional distance = 1354 >> proper extension: 01mqz0; 040db; 03flwk; 048_p; 043hg; 01vsy9_; 016z68; 0h1q6; 047jhq; 025_ql1; >> query: (?x5707, ?x6769) <- award_winner(?x1670, ?x5707), place_of_birth(?x5707, ?x6769) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #203 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 482 *> proper extension: 04r68; *> query: (?x5707, 04ly1) <- award_winner(?x1670, ?x5707), place_of_birth(?x5707, ?x6769), dog_breed(?x6769, ?x1706) *> conf = 0.01 ranks of expected_values: 33 EVAL 0d810y location 04ly1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 76.000 76.000 0.420 http://example.org/people/person/places_lived./people/place_lived/location #20687-03yj_0n PRED entity: 03yj_0n PRED relation: award_nominee PRED expected values: 06dn58 => 87 concepts (39 used for prediction) PRED predicted values (max 10 best out of 655): 06dn58 (0.81 #81145, 0.81 #44044, 0.81 #81144), 01_xtx (0.81 #81145, 0.81 #44044, 0.81 #81144), 03yj_0n (0.78 #802, 0.73 #5438, 0.69 #3120), 015rkw (0.42 #7319, 0.27 #6955, 0.24 #46363), 051wwp (0.36 #8109, 0.27 #6955, 0.24 #46363), 02l4rh (0.36 #8540, 0.27 #6955, 0.24 #46363), 01sp81 (0.36 #7140, 0.27 #6955, 0.17 #57956), 02l4pj (0.36 #7723, 0.27 #6955, 0.17 #57956), 01ksr1 (0.36 #7690, 0.27 #6955, 0.04 #23916), 016gr2 (0.33 #7201, 0.27 #6955, 0.24 #46363) >> Best rule #81145 for best value: >> intensional similarity = 3 >> extensional distance = 1481 >> proper extension: 0284n42; 01sbf2; 01ycck; 0gv40; 02lymt; 03sww; 01c6l; 037d35; 013423; 0d_skg; ... >> query: (?x3594, ?x7776) <- type_of_union(?x3594, ?x566), award_nominee(?x7776, ?x3594), award_nominee(?x561, ?x7776) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 03yj_0n award_nominee 06dn58 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 39.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20686-01hc1j PRED entity: 01hc1j PRED relation: institution! PRED expected values: 02_xgp2 => 137 concepts (81 used for prediction) PRED predicted values (max 10 best out of 15): 02_xgp2 (0.68 #879, 0.68 #367, 0.67 #385), 04zx3q1 (0.47 #379, 0.41 #138, 0.38 #86), 013zdg (0.33 #1156, 0.33 #382, 0.30 #963), 0bjrnt (0.33 #1156, 0.31 #88, 0.30 #963), 01rr_d (0.33 #1156, 0.30 #963, 0.29 #1441), 022h5x (0.33 #1156, 0.30 #963, 0.29 #1441), 02m4yg (0.33 #1156, 0.30 #963, 0.29 #1441), 01ysy9 (0.33 #1156, 0.30 #963, 0.29 #1441), 01gkg3 (0.33 #1156, 0.30 #963, 0.29 #1441), 071tyz (0.30 #963, 0.29 #1441, 0.19 #90) >> Best rule #879 for best value: >> intensional similarity = 4 >> extensional distance = 288 >> proper extension: 0ylvj; >> query: (?x11768, 02_xgp2) <- major_field_of_study(?x11768, ?x947), institution(?x4981, ?x11768), institution(?x4981, ?x12475), ?x12475 = 02_jjm >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hc1j institution! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 81.000 0.679 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20685-015c4g PRED entity: 015c4g PRED relation: award_winner! PRED expected values: 02q690_ => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 138): 013b2h (0.17 #6119, 0.13 #913, 0.11 #1469), 02cg41 (0.17 #6119, 0.10 #958, 0.10 #11403), 0466p0j (0.17 #6119, 0.10 #11403, 0.10 #909), 09gkdln (0.17 #6119, 0.10 #11403, 0.05 #1927), 05qb8vx (0.17 #6119, 0.10 #11403, 0.04 #3615), 09k5jh7 (0.17 #6119, 0.10 #11403, 0.03 #500), 05zksls (0.17 #6119, 0.10 #11403, 0.02 #313), 0fk0xk (0.17 #6119, 0.04 #3615, 0.02 #14184), 09pnw5 (0.14 #101, 0.04 #518, 0.04 #379), 02rjjll (0.11 #839, 0.09 #1395, 0.04 #4037) >> Best rule #6119 for best value: >> intensional similarity = 2 >> extensional distance = 1364 >> proper extension: 035_2h; 01j53q; >> query: (?x4436, ?x2220) <- award_winner(?x4436, ?x3442), award_winner(?x2220, ?x3442) >> conf = 0.17 => this is the best rule for 8 predicted values *> Best rule #1871 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 545 *> proper extension: 06jntd; *> query: (?x4436, 02q690_) <- award_winner(?x9222, ?x4436), actor(?x9222, ?x92) *> conf = 0.04 ranks of expected_values: 38 EVAL 015c4g award_winner! 02q690_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 110.000 110.000 0.173 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20684-017z49 PRED entity: 017z49 PRED relation: honored_for! PRED expected values: 0clfdj => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 117): 0hr6lkl (0.17 #12, 0.08 #256, 0.06 #1842), 0hndn2q (0.17 #32, 0.08 #276, 0.04 #642), 0gmdkyy (0.17 #24, 0.08 #268, 0.04 #634), 0dznvw (0.12 #240, 0.02 #484, 0.02 #606), 03gwpw2 (0.06 #127, 0.06 #1103, 0.06 #737), 09gkdln (0.06 #228, 0.05 #1082, 0.04 #1814), 04n2r9h (0.06 #158, 0.05 #280, 0.04 #2232), 02pgky2 (0.06 #198, 0.04 #1174, 0.04 #808), 02wzl1d (0.06 #129, 0.04 #1105, 0.04 #983), 03gyp30 (0.06 #224, 0.04 #1200, 0.03 #834) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02d44q; 0g5879y; 0bmhvpr; 0cmdwwg; >> query: (?x3482, 0hr6lkl) <- film_release_region(?x3482, ?x1499), nominated_for(?x3499, ?x3482), ?x1499 = 01znc_, ?x3499 = 03qgjwc >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #734 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 69 *> proper extension: 04mzf8; 070fnm; 0c_j9x; 0yx7h; 04nnpw; 02jkkv; *> query: (?x3482, 0clfdj) <- titles(?x812, ?x3482), award(?x3482, ?x2532), film(?x72, ?x3482), ?x812 = 01jfsb *> conf = 0.03 ranks of expected_values: 37 EVAL 017z49 honored_for! 0clfdj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 115.000 115.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #20683-0dh73w PRED entity: 0dh73w PRED relation: nominated_for PRED expected values: 04q827 => 118 concepts (69 used for prediction) PRED predicted values (max 10 best out of 370): 08cfr1 (0.41 #4852, 0.41 #4851, 0.29 #3234), 0gy0l_ (0.41 #4852, 0.41 #4851, 0.29 #3234), 01jr4j (0.41 #4852, 0.41 #4851, 0.29 #3234), 05r3qc (0.41 #4852, 0.41 #4851, 0.29 #3234), 016y_f (0.41 #4852, 0.41 #4851, 0.29 #3234), 05_5rjx (0.41 #4852, 0.41 #4851, 0.29 #3234), 05cj_j (0.41 #4852, 0.41 #4851, 0.29 #3234), 0bcndz (0.18 #247, 0.13 #1864, 0.08 #5099), 083skw (0.18 #380, 0.07 #1997, 0.03 #5232), 0jqd3 (0.13 #2624, 0.11 #5859, 0.10 #6470) >> Best rule #4852 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 04kj2v; 05728w1; 04gmp_z; 0bytkq; 0d5wn3; 07hhnl; 0bjkpt; 04_1nk; 03wd5tk; 0cdf37; ... >> query: (?x4168, ?x1708) <- award_winner(?x602, ?x4168), film_production_design_by(?x1708, ?x4168), genre(?x1708, ?x258) >> conf = 0.41 => this is the best rule for 7 predicted values No rule for expected values ranks of expected_values: EVAL 0dh73w nominated_for 04q827 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 69.000 0.413 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20682-0tzt_ PRED entity: 0tzt_ PRED relation: state PRED expected values: 05k7sb => 157 concepts (82 used for prediction) PRED predicted values (max 10 best out of 60): 05k7sb (0.56 #87, 0.56 #24, 0.51 #3285), 0k3jq (0.29 #2510, 0.28 #5962, 0.28 #3027), 09c7w0 (0.29 #2510, 0.28 #5962, 0.28 #3027), 01n7q (0.25 #2955, 0.23 #3213, 0.18 #1481), 07z1m (0.19 #281, 0.07 #452, 0.07 #106), 04rrd (0.12 #284, 0.04 #369, 0.03 #2876), 01x73 (0.11 #454, 0.08 #969, 0.07 #108), 059rby (0.09 #523, 0.08 #780, 0.05 #2079), 07b_l (0.08 #731, 0.08 #1592, 0.08 #903), 03v0t (0.08 #3241, 0.06 #306, 0.05 #1423) >> Best rule #87 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0t_4_; >> query: (?x13065, ?x2020) <- contains(?x11677, ?x13065), contains(?x2020, ?x13065), adjoins(?x11677, ?x6296), ?x2020 = 05k7sb, citytown(?x9947, ?x13065) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tzt_ state 05k7sb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 82.000 0.556 http://example.org/base/biblioness/bibs_location/state #20681-0329r5 PRED entity: 0329r5 PRED relation: position PRED expected values: 02nzb8 => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 8): 02nzb8 (0.96 #44, 0.95 #80, 0.95 #84), 03f0fp (0.51 #135, 0.50 #156, 0.32 #88), 02qvgy (0.51 #135), 02md_2 (0.50 #156, 0.32 #88, 0.31 #162), 05b3ts (0.32 #88, 0.30 #92, 0.25 #96), 04nfpk (0.32 #88, 0.30 #92, 0.25 #96), 02g_6x (0.32 #88, 0.30 #92, 0.25 #96), 01r3hr (0.32 #88, 0.30 #92, 0.25 #96) >> Best rule #44 for best value: >> intensional similarity = 10 >> extensional distance = 151 >> proper extension: 01k2yr; 0371rb; 0gxkm; 02mplj; 04mp9q; 037mjv; 0196bp; 0k_l4; 023fb; 01rlzn; ... >> query: (?x7616, ?x60) <- position(?x7616, ?x530), position(?x7616, ?x203), position(?x7616, ?x63), ?x530 = 02_j1w, team(?x60, ?x7616), ?x63 = 02sdk9v, sport(?x7616, ?x471), ?x471 = 02vx4, ?x203 = 0dgrmp, position(?x59, ?x60) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0329r5 position 02nzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.964 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #20680-02vp1f_ PRED entity: 02vp1f_ PRED relation: titles! PRED expected values: 07s9rl0 => 97 concepts (59 used for prediction) PRED predicted values (max 10 best out of 149): 07s9rl0 (0.50 #1, 0.48 #2161, 0.37 #3395), 07ssc (0.38 #2169, 0.14 #730, 0.14 #1551), 03k9fj (0.34 #3599, 0.31 #4423, 0.26 #4937), 01hmnh (0.34 #3599, 0.31 #309, 0.31 #230), 01z4y (0.21 #5178, 0.19 #5383, 0.16 #4662), 01jfsb (0.16 #740, 0.14 #1050, 0.14 #1458), 017fp (0.15 #125, 0.14 #2183, 0.12 #1565), 07c52 (0.15 #1265, 0.15 #2909, 0.15 #2807), 0f8l9c (0.11 #928, 0.09 #3291, 0.09 #5040), 0d060g (0.11 #928, 0.09 #3291, 0.09 #5040) >> Best rule #1 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 02r858_; >> query: (?x251, 07s9rl0) <- award(?x251, ?x5824), genre(?x251, ?x811), currency(?x251, ?x170), ?x170 = 09nqf, ?x5824 = 02qysm0 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vp1f_ titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 59.000 0.500 http://example.org/media_common/netflix_genre/titles #20679-0bbc17 PRED entity: 0bbc17 PRED relation: genre! PRED expected values: 0kv2hv => 31 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1874): 02v63m (0.67 #7644, 0.50 #3915, 0.43 #9510), 02v5_g (0.67 #8272, 0.50 #4543, 0.43 #10138), 047vp1n (0.60 #6910, 0.33 #14370, 0.33 #1315), 034xyf (0.57 #12681, 0.50 #5221, 0.35 #16410), 04yc76 (0.57 #11647, 0.40 #6052, 0.33 #2322), 02tgz4 (0.57 #12753, 0.33 #3428, 0.25 #5293), 03z20c (0.57 #11681, 0.33 #2356, 0.25 #4221), 065zlr (0.57 #11603, 0.33 #2278, 0.25 #4143), 0kv2hv (0.57 #11328, 0.33 #2003, 0.25 #3868), 02v8kmz (0.50 #3758, 0.47 #14947, 0.44 #16814) >> Best rule #7644 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 01585b; >> query: (?x12586, 02v63m) <- genre(?x4500, ?x12586), genre(?x1811, ?x12586), film_crew_role(?x1811, ?x3305), film_crew_role(?x1811, ?x137), film(?x545, ?x1811), ?x4500 = 01pj_5, titles(?x2480, ?x1811), nominated_for(?x298, ?x1811), ?x137 = 09zzb8, film_crew_role(?x835, ?x3305), ?x835 = 0164qt >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #11328 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 5 *> proper extension: 06cvj; 02l7c8; 09q17; 011ys5; *> query: (?x12586, 0kv2hv) <- genre(?x1811, ?x12586), genre(?x1728, ?x12586), ?x1811 = 01pgp6, film_release_region(?x1728, ?x94), film(?x166, ?x1728), film(?x4228, ?x1728), film(?x3651, ?x1728), featured_film_locations(?x1728, ?x1036), type_of_union(?x4228, ?x566), people(?x2510, ?x3651) *> conf = 0.57 ranks of expected_values: 9 EVAL 0bbc17 genre! 0kv2hv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 31.000 14.000 0.667 http://example.org/film/film/genre #20678-0345gh PRED entity: 0345gh PRED relation: institution! PRED expected values: 014mlp 0bkj86 => 169 concepts (162 used for prediction) PRED predicted values (max 10 best out of 21): 019v9k (0.73 #655, 0.67 #722, 0.67 #365), 014mlp (0.73 #71, 0.72 #673, 0.71 #1028), 0bkj86 (0.65 #98, 0.53 #74, 0.44 #744), 016t_3 (0.54 #649, 0.53 #716, 0.50 #138), 04zx3q1 (0.47 #68, 0.41 #92, 0.33 #738), 027f2w (0.41 #100, 0.29 #746, 0.28 #2474), 0bjrnt (0.41 #96, 0.28 #2474, 0.27 #72), 07s6fsf (0.40 #67, 0.39 #714, 0.36 #647), 02mjs7 (0.33 #70, 0.28 #2474, 0.25 #4), 013zdg (0.28 #2474, 0.25 #541, 0.25 #7) >> Best rule #655 for best value: >> intensional similarity = 6 >> extensional distance = 107 >> proper extension: 05xb7q; 03fcbb; >> query: (?x4692, 019v9k) <- contains(?x362, ?x4692), institution(?x4981, ?x4692), institution(?x865, ?x4692), ?x865 = 02h4rq6, ?x4981 = 03bwzr4, category(?x4692, ?x134) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #71 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 01dbns; 01c57n; *> query: (?x4692, 014mlp) <- institution(?x865, ?x4692), major_field_of_study(?x4692, ?x6364), major_field_of_study(?x865, ?x254), ?x6364 = 05qt0 *> conf = 0.73 ranks of expected_values: 2, 3 EVAL 0345gh institution! 0bkj86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 169.000 162.000 0.734 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0345gh institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 169.000 162.000 0.734 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20677-0525b PRED entity: 0525b PRED relation: film PRED expected values: 01pv91 085bd1 => 83 concepts (50 used for prediction) PRED predicted values (max 10 best out of 381): 03cfkrw (0.20 #747, 0.06 #69464, 0.04 #56995), 02p76f9 (0.20 #1421), 0pv54 (0.20 #955), 02prwdh (0.20 #937), 0645k5 (0.20 #470), 01f39b (0.06 #2757, 0.02 #13443, 0.02 #20567), 02b6n9 (0.06 #69464, 0.05 #89059, 0.04 #56995), 02qcr (0.06 #69464, 0.05 #89059, 0.04 #56995), 05dptj (0.06 #69464, 0.05 #89059, 0.04 #56995), 04nnpw (0.06 #69464, 0.05 #89059, 0.04 #56995) >> Best rule #747 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 016z68; >> query: (?x11858, 03cfkrw) <- nominated_for(?x11858, ?x2550), ?x2550 = 07j8r, award(?x11858, ?x704) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0525b film 085bd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 50.000 0.200 http://example.org/film/actor/film./film/performance/film EVAL 0525b film 01pv91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 50.000 0.200 http://example.org/film/actor/film./film/performance/film #20676-09pj68 PRED entity: 09pj68 PRED relation: award_winner PRED expected values: 09hd16 => 25 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1589): 09hd16 (0.64 #1524, 0.33 #3658, 0.27 #9155), 0h53p1 (0.64 #1524, 0.33 #3454, 0.27 #9155), 0d7hg4 (0.64 #1524, 0.33 #3426, 0.27 #9155), 0h584v (0.64 #1524, 0.33 #3660, 0.27 #9155), 0884hk (0.64 #1524, 0.33 #3657, 0.27 #9155), 09_99w (0.64 #1524, 0.33 #4290, 0.21 #21385), 0brkwj (0.64 #1524, 0.33 #4215, 0.20 #13738), 083chw (0.64 #1524, 0.27 #16798, 0.22 #16799), 015pxr (0.64 #1524, 0.27 #9155, 0.25 #4876), 0bt4r4 (0.64 #1524, 0.27 #9155, 0.22 #16799) >> Best rule #1524 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 09qvms; >> query: (?x7573, ?x2650) <- award_winner(?x7573, ?x4035), award_winner(?x7573, ?x548), award_winner(?x7573, ?x237), honored_for(?x7573, ?x5808), honored_for(?x7573, ?x1415), ?x5808 = 05lfwd, ?x548 = 014x77, ceremony(?x746, ?x7573), profession(?x237, ?x319), award(?x237, ?x401), produced_by(?x1012, ?x4035), ?x1415 = 09p0ct, award_winner(?x2650, ?x4035), award_nominee(?x364, ?x237), place_of_birth(?x4035, ?x739) >> conf = 0.64 => this is the best rule for 33 predicted values ranks of expected_values: 1 EVAL 09pj68 award_winner 09hd16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 14.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20675-026c0p PRED entity: 026c0p PRED relation: gender PRED expected values: 05zppz => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #47, 0.85 #91, 0.85 #13), 02zsn (0.51 #153, 0.46 #209, 0.46 #206) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 238 >> proper extension: 017r2; 03jm6c; 053yx; 0127gn; 0flpy; 0bdlj; 03h_yfh; 023361; 03_js; 0c_md_; ... >> query: (?x12700, 05zppz) <- people(?x4322, ?x12700), place_of_death(?x12700, ?x4698), place_of_birth(?x12700, ?x14446), profession(?x12700, ?x1032) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026c0p gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.850 http://example.org/people/person/gender #20674-04wx2v PRED entity: 04wx2v PRED relation: location PRED expected values: 0cc56 => 95 concepts (61 used for prediction) PRED predicted values (max 10 best out of 51): 030qb3t (0.19 #6510, 0.19 #9722, 0.17 #11329), 0cc56 (0.09 #1662, 0.06 #56, 0.05 #8893), 0cr3d (0.09 #144, 0.06 #31476, 0.05 #35493), 04jpl (0.07 #2427, 0.06 #6445, 0.06 #17), 059rby (0.04 #8853, 0.04 #6444, 0.04 #5641), 05qtj (0.04 #3454, 0.02 #9880, 0.02 #31572), 01531 (0.03 #1763, 0.03 #31489, 0.03 #8994), 0d6lp (0.03 #2577, 0.02 #31499, 0.02 #35516), 01n7q (0.03 #6490, 0.03 #9702, 0.03 #8899), 0rh6k (0.03 #4, 0.02 #4021, 0.02 #31336) >> Best rule #6510 for best value: >> intensional similarity = 2 >> extensional distance = 555 >> proper extension: 03f6fl0; 081k8; 0130sy; 067xw; 02n9k; 02s58t; 040dv; 0163t3; 03f1zhf; 020_4z; ... >> query: (?x9437, 030qb3t) <- languages(?x9437, ?x254), ?x254 = 02h40lc >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1662 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 233 *> proper extension: 033hqf; 0bz5v2; 04cf09; 01wjrn; 041mt; 049_zz; 0mj0c; 02lt8; 02v406; 07_grx; ... *> query: (?x9437, 0cc56) <- student(?x11036, ?x9437), location(?x9437, ?x739), ?x739 = 02_286 *> conf = 0.09 ranks of expected_values: 2 EVAL 04wx2v location 0cc56 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 61.000 0.187 http://example.org/people/person/places_lived./people/place_lived/location #20673-02qfhb PRED entity: 02qfhb PRED relation: film PRED expected values: 03wjm2 => 113 concepts (99 used for prediction) PRED predicted values (max 10 best out of 895): 024lt6 (0.64 #37468, 0.58 #89206, 0.57 #96343), 043t8t (0.33 #2573, 0.04 #155227, 0.03 #160581), 01gkp1 (0.22 #2599, 0.04 #155227, 0.03 #160581), 01chpn (0.22 #2892, 0.04 #155227, 0.03 #160581), 04jpk2 (0.17 #4153, 0.05 #5937, 0.02 #7721), 01shy7 (0.16 #5776, 0.11 #2208, 0.08 #3992), 026wlxw (0.12 #1413, 0.11 #3197, 0.02 #15685), 0gy2y8r (0.12 #669, 0.11 #2453), 06_wqk4 (0.12 #127, 0.07 #9047, 0.05 #12615), 0bvn25 (0.12 #50, 0.05 #5402, 0.03 #62494) >> Best rule #37468 for best value: >> intensional similarity = 3 >> extensional distance = 288 >> proper extension: 057hz; >> query: (?x4929, ?x9941) <- film(?x4929, ?x3441), award_winner(?x9941, ?x4929), participant(?x2012, ?x4929) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #41006 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 290 *> proper extension: 017s11; 025jfl; 0288fyj; 08wr3kg; 01x15dc; 025hwq; *> query: (?x4929, 03wjm2) <- award_nominee(?x4929, ?x397), sibling(?x397, ?x12975) *> conf = 0.02 ranks of expected_values: 385 EVAL 02qfhb film 03wjm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 113.000 99.000 0.644 http://example.org/film/actor/film./film/performance/film #20672-0cyhq PRED entity: 0cyhq PRED relation: people! PRED expected values: 0gk4g => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 43): 0gk4g (0.30 #208, 0.17 #670, 0.17 #1792), 01_qc_ (0.25 #28, 0.07 #490, 0.06 #94), 0dq9p (0.22 #215, 0.15 #677, 0.10 #149), 04p3w (0.12 #11, 0.10 #407, 0.10 #143), 02k6hp (0.12 #37, 0.06 #103, 0.05 #169), 0j8hd (0.12 #113, 0.10 #179, 0.03 #971), 02y0js (0.10 #398, 0.08 #1058, 0.08 #860), 0qcr0 (0.08 #991, 0.08 #2707, 0.08 #1255), 01psyx (0.07 #441, 0.03 #507, 0.03 #1101), 01l2m3 (0.06 #874, 0.05 #1072, 0.04 #280) >> Best rule #208 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0cm03; >> query: (?x10883, 0gk4g) <- type_of_union(?x10883, ?x566), organization(?x10883, ?x8603), ?x8603 = 02_l9 >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cyhq people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.304 http://example.org/people/cause_of_death/people #20671-01pny5 PRED entity: 01pny5 PRED relation: type_of_union PRED expected values: 01g63y => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.74 #5, 0.72 #49, 0.71 #17), 01g63y (0.20 #469, 0.19 #393, 0.16 #10), 01bl8s (0.20 #469, 0.19 #393, 0.04 #7), 0jgjn (0.20 #469, 0.19 #393, 0.03 #36) >> Best rule #5 for best value: >> intensional similarity = 6 >> extensional distance = 21 >> proper extension: 01w9ph_; >> query: (?x12791, 04ztj) <- artists(?x2809, ?x12791), artists(?x1572, ?x12791), ?x2809 = 05w3f, ?x1572 = 06by7, profession(?x12791, ?x131), role(?x12791, ?x227) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #469 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4047 *> proper extension: 06f5j; *> query: (?x12791, ?x566) <- profession(?x12791, ?x2659), profession(?x5879, ?x2659), profession(?x1521, ?x2659), role(?x1521, ?x214), category(?x5879, ?x134), type_of_union(?x1521, ?x566) *> conf = 0.20 ranks of expected_values: 2 EVAL 01pny5 type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 124.000 0.739 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20670-019m9h PRED entity: 019m9h PRED relation: colors PRED expected values: 04mkbj 038hg => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 19): 06fvc (0.53 #1223, 0.48 #1337, 0.46 #1375), 01g5v (0.45 #115, 0.44 #79, 0.44 #1243), 019sc (0.45 #115, 0.40 #629, 0.39 #1533), 038hg (0.33 #50, 0.31 #222, 0.26 #355), 04d18d (0.29 #916, 0.12 #1488, 0.11 #1183), 067z2v (0.29 #916, 0.12 #1488, 0.11 #1183), 01l849 (0.19 #96, 0.12 #1488, 0.12 #1623), 06kqt3 (0.15 #73, 0.12 #1488, 0.11 #92), 088fh (0.12 #1488, 0.12 #1341, 0.11 #1183), 02rnmb (0.12 #1488, 0.11 #1183, 0.11 #622) >> Best rule #1223 for best value: >> intensional similarity = 11 >> extensional distance = 131 >> proper extension: 03j7cf; >> query: (?x8703, 06fvc) <- position(?x8703, ?x203), ?x203 = 0dgrmp, colors(?x8703, ?x663), colors(?x10196, ?x663), colors(?x6645, ?x663), colors(?x1576, ?x663), colors(?x4692, ?x663), ?x4692 = 0345gh, position(?x1576, ?x180), team(?x935, ?x6645), team(?x10129, ?x10196) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 10 *> proper extension: 02mplj; 032498; 0498yf; *> query: (?x8703, 038hg) <- position(?x8703, ?x530), position(?x8703, ?x203), position(?x8703, ?x63), ?x203 = 0dgrmp, colors(?x8703, ?x663), ?x663 = 083jv, ?x63 = 02sdk9v, sport(?x8703, ?x471), ?x471 = 02vx4, teams(?x1167, ?x8703), ?x530 = 02_j1w, country(?x1167, ?x583) *> conf = 0.33 ranks of expected_values: 4, 12 EVAL 019m9h colors 038hg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 99.000 0.526 http://example.org/sports/sports_team/colors EVAL 019m9h colors 04mkbj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 99.000 99.000 0.526 http://example.org/sports/sports_team/colors #20669-02dtg PRED entity: 02dtg PRED relation: dog_breed PRED expected values: 01t032 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 1): 01t032 (0.84 #11, 0.83 #9, 0.83 #13) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0fsb8; >> query: (?x479, 01t032) <- dog_breed(?x479, ?x1706), teams(?x479, ?x7643), location(?x115, ?x479) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02dtg dog_breed 01t032 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.837 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #20668-012x2b PRED entity: 012x2b PRED relation: participant! PRED expected values: 0bq2g => 101 concepts (50 used for prediction) PRED predicted values (max 10 best out of 202): 024dgj (0.81 #8299, 0.80 #12129, 0.80 #19154), 0bbf1f (0.14 #206, 0.02 #7864, 0.01 #18720), 01p4vl (0.14 #493, 0.01 #8151), 029_3 (0.14 #284), 0gx_p (0.07 #1697, 0.04 #1059, 0.03 #2335), 05ty4m (0.05 #2574, 0.03 #3850, 0.02 #4488), 09r9dp (0.05 #2820, 0.02 #4096, 0.02 #4734), 0237fw (0.04 #7826, 0.03 #11658, 0.02 #18682), 0c6qh (0.04 #811, 0.03 #1449, 0.03 #2087), 0456xp (0.04 #703, 0.03 #1341, 0.03 #1979) >> Best rule #8299 for best value: >> intensional similarity = 4 >> extensional distance = 259 >> proper extension: 07c0j; >> query: (?x9601, ?x3503) <- nominated_for(?x9601, ?x11958), participant(?x9601, ?x3503), featured_film_locations(?x11958, ?x362), film(?x2092, ?x11958) >> conf = 0.81 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 012x2b participant! 0bq2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 50.000 0.807 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #20667-01jgkj2 PRED entity: 01jgkj2 PRED relation: artist! PRED expected values: 03rhqg => 138 concepts (79 used for prediction) PRED predicted values (max 10 best out of 115): 033hn8 (0.24 #156, 0.15 #14, 0.14 #582), 015_1q (0.20 #2153, 0.20 #446, 0.19 #588), 03mp8k (0.19 #210, 0.14 #636, 0.12 #494), 043g7l (0.19 #174, 0.12 #458, 0.11 #600), 03rhqg (0.16 #584, 0.15 #1013, 0.14 #442), 0g768 (0.15 #38, 0.14 #606, 0.11 #1461), 0181dw (0.15 #43, 0.14 #611, 0.11 #1608), 016ckq (0.14 #612, 0.10 #44, 0.10 #186), 01cszh (0.14 #153, 0.12 #579, 0.10 #11), 0fb0v (0.14 #149, 0.10 #7, 0.10 #1004) >> Best rule #156 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01m65sp; 01vv6_6; 0g476; >> query: (?x9176, 033hn8) <- artists(?x671, ?x9176), actor(?x5529, ?x9176), ?x5529 = 026bfsh >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #584 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 123 *> proper extension: 0qmny; *> query: (?x9176, 03rhqg) <- artists(?x3928, ?x9176), ?x3928 = 0gywn *> conf = 0.16 ranks of expected_values: 5 EVAL 01jgkj2 artist! 03rhqg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 79.000 0.238 http://example.org/music/record_label/artist #20666-06gcn PRED entity: 06gcn PRED relation: influenced_by! PRED expected values: 07hgm => 90 concepts (39 used for prediction) PRED predicted values (max 10 best out of 55): 05xq9 (0.20 #1755, 0.12 #4348, 0.08 #10569), 0478__m (0.20 #1738, 0.12 #4331, 0.04 #10552), 01kcms4 (0.13 #7545, 0.06 #8063, 0.03 #11691), 01vsy7t (0.12 #4850, 0.07 #7442, 0.06 #8479), 07hgm (0.12 #5054, 0.06 #8683, 0.06 #9201), 070b4 (0.12 #5031, 0.06 #8660, 0.05 #9697), 01ww_vs (0.12 #5137, 0.06 #8766, 0.02 #13426), 02cpp (0.12 #4912, 0.06 #8541, 0.02 #13201), 03g5jw (0.08 #10413, 0.07 #10931, 0.07 #12999), 0167xy (0.07 #6656, 0.04 #19100, 0.03 #11839) >> Best rule #1755 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 01vsqvs; >> query: (?x7620, 05xq9) <- artists(?x9853, ?x7620), artists(?x9063, ?x7620), artists(?x2664, ?x7620), ?x9063 = 0cx7f, ?x9853 = 02qm5j, origin(?x7620, ?x362), artists(?x2664, ?x3122), artists(?x2664, ?x215), location(?x3122, ?x4622), ?x215 = 07s3vqk >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #5054 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 0qmpd; *> query: (?x7620, 07hgm) <- artists(?x3734, ?x7620), artists(?x3370, ?x7620), group(?x75, ?x7620), artists(?x3734, ?x3735), ?x75 = 07y_7, ?x3735 = 0lzkm, artists(?x3370, ?x3869), ?x3869 = 06gd4, parent_genre(?x3370, ?x283) *> conf = 0.12 ranks of expected_values: 5 EVAL 06gcn influenced_by! 07hgm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 39.000 0.200 http://example.org/influence/influence_node/influenced_by #20665-02607j PRED entity: 02607j PRED relation: student PRED expected values: 0252fh => 203 concepts (166 used for prediction) PRED predicted values (max 10 best out of 1487): 073v6 (0.12 #2616, 0.11 #10972, 0.11 #6794), 08k1lz (0.11 #12179, 0.11 #10090, 0.11 #8001), 0f7h2g (0.11 #12068, 0.11 #9979, 0.11 #7890), 063vn (0.11 #10742, 0.10 #17009, 0.09 #19098), 0d3k14 (0.11 #12296, 0.10 #18563, 0.09 #20652), 02vntj (0.08 #702, 0.07 #23681, 0.06 #2791), 0fpzt5 (0.08 #1533, 0.06 #3622, 0.06 #5711), 014vk4 (0.08 #2008, 0.06 #4097, 0.06 #6186), 03f1r6t (0.08 #901, 0.06 #2990, 0.06 #5079), 0hfml (0.08 #1290, 0.06 #3379, 0.06 #5468) >> Best rule #2616 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0c0sl; >> query: (?x3394, 073v6) <- service_language(?x3394, ?x254), currency(?x3394, ?x170), registering_agency(?x3394, ?x1982), ?x254 = 02h40lc >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #32676 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 016sd3; *> query: (?x3394, 0252fh) <- colors(?x3394, ?x663), country(?x3394, ?x94), category(?x3394, ?x134), ?x663 = 083jv *> conf = 0.02 ranks of expected_values: 675 EVAL 02607j student 0252fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 203.000 166.000 0.118 http://example.org/education/educational_institution/students_graduates./education/education/student #20664-08s6mr PRED entity: 08s6mr PRED relation: film! PRED expected values: 01cj6y => 76 concepts (34 used for prediction) PRED predicted values (max 10 best out of 874): 02ld6x (0.57 #2078, 0.51 #6234, 0.50 #10389), 0fvf9q (0.51 #6234, 0.43 #49876, 0.40 #51955), 0csdzz (0.51 #6234, 0.43 #49876, 0.40 #51955), 025jfl (0.51 #6234, 0.43 #49876, 0.40 #51955), 0p8r1 (0.50 #2660, 0.05 #21362, 0.04 #17205), 0l6px (0.27 #4541, 0.21 #6619, 0.20 #385), 01f6zc (0.21 #7174, 0.18 #5096, 0.10 #940), 0525b (0.20 #1909, 0.18 #6065, 0.14 #8143), 016gr2 (0.20 #192, 0.18 #4348, 0.14 #6426), 01j5ws (0.20 #2588, 0.02 #17133, 0.01 #23367) >> Best rule #2078 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 09gq0x5; 08sfxj; 011ywj; >> query: (?x7590, ?x286) <- award_winner(?x7590, ?x286), nominated_for(?x1053, ?x7590), film(?x1739, ?x7590), ?x1739 = 015rkw >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #11143 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 09xbpt; 03twd6; 035w2k; 047vnkj; 03rg2b; 0322yj; *> query: (?x7590, 01cj6y) <- award_winner(?x7590, ?x286), language(?x7590, ?x5607), film_crew_role(?x7590, ?x281), ?x5607 = 064_8sq *> conf = 0.01 ranks of expected_values: 776 EVAL 08s6mr film! 01cj6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 34.000 0.568 http://example.org/film/actor/film./film/performance/film #20663-0gys2jp PRED entity: 0gys2jp PRED relation: category PRED expected values: 08mbj5d => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.33 #3, 0.33 #8, 0.31 #7) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 03cv_gy; >> query: (?x11701, 08mbj5d) <- country(?x11701, ?x2346), genre(?x11701, ?x3312), film_release_distribution_medium(?x11701, ?x81), film(?x4835, ?x11701), ?x3312 = 02p0szs >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gys2jp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.333 http://example.org/common/topic/webpage./common/webpage/category #20662-01fs_4 PRED entity: 01fs_4 PRED relation: company PRED expected values: 01skqzw => 131 concepts (111 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.07 #1353, 0.04 #5024, 0.02 #5798), 03ksy (0.03 #436, 0.02 #1402), 06hhp (0.03 #516), 0bwfn (0.03 #501), 02sjgpq (0.03 #496), 07vjm (0.03 #485), 01w3v (0.03 #401), 016ckq (0.03 #693, 0.02 #886), 01n2m6 (0.03 #725), 03mp8k (0.03 #718) >> Best rule #1353 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 0j5b8; >> query: (?x3868, 09c7w0) <- people(?x268, ?x3868), people(?x1050, ?x3868), religion(?x3868, ?x7131) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01fs_4 company 01skqzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 111.000 0.067 http://example.org/people/person/employment_history./business/employment_tenure/company #20661-0k57l PRED entity: 0k57l PRED relation: profession PRED expected values: 0dxtg => 95 concepts (93 used for prediction) PRED predicted values (max 10 best out of 101): 0dxtg (0.83 #895, 0.80 #160, 0.80 #1483), 01d_h8 (0.72 #1035, 0.68 #7066, 0.67 #9124), 0cbd2 (0.44 #5448, 0.42 #6625, 0.41 #6478), 02krf9 (0.41 #1348, 0.41 #1495, 0.31 #1054), 03gjzk (0.41 #1337, 0.37 #10897, 0.36 #1484), 0np9r (0.38 #10020, 0.24 #1342, 0.18 #1489), 0kyk (0.28 #5469, 0.28 #6499, 0.28 #4586), 01c72t (0.27 #5000, 0.17 #2374, 0.16 #2668), 05z96 (0.22 #2834, 0.21 #2687, 0.21 #2981), 09jwl (0.20 #4722, 0.18 #3545, 0.18 #1340) >> Best rule #895 for best value: >> intensional similarity = 7 >> extensional distance = 21 >> proper extension: 015pxr; 0hskw; 03g5_y; 0q9t7; 0894_x; >> query: (?x8472, 0dxtg) <- profession(?x8472, ?x1146), profession(?x8472, ?x1032), profession(?x8472, ?x524), ?x524 = 02jknp, ?x1032 = 02hrh1q, ?x1146 = 018gz8, people(?x5741, ?x8472) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k57l profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 93.000 0.826 http://example.org/people/person/profession #20660-0g5838s PRED entity: 0g5838s PRED relation: language PRED expected values: 06mp7 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 33): 064_8sq (0.15 #312, 0.14 #194, 0.13 #603), 06nm1 (0.11 #707, 0.10 #650, 0.10 #879), 02bjrlw (0.09 #175, 0.08 #117, 0.08 #59), 04306rv (0.08 #644, 0.08 #296, 0.07 #3003), 03_9r (0.08 #125, 0.08 #67, 0.07 #183), 06b_j (0.07 #195, 0.06 #137, 0.06 #79), 0t_2 (0.06 #13, 0.05 #246, 0.03 #362), 05zjd (0.05 #198, 0.04 #24, 0.04 #82), 0653m (0.04 #651, 0.04 #1108, 0.04 #594), 0jzc (0.04 #658, 0.04 #134, 0.04 #76) >> Best rule #312 for best value: >> intensional similarity = 4 >> extensional distance = 149 >> proper extension: 09p7fh; 0pd6l; 0m_h6; >> query: (?x3076, 064_8sq) <- nominated_for(?x1245, ?x3076), ?x1245 = 0gqwc, genre(?x3076, ?x53), nominated_for(?x2891, ?x3076) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 0ds35l9; 0c3ybss; 0c0nhgv; 0cnztc4; 047msdk; 0fpkhkz; 04jkpgv; 02r1c18; 0by1wkq; 0gz6b6g; ... *> query: (?x3076, 06mp7) <- film_release_region(?x3076, ?x1499), ?x1499 = 01znc_, film_festivals(?x3076, ?x4903), language(?x3076, ?x254) *> conf = 0.04 ranks of expected_values: 12 EVAL 0g5838s language 06mp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 61.000 61.000 0.146 http://example.org/film/film/language #20659-017s11 PRED entity: 017s11 PRED relation: production_companies! PRED expected values: 0gzy02 07p62k 01y9jr => 129 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1134): 02rb84n (0.50 #21438, 0.47 #23583, 0.45 #13938), 04j14qc (0.50 #21438, 0.47 #23583, 0.45 #13938), 07s846j (0.50 #21438, 0.47 #23583, 0.45 #13938), 01chpn (0.50 #21438, 0.47 #23583, 0.45 #13938), 05g8pg (0.50 #21438, 0.47 #23583, 0.45 #13938), 01l_pn (0.50 #21438, 0.47 #23583, 0.45 #13938), 0dgq80b (0.50 #21438, 0.47 #23583, 0.45 #13938), 087vnr5 (0.50 #21438, 0.47 #23583, 0.45 #13938), 0dc7hc (0.50 #21438, 0.47 #23583, 0.45 #13938), 01qvz8 (0.50 #21438, 0.47 #23583, 0.45 #13938) >> Best rule #21438 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0kk9v; 056ws9; 04rcl7; 02x2097; >> query: (?x541, ?x770) <- nominated_for(?x541, ?x770), production_companies(?x80, ?x541), award_winner(?x1105, ?x541) >> conf = 0.50 => this is the best rule for 17 predicted values *> Best rule #15011 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 0jz9f; 016tt2; 025jfl; 0g1rw; 05qd_; 016tw3; 054lpb6; 030_1m; 017jv5; 03xsby; ... *> query: (?x541, ?x6882) <- film(?x541, ?x6882), award_nominee(?x541, ?x163), currency(?x6882, ?x170) *> conf = 0.41 ranks of expected_values: 36, 74, 400 EVAL 017s11 production_companies! 01y9jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 129.000 123.000 0.504 http://example.org/film/film/production_companies EVAL 017s11 production_companies! 07p62k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 129.000 123.000 0.504 http://example.org/film/film/production_companies EVAL 017s11 production_companies! 0gzy02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 129.000 123.000 0.504 http://example.org/film/film/production_companies #20658-0157m PRED entity: 0157m PRED relation: participant PRED expected values: 06pj8 => 214 concepts (201 used for prediction) PRED predicted values (max 10 best out of 452): 09b6zr (0.84 #10905, 0.83 #12190, 0.83 #10904), 0gx_p (0.20 #422, 0.12 #13898, 0.09 #13256), 0q5hw (0.17 #4041, 0.09 #8531, 0.08 #9813), 02mjmr (0.14 #821, 0.10 #7236, 0.08 #4029), 01dw4q (0.14 #669, 0.09 #3235, 0.08 #4518), 0tc7 (0.14 #794, 0.09 #3360, 0.08 #4002), 015lhm (0.14 #1021, 0.09 #3587, 0.08 #4229), 0157m (0.14 #746, 0.09 #3312, 0.08 #3954), 07g2v (0.14 #878, 0.08 #4086, 0.06 #5368), 01kwsg (0.14 #967, 0.08 #4175, 0.06 #5457) >> Best rule #10905 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 0pz91; 014q2g; 09qh1; 0ph2w; 01pfkw; 04__f; >> query: (?x1620, ?x1384) <- participant(?x1384, ?x1620), celebrities_impersonated(?x3649, ?x1620), award_nominee(?x71, ?x1384) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #3987 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0d06m5; *> query: (?x1620, 06pj8) <- participant(?x1620, ?x286), company(?x1620, ?x94), person(?x1015, ?x1620) *> conf = 0.08 ranks of expected_values: 57 EVAL 0157m participant 06pj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 214.000 201.000 0.839 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #20657-02ny6g PRED entity: 02ny6g PRED relation: film! PRED expected values: 01tfck => 76 concepts (49 used for prediction) PRED predicted values (max 10 best out of 694): 01gw4f (0.17 #862, 0.07 #31176, 0.07 #33255), 0dzf_ (0.17 #810, 0.04 #44456, 0.02 #46535), 0bl2g (0.17 #54, 0.03 #43700, 0.02 #14601), 055c8 (0.17 #543, 0.03 #42111, 0.02 #35876), 057_yx (0.17 #1838, 0.03 #5995), 02qgqt (0.17 #18, 0.02 #39508, 0.02 #47822), 0fby2t (0.17 #754, 0.02 #44400, 0.01 #61029), 01r93l (0.17 #748, 0.02 #36081, 0.01 #63101), 03wy70 (0.17 #1285, 0.01 #36618, 0.01 #42853), 01xcfy (0.17 #493, 0.01 #35826) >> Best rule #862 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 04kkz8; 040_lv; >> query: (?x3639, 01gw4f) <- film(?x8642, ?x3639), genre(?x3639, ?x225), ?x8642 = 05dbyt, music(?x3639, ?x5556) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #60275 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 798 *> proper extension: 03y3bp7; 01b7h8; 025x1t; 0gxsh4; 0clpml; 06ys2; *> query: (?x3639, ?x971) <- nominated_for(?x4777, ?x3639), award(?x4777, ?x401), participant(?x4777, ?x971), gender(?x4777, ?x231) *> conf = 0.03 ranks of expected_values: 185 EVAL 02ny6g film! 01tfck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 49.000 0.167 http://example.org/film/actor/film./film/performance/film #20656-02301 PRED entity: 02301 PRED relation: category PRED expected values: 08mbj5d => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #46, 0.89 #91, 0.89 #67) >> Best rule #46 for best value: >> intensional similarity = 4 >> extensional distance = 167 >> proper extension: 0ymgk; >> query: (?x2730, 08mbj5d) <- student(?x2730, ?x8408), award_winner(?x3662, ?x8408), currency(?x2730, ?x170), award_winner(?x6213, ?x8408) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02301 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.899 http://example.org/common/topic/webpage./common/webpage/category #20655-01cmp9 PRED entity: 01cmp9 PRED relation: nominated_for! PRED expected values: 040njc 0gr0m => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 273): 09sb52 (0.67 #17859, 0.67 #10107, 0.67 #18723), 099tbz (0.67 #17859, 0.67 #10107, 0.67 #18723), 026mmy (0.67 #17859, 0.67 #10107, 0.67 #18723), 02g3v6 (0.50 #233, 0.21 #1308, 0.21 #6666), 040njc (0.50 #3446, 0.43 #1941, 0.42 #4091), 0gr0m (0.43 #2199, 0.37 #3489, 0.36 #1984), 02x2gy0 (0.42 #298, 0.27 #1373, 0.21 #6666), 0f4x7 (0.40 #1957, 0.38 #4107, 0.37 #3462), 0l8z1 (0.37 #1334, 0.32 #2194, 0.30 #3484), 04kxsb (0.37 #2013, 0.37 #3518, 0.37 #4163) >> Best rule #17859 for best value: >> intensional similarity = 3 >> extensional distance = 950 >> proper extension: 06w7mlh; >> query: (?x6048, ?x2577) <- nominated_for(?x1585, ?x6048), award(?x6048, ?x2577), nominated_for(?x2577, ?x86) >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #3446 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 145 *> proper extension: 0j8f09z; *> query: (?x6048, 040njc) <- nominated_for(?x1307, ?x6048), ?x1307 = 0gq9h, film_crew_role(?x6048, ?x137) *> conf = 0.50 ranks of expected_values: 5, 6 EVAL 01cmp9 nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 121.000 121.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01cmp9 nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 121.000 121.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20654-0jgx PRED entity: 0jgx PRED relation: jurisdiction_of_office! PRED expected values: 060c4 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.79 #508, 0.79 #178, 0.78 #662), 0pqc5 (0.72 #466, 0.36 #1566, 0.36 #1588), 0f6c3 (0.24 #953, 0.22 #1217, 0.20 #1261), 0fkvn (0.23 #1257, 0.22 #1213, 0.21 #1191), 01zq91 (0.22 #14, 0.19 #190, 0.18 #256), 0p5vf (0.22 #12, 0.17 #342, 0.16 #320), 09n5b9 (0.21 #957, 0.18 #1221, 0.17 #1265), 0dq3c (0.18 #23, 0.15 #705, 0.14 #67), 0789n (0.18 #31, 0.14 #75, 0.14 #53), 01gkgk (0.18 #27, 0.14 #71, 0.14 #49) >> Best rule #508 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0169t; 07dvs; 03gyl; 088vb; 07t_x; 03548; 05cc1; 01nln; 05b7q; 0fv4v; ... >> query: (?x3855, 060c4) <- country(?x2978, ?x3855), ?x2978 = 03_8r, medal(?x3855, ?x422) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgx jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.789 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #20653-03_l8m PRED entity: 03_l8m PRED relation: languages PRED expected values: 02h40lc => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.44 #80, 0.36 #120, 0.35 #160), 064_8sq (0.03 #720, 0.03 #369, 0.03 #954), 03k50 (0.02 #319, 0.02 #709, 0.01 #3673), 06nm1 (0.02 #360, 0.01 #282, 0.01 #789), 02bjrlw (0.02 #79, 0.01 #706, 0.01 #119), 03_9r (0.02 #83, 0.01 #123, 0.01 #163), 04rlf (0.01 #118, 0.01 #158, 0.01 #198), 05lls (0.01 #118, 0.01 #158, 0.01 #198), 04306rv (0.01 #357), 07c9s (0.01 #718, 0.01 #328) >> Best rule #80 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 049gc; >> query: (?x5103, 02h40lc) <- location(?x5103, ?x1131), award_winner(?x221, ?x5103), student(?x888, ?x5103) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_l8m languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.441 http://example.org/people/person/languages #20652-0mk59 PRED entity: 0mk59 PRED relation: currency PRED expected values: 09nqf => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.88 #3, 0.88 #2, 0.84 #51) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0fw4v; >> query: (?x14511, ?x170) <- source(?x14511, ?x958), administrative_parent(?x14511, ?x3086), time_zones(?x14511, ?x2088), currency(?x3086, ?x170) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mk59 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.875 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #20651-024y8p PRED entity: 024y8p PRED relation: fraternities_and_sororities PRED expected values: 0325pb => 224 concepts (224 used for prediction) PRED predicted values (max 10 best out of 2): 0325pb (0.62 #37, 0.56 #41, 0.54 #27), 04m8fy (0.08 #28, 0.07 #380, 0.06 #18) >> Best rule #37 for best value: >> intensional similarity = 6 >> extensional distance = 32 >> proper extension: 01jswq; 0m9_5; >> query: (?x1635, 0325pb) <- institution(?x1368, ?x1635), currency(?x1635, ?x170), major_field_of_study(?x1635, ?x4321), fraternities_and_sororities(?x1635, ?x4348), state_province_region(?x1635, ?x760), ?x4321 = 0g26h >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024y8p fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 224.000 224.000 0.618 http://example.org/education/university/fraternities_and_sororities #20650-0hv4t PRED entity: 0hv4t PRED relation: nominated_for! PRED expected values: 0gq_v 02qvyrt => 83 concepts (73 used for prediction) PRED predicted values (max 10 best out of 201): 09cm54 (0.68 #7085, 0.68 #9301, 0.67 #3762), 0gqy2 (0.45 #329, 0.37 #771, 0.31 #992), 02qvyrt (0.40 #966, 0.32 #81, 0.30 #745), 0gq_v (0.39 #461, 0.36 #682, 0.36 #4002), 099c8n (0.37 #935, 0.37 #714, 0.27 #50), 0gr4k (0.36 #687, 0.35 #908, 0.33 #245), 09qv_s (0.35 #99, 0.24 #11296, 0.20 #14834), 02r22gf (0.34 #910, 0.24 #25, 0.22 #689), 02ppm4q (0.32 #986, 0.27 #101, 0.22 #765), 0gqyl (0.30 #951, 0.29 #509, 0.29 #730) >> Best rule #7085 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 06mmr; >> query: (?x6653, ?x746) <- award(?x6653, ?x746), award_winner(?x6653, ?x406), award(?x276, ?x746) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #966 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 132 *> proper extension: 0sxg4; 02qr69m; 07w8fz; 09gb_4p; *> query: (?x6653, 02qvyrt) <- award_winner(?x6653, ?x406), nominated_for(?x1198, ?x6653), ?x1198 = 02pqp12 *> conf = 0.40 ranks of expected_values: 3, 4 EVAL 0hv4t nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 73.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0hv4t nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 73.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20649-072zl1 PRED entity: 072zl1 PRED relation: film! PRED expected values: 0lpjn => 81 concepts (47 used for prediction) PRED predicted values (max 10 best out of 798): 02q42j_ (0.45 #62384, 0.45 #64464, 0.43 #85259), 0b13g7 (0.45 #62384, 0.45 #64464, 0.43 #85259), 02ck7w (0.12 #3018, 0.12 #939, 0.09 #7177), 0jfx1 (0.12 #2485, 0.12 #406, 0.06 #8723), 07lt7b (0.12 #2193, 0.12 #114, 0.03 #8431), 015pkc (0.12 #2357, 0.12 #278, 0.03 #14832), 0159h6 (0.12 #2152, 0.12 #73, 0.03 #12548), 03x400 (0.12 #3238, 0.12 #1159, 0.02 #18715), 013cr (0.12 #2305, 0.12 #226, 0.02 #12701), 02f2p7 (0.12 #3024, 0.12 #945, 0.01 #11341) >> Best rule #62384 for best value: >> intensional similarity = 4 >> extensional distance = 928 >> proper extension: 047svrl; >> query: (?x7320, ?x3568) <- film_crew_role(?x7320, ?x137), film(?x11983, ?x7320), nominated_for(?x3568, ?x7320), nominated_for(?x11983, ?x3252) >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #8796 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 91 *> proper extension: 0hv81; *> query: (?x7320, 0lpjn) <- nominated_for(?x484, ?x7320), film_crew_role(?x7320, ?x137), ?x484 = 0gq_v, titles(?x162, ?x7320) *> conf = 0.03 ranks of expected_values: 106 EVAL 072zl1 film! 0lpjn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 81.000 47.000 0.454 http://example.org/film/actor/film./film/performance/film #20648-09sr0 PRED entity: 09sr0 PRED relation: nominated_for! PRED expected values: 02n9nmz => 124 concepts (95 used for prediction) PRED predicted values (max 10 best out of 202): 019f4v (0.73 #6363, 0.69 #4089, 0.68 #1363), 02qt02v (0.69 #4089, 0.68 #1363, 0.68 #4998), 0gq_v (0.49 #4787, 0.44 #1380, 0.44 #1835), 040njc (0.49 #4095, 0.40 #4776, 0.40 #6140), 02n9nmz (0.43 #278, 0.24 #21609, 0.18 #1869), 0f4x7 (0.42 #4111, 0.38 #6156, 0.36 #249), 099c8n (0.41 #277, 0.27 #5275, 0.26 #504), 054krc (0.36 #287, 0.33 #1878, 0.33 #514), 04kxsb (0.36 #313, 0.32 #4175, 0.31 #1449), 02pqp12 (0.36 #4141, 0.31 #1870, 0.30 #6186) >> Best rule #6363 for best value: >> intensional similarity = 4 >> extensional distance = 291 >> proper extension: 06mmr; >> query: (?x9056, ?x1703) <- award(?x9056, ?x1703), nominated_for(?x1703, ?x4431), ceremony(?x1703, ?x78), ?x4431 = 0pd4f >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #278 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 04x4vj; *> query: (?x9056, 02n9nmz) <- films(?x7455, ?x9056), nominated_for(?x384, ?x9056), ?x384 = 03hkv_r *> conf = 0.43 ranks of expected_values: 5 EVAL 09sr0 nominated_for! 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 95.000 0.734 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20647-0lcx PRED entity: 0lcx PRED relation: people! PRED expected values: 03ts0c => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 41): 041rx (0.33 #235, 0.33 #4, 0.27 #698), 09vc4s (0.33 #9, 0.05 #394, 0.05 #472), 03ts0c (0.29 #953, 0.26 #566, 0.24 #1261), 013xrm (0.24 #714, 0.22 #251, 0.22 #174), 013b6_ (0.17 #772, 0.11 #207, 0.11 #3936), 07mqps (0.17 #772, 0.11 #3936, 0.11 #2855), 01p7s6 (0.17 #772, 0.11 #2855, 0.10 #3473), 02ctzb (0.12 #2251, 0.05 #400, 0.05 #478), 0x67 (0.11 #1861, 0.11 #3405, 0.11 #1553), 048z7l (0.11 #927, 0.09 #2700, 0.09 #2468) >> Best rule #235 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0399p; >> query: (?x4028, 041rx) <- influenced_by(?x4028, ?x11097), influenced_by(?x4028, ?x7509), ?x7509 = 048cl, ?x11097 = 02wh0 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #953 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 0d3f83; *> query: (?x4028, 03ts0c) <- nationality(?x4028, ?x789), gender(?x4028, ?x231), ?x231 = 05zppz, ?x789 = 0f8l9c *> conf = 0.29 ranks of expected_values: 3 EVAL 0lcx people! 03ts0c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 132.000 132.000 0.333 http://example.org/people/ethnicity/people #20646-03xn3s2 PRED entity: 03xn3s2 PRED relation: nominated_for PRED expected values: 02h2vv => 95 concepts (29 used for prediction) PRED predicted values (max 10 best out of 360): 07c72 (0.60 #22717, 0.49 #25964, 0.43 #1623), 0330r (0.25 #1415, 0.03 #3038, 0.02 #4660), 050f0s (0.23 #24341, 0.20 #29213, 0.19 #42197), 0d68qy (0.10 #374, 0.05 #6864, 0.04 #21468), 02k_4g (0.10 #108, 0.02 #19579, 0.02 #4975), 01fszq (0.10 #1505, 0.02 #7995, 0.02 #11241), 01rp13 (0.10 #1018, 0.02 #40572, 0.01 #43821), 020bv3 (0.10 #295, 0.02 #1918, 0.01 #21389), 039cq4 (0.08 #10822, 0.07 #15690, 0.05 #7576), 0kfv9 (0.05 #21361, 0.04 #24608, 0.03 #5134) >> Best rule #22717 for best value: >> intensional similarity = 3 >> extensional distance = 306 >> proper extension: 02wrhj; >> query: (?x6825, ?x3180) <- actor(?x3180, ?x6825), tv_program(?x636, ?x3180), honored_for(?x1265, ?x3180) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #40572 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 600 *> proper extension: 070m12; 0g9zcgx; 027rfxc; 02vkvcz; *> query: (?x6825, ?x2078) <- gender(?x6825, ?x514), award(?x6825, ?x4225), ?x514 = 02zsn, award(?x2078, ?x4225) *> conf = 0.02 ranks of expected_values: 123 EVAL 03xn3s2 nominated_for 02h2vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 95.000 29.000 0.598 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20645-0sx5w PRED entity: 0sx5w PRED relation: company PRED expected values: 01ym8l => 197 concepts (176 used for prediction) PRED predicted values (max 10 best out of 96): 05s34b (0.33 #173, 0.06 #2493), 09c7w0 (0.29 #7543, 0.21 #3676, 0.21 #8513), 07wj1 (0.14 #533, 0.10 #919, 0.04 #5561), 05gnf (0.14 #487, 0.01 #9775, 0.01 #11326), 07vsl (0.12 #2893, 0.11 #3862, 0.06 #3088), 07wrz (0.12 #3325, 0.08 #4677, 0.07 #5257), 01w5m (0.12 #3338, 0.08 #4690, 0.07 #5270), 01rs59 (0.11 #3810, 0.06 #3036, 0.04 #7677), 02975m (0.10 #756, 0.08 #1142, 0.07 #1915), 02jd_7 (0.08 #1501, 0.08 #1694, 0.06 #2661) >> Best rule #173 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01d_4t; >> query: (?x10645, 05s34b) <- profession(?x10645, ?x8709), profession(?x10645, ?x6183), nationality(?x10645, ?x94), ?x8709 = 08z956, ?x6183 = 02dsz >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0sx5w company 01ym8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 197.000 176.000 0.333 http://example.org/people/person/employment_history./business/employment_tenure/company #20644-04smdd PRED entity: 04smdd PRED relation: film! PRED expected values: 01cpqk => 96 concepts (54 used for prediction) PRED predicted values (max 10 best out of 999): 01j5ts (0.83 #4164, 0.71 #97833, 0.65 #104078), 0jmj (0.83 #4164, 0.71 #97833, 0.56 #93669), 01mqnr (0.71 #97833, 0.46 #4163, 0.42 #97832), 0grrq8 (0.56 #93669, 0.46 #4163, 0.43 #54116), 02x7vq (0.40 #981, 0.03 #5145, 0.02 #13465), 01438g (0.28 #2604, 0.02 #13007, 0.01 #15088), 02wr6r (0.20 #1667, 0.03 #5831, 0.02 #14151), 01r7t9 (0.20 #1880, 0.03 #6044, 0.02 #18527), 01v3vp (0.20 #709, 0.03 #4873), 01cpqk (0.20 #1143, 0.03 #104079, 0.02 #13627) >> Best rule #4164 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0dc7hc; >> query: (?x4347, ?x241) <- music(?x4347, ?x11497), nominated_for(?x241, ?x4347), film(?x241, ?x11610), ?x11610 = 03cffvv >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #1143 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0blpg; *> query: (?x4347, 01cpqk) <- produced_by(?x4347, ?x4562), written_by(?x4347, ?x986), nominated_for(?x241, ?x4347), ?x4562 = 0grrq8 *> conf = 0.20 ranks of expected_values: 10 EVAL 04smdd film! 01cpqk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 96.000 54.000 0.826 http://example.org/film/actor/film./film/performance/film #20643-0c35b1 PRED entity: 0c35b1 PRED relation: film PRED expected values: 0963mq => 76 concepts (48 used for prediction) PRED predicted values (max 10 best out of 390): 0ds3t5x (0.07 #54, 0.02 #5409, 0.01 #12549), 01jrbv (0.05 #552, 0.01 #5907, 0.01 #9477), 03hp2y1 (0.05 #1606, 0.01 #3391, 0.01 #12316), 0422v0 (0.05 #1779), 01f69m (0.05 #1732), 03cvvlg (0.05 #1442), 03np63f (0.05 #1374), 0_9wr (0.05 #1230), 0y_hb (0.05 #1111), 0241y7 (0.05 #1070) >> Best rule #54 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 01l9v7n; 012wg; 04ls53; 02bn75; >> query: (?x7779, 0ds3t5x) <- nominated_for(?x7779, ?x6448), student(?x2909, ?x7779), ?x2909 = 017z88 >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c35b1 film 0963mq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 48.000 0.071 http://example.org/film/actor/film./film/performance/film #20642-0h3tv PRED entity: 0h3tv PRED relation: month PRED expected values: 040fb 02xx5 => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 2): 02xx5 (0.90 #52, 0.89 #60, 0.89 #40), 040fb (0.85 #73, 0.84 #59, 0.84 #49) >> Best rule #52 for best value: >> intensional similarity = 6 >> extensional distance = 37 >> proper extension: 03902; >> query: (?x10143, 02xx5) <- location_of_ceremony(?x566, ?x10143), month(?x10143, ?x3270), month(?x10143, ?x3107), month(?x5267, ?x3270), ?x3107 = 05lf_, ?x5267 = 0d9jr >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0h3tv month 02xx5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0h3tv month 040fb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #20641-0gy3w PRED entity: 0gy3w PRED relation: contains! PRED expected values: 09c7w0 => 183 concepts (68 used for prediction) PRED predicted values (max 10 best out of 252): 09c7w0 (0.85 #25968, 0.84 #28656, 0.83 #8059), 04_1l0v (0.41 #55541, 0.01 #29103), 03v1s (0.25 #26, 0.17 #8082, 0.10 #2711), 05tbn (0.25 #224, 0.11 #25293, 0.11 #37841), 04tgp (0.25 #1174, 0.08 #9232, 0.07 #3859), 0dclg (0.25 #144, 0.06 #5515, 0.05 #7305), 059rby (0.21 #9868, 0.18 #51078, 0.14 #32264), 01n7q (0.17 #14401, 0.17 #16193, 0.13 #54722), 0f2tj (0.17 #6632, 0.03 #33506, 0.03 #36192), 02jx1 (0.15 #43976, 0.13 #49353, 0.12 #47561) >> Best rule #25968 for best value: >> intensional similarity = 7 >> extensional distance = 91 >> proper extension: 04gd8j; >> query: (?x7576, 09c7w0) <- currency(?x7576, ?x170), contains(?x3908, ?x7576), major_field_of_study(?x7576, ?x7134), major_field_of_study(?x9847, ?x7134), major_field_of_study(?x4955, ?x7134), ?x4955 = 09f2j, ?x9847 = 0187nd >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gy3w contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 68.000 0.849 http://example.org/location/location/contains #20640-03txms PRED entity: 03txms PRED relation: basic_title PRED expected values: 01gkgk => 221 concepts (221 used for prediction) PRED predicted values (max 10 best out of 17): 01gkgk (0.62 #142, 0.56 #176, 0.55 #227), 060c4 (0.57 #412, 0.54 #463, 0.50 #71), 0fkvn (0.38 #413, 0.35 #345, 0.33 #549), 0789n (0.33 #402, 0.28 #487, 0.24 #419), 0dq3c (0.33 #70, 0.26 #428, 0.24 #411), 02079p (0.20 #28, 0.17 #62, 0.12 #147), 060bp (0.19 #376, 0.19 #325, 0.17 #69), 0pqc5 (0.18 #733, 0.06 #550, 0.06 #329), 0fj45 (0.17 #83, 0.12 #168, 0.12 #356), 0p5vf (0.12 #353, 0.10 #693, 0.10 #421) >> Best rule #142 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0bymv; 0d3qd0; 02hy5d; >> query: (?x7961, 01gkgk) <- legislative_sessions(?x7961, ?x4730), legislative_sessions(?x7961, ?x845), religion(?x7961, ?x2769), ?x4730 = 02cg7g, ?x845 = 07p__7 >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03txms basic_title 01gkgk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 221.000 221.000 0.625 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #20639-015dcj PRED entity: 015dcj PRED relation: type_of_union PRED expected values: 04ztj => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.83 #65, 0.81 #53, 0.81 #93), 01g63y (0.20 #82, 0.19 #110, 0.19 #74) >> Best rule #65 for best value: >> intensional similarity = 2 >> extensional distance = 241 >> proper extension: 06nz46; 0627sn; 023361; 02h48; >> query: (?x6358, 04ztj) <- nominated_for(?x6358, ?x984), people(?x13131, ?x6358) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015dcj type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.827 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20638-01hp5 PRED entity: 01hp5 PRED relation: nominated_for! PRED expected values: 02g3v6 => 81 concepts (70 used for prediction) PRED predicted values (max 10 best out of 188): 0gqy2 (0.69 #2480, 0.22 #356, 0.20 #3305), 0gq9h (0.51 #2419, 0.28 #7140, 0.27 #295), 0gs9p (0.42 #2421, 0.26 #7142, 0.22 #4546), 019f4v (0.41 #2410, 0.29 #286, 0.23 #7131), 0gr42 (0.36 #558, 0.26 #86, 0.13 #1502), 0k611 (0.36 #2430, 0.27 #306, 0.24 #542), 0p9sw (0.33 #490, 0.26 #18, 0.22 #2378), 040njc (0.33 #2365, 0.20 #477, 0.17 #7086), 0gr4k (0.31 #2384, 0.20 #7105, 0.16 #9230), 04dn09n (0.31 #2391, 0.27 #267, 0.18 #7112) >> Best rule #2480 for best value: >> intensional similarity = 3 >> extensional distance = 270 >> proper extension: 01j7mr; 05_z42; 039cq4; 053x8hr; 0c1j_; 02qr46y; >> query: (?x751, 0gqy2) <- nominated_for(?x112, ?x751), award(?x8450, ?x112), ?x8450 = 0h953 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #491 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 104 *> proper extension: 05jf85; *> query: (?x751, 02g3v6) <- genre(?x751, ?x53), film(?x406, ?x751), nominated_for(?x640, ?x751), ?x640 = 02hsq3m *> conf = 0.28 ranks of expected_values: 11 EVAL 01hp5 nominated_for! 02g3v6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 81.000 70.000 0.695 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20637-0hr3g PRED entity: 0hr3g PRED relation: music! PRED expected values: 08ct6 => 173 concepts (158 used for prediction) PRED predicted values (max 10 best out of 962): 06c0ns (0.20 #707, 0.02 #18960, 0.02 #20988), 018js4 (0.20 #10, 0.02 #18263, 0.02 #20291), 042y1c (0.14 #1249, 0.11 #2263, 0.07 #4291), 0pd6l (0.14 #1404, 0.09 #3432, 0.03 #7488), 02_kd (0.14 #1366, 0.09 #3394, 0.03 #7450), 0g68zt (0.14 #1326, 0.09 #3354, 0.03 #7410), 0c_j9x (0.14 #1243, 0.09 #3271, 0.03 #7327), 03cfkrw (0.14 #1457, 0.09 #3485, 0.03 #7541), 0bscw (0.14 #1147, 0.09 #3175, 0.03 #7231), 0bmhn (0.14 #1939, 0.03 #8023, 0.02 #10051) >> Best rule #707 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 05whq_9; 041jlr; >> query: (?x9297, 06c0ns) <- type_of_union(?x9297, ?x566), place_of_birth(?x9297, ?x2611), profession(?x9297, ?x563), ?x2611 = 02h6_6p >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hr3g music! 08ct6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 158.000 0.200 http://example.org/film/film/music #20636-077rj PRED entity: 077rj PRED relation: category PRED expected values: 08mbj5d => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #2, 0.80 #32, 0.78 #38) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 07j8kh; >> query: (?x5896, 08mbj5d) <- award(?x5896, ?x1869), award_winner(?x7105, ?x5896), ?x1869 = 04njml, artists(?x307, ?x5896) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 077rj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.850 http://example.org/common/topic/webpage./common/webpage/category #20635-0p5mw PRED entity: 0p5mw PRED relation: performance_role PRED expected values: 06w87 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 44): 03bx0bm (0.44 #828, 0.43 #360, 0.43 #233), 0l14md (0.23 #816, 0.23 #432, 0.21 #136), 026t6 (0.17 #812, 0.17 #132, 0.15 #428), 0l14qv (0.16 #814, 0.14 #430, 0.13 #346), 013y1f (0.15 #104, 0.13 #829, 0.12 #149), 0l15bq (0.15 #235, 0.14 #150, 0.09 #105), 02k84w (0.12 #174, 0.12 #470, 0.04 #129), 05r5c (0.12 #137, 0.11 #222, 0.10 #691), 02snj9 (0.09 #75, 0.07 #163, 0.07 #459), 03gvt (0.09 #79, 0.07 #167, 0.06 #252) >> Best rule #828 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 02zrv7; >> query: (?x1887, 03bx0bm) <- category(?x1887, ?x134), performance_role(?x1887, ?x1969), ?x134 = 08mbj5d, role(?x366, ?x1969) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0p5mw performance_role 06w87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 108.000 0.444 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #20634-0jnwx PRED entity: 0jnwx PRED relation: genre PRED expected values: 03k9fj 02l7c8 => 87 concepts (38 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.81 #706, 0.79 #236, 0.70 #472), 017fp (0.76 #720, 0.12 #250, 0.12 #486), 03k9fj (0.67 #11, 0.59 #128, 0.59 #364), 01hmnh (0.66 #823, 0.63 #371, 0.55 #1177), 09b3v (0.66 #823, 0.55 #1177, 0.53 #2352), 02l7c8 (0.42 #487, 0.33 #251, 0.32 #2837), 04xvlr (0.40 #473, 0.35 #707, 0.23 #1061), 02kdv5l (0.37 #943, 0.29 #3296, 0.28 #3414), 01jfsb (0.30 #3423, 0.29 #3305, 0.28 #3540), 060__y (0.28 #488, 0.17 #1076, 0.17 #1429) >> Best rule #706 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 0c9k8; 03hkch7; 0g0x9c; 0170xl; >> query: (?x1893, 07s9rl0) <- titles(?x307, ?x1893), genre(?x1893, ?x8681), nominated_for(?x298, ?x1893), taxonomy(?x8681, ?x939) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0mbql; *> query: (?x1893, 03k9fj) <- genre(?x1893, ?x6459), ?x6459 = 0bj8m2, film(?x3758, ?x1893), currency(?x1893, ?x170) *> conf = 0.67 ranks of expected_values: 3, 6 EVAL 0jnwx genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 38.000 0.813 http://example.org/film/film/genre EVAL 0jnwx genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 38.000 0.813 http://example.org/film/film/genre #20633-0nqph PRED entity: 0nqph PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 21): 0pqc5 (0.75 #166, 0.75 #74, 0.63 #143), 0f6c3 (0.31 #744, 0.22 #1044, 0.21 #1113), 060c4 (0.30 #877, 0.26 #1131, 0.23 #854), 09n5b9 (0.28 #748, 0.21 #1048, 0.20 #1117), 0fkvn (0.27 #740, 0.19 #1040, 0.18 #1109), 060bp (0.27 #875, 0.21 #1129, 0.20 #668), 01q24l (0.11 #83, 0.11 #152, 0.10 #1004), 0fkzq (0.09 #753, 0.06 #1099, 0.06 #1122), 0789n (0.07 #746, 0.05 #1115, 0.04 #1046), 0p5vf (0.07 #841, 0.06 #772, 0.06 #795) >> Best rule #166 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 0rh6k; 02cl1; 02_286; 0fvvz; 04f_d; 0dclg; 0f__1; 0fvzg; 019k6n; 0d6lp; ... >> query: (?x13949, 0pqc5) <- contains(?x9712, ?x13949), citytown(?x12795, ?x13949), contains(?x9712, ?x9713), ?x9713 = 0f2s6, teams(?x13949, ?x387) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nqph jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.750 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #20632-0ylgz PRED entity: 0ylgz PRED relation: colors PRED expected values: 088fh => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.72 #1102, 0.53 #1622, 0.50 #1762), 01l849 (0.34 #1761, 0.34 #1621, 0.29 #241), 06fvc (0.33 #243, 0.32 #223, 0.29 #203), 01g5v (0.30 #1404, 0.30 #1264, 0.28 #1204), 088fh (0.27 #67, 0.10 #2641, 0.09 #287), 03wkwg (0.18 #35, 0.15 #135, 0.10 #2641), 067z2v (0.17 #149, 0.12 #229, 0.12 #249), 04mkbj (0.14 #50, 0.12 #110, 0.12 #1770), 01jnf1 (0.13 #71, 0.12 #91, 0.07 #291), 038hg (0.12 #192, 0.11 #1092, 0.10 #1772) >> Best rule #1102 for best value: >> intensional similarity = 4 >> extensional distance = 218 >> proper extension: 01ngz1; 01rr31; 02km0m; 027ydt; 02l1fn; 01rc6f; 04zwc; 01tntf; 035gt8; 036hnm; ... >> query: (?x9651, 083jv) <- colors(?x9651, ?x4557), institution(?x1368, ?x9651), colors(?x8051, ?x4557), ?x8051 = 04mp75 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 13 *> proper extension: 03ksy; 014zws; 0ymff; 0ym17; 0lk0l; *> query: (?x9651, 088fh) <- student(?x9651, ?x13289), institution(?x1368, ?x9651), place_of_death(?x13289, ?x362), citytown(?x9651, ?x1841), ?x362 = 04jpl, place_of_birth(?x13289, ?x6885) *> conf = 0.27 ranks of expected_values: 5 EVAL 0ylgz colors 088fh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 145.000 145.000 0.723 http://example.org/education/educational_institution/colors #20631-01tzfz PRED entity: 01tzfz PRED relation: contains! PRED expected values: 0m0bj => 152 concepts (79 used for prediction) PRED predicted values (max 10 best out of 292): 09c7w0 (0.86 #45642, 0.85 #57278, 0.77 #48327), 07ssc (0.80 #2715, 0.79 #57275, 0.76 #67153), 0978r (0.32 #13623, 0.25 #18989, 0.21 #7359), 059rby (0.27 #3597, 0.26 #4491, 0.22 #6280), 01n7q (0.27 #3655, 0.25 #78, 0.17 #6338), 04jpl (0.27 #17016, 0.25 #18806, 0.25 #7176), 0d060g (0.26 #17902, 0.25 #14325, 0.24 #12536), 0glh3 (0.20 #1604, 0.06 #8049, 0.04 #7864), 0127c4 (0.20 #1717, 0.05 #6188, 0.04 #7977), 0j7ng (0.20 #1630, 0.05 #6101, 0.04 #7890) >> Best rule #45642 for best value: >> intensional similarity = 5 >> extensional distance = 222 >> proper extension: 02t4yc; 02xpy5; 063576; 05gm16l; 02zy1z; >> query: (?x10373, 09c7w0) <- organization(?x5510, ?x10373), contains(?x1310, ?x10373), institution(?x865, ?x10373), currency(?x10373, ?x1099), nationality(?x57, ?x1310) >> conf = 0.86 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01tzfz contains! 0m0bj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 152.000 79.000 0.862 http://example.org/location/location/contains #20630-01qvz8 PRED entity: 01qvz8 PRED relation: genre PRED expected values: 0lsxr => 106 concepts (105 used for prediction) PRED predicted values (max 10 best out of 101): 07s9rl0 (0.72 #10467, 0.69 #601, 0.67 #2888), 01z4y (0.61 #9024, 0.52 #7942, 0.52 #10947), 01jfsb (0.48 #372, 0.42 #612, 0.40 #8794), 02kdv5l (0.44 #123, 0.41 #3010, 0.36 #4933), 03k9fj (0.34 #851, 0.28 #971, 0.28 #2538), 0lsxr (0.31 #608, 0.26 #368, 0.24 #1933), 082gq (0.22 #870, 0.22 #4569, 0.22 #5655), 06n90 (0.22 #133, 0.18 #2179, 0.17 #8795), 04xvlr (0.22 #4569, 0.22 #5655, 0.21 #362), 060__y (0.22 #4569, 0.22 #5655, 0.20 #3023) >> Best rule #10467 for best value: >> intensional similarity = 3 >> extensional distance = 1386 >> proper extension: 0413cff; 07s3m4g; 015qy1; >> query: (?x4709, 07s9rl0) <- genre(?x4709, ?x258), genre(?x1861, ?x258), ?x1861 = 0b76t12 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #608 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 03h3x5; 07nxvj; 0gltv; *> query: (?x4709, 0lsxr) <- cinematography(?x4709, ?x7384), nominated_for(?x541, ?x4709), genre(?x4709, ?x239), film_format(?x4709, ?x909) *> conf = 0.31 ranks of expected_values: 6 EVAL 01qvz8 genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 106.000 105.000 0.725 http://example.org/film/film/genre #20629-0cjdk PRED entity: 0cjdk PRED relation: service_location PRED expected values: 09c7w0 => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 93): 09c7w0 (0.83 #4528, 0.82 #4232, 0.78 #5613), 02j71 (0.26 #5531, 0.25 #4248, 0.24 #5236), 0d060g (0.26 #5619, 0.24 #5226, 0.24 #5128), 07ssc (0.19 #5234, 0.16 #5627, 0.15 #5136), 0chghy (0.17 #208, 0.14 #5132, 0.12 #5525), 0345h (0.11 #5246, 0.10 #5148, 0.09 #5639), 0f8l9c (0.09 #5240, 0.08 #5633, 0.07 #5142), 05v8c (0.07 #4247, 0.03 #5235, 0.03 #5137), 01b8jj (0.06 #1954, 0.05 #3428, 0.04 #2839), 0mgp (0.06 #1939, 0.05 #3413, 0.04 #2824) >> Best rule #4528 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 02zs4; 05krk; 087c7; 06pwq; 0cv9b; 04rwx; 049dk; 07wrz; 01xdn1; 0cchk3; ... >> query: (?x2554, 09c7w0) <- state_province_region(?x2554, ?x1227), citytown(?x2554, ?x1523), service_language(?x2554, ?x254) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cjdk service_location 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.831 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #20628-081pw PRED entity: 081pw PRED relation: films PRED expected values: 02725hs 05cvgl 02qhlwd 04nm0n0 0bs4r 0yxf4 014knw 03xj05 => 71 concepts (55 used for prediction) PRED predicted values (max 10 best out of 483): 091z_p (0.33 #73, 0.05 #6473, 0.05 #5488), 049xgc (0.33 #744, 0.05 #9611, 0.04 #7638), 02dwj (0.33 #737, 0.05 #9604, 0.03 #18472), 091rc5 (0.33 #1211, 0.05 #9586, 0.03 #18454), 06rzwx (0.33 #824, 0.05 #18559, 0.04 #7718), 0p3_y (0.33 #1099, 0.04 #7501, 0.03 #18342), 01hq1 (0.33 #1358, 0.03 #18601, 0.03 #21553), 01pv91 (0.33 #609, 0.03 #18344, 0.03 #21296), 09w6br (0.33 #1440, 0.03 #18683, 0.03 #9815), 0ptdz (0.33 #1464, 0.03 #9839, 0.02 #18707) >> Best rule #73 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 075k5; >> query: (?x326, 091z_p) <- films(?x326, ?x7170), films(?x326, ?x3496), film_release_distribution_medium(?x7170, ?x81), currency(?x3496, ?x170), combatants(?x326, ?x8687), ?x8687 = 059z0, film_release_region(?x7170, ?x87) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1410 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 01vq3; *> query: (?x326, 014knw) <- films(?x326, ?x7170), films(?x326, ?x4037), ?x7170 = 02pxst, nominated_for(?x496, ?x4037), film_release_distribution_medium(?x4037, ?x81), featured_film_locations(?x4037, ?x1264) *> conf = 0.33 ranks of expected_values: 13, 404, 477 EVAL 081pw films 03xj05 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 55.000 0.333 http://example.org/film/film_subject/films EVAL 081pw films 014knw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 71.000 55.000 0.333 http://example.org/film/film_subject/films EVAL 081pw films 0yxf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 55.000 0.333 http://example.org/film/film_subject/films EVAL 081pw films 0bs4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 55.000 0.333 http://example.org/film/film_subject/films EVAL 081pw films 04nm0n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 55.000 0.333 http://example.org/film/film_subject/films EVAL 081pw films 02qhlwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 55.000 0.333 http://example.org/film/film_subject/films EVAL 081pw films 05cvgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 55.000 0.333 http://example.org/film/film_subject/films EVAL 081pw films 02725hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 55.000 0.333 http://example.org/film/film_subject/films #20627-0320fn PRED entity: 0320fn PRED relation: genre PRED expected values: 0219x_ => 83 concepts (82 used for prediction) PRED predicted values (max 10 best out of 98): 0219x_ (0.46 #386, 0.33 #26, 0.28 #626), 02l7c8 (0.38 #376, 0.38 #616, 0.33 #16), 06cvj (0.33 #3, 0.23 #363, 0.20 #123), 0vgkd (0.33 #10, 0.15 #370, 0.06 #8162), 0d63kt (0.33 #86, 0.14 #686, 0.06 #8162), 01jfsb (0.33 #2772, 0.30 #9135, 0.29 #1452), 04xvlr (0.32 #2641, 0.17 #4441, 0.17 #4561), 02kdv5l (0.28 #2762, 0.26 #6722, 0.25 #8525), 01t_vv (0.23 #414, 0.08 #2934, 0.08 #1614), 03k9fj (0.23 #3251, 0.23 #1331, 0.21 #5051) >> Best rule #386 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0209xj; 0f4_l; 01qncf; 017z49; 0bmhvpr; 0sxmx; 02qpt1w; 06__m6; 0gd92; 0h95927; ... >> query: (?x4009, 0219x_) <- award(?x4009, ?x2902), film(?x532, ?x4009), written_by(?x4009, ?x3960), ?x2902 = 02x4sn8 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0320fn genre 0219x_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 82.000 0.462 http://example.org/film/film/genre #20626-065jlv PRED entity: 065jlv PRED relation: people! PRED expected values: 02g7sp => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 29): 02w7gg (0.67 #156, 0.20 #2, 0.18 #79), 0d7wh (0.30 #17, 0.27 #94, 0.06 #171), 041rx (0.16 #235, 0.14 #389, 0.12 #466), 0x67 (0.10 #1088, 0.10 #857, 0.10 #934), 03lmx1 (0.10 #14, 0.09 #91, 0.03 #245), 02ctzb (0.10 #15, 0.02 #554, 0.02 #1016), 02g7sp (0.10 #18, 0.02 #788, 0.01 #865), 03bkbh (0.09 #109, 0.02 #2496, 0.02 #2342), 033tf_ (0.09 #1008, 0.08 #315, 0.08 #2240), 0xnvg (0.06 #321, 0.06 #475, 0.06 #552) >> Best rule #156 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 05cj4r; 09fqtq; 016gr2; 06t61y; 02k6rq; 015gw6; 0l6px; 01hkhq; 01ksr1; 02l4pj; ... >> query: (?x1951, 02w7gg) <- award_winner(?x1951, ?x926), ?x926 = 01sp81, award_nominee(?x1951, ?x1223) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #18 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 05m7zg; *> query: (?x1951, 02g7sp) <- film(?x1951, ?x7305), location(?x1951, ?x362), ?x7305 = 031786 *> conf = 0.10 ranks of expected_values: 7 EVAL 065jlv people! 02g7sp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 94.000 94.000 0.667 http://example.org/people/ethnicity/people #20625-051ys82 PRED entity: 051ys82 PRED relation: country PRED expected values: 09c7w0 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 59): 09c7w0 (0.88 #1230, 0.87 #236, 0.85 #531), 0345h (0.21 #2661, 0.15 #1546, 0.13 #200), 0f8l9c (0.19 #2653, 0.18 #17, 0.14 #662), 01jfsb (0.10 #704, 0.09 #2107, 0.07 #2753), 03_3d (0.09 #2644, 0.09 #8, 0.06 #124), 0chghy (0.08 #186, 0.07 #2647, 0.05 #831), 03rjj (0.06 #182, 0.06 #2643, 0.06 #65), 0k6nt (0.06 #77, 0.02 #312, 0.02 #1520), 05qhw (0.06 #72, 0.02 #1520, 0.01 #659), 0154j (0.04 #651, 0.03 #181, 0.03 #1467) >> Best rule #1230 for best value: >> intensional similarity = 3 >> extensional distance = 222 >> proper extension: 018nnz; 02q56mk; 0cwfgz; 059lwy; 03tbg6; >> query: (?x6005, 09c7w0) <- nominated_for(?x857, ?x6005), film(?x875, ?x6005), country(?x6005, ?x279) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051ys82 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.884 http://example.org/film/film/country #20624-017959 PRED entity: 017959 PRED relation: group! PRED expected values: 02hnl => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 120): 02hnl (0.77 #1664, 0.76 #1752, 0.76 #630), 04rzd (0.75 #30, 0.21 #461, 0.20 #719), 0l14md (0.73 #695, 0.71 #437, 0.68 #609), 0mkg (0.50 #10, 0.12 #87, 0.10 #1647), 03qjg (0.39 #735, 0.39 #649, 0.38 #477), 05r5c (0.39 #696, 0.38 #94, 0.38 #7), 013y1f (0.38 #25, 0.29 #456, 0.29 #714), 02fsn (0.38 #47, 0.15 #134, 0.12 #87), 018j2 (0.38 #31, 0.12 #87, 0.12 #634), 0gghm (0.38 #38, 0.12 #87, 0.07 #1725) >> Best rule #1664 for best value: >> intensional similarity = 4 >> extensional distance = 183 >> proper extension: 0m19t; 07qnf; 0167_s; 02r1tx7; 01qqwp9; 07yg2; 03xhj6; 0394y; 018gm9; 02t3ln; ... >> query: (?x9638, 02hnl) <- artists(?x1000, ?x9638), group(?x228, ?x9638), role(?x5926, ?x228), ?x5926 = 0cfdd >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017959 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.768 http://example.org/music/performance_role/regular_performances./music/group_membership/group #20623-02f9wb PRED entity: 02f9wb PRED relation: award_winner! PRED expected values: 04gnbv1 => 78 concepts (32 used for prediction) PRED predicted values (max 10 best out of 677): 02f9wb (0.35 #41815, 0.28 #45033, 0.28 #40206), 04gnbv1 (0.35 #41815, 0.28 #45033, 0.28 #40206), 04x4s2 (0.35 #41815, 0.28 #45033, 0.28 #40206), 0c7t58 (0.35 #41815, 0.28 #45033, 0.28 #40206), 09_99w (0.28 #19299, 0.05 #11007, 0.05 #14224), 0bbxd3 (0.28 #19299), 01vz80y (0.28 #19299), 03cbtlj (0.22 #51464, 0.16 #33770, 0.16 #43424), 0b1f49 (0.22 #51464, 0.16 #33770, 0.16 #43424), 015pxr (0.18 #335, 0.12 #1943, 0.08 #5159) >> Best rule #41815 for best value: >> intensional similarity = 4 >> extensional distance = 986 >> proper extension: 04lgymt; 04rcr; 07c0j; 011zf2; 0ggl02; 05crg7; 0288fyj; 046b0s; 014hr0; 01x15dc; ... >> query: (?x5958, ?x3762) <- award_winner(?x8229, ?x5958), student(?x735, ?x8229), award_winner(?x944, ?x8229), award_winner(?x3762, ?x8229) >> conf = 0.35 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02f9wb award_winner! 04gnbv1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 32.000 0.355 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #20622-07c52 PRED entity: 07c52 PRED relation: film_distribution_medium! PRED expected values: 03g90h => 67 concepts (51 used for prediction) PRED predicted values (max 10 best out of 260): 0372j5 (0.67 #1820, 0.50 #1610, 0.40 #1399), 031778 (0.67 #1720, 0.50 #1510, 0.40 #1299), 01vksx (0.67 #1694, 0.50 #1484, 0.40 #1273), 0d90m (0.67 #1677, 0.50 #1467, 0.40 #1256), 0hv8w (0.60 #1371, 0.50 #1582, 0.50 #1160), 01pj_5 (0.50 #1560, 0.50 #1138, 0.40 #1349), 03y0pn (0.50 #1824, 0.50 #1614, 0.40 #1403), 03hxsv (0.50 #1809, 0.50 #1599, 0.40 #1388), 0d4htf (0.50 #1793, 0.50 #1583, 0.40 #1372), 0cz8mkh (0.50 #1709, 0.50 #1499, 0.40 #1288) >> Best rule #1820 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0dq6p; >> query: (?x2008, 0372j5) <- film_distribution_medium(?x6614, ?x2008), film(?x6613, ?x6614), artists(?x3061, ?x6613), vacationer(?x151, ?x6613) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #634 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 029j_; *> query: (?x2008, 03g90h) <- film_release_distribution_medium(?x7538, ?x2008), film_release_distribution_medium(?x3619, ?x2008), film_release_distribution_medium(?x204, ?x2008), ?x3619 = 0fphgb, film_distribution_medium(?x1315, ?x2008), ?x7538 = 035zr0, ?x204 = 028_yv *> conf = 0.33 ranks of expected_values: 79 EVAL 07c52 film_distribution_medium! 03g90h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 67.000 51.000 0.667 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #20621-03mqtr PRED entity: 03mqtr PRED relation: genre! PRED expected values: 02rqwhl 03hkch7 0q9sg 05mrf_p 02v_r7d 03bdkd => 50 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1983): 0q9sg (0.80 #3629, 0.80 #5448, 0.79 #5451), 05mrf_p (0.80 #3629, 0.80 #5448, 0.79 #5451), 02jxrw (0.80 #3629, 0.80 #5448, 0.79 #5451), 0ywrc (0.80 #3629, 0.80 #5448, 0.79 #5451), 01lsl (0.80 #3629, 0.80 #5448, 0.79 #5451), 09sr0 (0.80 #3629, 0.80 #5448, 0.79 #5451), 0209hj (0.80 #3629, 0.80 #5448, 0.79 #5451), 09rvcvl (0.80 #3629, 0.80 #5448, 0.79 #5451), 07vf5c (0.80 #3629, 0.80 #5448, 0.79 #5451), 04sntd (0.80 #3629, 0.80 #5448, 0.79 #5451) >> Best rule #3629 for best value: >> intensional similarity = 13 >> extensional distance = 1 >> proper extension: 07s9rl0; >> query: (?x3506, ?x240) <- titles(?x3506, ?x7895), titles(?x3506, ?x5328), titles(?x3506, ?x4538), titles(?x3506, ?x4188), titles(?x3506, ?x2336), titles(?x3506, ?x1820), titles(?x3506, ?x240), ?x1820 = 09cr8, ?x4538 = 0q9sg, ?x7895 = 02rlj20, ?x4188 = 02qhlwd, ?x2336 = 016z9n, ?x5328 = 03cv_gy >> conf = 0.80 => this is the best rule for 28 predicted values ranks of expected_values: 1, 2, 444, 582, 1076, 1677 EVAL 03mqtr genre! 03bdkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 20.000 0.797 http://example.org/film/film/genre EVAL 03mqtr genre! 02v_r7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 20.000 0.797 http://example.org/film/film/genre EVAL 03mqtr genre! 05mrf_p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 20.000 0.797 http://example.org/film/film/genre EVAL 03mqtr genre! 0q9sg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 20.000 0.797 http://example.org/film/film/genre EVAL 03mqtr genre! 03hkch7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 20.000 0.797 http://example.org/film/film/genre EVAL 03mqtr genre! 02rqwhl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 20.000 0.797 http://example.org/film/film/genre #20620-014mlp PRED entity: 014mlp PRED relation: split_to PRED expected values: 019v9k => 24 concepts (21 used for prediction) PRED predicted values (max 10 best out of 19): 07s6fsf (0.33 #12, 0.17 #1348, 0.12 #1450), 022h5x (0.01 #1437), 028dcg (0.01 #1437), 02cq61 (0.01 #1437), 01rr_d (0.01 #1437), 02m4yg (0.01 #1437), 03bwzr4 (0.01 #1437), 02_xgp2 (0.01 #1437), 03mkk4 (0.01 #1437), 027f2w (0.01 #1437) >> Best rule #12 for best value: >> intensional similarity = 22 >> extensional distance = 1 >> proper extension: 07s6fsf; >> query: (?x1368, 07s6fsf) <- institution(?x1368, ?x11474), institution(?x1368, ?x8694), institution(?x1368, ?x5737), institution(?x1368, ?x2150), institution(?x1368, ?x1681), institution(?x1368, ?x1476), institution(?x1368, ?x388), student(?x1368, ?x3210), ?x5737 = 02zd2b, major_field_of_study(?x1368, ?x6575), major_field_of_study(?x1368, ?x2014), colors(?x1476, ?x7179), student(?x11474, ?x5346), nominated_for(?x3210, ?x1230), major_field_of_study(?x7618, ?x2014), ?x8694 = 011xy1, ?x1681 = 07szy, currency(?x1476, ?x170), ?x7618 = 01bk1y, ?x2150 = 07w3r, industry(?x1908, ?x6575), ?x388 = 05krk >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1437 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 4 *> proper extension: 03bwzr4; *> query: (?x1368, ?x620) <- institution(?x1368, ?x11768), institution(?x1368, ?x6455), institution(?x1368, ?x3813), institution(?x1368, ?x3779), institution(?x1368, ?x1476), student(?x1368, ?x3572), ?x11768 = 01hc1j, currency(?x3779, ?x170), institution(?x620, ?x3779), ?x3813 = 07vfj, award(?x3572, ?x68), location(?x3572, ?x739), ?x6455 = 026vcc, gender(?x3572, ?x231), contains(?x94, ?x1476), type_of_union(?x3572, ?x566), ?x94 = 09c7w0, student(?x3779, ?x2409) *> conf = 0.01 ranks of expected_values: 11 EVAL 014mlp split_to 019v9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 24.000 21.000 0.333 http://example.org/dataworld/gardening_hint/split_to #20619-0p_47 PRED entity: 0p_47 PRED relation: person! PRED expected values: 04xbq3 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 13): 0g9lm2 (0.08 #233, 0.02 #304, 0.02 #731), 0dtw1x (0.04 #499, 0.03 #1136, 0.03 #1420), 0bx_hnp (0.03 #560, 0.01 #2042), 06t2t2 (0.02 #352), 0dzlbx (0.02 #305), 05qbckf (0.02 #293), 053tj7 (0.02 #1141, 0.01 #1705, 0.01 #1425), 02v570 (0.02 #756, 0.02 #826), 037q31 (0.01 #541, 0.01 #1178, 0.01 #1462), 05_61y (0.01 #539) >> Best rule #233 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 0mdqp; 06pj8; 0721cy; 021yw7; 01d8yn; 0fby2t; 0gyx4; 0kjrx; >> query: (?x3917, 0g9lm2) <- story_by(?x10470, ?x3917), participant(?x1817, ?x3917) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0p_47 person! 04xbq3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.083 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #20618-01tc9r PRED entity: 01tc9r PRED relation: music! PRED expected values: 016ky6 03cvvlg => 135 concepts (13 used for prediction) PRED predicted values (max 10 best out of 896): 019vhk (0.73 #8895), 016ky6 (0.70 #6917, 0.67 #7906), 017jd9 (0.70 #6917, 0.67 #7906), 01s7w3 (0.09 #3812, 0.05 #5788, 0.05 #9743), 02rrfzf (0.08 #2290, 0.04 #6242, 0.03 #12173), 03h3x5 (0.06 #2224, 0.03 #9143, 0.02 #6176), 09cxm4 (0.06 #2767, 0.03 #5731, 0.02 #8697), 0_7w6 (0.06 #2152, 0.03 #5116, 0.02 #8082), 02z3r8t (0.06 #2036, 0.02 #5988, 0.02 #6977), 078mm1 (0.06 #2779, 0.02 #6731, 0.02 #7720) >> Best rule #8895 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 03f68r6; >> query: (?x3910, ?x2852) <- music(?x69, ?x3910), nominated_for(?x3910, ?x2852), award_winner(?x1079, ?x3910) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #6917 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 01nqfh_; 0244r8; 04zwjd; 0bs1yy; 02bh9; 0b6yp2; 01pr6q7; 04pf4r; 06fxnf; 02jxmr; ... *> query: (?x3910, ?x4610) <- music(?x1463, ?x3910), award_winner(?x4610, ?x3910), film_release_region(?x1463, ?x87) *> conf = 0.70 ranks of expected_values: 2 EVAL 01tc9r music! 03cvvlg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 13.000 0.731 http://example.org/film/film/music EVAL 01tc9r music! 016ky6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 13.000 0.731 http://example.org/film/film/music #20617-0fm3kw PRED entity: 0fm3kw PRED relation: award! PRED expected values: 014x77 01xcfy 02s5v5 => 62 concepts (35 used for prediction) PRED predicted values (max 10 best out of 2939): 02rxbmt (0.40 #1570, 0.33 #4954, 0.29 #8338), 01vvycq (0.37 #37387, 0.11 #108502, 0.11 #101729), 03f2_rc (0.37 #30585, 0.15 #37355, 0.12 #81374), 063b4k (0.33 #6697, 0.29 #10081, 0.24 #16855), 07lt7b (0.33 #10311, 0.24 #13699, 0.19 #33853), 01tspc6 (0.33 #10388, 0.24 #13776, 0.14 #7002), 0dvld (0.29 #32223, 0.25 #11909, 0.18 #15297), 028knk (0.29 #30993, 0.25 #10679, 0.18 #14067), 09l3p (0.29 #31686, 0.17 #11372, 0.13 #38456), 014zcr (0.29 #50832, 0.19 #67762, 0.18 #71148) >> Best rule #1570 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 054knh; >> query: (?x7774, 02rxbmt) <- nominated_for(?x7774, ?x8496), nominated_for(?x7774, ?x4696), nominated_for(?x7774, ?x1786), ?x8496 = 0cvkv5, ?x1786 = 091z_p, genre(?x4696, ?x162), film_release_region(?x4696, ?x94) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #10949 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 09qwmm; 02x1dht; 0gqwc; 099cng; 09ly2r6; *> query: (?x7774, 01xcfy) <- nominated_for(?x7774, ?x8496), nominated_for(?x7774, ?x7735), ?x8496 = 0cvkv5, award(?x1880, ?x7774), nominated_for(?x5959, ?x7735), titles(?x2152, ?x7735) *> conf = 0.25 ranks of expected_values: 33, 147, 633 EVAL 0fm3kw award! 02s5v5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 62.000 35.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fm3kw award! 01xcfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 62.000 35.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fm3kw award! 014x77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 35.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20616-0fh2v5 PRED entity: 0fh2v5 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #11, 0.83 #96, 0.83 #71), 07z4p (0.23 #303, 0.09 #15, 0.04 #80), 02nxhr (0.23 #303, 0.06 #72, 0.04 #107), 07c52 (0.23 #303, 0.05 #13, 0.04 #38) >> Best rule #11 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0djb3vw; >> query: (?x9901, 029j_) <- production_companies(?x9901, ?x6560), genre(?x9901, ?x53), ?x6560 = 04rtpt, language(?x9901, ?x90), film_crew_role(?x9901, ?x137) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fh2v5 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #20615-01r3w7 PRED entity: 01r3w7 PRED relation: colors PRED expected values: 01g5v => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.42 #142, 0.39 #122, 0.38 #262), 01l849 (0.33 #61, 0.28 #981, 0.27 #101), 01g5v (0.33 #64, 0.27 #1584, 0.26 #1044), 06fvc (0.20 #3, 0.15 #1143, 0.15 #1083), 019sc (0.19 #148, 0.18 #228, 0.18 #1088), 03wkwg (0.18 #116, 0.16 #236, 0.16 #96), 036k5h (0.16 #246, 0.13 #126, 0.12 #306), 0jc_p (0.14 #245, 0.14 #305, 0.13 #65), 038hg (0.11 #93, 0.10 #1153, 0.10 #1053), 04mkbj (0.10 #991, 0.10 #771, 0.10 #751) >> Best rule #142 for best value: >> intensional similarity = 8 >> extensional distance = 24 >> proper extension: 0jpkw; >> query: (?x7447, 083jv) <- major_field_of_study(?x7447, ?x1154), major_field_of_study(?x7447, ?x742), currency(?x7447, ?x170), colors(?x7447, ?x13863), ?x1154 = 02lp1, ?x742 = 05qjt, institution(?x865, ?x7447), currency(?x54, ?x170) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #64 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 13 *> proper extension: 07w0v; 0bthb; 01jq34; 0j_sncb; 01swxv; 012vwb; 025v3k; 0hd7j; 09f2j; 01j_5k; ... *> query: (?x7447, 01g5v) <- major_field_of_study(?x7447, ?x6756), major_field_of_study(?x7447, ?x1154), currency(?x7447, ?x170), colors(?x7447, ?x13863), ?x1154 = 02lp1, student(?x7447, ?x4586), category(?x7447, ?x134), ?x6756 = 0_jm *> conf = 0.33 ranks of expected_values: 3 EVAL 01r3w7 colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 151.000 151.000 0.423 http://example.org/education/educational_institution/colors #20614-012gq6 PRED entity: 012gq6 PRED relation: profession PRED expected values: 09jwl => 146 concepts (110 used for prediction) PRED predicted values (max 10 best out of 95): 01d_h8 (0.62 #13684, 0.50 #8832, 0.49 #10449), 09jwl (0.52 #606, 0.48 #1342, 0.44 #753), 0cbd2 (0.51 #3980, 0.50 #5450, 0.48 #5597), 0nbcg (0.45 #619, 0.42 #1355, 0.36 #766), 03gjzk (0.44 #308, 0.43 #1486, 0.42 #5310), 02jknp (0.42 #10451, 0.42 #13686, 0.41 #8834), 0dz3r (0.42 #1327, 0.26 #591, 0.25 #149), 016z4k (0.42 #1329, 0.25 #1770, 0.24 #9565), 0kyk (0.35 #4442, 0.34 #3560, 0.33 #5618), 0np9r (0.29 #5315, 0.26 #7061, 0.25 #7650) >> Best rule #13684 for best value: >> intensional similarity = 3 >> extensional distance = 1573 >> proper extension: 0gry51; >> query: (?x3504, 01d_h8) <- profession(?x3504, ?x987), profession(?x3260, ?x987), ?x3260 = 05ldnp >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #606 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 01wz_ml; *> query: (?x3504, 09jwl) <- influenced_by(?x2894, ?x3504), gender(?x3504, ?x231), inductee(?x9953, ?x3504) *> conf = 0.52 ranks of expected_values: 2 EVAL 012gq6 profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 110.000 0.619 http://example.org/people/person/profession #20613-0bxs_d PRED entity: 0bxs_d PRED relation: honored_for PRED expected values: 015g28 0ph24 => 24 concepts (18 used for prediction) PRED predicted values (max 10 best out of 734): 01j7mr (0.67 #3752, 0.58 #4345, 0.57 #2574), 0kfv9 (0.60 #1285, 0.44 #3055, 0.35 #4826), 07s8z_l (0.50 #2323, 0.44 #3501, 0.43 #2912), 039cq4 (0.50 #3948, 0.42 #4541, 0.33 #3359), 04xbq3 (0.46 #4134, 0.33 #4641, 0.33 #4048), 07c72 (0.46 #4134, 0.33 #185, 0.25 #4319), 0fkwzs (0.46 #4134, 0.22 #7680, 0.19 #5314), 0kfpm (0.46 #4134, 0.12 #4131, 0.11 #5312), 05jyb2 (0.46 #4134, 0.09 #4723, 0.08 #4130), 03nymk (0.46 #4134) >> Best rule #3752 for best value: >> intensional similarity = 17 >> extensional distance = 10 >> proper extension: 0lp_cd3; >> query: (?x8238, 01j7mr) <- ceremony(?x2192, ?x8238), ceremony(?x870, ?x8238), award(?x5925, ?x2192), award(?x5130, ?x2192), award(?x294, ?x2192), award(?x6569, ?x870), ?x6569 = 03q43g, ?x5925 = 023kzp, award_winner(?x8238, ?x8081), ?x5130 = 03pp73, actor(?x758, ?x8081), film(?x8081, ?x755), category_of(?x870, ?x2758), award_winner(?x6341, ?x8081), award_winner(?x294, ?x1020), nominated_for(?x2192, ?x715), nominated_for(?x870, ?x871) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5314 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 15 *> proper extension: 0hr3c8y; 09qvms; 092_25; 03gyp30; 09g90vz; 0g55tzk; *> query: (?x8238, ?x1849) <- ceremony(?x2192, ?x8238), ceremony(?x2041, ?x8238), ceremony(?x870, ?x8238), ceremony(?x870, ?x2292), award(?x5925, ?x2192), award(?x5545, ?x2192), award(?x11697, ?x870), award(?x8203, ?x870), award(?x6569, ?x870), award(?x5690, ?x870), award(?x3417, ?x870), ?x6569 = 03q43g, ?x5925 = 023kzp, ?x5545 = 017khj, nominated_for(?x870, ?x758), award_winner(?x870, ?x3261), ?x5690 = 0h27vc, people(?x6260, ?x11697), award(?x1991, ?x2041), ?x1991 = 02lf70, film(?x3417, ?x697), ?x8203 = 07k51gd, honored_for(?x2292, ?x1849) *> conf = 0.19 ranks of expected_values: 80, 175 EVAL 0bxs_d honored_for 0ph24 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 24.000 18.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0bxs_d honored_for 015g28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 24.000 18.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #20612-01g0jn PRED entity: 01g0jn PRED relation: nationality PRED expected values: 09c7w0 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 106): 09c7w0 (0.86 #10849, 0.86 #11049, 0.80 #1202), 04rrx (0.25 #12153, 0.01 #11249), 0wh3 (0.25 #12153), 02jx1 (0.20 #133, 0.19 #2943, 0.18 #3447), 07ssc (0.20 #115, 0.17 #915, 0.15 #1015), 059j2 (0.11 #329, 0.02 #2939, 0.02 #3443), 03rk0 (0.09 #10994, 0.08 #11194, 0.08 #11295), 0f8l9c (0.08 #922, 0.07 #1022, 0.04 #2932), 0chghy (0.06 #1914, 0.06 #2920, 0.05 #3424), 0h7x (0.06 #535, 0.06 #635, 0.04 #935) >> Best rule #10849 for best value: >> intensional similarity = 3 >> extensional distance = 733 >> proper extension: 02s2ft; 0dbpyd; 01tvz5j; 02mslq; 044rvb; 021sv1; 01cv3n; 05b__vr; 02773nt; 0265v21; ... >> query: (?x12116, 09c7w0) <- gender(?x12116, ?x231), student(?x1681, ?x12116), fraternities_and_sororities(?x1681, ?x3697) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01g0jn nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.864 http://example.org/people/person/nationality #20611-02j9lm PRED entity: 02j9lm PRED relation: profession PRED expected values: 02hrh1q => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.88 #4815, 0.88 #765, 0.87 #7965), 03gjzk (0.51 #2266, 0.50 #1966, 0.29 #3766), 0dxtg (0.50 #1964, 0.46 #2264, 0.29 #3764), 01d_h8 (0.42 #3756, 0.41 #1206, 0.38 #3156), 02jknp (0.25 #3758, 0.22 #6458, 0.21 #3158), 09jwl (0.20 #1520, 0.20 #1070, 0.20 #1670), 0np9r (0.20 #6322, 0.20 #6622, 0.18 #4972), 02krf9 (0.20 #1978, 0.19 #2278, 0.14 #28), 0cbd2 (0.16 #7207, 0.15 #7807, 0.15 #4357), 0d1pc (0.15 #1852, 0.13 #3652, 0.13 #4702) >> Best rule #4815 for best value: >> intensional similarity = 2 >> extensional distance = 601 >> proper extension: 02zq43; 069ld1; 05zbm4; 05tk7y; 0277990; 026zvx7; 027xbpw; 03np3w; 027r8p; 02jtjz; ... >> query: (?x2900, 02hrh1q) <- award_nominee(?x2900, ?x221), actor(?x3610, ?x2900) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02j9lm profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.877 http://example.org/people/person/profession #20610-0h336 PRED entity: 0h336 PRED relation: profession PRED expected values: 0cbd2 05z96 05t4q => 156 concepts (76 used for prediction) PRED predicted values (max 10 best out of 95): 0d8qb (0.84 #4642, 0.25 #814, 0.17 #520), 0cbd2 (0.75 #596, 0.66 #9727, 0.60 #6044), 0dxtg (0.62 #10912, 0.62 #7524, 0.49 #2664), 0fj9f (0.62 #2558, 0.38 #791, 0.25 #350), 01c72t (0.57 #1055, 0.56 #1202, 0.48 #2086), 02hrh1q (0.50 #10177, 0.49 #10766, 0.49 #3107), 05z96 (0.50 #338, 0.40 #926, 0.36 #3828), 09jwl (0.37 #3259, 0.37 #3112, 0.34 #2081), 01d_h8 (0.37 #3098, 0.32 #10904, 0.22 #7516), 04gc2 (0.36 #3828, 0.33 #484, 0.33 #190) >> Best rule #4642 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 05f7snc; >> query: (?x10605, 0d8qb) <- profession(?x10605, ?x7397), gender(?x10605, ?x231), profession(?x3542, ?x7397), ?x3542 = 03hnd >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #596 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 0nk72; *> query: (?x10605, 0cbd2) <- influenced_by(?x10605, ?x7250), profession(?x10605, ?x2225), ?x7250 = 03sbs, nationality(?x10605, ?x1264), ?x2225 = 0kyk *> conf = 0.75 ranks of expected_values: 2, 7, 28 EVAL 0h336 profession 05t4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 156.000 76.000 0.836 http://example.org/people/person/profession EVAL 0h336 profession 05z96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 156.000 76.000 0.836 http://example.org/people/person/profession EVAL 0h336 profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 156.000 76.000 0.836 http://example.org/people/person/profession #20609-0cqhk0 PRED entity: 0cqhk0 PRED relation: ceremony PRED expected values: 092t4b 058m5m4 027hjff => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 134): 0gpjbt (0.36 #1489, 0.34 #1890, 0.33 #2023), 09n4nb (0.35 #1508, 0.34 #1909, 0.33 #2042), 0466p0j (0.35 #1535, 0.33 #1936, 0.33 #2069), 02cg41 (0.35 #1582, 0.33 #1983, 0.32 #2116), 02rjjll (0.34 #1468, 0.33 #1869, 0.32 #2002), 056878 (0.34 #1492, 0.33 #1893, 0.32 #2026), 05pd94v (0.33 #1465, 0.33 #1866, 0.32 #1999), 01c6qp (0.33 #1479, 0.33 #1880, 0.32 #2013), 01mh_q (0.32 #1547, 0.31 #1948, 0.30 #2081), 01bx35 (0.32 #1470, 0.31 #1871, 0.30 #2004) >> Best rule #1489 for best value: >> intensional similarity = 3 >> extensional distance = 229 >> proper extension: 0bwgmzd; >> query: (?x678, 0gpjbt) <- ceremony(?x678, ?x1112), honored_for(?x1112, ?x861), award_winner(?x1112, ?x56) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #54 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 09qs08; 0cqhmg; *> query: (?x678, 027hjff) <- award_winner(?x678, ?x6634), award_winner(?x678, ?x3815), award_winner(?x678, ?x3694), profession(?x3815, ?x1032), ?x3694 = 016tb7, award_winner(?x1631, ?x6634) *> conf = 0.25 ranks of expected_values: 22, 23, 24 EVAL 0cqhk0 ceremony 027hjff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 42.000 42.000 0.364 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0cqhk0 ceremony 058m5m4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 42.000 42.000 0.364 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0cqhk0 ceremony 092t4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 42.000 42.000 0.364 http://example.org/award/award_category/winners./award/award_honor/ceremony #20608-03wjm2 PRED entity: 03wjm2 PRED relation: production_companies PRED expected values: 05rrtf => 98 concepts (91 used for prediction) PRED predicted values (max 10 best out of 70): 020h2v (0.33 #3417, 0.32 #2833, 0.32 #5838), 054g1r (0.33 #3417, 0.32 #2833, 0.32 #5838), 01gb54 (0.29 #121, 0.14 #204, 0.12 #370), 016tw3 (0.29 #95, 0.11 #2677, 0.11 #427), 046b0s (0.25 #24, 0.05 #522, 0.05 #1607), 086k8 (0.19 #334, 0.11 #1418, 0.11 #417), 054lpb6 (0.14 #98, 0.13 #513, 0.08 #2096), 016tt2 (0.14 #87, 0.12 #336, 0.08 #253), 05qd_ (0.14 #93, 0.10 #1842, 0.09 #425), 03sb38 (0.14 #221, 0.08 #304, 0.06 #387) >> Best rule #3417 for best value: >> intensional similarity = 4 >> extensional distance = 626 >> proper extension: 0ds3t5x; 04vvh9; 0jymd; 0yxm1; 03rg2b; 064ndc; 0267wwv; >> query: (?x11945, ?x5636) <- produced_by(?x11945, ?x2789), country(?x11945, ?x94), type_of_union(?x2789, ?x566), film(?x5636, ?x11945) >> conf = 0.33 => this is the best rule for 2 predicted values *> Best rule #141 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 09g8vhw; 033fqh; 0dt8xq; 03h4fq7; 043mk4y; *> query: (?x11945, 05rrtf) <- produced_by(?x11945, ?x2789), country(?x11945, ?x512), ?x2789 = 01zfmm, country(?x1156, ?x512), nationality(?x111, ?x512) *> conf = 0.14 ranks of expected_values: 11 EVAL 03wjm2 production_companies 05rrtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 98.000 91.000 0.333 http://example.org/film/film/production_companies #20607-011_vz PRED entity: 011_vz PRED relation: group! PRED expected values: 02hnl => 77 concepts (54 used for prediction) PRED predicted values (max 10 best out of 120): 02hnl (0.78 #2016, 0.78 #2103, 0.76 #2190), 03bx0bm (0.61 #2011, 0.61 #885, 0.58 #2185), 0l14qv (0.46 #868, 0.38 #1214, 0.29 #350), 03qjg (0.42 #1255, 0.32 #909, 0.30 #1516), 013y1f (0.40 #1234, 0.36 #888, 0.16 #1495), 01vj9c (0.38 #529, 0.28 #1481, 0.27 #2087), 01v1d8 (0.33 #54, 0.25 #313, 0.12 #572), 07y_7 (0.29 #347, 0.25 #88, 0.14 #865), 06ncr (0.25 #123, 0.20 #1246, 0.18 #900), 0l14j_ (0.25 #136, 0.14 #395, 0.12 #2126) >> Best rule #2016 for best value: >> intensional similarity = 4 >> extensional distance = 174 >> proper extension: 03t9sp; 018ndc; 015srx; >> query: (?x9155, 02hnl) <- group(?x316, ?x9155), role(?x74, ?x316), instrumentalists(?x316, ?x115), artist(?x5634, ?x9155) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011_vz group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 54.000 0.784 http://example.org/music/performance_role/regular_performances./music/group_membership/group #20606-0d19y2 PRED entity: 0d19y2 PRED relation: symptom_of! PRED expected values: 0gxb2 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 68): 0gxb2 (0.67 #141, 0.65 #479, 0.50 #196), 01cdt5 (0.67 #141, 0.65 #479, 0.47 #990), 063yv (0.67 #141, 0.65 #479, 0.24 #87), 0hgxh (0.33 #113, 0.25 #529, 0.25 #237), 02y0js (0.33 #124, 0.25 #191, 0.24 #87), 0dq9p (0.33 #71, 0.24 #87, 0.20 #299), 01l2m3 (0.33 #70, 0.24 #87, 0.20 #298), 08g5q7 (0.25 #183, 0.24 #87, 0.14 #1245), 098s1 (0.24 #87, 0.20 #291, 0.18 #712), 01pf6 (0.24 #87, 0.20 #292, 0.17 #401) >> Best rule #141 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 0h1n9; >> query: (?x13131, ?x9509) <- risk_factors(?x11659, ?x13131), symptom_of(?x13373, ?x13131), symptom_of(?x4905, ?x13131), symptom_of(?x3679, ?x13131), ?x4905 = 01j6t0, ?x13373 = 0f3kl, ?x3679 = 02tfl8, symptom_of(?x9509, ?x11659) >> conf = 0.67 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0d19y2 symptom_of! 0gxb2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.667 http://example.org/medicine/symptom/symptom_of #20605-087vz PRED entity: 087vz PRED relation: olympics PRED expected values: 0l998 => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 28): 0kbvb (0.80 #639, 0.80 #417, 0.77 #499), 0lgxj (0.78 #46, 0.73 #301, 0.73 #291), 0jdk_ (0.77 #652, 0.76 #2497, 0.76 #662), 0kbws (0.77 #644, 0.73 #504, 0.70 #228), 06sks6 (0.74 #732, 0.73 #650, 0.73 #482), 0l998 (0.65 #222, 0.64 #416, 0.64 #276), 0sx8l (0.62 #744, 0.62 #494, 0.61 #883), 09n48 (0.62 #744, 0.62 #494, 0.61 #883), 0blfl (0.62 #744, 0.62 #494, 0.61 #883), 018ctl (0.50 #251, 0.47 #640, 0.46 #472) >> Best rule #639 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 09c7w0; 0b90_r; 0154j; 03rjj; 0d060g; 0chghy; 05qhw; 07ssc; 015fr; 0f8l9c; ... >> query: (?x3728, 0kbvb) <- combatants(?x3728, ?x151), contains(?x3728, ?x1791), olympics(?x3728, ?x584), country(?x4355, ?x3728) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #222 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 01rdm0; *> query: (?x3728, 0l998) <- combatants(?x3728, ?x5114), combatants(?x326, ?x3728), ?x5114 = 05vz3zq, organization(?x3728, ?x4230) *> conf = 0.65 ranks of expected_values: 6 EVAL 087vz olympics 0l998 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 164.000 164.000 0.800 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #20604-087vnr5 PRED entity: 087vnr5 PRED relation: film! PRED expected values: 01vhb0 01j5ws 014g_s => 72 concepts (46 used for prediction) PRED predicted values (max 10 best out of 962): 018grr (0.45 #60338, 0.45 #79069, 0.42 #47850), 017s11 (0.45 #60338, 0.45 #79069, 0.42 #47850), 04t2l2 (0.25 #28, 0.05 #4189, 0.03 #2108), 0kszw (0.25 #420, 0.04 #10824, 0.03 #8743), 06t74h (0.25 #697, 0.03 #9020, 0.02 #4858), 021yzs (0.25 #851, 0.02 #7094, 0.01 #9174), 02lkcc (0.25 #243, 0.02 #35611, 0.02 #33531), 023v4_ (0.25 #885, 0.02 #41609, 0.01 #9208), 02j490 (0.25 #1820, 0.02 #18464, 0.01 #35108), 08x5c_ (0.25 #1948, 0.02 #37316, 0.02 #39396) >> Best rule #60338 for best value: >> intensional similarity = 4 >> extensional distance = 713 >> proper extension: 02d44q; 0gtvrv3; 047svrl; 0gh8zks; 0hgnl3t; 07k2mq; 0372j5; >> query: (?x8492, ?x541) <- nominated_for(?x541, ?x8492), film_crew_role(?x8492, ?x137), film_release_region(?x8492, ?x94), film(?x3293, ?x8492) >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #17159 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 152 *> proper extension: 01_1hw; *> query: (?x8492, 01j5ws) <- genre(?x8492, ?x604), produced_by(?x8492, ?x5019), film(?x3293, ?x8492), ?x604 = 0lsxr *> conf = 0.02 ranks of expected_values: 543, 756, 956 EVAL 087vnr5 film! 014g_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 46.000 0.454 http://example.org/film/actor/film./film/performance/film EVAL 087vnr5 film! 01j5ws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 46.000 0.454 http://example.org/film/actor/film./film/performance/film EVAL 087vnr5 film! 01vhb0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 46.000 0.454 http://example.org/film/actor/film./film/performance/film #20603-06b_j PRED entity: 06b_j PRED relation: languages_spoken! PRED expected values: 018s6c => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 68): 059_w (0.50 #155, 0.33 #25, 0.25 #285), 0c41n (0.50 #195, 0.33 #65, 0.17 #325), 0fk3s (0.50 #188, 0.33 #58, 0.17 #318), 03x1x (0.50 #177, 0.33 #47, 0.17 #307), 0g8_vp (0.50 #147, 0.33 #17, 0.17 #277), 02vsw1 (0.42 #302, 0.38 #367, 0.33 #497), 03w9bjf (0.39 #759, 0.33 #954, 0.33 #44), 04gfy7 (0.33 #768, 0.29 #963, 0.20 #2590), 078vc (0.33 #39, 0.28 #689, 0.27 #559), 071x0k (0.33 #8, 0.25 #268, 0.25 #138) >> Best rule #155 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 064_8sq; >> query: (?x5671, 059_w) <- language(?x10060, ?x5671), language(?x8605, ?x5671), language(?x1595, ?x5671), countries_spoken_in(?x5671, ?x279), cinematography(?x8605, ?x5014), production_companies(?x8605, ?x541), service_language(?x555, ?x5671), genre(?x1595, ?x225), languages_spoken(?x3584, ?x5671), ?x10060 = 02jxrw, ?x225 = 02kdv5l >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #574 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 13 *> proper extension: 07zrf; 0653m; 03hkp; 02hxcvy; 02hwyss; 032f6; *> query: (?x5671, 018s6c) <- language(?x8605, ?x5671), language(?x1595, ?x5671), countries_spoken_in(?x5671, ?x279), cinematography(?x8605, ?x5014), production_companies(?x8605, ?x541), country(?x8605, ?x94), music(?x8605, ?x8374), film(?x5636, ?x8605), featured_film_locations(?x1595, ?x108), languages_spoken(?x3584, ?x5671) *> conf = 0.13 ranks of expected_values: 35 EVAL 06b_j languages_spoken! 018s6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 75.000 75.000 0.500 http://example.org/people/ethnicity/languages_spoken #20602-0134pk PRED entity: 0134pk PRED relation: artist! PRED expected values: 04rqd => 97 concepts (75 used for prediction) PRED predicted values (max 10 best out of 4): 04rqd (0.43 #43, 0.43 #63, 0.41 #55), 04y652m (0.32 #66, 0.26 #54, 0.25 #6), 04f73rc (0.03 #56, 0.03 #68, 0.03 #64), 0jrv_ (0.03 #53, 0.03 #65, 0.03 #61) >> Best rule #43 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 0152cw; 01vwbts; 01xzb6; >> query: (?x9868, 04rqd) <- artist(?x6672, ?x9868), artist(?x3265, ?x9868), artists(?x378, ?x9868), award(?x9868, ?x2877), ?x2877 = 02f5qb >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0134pk artist! 04rqd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 75.000 0.435 http://example.org/broadcast/content/artist #20601-0dzs0 PRED entity: 0dzs0 PRED relation: source PRED expected values: 0jbk9 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #48, 0.90 #23, 0.85 #3) >> Best rule #48 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x13817, 0jbk9) <- category(?x13817, ?x134), ?x134 = 08mbj5d, place(?x13817, ?x13817) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dzs0 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #20600-01ly5m PRED entity: 01ly5m PRED relation: location_of_ceremony! PRED expected values: 04ztj => 237 concepts (237 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #65, 0.83 #53, 0.82 #125), 01g63y (0.11 #18, 0.11 #14, 0.10 #26), 0jgjn (0.11 #20, 0.11 #16, 0.10 #28), 01bl8s (0.06 #51, 0.05 #59, 0.03 #115) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0hzlz; >> query: (?x2911, 04ztj) <- film_release_region(?x428, ?x2911), teams(?x2911, ?x6526), place_of_birth(?x1940, ?x2911), location(?x5283, ?x2911) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ly5m location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 237.000 237.000 0.885 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #20599-0bh8yn3 PRED entity: 0bh8yn3 PRED relation: film_crew_role PRED expected values: 02r96rf => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 21): 02r96rf (0.81 #65, 0.75 #470, 0.75 #3), 02rh1dz (0.34 #9, 0.25 #102, 0.22 #71), 02ynfr (0.27 #74, 0.19 #12, 0.17 #136), 0d2b38 (0.22 #21, 0.21 #488, 0.15 #394), 015h31 (0.18 #381, 0.17 #475, 0.12 #8), 0215hd (0.16 #15, 0.14 #482, 0.14 #77), 01xy5l_ (0.16 #11, 0.13 #228, 0.11 #384), 089g0h (0.14 #483, 0.11 #1796, 0.10 #1891), 033smt (0.12 #23, 0.11 #396, 0.11 #490), 04pyp5 (0.11 #44, 0.06 #637, 0.06 #1166) >> Best rule #65 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 0fpkhkz; >> query: (?x1701, 02r96rf) <- film_release_region(?x1701, ?x1003), film_release_region(?x1701, ?x404), award_winner(?x1701, ?x9084), ?x1003 = 03gj2, ?x404 = 047lj >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bh8yn3 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.811 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20598-0nccd PRED entity: 0nccd PRED relation: contains! PRED expected values: 04jpl => 107 concepts (49 used for prediction) PRED predicted values (max 10 best out of 253): 09c7w0 (0.63 #18769, 0.52 #9832, 0.50 #23242), 04jpl (0.60 #2702, 0.57 #5383, 0.44 #6276), 02j9z (0.48 #23238, 0.28 #20555, 0.25 #37575), 04_1l0v (0.40 #23688, 0.30 #30841, 0.27 #25475), 0cxgc (0.33 #652, 0.14 #6013, 0.03 #31939), 0nccd (0.28 #20555, 0.24 #23239, 0.23 #42019), 0d060g (0.27 #39348, 0.05 #15205, 0.04 #16991), 02qkt (0.23 #36101, 0.12 #37892, 0.11 #26266), 0f485 (0.22 #7075, 0.17 #4394, 0.06 #14225), 013p59 (0.20 #2592, 0.17 #4378, 0.11 #7059) >> Best rule #18769 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 059rby; 0f2wj; 01n7q; 030qb3t; 01x73; 0dclg; 03h64; 0d6lp; 0ncj8; 0498y; ... >> query: (?x4049, 09c7w0) <- contains(?x1310, ?x4049), location(?x4282, ?x4049), location_of_ceremony(?x5091, ?x4049), nationality(?x57, ?x1310) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #2702 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0n9dn; 0f8j6; *> query: (?x4049, 04jpl) <- contains(?x12774, ?x4049), contains(?x512, ?x4049), location(?x4282, ?x4049), ?x12774 = 036wy, ?x512 = 07ssc *> conf = 0.60 ranks of expected_values: 2 EVAL 0nccd contains! 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 49.000 0.633 http://example.org/location/location/contains #20597-063y9fp PRED entity: 063y9fp PRED relation: film! PRED expected values: 04mkft => 51 concepts (38 used for prediction) PRED predicted values (max 10 best out of 76): 03yxwq (0.45 #754, 0.44 #2347, 0.43 #2117), 02hvd (0.45 #754, 0.44 #2347, 0.43 #2117), 086k8 (0.33 #2, 0.25 #302, 0.25 #152), 05qd_ (0.33 #234, 0.25 #84, 0.18 #461), 027jw0c (0.33 #55, 0.02 #2193), 016tw3 (0.27 #463, 0.25 #161, 0.16 #2050), 04mkft (0.25 #336, 0.25 #186, 0.09 #412), 017s11 (0.25 #153, 0.18 #455, 0.17 #228), 03xq0f (0.25 #155, 0.18 #909, 0.13 #607), 054g1r (0.25 #335, 0.14 #562, 0.09 #411) >> Best rule #754 for best value: >> intensional similarity = 5 >> extensional distance = 161 >> proper extension: 0b60sq; 0267wwv; >> query: (?x9169, ?x4585) <- genre(?x9169, ?x1013), story_by(?x9169, ?x4238), production_companies(?x9169, ?x4585), genre(?x6788, ?x1013), ?x6788 = 01f8f7 >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #336 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 0cpllql; 0k54q; 0241y7; 03d8jd1; *> query: (?x9169, 04mkft) <- film(?x10152, ?x9169), film(?x1382, ?x9169), profession(?x10152, ?x319), nominated_for(?x10152, ?x7657), genre(?x9169, ?x225), award_nominee(?x10152, ?x129), ?x1382 = 01yh3y *> conf = 0.25 ranks of expected_values: 7 EVAL 063y9fp film! 04mkft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 51.000 38.000 0.452 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #20596-09yrh PRED entity: 09yrh PRED relation: friend PRED expected values: 07cjqy => 127 concepts (116 used for prediction) PRED predicted values (max 10 best out of 145): 01rr9f (0.81 #297, 0.80 #445, 0.06 #7561), 07cjqy (0.81 #297, 0.80 #445), 09yrh (0.20 #76, 0.06 #225, 0.05 #1862), 03v3xp (0.11 #1337, 0.11 #1785, 0.09 #2378), 0c9c0 (0.11 #1337, 0.11 #1785, 0.09 #2378), 0c6qh (0.11 #1337, 0.09 #2378, 0.09 #2377), 01hcj2 (0.11 #1785, 0.09 #2378, 0.09 #2377), 02js6_ (0.11 #1785, 0.09 #2378, 0.09 #2377), 01pw2f1 (0.11 #1785, 0.09 #2378, 0.09 #2377), 01gbbz (0.09 #2378, 0.09 #2377, 0.09 #1336) >> Best rule #297 for best value: >> intensional similarity = 2 >> extensional distance = 49 >> proper extension: 04d_mtq; >> query: (?x4536, ?x513) <- vacationer(?x3501, ?x4536), friend(?x513, ?x4536) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 09yrh friend 07cjqy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 116.000 0.807 http://example.org/celebrities/celebrity/celebrity_friends./celebrities/friendship/friend #20595-04f525m PRED entity: 04f525m PRED relation: film PRED expected values: 05q54f5 08nvyr 0dkv90 0g57wgv => 140 concepts (116 used for prediction) PRED predicted values (max 10 best out of 1673): 0404j37 (0.74 #25237, 0.72 #1578, 0.70 #25236), 035s95 (0.72 #1578, 0.64 #45749, 0.63 #52058), 04z257 (0.72 #1578, 0.64 #45749, 0.63 #52058), 0gtxj2q (0.72 #1578, 0.64 #45749, 0.63 #52058), 02rtqvb (0.72 #1578, 0.64 #45749, 0.63 #52058), 02gpkt (0.50 #2736, 0.22 #9044, 0.17 #5890), 07f_7h (0.50 #1946, 0.22 #8254, 0.17 #5100), 05n6sq (0.50 #2568, 0.22 #8876, 0.17 #5722), 03mh_tp (0.33 #445, 0.29 #6754, 0.28 #16217), 05b6rdt (0.33 #969, 0.25 #2547, 0.20 #4124) >> Best rule #25237 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0kcdl; >> query: (?x963, ?x6448) <- nominated_for(?x963, ?x6448), citytown(?x963, ?x242), film(?x3036, ?x6448) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #22760 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 02b07b; *> query: (?x963, 08nvyr) <- industry(?x963, ?x373), ?x373 = 02vxn, child(?x1914, ?x963) *> conf = 0.09 ranks of expected_values: 603, 882, 1494 EVAL 04f525m film 0g57wgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 116.000 0.735 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 04f525m film 0dkv90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 140.000 116.000 0.735 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 04f525m film 08nvyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 140.000 116.000 0.735 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 04f525m film 05q54f5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 140.000 116.000 0.735 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #20594-07r4c PRED entity: 07r4c PRED relation: artists! PRED expected values: 088vmr => 140 concepts (75 used for prediction) PRED predicted values (max 10 best out of 271): 06by7 (0.85 #5252, 0.80 #15427, 0.77 #9254), 064t9 (0.72 #7707, 0.68 #9245, 0.67 #2165), 017_qw (0.61 #6835, 0.24 #3754, 0.14 #4062), 0xhtw (0.58 #11717, 0.27 #1553, 0.25 #1861), 06j6l (0.57 #12057, 0.50 #2202, 0.41 #5280), 05bt6j (0.53 #7739, 0.50 #2197, 0.40 #4659), 0dl5d (0.48 #6174, 0.42 #1864, 0.40 #17), 0glt670 (0.44 #1271, 0.38 #7122, 0.36 #3426), 025sc50 (0.44 #3436, 0.33 #2204, 0.33 #665), 01fh36 (0.42 #2239, 0.40 #392, 0.28 #5317) >> Best rule #5252 for best value: >> intensional similarity = 6 >> extensional distance = 44 >> proper extension: 01nqfh_; 01qkqwg; 0k1bs; 01k47c; 02yygk; 01tw31; 01wx756; >> query: (?x6208, 06by7) <- artists(?x505, ?x6208), artists(?x302, ?x6208), instrumentalists(?x74, ?x6208), ?x505 = 03_d0, artists(?x302, ?x8053), ?x8053 = 032nl2 >> conf = 0.85 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07r4c artists! 088vmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 75.000 0.848 http://example.org/music/genre/artists #20593-0266s9 PRED entity: 0266s9 PRED relation: honored_for! PRED expected values: 07y_p6 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 87): 05c1t6z (0.42 #478, 0.26 #1766, 0.25 #244), 0bxs_d (0.42 #446, 0.12 #329, 0.09 #1149), 09p3h7 (0.42 #407, 0.12 #6321, 0.04 #1227), 03nnm4t (0.39 #527, 0.25 #293, 0.22 #1815), 0275n3y (0.38 #294, 0.16 #528, 0.11 #763), 0gvstc3 (0.29 #494, 0.22 #1782, 0.22 #1197), 07y_p6 (0.25 #429, 0.12 #312, 0.12 #6321), 09pj68 (0.25 #319, 0.09 #788, 0.07 #1139), 09qvms (0.25 #243, 0.08 #360, 0.06 #477), 09p2r9 (0.25 #308, 0.04 #777, 0.03 #1830) >> Best rule #478 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0gpjbt; >> query: (?x11806, 05c1t6z) <- honored_for(?x4760, ?x11806), honored_for(?x1193, ?x11806), ?x4760 = 02q690_, award_winner(?x1193, ?x221) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #429 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 01g03q; *> query: (?x11806, 07y_p6) <- genre(?x11806, ?x53), honored_for(?x6238, ?x11806), award_winner(?x6238, ?x1119), ?x1119 = 039bp *> conf = 0.25 ranks of expected_values: 7 EVAL 0266s9 honored_for! 07y_p6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 87.000 0.419 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #20592-017f4y PRED entity: 017f4y PRED relation: instrumentalists! PRED expected values: 02hnl => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 126): 05148p4 (0.55 #787, 0.46 #701, 0.42 #1540), 06w7v (0.45 #342, 0.45 #325, 0.33 #4974), 02hnl (0.45 #801, 0.27 #715, 0.26 #544), 026t6 (0.45 #772, 0.19 #1457, 0.18 #515), 01vj9c (0.35 #4026, 0.33 #4974, 0.31 #2310), 05842k (0.35 #4026, 0.31 #2310, 0.31 #1967), 018j2 (0.33 #4974, 0.31 #3337, 0.31 #2310), 013y1f (0.33 #4974, 0.31 #3337, 0.31 #2310), 042v_gx (0.33 #4974, 0.31 #3337, 0.31 #2310), 02dlh2 (0.31 #3337, 0.31 #2310, 0.31 #1967) >> Best rule #787 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 018y81; >> query: (?x10738, 05148p4) <- instrumentalists(?x315, ?x10738), role(?x10738, ?x432), ?x315 = 0l14md >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #801 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 018y81; *> query: (?x10738, 02hnl) <- instrumentalists(?x315, ?x10738), role(?x10738, ?x432), ?x315 = 0l14md *> conf = 0.45 ranks of expected_values: 3 EVAL 017f4y instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 169.000 169.000 0.551 http://example.org/music/instrument/instrumentalists #20591-03x1s8 PRED entity: 03x1s8 PRED relation: state_province_region PRED expected values: 0gyh => 108 concepts (52 used for prediction) PRED predicted values (max 10 best out of 75): 0gyh (0.84 #250, 0.78 #374, 0.68 #747), 09c7w0 (0.35 #5604, 0.29 #4983, 0.28 #3861), 0fttg (0.28 #3861, 0.28 #3737, 0.27 #4357), 0jt5zcn (0.25 #34, 0.15 #656, 0.11 #1155), 01n7q (0.25 #516, 0.23 #2508, 0.23 #3006), 059rby (0.22 #4988, 0.22 #5236, 0.19 #5734), 0d0x8 (0.16 #170, 0.15 #294, 0.12 #542), 04_1l0v (0.12 #1245), 05kr_ (0.11 #155, 0.11 #279, 0.08 #651), 07h34 (0.08 #178, 0.08 #302, 0.06 #550) >> Best rule #250 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 04qhdf; 08t9df; 09glbnt; 01w3vc; 0ljc_; 0gmf0nj; 0kcd5; 05frqx; >> query: (?x12126, ?x2831) <- citytown(?x12126, ?x12384), administrative_division(?x12384, ?x2831), district_represented(?x176, ?x2831), state_province_region(?x1201, ?x2831) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x1s8 state_province_region 0gyh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 52.000 0.839 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #20590-015qqg PRED entity: 015qqg PRED relation: nominated_for! PRED expected values: 027dtxw 019f4v => 74 concepts (64 used for prediction) PRED predicted values (max 10 best out of 191): 0gqy2 (0.68 #6034, 0.66 #6033, 0.66 #6482), 019f4v (0.51 #494, 0.50 #271, 0.50 #1834), 02n9nmz (0.50 #275, 0.38 #1838, 0.34 #2284), 03hkv_r (0.42 #1800, 0.39 #237, 0.35 #2246), 0gq_v (0.40 #2697, 0.38 #465, 0.37 #242), 02ppm4q (0.40 #548, 0.37 #1442, 0.34 #325), 04kxsb (0.39 #305, 0.29 #1868, 0.27 #751), 0f4x7 (0.38 #1365, 0.37 #1811, 0.37 #2257), 0p9sw (0.33 #2698, 0.31 #20, 0.28 #914), 09qwmm (0.32 #249, 0.24 #472, 0.12 #8499) >> Best rule #6034 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 07bz5; >> query: (?x4870, ?x198) <- award_winner(?x4870, ?x1126), award(?x4870, ?x198), award(?x71, ?x198) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #494 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 43 *> proper extension: 0m313; 095zlp; 0pv2t; 0pv3x; 035yn8; 03hj3b3; 070fnm; 0ct5zc; 083skw; 0gxfz; ... *> query: (?x4870, 019f4v) <- award_winner(?x4870, ?x1126), nominated_for(?x1972, ?x4870), nominated_for(?x1245, ?x4870), ?x1972 = 0gqyl, ?x1245 = 0gqwc *> conf = 0.51 ranks of expected_values: 2, 27 EVAL 015qqg nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 64.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 015qqg nominated_for! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 74.000 64.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20589-06cs1 PRED entity: 06cs1 PRED relation: school_type! PRED expected values: 02bhj4 => 23 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1347): 02x9g_ (0.93 #577, 0.74 #579, 0.61 #578), 01bm_ (0.93 #577, 0.74 #579, 0.61 #578), 06pwq (0.93 #577, 0.74 #579, 0.61 #578), 06fq2 (0.93 #577, 0.74 #579, 0.61 #578), 0kz2w (0.93 #577, 0.74 #579, 0.61 #578), 08815 (0.93 #577, 0.74 #579, 0.61 #578), 01w5m (0.93 #577, 0.74 #579, 0.61 #578), 09kvv (0.93 #577, 0.74 #579, 0.61 #578), 0pspl (0.93 #577, 0.74 #579, 0.61 #578), 0qlnr (0.93 #577, 0.74 #579, 0.61 #578) >> Best rule #577 for best value: >> intensional similarity = 30 >> extensional distance = 1 >> proper extension: 05jxkf; >> query: (?x3355, ?x1520) <- school_type(?x11278, ?x3355), school_type(?x3354, ?x3355), institution(?x6117, ?x3354), institution(?x1368, ?x3354), institution(?x865, ?x3354), major_field_of_study(?x3354, ?x9111), major_field_of_study(?x3354, ?x254), colors(?x3354, ?x3189), ?x6117 = 02m4yg, major_field_of_study(?x11278, ?x10046), school_type(?x11278, ?x1962), school_type(?x11278, ?x1044), ?x1368 = 014mlp, organization(?x346, ?x3354), ?x1962 = 01_srz, ?x9111 = 04sh3, company(?x346, ?x94), organization(?x346, ?x11632), ?x3189 = 01g5v, major_field_of_study(?x254, ?x2314), institution(?x865, ?x7546), institution(?x865, ?x3351), student(?x254, ?x2343), ?x7546 = 01_qgp, ?x3351 = 01q2sk, ?x11632 = 0mbwf, ?x10046 = 041y2, student(?x865, ?x1117), currency(?x11278, ?x170), school_type(?x1520, ?x1044) >> conf = 0.93 => this is the best rule for 138 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 23 EVAL 06cs1 school_type! 02bhj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 23.000 17.000 0.927 http://example.org/education/educational_institution/school_type #20588-0h63gl9 PRED entity: 0h63gl9 PRED relation: film_release_region PRED expected values: 07ssc 02vzc => 60 concepts (57 used for prediction) PRED predicted values (max 10 best out of 119): 07ssc (0.83 #722, 0.82 #1006, 0.79 #1148), 02vzc (0.81 #754, 0.80 #1038, 0.77 #1180), 03_3d (0.77 #714, 0.77 #998, 0.73 #1140), 015qh (0.57 #745, 0.53 #1029, 0.44 #35), 047yc (0.53 #732, 0.51 #1016, 0.43 #1158), 06qd3 (0.53 #741, 0.50 #1025, 0.49 #1167), 03rk0 (0.53 #757, 0.48 #1041, 0.38 #1183), 016wzw (0.52 #765, 0.48 #1049, 0.40 #1191), 06t8v (0.51 #776, 0.47 #1060, 0.42 #1202), 01ls2 (0.50 #719, 0.45 #1003, 0.38 #1145) >> Best rule #722 for best value: >> intensional similarity = 6 >> extensional distance = 154 >> proper extension: 0ds35l9; 0gtsx8c; 02vxq9m; 0c3ybss; 011yrp; 0gx1bnj; 0h1cdwq; 0dscrwf; 02x3lt7; 0gx9rvq; ... >> query: (?x6621, 07ssc) <- film_release_region(?x6621, ?x2645), film_release_region(?x6621, ?x1499), film_release_region(?x6621, ?x172), ?x172 = 0154j, ?x1499 = 01znc_, ?x2645 = 03h64 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0h63gl9 film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 57.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h63gl9 film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 57.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20587-01jr6 PRED entity: 01jr6 PRED relation: jurisdiction_of_office! PRED expected values: 01q24l => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.48 #531, 0.47 #1301, 0.46 #465), 060bp (0.43 #463, 0.40 #1299, 0.39 #529), 0f6c3 (0.24 #623, 0.20 #1305, 0.19 #1063), 0fkvn (0.21 #620, 0.21 #1302, 0.18 #1148), 09n5b9 (0.20 #627, 0.18 #1067, 0.18 #1309), 01q24l (0.19 #233, 0.17 #1025, 0.13 #695), 0p5vf (0.15 #254, 0.13 #210, 0.11 #474), 0dq3c (0.12 #46, 0.11 #530, 0.09 #464), 01zq91 (0.11 #256, 0.09 #212, 0.07 #476), 04syw (0.10 #336, 0.09 #248, 0.09 #534) >> Best rule #531 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 047lj; 01ls2; 05v10; 088vb; 0j4b; 07f1x; 02kcz; 07f5x; >> query: (?x3976, 060c4) <- teams(?x3976, ?x6294), adjoins(?x2552, ?x3976), contains(?x94, ?x3976) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 52 *> proper extension: 06pwq; *> query: (?x3976, 01q24l) <- state(?x3976, ?x1227), ?x1227 = 01n7q *> conf = 0.19 ranks of expected_values: 6 EVAL 01jr6 jurisdiction_of_office! 01q24l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 123.000 123.000 0.480 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #20586-079dy PRED entity: 079dy PRED relation: entity_involved! PRED expected values: 018w0j => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 77): 09x7p1 (0.50 #545, 0.46 #3592, 0.45 #1059), 0gfhg1y (0.46 #3592, 0.29 #1260, 0.25 #553), 024jvz (0.46 #3592, 0.25 #537, 0.23 #3593), 02kxjx (0.40 #687, 0.40 #623, 0.33 #174), 0f6rc (0.33 #86, 0.25 #1178, 0.20 #599), 086m1 (0.33 #19, 0.25 #275, 0.19 #1432), 01y998 (0.33 #82, 0.20 #595, 0.10 #1879), 0dl4z (0.31 #2633, 0.14 #5082, 0.09 #3855), 03jqfx (0.28 #3554, 0.14 #5082, 0.10 #5239), 07_nf (0.27 #3030, 0.22 #3415, 0.22 #3351) >> Best rule #545 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 02mjmr; >> query: (?x12920, 09x7p1) <- entity_involved(?x2391, ?x12920), ?x2391 = 0d06vc, person(?x6093, ?x12920), jurisdiction_of_office(?x12920, ?x4092), basic_title(?x12920, ?x346), type_of_union(?x12920, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #291 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 034ls; *> query: (?x12920, 018w0j) <- religion(?x12920, ?x492), entity_involved(?x2391, ?x12920), profession(?x12920, ?x353), jurisdiction_of_office(?x12920, ?x4092), profession(?x6849, ?x353), ?x6849 = 023n39 *> conf = 0.25 ranks of expected_values: 12 EVAL 079dy entity_involved! 018w0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 166.000 166.000 0.500 http://example.org/base/culturalevent/event/entity_involved #20585-01f1jy PRED entity: 01f1jy PRED relation: olympics! PRED expected values: 0d060g 0f8l9c => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 352): 0f8l9c (0.93 #2492, 0.90 #2622, 0.89 #2884), 07ssc (0.83 #2225, 0.80 #2467, 0.80 #2358), 0b90_r (0.83 #1562, 0.52 #402, 0.50 #2214), 0k6nt (0.79 #2625, 0.76 #2366, 0.71 #2233), 0d060g (0.77 #2855, 0.77 #2736, 0.75 #2216), 0chghy (0.75 #2334, 0.75 #2220, 0.63 #2874), 03gj2 (0.72 #2367, 0.71 #2234, 0.69 #2626), 0345h (0.62 #1601, 0.60 #1989, 0.57 #1341), 087vz (0.62 #1505, 0.52 #402, 0.50 #2280), 0154j (0.60 #2348, 0.59 #2607, 0.54 #2215) >> Best rule #2492 for best value: >> intensional similarity = 71 >> extensional distance = 26 >> proper extension: 06sks6; >> query: (?x1617, 0f8l9c) <- olympics(?x11872, ?x1617), olympics(?x5114, ?x1617), olympics(?x2984, ?x1617), olympics(?x1355, ?x1617), sports(?x1617, ?x1175), film_release_region(?x11809, ?x2984), film_release_region(?x11209, ?x2984), film_release_region(?x11065, ?x2984), film_release_region(?x9345, ?x2984), film_release_region(?x9294, ?x2984), film_release_region(?x7265, ?x2984), film_release_region(?x6620, ?x2984), film_release_region(?x6218, ?x2984), film_release_region(?x6181, ?x2984), film_release_region(?x5873, ?x2984), film_release_region(?x5849, ?x2984), film_release_region(?x5139, ?x2984), film_release_region(?x4971, ?x2984), film_release_region(?x3524, ?x2984), film_release_region(?x3514, ?x2984), film_release_region(?x2434, ?x2984), film_release_region(?x1452, ?x2984), film_release_region(?x430, ?x2984), film_release_region(?x251, ?x2984), ?x5849 = 02h22, ?x9345 = 014knw, olympics(?x2984, ?x8189), olympics(?x2984, ?x5176), olympics(?x2984, ?x867), ?x5873 = 0cq86w, ?x3514 = 04vh83, ?x5139 = 07bzz7, ?x251 = 02vp1f_, nominated_for(?x1822, ?x7265), ?x11065 = 0n08r, olympics(?x1175, ?x418), combatants(?x5114, ?x7287), combatants(?x5114, ?x5147), produced_by(?x7265, ?x3637), ?x8189 = 015l4k, ?x867 = 0l6ny, contains(?x5114, ?x8745), ?x1452 = 0jqn5, official_language(?x11872, ?x732), film(?x8151, ?x7265), ?x430 = 0m2kd, country(?x1175, ?x512), ?x5176 = 0sx92, medal(?x11872, ?x422), capital(?x2984, ?x10334), ?x11809 = 0b85mm, nationality(?x2693, ?x5114), ?x6620 = 0mbql, ?x2434 = 085ccd, ?x1355 = 0h7x, ?x6218 = 03rg2b, ?x9294 = 0m3gy, ?x4971 = 01jwxx, film_release_region(?x7265, ?x2645), country(?x4045, ?x11872), ?x11209 = 04fjzv, country(?x359, ?x5114), films(?x1175, ?x10459), organization(?x11872, ?x312), language(?x7265, ?x254), ?x6181 = 0hv27, ?x2645 = 03h64, ?x3524 = 06r2_, ?x5147 = 0d04z6, contains(?x7287, ?x11116), country(?x471, ?x7287) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 01f1jy olympics! 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.929 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 01f1jy olympics! 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 24.000 24.000 0.929 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #20584-0ptxj PRED entity: 0ptxj PRED relation: nominated_for! PRED expected values: 0p9tm => 119 concepts (60 used for prediction) PRED predicted values (max 10 best out of 239): 0283_zv (0.82 #1013, 0.82 #7589, 0.81 #6832), 0jyb4 (0.52 #2026, 0.50 #508, 0.45 #1520), 0d1qmz (0.15 #103, 0.06 #611, 0.06 #6177), 0fztbq (0.15 #245, 0.06 #753, 0.05 #6319), 01kf4tt (0.15 #75, 0.06 #583, 0.05 #6149), 0g5pvv (0.15 #167, 0.05 #6241, 0.04 #7503), 02qlp4 (0.14 #253, 0.03 #5315, 0.03 #5570), 02qrv7 (0.11 #33, 0.06 #541, 0.06 #6107), 025twgt (0.11 #246, 0.06 #754, 0.05 #6320), 0164qt (0.11 #18, 0.06 #526, 0.04 #6092) >> Best rule #1013 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 0m313; 0ds3t5x; 016fyc; 0pc62; 0209xj; 0jzw; 0bshwmp; 04vr_f; 026390q; 0416y94; ... >> query: (?x5212, ?x1822) <- produced_by(?x5212, ?x11625), nominated_for(?x5212, ?x1822), honored_for(?x3173, ?x5212), award_winner(?x5212, ?x669) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1229 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 024mxd; 02kfzz; 04tng0; *> query: (?x5212, 0p9tm) <- produced_by(?x5212, ?x11625), nominated_for(?x5212, ?x1822), honored_for(?x3173, ?x5212), film(?x772, ?x5212) *> conf = 0.02 ranks of expected_values: 187 EVAL 0ptxj nominated_for! 0p9tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 119.000 60.000 0.822 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #20583-04cj79 PRED entity: 04cj79 PRED relation: nominated_for! PRED expected values: 019f4v => 101 concepts (99 used for prediction) PRED predicted values (max 10 best out of 220): 09sdmz (0.71 #137, 0.35 #367, 0.26 #2667), 099c8n (0.65 #54, 0.61 #284, 0.38 #1894), 0f_nbyh (0.53 #237, 0.47 #7, 0.19 #2537), 09sb52 (0.53 #263, 0.41 #33, 0.25 #1643), 0gq9h (0.49 #9720, 0.42 #2589, 0.41 #289), 02pqp12 (0.45 #1665, 0.42 #2585, 0.35 #55), 02qyntr (0.45 #2702, 0.38 #1782, 0.35 #172), 040njc (0.43 #1616, 0.41 #2536, 0.33 #236), 0gs9p (0.41 #291, 0.40 #9722, 0.39 #1671), 03hkv_r (0.41 #244, 0.29 #14, 0.25 #1624) >> Best rule #137 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 0g4vmj8; >> query: (?x3605, 09sdmz) <- nominated_for(?x2733, ?x3605), nominated_for(?x2880, ?x3605), nominated_for(?x451, ?x3605), ?x2880 = 02ppm4q, ?x451 = 099jhq >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #9712 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 579 *> proper extension: 02qr69m; 0cbl95; *> query: (?x3605, 019f4v) <- nominated_for(?x2733, ?x3605), nominated_for(?x2880, ?x3605), nominated_for(?x2880, ?x2057), ?x2057 = 0jym0 *> conf = 0.39 ranks of expected_values: 11 EVAL 04cj79 nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 101.000 99.000 0.706 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20582-03gvt PRED entity: 03gvt PRED relation: instrumentalists PRED expected values: 032t2z 0163r3 => 72 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1009): 01sb5r (0.75 #6724, 0.71 #13218, 0.62 #15581), 018y81 (0.75 #6833, 0.64 #13327, 0.58 #9785), 01vw20_ (0.75 #6654, 0.58 #9606, 0.57 #13148), 0473q (0.75 #6883, 0.50 #13377, 0.50 #3931), 050z2 (0.74 #5906, 0.67 #4135, 0.64 #3544), 04m2zj (0.74 #5906, 0.67 #4135, 0.64 #3544), 01vrnsk (0.64 #13949, 0.64 #4136, 0.62 #4726), 032t2z (0.64 #4136, 0.62 #6523, 0.62 #4726), 0161sp (0.64 #4136, 0.62 #6648, 0.62 #4726), 06rgq (0.64 #4136, 0.62 #4726, 0.61 #4725) >> Best rule #6724 for best value: >> intensional similarity = 16 >> extensional distance = 6 >> proper extension: 0l14qv; >> query: (?x3716, 01sb5r) <- role(?x3716, ?x2798), role(?x3716, ?x868), instrumentalists(?x3716, ?x4620), role(?x3239, ?x3716), role(?x1432, ?x3716), ?x1432 = 0395lw, role(?x3716, ?x614), ?x2798 = 03qjg, performance_role(?x4052, ?x3716), place_of_birth(?x4620, ?x14311), location_of_ceremony(?x4620, ?x362), role(?x4471, ?x3239), profession(?x4620, ?x131), ?x4471 = 026g73, role(?x74, ?x868), influenced_by(?x4593, ?x4620) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #4136 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 0342h; *> query: (?x3716, ?x211) <- role(?x3716, ?x2798), role(?x3716, ?x868), instrumentalists(?x3716, ?x9074), instrumentalists(?x3716, ?x4620), role(?x1432, ?x3716), role(?x212, ?x3716), ?x1432 = 0395lw, role(?x3716, ?x1574), ?x2798 = 03qjg, performance_role(?x4052, ?x3716), ?x4620 = 01vsy7t, ?x212 = 026t6, role(?x1715, ?x868), artist(?x2149, ?x9074), role(?x211, ?x3716), ?x1574 = 0l15bq, role(?x74, ?x868) *> conf = 0.64 ranks of expected_values: 8, 235 EVAL 03gvt instrumentalists 0163r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 72.000 35.000 0.750 http://example.org/music/instrument/instrumentalists EVAL 03gvt instrumentalists 032t2z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 72.000 35.000 0.750 http://example.org/music/instrument/instrumentalists #20581-0jwl2 PRED entity: 0jwl2 PRED relation: genre PRED expected values: 0hcr => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 80): 0hcr (0.81 #266, 0.62 #430, 0.52 #594), 05p553 (0.79 #2220, 0.74 #2303, 0.52 #579), 07s9rl0 (0.67 #165, 0.65 #1067, 0.51 #3284), 01z4y (0.52 #2234, 0.48 #2317, 0.33 #183), 06n90 (0.42 #1326, 0.41 #1244, 0.37 #1162), 03k9fj (0.40 #1324, 0.38 #1242, 0.36 #1570), 01htzx (0.39 #1248, 0.39 #1330, 0.38 #1576), 0c4xc (0.36 #2258, 0.34 #2341, 0.23 #1766), 01hmnh (0.36 #673, 0.35 #755, 0.35 #345), 01t_vv (0.33 #199, 0.25 #35, 0.22 #2250) >> Best rule #266 for best value: >> intensional similarity = 9 >> extensional distance = 14 >> proper extension: 020qr4; >> query: (?x4339, 0hcr) <- program(?x1762, ?x4339), genre(?x4339, ?x10159), genre(?x4339, ?x10023), ?x10023 = 0pr6f, genre(?x4275, ?x10159), genre(?x1395, ?x10159), ?x1395 = 019nnl, languages(?x4275, ?x254), actor(?x4275, ?x4112) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jwl2 genre 0hcr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.812 http://example.org/tv/tv_program/genre #20580-0399p PRED entity: 0399p PRED relation: influenced_by PRED expected values: 02zjd 06myp 03j2gxx => 198 concepts (88 used for prediction) PRED predicted values (max 10 best out of 517): 03_87 (0.50 #6551, 0.50 #3587, 0.43 #15041), 05qmj (0.50 #3153, 0.48 #12484, 0.40 #9082), 0tfc (0.42 #5487, 0.35 #5912, 0.20 #2524), 0gz_ (0.40 #3066, 0.40 #2219, 0.39 #12397), 042q3 (0.40 #3322, 0.40 #2475, 0.36 #9251), 0j3v (0.40 #2176, 0.36 #8952, 0.25 #5139), 043s3 (0.40 #2231, 0.30 #12409, 0.29 #5619), 03f0324 (0.33 #2690, 0.33 #1843, 0.33 #1418), 0w6w (0.33 #2965, 0.33 #1269, 0.24 #12719), 01rgr (0.33 #2011, 0.33 #1586, 0.20 #3705) >> Best rule #6551 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 04135; >> query: (?x8233, 03_87) <- influenced_by(?x8233, ?x5612), influenced_by(?x8233, ?x3994), people(?x5540, ?x3994), profession(?x3994, ?x3342), ?x5612 = 058vp >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2062 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 07h1q; *> query: (?x8233, 06myp) <- influenced_by(?x8233, ?x11097), influenced_by(?x8233, ?x7250), influenced_by(?x8233, ?x4265), religion(?x8233, ?x2694), ?x4265 = 06whf, ?x7250 = 03sbs, ?x11097 = 02wh0 *> conf = 0.33 ranks of expected_values: 24, 32, 107 EVAL 0399p influenced_by 03j2gxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 198.000 88.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 0399p influenced_by 06myp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 198.000 88.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 0399p influenced_by 02zjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 198.000 88.000 0.500 http://example.org/influence/influence_node/influenced_by #20579-01pg1d PRED entity: 01pg1d PRED relation: film PRED expected values: 016y_f => 106 concepts (62 used for prediction) PRED predicted values (max 10 best out of 496): 01g03q (0.49 #21433, 0.36 #48228, 0.36 #51802), 01xbxn (0.25 #4965, 0.01 #12109), 03nfnx (0.18 #3188, 0.01 #24622), 02qydsh (0.18 #3283), 0642xf3 (0.18 #2658), 02lk60 (0.18 #2576), 0234j5 (0.17 #4994, 0.09 #3208, 0.04 #110748), 06cm5 (0.17 #4641, 0.04 #110748), 0gmgwnv (0.12 #1078, 0.02 #27871), 0dl6fv (0.12 #1486) >> Best rule #21433 for best value: >> intensional similarity = 3 >> extensional distance = 527 >> proper extension: 08wr3kg; >> query: (?x10851, ?x9350) <- gender(?x10851, ?x514), nominated_for(?x10851, ?x9350), ?x514 = 02zsn >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #4319 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 03q1vd; *> query: (?x10851, 016y_f) <- film(?x10851, ?x4093), ?x4093 = 0h6r5, location(?x10851, ?x2020) *> conf = 0.08 ranks of expected_values: 137 EVAL 01pg1d film 016y_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 106.000 62.000 0.494 http://example.org/film/actor/film./film/performance/film #20578-01rcmg PRED entity: 01rcmg PRED relation: film PRED expected values: 033pf1 => 87 concepts (62 used for prediction) PRED predicted values (max 10 best out of 719): 01bv8b (0.39 #46446, 0.36 #35728, 0.35 #53594), 0ctzf1 (0.07 #28583, 0.06 #25010, 0.06 #44659), 024rwx (0.07 #28583, 0.06 #25010, 0.06 #44659), 0jwl2 (0.07 #28583, 0.06 #25010, 0.06 #44659), 02qr3k8 (0.07 #1285, 0.03 #29868, 0.02 #72746), 03mh94 (0.07 #64, 0.01 #3637, 0.01 #28647), 09sr0 (0.06 #1516, 0.02 #10448, 0.02 #14021), 011ywj (0.05 #6792, 0.04 #1432, 0.03 #10364), 016z9n (0.05 #7516, 0.04 #370, 0.02 #28953), 07g9f (0.05 #78609, 0.03 #110770, 0.03 #82183) >> Best rule #46446 for best value: >> intensional similarity = 3 >> extensional distance = 926 >> proper extension: 03h40_7; 06mm1x; >> query: (?x8439, ?x2710) <- award_nominee(?x8439, ?x3446), student(?x3564, ?x8439), nominated_for(?x8439, ?x2710) >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01rcmg film 033pf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 62.000 0.391 http://example.org/film/actor/film./film/performance/film #20577-02q0k7v PRED entity: 02q0k7v PRED relation: film! PRED expected values: 06dv3 => 90 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1212): 0hvb2 (0.29 #300, 0.09 #16957, 0.05 #27369), 0169dl (0.14 #402, 0.14 #4566, 0.11 #8731), 09l3p (0.14 #748, 0.11 #2830, 0.04 #23652), 02yxwd (0.14 #744, 0.09 #4908, 0.07 #17401), 09fb5 (0.14 #58, 0.09 #4222, 0.07 #8387), 035rnz (0.14 #694, 0.07 #6941, 0.05 #4858), 023zsh (0.14 #1669, 0.07 #7916, 0.03 #24573), 03cglm (0.14 #1046, 0.07 #7293, 0.02 #17703), 01wbg84 (0.14 #47, 0.07 #16704, 0.05 #31281), 016k6x (0.14 #890, 0.07 #17547, 0.05 #5054) >> Best rule #300 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 03bx2lk; >> query: (?x7694, 0hvb2) <- film(?x3101, ?x7694), genre(?x7694, ?x53), edited_by(?x7694, ?x11314), ?x3101 = 0dvmd >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2115 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 07s3m4g; *> query: (?x7694, 06dv3) <- category(?x7694, ?x134), genre(?x7694, ?x4205), ?x4205 = 0c3351 *> conf = 0.11 ranks of expected_values: 66 EVAL 02q0k7v film! 06dv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 90.000 55.000 0.286 http://example.org/film/actor/film./film/performance/film #20576-08zrbl PRED entity: 08zrbl PRED relation: region PRED expected values: 07ssc => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 16): 07ssc (0.82 #124, 0.78 #100, 0.66 #147), 09c7w0 (0.10 #443, 0.09 #631, 0.03 #165), 09nm_ (0.09 #23, 0.07 #46, 0.02 #257), 09blyk (0.03 #118), 04xvlr (0.03 #118), 07s9rl0 (0.03 #118), 06t2t (0.02 #180), 02vzc (0.02 #177), 06bnz (0.02 #176), 06qd3 (0.02 #175) >> Best rule #124 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0522wp; >> query: (?x7911, 07ssc) <- film(?x609, ?x7911), film_distribution_medium(?x7911, ?x2099), category(?x7911, ?x134), ?x609 = 03xq0f >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08zrbl region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.818 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #20575-07n3s PRED entity: 07n3s PRED relation: award_winner! PRED expected values: 0466p0j => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 77): 056878 (0.20 #596, 0.16 #1301, 0.16 #737), 0466p0j (0.15 #1486, 0.15 #640, 0.12 #781), 019bk0 (0.15 #1567, 0.14 #298, 0.10 #580), 0gpjbt (0.14 #311, 0.12 #875, 0.12 #1016), 08pc1x (0.14 #421, 0.05 #703, 0.04 #844), 013b2h (0.12 #1490, 0.11 #4594, 0.10 #4736), 01xqqp (0.12 #1506, 0.08 #1929, 0.08 #2070), 01s695 (0.12 #1836, 0.10 #4517, 0.09 #4659), 02rjjll (0.11 #4519, 0.11 #4661, 0.09 #3953), 05pd94v (0.11 #4516, 0.10 #4658, 0.09 #1553) >> Best rule #596 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 01cv3n; 01vsnff; 0259r0; 01w806h; 01s21dg; 02bgmr; 0167v4; 01qmy04; >> query: (?x11929, 056878) <- artists(?x2996, ?x11929), origin(?x11929, ?x2850), ?x2996 = 01243b, award(?x11929, ?x12701) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1486 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 0kzy0; 06k02; 01271h; 016ntp; 03bnv; 06gd4; 0fhxv; 018x3; 01kd57; 01mxt_; ... *> query: (?x11929, 0466p0j) <- artists(?x5379, ?x11929), category(?x11929, ?x134), award(?x11929, ?x12701), ?x5379 = 08jyyk *> conf = 0.15 ranks of expected_values: 2 EVAL 07n3s award_winner! 0466p0j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20574-0clfdj PRED entity: 0clfdj PRED relation: ceremony! PRED expected values: 099ck7 => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 314): 0gqy2 (0.58 #10855, 0.56 #10599, 0.53 #8556), 0gq_d (0.56 #10891, 0.55 #10635, 0.49 #9869), 0k611 (0.56 #10806, 0.54 #8507, 0.54 #10550), 0gqwc (0.56 #10794, 0.54 #10538, 0.49 #8495), 0gvx_ (0.55 #10870, 0.54 #10614, 0.53 #8571), 018wng (0.54 #10770, 0.53 #10514, 0.53 #8471), 0f4x7 (0.54 #10760, 0.53 #10504, 0.51 #8461), 0gqyl (0.54 #10813, 0.53 #10557, 0.50 #1347), 0p9sw (0.54 #10755, 0.52 #10499, 0.51 #8456), 0gq9h (0.53 #10795, 0.51 #10539, 0.51 #8496) >> Best rule #10855 for best value: >> intensional similarity = 16 >> extensional distance = 103 >> proper extension: 0fz20l; 0fv89q; >> query: (?x472, 0gqy2) <- award_winner(?x472, ?x3604), honored_for(?x472, ?x972), nominated_for(?x628, ?x972), nominated_for(?x2222, ?x972), nominated_for(?x637, ?x972), award(?x972, ?x298), nominated_for(?x637, ?x5304), nominated_for(?x637, ?x2519), award(?x666, ?x637), ?x2519 = 09p7fh, award(?x3604, ?x112), currency(?x972, ?x170), ?x5304 = 0y_9q, award_winner(?x1739, ?x3604), award_winner(?x2222, ?x771), ceremony(?x2222, ?x78) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #2553 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 6 *> proper extension: 019bk0; 056878; *> query: (?x472, ?x1336) <- award_winner(?x472, ?x9314), award_winner(?x472, ?x7746), award_winner(?x472, ?x6998), award_winner(?x472, ?x3910), award(?x9314, ?x1336), ceremony(?x451, ?x472), award_nominee(?x539, ?x9314), award_nominee(?x9314, ?x1486), award_winner(?x1739, ?x6998), film(?x6998, ?x9981), award(?x6998, ?x704), film_crew_role(?x9981, ?x137), production_companies(?x9981, ?x4564), ?x3910 = 01tc9r, film(?x7746, ?x582), profession(?x7746, ?x1032) *> conf = 0.34 ranks of expected_values: 98 EVAL 0clfdj ceremony! 099ck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 46.000 46.000 0.581 http://example.org/award/award_category/winners./award/award_honor/ceremony #20573-018_q8 PRED entity: 018_q8 PRED relation: company! PRED expected values: 060c4 => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 34): 060c4 (0.67 #4733, 0.64 #777, 0.61 #1637), 01yc02 (0.57 #223, 0.50 #610, 0.50 #395), 0dq3c (0.53 #2582, 0.51 #2711, 0.49 #3012), 014l7h (0.40 #111, 0.38 #326, 0.29 #1487), 01kr6k (0.31 #712, 0.29 #2561, 0.27 #2862), 02211by (0.29 #778, 0.28 #1638, 0.21 #2498), 02k13d (0.24 #1475, 0.24 #1389, 0.20 #400), 028fjr (0.17 #5162, 0.12 #4860, 0.11 #1677), 04192r (0.17 #640, 0.14 #812, 0.14 #253), 01rk91 (0.17 #1635, 0.14 #775, 0.09 #2581) >> Best rule #4733 for best value: >> intensional similarity = 3 >> extensional distance = 195 >> proper extension: 0157m; 0jmj7; 0jnrk; 0jm9w; 0jnq8; 0d6qjf; >> query: (?x7326, 060c4) <- company(?x4682, ?x7326), company(?x4682, ?x9469), ?x9469 = 04sv4 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018_q8 company! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 170.000 0.670 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #20572-01gsrl PRED entity: 01gsrl PRED relation: legislative_sessions PRED expected values: 01grpc => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 46): 01gsrl (0.76 #145, 0.75 #144, 0.70 #1021), 01gtbb (0.76 #145, 0.75 #144, 0.70 #1021), 043djx (0.76 #145, 0.75 #144, 0.70 #1021), 01gtcc (0.76 #145, 0.75 #144, 0.70 #1021), 01gtcq (0.76 #145, 0.75 #144, 0.70 #1021), 01h7xx (0.76 #145, 0.75 #144, 0.70 #1021), 01gtc0 (0.76 #145, 0.75 #144, 0.70 #1021), 01gt99 (0.75 #144, 0.70 #1021, 0.68 #1613), 01gtdd (0.75 #144, 0.70 #1021, 0.68 #1613), 01grpc (0.68 #1613, 0.68 #973, 0.67 #1466) >> Best rule #145 for best value: >> intensional similarity = 43 >> extensional distance = 1 >> proper extension: 01gst_; >> query: (?x4437, ?x759) <- district_represented(?x4437, ?x4776), district_represented(?x4437, ?x3778), district_represented(?x4437, ?x3670), district_represented(?x4437, ?x2713), district_represented(?x4437, ?x2020), district_represented(?x4437, ?x1767), district_represented(?x4437, ?x1426), district_represented(?x4437, ?x760), district_represented(?x4437, ?x728), district_represented(?x4437, ?x335), legislative_sessions(?x4437, ?x9416), legislative_sessions(?x4437, ?x6712), legislative_sessions(?x4437, ?x5256), legislative_sessions(?x4437, ?x5005), legislative_sessions(?x4437, ?x2712), ?x1767 = 04rrd, legislative_sessions(?x4812, ?x5256), ?x335 = 059rby, ?x2020 = 05k7sb, ?x3670 = 05tbn, district_represented(?x2712, ?x4622), district_represented(?x2712, ?x3908), district_represented(?x2712, ?x3818), district_represented(?x2712, ?x1025), legislative_sessions(?x4665, ?x5256), ?x9416 = 01gsry, ?x4776 = 06yxd, ?x3818 = 03v0t, ?x6712 = 01gst9, ?x5005 = 01gstn, ?x728 = 059f4, ?x760 = 05fkf, legislative_sessions(?x759, ?x2712), ?x4812 = 01grpc, ?x4622 = 04tgp, ?x1025 = 04ych, ?x4665 = 07t58, ?x3908 = 04ly1, ?x2713 = 06btq, legislative_sessions(?x7891, ?x5256), ?x3778 = 07h34, ?x1426 = 07z1m, legislative_sessions(?x2712, ?x10291) >> conf = 0.76 => this is the best rule for 7 predicted values *> Best rule #1613 for first EXPECTED value: *> intensional similarity = 31 *> extensional distance = 24 *> proper extension: 02bp37; *> query: (?x4437, ?x1754) <- district_represented(?x4437, ?x7518), district_represented(?x4437, ?x1767), district_represented(?x4437, ?x1426), district_represented(?x4437, ?x760), district_represented(?x4437, ?x335), legislative_sessions(?x4437, ?x5256), ?x1767 = 04rrd, legislative_sessions(?x1754, ?x5256), ?x335 = 059rby, legislative_sessions(?x9046, ?x4437), state(?x10211, ?x7518), country(?x7518, ?x94), time_zones(?x7518, ?x2674), religion(?x7518, ?x8613), state_province_region(?x2959, ?x7518), jurisdiction_of_office(?x900, ?x7518), currency(?x2959, ?x170), major_field_of_study(?x2959, ?x254), contains(?x1426, ?x347), state_province_region(?x4077, ?x1426), district_represented(?x845, ?x7518), contains(?x760, ?x552), location(?x8744, ?x1426), student(?x3439, ?x9046), ?x2674 = 02hcv8, location_of_ceremony(?x566, ?x760), ?x8613 = 04pk9, ?x254 = 02h40lc, ?x8744 = 0gs5q, ?x845 = 07p__7, contains(?x8260, ?x1426) *> conf = 0.68 ranks of expected_values: 10 EVAL 01gsrl legislative_sessions 01grpc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 41.000 41.000 0.765 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #20571-0cq86w PRED entity: 0cq86w PRED relation: film_crew_role PRED expected values: 0dxtw => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.70 #1120, 0.70 #1231, 0.69 #787), 02r96rf (0.59 #1115, 0.59 #1226, 0.56 #782), 09vw2b7 (0.56 #1119, 0.56 #1230, 0.54 #786), 0dxtw (0.32 #1235, 0.32 #1124, 0.32 #791), 02vs3x5 (0.29 #25, 0.09 #210, 0.07 #544), 01vx2h (0.28 #421, 0.27 #384, 0.26 #1125), 02rh1dz (0.14 #11, 0.12 #419, 0.12 #641), 02ynfr (0.13 #313, 0.13 #1129, 0.13 #1240), 015h31 (0.11 #418, 0.08 #306, 0.08 #640), 0215hd (0.11 #1132, 0.11 #1243, 0.10 #799) >> Best rule #1120 for best value: >> intensional similarity = 4 >> extensional distance = 863 >> proper extension: 03h_yy; 02v63m; 0c00zd0; 0k4d7; 0gyy53; 014zwb; 07bwr; 04xx9s; 047rkcm; 03cyslc; ... >> query: (?x5873, 0ch6mp2) <- nominated_for(?x6514, ?x5873), language(?x5873, ?x254), film_crew_role(?x5873, ?x137), award_winner(?x2168, ?x6514) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1235 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 901 *> proper extension: 01j8wk; 05n6sq; *> query: (?x5873, 0dxtw) <- nominated_for(?x6514, ?x5873), language(?x5873, ?x254), film_crew_role(?x5873, ?x137) *> conf = 0.32 ranks of expected_values: 4 EVAL 0cq86w film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 71.000 71.000 0.702 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20570-05pdbs PRED entity: 05pdbs PRED relation: award_winner! PRED expected values: 01s695 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 108): 05pd94v (0.75 #2, 0.20 #272, 0.17 #6213), 02cg41 (0.25 #120, 0.17 #6213, 0.16 #255), 02rjjll (0.24 #275, 0.17 #6213, 0.16 #140), 0jzphpx (0.21 #171, 0.17 #6213, 0.15 #1621), 09n4nb (0.20 #315, 0.17 #6213, 0.15 #1621), 01c6qp (0.17 #6213, 0.17 #18, 0.15 #1621), 01bx35 (0.17 #6213, 0.17 #7, 0.15 #1621), 01s695 (0.17 #6213, 0.16 #138, 0.15 #1621), 0gx1673 (0.17 #6213, 0.15 #1621, 0.13 #3917), 092868 (0.17 #6213, 0.15 #1621, 0.13 #3917) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01l4zqz; 011zf2; 03xgm3; 07z542; 01qkqwg; 01309x; 01m15br; 02cx90; 02j3d4; 026spg; >> query: (?x1238, 05pd94v) <- award_nominee(?x506, ?x1238), award_winner(?x215, ?x1238), award_winner(?x1362, ?x1238), ?x506 = 026ps1 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6213 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1323 *> proper extension: 0c01c; 02wr2r; 035_2h; 0hm0k; 039cq4; 06_bq1; 01d6jf; 0gdhhy; 06y3r; 037q1z; ... *> query: (?x1238, ?x139) <- award_winner(?x1238, ?x1563), award_winner(?x5298, ?x1238), award_winner(?x139, ?x1563) *> conf = 0.17 ranks of expected_values: 8 EVAL 05pdbs award_winner! 01s695 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 97.000 97.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20569-0p07l PRED entity: 0p07l PRED relation: contains PRED expected values: 0k9wp => 107 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1543): 0k9wp (0.40 #53020, 0.29 #61859, 0.25 #58913), 018t8f (0.40 #53020, 0.29 #61859, 0.25 #58913), 09c7w0 (0.29 #55967, 0.29 #53021, 0.24 #64806), 01n4w (0.29 #55967, 0.29 #53021, 0.24 #64806), 0p07l (0.29 #55967, 0.29 #53021, 0.24 #64806), 02_286 (0.28 #123741, 0.05 #47128, 0.02 #23636), 0n6bs (0.28 #123741, 0.05 #47128, 0.01 #50457), 0vrmb (0.28 #123741, 0.02 #25839, 0.02 #11109), 0cc56 (0.28 #123741, 0.02 #23677, 0.01 #35456), 01pj7 (0.28 #123741, 0.02 #23785, 0.01 #35564) >> Best rule #53020 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 0d8h4; 01279v; >> query: (?x14300, ?x5983) <- contains(?x14300, ?x7770), contains(?x7770, ?x5983), adjoins(?x14300, ?x14360) >> conf = 0.40 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0p07l contains 0k9wp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 51.000 0.405 http://example.org/location/location/contains #20568-015_1q PRED entity: 015_1q PRED relation: artist PRED expected values: 01vvycq 0137n0 04dqdk 05d8vw 015mrk 01309x 01817f 03j24kf 01s21dg 04f7c55 01wgjj5 01wmjkb 01f9zw 04d_mtq 016t00 => 160 concepts (110 used for prediction) PRED predicted values (max 10 best out of 802): 01vxlbm (0.43 #7828, 0.40 #5751, 0.33 #4367), 017lb_ (0.43 #8104, 0.40 #6027, 0.33 #1183), 0c9l1 (0.43 #8224, 0.40 #6147, 0.33 #1303), 0fpj4lx (0.43 #7819, 0.33 #4358, 0.33 #898), 0bk1p (0.40 #6078, 0.33 #6770, 0.33 #4002), 01k23t (0.40 #5992, 0.33 #3224, 0.25 #5300), 01k3qj (0.40 #5978, 0.33 #6670, 0.25 #5286), 0kr_t (0.40 #5858, 0.33 #3782, 0.17 #6550), 09qr6 (0.40 #5594, 0.33 #3518, 0.17 #6286), 01j6mff (0.40 #6103, 0.29 #8180, 0.18 #15106) >> Best rule #7828 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 011k1h; 073tm9; 03mp8k; >> query: (?x3265, 01vxlbm) <- artist(?x3265, ?x10025), artist(?x3265, ?x4740), artist(?x3265, ?x2731), artist(?x3265, ?x1953), ?x1953 = 019g40, profession(?x4740, ?x220), award(?x2731, ?x567), artists(?x505, ?x10025) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1729 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 02p11jq; *> query: (?x3265, 01wgjj5) <- artist(?x3265, ?x4740), artist(?x3265, ?x1953), artist(?x3265, ?x1800), artist(?x3265, ?x1684), artists(?x505, ?x1953), award(?x1684, ?x2139), ?x1800 = 015_30, profession(?x4740, ?x220) *> conf = 0.33 ranks of expected_values: 40, 51, 98, 108, 113, 160, 253, 275, 408, 588, 652, 733, 744 EVAL 015_1q artist 016t00 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 04d_mtq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 01f9zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 01wmjkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 01wgjj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 04f7c55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 01s21dg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 03j24kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 01817f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 01309x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 015mrk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 05d8vw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 04dqdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 0137n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 160.000 110.000 0.429 http://example.org/music/record_label/artist EVAL 015_1q artist 01vvycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 160.000 110.000 0.429 http://example.org/music/record_label/artist #20567-0fk98 PRED entity: 0fk98 PRED relation: location! PRED expected values: 05j12n => 105 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1413): 0738y5 (0.65 #75446, 0.61 #108146, 0.55 #37721), 0cqcgj (0.55 #37721, 0.53 #50294, 0.50 #140840), 02hkv5 (0.33 #2235, 0.20 #4749, 0.10 #9777), 02756j (0.25 #6309, 0.18 #11337, 0.11 #16365), 02qy3py (0.23 #22629, 0.23 #25145, 0.22 #27660), 02xgdv (0.20 #8957, 0.12 #6443, 0.12 #13985), 040wdl (0.20 #7885, 0.12 #5371, 0.12 #12913), 0b5x23 (0.20 #9771, 0.12 #7257, 0.12 #14799), 08d6bd (0.20 #8851, 0.12 #6337, 0.12 #13879), 05nw9m (0.20 #9443, 0.12 #6929, 0.11 #19501) >> Best rule #75446 for best value: >> intensional similarity = 5 >> extensional distance = 113 >> proper extension: 0qymv; 0kf9p; 0sq2v; 0r0ss; 04cx5; 06kx2; 0r172; >> query: (?x13551, ?x9506) <- country(?x13551, ?x2146), place_of_birth(?x10331, ?x13551), place_of_birth(?x9506, ?x13551), languages(?x9506, ?x1882), profession(?x10331, ?x319) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #30176 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0pbhz; 09b83; 0m_1s; *> query: (?x13551, ?x111) <- country(?x13551, ?x2146), capital(?x7297, ?x13551), location_of_ceremony(?x4740, ?x2146), nationality(?x111, ?x2146) *> conf = 0.02 ranks of expected_values: 616 EVAL 0fk98 location! 05j12n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 105.000 58.000 0.652 http://example.org/people/person/places_lived./people/place_lived/location #20566-07xzm PRED entity: 07xzm PRED relation: instrumentalists PRED expected values: 01wp8w7 03qmj9 0259r0 01vsyjy 02vr7 => 84 concepts (46 used for prediction) PRED predicted values (max 10 best out of 3648): 01vtg4q (0.75 #12647, 0.54 #614, 0.50 #13865), 01sb5r (0.67 #9373, 0.62 #12424, 0.54 #614), 02vr7 (0.67 #9598, 0.54 #614, 0.50 #3508), 09prnq (0.67 #9252, 0.54 #614, 0.42 #19003), 01w8n89 (0.67 #9345, 0.54 #614, 0.41 #607), 01vw20_ (0.62 #12352, 0.54 #614, 0.50 #17834), 01vrncs (0.60 #7356, 0.57 #10408, 0.54 #614), 02rn_bj (0.60 #7770, 0.57 #10822, 0.54 #614), 01vvycq (0.60 #7341, 0.55 #611, 0.54 #614), 0phx4 (0.60 #17879, 0.54 #614, 0.50 #13615) >> Best rule #12647 for best value: >> intensional similarity = 19 >> extensional distance = 6 >> proper extension: 01s0ps; >> query: (?x1212, 01vtg4q) <- role(?x1212, ?x2206), role(?x1212, ?x2048), role(?x1212, ?x1647), role(?x1212, ?x780), role(?x1212, ?x315), instrumentalists(?x1212, ?x672), role(?x1212, ?x1332), role(?x3716, ?x780), ?x3716 = 03gvt, ?x315 = 0l14md, role(?x219, ?x780), role(?x1332, ?x3161), role(?x1332, ?x569), role(?x120, ?x1332), ?x2206 = 07gql, ?x3161 = 01v1d8, role(?x487, ?x1332), ?x1647 = 05ljv7, ?x2048 = 018j2 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #9598 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 4 *> proper extension: 018vs; *> query: (?x1212, 02vr7) <- role(?x1212, ?x8957), role(?x1212, ?x2158), role(?x1212, ?x1655), role(?x1212, ?x780), role(?x1212, ?x432), role(?x1212, ?x315), instrumentalists(?x1212, ?x672), role(?x1212, ?x75), role(?x3716, ?x780), ?x3716 = 03gvt, ?x315 = 0l14md, family(?x1212, ?x7256), role(?x780, ?x2904), ?x2158 = 01dnws, role(?x1291, ?x432), ?x8957 = 03f5mt, role(?x432, ?x433), role(?x11186, ?x432), role(?x4207, ?x432), ?x4207 = 03k0yw, ?x11186 = 01304j, ?x1655 = 01hww_ *> conf = 0.67 ranks of expected_values: 3, 69, 80, 251, 333 EVAL 07xzm instrumentalists 02vr7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 46.000 0.750 http://example.org/music/instrument/instrumentalists EVAL 07xzm instrumentalists 01vsyjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 84.000 46.000 0.750 http://example.org/music/instrument/instrumentalists EVAL 07xzm instrumentalists 0259r0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 46.000 0.750 http://example.org/music/instrument/instrumentalists EVAL 07xzm instrumentalists 03qmj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 84.000 46.000 0.750 http://example.org/music/instrument/instrumentalists EVAL 07xzm instrumentalists 01wp8w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 84.000 46.000 0.750 http://example.org/music/instrument/instrumentalists #20565-0bh8yn3 PRED entity: 0bh8yn3 PRED relation: language PRED expected values: 02h40lc => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 43): 02h40lc (0.90 #1606, 0.90 #1725, 0.89 #541), 064_8sq (0.22 #202, 0.17 #82, 0.15 #738), 0t_2 (0.20 #14, 0.05 #312, 0.03 #493), 06nm1 (0.18 #250, 0.17 #132, 0.17 #71), 06b_j (0.17 #144, 0.17 #83, 0.12 #262), 02bjrlw (0.17 #61, 0.07 #658, 0.07 #835), 04306rv (0.11 #185, 0.10 #780, 0.09 #662), 0jzc (0.11 #200, 0.06 #259, 0.04 #378), 032f6 (0.11 #236, 0.05 #354, 0.04 #414), 0653m (0.11 #192, 0.04 #1379, 0.04 #551) >> Best rule #1606 for best value: >> intensional similarity = 4 >> extensional distance = 503 >> proper extension: 015qsq; 0sxfd; 064r97z; 0kbwb; 01gvsn; >> query: (?x1701, 02h40lc) <- production_companies(?x1701, ?x382), genre(?x1701, ?x225), award_winner(?x1701, ?x9084), award_nominee(?x847, ?x382) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bh8yn3 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.897 http://example.org/film/film/language #20564-0640m69 PRED entity: 0640m69 PRED relation: film_crew_role PRED expected values: 0ch6mp2 0d2b38 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.78 #440, 0.77 #404, 0.76 #696), 02r96rf (0.70 #39, 0.68 #399, 0.68 #435), 09vw2b7 (0.68 #403, 0.68 #439, 0.66 #695), 0dxtw (0.40 #408, 0.40 #12, 0.40 #444), 01pvkk (0.33 #13, 0.28 #990, 0.27 #701), 02ynfr (0.20 #89, 0.17 #197, 0.17 #705), 0215hd (0.17 #92, 0.15 #56, 0.14 #200), 01xy5l_ (0.14 #87, 0.12 #267, 0.12 #195), 02rh1dz (0.13 #11, 0.11 #407, 0.11 #443), 089fss (0.13 #6, 0.09 #186, 0.09 #2536) >> Best rule #440 for best value: >> intensional similarity = 4 >> extensional distance = 477 >> proper extension: 0djb3vw; 04dsnp; 091z_p; 02phtzk; 064lsn; 0gy0l_; 0267wwv; >> query: (?x11980, 0ch6mp2) <- produced_by(?x11980, ?x1335), production_companies(?x11980, ?x1478), film_crew_role(?x11980, ?x137), genre(?x11980, ?x258) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11 EVAL 0640m69 film_crew_role 0d2b38 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 71.000 71.000 0.777 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0640m69 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.777 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20563-01m15br PRED entity: 01m15br PRED relation: artists! PRED expected values: 03_d0 01lyv => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 180): 06by7 (0.50 #1269, 0.50 #333, 0.49 #2205), 064t9 (0.45 #2508, 0.44 #4687, 0.41 #2819), 03_d0 (0.40 #12, 0.21 #11526, 0.21 #947), 016clz (0.30 #628, 0.29 #2188, 0.29 #1252), 0glt670 (0.28 #4716, 0.21 #2848, 0.18 #4093), 0xhtw (0.27 #1264, 0.27 #2200, 0.25 #1888), 06j6l (0.27 #4723, 0.25 #2855, 0.25 #984), 025sc50 (0.24 #4725, 0.22 #2857, 0.20 #2546), 05bt6j (0.24 #2540, 0.23 #356, 0.21 #1292), 01lyv (0.21 #11526, 0.20 #3464, 0.20 #35) >> Best rule #1269 for best value: >> intensional similarity = 2 >> extensional distance = 228 >> proper extension: 0f0y8; 03c7ln; 01vw87c; 0fp_v1x; 032t2z; 0kzy0; 06y9c2; 02whj; 07_3qd; 0ftps; ... >> query: (?x4044, 06by7) <- role(?x4044, ?x1969), artist(?x9671, ?x4044) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 028q6; 026ps1; 011zf2; 03xgm3; 07z542; 01qkqwg; 01309x; 02cx90; *> query: (?x4044, 03_d0) <- award_nominee(?x4044, ?x1333), ?x1333 = 01l4zqz, artists(?x597, ?x4044) *> conf = 0.40 ranks of expected_values: 3, 10 EVAL 01m15br artists! 01lyv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 98.000 98.000 0.504 http://example.org/music/genre/artists EVAL 01m15br artists! 03_d0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 98.000 0.504 http://example.org/music/genre/artists #20562-02rhwjr PRED entity: 02rhwjr PRED relation: program! PRED expected values: 01nzs7 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 100): 0gsg7 (0.33 #114, 0.33 #2, 0.29 #562), 0kc9f (0.33 #43, 0.20 #99, 0.17 #211), 0187wh (0.21 #586, 0.20 #82, 0.18 #867), 01bfjy (0.21 #666, 0.06 #722, 0.06 #1619), 05gnf (0.21 #2368, 0.20 #2649, 0.20 #2707), 0cjdk (0.20 #1462, 0.17 #117, 0.16 #1014), 0kctd (0.18 #420, 0.17 #196, 0.17 #140), 02kx91 (0.17 #215, 0.14 #271, 0.06 #775), 018kcp (0.17 #221, 0.14 #277, 0.06 #781), 04qb6g (0.17 #212, 0.14 #268, 0.06 #772) >> Best rule #114 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 07c72; 0431v3; >> query: (?x13557, 0gsg7) <- actor(?x13557, ?x12753), artists(?x505, ?x12753), origin(?x12753, ?x252), genre(?x13557, ?x53), location(?x12753, ?x13585), artists(?x505, ?x7477), ?x7477 = 028hc2 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1626 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 53 *> proper extension: 0283ph; 01j95; *> query: (?x13557, 01nzs7) <- genre(?x13557, ?x225), genre(?x11066, ?x225), genre(?x7425, ?x225), genre(?x1077, ?x225), genre(?x186, ?x225), ?x7425 = 042fgh, film_release_region(?x186, ?x47), ?x11066 = 025s1wg, film(?x1018, ?x186), nominated_for(?x112, ?x1077) *> conf = 0.07 ranks of expected_values: 26 EVAL 02rhwjr program! 01nzs7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 67.000 67.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #20561-0345kr PRED entity: 0345kr PRED relation: category PRED expected values: 08mbj5d => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #5, 0.83 #4, 0.78 #27) >> Best rule #5 for best value: >> intensional similarity = 18 >> extensional distance = 10 >> proper extension: 017l96; 015_1q; 011k11; 0g768; 037h1k; 0dd2f; >> query: (?x14392, ?x134) <- artist(?x14392, ?x10938), category(?x10938, ?x134), group(?x1166, ?x10938), group(?x645, ?x10938), group(?x614, ?x10938), group(?x315, ?x10938), ?x614 = 0mkg, ?x315 = 0l14md, ?x1166 = 05148p4, role(?x6039, ?x645), group(?x645, ?x9868), group(?x645, ?x5227), group(?x645, ?x4658), ?x4658 = 018gm9, ?x9868 = 0134pk, role(?x679, ?x645), ?x5227 = 01j59b0, ?x6039 = 05kms >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0345kr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.833 http://example.org/common/topic/webpage./common/webpage/category #20560-0558_1 PRED entity: 0558_1 PRED relation: registering_agency PRED expected values: 03z19 => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.88 #12, 0.85 #42, 0.84 #40) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 02s62q; 033x5p; 02d9nr; 06182p; 01cf5; 01qdhx; >> query: (?x11688, 03z19) <- currency(?x11688, ?x170), country(?x11688, ?x94), citytown(?x11688, ?x5658), contains(?x3634, ?x11688) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0558_1 registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 170.000 0.880 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #20559-0121rx PRED entity: 0121rx PRED relation: inductee! PRED expected values: 06szd3 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 4): 06szd3 (0.26 #29, 0.20 #2, 0.08 #47), 0g2c8 (0.08 #73, 0.08 #46, 0.06 #100), 0qjfl (0.07 #3, 0.06 #30, 0.03 #111), 04dm2n (0.03 #17, 0.03 #35, 0.03 #26) >> Best rule #29 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 02x0bdb; 01d5vk; 0grmhb; >> query: (?x13073, 06szd3) <- profession(?x13073, ?x1041), profession(?x13073, ?x1032), ?x1032 = 02hrh1q, ?x1041 = 03gjzk, people(?x4959, ?x13073) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0121rx inductee! 06szd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.258 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #20558-09f2j PRED entity: 09f2j PRED relation: fraternities_and_sororities PRED expected values: 035tlh => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2): 035tlh (0.57 #27, 0.54 #92, 0.53 #68), 04m8fy (0.10 #2, 0.06 #93, 0.05 #55) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 02t4yc; >> query: (?x4955, 035tlh) <- fraternities_and_sororities(?x4955, ?x3697), student(?x4955, ?x3395), languages(?x3395, ?x254) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09f2j fraternities_and_sororities 035tlh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.569 http://example.org/education/university/fraternities_and_sororities #20557-05dppk PRED entity: 05dppk PRED relation: nominated_for PRED expected values: 0kvgnq => 117 concepts (35 used for prediction) PRED predicted values (max 10 best out of 455): 016z5x (0.50 #3241, 0.48 #14591, 0.42 #11348), 0kvgnq (0.50 #3241, 0.48 #14591, 0.42 #11348), 04smdd (0.50 #3241, 0.48 #14591, 0.42 #11348), 03wy8t (0.50 #3241, 0.42 #11348, 0.41 #9727), 03m8y5 (0.50 #3241, 0.42 #11348, 0.41 #9727), 0c_j9x (0.20 #346, 0.04 #3587, 0.04 #5208), 070fnm (0.20 #289, 0.04 #5151, 0.03 #6772), 04vq33 (0.20 #1606, 0.02 #17819, 0.01 #19439), 0gh8zks (0.20 #489), 048qrd (0.20 #305) >> Best rule #3241 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 09cdxn; >> query: (?x2530, ?x518) <- award_winner(?x1243, ?x2530), cinematography(?x518, ?x2530), place_of_death(?x2530, ?x5168), ?x1243 = 0gr0m >> conf = 0.50 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 05dppk nominated_for 0kvgnq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 35.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20556-01_qp_ PRED entity: 01_qp_ PRED relation: parent_genre! PRED expected values: 0xjl2 => 63 concepts (26 used for prediction) PRED predicted values (max 10 best out of 297): 0y3_8 (0.33 #1110, 0.33 #842, 0.33 #266), 01b4p4 (0.33 #1236, 0.33 #968, 0.33 #167), 03xnwz (0.33 #1097, 0.33 #829, 0.30 #1365), 0133k0 (0.33 #471, 0.33 #202, 0.22 #1271), 0163zw (0.33 #451, 0.33 #182, 0.11 #1251), 029fbr (0.33 #1223, 0.30 #1491, 0.27 #1760), 01_qp_ (0.33 #177, 0.22 #1246, 0.22 #978), 01_sz1 (0.33 #68, 0.22 #1137, 0.22 #869), 07gxw (0.33 #48, 0.22 #1117, 0.20 #1385), 0dn16 (0.33 #12, 0.17 #281, 0.12 #2154) >> Best rule #1110 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 03ckfl9; >> query: (?x12149, 0y3_8) <- artists(?x12149, ?x11633), artists(?x12149, ?x5916), ?x11633 = 01ww_vs, parent_genre(?x14058, ?x12149), artist(?x3240, ?x5916), ?x3240 = 017l96, group(?x227, ?x5916) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #575 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 6 *> proper extension: 0g64p; *> query: (?x12149, 0xjl2) <- parent_genre(?x14058, ?x12149), ?x14058 = 088vmr *> conf = 0.25 ranks of expected_values: 14 EVAL 01_qp_ parent_genre! 0xjl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 63.000 26.000 0.333 http://example.org/music/genre/parent_genre #20555-05qgc PRED entity: 05qgc PRED relation: student PRED expected values: 05bnp0 => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 244): 01yk13 (0.64 #1459, 0.25 #742, 0.25 #255), 0405l (0.64 #1459, 0.25 #946, 0.25 #459), 016h4r (0.64 #1459, 0.25 #799, 0.25 #312), 0cbdf1 (0.64 #1459, 0.25 #965, 0.25 #478), 012x2b (0.25 #673, 0.20 #1162, 0.17 #1408), 06ltr (0.25 #604, 0.20 #1093, 0.17 #1339), 04sry (0.25 #635, 0.20 #1124, 0.17 #1370), 0bg539 (0.25 #507, 0.20 #996, 0.17 #1242), 06pjs (0.25 #666, 0.20 #1155, 0.17 #1401), 03l3ln (0.25 #628, 0.20 #1117, 0.17 #1363) >> Best rule #1459 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 0hcr; >> query: (?x14103, ?x879) <- major_field_of_study(?x3386, ?x14103), disciplines_or_subjects(?x14436, ?x14103), ?x3386 = 03mkk4, award(?x8938, ?x14436), award(?x2845, ?x14436), profession(?x8938, ?x987), nationality(?x8938, ?x94), ?x987 = 0dxtg, disciplines_or_subjects(?x14436, ?x5864), gender(?x8938, ?x231), location(?x2845, ?x739), influenced_by(?x2845, ?x1029), type_of_union(?x8938, ?x566), influenced_by(?x8938, ?x587), ?x566 = 04ztj, major_field_of_study(?x741, ?x5864), student(?x5864, ?x879) >> conf = 0.64 => this is the best rule for 4 predicted values *> Best rule #5608 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 18 *> proper extension: 0557q; *> query: (?x14103, 05bnp0) <- major_field_of_study(?x3386, ?x14103), student(?x3386, ?x2551), institution(?x3386, ?x10576), institution(?x3386, ?x6894), institution(?x3386, ?x5807), institution(?x3386, ?x3424), institution(?x3386, ?x3387), institution(?x3386, ?x1924), ?x1924 = 0bjqh, nominated_for(?x2551, ?x414), ?x3424 = 01w5m, student(?x5807, ?x690), award_winner(?x1193, ?x2551), major_field_of_study(?x10576, ?x1154), award_nominee(?x2551, ?x92), award(?x2551, ?x704), institution(?x620, ?x5807), ?x620 = 07s6fsf, participant(?x2551, ?x6314), film(?x2551, ?x2519), ?x6894 = 0cwx_, school(?x5419, ?x5807), ?x3387 = 02fgdx *> conf = 0.10 ranks of expected_values: 43 EVAL 05qgc student 05bnp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 38.000 38.000 0.643 http://example.org/education/field_of_study/students_majoring./education/education/student #20554-026lgs PRED entity: 026lgs PRED relation: nominated_for! PRED expected values: 03h26tm => 88 concepts (36 used for prediction) PRED predicted values (max 10 best out of 699): 01f7j9 (0.77 #63002, 0.76 #72342, 0.76 #44335), 06t74h (0.38 #72339, 0.28 #72338, 0.26 #79342), 01ggc9 (0.38 #72339, 0.28 #72338, 0.26 #79342), 06bzwt (0.38 #72339, 0.28 #72338, 0.26 #79342), 016ks_ (0.38 #72339, 0.28 #72338, 0.26 #79342), 016z2j (0.33 #484, 0.03 #7488, 0.03 #16826), 01wmxfs (0.33 #153, 0.01 #53820), 0js9s (0.29 #32671, 0.03 #17773, 0.02 #43433), 015wnl (0.28 #72338, 0.23 #37336, 0.21 #42002), 035kl6 (0.28 #72338, 0.23 #37336, 0.21 #42002) >> Best rule #63002 for best value: >> intensional similarity = 4 >> extensional distance = 422 >> proper extension: 07s8z_l; >> query: (?x5418, ?x7701) <- award_winner(?x5418, ?x7701), award_winner(?x5418, ?x2182), produced_by(?x4518, ?x2182), film_release_region(?x4518, ?x87) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #42186 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 269 *> proper extension: 01j95; *> query: (?x5418, 03h26tm) <- award_winner(?x5418, ?x2182), produced_by(?x570, ?x2182), written_by(?x2470, ?x2182) *> conf = 0.01 ranks of expected_values: 614 EVAL 026lgs nominated_for! 03h26tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 88.000 36.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20553-05hmp6 PRED entity: 05hmp6 PRED relation: instance_of_recurring_event PRED expected values: 0g_w => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 3): 0g_w (0.91 #181, 0.88 #164, 0.88 #123), 0c4ys (0.29 #97, 0.12 #263, 0.11 #271), 0gcf2r (0.12 #228, 0.12 #212, 0.09 #236) >> Best rule #181 for best value: >> intensional similarity = 15 >> extensional distance = 56 >> proper extension: 02yw5r; 073hmq; 02yv_b; 0ftlkg; 02hn5v; 073h9x; 0bc773; 02glmx; 02pgky2; 073hgx; ... >> query: (?x6323, 0g_w) <- award_winner(?x6323, ?x11625), award_winner(?x6323, ?x4572), award_winner(?x6323, ?x2449), award_winner(?x6323, ?x2167), ceremony(?x3066, ?x6323), nominated_for(?x2167, ?x2168), award_winner(?x2060, ?x2167), film(?x11625, ?x5212), ?x3066 = 0gqy2, nominated_for(?x2449, ?x878), profession(?x4572, ?x319), award_nominee(?x2449, ?x2801), film_release_region(?x878, ?x94), titles(?x162, ?x878), currency(?x878, ?x170) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05hmp6 instance_of_recurring_event 0g_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.914 http://example.org/time/event/instance_of_recurring_event #20552-0170xl PRED entity: 0170xl PRED relation: country PRED expected values: 09c7w0 07ssc => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 94): 09c7w0 (0.82 #309, 0.82 #617, 0.82 #925), 07ssc (0.30 #17, 0.29 #262, 0.27 #508), 0f8l9c (0.19 #142, 0.15 #265, 0.10 #1254), 0d060g (0.13 #70, 0.12 #192, 0.09 #685), 0345h (0.12 #211, 0.12 #150, 0.11 #335), 03_3d (0.10 #8, 0.04 #499, 0.04 #69), 0chghy (0.09 #196, 0.09 #74, 0.04 #1122), 04xvlr (0.08 #3090, 0.07 #1792, 0.07 #3402), 07s9rl0 (0.08 #3090, 0.07 #1792, 0.07 #3402), 059j2 (0.06 #210, 0.04 #149, 0.02 #334) >> Best rule #309 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 03ckwzc; 080lkt7; >> query: (?x11213, 09c7w0) <- titles(?x162, ?x11213), music(?x11213, ?x4644), films(?x10705, ?x11213), ?x162 = 04xvlr >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0170xl country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.823 http://example.org/film/film/country EVAL 0170xl country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.823 http://example.org/film/film/country #20551-0435vm PRED entity: 0435vm PRED relation: genre PRED expected values: 02kdv5l => 107 concepts (60 used for prediction) PRED predicted values (max 10 best out of 117): 02kdv5l (0.85 #4543, 0.60 #1553, 0.51 #1912), 0lsxr (0.55 #366, 0.53 #128, 0.50 #9), 05p553 (0.41 #242, 0.40 #4665, 0.40 #2632), 03k9fj (0.39 #1922, 0.38 #4553, 0.29 #1563), 02n4kr (0.33 #604, 0.29 #724, 0.29 #843), 02l7c8 (0.33 #3481, 0.30 #5276, 0.30 #4436), 0vgkd (0.24 #249, 0.08 #1204, 0.07 #1323), 01hmnh (0.22 #1927, 0.20 #2883, 0.19 #4558), 060__y (0.20 #135, 0.19 #3482, 0.18 #5277), 04xvlr (0.19 #3467, 0.18 #5262, 0.17 #4422) >> Best rule #4543 for best value: >> intensional similarity = 6 >> extensional distance = 490 >> proper extension: 04svwx; >> query: (?x3925, 02kdv5l) <- country(?x3925, ?x94), genre(?x3925, ?x1013), genre(?x8370, ?x1013), genre(?x5054, ?x1013), ?x5054 = 09zf_q, ?x8370 = 07ghq >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0435vm genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 60.000 0.848 http://example.org/film/film/genre #20550-01vn35l PRED entity: 01vn35l PRED relation: award PRED expected values: 01by1l 03tcnt => 136 concepts (110 used for prediction) PRED predicted values (max 10 best out of 263): 01by1l (0.86 #8426, 0.64 #10406, 0.46 #17535), 09sb52 (0.57 #24991, 0.39 #21031, 0.33 #28159), 02v1m7 (0.38 #4467, 0.17 #8427, 0.15 #10407), 03t5kl (0.31 #4577, 0.13 #8537, 0.12 #10517), 023vrq (0.28 #4675, 0.11 #10615, 0.10 #8635), 054ks3 (0.28 #2516, 0.25 #6872, 0.24 #3308), 03qbh5 (0.28 #4555, 0.27 #2179, 0.25 #8515), 02f72_ (0.26 #4579, 0.15 #10519, 0.15 #8539), 02f76h (0.23 #4530, 0.09 #10470, 0.09 #8490), 02f72n (0.22 #4500, 0.14 #10440, 0.13 #8460) >> Best rule #8426 for best value: >> intensional similarity = 4 >> extensional distance = 185 >> proper extension: 04lgymt; 0jdhp; 0ggl02; 01x15dc; 02_jkc; 016732; 01wyq0w; 010xjr; >> query: (?x2876, 01by1l) <- award_nominee(?x3403, ?x2876), award(?x2876, ?x2877), award(?x1271, ?x2877), ?x1271 = 01v0sx2 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 24 EVAL 01vn35l award 03tcnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 136.000 110.000 0.861 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vn35l award 01by1l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 110.000 0.861 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20549-07fvf1 PRED entity: 07fvf1 PRED relation: gender PRED expected values: 05zppz => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #33, 0.85 #35, 0.85 #3), 02zsn (0.50 #146, 0.29 #46, 0.27 #72) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 311 >> proper extension: 0p51w; >> query: (?x3629, 05zppz) <- nationality(?x3629, ?x94), produced_by(?x1965, ?x3629), nominated_for(?x3629, ?x2710) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07fvf1 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.856 http://example.org/people/person/gender #20548-03s5t PRED entity: 03s5t PRED relation: district_represented! PRED expected values: 024tkd => 216 concepts (216 used for prediction) PRED predicted values (max 10 best out of 52): 024tkd (0.78 #504, 0.66 #1128, 0.62 #1284), 02bp37 (0.66 #477, 0.57 #1101, 0.55 #1257), 02bqm0 (0.63 #493, 0.53 #1117, 0.51 #1273), 03rl1g (0.61 #469, 0.55 #573, 0.55 #1093), 02bqmq (0.59 #483, 0.51 #1107, 0.50 #67), 043djx (0.57 #577, 0.56 #473, 0.55 #1097), 02bqn1 (0.54 #475, 0.50 #59, 0.43 #7), 01h7xx (0.51 #508, 0.50 #612, 0.49 #1132), 02cg7g (0.51 #490, 0.40 #1114, 0.38 #1270), 02gkzs (0.49 #487, 0.38 #1111, 0.38 #71) >> Best rule #504 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0g0syc; >> query: (?x2768, 024tkd) <- district_represented(?x6728, ?x2768), district_represented(?x3540, ?x2768), ?x6728 = 070mff, ?x3540 = 024tcq >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s5t district_represented! 024tkd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 216.000 216.000 0.780 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #20547-02_286 PRED entity: 02_286 PRED relation: month PRED expected values: 0lkm => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 1): 0lkm (0.88 #24, 0.88 #39, 0.87 #31) >> Best rule #24 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 02h6_6p; 03hrz; 049d1; 0ply0; 0l0mk; 06mxs; 0f2rq; 08966; 02sn34; 0f04v; ... >> query: (?x739, 0lkm) <- location(?x163, ?x739), citytown(?x166, ?x739), month(?x739, ?x1459) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_286 month 0lkm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 178.000 0.884 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #20546-07tj4c PRED entity: 07tj4c PRED relation: film! PRED expected values: 0jz9f => 63 concepts (52 used for prediction) PRED predicted values (max 10 best out of 61): 086k8 (0.18 #152, 0.17 #228, 0.15 #1516), 0jz9f (0.16 #1, 0.08 #452, 0.08 #679), 016tw3 (0.15 #11, 0.13 #614, 0.13 #2136), 016tt2 (0.14 #154, 0.14 #79, 0.12 #230), 05qd_ (0.12 #235, 0.11 #1447, 0.11 #9), 017s11 (0.12 #2128, 0.12 #304, 0.11 #454), 024rgt (0.10 #20, 0.05 #850, 0.05 #321), 03xq0f (0.09 #608, 0.09 #2130, 0.09 #2742), 054g1r (0.08 #486, 0.08 #713, 0.07 #1322), 01gb54 (0.07 #29, 0.06 #179, 0.06 #707) >> Best rule #152 for best value: >> intensional similarity = 3 >> extensional distance = 137 >> proper extension: 02xhpl; >> query: (?x11001, 086k8) <- award_winner(?x11001, ?x3553), nominated_for(?x384, ?x11001), honored_for(?x4864, ?x11001) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 0gfsq9; 07f_t4; 02mpyh; *> query: (?x11001, 0jz9f) <- film(?x1950, ?x11001), executive_produced_by(?x11001, ?x4060), genre(?x11001, ?x1509), ?x1509 = 060__y *> conf = 0.16 ranks of expected_values: 2 EVAL 07tj4c film! 0jz9f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 52.000 0.180 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #20545-013knm PRED entity: 013knm PRED relation: award PRED expected values: 02ppm4q => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 257): 02ppm4q (0.72 #36057, 0.70 #26545, 0.70 #15844), 05pcn59 (0.37 #7209, 0.33 #2851, 0.24 #4435), 05b4l5x (0.36 #2778, 0.32 #4362, 0.31 #5552), 05zr6wv (0.30 #7147, 0.25 #17, 0.14 #2789), 094qd5 (0.25 #4400, 0.23 #5590, 0.21 #2816), 0f4x7 (0.25 #31, 0.19 #21789, 0.18 #1615), 0fbvqf (0.25 #47, 0.19 #21789, 0.18 #1631), 0cqhmg (0.25 #1146, 0.19 #21789, 0.15 #33678), 09qs08 (0.25 #931, 0.19 #21789, 0.15 #33678), 02x4w6g (0.25 #111, 0.19 #21789, 0.15 #33678) >> Best rule #36057 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 04b19t; 0627sn; 099ks0; 0f1jhc; >> query: (?x3708, ?x2577) <- award_winner(?x2577, ?x3708), award(?x91, ?x2577) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013knm award 02ppm4q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20544-0nm9h PRED entity: 0nm9h PRED relation: location_of_ceremony! PRED expected values: 04ztj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.50 #5, 0.29 #317, 0.28 #293), 01g63y (0.03 #34, 0.03 #38, 0.02 #114) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 09c7w0; 050ks; >> query: (?x12290, 04ztj) <- contains(?x7058, ?x12290), contains(?x12290, ?x12289), ?x12289 = 0c4kv >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nm9h location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.500 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #20543-09v42sf PRED entity: 09v42sf PRED relation: genre PRED expected values: 0hn10 0vgkd => 142 concepts (83 used for prediction) PRED predicted values (max 10 best out of 108): 07s9rl0 (0.86 #9329, 0.80 #6406, 0.80 #6758), 02kdv5l (0.61 #7111, 0.53 #6525, 0.48 #2680), 0lsxr (0.57 #587, 0.49 #2218, 0.47 #1401), 03k9fj (0.43 #7118, 0.37 #2338, 0.34 #6532), 0c3351 (0.40 #1544, 0.28 #3527, 0.28 #3644), 01hmnh (0.40 #711, 0.27 #7123, 0.27 #6537), 060__y (0.36 #942, 0.30 #2225, 0.27 #3041), 02l7c8 (0.33 #1874, 0.33 #13, 0.32 #6302), 04xvlr (0.30 #6407, 0.30 #6641, 0.29 #6759), 04xvh5 (0.30 #726, 0.10 #8420, 0.10 #8538) >> Best rule #9329 for best value: >> intensional similarity = 7 >> extensional distance = 367 >> proper extension: 0cq8nx; >> query: (?x10535, 07s9rl0) <- country(?x10535, ?x94), films(?x271, ?x10535), genre(?x10535, ?x812), genre(?x11735, ?x812), genre(?x4007, ?x812), ?x4007 = 03hmt9b, ?x11735 = 02x2jl_ >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2686 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 54 *> proper extension: 05cj_j; 06rmdr; 0y_pg; 052_mn; *> query: (?x10535, 0vgkd) <- country(?x10535, ?x94), genre(?x10535, ?x812), genre(?x10535, ?x258), language(?x10535, ?x254), ?x258 = 05p553, ?x812 = 01jfsb *> conf = 0.18 ranks of expected_values: 14, 18 EVAL 09v42sf genre 0vgkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 142.000 83.000 0.856 http://example.org/film/film/genre EVAL 09v42sf genre 0hn10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 142.000 83.000 0.856 http://example.org/film/film/genre #20542-05m883 PRED entity: 05m883 PRED relation: student! PRED expected values: 019n9w => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 91): 065y4w7 (0.09 #1592, 0.05 #540, 0.05 #2118), 01w5m (0.07 #105, 0.05 #1157, 0.04 #13784), 04b_46 (0.05 #1805, 0.05 #753, 0.03 #2331), 015zyd (0.05 #527, 0.03 #1579, 0.02 #2631), 03ksy (0.05 #2210, 0.04 #2736, 0.04 #4840), 09f2j (0.05 #159, 0.04 #13838, 0.04 #6471), 07tgn (0.05 #17, 0.03 #1069, 0.02 #2121), 0yjf0 (0.05 #48), 015nl4 (0.04 #6379, 0.03 #21112, 0.03 #20586), 017z88 (0.04 #6394, 0.03 #6920, 0.03 #13761) >> Best rule #1592 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 0djywgn; 03p01x; 03c9pqt; 0g_rs_; >> query: (?x1179, 065y4w7) <- profession(?x1179, ?x987), ?x987 = 0dxtg, executive_produced_by(?x1420, ?x1179) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05m883 student! 019n9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 86.000 0.087 http://example.org/education/educational_institution/students_graduates./education/education/student #20541-027f2w PRED entity: 027f2w PRED relation: institution PRED expected values: 0820xz 016ndm 020vx9 => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 602): 02zd460 (0.79 #8153, 0.78 #9865, 0.76 #9295), 07tgn (0.75 #5714, 0.73 #8566, 0.71 #7996), 09vzz (0.75 #6790, 0.50 #8500, 0.50 #6218), 05zl0 (0.73 #7049, 0.71 #8189, 0.67 #8759), 07tg4 (0.73 #6922, 0.67 #8632, 0.67 #4639), 01jt2w (0.71 #8263, 0.71 #7693, 0.71 #5410), 017j69 (0.71 #7555, 0.71 #5272, 0.67 #4702), 078bz (0.71 #7483, 0.71 #5200, 0.67 #4630), 0ks67 (0.71 #5318, 0.67 #4748, 0.64 #8171), 01jsk6 (0.71 #5560, 0.67 #4990, 0.59 #9555) >> Best rule #8153 for best value: >> intensional similarity = 26 >> extensional distance = 12 >> proper extension: 03mkk4; >> query: (?x2636, 02zd460) <- institution(?x2636, ?x11607), institution(?x2636, ?x9200), institution(?x2636, ?x8354), institution(?x2636, ?x7363), institution(?x2636, ?x6177), institution(?x2636, ?x331), institution(?x2636, ?x122), ?x122 = 08815, student(?x2636, ?x4292), list(?x331, ?x2197), major_field_of_study(?x9200, ?x5614), school(?x2820, ?x9200), organization(?x4095, ?x7363), currency(?x11607, ?x10674), school_type(?x8354, ?x1044), contains(?x94, ?x6177), company(?x3335, ?x7363), ?x5614 = 03qsdpk, category(?x11607, ?x134), school(?x4979, ?x331), major_field_of_study(?x6177, ?x4321), currency(?x8354, ?x170), ?x2197 = 09g7thr, student(?x331, ?x2993), major_field_of_study(?x331, ?x254), ?x4979 = 0f4vx0 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #4686 for first EXPECTED value: *> intensional similarity = 31 *> extensional distance = 4 *> proper extension: 07s6fsf; *> query: (?x2636, 016ndm) <- institution(?x2636, ?x11607), institution(?x2636, ?x10832), institution(?x2636, ?x9200), institution(?x2636, ?x4599), institution(?x2636, ?x4390), institution(?x2636, ?x3208), institution(?x2636, ?x2760), institution(?x2636, ?x2327), institution(?x2636, ?x2171), institution(?x2636, ?x331), ?x9200 = 0dzst, ?x2760 = 07wlf, ?x331 = 01jssp, ?x2327 = 07wjk, institution(?x865, ?x4599), state_province_region(?x4599, ?x4600), school(?x3089, ?x4599), major_field_of_study(?x4599, ?x4268), school_type(?x10832, ?x3092), school(?x1639, ?x4599), student(?x4390, ?x1857), major_field_of_study(?x4390, ?x6364), ?x6364 = 05qt0, contains(?x2146, ?x11607), citytown(?x3208, ?x1860), ?x865 = 02h4rq6, major_field_of_study(?x3208, ?x6756), award_winner(?x3486, ?x2171), ?x4268 = 02822, colors(?x3208, ?x1101), ?x6756 = 0_jm *> conf = 0.67 ranks of expected_values: 18, 373, 385 EVAL 027f2w institution 020vx9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 23.000 23.000 0.786 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 027f2w institution 016ndm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 23.000 23.000 0.786 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 027f2w institution 0820xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 23.000 23.000 0.786 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20540-0f6_dy PRED entity: 0f6_dy PRED relation: location PRED expected values: 050ks => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 70): 0f2nf (0.49 #14475, 0.47 #35389, 0.44 #53087), 02_286 (0.14 #22554, 0.14 #25773, 0.13 #34621), 030qb3t (0.14 #11340, 0.14 #26624, 0.13 #13753), 04jpl (0.12 #821, 0.10 #17, 0.08 #1625), 0c_m3 (0.10 #271, 0.04 #1075, 0.02 #27346), 019fh (0.10 #191, 0.04 #995, 0.02 #27346), 0vzm (0.10 #173, 0.04 #977, 0.02 #1781), 05fjy (0.10 #279, 0.04 #1083, 0.02 #1887), 0846v (0.10 #165, 0.04 #969, 0.01 #2577), 0__wm (0.10 #548, 0.04 #1352) >> Best rule #14475 for best value: >> intensional similarity = 3 >> extensional distance = 755 >> proper extension: 029_3; 01p0vf; 0k9j_; >> query: (?x2123, ?x9336) <- award_nominee(?x230, ?x2123), film(?x2123, ?x813), place_of_birth(?x2123, ?x9336) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1143 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 09wj5; 01rh0w; 0241jw; 05th8t; 01v9l67; 015t56; 016ypb; 0f0kz; 01846t; 0154qm; ... *> query: (?x2123, 050ks) <- award_nominee(?x2616, ?x2123), award_nominee(?x230, ?x2123), ?x230 = 02bfmn, nominated_for(?x2616, ?x2973) *> conf = 0.04 ranks of expected_values: 23 EVAL 0f6_dy location 050ks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 98.000 98.000 0.487 http://example.org/people/person/places_lived./people/place_lived/location #20539-06x2ww PRED entity: 06x2ww PRED relation: artist PRED expected values: 06cc_1 02fn5r => 72 concepts (54 used for prediction) PRED predicted values (max 10 best out of 920): 0gbwp (0.60 #1920, 0.33 #270, 0.25 #4394), 02f1c (0.50 #1462, 0.33 #3111, 0.33 #636), 09hnb (0.50 #984, 0.33 #2633, 0.33 #158), 01wg25j (0.50 #1439, 0.33 #613, 0.25 #3088), 0167xy (0.50 #1563, 0.33 #737, 0.25 #3212), 0167_s (0.50 #949, 0.33 #123, 0.20 #1773), 0qf11 (0.50 #1119, 0.33 #293, 0.20 #1943), 016h4r (0.50 #1065, 0.33 #239, 0.20 #1889), 0p7h7 (0.50 #1141, 0.33 #315, 0.20 #1965), 016qtt (0.50 #829, 0.33 #3, 0.20 #1653) >> Best rule #1920 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 01clyr; 01cl0d; >> query: (?x8027, 0gbwp) <- artist(?x8027, ?x7359), artist(?x8027, ?x1970), instrumentalists(?x1969, ?x1970), artists(?x1000, ?x1970), ?x1969 = 04rzd, profession(?x1970, ?x2348), ?x7359 = 01k_n63, ?x2348 = 0nbcg >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #854 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 03rhqg; *> query: (?x8027, 06cc_1) <- artist(?x8027, ?x1970), artist(?x8027, ?x1270), instrumentalists(?x2158, ?x1970), instrumentalists(?x1969, ?x1970), artists(?x1928, ?x1970), ?x1969 = 04rzd, ?x1928 = 0mhfr, ?x2158 = 01dnws, profession(?x1970, ?x131), role(?x1270, ?x432) *> conf = 0.25 ranks of expected_values: 168, 445 EVAL 06x2ww artist 02fn5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 54.000 0.600 http://example.org/music/record_label/artist EVAL 06x2ww artist 06cc_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 72.000 54.000 0.600 http://example.org/music/record_label/artist #20538-057bc6m PRED entity: 057bc6m PRED relation: nominated_for PRED expected values: 01wb95 => 102 concepts (65 used for prediction) PRED predicted values (max 10 best out of 280): 0k4kk (0.58 #8092, 0.50 #8094, 0.49 #9712), 0bl06 (0.58 #8092, 0.50 #8094, 0.49 #9712), 014knw (0.58 #8092, 0.50 #8094, 0.49 #9712), 0jwvf (0.50 #8094, 0.49 #9712, 0.49 #8091), 06pyc2 (0.50 #8094, 0.49 #9712, 0.49 #8091), 09qycb (0.50 #8094, 0.49 #9712, 0.49 #8091), 048rn (0.50 #8094, 0.49 #9712, 0.49 #8091), 0k5g9 (0.38 #400, 0.15 #1618, 0.15 #14571), 0bcndz (0.31 #3483, 0.20 #9962, 0.18 #1865), 0h3k3f (0.25 #1331, 0.15 #1618, 0.15 #14571) >> Best rule #8092 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 03mdw3c; >> query: (?x8401, ?x9183) <- film_sets_designed(?x8401, ?x9183), film_sets_designed(?x8401, ?x5697), language(?x5697, ?x254), award_winner(?x9183, ?x4405) >> conf = 0.58 => this is the best rule for 3 predicted values *> Best rule #14571 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 04kj2v; 03gyh_z; 07hhnl; 0cdf37; 0fmqp6; 0523v5y; 0521d_3; 0py5b; 05b5_tj; 034qt_; *> query: (?x8401, ?x1746) <- award_nominee(?x8401, ?x4896), award_nominee(?x8401, ?x4423), film_sets_designed(?x4423, ?x951), nominated_for(?x4896, ?x1746) *> conf = 0.15 ranks of expected_values: 34 EVAL 057bc6m nominated_for 01wb95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 102.000 65.000 0.581 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20537-06x6s PRED entity: 06x6s PRED relation: team! PRED expected values: 02y8bn => 121 concepts (93 used for prediction) PRED predicted values (max 10 best out of 101): 02y8bn (0.17 #318, 0.12 #1456, 0.10 #432), 07h1h5 (0.17 #241, 0.03 #6859, 0.03 #8240), 05s_c38 (0.17 #253, 0.03 #5953, 0.03 #6180), 09m465 (0.17 #294, 0.02 #9206, 0.02 #9781), 019g65 (0.13 #2581, 0.13 #2811, 0.12 #3153), 0hcs3 (0.13 #2828, 0.12 #3170, 0.09 #3285), 03n69x (0.11 #4461, 0.11 #4576, 0.09 #5716), 040j2_ (0.10 #4262, 0.09 #4489, 0.09 #3577), 054c1 (0.09 #548, 0.06 #1005, 0.05 #2595), 0cg39k (0.09 #523, 0.06 #980, 0.04 #3483) >> Best rule #318 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0jnmj; 01rlzn; 0j6tr; >> query: (?x13860, 02y8bn) <- teams(?x4356, ?x13860), team(?x2918, ?x13860), colors(?x13860, ?x12067), sport(?x13860, ?x453), place_of_birth(?x543, ?x4356), time_zones(?x4356, ?x1638), ?x12067 = 06kqt3 >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06x6s team! 02y8bn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 93.000 0.167 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #20536-0652ty PRED entity: 0652ty PRED relation: profession PRED expected values: 02hrh1q => 80 concepts (55 used for prediction) PRED predicted values (max 10 best out of 141): 02hrh1q (0.95 #7612, 0.94 #7758, 0.91 #2641), 0nbcg (0.62 #761, 0.15 #3389, 0.10 #6168), 0cbd2 (0.61 #883, 0.29 #591, 0.29 #1175), 03gjzk (0.48 #3664, 0.48 #3810, 0.42 #1620), 09jwl (0.43 #748, 0.27 #163, 0.21 #3376), 0kyk (0.36 #905, 0.29 #613, 0.27 #174), 018gz8 (0.32 #892, 0.28 #1622, 0.21 #4251), 0np9r (0.31 #6449, 0.30 #5572, 0.18 #165), 0dz3r (0.30 #733, 0.14 #3361, 0.09 #6140), 02krf9 (0.22 #2508, 0.22 #2070, 0.21 #2216) >> Best rule #7612 for best value: >> intensional similarity = 6 >> extensional distance = 1884 >> proper extension: 01sl1q; 044mz_; 0184jc; 04bdxl; 02s2ft; 05vsxz; 06qgvf; 0grwj; 05d7rk; 01vvydl; ... >> query: (?x11069, 02hrh1q) <- profession(?x11069, ?x319), film(?x11069, ?x153), profession(?x8898, ?x319), profession(?x1117, ?x319), ?x8898 = 0h7pj, ?x1117 = 03lt8g >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0652ty profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 55.000 0.953 http://example.org/people/person/profession #20535-05mrf_p PRED entity: 05mrf_p PRED relation: film_crew_role PRED expected values: 02_n3z 02r96rf => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.81 #793, 0.80 #34, 0.78 #331), 02r96rf (0.78 #37, 0.78 #169, 0.70 #70), 09vw2b7 (0.77 #40, 0.75 #172, 0.75 #73), 089g0h (0.56 #50, 0.53 #83, 0.22 #182), 0d2b38 (0.52 #56, 0.50 #89, 0.23 #188), 0dxtw (0.50 #176, 0.42 #77, 0.40 #902), 02_n3z (0.46 #68, 0.27 #35, 0.12 #2), 02ynfr (0.22 #344, 0.20 #47, 0.20 #179), 015h31 (0.22 #174, 0.18 #75, 0.17 #42), 033smt (0.19 #91, 0.16 #58, 0.13 #190) >> Best rule #793 for best value: >> intensional similarity = 4 >> extensional distance = 547 >> proper extension: 02rb607; 0g5qmbz; >> query: (?x5074, 09zzb8) <- film_crew_role(?x5074, ?x2154), award_winner(?x5074, ?x2279), film_crew_role(?x3531, ?x2154), ?x3531 = 0c34mt >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 86 *> proper extension: 02qsqmq; 047vp1n; *> query: (?x5074, 02r96rf) <- film_crew_role(?x5074, ?x2472), film(?x2259, ?x5074), film_release_region(?x5074, ?x94), ?x2472 = 01xy5l_ *> conf = 0.78 ranks of expected_values: 2, 7 EVAL 05mrf_p film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 95.000 0.809 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05mrf_p film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 95.000 95.000 0.809 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20534-0gr42 PRED entity: 0gr42 PRED relation: award! PRED expected values: 0h5j77 => 57 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2487): 06mn7 (0.83 #6750, 0.81 #3374, 0.81 #20249), 06rnl9 (0.83 #6750, 0.81 #3374, 0.81 #20249), 02h1rt (0.83 #6750, 0.81 #3374, 0.81 #20249), 03r1pr (0.83 #6750, 0.81 #3374, 0.81 #20249), 04rcl7 (0.83 #6750, 0.81 #3374, 0.81 #20249), 0gl88b (0.50 #3905, 0.33 #529, 0.12 #43886), 0bytkq (0.50 #4221, 0.33 #845, 0.12 #43886), 02vxyl5 (0.50 #6580, 0.33 #3204, 0.10 #30204), 01g6bk (0.40 #16697), 05cv8 (0.40 #16448) >> Best rule #6750 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0gq_v; >> query: (?x2209, ?x788) <- award_winner(?x2209, ?x788), ceremony(?x2209, ?x78), nominated_for(?x2209, ?x8941), nominated_for(?x2209, ?x2223), genre(?x8941, ?x53), ?x2223 = 01_1pv >> conf = 0.83 => this is the best rule for 5 predicted values *> Best rule #8920 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 05b4l5x; *> query: (?x2209, 0h5j77) <- award_winner(?x2209, ?x788), award(?x382, ?x2209), nominated_for(?x2209, ?x5825), nominated_for(?x2209, ?x1385), ?x1385 = 044g_k, genre(?x5825, ?x571) *> conf = 0.25 ranks of expected_values: 135 EVAL 0gr42 award! 0h5j77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 57.000 23.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20533-01wc7p PRED entity: 01wc7p PRED relation: student! PRED expected values: 049dk 015q1n => 144 concepts (133 used for prediction) PRED predicted values (max 10 best out of 161): 0lfgr (0.17 #1097, 0.17 #570, 0.11 #2151), 0lyjf (0.17 #684, 0.11 #2265, 0.06 #3846), 01dq0z (0.14 #2100, 0.06 #4208, 0.03 #7370), 01w5m (0.12 #3267, 0.06 #3794, 0.03 #32779), 023znp (0.11 #2227, 0.02 #7497, 0.02 #8551), 0bwfn (0.07 #32949, 0.06 #26625, 0.06 #29787), 03ksy (0.06 #8538, 0.05 #4322, 0.05 #5376), 017z88 (0.06 #3244, 0.06 #3771, 0.05 #4298), 01vc5m (0.06 #2729, 0.05 #4837, 0.04 #6418), 015zyd (0.06 #2636, 0.05 #4744, 0.04 #6325) >> Best rule #1097 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0163t3; >> query: (?x5848, 0lfgr) <- person(?x3480, ?x5848), person(?x9646, ?x5848), actor(?x4517, ?x5848) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #18657 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 147 *> proper extension: 06y9c2; 01mt1fy; 01nfys; *> query: (?x5848, 015q1n) <- participant(?x702, ?x5848), artist(?x2190, ?x702), award(?x702, ?x350) *> conf = 0.01 ranks of expected_values: 111 EVAL 01wc7p student! 015q1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 144.000 133.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01wc7p student! 049dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 133.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #20532-0tln7 PRED entity: 0tln7 PRED relation: contains PRED expected values: 019tfm => 67 concepts (35 used for prediction) PRED predicted values (max 10 best out of 165): 019tfm (0.79 #29471, 0.68 #23579, 0.67 #20633), 03x33n (0.25 #531, 0.07 #3479, 0.07 #6426), 0p4gy (0.06 #11478, 0.01 #38314), 0lyjf (0.02 #12423, 0.01 #15371, 0.01 #18317), 0fvvz (0.02 #64838, 0.01 #38314), 03_fmr (0.01 #19453, 0.01 #25345, 0.01 #45975), 021q2j (0.01 #18949, 0.01 #24841, 0.01 #45471), 03bmmc (0.01 #18465, 0.01 #24357, 0.01 #44987), 01t0dy (0.01 #18534, 0.01 #24426, 0.01 #45056), 01sn04 (0.01 #17813, 0.01 #23705, 0.01 #44335) >> Best rule #29471 for best value: >> intensional similarity = 3 >> extensional distance = 174 >> proper extension: 0fngy; >> query: (?x5015, ?x14319) <- citytown(?x14319, ?x5015), country(?x5015, ?x94), contains(?x3908, ?x14319) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tln7 contains 019tfm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 35.000 0.789 http://example.org/location/location/contains #20531-034m8 PRED entity: 034m8 PRED relation: organization PRED expected values: 0j7v_ => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 17): 0b6css (0.57 #10, 0.48 #52, 0.45 #136), 04k4l (0.56 #2047, 0.56 #2069, 0.50 #25), 01rz1 (0.46 #43, 0.45 #148, 0.41 #169), 0_2v (0.43 #129, 0.42 #150, 0.42 #45), 018cqq (0.35 #53, 0.34 #158, 0.32 #200), 0gkjy (0.30 #788, 0.26 #683, 0.26 #1524), 0j7v_ (0.29 #5, 0.28 #639, 0.28 #131), 02jxk (0.27 #44, 0.27 #149, 0.24 #65), 034h1h (0.21 #2225, 0.18 #2523, 0.03 #2952), 059dn (0.14 #15, 0.09 #120, 0.09 #183) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 012wgb; >> query: (?x9459, 0b6css) <- adjoins(?x9459, ?x1144), country(?x13946, ?x9459), ?x1144 = 0j3b >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #5 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 012wgb; *> query: (?x9459, 0j7v_) <- adjoins(?x9459, ?x1144), country(?x13946, ?x9459), ?x1144 = 0j3b *> conf = 0.29 ranks of expected_values: 7 EVAL 034m8 organization 0j7v_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 148.000 148.000 0.571 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #20530-07m69t PRED entity: 07m69t PRED relation: team PRED expected values: 02s2ys => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 743): 085v7 (0.82 #2664, 0.81 #3197, 0.81 #3465), 0bl8l (0.82 #2664, 0.81 #3197, 0.81 #3465), 01cwm1 (0.82 #2664, 0.81 #3197, 0.81 #3465), 02s2ys (0.82 #2664, 0.81 #3197, 0.81 #3465), 02b10g (0.82 #2664, 0.81 #3197, 0.81 #3465), 0284h6 (0.40 #430, 0.33 #696, 0.11 #1494), 02b1k5 (0.33 #67, 0.20 #333, 0.17 #599), 02b1mc (0.33 #24, 0.20 #290, 0.17 #556), 02b17f (0.33 #167, 0.20 #433, 0.17 #699), 01_8n9 (0.33 #147, 0.20 #413, 0.17 #679) >> Best rule #2664 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 05_6_y; 0bn9sc; 0487c3; 080dyk; 02d9k; 0784v1; 09ntbc; 0c11mj; 083qy7; 071pf2; ... >> query: (?x8598, ?x5207) <- team(?x8598, ?x6831), nationality(?x8598, ?x94), sport(?x6831, ?x471), team(?x8598, ?x5207), ?x471 = 02vx4 >> conf = 0.82 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 07m69t team 02s2ys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 78.000 78.000 0.819 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #20529-01fmys PRED entity: 01fmys PRED relation: film! PRED expected values: 05s_k6 => 83 concepts (79 used for prediction) PRED predicted values (max 10 best out of 58): 086k8 (0.62 #75, 0.29 #294, 0.25 #2), 04mkft (0.50 #107, 0.29 #326, 0.27 #180), 025tlyv (0.29 #276, 0.27 #203, 0.24 #349), 024rgt (0.29 #237, 0.18 #164, 0.12 #91), 016tw3 (0.25 #9, 0.18 #155, 0.18 #301), 06jntd (0.19 #1051, 0.09 #175, 0.08 #1782), 05qd_ (0.19 #956, 0.18 #1248, 0.18 #1175), 017s11 (0.15 #441, 0.14 #1098, 0.13 #514), 05s_k6 (0.12 #135, 0.12 #354, 0.10 #1084), 020h2v (0.12 #43, 0.11 #2494, 0.07 #262) >> Best rule #75 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 031778; >> query: (?x2050, 086k8) <- film_distribution_medium(?x2050, ?x627), film(?x9354, ?x2050), ?x627 = 02nxhr, region(?x2050, ?x512) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #135 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 031778; *> query: (?x2050, 05s_k6) <- film_distribution_medium(?x2050, ?x627), film(?x9354, ?x2050), ?x627 = 02nxhr, region(?x2050, ?x512) *> conf = 0.12 ranks of expected_values: 9 EVAL 01fmys film! 05s_k6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 83.000 79.000 0.625 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #20528-02wgk1 PRED entity: 02wgk1 PRED relation: film! PRED expected values: 08swgx => 79 concepts (61 used for prediction) PRED predicted values (max 10 best out of 888): 02bh9 (0.73 #99411, 0.72 #103555, 0.61 #2072), 0284n42 (0.61 #2072, 0.53 #2071, 0.48 #12428), 072vj (0.45 #33138, 0.42 #74563, 0.29 #28994), 0f0kz (0.36 #4654, 0.06 #8797, 0.04 #12939), 0kszw (0.36 #4557, 0.05 #10771, 0.03 #14913), 0jfx1 (0.27 #4544, 0.09 #2073, 0.07 #10758), 0h7pj (0.25 #3608, 0.03 #13963, 0.02 #34673), 0q9kd (0.18 #4147, 0.04 #8290, 0.02 #24856), 02_p8v (0.18 #5061, 0.02 #25770, 0.01 #44413), 0b_dy (0.18 #4673, 0.02 #8816, 0.02 #35738) >> Best rule #99411 for best value: >> intensional similarity = 4 >> extensional distance = 1208 >> proper extension: 047gn4y; 04kkz8; 0c00zd0; 0m491; 020y73; 075cph; 07x4qr; 014zwb; 023gxx; 0d1qmz; ... >> query: (?x4502, ?x3410) <- genre(?x4502, ?x53), nominated_for(?x3410, ?x4502), film(?x96, ?x4502), film(?x3410, ?x1797) >> conf = 0.73 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02wgk1 film! 08swgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 61.000 0.730 http://example.org/film/actor/film./film/performance/film #20527-03bzjpm PRED entity: 03bzjpm PRED relation: film_crew_role PRED expected values: 09vw2b7 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 29): 02r96rf (0.66 #990, 0.65 #1575, 0.62 #1721), 09vw2b7 (0.64 #1103, 0.63 #994, 0.60 #1725), 0dxtw (0.38 #415, 0.37 #998, 0.35 #1729), 01vx2h (0.37 #85, 0.36 #159, 0.34 #195), 01pvkk (0.33 #345, 0.29 #1330, 0.29 #1256), 02ynfr (0.19 #349, 0.19 #127, 0.16 #385), 01xy5l_ (0.14 #88, 0.14 #711, 0.13 #51), 0215hd (0.14 #1115, 0.14 #715, 0.13 #239), 0d2b38 (0.14 #136, 0.12 #2377, 0.11 #99), 02_n3z (0.13 #37, 0.12 #221, 0.12 #2377) >> Best rule #990 for best value: >> intensional similarity = 5 >> extensional distance = 611 >> proper extension: 0djb3vw; 0h95zbp; >> query: (?x7563, 02r96rf) <- film_release_distribution_medium(?x7563, ?x81), ?x81 = 029j_, production_companies(?x7563, ?x382), language(?x7563, ?x254), film_crew_role(?x7563, ?x137) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #1103 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 674 *> proper extension: 02y_lrp; 02_fm2; 02vp1f_; 047gn4y; 09gq0x5; 09tqkv2; 07nt8p; 0g3zrd; 03kg2v; 0ds2n; ... *> query: (?x7563, 09vw2b7) <- titles(?x1403, ?x7563), genre(?x7563, ?x53), currency(?x7563, ?x170), film(?x2549, ?x7563), film_crew_role(?x7563, ?x137) *> conf = 0.64 ranks of expected_values: 2 EVAL 03bzjpm film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.656 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20526-02wrhj PRED entity: 02wrhj PRED relation: profession PRED expected values: 018gz8 0np9r => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 64): 02hrh1q (0.91 #3615, 0.89 #3765, 0.88 #3315), 0np9r (0.67 #622, 0.29 #322, 0.23 #2122), 03gjzk (0.53 #3016, 0.46 #1966, 0.45 #1816), 0dxtg (0.50 #164, 0.44 #1964, 0.42 #1814), 02jknp (0.50 #458, 0.33 #758, 0.29 #308), 018gz8 (0.50 #168, 0.22 #1068, 0.22 #618), 01d_h8 (0.36 #1506, 0.34 #3006, 0.28 #15164), 09jwl (0.21 #920, 0.18 #14124, 0.17 #8271), 01c72t (0.20 #25, 0.14 #325, 0.14 #17264), 01c8w0 (0.20 #9, 0.14 #17264, 0.12 #16060) >> Best rule #3615 for best value: >> intensional similarity = 3 >> extensional distance = 283 >> proper extension: 01r42_g; 0gcdzz; 04mz10g; 01l1sq; 01bpc9; 0443y3; 0783m_; 07sgfsl; 03zyvw; 0bt7ws; ... >> query: (?x1765, 02hrh1q) <- actor(?x11377, ?x1765), award_winner(?x1764, ?x1765), program(?x11291, ?x11377) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #622 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01yh3y; 041c4; 02y0yt; 034rd9; *> query: (?x1765, 0np9r) <- actor(?x2555, ?x1765), film(?x1765, ?x10072), nationality(?x1765, ?x279), ?x10072 = 099bhp *> conf = 0.67 ranks of expected_values: 2, 6 EVAL 02wrhj profession 0np9r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.905 http://example.org/people/person/profession EVAL 02wrhj profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 130.000 130.000 0.905 http://example.org/people/person/profession #20525-06cgf PRED entity: 06cgf PRED relation: film! PRED expected values: 01vvybv => 52 concepts (42 used for prediction) PRED predicted values (max 10 best out of 804): 05txrz (0.25 #2850, 0.21 #4933, 0.19 #7016), 0fby2t (0.25 #2838, 0.21 #4921, 0.19 #7004), 0dt645q (0.21 #12181), 02gf_l (0.18 #1270, 0.02 #22107, 0.01 #26276), 03fghg (0.18 #10645), 0pz91 (0.17 #2295, 0.14 #4378, 0.12 #6461), 086nl7 (0.17 #2870, 0.14 #4953, 0.12 #7036), 04t2l2 (0.17 #2111, 0.14 #4194, 0.12 #6277), 01fyzy (0.17 #3146, 0.14 #5229, 0.12 #7312), 03q64h (0.14 #12453) >> Best rule #2850 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0bvn25; 0k4f3; 074w86; 0fz3b1; 01k0xy; 0gd92; 06fpsx; 0f2sx4; 0456zg; 0p7pw; >> query: (?x10873, 05txrz) <- currency(?x10873, ?x170), genre(?x10873, ?x12008), ?x12008 = 0gsy3b, film_release_region(?x10873, ?x94) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #14270 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 73 *> proper extension: 01cgz; *> query: (?x10873, 01vvybv) <- films(?x2286, ?x10873), titles(?x2286, ?x414) *> conf = 0.01 ranks of expected_values: 506 EVAL 06cgf film! 01vvybv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 42.000 0.250 http://example.org/film/actor/film./film/performance/film #20524-01q2nx PRED entity: 01q2nx PRED relation: country PRED expected values: 09c7w0 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 75): 09c7w0 (0.87 #492, 0.85 #676, 0.85 #737), 02jx1 (0.43 #5286, 0.37 #5532, 0.37 #3873), 07ssc (0.37 #5532, 0.37 #3873, 0.37 #5041), 0h7x (0.37 #5532, 0.37 #3873, 0.37 #5041), 0345h (0.36 #3627, 0.16 #1873, 0.16 #579), 0d060g (0.36 #3627, 0.06 #192, 0.06 #437), 0chghy (0.36 #3627, 0.05 #932, 0.05 #1176), 03rt9 (0.36 #3627, 0.03 #5594, 0.02 #260), 0d05w3 (0.17 #44, 0.14 #105, 0.11 #166), 0f8l9c (0.15 #633, 0.13 #203, 0.12 #816) >> Best rule #492 for best value: >> intensional similarity = 5 >> extensional distance = 133 >> proper extension: 02v8kmz; 06wzvr; 034qrh; 03s6l2; 04fzfj; 03ckwzc; 0f4_l; 02yvct; 0f40w; 0661ql3; ... >> query: (?x5275, 09c7w0) <- film(?x4731, ?x5275), genre(?x5275, ?x53), people(?x9428, ?x4731), ?x9428 = 048z7l, currency(?x5275, ?x170) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q2nx country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.867 http://example.org/film/film/country #20523-01g5kv PRED entity: 01g5kv PRED relation: gender PRED expected values: 02zsn => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #13, 0.82 #15, 0.82 #19), 02zsn (0.46 #101, 0.41 #6, 0.37 #8) >> Best rule #13 for best value: >> intensional similarity = 6 >> extensional distance = 284 >> proper extension: 03gm48; 06w33f8; 0j_c; 01ycck; 03xp8d5; 03nk3t; 0gv5c; 051wwp; 03cn92; 01d5vk; ... >> query: (?x13000, 05zppz) <- profession(?x13000, ?x1032), profession(?x13000, ?x524), profession(?x13000, ?x319), ?x1032 = 02hrh1q, ?x524 = 02jknp, ?x319 = 01d_h8 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #101 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4061 *> proper extension: 0jrg; *> query: (?x13000, ?x231) <- nationality(?x13000, ?x94), nationality(?x9493, ?x94), nationality(?x3633, ?x94), gender(?x9493, ?x231), type_of_union(?x3633, ?x566) *> conf = 0.46 ranks of expected_values: 2 EVAL 01g5kv gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 53.000 53.000 0.829 http://example.org/people/person/gender #20522-0167v PRED entity: 0167v PRED relation: organization PRED expected values: 0j7v_ => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 49): 0j7v_ (0.51 #4, 0.26 #277, 0.26 #193), 041288 (0.45 #15, 0.36 #183, 0.35 #267), 0b6css (0.34 #51, 0.32 #114, 0.32 #177), 04k4l (0.28 #24, 0.24 #150, 0.24 #66), 0gkjy (0.28 #111, 0.25 #174, 0.25 #48), 01rz1 (0.25 #106, 0.24 #85, 0.23 #379), 034h1h (0.21 #491, 0.18 #596, 0.02 #809), 018cqq (0.16 #241, 0.15 #388, 0.15 #31), 02jxk (0.13 #23, 0.12 #107, 0.11 #275), 02_l9 (0.07 #601) >> Best rule #4 for best value: >> intensional similarity = 2 >> extensional distance = 65 >> proper extension: 0h44w; >> query: (?x5445, 0j7v_) <- countries_spoken_in(?x254, ?x5445), ?x254 = 02h40lc >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0167v organization 0j7v_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.507 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #20521-0fxyd PRED entity: 0fxyd PRED relation: currency PRED expected values: 09nqf => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.86 #48, 0.86 #47, 0.85 #50) >> Best rule #48 for best value: >> intensional similarity = 5 >> extensional distance = 258 >> proper extension: 0f4y_; 0mlyw; 0nvd8; 0nh57; 0cc1v; 043z0; 0nm8n; 0drr3; 09dfcj; 0mlzk; ... >> query: (?x4202, ?x170) <- adjoins(?x9535, ?x4202), adjoins(?x7500, ?x4202), source(?x4202, ?x958), currency(?x9535, ?x170), contains(?x3670, ?x7500) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fxyd currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.862 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #20520-015f7 PRED entity: 015f7 PRED relation: artists! PRED expected values: 08cyft => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 213): 05bt6j (0.64 #351, 0.32 #5572, 0.32 #2808), 06by7 (0.58 #21, 0.57 #1864, 0.49 #4321), 06j6l (0.50 #1276, 0.39 #662, 0.38 #2198), 0glt670 (0.46 #3419, 0.45 #2805, 0.43 #3726), 0gywn (0.38 #1285, 0.34 #2207, 0.33 #671), 016clz (0.32 #2770, 0.32 #927, 0.29 #1541), 0155w (0.29 #1947, 0.27 #4404, 0.25 #104), 0m0jc (0.25 #9, 0.23 #1238, 0.21 #931), 0xhtw (0.25 #16, 0.21 #938, 0.19 #13838), 03lty (0.25 #27, 0.21 #949, 0.12 #23678) >> Best rule #351 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0hvbj; 02twdq; 016vn3; >> query: (?x3397, 05bt6j) <- artist(?x5666, ?x3397), artists(?x996, ?x3397), ?x996 = 0dn16 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 02jg92; *> query: (?x3397, 08cyft) <- artist(?x5666, ?x3397), role(?x3397, ?x316), participant(?x3397, ?x1896) *> conf = 0.17 ranks of expected_values: 22 EVAL 015f7 artists! 08cyft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 134.000 134.000 0.643 http://example.org/music/genre/artists #20519-0bksh PRED entity: 0bksh PRED relation: award PRED expected values: 09sb52 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 264): 05zr6wv (0.72 #37546, 0.69 #37545, 0.69 #9086), 09sb52 (0.47 #2016, 0.42 #2806, 0.39 #4781), 01by1l (0.23 #5640, 0.12 #900, 0.11 #8800), 05b4l5x (0.23 #1191, 0.23 #1586, 0.20 #401), 05ztrmj (0.21 #2548, 0.17 #968, 0.16 #5313), 01bgqh (0.18 #5573, 0.12 #833, 0.11 #3598), 03qbh5 (0.17 #5728, 0.10 #1383, 0.10 #988), 094qd5 (0.17 #4390, 0.17 #3995, 0.16 #2020), 057xs89 (0.16 #2524, 0.16 #26080, 0.14 #27661), 0gqwc (0.16 #4419, 0.16 #4024, 0.15 #9951) >> Best rule #37546 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x4782, ?x401) <- award_winner(?x401, ?x4782), award(?x56, ?x401) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #2016 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 014x77; 0prjs; 0h32q; 0hwbd; 01vxqyl; *> query: (?x4782, 09sb52) <- nominated_for(?x4782, ?x3601), celebrity(?x4782, ?x1896), film_crew_role(?x3601, ?x137) *> conf = 0.47 ranks of expected_values: 2 EVAL 0bksh award 09sb52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20518-0bl3nn PRED entity: 0bl3nn PRED relation: genre PRED expected values: 0btmb => 108 concepts (48 used for prediction) PRED predicted values (max 10 best out of 91): 03k9fj (0.72 #4358, 0.71 #4594, 0.69 #2238), 07s9rl0 (0.58 #1055, 0.57 #1641, 0.57 #3639), 0btmb (0.41 #554, 0.36 #320, 0.24 #906), 05p553 (0.39 #3052, 0.39 #3877, 0.37 #2230), 0bkbm (0.36 #38, 0.15 #624, 0.15 #741), 0lsxr (0.36 #5064, 0.33 #3765, 0.29 #5535), 02l7c8 (0.32 #366, 0.27 #3888, 0.27 #3299), 02n4kr (0.23 #3764, 0.18 #1413, 0.17 #5063), 04xvlr (0.21 #1525, 0.17 #1056, 0.16 #3286), 03npn (0.19 #124, 0.16 #3763, 0.14 #358) >> Best rule #4358 for best value: >> intensional similarity = 6 >> extensional distance = 461 >> proper extension: 07ng9k; 08fbnx; 01f39b; 0bh72t; 02r9p0c; 02r2j8; 0199wf; 02kwcj; >> query: (?x7239, 03k9fj) <- language(?x7239, ?x254), genre(?x7239, ?x6888), genre(?x6053, ?x6888), genre(?x4032, ?x6888), ?x6053 = 05qbbfb, ?x4032 = 0g9yrw >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #554 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 05qbckf; 0cc846d; 04tqtl; 05f4_n0; 0dzlbx; 048vhl; 0dnkmq; 034b6k; *> query: (?x7239, 0btmb) <- executive_produced_by(?x7239, ?x5869), genre(?x7239, ?x6888), film_crew_role(?x7239, ?x137), ?x6888 = 04pbhw *> conf = 0.41 ranks of expected_values: 3 EVAL 0bl3nn genre 0btmb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 48.000 0.724 http://example.org/film/film/genre #20517-0bk1p PRED entity: 0bk1p PRED relation: group! PRED expected values: 05r5c => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 118): 03qjg (0.45 #448, 0.34 #1339, 0.33 #2311), 013y1f (0.31 #428, 0.18 #1319, 0.17 #2291), 05r5c (0.30 #2763, 0.28 #2275, 0.28 #412), 0l14qv (0.28 #3167, 0.26 #1868, 0.25 #2761), 06ncr (0.20 #3196, 0.17 #439, 0.17 #925), 018j2 (0.17 #433, 0.09 #1324, 0.08 #4408), 04rzd (0.17 #27, 0.14 #3189, 0.14 #2295), 042v_gx (0.17 #8, 0.12 #3170, 0.11 #2764), 0mkg (0.17 #10, 0.10 #3172, 0.10 #4390), 02k856 (0.17 #45, 0.08 #126, 0.08 #207) >> Best rule #448 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 07qnf; 02r3zy; 07c0j; 01fl3; 01czx; 0dvqq; 0249kn; 05563d; 07yg2; 0394y; ... >> query: (?x8999, 03qjg) <- group(?x745, ?x8999), group(?x3867, ?x8999), artist(?x2149, ?x8999), ?x745 = 01vj9c >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2763 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 99 *> proper extension: 02_5x9; 02mq_y; 0123r4; *> query: (?x8999, 05r5c) <- group(?x1750, ?x8999), group(?x3867, ?x8999), role(?x1750, ?x74), artists(?x671, ?x8999) *> conf = 0.30 ranks of expected_values: 3 EVAL 0bk1p group! 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 113.000 0.448 http://example.org/music/performance_role/regular_performances./music/group_membership/group #20516-06pcz0 PRED entity: 06pcz0 PRED relation: film PRED expected values: 0cp0ph6 => 94 concepts (83 used for prediction) PRED predicted values (max 10 best out of 426): 04gv3db (0.29 #8947, 0.25 #7157, 0.23 #10737), 0bmssv (0.29 #8947, 0.25 #7157, 0.23 #10737), 0blpg (0.11 #656, 0.01 #11393, 0.01 #4234), 01l_pn (0.06 #966, 0.03 #18860, 0.02 #11703), 0cn_b8 (0.06 #615, 0.02 #2404, 0.02 #11352), 01xbxn (0.06 #1394, 0.02 #12131, 0.02 #4972), 09146g (0.06 #299, 0.02 #11036, 0.02 #3877), 06ztvyx (0.06 #432, 0.02 #11169, 0.02 #18326), 0cfhfz (0.06 #493, 0.02 #4071, 0.02 #11230), 07sgdw (0.06 #811, 0.02 #4389, 0.02 #15126) >> Best rule #8947 for best value: >> intensional similarity = 3 >> extensional distance = 172 >> proper extension: 04m_zp; >> query: (?x11437, ?x4178) <- profession(?x11437, ?x1032), written_by(?x4178, ?x11437), ?x1032 = 02hrh1q >> conf = 0.29 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 06pcz0 film 0cp0ph6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 83.000 0.289 http://example.org/film/actor/film./film/performance/film #20515-01mskc3 PRED entity: 01mskc3 PRED relation: location PRED expected values: 02xry => 122 concepts (109 used for prediction) PRED predicted values (max 10 best out of 136): 02_286 (0.35 #37736, 0.33 #39340, 0.28 #6453), 030qb3t (0.28 #37782, 0.26 #39386, 0.23 #23343), 0nbwf (0.20 #1206, 0.03 #2810), 04jpl (0.11 #39320, 0.07 #7235, 0.07 #23277), 059rby (0.11 #3224, 0.10 #1620, 0.10 #4026), 0r0m6 (0.11 #3424, 0.08 #4226, 0.06 #6632), 0cr3d (0.10 #11375, 0.08 #10573, 0.08 #16187), 01qh7 (0.10 #1761, 0.05 #3365, 0.04 #4167), 01b8jj (0.08 #3799, 0.06 #4601, 0.05 #5403), 01n7q (0.07 #11293, 0.06 #4073, 0.05 #3271) >> Best rule #37736 for best value: >> intensional similarity = 3 >> extensional distance = 958 >> proper extension: 0f1vrl; 05nzw6; 01qklj; 09fqd3; >> query: (?x11953, 02_286) <- profession(?x11953, ?x955), location(?x11953, ?x3501), dog_breed(?x3501, ?x3095) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #84223 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2484 *> proper extension: 07m69t; 01h2_6; 0443c; *> query: (?x11953, ?x94) <- location(?x11953, ?x3501), contains(?x94, ?x3501) *> conf = 0.03 ranks of expected_values: 70 EVAL 01mskc3 location 02xry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 122.000 109.000 0.354 http://example.org/people/person/places_lived./people/place_lived/location #20514-0cwrr PRED entity: 0cwrr PRED relation: program! PRED expected values: 05xbx => 74 concepts (72 used for prediction) PRED predicted values (max 10 best out of 59): 05gnf (0.36 #14, 0.21 #816, 0.21 #1104), 09d5h (0.29 #3, 0.17 #805, 0.14 #1268), 0gsg7 (0.22 #1092, 0.20 #1209, 0.20 #1733), 0cjdk (0.19 #62, 0.18 #119, 0.17 #462), 03mdt (0.14 #178, 0.14 #121, 0.14 #693), 02hmvw (0.14 #100, 0.11 #328, 0.09 #443), 03lpbx (0.12 #90, 0.09 #318, 0.08 #433), 0187wh (0.11 #483, 0.09 #598, 0.09 #655), 0ljc_ (0.10 #257, 0.09 #86, 0.09 #314), 0146mv (0.09 #84, 0.08 #255, 0.07 #312) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 01hn_t; 06qw_; >> query: (?x802, 05gnf) <- actor(?x802, ?x803), tv_program(?x8922, ?x802), people(?x6484, ?x8922) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 01hn_t; 06qw_; *> query: (?x802, 05xbx) <- actor(?x802, ?x803), tv_program(?x8922, ?x802), people(?x6484, ?x8922) *> conf = 0.07 ranks of expected_values: 16 EVAL 0cwrr program! 05xbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 74.000 72.000 0.357 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #20513-0244r8 PRED entity: 0244r8 PRED relation: place_of_birth PRED expected values: 0chgzm => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 116): 01vqq1 (0.25 #423, 0.08 #1831, 0.05 #2535), 02_286 (0.17 #1427, 0.11 #6357, 0.11 #5653), 094jv (0.17 #765, 0.08 #1469, 0.05 #2173), 0dclg (0.17 #782, 0.02 #19093, 0.02 #24021), 04sqj (0.17 #998, 0.02 #55628), 05l5n (0.08 #1473, 0.05 #2177, 0.02 #2881), 0b_yz (0.08 #1841, 0.05 #2545, 0.01 #4657), 030qb3t (0.08 #4278, 0.07 #8504, 0.07 #5688), 013t2y (0.05 #2626), 0bxbb (0.05 #2347) >> Best rule #423 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0130sy; >> query: (?x1489, 01vqq1) <- type_of_union(?x1489, ?x566), artists(?x6385, ?x1489), ?x6385 = 05g9_ >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #5944 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 06449; 05ccxr; 0164y7; *> query: (?x1489, 0chgzm) <- award_winner(?x1489, ?x3069), music(?x1077, ?x1489), nominated_for(?x262, ?x1077) *> conf = 0.01 ranks of expected_values: 91 EVAL 0244r8 place_of_birth 0chgzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 112.000 112.000 0.250 http://example.org/people/person/place_of_birth #20512-01d0fp PRED entity: 01d0fp PRED relation: award PRED expected values: 0641kkh => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 264): 09td7p (0.73 #1605, 0.72 #30086, 0.71 #18449), 05b4l5x (0.33 #407, 0.20 #808, 0.20 #1209), 0gq9h (0.30 #75, 0.19 #4086, 0.13 #7695), 040njc (0.30 #8, 0.16 #4019, 0.13 #25271), 05p09zm (0.24 #922, 0.22 #3730, 0.21 #521), 0gr4k (0.20 #33, 0.13 #25271, 0.12 #22061), 03hkv_r (0.20 #16, 0.13 #25271, 0.12 #22061), 02x17s4 (0.20 #121, 0.13 #25271, 0.12 #22061), 04dn09n (0.20 #43, 0.13 #25271, 0.07 #7663), 02n9nmz (0.20 #67, 0.13 #25271, 0.05 #7687) >> Best rule #1605 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 0bx_q; >> query: (?x4930, ?x1972) <- profession(?x4930, ?x4773), ?x4773 = 0d1pc, award_winner(?x1972, ?x4930) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1535 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 0bx_q; *> query: (?x4930, 0641kkh) <- profession(?x4930, ?x4773), ?x4773 = 0d1pc, award_winner(?x1972, ?x4930) *> conf = 0.05 ranks of expected_values: 123 EVAL 01d0fp award 0641kkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 93.000 93.000 0.729 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20511-047p798 PRED entity: 047p798 PRED relation: film! PRED expected values: 049g_xj 022411 => 100 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1002): 0j_c (0.18 #12889, 0.09 #31610, 0.07 #25369), 044qx (0.15 #13210, 0.07 #25690, 0.07 #31931), 014zcr (0.14 #37, 0.10 #10437, 0.07 #14597), 0170s4 (0.14 #397, 0.07 #10797, 0.07 #2477), 032xhg (0.14 #64, 0.06 #4224, 0.05 #6304), 0863x_ (0.14 #839, 0.05 #15399, 0.04 #21639), 0h5g_ (0.14 #74, 0.05 #6314, 0.04 #8394), 06cgy (0.14 #250, 0.04 #68899, 0.03 #62657), 0mdqp (0.14 #118, 0.04 #83333, 0.03 #12598), 081lh (0.14 #161, 0.03 #68810, 0.02 #89618) >> Best rule #12889 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 09qycb; >> query: (?x10475, 0j_c) <- language(?x10475, ?x254), film_festivals(?x10475, ?x6557), titles(?x2480, ?x10475), film(?x2221, ?x10475), people(?x1446, ?x2221) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #33524 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 80 *> proper extension: 028_yv; 01vksx; 03cvwkr; 0cwy47; 0dtfn; 0gtvrv3; 0168ls; 0dr_4; 035yn8; 0fq7dv_; ... *> query: (?x10475, 049g_xj) <- language(?x10475, ?x254), ?x254 = 02h40lc, currency(?x10475, ?x170), film_release_region(?x10475, ?x87), film_regional_debut_venue(?x10475, ?x6557) *> conf = 0.01 ranks of expected_values: 909 EVAL 047p798 film! 022411 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 73.000 0.176 http://example.org/film/actor/film./film/performance/film EVAL 047p798 film! 049g_xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 100.000 73.000 0.176 http://example.org/film/actor/film./film/performance/film #20510-017f3m PRED entity: 017f3m PRED relation: actor PRED expected values: 02lfcm => 63 concepts (49 used for prediction) PRED predicted values (max 10 best out of 736): 0438pz (0.39 #5533, 0.36 #12904, 0.35 #9218), 05gnf (0.39 #5533, 0.35 #9218, 0.34 #12903), 046zh (0.36 #12904, 0.34 #12903, 0.34 #16592), 06r3p2 (0.36 #12904, 0.34 #12903, 0.34 #16592), 07qcbw (0.36 #12904, 0.34 #12903, 0.34 #16592), 08xwck (0.10 #13826, 0.09 #14748, 0.09 #5532), 0582cf (0.07 #1617, 0.04 #9911, 0.03 #10832), 03q5dr (0.06 #7188, 0.06 #6267, 0.05 #9030), 01ggc9 (0.05 #1683, 0.05 #3526, 0.03 #8134), 0725ny (0.05 #1559, 0.03 #15383, 0.03 #17227) >> Best rule #5533 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 07s8z_l; >> query: (?x4898, ?x1204) <- producer_type(?x4898, ?x632), program(?x8522, ?x4898), award_winner(?x4898, ?x1204) >> conf = 0.39 => this is the best rule for 2 predicted values *> Best rule #1879 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 064q5v; *> query: (?x4898, 02lfcm) <- award_winner(?x4898, ?x1204), award(?x4898, ?x3486), ?x3486 = 0m7yy *> conf = 0.02 ranks of expected_values: 419 EVAL 017f3m actor 02lfcm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 63.000 49.000 0.392 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #20509-02qd04y PRED entity: 02qd04y PRED relation: genre PRED expected values: 07s9rl0 => 88 concepts (52 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.93 #5949, 0.88 #5830, 0.87 #4282), 0d05w3 (0.72 #1069, 0.56 #1307, 0.56 #1306), 012w70 (0.72 #1069, 0.56 #1307, 0.56 #1306), 0653m (0.72 #1069, 0.56 #1307, 0.56 #1306), 03h64 (0.72 #1069, 0.56 #1307, 0.56 #1306), 01jfsb (0.67 #12, 0.35 #248, 0.34 #1556), 01hmnh (0.61 #847, 0.30 #254, 0.17 #18), 03k9fj (0.41 #840, 0.35 #247, 0.29 #365), 08322 (0.38 #223, 0.10 #341, 0.07 #459), 02l7c8 (0.37 #1085, 0.36 #607, 0.36 #726) >> Best rule #5949 for best value: >> intensional similarity = 3 >> extensional distance = 1080 >> proper extension: 0c0wvx; >> query: (?x9175, 07s9rl0) <- genre(?x9175, ?x162), genre(?x3596, ?x162), ?x3596 = 0cc5qkt >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qd04y genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 52.000 0.930 http://example.org/film/film/genre #20508-0ndsl1x PRED entity: 0ndsl1x PRED relation: film! PRED expected values: 03xq0f => 63 concepts (38 used for prediction) PRED predicted values (max 10 best out of 56): 03xq0f (0.58 #226, 0.56 #300, 0.17 #152), 0c41qv (0.45 #1864, 0.44 #2014, 0.43 #966), 016tw3 (0.19 #232, 0.17 #901, 0.15 #1799), 086k8 (0.19 #224, 0.15 #298, 0.14 #1941), 05qd_ (0.19 #230, 0.13 #1947, 0.13 #603), 017s11 (0.17 #77, 0.13 #374, 0.13 #894), 03rwz3 (0.17 #117, 0.13 #191, 0.07 #414), 054g1r (0.17 #108, 0.13 #330, 0.08 #925), 020h2v (0.17 #118, 0.06 #639, 0.04 #935), 0fqy4p (0.17 #101, 0.01 #2726, 0.01 #622) >> Best rule #226 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 0gtsx8c; >> query: (?x9002, 03xq0f) <- film_release_region(?x9002, ?x390), film(?x400, ?x9002), ?x390 = 0chghy, film_distribution_medium(?x9002, ?x2099) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ndsl1x film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 38.000 0.583 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #20507-0151ns PRED entity: 0151ns PRED relation: nominated_for PRED expected values: 0gtv7pk => 112 concepts (57 used for prediction) PRED predicted values (max 10 best out of 549): 01shy7 (0.34 #8102, 0.32 #11345, 0.29 #19448), 016z5x (0.34 #8102, 0.32 #11345, 0.29 #19448), 080dfr7 (0.34 #8102, 0.32 #11345, 0.29 #19448), 03cwwl (0.34 #8102, 0.32 #11345, 0.29 #19448), 06_sc3 (0.34 #8102, 0.32 #11345, 0.29 #19448), 02j69w (0.34 #8102, 0.32 #11345, 0.29 #19448), 0401sg (0.34 #8102, 0.32 #11345, 0.29 #19448), 0gtv7pk (0.34 #8102, 0.32 #11345, 0.29 #19448), 039fgy (0.17 #3334), 0ds33 (0.09 #4924, 0.04 #9787, 0.03 #14649) >> Best rule #8102 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 01sbhvd; >> query: (?x558, ?x409) <- award(?x558, ?x3064), ?x3064 = 05q5t0b, film(?x558, ?x409) >> conf = 0.34 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 8 EVAL 0151ns nominated_for 0gtv7pk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 112.000 57.000 0.337 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20506-02w86hz PRED entity: 02w86hz PRED relation: currency PRED expected values: 02gsvk => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.84 #386, 0.84 #281, 0.83 #477), 02gsvk (0.64 #48, 0.60 #41, 0.43 #20), 01nv4h (0.09 #93, 0.06 #107, 0.06 #135), 088n7 (0.03 #161, 0.03 #133, 0.02 #371), 02l6h (0.03 #333, 0.03 #340, 0.03 #410), 0ptk_ (0.02 #94) >> Best rule #386 for best value: >> intensional similarity = 7 >> extensional distance = 256 >> proper extension: 05css_; 0g5ptf; >> query: (?x3742, 09nqf) <- film(?x12062, ?x3742), genre(?x3742, ?x1510), genre(?x3742, ?x225), titles(?x3741, ?x3742), ?x225 = 02kdv5l, type_of_union(?x12062, ?x566), genre(?x419, ?x1510) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 01p3ty; *> query: (?x3742, 02gsvk) <- film(?x6249, ?x3742), film_release_region(?x3742, ?x94), titles(?x3741, ?x3742), ?x3741 = 01chg, nationality(?x51, ?x94), combatants(?x326, ?x94) *> conf = 0.64 ranks of expected_values: 2 EVAL 02w86hz currency 02gsvk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.845 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #20505-0gxfz PRED entity: 0gxfz PRED relation: list PRED expected values: 05glt => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.40 #9, 0.30 #23, 0.29 #16) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 0ds33; 0cwy47; 03kxj2; 016kz1; 02_sr1; 0gcpc; 0cqnss; 0cy__l; 0jwvf; 0k7tq; ... >> query: (?x2721, 05glt) <- award(?x2721, ?x591), music(?x2721, ?x6971), language(?x2721, ?x254), film_sets_designed(?x786, ?x2721) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gxfz list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.396 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #20504-0m_mm PRED entity: 0m_mm PRED relation: list PRED expected values: 05glt => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.30 #65, 0.26 #23, 0.23 #16) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 159 >> proper extension: 0cwrr; 07bz5; >> query: (?x984, 05glt) <- honored_for(?x3029, ?x984), nominated_for(?x6358, ?x984), award(?x6358, ?x591), place_of_death(?x6358, ?x191) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m_mm list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.304 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #20503-08fn5b PRED entity: 08fn5b PRED relation: film! PRED expected values: 04m064 => 99 concepts (52 used for prediction) PRED predicted values (max 10 best out of 905): 0dh73w (0.52 #47792, 0.46 #2078, 0.45 #95586), 0bwh6 (0.52 #47792, 0.46 #2078, 0.45 #95586), 05ldnp (0.46 #2078, 0.45 #95586, 0.45 #97664), 01gb54 (0.46 #2078, 0.45 #95586, 0.45 #97664), 0f5xn (0.10 #969, 0.04 #38371, 0.03 #9280), 079vf (0.10 #8, 0.03 #37410, 0.02 #14554), 0p8r1 (0.08 #8896, 0.02 #6819, 0.02 #46298), 06ltr (0.07 #946, 0.04 #9257, 0.04 #11335), 0lpjn (0.07 #480, 0.03 #15026, 0.03 #60743), 03knl (0.07 #158, 0.02 #14704, 0.02 #10547) >> Best rule #47792 for best value: >> intensional similarity = 4 >> extensional distance = 463 >> proper extension: 04glx0; >> query: (?x4167, ?x1365) <- nominated_for(?x1365, ?x4167), honored_for(?x5592, ?x4167), award_winner(?x289, ?x1365), film(?x1365, ?x1118) >> conf = 0.52 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 08fn5b film! 04m064 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 52.000 0.516 http://example.org/film/actor/film./film/performance/film #20502-02yv_b PRED entity: 02yv_b PRED relation: ceremony! PRED expected values: 0gq_v 0gr4k 0gr51 0gr07 => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 359): 0gq_v (0.89 #2919, 0.85 #1709, 0.84 #4133), 0gr51 (0.88 #3698, 0.85 #5152, 0.84 #1516), 0gr4k (0.85 #2925, 0.85 #4382, 0.85 #1715), 0gr07 (0.83 #2576, 0.80 #1121, 0.80 #153), 0gqzz (0.78 #3634, 0.75 #6297, 0.75 #8473), 0czp_ (0.78 #3634, 0.75 #6297, 0.75 #8473), 02x201b (0.78 #3634, 0.75 #6297, 0.75 #8473), 019f4v (0.41 #3148, 0.34 #5089, 0.30 #4606), 040njc (0.41 #3148, 0.34 #5089, 0.30 #4606), 02pqp12 (0.41 #3148, 0.34 #5089, 0.30 #4606) >> Best rule #2919 for best value: >> intensional similarity = 17 >> extensional distance = 25 >> proper extension: 0bz6l9; 0dthsy; 02yvhx; 0bzjvm; 0bvhz9; >> query: (?x1819, 0gq_v) <- ceremony(?x3617, ?x1819), ceremony(?x1972, ?x1819), ceremony(?x1323, ?x1819), ceremony(?x1245, ?x1819), honored_for(?x1819, ?x1077), ?x1323 = 0gqz2, award_winner(?x1819, ?x5246), award_winner(?x1819, ?x395), ?x1245 = 0gqwc, ?x3617 = 0gvx_, ?x1972 = 0gqyl, award_winner(?x2853, ?x395), participant(?x5246, ?x105), award_winner(?x192, ?x395), nominated_for(?x2853, ?x7336), ?x7336 = 0bdjd, award_winner(?x394, ?x395) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 02yv_b ceremony! 0gr07 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02yv_b ceremony! 0gr51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02yv_b ceremony! 0gr4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02yv_b ceremony! 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony #20501-0sxmx PRED entity: 0sxmx PRED relation: film! PRED expected values: 045gzq => 92 concepts (38 used for prediction) PRED predicted values (max 10 best out of 969): 06r_by (0.47 #27011, 0.43 #4156, 0.40 #37402), 05kfs (0.40 #37402, 0.40 #47791, 0.38 #51947), 018yj6 (0.33 #3606, 0.06 #7761, 0.05 #9839), 02rf1y (0.20 #959, 0.18 #5115, 0.01 #36283), 03f1d47 (0.20 #890, 0.17 #2968), 0hvb2 (0.20 #298, 0.11 #6531, 0.02 #23153), 0652ty (0.20 #1832, 0.11 #8065, 0.02 #22608), 0kjgl (0.20 #1377, 0.06 #7610, 0.02 #34623), 01fkv0 (0.20 #167, 0.06 #6400, 0.01 #10555), 043js (0.20 #452, 0.06 #6685, 0.01 #12917) >> Best rule #27011 for best value: >> intensional similarity = 4 >> extensional distance = 239 >> proper extension: 0b76d_m; 034qmv; 0sxg4; 09xbpt; 0dnvn3; 0m2kd; 04ddm4; 03s6l2; 06z8s_; 04tc1g; ... >> query: (?x4734, ?x6062) <- film(?x8927, ?x4734), language(?x4734, ?x254), nominated_for(?x6062, ?x4734), sibling(?x8927, ?x5834) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #12452 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 01qb559; *> query: (?x4734, 045gzq) <- film(?x12123, ?x4734), nominated_for(?x500, ?x4734), ?x500 = 0p9sw, location_of_ceremony(?x12123, ?x13006) *> conf = 0.01 ranks of expected_values: 603 EVAL 0sxmx film! 045gzq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 92.000 38.000 0.465 http://example.org/film/actor/film./film/performance/film #20500-01q415 PRED entity: 01q415 PRED relation: award PRED expected values: 01ppdy => 106 concepts (104 used for prediction) PRED predicted values (max 10 best out of 298): 019f4v (0.33 #64, 0.23 #6847, 0.21 #3256), 0gs9p (0.33 #76, 0.22 #6859, 0.21 #3268), 02rdyk7 (0.33 #88, 0.15 #3280, 0.13 #5674), 0gr51 (0.30 #3289, 0.27 #97, 0.25 #5683), 0gq9h (0.30 #6857, 0.27 #74, 0.22 #3266), 0f4x7 (0.29 #6813, 0.16 #8808, 0.12 #33518), 09sb52 (0.29 #15201, 0.28 #9615, 0.27 #12009), 040njc (0.27 #8, 0.24 #6791, 0.20 #3200), 05b1610 (0.27 #37, 0.21 #835, 0.21 #1234), 02x4sn8 (0.27 #154, 0.09 #5740, 0.09 #3346) >> Best rule #64 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0184jw; >> query: (?x2248, 019f4v) <- location(?x2248, ?x3634), award_winner(?x601, ?x2248), ?x601 = 0gr4k >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #36313 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2328 *> proper extension: 01v0sx2; 0143q0; 04qzm; 04k05; 017_hq; 016m5c; 014_xj; 0jg77; 06lxn; *> query: (?x2248, ?x1869) <- award_winner(?x746, ?x2248), award(?x647, ?x746), award(?x647, ?x1869) *> conf = 0.05 ranks of expected_values: 120 EVAL 01q415 award 01ppdy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 106.000 104.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20499-04rkkv PRED entity: 04rkkv PRED relation: institution! PRED expected values: 014mlp => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 22): 014mlp (0.68 #30, 0.68 #176, 0.68 #79), 02h4rq6 (0.64 #27, 0.61 #173, 0.61 #344), 019v9k (0.60 #180, 0.59 #83, 0.58 #351), 02_xgp2 (0.44 #184, 0.43 #282, 0.43 #38), 016t_3 (0.43 #28, 0.41 #174, 0.40 #223), 0bkj86 (0.41 #33, 0.40 #82, 0.39 #179), 03bwzr4 (0.39 #40, 0.37 #849, 0.36 #138), 07s6fsf (0.33 #25, 0.29 #342, 0.28 #74), 028dcg (0.30 #936, 0.28 #1135, 0.25 #20), 03mkk4 (0.30 #936, 0.28 #1135, 0.20 #37) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 150 >> proper extension: 041pnt; >> query: (?x8357, 014mlp) <- student(?x8357, ?x5758), major_field_of_study(?x8357, ?x5740), participant(?x6424, ?x5758), profession(?x5758, ?x319) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04rkkv institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.684 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20498-02v2jy PRED entity: 02v2jy PRED relation: profession PRED expected values: 015cjr => 162 concepts (85 used for prediction) PRED predicted values (max 10 best out of 92): 0cbd2 (0.71 #5556, 0.26 #5848, 0.25 #1175), 01d_h8 (0.65 #6139, 0.60 #590, 0.57 #7599), 02jknp (0.59 #6141, 0.50 #8185, 0.50 #4826), 09jwl (0.48 #1769, 0.45 #3083, 0.44 #3375), 02krf9 (0.42 #463, 0.34 #1485, 0.33 #317), 016z4k (0.34 #1756, 0.33 #150, 0.30 #3070), 0nbcg (0.34 #1782, 0.32 #3096, 0.31 #1052), 0kyk (0.33 #174, 0.29 #5577, 0.25 #758), 012t_z (0.33 #13, 0.27 #597, 0.16 #2933), 018gz8 (0.33 #161, 0.25 #7608, 0.22 #5271) >> Best rule #5556 for best value: >> intensional similarity = 4 >> extensional distance = 147 >> proper extension: 03qcq; 04zd4m; 0379s; 032l1; 0fx02; 052h3; 014635; 0zm1; 07_m9_; 03f0324; ... >> query: (?x13097, 0cbd2) <- people(?x6720, ?x13097), profession(?x13097, ?x1041), profession(?x7228, ?x1041), ?x7228 = 054187 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #194 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 0fb1q; *> query: (?x13097, 015cjr) <- award(?x13097, ?x2750), award(?x13097, ?x537), ?x2750 = 02vm9nd, ?x537 = 0gkvb7, profession(?x13097, ?x987) *> conf = 0.33 ranks of expected_values: 11 EVAL 02v2jy profession 015cjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 162.000 85.000 0.711 http://example.org/people/person/profession #20497-01v27pl PRED entity: 01v27pl PRED relation: artist! PRED expected values: 07s363 => 65 concepts (57 used for prediction) PRED predicted values (max 10 best out of 118): 015_1q (0.50 #447, 0.40 #304, 0.33 #590), 0br2wp (0.33 #128, 0.11 #698), 033hn8 (0.25 #156, 0.20 #298, 0.19 #726), 03mp8k (0.25 #210, 0.20 #352, 0.17 #495), 043g7l (0.25 #174, 0.20 #316, 0.17 #459), 073tm9 (0.25 #179, 0.20 #321, 0.17 #464), 02p11jq (0.25 #155, 0.20 #297, 0.17 #440), 011k11 (0.22 #606, 0.20 #320, 0.17 #463), 01w40h (0.22 #599, 0.13 #1169, 0.11 #741), 01clyr (0.20 #1458, 0.11 #604, 0.10 #5309) >> Best rule #447 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 01vrwfv; >> query: (?x11667, 015_1q) <- artists(?x10319, ?x11667), artists(?x996, ?x11667), category(?x11667, ?x134), ?x10319 = 01gjw, origin(?x11667, ?x9310), artists(?x996, ?x5906), artists(?x996, ?x5878), artist(?x4797, ?x5878), place_of_birth(?x5878, ?x2850), participant(?x3481, ?x5906), origin(?x5906, ?x4499), people(?x3584, ?x5878) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1139 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 38 *> proper extension: 03n0q5; 024yxd; *> query: (?x11667, ?x9309) <- origin(?x11667, ?x9310), category(?x11667, ?x134), contains(?x9310, ?x2079), citytown(?x9309, ?x9310), industry(?x9309, ?x245), country(?x9310, ?x1453), major_field_of_study(?x2079, ?x1154), ?x1154 = 02lp1, colors(?x2079, ?x3189) *> conf = 0.02 ranks of expected_values: 111 EVAL 01v27pl artist! 07s363 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 65.000 57.000 0.500 http://example.org/music/record_label/artist #20496-01w60_p PRED entity: 01w60_p PRED relation: award PRED expected values: 01d38g => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 299): 01cw51 (0.79 #13533, 0.79 #15923, 0.75 #15524), 02f79n (0.79 #13533, 0.79 #15923, 0.75 #15524), 05qck (0.79 #13533, 0.79 #15923, 0.75 #15524), 01by1l (0.50 #3297, 0.44 #3695, 0.40 #1307), 01bgqh (0.44 #3625, 0.38 #441, 0.34 #3227), 09sb52 (0.34 #25517, 0.28 #27110, 0.24 #36274), 054ks3 (0.33 #540, 0.32 #142, 0.31 #3724), 01ck6h (0.33 #3705, 0.31 #1715, 0.29 #521), 0c4z8 (0.31 #3654, 0.27 #1266, 0.25 #8430), 01c92g (0.31 #3680, 0.25 #496, 0.23 #98) >> Best rule #13533 for best value: >> intensional similarity = 3 >> extensional distance = 259 >> proper extension: 07q1v4; >> query: (?x2169, ?x2563) <- role(?x2169, ?x227), award(?x2169, ?x2420), award_winner(?x2563, ?x2169) >> conf = 0.79 => this is the best rule for 3 predicted values *> Best rule #25476 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 701 *> proper extension: 01p6xx; 01fxfk; *> query: (?x2169, ?x567) <- award_nominee(?x215, ?x2169), artists(?x378, ?x215), award_winner(?x567, ?x215) *> conf = 0.19 ranks of expected_values: 22 EVAL 01w60_p award 01d38g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 125.000 125.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20495-06m_5 PRED entity: 06m_5 PRED relation: country! PRED expected values: 0bynt 01lb14 071t0 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 54): 0bynt (0.85 #2875, 0.85 #3254, 0.85 #2821), 071t0 (0.81 #77, 0.79 #185, 0.76 #455), 03_8r (0.79 #22, 0.70 #1804, 0.70 #1912), 07jbh (0.69 #88, 0.64 #34, 0.58 #196), 0486tv (0.69 #94, 0.64 #40, 0.50 #202), 06wrt (0.68 #448, 0.64 #16, 0.62 #70), 01lb14 (0.66 #447, 0.64 #15, 0.62 #69), 03hr1p (0.64 #24, 0.63 #456, 0.57 #672), 07gyv (0.64 #7, 0.58 #169, 0.56 #61), 09w1n (0.64 #25, 0.56 #79, 0.50 #187) >> Best rule #2875 for best value: >> intensional similarity = 2 >> extensional distance = 127 >> proper extension: 02jxk; >> query: (?x8420, 0bynt) <- member_states(?x7695, ?x8420), ?x7695 = 085h1 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 7 EVAL 06m_5 country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.853 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06m_5 country! 01lb14 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 130.000 130.000 0.853 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06m_5 country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.853 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #20494-0dzst PRED entity: 0dzst PRED relation: major_field_of_study PRED expected values: 062z7 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 97): 062z7 (0.50 #918, 0.44 #806, 0.43 #246), 01tbp (0.46 #274, 0.44 #834, 0.39 #946), 05qfh (0.44 #925, 0.42 #813, 0.39 #701), 0g26h (0.43 #259, 0.39 #2836, 0.38 #3285), 01lj9 (0.41 #592, 0.41 #928, 0.37 #704), 041y2 (0.39 #293, 0.32 #629, 0.30 #965), 02_7t (0.39 #279, 0.32 #615, 0.29 #839), 037mh8 (0.39 #954, 0.33 #730, 0.32 #618), 01540 (0.31 #835, 0.31 #1171, 0.30 #947), 02ky346 (0.30 #910, 0.29 #574, 0.25 #798) >> Best rule #918 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 07vht; 0bqxw; 01l8t8; >> query: (?x9200, 062z7) <- major_field_of_study(?x9200, ?x742), school_type(?x9200, ?x1044), organization(?x9200, ?x5487) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dzst major_field_of_study 062z7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20493-047vp1n PRED entity: 047vp1n PRED relation: film_format PRED expected values: 0cj16 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 3): 0cj16 (0.34 #53, 0.33 #29, 0.32 #63), 07fb8_ (0.33 #7, 0.20 #12, 0.18 #165), 017fx5 (0.06 #59, 0.03 #156, 0.03 #222) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 0bs8hvm; >> query: (?x7314, 0cj16) <- film_festivals(?x7314, ?x9080), titles(?x2480, ?x7314), film_crew_role(?x7314, ?x1284), ?x1284 = 0ch6mp2 >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047vp1n film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.340 http://example.org/film/film/film_format #20492-0584j4n PRED entity: 0584j4n PRED relation: award PRED expected values: 0gq_v => 83 concepts (71 used for prediction) PRED predicted values (max 10 best out of 250): 0gq_v (0.84 #3678, 0.84 #2866, 0.81 #1647), 09sb52 (0.28 #15473, 0.27 #12630, 0.26 #15879), 0gq9h (0.17 #2109, 0.15 #6170, 0.14 #4952), 01l29r (0.17 #2200, 0.05 #5855, 0.04 #5449), 0ck27z (0.12 #16337, 0.12 #17961, 0.12 #19180), 0f4x7 (0.11 #6123, 0.11 #12620, 0.09 #13432), 0gqy2 (0.11 #12755, 0.09 #13567, 0.09 #6258), 040njc (0.11 #2039, 0.09 #12597, 0.08 #13409), 01lj_c (0.11 #2333, 0.01 #8830, 0.01 #7612), 05pcn59 (0.11 #12671, 0.10 #13483, 0.07 #15514) >> Best rule #3678 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0523v5y; 0521d_3; 034qt_; >> query: (?x4897, 0gq_v) <- award_nominee(?x9825, ?x4897), film_sets_designed(?x9825, ?x6309), film(?x2465, ?x6309), genre(?x6309, ?x53) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0584j4n award 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 71.000 0.844 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20491-073w14 PRED entity: 073w14 PRED relation: film PRED expected values: 09gmmt6 => 106 concepts (73 used for prediction) PRED predicted values (max 10 best out of 900): 02yvct (0.28 #5703, 0.05 #103481, 0.04 #62445), 0418wg (0.22 #5752, 0.10 #2184, 0.10 #400), 078sj4 (0.20 #453, 0.07 #9373, 0.01 #7589), 09xbpt (0.17 #5399, 0.10 #47, 0.05 #103481), 06z8s_ (0.17 #5482, 0.10 #130, 0.04 #62445), 0gj8t_b (0.17 #5533, 0.01 #12669, 0.01 #9101), 03p2xc (0.14 #10161, 0.10 #1241, 0.01 #36922), 01chpn (0.13 #10026, 0.10 #2890, 0.10 #1106), 0c00zd0 (0.11 #5611, 0.09 #3827, 0.03 #57092), 013q07 (0.11 #5708, 0.04 #12844, 0.02 #9276) >> Best rule #5703 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 058kqy; 032_jg; 0blbxk; 055c8; 013knm; 0b1f49; 018009; 02xwgr; 046zh; 0p__8; ... >> query: (?x4345, 02yvct) <- location(?x4345, ?x2474), award_nominee(?x2499, ?x4345), ?x2499 = 0c6qh >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 073w14 film 09gmmt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 73.000 0.278 http://example.org/film/actor/film./film/performance/film #20490-0151w_ PRED entity: 0151w_ PRED relation: film PRED expected values: 02j69w => 136 concepts (116 used for prediction) PRED predicted values (max 10 best out of 1154): 07kh6f3 (0.70 #5342, 0.69 #12464, 0.64 #72989), 011ypx (0.70 #5342, 0.69 #12464, 0.64 #72989), 048vhl (0.70 #5342, 0.69 #12464, 0.64 #72989), 0ds33 (0.70 #5342, 0.64 #72989, 0.58 #149560), 0dsvzh (0.39 #1781, 0.21 #35607, 0.20 #17806), 034qzw (0.07 #331, 0.05 #2112, 0.03 #34157), 03nfnx (0.07 #1394, 0.04 #4955, 0.04 #12077), 0bvn25 (0.07 #49, 0.03 #26755, 0.03 #67697), 0f42nz (0.06 #25829, 0.05 #22269, 0.02 #47191), 01shy7 (0.05 #14665, 0.05 #11104, 0.05 #59169) >> Best rule #5342 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 027dtv3; 01yg9y; 022yb4; 023s8; >> query: (?x989, ?x508) <- award_winner(?x969, ?x989), nominated_for(?x989, ?x508), participant(?x287, ?x989) >> conf = 0.70 => this is the best rule for 4 predicted values *> Best rule #11480 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 137 *> proper extension: 04wp3s; *> query: (?x989, 02j69w) <- award_winner(?x144, ?x989), participant(?x287, ?x989) *> conf = 0.01 ranks of expected_values: 620 EVAL 0151w_ film 02j69w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 136.000 116.000 0.699 http://example.org/film/actor/film./film/performance/film #20489-09b0xs PRED entity: 09b0xs PRED relation: profession PRED expected values: 015h31 => 85 concepts (84 used for prediction) PRED predicted values (max 10 best out of 52): 02hrh1q (0.68 #3788, 0.68 #10030, 0.68 #2914), 09jwl (0.47 #2773, 0.34 #3500, 0.33 #3209), 02jknp (0.43 #7, 0.38 #2321, 0.28 #5661), 0np9r (0.38 #2321, 0.29 #19, 0.28 #5661), 01c8w0 (0.38 #2321, 0.28 #5661, 0.28 #7840), 0cbd2 (0.38 #2321, 0.26 #9727, 0.25 #10599), 015h31 (0.38 #2321, 0.26 #9727, 0.25 #10599), 0196pc (0.38 #2321, 0.26 #9727, 0.25 #10599), 0dz3r (0.30 #2758, 0.27 #3485, 0.26 #3631), 0nbcg (0.29 #2784, 0.28 #3220, 0.28 #3511) >> Best rule #3788 for best value: >> intensional similarity = 2 >> extensional distance = 899 >> proper extension: 049tjg; 01nzs7; 02wrhj; 027_tg; 01j7pt; 0kcdl; 0kctd; 0kcd5; >> query: (?x3145, 02hrh1q) <- nominated_for(?x3145, ?x11377), actor(?x11377, ?x1765) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2321 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 219 *> proper extension: 07s6tbm; 0162c8; 0b05xm; 03b78r; 0b4rf3; *> query: (?x3145, ?x319) <- award_nominee(?x3145, ?x10152), program(?x3145, ?x3144), profession(?x10152, ?x319) *> conf = 0.38 ranks of expected_values: 7 EVAL 09b0xs profession 015h31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 84.000 0.680 http://example.org/people/person/profession #20488-01hdht PRED entity: 01hdht PRED relation: people! PRED expected values: 0gk4g => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 34): 0gk4g (0.30 #76, 0.21 #406, 0.16 #1264), 02k6hp (0.25 #37, 0.10 #103, 0.07 #763), 012hw (0.25 #52, 0.05 #514, 0.04 #580), 04p3w (0.19 #341, 0.09 #473, 0.08 #539), 0dq9p (0.11 #677, 0.11 #413, 0.10 #1271), 01mtqf (0.10 #70, 0.04 #730, 0.03 #796), 014w_8 (0.10 #105, 0.03 #831, 0.03 #897), 02y0js (0.09 #464, 0.08 #596, 0.08 #530), 051_y (0.09 #510, 0.08 #576, 0.04 #642), 08g5q7 (0.08 #636, 0.08 #570, 0.06 #372) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0cm03; >> query: (?x11626, 0gk4g) <- gender(?x11626, ?x231), student(?x3439, ?x11626), organization(?x11626, ?x8603), ?x8603 = 02_l9 >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hdht people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.300 http://example.org/people/cause_of_death/people #20487-06bd5j PRED entity: 06bd5j PRED relation: currency PRED expected values: 09nqf => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.84 #22, 0.83 #15, 0.82 #274), 01nv4h (0.03 #142, 0.03 #149, 0.03 #352), 02l6h (0.03 #102, 0.03 #39, 0.02 #60), 02gsvk (0.01 #62) >> Best rule #22 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 0c0nhgv; 016dj8; >> query: (?x5627, 09nqf) <- nominated_for(?x541, ?x5627), film(?x541, ?x3000), film(?x541, ?x770), ?x770 = 01r97z, ?x3000 = 045j3w >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06bd5j currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.844 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #20486-0f1sm PRED entity: 0f1sm PRED relation: locations! PRED expected values: 0b_6rk => 177 concepts (153 used for prediction) PRED predicted values (max 10 best out of 109): 0b_6zk (0.37 #281, 0.27 #31, 0.24 #531), 0b_6rk (0.26 #296, 0.24 #546, 0.18 #46), 0b_6qj (0.26 #317, 0.24 #567, 0.18 #67), 0b_6v_ (0.26 #314, 0.16 #1439, 0.12 #1814), 0b_6lb (0.21 #327, 0.21 #577, 0.15 #1827), 0bzrsh (0.21 #329, 0.21 #579, 0.15 #1829), 0b_6pv (0.21 #330, 0.18 #580, 0.17 #1080), 0b_6s7 (0.18 #565, 0.16 #315, 0.12 #1065), 0b_6x2 (0.17 #1784, 0.15 #1034, 0.15 #534), 0bzrxn (0.16 #1429, 0.15 #1804, 0.15 #554) >> Best rule #281 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0kcw2; >> query: (?x9445, 0b_6zk) <- administrative_division(?x9445, ?x1755), county(?x9445, ?x3164), location(?x427, ?x9445), locations(?x4803, ?x9445) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #296 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 0kcw2; *> query: (?x9445, 0b_6rk) <- administrative_division(?x9445, ?x1755), county(?x9445, ?x3164), location(?x427, ?x9445), locations(?x4803, ?x9445) *> conf = 0.26 ranks of expected_values: 2 EVAL 0f1sm locations! 0b_6rk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 177.000 153.000 0.368 http://example.org/time/event/locations #20485-02hxcvy PRED entity: 02hxcvy PRED relation: languages_spoken! PRED expected values: 04gfy7 => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 69): 07hwkr (0.60 #950, 0.56 #1755, 0.55 #2291), 04gfy7 (0.60 #458, 0.33 #190, 0.33 #123), 02vsw1 (0.36 #512, 0.27 #1384, 0.27 #1316), 059_w (0.33 #26, 0.27 #495, 0.20 #227), 09zyn5 (0.33 #63, 0.27 #532, 0.20 #264), 071x0k (0.33 #8, 0.20 #343, 0.20 #209), 05l3g_ (0.33 #52, 0.20 #387, 0.20 #253), 0bhsnb (0.33 #66, 0.20 #468, 0.20 #267), 0bbz66j (0.33 #42, 0.20 #243, 0.18 #511), 0x67 (0.33 #10, 0.20 #211, 0.18 #479) >> Best rule #950 for best value: >> intensional similarity = 15 >> extensional distance = 18 >> proper extension: 01lqm; >> query: (?x9113, 07hwkr) <- countries_spoken_in(?x9113, ?x10457), language(?x8074, ?x9113), language(?x2814, ?x9113), language(?x257, ?x9113), official_language(?x613, ?x9113), nominated_for(?x2382, ?x8074), nominated_for(?x112, ?x2814), nominated_for(?x2065, ?x257), currency(?x8074, ?x10674), organization(?x10457, ?x3750), nominated_for(?x2814, ?x3133), ?x3750 = 0_2v, film(?x92, ?x2814), nominated_for(?x1937, ?x257), film_release_distribution_medium(?x257, ?x81) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #458 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 8 *> proper extension: 09bnf; *> query: (?x9113, 04gfy7) <- countries_spoken_in(?x9113, ?x10457), countries_spoken_in(?x9113, ?x2146), languages(?x12675, ?x9113), capital(?x10457, ?x13165), ?x2146 = 03rk0, olympics(?x10457, ?x2966), religion(?x10457, ?x109), locations(?x12844, ?x10457), contains(?x10457, ?x1961), country(?x668, ?x10457), form_of_government(?x10457, ?x1926), official_language(?x10457, ?x11341), jurisdiction_of_office(?x182, ?x10457), profession(?x12675, ?x1032) *> conf = 0.60 ranks of expected_values: 2 EVAL 02hxcvy languages_spoken! 04gfy7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 44.000 0.600 http://example.org/people/ethnicity/languages_spoken #20484-05x8n PRED entity: 05x8n PRED relation: award_winner! PRED expected values: 0265wl => 128 concepts (94 used for prediction) PRED predicted values (max 10 best out of 235): 0262zm (0.50 #2229, 0.41 #1799, 0.38 #428), 0265vt (0.46 #2465, 0.45 #2146, 0.45 #2035), 01yz0x (0.44 #3866, 0.44 #3609, 0.43 #5761), 040vk98 (0.41 #3435, 0.40 #3865, 0.39 #889), 040_9s0 (0.40 #3865, 0.39 #1288, 0.38 #5156), 02664f (0.38 #428, 0.37 #4295, 0.36 #2145), 0265wl (0.36 #1950, 0.29 #2380, 0.29 #2810), 039yzf (0.22 #1206, 0.21 #2493, 0.18 #2063), 01l78d (0.18 #6727, 0.03 #9293, 0.02 #20113), 0c_dx (0.17 #5861, 0.11 #1562, 0.07 #6715) >> Best rule #2229 for best value: >> intensional similarity = 6 >> extensional distance = 22 >> proper extension: 040db; 0210f1; 041xl; 0821j; >> query: (?x6688, 0262zm) <- award(?x6688, ?x4418), award(?x6688, ?x3337), ?x4418 = 02664f, award(?x1727, ?x3337), ?x1727 = 0c3kw, disciplines_or_subjects(?x3337, ?x5864) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1950 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 07w21; 09dt7; 01963w; 0c3kw; 01dzz7; 01dhmw; 05jm7; 04mhl; 0b0pf; 048_p; ... *> query: (?x6688, 0265wl) <- award(?x6688, ?x9285), award(?x6688, ?x8880), ?x9285 = 0265vt, ?x8880 = 0262x6, award_winner(?x14213, ?x6688) *> conf = 0.36 ranks of expected_values: 7 EVAL 05x8n award_winner! 0265wl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 128.000 94.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #20483-016z9n PRED entity: 016z9n PRED relation: films! PRED expected values: 06c97 => 61 concepts (20 used for prediction) PRED predicted values (max 10 best out of 43): 01fzpw (0.33 #119), 081pw (0.25 #159, 0.05 #629, 0.04 #943), 05qt0 (0.25 #212), 05489 (0.06 #678, 0.05 #364, 0.03 #992), 0fx2s (0.05 #385, 0.05 #699, 0.02 #1486), 0bq3x (0.05 #500, 0.04 #1286, 0.02 #2240), 07c52 (0.05 #490, 0.02 #803, 0.02 #1117), 03hzt (0.05 #761, 0.03 #447, 0.02 #1075), 04gb7 (0.03 #828, 0.03 #1142, 0.02 #1301), 06d4h (0.03 #1933, 0.03 #983, 0.03 #2574) >> Best rule #119 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 09cr8; >> query: (?x2336, 01fzpw) <- film(?x5995, ?x2336), ?x5995 = 0k2mxq, nominated_for(?x591, ?x2336), nominated_for(?x669, ?x2336) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016z9n films! 06c97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 20.000 0.333 http://example.org/film/film_subject/films #20482-02vzc PRED entity: 02vzc PRED relation: combatants! PRED expected values: 02kxg_ => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 72): 0cm2xh (0.47 #196, 0.43 #134, 0.30 #320), 0gfq9 (0.47 #192, 0.36 #130, 0.15 #68), 08qz1l (0.47 #226, 0.36 #164, 0.15 #2582), 0cwt70 (0.43 #162, 0.33 #224, 0.14 #38), 03gqgt3 (0.41 #735, 0.37 #1479, 0.36 #1665), 048n7 (0.34 #703, 0.34 #1013, 0.31 #1633), 01h6pn (0.33 #197, 0.31 #73, 0.29 #135), 01fc7p (0.33 #187, 0.21 #125, 0.15 #2543), 018w0j (0.31 #95, 0.22 #343, 0.17 #1025), 02kxg_ (0.29 #155, 0.29 #31, 0.27 #217) >> Best rule #196 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 03b79; 01h3dj; 0212ny; >> query: (?x1892, 0cm2xh) <- combatants(?x7241, ?x1892), combatants(?x756, ?x1892), ?x7241 = 06k75 >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #155 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 0bq0p9; 03pn9; 02psqkz; 0cdbq; 05kyr; 0193qj; 088q1s; 01rdm0; *> query: (?x1892, 02kxg_) <- combatants(?x7241, ?x1892), capital(?x1892, ?x11237), ?x7241 = 06k75 *> conf = 0.29 ranks of expected_values: 10 EVAL 02vzc combatants! 02kxg_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 166.000 166.000 0.467 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #20481-0h7t36 PRED entity: 0h7t36 PRED relation: category PRED expected values: 08mbj5d => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.31 #6, 0.29 #5, 0.27 #17) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 0j_tw; >> query: (?x10800, 08mbj5d) <- featured_film_locations(?x10800, ?x739), film_festivals(?x10800, ?x9189), genre(?x10800, ?x53) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h7t36 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.311 http://example.org/common/topic/webpage./common/webpage/category #20480-08jgk1 PRED entity: 08jgk1 PRED relation: producer_type PRED expected values: 0ckd1 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.74 #5, 0.71 #12, 0.71 #10) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 09kn9; 05sy2k_; 0cpz4k; 01hn_t; 099pks; 06r4f; 02xhwm; 06qw_; >> query: (?x1631, 0ckd1) <- program(?x6678, ?x1631), tv_program(?x2819, ?x1631), actor(?x1631, ?x237) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08jgk1 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.735 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #20479-01y_px PRED entity: 01y_px PRED relation: award_nominee PRED expected values: 0p_pd 01f7dd => 94 concepts (38 used for prediction) PRED predicted values (max 10 best out of 956): 0p_pd (0.81 #4681, 0.81 #77229, 0.81 #86587), 01f7dd (0.81 #4681, 0.81 #77229, 0.81 #86587), 06dn58 (0.31 #51487, 0.24 #53830, 0.02 #39174), 06j0md (0.31 #51487, 0.24 #53830, 0.01 #49176), 033jj1 (0.31 #51487, 0.24 #53830), 01z5tr (0.31 #51487, 0.24 #53830), 033jkj (0.31 #51487, 0.24 #53830), 06yrj6 (0.31 #51487), 01y_px (0.24 #53830, 0.17 #18720, 0.11 #49144), 018ygt (0.24 #53830, 0.07 #67869, 0.05 #4680) >> Best rule #4681 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 07lmxq; >> query: (?x2263, ?x397) <- film(?x2263, ?x2878), ?x2878 = 0hx4y, award_nominee(?x397, ?x2263) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2 EVAL 01y_px award_nominee 01f7dd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 38.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 01y_px award_nominee 0p_pd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 38.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20478-09k0h5 PRED entity: 09k0h5 PRED relation: child PRED expected values: 02pfymy => 166 concepts (127 used for prediction) PRED predicted values (max 10 best out of 322): 01scmq (0.57 #1187, 0.33 #678, 0.29 #2544), 01jx9 (0.50 #724, 0.50 #215, 0.38 #1741), 0dwcl (0.50 #819, 0.50 #310, 0.38 #1836), 031rq5 (0.36 #3107, 0.30 #2597, 0.25 #4982), 025txrl (0.33 #800, 0.25 #1817, 0.25 #291), 09j_g (0.33 #728, 0.25 #1745, 0.25 #219), 086h6p (0.31 #4758, 0.14 #1677, 0.07 #4398), 02qdyj (0.31 #4758, 0.14 #1581, 0.07 #4302), 02wbnv (0.31 #4758, 0.14 #1686, 0.07 #4407), 01swdw (0.31 #4758, 0.14 #1645, 0.07 #4366) >> Best rule #1187 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 02bh8z; >> query: (?x13291, ?x13935) <- child(?x13291, ?x14600), child(?x13291, ?x11071), citytown(?x11071, ?x10916), state_province_region(?x11071, ?x1227), ?x1227 = 01n7q, child(?x11071, ?x13935), industry(?x13291, ?x245), county(?x10916, ?x7964), organization(?x4682, ?x14600) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4757 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 14 *> proper extension: 05w3y; *> query: (?x13291, ?x10436) <- child(?x13291, ?x14600), child(?x13291, ?x11071), citytown(?x13291, ?x8951), child(?x10436, ?x14600), child(?x11071, ?x13935), citytown(?x11303, ?x8951), category(?x10436, ?x134), child(?x11303, ?x6141), place_founded(?x11303, ?x6960) *> conf = 0.24 ranks of expected_values: 11 EVAL 09k0h5 child 02pfymy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 166.000 127.000 0.571 http://example.org/organization/organization/child./organization/organization_relationship/child #20477-0cd2vh9 PRED entity: 0cd2vh9 PRED relation: production_companies PRED expected values: 0c_j5d => 65 concepts (46 used for prediction) PRED predicted values (max 10 best out of 49): 0c_j5d (0.29 #5, 0.20 #87, 0.19 #169), 086k8 (0.19 #2, 0.19 #166, 0.18 #84), 02hvd (0.14 #38, 0.09 #202, 0.07 #120), 05qd_ (0.11 #1327, 0.11 #1905, 0.07 #1987), 016tw3 (0.10 #1907, 0.10 #1329, 0.09 #93), 0338lq (0.10 #6, 0.07 #88, 0.07 #170), 024rgt (0.10 #24, 0.04 #270, 0.04 #1342), 01795t (0.10 #21, 0.04 #267, 0.03 #1917), 017s11 (0.08 #1321, 0.07 #1899, 0.06 #661), 01gb54 (0.08 #283, 0.06 #1355, 0.06 #1933) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0d_wms; 048tv9; 0f61tk; 042g97; 091xrc; >> query: (?x1640, 0c_j5d) <- country(?x1640, ?x94), genre(?x1640, ?x11401), currency(?x1640, ?x170), ?x11401 = 0btmb >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cd2vh9 production_companies 0c_j5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 46.000 0.286 http://example.org/film/film/production_companies #20476-05sxr_ PRED entity: 05sxr_ PRED relation: film_crew_role PRED expected values: 01pvkk => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 24): 09zzb8 (0.91 #1431, 0.84 #384, 0.84 #558), 09vw2b7 (0.80 #1263, 0.77 #390, 0.71 #633), 01pvkk (0.30 #393, 0.30 #601, 0.30 #567), 02rh1dz (0.21 #322, 0.17 #426, 0.17 #566), 02ynfr (0.21 #397, 0.19 #536, 0.19 #640), 0d2b38 (0.18 #268, 0.14 #337, 0.13 #407), 0215hd (0.16 #1273, 0.15 #400, 0.14 #643), 094hwz (0.16 #257, 0.13 #2816, 0.09 #326), 033smt (0.14 #270, 0.13 #2816, 0.08 #339), 089g0h (0.13 #2816, 0.12 #1274, 0.12 #644) >> Best rule #1431 for best value: >> intensional similarity = 7 >> extensional distance = 855 >> proper extension: 083shs; 0c3ybss; 011yrp; 05p1tzf; 03s6l2; 02x3lt7; 0dsvzh; 0b73_1d; 05jzt3; 0_b3d; ... >> query: (?x10684, 09zzb8) <- film(?x1690, ?x10684), language(?x10684, ?x254), film_crew_role(?x10684, ?x2095), film_crew_role(?x5509, ?x2095), film_crew_role(?x1224, ?x2095), ?x5509 = 0cy__l, ?x1224 = 020fcn >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #393 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 303 *> proper extension: 02q6gfp; 0sxkh; 08tq4x; 02wyzmv; 0g57wgv; *> query: (?x10684, 01pvkk) <- film(?x1690, ?x10684), language(?x10684, ?x254), film_crew_role(?x10684, ?x2095), film_crew_role(?x10684, ?x1284), ?x2095 = 0dxtw, genre(?x10684, ?x53), ?x1284 = 0ch6mp2 *> conf = 0.30 ranks of expected_values: 3 EVAL 05sxr_ film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.914 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20475-07xtqq PRED entity: 07xtqq PRED relation: nominated_for! PRED expected values: 09rp4r_ => 64 concepts (33 used for prediction) PRED predicted values (max 10 best out of 882): 0q9kd (0.41 #16337, 0.34 #49002, 0.28 #37336), 01j5ts (0.41 #16337, 0.34 #49002, 0.28 #37336), 01rw116 (0.41 #16337, 0.34 #49002, 0.28 #37336), 01l_yg (0.41 #16337, 0.34 #49002, 0.28 #37336), 06b_0 (0.33 #1637, 0.06 #10973, 0.03 #77006), 0154qm (0.30 #12362, 0.13 #44335, 0.10 #14004), 09rp4r_ (0.28 #23336, 0.03 #16655, 0.02 #18987), 03h26tm (0.20 #2514, 0.02 #18851, 0.01 #23518), 019pm_ (0.19 #12251), 0fvf9q (0.17 #18, 0.10 #7020, 0.03 #9354) >> Best rule #16337 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 05q_dw; 04ghz4m; >> query: (?x407, ?x71) <- film(?x71, ?x407), nominated_for(?x3209, ?x407), ?x3209 = 02w9sd7 >> conf = 0.41 => this is the best rule for 4 predicted values *> Best rule #23336 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 241 *> proper extension: 04ddm4; 0fr63l; 0gkz15s; 0bwfwpj; 01c22t; 01f7gh; 0bscw; 07y9w5; 02r1c18; 0gxtknx; ... *> query: (?x407, ?x1622) <- film(?x71, ?x407), nominated_for(?x112, ?x407), crewmember(?x407, ?x1622) *> conf = 0.28 ranks of expected_values: 7 EVAL 07xtqq nominated_for! 09rp4r_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 64.000 33.000 0.411 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20474-08f3b1 PRED entity: 08f3b1 PRED relation: profession PRED expected values: 01xr66 => 99 concepts (60 used for prediction) PRED predicted values (max 10 best out of 89): 02hrh1q (0.80 #6600, 0.77 #4309, 0.77 #7172), 01d_h8 (0.49 #7022, 0.48 #6021, 0.45 #3300), 02jknp (0.42 #7023, 0.40 #7, 0.37 #1583), 0np9r (0.41 #7608, 0.24 #1595, 0.16 #1021), 04gc2 (0.39 #2044, 0.38 #2187, 0.38 #2330), 09jwl (0.32 #7892, 0.28 #6175, 0.27 #4601), 018gz8 (0.30 #1591, 0.19 #6030, 0.18 #7604), 012t_z (0.30 #155, 0.27 #441, 0.20 #1157), 02krf9 (0.26 #6040, 0.19 #7614, 0.15 #7041), 0dz3r (0.24 #4586, 0.23 #4729, 0.22 #4872) >> Best rule #6600 for best value: >> intensional similarity = 3 >> extensional distance = 694 >> proper extension: 0g9zcgx; 0134wr; 012x1l; >> query: (?x744, 02hrh1q) <- award(?x744, ?x1007), gender(?x744, ?x514), ?x514 = 02zsn >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1205 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 03gkn5; *> query: (?x744, 01xr66) <- profession(?x744, ?x353), student(?x4321, ?x744), politician(?x1912, ?x744) *> conf = 0.10 ranks of expected_values: 24 EVAL 08f3b1 profession 01xr66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 99.000 60.000 0.802 http://example.org/people/person/profession #20473-016z68 PRED entity: 016z68 PRED relation: gender PRED expected values: 05zppz => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.96 #9, 0.90 #11, 0.73 #19), 02zsn (0.50 #222, 0.46 #245, 0.46 #151) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 0126rp; >> query: (?x11396, 05zppz) <- award(?x11396, ?x458), ?x458 = 0789_m, location(?x11396, ?x6357) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016z68 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.963 http://example.org/people/person/gender #20472-02g1jh PRED entity: 02g1jh PRED relation: music! PRED expected values: 0ds2n => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 679): 053x8hr (0.47 #15003, 0.47 #19004, 0.41 #11002), 01s7w3 (0.06 #11860, 0.04 #4858, 0.04 #15861), 02rrfzf (0.04 #4320, 0.04 #5320, 0.04 #8321), 09d3b7 (0.04 #4830, 0.04 #5830, 0.03 #9831), 07bzz7 (0.04 #4520, 0.03 #9521, 0.03 #11522), 02ht1k (0.04 #4361, 0.03 #9362, 0.02 #17364), 0401sg (0.04 #2051, 0.02 #17054, 0.02 #13053), 06929s (0.04 #2416, 0.01 #13418, 0.01 #15419), 04tqtl (0.03 #4304, 0.03 #5304, 0.03 #9305), 0pdp8 (0.03 #4220, 0.03 #5220, 0.03 #9221) >> Best rule #15003 for best value: >> intensional similarity = 3 >> extensional distance = 158 >> proper extension: 012d40; 02zmh5; 01cwhp; 016pns; 014488; 04qmr; 02cx72; 01wv9p; 0178rl; 0dw4g; ... >> query: (?x7027, ?x787) <- artists(?x4910, ?x7027), award_winner(?x1854, ?x7027), nominated_for(?x7027, ?x787) >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02g1jh music! 0ds2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 105.000 0.466 http://example.org/film/film/music #20471-02pw_n PRED entity: 02pw_n PRED relation: genre PRED expected values: 06cvj => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 85): 01z4y (0.53 #1657, 0.52 #2368, 0.52 #2960), 02kdv5l (0.34 #120, 0.29 #1067, 0.28 #238), 01jfsb (0.33 #248, 0.32 #130, 0.32 #1077), 04xvlr (0.32 #1, 0.16 #829, 0.16 #1776), 01hmnh (0.26 #17, 0.19 #135, 0.15 #1436), 060__y (0.26 #16, 0.15 #1554, 0.15 #3567), 03k9fj (0.23 #129, 0.23 #247, 0.21 #1076), 06n90 (0.21 #13, 0.19 #131, 0.14 #1078), 06cvj (0.21 #1304, 0.21 #1422, 0.11 #594), 0lsxr (0.20 #1191, 0.18 #1546, 0.18 #244) >> Best rule #1657 for best value: >> intensional similarity = 3 >> extensional distance = 642 >> proper extension: 02_1sj; 026p_bs; 02z3r8t; 03ckwzc; 0963mq; 02vqhv0; 035s95; 04q00lw; 05fgt1; 0pvms; ... >> query: (?x6619, ?x2480) <- genre(?x6619, ?x53), titles(?x2480, ?x6619), film(?x8274, ?x6619) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1304 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 484 *> proper extension: 0g5q34q; 0gh6j94; 076xkdz; *> query: (?x6619, 06cvj) <- genre(?x6619, ?x258), ?x258 = 05p553, film_release_distribution_medium(?x6619, ?x81) *> conf = 0.21 ranks of expected_values: 9 EVAL 02pw_n genre 06cvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 47.000 47.000 0.527 http://example.org/film/film/genre #20470-0mnm2 PRED entity: 0mnm2 PRED relation: place! PRED expected values: 0mnm2 => 147 concepts (102 used for prediction) PRED predicted values (max 10 best out of 210): 0dzt9 (0.25 #264, 0.20 #779, 0.17 #2325), 0rh6k (0.20 #1032, 0.03 #6187, 0.03 #7218), 0t_gg (0.20 #1148, 0.02 #10428), 06wxw (0.17 #1645, 0.07 #3707, 0.06 #4223), 013h9 (0.17 #2372, 0.05 #35082, 0.05 #4950), 0mnzd (0.12 #2603, 0.11 #3119, 0.07 #3634), 0mnk7 (0.12 #2883, 0.11 #3399, 0.04 #5461), 0mm_4 (0.11 #3397, 0.07 #3912, 0.05 #4944), 010m55 (0.11 #3389, 0.04 #5451, 0.03 #8029), 0mnm2 (0.10 #40245, 0.09 #47476, 0.09 #50571) >> Best rule #264 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0mp3l; 0dzt9; >> query: (?x7548, 0dzt9) <- state(?x7548, ?x1426), contains(?x7548, ?x11185), ?x1426 = 07z1m, student(?x11185, ?x10593) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #40245 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 278 *> proper extension: 06m_5; 06nrt; 06rf7; 0g7pm; *> query: (?x7548, ?x94) <- contains(?x7548, ?x3949), colors(?x3949, ?x663), category(?x3949, ?x134), contains(?x94, ?x3949) *> conf = 0.10 ranks of expected_values: 10 EVAL 0mnm2 place! 0mnm2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 147.000 102.000 0.250 http://example.org/location/hud_county_place/place #20469-0r8c8 PRED entity: 0r8c8 PRED relation: county PRED expected values: 0l38x => 85 concepts (52 used for prediction) PRED predicted values (max 10 best out of 70): 0l38x (0.73 #5928, 0.67 #1380, 0.66 #5330), 0kpys (0.31 #603, 0.29 #801, 0.22 #210), 01n7q (0.28 #5927, 0.27 #1379, 0.26 #5329), 0cb4j (0.16 #1382, 0.15 #1776, 0.14 #790), 0fc2c (0.11 #248, 0.01 #2418, 0.01 #2614), 0y62n (0.11 #277, 0.01 #2643, 0.01 #2842), 0kvt9 (0.11 #488, 0.07 #883, 0.05 #1672), 0l2q3 (0.08 #1289, 0.07 #1092, 0.05 #1487), 0l2lk (0.06 #2016, 0.05 #438, 0.04 #2412), 0m2fr (0.06 #2044, 0.05 #2636, 0.04 #2440) >> Best rule #5928 for best value: >> intensional similarity = 3 >> extensional distance = 251 >> proper extension: 03qzj4; >> query: (?x6367, ?x11967) <- contains(?x11967, ?x6367), time_zones(?x11967, ?x2950), county(?x10865, ?x11967) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r8c8 county 0l38x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 52.000 0.733 http://example.org/location/hud_county_place/county #20468-0325dj PRED entity: 0325dj PRED relation: institution! PRED expected values: 03bwzr4 => 124 concepts (120 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.80 #195, 0.78 #171, 0.78 #339), 014mlp (0.66 #658, 0.66 #391, 0.65 #512), 019v9k (0.63 #250, 0.63 #346, 0.62 #322), 03bwzr4 (0.51 #256, 0.49 #352, 0.48 #208), 016t_3 (0.44 #244, 0.40 #340, 0.38 #196), 07s6fsf (0.43 #193, 0.41 #169, 0.41 #265), 02_xgp2 (0.41 #254, 0.39 #350, 0.38 #206), 0bkj86 (0.35 #249, 0.33 #345, 0.32 #201), 0bjrnt (0.30 #1308, 0.29 #1598, 0.28 #1725), 071tyz (0.30 #1308, 0.29 #1598, 0.28 #1725) >> Best rule #195 for best value: >> intensional similarity = 5 >> extensional distance = 136 >> proper extension: 01jssp; 04wlz2; 05krk; 06pwq; 02w2bc; 065y4w7; 01w3v; 01hhvg; 07w0v; 01b1mj; ... >> query: (?x11387, 02h4rq6) <- currency(?x11387, ?x170), colors(?x11387, ?x4557), school(?x387, ?x11387), ?x170 = 09nqf, institution(?x9054, ?x11387) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #256 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 139 *> proper extension: 0fht9f; *> query: (?x11387, 03bwzr4) <- school(?x387, ?x11387), teams(?x13949, ?x387), team(?x180, ?x387), draft(?x387, ?x465) *> conf = 0.51 ranks of expected_values: 4 EVAL 0325dj institution! 03bwzr4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 124.000 120.000 0.797 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20467-01k70_ PRED entity: 01k70_ PRED relation: people! PRED expected values: 063k3h => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 27): 0x67 (0.26 #10, 0.19 #87, 0.12 #241), 041rx (0.13 #312, 0.12 #1775, 0.12 #81), 02ctzb (0.11 #169, 0.09 #15, 0.08 #92), 033tf_ (0.08 #84, 0.08 #777, 0.07 #161), 02w7gg (0.06 #233, 0.06 #310, 0.06 #926), 0xnvg (0.05 #783, 0.04 #13, 0.04 #552), 07hwkr (0.04 #12, 0.04 #320, 0.04 #89), 03295l (0.04 #24, 0.04 #178), 07bch9 (0.04 #100, 0.04 #177, 0.03 #331), 048z7l (0.04 #117, 0.04 #194, 0.02 #271) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 058s57; 0pyg6; 0253b6; 025ldg; 018z_c; 03f3yfj; 04j_gs; 02ktrs; >> query: (?x4433, 0x67) <- award_nominee(?x690, ?x4433), person(?x3775, ?x4433), award_winner(?x2751, ?x4433) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #339 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 322 *> proper extension: 01l1b90; 0gkg6; 032l1; 05r5w; 040_9; 015njf; 06hmd; 01tz6vs; 01v9724; 06c97; ... *> query: (?x4433, 063k3h) <- profession(?x4433, ?x2225), ?x2225 = 0kyk *> conf = 0.02 ranks of expected_values: 20 EVAL 01k70_ people! 063k3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 67.000 67.000 0.261 http://example.org/people/ethnicity/people #20466-071vr PRED entity: 071vr PRED relation: month PRED expected values: 03_ly 028kb => 210 concepts (210 used for prediction) PRED predicted values (max 10 best out of 2): 03_ly (0.93 #47, 0.91 #49, 0.91 #25), 028kb (0.93 #48, 0.91 #46, 0.91 #4) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 03khn; >> query: (?x6960, 03_ly) <- month(?x6960, ?x4869), month(?x6960, ?x2140), ?x4869 = 02xx5, ?x2140 = 040fb >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 071vr month 028kb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 210.000 0.932 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 071vr month 03_ly CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 210.000 0.932 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #20465-06cmp PRED entity: 06cmp PRED relation: capital PRED expected values: 06c62 => 125 concepts (87 used for prediction) PRED predicted values (max 10 best out of 119): 04jpl (0.25 #600, 0.12 #1080, 0.12 #1794), 01914 (0.20 #479, 0.06 #1078, 0.05 #1435), 0dlwj (0.20 #580, 0.03 #2370, 0.03 #2848), 04swd (0.19 #1114, 0.16 #1828, 0.13 #2424), 0156q (0.14 #847, 0.13 #2398, 0.12 #1088), 095w_ (0.14 #844, 0.10 #1323, 0.09 #1442), 05qtj (0.12 #616, 0.06 #2406, 0.03 #1929), 02cft (0.12 #622, 0.06 #1102, 0.04 #1697), 0rh6k (0.12 #597, 0.06 #1077, 0.04 #1791), 06c62 (0.10 #3104, 0.05 #3581, 0.05 #3252) >> Best rule #600 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0f8l9c; >> query: (?x6329, 04jpl) <- nationality(?x7885, ?x6329), influenced_by(?x10598, ?x7885), capital(?x6329, ?x8810), ?x10598 = 0mb0, contains(?x8809, ?x8810) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3104 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 05v8c; 01znc_; 06t2t; 04hqz; 024pcx; *> query: (?x6329, ?x6959) <- nationality(?x13877, ?x6329), contains(?x455, ?x6329), capital(?x6329, ?x8810), place_of_birth(?x13877, ?x6959), official_language(?x6329, ?x11038) *> conf = 0.10 ranks of expected_values: 10 EVAL 06cmp capital 06c62 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 125.000 87.000 0.250 http://example.org/location/country/capital #20464-016vg8 PRED entity: 016vg8 PRED relation: award_nominee! PRED expected values: 019pm_ => 104 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1111): 014488 (0.83 #6929, 0.83 #5357, 0.82 #32343), 019pm_ (0.83 #5217, 0.82 #32343, 0.82 #85493), 07h565 (0.83 #5933, 0.82 #32343, 0.82 #85493), 01pgzn_ (0.82 #32343, 0.82 #85493, 0.82 #6928), 023kzp (0.82 #32343, 0.82 #85493, 0.82 #6928), 02wgln (0.82 #32343, 0.82 #85493, 0.82 #6928), 05cx7x (0.82 #32343, 0.82 #85493, 0.82 #6928), 014gf8 (0.82 #32343, 0.82 #85493, 0.82 #6928), 016vg8 (0.67 #5711, 0.57 #1093, 0.17 #12643), 0h0wc (0.32 #12093, 0.13 #62388, 0.08 #28263) >> Best rule #6929 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 06151l; 0278x6s; 073x6y; >> query: (?x4662, ?x3324) <- award_nominee(?x4662, ?x3324), profession(?x4662, ?x1032), ?x3324 = 014488 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #5217 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 06151l; 0278x6s; 073x6y; *> query: (?x4662, 019pm_) <- award_nominee(?x4662, ?x3324), profession(?x4662, ?x1032), ?x3324 = 014488 *> conf = 0.83 ranks of expected_values: 2 EVAL 016vg8 award_nominee! 019pm_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 45.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20463-01t6b4 PRED entity: 01t6b4 PRED relation: award_winner! PRED expected values: 0hn821n => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 125): 0gx_st (0.30 #13527, 0.28 #15322, 0.21 #9800), 02q690_ (0.30 #13527, 0.28 #15322, 0.21 #9800), 0hn821n (0.30 #13527, 0.28 #15322, 0.21 #9800), 09pnw5 (0.21 #652, 0.13 #2170, 0.12 #2722), 0h_9252 (0.21 #608, 0.07 #2954, 0.04 #5300), 04n2r9h (0.20 #43, 0.07 #595, 0.04 #733), 09bymc (0.20 #118, 0.02 #1360, 0.02 #4672), 0275n3y (0.17 #210, 0.07 #624, 0.07 #2970), 0d__c3 (0.17 #260, 0.03 #3020, 0.02 #6056), 0c53zb (0.17 #197, 0.03 #2957, 0.02 #4613) >> Best rule #13527 for best value: >> intensional similarity = 3 >> extensional distance = 772 >> proper extension: 02rf51g; >> query: (?x1285, ?x2213) <- award_winner(?x10447, ?x1285), honored_for(?x2213, ?x10447), type_of_union(?x1285, ?x566) >> conf = 0.30 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 01t6b4 award_winner! 0hn821n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 171.000 171.000 0.296 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20462-0h3bn PRED entity: 0h3bn PRED relation: symptom_of! PRED expected values: 012qjw => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 71): 01j6t0 (0.94 #1468, 0.90 #1061, 0.90 #1029), 012qjw (0.75 #603, 0.67 #344, 0.67 #119), 0cjf0 (0.72 #1226, 0.67 #1071, 0.67 #119), 0brgy (0.67 #119, 0.67 #116, 0.67 #114), 0hgxh (0.67 #119, 0.67 #116, 0.67 #114), 01cdt5 (0.67 #119, 0.67 #116, 0.67 #114), 0j5fv (0.67 #119, 0.67 #116, 0.67 #114), 098s1 (0.67 #119, 0.67 #116, 0.67 #114), 01pf6 (0.67 #119, 0.67 #116, 0.67 #114), 0hg45 (0.67 #119, 0.67 #116, 0.67 #114) >> Best rule #1468 for best value: >> intensional similarity = 32 >> extensional distance = 32 >> proper extension: 01g2q; >> query: (?x14096, 01j6t0) <- symptom_of(?x13373, ?x14096), symptom_of(?x9509, ?x14096), symptom_of(?x9509, ?x14562), symptom_of(?x9509, ?x13744), symptom_of(?x9509, ?x11659), symptom_of(?x9509, ?x11064), symptom_of(?x9509, ?x10480), symptom_of(?x9509, ?x4322), symptom_of(?x9509, ?x3799), symptom_of(?x10717, ?x13744), people(?x13744, ?x4204), symptom_of(?x13373, ?x13485), symptom_of(?x13373, ?x7006), symptom_of(?x13373, ?x4959), symptom_of(?x13373, ?x3680), ?x4204 = 02dth1, ?x13485 = 07s4l, risk_factors(?x13744, ?x8524), risk_factors(?x13744, ?x231), ?x8524 = 01hbgs, ?x10717 = 0cjf0, ?x4959 = 01dcqj, ?x11064 = 01n3bm, ?x3680 = 025hl8, ?x3799 = 04psf, ?x231 = 05zppz, ?x4322 = 0gk4g, ?x7006 = 02psvcf, award(?x14562, ?x12628), ?x11659 = 072hv, ?x12628 = 0dt49, ?x10480 = 0h1n9 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #603 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 6 *> proper extension: 035482; *> query: (?x14096, 012qjw) <- symptom_of(?x13373, ?x14096), symptom_of(?x9509, ?x14096), symptom_of(?x3679, ?x14096), ?x9509 = 0gxb2, symptom_of(?x13373, ?x14430), symptom_of(?x13373, ?x13485), symptom_of(?x13373, ?x13131), symptom_of(?x13373, ?x10480), symptom_of(?x13373, ?x7006), symptom_of(?x13373, ?x4959), ?x14430 = 024c2, symptom_of(?x9510, ?x7006), symptom_of(?x4905, ?x7006), ?x13131 = 0d19y2, people(?x4959, ?x13073), people(?x7006, ?x1946), ?x4905 = 01j6t0, ?x10480 = 0h1n9, ?x13073 = 0121rx, ?x9510 = 0hgxh, symptom_of(?x3679, ?x5118), risk_factors(?x4959, ?x8023), ?x8023 = 0jpmt, risk_factors(?x5118, ?x11393), ?x11393 = 098s1, risk_factors(?x7006, ?x7007), symptom_of(?x10717, ?x13485), ?x10717 = 0cjf0 *> conf = 0.75 ranks of expected_values: 2 EVAL 0h3bn symptom_of! 012qjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 52.000 0.941 http://example.org/medicine/symptom/symptom_of #20461-03q43g PRED entity: 03q43g PRED relation: profession PRED expected values: 03gjzk => 75 concepts (23 used for prediction) PRED predicted values (max 10 best out of 67): 01d_h8 (0.53 #2492, 0.49 #3223, 0.48 #298), 0cbd2 (0.52 #1468, 0.49 #2053, 0.48 #1760), 02jknp (0.46 #2494, 0.42 #3225, 0.31 #300), 03gjzk (0.44 #306, 0.40 #1461, 0.37 #2500), 0kyk (0.37 #1488, 0.34 #1780, 0.32 #2073), 02krf9 (0.20 #24, 0.16 #2510, 0.15 #3241), 09jwl (0.18 #1331, 0.15 #893, 0.12 #1917), 0d1pc (0.14 #1655, 0.13 #778, 0.12 #1070), 05z96 (0.13 #1793, 0.13 #1501, 0.12 #2086), 02hv44_ (0.11 #2101, 0.10 #2247, 0.10 #1516) >> Best rule #2492 for best value: >> intensional similarity = 4 >> extensional distance = 529 >> proper extension: 04rs03; 042rnl; 01pr_j6; 01p45_v; 0177s6; 01t07j; 025tdwc; 01wj9y9; 04k25; 02645b; ... >> query: (?x6569, 01d_h8) <- profession(?x6569, ?x1032), profession(?x6569, ?x987), ?x987 = 0dxtg, ?x1032 = 02hrh1q >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #306 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 03qcq; 014zfs; 01wcp_g; 06dl_; 0184dt; 01w8sf; 01gp_x; 02ld6x; 0q5hw; 053yx; ... *> query: (?x6569, 03gjzk) <- award_nominee(?x436, ?x6569), student(?x8191, ?x6569), influenced_by(?x6569, ?x11357) *> conf = 0.44 ranks of expected_values: 4 EVAL 03q43g profession 03gjzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 23.000 0.535 http://example.org/people/person/profession #20460-03k50 PRED entity: 03k50 PRED relation: languages_spoken! PRED expected values: 02sch9 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 68): 07hwkr (0.50 #624, 0.50 #556, 0.47 #1306), 0bhsnb (0.40 #407, 0.33 #475, 0.33 #135), 02vsw1 (0.40 #588, 0.33 #180, 0.31 #724), 059_w (0.33 #162, 0.33 #94, 0.20 #638), 0c41n (0.33 #204, 0.33 #136, 0.20 #408), 0fk3s (0.33 #197, 0.33 #129, 0.20 #401), 03x1x (0.33 #185, 0.33 #117, 0.20 #389), 0g8_vp (0.33 #154, 0.33 #86, 0.20 #358), 04czx7 (0.33 #133, 0.20 #677, 0.20 #609), 0d2by (0.33 #97, 0.20 #641, 0.20 #573) >> Best rule #624 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 02bjrlw; 04306rv; 03_9r; 06nm1; 0653m; 012w70; 05zjd; >> query: (?x1882, 07hwkr) <- titles(?x1882, ?x5247), titles(?x1882, ?x4579), languages_spoken(?x5025, ?x1882), category(?x4579, ?x134), country(?x5247, ?x2146), languages(?x491, ?x1882), genre(?x5247, ?x53) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 02hxcvy; *> query: (?x1882, 02sch9) <- language(?x9805, ?x1882), language(?x5247, ?x1882), language(?x2381, ?x1882), languages(?x491, ?x1882), ?x5247 = 0f42nz, genre(?x9805, ?x53), countries_spoken_in(?x1882, ?x792), ?x2381 = 04q00lw *> conf = 0.33 ranks of expected_values: 31 EVAL 03k50 languages_spoken! 02sch9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 47.000 47.000 0.500 http://example.org/people/ethnicity/languages_spoken #20459-016ynj PRED entity: 016ynj PRED relation: location PRED expected values: 06y57 => 110 concepts (108 used for prediction) PRED predicted values (max 10 best out of 150): 06y57 (0.27 #1061, 0.02 #1867, 0.02 #26008), 02_286 (0.24 #37, 0.13 #19351, 0.13 #32222), 0k049 (0.24 #805, 0.11 #21728, 0.11 #23340), 030qb3t (0.13 #29052, 0.13 #23423, 0.13 #25031), 05k7sb (0.12 #109, 0.03 #6545, 0.02 #5741), 0chgzm (0.09 #1216, 0.02 #2022, 0.02 #3631), 04vmp (0.07 #6790, 0.06 #5986, 0.04 #7594), 0r0m6 (0.06 #3438, 0.06 #2633, 0.06 #4242), 059rby (0.06 #3236, 0.06 #4040, 0.05 #2431), 04jpl (0.06 #4845, 0.05 #16915, 0.04 #822) >> Best rule #1061 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 08jbxf; >> query: (?x8301, 06y57) <- nationality(?x8301, ?x390), profession(?x8301, ?x1032), ?x390 = 0chghy >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016ynj location 06y57 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 108.000 0.267 http://example.org/people/person/places_lived./people/place_lived/location #20458-0dky9n PRED entity: 0dky9n PRED relation: gender PRED expected values: 05zppz => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #51, 0.88 #59, 0.86 #149), 02zsn (0.56 #151, 0.46 #342, 0.25 #185) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 014dq7; 0kvnn; 01mv_n; 01vz0g4; 013sg6; 044bn; 02_33l; 03lpd0; 02hg53; 0c5vh; ... >> query: (?x877, 05zppz) <- place_of_death(?x877, ?x1523), place_of_birth(?x877, ?x1658), ?x1523 = 030qb3t, people(?x9771, ?x877) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dky9n gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 171.000 0.885 http://example.org/people/person/gender #20457-025sc50 PRED entity: 025sc50 PRED relation: parent_genre! PRED expected values: 02b71x => 38 concepts (26 used for prediction) PRED predicted values (max 10 best out of 283): 01ym9b (0.50 #307, 0.40 #842, 0.33 #1109), 03xnwz (0.40 #830, 0.33 #1097, 0.25 #295), 016_rm (0.40 #732, 0.31 #2869, 0.29 #1802), 016_nr (0.40 #597, 0.30 #2201, 0.27 #2467), 059kh (0.29 #1647, 0.25 #310, 0.25 #43), 06cp5 (0.29 #1680, 0.25 #343, 0.22 #1947), 017371 (0.29 #1483, 0.25 #147, 0.22 #2018), 0283d (0.29 #1690, 0.25 #353, 0.20 #888), 036jv (0.29 #1766, 0.25 #429, 0.20 #964), 01flzq (0.29 #1702, 0.25 #365, 0.20 #900) >> Best rule #307 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0glt670; >> query: (?x3562, 01ym9b) <- parent_genre(?x3562, ?x3319), artists(?x3562, ?x11123), artists(?x3562, ?x10148), artists(?x3562, ?x3930), ?x11123 = 0k6yt1, ?x3930 = 01svw8n, ?x10148 = 02h9_l >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2265 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 02c8d7; 02lnbg; 0ggx5q; 05lwjc; *> query: (?x3562, 02b71x) <- artists(?x3562, ?x2737), artists(?x3562, ?x2227), ?x2227 = 07ss8_, currency(?x2737, ?x170), profession(?x2737, ?x955) *> conf = 0.20 ranks of expected_values: 53 EVAL 025sc50 parent_genre! 02b71x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 38.000 26.000 0.500 http://example.org/music/genre/parent_genre #20456-0c3xw46 PRED entity: 0c3xw46 PRED relation: film_crew_role PRED expected values: 09zzb8 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 22): 09zzb8 (0.76 #531, 0.71 #601, 0.70 #671), 0dxtw (0.39 #540, 0.36 #680, 0.35 #1033), 01vx2h (0.38 #46, 0.34 #117, 0.33 #541), 01pvkk (0.28 #893, 0.28 #154, 0.28 #1070), 02rh1dz (0.14 #44, 0.12 #151, 0.12 #115), 0215hd (0.14 #618, 0.13 #89, 0.13 #723), 089g0h (0.12 #619, 0.11 #724, 0.11 #90), 0d2b38 (0.11 #625, 0.11 #730, 0.10 #590), 01xy5l_ (0.11 #544, 0.10 #614, 0.10 #1143), 015h31 (0.09 #678, 0.08 #1031, 0.08 #713) >> Best rule #531 for best value: >> intensional similarity = 4 >> extensional distance = 596 >> proper extension: 03t97y; 01kff7; 0bscw; 0qm8b; 05p3738; 014zwb; 07w8fz; 0g54xkt; 01s3vk; 071nw5; ... >> query: (?x3812, 09zzb8) <- film_crew_role(?x3812, ?x1284), production_companies(?x3812, ?x5908), film(?x1896, ?x3812), ?x1284 = 0ch6mp2 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c3xw46 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.764 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20455-09h_q PRED entity: 09h_q PRED relation: people! PRED expected values: 01l2m3 => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 45): 0dq9p (0.20 #17, 0.17 #1205, 0.11 #2657), 09jg8 (0.20 #34, 0.07 #100, 0.06 #232), 0gk4g (0.14 #6083, 0.14 #3310, 0.14 #802), 02y0js (0.12 #200, 0.12 #134, 0.12 #398), 01psyx (0.12 #375, 0.09 #705, 0.06 #1563), 07jwr (0.09 #735, 0.06 #207, 0.05 #3837), 01l2m3 (0.08 #940, 0.07 #2260, 0.07 #82), 01_qc_ (0.08 #952, 0.05 #1876, 0.04 #2470), 0qcr0 (0.08 #397, 0.06 #5546, 0.06 #3301), 06z5s (0.08 #487, 0.03 #3391, 0.03 #2071) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0lrh; 049gc; >> query: (?x8080, 0dq9p) <- influenced_by(?x8374, ?x8080), place_of_death(?x8080, ?x739), award_winner(?x6918, ?x8374), ?x739 = 02_286 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #940 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 0465_; 01w9ph_; 0kc6; *> query: (?x8080, 01l2m3) <- influenced_by(?x8374, ?x8080), place_of_death(?x8080, ?x739), award(?x8374, ?x1079), artists(?x1067, ?x8374) *> conf = 0.08 ranks of expected_values: 7 EVAL 09h_q people! 01l2m3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 173.000 173.000 0.200 http://example.org/people/cause_of_death/people #20454-01pf21 PRED entity: 01pf21 PRED relation: company! PRED expected values: 0krdk 01yc02 => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 39): 0krdk (0.80 #579, 0.75 #292, 0.74 #3580), 01yc02 (0.62 #89, 0.50 #212, 0.45 #3130), 02211by (0.25 #5138, 0.25 #5137, 0.25 #5963), 02y6fz (0.25 #5138, 0.25 #5137, 0.25 #5963), 09lq2c (0.25 #5138, 0.25 #5137, 0.25 #5963), 0142rn (0.25 #5963, 0.20 #1376, 0.20 #1334), 021q0l (0.25 #5963, 0.17 #7037, 0.11 #6170), 01rk91 (0.25 #5963, 0.17 #7037, 0.11 #6170), 04192r (0.25 #5963, 0.16 #1802, 0.15 #2090), 028fjr (0.25 #5963, 0.11 #6170, 0.11 #6087) >> Best rule #579 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 061v5m; 0lwkh; >> query: (?x9675, 0krdk) <- industry(?x9675, ?x14344), company(?x5161, ?x9675), company(?x4682, ?x9675), currency(?x9675, ?x170), ?x4682 = 0dq_5, ?x5161 = 09d6p2 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01pf21 company! 01yc02 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 220.000 220.000 0.800 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 01pf21 company! 0krdk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 220.000 220.000 0.800 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #20453-02rky4 PRED entity: 02rky4 PRED relation: colors PRED expected values: 083jv => 206 concepts (206 used for prediction) PRED predicted values (max 10 best out of 19): 01l849 (0.50 #229, 0.43 #267, 0.36 #457), 083jv (0.47 #572, 0.46 #534, 0.46 #477), 01g5v (0.40 #80, 0.36 #270, 0.34 #555), 06fvc (0.40 #79, 0.33 #22, 0.25 #136), 019sc (0.25 #426, 0.25 #236, 0.21 #274), 04mkbj (0.25 #163, 0.22 #201, 0.12 #315), 04d18d (0.20 #113, 0.08 #246, 0.07 #284), 0jc_p (0.19 #499, 0.12 #461, 0.11 #613), 036k5h (0.18 #348, 0.16 #405, 0.15 #253), 09ggk (0.14 #301, 0.14 #130, 0.12 #320) >> Best rule #229 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01qrb2; >> query: (?x10368, 01l849) <- school(?x2820, ?x10368), child(?x10513, ?x10368), contains(?x1227, ?x10368), student(?x10368, ?x5617) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #572 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 01j_cy; 0l2tk; *> query: (?x10368, 083jv) <- school(?x2820, ?x10368), organization(?x346, ?x10368), country(?x10368, ?x94), contains(?x1227, ?x10368) *> conf = 0.47 ranks of expected_values: 2 EVAL 02rky4 colors 083jv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 206.000 0.500 http://example.org/education/educational_institution/colors #20452-02x8z_ PRED entity: 02x8z_ PRED relation: profession PRED expected values: 0nbcg => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 57): 02hrh1q (0.77 #5207, 0.72 #1499, 0.70 #11133), 0nbcg (0.57 #627, 0.55 #1960, 0.54 #181), 0dz3r (0.48 #151, 0.46 #597, 0.44 #1930), 016z4k (0.46 #153, 0.45 #748, 0.45 #4), 01d_h8 (0.39 #1490, 0.36 #898, 0.34 #5050), 039v1 (0.38 #632, 0.36 #1965, 0.34 #186), 01c72t (0.34 #321, 0.33 #1804, 0.30 #24), 0dxtg (0.32 #5058, 0.28 #7580, 0.27 #8024), 03gjzk (0.28 #4764, 0.26 #5060, 0.24 #7582), 02jknp (0.22 #5052, 0.21 #8610, 0.21 #9054) >> Best rule #5207 for best value: >> intensional similarity = 3 >> extensional distance = 681 >> proper extension: 01wz01; >> query: (?x4528, 02hrh1q) <- award_nominee(?x1128, ?x4528), award_winner(?x11422, ?x4528), location(?x4528, ?x1523) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #627 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 208 *> proper extension: 0bkg4; 04cr6qv; 02r3cn; 018y81; 01ydzx; 04_jsg; 01w9mnm; 01vzz1c; *> query: (?x4528, 0nbcg) <- category(?x4528, ?x134), profession(?x4528, ?x1183), role(?x4528, ?x1166) *> conf = 0.57 ranks of expected_values: 2 EVAL 02x8z_ profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.766 http://example.org/people/person/profession #20451-02v703 PRED entity: 02v703 PRED relation: category_of PRED expected values: 0c4ys => 60 concepts (48 used for prediction) PRED predicted values (max 10 best out of 3): 0c4ys (0.91 #127, 0.91 #85, 0.91 #22), 0gcf2r (0.16 #447, 0.14 #533, 0.13 #576), 0g_w (0.11 #448, 0.09 #534, 0.09 #577) >> Best rule #127 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 03x3wf; 01dpdh; 03t5b6; 01ck6v; 023vrq; 02ddq4; 03r00m; >> query: (?x7594, 0c4ys) <- award(?x1817, ?x7594), ceremony(?x7594, ?x8500), award_winner(?x8500, ?x827), ?x827 = 02l840 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v703 category_of 0c4ys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 48.000 0.914 http://example.org/award/award_category/category_of #20450-0187nd PRED entity: 0187nd PRED relation: student PRED expected values: 03jm6c 059j1m 0c4y8 => 103 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1599): 0ff3y (0.10 #6230, 0.10 #4145, 0.05 #16652), 01cv3n (0.10 #2172, 0.05 #4257, 0.03 #14679), 0d3k14 (0.08 #16438, 0.04 #14354, 0.04 #22691), 06hx2 (0.08 #15657, 0.04 #13573, 0.04 #1066), 0405l (0.07 #6015, 0.06 #3930, 0.05 #16437), 083chw (0.07 #4194, 0.06 #2109, 0.04 #22955), 030hcs (0.07 #4442, 0.06 #2357, 0.04 #273), 022411 (0.07 #5849, 0.06 #3764, 0.04 #1680), 02vntj (0.07 #4870, 0.06 #2785, 0.03 #15292), 01cj6y (0.07 #4897, 0.06 #2812, 0.03 #15319) >> Best rule #6230 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 02l9wl; 01d34b; 02ldmw; 0fr9jp; 05nrkb; >> query: (?x9847, 0ff3y) <- student(?x9847, ?x7138), participant(?x9925, ?x7138), film(?x7138, ?x1822) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #3729 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 02g839; *> query: (?x9847, 0c4y8) <- student(?x9847, ?x7138), participant(?x9925, ?x7138), colors(?x9847, ?x332) *> conf = 0.06 ranks of expected_values: 33 EVAL 0187nd student 0c4y8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 103.000 97.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0187nd student 059j1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 97.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0187nd student 03jm6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 97.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student #20449-02yl42 PRED entity: 02yl42 PRED relation: influenced_by PRED expected values: 07w21 => 147 concepts (48 used for prediction) PRED predicted values (max 10 best out of 388): 0g5ff (0.42 #1444, 0.41 #2282, 0.18 #1025), 03_87 (0.40 #3128, 0.35 #3547, 0.33 #3966), 032l1 (0.36 #3020, 0.35 #3439, 0.33 #3858), 09dt7 (0.35 #2126, 0.33 #1288, 0.18 #869), 0l99s (0.29 #636, 0.12 #3151, 0.12 #16367), 0gd_s (0.29 #721, 0.12 #2397, 0.09 #1140), 084w8 (0.28 #2937, 0.23 #3775, 0.19 #3356), 080r3 (0.27 #999, 0.20 #161, 0.08 #1418), 041h0 (0.25 #1267, 0.24 #2105, 0.09 #848), 06bng (0.25 #1527, 0.24 #2365, 0.09 #1108) >> Best rule #1444 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0c3kw; >> query: (?x3663, 0g5ff) <- influenced_by(?x3663, ?x1089), award(?x3663, ?x5050), ?x5050 = 0265wl, student(?x10497, ?x3663) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #847 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0210f1; *> query: (?x3663, 07w21) <- award(?x3663, ?x12418), gender(?x3663, ?x231), award_winner(?x12729, ?x3663), ?x12418 = 045xh *> conf = 0.09 ranks of expected_values: 113 EVAL 02yl42 influenced_by 07w21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 147.000 48.000 0.417 http://example.org/influence/influence_node/influenced_by #20448-011ycb PRED entity: 011ycb PRED relation: film_release_region PRED expected values: 05r4w 07ssc => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 150): 03rjj (0.82 #1144, 0.80 #1306, 0.79 #494), 05r4w (0.81 #1140, 0.80 #1302, 0.78 #1464), 07ssc (0.79 #504, 0.78 #1154, 0.75 #1478), 03h64 (0.76 #1209, 0.71 #559, 0.71 #1371), 035qy (0.74 #1174, 0.69 #1336, 0.67 #1498), 0154j (0.71 #1143, 0.66 #493, 0.66 #1305), 05b4w (0.71 #1368, 0.70 #1206, 0.67 #556), 015fr (0.68 #1156, 0.68 #506, 0.66 #1318), 0b90_r (0.63 #1304, 0.62 #1142, 0.60 #1466), 03spz (0.61 #1240, 0.56 #590, 0.54 #1402) >> Best rule #1144 for best value: >> intensional similarity = 4 >> extensional distance = 166 >> proper extension: 0fq27fp; >> query: (?x5013, 03rjj) <- film_release_region(?x5013, ?x1453), film_release_region(?x5013, ?x304), ?x1453 = 06qd3, ?x304 = 0d0vqn >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1140 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 166 *> proper extension: 0fq27fp; *> query: (?x5013, 05r4w) <- film_release_region(?x5013, ?x1453), film_release_region(?x5013, ?x304), ?x1453 = 06qd3, ?x304 = 0d0vqn *> conf = 0.81 ranks of expected_values: 2, 3 EVAL 011ycb film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 011ycb film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20447-01k3s2 PRED entity: 01k3s2 PRED relation: contains! PRED expected values: 0nlh7 => 185 concepts (60 used for prediction) PRED predicted values (max 10 best out of 246): 09c7w0 (0.60 #48302, 0.60 #19677, 0.59 #51881), 05kr_ (0.58 #26059, 0.56 #27849, 0.42 #9066), 02jx1 (0.42 #42122, 0.41 #43912, 0.41 #50174), 07ssc (0.33 #17915, 0.29 #42067, 0.27 #43857), 0nlh7 (0.31 #9836), 036k0s (0.25 #5523, 0.17 #9100, 0.14 #9995), 05k7sb (0.25 #1920, 0.10 #17121, 0.09 #12650), 05tbn (0.25 #2905, 0.09 #6481, 0.07 #11847), 0zqq8 (0.25 #3131, 0.09 #6707, 0.03 #18333), 0chgr2 (0.25 #3205, 0.09 #6781, 0.02 #24669) >> Best rule #48302 for best value: >> intensional similarity = 5 >> extensional distance = 84 >> proper extension: 01jtp7; 01vc5m; 02ccqg; 01f1r4; 013nky; 02ckl3; 01hc1j; 01pxcf; >> query: (?x4342, 09c7w0) <- major_field_of_study(?x4342, ?x2981), student(?x4342, ?x13591), ?x2981 = 02j62, currency(?x4342, ?x2244), award(?x13591, ?x11230) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #9836 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 02583l; 0778_3; *> query: (?x4342, ?x10718) <- currency(?x4342, ?x2244), ?x2244 = 0ptk_, contains(?x10063, ?x4342), organization(?x346, ?x4342), administrative_division(?x10718, ?x10063) *> conf = 0.31 ranks of expected_values: 5 EVAL 01k3s2 contains! 0nlh7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 185.000 60.000 0.605 http://example.org/location/location/contains #20446-0hw1j PRED entity: 0hw1j PRED relation: nationality PRED expected values: 09c7w0 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.75 #1, 0.74 #2506, 0.73 #1005), 02jx1 (0.12 #133, 0.11 #1938, 0.10 #3240), 07ssc (0.11 #617, 0.10 #718, 0.10 #517), 03rk0 (0.06 #7558, 0.06 #7959, 0.05 #8359), 0d060g (0.05 #509, 0.04 #2112, 0.04 #710), 03rt9 (0.03 #113, 0.02 #916, 0.01 #716), 03gj2 (0.03 #226), 0chghy (0.02 #1214, 0.02 #913, 0.02 #1815), 0f8l9c (0.02 #122, 0.02 #1927, 0.02 #1226), 0345h (0.02 #633, 0.02 #934, 0.02 #834) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02q4mt; >> query: (?x3736, 09c7w0) <- profession(?x3736, ?x987), award_nominee(?x3736, ?x4397), ?x4397 = 0gyx4 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hw1j nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.750 http://example.org/people/person/nationality #20445-0sx5w PRED entity: 0sx5w PRED relation: profession PRED expected values: 018gz8 => 158 concepts (124 used for prediction) PRED predicted values (max 10 best out of 114): 03gjzk (0.81 #10547, 0.72 #4778, 0.69 #1026), 09jwl (0.78 #12712, 0.40 #9110, 0.40 #9975), 0dxtg (0.67 #10546, 0.67 #159, 0.57 #1457), 01d_h8 (0.67 #729, 0.64 #4769, 0.49 #10538), 018gz8 (0.67 #740, 0.50 #162, 0.46 #1028), 0dz3r (0.54 #869, 0.35 #12695, 0.35 #9093), 0nbcg (0.53 #12724, 0.41 #9122, 0.32 #9987), 0cbd2 (0.52 #1594, 0.49 #9386, 0.49 #2604), 04gc2 (0.38 #1772, 0.30 #2349, 0.22 #3505), 016z4k (0.36 #9095, 0.32 #9960, 0.31 #12697) >> Best rule #10547 for best value: >> intensional similarity = 3 >> extensional distance = 196 >> proper extension: 01r216; >> query: (?x10645, 03gjzk) <- award(?x10645, ?x3064), producer_type(?x10645, ?x632), gender(?x10645, ?x231) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #740 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0738b8; 06crng; *> query: (?x10645, 018gz8) <- people(?x1050, ?x10645), influenced_by(?x10645, ?x9024), nationality(?x10645, ?x94), ?x9024 = 01k9lpl *> conf = 0.67 ranks of expected_values: 5 EVAL 0sx5w profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 158.000 124.000 0.813 http://example.org/people/person/profession #20444-01kxxq PRED entity: 01kxxq PRED relation: institution PRED expected values: 017cy9 0gjv_ => 22 concepts (19 used for prediction) PRED predicted values (max 10 best out of 704): 07szy (0.82 #7654, 0.78 #5749, 0.75 #8922), 01w5m (0.80 #7096, 0.78 #5825, 0.75 #8998), 09f2j (0.80 #7163, 0.78 #5892, 0.75 #9065), 03ksy (0.80 #7097, 0.78 #5826, 0.75 #5192), 07wjk (0.78 #5774, 0.75 #5140, 0.73 #8313), 07tg4 (0.78 #5799, 0.73 #8338, 0.73 #7704), 08815 (0.78 #9511, 0.73 #8242, 0.70 #6974), 02zd460 (0.75 #9079, 0.73 #7811, 0.71 #4006), 025v3k (0.75 #5209, 0.73 #7748, 0.71 #3943), 06pwq (0.75 #5080, 0.71 #3814, 0.70 #6985) >> Best rule #7654 for best value: >> intensional similarity = 20 >> extensional distance = 9 >> proper extension: 022h5x; >> query: (?x9742, 07szy) <- institution(?x9742, ?x11975), institution(?x9742, ?x6908), institution(?x9742, ?x5035), ?x11975 = 050xpd, student(?x9742, ?x6308), school_type(?x5035, ?x3092), major_field_of_study(?x5035, ?x2921), major_field_of_study(?x5035, ?x1668), ?x1668 = 01mkq, organization(?x5510, ?x6908), category(?x5035, ?x134), institution(?x3437, ?x6908), institution(?x1368, ?x6908), ?x3437 = 02_xgp2, currency(?x6908, ?x1099), institution(?x734, ?x5035), contains(?x390, ?x5035), ?x734 = 04zx3q1, ?x1368 = 014mlp, ?x2921 = 06n6p >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #8490 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 9 *> proper extension: 02m4yg; *> query: (?x9742, 0gjv_) <- institution(?x9742, ?x11975), institution(?x9742, ?x9025), institution(?x9742, ?x6908), institution(?x9742, ?x5035), ?x11975 = 050xpd, contains(?x390, ?x5035), institution(?x4981, ?x6908), currency(?x5035, ?x7888), ?x4981 = 03bwzr4, student(?x5035, ?x1738), school_type(?x5035, ?x3092), contains(?x1310, ?x6908), organization(?x5510, ?x5035), colors(?x6908, ?x332), contains(?x2204, ?x9025), student(?x6908, ?x11465), ?x1310 = 02jx1, citytown(?x12588, ?x2204), major_field_of_study(?x5035, ?x1668), ?x5510 = 07xl34 *> conf = 0.64 ranks of expected_values: 45, 61 EVAL 01kxxq institution 0gjv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 22.000 19.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01kxxq institution 017cy9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 22.000 19.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20443-045m1_ PRED entity: 045m1_ PRED relation: people! PRED expected values: 041rx => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 42): 041rx (0.69 #1624, 0.63 #2933, 0.44 #929), 013xrm (0.44 #945, 0.23 #1409, 0.22 #2641), 0x67 (0.33 #241, 0.33 #87, 0.11 #1784), 01qhm_ (0.25 #468, 0.20 #545, 0.17 #622), 0g6ff (0.25 #406, 0.17 #715, 0.10 #1023), 013b6_ (0.22 #978, 0.08 #1673, 0.08 #1442), 0xnvg (0.20 #552, 0.17 #629, 0.12 #1325), 033tf_ (0.20 #546, 0.17 #623, 0.11 #4941), 07bch9 (0.17 #794, 0.16 #1566, 0.12 #2028), 0xff (0.17 #831) >> Best rule #1624 for best value: >> intensional similarity = 6 >> extensional distance = 47 >> proper extension: 02784z; >> query: (?x7885, 041rx) <- nationality(?x7885, ?x6329), religion(?x7885, ?x7131), gender(?x7885, ?x231), official_language(?x6329, ?x11038), contains(?x455, ?x6329), ?x7131 = 03_gx >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045m1_ people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.694 http://example.org/people/ethnicity/people #20442-01pwz PRED entity: 01pwz PRED relation: taxonomy PRED expected values: 04n6k => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.23 #32, 0.23 #27, 0.23 #31) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 05rd8; 03nh9; >> query: (?x7054, 04n6k) <- films(?x7054, ?x689), genre(?x689, ?x53), language(?x689, ?x254) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pwz taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.228 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #20441-0d05w3 PRED entity: 0d05w3 PRED relation: exported_to PRED expected values: 01f08r 027jk 06s_2 => 194 concepts (138 used for prediction) PRED predicted values (max 10 best out of 116): 09c7w0 (0.42 #1376, 0.40 #1985, 0.40 #1325), 04wlh (0.36 #2035, 0.32 #1068, 0.04 #861), 0jdd (0.20 #286, 0.14 #592, 0.14 #540), 06s_2 (0.20 #300, 0.14 #554, 0.14 #914), 01f08r (0.20 #288, 0.14 #594, 0.10 #440), 07ssc (0.20 #261, 0.14 #2300, 0.12 #1994), 0345h (0.20 #270, 0.11 #1598, 0.11 #2715), 0d05w3 (0.20 #283, 0.10 #2016, 0.10 #2220), 03_3d (0.20 #256, 0.10 #1989, 0.10 #408), 059j2 (0.20 #269, 0.10 #421, 0.09 #1597) >> Best rule #1376 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0853g; >> query: (?x2346, 09c7w0) <- exported_to(?x2346, ?x8620), contains(?x2346, ?x1885), olympics(?x8620, ?x784) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #300 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 3 *> proper extension: 01j_x; *> query: (?x2346, 06s_2) <- entity_involved(?x7455, ?x2346), split_to(?x2346, ?x4271) *> conf = 0.20 ranks of expected_values: 4, 5, 37 EVAL 0d05w3 exported_to 06s_2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 194.000 138.000 0.419 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to EVAL 0d05w3 exported_to 027jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 194.000 138.000 0.419 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to EVAL 0d05w3 exported_to 01f08r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 194.000 138.000 0.419 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #20440-017z88 PRED entity: 017z88 PRED relation: institution! PRED expected values: 016t_3 => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 24): 019v9k (0.73 #251, 0.62 #299, 0.61 #371), 014mlp (0.70 #247, 0.68 #751, 0.68 #535), 02h4rq6 (0.67 #1986, 0.67 #292, 0.63 #268), 02_xgp2 (0.59 #303, 0.58 #279, 0.51 #255), 016t_3 (0.54 #293, 0.54 #269, 0.50 #52), 03bwzr4 (0.54 #305, 0.52 #281, 0.50 #184), 04zx3q1 (0.50 #50, 0.39 #291, 0.33 #2), 07s6fsf (0.50 #49, 0.33 #1984, 0.31 #169), 0bkj86 (0.48 #298, 0.46 #274, 0.42 #177), 027f2w (0.36 #300, 0.35 #276, 0.33 #11) >> Best rule #251 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 0p7tb; >> query: (?x2909, 019v9k) <- contains(?x94, ?x2909), currency(?x2909, ?x170), company(?x3687, ?x2909) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #293 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 0bqxw; 03p7gb; 09hgk; 01rc6f; 02x9g_; 019_6d; *> query: (?x2909, 016t_3) <- school_type(?x2909, ?x3205), company(?x3687, ?x2909), institution(?x8398, ?x2909) *> conf = 0.54 ranks of expected_values: 5 EVAL 017z88 institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 151.000 151.000 0.730 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20439-02vntj PRED entity: 02vntj PRED relation: award PRED expected values: 09qwmm 05ztrmj => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 263): 02ppm4q (0.70 #25824, 0.70 #3178, 0.70 #22248), 09td7p (0.70 #25824, 0.70 #3178, 0.70 #22248), 02z1nbg (0.70 #25824, 0.70 #3178, 0.70 #22248), 07bdd_ (0.40 #63, 0.15 #5561, 0.05 #19133), 057xs89 (0.33 #1344, 0.17 #3331, 0.15 #5561), 05pcn59 (0.31 #4446, 0.30 #3255, 0.29 #5240), 05p09zm (0.25 #1706, 0.23 #4487, 0.23 #5281), 05b4l5x (0.25 #1594, 0.21 #1991, 0.17 #5964), 027dtxw (0.25 #1195, 0.15 #5561, 0.13 #2383), 05zr6wv (0.23 #3195, 0.18 #4386, 0.17 #5180) >> Best rule #25824 for best value: >> intensional similarity = 2 >> extensional distance = 1585 >> proper extension: 0l56b; 0kk9v; >> query: (?x4247, ?x1441) <- award_nominee(?x4247, ?x123), award_winner(?x1441, ?x4247) >> conf = 0.70 => this is the best rule for 3 predicted values *> Best rule #177 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 016tw3; *> query: (?x4247, 05ztrmj) <- award_nominee(?x4247, ?x6086), ?x6086 = 058frd, award(?x4247, ?x704) *> conf = 0.20 ranks of expected_values: 13, 35 EVAL 02vntj award 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 103.000 103.000 0.703 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02vntj award 09qwmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 103.000 103.000 0.703 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20438-0klh7 PRED entity: 0klh7 PRED relation: languages PRED expected values: 02h40lc => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 18): 02h40lc (0.46 #158, 0.45 #353, 0.44 #314), 064_8sq (0.06 #524, 0.05 #680, 0.04 #641), 02bjrlw (0.04 #510, 0.04 #666, 0.04 #627), 0t_2 (0.04 #2148, 0.02 #165, 0.01 #674), 03hkp (0.04 #2148, 0.02 #166), 032f6 (0.04 #2148), 0880p (0.04 #2148), 06b_j (0.04 #2148), 04306rv (0.02 #629, 0.01 #473, 0.01 #198), 03k50 (0.02 #981, 0.02 #669, 0.02 #513) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 03zqc1; >> query: (?x2849, 02h40lc) <- film(?x2849, ?x1230), student(?x8398, ?x2849), actor(?x4529, ?x2849) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0klh7 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.464 http://example.org/people/person/languages #20437-03s0w PRED entity: 03s0w PRED relation: currency PRED expected values: 09nqf => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.88 #4, 0.88 #10, 0.87 #22), 0ptk_ (0.04 #20, 0.03 #53, 0.03 #74), 02l6h (0.02 #42, 0.02 #51, 0.02 #57) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 011hq1; >> query: (?x961, 09nqf) <- category(?x961, ?x134), religion(?x961, ?x109), location(?x1987, ?x961) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s0w currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.879 http://example.org/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency #20436-050yyb PRED entity: 050yyb PRED relation: ceremony! PRED expected values: 0gqng 0gqwc => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 322): 0gqwc (0.90 #4831, 0.89 #5785, 0.89 #5069), 0gqng (0.88 #2395, 0.82 #5023, 0.81 #4067), 0gkts9 (0.41 #2261, 0.20 #3933, 0.16 #5604), 02qyp19 (0.37 #4542, 0.36 #4782, 0.34 #4783), 02pqp12 (0.37 #4542, 0.36 #4782, 0.34 #4783), 040njc (0.37 #4542, 0.36 #4782, 0.34 #4783), 03hl6lc (0.37 #4542, 0.36 #4782, 0.34 #4783), 054krc (0.36 #1489, 0.36 #1967, 0.36 #4782), 04dn09n (0.36 #1463, 0.36 #1941, 0.36 #4782), 054knh (0.36 #1620, 0.36 #2098, 0.31 #1859) >> Best rule #4831 for best value: >> intensional similarity = 15 >> extensional distance = 50 >> proper extension: 0dznvw; >> query: (?x2294, 0gqwc) <- honored_for(?x2294, ?x4040), award_winner(?x2294, ?x7333), ceremony(?x1703, ?x2294), ceremony(?x1243, ?x2294), ceremony(?x720, ?x2294), student(?x741, ?x7333), ?x1243 = 0gr0m, nominated_for(?x1703, ?x8217), nominated_for(?x1703, ?x5843), nominated_for(?x1703, ?x878), ?x878 = 0147sh, ?x5843 = 0kbhf, award(?x382, ?x720), film_release_region(?x4040, ?x87), ?x8217 = 04v89z >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 050yyb ceremony! 0gqwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.904 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 050yyb ceremony! 0gqng CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.904 http://example.org/award/award_category/winners./award/award_honor/ceremony #20435-095w_ PRED entity: 095w_ PRED relation: capital! PRED expected values: 03l5m1 => 167 concepts (104 used for prediction) PRED predicted values (max 10 best out of 134): 020d5 (0.13 #1843, 0.11 #2244, 0.07 #2377), 01mk6 (0.11 #497, 0.03 #2901, 0.02 #3034), 09c7w0 (0.09 #8006, 0.08 #8675, 0.08 #12150), 0d060g (0.09 #8006, 0.08 #8675, 0.08 #12150), 07ssc (0.09 #8006, 0.08 #8675, 0.08 #12150), 02psqkz (0.09 #1800, 0.09 #1666, 0.07 #2334), 03f4n1 (0.09 #1864, 0.09 #1730, 0.05 #1196), 0dv0z (0.09 #1710, 0.05 #1310, 0.04 #1844), 0c4b8 (0.06 #3021, 0.04 #4089, 0.04 #4756), 0hzlz (0.06 #2959, 0.04 #4027, 0.04 #4694) >> Best rule #1843 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 07ytt; >> query: (?x1374, 020d5) <- time_zones(?x1374, ?x2864), contains(?x1003, ?x1374), capital(?x9006, ?x1374), entity_involved(?x612, ?x9006) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #2920 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 03khn; *> query: (?x1374, 03l5m1) <- category(?x1374, ?x134), capital(?x9006, ?x1374), combatants(?x612, ?x9006) *> conf = 0.03 ranks of expected_values: 87 EVAL 095w_ capital! 03l5m1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 167.000 104.000 0.130 http://example.org/location/country/capital #20434-01dpsv PRED entity: 01dpsv PRED relation: profession PRED expected values: 02hrh1q 0nbcg => 108 concepts (85 used for prediction) PRED predicted values (max 10 best out of 61): 02hrh1q (0.91 #10322, 0.89 #10469, 0.87 #9734), 09jwl (0.69 #311, 0.68 #3259, 0.66 #2375), 0dxtg (0.56 #3992, 0.27 #2959, 0.26 #1483), 0nbcg (0.51 #2388, 0.46 #3566, 0.45 #3124), 01d_h8 (0.48 #3984, 0.35 #1475, 0.31 #2951), 0n1h (0.40 #10, 0.27 #745, 0.24 #304), 01c72t (0.32 #6359, 0.29 #3558, 0.28 #3116), 02jknp (0.27 #3986, 0.22 #1477, 0.21 #2953), 039v1 (0.26 #623, 0.25 #2393, 0.24 #329), 02krf9 (0.25 #4005, 0.09 #1496, 0.08 #11069) >> Best rule #10322 for best value: >> intensional similarity = 3 >> extensional distance = 1652 >> proper extension: 04bdqk; 015010; 04kwbt; >> query: (?x12659, 02hrh1q) <- award(?x12659, ?x1361), profession(?x12659, ?x131), film(?x12659, ?x1120) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 01dpsv profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 85.000 0.908 http://example.org/people/person/profession EVAL 01dpsv profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 85.000 0.908 http://example.org/people/person/profession #20433-0m75g PRED entity: 0m75g PRED relation: contains! PRED expected values: 094vy => 158 concepts (127 used for prediction) PRED predicted values (max 10 best out of 291): 09c7w0 (0.60 #15187, 0.59 #94692, 0.59 #108992), 04_1l0v (0.43 #18313, 0.37 #16526, 0.35 #36180), 0m75g (0.28 #27694, 0.26 #48239, 0.18 #95587), 02qkt (0.23 #55730, 0.18 #95037, 0.16 #88781), 0345h (0.15 #3653, 0.15 #4546, 0.14 #974), 01n7q (0.14 #6328, 0.12 #23302, 0.11 #65287), 04jpl (0.14 #5380, 0.11 #22, 0.10 #7166), 06q1r (0.14 #13748, 0.08 #3029, 0.08 #87892), 059rby (0.14 #7164, 0.13 #15204, 0.13 #18778), 0f8l9c (0.13 #74187, 0.07 #101889, 0.06 #80441) >> Best rule #15187 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0dhdp; 0f04c; 02_n7; 0sq2v; 0xt3t; 01vc3y; >> query: (?x7213, 09c7w0) <- place_of_birth(?x3079, ?x7213), student(?x6760, ?x3079), award_nominee(?x3079, ?x2307), film(?x3079, ?x1710) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #5020 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 020d8d; *> query: (?x7213, 094vy) <- place_of_birth(?x1818, ?x7213), origin(?x9206, ?x7213), administrative_parent(?x7213, ?x10789), profession(?x1818, ?x131) *> conf = 0.04 ranks of expected_values: 87 EVAL 0m75g contains! 094vy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 158.000 127.000 0.600 http://example.org/location/location/contains #20432-01kkx2 PRED entity: 01kkx2 PRED relation: location_of_ceremony PRED expected values: 02cft => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 37): 0cv3w (0.08 #392, 0.04 #2431, 0.03 #1113), 0f2w0 (0.08 #379, 0.03 #1579, 0.02 #2418), 0kc40 (0.05 #580, 0.05 #700, 0.04 #941), 0ycht (0.05 #591, 0.05 #2151, 0.03 #1551), 0r62v (0.05 #494, 0.02 #1934, 0.02 #2173), 0b90_r (0.05 #480, 0.02 #3356, 0.01 #2996), 0r3wm (0.05 #567, 0.01 #3083, 0.01 #3204), 0xmqf (0.05 #593), 02_286 (0.04 #851, 0.04 #731, 0.02 #3366), 03s5t (0.04 #991, 0.03 #1470, 0.03 #1711) >> Best rule #392 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 03h_fk5; 02jq1; 01dkpb; 02_01w; >> query: (?x12037, 0cv3w) <- celebrities_impersonated(?x3649, ?x12037), place_of_death(?x12037, ?x4801), languages(?x12037, ?x90) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kkx2 location_of_ceremony 02cft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 140.000 0.083 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #20431-021y7yw PRED entity: 021y7yw PRED relation: genre PRED expected values: 01lrrt => 74 concepts (72 used for prediction) PRED predicted values (max 10 best out of 76): 01jfsb (0.37 #12, 0.31 #249, 0.30 #1668), 05p553 (0.36 #1778, 0.35 #1896, 0.35 #2370), 04xvlr (0.32 #356, 0.21 #1, 0.17 #2959), 02kdv5l (0.32 #2, 0.29 #7698, 0.28 #711), 03k9fj (0.26 #11, 0.25 #720, 0.23 #1549), 0lsxr (0.21 #9, 0.19 #246, 0.18 #364), 01hmnh (0.17 #725, 0.16 #2855, 0.16 #1554), 04xvh5 (0.16 #32, 0.12 #387, 0.08 #3109), 0bkbm (0.16 #37, 0.03 #1575, 0.03 #2876), 017fp (0.14 #370, 0.11 #15, 0.10 #3092) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0m313; 02vxq9m; 08gsvw; 0164qt; 0dgst_d; 014kq6; 0g54xkt; 03r0g9; 01242_; 0prh7; ... >> query: (?x2458, 01jfsb) <- film(?x2805, ?x2458), film_crew_role(?x2458, ?x137), ?x2805 = 0lpjn, nominated_for(?x618, ?x2458) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #404 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 582 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x2458, 01lrrt) <- titles(?x53, ?x2458), titles(?x53, ?x7204), ?x7204 = 0280061 *> conf = 0.02 ranks of expected_values: 54 EVAL 021y7yw genre 01lrrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 74.000 72.000 0.368 http://example.org/film/film/genre #20430-01j_cy PRED entity: 01j_cy PRED relation: institution! PRED expected values: 014mlp 013zdg => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 13): 014mlp (0.75 #144, 0.74 #63, 0.73 #340), 013zdg (0.43 #19, 0.37 #50, 0.35 #83), 022h5x (0.36 #27, 0.25 #43, 0.19 #350), 03mkk4 (0.29 #21, 0.26 #52, 0.25 #133), 0bjrnt (0.29 #18, 0.22 #82, 0.19 #130), 02mjs7 (0.29 #16, 0.13 #47, 0.13 #80), 01rr_d (0.21 #25, 0.19 #89, 0.18 #71), 02m4yg (0.14 #24, 0.14 #70, 0.10 #317), 01ysy9 (0.14 #29, 0.05 #75, 0.04 #60), 071tyz (0.08 #84, 0.07 #313, 0.07 #51) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 02d9nr; >> query: (?x1675, 014mlp) <- country(?x1675, ?x94), colors(?x1675, ?x663), student(?x1675, ?x1875) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01j_cy institution! 013zdg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.747 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01j_cy institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.747 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20429-01wsl7c PRED entity: 01wsl7c PRED relation: role PRED expected values: 018vs 01vj9c 011_6p => 142 concepts (102 used for prediction) PRED predicted values (max 10 best out of 106): 0l14j_ (0.32 #3638, 0.23 #4290, 0.23 #3169), 02hnl (0.32 #3638, 0.23 #4290, 0.23 #3169), 01vdm0 (0.32 #773, 0.31 #2171, 0.30 #3571), 018vs (0.32 #755, 0.22 #104, 0.20 #2153), 013y1f (0.25 #777, 0.22 #312, 0.21 #405), 01vj9c (0.25 #757, 0.18 #1504, 0.18 #2155), 026t6 (0.24 #747, 0.21 #2145, 0.18 #3451), 04rzd (0.22 #134, 0.08 #413, 0.08 #320), 05ljv7 (0.22 #128, 0.05 #779, 0.05 #221), 0l14qv (0.18 #3547, 0.17 #749, 0.17 #3922) >> Best rule #3638 for best value: >> intensional similarity = 4 >> extensional distance = 340 >> proper extension: 01vd7hn; 03_0p; >> query: (?x1997, ?x1750) <- role(?x1997, ?x3991), instrumentalists(?x1750, ?x1997), role(?x2798, ?x3991), ?x2798 = 03qjg >> conf = 0.32 => this is the best rule for 2 predicted values *> Best rule #755 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 61 *> proper extension: 01vsxdm; 0kxbc; 02bgmr; 01ydzx; 0191h5; 01w9mnm; 06br6t; *> query: (?x1997, 018vs) <- artists(?x7440, ?x1997), artists(?x302, ?x1997), ?x302 = 016clz, artists(?x7440, ?x8149), ?x8149 = 01whg97, role(?x1997, ?x227) *> conf = 0.32 ranks of expected_values: 4, 6, 90 EVAL 01wsl7c role 011_6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 142.000 102.000 0.318 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01wsl7c role 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 142.000 102.000 0.318 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01wsl7c role 018vs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 142.000 102.000 0.318 http://example.org/music/artist/track_contributions./music/track_contribution/role #20428-0821j PRED entity: 0821j PRED relation: tv_program PRED expected values: 07g9f => 108 concepts (106 used for prediction) PRED predicted values (max 10 best out of 10): 039cq4 (0.06 #483, 0.05 #831, 0.05 #570), 01j95 (0.04 #260, 0.01 #434, 0.01 #521), 0180mw (0.03 #307, 0.01 #394), 09fc83 (0.03 #299, 0.01 #386), 07c72 (0.02 #2111, 0.01 #3679, 0.01 #456), 03g9xj (0.01 #2153), 01j7mr (0.01 #461, 0.01 #2116, 0.01 #548), 0b005 (0.01 #482, 0.01 #569), 05_z42 (0.01 #476, 0.01 #563), 0h3mh3q (0.01 #2152) >> Best rule #483 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 04rcr; >> query: (?x8718, 039cq4) <- award_nominee(?x8718, ?x1752), award_winner(?x3337, ?x8718), influenced_by(?x576, ?x8718) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0821j tv_program 07g9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 106.000 0.056 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #20427-07xvf PRED entity: 07xvf PRED relation: honored_for! PRED expected values: 02yv_b => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 93): 03gyp30 (0.05 #346, 0.04 #468, 0.02 #834), 0275n3y (0.05 #308, 0.04 #796, 0.02 #3358), 0bvhz9 (0.05 #358, 0.02 #3408, 0.02 #846), 02wzl1d (0.05 #251, 0.02 #739, 0.02 #617), 0hr6lkl (0.05 #256, 0.02 #744, 0.02 #622), 02yvhx (0.05 #309, 0.02 #1407, 0.02 #6345), 03gwpw2 (0.04 #371, 0.03 #249, 0.03 #2201), 04n2r9h (0.03 #1256, 0.03 #768, 0.03 #646), 09gkdln (0.03 #838, 0.03 #716, 0.03 #6328), 09k5jh7 (0.03 #803, 0.03 #681, 0.03 #315) >> Best rule #346 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0bhwhj; >> query: (?x7373, 03gyp30) <- produced_by(?x7373, ?x9363), films(?x326, ?x7373), award(?x7373, ?x6860), crewmember(?x7373, ?x9391) >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #6345 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 691 *> proper extension: 07bz5; *> query: (?x7373, ?x78) <- award(?x7373, ?x6860), ceremony(?x6860, ?x78) *> conf = 0.02 ranks of expected_values: 62 EVAL 07xvf honored_for! 02yv_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 111.000 111.000 0.053 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #20426-05nn4k PRED entity: 05nn4k PRED relation: produced_by! PRED expected values: 0h95zbp 01j5ql 06znpjr => 116 concepts (83 used for prediction) PRED predicted values (max 10 best out of 533): 0b73_1d (0.17 #2880, 0.17 #1944, 0.11 #3816), 05fgt1 (0.17 #3021, 0.17 #2085, 0.11 #3957), 011yhm (0.17 #3431, 0.17 #2495, 0.11 #4367), 02704ff (0.17 #3345, 0.17 #2409, 0.11 #4281), 07bwr (0.17 #3273, 0.17 #2337, 0.11 #4209), 01jzyf (0.17 #3135, 0.17 #2199, 0.11 #4071), 01vfqh (0.17 #2928, 0.17 #1992, 0.11 #3864), 0b6tzs (0.17 #2890, 0.17 #1954, 0.11 #3826), 06znpjr (0.09 #9361, 0.09 #9362), 01j5ql (0.09 #9361, 0.09 #9362) >> Best rule #2880 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 017c87; >> query: (?x4660, 0b73_1d) <- student(?x620, ?x4660), produced_by(?x153, ?x4660), featured_film_locations(?x153, ?x739) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #9361 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 0143wl; 01nbq4; *> query: (?x4660, ?x886) <- company(?x4660, ?x3323), production_companies(?x886, ?x3323), film(?x4657, ?x886) *> conf = 0.09 ranks of expected_values: 9, 10, 12 EVAL 05nn4k produced_by! 06znpjr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 116.000 83.000 0.167 http://example.org/film/film/produced_by EVAL 05nn4k produced_by! 01j5ql CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 116.000 83.000 0.167 http://example.org/film/film/produced_by EVAL 05nn4k produced_by! 0h95zbp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 116.000 83.000 0.167 http://example.org/film/film/produced_by #20425-01x8f6 PRED entity: 01x8f6 PRED relation: category PRED expected values: 08mbj5d => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14817, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x8f6 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #20424-0104lr PRED entity: 0104lr PRED relation: contains! PRED expected values: 07b_l => 133 concepts (116 used for prediction) PRED predicted values (max 10 best out of 328): 09c7w0 (0.71 #19728, 0.70 #62765, 0.70 #78009), 07b_l (0.46 #85181, 0.44 #83387, 0.18 #23535), 04_1l0v (0.26 #21969, 0.20 #18382, 0.20 #17486), 07ssc (0.26 #7204, 0.20 #1825, 0.18 #9892), 05fjf (0.20 #1271, 0.12 #3961, 0.08 #5754), 0345h (0.17 #2771, 0.05 #79881, 0.04 #4566), 02jx1 (0.17 #87, 0.13 #7259, 0.10 #984), 04rrx (0.17 #127, 0.10 #1024, 0.10 #8195), 0h7x (0.17 #91, 0.10 #988, 0.09 #2780), 0mpzm (0.17 #641, 0.10 #1538, 0.05 #2434) >> Best rule #19728 for best value: >> intensional similarity = 5 >> extensional distance = 50 >> proper extension: 0j7ng; 0s9b_; >> query: (?x14085, 09c7w0) <- category(?x14085, ?x134), ?x134 = 08mbj5d, location(?x4608, ?x14085), artists(?x3928, ?x4608), ?x3928 = 0gywn >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #85181 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 303 *> proper extension: 027rn; 02j9z; 0121c1; 01qcx_; *> query: (?x14085, ?x3634) <- location(?x4608, ?x14085), award_winner(?x4608, ?x4609), place_of_birth(?x4608, ?x13535), contains(?x3634, ?x13535), location(?x13926, ?x13535) *> conf = 0.46 ranks of expected_values: 2 EVAL 0104lr contains! 07b_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 116.000 0.712 http://example.org/location/location/contains #20423-037vqt PRED entity: 037vqt PRED relation: award_winner PRED expected values: 03cvfg => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 037vqt award_winner 03cvfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/award/award_category/winners./award/award_honor/award_winner #20422-0gmd3k7 PRED entity: 0gmd3k7 PRED relation: film_release_region PRED expected values: 0d0vqn 07ylj 06bnz 05b4w => 91 concepts (87 used for prediction) PRED predicted values (max 10 best out of 158): 0d0vqn (0.92 #561, 0.91 #1396, 0.91 #1257), 05b4w (0.83 #1862, 0.82 #2141, 0.81 #1723), 06bnz (0.80 #1845, 0.78 #2124, 0.75 #2681), 06qd3 (0.67 #1699, 0.63 #2534, 0.63 #2674), 047yc (0.67 #299, 0.59 #1273, 0.58 #577), 03rk0 (0.63 #325, 0.52 #1299, 0.50 #1855), 0h7x (0.61 #1696, 0.59 #2253, 0.58 #2392), 05qx1 (0.59 #311, 0.53 #1424, 0.52 #728), 06c1y (0.52 #313, 0.47 #591, 0.45 #730), 047lj (0.52 #287, 0.40 #1678, 0.39 #565) >> Best rule #561 for best value: >> intensional similarity = 8 >> extensional distance = 34 >> proper extension: 02vxq9m; 017gl1; 02d44q; 0jjy0; 0_92w; 0872p_c; 053rxgm; 017gm7; 011yqc; 035yn8; ... >> query: (?x6283, 0d0vqn) <- film_release_region(?x6283, ?x1558), film_release_region(?x6283, ?x429), film_release_region(?x6283, ?x172), film(?x777, ?x6283), ?x172 = 0154j, ?x429 = 03rt9, ?x1558 = 01mjq, film(?x1104, ?x6283) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 18 EVAL 0gmd3k7 film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 87.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gmd3k7 film_release_region 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 87.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gmd3k7 film_release_region 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 91.000 87.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gmd3k7 film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 87.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20421-03s6l2 PRED entity: 03s6l2 PRED relation: crewmember PRED expected values: 021yc7p => 75 concepts (58 used for prediction) PRED predicted values (max 10 best out of 32): 0b79gfg (0.09 #18, 0.05 #161, 0.04 #448), 027y151 (0.09 #40, 0.02 #135, 0.02 #277), 051z6rz (0.09 #29, 0.02 #1422, 0.02 #747), 0bbxx9b (0.09 #21, 0.02 #404, 0.02 #451), 04wp63 (0.07 #137, 0.04 #185, 0.03 #425), 027rwmr (0.07 #53, 0.04 #196, 0.03 #243), 095zvfg (0.07 #85, 0.02 #133, 0.02 #1188), 0b6mgp_ (0.05 #117, 0.02 #595, 0.02 #165), 05bm4sm (0.05 #121, 0.02 #599, 0.02 #216), 02mt4k (0.05 #143, 0.03 #1006, 0.03 #1584) >> Best rule #18 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 04jkpgv; 0fy34l; 031778; 02q56mk; 03cw411; 02b61v; 017180; 05pxnmb; 023g6w; >> query: (?x603, 0b79gfg) <- honored_for(?x6288, ?x603), country(?x603, ?x1264), ?x1264 = 0345h >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #103 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 0ddjy; 0gfsq9; *> query: (?x603, 021yc7p) <- honored_for(?x6288, ?x603), executive_produced_by(?x603, ?x4060), award_winner(?x603, ?x286) *> conf = 0.02 ranks of expected_values: 19 EVAL 03s6l2 crewmember 021yc7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 75.000 58.000 0.091 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #20420-03cmsqb PRED entity: 03cmsqb PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.82 #40, 0.77 #653, 0.74 #926), 0dxtw (0.37 #656, 0.36 #43, 0.36 #554), 01pvkk (0.28 #930, 0.28 #1681, 0.27 #44), 02ynfr (0.19 #661, 0.18 #48, 0.17 #116), 0215hd (0.18 #51, 0.14 #937, 0.13 #119), 01xy5l_ (0.18 #46, 0.13 #12, 0.13 #114), 089g0h (0.15 #52, 0.12 #938, 0.11 #120), 02rh1dz (0.13 #655, 0.11 #553, 0.10 #928), 0d2b38 (0.12 #58, 0.11 #126, 0.10 #671), 033smt (0.12 #60, 0.06 #26, 0.05 #673) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0gtv7pk; 0gjcrrw; 04fjzv; >> query: (?x7968, 0ch6mp2) <- nominated_for(?x1007, ?x7968), film_crew_role(?x7968, ?x137), ?x1007 = 03c7tr1 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cmsqb film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.818 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20419-07tg4 PRED entity: 07tg4 PRED relation: student PRED expected values: 01w923 0c9c0 0bd2n4 0ksrf8 05mcjs 01m7f5r 0h10vt 01t_wfl 02lfwp => 82 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1122): 05mcjs (0.33 #1130), 0tfc (0.25 #4022, 0.07 #20440, 0.06 #16335), 0638kv (0.25 #2879, 0.05 #19297, 0.04 #23402), 01fx5l (0.25 #3123, 0.05 #19541, 0.03 #33910), 0ky1 (0.25 #3803, 0.02 #20221, 0.02 #24326), 06y8v (0.25 #3243, 0.02 #19661, 0.02 #23766), 06lbp (0.25 #3147, 0.02 #19565, 0.02 #23670), 06ltr (0.25 #2954, 0.02 #19372, 0.02 #23477), 03f77 (0.25 #2927, 0.02 #19345, 0.02 #23450), 026lj (0.25 #2327, 0.02 #18745, 0.02 #22850) >> Best rule #1130 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01clyb; >> query: (?x2999, 05mcjs) <- student(?x2999, ?x12677), ?x12677 = 01385g, major_field_of_study(?x2999, ?x742) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 502 EVAL 07tg4 student 02lfwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 39.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07tg4 student 01t_wfl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 39.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07tg4 student 0h10vt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 82.000 39.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07tg4 student 01m7f5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 39.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07tg4 student 05mcjs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 39.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07tg4 student 0ksrf8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 39.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07tg4 student 0bd2n4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 39.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07tg4 student 0c9c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 39.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07tg4 student 01w923 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 39.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #20418-02_340 PRED entity: 02_340 PRED relation: produced_by! PRED expected values: 06q8qh => 93 concepts (43 used for prediction) PRED predicted values (max 10 best out of 144): 0n6ds (0.33 #867), 08c6k9 (0.33 #807), 02rrh1w (0.33 #729), 07k8rt4 (0.33 #399), 024lff (0.33 #329), 025n07 (0.33 #270), 0gfsq9 (0.33 #246), 07p62k (0.33 #192), 0gvrws1 (0.33 #172), 0bq8tmw (0.33 #142) >> Best rule #867 for best value: >> intensional similarity = 2 >> extensional distance = 1 >> proper extension: 043q6n_; >> query: (?x6579, 0n6ds) <- award_nominee(?x6579, ?x1416), ?x1416 = 0162c8 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02_340 produced_by! 06q8qh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 43.000 0.333 http://example.org/film/film/produced_by #20417-0y2tr PRED entity: 0y2tr PRED relation: parent_genre PRED expected values: 0xhtw => 54 concepts (28 used for prediction) PRED predicted values (max 10 best out of 291): 016clz (0.62 #1289, 0.33 #807, 0.33 #4), 01243b (0.42 #1312, 0.37 #2118, 0.36 #2443), 064t9 (0.33 #814, 0.27 #1135, 0.25 #332), 03lty (0.31 #3737, 0.22 #1464, 0.21 #1787), 059kh (0.25 #193, 0.22 #836, 0.18 #1157), 01pfpt (0.25 #218, 0.19 #1607, 0.15 #2252), 05c6073 (0.25 #279, 0.18 #1243, 0.12 #1404), 0y3_8 (0.25 #191, 0.12 #513, 0.12 #352), 05bt6j (0.25 #188, 0.12 #510, 0.12 #349), 08cyft (0.25 #198, 0.12 #359, 0.12 #2129) >> Best rule #1289 for best value: >> intensional similarity = 9 >> extensional distance = 22 >> proper extension: 034487; 05jg58; 01skxk; 0175zz; 01rthc; >> query: (?x14252, 016clz) <- parent_genre(?x14252, ?x2491), artists(?x2491, ?x12497), artists(?x2491, ?x11633), artists(?x2491, ?x9735), artists(?x2491, ?x6368), ?x12497 = 079kr, ?x9735 = 01wxdn3, category(?x11633, ?x134), ?x6368 = 0178kd >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3066 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 89 *> proper extension: 03x2qp; *> query: (?x14252, ?x9248) <- artists(?x14252, ?x10756), artists(?x9248, ?x10756), artists(?x7083, ?x10756), category(?x10756, ?x134), ?x7083 = 02yv6b, artists(?x9248, ?x12506), ?x12506 = 01518s *> conf = 0.20 ranks of expected_values: 13 EVAL 0y2tr parent_genre 0xhtw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 54.000 28.000 0.625 http://example.org/music/genre/parent_genre #20416-04fv5b PRED entity: 04fv5b PRED relation: genre PRED expected values: 0fdjb => 107 concepts (63 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.72 #6929, 0.69 #5400, 0.68 #2930), 05p553 (0.55 #4228, 0.46 #1175, 0.42 #1292), 02b5_l (0.44 #280, 0.12 #631, 0.09 #514), 02kdv5l (0.43 #2580, 0.43 #705, 0.43 #2228), 03k9fj (0.33 #2821, 0.30 #1297, 0.29 #829), 01q03 (0.33 #240, 0.33 #123, 0.17 #6), 04xvh5 (0.33 #149, 0.33 #32, 0.14 #500), 0fdjb (0.33 #45, 0.18 #513, 0.17 #162), 02l7c8 (0.32 #1184, 0.32 #1067, 0.30 #5764), 01hmnh (0.26 #2827, 0.20 #2475, 0.20 #835) >> Best rule #6929 for best value: >> intensional similarity = 4 >> extensional distance = 783 >> proper extension: 018nnz; >> query: (?x5361, 07s9rl0) <- production_companies(?x5361, ?x1914), genre(?x5361, ?x604), genre(?x7711, ?x604), ?x7711 = 0pd64 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #45 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0fg04; 03t79f; 0h21v2; 01xq8v; *> query: (?x5361, 0fdjb) <- nominated_for(?x688, ?x5361), titles(?x571, ?x5361), ?x571 = 03npn, crewmember(?x5361, ?x9151) *> conf = 0.33 ranks of expected_values: 8 EVAL 04fv5b genre 0fdjb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 107.000 63.000 0.720 http://example.org/film/film/genre #20415-016vg8 PRED entity: 016vg8 PRED relation: film PRED expected values: 0kvgtf 0bwhdbl => 95 concepts (81 used for prediction) PRED predicted values (max 10 best out of 679): 0jzw (0.12 #117, 0.03 #1897, 0.03 #72984), 03p2xc (0.10 #3017, 0.04 #1237, 0.03 #72984), 034qzw (0.10 #2111, 0.03 #16352, 0.02 #5671), 01l_pn (0.08 #961, 0.04 #4521, 0.03 #11642), 0kvgxk (0.08 #326, 0.03 #3886, 0.03 #72984), 07jxpf (0.08 #679, 0.03 #72984, 0.02 #4239), 026n4h6 (0.08 #240, 0.03 #72984, 0.02 #3800), 03bx2lk (0.06 #1962, 0.05 #3742, 0.04 #182), 09g8vhw (0.06 #2103, 0.04 #3883, 0.02 #18124), 02q0k7v (0.06 #3109, 0.02 #21362) >> Best rule #117 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 06151l; 0z4s; 0c4f4; 0hvb2; 01pgzn_; 015t56; 019pm_; 08swgx; 014488; 03_6y; ... >> query: (?x4662, 0jzw) <- profession(?x4662, ?x1032), award_nominee(?x6935, ?x4662), ?x6935 = 01d1st >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #4177 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 04shbh; *> query: (?x4662, 0kvgtf) <- participant(?x2443, ?x4662), participant(?x4662, ?x262), award_winner(?x408, ?x4662) *> conf = 0.02 ranks of expected_values: 435, 650 EVAL 016vg8 film 0bwhdbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 95.000 81.000 0.125 http://example.org/film/actor/film./film/performance/film EVAL 016vg8 film 0kvgtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 95.000 81.000 0.125 http://example.org/film/actor/film./film/performance/film #20414-0b2km_ PRED entity: 0b2km_ PRED relation: films! PRED expected values: 07jq_ => 89 concepts (35 used for prediction) PRED predicted values (max 10 best out of 62): 081pw (0.20 #938, 0.15 #627, 0.10 #3), 07c52 (0.14 #331, 0.06 #1423, 0.05 #1112), 0fzyg (0.10 #54, 0.08 #210, 0.05 #1612), 04gb7 (0.09 #356, 0.04 #1603, 0.04 #1448), 01w1sx (0.09 #401, 0.04 #559, 0.04 #1648), 06by7 (0.08 #173, 0.02 #486, 0.02 #797), 030qb3t (0.08 #172, 0.02 #485, 0.02 #796), 07jq_ (0.08 #705, 0.05 #1173, 0.05 #1484), 06d4h (0.07 #1601, 0.06 #2224, 0.06 #1446), 07_nf (0.06 #1002, 0.04 #691, 0.02 #3812) >> Best rule #938 for best value: >> intensional similarity = 5 >> extensional distance = 155 >> proper extension: 0c0wvx; 02qjv1p; >> query: (?x10024, 081pw) <- genre(?x10024, ?x3515), genre(?x10024, ?x53), ?x3515 = 082gq, genre(?x6448, ?x53), ?x6448 = 0404j37 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #705 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 50 *> proper extension: 0qm8b; 0c38gj; *> query: (?x10024, 07jq_) <- produced_by(?x10024, ?x6690), film(?x426, ?x10024), genre(?x10024, ?x3515), film_crew_role(?x10024, ?x137), ?x3515 = 082gq *> conf = 0.08 ranks of expected_values: 8 EVAL 0b2km_ films! 07jq_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 89.000 35.000 0.197 http://example.org/film/film_subject/films #20413-0bh8drv PRED entity: 0bh8drv PRED relation: nominated_for! PRED expected values: 02ppm4q => 109 concepts (101 used for prediction) PRED predicted values (max 10 best out of 192): 02z1nbg (0.70 #1176, 0.69 #1882, 0.67 #6352), 02qvyrt (0.43 #95, 0.27 #5035, 0.16 #5975), 0gq9h (0.33 #5002, 0.33 #4060, 0.32 #3119), 019f4v (0.33 #3110, 0.31 #4051, 0.29 #4993), 0gs96 (0.32 #5028, 0.29 #88, 0.18 #3145), 0gs9p (0.32 #4062, 0.30 #5004, 0.29 #3121), 0gq_v (0.31 #4960, 0.29 #20, 0.25 #490), 0gqwc (0.31 #5000, 0.29 #60, 0.16 #12760), 040njc (0.29 #7, 0.27 #4947, 0.26 #3064), 0gr0m (0.29 #59, 0.25 #4999, 0.21 #4057) >> Best rule #1176 for best value: >> intensional similarity = 3 >> extensional distance = 201 >> proper extension: 0cwrr; >> query: (?x7516, ?x3902) <- nominated_for(?x5591, ?x7516), category(?x7516, ?x134), award(?x7516, ?x3902) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #115 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0m313; 01pv91; 04grkmd; 011yg9; 04jpg2p; *> query: (?x7516, 02ppm4q) <- film_release_region(?x7516, ?x94), currency(?x7516, ?x1099), film(?x988, ?x7516), ?x988 = 01tspc6 *> conf = 0.29 ranks of expected_values: 19 EVAL 0bh8drv nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 109.000 101.000 0.698 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20412-029m83 PRED entity: 029m83 PRED relation: executive_produced_by! PRED expected values: 0fjyzt => 149 concepts (95 used for prediction) PRED predicted values (max 10 best out of 300): 0k4p0 (0.10 #3173, 0.10 #3174, 0.10 #13751), 0mcl0 (0.10 #3173, 0.10 #3174, 0.10 #13751), 01gvsn (0.10 #3173, 0.10 #3174, 0.09 #1057), 01fwzk (0.10 #3173, 0.10 #13751, 0.09 #1057), 01q7h2 (0.10 #3173, 0.09 #1057, 0.09 #1586), 0fh694 (0.10 #13751, 0.09 #5820, 0.05 #2682), 03cw411 (0.10 #13751, 0.03 #9522, 0.03 #10579), 02cbhg (0.10 #13751, 0.03 #9522, 0.03 #10579), 04x4vj (0.09 #5820, 0.04 #9521, 0.03 #8464), 03m4mj (0.09 #5820, 0.04 #9521, 0.03 #4232) >> Best rule #3173 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 01f7v_; 0362q0; 02qzjj; 06y0xx; >> query: (?x8041, ?x3882) <- executive_produced_by(?x2323, ?x8041), film(?x8041, ?x5712), film(?x8041, ?x3882), currency(?x5712, ?x170) >> conf = 0.10 => this is the best rule for 5 predicted values *> Best rule #2954 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 01f7v_; 0362q0; 02qzjj; 06y0xx; *> query: (?x8041, 0fjyzt) <- executive_produced_by(?x2323, ?x8041), film(?x8041, ?x5712), currency(?x5712, ?x170) *> conf = 0.02 ranks of expected_values: 260 EVAL 029m83 executive_produced_by! 0fjyzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 149.000 95.000 0.105 http://example.org/film/film/executive_produced_by #20411-01mh_q PRED entity: 01mh_q PRED relation: ceremony! PRED expected values: 02581c 025m98 01cw7s 024_41 01ckcd 024fxq => 32 concepts (31 used for prediction) PRED predicted values (max 10 best out of 255): 01cw7s (0.83 #3104, 0.83 #2919, 0.80 #2733), 024_41 (0.83 #3130, 0.80 #2759, 0.75 #2945), 01ckcd (0.83 #2955, 0.78 #2398, 0.78 #2027), 024fxq (0.83 #2967, 0.78 #2039, 0.67 #2410), 025m98 (0.80 #2723, 0.78 #2167, 0.75 #2909), 02581c (0.78 #2303, 0.78 #1932, 0.75 #3045), 02581q (0.67 #2794, 0.62 #1679, 0.60 #2608), 02g3gj (0.62 #1688, 0.60 #2431, 0.58 #2803), 03qpp9 (0.62 #1842, 0.60 #2585, 0.58 #2957), 03t5kl (0.62 #1791, 0.57 #1419, 0.51 #1116) >> Best rule #3104 for best value: >> intensional similarity = 24 >> extensional distance = 10 >> proper extension: 05pd94v; >> query: (?x6487, 01cw7s) <- award_winner(?x6487, ?x12724), award_winner(?x6487, ?x2638), award(?x12724, ?x1232), ceremony(?x12833, ?x6487), ceremony(?x4382, ?x6487), ceremony(?x3978, ?x6487), ceremony(?x2703, ?x6487), ceremony(?x2212, ?x6487), ceremony(?x1565, ?x6487), ceremony(?x1389, ?x6487), ceremony(?x247, ?x6487), ?x1389 = 01c427, award_winner(?x2703, ?x4184), award_winner(?x2638, ?x3146), ?x12833 = 0257pw, ?x247 = 02wh75, ?x2212 = 02nbqh, ?x4184 = 01m3x5p, nominated_for(?x12724, ?x508), ?x1565 = 01c4_6, profession(?x2638, ?x220), ?x4382 = 03tk6z, award(?x140, ?x3978), ?x3146 = 0ggjt >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6 EVAL 01mh_q ceremony! 024fxq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mh_q ceremony! 01ckcd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mh_q ceremony! 024_41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mh_q ceremony! 01cw7s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mh_q ceremony! 025m98 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01mh_q ceremony! 02581c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony #20410-086qd PRED entity: 086qd PRED relation: award_nominee! PRED expected values: 01kp_1t => 122 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1358): 01wwvc5 (0.81 #53612, 0.81 #123547, 0.80 #135198), 01mxqyk (0.76 #158504, 0.75 #104900, 0.74 #111894), 02l840 (0.29 #157, 0.22 #2488, 0.13 #14141), 02x_h0 (0.22 #3614, 0.14 #1283, 0.04 #15267), 01w272y (0.14 #763, 0.11 #3094, 0.06 #5424), 0770cd (0.14 #381, 0.11 #2712, 0.06 #5042), 01yzl2 (0.14 #1282, 0.11 #3613, 0.06 #15266), 01wbgdv (0.14 #227, 0.11 #2558, 0.04 #7219), 0163kf (0.14 #2283, 0.11 #4614, 0.04 #16267), 01wmxfs (0.14 #158, 0.11 #2489, 0.04 #14142) >> Best rule #53612 for best value: >> intensional similarity = 3 >> extensional distance = 154 >> proper extension: 037hgm; >> query: (?x2138, ?x2451) <- nominated_for(?x2138, ?x7354), artists(?x671, ?x2138), award_nominee(?x2138, ?x2451) >> conf = 0.81 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 086qd award_nominee! 01kp_1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 69.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20409-01gqg3 PRED entity: 01gqg3 PRED relation: entity_involved PRED expected values: 04pwg => 59 concepts (38 used for prediction) PRED predicted values (max 10 best out of 338): 01s47p (0.40 #786, 0.33 #950, 0.29 #1111), 01m41_ (0.38 #2241, 0.33 #2404, 0.23 #2567), 0285m87 (0.33 #86, 0.31 #4927, 0.27 #1948), 024pcx (0.31 #4927, 0.27 #1948, 0.25 #802), 025ndl (0.31 #4927, 0.27 #1948, 0.25 #802), 03gk2 (0.31 #4927, 0.27 #1948, 0.25 #802), 06mkj (0.31 #4927, 0.27 #1948, 0.25 #802), 0j5b8 (0.31 #2185, 0.27 #2348, 0.18 #2511), 01fvhp (0.25 #574, 0.25 #253, 0.20 #735), 03gj2 (0.25 #492, 0.25 #171, 0.20 #653) >> Best rule #786 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0k4y6; >> query: (?x10176, 01s47p) <- taxonomy(?x10176, ?x939), combatants(?x10176, ?x9328), combatants(?x10176, ?x1778), locations(?x10176, ?x87), ?x1778 = 03gk2, entity_involved(?x6982, ?x9328), nationality(?x5249, ?x9328) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01gqg3 entity_involved 04pwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 38.000 0.400 http://example.org/base/culturalevent/event/entity_involved #20408-022_lg PRED entity: 022_lg PRED relation: film PRED expected values: 04gknr => 95 concepts (44 used for prediction) PRED predicted values (max 10 best out of 482): 04sh80 (0.05 #815, 0.02 #4127, 0.02 #4955), 03wy8t (0.05 #756, 0.02 #4068, 0.02 #4896), 0322yj (0.05 #824, 0.01 #2480, 0.01 #3308), 07bxqz (0.05 #814, 0.01 #2470, 0.01 #3298), 0bs5vty (0.05 #772, 0.01 #2428, 0.01 #3256), 0315rp (0.05 #692, 0.01 #2348, 0.01 #3176), 011xg5 (0.05 #689, 0.01 #2345, 0.01 #3173), 0k_9j (0.05 #673, 0.01 #2329, 0.01 #3157), 02mmwk (0.05 #615, 0.01 #2271, 0.01 #3099), 0jyb4 (0.05 #544, 0.01 #2200, 0.01 #3028) >> Best rule #815 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 016bx2; >> query: (?x1431, 04sh80) <- award(?x1431, ?x198), ?x198 = 040njc, profession(?x1431, ?x319), spouse(?x7632, ?x1431) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 022_lg film 04gknr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 44.000 0.053 http://example.org/film/director/film #20407-0v0d9 PRED entity: 0v0d9 PRED relation: contains! PRED expected values: 09c7w0 => 61 concepts (34 used for prediction) PRED predicted values (max 10 best out of 129): 09c7w0 (0.96 #15212, 0.85 #897, 0.77 #3), 0d060g (0.46 #5382, 0.09 #2684, 0.06 #7173), 07z1m (0.27 #4566, 0.14 #2778, 0.14 #3672), 02_286 (0.22 #4517, 0.03 #2729, 0.03 #3623), 01cx_ (0.19 #1090, 0.15 #1985, 0.15 #196), 059rby (0.18 #4494, 0.14 #6286, 0.11 #13441), 05kr_ (0.17 #5495, 0.09 #2684, 0.05 #7286), 02jx1 (0.16 #22463, 0.13 #24255, 0.13 #25152), 01x73 (0.16 #2801, 0.15 #3695, 0.09 #2684), 07ssc (0.15 #25097, 0.13 #26890, 0.13 #27785) >> Best rule #15212 for best value: >> intensional similarity = 4 >> extensional distance = 932 >> proper extension: 0b90_r; 0d060g; 05qx1; 03h2c; 0345_; 0k3nk; 05c74; 020d5; 01p8s; 0164b; ... >> query: (?x9505, 09c7w0) <- contains(?x2020, ?x9505), adjoins(?x335, ?x2020), contains(?x2020, ?x10440), ?x10440 = 0typ5 >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0v0d9 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 34.000 0.955 http://example.org/location/location/contains #20406-01sxd1 PRED entity: 01sxd1 PRED relation: type_of_union PRED expected values: 04ztj => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.91 #69, 0.89 #106, 0.89 #126), 01g63y (0.34 #272, 0.31 #188, 0.28 #314), 0jgjn (0.34 #272, 0.25 #529, 0.09 #28) >> Best rule #69 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 03xmy1; >> query: (?x7018, 04ztj) <- location_of_ceremony(?x7018, ?x11868), profession(?x7018, ?x2348), spouse(?x3849, ?x7018), profession(?x8556, ?x2348), ?x8556 = 01wqflx >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sxd1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.909 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20405-04ljl_l PRED entity: 04ljl_l PRED relation: award! PRED expected values: 0q9kd 02pby8 02784z 021npv => 45 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2316): 01ggc9 (0.69 #49486, 0.68 #32987, 0.67 #32986), 04__f (0.69 #49486, 0.67 #32986, 0.66 #29688), 0f276 (0.69 #49486, 0.67 #32986, 0.66 #29688), 02t_99 (0.62 #7901, 0.38 #11198, 0.04 #14495), 030g9z (0.62 #9163, 0.25 #12460, 0.14 #36286), 015grj (0.60 #3510, 0.11 #13401, 0.08 #16700), 01ycbq (0.60 #3810, 0.07 #13701, 0.05 #17000), 01tnxc (0.60 #5600, 0.04 #15491, 0.03 #18790), 0p__8 (0.50 #8296, 0.33 #1702, 0.25 #11593), 0170qf (0.40 #3872, 0.33 #575, 0.25 #7169) >> Best rule #49486 for best value: >> intensional similarity = 3 >> extensional distance = 214 >> proper extension: 09v7wsg; >> query: (?x102, ?x133) <- nominated_for(?x102, ?x1185), award_winner(?x102, ?x133), nominated_for(?x556, ?x1185) >> conf = 0.69 => this is the best rule for 3 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 057xs89; *> query: (?x102, 0q9kd) <- award(?x123, ?x102), award(?x1812, ?x102), nominated_for(?x102, ?x103), ?x1812 = 0fdv3 *> conf = 0.33 ranks of expected_values: 84, 701, 711 EVAL 04ljl_l award! 021npv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 45.000 17.000 0.686 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04ljl_l award! 02784z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 45.000 17.000 0.686 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04ljl_l award! 02pby8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 45.000 17.000 0.686 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04ljl_l award! 0q9kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 45.000 17.000 0.686 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20404-01wv9p PRED entity: 01wv9p PRED relation: religion PRED expected values: 0c8wxp => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 21): 0c8wxp (0.23 #6, 0.21 #861, 0.20 #1581), 0kpl (0.08 #145, 0.08 #1135, 0.08 #595), 03_gx (0.07 #14, 0.06 #284, 0.06 #3209), 01lp8 (0.06 #91, 0.06 #811, 0.06 #361), 06nzl (0.06 #465, 0.05 #15, 0.04 #105), 092bf5 (0.05 #376, 0.04 #871, 0.03 #511), 019cr (0.04 #236, 0.04 #191, 0.03 #11), 0kq2 (0.04 #108, 0.04 #153, 0.03 #333), 0flw86 (0.04 #1307, 0.04 #1082, 0.03 #1532), 04pk9 (0.04 #200, 0.02 #65, 0.02 #560) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 01qn8k; >> query: (?x4123, 0c8wxp) <- profession(?x4123, ?x131), actor(?x5529, ?x4123), friend(?x6187, ?x4123) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wv9p religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.225 http://example.org/people/person/religion #20403-0d_2fb PRED entity: 0d_2fb PRED relation: featured_film_locations PRED expected values: 06q1r => 76 concepts (46 used for prediction) PRED predicted values (max 10 best out of 49): 02_286 (0.21 #1946, 0.18 #3148, 0.17 #20), 030qb3t (0.13 #1242, 0.12 #1484, 0.12 #2445), 04jpl (0.13 #1695, 0.07 #1454, 0.06 #6992), 080h2 (0.09 #1227, 0.07 #1469, 0.05 #2672), 0cv3w (0.07 #1033, 0.02 #2236, 0.01 #4639), 02cl1 (0.07 #978), 052p7 (0.04 #1984, 0.03 #3186, 0.02 #1261), 0rh6k (0.04 #3129, 0.04 #5296, 0.04 #1927), 01_d4 (0.04 #1973, 0.03 #2213, 0.02 #3655), 06y57 (0.04 #2029, 0.02 #3231, 0.02 #2751) >> Best rule #1946 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 0gtvrv3; >> query: (?x2339, 02_286) <- film(?x3461, ?x2339), film_crew_role(?x2339, ?x2154), ?x2154 = 01vx2h, category(?x2339, ?x134) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d_2fb featured_film_locations 06q1r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 46.000 0.205 http://example.org/film/film/featured_film_locations #20402-0jfx1 PRED entity: 0jfx1 PRED relation: vacationer! PRED expected values: 05qtj => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 39): 03gh4 (0.13 #569, 0.13 #813, 0.11 #691), 0cv3w (0.08 #911, 0.07 #1157, 0.06 #1401), 05qtj (0.07 #682, 0.07 #804, 0.07 #1172), 0f2v0 (0.07 #673, 0.06 #795, 0.06 #551), 0b90_r (0.05 #736, 0.05 #492, 0.05 #614), 04jpl (0.05 #742, 0.05 #498, 0.04 #864), 06c62 (0.05 #819, 0.04 #697, 0.03 #575), 0261m (0.04 #590, 0.04 #712, 0.04 #834), 02_286 (0.04 #626, 0.04 #748, 0.03 #870), 0r0m6 (0.04 #1169, 0.03 #1413, 0.01 #4104) >> Best rule #569 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 01trhmt; 033wx9; 01wyz92; 027r8p; 01vw20h; 0127s7; 0h0yt; 09h4b5; 01vz0g4; 0k6yt1; ... >> query: (?x2444, 03gh4) <- award_nominee(?x2444, ?x398), award_nominee(?x4440, ?x2444), friend(?x4314, ?x2444) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #682 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 105 *> proper extension: 06y9c2; 02d9k; 012_53; 08b8vd; 01s3kv; 06gb2q; *> query: (?x2444, 05qtj) <- type_of_union(?x2444, ?x566), friend(?x4314, ?x2444) *> conf = 0.07 ranks of expected_values: 3 EVAL 0jfx1 vacationer! 05qtj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 119.000 0.135 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #20401-02ktrs PRED entity: 02ktrs PRED relation: award_winner! PRED expected values: 0275n3y 0bvhz9 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 135): 0jt3qpk (0.25 #733, 0.15 #457, 0.12 #319), 0gkxgfq (0.23 #794, 0.12 #380, 0.11 #518), 02rjjll (0.13 #1385, 0.12 #3869, 0.12 #1109), 013b2h (0.13 #3941, 0.11 #2423, 0.11 #1733), 05c1t6z (0.12 #705, 0.12 #15, 0.07 #429), 03nnm4t (0.12 #72, 0.07 #762, 0.07 #486), 0gx_st (0.12 #37, 0.05 #727, 0.04 #313), 056878 (0.12 #1688, 0.10 #3896, 0.09 #2378), 019bk0 (0.12 #1672, 0.10 #3880, 0.09 #2500), 03gwpw2 (0.12 #147, 0.08 #285, 0.06 #9) >> Best rule #733 for best value: >> intensional similarity = 2 >> extensional distance = 38 >> proper extension: 05sj55; >> query: (?x11519, 0jt3qpk) <- award_winner(?x3624, ?x11519), program(?x11519, ?x9788) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #5107 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 530 *> proper extension: 06lxn; *> query: (?x11519, ?x472) <- artists(?x671, ?x11519), award_winner(?x2577, ?x11519), ceremony(?x2577, ?x472) *> conf = 0.07 ranks of expected_values: 67, 117 EVAL 02ktrs award_winner! 0bvhz9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 108.000 108.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02ktrs award_winner! 0275n3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 108.000 108.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20400-02d478 PRED entity: 02d478 PRED relation: music PRED expected values: 09swkk => 76 concepts (44 used for prediction) PRED predicted values (max 10 best out of 79): 01tc9r (0.15 #275, 0.04 #3870, 0.04 #3233), 06fxnf (0.14 #69, 0.07 #491, 0.05 #1544), 04ls53 (0.14 #79, 0.07 #501, 0.02 #1344), 06449 (0.14 #42, 0.07 #464), 0150t6 (0.11 #1521, 0.07 #468, 0.06 #889), 0146pg (0.10 #642, 0.08 #2753, 0.07 #2118), 02bh9 (0.08 #261, 0.07 #2159, 0.06 #2370), 01njxvw (0.08 #401, 0.02 #1877, 0.02 #2088), 05cgy8 (0.08 #330), 01wl38s (0.08 #219) >> Best rule #275 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 04jn6y7; >> query: (?x4067, 01tc9r) <- film(?x3733, ?x4067), film(?x1286, ?x4067), ?x1286 = 07vc_9, award_nominee(?x968, ?x3733) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #717 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 0jzw; 04vr_f; 0dtfn; 011yd2; 0c_j9x; 07cz2; 07024; 0f4yh; 03hmt9b; 0h6r5; ... *> query: (?x4067, 09swkk) <- nominated_for(?x6909, ?x4067), nominated_for(?x68, ?x4067), nominated_for(?x2116, ?x4067), award_winner(?x68, ?x986), ?x6909 = 02qyntr *> conf = 0.03 ranks of expected_values: 25 EVAL 02d478 music 09swkk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 76.000 44.000 0.154 http://example.org/film/film/music #20399-033qdy PRED entity: 033qdy PRED relation: films! PRED expected values: 0bq3x => 88 concepts (40 used for prediction) PRED predicted values (max 10 best out of 70): 0cm2xh (0.17 #47, 0.02 #1307, 0.02 #2413), 081pw (0.11 #1106, 0.10 #1580, 0.10 #1739), 0bq3x (0.10 #345, 0.05 #3672, 0.04 #5566), 07s2s (0.08 #257, 0.05 #1994, 0.04 #2308), 07_nf (0.08 #225, 0.04 #1803, 0.04 #2118), 0kbq (0.08 #263, 0.04 #1051, 0.03 #1524), 08b3m (0.08 #305, 0.01 #934, 0.01 #1093), 026bk (0.08 #269, 0.01 #898, 0.01 #1057), 03qsdpk (0.08 #209, 0.01 #838, 0.01 #997), 07jq_ (0.07 #555, 0.04 #1342, 0.04 #712) >> Best rule #47 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 050xxm; 0cc5qkt; 02ylg6; 09tcg4; >> query: (?x6624, 0cm2xh) <- film_crew_role(?x6624, ?x468), ?x468 = 02r96rf, film(?x3461, ?x6624), country(?x6624, ?x94), titles(?x812, ?x6624), ?x3461 = 02l4pj >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #345 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 37 *> proper extension: 0c9t0y; 0m3gy; *> query: (?x6624, 0bq3x) <- film(?x1104, ?x6624), genre(?x6624, ?x571), titles(?x4205, ?x6624), music(?x6624, ?x3410), ?x571 = 03npn *> conf = 0.10 ranks of expected_values: 3 EVAL 033qdy films! 0bq3x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 88.000 40.000 0.167 http://example.org/film/film_subject/films #20398-01fc50 PRED entity: 01fc50 PRED relation: genre! PRED expected values: 02cbhg 02p86pb => 33 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1869): 01hvjx (0.71 #7856, 0.33 #11590, 0.25 #9723), 027pfg (0.60 #6862, 0.56 #12463, 0.50 #10596), 08fn5b (0.60 #6321, 0.50 #4454, 0.50 #2587), 0hfzr (0.60 #6331, 0.50 #4464, 0.50 #2597), 083skw (0.60 #6035, 0.50 #4168, 0.33 #11636), 02p86pb (0.60 #7177, 0.50 #5310, 0.33 #12778), 0fjyzt (0.60 #6571, 0.50 #4704, 0.33 #12172), 0ctb4g (0.60 #6178, 0.50 #4311, 0.33 #11779), 0y_9q (0.60 #6545, 0.50 #4678, 0.33 #12146), 0cf8qb (0.60 #6985, 0.50 #5118, 0.33 #12586) >> Best rule #7856 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 04t36; 0vgkd; >> query: (?x12340, 01hvjx) <- genre(?x6924, ?x12340), genre(?x5129, ?x12340), genre(?x3549, ?x12340), award(?x6924, ?x10747), ?x3549 = 017kct, nominated_for(?x1243, ?x5129), nominated_for(?x746, ?x5129), language(?x5129, ?x254), production_companies(?x6924, ?x1104), ?x746 = 04dn09n, story_by(?x6924, ?x5346), ?x1243 = 0gr0m, award_winner(?x5129, ?x2551) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #7177 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 060__y; *> query: (?x12340, 02p86pb) <- genre(?x6924, ?x12340), genre(?x4734, ?x12340), genre(?x810, ?x12340), ?x6924 = 08cfr1, production_companies(?x810, ?x902), award_winner(?x810, ?x1622), currency(?x810, ?x170), ?x170 = 09nqf, nominated_for(?x1243, ?x810), film(?x777, ?x4734), ?x1243 = 0gr0m, ?x1622 = 09rp4r_ *> conf = 0.60 ranks of expected_values: 6, 82 EVAL 01fc50 genre! 02p86pb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 33.000 14.000 0.714 http://example.org/film/film/genre EVAL 01fc50 genre! 02cbhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 33.000 14.000 0.714 http://example.org/film/film/genre #20397-043hg PRED entity: 043hg PRED relation: award_winner! PRED expected values: 0bqsk5 => 108 concepts (84 used for prediction) PRED predicted values (max 10 best out of 264): 02x4wr9 (0.32 #1299, 0.32 #1001, 0.31 #1298), 02x4sn8 (0.31 #1298, 0.31 #30626, 0.31 #31923), 03hl6lc (0.31 #1298, 0.31 #30626, 0.31 #31923), 0gr51 (0.31 #1298, 0.31 #30626, 0.31 #31923), 02wkmx (0.31 #1298, 0.31 #30626, 0.31 #31923), 0g9wd99 (0.31 #1298, 0.31 #30626, 0.31 #31923), 02qyp19 (0.31 #1298, 0.30 #33656, 0.30 #33655), 09d28z (0.28 #1169, 0.22 #1602, 0.20 #303), 02pqp12 (0.28 #936, 0.20 #70, 0.16 #1369), 04dn09n (0.24 #910, 0.16 #1343, 0.16 #1777) >> Best rule #1299 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 014zcr; 06cv1; 05kfs; 02kxbwx; 0h1p; 021bk; 0184dt; 02kxbx3; 0bzyh; 06m6z6; ... >> query: (?x6748, ?x2532) <- type_of_union(?x6748, ?x1873), profession(?x6748, ?x524), award(?x6748, ?x2532), ?x2532 = 02x4wr9 >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #32357 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2142 *> proper extension: 04411; 040db; 01w8sf; 03pm9; 03hnd; 0lcx; 01tz6vs; 0hky; 037jz; 082_p; ... *> query: (?x6748, ?x601) <- type_of_union(?x6748, ?x1873), profession(?x6748, ?x524), award(?x6748, ?x2532), award(?x826, ?x2532), award_winner(?x601, ?x826) *> conf = 0.02 ranks of expected_values: 211 EVAL 043hg award_winner! 0bqsk5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 108.000 84.000 0.320 http://example.org/award/award_category/winners./award/award_honor/award_winner #20396-09rsjpv PRED entity: 09rsjpv PRED relation: music PRED expected values: 01m3b1t => 123 concepts (100 used for prediction) PRED predicted values (max 10 best out of 89): 0146pg (0.38 #10, 0.36 #430, 0.36 #220), 02jxkw (0.20 #774, 0.11 #2673, 0.08 #3307), 02bh9 (0.11 #2582, 0.10 #3004, 0.09 #3427), 03h610 (0.10 #4088, 0.07 #709, 0.05 #2608), 02cyfz (0.08 #34, 0.07 #454, 0.07 #244), 07qy0b (0.07 #469, 0.05 #892, 0.04 #3214), 02jxmr (0.07 #706, 0.03 #7247, 0.03 #11473), 02ryx0 (0.07 #742, 0.03 #2641, 0.02 #3063), 01vrncs (0.07 #648), 081pw (0.06 #843) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0dtfn; 0fdv3; 0ddjy; 0ddt_; 0184tc; 0g9yrw; 01hw5kk; 04mcw4; 0k_9j; 0f3m1; ... >> query: (?x3517, 0146pg) <- executive_produced_by(?x3517, ?x1387), ?x1387 = 0343h, film(?x574, ?x3517), genre(?x3517, ?x53) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #3725 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 05znbh7; *> query: (?x3517, 01m3b1t) <- film_release_distribution_medium(?x3517, ?x81), genre(?x3517, ?x2605), country(?x3517, ?x94), ?x2605 = 03g3w *> conf = 0.03 ranks of expected_values: 26 EVAL 09rsjpv music 01m3b1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 123.000 100.000 0.385 http://example.org/film/film/music #20395-05kwx2 PRED entity: 05kwx2 PRED relation: film PRED expected values: 031hcx 031786 => 78 concepts (47 used for prediction) PRED predicted values (max 10 best out of 357): 02wk7b (0.74 #5339, 0.60 #23140, 0.60 #10678), 0m313 (0.42 #3572, 0.05 #44501, 0.03 #39162), 085bd1 (0.20 #447, 0.06 #2227, 0.01 #7566), 031778 (0.20 #313, 0.05 #3872, 0.02 #7432), 031786 (0.20 #1265, 0.05 #4824, 0.02 #8384), 0qf2t (0.20 #828), 07yvsn (0.20 #553), 011yl_ (0.16 #4141, 0.05 #44501, 0.03 #39162), 031hcx (0.16 #4823, 0.03 #8383, 0.03 #39162), 09tkzy (0.16 #5016, 0.03 #39162, 0.03 #26701) >> Best rule #5339 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 03hzl42; 016ggh; 02my3z; >> query: (?x6227, ?x1813) <- nominated_for(?x6227, ?x1813), award_nominee(?x1738, ?x6227), ?x1738 = 0170pk >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1265 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 03y1mlp; *> query: (?x6227, 031786) <- nominated_for(?x6227, ?x6272), ?x6272 = 041td_, profession(?x6227, ?x1032) *> conf = 0.20 ranks of expected_values: 5, 9 EVAL 05kwx2 film 031786 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 47.000 0.742 http://example.org/film/actor/film./film/performance/film EVAL 05kwx2 film 031hcx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 78.000 47.000 0.742 http://example.org/film/actor/film./film/performance/film #20394-06j8wx PRED entity: 06j8wx PRED relation: film PRED expected values: 0h2zvzr => 92 concepts (55 used for prediction) PRED predicted values (max 10 best out of 638): 08y2fn (0.53 #83822, 0.08 #5350, 0.08 #26750), 08s6mr (0.25 #1314, 0.05 #4880, 0.03 #33884), 04sskp (0.25 #1392, 0.03 #6742, 0.01 #8525), 076tq0z (0.25 #459, 0.03 #5809, 0.01 #7592), 04k9y6 (0.25 #1037, 0.03 #6387), 05fcbk7 (0.25 #460, 0.03 #5810), 03shpq (0.25 #1440, 0.01 #8573), 01chpn (0.25 #1105, 0.01 #8238), 0bv8h2 (0.25 #586, 0.01 #7719), 03cvvlg (0.25 #1438) >> Best rule #83822 for best value: >> intensional similarity = 3 >> extensional distance = 1458 >> proper extension: 02wb6yq; 04pg29; 0f3zsq; 01vq3nl; >> query: (?x5422, ?x7424) <- profession(?x5422, ?x1032), ?x1032 = 02hrh1q, nominated_for(?x5422, ?x7424) >> conf = 0.53 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06j8wx film 0h2zvzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 55.000 0.526 http://example.org/film/actor/film./film/performance/film #20393-0gwgn1k PRED entity: 0gwgn1k PRED relation: film_release_region PRED expected values: 03spz => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 85): 059j2 (0.86 #319, 0.84 #465, 0.26 #3679), 02vzc (0.80 #337, 0.79 #483, 0.26 #3697), 03_3d (0.78 #297, 0.78 #443, 0.25 #3657), 035qy (0.73 #322, 0.71 #468, 0.21 #3682), 0154j (0.71 #295, 0.70 #441, 0.21 #3655), 06t2t (0.63 #492, 0.62 #346, 0.16 #3706), 03spz (0.59 #378, 0.59 #524, 0.16 #3738), 05v8c (0.54 #304, 0.53 #450, 0.15 #3664), 06qd3 (0.49 #472, 0.48 #326, 0.14 #3686), 047yc (0.43 #315, 0.41 #461, 0.11 #3675) >> Best rule #319 for best value: >> intensional similarity = 4 >> extensional distance = 263 >> proper extension: 0b76d_m; 0ds35l9; 0g56t9t; 0gtsx8c; 02vxq9m; 028_yv; 0c3ybss; 02vp1f_; 011yrp; 07gp9; ... >> query: (?x9322, 059j2) <- film_release_region(?x9322, ?x2645), film_release_region(?x9322, ?x304), ?x2645 = 03h64, ?x304 = 0d0vqn >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #378 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 263 *> proper extension: 0b76d_m; 0ds35l9; 0g56t9t; 0gtsx8c; 02vxq9m; 028_yv; 0c3ybss; 02vp1f_; 011yrp; 07gp9; ... *> query: (?x9322, 03spz) <- film_release_region(?x9322, ?x2645), film_release_region(?x9322, ?x304), ?x2645 = 03h64, ?x304 = 0d0vqn *> conf = 0.59 ranks of expected_values: 7 EVAL 0gwgn1k film_release_region 03spz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 45.000 45.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20392-07y8l9 PRED entity: 07y8l9 PRED relation: film PRED expected values: 02vyyl8 => 82 concepts (66 used for prediction) PRED predicted values (max 10 best out of 405): 04f52jw (0.33 #2218, 0.20 #439), 07pd_j (0.25 #4738, 0.05 #39140, 0.04 #67608), 01738w (0.25 #4681, 0.03 #13576, 0.03 #53373), 01k0xy (0.25 #4831, 0.03 #53373, 0.03 #74726), 01k0vq (0.25 #4865, 0.03 #53373, 0.03 #74726), 05m_jsg (0.20 #641, 0.17 #2420, 0.03 #53373), 0bvn25 (0.20 #50, 0.17 #1829, 0.02 #21399), 0fphf3v (0.20 #1353, 0.17 #3132, 0.01 #26260), 0bs5f0b (0.20 #1613, 0.17 #3392), 07f_t4 (0.20 #1323, 0.17 #3102) >> Best rule #2218 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 04mhbh; >> query: (?x5488, 04f52jw) <- film(?x5488, ?x6762), ?x6762 = 047rkcm, gender(?x5488, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13414 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 189 *> proper extension: 047jhq; *> query: (?x5488, 02vyyl8) <- actor(?x6375, ?x5488), languages(?x5488, ?x254) *> conf = 0.01 ranks of expected_values: 281 EVAL 07y8l9 film 02vyyl8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 82.000 66.000 0.333 http://example.org/film/actor/film./film/performance/film #20391-05sq20 PRED entity: 05sq20 PRED relation: artist! PRED expected values: 01cszh 03rhqg => 84 concepts (49 used for prediction) PRED predicted values (max 10 best out of 83): 033hn8 (0.33 #156, 0.12 #298, 0.11 #440), 03rhqg (0.25 #300, 0.24 #442, 0.20 #16), 015_1q (0.21 #588, 0.20 #20, 0.19 #446), 043ljr (0.20 #17, 0.17 #159, 0.12 #301), 0bfp0l (0.20 #107, 0.17 #249, 0.12 #391), 02p11jq (0.17 #581, 0.17 #155, 0.08 #865), 01cl2y (0.17 #173, 0.10 #31, 0.06 #315), 017l96 (0.15 #587, 0.08 #161, 0.08 #1583), 01cf93 (0.12 #343, 0.08 #201, 0.05 #2626), 041bnw (0.12 #354, 0.08 #212, 0.04 #496) >> Best rule #156 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01lmj3q; 01ww2fs; 0ggjt; 03cfjg; 0x3b7; 02cx90; 01k_r5b; 05sq0m; 051m56; >> query: (?x6562, 033hn8) <- award_winner(?x6467, ?x6562), award_nominee(?x133, ?x6562), ?x6467 = 01l47f5 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #300 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 01fh9; *> query: (?x6562, 03rhqg) <- award_nominee(?x2518, ?x6562), profession(?x6562, ?x1032), ?x2518 = 016sp_ *> conf = 0.25 ranks of expected_values: 2, 39 EVAL 05sq20 artist! 03rhqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 49.000 0.333 http://example.org/music/record_label/artist EVAL 05sq20 artist! 01cszh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 84.000 49.000 0.333 http://example.org/music/record_label/artist #20390-01q4qv PRED entity: 01q4qv PRED relation: award_winner! PRED expected values: 0bz6l9 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 135): 0bz6l9 (0.18 #5462, 0.18 #5745, 0.18 #6026), 0n8_m93 (0.12 #117, 0.06 #257, 0.06 #5604), 0bvfqq (0.12 #33, 0.06 #5604, 0.04 #6587), 02yv_b (0.12 #25, 0.06 #5604, 0.04 #6587), 0fzrtf (0.12 #62, 0.06 #5604, 0.04 #6587), 0dthsy (0.12 #67, 0.06 #5604, 0.04 #6587), 09pj68 (0.12 #104, 0.03 #1644, 0.02 #3324), 02q690_ (0.08 #1605, 0.04 #1745, 0.03 #3285), 027n06w (0.06 #1613, 0.03 #2873, 0.02 #3293), 0fv89q (0.06 #402, 0.06 #262, 0.06 #5604) >> Best rule #5462 for best value: >> intensional similarity = 4 >> extensional distance = 1523 >> proper extension: 02b9g4; >> query: (?x3177, ?x3332) <- nominated_for(?x3177, ?x11110), award(?x3177, ?x601), honored_for(?x3332, ?x11110), ceremony(?x601, ?x78) >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q4qv award_winner! 0bz6l9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.177 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20389-01nm3s PRED entity: 01nm3s PRED relation: film PRED expected values: 01jnc_ => 92 concepts (62 used for prediction) PRED predicted values (max 10 best out of 514): 01jzyf (0.27 #4158, 0.22 #2381, 0.11 #604), 0872p_c (0.22 #1951, 0.22 #174, 0.18 #3728), 02tgz4 (0.22 #3278, 0.22 #1501, 0.18 #5055), 0422v0 (0.22 #3548, 0.22 #1771, 0.18 #5325), 031t2d (0.22 #2029, 0.18 #3806, 0.11 #252), 0407yj_ (0.22 #2257, 0.18 #4034, 0.11 #480), 025s1wg (0.22 #3470, 0.18 #5247, 0.11 #1693), 03m8y5 (0.22 #404, 0.18 #3958, 0.11 #2181), 06lpmt (0.11 #2456, 0.11 #679, 0.09 #4233), 0661m4p (0.11 #2149, 0.11 #372, 0.09 #3926) >> Best rule #4158 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 046chh; >> query: (?x4004, 01jzyf) <- film(?x4004, ?x3151), film(?x4004, ?x1331), ?x1331 = 01vfqh, film_release_region(?x3151, ?x87) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nm3s film 01jnc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 62.000 0.273 http://example.org/film/actor/film./film/performance/film #20388-04kqk PRED entity: 04kqk PRED relation: company! PRED expected values: 0dq_5 => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 44): 0dq_5 (0.54 #2510, 0.52 #3216, 0.50 #2322), 0krdk (0.54 #2499, 0.50 #2311, 0.43 #5703), 0dq3c (0.44 #1554, 0.42 #2494, 0.34 #3200), 05_wyz (0.42 #2511, 0.36 #2323, 0.35 #2417), 060c4 (0.41 #2307, 0.39 #3013, 0.39 #2401), 01yc02 (0.36 #2313, 0.33 #2501, 0.29 #3677), 09d6p2 (0.29 #2512, 0.27 #2324, 0.26 #2418), 01kr6k (0.23 #2332, 0.22 #2426, 0.20 #3743), 02211by (0.17 #7351, 0.17 #2496, 0.15 #2967), 02y6fz (0.17 #7351, 0.14 #307, 0.12 #7209) >> Best rule #2510 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 01zpmq; >> query: (?x13750, 0dq_5) <- place_founded(?x13750, ?x3677), citytown(?x13750, ?x3125), state_province_region(?x13750, ?x1227), adjoins(?x7369, ?x3677) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04kqk company! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 182.000 0.542 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #20387-02lm0t PRED entity: 02lm0t PRED relation: location PRED expected values: 01cx_ => 161 concepts (38 used for prediction) PRED predicted values (max 10 best out of 231): 09c7w0 (0.45 #4825, 0.33 #5627, 0.01 #29731), 030qb3t (0.29 #21780, 0.26 #23388, 0.20 #24191), 059rby (0.19 #19303, 0.18 #1624, 0.12 #820), 0cr3d (0.19 #3360, 0.14 #25056, 0.13 #25859), 01n7q (0.17 #19350, 0.12 #867, 0.09 #1671), 02_286 (0.16 #19324, 0.14 #23342, 0.13 #21734), 0rh6k (0.12 #808, 0.12 #4, 0.09 #1612), 01ktz1 (0.12 #926, 0.12 #122, 0.09 #1730), 06wxw (0.12 #228, 0.09 #5050, 0.09 #1836), 0r0m6 (0.12 #1022, 0.09 #1826, 0.09 #21915) >> Best rule #4825 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 099p5; 03_2y; 032r1; >> query: (?x13105, 09c7w0) <- gender(?x13105, ?x231), location(?x13105, ?x4776), nationality(?x13105, ?x94), contains(?x4776, ?x6770), ?x6770 = 01hnb >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #24271 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 78 *> proper extension: 036hf4; *> query: (?x13105, 01cx_) <- gender(?x13105, ?x231), participant(?x13105, ?x1898), type_of_union(?x13105, ?x566), ?x231 = 05zppz, currency(?x13105, ?x170) *> conf = 0.04 ranks of expected_values: 92 EVAL 02lm0t location 01cx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 161.000 38.000 0.455 http://example.org/people/person/places_lived./people/place_lived/location #20386-016ztl PRED entity: 016ztl PRED relation: genre PRED expected values: 03k9fj 0jxy => 135 concepts (69 used for prediction) PRED predicted values (max 10 best out of 112): 05p553 (0.90 #6211, 0.63 #2029, 0.60 #3822), 02kdv5l (0.83 #2863, 0.72 #7523, 0.69 #1787), 01hmnh (0.77 #6924, 0.71 #7282, 0.69 #3460), 0jxy (0.70 #2787, 0.67 #878, 0.60 #1473), 03k9fj (0.69 #6936, 0.66 #5621, 0.60 #726), 03g3w (0.60 #5394, 0.16 #3961, 0.14 #1095), 02l7c8 (0.47 #1683, 0.44 #6224, 0.38 #1564), 01jfsb (0.45 #7534, 0.44 #4903, 0.43 #2874), 04t36 (0.44 #5854, 0.25 #1912, 0.23 #1553), 0lsxr (0.43 #1199, 0.32 #2034, 0.29 #4423) >> Best rule #6211 for best value: >> intensional similarity = 6 >> extensional distance = 215 >> proper extension: 011yrp; 03m8y5; 0sxns; 09dv8h; 0h63gl9; 0jqkh; 05dptj; 02x0fs9; >> query: (?x5955, 05p553) <- genre(?x5955, ?x2540), genre(?x5955, ?x53), ?x53 = 07s9rl0, film(?x2156, ?x5955), genre(?x9668, ?x2540), ?x9668 = 025ljp >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2787 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 25 *> proper extension: 05dfy_; *> query: (?x5955, 0jxy) <- genre(?x5955, ?x53), genre(?x6543, ?x53), genre(?x6536, ?x53), titles(?x53, ?x253), actor(?x5955, ?x1382), film_release_region(?x6536, ?x87), nominated_for(?x1336, ?x6543) *> conf = 0.70 ranks of expected_values: 4, 5 EVAL 016ztl genre 0jxy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 135.000 69.000 0.899 http://example.org/film/film/genre EVAL 016ztl genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 135.000 69.000 0.899 http://example.org/film/film/genre #20385-0kszw PRED entity: 0kszw PRED relation: award PRED expected values: 0bfvw2 0gqwc => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 234): 0gqwc (0.41 #461, 0.18 #852, 0.15 #2416), 02z0dfh (0.38 #462, 0.13 #29329, 0.12 #30894), 0ck27z (0.32 #11427, 0.21 #15337, 0.20 #15728), 09qv_s (0.30 #143, 0.16 #1316, 0.09 #3662), 04kxsb (0.30 #118, 0.15 #900, 0.12 #2073), 0gqy2 (0.27 #1327, 0.25 #154, 0.12 #936), 0f4x7 (0.25 #31, 0.16 #1204, 0.13 #3550), 05pcn59 (0.24 #2814, 0.23 #4769, 0.23 #3596), 09sdmz (0.22 #1368, 0.15 #195, 0.09 #977), 0bdwqv (0.20 #162, 0.15 #944, 0.11 #1335) >> Best rule #461 for best value: >> intensional similarity = 2 >> extensional distance = 27 >> proper extension: 04jlgp; 03dpqd; >> query: (?x2531, 0gqwc) <- award_winner(?x2880, ?x2531), ?x2880 = 02ppm4q >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1, 67 EVAL 0kszw award 0gqwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.414 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0kszw award 0bfvw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 109.000 109.000 0.414 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20384-02k6rq PRED entity: 02k6rq PRED relation: gender PRED expected values: 05zppz => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #157, 0.73 #159, 0.72 #195), 02zsn (0.36 #12, 0.35 #6, 0.34 #20) >> Best rule #157 for best value: >> intensional similarity = 2 >> extensional distance = 1867 >> proper extension: 01sb5r; 07c37; 03z0l6; >> query: (?x2045, 05zppz) <- student(?x6127, ?x2045), institution(?x734, ?x6127) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02k6rq gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.727 http://example.org/people/person/gender #20383-0jzc PRED entity: 0jzc PRED relation: countries_spoken_in PRED expected values: 02k54 => 79 concepts (48 used for prediction) PRED predicted values (max 10 best out of 310): 01z88t (0.73 #1620, 0.71 #4696, 0.71 #2592), 04gqr (0.73 #1620, 0.71 #4696, 0.71 #2592), 02khs (0.73 #1620, 0.71 #4696, 0.71 #2592), 0604m (0.73 #1620, 0.71 #4696, 0.71 #2592), 05l8y (0.71 #2592, 0.71 #1782, 0.70 #2264), 06tgw (0.71 #2592, 0.71 #1782, 0.70 #2264), 01p1b (0.71 #2592, 0.71 #1782, 0.70 #2264), 07ytt (0.50 #624, 0.40 #1437, 0.40 #949), 0162v (0.50 #531, 0.40 #693, 0.33 #208), 034m8 (0.50 #626, 0.40 #788, 0.33 #303) >> Best rule #1620 for best value: >> intensional similarity = 10 >> extensional distance = 12 >> proper extension: 06mp7; 05zjd; 02bv9; 01gp_d; >> query: (?x5359, ?x7413) <- language(?x6365, ?x5359), language(?x763, ?x5359), genre(?x6365, ?x258), official_language(?x7413, ?x5359), film(?x2390, ?x6365), countries_spoken_in(?x5359, ?x279), currency(?x7413, ?x170), service_language(?x610, ?x5359), nominated_for(?x2422, ?x763), film(?x3139, ?x763) >> conf = 0.73 => this is the best rule for 4 predicted values *> Best rule #970 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 04306rv; 06b_j; *> query: (?x5359, ?x1499) <- language(?x6365, ?x5359), language(?x1685, ?x5359), genre(?x6365, ?x8467), ?x1685 = 072x7s, ?x8467 = 0gf28, official_language(?x4302, ?x5359), film_release_region(?x559, ?x4302), adjoins(?x4302, ?x1499), film(?x1765, ?x6365), olympics(?x4302, ?x778), medal(?x4302, ?x422) *> conf = 0.24 ranks of expected_values: 111 EVAL 0jzc countries_spoken_in 02k54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 79.000 48.000 0.726 http://example.org/language/human_language/countries_spoken_in #20382-01l0__ PRED entity: 01l0__ PRED relation: team! PRED expected values: 0d3mlc => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 74): 0d3mlc (0.33 #100, 0.25 #216, 0.20 #449), 0f1pyf (0.25 #140, 0.20 #490, 0.13 #581), 0g9zjp (0.25 #204, 0.20 #554, 0.13 #581), 0djvzd (0.25 #157, 0.06 #849, 0.05 #1427), 02vl_pz (0.20 #253, 0.16 #233, 0.14 #601), 0dhrqx (0.20 #285, 0.16 #233, 0.14 #633), 02rnns (0.20 #510, 0.13 #581, 0.12 #968), 07nvmx (0.20 #544, 0.13 #581, 0.11 #1038), 0879xc (0.20 #502, 0.13 #581, 0.09 #1156), 09l9xt (0.20 #494, 0.13 #581, 0.09 #1156) >> Best rule #100 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 049f05; >> query: (?x10557, 0d3mlc) <- position(?x10557, ?x530), position(?x10557, ?x203), position(?x10557, ?x63), position(?x10557, ?x60), current_club(?x7294, ?x10557), current_club(?x59, ?x10557), ?x203 = 0dgrmp, ?x60 = 02nzb8, ?x63 = 02sdk9v, ?x7294 = 03xh50, ?x530 = 02_j1w, team(?x927, ?x59), sport(?x59, ?x471), team(?x927, ?x1697), teams(?x390, ?x59), gender(?x927, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l0__ team! 0d3mlc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.333 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #20381-03h_fk5 PRED entity: 03h_fk5 PRED relation: award PRED expected values: 01by1l 02f716 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 294): 01dpdh (0.76 #47851, 0.76 #25884, 0.74 #32941), 01c9f2 (0.76 #47851, 0.76 #25884, 0.74 #32941), 02gm9n (0.76 #47851, 0.76 #25884, 0.74 #32941), 024fz9 (0.76 #47851, 0.76 #25884, 0.74 #32941), 03x3wf (0.76 #47851, 0.76 #25884, 0.74 #32941), 01by1l (0.52 #1680, 0.49 #4817, 0.44 #504), 0f4x7 (0.43 #31, 0.37 #7091, 0.35 #8267), 01c92g (0.41 #4802, 0.26 #3626, 0.22 #4018), 0c4z8 (0.39 #4776, 0.25 #13408, 0.24 #15760), 02x17c2 (0.33 #603, 0.20 #4916, 0.18 #18827) >> Best rule #47851 for best value: >> intensional similarity = 2 >> extensional distance = 1907 >> proper extension: 03j90; >> query: (?x2807, ?x4183) <- award_winner(?x4183, ?x2807), ceremony(?x4183, ?x139) >> conf = 0.76 => this is the best rule for 5 predicted values *> Best rule #1680 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 21 *> proper extension: 013qvn; *> query: (?x2807, 01by1l) <- celebrities_impersonated(?x3649, ?x2807), artist(?x3265, ?x2807) *> conf = 0.52 ranks of expected_values: 6, 16 EVAL 03h_fk5 award 02f716 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 150.000 150.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03h_fk5 award 01by1l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 150.000 150.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20380-01nn6c PRED entity: 01nn6c PRED relation: artist! PRED expected values: 017l96 043g7l => 206 concepts (198 used for prediction) PRED predicted values (max 10 best out of 119): 033hn8 (0.29 #714, 0.13 #6036, 0.11 #14719), 01dtcb (0.25 #186, 0.21 #2708, 0.20 #1588), 03rhqg (0.23 #2397, 0.22 #2818, 0.22 #2117), 01clyr (0.21 #733, 0.14 #3815, 0.13 #11237), 08pn_9 (0.21 #832, 0.04 #6014, 0.04 #6154), 01cl0d (0.20 #54, 0.15 #4537, 0.12 #1035), 01cl2y (0.20 #29, 0.14 #730, 0.14 #590), 015mlw (0.20 #86, 0.07 #647, 0.07 #3869), 02p4jf0 (0.20 #75, 0.06 #3018, 0.04 #2597), 0fb0v (0.17 #1268, 0.15 #428, 0.15 #4070) >> Best rule #714 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0134tg; 033s6; 0p76z; >> query: (?x3266, 033hn8) <- artists(?x6107, ?x3266), artists(?x1000, ?x3266), ?x6107 = 0126t5, artist(?x2149, ?x3266), ?x1000 = 0xhtw >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3801 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 05crg7; 06br6t; *> query: (?x3266, 017l96) <- role(?x3266, ?x212), artists(?x2809, ?x3266), ?x2809 = 05w3f *> conf = 0.16 ranks of expected_values: 11, 22 EVAL 01nn6c artist! 043g7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 206.000 198.000 0.286 http://example.org/music/record_label/artist EVAL 01nn6c artist! 017l96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 206.000 198.000 0.286 http://example.org/music/record_label/artist #20379-01h4rj PRED entity: 01h4rj PRED relation: nationality PRED expected values: 09c7w0 => 126 concepts (125 used for prediction) PRED predicted values (max 10 best out of 77): 09c7w0 (0.90 #3003, 0.89 #6117, 0.89 #6518), 0gx1l (0.33 #11743), 0kpys (0.33 #11743), 03_3d (0.26 #4205, 0.03 #1106, 0.02 #2307), 02jx1 (0.19 #1233, 0.19 #1733, 0.18 #1833), 0d060g (0.12 #807, 0.12 #1607, 0.12 #907), 07ssc (0.12 #715, 0.11 #1215, 0.11 #515), 03rjj (0.12 #905, 0.09 #2206, 0.08 #805), 0f8l9c (0.11 #1922, 0.09 #1722, 0.09 #1622), 03rk0 (0.11 #5759, 0.10 #5860, 0.09 #1746) >> Best rule #3003 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 044mfr; 0bbxd3; >> query: (?x9709, 09c7w0) <- place_of_birth(?x9709, ?x1523), ?x1523 = 030qb3t, gender(?x9709, ?x231) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01h4rj nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 125.000 0.905 http://example.org/people/person/nationality #20378-02r7lqg PRED entity: 02r7lqg PRED relation: sport PRED expected values: 03tmr => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 58): 03tmr (0.89 #334, 0.88 #325, 0.88 #435), 02vx4 (0.82 #557, 0.79 #614, 0.77 #519), 0jm_ (0.60 #188, 0.59 #482, 0.55 #244), 09xp_ (0.50 #181, 0.11 #261, 0.10 #416), 018jz (0.47 #421, 0.30 #200, 0.29 #320), 018w8 (0.15 #575, 0.12 #115, 0.12 #96), 039yzs (0.15 #575, 0.12 #322, 0.11 #261), 0z74 (0.11 #261, 0.10 #416, 0.02 #506), 037hz (0.02 #102), 01yfj (0.02 #102) >> Best rule #334 for best value: >> intensional similarity = 17 >> extensional distance = 16 >> proper extension: 0j86l; >> query: (?x10142, 03tmr) <- position(?x10142, ?x5234), position(?x10142, ?x3724), teams(?x10141, ?x10142), team(?x2918, ?x10142), origin(?x5858, ?x10141), award(?x5858, ?x9828), contains(?x94, ?x10141), artists(?x671, ?x5858), artist(?x8489, ?x5858), ?x5234 = 02qvdc, location(?x1486, ?x10141), position(?x13661, ?x3724), position(?x12977, ?x3724), position(?x5233, ?x3724), ?x12977 = 0jnkr, ?x5233 = 0j5m6, ?x13661 = 0jnr3 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r7lqg sport 03tmr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.889 http://example.org/sports/sports_team/sport #20377-02qhqz4 PRED entity: 02qhqz4 PRED relation: genre PRED expected values: 02kdv5l => 48 concepts (28 used for prediction) PRED predicted values (max 10 best out of 114): 05p553 (0.97 #2107, 0.65 #821, 0.63 #939), 02kdv5l (0.93 #1055, 0.91 #1291, 0.90 #1174), 01hmnh (0.91 #252, 0.91 #1538, 0.89 #2003), 01jfsb (0.70 #2463, 0.67 #3170, 0.61 #247), 07s9rl0 (0.67 #118, 0.66 #2103, 0.57 #2335), 0btmb (0.61 #318, 0.50 #84, 0.47 #784), 0lsxr (0.34 #3049, 0.28 #2460, 0.27 #3167), 02l7c8 (0.33 #2118, 0.24 #2350, 0.21 #1768), 01t_vv (0.23 #2154, 0.09 #1169, 0.07 #2270), 06nbt (0.20 #1170, 0.09 #1169, 0.07 #2126) >> Best rule #2107 for best value: >> intensional similarity = 15 >> extensional distance = 347 >> proper extension: 027qgy; 047q2k1; 0209xj; 0hmr4; 0pv2t; 026390q; 03m4mj; 0sxfd; 05cj_j; 06rmdr; ... >> query: (?x2153, 05p553) <- genre(?x2153, ?x10185), genre(?x2153, ?x6888), genre(?x7208, ?x6888), genre(?x6528, ?x6888), genre(?x1511, ?x6888), film_release_region(?x6528, ?x4743), film_release_region(?x6528, ?x1499), film_release_region(?x6528, ?x1355), ?x7208 = 0b6l1st, ?x1511 = 0340hj, genre(?x10072, ?x10185), ?x1355 = 0h7x, ?x1499 = 01znc_, ?x10072 = 099bhp, ?x4743 = 03spz >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #1055 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 41 *> proper extension: 034b6k; *> query: (?x2153, 02kdv5l) <- genre(?x2153, ?x6888), genre(?x2153, ?x2540), ?x6888 = 04pbhw, genre(?x8717, ?x2540), genre(?x1076, ?x2540), genre(?x10555, ?x2540), ?x10555 = 06xkst, film(?x1735, ?x2153), nominated_for(?x930, ?x1076), genre(?x8717, ?x53), production_companies(?x1076, ?x1686) *> conf = 0.93 ranks of expected_values: 2 EVAL 02qhqz4 genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 48.000 28.000 0.968 http://example.org/film/film/genre #20376-019g40 PRED entity: 019g40 PRED relation: instrumentalists! PRED expected values: 0342h => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 82): 0342h (0.61 #621, 0.60 #4421, 0.58 #2206), 05148p4 (0.60 #21, 0.50 #637, 0.50 #109), 018vs (0.43 #629, 0.39 #2214, 0.31 #2567), 03bx0bm (0.38 #705, 0.36 #2290), 02hnl (0.25 #651, 0.20 #2236, 0.19 #2589), 0l14md (0.20 #8, 0.17 #96, 0.14 #184), 026t6 (0.20 #3, 0.17 #91, 0.14 #179), 026g73 (0.20 #70, 0.17 #158, 0.14 #246), 04rzd (0.18 #654, 0.10 #302, 0.09 #2239), 03qjg (0.17 #2518, 0.17 #2253, 0.17 #2694) >> Best rule #621 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 02fybl; >> query: (?x1953, 0342h) <- profession(?x1953, ?x220), ?x220 = 016z4k, role(?x1953, ?x1466), participant(?x2227, ?x1953) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019g40 instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.607 http://example.org/music/instrument/instrumentalists #20375-06krf3 PRED entity: 06krf3 PRED relation: currency PRED expected values: 09nqf => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.84 #15, 0.83 #8, 0.82 #43), 01nv4h (0.05 #23, 0.04 #65, 0.04 #30), 02l6h (0.03 #39, 0.03 #25, 0.02 #67), 02gsvk (0.02 #55, 0.02 #62, 0.01 #27) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 02pg45; >> query: (?x1006, 09nqf) <- genre(?x1006, ?x53), award(?x1006, ?x154), ?x154 = 05b4l5x >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06krf3 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.840 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #20374-05br10 PRED entity: 05br10 PRED relation: cinematography! PRED expected values: 02qzh2 => 95 concepts (14 used for prediction) PRED predicted values (max 10 best out of 340): 03wy8t (0.06 #984, 0.04 #1664, 0.04 #2344), 03cw411 (0.05 #1139, 0.04 #1479, 0.04 #2159), 084qpk (0.05 #1043, 0.04 #1383, 0.04 #1723), 0jvt9 (0.05 #1127, 0.02 #1467, 0.02 #2147), 0kbhf (0.04 #1554, 0.04 #2234, 0.04 #1894), 083skw (0.04 #1442, 0.04 #2122, 0.04 #1782), 0jymd (0.04 #2508, 0.03 #1148, 0.02 #1488), 02yy9r (0.03 #1020, 0.03 #1360, 0.02 #1700), 04hk0w (0.03 #1018, 0.03 #1358, 0.02 #1698), 0bbgvp (0.03 #1014, 0.03 #1354, 0.02 #1694) >> Best rule #984 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 06t8b; 0164w8; >> query: (?x10704, 03wy8t) <- nominated_for(?x10704, ?x5122), award_winner(?x6686, ?x10704), cinematography(?x670, ?x10704), film(?x3705, ?x5122) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05br10 cinematography! 02qzh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 14.000 0.065 http://example.org/film/film/cinematography #20373-016z2j PRED entity: 016z2j PRED relation: film PRED expected values: 07w8fz 0320fn => 115 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1096): 016z5x (0.74 #8896, 0.62 #40922, 0.59 #88963), 0dzlbx (0.74 #8896, 0.62 #40922, 0.59 #88963), 0vjr (0.74 #8896, 0.62 #40922, 0.59 #88963), 04gv3db (0.18 #748, 0.02 #25655, 0.02 #7864), 065_cjc (0.18 #1189, 0.01 #4747, 0.01 #6526), 0dr_9t7 (0.18 #742), 01shy7 (0.09 #420, 0.07 #9316, 0.07 #11095), 0ds5_72 (0.09 #1450, 0.04 #15683, 0.04 #3229), 09lxv9 (0.09 #1498, 0.04 #5056, 0.02 #42420), 0b7l4x (0.09 #1032, 0.04 #6369, 0.02 #13486) >> Best rule #8896 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 04shbh; >> query: (?x2373, ?x518) <- celebrity(?x1564, ?x2373), award_winner(?x518, ?x2373), film(?x2373, ?x1956) >> conf = 0.74 => this is the best rule for 3 predicted values *> Best rule #145914 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1678 *> proper extension: 03b78r; 024y6w; 02v49c; *> query: (?x2373, ?x3404) <- award_nominee(?x2373, ?x10473), film(?x10473, ?x3404) *> conf = 0.03 ranks of expected_values: 178, 710 EVAL 016z2j film 0320fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 115.000 89.000 0.736 http://example.org/film/actor/film./film/performance/film EVAL 016z2j film 07w8fz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 115.000 89.000 0.736 http://example.org/film/actor/film./film/performance/film #20372-014knw PRED entity: 014knw PRED relation: film_release_region PRED expected values: 02_286 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 266): 09c7w0 (0.92 #9099, 0.92 #8937, 0.92 #4712), 059j2 (0.86 #2145, 0.86 #2470, 0.86 #1333), 0chghy (0.85 #2121, 0.84 #1309, 0.83 #661), 07ssc (0.82 #1316, 0.82 #1478, 0.82 #668), 0345h (0.82 #2147, 0.81 #2472, 0.81 #1497), 035qy (0.81 #1337, 0.79 #1499, 0.77 #689), 0jgd (0.81 #653, 0.80 #2113, 0.79 #1301), 03gj2 (0.81 #1325, 0.77 #1487, 0.77 #2462), 0154j (0.77 #1303, 0.74 #2440, 0.73 #1465), 0b90_r (0.75 #654, 0.72 #1302, 0.71 #1464) >> Best rule #9099 for best value: >> intensional similarity = 4 >> extensional distance = 1007 >> proper extension: 0g56t9t; 02v8kmz; 047q2k1; 0dckvs; 016z5x; 04nl83; 04ddm4; 061681; 0gkz15s; 06z8s_; ... >> query: (?x9345, 09c7w0) <- film_release_region(?x9345, ?x583), nominated_for(?x7091, ?x9345), combatants(?x94, ?x583), country(?x150, ?x583) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #23 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01lbcqx; *> query: (?x9345, 02_286) <- language(?x9345, ?x254), genre(?x9345, ?x53), written_by(?x9345, ?x9320), ?x9320 = 0c921 *> conf = 0.11 ranks of expected_values: 88 EVAL 014knw film_release_region 02_286 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 89.000 89.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20371-0bw87 PRED entity: 0bw87 PRED relation: award PRED expected values: 094qd5 0cqgl9 => 127 concepts (120 used for prediction) PRED predicted values (max 10 best out of 283): 094qd5 (0.78 #17655, 0.75 #804, 0.74 #2008), 02py7pj (0.78 #17655, 0.75 #804, 0.74 #2008), 09cn0c (0.75 #804, 0.74 #2008, 0.73 #43344), 02y_j8g (0.75 #804, 0.74 #2008, 0.73 #43344), 0cqh6z (0.50 #871, 0.04 #13708, 0.03 #10099), 05pcn59 (0.41 #1685, 0.38 #481, 0.32 #1284), 09sb52 (0.36 #1245, 0.33 #10473, 0.33 #12879), 0f4x7 (0.36 #31, 0.26 #2841, 0.18 #1236), 0gqyl (0.30 #907, 0.15 #5320, 0.14 #13744), 0ck27z (0.30 #894, 0.14 #10523, 0.14 #12929) >> Best rule #17655 for best value: >> intensional similarity = 3 >> extensional distance = 909 >> proper extension: 03yf3z; 0g5ff; 03j90; >> query: (?x6660, ?x1245) <- award_winner(?x1245, ?x6660), student(?x7092, ?x6660), ceremony(?x1245, ?x78) >> conf = 0.78 => this is the best rule for 2 predicted values ranks of expected_values: 1, 37 EVAL 0bw87 award 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 127.000 120.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bw87 award 094qd5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 120.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20370-01f7gh PRED entity: 01f7gh PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #36, 0.86 #91, 0.85 #86), 02nxhr (0.06 #72, 0.06 #17, 0.06 #7), 07c52 (0.03 #193, 0.03 #138, 0.03 #158), 07z4p (0.02 #45, 0.02 #50, 0.02 #215) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 94 >> proper extension: 0dnvn3; 03s6l2; 05m_jsg; 0blpg; 0bbw2z6; 02wwmhc; 09v8clw; >> query: (?x1430, 029j_) <- nominated_for(?x574, ?x1430), nominated_for(?x4680, ?x1430), film_crew_role(?x1430, ?x468), ?x468 = 02r96rf >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f7gh film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.865 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #20369-028qdb PRED entity: 028qdb PRED relation: instrumentalists! PRED expected values: 05r5c => 89 concepts (82 used for prediction) PRED predicted values (max 10 best out of 127): 0342h (0.67 #448, 0.65 #2118, 0.64 #2206), 05r5c (0.57 #626, 0.50 #2121, 0.49 #2916), 018vs (0.56 #455, 0.43 #630, 0.38 #99), 05148p4 (0.48 #638, 0.48 #463, 0.42 #531), 03q5t (0.32 #1496, 0.31 #2907, 0.30 #2201), 0dwtp (0.32 #1496, 0.31 #2907, 0.30 #2201), 013y1f (0.32 #1496, 0.31 #2907, 0.30 #2201), 0bxl5 (0.32 #1496, 0.31 #2907, 0.30 #2201), 01vdm0 (0.32 #1496, 0.31 #2907, 0.30 #2201), 0214km (0.32 #1496, 0.30 #2201, 0.30 #2906) >> Best rule #448 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 01vzz1c; >> query: (?x4206, 0342h) <- artists(?x378, ?x4206), ?x378 = 07sbbz2, role(?x4206, ?x316) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #626 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 03c7ln; 0kzy0; 01x66d; 01sb5r; 01386_; 04m2zj; 0k60; 01j590z; 01mxnvc; *> query: (?x4206, 05r5c) <- instrumentalists(?x228, ?x4206), artists(?x378, ?x4206), ?x228 = 0l14qv *> conf = 0.57 ranks of expected_values: 2 EVAL 028qdb instrumentalists! 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 82.000 0.667 http://example.org/music/instrument/instrumentalists #20368-012b30 PRED entity: 012b30 PRED relation: artist PRED expected values: 0bg539 08n__5 0fpj9pm => 179 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1360): 01vv6_6 (0.43 #1900, 0.12 #11050, 0.12 #6888), 01k23t (0.40 #4717, 0.38 #6379, 0.31 #7210), 03xhj6 (0.40 #4463, 0.31 #6956, 0.31 #6125), 02vr7 (0.40 #4766, 0.31 #7259, 0.31 #6428), 081wh1 (0.34 #16633, 0.14 #2156, 0.04 #9980), 011_vz (0.34 #16633, 0.04 #9980, 0.04 #15640), 020_4z (0.31 #7383, 0.31 #6552, 0.30 #4890), 016376 (0.31 #7396, 0.31 #6565, 0.20 #4903), 0g824 (0.31 #6271, 0.30 #4609, 0.25 #7102), 03g5jw (0.31 #5902, 0.30 #4240, 0.25 #6733) >> Best rule #1900 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 01w31x; >> query: (?x12061, 01vv6_6) <- artist(?x12061, ?x8560), artist(?x12061, ?x7375), artist(?x12061, ?x3118), ?x8560 = 02y7sr, profession(?x3118, ?x131), type_of_union(?x7375, ?x566), student(?x8694, ?x3118) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #9980 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 05w3y; *> query: (?x12061, ?x133) <- artist(?x12061, ?x3118), artists(?x302, ?x3118), citytown(?x12061, ?x5267), artists(?x302, ?x133) *> conf = 0.04 ranks of expected_values: 804 EVAL 012b30 artist 0fpj9pm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 179.000 39.000 0.429 http://example.org/music/record_label/artist EVAL 012b30 artist 08n__5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 179.000 39.000 0.429 http://example.org/music/record_label/artist EVAL 012b30 artist 0bg539 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 179.000 39.000 0.429 http://example.org/music/record_label/artist #20367-03ds83 PRED entity: 03ds83 PRED relation: profession PRED expected values: 0d1pc => 102 concepts (101 used for prediction) PRED predicted values (max 10 best out of 50): 01d_h8 (0.43 #1049, 0.41 #6, 0.36 #1496), 03gjzk (0.31 #1058, 0.26 #1505, 0.26 #2250), 0dxtg (0.30 #6855, 0.29 #1057, 0.27 #6570), 0d1pc (0.30 #6855, 0.20 #51, 0.19 #8346), 02jknp (0.25 #1051, 0.22 #3584, 0.21 #8503), 09jwl (0.22 #317, 0.21 #3297, 0.21 #3148), 0np9r (0.19 #8346, 0.15 #10006, 0.14 #10751), 02krf9 (0.19 #8346, 0.14 #1070, 0.09 #8969), 018gz8 (0.19 #8346, 0.13 #1954, 0.13 #7170), 0cbd2 (0.19 #8346, 0.11 #14911, 0.11 #13866) >> Best rule #1049 for best value: >> intensional similarity = 3 >> extensional distance = 369 >> proper extension: 0g1rw; 030_1m; 017jv5; 02vyh; 01gb54; 07bzp; 02j_j0; 03rwz3; >> query: (?x5443, 01d_h8) <- award_winner(?x5443, ?x400), award_nominee(?x147, ?x400), executive_produced_by(?x4768, ?x400) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #6855 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1399 *> proper extension: 09mq4m; 04ktcgn; 070w7s; 01wz_ml; 094wz7q; 092ys_y; 03bx_5q; 03ckvj9; 05bm4sm; 01jllg1; ... *> query: (?x5443, ?x4773) <- award_winner(?x5443, ?x932), profession(?x5443, ?x1032), profession(?x932, ?x4773) *> conf = 0.30 ranks of expected_values: 4 EVAL 03ds83 profession 0d1pc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 101.000 0.426 http://example.org/people/person/profession #20366-0ndsl1x PRED entity: 0ndsl1x PRED relation: film_crew_role PRED expected values: 09vw2b7 0ch6mp2 01vx2h => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.75 #452, 0.74 #1166, 0.74 #1129), 09vw2b7 (0.69 #563, 0.68 #451, 0.64 #714), 0dxtw (0.46 #568, 0.44 #493, 0.44 #681), 01vx2h (0.44 #569, 0.44 #123, 0.42 #457), 01pvkk (0.30 #458, 0.30 #570, 0.29 #683), 02rh1dz (0.26 #121, 0.23 #158, 0.21 #567), 02ynfr (0.20 #165, 0.19 #91, 0.18 #499), 094hwz (0.20 #53, 0.10 #127, 0.09 #2058), 04pyp5 (0.20 #55, 0.09 #2058, 0.08 #129), 02vs3x5 (0.20 #62, 0.09 #2058, 0.06 #545) >> Best rule #452 for best value: >> intensional similarity = 4 >> extensional distance = 137 >> proper extension: 0963mq; >> query: (?x9002, 0ch6mp2) <- film(?x4536, ?x9002), crewmember(?x9002, ?x9151), currency(?x4536, ?x170), participant(?x513, ?x4536) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 0ndsl1x film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 73.000 0.748 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ndsl1x film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.748 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ndsl1x film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.748 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20365-01bm_ PRED entity: 01bm_ PRED relation: major_field_of_study PRED expected values: 0193x 01540 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 92): 062z7 (0.52 #230, 0.47 #440, 0.46 #545), 0g26h (0.47 #453, 0.37 #348, 0.35 #1503), 01tbp (0.44 #574, 0.40 #364, 0.39 #784), 05qfh (0.43 #342, 0.41 #552, 0.41 #762), 01lj9 (0.40 #345, 0.39 #240, 0.38 #135), 02_7t (0.40 #474, 0.35 #264, 0.29 #579), 01540 (0.37 #365, 0.34 #575, 0.30 #470), 0db86 (0.37 #357, 0.25 #777, 0.24 #147), 041y2 (0.35 #483, 0.29 #588, 0.27 #798), 02jfc (0.33 #488, 0.26 #278, 0.24 #173) >> Best rule #230 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 09c7w0; 059j2; 03rj0; 04hzj; 05c74; >> query: (?x6925, 062z7) <- contains(?x2713, ?x6925), organization(?x6925, ?x5487), company(?x3131, ?x6925) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #365 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 0d06m5; 0d05fv; *> query: (?x6925, 01540) <- organization(?x6925, ?x5487), list(?x6925, ?x2197), category(?x6925, ?x134) *> conf = 0.37 ranks of expected_values: 7, 19 EVAL 01bm_ major_field_of_study 01540 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 82.000 82.000 0.516 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01bm_ major_field_of_study 0193x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 82.000 82.000 0.516 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20364-02q636 PRED entity: 02q636 PRED relation: registering_agency PRED expected values: 03z19 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.88 #12, 0.86 #7, 0.85 #17) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 03p7gb; >> query: (?x2980, 03z19) <- state_province_region(?x2980, ?x335), school_type(?x2980, ?x1044), currency(?x2980, ?x170) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q636 registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.878 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #20363-01qbg5 PRED entity: 01qbg5 PRED relation: film! PRED expected values: 023v4_ => 77 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1059): 0hwbd (0.64 #54223, 0.64 #54221, 0.58 #47960), 0306bt (0.58 #47960, 0.42 #52135, 0.41 #56310), 03mdt (0.45 #16679, 0.41 #52134, 0.40 #56309), 01_p6t (0.18 #1024, 0.02 #13532, 0.01 #17704), 03x400 (0.18 #1161, 0.01 #42865), 0p8r1 (0.17 #11009, 0.02 #38116, 0.02 #40202), 0jfx1 (0.17 #4575, 0.05 #12914, 0.02 #31679), 0z4s (0.17 #4237, 0.03 #18835, 0.02 #20919), 01chc7 (0.17 #4729, 0.03 #15154, 0.03 #8899), 0143wl (0.17 #5240, 0.02 #11494, 0.02 #13579) >> Best rule #54223 for best value: >> intensional similarity = 3 >> extensional distance = 801 >> proper extension: 0123qq; >> query: (?x7319, ?x5821) <- nominated_for(?x5821, ?x7319), nationality(?x5821, ?x94), participant(?x4397, ?x5821) >> conf = 0.64 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01qbg5 film! 023v4_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 37.000 0.641 http://example.org/film/actor/film./film/performance/film #20362-0l14qv PRED entity: 0l14qv PRED relation: role! PRED expected values: 03f5mt => 93 concepts (76 used for prediction) PRED predicted values (max 10 best out of 110): 0l14qv (0.87 #2662, 0.81 #3559, 0.78 #3912), 0gkd1 (0.85 #884, 0.84 #68, 0.84 #1633), 0l1589 (0.85 #884, 0.84 #68, 0.84 #1633), 018j2 (0.85 #884, 0.84 #68, 0.84 #1633), 06ncr (0.85 #884, 0.84 #68, 0.84 #1633), 03q5t (0.85 #884, 0.84 #68, 0.84 #1633), 06w7v (0.85 #884, 0.84 #68, 0.84 #1633), 02k84w (0.85 #884, 0.84 #68, 0.84 #1633), 01wy6 (0.85 #884, 0.84 #68, 0.84 #1633), 02w4b (0.85 #884, 0.84 #68, 0.84 #1633) >> Best rule #2662 for best value: >> intensional similarity = 10 >> extensional distance = 13 >> proper extension: 02snj9; >> query: (?x228, 0l14qv) <- group(?x228, ?x9589), group(?x228, ?x5512), instrumentalists(?x228, ?x140), role(?x228, ?x3215), role(?x642, ?x228), performance_role(?x130, ?x228), ?x3215 = 0bxl5, performance_role(?x228, ?x1225), artists(?x671, ?x9589), category(?x5512, ?x134) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1626 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 7 *> proper extension: 011k_j; *> query: (?x228, 03f5mt) <- group(?x228, ?x5385), role(?x228, ?x1148), instrumentalists(?x228, ?x140), role(?x4574, ?x228), ?x5385 = 0134tg, performance_role(?x228, ?x212), role(?x228, ?x74), award_nominee(?x4574, ?x2138), role(?x1292, ?x1148), award_winner(?x724, ?x4574) *> conf = 0.67 ranks of expected_values: 59 EVAL 0l14qv role! 03f5mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 93.000 76.000 0.867 http://example.org/music/performance_role/regular_performances./music/group_membership/role #20361-018vs PRED entity: 018vs PRED relation: instrumentalists PRED expected values: 02whj 01wdqrx 0285c 01271h 01wy61y 027dpx 0g824 01tv3x2 01wf86y 06rgq 01w5gg6 => 61 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1012): 01gg59 (0.67 #3839, 0.60 #1539, 0.50 #5221), 01p0vf (0.62 #5815, 0.54 #6275, 0.50 #754), 0fp_v1x (0.60 #1391, 0.57 #2309, 0.50 #3231), 09889g (0.60 #1599, 0.50 #680, 0.50 #221), 01t110 (0.60 #1651, 0.50 #732, 0.50 #273), 01nqfh_ (0.60 #1397, 0.50 #478, 0.50 #19), 02whj (0.60 #1414, 0.50 #36, 0.44 #3714), 06cc_1 (0.57 #2317, 0.50 #3239, 0.50 #480), 01wp8w7 (0.57 #2352, 0.50 #3274, 0.50 #56), 04f7c55 (0.57 #2087, 0.43 #2546, 0.40 #1628) >> Best rule #3839 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 02sgy; 02hnl; 06ncr; >> query: (?x716, 01gg59) <- role(?x716, ?x2592), instrumentalists(?x716, ?x211), group(?x716, ?x11425), role(?x227, ?x716), ?x11425 = 02vnpv, ?x2592 = 0j871, role(?x677, ?x716) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1414 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 0l14md; *> query: (?x716, 02whj) <- role(?x716, ?x2592), instrumentalists(?x716, ?x2784), group(?x716, ?x11425), role(?x227, ?x716), ?x11425 = 02vnpv, ?x2592 = 0j871, ?x2784 = 0137g1 *> conf = 0.60 ranks of expected_values: 7, 17, 35, 37, 119, 145, 170, 187, 192, 199, 702 EVAL 018vs instrumentalists 01w5gg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018vs instrumentalists 06rgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018vs instrumentalists 01wf86y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018vs instrumentalists 01tv3x2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018vs instrumentalists 0g824 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018vs instrumentalists 027dpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018vs instrumentalists 01wy61y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018vs instrumentalists 01271h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018vs instrumentalists 0285c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018vs instrumentalists 01wdqrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018vs instrumentalists 02whj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 61.000 51.000 0.667 http://example.org/music/instrument/instrumentalists #20360-019g65 PRED entity: 019g65 PRED relation: people! PRED expected values: 033tf_ => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 36): 0x67 (0.64 #770, 0.63 #694, 0.62 #618), 09vc4s (0.40 #85, 0.20 #161, 0.08 #313), 041rx (0.25 #1904, 0.25 #1600, 0.24 #1752), 07bch9 (0.20 #175, 0.20 #99, 0.08 #327), 025rpb0 (0.20 #197, 0.15 #349), 0g48m4 (0.20 #5, 0.08 #233, 0.07 #385), 033tf_ (0.14 #1907, 0.11 #1755, 0.11 #1603), 02ctzb (0.12 #775, 0.06 #471, 0.04 #1915), 02w7gg (0.09 #1750, 0.09 #2738, 0.09 #2130), 0xnvg (0.09 #1913, 0.07 #1761, 0.06 #1989) >> Best rule #770 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 016cff; >> query: (?x10287, 0x67) <- athlete(?x1083, ?x10287), nationality(?x10287, ?x94), gender(?x10287, ?x231), people(?x11321, ?x10287) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #1907 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 713 *> proper extension: 01vvydl; 023tp8; 0jf1b; 034x61; 01t6b4; 058s57; 030x48; 015f7; 0gbwp; 014g22; ... *> query: (?x10287, 033tf_) <- type_of_union(?x10287, ?x566), nationality(?x10287, ?x94), ?x566 = 04ztj, people(?x11321, ?x10287), ?x94 = 09c7w0 *> conf = 0.14 ranks of expected_values: 7 EVAL 019g65 people! 033tf_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 86.000 86.000 0.643 http://example.org/people/ethnicity/people #20359-0chghy PRED entity: 0chghy PRED relation: nationality! PRED expected values: 0244r8 07nv3_ 03dpqd 02dbn2 03x22w 01pcz9 05qhnq 02fgm7 013tcv => 215 concepts (117 used for prediction) PRED predicted values (max 10 best out of 4072): 0d1_f (0.73 #44223, 0.10 #41165, 0.10 #37144), 059xvg (0.30 #41236, 0.20 #37215, 0.17 #49277), 0p__8 (0.30 #42023, 0.20 #38002, 0.17 #50064), 054k_8 (0.29 #25800, 0.20 #41882, 0.17 #21780), 099d4 (0.29 #27810, 0.20 #43892, 0.17 #23790), 06kkgw (0.29 #27416, 0.20 #43498, 0.17 #23396), 01ypsj (0.29 #27186, 0.20 #43268, 0.17 #23166), 02lnhv (0.29 #24411, 0.20 #40493, 0.17 #20391), 026lyl4 (0.29 #27885, 0.17 #23865, 0.13 #47988), 03hzl42 (0.22 #100509, 0.10 #41531, 0.10 #37510) >> Best rule #44223 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06frc; >> query: (?x390, ?x3444) <- entity_involved(?x3278, ?x390), jurisdiction_of_office(?x3444, ?x390), contains(?x10150, ?x390) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #21911 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0hkt6; *> query: (?x390, 01pcz9) <- taxonomy(?x390, ?x939), religion(?x390, ?x492), films(?x390, ?x5835) *> conf = 0.17 ranks of expected_values: 1750, 2999 EVAL 0chghy nationality! 013tcv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 117.000 0.730 http://example.org/people/person/nationality EVAL 0chghy nationality! 02fgm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 117.000 0.730 http://example.org/people/person/nationality EVAL 0chghy nationality! 05qhnq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 117.000 0.730 http://example.org/people/person/nationality EVAL 0chghy nationality! 01pcz9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 215.000 117.000 0.730 http://example.org/people/person/nationality EVAL 0chghy nationality! 03x22w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 117.000 0.730 http://example.org/people/person/nationality EVAL 0chghy nationality! 02dbn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 117.000 0.730 http://example.org/people/person/nationality EVAL 0chghy nationality! 03dpqd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 117.000 0.730 http://example.org/people/person/nationality EVAL 0chghy nationality! 07nv3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 117.000 0.730 http://example.org/people/person/nationality EVAL 0chghy nationality! 0244r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 117.000 0.730 http://example.org/people/person/nationality #20358-0jwmp PRED entity: 0jwmp PRED relation: nominated_for! PRED expected values: 07h07 => 108 concepts (40 used for prediction) PRED predicted values (max 10 best out of 757): 04pqqb (0.49 #21046, 0.46 #77162, 0.44 #81840), 01ycfv (0.41 #60791, 0.02 #15977), 06cgy (0.40 #9357, 0.40 #7327, 0.39 #7017), 086k8 (0.40 #58, 0.19 #2396, 0.14 #9415), 015vq_ (0.24 #28060, 0.23 #67809, 0.23 #4677), 04yt7 (0.24 #28060, 0.23 #67809, 0.23 #4677), 01tsbmv (0.24 #28060, 0.23 #67809, 0.23 #4677), 01nwwl (0.24 #28060, 0.23 #67809, 0.23 #4677), 0184jw (0.20 #1667, 0.10 #77163, 0.06 #4005), 06dv3 (0.20 #36, 0.06 #2374, 0.05 #9393) >> Best rule #21046 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 0bx_hnp; >> query: (?x3392, ?x4854) <- produced_by(?x3392, ?x4854), film_release_region(?x3392, ?x2513), nominated_for(?x5495, ?x3392), ?x2513 = 05b4w >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #19563 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 0bx_hnp; *> query: (?x3392, 07h07) <- produced_by(?x3392, ?x4854), film_release_region(?x3392, ?x2513), nominated_for(?x5495, ?x3392), ?x2513 = 05b4w *> conf = 0.02 ranks of expected_values: 207 EVAL 0jwmp nominated_for! 07h07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 108.000 40.000 0.486 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20357-03v6t PRED entity: 03v6t PRED relation: major_field_of_study PRED expected values: 0g26h 0w7s => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 112): 04rjg (0.62 #18, 0.56 #471, 0.52 #697), 03g3w (0.57 #816, 0.51 #1155, 0.50 #24), 01lj9 (0.50 #35, 0.44 #488, 0.43 #1166), 0fdys (0.50 #34, 0.40 #374, 0.37 #260), 037mh8 (0.50 #61, 0.37 #853, 0.33 #401), 05qjt (0.46 #913, 0.45 #1026, 0.42 #1252), 0g26h (0.41 #2526, 0.40 #1621, 0.39 #1960), 02_7t (0.38 #511, 0.38 #58, 0.32 #624), 06ms6 (0.38 #15, 0.35 #807, 0.32 #581), 01540 (0.38 #54, 0.35 #846, 0.31 #959) >> Best rule #18 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 06pwq; 065y4w7; 0l2tk; 07tds; 09f2j; 01bm_; >> query: (?x1667, 04rjg) <- major_field_of_study(?x1667, ?x9079), major_field_of_study(?x1667, ?x1682), ?x9079 = 0l5mz, institution(?x734, ?x1667), ?x1682 = 02ky346 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2526 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 114 *> proper extension: 01wdl3; 033q4k; 02hft3; 04344j; 0m9_5; 02zcnq; 01rgdw; 0146hc; 01jq4b; 01jq0j; ... *> query: (?x1667, 0g26h) <- major_field_of_study(?x1667, ?x9079), major_field_of_study(?x865, ?x9079), fraternities_and_sororities(?x1667, ?x3697) *> conf = 0.41 ranks of expected_values: 7, 52 EVAL 03v6t major_field_of_study 0w7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 85.000 85.000 0.625 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03v6t major_field_of_study 0g26h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 85.000 0.625 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20356-0k611 PRED entity: 0k611 PRED relation: ceremony PRED expected values: 073h9x 0bz6sb 02pgky2 03tn9w 0dznvw => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 81): 073h9x (0.83 #600, 0.50 #357, 0.45 #438), 02pgky2 (0.78 #621, 0.50 #378, 0.40 #459), 03tn9w (0.78 #624, 0.44 #381, 0.36 #138), 0bz6sb (0.74 #606, 0.45 #444, 0.44 #363), 0gpjbt (0.61 #1072, 0.51 #1315, 0.36 #2206), 0dznvw (0.61 #643, 0.38 #400, 0.36 #157), 09n4nb (0.60 #1085, 0.49 #1328, 0.36 #2219), 0466p0j (0.59 #1101, 0.49 #1344, 0.35 #2235), 05pd94v (0.59 #1054, 0.49 #1297, 0.34 #2188), 056878 (0.58 #1074, 0.49 #1317, 0.35 #2208) >> Best rule #600 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x1703, 073h9x) <- award(?x707, ?x1703), ceremony(?x1703, ?x9921), ?x9921 = 0bvhz9, award_winner(?x1703, ?x323) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6 EVAL 0k611 ceremony 0dznvw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 56.000 0.826 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0k611 ceremony 03tn9w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.826 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0k611 ceremony 02pgky2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.826 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0k611 ceremony 0bz6sb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.826 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0k611 ceremony 073h9x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.826 http://example.org/award/award_category/winners./award/award_honor/ceremony #20355-012d40 PRED entity: 012d40 PRED relation: languages PRED expected values: 06nm1 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 18): 03k50 (0.16 #167, 0.09 #233, 0.07 #1553), 064_8sq (0.10 #736, 0.10 #1033, 0.10 #868), 03115z (0.07 #2939, 0.06 #2806, 0.01 #154), 04h9h (0.07 #2939, 0.06 #2806), 0459q4 (0.07 #2939, 0.06 #2806), 06nm1 (0.07 #2939, 0.04 #201, 0.03 #1554), 02hwyss (0.07 #2939), 07c9s (0.06 #173, 0.06 #239, 0.04 #404), 02bjrlw (0.06 #727, 0.06 #859, 0.05 #1024), 0999q (0.04 #182, 0.02 #248, 0.02 #1568) >> Best rule #167 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 02qjj7; 01gj8_; 02qy3py; 09tqx3; 0738y5; 06zmg7m; 01wkmgb; 0894_x; 02qvhbb; 0b66qd; >> query: (?x147, 03k50) <- languages(?x147, ?x254), profession(?x147, ?x524), ?x524 = 02jknp >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #2939 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1869 *> proper extension: 06151l; 0lbj1; 023tp8; 09fqtq; 03zqc1; 0lzb8; 064nh4k; 01k5t_3; 01yb09; 04y79_n; ... *> query: (?x147, ?x254) <- film(?x147, ?x5313), nationality(?x147, ?x2645), language(?x5313, ?x254) *> conf = 0.07 ranks of expected_values: 6 EVAL 012d40 languages 06nm1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 123.000 123.000 0.156 http://example.org/people/person/languages #20354-0dz46 PRED entity: 0dz46 PRED relation: profession PRED expected values: 0kyk => 123 concepts (109 used for prediction) PRED predicted values (max 10 best out of 73): 02hrh1q (0.70 #11201, 0.70 #11052, 0.70 #6581), 0dxtg (0.51 #1952, 0.48 #2996, 0.48 #3294), 0kyk (0.45 #478, 0.39 #2417, 0.37 #1224), 01d_h8 (0.36 #1945, 0.36 #5226, 0.35 #5076), 03gjzk (0.35 #2387, 0.28 #1954, 0.27 #314), 02jknp (0.32 #5077, 0.32 #5526, 0.31 #5227), 018gz8 (0.24 #1956, 0.24 #316, 0.23 #3298), 09jwl (0.23 #19, 0.19 #318, 0.19 #766), 05z96 (0.19 #2430, 0.17 #939, 0.16 #491), 01c72t (0.14 #24, 0.11 #1367, 0.11 #9721) >> Best rule #11201 for best value: >> intensional similarity = 3 >> extensional distance = 1667 >> proper extension: 01gvr1; 066m4g; 01wdqrx; 0blbxk; 04mn81; 03jm6c; 03j0br4; 0m32_; 01vx5w7; 01k98nm; ... >> query: (?x8997, 02hrh1q) <- gender(?x8997, ?x231), award(?x8997, ?x8998), location(?x8997, ?x2254) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #478 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 123 *> proper extension: 0gthm; *> query: (?x8997, 0kyk) <- nationality(?x8997, ?x94), award(?x8997, ?x8998), disciplines_or_subjects(?x8998, ?x5864), influenced_by(?x8997, ?x477) *> conf = 0.45 ranks of expected_values: 3 EVAL 0dz46 profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 109.000 0.700 http://example.org/people/person/profession #20353-0bh8x1y PRED entity: 0bh8x1y PRED relation: film_release_region PRED expected values: 0ctw_b 04hqz => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 162): 0f8l9c (0.92 #481, 0.90 #944, 0.89 #790), 059j2 (0.89 #646, 0.87 #1109, 0.85 #1726), 035qy (0.88 #1112, 0.88 #495, 0.86 #958), 03gj2 (0.88 #486, 0.85 #795, 0.85 #949), 0k6nt (0.87 #639, 0.80 #1102, 0.79 #1719), 06mkj (0.87 #1136, 0.87 #828, 0.86 #982), 03rjj (0.86 #931, 0.85 #1085, 0.85 #777), 0154j (0.85 #621, 0.83 #776, 0.81 #467), 01znc_ (0.85 #504, 0.82 #967, 0.82 #813), 0jgd (0.83 #465, 0.82 #619, 0.81 #928) >> Best rule #481 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 0407yfx; 0j43swk; 030z4z; >> query: (?x4668, 0f8l9c) <- film_release_region(?x4668, ?x2146), film(?x2280, ?x4668), ?x2146 = 03rk0, award(?x4668, ?x13107) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 159 *> proper extension: 0crh5_f; 0bmc4cm; 07l50vn; 0h95zbp; 07s3m4g; 0gh6j94; 0g5qmbz; *> query: (?x4668, 0ctw_b) <- film_release_region(?x4668, ?x550), film_release_region(?x4668, ?x456), ?x456 = 05qhw, ?x550 = 05v8c *> conf = 0.60 ranks of expected_values: 22, 38 EVAL 0bh8x1y film_release_region 04hqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 72.000 72.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bh8x1y film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 72.000 72.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20352-0dzbl PRED entity: 0dzbl PRED relation: student PRED expected values: 0fx02 013pk3 => 152 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1797): 03_dj (0.33 #8248, 0.33 #6163, 0.06 #35363), 05np2 (0.33 #7459, 0.33 #5374, 0.06 #34574), 0cj2w (0.33 #6050, 0.33 #3964, 0.06 #18566), 07g2b (0.33 #4246, 0.18 #33371, 0.17 #37545), 0ff3y (0.33 #6234, 0.12 #10404, 0.11 #37522), 01vwbts (0.33 #2894, 0.12 #9150, 0.10 #13322), 0136g9 (0.33 #4373, 0.11 #10630, 0.10 #18974), 0m76b (0.33 #5923, 0.11 #18439, 0.10 #20524), 02tc5y (0.33 #5907, 0.11 #12164, 0.10 #14249), 01xsbh (0.33 #2455, 0.11 #10798, 0.10 #12883) >> Best rule #8248 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 011xy1; >> query: (?x13049, 03_dj) <- company(?x12076, ?x13049), student(?x13049, ?x10499), student(?x13049, ?x3079), contains(?x1310, ?x13049), ?x3079 = 0686zv, nationality(?x10499, ?x512) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #24265 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 02gkxp; *> query: (?x13049, 013pk3) <- company(?x12076, ?x13049), student(?x13049, ?x3079), contains(?x1310, ?x13049), student(?x6760, ?x3079), award_winner(?x2307, ?x3079) *> conf = 0.04 ranks of expected_values: 1030 EVAL 0dzbl student 013pk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 152.000 83.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0dzbl student 0fx02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 152.000 83.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #20351-05233hy PRED entity: 05233hy PRED relation: place_of_death PRED expected values: 0k_q_ => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 23): 030qb3t (0.32 #606, 0.14 #995, 0.14 #411), 0k_p5 (0.17 #88, 0.07 #282, 0.05 #477), 0r00l (0.17 #162, 0.02 #1135, 0.01 #2107), 0f2wj (0.09 #401, 0.08 #790, 0.07 #206), 02_286 (0.08 #986, 0.06 #1958, 0.06 #1764), 0k049 (0.08 #976, 0.05 #1754, 0.05 #1560), 0mzww (0.07 #297, 0.05 #492, 0.04 #881), 0f2tj (0.07 #291, 0.04 #875), 071vr (0.05 #491, 0.04 #1557, 0.04 #1362), 06_kh (0.05 #394, 0.04 #783, 0.04 #589) >> Best rule #606 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 03mdw3c; >> query: (?x12512, 030qb3t) <- nominated_for(?x12512, ?x2640), film_sets_designed(?x12512, ?x5198), genre(?x5198, ?x571) >> conf = 0.32 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05233hy place_of_death 0k_q_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 74.000 0.320 http://example.org/people/deceased_person/place_of_death #20350-092j54 PRED entity: 092j54 PRED relation: school PRED expected values: 01qd_r 02q253 => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 659): 07w0v (0.67 #991, 0.60 #1093, 0.54 #1491), 01pl14 (0.60 #891, 0.54 #1488, 0.54 #1195), 05krk (0.60 #890, 0.50 #599, 0.46 #1487), 03tw2s (0.50 #1047, 0.50 #754, 0.44 #401), 012vwb (0.50 #627, 0.44 #401, 0.44 #398), 0g8rj (0.50 #743, 0.44 #401, 0.44 #398), 0frm7n (0.50 #640, 0.44 #401, 0.44 #398), 07t90 (0.50 #540, 0.44 #401, 0.44 #398), 0187nd (0.50 #582, 0.40 #872, 0.38 #399), 01qgr3 (0.44 #401, 0.44 #398, 0.40 #957) >> Best rule #991 for best value: >> intensional similarity = 34 >> extensional distance = 4 >> proper extension: 047dpm0; >> query: (?x4171, 07w0v) <- draft(?x7078, ?x4171), draft(?x5773, ?x4171), draft(?x729, ?x4171), draft(?x684, ?x4171), team(?x180, ?x729), colors(?x5773, ?x332), school(?x4171, ?x6333), school(?x4171, ?x5907), school(?x4171, ?x5621), school(?x4171, ?x735), team(?x1177, ?x729), ?x735 = 065y4w7, currency(?x5907, ?x170), student(?x5907, ?x3762), major_field_of_study(?x5907, ?x2172), school(?x7078, ?x331), organization(?x346, ?x5907), sport(?x684, ?x1083), contains(?x760, ?x5907), team(?x11323, ?x729), major_field_of_study(?x6333, ?x742), colors(?x684, ?x663), organization(?x6333, ?x5487), ?x331 = 01jssp, company(?x3520, ?x6333), institution(?x865, ?x6333), jurisdiction_of_office(?x900, ?x760), adjoins(?x760, ?x1426), ?x5621 = 01vs5c, district_represented(?x605, ?x760), religion(?x760, ?x2672), school(?x5773, ?x621), position(?x180, ?x8329), ?x2672 = 01y0s9 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #399 for first EXPECTED value: *> intensional similarity = 29 *> extensional distance = 1 *> proper extension: 0f4vx0; *> query: (?x4171, ?x2830) <- draft(?x5822, ?x4171), draft(?x5773, ?x4171), draft(?x729, ?x4171), draft(?x684, ?x4171), team(?x10168, ?x729), team(?x2247, ?x729), colors(?x5773, ?x332), school(?x4171, ?x6333), school(?x4171, ?x5907), school(?x4171, ?x735), team(?x1177, ?x729), ?x735 = 065y4w7, currency(?x5907, ?x170), draft(?x5822, ?x6462), student(?x5907, ?x3762), major_field_of_study(?x5907, ?x2172), position(?x2247, ?x1717), ?x6333 = 07ccs, school(?x684, ?x2830), school(?x729, ?x1681), position(?x706, ?x10168), sport(?x684, ?x1083), school(?x8111, ?x5907), ?x8111 = 07147, teams(?x3125, ?x5773), institution(?x620, ?x5907), school(?x6462, ?x7071), ?x7071 = 02y9bj, ?x620 = 07s6fsf *> conf = 0.38 ranks of expected_values: 51, 53 EVAL 092j54 school 02q253 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 17.000 17.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 092j54 school 01qd_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 17.000 17.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #20349-05r7t PRED entity: 05r7t PRED relation: medal PRED expected values: 02lq5w => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 2): 02lpp7 (0.89 #56, 0.79 #50, 0.79 #104), 02lq5w (0.81 #55, 0.80 #103, 0.78 #63) >> Best rule #56 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 05qhw; 0f8l9c; 015qh; 06bnz; 03rk0; 05b4w; 04hqz; >> query: (?x6559, 02lpp7) <- film_release_region(?x6520, ?x6559), ?x6520 = 02bg55, olympics(?x6559, ?x867), contains(?x6559, ?x13728) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 05qhw; 0f8l9c; 015qh; 06bnz; 03rk0; 05b4w; 04hqz; *> query: (?x6559, 02lq5w) <- film_release_region(?x6520, ?x6559), ?x6520 = 02bg55, olympics(?x6559, ?x867), contains(?x6559, ?x13728) *> conf = 0.81 ranks of expected_values: 2 EVAL 05r7t medal 02lq5w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 191.000 191.000 0.889 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #20348-0bmfnjs PRED entity: 0bmfnjs PRED relation: film! PRED expected values: 0lpjn => 109 concepts (34 used for prediction) PRED predicted values (max 10 best out of 937): 0f0kz (0.20 #517, 0.06 #8855, 0.06 #4687), 03ym1 (0.13 #1015, 0.06 #5185, 0.04 #7269), 0svqs (0.13 #877, 0.06 #5047, 0.04 #9215), 02ck7w (0.13 #942, 0.06 #5112, 0.03 #7196), 02gvwz (0.13 #188, 0.06 #4358, 0.03 #6442), 0241jw (0.13 #296, 0.06 #4466, 0.03 #6550), 01v9l67 (0.13 #466, 0.06 #4636, 0.03 #6720), 0js9s (0.13 #1158, 0.06 #5328, 0.03 #7412), 01ps2h8 (0.13 #943, 0.06 #5113, 0.03 #13451), 0154qm (0.13 #563, 0.05 #21407, 0.05 #15155) >> Best rule #517 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 04pk1f; >> query: (?x8682, 0f0kz) <- nominated_for(?x2222, ?x8682), film_release_region(?x8682, ?x205), ?x2222 = 0gs96, ?x205 = 03rjj >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2565 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 25 *> proper extension: 01tspc6; *> query: (?x8682, 0lpjn) <- nominated_for(?x2222, ?x8682), nominated_for(?x941, ?x8682), ?x941 = 0fq9zdn, nominated_for(?x2222, ?x4610), award(?x1255, ?x2222), film_release_region(?x4610, ?x87) *> conf = 0.11 ranks of expected_values: 15 EVAL 0bmfnjs film! 0lpjn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 109.000 34.000 0.200 http://example.org/film/actor/film./film/performance/film #20347-01q_ph PRED entity: 01q_ph PRED relation: special_performance_type PRED expected values: 01pb34 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.19 #8, 0.14 #48, 0.12 #33), 09_gdc (0.11 #12, 0.04 #57, 0.03 #83), 01kyvx (0.01 #473, 0.01 #479, 0.01 #41) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 04bs3j; 0484q; >> query: (?x400, 01pb34) <- celebrity(?x400, ?x3581), participant(?x400, ?x794), influenced_by(?x400, ?x7717) >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q_ph special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.188 http://example.org/film/actor/film./film/performance/special_performance_type #20346-027b43 PRED entity: 027b43 PRED relation: school_type PRED expected values: 05jxkf => 210 concepts (210 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.49 #220, 0.49 #412, 0.49 #268), 01rs41 (0.43 #341, 0.33 #1085, 0.32 #1205), 01_9fk (0.33 #50, 0.33 #2, 0.25 #26), 01_srz (0.33 #51, 0.07 #219, 0.07 #267), 05pcjw (0.32 #337, 0.28 #145, 0.28 #193), 07tf8 (0.17 #249, 0.17 #57, 0.16 #153), 04399 (0.06 #158, 0.04 #206, 0.04 #302), 0bwd5 (0.03 #379, 0.03 #235, 0.03 #283), 02p0qmm (0.03 #1666, 0.03 #250, 0.03 #922), 04qbv (0.03 #1096, 0.02 #1048, 0.02 #1984) >> Best rule #220 for best value: >> intensional similarity = 6 >> extensional distance = 93 >> proper extension: 052nd; 07vk2; 04zwc; 01y06y; >> query: (?x12132, 05jxkf) <- contains(?x4198, ?x12132), major_field_of_study(?x12132, ?x2981), state(?x9371, ?x4198), institution(?x1200, ?x12132), ?x1200 = 016t_3, adjoins(?x1274, ?x4198) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027b43 school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 210.000 0.495 http://example.org/education/educational_institution/school_type #20345-01jkqfz PRED entity: 01jkqfz PRED relation: group! PRED expected values: 06w87 02w3w => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 115): 0342h (0.93 #1326, 0.92 #1238, 0.90 #2031), 0l14md (0.63 #801, 0.63 #1241, 0.60 #1329), 028tv0 (0.48 #807, 0.43 #1335, 0.42 #1247), 03qjg (0.32 #664, 0.30 #841, 0.25 #48), 05r5c (0.32 #978, 0.25 #1330, 0.24 #2035), 0l14qv (0.30 #799, 0.28 #1327, 0.27 #1239), 01vj9c (0.30 #808, 0.28 #1689, 0.27 #2041), 04rzd (0.27 #648, 0.15 #1353, 0.15 #1265), 07c6l (0.25 #11, 0.13 #980, 0.09 #627), 085jw (0.25 #55, 0.07 #495, 0.07 #2115) >> Best rule #1326 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 0123r4; >> query: (?x8393, 0342h) <- group(?x716, ?x8393), ?x716 = 018vs, artists(?x2664, ?x8393) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #692 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 06lxn; *> query: (?x8393, 02w3w) <- artist(?x2299, ?x8393), group(?x716, ?x8393), award_winner(?x2638, ?x8393) *> conf = 0.09 ranks of expected_values: 20, 83 EVAL 01jkqfz group! 02w3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 98.000 98.000 0.930 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01jkqfz group! 06w87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 98.000 98.000 0.930 http://example.org/music/performance_role/regular_performances./music/group_membership/group #20344-09d5h PRED entity: 09d5h PRED relation: child PRED expected values: 030_1m => 196 concepts (174 used for prediction) PRED predicted values (max 10 best out of 144): 0c41qv (0.20 #588, 0.17 #3335, 0.12 #5051), 030_1m (0.20 #531, 0.11 #1905, 0.10 #6365), 031rq5 (0.20 #570, 0.11 #1944, 0.10 #6404), 05s_k6 (0.20 #643, 0.11 #2017, 0.08 #3390), 017s11 (0.20 #521, 0.11 #1895, 0.05 #6355), 024rdh (0.20 #571, 0.11 #1945, 0.05 #6405), 07vj4v (0.17 #1162, 0.03 #9918), 032j_n (0.14 #4054, 0.12 #4912, 0.11 #6112), 0hv0d (0.12 #1674, 0.11 #2188, 0.11 #2016), 03jvmp (0.12 #1399, 0.10 #2256, 0.08 #3113) >> Best rule #588 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 016tt2; 03rwz3; >> query: (?x2062, 0c41qv) <- service_language(?x2062, ?x254), award_winner(?x2062, ?x1394), film(?x2062, ?x4504) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #531 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 016tt2; 03rwz3; *> query: (?x2062, 030_1m) <- service_language(?x2062, ?x254), award_winner(?x2062, ?x1394), film(?x2062, ?x4504) *> conf = 0.20 ranks of expected_values: 2 EVAL 09d5h child 030_1m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 196.000 174.000 0.200 http://example.org/organization/organization/child./organization/organization_relationship/child #20343-02htv6 PRED entity: 02htv6 PRED relation: country PRED expected values: 09c7w0 => 168 concepts (138 used for prediction) PRED predicted values (max 10 best out of 3): 09c7w0 (0.87 #65, 0.87 #37, 0.87 #59), 0dclg (0.47 #72, 0.32 #71, 0.31 #55), 05tbn (0.32 #71, 0.31 #55, 0.18 #20) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 03lb_v; 01z_jj; >> query: (?x12028, 09c7w0) <- registering_agency(?x12028, ?x1982), currency(?x12028, ?x170), ?x170 = 09nqf, state_province_region(?x12028, ?x3670) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02htv6 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 138.000 0.872 http://example.org/organization/organization/headquarters./location/mailing_address/country #20342-01w9k25 PRED entity: 01w9k25 PRED relation: gender PRED expected values: 05zppz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #89, 0.81 #85, 0.80 #93), 02zsn (0.38 #14, 0.38 #8, 0.30 #144) >> Best rule #89 for best value: >> intensional similarity = 3 >> extensional distance = 435 >> proper extension: 02fybl; >> query: (?x10289, 05zppz) <- profession(?x10289, ?x131), role(?x10289, ?x316), instrumentalists(?x316, ?x115) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w9k25 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.815 http://example.org/people/person/gender #20341-01m8dg PRED entity: 01m8dg PRED relation: contains! PRED expected values: 09c7w0 => 68 concepts (32 used for prediction) PRED predicted values (max 10 best out of 249): 09c7w0 (0.98 #21479, 0.93 #10741, 0.85 #5369), 029jpy (0.37 #26848, 0.05 #1109, 0.04 #2003), 0k3k1 (0.23 #1390, 0.19 #2284, 0.13 #3178), 07ssc (0.22 #25090, 0.07 #24194, 0.06 #27779), 01cx_ (0.19 #5562, 0.16 #3773, 0.15 #6458), 02jx1 (0.19 #25145, 0.09 #24249, 0.07 #27834), 0k3hn (0.18 #1268, 0.11 #2162, 0.10 #3056), 01x73 (0.16 #7275, 0.16 #9958, 0.16 #8169), 07z1m (0.15 #7252, 0.14 #9935, 0.14 #8146), 059rby (0.15 #11653, 0.14 #12547, 0.11 #18810) >> Best rule #21479 for best value: >> intensional similarity = 5 >> extensional distance = 688 >> proper extension: 0rs6x; 015zyd; 0rh6k; 05kkh; 0k049; 01fq7; 06_kh; 01rtm4; 01jssp; 059rby; ... >> query: (?x13869, 09c7w0) <- category(?x13869, ?x134), contains(?x2020, ?x13869), ?x134 = 08mbj5d, contains(?x2020, ?x2484), ?x2484 = 01k7xz >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m8dg contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 32.000 0.984 http://example.org/location/location/contains #20340-0kvbl6 PRED entity: 0kvbl6 PRED relation: music PRED expected values: 02jxmr => 115 concepts (65 used for prediction) PRED predicted values (max 10 best out of 98): 02jxmr (0.33 #74, 0.07 #284, 0.06 #705), 07q1v4 (0.33 #15, 0.01 #2331, 0.01 #9728), 01l1rw (0.14 #313, 0.09 #1364, 0.06 #734), 0146pg (0.13 #2326, 0.13 #2536, 0.12 #3803), 0237jb (0.09 #13301, 0.08 #12246, 0.08 #11191), 09v6tz (0.09 #13301, 0.08 #12246, 0.08 #11191), 02bh9 (0.08 #4897, 0.06 #682, 0.06 #471), 04f9r2 (0.07 #400, 0.06 #821, 0.06 #610), 01nc3rh (0.07 #392, 0.06 #813, 0.06 #602), 03kwtb (0.07 #230, 0.06 #651, 0.06 #440) >> Best rule #74 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02z9rr; >> query: (?x6334, 02jxmr) <- award(?x6334, ?x350), nominated_for(?x6334, ?x6352), ?x6352 = 08mg_b, film_crew_role(?x6334, ?x137) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kvbl6 music 02jxmr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 65.000 0.333 http://example.org/film/film/music #20339-0jlv5 PRED entity: 0jlv5 PRED relation: award PRED expected values: 09qwmm 02ppm4q 03qgjwc => 101 concepts (71 used for prediction) PRED predicted values (max 10 best out of 298): 03qgjwc (0.77 #7591, 0.76 #4394, 0.76 #7590), 0gqwc (0.71 #473, 0.46 #74, 0.18 #1272), 0gqyl (0.55 #104, 0.41 #503, 0.13 #5298), 09sb52 (0.52 #41, 0.42 #440, 0.39 #839), 09qwmm (0.47 #433, 0.29 #34, 0.13 #22767), 02y_rq5 (0.45 #493, 0.25 #94, 0.07 #6086), 02ppm4q (0.38 #156, 0.34 #555, 0.14 #19970), 09td7p (0.34 #120, 0.20 #519, 0.07 #1318), 099cng (0.33 #484, 0.22 #85, 0.07 #1283), 05pcn59 (0.32 #878, 0.20 #2076, 0.20 #1278) >> Best rule #7591 for best value: >> intensional similarity = 3 >> extensional distance = 704 >> proper extension: 06lxn; >> query: (?x6709, ?x3499) <- award_winner(?x3499, ?x6709), category(?x6709, ?x134), award(?x241, ?x3499) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 7 EVAL 0jlv5 award 03qgjwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 71.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0jlv5 award 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 71.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0jlv5 award 09qwmm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 101.000 71.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20338-0h3mh3q PRED entity: 0h3mh3q PRED relation: honored_for! PRED expected values: 0hr3c8y 0hndn2q => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 96): 05c1t6z (0.36 #1463, 0.26 #2311, 0.23 #858), 02q690_ (0.35 #1505, 0.27 #1384, 0.25 #2353), 0gvstc3 (0.31 #1478, 0.22 #2326, 0.21 #1115), 03nnm4t (0.29 #1514, 0.20 #2362, 0.19 #1393), 0gx_st (0.18 #1481, 0.14 #6295, 0.14 #1360), 04n2r9h (0.17 #35, 0.11 #277, 0.11 #156), 0hndn2q (0.16 #6783, 0.14 #6295, 0.09 #9816), 0hr3c8y (0.16 #6783, 0.14 #6295, 0.09 #9816), 059x66 (0.16 #6783, 0.09 #9816, 0.09 #9815), 0bzmt8 (0.16 #6783, 0.09 #9816, 0.09 #9815) >> Best rule #1463 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 0gpjbt; >> query: (?x9514, 05c1t6z) <- honored_for(?x1764, ?x9514), honored_for(?x1764, ?x3626), award_winner(?x1764, ?x1040), ?x3626 = 01j7mr >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #6783 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 838 *> proper extension: 04gknr; 02x8fs; 02gs6r; 047gpsd; 07ghq; 0564x; *> query: (?x9514, ?x873) <- award_winner(?x9514, ?x8734), award_winner(?x873, ?x8734), award(?x8734, ?x375) *> conf = 0.16 ranks of expected_values: 7, 8 EVAL 0h3mh3q honored_for! 0hndn2q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 87.000 0.365 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0h3mh3q honored_for! 0hr3c8y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 87.000 0.365 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #20337-06tw8 PRED entity: 06tw8 PRED relation: administrative_parent PRED expected values: 02j71 => 163 concepts (119 used for prediction) PRED predicted values (max 10 best out of 28): 02j71 (0.86 #1659, 0.86 #8844, 0.85 #9669), 09c7w0 (0.48 #3444, 0.36 #3998, 0.33 #5106), 0dg3n1 (0.26 #3581, 0.26 #3580, 0.18 #5242), 05g2v (0.26 #3581, 0.26 #3580, 0.18 #5242), 04wsz (0.18 #16213, 0.16 #14947, 0.05 #15227), 0d060g (0.07 #4557, 0.04 #2759, 0.03 #13140), 03rjj (0.07 #11465, 0.02 #11323, 0.02 #15793), 0d05w3 (0.06 #14152, 0.04 #15978, 0.02 #2109), 059rby (0.06 #14674, 0.06 #14815, 0.04 #16362), 0345h (0.06 #2779, 0.03 #15815, 0.03 #16098) >> Best rule #1659 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 01n6c; 06sw9; 04vs9; 01nqj; >> query: (?x5457, 02j71) <- jurisdiction_of_office(?x265, ?x5457), country(?x1121, ?x5457), contains(?x2467, ?x5457), ?x2467 = 0dg3n1 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06tw8 administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 119.000 0.857 http://example.org/base/aareas/schema/administrative_area/administrative_parent #20336-0l6mp PRED entity: 0l6mp PRED relation: olympics! PRED expected values: 0jgd 0154j => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 366): 09c7w0 (0.83 #2155, 0.82 #2062, 0.79 #2740), 0345h (0.79 #2740, 0.56 #1883, 0.54 #1070), 01mjq (0.78 #1887, 0.60 #1093, 0.50 #706), 06bnz (0.78 #1889, 0.60 #780, 0.55 #872), 0163v (0.78 #1897, 0.50 #618, 0.40 #1103), 0154j (0.71 #1571, 0.70 #3036, 0.70 #2937), 06f32 (0.67 #3420, 0.67 #1957, 0.60 #780), 01pj7 (0.67 #3420, 0.60 #1098, 0.56 #1892), 059z0 (0.67 #3420, 0.60 #780, 0.55 #872), 0g8bw (0.67 #3420, 0.60 #780, 0.54 #1070) >> Best rule #2155 for best value: >> intensional similarity = 68 >> extensional distance = 10 >> proper extension: 016r9z; >> query: (?x2233, 09c7w0) <- sports(?x2233, ?x4503), sports(?x2233, ?x779), olympics(?x7747, ?x2233), olympics(?x1892, ?x2233), olympics(?x1499, ?x2233), olympics(?x390, ?x2233), olympics(?x279, ?x2233), olympics(?x205, ?x2233), olympics(?x87, ?x2233), ?x779 = 096f8, film_release_region(?x6932, ?x7747), film_release_region(?x3201, ?x7747), film_release_region(?x2893, ?x7747), film_release_region(?x2656, ?x7747), film_release_region(?x1463, ?x7747), ?x6932 = 027pfg, teams(?x7747, ?x11379), ?x2893 = 01jrbb, adjoins(?x1122, ?x7747), exported_to(?x1780, ?x7747), combatants(?x7747, ?x756), ?x3201 = 01ffx4, ?x390 = 0chghy, sports(?x2233, ?x359), olympics(?x4503, ?x778), film_release_region(?x10475, ?x1892), film_release_region(?x7524, ?x1892), film_release_region(?x7170, ?x1892), film_release_region(?x6603, ?x1892), film_release_region(?x5255, ?x1892), film_release_region(?x4041, ?x1892), film_release_region(?x3812, ?x1892), film_release_region(?x3226, ?x1892), film_release_region(?x3137, ?x1892), film_release_region(?x2783, ?x1892), film_release_region(?x2598, ?x1892), film_release_region(?x2394, ?x1892), film_release_region(?x1546, ?x1892), film_release_region(?x1202, ?x1892), film_release_region(?x1002, ?x1892), film_release_region(?x66, ?x1892), country(?x453, ?x1892), ?x4041 = 0gy2y8r, ?x10475 = 047p798, ?x6603 = 094g2z, ?x3226 = 0gyfp9c, ?x2656 = 03qnc6q, ?x3137 = 0htww, ?x66 = 014lc_, ?x2598 = 07f_7h, ?x7170 = 02pxst, ?x1202 = 0gj8t_b, ?x1546 = 0d6b7, form_of_government(?x87, ?x1926), ?x3812 = 0c3xw46, contains(?x1499, ?x8809), ?x5255 = 01sby_, ?x205 = 03rjj, ?x2783 = 0879bpq, film_release_region(?x7275, ?x87), ?x2394 = 0661ql3, ?x7524 = 01cm8w, ?x279 = 0d060g, organization(?x1892, ?x127), currency(?x87, ?x170), ?x1002 = 0_b3d, ?x7275 = 0g4vmj8, ?x1463 = 0gtvrv3 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1571 for first EXPECTED value: *> intensional similarity = 61 *> extensional distance = 5 *> proper extension: 0blg2; *> query: (?x2233, 0154j) <- sports(?x2233, ?x1557), sports(?x2233, ?x779), sports(?x2233, ?x171), olympics(?x7747, ?x2233), olympics(?x2346, ?x2233), olympics(?x1892, ?x2233), olympics(?x1353, ?x2233), olympics(?x421, ?x2233), olympics(?x390, ?x2233), ?x779 = 096f8, film_release_region(?x6932, ?x7747), film_release_region(?x6095, ?x7747), film_release_region(?x6014, ?x7747), film_release_region(?x4610, ?x7747), film_release_region(?x3217, ?x7747), film_release_region(?x3201, ?x7747), film_release_region(?x2893, ?x7747), film_release_region(?x2656, ?x7747), film_release_region(?x2050, ?x7747), film_release_region(?x1932, ?x7747), film_release_region(?x1927, ?x7747), ?x6932 = 027pfg, teams(?x7747, ?x11379), ?x2893 = 01jrbb, adjoins(?x1122, ?x7747), exported_to(?x1780, ?x7747), combatants(?x7747, ?x756), ?x3201 = 01ffx4, ?x390 = 0chghy, sports(?x2233, ?x359), ?x1892 = 02vzc, participating_countries(?x2233, ?x94), ?x3217 = 0gffmn8, contains(?x2346, ?x1885), ?x421 = 03_r3, country(?x1557, ?x5274), film_release_region(?x186, ?x2346), olympics(?x6733, ?x2233), exported_to(?x5457, ?x2346), sports(?x358, ?x1557), ?x2656 = 03qnc6q, country(?x206, ?x2346), country(?x150, ?x2346), ?x1932 = 0btyf5z, ?x2050 = 01fmys, combatants(?x7287, ?x2346), participating_countries(?x4255, ?x7747), titles(?x7747, ?x5399), ?x6014 = 031ldd, ?x4610 = 017jd9, ?x6095 = 0bq6ntw, administrative_area_type(?x7747, ?x2792), ?x1927 = 0by1wkq, ?x171 = 0d1tm, ?x5274 = 04g61, jurisdiction_of_office(?x182, ?x7747), nationality(?x754, ?x2346), film_release_region(?x3897, ?x1353), film_release_region(?x1163, ?x1353), ?x3897 = 02dpl9, ?x1163 = 0c0nhgv *> conf = 0.71 ranks of expected_values: 6, 15 EVAL 0l6mp olympics! 0154j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 37.000 37.000 0.833 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l6mp olympics! 0jgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 37.000 37.000 0.833 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #20335-04snp2 PRED entity: 04snp2 PRED relation: location PRED expected values: 01x73 => 101 concepts (97 used for prediction) PRED predicted values (max 10 best out of 132): 01m1zk (0.47 #59552, 0.46 #20113, 0.45 #32184), 02_286 (0.32 #4058, 0.25 #841, 0.20 #3254), 0hptm (0.12 #1107, 0.10 #1911, 0.08 #2715), 0xrzh (0.12 #1002, 0.10 #1806, 0.08 #2610), 0s987 (0.12 #1420, 0.10 #2224, 0.08 #3028), 0k_s5 (0.12 #1413, 0.10 #2217, 0.08 #3021), 071vr (0.12 #1141, 0.10 #1945, 0.08 #2749), 0f2rq (0.12 #1085, 0.10 #1889, 0.08 #2693), 0cr3d (0.12 #3362, 0.06 #21062, 0.06 #4166), 0wh3 (0.10 #1663, 0.08 #2467, 0.01 #5685) >> Best rule #59552 for best value: >> intensional similarity = 3 >> extensional distance = 2279 >> proper extension: 05fh2; >> query: (?x4238, ?x4074) <- place_of_birth(?x4238, ?x4074), location(?x6765, ?x4074), contains(?x94, ?x4074) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #59553 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2279 *> proper extension: 05fh2; *> query: (?x4238, ?x94) <- place_of_birth(?x4238, ?x4074), location(?x6765, ?x4074), contains(?x94, ?x4074) *> conf = 0.03 ranks of expected_values: 40 EVAL 04snp2 location 01x73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 101.000 97.000 0.470 http://example.org/people/person/places_lived./people/place_lived/location #20334-03yfh3 PRED entity: 03yfh3 PRED relation: current_club! PRED expected values: 03_qrp => 125 concepts (59 used for prediction) PRED predicted values (max 10 best out of 44): 02ltg3 (0.50 #62, 0.38 #455, 0.30 #482), 02bh_v (0.50 #74, 0.19 #467, 0.17 #214), 02s2lg (0.45 #481, 0.25 #61, 0.22 #369), 02pp1 (0.40 #163, 0.33 #190, 0.31 #471), 03_qj1 (0.40 #123, 0.29 #263, 0.21 #629), 032jlh (0.33 #24, 0.30 #526, 0.29 #642), 03dj48 (0.31 #469, 0.20 #161, 0.17 #216), 02rqxc (0.26 #511, 0.25 #627, 0.20 #121), 033nzk (0.25 #310, 0.22 #337, 0.17 #561), 01l3wr (0.25 #77, 0.22 #443, 0.17 #217) >> Best rule #62 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0175rc; >> query: (?x13947, 02ltg3) <- position(?x13947, ?x530), colors(?x13947, ?x1101), ?x1101 = 06fvc, team(?x203, ?x13947), team(?x7026, ?x13947), ?x530 = 02_j1w, ?x7026 = 09r1j5 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #573 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 22 *> proper extension: 03x746; 04112r; 03c0t9; 01k9cc; 03j6_5; 0498yf; 04ck0_; *> query: (?x13947, 03_qrp) <- position(?x13947, ?x60), colors(?x13947, ?x4557), colors(?x13947, ?x1101), ?x1101 = 06fvc, team(?x203, ?x13947), current_club(?x7294, ?x13947), ?x203 = 0dgrmp, colors(?x10409, ?x4557), colors(?x1115, ?x4557), team(?x9266, ?x10409), ?x1115 = 01y3c *> conf = 0.12 ranks of expected_values: 22 EVAL 03yfh3 current_club! 03_qrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 125.000 59.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #20333-03f4xvm PRED entity: 03f4xvm PRED relation: category PRED expected values: 08mbj5d => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #3, 0.85 #51, 0.84 #21) >> Best rule #3 for best value: >> intensional similarity = 2 >> extensional distance = 26 >> proper extension: 07pzc; >> query: (?x4548, 08mbj5d) <- award(?x4548, ?x8705), ?x8705 = 01c9dd >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f4xvm category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.857 http://example.org/common/topic/webpage./common/webpage/category #20332-01svry PRED entity: 01svry PRED relation: film_crew_role PRED expected values: 09zzb8 02r96rf => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 25): 02r96rf (0.82 #93, 0.75 #153, 0.74 #213), 01vx2h (0.75 #100, 0.55 #220, 0.50 #130), 09zzb8 (0.75 #1033, 0.73 #331, 0.73 #1093), 01xy5l_ (0.53 #133, 0.51 #193, 0.50 #103), 015h31 (0.46 #98, 0.33 #8, 0.26 #218), 026sdt1 (0.33 #22, 0.03 #172, 0.02 #202), 01pvkk (0.29 #131, 0.29 #311, 0.27 #1316), 02rh1dz (0.21 #99, 0.15 #219, 0.13 #129), 020xn5 (0.21 #97, 0.15 #127, 0.12 #217), 02ynfr (0.18 #225, 0.16 #315, 0.16 #255) >> Best rule #93 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 03hxsv; 02z0f6l; 0gy30w; 0270k40; >> query: (?x6731, 02r96rf) <- film(?x1057, ?x6731), film_crew_role(?x6731, ?x8411), film_crew_role(?x6731, ?x4305), ?x4305 = 0215hd, ?x8411 = 033smt >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01svry film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.821 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01svry film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 60.000 60.000 0.821 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20331-03gj2 PRED entity: 03gj2 PRED relation: country! PRED expected values: 0c0yh4 => 188 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1747): 01m13b (0.71 #10378, 0.53 #15495, 0.45 #22318), 023g6w (0.57 #11633, 0.32 #23573, 0.30 #35513), 020y73 (0.36 #7170, 0.30 #5465, 0.21 #10581), 034qmv (0.36 #6835, 0.30 #5130, 0.21 #10246), 049mql (0.36 #10875, 0.27 #7464, 0.26 #24523), 0bmch_x (0.36 #11020, 0.27 #7609, 0.25 #14432), 0fy34l (0.36 #10529, 0.27 #7118, 0.20 #5413), 0dscrwf (0.29 #10299, 0.27 #6888, 0.26 #23947), 0401sg (0.29 #10324, 0.27 #6913, 0.25 #13736), 0cp08zg (0.29 #11499, 0.27 #8088, 0.25 #14911) >> Best rule #10378 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 05r4w; 09c7w0; 03_3d; 0d0vqn; 07ssc; 0f8l9c; 0k6nt; 059j2; 0345h; 02vzc; ... >> query: (?x1003, 01m13b) <- film_release_region(?x5139, ?x1003), combatants(?x326, ?x1003), ?x5139 = 07bzz7 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 022dp5; 012f86; *> query: (?x1003, 0c0yh4) <- split_to(?x8649, ?x1003), people(?x8649, ?x548) *> conf = 0.14 ranks of expected_values: 642 EVAL 03gj2 country! 0c0yh4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 188.000 76.000 0.714 http://example.org/film/film/country #20330-0ctw_b PRED entity: 0ctw_b PRED relation: teams PRED expected values: 03_44z => 228 concepts (228 used for prediction) PRED predicted values (max 10 best out of 267): 038zh6 (0.12 #2144, 0.07 #1426, 0.06 #2503), 020wyp (0.08 #691, 0.07 #1050, 0.07 #1768), 0cnk2q (0.08 #360, 0.07 #719, 0.07 #1437), 023zd7 (0.08 #521, 0.07 #1598, 0.07 #1239), 02bh_v (0.08 #574, 0.07 #1651, 0.07 #1292), 03dj48 (0.08 #605, 0.07 #1323, 0.06 #2041), 0bszz (0.08 #714, 0.05 #3586, 0.04 #17595), 09glnr (0.08 #703, 0.05 #3575, 0.04 #5012), 049dzz (0.08 #597, 0.05 #3469, 0.04 #4906), 0264v8r (0.08 #409, 0.05 #3281, 0.04 #4718) >> Best rule #2144 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0j5g9; >> query: (?x1023, 038zh6) <- contains(?x1023, ?x2396), featured_film_locations(?x522, ?x1023), nationality(?x226, ?x1023) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ctw_b teams 03_44z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 228.000 0.125 http://example.org/sports/sports_team_location/teams #20329-058m5m4 PRED entity: 058m5m4 PRED relation: award_winner PRED expected values: 020_95 0d810y 018ygt => 18 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1715): 018ygt (0.33 #949, 0.29 #1517, 0.27 #10064), 0d810y (0.33 #878, 0.29 #1517, 0.22 #3035), 048q6x (0.33 #781, 0.29 #1517, 0.22 #3035), 04954 (0.33 #1089, 0.29 #1517, 0.11 #19755), 026rm_y (0.33 #1227, 0.23 #2745, 0.17 #4265), 044lyq (0.33 #1057, 0.22 #3035, 0.19 #1518), 077yk0 (0.33 #973, 0.19 #1518, 0.11 #19755), 02lxj_ (0.33 #219, 0.19 #1518, 0.10 #10635), 0382m4 (0.33 #869, 0.15 #2387, 0.11 #8464), 04znsy (0.33 #1282, 0.15 #2800, 0.11 #4320) >> Best rule #949 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 09g90vz; >> query: (?x3609, 018ygt) <- award_winner(?x3609, ?x5690), award_winner(?x3609, ?x5505), award_winner(?x3609, ?x5215), award_winner(?x3609, ?x2551), award_nominee(?x5041, ?x5505), honored_for(?x3609, ?x2528), nominated_for(?x2551, ?x414), type_of_union(?x2551, ?x566), ceremony(?x618, ?x3609), award_nominee(?x2551, ?x92), award(?x2551, ?x749), film(?x2551, ?x2519), student(?x122, ?x2551), nationality(?x5690, ?x94), ?x5215 = 07s95_l >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 75 EVAL 058m5m4 award_winner 018ygt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 18.000 14.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 058m5m4 award_winner 0d810y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 18.000 14.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 058m5m4 award_winner 020_95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 18.000 14.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20328-02nczh PRED entity: 02nczh PRED relation: film_crew_role PRED expected values: 09zzb8 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.85 #289, 0.84 #541, 0.83 #649), 02r96rf (0.81 #292, 0.68 #688, 0.67 #906), 09vw2b7 (0.78 #296, 0.75 #548, 0.71 #692), 01vx2h (0.53 #300, 0.44 #696, 0.42 #914), 01pvkk (0.34 #481, 0.33 #301, 0.32 #445), 089fss (0.33 #295, 0.09 #79, 0.09 #909), 02rh1dz (0.18 #299, 0.17 #913, 0.16 #1094), 015h31 (0.17 #10, 0.16 #406, 0.12 #694), 0215hd (0.17 #19, 0.16 #523, 0.15 #451), 0d2b38 (0.17 #26, 0.15 #314, 0.14 #458) >> Best rule #289 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 08c6k9; >> query: (?x6427, 09zzb8) <- film_crew_role(?x6427, ?x3197), film_crew_role(?x6427, ?x2095), ?x2095 = 0dxtw, film(?x6618, ?x6427), ?x3197 = 02ynfr >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02nczh film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.849 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20327-0164v PRED entity: 0164v PRED relation: official_language PRED expected values: 064_8sq => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 44): 064_8sq (0.33 #60, 0.29 #192, 0.28 #236), 02h40lc (0.31 #178, 0.30 #222, 0.30 #1806), 06nm1 (0.18 #800, 0.18 #844, 0.17 #96), 0jzc (0.14 #190, 0.13 #234, 0.13 #58), 04306rv (0.08 #269, 0.07 #929, 0.06 #753), 05zjd (0.08 #328, 0.07 #548, 0.07 #196), 071fb (0.07 #56, 0.05 #188, 0.05 #232), 0653m (0.06 #97, 0.04 #493, 0.04 #713), 02bv9 (0.05 #153, 0.04 #637, 0.03 #285), 02bjrlw (0.05 #265, 0.03 #749, 0.03 #925) >> Best rule #60 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 07p7g; >> query: (?x8857, 064_8sq) <- contains(?x2467, ?x8857), administrative_parent(?x8857, ?x551), ?x2467 = 0dg3n1 >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0164v official_language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.333 http://example.org/location/country/official_language #20326-06y0xx PRED entity: 06y0xx PRED relation: profession PRED expected values: 0dxtg => 112 concepts (58 used for prediction) PRED predicted values (max 10 best out of 49): 0dxtg (0.71 #1172, 0.70 #1752, 0.70 #2767), 0cbd2 (0.23 #8130, 0.20 #3342, 0.19 #3487), 0np9r (0.19 #8142, 0.16 #163, 0.15 #1613), 09jwl (0.18 #5238, 0.17 #7995, 0.16 #7415), 0kyk (0.16 #8150, 0.09 #6119, 0.08 #1476), 018gz8 (0.15 #1609, 0.13 #3930, 0.12 #6107), 0dgd_ (0.13 #752, 0.09 #317, 0.08 #1767), 0nbcg (0.12 #8007, 0.11 #7427, 0.11 #5976), 0n1h (0.11 #6248, 0.06 #590, 0.05 #7989), 0dz3r (0.11 #7401, 0.11 #7981, 0.10 #5224) >> Best rule #1172 for best value: >> intensional similarity = 4 >> extensional distance = 142 >> proper extension: 0jf1b; 030pr; 0gyx4; 06b_0; 0bq4j6; 0l9k1; 0pksh; >> query: (?x12366, 0dxtg) <- nationality(?x12366, ?x94), film(?x12366, ?x886), produced_by(?x6499, ?x12366), film_release_distribution_medium(?x886, ?x81) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06y0xx profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 58.000 0.708 http://example.org/people/person/profession #20325-025sf8g PRED entity: 025sf8g PRED relation: nutrient! PRED expected values: 0dcfv => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 71): 014j1m (0.92 #353, 0.92 #345, 0.92 #336), 05z55 (0.90 #99, 0.89 #255, 0.89 #247), 0dcfv (0.90 #99, 0.89 #58, 0.89 #146), 04k8n (0.24 #47, 0.04 #45, 0.03 #48), 05wvs (0.24 #47, 0.04 #45, 0.03 #48), 01sh2 (0.24 #47, 0.04 #45, 0.03 #48), 0f4kp (0.04 #45, 0.03 #48, 0.03 #42), 07q0m (0.04 #45, 0.03 #48, 0.03 #42), 0fzjh (0.04 #45, 0.03 #48, 0.03 #42), 025rw19 (0.04 #45, 0.03 #48, 0.03 #42) >> Best rule #353 for best value: >> intensional similarity = 106 >> extensional distance = 24 >> proper extension: 03d49; >> query: (?x6026, ?x6191) <- nutrient(?x9005, ?x6026), nutrient(?x8298, ?x6026), nutrient(?x7719, ?x6026), nutrient(?x7057, ?x6026), nutrient(?x6159, ?x6026), nutrient(?x6032, ?x6026), nutrient(?x5009, ?x6026), nutrient(?x4068, ?x6026), nutrient(?x3900, ?x6026), nutrient(?x1959, ?x6026), nutrient(?x1303, ?x6026), nutrient(?x1257, ?x6026), ?x7057 = 0fbdb, nutrient(?x6032, ?x13944), nutrient(?x6032, ?x13498), nutrient(?x6032, ?x12902), nutrient(?x6032, ?x12454), nutrient(?x6032, ?x12083), nutrient(?x6032, ?x11758), nutrient(?x6032, ?x11409), nutrient(?x6032, ?x11270), nutrient(?x6032, ?x10891), nutrient(?x6032, ?x10098), nutrient(?x6032, ?x9915), nutrient(?x6032, ?x9733), nutrient(?x6032, ?x9426), nutrient(?x6032, ?x9365), nutrient(?x6032, ?x8487), nutrient(?x6032, ?x8442), nutrient(?x6032, ?x8413), nutrient(?x6032, ?x7431), nutrient(?x6032, ?x7364), nutrient(?x6032, ?x7219), nutrient(?x6032, ?x7135), nutrient(?x6032, ?x6586), nutrient(?x6032, ?x6286), nutrient(?x6032, ?x6192), nutrient(?x6032, ?x6160), nutrient(?x6032, ?x6033), nutrient(?x6032, ?x5549), nutrient(?x6032, ?x5526), nutrient(?x6032, ?x5451), nutrient(?x6032, ?x5010), nutrient(?x6032, ?x3264), nutrient(?x6032, ?x3203), nutrient(?x6032, ?x2702), nutrient(?x6032, ?x1960), nutrient(?x6032, ?x1258), ?x9915 = 025tkqy, ?x8442 = 02kcv4x, ?x1960 = 07hnp, ?x12083 = 01n78x, ?x9365 = 04k8n, ?x13944 = 0f4kp, ?x7135 = 025rsfk, ?x10098 = 0h1_c, ?x5549 = 025s7j4, ?x6160 = 041r51, ?x6586 = 05gh50, nutrient(?x8298, ?x10709), nutrient(?x8298, ?x9708), nutrient(?x8298, ?x7894), nutrient(?x8298, ?x2018), ?x7364 = 09gvd, ?x12902 = 0fzjh, ?x2702 = 0838f, ?x3203 = 04kl74p, ?x1303 = 0fj52s, ?x6286 = 02y_3rf, nutrient(?x9005, ?x9795), ?x7219 = 0h1vg, ?x10709 = 0h1sz, ?x13498 = 07q0m, ?x12454 = 025rw19, ?x10891 = 0g5gq, nutrient(?x3900, ?x12481), ?x5526 = 09pbb, ?x6159 = 033cnk, ?x1257 = 09728, ?x6192 = 06jry, ?x11758 = 0q01m, ?x5009 = 0fjfh, ?x11409 = 0h1yf, ?x4068 = 0fbw6, ?x3264 = 0dcfv, nutrient(?x9732, ?x9733), nutrient(?x6191, ?x9733), ?x9795 = 05v_8y, ?x6191 = 014j1m, ?x6033 = 04zjxcz, ?x7431 = 09gwd, ?x7894 = 0f4hc, ?x9708 = 061xhr, ?x8413 = 02kc4sf, ?x9426 = 0h1yy, ?x7719 = 0dj75, nutrient(?x1959, ?x6517), ?x9732 = 05z55, ?x1258 = 0h1wg, ?x5451 = 05wvs, ?x8487 = 014yzm, ?x12481 = 027g6p7, ?x2018 = 01sh2, ?x11270 = 02kc008, ?x5010 = 0h1vz, ?x6517 = 02kd8zw >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #99 for first EXPECTED value: *> intensional similarity = 112 *> extensional distance = 9 *> proper extension: 025sf0_; *> query: (?x6026, ?x9732) <- nutrient(?x10612, ?x6026), nutrient(?x9489, ?x6026), nutrient(?x9005, ?x6026), nutrient(?x8298, ?x6026), nutrient(?x7719, ?x6026), nutrient(?x7057, ?x6026), nutrient(?x6285, ?x6026), nutrient(?x6159, ?x6026), nutrient(?x6032, ?x6026), nutrient(?x5373, ?x6026), nutrient(?x5337, ?x6026), nutrient(?x5009, ?x6026), nutrient(?x4068, ?x6026), nutrient(?x3468, ?x6026), nutrient(?x2701, ?x6026), nutrient(?x1303, ?x6026), nutrient(?x1257, ?x6026), ?x7057 = 0fbdb, ?x6032 = 01nkt, ?x1303 = 0fj52s, ?x6285 = 01645p, ?x7719 = 0dj75, ?x9005 = 04zpv, ?x8298 = 037ls6, ?x5373 = 0971v, ?x2701 = 0hkxq, ?x10612 = 0frq6, ?x4068 = 0fbw6, ?x6159 = 033cnk, ?x9489 = 07j87, nutrient(?x5337, ?x12454), nutrient(?x5337, ?x9915), nutrient(?x5337, ?x8243), nutrient(?x5337, ?x5549), nutrient(?x5337, ?x2702), nutrient(?x5337, ?x2018), ?x5009 = 0fjfh, ?x1257 = 09728, ?x2702 = 0838f, nutrient(?x3468, ?x14210), nutrient(?x3468, ?x13944), nutrient(?x3468, ?x13126), nutrient(?x3468, ?x12902), nutrient(?x3468, ?x12083), nutrient(?x3468, ?x11409), nutrient(?x3468, ?x11270), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10709), nutrient(?x3468, ?x10453), nutrient(?x3468, ?x10098), nutrient(?x3468, ?x9733), nutrient(?x3468, ?x9619), nutrient(?x3468, ?x9436), nutrient(?x3468, ?x9426), nutrient(?x3468, ?x9365), nutrient(?x3468, ?x8442), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x7652), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7364), nutrient(?x3468, ?x7362), nutrient(?x3468, ?x7219), nutrient(?x3468, ?x7135), nutrient(?x3468, ?x6586), nutrient(?x3468, ?x6286), nutrient(?x3468, ?x6033), nutrient(?x3468, ?x5526), nutrient(?x3468, ?x5451), nutrient(?x3468, ?x3469), nutrient(?x3468, ?x3203), nutrient(?x3468, ?x1960), nutrient(?x3468, ?x1304), ?x5451 = 05wvs, ?x10891 = 0g5gq, ?x13944 = 0f4kp, ?x9619 = 0h1tg, ?x12902 = 0fzjh, ?x10098 = 0h1_c, ?x14210 = 0f4k5, ?x8442 = 02kcv4x, ?x10453 = 075pwf, ?x1304 = 08lb68, ?x5526 = 09pbb, ?x9436 = 025sqz8, ?x6586 = 05gh50, ?x8243 = 014d7f, ?x11270 = 02kc008, ?x9915 = 025tkqy, ?x11409 = 0h1yf, ?x7219 = 0h1vg, nutrient(?x9732, ?x10709), nutrient(?x6191, ?x10709), ?x7362 = 02kc5rj, ?x3469 = 0h1zw, ?x3203 = 04kl74p, ?x9426 = 0h1yy, ?x6191 = 014j1m, ?x6033 = 04zjxcz, ?x6286 = 02y_3rf, ?x7431 = 09gwd, ?x13126 = 02kc_w5, ?x7652 = 025s0s0, ?x7135 = 025rsfk, ?x9365 = 04k8n, ?x1960 = 07hnp, ?x12083 = 01n78x, ?x12454 = 025rw19, ?x8413 = 02kc4sf, ?x7364 = 09gvd, ?x2018 = 01sh2, ?x9733 = 0h1tz, ?x5549 = 025s7j4 *> conf = 0.90 ranks of expected_values: 3 EVAL 025sf8g nutrient! 0dcfv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 59.000 59.000 0.923 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #20324-0xkyn PRED entity: 0xkyn PRED relation: place! PRED expected values: 0xkyn => 106 concepts (65 used for prediction) PRED predicted values (max 10 best out of 122): 0d35y (0.30 #5158, 0.26 #12387, 0.26 #12903), 0qplq (0.30 #5158, 0.25 #383, 0.24 #9289), 0xkyn (0.30 #5158, 0.24 #9289, 0.18 #16520), 0xkq4 (0.18 #16520, 0.17 #9807, 0.16 #14453), 010cw1 (0.18 #16520, 0.17 #9807, 0.16 #14453), 0hptm (0.08 #672, 0.06 #1187, 0.05 #1702), 0fvxz (0.08 #537, 0.06 #1052, 0.04 #2083), 0h6l4 (0.08 #891, 0.06 #1406, 0.04 #2437), 0xn7q (0.08 #861, 0.06 #1376, 0.04 #2407), 0xn5b (0.08 #649, 0.06 #1164, 0.04 #2195) >> Best rule #5158 for best value: >> intensional similarity = 5 >> extensional distance = 101 >> proper extension: 0rgxp; 0s9b_; >> query: (?x11863, ?x4419) <- location(?x2226, ?x11863), source(?x11863, ?x958), award_nominee(?x2226, ?x2227), artists(?x671, ?x2226), location(?x2226, ?x4419) >> conf = 0.30 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0xkyn place! 0xkyn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 65.000 0.296 http://example.org/location/hud_county_place/place #20323-016h4r PRED entity: 016h4r PRED relation: type_of_union PRED expected values: 04ztj => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.81 #61, 0.77 #69, 0.77 #89), 01g63y (0.21 #178, 0.21 #170, 0.20 #182), 0jgjn (0.02 #16, 0.01 #20, 0.01 #24) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 012v1t; >> query: (?x3495, 04ztj) <- gender(?x3495, ?x231), student(?x1368, ?x3495), student(?x3995, ?x3495) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016h4r type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.810 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20322-0cc56 PRED entity: 0cc56 PRED relation: location! PRED expected values: 01wbg84 03m8lq 02_hj4 01gq0b 0lgm5 07cjqy 0cqt90 024bbl 04wx2v => 173 concepts (147 used for prediction) PRED predicted values (max 10 best out of 2275): 06mn7 (0.71 #318739, 0.70 #350617, 0.70 #357972), 01ps2h8 (0.71 #318739, 0.70 #350617, 0.70 #357972), 05bpg3 (0.71 #318739, 0.70 #350617, 0.70 #357972), 01_6dw (0.71 #318739, 0.70 #350617, 0.70 #357972), 01f5q5 (0.71 #318739, 0.70 #350617, 0.70 #357972), 01kkx2 (0.71 #318739, 0.70 #350617, 0.70 #357972), 01n9d9 (0.71 #318739, 0.70 #350617, 0.70 #357972), 011vx3 (0.71 #318739, 0.70 #350617, 0.70 #357972), 041_y (0.71 #318739, 0.70 #350617, 0.70 #357972), 030dx5 (0.71 #318739, 0.70 #350617, 0.70 #357972) >> Best rule #318739 for best value: >> intensional similarity = 4 >> extensional distance = 430 >> proper extension: 02gw_w; >> query: (?x1131, ?x4816) <- place_of_birth(?x4816, ?x1131), place_of_birth(?x4353, ?x1131), location(?x4816, ?x108), award(?x4353, ?x198) >> conf = 0.71 => this is the best rule for 10 predicted values *> Best rule #10542 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 05l64; *> query: (?x1131, 0cqt90) <- administrative_parent(?x1131, ?x739), vacationer(?x1131, ?x3585), place_of_death(?x1047, ?x1131) *> conf = 0.25 ranks of expected_values: 30, 44, 538, 657, 1283, 1488 EVAL 0cc56 location! 04wx2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 147.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cc56 location! 024bbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 147.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cc56 location! 0cqt90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 173.000 147.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cc56 location! 07cjqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 147.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cc56 location! 0lgm5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 147.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cc56 location! 01gq0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 147.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cc56 location! 02_hj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 173.000 147.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cc56 location! 03m8lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 173.000 147.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cc56 location! 01wbg84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 173.000 147.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location #20321-0d05q4 PRED entity: 0d05q4 PRED relation: country! PRED expected values: 06f41 03_8r => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 52): 03_8r (0.79 #489, 0.76 #1113, 0.76 #385), 03hr1p (0.79 #490, 0.73 #386, 0.69 #646), 06f41 (0.79 #481, 0.72 #637, 0.70 #377), 07jbh (0.74 #499, 0.70 #395, 0.62 #655), 064vjs (0.74 #497, 0.68 #185, 0.67 #653), 06wrt (0.71 #483, 0.70 #379, 0.67 #639), 01lb14 (0.71 #482, 0.67 #638, 0.67 #378), 0194d (0.68 #513, 0.64 #669, 0.64 #409), 07bs0 (0.68 #480, 0.64 #636, 0.61 #376), 07gyv (0.65 #475, 0.62 #943, 0.60 #787) >> Best rule #489 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 015qh; 06bnz; 05b4w; >> query: (?x4092, 03_8r) <- film_release_region(?x186, ?x4092), combatants(?x94, ?x4092), administrative_area_type(?x4092, ?x2792) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0d05q4 country! 03_8r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.794 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d05q4 country! 06f41 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 131.000 0.794 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #20320-026p_bs PRED entity: 026p_bs PRED relation: film_release_region PRED expected values: 02vzc => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 253): 0f8l9c (0.92 #8185, 0.92 #4192, 0.91 #8358), 0345h (0.86 #1085, 0.75 #10798, 0.73 #6634), 03h64 (0.86 #1124, 0.73 #10837, 0.70 #8238), 01znc_ (0.86 #1095, 0.66 #4216, 0.65 #10808), 059j2 (0.84 #10796, 0.75 #9410, 0.75 #6632), 02vzc (0.84 #10820, 0.83 #8221, 0.83 #9434), 05r4w (0.83 #2604, 0.83 #10756, 0.81 #8157), 03_3d (0.81 #1050, 0.79 #2611, 0.75 #10763), 03rjj (0.81 #1048, 0.78 #10761, 0.77 #9375), 0chghy (0.81 #10769, 0.79 #8170, 0.79 #8343) >> Best rule #8185 for best value: >> intensional similarity = 5 >> extensional distance = 124 >> proper extension: 05p1tzf; 02x3lt7; 0401sg; 01vksx; 0m_mm; 0h3xztt; 03bx2lk; 05z_kps; 047msdk; 0gmcwlb; ... >> query: (?x650, 0f8l9c) <- language(?x650, ?x254), film_release_region(?x650, ?x1355), ?x1355 = 0h7x, ?x254 = 02h40lc, film(?x8002, ?x650) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #10820 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 217 *> proper extension: 0fq27fp; *> query: (?x650, 02vzc) <- film_release_region(?x650, ?x2152), film_release_region(?x650, ?x985), film_release_region(?x650, ?x94), genre(?x650, ?x53), ?x2152 = 06mkj, ?x94 = 09c7w0, ?x985 = 0k6nt *> conf = 0.84 ranks of expected_values: 6 EVAL 026p_bs film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 139.000 139.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20319-015y_n PRED entity: 015y_n PRED relation: artists PRED expected values: 04n32 01nn3m => 61 concepts (16 used for prediction) PRED predicted values (max 10 best out of 3273): 015882 (0.62 #4419, 0.40 #2273, 0.33 #5491), 0407f (0.58 #5640, 0.38 #4568, 0.33 #3495), 01htxr (0.50 #1626, 0.40 #2699, 0.36 #3217), 01vsy9_ (0.50 #1874, 0.40 #2947, 0.33 #4020), 03f2_rc (0.50 #1103, 0.40 #2176, 0.33 #3249), 04n32 (0.50 #1929, 0.40 #3002, 0.33 #4075), 026ps1 (0.50 #1100, 0.40 #2173, 0.33 #3246), 01n44c (0.50 #1535, 0.40 #2608, 0.33 #3681), 019f9z (0.50 #5955, 0.38 #4883, 0.34 #9174), 01wp8w7 (0.50 #4395, 0.33 #5467, 0.33 #3322) >> Best rule #4419 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 07sbbz2; 06by7; 02w4v; >> query: (?x12513, 015882) <- artists(?x12513, ?x3241), artists(?x12513, ?x3017), award(?x3017, ?x591), type_of_union(?x3017, ?x566), spouse(?x6525, ?x3017), profession(?x3017, ?x319), film(?x3017, ?x2112), ?x3241 = 0pj9t >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1929 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 03_d0; *> query: (?x12513, 04n32) <- artists(?x12513, ?x10559), artists(?x12513, ?x7414), artists(?x12513, ?x3241), artists(?x12513, ?x3017), artists(?x12513, ?x1338), ?x3017 = 02_fj, ?x10559 = 0dbb3, ?x3241 = 0pj9t, artists(?x8138, ?x7414), ?x8138 = 0161rf, award_nominee(?x1338, ?x827) *> conf = 0.50 ranks of expected_values: 6, 214 EVAL 015y_n artists 01nn3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 16.000 0.625 http://example.org/music/genre/artists EVAL 015y_n artists 04n32 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 61.000 16.000 0.625 http://example.org/music/genre/artists #20318-05xvj PRED entity: 05xvj PRED relation: season PRED expected values: 025ygqm 03c74_8 027pwzc => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 4): 027pwzc (0.79 #92, 0.71 #156, 0.70 #148), 025ygqm (0.78 #146, 0.76 #78, 0.75 #154), 03c74_8 (0.46 #53, 0.34 #210, 0.33 #30), 04n36qk (0.34 #210, 0.21 #255, 0.17 #28) >> Best rule #92 for best value: >> intensional similarity = 11 >> extensional distance = 17 >> proper extension: 07147; >> query: (?x12042, 027pwzc) <- school(?x12042, ?x735), season(?x12042, ?x2406), position(?x12042, ?x2010), ?x2406 = 03c6sl9, major_field_of_study(?x735, ?x10046), student(?x735, ?x65), school(?x3089, ?x735), ?x3089 = 03nt7j, citytown(?x735, ?x1523), ?x10046 = 041y2, currency(?x735, ?x170) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 05xvj season 027pwzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.789 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 05xvj season 03c74_8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.789 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 05xvj season 025ygqm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.789 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #20317-0gl5_ PRED entity: 0gl5_ PRED relation: institution! PRED expected values: 028dcg => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 13): 028dcg (0.23 #80, 0.13 #66, 0.12 #108), 0bjrnt (0.21 #58, 0.18 #100, 0.18 #115), 03mkk4 (0.20 #134, 0.17 #74, 0.17 #290), 01ysy9 (0.20 #27, 0.08 #41, 0.08 #69), 01rr_d (0.18 #64, 0.17 #78, 0.17 #308), 022h5x (0.17 #381, 0.15 #141, 0.13 #311), 02mjs7 (0.13 #71, 0.12 #85, 0.11 #99), 071tyz (0.10 #59, 0.09 #87, 0.08 #73), 02cq61 (0.08 #107, 0.08 #122, 0.08 #309), 02m4yg (0.08 #63, 0.07 #105, 0.07 #120) >> Best rule #80 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 02d9nr; >> query: (?x6912, 028dcg) <- student(?x6912, ?x1992), place_of_birth(?x1992, ?x9233), celebrity(?x1992, ?x1089) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gl5_ institution! 028dcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.226 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20316-03nb5v PRED entity: 03nb5v PRED relation: student! PRED expected values: 0fr9jp => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 88): 03y5ky (0.33 #211, 0.02 #1792, 0.02 #2319), 03ksy (0.11 #3795, 0.11 #2741, 0.11 #4322), 05q2c (0.10 #841), 03l6bs (0.10 #726), 01jq34 (0.08 #1638, 0.06 #2165, 0.06 #2692), 0bwfn (0.07 #16088, 0.06 #5018, 0.06 #3964), 0cwx_ (0.05 #1295, 0.04 #1822, 0.03 #2349), 04b_46 (0.05 #1281, 0.03 #11822, 0.03 #3389), 08815 (0.05 #1056, 0.03 #14760, 0.03 #9488), 05zl0 (0.05 #1256, 0.02 #3364, 0.02 #1783) >> Best rule #211 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0277990; >> query: (?x6550, 03y5ky) <- tv_program(?x6550, ?x1876), location(?x6550, ?x1274), ?x1274 = 04ykg >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #9831 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 263 *> proper extension: 024dgj; *> query: (?x6550, 0fr9jp) <- place_of_birth(?x6550, ?x5771), citytown(?x3948, ?x5771), participant(?x3307, ?x6550) *> conf = 0.03 ranks of expected_values: 23 EVAL 03nb5v student! 0fr9jp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 129.000 129.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #20315-01_x6d PRED entity: 01_x6d PRED relation: people! PRED expected values: 041rx => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 41): 041rx (0.26 #1753, 0.21 #4, 0.17 #2361), 0x67 (0.22 #1758, 0.21 #465, 0.18 #85), 048z7l (0.09 #39, 0.07 #267, 0.07 #115), 02w7gg (0.09 #1751, 0.07 #2588, 0.06 #3579), 0xnvg (0.08 #2065, 0.07 #1761, 0.07 #88), 07bch9 (0.06 #706, 0.05 #2075, 0.05 #1847), 02ctzb (0.06 #698, 0.04 #242, 0.04 #1763), 07hwkr (0.05 #2064, 0.05 #1456, 0.04 #2445), 02g7sp (0.05 #473, 0.04 #93, 0.03 #169), 01qhm_ (0.04 #1755, 0.04 #1222, 0.04 #2363) >> Best rule #1753 for best value: >> intensional similarity = 3 >> extensional distance = 381 >> proper extension: 022769; >> query: (?x4466, 041rx) <- award_winner(?x4466, ?x163), people(?x1446, ?x4466), place_of_birth(?x4466, ?x4733) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_x6d people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.256 http://example.org/people/ethnicity/people #20314-04rqd PRED entity: 04rqd PRED relation: artist PRED expected values: 01vvycq 09889g 0134pk => 121 concepts (93 used for prediction) PRED predicted values (max 10 best out of 100): 0134pk (0.50 #256, 0.40 #392, 0.33 #122), 09889g (0.33 #102, 0.25 #236, 0.20 #372), 01vvycq (0.33 #70, 0.25 #204, 0.20 #340), 01vrt_c (0.25 #207, 0.20 #343, 0.14 #411), 027kwc (0.25 #268, 0.20 #404, 0.14 #472), 063t3j (0.25 #267, 0.20 #403, 0.14 #471), 012x1l (0.25 #264, 0.20 #400, 0.14 #468), 016vn3 (0.25 #260, 0.20 #396, 0.14 #464), 0mjn2 (0.25 #259, 0.20 #395, 0.14 #463), 02h9_l (0.25 #258, 0.20 #394, 0.14 #462) >> Best rule #256 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03gfvsz; >> query: (?x12476, 0134pk) <- artist(?x12476, ?x7013), artist(?x12476, ?x140), group(?x3472, ?x7013), actor(?x5529, ?x140), award_nominee(?x527, ?x140) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 04rqd artist 0134pk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 93.000 0.500 http://example.org/broadcast/content/artist EVAL 04rqd artist 09889g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 93.000 0.500 http://example.org/broadcast/content/artist EVAL 04rqd artist 01vvycq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 93.000 0.500 http://example.org/broadcast/content/artist #20313-04q827 PRED entity: 04q827 PRED relation: nominated_for! PRED expected values: 099jhq 02w9sd7 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 200): 02qt02v (0.68 #1328, 0.68 #1329, 0.67 #8859), 040njc (0.59 #892, 0.59 #1113, 0.56 #1557), 0p9sw (0.55 #1126, 0.53 #684, 0.44 #2675), 02pqp12 (0.54 #936, 0.48 #1601, 0.45 #1157), 094qd5 (0.53 #474, 0.24 #11078, 0.19 #15945), 02qyntr (0.50 #827, 0.48 #1713, 0.46 #1048), 04dn09n (0.50 #1137, 0.47 #1581, 0.44 #2686), 0gr0m (0.50 #716, 0.47 #1158, 0.42 #2707), 02r22gf (0.47 #689, 0.46 #910, 0.44 #1575), 0gq_v (0.46 #904, 0.40 #1125, 0.39 #2674) >> Best rule #1328 for best value: >> intensional similarity = 6 >> extensional distance = 56 >> proper extension: 0gmcwlb; 0j80w; >> query: (?x10806, ?x618) <- nominated_for(?x3260, ?x10806), award(?x10806, ?x1716), award(?x10806, ?x1313), award(?x10806, ?x618), award_winner(?x1716, ?x624), ?x1313 = 0gs9p >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #11078 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1088 *> proper extension: 07s8z_l; 01j95; *> query: (?x10806, ?x451) <- award_winner(?x10806, ?x3186), award(?x3186, ?x451), nominated_for(?x3186, ?x2107) *> conf = 0.24 ranks of expected_values: 30, 37 EVAL 04q827 nominated_for! 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 83.000 83.000 0.680 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04q827 nominated_for! 099jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 83.000 83.000 0.680 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20312-028_yv PRED entity: 028_yv PRED relation: titles! PRED expected values: 04xvlr => 85 concepts (50 used for prediction) PRED predicted values (max 10 best out of 68): 04xvlr (0.38 #3, 0.26 #1341, 0.23 #412), 07ssc (0.25 #9, 0.11 #2682, 0.09 #2991), 0lsxr (0.21 #3085, 0.18 #2878, 0.17 #3805), 01z4y (0.19 #4767, 0.17 #2400, 0.17 #3534), 01jfsb (0.18 #223, 0.18 #120, 0.13 #1867), 024qqx (0.15 #489, 0.10 #1109, 0.10 #1418), 02l7c8 (0.12 #24, 0.03 #433, 0.03 #3006), 017fp (0.11 #432, 0.08 #3005, 0.08 #4755), 09blyk (0.11 #250, 0.09 #147, 0.05 #2306), 01hmnh (0.10 #332, 0.09 #1670, 0.09 #1466) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 058kh7; >> query: (?x204, 04xvlr) <- featured_film_locations(?x204, ?x739), film(?x382, ?x204), film(?x6259, ?x204), ?x6259 = 01x4sb >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028_yv titles! 04xvlr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 50.000 0.375 http://example.org/media_common/netflix_genre/titles #20311-04jpl PRED entity: 04jpl PRED relation: citytown! PRED expected values: 015g1w 03_c8p 02m97n => 198 concepts (181 used for prediction) PRED predicted values (max 10 best out of 921): 0m4yg (0.54 #111739, 0.50 #95551, 0.50 #96322), 02bzh0 (0.54 #111739, 0.50 #95551, 0.50 #96322), 01314k (0.54 #111739, 0.50 #95551, 0.50 #96322), 02kzfw (0.54 #111739, 0.50 #95551, 0.50 #96322), 01g0p5 (0.54 #111739, 0.50 #95551, 0.50 #96322), 0dplh (0.54 #111739, 0.50 #95551, 0.50 #96322), 0f8j6 (0.54 #111739, 0.50 #95551, 0.50 #96322), 0ncy4 (0.54 #111739, 0.50 #95551, 0.50 #96322), 0n96z (0.54 #111739, 0.50 #95551, 0.50 #96322), 02gw_w (0.54 #111739, 0.50 #95551, 0.50 #96322) >> Best rule #111739 for best value: >> intensional similarity = 2 >> extensional distance = 236 >> proper extension: 0t015; 0fm2_; 0_7z2; 0_ytw; 013cz2; 013hxv; 0j8p6; 0fvwg; 0mnm2; 0_lr1; ... >> query: (?x362, ?x639) <- citytown(?x752, ?x362), contains(?x362, ?x639) >> conf = 0.54 => this is the best rule for 39 predicted values *> Best rule #3631 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 8 *> proper extension: 07751; 02fzs; *> query: (?x362, 03_c8p) <- film_regional_debut_venue(?x1597, ?x362), vacationer(?x362, ?x827) *> conf = 0.10 ranks of expected_values: 93, 764 EVAL 04jpl citytown! 02m97n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 198.000 181.000 0.538 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 04jpl citytown! 03_c8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 198.000 181.000 0.538 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 04jpl citytown! 015g1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 198.000 181.000 0.538 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #20310-02cpp PRED entity: 02cpp PRED relation: group! PRED expected values: 05148p4 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 76): 05148p4 (0.69 #4074, 0.69 #4955, 0.68 #4867), 018vs (0.61 #4067, 0.61 #4860, 0.61 #4948), 0l14md (0.60 #4061, 0.57 #4942, 0.56 #4854), 028tv0 (0.52 #981, 0.50 #893, 0.39 #4066), 03qjg (0.35 #1283, 0.23 #4895, 0.22 #4983), 0l14qv (0.30 #529, 0.23 #4940, 0.23 #1146), 013y1f (0.30 #529, 0.23 #1146, 0.23 #1235), 06ncr (0.30 #529, 0.23 #1146, 0.23 #1235), 07y_7 (0.30 #529, 0.23 #1146, 0.23 #1235), 0l14j_ (0.30 #529, 0.23 #1146, 0.23 #1235) >> Best rule #4074 for best value: >> intensional similarity = 3 >> extensional distance = 129 >> proper extension: 089tm; 01t_xp_; 01pfr3; 04rcr; 0150jk; 02r3zy; 067mj; 01wv9xn; 03t9sp; 01fl3; ... >> query: (?x5916, 05148p4) <- award(?x5916, ?x1565), artist(?x2149, ?x5916), group(?x227, ?x5916) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cpp group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.687 http://example.org/music/performance_role/regular_performances./music/group_membership/group #20309-07h07 PRED entity: 07h07 PRED relation: award PRED expected values: 03hl6lc => 113 concepts (92 used for prediction) PRED predicted values (max 10 best out of 300): 0789r6 (0.72 #32356, 0.72 #23565, 0.71 #22363), 03hl6lc (0.59 #972, 0.24 #4964, 0.20 #7360), 07bdd_ (0.57 #3656, 0.52 #4454, 0.09 #14034), 09sb52 (0.51 #14809, 0.28 #14011, 0.27 #13612), 0gr4k (0.50 #830, 0.34 #5621, 0.32 #7218), 03hkv_r (0.44 #813, 0.25 #4805, 0.22 #7201), 05p1dby (0.39 #3696, 0.37 #4494, 0.07 #14074), 0gq9h (0.35 #3667, 0.33 #4465, 0.24 #872), 040njc (0.32 #805, 0.24 #5596, 0.22 #4797), 0gs9p (0.29 #874, 0.27 #5665, 0.25 #7262) >> Best rule #32356 for best value: >> intensional similarity = 3 >> extensional distance = 1563 >> proper extension: 05cljf; 01vrx3g; 0m2l9; 01zkxv; 01n5309; 01w61th; 01kwlwp; 018y2s; 09qr6; 06w2sn5; ... >> query: (?x4008, ?x746) <- award_nominee(?x3170, ?x4008), award_winner(?x746, ?x4008), award(?x276, ?x746) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #972 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 03m_k0; 02_4fn; 01_f_5; 026670; *> query: (?x4008, 03hl6lc) <- award(?x4008, ?x899), ?x899 = 02x1dht, award_winner(?x13075, ?x4008) *> conf = 0.59 ranks of expected_values: 2 EVAL 07h07 award 03hl6lc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 92.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20308-0cqhk0 PRED entity: 0cqhk0 PRED relation: nominated_for PRED expected values: 030k94 => 47 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1527): 072kp (0.70 #15813, 0.51 #4739, 0.33 #1580), 03ln8b (0.70 #15813, 0.51 #4739, 0.33 #1580), 0124k9 (0.60 #3377, 0.51 #4739, 0.50 #1798), 0q9jk (0.60 #4388, 0.50 #2809, 0.12 #5968), 02nf2c (0.60 #3279, 0.50 #1700, 0.11 #4859), 014gjp (0.60 #4301, 0.50 #2722, 0.11 #5881), 01vnbh (0.60 #3978, 0.50 #2399, 0.09 #5558), 0kfpm (0.51 #4739, 0.50 #1682, 0.40 #3261), 01b9w3 (0.51 #4739, 0.40 #3818, 0.25 #2239), 039cq4 (0.51 #4739, 0.33 #1057, 0.25 #2637) >> Best rule #15813 for best value: >> intensional similarity = 5 >> extensional distance = 151 >> proper extension: 0fqnzts; >> query: (?x678, ?x631) <- award(?x4119, ?x678), award(?x513, ?x678), participant(?x513, ?x2451), participant(?x406, ?x4119), award(?x631, ?x678) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #4739 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 09qrn4; *> query: (?x678, ?x631) <- award(?x10153, ?x678), award(?x4676, ?x678), nominated_for(?x678, ?x1631), ?x4676 = 04cl1, actor(?x631, ?x10153) *> conf = 0.51 ranks of expected_values: 20 EVAL 0cqhk0 nominated_for 030k94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 47.000 16.000 0.704 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20307-05r4w PRED entity: 05r4w PRED relation: country! PRED expected values: 06z6r => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 37): 06z6r (0.86 #759, 0.85 #1092, 0.84 #1721), 01cgz (0.71 #454, 0.70 #343, 0.68 #1120), 0w0d (0.70 #1192, 0.67 #1451, 0.66 #1155), 07bs0 (0.70 #342, 0.67 #268, 0.64 #379), 09w1n (0.70 #347, 0.67 #273, 0.64 #384), 07jjt (0.70 #346, 0.67 #272, 0.64 #383), 01sgl (0.70 #362, 0.67 #288, 0.64 #399), 0d1tm (0.70 #334, 0.67 #260, 0.64 #371), 09_bl (0.70 #339, 0.67 #265, 0.64 #376), 06z68 (0.60 #353, 0.57 #168, 0.56 #279) >> Best rule #759 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 02j71; >> query: (?x87, 06z6r) <- service_location(?x896, ?x87), currency(?x87, ?x170), taxonomy(?x87, ?x939) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05r4w country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 171.000 0.862 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #20306-07ylj PRED entity: 07ylj PRED relation: country! PRED expected values: 09f6b => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 36): 064vjs (0.72 #18, 0.69 #162, 0.67 #234), 07gyv (0.70 #42, 0.68 #6, 0.62 #150), 019tzd (0.68 #25, 0.59 #61, 0.51 #169), 09w1n (0.68 #14, 0.56 #158, 0.51 #122), 0194d (0.67 #246, 0.64 #174, 0.63 #66), 01z27 (0.64 #11, 0.56 #155, 0.52 #47), 07rlg (0.64 #1, 0.56 #37, 0.49 #145), 07bs0 (0.64 #10, 0.53 #226, 0.51 #154), 09_bl (0.60 #8, 0.44 #44, 0.39 #80), 02vx4 (0.56 #4, 0.52 #40, 0.44 #148) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 09c7w0; 01ls2; 03rk0; 05b4w; >> query: (?x1203, 064vjs) <- film_release_region(?x9194, ?x1203), film_release_region(?x3276, ?x1203), ?x9194 = 0fpgp26, ?x3276 = 0gjc4d3 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #33 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 09c7w0; 01ls2; 03rk0; 05b4w; *> query: (?x1203, 09f6b) <- film_release_region(?x9194, ?x1203), film_release_region(?x3276, ?x1203), ?x9194 = 0fpgp26, ?x3276 = 0gjc4d3 *> conf = 0.44 ranks of expected_values: 21 EVAL 07ylj country! 09f6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 126.000 126.000 0.720 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #20305-05r5c PRED entity: 05r5c PRED relation: instrumentalists PRED expected values: 086qd 01w60_p 028qdb 026spg 03f0fnk 0kvjrw 06tp4h 01ydzx 0132k4 01jgkj2 01rwcgb 01s7ns 01p0w_ 0h6sv => 88 concepts (64 used for prediction) PRED predicted values (max 10 best out of 757): 04m2zj (0.71 #2685, 0.43 #5454, 0.40 #261), 0m_v0 (0.65 #3460, 0.60 #3461, 0.54 #1732), 0cj2w (0.65 #3460, 0.60 #3461, 0.54 #1732), 01r0t_j (0.65 #3460, 0.43 #4152, 0.43 #5538), 0m2l9 (0.62 #1039, 0.60 #6232, 0.60 #3461), 01p0w_ (0.62 #1039, 0.60 #6232, 0.56 #1038), 01wwvt2 (0.62 #1039, 0.60 #6232, 0.56 #1038), 01p45_v (0.62 #1039, 0.60 #6232, 0.56 #1038), 016jfw (0.62 #1039, 0.56 #1038, 0.54 #6231), 03rl84 (0.62 #1039, 0.56 #1038, 0.54 #6231) >> Best rule #2685 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0342h; 0l14qv; 02sgy; 0l14j_; 0gkd1; >> query: (?x316, 04m2zj) <- performance_role(?x2698, ?x316), role(?x5126, ?x316), instrumentalists(?x316, ?x130), ?x5126 = 03h502k, role(?x316, ?x75), role(?x74, ?x316) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1039 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 03ndd; *> query: (?x316, ?x9008) <- role(?x9008, ?x316), role(?x74, ?x316), instrumentalists(?x316, ?x7794), group(?x316, ?x997), location(?x9008, ?x13208), ?x7794 = 01k23t *> conf = 0.62 ranks of expected_values: 6, 11, 74, 99, 148, 150, 161, 199, 238, 273, 356, 397, 408 EVAL 05r5c instrumentalists 0h6sv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 01p0w_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 01s7ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 01rwcgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 01jgkj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 0132k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 01ydzx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 06tp4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 0kvjrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 03f0fnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 026spg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 028qdb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 01w60_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists EVAL 05r5c instrumentalists 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 88.000 64.000 0.714 http://example.org/music/instrument/instrumentalists #20304-09h_q PRED entity: 09h_q PRED relation: artists! PRED expected values: 0l8gh => 157 concepts (75 used for prediction) PRED predicted values (max 10 best out of 231): 03_d0 (0.85 #945, 0.56 #4367, 0.45 #2501), 017_qw (0.80 #10958, 0.79 #7843, 0.62 #7532), 06q6jz (0.67 #810, 0.47 #2054, 0.29 #1743), 06j6l (0.62 #982, 0.37 #4093, 0.36 #3782), 0gywn (0.62 #991, 0.31 #9707, 0.31 #12508), 06by7 (0.57 #4065, 0.55 #3754, 0.54 #954), 064t9 (0.49 #12463, 0.48 #13707, 0.47 #9662), 0155w (0.46 #1040, 0.29 #5086, 0.29 #6953), 0d6n1 (0.35 #2006, 0.33 #762, 0.33 #140), 0557q (0.33 #168, 0.25 #479, 0.14 #5769) >> Best rule #945 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0f6lx; >> query: (?x8080, 03_d0) <- artists(?x888, ?x8080), award_winner(?x1088, ?x8080), influenced_by(?x1092, ?x8080), major_field_of_study(?x2767, ?x888) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #2044 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 02r38; *> query: (?x8080, 0l8gh) <- artists(?x5640, ?x8080), artists(?x3597, ?x8080), artists(?x5640, ?x8311), ?x3597 = 021dvj, role(?x8311, ?x227) *> conf = 0.29 ranks of expected_values: 12 EVAL 09h_q artists! 0l8gh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 157.000 75.000 0.846 http://example.org/music/genre/artists #20303-02k54 PRED entity: 02k54 PRED relation: countries_spoken_in! PRED expected values: 0jzc => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 54): 02h40lc (0.41 #674, 0.33 #450, 0.33 #1570), 064_8sq (0.25 #1754, 0.23 #466, 0.17 #4834), 06nm1 (0.23 #456, 0.23 #680, 0.19 #1464), 0cjk9 (0.21 #4, 0.12 #228, 0.12 #284), 0jzc (0.20 #408, 0.19 #576, 0.19 #1528), 02ztjwg (0.17 #1037, 0.13 #1933, 0.11 #589), 071fb (0.17 #1750, 0.08 #3038, 0.07 #4102), 02hxcvy (0.14 #143, 0.13 #423, 0.11 #647), 0121sr (0.14 #155, 0.10 #435, 0.08 #659), 04306rv (0.13 #1461, 0.13 #397, 0.13 #1013) >> Best rule #674 for best value: >> intensional similarity = 2 >> extensional distance = 37 >> proper extension: 01f08r; 03_xj; 07fr_; >> query: (?x608, 02h40lc) <- location_of_ceremony(?x566, ?x608), currency(?x608, ?x170) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #408 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 025ndl; 070zc; *> query: (?x608, 0jzc) <- adjoins(?x4120, ?x608), locations(?x6982, ?x608), combatants(?x7419, ?x608) *> conf = 0.20 ranks of expected_values: 5 EVAL 02k54 countries_spoken_in! 0jzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 161.000 161.000 0.410 http://example.org/language/human_language/countries_spoken_in #20302-0pd4f PRED entity: 0pd4f PRED relation: language PRED expected values: 02h40lc => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 29): 02h40lc (0.97 #165, 0.97 #3137, 0.94 #3411), 06nm1 (0.15 #63, 0.12 #611, 0.11 #391), 04h9h (0.10 #92, 0.05 #147, 0.05 #38), 05zjd (0.06 #75, 0.02 #3101, 0.02 #1392), 03_9r (0.05 #390, 0.05 #3088, 0.05 #2755), 0653m (0.05 #392, 0.04 #1381, 0.04 #996), 05qqm (0.04 #90, 0.03 #36, 0.02 #254), 012w70 (0.04 #65, 0.03 #942, 0.03 #997), 0349s (0.04 #94, 0.02 #585, 0.02 #40), 03hkp (0.03 #13, 0.03 #122, 0.02 #231) >> Best rule #165 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 048rn; >> query: (?x4431, 02h40lc) <- list(?x4431, ?x3004), film(?x4279, ?x4431), language(?x4431, ?x90) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pd4f language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.971 http://example.org/film/film/language #20301-07h34 PRED entity: 07h34 PRED relation: contains PRED expected values: 0_wm_ => 198 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2785): 05cl8y (0.53 #149390, 0.47 #143529, 0.47 #172827), 07h34 (0.52 #172826, 0.49 #169896, 0.47 #193330), 09c7w0 (0.52 #172826, 0.49 #169896, 0.47 #193330), 0rqyx (0.33 #678, 0.25 #3608, 0.11 #9468), 0ggh3 (0.33 #1097, 0.25 #4027, 0.11 #9887), 0c5v2 (0.33 #2367, 0.25 #5297, 0.11 #11157), 09s5q8 (0.33 #782, 0.25 #3712, 0.11 #9572), 0j_sncb (0.33 #345, 0.25 #3275, 0.11 #9135), 0rhp6 (0.33 #1113, 0.25 #4043, 0.11 #9903), 0rsjf (0.33 #858, 0.25 #3788, 0.11 #9648) >> Best rule #149390 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 01vsb_; >> query: (?x3778, ?x11060) <- contains(?x94, ?x3778), state_province_region(?x11060, ?x3778), category(?x11060, ?x134) >> conf = 0.53 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07h34 contains 0_wm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 198.000 101.000 0.533 http://example.org/location/location/contains #20300-05mlqj PRED entity: 05mlqj PRED relation: profession PRED expected values: 02hrh1q 0np9r => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 53): 02hrh1q (0.88 #1515, 0.88 #315, 0.87 #2265), 01d_h8 (0.33 #3006, 0.32 #2856, 0.32 #1206), 0dxtg (0.31 #3014, 0.28 #3164, 0.28 #6614), 03gjzk (0.25 #7051, 0.25 #166, 0.23 #3016), 02krf9 (0.25 #7051, 0.19 #478, 0.10 #3028), 05z96 (0.25 #7051, 0.08 #8102, 0.06 #494), 01___w (0.25 #7051, 0.06 #497), 09jwl (0.25 #170, 0.17 #3170, 0.17 #4220), 0np9r (0.25 #172, 0.15 #622, 0.14 #5722), 02jknp (0.23 #3008, 0.21 #2858, 0.20 #6608) >> Best rule #1515 for best value: >> intensional similarity = 3 >> extensional distance = 1085 >> proper extension: 05b__vr; 016pns; 02yj7w; 01kh2m1; 013w7j; 0k6yt1; 02ktrs; >> query: (?x9384, 02hrh1q) <- film(?x9384, ?x6293), award_nominee(?x9384, ?x879), produced_by(?x6293, ?x2182) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 05mlqj profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 68.000 68.000 0.877 http://example.org/people/person/profession EVAL 05mlqj profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.877 http://example.org/people/person/profession #20299-01p1v PRED entity: 01p1v PRED relation: olympics PRED expected values: 06sks6 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 38): 06sks6 (0.89 #1925, 0.89 #1544, 0.88 #2497), 0kbvv (0.71 #23, 0.65 #251, 0.61 #175), 0jdk_ (0.50 #176, 0.50 #138, 0.50 #100), 0swbd (0.50 #123, 0.44 #85, 0.39 #47), 09n48 (0.47 #231, 0.47 #155, 0.47 #117), 0l6mp (0.41 #837, 0.26 #53, 0.25 #129), 0ldqf (0.41 #837, 0.25 #148, 0.16 #110), 0lv1x (0.41 #837, 0.21 #12, 0.19 #126), 018ljb (0.41 #837, 0.12 #146, 0.11 #32), 0lgxj (0.41 #1522, 0.36 #2399, 0.36 #2398) >> Best rule #1925 for best value: >> intensional similarity = 2 >> extensional distance = 150 >> proper extension: 07ww5; 06nnj; >> query: (?x1917, 06sks6) <- adjoins(?x142, ?x1917), olympics(?x1917, ?x778) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p1v olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.888 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #20298-08q3s0 PRED entity: 08q3s0 PRED relation: producer_type PRED expected values: 0ckd1 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.63 #5, 0.62 #6, 0.50 #1) >> Best rule #5 for best value: >> intensional similarity = 2 >> extensional distance = 251 >> proper extension: 04rtpt; >> query: (?x5387, 0ckd1) <- program(?x5387, ?x5810), actor(?x5810, ?x56) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08q3s0 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.628 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #20297-02hwww PRED entity: 02hwww PRED relation: institution! PRED expected values: 02h4rq6 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 16): 02h4rq6 (0.74 #175, 0.72 #158, 0.62 #210), 03bwzr4 (0.55 #218, 0.50 #183, 0.47 #166), 016t_3 (0.54 #211, 0.52 #228, 0.50 #176), 0bkj86 (0.53 #179, 0.50 #162, 0.49 #231), 07s6fsf (0.33 #18, 0.31 #209, 0.30 #226), 01ysy9 (0.28 #1438, 0.08 #224, 0.06 #172), 01gkg3 (0.28 #1438, 0.01 #372, 0.01 #253), 013zdg (0.26 #178, 0.25 #161, 0.24 #247), 02mjs7 (0.22 #160, 0.21 #177, 0.13 #229), 03mkk4 (0.21 #181, 0.19 #164, 0.17 #216) >> Best rule #175 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 06pwq; 0gkkf; 0yjf0; 01w5m; 09f2j; >> query: (?x11607, 02h4rq6) <- citytown(?x11607, ?x7412), institution(?x1390, ?x11607), ?x1390 = 0bjrnt, major_field_of_study(?x11607, ?x1154) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hwww institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.735 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20296-02cbg0 PRED entity: 02cbg0 PRED relation: cinematography PRED expected values: 04qvl7 => 94 concepts (58 used for prediction) PRED predicted values (max 10 best out of 33): 08mhyd (0.07 #32, 0.06 #96, 0.02 #159), 06r_by (0.07 #23, 0.03 #339, 0.02 #87), 09bxq9 (0.07 #40, 0.01 #608, 0.01 #735), 016k6x (0.06 #64, 0.03 #3059, 0.03 #3252), 04qvl7 (0.05 #443, 0.04 #65, 0.03 #1272), 02404v (0.04 #165, 0.02 #102), 027t8fw (0.03 #985, 0.03 #1112, 0.02 #473), 02g9z1 (0.03 #2612, 0.02 #2932, 0.02 #2291), 0cqh57 (0.02 #414, 0.02 #225, 0.02 #351), 03cx282 (0.02 #395, 0.02 #458, 0.02 #970) >> Best rule #32 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 06bc59; >> query: (?x8436, 08mhyd) <- genre(?x8436, ?x3613), nominated_for(?x4969, ?x8436), film_format(?x8436, ?x6392), ?x3613 = 09blyk >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #443 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 128 *> proper extension: 024l2y; 0j_tw; 0htww; 0pd64; *> query: (?x8436, 04qvl7) <- film_crew_role(?x8436, ?x2095), ?x2095 = 0dxtw, films(?x8435, ?x8436), language(?x8436, ?x13310) *> conf = 0.05 ranks of expected_values: 5 EVAL 02cbg0 cinematography 04qvl7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 58.000 0.071 http://example.org/film/film/cinematography #20295-04shbh PRED entity: 04shbh PRED relation: profession PRED expected values: 02hrh1q => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 60): 02hrh1q (0.90 #2415, 0.89 #1665, 0.89 #5415), 01d_h8 (0.40 #456, 0.36 #3906, 0.36 #2256), 0dxtg (0.31 #7364, 0.30 #7814, 0.30 #6764), 03gjzk (0.27 #3916, 0.24 #4966, 0.22 #7816), 09jwl (0.25 #20, 0.22 #1220, 0.21 #1070), 02jknp (0.24 #6758, 0.24 #7508, 0.23 #6908), 0d1pc (0.22 #1252, 0.21 #1102, 0.18 #1402), 0q04f (0.22 #251, 0.02 #3851, 0.02 #6851), 016z4k (0.20 #13951, 0.16 #1204, 0.16 #1054), 0nbcg (0.20 #13951, 0.15 #1233, 0.15 #1083) >> Best rule #2415 for best value: >> intensional similarity = 3 >> extensional distance = 270 >> proper extension: 0c3jz; >> query: (?x1018, 02hrh1q) <- participant(?x1018, ?x1017), nominated_for(?x1018, ?x787), place_of_birth(?x1018, ?x11586) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04shbh profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.901 http://example.org/people/person/profession #20294-014kq6 PRED entity: 014kq6 PRED relation: written_by PRED expected values: 0fx02 => 72 concepts (46 used for prediction) PRED predicted values (max 10 best out of 72): 041c4 (0.17 #676, 0.17 #498, 0.06 #3038), 01vs_v8 (0.09 #7767, 0.08 #6753, 0.08 #7430), 0dn44 (0.08 #665), 03dq9 (0.08 #644), 0dpqk (0.08 #497), 04yt7 (0.08 #469), 03knl (0.08 #14507, 0.07 #13831, 0.06 #14506), 03kpvp (0.05 #675, 0.02 #3037, 0.02 #2024), 0lpjn (0.05 #675, 0.02 #3037, 0.02 #2024), 02zyy4 (0.05 #675, 0.02 #3037, 0.02 #2024) >> Best rule #676 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01sxly; 04zl8; 04ltlj; >> query: (?x2160, ?x4988) <- film(?x4988, ?x2160), film_release_distribution_medium(?x2160, ?x81), ?x4988 = 041c4, language(?x2160, ?x90) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #2469 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 178 *> proper extension: 0d90m; 01vksx; 053rxgm; 02v63m; 0dr3sl; 0ckrgs; 07cyl; 024lff; 0198b6; 057lbk; ... *> query: (?x2160, 0fx02) <- film(?x971, ?x2160), prequel(?x3693, ?x2160), genre(?x2160, ?x225) *> conf = 0.01 ranks of expected_values: 48 EVAL 014kq6 written_by 0fx02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 72.000 46.000 0.167 http://example.org/film/film/written_by #20293-0b73_1d PRED entity: 0b73_1d PRED relation: production_companies PRED expected values: 030_1_ => 115 concepts (103 used for prediction) PRED predicted values (max 10 best out of 65): 05qd_ (0.35 #501, 0.34 #2502, 0.33 #3503), 086k8 (0.12 #252, 0.12 #669, 0.12 #168), 017s11 (0.12 #86, 0.09 #253, 0.09 #922), 016tt2 (0.11 #421, 0.08 #2923, 0.08 #170), 0flw6 (0.10 #3923, 0.04 #3922, 0.04 #5182), 030_1_ (0.10 #183, 0.08 #1685, 0.07 #434), 0kx4m (0.10 #175, 0.03 #426, 0.03 #844), 016tw3 (0.09 #2514, 0.09 #931, 0.09 #1929), 01gb54 (0.09 #705, 0.08 #204, 0.07 #2205), 054lpb6 (0.08 #2015, 0.07 #1932, 0.07 #2517) >> Best rule #501 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 0m313; 02vxq9m; 01jc6q; 0yyg4; 01gc7; 011yxg; 0gzy02; 04v8x9; 01h7bb; 050r1z; ... >> query: (?x825, ?x902) <- award(?x825, ?x11466), film(?x902, ?x825), nominated_for(?x500, ?x825), ?x500 = 0p9sw >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #183 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 0g22z; 07gp9; 016fyc; 0ds11z; 0g5qs2k; 0ds33; 0bth54; 0pc62; 0fg04; 01r97z; ... *> query: (?x825, 030_1_) <- award(?x825, ?x11466), film(?x902, ?x825), edited_by(?x825, ?x826), nominated_for(?x198, ?x825) *> conf = 0.10 ranks of expected_values: 6 EVAL 0b73_1d production_companies 030_1_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 115.000 103.000 0.346 http://example.org/film/film/production_companies #20292-03ttfc PRED entity: 03ttfc PRED relation: geographic_distribution PRED expected values: 09c7w0 => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 234): 09c7w0 (0.78 #1147, 0.75 #1223, 0.67 #1377), 07ssc (0.50 #695, 0.40 #847, 0.40 #86), 09pmkv (0.50 #703, 0.40 #855, 0.40 #94), 03spz (0.50 #53, 0.29 #510, 0.20 #890), 03_3d (0.43 #537, 0.30 #841, 0.29 #613), 06qd3 (0.43 #557, 0.30 #861, 0.29 #633), 0chghy (0.43 #617, 0.29 #541, 0.27 #923), 06t2t (0.40 #112, 0.38 #721, 0.30 #873), 0f8l9c (0.40 #165, 0.29 #622, 0.29 #546), 03rk0 (0.40 #108, 0.29 #489, 0.25 #717) >> Best rule #1147 for best value: >> intensional similarity = 9 >> extensional distance = 21 >> proper extension: 01qhm_; 0x67; 07hwkr; 0xnvg; 0g6ff; 07bch9; 03295l; 059_w; 01336l; 0dbxy; ... >> query: (?x1575, 09c7w0) <- geographic_distribution(?x1575, ?x142), film_release_region(?x7009, ?x142), film_release_region(?x4352, ?x142), film_release_region(?x3377, ?x142), locations(?x4908, ?x142), ?x7009 = 0bs8s1p, countries_spoken_in(?x90, ?x142), ?x4352 = 09v71cj, ?x3377 = 0gj8nq2 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03ttfc geographic_distribution 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.783 http://example.org/people/ethnicity/geographic_distribution #20291-048xg8 PRED entity: 048xg8 PRED relation: current_club! PRED expected values: 01_lhg => 104 concepts (78 used for prediction) PRED predicted values (max 10 best out of 29): 02rqxc (0.60 #67, 0.44 #681, 0.40 #96), 01_lhg (0.50 #241, 0.45 #503, 0.40 #37), 02ltg3 (0.50 #153, 0.38 #211, 0.27 #444), 02s2lg (0.40 #93, 0.20 #64, 0.20 #35), 03d8m4 (0.38 #682, 0.22 #330, 0.20 #68), 03yl2t (0.35 #850, 0.20 #91, 0.18 #1230), 03z8bw (0.33 #166, 0.27 #428, 0.25 #224), 01352_ (0.31 #699, 0.20 #85, 0.18 #464), 03y_f8 (0.27 #440, 0.26 #849, 0.22 #323), 02s9vc (0.25 #225, 0.22 #312, 0.21 #634) >> Best rule #67 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 0177gl; >> query: (?x5027, 02rqxc) <- position(?x5027, ?x203), position(?x5027, ?x63), sport(?x5027, ?x471), ?x203 = 0dgrmp, current_club(?x7616, ?x5027), ?x63 = 02sdk9v, team(?x530, ?x5027), ?x7616 = 0329r5, ?x530 = 02_j1w >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 049msk; *> query: (?x5027, 01_lhg) <- position(?x5027, ?x203), position(?x5027, ?x60), ?x60 = 02nzb8, team(?x530, ?x5027), ?x530 = 02_j1w, ?x203 = 0dgrmp, team(?x5471, ?x5027), ?x5471 = 03zv9, current_club(?x7616, ?x5027) *> conf = 0.50 ranks of expected_values: 2 EVAL 048xg8 current_club! 01_lhg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 78.000 0.600 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #20290-02qwg PRED entity: 02qwg PRED relation: artist! PRED expected values: 01cl2y => 137 concepts (101 used for prediction) PRED predicted values (max 10 best out of 107): 015_1q (0.25 #3196, 0.24 #2781, 0.23 #709), 033hn8 (0.20 #14, 0.17 #1118, 0.11 #704), 01w40h (0.20 #28, 0.13 #718, 0.13 #994), 011k1h (0.20 #10, 0.13 #700, 0.12 #1114), 0181dw (0.20 #41, 0.12 #869, 0.11 #3218), 0k_kr (0.20 #43, 0.07 #871, 0.06 #1562), 01cl2y (0.17 #444, 0.11 #720, 0.08 #1549), 0g768 (0.16 #1140, 0.15 #1002, 0.13 #1555), 0fb0v (0.16 #283, 0.10 #7, 0.10 #559), 02p11jq (0.15 #427, 0.11 #703, 0.11 #151) >> Best rule #3196 for best value: >> intensional similarity = 3 >> extensional distance = 235 >> proper extension: 02bwjv; >> query: (?x3403, 015_1q) <- award_winner(?x1089, ?x3403), artist(?x2931, ?x3403), gender(?x3403, ?x231) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #444 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 0288fyj; 06fmdb; 01vb6z; 02qtywd; *> query: (?x3403, 01cl2y) <- award_winner(?x1089, ?x3403), award_winner(?x725, ?x3403), ?x725 = 01bx35 *> conf = 0.17 ranks of expected_values: 7 EVAL 02qwg artist! 01cl2y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 137.000 101.000 0.253 http://example.org/music/record_label/artist #20289-0fnb4 PRED entity: 0fnb4 PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 19): 0pqc5 (0.43 #427, 0.35 #826, 0.33 #898), 060c4 (0.26 #2015, 0.23 #1968, 0.07 #3003), 060bp (0.25 #2013, 0.23 #1966, 0.05 #3001), 0fkvn (0.17 #2039, 0.09 #2154, 0.09 #2200), 01q24l (0.14 #436, 0.10 #625, 0.08 #741), 0f6c3 (0.14 #2043, 0.09 #2135, 0.09 #2158), 09n5b9 (0.11 #2047, 0.08 #2116, 0.08 #2139), 0fkzq (0.05 #2052, 0.03 #2144, 0.03 #2167), 04syw (0.04 #2019, 0.03 #1972, 0.01 #1739), 0dq3c (0.03 #1688, 0.03 #2014, 0.02 #1757) >> Best rule #427 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 07bcn; >> query: (?x13165, 0pqc5) <- capital(?x10457, ?x13165), citytown(?x1961, ?x13165), organization(?x5510, ?x1961), religion(?x10457, ?x7422), contains(?x6304, ?x10457), ?x7422 = 092bf5, adjoins(?x2146, ?x10457) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2039 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 305 *> proper extension: 0ldff; 0290rb; 0vh3; 086g2; 01l_9d; 01gh6z; 026mx4; 01vskn; 018ckn; *> query: (?x13165, 0fkvn) <- country(?x13165, ?x10457), contains(?x6304, ?x10457), administrative_area_type(?x10457, ?x2792), organization(?x10457, ?x127), olympics(?x10457, ?x2966) *> conf = 0.17 ranks of expected_values: 4 EVAL 0fnb4 jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 162.000 162.000 0.429 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #20288-031296 PRED entity: 031296 PRED relation: nominated_for PRED expected values: 0431v3 => 107 concepts (62 used for prediction) PRED predicted values (max 10 best out of 268): 02_1ky (0.53 #22694, 0.51 #43773, 0.51 #29181), 02k_4g (0.40 #108, 0.10 #1728, 0.09 #69717), 0crd8q6 (0.29 #38909, 0.21 #98899, 0.17 #51879), 023vcd (0.29 #38909, 0.21 #98899, 0.17 #51879), 08jgk1 (0.20 #231, 0.07 #87549, 0.07 #84306), 0gmgwnv (0.20 #979, 0.07 #87549, 0.07 #84306), 026p4q7 (0.20 #367, 0.07 #87549, 0.07 #84306), 0221zw (0.20 #523, 0.07 #87549, 0.07 #84306), 02prw4h (0.20 #170, 0.07 #87549, 0.07 #84306), 03ln8b (0.12 #5163, 0.11 #3543, 0.10 #1923) >> Best rule #22694 for best value: >> intensional similarity = 3 >> extensional distance = 297 >> proper extension: 07sgfsl; 05bpg3; 03j149k; 0404wqb; >> query: (?x3709, ?x3169) <- award_nominee(?x3709, ?x1290), award_winner(?x873, ?x3709), actor(?x3169, ?x3709) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #87549 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1390 *> proper extension: 0gv2r; *> query: (?x3709, ?x782) <- award_nominee(?x3709, ?x7489), nominated_for(?x7489, ?x782), award_winner(?x678, ?x3709) *> conf = 0.07 ranks of expected_values: 33 EVAL 031296 nominated_for 0431v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 107.000 62.000 0.528 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20287-03q0r1 PRED entity: 03q0r1 PRED relation: language PRED expected values: 02bjrlw 0880p => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 41): 064_8sq (0.17 #138, 0.17 #1117, 0.16 #486), 06nm1 (0.15 #67, 0.13 #647, 0.11 #875), 04306rv (0.13 #642, 0.11 #1101, 0.11 #470), 02bjrlw (0.10 #639, 0.07 #1098, 0.07 #1330), 07c9s (0.10 #17, 0.08 #75, 0.07 #1330), 06b_j (0.09 #659, 0.07 #253, 0.07 #887), 03hkp (0.07 #1330, 0.05 #536, 0.04 #708), 0jzc (0.07 #1330, 0.04 #541, 0.04 #656), 0653m (0.07 #1330, 0.04 #590, 0.04 #359), 05zjd (0.07 #1330, 0.04 #256, 0.03 #142) >> Best rule #138 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 04lqvly; >> query: (?x3854, 064_8sq) <- nominated_for(?x1723, ?x3854), film_release_distribution_medium(?x3854, ?x81), film_crew_role(?x3854, ?x1966), ?x1723 = 09tqxt >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #639 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 147 *> proper extension: 02vw1w2; *> query: (?x3854, 02bjrlw) <- film_release_distribution_medium(?x3854, ?x81), film(?x400, ?x3854), prequel(?x2933, ?x3854) *> conf = 0.10 ranks of expected_values: 4, 38 EVAL 03q0r1 language 0880p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 87.000 87.000 0.172 http://example.org/film/film/language EVAL 03q0r1 language 02bjrlw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 87.000 0.172 http://example.org/film/film/language #20286-01r9fv PRED entity: 01r9fv PRED relation: award PRED expected values: 02f72n 02f6ym => 100 concepts (69 used for prediction) PRED predicted values (max 10 best out of 255): 054ks3 (0.71 #4930, 0.37 #940, 0.36 #142), 01bgqh (0.71 #6028, 0.56 #841, 0.49 #1240), 02f5qb (0.52 #954, 0.48 #3348, 0.46 #1353), 02f6xy (0.48 #996, 0.35 #1395, 0.21 #198), 01by1l (0.47 #6097, 0.47 #9688, 0.41 #910), 02f72n (0.44 #944, 0.37 #3338, 0.27 #1343), 03qbh5 (0.41 #1001, 0.38 #1400, 0.29 #8183), 0c4z8 (0.41 #870, 0.32 #4860, 0.30 #1269), 03qbnj (0.37 #1027, 0.25 #6214, 0.17 #5017), 0gqz2 (0.34 #4869, 0.19 #879, 0.16 #9657) >> Best rule #4930 for best value: >> intensional similarity = 4 >> extensional distance = 167 >> proper extension: 05dbf; 02qgyv; 02v3yy; 01jpmpv; 0k269; 01vvdm; 01wb8bs; 01g23m; 02zft0; 0c3p7; ... >> query: (?x1544, 054ks3) <- award(?x1544, ?x4796), award(?x2461, ?x4796), ?x2461 = 01cwhp, nominated_for(?x4796, ?x7750) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #944 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 0lbj1; 01vrz41; 01v_pj6; 01vs_v8; 09hnb; 0137g1; 03h_fk5; 0161sp; 01wj18h; 03bnv; ... *> query: (?x1544, 02f72n) <- artists(?x1000, ?x1544), type_of_union(?x1544, ?x566), award(?x1544, ?x4892), ?x4892 = 02f72_ *> conf = 0.44 ranks of expected_values: 6, 11 EVAL 01r9fv award 02f6ym CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 100.000 69.000 0.710 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01r9fv award 02f72n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 100.000 69.000 0.710 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20285-03rj0 PRED entity: 03rj0 PRED relation: organization PRED expected values: 02vk52z => 80 concepts (79 used for prediction) PRED predicted values (max 10 best out of 49): 02vk52z (0.90 #400, 0.85 #863, 0.84 #148), 0_2v (0.64 #87, 0.54 #45, 0.51 #234), 0b6css (0.62 #52, 0.60 #31, 0.59 #94), 04k4l (0.52 #109, 0.50 #151, 0.50 #88), 02jxk (0.46 #44, 0.44 #107, 0.42 #170), 041288 (0.33 #792, 0.33 #1066, 0.32 #498), 0gkjy (0.25 #406, 0.25 #847, 0.23 #532), 0j7v_ (0.24 #782, 0.24 #888, 0.24 #909), 059dn (0.23 #56, 0.20 #35, 0.19 #161), 034h1h (0.21 #1187, 0.18 #1252, 0.11 #555) >> Best rule #400 for best value: >> intensional similarity = 2 >> extensional distance = 89 >> proper extension: 02j71; >> query: (?x2267, 02vk52z) <- currency(?x2267, ?x170), taxonomy(?x2267, ?x939) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rj0 organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 79.000 0.901 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #20284-0lp_cd3 PRED entity: 0lp_cd3 PRED relation: ceremony! PRED expected values: 04ldyx1 => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 302): 09qrn4 (0.95 #9295, 0.78 #2177, 0.75 #1926), 0gqwc (0.85 #8597, 0.78 #8096, 0.77 #9098), 0gqyl (0.85 #8117, 0.75 #8618, 0.72 #9119), 0gqy2 (0.84 #8660, 0.83 #8159, 0.82 #9161), 0gq_d (0.80 #8696, 0.78 #8195, 0.76 #7440), 0p9sw (0.78 #8056, 0.77 #8557, 0.74 #9058), 027gs1_ (0.78 #2201, 0.75 #1950, 0.71 #9296), 0bdw1g (0.78 #2039, 0.75 #1788, 0.71 #9296), 0bfvd4 (0.78 #2095, 0.75 #1844, 0.71 #9296), 0cjyzs (0.78 #2089, 0.75 #1838, 0.71 #9296) >> Best rule #9295 for best value: >> intensional similarity = 14 >> extensional distance = 63 >> proper extension: 0fz20l; 0fz2y7; >> query: (?x1764, ?x5235) <- award_winner(?x1764, ?x12148), award_winner(?x1764, ?x7310), instance_of_recurring_event(?x1764, ?x2758), honored_for(?x1764, ?x493), ceremony(?x435, ?x1764), gender(?x12148, ?x514), category_of(?x5235, ?x2758), risk_factors(?x3680, ?x514), award(?x7138, ?x5235), award(?x6591, ?x5235), ?x7138 = 0l786, nominated_for(?x7310, ?x1135), ?x6591 = 02f6s3, film(?x7310, ?x1619) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #9296 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 63 *> proper extension: 0fz20l; 0fz2y7; *> query: (?x1764, ?x375) <- award_winner(?x1764, ?x12148), award_winner(?x1764, ?x7310), instance_of_recurring_event(?x1764, ?x2758), honored_for(?x1764, ?x493), ceremony(?x435, ?x1764), gender(?x12148, ?x514), category_of(?x5235, ?x2758), category_of(?x375, ?x2758), risk_factors(?x3680, ?x514), award(?x7138, ?x5235), award(?x6591, ?x5235), ?x7138 = 0l786, nominated_for(?x7310, ?x1135), ?x6591 = 02f6s3, film(?x7310, ?x1619) *> conf = 0.71 ranks of expected_values: 43 EVAL 0lp_cd3 ceremony! 04ldyx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 46.000 46.000 0.954 http://example.org/award/award_category/winners./award/award_honor/ceremony #20283-02_1ky PRED entity: 02_1ky PRED relation: nominated_for! PRED expected values: 02pzz3p => 86 concepts (82 used for prediction) PRED predicted values (max 10 best out of 174): 0fbvqf (0.37 #518, 0.29 #4358, 0.29 #2198), 02pzxlw (0.35 #377, 0.19 #1577, 0.18 #18727), 027qq9b (0.35 #387, 0.19 #1587, 0.18 #18727), 02pzz3p (0.35 #355, 0.18 #18727, 0.18 #18969), 0gq9h (0.33 #16625, 0.29 #17105, 0.26 #17827), 0bdx29 (0.32 #564, 0.31 #4404, 0.29 #5124), 0gs9p (0.31 #16627, 0.25 #17107, 0.23 #17829), 027gs1_ (0.31 #2589, 0.28 #9070, 0.27 #8590), 0fbtbt (0.31 #4482, 0.29 #4962, 0.29 #5202), 02pz3j5 (0.29 #357, 0.18 #18727, 0.18 #18969) >> Best rule #518 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 02k_4g; >> query: (?x10911, 0fbvqf) <- program(?x1762, ?x10911), actor(?x10911, ?x1676), languages(?x10911, ?x254), program_creator(?x10911, ?x2176), ?x1762 = 0gsg7 >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #355 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 01b66d; *> query: (?x10911, 02pzz3p) <- program(?x1762, ?x10911), actor(?x10911, ?x1676), tv_program(?x2176, ?x10911), currency(?x1762, ?x170), award_winner(?x687, ?x1762) *> conf = 0.35 ranks of expected_values: 4 EVAL 02_1ky nominated_for! 02pzz3p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 82.000 0.368 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20282-05slvm PRED entity: 05slvm PRED relation: nationality PRED expected values: 0d060g => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.80 #1202, 0.77 #1302, 0.74 #1504), 0d060g (0.40 #5717, 0.33 #5819, 0.04 #708), 02jx1 (0.34 #4411, 0.34 #4612, 0.23 #233), 0hzlz (0.34 #4411, 0.34 #4612, 0.20 #23), 07ssc (0.34 #4411, 0.34 #4612, 0.09 #2019), 05kr_ (0.33 #5819), 03rk0 (0.06 #5561, 0.05 #6066, 0.05 #6166), 0chghy (0.02 #1011, 0.02 #1111, 0.02 #1814), 03rjj (0.02 #2512, 0.02 #2612, 0.01 #1809), 0f8l9c (0.02 #1323, 0.02 #6042, 0.02 #6142) >> Best rule #1202 for best value: >> intensional similarity = 3 >> extensional distance = 1241 >> proper extension: 0jcx; 0f2c8g; 03c_pqj; 09r_wb; 06g4_; 0brddh; >> query: (?x4125, 09c7w0) <- place_of_birth(?x4125, ?x9699), place_of_birth(?x3560, ?x9699), student(?x8398, ?x3560) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5717 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2574 *> proper extension: 025vry; 0784v1; 0zjpz; 0c11mj; 01tp5bj; 01pnn3; 03xl77; 01qx13; 01gx5f; 01w8n89; ... *> query: (?x4125, ?x94) <- place_of_birth(?x4125, ?x9699), place_of_birth(?x6857, ?x9699), nationality(?x6857, ?x94) *> conf = 0.40 ranks of expected_values: 2 EVAL 05slvm nationality 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 65.000 0.799 http://example.org/people/person/nationality #20281-04lh6 PRED entity: 04lh6 PRED relation: location! PRED expected values: 0bz5v2 01pkhw => 123 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2105): 01vrnsk (0.49 #12549, 0.47 #200752, 0.47 #2511), 025t9b (0.49 #12549, 0.47 #200752, 0.47 #2511), 0151ns (0.33 #84, 0.13 #17652, 0.11 #5105), 01vh3r (0.33 #2331, 0.13 #19899, 0.09 #12370), 03d_w3h (0.33 #149, 0.13 #17717, 0.09 #10188), 044mvs (0.33 #2059, 0.13 #19627, 0.09 #12098), 01cyjx (0.33 #1376, 0.13 #18944, 0.08 #28982), 03hh89 (0.33 #1108, 0.13 #18676, 0.08 #28714), 01vsy3q (0.33 #987, 0.11 #6008, 0.09 #36120), 0456xp (0.33 #169, 0.11 #5190, 0.09 #10208) >> Best rule #12549 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01c1nm; >> query: (?x9026, ?x3321) <- location_of_ceremony(?x2799, ?x9026), administrative_parent(?x9026, ?x13280), place_of_birth(?x3321, ?x9026) >> conf = 0.49 => this is the best rule for 2 predicted values *> Best rule #5193 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 02wt0; *> query: (?x9026, 0bz5v2) <- location_of_ceremony(?x2799, ?x9026), administrative_parent(?x9026, ?x13280), featured_film_locations(?x1002, ?x9026) *> conf = 0.11 ranks of expected_values: 482, 824 EVAL 04lh6 location! 01pkhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 123.000 87.000 0.494 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04lh6 location! 0bz5v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 123.000 87.000 0.494 http://example.org/people/person/places_lived./people/place_lived/location #20280-0237fw PRED entity: 0237fw PRED relation: vacationer! PRED expected values: 07_pf => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 91): 0261m (0.33 #100, 0.06 #841, 0.05 #1337), 03gh4 (0.21 #1191, 0.20 #1316, 0.20 #202), 05qtj (0.20 #193, 0.18 #935, 0.17 #1182), 0f2v0 (0.20 #184, 0.08 #926, 0.08 #1173), 0n3g (0.20 #199, 0.03 #941, 0.02 #1188), 06c62 (0.14 #331, 0.06 #1197, 0.06 #1322), 02_286 (0.14 #259, 0.05 #878, 0.04 #1125), 0chghy (0.14 #254, 0.04 #1245, 0.04 #749), 0jbs5 (0.14 #344, 0.04 #963, 0.04 #1335), 0r0m6 (0.14 #313, 0.03 #1179, 0.03 #1304) >> Best rule #100 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0169dl; >> query: (?x2443, 0261m) <- participant(?x262, ?x2443), film(?x2443, ?x3612), ?x3612 = 04z257 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #973 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 01cwhp; 01ttg5; *> query: (?x2443, 07_pf) <- location(?x2443, ?x1131), vacationer(?x151, ?x2443), award_winner(?x2443, ?x3078) *> conf = 0.03 ranks of expected_values: 40 EVAL 0237fw vacationer! 07_pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 116.000 116.000 0.333 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #20279-0296rz PRED entity: 0296rz PRED relation: film! PRED expected values: 04vq3h => 67 concepts (26 used for prediction) PRED predicted values (max 10 best out of 848): 0g9zcgx (0.33 #8328, 0.28 #18737, 0.02 #18738), 02b29 (0.20 #31232, 0.15 #4163, 0.14 #27069), 04sry (0.15 #1276, 0.04 #3357, 0.02 #5439), 0170s4 (0.15 #397, 0.04 #2478, 0.02 #4560), 01kb2j (0.15 #910, 0.02 #27979, 0.02 #44636), 0q9kd (0.11 #27068, 0.11 #49971, 0.11 #14572), 04w1j9 (0.11 #27068, 0.11 #49971, 0.11 #14572), 053xw6 (0.09 #3333, 0.08 #1252, 0.04 #7497), 0h5g_ (0.09 #2154, 0.08 #73, 0.03 #18811), 02qgqt (0.09 #2099, 0.08 #18, 0.03 #18756) >> Best rule #8328 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 0bx_hnp; >> query: (?x10300, ?x406) <- produced_by(?x10300, ?x71), genre(?x10300, ?x6887), ?x6887 = 03bxz7, nominated_for(?x406, ?x10300) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3783 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 025scjj; *> query: (?x10300, 04vq3h) <- genre(?x10300, ?x3506), ?x3506 = 03mqtr, nominated_for(?x1243, ?x10300), written_by(?x10300, ?x6914) *> conf = 0.04 ranks of expected_values: 169 EVAL 0296rz film! 04vq3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 67.000 26.000 0.326 http://example.org/film/actor/film./film/performance/film #20278-0mwq7 PRED entity: 0mwq7 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 114 concepts (50 used for prediction) PRED predicted values (max 10 best out of 8): 09c7w0 (0.88 #227, 0.88 #215, 0.87 #312), 04rrd (0.10 #214, 0.08 #374, 0.08 #327), 05tbn (0.09 #361, 0.08 #552, 0.08 #229), 027rqbx (0.08 #422, 0.06 #658), 07z1m (0.08 #422, 0.06 #658), 03rt9 (0.03 #148, 0.02 #317, 0.02 #348), 02jx1 (0.01 #532, 0.01 #635, 0.01 #548), 0d060g (0.01 #95, 0.01 #107) >> Best rule #227 for best value: >> intensional similarity = 5 >> extensional distance = 244 >> proper extension: 0mrq3; >> query: (?x13304, ?x94) <- adjoins(?x12233, ?x13304), adjoins(?x11541, ?x12233), second_level_divisions(?x94, ?x12233), contains(?x3670, ?x13304), county_seat(?x11541, ?x10096) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwq7 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 50.000 0.882 http://example.org/location/country/second_level_divisions #20277-015t7v PRED entity: 015t7v PRED relation: people! PRED expected values: 02w7gg => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 31): 02w7gg (0.16 #233, 0.15 #156, 0.12 #2), 041rx (0.12 #2006, 0.11 #1544, 0.11 #697), 0x67 (0.10 #703, 0.10 #549, 0.10 #318), 033tf_ (0.08 #315, 0.08 #546, 0.07 #2394), 0d7wh (0.06 #248, 0.06 #171, 0.01 #2019), 09vc4s (0.06 #9, 0.05 #86, 0.03 #317), 01qhm_ (0.06 #6, 0.03 #314, 0.03 #1854), 0222qb (0.06 #44, 0.01 #3586, 0.01 #3432), 022dp5 (0.06 #50), 09kr66 (0.06 #43) >> Best rule #233 for best value: >> intensional similarity = 2 >> extensional distance = 332 >> proper extension: 032t2z; 026y23w; 01_k0d; 01w9mnm; >> query: (?x4999, 02w7gg) <- nationality(?x4999, ?x512), ?x512 = 07ssc >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015t7v people! 02w7gg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.156 http://example.org/people/ethnicity/people #20276-06t8v PRED entity: 06t8v PRED relation: medal PRED expected values: 02lpp7 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.80 #7, 0.80 #1, 0.77 #4) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 084n_; >> query: (?x3277, 02lpp7) <- adjoins(?x205, ?x3277), country(?x6642, ?x3277), organization(?x3277, ?x127) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06t8v medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.804 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #20275-030cx PRED entity: 030cx PRED relation: titles! PRED expected values: 07c52 => 70 concepts (51 used for prediction) PRED predicted values (max 10 best out of 66): 07c52 (0.78 #133, 0.77 #339, 0.72 #30), 07s9rl0 (0.28 #4653, 0.27 #4033, 0.27 #3929), 04xvlr (0.19 #3828, 0.18 #3621, 0.18 #4243), 01z4y (0.18 #4482, 0.15 #3549, 0.14 #4585), 03mdt (0.17 #148, 0.12 #870, 0.11 #457), 0215n (0.11 #593, 0.10 #695, 0.05 #2556), 01jfsb (0.10 #3533, 0.08 #4052, 0.08 #4466), 017fp (0.09 #3744, 0.09 #4159, 0.09 #3641), 07ssc (0.09 #5183, 0.08 #5079, 0.08 #4870), 05gnf (0.08 #2684) >> Best rule #133 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 072kp; 0124k9; 03ln8b; 01q_y0; 02hct1; 0d68qy; 01bv8b; 0557yqh; 01s81; 0l76z; ... >> query: (?x4535, 07c52) <- nominated_for(?x3906, ?x4535), nominated_for(?x71, ?x4535), ?x3906 = 03ccq3s, producer_type(?x4535, ?x632) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030cx titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 51.000 0.783 http://example.org/media_common/netflix_genre/titles #20274-02pbrn PRED entity: 02pbrn PRED relation: category PRED expected values: 08mbj5d => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #29, 0.85 #18, 0.84 #26) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 463 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01wv9xn; 0frsw; 01vrwfv; 014_lq; 02jqjm; 0178kd; 0143q0; ... >> query: (?x12304, 08mbj5d) <- artist(?x8919, ?x12304), artists(?x5355, ?x12304), award_winner(?x2561, ?x12304) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pbrn category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.862 http://example.org/common/topic/webpage./common/webpage/category #20273-019vhk PRED entity: 019vhk PRED relation: award_winner PRED expected values: 0gnbw => 107 concepts (51 used for prediction) PRED predicted values (max 10 best out of 504): 01tc9r (0.49 #9872, 0.49 #29616, 0.47 #57603), 05hj_k (0.49 #9872, 0.47 #57603, 0.46 #59250), 07s93v (0.49 #9872, 0.47 #57603, 0.46 #59250), 04sry (0.49 #9872, 0.47 #57603, 0.46 #59250), 016yvw (0.49 #9872, 0.47 #57603, 0.46 #59250), 0bksh (0.49 #9872, 0.47 #57603, 0.46 #59250), 0dvmd (0.49 #9872, 0.47 #57603, 0.46 #59250), 01vrx35 (0.49 #9872, 0.47 #57603, 0.46 #59250), 01vswx5 (0.49 #9872, 0.47 #57603, 0.46 #59250), 095zvfg (0.49 #9872, 0.46 #59250, 0.45 #59249) >> Best rule #9872 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 0ddjy; 0qf2t; 01hv3t; 09v8clw; >> query: (?x2852, ?x1616) <- titles(?x53, ?x2852), film(?x166, ?x2852), nominated_for(?x1616, ?x2852), honored_for(?x5465, ?x2852) >> conf = 0.49 => this is the best rule for 13 predicted values *> Best rule #25847 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 223 *> proper extension: 05z7c; 02qr69m; 0pd57; 0kb07; 0295sy; 0kbhf; 0gl3hr; 0gy4k; 0bbgvp; *> query: (?x2852, 0gnbw) <- film(?x489, ?x2852), genre(?x2852, ?x53), nominated_for(?x484, ?x2852), ?x484 = 0gq_v *> conf = 0.01 ranks of expected_values: 313 EVAL 019vhk award_winner 0gnbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 107.000 51.000 0.494 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #20272-0fgrm PRED entity: 0fgrm PRED relation: nominated_for! PRED expected values: 054krc => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 199): 0gq9h (0.26 #1264, 0.25 #64, 0.22 #1744), 0k611 (0.25 #75, 0.19 #1275, 0.18 #1755), 0gs9p (0.25 #66, 0.18 #8708, 0.18 #9428), 019f4v (0.25 #55, 0.17 #9417, 0.17 #10377), 099c8n (0.25 #58, 0.17 #1498, 0.17 #4859), 0gr4k (0.25 #27, 0.15 #9389, 0.14 #11551), 04dn09n (0.25 #36, 0.13 #10358, 0.13 #10118), 02pqp12 (0.25 #60, 0.12 #4861, 0.11 #9422), 02x17s4 (0.25 #97, 0.12 #1297, 0.09 #1537), 09qv_s (0.25 #117, 0.12 #1317, 0.09 #1797) >> Best rule #1264 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 0qmfk; >> query: (?x4650, 0gq9h) <- film(?x3229, ?x4650), nominated_for(?x3911, ?x4650), film_release_region(?x4650, ?x550), film_release_region(?x4650, ?x94) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #14408 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1584 *> proper extension: 06g60w; 04glx0; *> query: (?x4650, ?x7099) <- nominated_for(?x8374, ?x4650), award(?x8374, ?x7099), nominated_for(?x7099, ?x1944) *> conf = 0.22 ranks of expected_values: 18 EVAL 0fgrm nominated_for! 054krc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 68.000 68.000 0.256 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20271-019f2f PRED entity: 019f2f PRED relation: people! PRED expected values: 033tf_ => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 40): 065b6q (0.27 #3, 0.05 #157, 0.04 #234), 033tf_ (0.20 #7, 0.19 #315, 0.17 #1321), 0x67 (0.18 #704, 0.18 #2408, 0.17 #2485), 041rx (0.17 #2788, 0.16 #1085, 0.16 #81), 0xnvg (0.13 #321, 0.10 #167, 0.09 #1327), 07mqps (0.11 #96, 0.03 #327, 0.02 #2803), 07hwkr (0.10 #166, 0.05 #89, 0.05 #474), 0d7wh (0.08 #248, 0.05 #171, 0.02 #1254), 02ctzb (0.08 #246, 0.03 #554, 0.03 #2799), 02w7gg (0.08 #4720, 0.07 #5490, 0.06 #541) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 01j851; >> query: (?x2589, 065b6q) <- religion(?x2589, ?x492), award(?x2589, ?x3184), ?x3184 = 0gkts9 >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 01j851; *> query: (?x2589, 033tf_) <- religion(?x2589, ?x492), award(?x2589, ?x3184), ?x3184 = 0gkts9 *> conf = 0.20 ranks of expected_values: 2 EVAL 019f2f people! 033tf_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 123.000 123.000 0.267 http://example.org/people/ethnicity/people #20270-048rn PRED entity: 048rn PRED relation: country PRED expected values: 09c7w0 => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 107): 09c7w0 (0.86 #490, 0.84 #918, 0.83 #857), 07ssc (0.22 #2410, 0.20 #2471, 0.20 #2532), 0d060g (0.15 #558, 0.06 #2093, 0.06 #1785), 0345h (0.12 #1374, 0.11 #1312, 0.11 #1250), 0f8l9c (0.10 #569, 0.10 #81, 0.08 #142), 03rk0 (0.10 #101, 0.08 #162, 0.05 #345), 03_3d (0.06 #863, 0.04 #557, 0.04 #2585), 0h9qh (0.06 #2701, 0.06 #2764, 0.06 #2700), 03k9fj (0.06 #2701, 0.06 #2764, 0.06 #2700), 0chghy (0.04 #562, 0.04 #1483, 0.03 #1112) >> Best rule #490 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 06wzvr; 0gxfz; >> query: (?x5198, 09c7w0) <- film_sets_designed(?x200, ?x5198), genre(?x5198, ?x571), costume_design_by(?x5198, ?x4526) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 048rn country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.861 http://example.org/film/film/country #20269-099bk PRED entity: 099bk PRED relation: influenced_by PRED expected values: 03sbs => 152 concepts (89 used for prediction) PRED predicted values (max 10 best out of 333): 03cdg (0.40 #2121, 0.13 #7736, 0.12 #8599), 03_87 (0.39 #19618, 0.20 #1927, 0.18 #32550), 081k8 (0.36 #6194, 0.25 #2745, 0.21 #32504), 03sbs (0.34 #15756, 0.34 #21361, 0.33 #221), 0420y (0.33 #400, 0.29 #6439, 0.25 #2556), 0tfc (0.33 #412, 0.25 #3862, 0.25 #3002), 042q3 (0.33 #362, 0.25 #2952, 0.23 #15031), 03_f0 (0.33 #265, 0.14 #6738, 0.13 #7171), 015n8 (0.30 #4717, 0.29 #6446, 0.26 #10332), 026lj (0.29 #6084, 0.27 #6951, 0.25 #909) >> Best rule #2121 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 06y9c2; >> query: (?x3993, 03cdg) <- nationality(?x3993, ?x1264), ?x1264 = 0345h, student(?x9988, ?x3993), student(?x8221, ?x3993), type_of_union(?x3993, ?x566) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #15756 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 07kb5; 04411; 045bg; 028p0; 026lj; 040db; 0379s; 0372p; 0lcx; 043s3; ... *> query: (?x3993, 03sbs) <- influenced_by(?x4003, ?x3993), profession(?x4003, ?x353), interests(?x4003, ?x713), influenced_by(?x3993, ?x3994) *> conf = 0.34 ranks of expected_values: 4 EVAL 099bk influenced_by 03sbs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 152.000 89.000 0.400 http://example.org/influence/influence_node/influenced_by #20268-0322yj PRED entity: 0322yj PRED relation: genre PRED expected values: 06cvj => 115 concepts (97 used for prediction) PRED predicted values (max 10 best out of 91): 01z4y (0.61 #10286, 0.57 #239, 0.53 #9688), 01jfsb (0.44 #728, 0.43 #2520, 0.43 #1682), 02kdv5l (0.36 #241, 0.36 #2510, 0.36 #1672), 03k9fj (0.26 #250, 0.26 #370, 0.26 #965), 0lsxr (0.23 #367, 0.21 #843, 0.21 #1201), 06cvj (0.23 #4068, 0.09 #1673, 0.09 #3112), 06n90 (0.22 #13, 0.20 #729, 0.18 #2521), 04xvlr (0.22 #1, 0.19 #4785, 0.19 #240), 060__y (0.22 #16, 0.18 #3605, 0.17 #135), 017fp (0.22 #15, 0.10 #4799, 0.10 #7073) >> Best rule #10286 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 02xhwm; >> query: (?x12437, ?x2480) <- titles(?x2480, ?x12437), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #4068 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 482 *> proper extension: 01k5y0; *> query: (?x12437, 06cvj) <- film(?x4564, ?x12437), film(?x496, ?x12437), genre(?x12437, ?x258), ?x258 = 05p553 *> conf = 0.23 ranks of expected_values: 6 EVAL 0322yj genre 06cvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 115.000 97.000 0.612 http://example.org/film/film/genre #20267-0jmcb PRED entity: 0jmcb PRED relation: teams! PRED expected values: 0h7h6 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 62): 01cx_ (0.33 #635, 0.25 #1448, 0.20 #1988), 0f2tj (0.33 #422, 0.20 #2046, 0.14 #3132), 01_d4 (0.25 #1143, 0.17 #2224, 0.10 #4665), 0nqph (0.25 #1071, 0.09 #5949, 0.04 #9744), 030qb3t (0.20 #1674, 0.17 #2488, 0.11 #4112), 0ply0 (0.20 #1995, 0.14 #3081, 0.14 #2811), 02cl1 (0.17 #2458, 0.11 #3811, 0.10 #5168), 02_286 (0.17 #2460, 0.11 #4354, 0.08 #6526), 02dtg (0.17 #2180, 0.09 #5706, 0.05 #7604), 01sn3 (0.14 #3095, 0.12 #3365, 0.10 #4992) >> Best rule #635 for best value: >> intensional similarity = 25 >> extensional distance = 1 >> proper extension: 0bwjj; >> query: (?x2568, 01cx_) <- team(?x6848, ?x2568), team(?x4570, ?x2568), team(?x1579, ?x2568), ?x4570 = 03558l, position(?x2568, ?x5755), position(?x2568, ?x4747), school(?x2568, ?x2895), school(?x2568, ?x2399), ?x6848 = 02_ssl, draft(?x2568, ?x12852), draft(?x2568, ?x8586), draft(?x2568, ?x8542), ?x8586 = 038981, ?x5755 = 0355dz, sport(?x2568, ?x4833), ?x8542 = 09th87, ?x2895 = 0l2tk, ?x4747 = 02sf_r, ?x12852 = 06439y, currency(?x2399, ?x170), major_field_of_study(?x2399, ?x1527), ?x4833 = 018w8, contains(?x94, ?x2399), ?x1579 = 0ctt4z, school_type(?x2399, ?x1507) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #15798 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 175 *> proper extension: 04913k; 021f30; 0132_h; 01v3x8; 02gtm4; 04b5l3; 02h8p8; 02hfgl; 03qrh9; 04c9bn; *> query: (?x2568, 0h7h6) <- team(?x4570, ?x2568), team(?x4570, ?x12141), team(?x4570, ?x9931), team(?x4570, ?x6803), team(?x4570, ?x2398), team(?x4570, ?x1578), company(?x4486, ?x1578), draft(?x2398, ?x4979), team(?x4834, ?x1578), draft(?x12141, ?x2569), teams(?x5259, ?x6803), sport(?x2398, ?x4833), team(?x12339, ?x12141), school(?x1578, ?x1783), school(?x12141, ?x581), colors(?x9931, ?x663), ?x663 = 083jv *> conf = 0.01 ranks of expected_values: 59 EVAL 0jmcb teams! 0h7h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 64.000 64.000 0.333 http://example.org/sports/sports_team_location/teams #20266-0c2ry PRED entity: 0c2ry PRED relation: location PRED expected values: 02_n7 => 130 concepts (127 used for prediction) PRED predicted values (max 10 best out of 155): 030qb3t (0.28 #4911, 0.25 #19390, 0.25 #4107), 0cr3d (0.25 #145, 0.08 #1755, 0.04 #31514), 0cc56 (0.25 #57, 0.07 #8907, 0.06 #29818), 059_c (0.25 #58, 0.03 #1668), 02_286 (0.17 #13716, 0.17 #49103, 0.17 #14520), 013yq (0.14 #923, 0.05 #16210, 0.04 #19426), 05k7sb (0.14 #913, 0.04 #2523, 0.04 #5741), 0f2r6 (0.14 #837, 0.03 #5665, 0.02 #6470), 05tbn (0.14 #992, 0.03 #5820, 0.02 #2602), 04jpl (0.09 #4041, 0.08 #1627, 0.07 #4845) >> Best rule #4911 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 01d0fp; 0436kgz; >> query: (?x4057, 030qb3t) <- participant(?x4057, ?x1567), spouse(?x4057, ?x5484), participant(?x4057, ?x544) >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c2ry location 02_n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 127.000 0.284 http://example.org/people/person/places_lived./people/place_lived/location #20265-0cg9f PRED entity: 0cg9f PRED relation: location_of_ceremony PRED expected values: 0cv3w => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 23): 0d6hn (0.17 #94, 0.07 #333, 0.02 #453), 02_286 (0.07 #252, 0.02 #372, 0.01 #610), 05qtj (0.07 #294, 0.02 #414), 0d6lp (0.07 #276, 0.02 #396), 0cv3w (0.03 #513, 0.03 #2540, 0.02 #2659), 0k049 (0.02 #2509, 0.02 #2628, 0.02 #3104), 030qb3t (0.02 #378, 0.01 #2762, 0.01 #3000), 0qr8z (0.02 #434), 0kc40 (0.02 #581, 0.01 #700, 0.01 #819), 059rby (0.02 #486, 0.01 #605, 0.01 #724) >> Best rule #94 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03vrp; >> query: (?x12584, 0d6hn) <- profession(?x12584, ?x1032), people(?x10900, ?x12584), ?x10900 = 08g5q7, award(?x12584, ?x458) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #513 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 0lh0c; *> query: (?x12584, 0cv3w) <- profession(?x12584, ?x1032), celebrities_impersonated(?x3649, ?x12584), people(?x6736, ?x12584) *> conf = 0.03 ranks of expected_values: 5 EVAL 0cg9f location_of_ceremony 0cv3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 120.000 120.000 0.167 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #20264-0kc40 PRED entity: 0kc40 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #45, 0.87 #37, 0.86 #93), 01g63y (0.53 #138, 0.26 #230, 0.13 #343), 0jgjn (0.53 #138, 0.17 #16, 0.12 #8), 01bl8s (0.02 #59, 0.01 #107, 0.01 #103) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0f8l9c; >> query: (?x11794, 04ztj) <- location_of_ceremony(?x8460, ?x11794), profession(?x8460, ?x1146), people(?x4322, ?x8460) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kc40 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.875 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #20263-04gdr PRED entity: 04gdr PRED relation: contains! PRED expected values: 0f8l9c => 178 concepts (47 used for prediction) PRED predicted values (max 10 best out of 295): 05k7sb (0.90 #26120, 0.03 #20737, 0.03 #21635), 0f8l9c (0.83 #12543, 0.80 #14335, 0.80 #10751), 04jpl (0.71 #25113, 0.67 #27800, 0.63 #31383), 09c7w0 (0.67 #3583, 0.62 #4478, 0.60 #25990), 03rjj (0.54 #17033, 0.33 #905, 0.31 #36751), 02jx1 (0.48 #25178, 0.45 #27865, 0.43 #31448), 0345h (0.40 #17105, 0.22 #36823, 0.22 #37718), 0gtzp (0.38 #11647, 0.36 #33156, 0.36 #41219), 0kpys (0.33 #27063, 0.33 #30646, 0.29 #41400), 02j9z (0.33 #23325, 0.27 #37664, 0.14 #1818) >> Best rule #26120 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 017v71; 0k3jq; 0k3j0; 03kxzm; >> query: (?x10739, 05k7sb) <- contains(?x4627, ?x10739), location(?x12258, ?x4627), vacationer(?x4627, ?x436), taxonomy(?x4627, ?x939), ?x12258 = 019fz >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #12543 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 33 *> proper extension: 0r2l7; *> query: (?x10739, ?x789) <- contains(?x4627, ?x10739), place_of_birth(?x771, ?x4627), second_level_divisions(?x789, ?x4627), location(?x598, ?x4627), place_of_death(?x3428, ?x4627) *> conf = 0.83 ranks of expected_values: 2 EVAL 04gdr contains! 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 178.000 47.000 0.904 http://example.org/location/location/contains #20262-0h5f5n PRED entity: 0h5f5n PRED relation: people! PRED expected values: 041rx => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 41): 041rx (0.30 #3, 0.24 #1523, 0.22 #1599), 0x67 (0.16 #4569, 0.13 #1529, 0.11 #1681), 033tf_ (0.12 #1602, 0.11 #1526, 0.10 #158), 0xnvg (0.09 #1608, 0.08 #1532, 0.06 #4572), 0d7wh (0.08 #700, 0.08 #320, 0.07 #624), 048z7l (0.08 #267, 0.06 #495, 0.05 #875), 07hwkr (0.07 #1531, 0.05 #1607, 0.05 #4571), 07bch9 (0.06 #1542, 0.05 #4582, 0.04 #1618), 0dryh9k (0.05 #4575, 0.02 #6399, 0.02 #6019), 01qhm_ (0.04 #1601, 0.03 #1525, 0.03 #4565) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 019z7q; 012t1; 085pr; 0gv5c; 012wg; 0282x; 081l_; 0p50v; 0mb5x; 0c4y8; ... >> query: (?x361, 041rx) <- award(?x361, ?x68), written_by(?x2932, ?x361), profession(?x361, ?x6421), ?x6421 = 02hv44_ >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h5f5n people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.300 http://example.org/people/ethnicity/people #20261-0cshrf PRED entity: 0cshrf PRED relation: genre! PRED expected values: 032_wv 01hvjx => 30 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1855): 01hvjx (0.67 #9669, 0.50 #2242, 0.45 #11525), 02x0fs9 (0.60 #7277, 0.50 #5420, 0.50 #3563), 021pqy (0.60 #6365, 0.50 #8222, 0.50 #4508), 04h41v (0.60 #6631, 0.50 #4774, 0.50 #2917), 0g5q34q (0.60 #6633, 0.50 #4776, 0.50 #2919), 07l4zhn (0.60 #6570, 0.50 #4713, 0.50 #2856), 0sxlb (0.60 #7205, 0.50 #5348, 0.50 #3491), 0yx1m (0.60 #7037, 0.50 #5180, 0.45 #12606), 0cbv4g (0.60 #6512, 0.50 #4655, 0.45 #12081), 0gd92 (0.60 #6906, 0.50 #5049, 0.42 #14331) >> Best rule #9669 for best value: >> intensional similarity = 15 >> extensional distance = 7 >> proper extension: 04t36; 03k9fj; >> query: (?x7217, 01hvjx) <- genre(?x8054, ?x7217), genre(?x6365, ?x7217), genre(?x5991, ?x7217), genre(?x5984, ?x7217), ?x6365 = 03n3gl, production_companies(?x5991, ?x617), film(?x157, ?x8054), film(?x643, ?x5991), film_release_distribution_medium(?x5984, ?x81), film_crew_role(?x5991, ?x1284), nominated_for(?x5351, ?x5991), featured_film_locations(?x8054, ?x362), film(?x318, ?x5984), ?x1284 = 0ch6mp2, language(?x5984, ?x254) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 110 EVAL 0cshrf genre! 01hvjx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 10.000 0.667 http://example.org/film/film/genre EVAL 0cshrf genre! 032_wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 30.000 10.000 0.667 http://example.org/film/film/genre #20260-0g8rj PRED entity: 0g8rj PRED relation: fraternities_and_sororities PRED expected values: 0325pb => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 3): 0325pb (0.50 #89, 0.42 #104, 0.37 #116), 035tlh (0.36 #53, 0.36 #80, 0.36 #90), 04m8fy (0.08 #54, 0.06 #48, 0.05 #57) >> Best rule #89 for best value: >> intensional similarity = 2 >> extensional distance = 74 >> proper extension: 0fht9f; >> query: (?x5486, 0325pb) <- school(?x5204, ?x5486), position_s(?x5204, ?x180) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g8rj fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.500 http://example.org/education/university/fraternities_and_sororities #20259-098s2w PRED entity: 098s2w PRED relation: film! PRED expected values: 02r_d4 => 88 concepts (40 used for prediction) PRED predicted values (max 10 best out of 630): 0f4vbz (0.67 #76870, 0.61 #29084, 0.54 #56093), 015v3r (0.17 #535, 0.03 #78951, 0.01 #76871), 0gn30 (0.17 #944, 0.03 #54959, 0.03 #59114), 015c4g (0.17 #778, 0.02 #4936, 0.02 #9090), 024bbl (0.17 #834, 0.02 #2911, 0.02 #46540), 04bd8y (0.17 #127, 0.02 #29212, 0.01 #70764), 0dzf_ (0.17 #807, 0.02 #54822, 0.02 #58977), 032qgs (0.17 #2070), 0736qr (0.17 #1985), 016zdd (0.17 #1809) >> Best rule #76870 for best value: >> intensional similarity = 3 >> extensional distance = 739 >> proper extension: 0123qq; >> query: (?x6493, ?x2258) <- nominated_for(?x2258, ?x6493), participant(?x3056, ?x2258), award_nominee(?x382, ?x3056) >> conf = 0.67 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 098s2w film! 02r_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 40.000 0.665 http://example.org/film/actor/film./film/performance/film #20258-0pz7h PRED entity: 0pz7h PRED relation: profession PRED expected values: 09jwl => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 72): 01d_h8 (0.50 #870, 0.49 #4039, 0.42 #726), 02jknp (0.42 #4040, 0.27 #871, 0.23 #3464), 0kyk (0.34 #1033, 0.31 #1609, 0.28 #1897), 02krf9 (0.30 #2593, 0.28 #886, 0.28 #7634), 0fj9f (0.29 #338, 0.17 #194, 0.05 #1058), 09jwl (0.20 #1743, 0.18 #4480, 0.17 #2463), 0d1pc (0.15 #1342, 0.14 #2206, 0.12 #2639), 012t_z (0.14 #300, 0.05 #876, 0.05 #732), 015cjr (0.14 #477, 0.11 #333, 0.08 #189), 0dz3r (0.13 #1730, 0.13 #4467, 0.12 #5619) >> Best rule #870 for best value: >> intensional similarity = 2 >> extensional distance = 200 >> proper extension: 0162c8; 05y5fw; 01rlxt; 0564mx; 08qmfm; 03cs_xw; 08nz99; 03xpfzg; >> query: (?x906, 01d_h8) <- award_nominee(?x906, ?x237), producer_type(?x906, ?x632) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1743 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 432 *> proper extension: 03qcq; 06cv1; 06w2sn5; 033wx9; 01wgxtl; 01vw20_; 01q32bd; 01vswwx; 02x_h0; 05szp; ... *> query: (?x906, 09jwl) <- award_nominee(?x906, ?x237), participant(?x906, ?x692) *> conf = 0.20 ranks of expected_values: 6 EVAL 0pz7h profession 09jwl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 89.000 89.000 0.500 http://example.org/people/person/profession #20257-0hcr PRED entity: 0hcr PRED relation: student PRED expected values: 017c87 => 104 concepts (68 used for prediction) PRED predicted values (max 10 best out of 273): 012x2b (0.40 #5687, 0.33 #1860, 0.25 #4251), 05j0wc (0.33 #1872, 0.25 #4263, 0.20 #5699), 01_rh4 (0.33 #1739, 0.20 #5566, 0.07 #12022), 067sqt (0.33 #1896, 0.20 #5723, 0.03 #14095), 08p1gp (0.33 #1891, 0.20 #5718, 0.03 #14090), 01rs5p (0.33 #1883, 0.20 #5710, 0.03 #14082), 016tbr (0.33 #1871, 0.20 #5698, 0.03 #14070), 07m77x (0.33 #1850, 0.20 #5677, 0.03 #14049), 0fn5bx (0.33 #1789, 0.20 #5616, 0.03 #13988), 02jsgf (0.33 #1762, 0.20 #5589, 0.03 #13961) >> Best rule #5687 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 0jjw; >> query: (?x2540, 012x2b) <- major_field_of_study(?x4955, ?x2540), major_field_of_study(?x2056, ?x2540), ?x4955 = 09f2j, major_field_of_study(?x3386, ?x2540), major_field_of_study(?x1368, ?x2540), ?x3386 = 03mkk4, ?x1368 = 014mlp, currency(?x2056, ?x170) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #5976 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 05qgc; *> query: (?x2540, ?x744) <- major_field_of_study(?x3386, ?x2540), major_field_of_study(?x1771, ?x2540), disciplines_or_subjects(?x13285, ?x2540), ?x3386 = 03mkk4, institution(?x1771, ?x99), student(?x1771, ?x744) *> conf = 0.03 ranks of expected_values: 227 EVAL 0hcr student 017c87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 104.000 68.000 0.400 http://example.org/education/field_of_study/students_majoring./education/education/student #20256-03176f PRED entity: 03176f PRED relation: language PRED expected values: 02h40lc => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 48): 02h40lc (0.94 #593, 0.92 #1850, 0.91 #1552), 064_8sq (0.22 #199, 0.20 #613, 0.20 #140), 04306rv (0.22 #182, 0.16 #241, 0.15 #359), 06nm1 (0.20 #11, 0.12 #70, 0.12 #602), 0k0sv (0.20 #24, 0.12 #83, 0.05 #260), 06b_j (0.17 #200, 0.07 #2228, 0.07 #1453), 0653m (0.15 #484, 0.05 #2337, 0.05 #1681), 02bjrlw (0.11 #178, 0.10 #592, 0.08 #889), 012w70 (0.11 #485, 0.04 #1682, 0.04 #2338), 03_9r (0.07 #3234, 0.07 #482, 0.06 #187) >> Best rule #593 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 0jzw; 0c_j9x; 03cp4cn; >> query: (?x4235, 02h40lc) <- nominated_for(?x4235, ?x2006), film(?x981, ?x4235), story_by(?x4235, ?x13644), film(?x382, ?x4235) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03176f language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.940 http://example.org/film/film/language #20255-0ljsz PRED entity: 0ljsz PRED relation: citytown! PRED expected values: 05zl0 => 162 concepts (72 used for prediction) PRED predicted values (max 10 best out of 731): 03t4nx (0.69 #38016, 0.66 #3235, 0.62 #16986), 059wk (0.08 #3693, 0.03 #5310, 0.03 #33620), 0ks67 (0.06 #47725, 0.02 #1065), 01dyk8 (0.06 #47725, 0.02 #3687, 0.02 #6112), 01n951 (0.06 #47725, 0.02 #4428, 0.02 #6044), 02m0sc (0.06 #47725), 01q7q2 (0.06 #47725), 01tpvt (0.06 #47725), 01nds (0.06 #7045, 0.06 #7853, 0.06 #2195), 064f29 (0.06 #1932, 0.05 #5166, 0.04 #6782) >> Best rule #38016 for best value: >> intensional similarity = 4 >> extensional distance = 191 >> proper extension: 0_lr1; 024bqj; >> query: (?x10988, ?x10036) <- contains(?x6895, ?x10988), contains(?x10988, ?x10036), citytown(?x12284, ?x10988), school_type(?x10036, ?x3205) >> conf = 0.69 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ljsz citytown! 05zl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 162.000 72.000 0.688 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #20254-0cmc26r PRED entity: 0cmc26r PRED relation: film_release_region PRED expected values: 0chghy 06t2t 012wgb => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 103): 0chghy (0.89 #7, 0.86 #707, 0.86 #287), 03rt9 (0.66 #1271, 0.65 #289, 0.64 #709), 06t2t (0.66 #1312, 0.64 #50, 0.59 #750), 05v8c (0.64 #11, 0.61 #291, 0.59 #711), 03rj0 (0.59 #48, 0.56 #328, 0.55 #1310), 01mjq (0.55 #1296, 0.51 #734, 0.50 #314), 06mzp (0.54 #15, 0.53 #715, 0.51 #295), 05qx1 (0.47 #31, 0.40 #311, 0.39 #731), 047yc (0.47 #1283, 0.45 #21, 0.41 #301), 015qh (0.47 #1294, 0.45 #32, 0.37 #1434) >> Best rule #7 for best value: >> intensional similarity = 6 >> extensional distance = 108 >> proper extension: 0g5qmbz; >> query: (?x4111, 0chghy) <- film_release_region(?x4111, ?x1499), film_release_region(?x4111, ?x1453), film_release_region(?x4111, ?x87), ?x87 = 05r4w, ?x1453 = 06qd3, ?x1499 = 01znc_ >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 37 EVAL 0cmc26r film_release_region 012wgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 84.000 84.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cmc26r film_release_region 06t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cmc26r film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20253-065ym0c PRED entity: 065ym0c PRED relation: genre PRED expected values: 01rbb => 101 concepts (100 used for prediction) PRED predicted values (max 10 best out of 118): 07s9rl0 (0.77 #3397, 0.77 #728, 0.74 #5221), 02l7c8 (0.74 #7064, 0.40 #743, 0.37 #2196), 04t2t (0.53 #10699, 0.53 #6440, 0.52 #10454), 0d05w3 (0.53 #6440, 0.52 #10454, 0.52 #10698), 05p553 (0.49 #9971, 0.34 #7052, 0.34 #2670), 01jfsb (0.46 #618, 0.35 #5720, 0.33 #5476), 03q4nz (0.44 #140, 0.33 #383, 0.33 #261), 03k9fj (0.37 #859, 0.37 #1343, 0.35 #2677), 0lsxr (0.33 #372, 0.33 #250, 0.33 #129), 04xvlr (0.33 #123, 0.27 #366, 0.23 #2182) >> Best rule #3397 for best value: >> intensional similarity = 4 >> extensional distance = 186 >> proper extension: 07bz5; 0lcdk; 0542n; 087z2; >> query: (?x10080, 07s9rl0) <- award(?x10080, ?x12715), disciplines_or_subjects(?x12715, ?x373), major_field_of_study(?x1695, ?x373), major_field_of_study(?x735, ?x373) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1684 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 80 *> proper extension: 0bh8yn3; *> query: (?x10080, 01rbb) <- film_regional_debut_venue(?x10080, ?x13344), genre(?x10080, ?x225), film_release_region(?x10080, ?x2346), award_winner(?x10080, ?x12529), exported_to(?x2346, ?x291), country(?x1889, ?x2346) *> conf = 0.01 ranks of expected_values: 116 EVAL 065ym0c genre 01rbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 101.000 100.000 0.771 http://example.org/film/film/genre #20252-01n_g9 PRED entity: 01n_g9 PRED relation: fraternities_and_sororities PRED expected values: 0325pb => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 3): 0325pb (0.30 #25, 0.26 #16, 0.25 #4), 035tlh (0.25 #14, 0.25 #5, 0.25 #26), 04m8fy (0.04 #6, 0.03 #9, 0.03 #12) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 189 >> proper extension: 0fht9f; 0frm7n; >> query: (?x7716, 0325pb) <- school(?x7725, ?x7716), draft(?x7725, ?x1161), team(?x261, ?x7725) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n_g9 fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.298 http://example.org/education/university/fraternities_and_sororities #20251-05wh0sh PRED entity: 05wh0sh PRED relation: influenced_by! PRED expected values: 01hb6v => 131 concepts (65 used for prediction) PRED predicted values (max 10 best out of 340): 04z0g (0.36 #3332, 0.05 #20860, 0.04 #22926), 047g6 (0.29 #3573, 0.09 #23167, 0.08 #23205), 032r1 (0.29 #3567, 0.07 #23161, 0.04 #21095), 09gnn (0.29 #3512, 0.07 #23206, 0.07 #33530), 0b78hw (0.21 #3260, 0.07 #23206, 0.07 #33530), 03_hd (0.21 #3273, 0.04 #22867, 0.04 #24419), 016lh0 (0.21 #3309, 0.03 #20837, 0.03 #22903), 045bg (0.20 #36, 0.11 #1067, 0.10 #5705), 03cdg (0.20 #467, 0.11 #1498, 0.07 #3559), 01hb6v (0.19 #5248, 0.14 #3702, 0.14 #3186) >> Best rule #3332 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 026lj; 052h3; 0cpvcd; 032r1; 0tfc; >> query: (?x3341, 04z0g) <- gender(?x3341, ?x231), profession(?x3341, ?x10210), ?x231 = 05zppz, ?x10210 = 0frz0 >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #5248 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 024jwt; *> query: (?x3341, 01hb6v) <- student(?x1305, ?x3341), religion(?x3341, ?x2694), type_of_union(?x3341, ?x566), institution(?x1305, ?x388), influenced_by(?x9851, ?x3341) *> conf = 0.19 ranks of expected_values: 10 EVAL 05wh0sh influenced_by! 01hb6v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 131.000 65.000 0.357 http://example.org/influence/influence_node/influenced_by #20250-01rk91 PRED entity: 01rk91 PRED relation: company PRED expected values: 05gnf => 37 concepts (20 used for prediction) PRED predicted values (max 10 best out of 633): 060ppp (0.78 #4296, 0.67 #3958, 0.67 #2269), 019rl6 (0.75 #3197, 0.67 #4210, 0.67 #3872), 01qygl (0.75 #3234, 0.67 #4247, 0.67 #2220), 0z90c (0.71 #2873, 0.67 #2194, 0.62 #3546), 0300cp (0.67 #4099, 0.67 #3761, 0.67 #2072), 087c7 (0.67 #4057, 0.67 #2030, 0.62 #3044), 01s73z (0.67 #4159, 0.67 #2132, 0.62 #3146), 02r5dz (0.67 #4121, 0.67 #2094, 0.60 #1414), 07xyn1 (0.67 #4235, 0.67 #2208, 0.60 #1528), 09b3v (0.67 #4138, 0.67 #2111, 0.57 #2790) >> Best rule #4296 for best value: >> intensional similarity = 16 >> extensional distance = 7 >> proper extension: 01kr6k; >> query: (?x233, 060ppp) <- company(?x233, ?x10699), company(?x233, ?x9469), company(?x233, ?x7457), service_location(?x9469, ?x94), service_language(?x9469, ?x732), service_language(?x9469, ?x254), place_founded(?x10699, ?x1860), ?x254 = 02h40lc, award_winner(?x3776, ?x7457), list(?x9469, ?x5997), industry(?x9469, ?x245), contact_category(?x9469, ?x897), ?x5997 = 04k4rt, language(?x8631, ?x732), ?x8631 = 01_1hw, countries_spoken_in(?x732, ?x172) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1680 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 3 *> proper extension: 09d6p2; *> query: (?x233, ?x555) <- company(?x233, ?x10699), company(?x233, ?x9469), company(?x233, ?x7457), company(?x233, ?x6676), service_location(?x9469, ?x94), company(?x554, ?x9469), ?x10699 = 0206k5, service_language(?x9469, ?x254), list(?x6676, ?x5997), company(?x554, ?x13349), company(?x554, ?x11636), company(?x554, ?x8931), company(?x554, ?x7218), company(?x554, ?x555), ?x7218 = 019rl6, ?x254 = 02h40lc, ?x11636 = 03s7h, ?x8931 = 01qygl, contact_category(?x7457, ?x897), ?x13349 = 05b5c, category(?x9469, ?x134), ?x897 = 03w5xm *> conf = 0.38 ranks of expected_values: 85 EVAL 01rk91 company 05gnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 37.000 20.000 0.778 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #20249-011yph PRED entity: 011yph PRED relation: nominated_for! PRED expected values: 02qyp19 => 73 concepts (66 used for prediction) PRED predicted values (max 10 best out of 201): 019f4v (0.60 #1843, 0.46 #722, 0.30 #2291), 0k611 (0.53 #1859, 0.51 #738, 0.26 #2307), 02qyp19 (0.52 #1346, 0.40 #1, 0.34 #673), 04dn09n (0.47 #1825, 0.46 #704, 0.43 #1377), 0gr4k (0.44 #1818, 0.31 #697, 0.21 #1594), 0gqyl (0.43 #744, 0.40 #72, 0.33 #1865), 0f4x7 (0.40 #1817, 0.29 #696, 0.24 #6724), 0gr0m (0.39 #1847, 0.26 #726, 0.20 #2519), 09td7p (0.38 #757, 0.30 #85, 0.17 #309), 09qv_s (0.38 #777, 0.17 #1450, 0.13 #1898) >> Best rule #1843 for best value: >> intensional similarity = 4 >> extensional distance = 176 >> proper extension: 02h22; 0k2m6; 0j8f09z; >> query: (?x616, 019f4v) <- genre(?x616, ?x258), nominated_for(?x1313, ?x616), film_release_region(?x616, ?x94), ?x1313 = 0gs9p >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1346 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 98 *> proper extension: 0by1wkq; 0cmdwwg; *> query: (?x616, 02qyp19) <- genre(?x616, ?x258), nominated_for(?x3435, ?x616), film(?x2556, ?x616), ?x3435 = 03hl6lc *> conf = 0.52 ranks of expected_values: 3 EVAL 011yph nominated_for! 02qyp19 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 73.000 66.000 0.601 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20248-0gthm PRED entity: 0gthm PRED relation: special_performance_type PRED expected values: 01pb34 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.14 #23, 0.12 #113, 0.11 #118), 09_gdc (0.03 #127, 0.02 #87, 0.02 #102), 01kyvx (0.01 #409) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0144l1; 01vsnff; 03j24kf; 01mwsnc; 01vrnsk; >> query: (?x9854, 01pb34) <- profession(?x9854, ?x353), person(?x1315, ?x9854), type_of_appearance(?x9854, ?x3429) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gthm special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.143 http://example.org/film/actor/film./film/performance/special_performance_type #20247-01nv4h PRED entity: 01nv4h PRED relation: currency! PRED expected values: 0kt_4 => 8 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1813): 0ctb4g (0.72 #5199, 0.71 #1297, 0.71 #1296), 0pv3x (0.72 #5199, 0.71 #1297, 0.71 #1296), 08zrbl (0.72 #5199, 0.71 #1297, 0.71 #1296), 09p0ct (0.72 #5199, 0.71 #1297, 0.71 #1296), 0cmc26r (0.72 #5199, 0.71 #1297, 0.71 #1296), 0jyb4 (0.72 #5199, 0.71 #1297, 0.71 #1296), 016z7s (0.72 #5199, 0.71 #1297, 0.71 #1296), 0209hj (0.72 #5199, 0.71 #1297, 0.71 #1296), 0gmgwnv (0.72 #5199, 0.71 #1297, 0.71 #1296), 08vd2q (0.72 #5199, 0.71 #1297, 0.71 #1296) >> Best rule #5199 for best value: >> intensional similarity = 40 >> extensional distance = 2 >> proper extension: 02l6h; >> query: (?x1099, ?x414) <- currency(?x6908, ?x1099), currency(?x6132, ?x1099), currency(?x9239, ?x1099), currency(?x5539, ?x1099), student(?x6132, ?x11469), student(?x5539, ?x2609), currency(?x5185, ?x1099), currency(?x5139, ?x1099), currency(?x4864, ?x1099), currency(?x2550, ?x1099), major_field_of_study(?x6132, ?x2314), contains(?x455, ?x5539), film_crew_role(?x5185, ?x137), nominated_for(?x1414, ?x5185), film_release_region(?x2550, ?x87), nominated_for(?x591, ?x5185), award(?x5185, ?x5824), honored_for(?x11087, ?x2550), student(?x9239, ?x2214), nominated_for(?x11858, ?x2550), film(?x6947, ?x5139), titles(?x307, ?x5139), award(?x11858, ?x704), nominated_for(?x68, ?x2550), language(?x2550, ?x254), ?x254 = 02h40lc, nominated_for(?x3580, ?x4864), award_winner(?x1854, ?x6947), genre(?x4864, ?x258), award_nominee(?x11858, ?x1871), citytown(?x9239, ?x362), nominated_for(?x2214, ?x253), film(?x11858, ?x414), language(?x5185, ?x403), institution(?x620, ?x6908), participant(?x891, ?x3580), award(?x3580, ?x435), award_nominee(?x11469, ?x217), film(?x1414, ?x1218), colors(?x6908, ?x332) >> conf = 0.72 => this is the best rule for 77 predicted values *> Best rule #1297 for first EXPECTED value: *> intensional similarity = 43 *> extensional distance = 1 *> proper extension: 09nqf; *> query: (?x1099, ?x278) <- currency(?x13827, ?x1099), currency(?x13052, ?x1099), currency(?x11602, ?x1099), currency(?x6132, ?x1099), currency(?x2196, ?x1099), currency(?x6120, ?x1099), currency(?x5539, ?x1099), student(?x6132, ?x1291), student(?x5539, ?x2609), currency(?x7204, ?x1099), currency(?x5185, ?x1099), currency(?x2550, ?x1099), major_field_of_study(?x6132, ?x2314), contains(?x455, ?x5539), film_crew_role(?x5185, ?x137), nominated_for(?x1414, ?x5185), film_release_region(?x2550, ?x87), nominated_for(?x8843, ?x5185), nominated_for(?x6729, ?x5185), award(?x5185, ?x5824), honored_for(?x11087, ?x2550), nominated_for(?x11858, ?x2550), ?x11858 = 0525b, state_province_region(?x13052, ?x12774), colors(?x11602, ?x3315), ?x6729 = 099ck7, citytown(?x5539, ?x12756), colors(?x5539, ?x3189), institution(?x1200, ?x6120), featured_film_locations(?x11429, ?x13827), titles(?x53, ?x7204), award(?x3201, ?x8843), language(?x5185, ?x403), organization(?x5510, ?x2196), organizations_founded(?x11554, ?x6132), film_release_region(?x7204, ?x172), student(?x13827, ?x1997), institution(?x1771, ?x11602), currency(?x248, ?x1099), ?x1200 = 016t_3, adjoins(?x455, ?x1144), film(?x2891, ?x5185), nominated_for(?x8843, ?x278) *> conf = 0.71 ranks of expected_values: 375 EVAL 01nv4h currency! 0kt_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 8.000 5.000 0.720 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #20246-0f1nl PRED entity: 0f1nl PRED relation: major_field_of_study PRED expected values: 05qjt 04_tv 02j62 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 111): 02j62 (0.58 #498, 0.49 #732, 0.45 #2254), 04rjg (0.51 #487, 0.50 #136, 0.45 #721), 03g3w (0.47 #494, 0.42 #143, 0.39 #728), 02h40lc (0.44 #4, 0.33 #121, 0.25 #472), 05qjt (0.44 #476, 0.40 #710, 0.35 #1296), 062z7 (0.44 #495, 0.38 #729, 0.34 #2719), 0g26h (0.43 #627, 0.39 #862, 0.38 #1213), 02_7t (0.42 #179, 0.33 #62, 0.30 #647), 04x_3 (0.42 #142, 0.33 #25, 0.29 #493), 0fdys (0.39 #507, 0.35 #741, 0.25 #156) >> Best rule #498 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 01nmgc; >> query: (?x2497, 02j62) <- institution(?x865, ?x2497), ?x865 = 02h4rq6, list(?x2497, ?x2197) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 36 EVAL 0f1nl major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.576 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0f1nl major_field_of_study 04_tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 102.000 102.000 0.576 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0f1nl major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 102.000 0.576 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20245-02h6_6p PRED entity: 02h6_6p PRED relation: month PRED expected values: 06vkl 040fb => 285 concepts (285 used for prediction) PRED predicted values (max 10 best out of 2): 040fb (0.90 #130, 0.90 #90, 0.89 #114), 06vkl (0.89 #157, 0.88 #153, 0.84 #155) >> Best rule #130 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 01f62; >> query: (?x2611, 040fb) <- location(?x2610, ?x2611), month(?x2611, ?x9905), ?x9905 = 028kb, contains(?x1264, ?x2611) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02h6_6p month 040fb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 285.000 285.000 0.902 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 02h6_6p month 06vkl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 285.000 285.000 0.902 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #20244-03xb2w PRED entity: 03xb2w PRED relation: gender PRED expected values: 05zppz => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #9, 0.75 #19, 0.73 #17), 02zsn (0.45 #4, 0.40 #8, 0.38 #22) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 03mv0b; >> query: (?x4935, 05zppz) <- profession(?x4935, ?x1146), profession(?x4935, ?x319), ?x1146 = 018gz8, ?x319 = 01d_h8 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xb2w gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.828 http://example.org/people/person/gender #20243-0r1jr PRED entity: 0r1jr PRED relation: featured_film_locations! PRED expected values: 02fqxm => 131 concepts (80 used for prediction) PRED predicted values (max 10 best out of 584): 04dsnp (0.10 #2277, 0.07 #9647, 0.05 #5962), 0473rc (0.09 #1191, 0.08 #4139, 0.07 #7824), 02fqxm (0.09 #1471, 0.07 #5156, 0.04 #8104), 07bx6 (0.09 #1284, 0.06 #4969, 0.04 #2021), 02sg5v (0.09 #791, 0.04 #1528, 0.04 #4476), 04j14qc (0.09 #1338, 0.04 #2075, 0.03 #7971), 03hkch7 (0.09 #963, 0.02 #9807, 0.02 #4648), 025rxjq (0.09 #1312, 0.02 #3523, 0.02 #13841), 011yth (0.09 #871, 0.02 #3082, 0.02 #4556), 02phtzk (0.09 #1067, 0.02 #3278, 0.02 #4752) >> Best rule #2277 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 02cl1; 0xrzh; 0c8tk; 02hrh0_; 0cvw9; 0chgzm; 0rgxp; 0fp5z; 0ycht; >> query: (?x2633, 04dsnp) <- place_of_birth(?x12240, ?x2633), administrative_division(?x2633, ?x3677), contains(?x94, ?x2633), student(?x6760, ?x12240) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #1471 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0mhdz; *> query: (?x2633, 02fqxm) <- state(?x2633, ?x1227), contains(?x2632, ?x2633), ?x2632 = 06pvr *> conf = 0.09 ranks of expected_values: 3 EVAL 0r1jr featured_film_locations! 02fqxm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 131.000 80.000 0.097 http://example.org/film/film/featured_film_locations #20242-03f1r6t PRED entity: 03f1r6t PRED relation: student! PRED expected values: 06ms6 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 25): 041y2 (0.14 #51, 0.03 #175, 0.02 #237), 02822 (0.09 #965, 0.04 #1027, 0.02 #1585), 02h40lc (0.07 #3, 0.06 #127, 0.05 #252), 029g_vk (0.07 #41, 0.02 #227, 0.02 #290), 03qsdpk (0.07 #970, 0.02 #1032, 0.02 #1528), 0g26h (0.06 #94, 0.02 #842, 0.01 #656), 0fdys (0.06 #153, 0.05 #963, 0.03 #590), 03g3w (0.05 #955, 0.03 #83, 0.01 #519), 062z7 (0.03 #956, 0.01 #1824, 0.01 #708), 01lhf (0.03 #180, 0.02 #242, 0.02 #305) >> Best rule #51 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 020ffd; >> query: (?x5222, 041y2) <- profession(?x5222, ?x1032), award_nominee(?x5222, ?x5413), ?x5413 = 01yg9y >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #946 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 213 *> proper extension: 099bk; 0frmb1; *> query: (?x5222, 06ms6) <- student(?x1368, ?x5222), nationality(?x5222, ?x94) *> conf = 0.01 ranks of expected_values: 23 EVAL 03f1r6t student! 06ms6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 100.000 100.000 0.143 http://example.org/education/field_of_study/students_majoring./education/education/student #20241-02hft3 PRED entity: 02hft3 PRED relation: category PRED expected values: 08mbj5d => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #6, 0.91 #66, 0.90 #53) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0352gk; >> query: (?x1977, 08mbj5d) <- colors(?x1977, ?x3315), contains(?x94, ?x1977), ?x3315 = 0jc_p, ?x94 = 09c7w0 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hft3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.913 http://example.org/common/topic/webpage./common/webpage/category #20240-0bh8tgs PRED entity: 0bh8tgs PRED relation: genre PRED expected values: 082gq => 69 concepts (59 used for prediction) PRED predicted values (max 10 best out of 90): 07s9rl0 (0.66 #6444, 0.58 #4536, 0.58 #5609), 05p553 (0.47 #4, 0.44 #123, 0.38 #242), 0lsxr (0.33 #1558, 0.26 #2392, 0.21 #9), 01hmnh (0.32 #1088, 0.20 #3716, 0.16 #135), 02l7c8 (0.31 #6457, 0.30 #490, 0.27 #4191), 02n4kr (0.23 #1557, 0.17 #2391, 0.15 #365), 0jtdp (0.21 #12, 0.03 #2037, 0.02 #607), 0hcr (0.17 #1094, 0.10 #3722, 0.09 #617), 04xvlr (0.16 #478, 0.14 #716, 0.14 #4777), 03npn (0.16 #1556, 0.11 #2390, 0.11 #245) >> Best rule #6444 for best value: >> intensional similarity = 4 >> extensional distance = 1416 >> proper extension: 07ng9k; 0436yk; 02pb2bp; 027pfb2; 0cks1m; 08cfr1; 03kx49; 02r2j8; 02v5xg; 058kh7; ... >> query: (?x5089, 07s9rl0) <- film(?x489, ?x5089), genre(?x5089, ?x812), genre(?x10082, ?x812), ?x10082 = 05zwrg0 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #6472 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1416 *> proper extension: 07ng9k; 0436yk; 02pb2bp; 027pfb2; 0cks1m; 08cfr1; 03kx49; 02r2j8; 02v5xg; 058kh7; ... *> query: (?x5089, 082gq) <- film(?x489, ?x5089), genre(?x5089, ?x812), genre(?x10082, ?x812), ?x10082 = 05zwrg0 *> conf = 0.09 ranks of expected_values: 17 EVAL 0bh8tgs genre 082gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 69.000 59.000 0.664 http://example.org/film/film/genre #20239-06pr6 PRED entity: 06pr6 PRED relation: location! PRED expected values: 09h_q 02cj_f => 260 concepts (131 used for prediction) PRED predicted values (max 10 best out of 2317): 07xr3w (0.53 #67893, 0.51 #12571, 0.50 #266569), 02cj_f (0.53 #67893, 0.51 #12571, 0.50 #266569), 03f47xl (0.53 #67893, 0.51 #12571, 0.50 #266569), 026rm_y (0.25 #6800, 0.17 #31943, 0.11 #14343), 01nz1q6 (0.25 #7210, 0.13 #24810, 0.12 #9724), 01dhpj (0.25 #6667, 0.12 #9181, 0.08 #39354), 08c7cz (0.25 #6547, 0.11 #31690, 0.11 #14090), 0dx97 (0.22 #13636, 0.22 #11121, 0.18 #16151), 01vh3r (0.22 #14907, 0.22 #12392, 0.18 #17422), 03bxh (0.22 #13722, 0.22 #11207, 0.18 #16237) >> Best rule #67893 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 02p3my; >> query: (?x7184, ?x3348) <- location(?x7245, ?x7184), capital(?x4492, ?x7184), film(?x7245, ?x755), place_of_birth(?x3348, ?x7184) >> conf = 0.53 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 75 EVAL 06pr6 location! 02cj_f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 260.000 131.000 0.530 http://example.org/people/person/places_lived./people/place_lived/location EVAL 06pr6 location! 09h_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 260.000 131.000 0.530 http://example.org/people/person/places_lived./people/place_lived/location #20238-0mhfr PRED entity: 0mhfr PRED relation: artists PRED expected values: 036px 017959 0p8h0 => 57 concepts (30 used for prediction) PRED predicted values (max 10 best out of 969): 0qf11 (0.60 #4473, 0.60 #3443, 0.56 #7567), 01gf5h (0.60 #4176, 0.60 #3146, 0.56 #7270), 02p68d (0.60 #4818, 0.60 #3788, 0.56 #7912), 033s6 (0.60 #4935, 0.60 #3905, 0.44 #8029), 0b_j2 (0.60 #4679, 0.60 #3649, 0.44 #7773), 01dw_f (0.60 #4763, 0.60 #3733, 0.44 #7857), 095x_ (0.60 #4810, 0.60 #3780, 0.44 #7904), 02cw1m (0.60 #4951, 0.60 #3921, 0.44 #8045), 01vsy7t (0.60 #4497, 0.60 #3467, 0.44 #7591), 04bgy (0.60 #4669, 0.60 #3639, 0.44 #7763) >> Best rule #4473 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 064t9; >> query: (?x1928, 0qf11) <- artists(?x1928, ?x6854), artists(?x1928, ?x4080), artists(?x1928, ?x1970), parent_genre(?x114, ?x1928), ?x6854 = 0178_w, instrumentalists(?x227, ?x1970), ?x4080 = 0dl567 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3926 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 0xhtw; 05bt6j; *> query: (?x1928, 017959) <- artists(?x1928, ?x6854), artists(?x1928, ?x5285), artists(?x1928, ?x4790), parent_genre(?x114, ?x1928), ?x6854 = 0178_w, ?x4790 = 01kph_c, role(?x5285, ?x228), award(?x5285, ?x724) *> conf = 0.40 ranks of expected_values: 153, 183, 251 EVAL 0mhfr artists 0p8h0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 30.000 0.600 http://example.org/music/genre/artists EVAL 0mhfr artists 017959 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 57.000 30.000 0.600 http://example.org/music/genre/artists EVAL 0mhfr artists 036px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 57.000 30.000 0.600 http://example.org/music/genre/artists #20237-01jgkj2 PRED entity: 01jgkj2 PRED relation: award PRED expected values: 01c92g 054ks3 03qbh5 => 109 concepts (82 used for prediction) PRED predicted values (max 10 best out of 262): 01d38g (0.78 #4001, 0.75 #9203, 0.74 #16806), 054ks3 (0.53 #2539, 0.27 #1739, 0.18 #5740), 031b3h (0.50 #197, 0.16 #3797, 0.13 #32021), 01bgqh (0.49 #1643, 0.42 #43, 0.31 #3643), 01by1l (0.42 #3711, 0.37 #5712, 0.31 #5312), 01c99j (0.38 #1822, 0.20 #5823, 0.17 #222), 0c4z8 (0.35 #1672, 0.33 #2472, 0.25 #72), 03qbh5 (0.33 #201, 0.24 #3801, 0.24 #1801), 01cw7s (0.33 #261, 0.15 #32422, 0.13 #32021), 0ck27z (0.31 #8093, 0.31 #6492, 0.26 #16095) >> Best rule #4001 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 0cg9y; 07mvp; 011z3g; 0178_w; 012x03; >> query: (?x9176, ?x567) <- artists(?x3319, ?x9176), ?x3319 = 06j6l, award_winner(?x567, ?x9176) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2539 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 89 *> proper extension: 0pgjm; 02lz1s; 01_x6v; 05pq9; 03n0q5; 02v3yy; 01jpmpv; 021yw7; 01r6jt2; 01vvdm; ... *> query: (?x9176, 054ks3) <- nationality(?x9176, ?x94), award(?x9176, ?x1323), profession(?x9176, ?x131), ?x1323 = 0gqz2 *> conf = 0.53 ranks of expected_values: 2, 8, 13 EVAL 01jgkj2 award 03qbh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 109.000 82.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01jgkj2 award 054ks3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 82.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01jgkj2 award 01c92g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 109.000 82.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20236-0b68vs PRED entity: 0b68vs PRED relation: artists! PRED expected values: 03_d0 025sc50 => 128 concepts (99 used for prediction) PRED predicted values (max 10 best out of 199): 06by7 (0.66 #331, 0.43 #4049, 0.42 #2192), 03_d0 (0.30 #321, 0.23 #2182, 0.20 #1872), 01lyv (0.28 #343, 0.22 #2824, 0.21 #3133), 02yv6b (0.28 #408, 0.20 #5883, 0.19 #6811), 05bt6j (0.26 #4071, 0.25 #3762, 0.25 #353), 016clz (0.25 #1243, 0.25 #932, 0.25 #5), 0glt670 (0.25 #41, 0.23 #5924, 0.22 #8097), 016jny (0.25 #105, 0.20 #5883, 0.19 #6811), 0ggx5q (0.25 #78, 0.20 #5883, 0.19 #6811), 05r6t (0.25 #82, 0.08 #9684, 0.07 #18652) >> Best rule #331 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 02whj; 01r9fv; 0cg9y; 03f0vvr; 01vsy3q; 015xp4; 0134tg; 03h_fqv; 02jq1; 015cxv; ... >> query: (?x1181, 06by7) <- award_winner(?x2139, ?x1181), artists(?x7440, ?x1181), ?x7440 = 0155w >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #321 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 02whj; 01r9fv; 0cg9y; 03f0vvr; 01vsy3q; 015xp4; 0134tg; 03h_fqv; 02jq1; 015cxv; ... *> query: (?x1181, 03_d0) <- award_winner(?x2139, ?x1181), artists(?x7440, ?x1181), ?x7440 = 0155w *> conf = 0.30 ranks of expected_values: 2, 14 EVAL 0b68vs artists! 025sc50 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 128.000 99.000 0.656 http://example.org/music/genre/artists EVAL 0b68vs artists! 03_d0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 99.000 0.656 http://example.org/music/genre/artists #20235-03kwtb PRED entity: 03kwtb PRED relation: nominated_for PRED expected values: 0456zg => 139 concepts (70 used for prediction) PRED predicted values (max 10 best out of 483): 0bpbhm (0.36 #25938, 0.35 #34047, 0.35 #50267), 034qmv (0.36 #25938, 0.35 #34047, 0.35 #50267), 01f8f7 (0.12 #4323, 0.12 #12428, 0.11 #7565), 01f85k (0.12 #4268, 0.12 #12373, 0.11 #7510), 0qmhk (0.12 #4112, 0.11 #7354, 0.09 #10596), 07s846j (0.11 #2234, 0.11 #612, 0.06 #8718), 0dgq_kn (0.11 #2566, 0.11 #944, 0.06 #9050), 07zhjj (0.11 #9444, 0.07 #14307, 0.06 #6202), 0p_sc (0.11 #106, 0.06 #3349, 0.06 #8212), 049xgc (0.11 #2508, 0.06 #8992, 0.04 #20339) >> Best rule #25938 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 07s3vqk; 02mslq; 0146pg; 01vvycq; 07q1v4; 01vrncs; 01kx_81; 01vsxdm; 0244r8; 0l12d; ... >> query: (?x1292, ?x148) <- award_winner(?x2585, ?x1292), music(?x148, ?x1292), role(?x1292, ?x314) >> conf = 0.36 => this is the best rule for 2 predicted values *> Best rule #32083 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 01wl38s; 03qd_; 0kvrb; 07qy0b; 02bh9; 01pr6q7; 04pf4r; 06fxnf; 03h610; 02qfhb; ... *> query: (?x1292, 0456zg) <- award_nominee(?x1291, ?x1292), type_of_union(?x1292, ?x566), music(?x148, ?x1292) *> conf = 0.02 ranks of expected_values: 93 EVAL 03kwtb nominated_for 0456zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 139.000 70.000 0.361 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20234-015wc0 PRED entity: 015wc0 PRED relation: music! PRED expected values: 016y_f => 140 concepts (96 used for prediction) PRED predicted values (max 10 best out of 903): 09d3b7 (0.07 #5878, 0.03 #13942, 0.03 #14950), 0gvvm6l (0.07 #2818, 0.07 #1810, 0.06 #4834), 09d38d (0.06 #5010, 0.03 #11058, 0.02 #16098), 02rrfzf (0.05 #7380, 0.04 #12420, 0.04 #13428), 01s7w3 (0.05 #13971, 0.05 #14979, 0.04 #10947), 0pd6l (0.05 #387, 0.03 #2403, 0.03 #1395), 02_kd (0.05 #349, 0.03 #2365, 0.03 #1357), 0g68zt (0.05 #309, 0.03 #2325, 0.03 #1317), 0c_j9x (0.05 #228, 0.03 #2244, 0.03 #1236), 0ckrnn (0.05 #956, 0.03 #2972, 0.03 #1964) >> Best rule #5878 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 0bk1p; >> query: (?x9946, 09d3b7) <- award(?x9946, ?x2379), ?x2379 = 02qvyrt, music(?x2094, ?x9946) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015wc0 music! 016y_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 96.000 0.070 http://example.org/film/film/music #20233-0bxc4 PRED entity: 0bxc4 PRED relation: place PRED expected values: 0bxc4 => 140 concepts (73 used for prediction) PRED predicted values (max 10 best out of 219): 0bxc4 (0.38 #27882, 0.17 #16004, 0.06 #28399), 0xszy (0.38 #27882, 0.04 #3882, 0.03 #4916), 0bxbb (0.17 #16004, 0.14 #677, 0.08 #1709), 0d8jf (0.17 #16004, 0.08 #1682, 0.08 #1166), 030qb3t (0.17 #30, 0.06 #28399, 0.02 #24265), 094jv (0.17 #36, 0.06 #28399, 0.02 #24265), 0fvwg (0.14 #701, 0.08 #1733, 0.06 #2249), 0pc6x (0.14 #754, 0.08 #1786, 0.06 #2302), 0ttxp (0.14 #780, 0.08 #1812, 0.06 #2328), 0bxbr (0.14 #664, 0.06 #2212, 0.06 #3244) >> Best rule #27882 for best value: >> intensional similarity = 3 >> extensional distance = 236 >> proper extension: 07sb1; >> query: (?x13690, ?x10059) <- citytown(?x10166, ?x13690), citytown(?x10166, ?x10059), time_zones(?x13690, ?x2674) >> conf = 0.38 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0bxc4 place 0bxc4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 73.000 0.376 http://example.org/location/hud_county_place/place #20232-02yvhx PRED entity: 02yvhx PRED relation: ceremony! PRED expected values: 0gq_d => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 340): 0gq_d (0.94 #3955, 0.91 #3716, 0.91 #2521), 02x201b (0.76 #5249, 0.34 #1194, 0.23 #7637), 0czp_ (0.76 #5249, 0.13 #5487, 0.10 #4006), 02x17c2 (0.40 #851, 0.34 #1194, 0.33 #1329), 02x1dht (0.40 #751, 0.33 #1229, 0.33 #4056), 02x2gy0 (0.40 #797, 0.33 #1275, 0.33 #4056), 02x17s4 (0.40 #790, 0.33 #1268, 0.33 #4056), 025m8l (0.34 #1194, 0.33 #1193, 0.33 #1025), 025m8y (0.34 #1194, 0.33 #1014, 0.33 #59), 026mfs (0.34 #1194, 0.33 #1033, 0.33 #78) >> Best rule #3955 for best value: >> intensional similarity = 14 >> extensional distance = 46 >> proper extension: 0bzk2h; 0bzm__; 0c6vcj; 0fz0c2; 0fy59t; >> query: (?x5703, 0gq_d) <- award_winner(?x5703, ?x1039), honored_for(?x5703, ?x8063), honored_for(?x5703, ?x4963), honored_for(?x5703, ?x308), award_winner(?x8063, ?x2926), nominated_for(?x163, ?x4963), ceremony(?x1323, ?x5703), award_winner(?x308, ?x574), titles(?x162, ?x308), genre(?x308, ?x53), nominated_for(?x68, ?x308), film_release_distribution_medium(?x8063, ?x81), ?x1323 = 0gqz2, film(?x166, ?x4963) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02yvhx ceremony! 0gq_d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.938 http://example.org/award/award_category/winners./award/award_honor/ceremony #20231-046qpy PRED entity: 046qpy PRED relation: industry PRED expected values: 01mw1 01mf0 => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 37): 01mw1 (0.80 #1507, 0.79 #236, 0.76 #283), 02vxn (0.31 #1838, 0.30 #1933, 0.28 #1981), 0sydc (0.20 #126, 0.07 #1114, 0.05 #1820), 01mf0 (0.20 #847, 0.19 #1979, 0.18 #1271), 019z7b (0.20 #847, 0.19 #1979, 0.18 #1271), 02jjt (0.18 #525, 0.17 #337, 0.15 #384), 03qh03g (0.14 #1889, 0.14 #1936, 0.13 #1984), 029g_vk (0.10 #1799, 0.09 #1847, 0.08 #1895), 015p1m (0.08 #356, 0.08 #403, 0.07 #497), 02h400t (0.08 #354, 0.08 #401, 0.07 #495) >> Best rule #1507 for best value: >> intensional similarity = 13 >> extensional distance = 73 >> proper extension: 05925; 02qdyj; 073tm9; 04sv4; 01tlrp; 02mdty; 01qvcr; 0317zz; 055z7; 0dwcl; ... >> query: (?x10646, 01mw1) <- industry(?x10646, ?x10022), industry(?x14248, ?x10022), industry(?x11304, ?x10022), industry(?x11303, ?x10022), industry(?x11273, ?x10022), industry(?x9309, ?x10022), industry(?x6717, ?x10022), ?x11303 = 03_c8p, ?x9309 = 059wk, ?x14248 = 03_kl4, service_location(?x6717, ?x94), ?x11273 = 027lf1, citytown(?x11304, ?x362) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 046qpy industry 01mf0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 49.000 49.000 0.800 http://example.org/business/business_operation/industry EVAL 046qpy industry 01mw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.800 http://example.org/business/business_operation/industry #20230-03g9xj PRED entity: 03g9xj PRED relation: honored_for! PRED expected values: 03nnm4t => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 72): 0gvstc3 (0.19 #1735, 0.19 #1003, 0.18 #1125), 05c1t6z (0.19 #1475, 0.19 #987, 0.18 #1719), 02q690_ (0.17 #1640, 0.17 #2006, 0.17 #1762), 0lp_cd3 (0.17 #993, 0.14 #749, 0.14 #1359), 03nnm4t (0.16 #1771, 0.15 #1161, 0.14 #1039), 09p3h7 (0.11 #182, 0.07 #304, 0.06 #670), 0bxs_d (0.11 #954, 0.07 #1198, 0.07 #2296), 07z31v (0.11 #879, 0.05 #2099, 0.05 #2953), 0gx_st (0.10 #1494, 0.09 #884, 0.09 #2226), 07y_p6 (0.09 #937, 0.07 #1181, 0.06 #815) >> Best rule #1735 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 06hwzy; >> query: (?x9649, 0gvstc3) <- genre(?x9649, ?x811), producer_type(?x9649, ?x632), titles(?x811, ?x148) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1771 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 104 *> proper extension: 06hwzy; *> query: (?x9649, 03nnm4t) <- genre(?x9649, ?x811), producer_type(?x9649, ?x632), titles(?x811, ?x148) *> conf = 0.16 ranks of expected_values: 5 EVAL 03g9xj honored_for! 03nnm4t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 89.000 89.000 0.189 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #20229-019dmc PRED entity: 019dmc PRED relation: people PRED expected values: 03hfxx 02x02kb => 30 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1172): 01938t (0.33 #2998, 0.33 #282, 0.25 #2319), 02dth1 (0.33 #823, 0.33 #144, 0.25 #2181), 0gr36 (0.33 #1458, 0.33 #100, 0.25 #2137), 0137hn (0.33 #2991, 0.29 #3671, 0.22 #5029), 06y7d (0.33 #604, 0.25 #2641, 0.17 #3320), 02cvp8 (0.33 #540, 0.25 #2577, 0.17 #3256), 08bqy9 (0.33 #256, 0.25 #2293, 0.17 #2972), 01kws3 (0.33 #213, 0.25 #2250, 0.17 #2929), 0136p1 (0.33 #61, 0.25 #2098, 0.17 #2777), 029m83 (0.33 #347, 0.25 #2384, 0.17 #3063) >> Best rule #2998 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 01dcqj; 01l2m3; 0dq9p; >> query: (?x12624, 01938t) <- people(?x12624, ?x13098), people(?x12624, ?x7958), award(?x7958, ?x3209), legislative_sessions(?x13098, ?x2019), location(?x13098, ?x3670), award_winner(?x1126, ?x7958), film(?x7958, ?x689), award(?x407, ?x3209) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 019dmc people 02x02kb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 16.000 0.333 http://example.org/people/cause_of_death/people EVAL 019dmc people 03hfxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 16.000 0.333 http://example.org/people/cause_of_death/people #20228-02cm61 PRED entity: 02cm61 PRED relation: major_field_of_study! PRED expected values: 071tyz => 58 concepts (52 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.89 #647, 0.83 #280, 0.81 #66), 02h4rq6 (0.87 #234, 0.87 #107, 0.87 #85), 019v9k (0.80 #112, 0.79 #133, 0.77 #218), 03bwzr4 (0.73 #116, 0.67 #158, 0.67 #94), 04zx3q1 (0.73 #42, 0.57 #212, 0.57 #84), 013zdg (0.51 #448, 0.47 #1078, 0.40 #902), 022h5x (0.44 #104, 0.42 #210, 0.40 #902), 01ysy9 (0.43 #534, 0.42 #210, 0.39 #405), 0bjrnt (0.43 #534, 0.40 #449, 0.39 #405), 071tyz (0.43 #534, 0.39 #405, 0.38 #558) >> Best rule #647 for best value: >> intensional similarity = 9 >> extensional distance = 73 >> proper extension: 09xq9d; 0299ct; 01vrkm; 01h788; >> query: (?x12363, 014mlp) <- major_field_of_study(?x1526, ?x12363), major_field_of_study(?x2175, ?x12363), contains(?x94, ?x2175), colors(?x2175, ?x663), category(?x2175, ?x134), student(?x1526, ?x476), organization(?x346, ?x2175), institution(?x1526, ?x8973), ?x8973 = 0677j >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #534 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 58 *> proper extension: 06274w; *> query: (?x12363, ?x11690) <- major_field_of_study(?x5288, ?x12363), taxonomy(?x12363, ?x939), institution(?x620, ?x5288), major_field_of_study(?x5288, ?x10391), major_field_of_study(?x6315, ?x10391), major_field_of_study(?x4099, ?x10391), contains(?x94, ?x5288), school_type(?x5288, ?x3092), student(?x5288, ?x460), ?x6315 = 08qnnv, ?x4099 = 01f1r4, major_field_of_study(?x11690, ?x10391) *> conf = 0.43 ranks of expected_values: 10 EVAL 02cm61 major_field_of_study! 071tyz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 58.000 52.000 0.893 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #20227-0l8sx PRED entity: 0l8sx PRED relation: service_location PRED expected values: 09c7w0 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 126): 09c7w0 (0.86 #5112, 0.82 #6289, 0.81 #7175), 02j71 (0.33 #215, 0.30 #1589, 0.26 #4931), 06mkj (0.33 #235, 0.08 #2100, 0.08 #2394), 0d060g (0.29 #4921, 0.27 #5118, 0.25 #990), 03_3d (0.25 #1087, 0.20 #1578, 0.12 #989), 07ssc (0.17 #4929, 0.16 #8569, 0.15 #8471), 0chghy (0.17 #4925, 0.14 #5122, 0.12 #994), 01n7q (0.12 #1106, 0.12 #1008, 0.10 #1597), 05v8c (0.12 #1097, 0.12 #999, 0.10 #1588), 06t2t (0.12 #1119, 0.12 #1021, 0.10 #1610) >> Best rule #5112 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 0k9ts; 05b5c; >> query: (?x1908, 09c7w0) <- list(?x1908, ?x5997), service_language(?x1908, ?x254), industry(?x1908, ?x2271) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l8sx service_location 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.864 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #20226-026njb5 PRED entity: 026njb5 PRED relation: film_release_region PRED expected values: 03rjj 06c1y 077qn 07f1x => 99 concepts (84 used for prediction) PRED predicted values (max 10 best out of 150): 03h64 (0.89 #1625, 0.84 #3030, 0.83 #3340), 0jgd (0.87 #1563, 0.84 #3123, 0.84 #3278), 0b90_r (0.87 #1564, 0.83 #2969, 0.82 #3279), 01znc_ (0.85 #1601, 0.82 #3316, 0.81 #3006), 06bnz (0.85 #1606, 0.77 #3011, 0.76 #3321), 03rjj (0.84 #3125, 0.83 #3280, 0.82 #2970), 06t2t (0.83 #1621, 0.78 #3336, 0.77 #3026), 03_3d (0.80 #1099, 0.79 #1567, 0.79 #2972), 05v8c (0.79 #1576, 0.67 #2981, 0.66 #3291), 01mjq (0.75 #1136, 0.66 #1604, 0.65 #3009) >> Best rule #1625 for best value: >> intensional similarity = 10 >> extensional distance = 51 >> proper extension: 0gtsx8c; 0h1cdwq; 087wc7n; 08hmch; 0gffmn8; 0c3xw46; 047vnkj; 0glqh5_; 0dll_t2; 03mgx6z; ... >> query: (?x3287, 03h64) <- film_release_region(?x3287, ?x1790), film_release_region(?x3287, ?x1353), film_release_region(?x3287, ?x1264), film_release_region(?x3287, ?x512), film_release_region(?x3287, ?x429), ?x512 = 07ssc, ?x429 = 03rt9, ?x1353 = 035qy, ?x1264 = 0345h, ?x1790 = 01pj7 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #3125 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 120 *> proper extension: 0dscrwf; 02x3lt7; 0gkz15s; 0bwfwpj; 01c22t; 0872p_c; 053rxgm; 017gm7; 07qg8v; 04n52p6; ... *> query: (?x3287, 03rjj) <- film_release_region(?x3287, ?x512), film_release_region(?x3287, ?x429), ?x512 = 07ssc, ?x429 = 03rt9, nominated_for(?x5886, ?x3287), nominated_for(?x5886, ?x4329), genre(?x4329, ?x258) *> conf = 0.84 ranks of expected_values: 6, 20, 27, 29 EVAL 026njb5 film_release_region 07f1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 99.000 84.000 0.887 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 026njb5 film_release_region 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 99.000 84.000 0.887 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 026njb5 film_release_region 06c1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 99.000 84.000 0.887 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 026njb5 film_release_region 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 99.000 84.000 0.887 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20225-01722w PRED entity: 01722w PRED relation: student PRED expected values: 0kszw => 106 concepts (10 used for prediction) PRED predicted values (max 10 best out of 486): 034bs (0.20 #9024, 0.17 #11112, 0.17 #4848), 0h0p_ (0.20 #3128, 0.17 #5218, 0.14 #7306), 06dl_ (0.20 #2381, 0.17 #4471, 0.14 #6559), 05vsxz (0.20 #2094, 0.17 #4184, 0.14 #6272), 0cqt90 (0.20 #2725, 0.10 #8991, 0.08 #11079), 02lgj6 (0.20 #2314, 0.10 #8580, 0.08 #10668), 0136g9 (0.20 #2290, 0.10 #8556, 0.08 #10644), 02d4ct (0.20 #2450, 0.10 #8716, 0.08 #10804), 063t3j (0.20 #4105, 0.10 #10371, 0.08 #12459), 01xsc9 (0.20 #4049, 0.10 #10315, 0.08 #12403) >> Best rule #9024 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0b6k40; 02mw6c; 0dbns; 0dzbl; 0d5fb; >> query: (?x8294, 034bs) <- school_type(?x8294, ?x12633), student(?x8294, ?x3849), ?x12633 = 01jlsn, nominated_for(?x3849, ?x3681), profession(?x3849, ?x319) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01722w student 0kszw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 10.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #20224-016h9b PRED entity: 016h9b PRED relation: group PRED expected values: 0134wr => 99 concepts (59 used for prediction) PRED predicted values (max 10 best out of 73): 0cbm64 (0.19 #77, 0.11 #185, 0.03 #1052), 01v0sx2 (0.12 #5, 0.11 #113, 0.06 #221), 015srx (0.12 #41, 0.07 #149, 0.05 #366), 0b_xm (0.12 #493, 0.02 #2338, 0.02 #2447), 0123r4 (0.11 #585, 0.05 #477, 0.05 #1994), 0134wr (0.07 #174, 0.06 #66, 0.05 #391), 0qmpd (0.07 #509, 0.06 #76, 0.01 #1051), 0bk1p (0.07 #507, 0.04 #832, 0.02 #2352), 047cx (0.07 #463, 0.01 #3179, 0.01 #3396), 07mvp (0.07 #696, 0.06 #804, 0.03 #1453) >> Best rule #77 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 015076; >> query: (?x2865, 0cbm64) <- instrumentalists(?x316, ?x2865), sibling(?x6129, ?x2865), profession(?x2865, ?x220) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #174 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 0356dp; *> query: (?x2865, 0134wr) <- sibling(?x2865, ?x6129), artists(?x671, ?x6129), profession(?x6129, ?x131) *> conf = 0.07 ranks of expected_values: 6 EVAL 016h9b group 0134wr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 99.000 59.000 0.188 http://example.org/music/group_member/membership./music/group_membership/group #20223-0g9lm2 PRED entity: 0g9lm2 PRED relation: person PRED expected values: 01jgpsh => 121 concepts (81 used for prediction) PRED predicted values (max 10 best out of 189): 09b6zr (0.23 #1162, 0.17 #2071, 0.13 #2253), 06c97 (0.19 #1732, 0.12 #99, 0.11 #280), 0h5f5n (0.17 #1269, 0.16 #2178, 0.11 #1270), 0jw67 (0.14 #1155, 0.10 #2064, 0.07 #1701), 01n4f8 (0.12 #29, 0.11 #210, 0.09 #1116), 0d3k14 (0.12 #164, 0.11 #345, 0.07 #1797), 02l840 (0.12 #14, 0.11 #195, 0.05 #1101), 0gr69 (0.12 #127, 0.11 #308, 0.05 #1214), 0f6_x (0.12 #70, 0.11 #251, 0.05 #1157), 04bdxl (0.12 #1, 0.11 #182, 0.05 #1088) >> Best rule #1162 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0g56t9t; 04dsnp; 0gj9tn5; 0cz_ym; 05qbckf; 013q07; 01jrbv; 04jpk2; 06929s; 0bhwhj; ... >> query: (?x4359, 09b6zr) <- written_by(?x4359, ?x361), nominated_for(?x68, ?x4359), person(?x4359, ?x496), nominated_for(?x166, ?x4359) >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g9lm2 person 01jgpsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 81.000 0.227 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #20222-02n9nmz PRED entity: 02n9nmz PRED relation: nominated_for PRED expected values: 04vr_f 05cvgl 03r0g9 06_x996 01chpn 025rxjq 02cbhg 09sr0 => 53 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1425): 04j4tx (0.78 #7474, 0.67 #6571, 0.65 #34414), 01cmp9 (0.75 #11327, 0.67 #8338, 0.67 #6842), 0dr_4 (0.68 #12165, 0.67 #13660, 0.60 #10670), 0ch26b_ (0.67 #7729, 0.67 #6233, 0.56 #12213), 011yth (0.67 #7727, 0.67 #6231, 0.50 #10716), 04vr_f (0.67 #7621, 0.67 #6125, 0.50 #3135), 04q827 (0.67 #8872, 0.67 #7376, 0.50 #4386), 09sr0 (0.67 #7229, 0.55 #11714, 0.50 #8725), 05cvgl (0.67 #6342, 0.55 #10827, 0.33 #7838), 0298n7 (0.67 #8573, 0.50 #7077, 0.50 #5582) >> Best rule #7474 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 03hkv_r; 0gr4k; 0gq9h; 0gs9p; >> query: (?x1180, ?x4231) <- nominated_for(?x1180, ?x7015), nominated_for(?x1180, ?x4610), ?x4610 = 017jd9, ?x7015 = 0_9wr, award(?x4231, ?x1180), award_winner(?x1180, ?x361) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #7621 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 099c8n; *> query: (?x1180, 04vr_f) <- nominated_for(?x1180, ?x7015), nominated_for(?x1180, ?x4610), nominated_for(?x1180, ?x3882), nominated_for(?x1180, ?x2613), ?x4610 = 017jd9, ?x2613 = 02q56mk, award(?x3882, ?x484), film_release_region(?x7015, ?x94) *> conf = 0.67 ranks of expected_values: 6, 8, 9, 12, 59, 290, 384, 523 EVAL 02n9nmz nominated_for 09sr0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 53.000 25.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02n9nmz nominated_for 02cbhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 53.000 25.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02n9nmz nominated_for 025rxjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02n9nmz nominated_for 01chpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 25.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02n9nmz nominated_for 06_x996 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 53.000 25.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02n9nmz nominated_for 03r0g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 53.000 25.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02n9nmz nominated_for 05cvgl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 53.000 25.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02n9nmz nominated_for 04vr_f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 53.000 25.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20221-0dq626 PRED entity: 0dq626 PRED relation: currency PRED expected values: 09nqf => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.81 #204, 0.81 #57, 0.80 #141), 01nv4h (0.04 #37, 0.04 #44, 0.03 #79), 088n7 (0.01 #161), 02l6h (0.01 #186) >> Best rule #204 for best value: >> intensional similarity = 5 >> extensional distance = 537 >> proper extension: 03n785; >> query: (?x377, 09nqf) <- film(?x376, ?x377), production_companies(?x377, ?x3920), genre(?x377, ?x53), child(?x3920, ?x166), company(?x2426, ?x3920) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dq626 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.807 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #20220-0p50v PRED entity: 0p50v PRED relation: people! PRED expected values: 02y0js => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 29): 0qcr0 (0.12 #1, 0.04 #595, 0.03 #1321), 0dq9p (0.12 #17, 0.04 #479, 0.03 #3119), 02knxx (0.12 #32, 0.02 #494, 0.01 #428), 0gk4g (0.08 #142, 0.07 #406, 0.07 #472), 02k6hp (0.05 #169, 0.03 #235, 0.03 #301), 02y0js (0.05 #200, 0.05 #266, 0.03 #134), 04p3w (0.03 #209, 0.03 #275, 0.03 #473), 01psyx (0.03 #243, 0.03 #309, 0.03 #375), 01_qc_ (0.03 #490), 051_y (0.03 #180, 0.02 #642) >> Best rule #1 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 019_1h; >> query: (?x8268, 0qcr0) <- gender(?x8268, ?x231), nominated_for(?x8268, ?x8773), nominated_for(?x8268, ?x2168), ?x2168 = 0bx0l, nominated_for(?x198, ?x8773) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #200 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 040_9; 019gz; *> query: (?x8268, 02y0js) <- nationality(?x8268, ?x512), story_by(?x8773, ?x8268), religion(?x8268, ?x8140) *> conf = 0.05 ranks of expected_values: 6 EVAL 0p50v people! 02y0js CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 151.000 151.000 0.125 http://example.org/people/cause_of_death/people #20219-073h1t PRED entity: 073h1t PRED relation: ceremony! PRED expected values: 0gq_v 0p9sw 0gqyl 0gr42 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 343): 0gqyl (0.89 #6342, 0.88 #7068, 0.88 #6100), 0p9sw (0.87 #7258, 0.87 #7017, 0.85 #6776), 0gr42 (0.84 #4420, 0.84 #4179, 0.84 #5626), 0gq_v (0.84 #4843, 0.83 #3878, 0.82 #3395), 0czp_ (0.74 #10150, 0.43 #2367, 0.27 #6227), 02x201b (0.74 #10150, 0.38 #1621, 0.32 #9665), 0gqzz (0.74 #10150, 0.35 #3663, 0.35 #3180), 01by1l (0.35 #6762, 0.34 #6589, 0.26 #9181), 02ddqh (0.35 #6762, 0.32 #6623, 0.26 #9181), 01bgqh (0.35 #6762, 0.32 #6546, 0.26 #9181) >> Best rule #6342 for best value: >> intensional similarity = 13 >> extensional distance = 35 >> proper extension: 0c4hgj; >> query: (?x1998, 0gqyl) <- ceremony(?x4573, ?x1998), ceremony(?x1703, ?x1998), award_winner(?x1998, ?x2871), award_winner(?x1998, ?x2135), ?x1703 = 0k611, nationality(?x2871, ?x94), ?x4573 = 0gq_d, place_of_death(?x2871, ?x4151), award_winner(?x1452, ?x2135), award(?x2135, ?x198), award_nominee(?x2135, ?x798), award_winner(?x4680, ?x2871), nominated_for(?x2135, ?x531) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 073h1t ceremony! 0gr42 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.892 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073h1t ceremony! 0gqyl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.892 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073h1t ceremony! 0p9sw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.892 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073h1t ceremony! 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.892 http://example.org/award/award_category/winners./award/award_honor/ceremony #20218-01wl38s PRED entity: 01wl38s PRED relation: role PRED expected values: 01s0ps => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 117): 0342h (0.48 #1161, 0.40 #2324, 0.38 #292), 0l14qv (0.35 #293, 0.18 #389, 0.17 #1162), 02sgy (0.32 #1254, 0.26 #1836, 0.26 #1835), 06ncr (0.32 #1254, 0.26 #1836, 0.26 #1835), 03bx0bm (0.25 #482, 0.04 #3191, 0.04 #3482), 05842k (0.25 #360, 0.23 #1229, 0.18 #2392), 042v_gx (0.23 #2328, 0.22 #586, 0.21 #1165), 018vs (0.23 #1170, 0.21 #301, 0.19 #109), 01vj9c (0.22 #399, 0.21 #303, 0.18 #1172), 0bxl5 (0.17 #353, 0.10 #3385, 0.08 #449) >> Best rule #1161 for best value: >> intensional similarity = 3 >> extensional distance = 201 >> proper extension: 02fybl; >> query: (?x565, 0342h) <- profession(?x565, ?x987), role(?x565, ?x227), role(?x565, ?x212) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #345 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 028qdb; 06br6t; *> query: (?x565, 01s0ps) <- role(?x565, ?x1166), artists(?x1000, ?x565), ?x1166 = 05148p4 *> conf = 0.13 ranks of expected_values: 19 EVAL 01wl38s role 01s0ps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 85.000 85.000 0.478 http://example.org/music/artist/track_contributions./music/track_contribution/role #20217-0l5yl PRED entity: 0l5yl PRED relation: gender PRED expected values: 05zppz => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #11, 0.89 #15, 0.89 #29), 02zsn (0.57 #139, 0.39 #6, 0.37 #74) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 01pfkw; >> query: (?x8286, 05zppz) <- participant(?x8286, ?x1545), nationality(?x8286, ?x94), celebrities_impersonated(?x3649, ?x8286) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l5yl gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.906 http://example.org/people/person/gender #20216-01q2nx PRED entity: 01q2nx PRED relation: music PRED expected values: 02cyfz => 68 concepts (56 used for prediction) PRED predicted values (max 10 best out of 62): 02bh9 (0.33 #471, 0.25 #261, 0.08 #1943), 01tc9r (0.25 #65, 0.11 #905, 0.09 #1325), 0417z2 (0.22 #1012, 0.20 #1222, 0.14 #802), 0146pg (0.17 #430, 0.08 #1902, 0.08 #3381), 02jxkw (0.14 #772, 0.11 #982, 0.10 #1192), 05y7hc (0.14 #756, 0.11 #966, 0.10 #1176), 0150t6 (0.11 #886, 0.05 #3417, 0.04 #1306), 01x6v6 (0.09 #1383, 0.08 #1594, 0.02 #3494), 016szr (0.06 #1552, 0.02 #1341, 0.02 #1763), 04pf4r (0.05 #3016, 0.04 #3439, 0.04 #4288) >> Best rule #471 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 024mpp; 011xg5; >> query: (?x5275, 02bh9) <- genre(?x5275, ?x53), crewmember(?x5275, ?x929), featured_film_locations(?x5275, ?x108), ?x929 = 027rwmr >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2138 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 120 *> proper extension: 076xkdz; *> query: (?x5275, 02cyfz) <- genre(?x5275, ?x1013), production_companies(?x5275, ?x1686), film_release_distribution_medium(?x5275, ?x81), ?x1013 = 06n90 *> conf = 0.04 ranks of expected_values: 14 EVAL 01q2nx music 02cyfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 68.000 56.000 0.333 http://example.org/film/film/music #20215-0479b PRED entity: 0479b PRED relation: film PRED expected values: 0k2cb => 88 concepts (53 used for prediction) PRED predicted values (max 10 best out of 773): 0x25q (0.59 #87427, 0.48 #51740, 0.47 #48171), 0bpm4yw (0.08 #720, 0.07 #2504, 0.01 #13209), 01shy7 (0.06 #421, 0.05 #2205, 0.05 #3989), 031hcx (0.06 #1271, 0.05 #3055, 0.02 #13760), 062zm5h (0.06 #854, 0.05 #2638, 0.02 #4422), 0dfw0 (0.06 #836, 0.05 #2620, 0.01 #15109), 0fdv3 (0.06 #280, 0.05 #2064, 0.01 #14553), 03lrht (0.05 #2041, 0.05 #257, 0.02 #5609), 06ztvyx (0.05 #2213, 0.03 #429, 0.01 #12918), 08r4x3 (0.05 #154, 0.04 #1938, 0.04 #3722) >> Best rule #87427 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 033071; >> query: (?x6917, ?x1450) <- profession(?x6917, ?x319), nominated_for(?x6917, ?x1450), film(?x6917, ?x1807) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #6161 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 236 *> proper extension: 06c0j; *> query: (?x6917, 0k2cb) <- people(?x743, ?x6917), award_winner(?x401, ?x6917), participant(?x719, ?x6917) *> conf = 0.02 ranks of expected_values: 251 EVAL 0479b film 0k2cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 88.000 53.000 0.585 http://example.org/film/actor/film./film/performance/film #20214-0343h PRED entity: 0343h PRED relation: influenced_by PRED expected values: 0453t => 183 concepts (142 used for prediction) PRED predicted values (max 10 best out of 326): 0j_c (0.25 #66, 0.20 #932, 0.20 #499), 0ff2k (0.25 #399, 0.20 #1265, 0.14 #3430), 07w21 (0.25 #9, 0.20 #875, 0.14 #3040), 03f0324 (0.23 #6218, 0.14 #10121, 0.07 #44388), 014z8v (0.20 #986, 0.12 #13996, 0.11 #9656), 03dbds (0.20 #1106, 0.08 #5005, 0.04 #11077), 06pj8 (0.16 #26456, 0.04 #7801), 081lh (0.16 #9556, 0.09 #24308, 0.06 #30381), 0p_47 (0.16 #9642, 0.07 #24394, 0.06 #30467), 03_87 (0.15 #6269, 0.11 #16679, 0.09 #46173) >> Best rule #66 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0534v; >> query: (?x1387, 0j_c) <- edited_by(?x2366, ?x1387), influenced_by(?x1387, ?x3028), executive_produced_by(?x1386, ?x1387) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0343h influenced_by 0453t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 183.000 142.000 0.250 http://example.org/influence/influence_node/influenced_by #20213-01tsq8 PRED entity: 01tsq8 PRED relation: contains! PRED expected values: 0bzty => 114 concepts (44 used for prediction) PRED predicted values (max 10 best out of 347): 0bzty (0.89 #4477, 0.83 #20586, 0.81 #9843), 09c7w0 (0.57 #21488, 0.51 #23278, 0.50 #24174), 07ssc (0.53 #26886, 0.30 #37639, 0.28 #28674), 02jx1 (0.44 #28729, 0.42 #29624, 0.30 #36797), 02j71 (0.43 #3580, 0.38 #5372, 0.32 #11633), 0d060g (0.41 #20599, 0.28 #22392, 0.16 #28655), 01n7q (0.27 #35893, 0.22 #36788, 0.12 #21562), 02j9z (0.26 #11660, 0.20 #16134, 0.18 #17031), 0345h (0.25 #34020, 0.24 #22461, 0.21 #11714), 059j2 (0.25 #34020, 0.22 #5450, 0.21 #39400) >> Best rule #4477 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 04_xrs; 05314s; 03tm68; 040hg8; 04_xr8; 098phg; 0536sd; 04_x4s; >> query: (?x10691, ?x10706) <- contains(?x12142, ?x10691), contains(?x205, ?x10691), ?x205 = 03rjj, administrative_parent(?x12142, ?x10706), contains(?x10706, ?x1356) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tsq8 contains! 0bzty CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 44.000 0.889 http://example.org/location/location/contains #20212-01mqh5 PRED entity: 01mqh5 PRED relation: film PRED expected values: 0dcz8_ => 158 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1166): 01dc0c (0.25 #1451, 0.04 #12173, 0.03 #15747), 03z20c (0.12 #2264, 0.12 #477, 0.06 #20134), 029zqn (0.12 #2054, 0.12 #267, 0.05 #5628), 06z8s_ (0.12 #1917, 0.12 #130, 0.04 #7278), 0c1sgd3 (0.12 #2595, 0.12 #808, 0.02 #6169), 0cqr0q (0.12 #3283, 0.12 #1496, 0.02 #6857), 04qk12 (0.12 #3245, 0.12 #1458, 0.02 #6819), 0dll_t2 (0.12 #2758, 0.12 #971, 0.02 #6332), 032_wv (0.12 #1985, 0.12 #198, 0.02 #5559), 05jzt3 (0.12 #1915, 0.12 #128, 0.02 #5489) >> Best rule #1451 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0c6qh; >> query: (?x11317, 01dc0c) <- friend(?x11317, ?x1299), student(?x3490, ?x11317), film(?x11317, ?x4663) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #6943 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 05bnp0; 0prfz; 0n6f8; 0prjs; 031zkw; 063vn; 01vwllw; 016h4r; 0blt6; 01ft2l; ... *> query: (?x11317, 0dcz8_) <- student(?x1681, ?x11317), participant(?x4929, ?x11317), student(?x8398, ?x11317) *> conf = 0.02 ranks of expected_values: 307 EVAL 01mqh5 film 0dcz8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 158.000 114.000 0.250 http://example.org/film/actor/film./film/performance/film #20211-0g5lhl7 PRED entity: 0g5lhl7 PRED relation: service_location PRED expected values: 07ssc => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 58): 09c7w0 (0.83 #4521, 0.76 #7675, 0.74 #7379), 0d060g (0.34 #4527, 0.25 #7681, 0.23 #7385), 02j71 (0.26 #7691, 0.24 #7395, 0.21 #8279), 07ssc (0.25 #311, 0.18 #6589, 0.17 #4535), 0f8l9c (0.14 #1498, 0.10 #4541, 0.08 #7695), 03_3d (0.14 #1483, 0.04 #7384, 0.04 #7680), 0chghy (0.13 #7389, 0.13 #7685, 0.12 #1586), 015fr (0.12 #1593, 0.07 #4538, 0.04 #4341), 034cm (0.12 #1628, 0.03 #4573, 0.02 #5953), 01p1v (0.12 #1610, 0.03 #4555, 0.02 #5935) >> Best rule #4521 for best value: >> intensional similarity = 2 >> extensional distance = 27 >> proper extension: 03d6fyn; 05w3y; 01dycg; 059yj; 01bvx1; >> query: (?x2776, 09c7w0) <- service_language(?x2776, ?x254), place_founded(?x2776, ?x362) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #311 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2 *> proper extension: 02mw6c; *> query: (?x2776, 07ssc) <- company(?x900, ?x2776), ?x900 = 0fkvn *> conf = 0.25 ranks of expected_values: 4 EVAL 0g5lhl7 service_location 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 151.000 151.000 0.828 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #20210-01nn6c PRED entity: 01nn6c PRED relation: profession PRED expected values: 0kyk => 180 concepts (95 used for prediction) PRED predicted values (max 10 best out of 87): 0nbcg (0.93 #2678, 0.65 #912, 0.65 #7542), 02hnl (0.88 #10613), 026t6 (0.88 #10613), 02hrh1q (0.85 #10479, 0.76 #1779, 0.75 #5608), 039v1 (0.57 #1653, 0.51 #2095, 0.49 #1947), 01d_h8 (0.56 #1770, 0.47 #5599, 0.44 #6781), 016z4k (0.54 #150, 0.53 #7958, 0.52 #738), 01c72t (0.43 #6503, 0.38 #8418, 0.37 #3701), 0dxtg (0.41 #1778, 0.40 #5313, 0.39 #2367), 03gjzk (0.35 #6644, 0.35 #5609, 0.34 #6791) >> Best rule #2678 for best value: >> intensional similarity = 5 >> extensional distance = 98 >> proper extension: 01vzz1c; >> query: (?x3266, 0nbcg) <- profession(?x3266, ?x1359), artists(?x1000, ?x3266), currency(?x3266, ?x1099), profession(?x8114, ?x1359), ?x8114 = 02mx98 >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #2382 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 099bk; *> query: (?x3266, 0kyk) <- student(?x12958, ?x3266), religion(?x3266, ?x2694), ?x2694 = 0kpl, gender(?x3266, ?x231) *> conf = 0.33 ranks of expected_values: 13 EVAL 01nn6c profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 180.000 95.000 0.930 http://example.org/people/person/profession #20209-071cn PRED entity: 071cn PRED relation: location! PRED expected values: 03kxdw => 156 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2122): 02l840 (0.22 #5146, 0.17 #10168, 0.11 #2635), 0227tr (0.20 #478, 0.17 #10522, 0.11 #2989), 0f_y9 (0.20 #1473, 0.11 #6495, 0.11 #3984), 06s6hs (0.20 #1182, 0.11 #6204, 0.11 #3693), 0fpzt5 (0.20 #1799, 0.11 #6821, 0.11 #4310), 096lf_ (0.20 #2037, 0.11 #7059, 0.11 #4548), 04n_g (0.20 #750, 0.11 #5772, 0.11 #3261), 073x6y (0.20 #1362, 0.11 #6384, 0.11 #3873), 015882 (0.20 #320, 0.11 #5342, 0.11 #2831), 032xhg (0.20 #55, 0.11 #5077, 0.11 #2566) >> Best rule #5146 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 013yq; 0fsb8; >> query: (?x3786, 02l840) <- locations(?x9974, ?x3786), ?x9974 = 0b_6pv, location(?x1852, ?x3786), county_seat(?x8854, ?x3786) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 071cn location! 03kxdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 156.000 109.000 0.222 http://example.org/people/person/places_lived./people/place_lived/location #20208-05cv8 PRED entity: 05cv8 PRED relation: location PRED expected values: 04rrd => 97 concepts (76 used for prediction) PRED predicted values (max 10 best out of 118): 02_286 (0.18 #7279, 0.15 #4865, 0.14 #38659), 0cr3d (0.15 #1754, 0.12 #2559, 0.10 #949), 01cx_ (0.15 #967, 0.08 #1772, 0.05 #2577), 030qb3t (0.15 #4911, 0.13 #7325, 0.12 #13761), 0cc56 (0.12 #1666, 0.07 #2471, 0.05 #861), 01n7q (0.10 #867, 0.08 #63, 0.05 #9718), 0hptm (0.08 #1912, 0.05 #1107, 0.05 #2717), 0ccvx (0.08 #222, 0.05 #1026, 0.04 #7464), 05tbn (0.08 #188, 0.04 #1797, 0.02 #2602), 027l4q (0.08 #498, 0.04 #2107, 0.02 #2912) >> Best rule #7279 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 012v1t; >> query: (?x10578, 02_286) <- nationality(?x10578, ?x94), ?x94 = 09c7w0, student(?x1368, ?x10578), gender(?x10578, ?x231) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05cv8 location 04rrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 76.000 0.177 http://example.org/people/person/places_lived./people/place_lived/location #20207-0bk1p PRED entity: 0bk1p PRED relation: influenced_by! PRED expected values: 0478__m => 108 concepts (57 used for prediction) PRED predicted values (max 10 best out of 443): 03g5jw (0.29 #44, 0.17 #1589, 0.13 #6743), 05xq9 (0.29 #199, 0.05 #3289, 0.05 #3805), 01vrncs (0.23 #3120, 0.16 #4667, 0.13 #5182), 0ph2w (0.20 #2216, 0.17 #1701, 0.06 #6855), 0167xy (0.14 #434, 0.10 #1979, 0.06 #6616), 01kcms4 (0.14 #286, 0.10 #1831, 0.06 #2861), 01vsy7t (0.14 #183, 0.10 #3273, 0.07 #4820), 01vs4f3 (0.14 #351, 0.09 #28890, 0.04 #10144), 0m2l9 (0.14 #13, 0.07 #4650, 0.07 #1558), 0282x (0.14 #224, 0.05 #11567, 0.03 #16730) >> Best rule #44 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 07c0j; 01kcms4; 033s6; 07hgm; 016vn3; >> query: (?x8999, 03g5jw) <- artists(?x671, ?x8999), ?x671 = 064t9, group(?x227, ?x8999), influenced_by(?x3929, ?x8999) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3272 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 05rx__; *> query: (?x8999, 0478__m) <- influenced_by(?x3929, ?x8999), award_winner(?x567, ?x3929), origin(?x3929, ?x362), award_winner(?x5310, ?x3929) *> conf = 0.05 ranks of expected_values: 76 EVAL 0bk1p influenced_by! 0478__m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 108.000 57.000 0.286 http://example.org/influence/influence_node/influenced_by #20206-07f0tw PRED entity: 07f0tw PRED relation: film PRED expected values: 07vfy4 09yxcz => 124 concepts (54 used for prediction) PRED predicted values (max 10 best out of 298): 0f42nz (0.54 #2703, 0.29 #4495, 0.23 #13457), 02w86hz (0.33 #612, 0.07 #4197, 0.03 #11365), 04jwjq (0.15 #1885, 0.08 #10845, 0.08 #5469), 02tcgh (0.14 #5296, 0.08 #3504, 0.05 #12547), 0h2zvzr (0.08 #6819, 0.07 #5027, 0.06 #8611), 047q2k1 (0.08 #1825, 0.05 #12547, 0.03 #10785), 052_mn (0.08 #3197, 0.04 #13951, 0.04 #6781), 08g_jw (0.08 #3485, 0.04 #7069, 0.03 #8861), 0fpv_3_ (0.08 #2165, 0.04 #5749, 0.03 #7541), 0dc7hc (0.07 #5177, 0.02 #12345) >> Best rule #2703 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01zp33; 04y0yc; 03x31g; >> query: (?x10462, 0f42nz) <- location(?x10462, ?x12210), award(?x10462, ?x10156), ?x10156 = 03r8v_, gender(?x10462, ?x514) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #14230 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 94 *> proper extension: 0674cw; 07yw6t; 0fr7nt; 03_80b; 0cvbb9q; 04fkg4; 07s9tsr; 0cct7p; 05b1062; 090gpr; ... *> query: (?x10462, 09yxcz) <- award(?x10462, ?x10156), nationality(?x10462, ?x2146), ?x2146 = 03rk0, profession(?x10462, ?x1032) *> conf = 0.03 ranks of expected_values: 67, 87 EVAL 07f0tw film 09yxcz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 124.000 54.000 0.538 http://example.org/film/actor/film./film/performance/film EVAL 07f0tw film 07vfy4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 124.000 54.000 0.538 http://example.org/film/actor/film./film/performance/film #20205-0y_hb PRED entity: 0y_hb PRED relation: nominated_for! PRED expected values: 02w9sd7 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 209): 09cm54 (0.68 #712, 0.68 #10920, 0.67 #10919), 027c95y (0.68 #712, 0.68 #10920, 0.67 #10919), 0gs9p (0.67 #2199, 0.65 #1486, 0.62 #774), 019f4v (0.59 #2189, 0.58 #764, 0.56 #1476), 0k611 (0.53 #2208, 0.51 #1495, 0.51 #783), 040njc (0.46 #2144, 0.45 #1431, 0.44 #719), 0gq_v (0.45 #731, 0.43 #256, 0.42 #2156), 04dn09n (0.45 #2170, 0.44 #1457, 0.43 #1220), 0gqy2 (0.44 #833, 0.40 #1308, 0.40 #2258), 0gr0m (0.42 #770, 0.39 #1245, 0.37 #2195) >> Best rule #712 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 0g60z; 0180mw; >> query: (?x6300, ?x1770) <- award(?x6300, ?x1770), nominated_for(?x6300, ?x4093), nominated_for(?x384, ?x6300) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #2137 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 245 *> proper extension: 02fn5r; *> query: (?x6300, ?x384) <- nominated_for(?x6300, ?x4093), nominated_for(?x384, ?x4093) *> conf = 0.25 ranks of expected_values: 34 EVAL 0y_hb nominated_for! 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 92.000 92.000 0.681 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20204-0275n3y PRED entity: 0275n3y PRED relation: honored_for PRED expected values: 01_mdl 042fgh => 47 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1545): 01j7mr (0.60 #3647, 0.33 #782, 0.33 #208), 0828jw (0.50 #3200, 0.33 #2628, 0.33 #907), 09p0ct (0.50 #2943, 0.33 #2371, 0.13 #4012), 092vkg (0.50 #2924, 0.33 #2352, 0.13 #4012), 05jzt3 (0.50 #2915, 0.33 #2343, 0.13 #4012), 0hz55 (0.43 #5442, 0.33 #283, 0.25 #3150), 0l76z (0.43 #5421, 0.18 #8296, 0.13 #4012), 0d68qy (0.40 #4160, 0.33 #722, 0.33 #148), 08jgk1 (0.40 #3529, 0.33 #2385, 0.33 #90), 02rzdcp (0.40 #3631, 0.33 #766, 0.33 #192) >> Best rule #3647 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0gvstc3; 0gx_st; >> query: (?x5592, 01j7mr) <- award_winner(?x5592, ?x5809), honored_for(?x5592, ?x6489), honored_for(?x5592, ?x3180), honored_for(?x5592, ?x2920), type_of_union(?x5809, ?x566), ?x3180 = 07c72, place_of_birth(?x5809, ?x6703), nominated_for(?x2341, ?x2920), titles(?x307, ?x2920), award(?x276, ?x2341), film(?x157, ?x6489) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6882 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 8 *> proper extension: 0c53zb; 0d__c3; *> query: (?x5592, ?x1072) <- award_winner(?x5592, ?x5959), award_winner(?x5592, ?x5809), award_winner(?x5592, ?x2464), honored_for(?x5592, ?x6489), honored_for(?x5592, ?x4541), featured_film_locations(?x6489, ?x1646), film_crew_role(?x4541, ?x137), film(?x3281, ?x4541), film(?x5809, ?x7626), film(?x5959, ?x542), language(?x4541, ?x254), film(?x2464, ?x1072), award(?x4541, ?x1079) *> conf = 0.14 ranks of expected_values: 215, 303 EVAL 0275n3y honored_for 042fgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 46.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0275n3y honored_for 01_mdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 47.000 46.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #20203-08d6bd PRED entity: 08d6bd PRED relation: languages PRED expected values: 0688f => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 22): 02h40lc (0.91 #1104, 0.90 #1636, 0.90 #1902), 0688f (0.25 #104, 0.17 #408, 0.11 #256), 09s02 (0.25 #111, 0.11 #263, 0.11 #1023), 07c9s (0.24 #1000, 0.24 #658, 0.14 #1038), 09bnf (0.14 #684, 0.09 #874, 0.08 #1064), 064_8sq (0.11 #1800, 0.10 #1724, 0.09 #2332), 01c7y (0.11 #258, 0.11 #1018, 0.10 #3689), 02hxcvy (0.11 #253, 0.10 #291, 0.10 #3689), 055qm (0.11 #1011, 0.10 #3689, 0.09 #3612), 0999q (0.11 #1010, 0.10 #668, 0.07 #1276) >> Best rule #1104 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 047c9l; >> query: (?x6442, 02h40lc) <- award_winner(?x4687, ?x6442), student(?x865, ?x6442), languages(?x6442, ?x1882), award(?x11799, ?x4687), spouse(?x11799, ?x10033) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #104 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 047jhq; *> query: (?x6442, 0688f) <- award_winner(?x4687, ?x6442), location(?x6442, ?x7412), people(?x7838, ?x6442), languages(?x6442, ?x1882), ?x7838 = 02sch9 *> conf = 0.25 ranks of expected_values: 2 EVAL 08d6bd languages 0688f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 174.000 174.000 0.907 http://example.org/people/person/languages #20202-0k6nt PRED entity: 0k6nt PRED relation: nationality! PRED expected values: 03xmy1 07h1h5 => 189 concepts (117 used for prediction) PRED predicted values (max 10 best out of 4198): 0k525 (0.49 #211241, 0.11 #7539, 0.10 #23789), 01y64_ (0.49 #211241, 0.11 #5403, 0.09 #13528), 02v406 (0.49 #211241, 0.11 #5307, 0.09 #13432), 0889x (0.49 #211241, 0.11 #7813, 0.09 #15938), 0d5_f (0.49 #211241, 0.11 #5344, 0.09 #13469), 0lkr7 (0.49 #211241, 0.11 #9663, 0.05 #25913), 0132k4 (0.49 #211241, 0.11 #10285, 0.05 #26535), 0k1bs (0.49 #211241, 0.11 #10122, 0.05 #26372), 04smkr (0.49 #211241, 0.11 #8737, 0.05 #24987), 017l4 (0.49 #211241) >> Best rule #211241 for best value: >> intensional similarity = 2 >> extensional distance = 55 >> proper extension: 01vfwd; 02bd41; >> query: (?x985, ?x2281) <- geographic_distribution(?x7790, ?x985), people(?x7790, ?x2281) >> conf = 0.49 => this is the best rule for 10 predicted values *> Best rule #264054 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 81 *> proper extension: 0qb1z; 016wrq; 0prxp; 01w2dq; 014kj2; 0pfd9; *> query: (?x985, ?x3586) <- contains(?x455, ?x985), teams(?x985, ?x3587), team(?x3586, ?x3587) *> conf = 0.16 ranks of expected_values: 63 EVAL 0k6nt nationality! 07h1h5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 189.000 117.000 0.492 http://example.org/people/person/nationality EVAL 0k6nt nationality! 03xmy1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 189.000 117.000 0.492 http://example.org/people/person/nationality #20201-0353tm PRED entity: 0353tm PRED relation: story_by PRED expected values: 0g28b1 => 82 concepts (67 used for prediction) PRED predicted values (max 10 best out of 131): 041h0 (0.20 #5, 0.07 #1728, 0.04 #3244), 02gn9g (0.17 #426, 0.04 #1934, 0.03 #3450), 03h2p5 (0.17 #363, 0.04 #1871, 0.03 #3387), 0343h (0.16 #1523, 0.15 #2173, 0.07 #3041), 041jlr (0.12 #584, 0.10 #799), 042xh (0.12 #1504, 0.09 #1719, 0.08 #2369), 079vf (0.09 #2591, 0.07 #1507, 0.06 #2157), 04hw4b (0.07 #1628, 0.06 #2278, 0.04 #1413), 0fx02 (0.07 #5451, 0.05 #6748, 0.05 #5885), 01v9724 (0.06 #1174, 0.06 #959, 0.01 #5490) >> Best rule #5 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0404j37; >> query: (?x9213, 041h0) <- film(?x2925, ?x9213), film(?x2141, ?x9213), ?x2141 = 03_wj_, location(?x2925, ?x2277), award(?x2925, ?x528), genre(?x9213, ?x571) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0353tm story_by 0g28b1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 67.000 0.200 http://example.org/film/film/story_by #20200-0x67 PRED entity: 0x67 PRED relation: people PRED expected values: 0411q 03f5spx 016kjs 016_mj 09k2t1 07ss8_ 01w724 07sgfsl 01wn718 02xwq9 0205dx 09889g 0d9xq 046m59 095b70 03b78r 01q9b9 0f6lx 030wkp 0234pg 022q32 0hcs3 => 49 concepts (39 used for prediction) PRED predicted values (max 10 best out of 3955): 052hl (0.67 #3677, 0.40 #10923, 0.29 #15270), 01vrt_c (0.50 #3031, 0.33 #13175, 0.30 #10277), 03rx9 (0.50 #4040, 0.30 #11286, 0.29 #15633), 05xpv (0.50 #3933, 0.30 #11179, 0.21 #15526), 02z1yj (0.50 #4063, 0.30 #11309, 0.21 #15656), 0427y (0.50 #4026, 0.30 #11272, 0.21 #15619), 016z2j (0.38 #6044, 0.33 #3147, 0.27 #16190), 01vwllw (0.38 #6151, 0.25 #13398, 0.21 #14847), 046zh (0.38 #6413, 0.20 #16559, 0.19 #35415), 01rrd4 (0.38 #6554, 0.20 #16700, 0.17 #13801) >> Best rule #3677 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 041rx; 01qhm_; 048z7l; 09kr66; >> query: (?x2510, 052hl) <- people(?x2510, ?x8200), people(?x2510, ?x7183), people(?x2510, ?x5356), people(?x2510, ?x3975), written_by(?x1210, ?x7183), influenced_by(?x318, ?x7183), languages_spoken(?x2510, ?x254), artists(?x283, ?x8200), location(?x3975, ?x4253), ?x1210 = 018f8, role(?x5356, ?x716) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #14490 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 10 *> proper extension: 02w7gg; 033tf_; 07hwkr; 0xnvg; 0dbxy; 013b6_; 038723; *> query: (?x2510, ?x318) <- people(?x2510, ?x8200), people(?x2510, ?x7183), people(?x2510, ?x5356), people(?x2510, ?x4606), people(?x2510, ?x3975), written_by(?x1210, ?x7183), influenced_by(?x318, ?x7183), languages_spoken(?x2510, ?x254), artists(?x283, ?x8200), location(?x3975, ?x4253), role(?x5356, ?x2888), award_nominee(?x406, ?x4606) *> conf = 0.12 ranks of expected_values: 761, 778, 789, 795, 797, 1198, 1212, 1259, 1280, 1285, 1443, 1472, 1483, 2008, 2241, 2329, 2849, 3242, 3348, 3613, 3688, 3942 EVAL 0x67 people 0hcs3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 022q32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 0234pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 030wkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 0f6lx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 01q9b9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 03b78r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 095b70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 046m59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 0d9xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 09889g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 0205dx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 02xwq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 01wn718 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 07sgfsl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 01w724 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 07ss8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 09k2t1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 016_mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 016kjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 03f5spx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 39.000 0.667 http://example.org/people/ethnicity/people EVAL 0x67 people 0411q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 39.000 0.667 http://example.org/people/ethnicity/people #20199-02kxwk PRED entity: 02kxwk PRED relation: film PRED expected values: 04w7rn 02q5g1z => 107 concepts (50 used for prediction) PRED predicted values (max 10 best out of 523): 0b6m5fy (0.58 #69533, 0.58 #69532, 0.45 #35656), 01vnbh (0.58 #69533, 0.58 #69532, 0.43 #7131), 03rtz1 (0.58 #69533, 0.58 #69532, 0.38 #74884), 01g03q (0.58 #69533, 0.58 #69532, 0.38 #74884), 011yg9 (0.08 #1025, 0.05 #44569, 0.05 #2808), 017180 (0.08 #1184, 0.05 #44569, 0.04 #49921), 0194zl (0.08 #843, 0.05 #44569, 0.04 #49921), 02q5g1z (0.08 #269, 0.05 #44569, 0.04 #49921), 011yqc (0.08 #232, 0.05 #44569, 0.02 #3797), 01flv_ (0.08 #1062, 0.04 #49921, 0.03 #78452) >> Best rule #69533 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 02wrhj; 0q1lp; 033071; >> query: (?x4367, ?x9350) <- film(?x4367, ?x148), nominated_for(?x4367, ?x9350) >> conf = 0.58 => this is the best rule for 4 predicted values *> Best rule #269 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 02lxj_; 0c94fn; 04ktcgn; 01cwhp; 092ys_y; 04_1nk; *> query: (?x4367, 02q5g1z) <- award_winner(?x1597, ?x4367), profession(?x4367, ?x1032), ?x1597 = 0dr_4 *> conf = 0.08 ranks of expected_values: 8 EVAL 02kxwk film 02q5g1z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 107.000 50.000 0.585 http://example.org/film/actor/film./film/performance/film EVAL 02kxwk film 04w7rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 50.000 0.585 http://example.org/film/actor/film./film/performance/film #20198-059_w PRED entity: 059_w PRED relation: languages_spoken PRED expected values: 0t_2 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 74): 0t_2 (0.60 #1389, 0.53 #1601, 0.48 #1815), 01jb8r (0.33 #45, 0.25 #310, 0.11 #1636), 01r2l (0.25 #284, 0.09 #2513, 0.09 #1663), 0459q4 (0.25 #297, 0.09 #1676, 0.08 #1198), 012w70 (0.25 #274, 0.09 #1653, 0.08 #1175), 0653m (0.25 #273, 0.08 #1174, 0.08 #1121), 06b_j (0.24 #1449, 0.20 #441, 0.18 #1661), 02bjrlw (0.22 #849, 0.20 #425, 0.15 #1167), 04306rv (0.22 #852, 0.20 #428, 0.15 #1170), 02bv9 (0.22 #870, 0.20 #446, 0.15 #1188) >> Best rule #1389 for best value: >> intensional similarity = 8 >> extensional distance = 13 >> proper extension: 0g48m4; 0cn68; 04gfy7; >> query: (?x7139, 0t_2) <- geographic_distribution(?x7139, ?x94), people(?x7139, ?x8018), people(?x7139, ?x7823), languages_spoken(?x7139, ?x254), participant(?x8018, ?x5657), location(?x7823, ?x1131), category(?x8018, ?x134), actor(?x6070, ?x7823) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059_w languages_spoken 0t_2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.600 http://example.org/people/ethnicity/languages_spoken #20197-01n8gr PRED entity: 01n8gr PRED relation: artist! PRED expected values: 01w40h 01cl2y 0181dw => 142 concepts (101 used for prediction) PRED predicted values (max 10 best out of 108): 015_1q (0.94 #2744, 0.66 #2199, 0.58 #4921), 0n85g (0.25 #4961, 0.22 #876, 0.22 #2239), 011k1h (0.23 #146, 0.18 #827, 0.16 #4912), 0k_kr (0.23 #177, 0.14 #41, 0.08 #1403), 0mzkr (0.21 #4926, 0.18 #841, 0.17 #2204), 03rhqg (0.20 #1378, 0.17 #288, 0.16 #7097), 01w40h (0.18 #844, 0.15 #163, 0.14 #707), 043g7l (0.17 #2755, 0.15 #847, 0.14 #2210), 03mp8k (0.16 #2788, 0.15 #199, 0.14 #63), 01dtcb (0.16 #723, 0.14 #315, 0.13 #587) >> Best rule #2744 for best value: >> intensional similarity = 5 >> extensional distance = 157 >> proper extension: 01fl3; 0mgcr; 03xhj6; 01q99h; 015cqh; 033s6; 016ppr; 01s560x; 011xhx; 0p8h0; >> query: (?x3358, 015_1q) <- artist(?x10727, ?x3358), artist(?x10727, ?x6986), artist(?x10727, ?x2876), ?x2876 = 01vn35l, ?x6986 = 02vgh >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #844 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 71 *> proper extension: 017mbb; *> query: (?x3358, 01w40h) <- artist(?x10727, ?x3358), artist(?x10727, ?x2876), ?x2876 = 01vn35l, role(?x3358, ?x1466) *> conf = 0.18 ranks of expected_values: 7, 14, 16 EVAL 01n8gr artist! 0181dw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 142.000 101.000 0.937 http://example.org/music/record_label/artist EVAL 01n8gr artist! 01cl2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 142.000 101.000 0.937 http://example.org/music/record_label/artist EVAL 01n8gr artist! 01w40h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 142.000 101.000 0.937 http://example.org/music/record_label/artist #20196-04954 PRED entity: 04954 PRED relation: location PRED expected values: 059rby => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 171): 0dclg (0.48 #21721, 0.48 #20112, 0.47 #43443), 02_286 (0.19 #34631, 0.17 #32217, 0.16 #16126), 030qb3t (0.19 #16172, 0.16 #21804, 0.16 #9735), 0cr3d (0.09 #34739, 0.06 #53246, 0.05 #17038), 01b8jj (0.07 #593, 0.03 #4616, 0.02 #6225), 0k33p (0.07 #482, 0.03 #3700, 0.02 #1287), 04lh6 (0.07 #1241, 0.03 #3654, 0.02 #4459), 01531 (0.06 #4986, 0.04 #7398, 0.04 #34752), 02jx1 (0.06 #2484, 0.05 #876, 0.04 #1680), 05jbn (0.06 #2666, 0.04 #5081, 0.03 #3471) >> Best rule #21721 for best value: >> intensional similarity = 3 >> extensional distance = 408 >> proper extension: 01507p; >> query: (?x7530, ?x2254) <- place_of_birth(?x7530, ?x2254), actor(?x9029, ?x7530), nominated_for(?x7530, ?x2116) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #26565 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 717 *> proper extension: 0bz5v2; 07ymr5; 04smkr; 03kpvp; 06vsbt; 0d02km; 03_wpf; 040696; 02bwjv; 03h3vtz; ... *> query: (?x7530, 059rby) <- award_nominee(?x496, ?x7530), film(?x7530, ?x796), award_winner(?x7530, ?x525) *> conf = 0.03 ranks of expected_values: 40 EVAL 04954 location 059rby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 115.000 115.000 0.478 http://example.org/people/person/places_lived./people/place_lived/location #20195-02f73b PRED entity: 02f73b PRED relation: award! PRED expected values: 01pfr3 02r3zy 01vrz41 01j4ls 016lmg 09jm8 016t0h => 42 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2016): 01vw20h (0.79 #33292, 0.57 #11250, 0.50 #7922), 0838y (0.79 #33292, 0.33 #5273, 0.33 #1945), 0178kd (0.79 #33292, 0.33 #1829, 0.17 #8485), 02r3zy (0.71 #10235, 0.50 #20221, 0.50 #6907), 01vrz41 (0.57 #10274, 0.50 #6946, 0.47 #16931), 01wf86y (0.57 #12141, 0.50 #8813, 0.33 #5485), 011z3g (0.57 #11905, 0.33 #8577, 0.33 #5249), 02qwg (0.53 #17557, 0.50 #20886, 0.50 #7572), 0lbj1 (0.50 #6700, 0.47 #16685, 0.45 #20014), 03j24kf (0.50 #7998, 0.43 #11326, 0.37 #17983) >> Best rule #33292 for best value: >> intensional similarity = 6 >> extensional distance = 151 >> proper extension: 02gx2k; 02flpc; 02681xs; 026rsl9; 02w7fs; 02681_5; >> query: (?x7535, ?x4476) <- award(?x7331, ?x7535), award(?x5547, ?x7535), award_winner(?x247, ?x5547), artist(?x1954, ?x7331), participant(?x7331, ?x338), award_winner(?x7535, ?x4476) >> conf = 0.79 => this is the best rule for 3 predicted values *> Best rule #10235 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 01by1l; *> query: (?x7535, 02r3zy) <- award(?x5547, ?x7535), award(?x4628, ?x7535), award(?x1896, ?x7535), ?x5547 = 0dw4g, participant(?x2258, ?x4628), origin(?x4628, ?x9417), ?x1896 = 0j1yf *> conf = 0.71 ranks of expected_values: 4, 5, 46, 51, 60, 96, 109 EVAL 02f73b award! 016t0h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 42.000 17.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f73b award! 09jm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 42.000 17.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f73b award! 016lmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 42.000 17.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f73b award! 01j4ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 42.000 17.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f73b award! 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 42.000 17.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f73b award! 02r3zy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 42.000 17.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f73b award! 01pfr3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 42.000 17.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20194-058x5 PRED entity: 058x5 PRED relation: religion! PRED expected values: 0gyh => 31 concepts (22 used for prediction) PRED predicted values (max 10 best out of 162): 04rrx (0.85 #769, 0.83 #676, 0.78 #583), 02xry (0.83 #682, 0.78 #589, 0.78 #497), 03v0t (0.78 #601, 0.78 #509, 0.77 #787), 05k7sb (0.78 #493, 0.77 #771, 0.75 #678), 0gyh (0.78 #500, 0.77 #778, 0.75 #685), 03v1s (0.78 #472, 0.77 #750, 0.75 #657), 050ks (0.78 #530, 0.77 #808, 0.75 #715), 0rh6k (0.78 #465, 0.75 #650, 0.75 #371), 03s0w (0.78 #478, 0.71 #292, 0.69 #756), 05tbn (0.78 #506, 0.71 #320, 0.69 #784) >> Best rule #769 for best value: >> intensional similarity = 10 >> extensional distance = 11 >> proper extension: 01s5nb; >> query: (?x1363, 04rrx) <- religion(?x5575, ?x1363), religion(?x4198, ?x1363), religion(?x1767, ?x1363), ?x1767 = 04rrd, district_represented(?x605, ?x4198), adjoins(?x1274, ?x4198), contains(?x4198, ?x7067), country(?x5575, ?x94), administrative_parent(?x11575, ?x5575), adjoins(?x5575, ?x3634) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #500 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 7 *> proper extension: 0631_; 05w5d; *> query: (?x1363, 0gyh) <- religion(?x5575, ?x1363), religion(?x4198, ?x1363), religion(?x1767, ?x1363), ?x1767 = 04rrd, district_represented(?x653, ?x4198), ?x5575 = 05fjy, ?x653 = 070m6c, jurisdiction_of_office(?x900, ?x4198), contains(?x4198, ?x7067) *> conf = 0.78 ranks of expected_values: 5 EVAL 058x5 religion! 0gyh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 31.000 22.000 0.846 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #20193-0274ck PRED entity: 0274ck PRED relation: artists! PRED expected values: 0xhtw 0hdf8 0jrv_ => 135 concepts (68 used for prediction) PRED predicted values (max 10 best out of 255): 03lty (0.71 #333, 0.67 #640, 0.40 #11367), 0xhtw (0.60 #11356, 0.59 #11662, 0.57 #322), 06by7 (0.56 #11666, 0.56 #11360, 0.54 #4007), 016clz (0.44 #924, 0.43 #310, 0.36 #11650), 01_bkd (0.44 #668, 0.29 #361, 0.26 #612), 05r6t (0.43 #388, 0.33 #695, 0.32 #8670), 0172rj (0.43 #419, 0.11 #726, 0.06 #11645), 064t9 (0.41 #8295, 0.41 #9213, 0.41 #3081), 05w3f (0.41 #957, 0.26 #612, 0.26 #3717), 0cx7f (0.41 #1057, 0.14 #6582, 0.14 #4124) >> Best rule #333 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01shhf; >> query: (?x764, 03lty) <- artists(?x12808, ?x764), artists(?x6350, ?x764), ?x6350 = 0296y, parent_genre(?x12808, ?x1572) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #11356 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 280 *> proper extension: 089tm; 01t_xp_; 01pfr3; 04rcr; 0150jk; 02r3zy; 067mj; 01vsxdm; 01wv9xn; 05crg7; ... *> query: (?x764, 0xhtw) <- artists(?x6350, ?x764), artists(?x6350, ?x8012), ?x8012 = 01wt4wc, parent_genre(?x6349, ?x6350) *> conf = 0.60 ranks of expected_values: 2, 15, 22 EVAL 0274ck artists! 0jrv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 135.000 68.000 0.714 http://example.org/music/genre/artists EVAL 0274ck artists! 0hdf8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 135.000 68.000 0.714 http://example.org/music/genre/artists EVAL 0274ck artists! 0xhtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 68.000 0.714 http://example.org/music/genre/artists #20192-0n839 PRED entity: 0n839 PRED relation: gender PRED expected values: 05zppz => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #39, 0.89 #37, 0.84 #41), 02zsn (0.46 #155, 0.44 #10, 0.36 #32) >> Best rule #39 for best value: >> intensional similarity = 2 >> extensional distance = 78 >> proper extension: 019fz; >> query: (?x11949, 05zppz) <- type_of_union(?x11949, ?x566), organizations_founded(?x11949, ?x1693) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n839 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.887 http://example.org/people/person/gender #20191-04x_3 PRED entity: 04x_3 PRED relation: major_field_of_study! PRED expected values: 05krk 01ptt7 01jswq 07wlf 03ksy 01pq4w 017j69 029d_ 012mzw 01n_g9 01dyk8 02z6fs 0jpkw 01z3bz 018sg9 => 62 concepts (34 used for prediction) PRED predicted values (max 10 best out of 602): 03ksy (0.75 #5950, 0.71 #4887, 0.67 #4355), 07tg4 (0.75 #5393, 0.62 #6457, 0.53 #8584), 0bwfn (0.71 #5052, 0.62 #7179, 0.57 #7711), 09f2j (0.64 #8132, 0.58 #6005, 0.57 #4942), 07wrz (0.62 #5371, 0.62 #6435, 0.58 #5903), 01mpwj (0.62 #5419, 0.58 #5951, 0.46 #6483), 07wjk (0.62 #5372, 0.54 #6436, 0.53 #8563), 07tgn (0.62 #5331, 0.46 #6395, 0.44 #9054), 017j69 (0.62 #6522, 0.57 #4927, 0.53 #8649), 065y4w7 (0.60 #3735, 0.57 #4798, 0.50 #7988) >> Best rule #5950 for best value: >> intensional similarity = 14 >> extensional distance = 10 >> proper extension: 03nfmq; >> query: (?x2601, 03ksy) <- major_field_of_study(?x10832, ?x2601), major_field_of_study(?x6973, ?x2601), major_field_of_study(?x6432, ?x2601), major_field_of_study(?x5288, ?x2601), major_field_of_study(?x5167, ?x2601), major_field_of_study(?x4672, ?x2601), major_field_of_study(?x1667, ?x2601), ?x5288 = 02zd460, ?x4672 = 07tds, currency(?x1667, ?x170), institution(?x865, ?x10832), school_type(?x5167, ?x3092), contains(?x94, ?x6973), currency(?x6432, ?x1099) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9, 17, 22, 27, 35, 64, 87, 114, 141, 204, 213, 252, 354 EVAL 04x_3 major_field_of_study! 018sg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 01z3bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 0jpkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 02z6fs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 01dyk8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 01n_g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 012mzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 029d_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 017j69 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 01pq4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 03ksy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 07wlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 01jswq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 01ptt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04x_3 major_field_of_study! 05krk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 62.000 34.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20190-027z0pl PRED entity: 027z0pl PRED relation: gender PRED expected values: 05zppz => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #5, 0.88 #45, 0.87 #37), 02zsn (0.24 #167, 0.24 #84, 0.24 #86) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 04411; 028rk; 0jcx; 09bg4l; 03kdl; 0dx97; 07cbs; 0f7fy; 04pg29; 07bty; ... >> query: (?x10430, 05zppz) <- profession(?x10430, ?x319), organizations_founded(?x10430, ?x7690), company(?x346, ?x7690) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027z0pl gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.889 http://example.org/people/person/gender #20189-06yxd PRED entity: 06yxd PRED relation: religion PRED expected values: 01lp8 => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 27): 01lp8 (0.88 #365, 0.88 #337, 0.77 #281), 021_0p (0.65 #348, 0.59 #264, 0.58 #376), 03_gx (0.59 #92, 0.48 #372, 0.48 #344), 01s5nb (0.46 #353, 0.44 #381, 0.43 #1825), 058x5 (0.43 #1825, 0.40 #339, 0.39 #255), 0flw86 (0.43 #1825, 0.39 #1321, 0.38 #1013), 092bf5 (0.43 #1825, 0.28 #711, 0.27 #1272), 02t7t (0.27 #351, 0.26 #295, 0.25 #379), 072w0 (0.27 #382, 0.25 #354, 0.23 #1460), 03j6c (0.23 #1460, 0.09 #1276, 0.09 #1416) >> Best rule #365 for best value: >> intensional similarity = 2 >> extensional distance = 46 >> proper extension: 0n3g; >> query: (?x4776, 01lp8) <- religion(?x4776, ?x1624), ?x1624 = 051kv >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06yxd religion 01lp8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.875 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #20188-07wjk PRED entity: 07wjk PRED relation: major_field_of_study PRED expected values: 02ky346 04rjg => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 110): 02ky346 (0.51 #1854, 0.24 #2522, 0.22 #1868), 04rjg (0.48 #1435, 0.48 #1872, 0.47 #2744), 01lj9 (0.48 #1450, 0.43 #1668, 0.40 #1014), 05qfh (0.37 #1884, 0.36 #1447, 0.34 #2538), 04x_3 (0.37 #1878, 0.33 #2532, 0.30 #2096), 0g26h (0.37 #2325, 0.36 #1452, 0.33 #1670), 041y2 (0.33 #1046, 0.28 #1482, 0.26 #1919), 06ms6 (0.33 #996, 0.25 #2523, 0.25 #1105), 04rlf (0.33 #1039, 0.13 #2021, 0.12 #2239), 02_7t (0.33 #1908, 0.28 #1471, 0.27 #2126) >> Best rule #1854 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 026gvfj; 01d34b; 05bjp6; >> query: (?x2327, ?x1682) <- student(?x2327, ?x10626), participant(?x10626, ?x1126), place_of_birth(?x10626, ?x12135), student(?x1682, ?x10626) >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07wjk major_field_of_study 04rjg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.511 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07wjk major_field_of_study 02ky346 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.511 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20187-02c6d PRED entity: 02c6d PRED relation: film_release_region PRED expected values: 015fr 035qy => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 133): 09c7w0 (0.93 #7165, 0.93 #6028, 0.92 #8793), 0345h (0.81 #1175, 0.80 #3132, 0.79 #2807), 03gj2 (0.81 #1166, 0.80 #3123, 0.75 #2961), 035qy (0.81 #1177, 0.78 #2809, 0.72 #3296), 07ssc (0.79 #2950, 0.79 #1155, 0.78 #2787), 05qhw (0.79 #1153, 0.72 #2785, 0.71 #3110), 03h64 (0.75 #397, 0.74 #3331, 0.74 #3169), 015fr (0.74 #1157, 0.72 #2952, 0.70 #3114), 03rt9 (0.70 #1152, 0.63 #3109, 0.61 #2947), 01znc_ (0.70 #2980, 0.69 #3304, 0.67 #2817) >> Best rule #7165 for best value: >> intensional similarity = 4 >> extensional distance = 980 >> proper extension: 01br2w; 0b60sq; 04dsnp; 0cnztc4; 0d6b7; 0gj9qxr; 091z_p; 064n1pz; 016kz1; 04lqvlr; ... >> query: (?x1252, 09c7w0) <- titles(?x600, ?x1252), film_release_region(?x1252, ?x172), country(?x150, ?x172), olympics(?x172, ?x418) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1177 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 0dtw1x; 04969y; 0bmc4cm; 026njb5; *> query: (?x1252, 035qy) <- titles(?x600, ?x1252), film_release_region(?x1252, ?x172), ?x172 = 0154j, executive_produced_by(?x1252, ?x8462) *> conf = 0.81 ranks of expected_values: 4, 8 EVAL 02c6d film_release_region 035qy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 83.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02c6d film_release_region 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 83.000 83.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20186-023vrq PRED entity: 023vrq PRED relation: ceremony PRED expected values: 019bk0 056878 02cg41 => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 128): 056878 (0.67 #788, 0.58 #1423, 0.57 #1042), 02cg41 (0.58 #1509, 0.57 #1128, 0.56 #874), 019bk0 (0.56 #774, 0.54 #1409, 0.50 #1028), 0gx1673 (0.50 #1122, 0.50 #487, 0.44 #868), 0hhtgcw (0.27 #4575, 0.26 #4066, 0.21 #3811), 02hn5v (0.21 #3811, 0.15 #1431, 0.13 #1558), 050yyb (0.21 #3811, 0.14 #1429, 0.13 #1556), 09p2r9 (0.21 #3811, 0.07 #1096, 0.03 #1350), 09bymc (0.21 #3811, 0.07 #1123, 0.03 #2266), 05c1t6z (0.18 #1535, 0.15 #2170, 0.13 #1789) >> Best rule #788 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 09sb52; 01c427; 025m8l; 01cky2; 01cw7s; 03r00m; >> query: (?x9295, 056878) <- ceremony(?x9295, ?x139), award(?x8124, ?x9295), award(?x3176, ?x9295), ?x3176 = 01w7nww, award_nominee(?x5340, ?x8124) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 023vrq ceremony 02cg41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 023vrq ceremony 056878 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 023vrq ceremony 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony #20185-01pfr3 PRED entity: 01pfr3 PRED relation: award PRED expected values: 01c427 02f73b => 84 concepts (64 used for prediction) PRED predicted values (max 10 best out of 233): 02f5qb (0.63 #6077, 0.53 #4497, 0.50 #942), 02f73b (0.54 #6205, 0.50 #1070, 0.43 #4625), 01bgqh (0.53 #3991, 0.39 #14658, 0.39 #5966), 02f77l (0.50 #1038, 0.47 #4198, 0.40 #4593), 02v1m7 (0.50 #901, 0.41 #4061, 0.40 #6036), 01d38t (0.50 #1112, 0.33 #2297, 0.30 #4667), 01ckrr (0.50 #1014, 0.24 #4174, 0.20 #3384), 01c427 (0.47 #5218, 0.31 #13119, 0.31 #14304), 01c9jp (0.42 #4923, 0.33 #6503, 0.31 #7293), 01ck6h (0.39 #8020, 0.22 #2095, 0.18 #2885) >> Best rule #6077 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 015mrk; >> query: (?x475, 02f5qb) <- award(?x475, ?x3365), artists(?x302, ?x475), ?x3365 = 02f716 >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #6205 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 015mrk; *> query: (?x475, 02f73b) <- award(?x475, ?x3365), artists(?x302, ?x475), ?x3365 = 02f716 *> conf = 0.54 ranks of expected_values: 2, 8 EVAL 01pfr3 award 02f73b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 64.000 0.627 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01pfr3 award 01c427 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 84.000 64.000 0.627 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20184-0p8h0 PRED entity: 0p8h0 PRED relation: group! PRED expected values: 018j2 => 109 concepts (75 used for prediction) PRED predicted values (max 10 best out of 120): 05148p4 (0.71 #2885, 0.71 #2629, 0.70 #2970), 03bx0bm (0.69 #276, 0.59 #613, 0.58 #2975), 0l14md (0.59 #2617, 0.59 #2873, 0.58 #2958), 028tv0 (0.52 #264, 0.44 #601, 0.43 #939), 0l14qv (0.39 #594, 0.38 #257, 0.35 #932), 0mkg (0.31 #937, 0.20 #10, 0.15 #599), 01vj9c (0.30 #13, 0.28 #2964, 0.27 #940), 06ncr (0.30 #36, 0.22 #625, 0.22 #963), 0l14j_ (0.20 #48, 0.12 #2828, 0.12 #2914), 07y_7 (0.20 #2, 0.12 #2782, 0.11 #2953) >> Best rule #2885 for best value: >> intensional similarity = 5 >> extensional distance = 180 >> proper extension: 01t_xp_; 03t9sp; 014kyy; >> query: (?x13145, 05148p4) <- group(?x227, ?x13145), instrumentalists(?x227, ?x5550), role(?x227, ?x74), role(?x1292, ?x227), ?x5550 = 01bczm >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #957 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 49 *> proper extension: 0249kn; 013w2r; 07mvp; 015cxv; 014pg1; 09z1lg; 09jvl; 07n3s; 079kr; *> query: (?x13145, 018j2) <- group(?x316, ?x13145), group(?x227, ?x13145), ?x227 = 0342h, role(?x4528, ?x316), role(?x74, ?x316), ?x4528 = 02x8z_ *> conf = 0.14 ranks of expected_values: 17 EVAL 0p8h0 group! 018j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 109.000 75.000 0.709 http://example.org/music/performance_role/regular_performances./music/group_membership/group #20183-01vs_v8 PRED entity: 01vs_v8 PRED relation: artist! PRED expected values: 03gfvsz => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 4): 03gfvsz (0.30 #164, 0.25 #37, 0.24 #152), 01fjfv (0.17 #8, 0.14 #14, 0.09 #44), 04rqd (0.05 #41, 0.03 #228, 0.03 #493), 04y652m (0.04 #52, 0.03 #227, 0.03 #70) >> Best rule #164 for best value: >> intensional similarity = 2 >> extensional distance = 61 >> proper extension: 046p9; 033s6; >> query: (?x2237, 03gfvsz) <- award(?x2237, ?x4958), ?x4958 = 03qbnj >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vs_v8 artist! 03gfvsz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.302 http://example.org/broadcast/content/artist #20182-07kb7vh PRED entity: 07kb7vh PRED relation: titles! PRED expected values: 01z4y => 68 concepts (45 used for prediction) PRED predicted values (max 10 best out of 48): 07s9rl0 (0.33 #309, 0.33 #2175, 0.30 #2277), 01z4y (0.33 #36, 0.21 #653, 0.21 #968), 01hmnh (0.24 #438, 0.18 #1169, 0.18 #1064), 04xvlr (0.21 #2280, 0.19 #1458, 0.18 #2178), 024qqx (0.18 #492, 0.15 #803, 0.13 #1328), 07ssc (0.14 #113, 0.12 #215, 0.11 #524), 03mqtr (0.14 #149, 0.12 #251, 0.11 #354), 0f8l9c (0.14 #121, 0.01 #635, 0.01 #844), 064_8sq (0.14 #165), 01jfsb (0.13 #1267, 0.12 #1577, 0.12 #1679) >> Best rule #309 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02z44tp; >> query: (?x4131, 07s9rl0) <- film(?x4992, ?x4131), ?x4992 = 0lkr7, film_crew_role(?x4131, ?x1171), film_crew_role(?x504, ?x1171), film_release_region(?x504, ?x87) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #36 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 02ryz24; *> query: (?x4131, 01z4y) <- film(?x10905, ?x4131), film(?x4992, ?x4131), ?x4992 = 0lkr7, film_crew_role(?x4131, ?x137), language(?x4131, ?x254), influenced_by(?x1593, ?x10905) *> conf = 0.33 ranks of expected_values: 2 EVAL 07kb7vh titles! 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 45.000 0.333 http://example.org/media_common/netflix_genre/titles #20181-01hcj2 PRED entity: 01hcj2 PRED relation: participant PRED expected values: 0bksh => 138 concepts (53 used for prediction) PRED predicted values (max 10 best out of 252): 0bksh (0.81 #23624, 0.81 #14684, 0.80 #26179), 03lt8g (0.81 #23624, 0.81 #14684, 0.80 #26179), 070yzk (0.11 #10213), 02v60l (0.11 #10213), 024dgj (0.11 #10213), 01j5ws (0.11 #10213), 01mqc_ (0.09 #11491, 0.06 #24902, 0.06 #33202), 0c6qh (0.06 #24902, 0.06 #33202, 0.06 #17239), 07r1h (0.06 #7432, 0.05 #9985, 0.05 #6794), 0bbf1f (0.06 #7218, 0.05 #6580, 0.05 #9771) >> Best rule #23624 for best value: >> intensional similarity = 3 >> extensional distance = 436 >> proper extension: 04shbh; >> query: (?x9545, ?x513) <- participant(?x513, ?x9545), award(?x9545, ?x757), film(?x9545, ?x428) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01hcj2 participant 0bksh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 53.000 0.810 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #20180-07c52 PRED entity: 07c52 PRED relation: major_field_of_study! PRED expected values: 0hd7j => 84 concepts (80 used for prediction) PRED predicted values (max 10 best out of 632): 01w5m (0.64 #11340, 0.63 #23155, 0.53 #25521), 06pwq (0.62 #24233, 0.60 #26597, 0.48 #28370), 09f2j (0.62 #24399, 0.56 #23214, 0.55 #11399), 02zd460 (0.57 #5506, 0.50 #4325, 0.47 #24415), 07szy (0.55 #11264, 0.53 #26628, 0.50 #24264), 08815 (0.53 #24222, 0.52 #23037, 0.45 #11222), 03ksy (0.53 #24341, 0.45 #11341, 0.45 #25522), 07vyf (0.50 #4287, 0.43 #5468, 0.36 #10784), 01w3v (0.50 #24236, 0.43 #26600, 0.36 #11236), 0gl5_ (0.50 #4405, 0.36 #10902, 0.32 #24495) >> Best rule #11340 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 02h40lc; 06ms6; 0fdys; 01zc2w; 01x3g; >> query: (?x2008, 01w5m) <- major_field_of_study(?x2008, ?x4268), ?x4268 = 02822, major_field_of_study(?x4410, ?x2008), major_field_of_study(?x865, ?x2008) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #5480 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 04rjg; 04gb7; 03qsdpk; 05qt0; *> query: (?x2008, 0hd7j) <- major_field_of_study(?x2008, ?x4268), films(?x2008, ?x8330), major_field_of_study(?x388, ?x4268), ?x388 = 05krk, written_by(?x8330, ?x4385) *> conf = 0.29 ranks of expected_values: 88 EVAL 07c52 major_field_of_study! 0hd7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 84.000 80.000 0.636 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20179-07h1tr PRED entity: 07h1tr PRED relation: gender PRED expected values: 05zppz => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #47, 0.84 #37, 0.84 #39), 02zsn (0.25 #82, 0.25 #99, 0.24 #78) >> Best rule #47 for best value: >> intensional similarity = 1 >> extensional distance = 755 >> proper extension: 0j3v; 0dzkq; 075wq; 07c37; 0bk4s; 02ln1; 03d6q; 02wh0; >> query: (?x2716, 05zppz) <- place_of_death(?x2716, ?x1659) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07h1tr gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.845 http://example.org/people/person/gender #20178-01s7ns PRED entity: 01s7ns PRED relation: instrumentalists! PRED expected values: 05r5c => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 61): 05r5c (0.48 #360, 0.47 #2918, 0.39 #1152), 05148p4 (0.37 #373, 0.33 #1165, 0.32 #2931), 018vs (0.28 #2923, 0.24 #365, 0.23 #1157), 03qjg (0.19 #404, 0.16 #1196, 0.14 #2962), 02hnl (0.17 #387, 0.16 #2945, 0.14 #1091), 0l14md (0.12 #183, 0.11 #2917, 0.10 #1855), 026t6 (0.11 #2913, 0.11 #1059, 0.09 #1147), 06ncr (0.09 #397, 0.07 #2955, 0.05 #2865), 07y_7 (0.09 #354, 0.06 #2912, 0.05 #1410), 03gvt (0.09 #418, 0.06 #2976, 0.05 #1210) >> Best rule #360 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 095x_; >> query: (?x11026, 05r5c) <- profession(?x11026, ?x131), artists(?x5300, ?x11026), category(?x11026, ?x134), ?x5300 = 02k_kn >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01s7ns instrumentalists! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.481 http://example.org/music/instrument/instrumentalists #20177-01hmnh PRED entity: 01hmnh PRED relation: disciplines_or_subjects! PRED expected values: 0262zm 058bzgm => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 145): 01yz0x (0.60 #1434, 0.44 #2187, 0.33 #1905), 02r6nbc (0.60 #1448, 0.33 #2201, 0.33 #1919), 01f7d (0.60 #1469, 0.33 #2222, 0.33 #1940), 02r771y (0.60 #1499, 0.33 #2252, 0.33 #1970), 0262zm (0.50 #1895, 0.44 #2177, 0.40 #1424), 06196 (0.40 #1463, 0.33 #2216, 0.33 #1934), 05x2s (0.40 #1483, 0.33 #2236, 0.33 #1954), 04jhhng (0.40 #1494, 0.33 #1965, 0.33 #742), 01ppdy (0.40 #1464, 0.33 #1935, 0.33 #712), 0c_dx (0.40 #1449, 0.33 #1920, 0.33 #697) >> Best rule #1434 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 05hgj; >> query: (?x1510, 01yz0x) <- disciplines_or_subjects(?x10678, ?x1510), disciplines_or_subjects(?x8909, ?x1510), ?x8909 = 040_9s0, award(?x3858, ?x10678), ?x3858 = 05jm7 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1895 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0707q; 0l67h; *> query: (?x1510, 0262zm) <- disciplines_or_subjects(?x14213, ?x1510), disciplines_or_subjects(?x10678, ?x1510), ?x10678 = 039yzf, award_winner(?x14213, ?x3338), award(?x1727, ?x14213) *> conf = 0.50 ranks of expected_values: 5, 11 EVAL 01hmnh disciplines_or_subjects! 058bzgm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 97.000 97.000 0.600 http://example.org/award/award_category/disciplines_or_subjects EVAL 01hmnh disciplines_or_subjects! 0262zm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 97.000 0.600 http://example.org/award/award_category/disciplines_or_subjects #20176-013ksx PRED entity: 013ksx PRED relation: contains! PRED expected values: 0mwl2 05tbn => 161 concepts (101 used for prediction) PRED predicted values (max 10 best out of 227): 0mwh1 (0.82 #59156, 0.75 #48404, 0.73 #61845), 05tbn (0.60 #81556, 0.58 #36754, 0.54 #20620), 0mwl2 (0.60 #81556, 0.58 #36754, 0.54 #20620), 0d060g (0.46 #88725, 0.10 #50210, 0.09 #55584), 04_1l0v (0.43 #13003, 0.42 #19276, 0.39 #27343), 059rby (0.33 #19, 0.25 #915, 0.18 #4501), 013ksx (0.33 #67220, 0.21 #86038, 0.02 #6276), 029jpy (0.25 #1111, 0.05 #19041, 0.04 #10079), 01n7q (0.20 #47585, 0.20 #30556, 0.20 #59235), 05fjf (0.19 #3062, 0.15 #5752, 0.12 #10237) >> Best rule #59156 for best value: >> intensional similarity = 3 >> extensional distance = 231 >> proper extension: 0ybkj; 0zygc; 0t_gg; 0136jw; 0rn0z; 0sc6p; 0s4sj; >> query: (?x3068, ?x2744) <- county(?x3068, ?x2744), adjoins(?x855, ?x2744), contains(?x2744, ?x1494) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #81556 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 340 *> proper extension: 0f4y_; 0m2gk; 0mlyw; 0nj1c; 0mm0p; 0nvd8; 0n5_g; 0ntwb; 0k3ll; 0mws3; ... *> query: (?x3068, ?x855) <- adjoins(?x3068, ?x854), source(?x3068, ?x958), ?x958 = 0jbk9, contains(?x855, ?x854) *> conf = 0.60 ranks of expected_values: 2, 3 EVAL 013ksx contains! 05tbn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 161.000 101.000 0.820 http://example.org/location/location/contains EVAL 013ksx contains! 0mwl2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 161.000 101.000 0.820 http://example.org/location/location/contains #20175-05jf85 PRED entity: 05jf85 PRED relation: nominated_for! PRED expected values: 03hl6lc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 224): 04dn09n (0.68 #8508, 0.66 #8507, 0.66 #7087), 040njc (0.68 #8508, 0.66 #8507, 0.66 #7087), 0gq9h (0.48 #1004, 0.41 #1476, 0.34 #1713), 0gs9p (0.48 #1006, 0.35 #1478, 0.29 #1715), 03hl6lc (0.43 #1072, 0.12 #8747, 0.10 #1308), 019f4v (0.39 #995, 0.33 #1467, 0.29 #1231), 0k611 (0.34 #1015, 0.30 #1487, 0.24 #1724), 02pqp12 (0.32 #1000, 0.25 #6376, 0.22 #14649), 0gr0m (0.30 #1473, 0.28 #1237, 0.27 #1710), 0f4x7 (0.30 #969, 0.27 #1441, 0.25 #6376) >> Best rule #8508 for best value: >> intensional similarity = 2 >> extensional distance = 987 >> proper extension: 06mmr; >> query: (?x306, ?x198) <- award(?x306, ?x198), award(?x71, ?x198) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #1072 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 147 *> proper extension: 02d44q; 0hgnl3t; *> query: (?x306, 03hl6lc) <- nominated_for(?x1862, ?x306), nominated_for(?x986, ?x306), ?x1862 = 0gr51 *> conf = 0.43 ranks of expected_values: 5 EVAL 05jf85 nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 72.000 72.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20174-0hv8w PRED entity: 0hv8w PRED relation: nominated_for! PRED expected values: 02ppm4q => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 209): 040njc (0.33 #1887, 0.21 #3533, 0.20 #5413), 0gq_v (0.30 #1900, 0.28 #3546, 0.21 #3781), 0gr4k (0.27 #1907, 0.25 #7522, 0.19 #5668), 0f4x7 (0.27 #1906, 0.22 #17632, 0.19 #13163), 0gs96 (0.27 #3612, 0.14 #1026, 0.14 #6667), 02hsq3m (0.26 #735, 0.25 #2145, 0.22 #500), 0gr0m (0.25 #3586, 0.23 #1940, 0.21 #3821), 0gqy2 (0.25 #1999, 0.20 #3880, 0.18 #5525), 05b1610 (0.25 #7522, 0.22 #17632, 0.19 #13163), 05p1dby (0.25 #7522, 0.22 #17632, 0.19 #13163) >> Best rule #1887 for best value: >> intensional similarity = 3 >> extensional distance = 140 >> proper extension: 014zcr; 0bxtg; 08f3b1; 0pz7h; 0tc7; 0c6qh; 05whq_9; 02mjmr; 0c9c0; 046lt; ... >> query: (?x5473, 040njc) <- list(?x5473, ?x3004), list(?x5930, ?x3004), film_festivals(?x5930, ?x7988) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4344 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 306 *> proper extension: 03d17dg; *> query: (?x5473, 02ppm4q) <- nominated_for(?x1387, ?x5473), award_winner(?x5473, ?x3662), executive_produced_by(?x696, ?x3662) *> conf = 0.12 ranks of expected_values: 46 EVAL 0hv8w nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 81.000 81.000 0.331 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20173-02x4sn8 PRED entity: 02x4sn8 PRED relation: award! PRED expected values: 0h1p 02mt4k 0gpprt => 42 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2200): 0693l (0.76 #20141, 0.71 #40284, 0.69 #46999), 02pv_d (0.76 #20141, 0.71 #40284, 0.69 #46999), 02kxbwx (0.76 #20141, 0.71 #40284, 0.69 #46999), 081lh (0.76 #20141, 0.71 #40284, 0.69 #46999), 06m6z6 (0.76 #20141, 0.71 #40284, 0.69 #46999), 014zcr (0.34 #52, 0.23 #3409, 0.18 #6763), 0151w_ (0.26 #234, 0.15 #3591, 0.12 #6945), 0c6qh (0.26 #662, 0.13 #4019, 0.11 #7373), 03_gd (0.26 #169, 0.11 #3526, 0.09 #6880), 05ldnp (0.23 #892, 0.13 #4249, 0.11 #7603) >> Best rule #20141 for best value: >> intensional similarity = 4 >> extensional distance = 158 >> proper extension: 0dzfdw; >> query: (?x2902, ?x826) <- award_winner(?x2902, ?x826), award(?x4320, ?x2902), award_nominee(?x2443, ?x4320), film(?x4320, ?x2287) >> conf = 0.76 => this is the best rule for 5 predicted values *> Best rule #541 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 099c8n; *> query: (?x2902, 0h1p) <- award(?x394, ?x2902), nominated_for(?x2902, ?x1370), ?x1370 = 0gmcwlb, country(?x394, ?x94) *> conf = 0.17 ranks of expected_values: 24, 111, 359 EVAL 02x4sn8 award! 0gpprt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 15.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4sn8 award! 02mt4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 42.000 15.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4sn8 award! 0h1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 42.000 15.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20172-01n5309 PRED entity: 01n5309 PRED relation: student! PRED expected values: 01mkq => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 27): 06ms6 (0.17 #74, 0.01 #508), 01x3g (0.17 #119), 05qfh (0.06 #151, 0.02 #647, 0.02 #399), 02822 (0.05 #651, 0.04 #713, 0.03 #1085), 04rlf (0.04 #419, 0.02 #605, 0.01 #915), 02vxn (0.03 #190, 0.02 #314, 0.02 #376), 0w7c (0.03 #662, 0.02 #476, 0.01 #1096), 03qsdpk (0.02 #656, 0.02 #718, 0.02 #408), 0fdys (0.02 #401, 0.02 #463, 0.02 #1269), 041y2 (0.02 #423, 0.01 #609, 0.01 #671) >> Best rule #74 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0d608; >> query: (?x692, 06ms6) <- place_of_birth(?x692, ?x2850), award_winner(?x6739, ?x692), celebrities_impersonated(?x692, ?x1335) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01n5309 student! 01mkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 108.000 0.167 http://example.org/education/field_of_study/students_majoring./education/education/student #20171-01c59k PRED entity: 01c59k PRED relation: gender PRED expected values: 05zppz => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #13, 0.90 #17, 0.89 #25), 02zsn (0.21 #84, 0.21 #82, 0.21 #86) >> Best rule #13 for best value: >> intensional similarity = 5 >> extensional distance = 83 >> proper extension: 0j_c; 01pp3p; 0627sn; 012vct; 01d5vk; 03mv0b; 05dxl_; 0py5b; 0gry51; >> query: (?x1775, 05zppz) <- people(?x4322, ?x1775), profession(?x1775, ?x1776), profession(?x1775, ?x524), ?x524 = 02jknp, film_crew_role(?x148, ?x1776) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c59k gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.918 http://example.org/people/person/gender #20170-0b_c7 PRED entity: 0b_c7 PRED relation: profession PRED expected values: 0fj9f => 115 concepts (96 used for prediction) PRED predicted values (max 10 best out of 73): 01d_h8 (0.82 #594, 0.80 #447, 0.78 #2505), 02hrh1q (0.82 #8542, 0.80 #7660, 0.79 #8101), 0cbd2 (0.53 #1477, 0.50 #2800, 0.47 #5448), 03gjzk (0.41 #1043, 0.40 #3395, 0.40 #602), 0kyk (0.36 #1499, 0.35 #2822, 0.32 #3851), 018gz8 (0.33 #751, 0.33 #1633, 0.32 #2074), 02krf9 (0.24 #5320, 0.23 #2966, 0.23 #5173), 05z96 (0.20 #41, 0.13 #3128, 0.13 #3275), 0fj9f (0.20 #53, 0.08 #6524, 0.08 #7553), 0np9r (0.19 #755, 0.16 #2078, 0.14 #1637) >> Best rule #594 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 04v048; >> query: (?x1742, 01d_h8) <- award(?x1742, ?x350), ?x350 = 05f4m9q, profession(?x1742, ?x524) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #53 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 3 *> proper extension: 05fh2; *> query: (?x1742, 0fj9f) <- place_of_birth(?x1742, ?x9660), ?x9660 = 031y2 *> conf = 0.20 ranks of expected_values: 9 EVAL 0b_c7 profession 0fj9f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 115.000 96.000 0.820 http://example.org/people/person/profession #20169-0kvgtf PRED entity: 0kvgtf PRED relation: language PRED expected values: 02h40lc => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.94 #772, 0.93 #2132, 0.93 #2192), 064_8sq (0.20 #21, 0.15 #373, 0.15 #553), 06nm1 (0.11 #662, 0.10 #961, 0.09 #1022), 03_9r (0.10 #9, 0.06 #2786, 0.06 #245), 02bjrlw (0.10 #472, 0.09 #771, 0.09 #594), 06b_j (0.07 #792, 0.06 #615, 0.06 #853), 0jzc (0.06 #2786, 0.05 #313, 0.04 #789), 03k50 (0.06 #2786, 0.05 #66, 0.03 #420), 0653m (0.06 #2786, 0.03 #663, 0.03 #1492), 04h9h (0.06 #2786, 0.03 #812, 0.03 #513) >> Best rule #772 for best value: >> intensional similarity = 4 >> extensional distance = 466 >> proper extension: 05f67hw; >> query: (?x3781, 02h40lc) <- films(?x942, ?x3781), films(?x942, ?x1421), language(?x3781, ?x732), film_release_region(?x1421, ?x87) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kvgtf language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.938 http://example.org/film/film/language #20168-0163r3 PRED entity: 0163r3 PRED relation: award_nominee PRED expected values: 042xrr => 114 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1231): 04lgymt (0.13 #72583, 0.06 #14151, 0.04 #63320), 02p65p (0.13 #72583, 0.04 #58561, 0.03 #53877), 0154qm (0.13 #72583, 0.04 #59274, 0.03 #78008), 01ksr1 (0.13 #72583, 0.02 #59281, 0.02 #78015), 01wcp_g (0.13 #72583, 0.02 #14336, 0.02 #2629), 01wmxfs (0.13 #72583, 0.02 #14209, 0.01 #63217), 02lhm2 (0.13 #72583, 0.02 #3625), 0c6qh (0.13 #72583, 0.02 #59076, 0.02 #77810), 0163r3 (0.13 #72583, 0.02 #25755, 0.01 #63217), 04mn81 (0.13 #72583, 0.02 #70669, 0.02 #63645) >> Best rule #72583 for best value: >> intensional similarity = 3 >> extensional distance = 406 >> proper extension: 01vw917; >> query: (?x6716, ?x100) <- award_nominee(?x6716, ?x1290), artist(?x2039, ?x6716), award_nominee(?x100, ?x1290) >> conf = 0.13 => this is the best rule for 35 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 13 EVAL 0163r3 award_nominee 042xrr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 114.000 37.000 0.135 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20167-01v9724 PRED entity: 01v9724 PRED relation: story_by! PRED expected values: 08y2fn => 126 concepts (120 used for prediction) PRED predicted values (max 10 best out of 220): 08lr6s (0.25 #353, 0.20 #1029, 0.02 #10495), 026p4q7 (0.25 #754, 0.12 #2108, 0.10 #3122), 01cycq (0.25 #930, 0.12 #2284, 0.10 #3298), 0gtt5fb (0.25 #872, 0.12 #2226, 0.10 #3240), 09txzv (0.17 #1405, 0.07 #4449, 0.03 #8167), 0ccd3x (0.17 #3877, 0.06 #6243, 0.05 #4891), 0dp7wt (0.14 #1943, 0.02 #9719, 0.02 #10395), 02mmwk (0.14 #1929, 0.02 #9705, 0.02 #10381), 04x4gw (0.14 #2023, 0.02 #10475, 0.02 #10813), 05sxr_ (0.14 #2007, 0.02 #10459, 0.02 #10797) >> Best rule #353 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02lt8; 05gpy; >> query: (?x5435, 08lr6s) <- profession(?x5435, ?x353), influenced_by(?x6400, ?x5435), influenced_by(?x5034, ?x5435), ?x6400 = 06lbp, award_winner(?x1288, ?x5034) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01v9724 story_by! 08y2fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 120.000 0.250 http://example.org/film/film/story_by #20166-023g6w PRED entity: 023g6w PRED relation: production_companies PRED expected values: 02slt7 => 70 concepts (54 used for prediction) PRED predicted values (max 10 best out of 55): 017s11 (0.40 #2670, 0.36 #1834, 0.35 #1000), 03xsby (0.40 #2670, 0.36 #1834, 0.35 #1000), 01gb54 (0.33 #38, 0.10 #287, 0.09 #453), 05rrtf (0.33 #58, 0.08 #224, 0.03 #557), 05qd_ (0.31 #176, 0.15 #259, 0.10 #593), 02j_j0 (0.27 #380, 0.20 #547, 0.18 #714), 02slt7 (0.25 #113, 0.12 #696, 0.12 #362), 03sb38 (0.20 #387, 0.20 #554, 0.17 #721), 086k8 (0.13 #417, 0.11 #1085, 0.11 #585), 016tw3 (0.13 #511, 0.10 #344, 0.10 #261) >> Best rule #2670 for best value: >> intensional similarity = 3 >> extensional distance = 720 >> proper extension: 016ztl; >> query: (?x8679, ?x1914) <- film(?x1914, ?x8679), titles(?x162, ?x8679), place_founded(?x1914, ?x1036) >> conf = 0.40 => this is the best rule for 2 predicted values *> Best rule #113 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01m13b; 0fpkhkz; *> query: (?x8679, 02slt7) <- film(?x1324, ?x8679), written_by(?x8679, ?x2671), ?x2671 = 04k25, country(?x8679, ?x304) *> conf = 0.25 ranks of expected_values: 7 EVAL 023g6w production_companies 02slt7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 70.000 54.000 0.402 http://example.org/film/film/production_companies #20165-01mz9lt PRED entity: 01mz9lt PRED relation: music! PRED expected values: 052_mn => 120 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1022): 03_gz8 (0.10 #1670, 0.07 #2685, 0.07 #3700), 03hmt9b (0.10 #1405, 0.07 #2420, 0.07 #3435), 09yxcz (0.10 #1972, 0.07 #2987, 0.07 #4002), 021pqy (0.10 #1473, 0.07 #2488, 0.07 #3503), 02w86hz (0.10 #1376, 0.07 #2391, 0.07 #3406), 0209hj (0.10 #1069, 0.01 #8174, 0.01 #9190), 01s7w3 (0.06 #16100, 0.06 #17115, 0.06 #21176), 02ht1k (0.05 #11535, 0.03 #20672, 0.03 #16611), 02rrfzf (0.04 #13523, 0.04 #14538, 0.04 #19614), 0h3k3f (0.04 #10998, 0.03 #17089, 0.03 #12013) >> Best rule #1670 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 01gg59; 0pj8m; 01rwcgb; 01vzz1c; 0bkg87; 0djc3s; 01q8wk7; 05jrj4; >> query: (?x11462, 03_gz8) <- nationality(?x11462, ?x2146), profession(?x11462, ?x8353), artists(?x4910, ?x11462), place_of_birth(?x11462, ?x7412), ?x2146 = 03rk0 >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01mz9lt music! 052_mn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 59.000 0.100 http://example.org/film/film/music #20164-0nqph PRED entity: 0nqph PRED relation: category PRED expected values: 08mbj5d => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.79 #29, 0.78 #82, 0.78 #86) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 0104lr; >> query: (?x13949, 08mbj5d) <- time_zones(?x13949, ?x1638), ?x1638 = 02fqwt, place(?x13949, ?x13949) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nqph category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.792 http://example.org/common/topic/webpage./common/webpage/category #20163-07dfk PRED entity: 07dfk PRED relation: citytown! PRED expected values: 06p8m 01tlrp => 250 concepts (201 used for prediction) PRED predicted values (max 10 best out of 671): 024bqj (0.53 #38152, 0.26 #108247, 0.15 #130835), 03pmfw (0.44 #47496, 0.38 #47497, 0.37 #35036), 0212zp (0.35 #6228, 0.07 #95002, 0.06 #10120), 0211jt (0.35 #6228, 0.06 #10120, 0.06 #21019), 09k0h5 (0.33 #714, 0.28 #48276, 0.12 #5385), 025txrl (0.33 #628, 0.20 #6856, 0.14 #1406), 01swdw (0.33 #607, 0.12 #5278, 0.10 #6835), 026v1z (0.33 #555, 0.12 #5226, 0.10 #6783), 01dtcb (0.30 #6608, 0.14 #1158, 0.12 #19841), 05cl8y (0.30 #6637, 0.14 #1187, 0.09 #29992) >> Best rule #38152 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0l35f; >> query: (?x9559, ?x8951) <- mode_of_transportation(?x9559, ?x6665), ?x6665 = 025t3bg, contains(?x9559, ?x8951) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #48276 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 0k_mf; 01pt5w; *> query: (?x9559, ?x13291) <- citytown(?x11071, ?x9559), citytown(?x8125, ?x9559), organization(?x4682, ?x8125), child(?x13291, ?x11071) *> conf = 0.28 ranks of expected_values: 13 EVAL 07dfk citytown! 01tlrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 250.000 201.000 0.528 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 07dfk citytown! 06p8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 250.000 201.000 0.528 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #20162-01hkhq PRED entity: 01hkhq PRED relation: profession PRED expected values: 02hrh1q => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 101): 02hrh1q (0.89 #3465, 0.89 #6916, 0.89 #3765), 03gjzk (0.59 #2116, 0.34 #1216, 0.30 #1066), 0dxtg (0.57 #2114, 0.37 #4801, 0.35 #1064), 01d_h8 (0.49 #1056, 0.43 #1206, 0.39 #906), 02jknp (0.37 #4801, 0.28 #14103, 0.26 #1058), 0fj9f (0.33 #56, 0.21 #656, 0.17 #1406), 0cbd2 (0.32 #1807, 0.25 #607, 0.22 #1507), 0kyk (0.28 #14103, 0.26 #1831, 0.26 #781), 018gz8 (0.28 #14103, 0.16 #3468, 0.15 #3768), 0np9r (0.28 #14103, 0.14 #16826, 0.14 #16526) >> Best rule #3465 for best value: >> intensional similarity = 3 >> extensional distance = 291 >> proper extension: 01m65sp; 01tpl1p; 0d0l91; >> query: (?x2493, 02hrh1q) <- people(?x743, ?x2493), location(?x2493, ?x1523), actor(?x6023, ?x2493) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hkhq profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.894 http://example.org/people/person/profession #20161-0d05w3 PRED entity: 0d05w3 PRED relation: nationality! PRED expected values: 01d1yr 02p59ry 04jb97 0139q5 => 233 concepts (103 used for prediction) PRED predicted values (max 10 best out of 4051): 04h68j (0.53 #412531, 0.22 #279063, 0.20 #31513), 0139q5 (0.53 #412531, 0.22 #279063, 0.20 #279062), 041xl (0.53 #412531, 0.22 #279063, 0.20 #279062), 01l_vgt (0.53 #412531, 0.22 #279063, 0.20 #279062), 02wk4d (0.53 #412531, 0.22 #279063, 0.20 #279062), 02p59ry (0.53 #412531, 0.22 #279063, 0.20 #279062), 034rd (0.40 #30052, 0.17 #34096, 0.15 #54316), 059xvg (0.33 #33402, 0.25 #41490, 0.25 #37446), 07m69t (0.33 #35049, 0.25 #43137, 0.25 #39093), 0202p_ (0.33 #36013, 0.25 #44101, 0.25 #40057) >> Best rule #412531 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 022_6; 0dbdy; 0121c1; 06y9v; 01w0v; 02ly_; 0125q1; 028n3; 0dyjz; 07c98; ... >> query: (?x2346, ?x10186) <- administrative_parent(?x206, ?x2346), contains(?x2346, ?x12917), place_of_birth(?x10186, ?x12917) >> conf = 0.53 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 6, 3792, 3844 EVAL 0d05w3 nationality! 0139q5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 233.000 103.000 0.534 http://example.org/people/person/nationality EVAL 0d05w3 nationality! 04jb97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 233.000 103.000 0.534 http://example.org/people/person/nationality EVAL 0d05w3 nationality! 02p59ry CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 233.000 103.000 0.534 http://example.org/people/person/nationality EVAL 0d05w3 nationality! 01d1yr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 233.000 103.000 0.534 http://example.org/people/person/nationality #20160-0j5q3 PRED entity: 0j5q3 PRED relation: award_winner! PRED expected values: 057xs89 => 91 concepts (84 used for prediction) PRED predicted values (max 10 best out of 272): 03c7tr1 (0.34 #4722, 0.30 #34344, 0.30 #34343), 05zvj3m (0.34 #4722, 0.30 #34344, 0.30 #34343), 09sb52 (0.13 #7336, 0.10 #13774, 0.09 #11200), 05pcn59 (0.11 #1798, 0.08 #2228, 0.07 #4373), 0gqwc (0.10 #2221, 0.10 #74, 0.09 #503), 0ck27z (0.10 #92, 0.09 #521, 0.08 #951), 094qd5 (0.10 #44, 0.09 #473, 0.08 #903), 04kxsb (0.10 #126, 0.09 #555, 0.08 #985), 05p09zm (0.09 #5275, 0.08 #4416, 0.08 #2271), 03nqnk3 (0.09 #562, 0.05 #133, 0.04 #992) >> Best rule #4722 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 02dth1; >> query: (?x7056, ?x1007) <- place_of_birth(?x7056, ?x3125), award(?x7056, ?x1007), friend(?x7056, ?x2796) >> conf = 0.34 => this is the best rule for 2 predicted values *> Best rule #3592 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 0grwj; 014zcr; 01tvz5j; 041h0; 0147dk; 03f2_rc; 0mdqp; 01hxs4; 05zbm4; 03_vx9; ... *> query: (?x7056, 057xs89) <- people(?x743, ?x7056), friend(?x2796, ?x7056), gender(?x7056, ?x514) *> conf = 0.04 ranks of expected_values: 100 EVAL 0j5q3 award_winner! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 91.000 84.000 0.338 http://example.org/award/award_category/winners./award/award_honor/award_winner #20159-06f32 PRED entity: 06f32 PRED relation: form_of_government PRED expected values: 01fpfn => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 4): 01q20 (0.44 #23, 0.37 #31, 0.33 #11), 01fpfn (0.44 #118, 0.41 #318, 0.40 #162), 018wl5 (0.43 #69, 0.42 #9, 0.38 #113), 026wp (0.17 #48, 0.11 #56, 0.10 #108) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 047yc; 015qh; >> query: (?x2629, 01q20) <- film_release_region(?x1999, ?x2629), combatants(?x2629, ?x94), ?x1999 = 0gd0c7x, ?x94 = 09c7w0 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #118 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 04thp; *> query: (?x2629, 01fpfn) <- contains(?x6956, ?x2629), contains(?x6304, ?x2629), ?x6304 = 02qkt, ?x6956 = 0j0k *> conf = 0.44 ranks of expected_values: 2 EVAL 06f32 form_of_government 01fpfn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 150.000 150.000 0.438 http://example.org/location/country/form_of_government #20158-071t0 PRED entity: 071t0 PRED relation: country PRED expected values: 03rt9 03gj2 09pmkv 07ww5 01znc_ 056vv 0jgx 07dvs 07fj_ 05r7t 06m_5 01crd5 => 40 concepts (33 used for prediction) PRED predicted values (max 10 best out of 229): 01crd5 (0.71 #1386, 0.67 #1059, 0.64 #2039), 01znc_ (0.71 #1211, 0.64 #1865, 0.61 #2948), 03gj2 (0.67 #1530, 0.64 #652, 0.64 #1858), 03rt9 (0.67 #2073, 0.61 #2948, 0.60 #875), 087vz (0.64 #652, 0.57 #2946, 0.51 #1524), 01rdm0 (0.64 #652, 0.51 #1524, 0.50 #1307), 059z0 (0.64 #652, 0.51 #1524, 0.50 #1307), 0193qj (0.64 #652, 0.51 #1524, 0.50 #1307), 02psqkz (0.64 #652, 0.51 #1524, 0.50 #1307), 03bxbql (0.64 #652, 0.51 #1524, 0.50 #1307) >> Best rule #1386 for best value: >> intensional similarity = 38 >> extensional distance = 5 >> proper extension: 01cgz; 06f41; >> query: (?x3015, 01crd5) <- country(?x3015, ?x8958), country(?x3015, ?x4743), country(?x3015, ?x2756), country(?x3015, ?x2152), country(?x3015, ?x2146), country(?x3015, ?x291), film_release_region(?x9565, ?x4743), film_release_region(?x6761, ?x4743), film_release_region(?x5496, ?x4743), film_release_region(?x5070, ?x4743), film_release_region(?x4518, ?x4743), film_release_region(?x4040, ?x4743), film_release_region(?x3000, ?x4743), film_release_region(?x2788, ?x4743), film_release_region(?x2714, ?x4743), film_release_region(?x2709, ?x4743), ?x2709 = 06ztvyx, ?x3000 = 045j3w, location(?x12255, ?x4743), ?x2146 = 03rk0, religion(?x4743, ?x7131), jurisdiction_of_office(?x182, ?x4743), olympics(?x4743, ?x2966), ?x6761 = 05ft32, ?x2788 = 05q4y12, ?x4040 = 02mt51, ?x291 = 0h3y, ?x2152 = 06mkj, ?x2966 = 06sks6, ?x2714 = 0kv238, olympics(?x3015, ?x1931), ?x4518 = 0hgnl3t, ?x9565 = 0hz6mv2, administrative_area_type(?x8958, ?x2792), ?x5496 = 07l50vn, ?x5070 = 0dt8xq, currency(?x2756, ?x170), organization(?x4743, ?x127) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 13, 21, 33, 34, 44, 84, 111, 140 EVAL 071t0 country 01crd5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 06m_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 05r7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 07fj_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 07dvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 056vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 07ww5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 09pmkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 071t0 country 03rt9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 33.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #20157-0m4yg PRED entity: 0m4yg PRED relation: school_type PRED expected values: 05jxkf => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 21): 05jxkf (0.53 #436, 0.48 #580, 0.47 #124), 07tf8 (0.33 #9, 0.25 #33, 0.20 #225), 01rs41 (0.27 #245, 0.26 #413, 0.26 #822), 05pcjw (0.26 #1130, 0.26 #818, 0.26 #337), 01jlsn (0.20 #89, 0.18 #113, 0.13 #137), 0m4mb (0.20 #83, 0.09 #107, 0.07 #131), 02p0qmm (0.12 #394, 0.10 #442, 0.10 #298), 01y64 (0.12 #348, 0.12 #252, 0.10 #420), 01_9fk (0.11 #651, 0.10 #410, 0.10 #218), 0bwd5 (0.07 #139, 0.06 #187, 0.05 #211) >> Best rule #436 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 071_8; 01dbns; 013719; 0ym69; >> query: (?x9844, 05jxkf) <- currency(?x9844, ?x1099), state_province_region(?x9844, ?x362), major_field_of_study(?x9844, ?x6760) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m4yg school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.525 http://example.org/education/educational_institution/school_type #20156-018ygt PRED entity: 018ygt PRED relation: student! PRED expected values: 026gvfj => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 166): 03ksy (0.22 #106, 0.14 #1678, 0.09 #1154), 0g8rj (0.11 #176, 0.05 #1748, 0.05 #700), 07wjk (0.11 #63, 0.03 #1111, 0.02 #13691), 04bfg (0.11 #225), 01w5m (0.09 #629, 0.08 #1677, 0.05 #13733), 01mpwj (0.09 #631, 0.08 #1679, 0.04 #2727), 08815 (0.07 #6815, 0.05 #3670, 0.04 #5243), 065y4w7 (0.06 #1062, 0.05 #16787, 0.05 #5779), 02l9wl (0.05 #1822, 0.05 #2346, 0.05 #774), 05qgd9 (0.05 #2032, 0.05 #984, 0.03 #3080) >> Best rule #106 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0284gcb; >> query: (?x6324, 03ksy) <- award_winner(?x832, ?x6324), nominated_for(?x6324, ?x1135), ?x832 = 02778pf >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #9020 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 141 *> proper extension: 01rc4p; 037s5h; 01hdht; *> query: (?x6324, 026gvfj) <- location(?x6324, ?x335), student(?x5750, ?x6324), spouse(?x6324, ?x4930) *> conf = 0.03 ranks of expected_values: 26 EVAL 018ygt student! 026gvfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 133.000 133.000 0.222 http://example.org/education/educational_institution/students_graduates./education/education/student #20155-02lvtb PRED entity: 02lvtb PRED relation: type_of_union PRED expected values: 04ztj => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.81 #193, 0.81 #209, 0.81 #185), 01g63y (0.15 #22, 0.13 #66, 0.13 #243), 01bl8s (0.02 #71, 0.02 #35, 0.02 #75), 0jgjn (0.01 #68) >> Best rule #193 for best value: >> intensional similarity = 3 >> extensional distance = 344 >> proper extension: 0h1_w; 030pr; 0hwd8; 04n_g; 03q95r; 04107; 03wd5tk; 0c_drn; 03bw6; 0gm34; ... >> query: (?x5119, 04ztj) <- award(?x5119, ?x537), people(?x5118, ?x5119), risk_factors(?x5118, ?x231) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lvtb type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.812 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20154-0pv2t PRED entity: 0pv2t PRED relation: genre PRED expected values: 06cvj 02l7c8 => 106 concepts (105 used for prediction) PRED predicted values (max 10 best out of 116): 02l7c8 (0.71 #5702, 0.53 #7523, 0.53 #1091), 04xvlr (0.43 #1939, 0.24 #243, 0.23 #486), 01jfsb (0.33 #3528, 0.33 #4256, 0.33 #3892), 0lsxr (0.32 #129, 0.32 #8, 0.25 #1099), 02kdv5l (0.29 #7647, 0.29 #1093, 0.28 #3882), 060__y (0.25 #1955, 0.23 #138, 0.19 #502), 03k9fj (0.23 #7656, 0.21 #11535, 0.21 #6684), 06cvj (0.23 #1578, 0.10 #488, 0.10 #8011), 03bxz7 (0.23 #177, 0.19 #419, 0.15 #662), 06l3bl (0.23 #160, 0.13 #281, 0.13 #402) >> Best rule #5702 for best value: >> intensional similarity = 4 >> extensional distance = 812 >> proper extension: 09rfpk; >> query: (?x833, ?x1403) <- country(?x833, ?x94), titles(?x1403, ?x833), nominated_for(?x340, ?x833), genre(?x83, ?x1403) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 0pv2t genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 105.000 0.713 http://example.org/film/film/genre EVAL 0pv2t genre 06cvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 106.000 105.000 0.713 http://example.org/film/film/genre #20153-09pmkv PRED entity: 09pmkv PRED relation: film_release_region! PRED expected values: 04n52p6 0fphgb 02w86hz 07s846j 047vnkj 0m63c => 150 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1232): 0gd0c7x (0.88 #3907, 0.80 #24851, 0.79 #29779), 040rmy (0.88 #3963, 0.66 #8891, 0.65 #2731), 07jqjx (0.88 #4779, 0.65 #3547, 0.60 #9707), 0645k5 (0.88 #4007, 0.63 #15095, 0.61 #2775), 0gmcwlb (0.83 #3833, 0.78 #2601, 0.69 #8761), 06fcqw (0.83 #4456, 0.74 #3224, 0.73 #15544), 01vksx (0.83 #3785, 0.74 #2553, 0.73 #24729), 04n52p6 (0.83 #3871, 0.74 #2639, 0.68 #14959), 0jjy0 (0.83 #3808, 0.71 #24752, 0.70 #2576), 03nsm5x (0.83 #4654, 0.65 #3422, 0.63 #25598) >> Best rule #3907 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 05r4w; 09c7w0; 0jgd; 0154j; 03_3d; 0d060g; 0d0vqn; 0chghy; 05qhw; 07ssc; ... >> query: (?x1122, 0gd0c7x) <- film_release_region(?x3201, ?x1122), olympics(?x1122, ?x2966), ?x3201 = 01ffx4 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3871 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 05r4w; 09c7w0; 0jgd; 0154j; 03_3d; 0d060g; 0d0vqn; 0chghy; 05qhw; 07ssc; ... *> query: (?x1122, 04n52p6) <- film_release_region(?x3201, ?x1122), olympics(?x1122, ?x2966), ?x3201 = 01ffx4 *> conf = 0.83 ranks of expected_values: 8, 13, 32, 105, 221, 332 EVAL 09pmkv film_release_region! 0m63c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 150.000 102.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09pmkv film_release_region! 047vnkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 150.000 102.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09pmkv film_release_region! 07s846j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 150.000 102.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09pmkv film_release_region! 02w86hz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 150.000 102.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09pmkv film_release_region! 0fphgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 150.000 102.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09pmkv film_release_region! 04n52p6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 150.000 102.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20152-01qz5 PRED entity: 01qz5 PRED relation: titles! PRED expected values: 07s9rl0 => 104 concepts (60 used for prediction) PRED predicted values (max 10 best out of 60): 07s9rl0 (0.77 #3436, 0.47 #605, 0.45 #404), 01z4y (0.60 #34, 0.50 #134, 0.35 #337), 0bynt (0.48 #302, 0.40 #301, 0.04 #2421), 0nbjq (0.40 #301, 0.04 #2421), 01cgz (0.33 #227, 0.02 #2345, 0.02 #3462), 017fp (0.24 #222, 0.15 #3457, 0.13 #425), 03g3w (0.22 #5163, 0.22 #4859, 0.21 #604), 04t36 (0.20 #7, 0.13 #911, 0.12 #711), 01jfsb (0.20 #118, 0.12 #2946, 0.12 #722), 06qm3 (0.20 #49, 0.10 #149, 0.02 #1961) >> Best rule #3436 for best value: >> intensional similarity = 3 >> extensional distance = 518 >> proper extension: 01qn7n; 03y317; >> query: (?x8188, 07s9rl0) <- titles(?x162, ?x8188), titles(?x162, ?x7243), ?x7243 = 0yzbg >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qz5 titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 60.000 0.773 http://example.org/media_common/netflix_genre/titles #20151-0crs0b8 PRED entity: 0crs0b8 PRED relation: nominated_for! PRED expected values: 0fq9zdn => 119 concepts (101 used for prediction) PRED predicted values (max 10 best out of 209): 0gs9p (0.40 #307, 0.36 #1030, 0.33 #4404), 040njc (0.40 #248, 0.36 #971, 0.29 #6032), 0gq9h (0.33 #1510, 0.26 #4402, 0.25 #4643), 0gr0m (0.29 #4399, 0.29 #4640, 0.28 #1507), 02qyntr (0.29 #4521, 0.29 #4762, 0.25 #183), 0gr51 (0.29 #1044, 0.22 #1767, 0.20 #1285), 0gr4k (0.28 #1473, 0.23 #750, 0.21 #6052), 0k611 (0.28 #1521, 0.21 #4413, 0.20 #4654), 02pqp12 (0.28 #4398, 0.27 #4639, 0.26 #6085), 0gs96 (0.28 #4430, 0.25 #4671, 0.20 #333) >> Best rule #307 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 02rb607; >> query: (?x9209, 0gs9p) <- story_by(?x9209, ?x11271), genre(?x9209, ?x604), country(?x9209, ?x512), film_festivals(?x9209, ?x10083), film_release_region(?x9209, ?x94), ?x604 = 0lsxr >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #4384 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 56 *> proper extension: 02gd6x; *> query: (?x9209, 0fq9zdn) <- genre(?x9209, ?x11108), genre(?x5122, ?x11108), production_companies(?x9209, ?x7303), titles(?x512, ?x9209), ?x5122 = 07z6xs, region(?x54, ?x512) *> conf = 0.12 ranks of expected_values: 64 EVAL 0crs0b8 nominated_for! 0fq9zdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 119.000 101.000 0.400 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20150-069ld1 PRED entity: 069ld1 PRED relation: award_nominee! PRED expected values: 016yr0 => 76 concepts (21 used for prediction) PRED predicted values (max 10 best out of 589): 04lp8k (0.81 #32598, 0.81 #39585, 0.81 #41915), 01j5x6 (0.81 #39585, 0.81 #41915, 0.81 #20956), 069ld1 (0.50 #2505, 0.44 #7162, 0.41 #11640), 016yr0 (0.41 #11640, 0.29 #16296, 0.25 #7994), 03lmzl (0.41 #11640, 0.29 #16296, 0.24 #23284), 03f4xvm (0.41 #11640, 0.29 #16296, 0.24 #23284), 027bs_2 (0.38 #1650, 0.33 #6306, 0.25 #44245), 01900g (0.33 #5704, 0.25 #1048, 0.25 #44245), 02x0dzw (0.33 #6562, 0.25 #1906, 0.01 #20531), 0292l3 (0.33 #4956, 0.25 #300) >> Best rule #32598 for best value: >> intensional similarity = 3 >> extensional distance = 1085 >> proper extension: 06449; 011hdn; 03_2y; >> query: (?x890, ?x3866) <- award_nominee(?x890, ?x3866), student(?x3424, ?x3866), location(?x890, ?x3014) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #11640 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 517 *> proper extension: 01rrwf6; 04n7njg; 045bs6; 01wk7b7; 0309jm; 01rnxn; 030x48; 05r5w; 04h07s; 044mfr; ... *> query: (?x890, ?x879) <- actor(?x8870, ?x890), actor(?x8870, ?x879), honored_for(?x762, ?x8870) *> conf = 0.41 ranks of expected_values: 4 EVAL 069ld1 award_nominee! 016yr0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 21.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20149-01wv9p PRED entity: 01wv9p PRED relation: award PRED expected values: 026rsl9 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 306): 01bgqh (0.43 #1243, 0.41 #2443, 0.41 #2843), 02f73b (0.43 #1487, 0.22 #3487, 0.21 #2687), 01by1l (0.36 #4511, 0.36 #1311, 0.36 #2511), 02f6ym (0.36 #1458, 0.30 #3458, 0.20 #3058), 02f705 (0.36 #1352, 0.21 #4552, 0.17 #5352), 02f716 (0.36 #1376, 0.17 #576, 0.16 #4576), 054ks3 (0.34 #2541, 0.32 #2941, 0.32 #2141), 05pcn59 (0.33 #481, 0.26 #10481, 0.18 #14881), 02f777 (0.33 #709, 0.23 #3509, 0.14 #4709), 05p09zm (0.33 #523, 0.17 #6523, 0.16 #9323) >> Best rule #1243 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02wb6yq; >> query: (?x4123, 01bgqh) <- nominated_for(?x4123, ?x10742), origin(?x4123, ?x94), friend(?x6187, ?x4123) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1137 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 11 *> proper extension: 01v27pl; *> query: (?x4123, 026rsl9) <- artists(?x10319, ?x4123), ?x10319 = 01gjw *> conf = 0.08 ranks of expected_values: 113 EVAL 01wv9p award 026rsl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 132.000 132.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20148-083q7 PRED entity: 083q7 PRED relation: place_of_death PRED expected values: 0rh6k => 205 concepts (174 used for prediction) PRED predicted values (max 10 best out of 62): 0hkpn (0.33 #317, 0.14 #2259, 0.11 #3036), 02_286 (0.25 #2343, 0.20 #3707, 0.18 #3901), 04jpl (0.14 #2143, 0.12 #2726, 0.11 #3310), 01ktz1 (0.14 #5253, 0.08 #23561, 0.08 #23171), 030qb3t (0.14 #25332, 0.13 #27666, 0.13 #23388), 0f2rq (0.11 #3194, 0.09 #4753, 0.08 #5144), 0rh6k (0.10 #7789, 0.10 #8567, 0.08 #15762), 0f2wj (0.10 #11106, 0.09 #13440, 0.09 #3900), 0156q (0.10 #3522, 0.09 #4301, 0.09 #3911), 0d6lp (0.10 #3740, 0.08 #5299, 0.07 #6470) >> Best rule #317 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 05hks; >> query: (?x1159, 0hkpn) <- entity_involved(?x5503, ?x1159), type_of_union(?x1159, ?x566), profession(?x1159, ?x2606), ?x5503 = 0cm2xh >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7789 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 09jrf; *> query: (?x1159, 0rh6k) <- entity_involved(?x5503, ?x1159), type_of_union(?x1159, ?x566), student(?x1768, ?x1159), ?x566 = 04ztj *> conf = 0.10 ranks of expected_values: 7 EVAL 083q7 place_of_death 0rh6k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 205.000 174.000 0.333 http://example.org/people/deceased_person/place_of_death #20147-05f4m9q PRED entity: 05f4m9q PRED relation: nominated_for PRED expected values: 04ddm4 04fzfj 03z20c 04fv5b 01mszz 0296rz => 46 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1391): 01qvz8 (0.76 #4560, 0.71 #2211, 0.68 #25867), 082scv (0.76 #4560, 0.68 #25867, 0.66 #24340), 05b_gq (0.76 #4560, 0.68 #25867, 0.66 #24340), 0n83s (0.76 #4560, 0.68 #25867, 0.66 #24340), 03cvwkr (0.76 #4560, 0.68 #25867, 0.66 #24340), 026n4h6 (0.76 #4560, 0.68 #25867, 0.66 #24340), 01mszz (0.57 #2438, 0.50 #919, 0.27 #3959), 074rg9 (0.57 #2348, 0.25 #829, 0.22 #25866), 01771z (0.57 #1892, 0.25 #373, 0.18 #3413), 037xlx (0.55 #3881, 0.50 #841, 0.29 #2360) >> Best rule #4560 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 03m73lj; >> query: (?x350, ?x915) <- nominated_for(?x350, ?x1173), nominated_for(?x350, ?x508), award(?x915, ?x350), ?x1173 = 0872p_c, film_crew_role(?x508, ?x137) >> conf = 0.76 => this is the best rule for 6 predicted values *> Best rule #2438 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 03c7tr1; 07cbcy; *> query: (?x350, 01mszz) <- award(?x3873, ?x350), nominated_for(?x350, ?x4127), gender(?x3873, ?x231), type_of_union(?x3873, ?x566), ?x4127 = 049mql *> conf = 0.57 ranks of expected_values: 7, 15, 17, 19, 20, 220 EVAL 05f4m9q nominated_for 0296rz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 17.000 0.762 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05f4m9q nominated_for 01mszz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 46.000 17.000 0.762 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05f4m9q nominated_for 04fv5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 46.000 17.000 0.762 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05f4m9q nominated_for 03z20c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 46.000 17.000 0.762 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05f4m9q nominated_for 04fzfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 46.000 17.000 0.762 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05f4m9q nominated_for 04ddm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 46.000 17.000 0.762 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20146-02_1kl PRED entity: 02_1kl PRED relation: actor PRED expected values: 030hbp => 73 concepts (53 used for prediction) PRED predicted values (max 10 best out of 789): 026n9h3 (0.38 #10161, 0.37 #12934, 0.37 #15704), 0443y3 (0.23 #2936, 0.20 #165, 0.18 #1088), 031ydm (0.20 #339, 0.18 #1262, 0.17 #37867), 03zyvw (0.17 #37867, 0.15 #3064, 0.12 #4912), 03q5dr (0.17 #37867, 0.15 #3509, 0.10 #738), 02q3bb (0.17 #37867, 0.12 #11086, 0.09 #20325), 04vq3h (0.17 #37867, 0.12 #11086, 0.09 #20325), 038bht (0.17 #37867, 0.12 #11086, 0.09 #20325), 01pgzn_ (0.17 #37867, 0.10 #179, 0.09 #1102), 01qr1_ (0.17 #37867, 0.10 #279, 0.09 #1202) >> Best rule #10161 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 01b7h8; >> query: (?x7175, ?x6970) <- nominated_for(?x6970, ?x7175), producer_type(?x7175, ?x632), languages(?x7175, ?x254), award_nominee(?x439, ?x6970) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #16484 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 128 *> proper extension: 0bx_hnp; 03cf9ly; *> query: (?x7175, 030hbp) <- nominated_for(?x6970, ?x7175), languages(?x7175, ?x254), program(?x1762, ?x7175), award_nominee(?x6970, ?x911) *> conf = 0.02 ranks of expected_values: 386 EVAL 02_1kl actor 030hbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 73.000 53.000 0.383 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #20145-015v3r PRED entity: 015v3r PRED relation: award PRED expected values: 05pcn59 09sdmz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 274): 09sb52 (0.43 #440, 0.38 #841, 0.35 #18486), 05p09zm (0.29 #523, 0.25 #924, 0.21 #2127), 05pcn59 (0.25 #2486, 0.21 #2085, 0.21 #6095), 057xs89 (0.25 #960, 0.14 #559, 0.13 #2564), 03c7tr1 (0.20 #2062, 0.17 #2463, 0.13 #2864), 09sdmz (0.20 #1406, 0.15 #37300, 0.13 #36096), 02z0dfh (0.20 #1277, 0.14 #28475, 0.13 #36096), 027dtxw (0.20 #1207, 0.13 #36096, 0.12 #34491), 09td7p (0.20 #1322, 0.13 #36096, 0.12 #34491), 02ppm4q (0.20 #1358, 0.13 #36096, 0.12 #34491) >> Best rule #440 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 05fnl9; 0320jz; 02cllz; 03l3jy; 01pk8v; >> query: (?x3138, 09sb52) <- film(?x3138, ?x2037), ?x2037 = 0gvrws1, award_nominee(?x3138, ?x156) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2486 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 112 *> proper extension: 04d_mtq; *> query: (?x3138, 05pcn59) <- people(?x3584, ?x3138), gender(?x3138, ?x231), vacationer(?x4698, ?x3138) *> conf = 0.25 ranks of expected_values: 3, 6 EVAL 015v3r award 09sdmz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 112.000 112.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 015v3r award 05pcn59 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 112.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20144-02g8h PRED entity: 02g8h PRED relation: influenced_by! PRED expected values: 03g5_y => 98 concepts (27 used for prediction) PRED predicted values (max 10 best out of 224): 0c00lh (0.09 #1259, 0.03 #2808, 0.02 #4359), 040db (0.08 #1625, 0.05 #8868, 0.05 #9904), 06whf (0.08 #1714, 0.02 #8957, 0.02 #8440), 03_87 (0.08 #1811, 0.02 #9054, 0.02 #10607), 07h07 (0.08 #1701, 0.01 #8427, 0.01 #8944), 014z8v (0.07 #8274, 0.05 #7236, 0.05 #4649), 01hmk9 (0.07 #8274, 0.05 #4649, 0.04 #5166), 01xwv7 (0.07 #3524, 0.07 #5592, 0.07 #3007), 016_mj (0.06 #1087, 0.06 #5221, 0.06 #3153), 0dzf_ (0.06 #1214, 0.05 #7236, 0.05 #7755) >> Best rule #1259 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 05ty4m; 06pjs; >> query: (?x318, 0c00lh) <- influenced_by(?x318, ?x4112), location(?x318, ?x739), produced_by(?x9129, ?x318) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #8275 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 203 *> proper extension: 0716b6; *> query: (?x318, ?x1835) <- influenced_by(?x318, ?x7183), influenced_by(?x1835, ?x7183), category(?x7183, ?x134) *> conf = 0.04 ranks of expected_values: 46 EVAL 02g8h influenced_by! 03g5_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 98.000 27.000 0.091 http://example.org/influence/influence_node/influenced_by #20143-017zq0 PRED entity: 017zq0 PRED relation: student PRED expected values: 0cmt6q => 119 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1299): 02jsgf (0.11 #680, 0.10 #2767, 0.09 #4854), 02ndbd (0.11 #113, 0.10 #2200, 0.09 #4287), 01gv_f (0.10 #2710, 0.05 #623, 0.04 #4797), 0chsq (0.10 #2150, 0.05 #63, 0.04 #4237), 02nwxc (0.10 #3082, 0.05 #995, 0.04 #5169), 06y9c2 (0.10 #2174, 0.05 #87, 0.04 #4261), 073v6 (0.09 #4701, 0.05 #527, 0.05 #2614), 01f7j9 (0.05 #330, 0.05 #2417, 0.04 #4504), 013zyw (0.05 #1007, 0.05 #3094, 0.04 #5181), 0glmv (0.05 #516, 0.05 #2603, 0.04 #4690) >> Best rule #680 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0178g; 02ktt7; >> query: (?x1440, 02jsgf) <- organization(?x346, ?x1440), state_province_region(?x1440, ?x3818), ?x3818 = 03v0t, category(?x1440, ?x134) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 017zq0 student 0cmt6q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 54.000 0.105 http://example.org/education/educational_institution/students_graduates./education/education/student #20142-01xqqp PRED entity: 01xqqp PRED relation: award_winner PRED expected values: 015rmq 01wwvd2 01z9_x => 30 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1564): 01w60_p (0.67 #13923, 0.40 #9380, 0.38 #18468), 032nwy (0.62 #19734, 0.50 #4597, 0.50 #3085), 06fmdb (0.60 #12107, 0.60 #11387, 0.50 #12901), 0gcs9 (0.60 #21623, 0.57 #23136, 0.50 #20111), 01vw20h (0.60 #21875, 0.50 #23388, 0.33 #2204), 02r3zy (0.60 #9221, 0.40 #10735, 0.33 #12249), 03h_fk5 (0.60 #9485, 0.40 #10999, 0.33 #12513), 0hl3d (0.50 #18199, 0.50 #15170, 0.50 #13654), 01lmj3q (0.50 #18202, 0.50 #12142, 0.50 #6090), 02cx90 (0.50 #21844, 0.50 #20332, 0.50 #3683) >> Best rule #13923 for best value: >> intensional similarity = 25 >> extensional distance = 4 >> proper extension: 09n4nb; >> query: (?x6869, 01w60_p) <- ceremony(?x12458, ?x6869), ceremony(?x8505, ?x6869), ceremony(?x8141, ?x6869), ceremony(?x5765, ?x6869), ceremony(?x4382, ?x6869), ceremony(?x2420, ?x6869), ?x8141 = 024_41, award_winner(?x6869, ?x13842), award_winner(?x6869, ?x6207), ?x12458 = 024_dt, ?x2420 = 026mfs, award(?x6418, ?x4382), award(?x4675, ?x4382), award(?x4381, ?x4382), award(?x2662, ?x4382), award(?x1270, ?x4382), ?x2662 = 045zr, ?x5765 = 024_fw, type_of_union(?x13842, ?x566), ?x4675 = 026spg, ?x6207 = 01htxr, ?x1270 = 0137n0, ?x8505 = 02fm4d, ?x6418 = 013423, instrumentalists(?x227, ?x4381) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #28774 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 26 *> proper extension: 0hhtgcw; *> query: (?x6869, ?x2584) <- award_winner(?x6869, ?x6207), award_winner(?x6869, ?x5543), award_winner(?x6869, ?x2461), award_winner(?x6869, ?x1128), artists(?x3061, ?x5543), award_nominee(?x2461, ?x538), award(?x2461, ?x724), ?x3061 = 05bt6j, artist(?x2039, ?x1128), award_winner(?x2584, ?x2461), place_of_birth(?x6207, ?x12738), type_of_union(?x6207, ?x566), ?x724 = 01bgqh, participant(?x3884, ?x6207), award_winner(?x537, ?x538), instrumentalists(?x227, ?x5543), award_nominee(?x538, ?x772) *> conf = 0.23 ranks of expected_values: 239, 253, 383 EVAL 01xqqp award_winner 01z9_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 30.000 20.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01xqqp award_winner 01wwvd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 30.000 20.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01xqqp award_winner 015rmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 30.000 20.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20141-02p7_k PRED entity: 02p7_k PRED relation: award_nominee PRED expected values: 050t68 => 88 concepts (40 used for prediction) PRED predicted values (max 10 best out of 718): 03q95r (0.83 #11612, 0.82 #13935, 0.81 #9289), 016ks_ (0.83 #11612, 0.82 #13935, 0.81 #9289), 01vyv9 (0.83 #11612, 0.82 #13935, 0.81 #9289), 0f6_dy (0.83 #11612, 0.82 #13935, 0.81 #9289), 05th8t (0.83 #11612, 0.82 #13935, 0.81 #9289), 050t68 (0.71 #5536, 0.48 #7858, 0.16 #88259), 02p7_k (0.53 #5460, 0.38 #7782, 0.32 #10105), 02ck7w (0.52 #10526, 0.22 #13937, 0.16 #12849), 0241jw (0.52 #9678, 0.22 #13937, 0.16 #88259), 09wj5 (0.48 #9405, 0.22 #13937, 0.16 #11728) >> Best rule #11612 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 09wj5; 0785v8; 05ml_s; 01rh0w; 0241jw; 0f6_dy; 03mcwq3; 05th8t; 01v9l67; 015t56; ... >> query: (?x3660, ?x819) <- award_nominee(?x819, ?x3660), award_nominee(?x230, ?x3660), ?x230 = 02bfmn, award_nominee(?x3660, ?x450) >> conf = 0.83 => this is the best rule for 5 predicted values *> Best rule #5536 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 0652ty; *> query: (?x3660, 050t68) <- film(?x3660, ?x8770), film(?x3660, ?x2336), ?x2336 = 016z9n, film_release_region(?x8770, ?x94) *> conf = 0.71 ranks of expected_values: 6 EVAL 02p7_k award_nominee 050t68 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 40.000 0.832 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20140-0cbv4g PRED entity: 0cbv4g PRED relation: nominated_for! PRED expected values: 03hkv_r 03hj5vf => 114 concepts (108 used for prediction) PRED predicted values (max 10 best out of 292): 027b9k6 (0.67 #15743, 0.67 #7756, 0.66 #15742), 05ztjjw (0.67 #237, 0.20 #921, 0.17 #1149), 0gq9h (0.58 #2110, 0.56 #970, 0.51 #2566), 019f4v (0.53 #508, 0.50 #2104, 0.46 #964), 0gs9p (0.50 #1656, 0.49 #972, 0.48 #2112), 040njc (0.48 #1602, 0.42 #462, 0.37 #918), 099c8n (0.48 #1195, 0.44 #967, 0.33 #5073), 0k611 (0.46 #979, 0.38 #2119, 0.36 #2575), 02pqp12 (0.46 #1652, 0.37 #512, 0.35 #1880), 0gr4k (0.43 #2534, 0.38 #3446, 0.33 #1622) >> Best rule #15743 for best value: >> intensional similarity = 3 >> extensional distance = 973 >> proper extension: 06mmr; >> query: (?x5293, ?x4226) <- award(?x5293, ?x4226), award_winner(?x4226, ?x748), award(?x2493, ?x4226) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #926 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 02wwmhc; *> query: (?x5293, 03hkv_r) <- film_crew_role(?x5293, ?x137), honored_for(?x4781, ?x5293), film(?x2551, ?x5293), honored_for(?x3684, ?x5293) *> conf = 0.22 ranks of expected_values: 45, 83 EVAL 0cbv4g nominated_for! 03hj5vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 114.000 108.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0cbv4g nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 114.000 108.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20139-05cgv PRED entity: 05cgv PRED relation: jurisdiction_of_office! PRED expected values: 060c4 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 43): 060c4 (0.77 #46, 0.75 #1390, 0.75 #420), 060bp (0.68 #771, 0.66 #749, 0.65 #793), 0pqc5 (0.58 #686, 0.52 #488, 0.36 #2960), 0f6c3 (0.41 #1042, 0.39 #1130, 0.39 #1020), 0fkvn (0.39 #1038, 0.39 #1457, 0.36 #91), 09n5b9 (0.37 #1046, 0.35 #1134, 0.35 #1024), 0p5vf (0.36 #12, 0.19 #188, 0.18 #452), 04syw (0.18 #1372, 0.17 #160, 0.17 #512), 01zq91 (0.16 #190, 0.16 #14, 0.16 #2691), 0377k9 (0.16 #15, 0.16 #2691, 0.12 #147) >> Best rule #46 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 0160w; 0169t; 088q4; 0166v; 07dzf; 088vb; 06tw8; 0j4b; 05cc1; 01nln; ... >> query: (?x1241, 060c4) <- organization(?x1241, ?x9102), olympics(?x1241, ?x778), ?x9102 = 041288 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05cgv jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.774 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #20138-01_p6t PRED entity: 01_p6t PRED relation: profession PRED expected values: 02hrh1q => 104 concepts (81 used for prediction) PRED predicted values (max 10 best out of 57): 02hrh1q (0.92 #3248, 0.89 #602, 0.89 #11188), 0dxtg (0.56 #2512, 0.55 #2659, 0.55 #1336), 0nbcg (0.55 #177, 0.50 #5176, 0.42 #3999), 02jknp (0.45 #3535, 0.28 #2653, 0.27 #1330), 016z4k (0.44 #4415, 0.42 #151, 0.38 #298), 0dz3r (0.40 #149, 0.38 #5148, 0.37 #5589), 01c72t (0.34 #5609, 0.23 #2815, 0.23 #5168), 05t4q (0.33 #60), 039v1 (0.33 #182, 0.24 #3710, 0.23 #4004), 02krf9 (0.28 #2671, 0.26 #2524, 0.24 #1348) >> Best rule #3248 for best value: >> intensional similarity = 3 >> extensional distance = 526 >> proper extension: 05m63c; 01ty7ll; 033hqf; 01q7cb_; 01pw2f1; 0285c; 05hdf; 0c01c; 02lf1j; 01pnn3; ... >> query: (?x5758, 02hrh1q) <- profession(?x5758, ?x319), participant(?x6424, ?x5758), film(?x5758, ?x414) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_p6t profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 81.000 0.919 http://example.org/people/person/profession #20137-0292l3 PRED entity: 0292l3 PRED relation: award PRED expected values: 09sb52 => 106 concepts (95 used for prediction) PRED predicted values (max 10 best out of 268): 09sb52 (0.37 #9333, 0.36 #8525, 0.33 #10141), 0bdwqv (0.33 #173, 0.10 #3405, 0.08 #11081), 0f4x7 (0.16 #19797, 0.15 #31920, 0.15 #31921), 0gqy2 (0.16 #19797, 0.15 #31920, 0.15 #31921), 05zr6wv (0.16 #19797, 0.15 #31920, 0.15 #31921), 09qwmm (0.16 #19797, 0.15 #31920, 0.15 #31921), 05ztrmj (0.16 #19797, 0.15 #31920, 0.15 #31921), 02x73k6 (0.16 #19797, 0.15 #31920, 0.15 #31921), 02w9sd7 (0.16 #19797, 0.15 #31920, 0.15 #31921), 02x4x18 (0.16 #19797, 0.15 #31920, 0.15 #31921) >> Best rule #9333 for best value: >> intensional similarity = 3 >> extensional distance = 818 >> proper extension: 0134w7; 0157m; 06k02; 01dw9z; 03sww; 0c12h; 02_0d2; 01p0vf; 01hmk9; 040z9; ... >> query: (?x1445, 09sb52) <- award_winner(?x1937, ?x1445), film(?x1445, ?x657), award_nominee(?x1554, ?x1445) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0292l3 award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 95.000 0.366 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20136-03l2n PRED entity: 03l2n PRED relation: source PRED expected values: 0jbk9 => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #32, 0.94 #27, 0.93 #83) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 0r4xt; >> query: (?x4733, 0jbk9) <- county(?x4733, ?x10367), location_of_ceremony(?x566, ?x4733), ?x566 = 04ztj >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03l2n source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 181.000 0.938 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #20135-06pcz0 PRED entity: 06pcz0 PRED relation: nominated_for PRED expected values: 03ynwqj => 96 concepts (17 used for prediction) PRED predicted values (max 10 best out of 166): 04gv3db (0.53 #3246, 0.52 #6492, 0.49 #11360), 0bmssv (0.53 #3246, 0.52 #6492, 0.49 #11360), 03ynwqj (0.33 #3247, 0.24 #12983, 0.24 #25965), 02vrgnr (0.33 #3247, 0.24 #12983, 0.24 #25965), 017z49 (0.33 #3247, 0.24 #12983, 0.24 #25965), 03b_fm5 (0.09 #22718), 04zl8 (0.04 #2469, 0.03 #5715, 0.02 #10582), 08jgk1 (0.03 #3478, 0.03 #21325, 0.03 #18080), 039cq4 (0.03 #2709, 0.03 #12446, 0.03 #1086), 06fpsx (0.03 #2819, 0.03 #1196, 0.02 #6065) >> Best rule #3246 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 06b_0; >> query: (?x11437, ?x4178) <- type_of_union(?x11437, ?x566), profession(?x11437, ?x319), written_by(?x4178, ?x11437), film(?x11437, ?x3482) >> conf = 0.53 => this is the best rule for 2 predicted values *> Best rule #3247 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 118 *> proper extension: 06b_0; *> query: (?x11437, ?x3482) <- type_of_union(?x11437, ?x566), profession(?x11437, ?x319), written_by(?x4178, ?x11437), film(?x11437, ?x3482) *> conf = 0.33 ranks of expected_values: 3 EVAL 06pcz0 nominated_for 03ynwqj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 17.000 0.532 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #20134-07z6xs PRED entity: 07z6xs PRED relation: nominated_for! PRED expected values: 02x258x => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 166): 0gq9h (0.43 #63, 0.29 #1020, 0.28 #240), 019f4v (0.37 #55, 0.28 #240, 0.28 #1012), 0gq_v (0.37 #20, 0.28 #240, 0.25 #260), 0k611 (0.35 #74, 0.28 #240, 0.22 #1748), 0gr4k (0.31 #984, 0.22 #27, 0.17 #6003), 0gs9p (0.30 #65, 0.28 #240, 0.26 #1022), 0p9sw (0.30 #21, 0.28 #240, 0.18 #1695), 0gqy2 (0.29 #123, 0.28 #240, 0.23 #1080), 04dn09n (0.28 #240, 0.27 #36, 0.20 #1710), 040njc (0.28 #240, 0.27 #7, 0.19 #1681) >> Best rule #63 for best value: >> intensional similarity = 4 >> extensional distance = 156 >> proper extension: 05sy0cv; 0gxsh4; >> query: (?x5122, 0gq9h) <- nominated_for(?x10704, ?x5122), cinematography(?x6222, ?x10704), nominated_for(?x500, ?x6222), gender(?x10704, ?x231) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #8128 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1294 *> proper extension: 02md2d; *> query: (?x5122, ?x198) <- nominated_for(?x10704, ?x5122), nominated_for(?x3080, ?x5122), award_winner(?x2914, ?x10704), nominated_for(?x198, ?x2914), award_winner(?x1442, ?x3080) *> conf = 0.15 ranks of expected_values: 44 EVAL 07z6xs nominated_for! 02x258x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 56.000 56.000 0.430 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20133-0778_3 PRED entity: 0778_3 PRED relation: student PRED expected values: 01_njt => 143 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1042): 077yk0 (0.25 #1136, 0.22 #9501, 0.20 #5319), 019fnv (0.25 #1732, 0.20 #5915, 0.20 #3823), 02_t2t (0.25 #1445, 0.20 #5628, 0.20 #3536), 0lccn (0.25 #351, 0.20 #4534, 0.20 #2442), 01kt17 (0.25 #1593, 0.20 #5776, 0.20 #3684), 027y_ (0.25 #1516, 0.20 #5699, 0.20 #3607), 084m3 (0.25 #1295, 0.20 #5478, 0.20 #3386), 018gqj (0.25 #1052, 0.20 #5235, 0.20 #3143), 07cn2c (0.25 #685, 0.20 #4868, 0.20 #2776), 0783m_ (0.22 #8725, 0.04 #18825, 0.04 #79491) >> Best rule #1136 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01zh3_; >> query: (?x12918, 077yk0) <- organization(?x5161, ?x12918), state_province_region(?x12918, ?x6842), ?x6842 = 0694j, company(?x5161, ?x902) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0778_3 student 01_njt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 57.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #20132-02xc1w4 PRED entity: 02xc1w4 PRED relation: location PRED expected values: 05tbn => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 90): 068p2 (0.47 #69181, 0.47 #64356, 0.44 #57920), 02_286 (0.25 #2450, 0.17 #4863, 0.14 #11305), 05k7sb (0.25 #2522, 0.05 #11377, 0.03 #12181), 030qb3t (0.14 #1692, 0.12 #2496, 0.12 #16175), 0cc56 (0.14 #1666, 0.04 #12129, 0.03 #12933), 01lfy (0.14 #1997), 05fjy (0.14 #1888), 0ccvx (0.12 #2635, 0.10 #3439, 0.08 #5048), 094jv (0.12 #2506, 0.10 #3310, 0.03 #11361), 059rby (0.12 #2429, 0.09 #4037, 0.08 #4842) >> Best rule #69181 for best value: >> intensional similarity = 3 >> extensional distance = 2279 >> proper extension: 05fh2; >> query: (?x5664, ?x4499) <- place_of_birth(?x5664, ?x4499), location(?x396, ?x4499), contains(?x94, ?x4499) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #4209 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0d02km; *> query: (?x5664, 05tbn) <- nominated_for(?x5664, ?x2009), place_of_birth(?x5664, ?x4499), ?x4499 = 068p2, profession(?x5664, ?x319) *> conf = 0.09 ranks of expected_values: 15 EVAL 02xc1w4 location 05tbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 111.000 111.000 0.470 http://example.org/people/person/places_lived./people/place_lived/location #20131-01hmk9 PRED entity: 01hmk9 PRED relation: place_of_death PRED expected values: 0281y0 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 81): 06_kh (0.33 #5, 0.06 #5053, 0.05 #6803), 030qb3t (0.18 #5847, 0.17 #9736, 0.17 #5265), 02_286 (0.17 #3119, 0.11 #5838, 0.11 #2536), 0k049 (0.12 #5246, 0.10 #4857, 0.09 #3304), 0sf9_ (0.09 #2912, 0.09 #6020, 0.07 #9131), 04jpl (0.07 #2724, 0.05 #2530, 0.05 #5444), 0f2wj (0.05 #4671, 0.04 #9726, 0.04 #7392), 05qtj (0.05 #2781, 0.05 #5501, 0.05 #1616), 05jbn (0.03 #847, 0.03 #1041, 0.02 #1235), 0fn7r (0.03 #935, 0.03 #1129, 0.02 #1517) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 014z8v; >> query: (?x7183, 06_kh) <- people(?x12870, ?x7183), influenced_by(?x8065, ?x7183), ?x8065 = 02633g >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3217 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 76 *> proper extension: 03wd5tk; 0c_drn; 014g91; 05hjmd; *> query: (?x7183, 0281y0) <- people(?x12870, ?x7183), place_of_birth(?x7183, ?x3964), award_winner(?x7183, ?x2300) *> conf = 0.01 ranks of expected_values: 67 EVAL 01hmk9 place_of_death 0281y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 118.000 118.000 0.333 http://example.org/people/deceased_person/place_of_death #20130-0bvn25 PRED entity: 0bvn25 PRED relation: featured_film_locations PRED expected values: 030qb3t => 61 concepts (37 used for prediction) PRED predicted values (max 10 best out of 51): 02_286 (0.20 #20, 0.17 #740, 0.16 #1944), 0cr3d (0.20 #66, 0.01 #1990), 03gh4 (0.14 #355, 0.01 #1077, 0.01 #1318), 0135g (0.14 #345, 0.01 #585, 0.01 #1067), 04jpl (0.10 #1452, 0.07 #489, 0.06 #729), 030qb3t (0.07 #2687, 0.07 #1482, 0.07 #3892), 080h2 (0.03 #1948, 0.03 #6261, 0.02 #3155), 0rh6k (0.03 #4095, 0.03 #3132, 0.03 #3614), 01x73 (0.03 #6261, 0.02 #8674, 0.02 #2165), 07z1m (0.03 #6261, 0.02 #8674, 0.02 #2165) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0d8w2n; >> query: (?x365, 02_286) <- genre(?x365, ?x12008), ?x12008 = 0gsy3b, films(?x14068, ?x365) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2687 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 571 *> proper extension: 06dfz1; *> query: (?x365, 030qb3t) <- nominated_for(?x4371, ?x365), currency(?x4371, ?x170), nationality(?x4371, ?x279) *> conf = 0.07 ranks of expected_values: 6 EVAL 0bvn25 featured_film_locations 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 61.000 37.000 0.200 http://example.org/film/film/featured_film_locations #20129-02f1c PRED entity: 02f1c PRED relation: award_winner! PRED expected values: 01s695 01bx35 013b2h => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 137): 01s695 (0.22 #144, 0.16 #10153, 0.11 #4092), 01xqqp (0.17 #237, 0.16 #10153, 0.10 #14667), 056878 (0.17 #173, 0.10 #14667, 0.09 #4121), 019bk0 (0.17 #157, 0.10 #14667, 0.09 #4105), 01bx35 (0.16 #10153, 0.14 #853, 0.10 #5083), 013b2h (0.16 #10153, 0.12 #4169, 0.12 #5156), 01c6qp (0.16 #10153, 0.12 #442, 0.11 #583), 01mhwk (0.16 #10153, 0.10 #14667, 0.10 #182), 09n4nb (0.16 #10153, 0.10 #14667, 0.09 #4137), 01mh_q (0.16 #10153, 0.10 #14667, 0.07 #230) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 01cblr; >> query: (?x8799, 01s695) <- award(?x8799, ?x2238), artist(?x2931, ?x8799), ?x2238 = 025m8l >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 6 EVAL 02f1c award_winner! 013b2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 133.000 133.000 0.220 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02f1c award_winner! 01bx35 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 133.000 133.000 0.220 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02f1c award_winner! 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.220 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #20128-02sj1x PRED entity: 02sj1x PRED relation: music! PRED expected values: 0jvt9 => 147 concepts (122 used for prediction) PRED predicted values (max 10 best out of 959): 0pd57 (0.76 #15082, 0.73 #14075, 0.10 #40211), 0bcndz (0.68 #13069, 0.67 #11058, 0.07 #2011), 034xyf (0.68 #13069, 0.67 #11058, 0.07 #2011), 019kyn (0.08 #474, 0.02 #4495, 0.01 #10526), 0kb57 (0.08 #304, 0.02 #4325, 0.01 #10356), 0k4d7 (0.08 #238, 0.02 #4259, 0.01 #10290), 0gt14 (0.08 #1997, 0.02 #5013, 0.01 #9034), 0k7tq (0.08 #1686, 0.02 #4702, 0.01 #8723), 04v8h1 (0.08 #1474, 0.02 #4490, 0.01 #8511), 0gcpc (0.08 #1425, 0.02 #4441, 0.01 #8462) >> Best rule #15082 for best value: >> intensional similarity = 3 >> extensional distance = 86 >> proper extension: 02rgz4; 07z4fy; >> query: (?x3519, ?x4179) <- music(?x9059, ?x3519), films(?x9203, ?x9059), nominated_for(?x3519, ?x4179) >> conf = 0.76 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02sj1x music! 0jvt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 122.000 0.765 http://example.org/film/film/music #20127-0311wg PRED entity: 0311wg PRED relation: nationality PRED expected values: 09c7w0 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 66): 09c7w0 (0.85 #1, 0.79 #1106, 0.77 #1409), 02jx1 (0.11 #333, 0.10 #5965, 0.10 #2447), 07ssc (0.09 #315, 0.09 #516, 0.09 #918), 0d060g (0.06 #3427, 0.06 #1010, 0.06 #1313), 03rk0 (0.06 #9792, 0.06 #7081, 0.06 #7181), 01t3h6 (0.03 #6434), 0c1xm (0.03 #6434), 0rt80 (0.03 #6434), 043yj (0.03 #6434), 0ycht (0.03 #6434) >> Best rule #1 for best value: >> intensional similarity = 2 >> extensional distance = 282 >> proper extension: 0grwj; 0f0y8; 01l1b90; 0lzb8; 01kwlwp; 03gm48; 03knl; 05mt_q; 02lkcc; 0203v; ... >> query: (?x2296, 09c7w0) <- people(?x2510, ?x2296), ?x2510 = 0x67 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0311wg nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.852 http://example.org/people/person/nationality #20126-012m_ PRED entity: 012m_ PRED relation: contains PRED expected values: 05ywg => 147 concepts (42 used for prediction) PRED predicted values (max 10 best out of 2263): 095w_ (0.71 #79496, 0.29 #44162, 0.08 #41369), 01mjq (0.58 #94217, 0.44 #76551, 0.13 #53171), 012m_ (0.58 #94217, 0.44 #76551, 0.10 #103050), 06c1y (0.58 #94217, 0.17 #56113, 0.17 #14896), 05ywg (0.57 #70661, 0.20 #6050, 0.17 #11939), 06fz_ (0.33 #12808, 0.20 #51081, 0.20 #30472), 07_pf (0.33 #16343, 0.11 #66394, 0.06 #32386), 0prxp (0.33 #16955, 0.11 #67006, 0.05 #64061), 01n43d (0.33 #16394, 0.11 #66445, 0.05 #63500), 03ryn (0.27 #53462, 0.22 #56406, 0.17 #88798) >> Best rule #79496 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0160w; >> query: (?x9006, ?x1374) <- contains(?x9006, ?x5127), form_of_government(?x9006, ?x6065), ?x6065 = 01q20, capital(?x9006, ?x1374) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #70661 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 09lxtg; *> query: (?x9006, ?x1458) <- contains(?x9006, ?x10801), jurisdiction_of_office(?x3119, ?x10801), form_of_government(?x9006, ?x6065), contains(?x10801, ?x1458) *> conf = 0.57 ranks of expected_values: 5 EVAL 012m_ contains 05ywg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 147.000 42.000 0.714 http://example.org/location/location/contains #20125-03xb2w PRED entity: 03xb2w PRED relation: film PRED expected values: 02ph9tm => 78 concepts (63 used for prediction) PRED predicted values (max 10 best out of 633): 0ch3qr1 (0.18 #974, 0.09 #8926, 0.07 #57125), 087pfc (0.12 #1527, 0.09 #8926, 0.07 #57125), 02hxhz (0.12 #122, 0.09 #8926, 0.07 #57125), 0gwgn1k (0.12 #1545, 0.09 #8926, 0.07 #57125), 0bshwmp (0.12 #158, 0.09 #8926, 0.04 #41059), 02ph9tm (0.12 #1098, 0.09 #8926, 0.04 #41059), 0gvrws1 (0.12 #319, 0.07 #57125, 0.04 #89257), 03bzjpm (0.12 #1312, 0.03 #3097, 0.02 #8452), 0b6f8pf (0.09 #8926, 0.07 #57125, 0.04 #89257), 074w86 (0.09 #8926, 0.06 #672, 0.04 #41059) >> Best rule #974 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 017s11; 016tw3; >> query: (?x4935, 0ch3qr1) <- award_nominee(?x4935, ?x1335), ?x1335 = 0pz91, award(?x4935, ?x102) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #1098 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 017s11; 016tw3; *> query: (?x4935, 02ph9tm) <- award_nominee(?x4935, ?x1335), ?x1335 = 0pz91, award(?x4935, ?x102) *> conf = 0.12 ranks of expected_values: 6 EVAL 03xb2w film 02ph9tm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 78.000 63.000 0.176 http://example.org/film/actor/film./film/performance/film #20124-03c_8t PRED entity: 03c_8t PRED relation: student! PRED expected values: 08815 => 168 concepts (142 used for prediction) PRED predicted values (max 10 best out of 190): 07wjk (0.27 #3218, 0.25 #3744, 0.12 #8479), 03ksy (0.26 #29566, 0.25 #631, 0.20 #1157), 052nd (0.25 #535, 0.25 #9, 0.20 #1061), 01w5m (0.25 #104, 0.12 #630, 0.11 #39033), 0bwfn (0.22 #36573, 0.21 #38677, 0.20 #39203), 09f2j (0.17 #31723, 0.14 #33301, 0.12 #36457), 0778_3 (0.12 #1022, 0.10 #1548, 0.06 #3652), 06182p (0.12 #823, 0.10 #1349, 0.05 #3979), 041pnt (0.12 #995, 0.10 #1521, 0.02 #3625), 0hsb3 (0.12 #733, 0.10 #1259, 0.02 #3363) >> Best rule #3218 for best value: >> intensional similarity = 5 >> extensional distance = 46 >> proper extension: 02lf0c; 03_gd; 01ycbq; 073v6; 04m_zp; 0pmw9; 012j5h; 01d1yr; 022q4l9; 084m3; ... >> query: (?x13700, 07wjk) <- student(?x735, ?x13700), nationality(?x13700, ?x279), gender(?x13700, ?x231), ?x279 = 0d060g, ?x231 = 05zppz >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #36301 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 553 *> proper extension: 043q6n_; 037d35; 01l1ls; 0f1jhc; *> query: (?x13700, 08815) <- student(?x735, ?x13700), school(?x580, ?x735), school_type(?x735, ?x1044), list(?x735, ?x2197) *> conf = 0.10 ranks of expected_values: 13 EVAL 03c_8t student! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 168.000 142.000 0.271 http://example.org/education/educational_institution/students_graduates./education/education/student #20123-0b9l3x PRED entity: 0b9l3x PRED relation: profession PRED expected values: 02jknp => 80 concepts (78 used for prediction) PRED predicted values (max 10 best out of 52): 01d_h8 (0.76 #3410, 0.57 #6, 0.36 #154), 02jknp (0.57 #8, 0.50 #156, 0.37 #3412), 0dxtg (0.39 #3418, 0.36 #1198, 0.33 #1346), 0cbd2 (0.34 #1191, 0.33 #1339, 0.14 #6669), 03gjzk (0.25 #1495, 0.25 #3419, 0.24 #1643), 02krf9 (0.21 #175, 0.14 #27, 0.11 #3431), 0kyk (0.19 #1362, 0.19 #1214, 0.10 #3879), 09jwl (0.16 #8014, 0.15 #10236, 0.15 #10087), 0n1h (0.14 #3416, 0.04 #9932, 0.04 #10080), 0nbcg (0.10 #9506, 0.10 #10099, 0.10 #9062) >> Best rule #3410 for best value: >> intensional similarity = 2 >> extensional distance = 1273 >> proper extension: 04b19t; 01wz_ml; 0b05xm; 023l9y; 0dfjb8; 0c8hct; 01wbsdz; 01d5vk; 03mv0b; 04m_kpx; ... >> query: (?x5046, 01d_h8) <- profession(?x5046, ?x2265), film_crew_role(?x3693, ?x2265) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0klh7; *> query: (?x5046, 02jknp) <- award(?x5046, ?x1243), nominated_for(?x5046, ?x5930), ?x5930 = 07cw4 *> conf = 0.57 ranks of expected_values: 2 EVAL 0b9l3x profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 78.000 0.765 http://example.org/people/person/profession #20122-0175wg PRED entity: 0175wg PRED relation: award_nominee PRED expected values: 02zq43 => 66 concepts (38 used for prediction) PRED predicted values (max 10 best out of 654): 02fz3w (0.81 #86080, 0.81 #72121, 0.81 #48855), 02zq43 (0.16 #88408, 0.15 #83753, 0.15 #27914), 0175wg (0.16 #88408, 0.15 #83753, 0.15 #27914), 02q42j_ (0.16 #88408), 0ksf29 (0.16 #88408), 0136g9 (0.16 #88408), 03v1jf (0.15 #83753, 0.08 #88407, 0.07 #60488), 01fh9 (0.15 #83753, 0.08 #88407, 0.07 #60488), 0hvb2 (0.15 #27914, 0.07 #9696, 0.06 #5044), 02p65p (0.15 #27914, 0.05 #4678, 0.05 #9330) >> Best rule #86080 for best value: >> intensional similarity = 3 >> extensional distance = 1878 >> proper extension: 018ndc; 06mj4; 026v1z; >> query: (?x5743, ?x9236) <- award_nominee(?x5743, ?x489), award_nominee(?x9236, ?x5743), student(?x11459, ?x489) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #88408 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1902 *> proper extension: 01l1b90; 01yznp; 0h1_w; 01rrwf6; 041ly3; 012c6x; 0152cw; 0f0p0; 01yh3y; 028lc8; ... *> query: (?x5743, ?x1714) <- film(?x5743, ?x9996), nominated_for(?x1714, ?x9996) *> conf = 0.16 ranks of expected_values: 2 EVAL 0175wg award_nominee 02zq43 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 38.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20121-045gzq PRED entity: 045gzq PRED relation: student! PRED expected values: 0w7c => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 7): 02822 (0.04 #527, 0.03 #1589, 0.03 #838), 03g3w (0.02 #207, 0.02 #1202, 0.01 #1391), 03qsdpk (0.02 #1719, 0.02 #1594, 0.02 #2281), 0w7c (0.02 #538, 0.01 #228, 0.01 #1223), 02vxn (0.01 #500, 0.01 #314, 0.01 #252), 01zc2w (0.01 #482, 0.01 #855, 0.01 #1229), 0fdys (0.01 #1148, 0.01 #1900) >> Best rule #527 for best value: >> intensional similarity = 5 >> extensional distance = 394 >> proper extension: 078g3l; 01m42d0; 04bdqk; 0g10g; 01f9mq; 04xbr4; >> query: (?x13928, 02822) <- film(?x13928, ?x5277), profession(?x13928, ?x1032), student(?x2399, ?x13928), ?x1032 = 02hrh1q, film_distribution_medium(?x5277, ?x2099) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #538 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 394 *> proper extension: 078g3l; 01m42d0; 04bdqk; 0g10g; 01f9mq; 04xbr4; *> query: (?x13928, 0w7c) <- film(?x13928, ?x5277), profession(?x13928, ?x1032), student(?x2399, ?x13928), ?x1032 = 02hrh1q, film_distribution_medium(?x5277, ?x2099) *> conf = 0.02 ranks of expected_values: 4 EVAL 045gzq student! 0w7c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 101.000 101.000 0.035 http://example.org/education/field_of_study/students_majoring./education/education/student #20120-06jzh PRED entity: 06jzh PRED relation: type_of_union PRED expected values: 04ztj => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.85 #25, 0.84 #41, 0.71 #199), 01g63y (0.57 #61, 0.48 #170, 0.45 #227), 0jgjn (0.01 #16) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 194 >> proper extension: 06cv1; 081lh; 01w02sy; 05y5fw; 05szp; 096hm; >> query: (?x540, 04ztj) <- award_nominee(?x540, ?x539), nominated_for(?x540, ?x589), spouse(?x1208, ?x540) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06jzh type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.852 http://example.org/people/person/spouse_s./people/marriage/type_of_union #20119-01nhkxp PRED entity: 01nhkxp PRED relation: nationality PRED expected values: 09c7w0 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.85 #1301, 0.82 #601, 0.80 #4007), 0k3jc (0.34 #8612, 0.33 #10018), 05k7sb (0.34 #8612, 0.33 #10018), 02jx1 (0.26 #1533, 0.22 #1133, 0.19 #4741), 07ssc (0.13 #1115, 0.12 #915, 0.11 #2116), 03rk0 (0.10 #446, 0.06 #8960, 0.06 #9160), 0chghy (0.10 #510, 0.01 #8824, 0.01 #8321), 0hzlz (0.10 #423, 0.01 #2124, 0.01 #1123), 0d060g (0.07 #907, 0.06 #2108, 0.06 #3412), 0h7x (0.06 #635, 0.02 #735, 0.02 #835) >> Best rule #1301 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 03lh3v; 03n69x; 03l295; 03l26m; 03fnyk; 054c1; 0dq9wx; 01g0jn; 02_nkp; 01jz6d; ... >> query: (?x9577, 09c7w0) <- people(?x4195, ?x9577), currency(?x9577, ?x170), gender(?x9577, ?x514), risk_factors(?x3984, ?x4195) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nhkxp nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.849 http://example.org/people/person/nationality #20118-013yq PRED entity: 013yq PRED relation: location! PRED expected values: 01trhmt 03n69x 01wbsdz 040j2_ => 154 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1987): 0cnl1c (0.50 #54855, 0.47 #251841, 0.47 #177037), 04zkj5 (0.50 #54855, 0.47 #251841, 0.47 #177037), 01vw_dv (0.50 #54855, 0.47 #251841, 0.47 #177037), 0163kf (0.50 #54855, 0.47 #251841, 0.47 #177037), 03m_k0 (0.50 #54855, 0.47 #251841, 0.47 #177037), 030wkp (0.50 #54855, 0.47 #251841, 0.47 #177037), 0gm8_p (0.50 #54855, 0.47 #251841, 0.47 #177037), 01w5jwb (0.40 #92256, 0.37 #127163, 0.30 #92255), 01vw37m (0.37 #127163, 0.30 #92255, 0.30 #162072), 02wwwv5 (0.37 #127163, 0.30 #92255, 0.30 #162072) >> Best rule #54855 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 059rby; 0yc84; 0cc56; 01n7q; 0pmpl; 0kpys; 03v0t; 0r0m6; 0ccvx; 02j3w; ... >> query: (?x2277, ?x3058) <- contains(?x94, ?x2277), location(?x7040, ?x2277), place_of_birth(?x3058, ?x2277), person(?x3480, ?x7040) >> conf = 0.50 => this is the best rule for 7 predicted values *> Best rule #177036 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 207 *> proper extension: 06_kh; 09bjv; 0cb4j; 0f2wj; 05fkf; 0vmt; 0r62v; 03s0w; 04ych; 0xkq4; ... *> query: (?x2277, ?x286) <- contains(?x94, ?x2277), location(?x5246, ?x2277), place_of_birth(?x3058, ?x2277), participant(?x286, ?x5246) *> conf = 0.07 ranks of expected_values: 159, 204 EVAL 013yq location! 040j2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 106.000 0.502 http://example.org/people/person/places_lived./people/place_lived/location EVAL 013yq location! 01wbsdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 154.000 106.000 0.502 http://example.org/people/person/places_lived./people/place_lived/location EVAL 013yq location! 03n69x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 106.000 0.502 http://example.org/people/person/places_lived./people/place_lived/location EVAL 013yq location! 01trhmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 154.000 106.000 0.502 http://example.org/people/person/places_lived./people/place_lived/location #20117-0ckhc PRED entity: 0ckhc PRED relation: state PRED expected values: 081yw => 140 concepts (108 used for prediction) PRED predicted values (max 10 best out of 47): 01n7q (0.55 #1312, 0.38 #2006, 0.36 #2699), 081yw (0.18 #1470, 0.18 #6490, 0.17 #2165), 09c7w0 (0.18 #1470, 0.18 #6490, 0.17 #2165), 0d1xx (0.18 #1470, 0.18 #6490, 0.17 #2165), 059rby (0.18 #692, 0.10 #88, 0.09 #1907), 05kj_ (0.09 #4411, 0.05 #1302, 0.05 #90), 02xry (0.06 #27, 0.05 #6342, 0.04 #1583), 07b_l (0.06 #39, 0.05 #125, 0.03 #384), 059_c (0.06 #13, 0.05 #1311, 0.03 #2005), 04ych (0.06 #12, 0.03 #357, 0.03 #874) >> Best rule #1312 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 0l1pj; >> query: (?x12182, 01n7q) <- place_of_birth(?x9781, ?x12182), time_zones(?x12182, ?x2950), ?x2950 = 02lcqs, country(?x12182, ?x94) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1470 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 013ksx; 0kpzy; 0r6ff; 0xn7q; 0vrmb; *> query: (?x12182, ?x94) <- place_of_birth(?x9781, ?x12182), adjoins(?x12182, ?x9605), contains(?x94, ?x12182), source(?x12182, ?x958) *> conf = 0.18 ranks of expected_values: 2 EVAL 0ckhc state 081yw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 140.000 108.000 0.550 http://example.org/base/biblioness/bibs_location/state #20116-0fqyzz PRED entity: 0fqyzz PRED relation: people! PRED expected values: 0g5y6 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 34): 041rx (0.63 #312, 0.17 #1545, 0.16 #1313), 0222qb (0.33 #44, 0.02 #121, 0.02 #1353), 0x67 (0.11 #5021, 0.10 #2247, 0.09 #5714), 033tf_ (0.11 #1316, 0.09 #1703, 0.08 #1548), 02w7gg (0.10 #1080, 0.08 #541, 0.08 #618), 0g5y6 (0.08 #345, 0.03 #499, 0.03 #730), 048z7l (0.08 #348, 0.04 #425, 0.03 #117), 013xrm (0.07 #2160, 0.07 #328, 0.04 #482), 0d7wh (0.07 #2160, 0.06 #710, 0.05 #479), 0xnvg (0.07 #90, 0.06 #552, 0.06 #629) >> Best rule #312 for best value: >> intensional similarity = 2 >> extensional distance = 164 >> proper extension: 01w3v; 0mcf4; >> query: (?x3828, 041rx) <- religion(?x3828, ?x7131), ?x7131 = 03_gx >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #345 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 164 *> proper extension: 01w3v; 0mcf4; *> query: (?x3828, 0g5y6) <- religion(?x3828, ?x7131), ?x7131 = 03_gx *> conf = 0.08 ranks of expected_values: 6 EVAL 0fqyzz people! 0g5y6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 109.000 109.000 0.627 http://example.org/people/ethnicity/people #20115-0r62v PRED entity: 0r62v PRED relation: location_of_ceremony! PRED expected values: 03xl77 01f6zc => 166 concepts (111 used for prediction) PRED predicted values (max 10 best out of 247): 02p5hf (0.25 #466, 0.09 #713, 0.07 #962), 01xg_w (0.25 #457, 0.09 #704, 0.07 #953), 02bwc7 (0.25 #349, 0.09 #596, 0.07 #845), 0c6qh (0.25 #301, 0.09 #548, 0.07 #797), 03pmty (0.25 #263, 0.09 #510, 0.07 #759), 01j5x6 (0.25 #261, 0.09 #508, 0.07 #757), 01pcq3 (0.25 #258, 0.09 #505, 0.07 #754), 0l8v5 (0.25 #252, 0.09 #499, 0.07 #748), 03ft8 (0.25 #281), 02m30v (0.10 #1736, 0.08 #2974, 0.07 #990) >> Best rule #466 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03_3d; >> query: (?x957, 02p5hf) <- location_of_ceremony(?x329, ?x957), location_of_ceremony(?x269, ?x957), program_creator(?x273, ?x329), profession(?x269, ?x319) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r62v location_of_ceremony! 01f6zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 166.000 111.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony EVAL 0r62v location_of_ceremony! 03xl77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 166.000 111.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #20114-0g2c8 PRED entity: 0g2c8 PRED relation: inductee PRED expected values: 089tm 07c0j 01fl3 0lccn 01wj92r 053yx 02jq1 0ddkf 07m4c 03h_yfh 017lb_ 0dbb3 => 140 concepts (86 used for prediction) PRED predicted values (max 10 best out of 74): 0127xk (0.33 #285, 0.29 #359, 0.25 #508), 015cbq (0.33 #279, 0.29 #353, 0.25 #502), 029b9k (0.33 #272, 0.29 #346, 0.25 #495), 0grwj (0.33 #223, 0.29 #297, 0.25 #446), 018417 (0.17 #295, 0.14 #369, 0.12 #518), 02v2jy (0.17 #294, 0.14 #368, 0.12 #517), 02l0xc (0.17 #293, 0.14 #367, 0.12 #516), 01m4kpp (0.17 #292, 0.14 #366, 0.12 #515), 0488g9 (0.17 #288, 0.14 #362, 0.12 #511), 0pqzh (0.17 #287, 0.14 #361, 0.12 #510) >> Best rule #285 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 06szd3; 0qjfl; 04045y; 04dm2n; >> query: (?x1091, 0127xk) <- inductee(?x1091, ?x12357), inductee(?x1091, ?x1521), location(?x1521, ?x335), award(?x1521, ?x2322), category(?x12357, ?x134) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g2c8 inductee 0dbb3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 017lb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 03h_yfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 07m4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 0ddkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 02jq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 053yx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 01wj92r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 0lccn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 01fl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 07c0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0g2c8 inductee 089tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 86.000 0.333 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #20113-06bss PRED entity: 06bss PRED relation: place_of_death PRED expected values: 0mnzd => 144 concepts (103 used for prediction) PRED predicted values (max 10 best out of 33): 0rh6k (0.16 #1366, 0.13 #1562, 0.12 #1956), 0bxbr (0.08 #671, 0.07 #867, 0.06 #1063), 03pcgf (0.08 #771, 0.07 #967, 0.06 #1163), 030qb3t (0.07 #800, 0.06 #996, 0.04 #7438), 02_286 (0.06 #2554, 0.05 #2749, 0.04 #10363), 0dq16 (0.05 #1433, 0.04 #1629, 0.04 #2023), 05jbn (0.04 #16221, 0.04 #2025, 0.03 #2220), 0ygbf (0.04 #16221), 04n3l (0.04 #16221), 059rby (0.04 #16221) >> Best rule #1366 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0fd_1; 03_nq; >> query: (?x6742, 0rh6k) <- legislative_sessions(?x6742, ?x952), nationality(?x6742, ?x94), district_represented(?x952, ?x177), ?x177 = 05kkh >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06bss place_of_death 0mnzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 103.000 0.158 http://example.org/people/deceased_person/place_of_death #20112-035yn8 PRED entity: 035yn8 PRED relation: film_release_region PRED expected values: 0jgd 03_3d 06mzp 06mkj 05b4w => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 155): 06mkj (0.91 #514, 0.89 #667, 0.88 #973), 0jgd (0.90 #1074, 0.88 #768, 0.86 #1227), 0k6nt (0.88 #2781, 0.86 #789, 0.86 #483), 05qhw (0.88 #779, 0.86 #473, 0.84 #932), 05b4w (0.83 #522, 0.80 #981, 0.80 #1440), 03_3d (0.83 #1995, 0.82 #2762, 0.82 #617), 06t2t (0.78 #978, 0.75 #519, 0.74 #1437), 0d060g (0.77 #465, 0.76 #771, 0.75 #924), 06bnz (0.75 #1420, 0.75 #961, 0.75 #502), 06f32 (0.64 #524, 0.57 #1442, 0.57 #983) >> Best rule #514 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 014lc_; 017gl1; 06wbm8q; 06w839_; 02xbyr; 0m63c; >> query: (?x1744, 06mkj) <- film_release_region(?x1744, ?x1023), music(?x1744, ?x4052), ?x1023 = 0ctw_b, nominated_for(?x484, ?x1744) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 6, 19 EVAL 035yn8 film_release_region 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 131.000 131.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035yn8 film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035yn8 film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 131.000 131.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035yn8 film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 131.000 131.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035yn8 film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20111-03m6pk PRED entity: 03m6pk PRED relation: profession PRED expected values: 02hrh1q => 145 concepts (104 used for prediction) PRED predicted values (max 10 best out of 77): 02hrh1q (0.91 #6283, 0.90 #7328, 0.89 #8820), 0nbcg (0.61 #3464, 0.60 #2568, 0.58 #4212), 016z4k (0.59 #4184, 0.58 #4035, 0.55 #2540), 0dz3r (0.57 #3284, 0.57 #3434, 0.56 #1194), 01d_h8 (0.51 #6722, 0.51 #5677, 0.45 #5826), 039v1 (0.45 #4068, 0.43 #4217, 0.43 #3319), 03gjzk (0.40 #314, 0.37 #6732, 0.33 #5687), 01c72t (0.39 #1812, 0.31 #2411, 0.30 #3906), 0dxtg (0.38 #5685, 0.37 #6730, 0.33 #5834), 02jknp (0.33 #455, 0.27 #11646, 0.27 #12540) >> Best rule #6283 for best value: >> intensional similarity = 4 >> extensional distance = 233 >> proper extension: 01r42_g; >> query: (?x6663, 02hrh1q) <- participant(?x5665, ?x6663), nominated_for(?x6663, ?x7645), type_of_union(?x5665, ?x566), people(?x6736, ?x6663) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03m6pk profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 104.000 0.915 http://example.org/people/person/profession #20110-015_30 PRED entity: 015_30 PRED relation: award PRED expected values: 02gm9n => 146 concepts (118 used for prediction) PRED predicted values (max 10 best out of 321): 0gqy2 (0.67 #18329, 0.15 #5293, 0.14 #6478), 0gqz2 (0.64 #868, 0.20 #3238, 0.20 #4818), 0l8z1 (0.55 #853, 0.12 #4803, 0.10 #3223), 02qvyrt (0.45 #911, 0.14 #39109, 0.13 #31997), 05pcn59 (0.43 #21015, 0.18 #2054, 0.14 #11929), 02gdjb (0.36 #1001, 0.15 #10876, 0.13 #1396), 02x201b (0.36 #1056, 0.15 #45828, 0.15 #46619), 04njml (0.36 #886, 0.07 #1281, 0.06 #12737), 09sb52 (0.35 #2016, 0.31 #11101, 0.31 #20977), 026mfs (0.34 #8813, 0.13 #2493, 0.13 #18689) >> Best rule #18329 for best value: >> intensional similarity = 3 >> extensional distance = 259 >> proper extension: 0hwd8; 0436zq; 04bdlg; >> query: (?x1800, 0gqy2) <- award(?x1800, ?x1312), award(?x2991, ?x1312), ?x2991 = 01rnxn >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #34368 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1084 *> proper extension: 03_gx; *> query: (?x1800, ?x724) <- artists(?x5300, ?x1800), artists(?x5300, ?x6715), award(?x6715, ?x724) *> conf = 0.03 ranks of expected_values: 283 EVAL 015_30 award 02gm9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 146.000 118.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20109-06zn1c PRED entity: 06zn1c PRED relation: language PRED expected values: 02h40lc => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 60): 02h40lc (0.90 #1846, 0.89 #1666, 0.89 #2025), 03_9r (0.50 #10, 0.43 #128, 0.11 #1019), 03115z (0.25 #38, 0.05 #156, 0.03 #4003), 064_8sq (0.22 #258, 0.15 #1746, 0.15 #1211), 06nm1 (0.21 #2505, 0.11 #901, 0.11 #485), 02bjrlw (0.21 #2505, 0.11 #593, 0.11 #832), 0653m (0.21 #2505, 0.10 #130, 0.09 #6155), 0349s (0.21 #2505, 0.09 #6155, 0.06 #6695), 012w70 (0.21 #2505, 0.06 #6695, 0.05 #6336), 02hwyss (0.21 #2505, 0.05 #6336, 0.03 #4003) >> Best rule #1846 for best value: >> intensional similarity = 5 >> extensional distance = 320 >> proper extension: 02rx2m5; 04vh83; 0kxf1; 02dwj; 07gghl; 0294mx; 09yxcz; 0cbl95; >> query: (?x10749, 02h40lc) <- award_winner(?x10749, ?x9296), nominated_for(?x350, ?x10749), films(?x10489, ?x10749), genre(?x10749, ?x1510), genre(?x419, ?x1510) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06zn1c language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.898 http://example.org/film/film/language #20108-03b04g PRED entity: 03b04g PRED relation: current_club! PRED expected values: 033nzk => 150 concepts (80 used for prediction) PRED predicted values (max 10 best out of 47): 01_lhg (0.50 #63, 0.43 #91, 0.38 #149), 03yl2t (0.31 #459, 0.18 #749, 0.17 #575), 02ltg3 (0.23 #462, 0.20 #607, 0.18 #723), 032jlh (0.22 #340, 0.18 #827, 0.17 #597), 02pp1 (0.21 #567, 0.15 #1144, 0.13 #855), 03d8m4 (0.20 #235, 0.16 #782, 0.15 #1144), 02s2lg (0.19 #461, 0.17 #61, 0.15 #1144), 01352_ (0.17 #598, 0.17 #341, 0.17 #82), 03_qj1 (0.17 #552, 0.17 #65, 0.15 #1144), 02w64f (0.17 #571, 0.15 #1144, 0.12 #484) >> Best rule #63 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 01453; >> query: (?x10977, 01_lhg) <- position(?x10977, ?x530), position(?x10977, ?x203), position(?x10977, ?x63), ?x63 = 02sdk9v, team(?x8360, ?x10977), current_club(?x978, ?x10977), ?x530 = 02_j1w, ?x203 = 0dgrmp, ?x8360 = 0c2rr7 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1088 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 48 *> proper extension: 02nt75; 01r5xw; 03j0ss; *> query: (?x10977, 033nzk) <- position(?x10977, ?x530), sport(?x10977, ?x471), current_club(?x978, ?x10977), team(?x60, ?x10977), colors(?x10977, ?x663), ?x60 = 02nzb8, team(?x530, ?x59) *> conf = 0.16 ranks of expected_values: 14 EVAL 03b04g current_club! 033nzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 150.000 80.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #20107-0jz71 PRED entity: 0jz71 PRED relation: film_release_region PRED expected values: 03rjj => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 133): 09c7w0 (0.92 #4973, 0.90 #1031, 0.89 #345), 0d0vqn (0.89 #2581, 0.85 #2923, 0.40 #695), 06mkj (0.82 #2641, 0.78 #2983, 0.33 #755), 0chghy (0.77 #2586, 0.73 #2928, 0.33 #700), 03rjj (0.77 #2578, 0.74 #2920, 0.36 #692), 07ssc (0.74 #2593, 0.72 #2935, 0.34 #707), 03gj2 (0.72 #2604, 0.67 #2946, 0.31 #718), 03h64 (0.72 #2653, 0.67 #2995, 0.29 #767), 0345h (0.71 #2613, 0.69 #2955, 0.38 #727), 01znc_ (0.67 #2624, 0.62 #2966, 0.20 #5023) >> Best rule #4973 for best value: >> intensional similarity = 2 >> extensional distance = 1328 >> proper extension: 0ckr7s; 09xbpt; 047gn4y; 0dq626; 0dtw1x; 03s6l2; 026p_bs; 026mfbr; 04gknr; 04kkz8; ... >> query: (?x10829, 09c7w0) <- film_release_region(?x10829, ?x789), country(?x150, ?x789) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #2578 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 297 *> proper extension: 0gtsx8c; 0c40vxk; 0gx9rvq; 0401sg; 087wc7n; 08hmch; 0jjy0; 0gj8t_b; 07g_0c; 04zyhx; ... *> query: (?x10829, 03rjj) <- film_release_region(?x10829, ?x1892), film_release_region(?x10829, ?x789), ?x789 = 0f8l9c, ?x1892 = 02vzc *> conf = 0.77 ranks of expected_values: 5 EVAL 0jz71 film_release_region 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 49.000 49.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20106-040696 PRED entity: 040696 PRED relation: nationality PRED expected values: 06bnz => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 14): 0chghy (0.36 #2683, 0.32 #1489, 0.12 #9), 0d060g (0.36 #2683, 0.32 #1489, 0.12 #6), 06qd3 (0.36 #2683, 0.32 #1489, 0.12 #35), 0ctw_b (0.36 #2683, 0.32 #1489, 0.12 #26), 07ssc (0.36 #2683, 0.32 #1489, 0.12 #113), 02jx1 (0.10 #1223, 0.10 #826, 0.10 #3709), 03rk0 (0.06 #6202, 0.06 #3524, 0.05 #6798), 0345h (0.02 #3509, 0.02 #6783, 0.02 #3111), 0f8l9c (0.02 #1609, 0.02 #3500, 0.02 #6774), 03rjj (0.02 #3483, 0.02 #699, 0.02 #301) >> Best rule #2683 for best value: >> intensional similarity = 3 >> extensional distance = 1285 >> proper extension: 07hhnl; 0cdf37; 07g7h2; 01zwy; 01c7qd; 058z1hb; >> query: (?x7245, ?x390) <- award_winner(?x7245, ?x4318), nationality(?x4318, ?x390), nominated_for(?x4318, ?x1685) >> conf = 0.36 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 040696 nationality 06bnz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.359 http://example.org/people/person/nationality #20105-0km5c PRED entity: 0km5c PRED relation: dog_breed! PRED expected values: 02cl1 013yq 0f2v0 0chrx => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1088): 0h7h6 (0.60 #18, 0.60 #16, 0.24 #27), 0_xdd (0.60 #18, 0.60 #16, 0.05 #28), 02_286 (0.60 #16, 0.50 #30, 0.40 #26), 0f2v0 (0.60 #16, 0.50 #32, 0.33 #22), 06y57 (0.60 #16, 0.40 #25), 080h2 (0.60 #16, 0.25 #17, 0.24 #27), 0ygbf (0.60 #16, 0.25 #17, 0.21 #6), 04jpl (0.60 #16, 0.25 #17), 01b8jj (0.60 #16), 0chrx (0.50 #33, 0.33 #23, 0.33 #14) >> Best rule #18 for best value: >> intensional similarity = 132 >> extensional distance = 1 >> proper extension: 01_gx_; >> query: (?x1706, ?x1658) <- dog_breed(?x9605, ?x1706), dog_breed(?x8993, ?x1706), dog_breed(?x8468, ?x1706), dog_breed(?x6960, ?x1706), dog_breed(?x6769, ?x1706), dog_breed(?x6453, ?x1706), dog_breed(?x6088, ?x1706), dog_breed(?x5893, ?x1706), dog_breed(?x5267, ?x1706), dog_breed(?x5193, ?x1706), dog_breed(?x4978, ?x1706), dog_breed(?x4499, ?x1706), dog_breed(?x4419, ?x1706), dog_breed(?x4362, ?x1706), dog_breed(?x4350, ?x1706), dog_breed(?x4090, ?x1706), dog_breed(?x3373, ?x1706), dog_breed(?x3269, ?x1706), dog_breed(?x2740, ?x1706), dog_breed(?x1705, ?x1706), ?x2740 = 0f__1, ?x6960 = 071vr, ?x4350 = 0_vn7, locations(?x9146, ?x4978), place_of_birth(?x8693, ?x4978), place_of_birth(?x4976, ?x4978), participant(?x8693, ?x3244), contains(?x4978, ?x3779), celebrity(?x5691, ?x8693), ?x9146 = 0b_6qj, ?x6769 = 0f2tj, nominated_for(?x8693, ?x607), administrative_division(?x4978, ?x3778), ?x8468 = 0nbwf, place_of_birth(?x8012, ?x4362), people(?x5741, ?x8693), ?x3373 = 0ply0, ?x4419 = 0d35y, place_of_death(?x9765, ?x4978), place_of_death(?x3539, ?x4978), ?x3269 = 0vzm, ?x5893 = 07bcn, teams(?x6088, ?x2174), source(?x4362, ?x958), place_of_birth(?x13352, ?x6088), place_of_birth(?x4662, ?x6088), place_of_birth(?x2794, ?x6088), location(?x4977, ?x4978), location(?x4873, ?x4978), location(?x3159, ?x4978), location(?x226, ?x4978), ?x5267 = 0d9jr, ?x958 = 0jbk9, ?x9605 = 02frhbc, award_nominee(?x8693, ?x4360), profession(?x8693, ?x220), county(?x6088, ?x7567), award_nominee(?x3539, ?x1247), award_winner(?x139, ?x3159), time_zones(?x4978, ?x1638), award_winner(?x367, ?x3159), major_field_of_study(?x3779, ?x1668), contains(?x94, ?x4362), school(?x2820, ?x3779), profession(?x2794, ?x2225), contains(?x4105, ?x6088), location_of_ceremony(?x566, ?x5193), award_nominee(?x3159, ?x1794), award_nominee(?x4662, ?x8445), award_nominee(?x4662, ?x7525), award_nominee(?x4662, ?x2762), award_nominee(?x4662, ?x2352), award_nominee(?x4662, ?x221), colors(?x3779, ?x4557), actor(?x7365, ?x4662), profession(?x3539, ?x2348), institution(?x620, ?x3779), film(?x4662, ?x408), student(?x546, ?x2794), award(?x8693, ?x462), award_winner(?x2420, ?x3159), ?x2762 = 015t56, contact_category(?x3779, ?x897), featured_film_locations(?x2331, ?x6088), languages(?x4977, ?x254), place_of_birth(?x819, ?x5193), religion(?x8693, ?x109), ?x2352 = 01pgzn_, gender(?x8693, ?x514), award_nominee(?x819, ?x230), ?x2225 = 0kyk, profession(?x4873, ?x131), artist(?x2241, ?x4873), participant(?x4662, ?x2443), origin(?x2683, ?x5193), vacationer(?x4978, ?x3421), award_nominee(?x4976, ?x515), ?x6453 = 01smm, ?x1705 = 094jv, award_nominee(?x9152, ?x819), instrumentalists(?x212, ?x4873), gender(?x9765, ?x231), film(?x8693, ?x2586), award(?x3539, ?x341), ?x221 = 06151l, award_nominee(?x226, ?x1795), religion(?x9765, ?x1624), role(?x4873, ?x314), artists(?x1000, ?x4873), award_nominee(?x1051, ?x3539), location(?x8693, ?x1658), award_nominee(?x450, ?x4662), contains(?x8260, ?x4105), ?x4499 = 068p2, ?x2820 = 0jmj7, organizations_founded(?x9765, ?x9691), jurisdiction_of_office(?x1195, ?x5193), ?x7525 = 01mqc_, people(?x11067, ?x226), role(?x4873, ?x74), administrative_division(?x5193, ?x3704), award(?x4662, ?x2456), award_nominee(?x444, ?x4976), people(?x268, ?x13352), ?x8993 = 0fsb8, people(?x3584, ?x4662), participant(?x226, ?x262), ?x8445 = 0btpx, religion(?x4105, ?x962), teams(?x4978, ?x5229), ?x4090 = 01sn3, currency(?x3779, ?x170) >> conf = 0.60 => this is the best rule for 2 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 133 *> extensional distance = 1 *> proper extension: 01_gx_; *> query: (?x1706, ?x3501) <- dog_breed(?x9605, ?x1706), dog_breed(?x8993, ?x1706), dog_breed(?x8468, ?x1706), dog_breed(?x6960, ?x1706), dog_breed(?x6769, ?x1706), dog_breed(?x6453, ?x1706), dog_breed(?x6088, ?x1706), dog_breed(?x5893, ?x1706), dog_breed(?x5267, ?x1706), dog_breed(?x5193, ?x1706), dog_breed(?x4978, ?x1706), dog_breed(?x4499, ?x1706), dog_breed(?x4419, ?x1706), dog_breed(?x4362, ?x1706), dog_breed(?x4350, ?x1706), dog_breed(?x4090, ?x1706), dog_breed(?x3373, ?x1706), dog_breed(?x3269, ?x1706), dog_breed(?x2740, ?x1706), dog_breed(?x1705, ?x1706), ?x2740 = 0f__1, ?x6960 = 071vr, ?x4350 = 0_vn7, locations(?x9146, ?x4978), place_of_birth(?x8693, ?x4978), place_of_birth(?x4976, ?x4978), participant(?x8693, ?x3244), contains(?x4978, ?x3779), celebrity(?x5691, ?x8693), ?x9146 = 0b_6qj, ?x6769 = 0f2tj, nominated_for(?x8693, ?x607), administrative_division(?x4978, ?x3778), ?x8468 = 0nbwf, place_of_birth(?x8012, ?x4362), people(?x5741, ?x8693), ?x3373 = 0ply0, ?x4419 = 0d35y, place_of_death(?x9765, ?x4978), place_of_death(?x3539, ?x4978), ?x3269 = 0vzm, ?x5893 = 07bcn, teams(?x6088, ?x2174), source(?x4362, ?x958), place_of_birth(?x13352, ?x6088), place_of_birth(?x4662, ?x6088), place_of_birth(?x2794, ?x6088), location(?x4977, ?x4978), location(?x4873, ?x4978), location(?x3159, ?x4978), location(?x226, ?x4978), ?x5267 = 0d9jr, ?x958 = 0jbk9, ?x9605 = 02frhbc, award_nominee(?x8693, ?x4360), profession(?x8693, ?x220), county(?x6088, ?x7567), award_nominee(?x3539, ?x1247), award_winner(?x139, ?x3159), time_zones(?x4978, ?x1638), award_winner(?x367, ?x3159), major_field_of_study(?x3779, ?x1668), contains(?x94, ?x4362), school(?x2820, ?x3779), profession(?x2794, ?x2225), contains(?x4105, ?x6088), location_of_ceremony(?x566, ?x5193), award_nominee(?x3159, ?x1794), award_nominee(?x4662, ?x8445), award_nominee(?x4662, ?x7525), award_nominee(?x4662, ?x2762), award_nominee(?x4662, ?x2352), award_nominee(?x4662, ?x221), location(?x4977, ?x3501), colors(?x3779, ?x4557), actor(?x7365, ?x4662), profession(?x3539, ?x2348), institution(?x620, ?x3779), film(?x4662, ?x408), student(?x546, ?x2794), award(?x8693, ?x462), award_winner(?x2420, ?x3159), ?x2762 = 015t56, contact_category(?x3779, ?x897), featured_film_locations(?x2331, ?x6088), languages(?x4977, ?x254), place_of_birth(?x819, ?x5193), religion(?x8693, ?x109), ?x2352 = 01pgzn_, gender(?x8693, ?x514), award_nominee(?x819, ?x230), ?x2225 = 0kyk, profession(?x4873, ?x131), artist(?x2241, ?x4873), participant(?x4662, ?x2443), origin(?x2683, ?x5193), vacationer(?x4978, ?x3421), award_nominee(?x4976, ?x515), ?x6453 = 01smm, ?x1705 = 094jv, award_nominee(?x9152, ?x819), instrumentalists(?x212, ?x4873), gender(?x9765, ?x231), film(?x8693, ?x2586), award(?x3539, ?x341), ?x221 = 06151l, award_nominee(?x226, ?x1795), religion(?x9765, ?x1624), role(?x4873, ?x314), artists(?x1000, ?x4873), award_nominee(?x1051, ?x3539), location(?x8693, ?x1658), award_nominee(?x450, ?x4662), contains(?x8260, ?x4105), ?x4499 = 068p2, ?x2820 = 0jmj7, organizations_founded(?x9765, ?x9691), jurisdiction_of_office(?x1195, ?x5193), ?x7525 = 01mqc_, people(?x11067, ?x226), role(?x4873, ?x74), administrative_division(?x5193, ?x3704), award(?x4662, ?x2456), award_nominee(?x444, ?x4976), people(?x268, ?x13352), ?x8993 = 0fsb8, people(?x3584, ?x4662), participant(?x226, ?x262), ?x8445 = 0btpx, religion(?x4105, ?x962), teams(?x4978, ?x5229), ?x4090 = 01sn3, currency(?x3779, ?x170) *> conf = 0.60 ranks of expected_values: 4, 10, 11, 12 EVAL 0km5c dog_breed! 0chrx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 5.000 5.000 0.600 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 0km5c dog_breed! 0f2v0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 5.000 5.000 0.600 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 0km5c dog_breed! 013yq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 5.000 5.000 0.600 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 0km5c dog_breed! 02cl1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 5.000 5.000 0.600 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #20104-051m56 PRED entity: 051m56 PRED relation: artist! PRED expected values: 043ljr => 107 concepts (67 used for prediction) PRED predicted values (max 10 best out of 103): 015_1q (0.27 #583, 0.20 #1429, 0.20 #2558), 03mp8k (0.23 #631, 0.10 #1477, 0.09 #1336), 03rhqg (0.23 #156, 0.20 #15, 0.19 #297), 06x2ww (0.23 #190, 0.08 #2398, 0.08 #4659), 011k1h (0.20 #574, 0.13 #715, 0.10 #1420), 043g7l (0.18 #595, 0.10 #31, 0.10 #313), 0bfp0l (0.15 #247, 0.10 #106, 0.10 #388), 04gmlt (0.15 #194, 0.08 #2398, 0.08 #4659), 043ljr (0.15 #157, 0.08 #2398, 0.08 #4659), 01clyr (0.15 #738, 0.08 #2997, 0.08 #174) >> Best rule #583 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 0134s5; 0d193h; 0134tg; 015srx; 01q99h; 0178kd; 02vgh; 01jcxwp; 046p9; 017lb_; ... >> query: (?x8832, 015_1q) <- origin(?x8832, ?x859), artist(?x2299, ?x8832), ?x2299 = 033hn8 >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #157 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 01k_n63; *> query: (?x8832, 043ljr) <- award_nominee(?x8832, ?x3419), award_nominee(?x8832, ?x2300), ?x2300 = 01ww2fs, award_winner(?x1413, ?x3419) *> conf = 0.15 ranks of expected_values: 9 EVAL 051m56 artist! 043ljr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 107.000 67.000 0.267 http://example.org/music/record_label/artist #20103-08tq4x PRED entity: 08tq4x PRED relation: country PRED expected values: 0jdx => 81 concepts (77 used for prediction) PRED predicted values (max 10 best out of 211): 09c7w0 (0.88 #3974, 0.85 #4139, 0.84 #1037), 0345h (0.51 #3410, 0.27 #1834, 0.19 #2582), 06mkj (0.25 #3689, 0.18 #2638, 0.18 #3746), 059j2 (0.25 #3689, 0.17 #2471, 0.16 #3803), 0chghy (0.19 #2582, 0.19 #2249, 0.18 #2638), 0jgd (0.19 #2582, 0.19 #2249, 0.18 #2638), 03_3d (0.19 #2582, 0.19 #2249, 0.18 #2638), 0d0vqn (0.19 #2582, 0.19 #2249, 0.18 #2638), 0k6nt (0.19 #2582, 0.19 #2249, 0.18 #2638), 06bnz (0.18 #3746, 0.16 #3803, 0.09 #3409) >> Best rule #3974 for best value: >> intensional similarity = 7 >> extensional distance = 1543 >> proper extension: 0cvkv5; >> query: (?x4355, 09c7w0) <- genre(?x4355, ?x53), country(?x4355, ?x1353), film_release_region(?x6931, ?x1353), participating_countries(?x418, ?x1353), combatants(?x326, ?x1353), ?x6931 = 09v3jyg, locations(?x9939, ?x1353) >> conf = 0.88 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08tq4x country 0jdx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 77.000 0.881 http://example.org/film/film/country #20102-0947l PRED entity: 0947l PRED relation: month PRED expected values: 05cw8 03_ly => 243 concepts (243 used for prediction) PRED predicted values (max 10 best out of 2): 03_ly (0.94 #84, 0.93 #110, 0.92 #52), 05cw8 (0.91 #83, 0.91 #71, 0.90 #31) >> Best rule #84 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 0g6xq; >> query: (?x8956, 03_ly) <- month(?x8956, ?x9905), month(?x8956, ?x1650), country(?x8956, ?x205), ?x1650 = 06vkl, ?x9905 = 028kb >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0947l month 03_ly CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 243.000 243.000 0.941 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0947l month 05cw8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 243.000 243.000 0.941 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #20101-07jjt PRED entity: 07jjt PRED relation: sports! PRED expected values: 0kbvb => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 26): 0kbvb (0.83 #666, 0.82 #823, 0.81 #441), 0lk8j (0.82 #843, 0.82 #928, 0.82 #711), 0l6vl (0.82 #843, 0.82 #928, 0.82 #711), 0blg2 (0.82 #843, 0.82 #928, 0.82 #711), 0l6mp (0.81 #441, 0.81 #198, 0.80 #112), 06sks6 (0.81 #441, 0.81 #198, 0.80 #112), 018wrk (0.81 #441, 0.81 #198, 0.80 #112), 0c_tl (0.81 #441, 0.81 #198, 0.80 #112), 018ljb (0.64 #784, 0.64 #595, 0.60 #352), 0sx92 (0.53 #111, 0.50 #52, 0.49 #283) >> Best rule #666 for best value: >> intensional similarity = 39 >> extensional distance = 10 >> proper extension: 01hp22; >> query: (?x2885, 0kbvb) <- olympics(?x2885, ?x2966), country(?x2885, ?x7430), country(?x2885, ?x5147), country(?x2885, ?x3730), country(?x2885, ?x1603), country(?x2885, ?x1471), country(?x2885, ?x1203), country(?x2885, ?x792), country(?x2885, ?x344), sports(?x584, ?x2885), ?x1603 = 06bnz, combatants(?x13069, ?x7430), olympics(?x7430, ?x1617), film_release_region(?x5825, ?x7430), ?x5825 = 067ghz, adjoins(?x1499, ?x3730), ?x344 = 04gzd, medal(?x3730, ?x422), jurisdiction_of_office(?x182, ?x7430), film_release_region(?x4684, ?x792), film_release_region(?x791, ?x792), ?x2966 = 06sks6, country(?x2204, ?x792), ?x791 = 087wc7n, ?x4684 = 03nm_fh, ?x13069 = 01rdm0, country(?x3598, ?x3730), country(?x3015, ?x3730), form_of_government(?x5147, ?x48), jurisdiction_of_office(?x900, ?x5147), organization(?x792, ?x127), countries_spoken_in(?x254, ?x792), official_language(?x792, ?x13263), ?x3598 = 03rbzn, ?x3015 = 071t0, adjoins(?x3432, ?x792), ?x1203 = 07ylj, contains(?x792, ?x841), ?x1471 = 07t21 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07jjt sports! 0kbvb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.833 http://example.org/user/jg/default_domain/olympic_games/sports #20100-06tp4h PRED entity: 06tp4h PRED relation: artist! PRED expected values: 03rhqg => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 100): 01f_3w (0.33 #34, 0.15 #594, 0.14 #734), 033hn8 (0.33 #14, 0.14 #4354, 0.12 #6454), 015_1q (0.31 #580, 0.21 #720, 0.21 #1420), 0fb0v (0.29 #707, 0.23 #567, 0.16 #987), 0181dw (0.24 #1442, 0.23 #602, 0.17 #2142), 01dtcb (0.20 #327, 0.20 #187, 0.16 #1867), 081g_l (0.20 #164, 0.12 #864, 0.11 #1004), 0g768 (0.18 #2557, 0.15 #2137, 0.15 #4377), 01trtc (0.17 #1473, 0.15 #2593, 0.15 #2173), 03rhqg (0.16 #4356, 0.15 #6596, 0.15 #12336) >> Best rule #34 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01wqmm8; >> query: (?x6613, 01f_3w) <- artists(?x3061, ?x6613), participant(?x8285, ?x6613), nationality(?x6613, ?x94), ?x8285 = 03yrkt >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4356 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 99 *> proper extension: 08w4pm; 01lf293; 07hgm; *> query: (?x6613, 03rhqg) <- artists(?x3061, ?x6613), artist(?x5634, ?x6613), ?x3061 = 05bt6j, origin(?x6613, ?x10054) *> conf = 0.16 ranks of expected_values: 10 EVAL 06tp4h artist! 03rhqg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 123.000 123.000 0.333 http://example.org/music/record_label/artist #20099-0nlg4 PRED entity: 0nlg4 PRED relation: contains PRED expected values: 0nc7s => 144 concepts (58 used for prediction) PRED predicted values (max 10 best out of 2327): 0nbfm (0.40 #7603, 0.20 #1705, 0.14 #10550), 09bkv (0.40 #7419, 0.20 #1521, 0.14 #10366), 015g1w (0.40 #7032, 0.20 #1134, 0.14 #9979), 04jpl (0.39 #170933, 0.33 #94304, 0.04 #47152), 0bvqq (0.20 #6474, 0.20 #576, 0.14 #9421), 0m4yg (0.20 #7375, 0.20 #1477, 0.12 #13269), 015wy_ (0.20 #7870, 0.17 #170932, 0.15 #94302), 0gl6x (0.20 #7427, 0.17 #170932, 0.15 #94302), 0fvd03 (0.20 #8233, 0.17 #170932, 0.15 #94302), 026m3y (0.20 #7506, 0.17 #170932, 0.15 #94302) >> Best rule #7603 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0f485; >> query: (?x11888, 0nbfm) <- location_of_ceremony(?x566, ?x11888), adjoins(?x11049, ?x11888), contains(?x512, ?x11888), ?x512 = 07ssc, ?x566 = 04ztj >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #8576 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0f485; *> query: (?x11888, 0nc7s) <- location_of_ceremony(?x566, ?x11888), adjoins(?x11049, ?x11888), contains(?x512, ?x11888), ?x512 = 07ssc, ?x566 = 04ztj *> conf = 0.20 ranks of expected_values: 173 EVAL 0nlg4 contains 0nc7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 144.000 58.000 0.400 http://example.org/location/location/contains #20098-0q9sg PRED entity: 0q9sg PRED relation: films! PRED expected values: 01w1sx => 101 concepts (54 used for prediction) PRED predicted values (max 10 best out of 49): 01w1sx (0.12 #91, 0.06 #247, 0.03 #403), 03r8gp (0.12 #90, 0.06 #246, 0.03 #1184), 0d1w9 (0.06 #348, 0.06 #192, 0.02 #3479), 0bt9lr (0.06 #420, 0.01 #3551, 0.01 #733), 01cgz (0.06 #175, 0.03 #331, 0.02 #625), 05489 (0.05 #1461, 0.05 #2870, 0.05 #3026), 0fx2s (0.05 #1482, 0.05 #2423, 0.05 #2579), 07s2s (0.05 #1980, 0.05 #2137, 0.04 #724), 07c52 (0.04 #1114, 0.04 #1585, 0.03 #2838), 0fzyg (0.04 #5063, 0.03 #5221, 0.02 #4435) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 059lwy; >> query: (?x4538, 01w1sx) <- honored_for(?x5441, ?x4538), genre(?x4538, ?x53), ?x5441 = 04cbbz, award(?x4538, ?x154) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q9sg films! 01w1sx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 54.000 0.125 http://example.org/film/film_subject/films #20097-01m2n1 PRED entity: 01m2n1 PRED relation: contains! PRED expected values: 0k3k1 => 77 concepts (29 used for prediction) PRED predicted values (max 10 best out of 259): 029jpy (0.49 #16992, 0.37 #17888, 0.04 #1108), 07c5l (0.37 #17888, 0.01 #21863), 01cx_ (0.20 #2877, 0.16 #1983, 0.04 #195), 0k3k1 (0.17 #496, 0.13 #1390, 0.11 #2284), 01n7q (0.16 #16174, 0.14 #7230, 0.13 #5442), 04_1l0v (0.14 #7602, 0.11 #9390, 0.07 #12072), 059rby (0.14 #6278, 0.11 #10748, 0.09 #16116), 0k3hn (0.13 #1268, 0.13 #374, 0.07 #3056), 0vmt (0.12 #3630, 0.08 #4525, 0.02 #16151), 07ssc (0.10 #14338, 0.10 #15233, 0.07 #11654) >> Best rule #16992 for best value: >> intensional similarity = 5 >> extensional distance = 691 >> proper extension: 0rs6x; 0k049; 05zjtn4; 01fq7; 06_kh; 01jssp; 04wlz2; 05krk; 0s3y5; 0plyy; ... >> query: (?x14202, ?x3448) <- contains(?x2020, ?x14202), contains(?x94, ?x14202), ?x94 = 09c7w0, contains(?x3448, ?x2020), district_represented(?x176, ?x2020) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #496 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 01l9vr; *> query: (?x14202, 0k3k1) <- contains(?x2020, ?x14202), contains(?x94, ?x14202), ?x94 = 09c7w0, ?x2020 = 05k7sb, time_zones(?x14202, ?x2674) *> conf = 0.17 ranks of expected_values: 4 EVAL 01m2n1 contains! 0k3k1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 29.000 0.486 http://example.org/location/location/contains #20096-01d30f PRED entity: 01d30f PRED relation: profession! PRED expected values: 03hnd 02gyl0 => 37 concepts (6 used for prediction) PRED predicted values (max 10 best out of 4227): 05wm88 (0.71 #12222, 0.50 #8008, 0.47 #16437), 02b29 (0.71 #10656, 0.50 #6442, 0.47 #14871), 015pxr (0.71 #9029, 0.50 #4815, 0.41 #13244), 06m6z6 (0.71 #9645, 0.50 #5431, 0.41 #13860), 052hl (0.71 #10616, 0.35 #14831, 0.25 #6402), 015njf (0.71 #9972, 0.35 #14187, 0.25 #5758), 026dx (0.71 #9936, 0.35 #14151, 0.20 #18366), 09px1w (0.71 #11082, 0.35 #15297, 0.20 #19512), 0hqly (0.71 #12018, 0.35 #16233, 0.15 #20448), 02633g (0.71 #11056, 0.35 #15271, 0.15 #19486) >> Best rule #12222 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 01d_h8; 02jknp; 0dxtg; 02hrh1q; 03gjzk; >> query: (?x7998, 05wm88) <- profession(?x6996, ?x7998), profession(?x5785, ?x7998), profession(?x1376, ?x7998), award(?x1376, ?x575), location(?x1376, ?x4356), ?x5785 = 013zyw, people(?x5540, ?x6996) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #18347 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 44 *> proper extension: 05sxg2; 0dz3r; 016z4k; 07s467s; 0n1h; 012t_z; 018gz8; 09jwl; 0np9r; 01c72t; ... *> query: (?x7998, 02gyl0) <- profession(?x10061, ?x7998), profession(?x5785, ?x7998), profession(?x1376, ?x7998), award(?x1376, ?x575), location(?x1376, ?x4356), currency(?x5785, ?x170), produced_by(?x2882, ?x10061) *> conf = 0.07 ranks of expected_values: 3880, 4031 EVAL 01d30f profession! 02gyl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 6.000 0.714 http://example.org/people/person/profession EVAL 01d30f profession! 03hnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 6.000 0.714 http://example.org/people/person/profession #20095-03z20c PRED entity: 03z20c PRED relation: film! PRED expected values: 06rq2l => 85 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1008): 0gn30 (0.50 #5080, 0.09 #19576, 0.05 #25787), 04yywz (0.50 #4159, 0.03 #24866, 0.02 #18655), 0pz91 (0.44 #16565, 0.43 #8489, 0.42 #76625), 01fyzy (0.44 #16565, 0.42 #76625, 0.33 #37277), 02r251z (0.44 #16565, 0.42 #76625, 0.33 #37277), 024rgt (0.44 #16565, 0.42 #76625, 0.33 #37277), 06rq2l (0.43 #9849, 0.33 #7779, 0.33 #3638), 018y2s (0.33 #182, 0.29 #8463, 0.17 #6393), 09yrh (0.33 #795, 0.17 #7006, 0.14 #9076), 0mdqp (0.33 #2188, 0.17 #6329, 0.14 #8399) >> Best rule #5080 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0cf08; >> query: (?x2907, 0gn30) <- film(?x10361, ?x2907), film(?x1065, ?x2907), award_nominee(?x1065, ?x1379), nominated_for(?x2907, ?x4054), ?x10361 = 0234pg >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9849 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 02ph9tm; 02d003; 026wlxw; *> query: (?x2907, 06rq2l) <- film(?x8145, ?x2907), film(?x1065, ?x2907), ?x1065 = 049dyj, country(?x2907, ?x94), award_winner(?x6884, ?x8145) *> conf = 0.43 ranks of expected_values: 7 EVAL 03z20c film! 06rq2l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 43.000 0.500 http://example.org/film/actor/film./film/performance/film #20094-05rwpb PRED entity: 05rwpb PRED relation: artists PRED expected values: 09prnq => 65 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1042): 03t9sp (0.78 #13134, 0.73 #14218, 0.71 #10964), 03fbc (0.78 #13215, 0.73 #14299, 0.71 #11045), 01vxlbm (0.71 #11183, 0.67 #13353, 0.60 #6846), 03f5spx (0.67 #13069, 0.57 #10899, 0.55 #14153), 05k79 (0.60 #6651, 0.57 #10988, 0.44 #13158), 01w8n89 (0.60 #6822, 0.53 #17673, 0.52 #20922), 0191h5 (0.60 #7156, 0.45 #14747, 0.44 #13663), 01w806h (0.60 #6766, 0.43 #11103, 0.36 #14357), 07r4c (0.60 #7066, 0.43 #11403, 0.36 #14657), 01vv7sc (0.60 #6568, 0.43 #10905, 0.36 #14159) >> Best rule #13134 for best value: >> intensional similarity = 9 >> extensional distance = 7 >> proper extension: 06by7; 07d2d; >> query: (?x9280, 03t9sp) <- artists(?x9280, ?x11689), artists(?x9280, ?x7951), ?x11689 = 06p03s, artists(?x3562, ?x7951), artist(?x10426, ?x7951), award_nominee(?x3890, ?x7951), ?x3562 = 025sc50, ?x10426 = 01trtc, award(?x7951, ?x724) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #17523 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 17 *> proper extension: 03_d0; 0xhtw; 0dl5d; 05w3f; 0126t5; 01fh36; *> query: (?x9280, 09prnq) <- artists(?x9280, ?x11689), artists(?x671, ?x11689), gender(?x11689, ?x514), role(?x11689, ?x432), ?x671 = 064t9, instrumentalists(?x227, ?x11689), ?x432 = 042v_gx *> conf = 0.26 ranks of expected_values: 298 EVAL 05rwpb artists 09prnq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 65.000 23.000 0.778 http://example.org/music/genre/artists #20093-01jrbb PRED entity: 01jrbb PRED relation: language PRED expected values: 02h40lc => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.90 #1378, 0.89 #1137, 0.89 #1198), 064_8sq (0.18 #380, 0.18 #440, 0.16 #499), 03_9r (0.18 #667, 0.07 #128, 0.06 #428), 06nm1 (0.14 #70, 0.13 #369, 0.12 #1027), 04306rv (0.13 #423, 0.11 #363, 0.10 #5), 0k0sv (0.10 #24, 0.04 #262, 0.03 #501), 06b_j (0.09 #201, 0.08 #381, 0.07 #141), 02bjrlw (0.08 #359, 0.08 #419, 0.06 #777), 05zjd (0.06 #444, 0.04 #144, 0.03 #683), 0653m (0.04 #609, 0.04 #909, 0.04 #549) >> Best rule #1378 for best value: >> intensional similarity = 4 >> extensional distance = 311 >> proper extension: 0dnvn3; 03h_yy; 03ckwzc; 0963mq; 03t97y; 0c00zd0; 01j8wk; 014zwb; 0c57yj; 05_5rjx; ... >> query: (?x2893, 02h40lc) <- crewmember(?x2893, ?x1933), film(?x1052, ?x2893), genre(?x2893, ?x2540), genre(?x419, ?x2540) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jrbb language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.904 http://example.org/film/film/language #20092-01b39j PRED entity: 01b39j PRED relation: list PRED expected values: 04k4rt => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.87 #33, 0.82 #26, 0.81 #40), 04k4rt (0.61 #218, 0.57 #95, 0.55 #214), 01pd60 (0.61 #218, 0.52 #48, 0.50 #160), 09g7thr (0.40 #324, 0.30 #465, 0.22 #606) >> Best rule #33 for best value: >> intensional similarity = 6 >> extensional distance = 21 >> proper extension: 01yfp7; 0vlf; >> query: (?x8934, 01ptsx) <- company(?x4682, ?x8934), company(?x346, ?x8934), contact_category(?x8934, ?x897), ?x346 = 060c4, ?x4682 = 0dq_5, currency(?x8934, ?x170) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #218 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 56 *> proper extension: 02y7t7; *> query: (?x8934, ?x7472) <- company(?x1491, ?x8934), industry(?x8934, ?x14344), ?x1491 = 0krdk, industry(?x5956, ?x14344), list(?x5956, ?x7472) *> conf = 0.61 ranks of expected_values: 2 EVAL 01b39j list 04k4rt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 135.000 0.870 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #20091-035wtd PRED entity: 035wtd PRED relation: major_field_of_study PRED expected values: 05lls => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 132): 05lls (0.56 #2484, 0.29 #507, 0.17 #383), 05qjc (0.56 #2484, 0.01 #5019, 0.01 #7376), 01mkq (0.55 #6844, 0.50 #140, 0.43 #2126), 02lp1 (0.50 #385, 0.50 #136, 0.43 #2122), 01lj9 (0.50 #166, 0.38 #5629, 0.33 #415), 04rjg (0.50 #145, 0.35 #2131, 0.33 #394), 0w7c (0.50 #185, 0.33 #434, 0.33 #61), 062z7 (0.50 #153, 0.33 #402, 0.33 #29), 01tbp (0.50 #186, 0.33 #435, 0.33 #62), 041y2 (0.50 #205, 0.33 #454, 0.33 #81) >> Best rule #2484 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 02d9nr; 0fr9jp; 0194_r; 0dzbl; 017hnw; >> query: (?x4145, ?x888) <- state_province_region(?x4145, ?x6521), student(?x4145, ?x5105), citytown(?x4145, ?x2941), student(?x888, ?x5105) >> conf = 0.56 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 035wtd major_field_of_study 05lls CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.561 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #20090-01vzxld PRED entity: 01vzxld PRED relation: profession PRED expected values: 01c979 => 120 concepts (63 used for prediction) PRED predicted values (max 10 best out of 69): 09jwl (0.59 #3255, 0.58 #901, 0.56 #2520), 016z4k (0.58 #886, 0.48 #592, 0.48 #2505), 01d_h8 (0.37 #741, 0.34 #2654, 0.33 #2801), 0dxtg (0.30 #8708, 0.29 #7824, 0.27 #4576), 0n1h (0.29 #305, 0.26 #1629, 0.25 #894), 01c72t (0.28 #1789, 0.26 #3260, 0.23 #2525), 039v1 (0.27 #918, 0.23 #5039, 0.23 #5630), 03gjzk (0.25 #7825, 0.25 #3398, 0.25 #8709), 02jknp (0.25 #7, 0.20 #7818, 0.19 #3097), 0d1pc (0.22 #343, 0.14 #2698, 0.14 #2845) >> Best rule #3255 for best value: >> intensional similarity = 3 >> extensional distance = 260 >> proper extension: 01x66d; 01wz3cx; 0pyg6; 01zmpg; 01hw6wq; 014q2g; 01w02sy; 02bh9; 016ksk; 01q32bd; ... >> query: (?x10181, 09jwl) <- award(?x10181, ?x567), profession(?x10181, ?x131), ?x131 = 0dz3r >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #817 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 04wqr; 04smkr; *> query: (?x10181, 01c979) <- participant(?x10181, ?x702), award_winner(?x10180, ?x10181), languages(?x10181, ?x254) *> conf = 0.01 ranks of expected_values: 61 EVAL 01vzxld profession 01c979 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 120.000 63.000 0.588 http://example.org/people/person/profession #20089-06c62 PRED entity: 06c62 PRED relation: location_of_ceremony! PRED expected values: 02s58t => 297 concepts (253 used for prediction) PRED predicted values (max 10 best out of 302): 03lt8g (0.25 #275, 0.20 #779, 0.17 #1794), 0gdqy (0.25 #475, 0.20 #979, 0.17 #1994), 0c9c0 (0.25 #318, 0.20 #822, 0.17 #1837), 06wvj (0.25 #311, 0.20 #815, 0.17 #1830), 06x58 (0.25 #293, 0.20 #797, 0.17 #1812), 02m30v (0.20 #1261, 0.17 #2023, 0.17 #1769), 03j24kf (0.20 #1121, 0.17 #1883, 0.17 #1629), 01rwcgb (0.20 #1236, 0.17 #1998, 0.17 #1744), 01vsy7t (0.20 #1119, 0.17 #1881, 0.17 #1627), 014v1q (0.20 #1254, 0.17 #2016, 0.17 #1762) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 07ytt; >> query: (?x6959, 03lt8g) <- films(?x6959, ?x1077), capital(?x205, ?x6959), written_by(?x1077, ?x1532) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06c62 location_of_ceremony! 02s58t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 297.000 253.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #20088-02jtjz PRED entity: 02jtjz PRED relation: film PRED expected values: 061681 => 99 concepts (70 used for prediction) PRED predicted values (max 10 best out of 784): 0fhzwl (0.69 #10736, 0.49 #35785, 0.48 #76943), 0fpmrm3 (0.19 #2214, 0.03 #101992, 0.02 #42943), 0234j5 (0.11 #3213, 0.03 #101992, 0.02 #42943), 027r9t (0.08 #4826, 0.07 #1248, 0.05 #13773), 01shy7 (0.07 #423, 0.06 #9369, 0.06 #11159), 05fm6m (0.07 #1321, 0.05 #4899, 0.04 #8478), 0blpg (0.07 #656, 0.05 #7813, 0.05 #9602), 02cbhg (0.07 #1404, 0.04 #8561, 0.04 #10350), 0bxsk (0.07 #1210, 0.04 #2999, 0.03 #4788), 0jsf6 (0.07 #2879, 0.04 #1090, 0.03 #4668) >> Best rule #10736 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 0mm1q; 017yxq; >> query: (?x3866, ?x8870) <- participant(?x3866, ?x3466), film(?x3866, ?x1470), nominated_for(?x3866, ?x8870) >> conf = 0.69 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02jtjz film 061681 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 70.000 0.687 http://example.org/film/actor/film./film/performance/film #20087-03h_fk5 PRED entity: 03h_fk5 PRED relation: artists! PRED expected values: 07sbbz2 0155w => 144 concepts (90 used for prediction) PRED predicted values (max 10 best out of 240): 064t9 (0.49 #3701, 0.46 #2164, 0.46 #14147), 0155w (0.41 #1335, 0.31 #4101, 0.25 #106), 03_d0 (0.35 #1240, 0.30 #3391, 0.26 #5542), 06j6l (0.35 #1276, 0.30 #5578, 0.29 #1891), 0xhtw (0.35 #1861, 0.31 #4012, 0.30 #5548), 05bt6j (0.32 #3731, 0.29 #12024, 0.27 #2194), 016jny (0.29 #1948, 0.22 #1333, 0.20 #5635), 02yv6b (0.29 #1942, 0.21 #4093, 0.21 #5629), 025sc50 (0.27 #3737, 0.25 #14183, 0.21 #18797), 07sbbz2 (0.25 #7, 0.24 #1236, 0.24 #1851) >> Best rule #3701 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 0152cw; 01v0sx2; 0dtd6; 0frsw; 0178kd; 07r1_; 046p9; 017mbb; 017959; 016vn3; ... >> query: (?x2807, 064t9) <- award(?x2807, ?x2877), artists(?x302, ?x2807), ?x2877 = 02f5qb >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1335 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 01wg25j; *> query: (?x2807, 0155w) <- gender(?x2807, ?x231), inductee(?x1091, ?x2807), instrumentalists(?x227, ?x2807) *> conf = 0.41 ranks of expected_values: 2, 10 EVAL 03h_fk5 artists! 0155w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 90.000 0.493 http://example.org/music/genre/artists EVAL 03h_fk5 artists! 07sbbz2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 144.000 90.000 0.493 http://example.org/music/genre/artists #20086-059x0w PRED entity: 059x0w PRED relation: profession PRED expected values: 01d_h8 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 82): 01d_h8 (0.85 #2556, 0.85 #1506, 0.84 #2256), 02hrh1q (0.85 #315, 0.78 #4367, 0.77 #3767), 0dxtg (0.56 #1814, 0.51 #2865, 0.51 #2715), 03gjzk (0.56 #166, 0.46 #1666, 0.45 #2116), 02jknp (0.51 #1808, 0.48 #2558, 0.48 #2709), 012t_z (0.33 #163, 0.24 #463, 0.20 #913), 0fj9f (0.28 #10204, 0.26 #656, 0.23 #806), 01c72t (0.28 #10204, 0.11 #4977, 0.09 #5428), 0cbd2 (0.21 #1357, 0.20 #1957, 0.20 #7), 02krf9 (0.20 #28, 0.15 #1678, 0.15 #2128) >> Best rule #2556 for best value: >> intensional similarity = 2 >> extensional distance = 339 >> proper extension: 04dyqk; >> query: (?x9316, 01d_h8) <- produced_by(?x11332, ?x9316), titles(?x600, ?x11332) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059x0w profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.850 http://example.org/people/person/profession #20085-087z12 PRED entity: 087z12 PRED relation: religion PRED expected values: 0flw86 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 14): 03j6c (0.55 #66, 0.50 #156, 0.42 #111), 0flw86 (0.33 #2, 0.18 #47, 0.11 #137), 0c8wxp (0.15 #366, 0.14 #411, 0.14 #456), 03kbr (0.09 #75), 0kpl (0.07 #641, 0.06 #145, 0.05 #1091), 03_gx (0.05 #915, 0.05 #1185, 0.05 #1050), 092bf5 (0.03 #331, 0.03 #376, 0.02 #466), 06yyp (0.03 #247, 0.02 #202, 0.02 #292), 01lp8 (0.02 #181, 0.02 #3022, 0.02 #496), 042s9 (0.02 #3022) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 038b_x; 0239zv; 07t3x8; 03f22dp; >> query: (?x7531, 03j6c) <- student(?x4289, ?x7531), award(?x7531, ?x1937), ?x1937 = 03r8tl, type_of_union(?x7531, ?x566) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 01zh29; *> query: (?x7531, 0flw86) <- student(?x4289, ?x7531), award(?x7531, ?x1937), film(?x7531, ?x8074), ?x8074 = 052_mn *> conf = 0.33 ranks of expected_values: 2 EVAL 087z12 religion 0flw86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 75.000 0.545 http://example.org/people/person/religion #20084-02f5qb PRED entity: 02f5qb PRED relation: award! PRED expected values: 01w61th 07ss8_ 03bnv 015f7 01pq5j7 0gr69 03f3yfj => 49 concepts (31 used for prediction) PRED predicted values (max 10 best out of 3720): 089pg7 (0.76 #82839, 0.74 #99407, 0.73 #49700), 01bczm (0.60 #14848, 0.50 #18162, 0.41 #31413), 089tm (0.60 #13317, 0.50 #16631, 0.35 #29882), 01vvycq (0.60 #13397, 0.50 #16711, 0.33 #6773), 01wgxtl (0.57 #20601, 0.50 #10662, 0.33 #7350), 01pfr3 (0.53 #29909, 0.50 #16658, 0.40 #13344), 01w7nww (0.50 #10789, 0.43 #24040, 0.43 #20728), 05szp (0.50 #11831, 0.43 #21770, 0.36 #25082), 01dwrc (0.50 #11595, 0.43 #21534, 0.33 #18221), 07c0j (0.50 #10207, 0.40 #13519, 0.35 #30084) >> Best rule #82839 for best value: >> intensional similarity = 5 >> extensional distance = 239 >> proper extension: 040v3t; >> query: (?x2877, ?x7781) <- award(?x9497, ?x2877), award(?x3065, ?x2877), profession(?x3065, ?x131), influenced_by(?x9497, ?x3316), award_winner(?x2877, ?x7781) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #14143 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 01ckcd; *> query: (?x2877, 03bnv) <- award(?x9497, ?x2877), award(?x9206, ?x2877), award(?x1398, ?x2877), ?x9497 = 07hgm, celebrity(?x4126, ?x1398), award_winner(?x3926, ?x1398), artist(?x4483, ?x9206) *> conf = 0.40 ranks of expected_values: 21, 26, 32, 44, 82, 208, 286 EVAL 02f5qb award! 03f3yfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 49.000 31.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f5qb award! 0gr69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 49.000 31.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f5qb award! 01pq5j7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 49.000 31.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f5qb award! 015f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 49.000 31.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f5qb award! 03bnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 49.000 31.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f5qb award! 07ss8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 31.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f5qb award! 01w61th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 31.000 0.761 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20083-025rzfc PRED entity: 025rzfc PRED relation: combatants PRED expected values: 02psqkz => 64 concepts (52 used for prediction) PRED predicted values (max 10 best out of 201): 07ssc (0.67 #4892, 0.67 #901, 0.61 #4242), 09c7w0 (0.59 #1524, 0.43 #3326, 0.43 #639), 02psqkz (0.57 #688, 0.41 #253, 0.38 #813), 088q1s (0.43 #727, 0.13 #4970, 0.12 #4320), 0cdbq (0.42 #1070, 0.30 #1959, 0.29 #1706), 01tdpv (0.42 #1116, 0.25 #357, 0.24 #1752), 01k6y1 (0.42 #1071, 0.24 #1707, 0.22 #2217), 0chghy (0.41 #1532, 0.41 #253, 0.35 #1404), 015qh (0.41 #253, 0.38 #254, 0.33 #917), 06c1y (0.41 #253, 0.38 #254, 0.25 #156) >> Best rule #4892 for best value: >> intensional similarity = 8 >> extensional distance = 37 >> proper extension: 0cmc2; 0gjw_; >> query: (?x9814, 07ssc) <- locations(?x9814, ?x5114), combatants(?x9814, ?x13069), combatants(?x9814, ?x456), organization(?x13069, ?x4230), olympics(?x456, ?x391), teams(?x456, ?x11268), combatants(?x94, ?x456), film_release_region(?x66, ?x456) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #688 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 5 *> proper extension: 02kxg_; *> query: (?x9814, 02psqkz) <- combatants(?x9814, ?x13069), combatants(?x9814, ?x5114), ?x13069 = 01rdm0, combatants(?x5114, ?x3728), olympics(?x5114, ?x6464), olympics(?x5114, ?x2369), ?x2369 = 0lbbj, ?x3728 = 087vz, ?x6464 = 0lbd9 *> conf = 0.57 ranks of expected_values: 3 EVAL 025rzfc combatants 02psqkz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 52.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #20082-03ntbmw PRED entity: 03ntbmw PRED relation: produced_by PRED expected values: 05zrx3v => 67 concepts (50 used for prediction) PRED predicted values (max 10 best out of 104): 0fvf9q (0.13 #6, 0.07 #393, 0.05 #783), 013pp3 (0.12 #4652, 0.11 #9306, 0.11 #6978), 0633p0 (0.12 #4652, 0.10 #15121, 0.10 #13956), 0c9xjl (0.10 #5040, 0.02 #5039, 0.02 #14731), 0b13g7 (0.07 #118, 0.05 #1284, 0.03 #2057), 02q42j_ (0.07 #210, 0.05 #1376, 0.02 #3699), 029m83 (0.07 #275, 0.03 #1441, 0.02 #662), 02r251z (0.07 #242, 0.02 #4894, 0.02 #4118), 092kgw (0.07 #196, 0.02 #1362, 0.01 #4460), 0pz91 (0.07 #46, 0.02 #4698, 0.01 #5473) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 02qcr; >> query: (?x12403, 0fvf9q) <- film(?x2275, ?x12403), film_release_distribution_medium(?x12403, ?x81), genre(?x12403, ?x53), ?x2275 = 05dbf >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03ntbmw produced_by 05zrx3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 50.000 0.133 http://example.org/film/film/produced_by #20081-0mkv3 PRED entity: 0mkv3 PRED relation: source PRED expected values: 0jbk9 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #40, 0.91 #34, 0.91 #32) >> Best rule #40 for best value: >> intensional similarity = 2 >> extensional distance = 365 >> proper extension: 0nm8n; >> query: (?x12400, 0jbk9) <- second_level_divisions(?x94, ?x12400), ?x94 = 09c7w0 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mkv3 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #20080-061k5 PRED entity: 061k5 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.60 #5, 0.52 #85, 0.51 #81), 0jgjn (0.20 #221, 0.12 #32, 0.12 #28), 01g63y (0.05 #50, 0.03 #74, 0.03 #78), 01bl8s (0.03 #79, 0.02 #83, 0.01 #103) >> Best rule #5 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0947l; 031y2; >> query: (?x13218, 04ztj) <- country(?x13218, ?x205), ?x205 = 03rjj, place_of_birth(?x9606, ?x13218), profession(?x9606, ?x319), award(?x9606, ?x198) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 061k5 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.600 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #20079-07ssc PRED entity: 07ssc PRED relation: country! PRED expected values: 02bkg 02y8z 09wz9 => 225 concepts (225 used for prediction) PRED predicted values (max 10 best out of 14): 02y8z (0.76 #510, 0.73 #734, 0.72 #776), 01z27 (0.68 #676, 0.65 #522, 0.62 #802), 03fyrh (0.62 #302, 0.62 #414, 0.61 #736), 02bkg (0.55 #673, 0.53 #435, 0.52 #519), 09_9n (0.54 #419, 0.39 #531, 0.39 #503), 09_94 (0.54 #411, 0.39 #495, 0.38 #299), 01dys (0.52 #521, 0.46 #409, 0.46 #801), 09wz9 (0.50 #497, 0.50 #301, 0.48 #525), 06zgc (0.50 #304, 0.46 #416, 0.45 #360), 09_b4 (0.46 #417, 0.45 #361, 0.45 #683) >> Best rule #510 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 0d0kn; >> query: (?x512, 02y8z) <- film_release_region(?x3491, ?x512), country(?x1156, ?x512), ?x3491 = 0gtvpkw >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 8 EVAL 07ssc country! 09wz9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 225.000 225.000 0.762 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07ssc country! 02y8z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 225.000 225.000 0.762 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07ssc country! 02bkg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 225.000 225.000 0.762 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #20078-01wy6 PRED entity: 01wy6 PRED relation: role! PRED expected values: 01mxnvc => 83 concepts (51 used for prediction) PRED predicted values (max 10 best out of 738): 016h9b (0.67 #5094, 0.62 #6868, 0.50 #9822), 04mx7s (0.67 #5247, 0.50 #7021, 0.50 #4952), 0473q (0.67 #5218, 0.50 #6992, 0.50 #3153), 0bg539 (0.50 #6823, 0.50 #5049, 0.50 #4754), 05qhnq (0.50 #6989, 0.50 #5215, 0.50 #3150), 08n__5 (0.50 #4886, 0.50 #3411, 0.50 #3116), 0167v4 (0.50 #5265, 0.50 #3200, 0.38 #7039), 01mxnvc (0.50 #5290, 0.50 #3225, 0.38 #7064), 01v_pj6 (0.50 #5060, 0.50 #2995, 0.38 #6834), 02mx98 (0.50 #7018, 0.50 #5244, 0.33 #819) >> Best rule #5094 for best value: >> intensional similarity = 22 >> extensional distance = 4 >> proper extension: 05148p4; >> query: (?x2460, 016h9b) <- role(?x2460, ?x3161), role(?x2460, ?x1663), role(?x2460, ?x314), role(?x2460, ?x6039), role(?x2460, ?x2592), role(?x3161, ?x2297), ?x2297 = 051hrr, role(?x5391, ?x3161), role(?x2306, ?x3161), role(?x4078, ?x1663), role(?x7449, ?x3161), role(?x745, ?x3161), ?x314 = 02sgy, instrumentalists(?x3161, ?x140), ?x7449 = 01vnt4, instrumentalists(?x6039, ?x3030), artist(?x2149, ?x2306), role(?x6039, ?x1466), ?x2592 = 0j871, ?x4078 = 011k_j, ?x745 = 01vj9c, ?x5391 = 03h_fqv >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5290 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 4 *> proper extension: 05148p4; *> query: (?x2460, 01mxnvc) <- role(?x2460, ?x3161), role(?x2460, ?x1663), role(?x2460, ?x314), role(?x2460, ?x6039), role(?x2460, ?x2592), role(?x3161, ?x2297), ?x2297 = 051hrr, role(?x5391, ?x3161), role(?x2306, ?x3161), role(?x4078, ?x1663), role(?x7449, ?x3161), role(?x745, ?x3161), ?x314 = 02sgy, instrumentalists(?x3161, ?x140), ?x7449 = 01vnt4, instrumentalists(?x6039, ?x3030), artist(?x2149, ?x2306), role(?x6039, ?x1466), ?x2592 = 0j871, ?x4078 = 011k_j, ?x745 = 01vj9c, ?x5391 = 03h_fqv *> conf = 0.50 ranks of expected_values: 8 EVAL 01wy6 role! 01mxnvc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 83.000 51.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role #20077-02hxhz PRED entity: 02hxhz PRED relation: film! PRED expected values: 01wbg84 => 150 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1289): 02r251z (0.44 #170705, 0.44 #189437, 0.44 #185273), 017s11 (0.44 #170705, 0.44 #189437, 0.44 #185273), 07ymr5 (0.33 #311, 0.04 #10723, 0.01 #39865), 0252fh (0.33 #1354, 0.02 #40908, 0.02 #24254), 01vwllw (0.33 #547, 0.02 #25528, 0.02 #27611), 092ggq (0.33 #1196, 0.01 #40750), 03h_0_z (0.33 #1083, 0.01 #40637), 04h07s (0.33 #795, 0.01 #40349), 0mdqp (0.21 #2202, 0.17 #4284, 0.08 #4165), 0lx2l (0.21 #2502, 0.17 #4584, 0.08 #4165) >> Best rule #170705 for best value: >> intensional similarity = 4 >> extensional distance = 454 >> proper extension: 018js4; 034qrh; 01ln5z; 0p9lw; 033g4d; 0b76t12; 0cc7hmk; 0f40w; 065z3_x; 0k5g9; ... >> query: (?x821, ?x541) <- nominated_for(?x541, ?x821), titles(?x2480, ?x821), featured_film_locations(?x821, ?x739), film(?x2127, ?x821) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #6296 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 057lbk; 05650n; 048yqf; *> query: (?x821, 01wbg84) <- executive_produced_by(?x821, ?x1335), nominated_for(?x350, ?x821), produced_by(?x821, ?x7090), region(?x821, ?x512) *> conf = 0.05 ranks of expected_values: 159 EVAL 02hxhz film! 01wbg84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 150.000 92.000 0.442 http://example.org/film/actor/film./film/performance/film #20076-0f40w PRED entity: 0f40w PRED relation: film! PRED expected values: 05qd_ => 73 concepts (47 used for prediction) PRED predicted values (max 10 best out of 49): 086k8 (0.36 #77, 0.20 #2, 0.17 #677), 016tw3 (0.31 #236, 0.27 #161, 0.18 #86), 017s11 (0.31 #228, 0.27 #153, 0.17 #378), 05qd_ (0.24 #384, 0.21 #534, 0.20 #9), 016tt2 (0.20 #154, 0.19 #229, 0.13 #982), 03xq0f (0.20 #155, 0.19 #230, 0.11 #1285), 01gb54 (0.20 #179, 0.19 #254, 0.10 #29), 024rgt (0.13 #170, 0.12 #245, 0.09 #620), 020h2v (0.13 #195, 0.12 #270, 0.05 #1098), 024rbz (0.12 #237, 0.07 #162, 0.04 #1292) >> Best rule #77 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0413cff; >> query: (?x2288, 086k8) <- genre(?x2288, ?x812), ?x812 = 01jfsb, person(?x2288, ?x6008), country(?x2288, ?x94) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #384 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 0bx_hnp; *> query: (?x2288, 05qd_) <- genre(?x2288, ?x812), film_crew_role(?x2288, ?x137), produced_by(?x2288, ?x6187), person(?x2288, ?x6008) *> conf = 0.24 ranks of expected_values: 4 EVAL 0f40w film! 05qd_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 73.000 47.000 0.364 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #20075-02qgqt PRED entity: 02qgqt PRED relation: film PRED expected values: 0c0nhgv 027r9t => 89 concepts (36 used for prediction) PRED predicted values (max 10 best out of 479): 08r4x3 (0.65 #153, 0.02 #1923, 0.02 #7233), 0h7t36 (0.58 #14163, 0.58 #10621, 0.43 #23020), 09cr8 (0.12 #282, 0.03 #7362, 0.02 #5592), 04vr_f (0.12 #169, 0.02 #1939, 0.01 #5479), 0jzw (0.12 #119, 0.01 #8969), 013q0p (0.12 #804), 07jxpf (0.12 #680), 06q8qh (0.12 #603), 026n4h6 (0.12 #240), 02prw4h (0.12 #182) >> Best rule #153 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 07h565; >> query: (?x157, 08r4x3) <- award_nominee(?x4662, ?x157), award_winner(?x1564, ?x157), ?x4662 = 016vg8 >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02qgqt film 027r9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 36.000 0.647 http://example.org/film/actor/film./film/performance/film EVAL 02qgqt film 0c0nhgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 36.000 0.647 http://example.org/film/actor/film./film/performance/film #20074-03d8m4 PRED entity: 03d8m4 PRED relation: current_club PRED expected values: 01dwyd => 71 concepts (46 used for prediction) PRED predicted values (max 10 best out of 737): 06l22 (0.40 #487, 0.19 #771, 0.18 #913), 045xx (0.40 #496, 0.15 #780, 0.14 #922), 03tck1 (0.40 #543, 0.12 #827, 0.11 #969), 0177gl (0.40 #478, 0.08 #762, 0.07 #904), 03x6m (0.33 #217, 0.33 #71, 0.27 #788), 0kqbh (0.33 #421, 0.33 #131, 0.12 #848), 0371rb (0.33 #306, 0.33 #16, 0.10 #1160), 0xbm (0.33 #310, 0.23 #737, 0.21 #879), 0y54 (0.33 #8, 0.20 #441, 0.19 #725), 075q_ (0.33 #4, 0.20 #437, 0.12 #721) >> Best rule #487 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 02rqxc; 0329r5; 01352_; >> query: (?x4972, 06l22) <- current_club(?x4972, ?x10196), current_club(?x4972, ?x9273), current_club(?x4972, ?x5993), current_club(?x4972, ?x3620), position(?x3620, ?x530), position(?x9273, ?x63), teams(?x9402, ?x3620), ?x10196 = 02rh_0, ?x530 = 02_j1w, sport(?x5993, ?x471) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #146 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 03y_f8; *> query: (?x4972, ?x59) <- current_club(?x4972, ?x3620), ?x3620 = 01w_d6, team(?x530, ?x4972), team(?x530, ?x1599), team(?x530, ?x59), ?x1599 = 025txtg, position(?x62, ?x530), position(?x3719, ?x530) *> conf = 0.02 ranks of expected_values: 301 EVAL 03d8m4 current_club 01dwyd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 71.000 46.000 0.400 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #20073-02z2mr7 PRED entity: 02z2mr7 PRED relation: film_crew_role PRED expected values: 02_n3z 09vw2b7 089g0h => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 31): 09vw2b7 (0.82 #147, 0.71 #77, 0.71 #708), 089g0h (0.54 #721, 0.20 #510, 0.17 #3919), 0d2b38 (0.52 #727, 0.38 #131, 0.29 #201), 02_n3z (0.48 #703, 0.17 #3919, 0.17 #36), 01xy5l_ (0.46 #716, 0.18 #505, 0.17 #3919), 01vx2h (0.45 #713, 0.43 #82, 0.36 #152), 0dxtw (0.41 #957, 0.41 #992, 0.40 #1449), 01pvkk (0.34 #1241, 0.33 #573, 0.33 #12), 02rh1dz (0.33 #9, 0.18 #150, 0.18 #921), 02ynfr (0.29 #87, 0.27 #157, 0.21 #297) >> Best rule #147 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0ds3t5x; 011yd2; 02q87z6; 05k4my; >> query: (?x5725, 09vw2b7) <- film(?x7093, ?x5725), ?x7093 = 03kbb8, country(?x5725, ?x512), contains(?x512, ?x362) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 02z2mr7 film_crew_role 089g0h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.818 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02z2mr7 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.818 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02z2mr7 film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.818 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #20072-0dw4g PRED entity: 0dw4g PRED relation: artist! PRED expected values: 025t8bv => 107 concepts (88 used for prediction) PRED predicted values (max 10 best out of 116): 0fb0v (0.29 #145, 0.10 #4289, 0.10 #5531), 01clyr (0.25 #307, 0.19 #583, 0.15 #1273), 03rhqg (0.23 #4298, 0.23 #3054, 0.21 #3468), 017l96 (0.18 #1123, 0.14 #571, 0.13 #2504), 0mcf4 (0.17 #747, 0.15 #471, 0.10 #1299), 0181dw (0.17 #1696, 0.15 #2248, 0.12 #3770), 0n85g (0.17 #2131, 0.13 #2546, 0.13 #1717), 0g768 (0.16 #3073, 0.15 #3487, 0.15 #2105), 033hn8 (0.16 #4710, 0.15 #2499, 0.15 #6091), 02p11jq (0.15 #1669, 0.13 #2221, 0.11 #3051) >> Best rule #145 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01pfr3; 081wh1; 0134pk; >> query: (?x5547, 0fb0v) <- award(?x5547, ?x462), group(?x227, ?x5547), ?x462 = 05zkcn5 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #335 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 01y8d4; *> query: (?x5547, 025t8bv) <- peers(?x1573, ?x5547), award_winner(?x2169, ?x5547), location(?x2169, ?x3778) *> conf = 0.06 ranks of expected_values: 42 EVAL 0dw4g artist! 025t8bv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 107.000 88.000 0.286 http://example.org/music/record_label/artist #20071-012d40 PRED entity: 012d40 PRED relation: award PRED expected values: 05zr6wv => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 273): 05zr6wv (0.77 #4815, 0.75 #6020, 0.71 #20062), 09sb52 (0.42 #8868, 0.37 #8066, 0.33 #9670), 01by1l (0.39 #113, 0.31 #4526, 0.31 #4928), 01bgqh (0.36 #43, 0.24 #4456, 0.23 #4858), 0gq9h (0.26 #880, 0.19 #1682, 0.17 #8504), 0c4z8 (0.25 #72, 0.21 #4485, 0.20 #4887), 03qbh5 (0.25 #206, 0.19 #4619, 0.19 #5021), 02f73b (0.25 #286, 0.09 #687, 0.09 #4699), 040njc (0.22 #810, 0.17 #6830, 0.17 #1612), 054ks3 (0.21 #143, 0.18 #4556, 0.18 #4958) >> Best rule #4815 for best value: >> intensional similarity = 3 >> extensional distance = 341 >> proper extension: 0c_mvb; 0lzkm; 0f6lx; 06lxn; >> query: (?x147, ?x401) <- award_winner(?x4462, ?x147), artists(?x13968, ?x147), award_winner(?x401, ?x147) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012d40 award 05zr6wv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20070-02825kb PRED entity: 02825kb PRED relation: nominated_for! PRED expected values: 03hj5vf => 78 concepts (77 used for prediction) PRED predicted values (max 10 best out of 228): 0gq9h (0.28 #6545, 0.28 #1264, 0.25 #784), 0gr0m (0.27 #1261, 0.25 #1021, 0.23 #781), 0gs9p (0.26 #1266, 0.25 #6547, 0.25 #1026), 019f4v (0.24 #6536, 0.23 #1015, 0.22 #5336), 0gq_v (0.24 #980, 0.23 #1220, 0.23 #740), 0k611 (0.23 #1035, 0.22 #1275, 0.21 #6556), 099vwn (0.22 #16330, 0.20 #3841, 0.19 #16331), 0gs96 (0.22 #1052, 0.19 #1292, 0.16 #4653), 0l8z1 (0.22 #1013, 0.17 #1253, 0.17 #773), 0gqwc (0.20 #4623, 0.16 #6543, 0.14 #1022) >> Best rule #6545 for best value: >> intensional similarity = 4 >> extensional distance = 771 >> proper extension: 0ddfwj1; 02z9hqn; 02vr3gz; 08sfxj; 05r3qc; 047bynf; 02qkwl; 023cjg; 03h0byn; 07ykkx5; ... >> query: (?x6984, 0gq9h) <- genre(?x6984, ?x53), film(?x436, ?x6984), ?x53 = 07s9rl0, nominated_for(?x2597, ?x6984) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #14167 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1454 *> proper extension: 01tspc6; 02_1kl; 04z_x4v; 05nlzq; *> query: (?x6984, ?x198) <- nominated_for(?x2597, ?x6984), nominated_for(?x6985, ?x6984), nominated_for(?x2597, ?x4007), award(?x4007, ?x198) *> conf = 0.07 ranks of expected_values: 81 EVAL 02825kb nominated_for! 03hj5vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 78.000 77.000 0.283 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #20069-07g1sm PRED entity: 07g1sm PRED relation: film! PRED expected values: 01gw4f 01_f_5 => 107 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1465): 02vyw (0.47 #72803, 0.45 #43679, 0.42 #18721), 0kb3n (0.47 #72803, 0.45 #43679, 0.42 #18721), 012201 (0.47 #72803, 0.45 #43679, 0.42 #18721), 0854hr (0.47 #72803, 0.45 #43679, 0.39 #20803), 09dvgb8 (0.45 #43679, 0.39 #20803, 0.38 #83207), 02q9kqf (0.45 #43679, 0.39 #20803, 0.38 #83207), 02hy9p (0.11 #81126, 0.10 #14561), 0f0kz (0.10 #2595, 0.06 #12996, 0.04 #8835), 016z2j (0.10 #2468, 0.06 #12869, 0.03 #44067), 06cgy (0.09 #250, 0.06 #85288, 0.04 #21053) >> Best rule #72803 for best value: >> intensional similarity = 4 >> extensional distance = 416 >> proper extension: 025x1t; 0gxsh4; >> query: (?x7016, ?x7780) <- award_winner(?x7016, ?x3662), nominated_for(?x7780, ?x7016), location(?x7780, ?x191), produced_by(?x696, ?x3662) >> conf = 0.47 => this is the best rule for 4 predicted values *> Best rule #85288 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 526 *> proper extension: 01cgz; *> query: (?x7016, ?x446) <- films(?x5673, ?x7016), films(?x5673, ?x8028), film(?x446, ?x8028) *> conf = 0.06 ranks of expected_values: 44, 45 EVAL 07g1sm film! 01_f_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 107.000 47.000 0.473 http://example.org/film/actor/film./film/performance/film EVAL 07g1sm film! 01gw4f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 107.000 47.000 0.473 http://example.org/film/actor/film./film/performance/film #20068-0j3d9tn PRED entity: 0j3d9tn PRED relation: film_release_region PRED expected values: 03_3d 03rt9 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 158): 0jgd (0.89 #454, 0.86 #604, 0.86 #904), 05qhw (0.87 #915, 0.87 #765, 0.87 #465), 03_3d (0.87 #456, 0.84 #306, 0.84 #606), 03gj2 (0.83 #925, 0.83 #1526, 0.83 #775), 05v8c (0.83 #467, 0.80 #617, 0.78 #317), 03spz (0.81 #391, 0.80 #541, 0.78 #691), 01znc_ (0.80 #492, 0.79 #792, 0.78 #642), 03rk0 (0.78 #506, 0.74 #656, 0.72 #356), 09pmkv (0.78 #327, 0.64 #627, 0.63 #477), 03rt9 (0.76 #914, 0.76 #1215, 0.76 #764) >> Best rule #454 for best value: >> intensional similarity = 7 >> extensional distance = 44 >> proper extension: 0dscrwf; 0gmcwlb; 03qnvdl; 04n52p6; 02r8hh_; 0gd0c7x; 0gvrws1; 02yvct; 0661ql3; 02vr3gz; ... >> query: (?x5162, 0jgd) <- film_release_region(?x5162, ?x3749), film_release_region(?x5162, ?x2152), film_release_region(?x5162, ?x512), nominated_for(?x618, ?x5162), ?x512 = 07ssc, ?x3749 = 03ryn, ?x2152 = 06mkj >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #456 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 44 *> proper extension: 0dscrwf; 0gmcwlb; 03qnvdl; 04n52p6; 02r8hh_; 0gd0c7x; 0gvrws1; 02yvct; 0661ql3; 02vr3gz; ... *> query: (?x5162, 03_3d) <- film_release_region(?x5162, ?x3749), film_release_region(?x5162, ?x2152), film_release_region(?x5162, ?x512), nominated_for(?x618, ?x5162), ?x512 = 07ssc, ?x3749 = 03ryn, ?x2152 = 06mkj *> conf = 0.87 ranks of expected_values: 3, 10 EVAL 0j3d9tn film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 65.000 65.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j3d9tn film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 65.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20067-031b3h PRED entity: 031b3h PRED relation: award! PRED expected values: 01wmxfs 019f9z => 42 concepts (9 used for prediction) PRED predicted values (max 10 best out of 1874): 015mrk (0.81 #20140, 0.79 #30204, 0.78 #30203), 01w7nww (0.81 #20140, 0.79 #30204, 0.78 #30203), 02ktrs (0.79 #30204, 0.78 #30203, 0.76 #16781), 0288fyj (0.79 #30204, 0.78 #30203, 0.76 #16781), 0gbwp (0.71 #14532, 0.60 #11176, 0.50 #7819), 04xrx (0.60 #10765, 0.57 #14121, 0.50 #7408), 0163kf (0.50 #9899, 0.43 #16612, 0.40 #13256), 01vt9p3 (0.50 #8185, 0.33 #4829, 0.29 #14898), 019f9z (0.50 #8626, 0.33 #5270, 0.29 #15339), 01s1zk (0.43 #15621, 0.40 #12265, 0.33 #2195) >> Best rule #20140 for best value: >> intensional similarity = 5 >> extensional distance = 74 >> proper extension: 02qkk9_; >> query: (?x3937, ?x3065) <- award_winner(?x3937, ?x3065), award_winner(?x3937, ?x2335), award(?x3065, ?x1389), ?x1389 = 01c427, award_nominee(?x527, ?x2335) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #8626 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 01d38g; *> query: (?x3937, 019f9z) <- award_winner(?x3937, ?x4474), award_winner(?x3937, ?x2335), award(?x3493, ?x3937), ?x4474 = 01vvyvk, ?x3493 = 044gyq, award_nominee(?x527, ?x2335) *> conf = 0.50 ranks of expected_values: 9, 87 EVAL 031b3h award! 019f9z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 42.000 9.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 031b3h award! 01wmxfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 42.000 9.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20066-0dj5q PRED entity: 0dj5q PRED relation: basic_title PRED expected values: 060bp => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 16): 0fkvn (0.32 #377, 0.32 #360, 0.30 #241), 0dq3c (0.29 #274, 0.25 #240, 0.24 #308), 0789n (0.24 #179, 0.21 #128, 0.20 #247), 060bp (0.22 #205, 0.17 #35, 0.15 #324), 01gkgk (0.18 #90, 0.17 #532, 0.17 #481), 0p5vf (0.17 #215, 0.17 #45, 0.15 #249), 09d6p2 (0.11 #212, 0.07 #331, 0.06 #195), 01q24l (0.10 #284, 0.09 #97, 0.08 #318), 02079p (0.09 #95, 0.05 #384, 0.05 #282), 0f6c3 (0.09 #92, 0.02 #415, 0.02 #483) >> Best rule #377 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 08f3b1; >> query: (?x6735, 0fkvn) <- gender(?x6735, ?x231), type_of_union(?x6735, ?x566), people(?x6734, ?x6735), jurisdiction_of_office(?x6735, ?x789) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #205 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 0kn4c; *> query: (?x6735, 060bp) <- gender(?x6735, ?x231), ?x231 = 05zppz, jurisdiction_of_office(?x6735, ?x789), basic_title(?x6735, ?x346), entity_involved(?x12031, ?x6735) *> conf = 0.22 ranks of expected_values: 4 EVAL 0dj5q basic_title 060bp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 144.000 144.000 0.324 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #20065-070g7 PRED entity: 070g7 PRED relation: currency PRED expected values: 09nqf => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.90 #141, 0.84 #211, 0.80 #372), 01nv4h (0.14 #51, 0.12 #58, 0.07 #107), 02l6h (0.14 #53, 0.12 #60, 0.06 #207), 088n7 (0.01 #399, 0.01 #392), 02gsvk (0.01 #552) >> Best rule #141 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 01kff7; 0fdv3; 01kf3_9; 047qxs; 012kyx; 026f__m; 09lxv9; >> query: (?x4203, 09nqf) <- genre(?x4203, ?x1013), film(?x9783, ?x4203), language(?x4203, ?x254), film(?x1689, ?x4203), ?x1013 = 06n90 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 070g7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.904 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #20064-0488g PRED entity: 0488g PRED relation: religion PRED expected values: 05sfs => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 23): 05sfs (0.75 #280, 0.72 #305, 0.72 #254), 01s5nb (0.42 #64, 0.42 #292, 0.41 #89), 0flw86 (0.39 #911, 0.38 #733, 0.38 #632), 02t7t (0.26 #87, 0.25 #290, 0.25 #264), 092bf5 (0.25 #335, 0.25 #32, 0.25 #7), 03j6c (0.25 #35, 0.25 #10, 0.09 #1072), 0kpl (0.25 #29, 0.25 #4, 0.03 #635), 07w8f (0.25 #44, 0.25 #19, 0.02 #221), 072w0 (0.24 #116, 0.23 #90, 0.23 #293), 04t_mf (0.04 #1078, 0.04 #1179, 0.02 #1128) >> Best rule #280 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 0h5qxv; >> query: (?x1782, 05sfs) <- adjoins(?x1782, ?x1024), district_represented(?x605, ?x1782), jurisdiction_of_office(?x3959, ?x1782) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0488g religion 05sfs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.755 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #20063-026spg PRED entity: 026spg PRED relation: student! PRED expected values: 065y4w7 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 107): 0bwfn (0.09 #32362, 0.08 #40778, 0.08 #41830), 02g839 (0.09 #4233, 0.09 #2655, 0.08 #7915), 01d34b (0.08 #781, 0.05 #2885, 0.03 #4463), 017z88 (0.07 #6920, 0.06 #2712, 0.06 #16388), 01t0dy (0.06 #217, 0.03 #1795, 0.03 #743), 01w5m (0.06 #2735, 0.05 #45343, 0.05 #12729), 09f2j (0.06 #4367, 0.05 #6997, 0.05 #12783), 03x33n (0.05 #655, 0.04 #2759, 0.03 #5389), 01g0p5 (0.05 #733, 0.03 #2837, 0.02 #7045), 078bz (0.05 #603, 0.03 #2707, 0.02 #4285) >> Best rule #32362 for best value: >> intensional similarity = 3 >> extensional distance = 729 >> proper extension: 079vf; 0dbpyd; 05ty4m; 03m8lq; 03qd_; 0blbxk; 05k2s_; 07csf4; 06hhrs; 0738b8; ... >> query: (?x4675, 0bwfn) <- profession(?x4675, ?x220), award_winner(?x1238, ?x4675), student(?x6545, ?x4675) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #45252 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1314 *> proper extension: 0cm03; 0frmb1; 0xnc3; 02vptk_; 09jrf; *> query: (?x4675, 065y4w7) <- nationality(?x4675, ?x94), student(?x6545, ?x4675), currency(?x6545, ?x170) *> conf = 0.05 ranks of expected_values: 11 EVAL 026spg student! 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 138.000 138.000 0.088 http://example.org/education/educational_institution/students_graduates./education/education/student #20062-03rtz1 PRED entity: 03rtz1 PRED relation: genre PRED expected values: 07s9rl0 05p553 => 67 concepts (65 used for prediction) PRED predicted values (max 10 best out of 90): 07s9rl0 (0.64 #6433, 0.60 #2548, 0.58 #3521), 01z4y (0.61 #2670, 0.54 #2669, 0.53 #1576), 01jfsb (0.53 #2317, 0.37 #498, 0.33 #256), 02kdv5l (0.50 #2306, 0.50 #124, 0.43 #487), 03k9fj (0.50 #1345, 0.33 #255, 0.32 #2316), 05p553 (0.40 #126, 0.38 #247, 0.38 #489), 02l7c8 (0.29 #2565, 0.27 #2443, 0.27 #4267), 0hcr (0.24 #1356, 0.10 #2327, 0.07 #6456), 06n90 (0.23 #2318, 0.23 #1347, 0.22 #257), 0lsxr (0.22 #2313, 0.21 #131, 0.20 #10) >> Best rule #6433 for best value: >> intensional similarity = 3 >> extensional distance = 1561 >> proper extension: 0vgkd; >> query: (?x1120, 07s9rl0) <- genre(?x1120, ?x1510), genre(?x6018, ?x1510), ?x6018 = 04k9y6 >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 03rtz1 genre 05p553 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 67.000 65.000 0.644 http://example.org/film/film/genre EVAL 03rtz1 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 65.000 0.644 http://example.org/film/film/genre #20061-021yw7 PRED entity: 021yw7 PRED relation: religion PRED expected values: 0kpl => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 23): 0c8wxp (0.20 #1806, 0.19 #1221, 0.19 #1851), 0kpl (0.11 #100, 0.11 #505, 0.10 #550), 03_gx (0.08 #1139, 0.08 #554, 0.08 #1274), 0kq2 (0.05 #558, 0.05 #423, 0.04 #513), 01lp8 (0.04 #676, 0.03 #1621, 0.02 #406), 04pk9 (0.04 #20, 0.03 #65, 0.02 #515), 03j6c (0.04 #21, 0.03 #606, 0.03 #426), 0flw86 (0.04 #2, 0.02 #1622, 0.01 #2433), 058x5 (0.04 #4), 019cr (0.03 #821, 0.03 #596, 0.03 #506) >> Best rule #1806 for best value: >> intensional similarity = 3 >> extensional distance = 572 >> proper extension: 0zjpz; 01wgjj5; 0hhqw; 0kj34; 01wkmgb; 06c0j; >> query: (?x3673, 0c8wxp) <- profession(?x3673, ?x319), gender(?x3673, ?x231), participant(?x5268, ?x3673) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #100 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 03cs_z7; 03ft8; 01y0y6; 024swd; 02nygk; *> query: (?x3673, 0kpl) <- profession(?x3673, ?x319), student(?x2056, ?x3673), program_creator(?x1395, ?x3673) *> conf = 0.11 ranks of expected_values: 2 EVAL 021yw7 religion 0kpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.197 http://example.org/people/person/religion #20060-0f13b PRED entity: 0f13b PRED relation: profession PRED expected values: 02hrh1q 0lgw7 => 120 concepts (56 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.91 #4334, 0.91 #1740, 0.91 #4046), 0np9r (0.55 #162, 0.18 #5637, 0.16 #4340), 03gjzk (0.48 #589, 0.44 #5920, 0.43 #1309), 016z4k (0.35 #5767, 0.30 #6775, 0.13 #2597), 0dz3r (0.34 #5765, 0.32 #6773, 0.15 #290), 0kyk (0.33 #27, 0.33 #4755, 0.20 #747), 018gz8 (0.33 #15, 0.30 #591, 0.25 #3185), 02hv44_ (0.33 #53, 0.11 #1205, 0.11 #1349), 01xr66 (0.33 #4755, 0.03 #1068, 0.02 #3518), 0cbd2 (0.31 #870, 0.29 #1302, 0.29 #2743) >> Best rule #4334 for best value: >> intensional similarity = 3 >> extensional distance = 477 >> proper extension: 03qd_; 03gm48; 0gcdzz; 0m31m; 0gd_b_; 03f4xvm; 02tkzn; 03x22w; 030b93; 04wf_b; ... >> query: (?x8485, 02hrh1q) <- student(?x4955, ?x8485), profession(?x8485, ?x319), actor(?x7488, ?x8485) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 52 EVAL 0f13b profession 0lgw7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 120.000 56.000 0.914 http://example.org/people/person/profession EVAL 0f13b profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 56.000 0.914 http://example.org/people/person/profession #20059-01m3b1t PRED entity: 01m3b1t PRED relation: nationality PRED expected values: 09c7w0 => 197 concepts (169 used for prediction) PRED predicted values (max 10 best out of 42): 09c7w0 (0.85 #3907, 0.76 #1502, 0.74 #14716), 02jx1 (0.19 #5744, 0.19 #6144, 0.19 #5944), 07ssc (0.14 #615, 0.12 #2720, 0.11 #5726), 0d060g (0.08 #708, 0.07 #507, 0.07 #1308), 06q1r (0.07 #77, 0.06 #778, 0.05 #377), 0345h (0.07 #31, 0.06 #2836, 0.06 #2235), 03rjj (0.07 #5, 0.06 #906, 0.04 #706), 0jgd (0.07 #2, 0.04 #903, 0.02 #1905), 03rk0 (0.06 #13861, 0.06 #14361, 0.06 #14061), 0f8l9c (0.06 #923, 0.04 #2226, 0.03 #22) >> Best rule #3907 for best value: >> intensional similarity = 3 >> extensional distance = 167 >> proper extension: 02d45s; 054c1; 01g0jn; 01jz6d; >> query: (?x7240, 09c7w0) <- award_winner(?x2212, ?x7240), people(?x2510, ?x7240), ?x2510 = 0x67 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m3b1t nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 197.000 169.000 0.852 http://example.org/people/person/nationality #20058-063fh9 PRED entity: 063fh9 PRED relation: country PRED expected values: 05qhw => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 38): 01hmnh (0.68 #1088, 0.31 #629, 0.29 #915), 03rt9 (0.37 #5115, 0.36 #6495, 0.36 #5057), 02jx1 (0.37 #5115, 0.36 #6495, 0.36 #5057), 0f8l9c (0.24 #189, 0.21 #589, 0.15 #6725), 0345h (0.22 #369, 0.19 #312, 0.17 #483), 03rjj (0.15 #6725, 0.10 #177, 0.06 #863), 0d060g (0.15 #6725, 0.09 #465, 0.09 #980), 0ctw_b (0.15 #6725, 0.06 #422, 0.06 #136), 03_3d (0.15 #6725, 0.05 #5236, 0.04 #6559), 06mkj (0.15 #6725, 0.05 #209, 0.03 #1010) >> Best rule #1088 for best value: >> intensional similarity = 5 >> extensional distance = 134 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x6642, ?x1510) <- titles(?x1510, ?x6642), titles(?x512, ?x6642), ?x512 = 07ssc, titles(?x1510, ?x1842), ?x1842 = 015x74 >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #586 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 09rfpk; *> query: (?x6642, 05qhw) <- film_crew_role(?x6642, ?x468), titles(?x512, ?x6642), film_release_region(?x6642, ?x94), ?x512 = 07ssc *> conf = 0.01 ranks of expected_values: 36 EVAL 063fh9 country 05qhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 121.000 121.000 0.678 http://example.org/film/film/country #20057-04l58n PRED entity: 04l58n PRED relation: colors PRED expected values: 01g5v => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 19): 01l849 (0.61 #713, 0.25 #74, 0.23 #1552), 06fvc (0.60 #149, 0.50 #458, 0.50 #312), 019sc (0.57 #499, 0.52 #517, 0.50 #317), 01g5v (0.39 #1499, 0.33 #110, 0.32 #1594), 088fh (0.37 #570, 0.20 #135, 0.20 #1150), 03vtbc (0.33 #110, 0.23 #1552, 0.21 #427), 06kqt3 (0.33 #110, 0.18 #37, 0.17 #787), 02rnmb (0.23 #1552, 0.21 #761, 0.20 #558), 09ggk (0.23 #1552, 0.13 #1591, 0.11 #1381), 0680m7 (0.20 #1150, 0.19 #310, 0.18 #1646) >> Best rule #713 for best value: >> intensional similarity = 11 >> extensional distance = 42 >> proper extension: 07k53y; 0fht9f; 03b3j; 0hn6d; 061xq; 051q5; 0713r; 02pqcfz; 06rny; 02r2qt7; ... >> query: (?x12541, 01l849) <- teams(?x9445, ?x12541), colors(?x12541, ?x8047), colors(?x7154, ?x8047), colors(?x5651, ?x8047), team(?x2918, ?x12541), currency(?x7154, ?x5696), ?x5651 = 027mdh, student(?x7154, ?x1645), colors(?x6180, ?x8047), institution(?x1526, ?x7154), position(?x6180, ?x60) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1499 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 271 *> proper extension: 03k2hn; *> query: (?x12541, 01g5v) <- colors(?x12541, ?x663), colors(?x8537, ?x663), colors(?x7247, ?x663), colors(?x6570, ?x663), colors(?x2174, ?x663), position_s(?x6570, ?x1240), ?x8537 = 02029f, colors(?x13088, ?x663), colors(?x9988, ?x663), colors(?x4209, ?x663), school(?x2820, ?x4209), teams(?x6088, ?x2174), current_club(?x2427, ?x7247), ?x9988 = 0pz6q, institution(?x620, ?x4209), contains(?x1310, ?x13088) *> conf = 0.39 ranks of expected_values: 4 EVAL 04l58n colors 01g5v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 94.000 0.614 http://example.org/sports/sports_team/colors #20056-0r15k PRED entity: 0r15k PRED relation: contains! PRED expected values: 01n7q => 132 concepts (117 used for prediction) PRED predicted values (max 10 best out of 499): 01n7q (0.70 #67064, 0.67 #5441, 0.67 #2759), 07ssc (0.19 #96618, 0.19 #97512, 0.19 #98407), 030qb3t (0.19 #5464, 0.09 #100, 0.07 #13511), 06pvr (0.17 #18046, 0.14 #42191, 0.07 #7318), 04_1l0v (0.16 #39791, 0.13 #45157, 0.09 #64828), 02jx1 (0.15 #980, 0.14 #97567, 0.13 #96673), 05k7sb (0.15 #1026, 0.09 #60934, 0.08 #74352), 0k_s5 (0.15 #6053, 0.03 #14100, 0.02 #8736), 03rk0 (0.13 #4606, 0.07 #13547, 0.07 #27853), 05kj_ (0.13 #42066, 0.03 #6299, 0.03 #7193) >> Best rule #67064 for best value: >> intensional similarity = 3 >> extensional distance = 231 >> proper extension: 01xhb_; 01zk9d; >> query: (?x9405, ?x1227) <- citytown(?x1478, ?x9405), state_province_region(?x1478, ?x1227), organization(?x346, ?x1478) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r15k contains! 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 117.000 0.704 http://example.org/location/location/contains #20055-07l8f PRED entity: 07l8f PRED relation: season PRED expected values: 025ygws => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 8): 025ygws (0.82 #98, 0.76 #106, 0.75 #130), 027pwzc (0.76 #101, 0.71 #109, 0.67 #29), 05kcgsf (0.67 #25, 0.57 #41, 0.53 #97), 04110b0 (0.41 #100, 0.35 #108, 0.33 #28), 02h7s73 (0.35 #110, 0.35 #102, 0.33 #30), 03c6s24 (0.29 #111, 0.25 #159, 0.25 #135), 03c74_8 (0.29 #107, 0.24 #99, 0.21 #91), 04n36qk (0.17 #32, 0.09 #144, 0.09 #72) >> Best rule #98 for best value: >> intensional similarity = 8 >> extensional distance = 15 >> proper extension: 051wf; >> query: (?x6823, 025ygws) <- season(?x6823, ?x2406), draft(?x6823, ?x4779), draft(?x6823, ?x1161), sport(?x6823, ?x5063), school(?x4779, ?x1884), major_field_of_study(?x1884, ?x1668), ?x1161 = 02x2khw, student(?x1884, ?x1815) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07l8f season 025ygws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.824 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #20054-01m15br PRED entity: 01m15br PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #19, 0.85 #3, 0.84 #23), 02zsn (0.52 #191, 0.38 #6, 0.37 #8) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 03m6pk; 02fybl; 01r4zfk; >> query: (?x4044, 05zppz) <- role(?x4044, ?x1969), profession(?x4044, ?x220), nationality(?x4044, ?x94) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m15br gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.856 http://example.org/people/person/gender #20053-0gj96ln PRED entity: 0gj96ln PRED relation: film! PRED expected values: 01w5jwb => 78 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1246): 02lk1s (0.65 #35316, 0.48 #58175, 0.46 #56096), 0pz91 (0.50 #6442, 0.32 #43629, 0.22 #43628), 01fyzy (0.50 #7289, 0.04 #43630, 0.02 #68571), 07y8l9 (0.33 #9279, 0.22 #17589, 0.04 #34208), 0d608 (0.33 #7532, 0.03 #59477, 0.02 #53241), 02k21g (0.33 #7023, 0.01 #34030, 0.01 #36109), 0p8r1 (0.25 #25513, 0.06 #49862, 0.03 #54602), 07lt7b (0.20 #113, 0.17 #4267, 0.17 #2190), 03v0vd (0.20 #1625, 0.17 #3702, 0.11 #14089), 023kzp (0.20 #1053, 0.17 #3130, 0.11 #13517) >> Best rule #35316 for best value: >> intensional similarity = 5 >> extensional distance = 69 >> proper extension: 02y_lrp; >> query: (?x6168, ?x881) <- film(?x368, ?x6168), written_by(?x6168, ?x881), profession(?x881, ?x1146), ?x1146 = 018gz8, award_nominee(?x881, ?x829) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gj96ln film! 01w5jwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 37.000 0.655 http://example.org/film/actor/film./film/performance/film #20052-01kwh5j PRED entity: 01kwh5j PRED relation: gender PRED expected values: 02zsn => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #110, 0.73 #104, 0.71 #176), 02zsn (0.64 #14, 0.60 #20, 0.57 #16) >> Best rule #110 for best value: >> intensional similarity = 9 >> extensional distance = 1224 >> proper extension: 03ckxdg; 05drq5; 022_lg; 017r2; 09gffmz; 070w7s; 0p8jf; 02bfxb; 02_4fn; 0b05xm; ... >> query: (?x8988, 05zppz) <- profession(?x8988, ?x1383), profession(?x9471, ?x1383), profession(?x8482, ?x1383), profession(?x4085, ?x1383), profession(?x3788, ?x1383), ?x3788 = 0chrwb, film(?x8482, ?x1904), type_of_union(?x9471, ?x566), award_nominee(?x123, ?x4085) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 12 *> proper extension: 01kymm; 084m3; 01wphh2; 015p37; 01rddlc; 0l_dv; 03d29b; *> query: (?x8988, 02zsn) <- actor(?x8717, ?x8988), special_performance_type(?x8988, ?x296), category(?x8988, ?x134), genre(?x8717, ?x1403), genre(?x11998, ?x1403), genre(?x5839, ?x1403), ?x11998 = 0bbgvp, ?x5839 = 05650n *> conf = 0.64 ranks of expected_values: 2 EVAL 01kwh5j gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 95.000 0.788 http://example.org/people/person/gender #20051-0ddfwj1 PRED entity: 0ddfwj1 PRED relation: film_release_region PRED expected values: 0h7x => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 272): 03rjj (0.87 #5039, 0.87 #3781, 0.85 #1109), 0chghy (0.86 #3787, 0.83 #5045, 0.81 #4573), 0jgd (0.82 #3779, 0.81 #949, 0.80 #5981), 01znc_ (0.80 #3818, 0.78 #5076, 0.76 #6020), 0b90_r (0.79 #5038, 0.75 #5982, 0.74 #634), 06t2t (0.77 #5096, 0.70 #6040, 0.69 #3838), 03spz (0.72 #1201, 0.72 #1043, 0.71 #727), 06t8v (0.68 #236, 0.65 #708, 0.61 #1024), 05v8c (0.61 #5050, 0.59 #3792, 0.55 #5994), 03rj0 (0.60 #5094, 0.59 #218, 0.58 #690) >> Best rule #5039 for best value: >> intensional similarity = 9 >> extensional distance = 164 >> proper extension: 0gtsx8c; 02vxq9m; 03g90h; 01gc7; 0ds3t5x; 0gtv7pk; 0h1cdwq; 0g5qs2k; 0dscrwf; 02x3lt7; ... >> query: (?x370, 03rjj) <- film_release_region(?x370, ?x2152), film_release_region(?x370, ?x1353), film_release_region(?x370, ?x789), film_release_region(?x370, ?x87), ?x789 = 0f8l9c, ?x87 = 05r4w, film(?x794, ?x370), ?x2152 = 06mkj, ?x1353 = 035qy >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #4596 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 154 *> proper extension: 0gj9qxr; 040rmy; 026njb5; 064lsn; *> query: (?x370, 0h7x) <- film_release_region(?x370, ?x789), film_release_region(?x370, ?x304), film_release_region(?x370, ?x87), ?x789 = 0f8l9c, ?x87 = 05r4w, film_crew_role(?x370, ?x137), titles(?x2480, ?x370), ?x304 = 0d0vqn *> conf = 0.47 ranks of expected_values: 20 EVAL 0ddfwj1 film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 112.000 112.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #20050-01mqc_ PRED entity: 01mqc_ PRED relation: film PRED expected values: 035s95 04x4nv => 108 concepts (62 used for prediction) PRED predicted values (max 10 best out of 729): 02p76f9 (0.73 #1784, 0.65 #17833, 0.64 #24967), 016z9n (0.34 #2153, 0.05 #369, 0.03 #105222), 02b6n9 (0.11 #1566, 0.06 #3350, 0.03 #105222), 026n4h6 (0.11 #242, 0.06 #2026, 0.03 #105222), 0jzw (0.11 #119, 0.06 #1903, 0.03 #105222), 07jxpf (0.11 #681, 0.03 #2465, 0.03 #105222), 0kvgxk (0.11 #328, 0.03 #105222, 0.01 #16377), 01l_pn (0.06 #2747, 0.05 #963, 0.03 #105222), 074rg9 (0.06 #2755, 0.05 #971, 0.03 #105222), 03ct7jd (0.06 #2618, 0.05 #834, 0.03 #105222) >> Best rule #1784 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 07h565; >> query: (?x7525, ?x1045) <- nominated_for(?x7525, ?x1045), award_nominee(?x5834, ?x7525), ?x5834 = 01z7s_ >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2124 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 03_6y; 01z7s_; *> query: (?x7525, 035s95) <- award(?x7525, ?x102), award_nominee(?x7525, ?x450), ?x450 = 0z4s *> conf = 0.06 ranks of expected_values: 13, 193 EVAL 01mqc_ film 04x4nv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 108.000 62.000 0.727 http://example.org/film/actor/film./film/performance/film EVAL 01mqc_ film 035s95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 108.000 62.000 0.727 http://example.org/film/actor/film./film/performance/film #20049-01t6xz PRED entity: 01t6xz PRED relation: people! PRED expected values: 041rx => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 40): 041rx (0.16 #466, 0.13 #697, 0.13 #81), 033tf_ (0.15 #7, 0.13 #238, 0.10 #469), 0x67 (0.10 #2859, 0.10 #3013, 0.09 #1088), 02w7gg (0.09 #233, 0.08 #772, 0.07 #1696), 0xnvg (0.08 #13, 0.07 #244, 0.06 #475), 022dp5 (0.08 #50, 0.02 #435, 0.01 #897), 03lmx1 (0.08 #14, 0.01 #168, 0.01 #245), 0d2by (0.08 #33), 07hwkr (0.07 #397, 0.07 #243, 0.06 #89), 07bch9 (0.06 #254, 0.04 #485, 0.04 #408) >> Best rule #466 for best value: >> intensional similarity = 2 >> extensional distance = 391 >> proper extension: 01xyt7; >> query: (?x6481, 041rx) <- place_of_birth(?x6481, ?x682), participant(?x10792, ?x6481) >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01t6xz people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.155 http://example.org/people/ethnicity/people #20048-059y0 PRED entity: 059y0 PRED relation: peers PRED expected values: 06crk => 214 concepts (124 used for prediction) PRED predicted values (max 10 best out of 128): 0jcx (0.86 #2889, 0.86 #3647, 0.81 #5532), 059y0 (0.17 #855, 0.14 #229, 0.12 #480), 09889g (0.15 #1797, 0.14 #2803, 0.14 #1547), 029rk (0.14 #219, 0.12 #470, 0.11 #595), 06y7d (0.14 #242, 0.11 #618, 0.08 #994), 01w9ph_ (0.13 #1202, 0.09 #1579, 0.08 #1829), 026lj (0.12 #267, 0.07 #2652, 0.06 #10295), 03_js (0.12 #336, 0.07 #3099, 0.07 #2847), 0424m (0.12 #307, 0.06 #10295, 0.04 #1812), 07g2b (0.12 #1760, 0.11 #2388, 0.10 #3144) >> Best rule #2889 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 01vvycq; 0l12d; >> query: (?x10913, ?x2397) <- type_of_union(?x10913, ?x566), peers(?x2397, ?x10913), location(?x10913, ?x985), award_winner(?x11301, ?x10913) >> conf = 0.86 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 059y0 peers 06crk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 214.000 124.000 0.864 http://example.org/influence/influence_node/peers./influence/peer_relationship/peers #20047-0bdxs5 PRED entity: 0bdxs5 PRED relation: artist! PRED expected values: 03gfvsz => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 4): 03gfvsz (0.20 #1, 0.14 #19, 0.11 #13), 01fjfv (0.05 #32, 0.05 #38, 0.04 #14), 04rqd (0.04 #23, 0.03 #460, 0.03 #29), 04y652m (0.02 #459, 0.01 #576, 0.01 #94) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01w20rx; >> query: (?x8693, 03gfvsz) <- location(?x8693, ?x4852), ?x4852 = 0_xdd, award(?x8693, ?x462) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bdxs5 artist! 03gfvsz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.200 http://example.org/broadcast/content/artist #20046-0jhd PRED entity: 0jhd PRED relation: teams PRED expected values: 0303jw => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 130): 038zh6 (0.05 #2150, 0.05 #350, 0.03 #5750), 086x3 (0.05 #360, 0.04 #720, 0.02 #1800), 024nj1 (0.05 #354, 0.04 #714, 0.02 #1794), 035l_9 (0.05 #315, 0.04 #675, 0.02 #1755), 02ryyk (0.05 #348, 0.04 #708, 0.02 #2868), 0329nn (0.05 #98, 0.03 #818, 0.03 #1178), 03ytp3 (0.05 #307, 0.03 #1387, 0.02 #2467), 023zd7 (0.05 #162, 0.02 #1602, 0.02 #2322), 03lygq (0.05 #258, 0.02 #1698, 0.02 #2418), 02bh_v (0.05 #215, 0.02 #1655, 0.02 #2375) >> Best rule #2150 for best value: >> intensional similarity = 2 >> extensional distance = 40 >> proper extension: 049nq; >> query: (?x8588, 038zh6) <- administrative_parent(?x11419, ?x8588), form_of_government(?x8588, ?x48) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jhd teams 0303jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 108.000 0.048 http://example.org/sports/sports_team_location/teams #20045-02dbn2 PRED entity: 02dbn2 PRED relation: film PRED expected values: 02pxst => 104 concepts (55 used for prediction) PRED predicted values (max 10 best out of 353): 0fdv3 (0.70 #283, 0.40 #3573, 0.13 #7146), 0ddt_ (0.30 #475, 0.13 #7146, 0.01 #2261), 03bx2lk (0.20 #185, 0.02 #3758, 0.02 #1971), 02z3r8t (0.20 #108, 0.02 #5467, 0.02 #3681), 09bw4_ (0.20 #1473), 0cpllql (0.20 #86), 0dtfn (0.13 #7146, 0.01 #3783, 0.01 #5569), 01shy7 (0.10 #424, 0.03 #12928, 0.02 #34360), 017gm7 (0.10 #211, 0.02 #1997, 0.02 #12715), 017gl1 (0.10 #143, 0.02 #1929, 0.02 #12647) >> Best rule #283 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06cl2w; >> query: (?x4809, 0fdv3) <- film(?x4809, ?x4902), location(?x4809, ?x5036), ?x4902 = 0dfw0 >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02dbn2 film 02pxst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 55.000 0.700 http://example.org/film/actor/film./film/performance/film #20044-0dclg PRED entity: 0dclg PRED relation: locations! PRED expected values: 0b_6h7 => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 120): 0b_6pv (0.22 #205, 0.12 #5121, 0.12 #4743), 0b_6rk (0.15 #802, 0.13 #928, 0.13 #5088), 0b_6qj (0.15 #823, 0.13 #949, 0.13 #5109), 0b_6zk (0.15 #787, 0.12 #1039, 0.12 #5073), 0b_6q5 (0.14 #5136, 0.11 #850, 0.11 #472), 0b_75k (0.13 #5091, 0.12 #679, 0.11 #301), 0b_6mr (0.13 #5129, 0.12 #4751, 0.11 #843), 0bzrsh (0.13 #5120, 0.11 #456, 0.08 #5751), 0b_6xf (0.11 #861, 0.11 #483, 0.11 #231), 0b_6x2 (0.11 #160, 0.10 #5076, 0.10 #4698) >> Best rule #205 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0f2w0; 0fr0t; 0d35y; 02hrh0_; 0f2tj; 0f2s6; >> query: (?x2254, 0b_6pv) <- place_of_birth(?x487, ?x2254), location_of_ceremony(?x2357, ?x2254), dog_breed(?x2254, ?x1706) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #5081 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 05v8c; 03shp; 03spz; 0d04z6; *> query: (?x2254, 0b_6h7) <- place_of_birth(?x11346, ?x2254), locations(?x4803, ?x2254), profession(?x11346, ?x1032) *> conf = 0.08 ranks of expected_values: 16 EVAL 0dclg locations! 0b_6h7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 149.000 149.000 0.222 http://example.org/time/event/locations #20043-039zft PRED entity: 039zft PRED relation: genre PRED expected values: 0bj8m2 => 136 concepts (52 used for prediction) PRED predicted values (max 10 best out of 115): 05p553 (0.62 #4304, 0.46 #4655, 0.44 #1862), 03g3w (0.57 #1300, 0.31 #835, 0.24 #952), 02kdv5l (0.49 #2325, 0.48 #5934, 0.47 #2441), 01jfsb (0.48 #2799, 0.41 #2218, 0.39 #1173), 04t36 (0.44 #470, 0.33 #6, 0.30 #2096), 02l7c8 (0.43 #1293, 0.40 #4083, 0.39 #3966), 04xvlr (0.43 #3951, 0.35 #5817, 0.33 #1), 017fp (0.40 #5831, 0.25 #363, 0.19 #1292), 03bxz7 (0.36 #5868, 0.10 #1329, 0.09 #4002), 0219x_ (0.33 #25, 0.10 #605, 0.08 #837) >> Best rule #4304 for best value: >> intensional similarity = 7 >> extensional distance = 87 >> proper extension: 04svwx; >> query: (?x5538, 05p553) <- genre(?x5538, ?x2540), genre(?x5538, ?x811), ?x2540 = 0hcr, genre(?x8359, ?x811), genre(?x3946, ?x811), ?x3946 = 027s39y, ?x8359 = 015ynm >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #511 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 02ctc6; 023p7l; *> query: (?x5538, 0bj8m2) <- film_release_distribution_medium(?x5538, ?x81), genre(?x5538, ?x4088), genre(?x5538, ?x2540), ?x4088 = 04xvh5, ?x2540 = 0hcr, film(?x5537, ?x5538), language(?x5538, ?x254) *> conf = 0.22 ranks of expected_values: 17 EVAL 039zft genre 0bj8m2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 136.000 52.000 0.618 http://example.org/film/film/genre #20042-02xfj0 PRED entity: 02xfj0 PRED relation: category PRED expected values: 08mbj5d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.74 #20, 0.69 #25, 0.54 #7) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 339 >> proper extension: 01wy61y; 021r7r; 017f4y; 01ww_vs; >> query: (?x7560, 08mbj5d) <- profession(?x7560, ?x6183), profession(?x6659, ?x6183), ?x6659 = 01vw_dv >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xfj0 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.742 http://example.org/common/topic/webpage./common/webpage/category #20041-071vr PRED entity: 071vr PRED relation: place_of_death! PRED expected values: 053vcrp => 195 concepts (182 used for prediction) PRED predicted values (max 10 best out of 793): 02whj (0.14 #88627, 0.07 #70600, 0.04 #15763), 02fn5 (0.14 #88627, 0.04 #60081, 0.04 #49558), 0br1w (0.07 #70600, 0.04 #60081, 0.04 #49558), 0521d_3 (0.05 #33780, 0.05 #21769, 0.04 #603), 0584j4n (0.05 #33780, 0.05 #21769, 0.04 #216), 03bnv (0.05 #33780, 0.05 #21769, 0.04 #124), 01vyv9 (0.05 #33780, 0.05 #21769, 0.04 #191), 015c4g (0.05 #33780, 0.05 #21769, 0.04 #21768), 01nzz8 (0.05 #33780, 0.05 #21769, 0.03 #4004), 0dh73w (0.05 #33780, 0.05 #21769, 0.02 #15179) >> Best rule #88627 for best value: >> intensional similarity = 3 >> extensional distance = 196 >> proper extension: 023vwt; 0d7_n; 05mwx; >> query: (?x6960, ?x1092) <- place_of_birth(?x1182, ?x6960), location(?x1092, ?x6960), place_of_death(?x1092, ?x1523) >> conf = 0.14 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 071vr place_of_death! 053vcrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 195.000 182.000 0.143 http://example.org/people/deceased_person/place_of_death #20040-0myk8 PRED entity: 0myk8 PRED relation: role! PRED expected values: 042v_gx => 66 concepts (55 used for prediction) PRED predicted values (max 10 best out of 110): 05r5c (0.88 #3312, 0.84 #5568, 0.82 #4105), 02sgy (0.83 #575, 0.81 #2624, 0.80 #1944), 01xqw (0.83 #575, 0.81 #2624, 0.80 #1944), 07y_7 (0.83 #575, 0.81 #2624, 0.80 #1944), 0l14md (0.82 #4653, 0.80 #1717, 0.80 #1616), 026t6 (0.80 #5887, 0.80 #5785, 0.80 #5668), 0bxl5 (0.80 #1895, 0.78 #1329, 0.75 #2122), 0dwtp (0.80 #1961, 0.77 #2306, 0.70 #1627), 013y1f (0.79 #5442, 0.78 #5329, 0.77 #797), 0395lw (0.78 #1521, 0.75 #2085, 0.75 #1061) >> Best rule #3312 for best value: >> intensional similarity = 32 >> extensional distance = 23 >> proper extension: 025cbm; >> query: (?x2956, 05r5c) <- role(?x1574, ?x2956), role(?x1437, ?x2956), role(?x1225, ?x2956), role(?x960, ?x2956), role(?x716, ?x2956), role(?x228, ?x2956), role(?x1225, ?x5676), role(?x1225, ?x5480), role(?x1225, ?x4078), role(?x1225, ?x2944), role(?x1225, ?x1332), role(?x1225, ?x894), role(?x1225, ?x569), role(?x1225, ?x212), ?x228 = 0l14qv, ?x212 = 026t6, performance_role(?x1817, ?x1225), ?x716 = 018vs, performance_role(?x315, ?x1225), ?x4078 = 011k_j, ?x5480 = 01w4c9, ?x1574 = 0l15bq, ?x569 = 07c6l, ?x5676 = 0151b0, role(?x74, ?x894), ?x1332 = 03qlv7, ?x2944 = 0l14j_, role(?x75, ?x960), ?x1437 = 01vdm0, instrumentalists(?x894, ?x3890), ?x3890 = 01gg59, role(?x894, ?x1212) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3652 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 29 *> proper extension: 01vj9c; 0l1589; *> query: (?x2956, 042v_gx) <- role(?x1437, ?x2956), role(?x745, ?x2956), role(?x2956, ?x1495), ?x1495 = 013y1f, role(?x2956, ?x75), ?x1437 = 01vdm0, role(?x8539, ?x745), role(?x8490, ?x745), role(?x7112, ?x745), role(?x745, ?x4471), role(?x745, ?x2798), role(?x745, ?x432), ?x4471 = 026g73, group(?x745, ?x498), ?x2798 = 03qjg, role(?x2253, ?x745), ?x7112 = 0133x7, ?x8539 = 01w9mnm, role(?x211, ?x745), role(?x745, ?x645), award(?x8490, ?x6220), ?x6220 = 02f6ym, ?x2253 = 01679d, role(?x217, ?x432) *> conf = 0.74 ranks of expected_values: 18 EVAL 0myk8 role! 042v_gx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 66.000 55.000 0.880 http://example.org/music/performance_role/track_performances./music/track_contribution/role #20039-0zrlp PRED entity: 0zrlp PRED relation: place_of_birth! PRED expected values: 02m3sd => 120 concepts (60 used for prediction) PRED predicted values (max 10 best out of 706): 024y6w (0.33 #1750, 0.01 #78392), 040_t (0.11 #3914, 0.03 #6527, 0.01 #9140), 070j61 (0.11 #4189, 0.03 #6802, 0.01 #9415), 012gx2 (0.11 #3807, 0.03 #6420, 0.01 #9033), 0dl567 (0.11 #3421, 0.03 #6034, 0.01 #8647), 0132k4 (0.11 #4059, 0.03 #6672, 0.01 #9285), 02dth1 (0.11 #3447, 0.03 #6060, 0.01 #8673), 01tvz5j (0.11 #2663, 0.03 #5276, 0.01 #7889), 063lqs (0.11 #3355, 0.03 #5968, 0.01 #11194), 016kb7 (0.03 #6863, 0.01 #78392) >> Best rule #1750 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0kdqw; >> query: (?x9970, 024y6w) <- contains(?x9535, ?x9970), adjoins(?x9539, ?x9535), adjoins(?x4614, ?x9535), ?x4614 = 0mwzv, source(?x9535, ?x958), ?x9539 = 0mws3 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #78392 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 149 *> proper extension: 0lg0r; *> query: (?x9970, ?x395) <- county_seat(?x9535, ?x9970), contains(?x3670, ?x9970), currency(?x9535, ?x170), location(?x395, ?x3670) *> conf = 0.01 ranks of expected_values: 228 EVAL 0zrlp place_of_birth! 02m3sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 120.000 60.000 0.333 http://example.org/people/person/place_of_birth #20038-08jyyk PRED entity: 08jyyk PRED relation: artists PRED expected values: 0m19t 02whj 09prnq 0gdh5 0khth 047cx 05xq9 02bgmr 01386_ 01w5n51 0lsw9 0dw3l 011_vz 012ycy => 64 concepts (29 used for prediction) PRED predicted values (max 10 best out of 3947): 01vsxdm (0.70 #18527, 0.67 #16478, 0.60 #17502), 03t9sp (0.64 #19570, 0.60 #6258, 0.57 #22643), 06p03s (0.64 #20411, 0.60 #7099, 0.50 #28606), 01386_ (0.60 #17950, 0.60 #7710, 0.58 #21023), 01j59b0 (0.60 #18873, 0.60 #17848, 0.56 #16824), 01pny5 (0.60 #10212, 0.60 #8164, 0.40 #26599), 03k3b (0.60 #7839, 0.57 #13982, 0.30 #19104), 011_vz (0.60 #18205, 0.56 #17181, 0.50 #21278), 048tgl (0.60 #19292, 0.56 #17243, 0.50 #18267), 020_4z (0.60 #10100, 0.50 #19317, 0.47 #26487) >> Best rule #18527 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 02t8gf; >> query: (?x5379, 01vsxdm) <- artists(?x5379, ?x9463), artists(?x5379, ?x2799), artists(?x5379, ?x2784), ?x9463 = 01shhf, role(?x2799, ?x227), award(?x2784, ?x1565), category(?x2784, ?x134), award_winner(?x6947, ?x2799) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #17950 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 05jt_; *> query: (?x5379, 01386_) <- artists(?x5379, ?x9463), artists(?x5379, ?x2799), ?x9463 = 01shhf, languages(?x2799, ?x254), profession(?x2799, ?x131), spouse(?x10924, ?x2799), instrumentalists(?x227, ?x2799) *> conf = 0.60 ranks of expected_values: 4, 8, 44, 47, 54, 58, 65, 69, 110, 129, 138, 227, 323, 566 EVAL 08jyyk artists 012ycy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 011_vz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 0dw3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 0lsw9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 01w5n51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 01386_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 02bgmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 05xq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 047cx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 0khth CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 0gdh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 09prnq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 02whj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 64.000 29.000 0.700 http://example.org/music/genre/artists EVAL 08jyyk artists 0m19t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 64.000 29.000 0.700 http://example.org/music/genre/artists #20037-03f2_rc PRED entity: 03f2_rc PRED relation: category PRED expected values: 08mbj5d => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #17, 0.84 #12, 0.82 #28) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 07s3vqk; 0197tq; 026ps1; 02r3zy; 01wbgdv; 018y2s; 0137n0; 01wp8w7; 05crg7; 01w60_p; ... >> query: (?x538, 08mbj5d) <- award_nominee(?x538, ?x772), award(?x538, ?x4018), ?x4018 = 03qbh5 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f2_rc category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.903 http://example.org/common/topic/webpage./common/webpage/category #20036-0l1pj PRED entity: 0l1pj PRED relation: place_of_death! PRED expected values: 03bdm4 0gry51 => 108 concepts (47 used for prediction) PRED predicted values (max 10 best out of 642): 01kkx2 (0.20 #660, 0.09 #1414, 0.04 #2168), 0223g8 (0.20 #586, 0.09 #1340, 0.04 #2094), 019fnv (0.20 #537, 0.09 #1291, 0.04 #2045), 015wc0 (0.09 #1273, 0.02 #2781, 0.02 #4292), 03mstc (0.09 #1234, 0.02 #2742, 0.02 #4253), 0lh0c (0.09 #1041, 0.02 #2549, 0.02 #4060), 057dxsg (0.09 #926, 0.02 #2434, 0.02 #3945), 051ysmf (0.04 #2204, 0.02 #2958, 0.02 #3714), 0m0hw (0.04 #1815, 0.02 #2569, 0.02 #3325), 0282x (0.04 #1748, 0.02 #2502, 0.02 #3258) >> Best rule #660 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 065y4w7; 02gnmp; >> query: (?x7886, 01kkx2) <- contains(?x1523, ?x7886), contains(?x94, ?x7886), time_zones(?x7886, ?x2950), ?x94 = 09c7w0, ?x1523 = 030qb3t >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0l1pj place_of_death! 0gry51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 47.000 0.200 http://example.org/people/deceased_person/place_of_death EVAL 0l1pj place_of_death! 03bdm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 47.000 0.200 http://example.org/people/deceased_person/place_of_death #20035-0rvty PRED entity: 0rvty PRED relation: place! PRED expected values: 0rvty => 90 concepts (34 used for prediction) PRED predicted values (max 10 best out of 41): 013yq (0.25 #45, 0.06 #2106, 0.06 #9281), 0rt80 (0.17 #1007, 0.09 #1522, 0.08 #2038), 0rwq6 (0.17 #954, 0.09 #1469, 0.08 #1985), 0rwgm (0.17 #936, 0.09 #1451, 0.08 #1967), 0rv97 (0.17 #748, 0.09 #1263, 0.08 #1779), 01ktz1 (0.08 #1592, 0.04 #2622), 0nzw2 (0.07 #6701), 0rw2x (0.06 #2501, 0.04 #3016), 0rxyk (0.06 #9281), 0rvty (0.06 #9281) >> Best rule #45 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 013yq; 0rxyk; >> query: (?x6966, 013yq) <- time_zones(?x6966, ?x2674), featured_film_locations(?x2362, ?x6966), ?x2674 = 02hcv8, ?x2362 = 05p1qyh, contains(?x94, ?x6966) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #9281 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 203 *> proper extension: 0fn2g; *> query: (?x6966, ?x1523) <- featured_film_locations(?x2362, ?x6966), film_release_region(?x2362, ?x94), genre(?x2362, ?x225), featured_film_locations(?x2362, ?x1523) *> conf = 0.06 ranks of expected_values: 10 EVAL 0rvty place! 0rvty CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 90.000 34.000 0.250 http://example.org/location/hud_county_place/place #20034-01jqr_5 PRED entity: 01jqr_5 PRED relation: profession PRED expected values: 01445t => 111 concepts (72 used for prediction) PRED predicted values (max 10 best out of 86): 09jwl (0.65 #1499, 0.59 #5799, 0.58 #3128), 0nbcg (0.55 #1512, 0.49 #3290, 0.45 #3439), 0fj9f (0.53 #202, 0.33 #54, 0.15 #350), 016z4k (0.53 #1484, 0.38 #3411, 0.37 #4746), 0dz3r (0.45 #2074, 0.38 #4744, 0.38 #3409), 0gl2ny2 (0.44 #1695, 0.38 #1843, 0.38 #1991), 01445t (0.35 #1355, 0.33 #1059, 0.30 #1207), 0kyk (0.33 #30, 0.16 #2547, 0.16 #178), 03jgz (0.33 #65, 0.03 #213, 0.01 #5697), 039h8_ (0.33 #130) >> Best rule #1499 for best value: >> intensional similarity = 2 >> extensional distance = 72 >> proper extension: 0249kn; 018ndc; 015cxv; >> query: (?x2511, 09jwl) <- artists(?x3108, ?x2511), ?x3108 = 02w4v >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #1355 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 01ztgm; 015z4j; 038bht; 01kmd4; 016cff; 029b9k; 0f2zc; 015cbq; 02m501; 0hcs3; ... *> query: (?x2511, 01445t) <- nationality(?x2511, ?x94), ?x94 = 09c7w0, athlete(?x1083, ?x2511), profession(?x2511, ?x1032) *> conf = 0.35 ranks of expected_values: 7 EVAL 01jqr_5 profession 01445t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 111.000 72.000 0.649 http://example.org/people/person/profession #20033-039bp PRED entity: 039bp PRED relation: languages PRED expected values: 02h40lc => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 14): 02h40lc (0.34 #275, 0.31 #236, 0.28 #782), 064_8sq (0.07 #2928, 0.03 #1029, 0.03 #717), 06nm1 (0.07 #2928, 0.03 #123, 0.02 #357), 02bjrlw (0.07 #2928, 0.02 #352, 0.02 #547), 04306rv (0.07 #2928, 0.01 #705), 01gp_d (0.07 #2928), 06b_j (0.07 #2928), 0f8l9c (0.07 #2928), 03k50 (0.03 #277, 0.02 #433, 0.02 #667), 07c9s (0.03 #286, 0.02 #442, 0.01 #2354) >> Best rule #275 for best value: >> intensional similarity = 2 >> extensional distance = 127 >> proper extension: 012v1t; >> query: (?x1119, 02h40lc) <- student(?x1368, ?x1119), people(?x3584, ?x1119) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039bp languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.341 http://example.org/people/person/languages #20032-01vrkdt PRED entity: 01vrkdt PRED relation: role PRED expected values: 01vj9c => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 122): 042v_gx (0.54 #216, 0.41 #424, 0.27 #112), 05r5c (0.44 #215, 0.44 #527, 0.39 #1780), 018vs (0.32 #117, 0.32 #221, 0.23 #429), 01vdm0 (0.27 #1805, 0.27 #2326, 0.25 #240), 05842k (0.27 #287, 0.20 #495, 0.18 #1225), 028tv0 (0.26 #1252, 0.24 #1461, 0.24 #938), 013y1f (0.22 #245, 0.14 #453, 0.14 #557), 01vj9c (0.20 #431, 0.16 #223, 0.14 #2309), 026t6 (0.18 #211, 0.16 #1149, 0.15 #2297), 0l14qv (0.15 #2299, 0.14 #1778, 0.14 #1257) >> Best rule #216 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 0p5mw; >> query: (?x3962, 042v_gx) <- role(?x3962, ?x314), award(?x3962, ?x1854), ?x314 = 02sgy >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #431 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 02nfjp; 015076; *> query: (?x3962, 01vj9c) <- role(?x3962, ?x227), award_winner(?x1854, ?x3962), ?x227 = 0342h *> conf = 0.20 ranks of expected_values: 8 EVAL 01vrkdt role 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 114.000 114.000 0.544 http://example.org/music/artist/track_contributions./music/track_contribution/role #20031-04ykg PRED entity: 04ykg PRED relation: contains PRED expected values: 0nh0f 0nh57 => 174 concepts (133 used for prediction) PRED predicted values (max 10 best out of 2672): 0b2lw (0.85 #290266, 0.83 #64503, 0.81 #90893), 0nh57 (0.60 #205242), 04ld32 (0.51 #117284, 0.48 #43978, 0.48 #146612), 026v5 (0.51 #117284, 0.48 #43978, 0.48 #146612), 04ykg (0.48 #205243, 0.38 #381147, 0.31 #296130), 09c7w0 (0.48 #205243, 0.38 #381147, 0.31 #296130), 0nh0f (0.48 #205243, 0.38 #381147, 0.31 #296130), 0bwfn (0.12 #143679, 0.09 #172995, 0.09 #216970), 065r8g (0.12 #143679, 0.09 #172995, 0.09 #216970), 05q2c (0.12 #143679, 0.09 #172995, 0.09 #216970) >> Best rule #290266 for best value: >> intensional similarity = 2 >> extensional distance = 250 >> proper extension: 0h9vh; >> query: (?x1274, ?x7328) <- contains(?x1274, ?x3204), administrative_division(?x7328, ?x1274) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #205242 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 153 *> proper extension: 02qkt; 06mx8; 07c5l; 04wsz; 02v3m7; 05g56; 065ky; *> query: (?x1274, ?x10566) <- contains(?x1274, ?x9546), adjoins(?x10566, ?x9546) *> conf = 0.60 ranks of expected_values: 2, 7 EVAL 04ykg contains 0nh57 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 174.000 133.000 0.853 http://example.org/location/location/contains EVAL 04ykg contains 0nh0f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 174.000 133.000 0.853 http://example.org/location/location/contains #20030-037d35 PRED entity: 037d35 PRED relation: nationality PRED expected values: 09c7w0 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 22): 09c7w0 (0.86 #2212, 0.84 #101, 0.84 #1), 0njj0 (0.32 #5325), 04rrx (0.32 #5325), 02jx1 (0.11 #3048, 0.10 #4551, 0.10 #3851), 07ssc (0.10 #616, 0.10 #516, 0.09 #3030), 03rk0 (0.06 #948, 0.06 #8580, 0.05 #8780), 0d060g (0.05 #808, 0.05 #207, 0.05 #2519), 03rjj (0.04 #5, 0.02 #306, 0.02 #907), 0d05w3 (0.03 #851, 0.02 #451, 0.02 #952), 0chghy (0.02 #3025, 0.02 #3828, 0.02 #3928) >> Best rule #2212 for best value: >> intensional similarity = 3 >> extensional distance = 988 >> proper extension: 02qjj7; 05fg2; 03cvfg; 0n00; 0mj0c; 01jb26; 034ls; 08k1lz; 02vptk_; 0gs7x; ... >> query: (?x6041, 09c7w0) <- student(?x4955, ?x6041), school(?x1632, ?x4955), major_field_of_study(?x4955, ?x373) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 037d35 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.859 http://example.org/people/person/nationality #20029-06thjt PRED entity: 06thjt PRED relation: institution! PRED expected values: 016t_3 => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.61 #788, 0.61 #2010, 0.61 #1754), 02_xgp2 (0.57 #105, 0.51 #1257, 0.50 #728), 019v9k (0.56 #2132, 0.56 #1759, 0.55 #2015), 016t_3 (0.50 #51, 0.45 #1525, 0.45 #720), 07s6fsf (0.50 #48, 0.33 #1, 0.29 #901), 03bwzr4 (0.43 #730, 0.43 #107, 0.39 #1535), 04zx3q1 (0.43 #95, 0.33 #2, 0.30 #1316), 013zdg (0.43 #100, 0.28 #723, 0.25 #31), 02cq61 (0.43 #111, 0.23 #343, 0.18 #274), 027f2w (0.33 #9, 0.29 #102, 0.25 #56) >> Best rule #788 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 01j_9c; 021996; >> query: (?x10478, 02h4rq6) <- student(?x10478, ?x4831), contains(?x94, ?x10478), instrumentalists(?x316, ?x4831), currency(?x10478, ?x170) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #51 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 0g2jl; *> query: (?x10478, 016t_3) <- student(?x10478, ?x9610), student(?x10478, ?x3868), student(?x10478, ?x2208), location(?x2208, ?x151), award(?x3868, ?x757), ?x9610 = 0c4y8 *> conf = 0.50 ranks of expected_values: 4 EVAL 06thjt institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 161.000 161.000 0.612 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #20028-0cv9t5 PRED entity: 0cv9t5 PRED relation: artist PRED expected values: 0411q => 51 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1390): 01vtj38 (0.50 #1368, 0.33 #529, 0.25 #4727), 0136p1 (0.50 #945, 0.33 #106, 0.21 #6820), 06449 (0.43 #2708, 0.16 #6903, 0.11 #6064), 01wg25j (0.42 #4821, 0.35 #8175, 0.28 #6498), 01vxlbm (0.40 #1946, 0.33 #267, 0.26 #6981), 01vrnsk (0.40 #3853, 0.25 #4691, 0.22 #6368), 0kvnn (0.40 #5337, 0.14 #2819, 0.09 #17084), 01vsl3_ (0.33 #174, 0.30 #3534, 0.25 #1013), 024zq (0.33 #4609, 0.30 #3771, 0.22 #6286), 0fhxv (0.33 #326, 0.29 #2845, 0.25 #1165) >> Best rule #1368 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 02bh8z; >> query: (?x13994, 01vtj38) <- artist(?x13994, ?x8600), artist(?x13994, ?x4461), artist(?x13994, ?x3316), people(?x6260, ?x3316), nationality(?x8600, ?x205), ?x4461 = 0fcsd, student(?x9293, ?x8600), award_nominee(?x1060, ?x3316), artists(?x378, ?x3316), risk_factors(?x10613, ?x6260) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4206 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 10 *> proper extension: 02p11jq; *> query: (?x13994, 0411q) <- artist(?x13994, ?x8600), artist(?x13994, ?x3316), people(?x6260, ?x3316), nationality(?x8600, ?x205), influenced_by(?x4960, ?x3316), gender(?x8600, ?x231), role(?x3316, ?x1466), profession(?x8600, ?x1614), type_of_union(?x3316, ?x566), artists(?x378, ?x3316) *> conf = 0.17 ranks of expected_values: 245 EVAL 0cv9t5 artist 0411q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 51.000 21.000 0.500 http://example.org/music/record_label/artist #20027-05hjmd PRED entity: 05hjmd PRED relation: award_nominee PRED expected values: 086k8 => 119 concepts (59 used for prediction) PRED predicted values (max 10 best out of 864): 016tt2 (0.77 #138293, 0.76 #131259, 0.76 #133604), 05qd_ (0.24 #30653, 0.23 #25964, 0.23 #28309), 086k8 (0.22 #126569, 0.22 #25844, 0.21 #30533), 05hjmd (0.22 #126569, 0.12 #67976, 0.11 #82037), 0py5b (0.22 #126569, 0.11 #82037), 03g62 (0.22 #126569, 0.11 #82037), 02f6s3 (0.22 #126569, 0.11 #82037), 0c12h (0.22 #126569, 0.11 #82037), 01pr6q7 (0.22 #126569, 0.11 #82037), 017s11 (0.18 #32921, 0.17 #44642, 0.17 #39954) >> Best rule #138293 for best value: >> intensional similarity = 3 >> extensional distance = 1112 >> proper extension: 086k8; 017s11; 016tt2; 0g1rw; 0kx4m; 05qd_; 016tw3; 07c0j; 030_1m; 09gffmz; ... >> query: (?x11030, ?x574) <- award_nominee(?x11030, ?x6488), nominated_for(?x11030, ?x9993), award_winner(?x574, ?x11030) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #126569 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 994 *> proper extension: 0kcd5; *> query: (?x11030, ?x3811) <- nominated_for(?x11030, ?x9993), nominated_for(?x3811, ?x9993), nominated_for(?x382, ?x9993), film(?x382, ?x83) *> conf = 0.22 ranks of expected_values: 3 EVAL 05hjmd award_nominee 086k8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 59.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #20026-0fr0t PRED entity: 0fr0t PRED relation: location! PRED expected values: 02z3zp => 205 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2070): 0fwy0h (0.46 #254052, 0.45 #176068, 0.45 #198710), 023kzp (0.36 #8759, 0.16 #26366, 0.16 #21336), 01t6b4 (0.33 #218, 0.18 #7763, 0.16 #22855), 0127xk (0.33 #2239, 0.09 #9784, 0.08 #24876), 02wlk (0.33 #2413, 0.04 #25050), 05v45k (0.33 #2401, 0.04 #25038), 0dxmyh (0.33 #2077, 0.04 #24714), 024zq (0.33 #1169, 0.04 #23806), 0227tr (0.22 #5509, 0.20 #2994, 0.12 #28147), 01qklj (0.22 #6967, 0.20 #4452, 0.07 #44697) >> Best rule #254052 for best value: >> intensional similarity = 4 >> extensional distance = 125 >> proper extension: 0pmpl; 05l5n; 0r1jr; 03pbf; 0gqkd; 0zlgm; 09c6w; 096g3; 0rh7t; 013gxt; ... >> query: (?x3983, ?x4817) <- administrative_division(?x3983, ?x8727), location(?x7345, ?x3983), place_of_birth(?x4817, ?x3983), profession(?x7345, ?x220) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #9193 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0f2w0; *> query: (?x3983, 02z3zp) <- dog_breed(?x3983, ?x1706), locations(?x4368, ?x3983), location_of_ceremony(?x9604, ?x3983), location_of_ceremony(?x566, ?x3983) *> conf = 0.09 ranks of expected_values: 349 EVAL 0fr0t location! 02z3zp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 205.000 108.000 0.464 http://example.org/people/person/places_lived./people/place_lived/location #20025-0bz5v2 PRED entity: 0bz5v2 PRED relation: location PRED expected values: 04lh6 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 161): 0k33p (0.77 #12033, 0.47 #70591, 0.44 #57753), 030qb3t (0.24 #36176, 0.21 #29760, 0.21 #44198), 0rh6k (0.12 #4, 0.06 #806, 0.05 #2410), 0xmp9 (0.12 #685, 0.06 #1487, 0.03 #3893), 0gkgp (0.12 #457, 0.06 #1259, 0.03 #2061), 04jpl (0.12 #34507, 0.11 #36111, 0.10 #44133), 0cymp (0.11 #1049, 0.03 #1851, 0.03 #5059), 0cc56 (0.10 #9681, 0.09 #10483, 0.09 #11286), 0cr3d (0.09 #1748, 0.08 #29822, 0.07 #22603), 013yq (0.08 #2524, 0.05 #4930, 0.03 #3326) >> Best rule #12033 for best value: >> intensional similarity = 3 >> extensional distance = 245 >> proper extension: 012v1t; >> query: (?x1040, ?x9878) <- location(?x1040, ?x739), ?x739 = 02_286, place_of_birth(?x1040, ?x9878) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #6851 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 129 *> proper extension: 04n_g; *> query: (?x1040, 04lh6) <- location(?x1040, ?x739), ?x739 = 02_286, award_winner(?x1265, ?x1040) *> conf = 0.02 ranks of expected_values: 117 EVAL 0bz5v2 location 04lh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 96.000 96.000 0.773 http://example.org/people/person/places_lived./people/place_lived/location #20024-0h1_w PRED entity: 0h1_w PRED relation: profession PRED expected values: 02hrh1q => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 145): 02hrh1q (0.91 #8265, 0.88 #13965, 0.88 #2415), 01d_h8 (0.39 #1356, 0.38 #7956, 0.37 #8106), 02jknp (0.34 #1358, 0.28 #8408, 0.27 #2708), 0dxtg (0.31 #13214, 0.30 #4214, 0.29 #7664), 03gjzk (0.24 #7666, 0.24 #8716, 0.23 #9166), 0cbd2 (0.23 #3007, 0.21 #3157, 0.21 #2107), 09jwl (0.16 #11870, 0.16 #18321, 0.16 #17120), 0kyk (0.15 #3031, 0.14 #3181, 0.13 #2131), 0np9r (0.14 #15322, 0.13 #11272, 0.13 #14272), 018gz8 (0.13 #11268, 0.13 #768, 0.13 #5118) >> Best rule #8265 for best value: >> intensional similarity = 4 >> extensional distance = 702 >> proper extension: 01sl1q; 05vsxz; 06gp3f; 01qscs; 0p_pd; 03rs8y; 0z4s; 03w1v2; 027dtv3; 01gvr1; ... >> query: (?x457, 02hrh1q) <- award(?x457, ?x3247), nominated_for(?x457, ?x5711), award(?x1870, ?x3247), ?x1870 = 0hvb2 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h1_w profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.906 http://example.org/people/person/profession #20023-07lwsz PRED entity: 07lwsz PRED relation: place_of_birth PRED expected values: 0ccvx => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 106): 030qb3t (0.22 #1462, 0.17 #54, 0.12 #758), 02_286 (0.17 #19, 0.09 #6357, 0.09 #14103), 0d9jr (0.17 #194, 0.02 #5828, 0.01 #4419), 0dzt9 (0.12 #1074, 0.11 #1778, 0.03 #2482), 01_d4 (0.08 #2178, 0.07 #6404, 0.06 #4995), 0cr3d (0.08 #2206, 0.05 #4319, 0.05 #11361), 09c7w0 (0.05 #4225, 0.04 #9156, 0.04 #12677), 0rh6k (0.04 #4931, 0.03 #7044, 0.03 #2114), 01nl79 (0.03 #6175, 0.01 #8287, 0.01 #14625), 04sqj (0.03 #3814, 0.03 #2406, 0.03 #3110) >> Best rule #1462 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0bbxd3; >> query: (?x3571, 030qb3t) <- program(?x3571, ?x10089), ?x10089 = 07g9f, profession(?x3571, ?x319) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #2969 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 05ty4m; 02lf0c; 02q_cc; 01rzqj; 0cj2nl; 030_3z; 09_99w; 02qjpv5; 01qg7c; 05vtbl; ... *> query: (?x3571, 0ccvx) <- program(?x3571, ?x4932), award_nominee(?x3571, ?x3366), executive_produced_by(?x8770, ?x3571) *> conf = 0.03 ranks of expected_values: 32 EVAL 07lwsz place_of_birth 0ccvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 125.000 125.000 0.222 http://example.org/people/person/place_of_birth #20022-048lv PRED entity: 048lv PRED relation: award PRED expected values: 057xs89 02w9sd7 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 255): 0f4x7 (0.71 #15373, 0.70 #24443, 0.70 #24047), 099jhq (0.71 #15373, 0.70 #24443, 0.70 #24047), 027b9j5 (0.71 #15373, 0.70 #24443, 0.70 #24047), 027dtxw (0.30 #398, 0.20 #4, 0.15 #20893), 0gq9h (0.22 #1256, 0.13 #32327, 0.12 #11504), 0bfvd4 (0.22 #504, 0.15 #20893, 0.14 #24442), 0ck27z (0.20 #89, 0.18 #22864, 0.15 #20893), 02x8n1n (0.20 #115, 0.14 #24442, 0.13 #32327), 0bp_b2 (0.20 #18, 0.14 #24442, 0.13 #32327), 0fbvqf (0.20 #46, 0.14 #24442, 0.13 #32327) >> Best rule #15373 for best value: >> intensional similarity = 3 >> extensional distance = 1229 >> proper extension: 030_1_; 0khth; 014l4w; 07mvp; 04k05; >> query: (?x1384, ?x451) <- award_winner(?x92, ?x1384), award(?x1384, ?x458), award_winner(?x451, ?x1384) >> conf = 0.71 => this is the best rule for 3 predicted values *> Best rule #22864 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1531 *> proper extension: 01_8w2; 0gsgr; *> query: (?x1384, ?x618) <- award_winner(?x2028, ?x1384), award_winner(?x618, ?x2028) *> conf = 0.18 ranks of expected_values: 14, 30 EVAL 048lv award 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 96.000 96.000 0.715 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 048lv award 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 96.000 96.000 0.715 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20021-050z2 PRED entity: 050z2 PRED relation: role PRED expected values: 0dwsp 0680x0 0dwt5 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 101): 0l15bq (0.46 #1271, 0.45 #1031, 0.44 #1030), 01qbl (0.45 #1031, 0.44 #1030, 0.42 #1676), 06w7v (0.33 #64, 0.17 #302, 0.14 #2138), 03qjg (0.30 #2317, 0.24 #4963, 0.23 #4962), 048j4l (0.30 #2317, 0.23 #4962, 0.23 #2805), 02qjv (0.20 #91, 0.17 #250, 0.17 #170), 07kc_ (0.20 #90, 0.17 #169, 0.14 #407), 051hrr (0.20 #115, 0.17 #194, 0.14 #432), 0bxl5 (0.17 #289, 0.13 #842, 0.13 #763), 01v1d8 (0.17 #288, 0.13 #841, 0.13 #762) >> Best rule #1271 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 01x0yrt; >> query: (?x4052, ?x212) <- performance_role(?x4052, ?x212), performance_role(?x4471, ?x212), role(?x212, ?x75), ?x4471 = 026g73 >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1594 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 04pf4r; *> query: (?x4052, ?x314) <- performance_role(?x4052, ?x212), performance_role(?x314, ?x212), instrumentalists(?x212, ?x226), artists(?x284, ?x4052) *> conf = 0.10 ranks of expected_values: 16, 27, 37 EVAL 050z2 role 0dwt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 125.000 125.000 0.462 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 050z2 role 0680x0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 125.000 125.000 0.462 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 050z2 role 0dwsp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 125.000 125.000 0.462 http://example.org/music/artist/track_contributions./music/track_contribution/role #20020-04sv4 PRED entity: 04sv4 PRED relation: place_founded PRED expected values: 0djd3 => 152 concepts (134 used for prediction) PRED predicted values (max 10 best out of 76): 01_d4 (0.33 #81, 0.11 #1133, 0.11 #1001), 06pwq (0.33 #137, 0.11 #1124, 0.06 #3571), 02_286 (0.25 #864, 0.25 #337, 0.25 #271), 02kx3 (0.25 #523, 0.25 #457, 0.06 #1773), 0c75w (0.25 #248, 0.20 #576, 0.08 #1367), 080h2 (0.20 #534, 0.04 #1983, 0.03 #2114), 0r5wt (0.17 #620, 0.12 #821, 0.12 #687), 09d4_ (0.17 #627, 0.08 #1222, 0.08 #1418), 0bxbb (0.15 #1421, 0.11 #1093, 0.09 #1948), 07dfk (0.14 #1494, 0.12 #1626, 0.10 #4214) >> Best rule #81 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0537b; >> query: (?x9469, 01_d4) <- industry(?x9469, ?x245), list(?x9469, ?x5997), company(?x554, ?x9469), company(?x233, ?x9469), ?x554 = 02211by, ?x233 = 01rk91 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04sv4 place_founded 0djd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 152.000 134.000 0.333 http://example.org/organization/organization/place_founded #20019-01v2xl PRED entity: 01v2xl PRED relation: company! PRED expected values: 08jcfy => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 22): 021q0l (0.22 #481, 0.16 #292, 0.16 #575), 060c4 (0.19 #379, 0.13 #1461, 0.12 #1226), 07t3gd (0.13 #447, 0.08 #682, 0.07 #494), 07xl34 (0.12 #540, 0.10 #587, 0.10 #916), 021q1c (0.11 #623, 0.11 #293, 0.06 #1046), 05k17c (0.09 #625, 0.07 #154, 0.06 #578), 0hm4q (0.08 #440, 0.07 #487, 0.05 #675), 02md_2 (0.07 #158, 0.06 #205, 0.06 #252), 04n1q6 (0.07 #624, 0.05 #483, 0.04 #577), 0fkvn (0.06 #240, 0.04 #334, 0.02 #476) >> Best rule #481 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 01nrnm; 01sjz_; 017rbx; 014d4v; 0ym20; >> query: (?x11602, 021q0l) <- contains(?x4221, ?x11602), category(?x11602, ?x134), currency(?x11602, ?x1099), ?x1099 = 01nv4h, student(?x11602, ?x8306) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #424 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 29 *> proper extension: 01q460; 0352gk; 01p79b; 0qlnr; 02mg7n; 0l0wv; 016w7b; *> query: (?x11602, ?x346) <- colors(?x11602, ?x9778), colors(?x11602, ?x3315), colors(?x7900, ?x9778), colors(?x5737, ?x9778), organization(?x346, ?x5737), institution(?x620, ?x7900), ?x3315 = 0jc_p *> conf = 0.03 ranks of expected_values: 13 EVAL 01v2xl company! 08jcfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 158.000 158.000 0.220 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #20018-016622 PRED entity: 016622 PRED relation: role PRED expected values: 013y1f => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 103): 026t6 (0.85 #3175, 0.84 #718, 0.84 #4428), 0dwsp (0.81 #1147, 0.68 #516, 0.66 #514), 07y_7 (0.79 #834, 0.76 #1049, 0.74 #1154), 013y1f (0.77 #1714, 0.76 #1075, 0.76 #1042), 0l14md (0.76 #1037, 0.76 #942, 0.74 #1157), 0bxl5 (0.75 #678, 0.68 #516, 0.68 #1529), 06rvn (0.75 #711, 0.68 #516, 0.65 #1028), 04rzd (0.74 #1721, 0.71 #552, 0.70 #1611), 0mkg (0.74 #1160, 0.71 #525, 0.68 #516), 01wy6 (0.73 #1302, 0.68 #516, 0.68 #1197) >> Best rule #3175 for best value: >> intensional similarity = 24 >> extensional distance = 68 >> proper extension: 0dwtp; 0bm02; >> query: (?x3328, ?x1437) <- role(?x2944, ?x3328), role(?x1437, ?x3328), role(?x316, ?x3328), ?x316 = 05r5c, role(?x2944, ?x6039), role(?x2944, ?x2764), role(?x2944, ?x2459), group(?x2944, ?x7476), role(?x2944, ?x569), role(?x2944, ?x315), ?x2764 = 01s0ps, ?x315 = 0l14md, ?x6039 = 05kms, role(?x4186, ?x2944), ?x7476 = 048xh, role(?x3328, ?x2059), ?x2459 = 021bmf, family(?x1147, ?x1437), role(?x1437, ?x3967), role(?x11710, ?x1437), ?x11710 = 09g0h, ?x569 = 07c6l, nationality(?x4186, ?x512), ?x3967 = 01p970 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1714 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 33 *> proper extension: 06rvn; *> query: (?x3328, 013y1f) <- role(?x885, ?x3328), role(?x316, ?x3328), role(?x228, ?x3328), role(?x654, ?x3328), role(?x11916, ?x316), role(?x5878, ?x316), role(?x5550, ?x316), role(?x2690, ?x316), role(?x1181, ?x316), role(?x1004, ?x316), ?x2690 = 0892sx, ?x1004 = 01vv7sc, role(?x9413, ?x316), role(?x1472, ?x316), ?x9413 = 07m2y, ?x1472 = 0319l, ?x228 = 0l14qv, ?x1181 = 0b68vs, artists(?x671, ?x5878), group(?x316, ?x997), role(?x642, ?x316), performance_role(?x1969, ?x316), role(?x3328, ?x1432), role(?x214, ?x316), ?x11916 = 023slg, origin(?x5550, ?x13347), ?x885 = 0dwtp *> conf = 0.77 ranks of expected_values: 4 EVAL 016622 role 013y1f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 47.000 47.000 0.853 http://example.org/music/performance_role/track_performances./music/track_contribution/role #20017-05yvfd PRED entity: 05yvfd PRED relation: people! PRED expected values: 0dryh9k => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 48): 0dryh9k (0.57 #320, 0.55 #92, 0.42 #168), 041rx (0.35 #613, 0.29 #920, 0.29 #536), 033tf_ (0.20 #616, 0.17 #2217, 0.16 #1607), 0x67 (0.19 #3516, 0.18 #3212, 0.18 #2449), 0xnvg (0.15 #622, 0.11 #699, 0.10 #775), 0bpjh3 (0.14 #329, 0.09 #481, 0.08 #253), 01rv7x (0.11 #418, 0.04 #342, 0.02 #494), 02w7gg (0.09 #7775, 0.09 #7699, 0.09 #7471), 07bch9 (0.07 #2918, 0.07 #4062, 0.06 #5054), 07hwkr (0.07 #2755, 0.07 #4051, 0.07 #2831) >> Best rule #320 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 06gn7r; 01wj5hp; >> query: (?x9465, 0dryh9k) <- location(?x9465, ?x7412), religion(?x9465, ?x8967), ?x8967 = 03j6c, people(?x7838, ?x9465), profession(?x9465, ?x1032) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05yvfd people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.571 http://example.org/people/ethnicity/people #20016-046488 PRED entity: 046488 PRED relation: language PRED expected values: 064_8sq => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 47): 06nm1 (0.26 #242, 0.15 #649, 0.14 #10), 064_8sq (0.20 #543, 0.19 #2063, 0.18 #2123), 03_9r (0.17 #125, 0.11 #241, 0.10 #1114), 012w70 (0.17 #186, 0.09 #70, 0.08 #128), 0jzc (0.16 #716, 0.11 #251, 0.09 #77), 04306rv (0.15 #643, 0.13 #701, 0.13 #526), 02bjrlw (0.12 #523, 0.09 #2103, 0.09 #640), 06b_j (0.10 #486, 0.09 #1010, 0.09 #80), 032f6 (0.09 #113, 0.08 #171, 0.06 #403), 02hwyss (0.09 #99, 0.06 #331, 0.06 #389) >> Best rule #242 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 011yxg; 02r1c18; 0661ql3; 02mt51; 08nvyr; 03f7nt; 09r94m; 06823p; 0280061; 0gvvm6l; ... >> query: (?x4993, 06nm1) <- language(?x4993, ?x254), film_format(?x4993, ?x909), nominated_for(?x68, ?x4993), ?x68 = 02qyp19 >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #543 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 58 *> proper extension: 03ffcz; *> query: (?x4993, 064_8sq) <- language(?x4993, ?x254), film_release_distribution_medium(?x4993, ?x81), award_winner(?x4993, ?x192), titles(?x512, ?x4993), ?x512 = 07ssc *> conf = 0.20 ranks of expected_values: 2 EVAL 046488 language 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.263 http://example.org/film/film/language #20015-0193x PRED entity: 0193x PRED relation: major_field_of_study! PRED expected values: 027f2w => 59 concepts (27 used for prediction) PRED predicted values (max 10 best out of 17): 016t_3 (0.80 #142, 0.80 #89, 0.79 #179), 04zx3q1 (0.80 #141, 0.80 #88, 0.75 #123), 0bjrnt (0.51 #158, 0.45 #52, 0.44 #459), 01rr_d (0.51 #158, 0.45 #52, 0.39 #310), 027f2w (0.51 #158, 0.45 #52, 0.39 #310), 03mkk4 (0.51 #158, 0.45 #52, 0.39 #310), 028dcg (0.51 #158, 0.45 #52, 0.39 #310), 07s6fsf (0.51 #158, 0.45 #52, 0.39 #310), 071tyz (0.51 #158, 0.45 #52, 0.38 #272), 02mjs7 (0.51 #158, 0.45 #52, 0.38 #272) >> Best rule #142 for best value: >> intensional similarity = 12 >> extensional distance = 13 >> proper extension: 04rjg; >> query: (?x3489, 016t_3) <- major_field_of_study(?x865, ?x3489), major_field_of_study(?x3424, ?x3489), major_field_of_study(?x892, ?x3489), ?x892 = 07tgn, student(?x3424, ?x9584), student(?x3424, ?x7044), ?x9584 = 01c7p_, major_field_of_study(?x3424, ?x8221), award_winner(?x6071, ?x7044), ?x8221 = 037mh8, institution(?x620, ?x3424), school_type(?x3424, ?x1044) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #158 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 13 *> proper extension: 04rjg; *> query: (?x3489, ?x620) <- major_field_of_study(?x865, ?x3489), major_field_of_study(?x3424, ?x3489), major_field_of_study(?x892, ?x3489), ?x892 = 07tgn, student(?x3424, ?x9584), student(?x3424, ?x7044), ?x9584 = 01c7p_, major_field_of_study(?x3424, ?x8221), award_winner(?x6071, ?x7044), ?x8221 = 037mh8, institution(?x620, ?x3424), school_type(?x3424, ?x1044) *> conf = 0.51 ranks of expected_values: 5 EVAL 0193x major_field_of_study! 027f2w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 59.000 27.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #20014-0fkwzs PRED entity: 0fkwzs PRED relation: genre PRED expected values: 04t36 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 85): 07s9rl0 (0.78 #3364, 0.65 #4539, 0.62 #391), 03k9fj (0.59 #401, 0.18 #3374, 0.17 #4939), 0c4xc (0.58 #819, 0.57 #1053, 0.53 #351), 01htzx (0.52 #406, 0.40 #172, 0.38 #16), 06n90 (0.50 #13, 0.30 #169, 0.28 #403), 01t_vv (0.42 #343, 0.39 #655, 0.34 #1045), 01jfsb (0.38 #12, 0.21 #402, 0.14 #870), 01z77k (0.33 #104, 0.14 #416, 0.14 #4330), 06nbt (0.32 #330, 0.27 #642, 0.26 #564), 0vgkd (0.26 #322, 0.24 #790, 0.23 #1024) >> Best rule #3364 for best value: >> intensional similarity = 5 >> extensional distance = 123 >> proper extension: 02qfh; >> query: (?x8554, 07s9rl0) <- actor(?x8554, ?x381), genre(?x8554, ?x1510), award_nominee(?x381, ?x100), titles(?x1510, ?x83), genre(?x136, ?x1510) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #162 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 05sy2k_; 05f7w84; 0cskb; 043qqt5; *> query: (?x8554, 04t36) <- category(?x8554, ?x134), program(?x1182, ?x8554), actor(?x8554, ?x381), genre(?x8554, ?x1510), ?x1510 = 01hmnh *> conf = 0.10 ranks of expected_values: 36 EVAL 0fkwzs genre 04t36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 113.000 113.000 0.784 http://example.org/tv/tv_program/genre #20013-01qr1_ PRED entity: 01qr1_ PRED relation: film PRED expected values: 0456zg => 97 concepts (57 used for prediction) PRED predicted values (max 10 best out of 586): 0vjr (0.58 #44722, 0.39 #32199, 0.38 #85864), 0d68qy (0.58 #44722, 0.39 #32199, 0.38 #85864), 034fl9 (0.58 #44722, 0.39 #32199, 0.38 #85864), 02vw1w2 (0.10 #2002), 026q3s3 (0.10 #1992), 013q07 (0.08 #357, 0.02 #2145, 0.01 #16458), 02_1q9 (0.08 #16101, 0.07 #14312, 0.07 #8945), 0dd6bf (0.06 #3023), 07ghv5 (0.06 #2956), 0dh8v4 (0.06 #2729) >> Best rule #44722 for best value: >> intensional similarity = 3 >> extensional distance = 1049 >> proper extension: 023tp8; 01j5x6; 01yb09; 02pb53; 015pxr; 07hbxm; 02xb2bt; 0738b8; 02j8nx; 0347xl; ... >> query: (?x3557, ?x2528) <- award_nominee(?x3557, ?x906), nominated_for(?x3557, ?x2528), film(?x3557, ?x3088) >> conf = 0.58 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 01qr1_ film 0456zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 57.000 0.585 http://example.org/film/actor/film./film/performance/film #20012-015pxr PRED entity: 015pxr PRED relation: film PRED expected values: 04gv3db => 133 concepts (109 used for prediction) PRED predicted values (max 10 best out of 760): 01shy7 (0.10 #9362, 0.07 #14723, 0.07 #11149), 03lrht (0.10 #9196, 0.06 #14557, 0.05 #16344), 0prrm (0.09 #11586, 0.06 #6224, 0.06 #15160), 02825cv (0.09 #1142, 0.01 #56546, 0.01 #15441), 04gv3db (0.08 #9691, 0.07 #15052, 0.07 #11478), 016dj8 (0.08 #10052, 0.07 #15413, 0.05 #27925), 034qzw (0.08 #9272, 0.07 #11059, 0.06 #16420), 0bvn25 (0.08 #8988, 0.07 #12562, 0.05 #16136), 01rwyq (0.07 #11276, 0.04 #14850, 0.04 #16637), 01jft4 (0.07 #13778, 0.05 #10204, 0.04 #15565) >> Best rule #9362 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 01y8d4; >> query: (?x2143, 01shy7) <- influenced_by(?x2143, ?x986), award_winner(?x2143, ?x237), religion(?x2143, ?x2694) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #9691 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 01y8d4; *> query: (?x2143, 04gv3db) <- influenced_by(?x2143, ?x986), award_winner(?x2143, ?x237), religion(?x2143, ?x2694) *> conf = 0.08 ranks of expected_values: 5 EVAL 015pxr film 04gv3db CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 133.000 109.000 0.103 http://example.org/film/actor/film./film/performance/film #20011-03fqv5 PRED entity: 03fqv5 PRED relation: location PRED expected values: 01531 => 143 concepts (137 used for prediction) PRED predicted values (max 10 best out of 137): 01531 (0.51 #59533, 0.47 #74018, 0.44 #29770), 02_286 (0.22 #5668, 0.18 #37, 0.18 #6472), 030qb3t (0.11 #28244, 0.11 #20999, 0.10 #45135), 0cr3d (0.08 #2560, 0.07 #3364, 0.07 #7386), 0cc56 (0.07 #862, 0.07 #4080, 0.05 #5688), 0d6lp (0.05 #2583, 0.05 #3387, 0.05 #4191), 04jpl (0.05 #41851, 0.05 #21738, 0.05 #17), 03v1s (0.05 #3239, 0.05 #4043, 0.03 #2435), 059rby (0.05 #16, 0.04 #20932, 0.03 #34611), 06pvr (0.05 #135, 0.03 #1745) >> Best rule #59533 for best value: >> intensional similarity = 3 >> extensional distance = 959 >> proper extension: 07h1q; >> query: (?x12960, ?x3014) <- place_of_birth(?x12960, ?x3014), people(?x1050, ?x12960), gender(?x12960, ?x231) >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03fqv5 location 01531 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 137.000 0.506 http://example.org/people/person/places_lived./people/place_lived/location #20010-01w40h PRED entity: 01w40h PRED relation: artist PRED expected values: 01vw87c 01n8gr 01wz_ml 06m61 0flpy 05szp 01k3qj => 96 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1438): 016dsy (0.60 #2612, 0.43 #4180, 0.40 #1046), 03xhj6 (0.50 #8125, 0.43 #4201, 0.43 #3416), 01vn35l (0.43 #3300, 0.40 #1734, 0.40 #951), 04xrx (0.43 #3275, 0.40 #1709, 0.40 #926), 03y82t6 (0.43 #3443, 0.40 #1877, 0.40 #1094), 01k3qj (0.43 #3640, 0.40 #2074, 0.38 #5209), 01wx756 (0.43 #4659, 0.40 #1525, 0.20 #8583), 0565cz (0.43 #4095, 0.33 #178, 0.30 #8019), 089tm (0.43 #3932, 0.20 #2364, 0.20 #798), 015xp4 (0.40 #8182, 0.40 #1907, 0.33 #341) >> Best rule #2612 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 011k11; 0181dw; 01t04r; >> query: (?x4868, 016dsy) <- artist(?x4868, ?x9008), artist(?x4868, ?x4237), artist(?x4868, ?x3997), child(?x7793, ?x4868), category(?x4868, ?x134), ?x4237 = 01w524f, type_of_union(?x3997, ?x566), location(?x3997, ?x7769), award(?x9008, ?x528) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3640 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 011k1h; 015_1q; *> query: (?x4868, 01k3qj) <- artist(?x4868, ?x11446), artist(?x4868, ?x3997), child(?x7793, ?x4868), category(?x4868, ?x134), sibling(?x2273, ?x3997), spouse(?x3997, ?x5479), artists(?x114, ?x11446), award(?x3997, ?x528) *> conf = 0.43 ranks of expected_values: 6, 63, 73, 154, 379, 595, 1412 EVAL 01w40h artist 01k3qj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 72.000 0.600 http://example.org/music/record_label/artist EVAL 01w40h artist 05szp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 96.000 72.000 0.600 http://example.org/music/record_label/artist EVAL 01w40h artist 0flpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 96.000 72.000 0.600 http://example.org/music/record_label/artist EVAL 01w40h artist 06m61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 96.000 72.000 0.600 http://example.org/music/record_label/artist EVAL 01w40h artist 01wz_ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 96.000 72.000 0.600 http://example.org/music/record_label/artist EVAL 01w40h artist 01n8gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 96.000 72.000 0.600 http://example.org/music/record_label/artist EVAL 01w40h artist 01vw87c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 96.000 72.000 0.600 http://example.org/music/record_label/artist #20009-04wlz2 PRED entity: 04wlz2 PRED relation: colors PRED expected values: 083jv => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.45 #182, 0.40 #122, 0.37 #282), 06fvc (0.27 #43, 0.23 #183, 0.19 #63), 01l849 (0.26 #1101, 0.26 #261, 0.26 #381), 04mkbj (0.20 #10, 0.17 #30, 0.10 #290), 036k5h (0.20 #5, 0.17 #25, 0.09 #1045), 02rnmb (0.18 #53, 0.12 #73, 0.10 #93), 019sc (0.17 #1047, 0.17 #1107, 0.17 #1027), 04d18d (0.11 #239, 0.07 #279, 0.06 #419), 038hg (0.10 #112, 0.10 #92, 0.10 #1032), 03vtbc (0.10 #108, 0.10 #88, 0.09 #48) >> Best rule #182 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0288zy; 02kth6; 0bthb; 07w3r; 07wrz; 02bjhv; 01swxv; 086xm; 02fgdx; 02607j; ... >> query: (?x347, 083jv) <- contains(?x94, ?x347), institution(?x865, ?x347), ?x865 = 02h4rq6, currency(?x347, ?x170) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wlz2 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.447 http://example.org/education/educational_institution/colors #20008-027r8p PRED entity: 027r8p PRED relation: award PRED expected values: 0ck27z => 89 concepts (74 used for prediction) PRED predicted values (max 10 best out of 249): 0ck27z (0.67 #498, 0.64 #1308, 0.60 #93), 09sb52 (0.38 #851, 0.31 #12597, 0.31 #8952), 05p09zm (0.32 #2149, 0.17 #4579, 0.12 #934), 0gkts9 (0.25 #979, 0.15 #5671, 0.06 #10295), 0gqyl (0.25 #916, 0.14 #4966, 0.12 #10232), 0f4x7 (0.25 #841, 0.12 #2461, 0.12 #2056), 09qv_s (0.25 #962, 0.12 #2177, 0.08 #2582), 02ppm4q (0.25 #967, 0.10 #5017, 0.10 #7043), 09qvf4 (0.25 #1021, 0.06 #5071, 0.05 #2641), 07bdd_ (0.25 #876, 0.06 #13432, 0.05 #13837) >> Best rule #498 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03rs8y; 01z_g6; 04qz6n; 03kcyd; >> query: (?x3747, 0ck27z) <- award_nominee(?x3747, ?x7835), actor(?x6482, ?x3747), ?x7835 = 033m23, award(?x3747, ?x2041) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027r8p award 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 74.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #20007-09qh1 PRED entity: 09qh1 PRED relation: nationality PRED expected values: 02jx1 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 39): 0nrnz (0.32 #6256), 03s0w (0.32 #6256), 02jx1 (0.29 #4764, 0.27 #12511, 0.25 #9235), 07ssc (0.29 #4764, 0.27 #12511, 0.25 #9235), 0345h (0.17 #1814, 0.12 #921, 0.08 #2705), 0f8l9c (0.11 #120, 0.08 #219, 0.07 #417), 0h7x (0.08 #1818, 0.08 #925, 0.03 #6090), 03rk0 (0.07 #11959, 0.06 #12953, 0.06 #13745), 0d060g (0.05 #2285, 0.05 #6460, 0.05 #10629), 03rt9 (0.04 #903, 0.02 #3978, 0.02 #705) >> Best rule #6256 for best value: >> intensional similarity = 3 >> extensional distance = 460 >> proper extension: 028q6; 032nwy; 02pp_q_; 032t2z; 012cph; 01g4zr; 04zd4m; 01d494; 01c59k; 01c58j; ... >> query: (?x3627, ?x961) <- people(?x1158, ?x3627), place_of_death(?x3627, ?x10662), contains(?x961, ?x10662) >> conf = 0.32 => this is the best rule for 2 predicted values *> Best rule #4764 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 382 *> proper extension: 0qkj7; *> query: (?x3627, ?x512) <- location(?x3627, ?x9929), people(?x1158, ?x3627), contains(?x512, ?x9929) *> conf = 0.29 ranks of expected_values: 3 EVAL 09qh1 nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 150.000 150.000 0.316 http://example.org/people/person/nationality #20006-099tbz PRED entity: 099tbz PRED relation: award_winner PRED expected values: 02tr7d 016ypb 0294fd 07m9cm 048s0r 026r8q 0336mc => 42 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1466): 026r8q (0.50 #1581, 0.30 #4003, 0.08 #26654), 0237fw (0.30 #2912, 0.25 #490, 0.08 #26654), 01kwsg (0.30 #3457, 0.25 #1035, 0.08 #26654), 0dzf_ (0.30 #3413, 0.25 #991, 0.08 #26654), 01713c (0.30 #2730, 0.25 #308, 0.07 #19387), 02fn5 (0.30 #3339, 0.25 #917, 0.02 #5763), 055c8 (0.30 #3087, 0.08 #26654, 0.08 #26653), 01ycbq (0.30 #2826, 0.04 #27059, 0.04 #5250), 09l3p (0.30 #29077, 0.29 #29078, 0.29 #36343), 0686zv (0.30 #29077, 0.29 #29078, 0.29 #36343) >> Best rule #1581 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 09sb52; 057xs89; >> query: (?x995, 026r8q) <- award(?x6916, ?x995), award(?x2307, ?x995), ?x6916 = 01f7dd, ?x2307 = 011zd3, award_winner(?x995, ?x193) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 41, 45, 49, 60, 238, 241 EVAL 099tbz award_winner 0336mc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 42.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 099tbz award_winner 026r8q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 099tbz award_winner 048s0r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 42.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 099tbz award_winner 07m9cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 42.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 099tbz award_winner 0294fd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 099tbz award_winner 016ypb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 42.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 099tbz award_winner 02tr7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #20005-03czz87 PRED entity: 03czz87 PRED relation: program! PRED expected values: 09d5h => 118 concepts (108 used for prediction) PRED predicted values (max 10 best out of 56): 0cjdk (0.36 #349, 0.25 #178, 0.25 #64), 05gnf (0.27 #301, 0.25 #73, 0.24 #472), 01fsyp (0.25 #50, 0.12 #223, 0.12 #166), 01zcrv (0.25 #22, 0.12 #195, 0.12 #138), 03mdt (0.24 #639, 0.20 #580, 0.18 #755), 0gsg7 (0.20 #1735, 0.20 #3654, 0.20 #1505), 01fkr_ (0.20 #267, 0.09 #324, 0.05 #438), 07c52 (0.19 #516, 0.14 #1041, 0.14 #866), 09lmb (0.19 #516, 0.14 #1041, 0.14 #866), 02kx91 (0.12 #221, 0.12 #164, 0.10 #278) >> Best rule #349 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 01b7h8; >> query: (?x12387, 0cjdk) <- program(?x4374, ?x12387), honored_for(?x2213, ?x12387), country_of_origin(?x12387, ?x94), tv_program(?x12903, ?x12387), ?x2213 = 0gvstc3 >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1045 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 0bbm7r; 04mx8h4; *> query: (?x12387, 09d5h) <- genre(?x12387, ?x5728), category(?x12387, ?x134), honored_for(?x2213, ?x12387), award_winner(?x2213, ?x72) *> conf = 0.12 ranks of expected_values: 18 EVAL 03czz87 program! 09d5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 118.000 108.000 0.357 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #20004-0xrz2 PRED entity: 0xrz2 PRED relation: contains! PRED expected values: 05fjf => 113 concepts (83 used for prediction) PRED predicted values (max 10 best out of 195): 05fjf (0.76 #36703, 0.13 #14694, 0.11 #20064), 0n5bk (0.74 #37600, 0.74 #36702, 0.73 #23273), 07ssc (0.18 #25096, 0.16 #48379, 0.16 #50169), 01n7q (0.18 #35883, 0.17 #36781, 0.14 #44845), 04_1l0v (0.15 #34466, 0.14 #33571, 0.12 #40739), 02jx1 (0.14 #25151, 0.13 #9037, 0.12 #53804), 05k7sb (0.13 #19823, 0.08 #36836, 0.08 #35938), 059rby (0.13 #52842, 0.11 #57318, 0.11 #19), 0n5gq (0.11 #37601, 0.04 #1239, 0.03 #2135), 0cymp (0.11 #37601, 0.03 #289, 0.02 #14610) >> Best rule #36703 for best value: >> intensional similarity = 3 >> extensional distance = 243 >> proper extension: 0ybkj; 0136jw; 0zrlp; 0s6g4; 0sd7v; 0c5v2; 01m24m; 0s4sj; >> query: (?x8343, ?x6895) <- county(?x8343, ?x7492), adjoins(?x321, ?x7492), contains(?x6895, ?x7492) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xrz2 contains! 05fjf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 83.000 0.763 http://example.org/location/location/contains #20003-07c52 PRED entity: 07c52 PRED relation: titles PRED expected values: 0464pz 02hct1 02md2d 030cx 0l76z 0431v3 0c3xpwy 05lfwd 016zfm 01fszq 0123qq 01cvtf 0qmk5 02qr46y => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 1426): 0bt3j9 (0.50 #7138, 0.50 #7137, 0.50 #7136), 029zqn (0.50 #7138, 0.50 #7137, 0.50 #7136), 0k4p0 (0.50 #7138, 0.50 #7137, 0.50 #7136), 07gghl (0.50 #7138, 0.50 #7137, 0.50 #7136), 02_1sj (0.50 #7138, 0.50 #7137, 0.50 #7136), 011yth (0.50 #7138, 0.50 #7137, 0.50 #7136), 0gy0n (0.50 #7138, 0.50 #7137, 0.50 #7136), 015whm (0.50 #7138, 0.50 #7137, 0.50 #7136), 011ycb (0.50 #7138, 0.50 #7137, 0.50 #7136), 01jft4 (0.50 #7138, 0.50 #7137, 0.50 #7136) >> Best rule #7138 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 01hmnh; 01sgl; >> query: (?x2008, ?x1734) <- films(?x2008, ?x11534), films(?x2008, ?x1734), titles(?x2008, ?x4063), country_of_origin(?x4063, ?x512), language(?x1734, ?x254), film(?x2891, ?x11534) >> conf = 0.50 => this is the best rule for 15 predicted values *> Best rule #612 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 03mdt; *> query: (?x2008, 0l76z) <- titles(?x2008, ?x11818), titles(?x2008, ?x7317), titles(?x2008, ?x631), nominated_for(?x1712, ?x631), actor(?x7317, ?x2965), ?x11818 = 06k176, nominated_for(?x678, ?x7317) *> conf = 0.33 ranks of expected_values: 37, 38, 39, 577 EVAL 07c52 titles 02qr46y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 0qmk5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 01cvtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 0123qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 01fszq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 016zfm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 05lfwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 0c3xpwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 0431v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 0l76z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 030cx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 02md2d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 02hct1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 07c52 titles 0464pz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 78.000 78.000 0.500 http://example.org/media_common/netflix_genre/titles #20002-0225bv PRED entity: 0225bv PRED relation: school! PRED expected values: 0jmmn 06rpd => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 90): 07147 (0.40 #64, 0.10 #1414, 0.10 #1324), 0jmm4 (0.40 #70, 0.10 #790, 0.08 #880), 0289q (0.40 #41, 0.06 #941, 0.06 #401), 01ync (0.40 #37, 0.06 #1387, 0.05 #1297), 02__x (0.40 #49, 0.05 #1399, 0.04 #1309), 05m_8 (0.20 #633, 0.17 #1353, 0.17 #723), 051vz (0.20 #23, 0.16 #653, 0.12 #1373), 01yhm (0.20 #20, 0.13 #650, 0.12 #740), 05xvj (0.20 #85, 0.11 #985, 0.10 #1345), 05g49 (0.20 #43, 0.10 #133, 0.08 #673) >> Best rule #64 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0j_sncb; 01pq4w; >> query: (?x12485, 07147) <- student(?x12485, ?x2739), school(?x2820, ?x12485), school(?x2574, ?x12485), ?x2820 = 0jmj7, ?x2574 = 01y3v >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #791 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 82 *> proper extension: 02jyr8; 016sd3; *> query: (?x12485, 06rpd) <- contains(?x94, ?x12485), school(?x2574, ?x12485), school(?x685, ?x12485), ?x94 = 09c7w0 *> conf = 0.10 ranks of expected_values: 49, 68 EVAL 0225bv school! 06rpd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 132.000 132.000 0.400 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0225bv school! 0jmmn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 132.000 132.000 0.400 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #20001-082xp PRED entity: 082xp PRED relation: religion PRED expected values: 0n2g => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 35): 03_gx (0.33 #239, 0.25 #104, 0.25 #59), 0kpl (0.33 #190, 0.25 #1900, 0.23 #640), 01spm (0.33 #352, 0.20 #172, 0.11 #757), 0c8wxp (0.31 #681, 0.28 #726, 0.25 #2211), 019cr (0.30 #551, 0.21 #1001, 0.15 #1091), 0v53x (0.30 #569, 0.17 #1019, 0.14 #794), 01lp8 (0.25 #91, 0.12 #676, 0.11 #721), 092bf5 (0.25 #61, 0.11 #376, 0.09 #601), 05sfs (0.22 #363, 0.20 #543, 0.18 #588), 0n2g (0.22 #328, 0.20 #148, 0.10 #823) >> Best rule #239 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0b78hw; >> query: (?x11492, 03_gx) <- type_of_union(?x11492, ?x566), ?x566 = 04ztj, profession(?x11492, ?x7397), company(?x11492, ?x9823), ?x7397 = 03jgz >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #328 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 063vn; 03f5vvx; *> query: (?x11492, 0n2g) <- basic_title(?x11492, ?x182), student(?x9741, ?x11492), ?x182 = 060bp, profession(?x11492, ?x955) *> conf = 0.22 ranks of expected_values: 10 EVAL 082xp religion 0n2g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 138.000 138.000 0.333 http://example.org/people/person/religion #20000-0nt4s PRED entity: 0nt4s PRED relation: contains! PRED expected values: 03v1s => 134 concepts (98 used for prediction) PRED predicted values (max 10 best out of 111): 03v1s (0.81 #8971, 0.30 #15254, 0.22 #29618), 09c7w0 (0.78 #57449, 0.73 #50267, 0.68 #9869), 04_1l0v (0.78 #57449, 0.49 #29171, 0.47 #22883), 03rk0 (0.47 #9108, 0.44 #2826, 0.43 #7312), 0nt4s (0.42 #26027, 0.42 #85284, 0.42 #61941), 0chghy (0.33 #3608, 0.21 #7198, 0.20 #1815), 07c5l (0.33 #1291, 0.09 #20135, 0.07 #27320), 07b_l (0.17 #10091, 0.17 #10988, 0.14 #12783), 01n7q (0.14 #25206, 0.14 #66508, 0.14 #52139), 07ssc (0.14 #50299, 0.11 #51196, 0.10 #76336) >> Best rule #8971 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0235l; >> query: (?x11235, ?x448) <- source(?x11235, ?x958), ?x958 = 0jbk9, county(?x2879, ?x11235), capital(?x448, ?x2879) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nt4s contains! 03v1s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 98.000 0.812 http://example.org/location/location/contains #19999-03qnc6q PRED entity: 03qnc6q PRED relation: music PRED expected values: 0csdzz => 119 concepts (52 used for prediction) PRED predicted values (max 10 best out of 120): 01tc9r (0.14 #275, 0.08 #65, 0.04 #1959), 02cyfz (0.14 #244, 0.04 #876, 0.04 #1086), 0146pg (0.11 #1693, 0.10 #1482, 0.09 #2115), 012201 (0.08 #151, 0.08 #573, 0.04 #993), 01m5m5b (0.08 #188, 0.05 #2503, 0.04 #1450), 0bwh6 (0.08 #22, 0.04 #444, 0.03 #2548), 0c73z (0.08 #198), 03_f0 (0.08 #148), 04k15 (0.08 #60), 0csdzz (0.08 #609, 0.05 #2081, 0.05 #1659) >> Best rule #275 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 030cx; >> query: (?x2656, 01tc9r) <- nominated_for(?x372, ?x2656), nominated_for(?x2499, ?x2656), award_winner(?x372, ?x767), ?x2499 = 0c6qh >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #609 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 078sj4; *> query: (?x2656, 0csdzz) <- currency(?x2656, ?x170), honored_for(?x1442, ?x2656), featured_film_locations(?x2656, ?x3634), nominated_for(?x2656, ?x385) *> conf = 0.08 ranks of expected_values: 10 EVAL 03qnc6q music 0csdzz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 119.000 52.000 0.143 http://example.org/film/film/music #19998-017yfz PRED entity: 017yfz PRED relation: nationality PRED expected values: 09c7w0 => 138 concepts (113 used for prediction) PRED predicted values (max 10 best out of 52): 09c7w0 (0.91 #1104, 0.89 #7129, 0.87 #7329), 059rby (0.26 #9842, 0.01 #5118), 01w65s (0.26 #9842), 03v0t (0.26 #9842), 07h34 (0.26 #9842), 02_286 (0.26 #9842), 02jx1 (0.23 #634, 0.21 #434, 0.18 #1236), 07ssc (0.16 #1518, 0.16 #1218, 0.15 #516), 0h7x (0.14 #235, 0.09 #335, 0.05 #1438), 0345h (0.13 #331, 0.13 #2538, 0.12 #1834) >> Best rule #1104 for best value: >> intensional similarity = 5 >> extensional distance = 52 >> proper extension: 04sx9_; 02qw2xb; >> query: (?x4142, 09c7w0) <- gender(?x4142, ?x231), location(?x4142, ?x14263), location(?x4142, ?x3014), ?x3014 = 01531, source(?x14263, ?x958) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017yfz nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 113.000 0.907 http://example.org/people/person/nationality #19997-02wtp6 PRED entity: 02wtp6 PRED relation: film_release_region PRED expected values: 0jgd 07ssc 015fr 0k6nt 01znc_ 03rj0 03h64 03spz => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 169): 0f8l9c (0.93 #1513, 0.93 #2112, 0.91 #616), 0k6nt (0.84 #1068, 0.84 #4214, 0.81 #172), 03h64 (0.83 #1107, 0.81 #510, 0.80 #3202), 07ssc (0.80 #1508, 0.80 #3154, 0.80 #2107), 03spz (0.78 #390, 0.72 #3231, 0.65 #539), 0jgd (0.78 #4194, 0.76 #1946, 0.76 #1048), 01znc_ (0.77 #1083, 0.72 #3178, 0.71 #4229), 015fr (0.76 #1060, 0.76 #3155, 0.75 #4206), 06bnz (0.74 #3182, 0.69 #341, 0.66 #1087), 0d060g (0.71 #901, 0.68 #4196, 0.68 #3145) >> Best rule #1513 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 0g5qs2k; 0gvrws1; 0b_5d; 0dgpwnk; 0gl02yg; 0421v9q; 0g57wgv; >> query: (?x11351, 0f8l9c) <- film_crew_role(?x11351, ?x137), film_release_region(?x11351, ?x1355), ?x1355 = 0h7x, nominated_for(?x9606, ?x11351) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1068 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 06wbm8q; 03q0r1; *> query: (?x11351, 0k6nt) <- film_crew_role(?x11351, ?x137), production_companies(?x11351, ?x617), film_release_region(?x11351, ?x774), ?x774 = 06mzp *> conf = 0.84 ranks of expected_values: 2, 3, 4, 5, 6, 7, 8, 12 EVAL 02wtp6 film_release_region 03spz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02wtp6 film_release_region 03h64 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02wtp6 film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 125.000 125.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02wtp6 film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02wtp6 film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02wtp6 film_release_region 015fr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02wtp6 film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02wtp6 film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19996-0272kv PRED entity: 0272kv PRED relation: place_of_death PRED expected values: 0k049 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 25): 030qb3t (0.15 #5459, 0.14 #6235, 0.14 #6623), 02_286 (0.09 #4479, 0.09 #6226, 0.08 #6614), 0k049 (0.08 #5440, 0.07 #4469, 0.07 #391), 0f2wj (0.07 #400, 0.05 #1176, 0.04 #1758), 06_kh (0.07 #393, 0.04 #5442, 0.03 #6606), 027l4q (0.07 #526, 0.01 #720, 0.01 #914), 0f04c (0.07 #431, 0.01 #625), 01c40n (0.07 #415, 0.01 #609), 0cc56 (0.07 #405, 0.01 #2541, 0.01 #3318), 01b8w_ (0.07 #515, 0.01 #903) >> Best rule #5459 for best value: >> intensional similarity = 3 >> extensional distance = 354 >> proper extension: 01v90t; 01m42d0; 013sg6; 01kgg9; 02jxsq; 02j4sk; 015076; 06w38l; >> query: (?x9363, 030qb3t) <- award(?x9363, ?x77), people(?x9771, ?x9363), award(?x303, ?x77) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #5440 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 354 *> proper extension: 01v90t; 01m42d0; 013sg6; 01kgg9; 02jxsq; 02j4sk; 015076; 06w38l; *> query: (?x9363, 0k049) <- award(?x9363, ?x77), people(?x9771, ?x9363), award(?x303, ?x77) *> conf = 0.08 ranks of expected_values: 3 EVAL 0272kv place_of_death 0k049 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 127.000 127.000 0.149 http://example.org/people/deceased_person/place_of_death #19995-01wxyx1 PRED entity: 01wxyx1 PRED relation: award PRED expected values: 07cbcy => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 285): 09qrn4 (0.50 #1050, 0.04 #10366, 0.04 #8746), 09sb52 (0.34 #2876, 0.33 #41, 0.33 #3281), 027dtxw (0.33 #4, 0.25 #409, 0.20 #1219), 0bdwqv (0.33 #173, 0.12 #1793, 0.08 #39059), 05pcn59 (0.31 #4132, 0.29 #4942, 0.26 #4537), 05p09zm (0.26 #3770, 0.24 #6200, 0.24 #2960), 0gqy2 (0.25 #570, 0.20 #1380, 0.11 #39051), 05ztrmj (0.25 #590, 0.16 #5045, 0.15 #3020), 01c427 (0.25 #895, 0.11 #2110, 0.10 #23173), 04ljl_l (0.25 #408, 0.10 #1623, 0.08 #8104) >> Best rule #1050 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 012gq6; >> query: (?x2108, 09qrn4) <- religion(?x2108, ?x1985), film(?x2108, ?x10191), ?x10191 = 0crd8q6 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #42938 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1743 *> proper extension: 0kcdl; *> query: (?x2108, ?x112) <- nominated_for(?x2108, ?x1448), nominated_for(?x112, ?x1448), genre(?x1448, ?x53) *> conf = 0.12 ranks of expected_values: 41 EVAL 01wxyx1 award 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 136.000 136.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19994-02pzck PRED entity: 02pzck PRED relation: location PRED expected values: 0dclg => 84 concepts (62 used for prediction) PRED predicted values (max 10 best out of 144): 030qb3t (0.24 #5713, 0.23 #83, 0.23 #4105), 059rby (0.23 #16, 0.12 #2429, 0.12 #1625), 02_286 (0.19 #2450, 0.19 #1646, 0.17 #15321), 01qh7 (0.15 #157, 0.12 #2570, 0.12 #1766), 0cc56 (0.15 #57, 0.12 #2470, 0.07 #6491), 0cb4j (0.12 #1639, 0.06 #2443, 0.03 #4856), 0r0m6 (0.08 #218, 0.07 #1023, 0.05 #5044), 01n7q (0.08 #63, 0.06 #7302, 0.06 #2476), 0k049 (0.08 #8, 0.06 #2421, 0.06 #1617), 01531 (0.08 #158, 0.06 #2571, 0.06 #1767) >> Best rule #5713 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 05m63c; 0m2wm; 01pw2f1; 0285c; 047hpm; 02xbw2; 039crh; 01pctb; 02pk6x; 01p47r; >> query: (?x10212, 030qb3t) <- film(?x10212, ?x638), participant(?x10212, ?x6066), people(?x1446, ?x10212), participant(?x10212, ?x8793) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #7356 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 158 *> proper extension: 02cg2v; *> query: (?x10212, 0dclg) <- nationality(?x10212, ?x94), ?x94 = 09c7w0, religion(?x10212, ?x1985), participant(?x6066, ?x10212) *> conf = 0.04 ranks of expected_values: 40 EVAL 02pzck location 0dclg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 84.000 62.000 0.243 http://example.org/people/person/places_lived./people/place_lived/location #19993-045xh PRED entity: 045xh PRED relation: category_of PRED expected values: 045xh => 61 concepts (54 used for prediction) PRED predicted values (max 10 best out of 38): 0c4ys (0.26 #1041, 0.25 #931, 0.24 #1109), 0grw_ (0.20 #26, 0.02 #43, 0.01 #351), 01b8bn (0.12 #95, 0.06 #250, 0.05 #293), 01tgwv (0.12 #77, 0.04 #341, 0.04 #365), 0gcf2r (0.11 #932, 0.10 #820, 0.09 #1019), 0g_w (0.07 #1020, 0.07 #799, 0.07 #998), 04jhhng (0.06 #237, 0.05 #324, 0.04 #346), 05x2s (0.05 #277, 0.04 #343, 0.03 #390), 01ppdy (0.04 #336, 0.04 #360, 0.03 #405), 02tzwd (0.04 #340, 0.03 #453, 0.03 #475) >> Best rule #1041 for best value: >> intensional similarity = 7 >> extensional distance = 319 >> proper extension: 02qrbbx; >> query: (?x12418, 0c4ys) <- award(?x9794, ?x12418), award(?x2934, ?x12418), category(?x9794, ?x134), nationality(?x9794, ?x512), ?x134 = 08mbj5d, gender(?x2934, ?x231), award_winner(?x11020, ?x2934) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #352 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 24 *> proper extension: 027x4ws; 0j6j8; 02tzwd; 0g9wd99; *> query: (?x12418, ?x1375) <- disciplines_or_subjects(?x12418, ?x1510), award(?x5087, ?x12418), disciplines_or_subjects(?x13751, ?x1510), award_nominee(?x5087, ?x8718), ?x13751 = 03mv9j, award(?x5087, ?x1375) *> conf = 0.02 ranks of expected_values: 19 EVAL 045xh category_of 045xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 61.000 54.000 0.262 http://example.org/award/award_category/category_of #19992-02vyyl8 PRED entity: 02vyyl8 PRED relation: film! PRED expected values: 07y8l9 => 93 concepts (50 used for prediction) PRED predicted values (max 10 best out of 978): 03v1w7 (0.29 #4165, 0.11 #22906, 0.11 #89530), 02k21g (0.20 #2874, 0.20 #792, 0.03 #7038), 0p__8 (0.20 #3138, 0.20 #1056, 0.03 #7302), 0bq2g (0.20 #2686, 0.20 #604, 0.03 #25591), 03hh89 (0.20 #3045, 0.20 #963, 0.03 #9291), 0f502 (0.20 #2842, 0.20 #760, 0.03 #21583), 01ggc9 (0.20 #3811, 0.20 #1729, 0.02 #43376), 063g7l (0.20 #3977, 0.20 #1895, 0.02 #37297), 07r1h (0.20 #3170, 0.20 #1088, 0.02 #88536), 06pj8 (0.20 #2427, 0.20 #345, 0.02 #17002) >> Best rule #4165 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02hxhz; >> query: (?x5602, ?x6369) <- executive_produced_by(?x5602, ?x8503), award(?x5602, ?x1691), ?x1691 = 05zvj3m, produced_by(?x5602, ?x6369) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #65520 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 378 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x5602, 07y8l9) <- titles(?x2480, ?x5602), titles(?x2480, ?x6752), ?x6752 = 065_cjc *> conf = 0.02 ranks of expected_values: 301 EVAL 02vyyl8 film! 07y8l9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 93.000 50.000 0.286 http://example.org/film/actor/film./film/performance/film #19991-0gqy2 PRED entity: 0gqy2 PRED relation: award! PRED expected values: 0qm98 => 54 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1330): 0cq806 (0.57 #2963, 0.57 #2805, 0.43 #3794), 042y1c (0.57 #2198, 0.50 #3187, 0.47 #4175), 09m6kg (0.57 #1988, 0.50 #999, 0.43 #2977), 0bmhn (0.57 #2880, 0.50 #1891, 0.29 #3869), 0cq8nx (0.57 #2859, 0.50 #1870, 0.29 #3848), 07xtqq (0.57 #2003, 0.43 #2992, 0.41 #4968), 0p_th (0.57 #2120, 0.40 #4097, 0.36 #3109), 0pd64 (0.57 #2725, 0.36 #3714, 0.33 #4702), 0c5dd (0.57 #2074, 0.29 #3063, 0.27 #4051), 0pv3x (0.50 #3066, 0.50 #1088, 0.47 #4054) >> Best rule #2963 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0f4x7; 0gr4k; 0gs9p; >> query: (?x3066, ?x8773) <- nominated_for(?x3066, ?x8773), nominated_for(?x3066, ?x6213), ?x6213 = 0jsf6, ?x8773 = 0cq806, award(?x92, ?x3066) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2106 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0f4x7; 0gr4k; 0gs9p; *> query: (?x3066, 0qm98) <- nominated_for(?x3066, ?x8773), nominated_for(?x3066, ?x6213), ?x6213 = 0jsf6, ?x8773 = 0cq806, award(?x92, ?x3066) *> conf = 0.43 ranks of expected_values: 30 EVAL 0gqy2 award! 0qm98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 54.000 27.000 0.571 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19990-0gs96 PRED entity: 0gs96 PRED relation: ceremony PRED expected values: 0ftlkg 0bzkgg 05qb8vx 0c4hgj => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 85): 0bzkgg (0.76 #797, 0.67 #627, 0.57 #457), 05qb8vx (0.76 #807, 0.57 #467, 0.56 #637), 0bzk2h (0.71 #801, 0.67 #631, 0.57 #461), 0fy59t (0.67 #665, 0.57 #495, 0.50 #240), 0bzk8w (0.67 #770, 0.56 #600, 0.50 #175), 0fz20l (0.62 #803, 0.56 #633, 0.43 #463), 0c4hgj (0.62 #824, 0.33 #654, 0.29 #739), 0gpjbt (0.61 #1552, 0.38 #2402, 0.34 #2743), 09n4nb (0.60 #1566, 0.36 #2416, 0.34 #2757), 0466p0j (0.59 #1583, 0.36 #2433, 0.33 #2774) >> Best rule #797 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x2222, 0bzkgg) <- ceremony(?x2222, ?x1747), award(?x771, ?x2222), ?x1747 = 0bzm81, category_of(?x2222, ?x3459) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 7, 24 EVAL 0gs96 ceremony 0c4hgj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 49.000 49.000 0.762 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gs96 ceremony 05qb8vx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.762 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gs96 ceremony 0bzkgg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.762 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gs96 ceremony 0ftlkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 49.000 49.000 0.762 http://example.org/award/award_category/winners./award/award_honor/ceremony #19989-0f1sm PRED entity: 0f1sm PRED relation: place PRED expected values: 0f1sm => 140 concepts (128 used for prediction) PRED predicted values (max 10 best out of 225): 0f1sm (0.18 #26292, 0.16 #34024, 0.04 #57236), 01m1_t (0.18 #26292, 0.01 #20622), 01x73 (0.16 #34024, 0.04 #57236), 0m2gk (0.09 #516, 0.05 #11854), 09c7w0 (0.04 #57236), 0c1d0 (0.04 #4638, 0.04 #7215, 0.03 #17524), 030qb3t (0.04 #4638, 0.04 #7215, 0.03 #17524), 0k9p4 (0.04 #4638, 0.04 #7215, 0.03 #17524), 0n1rj (0.04 #4638, 0.04 #7215, 0.03 #17524), 06wxw (0.04 #4638, 0.04 #7215, 0.03 #17524) >> Best rule #26292 for best value: >> intensional similarity = 3 >> extensional distance = 254 >> proper extension: 0288zy; 018mm4; 01q2sk; 020923; 02zd460; 01s7j5; 0fr9jp; 0k_mf; 02tz9z; 0194_r; ... >> query: (?x9445, ?x3163) <- contains(?x3164, ?x9445), adjoins(?x3443, ?x3164), county(?x3163, ?x3164) >> conf = 0.18 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0f1sm place 0f1sm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 128.000 0.175 http://example.org/location/hud_county_place/place #19988-052nd PRED entity: 052nd PRED relation: student PRED expected values: 0fqyzz => 132 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1656): 02m7r (0.16 #35455, 0.13 #50055, 0.13 #45884), 0d6d2 (0.15 #5582, 0.05 #7667, 0.03 #36867), 0ff3y (0.10 #12489, 0.10 #8317, 0.09 #14575), 0d3k14 (0.10 #8102, 0.09 #14360, 0.07 #12274), 05bnp0 (0.10 #6266, 0.08 #4181, 0.07 #10438), 01n1gc (0.10 #6866, 0.08 #4781, 0.07 #13124), 02hsgn (0.10 #7075, 0.08 #4990, 0.07 #13333), 01d494 (0.10 #6519, 0.08 #4434, 0.05 #8605), 01x6v6 (0.10 #7425, 0.08 #5340, 0.05 #9511), 0432cd (0.10 #7571, 0.08 #5486, 0.05 #9657) >> Best rule #35455 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 02zc7f; >> query: (?x481, ?x2397) <- company(?x2397, ?x481), student(?x481, ?x2319), category(?x481, ?x134) >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 052nd student 0fqyzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 122.000 0.162 http://example.org/education/educational_institution/students_graduates./education/education/student #19987-03x6w8 PRED entity: 03x6w8 PRED relation: colors PRED expected values: 038hg => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 18): 01g5v (0.52 #1080, 0.40 #653, 0.39 #687), 038hg (0.36 #103, 0.22 #223, 0.15 #326), 088fh (0.34 #279, 0.24 #1078, 0.17 #125), 01l849 (0.24 #1078, 0.13 #1216, 0.12 #1113), 03vtbc (0.24 #1078, 0.13 #1216, 0.12 #1113), 0jc_p (0.24 #1078, 0.13 #1216, 0.12 #1113), 06kqt3 (0.24 #1078, 0.13 #1216, 0.12 #1113), 036k5h (0.24 #1078, 0.13 #1216, 0.12 #1113), 067z2v (0.24 #1078, 0.13 #1216, 0.12 #1113), 03wkwg (0.24 #1078, 0.13 #1216, 0.12 #1113) >> Best rule #1080 for best value: >> intensional similarity = 23 >> extensional distance = 200 >> proper extension: 03d555l; 03k2hn; 024nj1; >> query: (?x8826, 01g5v) <- colors(?x8826, ?x1101), colors(?x12301, ?x1101), colors(?x9860, ?x1101), colors(?x5229, ?x1101), colors(?x3188, ?x1101), colors(?x2677, ?x1101), colors(?x13707, ?x1101), colors(?x13491, ?x1101), colors(?x9724, ?x1101), colors(?x8694, ?x1101), colors(?x8223, ?x1101), colors(?x7912, ?x1101), ?x8223 = 014xf6, ?x13707 = 024cg8, ?x7912 = 06b19, ?x3188 = 04k3r_, contains(?x512, ?x13491), sport(?x12301, ?x471), position(?x2677, ?x63), position(?x9860, ?x60), position(?x5229, ?x180), ?x8694 = 011xy1, registering_agency(?x9724, ?x1982) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #103 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 6 *> proper extension: 01gjlw; *> query: (?x8826, ?x663) <- team(?x203, ?x8826), team(?x63, ?x8826), team(?x60, ?x8826), position(?x8826, ?x530), ?x203 = 0dgrmp, ?x60 = 02nzb8, ?x63 = 02sdk9v, ?x530 = 02_j1w, current_club(?x4805, ?x8826), current_club(?x4805, ?x11153), current_club(?x4805, ?x8326), current_club(?x4805, ?x6391), ?x6391 = 0177gl, ?x8326 = 045xx, teams(?x10143, ?x11153), team(?x12598, ?x11153), sport(?x11153, ?x471), ?x471 = 02vx4, team(?x6430, ?x11153), colors(?x11153, ?x663) *> conf = 0.36 ranks of expected_values: 2 EVAL 03x6w8 colors 038hg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.525 http://example.org/sports/sports_team/colors #19986-05kjlr PRED entity: 05kjlr PRED relation: award_winner PRED expected values: 05fg2 0d__g => 45 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2401): 05fg2 (0.33 #258, 0.25 #5208, 0.25 #2732), 06pj8 (0.25 #7859, 0.23 #22705, 0.21 #27656), 07w21 (0.25 #12447, 0.23 #9972, 0.22 #14921), 018fq (0.25 #6105, 0.15 #2474, 0.08 #16002), 0gd5z (0.25 #5471, 0.15 #2474, 0.06 #27218), 05cv8 (0.25 #7140, 0.15 #2474, 0.03 #12088), 0210f1 (0.22 #13936, 0.19 #11461, 0.19 #16410), 0p__8 (0.20 #21130, 0.08 #26078, 0.07 #33502), 01ycbq (0.17 #20214, 0.13 #25162, 0.05 #22687), 015cbq (0.17 #9482, 0.15 #2474, 0.06 #11957) >> Best rule #258 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0blst_; >> query: (?x13257, 05fg2) <- award_winner(?x13257, ?x11596), award_winner(?x13257, ?x5254), ?x11596 = 0d_w7, influenced_by(?x5254, ?x7251), profession(?x5254, ?x353), student(?x6919, ?x5254), gender(?x5254, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 122 EVAL 05kjlr award_winner 0d__g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 45.000 21.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 05kjlr award_winner 05fg2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 21.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #19985-01b_lz PRED entity: 01b_lz PRED relation: genre PRED expected values: 0gs6m => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 75): 05p553 (0.50 #1465, 0.45 #897, 0.45 #1547), 01z4y (0.35 #1478, 0.35 #1642, 0.33 #1560), 0hcr (0.29 #180, 0.29 #99, 0.27 #504), 0dm00 (0.29 #232, 0.29 #151, 0.04 #720), 01jfsb (0.29 #92, 0.15 #254, 0.14 #335), 02n4kr (0.29 #89, 0.14 #170, 0.11 #413), 0c4xc (0.25 #1503, 0.23 #1749, 0.22 #1912), 01t_vv (0.21 #683, 0.19 #764, 0.19 #1169), 01htzx (0.19 #1559, 0.19 #1641, 0.18 #1886), 06n90 (0.19 #2697, 0.18 #498, 0.18 #2615) >> Best rule #1465 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 01j95; >> query: (?x3326, 05p553) <- genre(?x3326, ?x10647), genre(?x9031, ?x10647), award_winner(?x3326, ?x2589) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #278 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0g60z; 039fgy; 0kfv9; 0d66j2; 063ykwt; 030p35; 01fx1l; 0gvsh7l; 07g9f; 01cvtf; ... *> query: (?x3326, 0gs6m) <- award(?x3326, ?x686), actor(?x3326, ?x2582), ?x686 = 0bdw1g, nominated_for(?x2589, ?x3326) *> conf = 0.08 ranks of expected_values: 37 EVAL 01b_lz genre 0gs6m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 87.000 87.000 0.504 http://example.org/tv/tv_program/genre #19984-04kzqz PRED entity: 04kzqz PRED relation: film! PRED expected values: 0372kf 021npv => 65 concepts (15 used for prediction) PRED predicted values (max 10 best out of 606): 06chf (0.28 #16674, 0.27 #16675, 0.12 #20842), 02tn0_ (0.28 #16674, 0.27 #16675, 0.12 #20842), 0h0wc (0.18 #6676, 0.03 #15014), 02qgyv (0.17 #6636, 0.02 #14974), 0170s4 (0.15 #6650, 0.02 #21240), 01kwsg (0.14 #7092, 0.02 #21682, 0.01 #5009), 02k21g (0.09 #7047, 0.03 #15385), 01gbn6 (0.08 #7881), 014zcr (0.08 #14628, 0.02 #6290, 0.02 #18796), 06cgy (0.06 #14842, 0.03 #19010, 0.01 #6504) >> Best rule #16674 for best value: >> intensional similarity = 5 >> extensional distance = 109 >> proper extension: 07s8z_l; >> query: (?x2026, ?x2803) <- award_winner(?x2026, ?x9785), award_winner(?x2026, ?x2803), produced_by(?x638, ?x9785), producer_type(?x9785, ?x632), nominated_for(?x298, ?x638) >> conf = 0.28 => this is the best rule for 2 predicted values *> Best rule #7175 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 94 *> proper extension: 07kb7vh; *> query: (?x2026, 0372kf) <- film(?x1870, ?x2026), film_release_distribution_medium(?x2026, ?x81), award_nominee(?x1870, ?x3267), ?x3267 = 011_3s *> conf = 0.02 ranks of expected_values: 160 EVAL 04kzqz film! 021npv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 15.000 0.275 http://example.org/film/actor/film./film/performance/film EVAL 04kzqz film! 0372kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 65.000 15.000 0.275 http://example.org/film/actor/film./film/performance/film #19983-01vrx35 PRED entity: 01vrx35 PRED relation: category PRED expected values: 08mbj5d => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #26, 0.82 #42, 0.82 #34) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 195 >> proper extension: 07qnf; 01fmz6; 07bzp; 01kcms4; 07m4c; 02hzz; 012vm6; >> query: (?x7668, 08mbj5d) <- artist(?x382, ?x7668), artist(?x382, ?x8078), ?x8078 = 0134wr >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrx35 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.838 http://example.org/common/topic/webpage./common/webpage/category #19982-0mlxt PRED entity: 0mlxt PRED relation: source PRED expected values: 0jbk9 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #11, 0.93 #18, 0.92 #17) >> Best rule #11 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 0f6_4; 0n3dv; >> query: (?x11974, 0jbk9) <- county_seat(?x11974, ?x7957), place_of_birth(?x8036, ?x7957), time_zones(?x7957, ?x2950), contains(?x7957, ?x1087), profession(?x8036, ?x987) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mlxt source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.930 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #19981-0134s5 PRED entity: 0134s5 PRED relation: group! PRED expected values: 02hnl => 130 concepts (97 used for prediction) PRED predicted values (max 10 best out of 119): 02hnl (0.78 #3912, 0.77 #3646, 0.77 #1705), 05148p4 (0.76 #1695, 0.75 #1429, 0.72 #3018), 0l14md (0.63 #1683, 0.62 #3890, 0.62 #1417), 03qjg (0.39 #665, 0.38 #489, 0.38 #224), 05r5c (0.33 #272, 0.25 #1684, 0.25 #625), 01vj9c (0.28 #3630, 0.28 #4601, 0.25 #3896), 0l14qv (0.25 #1681, 0.24 #3888, 0.23 #4593), 013y1f (0.25 #644, 0.22 #291, 0.21 #468), 042v_gx (0.25 #185, 0.14 #1685, 0.11 #3626), 04rzd (0.21 #473, 0.19 #1708, 0.18 #384) >> Best rule #3912 for best value: >> intensional similarity = 5 >> extensional distance = 139 >> proper extension: 0qmpd; 06br6t; >> query: (?x3420, 02hnl) <- group(?x227, ?x3420), ?x227 = 0342h, artists(?x1572, ?x3420), artists(?x1572, ?x7084), ?x7084 = 01vs4ff >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0134s5 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 97.000 0.780 http://example.org/music/performance_role/regular_performances./music/group_membership/group #19980-0230rx PRED entity: 0230rx PRED relation: colors PRED expected values: 083jv => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 15): 083jv (0.90 #1337, 0.70 #1448, 0.69 #291), 06fvc (0.51 #1374, 0.50 #129, 0.40 #544), 019sc (0.50 #1323, 0.42 #132, 0.31 #1452), 0jc_p (0.25 #40, 0.23 #199, 0.18 #1390), 038hg (0.25 #65, 0.23 #199, 0.15 #570), 04d18d (0.25 #35, 0.20 #89, 0.18 #1390), 01l849 (0.18 #1372, 0.18 #1390, 0.14 #830), 036k5h (0.18 #1390, 0.09 #1746, 0.09 #1745), 06kqt3 (0.18 #1390, 0.09 #1746, 0.09 #1745), 03vtbc (0.18 #1390, 0.08 #836, 0.08 #278) >> Best rule #1337 for best value: >> intensional similarity = 9 >> extensional distance = 203 >> proper extension: 04088s0; 026xxv_; >> query: (?x12905, 083jv) <- sport(?x12905, ?x471), colors(?x12905, ?x3621), colors(?x10071, ?x3621), colors(?x7092, ?x3621), colors(?x3813, ?x3621), ?x10071 = 0gl6x, ?x7092 = 01g7_r, contains(?x94, ?x3813), institution(?x620, ?x3813) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0230rx colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.902 http://example.org/sports/sports_team/colors #19979-01t3h6 PRED entity: 01t3h6 PRED relation: time_zones PRED expected values: 02hcv8 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.40 #562, 0.40 #211, 0.36 #198), 02fqwt (0.33 #1, 0.22 #144, 0.21 #222), 02lcqs (0.22 #57, 0.21 #564, 0.21 #135), 02hczc (0.20 #67, 0.18 #80, 0.16 #963), 042g7t (0.16 #963, 0.15 #977, 0.15 #1017), 05jphn (0.16 #963, 0.15 #977, 0.15 #1017), 03bdv (0.08 #175, 0.08 #305, 0.07 #188), 02llzg (0.07 #446, 0.07 #706, 0.05 #615), 02lcrv (0.03 #137, 0.03 #150, 0.02 #163), 03plfd (0.01 #647, 0.01 #1248, 0.01 #712) >> Best rule #562 for best value: >> intensional similarity = 4 >> extensional distance = 234 >> proper extension: 06pwq; 0x335; >> query: (?x14707, 02hcv8) <- state(?x14707, ?x1905), state_province_region(?x1306, ?x1905), currency(?x1905, ?x2244), contains(?x1905, ?x1196) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01t3h6 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.403 http://example.org/location/location/time_zones #19978-01wk7b7 PRED entity: 01wk7b7 PRED relation: type_of_union PRED expected values: 04ztj => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.85 #157, 0.85 #117, 0.82 #109), 01g63y (0.38 #10, 0.36 #110, 0.35 #114), 0jgjn (0.20 #446, 0.19 #455, 0.19 #433), 01bl8s (0.20 #446, 0.19 #455, 0.19 #433) >> Best rule #157 for best value: >> intensional similarity = 2 >> extensional distance = 355 >> proper extension: 02756j; 08h79x; 02vkvcz; >> query: (?x2435, 04ztj) <- gender(?x2435, ?x231), spouse(?x2435, ?x9585) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wk7b7 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.849 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19977-018pj3 PRED entity: 018pj3 PRED relation: category PRED expected values: 08mbj5d => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #12, 0.83 #10, 0.82 #37) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 258 >> proper extension: 013pk3; >> query: (?x2575, 08mbj5d) <- nationality(?x2575, ?x94), award_winner(?x2054, ?x2575), artist(?x3265, ?x2575) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018pj3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.842 http://example.org/common/topic/webpage./common/webpage/category #19976-05c5z8j PRED entity: 05c5z8j PRED relation: genre PRED expected values: 05mrx8 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 88): 07s9rl0 (0.75 #239, 0.72 #955, 0.69 #596), 01z4y (0.52 #3103, 0.51 #4655, 0.50 #2626), 07ssc (0.52 #3103, 0.51 #4655, 0.50 #2626), 05mrx8 (0.40 #224, 0.33 #105, 0.03 #462), 02kdv5l (0.38 #479, 0.31 #838, 0.28 #3584), 02l7c8 (0.38 #373, 0.37 #611, 0.35 #731), 03k9fj (0.36 #487, 0.35 #846, 0.26 #1322), 04xvlr (0.35 #240, 0.28 #359, 0.24 #597), 01jfsb (0.34 #488, 0.30 #3593, 0.30 #1801), 0gf28 (0.33 #183, 0.33 #64, 0.06 #540) >> Best rule #239 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0gydcp7; 0gyy53; 0ggbfwf; >> query: (?x4329, 07s9rl0) <- production_companies(?x4329, ?x9518), ?x9518 = 0283xx2, nominated_for(?x6654, ?x4329) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #224 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 05_61y; *> query: (?x4329, 05mrx8) <- nominated_for(?x1180, ?x4329), genre(?x4329, ?x7217), ?x7217 = 0cshrf *> conf = 0.40 ranks of expected_values: 4 EVAL 05c5z8j genre 05mrx8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 62.000 62.000 0.750 http://example.org/film/film/genre #19975-0p9rz PRED entity: 0p9rz PRED relation: genre PRED expected values: 07s9rl0 => 84 concepts (61 used for prediction) PRED predicted values (max 10 best out of 108): 07s9rl0 (0.97 #5251, 0.94 #5489, 0.85 #4775), 05p553 (0.67 #3584, 0.41 #1912, 0.41 #841), 07ssc (0.51 #3818, 0.48 #6682, 0.48 #6681), 04xvlr (0.50 #2, 0.39 #240, 0.27 #957), 03k9fj (0.39 #3473, 0.33 #1682, 0.29 #13), 082gq (0.36 #31, 0.19 #269, 0.19 #2416), 02kdv5l (0.32 #1672, 0.29 #2868, 0.28 #1434), 01jfsb (0.30 #2998, 0.29 #1683, 0.28 #5383), 06cvj (0.25 #2506, 0.25 #840, 0.25 #1911), 06n90 (0.25 #2506, 0.21 #1684, 0.13 #2999) >> Best rule #5251 for best value: >> intensional similarity = 5 >> extensional distance = 1038 >> proper extension: 0fq27fp; >> query: (?x9261, 07s9rl0) <- genre(?x9261, ?x4757), genre(?x6352, ?x4757), genre(?x3514, ?x4757), ?x3514 = 04vh83, ?x6352 = 08mg_b >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p9rz genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 61.000 0.967 http://example.org/film/film/genre #19974-01zfzb PRED entity: 01zfzb PRED relation: language PRED expected values: 02h40lc => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 33): 02h40lc (0.92 #238, 0.92 #179, 0.91 #297), 064_8sq (0.14 #971, 0.14 #317, 0.14 #613), 04306rv (0.14 #123, 0.13 #182, 0.12 #241), 06nm1 (0.14 #129, 0.13 #306, 0.12 #366), 03_9r (0.12 #69, 0.06 #1916, 0.06 #365), 06b_j (0.09 #496, 0.08 #141, 0.08 #437), 0653m (0.08 #367, 0.04 #130, 0.04 #426), 02bjrlw (0.08 #770, 0.07 #1069, 0.07 #474), 012w70 (0.06 #131, 0.05 #368, 0.04 #427), 0jzc (0.04 #493, 0.04 #434, 0.03 #789) >> Best rule #238 for best value: >> intensional similarity = 4 >> extensional distance = 135 >> proper extension: 0c5qvw; >> query: (?x5320, 02h40lc) <- music(?x5320, ?x8013), award(?x5320, ?x1691), genre(?x5320, ?x53), cinematography(?x5320, ?x7249) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01zfzb language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.920 http://example.org/film/film/language #19973-02j_j0 PRED entity: 02j_j0 PRED relation: production_companies! PRED expected values: 01cz7r => 144 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1841): 0bbw2z6 (0.50 #525, 0.07 #6095, 0.05 #11665), 0830vk (0.25 #390, 0.15 #13758, 0.12 #7074), 03clwtw (0.25 #770, 0.15 #26393, 0.14 #28621), 0fqt1ns (0.25 #506, 0.14 #6076, 0.11 #11646), 047vnkj (0.25 #577, 0.14 #6147, 0.11 #11717), 09g8vhw (0.25 #217, 0.13 #14699, 0.12 #25840), 06_wqk4 (0.25 #92, 0.13 #14574, 0.12 #25715), 03wbqc4 (0.25 #470, 0.10 #13838, 0.09 #26093), 0bq8tmw (0.25 #171, 0.10 #13539, 0.09 #25794), 047d21r (0.25 #399, 0.10 #13767, 0.09 #26022) >> Best rule #525 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03sb38; >> query: (?x6554, 0bbw2z6) <- production_companies(?x6009, ?x6554), production_companies(?x3430, ?x6554), genre(?x6009, ?x53), ?x3430 = 0ctb4g >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #30899 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 0xbm; 09glbnt; 0gjv_; 026m3y; 041sbd; 0bvz6; *> query: (?x6554, 01cz7r) <- citytown(?x6554, ?x10852), citytown(?x6554, ?x362), ?x362 = 04jpl, contains(?x512, ?x10852) *> conf = 0.03 ranks of expected_values: 996 EVAL 02j_j0 production_companies! 01cz7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 144.000 43.000 0.500 http://example.org/film/film/production_companies #19972-03nqnnk PRED entity: 03nqnnk PRED relation: film! PRED expected values: 03xsby => 85 concepts (64 used for prediction) PRED predicted values (max 10 best out of 76): 05qd_ (0.40 #310, 0.35 #460, 0.29 #385), 017s11 (0.40 #229, 0.25 #78, 0.25 #3), 086k8 (0.25 #2, 0.20 #228, 0.20 #152), 03xq0f (0.25 #5, 0.20 #231, 0.12 #1281), 024rgt (0.25 #20, 0.20 #246, 0.10 #546), 016tw3 (0.20 #161, 0.17 #687, 0.17 #612), 0fqy4p (0.20 #329, 0.14 #404, 0.08 #854), 020h2v (0.14 #421, 0.10 #571, 0.06 #1096), 054g1r (0.13 #1086, 0.07 #1688, 0.06 #1311), 016tt2 (0.12 #1430, 0.11 #2032, 0.11 #2407) >> Best rule #310 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03h_yy; 06r2h; >> query: (?x5929, 05qd_) <- film_release_distribution_medium(?x5929, ?x81), film(?x1343, ?x5929), ?x1343 = 030znt, produced_by(?x5929, ?x7617) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1367 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 053tj7; *> query: (?x5929, 03xsby) <- film_release_distribution_medium(?x5929, ?x81), person(?x5929, ?x540), ?x81 = 029j_, language(?x5929, ?x254) *> conf = 0.10 ranks of expected_values: 14 EVAL 03nqnnk film! 03xsby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 85.000 64.000 0.400 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #19971-0c0cs PRED entity: 0c0cs PRED relation: category PRED expected values: 08mbj5d => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #46, 0.83 #45, 0.79 #80) >> Best rule #46 for best value: >> intensional similarity = 7 >> extensional distance = 27 >> proper extension: 065y4w7; 043g7l; 0gl5_; >> query: (?x14381, ?x134) <- company(?x12453, ?x14381), profession(?x12453, ?x967), company(?x12453, ?x14028), religion(?x12453, ?x2591), award_nominee(?x12453, ?x4196), gender(?x12453, ?x514), category(?x14028, ?x134) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c0cs category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.828 http://example.org/common/topic/webpage./common/webpage/category #19970-0k3p PRED entity: 0k3p PRED relation: film_release_region! PRED expected values: 043tvp3 => 324 concepts (198 used for prediction) PRED predicted values (max 10 best out of 1328): 017jd9 (0.86 #60272, 0.83 #20488, 0.75 #11206), 0bpm4yw (0.83 #20441, 0.81 #32376, 0.76 #60225), 05qbckf (0.83 #20132, 0.81 #32067, 0.75 #10850), 08hmch (0.83 #20011, 0.81 #59795, 0.75 #10729), 0dzlbx (0.83 #20546, 0.76 #60330, 0.75 #11264), 0jjy0 (0.83 #20021, 0.76 #59805, 0.75 #10739), 02vxq9m (0.83 #19909, 0.76 #59693, 0.66 #92844), 01jrbb (0.83 #20252, 0.76 #60036, 0.56 #32187), 05p1tzf (0.83 #19951, 0.75 #10669, 0.71 #59735), 04w7rn (0.83 #20074, 0.75 #10792, 0.71 #59858) >> Best rule #60272 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 06bnz; >> query: (?x8252, 017jd9) <- contains(?x3407, ?x8252), location_of_ceremony(?x566, ?x8252), film_release_region(?x2350, ?x8252), ?x2350 = 0661m4p >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #60602 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 06bnz; *> query: (?x8252, 043tvp3) <- contains(?x3407, ?x8252), location_of_ceremony(?x566, ?x8252), film_release_region(?x2350, ?x8252), ?x2350 = 0661m4p *> conf = 0.81 ranks of expected_values: 20 EVAL 0k3p film_release_region! 043tvp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 324.000 198.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19969-02ptzz0 PRED entity: 02ptzz0 PRED relation: team! PRED expected values: 0cymln => 163 concepts (156 used for prediction) PRED predicted values (max 10 best out of 114): 012xdf (0.33 #1519, 0.33 #621, 0.30 #1745), 04g9sq (0.33 #670, 0.30 #1794, 0.29 #3033), 049sb (0.33 #97, 0.25 #1330, 0.20 #321), 02cg2v (0.30 #1795, 0.15 #4730, 0.12 #1344), 0hcs3 (0.22 #1441, 0.17 #7653, 0.15 #4038), 0d3f83 (0.22 #1410, 0.10 #3894, 0.09 #5701), 03n69x (0.21 #4979, 0.20 #3963, 0.16 #6337), 019g65 (0.21 #2776, 0.16 #3568, 0.13 #6395), 02_nkp (0.20 #1785, 0.12 #1334, 0.09 #2009), 02lm0t (0.17 #664, 0.15 #2577, 0.12 #3251) >> Best rule #1519 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 0jm6n; 0jm4b; >> query: (?x3798, 012xdf) <- teams(?x5837, ?x3798), sport(?x3798, ?x12913), location(?x156, ?x5837), colors(?x3798, ?x4557), position(?x3798, ?x1579), team(?x9266, ?x3798), category(?x5837, ?x134) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2548 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 0jml5; *> query: (?x3798, 0cymln) <- team(?x6848, ?x3798), team(?x9266, ?x3798), ?x6848 = 02_ssl, position(?x3798, ?x5755), nationality(?x9266, ?x94), ?x5755 = 0355dz *> conf = 0.15 ranks of expected_values: 14 EVAL 02ptzz0 team! 0cymln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 163.000 156.000 0.333 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #19968-03plfd PRED entity: 03plfd PRED relation: time_zones! PRED expected values: 04gzd 03khn 0lxg6 => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 1425): 0d060g (0.75 #6164, 0.57 #3705, 0.50 #4931), 09c7w0 (0.71 #3696, 0.62 #4922, 0.50 #6155), 0d0vqn (0.50 #2457, 0.33 #1244, 0.33 #1225), 05b4w (0.50 #2457, 0.33 #1362, 0.33 #1225), 04pnx (0.43 #4141, 0.38 #6600, 0.38 #5367), 0hzgf (0.39 #2458, 0.08 #7379), 01g6l8 (0.39 #2458, 0.08 #7379), 04_1l0v (0.38 #6644, 0.29 #4185, 0.25 #5411), 059j2 (0.33 #2524, 0.33 #1295, 0.30 #2460), 03rjj (0.33 #1239, 0.30 #2460, 0.17 #3687) >> Best rule #6164 for best value: >> intensional similarity = 53 >> extensional distance = 6 >> proper extension: 02lcqs; 05jphn; >> query: (?x10735, 0d060g) <- time_zones(?x1892, ?x10735), combatants(?x1892, ?x1003), film_release_region(?x11809, ?x1892), film_release_region(?x9501, ?x1892), film_release_region(?x8292, ?x1892), film_release_region(?x8258, ?x1892), film_release_region(?x8193, ?x1892), film_release_region(?x7126, ?x1892), film_release_region(?x6014, ?x1892), film_release_region(?x5992, ?x1892), film_release_region(?x5347, ?x1892), film_release_region(?x4950, ?x1892), film_release_region(?x4040, ?x1892), film_release_region(?x2501, ?x1892), film_release_region(?x1602, ?x1892), film_release_region(?x1259, ?x1892), film_release_region(?x1002, ?x1892), film_release_region(?x504, ?x1892), film_release_region(?x385, ?x1892), film_release_region(?x124, ?x1892), ?x7126 = 0ds1glg, ?x8292 = 0cmf0m0, country(?x6014, ?x2346), film(?x1864, ?x6014), film_release_region(?x6014, ?x1453), ?x1453 = 06qd3, music(?x4040, ?x1715), language(?x6014, ?x2890), ?x5992 = 0g5q34q, ?x4950 = 07k2mq, ?x2501 = 040rmy, olympics(?x1892, ?x391), country(?x3309, ?x1892), nominated_for(?x4353, ?x8258), nominated_for(?x68, ?x4040), ?x8193 = 03z9585, film_release_region(?x4040, ?x311), ?x3309 = 09w1n, olympics(?x1892, ?x452), ?x124 = 0g56t9t, ?x5347 = 02ylg6, countries_spoken_in(?x4442, ?x1892), featured_film_locations(?x4040, ?x739), ?x504 = 0g5qs2k, ?x1602 = 0gxtknx, ?x1259 = 04hwbq, films(?x4450, ?x8258), film_regional_debut_venue(?x11809, ?x13344), ?x9501 = 0g5qmbz, contains(?x1892, ?x7818), ?x385 = 0ds3t5x, ?x311 = 0j1z8, ?x1002 = 0_b3d >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3690 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 4 *> proper extension: 0gsrz4; *> query: (?x10735, ?x279) <- time_zones(?x1892, ?x10735), contains(?x455, ?x1892), film_release_region(?x8471, ?x1892), film_release_region(?x6376, ?x1892), film_release_region(?x5827, ?x1892), film_release_region(?x5070, ?x1892), film_release_region(?x4684, ?x1892), film_release_region(?x3745, ?x1892), film_release_region(?x1263, ?x1892), country(?x2266, ?x1892), country(?x520, ?x1892), film_release_region(?x3745, ?x2316), film_release_region(?x3745, ?x774), film_release_region(?x3745, ?x205), ?x2266 = 01lb14, nominated_for(?x198, ?x3745), administrative_parent(?x1892, ?x551), olympics(?x1892, ?x784), nominated_for(?x163, ?x3745), award(?x5070, ?x834), ?x774 = 06mzp, olympics(?x1892, ?x452), ?x784 = 018ctl, genre(?x5827, ?x53), film(?x3580, ?x5827), film_release_region(?x8471, ?x4059), film_release_region(?x8471, ?x279), organization(?x1892, ?x127), ?x4059 = 077qn, ?x2316 = 06t2t, film_crew_role(?x5070, ?x137), ?x205 = 03rjj, film(?x609, ?x4684), nominated_for(?x451, ?x1263), film_crew_role(?x4684, ?x1171), film(?x1414, ?x8471), production_companies(?x5070, ?x574), film(?x981, ?x1263), sports(?x1741, ?x520), featured_film_locations(?x8471, ?x2474), film_format(?x6376, ?x6392) *> conf = 0.09 ranks of expected_values: 1273, 1352 EVAL 03plfd time_zones! 0lxg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.750 http://example.org/location/location/time_zones EVAL 03plfd time_zones! 03khn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.750 http://example.org/location/location/time_zones EVAL 03plfd time_zones! 04gzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.750 http://example.org/location/location/time_zones #19967-05cqhl PRED entity: 05cqhl PRED relation: nationality PRED expected values: 09c7w0 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 21): 09c7w0 (0.84 #2103, 0.84 #3103, 0.83 #2503), 0f4zv (0.27 #11515), 059rby (0.27 #11515), 02jx1 (0.11 #5638, 0.09 #10345, 0.09 #12048), 07ssc (0.08 #4317, 0.08 #9827, 0.08 #12030), 03rk0 (0.05 #11561, 0.05 #11761, 0.05 #11661), 0d060g (0.05 #4809, 0.04 #6912, 0.04 #10919), 0ctw_b (0.04 #429, 0.04 #529, 0.03 #929), 0b90_r (0.03 #103, 0.02 #204, 0.02 #305), 0345h (0.02 #1633, 0.02 #12147, 0.02 #12447) >> Best rule #2103 for best value: >> intensional similarity = 3 >> extensional distance = 119 >> proper extension: 0bgrsl; >> query: (?x9571, 09c7w0) <- award_winner(?x7116, ?x9571), award_winner(?x3018, ?x9571), producer_type(?x9571, ?x632) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05cqhl nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.843 http://example.org/people/person/nationality #19966-01s695 PRED entity: 01s695 PRED relation: award_winner PRED expected values: 05pdbs 086qd 02fn5r 0137g1 0z05l 02f1c 01f2q5 => 31 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1977): 02fn5r (0.67 #15335, 0.62 #21320, 0.57 #19824), 02qlg7s (0.67 #13811, 0.40 #10815, 0.38 #21292), 0gcs9 (0.62 #21382, 0.60 #9409, 0.60 #7914), 01vw20h (0.60 #23126, 0.60 #9657, 0.57 #20134), 02l840 (0.60 #9079, 0.57 #19556, 0.50 #6089), 016srn (0.60 #7940, 0.48 #11976, 0.40 #22904), 01dwrc (0.60 #8362, 0.40 #9857, 0.30 #23326), 01wwvc5 (0.57 #18345, 0.33 #16849, 0.33 #15352), 02cx90 (0.55 #24595, 0.50 #23095, 0.50 #12618), 032nwy (0.50 #16521, 0.40 #22505, 0.40 #10532) >> Best rule #15335 for best value: >> intensional similarity = 23 >> extensional distance = 4 >> proper extension: 0jzphpx; 01mh_q; >> query: (?x342, 02fn5r) <- award_winner(?x342, ?x5391), award_winner(?x342, ?x2169), award_winner(?x342, ?x367), ceremony(?x12833, ?x342), ceremony(?x12813, ?x342), ceremony(?x4796, ?x342), ceremony(?x3978, ?x342), ceremony(?x3094, ?x342), ceremony(?x2324, ?x342), ?x3978 = 03t5b6, ?x2169 = 01w60_p, ?x3094 = 026mff, ?x12833 = 0257pw, ?x4796 = 01c99j, award_nominee(?x367, ?x366), role(?x367, ?x432), artist(?x2299, ?x367), award_winner(?x2324, ?x1373), ceremony(?x2324, ?x9431), award(?x5391, ?x7005), ?x12813 = 01c9d1, ?x9431 = 02cg41, artists(?x302, ?x5391) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 32, 42, 266, 267, 326, 352 EVAL 01s695 award_winner 01f2q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 31.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01s695 award_winner 02f1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 31.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01s695 award_winner 0z05l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 31.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01s695 award_winner 0137g1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01s695 award_winner 02fn5r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01s695 award_winner 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01s695 award_winner 05pdbs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 31.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19965-015cqh PRED entity: 015cqh PRED relation: artist! PRED expected values: 03gfvsz => 93 concepts (65 used for prediction) PRED predicted values (max 10 best out of 6): 03gfvsz (0.27 #79, 0.20 #213, 0.17 #85), 04y652m (0.15 #59, 0.12 #72, 0.11 #28), 04rqd (0.12 #160, 0.11 #117, 0.09 #198), 01fjfv (0.12 #157, 0.11 #114, 0.09 #195), 04f73rc (0.01 #118), 0jrv_ (0.01 #115) >> Best rule #79 for best value: >> intensional similarity = 7 >> extensional distance = 31 >> proper extension: 03k3b; >> query: (?x8335, 03gfvsz) <- artist(?x3265, ?x8335), group(?x1166, ?x8335), group(?x716, ?x8335), artists(?x1000, ?x8335), ?x1166 = 05148p4, ?x716 = 018vs, ?x1000 = 0xhtw >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015cqh artist! 03gfvsz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 65.000 0.273 http://example.org/broadcast/content/artist #19964-0lx2l PRED entity: 0lx2l PRED relation: vacationer! PRED expected values: 03gh4 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 104): 03gh4 (0.22 #2818, 0.19 #329, 0.18 #1074), 05qtj (0.19 #320, 0.15 #2809, 0.14 #941), 0cv3w (0.19 #305, 0.11 #2794, 0.11 #2170), 0160w (0.14 #126, 0.11 #747, 0.10 #375), 0f2v0 (0.14 #186, 0.10 #435, 0.08 #1306), 06c62 (0.14 #210, 0.10 #459, 0.06 #335), 02_286 (0.12 #263, 0.07 #138, 0.06 #759), 0r0m6 (0.12 #317, 0.06 #2182, 0.05 #2431), 0b90_r (0.11 #2741, 0.09 #1247, 0.09 #1495), 0d1qn (0.10 #428, 0.08 #55, 0.07 #179) >> Best rule #2818 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 02l840; >> query: (?x2534, 03gh4) <- award_nominee(?x722, ?x2534), participant(?x1817, ?x2534), vacationer(?x279, ?x2534) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lx2l vacationer! 03gh4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.222 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #19963-0lzb8 PRED entity: 0lzb8 PRED relation: award PRED expected values: 02tzwd => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 316): 09sb52 (0.32 #16199, 0.32 #15795, 0.31 #21855), 0bfvd4 (0.30 #1730, 0.25 #922, 0.20 #1326), 01by1l (0.28 #3343, 0.22 #12635, 0.20 #14655), 03qbh5 (0.28 #3437, 0.14 #12729, 0.13 #14749), 0fbtbt (0.27 #6696, 0.16 #4272, 0.13 #5888), 0ck27z (0.26 #11807, 0.26 #13423, 0.25 #14231), 0cjyzs (0.26 #6569, 0.17 #5761, 0.16 #2529), 0cqhk0 (0.25 #843, 0.25 #439, 0.16 #11751), 0789_m (0.25 #827, 0.20 #1635, 0.20 #1231), 0cqh46 (0.25 #858, 0.20 #1666, 0.20 #1262) >> Best rule #16199 for best value: >> intensional similarity = 4 >> extensional distance = 538 >> proper extension: 0dh73w; 03bdm4; >> query: (?x593, 09sb52) <- film(?x593, ?x66), student(?x735, ?x593), award_winner(?x594, ?x593), nominated_for(?x593, ?x623) >> conf = 0.32 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0lzb8 award 02tzwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 120.000 0.322 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19962-016clz PRED entity: 016clz PRED relation: parent_genre! PRED expected values: 0dls3 01738f => 68 concepts (51 used for prediction) PRED predicted values (max 10 best out of 309): 01ym9b (0.50 #2548, 0.33 #36, 0.31 #6074), 059kh (0.38 #4315, 0.33 #1295, 0.33 #541), 0pm85 (0.38 #4397, 0.33 #4900, 0.33 #1377), 0dls3 (0.38 #4317, 0.33 #1297, 0.25 #4065), 0621cs (0.38 #4404, 0.33 #1384, 0.25 #4152), 0y2tr (0.38 #4500, 0.33 #1480, 0.25 #4248), 08cg36 (0.38 #4504, 0.33 #1484, 0.25 #4252), 01b4p4 (0.38 #4429, 0.25 #4177, 0.14 #2918), 0y3_8 (0.33 #1293, 0.33 #539, 0.33 #37), 01cbwl (0.33 #4813, 0.33 #536, 0.33 #34) >> Best rule #2548 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0m0jc; 02x8m; >> query: (?x302, 01ym9b) <- artists(?x302, ?x5478), artists(?x302, ?x3160), artists(?x302, ?x1412), ?x3160 = 01w806h, ?x5478 = 01yzl2, group(?x227, ?x1412) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4317 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 01h0kx; *> query: (?x302, 0dls3) <- parent_genre(?x9831, ?x302), parent_genre(?x7577, ?x302), parent_genre(?x302, ?x1572), ?x7577 = 0bt7w, artists(?x9831, ?x475) *> conf = 0.38 ranks of expected_values: 4, 20 EVAL 016clz parent_genre! 01738f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 68.000 51.000 0.500 http://example.org/music/genre/parent_genre EVAL 016clz parent_genre! 0dls3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 51.000 0.500 http://example.org/music/genre/parent_genre #19961-07t21 PRED entity: 07t21 PRED relation: form_of_government PRED expected values: 06cx9 => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 4): 06cx9 (0.40 #285, 0.39 #373, 0.39 #401), 01d9r3 (0.39 #231, 0.37 #287, 0.34 #263), 01q20 (0.33 #158, 0.33 #86, 0.32 #402), 026wp (0.12 #4, 0.09 #144, 0.09 #68) >> Best rule #285 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 0n3g; >> query: (?x1471, 06cx9) <- adjoins(?x456, ?x1471), form_of_government(?x1471, ?x1926), contains(?x455, ?x1471) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07t21 form_of_government 06cx9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 170.000 0.397 http://example.org/location/country/form_of_government #19960-0661ql3 PRED entity: 0661ql3 PRED relation: film_crew_role PRED expected values: 02ynfr => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 21): 01pvkk (0.33 #367, 0.32 #577, 0.32 #517), 02ynfr (0.23 #371, 0.21 #581, 0.21 #401), 0d2b38 (0.20 #381, 0.20 #531, 0.20 #411), 015h31 (0.20 #366, 0.20 #396, 0.19 #516), 0215hd (0.20 #374, 0.19 #404, 0.18 #524), 089g0h (0.19 #375, 0.17 #525, 0.16 #405), 01xy5l_ (0.19 #69, 0.17 #99, 0.17 #369), 02_n3z (0.11 #361, 0.10 #511, 0.10 #391), 033smt (0.10 #533, 0.10 #413, 0.09 #383), 04pyp5 (0.08 #642, 0.07 #372, 0.07 #1398) >> Best rule #367 for best value: >> intensional similarity = 5 >> extensional distance = 178 >> proper extension: 05dy7p; >> query: (?x2394, 01pvkk) <- film_crew_role(?x2394, ?x2154), film_crew_role(?x2394, ?x1171), ?x2154 = 01vx2h, nominated_for(?x748, ?x2394), ?x1171 = 09vw2b7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #371 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 178 *> proper extension: 05dy7p; *> query: (?x2394, 02ynfr) <- film_crew_role(?x2394, ?x2154), film_crew_role(?x2394, ?x1171), ?x2154 = 01vx2h, nominated_for(?x748, ?x2394), ?x1171 = 09vw2b7 *> conf = 0.23 ranks of expected_values: 2 EVAL 0661ql3 film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.328 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19959-0137n0 PRED entity: 0137n0 PRED relation: award PRED expected values: 01dpdh => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 259): 01dpdh (0.78 #1589, 0.77 #7942, 0.72 #34953), 026mff (0.78 #1589, 0.77 #7942, 0.72 #34953), 01by1l (0.37 #1301, 0.30 #7257, 0.30 #7654), 09sb52 (0.34 #18702, 0.32 #20291, 0.26 #22678), 054ks3 (0.30 #535, 0.20 #932, 0.18 #2124), 0c4z8 (0.28 #1262, 0.24 #2057, 0.23 #1660), 026mg3 (0.25 #12, 0.15 #30583, 0.11 #20648), 01ck6h (0.22 #517, 0.17 #1311, 0.16 #2106), 01c92g (0.22 #1286, 0.21 #2081, 0.20 #1684), 02x17c2 (0.19 #610, 0.15 #1007, 0.14 #2199) >> Best rule #1589 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 01vw87c; 0kzy0; 02whj; 0gcs9; 024dgj; 0lzkm; 017vkx; 01vtqml; 016z1t; 01vsy3q; ... >> query: (?x1270, ?x2420) <- gender(?x1270, ?x231), award_winner(?x2420, ?x1270), group(?x1270, ?x1271) >> conf = 0.78 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0137n0 award 01dpdh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19958-0h10vt PRED entity: 0h10vt PRED relation: profession PRED expected values: 02hrh1q => 88 concepts (87 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.90 #462, 0.88 #1803, 0.88 #4188), 01d_h8 (0.34 #2688, 0.33 #304, 0.33 #1496), 0dxtg (0.29 #2100, 0.28 #908, 0.28 #4024), 03gjzk (0.28 #4024, 0.26 #761, 0.24 #2102), 02jknp (0.28 #4024, 0.24 #2690, 0.22 #3584), 09jwl (0.28 #4024, 0.20 #765, 0.17 #616), 0cbd2 (0.28 #4024, 0.17 #7, 0.15 #8203), 0np9r (0.28 #4024, 0.15 #4195, 0.14 #8963), 018gz8 (0.28 #4024, 0.14 #1806, 0.13 #316), 0n1h (0.28 #4024, 0.06 #1353, 0.05 #6569) >> Best rule #462 for best value: >> intensional similarity = 2 >> extensional distance = 155 >> proper extension: 017b2p; 05vzql; 040nwr; >> query: (?x9561, 02hrh1q) <- profession(?x9561, ?x4773), ?x4773 = 0d1pc >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h10vt profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 87.000 0.898 http://example.org/people/person/profession #19957-0830vk PRED entity: 0830vk PRED relation: category PRED expected values: 08mbj5d => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.30 #2, 0.28 #3, 0.28 #1) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 047svrl; >> query: (?x3601, 08mbj5d) <- film(?x4832, ?x3601), film(?x398, ?x3601), film_release_distribution_medium(?x3601, ?x81), award_winner(?x591, ?x398) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0830vk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.298 http://example.org/common/topic/webpage./common/webpage/category #19956-0837ql PRED entity: 0837ql PRED relation: award_nominee PRED expected values: 01wlt3k => 98 concepts (50 used for prediction) PRED predicted values (max 10 best out of 734): 01wgxtl (0.81 #109630, 0.81 #83972, 0.80 #109631), 0dt1cm (0.81 #109630, 0.81 #83972, 0.80 #109631), 02vwckw (0.81 #109630, 0.81 #83972, 0.80 #109631), 01wmxfs (0.81 #109630, 0.81 #83972, 0.80 #44319), 026yqrr (0.81 #109630, 0.81 #83972, 0.80 #44319), 01wlt3k (0.70 #4561, 0.36 #6894, 0.33 #2229), 0837ql (0.50 #3470, 0.36 #5803, 0.33 #1138), 06mt91 (0.36 #6219, 0.33 #1554, 0.30 #3886), 01vsgrn (0.33 #1304, 0.30 #3636, 0.29 #5969), 01w9k25 (0.33 #2119, 0.29 #6784, 0.20 #4451) >> Best rule #109630 for best value: >> intensional similarity = 4 >> extensional distance = 753 >> proper extension: 0280mv7; >> query: (?x4836, ?x827) <- award_nominee(?x8200, ?x4836), award_nominee(?x827, ?x4836), artists(?x283, ?x8200), gender(?x8200, ?x231) >> conf = 0.81 => this is the best rule for 5 predicted values *> Best rule #4561 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 026yqrr; *> query: (?x4836, 01wlt3k) <- award_nominee(?x4836, ?x4475), award_nominee(?x4836, ?x2227), ?x4475 = 01ws9n6, participant(?x6835, ?x2227) *> conf = 0.70 ranks of expected_values: 6 EVAL 0837ql award_nominee 01wlt3k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 98.000 50.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19955-0tj9 PRED entity: 0tj9 PRED relation: languages PRED expected values: 03k50 => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 22): 03k50 (0.40 #79, 0.36 #345, 0.33 #117), 07c9s (0.17 #126, 0.12 #697, 0.12 #1271), 01c7y (0.14 #182, 0.05 #715, 0.04 #410), 02hxcvy (0.09 #2554, 0.06 #4153, 0.06 #3658), 0688f (0.09 #2554, 0.06 #4153, 0.06 #3658), 064_8sq (0.09 #1921, 0.08 #280, 0.08 #2225), 0999q (0.06 #1281, 0.06 #707, 0.04 #745), 09s02 (0.06 #758, 0.04 #1294, 0.04 #377), 09bnf (0.05 #723, 0.04 #1297, 0.04 #761), 06mp7 (0.05 #314, 0.02 #1154, 0.01 #542) >> Best rule #79 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01n8_g; >> query: (?x12927, 03k50) <- film(?x12927, ?x657), award_winner(?x4687, ?x12927), special_performance_type(?x12927, ?x4832), ?x4687 = 03rbj2 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tj9 languages 03k50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.400 http://example.org/people/person/languages #19954-03kxdw PRED entity: 03kxdw PRED relation: film PRED expected values: 016017 => 88 concepts (46 used for prediction) PRED predicted values (max 10 best out of 836): 0symg (0.25 #1704, 0.03 #3495, 0.02 #17823), 04v89z (0.25 #1420, 0.02 #56946), 03nqnnk (0.25 #1024, 0.02 #17143, 0.01 #8188), 06r2h (0.25 #1518, 0.01 #19428), 03h_yy (0.25 #74, 0.01 #28730), 01fx6y (0.25 #1184), 0c57yj (0.25 #639), 090s_0 (0.25 #37), 01hvjx (0.07 #7539, 0.06 #2166, 0.03 #18285), 03kx49 (0.07 #12089, 0.06 #13880, 0.05 #10298) >> Best rule #1704 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 030znt; >> query: (?x8780, 0symg) <- film(?x8780, ?x2463), type_of_union(?x8780, ?x566), ?x2463 = 03lrqw >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #8878 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 65 *> proper extension: 01csvq; 01wmxfs; 07ymr5; 015pxr; 0gkydb; 021yw7; 028k57; 08hsww; 048wrb; 05vtbl; ... *> query: (?x8780, 016017) <- film(?x8780, ?x2463), profession(?x8780, ?x1943), profession(?x8780, ?x987), ?x1943 = 02krf9, ?x987 = 0dxtg *> conf = 0.01 ranks of expected_values: 460 EVAL 03kxdw film 016017 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 88.000 46.000 0.250 http://example.org/film/actor/film./film/performance/film #19953-0f04v PRED entity: 0f04v PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 263 concepts (263 used for prediction) PRED predicted values (max 10 best out of 21): 0pqc5 (0.79 #442, 0.73 #97, 0.67 #304), 060c4 (0.52 #1618, 0.50 #2312, 0.47 #2127), 060bp (0.50 #1731, 0.50 #1616, 0.47 #2008), 0f6c3 (0.41 #2479, 0.36 #2548, 0.34 #2178), 09n5b9 (0.38 #2483, 0.34 #2552, 0.33 #2182), 0fkvn (0.36 #2475, 0.34 #2544, 0.33 #2174), 01q24l (0.33 #14, 0.23 #1905, 0.20 #37), 0p5vf (0.12 #2137, 0.11 #2322, 0.10 #1512), 0fkzq (0.12 #2488, 0.10 #2557, 0.08 #3293), 01zq91 (0.11 #1745, 0.11 #1630, 0.10 #2022) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 01smm; >> query: (?x6703, 0pqc5) <- citytown(?x6404, ?x6703), list(?x6404, ?x5997), dog_breed(?x6703, ?x1706) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f04v jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 263.000 263.000 0.793 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #19952-032j_n PRED entity: 032j_n PRED relation: production_companies! PRED expected values: 047vp1n => 122 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1210): 04nnpw (0.46 #14851, 0.38 #20564, 0.32 #36558), 0gvvf4j (0.46 #14851, 0.38 #20564, 0.32 #36558), 02s4l6 (0.40 #17136, 0.32 #36558, 0.32 #36557), 04z257 (0.32 #36558, 0.32 #36557, 0.29 #54835), 04y9mm8 (0.32 #36558, 0.32 #36557, 0.29 #54835), 084qpk (0.32 #36558, 0.32 #36557, 0.29 #54835), 025s1wg (0.32 #36558, 0.32 #36557, 0.29 #54835), 02mc5v (0.32 #36558, 0.32 #36557, 0.29 #54835), 01svry (0.32 #36558, 0.32 #36557, 0.29 #54835), 03clwtw (0.32 #36558, 0.32 #36557, 0.27 #34272) >> Best rule #14851 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0kcdl; >> query: (?x10884, ?x4696) <- company(?x4060, ?x10884), nominated_for(?x10884, ?x4696), child(?x166, ?x10884) >> conf = 0.46 => this is the best rule for 2 predicted values *> Best rule #36558 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 0gy1_; 0ky6d; *> query: (?x10884, ?x144) <- company(?x7324, ?x10884), executive_produced_by(?x7678, ?x7324), executive_produced_by(?x144, ?x7324), genre(?x7678, ?x258) *> conf = 0.32 ranks of expected_values: 46 EVAL 032j_n production_companies! 047vp1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 122.000 96.000 0.461 http://example.org/film/film/production_companies #19951-01l3wr PRED entity: 01l3wr PRED relation: sport PRED expected values: 02vx4 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.93 #175, 0.93 #157, 0.91 #228), 0z74 (0.47 #366, 0.27 #683), 0jm_ (0.17 #314, 0.15 #359, 0.15 #396), 03tmr (0.12 #192, 0.10 #129, 0.08 #138), 018jz (0.10 #316, 0.09 #398, 0.08 #361), 018w8 (0.06 #195, 0.05 #132, 0.04 #141), 09xp_ (0.03 #399), 039yzs (0.01 #680) >> Best rule #175 for best value: >> intensional similarity = 9 >> extensional distance = 28 >> proper extension: 02pp1; 01352_; >> query: (?x10788, 02vx4) <- current_club(?x10788, ?x10248), position(?x10788, ?x530), team(?x8324, ?x10248), team(?x530, ?x7829), team(?x530, ?x7642), team(?x530, ?x1664), ?x1664 = 02jgm0, ?x7829 = 0882r_, ?x7642 = 05f5sr9 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l3wr sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.933 http://example.org/sports/sports_team/sport #19950-0_565 PRED entity: 0_565 PRED relation: source PRED expected values: 0jbk9 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #9, 0.92 #8, 0.91 #7) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 224 >> proper extension: 0mn0v; >> query: (?x12295, ?x958) <- county(?x12295, ?x12296), time_zones(?x12295, ?x2674), source(?x12296, ?x958) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_565 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.916 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #19949-02rv_dz PRED entity: 02rv_dz PRED relation: film! PRED expected values: 083chw => 111 concepts (43 used for prediction) PRED predicted values (max 10 best out of 923): 0c00lh (0.46 #12478, 0.37 #51998, 0.36 #35356), 025jfl (0.46 #12478, 0.37 #51998, 0.33 #79034), 017r13 (0.46 #12478, 0.37 #51998, 0.33 #79034), 0dvld (0.14 #5218, 0.09 #11456, 0.05 #9376), 0h0wc (0.12 #8739, 0.05 #21217, 0.05 #6660), 0lpjn (0.11 #4636, 0.06 #10874, 0.02 #71189), 09fb5 (0.09 #8376, 0.05 #20854, 0.04 #4218), 0bl2g (0.09 #8373, 0.04 #20851, 0.04 #2134), 0f4dx2 (0.09 #556, 0.08 #2635, 0.02 #10954), 0b_dy (0.09 #532, 0.08 #2611, 0.02 #29647) >> Best rule #12478 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0g5838s; 093dqjy; 02d478; 0462hhb; 0j3d9tn; 047myg9; >> query: (?x1531, ?x617) <- film_release_region(?x1531, ?x94), nominated_for(?x618, ?x1531), ?x618 = 09qwmm, nominated_for(?x617, ?x1531) >> conf = 0.46 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 02rv_dz film! 083chw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 43.000 0.464 http://example.org/film/actor/film./film/performance/film #19948-059_c PRED entity: 059_c PRED relation: category PRED expected values: 08mbj5d => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.77 #37, 0.76 #145, 0.76 #147) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 0t015; 0f2r6; 0fvvz; 0_ytw; 099ty; 0mp3l; 0ftvz; 013cz2; 019k6n; 071cn; ... >> query: (?x1138, 08mbj5d) <- contains(?x1138, ?x9745), country(?x1138, ?x94), school(?x2569, ?x9745) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059_c category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.774 http://example.org/common/topic/webpage./common/webpage/category #19947-014x77 PRED entity: 014x77 PRED relation: award PRED expected values: 099t8j 0fm3kw => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 253): 09td7p (0.72 #6852, 0.72 #31445, 0.70 #28218), 09cn0c (0.72 #6852, 0.72 #31445, 0.70 #28218), 09sb52 (0.43 #41, 0.39 #444, 0.36 #847), 057xs89 (0.29 #159, 0.14 #562, 0.13 #965), 0gqy2 (0.29 #163, 0.12 #11852, 0.12 #11045), 05pcn59 (0.25 #483, 0.23 #886, 0.22 #1289), 0gqwc (0.19 #6521, 0.14 #5715, 0.13 #476), 05p09zm (0.17 #525, 0.17 #928, 0.17 #1331), 03c7tr1 (0.16 #460, 0.15 #1266, 0.15 #863), 05zr6wv (0.15 #420, 0.13 #823, 0.12 #1226) >> Best rule #6852 for best value: >> intensional similarity = 3 >> extensional distance = 515 >> proper extension: 067jsf; 03f5vvx; 04ns3gy; >> query: (?x548, ?x2257) <- gender(?x548, ?x514), award_winner(?x2257, ?x548), ?x514 = 02zsn >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #6587 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 515 *> proper extension: 067jsf; 03f5vvx; 04ns3gy; *> query: (?x548, 099t8j) <- gender(?x548, ?x514), award_winner(?x2257, ?x548), ?x514 = 02zsn *> conf = 0.06 ranks of expected_values: 60 EVAL 014x77 award 0fm3kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 107.000 0.717 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 014x77 award 099t8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 107.000 107.000 0.717 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19946-016bx2 PRED entity: 016bx2 PRED relation: profession PRED expected values: 01d_h8 => 99 concepts (67 used for prediction) PRED predicted values (max 10 best out of 68): 01d_h8 (0.85 #888, 0.72 #6036, 0.71 #4270), 0cbd2 (0.46 #1330, 0.45 #5155, 0.43 #5449), 018gz8 (0.38 #2220, 0.34 #1044, 0.32 #2073), 03gjzk (0.37 #8104, 0.37 #4130, 0.34 #4277), 09jwl (0.29 #2958, 0.21 #3399, 0.21 #1046), 0kyk (0.29 #5176, 0.28 #1351, 0.28 #4586), 02krf9 (0.27 #4142, 0.26 #172, 0.26 #4289), 05z96 (0.16 #1364, 0.14 #2687, 0.14 #4599), 0nbcg (0.16 #2971, 0.16 #3412, 0.15 #765), 02hv44_ (0.15 #1379, 0.15 #203, 0.14 #3291) >> Best rule #888 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 0p51w; >> query: (?x5669, 01d_h8) <- nationality(?x5669, ?x1310), award(?x5669, ?x1307), ?x1307 = 0gq9h >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016bx2 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 67.000 0.853 http://example.org/people/person/profession #19945-0277470 PRED entity: 0277470 PRED relation: award_winner! PRED expected values: 0418154 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 95): 09v0p2c (0.50 #81, 0.25 #220, 0.21 #1530), 0418154 (0.40 #106, 0.28 #3062, 0.21 #1530), 02q690_ (0.31 #203, 0.30 #64, 0.28 #3062), 03gt46z (0.30 #62, 0.17 #2922, 0.17 #4871), 09g90vz (0.28 #3062, 0.21 #1530, 0.17 #2922), 058m5m4 (0.28 #3062, 0.21 #1530, 0.17 #2922), 02wzl1d (0.28 #3062, 0.21 #1530, 0.17 #2922), 027hjff (0.28 #3062, 0.17 #4871, 0.05 #1446), 0drtv8 (0.28 #3062, 0.17 #4871, 0.04 #482), 05zksls (0.28 #3062, 0.17 #4871, 0.03 #312) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02773nt; 02778pf; 0pz7h; 0p_2r; 0284gcb; 026w_gk; 02778yp; 02778tk; >> query: (?x1266, 09v0p2c) <- award_winner(?x830, ?x1266), nationality(?x1266, ?x94), ?x830 = 02773m2 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #106 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 02773nt; 02778pf; 0pz7h; 0p_2r; 0284gcb; 026w_gk; 02778yp; 02778tk; *> query: (?x1266, 0418154) <- award_winner(?x830, ?x1266), nationality(?x1266, ?x94), ?x830 = 02773m2 *> conf = 0.40 ranks of expected_values: 2 EVAL 0277470 award_winner! 0418154 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 81.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19944-02xry PRED entity: 02xry PRED relation: religion PRED expected values: 01y0s9 => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 20): 01y0s9 (0.59 #364, 0.57 #412, 0.50 #556), 0flw86 (0.56 #49, 0.43 #1803, 0.37 #313), 01s5nb (0.43 #1803, 0.41 #421, 0.41 #373), 058x5 (0.43 #1803, 0.39 #410, 0.37 #362), 072w0 (0.43 #1803, 0.23 #566, 0.22 #422), 03j6c (0.33 #10, 0.13 #322, 0.11 #58), 0kpl (0.33 #5, 0.11 #53, 0.04 #317), 07w8f (0.33 #18, 0.11 #66, 0.04 #114), 06yyp (0.04 #323, 0.02 #299, 0.02 #467), 0n2g (0.04 #462, 0.03 #702, 0.03 #750) >> Best rule #364 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 05j49; >> query: (?x2623, 01y0s9) <- contains(?x2623, ?x10838), district_represented(?x176, ?x2623), currency(?x10838, ?x170) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xry religion 01y0s9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.592 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #19943-02kxg_ PRED entity: 02kxg_ PRED relation: entity_involved PRED expected values: 07_m9_ 03_lf => 30 concepts (22 used for prediction) PRED predicted values (max 10 best out of 184): 059z0 (0.60 #154, 0.38 #693, 0.33 #74), 01rdm0 (0.60 #154, 0.33 #155, 0.33 #118), 09c7w0 (0.60 #154, 0.30 #2945, 0.29 #2944), 02psqkz (0.60 #154, 0.30 #2945, 0.29 #2944), 07ssc (0.60 #154, 0.30 #2945, 0.29 #2944), 01h3dj (0.50 #531, 0.28 #1626, 0.27 #1156), 03l5m1 (0.33 #85, 0.25 #704, 0.17 #548), 0c_jc (0.33 #209, 0.20 #362, 0.13 #1142), 01_4z (0.33 #170, 0.20 #323, 0.07 #1103), 05vz3zq (0.33 #45, 0.17 #508, 0.12 #772) >> Best rule #154 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 05nqz; >> query: (?x10764, ?x94) <- entity_involved(?x10764, ?x10986), entity_involved(?x10764, ?x3057), combatants(?x10764, ?x13069), combatants(?x10764, ?x94), ?x13069 = 01rdm0, combatants(?x1229, ?x3057), ?x1229 = 059j2, religion(?x10986, ?x1985), type_of_union(?x10986, ?x566) >> conf = 0.60 => this is the best rule for 5 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 05nqz; *> query: (?x10764, 07_m9_) <- entity_involved(?x10764, ?x10986), entity_involved(?x10764, ?x3057), combatants(?x10764, ?x13069), ?x13069 = 01rdm0, combatants(?x1229, ?x3057), ?x1229 = 059j2, religion(?x10986, ?x1985), type_of_union(?x10986, ?x566) *> conf = 0.33 ranks of expected_values: 12, 47 EVAL 02kxg_ entity_involved 03_lf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 30.000 22.000 0.600 http://example.org/base/culturalevent/event/entity_involved EVAL 02kxg_ entity_involved 07_m9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 30.000 22.000 0.600 http://example.org/base/culturalevent/event/entity_involved #19942-0j_t1 PRED entity: 0j_t1 PRED relation: genre PRED expected values: 07s9rl0 => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 75): 07s9rl0 (0.63 #1801, 0.62 #2041, 0.62 #1921), 02l7c8 (0.59 #856, 0.39 #16, 0.29 #1216), 05p553 (0.40 #5, 0.39 #845, 0.34 #245), 01hmnh (0.33 #858, 0.16 #2178, 0.15 #1098), 01jfsb (0.31 #252, 0.28 #2172, 0.28 #4452), 02kdv5l (0.29 #243, 0.27 #2163, 0.25 #3963), 03k9fj (0.25 #851, 0.23 #2171, 0.23 #1091), 0lsxr (0.18 #729, 0.17 #969, 0.17 #2169), 0hcr (0.16 #864, 0.14 #24, 0.07 #3984), 04xvlr (0.15 #362, 0.15 #1202, 0.15 #482) >> Best rule #1801 for best value: >> intensional similarity = 3 >> extensional distance = 881 >> proper extension: 07l50vn; 0cvkv5; 076xkdz; 0dmn0x; >> query: (?x2719, 07s9rl0) <- genre(?x2719, ?x307), nominated_for(?x746, ?x2719), award(?x2719, ?x289) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j_t1 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.629 http://example.org/film/film/genre #19941-02z5x7l PRED entity: 02z5x7l PRED relation: film! PRED expected values: 03cz9_ => 69 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1163): 04fzk (0.44 #25666, 0.38 #29825, 0.36 #31905), 065mm1 (0.40 #14270, 0.29 #24671, 0.12 #37150), 0dt645q (0.33 #20483, 0.29 #5924, 0.25 #10083), 03fghg (0.33 #230, 0.29 #4389, 0.25 #16866), 04j5fx (0.33 #1844, 0.19 #16400, 0.12 #18480), 01rddlc (0.33 #1905, 0.17 #8144, 0.14 #6064), 04f62k (0.33 #1985, 0.15 #22783, 0.08 #8224), 03cz4j (0.33 #1970, 0.15 #22768, 0.08 #8209), 0c6qh (0.29 #44089, 0.04 #25372, 0.03 #29531), 01yh3y (0.29 #23097, 0.15 #35576, 0.07 #25177) >> Best rule #25666 for best value: >> intensional similarity = 7 >> extensional distance = 25 >> proper extension: 0p9lw; 0crfwmx; 0fpkhkz; 0_7w6; 04g9gd; 07yvsn; 03x7hd; 02mt51; 02wgk1; 05sw5b; ... >> query: (?x6840, 04fzk) <- film(?x12353, ?x6840), gender(?x12353, ?x514), actor(?x6610, ?x12353), film(?x7764, ?x6610), location(?x12353, ?x3634), genre(?x6840, ?x714), nationality(?x12353, ?x94) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #6135 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 076xkdz; *> query: (?x6840, 03cz9_) <- country(?x6840, ?x252), genre(?x6840, ?x2540), ?x252 = 03_3d, actor(?x6840, ?x12353), ?x2540 = 0hcr, ?x12353 = 0ckm4x *> conf = 0.14 ranks of expected_values: 39 EVAL 02z5x7l film! 03cz9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 69.000 41.000 0.444 http://example.org/film/actor/film./film/performance/film #19940-06y611 PRED entity: 06y611 PRED relation: film! PRED expected values: 03xq0f => 113 concepts (108 used for prediction) PRED predicted values (max 10 best out of 70): 03xq0f (0.84 #1258, 0.79 #1111, 0.72 #299), 05qd_ (0.58 #1777, 0.19 #229, 0.18 #1115), 09b3v (0.55 #1842, 0.53 #5306, 0.53 #5677), 016tt2 (0.22 #298, 0.21 #889, 0.19 #1037), 086k8 (0.21 #1182, 0.19 #1035, 0.19 #1697), 017s11 (0.21 #888, 0.19 #1036, 0.19 #223), 016tw3 (0.19 #2586, 0.19 #3029, 0.18 #3324), 01gb54 (0.13 #1134, 0.13 #248, 0.12 #1281), 0jz9f (0.12 #75, 0.10 #148, 0.10 #809), 093h7p (0.11 #715, 0.10 #809, 0.05 #863) >> Best rule #1258 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 0cp08zg; >> query: (?x9804, 03xq0f) <- production_companies(?x9804, ?x2156), region(?x9804, ?x512), ?x512 = 07ssc >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06y611 film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 108.000 0.839 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #19939-01gw4f PRED entity: 01gw4f PRED relation: film PRED expected values: 07g1sm => 126 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1172): 0cf08 (0.69 #37516, 0.69 #30370, 0.68 #1787), 0q9sg (0.69 #37516, 0.69 #30370, 0.68 #1787), 06c0ns (0.33 #1223, 0.12 #57168, 0.10 #4797), 074rg9 (0.33 #975, 0.12 #57168, 0.06 #8123), 01mszz (0.33 #1085, 0.12 #57168, 0.06 #10020), 07sgdw (0.33 #812, 0.12 #57168, 0.03 #18678), 0946bb (0.33 #545, 0.12 #57168, 0.02 #57713), 02ny6g (0.33 #601, 0.12 #57168, 0.02 #29184), 059lwy (0.33 #1192, 0.12 #57168, 0.01 #47641), 037xlx (0.33 #993, 0.12 #57168) >> Best rule #37516 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 01gvyp; 06r3p2; >> query: (?x4867, ?x188) <- nominated_for(?x4867, ?x188), award(?x4867, ?x154), ?x154 = 05b4l5x, profession(?x4867, ?x1032) >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #5361 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0kh6b; *> query: (?x4867, ?x810) <- sibling(?x4867, ?x3662), people(?x3591, ?x4867), film(?x3662, ?x810) *> conf = 0.22 ranks of expected_values: 21 EVAL 01gw4f film 07g1sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 126.000 66.000 0.694 http://example.org/film/actor/film./film/performance/film #19938-01sbv9 PRED entity: 01sbv9 PRED relation: genre PRED expected values: 05p553 => 100 concepts (84 used for prediction) PRED predicted values (max 10 best out of 98): 05p553 (0.67 #5680, 0.50 #241, 0.43 #1067), 07s9rl0 (0.65 #9229, 0.62 #591, 0.62 #5084), 01jfsb (0.53 #5451, 0.45 #721, 0.31 #9241), 02kdv5l (0.50 #5441, 0.49 #2836, 0.48 #2364), 06n90 (0.46 #4266, 0.27 #2257, 0.25 #2847), 02l7c8 (0.30 #371, 0.28 #1551, 0.27 #6995), 0lsxr (0.25 #718, 0.21 #5448, 0.18 #9238), 082gq (0.23 #738, 0.15 #620, 0.10 #856), 04xvlr (0.20 #120, 0.19 #710, 0.18 #592), 060__y (0.18 #726, 0.17 #608, 0.14 #844) >> Best rule #5680 for best value: >> intensional similarity = 4 >> extensional distance = 847 >> proper extension: 02ppg1r; 0dr1c2; >> query: (?x10192, 05p553) <- genre(?x10192, ?x811), film(?x1835, ?x10192), genre(?x6216, ?x811), ?x6216 = 06fcqw >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sbv9 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 84.000 0.665 http://example.org/film/film/genre #19937-02xbyr PRED entity: 02xbyr PRED relation: film! PRED expected values: 08jtv5 => 81 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1175): 0p8r1 (0.26 #6826, 0.21 #15148, 0.21 #13067), 02_p5w (0.22 #6886, 0.16 #13127, 0.13 #15208), 02gf_l (0.19 #7509, 0.16 #13750, 0.13 #15831), 0kszw (0.15 #419, 0.13 #2499, 0.08 #10820), 0c9xjl (0.15 #973, 0.08 #11374, 0.04 #5133), 019vgs (0.15 #6901, 0.13 #15223, 0.09 #17303), 0fby2t (0.13 #2835, 0.05 #27800, 0.04 #31961), 016ypb (0.12 #4659, 0.08 #10900, 0.05 #499), 01v3vp (0.11 #6950, 0.11 #15272, 0.11 #13191), 01mylz (0.11 #8187, 0.11 #14428, 0.08 #16509) >> Best rule #6826 for best value: >> intensional similarity = 6 >> extensional distance = 25 >> proper extension: 016017; >> query: (?x4707, 0p8r1) <- film(?x665, ?x4707), film(?x2156, ?x4707), film_release_distribution_medium(?x4707, ?x81), genre(?x4707, ?x2540), ?x2156 = 01795t, ?x2540 = 0hcr >> conf = 0.26 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02xbyr film! 08jtv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 52.000 0.259 http://example.org/film/actor/film./film/performance/film #19936-0237fw PRED entity: 0237fw PRED relation: award_nominee! PRED expected values: 02vntj => 120 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1181): 02vntj (0.81 #65120, 0.81 #127915, 0.81 #132568), 01w23w (0.81 #65120, 0.81 #127915, 0.81 #132568), 0237fw (0.56 #522, 0.27 #211635, 0.13 #53492), 015t7v (0.27 #211635, 0.13 #53492, 0.11 #1186), 015t56 (0.27 #211635, 0.13 #53492, 0.11 #609), 013knm (0.27 #211635, 0.13 #53492, 0.11 #835), 016zp5 (0.27 #211635, 0.13 #53492, 0.11 #1290), 030xr_ (0.27 #211635, 0.13 #53492, 0.11 #1986), 09wj5 (0.27 #211635, 0.13 #53492, 0.11 #118), 011zd3 (0.27 #211635, 0.13 #53492, 0.11 #488) >> Best rule #65120 for best value: >> intensional similarity = 2 >> extensional distance = 131 >> proper extension: 01kwld; 01cwhp; 0154qm; 01k5zk; 01tj34; 01d1st; 05vk_d; 02m3sd; >> query: (?x2443, ?x2626) <- vacationer(?x151, ?x2443), award_nominee(?x2443, ?x2626) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0237fw award_nominee! 02vntj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 94.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19935-023l9y PRED entity: 023l9y PRED relation: role PRED expected values: 0l14j_ => 85 concepts (54 used for prediction) PRED predicted values (max 10 best out of 106): 0l14md (0.50 #4, 0.25 #762, 0.25 #3486), 0cfdd (0.50 #74, 0.11 #581, 0.08 #412), 05148p4 (0.38 #15, 0.26 #522, 0.25 #762), 0l15bq (0.38 #25, 0.22 #193, 0.12 #109), 03m5k (0.38 #12, 0.07 #253, 0.07 #592), 013y1f (0.34 #531, 0.25 #108, 0.25 #24), 0mkg (0.25 #7, 0.07 #514, 0.07 #253), 0dwtp (0.25 #11, 0.07 #253, 0.07 #592), 018j2 (0.25 #32, 0.07 #253, 0.07 #592), 02hnl (0.25 #762, 0.25 #3486, 0.24 #3745) >> Best rule #4 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0892sx; 0137g1; 050z2; 01l4g5; 0140t7; 01wxdn3; >> query: (?x4595, 0l14md) <- role(?x4595, ?x1267), role(?x4595, ?x228), ?x228 = 0l14qv, artists(?x497, ?x4595), ?x1267 = 07brj, profession(?x4595, ?x955) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #472 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 24 *> proper extension: 05cljf; 016kjs; 01vw20_; 01n8gr; 0jsg0m; 0kj34; 04mky3; *> query: (?x4595, 0l14j_) <- profession(?x4595, ?x955), instrumentalists(?x716, ?x4595), ?x955 = 0n1h, ?x716 = 018vs, artists(?x497, ?x4595) *> conf = 0.12 ranks of expected_values: 34 EVAL 023l9y role 0l14j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 85.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #19934-02rxj PRED entity: 02rxj PRED relation: taxonomy PRED expected values: 04n6k => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.46 #13, 0.44 #18, 0.44 #17) >> Best rule #13 for best value: >> intensional similarity = 12 >> extensional distance = 11 >> proper extension: 06yyp; >> query: (?x2260, 04n6k) <- religion(?x1471, ?x2260), religion(?x10648, ?x2260), religion(?x10328, ?x2260), location(?x10328, ?x8745), award_winner(?x9918, ?x10328), contains(?x455, ?x1471), medal(?x1471, ?x422), place_of_death(?x2536, ?x8745), film_release_region(?x2709, ?x1471), award(?x10648, ?x783), ?x2709 = 06ztvyx, citytown(?x9688, ?x8745) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rxj taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.462 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #19933-01p0vf PRED entity: 01p0vf PRED relation: instrumentalists! PRED expected values: 0dwr4 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 120): 018vs (0.48 #90, 0.40 #1836, 0.40 #1597), 028tv0 (0.40 #1905, 0.39 #1666, 0.37 #1030), 02hnl (0.31 #109, 0.23 #1139, 0.23 #662), 0l14qv (0.28 #83, 0.14 #162, 0.13 #241), 04rzd (0.24 #112, 0.11 #1858, 0.11 #1619), 07y_7 (0.21 #81, 0.11 #397, 0.09 #3895), 0l14md (0.16 #1115, 0.15 #718, 0.14 #638), 0gkd1 (0.14 #151, 0.09 #3895, 0.05 #72), 0dwt5 (0.14 #141, 0.09 #3895, 0.04 #3417), 06ncr (0.12 #989, 0.10 #1864, 0.10 #118) >> Best rule #90 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 03c7ln; 0m2l9; 032t2z; 0fpjd_g; 01vsnff; 09prnq; 02jg92; 01w724; 053yx; 01vn35l; ... >> query: (?x7053, 018vs) <- instrumentalists(?x1495, ?x7053), profession(?x7053, ?x1614), ?x1495 = 013y1f >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #3895 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 580 *> proper extension: 01pbxb; 0f0y8; 053y0s; 016qtt; 01vvydl; 028q6; 07s3vqk; 0197tq; 0411q; 05cljf; ... *> query: (?x7053, ?x228) <- instrumentalists(?x1495, ?x7053), profession(?x7053, ?x1614), performance_role(?x1495, ?x228) *> conf = 0.09 ranks of expected_values: 31 EVAL 01p0vf instrumentalists! 0dwr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 130.000 130.000 0.483 http://example.org/music/instrument/instrumentalists #19932-01q24l PRED entity: 01q24l PRED relation: jurisdiction_of_office PRED expected values: 0k049 0r1yc 0m2lt 0r4xt 01jr6 06hdk 0r4z7 0r00l 0nbzp => 22 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1556): 0dc95 (0.70 #1313, 0.57 #874, 0.56 #3505), 0f04v (0.70 #1313, 0.57 #874, 0.56 #3505), 0kpzy (0.70 #1313, 0.57 #874, 0.56 #3505), 0l2hf (0.70 #1313, 0.57 #874, 0.56 #3505), 0r6ff (0.70 #1313, 0.57 #874, 0.56 #3505), 0r6cx (0.70 #1313, 0.57 #874, 0.56 #3505), 01qh7 (0.70 #1313, 0.57 #874, 0.56 #3505), 09c7w0 (0.66 #5708, 0.56 #4391, 0.54 #4828), 0hjy (0.66 #5708, 0.54 #4828, 0.33 #920), 0m2lt (0.66 #5708, 0.54 #4828, 0.33 #1754) >> Best rule #1313 for best value: >> intensional similarity = 21 >> extensional distance = 1 >> proper extension: 0f6c3; >> query: (?x10525, ?x6703) <- jurisdiction_of_office(?x10525, ?x13190), jurisdiction_of_office(?x10525, ?x12153), jurisdiction_of_office(?x10525, ?x12107), jurisdiction_of_office(?x10525, ?x3794), jurisdiction_of_office(?x10525, ?x2495), basic_title(?x5804, ?x10525), jurisdiction_of_office(?x1195, ?x13190), politician(?x8714, ?x5804), adjoins(?x6703, ?x3794), category(?x3794, ?x134), profession(?x5804, ?x3342), contains(?x94, ?x12107), student(?x2775, ?x5804), ?x1195 = 0pqc5, time_zones(?x2495, ?x2950), ?x8714 = 0d075m, place_of_birth(?x2103, ?x2495), contains(?x2632, ?x3794), source(?x12153, ?x958), contains(?x279, ?x13190), location(?x140, ?x12107) >> conf = 0.70 => this is the best rule for 7 predicted values *> Best rule #5708 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 8 *> proper extension: 04syw; *> query: (?x10525, ?x94) <- jurisdiction_of_office(?x10525, ?x9958), jurisdiction_of_office(?x10525, ?x8811), jurisdiction_of_office(?x10525, ?x4090), jurisdiction_of_office(?x10525, ?x3794), basic_title(?x5804, ?x10525), adjoins(?x6703, ?x3794), jurisdiction_of_office(?x5804, ?x94), contains(?x1227, ?x3794), featured_film_locations(?x8397, ?x8811), film_release_distribution_medium(?x8397, ?x81), location(?x3585, ?x4090), vacationer(?x4090, ?x5657), ?x81 = 029j_, award_winner(?x1402, ?x3585), category(?x9958, ?x134), award(?x3585, ?x749), languages(?x3585, ?x254), story_by(?x8397, ?x6045), time_zones(?x8811, ?x2950) *> conf = 0.66 ranks of expected_values: 10, 207, 209, 210, 211, 212, 213, 294 EVAL 01q24l jurisdiction_of_office 0nbzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 16.000 0.701 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 01q24l jurisdiction_of_office 0r00l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 16.000 0.701 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 01q24l jurisdiction_of_office 0r4z7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 16.000 0.701 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 01q24l jurisdiction_of_office 06hdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 22.000 16.000 0.701 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 01q24l jurisdiction_of_office 01jr6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 16.000 0.701 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 01q24l jurisdiction_of_office 0r4xt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 16.000 0.701 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 01q24l jurisdiction_of_office 0m2lt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 22.000 16.000 0.701 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 01q24l jurisdiction_of_office 0r1yc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 16.000 0.701 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 01q24l jurisdiction_of_office 0k049 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 16.000 0.701 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #19931-03l2n PRED entity: 03l2n PRED relation: place_of_birth! PRED expected values: 06mmb 01jfrg 03c6vl => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1828): 05d8vw (0.36 #199548, 0.34 #72564, 0.34 #194365), 01vx5w7 (0.36 #199548, 0.34 #72564, 0.34 #194365), 01j851 (0.36 #199548, 0.34 #72564, 0.34 #194365), 014ps4 (0.36 #199548, 0.34 #72564, 0.34 #194365), 081jbk (0.36 #199548, 0.34 #72564, 0.34 #194365), 05m63c (0.36 #199548, 0.34 #72564, 0.34 #194365), 016ppr (0.29 #111435, 0.26 #119210, 0.25 #119211), 089tm (0.29 #111435, 0.26 #119210, 0.25 #119211), 01hb6v (0.10 #62198, 0.08 #31097, 0.07 #36282), 03n93 (0.07 #36282, 0.06 #62197, 0.06 #31096) >> Best rule #199548 for best value: >> intensional similarity = 3 >> extensional distance = 419 >> proper extension: 05mwx; >> query: (?x4733, ?x287) <- place_of_birth(?x9272, ?x4733), location(?x287, ?x4733), location(?x9272, ?x3125) >> conf = 0.36 => this is the best rule for 6 predicted values No rule for expected values ranks of expected_values: EVAL 03l2n place_of_birth! 03c6vl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 106.000 0.364 http://example.org/people/person/place_of_birth EVAL 03l2n place_of_birth! 01jfrg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 106.000 0.364 http://example.org/people/person/place_of_birth EVAL 03l2n place_of_birth! 06mmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 106.000 0.364 http://example.org/people/person/place_of_birth #19930-045c66 PRED entity: 045c66 PRED relation: award_nominee PRED expected values: 06jzh => 92 concepts (31 used for prediction) PRED predicted values (max 10 best out of 727): 06t74h (0.81 #49005, 0.81 #32669, 0.81 #32670), 0g8st4 (0.81 #49005, 0.81 #32669, 0.81 #32670), 0c3p7 (0.81 #49005, 0.81 #32669, 0.81 #32670), 0fthdk (0.81 #49005, 0.81 #32669, 0.81 #32670), 01kp66 (0.81 #49005, 0.81 #32669, 0.81 #32670), 09y20 (0.20 #324, 0.07 #58342, 0.02 #18991), 05vsxz (0.20 #9, 0.04 #7009, 0.03 #18676), 03f1zdw (0.20 #250, 0.04 #4916, 0.04 #7250), 0mdqp (0.20 #146, 0.03 #4812), 020_95 (0.20 #1277, 0.03 #8277, 0.03 #19944) >> Best rule #49005 for best value: >> intensional similarity = 3 >> extensional distance = 1212 >> proper extension: 07nznf; 0grwj; 07s3vqk; 0cnl80; 05ty4m; 0l8v5; 03w1v2; 01rr9f; 0f830f; 03m8lq; ... >> query: (?x1486, ?x4043) <- award_nominee(?x1486, ?x539), film(?x1486, ?x1487), award_nominee(?x4043, ?x1486) >> conf = 0.81 => this is the best rule for 5 predicted values *> Best rule #53674 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1244 *> proper extension: 0162c8; 0b_c7; 05218gr; 08wr3kg; 05nn4k; 05xbx; 040rjq; *> query: (?x1486, ?x540) <- award_nominee(?x1486, ?x539), nominated_for(?x1486, ?x2973), film(?x540, ?x2973) *> conf = 0.19 ranks of expected_values: 47 EVAL 045c66 award_nominee 06jzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 92.000 31.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19929-06d6y PRED entity: 06d6y PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.94 #7, 0.91 #25, 0.90 #31), 02zsn (0.31 #119, 0.31 #121, 0.30 #117) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 012cph; 03hnd; 0c5tl; 01tz6vs; 041xl; 02mpb; >> query: (?x8961, 05zppz) <- award(?x8961, ?x688), story_by(?x2772, ?x8961), influenced_by(?x2343, ?x8961) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06d6y gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.938 http://example.org/people/person/gender #19928-0fsm8c PRED entity: 0fsm8c PRED relation: film PRED expected values: 0by1wkq 08052t3 => 81 concepts (47 used for prediction) PRED predicted values (max 10 best out of 725): 034qzw (0.15 #333, 0.03 #75035, 0.03 #17862), 0bvn25 (0.12 #50, 0.03 #75035, 0.03 #17862), 02825cv (0.12 #1140, 0.02 #27938, 0.02 #33298), 0m9p3 (0.07 #2173, 0.06 #3959), 06fpsx (0.06 #1336, 0.06 #37519, 0.03 #75035), 05zpghd (0.06 #953, 0.04 #4525, 0.02 #2739), 026wlxw (0.06 #1415, 0.03 #75035, 0.03 #17862), 091rc5 (0.06 #854, 0.03 #75035, 0.03 #17862), 04gv3db (0.06 #751, 0.02 #7895, 0.02 #9681), 0888c3 (0.06 #1412, 0.01 #10342, 0.01 #13914) >> Best rule #333 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 08wq0g; 0cj36c; >> query: (?x1722, 034qzw) <- award_nominee(?x1722, ?x237), profession(?x1722, ?x319), ?x237 = 04t2l2 >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fsm8c film 08052t3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 47.000 0.152 http://example.org/film/actor/film./film/performance/film EVAL 0fsm8c film 0by1wkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 47.000 0.152 http://example.org/film/actor/film./film/performance/film #19927-0d_skg PRED entity: 0d_skg PRED relation: executive_produced_by! PRED expected values: 01n30p => 73 concepts (47 used for prediction) PRED predicted values (max 10 best out of 152): 03s6l2 (0.14 #23, 0.02 #1611, 0.01 #4259), 04jwly (0.14 #156, 0.02 #1744, 0.01 #2273), 0fh694 (0.14 #38, 0.01 #3743, 0.01 #1626), 0djlxb (0.14 #182, 0.01 #1770), 07w8fz (0.14 #179, 0.01 #1767), 0bs8s1p (0.14 #390, 0.01 #2507, 0.01 #3036), 078sj4 (0.14 #154), 04lhc4 (0.10 #3176, 0.02 #4767, 0.02 #4766), 0q9b0 (0.10 #3176, 0.02 #4767, 0.02 #4766), 0277j40 (0.10 #3176, 0.02 #4767, 0.02 #4766) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 017s11; >> query: (?x6690, 03s6l2) <- nominated_for(?x6690, ?x6899), award(?x6690, ?x198), ?x6899 = 04lhc4 >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d_skg executive_produced_by! 01n30p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 47.000 0.143 http://example.org/film/film/executive_produced_by #19926-03k545 PRED entity: 03k545 PRED relation: language PRED expected values: 02h40lc => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 7): 02h40lc (0.83 #7, 0.77 #16, 0.05 #205), 03k50 (0.06 #8, 0.03 #17, 0.01 #284), 06nm1 (0.01 #284, 0.01 #198), 04h9h (0.01 #284), 064_8sq (0.01 #284), 071fb (0.01 #284), 02bjrlw (0.01 #284) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0h5g_; 0169dl; 01j5ws; 011_3s; 04fzk; 01wy5m; 01bcq; 01x_d8; 01h1b; 08wjf4; ... >> query: (?x11470, 02h40lc) <- award_winner(?x3499, ?x11470), gender(?x11470, ?x514), film(?x11470, ?x1184), actor(?x596, ?x11470) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03k545 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.833 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #19925-0pvms PRED entity: 0pvms PRED relation: film! PRED expected values: 016tt2 => 97 concepts (90 used for prediction) PRED predicted values (max 10 best out of 122): 016tw3 (0.60 #162, 0.25 #237, 0.19 #837), 024rgt (0.33 #20, 0.06 #1449, 0.06 #846), 03xq0f (0.20 #156, 0.20 #80, 0.10 #2643), 086k8 (0.20 #77, 0.17 #1657, 0.16 #1054), 054g1r (0.20 #110, 0.08 #786, 0.07 #2673), 01795t (0.20 #93, 0.06 #619, 0.06 #994), 016tt2 (0.16 #980, 0.16 #455, 0.16 #380), 017s11 (0.16 #679, 0.15 #1508, 0.15 #304), 05qd_ (0.14 #2345, 0.13 #685, 0.13 #1664), 0g1rw (0.12 #234, 0.10 #684, 0.07 #1966) >> Best rule #162 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0c9t0y; >> query: (?x2565, 016tw3) <- film_release_distribution_medium(?x2565, ?x81), country(?x2565, ?x94), film(?x12041, ?x2565), ?x94 = 09c7w0, ?x12041 = 044zvm >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #980 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 179 *> proper extension: 05pbl56; 06v9_x; 0ds5_72; 085wqm; *> query: (?x2565, 016tt2) <- film_release_distribution_medium(?x2565, ?x81), film_crew_role(?x2565, ?x1171), featured_film_locations(?x2565, ?x9417), ?x1171 = 09vw2b7, music(?x2565, ?x3910) *> conf = 0.16 ranks of expected_values: 7 EVAL 0pvms film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 90.000 0.600 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #19924-0425hg PRED entity: 0425hg PRED relation: current_club! PRED expected values: 03zrhb => 33 concepts (24 used for prediction) PRED predicted values (max 10 best out of 28): 03y_f8 (0.03 #123, 0.03 #243, 0.03 #273), 032jlh (0.03 #57, 0.03 #27, 0.03 #177), 03ylxn (0.03 #25, 0.03 #85, 0.03 #235), 03yl2t (0.03 #124, 0.02 #244, 0.02 #274), 02ltg3 (0.03 #247, 0.03 #457, 0.02 #127), 03zrhb (0.03 #77, 0.02 #197, 0.02 #227), 01l3vx (0.03 #95, 0.02 #155, 0.02 #215), 02s9vc (0.02 #601, 0.02 #382, 0.02 #322), 03ys48 (0.02 #378, 0.02 #18, 0.02 #138), 03dj48 (0.02 #323, 0.02 #473, 0.02 #203) >> Best rule #123 for best value: >> intensional similarity = 9 >> extensional distance = 214 >> proper extension: 05kjc6; 0dy68h; 04b4yg; 0371rb; 024tsn; 02b1mc; 0g7s1n; 044l47; 02_cq0; 041xyk; ... >> query: (?x10781, 03y_f8) <- team(?x530, ?x10781), team(?x63, ?x10781), position(?x10781, ?x60), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x60 = 02nzb8, position(?x10781, ?x60), position(?x10781, ?x530), position(?x10781, ?x63) >> conf = 0.03 => this is the best rule for 1 predicted values *> Best rule #77 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 196 *> proper extension: 01kkg5; 02b1cn; 01_jky; 0449sw; 050fh; 01rl_3; 0177gl; 046f25; 0cxbth; 03_qrp; ... *> query: (?x10781, 03zrhb) <- team(?x530, ?x10781), team(?x63, ?x10781), position(?x10781, ?x60), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x60 = 02nzb8, position(?x10781, ?x60), position(?x10781, ?x63), position(?x10781, ?x530) *> conf = 0.03 ranks of expected_values: 6 EVAL 0425hg current_club! 03zrhb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 33.000 24.000 0.032 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #19923-021mkg PRED entity: 021mkg PRED relation: current_club! PRED expected values: 032jlh => 104 concepts (95 used for prediction) PRED predicted values (max 10 best out of 29): 03yl2t (0.62 #302, 0.30 #571, 0.28 #419), 03y_f8 (0.58 #211, 0.55 #181, 0.43 #331), 02ltg3 (0.58 #276, 0.30 #571, 0.28 #487), 03dj48 (0.50 #170, 0.42 #261, 0.24 #441), 035qgm (0.50 #136, 0.33 #47, 0.32 #358), 03d8m4 (0.41 #239, 0.35 #208, 0.23 #269), 032jlh (0.33 #55, 0.32 #506, 0.32 #358), 033nzk (0.33 #31, 0.32 #358, 0.29 #451), 02bh_v (0.33 #288, 0.20 #529, 0.16 #499), 03ys48 (0.32 #497, 0.30 #571, 0.29 #345) >> Best rule #302 for best value: >> intensional similarity = 23 >> extensional distance = 11 >> proper extension: 03x746; 025txtg; 01nd2c; 0hvjr; 01gjlw; 03j7cf; 03fnqj; 08vq2y; >> query: (?x7820, 03yl2t) <- position(?x7820, ?x530), position(?x7820, ?x203), position(?x7820, ?x63), current_club(?x8102, ?x7820), ?x203 = 0dgrmp, current_club(?x8102, ?x13223), current_club(?x8102, ?x12526), current_club(?x8102, ?x8750), current_club(?x8102, ?x8537), current_club(?x8102, ?x5993), teams(?x2051, ?x8102), ?x530 = 02_j1w, ?x8750 = 03x6m, colors(?x12526, ?x663), sport(?x8102, ?x471), ?x663 = 083jv, team(?x1142, ?x8102), team(?x982, ?x8537), team(?x5420, ?x5993), ?x63 = 02sdk9v, team(?x7907, ?x13223), ?x5420 = 0135nb, teams(?x14446, ?x8537) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 02hzx8; *> query: (?x7820, 032jlh) <- position(?x7820, ?x530), position(?x7820, ?x203), position(?x7820, ?x63), position(?x7820, ?x60), current_club(?x8102, ?x7820), ?x203 = 0dgrmp, ?x8102 = 03_qrp, ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x60 = 02nzb8, team(?x60, ?x7820), team(?x63, ?x7820), team(?x203, ?x7820), position(?x7820, ?x203), position(?x7820, ?x530) *> conf = 0.33 ranks of expected_values: 7 EVAL 021mkg current_club! 032jlh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 104.000 95.000 0.615 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #19922-01gp_x PRED entity: 01gp_x PRED relation: award_nominee! PRED expected values: 07lwsz => 68 concepts (51 used for prediction) PRED predicted values (max 10 best out of 801): 02q5xsx (0.81 #116591, 0.81 #116590, 0.81 #114258), 0b05xm (0.81 #116591, 0.81 #116590, 0.81 #114258), 07lwsz (0.81 #116591, 0.81 #116590, 0.81 #114258), 01gp_x (0.38 #578, 0.18 #97938, 0.18 #93270), 03d_w3h (0.25 #192), 0k2mxq (0.19 #1398, 0.01 #57355), 05fnl9 (0.18 #97938, 0.18 #93270, 0.18 #95604), 02779r4 (0.18 #97938, 0.18 #93270, 0.18 #95604), 070w7s (0.18 #9956, 0.12 #14620, 0.11 #16951), 026dg51 (0.18 #9510, 0.11 #14174, 0.10 #16505) >> Best rule #116591 for best value: >> intensional similarity = 2 >> extensional distance = 2068 >> proper extension: 0181hw; >> query: (?x2643, ?x4671) <- award_nominee(?x2643, ?x4671), nominated_for(?x4671, ?x10089) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 01gp_x award_nominee! 07lwsz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 51.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19921-033071 PRED entity: 033071 PRED relation: gender PRED expected values: 05zppz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #39, 0.84 #55, 0.84 #41), 02zsn (0.33 #46, 0.31 #74, 0.31 #52) >> Best rule #39 for best value: >> intensional similarity = 4 >> extensional distance = 498 >> proper extension: 0m2l9; 04rs03; 02pp_q_; 01vvycq; 04l3_z; 01p45_v; 01c58j; 0177s6; 02vmzp; 025tdwc; ... >> query: (?x11972, 05zppz) <- profession(?x11972, ?x524), ?x524 = 02jknp, type_of_union(?x11972, ?x566), ?x566 = 04ztj >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033071 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.840 http://example.org/people/person/gender #19920-0hd7j PRED entity: 0hd7j PRED relation: major_field_of_study PRED expected values: 07c52 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 114): 01mkq (0.56 #610, 0.48 #1326, 0.47 #1087), 02j62 (0.47 #1102, 0.41 #625, 0.40 #864), 062z7 (0.44 #622, 0.35 #861, 0.33 #1338), 01tbp (0.39 #653, 0.31 #1369, 0.29 #773), 04rjg (0.38 #615, 0.36 #1092, 0.35 #854), 01540 (0.36 #654, 0.29 #1370, 0.25 #1131), 02ky346 (0.31 #611, 0.26 #1327, 0.24 #1088), 05qjt (0.30 #603, 0.29 #1319, 0.29 #1080), 04x_3 (0.30 #621, 0.28 #1337, 0.27 #860), 05qfh (0.28 #631, 0.26 #1108, 0.24 #751) >> Best rule #610 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 02bqy; >> query: (?x4603, 01mkq) <- institution(?x734, ?x4603), major_field_of_study(?x4603, ?x1154), ?x1154 = 02lp1, school(?x684, ?x4603) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #4534 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 404 *> proper extension: 02cttt; 01ngz1; 01b1pf; 0pmcz; 0bqxw; 0b1xl; 025rcc; 03bmmc; 01y9qr; 09s5q8; ... *> query: (?x4603, ?x254) <- institution(?x865, ?x4603), major_field_of_study(?x4603, ?x1154), colors(?x4603, ?x7203), major_field_of_study(?x865, ?x254) *> conf = 0.06 ranks of expected_values: 70 EVAL 0hd7j major_field_of_study 07c52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 119.000 119.000 0.562 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19919-0f04v PRED entity: 0f04v PRED relation: location_of_ceremony! PRED expected values: 04ztj => 257 concepts (257 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.89 #97, 0.87 #109, 0.87 #25), 01g63y (0.05 #50, 0.03 #98, 0.03 #299), 0jgjn (0.04 #144, 0.04 #184, 0.03 #269), 01bl8s (0.02 #348, 0.02 #139, 0.02 #143) >> Best rule #97 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0853g; >> query: (?x6703, 04ztj) <- mode_of_transportation(?x6703, ?x4272), ?x4272 = 07jdr, place_of_birth(?x5809, ?x6703), citytown(?x6404, ?x6703) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f04v location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 257.000 257.000 0.886 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #19918-07s846j PRED entity: 07s846j PRED relation: film_release_region PRED expected values: 09pmkv 059j2 02vzc 05b4w 03spz => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 121): 03spz (0.93 #200, 0.89 #460, 0.89 #330), 059j2 (0.86 #148, 0.85 #408, 0.84 #928), 02vzc (0.86 #162, 0.85 #422, 0.84 #942), 05b4w (0.83 #951, 0.82 #821, 0.81 #1341), 09pmkv (0.70 #275, 0.68 #145, 0.67 #405), 06t8v (0.62 #833, 0.61 #183, 0.59 #313), 07f1x (0.57 #1003, 0.57 #223, 0.52 #353), 03ryn (0.57 #191, 0.55 #321, 0.50 #451), 0hzlz (0.46 #141, 0.45 #271, 0.43 #401), 07t21 (0.46 #154, 0.42 #1324, 0.41 #284) >> Best rule #200 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 047vnkj; 07jqjx; >> query: (?x4047, 03spz) <- film_release_region(?x4047, ?x756), film_release_region(?x4047, ?x608), nominated_for(?x163, ?x4047), ?x608 = 02k54, ?x756 = 06npd >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 07s846j film_release_region 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07s846j film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07s846j film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07s846j film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07s846j film_release_region 09pmkv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19917-0jmdb PRED entity: 0jmdb PRED relation: school PRED expected values: 02bhj4 => 98 concepts (64 used for prediction) PRED predicted values (max 10 best out of 190): 0bx8pn (0.40 #1166, 0.36 #1738, 0.33 #976), 01pq4w (0.40 #622, 0.12 #5952, 0.12 #814), 015q1n (0.38 #865, 0.33 #1055, 0.25 #3339), 0dzst (0.38 #910, 0.33 #1100, 0.17 #5667), 0pspl (0.33 #239, 0.25 #811, 0.24 #2475), 0j_sncb (0.33 #228, 0.25 #800, 0.22 #2514), 01jt2w (0.33 #319, 0.22 #1081, 0.14 #2223), 03tw2s (0.33 #301, 0.21 #6772, 0.17 #7153), 07vyf (0.33 #251, 0.19 #5961, 0.17 #2537), 02mj7c (0.33 #270, 0.17 #2556, 0.14 #2174) >> Best rule #1166 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 0jmhr; >> query: (?x660, 0bx8pn) <- team(?x1348, ?x660), teams(?x659, ?x660), draft(?x660, ?x12852), ?x12852 = 06439y, location(?x7345, ?x659), sport(?x660, ?x4833), nationality(?x7345, ?x94), source(?x659, ?x958) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #883 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0jmm4; *> query: (?x660, 02bhj4) <- team(?x4747, ?x660), teams(?x659, ?x660), draft(?x660, ?x12852), ?x12852 = 06439y, month(?x659, ?x1459), dog_breed(?x659, ?x5194), ?x4747 = 02sf_r, place_of_birth(?x1775, ?x659) *> conf = 0.12 ranks of expected_values: 55 EVAL 0jmdb school 02bhj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 98.000 64.000 0.400 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #19916-01vsl3_ PRED entity: 01vsl3_ PRED relation: artist! PRED expected values: 011k1h 017l96 => 142 concepts (129 used for prediction) PRED predicted values (max 10 best out of 113): 015_1q (0.40 #19, 0.38 #158, 0.31 #2382), 016ckq (0.40 #42, 0.12 #181, 0.11 #3240), 043g7l (0.25 #170, 0.18 #2394, 0.15 #2534), 0n85g (0.25 #200, 0.12 #2564, 0.11 #1034), 033hn8 (0.25 #152, 0.12 #3628, 0.11 #430), 03rhqg (0.23 #1683, 0.22 #293, 0.22 #3630), 0mzkr (0.22 #998, 0.20 #25, 0.19 #859), 03mp8k (0.20 #65, 0.12 #899, 0.12 #204), 03qx_f (0.20 #72, 0.11 #1045, 0.11 #350), 017l96 (0.20 #3077, 0.17 #991, 0.15 #1408) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0m2l9; >> query: (?x2799, 015_1q) <- influenced_by(?x2799, ?x5442), profession(?x2799, ?x131), ?x5442 = 02jq1 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3077 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 59 *> proper extension: 0641g8; 013qvn; *> query: (?x2799, 017l96) <- people(?x12333, ?x2799), artist(?x1954, ?x2799) *> conf = 0.20 ranks of expected_values: 10, 13 EVAL 01vsl3_ artist! 017l96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 142.000 129.000 0.400 http://example.org/music/record_label/artist EVAL 01vsl3_ artist! 011k1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 142.000 129.000 0.400 http://example.org/music/record_label/artist #19915-01p0w_ PRED entity: 01p0w_ PRED relation: artists! PRED expected values: 0cx7f => 151 concepts (78 used for prediction) PRED predicted values (max 10 best out of 258): 06by7 (0.60 #3439, 0.57 #4371, 0.57 #21), 064t9 (0.57 #1565, 0.56 #2809, 0.48 #5917), 0glt670 (0.43 #1594, 0.33 #2838, 0.30 #5946), 025sc50 (0.41 #1604, 0.29 #2848, 0.29 #5956), 0xhtw (0.38 #2190, 0.30 #6541, 0.30 #8405), 05bt6j (0.38 #1907, 0.30 #3151, 0.30 #3462), 0fd3y (0.36 #9, 0.21 #20217, 0.12 #941), 08cyft (0.36 #58, 0.15 #990, 0.08 #8759), 0155w (0.32 #729, 0.31 #418, 0.22 #1350), 06j6l (0.32 #1602, 0.31 #2846, 0.28 #5954) >> Best rule #3439 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 021bk; 04gycf; >> query: (?x12422, 06by7) <- instrumentalists(?x212, ?x12422), award_nominee(?x12422, ?x7053), award(?x12422, ?x1565), group(?x12422, ?x10561) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #140 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 089tm; 01pfr3; 03fbc; 0187x8; 016lmg; *> query: (?x12422, 0cx7f) <- award_winner(?x1584, ?x12422), artists(?x12498, ?x12422), ?x12498 = 05c6073 *> conf = 0.21 ranks of expected_values: 21 EVAL 01p0w_ artists! 0cx7f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 151.000 78.000 0.605 http://example.org/music/genre/artists #19914-04pk1f PRED entity: 04pk1f PRED relation: film_crew_role PRED expected values: 09vw2b7 0dxtw => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 30): 09vw2b7 (0.73 #998, 0.72 #74, 0.70 #1066), 0dxtw (0.41 #1070, 0.41 #1002, 0.41 #761), 02ynfr (0.26 #2743, 0.22 #287, 0.22 #321), 02rh1dz (0.26 #2743, 0.20 #43, 0.20 #316), 0215hd (0.26 #2743, 0.17 #1009, 0.14 #1077), 0d2b38 (0.26 #2743, 0.16 #331, 0.15 #92), 015h31 (0.26 #2743, 0.15 #178, 0.13 #315), 089g0h (0.26 #2743, 0.14 #1010, 0.12 #1318), 089fss (0.26 #2743, 0.12 #278, 0.12 #107), 02_n3z (0.26 #2743, 0.10 #993, 0.10 #1438) >> Best rule #998 for best value: >> intensional similarity = 4 >> extensional distance = 402 >> proper extension: 03n0cd; 03whyr; >> query: (?x6078, 09vw2b7) <- film_crew_role(?x6078, ?x1284), produced_by(?x6078, ?x3880), ?x1284 = 0ch6mp2, currency(?x6078, ?x170) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04pk1f film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.733 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 04pk1f film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.733 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19913-0jym0 PRED entity: 0jym0 PRED relation: country PRED expected values: 09c7w0 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 130): 09c7w0 (0.79 #2267, 0.78 #368, 0.78 #1532), 07ssc (0.28 #445, 0.27 #506, 0.24 #383), 0345h (0.15 #150, 0.13 #394, 0.11 #517), 0f8l9c (0.10 #3387, 0.09 #4366, 0.09 #2469), 0d060g (0.09 #375, 0.06 #2090, 0.05 #1967), 03rt9 (0.08 #137, 0.04 #198, 0.02 #565), 0154j (0.08 #128, 0.01 #5326, 0.01 #923), 05qhw (0.08 #138, 0.01 #5326), 04xvlr (0.06 #4775, 0.06 #5388, 0.06 #2939), 03rjj (0.05 #435, 0.04 #496, 0.04 #679) >> Best rule #2267 for best value: >> intensional similarity = 4 >> extensional distance = 751 >> proper extension: 09xbpt; 03h_yy; 04kkz8; 08hmch; 09gdm7q; 0g5pv3; 03s5lz; 0bh8yn3; 0c00zd0; 01b195; ... >> query: (?x2057, 09c7w0) <- film(?x541, ?x2057), nominated_for(?x4324, ?x2057), film(?x4324, ?x825), award_winner(?x4224, ?x4324) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jym0 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.794 http://example.org/film/film/country #19912-0njvn PRED entity: 0njvn PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 134 concepts (66 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.76 #113, 0.76 #136, 0.75 #102), 04_1l0v (0.16 #252, 0.12 #669, 0.12 #562), 04rrx (0.08 #158, 0.08 #194, 0.07 #124), 0d060g (0.07 #611, 0.07 #576, 0.01 #535), 02jx1 (0.03 #467, 0.03 #738, 0.02 #775), 026mj (0.01 #535), 07_f2 (0.01 #535), 05fjf (0.01 #535), 0694j (0.01 #535), 06yxd (0.01 #535) >> Best rule #113 for best value: >> intensional similarity = 5 >> extensional distance = 336 >> proper extension: 0fm9_; 02cl1; 0mw89; 0fr59; 0mwh1; 0mk7z; 0tz1x; 0m7d0; 03fb3t; 0cymp; ... >> query: (?x169, 09c7w0) <- currency(?x169, ?x170), ?x170 = 09nqf, contains(?x1906, ?x169), source(?x169, ?x958), ?x958 = 0jbk9 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0njvn second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 66.000 0.763 http://example.org/location/country/second_level_divisions #19911-026670 PRED entity: 026670 PRED relation: profession PRED expected values: 01d_h8 => 141 concepts (127 used for prediction) PRED predicted values (max 10 best out of 89): 02hrh1q (0.92 #11262, 0.90 #9042, 0.89 #7858), 01d_h8 (0.85 #154, 0.85 #4151, 0.85 #2227), 0cbd2 (0.47 #2672, 0.46 #4300, 0.45 #3856), 03gjzk (0.44 #1198, 0.44 #14, 0.43 #2235), 018gz8 (0.41 #608, 0.21 #1052, 0.20 #2089), 0kyk (0.33 #1806, 0.32 #2694, 0.32 #1065), 02krf9 (0.24 #3431, 0.23 #3727, 0.22 #3135), 0d1pc (0.19 #50, 0.12 #198, 0.12 #2419), 02hv44_ (0.18 #501, 0.16 #1093, 0.15 #797), 09jwl (0.18 #1499, 0.17 #10971, 0.17 #2535) >> Best rule #11262 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 0m2wm; 02zq43; 04wqr; 07lmxq; 03m8lq; 01j5x6; 01v3s2_; 0bz5v2; 04cf09; 01wjrn; ... >> query: (?x9754, 02hrh1q) <- nominated_for(?x9754, ?x2090), film(?x9754, ?x4565), profession(?x9754, ?x524) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #154 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 03y3dk; *> query: (?x9754, 01d_h8) <- produced_by(?x7911, ?x9754), award(?x9754, ?x68), spouse(?x9754, ?x4490) *> conf = 0.85 ranks of expected_values: 2 EVAL 026670 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 127.000 0.916 http://example.org/people/person/profession #19910-0152n0 PRED entity: 0152n0 PRED relation: country PRED expected values: 0ctw_b 0h7x => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 405): 0d0vqn (0.87 #1957, 0.83 #1569, 0.83 #200), 02vzc (0.87 #1996, 0.83 #200, 0.83 #398), 05qhw (0.85 #2747, 0.85 #1771, 0.84 #2945), 0k6nt (0.83 #200, 0.83 #398, 0.83 #397), 01mjq (0.83 #200, 0.83 #398, 0.83 #397), 059j2 (0.83 #200, 0.83 #398, 0.83 #397), 03gj2 (0.83 #200, 0.83 #398, 0.83 #397), 07t21 (0.83 #200, 0.83 #398, 0.83 #397), 0ctw_b (0.83 #1585, 0.83 #398, 0.83 #397), 0154j (0.83 #200, 0.83 #398, 0.83 #397) >> Best rule #1957 for best value: >> intensional similarity = 45 >> extensional distance = 13 >> proper extension: 01hp22; 07bs0; >> query: (?x6564, 0d0vqn) <- country(?x6564, ?x7479), country(?x6564, ?x3277), country(?x6564, ?x2513), country(?x6564, ?x1790), country(?x6564, ?x512), country(?x6564, ?x205), olympics(?x6564, ?x418), ?x205 = 03rjj, ?x1790 = 01pj7, ?x512 = 07ssc, currency(?x7479, ?x170), film_release_region(?x7897, ?x2513), film_release_region(?x5347, ?x2513), film_release_region(?x5270, ?x2513), film_release_region(?x5067, ?x2513), film_release_region(?x3938, ?x2513), film_release_region(?x3201, ?x2513), film_release_region(?x3000, ?x2513), film_release_region(?x1803, ?x2513), film_release_region(?x634, ?x2513), film_release_region(?x204, ?x2513), administrative_parent(?x2513, ?x551), combatants(?x151, ?x2513), ?x7897 = 03np63f, ?x5347 = 02ylg6, ?x3000 = 045j3w, administrative_area_type(?x2513, ?x2792), ?x1803 = 0g9wdmc, organization(?x3277, ?x127), olympics(?x2513, ?x5176), olympics(?x2513, ?x1931), olympics(?x2513, ?x1617), ?x5067 = 01rwpj, ?x1617 = 01f1jy, ?x204 = 028_yv, ?x5270 = 0bc1yhb, ?x3201 = 01ffx4, location_of_ceremony(?x566, ?x7479), ?x5176 = 0sx92, ?x3938 = 024mpp, contains(?x455, ?x2513), ?x634 = 0gx9rvq, participating_countries(?x1931, ?x183), olympics(?x1122, ?x1931), olympics(?x171, ?x1931) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1585 for first EXPECTED value: *> intensional similarity = 46 *> extensional distance = 10 *> proper extension: 01dys; 01z27; 02y8z; *> query: (?x6564, 0ctw_b) <- country(?x6564, ?x7479), country(?x6564, ?x2513), country(?x6564, ?x2152), country(?x6564, ?x1790), country(?x6564, ?x512), country(?x6564, ?x205), olympics(?x6564, ?x418), ?x205 = 03rjj, ?x1790 = 01pj7, film_release_region(?x8682, ?x512), film_release_region(?x8137, ?x512), film_release_region(?x7493, ?x512), film_release_region(?x6446, ?x512), film_release_region(?x6321, ?x512), film_release_region(?x6095, ?x512), film_release_region(?x1803, ?x512), film_release_region(?x573, ?x512), nationality(?x3295, ?x512), nationality(?x1223, ?x512), nationality(?x642, ?x512), combatants(?x512, ?x151), region(?x54, ?x512), country(?x6636, ?x512), country(?x4734, ?x512), ?x1803 = 0g9wdmc, ?x8137 = 0gtx63s, profession(?x642, ?x1183), ?x8682 = 0bmfnjs, ?x6446 = 089j8p, olympics(?x512, ?x2432), contains(?x512, ?x362), ?x6095 = 0bq6ntw, combatants(?x326, ?x512), ?x7493 = 0btpm6, ?x2152 = 06mkj, film(?x1342, ?x4734), ?x2432 = 0nbjq, ?x6321 = 0gg8z1f, film_crew_role(?x573, ?x137), form_of_government(?x7479, ?x1926), instrumentalists(?x74, ?x642), award_winner(?x72, ?x1223), film_release_distribution_medium(?x6636, ?x81), country(?x1339, ?x512), ?x2513 = 05b4w, student(?x13639, ?x3295) *> conf = 0.83 ranks of expected_values: 9, 22 EVAL 0152n0 country 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 25.000 25.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0152n0 country 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 25.000 25.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #19909-06sff PRED entity: 06sff PRED relation: official_language PRED expected values: 02bjrlw => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 38): 02bjrlw (0.44 #617, 0.41 #1103, 0.37 #926), 02h40lc (0.29 #1060, 0.28 #486, 0.28 #795), 064_8sq (0.17 #500, 0.15 #721, 0.14 #809), 0jzc (0.12 #146, 0.12 #234, 0.12 #190), 06nm1 (0.12 #625, 0.12 #580, 0.11 #934), 04306rv (0.08 #5, 0.05 #137, 0.05 #49), 071fb (0.05 #144, 0.04 #232, 0.04 #276), 0k0sv (0.05 #62, 0.05 #18, 0.05 #106), 06mp7 (0.05 #55, 0.05 #11, 0.05 #99), 0k0sb (0.05 #86, 0.05 #42, 0.04 #174) >> Best rule #617 for best value: >> intensional similarity = 3 >> extensional distance = 137 >> proper extension: 049nq; >> query: (?x5186, ?x90) <- administrative_parent(?x5186, ?x551), countries_spoken_in(?x90, ?x5186), languages(?x914, ?x90) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06sff official_language 02bjrlw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.443 http://example.org/location/country/official_language #19908-04511f PRED entity: 04511f PRED relation: award_nominee! PRED expected values: 08nz99 => 89 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1037): 08nz99 (0.87 #11670, 0.82 #18672, 0.82 #35009), 046mxj (0.87 #11670, 0.82 #18672, 0.82 #35009), 03ft8 (0.48 #49009, 0.09 #93362), 059j4x (0.42 #14004, 0.05 #20960, 0.04 #4621), 0d9_96 (0.42 #14004, 0.04 #3089, 0.03 #38098), 04511f (0.29 #105037, 0.20 #1002, 0.12 #3335), 0jbp0 (0.29 #105037, 0.16 #112039, 0.08 #58344), 0n8bn (0.29 #105037, 0.16 #112039, 0.08 #58344), 0m32_ (0.29 #105037, 0.16 #112039, 0.08 #58344), 0564mx (0.20 #1987, 0.04 #4320, 0.01 #20659) >> Best rule #11670 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0pz7h; 057d89; 06jrhz; 03cs_xw; >> query: (?x4299, ?x5431) <- award_nominee(?x4299, ?x5431), program_creator(?x10140, ?x4299), tv_program(?x4299, ?x4932) >> conf = 0.87 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 04511f award_nominee! 08nz99 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 48.000 0.866 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19907-09cr8 PRED entity: 09cr8 PRED relation: nominated_for! PRED expected values: 02x258x 0gqy2 02qyntr => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 194): 04kxsb (0.80 #312, 0.49 #540, 0.33 #1681), 09sb52 (0.80 #1825, 0.78 #7510, 0.69 #912), 0gqy2 (0.78 #7510, 0.69 #912, 0.68 #9794), 03hkv_r (0.78 #7510, 0.69 #912, 0.68 #9794), 0f4x7 (0.73 #252, 0.55 #709, 0.41 #480), 02rdyk7 (0.69 #912, 0.68 #9794, 0.68 #10023), 09d28z (0.69 #912, 0.68 #9794, 0.68 #10023), 027c924 (0.69 #912, 0.68 #9794, 0.68 #10023), 02w_6xj (0.69 #912, 0.68 #9794, 0.68 #10023), 099ck7 (0.60 #393, 0.19 #20489, 0.19 #20488) >> Best rule #312 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 09m6kg; 092vkg; 0f4vx; 019vhk; 03hkch7; 011yl_; 011yn5; 0gmgwnv; 03xf_m; 08nhfc1; ... >> query: (?x1820, 04kxsb) <- award(?x1820, ?x2853), language(?x1820, ?x254), award_winner(?x1820, ?x192), ?x2853 = 09qv_s >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #7510 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 596 *> proper extension: 0m123; 02_1ky; *> query: (?x1820, ?x2853) <- award(?x1820, ?x2853), nominated_for(?x2853, ?x144), award_winner(?x1820, ?x192), ceremony(?x2853, ?x873) *> conf = 0.78 ranks of expected_values: 3, 14, 39 EVAL 09cr8 nominated_for! 02qyntr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 101.000 101.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09cr8 nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 101.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09cr8 nominated_for! 02x258x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 101.000 101.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19906-049n7 PRED entity: 049n7 PRED relation: school PRED expected values: 01jq4b => 82 concepts (60 used for prediction) PRED predicted values (max 10 best out of 569): 01jq0j (0.50 #651, 0.43 #831, 0.33 #2461), 01tx9m (0.50 #638, 0.43 #818, 0.25 #2448), 025v3k (0.50 #591, 0.29 #771, 0.25 #2401), 07w0v (0.48 #3261, 0.46 #4343, 0.44 #1275), 0f1nl (0.43 #747, 0.33 #207, 0.33 #27), 01dzg0 (0.43 #876, 0.33 #156, 0.29 #1781), 06fq2 (0.39 #1393, 0.37 #1573, 0.36 #2658), 012vwb (0.39 #1314, 0.37 #1494, 0.32 #2579), 02pptm (0.33 #1405, 0.33 #140, 0.32 #1585), 01vs5c (0.33 #1351, 0.33 #266, 0.32 #1531) >> Best rule #651 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 05m_8; 01d5z; >> query: (?x1160, 01jq0j) <- school(?x1160, ?x6814), school(?x1160, ?x1884), season(?x1160, ?x9498), team(?x2010, ?x1160), draft(?x1160, ?x1161), ?x9498 = 027pwzc, ?x6814 = 03tw2s, ?x1884 = 0bx8pn >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3433 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 31 *> proper extension: 0jm3v; 0jm2v; 0jm74; 01k8vh; *> query: (?x1160, ?x1011) <- school(?x1160, ?x4363), school(?x1160, ?x735), draft(?x1160, ?x11905), school(?x11905, ?x8202), school(?x11905, ?x1011), student(?x4363, ?x158), major_field_of_study(?x735, ?x254), school_type(?x735, ?x1044), ?x254 = 02h40lc, ?x8202 = 06fq2 *> conf = 0.19 ranks of expected_values: 136 EVAL 049n7 school 01jq4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 82.000 60.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #19905-0kq2g PRED entity: 0kq2g PRED relation: adjoins! PRED expected values: 0l2nd => 130 concepts (52 used for prediction) PRED predicted values (max 10 best out of 415): 0kpzy (0.40 #1084, 0.33 #300, 0.28 #21186), 0l2sr (0.33 #454, 0.28 #21186, 0.25 #21188), 0l34j (0.33 #224, 0.28 #21186, 0.20 #1008), 0kq1l (0.28 #21186, 0.25 #21188, 0.25 #21187), 0kq0q (0.28 #21186, 0.23 #30613, 0.23 #39258), 0l2mg (0.28 #21186, 0.20 #1437, 0.03 #2222), 0l2nd (0.25 #21188, 0.25 #21187, 0.25 #17263), 0kq2g (0.25 #21188, 0.25 #21187, 0.25 #17263), 0l2xl (0.13 #1938, 0.12 #2724, 0.08 #3510), 0kv4k (0.12 #2830, 0.09 #5186, 0.08 #3616) >> Best rule #1084 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0l2sr; 0l2nd; >> query: (?x12056, 0kpzy) <- adjoins(?x5892, ?x12056), contains(?x1227, ?x12056), ?x1227 = 01n7q, second_level_divisions(?x94, ?x12056), ?x5892 = 0bxqq >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #21188 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 281 *> proper extension: 0f4y_; 05kr_; 0mlyw; 0mm0p; 0nvd8; 0mws3; 0nh57; 0cc1v; 043z0; 0nm8n; ... *> query: (?x12056, ?x7520) <- adjoins(?x5892, ?x12056), adjoins(?x9582, ?x5892), adjoins(?x7520, ?x5892), time_zones(?x9582, ?x2950), currency(?x5892, ?x170), adjoins(?x6815, ?x9582) *> conf = 0.25 ranks of expected_values: 7 EVAL 0kq2g adjoins! 0l2nd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 130.000 52.000 0.400 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #19904-01vqq1 PRED entity: 01vqq1 PRED relation: contains! PRED expected values: 0d060g => 171 concepts (97 used for prediction) PRED predicted values (max 10 best out of 265): 09c7w0 (0.71 #25989, 0.70 #12547, 0.70 #34060), 0d060g (0.70 #1804, 0.68 #34953, 0.67 #3595), 01n7q (0.66 #60138, 0.60 #34135, 0.58 #40412), 06pvr (0.38 #5539, 0.35 #6435, 0.33 #8228), 04_1l0v (0.36 #39886, 0.32 #50646, 0.32 #45269), 0kpys (0.22 #12725, 0.21 #14517, 0.20 #15412), 02qkt (0.17 #84605, 0.14 #57715, 0.09 #38884), 07ssc (0.17 #35883, 0.16 #69058, 0.15 #79809), 05kj_ (0.15 #40375, 0.15 #42170, 0.15 #41272), 0345h (0.15 #30553, 0.09 #52969, 0.08 #58348) >> Best rule #25989 for best value: >> intensional similarity = 5 >> extensional distance = 47 >> proper extension: 0kv5t; >> query: (?x10858, 09c7w0) <- time_zones(?x10858, ?x2950), place_of_birth(?x7759, ?x10858), award_winner(?x2071, ?x7759), contains(?x7468, ?x10858), ?x2950 = 02lcqs >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1804 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0dyg2; *> query: (?x10858, 0d060g) <- time_zones(?x10858, ?x2950), ?x2950 = 02lcqs, contains(?x7468, ?x10858), ?x7468 = 015jr *> conf = 0.70 ranks of expected_values: 2 EVAL 01vqq1 contains! 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 171.000 97.000 0.714 http://example.org/location/location/contains #19903-0pkgt PRED entity: 0pkgt PRED relation: student! PRED expected values: 0lyjf => 97 concepts (72 used for prediction) PRED predicted values (max 10 best out of 126): 0bwfn (0.25 #1329, 0.25 #802, 0.10 #3437), 09f2j (0.25 #159, 0.05 #5429, 0.04 #12280), 01qqv5 (0.25 #335, 0.02 #6659, 0.02 #7186), 02607j (0.25 #103, 0.02 #6427, 0.02 #6954), 01w5m (0.15 #3267, 0.08 #2213, 0.08 #18025), 017z88 (0.10 #3244, 0.09 #12203, 0.08 #2717), 07tgn (0.08 #2125, 0.06 #4760, 0.04 #5814), 015nl4 (0.08 #2175, 0.05 #8499, 0.05 #9026), 09r4xx (0.08 #2231, 0.03 #4866, 0.02 #5920), 03fmfs (0.08 #2216, 0.03 #4851, 0.02 #5905) >> Best rule #1329 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0178rl; >> query: (?x11238, 0bwfn) <- artists(?x10332, ?x11238), award(?x11238, ?x4317), award(?x11238, ?x3467), award(?x11238, ?x1869), ?x4317 = 05q8pss, ?x3467 = 02h3d1, ?x1869 = 04njml >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #12806 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 152 *> proper extension: 032t2z; 01w9mnm; 063tn; *> query: (?x11238, 0lyjf) <- artists(?x10332, ?x11238), artists(?x10332, ?x1894), nationality(?x11238, ?x94), major_field_of_study(?x2775, ?x10332), music(?x188, ?x1894) *> conf = 0.01 ranks of expected_values: 108 EVAL 0pkgt student! 0lyjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 97.000 72.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #19902-0cq8nx PRED entity: 0cq8nx PRED relation: film_sets_designed! PRED expected values: 076lxv => 102 concepts (79 used for prediction) PRED predicted values (max 10 best out of 23): 07h1tr (0.19 #54, 0.16 #80, 0.13 #28), 057bc6m (0.16 #61, 0.15 #87, 0.12 #164), 0cb77r (0.14 #51, 0.11 #129, 0.11 #77), 076lxv (0.12 #206, 0.12 #155, 0.11 #130), 0g1rw (0.11 #75, 0.10 #49, 0.10 #101), 0gv40 (0.11 #75, 0.10 #49, 0.10 #101), 012j8z (0.11 #75, 0.10 #49, 0.10 #101), 019fnv (0.11 #75, 0.10 #49, 0.10 #101), 07xr3w (0.11 #75, 0.10 #49, 0.10 #101), 076psv (0.07 #109, 0.07 #56, 0.07 #30) >> Best rule #54 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0m_q0; 0dnw1; 0bykpk; 04wddl; 0bmhn; >> query: (?x9611, 07h1tr) <- nominated_for(?x788, ?x9611), film_art_direction_by(?x9611, ?x2449), music(?x9611, ?x13352), award(?x9611, ?x601) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #206 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 04v8x9; 03hjv97; 0147sh; 01_mdl; 0c5dd; 04mzf8; 0dtfn; 0qm98; 02r79_h; 0bcndz; ... *> query: (?x9611, 076lxv) <- nominated_for(?x788, ?x9611), film_art_direction_by(?x9611, ?x2449), country(?x9611, ?x94), nominated_for(?x591, ?x9611) *> conf = 0.12 ranks of expected_values: 4 EVAL 0cq8nx film_sets_designed! 076lxv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 79.000 0.186 http://example.org/film/film_set_designer/film_sets_designed #19901-02r_d4 PRED entity: 02r_d4 PRED relation: film PRED expected values: 05zpghd 098s2w => 113 concepts (64 used for prediction) PRED predicted values (max 10 best out of 721): 02hct1 (0.59 #90910, 0.59 #60609, 0.59 #87344), 0124k9 (0.59 #90910, 0.59 #60609, 0.59 #87344), 02rv_dz (0.18 #239, 0.03 #69521), 01shy7 (0.12 #2205, 0.02 #20028, 0.02 #3988), 016dj8 (0.09 #1110, 0.03 #8240, 0.03 #69521), 02qr3k8 (0.09 #1283, 0.03 #69521, 0.02 #26237), 03bx2lk (0.09 #184, 0.03 #69521, 0.02 #19790), 0crd8q6 (0.09 #1626, 0.03 #69521, 0.02 #5192), 09146g (0.09 #297, 0.03 #69521, 0.02 #14555), 031t2d (0.09 #254, 0.03 #69521, 0.02 #14512) >> Best rule #90910 for best value: >> intensional similarity = 3 >> extensional distance = 1315 >> proper extension: 03gm48; 0f0p0; 03xmy1; 0m32_; 027l0b; 01v3vp; 02dbn2; 01pqy_; 03cn92; 02tkzn; ... >> query: (?x665, ?x1542) <- film(?x665, ?x559), award(?x665, ?x678), nominated_for(?x665, ?x1542) >> conf = 0.59 => this is the best rule for 2 predicted values *> Best rule #951 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 9 *> proper extension: 0cjdk; *> query: (?x665, 05zpghd) <- nominated_for(?x665, ?x2436), ?x2436 = 02hct1 *> conf = 0.09 ranks of expected_values: 19 EVAL 02r_d4 film 098s2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 64.000 0.589 http://example.org/film/actor/film./film/performance/film EVAL 02r_d4 film 05zpghd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 113.000 64.000 0.589 http://example.org/film/actor/film./film/performance/film #19900-053rxgm PRED entity: 053rxgm PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.85 #57, 0.80 #157, 0.80 #142), 02nxhr (0.22 #2, 0.06 #43, 0.05 #53), 07c52 (0.09 #54, 0.06 #44, 0.04 #200), 07z4p (0.07 #56, 0.07 #46, 0.03 #202), 0735l (0.07 #16) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 353 >> proper extension: 03t97y; 01kff7; 05p3738; 01s3vk; 08sk8l; 02nx2k; 05ch98; 01gglm; 07p12s; 0h63q6t; >> query: (?x1178, 029j_) <- film_crew_role(?x1178, ?x2154), film(?x2387, ?x1178), ?x2154 = 01vx2h >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 053rxgm film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.845 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19899-04fzfj PRED entity: 04fzfj PRED relation: nominated_for! PRED expected values: 05f4m9q => 91 concepts (84 used for prediction) PRED predicted values (max 10 best out of 221): 07cbcy (0.58 #2670, 0.35 #773, 0.26 #299), 04ljl_l (0.50 #2611, 0.35 #240, 0.29 #714), 05f4m9q (0.48 #2619, 0.35 #722, 0.32 #248), 05p09zm (0.44 #803, 0.38 #329, 0.31 #2700), 0gq9h (0.34 #1009, 0.33 #7175, 0.33 #5753), 0k611 (0.32 #1020, 0.27 #5764, 0.24 #7186), 019f4v (0.31 #5744, 0.28 #7166, 0.28 #1000), 0gs9p (0.30 #1011, 0.29 #7177, 0.28 #5755), 0p9sw (0.30 #968, 0.28 #3102, 0.26 #3339), 099c8n (0.29 #1478, 0.23 #1003, 0.20 #3611) >> Best rule #2670 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 07kb7vh; >> query: (?x723, 07cbcy) <- nominated_for(?x1105, ?x723), award(?x166, ?x1105), nominated_for(?x1105, ?x6206), ?x6206 = 0cwfgz >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #2619 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 148 *> proper extension: 07kb7vh; *> query: (?x723, 05f4m9q) <- nominated_for(?x1105, ?x723), award(?x166, ?x1105), nominated_for(?x1105, ?x6206), ?x6206 = 0cwfgz *> conf = 0.48 ranks of expected_values: 3 EVAL 04fzfj nominated_for! 05f4m9q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 84.000 0.580 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19898-03cd0x PRED entity: 03cd0x PRED relation: film_crew_role PRED expected values: 02rh1dz 01vx2h => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 32): 01vx2h (0.38 #283, 0.37 #351, 0.34 #249), 01pvkk (0.30 #284, 0.29 #352, 0.28 #491), 0215hd (0.25 #51, 0.12 #85, 0.12 #498), 01xy5l_ (0.25 #46, 0.12 #80, 0.10 #114), 0d2b38 (0.25 #58, 0.12 #194, 0.12 #229), 089g0h (0.25 #52, 0.11 #292, 0.11 #360), 02rh1dz (0.19 #282, 0.18 #350, 0.17 #248), 02ynfr (0.19 #254, 0.17 #495, 0.17 #288), 015h31 (0.12 #41, 0.12 #247, 0.11 #281), 020xn5 (0.12 #40, 0.09 #2819, 0.04 #211) >> Best rule #283 for best value: >> intensional similarity = 3 >> extensional distance = 288 >> proper extension: 0gtsx8c; >> query: (?x5388, 01vx2h) <- film_crew_role(?x5388, ?x137), crewmember(?x5388, ?x1983), language(?x5388, ?x254) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 03cd0x film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.376 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 03cd0x film_crew_role 02rh1dz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 83.000 83.000 0.376 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19897-0411q PRED entity: 0411q PRED relation: artist! PRED expected values: 01w565 0190vc 0cv9t5 => 112 concepts (79 used for prediction) PRED predicted values (max 10 best out of 111): 03rhqg (0.41 #3055, 0.19 #292, 0.18 #154), 015_1q (0.26 #20, 0.25 #296, 0.25 #158), 0n85g (0.22 #3101, 0.10 #338, 0.08 #7252), 01cl2y (0.19 #3070, 0.10 #307, 0.08 #31), 0mzkr (0.18 #3065, 0.11 #992, 0.10 #1130), 01w40h (0.18 #29, 0.17 #305, 0.14 #167), 0181dw (0.14 #1145, 0.13 #317, 0.13 #593), 011k1h (0.14 #148, 0.13 #10, 0.12 #3049), 017l96 (0.13 #295, 0.13 #19, 0.11 #157), 02p11jq (0.13 #13, 0.12 #289, 0.11 #151) >> Best rule #3055 for best value: >> intensional similarity = 3 >> extensional distance = 289 >> proper extension: 05563d; 07yg2; 02dw1_; 08w4pm; 0qmny; >> query: (?x219, 03rhqg) <- artist(?x9243, ?x219), artist(?x9243, ?x7620), ?x7620 = 06gcn >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #359 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 01pbxb; 0bvzp; 01vsy9_; *> query: (?x219, 0190vc) <- profession(?x219, ?x220), artist(?x5744, ?x219), inductee(?x1091, ?x219) *> conf = 0.08 ranks of expected_values: 27, 62 EVAL 0411q artist! 0cv9t5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 112.000 79.000 0.405 http://example.org/music/record_label/artist EVAL 0411q artist! 0190vc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 112.000 79.000 0.405 http://example.org/music/record_label/artist EVAL 0411q artist! 01w565 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 79.000 0.405 http://example.org/music/record_label/artist #19896-04jspq PRED entity: 04jspq PRED relation: produced_by! PRED expected values: 0crfwmx => 141 concepts (98 used for prediction) PRED predicted values (max 10 best out of 670): 03q0r1 (0.42 #18966, 0.40 #20861, 0.40 #22758), 0dyb1 (0.42 #18966, 0.40 #20861, 0.40 #22758), 01xdxy (0.42 #18966, 0.40 #20861, 0.40 #22758), 02c7k4 (0.42 #18966, 0.40 #20861, 0.40 #22758), 0407yj_ (0.42 #18966, 0.40 #20861, 0.40 #22758), 03x7hd (0.29 #43617, 0.28 #24654, 0.23 #12328), 06fcqw (0.29 #43617, 0.28 #24654, 0.23 #12328), 04hwbq (0.18 #9483, 0.17 #17070, 0.17 #12327), 015g28 (0.12 #1302, 0.12 #354, 0.05 #6991), 0bwfwpj (0.12 #1038, 0.05 #8625, 0.04 #10521) >> Best rule #18966 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0pksh; >> query: (?x6682, ?x2933) <- award_winner(?x6682, ?x1052), type_of_union(?x6682, ?x566), film(?x6682, ?x2933) >> conf = 0.42 => this is the best rule for 5 predicted values *> Best rule #10431 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 01c58j; 02508x; 01nbq4; *> query: (?x6682, ?x218) <- profession(?x6682, ?x319), company(?x6682, ?x10685), production_companies(?x218, ?x10685) *> conf = 0.09 ranks of expected_values: 60 EVAL 04jspq produced_by! 0crfwmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 141.000 98.000 0.416 http://example.org/film/film/produced_by #19895-0n_hp PRED entity: 0n_hp PRED relation: currency PRED expected values: 09nqf => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.89 #29, 0.89 #22, 0.83 #114), 02l6h (0.07 #4, 0.04 #54, 0.03 #18), 01nv4h (0.05 #52, 0.05 #31, 0.03 #129), 02gsvk (0.01 #511) >> Best rule #29 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 0jzw; 02vnmc9; >> query: (?x9129, ?x170) <- film_release_distribution_medium(?x9129, ?x81), crewmember(?x9129, ?x6546), nominated_for(?x2288, ?x9129), film(?x1814, ?x2288), currency(?x2288, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n_hp currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.889 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #19894-02s9vc PRED entity: 02s9vc PRED relation: teams! PRED expected values: 09c7w0 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 100): 059j2 (0.33 #308, 0.25 #578, 0.06 #1928), 0chghy (0.33 #10, 0.25 #820, 0.06 #1900), 0f04v (0.25 #1501, 0.25 #1231, 0.17 #1771), 02h6_6p (0.25 #1429, 0.17 #1699, 0.02 #2779), 0hzlz (0.25 #835, 0.04 #2185, 0.03 #2455), 04lh6 (0.17 #1806, 0.02 #2886, 0.02 #3426), 0jgd (0.06 #1893, 0.04 #2163, 0.03 #2433), 06qd3 (0.06 #1936, 0.04 #2206, 0.03 #2476), 0f8l9c (0.06 #1914, 0.04 #2184, 0.03 #2454), 0h3y (0.06 #1897, 0.04 #2167, 0.03 #2437) >> Best rule #308 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 02ltg3; >> query: (?x9926, 059j2) <- position(?x9926, ?x63), team(?x11941, ?x9926), current_club(?x9926, ?x13542), current_club(?x9926, ?x6477), current_club(?x9926, ?x1899), sport(?x9926, ?x471), team(?x1898, ?x1899), ?x471 = 02vx4, team(?x208, ?x6477), ?x13542 = 0mmd6 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02s9vc teams! 09c7w0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 57.000 0.333 http://example.org/sports/sports_team_location/teams #19893-06qc5 PRED entity: 06qc5 PRED relation: film_crew_role! PRED expected values: 0_b3d => 24 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1799): 0bth54 (0.80 #10164, 0.78 #8899, 0.77 #11430), 01gwk3 (0.80 #10929, 0.71 #13462, 0.71 #7138), 047wh1 (0.80 #10761, 0.71 #13294, 0.71 #6970), 0ct2tf5 (0.80 #11223, 0.71 #7432, 0.69 #12489), 05qbckf (0.80 #10343, 0.71 #6552, 0.69 #11609), 09sh8k (0.80 #10114, 0.71 #6323, 0.67 #7587), 076xkps (0.80 #11193, 0.71 #7402, 0.62 #12459), 04jpg2p (0.78 #8642, 0.70 #11169, 0.69 #12435), 0fdv3 (0.78 #7794, 0.70 #10321, 0.69 #11587), 03z20c (0.71 #13002, 0.70 #10469, 0.69 #11735) >> Best rule #10164 for best value: >> intensional similarity = 31 >> extensional distance = 8 >> proper extension: 01vx2h; >> query: (?x9094, 0bth54) <- film_crew_role(?x5109, ?x9094), film_crew_role(?x251, ?x9094), film(?x574, ?x251), film_release_region(?x5109, ?x2152), film_release_region(?x5109, ?x1790), film_release_region(?x5109, ?x1603), film_release_region(?x5109, ?x1229), film_release_region(?x5109, ?x142), award(?x5109, ?x112), ?x2152 = 06mkj, genre(?x251, ?x811), nominated_for(?x3458, ?x251), film_regional_debut_venue(?x5109, ?x739), genre(?x5109, ?x714), honored_for(?x873, ?x5109), ?x3458 = 0gqxm, ?x142 = 0jgd, ?x1790 = 01pj7, film(?x2033, ?x5109), titles(?x714, ?x2287), ?x2287 = 02s4l6, genre(?x4127, ?x714), genre(?x3268, ?x714), genre(?x943, ?x714), ?x3268 = 02x6dqb, ?x4127 = 049mql, ?x811 = 03k9fj, ?x1603 = 06bnz, ?x943 = 0963mq, ?x1229 = 059j2, film(?x609, ?x5109) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3895 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 2 *> proper extension: 01pvkk; *> query: (?x9094, 0_b3d) <- film_crew_role(?x7711, ?x9094), film_crew_role(?x6415, ?x9094), film_crew_role(?x5109, ?x9094), film_crew_role(?x3897, ?x9094), film_crew_role(?x3257, ?x9094), film_crew_role(?x251, ?x9094), ?x251 = 02vp1f_, genre(?x5109, ?x53), honored_for(?x873, ?x5109), award(?x5109, ?x112), featured_film_locations(?x3257, ?x2552), film(?x2033, ?x5109), film_distribution_medium(?x5109, ?x2099), ?x3897 = 02dpl9, ?x2099 = 0735l, film_release_region(?x5109, ?x985), country(?x6415, ?x94), language(?x3257, ?x254), production_companies(?x7711, ?x574), program(?x1762, ?x6415), film_release_region(?x3257, ?x390), award(?x2938, ?x112), nominated_for(?x112, ?x144), ?x2938 = 01nwwl, place_of_birth(?x2566, ?x2552), film_release_region(?x5873, ?x985), film_release_region(?x4610, ?x985), ?x4610 = 017jd9, olympics(?x985, ?x391), ?x5873 = 0cq86w *> conf = 0.50 ranks of expected_values: 476 EVAL 06qc5 film_crew_role! 0_b3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 24.000 16.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19892-03s9b PRED entity: 03s9b PRED relation: people! PRED expected values: 02knxx => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 35): 01psyx (0.25 #45, 0.03 #1101, 0.03 #1629), 0gk4g (0.12 #406, 0.08 #1198, 0.07 #274), 0dq9p (0.06 #1997, 0.05 #281, 0.05 #1865), 0qcr0 (0.05 #397, 0.04 #727, 0.03 #1717), 04p3w (0.05 #77, 0.04 #209, 0.03 #1001), 02knxx (0.05 #98, 0.02 #230, 0.02 #296), 02y0js (0.05 #992, 0.04 #1982, 0.04 #2312), 019dmc (0.04 #512, 0.04 #710, 0.04 #644), 08g5q7 (0.04 #372, 0.02 #1032, 0.02 #834), 01l2m3 (0.03 #1006, 0.03 #478, 0.03 #610) >> Best rule #45 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 02pb2bp; >> query: (?x6957, 01psyx) <- influenced_by(?x6957, ?x12355), film_festivals(?x12355, ?x9932) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #98 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 09qc1; 01nz1q6; *> query: (?x6957, 02knxx) <- location(?x6957, ?x9621), award_winner(?x77, ?x6957), type_of_appearance(?x6957, ?x3429) *> conf = 0.05 ranks of expected_values: 6 EVAL 03s9b people! 02knxx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 165.000 165.000 0.250 http://example.org/people/cause_of_death/people #19891-03cd0x PRED entity: 03cd0x PRED relation: nominated_for! PRED expected values: 05p09zm => 85 concepts (84 used for prediction) PRED predicted values (max 10 best out of 208): 07cbcy (0.50 #59, 0.43 #529, 0.39 #999), 05p09zm (0.50 #90, 0.43 #560, 0.38 #325), 0gq9h (0.38 #763, 0.32 #3583, 0.30 #5463), 05p1dby (0.36 #548, 0.26 #1018, 0.23 #8227), 019f4v (0.28 #3574, 0.26 #3809, 0.25 #5454), 0gs9p (0.27 #5465, 0.27 #3585, 0.26 #6405), 05q8pss (0.25 #147, 0.17 #1087, 0.15 #382), 0641kkh (0.24 #7521, 0.23 #8227, 0.20 #16455), 0bdwft (0.24 #7521, 0.23 #8227, 0.20 #16455), 0cqgl9 (0.24 #7521, 0.23 #8227, 0.20 #16455) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0gzlb9; >> query: (?x5388, 07cbcy) <- nominated_for(?x382, ?x5388), award(?x5388, ?x688), ?x688 = 05b1610, ?x382 = 086k8 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #90 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0gzlb9; *> query: (?x5388, 05p09zm) <- nominated_for(?x382, ?x5388), award(?x5388, ?x688), ?x688 = 05b1610, ?x382 = 086k8 *> conf = 0.50 ranks of expected_values: 2 EVAL 03cd0x nominated_for! 05p09zm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 84.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19890-01cw7s PRED entity: 01cw7s PRED relation: ceremony PRED expected values: 01mh_q => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 121): 01mh_q (0.78 #950, 0.77 #825, 0.77 #700), 0gx1673 (0.50 #355, 0.49 #855, 0.49 #730), 0n8_m93 (0.25 #228, 0.16 #1228, 0.16 #1353), 0bzm81 (0.25 #141, 0.16 #1141, 0.16 #1266), 02yxh9 (0.25 #211, 0.15 #1211, 0.15 #1336), 0bc773 (0.25 #168, 0.15 #1168, 0.15 #1293), 02yw5r (0.25 #133, 0.15 #1133, 0.15 #1258), 02yvhx (0.25 #190, 0.15 #1190, 0.15 #1315), 02hn5v (0.25 #157, 0.15 #1157, 0.15 #1282), 0bvfqq (0.25 #150, 0.15 #1150, 0.15 #1275) >> Best rule #950 for best value: >> intensional similarity = 5 >> extensional distance = 86 >> proper extension: 01ckbq; 01c92g; 01ck6h; 0257yf; 025m98; 03t5n3; 0249fn; 03ncb2; 03nc9d; 023vrq; ... >> query: (?x6652, 01mh_q) <- category_of(?x6652, ?x2421), ceremony(?x6652, ?x2186), award(?x1128, ?x6652), ceremony(?x10881, ?x2186), ?x10881 = 026mmy >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cw7s ceremony 01mh_q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.784 http://example.org/award/award_category/winners./award/award_honor/ceremony #19889-025m8l PRED entity: 025m8l PRED relation: award! PRED expected values: 03q0r1 => 50 concepts (11 used for prediction) PRED predicted values (max 10 best out of 787): 0c0zq (0.50 #5008, 0.43 #6033, 0.15 #9111), 0404j37 (0.50 #4773, 0.43 #5798, 0.12 #8876), 0m313 (0.50 #4106, 0.43 #5131, 0.11 #8209), 0hmr4 (0.50 #4166, 0.43 #5191, 0.11 #7243), 0b_5d (0.50 #4398, 0.43 #5423, 0.06 #8501), 0pv3x (0.43 #5234, 0.33 #4209, 0.14 #7286), 05sbv3 (0.33 #5084, 0.33 #2009, 0.29 #6109), 0gmcwlb (0.33 #4225, 0.29 #5250, 0.17 #8328), 09gq0x5 (0.33 #4275, 0.29 #5300, 0.17 #8378), 0hfzr (0.33 #4518, 0.29 #5543, 0.15 #8621) >> Best rule #5008 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 040njc; 0gq9h; 0gr51; >> query: (?x2238, 0c0zq) <- award(?x3434, ?x2238), award(?x2012, ?x2238), artists(?x2542, ?x2012), ?x3434 = 02bfxb, participant(?x2012, ?x4929) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 025m8l award! 03q0r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 11.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19888-09949m PRED entity: 09949m PRED relation: location_of_ceremony! PRED expected values: 04ztj => 209 concepts (209 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.84 #37, 0.83 #113, 0.82 #122), 01g63y (0.60 #117, 0.07 #22, 0.06 #38), 0jgjn (0.14 #24, 0.06 #40, 0.05 #221), 01bl8s (0.03 #43, 0.02 #200, 0.02 #75) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 05fkf; >> query: (?x8809, 04ztj) <- location(?x5366, ?x8809), vacationer(?x8809, ?x5889), award_nominee(?x4589, ?x5889), program(?x4589, ?x2137) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09949m location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 209.000 209.000 0.844 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #19887-04hqbbz PRED entity: 04hqbbz PRED relation: nationality PRED expected values: 03rk0 => 69 concepts (30 used for prediction) PRED predicted values (max 10 best out of 20): 05sb1 (0.82 #2131, 0.40 #2132, 0.34 #1217), 09c7w0 (0.79 #101, 0.75 #810, 0.75 #405), 065zr (0.40 #2132, 0.34 #1217, 0.32 #2234), 03rk0 (0.33 #46, 0.33 #1320, 0.32 #1827), 055vr (0.33 #1320, 0.32 #1827), 02jx1 (0.08 #2772, 0.08 #2672, 0.08 #2872), 07ssc (0.07 #2044, 0.07 #2954, 0.06 #2654), 0d060g (0.05 #1935, 0.04 #2544, 0.04 #2946), 0345h (0.04 #536, 0.04 #1553, 0.03 #1756), 0f8l9c (0.04 #527, 0.03 #1240, 0.03 #629) >> Best rule #2131 for best value: >> intensional similarity = 5 >> extensional distance = 1426 >> proper extension: 0457w0; 04mx7s; >> query: (?x9027, ?x2236) <- gender(?x9027, ?x231), place_of_birth(?x9027, ?x8514), contains(?x2236, ?x8514), ?x231 = 05zppz, member_states(?x7695, ?x2236) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 02qnk5c; *> query: (?x9027, 03rk0) <- gender(?x9027, ?x231), profession(?x9027, ?x1032), ?x231 = 05zppz, place_of_birth(?x9027, ?x8514), ?x8514 = 023vwt *> conf = 0.33 ranks of expected_values: 4 EVAL 04hqbbz nationality 03rk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 30.000 0.815 http://example.org/people/person/nationality #19886-0b4lkx PRED entity: 0b4lkx PRED relation: language PRED expected values: 02h40lc => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.96 #2867, 0.95 #2165, 0.94 #4864), 064_8sq (0.21 #137, 0.17 #544, 0.17 #195), 04306rv (0.18 #121, 0.17 #5, 0.14 #237), 06b_j (0.17 #22, 0.11 #138, 0.10 #196), 0jzc (0.10 #19, 0.05 #135, 0.04 #1185), 02bjrlw (0.08 #407, 0.08 #465, 0.08 #1341), 0653m (0.06 #69, 0.05 #766, 0.05 #185), 03_9r (0.05 #1176, 0.05 #4463, 0.05 #184), 03hkp (0.05 #130, 0.04 #246, 0.03 #14), 0459q4 (0.05 #210, 0.04 #268, 0.03 #152) >> Best rule #2867 for best value: >> intensional similarity = 3 >> extensional distance = 1035 >> proper extension: 0gtsx8c; >> query: (?x8000, 02h40lc) <- language(?x8000, ?x2502), film(?x398, ?x8000), production_companies(?x8000, ?x382) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b4lkx language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.959 http://example.org/film/film/language #19885-02tc5y PRED entity: 02tc5y PRED relation: participant! PRED expected values: 02vntj => 123 concepts (103 used for prediction) PRED predicted values (max 10 best out of 287): 0h5g_ (0.20 #671, 0.02 #7833, 0.02 #13041), 01r93l (0.11 #1598, 0.03 #7457, 0.02 #11363), 04shbh (0.11 #1370, 0.02 #7229, 0.02 #11135), 0gyx4 (0.06 #9420, 0.05 #11373, 0.05 #4212), 0c6qh (0.06 #2116, 0.04 #9277, 0.04 #4069), 0gy6z9 (0.06 #2180, 0.04 #4133, 0.03 #2831), 02g0mx (0.06 #2164, 0.04 #4117, 0.02 #8023), 0d_84 (0.06 #1964, 0.03 #5870, 0.03 #2615), 0dvmd (0.05 #11279, 0.05 #9326, 0.05 #1514), 01wxyx1 (0.05 #1432, 0.03 #2083, 0.03 #9244) >> Best rule #671 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 03jvmp; >> query: (?x10224, 0h5g_) <- nominated_for(?x10224, ?x6023), ?x6023 = 0bbm7r, award_nominee(?x10224, ?x818) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02tc5y participant! 02vntj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 103.000 0.200 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #19884-01c9dd PRED entity: 01c9dd PRED relation: award! PRED expected values: 01w7nwm 01v40wd 02x_h0 01vvyc_ => 45 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2507): 01vw20h (0.77 #20160, 0.71 #11356, 0.50 #4637), 011z3g (0.77 #20160, 0.43 #12021, 0.33 #8662), 0288fyj (0.77 #20160, 0.27 #13438, 0.20 #23521), 01vw8mh (0.57 #11479, 0.50 #4760, 0.50 #1401), 01w7nwm (0.50 #7582, 0.50 #4222, 0.50 #863), 01v40wd (0.50 #4437, 0.50 #1078, 0.43 #11156), 03j3pg9 (0.50 #2814, 0.43 #12892, 0.33 #9533), 01wmxfs (0.50 #179, 0.33 #6898, 0.29 #10257), 0bqvs2 (0.50 #2173, 0.33 #8892, 0.29 #12251), 01vvyc_ (0.50 #5055, 0.29 #11774, 0.27 #13438) >> Best rule #20160 for best value: >> intensional similarity = 5 >> extensional distance = 81 >> proper extension: 02v1ws; >> query: (?x8705, ?x140) <- award_winner(?x8705, ?x6264), award_winner(?x8705, ?x140), profession(?x6264, ?x1614), ?x1614 = 01c72t, category_of(?x8705, ?x2421) >> conf = 0.77 => this is the best rule for 3 predicted values *> Best rule #7582 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 031b3h; 03r00m; *> query: (?x8705, 01w7nwm) <- award(?x11897, ?x8705), award(?x2926, ?x8705), ?x11897 = 01f2q5, artists(?x671, ?x2926) *> conf = 0.50 ranks of expected_values: 5, 6, 10, 145 EVAL 01c9dd award! 01vvyc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 45.000 21.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c9dd award! 02x_h0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 45.000 21.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c9dd award! 01v40wd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 45.000 21.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c9dd award! 01w7nwm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 45.000 21.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19883-02pvqmz PRED entity: 02pvqmz PRED relation: genre PRED expected values: 0byb_x => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 199): 05p553 (0.76 #3465, 0.62 #4401, 0.60 #4062), 07s9rl0 (0.60 #253, 0.56 #4142, 0.56 #2196), 01z4y (0.53 #3479, 0.50 #1371, 0.42 #4076), 01t_vv (0.51 #3376, 0.24 #1134, 0.22 #2145), 01z77k (0.51 #3376, 0.22 #535, 0.21 #1298), 02xh1 (0.51 #3376, 0.20 #311, 0.18 #2364), 04t36 (0.51 #3376, 0.18 #2364, 0.14 #3885), 0byb_x (0.50 #238, 0.36 #915, 0.34 #3545), 0214st (0.50 #233, 0.23 #6013, 0.21 #910), 0c4xc (0.41 #1143, 0.37 #3503, 0.32 #1900) >> Best rule #3465 for best value: >> intensional similarity = 7 >> extensional distance = 100 >> proper extension: 0dk0dj; >> query: (?x10250, 05p553) <- program(?x10344, ?x10250), genre(?x10250, ?x5728), country_of_origin(?x10250, ?x512), genre(?x7433, ?x5728), program(?x14163, ?x7433), program(?x649, ?x7433), category(?x7433, ?x134) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #238 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 06mr2s; 01b7h8; *> query: (?x10250, 0byb_x) <- program(?x13510, ?x10250), program(?x2776, ?x10250), program(?x10344, ?x10250), languages(?x10250, ?x254), nominated_for(?x2776, ?x4063), citytown(?x2776, ?x362), program(?x10344, ?x6597), category(?x13510, ?x134), actor(?x6597, ?x988), citytown(?x13510, ?x8252) *> conf = 0.50 ranks of expected_values: 8 EVAL 02pvqmz genre 0byb_x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 75.000 75.000 0.765 http://example.org/tv/tv_program/genre #19882-0q5hw PRED entity: 0q5hw PRED relation: influenced_by! PRED expected values: 015pxr => 150 concepts (108 used for prediction) PRED predicted values (max 10 best out of 385): 0bqs56 (0.19 #2829, 0.08 #3862, 0.07 #6444), 01xdf5 (0.12 #3098, 0.08 #3614, 0.06 #2581), 01j7rd (0.12 #2650, 0.08 #3683, 0.05 #29422), 05ty4m (0.12 #2585, 0.08 #2069, 0.04 #27879), 01xwv7 (0.12 #3003, 0.06 #22612, 0.06 #3520), 0q5hw (0.12 #618, 0.06 #3198, 0.06 #2681), 01s7qqw (0.12 #726, 0.06 #2789, 0.05 #29422), 015pxr (0.12 #590, 0.06 #2653, 0.05 #29422), 04bs3j (0.12 #530, 0.06 #2593, 0.05 #29422), 01wp_jm (0.12 #2986, 0.05 #29422, 0.05 #48024) >> Best rule #2829 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0ph2w; >> query: (?x2817, 0bqs56) <- influenced_by(?x2817, ?x1145), award_winner(?x678, ?x2817), celebrities_impersonated(?x692, ?x2817) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #590 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 6 *> proper extension: 0gls4q_; *> query: (?x2817, 015pxr) <- award_nominee(?x8295, ?x2817), ?x8295 = 06yrj6 *> conf = 0.12 ranks of expected_values: 8 EVAL 0q5hw influenced_by! 015pxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 150.000 108.000 0.188 http://example.org/influence/influence_node/influenced_by #19881-08g_jw PRED entity: 08g_jw PRED relation: film_crew_role PRED expected values: 09vw2b7 02ynfr => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 22): 09vw2b7 (0.66 #313, 0.65 #381, 0.64 #759), 0dxtw (0.40 #317, 0.39 #385, 0.39 #763), 01vx2h (0.31 #798, 0.31 #386, 0.31 #318), 01pvkk (0.29 #765, 0.28 #799, 0.28 #1452), 02ynfr (0.18 #769, 0.17 #323, 0.17 #391), 0215hd (0.14 #806, 0.13 #326, 0.13 #394), 01xy5l_ (0.11 #801, 0.10 #321, 0.10 #13), 02rh1dz (0.11 #316, 0.11 #384, 0.10 #762), 0d2b38 (0.10 #812, 0.10 #984, 0.10 #1190), 02_n3z (0.10 #1, 0.09 #789, 0.08 #309) >> Best rule #313 for best value: >> intensional similarity = 3 >> extensional distance = 658 >> proper extension: 03g90h; 0gx1bnj; 0czyxs; 02_1sj; 026mfbr; 035xwd; 09p35z; 04gknr; 0963mq; 05q96q6; ... >> query: (?x10842, 09vw2b7) <- produced_by(?x10842, ?x10061), film_crew_role(?x10842, ?x137), film(?x6677, ?x10842) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 08g_jw film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 80.000 80.000 0.656 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 08g_jw film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.656 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19880-0cp9f9 PRED entity: 0cp9f9 PRED relation: award_winner! PRED expected values: 0gvstc3 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 106): 0gvstc3 (0.12 #5242, 0.11 #31, 0.10 #6815), 09pnw5 (0.12 #5242, 0.10 #6815, 0.10 #8389), 0bzjvm (0.12 #5242, 0.10 #6815, 0.10 #8389), 013b2h (0.08 #1644, 0.06 #2955, 0.05 #2693), 0gx_st (0.07 #557, 0.07 #33, 0.06 #688), 0bq_mx (0.07 #516, 0.05 #647, 0.04 #778), 056878 (0.06 #1601, 0.05 #2912, 0.04 #2650), 07y9ts (0.06 #586, 0.05 #717, 0.04 #62), 0466p0j (0.06 #2951, 0.04 #3737, 0.04 #5310), 09g90vz (0.06 #1686, 0.05 #1162, 0.05 #2341) >> Best rule #5242 for best value: >> intensional similarity = 3 >> extensional distance = 1424 >> proper extension: 018p5f; 04qzm; >> query: (?x8229, ?x1265) <- award_nominee(?x4618, ?x8229), award_winner(?x4921, ?x8229), award_winner(?x1265, ?x4618) >> conf = 0.12 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0cp9f9 award_winner! 0gvstc3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.116 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19879-0cw51 PRED entity: 0cw51 PRED relation: service_location! PRED expected values: 06_9lg => 127 concepts (74 used for prediction) PRED predicted values (max 10 best out of 18): 06_9lg (0.82 #824, 0.82 #784, 0.78 #686), 017vb_ (0.05 #1876, 0.05 #2014, 0.05 #2151), 01y81r (0.05 #1830, 0.05 #1968, 0.05 #2105), 08qnnv (0.04 #1428, 0.02 #1702, 0.02 #1839), 0cv9b (0.03 #3580, 0.02 #7444, 0.02 #7581), 03s7h (0.03 #3680), 01c6k4 (0.02 #7439, 0.02 #7576, 0.02 #7713), 02lv2v (0.02 #3098, 0.01 #4751), 064f29 (0.02 #7493, 0.02 #7630, 0.02 #7767), 069b85 (0.02 #3698, 0.01 #7285, 0.01 #7562) >> Best rule #824 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 027wvb; >> query: (?x10810, ?x10867) <- contains(?x2146, ?x10810), ?x2146 = 03rk0, administrative_division(?x10810, ?x12193), contains(?x12193, ?x10315), service_location(?x10867, ?x10315) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cw51 service_location! 06_9lg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 74.000 0.818 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #19878-0trv PRED entity: 0trv PRED relation: institution! PRED expected values: 07s6fsf => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 18): 03bwzr4 (0.79 #173, 0.79 #46, 0.75 #137), 07s6fsf (0.68 #165, 0.66 #129, 0.62 #92), 04zx3q1 (0.65 #166, 0.59 #130, 0.59 #93), 027f2w (0.62 #133, 0.62 #169, 0.51 #334), 01rr_d (0.32 #2274, 0.30 #2480, 0.30 #2179), 0bjrnt (0.32 #2274, 0.30 #2480, 0.30 #2179), 022h5x (0.32 #2274, 0.30 #2480, 0.30 #2179), 02m4yg (0.32 #2274, 0.30 #2480, 0.30 #2179), 071tyz (0.32 #2274, 0.30 #2480, 0.30 #2179), 01ysy9 (0.32 #2274, 0.30 #2480, 0.30 #2179) >> Best rule #173 for best value: >> intensional similarity = 9 >> extensional distance = 32 >> proper extension: 07tg4; 015cz0; 01pj48; >> query: (?x8706, 03bwzr4) <- citytown(?x8706, ?x4419), institution(?x3437, ?x8706), institution(?x1526, ?x8706), institution(?x1200, ?x8706), institution(?x865, ?x8706), ?x1526 = 0bkj86, ?x3437 = 02_xgp2, ?x1200 = 016t_3, ?x865 = 02h4rq6 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 32 *> proper extension: 07tg4; 015cz0; 01pj48; *> query: (?x8706, 07s6fsf) <- citytown(?x8706, ?x4419), institution(?x3437, ?x8706), institution(?x1526, ?x8706), institution(?x1200, ?x8706), institution(?x865, ?x8706), ?x1526 = 0bkj86, ?x3437 = 02_xgp2, ?x1200 = 016t_3, ?x865 = 02h4rq6 *> conf = 0.68 ranks of expected_values: 2 EVAL 0trv institution! 07s6fsf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 175.000 175.000 0.794 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #19877-01vvydl PRED entity: 01vvydl PRED relation: award_winner! PRED expected values: 01bx35 0jzphpx => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 117): 02cg41 (0.50 #125, 0.16 #545, 0.15 #685), 01s695 (0.25 #2, 0.20 #142, 0.11 #1822), 013b2h (0.25 #79, 0.14 #3719, 0.13 #359), 019bk0 (0.25 #15, 0.13 #295, 0.10 #715), 0hhtgcw (0.25 #85, 0.11 #505, 0.10 #645), 02hn5v (0.25 #41, 0.05 #461, 0.05 #601), 02rjjll (0.16 #424, 0.15 #564, 0.14 #844), 0466p0j (0.16 #495, 0.15 #635, 0.14 #915), 0gx1673 (0.16 #539, 0.15 #679, 0.06 #2919), 01bx35 (0.13 #286, 0.10 #5886, 0.10 #5746) >> Best rule #125 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01vrt_c; 01vsgrn; >> query: (?x140, 02cg41) <- award_nominee(?x1125, ?x140), ?x1125 = 016kjs, artist(?x8738, ?x140) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #286 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 03q2t9; 01mr2g6; 0g7k2g; *> query: (?x140, 01bx35) <- company(?x140, ?x10352), artist(?x2299, ?x140), instrumentalists(?x228, ?x140) *> conf = 0.13 ranks of expected_values: 10, 20 EVAL 01vvydl award_winner! 0jzphpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 123.000 123.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01vvydl award_winner! 01bx35 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 123.000 123.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19876-0h98b3k PRED entity: 0h98b3k PRED relation: ceremony! PRED expected values: 0fqpg6b => 23 concepts (21 used for prediction) PRED predicted values (max 10 best out of 286): 0gr51 (0.96 #1614, 0.94 #1102, 0.93 #847), 0gqyl (0.96 #1617, 0.86 #2387, 0.84 #2130), 0gqwc (0.95 #2109, 0.86 #1596, 0.83 #1084), 0k611 (0.91 #1097, 0.90 #1609, 0.90 #842), 0gqy2 (0.90 #2173, 0.90 #1660, 0.90 #893), 0gq_d (0.90 #1697, 0.89 #2210, 0.89 #1185), 0gvx_ (0.90 #907, 0.89 #1162, 0.88 #1674), 0gs96 (0.90 #860, 0.89 #1115, 0.81 #2140), 0gqz2 (0.89 #1087, 0.86 #832, 0.80 #1599), 0p9sw (0.88 #1555, 0.87 #2068, 0.80 #2325) >> Best rule #1614 for best value: >> intensional similarity = 17 >> extensional distance = 47 >> proper extension: 02yw5r; 0dth6b; 0bzkgg; 0bzk2h; 0bz6l9; 0fz20l; 05qb8vx; 0bz6sb; 0bzknt; 02pgky2; ... >> query: (?x13189, 0gr51) <- ceremony(?x7291, ?x13189), ceremony(?x3245, ?x13189), award_winner(?x13189, ?x8415), award(?x4742, ?x7291), award(?x7740, ?x7291), award(?x3246, ?x3245), ?x4742 = 0_b9f, honored_for(?x13189, ?x573), nominated_for(?x7291, ?x3398), award_winner(?x3245, ?x3281), award(?x2275, ?x3245), cinematography(?x1625, ?x7740), award_winner(?x8415, ?x1983), genre(?x3246, ?x53), nominated_for(?x7740, ?x7502), nominated_for(?x746, ?x3246), ?x3281 = 0154qm >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #5147 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 127 *> proper extension: 0h_9252; *> query: (?x13189, ?x14647) <- ceremony(?x7291, ?x13189), award_winner(?x13189, ?x11399), award_winner(?x7291, ?x7327), award(?x7740, ?x7291), award_nominee(?x804, ?x11399), nominated_for(?x11399, ?x308), nationality(?x7327, ?x390), award(?x11399, ?x14647), award_nominee(?x11399, ?x2938), student(?x8357, ?x11399), gender(?x11399, ?x231), ?x231 = 05zppz, titles(?x162, ?x308), nominated_for(?x68, ?x308) *> conf = 0.16 ranks of expected_values: 60 EVAL 0h98b3k ceremony! 0fqpg6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 23.000 21.000 0.959 http://example.org/award/award_category/winners./award/award_honor/ceremony #19875-0443c PRED entity: 0443c PRED relation: gender PRED expected values: 05zppz => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #17, 0.91 #15, 0.91 #51), 02zsn (0.46 #181, 0.27 #110, 0.27 #120) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0bn9sc; 040j2_; 02y0dd; >> query: (?x13779, 05zppz) <- team(?x13779, ?x8516), location(?x13779, ?x739), type_of_union(?x13779, ?x566) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0443c gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.923 http://example.org/people/person/gender #19874-072twv PRED entity: 072twv PRED relation: place_of_birth PRED expected values: 02cft => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 63): 02_286 (0.10 #9178, 0.09 #9883, 0.08 #7769), 030qb3t (0.09 #758, 0.07 #2871, 0.05 #54), 01_d4 (0.09 #770, 0.07 #5701, 0.05 #66), 0cr3d (0.07 #2911, 0.04 #798, 0.04 #3615), 0f2wj (0.07 #9864, 0.06 #8455, 0.06 #11979), 01nl79 (0.07 #1949, 0.03 #4062, 0.02 #4766), 02dtg (0.07 #2122, 0.03 #1418, 0.03 #3531), 02cft (0.05 #61982, 0.01 #3750), 01sn3 (0.05 #149, 0.05 #2966, 0.04 #853), 04swd (0.05 #316, 0.05 #3133, 0.04 #1020) >> Best rule #9178 for best value: >> intensional similarity = 2 >> extensional distance = 330 >> proper extension: 01d494; 0j3v; 0dzkq; 07c37; 02ln1; 0cl_m; 03j90; 047g6; 01h2_6; 011zwl; ... >> query: (?x2449, 02_286) <- place_of_death(?x2449, ?x682), student(?x5238, ?x2449) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #61982 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 4025 *> proper extension: 07qnf; 0784v1; 07m69t; 01ly8d; 06s27s; 01nvdc; 03cxqp5; *> query: (?x2449, ?x6357) <- nationality(?x2449, ?x429), capital(?x429, ?x6357) *> conf = 0.05 ranks of expected_values: 8 EVAL 072twv place_of_birth 02cft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 91.000 91.000 0.099 http://example.org/people/person/place_of_birth #19873-0fhxv PRED entity: 0fhxv PRED relation: profession PRED expected values: 02hrh1q => 145 concepts (104 used for prediction) PRED predicted values (max 10 best out of 73): 02hrh1q (0.86 #6339, 0.82 #2807, 0.80 #8996), 016z4k (0.69 #4709, 0.65 #3238, 0.65 #3091), 0nbcg (0.67 #618, 0.63 #1353, 0.60 #7387), 039v1 (0.50 #623, 0.40 #7392, 0.37 #7244), 01c72t (0.44 #1786, 0.42 #463, 0.40 #904), 0dxtg (0.43 #12, 0.31 #747, 0.30 #15045), 018gz8 (0.43 #16, 0.19 #2663, 0.15 #751), 01d_h8 (0.41 #5153, 0.39 #6331, 0.33 #2064), 02jknp (0.31 #742, 0.29 #7, 0.28 #5155), 0cbd2 (0.31 #741, 0.20 #9137, 0.17 #1623) >> Best rule #6339 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 06vsbt; >> query: (?x4646, 02hrh1q) <- student(?x11722, ?x4646), award_winner(?x2180, ?x4646), award_nominee(?x6783, ?x4646), participant(?x380, ?x4646) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fhxv profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 104.000 0.857 http://example.org/people/person/profession #19872-06x4c PRED entity: 06x4c PRED relation: nutrient! PRED expected values: 01645p => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 75): 0f25w9 (0.89 #355, 0.89 #644, 0.88 #326), 01645p (0.89 #704, 0.88 #514, 0.87 #719), 0frq6 (0.88 #691, 0.88 #507, 0.88 #501), 05z55 (0.88 #521, 0.88 #516, 0.87 #493), 07j87 (0.86 #282, 0.86 #350, 0.86 #362), 0971v (0.86 #282, 0.86 #350, 0.86 #362), 06x4c (0.86 #282, 0.86 #350, 0.86 #362), 0f4kp (0.04 #37, 0.03 #24, 0.03 #59), 07q0m (0.04 #37, 0.03 #24, 0.03 #59), 02kc_w5 (0.04 #37, 0.03 #24, 0.03 #59) >> Best rule #355 for best value: >> intensional similarity = 94 >> extensional distance = 17 >> proper extension: 0838f; 025rsfk; >> query: (?x5337, 0f25w9) <- nutrient(?x9005, ?x5337), nutrient(?x8298, ?x5337), nutrient(?x7719, ?x5337), nutrient(?x6191, ?x5337), nutrient(?x6159, ?x5337), nutrient(?x6032, ?x5337), nutrient(?x3900, ?x5337), nutrient(?x3468, ?x5337), nutrient(?x2701, ?x5337), nutrient(?x1303, ?x5337), nutrient(?x1257, ?x5337), ?x6191 = 014j1m, ?x3468 = 0cxn2, ?x7719 = 0dj75, ?x8298 = 037ls6, ?x2701 = 0hkxq, ?x1257 = 09728, ?x1303 = 0fj52s, ?x9005 = 04zpv, ?x6159 = 033cnk, nutrient(?x3900, ?x13944), nutrient(?x3900, ?x13498), nutrient(?x3900, ?x12902), nutrient(?x3900, ?x12454), nutrient(?x3900, ?x11758), nutrient(?x3900, ?x11592), nutrient(?x3900, ?x11270), nutrient(?x3900, ?x10891), nutrient(?x3900, ?x10195), nutrient(?x3900, ?x10098), nutrient(?x3900, ?x9840), nutrient(?x3900, ?x9795), nutrient(?x3900, ?x9733), nutrient(?x3900, ?x9619), nutrient(?x3900, ?x9490), nutrient(?x3900, ?x9436), nutrient(?x3900, ?x9426), nutrient(?x3900, ?x9365), nutrient(?x3900, ?x8487), nutrient(?x3900, ?x7894), nutrient(?x3900, ?x7720), nutrient(?x3900, ?x7652), nutrient(?x3900, ?x7364), nutrient(?x3900, ?x7362), nutrient(?x3900, ?x7219), nutrient(?x3900, ?x6192), nutrient(?x3900, ?x6033), nutrient(?x3900, ?x5526), nutrient(?x3900, ?x5451), nutrient(?x3900, ?x5374), nutrient(?x3900, ?x5010), nutrient(?x3900, ?x4069), nutrient(?x3900, ?x3901), nutrient(?x3900, ?x3469), nutrient(?x3900, ?x3203), nutrient(?x3900, ?x1960), ?x8487 = 014yzm, ?x7720 = 025s7x6, ?x10098 = 0h1_c, ?x5010 = 0h1vz, ?x7894 = 0f4hc, ?x13498 = 07q0m, ?x3203 = 04kl74p, ?x5451 = 05wvs, ?x9490 = 0h1sg, ?x9365 = 04k8n, ?x9436 = 025sqz8, ?x9426 = 0h1yy, ?x6033 = 04zjxcz, ?x7652 = 025s0s0, ?x12454 = 025rw19, ?x6032 = 01nkt, ?x5526 = 09pbb, ?x5374 = 025s0zp, ?x7364 = 09gvd, ?x11758 = 0q01m, ?x13944 = 0f4kp, ?x9619 = 0h1tg, ?x9795 = 05v_8y, ?x3469 = 0h1zw, ?x7362 = 02kc5rj, ?x9840 = 02p0tjr, ?x4069 = 0hqw8p_, ?x7219 = 0h1vg, ?x10891 = 0g5gq, ?x6192 = 06jry, ?x11592 = 025sf0_, nutrient(?x9489, ?x10195), ?x9733 = 0h1tz, ?x11270 = 02kc008, ?x3901 = 0466p20, ?x12902 = 0fzjh, ?x9489 = 07j87, ?x1960 = 07hnp >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #704 for first EXPECTED value: *> intensional similarity = 89 *> extensional distance = 44 *> proper extension: 027g6p7; *> query: (?x5337, 01645p) <- nutrient(?x6191, ?x5337), nutrient(?x4068, ?x5337), nutrient(?x3900, ?x5337), nutrient(?x3468, ?x5337), nutrient(?x2701, ?x5337), ?x6191 = 014j1m, nutrient(?x3468, ?x13944), nutrient(?x3468, ?x13545), nutrient(?x3468, ?x12902), nutrient(?x3468, ?x11784), nutrient(?x3468, ?x11409), nutrient(?x3468, ?x11270), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10709), nutrient(?x3468, ?x10195), nutrient(?x3468, ?x10098), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x9840), nutrient(?x3468, ?x9733), nutrient(?x3468, ?x9436), nutrient(?x3468, ?x9365), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x7894), nutrient(?x3468, ?x7720), nutrient(?x3468, ?x7652), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7364), nutrient(?x3468, ?x7362), nutrient(?x3468, ?x7219), nutrient(?x3468, ?x6586), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x6160), nutrient(?x3468, ?x6033), nutrient(?x3468, ?x6026), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5010), nutrient(?x3468, ?x3469), nutrient(?x3468, ?x3203), nutrient(?x3468, ?x2018), nutrient(?x3468, ?x1960), nutrient(?x3468, ?x1258), ?x10098 = 0h1_c, ?x6026 = 025sf8g, ?x9915 = 025tkqy, ?x1960 = 07hnp, ?x7720 = 025s7x6, ?x10891 = 0g5gq, ?x3203 = 04kl74p, ?x6160 = 041r51, ?x9365 = 04k8n, ?x7894 = 0f4hc, ?x1258 = 0h1wg, ?x7431 = 09gwd, nutrient(?x4068, ?x13498), nutrient(?x4068, ?x12868), nutrient(?x4068, ?x11592), nutrient(?x4068, ?x8243), nutrient(?x4068, ?x5374), nutrient(?x10612, ?x13545), ?x3900 = 061_f, ?x10195 = 0hkwr, ?x6033 = 04zjxcz, ?x3469 = 0h1zw, ?x5549 = 025s7j4, ?x7362 = 02kc5rj, ?x2018 = 01sh2, ?x6586 = 05gh50, ?x11592 = 025sf0_, ?x13498 = 07q0m, ?x5010 = 0h1vz, ?x9733 = 0h1tz, ?x11784 = 07zqy, ?x7364 = 09gvd, ?x8243 = 014d7f, ?x11409 = 0h1yf, ?x10612 = 0frq6, ?x9840 = 02p0tjr, ?x7652 = 025s0s0, ?x7219 = 0h1vg, ?x11270 = 02kc008, ?x2701 = 0hkxq, ?x9436 = 025sqz8, ?x12868 = 03d49, ?x8413 = 02kc4sf, ?x12902 = 0fzjh, ?x13944 = 0f4kp, ?x5374 = 025s0zp, ?x6192 = 06jry, ?x10709 = 0h1sz *> conf = 0.89 ranks of expected_values: 2 EVAL 06x4c nutrient! 01645p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.895 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #19871-05gml8 PRED entity: 05gml8 PRED relation: award_nominee PRED expected values: 050t68 => 112 concepts (72 used for prediction) PRED predicted values (max 10 best out of 973): 02p65p (0.82 #9360, 0.81 #149764, 0.81 #140401), 050t68 (0.82 #9360, 0.81 #149764, 0.81 #140401), 0lx2l (0.76 #145084, 0.76 #142743, 0.76 #152104), 0306ds (0.48 #7592, 0.15 #140402, 0.02 #115236), 026l37 (0.48 #8104, 0.02 #12784, 0.02 #10444), 05gml8 (0.44 #134, 0.40 #2473, 0.25 #74879), 05th8t (0.41 #7593, 0.15 #140402, 0.03 #12273), 053y4h (0.41 #8237, 0.15 #140402, 0.02 #50349), 06t74h (0.41 #7953, 0.15 #140402, 0.02 #50065), 06jzh (0.41 #7125, 0.15 #140402, 0.02 #11805) >> Best rule #9360 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 050t68; >> query: (?x709, ?x192) <- award_nominee(?x1641, ?x709), award_nominee(?x192, ?x709), film(?x709, ?x2163), ?x1641 = 07s8r0 >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 05gml8 award_nominee 050t68 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 72.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19870-0gqxm PRED entity: 0gqxm PRED relation: ceremony PRED expected values: 073hmq 0bzn6_ 05qb8vx 05q7cj 0bzmt8 073h5b => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 116): 05q7cj (0.90 #541, 0.50 #309, 0.20 #193), 0dth6b (0.86 #484, 0.33 #252, 0.20 #136), 05qb8vx (0.86 #511, 0.33 #279, 0.15 #859), 073h5b (0.81 #573, 0.50 #341, 0.22 #4647), 0bz6l9 (0.81 #504, 0.50 #272, 0.20 #156), 0bzjgq (0.81 #559, 0.33 #327, 0.20 #211), 0bzjvm (0.81 #552, 0.33 #320, 0.20 #204), 073hmq (0.76 #482, 0.50 #250, 0.27 #3483), 0fk0xk (0.76 #528, 0.50 #296, 0.20 #180), 0fzrtf (0.76 #514, 0.50 #282, 0.20 #166) >> Best rule #541 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x3458, 05q7cj) <- ceremony(?x3458, ?x5761), ceremony(?x3458, ?x2082), award(?x2871, ?x3458), ?x5761 = 02ywhz, ?x2082 = 0gmdkyy >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 8, 11, 20 EVAL 0gqxm ceremony 073h5b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 48.000 48.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqxm ceremony 0bzmt8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 48.000 48.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqxm ceremony 05q7cj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqxm ceremony 05qb8vx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 48.000 48.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqxm ceremony 0bzn6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 48.000 48.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqxm ceremony 073hmq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 48.000 48.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony #19869-01tw31 PRED entity: 01tw31 PRED relation: location PRED expected values: 0clz7 => 133 concepts (116 used for prediction) PRED predicted values (max 10 best out of 249): 01xd9 (0.44 #7305, 0.36 #10513, 0.03 #67465), 04lh6 (0.33 #1238, 0.22 #2041, 0.17 #2844), 02cft (0.28 #7527, 0.21 #10735, 0.02 #15548), 04jpl (0.17 #12835, 0.17 #1605, 0.12 #7237), 02_286 (0.17 #2445, 0.17 #839, 0.16 #85874), 094jv (0.17 #895, 0.11 #1698, 0.08 #2501), 0r02m (0.17 #1514, 0.11 #2317, 0.08 #3120), 0b2ds (0.17 #1157, 0.11 #1960, 0.08 #2763), 071vr (0.17 #1139, 0.11 #1942, 0.08 #2745), 021npd (0.17 #1574, 0.11 #2377, 0.03 #8794) >> Best rule #7305 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0f1pyf; 02y0dd; >> query: (?x10907, 01xd9) <- nationality(?x10907, ?x429), ?x429 = 03rt9, gender(?x10907, ?x231), location(?x10907, ?x11585) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #7390 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 0f1pyf; 02y0dd; *> query: (?x10907, 0clz7) <- nationality(?x10907, ?x429), ?x429 = 03rt9, gender(?x10907, ?x231), location(?x10907, ?x11585) *> conf = 0.03 ranks of expected_values: 76 EVAL 01tw31 location 0clz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 133.000 116.000 0.438 http://example.org/people/person/places_lived./people/place_lived/location #19868-02jxsq PRED entity: 02jxsq PRED relation: split_to! PRED expected values: 02jxsq => 79 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1): 067jsf (0.02 #991, 0.01 #1186) >> Best rule #991 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 084z0w; 07yw6t; 0fr7nt; 0cvbb9q; 0969vz; 087z12; 08kp57; 04ch23; 07jmnh; 026sv5l; >> query: (?x10200, 067jsf) <- profession(?x10200, ?x1032), award(?x10200, ?x4687), ?x4687 = 03rbj2 >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02jxsq split_to! 02jxsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 43.000 0.020 http://example.org/dataworld/gardening_hint/split_to #19867-030_3z PRED entity: 030_3z PRED relation: award PRED expected values: 040njc 0gq9h => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 280): 0gq9h (0.40 #9299, 0.37 #12106, 0.37 #11304), 0cjyzs (0.37 #8525, 0.35 #8927, 0.34 #9729), 0fbtbt (0.35 #8650, 0.34 #9052, 0.33 #2635), 09sb52 (0.33 #7257, 0.33 #7658, 0.31 #2044), 040njc (0.30 #9232, 0.27 #3617, 0.26 #14044), 0ck27z (0.21 #90, 0.13 #28563, 0.13 #28964), 019f4v (0.21 #8823, 0.18 #9288, 0.17 #13699), 0gs9p (0.21 #8823, 0.18 #9301, 0.17 #14113), 05b1610 (0.21 #8823, 0.15 #3245, 0.15 #28473), 0gr51 (0.21 #8823, 0.15 #28473, 0.14 #13733) >> Best rule #9299 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 04t2l2; 06dv3; 03_gd; 02kxbwx; 03h_9lg; 06pk8; 0151w_; 030pr; 0sz28; 0bwh6; ... >> query: (?x4552, 0gq9h) <- award_winner(?x4552, ?x847), produced_by(?x153, ?x4552), type_of_union(?x4552, ?x566) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 030_3z award 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 030_3z award 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 107.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19866-07gxw PRED entity: 07gxw PRED relation: parent_genre! PRED expected values: 0fd3y 0kz10 => 50 concepts (32 used for prediction) PRED predicted values (max 10 best out of 289): 0y3_8 (0.60 #560, 0.50 #2118, 0.38 #1860), 0133k0 (0.60 #717, 0.38 #2275, 0.38 #2017), 0193f (0.40 #1135, 0.38 #2175, 0.25 #1917), 01b4p4 (0.40 #683, 0.33 #162, 0.25 #2241), 01_sz1 (0.40 #587, 0.33 #66, 0.25 #2145), 01_qp_ (0.40 #693, 0.33 #172, 0.25 #2251), 0163zw (0.33 #177, 0.29 #1735, 0.25 #2256), 03mb9 (0.33 #81, 0.25 #2160, 0.25 #1902), 07gxw (0.33 #46, 0.25 #2125, 0.25 #1867), 0dn16 (0.33 #11, 0.20 #789, 0.20 #532) >> Best rule #560 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 05c6073; >> query: (?x3915, 0y3_8) <- artists(?x3915, ?x8131), artists(?x3915, ?x7331), artists(?x3915, ?x2005), ?x2005 = 05k79, artist(?x1954, ?x7331), celebrity(?x7331, ?x6187), ?x8131 = 02hzz >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1269 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 017_qw; *> query: (?x3915, 0kz10) <- artists(?x3915, ?x10624), artists(?x3915, ?x2005), artists(?x3915, ?x1004), artists(?x3916, ?x2005), award_winner(?x3631, ?x1004), ?x3916 = 08cyft, ?x10624 = 016nvh, award(?x1004, ?x528), student(?x11036, ?x1004) *> conf = 0.20 ranks of expected_values: 26, 49 EVAL 07gxw parent_genre! 0kz10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 50.000 32.000 0.600 http://example.org/music/genre/parent_genre EVAL 07gxw parent_genre! 0fd3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 50.000 32.000 0.600 http://example.org/music/genre/parent_genre #19865-0c0tzp PRED entity: 0c0tzp PRED relation: location PRED expected values: 0mzww => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 52): 0fpzwf (0.25 #282), 02_286 (0.13 #15322, 0.11 #16126, 0.11 #16931), 030qb3t (0.10 #21721, 0.10 #29043, 0.10 #35476), 01sn3 (0.07 #4238, 0.07 #3434, 0.07 #2629), 0cr3d (0.07 #2559, 0.04 #11405, 0.04 #13015), 04jpl (0.04 #18520, 0.04 #38626, 0.04 #33802), 0f2wj (0.04 #10490, 0.04 #8078, 0.04 #7274), 0cc56 (0.04 #12122, 0.03 #12927, 0.03 #13732), 05k7sb (0.04 #8153, 0.04 #7349, 0.03 #9761), 0v9qg (0.04 #7450, 0.02 #10666) >> Best rule #282 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0f7h2g; >> query: (?x12378, 0fpzwf) <- award_winner(?x199, ?x12378), gender(?x12378, ?x231), ?x199 = 0520r2x >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c0tzp location 0mzww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 106.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #19864-043qqt5 PRED entity: 043qqt5 PRED relation: actor PRED expected values: 06b4wb => 64 concepts (44 used for prediction) PRED predicted values (max 10 best out of 795): 03dbww (0.50 #1853, 0.07 #24093), 0sw6y (0.43 #856, 0.29 #3635, 0.29 #2709), 0488g9 (0.41 #8339, 0.41 #13899, 0.36 #16681), 0dbpyd (0.38 #10192, 0.37 #19461, 0.37 #8338), 02wrhj (0.29 #137, 0.14 #1990, 0.14 #4768), 024my5 (0.29 #606, 0.14 #2459, 0.12 #3385), 0sw62 (0.21 #2613, 0.18 #3539, 0.16 #4465), 01rcmg (0.18 #3430, 0.16 #4356, 0.14 #2504), 044f7 (0.15 #1381, 0.09 #6012, 0.06 #3234), 0534nr (0.15 #1727, 0.09 #6358, 0.06 #3580) >> Best rule #1853 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 026bfsh; >> query: (?x11477, ?x10680) <- actor(?x11477, ?x7446), place_of_death(?x7446, ?x1523), profession(?x7446, ?x1032), award_winner(?x10680, ?x7446) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3631 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 0jwl2; 0b005; 02vjhf; *> query: (?x11477, 06b4wb) <- genre(?x11477, ?x10023), genre(?x11477, ?x2540), program(?x2776, ?x11477), ?x10023 = 0pr6f, genre(?x124, ?x2540) *> conf = 0.06 ranks of expected_values: 175 EVAL 043qqt5 actor 06b4wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 64.000 44.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #19863-01wzs_q PRED entity: 01wzs_q PRED relation: special_performance_type PRED expected values: 01kyvx => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 5): 01kyvx (0.92 #33, 0.90 #22, 0.82 #32), 01pb34 (0.10 #145, 0.09 #139, 0.09 #128), 09_gdc (0.03 #127, 0.03 #120, 0.03 #144), 02t8yb (0.01 #273, 0.01 #167), 014kbl (0.01 #273) >> Best rule #33 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 01kwh5j; >> query: (?x14066, 01kyvx) <- profession(?x14066, ?x1383), nationality(?x14066, ?x252), ?x252 = 03_3d, ?x1383 = 0np9r >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wzs_q special_performance_type 01kyvx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.917 http://example.org/film/actor/film./film/performance/special_performance_type #19862-0584j4n PRED entity: 0584j4n PRED relation: profession PRED expected values: 089fss => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 62): 089fss (0.83 #1067, 0.83 #917, 0.81 #767), 02hrh1q (0.74 #5722, 0.73 #6323, 0.72 #7073), 02pjxr (0.44 #3905, 0.31 #3003, 0.30 #3304), 01d_h8 (0.40 #306, 0.31 #7214, 0.28 #6013), 02ynfr (0.31 #3003, 0.28 #2402, 0.24 #5707), 0dxtg (0.29 #3768, 0.28 #3919, 0.27 #3618), 03gjzk (0.23 #6173, 0.23 #6023, 0.23 #5272), 02jknp (0.21 #7216, 0.20 #308, 0.19 #9918), 02krf9 (0.20 #328, 0.11 #5256, 0.09 #6486), 01c72t (0.18 #1676, 0.16 #1826, 0.14 #3178) >> Best rule #1067 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 03csqj4; >> query: (?x4897, 089fss) <- film_sets_designed(?x4897, ?x2717), award_nominee(?x4897, ?x2716), nationality(?x4897, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0584j4n profession 089fss CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.826 http://example.org/people/person/profession #19861-0gqyl PRED entity: 0gqyl PRED relation: ceremony PRED expected values: 073h1t 0fz2y7 02glmx 0bzm__ 073hd1 0bzlrh 0d__c3 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 83): 0bzlrh (0.88 #1141, 0.75 #477, 0.62 #643), 073hd1 (0.81 #1139, 0.75 #475, 0.62 #641), 0bzm__ (0.81 #1133, 0.75 #469, 0.62 #635), 073h1t (0.81 #1097, 0.75 #433, 0.60 #101), 02glmx (0.75 #1129, 0.75 #465, 0.60 #133), 0d__c3 (0.75 #488, 0.62 #1152, 0.62 #654), 0fz2y7 (0.69 #1117, 0.62 #453, 0.62 #619), 05c1t6z (0.50 #344, 0.25 #1589, 0.24 #1340), 0gx_st (0.50 #358, 0.23 #3321, 0.21 #4152), 03nnm4t (0.50 #378, 0.21 #4152, 0.20 #1623) >> Best rule #1141 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x1972, 0bzlrh) <- award(?x91, ?x1972), ceremony(?x1972, ?x9899), ceremony(?x1972, ?x3173), ?x3173 = 0bzk2h, ?x9899 = 0c4hnm >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7 EVAL 0gqyl ceremony 0d__c3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqyl ceremony 0bzlrh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqyl ceremony 073hd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqyl ceremony 0bzm__ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqyl ceremony 02glmx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqyl ceremony 0fz2y7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqyl ceremony 073h1t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony #19860-03_wtr PRED entity: 03_wtr PRED relation: film PRED expected values: 0j43swk => 90 concepts (57 used for prediction) PRED predicted values (max 10 best out of 358): 0828jw (0.34 #75167, 0.33 #78746, 0.33 #42953), 0pv3x (0.23 #1968, 0.03 #64429, 0.03 #82326), 02wgk1 (0.15 #2545, 0.08 #756, 0.03 #82326), 0bz3jx (0.15 #2927), 0404j37 (0.08 #1137, 0.08 #2926, 0.06 #60850), 05sy_5 (0.08 #1054, 0.08 #2843, 0.03 #82326), 02q7yfq (0.08 #1203, 0.08 #2992, 0.03 #82326), 0bq6ntw (0.08 #1058, 0.08 #2847, 0.03 #82326), 04sntd (0.08 #488, 0.08 #2277, 0.03 #82326), 03qcfvw (0.08 #9, 0.08 #1798, 0.03 #82326) >> Best rule #75167 for best value: >> intensional similarity = 2 >> extensional distance = 1429 >> proper extension: 0qf43; 0d_84; 0h1_w; 041h0; 014x77; 01nqfh_; 025p38; 025vry; 0kr5_; 02w0dc0; ... >> query: (?x7646, ?x5810) <- nationality(?x7646, ?x94), award_winner(?x5810, ?x7646) >> conf = 0.34 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_wtr film 0j43swk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 57.000 0.343 http://example.org/film/actor/film./film/performance/film #19859-0qpjt PRED entity: 0qpjt PRED relation: place! PRED expected values: 0qpjt => 72 concepts (32 used for prediction) PRED predicted values (max 10 best out of 79): 0qplq (0.25 #383, 0.06 #898, 0.01 #1413), 0qpsn (0.25 #377, 0.06 #892), 0d35y (0.06 #618, 0.01 #1133), 0fr0t (0.06 #604, 0.01 #1119), 0qr8z (0.06 #743), 0m27n (0.05 #4123, 0.05 #4639), 0dwh5 (0.01 #1529), 0jbrr (0.01 #1483), 0r02m (0.01 #1468), 013m4v (0.01 #1443) >> Best rule #383 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0qpsn; >> query: (?x9010, 0qplq) <- contains(?x7409, ?x9010), contains(?x938, ?x9010), contains(?x94, ?x9010), ?x94 = 09c7w0, ?x938 = 0vmt, ?x7409 = 0m27n >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qpjt place! 0qpjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 32.000 0.250 http://example.org/location/hud_county_place/place #19858-094g2z PRED entity: 094g2z PRED relation: titles! PRED expected values: 01z4y => 70 concepts (35 used for prediction) PRED predicted values (max 10 best out of 52): 01z4y (0.71 #139, 0.70 #345, 0.67 #242), 09q17 (0.32 #1113, 0.06 #589, 0.05 #2467), 07s9rl0 (0.29 #1977, 0.28 #2809, 0.24 #3539), 04xvlr (0.21 #833, 0.19 #725, 0.18 #3542), 02l7c8 (0.19 #3641, 0.19 #3640, 0.16 #721), 05p553 (0.19 #3641, 0.19 #3640, 0.16 #721), 01hmnh (0.15 #2522, 0.14 #1793, 0.14 #2937), 024qqx (0.14 #2990, 0.07 #1533, 0.07 #1951), 01jfsb (0.12 #1577, 0.12 #2722, 0.11 #1891), 04t36 (0.11 #626, 0.07 #2816, 0.07 #419) >> Best rule #139 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 04t6fk; >> query: (?x6603, 01z4y) <- genre(?x6603, ?x4150), genre(?x6603, ?x258), film(?x719, ?x6603), ?x4150 = 06qm3, ?x258 = 05p553, film_crew_role(?x6603, ?x1284), language(?x6603, ?x254) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 094g2z titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 35.000 0.714 http://example.org/media_common/netflix_genre/titles #19857-01qb559 PRED entity: 01qb559 PRED relation: film! PRED expected values: 032zg9 => 81 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1172): 0h5g_ (0.54 #4228, 0.38 #10461, 0.37 #8384), 0284n42 (0.48 #29092, 0.48 #29091, 0.44 #2078), 0417z2 (0.48 #39484, 0.48 #54037, 0.45 #39483), 02zj61 (0.48 #39484, 0.48 #54037, 0.45 #39483), 020h2v (0.48 #39484, 0.48 #54037, 0.45 #39483), 01q_ph (0.35 #8367, 0.32 #10444, 0.17 #56), 0341n5 (0.33 #1746, 0.22 #3824, 0.03 #10057), 013knm (0.17 #636, 0.15 #4791, 0.13 #8947), 02nwxc (0.17 #1013, 0.15 #5168, 0.11 #3091), 098n_m (0.17 #953, 0.15 #5108, 0.11 #3031) >> Best rule #4228 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 02pg45; >> query: (?x7491, 0h5g_) <- film(?x13094, ?x7491), film(?x13094, ?x5372), ?x5372 = 03t79f, edited_by(?x7491, ?x707) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #13297 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 0fq27fp; *> query: (?x7491, 032zg9) <- genre(?x7491, ?x225), film_crew_role(?x7491, ?x137), currency(?x7491, ?x170), film_release_region(?x7491, ?x94) *> conf = 0.04 ranks of expected_values: 381 EVAL 01qb559 film! 032zg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 81.000 55.000 0.538 http://example.org/film/actor/film./film/performance/film #19856-055z7 PRED entity: 055z7 PRED relation: service_language PRED expected values: 064_8sq => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 29): 05zjd (0.25 #30, 0.17 #2264, 0.14 #68), 064_8sq (0.25 #27, 0.16 #1015, 0.15 #806), 04306rv (0.25 #21, 0.14 #59, 0.12 #477), 01r2l (0.25 #29, 0.14 #67, 0.10 #390), 03_9r (0.10 #251, 0.08 #783, 0.07 #384), 06b_j (0.10 #256, 0.07 #389, 0.07 #180), 02hwhyv (0.10 #261, 0.07 #394, 0.07 #185), 01jb8r (0.07 #437, 0.07 #190, 0.06 #494), 097kp (0.07 #189, 0.05 #246, 0.05 #265), 0459q4 (0.07 #187, 0.05 #244, 0.05 #263) >> Best rule #30 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 087c7; 04fv0k; >> query: (?x13035, 05zjd) <- company(?x4792, ?x13035), company(?x1491, ?x13035), country(?x13035, ?x94), service_location(?x13035, ?x279), ?x1491 = 0krdk, ?x4792 = 05_wyz >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 087c7; 04fv0k; *> query: (?x13035, 064_8sq) <- company(?x4792, ?x13035), company(?x1491, ?x13035), country(?x13035, ?x94), service_location(?x13035, ?x279), ?x1491 = 0krdk, ?x4792 = 05_wyz *> conf = 0.25 ranks of expected_values: 2 EVAL 055z7 service_language 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 172.000 172.000 0.250 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #19855-01dbns PRED entity: 01dbns PRED relation: list PRED expected values: 09g7thr => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 4): 09g7thr (0.73 #113, 0.69 #85, 0.57 #106), 01ptsx (0.15 #411, 0.14 #54, 0.12 #523), 04k4rt (0.14 #53, 0.11 #410, 0.10 #137), 01pd60 (0.10 #139, 0.10 #412, 0.08 #153) >> Best rule #113 for best value: >> intensional similarity = 11 >> extensional distance = 13 >> proper extension: 0dplh; >> query: (?x7950, 09g7thr) <- major_field_of_study(?x7950, ?x11038), major_field_of_study(?x7950, ?x9111), ?x9111 = 04sh3, institution(?x734, ?x7950), language(?x11037, ?x11038), language(?x9941, ?x11038), language(?x5317, ?x11038), award(?x11037, ?x8843), film(?x11867, ?x11037), film_release_region(?x5317, ?x87), film(?x2279, ?x9941) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dbns list 09g7thr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.733 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #19854-02t_99 PRED entity: 02t_99 PRED relation: student! PRED expected values: 02f4s3 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 168): 04b_46 (0.25 #754, 0.19 #2335, 0.14 #1281), 0bwfn (0.25 #2383, 0.15 #4491, 0.12 #802), 015nl4 (0.20 #67, 0.06 #10607, 0.05 #9026), 08815 (0.20 #2, 0.05 #4218, 0.03 #12123), 07tgn (0.20 #17, 0.03 #7395, 0.03 #3706), 033gn8 (0.20 #378, 0.02 #16188, 0.01 #18296), 0ym1n (0.20 #523), 017d77 (0.20 #35), 09f2j (0.14 #1213, 0.13 #1740, 0.06 #2267), 0lyjf (0.12 #684, 0.07 #1211, 0.07 #1738) >> Best rule #754 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0p__8; >> query: (?x4638, 04b_46) <- award_winner(?x688, ?x4638), ?x688 = 05b1610, location(?x4638, ?x1523), film(?x4638, ?x1184) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02t_99 student! 02f4s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 123.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #19853-016zp5 PRED entity: 016zp5 PRED relation: film PRED expected values: 04ltlj => 76 concepts (47 used for prediction) PRED predicted values (max 10 best out of 532): 017jd9 (0.58 #60336, 0.46 #15968, 0.35 #79858), 049xgc (0.58 #60336, 0.46 #15968, 0.35 #79858), 016z7s (0.58 #60336, 0.46 #15968, 0.35 #79858), 01lbcqx (0.10 #1435, 0.01 #3209, 0.01 #12080), 0hv4t (0.10 #1171, 0.01 #2945), 04vvh9 (0.10 #595, 0.01 #2369), 011ywj (0.08 #6744, 0.03 #17390, 0.02 #20939), 031hcx (0.07 #6585, 0.03 #11907, 0.02 #4811), 02gjrc (0.07 #15969, 0.06 #26615), 03177r (0.06 #5786, 0.03 #11108, 0.01 #34177) >> Best rule #60336 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 04yywz; 049tjg; 02g8h; 0d_84; 0h1_w; 02nb2s; 04bs3j; 0151ns; 03_vx9; 0456xp; ... >> query: (?x5495, ?x972) <- nominated_for(?x5495, ?x972), film(?x5495, ?x1688) >> conf = 0.58 => this is the best rule for 3 predicted values *> Best rule #7028 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 144 *> proper extension: 0130sy; *> query: (?x5495, 04ltlj) <- people(?x743, ?x5495), ?x743 = 02w7gg *> conf = 0.02 ranks of expected_values: 248 EVAL 016zp5 film 04ltlj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 47.000 0.585 http://example.org/film/actor/film./film/performance/film #19852-02tr7d PRED entity: 02tr7d PRED relation: award_winner! PRED expected values: 099tbz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 208): 099tbz (0.87 #487, 0.17 #9893, 0.15 #21079), 09sb52 (0.67 #470, 0.39 #8172, 0.37 #18067), 02ppm4q (0.39 #8172, 0.37 #18067, 0.37 #18928), 0bdx29 (0.39 #8172, 0.37 #18067, 0.37 #18928), 02x4x18 (0.39 #8172, 0.37 #18067, 0.37 #18928), 05zvq6g (0.20 #489, 0.17 #9893, 0.15 #21079), 0cjyzs (0.19 #1396, 0.03 #5266, 0.03 #15591), 0gqwc (0.18 #934, 0.07 #14196, 0.06 #32696), 02z1nbg (0.17 #9893, 0.17 #1053, 0.15 #21079), 0bdwft (0.17 #9893, 0.15 #928, 0.15 #21079) >> Best rule #487 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 016gr2; 0755wz; >> query: (?x1669, 099tbz) <- award_winner(?x374, ?x1669), award_nominee(?x2372, ?x1669), ?x374 = 05cj4r >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tr7d award_winner! 099tbz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.867 http://example.org/award/award_category/winners./award/award_honor/award_winner #19851-02x3lt7 PRED entity: 02x3lt7 PRED relation: genre PRED expected values: 07s9rl0 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 95): 07s9rl0 (0.66 #2892, 0.65 #1686, 0.63 #4581), 0hcr (0.61 #24, 0.49 #144, 0.41 #264), 03k9fj (0.55 #12, 0.47 #132, 0.39 #252), 09b3v (0.54 #4459, 0.52 #7352, 0.51 #5063), 05p553 (0.54 #1449, 0.54 #1328, 0.51 #1206), 01hmnh (0.47 #18, 0.40 #138, 0.34 #258), 01jfsb (0.36 #1939, 0.34 #2061, 0.33 #4108), 02kdv5l (0.33 #4098, 0.32 #483, 0.31 #723), 06cvj (0.24 #2895, 0.14 #1448, 0.14 #1205), 06n90 (0.23 #494, 0.23 #734, 0.16 #1820) >> Best rule #2892 for best value: >> intensional similarity = 3 >> extensional distance = 416 >> proper extension: 04svwx; >> query: (?x607, 07s9rl0) <- country(?x607, ?x94), genre(?x607, ?x1403), ?x1403 = 02l7c8 >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02x3lt7 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.660 http://example.org/film/film/genre #19850-06q2q PRED entity: 06q2q PRED relation: profession! PRED expected values: 0jcx 03s9v 059y0 => 66 concepts (23 used for prediction) PRED predicted values (max 10 best out of 3982): 01n1gc (0.67 #30704, 0.50 #18037, 0.40 #68705), 03rx9 (0.67 #32765, 0.50 #20098, 0.33 #7431), 045bg (0.50 #46445, 0.50 #17215, 0.49 #33780), 07dnx (0.50 #46445, 0.50 #19776, 0.49 #33780), 0453t (0.50 #46445, 0.50 #17522, 0.49 #33780), 0d4jl (0.50 #46445, 0.49 #33780, 0.48 #21113), 0bk5r (0.50 #46445, 0.49 #33780, 0.48 #21113), 07kb5 (0.50 #46445, 0.49 #33780, 0.48 #21113), 043s3 (0.50 #46445, 0.49 #33780, 0.48 #21113), 028p0 (0.50 #46445, 0.49 #33780, 0.48 #21113) >> Best rule #30704 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 02hrh1q; 016fly; >> query: (?x3802, 01n1gc) <- profession(?x12147, ?x3802), profession(?x11286, ?x3802), profession(?x9531, ?x3802), profession(?x5131, ?x3802), profession(?x3864, ?x3802), ?x5131 = 01tdnyh, influenced_by(?x1857, ?x11286), basic_title(?x3864, ?x182), student(?x3424, ?x12147), religion(?x9531, ?x8613) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #30554 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 02hrh1q; 016fly; *> query: (?x3802, 0jcx) <- profession(?x12147, ?x3802), profession(?x11286, ?x3802), profession(?x9531, ?x3802), profession(?x5131, ?x3802), profession(?x3864, ?x3802), ?x5131 = 01tdnyh, influenced_by(?x1857, ?x11286), basic_title(?x3864, ?x182), student(?x3424, ?x12147), religion(?x9531, ?x8613) *> conf = 0.50 ranks of expected_values: 17, 65, 160 EVAL 06q2q profession! 059y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 66.000 23.000 0.667 http://example.org/people/person/profession EVAL 06q2q profession! 03s9v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 66.000 23.000 0.667 http://example.org/people/person/profession EVAL 06q2q profession! 0jcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 66.000 23.000 0.667 http://example.org/people/person/profession #19849-016017 PRED entity: 016017 PRED relation: film! PRED expected values: 03kxdw 02js_6 => 84 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1207): 031k24 (0.53 #9694, 0.08 #10369, 0.03 #74643), 015vq_ (0.33 #11079, 0.08 #6930, 0.07 #9003), 01wbg84 (0.25 #2119, 0.17 #4192, 0.05 #22853), 06t74h (0.25 #2765, 0.17 #4838, 0.03 #74643), 019f2f (0.25 #2509, 0.17 #4582, 0.02 #23243), 0bl2g (0.25 #54, 0.08 #4200, 0.07 #8347), 034q3l (0.25 #1521, 0.08 #5667, 0.07 #9814), 01fh9 (0.25 #2388, 0.08 #4461, 0.06 #23122), 01q_ph (0.25 #2129, 0.08 #4202, 0.04 #10425), 0fgg4 (0.25 #2949, 0.08 #5022, 0.03 #74643) >> Best rule #9694 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 061681; 07w8fz; 08sk8l; >> query: (?x11149, 031k24) <- production_companies(?x11149, ?x3920), film(?x1865, ?x11149), award_nominee(?x1865, ?x5363), award_nominee(?x1865, ?x949), ?x5363 = 016yvw, ?x949 = 05zbm4 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #49655 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 279 *> proper extension: 02v5xg; *> query: (?x11149, 02js_6) <- film(?x4566, ?x11149), genre(?x11149, ?x258), instrumentalists(?x1166, ?x4566) *> conf = 0.01 ranks of expected_values: 1140 EVAL 016017 film! 02js_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 51.000 0.533 http://example.org/film/actor/film./film/performance/film EVAL 016017 film! 03kxdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 51.000 0.533 http://example.org/film/actor/film./film/performance/film #19848-056vv PRED entity: 056vv PRED relation: country! PRED expected values: 06wrt 071t0 064vjs => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 47): 071t0 (0.79 #210, 0.76 #680, 0.75 #633), 03hr1p (0.57 #22, 0.52 #634, 0.51 #163), 06wrt (0.55 #109, 0.46 #627, 0.45 #674), 064vjs (0.54 #28, 0.50 #217, 0.50 #122), 01z27 (0.54 #16, 0.43 #440, 0.43 #157), 0w0d (0.53 #201, 0.51 #295, 0.48 #483), 06f41 (0.53 #203, 0.50 #673, 0.50 #626), 07gyv (0.49 #196, 0.48 #666, 0.48 #619), 07bs0 (0.46 #13, 0.43 #107, 0.43 #202), 0194d (0.43 #135, 0.43 #230, 0.40 #653) >> Best rule #210 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 05r4w; 06npd; 0f8l9c; 0hzlz; 01mk6; >> query: (?x2979, 071t0) <- jurisdiction_of_office(?x182, ?x2979), film_release_region(?x791, ?x2979), ?x182 = 060bp >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4 EVAL 056vv country! 064vjs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.786 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 056vv country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.786 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 056vv country! 06wrt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.786 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #19847-01j5ts PRED entity: 01j5ts PRED relation: award PRED expected values: 0bfvw2 => 103 concepts (102 used for prediction) PRED predicted values (max 10 best out of 259): 027571b (0.70 #27923, 0.69 #12976, 0.69 #12189), 0bfvw2 (0.46 #801, 0.38 #408, 0.13 #29498), 09sb52 (0.46 #827, 0.35 #10264, 0.34 #11050), 094qd5 (0.39 #831, 0.18 #438, 0.09 #6730), 09qwmm (0.30 #820, 0.08 #427, 0.07 #3572), 0bb57s (0.28 #626, 0.13 #1019, 0.05 #6918), 05b4l5x (0.24 #399, 0.13 #792, 0.10 #3544), 03c7tr1 (0.22 #844, 0.12 #451, 0.11 #58), 02x4wr9 (0.22 #128, 0.04 #25956, 0.03 #7206), 099vwn (0.22 #205, 0.03 #5315, 0.03 #9642) >> Best rule #27923 for best value: >> intensional similarity = 3 >> extensional distance = 2059 >> proper extension: 012ljv; 084w8; 028q6; 0fvf9q; 0l6qt; 04qvl7; 06j0md; 0197tq; 0411q; 06gp3f; ... >> query: (?x241, ?x7192) <- profession(?x241, ?x319), award(?x241, ?x995), award_winner(?x7192, ?x241) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #801 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 02jt1k; 0h96g; 0hwbd; 02l3_5; 0hsn_; 01tl50z; 03crmd; 01gvxv; 01zz8t; *> query: (?x241, 0bfvw2) <- film(?x241, ?x407), award(?x241, ?x3722), ?x3722 = 0cqgl9 *> conf = 0.46 ranks of expected_values: 2 EVAL 01j5ts award 0bfvw2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 102.000 0.697 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19846-086qd PRED entity: 086qd PRED relation: artist! PRED expected values: 01cszh => 109 concepts (74 used for prediction) PRED predicted values (max 10 best out of 117): 015_1q (0.27 #725, 0.25 #20, 0.23 #584), 0k_kr (0.25 #44, 0.13 #185, 0.12 #749), 03rhqg (0.21 #439, 0.17 #1285, 0.17 #721), 03mp8k (0.19 #1195, 0.15 #349, 0.15 #1336), 0g768 (0.18 #460, 0.16 #1306, 0.15 #1165), 0181dw (0.17 #1311, 0.14 #1170, 0.13 #3714), 011k1h (0.17 #10, 0.16 #1138, 0.13 #151), 016ckq (0.17 #43, 0.16 #1312, 0.13 #184), 017l96 (0.17 #19, 0.15 #301, 0.13 #724), 0fb0v (0.17 #7, 0.12 #289, 0.11 #430) >> Best rule #725 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 01pbxb; 01czx; 0lrh; 01w524f; 048xh; 03c3yf; 0j6cj; 01whg97; 033s6; 01l7qw; >> query: (?x2138, 015_1q) <- influenced_by(?x4593, ?x2138), award(?x2138, ?x401), artists(?x671, ?x2138) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1280 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 84 *> proper extension: 020_4z; 0ql36; *> query: (?x2138, 01cszh) <- artists(?x3928, ?x2138), artists(?x3319, ?x2138), ?x3319 = 06j6l, ?x3928 = 0gywn *> conf = 0.12 ranks of expected_values: 17 EVAL 086qd artist! 01cszh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 109.000 74.000 0.267 http://example.org/music/record_label/artist #19845-0l2tk PRED entity: 0l2tk PRED relation: school! PRED expected values: 0jmj7 => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 89): 0jmj7 (0.83 #1199, 0.66 #5610, 0.66 #5520), 01slc (0.36 #417, 0.17 #1229, 0.16 #2759), 01d6g (0.25 #610, 0.18 #790, 0.15 #700), 0jm3v (0.20 #99, 0.18 #369, 0.07 #1181), 051vz (0.20 #112, 0.18 #742, 0.17 #562), 06x68 (0.20 #97, 0.17 #547, 0.15 #637), 01ync (0.20 #128, 0.12 #758, 0.09 #398), 03m1n (0.20 #172, 0.12 #802, 0.09 #442), 0jm74 (0.20 #149, 0.12 #779, 0.08 #599), 043vc (0.20 #127, 0.12 #757, 0.08 #577) >> Best rule #1199 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 02zcz3; 01fsv9; >> query: (?x2895, 0jmj7) <- organization(?x346, ?x2895), school(?x1823, ?x2895), registering_agency(?x2895, ?x1982), institution(?x620, ?x2895) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l2tk school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.828 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #19844-0cm19f PRED entity: 0cm19f PRED relation: place_of_death PRED expected values: 04vmp => 57 concepts (54 used for prediction) PRED predicted values (max 10 best out of 21): 04vmp (0.36 #302, 0.33 #496, 0.27 #108), 04jpl (0.09 #201, 0.09 #7, 0.08 #395), 030qb3t (0.04 #4691, 0.04 #4886, 0.04 #5278), 01c40n (0.03 #609, 0.03 #804, 0.03 #999), 0k_p5 (0.03 #670, 0.02 #1644, 0.02 #1840), 0f2rq (0.03 #668, 0.01 #1642), 0f2wj (0.03 #984, 0.02 #1568, 0.02 #1373), 06_kh (0.02 #1172, 0.02 #1366, 0.01 #3704), 02_286 (0.02 #7607, 0.02 #4682, 0.02 #7804), 05qtj (0.02 #2982, 0.02 #3178, 0.02 #3373) >> Best rule #302 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0b5x23; >> query: (?x13496, 04vmp) <- sibling(?x11786, ?x13496), nationality(?x13496, ?x2146), ?x2146 = 03rk0, gender(?x13496, ?x231), ?x231 = 05zppz >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cm19f place_of_death 04vmp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 54.000 0.364 http://example.org/people/deceased_person/place_of_death #19843-0p7h7 PRED entity: 0p7h7 PRED relation: award_winner! PRED expected values: 056878 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 95): 01bx35 (0.30 #148, 0.20 #289, 0.18 #430), 02cg41 (0.27 #408, 0.13 #690, 0.11 #2100), 02rjjll (0.21 #569, 0.18 #428, 0.18 #710), 01mh_q (0.20 #230, 0.13 #794, 0.13 #653), 0jzphpx (0.20 #180, 0.13 #462, 0.13 #603), 01s695 (0.20 #285, 0.13 #426, 0.12 #3), 09n4nb (0.16 #471, 0.15 #612, 0.11 #753), 01mhwk (0.13 #746, 0.13 #323, 0.13 #464), 019bk0 (0.13 #439, 0.13 #580, 0.11 #721), 013b2h (0.13 #2054, 0.13 #2195, 0.12 #3605) >> Best rule #148 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 08w4pm; >> query: (?x4609, 01bx35) <- inductee(?x1091, ?x4609), artist(?x2241, ?x4609), ?x2241 = 02p11jq >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #596 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 07bzp; *> query: (?x4609, 056878) <- inductee(?x1091, ?x4609), award_winner(?x4609, ?x2806), artist(?x2241, ?x4609) *> conf = 0.13 ranks of expected_values: 11 EVAL 0p7h7 award_winner! 056878 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 105.000 105.000 0.300 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19842-02d478 PRED entity: 02d478 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.74 #1113, 0.71 #390, 0.68 #1265), 09zzb8 (0.73 #1105, 0.71 #382, 0.69 #1181), 02r96rf (0.63 #1184, 0.62 #385, 0.61 #1260), 09vw2b7 (0.61 #1112, 0.58 #389, 0.57 #1188), 0dxtw (0.39 #394, 0.34 #1269, 0.33 #1193), 01vx2h (0.30 #1194, 0.29 #1270, 0.28 #281), 089fss (0.20 #7, 0.12 #1869, 0.06 #388), 02ynfr (0.17 #399, 0.15 #1122, 0.14 #1198), 02rh1dz (0.14 #279, 0.09 #1192, 0.09 #1268), 0215hd (0.13 #1125, 0.13 #1201, 0.12 #1277) >> Best rule #1113 for best value: >> intensional similarity = 4 >> extensional distance = 877 >> proper extension: 025n07; 014zwb; >> query: (?x4067, 0ch6mp2) <- film(?x1286, ?x4067), titles(?x53, ?x4067), film_crew_role(?x4067, ?x2178), award_nominee(?x1286, ?x2353) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d478 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.739 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19841-01ww_vs PRED entity: 01ww_vs PRED relation: artists! PRED expected values: 06by7 => 100 concepts (42 used for prediction) PRED predicted values (max 10 best out of 249): 06by7 (0.62 #1241, 0.59 #2461, 0.58 #3377), 064t9 (0.56 #14, 0.53 #1232, 0.48 #2452), 017_qw (0.55 #6768, 0.12 #11649, 0.09 #11342), 016clz (0.48 #309, 0.41 #1528, 0.40 #2443), 05bt6j (0.44 #2483, 0.41 #1263, 0.38 #349), 0cx7f (0.39 #1659, 0.23 #1049, 0.16 #1965), 05w3f (0.39 #1562, 0.17 #7354, 0.17 #8270), 06j6l (0.35 #961, 0.29 #4924, 0.29 #5228), 0gywn (0.35 #970, 0.26 #4933, 0.25 #5237), 0dl5d (0.32 #1544, 0.16 #7336, 0.16 #629) >> Best rule #1241 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 0frsw; >> query: (?x11633, 06by7) <- artist(?x9243, ?x11633), artists(?x3370, ?x11633), ?x3370 = 059kh, instrumentalists(?x227, ?x11633) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ww_vs artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 42.000 0.618 http://example.org/music/genre/artists #19840-0ym17 PRED entity: 0ym17 PRED relation: student PRED expected values: 016h4r => 165 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1336): 0136g9 (0.33 #201, 0.12 #16945, 0.10 #6480), 03j2gxx (0.33 #1858, 0.12 #18602, 0.10 #8137), 082_p (0.33 #1547, 0.12 #18291, 0.10 #7826), 043s3 (0.33 #665, 0.12 #17409, 0.10 #6944), 016xh5 (0.33 #1067, 0.12 #17811, 0.08 #11532), 01pk8v (0.33 #952, 0.12 #17696, 0.08 #11417), 03f5vvx (0.33 #628, 0.06 #17372, 0.04 #34116), 034bgm (0.20 #6693, 0.11 #19251, 0.09 #8786), 0ff3y (0.17 #2070, 0.12 #18814, 0.11 #23000), 01l79yc (0.17 #1086, 0.12 #17830, 0.07 #34574) >> Best rule #201 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0f11p; >> query: (?x10859, 0136g9) <- contains(?x1841, ?x10859), institution(?x734, ?x10859), ?x1841 = 05l5n, major_field_of_study(?x10859, ?x1154) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #550 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0f11p; *> query: (?x10859, 016h4r) <- contains(?x1841, ?x10859), institution(?x734, ?x10859), ?x1841 = 05l5n, major_field_of_study(?x10859, ?x1154) *> conf = 0.17 ranks of expected_values: 41 EVAL 0ym17 student 016h4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 165.000 112.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #19839-01xbpn PRED entity: 01xbpn PRED relation: sport PRED expected values: 02vx4 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.90 #317, 0.86 #290, 0.86 #542), 018w8 (0.22 #13, 0.15 #427, 0.15 #400), 018jz (0.18 #311, 0.16 #383, 0.16 #392), 03tmr (0.18 #298, 0.18 #388, 0.18 #460), 0jm_ (0.16 #363, 0.15 #489, 0.15 #507), 039yzs (0.07 #367, 0.05 #610, 0.05 #430), 09xp_ (0.04 #501, 0.04 #438, 0.03 #483), 0z74 (0.03 #143, 0.02 #422) >> Best rule #317 for best value: >> intensional similarity = 9 >> extensional distance = 75 >> proper extension: 03z0dt; >> query: (?x3814, 02vx4) <- position(?x3814, ?x530), position(?x3814, ?x63), position(?x3814, ?x60), ?x60 = 02nzb8, ?x63 = 02sdk9v, teams(?x2204, ?x3814), contains(?x792, ?x2204), ?x530 = 02_j1w, locations(?x11802, ?x792) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xbpn sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.896 http://example.org/sports/sports_team/sport #19838-090gk3 PRED entity: 090gk3 PRED relation: profession PRED expected values: 02krf9 => 56 concepts (26 used for prediction) PRED predicted values (max 10 best out of 41): 01d_h8 (0.36 #304, 0.34 #751, 0.33 #453), 0dxtg (0.30 #163, 0.25 #14, 0.24 #1953), 02jknp (0.25 #8, 0.25 #455, 0.24 #306), 03gjzk (0.17 #1059, 0.17 #1954, 0.17 #1805), 09jwl (0.15 #3746, 0.15 #3001, 0.14 #2852), 0cbd2 (0.15 #156, 0.12 #2542, 0.11 #2393), 0d1pc (0.14 #1244, 0.12 #1393, 0.12 #1543), 0np9r (0.11 #1065, 0.11 #2705, 0.11 #3301), 018gz8 (0.11 #2701, 0.11 #3148, 0.11 #3595), 016z4k (0.11 #1346, 0.10 #1496, 0.09 #1197) >> Best rule #304 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 0cfywh; >> query: (?x12520, 01d_h8) <- place_of_birth(?x12520, ?x7412), nationality(?x12520, ?x2146), ?x2146 = 03rk0 >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1071 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 646 *> proper extension: 07nznf; 02s2ft; 079vf; 05bnp0; 01k7d9; 02p65p; 0337vz; 06gp3f; 01xdf5; 04t2l2; ... *> query: (?x12520, 02krf9) <- profession(?x12520, ?x1032), nationality(?x12520, ?x2146), ?x1032 = 02hrh1q, place_of_birth(?x12520, ?x7412), place_of_death(?x2145, ?x7412) *> conf = 0.08 ranks of expected_values: 12 EVAL 090gk3 profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 56.000 26.000 0.356 http://example.org/people/person/profession #19837-07dzf PRED entity: 07dzf PRED relation: taxonomy PRED expected values: 04n6k => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.82 #20, 0.79 #21, 0.74 #9) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 0rh6k; 05kkh; 059rby; 03v1s; 05kj_; 0vmt; 01n7q; 04ykg; 06mz5; 07z1m; ... >> query: (?x5360, 04n6k) <- adjoins(?x5360, ?x910), administrative_parent(?x5360, ?x551), religion(?x5360, ?x109) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07dzf taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.816 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #19836-0cnztc4 PRED entity: 0cnztc4 PRED relation: genre PRED expected values: 03bxz7 => 131 concepts (52 used for prediction) PRED predicted values (max 10 best out of 113): 02l7c8 (0.67 #474, 0.50 #244, 0.41 #2548), 03bxz7 (0.60 #395, 0.50 #625, 0.50 #165), 04xvlr (0.50 #461, 0.48 #1728, 0.44 #806), 082gq (0.50 #600, 0.40 #370, 0.38 #2214), 05p553 (0.48 #2767, 0.34 #3808, 0.33 #5538), 03k9fj (0.38 #4507, 0.38 #700, 0.37 #5198), 06l3bl (0.38 #2222, 0.33 #33, 0.29 #1760), 02n4kr (0.36 #1157, 0.29 #1503, 0.23 #1849), 04xvh5 (0.33 #2218, 0.33 #834, 0.33 #29), 03g3w (0.33 #2210, 0.29 #1748, 0.22 #826) >> Best rule #474 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0gyy53; 0bs5k8r; >> query: (?x1283, 02l7c8) <- genre(?x1283, ?x1509), film_festivals(?x1283, ?x6828), film_crew_role(?x1283, ?x137), film_release_distribution_medium(?x1283, ?x81), film_format(?x1283, ?x6392), ?x137 = 09zzb8, ?x1509 = 060__y >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #395 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 0gmgwnv; *> query: (?x1283, 03bxz7) <- genre(?x1283, ?x3312), film_release_region(?x1283, ?x2152), film_release_region(?x1283, ?x1499), film_release_region(?x1283, ?x1497), film_release_region(?x1283, ?x583), ?x583 = 015fr, ?x3312 = 02p0szs, ?x1499 = 01znc_, ?x2152 = 06mkj, country(?x1283, ?x172), adjoins(?x1353, ?x1497) *> conf = 0.60 ranks of expected_values: 2 EVAL 0cnztc4 genre 03bxz7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 52.000 0.667 http://example.org/film/film/genre #19835-0k2sk PRED entity: 0k2sk PRED relation: region PRED expected values: 07ssc => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 6): 07ssc (0.59 #261, 0.57 #167, 0.56 #446), 09c7w0 (0.06 #47, 0.03 #441, 0.03 #535), 059j2 (0.06 #55, 0.01 #147, 0.01 #193), 0d060g (0.06 #50), 09nm_ (0.01 #254, 0.01 #161, 0.01 #207), 02jx1 (0.01 #2279) >> Best rule #261 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 064n1pz; 04nlb94; >> query: (?x1076, 07ssc) <- nominated_for(?x384, ?x1076), film_distribution_medium(?x1076, ?x2099), award(?x164, ?x384) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k2sk region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.586 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #19834-05z7c PRED entity: 05z7c PRED relation: nominated_for! PRED expected values: 0k0rf => 69 concepts (43 used for prediction) PRED predicted values (max 10 best out of 140): 02r_pp (0.83 #494, 0.82 #4198, 0.82 #1480), 05z7c (0.78 #305, 0.71 #58, 0.07 #798), 0k0rf (0.67 #388, 0.57 #141, 0.05 #881), 026p_bs (0.14 #12, 0.11 #259, 0.01 #752), 025twgf (0.14 #224, 0.11 #471, 0.01 #1211), 0d1qmz (0.09 #840, 0.06 #3063, 0.05 #3311), 02qrv7 (0.09 #773, 0.06 #2996, 0.05 #3244), 0fztbq (0.08 #979, 0.05 #3202, 0.05 #3450), 025twgt (0.08 #980, 0.05 #3203, 0.05 #3451), 01kf4tt (0.08 #813, 0.05 #3036, 0.04 #3284) >> Best rule #494 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 05css_; >> query: (?x2094, ?x2368) <- nominated_for(?x2094, ?x7149), nominated_for(?x2094, ?x2368), nominated_for(?x2094, ?x1708), ?x1708 = 05cj_j, ?x7149 = 01jr4j >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #388 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 05css_; *> query: (?x2094, 0k0rf) <- nominated_for(?x2094, ?x7149), nominated_for(?x2094, ?x1708), ?x1708 = 05cj_j, ?x7149 = 01jr4j *> conf = 0.67 ranks of expected_values: 3 EVAL 05z7c nominated_for! 0k0rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 43.000 0.833 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #19833-015fs3 PRED entity: 015fs3 PRED relation: major_field_of_study PRED expected values: 01mkq => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 113): 02j62 (0.56 #29, 0.44 #513, 0.43 #2209), 01mkq (0.44 #15, 0.42 #1346, 0.39 #862), 0g26h (0.43 #647, 0.42 #1131, 0.42 #1495), 02lp1 (0.43 #1343, 0.42 #1101, 0.42 #617), 04rjg (0.42 #504, 0.39 #383, 0.34 #625), 0_jm (0.35 #1026, 0.34 #1389, 0.32 #784), 0fdys (0.33 #38, 0.29 #522, 0.18 #2218), 01lj9 (0.33 #39, 0.26 #523, 0.24 #402), 05qjt (0.33 #8, 0.24 #2188, 0.23 #492), 01zc2w (0.33 #71, 0.16 #555, 0.14 #434) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 08815; 03ksy; 01k3s2; 0217m9; 01_qgp; 02237m; >> query: (?x11215, 02j62) <- major_field_of_study(?x11215, ?x7070), institution(?x865, ?x11215), contains(?x94, ?x11215), ?x7070 = 0mg1w >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 08815; 03ksy; 01k3s2; 0217m9; 01_qgp; 02237m; *> query: (?x11215, 01mkq) <- major_field_of_study(?x11215, ?x7070), institution(?x865, ?x11215), contains(?x94, ?x11215), ?x7070 = 0mg1w *> conf = 0.44 ranks of expected_values: 2 EVAL 015fs3 major_field_of_study 01mkq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.556 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19832-0pm85 PRED entity: 0pm85 PRED relation: artists PRED expected values: 082brv 0143q0 0fpj9pm => 78 concepts (35 used for prediction) PRED predicted values (max 10 best out of 987): 070b4 (0.71 #8357, 0.50 #4049, 0.40 #7279), 01vrt_c (0.60 #5460, 0.50 #4384, 0.50 #3308), 016fmf (0.60 #5597, 0.50 #4521, 0.50 #3445), 01ydzx (0.60 #5990, 0.50 #3838, 0.43 #8146), 02k5sc (0.60 #6092, 0.50 #3940, 0.43 #8248), 01w524f (0.60 #5758, 0.50 #3606, 0.43 #7914), 01vv7sc (0.60 #5446, 0.50 #3294, 0.33 #1140), 048tgl (0.52 #12931, 0.50 #18325, 0.50 #5216), 03xl77 (0.50 #4551, 0.50 #3475, 0.50 #2397), 0kxbc (0.50 #4824, 0.50 #3748, 0.50 #2670) >> Best rule #8357 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 029fbr; >> query: (?x10065, 070b4) <- artists(?x10065, ?x9603), artists(?x10065, ?x6818), artists(?x10065, ?x6699), artists(?x10065, ?x6035), ?x9603 = 012ycy, category(?x6818, ?x134), group(?x227, ?x6699), participant(?x6035, ?x2352), participant(?x6035, ?x1093) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1665 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 016clz; *> query: (?x10065, 0143q0) <- parent_genre(?x2249, ?x10065), parent_genre(?x10065, ?x1572), artists(?x10065, ?x10864), artists(?x10065, ?x9603), artists(?x10065, ?x8215), artists(?x10065, ?x7781), artists(?x10065, ?x6699), ?x10864 = 057xn_m, ?x8215 = 04_jsg, ?x7781 = 089pg7, ?x6699 = 09lwrt, ?x9603 = 012ycy *> conf = 0.33 ranks of expected_values: 109, 122, 497 EVAL 0pm85 artists 0fpj9pm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 78.000 35.000 0.714 http://example.org/music/genre/artists EVAL 0pm85 artists 0143q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 78.000 35.000 0.714 http://example.org/music/genre/artists EVAL 0pm85 artists 082brv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 78.000 35.000 0.714 http://example.org/music/genre/artists #19831-04qsdh PRED entity: 04qsdh PRED relation: profession PRED expected values: 05z96 => 99 concepts (96 used for prediction) PRED predicted values (max 10 best out of 57): 0kyk (0.35 #29, 0.10 #1927, 0.10 #4993), 0cbd2 (0.31 #7, 0.14 #8329, 0.14 #6869), 01d_h8 (0.30 #8620, 0.30 #2634, 0.29 #4094), 03gjzk (0.24 #2934, 0.23 #2788, 0.23 #3226), 0np9r (0.21 #2210, 0.21 #1626, 0.15 #8926), 02jknp (0.21 #7162, 0.21 #7308, 0.20 #4242), 015cjr (0.20 #49, 0.04 #2239, 0.03 #1655), 09jwl (0.17 #4398, 0.17 #1916, 0.17 #1770), 018gz8 (0.13 #746, 0.13 #892, 0.13 #1622), 0dz3r (0.12 #1754, 0.11 #4382, 0.11 #5258) >> Best rule #29 for best value: >> intensional similarity = 2 >> extensional distance = 63 >> proper extension: 0fx02; >> query: (?x8045, 0kyk) <- profession(?x8045, ?x9081), ?x9081 = 0d8qb >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 63 *> proper extension: 0fx02; *> query: (?x8045, 05z96) <- profession(?x8045, ?x9081), ?x9081 = 0d8qb *> conf = 0.06 ranks of expected_values: 18 EVAL 04qsdh profession 05z96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 99.000 96.000 0.354 http://example.org/people/person/profession #19830-03_wpf PRED entity: 03_wpf PRED relation: film PRED expected values: 03mgx6z => 65 concepts (39 used for prediction) PRED predicted values (max 10 best out of 100): 0828jw (0.58 #37605, 0.35 #66262, 0.35 #68053), 017jd9 (0.03 #16115, 0.03 #59098, 0.03 #14324), 017gm7 (0.03 #16115, 0.03 #59098, 0.03 #14324), 05sy_5 (0.03 #16115, 0.03 #59098, 0.03 #14324), 02wgk1 (0.03 #16115, 0.03 #59098, 0.03 #14324), 024mpp (0.03 #16115, 0.03 #59098, 0.03 #14324), 0x25q (0.03 #16115, 0.03 #59098, 0.03 #14324), 04sntd (0.03 #16115, 0.03 #59098, 0.03 #14324), 05fcbk7 (0.03 #16115, 0.03 #59098, 0.03 #14324), 01dyvs (0.03 #16115, 0.03 #59098, 0.03 #14324) >> Best rule #37605 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 04yywz; 049tjg; 02g8h; 0d_84; 0h1_w; 02nb2s; 04bs3j; 014x77; 0151ns; 0lzb8; ... >> query: (?x6747, ?x5810) <- nominated_for(?x6747, ?x5810), film(?x6747, ?x2471) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_wpf film 03mgx6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 39.000 0.585 http://example.org/film/actor/film./film/performance/film #19829-0jqkh PRED entity: 0jqkh PRED relation: genre PRED expected values: 0219x_ => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 85): 01jfsb (0.50 #375, 0.33 #254, 0.33 #12), 0lsxr (0.38 #371, 0.33 #250, 0.33 #8), 03npn (0.38 #369, 0.17 #248, 0.07 #4248), 02kdv5l (0.33 #244, 0.33 #2, 0.27 #971), 0556j8 (0.33 #43, 0.06 #891, 0.03 #1254), 02l7c8 (0.33 #864, 0.30 #1348, 0.27 #2441), 04xvlr (0.25 #122, 0.19 #727, 0.18 #1333), 09blyk (0.25 #395, 0.17 #274, 0.08 #1576), 03bxz7 (0.25 #177, 0.09 #1388, 0.08 #782), 0219x_ (0.25 #148, 0.09 #753, 0.09 #875) >> Best rule #375 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 033qdy; 0270k40; >> query: (?x7666, 01jfsb) <- film(?x9934, ?x7666), film(?x8065, ?x7666), ?x9934 = 09nz_c, type_of_union(?x8065, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #148 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 03sxd2; 01rwyq; *> query: (?x7666, 0219x_) <- film(?x9934, ?x7666), film(?x9153, ?x7666), award(?x9934, ?x1921), ?x9153 = 06pjs *> conf = 0.25 ranks of expected_values: 10 EVAL 0jqkh genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 44.000 44.000 0.500 http://example.org/film/film/genre #19828-0j_tw PRED entity: 0j_tw PRED relation: film! PRED expected values: 05nzw6 => 76 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1278): 02xv8m (0.20 #4830, 0.14 #2750, 0.05 #6910), 06cgy (0.20 #250, 0.10 #4411, 0.07 #18971), 0klh7 (0.20 #488, 0.05 #6729, 0.03 #21289), 013cr (0.20 #225, 0.05 #21026, 0.03 #23107), 0k8y7 (0.20 #745, 0.04 #11146, 0.02 #15306), 0b_4z (0.20 #1981, 0.03 #26944, 0.02 #16542), 02dth1 (0.20 #723, 0.03 #25686, 0.02 #15284), 01x209s (0.20 #1147, 0.02 #15708, 0.02 #17788), 04sry (0.20 #1277, 0.02 #15838, 0.02 #17918), 0chw_ (0.20 #1554, 0.02 #16115, 0.02 #18195) >> Best rule #4830 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 058kh7; >> query: (?x2104, 02xv8m) <- film(?x10626, ?x2104), film(?x7530, ?x2104), genre(?x2104, ?x258), ?x7530 = 04954, location(?x10626, ?x1310) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #53203 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 210 *> proper extension: 0p_tz; 01gvts; *> query: (?x2104, 05nzw6) <- films(?x5931, ?x2104), film(?x919, ?x2104), language(?x2104, ?x90), featured_film_locations(?x2104, ?x4980) *> conf = 0.02 ranks of expected_values: 528 EVAL 0j_tw film! 05nzw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 69.000 0.200 http://example.org/film/actor/film./film/performance/film #19827-015c2f PRED entity: 015c2f PRED relation: place_of_birth PRED expected values: 02_286 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 78): 02_286 (0.13 #19, 0.08 #1427, 0.08 #3539), 030qb3t (0.09 #758, 0.07 #3574, 0.06 #4982), 03l2n (0.07 #169, 0.03 #873, 0.03 #1577), 013kcv (0.07 #23, 0.03 #727), 02dtg (0.07 #10, 0.01 #44367, 0.01 #61970), 011wdm (0.07 #528), 0mzww (0.07 #259), 02j3w (0.07 #158), 04f_d (0.07 #73), 0rh6k (0.06 #706, 0.02 #2114, 0.01 #24646) >> Best rule #19 for best value: >> intensional similarity = 2 >> extensional distance = 13 >> proper extension: 03z509; 02l3_5; 04pp9s; 0mbs8; 06r3p2; >> query: (?x2813, 02_286) <- award_winner(?x3184, ?x2813), ?x3184 = 0gkts9 >> conf = 0.13 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015c2f place_of_birth 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.133 http://example.org/people/person/place_of_birth #19826-027s39y PRED entity: 027s39y PRED relation: honored_for! PRED expected values: 0418154 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 102): 0gmdkyy (0.14 #24, 0.10 #262, 0.04 #500), 0hndn2q (0.14 #32, 0.10 #270, 0.03 #389), 0h_cssd (0.14 #22, 0.10 #260, 0.03 #379), 02glmx (0.12 #1430, 0.10 #7742, 0.09 #7981), 0418154 (0.12 #1430, 0.10 #7742, 0.09 #7981), 05qb8vx (0.12 #1430, 0.10 #7742, 0.09 #7981), 02wzl1d (0.12 #1430, 0.10 #7742, 0.09 #7981), 0gvstc3 (0.11 #980, 0.07 #861, 0.06 #1695), 03gwpw2 (0.09 #958, 0.09 #839, 0.06 #481), 09bymc (0.09 #7981, 0.08 #10126, 0.08 #10125) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0ds3t5x; 0g9wdmc; 0170th; 01jrbb; 05p09dd; 04k9y6; 07jnt; 04lhc4; 0_9wr; 0bnzd; ... >> query: (?x3946, 0gmdkyy) <- nominated_for(?x500, ?x3946), music(?x3946, ?x6783), ?x6783 = 01x6v6, award(?x3946, ?x1053) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1430 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 187 *> proper extension: 03d17dg; *> query: (?x3946, ?x944) <- nominated_for(?x1052, ?x3946), award_winner(?x944, ?x1052), story_by(?x2893, ?x1052) *> conf = 0.12 ranks of expected_values: 5 EVAL 027s39y honored_for! 0418154 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 114.000 114.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #19825-0_j_z PRED entity: 0_j_z PRED relation: place PRED expected values: 0_j_z => 85 concepts (52 used for prediction) PRED predicted values (max 10 best out of 144): 0c1d0 (0.45 #7224, 0.28 #12909, 0.25 #217), 0_jws (0.28 #12909, 0.25 #491, 0.17 #12392), 0_j_z (0.28 #12909, 0.17 #12392, 0.08 #14459), 0mw5x (0.18 #3611, 0.18 #4127, 0.07 #3095), 0_jsl (0.12 #1021, 0.01 #4633, 0.01 #5149), 0r00l (0.11 #1378, 0.04 #1894, 0.03 #2410), 013yq (0.11 #1076, 0.04 #1592, 0.03 #2108), 030qb3t (0.11 #1061, 0.04 #1577, 0.03 #2093), 02_286 (0.11 #1045, 0.04 #1561, 0.03 #2077), 0r04p (0.11 #1144, 0.04 #1660, 0.01 #5272) >> Best rule #7224 for best value: >> intensional similarity = 4 >> extensional distance = 137 >> proper extension: 0f04v; >> query: (?x13019, ?x8263) <- county(?x13019, ?x10067), time_zones(?x13019, ?x2674), administrative_division(?x8263, ?x10067), adjoins(?x10067, ?x4990) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #12909 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 257 *> proper extension: 0xddr; 0_75d; 0s5cg; 0qzhw; 0zrlp; 0rgxp; 0s6g4; 01m23s; 0sc6p; 0s4sj; *> query: (?x13019, ?x8263) <- county(?x13019, ?x10067), contains(?x2713, ?x13019), adjoins(?x10067, ?x4990), county(?x8263, ?x10067) *> conf = 0.28 ranks of expected_values: 3 EVAL 0_j_z place 0_j_z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 52.000 0.446 http://example.org/location/hud_county_place/place #19824-05ldnp PRED entity: 05ldnp PRED relation: award_winner! PRED expected values: 0bvfqq => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 128): 09p2r9 (0.30 #836, 0.15 #93, 0.02 #3709), 0275n3y (0.30 #836, 0.08 #75, 0.06 #3552), 050yyb (0.30 #836, 0.08 #38, 0.02 #734), 09qvms (0.30 #836, 0.07 #3490, 0.05 #5714), 0drtv8 (0.30 #836, 0.07 #206, 0.04 #1041), 0bvhz9 (0.30 #836, 0.04 #129, 0.03 #269), 02wzl1d (0.10 #290, 0.09 #429, 0.08 #568), 02q690_ (0.10 #1040, 0.09 #1457, 0.07 #1596), 05c1t6z (0.09 #990, 0.09 #1407, 0.07 #3492), 03nnm4t (0.09 #1049, 0.08 #1466, 0.07 #214) >> Best rule #836 for best value: >> intensional similarity = 3 >> extensional distance = 122 >> proper extension: 016hvl; 0d05fv; >> query: (?x3260, ?x2294) <- written_by(?x10806, ?x3260), award_winner(?x68, ?x3260), honored_for(?x2294, ?x10806) >> conf = 0.30 => this is the best rule for 6 predicted values *> Best rule #451 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 02kxbwx; 05_k56; 05183k; 07s93v; 01f7j9; 02fcs2; 05jcn8; 07h07; 01tt43d; 05mcjs; ... *> query: (?x3260, 0bvfqq) <- award(?x3260, ?x68), ?x68 = 02qyp19, written_by(?x787, ?x3260) *> conf = 0.04 ranks of expected_values: 48 EVAL 05ldnp award_winner! 0bvfqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 112.000 112.000 0.300 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19823-0299hs PRED entity: 0299hs PRED relation: language PRED expected values: 02h40lc => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 53): 02h40lc (0.91 #833, 0.90 #3771, 0.90 #2392), 04306rv (0.24 #478, 0.22 #242, 0.21 #656), 064_8sq (0.22 #259, 0.21 #81, 0.21 #22), 06nm1 (0.18 #307, 0.16 #1624, 0.16 #1021), 02bjrlw (0.17 #238, 0.16 #356, 0.14 #60), 0jzc (0.13 #138, 0.07 #79, 0.07 #20), 012w70 (0.13 #131, 0.05 #903, 0.05 #1141), 06b_j (0.11 #854, 0.09 #319, 0.08 #1935), 03_9r (0.08 #2341, 0.07 #2876, 0.07 #128), 0653m (0.08 #1564, 0.07 #1201, 0.07 #1262) >> Best rule #833 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 02qhqz4; 062zjtt; 03d8jd1; >> query: (?x3433, 02h40lc) <- genre(?x3433, ?x6888), film(?x788, ?x3433), country(?x3433, ?x94), film(?x4748, ?x3433), ?x6888 = 04pbhw >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0299hs language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.909 http://example.org/film/film/language #19822-0l8sx PRED entity: 0l8sx PRED relation: citytown PRED expected values: 02_286 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 201): 02_286 (0.76 #7383, 0.75 #6645, 0.73 #8121), 0r04p (0.33 #105, 0.13 #3052, 0.12 #4526), 04jpl (0.32 #16213, 0.30 #21004, 0.30 #21003), 030qb3t (0.32 #16213, 0.30 #21004, 0.30 #21003), 0r00l (0.32 #16213, 0.30 #21004, 0.30 #21003), 013yq (0.32 #16213, 0.30 #21004, 0.30 #21003), 0d9jr (0.32 #16213, 0.30 #21004, 0.30 #21003), 081yw (0.32 #16213, 0.30 #21004, 0.30 #21003), 0h7h6 (0.29 #767, 0.12 #4084, 0.09 #8874), 07dfk (0.21 #10529, 0.19 #23795, 0.19 #13847) >> Best rule #7383 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0gsg7; 09d5h; 01w5m; 05njw; >> query: (?x1908, 02_286) <- list(?x1908, ?x5997), state_province_region(?x1908, ?x335), ?x335 = 059rby, company(?x346, ?x1908) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l8sx citytown 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.762 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #19821-0ycp3 PRED entity: 0ycp3 PRED relation: artists! PRED expected values: 09nwwf => 112 concepts (74 used for prediction) PRED predicted values (max 10 best out of 292): 01fh36 (0.63 #3786, 0.27 #8116, 0.23 #3170), 064t9 (0.55 #21009, 0.50 #3098, 0.50 #2173), 0dl5d (0.50 #2179, 0.43 #1254, 0.37 #3720), 05bt6j (0.50 #2202, 0.38 #350, 0.37 #6220), 03_d0 (0.44 #3712, 0.39 #8042, 0.31 #3096), 08jyyk (0.40 #2225, 0.36 #1300, 0.24 #6861), 02t8gf (0.39 #1992, 0.16 #6936, 0.06 #9102), 026z9 (0.38 #383, 0.12 #4701, 0.12 #1618), 05w3f (0.36 #1271, 0.35 #1579, 0.30 #2196), 05r6t (0.36 #6876, 0.22 #21614, 0.21 #17068) >> Best rule #3786 for best value: >> intensional similarity = 7 >> extensional distance = 25 >> proper extension: 03j0br4; 01gx5f; >> query: (?x6876, 01fh36) <- artist(?x2931, ?x6876), ?x2931 = 03rhqg, artists(?x9063, ?x6876), artists(?x9063, ?x1092), artists(?x9063, ?x498), ?x1092 = 02whj, ?x498 = 0m19t >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #6931 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 43 *> proper extension: 0326tc; *> query: (?x6876, 09nwwf) <- artists(?x3753, ?x6876), artists(?x302, ?x6876), artists(?x3753, ?x10106), artists(?x3753, ?x8308), ?x302 = 016clz, ?x10106 = 016lj_, instrumentalists(?x227, ?x8308) *> conf = 0.31 ranks of expected_values: 14 EVAL 0ycp3 artists! 09nwwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 112.000 74.000 0.630 http://example.org/music/genre/artists #19820-03qbm PRED entity: 03qbm PRED relation: company! PRED expected values: 028fjr => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 37): 0dq_5 (0.93 #2338, 0.89 #345, 0.88 #809), 0krdk (0.88 #2282, 0.83 #3074, 0.80 #566), 060c4 (0.81 #3770, 0.73 #145, 0.71 #4606), 05_wyz (0.62 #206, 0.58 #485, 0.56 #346), 0dq3c (0.50 #1443, 0.48 #979, 0.46 #282), 09d6p2 (0.47 #486, 0.46 #282, 0.46 #207), 01kr6k (0.46 #282, 0.42 #1626, 0.38 #215), 02k13d (0.42 #1626, 0.23 #5583, 0.20 #4233), 028fjr (0.42 #1626, 0.23 #5583, 0.20 #93), 02211by (0.23 #192, 0.22 #378, 0.22 #332) >> Best rule #2338 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 049n7; 01s0l0; >> query: (?x11080, 0dq_5) <- category(?x11080, ?x134), company(?x1907, ?x11080), ?x134 = 08mbj5d, company(?x1907, ?x2276), ?x2276 = 04qhdf >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1626 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 36 *> proper extension: 05cl8y; *> query: (?x11080, ?x265) <- industry(?x11080, ?x11520), industry(?x10926, ?x11520), state_province_region(?x11080, ?x335), company(?x265, ?x10926), taxonomy(?x11520, ?x939), citytown(?x10926, ?x6555) *> conf = 0.42 ranks of expected_values: 9 EVAL 03qbm company! 028fjr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 188.000 188.000 0.925 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #19819-018qpb PRED entity: 018qpb PRED relation: people! PRED expected values: 0j8hd => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 25): 0gk4g (0.17 #472, 0.14 #76, 0.14 #10), 02knxx (0.14 #230, 0.14 #164, 0.14 #98), 0dq9p (0.14 #83, 0.14 #17, 0.08 #281), 0gg4h (0.14 #102, 0.14 #36, 0.07 #234), 0c58k (0.14 #30, 0.03 #294, 0.02 #360), 0qcr0 (0.08 #463, 0.04 #331, 0.04 #397), 04p3w (0.08 #473, 0.02 #341, 0.02 #2057), 02y0js (0.07 #200, 0.07 #134, 0.05 #266), 01l2m3 (0.07 #214, 0.07 #148, 0.03 #280), 0dcsx (0.04 #477, 0.04 #345, 0.04 #411) >> Best rule #472 for best value: >> intensional similarity = 4 >> extensional distance = 112 >> proper extension: 05hjmd; >> query: (?x12582, 0gk4g) <- gender(?x12582, ?x514), type_of_union(?x12582, ?x566), ?x566 = 04ztj, place_of_burial(?x12582, ?x11327) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018qpb people! 0j8hd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 114.000 0.167 http://example.org/people/cause_of_death/people #19818-0315w4 PRED entity: 0315w4 PRED relation: film_crew_role PRED expected values: 01pvkk => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 29): 0dxtw (0.55 #387, 0.48 #352, 0.45 #558), 01pvkk (0.33 #388, 0.33 #353, 0.31 #559), 02ynfr (0.26 #392, 0.21 #357, 0.18 #219), 02rh1dz (0.25 #8, 0.24 #386, 0.20 #42), 0d2b38 (0.25 #24, 0.20 #58, 0.19 #367), 0215hd (0.25 #17, 0.20 #51, 0.18 #360), 02_n3z (0.25 #1, 0.20 #35, 0.16 #206), 089fss (0.25 #5, 0.20 #39, 0.09 #383), 03f_s3 (0.25 #33, 0.20 #67, 0.05 #135), 0263ycg (0.25 #16, 0.20 #50, 0.05 #359) >> Best rule #387 for best value: >> intensional similarity = 5 >> extensional distance = 241 >> proper extension: 0gx1bnj; 0h1cdwq; 0gj8nq2; 09g7vfw; 04ydr95; 0435vm; 024mpp; 0h95zbp; 02qyv3h; 06fqlk; ... >> query: (?x4799, 0dxtw) <- film_crew_role(?x4799, ?x2154), film_crew_role(?x4799, ?x137), country(?x4799, ?x94), ?x137 = 09zzb8, ?x2154 = 01vx2h >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #388 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 241 *> proper extension: 0gx1bnj; 0h1cdwq; 0gj8nq2; 09g7vfw; 04ydr95; 0435vm; 024mpp; 0h95zbp; 02qyv3h; 06fqlk; ... *> query: (?x4799, 01pvkk) <- film_crew_role(?x4799, ?x2154), film_crew_role(?x4799, ?x137), country(?x4799, ?x94), ?x137 = 09zzb8, ?x2154 = 01vx2h *> conf = 0.33 ranks of expected_values: 2 EVAL 0315w4 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.547 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19817-0hr3g PRED entity: 0hr3g PRED relation: languages PRED expected values: 04306rv => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 16): 02h40lc (0.31 #314, 0.28 #821, 0.28 #626), 06b_j (0.08 #94, 0.05 #718, 0.04 #991), 02bjrlw (0.07 #352, 0.07 #391, 0.07 #118), 064_8sq (0.07 #132, 0.06 #171, 0.05 #249), 06nm1 (0.05 #240, 0.03 #825, 0.03 #513), 0t_2 (0.04 #282, 0.01 #1998, 0.01 #1023), 03_9r (0.03 #512, 0.02 #1175, 0.02 #1214), 03k50 (0.03 #3203, 0.03 #3437, 0.02 #4022), 04306rv (0.03 #1953, 0.01 #2266, 0.01 #1134), 01c7y (0.02 #616, 0.01 #1201) >> Best rule #314 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0459z; >> query: (?x9297, 02h40lc) <- location(?x9297, ?x2611), influenced_by(?x11097, ?x9297), instrumentalists(?x316, ?x9297), influenced_by(?x9297, ?x1211) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #1953 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 231 *> proper extension: 02wrhj; *> query: (?x9297, 04306rv) <- location(?x9297, ?x2611), capital(?x1679, ?x2611), month(?x2611, ?x1459), ?x1459 = 04w_7 *> conf = 0.03 ranks of expected_values: 9 EVAL 0hr3g languages 04306rv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 137.000 137.000 0.308 http://example.org/people/person/languages #19816-01vj9c PRED entity: 01vj9c PRED relation: role PRED expected values: 02hrlh => 77 concepts (69 used for prediction) PRED predicted values (max 10 best out of 99): 03bx0bm (0.83 #2300, 0.83 #2128, 0.83 #2357), 018j2 (0.83 #2128, 0.83 #2357, 0.83 #227), 06w7v (0.83 #2128, 0.83 #2357, 0.83 #227), 02k856 (0.83 #2128, 0.83 #2357, 0.83 #227), 0319l (0.83 #2128, 0.83 #2357, 0.83 #227), 02pprs (0.83 #2128, 0.83 #2357, 0.83 #227), 01v1d8 (0.83 #2128, 0.83 #2357, 0.83 #227), 026g73 (0.83 #2128, 0.83 #2357, 0.83 #227), 0j862 (0.83 #2128, 0.83 #2357, 0.83 #227), 01vj9c (0.76 #2509, 0.67 #2286, 0.67 #1829) >> Best rule #2300 for best value: >> intensional similarity = 13 >> extensional distance = 16 >> proper extension: 03qlv7; 06ncr; 0l1589; 07_l6; 02w3w; 05kms; >> query: (?x745, 03bx0bm) <- role(?x745, ?x5926), role(?x745, ?x2798), role(?x745, ?x716), role(?x7459, ?x745), group(?x745, ?x498), profession(?x7459, ?x220), role(?x1004, ?x745), role(?x745, ?x645), artists(?x378, ?x7459), instrumentalists(?x5926, ?x140), role(?x214, ?x745), ?x716 = 018vs, ?x2798 = 03qjg >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1212 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 02snj9; *> query: (?x745, ?x212) <- role(?x745, ?x2764), role(?x745, ?x2310), role(?x745, ?x569), role(?x745, ?x314), role(?x211, ?x745), ?x314 = 02sgy, role(?x212, ?x2310), instrumentalists(?x2310, ?x2575), group(?x745, ?x1749), group(?x2310, ?x3109), ?x569 = 07c6l, artists(?x671, ?x1749), ?x2764 = 01s0ps *> conf = 0.49 ranks of expected_values: 77 EVAL 01vj9c role 02hrlh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 77.000 69.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/role #19815-02v63m PRED entity: 02v63m PRED relation: nominated_for! PRED expected values: 05zvj3m => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 168): 0gq_v (0.42 #502, 0.17 #4358, 0.16 #11347), 02x4sn8 (0.40 #120, 0.04 #2048, 0.04 #6145), 02hsq3m (0.27 #753, 0.18 #1235, 0.16 #1476), 02g3v6 (0.25 #504, 0.21 #745, 0.18 #1227), 05ztjjw (0.24 #733, 0.16 #1215, 0.14 #251), 057xs89 (0.22 #845, 0.17 #604, 0.14 #1327), 02r22gf (0.22 #752, 0.17 #511, 0.13 #1234), 05pcn59 (0.22 #14703, 0.20 #68, 0.17 #13979), 05zvj3m (0.22 #14703, 0.17 #13979, 0.13 #797), 02x4x18 (0.22 #14703, 0.17 #13979, 0.02 #2031) >> Best rule #502 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0dtfn; 0c8tkt; 0ddjy; 0prrm; 0f3m1; 0199wf; >> query: (?x1184, 0gq_v) <- film(?x6059, ?x1184), film(?x2969, ?x1184), ?x6059 = 01tnbn, actor(?x1395, ?x2969) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #14703 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1584 *> proper extension: 01tspc6; 06g60w; 04z_x4v; *> query: (?x1184, ?x1336) <- nominated_for(?x413, ?x1184), award(?x413, ?x1336), nominated_for(?x1336, ?x144) *> conf = 0.22 ranks of expected_values: 9 EVAL 02v63m nominated_for! 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 75.000 75.000 0.417 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19814-01gg59 PRED entity: 01gg59 PRED relation: artists! PRED expected values: 0ggx5q => 114 concepts (111 used for prediction) PRED predicted values (max 10 best out of 200): 03_d0 (0.50 #6098, 0.20 #315, 0.19 #1227), 06by7 (0.45 #628, 0.44 #6716, 0.43 #7630), 0m0jc (0.25 #8, 0.09 #616, 0.07 #7618), 059kh (0.25 #48, 0.08 #1264, 0.07 #656), 06j6l (0.24 #7657, 0.22 #6743, 0.19 #7048), 01lyv (0.23 #6729, 0.20 #7034, 0.18 #7643), 016clz (0.21 #9135, 0.20 #7614, 0.20 #9748), 0gywn (0.19 #7665, 0.16 #6751, 0.15 #7056), 025sc50 (0.18 #7659, 0.15 #7963, 0.15 #6745), 0glt670 (0.17 #7650, 0.14 #7954, 0.14 #10395) >> Best rule #6098 for best value: >> intensional similarity = 3 >> extensional distance = 273 >> proper extension: 0hnlx; 01jrz5j; 01wbgdv; 015rmq; 067mj; 03t9sp; 02qlg7s; 017j6; 01gx5f; 044gyq; ... >> query: (?x3890, 03_d0) <- artists(?x4910, ?x3890), artists(?x4910, ?x9163), ?x9163 = 02sjp >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #74 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 012pd4; *> query: (?x3890, 0ggx5q) <- artists(?x7052, ?x3890), artists(?x4910, ?x3890), ?x4910 = 017_qw, ?x7052 = 0l14gg *> conf = 0.12 ranks of expected_values: 20 EVAL 01gg59 artists! 0ggx5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 114.000 111.000 0.502 http://example.org/music/genre/artists #19813-0192hw PRED entity: 0192hw PRED relation: film_release_region PRED expected values: 0b90_r 01mjq => 162 concepts (139 used for prediction) PRED predicted values (max 10 best out of 257): 0d0vqn (0.95 #5685, 0.95 #7358, 0.94 #6526), 059j2 (0.90 #8712, 0.90 #10548, 0.90 #8877), 03rjj (0.90 #4680, 0.89 #7021, 0.88 #8019), 0154j (0.88 #7020, 0.88 #8516, 0.87 #8018), 015fr (0.87 #5864, 0.86 #5030, 0.86 #9359), 0jgd (0.86 #3517, 0.86 #4511, 0.84 #7018), 02vzc (0.86 #1898, 0.84 #8899, 0.83 #8734), 03h64 (0.86 #9414, 0.86 #8087, 0.83 #14274), 0k6nt (0.84 #7043, 0.82 #7710, 0.81 #8539), 06t2t (0.84 #7084, 0.82 #7751, 0.79 #8580) >> Best rule #5685 for best value: >> intensional similarity = 10 >> extensional distance = 59 >> proper extension: 0jjy0; 0gtvpkw; >> query: (?x3257, 0d0vqn) <- featured_film_locations(?x3257, ?x6054), featured_film_locations(?x3257, ?x4335), film_release_region(?x3257, ?x1003), film_release_region(?x3257, ?x87), place_of_death(?x11755, ?x6054), ?x1003 = 03gj2, month(?x6054, ?x1459), ?x87 = 05r4w, place_of_birth(?x2873, ?x4335), contains(?x2146, ?x4335) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #8017 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 102 *> proper extension: 087wc7n; 0407yj_; 045j3w; 0j43swk; 07s3m4g; 0gwjw0c; 0fpgp26; *> query: (?x3257, 0b90_r) <- film_release_region(?x3257, ?x2152), film_release_region(?x3257, ?x1499), film_release_region(?x3257, ?x1353), film_release_region(?x3257, ?x456), film_release_region(?x3257, ?x390), film_release_region(?x3257, ?x87), language(?x3257, ?x254), ?x456 = 05qhw, ?x2152 = 06mkj, ?x1353 = 035qy, ?x1499 = 01znc_, ?x390 = 0chghy, ?x87 = 05r4w *> conf = 0.82 ranks of expected_values: 14, 23 EVAL 0192hw film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 162.000 139.000 0.951 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0192hw film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 162.000 139.000 0.951 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19812-01jwxx PRED entity: 01jwxx PRED relation: film_regional_debut_venue PRED expected values: 081m_ => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 16): 015hr (0.33 #47, 0.17 #412, 0.17 #479), 07751 (0.33 #42, 0.08 #306, 0.08 #541), 018cvf (0.29 #481, 0.27 #313, 0.27 #414), 0prpt (0.21 #493, 0.21 #426, 0.21 #325), 0kfhjq0 (0.11 #312, 0.10 #513, 0.10 #547), 0j63cyr (0.10 #511, 0.09 #478, 0.09 #545), 07zmj (0.07 #529, 0.07 #429, 0.07 #496), 0gg7gsl (0.05 #405, 0.05 #505, 0.05 #539), 04jpl (0.03 #466, 0.03 #499, 0.03 #533), 030qb3t (0.03 #401, 0.03 #468, 0.03 #535) >> Best rule #47 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0168ls; >> query: (?x4971, 015hr) <- film(?x7385, ?x4971), film(?x2424, ?x4971), film(?x269, ?x4971), film_regional_debut_venue(?x4971, ?x739), ?x269 = 0byfz, nominated_for(?x2424, ?x697), people(?x13231, ?x7385) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jwxx film_regional_debut_venue 081m_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #19811-0cv3w PRED entity: 0cv3w PRED relation: origin! PRED expected values: 089pg7 => 150 concepts (133 used for prediction) PRED predicted values (max 10 best out of 484): 04n2vgk (0.33 #411, 0.22 #1441, 0.10 #4018), 07n3s (0.33 #488, 0.11 #1518), 03j3pg9 (0.33 #435, 0.11 #1465), 09nhvw (0.33 #416, 0.11 #1446), 01wj5hp (0.33 #384, 0.11 #1414), 03f7m4h (0.33 #364, 0.11 #1394), 01jllg1 (0.33 #361, 0.11 #1391), 01w5n51 (0.33 #333, 0.11 #1363), 01jfr3y (0.33 #252, 0.11 #1282), 01n44c (0.33 #215, 0.11 #1245) >> Best rule #411 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0cr3d; >> query: (?x3026, 04n2vgk) <- location(?x6920, ?x3026), ?x6920 = 02lgfh, featured_film_locations(?x349, ?x3026) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cv3w origin! 089pg7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 133.000 0.333 http://example.org/music/artist/origin #19810-083chw PRED entity: 083chw PRED relation: film PRED expected values: 02rv_dz 047csmy 027r9t => 89 concepts (54 used for prediction) PRED predicted values (max 10 best out of 276): 01g03q (0.48 #48335, 0.40 #87734, 0.39 #42964), 08jgk1 (0.33 #60869, 0.32 #78777, 0.08 #8951), 0bvn25 (0.15 #1840, 0.03 #84150, 0.01 #3630), 06fpsx (0.15 #3129, 0.03 #84150), 08r4x3 (0.14 #154, 0.04 #62660, 0.03 #84150), 02chhq (0.14 #1388, 0.04 #62660, 0.03 #84150), 049xgc (0.14 #973, 0.03 #84150, 0.01 #6343), 05k4my (0.14 #1652, 0.03 #84150, 0.01 #7022), 01633c (0.14 #1328, 0.03 #84150, 0.01 #6698), 06t2t2 (0.14 #1658, 0.03 #84150) >> Best rule #48335 for best value: >> intensional similarity = 3 >> extensional distance = 1226 >> proper extension: 04yywz; 02nb2s; 02rgz4; 0151ns; 03_vx9; 01sxq9; 0h1m9; 01xcqc; 03xmy1; 03rl84; ... >> query: (?x275, ?x9350) <- profession(?x275, ?x319), nominated_for(?x275, ?x9350), location(?x275, ?x1523) >> conf = 0.48 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 083chw film 027r9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 54.000 0.476 http://example.org/film/actor/film./film/performance/film EVAL 083chw film 047csmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 54.000 0.476 http://example.org/film/actor/film./film/performance/film EVAL 083chw film 02rv_dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 54.000 0.476 http://example.org/film/actor/film./film/performance/film #19809-09bg4l PRED entity: 09bg4l PRED relation: basic_title PRED expected values: 060c4 => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 13): 060c4 (0.70 #513, 0.64 #564, 0.62 #394), 0fkvn (0.46 #327, 0.42 #293, 0.40 #514), 0789n (0.25 #43, 0.17 #60, 0.15 #520), 01gkgk (0.22 #448, 0.15 #516, 0.14 #601), 0fkzq (0.17 #81, 0.09 #235, 0.05 #524), 060bp (0.15 #631, 0.12 #137, 0.12 #120), 09d6p2 (0.12 #434, 0.09 #247, 0.06 #417), 01q24l (0.11 #182, 0.09 #642, 0.08 #353), 0pqc5 (0.11 #174, 0.08 #345, 0.04 #957), 01dz7z (0.10 #220, 0.05 #544) >> Best rule #513 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 042d1; 0835q; >> query: (?x3563, 060c4) <- profession(?x3563, ?x353), taxonomy(?x3563, ?x939), ?x939 = 04n6k, people(?x6260, ?x3563) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09bg4l basic_title 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.700 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #19808-014q2g PRED entity: 014q2g PRED relation: category PRED expected values: 08mbj5d => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #4, 0.84 #42, 0.82 #11) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 05563d; 0394y; 01kcms4; 01l_w0; 02cw1m; 0p76z; 03qkcn9; >> query: (?x2782, 08mbj5d) <- artists(?x2809, ?x2782), artists(?x1572, ?x2782), ?x2809 = 05w3f, ?x1572 = 06by7 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014q2g category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.844 http://example.org/common/topic/webpage./common/webpage/category #19807-032_wv PRED entity: 032_wv PRED relation: nominated_for! PRED expected values: 0gr4k => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 206): 02rdyk7 (0.83 #70, 0.28 #1646, 0.25 #1480), 0gqwc (0.67 #295, 0.19 #19283, 0.19 #19282), 0gs9p (0.61 #64, 0.39 #1474, 0.35 #299), 019f4v (0.56 #53, 0.40 #1463, 0.31 #288), 094qd5 (0.56 #271, 0.25 #8699, 0.22 #17867), 0gq9h (0.51 #62, 0.43 #1472, 0.35 #6175), 04dn09n (0.49 #35, 0.35 #270, 0.34 #1445), 02pqp12 (0.41 #58, 0.24 #293, 0.23 #1468), 040njc (0.39 #7, 0.30 #1417, 0.28 #1646), 0gr4k (0.37 #26, 0.28 #1646, 0.28 #261) >> Best rule #70 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 016yxn; >> query: (?x1298, 02rdyk7) <- genre(?x1298, ?x53), nominated_for(?x1063, ?x1298), music(?x1298, ?x11105), ?x1063 = 02rdxsh >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 016yxn; *> query: (?x1298, 0gr4k) <- genre(?x1298, ?x53), nominated_for(?x1063, ?x1298), music(?x1298, ?x11105), ?x1063 = 02rdxsh *> conf = 0.37 ranks of expected_values: 10 EVAL 032_wv nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 89.000 89.000 0.829 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19806-06yyp PRED entity: 06yyp PRED relation: religion! PRED expected values: 07ssc 05kr_ => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 314): 0rh6k (0.80 #1318, 0.60 #658, 0.55 #2755), 04rrx (0.73 #1354, 0.50 #3123, 0.50 #2791), 01n4w (0.67 #1367, 0.60 #2804, 0.54 #3468), 09c7w0 (0.67 #221, 0.57 #439, 0.54 #985), 02xry (0.67 #1361, 0.55 #2798, 0.50 #2686), 05k7sb (0.67 #1356, 0.50 #2793, 0.50 #2681), 01n7q (0.67 #1340, 0.50 #2777, 0.50 #680), 0gyh (0.67 #1365, 0.50 #2802, 0.48 #3577), 050ks (0.67 #1401, 0.45 #3170, 0.45 #2838), 05kkh (0.60 #1320, 0.55 #2757, 0.52 #3532) >> Best rule #1318 for best value: >> intensional similarity = 9 >> extensional distance = 13 >> proper extension: 072w0; >> query: (?x9040, 0rh6k) <- religion(?x11103, ?x9040), religion(?x2146, ?x9040), location_of_ceremony(?x566, ?x11103), month(?x11103, ?x4869), mode_of_transportation(?x11103, ?x6665), film_release_region(?x5644, ?x2146), administrative_parent(?x2146, ?x551), genre(?x5644, ?x225), contains(?x2146, ?x1391) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #693 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 8 *> proper extension: 05sfs; *> query: (?x9040, 05kr_) <- religion(?x10076, ?x9040), religion(?x2146, ?x9040), profession(?x10076, ?x524), country(?x3411, ?x2146), titles(?x2146, ?x257), film_release_region(?x3088, ?x2146), film_release_region(?x2441, ?x2146), nationality(?x111, ?x2146), ?x3088 = 06w839_, country(?x1352, ?x2146), nominated_for(?x2771, ?x2441) *> conf = 0.60 ranks of expected_values: 18, 26 EVAL 06yyp religion! 05kr_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 40.000 40.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 06yyp religion! 07ssc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 40.000 40.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #19805-0277c3 PRED entity: 0277c3 PRED relation: profession PRED expected values: 016z4k => 106 concepts (68 used for prediction) PRED predicted values (max 10 best out of 75): 016z4k (0.59 #149, 0.58 #4, 0.56 #874), 0dz3r (0.59 #147, 0.45 #2177, 0.42 #1742), 0n1h (0.42 #11, 0.31 #881, 0.24 #2186), 01c72t (0.36 #1182, 0.32 #457, 0.30 #1037), 01d_h8 (0.36 #731, 0.32 #1311, 0.31 #4220), 0dxtg (0.33 #738, 0.32 #1318, 0.29 #1898), 039v1 (0.33 #179, 0.31 #4103, 0.28 #2936), 0fnpj (0.30 #203, 0.14 #1073, 0.14 #4127), 03gjzk (0.24 #1319, 0.22 #1899, 0.21 #9320), 0cbd2 (0.21 #587, 0.19 #1892, 0.17 #297) >> Best rule #149 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0lbj1; 032t2z; >> query: (?x6124, 016z4k) <- artists(?x283, ?x6124), profession(?x6124, ?x11254), artist(?x2931, ?x6124), ?x11254 = 04f2zj >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0277c3 profession 016z4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 68.000 0.593 http://example.org/people/person/profession #19804-09q5w2 PRED entity: 09q5w2 PRED relation: film! PRED expected values: 022g44 => 111 concepts (24 used for prediction) PRED predicted values (max 10 best out of 994): 06chf (0.51 #14548, 0.43 #27021, 0.29 #43650), 0150t6 (0.51 #14548, 0.43 #27021, 0.29 #43650), 0244r8 (0.51 #14548, 0.43 #27021, 0.29 #43650), 0bn3jg (0.51 #14548, 0.43 #27021, 0.29 #43650), 05183k (0.51 #14548, 0.43 #27021, 0.29 #43650), 016tw3 (0.51 #14548, 0.43 #27021, 0.29 #43650), 01fh9 (0.22 #2394, 0.02 #25257, 0.02 #27336), 0f5xn (0.15 #5125, 0.07 #13437, 0.07 #11359), 0170pk (0.15 #4436, 0.06 #10670, 0.05 #12748), 04954 (0.12 #1307, 0.11 #3386, 0.03 #11698) >> Best rule #14548 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 02qm_f; 01pgp6; 0hfzr; 07j94; 011ykb; 07bx6; 0c0zq; 0h1x5f; >> query: (?x1077, ?x262) <- film(?x1104, ?x1077), nominated_for(?x298, ?x1077), ?x298 = 05ztjjw, nominated_for(?x262, ?x1077) >> conf = 0.51 => this is the best rule for 6 predicted values No rule for expected values ranks of expected_values: EVAL 09q5w2 film! 022g44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 24.000 0.511 http://example.org/film/actor/film./film/performance/film #19803-0fm3nb PRED entity: 0fm3nb PRED relation: disciplines_or_subjects PRED expected values: 02vxn => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 37): 02vxn (0.80 #577, 0.70 #729, 0.68 #423), 04g51 (0.45 #945, 0.44 #907, 0.44 #1100), 02xlf (0.27 #909, 0.26 #986, 0.24 #947), 01hmnh (0.19 #971, 0.18 #1009, 0.17 #932), 06n90 (0.19 #968, 0.16 #1006, 0.16 #891), 02jknp (0.14 #118, 0.14 #730, 0.14 #386), 05hgj (0.12 #985, 0.12 #908, 0.12 #1023), 0707q (0.07 #223, 0.05 #915, 0.05 #953), 0dwly (0.06 #1103, 0.06 #1141, 0.05 #948), 0jtdp (0.05 #506, 0.05 #660, 0.05 #698) >> Best rule #577 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 0dt49; >> query: (?x10999, 02vxn) <- award(?x534, ?x10999), disciplines_or_subjects(?x10999, ?x6760), major_field_of_study(?x7545, ?x6760), major_field_of_study(?x1368, ?x6760), ?x7545 = 0bwfn >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fm3nb disciplines_or_subjects 02vxn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.805 http://example.org/award/award_category/disciplines_or_subjects #19802-0x2p PRED entity: 0x2p PRED relation: school PRED expected values: 0trv => 50 concepts (46 used for prediction) PRED predicted values (max 10 best out of 202): 06pwq (0.60 #2105, 0.57 #1915, 0.56 #2485), 07w0v (0.60 #583, 0.38 #2490, 0.38 #2300), 06fq2 (0.45 #3572, 0.45 #1469, 0.45 #1278), 01lnyf (0.40 #447, 0.38 #1020, 0.33 #1592), 021w0_ (0.40 #908, 0.33 #145, 0.29 #2053), 01dzg0 (0.40 #546, 0.33 #166, 0.28 #2834), 07vyf (0.40 #442, 0.33 #62, 0.25 #1015), 01tx9m (0.40 #482, 0.33 #102, 0.21 #2010), 012mzw (0.40 #507, 0.33 #127, 0.18 #1461), 01qgr3 (0.40 #693, 0.29 #1839, 0.23 #3559) >> Best rule #2105 for best value: >> intensional similarity = 13 >> extensional distance = 13 >> proper extension: 01d6g; >> query: (?x2405, 06pwq) <- team(?x4244, ?x2405), ?x4244 = 028c_8, season(?x2405, ?x10017), season(?x2405, ?x8517), school(?x2405, ?x3416), draft(?x2405, ?x8786), draft(?x2405, ?x4779), ?x4779 = 02z6872, ?x8786 = 02pq_x5, ?x10017 = 026fmqm, position(?x2405, ?x11883), ?x8517 = 0285r5d, institution(?x865, ?x3416) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #331 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 021f30; *> query: (?x2405, 0trv) <- team(?x5727, ?x2405), team(?x4244, ?x2405), team(?x261, ?x2405), ?x4244 = 028c_8, colors(?x2405, ?x8271), ?x5727 = 02wszf, position(?x2405, ?x11883), ?x8271 = 02rnmb, team(?x5412, ?x2405), team(?x261, ?x7399), team(?x261, ?x4243), ?x7399 = 06wpc, position(?x580, ?x261), ?x4243 = 0713r *> conf = 0.25 ranks of expected_values: 34 EVAL 0x2p school 0trv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 50.000 46.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #19801-0hfjk PRED entity: 0hfjk PRED relation: genre! PRED expected values: 02__34 026qnh6 05hjnw 0cq8qq 0gfh84d 02ljhg 02q_ncg => 51 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1842): 02q_ncg (0.81 #5512, 0.76 #3674, 0.71 #18377), 02ljhg (0.81 #5512, 0.76 #3674, 0.71 #18377), 0b73_1d (0.81 #5512, 0.76 #3674, 0.71 #18377), 015gm8 (0.81 #5512, 0.76 #3674, 0.71 #18377), 08c4yn (0.81 #5512, 0.76 #3674, 0.71 #18377), 039c26 (0.81 #5512, 0.76 #3674, 0.71 #18377), 06cs95 (0.81 #5512, 0.76 #3674, 0.71 #18377), 04z257 (0.62 #11629, 0.60 #7956, 0.50 #6118), 05r3qc (0.60 #8441, 0.50 #12114, 0.50 #6603), 0bt4g (0.60 #10541, 0.50 #6866, 0.40 #8704) >> Best rule #5512 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 07s9rl0; 03k9fj; >> query: (?x8280, ?x825) <- genre(?x4865, ?x8280), genre(?x4811, ?x8280), genre(?x3294, ?x8280), ?x3294 = 0jvt9, genre(?x531, ?x8280), titles(?x8280, ?x825), ?x4811 = 0f4k49, country(?x4865, ?x94) >> conf = 0.81 => this is the best rule for 7 predicted values ranks of expected_values: 1, 2, 124, 527, 649, 1423, 1451 EVAL 0hfjk genre! 02q_ncg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 26.000 0.806 http://example.org/film/film/genre EVAL 0hfjk genre! 02ljhg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 26.000 0.806 http://example.org/film/film/genre EVAL 0hfjk genre! 0gfh84d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 26.000 0.806 http://example.org/film/film/genre EVAL 0hfjk genre! 0cq8qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 26.000 0.806 http://example.org/film/film/genre EVAL 0hfjk genre! 05hjnw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 26.000 0.806 http://example.org/film/film/genre EVAL 0hfjk genre! 026qnh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 51.000 26.000 0.806 http://example.org/film/film/genre EVAL 0hfjk genre! 02__34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 26.000 0.806 http://example.org/film/film/genre #19800-04dn09n PRED entity: 04dn09n PRED relation: ceremony PRED expected values: 026kq4q => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 126): 0h_cssd (0.56 #1159, 0.29 #1663, 0.29 #655), 02hn5v (0.50 #793, 0.48 #1675, 0.40 #1549), 02yw5r (0.50 #766, 0.48 #1648, 0.40 #1522), 0bzm81 (0.50 #775, 0.48 #1657, 0.40 #1531), 0n8_m93 (0.50 #859, 0.48 #1741, 0.40 #1615), 02yvhx (0.50 #826, 0.48 #1708, 0.40 #1582), 0bvfqq (0.50 #786, 0.48 #1668, 0.40 #1542), 02yxh9 (0.50 #846, 0.48 #1728, 0.40 #1602), 0bc773 (0.50 #805, 0.48 #1687, 0.40 #1561), 04110lv (0.50 #852, 0.48 #1734, 0.40 #1608) >> Best rule #1159 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 094qd5; 04kxsb; 02qyntr; >> query: (?x746, 0h_cssd) <- ceremony(?x746, ?x747), award(?x276, ?x746), nominated_for(?x746, ?x6149), ?x6149 = 016ks5 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #5546 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 208 *> proper extension: 03nl5k; *> query: (?x746, ?x725) <- ceremony(?x746, ?x747), award(?x6698, ?x746), award_winner(?x725, ?x6698), category(?x6698, ?x134) *> conf = 0.38 ranks of expected_values: 63 EVAL 04dn09n ceremony 026kq4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 70.000 70.000 0.556 http://example.org/award/award_category/winners./award/award_honor/ceremony #19799-0783m_ PRED entity: 0783m_ PRED relation: award PRED expected values: 09lvl1 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 226): 0ck27z (0.33 #2920, 0.28 #2112, 0.25 #3729), 09sb52 (0.26 #2868, 0.23 #8527, 0.22 #16612), 0cqhmg (0.20 #362, 0.17 #6870, 0.16 #13740), 09qj50 (0.17 #6870, 0.16 #13740, 0.15 #15764), 09qv3c (0.17 #6870, 0.16 #13740, 0.15 #15764), 09qs08 (0.15 #22634, 0.15 #21825, 0.13 #3233), 01by1l (0.14 #3345, 0.11 #6982, 0.11 #6577), 09qvc0 (0.13 #23847, 0.07 #1251, 0.06 #1655), 01bgqh (0.11 #3275, 0.08 #4891, 0.08 #6912), 0cjyzs (0.09 #6167, 0.05 #9805, 0.05 #6571) >> Best rule #2920 for best value: >> intensional similarity = 3 >> extensional distance = 570 >> proper extension: 01mv_n; 0k9j_; >> query: (?x2359, 0ck27z) <- award_nominee(?x7797, ?x2359), award_winner(?x678, ?x7797), actor(?x5060, ?x2359) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2718 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 497 *> proper extension: 01ry0f; *> query: (?x2359, 09lvl1) <- actor(?x5060, ?x2359), student(?x6611, ?x2359) *> conf = 0.01 ranks of expected_values: 202 EVAL 0783m_ award 09lvl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 77.000 77.000 0.325 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19798-0bq6ntw PRED entity: 0bq6ntw PRED relation: produced_by PRED expected values: 043q6n_ => 86 concepts (66 used for prediction) PRED predicted values (max 10 best out of 144): 0q9kd (0.25 #2, 0.02 #7348), 0d_skg (0.25 #229, 0.02 #5640, 0.02 #5253), 0184dt (0.07 #2015, 0.07 #469, 0.07 #855), 03ktjq (0.07 #4064, 0.06 #8320, 0.04 #7546), 04y8r (0.07 #844, 0.02 #3935, 0.02 #5868), 026gb3v (0.07 #1142, 0.01 #7328), 04pqqb (0.06 #4041, 0.05 #5974, 0.04 #6363), 02xnjd (0.06 #4136, 0.04 #8392, 0.04 #4523), 01sl1q (0.05 #387, 0.04 #6185, 0.03 #8507), 01vw37m (0.05 #387, 0.04 #6185, 0.03 #8507) >> Best rule #2 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 03t97y; >> query: (?x6095, 0q9kd) <- currency(?x6095, ?x170), film_crew_role(?x6095, ?x1171), film_crew_role(?x6095, ?x468), film(?x10780, ?x6095), ?x10780 = 014g_s, ?x1171 = 09vw2b7, ?x468 = 02r96rf, language(?x6095, ?x254) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3915 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 80 *> proper extension: 01q2nx; *> query: (?x6095, 043q6n_) <- currency(?x6095, ?x170), produced_by(?x6095, ?x2858), film_crew_role(?x6095, ?x2095), ?x2095 = 0dxtw, film(?x56, ?x6095), genre(?x6095, ?x812), ?x812 = 01jfsb *> conf = 0.02 ranks of expected_values: 65 EVAL 0bq6ntw produced_by 043q6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 86.000 66.000 0.250 http://example.org/film/film/produced_by #19797-0525b PRED entity: 0525b PRED relation: award_nominee! PRED expected values: 01_p6t => 77 concepts (29 used for prediction) PRED predicted values (max 10 best out of 651): 01kb2j (0.82 #9322, 0.81 #37285, 0.81 #34954), 0372kf (0.82 #9322, 0.81 #37285, 0.81 #34954), 05dbf (0.82 #9322, 0.81 #37285, 0.81 #34954), 01_p6t (0.82 #9322, 0.81 #37285, 0.81 #34954), 02bkdn (0.82 #9322, 0.81 #37285, 0.81 #34954), 0525b (0.11 #58260, 0.04 #67585, 0.03 #65253), 015rkw (0.11 #58260, 0.03 #2695, 0.03 #365), 05cj4r (0.11 #58260, 0.02 #14040, 0.01 #35012), 0jfx1 (0.11 #58260, 0.02 #2850, 0.02 #520), 01wy5m (0.11 #58260, 0.02 #1140, 0.01 #3470) >> Best rule #9322 for best value: >> intensional similarity = 3 >> extensional distance = 504 >> proper extension: 07s6tbm; 01gzm2; 033wx9; 05szp; 030b93; 01934k; 0564mx; 08qmfm; >> query: (?x11858, ?x1871) <- award_nominee(?x11858, ?x1871), gender(?x11858, ?x514), ?x514 = 02zsn >> conf = 0.82 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 0525b award_nominee! 01_p6t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 29.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19796-01zfzb PRED entity: 01zfzb PRED relation: honored_for! PRED expected values: 073h9x => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 110): 0bz6l9 (0.33 #41, 0.04 #1261, 0.04 #1139), 05c1t6z (0.11 #2207, 0.11 #499, 0.11 #1841), 02q690_ (0.11 #542, 0.08 #2250, 0.08 #1884), 09g90vz (0.11 #596, 0.05 #1938, 0.04 #2304), 0drtv8 (0.11 #177, 0.04 #1885, 0.04 #2251), 0gpjbt (0.11 #145), 0gvstc3 (0.09 #2223, 0.08 #1857, 0.06 #515), 09gkdln (0.08 #594, 0.05 #716, 0.05 #960), 0bx6zs (0.08 #599, 0.05 #1941, 0.04 #2307), 0dznvw (0.08 #728, 0.05 #972, 0.05 #1094) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0p9tm; >> query: (?x5320, 0bz6l9) <- genre(?x5320, ?x8280), film_sets_designed(?x8814, ?x5320), award_winner(?x5320, ?x5319), ?x8280 = 0hfjk >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1504 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 89 *> proper extension: 0209xj; 0416y94; 0sxfd; 0170th; 011ydl; 02ppg1r; 01s3vk; 0cbv4g; 02psgq; 0dpl44; ... *> query: (?x5320, 073h9x) <- genre(?x5320, ?x8280), genre(?x5320, ?x6674), nominated_for(?x1691, ?x5320), titles(?x8280, ?x531), ?x6674 = 01t_vv *> conf = 0.02 ranks of expected_values: 82 EVAL 01zfzb honored_for! 073h9x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 93.000 93.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #19795-016_nr PRED entity: 016_nr PRED relation: parent_genre! PRED expected values: 012yc => 58 concepts (36 used for prediction) PRED predicted values (max 10 best out of 264): 01ym9b (0.33 #557, 0.25 #298, 0.25 #39), 016_nr (0.33 #579, 0.25 #320, 0.25 #61), 016_rm (0.33 #709, 0.25 #450, 0.25 #191), 0263q4z (0.33 #743, 0.25 #484, 0.25 #225), 064t9 (0.27 #787, 0.17 #528, 0.14 #5215), 0283d (0.25 #342, 0.25 #83, 0.20 #860), 05jt_ (0.25 #358, 0.25 #99, 0.17 #617), 01738f (0.25 #352, 0.25 #93, 0.17 #611), 0hh2s (0.25 #368, 0.25 #109, 0.17 #627), 012yc (0.25 #379, 0.25 #120, 0.17 #638) >> Best rule #557 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01flzq; >> query: (?x5630, 01ym9b) <- parent_genre(?x2937, ?x5630), artists(?x5630, ?x2737), artists(?x2937, ?x3293), award_winner(?x3293, ?x286), ?x2737 = 0126y2 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #379 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 036jv; *> query: (?x5630, 012yc) <- artists(?x5630, ?x4476), artists(?x5630, ?x521), ?x4476 = 01vw20h, film(?x521, ?x1488), award(?x521, ?x401), friend(?x521, ?x6187) *> conf = 0.25 ranks of expected_values: 10 EVAL 016_nr parent_genre! 012yc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 58.000 36.000 0.333 http://example.org/music/genre/parent_genre #19794-03l26m PRED entity: 03l26m PRED relation: athlete! PRED expected values: 018w8 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 8): 039yzs (0.88 #212, 0.82 #161), 018w8 (0.82 #161, 0.71 #16, 0.50 #56), 02vx4 (0.62 #203, 0.59 #193, 0.58 #224), 0jm_ (0.55 #93, 0.52 #123, 0.42 #33), 018jz (0.25 #37, 0.21 #77, 0.20 #87), 07bs0 (0.19 #114), 03tmr (0.04 #101, 0.03 #151, 0.03 #162), 037hz (0.04 #120) >> Best rule #212 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 05_6_y; 0784v1; 09ntbc; 0c11mj; 071pf2; 0fv6dr; 09lhln; 02vl_pz; 0135nb; 026y23w; ... >> query: (?x11620, ?x12913) <- team(?x11620, ?x9983), nationality(?x11620, ?x94), colors(?x9983, ?x3189), sport(?x9983, ?x12913) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 07m69t; *> query: (?x11620, ?x12913) <- team(?x11620, ?x9983), nationality(?x11620, ?x94), location(?x11620, ?x739), sport(?x9983, ?x12913) *> conf = 0.82 ranks of expected_values: 2 EVAL 03l26m athlete! 018w8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 122.000 0.875 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #19793-011zf2 PRED entity: 011zf2 PRED relation: student! PRED expected values: 03ksy => 134 concepts (122 used for prediction) PRED predicted values (max 10 best out of 101): 02cw8s (0.15 #2695, 0.11 #4270, 0.02 #3220), 02_gzx (0.14 #907, 0.12 #1432, 0.07 #4057), 07tgn (0.14 #17, 0.02 #3167, 0.02 #60410), 025v3k (0.14 #118, 0.01 #4843, 0.01 #5368), 0ymcz (0.14 #412), 03ksy (0.12 #1154, 0.07 #1679, 0.04 #60497), 09f2j (0.12 #1207, 0.07 #1732, 0.03 #35335), 04sylm (0.12 #1126, 0.07 #1651, 0.03 #3751), 0342z_ (0.12 #1504, 0.07 #2029, 0.01 #4129), 02237m (0.12 #1445, 0.07 #1970, 0.01 #5120) >> Best rule #2695 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 0dj5q; >> query: (?x1399, 02cw8s) <- award_winner(?x159, ?x1399), nationality(?x1399, ?x789), ?x789 = 0f8l9c >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #1154 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 01nqfh_; 018d6l; *> query: (?x1399, 03ksy) <- instrumentalists(?x4311, ?x1399), ?x4311 = 01xqw, student(?x2909, ?x1399) *> conf = 0.12 ranks of expected_values: 6 EVAL 011zf2 student! 03ksy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 134.000 122.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #19792-06bpt_ PRED entity: 06bpt_ PRED relation: artists PRED expected values: 0bsj9 => 53 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1226): 016ntp (0.67 #2429, 0.57 #3511, 0.33 #264), 01j59b0 (0.57 #3720, 0.50 #2638, 0.45 #1083), 0ycp3 (0.57 #3864, 0.50 #2782, 0.33 #617), 01shhf (0.55 #6287, 0.55 #5205, 0.45 #1083), 07r1_ (0.50 #2802, 0.43 #3884, 0.38 #22744), 048tgl (0.50 #7409, 0.43 #4161, 0.36 #6327), 0285c (0.50 #2307, 0.43 #3389, 0.36 #5555), 01pfr3 (0.50 #2190, 0.43 #3272, 0.33 #25), 01gf5h (0.50 #2228, 0.43 #3310, 0.33 #63), 01vng3b (0.50 #2728, 0.43 #3810, 0.33 #563) >> Best rule #2429 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 016clz; 0xjl2; 0dls3; >> query: (?x7873, 016ntp) <- artists(?x7873, ?x8012), artists(?x7873, ?x7874), ?x7874 = 019389, instrumentalists(?x212, ?x8012), artists(?x5934, ?x8012), profession(?x8012, ?x131), location(?x8012, ?x4362), ?x5934 = 05r6t >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1037 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 0xhtw; *> query: (?x7873, 0bsj9) <- artists(?x7873, ?x10106), artists(?x7873, ?x8012), artists(?x7873, ?x7874), ?x7874 = 019389, ?x8012 = 01wt4wc, parent_genre(?x8289, ?x7873), group(?x227, ?x10106), artists(?x8289, ?x970) *> conf = 0.33 ranks of expected_values: 68 EVAL 06bpt_ artists 0bsj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 53.000 24.000 0.667 http://example.org/music/genre/artists #19791-0dxtg PRED entity: 0dxtg PRED relation: profession! PRED expected values: 012d40 01l1b90 0byfz 0m2l9 0d4fqn 02kxbwx 01v3s2_ 0yfp 06pk8 04wvhz 030pr 014zfs 016hvl 03cs_z7 0pgjm 05prs8 0ksf29 01n4f8 0f1vrl 0gz5hs 03j43 0h1p 0l56b 01r216 04y8r 028d4v 01wg982 05qsxy 05whq_9 04b19t 02ld6x 0bt4r4 0kh6b 076_74 029_3 01f7v_ 02v406 01trf3 04yt7 08qvhv 07rd7 086nl7 026l37 04gnbv1 024swd 045w_4 027hnjh 09qc1 070m12 05y5fw 053y4h 058nh2 05pzdk 0gn30 01rlxt 02lhm2 04pz5c 08n__5 026fd 0p__8 07r4c 03hy3g 0q9vf 04l19_ 01vb6z 0646qh 018ty9 06z4wj 01p1z_ 02vqpx8 025vldk 07h5d 087qxp 06b_0 0h0yt 01g1lp 01c1px 0c31_ 02_j8x 063_t 0k57l 02c0mv 033rq 0mfj2 0jpdn 0cj2k3 030vmc 06rq2l 01x4r3 03v0vd 0gp_x9 027j79k 0c4y8 07d3x 02m501 0htcn 04j0s3 09hd6f 046_v 014g9y 015p37 0c408_ 03vrv9 06pcz0 03wh95l 0ff2k 03yf4d 016lv3 06y0xx 01385g 01fxfk 01g5kv 09xvf7 06z9yh 0gry51 09t4hh => 53 concepts (25 used for prediction) PRED predicted values (max 10 best out of 3303): 0127m7 (0.75 #49879, 0.67 #53170, 0.57 #40010), 02ldv0 (0.71 #41137, 0.62 #51006, 0.56 #54297), 029m83 (0.71 #41538, 0.51 #29605, 0.50 #51407), 04cl1 (0.71 #40639, 0.51 #29605, 0.50 #50508), 06pcz0 (0.71 #42346, 0.51 #29605, 0.50 #52215), 06y0xx (0.71 #42528, 0.51 #29605, 0.50 #52397), 03b78r (0.71 #41368, 0.50 #51237, 0.50 #21629), 024jwt (0.71 #42201, 0.50 #52070, 0.50 #22462), 06w33f8 (0.71 #39847, 0.50 #49716, 0.50 #20108), 02lfwp (0.71 #42449, 0.50 #52318, 0.50 #22710) >> Best rule #49879 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 012t_z; 09jwl; >> query: (?x987, 0127m7) <- profession(?x11019, ?x987), profession(?x4134, ?x987), profession(?x397, ?x987), ?x11019 = 0hqly, gender(?x397, ?x231), award_nominee(?x4134, ?x1657) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #42346 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 02krf9; *> query: (?x987, 06pcz0) <- profession(?x10589, ?x987), profession(?x8235, ?x987), profession(?x8043, ?x987), ?x8235 = 064jjy, award_winner(?x10589, ?x7507), place_of_burial(?x8043, ?x8044), award_winner(?x3233, ?x10589) *> conf = 0.71 ranks of expected_values: 5, 6, 12, 14, 16, 19, 30, 31, 44, 52, 57, 67, 78, 79, 81, 82, 94, 97, 101, 105, 106, 113, 118, 120, 129, 153, 154, 161, 189, 209, 214, 216, 232, 233, 237, 238, 253, 254, 279, 377, 392, 429, 433, 450, 452, 453, 457, 479, 483, 484, 494, 537, 551, 553, 563, 566, 570, 591, 623, 782, 796, 845, 882, 883, 885, 912, 913, 914, 915, 916, 923, 924, 928, 930, 932, 948, 953, 959, 973, 1014, 1017, 1022, 1025, 1026, 1032, 1098, 1171, 1180, 1185, 1214, 1242, 1255, 1273, 1274, 1275, 1286, 1456, 1508, 1509, 1532, 1563, 1614, 1633, 1680, 1690, 1720, 1745, 1748, 1749, 1792, 2249, 2577, 2661, 2773 EVAL 0dxtg profession! 09t4hh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0gry51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 06z9yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 09xvf7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01g5kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01fxfk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01385g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 06y0xx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 016lv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 03yf4d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0ff2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 03wh95l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 06pcz0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 03vrv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0c408_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 015p37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 014g9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 046_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 09hd6f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 04j0s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0htcn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 02m501 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 07d3x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0c4y8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 027j79k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0gp_x9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 03v0vd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01x4r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 06rq2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 030vmc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0cj2k3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0jpdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0mfj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 033rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 02c0mv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0k57l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 063_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 02_j8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0c31_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01c1px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01g1lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0h0yt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 06b_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 087qxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 07h5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 025vldk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 02vqpx8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01p1z_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 06z4wj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 018ty9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0646qh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01vb6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 04l19_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0q9vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 03hy3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 07r4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0p__8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 026fd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 08n__5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 04pz5c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 02lhm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01rlxt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0gn30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 05pzdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 058nh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 053y4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 05y5fw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 070m12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 09qc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 027hnjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 045w_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 024swd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 04gnbv1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 026l37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 086nl7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 07rd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 08qvhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 04yt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01trf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 02v406 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01f7v_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 029_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 076_74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0kh6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0bt4r4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 02ld6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 04b19t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 05whq_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 05qsxy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01wg982 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 028d4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 04y8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01r216 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0l56b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0h1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 03j43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0gz5hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0f1vrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01n4f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0ksf29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 05prs8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0pgjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 03cs_z7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 016hvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 014zfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 030pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 04wvhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 06pk8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0yfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01v3s2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 02kxbwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0d4fqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0m2l9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 0byfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 01l1b90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 25.000 0.750 http://example.org/people/person/profession EVAL 0dxtg profession! 012d40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 53.000 25.000 0.750 http://example.org/people/person/profession #19790-01v40wd PRED entity: 01v40wd PRED relation: profession PRED expected values: 02hrh1q => 93 concepts (71 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.88 #1485, 0.87 #3102, 0.87 #4134), 09jwl (0.73 #19, 0.72 #2372, 0.69 #3549), 0cbd2 (0.60 #447, 0.13 #8397, 0.12 #10309), 016z4k (0.47 #3533, 0.46 #2356, 0.44 #3976), 01d_h8 (0.42 #888, 0.41 #1917, 0.40 #2946), 0kyk (0.37 #471, 0.15 #177, 0.14 #324), 039v1 (0.33 #36, 0.30 #2389, 0.27 #3861), 0dxtg (0.31 #2954, 0.30 #1925, 0.29 #896), 03gjzk (0.30 #898, 0.27 #9715, 0.27 #1927), 0n1h (0.30 #158, 0.27 #9715, 0.25 #1041) >> Best rule #1485 for best value: >> intensional similarity = 3 >> extensional distance = 217 >> proper extension: 0g476; >> query: (?x3893, 02hrh1q) <- profession(?x3893, ?x131), award(?x3893, ?x2180), participant(?x3893, ?x4474) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v40wd profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 71.000 0.877 http://example.org/people/person/profession #19789-03h26tm PRED entity: 03h26tm PRED relation: nominated_for PRED expected values: 026lgs => 93 concepts (38 used for prediction) PRED predicted values (max 10 best out of 473): 0ddjy (0.79 #35631, 0.79 #43729, 0.78 #11337), 04jpg2p (0.79 #35631, 0.79 #43729, 0.78 #11337), 011xg5 (0.38 #1277, 0.12 #46968, 0.12 #30774), 0315rp (0.38 #1284, 0.12 #46968, 0.12 #30774), 0hx4y (0.25 #426, 0.16 #59933, 0.15 #59934), 07nxnw (0.25 #4857, 0.23 #6476, 0.12 #1084), 0c3z0 (0.25 #4857, 0.23 #6476, 0.05 #4131), 031t2d (0.25 #4857, 0.23 #6476), 0yx_w (0.18 #3023), 07gp9 (0.16 #59933, 0.15 #59934, 0.14 #3275) >> Best rule #35631 for best value: >> intensional similarity = 3 >> extensional distance = 1058 >> proper extension: 01nzs7; >> query: (?x930, ?x2366) <- nominated_for(?x930, ?x7336), award_winner(?x2366, ?x930), film_release_region(?x7336, ?x87) >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #2477 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 15 *> proper extension: 0q9t7; *> query: (?x930, 026lgs) <- award_winner(?x78, ?x930), ?x78 = 073hkh *> conf = 0.06 ranks of expected_values: 59 EVAL 03h26tm nominated_for 026lgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 93.000 38.000 0.795 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #19788-015196 PRED entity: 015196 PRED relation: artists! PRED expected values: 0172rj => 93 concepts (32 used for prediction) PRED predicted values (max 10 best out of 258): 06by7 (0.88 #8595, 0.58 #6150, 0.52 #1553), 05bt6j (0.55 #6171, 0.27 #8616, 0.27 #4635), 064t9 (0.51 #4606, 0.46 #3077, 0.45 #5837), 016clz (0.33 #616, 0.29 #4902, 0.29 #5210), 011j5x (0.33 #31, 0.24 #1837, 0.24 #917), 0133_p (0.33 #151, 0.04 #456, 0.04 #6279), 0dl5d (0.27 #1857, 0.27 #938, 0.26 #1245), 01_bkd (0.24 #1837, 0.24 #917, 0.24 #1531), 09jw2 (0.24 #1837, 0.24 #917, 0.24 #1531), 07sbbz2 (0.24 #1837, 0.24 #917, 0.24 #1531) >> Best rule #8595 for best value: >> intensional similarity = 4 >> extensional distance = 442 >> proper extension: 053y0s; 07s3vqk; 01pfr3; 032nwy; 02mslq; 01nqfh_; 03f5spx; 01q7cb_; 07qnf; 02r3zy; ... >> query: (?x10756, 06by7) <- artists(?x2249, ?x10756), artists(?x2249, ?x9830), ?x9830 = 01m7pwq, parent_genre(?x302, ?x2249) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #6435 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 339 *> proper extension: 0152cw; 01vv7sc; 01k98nm; 01cblr; 01t110; 06tp4h; 017lb_; 01lf293; 0517bc; *> query: (?x10756, ?x302) <- artists(?x2249, ?x10756), parent_genre(?x11040, ?x2249), parent_genre(?x302, ?x2249), ?x11040 = 0173b0 *> conf = 0.07 ranks of expected_values: 61 EVAL 015196 artists! 0172rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 93.000 32.000 0.876 http://example.org/music/genre/artists #19787-01k165 PRED entity: 01k165 PRED relation: location PRED expected values: 01r32 => 188 concepts (118 used for prediction) PRED predicted values (max 10 best out of 213): 02_286 (0.91 #58736, 0.40 #68381, 0.27 #64361), 0rh6k (0.40 #3220, 0.33 #1611, 0.27 #4024), 030qb3t (0.27 #66820, 0.24 #68427, 0.17 #91757), 0d6lp (0.25 #167, 0.14 #17858, 0.09 #4991), 0ftvg (0.25 #513, 0.11 #2120, 0.10 #3729), 094jv (0.25 #92, 0.09 #4916, 0.07 #17783), 01cx_ (0.18 #9007, 0.13 #13029, 0.11 #49210), 01ktz1 (0.17 #925, 0.11 #1728, 0.10 #3337), 04jpl (0.17 #821, 0.09 #68361, 0.08 #16905), 05qtj (0.17 #1044, 0.08 #49288, 0.06 #50897) >> Best rule #58736 for best value: >> intensional similarity = 4 >> extensional distance = 382 >> proper extension: 0pyg6; 04bdqk; 0dszr0; >> query: (?x3099, 02_286) <- location(?x3099, ?x1658), location(?x1530, ?x1658), ?x1530 = 049g_xj, featured_film_locations(?x97, ?x1658) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #82827 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 823 *> proper extension: 01s21dg; 023n39; *> query: (?x3099, ?x1411) <- gender(?x3099, ?x231), student(?x11632, ?x3099), place_of_birth(?x3099, ?x1658), citytown(?x11632, ?x1411), school_type(?x11632, ?x3092) *> conf = 0.12 ranks of expected_values: 17 EVAL 01k165 location 01r32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 188.000 118.000 0.911 http://example.org/people/person/places_lived./people/place_lived/location #19786-059fjj PRED entity: 059fjj PRED relation: award_winner! PRED expected values: 0drtv8 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 122): 027hjff (0.28 #477, 0.24 #617, 0.09 #897), 092_25 (0.12 #352, 0.12 #72, 0.11 #212), 0drtv8 (0.12 #486, 0.12 #626, 0.02 #2166), 09p30_ (0.12 #84, 0.11 #224, 0.05 #504), 0bzlrh (0.12 #103, 0.11 #243, 0.03 #383), 0bx6zs (0.12 #126, 0.11 #266, 0.03 #406), 0bzknt (0.12 #81, 0.11 #221, 0.01 #8061), 0bvhz9 (0.12 #549, 0.10 #689, 0.01 #8109), 02q690_ (0.10 #485, 0.08 #625, 0.05 #905), 09gkdln (0.10 #541, 0.08 #681, 0.03 #1801) >> Best rule #477 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0gsg7; 0cjdk; >> query: (?x8113, 027hjff) <- award_winner(?x686, ?x8113), award_winner(?x5592, ?x8113), ?x5592 = 0275n3y >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #486 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 0gsg7; 0cjdk; *> query: (?x8113, 0drtv8) <- award_winner(?x686, ?x8113), award_winner(?x5592, ?x8113), ?x5592 = 0275n3y *> conf = 0.12 ranks of expected_values: 3 EVAL 059fjj award_winner! 0drtv8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 125.000 0.275 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19785-01fwk3 PRED entity: 01fwk3 PRED relation: award PRED expected values: 094qd5 => 83 concepts (72 used for prediction) PRED predicted values (max 10 best out of 257): 02z1nbg (0.71 #21665, 0.70 #11231, 0.69 #21664), 02ppm4q (0.44 #955, 0.13 #153, 0.09 #554), 09sb52 (0.41 #841, 0.30 #39, 0.29 #6856), 0ck27z (0.40 #89, 0.18 #6906, 0.13 #16537), 02z0dfh (0.32 #874, 0.13 #72, 0.07 #7290), 09td7p (0.30 #919, 0.10 #117, 0.09 #518), 094qd5 (0.29 #845, 0.15 #12837, 0.15 #23672), 0bdwft (0.27 #868, 0.20 #66, 0.09 #1269), 0bfvw2 (0.23 #15, 0.23 #817, 0.13 #24477), 099t8j (0.23 #939, 0.10 #137, 0.05 #538) >> Best rule #21665 for best value: >> intensional similarity = 3 >> extensional distance = 1979 >> proper extension: 03j90; >> query: (?x2715, ?x1972) <- nationality(?x2715, ?x94), award_winner(?x1972, ?x2715), award(?x91, ?x1972) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #845 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 110 *> proper extension: 01hkck; 01rnpy; 06r3p2; *> query: (?x2715, 094qd5) <- award(?x2715, ?x1972), ?x1972 = 0gqyl, profession(?x2715, ?x319) *> conf = 0.29 ranks of expected_values: 7 EVAL 01fwk3 award 094qd5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 83.000 72.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19784-0fdys PRED entity: 0fdys PRED relation: student PRED expected values: 060j8b 012x2b => 79 concepts (53 used for prediction) PRED predicted values (max 10 best out of 900): 0kn4c (0.50 #1138, 0.40 #2483, 0.33 #4936), 04z0g (0.40 #2355, 0.22 #5031, 0.14 #4141), 0bq2g (0.33 #71, 0.06 #1337, 0.03 #7429), 03j2gxx (0.25 #1316, 0.20 #2661, 0.20 #2438), 01yk13 (0.25 #1128, 0.20 #5372, 0.20 #2473), 09b6zr (0.25 #1204, 0.20 #2549, 0.17 #3662), 0q9zc (0.25 #1267, 0.20 #2612, 0.17 #3725), 083q7 (0.25 #1133, 0.20 #2478, 0.17 #3591), 02z1yj (0.25 #1292, 0.20 #2637, 0.17 #3750), 015p37 (0.25 #1313, 0.20 #2658, 0.17 #3771) >> Best rule #1138 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 01lhy; 03g3w; >> query: (?x3995, 0kn4c) <- major_field_of_study(?x10859, ?x3995), major_field_of_study(?x3485, ?x3995), ?x10859 = 0ym17, ?x3485 = 01mpwj, student(?x3995, ?x1188), major_field_of_study(?x3995, ?x3490), student(?x3490, ?x1125), major_field_of_study(?x263, ?x3490), major_field_of_study(?x3490, ?x1668) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7310 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 25 *> proper extension: 0pf2; 02_7t; 02stgt; *> query: (?x3995, 012x2b) <- major_field_of_study(?x10859, ?x3995), major_field_of_study(?x7545, ?x3995), major_field_of_study(?x2172, ?x3995), major_field_of_study(?x3995, ?x90), ?x7545 = 0bwfn, contains(?x512, ?x10859), currency(?x10859, ?x1099) *> conf = 0.11 ranks of expected_values: 50, 445 EVAL 0fdys student 012x2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 79.000 53.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 0fdys student 060j8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 79.000 53.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/student #19783-0l12d PRED entity: 0l12d PRED relation: award PRED expected values: 02qvyrt => 83 concepts (73 used for prediction) PRED predicted values (max 10 best out of 290): 0l8z1 (0.72 #7639, 0.70 #23726, 0.69 #17692), 02qvyrt (0.50 #5351, 0.44 #1733, 0.43 #2135), 01by1l (0.43 #110, 0.29 #914, 0.28 #3326), 0c4z8 (0.43 #72, 0.27 #2082, 0.27 #1680), 01c92g (0.43 #96, 0.21 #3312, 0.18 #900), 054ks3 (0.42 #1748, 0.41 #2150, 0.35 #5366), 0gqz2 (0.40 #1689, 0.39 #2091, 0.39 #5307), 025m8l (0.39 #5745, 0.27 #1725, 0.25 #2127), 02x17c2 (0.34 #5845, 0.19 #2629, 0.18 #1021), 09sb52 (0.30 #15721, 0.30 #15319, 0.27 #7680) >> Best rule #7639 for best value: >> intensional similarity = 3 >> extensional distance = 291 >> proper extension: 0gsg7; 09d5h; 0cjdk; 0kk9v; 05xbx; 05gnf; 01j7pt; 01zcrv; 0kctd; 0kcd5; >> query: (?x1656, ?x1079) <- nominated_for(?x1656, ?x3157), award_winner(?x1079, ?x1656), category(?x1656, ?x134) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #5351 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 129 *> proper extension: 037lyl; 01pbs9w; 01wd9lv; 0fpjyd; 02bn75; 01c7qd; 03f68r6; *> query: (?x1656, 02qvyrt) <- award(?x1656, ?x1854), award(?x6910, ?x1854), ?x6910 = 05y7hc *> conf = 0.50 ranks of expected_values: 2 EVAL 0l12d award 02qvyrt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 73.000 0.723 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19782-02k856 PRED entity: 02k856 PRED relation: role! PRED expected values: 01vsl3_ => 50 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1133): 050z2 (0.75 #3966, 0.75 #3495, 0.71 #9172), 023l9y (0.75 #3520, 0.71 #3049, 0.67 #10611), 04bpm6 (0.67 #4799, 0.62 #3851, 0.62 #8585), 05qhnq (0.67 #4564, 0.56 #5038, 0.50 #9296), 0137g1 (0.64 #9104, 0.57 #10046, 0.56 #4846), 03j24kf (0.62 #3996, 0.60 #2109, 0.57 #3054), 01wxdn3 (0.62 #3720, 0.57 #3249, 0.56 #5139), 03ryks (0.62 #4080, 0.56 #5500, 0.44 #4554), 02fn5r (0.60 #2358, 0.60 #2002, 0.50 #1885), 01d4cb (0.60 #2287, 0.50 #1814, 0.44 #5122) >> Best rule #3966 for best value: >> intensional similarity = 24 >> extensional distance = 6 >> proper extension: 026t6; 0mkg; >> query: (?x2923, 050z2) <- role(?x2923, ?x2888), role(?x2923, ?x1432), role(?x2923, ?x1166), role(?x2923, ?x780), role(?x2923, ?x227), ?x1166 = 05148p4, ?x2888 = 02fsn, role(?x2048, ?x2923), instrumentalists(?x2923, ?x2242), role(?x3202, ?x2923), ?x1432 = 0395lw, role(?x3374, ?x780), role(?x2963, ?x780), role(?x2784, ?x780), award_winner(?x3202, ?x1413), profession(?x3202, ?x131), award_nominee(?x3202, ?x4528), ?x2963 = 0gcs9, role(?x780, ?x868), ?x2784 = 0137g1, ?x227 = 0342h, ?x2048 = 018j2, award_winner(?x2186, ?x3202), ?x3374 = 01vsy95 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1068 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 2 *> proper extension: 018vs; *> query: (?x2923, 01vsl3_) <- role(?x2923, ?x2888), role(?x2923, ?x1472), role(?x2923, ?x1432), role(?x2923, ?x1166), role(?x2923, ?x780), role(?x2923, ?x745), ?x1166 = 05148p4, ?x2888 = 02fsn, role(?x1433, ?x2923), instrumentalists(?x2923, ?x2242), role(?x3735, ?x2923), role(?x3202, ?x2923), ?x1432 = 0395lw, ?x780 = 01qzyz, award_nominee(?x3146, ?x3202), gender(?x3202, ?x231), award_nominee(?x3202, ?x4528), ?x1472 = 0319l, award_winner(?x1413, ?x3202), ?x3735 = 0lzkm, performance_role(?x1495, ?x1433), role(?x569, ?x1433), ?x745 = 01vj9c, award_winner(?x3146, ?x2300) *> conf = 0.50 ranks of expected_values: 32 EVAL 02k856 role! 01vsl3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 50.000 34.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role #19781-02g8h PRED entity: 02g8h PRED relation: profession PRED expected values: 02hrh1q => 143 concepts (124 used for prediction) PRED predicted values (max 10 best out of 77): 02hrh1q (0.92 #9236, 0.91 #10965, 0.91 #11687), 02jknp (0.64 #3608, 0.57 #583, 0.57 #727), 0cbd2 (0.50 #5193, 0.48 #4184, 0.47 #6347), 09jwl (0.37 #8807, 0.37 #10391, 0.36 #9671), 0nbcg (0.27 #8818, 0.26 #11123, 0.26 #10402), 016z4k (0.26 #8795, 0.24 #9659, 0.23 #10379), 02krf9 (0.23 #599, 0.22 #743, 0.21 #3624), 0dz3r (0.22 #9657, 0.21 #10377, 0.21 #11098), 012t_z (0.18 #876, 0.09 #5632, 0.08 #4334), 01c72t (0.16 #4774, 0.15 #8811, 0.13 #10395) >> Best rule #9236 for best value: >> intensional similarity = 4 >> extensional distance = 662 >> proper extension: 044mz_; 02s2ft; 05bnp0; 02p65p; 06688p; 02bfmn; 083chw; 0d_84; 01wbg84; 01p7yb; ... >> query: (?x318, 02hrh1q) <- film(?x318, ?x3088), place_of_birth(?x318, ?x3046), student(?x3922, ?x318), profession(?x318, ?x319) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02g8h profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 124.000 0.917 http://example.org/people/person/profession #19780-027kmrb PRED entity: 027kmrb PRED relation: student! PRED expected values: 0lfgr => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 91): 05q2c (0.25 #841), 0bwfn (0.11 #14512, 0.09 #17674, 0.09 #12403), 03ksy (0.08 #1160, 0.02 #21726, 0.02 #20671), 02zcnq (0.08 #1200, 0.02 #11747), 02rv1w (0.08 #1439, 0.02 #12513, 0.01 #54846), 02ckl3 (0.08 #1499), 01p896 (0.08 #1425), 08qnnv (0.08 #1268), 0ks67 (0.08 #1243), 0pspl (0.08 #1163) >> Best rule #841 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0444x; >> query: (?x5647, 05q2c) <- citytown(?x5647, ?x739), type_of_union(?x5647, ?x566), profession(?x5647, ?x319) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #11644 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 01900g; *> query: (?x5647, 0lfgr) <- award_nominee(?x2451, ?x5647), film(?x2451, ?x195), ?x195 = 0b2v79 *> conf = 0.02 ranks of expected_values: 46 EVAL 027kmrb student! 0lfgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 125.000 125.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #19779-012jfb PRED entity: 012jfb PRED relation: person PRED expected values: 01zlh5 => 114 concepts (74 used for prediction) PRED predicted values (max 10 best out of 184): 020hh3 (0.33 #144, 0.06 #1059, 0.05 #876), 01nrz4 (0.33 #171, 0.05 #903, 0.03 #1086), 0157m (0.19 #946, 0.15 #1312, 0.13 #1679), 06c97 (0.12 #1198, 0.11 #832, 0.09 #1015), 079vf (0.11 #734, 0.06 #917, 0.05 #1283), 01kx_81 (0.11 #760, 0.05 #1309, 0.04 #1676), 01n4f8 (0.09 #947, 0.09 #1680, 0.08 #1313), 046lt (0.09 #1158, 0.08 #1891, 0.08 #1341), 02mjmr (0.08 #1338, 0.06 #1705, 0.06 #1155), 06c0j (0.06 #1092, 0.06 #1275, 0.05 #2375) >> Best rule #144 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 04969y; >> query: (?x6043, 020hh3) <- nominated_for(?x10550, ?x6043), ?x10550 = 03y8cbv, country(?x6043, ?x1264), film_release_region(?x66, ?x1264) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1054 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 056xkh; *> query: (?x6043, 01zlh5) <- genre(?x6043, ?x2605), person(?x6043, ?x3593), film_release_region(?x6043, ?x94), titles(?x1014, ?x6043) *> conf = 0.03 ranks of expected_values: 78 EVAL 012jfb person 01zlh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 114.000 74.000 0.333 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #19778-0404j37 PRED entity: 0404j37 PRED relation: currency PRED expected values: 09nqf => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.83 #64, 0.82 #15, 0.81 #120), 02l6h (0.05 #11, 0.03 #39, 0.03 #88), 01nv4h (0.03 #86, 0.03 #51, 0.03 #100), 02gsvk (0.01 #76, 0.01 #41, 0.01 #104), 0kz1h (0.01 #75, 0.01 #40, 0.01 #103) >> Best rule #64 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 0d90m; 03qcfvw; 01gc7; 0czyxs; 01k1k4; 0gtv7pk; 0g5qs2k; 0cpllql; 01r97z; 0dsvzh; ... >> query: (?x6448, 09nqf) <- nominated_for(?x1063, ?x6448), award(?x718, ?x1063), film_distribution_medium(?x6448, ?x2099) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0404j37 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.826 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #19777-01v5h PRED entity: 01v5h PRED relation: profession PRED expected values: 01d_h8 => 113 concepts (109 used for prediction) PRED predicted values (max 10 best out of 89): 01d_h8 (0.84 #3975, 0.80 #1476, 0.80 #741), 03gjzk (0.45 #13, 0.44 #1924, 0.43 #1483), 012t_z (0.30 #453, 0.26 #159, 0.23 #894), 02krf9 (0.29 #1936, 0.23 #3406, 0.22 #1495), 0fj9f (0.25 #347, 0.22 #1082, 0.13 #935), 0cbd2 (0.22 #5593, 0.21 #5005, 0.18 #4123), 09jwl (0.21 #3251, 0.19 #2222, 0.19 #5456), 018gz8 (0.18 #3102, 0.18 #5013, 0.17 #5601), 0dz3r (0.17 #443, 0.17 #2, 0.13 #149), 0nbcg (0.17 #30, 0.16 #3264, 0.15 #471) >> Best rule #3975 for best value: >> intensional similarity = 2 >> extensional distance = 339 >> proper extension: 024c1b; >> query: (?x8942, 01d_h8) <- produced_by(?x8941, ?x8942), film_release_distribution_medium(?x8941, ?x81) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v5h profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 109.000 0.839 http://example.org/people/person/profession #19776-0b6k___ PRED entity: 0b6k___ PRED relation: award! PRED expected values: 04qr6d 0738y5 => 52 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2445): 021j72 (0.79 #57233, 0.71 #16828, 0.70 #80808), 04qp06 (0.79 #57233, 0.71 #16828, 0.70 #80808), 04b19t (0.79 #57233, 0.71 #16828, 0.70 #80808), 08hhm6 (0.79 #57233, 0.71 #16828, 0.70 #80808), 01gg59 (0.60 #14547, 0.50 #7817, 0.33 #1085), 0fhxv (0.60 #14811, 0.50 #8081, 0.33 #1349), 01vrz41 (0.60 #13756, 0.50 #7026, 0.33 #294), 0gcs9 (0.60 #14280, 0.50 #7550, 0.33 #818), 0140t7 (0.60 #16136, 0.50 #9406, 0.33 #2674), 016szr (0.60 #14873, 0.50 #8143, 0.33 #1411) >> Best rule #57233 for best value: >> intensional similarity = 5 >> extensional distance = 118 >> proper extension: 02f6yz; >> query: (?x4443, ?x2618) <- award(?x10076, ?x4443), award(?x10076, ?x2238), award_winner(?x4443, ?x2618), ?x2238 = 025m8l, award_winner(?x10076, ?x3890) >> conf = 0.79 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 0b6k___ award! 0738y5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 26.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0b6k___ award! 04qr6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 26.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19775-02q690_ PRED entity: 02q690_ PRED relation: award_winner PRED expected values: 02778pf 0pz7h 015c4g 09px1w 06pjs => 26 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1958): 01t6b4 (0.60 #7722, 0.33 #1682, 0.31 #3018), 04glr5h (0.60 #8797, 0.33 #2757, 0.25 #7288), 018ygt (0.50 #10006, 0.50 #3961, 0.40 #8493), 02661h (0.50 #5669, 0.38 #14732, 0.31 #3018), 01zfmm (0.50 #6437, 0.33 #10970, 0.33 #1906), 0bt4r4 (0.50 #9485, 0.31 #3018, 0.29 #7549), 05np4c (0.50 #3513, 0.25 #10575, 0.19 #24175), 09f0bj (0.50 #3302, 0.25 #10575, 0.19 #24175), 095b70 (0.50 #3923, 0.25 #10575, 0.19 #24175), 0k2mxq (0.50 #3922, 0.25 #10575, 0.19 #24175) >> Best rule #7722 for best value: >> intensional similarity = 21 >> extensional distance = 3 >> proper extension: 05c1t6z; 0gvstc3; >> query: (?x4760, 01t6b4) <- ceremony(?x4921, ?x4760), ceremony(?x2071, ?x4760), ceremony(?x757, ?x4760), award(?x9011, ?x4921), award(?x6913, ?x4921), award(?x4035, ?x4921), award(?x2136, ?x4921), award(?x1676, ?x4921), honored_for(?x4760, ?x6884), ?x6913 = 01my4f, ?x2071 = 0bdw6t, people(?x1158, ?x2136), film(?x4035, ?x1012), award(?x687, ?x4921), award_nominee(?x2136, ?x4589), type_of_union(?x1676, ?x566), ?x757 = 09qj50, ?x6884 = 039cq4, nominated_for(?x4921, ?x337), ?x9011 = 03w9sgh, profession(?x2136, ?x319) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7670 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 3 *> proper extension: 05c1t6z; 0gvstc3; *> query: (?x4760, 0pz7h) <- ceremony(?x4921, ?x4760), ceremony(?x2071, ?x4760), ceremony(?x757, ?x4760), award(?x9011, ?x4921), award(?x6913, ?x4921), award(?x4035, ?x4921), award(?x2136, ?x4921), award(?x1676, ?x4921), honored_for(?x4760, ?x6884), ?x6913 = 01my4f, ?x2071 = 0bdw6t, people(?x1158, ?x2136), film(?x4035, ?x1012), award(?x687, ?x4921), award_nominee(?x2136, ?x4589), type_of_union(?x1676, ?x566), ?x757 = 09qj50, ?x6884 = 039cq4, nominated_for(?x4921, ?x337), ?x9011 = 03w9sgh, profession(?x2136, ?x319) *> conf = 0.40 ranks of expected_values: 17, 27, 128, 425, 686 EVAL 02q690_ award_winner 06pjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 26.000 18.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02q690_ award_winner 09px1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 26.000 18.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02q690_ award_winner 015c4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 26.000 18.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02q690_ award_winner 0pz7h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 26.000 18.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02q690_ award_winner 02778pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 26.000 18.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19774-018lg0 PRED entity: 018lg0 PRED relation: parent_genre PRED expected values: 02yv6b => 66 concepts (41 used for prediction) PRED predicted values (max 10 best out of 213): 06by7 (0.60 #497, 0.60 #336, 0.55 #1788), 05r6t (0.55 #1988, 0.36 #1179, 0.34 #2632), 011j5x (0.33 #20, 0.25 #180, 0.20 #502), 0xhtw (0.25 #172, 0.23 #979, 0.22 #3064), 02yv6b (0.25 #223, 0.20 #545, 0.20 #384), 0pm85 (0.25 #256, 0.20 #578, 0.20 #417), 07sbbz2 (0.20 #487, 0.20 #326, 0.11 #648), 02l96k (0.20 #551, 0.20 #390, 0.11 #712), 0glt670 (0.20 #2605, 0.14 #2122, 0.14 #1152), 0190_q (0.18 #827, 0.17 #1288, 0.15 #990) >> Best rule #497 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 0xhtw; >> query: (?x2072, 06by7) <- artists(?x2072, ?x10737), artists(?x2072, ?x6225), artists(?x2072, ?x5329), artists(?x2072, ?x1955), ?x6225 = 01vng3b, ?x5329 = 014_lq, ?x1955 = 0285c, parent_genre(?x2072, ?x837), parent_genre(?x837, ?x1572), category(?x10737, ?x134) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #223 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 03lty; *> query: (?x2072, 02yv6b) <- artists(?x2072, ?x10737), artists(?x2072, ?x6225), artists(?x2072, ?x5329), artists(?x2072, ?x1955), ?x6225 = 01vng3b, ?x5329 = 014_lq, ?x1955 = 0285c, ?x10737 = 0b1hw, parent_genre(?x2072, ?x837) *> conf = 0.25 ranks of expected_values: 5 EVAL 018lg0 parent_genre 02yv6b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 66.000 41.000 0.600 http://example.org/music/genre/parent_genre #19773-01vhb0 PRED entity: 01vhb0 PRED relation: film PRED expected values: 087vnr5 => 138 concepts (92 used for prediction) PRED predicted values (max 10 best out of 962): 02_1q9 (0.73 #12531, 0.68 #73405, 0.65 #50130), 05h43ls (0.13 #415, 0.03 #16526, 0.02 #3995), 03bzjpm (0.11 #3105, 0.11 #6685, 0.08 #8475), 0vjr (0.09 #59083, 0.09 #69824, 0.08 #134284), 0ch3qr1 (0.09 #977, 0.07 #2767, 0.07 #6347), 02f6g5 (0.09 #281, 0.04 #2071, 0.04 #5651), 01l_pn (0.08 #8128, 0.04 #2758, 0.04 #6338), 03bx2lk (0.07 #1975, 0.07 #5555, 0.06 #10925), 01qvz8 (0.07 #2597, 0.07 #6177, 0.04 #4387), 01kqq7 (0.07 #3421, 0.07 #7001, 0.04 #10581) >> Best rule #12531 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 0fthdk; >> query: (?x2308, ?x416) <- award_winner(?x2773, ?x2308), participant(?x2308, ?x8490), participant(?x2307, ?x2308), nominated_for(?x2308, ?x416) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #12196 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 0fthdk; *> query: (?x2308, 087vnr5) <- award_winner(?x2773, ?x2308), participant(?x2308, ?x8490), participant(?x2307, ?x2308), nominated_for(?x2308, ?x416) *> conf = 0.01 ranks of expected_values: 846 EVAL 01vhb0 film 087vnr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 138.000 92.000 0.735 http://example.org/film/actor/film./film/performance/film #19772-04l59s PRED entity: 04l59s PRED relation: team! PRED expected values: 01ddbl => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 8): 01ddbl (0.69 #91, 0.64 #50, 0.60 #166), 059yj (0.55 #46, 0.55 #37, 0.50 #87), 0h69c (0.13 #354, 0.12 #245, 0.11 #138), 07y9k (0.09 #483, 0.09 #491, 0.08 #499), 0355pl (0.08 #401, 0.08 #417, 0.08 #335), 03zv9 (0.07 #334, 0.07 #342, 0.06 #400), 0356lc (0.05 #480, 0.05 #488, 0.05 #496), 021q23 (0.05 #289, 0.02 #348, 0.02 #422) >> Best rule #91 for best value: >> intensional similarity = 21 >> extensional distance = 14 >> proper extension: 05gg4; 0289q; >> query: (?x14123, ?x13270) <- colors(?x14123, ?x5325), sport(?x14123, ?x453), sport(?x14124, ?x453), sport(?x5233, ?x453), sport(?x3723, ?x453), sport(?x3298, ?x453), team(?x2918, ?x3723), athlete(?x453, ?x11825), colors(?x5233, ?x332), colors(?x3723, ?x4557), colors(?x14124, ?x3621), teams(?x4499, ?x3723), ?x4499 = 068p2, team(?x13270, ?x14124), nationality(?x11825, ?x279), company(?x4486, ?x3298), ?x279 = 0d060g, type_of_union(?x11825, ?x566), colors(?x6644, ?x5325), ?x332 = 01l849, ?x6644 = 01jpyb >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04l59s team! 01ddbl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.688 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #19771-0jxy PRED entity: 0jxy PRED relation: genre! PRED expected values: 02z9hqn 0ckrgs 08fbnx 016ztl => 49 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1893): 02z9hqn (0.78 #16595, 0.50 #31480, 0.50 #29635), 0cpllql (0.67 #16685, 0.67 #12995, 0.50 #31436), 08fbnx (0.67 #17431, 0.62 #26649, 0.60 #10053), 02qydsh (0.67 #18129, 0.62 #27347, 0.50 #31035), 04f52jw (0.67 #17045, 0.62 #26263, 0.50 #29951), 0407yfx (0.67 #16947, 0.62 #26165, 0.50 #29853), 0g9yrw (0.67 #15433, 0.60 #9901, 0.50 #26497), 091xrc (0.67 #18412, 0.50 #27630, 0.50 #23943), 07nxnw (0.67 #17831, 0.50 #27049, 0.50 #23362), 05pdd86 (0.62 #26902, 0.60 #30590, 0.50 #32435) >> Best rule #16595 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 04pbhw; >> query: (?x5937, ?x869) <- genre(?x6840, ?x5937), genre(?x5430, ?x5937), genre(?x2153, ?x5937), genre(?x1419, ?x5937), actor(?x5430, ?x6786), genre(?x419, ?x5937), ?x2153 = 02qhqz4, actor(?x1419, ?x51), prequel(?x869, ?x6840), film(?x1418, ?x1419), nominated_for(?x13285, ?x6840), language(?x6786, ?x254), film_release_distribution_medium(?x6840, ?x81) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 99, 126 EVAL 0jxy genre! 016ztl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 49.000 25.000 0.779 http://example.org/film/film/genre EVAL 0jxy genre! 08fbnx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 49.000 25.000 0.779 http://example.org/film/film/genre EVAL 0jxy genre! 0ckrgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 25.000 0.779 http://example.org/film/film/genre EVAL 0jxy genre! 02z9hqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 25.000 0.779 http://example.org/film/film/genre #19770-09mq4m PRED entity: 09mq4m PRED relation: nationality PRED expected values: 07ssc => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 28): 09c7w0 (0.70 #8303, 0.70 #7003, 0.70 #8403), 02jx1 (0.35 #8903, 0.33 #33, 0.25 #333), 07ssc (0.22 #15, 0.13 #215, 0.11 #1615), 0d060g (0.07 #1007, 0.05 #2507, 0.05 #1407), 0chghy (0.07 #210, 0.06 #310, 0.04 #410), 01znc_ (0.07 #238, 0.04 #4601, 0.04 #6402), 03rk0 (0.06 #10249, 0.05 #10449, 0.05 #10349), 03rt9 (0.04 #4601, 0.04 #6402, 0.03 #513), 0f8l9c (0.04 #4601, 0.04 #6402, 0.02 #2522), 0jgd (0.04 #4601, 0.04 #6402, 0.02 #602) >> Best rule #8303 for best value: >> intensional similarity = 3 >> extensional distance = 1940 >> proper extension: 03b78r; 02v49c; >> query: (?x1826, 09c7w0) <- gender(?x1826, ?x231), award_nominee(?x1826, ?x3062), profession(?x1826, ?x131) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 01k_yf; 01w5n51; *> query: (?x1826, 07ssc) <- artists(?x2542, ?x1826), award(?x1826, ?x1232), ?x2542 = 03xnwz *> conf = 0.22 ranks of expected_values: 3 EVAL 09mq4m nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 113.000 0.699 http://example.org/people/person/nationality #19769-0776h1v PRED entity: 0776h1v PRED relation: disciplines_or_subjects PRED expected values: 02vxn => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 123): 02vxn (0.83 #438, 0.81 #479, 0.72 #518), 04g51 (0.63 #582, 0.51 #418, 0.44 #624), 02xlf (0.53 #420, 0.31 #584, 0.23 #626), 01hmnh (0.28 #405, 0.21 #569, 0.17 #611), 06n90 (0.26 #402, 0.19 #566, 0.16 #608), 04rlf (0.25 #157, 0.05 #105, 0.05 #599), 02jknp (0.23 #359, 0.12 #2, 0.09 #439), 05hgj (0.16 #419, 0.14 #583, 0.12 #24), 0dwly (0.12 #26, 0.09 #585, 0.06 #627), 0j7v_ (0.12 #20, 0.04 #579, 0.03 #621) >> Best rule #438 for best value: >> intensional similarity = 24 >> extensional distance = 45 >> proper extension: 0f_nbyh; 02wkmx; 0gq6s3; 018wng; 054ky1; 02qt02v; 0b6k___; 0fm3b5; 0b6jkkg; 09v0wy2; ... >> query: (?x13323, 02vxn) <- disciplines_or_subjects(?x13323, ?x6760), major_field_of_study(?x7545, ?x6760), major_field_of_study(?x6637, ?x6760), major_field_of_study(?x2775, ?x6760), disciplines_or_subjects(?x3245, ?x6760), disciplines_or_subjects(?x2478, ?x6760), disciplines_or_subjects(?x941, ?x6760), major_field_of_study(?x1368, ?x6760), student(?x6760, ?x6324), major_field_of_study(?x2775, ?x4100), major_field_of_study(?x2775, ?x2014), ?x3245 = 07h0cl, ?x7545 = 0bwfn, ?x4100 = 01lj9, school(?x660, ?x2775), student(?x2775, ?x1447), institution(?x620, ?x2775), ?x941 = 0fq9zdn, colors(?x6637, ?x332), ?x2014 = 04rjg, award(?x2493, ?x2478), ?x2493 = 01hkhq, award(?x6324, ?x102), award_nominee(?x406, ?x6324) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0776h1v disciplines_or_subjects 02vxn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 19.000 19.000 0.830 http://example.org/award/award_category/disciplines_or_subjects #19768-04grkmd PRED entity: 04grkmd PRED relation: titles! PRED expected values: 01z4y => 88 concepts (54 used for prediction) PRED predicted values (max 10 best out of 114): 01z4y (0.50 #138, 0.42 #2515, 0.41 #964), 07s9rl0 (0.40 #3830, 0.39 #4037, 0.39 #1962), 04xvlr (0.40 #3830, 0.39 #4037, 0.36 #2270), 0219x_ (0.36 #2270, 0.35 #5605, 0.23 #2685), 01hmnh (0.27 #334, 0.25 #129, 0.20 #231), 05p553 (0.23 #4765, 0.23 #2685, 0.22 #1961), 04rlf (0.23 #4765, 0.23 #2685, 0.22 #1961), 01t_vv (0.23 #4765, 0.23 #2685, 0.22 #1961), 0clz1b (0.23 #2685, 0.22 #1961, 0.22 #4036), 09blyk (0.20 #251, 0.07 #3773, 0.06 #4499) >> Best rule #138 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02x8fs; >> query: (?x3512, 01z4y) <- film(?x2086, ?x3512), film(?x4478, ?x3512), ?x4478 = 028k57, country(?x3512, ?x94), produced_by(?x3512, ?x3862) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04grkmd titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 54.000 0.500 http://example.org/media_common/netflix_genre/titles #19767-01p1z_ PRED entity: 01p1z_ PRED relation: profession PRED expected values: 0dxtg => 129 concepts (107 used for prediction) PRED predicted values (max 10 best out of 82): 01d_h8 (0.82 #4416, 0.81 #2358, 0.78 #3534), 0dxtg (0.78 #1777, 0.71 #2365, 0.61 #3541), 0cbd2 (0.57 #1918, 0.47 #4270, 0.44 #6035), 03gjzk (0.55 #4866, 0.34 #2366, 0.31 #3542), 018gz8 (0.35 #3250, 0.34 #3838, 0.28 #898), 0kyk (0.33 #6056, 0.28 #4291, 0.27 #7821), 0np9r (0.22 #4872, 0.15 #15609, 0.15 #15314), 09jwl (0.21 #3840, 0.21 #4723, 0.19 #3252), 05z96 (0.20 #4304, 0.16 #1952, 0.16 #1511), 025352 (0.20 #2116, 0.10 #6470, 0.08 #4027) >> Best rule #4416 for best value: >> intensional similarity = 5 >> extensional distance = 134 >> proper extension: 0fvf9q; 022_lg; 02645b; 05h72z; 01q4qv; 0b13g7; 076_74; 047q2wc; 01ycck; 07b3r9; ... >> query: (?x6993, 01d_h8) <- award(?x6993, ?x1862), award(?x6993, ?x198), ?x198 = 040njc, profession(?x6993, ?x524), nominated_for(?x1862, ?x69) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1777 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 0jf1b; 02kxbwx; 04wvhz; 01t07j; 03flwk; 016bx2; 02f93t; 01lc5; *> query: (?x6993, 0dxtg) <- award(?x6993, ?x1862), award(?x6993, ?x198), ?x198 = 040njc, profession(?x6993, ?x524), ?x1862 = 0gr51 *> conf = 0.78 ranks of expected_values: 2 EVAL 01p1z_ profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 107.000 0.816 http://example.org/people/person/profession #19766-0bqxw PRED entity: 0bqxw PRED relation: student PRED expected values: 037hgm => 123 concepts (108 used for prediction) PRED predicted values (max 10 best out of 1481): 07s8hms (0.17 #2718, 0.05 #21572, 0.04 #25762), 0146pg (0.17 #2179, 0.05 #21033, 0.04 #25223), 042kg (0.15 #33517, 0.13 #37706, 0.13 #48179), 0d3k14 (0.14 #10235, 0.11 #8140, 0.10 #12329), 0ff3y (0.13 #12544, 0.12 #14639, 0.08 #20924), 0gs7x (0.11 #8227, 0.10 #10322, 0.06 #12416), 07f7jp (0.11 #8266, 0.10 #10361, 0.06 #12455), 0hnjt (0.11 #7107, 0.10 #9202, 0.06 #11296), 0blt6 (0.11 #6858, 0.10 #8953, 0.06 #11047), 02hsgn (0.11 #7106, 0.06 #11295, 0.06 #13390) >> Best rule #2718 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01q940; 03mp8k; >> query: (?x4338, 07s8hms) <- company(?x11290, ?x4338), citytown(?x4338, ?x2277), celebrities_impersonated(?x3649, ?x11290) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #57386 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 05zjtn4; 065y4w7; 01w3v; 07w0v; 07wrz; 027xx3; 02fy0z; 02183k; 03ksy; 037fqp; ... *> query: (?x4338, 037hgm) <- major_field_of_study(?x4338, ?x4321), ?x4321 = 0g26h, colors(?x4338, ?x3189) *> conf = 0.01 ranks of expected_values: 1390 EVAL 0bqxw student 037hgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 123.000 108.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #19765-0sx8l PRED entity: 0sx8l PRED relation: medal PRED expected values: 02lq67 02lq5w => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2): 02lq67 (0.89 #53, 0.89 #51, 0.89 #42), 02lq5w (0.89 #42, 0.87 #50, 0.87 #45) >> Best rule #53 for best value: >> intensional similarity = 28 >> extensional distance = 36 >> proper extension: 0c_tl; >> query: (?x1741, 02lq67) <- sports(?x1741, ?x520), olympics(?x512, ?x1741), olympics(?x94, ?x1741), participating_countries(?x1741, ?x142), olympics(?x205, ?x1741), film_release_region(?x7126, ?x94), film_release_region(?x6095, ?x94), film_release_region(?x1642, ?x94), film_release_region(?x1625, ?x94), film_release_region(?x1228, ?x94), country(?x89, ?x94), contains(?x94, ?x95), service_location(?x6717, ?x94), ?x6717 = 064f29, nationality(?x6062, ?x94), nationality(?x5239, ?x94), nationality(?x2839, ?x94), ?x1642 = 0bq8tmw, ?x1625 = 01f8gz, ?x6095 = 0bq6ntw, ?x1228 = 05z_kps, second_level_divisions(?x94, ?x322), ?x5239 = 0gmtm, nationality(?x111, ?x512), titles(?x512, ?x1261), profession(?x2839, ?x319), ?x7126 = 0ds1glg, award(?x6062, ?x1243) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0sx8l medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.895 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 0sx8l medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.895 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #19764-0143q0 PRED entity: 0143q0 PRED relation: artists! PRED expected values: 05r6t 07ffjc 0pm85 08cg36 => 74 concepts (42 used for prediction) PRED predicted values (max 10 best out of 259): 05r6t (0.71 #390, 0.70 #1934, 0.70 #698), 06by7 (0.69 #10523, 0.68 #6812, 0.63 #4650), 059kh (0.62 #3443, 0.56 #2825, 0.50 #1591), 064t9 (0.55 #8666, 0.48 #9590, 0.48 #10206), 0xhtw (0.49 #5571, 0.44 #4645, 0.44 #4336), 05bt6j (0.46 #3438, 0.40 #43, 0.32 #2820), 0dl5d (0.40 #20, 0.25 #5574, 0.22 #7744), 03lty (0.38 #1263, 0.36 #4040, 0.36 #4348), 06j6l (0.33 #10548, 0.31 #5293, 0.28 #3134), 01cbwl (0.30 #657, 0.29 #349, 0.25 #1275) >> Best rule #390 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 03xl77; >> query: (?x6471, 05r6t) <- artists(?x10933, ?x6471), category(?x6471, ?x134), ?x10933 = 03p7rp, artist(?x4564, ?x6471) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14, 77, 159 EVAL 0143q0 artists! 08cg36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 74.000 42.000 0.714 http://example.org/music/genre/artists EVAL 0143q0 artists! 0pm85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 74.000 42.000 0.714 http://example.org/music/genre/artists EVAL 0143q0 artists! 07ffjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 74.000 42.000 0.714 http://example.org/music/genre/artists EVAL 0143q0 artists! 05r6t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 42.000 0.714 http://example.org/music/genre/artists #19763-0988cp PRED entity: 0988cp PRED relation: profession PRED expected values: 03gjzk => 95 concepts (69 used for prediction) PRED predicted values (max 10 best out of 49): 03gjzk (0.84 #1654, 0.83 #1058, 0.83 #1505), 02hrh1q (0.77 #6421, 0.69 #2100, 0.68 #4335), 01d_h8 (0.56 #453, 0.53 #900, 0.50 #1496), 02jknp (0.42 #4925, 0.28 #10286, 0.28 #10285), 02krf9 (0.38 #325, 0.33 #474, 0.31 #1666), 0cbd2 (0.22 #4924, 0.19 #1795, 0.18 #1646), 09jwl (0.20 #2105, 0.19 #4787, 0.18 #8811), 018gz8 (0.17 #4934, 0.14 #1507, 0.13 #1209), 0nbcg (0.13 #4800, 0.12 #8824, 0.12 #8079), 0dz3r (0.13 #4770, 0.13 #2088, 0.12 #6111) >> Best rule #1654 for best value: >> intensional similarity = 2 >> extensional distance = 215 >> proper extension: 04rtpt; >> query: (?x5677, 03gjzk) <- program(?x5677, ?x8132), award_winner(?x8132, ?x71) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0988cp profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 69.000 0.843 http://example.org/people/person/profession #19762-0178rl PRED entity: 0178rl PRED relation: profession PRED expected values: 025352 => 94 concepts (65 used for prediction) PRED predicted values (max 10 best out of 71): 025352 (0.71 #59, 0.42 #1391, 0.38 #1983), 0dxtg (0.67 #2233, 0.63 #2381, 0.61 #2529), 0kyk (0.67 #769, 0.63 #1657, 0.61 #1805), 01c72t (0.64 #171, 0.55 #1355, 0.52 #1947), 09jwl (0.60 #4758, 0.58 #4907, 0.57 #4609), 0nbcg (0.50 #1215, 0.48 #1067, 0.46 #4622), 01d_h8 (0.46 #2226, 0.45 #154, 0.44 #450), 0dz3r (0.44 #3704, 0.39 #4742, 0.39 #1038), 02hv44_ (0.43 #57, 0.19 #1981, 0.17 #3018), 016z4k (0.40 #4744, 0.38 #4893, 0.38 #3706) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 012wg; >> query: (?x5223, 025352) <- award(?x5223, ?x3467), ?x3467 = 02h3d1, story_by(?x4249, ?x5223), gender(?x5223, ?x231) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0178rl profession 025352 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 65.000 0.714 http://example.org/people/person/profession #19761-0162v PRED entity: 0162v PRED relation: location! PRED expected values: 01fwj8 => 94 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1678): 01w02sy (0.15 #596, 0.07 #5633, 0.06 #58529), 09yrh (0.12 #8470, 0.08 #28623, 0.07 #46253), 09fb5 (0.12 #7607, 0.07 #52946, 0.06 #57984), 023kzp (0.12 #8773, 0.07 #33962, 0.06 #39000), 01vh3r (0.12 #9896, 0.05 #55235, 0.05 #60273), 0c6qh (0.12 #8017, 0.05 #53356, 0.05 #63431), 01rh0w (0.11 #55415, 0.08 #55414, 0.07 #65489), 01c8v0 (0.11 #55415, 0.08 #55414, 0.07 #65489), 0465_ (0.10 #1297, 0.07 #6334, 0.07 #16411), 0prfz (0.10 #49, 0.07 #5086, 0.07 #17684) >> Best rule #596 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 01k6y1; 06jnv; >> query: (?x1957, 01w02sy) <- location(?x6835, ?x1957), form_of_government(?x1957, ?x6065), ?x6065 = 01q20 >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0162v location! 01fwj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 27.000 0.150 http://example.org/people/person/places_lived./people/place_lived/location #19760-06cc_1 PRED entity: 06cc_1 PRED relation: artist! PRED expected values: 06x2ww => 126 concepts (87 used for prediction) PRED predicted values (max 10 best out of 117): 015_1q (0.31 #993, 0.26 #158, 0.23 #1410), 0g768 (0.23 #593, 0.14 #2122, 0.14 #1010), 017l96 (0.19 #557, 0.15 #992, 0.14 #1270), 01cszh (0.19 #557, 0.15 #150, 0.12 #2097), 02p11jq (0.19 #557, 0.10 #1265, 0.08 #6136), 02zn1b (0.19 #557, 0.05 #983, 0.04 #844), 043g7l (0.19 #169, 0.13 #865, 0.11 #2116), 033hn8 (0.18 #292, 0.15 #153, 0.14 #2100), 0n85g (0.16 #1174, 0.10 #2147, 0.10 #618), 03mp8k (0.15 #204, 0.14 #2151, 0.13 #900) >> Best rule #993 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 015cxv; >> query: (?x568, 015_1q) <- award(?x568, ?x3647), artists(?x671, ?x568), ?x3647 = 01c9jp >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #882 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 03cd1q; *> query: (?x568, 06x2ww) <- award(?x568, ?x4958), ?x4958 = 03qbnj, award_nominee(?x568, ?x6382) *> conf = 0.04 ranks of expected_values: 54 EVAL 06cc_1 artist! 06x2ww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 126.000 87.000 0.305 http://example.org/music/record_label/artist #19759-0d0vqn PRED entity: 0d0vqn PRED relation: film_release_region! PRED expected values: 0g5ptf => 217 concepts (41 used for prediction) PRED predicted values (max 10 best out of 175): 0299hs (0.17 #581, 0.11 #1251, 0.09 #2325), 043h78 (0.12 #656, 0.12 #119, 0.11 #924), 03z106 (0.12 #54, 0.11 #188, 0.10 #1797), 015ynm (0.12 #113, 0.11 #247, 0.08 #650), 027x7z5 (0.12 #116, 0.11 #250, 0.04 #653), 0b85mm (0.12 #133, 0.11 #267, 0.04 #670), 02gpkt (0.12 #104, 0.11 #238, 0.04 #641), 064lsn (0.12 #91, 0.11 #225, 0.04 #628), 015qsq (0.12 #1, 0.11 #135, 0.04 #538), 02psgq (0.11 #882, 0.09 #1150, 0.08 #614) >> Best rule #581 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 02_n7; >> query: (?x304, 0299hs) <- service_location(?x555, ?x304), jurisdiction_of_office(?x182, ?x304), time_zones(?x304, ?x2864) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d0vqn film_release_region! 0g5ptf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 217.000 41.000 0.167 http://example.org/film/film/runtime./film/film_cut/film_release_region #19758-0r4z7 PRED entity: 0r4z7 PRED relation: jurisdiction_of_office! PRED expected values: 01q24l => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 20): 01q24l (0.53 #79, 0.32 #233, 0.30 #299), 060c4 (0.32 #1105, 0.32 #1127, 0.32 #1061), 060bp (0.27 #1103, 0.27 #1125, 0.27 #1059), 0fkvn (0.27 #598, 0.26 #643, 0.26 #576), 0f6c3 (0.21 #579, 0.21 #601, 0.20 #623), 09n5b9 (0.19 #583, 0.19 #605, 0.18 #627), 0p5vf (0.08 #342, 0.08 #496, 0.07 #408), 0fkx3 (0.08 #350, 0.07 #416, 0.05 #504), 0fkzq (0.08 #588, 0.07 #544, 0.07 #610), 04syw (0.07 #1108, 0.07 #1064, 0.06 #534) >> Best rule #79 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 0k049; 0qzhw; 0pc56; >> query: (?x10019, 01q24l) <- jurisdiction_of_office(?x1195, ?x10019), contains(?x9472, ?x10019), contains(?x1227, ?x10019), ?x1227 = 01n7q, adjoins(?x10399, ?x9472) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r4z7 jurisdiction_of_office! 01q24l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.531 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #19757-04bdxl PRED entity: 04bdxl PRED relation: location PRED expected values: 030qb3t => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 173): 0f2v0 (0.62 #3213, 0.42 #69071, 0.42 #69875), 02_286 (0.40 #37, 0.22 #2446, 0.22 #4054), 030qb3t (0.26 #2492, 0.24 #4100, 0.24 #5706), 0cr3d (0.20 #144, 0.06 #6570, 0.06 #60380), 0r0m6 (0.20 #217, 0.05 #5037, 0.04 #5840), 059rby (0.12 #19293, 0.10 #819, 0.05 #11260), 01n7q (0.11 #19340, 0.07 #1669, 0.05 #12110), 0f2rq (0.10 #1083, 0.03 #3493, 0.02 #4297), 0cc56 (0.08 #2466, 0.06 #4877, 0.05 #6483), 0rh6k (0.07 #19281, 0.04 #1610, 0.03 #7233) >> Best rule #3213 for best value: >> intensional similarity = 2 >> extensional distance = 70 >> proper extension: 0zjpz; 01vxlbm; 01pcvn; 026_dq6; 01npcy7; 022q32; 0dq9wx; >> query: (?x91, ?x3501) <- participant(?x5880, ?x91), place_of_birth(?x91, ?x3501) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2492 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 70 *> proper extension: 0zjpz; 01vxlbm; 01pcvn; 026_dq6; 01npcy7; 022q32; 0dq9wx; *> query: (?x91, 030qb3t) <- participant(?x5880, ?x91), place_of_birth(?x91, ?x3501) *> conf = 0.26 ranks of expected_values: 3 EVAL 04bdxl location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 101.000 0.625 http://example.org/people/person/places_lived./people/place_lived/location #19756-063tn PRED entity: 063tn PRED relation: profession PRED expected values: 0cbd2 => 168 concepts (112 used for prediction) PRED predicted values (max 10 best out of 79): 02hrh1q (0.84 #13469, 0.84 #13320, 0.84 #13022), 09jwl (0.80 #16022, 0.77 #10182, 0.77 #16469), 0nbcg (0.69 #11540, 0.64 #15284, 0.55 #9149), 01d_h8 (0.62 #14959, 0.29 #5382, 0.28 #10467), 0dz3r (0.61 #14205, 0.44 #10164, 0.42 #4332), 0np9r (0.56 #15424, 0.10 #10483, 0.09 #13178), 016z4k (0.52 #5529, 0.51 #4633, 0.47 #6126), 01c8w0 (0.45 #4937, 0.43 #3144, 0.40 #9), 05vyk (0.42 #1734, 0.36 #3230, 0.36 #2033), 0dxtg (0.38 #312, 0.33 #610, 0.31 #14967) >> Best rule #13469 for best value: >> intensional similarity = 4 >> extensional distance = 415 >> proper extension: 02r99xw; >> query: (?x9480, 02hrh1q) <- profession(?x9480, ?x1614), people(?x5590, ?x9480), languages(?x9480, ?x5671), languages_spoken(?x3584, ?x5671) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #603 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 03f2_rc; *> query: (?x9480, 0cbd2) <- profession(?x9480, ?x1614), people(?x5590, ?x9480), artists(?x597, ?x9480), languages(?x9480, ?x5671), music(?x4688, ?x9480) *> conf = 0.33 ranks of expected_values: 13 EVAL 063tn profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 168.000 112.000 0.842 http://example.org/people/person/profession #19755-01wgr PRED entity: 01wgr PRED relation: countries_spoken_in PRED expected values: 03gk2 01mk6 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 237): 07ytt (0.50 #1799, 0.50 #1431, 0.50 #706), 0d060g (0.50 #556, 0.48 #3465, 0.45 #2736), 09pmkv (0.50 #758, 0.43 #940, 0.40 #1671), 01ppq (0.43 #1063, 0.40 #335, 0.33 #881), 0hzlz (0.40 #206, 0.33 #752, 0.33 #572), 0162v (0.40 #418, 0.33 #600, 0.33 #54), 034m8 (0.40 #526, 0.33 #708, 0.33 #162), 05r7t (0.40 #487, 0.33 #669, 0.33 #123), 03h2c (0.40 #449, 0.33 #631, 0.33 #85), 06s0l (0.40 #497, 0.33 #679, 0.33 #133) >> Best rule #1799 for best value: >> intensional similarity = 11 >> extensional distance = 8 >> proper extension: 02bjrlw; 06nm1; 01r2l; >> query: (?x10429, 07ytt) <- language(?x6489, ?x10429), featured_film_locations(?x6489, ?x1646), film_crew_role(?x6489, ?x2154), written_by(?x6489, ?x2442), film_distribution_medium(?x6489, ?x2099), currency(?x6489, ?x170), film(?x4667, ?x6489), languages_spoken(?x3584, ?x10429), ?x2154 = 01vx2h, countries_spoken_in(?x10429, ?x1558), people(?x2510, ?x4667) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #957 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 064_8sq; *> query: (?x10429, 03gk2) <- language(?x6489, ?x10429), featured_film_locations(?x6489, ?x1646), film_crew_role(?x6489, ?x2154), written_by(?x6489, ?x2442), film_distribution_medium(?x6489, ?x2099), currency(?x6489, ?x170), film(?x4667, ?x6489), languages_spoken(?x3584, ?x10429), ?x2154 = 01vx2h, countries_spoken_in(?x10429, ?x1558), ?x4667 = 032zg9 *> conf = 0.14 ranks of expected_values: 118, 217 EVAL 01wgr countries_spoken_in 01mk6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 42.000 0.500 http://example.org/language/human_language/countries_spoken_in EVAL 01wgr countries_spoken_in 03gk2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 42.000 42.000 0.500 http://example.org/language/human_language/countries_spoken_in #19754-02l3_5 PRED entity: 02l3_5 PRED relation: award PRED expected values: 0bfvw2 09qj50 09td7p => 138 concepts (122 used for prediction) PRED predicted values (max 10 best out of 263): 02ppm4q (0.71 #47403, 0.71 #30664, 0.70 #37037), 09sb52 (0.42 #22333, 0.33 #16359, 0.31 #21535), 0cqhk0 (0.39 #3220, 0.18 #13967, 0.17 #434), 0gqy2 (0.33 #559, 0.18 #22455, 0.13 #37436), 02x73k6 (0.33 #457, 0.08 #16379, 0.07 #22353), 0ck27z (0.29 #14021, 0.27 #14818, 0.26 #15216), 0cqhmg (0.27 #3540, 0.14 #39033, 0.13 #37436), 09qj50 (0.24 #3228, 0.14 #14330, 0.14 #39033), 09qvc0 (0.18 #834, 0.17 #436, 0.14 #39033), 05pcn59 (0.18 #875, 0.13 #2865, 0.12 #18389) >> Best rule #47403 for best value: >> intensional similarity = 3 >> extensional distance = 2105 >> proper extension: 02pp_q_; 067jsf; 01h320; 034bs; 06whf; 0d0mbj; 015zql; 03hfxx; 0k_mt; 0d3k14; ... >> query: (?x8081, ?x3184) <- award_winner(?x3184, ?x8081), profession(?x8081, ?x1032), award(?x241, ?x3184) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3228 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 04nw9; 011zd3; 053y4h; 021b_; *> query: (?x8081, 09qj50) <- award(?x8081, ?x2603), film(?x8081, ?x755), ?x2603 = 09qs08 *> conf = 0.24 ranks of expected_values: 8, 20, 87 EVAL 02l3_5 award 09td7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 138.000 122.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02l3_5 award 09qj50 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 138.000 122.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02l3_5 award 0bfvw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 138.000 122.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19753-07wm6 PRED entity: 07wm6 PRED relation: major_field_of_study PRED expected values: 0193x => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 120): 01mkq (0.63 #1726, 0.55 #3320, 0.49 #2828), 04rjg (0.60 #1730, 0.55 #875, 0.42 #3324), 03g3w (0.57 #1736, 0.42 #3330, 0.40 #2838), 01lj9 (0.52 #894, 0.40 #1749, 0.35 #3343), 02lp1 (0.51 #1722, 0.45 #3316, 0.40 #2824), 062z7 (0.48 #882, 0.42 #1737, 0.38 #3331), 05qjt (0.42 #1718, 0.35 #3312, 0.30 #2208), 0fdys (0.35 #893, 0.34 #1748, 0.24 #3342), 05qfh (0.35 #1745, 0.33 #1012, 0.29 #890), 037mh8 (0.34 #1777, 0.31 #1044, 0.29 #922) >> Best rule #1726 for best value: >> intensional similarity = 7 >> extensional distance = 63 >> proper extension: 08qnnv; >> query: (?x12737, 01mkq) <- institution(?x3437, ?x12737), institution(?x1526, ?x12737), institution(?x865, ?x12737), student(?x12737, ?x4855), ?x865 = 02h4rq6, ?x3437 = 02_xgp2, ?x1526 = 0bkj86 >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #1744 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 63 *> proper extension: 08qnnv; *> query: (?x12737, 0193x) <- institution(?x3437, ?x12737), institution(?x1526, ?x12737), institution(?x865, ?x12737), student(?x12737, ?x4855), ?x865 = 02h4rq6, ?x3437 = 02_xgp2, ?x1526 = 0bkj86 *> conf = 0.26 ranks of expected_values: 18 EVAL 07wm6 major_field_of_study 0193x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 170.000 170.000 0.631 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19752-04fzfj PRED entity: 04fzfj PRED relation: language PRED expected values: 06nm1 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 29): 04306rv (0.22 #4, 0.13 #170, 0.10 #941), 06nm1 (0.15 #175, 0.15 #9, 0.12 #120), 02bjrlw (0.11 #1, 0.08 #167, 0.08 #1273), 0jzc (0.07 #18, 0.04 #73, 0.03 #184), 02hxcvy (0.06 #30, 0.02 #2080, 0.01 #747), 04h9h (0.05 #150, 0.04 #205, 0.04 #316), 0653m (0.04 #1058, 0.04 #1617, 0.04 #2005), 02ztjwg (0.04 #28, 0.02 #470, 0.01 #139), 012w70 (0.03 #1894, 0.02 #1452, 0.02 #1059), 05zjd (0.03 #77, 0.02 #794, 0.02 #1350) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 0c9k8; 0pd6l; 01pvxl; 04gcyg; >> query: (?x723, 04306rv) <- nominated_for(?x1561, ?x723), language(?x723, ?x5671), nominated_for(?x154, ?x723), ?x5671 = 06b_j >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #175 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 141 *> proper extension: 07g_0c; 03wbqc4; *> query: (?x723, 06nm1) <- crewmember(?x723, ?x666), film(?x722, ?x723), featured_film_locations(?x723, ?x739) *> conf = 0.15 ranks of expected_values: 2 EVAL 04fzfj language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.222 http://example.org/film/film/language #19751-02lcrv PRED entity: 02lcrv PRED relation: time_zones! PRED expected values: 0qf5p => 10 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1932): 0d060g (0.71 #1262, 0.67 #6289, 0.67 #9), 015jr (0.64 #3761, 0.64 #10047, 0.56 #1252), 04_1l0v (0.50 #502, 0.43 #3011, 0.43 #1755), 04pnx (0.50 #458, 0.43 #2967, 0.43 #1711), 0qf5p (0.48 #1253, 0.45 #5021, 0.45 #6276), 0l_qt (0.48 #1253, 0.45 #5021, 0.45 #6276), 0l_n1 (0.48 #1253, 0.45 #5021, 0.45 #6276), 0l_tn (0.48 #1253, 0.45 #5021, 0.45 #6276), 03s5t (0.43 #10046, 0.33 #153, 0.29 #2662), 081yw (0.43 #10046, 0.24 #3764, 0.23 #11300) >> Best rule #1262 for best value: >> intensional similarity = 17 >> extensional distance = 5 >> proper extension: 042g7t; >> query: (?x6498, 0d060g) <- time_zones(?x6497, ?x6498), time_zones(?x5244, ?x6498), time_zones(?x953, ?x6498), jurisdiction_of_office(?x1195, ?x6497), featured_film_locations(?x1866, ?x6497), currency(?x953, ?x170), jurisdiction_of_office(?x3959, ?x953), jurisdiction_of_office(?x900, ?x953), contains(?x953, ?x11121), location(?x744, ?x953), jurisdiction_of_office(?x3959, ?x390), adjoins(?x7468, ?x953), ?x390 = 0chghy, source(?x5244, ?x958), basic_title(?x1157, ?x900), ?x170 = 09nqf, nominated_for(?x91, ?x1866) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1253 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 4 *> proper extension: 02fqwt; 02hczc; 02hcv8; 02lcqs; *> query: (?x6498, ?x11121) <- time_zones(?x6497, ?x6498), time_zones(?x953, ?x6498), jurisdiction_of_office(?x1195, ?x6497), source(?x6497, ?x958), district_represented(?x845, ?x953), category(?x6497, ?x134), contains(?x953, ?x11121), ?x845 = 07p__7, featured_film_locations(?x1866, ?x6497), adjoins(?x953, ?x7468), film(?x1208, ?x1866), film(?x1865, ?x1866), nominated_for(?x1162, ?x1866), titles(?x53, ?x1866), jurisdiction_of_office(?x744, ?x953), currency(?x1866, ?x170), ?x958 = 0jbk9 *> conf = 0.48 ranks of expected_values: 5 EVAL 02lcrv time_zones! 0qf5p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 10.000 10.000 0.714 http://example.org/location/location/time_zones #19750-01t110 PRED entity: 01t110 PRED relation: instrumentalists! PRED expected values: 01xqw => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 121): 018vs (0.43 #257, 0.32 #1324, 0.30 #4195), 026t6 (0.39 #250, 0.17 #332, 0.14 #85), 02hnl (0.37 #278, 0.20 #360, 0.20 #1345), 06ncr (0.25 #40, 0.14 #247, 0.09 #287), 0l14j_ (0.25 #49, 0.14 #247, 0.06 #296), 07gql (0.25 #38, 0.14 #247, 0.06 #285), 018j2 (0.25 #34, 0.10 #198, 0.10 #1348), 07xzm (0.25 #18, 0.05 #347, 0.04 #1332), 0jtg0 (0.25 #46, 0.03 #4432, 0.03 #1360), 01dnws (0.25 #37, 0.03 #4432, 0.03 #5255) >> Best rule #257 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 01nqfh_; 04pf4r; 018y81; >> query: (?x6461, 018vs) <- instrumentalists(?x315, ?x6461), ?x315 = 0l14md, artists(?x302, ?x6461) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #310 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 01nqfh_; 04pf4r; 018y81; *> query: (?x6461, 01xqw) <- instrumentalists(?x315, ?x6461), ?x315 = 0l14md, artists(?x302, ?x6461) *> conf = 0.06 ranks of expected_values: 22 EVAL 01t110 instrumentalists! 01xqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 149.000 149.000 0.433 http://example.org/music/instrument/instrumentalists #19749-051wwp PRED entity: 051wwp PRED relation: profession PRED expected values: 0cbd2 02krf9 => 81 concepts (80 used for prediction) PRED predicted values (max 10 best out of 40): 03gjzk (0.48 #450, 0.40 #596, 0.37 #304), 018gz8 (0.44 #14, 0.28 #5112, 0.15 #452), 0kyk (0.28 #5112, 0.19 #27, 0.10 #6163), 0np9r (0.28 #5112, 0.15 #8053, 0.15 #7614), 015cjr (0.28 #5112, 0.06 #47, 0.04 #193), 0d8qb (0.28 #5112, 0.06 #77, 0.04 #223), 02hv44_ (0.28 #5112, 0.06 #493, 0.04 #201), 09jwl (0.25 #16, 0.17 #3521, 0.17 #3229), 02krf9 (0.21 #900, 0.20 #462, 0.19 #24), 0cbd2 (0.19 #444, 0.14 #9209, 0.14 #3949) >> Best rule #450 for best value: >> intensional similarity = 3 >> extensional distance = 363 >> proper extension: 014hdb; >> query: (?x4928, 03gjzk) <- award_winner(?x4928, ?x374), profession(?x4928, ?x987), ?x987 = 0dxtg >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #900 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 661 *> proper extension: 02qjj7; 016hvl; 01c59k; 04qr6d; 0738y5; 03z0l6; 0kc6; 0d0l91; 0jnb0; 05dxl_; ... *> query: (?x4928, 02krf9) <- profession(?x4928, ?x524), ?x524 = 02jknp *> conf = 0.21 ranks of expected_values: 9, 10 EVAL 051wwp profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 81.000 80.000 0.485 http://example.org/people/person/profession EVAL 051wwp profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 81.000 80.000 0.485 http://example.org/people/person/profession #19748-01271h PRED entity: 01271h PRED relation: artists! PRED expected values: 017_qw 0mmp3 => 106 concepts (57 used for prediction) PRED predicted values (max 10 best out of 245): 03lty (0.78 #638, 0.22 #942, 0.21 #15860), 0xhtw (0.56 #626, 0.34 #2761, 0.30 #320), 017_qw (0.52 #3417, 0.51 #4028, 0.34 #1891), 06by7 (0.52 #935, 0.51 #2766, 0.50 #5207), 08cyft (0.51 #1581, 0.47 #1276, 0.21 #15860), 064t9 (0.51 #1537, 0.43 #4283, 0.41 #3063), 05r6t (0.33 #691, 0.33 #81, 0.21 #15860), 05bt6j (0.33 #44, 0.25 #3095, 0.24 #3703), 059kh (0.33 #49, 0.21 #15860, 0.15 #963), 011j5x (0.33 #32, 0.21 #15860, 0.10 #336) >> Best rule #638 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01j59b0; 01shhf; 016lj_; 01518s; >> query: (?x2945, 03lty) <- artists(?x9248, ?x2945), category(?x2945, ?x134), ?x9248 = 02t8gf >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3417 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 132 *> proper extension: 01vsxdm; 0134s5; 03h610; 02jqjm; 0bpk2; 02cpp; 01l79yc; 015cxv; 0fpjyd; 0bk1p; ... *> query: (?x2945, 017_qw) <- artists(?x302, ?x2945), award(?x2945, ?x247), music(?x6007, ?x2945) *> conf = 0.52 ranks of expected_values: 3, 30 EVAL 01271h artists! 0mmp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 106.000 57.000 0.778 http://example.org/music/genre/artists EVAL 01271h artists! 017_qw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 57.000 0.778 http://example.org/music/genre/artists #19747-01swxv PRED entity: 01swxv PRED relation: major_field_of_study PRED expected values: 01lhf => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 116): 01mkq (0.51 #958, 0.41 #1430, 0.39 #3436), 02j62 (0.47 #971, 0.45 #853, 0.40 #3449), 037mh8 (0.40 #889, 0.20 #1007, 0.18 #5492), 05qfh (0.35 #977, 0.26 #3455, 0.25 #1449), 01tbp (0.35 #1000, 0.26 #2416, 0.25 #3478), 01lj9 (0.33 #155, 0.27 #981, 0.25 #3459), 04x_3 (0.31 #968, 0.29 #3446, 0.25 #2384), 01540 (0.31 #1001, 0.24 #765, 0.23 #3479), 05qjt (0.28 #3429, 0.27 #951, 0.26 #833), 02ky346 (0.27 #605, 0.27 #133, 0.25 #487) >> Best rule #958 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 027xx3; 02183k; 025v3k; 027mdh; 01q8hj; >> query: (?x2959, 01mkq) <- major_field_of_study(?x2959, ?x2606), school(?x684, ?x2959), ?x2606 = 062z7, colors(?x2959, ?x332) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #4839 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 175 *> proper extension: 01j_5k; 01stj9; *> query: (?x2959, ?x373) <- major_field_of_study(?x2959, ?x2606), school(?x684, ?x2959), major_field_of_study(?x2606, ?x373) *> conf = 0.18 ranks of expected_values: 30 EVAL 01swxv major_field_of_study 01lhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 160.000 160.000 0.510 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19746-0cqh6z PRED entity: 0cqh6z PRED relation: award! PRED expected values: 030b93 => 58 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2730): 0dvld (0.50 #15156, 0.26 #25219, 0.26 #28574), 026c1 (0.40 #13993, 0.40 #7283, 0.33 #573), 028knk (0.40 #13939, 0.35 #24002, 0.33 #519), 02jsgf (0.40 #14563, 0.35 #24626, 0.33 #1143), 030znt (0.40 #13746, 0.33 #326, 0.29 #23809), 01kb2j (0.40 #14896, 0.33 #1476, 0.26 #24959), 05dbf (0.40 #14003, 0.33 #583, 0.25 #20712), 01pcq3 (0.40 #13607, 0.33 #187, 0.20 #6897), 0blbxk (0.40 #13730, 0.33 #3666, 0.20 #7020), 0794g (0.40 #7611, 0.30 #14321, 0.17 #17676) >> Best rule #15156 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 05zr6wv; 0bdw1g; 09sb52; 0bdwft; 05pcn59; 027b9k6; >> query: (?x1111, 0dvld) <- award(?x3267, ?x1111), award(?x1890, ?x1111), ?x1890 = 01gq0b, award_winner(?x1254, ?x3267), award(?x337, ?x1111), nominated_for(?x1111, ?x1434) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5417 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 0ck27z; *> query: (?x1111, 030b93) <- award(?x3267, ?x1111), award(?x1890, ?x1111), ?x1890 = 01gq0b, ?x3267 = 011_3s, ceremony(?x1111, ?x873) *> conf = 0.33 ranks of expected_values: 41 EVAL 0cqh6z award! 030b93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 58.000 17.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19745-01yg9y PRED entity: 01yg9y PRED relation: category PRED expected values: 08mbj5d => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.81 #28, 0.78 #30, 0.77 #31) >> Best rule #28 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 0cg9y; 03j0br4; 0d9xq; 018d6l; 01lz4tf; 0bdlj; 03h_yfh; 04bbv7; 01wxdn3; 017f4y; ... >> query: (?x5413, 08mbj5d) <- profession(?x5413, ?x1032), artists(?x13359, ?x5413), ?x1032 = 02hrh1q >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01yg9y category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.806 http://example.org/common/topic/webpage./common/webpage/category #19744-0d19y2 PRED entity: 0d19y2 PRED relation: people PRED expected values: 01w1ywm => 91 concepts (71 used for prediction) PRED predicted values (max 10 best out of 3282): 053yx (0.50 #10337, 0.46 #20486, 0.45 #20485), 0407f (0.50 #7620, 0.33 #22645, 0.33 #21963), 0cgbf (0.46 #20486, 0.45 #20485, 0.44 #15701), 0jrny (0.46 #20486, 0.45 #20485, 0.44 #15701), 014zn0 (0.46 #20486, 0.45 #20485, 0.44 #15701), 0b22w (0.46 #20486, 0.45 #20485, 0.44 #15701), 01938t (0.46 #20486, 0.45 #20485, 0.44 #15701), 02h48 (0.46 #20486, 0.45 #20485, 0.44 #15701), 0432b (0.46 #20486, 0.45 #20485, 0.44 #15701), 0byfz (0.46 #20486, 0.45 #20485, 0.44 #15701) >> Best rule #10337 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 02y0js; >> query: (?x13131, 053yx) <- risk_factors(?x4291, ?x13131), symptom_of(?x3679, ?x13131), people(?x13131, ?x11239), people(?x13131, ?x10724), award(?x10724, ?x3066), award(?x10724, ?x1033), ?x1033 = 02x73k6, ?x3066 = 0gqy2, award_winner(?x1307, ?x11239), award_winner(?x9534, ?x11239) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #21851 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 01b_5g; *> query: (?x13131, ?x4142) <- risk_factors(?x8675, ?x13131), risk_factors(?x6655, ?x13131), risk_factors(?x4291, ?x13131), ?x8675 = 01gkcc, risk_factors(?x6655, ?x8524), risk_factors(?x6655, ?x6656), symptom_of(?x4905, ?x4291), symptom_of(?x6780, ?x6656), notable_people_with_this_condition(?x6656, ?x4142), risk_factors(?x1158, ?x8524), ?x1158 = 02y0js *> conf = 0.06 ranks of expected_values: 623 EVAL 0d19y2 people 01w1ywm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 91.000 71.000 0.500 http://example.org/people/cause_of_death/people #19743-02qfhb PRED entity: 02qfhb PRED relation: participant PRED expected values: 01gq0b => 73 concepts (50 used for prediction) PRED predicted values (max 10 best out of 190): 01gq0b (0.81 #9641, 0.80 #18002, 0.79 #10285), 01pllx (0.12 #548, 0.07 #1191, 0.03 #3118), 0jfx1 (0.12 #161, 0.07 #804, 0.02 #2731), 0lx2l (0.12 #168, 0.07 #811), 01mqh5 (0.09 #10284, 0.07 #3214, 0.06 #18645), 0c6qh (0.07 #808, 0.03 #5947, 0.03 #9162), 019pm_ (0.07 #830, 0.02 #4043, 0.02 #5969), 01p4vl (0.07 #1141, 0.01 #9495, 0.01 #14645), 01gw8b (0.07 #1243), 02bj6k (0.07 #1149) >> Best rule #9641 for best value: >> intensional similarity = 3 >> extensional distance = 352 >> proper extension: 0134w7; >> query: (?x4929, ?x1890) <- award_nominee(?x396, ?x4929), participant(?x1890, ?x4929), film(?x4929, ?x3441) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qfhb participant 01gq0b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 50.000 0.810 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #19742-01ljpm PRED entity: 01ljpm PRED relation: registering_agency PRED expected values: 03z19 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.91 #13, 0.89 #9, 0.89 #12) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 09wv__; >> query: (?x6501, 03z19) <- student(?x6501, ?x3694), currency(?x6501, ?x170), award_nominee(?x3694, ?x495), nominated_for(?x3694, ?x1295) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ljpm registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.907 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #19741-0cjsxp PRED entity: 0cjsxp PRED relation: nationality PRED expected values: 09c7w0 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.89 #5332, 0.84 #6134, 0.80 #5028), 02jx1 (0.37 #2815, 0.13 #1139, 0.11 #2242), 07ssc (0.37 #2815, 0.09 #1221, 0.09 #5746), 03rjj (0.37 #2815, 0.04 #5832, 0.02 #4228), 0498y (0.33 #8948, 0.33 #3018, 0.01 #5129), 03rk0 (0.06 #851, 0.05 #9094, 0.05 #9294), 0d060g (0.05 #1012, 0.05 #3025, 0.05 #2115), 03_3d (0.04 #5832, 0.03 #1913, 0.03 #3024), 0f8l9c (0.04 #5832, 0.02 #3241, 0.02 #322), 03rt9 (0.04 #5832, 0.02 #818, 0.02 #918) >> Best rule #5332 for best value: >> intensional similarity = 2 >> extensional distance = 1420 >> proper extension: 019g40; 02w5q6; 0132k4; 05lnk0; 027rfxc; 04mky3; 06czxq; >> query: (?x3842, 09c7w0) <- place_of_birth(?x3842, ?x5259), source(?x5259, ?x958) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cjsxp nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.891 http://example.org/people/person/nationality #19740-01wgr PRED entity: 01wgr PRED relation: language! PRED expected values: 02fj8n => 36 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1845): 03twd6 (0.83 #1729, 0.50 #3671, 0.33 #1944), 0gwjw0c (0.83 #1729, 0.50 #4617, 0.33 #2890), 047vnkj (0.83 #1729, 0.43 #7784, 0.33 #4326), 0g5qmbz (0.83 #1729, 0.36 #8412, 0.33 #4954), 011yrp (0.83 #1729, 0.33 #3490, 0.33 #1763), 0jdr0 (0.83 #1729, 0.33 #4938, 0.33 #3211), 03cw411 (0.83 #1729, 0.33 #4041, 0.33 #2314), 02qyv3h (0.83 #1729, 0.33 #4428, 0.33 #2701), 05qbckf (0.83 #1729, 0.33 #3753, 0.33 #297), 04zl8 (0.83 #1729, 0.33 #4336, 0.33 #880) >> Best rule #1729 for best value: >> intensional similarity = 18 >> extensional distance = 1 >> proper extension: 02h40lc; >> query: (?x10429, ?x124) <- language(?x6489, ?x10429), language(?x2968, ?x10429), language(?x2928, ?x10429), language(?x2550, ?x10429), language(?x1071, ?x10429), ?x2550 = 07j8r, ?x6489 = 06fqlk, ?x2928 = 07024, languages_spoken(?x3584, ?x10429), countries_spoken_in(?x10429, ?x1558), ?x2968 = 025n07, ?x1071 = 02d44q, film_release_region(?x5425, ?x1558), film_release_region(?x2318, ?x1558), film_release_region(?x124, ?x1558), ?x5425 = 02prwdh, country(?x453, ?x1558), ?x2318 = 06v9_x >> conf = 0.83 => this is the best rule for 186 predicted values *> Best rule #1241 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x10429, 02fj8n) <- language(?x6489, ?x10429), language(?x2968, ?x10429), language(?x2928, ?x10429), language(?x2550, ?x10429), language(?x1071, ?x10429), ?x2550 = 07j8r, ?x6489 = 06fqlk, ?x2928 = 07024, languages_spoken(?x3584, ?x10429), countries_spoken_in(?x10429, ?x1558), ?x2968 = 025n07, ?x1071 = 02d44q, film_release_region(?x5425, ?x1558), film_release_region(?x2318, ?x1558), ?x5425 = 02prwdh, country(?x453, ?x1558), ?x2318 = 06v9_x *> conf = 0.33 ranks of expected_values: 1048 EVAL 01wgr language! 02fj8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 12.000 0.825 http://example.org/film/film/language #19739-02s62q PRED entity: 02s62q PRED relation: student PRED expected values: 0l12d 06r_by 01g1lp => 171 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1699): 0hgqq (0.17 #842, 0.06 #9207, 0.04 #11299), 015qq1 (0.17 #1891, 0.06 #10256, 0.03 #56266), 04t969 (0.17 #1279, 0.05 #15918, 0.05 #20101), 09v6tz (0.17 #1341, 0.05 #15980, 0.04 #32710), 05kfs (0.17 #97, 0.05 #18919, 0.04 #14736), 02hsgn (0.17 #820, 0.05 #19642, 0.03 #7094), 0h0wc (0.17 #392, 0.04 #31761, 0.03 #8757), 023v4_ (0.17 #860, 0.04 #15499, 0.04 #10457), 01n1gc (0.17 #610, 0.04 #15249, 0.03 #19432), 07s8hms (0.17 #622, 0.04 #15261, 0.03 #19444) >> Best rule #842 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 08815; 02gr81; 09f2j; 01vg13; >> query: (?x2056, 0hgqq) <- student(?x2056, ?x123), state_province_region(?x2056, ?x2713), organization(?x346, ?x2056), ?x123 = 05bnp0 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #32429 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 01rr31; 015fsv; *> query: (?x2056, 06r_by) <- currency(?x2056, ?x170), major_field_of_study(?x2056, ?x2540), student(?x2540, ?x3673), genre(?x419, ?x2540) *> conf = 0.01 ranks of expected_values: 1554 EVAL 02s62q student 01g1lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 94.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 02s62q student 06r_by CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 94.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 02s62q student 0l12d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 94.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #19738-01wf86y PRED entity: 01wf86y PRED relation: artists! PRED expected values: 02lnbg => 128 concepts (75 used for prediction) PRED predicted values (max 10 best out of 252): 0m0jc (0.67 #316, 0.15 #5234, 0.14 #2776), 08cyft (0.67 #363, 0.12 #2516, 0.10 #5281), 016clz (0.50 #2772, 0.41 #2465, 0.40 #4000), 025sc50 (0.50 #973, 0.37 #5274, 0.33 #6195), 06j6l (0.44 #664, 0.36 #5273, 0.33 #3736), 02yv6b (0.44 #713, 0.17 #7780, 0.13 #15466), 02lnbg (0.43 #981, 0.33 #5282, 0.32 #6203), 011j5x (0.39 #2799, 0.33 #4027, 0.08 #4335), 05r6t (0.36 #2847, 0.35 #4075, 0.18 #2540), 0y3_8 (0.36 #2814, 0.32 #4042, 0.12 #6193) >> Best rule #316 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01vv7sc; 01v_pj6; 01vs_v8; 0478__m; >> query: (?x7581, 0m0jc) <- award(?x7581, ?x8331), instrumentalists(?x227, ?x7581), role(?x7581, ?x1166), ?x8331 = 056jm_ >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #981 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0p3r8; *> query: (?x7581, 02lnbg) <- artists(?x671, ?x7581), gender(?x7581, ?x514), person(?x3480, ?x7581), ?x514 = 02zsn *> conf = 0.43 ranks of expected_values: 7 EVAL 01wf86y artists! 02lnbg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 128.000 75.000 0.667 http://example.org/music/genre/artists #19737-07_pf PRED entity: 07_pf PRED relation: capital! PRED expected values: 02wm6l => 146 concepts (130 used for prediction) PRED predicted values (max 10 best out of 59): 0f8l9c (0.25 #21, 0.14 #156, 0.10 #291), 0gtzp (0.25 #135, 0.14 #270, 0.10 #405), 03rjj (0.14 #1898, 0.14 #1762, 0.14 #140), 0bzjf (0.14 #1898, 0.14 #1762, 0.14 #2034), 06frc (0.14 #242, 0.10 #377, 0.09 #784), 07ssc (0.14 #150, 0.09 #692, 0.07 #962), 014tss (0.14 #224, 0.09 #766, 0.07 #1036), 02jx1 (0.14 #167, 0.09 #709, 0.07 #979), 09c7w0 (0.14 #136, 0.09 #678, 0.06 #1627), 07ytt (0.09 #657, 0.05 #2149, 0.02 #5544) >> Best rule #21 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 03qhnx; >> query: (?x10496, 0f8l9c) <- featured_film_locations(?x4786, ?x10496), ?x4786 = 0bbw2z6 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07_pf capital! 02wm6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 146.000 130.000 0.250 http://example.org/location/country/capital #19736-0c3ns PRED entity: 0c3ns PRED relation: gender PRED expected values: 05zppz => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #9, 0.88 #37, 0.87 #35), 02zsn (0.28 #62, 0.28 #90, 0.27 #72) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 01y8d4; >> query: (?x2179, 05zppz) <- award_winner(?x2179, ?x4383), location(?x2179, ?x5036), written_by(?x1224, ?x2179) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c3ns gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.882 http://example.org/people/person/gender #19735-05dtsb PRED entity: 05dtsb PRED relation: profession PRED expected values: 02hrh1q => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 40): 02hrh1q (0.89 #315, 0.89 #2115, 0.88 #2715), 03gjzk (0.36 #166, 0.35 #1666, 0.35 #466), 0dxtg (0.35 #464, 0.31 #1664, 0.30 #1964), 01d_h8 (0.29 #5106, 0.29 #3006, 0.29 #2556), 02jknp (0.26 #9154, 0.25 #6302, 0.25 #6151), 02krf9 (0.26 #9154, 0.25 #6302, 0.25 #6151), 0d1pc (0.26 #9154, 0.25 #6302, 0.25 #6151), 0kyk (0.22 #31, 0.10 #6933, 0.09 #4231), 0np9r (0.20 #1822, 0.19 #1372, 0.19 #1072), 09jwl (0.16 #8573, 0.16 #7372, 0.15 #13074) >> Best rule #315 for best value: >> intensional similarity = 3 >> extensional distance = 415 >> proper extension: 06sn8m; 0hcvy; >> query: (?x6701, 02hrh1q) <- award(?x6701, ?x1670), actor(?x4932, ?x6701), student(?x1513, ?x6701) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05dtsb profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.890 http://example.org/people/person/profession #19734-085gk PRED entity: 085gk PRED relation: influenced_by! PRED expected values: 01d494 040db => 108 concepts (46 used for prediction) PRED predicted values (max 10 best out of 442): 01w9ph_ (0.50 #316, 0.14 #1337, 0.12 #6645), 05qw5 (0.50 #68, 0.12 #6645, 0.11 #22001), 03_87 (0.43 #1279, 0.40 #769, 0.12 #6645), 0683n (0.36 #1867, 0.29 #1357, 0.15 #2378), 040db (0.29 #1095, 0.27 #1605, 0.25 #74), 0d4jl (0.29 #1134, 0.20 #624, 0.18 #1644), 06whf (0.29 #1182, 0.20 #672, 0.18 #1692), 03f70xs (0.29 #1116, 0.20 #606, 0.09 #9712), 016hvl (0.29 #1056, 0.20 #546, 0.09 #1566), 04jwp (0.29 #1262, 0.20 #752, 0.05 #23539) >> Best rule #316 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03f70xs; >> query: (?x12402, 01w9ph_) <- influenced_by(?x1089, ?x12402), influenced_by(?x12402, ?x4072), people(?x5269, ?x12402), ?x1089 = 01vrncs >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1095 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02zjd; 0448r; *> query: (?x12402, 040db) <- influenced_by(?x587, ?x12402), location(?x12402, ?x335), ?x587 = 07g2b, nationality(?x12402, ?x94) *> conf = 0.29 ranks of expected_values: 5, 104 EVAL 085gk influenced_by! 040db CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 46.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 085gk influenced_by! 01d494 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 108.000 46.000 0.500 http://example.org/influence/influence_node/influenced_by #19733-02v406 PRED entity: 02v406 PRED relation: profession PRED expected values: 0dxtg => 212 concepts (86 used for prediction) PRED predicted values (max 10 best out of 96): 0dxtg (0.58 #10761, 0.56 #8408, 0.45 #5906), 09jwl (0.40 #2372, 0.40 #2078, 0.37 #10913), 0fj9f (0.38 #347, 0.20 #3292, 0.19 #1819), 03gjzk (0.35 #4285, 0.27 #5464, 0.27 #8409), 0nbcg (0.34 #2091, 0.31 #2385, 0.26 #6218), 0kyk (0.31 #1352, 0.25 #28, 0.24 #3267), 0cbd2 (0.28 #1330, 0.25 #1772, 0.25 #6), 0dz3r (0.27 #2063, 0.25 #2357, 0.20 #11045), 016z4k (0.26 #2065, 0.23 #2801, 0.23 #2359), 018gz8 (0.25 #309, 0.17 #9440, 0.16 #11205) >> Best rule #10761 for best value: >> intensional similarity = 4 >> extensional distance = 424 >> proper extension: 08q3s0; 0cvbb9q; 02404v; 04rg6; 05dxl_; 04dz_y7; 0gry51; 05b1062; >> query: (?x4217, 0dxtg) <- profession(?x4217, ?x1032), profession(?x4217, ?x524), ?x524 = 02jknp, ?x1032 = 02hrh1q >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v406 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 86.000 0.577 http://example.org/people/person/profession #19732-04qr6d PRED entity: 04qr6d PRED relation: type_of_union PRED expected values: 04ztj => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.82 #61, 0.81 #73, 0.81 #49), 01g63y (0.45 #177, 0.25 #312, 0.25 #311), 0jgjn (0.25 #312, 0.25 #311, 0.25 #306) >> Best rule #61 for best value: >> intensional similarity = 7 >> extensional distance = 82 >> proper extension: 01vs_v8; 0gv5c; 01pp3p; 0dfjb8; 01c6l; 021r7r; 04wg38; 0652ty; >> query: (?x8756, 04ztj) <- profession(?x8756, ?x987), profession(?x8756, ?x524), profession(?x8756, ?x319), religion(?x8756, ?x8967), ?x987 = 0dxtg, ?x524 = 02jknp, ?x319 = 01d_h8 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04qr6d type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.821 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19731-02bjrlw PRED entity: 02bjrlw PRED relation: languages! PRED expected values: 07f3xb 02byfd 0chw_ 01vh3r => 69 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1761): 01x2tm8 (0.36 #6126, 0.33 #490, 0.29 #7379), 040nwr (0.36 #6244, 0.33 #608, 0.24 #7497), 09r_wb (0.36 #6076, 0.33 #440, 0.24 #7329), 046rfv (0.36 #6060, 0.33 #424, 0.24 #7313), 03x31g (0.36 #6208, 0.33 #572, 0.24 #7461), 02pk6x (0.33 #2812, 0.33 #307, 0.29 #4064), 026rm_y (0.33 #2957, 0.33 #452, 0.29 #4209), 0pcc0 (0.33 #670, 0.33 #44, 0.17 #2549), 0151ns (0.33 #654, 0.33 #28, 0.17 #2533), 0dw6b (0.33 #2951, 0.33 #1072, 0.14 #4829) >> Best rule #6126 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 09s02; >> query: (?x90, 01x2tm8) <- languages(?x8445, ?x90), languages(?x1991, ?x90), languages_spoken(?x3584, ?x90), award(?x1991, ?x686), student(?x7070, ?x8445) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x90, 01vh3r) <- language(?x8664, ?x90), language(?x4431, ?x90), nominated_for(?x484, ?x4431), languages(?x1371, ?x90), languages(?x914, ?x90), ?x914 = 0htlr, ?x8664 = 03hfmm, ?x1371 = 0prjs *> conf = 0.33 ranks of expected_values: 34, 89, 555, 568 EVAL 02bjrlw languages! 01vh3r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 69.000 40.000 0.357 http://example.org/people/person/languages EVAL 02bjrlw languages! 0chw_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 69.000 40.000 0.357 http://example.org/people/person/languages EVAL 02bjrlw languages! 02byfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 69.000 40.000 0.357 http://example.org/people/person/languages EVAL 02bjrlw languages! 07f3xb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 69.000 40.000 0.357 http://example.org/people/person/languages #19730-02drd3 PRED entity: 02drd3 PRED relation: student! PRED expected values: 0g8fs => 101 concepts (87 used for prediction) PRED predicted values (max 10 best out of 142): 015zyd (0.33 #527, 0.06 #1579, 0.04 #6314), 01vc5m (0.33 #619), 0bwfn (0.11 #12901, 0.10 #6061, 0.10 #7639), 065y4w7 (0.10 #1592, 0.08 #5801, 0.07 #9485), 01w5m (0.10 #2208, 0.09 #9575, 0.08 #1682), 07tgn (0.10 #2121, 0.07 #9488, 0.07 #1069), 03ksy (0.07 #1157, 0.06 #12732, 0.05 #13784), 02301 (0.07 #1125, 0.02 #9018, 0.02 #10070), 0677j (0.07 #1379, 0.02 #1905, 0.02 #2431), 0gl5_ (0.07 #1295, 0.02 #1821, 0.01 #17604) >> Best rule #527 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0164nb; >> query: (?x12439, 015zyd) <- location(?x12439, ?x7930), ?x7930 = 0ggh3, nationality(?x12439, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2460 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 60 *> proper extension: 04zd4m; *> query: (?x12439, 0g8fs) <- place_of_death(?x12439, ?x6960), story_by(?x721, ?x12439) *> conf = 0.02 ranks of expected_values: 91 EVAL 02drd3 student! 0g8fs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 101.000 87.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #19729-01m42d0 PRED entity: 01m42d0 PRED relation: artists! PRED expected values: 0161rf => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 109): 064t9 (0.20 #329, 0.14 #6314, 0.12 #6629), 03_d0 (0.20 #327, 0.11 #4107, 0.10 #3477), 01lyv (0.20 #351, 0.07 #5706, 0.07 #6021), 0161rf (0.20 #438, 0.04 #3273, 0.04 #3903), 06by7 (0.17 #6323, 0.16 #10103, 0.10 #9473), 06j6l (0.14 #6351, 0.09 #10131, 0.09 #9501), 0gywn (0.12 #6361, 0.08 #9511, 0.07 #2581), 0155w (0.10 #6411, 0.08 #10191, 0.06 #9561), 0m40d (0.09 #4247, 0.08 #3617, 0.08 #3302), 0xhtw (0.08 #10098, 0.06 #1908, 0.05 #16083) >> Best rule #329 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02lvtb; >> query: (?x8010, 064t9) <- people(?x5118, ?x8010), profession(?x8010, ?x1032), ?x5118 = 01bcp7, award(?x8010, ?x2071) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #438 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 02lvtb; *> query: (?x8010, 0161rf) <- people(?x5118, ?x8010), profession(?x8010, ?x1032), ?x5118 = 01bcp7, award(?x8010, ?x2071) *> conf = 0.20 ranks of expected_values: 4 EVAL 01m42d0 artists! 0161rf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 109.000 0.200 http://example.org/music/genre/artists #19728-0bvg70 PRED entity: 0bvg70 PRED relation: profession PRED expected values: 03gjzk => 86 concepts (58 used for prediction) PRED predicted values (max 10 best out of 54): 03gjzk (0.85 #3247, 0.84 #2512, 0.84 #2659), 02hrh1q (0.69 #5745, 0.68 #5010, 0.68 #5598), 02krf9 (0.56 #613, 0.34 #2671, 0.31 #172), 0cbd2 (0.24 #153, 0.22 #5151, 0.21 #2946), 018gz8 (0.17 #5160, 0.15 #3543, 0.15 #309), 09jwl (0.17 #7516, 0.17 #4574, 0.17 #4868), 0dz3r (0.15 #2207, 0.11 #6912, 0.10 #7059), 0np9r (0.14 #607, 0.12 #2665, 0.12 #460), 0kyk (0.12 #5173, 0.10 #469, 0.10 #763), 0nbcg (0.12 #2235, 0.12 #6940, 0.12 #7529) >> Best rule #3247 for best value: >> intensional similarity = 2 >> extensional distance = 249 >> proper extension: 0grwj; 05g8ky; 0c4f4; 04n7njg; 01t6b4; 0bg539; 03cs_z7; 07s6tbm; 01pw2f1; 03jldb; ... >> query: (?x5659, 03gjzk) <- program(?x5659, ?x7813), profession(?x5659, ?x319) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bvg70 profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 58.000 0.849 http://example.org/people/person/profession #19727-027l4q PRED entity: 027l4q PRED relation: place_of_death! PRED expected values: 02j4sk => 73 concepts (51 used for prediction) PRED predicted values (max 10 best out of 711): 02fn5 (0.25 #174, 0.20 #926, 0.12 #1678), 0g72r (0.25 #699, 0.12 #2203, 0.04 #2956), 02tn0_ (0.25 #508, 0.12 #2012, 0.04 #2765), 02qdymm (0.20 #1369, 0.12 #2121, 0.04 #2874), 0bm9xk (0.20 #1366, 0.12 #2118, 0.04 #2871), 015rhv (0.20 #839, 0.12 #1591, 0.04 #2344), 0b82vw (0.04 #2321, 0.03 #3074, 0.02 #3828), 034qt_ (0.04 #3006, 0.03 #3759, 0.02 #4513), 02t0n9 (0.04 #2999, 0.03 #3752, 0.02 #4506), 05h7tk (0.04 #2986, 0.03 #3739, 0.02 #4493) >> Best rule #174 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 01c40n; >> query: (?x10298, 02fn5) <- contains(?x11940, ?x10298), contains(?x1523, ?x10298), contains(?x1227, ?x10298), ?x1227 = 01n7q, ?x11940 = 0k_s5, ?x1523 = 030qb3t >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027l4q place_of_death! 02j4sk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 51.000 0.250 http://example.org/people/deceased_person/place_of_death #19726-0165v PRED entity: 0165v PRED relation: organization PRED expected values: 04k4l => 127 concepts (119 used for prediction) PRED predicted values (max 10 best out of 18): 04k4l (0.56 #4, 0.55 #1698, 0.48 #290), 018cqq (0.55 #1698, 0.43 #143, 0.42 #121), 0b6css (0.55 #1698, 0.40 #538, 0.38 #142), 01rz1 (0.45 #111, 0.40 #133, 0.39 #67), 0_2v (0.42 #113, 0.40 #135, 0.40 #201), 041288 (0.34 #875, 0.33 #742, 0.32 #897), 02jxk (0.32 #2185, 0.31 #134, 0.31 #68), 0gkjy (0.32 #2185, 0.30 #733, 0.28 #1132), 0j7v_ (0.32 #2185, 0.26 #159, 0.26 #864), 059dn (0.32 #2185, 0.16 #2615, 0.14 #147) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 07twz; >> query: (?x9816, 04k4l) <- adjoins(?x9816, ?x583), ?x583 = 015fr, contains(?x7273, ?x9816) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0165v organization 04k4l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 119.000 0.556 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #19725-07w0v PRED entity: 07w0v PRED relation: major_field_of_study PRED expected values: 0jjw 0w7s => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 94): 05qjt (0.53 #839, 0.53 #735, 0.48 #1255), 062z7 (0.50 #1269, 0.44 #853, 0.42 #2933), 02ky346 (0.50 #636, 0.31 #1260, 0.28 #844), 05qfh (0.44 #1275, 0.44 #859, 0.44 #755), 037mh8 (0.43 #469, 0.37 #1301, 0.33 #885), 0fdys (0.41 #758, 0.37 #1278, 0.35 #862), 04sh3 (0.35 #788, 0.33 #892, 0.31 #1308), 01540 (0.33 #1295, 0.33 #879, 0.32 #2959), 06ms6 (0.33 #117, 0.30 #1261, 0.30 #533), 04_tv (0.33 #115, 0.19 #1051, 0.18 #4892) >> Best rule #839 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0kz2w; 07vfj; 0cwx_; >> query: (?x1011, 05qjt) <- student(?x1011, ?x400), organization(?x1011, ?x5487), currency(?x1011, ?x170), ?x5487 = 034h1h >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #545 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 0143hl; 027ydt; *> query: (?x1011, 0jjw) <- major_field_of_study(?x1011, ?x10391), school_type(?x1011, ?x1507), ?x10391 = 02jfc *> conf = 0.19 ranks of expected_values: 26, 46 EVAL 07w0v major_field_of_study 0w7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 136.000 136.000 0.535 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07w0v major_field_of_study 0jjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 136.000 136.000 0.535 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19724-02xnjd PRED entity: 02xnjd PRED relation: award PRED expected values: 07bdd_ => 115 concepts (90 used for prediction) PRED predicted values (max 10 best out of 349): 07bdd_ (0.60 #6969, 0.57 #8187, 0.53 #10217), 05p1dby (0.43 #3357, 0.41 #7011, 0.40 #8229), 0gq9h (0.42 #7793, 0.37 #19568, 0.37 #18349), 09sb52 (0.39 #22374, 0.33 #1665, 0.32 #2477), 040njc (0.39 #7723, 0.34 #7317, 0.34 #8535), 0gr42 (0.33 #117, 0.15 #19897, 0.15 #18678), 02x1z2s (0.33 #201, 0.09 #8322, 0.08 #7104), 0gq_d (0.33 #224, 0.07 #3473, 0.05 #2254), 018wng (0.33 #42, 0.05 #2072, 0.05 #2478), 0gvx_ (0.33 #188, 0.05 #2218, 0.05 #2624) >> Best rule #6969 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 05xbx; >> query: (?x7976, 07bdd_) <- award_nominee(?x1914, ?x7976), company(?x3960, ?x1914), production_companies(?x5520, ?x1914), nominated_for(?x9781, ?x5520) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xnjd award 07bdd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 90.000 0.602 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19723-011lpr PRED entity: 011lpr PRED relation: nationality PRED expected values: 09c7w0 => 95 concepts (89 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.87 #501, 0.86 #701, 0.85 #1703), 07ssc (0.40 #7337, 0.39 #7235, 0.16 #315), 01n7q (0.33 #7032, 0.33 #6630), 0kpys (0.32 #3513), 02jx1 (0.12 #633, 0.11 #333, 0.10 #4854), 06c1y (0.08 #139, 0.06 #239, 0.05 #339), 03rk0 (0.06 #6676, 0.06 #6776, 0.06 #7583), 0d060g (0.06 #1809, 0.05 #2113, 0.05 #2416), 0f8l9c (0.04 #622, 0.03 #3233, 0.03 #2832), 0345h (0.03 #3442, 0.03 #3242, 0.03 #2740) >> Best rule #501 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 04nw9; 01rgcg; 02q5xsx; 024swd; 02kmx6; 06z4wj; 06vqdf; 01kkx2; >> query: (?x13880, 09c7w0) <- profession(?x13880, ?x1041), ?x1041 = 03gjzk, place_of_death(?x13880, ?x9405), gender(?x13880, ?x231) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011lpr nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 89.000 0.872 http://example.org/people/person/nationality #19722-07pd_j PRED entity: 07pd_j PRED relation: film_release_region PRED expected values: 05r4w 0b90_r 0d0vqn 015fr 03gj2 01mjq => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 108): 0d0vqn (0.89 #603, 0.89 #454, 0.86 #7), 03gj2 (0.84 #24, 0.84 #471, 0.77 #620), 015fr (0.84 #17, 0.77 #464, 0.70 #613), 05r4w (0.84 #449, 0.81 #2, 0.80 #598), 035qy (0.83 #32, 0.78 #479, 0.67 #628), 0b90_r (0.80 #4, 0.69 #451, 0.62 #600), 0d060g (0.75 #6, 0.74 #453, 0.67 #602), 03rt9 (0.72 #15, 0.67 #462, 0.59 #611), 01mjq (0.58 #42, 0.55 #489, 0.47 #638), 04gzd (0.58 #10, 0.54 #457, 0.40 #606) >> Best rule #603 for best value: >> intensional similarity = 3 >> extensional distance = 282 >> proper extension: 0m2kd; 0gx9rvq; 01c22t; 0cz8mkh; 0j6b5; 085ccd; 07f_7h; 0g5879y; 0kv238; 0879bpq; ... >> query: (?x6684, 0d0vqn) <- film_release_region(?x6684, ?x142), film_crew_role(?x6684, ?x137), ?x142 = 0jgd >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 9 EVAL 07pd_j film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 71.000 71.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07pd_j film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07pd_j film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07pd_j film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07pd_j film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07pd_j film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19721-01cx_ PRED entity: 01cx_ PRED relation: source PRED expected values: 0jbk9 => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #95, 0.92 #114, 0.90 #126) >> Best rule #95 for best value: >> intensional similarity = 2 >> extensional distance = 179 >> proper extension: 0r066; >> query: (?x3052, 0jbk9) <- place_of_birth(?x1871, ?x3052), county(?x3052, ?x7309) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cx_ source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.934 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #19720-0154j PRED entity: 0154j PRED relation: exported_to! PRED expected values: 06q1r => 179 concepts (134 used for prediction) PRED predicted values (max 10 best out of 143): 06q1r (0.28 #334, 0.25 #974, 0.24 #1610), 04sj3 (0.24 #576, 0.22 #345, 0.18 #692), 05r4w (0.20 #3196, 0.15 #1797, 0.12 #2965), 0h3y (0.19 #527, 0.18 #936, 0.17 #296), 0jdd (0.19 #614, 0.15 #847, 0.14 #1079), 0j4b (0.19 #625, 0.15 #916, 0.12 #858), 0ctw_b (0.19 #536, 0.14 #420, 0.11 #305), 0d05w3 (0.19 #3226, 0.07 #2295, 0.07 #2235), 0l3h (0.14 #563, 0.14 #447, 0.14 #679), 07dzf (0.13 #1834, 0.11 #96, 0.11 #38) >> Best rule #334 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 05r4w; 0jgd; 0d0vqn; 05qhw; 03gj2; 03spz; >> query: (?x172, 06q1r) <- film_release_region(?x1919, ?x172), film_release_region(?x1228, ?x172), ?x1919 = 0_7w6, ?x1228 = 05z_kps >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0154j exported_to! 06q1r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 134.000 0.278 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #19719-01nds PRED entity: 01nds PRED relation: split_to PRED expected values: 01nds => 143 concepts (58 used for prediction) PRED predicted values (max 10 best out of 4): 09f2j (0.06 #621), 04n7ps6 (0.02 #1506, 0.01 #1605), 08z129 (0.01 #1764, 0.01 #1859), 07vfz (0.01 #1889) >> Best rule #621 for best value: >> intensional similarity = 7 >> extensional distance = 15 >> proper extension: 016tt2; 01bzw5; 024rgt; 0cjdk; 09f2j; 05cwl_; 01w5gp; 02gnmp; 06b7s9; 03b8c4; >> query: (?x11304, 09f2j) <- organization(?x4682, ?x11304), citytown(?x11304, ?x1658), citytown(?x11304, ?x1523), ?x1523 = 030qb3t, featured_film_locations(?x97, ?x1658), month(?x1658, ?x1459), origin(?x442, ?x1658) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nds split_to 01nds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 58.000 0.059 http://example.org/dataworld/gardening_hint/split_to #19718-06hgj PRED entity: 06hgj PRED relation: student! PRED expected values: 0bwfn 021q2j => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 228): 0dy04 (0.33 #1125, 0.25 #598, 0.06 #1652), 03ksy (0.25 #106, 0.17 #1160, 0.09 #3795), 0bwfn (0.25 #802, 0.17 #1329, 0.05 #9761), 07tds (0.25 #676, 0.17 #1203, 0.03 #23198), 011xy1 (0.22 #2426, 0.04 #3480, 0.03 #4007), 01w5m (0.17 #2740, 0.17 #1159, 0.09 #4848), 07tg4 (0.12 #3248, 0.06 #1667, 0.05 #11682), 07tgn (0.11 #2125, 0.08 #3179, 0.08 #5287), 0373qt (0.11 #2434, 0.06 #1907, 0.03 #4015), 065y4w7 (0.11 #2649, 0.05 #4757, 0.04 #15299) >> Best rule #1125 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0683n; >> query: (?x8452, 0dy04) <- profession(?x8452, ?x353), influenced_by(?x8452, ?x7296), ?x7296 = 04hcw, gender(?x8452, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #802 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0b78hw; 032r1; *> query: (?x8452, 0bwfn) <- profession(?x8452, ?x353), influenced_by(?x8452, ?x7296), location(?x8452, ?x1591), influenced_by(?x6723, ?x8452), ?x7296 = 04hcw *> conf = 0.25 ranks of expected_values: 3 EVAL 06hgj student! 021q2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 117.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06hgj student! 0bwfn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 117.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #19717-0bcndz PRED entity: 0bcndz PRED relation: nominated_for! PRED expected values: 0gs9p => 125 concepts (117 used for prediction) PRED predicted values (max 10 best out of 208): 0p9sw (0.77 #1892, 0.68 #19626, 0.67 #19864), 0gs9p (0.75 #1716, 0.75 #1479, 0.55 #2190), 027c95y (0.68 #19626, 0.67 #19864, 0.67 #13472), 019f4v (0.62 #53, 0.53 #1470, 0.53 #1707), 0k611 (0.57 #1725, 0.55 #1488, 0.42 #2199), 0gr4k (0.51 #1679, 0.49 #1442, 0.41 #733), 040njc (0.49 #1661, 0.49 #1424, 0.28 #5679), 0gqyl (0.48 #550, 0.32 #786, 0.31 #1495), 04kxsb (0.47 #1510, 0.45 #1747, 0.28 #1274), 0gqy2 (0.43 #592, 0.40 #1774, 0.39 #1537) >> Best rule #1892 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 0c5qvw; >> query: (?x1745, ?x500) <- award(?x1745, ?x591), award(?x1745, ?x500), ?x591 = 0f4x7, ceremony(?x500, ?x78) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1716 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 0c5qvw; *> query: (?x1745, 0gs9p) <- award(?x1745, ?x591), award(?x1745, ?x500), ?x591 = 0f4x7, ceremony(?x500, ?x78) *> conf = 0.75 ranks of expected_values: 2 EVAL 0bcndz nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 117.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19716-04gzd PRED entity: 04gzd PRED relation: film_release_region! PRED expected values: 0c0nhgv 0fpkhkz 0gxtknx 09146g 0gtsxr4 0gyfp9c 0gjc4d3 09g7vfw 02vr3gz 0gjcrrw 062zm5h 0dll_t2 0fphf3v 0bmfnjs => 160 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1547): 017jd9 (0.90 #1677, 0.90 #4029, 0.86 #13437), 062zm5h (0.85 #14670, 0.84 #1734, 0.84 #11142), 01fmys (0.84 #1375, 0.78 #3727, 0.73 #10783), 0bh8yn3 (0.84 #1340, 0.72 #3692, 0.69 #11924), 02vr3gz (0.81 #1571, 0.78 #3923, 0.69 #14507), 09g7vfw (0.81 #1523, 0.75 #14459, 0.75 #13283), 02fqrf (0.81 #1534, 0.75 #3886, 0.71 #10942), 0dll_t2 (0.81 #1811, 0.73 #14747, 0.71 #12395), 04w7rn (0.81 #1322, 0.70 #3674, 0.69 #10730), 03qnc6q (0.77 #1435, 0.75 #3787, 0.71 #12019) >> Best rule #1677 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 03rjj; 0d060g; 0d0vqn; 0chghy; 047lj; 05qhw; ... >> query: (?x344, 017jd9) <- film_release_region(?x3217, ?x344), film_release_region(?x1263, ?x344), ?x3217 = 0gffmn8, ?x1263 = 0dgst_d >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #14670 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 0154j; 03_3d; 015fr; 09pmkv; 07ylj; 05qx1; 05v10; 02vzc; 03rj0; 03ryn; ... *> query: (?x344, 062zm5h) <- film_release_region(?x3217, ?x344), film_release_region(?x2155, ?x344), executive_produced_by(?x3217, ?x14126), ?x2155 = 0407yfx *> conf = 0.85 ranks of expected_values: 2, 5, 6, 8, 13, 17, 28, 32, 47, 51, 57, 77, 78, 219 EVAL 04gzd film_release_region! 0bmfnjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 0fphf3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 0dll_t2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 062zm5h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 0gjcrrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 02vr3gz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 09g7vfw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 0gjc4d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 0gyfp9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 0gtsxr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 09146g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 0gxtknx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 0fpkhkz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04gzd film_release_region! 0c0nhgv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 160.000 94.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19715-02bm8 PRED entity: 02bm8 PRED relation: featured_film_locations! PRED expected values: 026qnh6 => 92 concepts (34 used for prediction) PRED predicted values (max 10 best out of 85): 09txzv (0.17 #848, 0.14 #2322, 0.14 #1585), 060v34 (0.17 #772, 0.14 #2246, 0.14 #1509), 0bbw2z6 (0.14 #1826, 0.07 #5511, 0.04 #9200), 05pt0l (0.14 #2757, 0.04 #4968, 0.02 #7182), 02b61v (0.14 #2644, 0.04 #4855, 0.02 #7069), 0dfw0 (0.14 #2573, 0.04 #4784, 0.02 #6998), 026qnh6 (0.14 #2565, 0.04 #4776, 0.02 #6990), 0x25q (0.14 #2432, 0.04 #4643, 0.02 #6857), 0ddt_ (0.14 #2425, 0.04 #4636, 0.02 #6850), 07cz2 (0.14 #2413, 0.04 #4624, 0.02 #6838) >> Best rule #848 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 03kjh; >> query: (?x14084, 09txzv) <- capital(?x12908, ?x14084), jurisdiction_of_office(?x10118, ?x12908), contains(?x390, ?x12908), ?x10118 = 0p5vf, ?x390 = 0chghy >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #2565 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 0dp90; *> query: (?x14084, 026qnh6) <- capital(?x12908, ?x14084), adjoins(?x12125, ?x12908), state_province_region(?x10889, ?x12125), contains(?x12125, ?x8823), adjoins(?x12125, ?x12854), location(?x927, ?x12125), ?x12854 = 06mtq *> conf = 0.14 ranks of expected_values: 7 EVAL 02bm8 featured_film_locations! 026qnh6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 92.000 34.000 0.167 http://example.org/film/film/featured_film_locations #19714-0dmn0x PRED entity: 0dmn0x PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.81 #566, 0.81 #1127, 0.76 #1090), 02r96rf (0.74 #190, 0.72 #1123, 0.67 #562), 0dxtw (0.40 #1131, 0.40 #1094, 0.38 #198), 01vx2h (0.40 #199, 0.35 #1132, 0.33 #645), 01pvkk (0.28 #1096, 0.28 #1394, 0.28 #831), 02ynfr (0.25 #204, 0.19 #650, 0.18 #835), 0215hd (0.20 #57, 0.15 #1140, 0.14 #653), 02_n3z (0.16 #1, 0.11 #38, 0.09 #634), 02rh1dz (0.13 #1130, 0.12 #197, 0.11 #828), 089g0h (0.12 #1141, 0.12 #580, 0.12 #1402) >> Best rule #566 for best value: >> intensional similarity = 4 >> extensional distance = 437 >> proper extension: 025n07; 0cqr0q; >> query: (?x9893, 0ch6mp2) <- nominated_for(?x84, ?x9893), film_crew_role(?x9893, ?x1171), titles(?x732, ?x9893), ?x1171 = 09vw2b7 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dmn0x film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.813 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19713-02qlkc3 PRED entity: 02qlkc3 PRED relation: nationality PRED expected values: 09c7w0 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 18): 09c7w0 (0.83 #1101, 0.82 #801, 0.82 #1201), 02jx1 (0.10 #4033, 0.10 #5233, 0.10 #5934), 07ssc (0.08 #5616, 0.08 #3215, 0.08 #7216), 03rk0 (0.06 #8748, 0.05 #8948, 0.05 #7347), 0d060g (0.04 #2207, 0.04 #1007, 0.04 #6308), 0ctw_b (0.02 #127, 0.02 #227, 0.02 #327), 0chghy (0.02 #2210, 0.02 #3910, 0.02 #3610), 0f8l9c (0.02 #5923, 0.02 #3722, 0.02 #5222), 03_3d (0.02 #6307, 0.01 #9308, 0.01 #9510), 03rjj (0.02 #6206, 0.02 #3805, 0.02 #6606) >> Best rule #1101 for best value: >> intensional similarity = 3 >> extensional distance = 174 >> proper extension: 0f721s; 04rtpt; >> query: (?x6087, 09c7w0) <- program(?x6087, ?x8132), award(?x8132, ?x757), program(?x1762, ?x8132) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qlkc3 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.830 http://example.org/people/person/nationality #19712-01y8zd PRED entity: 01y8zd PRED relation: colors PRED expected values: 04mkbj => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 20): 01l849 (0.42 #101, 0.39 #41, 0.35 #61), 083jv (0.36 #1022, 0.36 #782, 0.36 #862), 01g5v (0.30 #4, 0.28 #964, 0.28 #984), 06fvc (0.20 #3, 0.17 #23, 0.16 #483), 03vtbc (0.20 #8, 0.17 #28, 0.06 #148), 019sc (0.18 #867, 0.18 #1027, 0.18 #967), 09ggk (0.17 #56, 0.13 #96, 0.10 #76), 04mkbj (0.12 #110, 0.11 #50, 0.10 #70), 03wkwg (0.11 #55, 0.09 #95, 0.06 #415), 038hg (0.09 #1032, 0.08 #632, 0.08 #972) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 03zw80; 01fsv9; >> query: (?x3091, 01l849) <- contains(?x279, ?x3091), colors(?x3091, ?x3364), service_language(?x3091, ?x254) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #110 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 03zw80; 01fsv9; *> query: (?x3091, 04mkbj) <- contains(?x279, ?x3091), colors(?x3091, ?x3364), service_language(?x3091, ?x254) *> conf = 0.12 ranks of expected_values: 8 EVAL 01y8zd colors 04mkbj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 102.000 102.000 0.417 http://example.org/education/educational_institution/colors #19711-02b25y PRED entity: 02b25y PRED relation: artists! PRED expected values: 064t9 => 140 concepts (82 used for prediction) PRED predicted values (max 10 best out of 249): 03_d0 (0.73 #7084, 0.37 #935, 0.26 #2163), 06by7 (0.64 #1558, 0.45 #12010, 0.44 #21), 064t9 (0.62 #4009, 0.50 #629, 0.48 #9238), 0gywn (0.34 #4055, 0.28 #9284, 0.27 #9591), 06j6l (0.33 #4045, 0.32 #9274, 0.31 #9581), 05bt6j (0.32 #660, 0.32 #1581, 0.30 #4040), 016clz (0.32 #313, 0.29 #4001, 0.28 #1542), 025sc50 (0.29 #4047, 0.25 #9276, 0.25 #9583), 0glt670 (0.28 #9266, 0.28 #9573, 0.27 #9880), 0dl5d (0.27 #327, 0.26 #942, 0.13 #2170) >> Best rule #7084 for best value: >> intensional similarity = 2 >> extensional distance = 188 >> proper extension: 0h08p; >> query: (?x2584, 03_d0) <- artists(?x888, ?x2584), major_field_of_study(?x2767, ?x888) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #4009 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 01rm8b; 0fcsd; 01cblr; 0jltp; 012x1l; 027kwc; 0p8h0; *> query: (?x2584, 064t9) <- award(?x2584, ?x1389), artists(?x597, ?x2584), ?x1389 = 01c427 *> conf = 0.62 ranks of expected_values: 3 EVAL 02b25y artists! 064t9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 82.000 0.726 http://example.org/music/genre/artists #19710-01jdpf PRED entity: 01jdpf PRED relation: type_of_appearance! PRED expected values: 01vrz41 04sry 08cn_n => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 01jdpf type_of_appearance! 08cn_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance EVAL 01jdpf type_of_appearance! 04sry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance EVAL 01jdpf type_of_appearance! 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance #19709-01x4wq PRED entity: 01x4wq PRED relation: colors PRED expected values: 083jv => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.83 #1011, 0.83 #1471, 0.80 #1528), 01g5v (0.58 #879, 0.53 #1664, 0.52 #2372), 019sc (0.55 #2243, 0.52 #1092, 0.48 #598), 038hg (0.33 #69, 0.31 #2082, 0.24 #1681), 01l849 (0.31 #2082, 0.24 #1681, 0.23 #1469), 02rnmb (0.31 #2082, 0.23 #1469, 0.17 #2428), 036k5h (0.24 #1681, 0.18 #1546, 0.17 #2428), 088fh (0.24 #1681, 0.17 #2428, 0.15 #2217), 06kqt3 (0.23 #1469, 0.17 #2428, 0.15 #2217), 0jc_p (0.18 #1546, 0.17 #2428, 0.15 #2217) >> Best rule #1011 for best value: >> intensional similarity = 13 >> extensional distance = 50 >> proper extension: 019lxm; >> query: (?x6064, 083jv) <- position(?x6064, ?x530), position(?x6064, ?x60), colors(?x6064, ?x1101), ?x60 = 02nzb8, ?x530 = 02_j1w, team(?x2118, ?x6064), colors(?x481, ?x1101), colors(?x10142, ?x1101), colors(?x4306, ?x1101), colors(?x1297, ?x1101), ?x4306 = 037mp6, ?x10142 = 02r7lqg, ?x1297 = 03x746 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x4wq colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.827 http://example.org/sports/sports_team/colors #19708-0336mc PRED entity: 0336mc PRED relation: award_winner! PRED expected values: 099tbz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 224): 099tbz (0.67 #488, 0.50 #57, 0.14 #19830), 09td7p (0.37 #22848, 0.36 #34920, 0.36 #15519), 05p09zm (0.17 #555, 0.17 #124, 0.14 #19830), 05zr6wv (0.17 #449, 0.17 #18, 0.14 #19830), 02w9sd7 (0.17 #598, 0.17 #167, 0.14 #19830), 07cbcy (0.17 #509, 0.17 #78, 0.14 #19830), 057xs89 (0.17 #590, 0.17 #159, 0.14 #19830), 09cm54 (0.17 #527, 0.17 #96, 0.14 #19830), 05ztrmj (0.17 #613, 0.17 #182, 0.14 #19830), 07bdd_ (0.17 #496, 0.17 #65, 0.14 #19830) >> Best rule #488 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01q6bg; >> query: (?x8764, 099tbz) <- award_winner(?x8764, ?x8765), award_winner(?x8764, ?x4544), profession(?x4544, ?x1032), ?x8765 = 08qxx9 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0336mc award_winner! 099tbz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #19707-0p7qm PRED entity: 0p7qm PRED relation: language PRED expected values: 02h40lc => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 31): 02h40lc (0.94 #1873, 0.94 #1577, 0.89 #700), 064_8sq (0.18 #137, 0.17 #21, 0.16 #311), 04306rv (0.17 #121, 0.17 #5, 0.10 #295), 0jzc (0.17 #19, 0.11 #135, 0.03 #309), 06nm1 (0.17 #11, 0.10 #69, 0.10 #243), 06b_j (0.17 #22, 0.08 #138, 0.08 #80), 02bjrlw (0.17 #1, 0.08 #59, 0.07 #175), 07zrf (0.17 #3, 0.04 #119, 0.01 #177), 012w70 (0.17 #12, 0.03 #419, 0.03 #70), 03_9r (0.06 #126, 0.05 #68, 0.05 #417) >> Best rule #1873 for best value: >> intensional similarity = 3 >> extensional distance = 1215 >> proper extension: 0gj9qxr; 0413cff; 03_wm6; 07s3m4g; 02pcq92; >> query: (?x2924, 02h40lc) <- currency(?x2924, ?x170), language(?x2924, ?x2890), genre(?x2924, ?x225) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p7qm language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.939 http://example.org/film/film/language #19706-0cc97st PRED entity: 0cc97st PRED relation: language PRED expected values: 02h40lc => 72 concepts (68 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.94 #593, 0.92 #356, 0.92 #1606), 06nm1 (0.18 #11, 0.18 #129, 0.15 #483), 064_8sq (0.17 #199, 0.13 #793, 0.13 #2526), 04306rv (0.12 #182, 0.10 #596, 0.09 #776), 06mp7 (0.12 #134, 0.02 #728, 0.02 #787), 05qqm (0.09 #41, 0.04 #218, 0.01 #692), 0880p (0.09 #46, 0.02 #282), 06b_j (0.09 #377, 0.07 #1627, 0.07 #614), 02bjrlw (0.07 #60, 0.06 #592, 0.06 #355), 0653m (0.07 #71, 0.06 #425, 0.04 #189) >> Best rule #593 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 03ckwzc; 0963mq; >> query: (?x5713, 02h40lc) <- crewmember(?x5713, ?x1933), film_crew_role(?x5713, ?x2095), ?x2095 = 0dxtw, country(?x5713, ?x94) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cc97st language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 68.000 0.936 http://example.org/film/film/language #19705-05563d PRED entity: 05563d PRED relation: artists! PRED expected values: 061fhg 01fh36 => 66 concepts (28 used for prediction) PRED predicted values (max 10 best out of 294): 01fh36 (0.85 #2530, 0.41 #1918, 0.38 #2836), 064t9 (0.77 #3681, 0.75 #3987, 0.70 #2764), 06j6l (0.61 #4939, 0.47 #5552, 0.44 #4020), 0gywn (0.44 #5562, 0.42 #4949, 0.35 #3724), 016jny (0.40 #99, 0.33 #709, 0.29 #1935), 016jhr (0.40 #11, 0.33 #621, 0.26 #1527), 05bt6j (0.38 #2792, 0.31 #4015, 0.30 #6160), 0155w (0.37 #4997, 0.33 #3162, 0.32 #5610), 02x8m (0.35 #3686, 0.34 #3992, 0.33 #3380), 02k_kn (0.33 #366, 0.29 #976, 0.16 #4038) >> Best rule #2530 for best value: >> intensional similarity = 10 >> extensional distance = 32 >> proper extension: 032nwy; 01lvcs1; >> query: (?x3516, 01fh36) <- artists(?x2809, ?x3516), artists(?x505, ?x3516), ?x505 = 03_d0, parent_genre(?x497, ?x2809), artists(?x2809, ?x11186), artists(?x2809, ?x7221), artists(?x2809, ?x4783), ?x7221 = 0191h5, ?x4783 = 047cx, ?x11186 = 01304j >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 77 EVAL 05563d artists! 01fh36 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 28.000 0.853 http://example.org/music/genre/artists EVAL 05563d artists! 061fhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 66.000 28.000 0.853 http://example.org/music/genre/artists #19704-087pfc PRED entity: 087pfc PRED relation: language PRED expected values: 05zjd => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 32): 064_8sq (0.20 #20, 0.17 #307, 0.17 #134), 04h9h (0.20 #41, 0.08 #98, 0.06 #212), 06nm1 (0.17 #123, 0.15 #296, 0.11 #354), 04306rv (0.17 #118, 0.12 #1043, 0.11 #1221), 06b_j (0.17 #192, 0.08 #78, 0.07 #946), 02bjrlw (0.09 #1040, 0.09 #288, 0.08 #346), 0jzc (0.07 #246, 0.07 #305, 0.05 #363), 0653m (0.07 #297, 0.06 #355, 0.06 #414), 012w70 (0.06 #125, 0.04 #589, 0.03 #936), 06mp7 (0.06 #128, 0.03 #534, 0.01 #477) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02nx2k; >> query: (?x9174, 064_8sq) <- film(?x5485, ?x9174), titles(?x2480, ?x9174), ?x5485 = 01pk8v, production_companies(?x9174, ?x541) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #369 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 63 *> proper extension: 0gtsx8c; 02vxq9m; 0djb3vw; 0jjy0; 04w7rn; 09146g; 01fmys; 07f_7h; 06w839_; 047fjjr; ... *> query: (?x9174, 05zjd) <- film_release_region(?x9174, ?x1355), film_release_region(?x9174, ?x608), ?x608 = 02k54, language(?x9174, ?x254), origin(?x8600, ?x1355) *> conf = 0.03 ranks of expected_values: 12 EVAL 087pfc language 05zjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 72.000 72.000 0.200 http://example.org/film/film/language #19703-05l71 PRED entity: 05l71 PRED relation: company! PRED expected values: 02md_2 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 49): 014l7h (0.50 #267, 0.50 #219, 0.40 #2296), 060c4 (0.50 #382, 0.48 #2297, 0.48 #2251), 0dq3c (0.40 #2296, 0.40 #381, 0.40 #145), 02k13d (0.40 #2296, 0.33 #252, 0.33 #204), 02y6fz (0.33 #25, 0.20 #121, 0.04 #1141), 06b1q (0.25 #291, 0.25 #53, 0.20 #385), 02g_6x (0.25 #96, 0.25 #55, 0.12 #293), 0g686w (0.18 #571, 0.14 #766, 0.05 #2536), 0130xz (0.18 #570, 0.14 #765, 0.03 #2535), 01cpkt (0.18 #568, 0.14 #763, 0.03 #2533) >> Best rule #267 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0gsgr; >> query: (?x4170, 014l7h) <- company(?x7749, ?x4170), nationality(?x7749, ?x94), ?x94 = 09c7w0, inductee(?x14281, ?x7749), company(?x7749, ?x2062), ?x2062 = 09d5h >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #786 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 13 *> proper extension: 025_64l; *> query: (?x4170, 02md_2) <- position(?x4170, ?x3346), position(?x4170, ?x2312), position(?x4170, ?x1517), position(?x4170, ?x935), position_s(?x4170, ?x1240), ?x3346 = 02g_7z, ?x935 = 06b1q, sport(?x4170, ?x1083), ?x1517 = 02g_6j, ?x2312 = 02qpbqj *> conf = 0.07 ranks of expected_values: 36 EVAL 05l71 company! 02md_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 102.000 102.000 0.500 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #19702-0lpjn PRED entity: 0lpjn PRED relation: place_of_birth PRED expected values: 088cp => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 52): 0chgzm (0.33 #310, 0.25 #1014), 02_286 (0.07 #20438, 0.07 #40156, 0.07 #49312), 04jpl (0.07 #2120, 0.04 #1416, 0.02 #3529), 030qb3t (0.04 #1462, 0.04 #10616, 0.04 #7800), 02dtg (0.04 #1418, 0.01 #40851, 0.01 #48598), 0g5rg (0.04 #1971), 0b_yz (0.04 #1841), 0f2nf (0.04 #1755), 0d9y6 (0.04 #1601), 06y57 (0.04 #1588) >> Best rule #310 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0154qm; >> query: (?x2805, 0chgzm) <- film(?x2805, ?x2458), ?x2458 = 021y7yw, award_winner(?x2805, ?x748) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0lpjn place_of_birth 088cp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 97.000 0.333 http://example.org/people/person/place_of_birth #19701-03zqc1 PRED entity: 03zqc1 PRED relation: profession PRED expected values: 02hrh1q => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.91 #1215, 0.89 #4965, 0.89 #4065), 01d_h8 (0.42 #1956, 0.40 #2256, 0.37 #2106), 03gjzk (0.33 #6466, 0.30 #466, 0.29 #2116), 0dxtg (0.30 #6464, 0.27 #1664, 0.27 #10665), 0d1pc (0.28 #9151, 0.23 #952, 0.20 #802), 0np9r (0.28 #9151, 0.20 #2872, 0.20 #6022), 09jwl (0.25 #1970, 0.22 #3320, 0.21 #4670), 02jknp (0.24 #1658, 0.21 #8708, 0.21 #10059), 0dz3r (0.17 #1952, 0.14 #2252, 0.13 #3602), 0cbd2 (0.17 #1507, 0.17 #1807, 0.14 #10658) >> Best rule #1215 for best value: >> intensional similarity = 2 >> extensional distance = 174 >> proper extension: 01m65sp; 039crh; 01jb26; 044mfr; 0143wl; 01kmd4; 01p47r; 01507p; >> query: (?x516, 02hrh1q) <- actor(?x2078, ?x516), participant(?x5925, ?x516) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03zqc1 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.909 http://example.org/people/person/profession #19700-02hp70 PRED entity: 02hp70 PRED relation: category PRED expected values: 08mbj5d => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #9, 0.90 #31, 0.90 #25) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 167 >> proper extension: 03fcbb; >> query: (?x11397, 08mbj5d) <- institution(?x1771, ?x11397), ?x1771 = 019v9k, contains(?x94, ?x11397), ?x94 = 09c7w0 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hp70 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.905 http://example.org/common/topic/webpage./common/webpage/category #19699-0190_q PRED entity: 0190_q PRED relation: parent_genre! PRED expected values: 0g_bh => 65 concepts (25 used for prediction) PRED predicted values (max 10 best out of 273): 0xv2x (0.50 #390, 0.50 #126, 0.29 #1180), 03xnwz (0.50 #291, 0.43 #817, 0.25 #27), 0xjl2 (0.50 #301, 0.43 #827, 0.25 #37), 01_bkd (0.50 #309, 0.29 #1099, 0.29 #835), 029fbr (0.50 #415, 0.29 #941, 0.25 #151), 0bt7w (0.50 #352, 0.29 #878, 0.25 #88), 05jg58 (0.50 #97, 0.29 #1151, 0.25 #361), 01gbcf (0.43 #1058, 0.29 #794, 0.25 #268), 0g_bh (0.40 #634, 0.29 #1161, 0.25 #371), 0hdf8 (0.40 #584, 0.14 #1638, 0.14 #1111) >> Best rule #390 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 016clz; 05r6t; >> query: (?x2808, 0xv2x) <- parent_genre(?x497, ?x2808), artists(?x2808, ?x8215), artists(?x2808, ?x7896), artists(?x2808, ?x6234), artists(?x2808, ?x1684), ?x8215 = 04_jsg, ?x6234 = 0l8g0, parent_genre(?x2808, ?x1380), group(?x1750, ?x1684), category(?x7896, ?x134), ?x1750 = 02hnl >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #634 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 08jyyk; 01fh36; *> query: (?x2808, 0g_bh) <- parent_genre(?x497, ?x2808), artists(?x2808, ?x9999), artists(?x2808, ?x9196), artists(?x2808, ?x8215), group(?x1166, ?x9999), ?x9196 = 0qmpd, category(?x8215, ?x134), ?x1166 = 05148p4, ?x134 = 08mbj5d *> conf = 0.40 ranks of expected_values: 9 EVAL 0190_q parent_genre! 0g_bh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 65.000 25.000 0.500 http://example.org/music/genre/parent_genre #19698-0drtv8 PRED entity: 0drtv8 PRED relation: award_winner PRED expected values: 016ywr 092kgw 013pk3 059fjj => 45 concepts (34 used for prediction) PRED predicted values (max 10 best out of 2336): 070j61 (0.40 #4168, 0.17 #7226, 0.15 #16411), 0m66w (0.40 #3962, 0.17 #7020, 0.15 #16205), 01ycbq (0.33 #14052, 0.33 #4867, 0.22 #12521), 01zfmm (0.33 #18361, 0.29 #27551, 0.29 #6116), 06pj8 (0.33 #18361, 0.29 #17130, 0.29 #6116), 0bwh6 (0.33 #18361, 0.29 #6116, 0.27 #52023), 02z2xdf (0.33 #18361, 0.29 #6116, 0.25 #45903), 03qmx_f (0.33 #18361, 0.29 #6116, 0.25 #45903), 021yc7p (0.33 #215, 0.29 #7864, 0.22 #9395), 0b1f49 (0.33 #18361, 0.29 #6116, 0.20 #39782) >> Best rule #4168 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 09p3h7; 09pnw5; >> query: (?x4781, 070j61) <- honored_for(?x4781, ?x9133), honored_for(?x4781, ?x5070), award_winner(?x4781, ?x9743), ceremony(?x2585, ?x4781), award_nominee(?x9743, ?x541), prequel(?x9133, ?x4167), award(?x5070, ?x834), ?x2585 = 054ks3, produced_by(?x1318, ?x9743), nominated_for(?x298, ?x5070), film_crew_role(?x5070, ?x137), film_release_region(?x5070, ?x1003), production_companies(?x9133, ?x382), film_release_region(?x5849, ?x1003), olympics(?x1003, ?x418), ?x5849 = 02h22 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #45903 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 68 *> proper extension: 0fzrtf; *> query: (?x4781, ?x5643) <- honored_for(?x4781, ?x9133), honored_for(?x4781, ?x5070), award_winner(?x4781, ?x9743), award_winner(?x4781, ?x9439), ceremony(?x746, ?x4781), award_nominee(?x9743, ?x541), award(?x5070, ?x834), student(?x1103, ?x9743), location(?x9743, ?x335), nationality(?x9743, ?x94), film(?x902, ?x9133), produced_by(?x1218, ?x9439), film_release_region(?x5070, ?x87), nominated_for(?x5643, ?x5070), award_winner(?x834, ?x157), award(?x3505, ?x834), ?x3505 = 0p_qr *> conf = 0.25 ranks of expected_values: 69, 226, 233, 842 EVAL 0drtv8 award_winner 059fjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 45.000 34.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0drtv8 award_winner 013pk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 45.000 34.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0drtv8 award_winner 092kgw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 45.000 34.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0drtv8 award_winner 016ywr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 45.000 34.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19697-03kbb8 PRED entity: 03kbb8 PRED relation: film PRED expected values: 0cmdwwg => 126 concepts (102 used for prediction) PRED predicted values (max 10 best out of 955): 0djb3vw (0.47 #3565, 0.12 #39207, 0.11 #26732), 034qzw (0.07 #2112, 0.03 #19933, 0.03 #25279), 013q07 (0.06 #2134, 0.03 #27084, 0.03 #28866), 01shy7 (0.05 #53885, 0.04 #66360, 0.04 #69924), 03z20c (0.05 #9383, 0.04 #11165, 0.03 #472), 016dj8 (0.05 #1109, 0.04 #2891, 0.03 #10020), 02qydsh (0.05 #1492, 0.03 #10403, 0.03 #12185), 04x4vj (0.05 #770, 0.03 #9681, 0.02 #11463), 011ycb (0.05 #853, 0.03 #2635, 0.03 #6200), 01gkp1 (0.05 #812, 0.03 #6159, 0.03 #9723) >> Best rule #3565 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 07nznf; 0q9kd; 0grwj; 04t2l2; 06dv3; 014zcr; 05ty4m; 0bxtg; 0147dk; 0c1pj; ... >> query: (?x7093, ?x542) <- participant(?x2626, ?x7093), film(?x7093, ?x385), produced_by(?x542, ?x7093) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #2905 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 07nznf; 0q9kd; 0grwj; 04t2l2; 06dv3; 014zcr; 05ty4m; 0bxtg; 0147dk; 0c1pj; ... *> query: (?x7093, 0cmdwwg) <- participant(?x2626, ?x7093), film(?x7093, ?x385), produced_by(?x542, ?x7093) *> conf = 0.01 ranks of expected_values: 655 EVAL 03kbb8 film 0cmdwwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 126.000 102.000 0.466 http://example.org/film/actor/film./film/performance/film #19696-04n7jdv PRED entity: 04n7jdv PRED relation: artists PRED expected values: 017j6 => 48 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1087): 017j6 (0.62 #5679, 0.57 #3523, 0.50 #4600), 011_vz (0.60 #1920, 0.50 #5153, 0.50 #843), 01shhf (0.57 #3025, 0.50 #5180, 0.43 #4103), 01j59b0 (0.57 #2622, 0.50 #4777, 0.43 #3700), 0150jk (0.57 #2203, 0.38 #4358, 0.36 #6516), 048tgl (0.57 #3065, 0.38 #5220, 0.29 #4143), 014_lq (0.57 #2633, 0.38 #4788, 0.29 #3711), 01vsxdm (0.57 #2257, 0.38 #4412, 0.29 #3335), 0ycp3 (0.57 #2766, 0.38 #4921, 0.29 #3844), 01518s (0.57 #3198, 0.25 #5353, 0.18 #7511) >> Best rule #5679 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 07s7gk6; >> query: (?x14409, 017j6) <- artists(?x14409, ?x3682), artists(?x14409, ?x2987), artists(?x14409, ?x970), ?x3682 = 04qmr, ?x2987 = 01vw20_, participant(?x970, ?x932), participant(?x1503, ?x970), profession(?x970, ?x524) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04n7jdv artists 017j6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 32.000 0.625 http://example.org/music/genre/artists #19695-06znpjr PRED entity: 06znpjr PRED relation: film_crew_role PRED expected values: 0d2b38 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 19): 01xy5l_ (0.67 #157, 0.33 #106, 0.33 #6), 02_n3z (0.50 #101, 0.50 #51, 0.45 #76), 01pvkk (0.50 #29, 0.36 #231, 0.33 #206), 089g0h (0.45 #84, 0.44 #160, 0.42 #109), 0d2b38 (0.45 #90, 0.44 #166, 0.33 #115), 02vs3x5 (0.33 #13, 0.13 #2729, 0.12 #736), 033smt (0.22 #168, 0.18 #92, 0.13 #2729), 05smlt (0.20 #60, 0.17 #110, 0.13 #2729), 094hwz (0.13 #2729, 0.12 #736, 0.12 #539), 0263ycg (0.13 #2729, 0.12 #736, 0.10 #2575) >> Best rule #157 for best value: >> intensional similarity = 7 >> extensional distance = 16 >> proper extension: 07_k0c0; 05pdd86; >> query: (?x7878, 01xy5l_) <- titles(?x2480, ?x7878), film_crew_role(?x7878, ?x1776), film_crew_role(?x7878, ?x468), ?x468 = 02r96rf, genre(?x7878, ?x258), film(?x710, ?x7878), ?x1776 = 020xn5 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #90 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 9 *> proper extension: 053rxgm; 05f4_n0; 01xvjb; *> query: (?x7878, 0d2b38) <- country(?x7878, ?x94), film_crew_role(?x7878, ?x4305), film_crew_role(?x7878, ?x3197), film_crew_role(?x7878, ?x468), ?x3197 = 02ynfr, ?x4305 = 0215hd, ?x468 = 02r96rf *> conf = 0.45 ranks of expected_values: 5 EVAL 06znpjr film_crew_role 0d2b38 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 113.000 113.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19694-02w9sd7 PRED entity: 02w9sd7 PRED relation: award! PRED expected values: 0z4s 015grj 0bwh6 048lv 01nwwl 0cj8x 0flw6 0gyx4 0zcbl 0k525 => 42 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2333): 0d6d2 (0.82 #9951, 0.77 #43138, 0.69 #36501), 09fb5 (0.82 #9951, 0.77 #43138, 0.69 #36501), 016ywr (0.82 #9951, 0.77 #43138, 0.69 #36501), 016yvw (0.82 #9951, 0.77 #43138, 0.69 #36501), 057176 (0.82 #9951, 0.77 #43138, 0.69 #36501), 03ds3 (0.82 #9951, 0.77 #43138, 0.69 #36501), 015grj (0.58 #3533, 0.17 #216, 0.13 #46456), 017149 (0.58 #3423, 0.17 #106, 0.08 #10057), 01fh9 (0.50 #491, 0.27 #10442, 0.25 #3808), 01ycbq (0.50 #3826, 0.13 #46456, 0.09 #16587) >> Best rule #9951 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 040njc; 03hkv_r; 0gq_v; 0f4x7; 0gr4k; 04dn09n; 094qd5; 0l8z1; 019f4v; 0gqwc; ... >> query: (?x3209, ?x157) <- nominated_for(?x3209, ?x2734), award_winner(?x3209, ?x157), award(?x262, ?x3209), ?x2734 = 05cvgl >> conf = 0.82 => this is the best rule for 6 predicted values *> Best rule #3533 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0bm7fy; *> query: (?x3209, 015grj) <- award(?x11364, ?x3209), award(?x4360, ?x3209), ?x11364 = 016ggh, award_winner(?x2177, ?x4360), film(?x4360, ?x2218) *> conf = 0.58 ranks of expected_values: 7, 16, 22, 37, 39, 40, 93, 147, 175, 229 EVAL 02w9sd7 award! 0k525 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 14.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02w9sd7 award! 0zcbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 42.000 14.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02w9sd7 award! 0gyx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 42.000 14.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02w9sd7 award! 0flw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 42.000 14.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02w9sd7 award! 0cj8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 42.000 14.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02w9sd7 award! 01nwwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 42.000 14.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02w9sd7 award! 048lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 42.000 14.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02w9sd7 award! 0bwh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 42.000 14.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02w9sd7 award! 015grj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 42.000 14.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02w9sd7 award! 0z4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 42.000 14.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19693-014l6_ PRED entity: 014l6_ PRED relation: film_distribution_medium PRED expected values: 02nxhr => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 3): 0735l (0.64 #39, 0.63 #55, 0.60 #51), 02nxhr (0.26 #53, 0.25 #49, 0.24 #37), 07z4p (0.02 #40, 0.02 #253, 0.02 #52) >> Best rule #39 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 0cp08zg; >> query: (?x3220, 0735l) <- country(?x3220, ?x94), language(?x3220, ?x90), nominated_for(?x856, ?x3220), film_distribution_medium(?x3220, ?x81) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #53 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 202 *> proper extension: 0522wp; *> query: (?x3220, 02nxhr) <- film_distribution_medium(?x3220, ?x81), film(?x5636, ?x3220), film_release_distribution_medium(?x54, ?x81) *> conf = 0.26 ranks of expected_values: 2 EVAL 014l6_ film_distribution_medium 02nxhr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #19692-0284b56 PRED entity: 0284b56 PRED relation: film_release_region PRED expected values: 0d060g => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 139): 0d0vqn (0.90 #1046, 0.89 #1565, 0.88 #2603), 0345h (0.90 #214, 0.80 #1598, 0.76 #2636), 06mkj (0.86 #1627, 0.85 #243, 0.84 #1108), 05r4w (0.86 #1559, 0.83 #1040, 0.81 #2597), 059j2 (0.86 #1596, 0.81 #2634, 0.79 #731), 02vzc (0.85 #756, 0.83 #1102, 0.81 #1621), 03h64 (0.85 #255, 0.82 #1639, 0.77 #2677), 03_3d (0.85 #179, 0.79 #1044, 0.74 #1563), 07ssc (0.85 #193, 0.78 #1577, 0.75 #2615), 0chghy (0.84 #1570, 0.78 #2608, 0.75 #2262) >> Best rule #1046 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 07l50vn; >> query: (?x5706, 0d0vqn) <- film_release_region(?x5706, ?x789), ?x789 = 0f8l9c, film_crew_role(?x5706, ?x137), honored_for(?x762, ?x5706) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1564 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 205 *> proper extension: 087wc7n; 0crfwmx; 0gtsxr4; 0gffmn8; 0fpgp26; *> query: (?x5706, 0d060g) <- film_release_region(?x5706, ?x789), film_release_region(?x5706, ?x151), ?x789 = 0f8l9c, film(?x156, ?x5706), ?x151 = 0b90_r *> conf = 0.75 ranks of expected_values: 16 EVAL 0284b56 film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 95.000 95.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19691-0jmh7 PRED entity: 0jmh7 PRED relation: colors PRED expected values: 083jv => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.85 #788, 0.69 #497, 0.67 #574), 01g5v (0.52 #751, 0.30 #964, 0.29 #925), 06fvc (0.40 #789, 0.38 #440, 0.37 #652), 01l849 (0.33 #210, 0.33 #58, 0.33 #39), 02rnmb (0.31 #508, 0.25 #585, 0.22 #222), 0jc_p (0.22 #43, 0.17 #62, 0.15 #707), 038hg (0.20 #12, 0.15 #707, 0.15 #921), 09ggk (0.17 #72, 0.15 #707, 0.15 #921), 03vtbc (0.16 #484, 0.16 #522, 0.16 #369), 088fh (0.15 #921, 0.15 #920, 0.14 #572) >> Best rule #788 for best value: >> intensional similarity = 10 >> extensional distance = 216 >> proper extension: 0263cyj; 03dkx; >> query: (?x10409, 083jv) <- sport(?x10409, ?x4833), colors(?x10409, ?x4557), colors(?x2171, ?x4557), colors(?x9835, ?x4557), colors(?x8037, ?x4557), colors(?x7499, ?x4557), ?x2171 = 01jq34, position(?x8037, ?x2918), ?x7499 = 0132_h, ?x9835 = 02hqt6 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jmh7 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.849 http://example.org/sports/sports_team/colors #19690-0cp0ph6 PRED entity: 0cp0ph6 PRED relation: film_release_region PRED expected values: 0chghy 03gj2 01znc_ 02vzc => 89 concepts (86 used for prediction) PRED predicted values (max 10 best out of 102): 0chghy (0.89 #7, 0.87 #152, 0.84 #588), 03gj2 (0.89 #19, 0.82 #164, 0.82 #600), 01znc_ (0.87 #34, 0.84 #179, 0.79 #324), 02vzc (0.79 #44, 0.78 #1351, 0.78 #189), 03rk0 (0.79 #48, 0.69 #193, 0.65 #338), 047yc (0.76 #22, 0.65 #312, 0.64 #167), 016wzw (0.63 #56, 0.58 #201, 0.56 #346), 06f32 (0.60 #200, 0.58 #55, 0.54 #345), 047lj (0.58 #8, 0.50 #298, 0.49 #153), 07twz (0.58 #85, 0.44 #230, 0.44 #375) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0879bpq; 0gffmn8; 02vr3gz; 0gjcrrw; 0db94w; 0glqh5_; 03mgx6z; 043tvp3; >> query: (?x3565, 0chghy) <- film_release_region(?x3565, ?x2346), film_release_region(?x3565, ?x429), ?x2346 = 0d05w3, ?x429 = 03rt9 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0cp0ph6 film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 86.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cp0ph6 film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 86.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cp0ph6 film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 86.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cp0ph6 film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 86.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19689-074qgb PRED entity: 074qgb PRED relation: gender PRED expected values: 05zppz => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #7, 0.86 #9, 0.84 #15), 02zsn (0.46 #101, 0.25 #74, 0.23 #78) >> Best rule #7 for best value: >> intensional similarity = 5 >> extensional distance = 87 >> proper extension: 03b78r; 02v49c; >> query: (?x13536, 05zppz) <- profession(?x13536, ?x2848), profession(?x13536, ?x1943), ?x1943 = 02krf9, award_nominee(?x13536, ?x10011), film_crew_role(?x97, ?x2848) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 074qgb gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.888 http://example.org/people/person/gender #19688-09ftwr PRED entity: 09ftwr PRED relation: award PRED expected values: 019f4v => 88 concepts (78 used for prediction) PRED predicted values (max 10 best out of 260): 0cjyzs (0.72 #25404, 0.69 #25403, 0.69 #19357), 09d28z (0.72 #25404, 0.69 #25403, 0.69 #19357), 03hl6lc (0.53 #177, 0.42 #984, 0.38 #581), 04dn09n (0.51 #44, 0.41 #851, 0.37 #2060), 040njc (0.47 #8, 0.36 #1218, 0.35 #412), 0gs9p (0.43 #79, 0.40 #483, 0.35 #1289), 02qyp19 (0.43 #1, 0.38 #2017, 0.36 #808), 019f4v (0.43 #67, 0.38 #471, 0.31 #1277), 0fbtbt (0.39 #4667, 0.11 #5070, 0.10 #4264), 0gr4k (0.38 #33, 0.31 #1243, 0.31 #2049) >> Best rule #25404 for best value: >> intensional similarity = 3 >> extensional distance = 2323 >> proper extension: 06lxn; >> query: (?x2687, ?x2016) <- award_winner(?x2016, ?x2687), award(?x9500, ?x2016), nationality(?x9500, ?x94) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 05183k; 07s93v; 01gzm2; 01f7j9; 02ld6x; 02l5rm; 0693l; 013t9y; 06s1qy; 040rjq; *> query: (?x2687, 019f4v) <- award(?x2687, ?x1862), ?x1862 = 0gr51, award_winner(?x2016, ?x2687), produced_by(?x1069, ?x2687) *> conf = 0.43 ranks of expected_values: 8 EVAL 09ftwr award 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 88.000 78.000 0.717 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19687-05nlx4 PRED entity: 05nlx4 PRED relation: film! PRED expected values: 0170pk => 80 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1101): 0g9zcgx (0.43 #91410, 0.38 #24931, 0.38 #45704), 0f5xn (0.17 #967, 0.09 #3045, 0.08 #5123), 0f0kz (0.13 #10902, 0.07 #50370, 0.06 #54524), 079vf (0.13 #8, 0.06 #10398, 0.06 #14553), 01t6b4 (0.11 #108030, 0.11 #93488, 0.08 #10390), 03ym1 (0.09 #11400, 0.09 #1010, 0.06 #15555), 0c0k1 (0.09 #3584, 0.08 #5662, 0.06 #26437), 06ltr (0.09 #944, 0.07 #34185, 0.06 #38339), 0l6px (0.09 #387, 0.06 #19086, 0.06 #23240), 03h_9lg (0.09 #132, 0.06 #22985, 0.06 #14677) >> Best rule #91410 for best value: >> intensional similarity = 3 >> extensional distance = 570 >> proper extension: 02d413; 014_x2; 015qsq; 09sh8k; 02y_lrp; 0g22z; 0b2v79; 06w99h3; 027qgy; 09m6kg; ... >> query: (?x7199, ?x629) <- genre(?x7199, ?x225), featured_film_locations(?x7199, ?x6226), nominated_for(?x629, ?x7199) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #280 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 035w2k; 027j9wd; 033qdy; *> query: (?x7199, 0170pk) <- film_distribution_medium(?x7199, ?x2099), crewmember(?x7199, ?x1983), prequel(?x12423, ?x7199) *> conf = 0.09 ranks of expected_values: 27 EVAL 05nlx4 film! 0170pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 80.000 60.000 0.432 http://example.org/film/actor/film./film/performance/film #19686-0263ycg PRED entity: 0263ycg PRED relation: film_crew_role! PRED expected values: 03g90h 02w9k1c 03lfd_ 09rvwmy 04180vy => 28 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1760): 0bth54 (0.80 #15067, 0.80 #13818, 0.78 #17565), 05qbckf (0.80 #15241, 0.80 #13992, 0.78 #11492), 057lbk (0.80 #15545, 0.78 #13047, 0.78 #11796), 05m_jsg (0.80 #15479, 0.78 #11730, 0.70 #14230), 076xkps (0.80 #14836, 0.70 #16085, 0.67 #13587), 05zpghd (0.80 #14450, 0.67 #9450, 0.59 #16948), 07yk1xz (0.78 #12773, 0.78 #11522, 0.71 #10271), 047vnkj (0.78 #13171, 0.78 #11920, 0.70 #15669), 09sh8k (0.78 #12518, 0.71 #10016, 0.70 #15016), 0ct2tf5 (0.71 #11114, 0.71 #17363, 0.70 #16114) >> Best rule #15067 for best value: >> intensional similarity = 24 >> extensional distance = 8 >> proper extension: 09vw2b7; 0dxtw; >> query: (?x4177, 0bth54) <- film_crew_role(?x10596, ?x4177), film_crew_role(?x10475, ?x4177), film_crew_role(?x5081, ?x4177), film_crew_role(?x3743, ?x4177), film_crew_role(?x3714, ?x4177), film_crew_role(?x1707, ?x4177), genre(?x3714, ?x2753), genre(?x3714, ?x1509), award(?x3743, ?x451), film(?x3462, ?x3743), film_crew_role(?x10475, ?x4305), ?x2753 = 0219x_, nominated_for(?x1862, ?x3743), crewmember(?x5081, ?x9391), ?x4305 = 0215hd, honored_for(?x1553, ?x3743), film(?x692, ?x10475), production_companies(?x10596, ?x902), executive_produced_by(?x3743, ?x4060), ?x1862 = 0gr51, ?x1509 = 060__y, ?x9391 = 094tsh6, ?x1707 = 04n52p6, film_release_region(?x10475, ?x87) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #9988 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 4 *> proper extension: 01xy5l_; *> query: (?x4177, 04180vy) <- film_crew_role(?x10596, ?x4177), film_crew_role(?x6798, ?x4177), film_crew_role(?x5829, ?x4177), film_crew_role(?x3743, ?x4177), film_crew_role(?x3714, ?x4177), film_crew_role(?x2586, ?x4177), ?x3743 = 047d21r, ?x5829 = 035bcl, film_crew_role(?x2586, ?x5136), film_crew_role(?x2586, ?x1284), film(?x2549, ?x2586), language(?x2586, ?x254), ?x1284 = 0ch6mp2, production_companies(?x2586, ?x382), film(?x1205, ?x2586), nominated_for(?x3499, ?x3714), nominated_for(?x2478, ?x3714), nominated_for(?x601, ?x3714), currency(?x10596, ?x170), ?x5136 = 089g0h, ?x2478 = 02x4x18, film(?x818, ?x3714), ?x3499 = 03qgjwc, ?x6798 = 0g7pm1, nominated_for(?x3381, ?x2586), ?x601 = 0gr4k *> conf = 0.67 ranks of expected_values: 51, 81, 244, 294, 1239 EVAL 0263ycg film_crew_role! 04180vy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 28.000 16.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0263ycg film_crew_role! 09rvwmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 28.000 16.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0263ycg film_crew_role! 03lfd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 16.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0263ycg film_crew_role! 02w9k1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 28.000 16.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0263ycg film_crew_role! 03g90h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 28.000 16.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19685-09qv3c PRED entity: 09qv3c PRED relation: award! PRED expected values: 03nt59 => 46 concepts (21 used for prediction) PRED predicted values (max 10 best out of 907): 0d68qy (0.33 #243, 0.23 #20263, 0.22 #19249), 030cx (0.33 #447, 0.23 #20263, 0.22 #19249), 05f4vxd (0.33 #508, 0.23 #20263, 0.22 #19249), 02h2vv (0.33 #652, 0.22 #19249, 0.22 #20262), 039cq4 (0.33 #695, 0.22 #19249, 0.22 #20262), 016tvq (0.33 #832, 0.22 #19249, 0.22 #20262), 02czd5 (0.33 #829, 0.13 #3865, 0.12 #2853), 0266s9 (0.33 #990, 0.12 #3014, 0.03 #4026), 030k94 (0.32 #4050, 0.20 #1318, 0.10 #3342), 026y3cf (0.32 #4050, 0.12 #3030, 0.03 #4042) >> Best rule #243 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 09qvc0; >> query: (?x870, 0d68qy) <- award(?x8533, ?x870), ceremony(?x870, ?x1265), award(?x5558, ?x870), ?x5558 = 01nrgq, genre(?x8533, ?x258) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3650 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 0m7yy; *> query: (?x870, 03nt59) <- award(?x4517, ?x870), award_winner(?x870, ?x3261), tv_program(?x2285, ?x4517), actor(?x9029, ?x3261) *> conf = 0.03 ranks of expected_values: 475 EVAL 09qv3c award! 03nt59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 21.000 0.333 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19684-026qnh6 PRED entity: 026qnh6 PRED relation: featured_film_locations PRED expected values: 02bm8 => 86 concepts (33 used for prediction) PRED predicted values (max 10 best out of 90): 02_286 (0.18 #2658, 0.18 #498, 0.16 #1938), 0rh6k (0.09 #479, 0.06 #2159, 0.05 #2399), 04jpl (0.08 #248, 0.07 #2407, 0.07 #2167), 030qb3t (0.07 #1476, 0.06 #3878, 0.06 #4117), 01_d4 (0.05 #47, 0.04 #2685, 0.03 #5328), 080h2 (0.05 #24, 0.04 #2422, 0.04 #4102), 0345h (0.05 #33, 0.02 #1712, 0.02 #2671), 0156q (0.05 #41, 0.02 #999, 0.02 #1238), 035p3 (0.05 #232, 0.02 #1190, 0.02 #710), 0ctw_b (0.05 #23, 0.02 #2181, 0.02 #5041) >> Best rule #2658 for best value: >> intensional similarity = 5 >> extensional distance = 163 >> proper extension: 0c0nhgv; 0416y94; 0sxfd; 013q07; 07w8fz; 04vh83; 0f4k49; 09fc83; 0kb07; 02dwj; ... >> query: (?x4810, 02_286) <- genre(?x4810, ?x53), nominated_for(?x507, ?x4810), film(?x844, ?x4810), films(?x326, ?x4810), written_by(?x4810, ?x9281) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026qnh6 featured_film_locations 02bm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 33.000 0.182 http://example.org/film/film/featured_film_locations #19683-01kwlwp PRED entity: 01kwlwp PRED relation: artists! PRED expected values: 01flzb => 101 concepts (99 used for prediction) PRED predicted values (max 10 best out of 166): 064t9 (0.81 #2527, 0.42 #5353, 0.41 #6609), 06by7 (0.50 #23, 0.48 #2536, 0.42 #5362), 011j5x (0.50 #34, 0.05 #6629, 0.05 #8515), 06j6l (0.41 #2564, 0.23 #5390, 0.23 #4448), 05bt6j (0.28 #2559, 0.23 #6641, 0.22 #5385), 025sc50 (0.27 #2566, 0.20 #367, 0.19 #996), 0gywn (0.26 #2574, 0.18 #1004, 0.17 #5400), 016clz (0.25 #5, 0.23 #8486, 0.22 #6600), 0glt670 (0.24 #357, 0.19 #2556, 0.18 #4440), 0ggx5q (0.19 #2595, 0.12 #4165, 0.12 #4479) >> Best rule #2527 for best value: >> intensional similarity = 3 >> extensional distance = 435 >> proper extension: 0123r4; 03_gx; 01v27pl; >> query: (?x954, 064t9) <- artists(?x13245, ?x954), artists(?x13245, ?x9528), ?x9528 = 01kp_1t >> conf = 0.81 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kwlwp artists! 01flzb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 99.000 0.805 http://example.org/music/genre/artists #19682-0fcsd PRED entity: 0fcsd PRED relation: group! PRED expected values: 0l14md => 100 concepts (70 used for prediction) PRED predicted values (max 10 best out of 120): 03qjg (0.75 #304, 0.40 #132, 0.37 #1251), 0l14md (0.65 #1642, 0.63 #1212, 0.60 #3109), 028tv0 (0.49 #1648, 0.42 #271, 0.41 #529), 05r5c (0.42 #266, 0.28 #1213, 0.25 #1643), 013y1f (0.42 #285, 0.19 #1232, 0.15 #2781), 0l14qv (0.33 #263, 0.26 #1296, 0.25 #177), 01vj9c (0.32 #1219, 0.30 #100, 0.28 #2424), 0l14j_ (0.30 #136, 0.25 #222, 0.12 #3152), 042v_gx (0.25 #267, 0.12 #1644, 0.10 #3111), 06w7v (0.25 #330, 0.07 #2841, 0.07 #3190) >> Best rule #304 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 06nv27; 07m4c; >> query: (?x4461, 03qjg) <- artist(?x1954, ?x4461), group(?x1969, ?x4461), group(?x1750, ?x4461), artists(?x1380, ?x4461), ?x1750 = 02hnl, group(?x7172, ?x4461), ?x1969 = 04rzd >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1642 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 94 *> proper extension: 0123r4; *> query: (?x4461, 0l14md) <- group(?x1750, ?x4461), group(?x1466, ?x4461), group(?x227, ?x4461), ?x227 = 0342h, ?x1466 = 03bx0bm, instrumentalists(?x1750, ?x300), role(?x1750, ?x74) *> conf = 0.65 ranks of expected_values: 2 EVAL 0fcsd group! 0l14md CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 70.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group #19681-04969y PRED entity: 04969y PRED relation: film_release_region PRED expected values: 0chghy 03rt9 02vzc => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 114): 059j2 (0.87 #1381, 0.81 #1982, 0.81 #1081), 05r4w (0.84 #1352, 0.81 #1052, 0.80 #1502), 0jgd (0.83 #754, 0.80 #1354, 0.76 #1955), 03h64 (0.82 #1566, 0.79 #1416, 0.74 #2017), 0chghy (0.82 #1360, 0.78 #1510, 0.78 #1060), 02vzc (0.79 #1401, 0.78 #1101, 0.78 #1251), 015fr (0.79 #1367, 0.74 #767, 0.69 #1968), 05b4w (0.75 #1414, 0.66 #2315, 0.66 #1564), 06bnz (0.74 #1394, 0.71 #794, 0.61 #2295), 01znc_ (0.72 #1391, 0.68 #1091, 0.66 #1541) >> Best rule #1381 for best value: >> intensional similarity = 5 >> extensional distance = 222 >> proper extension: 0fq27fp; 0c40vxk; 0gj8t_b; 053tj7; 06v9_x; 0gfh84d; >> query: (?x903, 059j2) <- film_release_region(?x903, ?x205), film_release_region(?x903, ?x172), ?x205 = 03rjj, genre(?x903, ?x1014), ?x172 = 0154j >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1360 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 222 *> proper extension: 0fq27fp; 0c40vxk; 0gj8t_b; 053tj7; 06v9_x; 0gfh84d; *> query: (?x903, 0chghy) <- film_release_region(?x903, ?x205), film_release_region(?x903, ?x172), ?x205 = 03rjj, genre(?x903, ?x1014), ?x172 = 0154j *> conf = 0.82 ranks of expected_values: 5, 6, 11 EVAL 04969y film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 85.000 85.000 0.866 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04969y film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 85.000 85.000 0.866 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04969y film_release_region 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 85.000 85.000 0.866 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19680-0pkr1 PRED entity: 0pkr1 PRED relation: film PRED expected values: 034qmv => 121 concepts (64 used for prediction) PRED predicted values (max 10 best out of 332): 043n0v_ (0.62 #62701, 0.60 #75242, 0.59 #66284), 01f8f7 (0.29 #1203), 01f85k (0.29 #1127), 02yvct (0.14 #352, 0.02 #48719, 0.02 #12892), 05znbh7 (0.14 #1095, 0.01 #2886), 0233bn (0.14 #1308), 027m67 (0.14 #1273), 031ldd (0.14 #1042), 0mb8c (0.14 #908), 0198b6 (0.14 #643) >> Best rule #62701 for best value: >> intensional similarity = 4 >> extensional distance = 782 >> proper extension: 01gvr1; 0blbxk; 0b_dy; 055c8; 03kpvp; 06wm0z; 06g2d1; 01515w; 0436kgz; 0fthdk; ... >> query: (?x10695, ?x5038) <- film(?x10695, ?x5826), award_winner(?x5038, ?x10695), profession(?x10695, ?x319), award(?x5826, ?x7215) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3597 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 204 *> proper extension: 02l840; 01wz_ml; 01m3x5p; 0674cw; 02dbp7; 01sxd1; 01q9b9; 03f3yfj; 01wxdn3; 01wwnh2; ... *> query: (?x10695, 034qmv) <- gender(?x10695, ?x231), category(?x10695, ?x134), profession(?x10695, ?x319), ?x319 = 01d_h8 *> conf = 0.01 ranks of expected_values: 90 EVAL 0pkr1 film 034qmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 121.000 64.000 0.617 http://example.org/film/actor/film./film/performance/film #19679-01zqy6t PRED entity: 01zqy6t PRED relation: citytown! PRED expected values: 02bb47 02dqdp => 161 concepts (52 used for prediction) PRED predicted values (max 10 best out of 615): 02bb47 (0.73 #12113, 0.56 #24227, 0.43 #23418), 01zpmq (0.50 #275, 0.08 #1889, 0.06 #6735), 0k8z (0.25 #150, 0.08 #1764, 0.04 #4996), 045c7b (0.25 #214, 0.03 #6674, 0.03 #7482), 0473m9 (0.14 #2463, 0.11 #3271, 0.07 #5695), 064f29 (0.14 #2733, 0.07 #5965, 0.06 #3541), 056ws9 (0.11 #1076, 0.08 #1883, 0.07 #5922), 05njw (0.11 #1397, 0.08 #2204, 0.06 #3819), 043ttv (0.11 #1484, 0.08 #2291, 0.06 #3906), 01rs59 (0.11 #1255, 0.08 #2062, 0.06 #3677) >> Best rule #12113 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 07l5z; 0c5v2; >> query: (?x13529, ?x3212) <- contains(?x7964, ?x13529), contains(?x13529, ?x3212), citytown(?x581, ?x13529), county(?x2935, ?x7964) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01zqy6t citytown! 02dqdp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 161.000 52.000 0.729 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 01zqy6t citytown! 02bb47 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 52.000 0.729 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #19678-01dq0z PRED entity: 01dq0z PRED relation: time_zones PRED expected values: 02hcv8 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.76 #42, 0.46 #29, 0.30 #1486), 02lcqs (0.16 #1292, 0.16 #1815, 0.16 #1829), 02fqwt (0.16 #1815, 0.16 #1829, 0.16 #1746), 02hczc (0.16 #1815, 0.16 #1829, 0.16 #1746), 042g7t (0.16 #1815, 0.16 #1829, 0.16 #1746), 05jphn (0.16 #1815, 0.16 #1829, 0.16 #1746), 02llzg (0.09 #446, 0.05 #1697, 0.05 #1833), 03bdv (0.05 #1306, 0.04 #1606, 0.03 #1567), 03plfd (0.04 #452, 0.01 #1839, 0.01 #1362), 052vwh (0.03 #454) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 0n5fl; 0n5fz; 0n5df; 0n5gb; 0n5j7; 0n5bk; 0n5c9; 0n5jm; 0n5kc; 0xqf3; ... >> query: (?x13670, 02hcv8) <- contains(?x1196, ?x13670), location(?x6187, ?x1196), place_of_birth(?x2025, ?x1196), ?x6187 = 07r1h >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dq0z time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.765 http://example.org/location/location/time_zones #19677-02_sr1 PRED entity: 02_sr1 PRED relation: nominated_for! PRED expected values: 05ztrmj => 95 concepts (91 used for prediction) PRED predicted values (max 10 best out of 184): 05ztrmj (0.69 #4525, 0.67 #5956, 0.67 #7385), 0gq_v (0.58 #496, 0.52 #734, 0.50 #972), 09v51c2 (0.45 #206, 0.27 #445, 0.25 #12630), 0gq9h (0.42 #540, 0.42 #778, 0.39 #1016), 0gs9p (0.42 #542, 0.35 #780, 0.32 #1018), 019f4v (0.42 #531, 0.29 #3149, 0.29 #769), 09v0wy2 (0.36 #162, 0.25 #12630, 0.24 #15736), 09v92_x (0.36 #183, 0.24 #15736, 0.24 #15735), 09v4bym (0.36 #208, 0.20 #447, 0.12 #14780), 0gqy2 (0.35 #599, 0.29 #837, 0.29 #1075) >> Best rule #4525 for best value: >> intensional similarity = 4 >> extensional distance = 412 >> proper extension: 04glx0; 05sy0cv; >> query: (?x4038, ?x3508) <- nominated_for(?x1940, ?x4038), award(?x4038, ?x3508), profession(?x1940, ?x1614), category(?x1940, ?x134) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_sr1 nominated_for! 05ztrmj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 91.000 0.687 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19676-015bpl PRED entity: 015bpl PRED relation: written_by PRED expected values: 03ft8 => 72 concepts (39 used for prediction) PRED predicted values (max 10 best out of 79): 070j61 (0.29 #228, 0.05 #566), 06b_0 (0.14 #232, 0.02 #1243, 0.02 #570), 06y9bd (0.14 #298, 0.02 #636), 03fg0r (0.14 #147, 0.02 #485), 0693l (0.05 #1437, 0.05 #1102, 0.03 #1772), 0184dt (0.05 #1085, 0.04 #1420, 0.02 #2426), 03_gd (0.05 #1032, 0.04 #1367, 0.01 #1702), 06dkzt (0.05 #1612, 0.04 #1277, 0.02 #604), 026dx (0.04 #1163, 0.04 #1498, 0.01 #827), 09pl3f (0.04 #1195, 0.03 #859, 0.03 #1530) >> Best rule #228 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0992d9; >> query: (?x7989, 070j61) <- film(?x8741, ?x7989), film(?x7489, ?x7989), ?x8741 = 01p85y, award(?x7489, ?x102), participant(?x7489, ?x3308), genre(?x7989, ?x811) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015bpl written_by 03ft8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 39.000 0.286 http://example.org/film/film/written_by #19675-01vttb9 PRED entity: 01vttb9 PRED relation: location PRED expected values: 01sn3 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 165): 0ccvx (0.25 #1831, 0.14 #2635, 0.02 #9069), 052p7 (0.20 #932, 0.12 #1736, 0.07 #2540), 0cv3w (0.20 #964, 0.12 #1768, 0.07 #2572), 010v8k (0.20 #1185, 0.12 #1989, 0.07 #2793), 030qb3t (0.19 #8043, 0.15 #25014, 0.15 #22601), 02_286 (0.13 #30598, 0.12 #45879, 0.12 #27381), 0cr3d (0.12 #1754, 0.07 #3362, 0.07 #2558), 0cc56 (0.12 #1666, 0.07 #2470, 0.04 #3274), 0xms9 (0.12 #2226, 0.07 #3030, 0.01 #8660), 01n7q (0.07 #3280, 0.06 #4888, 0.04 #24994) >> Best rule #1831 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 013423; 0lsw9; >> query: (?x7556, 0ccvx) <- artists(?x8138, ?x7556), ?x8138 = 0161rf, award_winner(?x1079, ?x7556) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #16299 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 143 *> proper extension: 01y8d4; 03gvpk; 06y3r; *> query: (?x7556, 01sn3) <- award_winner(?x669, ?x7556), gender(?x7556, ?x231), place_of_death(?x7556, ?x1523) *> conf = 0.01 ranks of expected_values: 117 EVAL 01vttb9 location 01sn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 131.000 131.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #19674-0fdys PRED entity: 0fdys PRED relation: major_field_of_study! PRED expected values: 0dplh 05mv4 016ndm 03x83_ 017cy9 0b1xl 05zl0 0d07s 0hpv3 09vzz 024cg8 => 84 concepts (52 used for prediction) PRED predicted values (max 10 best out of 591): 0g8rj (0.67 #9179, 0.45 #15538, 0.43 #10769), 02bqy (0.60 #7596, 0.57 #10774, 0.56 #17134), 07tgn (0.60 #7430, 0.56 #16968, 0.50 #11668), 04rwx (0.60 #7452, 0.50 #11690, 0.50 #9040), 0kz2w (0.60 #7435, 0.50 #11673, 0.50 #4788), 017cy9 (0.60 #7565, 0.50 #3856, 0.45 #15512), 05mv4 (0.60 #7543, 0.50 #3834, 0.44 #17081), 07vfj (0.60 #7528, 0.44 #17066, 0.43 #10706), 01vc5m (0.60 #7504, 0.38 #11742, 0.25 #17042), 01j_cy (0.57 #11162, 0.57 #10632, 0.56 #13285) >> Best rule #9179 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 05qfh; 01lj9; >> query: (?x3995, 0g8rj) <- major_field_of_study(?x8008, ?x3995), major_field_of_study(?x7660, ?x3995), major_field_of_study(?x5288, ?x3995), major_field_of_study(?x3995, ?x90), student(?x3995, ?x1188), student(?x8008, ?x838), ?x5288 = 02zd460, major_field_of_study(?x5614, ?x3995), major_field_of_study(?x734, ?x3995), ?x7660 = 01qd_r >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7565 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 05qjt; 037mh8; *> query: (?x3995, 017cy9) <- major_field_of_study(?x8008, ?x3995), major_field_of_study(?x5288, ?x3995), major_field_of_study(?x3995, ?x90), student(?x3995, ?x1188), student(?x8008, ?x838), ?x5288 = 02zd460, major_field_of_study(?x5614, ?x3995), major_field_of_study(?x2759, ?x3995), ?x2759 = 071tyz *> conf = 0.60 ranks of expected_values: 6, 7, 14, 60, 67, 108, 118, 186, 269, 396, 485 EVAL 0fdys major_field_of_study! 024cg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0fdys major_field_of_study! 09vzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0fdys major_field_of_study! 0hpv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0fdys major_field_of_study! 0d07s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0fdys major_field_of_study! 05zl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0fdys major_field_of_study! 0b1xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0fdys major_field_of_study! 017cy9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0fdys major_field_of_study! 03x83_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0fdys major_field_of_study! 016ndm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0fdys major_field_of_study! 05mv4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0fdys major_field_of_study! 0dplh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 84.000 52.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19673-0fx02 PRED entity: 0fx02 PRED relation: profession PRED expected values: 0kyk => 105 concepts (73 used for prediction) PRED predicted values (max 10 best out of 100): 0dxtg (0.87 #4605, 0.87 #4901, 0.83 #2679), 01d_h8 (0.71 #1043, 0.66 #5042, 0.63 #4894), 02hrh1q (0.65 #1051, 0.62 #9645, 0.61 #10681), 02jknp (0.58 #1044, 0.56 #5043, 0.55 #6673), 0kyk (0.50 #8772, 0.43 #919, 0.43 #8179), 03gjzk (0.43 #4607, 0.42 #4903, 0.41 #2681), 0nbcg (0.37 #9959, 0.27 #1365, 0.26 #9663), 09jwl (0.36 #9650, 0.36 #9354, 0.35 #1500), 012qdp (0.33 #232, 0.01 #5714, 0.01 #1565), 01c72t (0.29 #1209, 0.26 #1357, 0.23 #468) >> Best rule #4605 for best value: >> intensional similarity = 4 >> extensional distance = 205 >> proper extension: 0l6qt; 04t2l2; 0byfz; 014zcr; 0h5f5n; 01q_ph; 0159h6; 0bxtg; 06cv1; 0c1pj; ... >> query: (?x3686, 0dxtg) <- nationality(?x3686, ?x512), profession(?x3686, ?x353), student(?x13297, ?x3686), written_by(?x1261, ?x3686) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #8772 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 588 *> proper extension: 04yywz; 01l1b90; 0hnlx; 01bpc9; 04xjp; 01zmpg; 013v5j; 04xrx; 0gkg6; 01vwyqp; ... *> query: (?x3686, 0kyk) <- nationality(?x3686, ?x512), profession(?x3686, ?x353), profession(?x5049, ?x353), ?x5049 = 04r68 *> conf = 0.50 ranks of expected_values: 5 EVAL 0fx02 profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 105.000 73.000 0.870 http://example.org/people/person/profession #19672-0fbx6 PRED entity: 0fbx6 PRED relation: film PRED expected values: 0kvgnq => 94 concepts (64 used for prediction) PRED predicted values (max 10 best out of 741): 0pv3x (0.76 #5365, 0.61 #48291, 0.59 #10730), 035xwd (0.08 #116, 0.01 #9057), 02qr3k8 (0.06 #4863, 0.02 #63887, 0.02 #47789), 031hcx (0.05 #4848, 0.03 #42924, 0.02 #10213), 03nqnnk (0.05 #4599, 0.01 #9964, 0.01 #20694), 0ds3t5x (0.05 #3630, 0.01 #105534, 0.01 #32242), 04jplwp (0.04 #1370, 0.04 #4946, 0.03 #42924), 0fphf3v (0.04 #1360, 0.04 #4936, 0.01 #12090), 05dptj (0.04 #1329, 0.03 #42924, 0.03 #98377), 0g3zrd (0.04 #368, 0.03 #42924, 0.03 #98377) >> Best rule #5365 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 01k5zk; 01wrcxr; 0jlv5; 0421st; >> query: (?x4254, ?x1199) <- nominated_for(?x4254, ?x1199), award(?x4254, ?x749), ?x749 = 094qd5 >> conf = 0.76 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fbx6 film 0kvgnq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 64.000 0.764 http://example.org/film/actor/film./film/performance/film #19671-018w8 PRED entity: 018w8 PRED relation: country PRED expected values: 0d060g 04gzd 0345h 01pj7 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 194): 0d060g (0.88 #3642, 0.86 #4024, 0.84 #4598), 0345h (0.83 #3666, 0.82 #4048, 0.80 #4622), 03_3d (0.81 #3641, 0.76 #4214, 0.76 #4023), 06qd3 (0.80 #2715, 0.75 #2524, 0.71 #1949), 05qhw (0.77 #3077, 0.76 #3651, 0.75 #2886), 07t21 (0.75 #2908, 0.74 #3673, 0.73 #2716), 0d0vqn (0.75 #2496, 0.73 #2687, 0.72 #2879), 06c1y (0.73 #2719, 0.64 #192, 0.62 #2528), 07ylj (0.73 #2707, 0.64 #192, 0.62 #2516), 01p1v (0.73 #2727, 0.64 #192, 0.50 #2919) >> Best rule #3642 for best value: >> intensional similarity = 8 >> extensional distance = 40 >> proper extension: 0d1tm; 096f8; 09_bl; 09_94; 09wz9; 07jjt; 03fyrh; 07jbh; 06zgc; 0486tv; ... >> query: (?x4833, 0d060g) <- sports(?x391, ?x4833), country(?x4833, ?x2152), country(?x4833, ?x789), ?x789 = 0f8l9c, film_release_region(?x11209, ?x2152), film_release_region(?x10404, ?x2152), ?x11209 = 04fjzv, ?x10404 = 01s9vc >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 20, 63 EVAL 018w8 country 01pj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 62.000 62.000 0.881 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 018w8 country 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.881 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 018w8 country 04gzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 62.000 62.000 0.881 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 018w8 country 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.881 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #19670-01wn718 PRED entity: 01wn718 PRED relation: people! PRED expected values: 0x67 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 53): 0x67 (0.61 #626, 0.43 #318, 0.38 #10), 041rx (0.17 #5016, 0.16 #5479, 0.16 #3087), 033tf_ (0.14 #1470, 0.14 #854, 0.13 #1778), 0xnvg (0.13 #475, 0.11 #1476, 0.08 #2015), 01rv7x (0.08 #39, 0.01 #1579), 01qhm_ (0.07 #853, 0.06 #1161, 0.06 #1469), 02w7gg (0.06 #1927, 0.06 #1696, 0.06 #2467), 09vc4s (0.06 #471, 0.06 #1472, 0.06 #779), 07hwkr (0.06 #1783, 0.06 #1398, 0.05 #1013), 07bch9 (0.06 #870, 0.06 #100, 0.06 #2411) >> Best rule #626 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 01gx5f; >> query: (?x3977, 0x67) <- artist(?x2190, ?x3977), artists(?x2937, ?x3977), place_of_birth(?x3977, ?x1860), ?x2937 = 0glt670 >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wn718 people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.606 http://example.org/people/ethnicity/people #19669-02wgk1 PRED entity: 02wgk1 PRED relation: honored_for! PRED expected values: 09bymc => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 116): 09gkdln (0.13 #226, 0.05 #1073, 0.04 #3251), 04n2r9h (0.07 #277, 0.05 #519, 0.04 #3786), 03tn9w (0.07 #321, 0.03 #684, 0.02 #563), 0bzkvd (0.07 #219, 0.03 #824, 0.01 #703), 05c1t6z (0.05 #3762, 0.05 #3884, 0.04 #3278), 03gwpw2 (0.05 #1941, 0.05 #731, 0.04 #3756), 02q690_ (0.05 #3804, 0.05 #3926, 0.05 #3199), 0275n3y (0.04 #3814, 0.04 #3936, 0.04 #3209), 0gvstc3 (0.04 #3778, 0.04 #3900, 0.04 #1963), 03nnm4t (0.04 #1998, 0.04 #3813, 0.04 #3935) >> Best rule #226 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0cd2vh9; 05qbckf; 047vnkj; 026lgs; 0dc_ms; 042fgh; 026hh0m; 0by17xn; >> query: (?x4502, 09gkdln) <- genre(?x4502, ?x1013), language(?x4502, ?x5671), ?x5671 = 06b_j, ?x1013 = 06n90 >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #3855 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 591 *> proper extension: 0gpjbt; 06hwzy; 025ljp; 0cnjm0; *> query: (?x4502, 09bymc) <- honored_for(?x2294, ?x4502), ceremony(?x484, ?x2294) *> conf = 0.03 ranks of expected_values: 36 EVAL 02wgk1 honored_for! 09bymc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 80.000 80.000 0.133 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #19668-02vzc PRED entity: 02vzc PRED relation: olympics PRED expected values: 0lv1x 01f1kd 015l4k => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 11): 0kbws (0.74 #196, 0.73 #155, 0.73 #54), 0kbvv (0.67 #784, 0.64 #38, 0.60 #1209), 0swbd (0.67 #784, 0.60 #1209, 0.58 #1394), 0lv1x (0.62 #25, 0.57 #45, 0.57 #35), 0sx8l (0.60 #325, 0.51 #732, 0.51 #731), 01f1kd (0.46 #29, 0.43 #39, 0.32 #69), 016r9z (0.46 #26, 0.36 #36, 0.33 #46), 015l4k (0.43 #40, 0.38 #30, 0.32 #70), 018wrk (0.31 #497, 0.31 #21, 0.23 #101), 0c_tl (0.31 #497, 0.17 #148, 0.15 #27) >> Best rule #196 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 03rjj; 07ylj; >> query: (?x1892, 0kbws) <- film_release_region(?x8891, ?x1892), ?x8891 = 0gwlfnb, country(?x453, ?x1892) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #25 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 06mx8; *> query: (?x1892, 0lv1x) <- contains(?x1892, ?x11236), region(?x1315, ?x1892), adjoins(?x11237, ?x11236) *> conf = 0.62 ranks of expected_values: 4, 6, 8 EVAL 02vzc olympics 015l4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 192.000 192.000 0.743 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 02vzc olympics 01f1kd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 192.000 192.000 0.743 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 02vzc olympics 0lv1x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 192.000 192.000 0.743 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #19667-013b6_ PRED entity: 013b6_ PRED relation: languages_spoken PRED expected values: 03hkp => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 53): 0t_2 (0.67 #2079, 0.62 #1602, 0.60 #965), 0cjk9 (0.40 #321, 0.11 #480, 0.09 #533), 03hkp (0.33 #171, 0.33 #65, 0.25 #277), 032f6 (0.33 #101, 0.33 #48, 0.20 #366), 0swlx (0.33 #49, 0.20 #367, 0.11 #526), 03k50 (0.33 #6, 0.08 #589, 0.08 #2710), 0688f (0.33 #35, 0.08 #618, 0.08 #1573), 02hxcvy (0.33 #31, 0.08 #1516, 0.07 #3106), 0y1mh (0.33 #17, 0.08 #653, 0.07 #812), 0c_v2 (0.33 #14, 0.04 #1499, 0.03 #2241) >> Best rule #2079 for best value: >> intensional similarity = 7 >> extensional distance = 28 >> proper extension: 01336l; >> query: (?x11490, 0t_2) <- people(?x11490, ?x4308), people(?x11490, ?x2319), languages_spoken(?x11490, ?x254), award_winner(?x217, ?x2319), instrumentalists(?x227, ?x217), profession(?x4308, ?x353), nationality(?x217, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 041rx; *> query: (?x11490, 03hkp) <- people(?x11490, ?x5720), people(?x11490, ?x2319), people(?x11490, ?x1089), languages_spoken(?x11490, ?x254), ?x2319 = 0lccn, award_winner(?x2826, ?x5720), award_nominee(?x5720, ?x6011), award(?x5720, ?x1079), celebrities_impersonated(?x8145, ?x1089) *> conf = 0.33 ranks of expected_values: 3 EVAL 013b6_ languages_spoken 03hkp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 65.000 0.667 http://example.org/people/ethnicity/languages_spoken #19666-0l786 PRED entity: 0l786 PRED relation: award_winner! PRED expected values: 026kqs9 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 131): 058m5m4 (0.40 #55, 0.04 #4503, 0.04 #7422), 04110lv (0.40 #109, 0.03 #1638, 0.03 #1916), 0418154 (0.20 #107, 0.06 #4277, 0.05 #4416), 09g90vz (0.20 #122, 0.05 #7628, 0.05 #7211), 0hr6lkl (0.20 #17, 0.03 #1546, 0.03 #1824), 09k5jh7 (0.20 #83, 0.02 #4253, 0.02 #4392), 0dth6b (0.12 #441, 0.05 #580, 0.05 #858), 092c5f (0.09 #153, 0.08 #4184, 0.06 #4323), 092_25 (0.09 #210, 0.06 #488, 0.06 #1322), 01mhwk (0.09 #736, 0.06 #458, 0.05 #597) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0sz28; 0237fw; 0c6qh; >> query: (?x7138, 058m5m4) <- participant(?x9925, ?x7138), participant(?x3870, ?x7138), award_winner(?x1770, ?x7138), ?x1770 = 09cm54 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #785 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 033hqf; *> query: (?x7138, 026kqs9) <- celebrities_impersonated(?x3649, ?x7138), participant(?x3870, ?x7138), film(?x7138, ?x1822) *> conf = 0.05 ranks of expected_values: 77 EVAL 0l786 award_winner! 026kqs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 128.000 128.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19665-059g4 PRED entity: 059g4 PRED relation: countries_within PRED expected values: 06ryl => 166 concepts (110 used for prediction) PRED predicted values (max 10 best out of 443): 0d060g (0.57 #914, 0.56 #2283, 0.54 #683), 05c74 (0.56 #2283, 0.54 #683, 0.51 #1262), 0164b (0.56 #2283, 0.54 #683, 0.51 #1262), 0j11 (0.56 #2283, 0.54 #683, 0.48 #224), 020d5 (0.56 #2283, 0.54 #683, 0.48 #224), 035v3 (0.56 #2283, 0.48 #224, 0.48 #223), 03h2c (0.54 #683, 0.51 #1262, 0.48 #224), 0824r (0.50 #685, 0.25 #455, 0.25 #454), 01n7q (0.50 #685, 0.25 #455, 0.25 #454), 0vbk (0.50 #685, 0.25 #455, 0.25 #454) >> Best rule #914 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01mjq; >> query: (?x8483, ?x151) <- contains(?x8483, ?x8260), contains(?x8483, ?x151), adjoins(?x8483, ?x12315), contains(?x8260, ?x108), combatants(?x151, ?x94) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 059g4 countries_within 06ryl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 166.000 110.000 0.571 http://example.org/base/locations/continents/countries_within #19664-0hvjr PRED entity: 0hvjr PRED relation: current_club! PRED expected values: 02pp1 => 94 concepts (65 used for prediction) PRED predicted values (max 10 best out of 60): 01l3vx (0.33 #4, 0.25 #33, 0.24 #600), 03ylxn (0.33 #23, 0.25 #52, 0.24 #600), 02bh_v (0.33 #19, 0.25 #48, 0.24 #600), 03d8m4 (0.25 #38, 0.24 #600, 0.14 #276), 03_qrp (0.25 #43, 0.24 #600, 0.14 #281), 03dj48 (0.24 #600, 0.20 #79, 0.14 #318), 03_44z (0.22 #353, 0.20 #116, 0.17 #412), 03ys48 (0.20 #106, 0.17 #193, 0.10 #1190), 03zrhb (0.20 #105, 0.17 #192, 0.10 #676), 033nzk (0.20 #91, 0.17 #178, 0.09 #573) >> Best rule #4 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 02gys2; >> query: (?x3216, 01l3vx) <- position(?x3216, ?x63), colors(?x3216, ?x8271), team(?x5420, ?x3216), ?x5420 = 0135nb, position(?x3216, ?x60), current_club(?x1598, ?x3216), ?x63 = 02sdk9v >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #350 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 01kwhf; 0j46b; 01rly6; 0ckf6; *> query: (?x3216, 02pp1) <- position(?x3216, ?x203), colors(?x3216, ?x8271), team(?x5420, ?x3216), team(?x5420, ?x11390), place_of_birth(?x5420, ?x13828), ?x11390 = 0fvly, ?x203 = 0dgrmp *> conf = 0.11 ranks of expected_values: 17 EVAL 0hvjr current_club! 02pp1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 94.000 65.000 0.333 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #19663-0bzlrh PRED entity: 0bzlrh PRED relation: ceremony! PRED expected values: 0gqyl => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 321): 0gqyl (0.87 #2989, 0.87 #2746, 0.86 #3232), 0gr42 (0.82 #2266, 0.80 #1779, 0.80 #2510), 018wdw (0.77 #1141, 0.75 #1630, 0.75 #6086), 0gqxm (0.75 #6086, 0.69 #1090, 0.62 #1579), 0gqzz (0.75 #6086, 0.31 #1011, 0.21 #1500), 0czp_ (0.75 #6086, 0.23 #1411, 0.14 #3118), 02x201b (0.75 #6086, 0.15 #1146, 0.12 #1635), 054krc (0.27 #2005, 0.27 #6818, 0.24 #3168), 02qyntr (0.27 #6818, 0.27 #1216, 0.25 #1951), 025m8y (0.27 #6818, 0.27 #1216, 0.24 #3168) >> Best rule #2989 for best value: >> intensional similarity = 17 >> extensional distance = 61 >> proper extension: 0c4hgj; >> query: (?x7515, 0gqyl) <- ceremony(?x2222, ?x7515), award_winner(?x7515, ?x4277), award(?x4277, ?x102), award_winner(?x834, ?x4277), award(?x144, ?x2222), nominated_for(?x2222, ?x11429), nominated_for(?x2222, ?x11385), nominated_for(?x2222, ?x10362), nominated_for(?x2222, ?x6121), nominated_for(?x2222, ?x5697), ?x11429 = 0_9l_, ceremony(?x2222, ?x4700), ?x6121 = 064lsn, ?x5697 = 0bl06, ?x4700 = 0bz6sb, ?x10362 = 0h0wd9, ?x11385 = 01c9d >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bzlrh ceremony! 0gqyl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.873 http://example.org/award/award_category/winners./award/award_honor/ceremony #19662-01d0fp PRED entity: 01d0fp PRED relation: type_of_union PRED expected values: 04ztj => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.88 #1, 0.85 #45, 0.84 #69), 01g63y (0.48 #2, 0.36 #34, 0.36 #14) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 01gw4f; >> query: (?x4930, 04ztj) <- award(?x4930, ?x1007), ?x1007 = 03c7tr1, spouse(?x4930, ?x6324) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d0fp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.879 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19661-07k2mq PRED entity: 07k2mq PRED relation: nominated_for! PRED expected values: 09qwmm => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 209): 09qwmm (0.58 #2390, 0.35 #6402, 0.21 #8055), 094qd5 (0.47 #2399, 0.45 #6411, 0.36 #8064), 0gq9h (0.46 #6437, 0.33 #8090, 0.33 #2425), 099cng (0.41 #2431, 0.24 #6443, 0.19 #21960), 0gs9p (0.39 #6439, 0.30 #2427, 0.29 #8092), 0gr4k (0.37 #6401, 0.27 #971, 0.25 #2389), 019f4v (0.35 #6429, 0.27 #10442, 0.26 #8082), 0gqyl (0.32 #6454, 0.28 #8107, 0.24 #2442), 040njc (0.31 #6381, 0.29 #2369, 0.25 #8034), 04dn09n (0.31 #6410, 0.26 #8063, 0.24 #2398) >> Best rule #2390 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 011yxg; 02rqwhl; 09z2b7; 04qw17; 048htn; 04jwly; 01242_; 03cfkrw; 0194zl; 0dgq_kn; ... >> query: (?x4950, 09qwmm) <- nominated_for(?x1245, ?x4950), nominated_for(?x4949, ?x4950), ?x1245 = 0gqwc, film_crew_role(?x4950, ?x468) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07k2mq nominated_for! 09qwmm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.578 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19660-022p06 PRED entity: 022p06 PRED relation: type_of_union PRED expected values: 04ztj => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.90 #25, 0.89 #41, 0.88 #73), 01g63y (0.12 #278, 0.11 #266, 0.11 #258), 01bl8s (0.01 #59, 0.01 #67, 0.01 #71) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 034rd; 02yy8; >> query: (?x4943, 04ztj) <- organizations_founded(?x4943, ?x788), nationality(?x4943, ?x94), people(?x5855, ?x4943) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022p06 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.900 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19659-0cwx_ PRED entity: 0cwx_ PRED relation: major_field_of_study PRED expected values: 05qfh => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 107): 03g3w (0.47 #1245, 0.47 #1134, 0.44 #135), 05qjt (0.47 #1229, 0.40 #230, 0.40 #1562), 062z7 (0.45 #1135, 0.43 #1246, 0.38 #25), 01lj9 (0.43 #1257, 0.40 #1590, 0.33 #591), 01tbp (0.43 #1275, 0.38 #54, 0.34 #1497), 05qfh (0.34 #1476, 0.34 #1254, 0.31 #1587), 06ms6 (0.34 #1237, 0.26 #1570, 0.24 #1459), 01540 (0.33 #1609, 0.32 #1276, 0.27 #1498), 04sh3 (0.32 #1287, 0.26 #1620, 0.26 #1176), 02ky346 (0.30 #1236, 0.25 #15, 0.24 #1458) >> Best rule #1245 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 019q50; >> query: (?x6894, 03g3w) <- list(?x6894, ?x2197), institution(?x1200, ?x6894), ?x1200 = 016t_3 >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1476 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 68 *> proper extension: 01mmslz; 0n00; 043gj; 01tdnyh; 0bdlj; 0l99s; 06rgq; 05yjhm; 018_lb; 0716t2; *> query: (?x6894, 05qfh) <- organization(?x6894, ?x5487), category(?x6894, ?x134) *> conf = 0.34 ranks of expected_values: 6 EVAL 0cwx_ major_field_of_study 05qfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 75.000 75.000 0.468 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19658-01tvz5j PRED entity: 01tvz5j PRED relation: profession PRED expected values: 01d_h8 09jwl => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 53): 0dxtg (0.38 #908, 0.30 #14, 0.30 #5217), 01d_h8 (0.37 #304, 0.36 #900, 0.33 #602), 03gjzk (0.36 #909, 0.30 #5217, 0.25 #2250), 02jknp (0.30 #5217, 0.22 #3130, 0.21 #902), 02krf9 (0.30 #5217, 0.22 #3130, 0.20 #27), 09jwl (0.21 #317, 0.18 #1211, 0.17 #4639), 0dz3r (0.18 #300, 0.11 #1194, 0.11 #3430), 016z4k (0.17 #302, 0.10 #1643, 0.09 #1196), 0cbd2 (0.15 #2093, 0.12 #603, 0.11 #12528), 0np9r (0.15 #8815, 0.11 #2703, 0.11 #11200) >> Best rule #908 for best value: >> intensional similarity = 3 >> extensional distance = 525 >> proper extension: 05qhnq; >> query: (?x426, 0dxtg) <- award_nominee(?x286, ?x426), written_by(?x3133, ?x286), award_winner(?x427, ?x286) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #304 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 033wx9; 09h4b5; 01vz0g4; *> query: (?x426, 01d_h8) <- award_nominee(?x286, ?x426), award(?x426, ?x1254), friend(?x2818, ?x426) *> conf = 0.37 ranks of expected_values: 2, 6 EVAL 01tvz5j profession 09jwl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 86.000 0.376 http://example.org/people/person/profession EVAL 01tvz5j profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.376 http://example.org/people/person/profession #19657-0c58k PRED entity: 0c58k PRED relation: risk_factors! PRED expected values: 07jwr 072hv => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 76): 0m32h (0.60 #498, 0.33 #1171, 0.33 #213), 01qqwn (0.60 #521, 0.33 #1194, 0.33 #236), 09d11 (0.50 #1072, 0.33 #2033, 0.33 #690), 072hv (0.50 #322, 0.12 #2247, 0.12 #1088), 02vrr (0.40 #492, 0.33 #1165, 0.33 #155), 01k9gb (0.40 #526, 0.33 #1199, 0.33 #91), 017s1k (0.40 #495, 0.33 #60, 0.22 #1168), 05mdx (0.33 #2030, 0.33 #1166, 0.33 #12), 01bcp7 (0.33 #56, 0.31 #1642, 0.24 #1790), 0dq9p (0.33 #13, 0.30 #1261, 0.22 #1167) >> Best rule #498 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 01hbgs; >> query: (?x8523, 0m32h) <- risk_factors(?x11064, ?x8523), risk_factors(?x5855, ?x8523), risk_factors(?x4322, ?x8523), people(?x5855, ?x12200), people(?x5855, ?x4112), ?x4112 = 014z8v, ?x4322 = 0gk4g, gender(?x12200, ?x231), symptom_of(?x3679, ?x11064) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #322 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0217g; *> query: (?x8523, 072hv) <- risk_factors(?x8675, ?x8523), risk_factors(?x5801, ?x8523), ?x8675 = 01gkcc, people(?x5801, ?x1056), type_of_union(?x1056, ?x566) *> conf = 0.50 ranks of expected_values: 4, 25 EVAL 0c58k risk_factors! 072hv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 80.000 80.000 0.600 http://example.org/medicine/disease/risk_factors EVAL 0c58k risk_factors! 07jwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 80.000 80.000 0.600 http://example.org/medicine/disease/risk_factors #19656-0chrx PRED entity: 0chrx PRED relation: time_zones PRED expected values: 02fqwt => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 13): 02fqwt (0.83 #1288, 0.64 #1796, 0.63 #1823), 02hcv8 (0.52 #757, 0.51 #822, 0.50 #1277), 02lcqs (0.33 #31, 0.33 #369, 0.32 #395), 02hczc (0.20 #2, 0.10 #184, 0.09 #769), 03bdv (0.12 #136, 0.07 #669, 0.06 #123), 02llzg (0.10 #1057, 0.10 #1096, 0.10 #537), 042g7t (0.06 #76, 0.03 #414, 0.02 #648), 03plfd (0.05 #1076, 0.05 #1102, 0.04 #1063), 0gsrz4 (0.04 #1061, 0.04 #1100, 0.02 #1595), 052vwh (0.02 #64, 0.02 #90, 0.01 #194) >> Best rule #1288 for best value: >> intensional similarity = 3 >> extensional distance = 324 >> proper extension: 0nj1c; 0mmr1; 0ntwb; >> query: (?x8451, ?x1638) <- source(?x8451, ?x958), adjoins(?x8451, ?x11644), time_zones(?x11644, ?x1638) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0chrx time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.829 http://example.org/location/location/time_zones #19655-01z452 PRED entity: 01z452 PRED relation: nominated_for! PRED expected values: 0gs9p 0gqyl => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 176): 027b9ly (0.68 #7049, 0.67 #4546, 0.67 #3409), 0gq9h (0.50 #3467, 0.46 #286, 0.45 #740), 040njc (0.49 #234, 0.37 #688, 0.32 #915), 0gqyl (0.44 #2121, 0.27 #303, 0.26 #757), 0gs9p (0.44 #742, 0.41 #3469, 0.41 #4151), 02pqp12 (0.43 #282, 0.35 #736, 0.30 #963), 019f4v (0.37 #3459, 0.32 #4141, 0.32 #278), 09qv_s (0.35 #336, 0.19 #790, 0.17 #1017), 0k611 (0.33 #3478, 0.30 #297, 0.28 #4160), 0gqy2 (0.33 #3524, 0.30 #343, 0.27 #797) >> Best rule #7049 for best value: >> intensional similarity = 3 >> extensional distance = 986 >> proper extension: 06mmr; >> query: (?x9258, ?x9343) <- award(?x9258, ?x9343), award(?x368, ?x9343), nominated_for(?x9343, ?x1531) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2121 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 365 *> proper extension: 02vxq9m; 0m2kd; 0dsvzh; 0p9lw; 0h3xztt; 018f8; 02prw4h; 032_wv; 0gxtknx; 0bq8tmw; ... *> query: (?x9258, 0gqyl) <- nominated_for(?x3435, ?x9258), nominated_for(?x3435, ?x5736), award(?x237, ?x3435), ?x5736 = 02qpt1w *> conf = 0.44 ranks of expected_values: 4, 5 EVAL 01z452 nominated_for! 0gqyl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 69.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01z452 nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 69.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19654-0140t7 PRED entity: 0140t7 PRED relation: profession PRED expected values: 016z4k => 106 concepts (75 used for prediction) PRED predicted values (max 10 best out of 65): 0nbcg (0.56 #1502, 0.55 #2091, 0.54 #325), 016z4k (0.50 #1770, 0.50 #1623, 0.47 #1181), 0dz3r (0.49 #738, 0.46 #296, 0.44 #1326), 01c72t (0.47 #23, 0.35 #317, 0.32 #4000), 039v1 (0.37 #1507, 0.32 #1360, 0.30 #2096), 01d_h8 (0.29 #9734, 0.28 #5901, 0.28 #6791), 0dxtg (0.28 #9742, 0.25 #10919, 0.25 #10330), 0n1h (0.27 #1925, 0.25 #748, 0.23 #1778), 0fnpj (0.26 #353, 0.21 #1236, 0.19 #1383), 03gjzk (0.23 #9743, 0.21 #10920, 0.21 #7537) >> Best rule #1502 for best value: >> intensional similarity = 3 >> extensional distance = 198 >> proper extension: 01r4zfk; >> query: (?x9321, 0nbcg) <- profession(?x9321, ?x1032), role(?x9321, ?x315), type_of_union(?x9321, ?x566) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1770 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 239 *> proper extension: 0565cz; *> query: (?x9321, 016z4k) <- artist(?x3887, ?x9321), award_nominee(?x9321, ?x9719), instrumentalists(?x212, ?x9321) *> conf = 0.50 ranks of expected_values: 2 EVAL 0140t7 profession 016z4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 75.000 0.555 http://example.org/people/person/profession #19653-0299hs PRED entity: 0299hs PRED relation: film! PRED expected values: 03vgp7 04t969 => 96 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1200): 0h96g (0.22 #2935, 0.14 #853, 0.09 #5017), 01s7zw (0.22 #2509, 0.14 #427, 0.09 #4591), 02zhkz (0.22 #3360, 0.14 #1278, 0.09 #5442), 0kszw (0.18 #4584, 0.05 #14994, 0.05 #31652), 01y0y6 (0.14 #643, 0.11 #2725, 0.09 #4807), 07rzf (0.14 #1884, 0.11 #3966, 0.08 #8130), 07r1h (0.14 #1091, 0.08 #7337, 0.03 #11501), 026c1 (0.14 #359, 0.08 #6605, 0.03 #10769), 02r34n (0.14 #187, 0.08 #6433, 0.03 #10597), 030xr_ (0.14 #1594, 0.06 #9922, 0.05 #18250) >> Best rule #2935 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 032xky; >> query: (?x3433, 0h96g) <- film(?x4748, ?x3433), genre(?x3433, ?x12344), genre(?x3433, ?x225), ?x12344 = 06qln, genre(?x7275, ?x225), ?x7275 = 0g4vmj8 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #6798 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 03g90h; 02w86hz; 0k54q; 042fgh; *> query: (?x3433, 03vgp7) <- film(?x4748, ?x3433), genre(?x3433, ?x1013), film_release_region(?x3433, ?x94), ?x1013 = 06n90 *> conf = 0.08 ranks of expected_values: 78 EVAL 0299hs film! 04t969 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 40.000 0.222 http://example.org/film/actor/film./film/performance/film EVAL 0299hs film! 03vgp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 96.000 40.000 0.222 http://example.org/film/actor/film./film/performance/film #19652-08gwzt PRED entity: 08gwzt PRED relation: location PRED expected values: 0fqxw => 94 concepts (34 used for prediction) PRED predicted values (max 10 best out of 89): 0k3p (0.40 #3613, 0.25 #394), 030qb3t (0.25 #1692, 0.25 #888, 0.11 #4107), 0k049 (0.25 #1617, 0.25 #813, 0.11 #4032), 03gh4 (0.25 #1106, 0.07 #7545, 0.01 #24452), 013ksx (0.25 #969, 0.07 #7408, 0.01 #24315), 02_286 (0.13 #7281, 0.03 #12916, 0.03 #26604), 01xd9 (0.11 #4914, 0.09 #5719, 0.08 #6524), 0b2h3 (0.11 #5116, 0.09 #5921, 0.05 #9946), 02tb17 (0.11 #4665, 0.06 #8690, 0.05 #10300), 0195j0 (0.08 #7065, 0.05 #9480, 0.03 #14310) >> Best rule #3613 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 015n8; >> query: (?x8926, 0k3p) <- nationality(?x8926, ?x1229), gender(?x8926, ?x231), ?x231 = 05zppz, ?x1229 = 059j2 >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08gwzt location 0fqxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 34.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #19651-06c53w PRED entity: 06c53w PRED relation: location_of_ceremony PRED expected values: 0c8tk => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 6): 0c8tk (0.06 #52, 0.01 #172, 0.01 #292), 0yyh (0.03 #104), 04jpl (0.03 #9), 0cv3w (0.02 #993, 0.02 #1351, 0.01 #1830), 04vmp (0.01 #190, 0.01 #310, 0.01 #430), 02_286 (0.01 #133, 0.01 #373, 0.01 #493) >> Best rule #52 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 040nwr; >> query: (?x12549, 0c8tk) <- type_of_union(?x12549, ?x566), nationality(?x12549, ?x2146), ?x566 = 04ztj, category(?x12549, ?x134), ?x2146 = 03rk0 >> conf = 0.06 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06c53w location_of_ceremony 0c8tk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.062 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #19650-09d6p2 PRED entity: 09d6p2 PRED relation: company PRED expected values: 0l8sx 01n073 03ksy 02bh8z 04htfd 02630g 01qd_r 02bm1v => 49 concepts (35 used for prediction) PRED predicted values (max 10 best out of 821): 087c7 (0.75 #4048, 0.64 #6234, 0.50 #6858), 03s7h (0.67 #7093, 0.65 #10231, 0.64 #6469), 0py9b (0.67 #3275, 0.50 #4206, 0.42 #7016), 0z90c (0.64 #6385, 0.62 #4199, 0.58 #7009), 0vlf (0.62 #4307, 0.55 #6493, 0.50 #7117), 01qygl (0.62 #4223, 0.55 #6409, 0.50 #3292), 01s73z (0.62 #4143, 0.55 #6329, 0.50 #3212), 0hpt3 (0.62 #4082, 0.50 #3151, 0.45 #6268), 0841v (0.57 #4012, 0.45 #6507, 0.42 #7131), 03mnk (0.57 #3797, 0.38 #4106, 0.36 #6292) >> Best rule #4048 for best value: >> intensional similarity = 15 >> extensional distance = 6 >> proper extension: 01yc02; 01kr6k; >> query: (?x5161, 087c7) <- company(?x5161, ?x14343), company(?x5161, ?x12350), company(?x5161, ?x11344), company(?x5161, ?x10699), company(?x5161, ?x8237), company(?x5161, ?x3367), ?x8237 = 07xyn1, organization(?x4682, ?x11344), child(?x14343, ?x13890), ?x3367 = 02r5dz, list(?x12350, ?x7472), category(?x10699, ?x134), citytown(?x12350, ?x4600), contact_category(?x12350, ?x897), place_founded(?x10699, ?x1860) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #4174 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 6 *> proper extension: 01yc02; 01kr6k; *> query: (?x5161, 02630g) <- company(?x5161, ?x14343), company(?x5161, ?x12350), company(?x5161, ?x11344), company(?x5161, ?x10699), company(?x5161, ?x8237), company(?x5161, ?x3367), ?x8237 = 07xyn1, organization(?x4682, ?x11344), child(?x14343, ?x13890), ?x3367 = 02r5dz, list(?x12350, ?x7472), category(?x10699, ?x134), citytown(?x12350, ?x4600), contact_category(?x12350, ?x897), place_founded(?x10699, ?x1860) *> conf = 0.50 ranks of expected_values: 14, 15, 27, 37, 47, 61, 64, 524 EVAL 09d6p2 company 02bm1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 49.000 35.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 09d6p2 company 01qd_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 35.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 09d6p2 company 02630g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 49.000 35.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 09d6p2 company 04htfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 49.000 35.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 09d6p2 company 02bh8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 49.000 35.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 09d6p2 company 03ksy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 49.000 35.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 09d6p2 company 01n073 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 49.000 35.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 09d6p2 company 0l8sx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 49.000 35.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #19649-07jnt PRED entity: 07jnt PRED relation: film_release_region PRED expected values: 0k6nt => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 141): 09c7w0 (0.93 #3303, 0.92 #3460, 0.92 #4717), 035qy (0.86 #821, 0.77 #2234, 0.73 #2548), 03rjj (0.85 #792, 0.83 #2205, 0.79 #2362), 0k6nt (0.84 #811, 0.80 #2381, 0.77 #2224), 015fr (0.81 #803, 0.78 #2216, 0.73 #2530), 03spz (0.78 #883, 0.65 #2296, 0.60 #2610), 0154j (0.76 #2204, 0.76 #791, 0.71 #2518), 06bnz (0.74 #2246, 0.73 #833, 0.68 #2560), 0b90_r (0.72 #790, 0.71 #2203, 0.65 #2517), 03rt9 (0.70 #801, 0.67 #2214, 0.62 #2528) >> Best rule #3303 for best value: >> intensional similarity = 3 >> extensional distance = 590 >> proper extension: 04q00lw; 019kyn; 064q5v; >> query: (?x6782, 09c7w0) <- film_release_region(?x6782, ?x87), award(?x6782, ?x3233), film(?x5542, ?x6782) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #811 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 103 *> proper extension: 014lc_; 0b76d_m; 0ds35l9; 02vxq9m; 0c3ybss; 02vp1f_; 01gc7; 07gp9; 0g5qs2k; 0dscrwf; ... *> query: (?x6782, 0k6nt) <- nominated_for(?x185, ?x6782), film_release_region(?x6782, ?x1229), film_release_region(?x6782, ?x550), ?x1229 = 059j2, ?x550 = 05v8c *> conf = 0.84 ranks of expected_values: 4 EVAL 07jnt film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 75.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19648-05ml_s PRED entity: 05ml_s PRED relation: film PRED expected values: 05t54s => 91 concepts (87 used for prediction) PRED predicted values (max 10 best out of 691): 039c26 (0.38 #114611, 0.35 #105660, 0.35 #93128), 016z9n (0.35 #3950, 0.07 #2159, 0.04 #91337), 076xkps (0.17 #1503, 0.06 #5084), 093dqjy (0.13 #2400, 0.08 #610, 0.06 #77015), 0466s8n (0.13 #3426, 0.01 #28490), 055td_ (0.12 #4317, 0.08 #736, 0.07 #2526), 042y1c (0.08 #380, 0.07 #2170, 0.06 #3961), 032sl_ (0.08 #1560, 0.07 #3350, 0.06 #5141), 0g56t9t (0.08 #10, 0.07 #1800, 0.06 #3591), 0dgrwqr (0.08 #1305, 0.07 #3095, 0.06 #4886) >> Best rule #114611 for best value: >> intensional similarity = 3 >> extensional distance = 1952 >> proper extension: 0gp9mp; 04b19t; 01pnn3; 07xr3w; 06n9lt; 0627sn; 03cp7b3; 01nc3rh; 0f3zsq; 065d1h; ... >> query: (?x819, ?x3303) <- profession(?x819, ?x1032), gender(?x819, ?x231), nominated_for(?x819, ?x3303) >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05ml_s film 05t54s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 87.000 0.379 http://example.org/film/actor/film./film/performance/film #19647-024rbz PRED entity: 024rbz PRED relation: child! PRED expected values: 02_l39 => 72 concepts (65 used for prediction) PRED predicted values (max 10 best out of 82): 049ql1 (0.50 #148, 0.45 #3259, 0.32 #1058), 02_l39 (0.50 #142, 0.32 #1058, 0.21 #1037), 0338lq (0.45 #3259, 0.32 #1058, 0.10 #1303), 026s90 (0.45 #3259, 0.03 #1014, 0.03 #1096), 01dtcb (0.39 #774, 0.30 #1264, 0.11 #3138), 09b3v (0.38 #1085, 0.27 #351, 0.26 #432), 01s73z (0.32 #1058, 0.25 #111, 0.10 #1303), 054lpb6 (0.32 #1058, 0.10 #1303, 0.06 #813), 02bh8z (0.27 #756, 0.20 #1246, 0.07 #2956), 086k8 (0.21 #978, 0.21 #1060, 0.09 #326) >> Best rule #148 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 016tw3; 03sb38; >> query: (?x1414, 049ql1) <- film(?x1414, ?x7081), film(?x1414, ?x3430), ?x3430 = 0ctb4g, country(?x7081, ?x94), nominated_for(?x112, ?x7081) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #142 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 016tw3; 03sb38; *> query: (?x1414, 02_l39) <- film(?x1414, ?x7081), film(?x1414, ?x3430), ?x3430 = 0ctb4g, country(?x7081, ?x94), nominated_for(?x112, ?x7081) *> conf = 0.50 ranks of expected_values: 2 EVAL 024rbz child! 02_l39 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 65.000 0.500 http://example.org/organization/organization/child./organization/organization_relationship/child #19646-033smt PRED entity: 033smt PRED relation: film_crew_role! PRED expected values: 09txzv 08phg9 => 48 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1622): 05qbckf (0.80 #22384, 0.78 #19923, 0.71 #17461), 05m_jsg (0.80 #22621, 0.78 #20160, 0.71 #17698), 076xkps (0.80 #23213, 0.78 #20752, 0.71 #18290), 03cp4cn (0.80 #22938, 0.78 #20477, 0.71 #18015), 047gn4y (0.80 #22184, 0.78 #19723, 0.60 #20954), 05p1qyh (0.78 #19971, 0.71 #17509, 0.70 #22432), 01gwk3 (0.78 #20499, 0.71 #18037, 0.70 #22960), 033f8n (0.78 #20292, 0.70 #22753, 0.60 #21523), 0ct2tf5 (0.71 #18319, 0.70 #33088, 0.70 #23242), 04ydr95 (0.71 #17653, 0.70 #22576, 0.70 #21346) >> Best rule #22384 for best value: >> intensional similarity = 14 >> extensional distance = 8 >> proper extension: 0215hd; >> query: (?x8411, 05qbckf) <- film_crew_role(?x8794, ?x8411), film_crew_role(?x8075, ?x8411), film_crew_role(?x6110, ?x8411), film_crew_role(?x5271, ?x8411), film_crew_role(?x1812, ?x8411), ?x5271 = 047vnkj, language(?x8794, ?x254), film(?x1896, ?x8075), genre(?x8794, ?x225), film_crew_role(?x1812, ?x2178), film(?x1387, ?x1812), ?x6110 = 05pdd86, film_release_distribution_medium(?x1812, ?x81), ?x2178 = 01pvkk >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #22793 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 8 *> proper extension: 0215hd; *> query: (?x8411, 08phg9) <- film_crew_role(?x8794, ?x8411), film_crew_role(?x8075, ?x8411), film_crew_role(?x6110, ?x8411), film_crew_role(?x5271, ?x8411), film_crew_role(?x1812, ?x8411), ?x5271 = 047vnkj, language(?x8794, ?x254), film(?x1896, ?x8075), genre(?x8794, ?x225), film_crew_role(?x1812, ?x2178), film(?x1387, ?x1812), ?x6110 = 05pdd86, film_release_distribution_medium(?x1812, ?x81), ?x2178 = 01pvkk *> conf = 0.70 ranks of expected_values: 129, 471 EVAL 033smt film_crew_role! 08phg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 48.000 34.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 033smt film_crew_role! 09txzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 34.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19645-03_3d PRED entity: 03_3d PRED relation: combatants! PRED expected values: 0d06vc => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 63): 081pw (0.44 #911, 0.43 #651, 0.43 #2341), 03gqgt3 (0.40 #381, 0.36 #511, 0.35 #706), 048n7 (0.33 #413, 0.30 #673, 0.29 #868), 01h6pn (0.33 #402, 0.19 #792, 0.17 #662), 0cm2xh (0.26 #661, 0.25 #401, 0.21 #856), 01gjd0 (0.25 #393, 0.22 #653, 0.21 #458), 018w0j (0.25 #426, 0.21 #491, 0.17 #686), 06k75 (0.25 #405, 0.18 #3525, 0.17 #730), 0c3mz (0.25 #429, 0.17 #689, 0.15 #819), 0d06vc (0.22 #655, 0.20 #525, 0.19 #785) >> Best rule #911 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 088xp; >> query: (?x252, 081pw) <- member_states(?x7416, ?x252), administrative_parent(?x536, ?x252), countries_spoken_in(?x254, ?x252) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #655 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 21 *> proper extension: 01f08r; *> query: (?x252, 0d06vc) <- location(?x3118, ?x252), exported_to(?x94, ?x252) *> conf = 0.22 ranks of expected_values: 10 EVAL 03_3d combatants! 0d06vc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 151.000 151.000 0.438 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #19644-02tc5y PRED entity: 02tc5y PRED relation: nationality PRED expected values: 02jx1 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.81 #5226, 0.79 #5427, 0.78 #7240), 07ssc (0.78 #4021, 0.47 #416, 0.37 #920), 02jx1 (0.60 #837, 0.58 #938, 0.56 #1239), 03rk0 (0.35 #1353, 0.11 #4368, 0.10 #4268), 0g14f (0.33 #11058, 0.27 #8542), 05l5n (0.25 #6938), 03rjj (0.10 #607, 0.07 #1312, 0.05 #1011), 0d060g (0.10 #609, 0.06 #6340, 0.06 #207), 0f8l9c (0.10 #624, 0.06 #222, 0.04 #1329), 0345h (0.09 #3348, 0.06 #633, 0.06 #231) >> Best rule #5226 for best value: >> intensional similarity = 3 >> extensional distance = 864 >> proper extension: 0dky9n; >> query: (?x10224, 09c7w0) <- nominated_for(?x10224, ?x6023), place_of_birth(?x10224, ?x1339), time_zones(?x1339, ?x5327) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #837 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 02pqgt8; 03bxh; 01p87y; 07ym0; 01m7f5r; 01qn8k; *> query: (?x10224, 02jx1) <- location(?x10224, ?x362), ?x362 = 04jpl, people(?x743, ?x10224) *> conf = 0.60 ranks of expected_values: 3 EVAL 02tc5y nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 118.000 118.000 0.806 http://example.org/people/person/nationality #19643-01rz1 PRED entity: 01rz1 PRED relation: citytown PRED expected values: 09b83 => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 15): 0rh6k (0.30 #2589, 0.25 #1480, 0.25 #370), 0177z (0.23 #3803, 0.23 #3432, 0.21 #4917), 02_286 (0.20 #753, 0.17 #1123, 0.14 #4826), 0195pd (0.20 #899, 0.12 #2011, 0.09 #3117), 0mp3l (0.20 #2632, 0.11 #5966, 0.10 #6334), 0dclg (0.17 #1150, 0.12 #1521, 0.10 #6332), 03902 (0.14 #4313, 0.12 #5795, 0.09 #3201), 04jpl (0.09 #2963, 0.08 #3704, 0.08 #3333), 0dttf (0.08 #3554, 0.07 #5039, 0.07 #4667), 0fvwg (0.05 #6085) >> Best rule #2589 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 07wbk; 0g8rj; 07x4c; 0d075m; 07wf9; 01hc1j; 05qgd9; >> query: (?x1062, 0rh6k) <- organizations_founded(?x5274, ?x1062), organization(?x5274, ?x127), organizations_founded(?x10499, ?x127) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01rz1 citytown 09b83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 21.000 0.300 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #19642-02d45s PRED entity: 02d45s PRED relation: type_of_union PRED expected values: 01g63y => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.78 #13, 0.71 #81, 0.71 #117), 01g63y (0.33 #6, 0.21 #22, 0.20 #18), 0jgjn (0.02 #12) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 164 >> proper extension: 01xcqc; 028lc8; 03fvqg; 02mxw0; 0gr36; 01rnxn; 015wnl; 02wrrm; 046qq; 015wfg; ... >> query: (?x10866, 04ztj) <- award(?x10866, ?x3066), film(?x10866, ?x1071), ?x3066 = 0gqy2 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 03zg2x; 03mp9s; *> query: (?x10866, 01g63y) <- award_nominee(?x10866, ?x4872), nominated_for(?x10866, ?x1071), ?x4872 = 02d42t *> conf = 0.33 ranks of expected_values: 2 EVAL 02d45s type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.777 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19641-0kvgtf PRED entity: 0kvgtf PRED relation: music PRED expected values: 01hgwkr => 81 concepts (52 used for prediction) PRED predicted values (max 10 best out of 73): 0146pg (0.15 #221, 0.05 #5499, 0.04 #6560), 05_pkf (0.09 #61, 0.01 #693, 0.01 #3439), 0kjrx (0.06 #9296, 0.06 #8664, 0.06 #7819), 02bh9 (0.06 #683, 0.05 #1104, 0.05 #262), 02jxkw (0.06 #142, 0.03 #774, 0.03 #1195), 01jpmpv (0.06 #55, 0.01 #2377, 0.01 #2588), 04pf4r (0.05 #279, 0.03 #68, 0.03 #1121), 0150t6 (0.04 #1943, 0.04 #2579, 0.03 #2368), 01tc9r (0.04 #1751, 0.04 #1538, 0.04 #697), 016szr (0.04 #713, 0.03 #1134, 0.02 #292) >> Best rule #221 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 016ztl; >> query: (?x3781, 0146pg) <- genre(?x3781, ?x239), films(?x942, ?x3781), edited_by(?x3781, ?x7855) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0kvgtf music 01hgwkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 52.000 0.153 http://example.org/film/film/music #19640-0glbqt PRED entity: 0glbqt PRED relation: nominated_for! PRED expected values: 0gqy2 => 107 concepts (100 used for prediction) PRED predicted values (max 10 best out of 209): 0gr4k (0.68 #6088, 0.68 #5619, 0.67 #8198), 0gr0m (0.68 #6088, 0.68 #5619, 0.67 #8198), 0l8z1 (0.68 #6088, 0.68 #5619, 0.67 #8198), 02z1nbg (0.68 #6088, 0.68 #5619, 0.67 #8198), 0gs96 (0.68 #6088, 0.68 #5619, 0.67 #8198), 0gqy2 (0.50 #352, 0.50 #118, 0.33 #1522), 0k611 (0.34 #6859, 0.33 #1472, 0.32 #5921), 0f4x7 (0.33 #258, 0.33 #1428, 0.28 #6815), 0p9sw (0.33 #721, 0.26 #2359, 0.26 #6810), 0gqwc (0.30 #6323, 0.29 #7728, 0.25 #58) >> Best rule #6088 for best value: >> intensional similarity = 4 >> extensional distance = 366 >> proper extension: 0cwrr; 04glx0; 05fgr_; 06mmr; >> query: (?x10531, ?x484) <- award_winner(?x10531, ?x1020), honored_for(?x9899, ?x10531), award(?x10531, ?x484), award_winner(?x294, ?x1020) >> conf = 0.68 => this is the best rule for 5 predicted values *> Best rule #352 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0pd6l; *> query: (?x10531, 0gqy2) <- award_winner(?x10531, ?x6514), nominated_for(?x198, ?x10531), titles(?x162, ?x10531), ?x6514 = 05km8z *> conf = 0.50 ranks of expected_values: 6 EVAL 0glbqt nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 107.000 100.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19639-03cmsqb PRED entity: 03cmsqb PRED relation: nominated_for! PRED expected values: 05p09zm => 114 concepts (111 used for prediction) PRED predicted values (max 10 best out of 191): 05p09zm (0.79 #238, 0.70 #2375, 0.69 #2374), 05b4l5x (0.44 #6, 0.24 #244, 0.19 #24688), 04ljl_l (0.39 #3, 0.18 #241, 0.15 #22553), 05p1dby (0.33 #80, 0.21 #318, 0.19 #24688), 0gq9h (0.31 #10980, 0.31 #6945, 0.31 #5520), 07cbcy (0.28 #61, 0.27 #299, 0.15 #22553), 0gs9p (0.27 #9559, 0.27 #10982, 0.27 #12405), 099c8n (0.27 #2190, 0.27 #1003, 0.26 #1478), 019f4v (0.27 #7174, 0.27 #6936, 0.26 #7412), 0gq_v (0.23 #5479, 0.22 #10939, 0.22 #12362) >> Best rule #238 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 0g5qs2k; >> query: (?x7968, ?x2325) <- award(?x7968, ?x2325), award(?x7968, ?x1007), nominated_for(?x574, ?x7968), nominated_for(?x350, ?x7968), ?x1007 = 03c7tr1 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cmsqb nominated_for! 05p09zm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 111.000 0.787 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19638-01vs_v8 PRED entity: 01vs_v8 PRED relation: influenced_by! PRED expected values: 0478__m => 114 concepts (110 used for prediction) PRED predicted values (max 10 best out of 378): 05ty4m (0.18 #7, 0.06 #9800, 0.05 #4646), 0bqs56 (0.18 #250, 0.05 #4889, 0.05 #16747), 01n5309 (0.18 #19, 0.03 #9812, 0.03 #1566), 0167xy (0.17 #950, 0.09 #7650, 0.06 #12805), 01xwqn (0.14 #444, 0.07 #10237, 0.04 #12815), 01j7rd (0.14 #72, 0.06 #9865, 0.04 #12443), 01s7qqw (0.14 #210, 0.06 #12581, 0.05 #1241), 05rx__ (0.14 #309, 0.04 #825, 0.04 #23507), 03g5jw (0.13 #560, 0.11 #7260, 0.08 #12415), 0478__m (0.11 #23714, 0.05 #182, 0.04 #698) >> Best rule #7 for best value: >> intensional similarity = 2 >> extensional distance = 20 >> proper extension: 022q4j; >> query: (?x2237, 05ty4m) <- influenced_by(?x2138, ?x2237), participant(?x1208, ?x2237) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #23714 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 353 *> proper extension: 07scx; *> query: (?x2237, ?x4593) <- influenced_by(?x2138, ?x2237), influenced_by(?x4593, ?x2138) *> conf = 0.11 ranks of expected_values: 10 EVAL 01vs_v8 influenced_by! 0478__m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 114.000 110.000 0.182 http://example.org/influence/influence_node/influenced_by #19637-0w0d PRED entity: 0w0d PRED relation: country PRED expected values: 0chghy 0345h 03h64 07bxhl 0d04z6 01nyl => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 320): 0chghy (0.89 #5770, 0.82 #301, 0.81 #3188), 0345h (0.88 #5022, 0.88 #3349, 0.88 #3197), 0jgd (0.83 #3640, 0.82 #3488, 0.82 #301), 0d04z6 (0.82 #301, 0.80 #2344, 0.78 #602), 0h7x (0.82 #301, 0.80 #2441, 0.78 #602), 05r4w (0.82 #301, 0.78 #602, 0.77 #3029), 01pj7 (0.82 #301, 0.78 #602, 0.75 #2904), 0ctw_b (0.82 #301, 0.78 #602, 0.75 #3345), 05vz3zq (0.82 #301, 0.78 #602, 0.73 #3179), 087vz (0.82 #301, 0.78 #602, 0.73 #3179) >> Best rule #5770 for best value: >> intensional similarity = 44 >> extensional distance = 42 >> proper extension: 064vjs; >> query: (?x1352, 0chghy) <- country(?x1352, ?x7360), country(?x1352, ?x1229), sports(?x867, ?x1352), country(?x3407, ?x1229), film_release_region(?x7628, ?x1229), film_release_region(?x7265, ?x1229), film_release_region(?x7114, ?x1229), film_release_region(?x6782, ?x1229), film_release_region(?x6620, ?x1229), film_release_region(?x6543, ?x1229), film_release_region(?x5496, ?x1229), film_release_region(?x4684, ?x1229), film_release_region(?x4040, ?x1229), film_release_region(?x3897, ?x1229), film_release_region(?x3757, ?x1229), film_release_region(?x1202, ?x1229), film_release_region(?x303, ?x1229), film_release_region(?x124, ?x1229), member_states(?x2106, ?x1229), ?x5496 = 07l50vn, ?x3897 = 02dpl9, ?x7628 = 0bcp9b, ?x6782 = 07jnt, ?x124 = 0g56t9t, nationality(?x731, ?x1229), ?x3757 = 02vr3gz, country(?x10585, ?x1229), ?x4040 = 02mt51, ?x4684 = 03nm_fh, combatants(?x1229, ?x390), administrative_area_type(?x1229, ?x2792), olympics(?x1229, ?x418), exported_to(?x5360, ?x1229), location(?x2580, ?x1229), organization(?x7360, ?x127), film(?x8151, ?x7265), honored_for(?x5369, ?x7265), ?x303 = 011yrp, ?x7114 = 06rzwx, country(?x1009, ?x1229), ?x10585 = 01gqfm, executive_produced_by(?x6620, ?x846), ?x6543 = 0421v9q, ?x1202 = 0gj8t_b >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 38, 110, 137 EVAL 0w0d country 01nyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 45.000 45.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0w0d country 0d04z6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 45.000 45.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0w0d country 07bxhl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 45.000 45.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0w0d country 03h64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 45.000 45.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0w0d country 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0w0d country 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #19636-02465 PRED entity: 02465 PRED relation: influenced_by PRED expected values: 0jt90f5 => 133 concepts (47 used for prediction) PRED predicted values (max 10 best out of 418): 05jm7 (0.30 #865, 0.14 #537, 0.09 #4329), 0yxl (0.30 #865, 0.09 #4329, 0.07 #8652), 03f0324 (0.29 #582, 0.11 #6208, 0.11 #11830), 0h0p_ (0.29 #621, 0.05 #6247, 0.05 #3217), 01hmk9 (0.21 #1516, 0.15 #4112, 0.13 #2814), 014z8v (0.18 #4012, 0.17 #1416, 0.15 #2714), 01k9lpl (0.17 #1606, 0.12 #2904, 0.11 #4202), 081lh (0.17 #1317, 0.11 #3913, 0.11 #2615), 081k8 (0.17 #14432, 0.12 #6212, 0.11 #1019), 032l1 (0.17 #6146, 0.14 #3116, 0.14 #14366) >> Best rule #865 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 025b3k; >> query: (?x11214, ?x3858) <- profession(?x11214, ?x2225), ?x2225 = 0kyk, influenced_by(?x3858, ?x11214), executive_produced_by(?x11213, ?x11214) >> conf = 0.30 => this is the best rule for 2 predicted values *> Best rule #927 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 02_j7t; 0gs5q; *> query: (?x11214, 0jt90f5) <- profession(?x11214, ?x2225), profession(?x11214, ?x1032), ?x2225 = 0kyk, ?x1032 = 02hrh1q, influenced_by(?x11214, ?x1029) *> conf = 0.03 ranks of expected_values: 203 EVAL 02465 influenced_by 0jt90f5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 133.000 47.000 0.300 http://example.org/influence/influence_node/influenced_by #19635-03v1w7 PRED entity: 03v1w7 PRED relation: award_winner! PRED expected values: 0ds11z => 108 concepts (27 used for prediction) PRED predicted values (max 10 best out of 278): 04jpg2p (0.41 #30651, 0.39 #6808, 0.38 #19301), 05szq8z (0.41 #30651, 0.38 #19301, 0.35 #1135), 027pfg (0.41 #30651, 0.38 #19301, 0.35 #1135), 01y9r2 (0.41 #30651, 0.38 #19301, 0.35 #1135), 0qmjd (0.41 #30651, 0.38 #19301, 0.35 #1135), 0jyb4 (0.41 #30651, 0.38 #19301, 0.35 #1135), 0ds11z (0.39 #6808, 0.34 #14758, 0.25 #6807), 02vyyl8 (0.39 #6808, 0.34 #14758, 0.25 #6807), 04pk1f (0.39 #6808, 0.25 #6807, 0.25 #20437), 04gknr (0.39 #6808, 0.25 #6807, 0.24 #5672) >> Best rule #30651 for best value: >> intensional similarity = 3 >> extensional distance = 877 >> proper extension: 01dw9z; 0c12h; 0k9j_; >> query: (?x6369, ?x2370) <- award_nominee(?x382, ?x6369), nominated_for(?x6369, ?x2370), award_winner(?x944, ?x6369) >> conf = 0.41 => this is the best rule for 6 predicted values *> Best rule #6808 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 133 *> proper extension: 0p51w; 01f8ld; 0jw67; 0171lb; 0534v; 0bs8d; 01_f_5; 013t9y; 06b_0; 06t8b; ... *> query: (?x6369, ?x2370) <- produced_by(?x2370, ?x6369), award_winner(?x944, ?x6369), award_winner(?x2370, ?x1197) *> conf = 0.39 ranks of expected_values: 7 EVAL 03v1w7 award_winner! 0ds11z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 108.000 27.000 0.411 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #19634-05d6q1 PRED entity: 05d6q1 PRED relation: film PRED expected values: 043sct5 04z_3pm 0bs8ndx 09v42sf => 118 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1692): 027qgy (0.76 #14305, 0.68 #19076, 0.67 #38150), 02nczh (0.73 #14304, 0.68 #27023, 0.64 #19075), 09v71cj (0.50 #653, 0.24 #8600, 0.13 #7010), 05c46y6 (0.50 #389, 0.12 #8336, 0.09 #13103), 0c3ybss (0.50 #26, 0.06 #7973, 0.06 #49302), 0gtvpkw (0.33 #2094, 0.24 #8452, 0.17 #13219), 01dvbd (0.33 #2032, 0.20 #6800, 0.18 #8390), 0h14ln (0.33 #2957, 0.20 #7725, 0.18 #9315), 0cc5qkt (0.33 #2117, 0.12 #8475, 0.09 #13242), 0ds3t5x (0.33 #1633, 0.08 #30245, 0.08 #4811) >> Best rule #14305 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 03rwz3; >> query: (?x8394, ?x238) <- industry(?x8394, ?x373), production_companies(?x6427, ?x8394), film(?x8394, ?x1228), nominated_for(?x8394, ?x238) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1219 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 03xq0f; *> query: (?x8394, 04z_3pm) <- film(?x8394, ?x6005), film(?x8394, ?x1498), ?x6005 = 051ys82, film_release_region(?x1498, ?x2152), ?x2152 = 06mkj *> conf = 0.25 ranks of expected_values: 136, 137, 956 EVAL 05d6q1 film 09v42sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 101.000 0.764 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 05d6q1 film 0bs8ndx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 118.000 101.000 0.764 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 05d6q1 film 04z_3pm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 118.000 101.000 0.764 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 05d6q1 film 043sct5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 118.000 101.000 0.764 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #19633-012t1 PRED entity: 012t1 PRED relation: profession PRED expected values: 0q04f => 127 concepts (121 used for prediction) PRED predicted values (max 10 best out of 82): 01d_h8 (0.86 #3683, 0.85 #3830, 0.85 #2506), 02hrh1q (0.81 #3543, 0.79 #4425, 0.77 #16923), 02jknp (0.67 #742, 0.59 #448, 0.57 #3243), 03gjzk (0.43 #5749, 0.42 #1485, 0.41 #2514), 0kyk (0.32 #4294, 0.26 #1353, 0.25 #1647), 018gz8 (0.25 #3987, 0.17 #5751, 0.17 #457), 02krf9 (0.19 #5761, 0.19 #1497, 0.17 #467), 09jwl (0.17 #3548, 0.17 #6489, 0.17 #13105), 0nbcg (0.17 #31, 0.14 #15881, 0.12 #619), 01c72t (0.17 #23, 0.14 #15881, 0.12 #2670) >> Best rule #3683 for best value: >> intensional similarity = 3 >> extensional distance = 342 >> proper extension: 02xnjd; 0glyyw; >> query: (?x1047, 01d_h8) <- nationality(?x1047, ?x94), produced_by(?x9138, ?x1047), profession(?x1047, ?x353) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #245 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 06n9lt; 032md; 01y8d4; *> query: (?x1047, 0q04f) <- place_of_birth(?x1047, ?x2850), place_of_death(?x1047, ?x1131), written_by(?x5134, ?x1047) *> conf = 0.10 ranks of expected_values: 47 EVAL 012t1 profession 0q04f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 127.000 121.000 0.863 http://example.org/people/person/profession #19632-0jt3qpk PRED entity: 0jt3qpk PRED relation: award_winner PRED expected values: 05f7snc 06jw0s 09pl3f 020ffd => 19 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1199): 047q2wc (0.59 #7643, 0.49 #10719, 0.48 #1526), 05f7snc (0.59 #7643, 0.49 #10719, 0.48 #1526), 020ffd (0.59 #7643, 0.49 #10719, 0.48 #1526), 06jw0s (0.59 #7643, 0.49 #10719, 0.48 #1526), 02b9g4 (0.59 #7643, 0.49 #10719, 0.48 #1526), 01w_10 (0.59 #7643, 0.48 #12253, 0.39 #6115), 01j7rd (0.50 #9489, 0.48 #11023, 0.08 #12559), 0cp9f9 (0.43 #8833, 0.33 #5766, 0.33 #4235), 063lqs (0.40 #6691, 0.33 #5162, 0.33 #574), 02xs0q (0.35 #9734, 0.33 #11268, 0.05 #12804) >> Best rule #7643 for best value: >> intensional similarity = 18 >> extensional distance = 3 >> proper extension: 073hd1; >> query: (?x2751, ?x5574) <- award_winner(?x2751, ?x5413), award_winner(?x2751, ?x4377), award_winner(?x2751, ?x439), award_winner(?x7721, ?x4377), ceremony(?x7644, ?x2751), award_nominee(?x6868, ?x439), award_nominee(?x3974, ?x4377), award_nominee(?x5413, ?x5574), profession(?x5413, ?x4725), nominated_for(?x439, ?x416), ?x4725 = 015cjr, award_winner(?x439, ?x2828), location(?x5413, ?x4627), ?x7721 = 0gkxgfq, award_winner(?x7644, ?x3183), award(?x105, ?x7644), award_winner(?x912, ?x6868), award_winner(?x6868, ?x10375) >> conf = 0.59 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3, 4, 67 EVAL 0jt3qpk award_winner 020ffd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 19.000 10.000 0.588 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0jt3qpk award_winner 09pl3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 19.000 10.000 0.588 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0jt3qpk award_winner 06jw0s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 19.000 10.000 0.588 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0jt3qpk award_winner 05f7snc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 19.000 10.000 0.588 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19631-016kv6 PRED entity: 016kv6 PRED relation: nominated_for! PRED expected values: 02x4x18 => 68 concepts (60 used for prediction) PRED predicted values (max 10 best out of 206): 0gs9p (0.32 #1471, 0.26 #4517, 0.25 #2878), 0gq9h (0.31 #1469, 0.30 #4515, 0.27 #2876), 019f4v (0.28 #1460, 0.25 #4506, 0.24 #2867), 0gqy2 (0.25 #119, 0.24 #937, 0.23 #938), 0k611 (0.24 #1479, 0.24 #937, 0.23 #938), 0bdwqv (0.24 #3048, 0.22 #7263, 0.19 #12884), 04dn09n (0.24 #937, 0.23 #938, 0.22 #14056), 0gr0m (0.24 #937, 0.23 #938, 0.21 #60), 02qyntr (0.24 #937, 0.23 #938, 0.20 #1582), 0p9sw (0.24 #937, 0.23 #938, 0.19 #489) >> Best rule #1471 for best value: >> intensional similarity = 2 >> extensional distance = 331 >> proper extension: 0lcdk; 0542n; 087z2; >> query: (?x3523, 0gs9p) <- award(?x3523, ?x2183), disciplines_or_subjects(?x2183, ?x6760) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1641 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 331 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x3523, ?x618) <- award(?x3523, ?x2183), disciplines_or_subjects(?x2183, ?x6760), award(?x1209, ?x2183), award(?x1209, ?x618) *> conf = 0.16 ranks of expected_values: 48 EVAL 016kv6 nominated_for! 02x4x18 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 68.000 60.000 0.321 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19630-02yplc PRED entity: 02yplc PRED relation: film PRED expected values: 07yvsn => 87 concepts (70 used for prediction) PRED predicted values (max 10 best out of 667): 039cq4 (0.60 #55311, 0.58 #64232, 0.40 #67801), 0f2sx4 (0.33 #1380, 0.04 #3164, 0.02 #6732), 0640m69 (0.22 #1756, 0.04 #3540, 0.02 #7108), 025s1wg (0.22 #1700, 0.02 #5268, 0.02 #17758), 07y9w5 (0.22 #227, 0.02 #7365, 0.02 #16285), 03nfnx (0.15 #3182, 0.08 #6750, 0.03 #17456), 03z20c (0.12 #2258, 0.11 #474, 0.06 #5826), 03bx2lk (0.11 #185, 0.08 #1969, 0.04 #5537), 03rtz1 (0.11 #168, 0.08 #1952, 0.04 #5520), 03cyslc (0.11 #1201, 0.08 #2985, 0.04 #6553) >> Best rule #55311 for best value: >> intensional similarity = 3 >> extensional distance = 1334 >> proper extension: 06jzh; 0785v8; 06lgq8; 02xb2bt; 01wb8bs; 05typm; 02qw2xb; >> query: (?x4263, ?x6884) <- nominated_for(?x4263, ?x6884), film(?x4263, ?x3761), nominated_for(?x601, ?x3761) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02yplc film 07yvsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 70.000 0.599 http://example.org/film/actor/film./film/performance/film #19629-0k6yt1 PRED entity: 0k6yt1 PRED relation: award PRED expected values: 02f76h => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 278): 09sb52 (0.31 #41, 0.31 #6858, 0.28 #14878), 01by1l (0.31 #3321, 0.31 #4123, 0.29 #3722), 05p09zm (0.30 #125, 0.17 #927, 0.16 #526), 01bgqh (0.26 #3251, 0.26 #1246, 0.24 #4053), 03qbh5 (0.22 #1409, 0.21 #3414, 0.17 #4617), 05pcn59 (0.22 #483, 0.20 #82, 0.18 #884), 05zr6wv (0.20 #17, 0.17 #418, 0.11 #819), 07cbcy (0.19 #79, 0.11 #881, 0.10 #480), 0c4z8 (0.18 #4082, 0.17 #3280, 0.16 #8092), 01c99j (0.18 #1430, 0.14 #4237, 0.12 #3435) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 08b8vd; >> query: (?x11123, 09sb52) <- friend(?x11123, ?x4628), religion(?x11123, ?x492), film(?x11123, ?x2968) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #1382 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 173 *> proper extension: 01q_ph; 0m0hw; *> query: (?x11123, 02f76h) <- award(?x11123, ?x3488), artist(?x1954, ?x11123), film(?x11123, ?x2968) *> conf = 0.08 ranks of expected_values: 61 EVAL 0k6yt1 award 02f76h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 94.000 94.000 0.315 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19628-03rk0 PRED entity: 03rk0 PRED relation: country! PRED expected values: 0c3zjn7 => 219 concepts (135 used for prediction) PRED predicted values (max 10 best out of 1716): 07vfy4 (0.75 #13538, 0.05 #201391, 0.05 #201390), 0h3xztt (0.40 #89693, 0.34 #108311, 0.34 #108310), 0h2zvzr (0.40 #89693, 0.34 #108311, 0.34 #108310), 0h95927 (0.40 #89693, 0.34 #108311, 0.34 #108310), 030z4z (0.40 #89693, 0.34 #108311, 0.34 #108310), 03hmt9b (0.40 #89693, 0.31 #226776, 0.12 #5691), 0_9l_ (0.40 #89693, 0.13 #13479, 0.12 #6711), 09gb_4p (0.40 #89693, 0.12 #5802, 0.11 #7494), 05k4my (0.40 #89693, 0.12 #6637, 0.06 #15098), 05qbbfb (0.40 #89693, 0.12 #6067, 0.06 #14528) >> Best rule #13538 for best value: >> intensional similarity = 2 >> extensional distance = 13 >> proper extension: 06mx8; >> query: (?x2146, ?x257) <- contains(?x2146, ?x1391), titles(?x2146, ?x257) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #5971 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0853g; *> query: (?x2146, 0c3zjn7) <- exported_to(?x2146, ?x3352), contains(?x2146, ?x1391), place_of_birth(?x1806, ?x2146) *> conf = 0.12 ranks of expected_values: 1143 EVAL 03rk0 country! 0c3zjn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 219.000 135.000 0.750 http://example.org/film/film/country #19627-02c638 PRED entity: 02c638 PRED relation: nominated_for! PRED expected values: 020_95 05bm4sm => 122 concepts (50 used for prediction) PRED predicted values (max 10 best out of 970): 0237jb (0.83 #6995, 0.79 #86284, 0.78 #83950), 05fyss (0.83 #6995, 0.79 #86284, 0.78 #83950), 02h1rt (0.50 #1041, 0.33 #8036, 0.27 #5704), 086k8 (0.39 #25649, 0.13 #55966, 0.10 #76955), 0js9s (0.38 #1428, 0.27 #6091, 0.25 #8423), 05bm4sm (0.38 #1257, 0.25 #8252, 0.18 #5920), 022411 (0.37 #39639, 0.37 #60630, 0.36 #74622), 02vg0 (0.37 #39639, 0.37 #60630, 0.36 #74622), 021yc7p (0.27 #2645, 0.12 #9639, 0.06 #30625), 05qd_ (0.26 #23490, 0.09 #2506, 0.05 #9500) >> Best rule #6995 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 015ynm; >> query: (?x2116, ?x6071) <- nominated_for(?x1983, ?x2116), ?x1983 = 04ktcgn, film_release_region(?x2116, ?x94), award_winner(?x2116, ?x6071) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #1257 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0bth54; 017gm7; 05qbckf; 017jd9; 0ndwt2w; 02dr9j; *> query: (?x2116, 05bm4sm) <- nominated_for(?x1983, ?x2116), ?x1983 = 04ktcgn, written_by(?x2116, ?x7761), language(?x2116, ?x254) *> conf = 0.38 ranks of expected_values: 6, 358 EVAL 02c638 nominated_for! 05bm4sm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 50.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 02c638 nominated_for! 020_95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 122.000 50.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #19626-01vng3b PRED entity: 01vng3b PRED relation: role PRED expected values: 01vj9c => 141 concepts (92 used for prediction) PRED predicted values (max 10 best out of 124): 02sgy (0.50 #5, 0.48 #2883, 0.43 #213), 05842k (0.50 #1921, 0.47 #1104, 0.46 #1819), 01vj9c (0.44 #2064, 0.37 #2891, 0.35 #3391), 042v_gx (0.43 #2885, 0.31 #3604, 0.25 #7), 05148p4 (0.35 #3391, 0.34 #1640, 0.33 #104), 0l14md (0.35 #3391, 0.34 #1640, 0.33 #5137), 03bx0bm (0.34 #1640, 0.31 #2257, 0.30 #3390), 013y1f (0.33 #104, 0.31 #103, 0.28 #2673), 01vdm0 (0.33 #2908, 0.32 #1363, 0.28 #7138), 02k856 (0.31 #103, 0.28 #2673, 0.26 #4725) >> Best rule #5 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02bgmr; >> query: (?x6225, 02sgy) <- artists(?x13553, ?x6225), ?x13553 = 0b_6yv, instrumentalists(?x1166, ?x6225), family(?x228, ?x1166), role(?x248, ?x1166) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2064 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 37 *> proper extension: 0bg539; 02ldv0; 03m6_z; *> query: (?x6225, 01vj9c) <- role(?x6225, ?x716), nationality(?x6225, ?x94), ?x716 = 018vs, gender(?x6225, ?x231), profession(?x6225, ?x131) *> conf = 0.44 ranks of expected_values: 3 EVAL 01vng3b role 01vj9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 141.000 92.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #19625-081k8 PRED entity: 081k8 PRED relation: influenced_by! PRED expected values: 07g2b 01v9724 01vs4f3 01hc9_ 0969fd => 164 concepts (64 used for prediction) PRED predicted values (max 10 best out of 365): 05jm7 (0.50 #3964, 0.29 #4923, 0.22 #4313), 07g2b (0.40 #2889, 0.33 #973, 0.33 #494), 04411 (0.40 #3378, 0.33 #982), 03_hd (0.40 #3524, 0.22 #4313, 0.16 #3354), 03sbs (0.40 #3623, 0.05 #17262, 0.05 #19663), 05qzv (0.33 #4208, 0.33 #1333, 0.29 #5167), 04hcw (0.33 #1229, 0.33 #271, 0.22 #4313), 02kz_ (0.33 #689, 0.33 #210, 0.22 #4313), 013pp3 (0.33 #688, 0.33 #209, 0.22 #4313), 045bg (0.33 #992, 0.22 #4313, 0.20 #3388) >> Best rule #3964 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0g5ff; >> query: (?x5004, 05jm7) <- influenced_by(?x10654, ?x5004), influenced_by(?x476, ?x5004), influenced_by(?x2994, ?x10654), ?x476 = 07w21, place_of_death(?x10654, ?x4861) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2889 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 028p0; 032l1; 01rgr; *> query: (?x5004, 07g2b) <- profession(?x5004, ?x2225), influenced_by(?x10895, ?x5004), influenced_by(?x1278, ?x5004), ?x1278 = 016hvl, influenced_by(?x1236, ?x10895) *> conf = 0.40 ranks of expected_values: 2, 43, 46, 55, 110 EVAL 081k8 influenced_by! 0969fd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 164.000 64.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 081k8 influenced_by! 01hc9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 164.000 64.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 081k8 influenced_by! 01vs4f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 164.000 64.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 081k8 influenced_by! 01v9724 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 164.000 64.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 081k8 influenced_by! 07g2b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 164.000 64.000 0.500 http://example.org/influence/influence_node/influenced_by #19624-01v6480 PRED entity: 01v6480 PRED relation: award_winner! PRED expected values: 02rjjll => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 129): 01mh_q (0.47 #1068, 0.10 #508, 0.07 #4570), 01s695 (0.24 #423, 0.20 #983, 0.14 #3), 013b2h (0.20 #499, 0.18 #1059, 0.10 #4561), 01c6qp (0.20 #438, 0.16 #998, 0.09 #4500), 02cg41 (0.19 #1105, 0.18 #545, 0.08 #4607), 02rjjll (0.16 #425, 0.15 #985, 0.09 #4487), 0466p0j (0.16 #495, 0.14 #1055, 0.09 #4557), 01xqqp (0.15 #1075, 0.14 #515, 0.07 #1915), 01mhwk (0.14 #40, 0.13 #1020, 0.12 #460), 019bk0 (0.14 #995, 0.12 #435, 0.08 #4497) >> Best rule #1068 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 07c0j; 028qdb; >> query: (?x6879, 01mh_q) <- award_winner(?x4183, ?x6879), award_winner(?x725, ?x6879), award_winner(?x725, ?x7882), ?x7882 = 01z9_x >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #425 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 04rcr; 02r3zy; 011zf2; 0288fyj; 0dw4g; 01jkqfz; 09jm8; 014kyy; 012x03; *> query: (?x6879, 02rjjll) <- award_winner(?x4183, ?x6879), award_winner(?x725, ?x6879), ?x725 = 01bx35, award(?x6879, ?x458) *> conf = 0.16 ranks of expected_values: 6 EVAL 01v6480 award_winner! 02rjjll CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 109.000 109.000 0.471 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19623-0dlngsd PRED entity: 0dlngsd PRED relation: executive_produced_by PRED expected values: 0fvf9q => 77 concepts (56 used for prediction) PRED predicted values (max 10 best out of 128): 01twdk (0.18 #871, 0.02 #1374, 0.02 #1877), 06q8hf (0.16 #1931, 0.10 #2690, 0.10 #6980), 041c4 (0.14 #125, 0.11 #631, 0.01 #2648), 01vh3r (0.14 #237, 0.11 #743), 0282x (0.14 #129), 05hj_k (0.14 #1862, 0.13 #2621, 0.12 #856), 0mm1q (0.13 #4041, 0.13 #4546, 0.04 #9333), 079vf (0.12 #1766, 0.06 #760, 0.05 #3283), 04jspq (0.12 #909, 0.04 #4697, 0.04 #6964), 063b4k (0.12 #1006, 0.04 #2012, 0.02 #2264) >> Best rule #871 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 04hwbq; 0gmcwlb; 0bq8tmw; 04n52p6; 05qbckf; 0gd0c7x; 0661m4p; 06wbm8q; 06ztvyx; 09g7vfw; ... >> query: (?x4615, 01twdk) <- executive_produced_by(?x4615, ?x8503), film_release_region(?x4615, ?x8593), film_crew_role(?x4615, ?x137), ?x8593 = 01crd5 >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #5810 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 334 *> proper extension: 048htn; 04t6fk; 0kv9d3; 05n6sq; 02_06s; 09y6pb; 03hp2y1; *> query: (?x4615, 0fvf9q) <- executive_produced_by(?x4615, ?x8503), nominated_for(?x2373, ?x4615), genre(?x4615, ?x53), film_crew_role(?x4615, ?x137) *> conf = 0.02 ranks of expected_values: 55 EVAL 0dlngsd executive_produced_by 0fvf9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 77.000 56.000 0.176 http://example.org/film/film/executive_produced_by #19622-0193x PRED entity: 0193x PRED relation: major_field_of_study! PRED expected values: 01j_cy 07wrz 01bm_ 05x_5 07wm6 => 52 concepts (27 used for prediction) PRED predicted values (max 10 best out of 634): 0bwfn (0.73 #1974, 0.67 #3101, 0.62 #3664), 07wrz (0.67 #1190, 0.64 #2318, 0.64 #1754), 0dzst (0.67 #3178, 0.62 #3741, 0.60 #923), 07w0v (0.67 #2838, 0.62 #3401, 0.55 #1711), 07vhb (0.67 #3000, 0.62 #3563, 0.40 #745), 01bm_ (0.67 #1387, 0.60 #823, 0.50 #3078), 07szy (0.64 #1732, 0.60 #604, 0.58 #2859), 01j_9c (0.64 #1703, 0.42 #2830, 0.40 #575), 05krk (0.60 #572, 0.50 #1136, 0.45 #1700), 065y4w7 (0.58 #2833, 0.54 #3396, 0.45 #1706) >> Best rule #1974 for best value: >> intensional similarity = 14 >> extensional distance = 9 >> proper extension: 01mkq; 03g3w; >> query: (?x3489, 0bwfn) <- major_field_of_study(?x5288, ?x3489), major_field_of_study(?x3424, ?x3489), major_field_of_study(?x1884, ?x3489), major_field_of_study(?x1220, ?x3489), ?x3424 = 01w5m, major_field_of_study(?x865, ?x3489), category(?x1220, ?x134), student(?x1220, ?x1221), ?x1884 = 0bx8pn, student(?x5288, ?x460), major_field_of_study(?x5288, ?x12158), major_field_of_study(?x5288, ?x7403), ?x7403 = 06mnr, ?x12158 = 09s1f >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1190 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 4 *> proper extension: 037mh8; *> query: (?x3489, 07wrz) <- major_field_of_study(?x10368, ?x3489), major_field_of_study(?x5288, ?x3489), major_field_of_study(?x3424, ?x3489), major_field_of_study(?x2999, ?x3489), major_field_of_study(?x1220, ?x3489), ?x3424 = 01w5m, major_field_of_study(?x3437, ?x3489), major_field_of_study(?x1526, ?x3489), ?x5288 = 02zd460, organization(?x4095, ?x1220), ?x3437 = 02_xgp2, ?x1526 = 0bkj86, school_type(?x10368, ?x3205), student(?x2999, ?x8306), ?x8306 = 0xnc3 *> conf = 0.67 ranks of expected_values: 2, 6, 15, 88, 106 EVAL 0193x major_field_of_study! 07wm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 52.000 27.000 0.727 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0193x major_field_of_study! 05x_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 52.000 27.000 0.727 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0193x major_field_of_study! 01bm_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 52.000 27.000 0.727 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0193x major_field_of_study! 07wrz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 27.000 0.727 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0193x major_field_of_study! 01j_cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 52.000 27.000 0.727 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19621-04wg38 PRED entity: 04wg38 PRED relation: nationality PRED expected values: 09c7w0 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.78 #4109, 0.76 #1, 0.76 #301), 02jx1 (0.12 #2238, 0.10 #634, 0.10 #734), 07ssc (0.11 #1216, 0.10 #2220, 0.10 #616), 03rk0 (0.08 #2651, 0.07 #2351, 0.06 #5154), 0d060g (0.05 #507, 0.05 #1910, 0.05 #2011), 0chghy (0.03 #611, 0.03 #711, 0.03 #2415), 03rjj (0.02 #2210, 0.02 #5113, 0.02 #5714), 0f8l9c (0.02 #2627, 0.02 #923, 0.02 #5130), 0345h (0.02 #5740, 0.02 #2636, 0.02 #131), 03rt9 (0.02 #1214, 0.02 #513, 0.02 #2618) >> Best rule #4109 for best value: >> intensional similarity = 2 >> extensional distance = 1515 >> proper extension: 0cl_m; 02vptk_; 03c_8t; >> query: (?x7731, 09c7w0) <- student(?x4889, ?x7731), colors(?x4889, ?x332) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wg38 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.780 http://example.org/people/person/nationality #19620-01hvjx PRED entity: 01hvjx PRED relation: film_crew_role PRED expected values: 01vx2h => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.74 #589, 0.72 #444, 0.71 #1022), 09zzb8 (0.74 #582, 0.73 #437, 0.71 #1270), 09vw2b7 (0.65 #588, 0.63 #189, 0.62 #1021), 0dxtw (0.41 #303, 0.41 #193, 0.36 #1025), 01vx2h (0.40 #194, 0.37 #304, 0.34 #593), 02rh1dz (0.19 #192, 0.17 #302, 0.14 #374), 02ynfr (0.16 #308, 0.16 #597, 0.15 #452), 0215hd (0.16 #201, 0.14 #600, 0.13 #455), 0d2b38 (0.16 #208, 0.12 #607, 0.12 #2911), 01xy5l_ (0.13 #196, 0.13 #595, 0.12 #2911) >> Best rule #589 for best value: >> intensional similarity = 3 >> extensional distance = 423 >> proper extension: 0cw3yd; >> query: (?x2349, 0ch6mp2) <- film_crew_role(?x2349, ?x468), film(?x286, ?x2349), executive_produced_by(?x2349, ?x163) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #194 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 122 *> proper extension: 0hgnl3t; *> query: (?x2349, 01vx2h) <- film(?x902, ?x2349), film_crew_role(?x2349, ?x468), ?x902 = 05qd_ *> conf = 0.40 ranks of expected_values: 5 EVAL 01hvjx film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 91.000 0.744 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19619-0432cd PRED entity: 0432cd PRED relation: profession PRED expected values: 01d_h8 => 118 concepts (47 used for prediction) PRED predicted values (max 10 best out of 75): 01d_h8 (0.74 #1931, 0.44 #598, 0.40 #746), 0dxtg (0.63 #1938, 0.35 #753, 0.34 #457), 0cbd2 (0.36 #451, 0.24 #1339, 0.23 #2228), 03gjzk (0.28 #754, 0.26 #1642, 0.25 #1939), 0kyk (0.26 #621, 0.19 #1657, 0.19 #2398), 02krf9 (0.20 #1951, 0.12 #26, 0.09 #3136), 09jwl (0.19 #2535, 0.17 #758, 0.16 #3424), 0np9r (0.18 #5944, 0.17 #6240, 0.17 #316), 018gz8 (0.17 #312, 0.15 #4162, 0.15 #3126), 021wpb (0.17 #348, 0.01 #2125, 0.01 #940) >> Best rule #1931 for best value: >> intensional similarity = 4 >> extensional distance = 144 >> proper extension: 0cc63l; 06gn7r; 024jwt; 02vtnf; 0l9k1; >> query: (?x7607, 01d_h8) <- award(?x7607, ?x458), religion(?x7607, ?x1985), profession(?x7607, ?x524), ?x524 = 02jknp >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0432cd profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 47.000 0.740 http://example.org/people/person/profession #19618-01713c PRED entity: 01713c PRED relation: award_winner! PRED expected values: 02pgky2 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 118): 092868 (0.17 #138, 0.01 #1945), 0418154 (0.15 #246, 0.10 #5839, 0.02 #663), 09qvms (0.15 #151, 0.06 #2931, 0.06 #4321), 03gwpw2 (0.10 #5839, 0.10 #148, 0.08 #9), 04n2r9h (0.10 #5839, 0.05 #183, 0.02 #461), 09bymc (0.10 #5839, 0.02 #4428, 0.02 #6375), 09p2r9 (0.10 #5839, 0.02 #4540, 0.02 #1621), 03gt46z (0.10 #5839, 0.02 #4371, 0.01 #6318), 050yyb (0.10 #5839, 0.02 #3929, 0.02 #4485), 0n8_m93 (0.10 #5839, 0.02 #1645, 0.01 #533) >> Best rule #138 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 01ly8d; >> query: (?x1582, 092868) <- nationality(?x1582, ?x2152), ?x2152 = 06mkj >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #5839 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1059 *> proper extension: 06688p; 07lmxq; 033hqf; 018dnt; 041ly3; 01wjrn; 02zyy4; 02wrhj; 02lq10; 05wjnt; ... *> query: (?x1582, ?x2294) <- nationality(?x1582, ?x2152), film(?x1582, ?x534), honored_for(?x2294, ?x534) *> conf = 0.10 ranks of expected_values: 11 EVAL 01713c award_winner! 02pgky2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 111.000 111.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19617-09lcsj PRED entity: 09lcsj PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.87 #101, 0.87 #179, 0.86 #137), 07z4p (0.12 #20, 0.11 #25, 0.10 #30), 02nxhr (0.06 #32, 0.05 #62, 0.05 #195), 07c52 (0.03 #206, 0.03 #161, 0.03 #108) >> Best rule #101 for best value: >> intensional similarity = 9 >> extensional distance = 99 >> proper extension: 09m6kg; 047gn4y; 03ckwzc; 06_wqk4; 053rxgm; 02r79_h; 05sxzwc; 05pbl56; 075wx7_; 02rb84n; ... >> query: (?x3537, 029j_) <- film_crew_role(?x3537, ?x4305), film_crew_role(?x3537, ?x2178), genre(?x3537, ?x162), film(?x4800, ?x3537), film_crew_role(?x9900, ?x2178), film_crew_role(?x8679, ?x2178), ?x9900 = 0qmfk, ?x8679 = 023g6w, ?x4305 = 0215hd >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09lcsj film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19616-012m_ PRED entity: 012m_ PRED relation: official_language PRED expected values: 02bjrlw 0k0sv 01wgr => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.86 #3194, 0.54 #1712, 0.51 #1864), 0jzc (0.26 #1721, 0.15 #657, 0.12 #505), 064_8sq (0.22 #165, 0.20 #355, 0.17 #545), 06nm1 (0.18 #2475, 0.17 #2779, 0.16 #3273), 02bv9 (0.18 #512, 0.10 #854, 0.09 #892), 02bjrlw (0.12 #115, 0.09 #229, 0.08 #267), 01wgr (0.12 #101, 0.07 #405, 0.07 #367), 06b_j (0.12 #52, 0.05 #1724, 0.05 #698), 0295r (0.11 #171, 0.07 #361, 0.06 #513), 06mp7 (0.11 #160, 0.06 #502, 0.05 #654) >> Best rule #3194 for best value: >> intensional similarity = 5 >> extensional distance = 79 >> proper extension: 027nb; 02khs; 06s6l; 07z5n; 05qkp; 0h8d; 047t_; 06ryl; 0166v; 04tr1; ... >> query: (?x9006, 02h40lc) <- official_language(?x9006, ?x8650), official_language(?x9006, ?x1049), countries_spoken_in(?x1049, ?x279), language(?x11685, ?x8650), ?x11685 = 017n9 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #115 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02jx1; 0h7x; *> query: (?x9006, 02bjrlw) <- nationality(?x11251, ?x9006), place_of_burial(?x11251, ?x3691), award_winner(?x8119, ?x11251), location(?x8299, ?x9006) *> conf = 0.12 ranks of expected_values: 6, 7, 12 EVAL 012m_ official_language 01wgr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 171.000 171.000 0.864 http://example.org/location/country/official_language EVAL 012m_ official_language 0k0sv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 171.000 171.000 0.864 http://example.org/location/country/official_language EVAL 012m_ official_language 02bjrlw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 171.000 171.000 0.864 http://example.org/location/country/official_language #19615-03t95n PRED entity: 03t95n PRED relation: nominated_for! PRED expected values: 01mkn_d => 92 concepts (38 used for prediction) PRED predicted values (max 10 best out of 835): 01mkn_d (0.79 #46791, 0.67 #25728, 0.59 #2339), 015t7v (0.25 #30406, 0.24 #65507, 0.21 #32749), 0bsb4j (0.25 #30406, 0.24 #65507, 0.21 #32749), 02lkcc (0.24 #65507, 0.21 #32749, 0.20 #32751), 07ncs0 (0.24 #65507, 0.21 #32749, 0.20 #32751), 01s7z0 (0.18 #51470, 0.16 #16373, 0.14 #14034), 016tw3 (0.12 #77205, 0.11 #53810, 0.05 #18712), 0146pg (0.09 #23510, 0.04 #9477, 0.04 #44572), 0284n42 (0.09 #2459, 0.04 #56268, 0.04 #67968), 06pj8 (0.07 #79979, 0.05 #23823, 0.04 #47225) >> Best rule #46791 for best value: >> intensional similarity = 4 >> extensional distance = 207 >> proper extension: 05h95s; 07s8z_l; 01j95; >> query: (?x6615, ?x6664) <- titles(?x811, ?x6615), category(?x6615, ?x134), award_winner(?x6615, ?x6664), ?x134 = 08mbj5d >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t95n nominated_for! 01mkn_d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 38.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #19614-0dq3c PRED entity: 0dq3c PRED relation: company PRED expected values: 02y7t7 061v5m 01b39j 0206k5 02l48d 0plw 06py2 => 51 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1598): 02r5dz (0.71 #6463, 0.67 #5005, 0.67 #4714), 07xyn1 (0.71 #6563, 0.67 #5105, 0.67 #4814), 09b3v (0.67 #5022, 0.67 #4731, 0.57 #6480), 0hpt3 (0.67 #4981, 0.67 #4690, 0.57 #6439), 04sv4 (0.67 #5125, 0.62 #6875, 0.57 #6583), 01yfp7 (0.67 #5053, 0.57 #6511, 0.57 #5636), 0sxdg (0.67 #5120, 0.57 #6578, 0.57 #5703), 0py9b (0.67 #5098, 0.57 #6556, 0.50 #6848), 0l8sx (0.67 #4979, 0.57 #6437, 0.50 #6729), 0gy1_ (0.67 #5225, 0.57 #6683, 0.50 #6975) >> Best rule #6463 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 05_wyz; >> query: (?x265, 02r5dz) <- company(?x265, ?x8931), company(?x265, ?x7633), company(?x265, ?x2975), company(?x265, ?x2270), company(?x265, ?x94), company(?x11869, ?x94), company(?x11088, ?x94), ?x7633 = 0z90c, service_location(?x2975, ?x279), citytown(?x8931, ?x6555), participant(?x11088, ?x543), type_of_union(?x11869, ?x566), ?x2270 = 0300cp >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5646 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 028fjr; *> query: (?x265, 061v5m) <- company(?x265, ?x6016), company(?x265, ?x5072), company(?x265, ?x4549), company(?x265, ?x1561), industry(?x5072, ?x5615), citytown(?x6016, ?x3794), production_companies(?x6918, ?x1561), state_province_region(?x4549, ?x335), music(?x6918, ?x8374), service_location(?x6016, ?x279), award_winner(?x1689, ?x1561) *> conf = 0.57 ranks of expected_values: 21, 22, 27, 30, 32, 136, 219 EVAL 0dq3c company 06py2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 51.000 43.000 0.714 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0dq3c company 0plw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 51.000 43.000 0.714 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0dq3c company 02l48d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 51.000 43.000 0.714 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0dq3c company 0206k5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 51.000 43.000 0.714 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0dq3c company 01b39j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 51.000 43.000 0.714 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0dq3c company 061v5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 51.000 43.000 0.714 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0dq3c company 02y7t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 51.000 43.000 0.714 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #19613-07024 PRED entity: 07024 PRED relation: nominated_for! PRED expected values: 05ztjjw => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 190): 0k611 (0.70 #1716, 0.67 #6214, 0.67 #1072), 02r22gf (0.70 #1716, 0.67 #6214, 0.67 #1072), 018wdw (0.70 #1716, 0.67 #6214, 0.67 #1072), 0gr0m (0.70 #1716, 0.67 #6214, 0.67 #1072), 09d28z (0.70 #1716, 0.67 #6214, 0.67 #1072), 0gr4k (0.45 #3238, 0.28 #4094, 0.24 #4736), 0gqy2 (0.37 #3318, 0.26 #6316, 0.26 #4174), 0gqyl (0.32 #3279, 0.25 #3216, 0.25 #3215), 05ztjjw (0.32 #864, 0.25 #3216, 0.25 #3215), 05pcn59 (0.27 #52, 0.25 #12860, 0.25 #3216) >> Best rule #1716 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 0hv81; >> query: (?x2928, ?x500) <- award_winner(?x2928, ?x496), film_crew_role(?x2928, ?x137), award(?x2928, ?x500), nominated_for(?x2928, ?x2262) >> conf = 0.70 => this is the best rule for 5 predicted values *> Best rule #864 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 0372j5; *> query: (?x2928, 05ztjjw) <- film(?x496, ?x2928), award(?x2928, ?x637), nominated_for(?x637, ?x144), region(?x2928, ?x512) *> conf = 0.32 ranks of expected_values: 9 EVAL 07024 nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 100.000 100.000 0.702 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19612-02cllz PRED entity: 02cllz PRED relation: award_winner! PRED expected values: 027dtxw => 118 concepts (116 used for prediction) PRED predicted values (max 10 best out of 239): 0ck27z (0.58 #2255, 0.56 #1822, 0.55 #958), 09sb52 (0.39 #865, 0.36 #2162, 0.35 #9940), 057xs89 (0.39 #865, 0.36 #2162, 0.35 #9940), 027dtxw (0.33 #4, 0.10 #436, 0.09 #1301), 09cm54 (0.33 #97, 0.09 #962, 0.06 #3123), 0bfvd4 (0.33 #116, 0.09 #981, 0.06 #1845), 02x4w6g (0.33 #115, 0.09 #980, 0.06 #1844), 03nqnk3 (0.30 #567, 0.27 #1432, 0.09 #1000), 0bb57s (0.18 #1107, 0.11 #1971, 0.11 #2404), 02ppm4q (0.18 #1021, 0.11 #1885, 0.11 #2318) >> Best rule #2255 for best value: >> intensional similarity = 2 >> extensional distance = 17 >> proper extension: 08_83x; >> query: (?x2457, 0ck27z) <- award_nominee(?x2457, ?x5144), ?x5144 = 017gxw >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0171cm; *> query: (?x2457, 027dtxw) <- award_nominee(?x2457, ?x5144), award_nominee(?x2457, ?x1222), ?x5144 = 017gxw, ?x1222 = 03f1zdw *> conf = 0.33 ranks of expected_values: 4 EVAL 02cllz award_winner! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 118.000 116.000 0.579 http://example.org/award/award_category/winners./award/award_honor/award_winner #19611-05sw5b PRED entity: 05sw5b PRED relation: genre PRED expected values: 0hcr => 70 concepts (66 used for prediction) PRED predicted values (max 10 best out of 87): 07s9rl0 (0.74 #5941, 0.58 #4002, 0.57 #3638), 02kdv5l (0.66 #1093, 0.49 #729, 0.33 #850), 0hcr (0.61 #387, 0.41 #508, 0.36 #629), 09b3v (0.54 #2181, 0.53 #4729, 0.53 #3759), 01hmnh (0.47 #381, 0.34 #502, 0.32 #744), 04xvlr (0.38 #244, 0.25 #123, 0.22 #2), 01jfsb (0.38 #1102, 0.32 #859, 0.32 #738), 04t36 (0.34 #369, 0.23 #490, 0.20 #611), 02l7c8 (0.33 #984, 0.29 #5956, 0.28 #4017), 06n90 (0.30 #1103, 0.28 #739, 0.22 #13) >> Best rule #5941 for best value: >> intensional similarity = 5 >> extensional distance = 1360 >> proper extension: 0fq27fp; 0cbl95; >> query: (?x4766, 07s9rl0) <- genre(?x4766, ?x258), genre(?x10349, ?x258), genre(?x1330, ?x258), ?x10349 = 09qycb, ?x1330 = 03m4mj >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #387 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 05h95s; 01hvv0; 07vqnc; *> query: (?x4766, 0hcr) <- titles(?x3920, ?x4766), award_nominee(?x7980, ?x3920), organizations_founded(?x1377, ?x7980) *> conf = 0.61 ranks of expected_values: 3 EVAL 05sw5b genre 0hcr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 70.000 66.000 0.739 http://example.org/film/film/genre #19610-020trj PRED entity: 020trj PRED relation: film PRED expected values: 013q0p => 111 concepts (89 used for prediction) PRED predicted values (max 10 best out of 615): 076zy_g (0.33 #5371, 0.32 #8952, 0.15 #14323), 02qzh2 (0.10 #693, 0.02 #2483, 0.02 #4273), 06fpsx (0.10 #1339, 0.02 #4919, 0.02 #8500), 03hp2y1 (0.10 #1611, 0.01 #37415, 0.01 #10563), 0f42nz (0.06 #6280, 0.03 #25972, 0.03 #40293), 034qzw (0.06 #7495, 0.05 #334, 0.04 #3914), 035xwd (0.05 #116, 0.03 #110985, 0.03 #119937), 04165w (0.05 #1318, 0.03 #110985, 0.03 #119937), 0prrm (0.05 #861, 0.03 #33085, 0.03 #4441), 0bvn25 (0.05 #50, 0.03 #3630, 0.02 #7211) >> Best rule #5371 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 098n_m; 02p59ry; >> query: (?x5833, ?x5155) <- profession(?x5833, ?x319), film(?x5833, ?x2755), place_of_birth(?x5833, ?x6050), produced_by(?x5155, ?x5833) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #33031 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 348 *> proper extension: 0q9kd; 012d40; 014zcr; 05ty4m; 01qscs; 09fb5; 032xhg; 02nb2s; 025p38; 09byk; ... *> query: (?x5833, 013q0p) <- profession(?x5833, ?x319), film(?x5833, ?x2755), ?x319 = 01d_h8, location(?x5833, ?x2623) *> conf = 0.02 ranks of expected_values: 378 EVAL 020trj film 013q0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 111.000 89.000 0.328 http://example.org/film/actor/film./film/performance/film #19609-011_3s PRED entity: 011_3s PRED relation: student! PRED expected values: 01s0_f => 117 concepts (115 used for prediction) PRED predicted values (max 10 best out of 162): 01s0_f (0.15 #60), 02l9wl (0.12 #3941, 0.10 #1833, 0.10 #2887), 03ksy (0.11 #6957, 0.05 #19605, 0.05 #5903), 09f2j (0.08 #159, 0.04 #17550, 0.04 #1213), 07wrz (0.08 #62, 0.02 #5332, 0.02 #6913), 02mj7c (0.08 #165, 0.01 #9124, 0.01 #20191), 02x9cv (0.08 #322), 02bqy (0.08 #182), 05krk (0.08 #7), 0bwfn (0.07 #1329, 0.06 #12396, 0.05 #6599) >> Best rule #60 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01rrwf6; 01d_4t; 031c2r; >> query: (?x3267, 01s0_f) <- actor(?x5047, ?x3267), place_of_birth(?x3267, ?x479), ?x479 = 02dtg >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011_3s student! 01s0_f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 115.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #19608-0g96wd PRED entity: 0g96wd PRED relation: people PRED expected values: 0h32q 05l0j5 => 22 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1766): 048cl (0.50 #2764, 0.10 #13105, 0.09 #24132), 0k4gf (0.50 #1880, 0.10 #12221, 0.07 #22564), 06cgy (0.33 #1921, 0.33 #197, 0.21 #15712), 032_jg (0.33 #1834, 0.33 #110, 0.20 #3559), 022_q8 (0.33 #2523, 0.33 #799, 0.20 #4248), 08f3b1 (0.33 #1816, 0.33 #92, 0.14 #5264), 014x77 (0.33 #1794, 0.33 #70, 0.13 #3519), 0h0yt (0.33 #2799, 0.33 #1075, 0.13 #4524), 01j2xj (0.33 #2424, 0.33 #700, 0.13 #4149), 025t9b (0.33 #2258, 0.33 #534, 0.13 #3983) >> Best rule #2764 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 013xrm; >> query: (?x12950, 048cl) <- people(?x12950, ?x11018), profession(?x11018, ?x987), influenced_by(?x11018, ?x12146), ?x12146 = 01lwx >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #15515 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 31 *> proper extension: 0bbz66j; *> query: (?x12950, ?x269) <- people(?x12950, ?x4996), languages_spoken(?x12950, ?x9617), film(?x4996, ?x8773), film(?x4996, ?x4971), award(?x8773, ?x198), ?x198 = 040njc, produced_by(?x8773, ?x2800), film(?x269, ?x4971) *> conf = 0.07 ranks of expected_values: 721 EVAL 0g96wd people 05l0j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 22.000 20.000 0.500 http://example.org/people/ethnicity/people EVAL 0g96wd people 0h32q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 20.000 0.500 http://example.org/people/ethnicity/people #19607-01vs73g PRED entity: 01vs73g PRED relation: artists! PRED expected values: 0y3_8 => 108 concepts (80 used for prediction) PRED predicted values (max 10 best out of 216): 06j6l (0.47 #5597, 0.42 #1590, 0.38 #972), 05bt6j (0.37 #42, 0.26 #1585, 0.25 #11760), 0gywn (0.36 #1599, 0.33 #2523, 0.32 #56), 0xhtw (0.33 #325, 0.24 #12351, 0.19 #8635), 02lnbg (0.29 #1600, 0.24 #982, 0.23 #57), 016clz (0.24 #313, 0.22 #7404, 0.22 #7713), 03lty (0.22 #336, 0.16 #12362, 0.12 #645), 03_d0 (0.22 #12, 0.21 #1246, 0.20 #4327), 0155w (0.22 #104, 0.19 #2571, 0.18 #1338), 02x8m (0.21 #5569, 0.18 #19, 0.17 #944) >> Best rule #5597 for best value: >> intensional similarity = 3 >> extensional distance = 383 >> proper extension: 0qmpd; >> query: (?x7908, 06j6l) <- artists(?x2937, ?x7908), artists(?x2937, ?x3834), ?x3834 = 01wzlxj >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #5596 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 383 *> proper extension: 0qmpd; *> query: (?x7908, 0y3_8) <- artists(?x2937, ?x7908), artists(?x2937, ?x3834), ?x3834 = 01wzlxj *> conf = 0.09 ranks of expected_values: 40 EVAL 01vs73g artists! 0y3_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 108.000 80.000 0.468 http://example.org/music/genre/artists #19606-02t_99 PRED entity: 02t_99 PRED relation: actor! PRED expected values: 070ltt => 140 concepts (102 used for prediction) PRED predicted values (max 10 best out of 100): 02zv4b (0.25 #25, 0.03 #553, 0.03 #1345), 026bfsh (0.07 #1416, 0.05 #11987, 0.05 #13572), 039cq4 (0.05 #392, 0.05 #2769, 0.05 #3297), 0cpz4k (0.05 #326, 0.04 #854), 03ln8b (0.05 #295, 0.03 #1351, 0.03 #13243), 0k0q73t (0.05 #505, 0.02 #1033, 0.02 #1561), 03bww6 (0.05 #396, 0.02 #924, 0.02 #1452), 06qv_ (0.05 #474, 0.02 #1002, 0.01 #2322), 0fpxp (0.05 #412, 0.02 #940, 0.01 #12039), 02xhwm (0.05 #479, 0.02 #1007) >> Best rule #25 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 015882; 05dbf; >> query: (?x4638, 02zv4b) <- participant(?x4638, ?x2534), people(?x1446, ?x4638), ?x2534 = 0lx2l, profession(?x4638, ?x1032) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02t_99 actor! 070ltt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 102.000 0.250 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #19605-07ylj PRED entity: 07ylj PRED relation: olympics PRED expected values: 0kbvv => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 37): 0kbws (0.69 #309, 0.63 #904, 0.61 #87), 0kbvv (0.57 #320, 0.43 #915, 0.43 #98), 0kbvb (0.54 #80, 0.53 #191, 0.51 #302), 018ctl (0.50 #81, 0.43 #743, 0.43 #742), 0jdk_ (0.43 #321, 0.39 #99, 0.35 #210), 0swbd (0.43 #84, 0.37 #306, 0.33 #195), 0jhn7 (0.39 #100, 0.33 #211, 0.31 #322), 0l6vl (0.39 #1151, 0.38 #1747, 0.38 #1709), 0l6ny (0.39 #1151, 0.38 #1747, 0.38 #1709), 0l998 (0.39 #1151, 0.38 #1747, 0.38 #1709) >> Best rule #309 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 05v8c; 06qd3; 03rj0; 06t2t; >> query: (?x1203, 0kbws) <- jurisdiction_of_office(?x265, ?x1203), film_release_region(?x1259, ?x1203), ?x1259 = 04hwbq >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #320 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 05v8c; 06qd3; 03rj0; 06t2t; *> query: (?x1203, 0kbvv) <- jurisdiction_of_office(?x265, ?x1203), film_release_region(?x1259, ?x1203), ?x1259 = 04hwbq *> conf = 0.57 ranks of expected_values: 2 EVAL 07ylj olympics 0kbvv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 116.000 0.686 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #19604-081nh PRED entity: 081nh PRED relation: award PRED expected values: 0p9sw => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 312): 05qck (0.71 #33630, 0.70 #34432, 0.70 #34431), 040njc (0.54 #8815, 0.32 #408, 0.30 #808), 019f4v (0.37 #8873, 0.22 #10875, 0.21 #466), 0gs9p (0.33 #8885, 0.23 #10887, 0.21 #478), 05p1dby (0.30 #906, 0.28 #1707, 0.24 #2107), 07bdd_ (0.30 #865, 0.24 #1666, 0.21 #465), 09sb52 (0.28 #24863, 0.26 #14052, 0.25 #23661), 040vk98 (0.26 #5233, 0.11 #10437, 0.11 #10036), 0f_nbyh (0.25 #810, 0.21 #8817, 0.21 #410), 0gqy2 (0.24 #3764, 0.20 #6168, 0.19 #8170) >> Best rule #33630 for best value: >> intensional similarity = 4 >> extensional distance = 1561 >> proper extension: 0kx4m; 030_1m; 0hpt3; 01795t; 046b0s; 024rgt; 03fbc; 0163m1; 0hvbj; 01fmz6; ... >> query: (?x2426, ?x720) <- award_winner(?x5409, ?x2426), award_winner(?x720, ?x2426), award_nominee(?x2426, ?x1377), award(?x382, ?x5409) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #423 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 04pg29; *> query: (?x2426, 0p9sw) <- nominated_for(?x2426, ?x2425), award_winner(?x4445, ?x2426), organizations_founded(?x2426, ?x99) *> conf = 0.05 ranks of expected_values: 135 EVAL 081nh award 0p9sw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 120.000 120.000 0.707 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19603-027ybp PRED entity: 027ybp PRED relation: institution! PRED expected values: 016t_3 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 19): 02_xgp2 (0.59 #210, 0.58 #250, 0.58 #50), 016t_3 (0.55 #43, 0.52 #303, 0.48 #203), 07s6fsf (0.52 #41, 0.43 #301, 0.42 #362), 04zx3q1 (0.35 #202, 0.33 #242, 0.31 #42), 027f2w (0.31 #47, 0.29 #207, 0.27 #247), 013zdg (0.28 #26, 0.25 #246, 0.24 #46), 022h5x (0.21 #177, 0.18 #378, 0.17 #257), 01rr_d (0.17 #214, 0.16 #355, 0.15 #516), 03mkk4 (0.16 #249, 0.15 #209, 0.15 #269), 028dcg (0.14 #176, 0.11 #56, 0.10 #377) >> Best rule #210 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 065y4w7; 02cttt; 07w0v; 03ksy; 0h6rm; 07tds; 0gl5_; 02sjgpq; 015fsv; 02z6fs; ... >> query: (?x9768, 02_xgp2) <- institution(?x4981, ?x9768), citytown(?x9768, ?x5174), ?x4981 = 03bwzr4, school_type(?x9768, ?x1507) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 03np_7; *> query: (?x9768, 016t_3) <- institution(?x4981, ?x9768), citytown(?x9768, ?x5174), ?x4981 = 03bwzr4, county(?x5174, ?x5173) *> conf = 0.55 ranks of expected_values: 2 EVAL 027ybp institution! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 147.000 0.589 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #19602-047dpm0 PRED entity: 047dpm0 PRED relation: draft! PRED expected values: 051vz 05xvj => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 84): 051vz (0.75 #1082, 0.60 #663, 0.58 #504), 01ypc (0.75 #1082, 0.60 #649, 0.57 #428), 07l8f (0.75 #1082, 0.60 #691, 0.57 #428), 0x0d (0.75 #1082, 0.60 #709, 0.57 #428), 05xvj (0.75 #1082, 0.57 #428, 0.54 #285), 03lpp_ (0.75 #1082, 0.57 #428, 0.51 #1081), 05tg3 (0.60 #792, 0.60 #743, 0.58 #504), 02c_4 (0.60 #769, 0.58 #504, 0.54 #285), 05tfm (0.60 #731, 0.58 #504, 0.54 #285), 06rpd (0.60 #774, 0.58 #504, 0.50 #486) >> Best rule #1082 for best value: >> intensional similarity = 48 >> extensional distance = 12 >> proper extension: 09th87; >> query: (?x11905, ?x260) <- draft(?x10279, ?x11905), draft(?x1160, ?x11905), draft(?x700, ?x11905), draft(?x580, ?x11905), school(?x11905, ?x6953), school(?x11905, ?x3777), draft(?x1160, ?x8786), team(?x12323, ?x10279), teams(?x2017, ?x1160), team(?x4244, ?x10279), colors(?x10279, ?x1101), colors(?x10279, ?x663), school(?x580, ?x8706), school(?x580, ?x6083), school(?x580, ?x4257), school(?x580, ?x3948), school(?x580, ?x2175), school(?x580, ?x1675), organization(?x346, ?x6083), sport(?x700, ?x5063), school(?x1160, ?x10104), contains(?x94, ?x6083), student(?x6083, ?x11630), currency(?x6083, ?x170), ?x1101 = 06fvc, ?x663 = 083jv, ?x2175 = 01ptt7, institution(?x1368, ?x6083), draft(?x260, ?x8786), school(?x8786, ?x4211), ?x4257 = 01q0kg, ?x1675 = 01j_cy, ?x8706 = 0trv, colors(?x580, ?x332), student(?x3948, ?x1068), major_field_of_study(?x3777, ?x3213), school(?x1239, ?x6953), major_field_of_study(?x3948, ?x10046), major_field_of_study(?x3948, ?x742), colors(?x1160, ?x12067), major_field_of_study(?x6953, ?x10380), profession(?x12323, ?x14261), ?x742 = 05qjt, school_type(?x3777, ?x1507), major_field_of_study(?x10104, ?x1527), contains(?x1274, ?x3948), ?x10046 = 041y2, service_language(?x3948, ?x254) >> conf = 0.75 => this is the best rule for 6 predicted values ranks of expected_values: 1, 5 EVAL 047dpm0 draft! 05xvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 17.000 17.000 0.749 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 047dpm0 draft! 051vz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 17.000 17.000 0.749 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #19601-0s69k PRED entity: 0s69k PRED relation: source PRED expected values: 0jbk9 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #19, 0.92 #23, 0.91 #39) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 157 >> proper extension: 0mn0v; >> query: (?x1964, 0jbk9) <- county(?x1964, ?x11658), time_zones(?x1964, ?x1638), location(?x194, ?x1964) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0s69k source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.931 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #19600-0p8bz PRED entity: 0p8bz PRED relation: contains! PRED expected values: 06q1r => 61 concepts (21 used for prediction) PRED predicted values (max 10 best out of 193): 09c7w0 (0.70 #5384, 0.63 #6280, 0.56 #7175), 02jx1 (0.55 #86, 0.55 #981, 0.37 #1877), 02qkt (0.27 #12550, 0.15 #3930, 0.14 #4828), 0345h (0.15 #2769, 0.05 #6358, 0.04 #8149), 03rk0 (0.13 #2824, 0.04 #9102, 0.04 #9999), 01n7q (0.13 #5458, 0.10 #7249, 0.10 #9043), 0f8l9c (0.12 #1837, 0.10 #2734, 0.03 #6323), 02j9z (0.12 #3612, 0.12 #4510, 0.05 #5379), 0j0k (0.10 #3961, 0.10 #4859), 0dg3n1 (0.10 #3738, 0.10 #4636) >> Best rule #5384 for best value: >> intensional similarity = 3 >> extensional distance = 1265 >> proper extension: 018mmj; 02j416; >> query: (?x14704, 09c7w0) <- contains(?x512, ?x14704), location_of_ceremony(?x566, ?x512), place_founded(?x11950, ?x512) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #351 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 254 *> proper extension: 04jpl; 0ymbl; 0dhdp; 0fm2_; 022_6; 02jx1; 0zc6f; 0crjn65; 0dbdy; 05l5n; ... *> query: (?x14704, 06q1r) <- contains(?x512, ?x14704), ?x512 = 07ssc *> conf = 0.08 ranks of expected_values: 12 EVAL 0p8bz contains! 06q1r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 61.000 21.000 0.704 http://example.org/location/location/contains #19599-01mqnr PRED entity: 01mqnr PRED relation: award_winner! PRED expected values: 059x66 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 137): 059x66 (0.33 #18, 0.17 #12182, 0.03 #1138), 027hjff (0.24 #337, 0.05 #4257, 0.04 #4537), 02wzl1d (0.17 #12182, 0.17 #11, 0.03 #4211), 02q690_ (0.17 #12182, 0.08 #345, 0.07 #765), 03nnm4t (0.17 #12182, 0.07 #774, 0.06 #1194), 0gx_st (0.17 #12182, 0.04 #737, 0.03 #1157), 04n2r9h (0.17 #45, 0.06 #325, 0.02 #5085), 092c5f (0.17 #14, 0.06 #4214, 0.05 #4494), 02jp5r (0.17 #69, 0.05 #909, 0.04 #349), 073h1t (0.17 #27, 0.04 #727, 0.04 #867) >> Best rule #18 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 07rd7; 0g9zcgx; 0cw67g; >> query: (?x8179, 059x66) <- nominated_for(?x8179, ?x5890), ?x5890 = 02lxrv, award(?x8179, ?x112) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mqnr award_winner! 059x66 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19598-04g51 PRED entity: 04g51 PRED relation: major_field_of_study! PRED expected values: 04rwx => 81 concepts (65 used for prediction) PRED predicted values (max 10 best out of 770): 06pwq (0.79 #17431, 0.75 #15110, 0.69 #23252), 08815 (0.75 #12195, 0.67 #12776, 0.62 #15099), 01w5m (0.74 #17536, 0.69 #15215, 0.67 #9987), 01j_cy (0.71 #11072, 0.50 #9910, 0.38 #12234), 07szy (0.71 #19784, 0.66 #23281, 0.63 #17460), 07wrz (0.69 #15162, 0.67 #12839, 0.63 #17483), 09f2j (0.67 #10044, 0.63 #18176, 0.63 #17593), 07tg4 (0.67 #12866, 0.62 #12285, 0.60 #8220), 027xx3 (0.67 #9962, 0.43 #11124, 0.33 #3577), 0bwfn (0.63 #17132, 0.62 #12487, 0.58 #20036) >> Best rule #17431 for best value: >> intensional similarity = 13 >> extensional distance = 17 >> proper extension: 02lp1; >> query: (?x5864, 06pwq) <- major_field_of_study(?x1771, ?x5864), major_field_of_study(?x1368, ?x5864), major_field_of_study(?x1200, ?x5864), major_field_of_study(?x734, ?x5864), ?x1200 = 016t_3, ?x734 = 04zx3q1, major_field_of_study(?x7582, ?x5864), major_field_of_study(?x2014, ?x5864), student(?x5864, ?x879), ?x1368 = 014mlp, currency(?x7582, ?x170), institution(?x1771, ?x5777), ?x5777 = 06bw5 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #9908 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 02_7t; *> query: (?x5864, 04rwx) <- major_field_of_study(?x1200, ?x5864), major_field_of_study(?x734, ?x5864), ?x1200 = 016t_3, ?x734 = 04zx3q1, major_field_of_study(?x4794, ?x5864), major_field_of_study(?x741, ?x5864), major_field_of_study(?x2014, ?x5864), school_type(?x741, ?x1044), institution(?x620, ?x741), currency(?x741, ?x170), ?x4794 = 027kp3 *> conf = 0.50 ranks of expected_values: 29 EVAL 04g51 major_field_of_study! 04rwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 81.000 65.000 0.789 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19597-05dptj PRED entity: 05dptj PRED relation: country PRED expected values: 07ssc => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 111): 07ssc (0.41 #1994, 0.35 #197, 0.25 #16), 02jx1 (0.41 #1994, 0.19 #2666, 0.04 #663), 0chghy (0.41 #1994, 0.07 #12, 0.04 #663), 0345h (0.11 #208, 0.11 #27, 0.09 #2447), 0f8l9c (0.09 #200, 0.08 #3711, 0.08 #2439), 0ctw_b (0.07 #23, 0.04 #663, 0.04 #204), 01z4y (0.06 #2604, 0.06 #61, 0.06 #3330), 04xvlr (0.06 #2604, 0.06 #61, 0.06 #3330), 03_3d (0.04 #663, 0.04 #3699, 0.04 #188), 0d060g (0.04 #663, 0.04 #2428, 0.04 #2489) >> Best rule #1994 for best value: >> intensional similarity = 4 >> extensional distance = 1096 >> proper extension: 01b9w3; 0d7vtk; 06k176; 02qr46y; 02rkkn1; >> query: (?x7671, ?x512) <- titles(?x162, ?x7671), nominated_for(?x11985, ?x7671), nominated_for(?x143, ?x7671), nationality(?x11985, ?x512) >> conf = 0.41 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 05dptj country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.405 http://example.org/film/film/country #19596-015ppk PRED entity: 015ppk PRED relation: nominated_for! PRED expected values: 0bp_b2 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 186): 0ck27z (0.84 #1169, 0.80 #2809, 0.79 #2105), 0bp_b2 (0.64 #17, 0.58 #251, 0.53 #951), 0bdw1g (0.47 #966, 0.38 #266, 0.36 #32), 09v7wsg (0.36 #173, 0.25 #407, 0.24 #1107), 0gq9h (0.35 #4740, 0.30 #11268, 0.28 #13139), 09qvf4 (0.34 #612, 0.23 #14949, 0.20 #2485), 0gq_v (0.34 #11226, 0.22 #4698, 0.20 #14031), 027gs1_ (0.33 #2524, 0.31 #651, 0.28 #2993), 09qv3c (0.31 #508, 0.22 #2381, 0.22 #1911), 02xcb6n (0.29 #431, 0.25 #15651, 0.23 #12609) >> Best rule #1169 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 03j63k; 0m123; 0300ml; >> query: (?x7116, ?x8250) <- award(?x7116, ?x8250), award(?x6482, ?x8250), ?x6482 = 0180mw, award(?x286, ?x8250) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0g60z; 080dwhx; 0kfv9; 03d34x8; 017f3m; 0hz55; 04p5cr; 0180mw; 0fhzwl; *> query: (?x7116, 0bp_b2) <- award(?x7116, ?x8250), program(?x1394, ?x7116), ?x8250 = 0cqhb3, actor(?x7116, ?x8431) *> conf = 0.64 ranks of expected_values: 2 EVAL 015ppk nominated_for! 0bp_b2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.835 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19595-07gp9 PRED entity: 07gp9 PRED relation: films! PRED expected values: 07s2s => 107 concepts (51 used for prediction) PRED predicted values (max 10 best out of 79): 0fx2s (0.09 #385, 0.05 #1482, 0.04 #698), 07s2s (0.07 #411, 0.03 #1351, 0.03 #99), 06ys2 (0.07 #129, 0.03 #285, 0.01 #1224), 0ddct (0.06 #244, 0.03 #1340, 0.01 #713), 081pw (0.04 #315, 0.04 #3618, 0.03 #3935), 02_h0 (0.04 #412, 0.03 #256, 0.02 #3715), 06d4h (0.04 #355, 0.03 #825, 0.02 #3658), 01vq3 (0.04 #353, 0.02 #1606, 0.02 #509), 0kbq (0.04 #730, 0.02 #1514, 0.01 #3720), 05489 (0.04 #1617, 0.03 #208, 0.03 #3667) >> Best rule #385 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 03lrqw; >> query: (?x324, 0fx2s) <- nominated_for(?x3458, ?x324), ?x3458 = 0gqxm, production_companies(?x324, ?x1561) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #411 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 03lrqw; *> query: (?x324, 07s2s) <- nominated_for(?x3458, ?x324), ?x3458 = 0gqxm, production_companies(?x324, ?x1561) *> conf = 0.07 ranks of expected_values: 2 EVAL 07gp9 films! 07s2s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 51.000 0.089 http://example.org/film/film_subject/films #19594-0m_q0 PRED entity: 0m_q0 PRED relation: nominated_for! PRED expected values: 0gs9p => 81 concepts (75 used for prediction) PRED predicted values (max 10 best out of 207): 0gs9p (0.68 #1676, 0.65 #752, 0.64 #2600), 040njc (0.50 #1624, 0.43 #2317, 0.43 #2548), 04dn09n (0.45 #1649, 0.44 #725, 0.42 #2342), 02pqp12 (0.35 #1672, 0.32 #2596, 0.32 #2365), 02qyntr (0.35 #1790, 0.34 #866, 0.31 #2483), 02ppm4q (0.35 #1034, 0.17 #6356, 0.16 #1727), 0l8z1 (0.34 #1666, 0.29 #2128, 0.29 #742), 04kxsb (0.33 #782, 0.32 #1706, 0.30 #4392), 0gs96 (0.32 #314, 0.29 #1700, 0.25 #545), 099c8n (0.32 #1670, 0.30 #746, 0.29 #6299) >> Best rule #1676 for best value: >> intensional similarity = 4 >> extensional distance = 188 >> proper extension: 0ptxj; 01mgw; >> query: (?x4457, 0gs9p) <- nominated_for(?x1307, ?x4457), film(?x6657, ?x4457), ?x1307 = 0gq9h, honored_for(?x4598, ?x4457) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m_q0 nominated_for! 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 75.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19593-028k2x PRED entity: 028k2x PRED relation: program! PRED expected values: 02fp82 => 75 concepts (67 used for prediction) PRED predicted values (max 10 best out of 58): 0kcd5 (0.33 #37, 0.03 #258, 0.01 #2037), 09gb9xh (0.25 #56, 0.08 #664, 0.07 #943), 05vtbl (0.25 #56, 0.08 #664, 0.07 #943), 0dbpyd (0.25 #56, 0.08 #664, 0.07 #943), 0gsg7 (0.24 #554, 0.24 #889, 0.23 #610), 05gnf (0.24 #901, 0.22 #125, 0.22 #679), 0cjdk (0.22 #116, 0.21 #337, 0.19 #226), 09d5h (0.17 #890, 0.17 #59, 0.13 #611), 02hmvw (0.16 #373, 0.03 #1766, 0.03 #2212), 03mdt (0.14 #615, 0.11 #894, 0.10 #449) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 06cs95; >> query: (?x7657, 0kcd5) <- actor(?x7657, ?x8875), nominated_for(?x129, ?x7657), ?x8875 = 01x9_8, genre(?x7657, ?x600) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #266 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 0kfpm; 0124k9; 01h72l; 01bv8b; 03y3bp7; 015w8_; 07c72; 05jyb2; 01b9w3; 02kk_c; ... *> query: (?x7657, 02fp82) <- actor(?x7657, ?x8875), nominated_for(?x129, ?x7657), genre(?x7657, ?x600), actor(?x9698, ?x8875) *> conf = 0.03 ranks of expected_values: 35 EVAL 028k2x program! 02fp82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 75.000 67.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #19592-0g7vxv PRED entity: 0g7vxv PRED relation: nationality PRED expected values: 02jx1 => 90 concepts (31 used for prediction) PRED predicted values (max 10 best out of 69): 09c7w0 (0.85 #2896, 0.78 #2795, 0.71 #2494), 02jx1 (0.45 #1624, 0.43 #231, 0.43 #132), 07ssc (0.43 #2210, 0.34 #2111, 0.31 #312), 0dg3n1 (0.40 #1591, 0.35 #3097, 0.34 #2694), 0f8l9c (0.39 #518, 0.10 #1713, 0.10 #1814), 034m8 (0.33 #93, 0.29 #192, 0.04 #688), 0d060g (0.30 #1698, 0.19 #2202, 0.14 #205), 035yg (0.29 #288, 0.03 #685, 0.03 #784), 03rk0 (0.27 #2241, 0.24 #1838, 0.23 #2142), 0chghy (0.19 #307, 0.08 #704, 0.08 #803) >> Best rule #2896 for best value: >> intensional similarity = 6 >> extensional distance = 1160 >> proper extension: 040db; >> query: (?x11481, 09c7w0) <- location(?x11481, ?x14378), nationality(?x11481, ?x10450), location_of_ceremony(?x566, ?x14378), administrative_area_type(?x10450, ?x2792), time_zones(?x14378, ?x5327), organization(?x10450, ?x127) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1624 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 317 *> proper extension: 02gvwz; 01l2fn; 021yzs; 03975z; 03z0l6; 01xsc9; 015010; *> query: (?x11481, 02jx1) <- location(?x11481, ?x14378), nationality(?x11481, ?x10450), location_of_ceremony(?x566, ?x14378), vacationer(?x10450, ?x5996), taxonomy(?x10450, ?x939), teams(?x10450, ?x1143) *> conf = 0.45 ranks of expected_values: 2 EVAL 0g7vxv nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 31.000 0.845 http://example.org/people/person/nationality #19591-03cvwkr PRED entity: 03cvwkr PRED relation: nominated_for! PRED expected values: 07bdd_ 05q5t0b => 88 concepts (87 used for prediction) PRED predicted values (max 10 best out of 221): 05f4m9q (0.68 #10816, 0.66 #12463, 0.66 #7523), 07bdd_ (0.53 #4046, 0.51 #2166, 0.19 #19050), 0k611 (0.38 #71, 0.24 #776, 0.24 #5947), 04ljl_l (0.36 #3998, 0.35 #2118, 0.12 #12936), 05p1dby (0.36 #4075, 0.23 #2195, 0.12 #12936), 0gqyl (0.35 #6660, 0.19 #783, 0.18 #5954), 02x4sn8 (0.34 #1054, 0.13 #2699, 0.10 #1289), 0gr0m (0.33 #763, 0.31 #293, 0.29 #528), 0gqwc (0.33 #6641, 0.15 #10639, 0.14 #9227), 05p09zm (0.33 #4086, 0.31 #2206, 0.12 #12936) >> Best rule #10816 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 06mmr; >> query: (?x915, ?x350) <- award(?x915, ?x350), award_winner(?x915, ?x916), award(?x71, ?x350) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #4046 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 146 *> proper extension: 016kz1; 06zn1c; *> query: (?x915, 07bdd_) <- nominated_for(?x2252, ?x915), nominated_for(?x154, ?x915), nominated_for(?x154, ?x6500), award(?x123, ?x2252), ?x6500 = 026hxwx *> conf = 0.53 ranks of expected_values: 2, 68 EVAL 03cvwkr nominated_for! 05q5t0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 88.000 87.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03cvwkr nominated_for! 07bdd_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 87.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19590-027cyf7 PRED entity: 027cyf7 PRED relation: award_winner PRED expected values: 02qgqt 09fb5 => 42 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1819): 02kxbx3 (0.38 #5696, 0.16 #13083, 0.10 #15547), 0h1p (0.33 #428, 0.12 #5354, 0.12 #12741), 0jgwf (0.33 #1839, 0.12 #6765, 0.08 #14152), 01q4qv (0.33 #685, 0.12 #5611, 0.04 #30243), 026670 (0.33 #2052, 0.12 #41879, 0.11 #36952), 0kvqv (0.33 #954, 0.06 #32022, 0.04 #13267), 071xj (0.33 #2111, 0.02 #31669, 0.02 #34135), 014hdb (0.33 #2134, 0.02 #14447, 0.02 #16911), 03hy3g (0.33 #1400, 0.02 #13713, 0.02 #44343), 01p1z_ (0.33 #1537, 0.02 #44343, 0.01 #16314) >> Best rule #5696 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 099c8n; >> query: (?x4135, 02kxbx3) <- award(?x8277, ?x4135), award(?x1135, ?x4135), ?x1135 = 04vr_f, nominated_for(?x8843, ?x8277), award(?x3201, ?x8843) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #32023 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 185 *> proper extension: 03ybrwc; 02vl9ln; *> query: (?x4135, ?x989) <- award(?x945, ?x4135), award_winner(?x4135, ?x2422), award_winner(?x2422, ?x989), award_nominee(?x286, ?x989) *> conf = 0.14 ranks of expected_values: 42, 47 EVAL 027cyf7 award_winner 09fb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 42.000 18.000 0.375 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027cyf7 award_winner 02qgqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 42.000 18.000 0.375 http://example.org/award/award_category/winners./award/award_honor/award_winner #19589-0gtx63s PRED entity: 0gtx63s PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 36): 0ch6mp2 (0.79 #2219, 0.77 #1762, 0.77 #2256), 09zzb8 (0.78 #596, 0.77 #1755, 0.75 #2249), 09vw2b7 (0.69 #1761, 0.69 #2218, 0.66 #2255), 01vx2h (0.47 #87, 0.46 #420, 0.39 #124), 0dxtw (0.40 #2260, 0.38 #2223, 0.38 #49), 02ynfr (0.30 #128, 0.25 #54, 0.25 #17), 02rh1dz (0.30 #122, 0.23 #418, 0.16 #643), 0215hd (0.25 #20, 0.22 #837, 0.19 #985), 089g0h (0.25 #21, 0.15 #986, 0.15 #1023), 0d2b38 (0.25 #27, 0.15 #844, 0.14 #397) >> Best rule #2219 for best value: >> intensional similarity = 6 >> extensional distance = 600 >> proper extension: 01gglm; >> query: (?x8137, 0ch6mp2) <- film(?x6211, ?x8137), film_crew_role(?x8137, ?x468), language(?x8137, ?x254), ?x254 = 02h40lc, ?x468 = 02r96rf, country(?x8137, ?x512) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gtx63s film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.787 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19588-016h4r PRED entity: 016h4r PRED relation: award PRED expected values: 026mfs => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 301): 09sb52 (0.60 #442, 0.34 #32121, 0.30 #12472), 099tbz (0.40 #459, 0.04 #3667, 0.04 #12489), 01by1l (0.35 #2517, 0.33 #15349, 0.33 #14547), 0gr4k (0.33 #33, 0.08 #28905, 0.06 #30108), 0gr51 (0.33 #99, 0.07 #28971, 0.06 #24560), 04dn09n (0.33 #44, 0.07 #28916, 0.06 #24505), 03hkv_r (0.33 #16, 0.05 #28888, 0.05 #24477), 0gqz2 (0.33 #2084, 0.24 #4891, 0.20 #8099), 054krc (0.29 #4898, 0.27 #8106, 0.27 #2091), 054ks3 (0.29 #2546, 0.29 #2145, 0.28 #4952) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0154qm; >> query: (?x3495, 09sb52) <- award_winner(?x2576, ?x3495), film(?x3495, ?x3275), ?x3275 = 0djlxb >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #930 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 03f1zhf; 0c73z; *> query: (?x3495, 026mfs) <- instrumentalists(?x316, ?x3495), profession(?x3495, ?x220), student(?x3995, ?x3495) *> conf = 0.17 ranks of expected_values: 52 EVAL 016h4r award 026mfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 136.000 136.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19587-05bmq PRED entity: 05bmq PRED relation: administrative_parent PRED expected values: 02j71 => 116 concepts (98 used for prediction) PRED predicted values (max 10 best out of 19): 02j71 (0.87 #1792, 0.85 #3850, 0.84 #6460), 09c7w0 (0.24 #547, 0.19 #8376, 0.18 #8791), 0dg3n1 (0.16 #11422, 0.14 #12398, 0.14 #11978), 03rjj (0.07 #7414, 0.06 #5214, 0.04 #6588), 07ssc (0.04 #969, 0.02 #1928, 0.02 #7285), 059rby (0.04 #11430, 0.03 #11290, 0.02 #12545), 0d060g (0.03 #10455, 0.02 #10873, 0.02 #10734), 0345h (0.03 #11309, 0.03 #11449, 0.02 #9092), 03_3d (0.02 #10177, 0.01 #11706, 0.01 #11568), 0d05w3 (0.02 #12166, 0.02 #2375, 0.02 #2512) >> Best rule #1792 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 01mk6; >> query: (?x9458, 02j71) <- jurisdiction_of_office(?x182, ?x9458), country(?x1121, ?x9458), adjoins(?x9458, ?x4421), ?x182 = 060bp >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05bmq administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 98.000 0.874 http://example.org/base/aareas/schema/administrative_area/administrative_parent #19586-09yrh PRED entity: 09yrh PRED relation: nationality PRED expected values: 09c7w0 => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 52): 09c7w0 (0.80 #11446, 0.80 #903, 0.79 #203), 02jx1 (0.40 #1709, 0.20 #133, 0.14 #1438), 07ssc (0.40 #1709, 0.15 #1420, 0.14 #1017), 03rjj (0.40 #1709, 0.07 #207, 0.06 #307), 01n7q (0.33 #14259, 0.11 #201, 0.11 #1506), 030qb3t (0.33 #14259, 0.11 #201, 0.11 #1506), 0f8l9c (0.22 #1507, 0.16 #1104, 0.11 #201), 035qy (0.22 #1507, 0.16 #1104, 0.11 #201), 06mkj (0.20 #147, 0.03 #1049, 0.02 #1452), 05v8c (0.20 #16) >> Best rule #11446 for best value: >> intensional similarity = 2 >> extensional distance = 1344 >> proper extension: 0274ck; 08n9ng; 04bbv7; 0466k4; >> query: (?x4536, 09c7w0) <- location(?x4536, ?x789), adjoins(?x172, ?x789) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09yrh nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.802 http://example.org/people/person/nationality #19585-07nxnw PRED entity: 07nxnw PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 29): 02r96rf (0.79 #864, 0.78 #967, 0.72 #588), 0ch6mp2 (0.76 #387, 0.75 #971, 0.75 #868), 09zzb8 (0.74 #310, 0.73 #964, 0.72 #861), 033smt (0.33 #128, 0.25 #26, 0.17 #60), 089g0h (0.33 #52, 0.25 #18, 0.14 #120), 01pvkk (0.32 #1281, 0.31 #319, 0.31 #973), 0d2b38 (0.29 #126, 0.15 #987, 0.15 #884), 02ynfr (0.21 #977, 0.21 #874, 0.19 #393), 0215hd (0.19 #119, 0.17 #51, 0.16 #153), 0263ycg (0.17 #50, 0.14 #118, 0.09 #2596) >> Best rule #864 for best value: >> intensional similarity = 4 >> extensional distance = 297 >> proper extension: 043sct5; 0h95zbp; >> query: (?x6881, 02r96rf) <- genre(?x6881, ?x225), ?x225 = 02kdv5l, language(?x6881, ?x254), film_crew_role(?x6881, ?x1171) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #387 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 219 *> proper extension: 02nx2k; *> query: (?x6881, 0ch6mp2) <- category(?x6881, ?x134), film_crew_role(?x6881, ?x1171), film(?x450, ?x6881), production_companies(?x6881, ?x382) *> conf = 0.76 ranks of expected_values: 2 EVAL 07nxnw film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.786 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19584-0bkq7 PRED entity: 0bkq7 PRED relation: award PRED expected values: 0gq9h => 99 concepts (89 used for prediction) PRED predicted values (max 10 best out of 207): 0gr4k (0.28 #2332, 0.28 #10965, 0.27 #10964), 0f4x7 (0.28 #2332, 0.28 #10965, 0.27 #10964), 019f4v (0.28 #2332, 0.28 #10965, 0.27 #10964), 0gqwc (0.28 #2332, 0.28 #10965, 0.27 #10964), 0m7yy (0.26 #1296, 0.09 #4793, 0.08 #5725), 0gr0m (0.19 #293, 0.15 #60, 0.14 #526), 0gq_v (0.17 #485, 0.16 #719, 0.16 #952), 0gq9h (0.16 #529, 0.15 #63, 0.15 #763), 0gs96 (0.16 #554, 0.15 #788, 0.14 #1021), 0p9sw (0.15 #720, 0.13 #953, 0.13 #486) >> Best rule #2332 for best value: >> intensional similarity = 4 >> extensional distance = 221 >> proper extension: 0g60z; 080dwhx; 072kp; 0ddd0gc; 0124k9; 0464pz; 0kfv9; 0d68qy; 0l76z; 017f3m; ... >> query: (?x8617, ?x591) <- award(?x8617, ?x1313), nominated_for(?x574, ?x8617), nominated_for(?x591, ?x8617), film(?x574, ?x97) >> conf = 0.28 => this is the best rule for 4 predicted values *> Best rule #529 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 016kz1; *> query: (?x8617, 0gq9h) <- language(?x8617, ?x254), film_sets_designed(?x200, ?x8617), award_winner(?x8617, ?x574), nominated_for(?x591, ?x8617) *> conf = 0.16 ranks of expected_values: 8 EVAL 0bkq7 award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 99.000 89.000 0.279 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19583-08phg9 PRED entity: 08phg9 PRED relation: film_crew_role PRED expected values: 09zzb8 0ch6mp2 033smt => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 19): 0ch6mp2 (0.83 #30, 0.77 #1082, 0.75 #381), 09zzb8 (0.75 #26, 0.74 #377, 0.73 #1078), 0dxtw (0.38 #382, 0.37 #1083, 0.37 #131), 02ynfr (0.19 #34, 0.19 #1086, 0.17 #385), 033smt (0.15 #42, 0.07 #142, 0.05 #718), 0263ycg (0.15 #36, 0.04 #186, 0.04 #211), 089fss (0.13 #29, 0.08 #229, 0.08 #129), 094hwz (0.09 #8, 0.07 #83, 0.07 #58), 02vs3x5 (0.07 #365, 0.06 #14, 0.06 #841), 04pyp5 (0.06 #10, 0.06 #1037, 0.06 #386) >> Best rule #30 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 02v8kmz; 03g90h; 047gn4y; 0bth54; 02z3r8t; 03ckwzc; 06_wqk4; 05sxzwc; 05pbl56; 09txzv; ... >> query: (?x5128, 0ch6mp2) <- currency(?x5128, ?x170), film_crew_role(?x5128, ?x5136), ?x5136 = 089g0h >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5 EVAL 08phg9 film_crew_role 033smt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 101.000 0.827 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 08phg9 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.827 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 08phg9 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.827 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19582-01cvtf PRED entity: 01cvtf PRED relation: actor PRED expected values: 02r_d4 => 94 concepts (69 used for prediction) PRED predicted values (max 10 best out of 900): 022yb4 (0.50 #652, 0.12 #1583, 0.11 #25162), 019pkm (0.41 #11185, 0.39 #4658, 0.39 #21433), 05cqhl (0.41 #11185, 0.39 #4658, 0.39 #21433), 05gnf (0.41 #11185, 0.39 #4658, 0.37 #29818), 030znt (0.37 #30750, 0.37 #33546, 0.37 #29817), 078jt5 (0.37 #30750, 0.37 #33546, 0.37 #29817), 04bcb1 (0.25 #371, 0.12 #1302, 0.04 #3164), 033db3 (0.25 #925, 0.12 #1856, 0.02 #3718), 03cvv4 (0.25 #752, 0.12 #1683, 0.02 #3545), 0mj1l (0.25 #147, 0.12 #1078, 0.02 #2940) >> Best rule #652 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02qkq0; 015ppk; >> query: (?x11250, 022yb4) <- nominated_for(?x1343, ?x11250), program_creator(?x11250, ?x9335), ?x9335 = 019pkm, nominated_for(?x783, ?x11250) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #24280 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 129 *> proper extension: 09rfpk; *> query: (?x11250, 02r_d4) <- nominated_for(?x1343, ?x11250), actor(?x11250, ?x10051), genre(?x11250, ?x53), award_winner(?x10051, ?x8431) *> conf = 0.02 ranks of expected_values: 235 EVAL 01cvtf actor 02r_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 94.000 69.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #19581-0p__8 PRED entity: 0p__8 PRED relation: profession PRED expected values: 0dxtg => 109 concepts (108 used for prediction) PRED predicted values (max 10 best out of 86): 0dxtg (0.85 #1924, 0.84 #3688, 0.83 #2806), 02jknp (0.63 #742, 0.61 #2947, 0.59 #3388), 03gjzk (0.47 #749, 0.45 #2807, 0.44 #1925), 0cbd2 (0.45 #5298, 0.44 #5445, 0.43 #3534), 09jwl (0.38 #605, 0.35 #1634, 0.33 #1046), 0kyk (0.30 #5320, 0.30 #3262, 0.30 #5467), 0nbcg (0.29 #618, 0.27 #1059, 0.26 #1647), 0d1pc (0.28 #10732, 0.26 #12498, 0.25 #12350), 02krf9 (0.26 #760, 0.22 #1936, 0.20 #2818), 0dgd_ (0.26 #12498, 0.25 #12350, 0.07 #470) >> Best rule #1924 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 014hdb; >> query: (?x5940, 0dxtg) <- written_by(?x146, ?x5940), award_winner(?x5940, ?x8740), nominated_for(?x8740, ?x4427) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p__8 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 108.000 0.853 http://example.org/people/person/profession #19580-073w14 PRED entity: 073w14 PRED relation: award_nominee! PRED expected values: 01v42g => 113 concepts (56 used for prediction) PRED predicted values (max 10 best out of 644): 02p65p (0.33 #7014, 0.22 #26, 0.17 #4685), 0hvb2 (0.29 #7380, 0.16 #128113, 0.16 #65222), 03n_7k (0.22 #514, 0.08 #5173, 0.02 #58748), 05yh_t (0.19 #8335, 0.16 #128113, 0.16 #65222), 073w14 (0.19 #8001, 0.16 #128113, 0.16 #65222), 01v42g (0.19 #7249, 0.16 #128113, 0.16 #65222), 016zp5 (0.17 #5950, 0.16 #128113, 0.16 #65222), 0h0wc (0.17 #5209, 0.14 #81526, 0.11 #550), 011_3s (0.17 #5391, 0.11 #732, 0.10 #3062), 050zr4 (0.17 #6500, 0.11 #1841, 0.10 #4171) >> Best rule #7014 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 05vsxz; 02p65p; 01v42g; 0509bl; 0c6qh; 02f2dn; 043js; 0154qm; 031296; 042xrr; ... >> query: (?x4345, 02p65p) <- film(?x4345, ?x1444), award_nominee(?x4345, ?x1290), ?x1290 = 0blbxk >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7249 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 05vsxz; 02p65p; 01v42g; 0509bl; 0c6qh; 02f2dn; 043js; 0154qm; 031296; 042xrr; ... *> query: (?x4345, 01v42g) <- film(?x4345, ?x1444), award_nominee(?x4345, ?x1290), ?x1290 = 0blbxk *> conf = 0.19 ranks of expected_values: 6 EVAL 073w14 award_nominee! 01v42g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 113.000 56.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19579-04hvw PRED entity: 04hvw PRED relation: organization PRED expected values: 07t65 0gkjy => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 48): 07t65 (0.90 #662, 0.90 #362, 0.89 #482), 0gkjy (0.56 #903, 0.27 #408, 0.26 #167), 0_2v (0.47 #44, 0.36 #24, 0.30 #304), 04k4l (0.32 #185, 0.32 #225, 0.31 #165), 01rz1 (0.30 #102, 0.30 #42, 0.26 #282), 018cqq (0.28 #50, 0.25 #110, 0.23 #30), 02jxk (0.20 #43, 0.17 #103, 0.14 #163), 034h1h (0.18 #1173), 059dn (0.09 #114, 0.05 #34, 0.05 #54), 02_l9 (0.07 #1177, 0.05 #334, 0.02 #1260) >> Best rule #662 for best value: >> intensional similarity = 3 >> extensional distance = 165 >> proper extension: 01n6c; >> query: (?x11774, 07t65) <- country(?x668, ?x11774), administrative_parent(?x11774, ?x551), organization(?x11774, ?x127) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04hvw organization 0gkjy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.904 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04hvw organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.904 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #19578-06688p PRED entity: 06688p PRED relation: location PRED expected values: 0s9b_ => 153 concepts (116 used for prediction) PRED predicted values (max 10 best out of 200): 02_286 (0.42 #42516, 0.38 #31294, 0.31 #41713), 030qb3t (0.34 #42562, 0.25 #50579, 0.24 #41759), 04jpl (0.29 #17, 0.28 #8831, 0.11 #31274), 05fkf (0.14 #38, 0.02 #32900, 0.01 #5647), 02h6_6p (0.14 #130), 0cr3d (0.14 #33807, 0.09 #64275, 0.08 #76303), 05qtj (0.12 #9053, 0.04 #6649, 0.03 #31496), 0j7ng (0.10 #1454, 0.07 #2256, 0.06 #3859), 01531 (0.10 #6567, 0.04 #65090, 0.04 #17787), 04vmp (0.08 #4358, 0.08 #5159, 0.05 #8364) >> Best rule #42516 for best value: >> intensional similarity = 4 >> extensional distance = 738 >> proper extension: 059x0w; 03f68r6; 069d71; >> query: (?x194, 02_286) <- location(?x194, ?x6960), nationality(?x194, ?x6401), county(?x6960, ?x9472), dog_breed(?x6960, ?x1706) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06688p location 0s9b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 116.000 0.416 http://example.org/people/person/places_lived./people/place_lived/location #19577-0gyv0b4 PRED entity: 0gyv0b4 PRED relation: produced_by PRED expected values: 054_mz => 80 concepts (73 used for prediction) PRED predicted values (max 10 best out of 132): 0693l (0.31 #15908, 0.25 #11632, 0.24 #14743), 02tn0_ (0.25 #328, 0.14 #2265, 0.14 #1878), 01qbjg (0.25 #270, 0.14 #2207, 0.14 #1820), 02q42j_ (0.25 #597, 0.02 #8349, 0.02 #11842), 013tcv (0.25 #697), 054_mz (0.20 #790, 0.17 #1178, 0.14 #1953), 04wvhz (0.20 #810, 0.03 #8175, 0.03 #6627), 0grrq8 (0.20 #938, 0.01 #12572, 0.01 #14129), 05zh9c (0.18 #3275, 0.04 #4439), 026c1 (0.18 #3169, 0.04 #4333) >> Best rule #15908 for best value: >> intensional similarity = 4 >> extensional distance = 710 >> proper extension: 02_1sj; 03ckwzc; 0963mq; 0j_tw; 04q00lw; 04g9gd; 04tz52; 05_5rjx; 01jr4j; 0bw20; ... >> query: (?x10446, ?x3117) <- language(?x10446, ?x254), film(?x1678, ?x10446), ?x254 = 02h40lc, film(?x3117, ?x10446) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #790 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 01xvjb; *> query: (?x10446, 054_mz) <- film(?x6236, ?x10446), award_winner(?x10446, ?x8134), nominated_for(?x298, ?x10446), ?x6236 = 01xv77 *> conf = 0.20 ranks of expected_values: 6 EVAL 0gyv0b4 produced_by 054_mz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 80.000 73.000 0.312 http://example.org/film/film/produced_by #19576-098n_m PRED entity: 098n_m PRED relation: type_of_union PRED expected values: 04ztj => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.80 #21, 0.78 #9, 0.77 #13), 01g63y (0.25 #317, 0.20 #2, 0.15 #66), 01bl8s (0.25 #317) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 301 >> proper extension: 0dfjb8; 0f2c8g; 01d5vk; 0652ty; >> query: (?x5371, 04ztj) <- profession(?x5371, ?x524), film(?x5371, ?x5372), ?x524 = 02jknp >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 098n_m type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.802 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19575-0g5q34q PRED entity: 0g5q34q PRED relation: currency PRED expected values: 0ptk_ => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.84 #351, 0.82 #372, 0.82 #113), 0kz1h (0.20 #40, 0.17 #54, 0.14 #61), 01nv4h (0.20 #37, 0.14 #722, 0.11 #982), 02l6h (0.18 #130, 0.14 #722, 0.11 #982), 02gsvk (0.14 #722, 0.11 #982, 0.02 #391), 088n7 (0.14 #722, 0.11 #982, 0.02 #420), 0ptk_ (0.11 #982) >> Best rule #351 for best value: >> intensional similarity = 11 >> extensional distance = 41 >> proper extension: 05cj_j; >> query: (?x5992, 09nqf) <- genre(?x5992, ?x809), genre(?x5992, ?x53), ?x809 = 0vgkd, genre(?x6624, ?x53), genre(?x5721, ?x53), genre(?x4007, ?x53), ?x4007 = 03hmt9b, titles(?x53, ?x253), genre(?x273, ?x53), ?x6624 = 033qdy, ?x5721 = 01d259 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #982 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1319 *> proper extension: 0bl1_; 03h4fq7; *> query: (?x5992, ?x170) <- genre(?x5992, ?x53), film_release_region(?x5992, ?x87), genre(?x9978, ?x53), genre(?x8218, ?x53), genre(?x5782, ?x53), genre(?x755, ?x53), film(?x875, ?x9978), language(?x755, ?x254), film_crew_role(?x8218, ?x137), currency(?x5782, ?x170) *> conf = 0.11 ranks of expected_values: 7 EVAL 0g5q34q currency 0ptk_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 149.000 149.000 0.837 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #19574-0814k3 PRED entity: 0814k3 PRED relation: actor! PRED expected values: 02vw1w2 => 120 concepts (112 used for prediction) PRED predicted values (max 10 best out of 28): 02vw1w2 (0.57 #62, 0.19 #342, 0.12 #258), 056k77g (0.40 #22, 0.07 #358, 0.06 #499), 016ztl (0.28 #436, 0.26 #239, 0.26 #379), 0b60sq (0.26 #338, 0.16 #759, 0.15 #366), 02q3fdr (0.21 #435, 0.21 #491, 0.20 #42), 031f_m (0.21 #500, 0.19 #612, 0.17 #668), 05dfy_ (0.20 #20, 0.07 #356, 0.06 #497), 07ghv5 (0.19 #352, 0.10 #773, 0.09 #240), 02gs6r (0.17 #263, 0.15 #123, 0.15 #488), 0dh8v4 (0.15 #348, 0.14 #68, 0.09 #236) >> Best rule #62 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 06v8s0; 0678gl; >> query: (?x12318, 02vw1w2) <- category(?x12318, ?x134), ?x134 = 08mbj5d, actor(?x10187, ?x12318), ?x10187 = 05t0zfv, gender(?x12318, ?x514) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0814k3 actor! 02vw1w2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 112.000 0.571 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #19573-035gjq PRED entity: 035gjq PRED relation: award_nominee PRED expected values: 026zvx7 => 113 concepts (53 used for prediction) PRED predicted values (max 10 best out of 825): 05lb87 (0.85 #2323, 0.81 #102173, 0.80 #32512), 026zvx7 (0.85 #2323, 0.81 #102173, 0.80 #32512), 03x16f (0.85 #2323, 0.81 #102173, 0.80 #32512), 035gjq (0.60 #222, 0.44 #2545, 0.28 #104497), 027n4zv (0.36 #4166, 0.12 #58057, 0.02 #104016), 0d810y (0.32 #3663, 0.17 #118429, 0.13 #76633), 09r9dp (0.32 #3172, 0.17 #118429, 0.13 #76633), 08pth9 (0.32 #3375, 0.17 #118429, 0.13 #76633), 048q6x (0.32 #3512, 0.12 #58057, 0.12 #60380), 02yj7w (0.28 #104497, 0.17 #118429, 0.13 #76633) >> Best rule #2323 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 06hgym; 03x16f; >> query: (?x1094, ?x444) <- participant(?x8160, ?x1094), award_nominee(?x3956, ?x1094), award_nominee(?x444, ?x1094), ?x3956 = 05dxl5 >> conf = 0.85 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 035gjq award_nominee 026zvx7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 53.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19572-025twgt PRED entity: 025twgt PRED relation: nominated_for PRED expected values: 014kq6 => 90 concepts (31 used for prediction) PRED predicted values (max 10 best out of 782): 025twgt (0.67 #719, 0.64 #1686, 0.62 #478), 014kq6 (0.67 #542, 0.64 #1509, 0.62 #301), 025twgf (0.59 #2417, 0.09 #4595, 0.08 #4840), 01kf5lf (0.58 #2902, 0.33 #1208, 0.32 #2660), 06ybb1 (0.14 #2962, 0.11 #1026, 0.11 #3687), 05b_gq (0.12 #411, 0.11 #894, 0.11 #652), 03176f (0.12 #2293, 0.09 #2778, 0.07 #2535), 01_mdl (0.12 #2927, 0.11 #991, 0.09 #3652), 059lwy (0.12 #3086, 0.11 #1150, 0.09 #3811), 01771z (0.12 #2979, 0.11 #1043, 0.09 #3704) >> Best rule #719 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0fsw_7; >> query: (?x11362, 025twgt) <- nominated_for(?x11362, ?x3643), genre(?x11362, ?x812), currency(?x11362, ?x170), ?x3643 = 0d1qmz, genre(?x8234, ?x812), ?x8234 = 06_sc3 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #542 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 0fsw_7; *> query: (?x11362, 014kq6) <- nominated_for(?x11362, ?x3643), genre(?x11362, ?x812), currency(?x11362, ?x170), ?x3643 = 0d1qmz, genre(?x8234, ?x812), ?x8234 = 06_sc3 *> conf = 0.67 ranks of expected_values: 2 EVAL 025twgt nominated_for 014kq6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 31.000 0.667 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #19571-0kv4k PRED entity: 0kv4k PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 108 concepts (47 used for prediction) PRED predicted values (max 10 best out of 7): 09c7w0 (0.93 #48, 0.89 #93, 0.86 #395), 01n7q (0.17 #47, 0.17 #471, 0.17 #24), 0k_s5 (0.17 #47, 0.17 #24, 0.12 #177), 059_c (0.17 #47, 0.17 #24, 0.11 #247), 0vmt (0.08 #560), 03rt9 (0.03 #168, 0.03 #237, 0.02 #414), 02jx1 (0.02 #271) >> Best rule #48 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 0cb4j; 0mlxt; >> query: (?x9896, 09c7w0) <- contains(?x1227, ?x9896), time_zones(?x9896, ?x2950), currency(?x9896, ?x170), ?x170 = 09nqf, ?x2950 = 02lcqs >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kv4k second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 47.000 0.927 http://example.org/location/country/second_level_divisions #19570-05x8n PRED entity: 05x8n PRED relation: award PRED expected values: 0265wl => 132 concepts (112 used for prediction) PRED predicted values (max 10 best out of 276): 01bb1c (0.73 #28947, 0.72 #44410, 0.71 #27356), 0265wl (0.58 #629, 0.56 #2215, 0.52 #3007), 039yzf (0.44 #2325, 0.42 #739, 0.29 #3117), 01tgwv (0.33 #3130, 0.25 #356, 0.22 #2734), 045xh (0.33 #3143, 0.22 #2351, 0.21 #6311), 0208wk (0.25 #734, 0.22 #2320, 0.17 #3904), 03mv9j (0.25 #388, 0.11 #16254, 0.10 #3162), 09sb52 (0.20 #16691, 0.19 #16294, 0.18 #29779), 019f4v (0.19 #7592, 0.15 #14336, 0.14 #21077), 058bzgm (0.19 #3139, 0.18 #6307, 0.17 #3535) >> Best rule #28947 for best value: >> intensional similarity = 6 >> extensional distance = 1072 >> proper extension: 08wq0g; 02k6rq; >> query: (?x6688, ?x1288) <- award_winner(?x1288, ?x6688), student(?x3948, ?x6688), award(?x12009, ?x1288), award(?x10275, ?x1288), award_winner(?x11712, ?x12009), profession(?x10275, ?x353) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #629 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 014ps4; 033cw; *> query: (?x6688, 0265wl) <- award(?x6688, ?x8880), award(?x6688, ?x1375), location(?x6688, ?x3670), gender(?x6688, ?x231), ?x8880 = 0262x6, ?x1375 = 0262zm *> conf = 0.58 ranks of expected_values: 2 EVAL 05x8n award 0265wl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 112.000 0.726 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19569-02q_cc PRED entity: 02q_cc PRED relation: produced_by! PRED expected values: 0295sy => 116 concepts (99 used for prediction) PRED predicted values (max 10 best out of 649): 04mcw4 (0.35 #25083, 0.29 #30656, 0.29 #30655), 0315rp (0.35 #25083, 0.29 #30656, 0.29 #30655), 0298n7 (0.35 #25083, 0.29 #30656, 0.29 #30655), 02rcdc2 (0.35 #25083, 0.29 #30656, 0.29 #30655), 0gmgwnv (0.35 #25083, 0.29 #30656, 0.29 #30655), 05qbbfb (0.20 #566, 0.11 #2425, 0.10 #3353), 0jjy0 (0.20 #99, 0.11 #1958, 0.10 #2886), 0k2sk (0.20 #98, 0.10 #1028, 0.07 #11147), 0h21v2 (0.20 #537, 0.10 #1467, 0.07 #5181), 0295sy (0.20 #520, 0.10 #1450, 0.05 #54796) >> Best rule #25083 for best value: >> intensional similarity = 3 >> extensional distance = 191 >> proper extension: 04g865; 013t9y; >> query: (?x846, ?x2903) <- produced_by(?x153, ?x846), location(?x846, ?x3976), nominated_for(?x846, ?x2903) >> conf = 0.35 => this is the best rule for 5 predicted values *> Best rule #520 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 023kzp; *> query: (?x846, 0295sy) <- nominated_for(?x846, ?x7755), ?x7755 = 0298n7, executive_produced_by(?x1076, ?x846) *> conf = 0.20 ranks of expected_values: 10 EVAL 02q_cc produced_by! 0295sy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 116.000 99.000 0.350 http://example.org/film/film/produced_by #19568-0bz3jx PRED entity: 0bz3jx PRED relation: language PRED expected values: 0jzc => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 42): 064_8sq (0.66 #680, 0.35 #1621, 0.32 #679), 0f8l9c (0.66 #680, 0.32 #679, 0.19 #1083), 04306rv (0.55 #1663, 0.28 #1606, 0.25 #60), 03_9r (0.31 #1668, 0.25 #574, 0.21 #1034), 012w70 (0.25 #632, 0.18 #518, 0.17 #690), 03k50 (0.25 #630, 0.17 #573, 0.14 #1033), 032f6 (0.25 #166, 0.03 #790, 0.02 #1425), 03hkp (0.25 #68, 0.03 #1384, 0.03 #1614), 02bjrlw (0.16 #1603, 0.14 #852, 0.13 #1660), 02hxcvy (0.14 #653, 0.12 #596, 0.10 #1056) >> Best rule #680 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 0dkv90; 0k2m6; 0bs8hvm; 0k20s; >> query: (?x6450, ?x5607) <- genre(?x6450, ?x53), titles(?x5607, ?x6450), written_by(?x6450, ?x826), language(?x6450, ?x254), language(?x80, ?x5607) >> conf = 0.66 => this is the best rule for 2 predicted values *> Best rule #299 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 06bc59; 03cwwl; *> query: (?x6450, 0jzc) <- film(?x368, ?x6450), country(?x6450, ?x789), genre(?x6450, ?x53), ?x789 = 0f8l9c, prequel(?x755, ?x6450) *> conf = 0.11 ranks of expected_values: 13 EVAL 0bz3jx language 0jzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 92.000 92.000 0.655 http://example.org/film/film/language #19567-01mxqyk PRED entity: 01mxqyk PRED relation: place_of_birth PRED expected values: 02dtg => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 81): 02_286 (0.09 #1427, 0.07 #62685, 0.06 #51421), 01_d4 (0.08 #66, 0.05 #770, 0.04 #2882), 0hptm (0.08 #225, 0.02 #3745, 0.02 #5153), 04jpl (0.05 #1416, 0.02 #18314, 0.02 #47185), 0dclg (0.05 #2190, 0.04 #78, 0.03 #3598), 01cx_ (0.04 #109, 0.02 #2221, 0.02 #5741), 0c_m3 (0.04 #197, 0.02 #2309), 013yq (0.04 #79, 0.01 #1487, 0.01 #5007), 0y62n (0.04 #339, 0.01 #1747, 0.01 #2451), 0rw2x (0.04 #607, 0.01 #2719) >> Best rule #1427 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 01gw4f; 023nlj; 030dx5; 02j4sk; 045g4l; 01xsc9; 0p_r5; >> query: (?x11621, 02_286) <- sibling(?x11621, ?x9528), type_of_union(?x11621, ?x566), profession(?x11621, ?x131) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #1418 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 01gw4f; 023nlj; 030dx5; 02j4sk; 045g4l; 01xsc9; 0p_r5; *> query: (?x11621, 02dtg) <- sibling(?x11621, ?x9528), type_of_union(?x11621, ?x566), profession(?x11621, ?x131) *> conf = 0.01 ranks of expected_values: 37 EVAL 01mxqyk place_of_birth 02dtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 105.000 105.000 0.087 http://example.org/people/person/place_of_birth #19566-01flzq PRED entity: 01flzq PRED relation: artists PRED expected values: 01wgxtl 01vvyd8 => 52 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1297): 01vw20h (0.50 #2521, 0.50 #1453, 0.43 #5726), 016ksk (0.50 #2452, 0.40 #3522, 0.33 #6728), 01vvydl (0.50 #2141, 0.40 #3211, 0.33 #6), 01vvyc_ (0.50 #2657, 0.40 #3727, 0.33 #522), 07pzc (0.50 #2969, 0.40 #4039, 0.33 #834), 01wlt3k (0.50 #5248, 0.33 #7387, 0.33 #976), 01wgxtl (0.50 #1282, 0.33 #215, 0.29 #5555), 03j3pg9 (0.50 #1961, 0.33 #894, 0.29 #6234), 03f7jfh (0.50 #1876, 0.33 #809, 0.29 #6149), 01jcxwp (0.43 #8110, 0.15 #10247, 0.12 #9178) >> Best rule #2521 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 036jv; >> query: (?x8123, 01vw20h) <- artists(?x8123, ?x7601), artists(?x8123, ?x5536), artists(?x8123, ?x5340), artists(?x8123, ?x3481), ?x5340 = 01vvzb1, ?x5536 = 01vsgrn, ?x7601 = 01vzx45, award_nominee(?x3481, ?x5906), award_nominee(?x5906, ?x883) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1282 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 016_nr; *> query: (?x8123, 01wgxtl) <- artists(?x8123, ?x6576), artists(?x8123, ?x5340), artists(?x8123, ?x3481), ?x5340 = 01vvzb1, ?x3481 = 01wyz92, award_nominee(?x1051, ?x6576), artist(?x3265, ?x6576), gender(?x6576, ?x231) *> conf = 0.50 ranks of expected_values: 7, 112 EVAL 01flzq artists 01vvyd8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 52.000 26.000 0.500 http://example.org/music/genre/artists EVAL 01flzq artists 01wgxtl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 52.000 26.000 0.500 http://example.org/music/genre/artists #19565-012wxt PRED entity: 012wxt PRED relation: profession! PRED expected values: 019n7x => 66 concepts (23 used for prediction) PRED predicted values (max 10 best out of 4213): 01vvycq (0.64 #12900, 0.35 #21386, 0.33 #17143), 0dpqk (0.57 #14344, 0.39 #31319, 0.38 #22830), 01vs_v8 (0.57 #13361, 0.33 #17604, 0.33 #4873), 03j24kf (0.50 #18485, 0.50 #14242, 0.41 #48191), 01vsl3_ (0.50 #13556, 0.42 #17799, 0.38 #22042), 0144l1 (0.50 #13341, 0.42 #17584, 0.37 #38803), 05wm88 (0.50 #16552, 0.39 #33527, 0.37 #42014), 03lgg (0.50 #14324, 0.36 #31299, 0.35 #22810), 01p45_v (0.50 #13143, 0.33 #17386, 0.33 #4655), 01bbwp (0.50 #15935, 0.33 #7447, 0.32 #32910) >> Best rule #12900 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 09lbv; >> query: (?x11201, 01vvycq) <- profession(?x2697, ?x11201), award(?x2697, ?x3631), celebrity(?x2697, ?x5881), artists(?x302, ?x2697), gender(?x2697, ?x514), ?x3631 = 02f73p >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #55168 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 34 *> proper extension: 021wpb; 01tkqy; *> query: (?x11201, ?x3930) <- profession(?x2697, ?x11201), award(?x2697, ?x154), participant(?x3930, ?x2697), award_nominee(?x3547, ?x2697), award(?x1424, ?x154), ?x1424 = 01rh0w *> conf = 0.38 ranks of expected_values: 30 EVAL 012wxt profession! 019n7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 66.000 23.000 0.643 http://example.org/people/person/profession #19564-02fsn PRED entity: 02fsn PRED relation: role! PRED expected values: 03h_fqv => 90 concepts (57 used for prediction) PRED predicted values (max 10 best out of 951): 01wxdn3 (0.71 #6480, 0.62 #9279, 0.62 #8814), 050z2 (0.71 #6260, 0.62 #8594, 0.60 #10931), 023l9y (0.71 #6283, 0.62 #8617, 0.60 #4412), 082brv (0.62 #8672, 0.60 #4467, 0.57 #7272), 04bpm6 (0.60 #4272, 0.57 #7077, 0.57 #6143), 01mxt_ (0.60 #4457, 0.57 #6794, 0.57 #6328), 016ntp (0.60 #4344, 0.57 #6215, 0.56 #10418), 01vsyg9 (0.60 #4458, 0.57 #6329, 0.56 #10532), 0137g1 (0.60 #10860, 0.57 #6189, 0.50 #12255), 0lzkm (0.60 #4373, 0.57 #6244, 0.50 #8578) >> Best rule #6480 for best value: >> intensional similarity = 15 >> extensional distance = 5 >> proper extension: 07brj; >> query: (?x2888, 01wxdn3) <- role(?x75, ?x2888), role(?x5718, ?x2888), role(?x1260, ?x2888), role(?x2888, ?x3991), role(?x2888, ?x1574), role(?x248, ?x2888), instrumentalists(?x2888, ?x425), ?x1260 = 07_3qd, type_of_union(?x5718, ?x566), ?x3991 = 05842k, people(?x2510, ?x5718), role(?x211, ?x1574), role(?x1574, ?x433), origin(?x5718, ?x1523), group(?x2888, ?x2521) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #9588 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 7 *> proper extension: 01p970; *> query: (?x2888, 03h_fqv) <- role(?x227, ?x2888), family(?x2888, ?x9885), group(?x2888, ?x9999), role(?x2888, ?x5480), role(?x2888, ?x614), ?x5480 = 01w4c9, performance_role(?x2888, ?x6938), instrumentalists(?x614, ?x317), group(?x227, ?x379), instrumentalists(?x227, ?x4191), type_of_union(?x4191, ?x566), artist(?x441, ?x9999), role(?x227, ?x569), role(?x1247, ?x227) *> conf = 0.33 ranks of expected_values: 148 EVAL 02fsn role! 03h_fqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 90.000 57.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #19563-049g_xj PRED entity: 049g_xj PRED relation: film PRED expected values: 047p798 => 116 concepts (71 used for prediction) PRED predicted values (max 10 best out of 954): 04vr_f (0.23 #1958, 0.03 #41102, 0.01 #68079), 049xgc (0.19 #2757, 0.01 #6331, 0.01 #8118), 0dr_4 (0.16 #2033, 0.03 #41102, 0.01 #12755), 017gl1 (0.07 #143, 0.06 #1930, 0.06 #3717), 01l_pn (0.07 #964, 0.06 #4538, 0.04 #15260), 017jd9 (0.07 #777, 0.06 #4351, 0.03 #2564), 02qzh2 (0.07 #691, 0.06 #4265, 0.03 #2478), 04y5j64 (0.07 #692, 0.06 #4266, 0.03 #6053), 0gkz15s (0.07 #112, 0.06 #3686, 0.03 #5473), 0gj8t_b (0.07 #181, 0.06 #3755, 0.03 #5542) >> Best rule #1958 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 07vc_9; 0hvb2; 02qgyv; 01s7zw; 0154qm; 07yp0f; 02yxwd; 0jmj; 042xrr; 016zp5; ... >> query: (?x1530, 04vr_f) <- award(?x1530, ?x618), award_nominee(?x1530, ?x3101), ?x3101 = 0dvmd >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #15952 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 181 *> proper extension: 01l_vgt; *> query: (?x1530, 047p798) <- participant(?x1530, ?x1987), religion(?x1987, ?x7131), award_winner(?x1987, ?x221) *> conf = 0.01 ranks of expected_values: 905 EVAL 049g_xj film 047p798 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 71.000 0.226 http://example.org/film/actor/film./film/performance/film #19562-01dvbd PRED entity: 01dvbd PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #83, 0.84 #31, 0.84 #78), 07z4p (0.13 #5, 0.03 #40, 0.03 #56), 07c52 (0.07 #3, 0.04 #54, 0.03 #105), 02nxhr (0.06 #12, 0.05 #27, 0.04 #99) >> Best rule #83 for best value: >> intensional similarity = 4 >> extensional distance = 333 >> proper extension: 0d1qmz; 0cqr0q; >> query: (?x3048, 029j_) <- genre(?x3048, ?x812), ?x812 = 01jfsb, nominated_for(?x5565, ?x3048), film(?x1289, ?x3048) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dvbd film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.845 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19561-01ggbx PRED entity: 01ggbx PRED relation: spouse! PRED expected values: 0b66qd => 112 concepts (37 used for prediction) PRED predicted values (max 10 best out of 122): 0b66qd (0.80 #10838, 0.77 #4414, 0.77 #2407), 04y0hj (0.04 #755, 0.03 #1156, 0.03 #1557), 0241wg (0.04 #520, 0.03 #921, 0.03 #1322), 049468 (0.04 #801, 0.03 #1202, 0.03 #2004), 017yxq (0.04 #704, 0.03 #1105, 0.02 #2308), 020trj (0.04 #620, 0.03 #1021, 0.02 #2224), 0bbf1f (0.04 #508, 0.03 #909, 0.02 #2112), 05dbf (0.04 #473, 0.03 #874, 0.02 #2077), 01qqtr (0.04 #721, 0.03 #1122, 0.01 #4332), 014g22 (0.04 #557, 0.03 #958, 0.01 #4168) >> Best rule #10838 for best value: >> intensional similarity = 3 >> extensional distance = 332 >> proper extension: 02lyx4; >> query: (?x13441, ?x14141) <- award(?x13441, ?x4443), spouse(?x13441, ?x14141), nominated_for(?x4443, ?x657) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ggbx spouse! 0b66qd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 37.000 0.800 http://example.org/people/person/spouse_s./people/marriage/spouse #19560-027gy0k PRED entity: 027gy0k PRED relation: currency PRED expected values: 09nqf => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.87 #22, 0.87 #29, 0.83 #64), 02l6h (0.12 #4, 0.01 #208, 0.01 #467), 01nv4h (0.03 #108, 0.03 #51, 0.03 #122), 088n7 (0.02 #56), 02gsvk (0.01 #147) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0ckt6; >> query: (?x6510, 09nqf) <- film(?x5507, ?x6510), titles(?x10308, ?x6510), athlete(?x1083, ?x5507), film_release_region(?x6510, ?x94) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027gy0k currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.868 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #19559-0psss PRED entity: 0psss PRED relation: award_winner! PRED expected values: 02y_j8g => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 248): 05zvq6g (0.37 #24629, 0.37 #20740, 0.34 #25926), 099tbz (0.22 #490, 0.09 #38032, 0.07 #7834), 0gqy2 (0.22 #595, 0.09 #38032, 0.05 #34572), 09sdmz (0.22 #637, 0.09 #38032, 0.05 #34572), 01by1l (0.18 #2705, 0.16 #4001, 0.12 #5297), 09sb52 (0.17 #7817, 0.12 #11706, 0.12 #13002), 05pcn59 (0.14 #82, 0.11 #514, 0.09 #38032), 027dtxw (0.14 #4, 0.11 #436, 0.09 #38032), 0fbtbt (0.14 #231, 0.11 #663, 0.03 #14921), 02y_j8g (0.14 #283, 0.07 #27223, 0.07 #2011) >> Best rule #24629 for best value: >> intensional similarity = 2 >> extensional distance = 1462 >> proper extension: 014hr0; 0khth; 014l4w; 07mvp; 04k05; 07k2d; >> query: (?x3280, ?x1008) <- award(?x3280, ?x1008), award_winner(?x2443, ?x3280) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #283 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 07mz77; *> query: (?x3280, 02y_j8g) <- nationality(?x3280, ?x789), film(?x3280, ?x1490), ?x1490 = 0fpkhkz *> conf = 0.14 ranks of expected_values: 10 EVAL 0psss award_winner! 02y_j8g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 114.000 114.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #19558-026_w57 PRED entity: 026_w57 PRED relation: actor! PRED expected values: 02_1q9 => 98 concepts (68 used for prediction) PRED predicted values (max 10 best out of 77): 0b76t12 (0.15 #2912, 0.13 #2381, 0.12 #6628), 026bfsh (0.05 #1946, 0.05 #360, 0.03 #3008), 0ddd0gc (0.04 #1870, 0.02 #5055, 0.01 #6117), 0828jw (0.04 #1954, 0.02 #2219, 0.02 #3016), 05f4vxd (0.04 #1938, 0.02 #2735, 0.02 #2203), 0kfv9 (0.04 #1877, 0.02 #2939, 0.01 #5062), 08jgk1 (0.03 #1872, 0.01 #2934, 0.01 #2137), 02_1q9 (0.03 #1855, 0.02 #2917, 0.01 #3448), 080dwhx (0.03 #1856, 0.02 #2653, 0.02 #2121), 02gjrc (0.03 #490, 0.02 #1019, 0.02 #3138) >> Best rule #2912 for best value: >> intensional similarity = 2 >> extensional distance = 687 >> proper extension: 06jntd; >> query: (?x3687, ?x1861) <- award_winner(?x1861, ?x3687), category(?x1861, ?x134) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #1855 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 342 *> proper extension: 0c01c; 02hsgn; 015np0; 02js_6; *> query: (?x3687, 02_1q9) <- nationality(?x3687, ?x94), award_winner(?x3687, ?x91), actor(?x3822, ?x3687) *> conf = 0.03 ranks of expected_values: 8 EVAL 026_w57 actor! 02_1q9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 98.000 68.000 0.150 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #19557-01cx_ PRED entity: 01cx_ PRED relation: month PRED expected values: 04wzr => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 1): 04wzr (0.93 #33, 0.91 #24, 0.90 #32) >> Best rule #33 for best value: >> intensional similarity = 2 >> extensional distance = 52 >> proper extension: 06t2t; >> query: (?x3052, 04wzr) <- mode_of_transportation(?x3052, ?x4272), month(?x3052, ?x1459) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cx_ month 04wzr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.926 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #19556-0cy__l PRED entity: 0cy__l PRED relation: film_release_region PRED expected values: 03rt9 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 124): 09c7w0 (0.93 #7746, 0.93 #3198, 0.93 #3704), 059j2 (0.84 #1722, 0.73 #375, 0.31 #5256), 0chghy (0.84 #1697, 0.75 #350, 0.30 #5231), 03rjj (0.80 #1690, 0.71 #343, 0.33 #175), 0jgd (0.78 #1687, 0.68 #340, 0.29 #5221), 03_3d (0.77 #1692, 0.73 #345, 0.36 #177), 0345h (0.77 #1724, 0.62 #377, 0.29 #5258), 03gj2 (0.77 #1714, 0.62 #367, 0.27 #5248), 035qy (0.75 #1726, 0.73 #379, 0.29 #211), 03h64 (0.74 #1761, 0.68 #414, 0.36 #246) >> Best rule #7746 for best value: >> intensional similarity = 3 >> extensional distance = 1311 >> proper extension: 0jvt9; 023p7l; 0m_q0; 01pvxl; 0dnw1; 0m63c; 030z4z; 02zk08; 063y9fp; 0bmhn; ... >> query: (?x5509, 09c7w0) <- film_release_region(?x5509, ?x87), film_release_region(?x5137, ?x87), ?x5137 = 0kb07 >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1701 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 243 *> proper extension: 0401sg; 087wc7n; 0crfwmx; 03mgx6z; 03nqnnk; 02qk3fk; 07s3m4g; 07ghq; 0m3gy; *> query: (?x5509, 03rt9) <- country(?x5509, ?x94), film_release_region(?x5509, ?x1499), ?x1499 = 01znc_ *> conf = 0.62 ranks of expected_values: 17 EVAL 0cy__l film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 64.000 64.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19555-0bh8drv PRED entity: 0bh8drv PRED relation: titles! PRED expected values: 07ssc => 118 concepts (79 used for prediction) PRED predicted values (max 10 best out of 59): 07ssc (0.50 #9, 0.47 #413, 0.27 #211), 04xvlr (0.45 #205, 0.42 #306, 0.25 #3), 03mqtr (0.27 #247, 0.25 #348, 0.12 #449), 01z4y (0.25 #35, 0.21 #3901, 0.21 #6463), 01hmnh (0.25 #127, 0.14 #531, 0.12 #936), 01jfsb (0.16 #3885, 0.15 #1132, 0.14 #1030), 07c52 (0.15 #1346, 0.15 #1854, 0.14 #1448), 024qqx (0.13 #1091, 0.12 #181, 0.12 #1193), 02l7c8 (0.12 #125, 0.09 #226, 0.08 #327), 04t36 (0.12 #108, 0.08 #512, 0.08 #917) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04qw17; 03c_cxn; >> query: (?x7516, 07ssc) <- film(?x988, ?x7516), nominated_for(?x5591, ?x7516), featured_film_locations(?x7516, ?x11117), ?x5591 = 022_q8 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bh8drv titles! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 79.000 0.500 http://example.org/media_common/netflix_genre/titles #19554-057hz PRED entity: 057hz PRED relation: award_winner! PRED expected values: 0hr3c8y => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 131): 0bz6sb (0.18 #344, 0.07 #2444, 0.04 #1744), 0gpjbt (0.11 #1149, 0.09 #309, 0.08 #1989), 09g90vz (0.11 #263, 0.09 #5443, 0.08 #6283), 0hr3c8y (0.11 #150, 0.08 #5330, 0.07 #6170), 01s695 (0.11 #143, 0.08 #1123, 0.06 #2523), 0418154 (0.11 #247, 0.07 #1647, 0.07 #1787), 058m5m4 (0.11 #195, 0.07 #5375, 0.06 #6215), 05zksls (0.11 #175, 0.07 #1715, 0.06 #1995), 0hndn2q (0.11 #180, 0.06 #740, 0.06 #2840), 092_25 (0.11 #212, 0.05 #13161, 0.05 #5392) >> Best rule #344 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0bkf4; >> query: (?x3644, 0bz6sb) <- diet(?x3644, ?x3130), award(?x3644, ?x1132), inductee(?x9953, ?x3644) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #150 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 02jt1k; *> query: (?x3644, 0hr3c8y) <- diet(?x3644, ?x3130), award(?x3644, ?x1132), ?x1132 = 0bdwft *> conf = 0.11 ranks of expected_values: 4 EVAL 057hz award_winner! 0hr3c8y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 139.000 139.000 0.182 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19553-06xpp7 PRED entity: 06xpp7 PRED relation: contains! PRED expected values: 01n7q => 68 concepts (55 used for prediction) PRED predicted values (max 10 best out of 148): 030qb3t (0.76 #9837, 0.37 #34882, 0.36 #33092), 03rjj (0.44 #25039, 0.42 #32197, 0.40 #27723), 0chghy (0.44 #25039, 0.42 #32197, 0.40 #27723), 02_286 (0.25 #936, 0.08 #3619, 0.08 #17885), 01n7q (0.21 #1865, 0.13 #9018, 0.12 #34958), 07ssc (0.17 #41179, 0.17 #42073, 0.17 #43864), 02jx1 (0.15 #41233, 0.15 #42127, 0.13 #27809), 059rby (0.14 #2703, 0.13 #38480, 0.11 #8961), 0z4_0 (0.08 #17885, 0.08 #27724, 0.08 #18780), 0r03f (0.08 #17885, 0.08 #27724, 0.08 #18780) >> Best rule #9837 for best value: >> intensional similarity = 3 >> extensional distance = 224 >> proper extension: 01hhvg; 07lx1s; 049dk; 02jyr8; 031n8c; 02bjhv; 07vht; 02zccd; 01y17m; 03zw80; ... >> query: (?x5522, ?x1523) <- contains(?x94, ?x5522), ?x94 = 09c7w0, citytown(?x5522, ?x1523) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1865 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 32 *> proper extension: 0kc6x; 016tt2; 0338lq; 046b0s; 024rgt; 0cjdk; 0k9ctht; 01w5gp; 06nzl; 0c41qv; ... *> query: (?x5522, 01n7q) <- citytown(?x5522, ?x1523), ?x1523 = 030qb3t *> conf = 0.21 ranks of expected_values: 5 EVAL 06xpp7 contains! 01n7q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 68.000 55.000 0.758 http://example.org/location/location/contains #19552-021_0p PRED entity: 021_0p PRED relation: religion! PRED expected values: 05k7sb 05fjy => 37 concepts (35 used for prediction) PRED predicted values (max 10 best out of 366): 05tbn (0.80 #844, 0.79 #1327, 0.78 #682), 04ykg (0.80 #826, 0.79 #1309, 0.78 #664), 05fjy (0.80 #859, 0.78 #697, 0.77 #1180), 0824r (0.80 #848, 0.78 #686, 0.77 #1169), 05fjf (0.78 #782, 0.78 #702, 0.77 #1104), 0rh6k (0.78 #728, 0.78 #648, 0.75 #485), 05k7sb (0.78 #673, 0.71 #1318, 0.71 #269), 059f4 (0.78 #657, 0.71 #1302, 0.71 #253), 05fkf (0.77 #1060, 0.71 #1303, 0.71 #254), 06btq (0.75 #514, 0.67 #677, 0.64 #1322) >> Best rule #844 for best value: >> intensional similarity = 15 >> extensional distance = 8 >> proper extension: 01y0s9; 05w5d; >> query: (?x8249, 05tbn) <- religion(?x3074, ?x8249), religion(?x4061, ?x8249), religion(?x3038, ?x8249), religion(?x2982, ?x8249), religion(?x2256, ?x8249), religion(?x938, ?x8249), ?x2982 = 01n4w, ?x4061 = 0498y, ?x938 = 0vmt, district_represented(?x6933, ?x2256), ?x3038 = 0d0x8, ?x6933 = 024tkd, location(?x5312, ?x2256), ?x5312 = 094xh, award_nominee(?x3074, ?x690) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #859 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 8 *> proper extension: 01y0s9; 05w5d; *> query: (?x8249, 05fjy) <- religion(?x3074, ?x8249), religion(?x4061, ?x8249), religion(?x3038, ?x8249), religion(?x2982, ?x8249), religion(?x2256, ?x8249), religion(?x938, ?x8249), ?x2982 = 01n4w, ?x4061 = 0498y, ?x938 = 0vmt, district_represented(?x6933, ?x2256), ?x3038 = 0d0x8, ?x6933 = 024tkd, location(?x5312, ?x2256), ?x5312 = 094xh, award_nominee(?x3074, ?x690) *> conf = 0.80 ranks of expected_values: 3, 7 EVAL 021_0p religion! 05fjy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 37.000 35.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 021_0p religion! 05k7sb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 37.000 35.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #19551-0rh6k PRED entity: 0rh6k PRED relation: featured_film_locations! PRED expected values: 04yc76 0170th 02vqsll 011wtv 0bl06 02q0k7v 026hh0m => 262 concepts (258 used for prediction) PRED predicted values (max 10 best out of 760): 0_b3d (0.25 #755, 0.07 #4926, 0.07 #6316), 0ywrc (0.25 #909, 0.07 #5080, 0.07 #6470), 04dsnp (0.20 #7014, 0.16 #9795, 0.14 #19528), 047csmy (0.17 #1763, 0.13 #8713, 0.12 #10104), 072x7s (0.17 #1497, 0.12 #801, 0.10 #8447), 04j14qc (0.17 #1958, 0.10 #7518, 0.09 #10299), 03hkch7 (0.17 #1604, 0.08 #4384, 0.07 #8554), 03np63f (0.17 #1937, 0.06 #11668, 0.05 #17926), 09fc83 (0.13 #7310, 0.09 #19824, 0.09 #21909), 06fqlk (0.12 #1154, 0.10 #8800, 0.09 #10191) >> Best rule #755 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 03gh4; >> query: (?x108, 0_b3d) <- featured_film_locations(?x103, ?x108), state_province_region(?x3228, ?x108), film_release_region(?x2878, ?x108) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2274 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 05kj_; 059f4; 04rrd; 07srw; 07b_l; 05tbn; 0j5g9; 0694j; 015jr; 0j95; *> query: (?x108, 04yc76) <- featured_film_locations(?x103, ?x108), state_province_region(?x3228, ?x108), adjoins(?x1426, ?x108) *> conf = 0.06 ranks of expected_values: 375, 574, 673 EVAL 0rh6k featured_film_locations! 026hh0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 262.000 258.000 0.250 http://example.org/film/film/featured_film_locations EVAL 0rh6k featured_film_locations! 02q0k7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 262.000 258.000 0.250 http://example.org/film/film/featured_film_locations EVAL 0rh6k featured_film_locations! 0bl06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 262.000 258.000 0.250 http://example.org/film/film/featured_film_locations EVAL 0rh6k featured_film_locations! 011wtv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 262.000 258.000 0.250 http://example.org/film/film/featured_film_locations EVAL 0rh6k featured_film_locations! 02vqsll CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 262.000 258.000 0.250 http://example.org/film/film/featured_film_locations EVAL 0rh6k featured_film_locations! 0170th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 262.000 258.000 0.250 http://example.org/film/film/featured_film_locations EVAL 0rh6k featured_film_locations! 04yc76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 262.000 258.000 0.250 http://example.org/film/film/featured_film_locations #19550-024_41 PRED entity: 024_41 PRED relation: award! PRED expected values: 014hr0 => 47 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2615): 0hl3d (0.79 #20297, 0.78 #3382, 0.78 #20296), 016k62 (0.50 #8280, 0.40 #4896, 0.25 #1514), 03_0p (0.40 #4895, 0.33 #8279, 0.25 #1513), 011zf2 (0.33 #7109, 0.25 #343, 0.20 #3725), 01wd9lv (0.31 #12021, 0.15 #42473, 0.12 #35706), 01vs_v8 (0.28 #41186, 0.17 #64859, 0.16 #68242), 01vrz41 (0.28 #10444, 0.22 #40896, 0.14 #17209), 01l3mk3 (0.28 #12461, 0.10 #42913, 0.09 #39530), 01vrlr4 (0.25 #1851, 0.24 #40601, 0.23 #6766), 0149xx (0.25 #1505, 0.24 #40601, 0.23 #6766) >> Best rule #20297 for best value: >> intensional similarity = 7 >> extensional distance = 69 >> proper extension: 01bgqh; 026mfs; 026mff; 02flpq; 026mmy; 0257__; >> query: (?x8141, ?x8129) <- award_winner(?x8141, ?x8129), ceremony(?x8141, ?x9431), ceremony(?x8141, ?x3121), ?x3121 = 09n4nb, award(?x9727, ?x8141), ?x9431 = 02cg41, award(?x8129, ?x2324) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #40601 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 102 *> proper extension: 05q8pss; *> query: (?x8141, ?x2897) <- award_winner(?x8141, ?x8080), award_winner(?x8141, ?x6399), student(?x10223, ?x8080), languages(?x6399, ?x3592), place_of_death(?x8080, ?x739), award_winner(?x6399, ?x2897) *> conf = 0.24 ranks of expected_values: 18 EVAL 024_41 award! 014hr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 47.000 21.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19549-01sgl PRED entity: 01sgl PRED relation: olympics PRED expected values: 0l6m5 0nbjq => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 18): 0l6m5 (0.43 #84, 0.30 #97, 0.29 #294), 018ctl (0.33 #67, 0.30 #309, 0.30 #97), 09n48 (0.33 #65, 0.30 #307, 0.30 #97), 0swff (0.33 #72, 0.30 #97, 0.28 #114), 0nbjq (0.30 #97, 0.29 #87, 0.28 #114), 01f1kd (0.30 #97, 0.28 #114, 0.25 #62), 0124ld (0.30 #97, 0.28 #114, 0.25 #61), 0sx92 (0.30 #97, 0.28 #114, 0.25 #59), 0blfl (0.30 #97, 0.28 #114, 0.25 #58), 0kbvv (0.30 #97, 0.28 #114, 0.19 #557) >> Best rule #84 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 0bynt; 01cgz; 06z6r; >> query: (?x6733, 0l6m5) <- country(?x6733, ?x7430), country(?x6733, ?x2513), country(?x6733, ?x2346), ?x2346 = 0d05w3, participating_countries(?x1608, ?x7430), olympics(?x6733, ?x391), ?x2513 = 05b4w, jurisdiction_of_office(?x182, ?x7430), ?x391 = 0l6vl >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 01sgl olympics 0nbjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 64.000 64.000 0.429 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 01sgl olympics 0l6m5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.429 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #19548-0jpkw PRED entity: 0jpkw PRED relation: colors PRED expected values: 01l849 => 229 concepts (229 used for prediction) PRED predicted values (max 10 best out of 19): 06fvc (0.53 #1371, 0.35 #1428, 0.34 #458), 01g5v (0.53 #2056, 0.51 #1619, 0.44 #2113), 01l849 (0.47 #419, 0.46 #210, 0.44 #267), 03wkwg (0.31 #204, 0.23 #356, 0.21 #394), 036k5h (0.23 #195, 0.19 #442, 0.18 #347), 088fh (0.20 #253, 0.18 #310, 0.14 #120), 067z2v (0.20 #255, 0.12 #388, 0.10 #749), 038hg (0.18 #2064, 0.14 #239, 0.13 #467), 02rnmb (0.17 #3044, 0.12 #297, 0.10 #430), 0jc_p (0.17 #61, 0.16 #498, 0.11 #745) >> Best rule #1371 for best value: >> intensional similarity = 5 >> extensional distance = 138 >> proper extension: 01d34b; >> query: (?x9880, 06fvc) <- colors(?x9880, ?x4557), colors(?x13795, ?x4557), colors(?x2303, ?x4557), ?x2303 = 02plv57, ?x13795 = 044p4_ >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #419 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 0ylsr; *> query: (?x9880, 01l849) <- colors(?x9880, ?x663), time_zones(?x9880, ?x2674), time_zones(?x7708, ?x2674), contains(?x7708, ?x47), colors(?x260, ?x663) *> conf = 0.47 ranks of expected_values: 3 EVAL 0jpkw colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 229.000 229.000 0.529 http://example.org/education/educational_institution/colors #19547-01_ztw PRED entity: 01_ztw PRED relation: artists! PRED expected values: 02w4v => 192 concepts (142 used for prediction) PRED predicted values (max 10 best out of 282): 025sc50 (0.61 #4041, 0.56 #1584, 0.49 #8340), 02w4v (0.56 #964, 0.25 #657, 0.25 #350), 06j6l (0.47 #4039, 0.44 #8338, 0.41 #17550), 0glt670 (0.39 #8331, 0.37 #4032, 0.34 #12016), 0y3_8 (0.36 #1274, 0.25 #1581, 0.22 #1888), 0gywn (0.35 #17560, 0.33 #4663, 0.32 #8348), 016clz (0.33 #926, 0.29 #40851, 0.25 #3075), 01lyv (0.33 #954, 0.25 #647, 0.25 #340), 03_d0 (0.27 #35942, 0.19 #40549, 0.17 #17515), 0dls3 (0.25 #358, 0.20 #51, 0.12 #665) >> Best rule #4041 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 04bgy; 024y6w; >> query: (?x5566, 025sc50) <- artists(?x671, ?x5566), ?x671 = 064t9, profession(?x5566, ?x4773), ?x4773 = 0d1pc >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #964 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 02w4fkq; 0jbyg; 0f_y9; 01nkxvx; *> query: (?x5566, 02w4v) <- artists(?x7329, ?x5566), artists(?x671, ?x5566), ?x671 = 064t9, people(?x1446, ?x5566), ?x7329 = 016jny *> conf = 0.56 ranks of expected_values: 2 EVAL 01_ztw artists! 02w4v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 142.000 0.605 http://example.org/music/genre/artists #19546-02nwxc PRED entity: 02nwxc PRED relation: award PRED expected values: 0ck27z 02y_rq5 05p09zm => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 235): 0ck27z (0.70 #24187, 0.69 #18543, 0.68 #10077), 09sb52 (0.36 #18180, 0.33 #1250, 0.33 #2459), 05pcn59 (0.25 #1291, 0.24 #2500, 0.24 #3709), 0cqhk0 (0.23 #37, 0.19 #24995, 0.18 #26208), 0cqh6z (0.19 #24995, 0.18 #26208, 0.18 #27418), 05zr6wv (0.18 #1226, 0.17 #3644, 0.17 #2435), 05p09zm (0.16 #1334, 0.16 #3752, 0.15 #2543), 0gqyl (0.15 #26207, 0.15 #29436, 0.14 #8867), 0bdw1g (0.15 #26207, 0.15 #29436, 0.12 #34676), 0bfvw2 (0.15 #26207, 0.15 #29436, 0.08 #18961) >> Best rule #24187 for best value: >> intensional similarity = 2 >> extensional distance = 1304 >> proper extension: 094wz7q; 02tkzn; 014l4w; 039x1k; 05g7q; 0b_dh; 04qb6g; >> query: (?x5662, ?x1670) <- award_winner(?x221, ?x5662), award_winner(?x1670, ?x5662) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 60 EVAL 02nwxc award 05p09zm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 108.000 108.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02nwxc award 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 108.000 108.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02nwxc award 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19545-019pcs PRED entity: 019pcs PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.89 #50, 0.88 #63, 0.88 #62) >> Best rule #50 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 027nb; >> query: (?x3635, 0hzc9wc) <- countries_within(?x2467, ?x3635), country(?x1121, ?x3635), olympics(?x3635, ?x784) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019pcs administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.892 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #19544-018cvf PRED entity: 018cvf PRED relation: film_regional_debut_venue! PRED expected values: 0fq27fp 026njb5 017z49 02rmd_2 0b44shh 07jnt => 35 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1835): 0cnztc4 (0.50 #684, 0.40 #1360, 0.33 #145), 028_yv (0.50 #540, 0.33 #137, 0.25 #811), 0jqn5 (0.50 #553, 0.33 #150, 0.25 #824), 047p7fr (0.50 #715, 0.33 #176, 0.25 #850), 0dgpwnk (0.50 #721, 0.33 #182, 0.25 #856), 026njb5 (0.50 #719, 0.33 #180, 0.25 #854), 0hv81 (0.50 #1703, 0.17 #1566, 0.11 #2243), 01s9vc (0.40 #1475, 0.33 #2285, 0.33 #1745), 0n08r (0.40 #1478, 0.33 #263, 0.33 #128), 0btpm6 (0.33 #2264, 0.27 #2807, 0.17 #1724) >> Best rule #684 for best value: >> intensional similarity = 33 >> extensional distance = 2 >> proper extension: 07751; >> query: (?x6601, 0cnztc4) <- film_regional_debut_venue(?x10535, ?x6601), film_regional_debut_venue(?x1163, ?x6601), film_regional_debut_venue(?x303, ?x6601), nominated_for(?x1703, ?x303), film_release_region(?x1163, ?x456), film_release_region(?x1163, ?x151), film_crew_role(?x1163, ?x2095), film_release_region(?x303, ?x390), nominated_for(?x112, ?x1163), ?x10535 = 09v42sf, genre(?x303, ?x53), ?x151 = 0b90_r, film_crew_role(?x303, ?x137), ?x1703 = 0k611, nominated_for(?x7526, ?x1163), organization(?x456, ?x127), country(?x1557, ?x456), country(?x1352, ?x456), combatants(?x456, ?x792), film_release_region(?x4040, ?x456), film_release_region(?x3619, ?x456), film_release_region(?x2628, ?x456), film_release_region(?x1386, ?x456), ?x2628 = 06wbm8q, award_nominee(?x7526, ?x519), ?x1352 = 0w0d, ?x1386 = 0dtfn, ?x4040 = 02mt51, ?x1557 = 07bs0, ?x3619 = 0fphgb, film(?x540, ?x1163), film_crew_role(?x10425, ?x2095), ?x10425 = 02x0fs9 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #719 for first EXPECTED value: *> intensional similarity = 33 *> extensional distance = 2 *> proper extension: 07751; *> query: (?x6601, 026njb5) <- film_regional_debut_venue(?x10535, ?x6601), film_regional_debut_venue(?x1163, ?x6601), film_regional_debut_venue(?x303, ?x6601), nominated_for(?x1703, ?x303), film_release_region(?x1163, ?x456), film_release_region(?x1163, ?x151), film_crew_role(?x1163, ?x2095), film_release_region(?x303, ?x390), nominated_for(?x112, ?x1163), ?x10535 = 09v42sf, genre(?x303, ?x53), ?x151 = 0b90_r, film_crew_role(?x303, ?x137), ?x1703 = 0k611, nominated_for(?x7526, ?x1163), organization(?x456, ?x127), country(?x1557, ?x456), country(?x1352, ?x456), combatants(?x456, ?x792), film_release_region(?x4040, ?x456), film_release_region(?x3619, ?x456), film_release_region(?x2628, ?x456), film_release_region(?x1386, ?x456), ?x2628 = 06wbm8q, award_nominee(?x7526, ?x519), ?x1352 = 0w0d, ?x1386 = 0dtfn, ?x4040 = 02mt51, ?x1557 = 07bs0, ?x3619 = 0fphgb, film(?x540, ?x1163), film_crew_role(?x10425, ?x2095), ?x10425 = 02x0fs9 *> conf = 0.50 ranks of expected_values: 6, 44, 81, 115, 183, 280 EVAL 018cvf film_regional_debut_venue! 07jnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 35.000 22.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 018cvf film_regional_debut_venue! 0b44shh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 35.000 22.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 018cvf film_regional_debut_venue! 02rmd_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 35.000 22.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 018cvf film_regional_debut_venue! 017z49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 35.000 22.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 018cvf film_regional_debut_venue! 026njb5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 35.000 22.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 018cvf film_regional_debut_venue! 0fq27fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 35.000 22.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #19543-016sd3 PRED entity: 016sd3 PRED relation: school! PRED expected values: 04f4z1k => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 19): 0f4vx0 (0.26 #257, 0.26 #333, 0.26 #238), 09l0x9 (0.25 #30, 0.17 #258, 0.17 #239), 02r6gw6 (0.25 #32, 0.14 #800, 0.13 #666), 02qw1zx (0.24 #232, 0.22 #327, 0.22 #251), 05vsb7 (0.20 #1, 0.19 #210, 0.18 #248), 0g3zpp (0.20 #2, 0.17 #21, 0.14 #249), 04f4z1k (0.20 #17, 0.14 #800, 0.13 #666), 02pq_x5 (0.18 #225, 0.17 #35, 0.16 #54), 03nt7j (0.17 #234, 0.17 #25, 0.15 #215), 092j54 (0.17 #27, 0.16 #217, 0.16 #255) >> Best rule #257 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 01pl14; 065y4w7; 07w0v; 0j_sncb; 01r3y2; 0hd7j; 019dwp; 01vs5c; 02y9bj; 02l424; ... >> query: (?x10838, 0f4vx0) <- contains(?x94, ?x10838), colors(?x10838, ?x663), school(?x1161, ?x10838) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01jygk; *> query: (?x10838, 04f4z1k) <- state_province_region(?x10838, ?x2623), category(?x10838, ?x134), ?x2623 = 02xry *> conf = 0.20 ranks of expected_values: 7 EVAL 016sd3 school! 04f4z1k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 126.000 126.000 0.263 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #19542-03rbzn PRED entity: 03rbzn PRED relation: country PRED expected values: 03rt9 0hzlz 0ctw_b 01znc_ 02k8k => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 413): 0d0vqn (0.87 #3059, 0.86 #2722, 0.82 #2383), 059j2 (0.82 #2227, 0.73 #3923, 0.73 #2397), 0d04z6 (0.80 #3139, 0.67 #1955, 0.67 #1614), 0ctw_b (0.79 #2732, 0.75 #2054, 0.73 #3239), 0hzlz (0.79 #2729, 0.75 #2051, 0.73 #2220), 0h7x (0.73 #3248, 0.73 #3928, 0.67 #4953), 015qh (0.73 #3080, 0.73 #2234, 0.67 #1896), 02k54 (0.73 #3064, 0.67 #1880, 0.67 #1539), 01pj7 (0.73 #2411, 0.73 #2241, 0.67 #2579), 06t8v (0.73 #2263, 0.67 #3109, 0.67 #1584) >> Best rule #3059 for best value: >> intensional similarity = 66 >> extensional distance = 13 >> proper extension: 064vjs; >> query: (?x3598, 0d0vqn) <- country(?x3598, ?x3730), country(?x3598, ?x2152), country(?x3598, ?x1203), country(?x3598, ?x404), film_release_region(?x11125, ?x2152), film_release_region(?x10246, ?x2152), film_release_region(?x10048, ?x2152), film_release_region(?x9941, ?x2152), film_release_region(?x9501, ?x2152), film_release_region(?x8867, ?x2152), film_release_region(?x8292, ?x2152), film_release_region(?x7629, ?x2152), film_release_region(?x6556, ?x2152), film_release_region(?x5877, ?x2152), film_release_region(?x5849, ?x2152), film_release_region(?x4998, ?x2152), film_release_region(?x4811, ?x2152), film_release_region(?x4047, ?x2152), film_release_region(?x4041, ?x2152), film_release_region(?x3453, ?x2152), film_release_region(?x3076, ?x2152), film_release_region(?x2783, ?x2152), film_release_region(?x2656, ?x2152), film_release_region(?x2340, ?x2152), film_release_region(?x2037, ?x2152), film_release_region(?x1546, ?x2152), film_release_region(?x1518, ?x2152), film_release_region(?x1118, ?x2152), film_release_region(?x504, ?x2152), film_release_region(?x249, ?x2152), olympics(?x1203, ?x391), ?x6556 = 05dss7, ?x5877 = 02qyv3h, ?x3453 = 0dgpwnk, ?x3730 = 03shp, ?x2340 = 0fpv_3_, member_states(?x2106, ?x2152), ?x11125 = 0gy4k, ?x2656 = 03qnc6q, ?x8867 = 03lfd_, ?x249 = 0c3ybss, ?x504 = 0g5qs2k, ?x4811 = 0f4k49, ?x1518 = 04w7rn, ?x10246 = 023vcd, ?x1546 = 0d6b7, country(?x1352, ?x1203), olympics(?x2152, ?x452), ?x10048 = 09tcg4, ?x1118 = 0_92w, film_release_region(?x7629, ?x2843), ?x4998 = 0dzlbx, organization(?x2152, ?x127), ?x4041 = 0gy2y8r, ?x2783 = 0879bpq, ?x2843 = 016wzw, ?x2037 = 0gvrws1, ?x9941 = 024lt6, ?x3076 = 0g5838s, ?x5849 = 02h22, ?x4047 = 07s846j, ?x9501 = 0g5qmbz, ?x1352 = 0w0d, ?x404 = 047lj, contains(?x7273, ?x1203), ?x8292 = 0cmf0m0 >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #2732 for first EXPECTED value: *> intensional similarity = 62 *> extensional distance = 12 *> proper extension: 06z6r; 01sgl; 01gqfm; *> query: (?x3598, 0ctw_b) <- country(?x3598, ?x2513), country(?x3598, ?x2346), country(?x3598, ?x2152), country(?x3598, ?x1003), country(?x3598, ?x344), country(?x3598, ?x142), ?x2152 = 06mkj, ?x142 = 0jgd, film_release_region(?x8193, ?x344), film_release_region(?x6931, ?x344), film_release_region(?x6121, ?x344), film_release_region(?x4690, ?x344), film_release_region(?x3784, ?x344), film_release_region(?x3606, ?x344), film_release_region(?x3217, ?x344), film_release_region(?x2961, ?x344), film_release_region(?x2893, ?x344), film_release_region(?x2628, ?x344), film_release_region(?x1956, ?x344), film_release_region(?x1392, ?x344), film_release_region(?x1259, ?x344), film_release_region(?x1202, ?x344), film_release_region(?x409, ?x344), ?x3606 = 0gh65c5, ?x2346 = 0d05w3, ?x2513 = 05b4w, ?x1956 = 05qbckf, ?x3217 = 0gffmn8, adjoins(?x1003, ?x1355), film_release_region(?x9900, ?x1003), film_release_region(?x6520, ?x1003), film_release_region(?x5013, ?x1003), film_release_region(?x3252, ?x1003), film_release_region(?x2656, ?x1003), film_release_region(?x2151, ?x1003), film_release_region(?x1602, ?x1003), film_release_region(?x664, ?x1003), ?x1392 = 017gm7, ?x6121 = 064lsn, ?x6520 = 02bg55, ?x3252 = 0gh8zks, ?x8193 = 03z9585, ?x1602 = 0gxtknx, ?x409 = 0gtv7pk, ?x2151 = 0yzvw, jurisdiction_of_office(?x182, ?x344), ?x4690 = 0gkz3nz, ?x1259 = 04hwbq, ?x2961 = 047p7fr, member_states(?x2106, ?x1003), ?x3784 = 0bmhvpr, ?x2893 = 01jrbb, ?x6931 = 09v3jyg, ?x2628 = 06wbm8q, partially_contains(?x1003, ?x10517), ?x664 = 0401sg, ?x5013 = 011ycb, ?x9900 = 0qmfk, nationality(?x1373, ?x1003), ?x1202 = 0gj8t_b, medal(?x344, ?x422), ?x2656 = 03qnc6q *> conf = 0.79 ranks of expected_values: 4, 5, 26, 35, 76 EVAL 03rbzn country 02k8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 36.000 36.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03rbzn country 01znc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 36.000 36.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03rbzn country 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 36.000 36.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03rbzn country 0hzlz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 36.000 36.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03rbzn country 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 36.000 36.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #19541-02fj8n PRED entity: 02fj8n PRED relation: film_crew_role PRED expected values: 015h31 01vx2h 02ynfr => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 32): 01vx2h (0.70 #486, 0.65 #384, 0.56 #418), 0dxtw (0.60 #485, 0.58 #179, 0.55 #383), 02rh1dz (0.42 #178, 0.40 #280, 0.31 #416), 02ynfr (0.35 #150, 0.29 #48, 0.28 #286), 015h31 (0.25 #483, 0.18 #756, 0.18 #1132), 0215hd (0.24 #528, 0.19 #2288, 0.19 #732), 01xy5l_ (0.23 #216, 0.22 #420, 0.17 #2628), 0d2b38 (0.19 #1393, 0.19 #432, 0.18 #773), 089g0h (0.17 #1871, 0.17 #2289, 0.16 #2634), 094hwz (0.16 #762, 0.16 #183, 0.15 #1138) >> Best rule #486 for best value: >> intensional similarity = 6 >> extensional distance = 38 >> proper extension: 0cpllql; 07sc6nw; 0cd2vh9; 0h14ln; 0270k40; >> query: (?x7463, 01vx2h) <- country(?x7463, ?x94), genre(?x7463, ?x1510), genre(?x7463, ?x812), ?x812 = 01jfsb, ?x1510 = 01hmnh, film_crew_role(?x7463, ?x137) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 5 EVAL 02fj8n film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 175.000 175.000 0.700 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02fj8n film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.700 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02fj8n film_crew_role 015h31 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 175.000 175.000 0.700 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19540-0407yfx PRED entity: 0407yfx PRED relation: language PRED expected values: 02h40lc => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 34): 02h40lc (0.90 #898, 0.89 #3037, 0.89 #1668), 064_8sq (0.23 #22, 0.14 #1452, 0.14 #1036), 06nm1 (0.23 #11, 0.14 #191, 0.12 #789), 03_9r (0.09 #190, 0.09 #1253, 0.08 #549), 02bjrlw (0.09 #1253, 0.06 #1431, 0.06 #60), 04306rv (0.09 #1376, 0.08 #960, 0.07 #3337), 06b_j (0.06 #742, 0.05 #1571, 0.05 #1927), 0653m (0.06 #672, 0.05 #3332, 0.03 #967), 012w70 (0.06 #673, 0.03 #968, 0.03 #1027), 032f6 (0.05 #3332, 0.05 #175, 0.02 #654) >> Best rule #898 for best value: >> intensional similarity = 4 >> extensional distance = 215 >> proper extension: 078sj4; >> query: (?x2155, 02h40lc) <- executive_produced_by(?x2155, ?x6682), profession(?x6682, ?x319), award_winner(?x78, ?x6682), film_release_distribution_medium(?x2155, ?x81) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0407yfx language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.903 http://example.org/film/film/language #19539-02psvcf PRED entity: 02psvcf PRED relation: people PRED expected values: 0202p_ => 69 concepts (53 used for prediction) PRED predicted values (max 10 best out of 882): 01w9ph_ (0.50 #7248, 0.17 #14156, 0.17 #9660), 015076 (0.50 #7447, 0.17 #14355, 0.11 #6208), 09889g (0.50 #9161, 0.12 #26435, 0.08 #35435), 0gyy0 (0.33 #12804, 0.30 #19712, 0.25 #7975), 04__f (0.33 #346, 0.25 #7938, 0.25 #6554), 0jrny (0.33 #12528, 0.25 #21516, 0.25 #9077), 0gr36 (0.33 #101, 0.25 #6309, 0.25 #4928), 040db (0.33 #70, 0.25 #6278, 0.25 #4208), 0121rx (0.33 #647, 0.25 #6855, 0.25 #4785), 02h48 (0.33 #2683, 0.25 #6134, 0.25 #5442) >> Best rule #7248 for best value: >> intensional similarity = 14 >> extensional distance = 2 >> proper extension: 01tf_6; >> query: (?x7006, 01w9ph_) <- people(?x7006, ?x1946), influenced_by(?x1946, ?x4072), influenced_by(?x1946, ?x916), influenced_by(?x8720, ?x1946), ?x916 = 019z7q, place_of_birth(?x1946, ?x6769), influenced_by(?x2161, ?x4072), location(?x4072, ?x739), student(?x481, ?x8720), people(?x4291, ?x4072), profession(?x1946, ?x353), gender(?x1946, ?x231), influenced_by(?x4072, ?x5435), ?x2161 = 040db >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9570 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 0gg4h; 0x2fg; *> query: (?x7006, 0202p_) <- people(?x7006, ?x1946), influenced_by(?x1946, ?x9854), influenced_by(?x1946, ?x916), influenced_by(?x8720, ?x1946), gender(?x916, ?x231), influenced_by(?x916, ?x3336), profession(?x1946, ?x353), people(?x11563, ?x916), place_of_burial(?x1946, ?x3153), nationality(?x1946, ?x94), person(?x1315, ?x9854), film(?x9854, ?x1444), location(?x1946, ?x739) *> conf = 0.25 ranks of expected_values: 324 EVAL 02psvcf people 0202p_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 53.000 0.500 http://example.org/people/cause_of_death/people #19538-01w9ph_ PRED entity: 01w9ph_ PRED relation: profession PRED expected values: 0cbd2 => 144 concepts (91 used for prediction) PRED predicted values (max 10 best out of 91): 01c72t (0.71 #309, 0.68 #1749, 0.56 #1461), 039v1 (0.62 #2481, 0.61 #2769, 0.57 #1185), 0dz3r (0.62 #2450, 0.57 #290, 0.57 #2738), 01d_h8 (0.57 #3318, 0.57 #2022, 0.55 #726), 0cbd2 (0.52 #2887, 0.50 #7496, 0.48 #4759), 0kyk (0.48 #10109, 0.43 #2331, 0.32 #2907), 016z4k (0.43 #1156, 0.42 #8501, 0.41 #7204), 0n1h (0.43 #1163, 0.41 #6779, 0.39 #2603), 03gjzk (0.38 #8654, 0.38 #10383, 0.36 #10671), 018gz8 (0.36 #8656, 0.30 #9088, 0.27 #9665) >> Best rule #309 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0144l1; 01vsyg9; >> query: (?x8004, 01c72t) <- artists(?x1000, ?x8004), ?x1000 = 0xhtw, peers(?x4608, ?x8004), role(?x8004, ?x1466) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2887 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 027d5g5; 01g4bk; 05q9g1; 04ch23; *> query: (?x8004, 0cbd2) <- profession(?x8004, ?x3746), profession(?x8004, ?x987), ?x987 = 0dxtg, type_of_union(?x8004, ?x11744), ?x3746 = 05z96 *> conf = 0.52 ranks of expected_values: 5 EVAL 01w9ph_ profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 144.000 91.000 0.714 http://example.org/people/person/profession #19537-01ydzx PRED entity: 01ydzx PRED relation: gender PRED expected values: 05zppz => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #51, 0.86 #75, 0.86 #19), 02zsn (0.36 #48, 0.34 #110, 0.27 #138) >> Best rule #51 for best value: >> intensional similarity = 5 >> extensional distance = 160 >> proper extension: 08wq0g; >> query: (?x6774, 05zppz) <- category(?x6774, ?x134), profession(?x6774, ?x131), instrumentalists(?x227, ?x6774), role(?x6774, ?x1466), nationality(?x6774, ?x1310) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ydzx gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.864 http://example.org/people/person/gender #19536-0v1xg PRED entity: 0v1xg PRED relation: time_zones PRED expected values: 02hcv8 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.82 #3, 0.81 #42, 0.60 #29), 02lcqs (0.26 #96, 0.25 #460, 0.24 #70), 02fqwt (0.19 #79, 0.19 #66, 0.18 #378), 02hczc (0.17 #1074, 0.17 #1114, 0.16 #941), 02lcrv (0.17 #1074, 0.17 #1114, 0.16 #941), 042g7t (0.16 #941, 0.15 #900, 0.15 #914), 02llzg (0.15 #225, 0.14 #160, 0.13 #329), 03bdv (0.06 #448, 0.05 #123, 0.05 #188), 03plfd (0.02 #140, 0.02 #413, 0.02 #426) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0vfs8; 0vm5t; >> query: (?x8757, 02hcv8) <- contains(?x1906, ?x8757), ?x1906 = 04rrx, place_of_birth(?x2237, ?x8757), award(?x2237, ?x154) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0v1xg time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.818 http://example.org/location/location/time_zones #19535-03mdw3c PRED entity: 03mdw3c PRED relation: film_production_design_by! PRED expected values: 06kl78 => 136 concepts (73 used for prediction) PRED predicted values (max 10 best out of 163): 029v40 (0.25 #477, 0.10 #640, 0.05 #1292), 0hwpz (0.25 #454, 0.10 #617, 0.05 #1269), 02n72k (0.25 #441, 0.10 #604, 0.05 #1256), 01npcx (0.25 #419, 0.10 #582, 0.05 #1234), 01f8hf (0.25 #406, 0.10 #569, 0.05 #1221), 03r0g9 (0.25 #386, 0.10 #549, 0.05 #1201), 014kq6 (0.25 #363, 0.10 #526, 0.05 #1178), 0dr_4 (0.25 #351, 0.10 #514, 0.05 #1166), 0g5pv3 (0.25 #345, 0.10 #508, 0.05 #1160), 02qrv7 (0.25 #344, 0.10 #507, 0.05 #1159) >> Best rule #477 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0d5wn3; >> query: (?x8844, 029v40) <- film_production_design_by(?x7246, ?x8844), film_format(?x7246, ?x6392), honored_for(?x1330, ?x7246), genre(?x7246, ?x53) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03mdw3c film_production_design_by! 06kl78 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 73.000 0.250 http://example.org/film/film/film_production_design_by #19534-0f1jhc PRED entity: 0f1jhc PRED relation: nationality PRED expected values: 09c7w0 => 82 concepts (74 used for prediction) PRED predicted values (max 10 best out of 21): 09c7w0 (0.86 #201, 0.86 #1604, 0.85 #1), 02jx1 (0.40 #6831, 0.10 #4952, 0.09 #3445), 07ssc (0.40 #6831, 0.08 #4232, 0.08 #3628), 0d060g (0.40 #6831, 0.06 #3318, 0.05 #5631), 0f8l9c (0.40 #6831, 0.03 #2126, 0.03 #2327), 03rjj (0.40 #6831, 0.03 #205, 0.03 #5), 03_3d (0.40 #6831, 0.02 #4323, 0.02 #6635), 0345h (0.40 #6831, 0.02 #1533, 0.02 #3342), 03rt9 (0.40 #6831, 0.01 #3324, 0.01 #5536), 03rk0 (0.06 #747, 0.06 #7078, 0.05 #7178) >> Best rule #201 for best value: >> intensional similarity = 2 >> extensional distance = 63 >> proper extension: 043q6n_; >> query: (?x9812, 09c7w0) <- student(?x4955, ?x9812), ?x4955 = 09f2j >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f1jhc nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 74.000 0.862 http://example.org/people/person/nationality #19533-05rfst PRED entity: 05rfst PRED relation: country PRED expected values: 0345h => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 76): 07ssc (0.35 #256, 0.35 #136, 0.31 #496), 0f8l9c (0.13 #139, 0.13 #439, 0.12 #19), 0345h (0.13 #207, 0.13 #1351, 0.12 #27), 03rt9 (0.12 #14, 0.02 #74, 0.02 #134), 09blyk (0.08 #601, 0.06 #2166, 0.06 #4098), 01jfsb (0.08 #601, 0.06 #2166, 0.06 #4098), 03rjj (0.06 #607, 0.06 #546, 0.05 #306), 0ctw_b (0.06 #143, 0.04 #263, 0.04 #83), 03_3d (0.05 #307, 0.05 #367, 0.05 #427), 0d060g (0.05 #1332, 0.04 #2053, 0.04 #2113) >> Best rule #256 for best value: >> intensional similarity = 6 >> extensional distance = 69 >> proper extension: 011yfd; 0j8f09z; >> query: (?x5674, 07ssc) <- nominated_for(?x1180, ?x5674), nominated_for(?x601, ?x5674), nominated_for(?x384, ?x5674), ?x601 = 0gr4k, ?x1180 = 02n9nmz, award(?x164, ?x384) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #207 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 04b2qn; *> query: (?x5674, 0345h) <- nominated_for(?x2577, ?x5674), film(?x426, ?x5674), ?x2577 = 099t8j, award_winner(?x426, ?x286) *> conf = 0.13 ranks of expected_values: 3 EVAL 05rfst country 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.352 http://example.org/film/film/country #19532-09nqf PRED entity: 09nqf PRED relation: currency! PRED expected values: 01mpwj 04ycjk 06b7s9 => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 2082): 01bk1y (0.70 #252, 0.65 #629, 0.62 #253), 0bwfn (0.70 #252, 0.65 #629, 0.62 #253), 09s5q8 (0.70 #252, 0.65 #629, 0.62 #253), 02zd460 (0.70 #252, 0.65 #629, 0.62 #253), 0l2tk (0.70 #252, 0.65 #629, 0.62 #253), 0bx8pn (0.70 #252, 0.65 #629, 0.62 #253), 09kvv (0.70 #252, 0.65 #629, 0.62 #253), 07szy (0.70 #252, 0.65 #629, 0.62 #253), 02cttt (0.70 #252, 0.65 #629, 0.62 #253), 0c5x_ (0.70 #252, 0.65 #629, 0.62 #253) >> Best rule #252 for best value: >> intensional similarity = 26 >> extensional distance = 1 >> proper extension: 01nv4h; >> query: (?x170, ?x99) <- currency(?x6947, ?x170), currency(?x2451, ?x170), currency(?x99, ?x170), currency(?x8569, ?x170), currency(?x6974, ?x170), currency(?x216, ?x170), currency(?x8574, ?x170), currency(?x5122, ?x170), currency(?x4409, ?x170), currency(?x3496, ?x170), currency(?x2612, ?x170), olympics(?x6974, ?x778), film(?x2451, ?x3276), currency(?x266, ?x170), genre(?x4409, ?x1403), award_winner(?x1089, ?x6947), vacationer(?x8569, ?x2352), award(?x8574, ?x112), film(?x382, ?x2612), nominated_for(?x500, ?x3496), role(?x6947, ?x212), ?x1403 = 02l7c8, award_nominee(?x1104, ?x2451), language(?x3496, ?x254), genre(?x2612, ?x53), film(?x3705, ?x5122) >> conf = 0.70 => this is the best rule for 53 predicted values *> Best rule #377 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 1 *> proper extension: 0ptk_; *> query: (?x170, ?x3087) <- currency(?x4750, ?x170), currency(?x10422, ?x170), currency(?x9258, ?x170), currency(?x5142, ?x170), currency(?x4287, ?x170), currency(?x2085, ?x170), currency(?x7747, ?x170), currency(?x3086, ?x170), award(?x10422, ?x102), nominated_for(?x1871, ?x5142), film_crew_role(?x4287, ?x137), currency(?x99, ?x170), genre(?x9258, ?x53), film(?x8285, ?x5142), religion(?x7747, ?x492), major_field_of_study(?x4750, ?x1154), nominated_for(?x384, ?x2085), contains(?x3086, ?x3087), currency(?x1961, ?x170) *> conf = 0.16 ranks of expected_values: 941, 1625, 1854 EVAL 09nqf currency! 06b7s9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.700 http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04ycjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.700 http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01mpwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.700 http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency #19531-023ny6 PRED entity: 023ny6 PRED relation: genre PRED expected values: 06q7n => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 83): 07s9rl0 (0.87 #1712, 0.61 #651, 0.59 #3175), 06n90 (0.48 #337, 0.33 #13, 0.32 #94), 0hcr (0.37 #341, 0.26 #98, 0.22 #17), 01t_vv (0.36 #194, 0.32 #518, 0.24 #275), 01htzx (0.33 #16, 0.26 #97, 0.26 #340), 03k9fj (0.26 #335, 0.22 #11, 0.19 #1722), 03npn (0.22 #7, 0.21 #88, 0.19 #331), 06nbt (0.22 #424, 0.20 #262, 0.18 #505), 01jfsb (0.21 #93, 0.17 #12, 0.15 #336), 0pr6f (0.19 #372, 0.16 #129, 0.12 #2653) >> Best rule #1712 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 07qht4; >> query: (?x9951, 07s9rl0) <- genre(?x9951, ?x1510), genre(?x715, ?x1510), ?x715 = 02py4c8, titles(?x1510, ?x83) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #204 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 0kfpm; 01q_y0; 0557yqh; 0l76z; 05p9_ql; 034fl9; 015pnb; *> query: (?x9951, 06q7n) <- country_of_origin(?x9951, ?x94), actor(?x9951, ?x2417), nominated_for(?x757, ?x9951), ?x757 = 09qj50 *> conf = 0.18 ranks of expected_values: 11 EVAL 023ny6 genre 06q7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 96.000 96.000 0.873 http://example.org/tv/tv_program/genre #19530-063hp4 PRED entity: 063hp4 PRED relation: currency PRED expected values: 09nqf => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.81 #155, 0.81 #162, 0.81 #197), 01nv4h (0.03 #107, 0.02 #198, 0.02 #359), 02gsvk (0.02 #125, 0.01 #405), 02l6h (0.02 #291, 0.01 #193, 0.01 #648) >> Best rule #155 for best value: >> intensional similarity = 4 >> extensional distance = 207 >> proper extension: 085ccd; 01738w; 032sl_; 03hp2y1; 037cr1; >> query: (?x6722, 09nqf) <- crewmember(?x6722, ?x10164), country(?x6722, ?x94), film(?x2416, ?x6722), production_companies(?x6722, ?x788) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 063hp4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.813 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #19529-03rx9 PRED entity: 03rx9 PRED relation: award PRED expected values: 02664f => 163 concepts (126 used for prediction) PRED predicted values (max 10 best out of 317): 0262yt (0.76 #34136, 0.74 #3216, 0.72 #49799), 02664f (0.70 #1019, 0.49 #3028, 0.12 #32129), 0262x6 (0.63 #3127, 0.60 #1118, 0.18 #46985), 0265wl (0.50 #1038, 0.46 #3047, 0.18 #46985), 039yzf (0.50 #1152, 0.29 #3161, 0.18 #46985), 040_9s0 (0.43 #3128, 0.30 #1119, 0.18 #2726), 045xh (0.30 #1178, 0.20 #3187, 0.12 #32129), 09sb52 (0.28 #41805, 0.27 #38591, 0.27 #39795), 01bgqh (0.25 #444, 0.15 #1650, 0.13 #6871), 0f4x7 (0.25 #432, 0.14 #12883, 0.13 #25330) >> Best rule #34136 for best value: >> intensional similarity = 4 >> extensional distance = 436 >> proper extension: 02sj1x; 01wb8bs; >> query: (?x9738, ?x575) <- award_winner(?x575, ?x9738), religion(?x9738, ?x2694), award(?x8718, ?x575), influenced_by(?x8718, ?x2161) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1019 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 05x8n; *> query: (?x9738, 02664f) <- award_winner(?x6687, ?x9738), gender(?x9738, ?x231), profession(?x9738, ?x353), ?x6687 = 0262yt *> conf = 0.70 ranks of expected_values: 2 EVAL 03rx9 award 02664f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 163.000 126.000 0.756 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19528-0453t PRED entity: 0453t PRED relation: influenced_by PRED expected values: 0bk5r 07dnx => 142 concepts (65 used for prediction) PRED predicted values (max 10 best out of 330): 032l1 (0.66 #7870, 0.38 #953, 0.33 #2682), 05qmj (0.50 #625, 0.42 #2354, 0.26 #13605), 03sbs (0.46 #2383, 0.31 #1086, 0.25 #13634), 0gz_ (0.38 #2264, 0.24 #13515, 0.20 #103), 081k8 (0.33 #2749, 0.33 #588, 0.29 #7937), 039n1 (0.33 #757, 0.25 #2486, 0.20 #325), 099bk (0.33 #543, 0.20 #111, 0.17 #2272), 0ct9_ (0.33 #711, 0.12 #2440, 0.08 #13845), 03f0324 (0.30 #2745, 0.20 #7933, 0.17 #584), 058vp (0.30 #2778, 0.17 #617, 0.11 #7966) >> Best rule #7870 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 02m4t; >> query: (?x2239, 032l1) <- influenced_by(?x2239, ?x2240), influenced_by(?x3335, ?x2240), people(?x5855, ?x2240), ?x3335 = 0jcx >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #14710 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 129 *> proper extension: 01d494; 0j3v; 02ln1; *> query: (?x2239, ?x4679) <- student(?x5638, ?x2239), gender(?x2239, ?x231), influenced_by(?x2239, ?x7495), peers(?x4679, ?x7495) *> conf = 0.13 ranks of expected_values: 62, 91 EVAL 0453t influenced_by 07dnx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 142.000 65.000 0.661 http://example.org/influence/influence_node/influenced_by EVAL 0453t influenced_by 0bk5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 142.000 65.000 0.661 http://example.org/influence/influence_node/influenced_by #19527-01c4_6 PRED entity: 01c4_6 PRED relation: award! PRED expected values: 016ntp 01w3lzq 0jg77 => 41 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2323): 0kr_t (0.80 #23475, 0.79 #26829, 0.79 #53667), 03h_fqv (0.80 #23475, 0.79 #26829, 0.79 #53667), 016l09 (0.80 #23475, 0.79 #26829, 0.79 #53667), 0lzkm (0.80 #23475, 0.79 #26829, 0.79 #53667), 0134pk (0.67 #16198, 0.29 #22904, 0.25 #12844), 01vs_v8 (0.60 #7288, 0.56 #13995, 0.50 #10641), 0478__m (0.60 #8029, 0.44 #14736, 0.27 #21442), 02qwg (0.58 #17697, 0.38 #10990, 0.33 #930), 01vrz41 (0.58 #17060, 0.25 #20413, 0.22 #13707), 0gcs9 (0.58 #17581, 0.25 #20934, 0.22 #24289) >> Best rule #23475 for best value: >> intensional similarity = 5 >> extensional distance = 57 >> proper extension: 06196; >> query: (?x1565, ?x3735) <- award(?x10561, ?x1565), award(?x5916, ?x1565), award_winner(?x1565, ?x3735), group(?x227, ?x5916), influenced_by(?x10561, ?x4942) >> conf = 0.80 => this is the best rule for 4 predicted values *> Best rule #16722 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 02v1m7; 02f716; 02f72_; 02f6yz; 01ckcd; *> query: (?x1565, 0jg77) <- award(?x10561, ?x1565), award(?x1004, ?x1565), instrumentalists(?x227, ?x1004), ?x10561 = 09jm8, ?x227 = 0342h *> conf = 0.33 ranks of expected_values: 50, 172, 561 EVAL 01c4_6 award! 0jg77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 41.000 25.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c4_6 award! 01w3lzq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 41.000 25.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c4_6 award! 016ntp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 25.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19526-05nqz PRED entity: 05nqz PRED relation: locations PRED expected values: 04w4s => 65 concepts (48 used for prediction) PRED predicted values (max 10 best out of 427): 0d05w3 (0.43 #411, 0.19 #2046, 0.18 #1319), 05qhw (0.38 #556, 0.33 #13, 0.27 #1099), 02j9z (0.35 #4551, 0.30 #4365, 0.23 #1647), 0d0kn (0.33 #44, 0.13 #7633, 0.12 #587), 04w8f (0.33 #67, 0.13 #7633, 0.12 #610), 0jhd (0.33 #128, 0.13 #7633, 0.12 #671), 02vzc (0.33 #43, 0.12 #586, 0.10 #1814), 0jgx (0.33 #74, 0.12 #617, 0.09 #1342), 047lj (0.33 #11, 0.12 #554, 0.09 #1279), 0cdbq (0.33 #83, 0.12 #626, 0.09 #1351) >> Best rule #411 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 01gjd0; >> query: (?x5352, 0d05w3) <- locations(?x5352, ?x5114), entity_involved(?x5352, ?x1892), combatants(?x7287, ?x5114), combatants(?x5147, ?x5114), combatants(?x1023, ?x5114), ?x7287 = 05b7q, participating_countries(?x1931, ?x5147), combatants(?x326, ?x5114), film_release_region(?x66, ?x1023), contains(?x7273, ?x5147) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4719 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 21 *> proper extension: 0cbvg; 07j9n; 01_3rn; 0dr7s; *> query: (?x5352, ?x456) <- locations(?x5352, ?x1471), locations(?x5352, ?x344), entity_involved(?x5352, ?x9940), contains(?x1471, ?x12817), adjoins(?x456, ?x344), capital(?x9940, ?x8601), category(?x12817, ?x134), locations(?x7241, ?x456), location(?x11500, ?x12817) *> conf = 0.21 ranks of expected_values: 22 EVAL 05nqz locations 04w4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 65.000 48.000 0.429 http://example.org/time/event/locations #19525-02sdx PRED entity: 02sdx PRED relation: religion PRED expected values: 0c8wxp => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 28): 0c8wxp (0.40 #96, 0.31 #411, 0.27 #231), 0kpl (0.26 #325, 0.25 #55, 0.24 #190), 03_gx (0.25 #59, 0.23 #329, 0.21 #284), 0kq2 (0.25 #63, 0.14 #468, 0.14 #513), 0n2g (0.09 #508, 0.08 #283, 0.06 #328), 04pk9 (0.09 #515, 0.06 #200, 0.02 #470), 03j6c (0.09 #2723, 0.04 #291, 0.04 #651), 019cr (0.06 #551, 0.04 #2657, 0.04 #731), 0631_ (0.06 #188, 0.05 #458, 0.04 #548), 07w8f (0.06 #215, 0.05 #530, 0.04 #305) >> Best rule #96 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0htlr; 01qq_lp; 02sjp; >> query: (?x11055, 0c8wxp) <- award_winner(?x11301, ?x11055), gender(?x11055, ?x231), type_of_union(?x11055, ?x566), location(?x11055, ?x6959), ?x6959 = 06c62 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02sdx religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.400 http://example.org/people/person/religion #19524-016nff PRED entity: 016nff PRED relation: award PRED expected values: 09sb52 => 107 concepts (70 used for prediction) PRED predicted values (max 10 best out of 251): 094qd5 (0.82 #3202, 0.76 #22024, 0.70 #28034), 09sb52 (0.58 #441, 0.37 #841, 0.36 #1241), 02y_rq5 (0.40 #1293, 0.33 #493, 0.09 #4896), 099cng (0.38 #484, 0.25 #1284, 0.09 #884), 09qwmm (0.35 #1234, 0.29 #434, 0.10 #4837), 099t8j (0.33 #537, 0.18 #1337, 0.07 #2937), 0bdwft (0.32 #1267, 0.21 #467, 0.13 #4870), 02z0dfh (0.26 #1273, 0.25 #473, 0.10 #4876), 02x4x18 (0.25 #1329, 0.21 #529, 0.11 #4932), 0cqgl9 (0.25 #1388, 0.12 #588, 0.10 #4991) >> Best rule #3202 for best value: >> intensional similarity = 4 >> extensional distance = 305 >> proper extension: 04cy8rb; 0dky9n; >> query: (?x6997, ?x749) <- nominated_for(?x6997, ?x4027), award_winner(?x749, ?x6997), nominated_for(?x749, ?x7580), ?x7580 = 04165w >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #441 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 03d_w3h; 07b2lv; 0lpjn; 07yp0f; 02jsgf; 04205z; 02cff1; 013zs9; 0bwgc_; *> query: (?x6997, 09sb52) <- film(?x6997, ?x2155), profession(?x6997, ?x1032), award(?x6997, ?x941), ?x941 = 0fq9zdn *> conf = 0.58 ranks of expected_values: 2 EVAL 016nff award 09sb52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 70.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19523-09z1lg PRED entity: 09z1lg PRED relation: group! PRED expected values: 018vs 03bx0bm => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 69): 02hnl (0.77 #1090, 0.76 #1002, 0.26 #1266), 05148p4 (0.69 #992, 0.68 #1080, 0.24 #1256), 03bx0bm (0.64 #998, 0.60 #1086, 0.18 #1880), 018vs (0.61 #985, 0.61 #1073, 0.21 #1249), 0l14md (0.59 #979, 0.56 #1067, 0.19 #1243), 028tv0 (0.38 #984, 0.37 #1072, 0.12 #1248), 01vj9c (0.26 #1074, 0.23 #986, 0.09 #1868), 03qjg (0.23 #1108, 0.22 #1020, 0.09 #1284), 05r5c (0.23 #980, 0.21 #1068, 0.10 #1244), 0l14qv (0.22 #1065, 0.22 #977, 0.07 #1859) >> Best rule #1090 for best value: >> intensional similarity = 3 >> extensional distance = 177 >> proper extension: 05563d; 018gm9; 03k3b; 01516r; 07rnh; 01518s; >> query: (?x9631, 02hnl) <- artists(?x284, ?x9631), artist(?x9492, ?x9631), group(?x227, ?x9631) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #998 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 131 *> proper extension: 089tm; 01t_xp_; 01pfr3; 04rcr; 0150jk; 02r3zy; 07c0j; 067mj; 01vsxdm; 03g5jw; ... *> query: (?x9631, 03bx0bm) <- award(?x9631, ?x884), artists(?x284, ?x9631), group(?x227, ?x9631) *> conf = 0.64 ranks of expected_values: 3, 4 EVAL 09z1lg group! 03bx0bm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 64.000 0.771 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 09z1lg group! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 64.000 0.771 http://example.org/music/performance_role/regular_performances./music/group_membership/group #19522-0bzm__ PRED entity: 0bzm__ PRED relation: ceremony! PRED expected values: 0p9sw 0gqyl => 31 concepts (28 used for prediction) PRED predicted values (max 10 best out of 348): 0p9sw (0.88 #4104, 0.87 #3863, 0.86 #2419), 0gqyl (0.88 #4155, 0.87 #3914, 0.85 #1510), 0gr42 (0.78 #4163, 0.78 #3922, 0.77 #1036), 0gqxm (0.70 #598, 0.63 #1081, 0.62 #358), 0gqzz (0.35 #519, 0.33 #38, 0.29 #279), 054krc (0.25 #722, 0.24 #5778, 0.24 #5777), 02x73k6 (0.25 #722, 0.24 #5778, 0.24 #5777), 04kxsb (0.25 #722, 0.24 #5778, 0.22 #964), 027dtxw (0.25 #722, 0.24 #5778, 0.22 #964), 02r22gf (0.25 #722, 0.23 #5780, 0.22 #964) >> Best rule #4104 for best value: >> intensional similarity = 22 >> extensional distance = 62 >> proper extension: 059x66; 0bz6l9; 0bzn6_; 0c53zb; 0c4hgj; 03tn9w; 0c6vcj; 0bzjvm; 073h5b; 0fzrhn; >> query: (?x6344, 0p9sw) <- ceremony(?x1243, ?x6344), nominated_for(?x1243, ?x10114), nominated_for(?x1243, ?x7883), nominated_for(?x1243, ?x6004), nominated_for(?x1243, ?x5795), nominated_for(?x1243, ?x4870), nominated_for(?x1243, ?x3992), nominated_for(?x1243, ?x2057), nominated_for(?x1243, ?x1076), nominated_for(?x1243, ?x697), ?x6004 = 0gw7p, award(?x185, ?x1243), ceremony(?x1243, ?x7936), ?x7883 = 0llcx, ?x4870 = 015qqg, ?x7936 = 04110lv, ?x10114 = 0bmhn, genre(?x5795, ?x53), ?x2057 = 0jym0, ?x697 = 0209hj, film_distribution_medium(?x1076, ?x2099), ?x3992 = 0pd6l >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0bzm__ ceremony! 0gqyl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 28.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzm__ ceremony! 0p9sw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 28.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony #19521-02z3zp PRED entity: 02z3zp PRED relation: influenced_by! PRED expected values: 015pxr => 122 concepts (80 used for prediction) PRED predicted values (max 10 best out of 390): 01xwv7 (0.14 #1453, 0.09 #4027, 0.07 #2482), 0bqs56 (0.12 #1278, 0.10 #3852, 0.08 #5395), 016_mj (0.12 #1084, 0.07 #3658, 0.06 #2628), 01s7qqw (0.10 #1239, 0.10 #724, 0.05 #26261), 01x4r3 (0.10 #895, 0.09 #1410, 0.06 #3984), 01xwqn (0.10 #1472, 0.07 #957, 0.06 #4046), 02yl42 (0.10 #4251, 0.08 #7337, 0.06 #14037), 05jm7 (0.09 #8373, 0.09 #7343, 0.08 #4257), 0jt90f5 (0.09 #3683, 0.07 #9343, 0.05 #4197), 01hb6v (0.07 #13996, 0.07 #10385, 0.06 #4210) >> Best rule #1453 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 08433; 02p21g; 0126rp; 0jt90f5; 046lt; 012gq6; 014z8v; 03s9b; 0gd9k; 0j6cj; ... >> query: (?x8163, 01xwv7) <- film(?x8163, ?x1956), influenced_by(?x8163, ?x986), influenced_by(?x2127, ?x8163) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1103 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 08433; 02p21g; 0126rp; 0jt90f5; 046lt; 012gq6; 014z8v; 03s9b; 0gd9k; 0j6cj; ... *> query: (?x8163, 015pxr) <- film(?x8163, ?x1956), influenced_by(?x8163, ?x986), influenced_by(?x2127, ?x8163) *> conf = 0.05 ranks of expected_values: 39 EVAL 02z3zp influenced_by! 015pxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 122.000 80.000 0.138 http://example.org/influence/influence_node/influenced_by #19520-04fv5b PRED entity: 04fv5b PRED relation: nominated_for! PRED expected values: 05f4m9q => 116 concepts (112 used for prediction) PRED predicted values (max 10 best out of 215): 05p1dby (0.67 #19523, 0.67 #19762, 0.66 #18332), 0gq9h (0.34 #5536, 0.32 #11249, 0.32 #12202), 0k611 (0.32 #5547, 0.26 #1739, 0.24 #11260), 019f4v (0.31 #1719, 0.30 #5527, 0.29 #3385), 0gs9p (0.30 #5538, 0.28 #12204, 0.28 #13870), 0p9sw (0.29 #5495, 0.22 #11208, 0.21 #10970), 0gr0m (0.27 #59, 0.22 #5533, 0.22 #1725), 0f4x7 (0.25 #3358, 0.24 #1692, 0.22 #3596), 0gq_v (0.25 #5494, 0.24 #11207, 0.23 #13826), 04dn09n (0.24 #1701, 0.23 #3367, 0.21 #12175) >> Best rule #19523 for best value: >> intensional similarity = 4 >> extensional distance = 972 >> proper extension: 06mmr; >> query: (?x5361, ?x2022) <- award(?x5361, ?x2022), nominated_for(?x2022, ?x148), award(?x166, ?x2022), award_winner(?x2022, ?x847) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #26672 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1584 *> proper extension: 06g60w; *> query: (?x5361, ?x350) <- nominated_for(?x8118, ?x5361), award(?x8118, ?x350), nominated_for(?x350, ?x103) *> conf = 0.22 ranks of expected_values: 14 EVAL 04fv5b nominated_for! 05f4m9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 116.000 112.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19519-01mgw PRED entity: 01mgw PRED relation: titles! PRED expected values: 0653m => 102 concepts (58 used for prediction) PRED predicted values (max 10 best out of 70): 07ssc (0.67 #1331, 0.21 #719, 0.12 #2559), 07s9rl0 (0.55 #102, 0.42 #710, 0.40 #1221), 01hmnh (0.43 #1731, 0.43 #1653, 0.21 #5415), 0653m (0.33 #40, 0.03 #1361, 0.03 #342), 04xvlr (0.32 #713, 0.29 #2249, 0.27 #1325), 01z4y (0.23 #2584, 0.20 #5348, 0.18 #2994), 03k9fj (0.21 #5415, 0.21 #2752, 0.20 #3366), 02l7c8 (0.21 #5415, 0.21 #2752, 0.20 #3366), 02kdv5l (0.21 #5415, 0.21 #2752, 0.20 #3366), 01jfsb (0.19 #1240, 0.16 #423, 0.16 #526) >> Best rule #1331 for best value: >> intensional similarity = 3 >> extensional distance = 201 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x7554, 07ssc) <- titles(?x2346, ?x7554), film_release_region(?x186, ?x2346), contains(?x2346, ?x1885) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 0dkv90; *> query: (?x7554, 0653m) <- nominated_for(?x5923, ?x7554), nominated_for(?x2489, ?x7554), genre(?x7554, ?x53), ?x5923 = 09v8db5, ?x2489 = 02x2gy0 *> conf = 0.33 ranks of expected_values: 4 EVAL 01mgw titles! 0653m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 58.000 0.665 http://example.org/media_common/netflix_genre/titles #19518-0f8j6 PRED entity: 0f8j6 PRED relation: place_of_death! PRED expected values: 01vn0t_ => 137 concepts (44 used for prediction) PRED predicted values (max 10 best out of 578): 04y9dk (0.14 #823, 0.03 #6115, 0.01 #12921), 01t_wfl (0.10 #2160, 0.09 #2915, 0.06 #3671), 015np0 (0.10 #1948, 0.06 #3459, 0.05 #4214), 01pq5j7 (0.09 #2500, 0.06 #3256, 0.05 #4011), 0151xv (0.09 #2857, 0.02 #7393, 0.02 #9663), 015nvj (0.09 #2837, 0.02 #7373, 0.02 #9643), 042xh (0.06 #18150, 0.06 #1512, 0.04 #5291), 026lj (0.06 #6047, 0.04 #4594, 0.04 #5351), 03sbs (0.06 #6047, 0.03 #6048), 03crcpt (0.06 #1512, 0.03 #15122, 0.03 #5290) >> Best rule #823 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 022_6; 0dbdy; 04p3c; 0m75g; 0cv5l; >> query: (?x14442, 04y9dk) <- country(?x14442, ?x1310), contains(?x362, ?x14442), location(?x5971, ?x14442), location_of_ceremony(?x566, ?x14442), ?x1310 = 02jx1 >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f8j6 place_of_death! 01vn0t_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 44.000 0.143 http://example.org/people/deceased_person/place_of_death #19517-0843m PRED entity: 0843m PRED relation: location_of_ceremony! PRED expected values: 04ztj => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.64 #33, 0.57 #98, 0.56 #166), 01g63y (0.14 #10, 0.04 #26, 0.02 #38), 0jgjn (0.14 #12, 0.04 #28, 0.02 #40), 01bl8s (0.01 #148) >> Best rule #33 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 07qzv; 0bwtj; >> query: (?x3877, 04ztj) <- citytown(?x2243, ?x3877), school_type(?x2243, ?x3092), adjoins(?x3877, ?x479), organization(?x346, ?x2243) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0843m location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.639 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #19516-07wlf PRED entity: 07wlf PRED relation: major_field_of_study PRED expected values: 04x_3 02xlf => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 113): 04rjg (0.43 #1991, 0.35 #2571, 0.30 #1527), 01tbp (0.43 #637, 0.40 #289, 0.38 #521), 01lj9 (0.43 #616, 0.38 #500, 0.27 #732), 0g26h (0.38 #1548, 0.38 #1199, 0.38 #1896), 062z7 (0.37 #1998, 0.30 #1534, 0.30 #1185), 036hv (0.36 #590, 0.31 #474, 0.23 #706), 05qjt (0.31 #471, 0.30 #1980, 0.29 #587), 05qfh (0.29 #2006, 0.29 #613, 0.23 #497), 041y2 (0.29 #653, 0.23 #537, 0.22 #1582), 06ms6 (0.29 #595, 0.23 #479, 0.19 #1988) >> Best rule #1991 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 0dplh; 0f1nl; 01mpwj; 04hgpt; 01_qgp; 0jpn8; 01nm8w; 02hp70; >> query: (?x2760, 04rjg) <- school_type(?x2760, ?x3092), major_field_of_study(?x2760, ?x1668), ?x1668 = 01mkq >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1184 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 05kj_; *> query: (?x2760, 04x_3) <- category(?x2760, ?x134), school(?x465, ?x2760), contains(?x94, ?x2760) *> conf = 0.26 ranks of expected_values: 14, 106 EVAL 07wlf major_field_of_study 02xlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 97.000 97.000 0.435 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07wlf major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 97.000 97.000 0.435 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19515-04hpck PRED entity: 04hpck PRED relation: location PRED expected values: 01tlmw => 109 concepts (99 used for prediction) PRED predicted values (max 10 best out of 177): 02_286 (0.30 #841, 0.15 #3254, 0.14 #31410), 030qb3t (0.20 #5713, 0.16 #12952, 0.15 #12148), 0cr3d (0.10 #949, 0.08 #3362, 0.06 #16230), 0cc56 (0.10 #861, 0.07 #3274, 0.04 #1665), 0ccvx (0.10 #1026, 0.02 #18722, 0.02 #7461), 02jx1 (0.10 #875, 0.02 #3288, 0.02 #1679), 0_jsl (0.10 #1595, 0.01 #3203), 04jpl (0.08 #4843, 0.08 #4038, 0.07 #22539), 0rh6k (0.08 #1612, 0.04 #6438, 0.03 #8851), 0r0m6 (0.05 #5848, 0.04 #3435, 0.03 #9065) >> Best rule #841 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 04yywz; 06cgy; 02t1cp; 016ks_; 0fgg4; 01x4sb; 04gvt5; 01r7t9; >> query: (?x1031, 02_286) <- film(?x1031, ?x4602), film(?x1031, ?x204), nominated_for(?x384, ?x4602), ?x204 = 028_yv, award(?x1031, ?x2192) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04hpck location 01tlmw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 99.000 0.300 http://example.org/people/person/places_lived./people/place_lived/location #19514-09sb52 PRED entity: 09sb52 PRED relation: nominated_for PRED expected values: 095zlp 0pv3x 02rx2m5 03hkch7 0462hhb 0404j37 04165w 04b2qn 03cvvlg => 42 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1797): 0m313 (0.66 #30579, 0.66 #30578, 0.54 #10711), 01cmp9 (0.66 #30579, 0.66 #30578, 0.36 #11590), 09cr8 (0.66 #30579, 0.66 #30578, 0.29 #10937), 02chhq (0.50 #4217, 0.20 #7274, 0.19 #27520), 021y7yw (0.50 #3380, 0.20 #6437, 0.19 #27520), 0462hhb (0.50 #3753, 0.19 #27520, 0.14 #11399), 05z_kps (0.50 #3207, 0.19 #27520, 0.10 #10700), 05zwrg0 (0.50 #4425, 0.19 #27520, 0.10 #10700), 095zlp (0.43 #10746, 0.30 #6157, 0.26 #12276), 0jqj5 (0.43 #11461, 0.19 #27520, 0.14 #12991) >> Best rule #30579 for best value: >> intensional similarity = 1 >> extensional distance = 234 >> proper extension: 0dt49; >> query: (?x704, ?x4610) <- award(?x4610, ?x704) >> conf = 0.66 => this is the best rule for 3 predicted values *> Best rule #3753 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0fq9zdn; 0fq9zdv; 0fq9zcx; *> query: (?x704, 0462hhb) <- award(?x57, ?x704), nominated_for(?x704, ?x1150), ?x1150 = 0h3xztt *> conf = 0.50 ranks of expected_values: 6, 9, 12, 14, 16, 21, 26, 133, 134 EVAL 09sb52 nominated_for 03cvvlg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 42.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sb52 nominated_for 04b2qn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 42.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sb52 nominated_for 04165w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 42.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sb52 nominated_for 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 42.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sb52 nominated_for 0462hhb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 42.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sb52 nominated_for 03hkch7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 42.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sb52 nominated_for 02rx2m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 42.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sb52 nominated_for 0pv3x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 42.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sb52 nominated_for 095zlp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 42.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19513-05wjnt PRED entity: 05wjnt PRED relation: film PRED expected values: 02ryz24 => 122 concepts (63 used for prediction) PRED predicted values (max 10 best out of 804): 08phg9 (0.20 #881, 0.03 #8013, 0.03 #11579), 07pd_j (0.20 #1183, 0.02 #15447, 0.01 #10098), 01k0xy (0.20 #1276, 0.02 #15540, 0.01 #28021), 03ynwqj (0.20 #1467, 0.02 #31778, 0.02 #33561), 0fphf3v (0.20 #1356, 0.02 #42365, 0.01 #78026), 0n1s0 (0.20 #1030, 0.02 #6379, 0.01 #15294), 0gj96ln (0.20 #1071, 0.02 #6420), 01sxdy (0.20 #2385, 0.02 #7734), 0c0zq (0.20 #1557, 0.02 #8689), 0gwgn1k (0.20 #1543, 0.01 #22939, 0.01 #10458) >> Best rule #881 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 07ymr5; 03l3jy; 05drr9; >> query: (?x2473, 08phg9) <- film(?x2473, ?x6832), ?x6832 = 03cyslc, profession(?x2473, ?x353), location(?x2473, ?x1196) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #36125 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 228 *> proper extension: 0b5x23; *> query: (?x2473, 02ryz24) <- people(?x1050, ?x2473), location(?x2473, ?x2474), languages(?x2473, ?x254), vacationer(?x2474, ?x971) *> conf = 0.02 ranks of expected_values: 186 EVAL 05wjnt film 02ryz24 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 122.000 63.000 0.200 http://example.org/film/actor/film./film/performance/film #19512-0s3pw PRED entity: 0s3pw PRED relation: teams PRED expected values: 02py8_w => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 129): 0jnlm (0.12 #352, 0.08 #712, 0.07 #1432), 0jm74 (0.12 #147, 0.08 #507, 0.07 #1227), 01slc (0.12 #143, 0.08 #503, 0.07 #1223), 01yjl (0.12 #57, 0.08 #417, 0.07 #1137), 01y3v (0.12 #48, 0.08 #408, 0.07 #1128), 0jnr_ (0.07 #981, 0.02 #3861), 07l2m (0.07 #815, 0.02 #3695), 02r7lqg (0.06 #1674, 0.02 #3114), 02663p2 (0.06 #1920, 0.02 #2280, 0.02 #3360), 0jm3b (0.02 #2381, 0.02 #3461, 0.02 #3821) >> Best rule #352 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0sc6p; >> query: (?x13681, 0jnlm) <- county(?x13681, ?x13596), contains(?x3818, ?x13681), ?x3818 = 03v0t, place_of_birth(?x6264, ?x13681) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0s3pw teams 02py8_w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 129.000 0.125 http://example.org/sports/sports_team_location/teams #19511-0dqyw PRED entity: 0dqyw PRED relation: location_of_ceremony! PRED expected values: 04ztj => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.70 #21, 0.69 #9, 0.67 #63), 0jgjn (0.11 #8, 0.07 #20, 0.04 #66), 01g63y (0.11 #6, 0.04 #64, 0.03 #76), 01bl8s (0.03 #48, 0.03 #44, 0.02 #57) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0ftjx; >> query: (?x10980, 04ztj) <- jurisdiction_of_office(?x1195, ?x10980), ?x1195 = 0pqc5, film_release_region(?x7538, ?x10980) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dqyw location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.700 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #19510-05zksls PRED entity: 05zksls PRED relation: award_winner PRED expected values: 03_gd 0flw6 04954 => 32 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2769): 06pj8 (0.40 #6430, 0.20 #9498, 0.18 #11033), 07m77x (0.33 #8919, 0.33 #4325, 0.21 #23018), 0flw6 (0.33 #2191, 0.33 #659, 0.17 #8319), 01f8ld (0.33 #1980, 0.33 #448, 0.17 #8108), 07k51gd (0.33 #8841, 0.33 #4247, 0.14 #23019), 0zcbl (0.33 #4100, 0.25 #5630, 0.18 #13301), 020_95 (0.33 #3908, 0.25 #5438, 0.18 #13109), 02qw2xb (0.33 #8787, 0.21 #23018, 0.14 #23019), 01gq0b (0.33 #3330, 0.20 #6393, 0.17 #7924), 09l3p (0.33 #3723, 0.18 #12924, 0.17 #8317) >> Best rule #6430 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 02wzl1d; 0hndn2q; 0drtv8; >> query: (?x2220, 06pj8) <- ceremony(?x8059, ?x2220), ?x8059 = 0drtkx, award_winner(?x2220, ?x5351), award_winner(?x2220, ?x879), honored_for(?x2220, ?x559), nominated_for(?x5351, ?x1531), produced_by(?x2386, ?x5351), award_winner(?x384, ?x5351), profession(?x879, ?x3746), award(?x879, ?x3066), award_nominee(?x879, ?x890), profession(?x10978, ?x3746), profession(?x8389, ?x3746), ?x3066 = 0gqy2, ?x10978 = 02ghq, ?x8389 = 0683n >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2191 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 09k5jh7; *> query: (?x2220, 0flw6) <- ceremony(?x1443, ?x2220), honored_for(?x2220, ?x559), award_winner(?x1443, ?x6716), nominated_for(?x1443, ?x8570), nominated_for(?x1443, ?x3211), nominated_for(?x1443, ?x1644), award(?x4428, ?x1443), ?x4428 = 02jxmr, ?x559 = 05p1tzf, film_crew_role(?x3211, ?x468), honored_for(?x8570, ?x4786), award(?x308, ?x1443), artists(?x1127, ?x6716), ?x1644 = 09txzv *> conf = 0.33 ranks of expected_values: 3, 233, 281 EVAL 05zksls award_winner 04954 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 32.000 15.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05zksls award_winner 0flw6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 32.000 15.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05zksls award_winner 03_gd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 32.000 15.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19509-0l35f PRED entity: 0l35f PRED relation: currency PRED expected values: 09nqf => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.84 #75, 0.83 #24, 0.83 #19) >> Best rule #75 for best value: >> intensional similarity = 3 >> extensional distance = 289 >> proper extension: 0mn0v; 0ml25; 0njcw; >> query: (?x7369, 09nqf) <- time_zones(?x7369, ?x2950), source(?x7369, ?x958), second_level_divisions(?x94, ?x7369) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l35f currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.842 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #19508-0109vk PRED entity: 0109vk PRED relation: time_zones PRED expected values: 02fqwt => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 7): 02fqwt (0.79 #53, 0.72 #1, 0.68 #27), 02hczc (0.54 #795, 0.17 #1045, 0.17 #1125), 02hcv8 (0.44 #328, 0.44 #341, 0.43 #354), 02lcqs (0.32 #213, 0.30 #200, 0.18 #135), 02llzg (0.05 #303, 0.05 #316, 0.05 #1115), 03bdv (0.04 #318, 0.04 #305, 0.04 #500), 03plfd (0.01 #1121, 0.01 #1344) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0mrs1; 0d1xh; 0mq17; 0mqs0; 0fxwx; 0mrhq; 0mpzm; 0mskq; 0ms1n; 0mr_8; >> query: (?x11846, 02fqwt) <- contains(?x3634, ?x11846), source(?x11846, ?x958), ?x3634 = 07b_l, ?x958 = 0jbk9 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0109vk time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.788 http://example.org/location/location/time_zones #19507-027pdrh PRED entity: 027pdrh PRED relation: edited_by! PRED expected values: 04lhc4 => 73 concepts (31 used for prediction) PRED predicted values (max 10 best out of 163): 02704ff (0.13 #587, 0.07 #913), 07bwr (0.13 #578, 0.07 #904), 05fgt1 (0.13 #534, 0.07 #860), 02r1c18 (0.13 #518, 0.07 #844), 01vfqh (0.13 #516, 0.07 #842), 0b6tzs (0.13 #509, 0.07 #835), 0hwpz (0.10 #293, 0.07 #456, 0.07 #945), 0f4yh (0.10 #225, 0.07 #388, 0.07 #877), 0dnqr (0.10 #216, 0.07 #379, 0.07 #868), 05z43v (0.10 #297, 0.07 #460, 0.07 #623) >> Best rule #587 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 06pj8; >> query: (?x2572, 02704ff) <- edited_by(?x7434, ?x2572), film(?x902, ?x7434), type_of_union(?x2572, ?x566), currency(?x7434, ?x170) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027pdrh edited_by! 04lhc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 31.000 0.133 http://example.org/film/film/edited_by #19506-0164qt PRED entity: 0164qt PRED relation: language PRED expected values: 06b_j => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 49): 04306rv (0.42 #1225, 0.40 #179, 0.33 #296), 06b_j (0.42 #1225, 0.35 #256, 0.33 #314), 064_8sq (0.42 #1225, 0.35 #255, 0.26 #313), 0jzc (0.42 #1225, 0.35 #253, 0.18 #311), 06nm1 (0.42 #1225, 0.33 #68, 0.22 #126), 02bjrlw (0.42 #1225, 0.22 #117, 0.20 #176), 012w70 (0.42 #1225, 0.20 #12, 0.12 #246), 02hwhyv (0.42 #1225, 0.20 #29, 0.11 #145), 03_9r (0.42 #1225, 0.20 #9, 0.08 #3449), 02ztjwg (0.42 #1225, 0.11 #147, 0.10 #206) >> Best rule #1225 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 0jzw; 01hqhm; 0ddjy; 05zlld0; 02nt3d; 03cp4cn; 0pd64; 02fqxm; >> query: (?x835, ?x254) <- production_companies(?x835, ?x788), nominated_for(?x1261, ?x835), award_winner(?x835, ?x3034), language(?x1261, ?x254) >> conf = 0.42 => this is the best rule for 11 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0164qt language 06b_j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.424 http://example.org/film/film/language #19505-0jgx PRED entity: 0jgx PRED relation: teams PRED expected values: 033g54 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 240): 037mp6 (0.14 #81, 0.06 #441, 0.04 #1161), 01l3vx (0.14 #44, 0.06 #404, 0.04 #764), 03z2rz (0.06 #645, 0.04 #1365, 0.04 #1005), 02w64f (0.06 #687, 0.04 #1407, 0.04 #1047), 03ytp3 (0.06 #667, 0.04 #1387, 0.04 #1027), 03zj_3 (0.06 #638, 0.04 #1358, 0.04 #998), 02rqxc (0.06 #449, 0.04 #809, 0.03 #2249), 098knd (0.06 #673, 0.04 #1393, 0.03 #2473), 03_3z4 (0.06 #691, 0.04 #1411, 0.03 #4291), 0cqt41 (0.04 #7590, 0.04 #8310, 0.03 #2910) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0cdbq; 049nq; >> query: (?x3855, 037mp6) <- nationality(?x6406, ?x3855), partially_contains(?x455, ?x3855), ?x455 = 02j9z >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jgx teams 033g54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 140.000 0.143 http://example.org/sports/sports_team_location/teams #19504-01k7b0 PRED entity: 01k7b0 PRED relation: genre PRED expected values: 060__y => 89 concepts (87 used for prediction) PRED predicted values (max 10 best out of 85): 05p553 (0.42 #6672, 0.41 #8819, 0.37 #3932), 02l7c8 (0.37 #7641, 0.37 #136, 0.36 #17), 04xvlr (0.34 #239, 0.26 #3690, 0.19 #2857), 017fp (0.34 #254, 0.26 #3690, 0.11 #4182), 01jfsb (0.31 #1084, 0.30 #4418, 0.30 #3107), 02kdv5l (0.31 #1073, 0.29 #3096, 0.28 #4407), 03k9fj (0.28 #1083, 0.27 #2154, 0.27 #8589), 060__y (0.26 #3690, 0.19 #5733, 0.19 #851), 0lsxr (0.26 #3690, 0.18 #3699, 0.17 #5129), 04xvh5 (0.26 #3690, 0.13 #34, 0.12 #510) >> Best rule #6672 for best value: >> intensional similarity = 4 >> extensional distance = 1083 >> proper extension: 0dgpwnk; 05c5z8j; 013q0p; 07sgdw; 0dt8xq; 05_5_22; 01mszz; 07gghl; 02825nf; 03pc89; ... >> query: (?x6680, 05p553) <- nominated_for(?x3519, ?x6680), genre(?x6680, ?x53), genre(?x4694, ?x53), ?x4694 = 02j69w >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #3690 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 686 *> proper extension: 028k2x; *> query: (?x6680, ?x53) <- nominated_for(?x7232, ?x6680), film(?x7232, ?x9100), profession(?x7232, ?x319), genre(?x9100, ?x53) *> conf = 0.26 ranks of expected_values: 8 EVAL 01k7b0 genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 89.000 87.000 0.416 http://example.org/film/film/genre #19503-0f6lx PRED entity: 0f6lx PRED relation: nationality PRED expected values: 09c7w0 => 197 concepts (155 used for prediction) PRED predicted values (max 10 best out of 55): 09c7w0 (0.91 #7126, 0.91 #4714, 0.90 #14150), 0488g (0.47 #14852, 0.47 #6418, 0.36 #2105), 02jx1 (0.39 #734, 0.33 #233, 0.32 #1134), 059rby (0.31 #13048), 07ssc (0.21 #1518, 0.20 #2019, 0.18 #515), 0345h (0.14 #3340, 0.09 #1835, 0.07 #6753), 0h7x (0.13 #635, 0.11 #1538, 0.10 #1638), 0f8l9c (0.11 #3331, 0.10 #1625, 0.09 #522), 0jgd (0.11 #102, 0.02 #3412, 0.02 #1806), 0d060g (0.07 #307, 0.06 #5822, 0.05 #14557) >> Best rule #7126 for best value: >> intensional similarity = 4 >> extensional distance = 241 >> proper extension: 05218gr; 053j4w4; >> query: (?x9021, 09c7w0) <- place_of_birth(?x9021, ?x9470), place_of_death(?x9021, ?x739), source(?x9470, ?x958), ?x958 = 0jbk9 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f6lx nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 197.000 155.000 0.909 http://example.org/people/person/nationality #19502-07tlfx PRED entity: 07tlfx PRED relation: film_crew_role PRED expected values: 0dxtw => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 25): 0dxtw (0.44 #421, 0.40 #561, 0.39 #1075), 01vx2h (0.36 #422, 0.32 #562, 0.32 #1419), 02vs3x5 (0.33 #22, 0.05 #159, 0.05 #295), 01pvkk (0.30 #1112, 0.28 #1420, 0.27 #1696), 0215hd (0.16 #429, 0.14 #1426, 0.13 #1083), 02rh1dz (0.14 #420, 0.11 #560, 0.11 #1074), 01xy5l_ (0.12 #425, 0.11 #1422, 0.11 #115), 089g0h (0.12 #430, 0.11 #1427, 0.10 #1703), 0d2b38 (0.11 #436, 0.10 #644, 0.10 #1709), 015h31 (0.10 #109, 0.08 #419, 0.08 #559) >> Best rule #421 for best value: >> intensional similarity = 4 >> extensional distance = 434 >> proper extension: 01br2w; 091z_p; 02phtzk; 064lsn; 0581vn8; >> query: (?x9978, 0dxtw) <- produced_by(?x9978, ?x2499), genre(?x9978, ?x53), film_crew_role(?x9978, ?x1171), ?x1171 = 09vw2b7 >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tlfx film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.438 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19501-0hzlz PRED entity: 0hzlz PRED relation: olympics PRED expected values: 018qb4 => 241 concepts (241 used for prediction) PRED predicted values (max 10 best out of 27): 0l6mp (0.59 #1201, 0.58 #796, 0.58 #255), 018ctl (0.59 #3869, 0.45 #3381, 0.43 #169), 0l998 (0.58 #790, 0.58 #249, 0.54 #466), 0l6ny (0.58 #792, 0.57 #170, 0.52 #251), 0jkvj (0.52 #753, 0.50 #293, 0.49 #564), 0l98s (0.52 #248, 0.50 #735, 0.49 #789), 0lbbj (0.51 #608, 0.48 #256, 0.48 #743), 0l6vl (0.43 #732, 0.42 #245, 0.41 #678), 0kbvv (0.40 #44, 0.36 #612, 0.36 #179), 0swbd (0.40 #36, 0.31 #604, 0.30 #793) >> Best rule #1201 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 0193qj; >> query: (?x792, 0l6mp) <- olympics(?x792, ?x2496), sports(?x2496, ?x171), combatants(?x326, ?x792) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 02j71; *> query: (?x792, 018qb4) <- adjustment_currency(?x792, ?x170), administrative_parent(?x10115, ?x792), jurisdiction_of_office(?x14293, ?x10115) *> conf = 0.33 ranks of expected_values: 13 EVAL 0hzlz olympics 018qb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 241.000 241.000 0.593 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #19500-0glbqt PRED entity: 0glbqt PRED relation: honored_for PRED expected values: 0cq7tx => 92 concepts (43 used for prediction) PRED predicted values (max 10 best out of 85): 0cq7tx (0.84 #1009, 0.82 #1347, 0.03 #6399), 074rg9 (0.11 #1278, 0.06 #941, 0.01 #5657), 03phtz (0.10 #1345, 0.07 #1008, 0.01 #3704), 0140g4 (0.10 #1179, 0.06 #842, 0.01 #5558), 01mszz (0.10 #1294, 0.05 #957, 0.01 #4999), 04cbbz (0.10 #1277, 0.06 #940, 0.01 #1447), 0q9sg (0.10 #1261, 0.05 #924, 0.01 #3620), 059lwy (0.10 #1309, 0.05 #972, 0.01 #5688), 0946bb (0.09 #1243, 0.06 #906, 0.01 #5622), 06c0ns (0.09 #1313, 0.06 #976, 0.01 #3672) >> Best rule #1009 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 0hmr4; 044g_k; 09g8vhw; 059lwy; >> query: (?x10531, ?x4404) <- award(?x10531, ?x484), film(?x2916, ?x10531), honored_for(?x4404, ?x10531) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0glbqt honored_for 0cq7tx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 43.000 0.836 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #19499-0dz8b PRED entity: 0dz8b PRED relation: genre! PRED expected values: 04sskp => 46 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1914): 07nt8p (0.67 #5990, 0.62 #9745, 0.60 #2241), 09fqgj (0.67 #7352, 0.62 #11107, 0.60 #3603), 02nx2k (0.67 #5001, 0.62 #10632, 0.60 #3128), 01mgw (0.67 #5101, 0.62 #10732, 0.56 #12609), 07f_t4 (0.67 #6995, 0.60 #3246, 0.57 #8872), 05pdd86 (0.67 #4846, 0.60 #16110, 0.56 #12354), 017gm7 (0.67 #3966, 0.50 #9597, 0.50 #5842), 017gl1 (0.67 #3895, 0.50 #9526, 0.50 #5771), 0fqt1ns (0.67 #4563, 0.50 #15827, 0.50 #10194), 0cc5mcj (0.67 #4152, 0.50 #15416, 0.50 #9783) >> Best rule #5990 for best value: >> intensional similarity = 18 >> extensional distance = 4 >> proper extension: 060__y; >> query: (?x4133, 07nt8p) <- genre(?x4132, ?x4133), genre(?x4087, ?x4133), genre(?x4087, ?x12344), genre(?x4087, ?x1510), genre(?x4087, ?x1403), genre(?x4087, ?x53), currency(?x4087, ?x170), nominated_for(?x102, ?x4087), production_companies(?x4087, ?x847), ?x1510 = 01hmnh, ?x4132 = 05c9zr, film(?x574, ?x4087), ?x1403 = 02l7c8, ?x170 = 09nqf, genre(?x4032, ?x12344), ?x53 = 07s9rl0, genre(?x1876, ?x12344), ?x4032 = 0g9yrw >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3319 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 3 *> proper extension: 04xvh5; *> query: (?x4133, 04sskp) <- genre(?x4132, ?x4133), genre(?x4087, ?x4133), genre(?x3063, ?x4133), ?x4087 = 01hw5kk, ?x4132 = 05c9zr, nominated_for(?x703, ?x3063), nominated_for(?x154, ?x3063), award(?x703, ?x102), profession(?x703, ?x524), film_release_region(?x3063, ?x94), award_nominee(?x157, ?x703), producer_type(?x703, ?x632), award_winner(?x8128, ?x703), film(?x3181, ?x3063), gender(?x703, ?x231) *> conf = 0.20 ranks of expected_values: 1627 EVAL 0dz8b genre! 04sskp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 14.000 0.667 http://example.org/film/film/genre #19498-01xpxv PRED entity: 01xpxv PRED relation: award PRED expected values: 0bfvd4 0gqy2 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 199): 0ck27z (0.27 #2927, 0.22 #3333, 0.19 #1712), 09sb52 (0.20 #10166, 0.19 #10571, 0.19 #8951), 0gqy2 (0.15 #165, 0.09 #6646, 0.08 #6241), 0f4x7 (0.15 #30, 0.07 #6511, 0.07 #10156), 0gqyl (0.15 #105, 0.06 #6586, 0.06 #10231), 0fbvqf (0.15 #47, 0.06 #2882, 0.05 #3288), 0gkts9 (0.15 #169, 0.04 #3004, 0.03 #6245), 02x4w6g (0.15 #114, 0.04 #6190, 0.04 #10240), 0bdw1g (0.15 #37, 0.03 #2872, 0.02 #3278), 0cqhk0 (0.15 #2871, 0.13 #1656, 0.12 #3277) >> Best rule #2927 for best value: >> intensional similarity = 3 >> extensional distance = 647 >> proper extension: 0gcdzz; 08_83x; 030b93; 02m92h; 0bbvr84; 0f87jy; 0hcvy; 03j9ml; >> query: (?x11423, 0ck27z) <- gender(?x11423, ?x231), actor(?x6248, ?x11423), nominated_for(?x870, ?x6248) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 015076; *> query: (?x11423, 0gqy2) <- nationality(?x11423, ?x94), film(?x11423, ?x3781), ?x3781 = 0kvgtf *> conf = 0.15 ranks of expected_values: 3, 17 EVAL 01xpxv award 0gqy2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 64.000 0.268 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01xpxv award 0bfvd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 64.000 64.000 0.268 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19497-01hn_t PRED entity: 01hn_t PRED relation: actor PRED expected values: 018ygt => 100 concepts (66 used for prediction) PRED predicted values (max 10 best out of 924): 01wk7b7 (0.60 #7637, 0.25 #4846, 0.20 #6706), 01vrncs (0.50 #4651, 0.40 #6511, 0.34 #9301), 01w724 (0.50 #4651, 0.40 #6511, 0.34 #9301), 03j24kf (0.50 #4651, 0.40 #6511, 0.34 #9301), 01vsl3_ (0.50 #4651, 0.40 #6511, 0.34 #9301), 018dyl (0.50 #4651, 0.40 #6511, 0.34 #9301), 02qwg (0.50 #4651, 0.40 #6511, 0.34 #9301), 0pj8m (0.50 #4651, 0.40 #6511, 0.34 #9301), 0gt_k (0.50 #4651, 0.40 #6511, 0.34 #9301), 03bnv (0.50 #4651, 0.40 #6511, 0.34 #9301) >> Best rule #7637 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0358x_; >> query: (?x4275, 01wk7b7) <- program(?x4274, ?x4275), actor(?x4275, ?x6947), genre(?x4275, ?x10159), profession(?x6947, ?x220), film(?x6947, ?x5684), instrumentalists(?x212, ?x6947), ?x5684 = 01f39b >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #28410 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 24 *> proper extension: 0cwrr; 0d68qy; 039c26; 02rzdcp; *> query: (?x4275, 018ygt) <- genre(?x4275, ?x12344), tv_program(?x8254, ?x4275), category(?x4275, ?x134), actor(?x4275, ?x4112), ?x134 = 08mbj5d, genre(?x124, ?x12344) *> conf = 0.04 ranks of expected_values: 626 EVAL 01hn_t actor 018ygt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 66.000 0.600 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #19496-03n08b PRED entity: 03n08b PRED relation: award_winner! PRED expected values: 05pcn59 => 87 concepts (80 used for prediction) PRED predicted values (max 10 best out of 217): 05zr6wv (0.37 #19402, 0.34 #13366, 0.31 #30615), 05zvj3m (0.37 #19402, 0.34 #13366, 0.31 #30615), 02f76h (0.27 #176, 0.02 #3625, 0.01 #2332), 09sb52 (0.24 #1334, 0.17 #8233, 0.16 #7371), 0cqhk0 (0.18 #37, 0.09 #2624, 0.08 #2193), 099tbz (0.12 #1351, 0.08 #8250, 0.07 #7388), 0ck27z (0.11 #8285, 0.11 #7423, 0.11 #7854), 01by1l (0.09 #9598, 0.09 #3562, 0.09 #7012), 05p09zm (0.09 #125, 0.08 #9917, 0.08 #17246), 03c7tr1 (0.09 #59, 0.08 #9917, 0.08 #17246) >> Best rule #19402 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 0khth; 014l4w; 07mvp; 04k05; 07k2d; >> query: (?x1461, ?x401) <- award(?x1461, ?x401), award_winner(?x1461, ?x1460) >> conf = 0.37 => this is the best rule for 2 predicted values *> Best rule #2238 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 271 *> proper extension: 025ldg; *> query: (?x1461, 05pcn59) <- award_nominee(?x1461, ?x1460), award_winner(?x1773, ?x1461), participant(?x1461, ?x7830) *> conf = 0.07 ranks of expected_values: 24 EVAL 03n08b award_winner! 05pcn59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 87.000 80.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #19495-095nx PRED entity: 095nx PRED relation: inductee! PRED expected values: 0k89p => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 8): 01nzmp (0.09 #43, 0.06 #79, 0.06 #34), 0g2c8 (0.07 #163, 0.06 #154, 0.05 #254), 0k89p (0.06 #40, 0.06 #31, 0.04 #58), 06szd3 (0.05 #47, 0.04 #56, 0.03 #110), 04045y (0.05 #51, 0.03 #132, 0.02 #96), 04dm2n (0.04 #62, 0.02 #89, 0.01 #143), 01b3l (0.03 #41, 0.02 #59, 0.02 #77), 0qjfl (0.03 #48, 0.01 #111, 0.01 #120) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 02qjj7; 0cg39k; 054fvj; 0cymln; 0hcs3; 06s27s; >> query: (?x13842, 01nzmp) <- nationality(?x13842, ?x94), team(?x13842, ?x12141), draft(?x12141, ?x2569) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 02qjj7; 0cg39k; 054fvj; 0cymln; 0hcs3; 06s27s; *> query: (?x13842, 0k89p) <- nationality(?x13842, ?x94), team(?x13842, ?x12141), draft(?x12141, ?x2569) *> conf = 0.06 ranks of expected_values: 3 EVAL 095nx inductee! 0k89p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 80.000 80.000 0.094 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #19494-01vrlr4 PRED entity: 01vrlr4 PRED relation: award PRED expected values: 0c4z8 => 105 concepts (87 used for prediction) PRED predicted values (max 10 best out of 300): 02h3d1 (0.77 #19665, 0.77 #16855, 0.76 #28500), 01ckrr (0.40 #4240, 0.05 #7451, 0.05 #1031), 024_41 (0.40 #299, 0.33 #701, 0.18 #22074), 024_fw (0.33 #647, 0.30 #245, 0.18 #22074), 0257w4 (0.33 #547, 0.20 #145, 0.01 #7368), 0gr4k (0.32 #6854, 0.27 #6051, 0.26 #6453), 0257__ (0.30 #381, 0.25 #783, 0.18 #22074), 04dn09n (0.30 #6865, 0.25 #6062, 0.23 #6464), 01by1l (0.29 #7334, 0.15 #20468, 0.15 #24081), 09sb52 (0.29 #11675, 0.28 #10071, 0.25 #10873) >> Best rule #19665 for best value: >> intensional similarity = 3 >> extensional distance = 1373 >> proper extension: 02r3zy; 03g5jw; 0dvqq; 018ndc; 0163m1; 0hvbj; 01yzl2; 01dwrc; 0gr69; 018p5f; ... >> query: (?x6311, ?x3467) <- award_winner(?x3467, ?x6311), award_nominee(?x999, ?x6311), ceremony(?x3467, ?x139) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #4084 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 125 *> proper extension: 01t_xp_; 067mj; 0dtd6; 047cx; 0bpk2; 048xh; 012x1l; *> query: (?x6311, 0c4z8) <- award(?x6311, ?x2874), award(?x2170, ?x2874), ?x2170 = 0144l1 *> conf = 0.22 ranks of expected_values: 17 EVAL 01vrlr4 award 0c4z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 105.000 87.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19493-03j3pg9 PRED entity: 03j3pg9 PRED relation: nationality PRED expected values: 09c7w0 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 71): 09c7w0 (0.84 #602, 0.84 #6630, 0.83 #902), 0d060g (0.38 #3108, 0.35 #7635, 0.05 #2814), 02jx1 (0.14 #734, 0.13 #3343, 0.13 #3845), 07ssc (0.08 #5735, 0.08 #6645, 0.08 #4731), 03rk0 (0.06 #5766, 0.06 #6676, 0.06 #9087), 0cr3d (0.05 #3310, 0.05 #3209, 0.04 #2807), 0345h (0.05 #432, 0.03 #832, 0.03 #2033), 0162v (0.04 #245, 0.03 #346, 0.02 #10243), 02k1b (0.04 #284, 0.02 #10243, 0.02 #485), 0f8l9c (0.02 #2728, 0.02 #2427, 0.02 #1624) >> Best rule #602 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 0443c; >> query: (?x10012, 09c7w0) <- location(?x10012, ?x2850), ?x2850 = 0cr3d, people(?x2510, ?x10012) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03j3pg9 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.844 http://example.org/people/person/nationality #19492-054knh PRED entity: 054knh PRED relation: ceremony PRED expected values: 026kq4q => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 126): 01mhwk (0.90 #2683, 0.90 #2557, 0.39 #5207), 01c6qp (0.84 #2664, 0.84 #2538, 0.42 #5188), 01mh_q (0.83 #2727, 0.82 #2601, 0.40 #5251), 09n4nb (0.82 #2564, 0.81 #2690, 0.44 #5214), 0gpjbt (0.82 #2547, 0.80 #2673, 0.44 #5197), 02cg41 (0.81 #2632, 0.79 #2758, 0.42 #5282), 01s695 (0.80 #2650, 0.80 #2524, 0.39 #5174), 01bx35 (0.79 #2654, 0.78 #2528, 0.40 #5178), 056878 (0.78 #2550, 0.78 #2676, 0.43 #5200), 013b2h (0.78 #2720, 0.77 #2594, 0.39 #5244) >> Best rule #2683 for best value: >> intensional similarity = 8 >> extensional distance = 79 >> proper extension: 02gx2k; 031b91; >> query: (?x7965, 01mhwk) <- ceremony(?x7965, ?x6595), award_winner(?x6595, ?x10219), award_winner(?x6595, ?x9808), award_winner(?x6595, ?x3017), sibling(?x10219, ?x6336), ?x3017 = 02_fj, film(?x9808, ?x3549), type_of_union(?x10219, ?x566) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #545 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 027s4dn; *> query: (?x7965, 026kq4q) <- ceremony(?x7965, ?x8128), ceremony(?x7965, ?x7573), ceremony(?x7965, ?x6595), ?x6595 = 026kqs9, ?x8128 = 09qftb, honored_for(?x7573, ?x861), award_winner(?x7573, ?x5387), award_nominee(?x2650, ?x5387) *> conf = 0.50 ranks of expected_values: 18 EVAL 054knh ceremony 026kq4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 52.000 52.000 0.901 http://example.org/award/award_category/winners./award/award_honor/ceremony #19491-0ckrgs PRED entity: 0ckrgs PRED relation: genre PRED expected values: 0jxy => 126 concepts (110 used for prediction) PRED predicted values (max 10 best out of 109): 07s9rl0 (0.92 #12999, 0.82 #9625, 0.65 #5051), 05p553 (0.90 #11433, 0.78 #12037, 0.75 #8908), 02kdv5l (0.84 #11191, 0.75 #5293, 0.73 #11552), 0jxy (0.80 #1486, 0.78 #2686, 0.75 #1726), 01jfsb (0.70 #11682, 0.66 #8435, 0.63 #12286), 02l7c8 (0.65 #3003, 0.50 #257, 0.47 #12773), 06n90 (0.53 #4943, 0.51 #7589, 0.50 #1095), 02n4kr (0.44 #10957, 0.36 #7344, 0.24 #12282), 03q4nz (0.43 #739, 0.42 #1700, 0.40 #499), 04xvlr (0.42 #7939, 0.17 #13000, 0.15 #9626) >> Best rule #12999 for best value: >> intensional similarity = 9 >> extensional distance = 930 >> proper extension: 02d413; 0b76d_m; 0g22z; 0sxg4; 01jc6q; 028_yv; 0yyg4; 0ds3t5x; 0djb3vw; 0n0bp; ... >> query: (?x3174, 07s9rl0) <- genre(?x3174, ?x2540), film(?x788, ?x3174), genre(?x3759, ?x2540), genre(?x428, ?x2540), genre(?x13769, ?x2540), language(?x428, ?x254), film_release_region(?x428, ?x87), ?x3759 = 023p7l, ?x13769 = 051kd >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1486 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 076xkdz; *> query: (?x3174, 0jxy) <- genre(?x3174, ?x811), film(?x296, ?x3174), film_release_region(?x3174, ?x94), actor(?x3174, ?x4134), film(?x788, ?x3174), gender(?x4134, ?x514), profession(?x4134, ?x987), ?x514 = 02zsn *> conf = 0.80 ranks of expected_values: 4 EVAL 0ckrgs genre 0jxy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 126.000 110.000 0.915 http://example.org/film/film/genre #19490-0m_mm PRED entity: 0m_mm PRED relation: film_release_region PRED expected values: 02vzc 03h64 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 159): 059j2 (0.87 #1366, 0.85 #2032, 0.84 #867), 02vzc (0.85 #1389, 0.83 #890, 0.82 #2055), 0jgd (0.80 #1333, 0.79 #1999, 0.78 #834), 07ssc (0.79 #848, 0.78 #2179, 0.78 #1347), 03h64 (0.78 #1406, 0.74 #2072, 0.73 #2238), 0345h (0.77 #1368, 0.77 #2200, 0.77 #2034), 03gj2 (0.74 #859, 0.73 #2190, 0.73 #2024), 035qy (0.73 #871, 0.71 #1370, 0.70 #2036), 015fr (0.72 #2181, 0.70 #2015, 0.70 #1349), 05b4w (0.71 #2069, 0.70 #1403, 0.68 #2235) >> Best rule #1366 for best value: >> intensional similarity = 6 >> extensional distance = 196 >> proper extension: 014lc_; 0g56t9t; 0gtv7pk; 0h1cdwq; 0gx9rvq; 087wc7n; 0bwfwpj; 08hmch; 053rxgm; 0dgst_d; ... >> query: (?x984, 059j2) <- film_release_region(?x984, ?x985), film_release_region(?x984, ?x390), film_release_region(?x984, ?x252), ?x390 = 0chghy, ?x985 = 0k6nt, ?x252 = 03_3d >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1389 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 196 *> proper extension: 014lc_; 0g56t9t; 0gtv7pk; 0h1cdwq; 0gx9rvq; 087wc7n; 0bwfwpj; 08hmch; 053rxgm; 0dgst_d; ... *> query: (?x984, 02vzc) <- film_release_region(?x984, ?x985), film_release_region(?x984, ?x390), film_release_region(?x984, ?x252), ?x390 = 0chghy, ?x985 = 0k6nt, ?x252 = 03_3d *> conf = 0.85 ranks of expected_values: 2, 5 EVAL 0m_mm film_release_region 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 69.000 0.869 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0m_mm film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 69.000 0.869 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19489-0g9yrw PRED entity: 0g9yrw PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #126, 0.83 #96, 0.82 #121), 02nxhr (0.21 #312, 0.10 #2, 0.06 #7), 07c52 (0.21 #312, 0.03 #48, 0.03 #38), 07z4p (0.21 #312, 0.02 #45, 0.02 #40) >> Best rule #126 for best value: >> intensional similarity = 4 >> extensional distance = 732 >> proper extension: 0192hw; >> query: (?x4032, 029j_) <- genre(?x4032, ?x811), featured_film_locations(?x4032, ?x2552), genre(?x297, ?x811), language(?x297, ?x2164) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g9yrw film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.834 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19488-01rp13 PRED entity: 01rp13 PRED relation: genre PRED expected values: 0c4xc => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 75): 0c4xc (0.69 #452, 0.69 #124, 0.64 #206), 07s9rl0 (0.56 #658, 0.53 #3123, 0.52 #741), 01t_vv (0.35 #525, 0.34 #443, 0.29 #33), 0hcr (0.32 #264, 0.24 #922, 0.23 #2729), 06nbt (0.24 #266, 0.19 #102, 0.18 #924), 01htzx (0.20 #263, 0.19 #1250, 0.17 #2728), 06n90 (0.20 #2724, 0.19 #3135, 0.16 #2971), 03k9fj (0.17 #2722, 0.17 #3133, 0.16 #257), 0lsxr (0.16 #255, 0.14 #9, 0.14 #666), 025s89p (0.16 #298, 0.11 #956, 0.08 #2435) >> Best rule #452 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0kfpm; 019nnl; 0124k9; 08jgk1; 01q_y0; 02hct1; 0557yqh; 02r5qtm; 030cx; 0l76z; ... >> query: (?x6341, 0c4xc) <- nominated_for(?x2016, ?x6341), nominated_for(?x3210, ?x6341), program(?x2554, ?x6341), ?x2016 = 0cjyzs >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rp13 genre 0c4xc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.690 http://example.org/tv/tv_program/genre #19487-065d1h PRED entity: 065d1h PRED relation: award PRED expected values: 09v8db5 => 71 concepts (44 used for prediction) PRED predicted values (max 10 best out of 215): 09v8db5 (0.50 #661, 0.33 #254, 0.25 #407), 09sb52 (0.40 #2075, 0.26 #9388, 0.25 #8576), 09v1lrz (0.33 #376, 0.25 #783, 0.25 #407), 09v478h (0.25 #1220, 0.25 #1176, 0.25 #407), 09v0wy2 (0.25 #644, 0.25 #407, 0.12 #1050), 09v51c2 (0.25 #407, 0.12 #1221, 0.12 #1139), 0dgshf6 (0.25 #407, 0.12 #1221, 0.12 #1008), 09v4bym (0.25 #407, 0.12 #1221, 0.09 #17881), 07kfzsg (0.25 #407, 0.12 #1196, 0.07 #13818), 09v92_x (0.25 #407, 0.12 #17880, 0.12 #2034) >> Best rule #661 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 054k_8; >> query: (?x10573, 09v8db5) <- film(?x10573, ?x6219), ?x6219 = 05znbh7, profession(?x10573, ?x319), profession(?x1738, ?x319), ?x1738 = 0170pk >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065d1h award 09v8db5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 44.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19486-08hhm6 PRED entity: 08hhm6 PRED relation: profession PRED expected values: 02hrh1q => 89 concepts (67 used for prediction) PRED predicted values (max 10 best out of 60): 02hrh1q (0.93 #159, 0.88 #12, 0.86 #306), 03gjzk (0.48 #4866, 0.37 #2072, 0.37 #3984), 09jwl (0.30 #5017, 0.19 #5752, 0.18 #5311), 0cbd2 (0.25 #6770, 0.25 #1329, 0.23 #3977), 0kyk (0.23 #4587, 0.17 #1351, 0.14 #2822), 02krf9 (0.21 #907, 0.20 #760, 0.20 #1054), 01c72t (0.20 #4581, 0.11 #5757, 0.11 #5316), 0np9r (0.18 #5019, 0.12 #2078, 0.11 #2519), 018gz8 (0.18 #2074, 0.18 #2515, 0.17 #3986), 0nbcg (0.14 #5030, 0.11 #5324, 0.11 #5765) >> Best rule #159 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 084z0w; 07yw6t; 0fr7nt; 0cvbb9q; 038b_x; 04c636; 0969vz; 02fbpz; 02n1gr; 01x2tm8; ... >> query: (?x6641, 02hrh1q) <- gender(?x6641, ?x231), award(?x6641, ?x4687), profession(?x6641, ?x319), ?x4687 = 03rbj2 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08hhm6 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 67.000 0.932 http://example.org/people/person/profession #19485-0g824 PRED entity: 0g824 PRED relation: instrumentalists! PRED expected values: 018vs => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 56): 018vs (0.28 #2952, 0.27 #3122, 0.26 #2868), 02hnl (0.16 #2973, 0.16 #3143, 0.15 #2721), 03qjg (0.14 #2990, 0.13 #2906, 0.13 #2738), 026t6 (0.11 #3114, 0.11 #2944, 0.11 #2860), 0l14md (0.11 #2947, 0.11 #3117, 0.10 #2695), 06ncr (0.11 #462, 0.08 #1807, 0.07 #2983), 018j2 (0.09 #2893, 0.09 #2725, 0.08 #3147), 06ch55 (0.09 #499, 0.04 #1844, 0.04 #3190), 04rzd (0.07 #1800, 0.07 #2976, 0.07 #2892), 07y_7 (0.06 #2691, 0.06 #2859, 0.06 #2943) >> Best rule #2952 for best value: >> intensional similarity = 2 >> extensional distance = 581 >> proper extension: 04gycf; 01wy61y; 023l9y; 01l4g5; 01wbsdz; 0flpy; 09lwrt; 018d6l; 01lz4tf; 03h_yfh; ... >> query: (?x6383, 018vs) <- instrumentalists(?x227, ?x6383), artists(?x505, ?x6383) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g824 instrumentalists! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.276 http://example.org/music/instrument/instrumentalists #19484-03f1d47 PRED entity: 03f1d47 PRED relation: award PRED expected values: 02f6ym => 94 concepts (78 used for prediction) PRED predicted values (max 10 best out of 290): 02f73b (0.67 #677, 0.42 #1471, 0.22 #5838), 02f6ym (0.67 #649, 0.35 #1443, 0.28 #3031), 01by1l (0.60 #110, 0.50 #8050, 0.43 #4874), 02f5qb (0.60 #550, 0.39 #1344, 0.23 #5711), 02f71y (0.60 #576, 0.35 #1370, 0.23 #4943), 02f716 (0.53 #571, 0.39 #1365, 0.20 #5732), 02f777 (0.47 #700, 0.40 #303, 0.23 #1494), 09sb52 (0.43 #13540, 0.38 #18304, 0.24 #15922), 03qbnj (0.40 #624, 0.32 #1418, 0.23 #5785), 02v1m7 (0.40 #508, 0.32 #1302, 0.16 #5669) >> Best rule #677 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 01jfr3y; >> query: (?x4983, 02f73b) <- award(?x4983, ?x4892), artists(?x3562, ?x4983), ?x3562 = 025sc50, ?x4892 = 02f72_ >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #649 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 01jfr3y; *> query: (?x4983, 02f6ym) <- award(?x4983, ?x4892), artists(?x3562, ?x4983), ?x3562 = 025sc50, ?x4892 = 02f72_ *> conf = 0.67 ranks of expected_values: 2 EVAL 03f1d47 award 02f6ym CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 78.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19483-0bmj62v PRED entity: 0bmj62v PRED relation: film_festivals! PRED expected values: 0bh8drv 0c0zq 07vfy4 => 35 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1830): 0b76d_m (0.33 #672, 0.29 #2881, 0.29 #2660), 02rb607 (0.33 #273, 0.25 #3146, 0.25 #1601), 047vp1n (0.33 #390, 0.25 #3263, 0.25 #1718), 0462hhb (0.33 #332, 0.25 #3205, 0.25 #1660), 0bcp9b (0.33 #841, 0.25 #1553, 0.25 #1503), 047p798 (0.33 #430, 0.25 #3303, 0.25 #1758), 03cw411 (0.33 #752, 0.25 #1414, 0.25 #971), 09v9mks (0.33 #814, 0.25 #1476, 0.25 #1033), 0g9wdmc (0.33 #705, 0.25 #1367, 0.25 #924), 0gvs1kt (0.33 #741, 0.25 #1403, 0.25 #960) >> Best rule #672 for best value: >> intensional similarity = 35 >> extensional distance = 1 >> proper extension: 0hrcs29; >> query: (?x10083, 0b76d_m) <- film_festivals(?x10082, ?x10083), film_festivals(?x9209, ?x10083), film_festivals(?x8162, ?x10083), film_festivals(?x5992, ?x10083), film_festivals(?x4602, ?x10083), film_festivals(?x3790, ?x10083), film(?x8394, ?x9209), film_release_region(?x9209, ?x2316), film_release_region(?x9209, ?x1174), film_release_region(?x9209, ?x304), film_release_region(?x9209, ?x172), production_companies(?x9209, ?x7303), film_crew_role(?x9209, ?x2154), film_crew_role(?x9209, ?x1284), ?x2154 = 01vx2h, film_release_region(?x8162, ?x205), ?x304 = 0d0vqn, ?x172 = 0154j, music(?x10082, ?x6251), ?x1284 = 0ch6mp2, genre(?x8162, ?x53), film(?x275, ?x8162), film_release_region(?x5992, ?x1353), ?x1353 = 035qy, film_crew_role(?x4602, ?x2178), genre(?x3790, ?x812), ?x2316 = 06t2t, award_winner(?x2988, ?x8394), ?x1174 = 047yc, featured_film_locations(?x4602, ?x2256), film_format(?x5992, ?x6392), nominated_for(?x384, ?x4602), film_crew_role(?x3790, ?x4305), language(?x10082, ?x254), language(?x5992, ?x5607) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #446 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 1 *> proper extension: 04_m9gk; *> query: (?x10083, ?x257) <- film_festivals(?x9209, ?x10083), film_festivals(?x1170, ?x10083), film(?x8394, ?x9209), film_release_region(?x9209, ?x985), production_companies(?x9209, ?x7303), ?x8394 = 05d6q1, ?x985 = 0k6nt, nominated_for(?x548, ?x1170), film_crew_role(?x1170, ?x137), film_release_region(?x1170, ?x2146), language(?x1170, ?x254), film_regional_debut_venue(?x1170, ?x6601), film_crew_role(?x9209, ?x468), film_format(?x1170, ?x6392), nationality(?x111, ?x2146), titles(?x2146, ?x257), film_release_region(?x6587, ?x2146), film_release_region(?x4615, ?x2146), film_release_region(?x4336, ?x2146), film_release_region(?x3757, ?x2146), ?x4615 = 0dlngsd, administrative_parent(?x3411, ?x2146), country(?x2446, ?x2146), ?x3757 = 02vr3gz, ?x111 = 05d7rk, ?x6587 = 07s3m4g, ?x4336 = 0bpm4yw, ?x468 = 02r96rf, participating_countries(?x418, ?x2146), adjoins(?x2236, ?x2146) *> conf = 0.03 ranks of expected_values: 243, 790, 1066 EVAL 0bmj62v film_festivals! 07vfy4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 35.000 20.000 0.333 http://example.org/film/film/film_festivals EVAL 0bmj62v film_festivals! 0c0zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 20.000 0.333 http://example.org/film/film/film_festivals EVAL 0bmj62v film_festivals! 0bh8drv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 20.000 0.333 http://example.org/film/film/film_festivals #19482-01p3ty PRED entity: 01p3ty PRED relation: award PRED expected values: 03r8v_ => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 177): 0m7yy (0.28 #131, 0.19 #2462, 0.11 #2695), 03rbj2 (0.25 #8394, 0.23 #10494, 0.23 #10493), 03r8v_ (0.25 #8394, 0.23 #10494, 0.23 #10493), 0b6jkkg (0.25 #8394, 0.23 #10494, 0.23 #10493), 07bdd_ (0.16 #53, 0.03 #8213, 0.03 #4481), 019f4v (0.16 #2385, 0.09 #2152, 0.06 #5416), 0gq9h (0.13 #2161, 0.13 #63, 0.10 #2394), 05f4m9q (0.13 #11, 0.03 #5373, 0.03 #2575), 05b1610 (0.13 #32, 0.03 #731, 0.03 #8192), 0gs9p (0.13 #2163, 0.12 #65, 0.09 #2396) >> Best rule #131 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 06mmr; >> query: (?x2617, 0m7yy) <- award_winner(?x2617, ?x9690), award(?x2617, ?x1937), film(?x9690, ?x657), company(?x2618, ?x9690) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #8394 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 819 *> proper extension: 04xbq3; *> query: (?x2617, ?x4687) <- film(?x1445, ?x2617), award(?x2617, ?x1937), nominated_for(?x2618, ?x2617), nominated_for(?x4687, ?x2617) *> conf = 0.25 ranks of expected_values: 3 EVAL 01p3ty award 03r8v_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 89.000 0.279 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19481-03vyh PRED entity: 03vyh PRED relation: artists PRED expected values: 01vvpjj => 52 concepts (9 used for prediction) PRED predicted values (max 10 best out of 1632): 01yzl2 (0.58 #2658, 0.54 #3738, 0.44 #4820), 03fbc (0.52 #5612, 0.38 #3446, 0.38 #4528), 03f5spx (0.50 #2220, 0.46 #3300, 0.38 #4382), 01dwrc (0.50 #2686, 0.46 #3766, 0.38 #4848), 01vvycq (0.50 #2209, 0.46 #3289, 0.38 #4371), 06mt91 (0.50 #2774, 0.46 #3854, 0.38 #4936), 03t9sp (0.46 #3365, 0.42 #5531, 0.42 #2285), 01w806h (0.46 #3504, 0.42 #2424, 0.38 #4586), 01vt5c_ (0.42 #2884, 0.38 #3964, 0.38 #5046), 011z3g (0.42 #2766, 0.38 #3846, 0.32 #6012) >> Best rule #2658 for best value: >> intensional similarity = 10 >> extensional distance = 10 >> proper extension: 016clz; 0m0jc; 02x8m; 0glt670; 0y3_8; 06j6l; 025sc50; 0ggx5q; 0bt7w; 03ckfl9; >> query: (?x6799, 01yzl2) <- artists(?x6799, ?x7578), artists(?x6799, ?x1068), artists(?x6799, ?x317), ?x7578 = 01k3qj, location(?x1068, ?x1523), ?x1523 = 030qb3t, nationality(?x1068, ?x1353), profession(?x1068, ?x131), instrumentalists(?x227, ?x317), category(?x317, ?x134) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #186 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 02v2lh; *> query: (?x6799, 01vvpjj) <- artists(?x6799, ?x7578), artists(?x6799, ?x1068), artists(?x6799, ?x317), artists(?x3562, ?x7578), ?x1068 = 01x66d, instrumentalists(?x227, ?x7578), nationality(?x7578, ?x94), ?x317 = 0c9d9, artist(?x2149, ?x7578), artists(?x3562, ?x9639), ?x9639 = 0gps0z *> conf = 0.33 ranks of expected_values: 57 EVAL 03vyh artists 01vvpjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 52.000 9.000 0.583 http://example.org/music/genre/artists #19480-0bx8pn PRED entity: 0bx8pn PRED relation: major_field_of_study PRED expected values: 02822 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 114): 02lp1 (0.65 #126, 0.61 #354, 0.51 #810), 04x_3 (0.48 #366, 0.34 #822, 0.32 #708), 01tbp (0.48 #168, 0.45 #396, 0.34 #852), 05qfh (0.48 #145, 0.39 #373, 0.36 #487), 01lj9 (0.43 #148, 0.39 #376, 0.29 #832), 04sh3 (0.43 #183, 0.29 #411, 0.23 #867), 05qjt (0.37 #806, 0.35 #350, 0.35 #122), 0g4gr (0.35 #369, 0.26 #141, 0.22 #1509), 037mh8 (0.35 #175, 0.31 #517, 0.30 #631), 0h5k (0.35 #135, 0.19 #363, 0.16 #819) >> Best rule #126 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0p5wz; 0bqxw; >> query: (?x1884, 02lp1) <- major_field_of_study(?x1884, ?x4321), ?x4321 = 0g26h, list(?x1884, ?x2197) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0p5wz; 0bqxw; *> query: (?x1884, 02822) <- major_field_of_study(?x1884, ?x4321), ?x4321 = 0g26h, list(?x1884, ?x2197) *> conf = 0.26 ranks of expected_values: 20 EVAL 0bx8pn major_field_of_study 02822 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 119.000 119.000 0.652 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19479-09f2j PRED entity: 09f2j PRED relation: major_field_of_study PRED expected values: 05qjt 02j62 0g26h 04sh3 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 86): 02j62 (0.72 #207, 0.55 #300, 0.51 #1138), 05qjt (0.60 #286, 0.44 #193, 0.40 #1124), 04x_3 (0.50 #297, 0.39 #204, 0.36 #1135), 0g26h (0.45 #307, 0.41 #1706, 0.40 #2078), 02h40lc (0.40 #283, 0.33 #190, 0.26 #562), 03nfmq (0.40 #305, 0.28 #212, 0.18 #677), 04sh3 (0.36 #51, 0.35 #330, 0.33 #237), 01r4k (0.35 #337, 0.22 #244, 0.19 #1175), 04g7x (0.35 #328, 0.21 #700, 0.19 #1166), 04gb7 (0.33 #215, 0.28 #1519, 0.25 #308) >> Best rule #207 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 04jhp; >> query: (?x4955, 02j62) <- major_field_of_study(?x4955, ?x5031), institution(?x620, ?x4955), ?x5031 = 0dc_v >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 7 EVAL 09f2j major_field_of_study 04sh3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 108.000 0.722 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 09f2j major_field_of_study 0g26h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 108.000 0.722 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 09f2j major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.722 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 09f2j major_field_of_study 05qjt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.722 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19478-07mqps PRED entity: 07mqps PRED relation: people PRED expected values: 04__f => 24 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1955): 09fb5 (0.50 #1749, 0.17 #6861, 0.13 #8567), 04xhwn (0.33 #8454, 0.33 #1640, 0.25 #6751), 01vwllw (0.33 #429, 0.25 #5540, 0.25 #3835), 08f3b1 (0.33 #90, 0.25 #5201, 0.25 #1792), 0g824 (0.33 #888, 0.25 #5999, 0.25 #2590), 0k9j_ (0.33 #1262, 0.25 #6373, 0.25 #4668), 074tb5 (0.33 #827, 0.25 #5938, 0.25 #4233), 0227vl (0.33 #1230, 0.25 #6341, 0.25 #2932), 04f7c55 (0.33 #803, 0.25 #5914, 0.25 #2505), 06qgvf (0.33 #7, 0.25 #5118, 0.25 #1709) >> Best rule #1749 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 02w7gg; 0xnvg; >> query: (?x5269, 09fb5) <- people(?x5269, ?x11090), people(?x5269, ?x4154), people(?x1446, ?x11090), award_nominee(?x4154, ?x3660), award_nominee(?x4154, ?x2516), ?x3660 = 02p7_k, ?x1446 = 033tf_, languages_spoken(?x5269, ?x3592), award(?x2516, ?x618) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #10225 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 21 *> proper extension: 0cn68; *> query: (?x5269, ?x3660) <- people(?x5269, ?x12571), people(?x5269, ?x7025), people(?x5269, ?x4154), award_nominee(?x4154, ?x3660), award(?x3660, ?x704), participant(?x7025, ?x3502), award_nominee(?x3660, ?x230), jurisdiction_of_office(?x12571, ?x94) *> conf = 0.08 ranks of expected_values: 635 EVAL 07mqps people 04__f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 24.000 19.000 0.500 http://example.org/people/ethnicity/people #19477-0k345 PRED entity: 0k345 PRED relation: parent_genre PRED expected values: 01wqlc => 59 concepts (51 used for prediction) PRED predicted values (max 10 best out of 194): 06by7 (0.50 #675, 0.50 #344, 0.40 #509), 016clz (0.25 #332, 0.20 #497, 0.17 #663), 011j5x (0.25 #350, 0.20 #515, 0.17 #681), 018ysx (0.25 #465, 0.20 #630, 0.17 #796), 01243b (0.17 #688, 0.11 #4137, 0.11 #5616), 05r6t (0.16 #5808, 0.16 #4163, 0.16 #5642), 03lty (0.16 #5772, 0.15 #5277, 0.14 #4293), 0glt670 (0.15 #2332, 0.12 #4630, 0.12 #5451), 0xhtw (0.13 #6415, 0.08 #4287, 0.08 #4451), 064t9 (0.12 #6911, 0.07 #2303, 0.07 #4777) >> Best rule #675 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 07v64s; >> query: (?x10307, 06by7) <- artists(?x10307, ?x7221), artists(?x10307, ?x3735), artists(?x10307, ?x3657), ?x3735 = 0lzkm, award_winner(?x342, ?x7221), people(?x3591, ?x7221), artist(?x2149, ?x3657), profession(?x7221, ?x131) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2185 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 80 *> proper extension: 034487; *> query: (?x10307, 01wqlc) <- artists(?x10307, ?x7221), artists(?x10307, ?x3735), award_winner(?x2186, ?x3735), artists(?x12498, ?x7221), artists(?x1380, ?x7221), ?x1380 = 0dl5d, award_winner(?x11456, ?x7221), parent_genre(?x3243, ?x12498), award(?x1322, ?x11456) *> conf = 0.02 ranks of expected_values: 90 EVAL 0k345 parent_genre 01wqlc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 59.000 51.000 0.500 http://example.org/music/genre/parent_genre #19476-03knl PRED entity: 03knl PRED relation: award_winner! PRED expected values: 02yvhx => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 123): 0jzphpx (0.07 #39, 0.06 #177, 0.03 #8595), 0bz6sb (0.07 #63, 0.06 #201, 0.02 #339), 09bymc (0.07 #394, 0.06 #256, 0.04 #118), 02rjjll (0.07 #557, 0.05 #971, 0.04 #1385), 09qvms (0.07 #1255, 0.06 #1531, 0.06 #5119), 09p3h7 (0.06 #208, 0.05 #1312, 0.05 #1588), 01s695 (0.06 #141, 0.05 #555, 0.05 #1521), 0gpjbt (0.06 #167, 0.05 #581, 0.04 #29), 0hndn2q (0.06 #178, 0.05 #316, 0.04 #592), 02cg41 (0.06 #261, 0.04 #123, 0.04 #8679) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 03pvt; 043tg; >> query: (?x971, 0jzphpx) <- place_of_birth(?x971, ?x4090), profession(?x971, ?x1032), type_of_appearance(?x971, ?x3429) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #5043 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 572 *> proper extension: 0520r2x; 0cb77r; 0284n42; 06n7h7; 03ldxq; 0p_2r; 05prs8; 0gp9mp; 0261g5l; 04myfb7; ... *> query: (?x971, 02yvhx) <- award_winner(?x3460, ?x971), award_winner(?x5388, ?x971), place_of_birth(?x971, ?x4090) *> conf = 0.02 ranks of expected_values: 100 EVAL 03knl award_winner! 02yvhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 108.000 108.000 0.074 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19475-05f67hw PRED entity: 05f67hw PRED relation: genre PRED expected values: 0jtdp => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 175): 07s9rl0 (0.75 #247, 0.66 #1849, 0.66 #3576), 082gq (0.40 #156, 0.20 #2374, 0.19 #4348), 05p553 (0.36 #1730, 0.35 #621, 0.35 #4690), 01jfsb (0.33 #3960, 0.32 #2850, 0.32 #4206), 02kdv5l (0.30 #372, 0.28 #4195, 0.28 #3949), 02l7c8 (0.25 #7041, 0.25 #264, 0.25 #6672), 04xvlr (0.25 #2, 0.21 #2590, 0.20 #3330), 01f9r0 (0.25 #78, 0.12 #324, 0.06 #694), 03bxz7 (0.25 #304, 0.10 #2646, 0.09 #3386), 017fp (0.25 #17, 0.09 #3345, 0.09 #1742) >> Best rule #247 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0ft18; 03m5y9p; >> query: (?x12555, 07s9rl0) <- language(?x12555, ?x254), ?x254 = 02h40lc, country(?x12555, ?x94), ?x94 = 09c7w0, films(?x5603, ?x12555), category(?x5603, ?x134) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #8623 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1695 *> proper extension: 01h72l; 0ggbfwf; *> query: (?x12555, ?x258) <- language(?x12555, ?x254), language(?x1386, ?x254), language(?x463, ?x254), languages(?x118, ?x254), languages(?x50, ?x254), country(?x1386, ?x94), genre(?x463, ?x258) *> conf = 0.03 ranks of expected_values: 48 EVAL 05f67hw genre 0jtdp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 75.000 75.000 0.750 http://example.org/film/film/genre #19474-05kh_ PRED entity: 05kh_ PRED relation: influenced_by! PRED expected values: 05rx__ => 112 concepts (81 used for prediction) PRED predicted values (max 10 best out of 295): 07dnx (0.14 #363, 0.06 #7078, 0.06 #6045), 051cc (0.14 #349, 0.03 #5515, 0.02 #6548), 01vsl3_ (0.14 #102, 0.02 #1136, 0.02 #5268), 05jm7 (0.11 #2208, 0.06 #5823, 0.05 #17563), 0683n (0.10 #7055, 0.09 #6022, 0.07 #340), 01hb6v (0.10 #5776, 0.09 #2161, 0.09 #6809), 0399p (0.09 #6012, 0.08 #7045, 0.06 #2397), 0dzkq (0.09 #5808, 0.08 #6841, 0.04 #2193), 040db (0.08 #5758, 0.08 #6791, 0.05 #17563), 07h1q (0.08 #6092, 0.08 #7125, 0.04 #12292) >> Best rule #363 for best value: >> intensional similarity = 2 >> extensional distance = 12 >> proper extension: 02m4t; 01d5g; >> query: (?x5601, 07dnx) <- films(?x5601, ?x6636), influenced_by(?x5601, ?x6810) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #3410 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 94 *> proper extension: 0d9kl; 057ph; 0dng4; *> query: (?x5601, 05rx__) <- celebrities_impersonated(?x3649, ?x5601), ?x3649 = 03m6t5 *> conf = 0.05 ranks of expected_values: 60 EVAL 05kh_ influenced_by! 05rx__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 112.000 81.000 0.143 http://example.org/influence/influence_node/influenced_by #19473-01rr9f PRED entity: 01rr9f PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.78 #7214, 0.77 #403, 0.76 #905), 0d060g (0.34 #9417, 0.34 #8616, 0.05 #7220), 03rjj (0.34 #9417, 0.34 #8616, 0.03 #1210), 02jx1 (0.16 #1739, 0.11 #1939, 0.11 #736), 07ssc (0.11 #1721, 0.10 #317, 0.09 #4523), 03rk0 (0.05 #10368, 0.05 #10268, 0.05 #10668), 0345h (0.05 #232, 0.03 #634, 0.03 #333), 0chghy (0.03 #512, 0.03 #813, 0.02 #4618), 0f8l9c (0.03 #1728, 0.02 #2730, 0.02 #3630), 0hzlz (0.03 #123, 0.02 #726, 0.01 #525) >> Best rule #7214 for best value: >> intensional similarity = 2 >> extensional distance = 1546 >> proper extension: 0784v1; 09lhln; 037mh8; 0ct_yc; 05fh2; 03c_8t; 04gtq43; >> query: (?x513, 09c7w0) <- place_of_birth(?x513, ?x6952), time_zones(?x6952, ?x1638) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rr9f nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.780 http://example.org/people/person/nationality #19472-01pqx6 PRED entity: 01pqx6 PRED relation: category_of! PRED expected values: 01pqx6 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 8): 058vy5 (0.06 #461, 0.06 #784, 0.03 #945), 01ppdy (0.06 #457, 0.06 #780, 0.03 #941), 0j6j8 (0.03 #934, 0.02 #1417), 01tgwv (0.02 #1112, 0.02 #1273), 01pqx6 (0.02 #1453), 0bqsk5 (0.02 #1453), 02tzwd (0.02 #1433, 0.02 #1595), 01cd7p (0.02 #1612) >> Best rule #461 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 05f4m9q; 0ddd9; 01yz0x; 0c_dx; 02xj3rw; 01ppdy; 058vy5; 0dt39; 0g9wd99; 0196kn; ... >> query: (?x14761, 058vy5) <- award_winner(?x14761, ?x14008), influenced_by(?x10578, ?x14008), gender(?x14008, ?x231), influenced_by(?x14008, ?x3336), ?x3336 = 032l1, type_of_union(?x14008, ?x566) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #1453 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 59 *> proper extension: 0bdw6t; *> query: (?x14761, ?x12729) <- award_winner(?x14761, ?x14008), influenced_by(?x10578, ?x14008), award_winner(?x12729, ?x14008), people(?x6821, ?x14008), student(?x741, ?x14008) *> conf = 0.02 ranks of expected_values: 5 EVAL 01pqx6 category_of! 01pqx6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 57.000 57.000 0.059 http://example.org/award/award_category/category_of #19471-05sq84 PRED entity: 05sq84 PRED relation: religion PRED expected values: 0c8wxp => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 16): 0n2g (0.25 #13, 0.11 #58, 0.06 #103), 0c8wxp (0.13 #1222, 0.13 #1584, 0.13 #1674), 03_gx (0.12 #194, 0.08 #1456, 0.08 #1817), 0kpl (0.10 #461, 0.09 #55, 0.09 #686), 03j6c (0.04 #201, 0.03 #787, 0.03 #652), 0631_ (0.03 #188, 0.02 #414, 0.02 #369), 092bf5 (0.03 #106, 0.03 #151, 0.02 #422), 0kq2 (0.03 #63, 0.03 #469, 0.03 #514), 0flw86 (0.02 #1444, 0.02 #1941, 0.02 #2212), 01lp8 (0.02 #181, 0.02 #1804, 0.02 #1443) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 016zp5; 013bd1; >> query: (?x1469, 0n2g) <- gender(?x1469, ?x231), nationality(?x1469, ?x1310), film(?x1469, ?x11668), ?x11668 = 04x4gw >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1222 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 851 *> proper extension: 05m63c; 01k5t_3; 045bs6; 01trhmt; 030x48; 07cjqy; 033w9g; 03xb2w; 01h8f; 05nzw6; ... *> query: (?x1469, 0c8wxp) <- film(?x1469, ?x2869), nominated_for(?x2869, ?x6332) *> conf = 0.13 ranks of expected_values: 2 EVAL 05sq84 religion 0c8wxp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.250 http://example.org/people/person/religion #19470-03h_fqv PRED entity: 03h_fqv PRED relation: role PRED expected values: 042v_gx 018j2 02fsn => 173 concepts (170 used for prediction) PRED predicted values (max 10 best out of 115): 0342h (0.59 #850, 0.58 #662, 0.53 #1038), 0l14qv (0.58 #663, 0.29 #287, 0.27 #569), 01vdm0 (0.50 #687, 0.39 #3892, 0.38 #3703), 018vs (0.37 #3769, 0.33 #1799, 0.29 #7263), 0l14md (0.37 #3769, 0.29 #7263, 0.25 #8305), 05148p4 (0.37 #3769, 0.27 #585, 0.25 #679), 02hnl (0.37 #3769, 0.25 #8305, 0.24 #8304), 01vj9c (0.33 #671, 0.29 #859, 0.22 #3876), 03gvt (0.33 #728, 0.27 #634, 0.24 #916), 01s0ps (0.29 #338, 0.25 #714, 0.18 #620) >> Best rule #850 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 01vtqml; >> query: (?x5391, 0342h) <- artists(?x7083, ?x5391), ?x7083 = 02yv6b, film(?x5391, ?x1481) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #3870 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 127 *> proper extension: 01pbxb; 03c7ln; 0197tq; 0m2l9; 026ps1; 01wl38s; 0kzy0; 025xt8y; 01vv7sc; 01x66d; ... *> query: (?x5391, 042v_gx) <- role(?x5391, ?x316), ?x316 = 05r5c, profession(?x5391, ?x220), artist(?x3874, ?x5391) *> conf = 0.26 ranks of expected_values: 11, 32, 47 EVAL 03h_fqv role 02fsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 173.000 170.000 0.588 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 03h_fqv role 018j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 173.000 170.000 0.588 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 03h_fqv role 042v_gx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 173.000 170.000 0.588 http://example.org/music/artist/track_contributions./music/track_contribution/role #19469-0c9k8 PRED entity: 0c9k8 PRED relation: nominated_for! PRED expected values: 0gq9h 04kxsb => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 192): 0gs9p (0.72 #448, 0.72 #447, 0.68 #672), 09d28z (0.72 #448, 0.72 #447, 0.68 #672), 02w_6xj (0.72 #448, 0.72 #447, 0.68 #672), 027c924 (0.72 #448, 0.72 #447, 0.68 #672), 0gq9h (0.69 #1171, 0.64 #1394, 0.62 #501), 02pqp12 (0.64 #275, 0.48 #947, 0.35 #1170), 040njc (0.64 #229, 0.46 #454, 0.46 #901), 02qyntr (0.59 #388, 0.42 #1283, 0.41 #1060), 0gr4k (0.42 #1364, 0.38 #1141, 0.38 #471), 04kxsb (0.42 #1423, 0.32 #305, 0.32 #754) >> Best rule #448 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 01fwzk; >> query: (?x2943, ?x1972) <- language(?x2943, ?x254), award(?x2943, ?x2880), award(?x2943, ?x1972), ?x2880 = 02ppm4q >> conf = 0.72 => this is the best rule for 4 predicted values *> Best rule #1171 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 151 *> proper extension: 0m313; 083shs; 09m6kg; 0yyg4; 01gc7; 011yxg; 07gp9; 0gzy02; 07xtqq; 095zlp; ... *> query: (?x2943, 0gq9h) <- award_winner(?x2943, ?x406), nominated_for(?x1703, ?x2943), film_release_distribution_medium(?x2943, ?x81), ?x1703 = 0k611 *> conf = 0.69 ranks of expected_values: 5, 10 EVAL 0c9k8 nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 83.000 83.000 0.724 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0c9k8 nominated_for! 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 83.000 83.000 0.724 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19468-01c7p_ PRED entity: 01c7p_ PRED relation: music! PRED expected values: 01jc6q => 113 concepts (89 used for prediction) PRED predicted values (max 10 best out of 809): 01cmp9 (0.50 #8117, 0.07 #31440, 0.07 #41588), 01vrwfv (0.50 #8117, 0.07 #31440, 0.07 #41588), 09d3b7 (0.11 #1860, 0.03 #9975, 0.02 #12003), 08rr3p (0.07 #1288, 0.02 #5346, 0.02 #6360), 08l0x2 (0.07 #1770, 0.02 #8871, 0.02 #15969), 01s7w3 (0.05 #14060, 0.04 #12032, 0.04 #16088), 01jc6q (0.05 #16, 0.04 #1032, 0.03 #2046), 02ht1k (0.04 #5443, 0.04 #6457, 0.02 #15584), 07bzz7 (0.04 #4589, 0.04 #1545, 0.02 #17772), 033g4d (0.04 #1127, 0.03 #4171, 0.01 #13298) >> Best rule #8117 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 02rgz4; >> query: (?x9584, ?x2901) <- profession(?x9584, ?x1614), nominated_for(?x9584, ?x2901), role(?x9584, ?x316) >> conf = 0.50 => this is the best rule for 2 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 031rx9; *> query: (?x9584, 01jc6q) <- award_nominee(?x3410, ?x9584), nominated_for(?x9584, ?x2901), award(?x2901, ?x724) *> conf = 0.05 ranks of expected_values: 7 EVAL 01c7p_ music! 01jc6q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 113.000 89.000 0.500 http://example.org/film/film/music #19467-057lbk PRED entity: 057lbk PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.90 #123, 0.87 #108, 0.87 #174), 0735l (0.26 #92, 0.21 #22, 0.21 #59), 02nxhr (0.10 #45, 0.09 #139, 0.09 #129), 07z4p (0.03 #27, 0.03 #152, 0.03 #193), 07c52 (0.03 #495, 0.03 #254, 0.03 #385) >> Best rule #123 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0d90m; 03qcfvw; 02y_lrp; 01h7bb; 060v34; 0fg04; 0_b3d; 08hmch; 0872p_c; 02pxmgz; ... >> query: (?x4378, 029j_) <- executive_produced_by(?x4378, ?x96), featured_film_locations(?x4378, ?x739), film_crew_role(?x4378, ?x2154), ?x2154 = 01vx2h >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 057lbk film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19466-07_nf PRED entity: 07_nf PRED relation: entity_involved PRED expected values: 01m59 => 86 concepts (50 used for prediction) PRED predicted values (max 10 best out of 196): 09b6zr (0.43 #1218, 0.17 #924, 0.15 #1805), 02mjmr (0.40 #615, 0.29 #1210, 0.17 #916), 09c7w0 (0.32 #4319, 0.31 #1919, 0.29 #3559), 05vz3zq (0.32 #4319, 0.29 #3559, 0.27 #3712), 02lmk (0.29 #1242, 0.20 #647, 0.09 #3624), 028rk (0.29 #1209, 0.20 #614, 0.06 #4776), 079dy (0.29 #1294, 0.17 #1000, 0.15 #1881), 05v8c (0.26 #4473, 0.26 #4472, 0.26 #4475), 0ctw_b (0.26 #4473, 0.26 #4472, 0.26 #4475), 06qd3 (0.26 #4473, 0.26 #4472, 0.26 #4475) >> Best rule #1218 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 09x7p1; >> query: (?x7455, 09b6zr) <- entity_involved(?x7455, ?x6768), locations(?x7455, ?x7456), person(?x6767, ?x6768), type_of_union(?x6768, ?x566), jurisdiction_of_office(?x6768, ?x94), politician(?x8714, ?x6768) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07_nf entity_involved 01m59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 50.000 0.429 http://example.org/base/culturalevent/event/entity_involved #19465-01kym3 PRED entity: 01kym3 PRED relation: film PRED expected values: 02z9hqn 026q3s3 => 117 concepts (75 used for prediction) PRED predicted values (max 10 best out of 922): 0cks1m (0.40 #112717, 0.02 #59040), 0p9lw (0.33 #146, 0.04 #30559, 0.03 #10880), 03x7hd (0.33 #561, 0.03 #30974, 0.03 #11295), 01shy7 (0.11 #4002, 0.06 #9369, 0.04 #71988), 01hvjx (0.11 #3953, 0.06 #9320, 0.04 #16476), 099bhp (0.10 #15930, 0.10 #30242, 0.09 #21297), 05sw5b (0.10 #15127, 0.08 #29439, 0.07 #13338), 047csmy (0.10 #15226, 0.08 #29538, 0.07 #25960), 0fgrm (0.09 #2577, 0.03 #11522, 0.02 #15100), 02fttd (0.09 #2615) >> Best rule #112717 for best value: >> intensional similarity = 4 >> extensional distance = 649 >> proper extension: 02xb2bt; >> query: (?x13574, ?x5633) <- film(?x13574, ?x3174), film(?x788, ?x3174), genre(?x3174, ?x811), prequel(?x5633, ?x3174) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #23386 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 03w1v2; 01jbx1; 07cjqy; 0f502; 0127s7; 02b9g4; 0d608; 01s0l0; 05vzql; 0d0l91; ... *> query: (?x13574, 02z9hqn) <- profession(?x13574, ?x1032), special_performance_type(?x13574, ?x296), ?x1032 = 02hrh1q, category(?x13574, ?x134) *> conf = 0.04 ranks of expected_values: 101, 108 EVAL 01kym3 film 026q3s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 117.000 75.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 01kym3 film 02z9hqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 117.000 75.000 0.400 http://example.org/film/actor/film./film/performance/film #19464-0wqwj PRED entity: 0wqwj PRED relation: place_founded! PRED expected values: 01s73z => 110 concepts (72 used for prediction) PRED predicted values (max 10 best out of 55): 01_4mn (0.11 #214, 0.05 #548, 0.04 #659), 0dq23 (0.11 #203, 0.05 #537, 0.04 #648), 01ynvx (0.11 #201, 0.05 #535, 0.04 #646), 01hlwv (0.11 #190, 0.05 #524, 0.04 #635), 032j_n (0.11 #177, 0.05 #511, 0.04 #622), 01dfb6 (0.11 #169, 0.05 #503, 0.04 #614), 043g7l (0.11 #142, 0.05 #476, 0.04 #587), 0xwj (0.11 #140, 0.05 #474, 0.04 #585), 01xdn1 (0.11 #121, 0.05 #455, 0.04 #566), 024rgt (0.11 #120, 0.05 #454, 0.04 #565) >> Best rule #214 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 013yq; 0d6lp; 0yc7f; 01j922; >> query: (?x13743, 01_4mn) <- place_of_birth(?x5442, ?x13743), profession(?x5442, ?x1032), place_of_death(?x5442, ?x5381), person(?x8501, ?x5442) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0wqwj place_founded! 01s73z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 72.000 0.111 http://example.org/organization/organization/place_founded #19463-01s3kv PRED entity: 01s3kv PRED relation: profession PRED expected values: 02hrh1q => 117 concepts (52 used for prediction) PRED predicted values (max 10 best out of 62): 02hrh1q (0.93 #4012, 0.91 #2975, 0.91 #458), 01d_h8 (0.60 #894, 0.50 #1634, 0.40 #302), 0cbd2 (0.46 #7263, 0.26 #895, 0.17 #1931), 09jwl (0.43 #6534, 0.40 #1054, 0.39 #5645), 02jknp (0.39 #896, 0.25 #1636, 0.23 #7264), 018gz8 (0.33 #4295, 0.33 #1644, 0.32 #904), 0kyk (0.33 #4295, 0.24 #917, 0.19 #7285), 016z4k (0.32 #1040, 0.29 #1336, 0.28 #1484), 0nbcg (0.32 #1067, 0.28 #5658, 0.26 #6547), 0dz3r (0.31 #1038, 0.24 #5629, 0.22 #4593) >> Best rule #4012 for best value: >> intensional similarity = 4 >> extensional distance = 556 >> proper extension: 01n7qlf; 065mm1; 0cgfb; 042gr4; 01kym3; >> query: (?x4625, 02hrh1q) <- profession(?x4625, ?x987), gender(?x4625, ?x514), ?x514 = 02zsn, film(?x4625, ?x2329) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01s3kv profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 52.000 0.928 http://example.org/people/person/profession #19462-01vsgrn PRED entity: 01vsgrn PRED relation: participant! PRED expected values: 01vrz41 => 113 concepts (57 used for prediction) PRED predicted values (max 10 best out of 304): 0168dy (0.83 #16532, 0.81 #9538, 0.80 #20985), 01vw20_ (0.83 #16532, 0.81 #9538, 0.80 #20985), 01vw20h (0.31 #636, 0.26 #3181, 0.05 #19712), 04xrx (0.11 #7631, 0.10 #182, 0.08 #2726), 015f7 (0.08 #241, 0.06 #2785, 0.06 #3422), 0127s7 (0.08 #392, 0.06 #2936, 0.06 #3573), 01trhmt (0.08 #178, 0.06 #2722, 0.05 #9080), 0227vl (0.08 #534, 0.06 #3078, 0.04 #3715), 0bdxs5 (0.08 #531, 0.06 #3075, 0.03 #9433), 0gs6vr (0.08 #430, 0.06 #2974, 0.03 #7425) >> Best rule #16532 for best value: >> intensional similarity = 3 >> extensional distance = 380 >> proper extension: 01twdk; 01xyt7; 0bkmf; 0d_w7; 01g0jn; >> query: (?x5536, ?x2987) <- award_winner(?x1323, ?x5536), participant(?x140, ?x5536), participant(?x5536, ?x2987) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #1990 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 094xh; 07pzc; 012x1l; *> query: (?x5536, 01vrz41) <- award(?x5536, ?x724), artist(?x8738, ?x5536), gender(?x5536, ?x231) *> conf = 0.02 ranks of expected_values: 235 EVAL 01vsgrn participant! 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 113.000 57.000 0.828 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #19461-0dzlbx PRED entity: 0dzlbx PRED relation: film_crew_role PRED expected values: 020xn5 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 24): 01pvkk (0.35 #99, 0.28 #2059, 0.28 #1877), 0215hd (0.21 #44, 0.14 #916, 0.13 #195), 05smlt (0.20 #16, 0.08 #46, 0.08 #228), 0ckd1 (0.20 #3, 0.05 #33, 0.04 #215), 02ynfr (0.18 #433, 0.17 #102, 0.16 #373), 015h31 (0.15 #369, 0.14 #429, 0.14 #579), 089g0h (0.13 #45, 0.11 #2126, 0.10 #2066), 02_n3z (0.13 #31, 0.10 #213, 0.09 #423), 033smt (0.11 #52, 0.07 #384, 0.06 #444), 089fss (0.09 #186, 0.09 #156, 0.07 #907) >> Best rule #99 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 0407yj_; 027r7k; >> query: (?x4998, 01pvkk) <- story_by(?x4998, ?x96), film(?x609, ?x4998), film_release_region(?x4998, ?x789), ?x789 = 0f8l9c >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #218 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 89 *> proper extension: 0kv9d3; *> query: (?x4998, 020xn5) <- story_by(?x4998, ?x96), country(?x4998, ?x94), executive_produced_by(?x4998, ?x4731), nominated_for(?x298, ?x4998) *> conf = 0.04 ranks of expected_values: 18 EVAL 0dzlbx film_crew_role 020xn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 106.000 106.000 0.346 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19460-02fz3w PRED entity: 02fz3w PRED relation: award_nominee PRED expected values: 02zq43 => 103 concepts (52 used for prediction) PRED predicted values (max 10 best out of 824): 02fz3w (0.46 #9302, 0.32 #1969, 0.26 #46511), 02zq43 (0.46 #9302, 0.29 #60, 0.14 #97672), 04v7kt (0.29 #2299, 0.14 #97672, 0.14 #48837), 06dv3 (0.26 #42, 0.14 #97672, 0.14 #48837), 027bs_2 (0.26 #1643, 0.14 #97672, 0.14 #48837), 05mc99 (0.26 #1687, 0.14 #97672, 0.14 #48837), 06qgvf (0.26 #9, 0.14 #97672, 0.14 #48837), 03n_7k (0.26 #512, 0.14 #97672, 0.02 #33071), 02g1jh (0.26 #46511), 02j8nx (0.26 #46511) >> Best rule #9302 for best value: >> intensional similarity = 3 >> extensional distance = 225 >> proper extension: 04lgymt; 016kjs; 03gr7w; 0288fyj; 03yf3z; 0bczgm; 0840vq; 0fwy0h; 01yzl2; 01dwrc; ... >> query: (?x9236, ?x381) <- award_nominee(?x2559, ?x9236), program(?x2559, ?x2583), award_nominee(?x2559, ?x381) >> conf = 0.46 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02fz3w award_nominee 02zq43 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 52.000 0.460 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19459-07z6xs PRED entity: 07z6xs PRED relation: film! PRED expected values: 05qd_ => 79 concepts (57 used for prediction) PRED predicted values (max 10 best out of 51): 086k8 (0.20 #446, 0.17 #372, 0.16 #520), 05qd_ (0.19 #83, 0.18 #157, 0.17 #231), 017s11 (0.13 #1119, 0.13 #671, 0.12 #299), 016tt2 (0.13 #4, 0.12 #522, 0.12 #448), 03xq0f (0.12 #598, 0.12 #747, 0.11 #5), 03xsby (0.10 #311, 0.05 #757, 0.04 #608), 0g1rw (0.09 #156, 0.09 #8, 0.08 #452), 0jz9f (0.09 #297, 0.08 #891, 0.08 #594), 04mkft (0.09 #35, 0.05 #479, 0.03 #553), 054g1r (0.08 #404, 0.07 #850, 0.06 #34) >> Best rule #446 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 02qrv7; 05cj_j; 08cfr1; 01jr4j; 029v40; >> query: (?x5122, 086k8) <- titles(?x53, ?x5122), film_production_design_by(?x5122, ?x3080), film(?x3705, ?x5122), genre(?x5122, ?x604) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #83 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 0422v0; *> query: (?x5122, 05qd_) <- titles(?x600, ?x5122), nominated_for(?x1243, ?x5122), genre(?x5122, ?x604), ?x600 = 02n4kr *> conf = 0.19 ranks of expected_values: 2 EVAL 07z6xs film! 05qd_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 57.000 0.198 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #19458-0hz28 PRED entity: 0hz28 PRED relation: industry! PRED expected values: 026db_ => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 864): 0plw (0.74 #247, 0.74 #246, 0.56 #240), 011k1h (0.74 #247, 0.74 #246, 0.56 #240), 07733f (0.74 #247, 0.74 #246, 0.56 #240), 01nn79 (0.74 #247, 0.74 #246, 0.55 #2680), 05b5c (0.74 #247, 0.74 #246, 0.55 #2680), 01tkfj (0.74 #247, 0.74 #246, 0.55 #2680), 07_dn (0.74 #247, 0.74 #246, 0.55 #2680), 0537b (0.74 #247, 0.74 #246, 0.55 #2680), 08z129 (0.74 #247, 0.74 #246, 0.55 #2680), 077w0b (0.74 #247, 0.74 #246, 0.55 #2680) >> Best rule #247 for best value: >> intensional similarity = 19 >> extensional distance = 1 >> proper extension: 01mw1; >> query: (?x12816, ?x2776) <- industry(?x11939, ?x12816), industry(?x9077, ?x12816), industry(?x6972, ?x12816), industry(?x1908, ?x12816), industry(?x1908, ?x13321), industry(?x1908, ?x6575), child(?x1908, ?x3381), list(?x1908, ?x5997), major_field_of_study(?x1368, ?x6575), company(?x1907, ?x1908), citytown(?x6972, ?x739), child(?x9077, ?x574), child(?x738, ?x6972), service_location(?x3381, ?x94), ?x1907 = 01yc02, ?x11939 = 01tlrp, company(?x96, ?x6972), industry(?x2776, ?x13321), citytown(?x574, ?x191) >> conf = 0.74 => this is the best rule for 101 predicted values *> Best rule #3404 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 6 *> proper extension: 01zhp; *> query: (?x12816, ?x99) <- industry(?x9923, ?x12816), industry(?x4585, ?x12816), industry(?x1908, ?x12816), company(?x346, ?x1908), organization(?x346, ?x99), company(?x346, ?x12452), company(?x346, ?x11188), company(?x346, ?x11051), company(?x346, ?x7442), company(?x346, ?x7390), company(?x346, ?x6638), company(?x346, ?x5108), company(?x4792, ?x9923), ?x6638 = 02630g, ?x4792 = 05_wyz, ?x7442 = 03v52f, ?x12452 = 0vlf, ?x5108 = 01s73z, ?x11188 = 0z07, ?x11051 = 07_dn, currency(?x9923, ?x170), ?x7390 = 018p5f, production_companies(?x383, ?x4585) *> conf = 0.01 ranks of expected_values: 571 EVAL 0hz28 industry! 026db_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 39.000 0.741 http://example.org/business/business_operation/industry #19457-046f3p PRED entity: 046f3p PRED relation: film_crew_role PRED expected values: 02r96rf => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.63 #1450, 0.61 #799, 0.60 #1991), 09vw2b7 (0.63 #1454, 0.63 #803, 0.58 #367), 01vx2h (0.31 #1458, 0.28 #1999, 0.26 #480), 02ynfr (0.31 #16, 0.16 #485, 0.16 #1463), 01pvkk (0.29 #518, 0.29 #481, 0.28 #1459), 0215hd (0.18 #271, 0.14 #379, 0.14 #1466), 089fss (0.15 #6, 0.12 #2496, 0.07 #78), 089g0h (0.15 #272, 0.12 #2496, 0.12 #1467), 01xy5l_ (0.14 #266, 0.12 #2496, 0.11 #1461), 02_n3z (0.12 #253, 0.12 #2496, 0.10 #361) >> Best rule #1450 for best value: >> intensional similarity = 2 >> extensional distance = 935 >> proper extension: 0fq27fp; >> query: (?x7664, 02r96rf) <- film_crew_role(?x7664, ?x137), currency(?x7664, ?x170) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 046f3p film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.633 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19456-026hh0m PRED entity: 026hh0m PRED relation: featured_film_locations PRED expected values: 0rh6k => 95 concepts (71 used for prediction) PRED predicted values (max 10 best out of 99): 02_286 (0.39 #498, 0.31 #1695, 0.31 #1216), 030qb3t (0.18 #517, 0.15 #995, 0.13 #1714), 052p7 (0.17 #57, 0.04 #774, 0.03 #1732), 04jpl (0.12 #9114, 0.10 #1684, 0.08 #726), 080h2 (0.11 #502, 0.11 #980, 0.10 #1220), 0rh6k (0.09 #479, 0.08 #1197, 0.08 #957), 035p3 (0.07 #710, 0.06 #1188, 0.05 #1428), 0h7h6 (0.05 #1718, 0.05 #521, 0.05 #282), 03gh4 (0.05 #1310, 0.05 #592, 0.04 #1549), 06y57 (0.05 #580, 0.05 #341, 0.03 #1298) >> Best rule #498 for best value: >> intensional similarity = 6 >> extensional distance = 42 >> proper extension: 0gzlb9; >> query: (?x10158, 02_286) <- executive_produced_by(?x10158, ?x8503), genre(?x10158, ?x1013), genre(?x10158, ?x225), film_crew_role(?x10158, ?x137), ?x1013 = 06n90, ?x225 = 02kdv5l >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #479 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 42 *> proper extension: 0gzlb9; *> query: (?x10158, 0rh6k) <- executive_produced_by(?x10158, ?x8503), genre(?x10158, ?x1013), genre(?x10158, ?x225), film_crew_role(?x10158, ?x137), ?x1013 = 06n90, ?x225 = 02kdv5l *> conf = 0.09 ranks of expected_values: 6 EVAL 026hh0m featured_film_locations 0rh6k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 95.000 71.000 0.386 http://example.org/film/film/featured_film_locations #19455-04g7x PRED entity: 04g7x PRED relation: major_field_of_study! PRED expected values: 02h4rq6 => 68 concepts (49 used for prediction) PRED predicted values (max 10 best out of 17): 02h4rq6 (0.89 #297, 0.86 #277, 0.85 #466), 019v9k (0.84 #339, 0.84 #394, 0.83 #530), 0bjrnt (0.67 #110, 0.66 #333, 0.50 #183), 071tyz (0.66 #333, 0.45 #124, 0.43 #198), 01ysy9 (0.50 #142, 0.35 #237, 0.33 #34), 01rr_d (0.46 #407, 0.45 #124, 0.45 #179), 013zdg (0.46 #407, 0.45 #124, 0.45 #179), 027f2w (0.46 #407, 0.45 #124, 0.45 #179), 03mkk4 (0.46 #407, 0.45 #124, 0.45 #179), 028dcg (0.46 #407, 0.45 #124, 0.45 #179) >> Best rule #297 for best value: >> intensional similarity = 10 >> extensional distance = 16 >> proper extension: 0h5k; 0jjw; 04gb7; >> query: (?x8962, 02h4rq6) <- major_field_of_study(?x9658, ?x8962), major_field_of_study(?x3513, ?x8962), major_field_of_study(?x735, ?x8962), major_field_of_study(?x581, ?x8962), ?x581 = 06pwq, colors(?x9658, ?x3621), ?x3621 = 088fh, institution(?x865, ?x3513), school(?x580, ?x735), student(?x735, ?x65) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04g7x major_field_of_study! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 49.000 0.889 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #19454-060ny2 PRED entity: 060ny2 PRED relation: legislative_sessions! PRED expected values: 02hy5d => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 450): 02hy5d (0.80 #636, 0.78 #586, 0.71 #534), 024_vw (0.71 #538, 0.70 #640, 0.70 #614), 0bymv (0.71 #519, 0.67 #466, 0.67 #439), 0d3qd0 (0.67 #489, 0.67 #470, 0.67 #439), 016lh0 (0.67 #439, 0.67 #157, 0.64 #259), 012v1t (0.67 #439, 0.67 #157, 0.64 #259), 03txms (0.67 #439, 0.67 #157, 0.64 #259), 0d06m5 (0.67 #439, 0.67 #157, 0.64 #259), 01lct6 (0.67 #157, 0.64 #259, 0.62 #491), 02mjmr (0.67 #439, 0.62 #105, 0.62 #646) >> Best rule #636 for best value: >> intensional similarity = 53 >> extensional distance = 8 >> proper extension: 02bn_p; >> query: (?x6139, 02hy5d) <- legislative_sessions(?x6139, ?x6933), legislative_sessions(?x6139, ?x2861), legislative_sessions(?x6139, ?x1028), legislative_sessions(?x6139, ?x1027), legislative_sessions(?x6139, ?x952), legislative_sessions(?x6139, ?x606), district_represented(?x6139, ?x6226), district_represented(?x6139, ?x2977), ?x2977 = 081mh, district_represented(?x1027, ?x4198), district_represented(?x1027, ?x1767), district_represented(?x1027, ?x1351), district_represented(?x1027, ?x1227), district_represented(?x1027, ?x961), district_represented(?x1027, ?x448), ?x1351 = 06mz5, legislative_sessions(?x6138, ?x6139), legislative_sessions(?x5266, ?x1027), legislative_sessions(?x2357, ?x1027), ?x1767 = 04rrd, contains(?x94, ?x6226), ?x1028 = 032ft5, ?x1227 = 01n7q, legislative_sessions(?x2860, ?x2861), vacationer(?x6226, ?x4593), vacationer(?x6226, ?x3422), vacationer(?x6226, ?x2987), vacationer(?x6226, ?x1733), geographic_distribution(?x7562, ?x6226), ?x5266 = 016lh0, gender(?x1733, ?x231), ?x606 = 03ww_x, participant(?x3422, ?x777), featured_film_locations(?x1810, ?x6226), participant(?x1733, ?x2763), ?x2860 = 0b3wk, profession(?x1733, ?x319), ?x6933 = 024tkd, ?x952 = 06f0dc, location_of_ceremony(?x940, ?x6226), artists(?x474, ?x3422), state(?x3704, ?x6226), currency(?x2987, ?x170), category(?x4593, ?x134), nominated_for(?x298, ?x1810), artists(?x302, ?x2987), participant(?x8691, ?x3422), ?x448 = 03v1s, ?x961 = 03s0w, ?x4198 = 05fky, ?x2357 = 0bymv, ?x302 = 016clz, people(?x9888, ?x6138) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 060ny2 legislative_sessions! 02hy5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.800 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #19453-04gxp2 PRED entity: 04gxp2 PRED relation: educational_institution! PRED expected values: 04gxp2 => 95 concepts (58 used for prediction) PRED predicted values (max 10 best out of 129): 07szy (0.16 #24813, 0.03 #35, 0.02 #574), 02bq1j (0.16 #24813, 0.02 #694, 0.02 #1233), 04gxp2 (0.16 #24813), 037q2p (0.03 #418, 0.02 #18337, 0.02 #2035), 02grjf (0.03 #454, 0.02 #18337), 01j_cy (0.03 #34, 0.02 #18337), 02w2bc (0.03 #11, 0.02 #18337), 015fs3 (0.03 #415), 0g8rj (0.02 #702, 0.02 #1241, 0.02 #1780), 078bz (0.02 #609, 0.02 #1148, 0.02 #1687) >> Best rule #24813 for best value: >> intensional similarity = 4 >> extensional distance = 447 >> proper extension: 08tyb_; >> query: (?x13215, ?x1681) <- student(?x13215, ?x9684), category(?x13215, ?x134), ?x134 = 08mbj5d, student(?x1681, ?x9684) >> conf = 0.16 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 04gxp2 educational_institution! 04gxp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 58.000 0.164 http://example.org/education/educational_institution_campus/educational_institution #19452-02z3r8t PRED entity: 02z3r8t PRED relation: language PRED expected values: 064_8sq => 84 concepts (82 used for prediction) PRED predicted values (max 10 best out of 56): 06b_j (0.89 #850, 0.88 #739, 0.85 #352), 064_8sq (0.40 #351, 0.36 #1574, 0.33 #738), 06nm1 (0.33 #10, 0.29 #175, 0.26 #1564), 04306rv (0.23 #1558, 0.21 #833, 0.19 #722), 02bjrlw (0.17 #1555, 0.15 #830, 0.11 #3502), 0653m (0.14 #176, 0.11 #3502, 0.10 #342), 03_9r (0.14 #1563, 0.11 #3502, 0.10 #340), 03hkp (0.12 #233, 0.05 #731, 0.03 #510), 06mp7 (0.12 #234, 0.03 #4229, 0.02 #843), 0jzc (0.11 #3502, 0.10 #349, 0.07 #847) >> Best rule #850 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 03xj05; >> query: (?x755, 06b_j) <- language(?x755, ?x12272), countries_spoken_in(?x12272, ?x1603), ?x1603 = 06bnz, country(?x755, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #351 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 04dsnp; *> query: (?x755, 064_8sq) <- language(?x755, ?x12272), countries_spoken_in(?x12272, ?x1603), ?x1603 = 06bnz, featured_film_locations(?x755, ?x739), film(?x754, ?x755) *> conf = 0.40 ranks of expected_values: 2 EVAL 02z3r8t language 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 82.000 0.889 http://example.org/film/film/language #19451-09nqf PRED entity: 09nqf PRED relation: currency! PRED expected values: 035hm 0168t => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 285): 06q1r (0.33 #26, 0.33 #21, 0.25 #47), 0kn68 (0.33 #19, 0.20 #123, 0.20 #103), 0c663 (0.33 #18, 0.20 #122, 0.20 #102), 01ngn3 (0.33 #17, 0.20 #121, 0.20 #101), 04q_g (0.33 #16, 0.20 #120, 0.20 #100), 06w92 (0.33 #15, 0.20 #119, 0.20 #99), 0c61p (0.33 #14, 0.20 #118, 0.20 #98), 01rxw2 (0.33 #13, 0.20 #117, 0.20 #97), 0bzty (0.33 #12, 0.20 #116, 0.20 #96), 015m08 (0.33 #11, 0.20 #115, 0.20 #95) >> Best rule #26 for best value: >> intensional similarity = 26 >> extensional distance = 1 >> proper extension: 01nv4h; >> query: (?x170, 06q1r) <- currency(?x8284, ?x170), currency(?x6119, ?x170), currency(?x5538, ?x170), currency(?x5128, ?x170), currency(?x4087, ?x170), currency(?x2506, ?x170), currency(?x1064, ?x170), currency(?x3183, ?x170), currency(?x2237, ?x170), currency(?x1955, ?x170), currency(?x99, ?x170), currency(?x122, ?x170), currency(?x466, ?x170), currency(?x47, ?x170), titles(?x1510, ?x4087), featured_film_locations(?x5128, ?x8363), nominated_for(?x277, ?x5128), artist(?x2931, ?x2237), award_winner(?x8284, ?x3080), written_by(?x5538, ?x6037), genre(?x2506, ?x225), film(?x157, ?x1064), award(?x3183, ?x537), executive_produced_by(?x6119, ?x4946), location_of_ceremony(?x1955, ?x1523), ?x157 = 02qgqt >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 1 *> proper extension: 02l6h; *> query: (?x170, ?x3432) <- currency(?x8130, ?x170), currency(?x4963, ?x170), currency(?x9794, ?x170), currency(?x7609, ?x170), currency(?x5882, ?x170), currency(?x4330, ?x170), currency(?x99, ?x170), currency(?x3696, ?x170), currency(?x122, ?x170), currency(?x6602, ?x170), currency(?x3432, ?x170), colors(?x6602, ?x332), gender(?x9794, ?x231), artist(?x1124, ?x5882), institution(?x1200, ?x3696), currency(?x94, ?x170), currency(?x1609, ?x170), profession(?x7609, ?x106), jurisdiction_of_office(?x182, ?x3432), film(?x413, ?x8130), nominated_for(?x112, ?x4963), genre(?x4963, ?x53), award_winner(?x372, ?x4330) *> conf = 0.33 ranks of expected_values: 69, 104 EVAL 09nqf currency! 0168t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 8.000 8.000 0.333 http://example.org/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 035hm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 8.000 8.000 0.333 http://example.org/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency #19450-0mkdm PRED entity: 0mkdm PRED relation: source PRED expected values: 0jbk9 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #15, 0.92 #14, 0.92 #21) >> Best rule #15 for best value: >> intensional similarity = 5 >> extensional distance = 63 >> proper extension: 0m2by; >> query: (?x7567, 0jbk9) <- county_seat(?x7567, ?x4316), adjoins(?x7567, ?x7568), contains(?x4105, ?x7567), administrative_division(?x6088, ?x7568), currency(?x7568, ?x170) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mkdm source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.923 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #19449-02rmd_2 PRED entity: 02rmd_2 PRED relation: film_regional_debut_venue PRED expected values: 018cvf => 106 concepts (99 used for prediction) PRED predicted values (max 10 best out of 28): 018cvf (0.18 #692, 0.17 #603, 0.16 #466), 04grdgy (0.17 #140, 0.15 #104, 0.14 #278), 015hr (0.11 #258, 0.08 #1294, 0.08 #1707), 0prpt (0.10 #478, 0.09 #615, 0.08 #1892), 0j63cyr (0.09 #670, 0.06 #915, 0.05 #984), 0kfhjq0 (0.08 #120, 0.06 #917, 0.06 #986), 07751 (0.06 #253, 0.04 #841, 0.04 #1702), 0gg7gsl (0.05 #594, 0.05 #457, 0.04 #76), 04_m9gk (0.04 #93, 0.04 #129, 0.03 #681), 09rwjly (0.04 #91, 0.04 #127, 0.02 #266) >> Best rule #692 for best value: >> intensional similarity = 5 >> extensional distance = 117 >> proper extension: 0h3y; 042rnl; 02z13jg; 01_vfy; 01q4qv; 01ycck; 01f7v_; 01c6l; 04ld94; 04r7p; ... >> query: (?x4372, ?x6601) <- film_festivals(?x4372, ?x9189), film_festivals(?x6832, ?x9189), film_release_region(?x6832, ?x94), instance_of_recurring_event(?x9189, ?x6601), nominated_for(?x541, ?x6832) >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rmd_2 film_regional_debut_venue 018cvf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 99.000 0.185 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #19448-03f4xvm PRED entity: 03f4xvm PRED relation: nationality PRED expected values: 09c7w0 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 75): 09c7w0 (0.83 #702, 0.76 #3207, 0.74 #1002), 02jx1 (0.23 #133, 0.22 #634, 0.19 #834), 07ssc (0.12 #1518, 0.12 #415, 0.09 #6026), 03rk0 (0.08 #4757, 0.08 #6057, 0.08 #5857), 0345h (0.07 #31, 0.04 #6514, 0.03 #3941), 0d060g (0.06 #1209, 0.05 #1711, 0.05 #3816), 0cr3d (0.05 #1102, 0.04 #601, 0.04 #2205), 03rjj (0.05 #706, 0.03 #105, 0.03 #3915), 0hzlz (0.05 #223, 0.02 #624, 0.02 #423), 0f8l9c (0.04 #6514, 0.03 #1525, 0.02 #4733) >> Best rule #702 for best value: >> intensional similarity = 2 >> extensional distance = 102 >> proper extension: 03nbbv; 02x8kk; 0kbn5; 01hkg9; >> query: (?x4548, 09c7w0) <- place_of_birth(?x4548, ?x2850), ?x2850 = 0cr3d >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f4xvm nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.827 http://example.org/people/person/nationality #19447-01kp_1t PRED entity: 01kp_1t PRED relation: award_nominee! PRED expected values: 086qd => 112 concepts (49 used for prediction) PRED predicted values (max 10 best out of 848): 02l840 (0.33 #157, 0.14 #23498, 0.10 #2491), 01k_mc (0.33 #1388, 0.08 #6056, 0.04 #8390), 02x_h0 (0.33 #1288, 0.06 #24629, 0.02 #47970), 01yzl2 (0.33 #1287, 0.04 #24628, 0.03 #40966), 0770cd (0.33 #381, 0.02 #23722, 0.01 #58734), 015bwt (0.33 #2245), 04znsy (0.33 #1980), 0169dl (0.17 #7520, 0.12 #12189, 0.10 #16857), 014zcr (0.12 #7048, 0.09 #11717, 0.07 #16385), 01vw20h (0.12 #24401, 0.06 #91034, 0.05 #59413) >> Best rule #157 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 04xrx; >> query: (?x9528, 02l840) <- award_nominee(?x9528, ?x11621), ?x11621 = 01mxqyk, award_winner(?x3121, ?x9528) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kp_1t award_nominee! 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 49.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19446-01dc0c PRED entity: 01dc0c PRED relation: film! PRED expected values: 01mylz => 97 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1125): 048lv (0.48 #41568, 0.48 #51962, 0.47 #60281), 018grr (0.29 #2416, 0.17 #338, 0.15 #4495), 02lq10 (0.29 #2434, 0.17 #356, 0.15 #4513), 01ggc9 (0.29 #3805, 0.17 #1727, 0.15 #5884), 02fb1n (0.29 #2411, 0.17 #333, 0.15 #4490), 01pnn3 (0.29 #2526, 0.17 #448, 0.15 #4605), 0gz5hs (0.29 #2395, 0.17 #317, 0.15 #4474), 0jfx1 (0.21 #6641, 0.12 #8719, 0.08 #4562), 06ltr (0.19 #11336, 0.17 #944, 0.14 #7180), 0l6px (0.19 #10779, 0.17 #387, 0.14 #6623) >> Best rule #41568 for best value: >> intensional similarity = 4 >> extensional distance = 163 >> proper extension: 025n07; >> query: (?x8474, ?x1384) <- nominated_for(?x1384, ?x8474), genre(?x8474, ?x604), film_release_region(?x8474, ?x94), ?x604 = 0lsxr >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #22727 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 95 *> proper extension: 0d1qmz; *> query: (?x8474, 01mylz) <- nominated_for(?x1384, ?x8474), nominated_for(?x8474, ?x394), film(?x820, ?x8474), honored_for(?x6861, ?x394) *> conf = 0.04 ranks of expected_values: 241 EVAL 01dc0c film! 01mylz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 97.000 40.000 0.484 http://example.org/film/actor/film./film/performance/film #19445-0gy6z9 PRED entity: 0gy6z9 PRED relation: film PRED expected values: 0gj9tn5 02qmsr => 112 concepts (79 used for prediction) PRED predicted values (max 10 best out of 857): 01bb9r (0.66 #24966, 0.59 #87382, 0.47 #105220), 03nt59 (0.66 #24966, 0.59 #87382, 0.47 #105220), 080dwhx (0.66 #24966, 0.59 #87382, 0.47 #105220), 01hqhm (0.36 #328, 0.03 #140903, 0.03 #137336), 047d21r (0.36 #10700, 0.10 #41018, 0.10 #41017), 0glqh5_ (0.36 #10700, 0.10 #41017, 0.01 #8054), 02mpyh (0.18 #1458, 0.03 #107005, 0.03 #110573), 02d003 (0.18 #1233, 0.03 #140903, 0.03 #137336), 095zlp (0.18 #60, 0.03 #140903, 0.03 #137336), 0c9t0y (0.18 #1250, 0.03 #140903, 0.03 #137336) >> Best rule #24966 for best value: >> intensional similarity = 3 >> extensional distance = 203 >> proper extension: 0c01c; >> query: (?x3293, ?x493) <- award_winner(?x286, ?x3293), nominated_for(?x3293, ?x493), participant(?x3293, ?x1299) >> conf = 0.66 => this is the best rule for 3 predicted values *> Best rule #2056 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 30 *> proper extension: 025504; *> query: (?x3293, 0gj9tn5) <- program(?x3293, ?x493), category(?x3293, ?x134) *> conf = 0.03 ranks of expected_values: 169, 624 EVAL 0gy6z9 film 02qmsr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 79.000 0.658 http://example.org/film/actor/film./film/performance/film EVAL 0gy6z9 film 0gj9tn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 112.000 79.000 0.658 http://example.org/film/actor/film./film/performance/film #19444-0kb1g PRED entity: 0kb1g PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.81 #92, 0.80 #143, 0.79 #127), 07c52 (0.08 #3, 0.05 #13, 0.04 #23), 07z4p (0.04 #15, 0.03 #25, 0.03 #172), 02nxhr (0.03 #169, 0.03 #219, 0.03 #149) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 646 >> proper extension: 0pvms; 05ch98; 0cqr0q; >> query: (?x9993, 029j_) <- genre(?x9993, ?x53), produced_by(?x9993, ?x11030), award_winner(?x11030, ?x574) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kb1g film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.810 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19443-0g28b1 PRED entity: 0g28b1 PRED relation: story_by! PRED expected values: 0353tm => 90 concepts (69 used for prediction) PRED predicted values (max 10 best out of 8): 06r2h (0.02 #977, 0.02 #1322, 0.02 #2011), 01y6dz (0.02 #1034, 0.02 #1379, 0.02 #2757), 02_1q9 (0.02 #1034, 0.02 #1379, 0.02 #2757), 014nq4 (0.02 #2863, 0.01 #795, 0.01 #1140), 02b61v (0.01 #899, 0.01 #1244, 0.01 #1933), 050f0s (0.01 #751, 0.01 #1096, 0.01 #1785), 063y9fp (0.01 #3047, 0.01 #3392), 062zjtt (0.01 #2901) >> Best rule #977 for best value: >> intensional similarity = 3 >> extensional distance = 138 >> proper extension: 0dbpyd; 01xdf5; 0p_pd; 03ckxdg; 050023; 026dcvf; 0d4fqn; 03qd_; 02773nt; 02lk1s; ... >> query: (?x4146, 06r2h) <- nominated_for(?x4146, ?x416), award_nominee(?x4146, ?x415), tv_program(?x4146, ?x3104) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g28b1 story_by! 0353tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 69.000 0.021 http://example.org/film/film/story_by #19442-01cmp9 PRED entity: 01cmp9 PRED relation: genre PRED expected values: 05p553 0lsxr => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 88): 04t36 (0.61 #5509, 0.54 #5510, 0.52 #6244), 05p553 (0.50 #858, 0.43 #614, 0.36 #1592), 02kdv5l (0.34 #2571, 0.31 #6612, 0.29 #1468), 01jfsb (0.34 #2094, 0.33 #2827, 0.33 #2582), 02l7c8 (0.34 #993, 0.27 #6138, 0.27 #1974), 03k9fj (0.34 #1110, 0.25 #1478, 0.25 #2581), 082gq (0.33 #32, 0.20 #154, 0.19 #1866), 0219x_ (0.33 #28, 0.20 #150, 0.14 #882), 02m4t (0.33 #69, 0.20 #191, 0.12 #313), 04xvlr (0.25 #489, 0.25 #367, 0.21 #733) >> Best rule #5509 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 03y317; >> query: (?x6048, ?x53) <- titles(?x53, ?x6048), genre(?x273, ?x53) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #858 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 02d003; 0g9z_32; 0270k40; *> query: (?x6048, 05p553) <- film(?x2353, ?x6048), ?x2353 = 02qgyv, film_crew_role(?x6048, ?x137), film_release_distribution_medium(?x6048, ?x81) *> conf = 0.50 ranks of expected_values: 2, 13 EVAL 01cmp9 genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 75.000 75.000 0.612 http://example.org/film/film/genre EVAL 01cmp9 genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 75.000 0.612 http://example.org/film/film/genre #19441-0466p0j PRED entity: 0466p0j PRED relation: ceremony! PRED expected values: 02g8mp 02gx2k 01cw51 02sp_v 02gdjb 024fxq 02gm9n => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 195): 02sp_v (0.80 #2062, 0.77 #1883, 0.75 #1704), 03nl5k (0.80 #2135, 0.77 #1956, 0.67 #1777), 01cw51 (0.78 #975, 0.73 #2049, 0.71 #796), 02g8mp (0.75 #1657, 0.73 #2015, 0.71 #583), 02gdjb (0.75 #1729, 0.73 #2087, 0.71 #834), 02gx2k (0.73 #2025, 0.73 #1309, 0.71 #593), 024fxq (0.73 #2142, 0.73 #1426, 0.71 #889), 02gm9n (0.67 #2143, 0.67 #1785, 0.64 #1427), 02flpq (0.67 #2114, 0.64 #1398, 0.62 #1935), 0gqy2 (0.66 #9942, 0.65 #10480, 0.46 #11375) >> Best rule #2062 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 01mhwk; >> query: (?x5656, 02sp_v) <- ceremony(?x2703, ?x5656), award_winner(?x5656, ?x4112), instance_of_recurring_event(?x5656, ?x2421), ?x2703 = 0257w4, influenced_by(?x4112, ?x4554) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 5, 6, 7, 8 EVAL 0466p0j ceremony! 02gm9n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0466p0j ceremony! 024fxq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0466p0j ceremony! 02gdjb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0466p0j ceremony! 02sp_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0466p0j ceremony! 01cw51 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0466p0j ceremony! 02gx2k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0466p0j ceremony! 02g8mp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony #19440-0ck91 PRED entity: 0ck91 PRED relation: gender PRED expected values: 02zsn => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.92 #12, 0.82 #2, 0.82 #8), 05zppz (0.81 #89, 0.81 #163, 0.80 #37) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 06r3p2; >> query: (?x11601, 02zsn) <- award(?x11601, ?x375), profession(?x11601, ?x319), ?x375 = 0bfvw2 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ck91 gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.923 http://example.org/people/person/gender #19439-019bk0 PRED entity: 019bk0 PRED relation: ceremony! PRED expected values: 02grdc 0c4z8 01c427 01c92g 01ck6h 02581c 02sp_v 02flpc 02h3d1 024vjd 03tk6z 023vrq 03nl5k => 44 concepts (39 used for prediction) PRED predicted values (max 10 best out of 201): 024vjd (0.88 #1773, 0.86 #1587, 0.76 #6684), 02sp_v (0.82 #1199, 0.81 #1755, 0.79 #1569), 02h3d1 (0.82 #1211, 0.81 #1767, 0.79 #1581), 02grdc (0.81 #1678, 0.79 #1492, 0.76 #6684), 03tk6z (0.79 #1595, 0.76 #6684, 0.75 #1781), 01c92g (0.76 #6684, 0.75 #1721, 0.75 #981), 02flpc (0.76 #6684, 0.75 #1763, 0.75 #1023), 03nl5k (0.76 #6684, 0.75 #1835, 0.73 #1279), 023vrq (0.76 #6684, 0.75 #1828, 0.71 #1642), 01c427 (0.76 #6684, 0.75 #1715, 0.71 #1529) >> Best rule #1773 for best value: >> intensional similarity = 17 >> extensional distance = 14 >> proper extension: 02rjjll; 01xqqp; >> query: (?x1362, 024vjd) <- award_winner(?x1362, ?x10565), award_winner(?x1362, ?x7553), award_winner(?x1362, ?x2926), ceremony(?x12813, ?x1362), ceremony(?x11010, ?x1362), ceremony(?x6623, ?x1362), award(?x2926, ?x2456), category(?x7553, ?x134), ceremony(?x12813, ?x9431), category_of(?x12813, ?x2421), ?x6623 = 0248jb, ?x9431 = 02cg41, artists(?x671, ?x7553), film(?x2926, ?x6507), ?x11010 = 02w7fs, award(?x10565, ?x2634), ?x2634 = 02f72n >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14 EVAL 019bk0 ceremony! 03nl5k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 023vrq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 03tk6z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 024vjd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 02h3d1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 02flpc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 02sp_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 02581c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 01ck6h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 01c92g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 01c427 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 0c4z8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 019bk0 ceremony! 02grdc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony #19438-09cn0c PRED entity: 09cn0c PRED relation: award! PRED expected values: 0hmr4 0gxfz 0dzz6g 0_b9f 09p3_s 02_06s 03b1l8 => 44 concepts (11 used for prediction) PRED predicted values (max 10 best out of 1263): 04jwly (0.50 #270, 0.25 #1275, 0.06 #2279), 04lhc4 (0.38 #691, 0.31 #1696, 0.09 #2700), 0ds3t5x (0.38 #1035, 0.25 #30, 0.09 #2039), 03b1l8 (0.38 #787, 0.19 #1792, 0.05 #1005), 03b1sb (0.38 #850, 0.19 #1855, 0.05 #1005), 0f4vx (0.38 #271, 0.12 #1276, 0.10 #2280), 0_b9f (0.25 #469, 0.19 #1474, 0.08 #2478), 04q827 (0.25 #949, 0.19 #1954, 0.08 #2958), 09k56b7 (0.25 #186, 0.19 #1191, 0.05 #2195), 011yhm (0.25 #664, 0.12 #1669, 0.09 #2673) >> Best rule #270 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 02x4x18; >> query: (?x9130, 04jwly) <- award_winner(?x9130, ?x2493), award_winner(?x9130, ?x1244), award_nominee(?x2493, ?x374), film(?x2493, ?x2111), ?x1244 = 0h1nt >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #787 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 02x4x18; *> query: (?x9130, 03b1l8) <- award_winner(?x9130, ?x2493), award_winner(?x9130, ?x1244), award_nominee(?x2493, ?x374), film(?x2493, ?x2111), ?x1244 = 0h1nt *> conf = 0.38 ranks of expected_values: 4, 7, 31, 44, 197, 220, 532 EVAL 09cn0c award! 03b1l8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 44.000 11.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 09cn0c award! 02_06s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 44.000 11.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 09cn0c award! 09p3_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 44.000 11.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 09cn0c award! 0_b9f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 44.000 11.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 09cn0c award! 0dzz6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 44.000 11.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 09cn0c award! 0gxfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 11.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 09cn0c award! 0hmr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 44.000 11.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19437-05kj_ PRED entity: 05kj_ PRED relation: partially_contains PRED expected values: 0k3nk => 187 concepts (123 used for prediction) PRED predicted values (max 10 best out of 38): 0k3nk (0.38 #54, 0.23 #135, 0.11 #413), 0lm0n (0.33 #1312, 0.33 #949, 0.31 #869), 04yf_ (0.25 #370, 0.19 #653, 0.18 #813), 06c6l (0.25 #71, 0.05 #230, 0.03 #1436), 02cgp8 (0.15 #146, 0.12 #867, 0.11 #1430), 05lx3 (0.14 #387, 0.12 #951, 0.11 #1072), 04ykz (0.13 #716, 0.13 #676, 0.12 #836), 0db94 (0.12 #77, 0.08 #158, 0.06 #197), 026zt (0.10 #223, 0.07 #1793, 0.07 #2727), 09glw (0.09 #100, 0.05 #3581, 0.04 #3989) >> Best rule #54 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 05c74; >> query: (?x726, 0k3nk) <- adjoins(?x726, ?x1879), contains(?x94, ?x726), ?x1879 = 05rgl >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05kj_ partially_contains 0k3nk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 123.000 0.375 http://example.org/location/location/partially_contains #19436-0828jw PRED entity: 0828jw PRED relation: nominated_for! PRED expected values: 0bdw6t 04ldyx1 => 81 concepts (78 used for prediction) PRED predicted values (max 10 best out of 222): 0m7yy (0.69 #4451, 0.67 #11004, 0.66 #12646), 0gqy2 (0.52 #10423, 0.25 #10891, 0.24 #11126), 0gq9h (0.39 #10831, 0.37 #11066, 0.36 #11300), 0gs9p (0.35 #10833, 0.32 #11068, 0.31 #11536), 019f4v (0.34 #10822, 0.32 #11057, 0.31 #11291), 0k611 (0.29 #10841, 0.28 #11076, 0.27 #11310), 027gs1_ (0.28 #2762, 0.27 #2996, 0.26 #3465), 040njc (0.28 #10776, 0.26 #11011, 0.26 #11245), 0gq_v (0.27 #10788, 0.27 #11257, 0.26 #11023), 02pz3j5 (0.26 #3046, 0.19 #16863, 0.19 #16394) >> Best rule #4451 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 021gzd; >> query: (?x5810, ?x783) <- actor(?x5810, ?x56), nominated_for(?x2650, ?x5810), award(?x5810, ?x783) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2660 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 02gl58; *> query: (?x5810, 0bdw6t) <- nominated_for(?x435, ?x5810), award_winner(?x5810, ?x56), program(?x2650, ?x5810) *> conf = 0.23 ranks of expected_values: 14, 65 EVAL 0828jw nominated_for! 04ldyx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 81.000 78.000 0.690 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0828jw nominated_for! 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 81.000 78.000 0.690 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19435-04y9dk PRED entity: 04y9dk PRED relation: award_nominee! PRED expected values: 01y64_ => 83 concepts (39 used for prediction) PRED predicted values (max 10 best out of 971): 0dvld (0.25 #1390, 0.16 #88482, 0.16 #90811), 02xs5v (0.25 #1782, 0.10 #51225, 0.06 #4110), 0h0wc (0.25 #551, 0.06 #2879, 0.03 #30817), 015t7v (0.25 #1186, 0.06 #3514, 0.03 #31452), 051wwp (0.25 #1162, 0.06 #3490, 0.03 #31428), 0dvmd (0.25 #694, 0.04 #30960, 0.02 #33290), 0blbxk (0.25 #262, 0.02 #30528, 0.02 #32596), 02s2ft (0.25 #7, 0.02 #30273, 0.02 #32596), 02kxwk (0.25 #1018, 0.02 #32596, 0.02 #31284), 01p7yb (0.25 #64, 0.02 #32596, 0.01 #30330) >> Best rule #1390 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 02f2dn; 01vxxb; >> query: (?x1975, 0dvld) <- film(?x1975, ?x5759), ?x5759 = 03prz_, gender(?x1975, ?x231) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #81497 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1602 *> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 04bdxl; 02s2ft; 05vsxz; 06qgvf; 0grwj; ... *> query: (?x1975, ?x2646) <- film(?x1975, ?x5759), nominated_for(?x2646, ?x5759), award(?x1975, ?x458) *> conf = 0.16 ranks of expected_values: 26 EVAL 04y9dk award_nominee! 01y64_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 83.000 39.000 0.250 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19434-03d34x8 PRED entity: 03d34x8 PRED relation: honored_for! PRED expected values: 0fqpc7d => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 98): 0gvstc3 (0.32 #728, 0.26 #1196, 0.24 #611), 02q690_ (0.28 #754, 0.25 #1807, 0.25 #1456), 0lp_cd3 (0.21 #133, 0.18 #1186, 0.17 #16), 0g5b0q5 (0.17 #13, 0.09 #247, 0.07 #364), 0fqpc7d (0.17 #28, 0.07 #379, 0.06 #613), 0hr3c8y (0.17 #6, 0.07 #942, 0.07 #1059), 02wzl1d (0.17 #7, 0.06 #1177, 0.05 #1411), 0gx_st (0.16 #731, 0.15 #1433, 0.15 #380), 0275n3y (0.15 #412, 0.12 #646, 0.12 #763), 0bx6zs (0.15 #458, 0.12 #692, 0.10 #809) >> Best rule #728 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 07s8z_l; >> query: (?x2009, 0gvstc3) <- genre(?x2009, ?x53), honored_for(?x1265, ?x2009), program_creator(?x2009, ?x3727), titles(?x53, ?x54) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #28 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 01kt_j; *> query: (?x2009, 0fqpc7d) <- genre(?x2009, ?x604), honored_for(?x1265, ?x2009), producer_type(?x2009, ?x632), ?x604 = 0lsxr *> conf = 0.17 ranks of expected_values: 5 EVAL 03d34x8 honored_for! 0fqpc7d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 109.000 109.000 0.320 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #19433-062z7 PRED entity: 062z7 PRED relation: major_field_of_study! PRED expected values: 06pwq 065y4w7 0dy04 0f102 086xm 01q460 0778p 0gjv_ 02_cx_ 01y20v 01lhdt 0dzst 02kbtf 01p896 03hpkp 09vzz => 124 concepts (83 used for prediction) PRED predicted values (max 10 best out of 496): 01j_9c (0.70 #11474, 0.60 #6220, 0.60 #5741), 06pwq (0.69 #28194, 0.69 #16729, 0.64 #12910), 01mpwj (0.67 #8218, 0.67 #7740, 0.62 #16812), 01w3v (0.67 #7660, 0.66 #28197, 0.60 #11479), 07wlf (0.67 #8190, 0.60 #7233, 0.40 #11531), 052nd (0.67 #7654, 0.40 #7175, 0.40 #6697), 01bm_ (0.64 #13586, 0.60 #11674, 0.60 #7376), 07tg4 (0.60 #11539, 0.60 #6285, 0.60 #4850), 07w0v (0.60 #11484, 0.60 #7186, 0.60 #5273), 0dzst (0.60 #11758, 0.60 #7460, 0.60 #6504) >> Best rule #11474 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 06ms6; 0g26h; >> query: (?x2606, 01j_9c) <- major_field_of_study(?x2606, ?x373), major_field_of_study(?x1884, ?x2606), major_field_of_study(?x892, ?x2606), major_field_of_study(?x734, ?x2606), ?x1884 = 0bx8pn, institution(?x620, ?x892), ?x734 = 04zx3q1 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #28194 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 27 *> proper extension: 036hv; 0h5k; 0pf2; 0dc_v; 036nz; 04g7x; 01r4k; *> query: (?x2606, 06pwq) <- major_field_of_study(?x2606, ?x373), major_field_of_study(?x4750, ?x2606), major_field_of_study(?x1884, ?x2606), major_field_of_study(?x734, ?x2606), school(?x580, ?x1884), organization(?x4750, ?x5487), ?x734 = 04zx3q1, school_type(?x4750, ?x1507) *> conf = 0.69 ranks of expected_values: 2, 10, 25, 26, 47, 52, 108, 123, 129, 149, 178, 198, 262, 276, 343, 438 EVAL 062z7 major_field_of_study! 09vzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 03hpkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 01p896 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 02kbtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 0dzst CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 01lhdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 01y20v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 02_cx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 0gjv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 0778p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 01q460 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 086xm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 0f102 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 0dy04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 062z7 major_field_of_study! 06pwq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 83.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19432-02cpp PRED entity: 02cpp PRED relation: category PRED expected values: 08mbj5d => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #15, 0.86 #60, 0.85 #56) >> Best rule #15 for best value: >> intensional similarity = 7 >> extensional distance = 21 >> proper extension: 0ggl02; >> query: (?x5916, 08mbj5d) <- award(?x5916, ?x8994), award(?x5916, ?x1565), ?x1565 = 01c4_6, award(?x11700, ?x8994), award(?x8060, ?x8994), ?x8060 = 06mj4, group(?x227, ?x11700) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cpp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.913 http://example.org/common/topic/webpage./common/webpage/category #19431-040b5k PRED entity: 040b5k PRED relation: country PRED expected values: 03h64 => 65 concepts (38 used for prediction) PRED predicted values (max 10 best out of 116): 09c7w0 (0.81 #682, 0.81 #1713, 0.80 #2013), 07ssc (0.51 #389, 0.31 #17, 0.25 #1185), 0653m (0.31 #433, 0.06 #1410, 0.06 #1349), 0f8l9c (0.21 #392, 0.14 #1188, 0.13 #140), 0345h (0.14 #28, 0.13 #462, 0.12 #1196), 03h64 (0.08 #2255, 0.08 #2254, 0.07 #418), 03rjj (0.08 #2255, 0.08 #2254, 0.07 #379), 0d060g (0.08 #2255, 0.08 #2254, 0.07 #9), 03_3d (0.08 #2255, 0.08 #2254, 0.07 #8), 0chghy (0.08 #2255, 0.08 #2254, 0.05 #447) >> Best rule #682 for best value: >> intensional similarity = 3 >> extensional distance = 306 >> proper extension: 03h_yy; 0963mq; 014zwb; 05_5rjx; 01q2nx; 02tktw; 04xx9s; >> query: (?x2889, 09c7w0) <- film_crew_role(?x2889, ?x468), genre(?x2889, ?x53), crewmember(?x2889, ?x3574) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #2255 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1145 *> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; 018js4; ... *> query: (?x2889, ?x1003) <- film_release_region(?x2889, ?x1003), film(?x382, ?x2889), genre(?x2889, ?x53) *> conf = 0.08 ranks of expected_values: 6 EVAL 040b5k country 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 65.000 38.000 0.815 http://example.org/film/film/country #19430-08k881 PRED entity: 08k881 PRED relation: student! PRED expected values: 0c5x_ => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 32): 0bwfn (0.07 #275, 0.05 #6072, 0.05 #4491), 04b_46 (0.05 #227, 0.02 #2862, 0.02 #4443), 017z88 (0.03 #1663, 0.03 #1136, 0.03 #82), 015nl4 (0.03 #1648, 0.03 #11134, 0.03 #15351), 065y4w7 (0.03 #4757, 0.03 #5811, 0.03 #17406), 017j69 (0.03 #145, 0.02 #672, 0.02 #1199), 09f2j (0.03 #1213, 0.03 #1740, 0.03 #4902), 03ksy (0.03 #4322, 0.02 #33312, 0.02 #30677), 08815 (0.02 #4745, 0.02 #5272, 0.02 #3691), 01w5m (0.02 #17497, 0.02 #33311, 0.02 #30676) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 04t2l2; 0cnl80; 083chw; 03x3qv; 01dw4q; 01rr9f; 058ncz; 02r_d4; 05b__vr; 064nh4k; ... >> query: (?x5770, 0bwfn) <- award(?x5770, ?x678), award_nominee(?x5770, ?x2307), ?x678 = 0cqhk0 >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08k881 student! 0c5x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 82.000 0.068 http://example.org/education/educational_institution/students_graduates./education/education/student #19429-06c1y PRED entity: 06c1y PRED relation: contains PRED expected values: 02pbzv => 194 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2796): 06fz_ (0.29 #12808, 0.17 #21636, 0.14 #3974), 018ym2 (0.20 #2882, 0.10 #8769, 0.08 #11715), 0lxg6 (0.20 #2907, 0.10 #8794, 0.08 #11740), 0ftjx (0.20 #1744, 0.10 #7631, 0.08 #10577), 01g_k3 (0.20 #2459, 0.10 #8346, 0.08 #11292), 0fhzy (0.20 #219, 0.10 #6106, 0.08 #9052), 0d34_ (0.14 #5500, 0.08 #29048, 0.08 #11389), 09f8q (0.14 #5237, 0.08 #28785, 0.08 #11126), 05bkf (0.14 #5164, 0.08 #28712, 0.08 #11053), 0d58_ (0.14 #4270, 0.08 #27818, 0.08 #10159) >> Best rule #12808 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 09b69; 03v9w; >> query: (?x1536, 06fz_) <- contains(?x455, ?x1536), ?x455 = 02j9z, contains(?x1536, ?x4962), partially_contains(?x1536, ?x10517) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #15973 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 07sb1; *> query: (?x1536, 02pbzv) <- contains(?x1536, ?x4962), time_zones(?x1536, ?x10735), ?x10735 = 03plfd *> conf = 0.06 ranks of expected_values: 363 EVAL 06c1y contains 02pbzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 194.000 91.000 0.286 http://example.org/location/location/contains #19428-03h610 PRED entity: 03h610 PRED relation: award PRED expected values: 02qvyrt 047sgz4 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 274): 0l8z1 (0.57 #468, 0.51 #64, 0.44 #872), 0gqz2 (0.49 #485, 0.41 #81, 0.34 #889), 09sb52 (0.43 #4486, 0.30 #8124, 0.30 #20648), 02qvyrt (0.40 #531, 0.39 #935, 0.35 #127), 054ks3 (0.38 #546, 0.27 #142, 0.25 #950), 025m8y (0.35 #99, 0.32 #503, 0.27 #907), 01by1l (0.32 #7387, 0.29 #4153, 0.26 #4963), 01bgqh (0.25 #7318, 0.23 #6106, 0.21 #4084), 03qbh5 (0.23 #6268, 0.15 #5056, 0.15 #4246), 02gdjb (0.22 #220, 0.21 #624, 0.14 #1432) >> Best rule #468 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 09hnb; 0cj2w; >> query: (?x4644, 0l8z1) <- award(?x4644, ?x1443), ?x1443 = 054krc, place_of_birth(?x4644, ?x739) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #531 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 09hnb; 0cj2w; *> query: (?x4644, 02qvyrt) <- award(?x4644, ?x1443), ?x1443 = 054krc, place_of_birth(?x4644, ?x739) *> conf = 0.40 ranks of expected_values: 4, 174 EVAL 03h610 award 047sgz4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 106.000 106.000 0.566 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03h610 award 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 106.000 106.000 0.566 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19427-06n3y PRED entity: 06n3y PRED relation: contains PRED expected values: 0p2n => 151 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2886): 06n3y (0.57 #134829, 0.54 #143623, 0.50 #170010), 04pnx (0.57 #134829, 0.54 #143623, 0.50 #170010), 07c5l (0.57 #134829, 0.54 #143623, 0.50 #170010), 0j3b (0.57 #134829, 0.54 #143623, 0.50 #170010), 02j71 (0.54 #108450, 0.49 #143624, 0.34 #219850), 09c7w0 (0.33 #2932, 0.25 #46894, 0.22 #49825), 056_y (0.33 #595, 0.25 #6455, 0.20 #9386), 01zv_ (0.33 #2684, 0.25 #8544, 0.20 #11475), 01f62 (0.33 #184, 0.25 #6044, 0.20 #8975), 01hlq3 (0.33 #2478, 0.25 #8338, 0.20 #11269) >> Best rule #134829 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 06t2t; 0697s; >> query: (?x12315, ?x7273) <- contains(?x12315, ?x1203), contains(?x7273, ?x1203), geographic_distribution(?x13662, ?x12315) >> conf = 0.57 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 06n3y contains 0p2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 151.000 83.000 0.566 http://example.org/location/location/contains #19426-0v9qg PRED entity: 0v9qg PRED relation: place_of_birth! PRED expected values: 034qt_ => 127 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1443): 03d_w3h (0.33 #67846, 0.33 #67847, 0.33 #18265), 034qt_ (0.33 #67846, 0.33 #67847, 0.33 #18265), 0chw_ (0.04 #193104, 0.02 #1875, 0.02 #7094), 03kbb8 (0.04 #193104, 0.02 #1467, 0.02 #6686), 0b_4z (0.04 #193104, 0.02 #2460, 0.02 #5070), 028r4y (0.04 #193104, 0.02 #1119, 0.02 #3729), 08yx9q (0.04 #193104, 0.02 #883, 0.02 #3493), 02kxwk (0.04 #193104, 0.02 #873, 0.02 #3483), 0klh7 (0.04 #193104, 0.02 #551, 0.02 #3161), 01yf85 (0.04 #193104, 0.02 #1813) >> Best rule #67846 for best value: >> intensional similarity = 4 >> extensional distance = 191 >> proper extension: 03v_5; 0d234; 0dq16; 09b9m; 0xpp5; 0rsjf; 0f67f; 0b2ds; 01m94f; 01vsl; ... >> query: (?x4025, ?x3934) <- location(?x3934, ?x4025), location(?x940, ?x4025), state(?x4025, ?x1906), profession(?x940, ?x1032) >> conf = 0.33 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0v9qg place_of_birth! 034qt_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 87.000 0.332 http://example.org/people/person/place_of_birth #19425-02n9jv PRED entity: 02n9jv PRED relation: specialization_of PRED expected values: 012t_z => 16 concepts (16 used for prediction) PRED predicted values (max 10 best out of 3): 04_tv (0.17 #41, 0.14 #73, 0.09 #338), 01c979 (0.14 #88, 0.06 #284, 0.06 #453), 0n1h (0.09 #164, 0.09 #367, 0.08 #467) >> Best rule #41 for best value: >> intensional similarity = 15 >> extensional distance = 4 >> proper extension: 02ynfr; >> query: (?x13982, 04_tv) <- film_crew_role(?x10799, ?x13982), film_crew_role(?x1048, ?x13982), ?x10799 = 07vn_9, language(?x1048, ?x254), film(?x2451, ?x1048), titles(?x3506, ?x1048), country(?x1048, ?x94), genre(?x1048, ?x3515), genre(?x1048, ?x162), award_winner(?x1048, ?x323), nominated_for(?x1162, ?x1048), ?x3515 = 082gq, ?x162 = 04xvlr, ?x254 = 02h40lc, executive_produced_by(?x1048, ?x3223) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02n9jv specialization_of 012t_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 16.000 16.000 0.167 http://example.org/people/profession/specialization_of #19424-0jsf6 PRED entity: 0jsf6 PRED relation: nominated_for! PRED expected values: 0gq_v 019f4v => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 200): 0gq_v (0.76 #1609, 0.74 #3198, 0.63 #5014), 019f4v (0.65 #2548, 0.64 #505, 0.63 #1867), 0k611 (0.48 #3245, 0.43 #1883, 0.41 #1656), 040njc (0.46 #1823, 0.45 #3185, 0.43 #2504), 03hkv_r (0.42 #1150, 0.14 #7280, 0.14 #469), 0gr0m (0.41 #1873, 0.41 #2554, 0.40 #3235), 02pqp12 (0.38 #1191, 0.32 #510, 0.30 #964), 02n9nmz (0.38 #1190, 0.15 #3460, 0.14 #7320), 094qd5 (0.36 #488, 0.20 #261, 0.19 #3666), 0p9sw (0.35 #1837, 0.35 #2518, 0.34 #3199) >> Best rule #1609 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 0k4kk; 0k4f3; 04954r; 0m_q0; 034xyf; 0m_h6; 04wddl; 0p9rz; 0ccck7; >> query: (?x6213, 0gq_v) <- nominated_for(?x6096, ?x6213), honored_for(?x2988, ?x6213), film_production_design_by(?x240, ?x6096), award_nominee(?x6096, ?x7438) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0jsf6 nominated_for! 019f4v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.761 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0jsf6 nominated_for! 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.761 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19423-044g_k PRED entity: 044g_k PRED relation: film_crew_role PRED expected values: 02rh1dz => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 29): 0d2b38 (0.30 #395, 0.29 #84, 0.22 #239), 089g0h (0.25 #47, 0.24 #326, 0.17 #668), 089fss (0.25 #36, 0.22 #222, 0.17 #471), 02rh1dz (0.24 #318, 0.23 #1415, 0.22 #1667), 02ynfr (0.22 #229, 0.19 #3292, 0.18 #322), 015h31 (0.22 #473, 0.20 #815, 0.16 #2131), 01xy5l_ (0.22 #476, 0.15 #383, 0.13 #818), 094hwz (0.18 #321, 0.15 #353, 0.12 #104), 0215hd (0.18 #325, 0.15 #667, 0.14 #77), 04pyp5 (0.18 #323, 0.11 #1420, 0.11 #230) >> Best rule #395 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 076xkdz; >> query: (?x1385, 0d2b38) <- country(?x1385, ?x94), film(?x3558, ?x1385), production_companies(?x1385, ?x382), award(?x1385, ?x1429), genre(?x1385, ?x225) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #318 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 0h63gl9; *> query: (?x1385, 02rh1dz) <- country(?x1385, ?x94), honored_for(?x1385, ?x1072), film_crew_role(?x1385, ?x137), edited_by(?x1385, ?x3042), genre(?x1385, ?x225) *> conf = 0.24 ranks of expected_values: 4 EVAL 044g_k film_crew_role 02rh1dz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 145.000 0.300 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19422-02bqmq PRED entity: 02bqmq PRED relation: legislative_sessions! PRED expected values: 0b3wk => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 6): 0b3wk (0.91 #195, 0.91 #183, 0.88 #104), 0x2sv (0.10 #202, 0.07 #209), 0h6dy (0.08 #203, 0.05 #210), 0l_j_ (0.06 #204, 0.04 #211), 030p4s (0.02 #206, 0.02 #213), 0162kb (0.02 #205) >> Best rule #195 for best value: >> intensional similarity = 29 >> extensional distance = 44 >> proper extension: 05l2z4; >> query: (?x3463, 0b3wk) <- legislative_sessions(?x1829, ?x3463), district_represented(?x3463, ?x6521), district_represented(?x3463, ?x1767), district_represented(?x3463, ?x1227), legislative_sessions(?x4665, ?x3463), contains(?x6521, ?x859), jurisdiction_of_office(?x900, ?x6521), currency(?x6521, ?x170), legislative_sessions(?x652, ?x1829), religion(?x1767, ?x10681), religion(?x1767, ?x7422), contains(?x94, ?x6521), ?x7422 = 092bf5, district_represented(?x9702, ?x1767), district_represented(?x6712, ?x1767), district_represented(?x5005, ?x1767), ?x10681 = 01s5nb, contains(?x1767, ?x1396), ?x9702 = 01gssz, adjoins(?x6521, ?x1782), legislative_sessions(?x6742, ?x3463), location(?x1376, ?x6521), adjoins(?x1767, ?x108), adjoins(?x151, ?x1227), state(?x581, ?x1227), place_of_birth(?x2285, ?x1227), adjoins(?x7518, ?x1767), ?x5005 = 01gstn, ?x6712 = 01gst9 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bqmq legislative_sessions! 0b3wk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.913 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #19421-05l64 PRED entity: 05l64 PRED relation: film_release_region! PRED expected values: 0jqp3 => 185 concepts (79 used for prediction) PRED predicted values (max 10 best out of 535): 028_yv (0.25 #11982, 0.22 #13311, 0.20 #15969), 0661m4p (0.25 #12248, 0.22 #32188, 0.20 #16235), 0kv238 (0.25 #12289, 0.20 #16276, 0.19 #30900), 01f8gz (0.25 #12156, 0.20 #1523, 0.19 #30767), 067ghz (0.23 #63250, 0.23 #72563, 0.20 #77885), 0fpgp26 (0.23 #63629, 0.23 #72942, 0.20 #78264), 062zm5h (0.23 #72453, 0.21 #63140, 0.20 #77775), 0gys2jp (0.22 #14592, 0.20 #34532, 0.20 #23895), 0jymd (0.22 #13795, 0.20 #15124, 0.15 #19111), 0k7tq (0.22 #14199, 0.20 #15528, 0.15 #19515) >> Best rule #11982 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0345h; >> query: (?x11197, 028_yv) <- time_zones(?x11197, ?x2864), ?x2864 = 02llzg, vacationer(?x11197, ?x1897), contains(?x2513, ?x11197), origin(?x11749, ?x11197), participant(?x1897, ?x1503) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #29367 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 0f8l9c; *> query: (?x11197, 0jqp3) <- time_zones(?x11197, ?x2864), ?x2864 = 02llzg, vacationer(?x11197, ?x1897), film(?x1897, ?x857), participant(?x1503, ?x1897), participant(?x1897, ?x5788) *> conf = 0.12 ranks of expected_values: 374 EVAL 05l64 film_release_region! 0jqp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 185.000 79.000 0.250 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19420-0vp5f PRED entity: 0vp5f PRED relation: contains! PRED expected values: 04rrx => 96 concepts (63 used for prediction) PRED predicted values (max 10 best out of 104): 04rrx (0.32 #54536, 0.06 #126, 0.06 #23370), 07c5l (0.32 #54536, 0.02 #27214, 0.02 #29896), 01n7q (0.20 #5441, 0.20 #2759, 0.18 #3653), 07ssc (0.16 #19699, 0.15 #25957, 0.15 #20593), 04_1l0v (0.13 #12072, 0.12 #8496, 0.10 #19224), 059rby (0.13 #23263, 0.11 #45613, 0.07 #52766), 02jx1 (0.12 #46574, 0.12 #19754, 0.11 #13496), 05k7sb (0.11 #9966, 0.08 #45726, 0.07 #23376), 05fjf (0.09 #23617, 0.08 #373, 0.08 #6631), 05kkh (0.08 #23252, 0.07 #45602, 0.06 #9842) >> Best rule #54536 for best value: >> intensional similarity = 3 >> extensional distance = 722 >> proper extension: 027rn; 0b90_r; 0chghy; 018jk2; 015fr; 07cfx; 0ctw_b; 07ylj; 0hkq4; 0162v; ... >> query: (?x12873, ?x1906) <- location(?x1660, ?x12873), contains(?x12411, ?x12873), contains(?x1906, ?x12411) >> conf = 0.32 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0vp5f contains! 04rrx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 63.000 0.321 http://example.org/location/location/contains #19419-047yc PRED entity: 047yc PRED relation: geographic_distribution! PRED expected values: 04mvp8 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 36): 0d29z (0.44 #137, 0.41 #176, 0.39 #293), 04mvp8 (0.26 #72, 0.26 #111, 0.24 #33), 0g6ff (0.14 #517, 0.08 #399, 0.08 #834), 0g48m4 (0.14 #509, 0.05 #1567, 0.04 #1645), 01rv7x (0.12 #138, 0.11 #372, 0.08 #255), 01xhh5 (0.12 #331, 0.09 #487, 0.09 #370), 013b6_ (0.07 #338, 0.07 #221, 0.06 #26), 012f86 (0.07 #187, 0.05 #343, 0.04 #109), 06mvq (0.05 #290, 0.05 #329, 0.04 #368), 0c41n (0.05 #547, 0.01 #1917, 0.01 #1722) >> Best rule #137 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 0154j; >> query: (?x1174, 0d29z) <- film_release_region(?x4446, ?x1174), adjustment_currency(?x1174, ?x170), ?x4446 = 0db94w >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #72 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0h3y; 07fj_; 07dzf; 027jk; 04vjh; *> query: (?x1174, 04mvp8) <- administrative_parent(?x1174, ?x551), countries_spoken_in(?x5359, ?x1174), ?x5359 = 0jzc *> conf = 0.26 ranks of expected_values: 2 EVAL 047yc geographic_distribution! 04mvp8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.440 http://example.org/people/ethnicity/geographic_distribution #19418-030znt PRED entity: 030znt PRED relation: award_nominee PRED expected values: 01bh6y => 110 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1156): 014v6f (0.81 #71987, 0.80 #58053, 0.76 #71988), 04vmqg (0.81 #71987, 0.80 #58053, 0.06 #2071), 06hgym (0.81 #71987, 0.80 #58053, 0.06 #1835), 048hf (0.81 #71987, 0.80 #58053, 0.03 #1728), 01rs5p (0.81 #71987, 0.80 #58053, 0.03 #2160), 01bh6y (0.81 #71987, 0.80 #58053, 0.02 #85919), 03lt8g (0.76 #71988, 0.76 #58054, 0.76 #32509), 05dxl5 (0.76 #71988, 0.76 #58054, 0.76 #69664), 03zqc1 (0.76 #32509, 0.75 #78954, 0.75 #46443), 02bkdn (0.10 #5040, 0.06 #397, 0.05 #2718) >> Best rule #71987 for best value: >> intensional similarity = 3 >> extensional distance = 1113 >> proper extension: 06vqdf; 0cbxl0; >> query: (?x1343, ?x444) <- award_nominee(?x444, ?x1343), award_winner(?x1117, ?x1343), nominated_for(?x1343, ?x293) >> conf = 0.81 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 030znt award_nominee 01bh6y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 110.000 37.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19417-01gw8b PRED entity: 01gw8b PRED relation: people! PRED expected values: 04psf => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 13): 04p3w (0.08 #143, 0.03 #275, 0.01 #4763), 0j8hd (0.05 #311, 0.03 #575, 0.02 #509), 0gk4g (0.04 #1792, 0.04 #6544, 0.04 #1000), 0dq9p (0.03 #1007, 0.02 #347, 0.02 #4769), 02knxx (0.03 #296, 0.02 #1022, 0.01 #560), 0jdk0 (0.03 #269, 0.02 #467, 0.01 #533), 01qqwn (0.03 #325, 0.01 #589), 02k6hp (0.02 #2479, 0.02 #1027, 0.02 #1819), 0qcr0 (0.02 #6535, 0.02 #2971, 0.02 #3961), 02y0js (0.01 #3830, 0.01 #992, 0.01 #4754) >> Best rule #143 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01nrq5; 04fhn_; >> query: (?x10617, 04p3w) <- actor(?x9029, ?x10617), ?x9029 = 034fl9, gender(?x10617, ?x514) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01gw8b people! 04psf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 110.000 0.077 http://example.org/people/cause_of_death/people #19416-02vx4 PRED entity: 02vx4 PRED relation: athlete PRED expected values: 083qy7 026y23w 02y9ln 0gtgp6 0g3b2z 0b7l1f 02y0dd => 88 concepts (69 used for prediction) PRED predicted values (max 10 best out of 79): 02y0dd (0.81 #798), 0b7l1f (0.81 #798), 0g3b2z (0.81 #798), 0gtgp6 (0.81 #798), 02y9ln (0.81 #798), 026y23w (0.81 #798), 083qy7 (0.81 #798), 02hg53 (0.40 #419, 0.33 #710, 0.33 #57), 054c1 (0.40 #418, 0.33 #709, 0.33 #56), 03n69x (0.40 #451, 0.29 #815, 0.29 #742) >> Best rule #798 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 039yzs; >> query: (?x471, ?x2201) <- sport(?x13233, ?x471), sport(?x8750, ?x471), sport(?x2096, ?x471), sport(?x733, ?x471), team(?x60, ?x733), teams(?x1791, ?x13233), team(?x2201, ?x8750), colors(?x2096, ?x663) >> conf = 0.81 => this is the best rule for 7 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7 EVAL 02vx4 athlete 02y0dd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 69.000 0.810 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete EVAL 02vx4 athlete 0b7l1f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 69.000 0.810 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete EVAL 02vx4 athlete 0g3b2z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 69.000 0.810 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete EVAL 02vx4 athlete 0gtgp6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 69.000 0.810 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete EVAL 02vx4 athlete 02y9ln CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 69.000 0.810 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete EVAL 02vx4 athlete 026y23w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 69.000 0.810 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete EVAL 02vx4 athlete 083qy7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 69.000 0.810 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #19415-016sp_ PRED entity: 016sp_ PRED relation: award PRED expected values: 01c9f2 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 271): 01by1l (0.50 #112, 0.36 #12976, 0.33 #11368), 01bgqh (0.32 #12906, 0.30 #11298, 0.28 #4464), 05pcn59 (0.30 #3699, 0.27 #4905, 0.27 #4101), 02581q (0.28 #1213, 0.26 #1615, 0.18 #30955), 02ddq4 (0.25 #343, 0.18 #30955, 0.18 #33772), 0248jb (0.25 #261, 0.13 #1869, 0.11 #1467), 02ddqh (0.25 #157, 0.13 #42217, 0.09 #1765), 03qbh5 (0.25 #13067, 0.24 #9449, 0.24 #3017), 02f5qb (0.24 #2565, 0.22 #2967, 0.20 #2163), 02f79n (0.24 #2752, 0.20 #3154, 0.12 #3556) >> Best rule #112 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01x15dc; 03mszl; >> query: (?x2518, 01by1l) <- award_nominee(?x4635, ?x2518), award_nominee(?x4343, ?x2518), ?x4343 = 02cx90, ?x4635 = 01l03w2 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #886 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 02jg92; 01vsy3q; 03f6fl0; 0147jt; 01797x; *> query: (?x2518, 01c9f2) <- artist(?x5634, ?x2518), location(?x2518, ?x4978), ?x4978 = 05jbn *> conf = 0.17 ranks of expected_values: 38 EVAL 016sp_ award 01c9f2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 121.000 121.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19414-04sry PRED entity: 04sry PRED relation: award_winner! PRED expected values: 027c924 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 276): 0gs9p (0.39 #31783, 0.37 #30946, 0.37 #7109), 0fbtbt (0.39 #31783, 0.37 #30946, 0.37 #7109), 0gr4k (0.39 #31783, 0.37 #30946, 0.37 #7109), 04dn09n (0.39 #31783, 0.37 #30946, 0.37 #7109), 03hkv_r (0.39 #31783, 0.37 #30946, 0.37 #7109), 0f_nbyh (0.39 #31783, 0.37 #30946, 0.37 #7109), 0gkr9q (0.39 #31783, 0.37 #30946, 0.37 #7109), 02g3ft (0.33 #81, 0.14 #499, 0.10 #3425), 0cjyzs (0.22 #3027, 0.19 #9301, 0.19 #9719), 0gq9h (0.20 #6763, 0.17 #73, 0.15 #1327) >> Best rule #31783 for best value: >> intensional similarity = 3 >> extensional distance = 1275 >> proper extension: 086k8; 017s11; 016tt2; 025jfl; 04rcr; 0g1rw; 05qd_; 04f525m; 02r3zy; 016tw3; ... >> query: (?x7310, ?x198) <- award_winner(?x1764, ?x7310), award(?x7310, ?x198), award_winner(?x372, ?x7310) >> conf = 0.39 => this is the best rule for 7 predicted values *> Best rule #3354 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 113 *> proper extension: 06cv1; 02lf0c; 0jf1b; 02kxbwx; 030pr; 01b9ck; 0136g9; 07s93v; 032v0v; 01gzm2; ... *> query: (?x7310, 027c924) <- film(?x7310, ?x1135), award_nominee(?x2444, ?x7310), produced_by(?x6900, ?x7310) *> conf = 0.16 ranks of expected_values: 12 EVAL 04sry award_winner! 027c924 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 126.000 126.000 0.387 http://example.org/award/award_category/winners./award/award_honor/award_winner #19413-0126t5 PRED entity: 0126t5 PRED relation: artists PRED expected values: 025xt8y 01vsyjy => 65 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1075): 020_4z (0.62 #10549, 0.54 #7341, 0.50 #5201), 01tw31 (0.60 #8445, 0.50 #5235, 0.38 #10583), 067mj (0.54 #6513, 0.50 #4373, 0.50 #3304), 01gx5f (0.54 #6706, 0.33 #1361, 0.29 #9914), 011z3g (0.50 #4873, 0.50 #3804, 0.50 #2736), 01wp8w7 (0.50 #4379, 0.50 #3310, 0.47 #7589), 01k47c (0.50 #5100, 0.50 #4031, 0.46 #7240), 03j0br4 (0.50 #3403, 0.50 #2335, 0.38 #4472), 02mslq (0.50 #3237, 0.50 #2169, 0.38 #4306), 0163m1 (0.50 #3553, 0.50 #2485, 0.38 #4622) >> Best rule #10549 for best value: >> intensional similarity = 7 >> extensional distance = 22 >> proper extension: 06cqb; 05w3f; 0gywn; 017510; 09jw2; >> query: (?x6107, 020_4z) <- artists(?x6107, ?x5141), artists(?x6107, ?x3657), artists(?x12114, ?x5141), parent_genre(?x1748, ?x6107), profession(?x5141, ?x131), ?x12114 = 0175yg, group(?x3657, ?x7013) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1119 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 06by7; *> query: (?x6107, 025xt8y) <- artists(?x6107, ?x8391), artists(?x6107, ?x5141), parent_genre(?x1748, ?x6107), ?x8391 = 01693z, performance_role(?x5141, ?x3716), performance_role(?x5141, ?x1495), ?x1495 = 013y1f, ?x3716 = 03gvt *> conf = 0.33 ranks of expected_values: 153, 427 EVAL 0126t5 artists 01vsyjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 28.000 0.625 http://example.org/music/genre/artists EVAL 0126t5 artists 025xt8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 65.000 28.000 0.625 http://example.org/music/genre/artists #19412-03q_w5 PRED entity: 03q_w5 PRED relation: group! PRED expected values: 018vs 02hnl => 78 concepts (60 used for prediction) PRED predicted values (max 10 best out of 88): 02hnl (0.71 #998, 0.50 #117, 0.40 #381), 018vs (0.64 #981, 0.50 #276, 0.50 #100), 05148p4 (0.64 #988, 0.40 #1341, 0.39 #1782), 05r5c (0.36 #976, 0.33 #7, 0.25 #271), 07gql (0.25 #213, 0.25 #125, 0.17 #477), 0mkg (0.25 #186, 0.25 #98, 0.17 #450), 03qjg (0.17 #1987, 0.16 #2078, 0.15 #1634), 06ncr (0.16 #1802, 0.15 #1713, 0.08 #920), 0l14qv (0.14 #974, 0.13 #1327, 0.11 #2302), 02fsn (0.13 #1371, 0.09 #1547, 0.07 #529) >> Best rule #998 for best value: >> intensional similarity = 8 >> extensional distance = 12 >> proper extension: 0dvqq; 016fmf; 018gm9; 03d9d6; 07h76; 09lwrt; 0838y; 089pg7; 017lb_; 06lxn; >> query: (?x10639, 02hnl) <- artists(?x7124, ?x10639), artists(?x2995, ?x10639), ?x2995 = 01cbwl, artist(?x2299, ?x10639), category(?x10639, ?x134), group(?x227, ?x10639), parent_genre(?x7124, ?x13938), ?x227 = 0342h >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03q_w5 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 60.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 03q_w5 group! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 60.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #19411-0gv5c PRED entity: 0gv5c PRED relation: nationality PRED expected values: 09c7w0 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 83): 09c7w0 (0.89 #5411, 0.85 #1803, 0.85 #1302), 07ssc (0.40 #10933, 0.40 #10831, 0.18 #515), 02jx1 (0.40 #10933, 0.40 #10831, 0.15 #533), 0d060g (0.40 #10933, 0.40 #10831, 0.14 #908), 0345h (0.40 #10933, 0.40 #10831, 0.10 #631), 03rt9 (0.40 #10933, 0.40 #10831, 0.05 #1915), 0f8l9c (0.40 #10933, 0.40 #10831, 0.05 #2704), 03rjj (0.40 #10933, 0.40 #10831, 0.05 #2704), 03_3d (0.40 #10933, 0.40 #10831, 0.05 #2704), 03rk0 (0.17 #2850, 0.14 #1648, 0.11 #4956) >> Best rule #5411 for best value: >> intensional similarity = 4 >> extensional distance = 603 >> proper extension: 01vw917; >> query: (?x4477, 09c7w0) <- place_of_birth(?x4477, ?x739), gender(?x4477, ?x231), featured_film_locations(?x89, ?x739), county(?x739, ?x9233) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gv5c nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.891 http://example.org/people/person/nationality #19410-0bzn6_ PRED entity: 0bzn6_ PRED relation: instance_of_recurring_event PRED expected values: 0g_w => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 36): 0g_w (0.94 #148, 0.93 #182, 0.92 #110), 0c4ys (0.21 #320, 0.17 #329, 0.17 #337), 0gcf2r (0.11 #338, 0.10 #248, 0.09 #391), 03r00m (0.01 #41), 02f79n (0.01 #41), 03qpp9 (0.01 #41), 01ckcd (0.01 #41), 02f777 (0.01 #41), 02f73b (0.01 #41), 01ck6v (0.01 #41) >> Best rule #148 for best value: >> intensional similarity = 14 >> extensional distance = 30 >> proper extension: 02ywhz; 073hd1; >> query: (?x3618, 0g_w) <- ceremony(?x3066, ?x3618), ceremony(?x2222, ?x3618), award_winner(?x3618, ?x702), ?x2222 = 0gs96, ?x3066 = 0gqy2, honored_for(?x3618, ?x2376), artists(?x302, ?x702), award(?x702, ?x2585), award(?x702, ?x1854), award(?x702, ?x567), award_winner(?x3835, ?x702), ceremony(?x567, ?x139), award_winner(?x1854, ?x84), ?x2585 = 054ks3 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bzn6_ instance_of_recurring_event 0g_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.938 http://example.org/time/event/instance_of_recurring_event #19409-01tx9m PRED entity: 01tx9m PRED relation: campuses! PRED expected values: 01tx9m => 192 concepts (123 used for prediction) PRED predicted values (max 10 best out of 271): 0221g_ (0.17 #119, 0.09 #665, 0.06 #2849), 03zw80 (0.17 #112, 0.02 #55178, 0.02 #8850), 07ccs (0.09 #754, 0.06 #2938, 0.06 #2392), 02fgdx (0.09 #638, 0.04 #4460, 0.03 #5007), 06fq2 (0.09 #838, 0.03 #30040, 0.02 #55178), 0ch280 (0.09 #1082, 0.02 #55178, 0.02 #9820), 01s7pm (0.09 #1009, 0.02 #55178, 0.02 #9747), 05zjtn4 (0.09 #550, 0.02 #55178, 0.02 #9288), 07w3r (0.07 #1688, 0.03 #4965, 0.03 #30040), 07tds (0.07 #1778, 0.03 #5601, 0.03 #30040) >> Best rule #119 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 021gk7; >> query: (?x6177, 0221g_) <- country(?x6177, ?x94), state_province_region(?x6177, ?x3634), ?x3634 = 07b_l, citytown(?x6177, ?x10364) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #30040 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 111 *> proper extension: 0frm7n; *> query: (?x6177, ?x735) <- school(?x12042, ?x6177), season(?x12042, ?x701), school(?x12042, ?x735), position(?x12042, ?x2010) *> conf = 0.03 ranks of expected_values: 74 EVAL 01tx9m campuses! 01tx9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 192.000 123.000 0.167 http://example.org/education/educational_institution/campuses #19408-0146hc PRED entity: 0146hc PRED relation: campuses! PRED expected values: 0146hc => 153 concepts (120 used for prediction) PRED predicted values (max 10 best out of 192): 01jq0j (0.19 #48066, 0.18 #45879, 0.10 #783), 0146hc (0.19 #48066, 0.18 #45879, 0.07 #6009), 019dwp (0.19 #48066, 0.18 #45879, 0.02 #2335), 012vwb (0.11 #101, 0.02 #2286, 0.01 #2832), 01vs5c (0.11 #176, 0.02 #2361, 0.01 #2907), 07w0v (0.11 #17, 0.01 #2748, 0.01 #3840), 01pl14 (0.11 #8, 0.01 #5469), 01jzyx (0.11 #170), 01j_06 (0.11 #28), 0lyjf (0.10 #695, 0.07 #6009, 0.07 #6008) >> Best rule #48066 for best value: >> intensional similarity = 3 >> extensional distance = 457 >> proper extension: 01dnnt; >> query: (?x5844, ?x4916) <- student(?x5844, ?x9232), student(?x4916, ?x9232), location(?x9232, ?x2624) >> conf = 0.19 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0146hc campuses! 0146hc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 153.000 120.000 0.191 http://example.org/education/educational_institution/campuses #19407-0362q0 PRED entity: 0362q0 PRED relation: profession PRED expected values: 01d_h8 => 101 concepts (46 used for prediction) PRED predicted values (max 10 best out of 49): 01d_h8 (0.86 #153, 0.81 #1036, 0.79 #1919), 03gjzk (0.57 #160, 0.47 #1337, 0.46 #1926), 02krf9 (0.32 #172, 0.29 #761, 0.28 #1055), 0cbd2 (0.28 #2067, 0.24 #1184, 0.19 #2361), 018gz8 (0.27 #2369, 0.15 #456, 0.14 #603), 0nbcg (0.20 #30, 0.10 #5324, 0.10 #3413), 01c72t (0.20 #22, 0.08 #2670, 0.07 #316), 01c8w0 (0.20 #8, 0.03 #4714, 0.03 #4861), 09jwl (0.15 #5311, 0.15 #3547, 0.14 #3400), 0np9r (0.13 #2373, 0.12 #166, 0.10 #5755) >> Best rule #153 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 07s93v; 032v0v; 01f7j9; 0bgrsl; 0693l; 01g1lp; 017c87; 05vtbl; 02qzjj; 040rjq; >> query: (?x5394, 01d_h8) <- executive_produced_by(?x12899, ?x5394), film(?x5394, ?x1728), profession(?x5394, ?x524), nationality(?x5394, ?x94) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0362q0 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 46.000 0.862 http://example.org/people/person/profession #19406-0ktpx PRED entity: 0ktpx PRED relation: film! PRED expected values: 0bdt8 => 78 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1134): 05_2h8 (0.54 #83358, 0.44 #22919, 0.44 #27086), 0hqcy (0.44 #22919, 0.44 #27086, 0.40 #6251), 017jv5 (0.44 #22919, 0.44 #27086, 0.40 #6251), 0gv5c (0.20 #79188, 0.19 #64601, 0.18 #60431), 01kgv4 (0.11 #1185, 0.06 #3268, 0.03 #17854), 01f873 (0.11 #1899, 0.06 #3982, 0.03 #28985), 02m501 (0.11 #1690, 0.03 #18359, 0.03 #20442), 02zyy4 (0.11 #272, 0.02 #4439, 0.02 #21107), 06lht1 (0.11 #890, 0.02 #7141, 0.02 #34228), 01r93l (0.09 #2832, 0.03 #27835, 0.02 #34087) >> Best rule #83358 for best value: >> intensional similarity = 3 >> extensional distance = 568 >> proper extension: 0275kr; >> query: (?x5818, ?x6549) <- nominated_for(?x6549, ?x5818), spouse(?x6549, ?x9777), award_winner(?x1243, ?x6549) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #3221 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 04hk0w; *> query: (?x5818, 0bdt8) <- cinematography(?x5818, ?x6549), genre(?x5818, ?x600), nominated_for(?x1850, ?x5818), ?x600 = 02n4kr *> conf = 0.06 ranks of expected_values: 32 EVAL 0ktpx film! 0bdt8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 78.000 40.000 0.543 http://example.org/film/actor/film./film/performance/film #19405-03qbnj PRED entity: 03qbnj PRED relation: award_winner PRED expected values: 0478__m 04vrxh => 44 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1841): 0dw4g (0.60 #13512, 0.57 #15966, 0.50 #18420), 02qwg (0.60 #12994, 0.50 #8084, 0.50 #5630), 0ddkf (0.60 #13772, 0.50 #6408, 0.43 #16226), 013423 (0.60 #13679, 0.50 #6315, 0.43 #16133), 05crg7 (0.50 #5259, 0.43 #15077, 0.40 #12623), 01htxr (0.50 #6269, 0.43 #16087, 0.40 #13633), 026spg (0.50 #5956, 0.43 #15774, 0.40 #13320), 0m_v0 (0.50 #5637, 0.43 #15455, 0.40 #13001), 0140t7 (0.50 #6871, 0.43 #16689, 0.40 #14235), 086qd (0.50 #5334, 0.40 #12698, 0.29 #15152) >> Best rule #13512 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0c4z8; >> query: (?x4958, 0dw4g) <- award(?x8921, ?x4958), award(?x3290, ?x4958), ?x3290 = 01vsykc, ceremony(?x4958, ?x139), ?x8921 = 016s0m >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #39255 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 157 *> proper extension: 03x3wf; 02hgm4; 0257w4; 0257yf; 024_fw; 03q_g6; 0249fn; 02flpq; 024_41; 03ncb2; ... *> query: (?x4958, ?x3290) <- award(?x3290, ?x4958), artists(?x671, ?x3290), ceremony(?x4958, ?x139), award_nominee(?x2698, ?x3290) *> conf = 0.36 ranks of expected_values: 42, 274 EVAL 03qbnj award_winner 04vrxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 44.000 27.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 03qbnj award_winner 0478__m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 44.000 27.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #19404-05nlzq PRED entity: 05nlzq PRED relation: actor PRED expected values: 0sw6y 0745k7 => 78 concepts (61 used for prediction) PRED predicted values (max 10 best out of 827): 04hxyv (0.50 #1822, 0.25 #2744, 0.06 #9204), 0sw6y (0.38 #2695, 0.25 #1773, 0.07 #6388), 09b0xs (0.37 #29534, 0.35 #13839, 0.35 #24919), 0488g9 (0.37 #29534, 0.35 #13839, 0.35 #24919), 03mstc (0.37 #29534, 0.35 #13839, 0.35 #24919), 01z_g6 (0.33 #417, 0.10 #22147, 0.09 #17532), 03rs8y (0.33 #29, 0.10 #22147, 0.09 #17532), 014zcr (0.33 #18, 0.10 #22147, 0.09 #17532), 04qz6n (0.33 #566, 0.10 #22147, 0.09 #17532), 01t6xz (0.33 #515, 0.10 #22147, 0.09 #17532) >> Best rule #1822 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 015w8_; 043qqt5; >> query: (?x9340, 04hxyv) <- actor(?x9340, ?x7001), genre(?x9340, ?x258), nominated_for(?x3263, ?x9340), ?x7001 = 029cpw >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2695 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 063zky; *> query: (?x9340, 0sw6y) <- actor(?x9340, ?x7266), country_of_origin(?x9340, ?x94), ?x7266 = 02gf_l *> conf = 0.38 ranks of expected_values: 2, 22 EVAL 05nlzq actor 0745k7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 78.000 61.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 05nlzq actor 0sw6y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 61.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #19403-01k53x PRED entity: 01k53x PRED relation: vacationer! PRED expected values: 02_286 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 38): 03gh4 (0.11 #328, 0.06 #825, 0.06 #204), 05qtj (0.09 #319, 0.06 #443, 0.06 #816), 0cv3w (0.08 #304, 0.05 #428, 0.04 #553), 0f2v0 (0.06 #310, 0.04 #434, 0.03 #1680), 0chghy (0.06 #133, 0.02 #257, 0.01 #879), 04jpl (0.05 #256, 0.02 #380, 0.02 #505), 0160w (0.04 #126, 0.03 #250, 0.02 #1122), 0261m (0.03 #349, 0.03 #473, 0.02 #846), 07fr_ (0.03 #343, 0.02 #840, 0.01 #467), 02_286 (0.03 #262, 0.02 #138, 0.02 #386) >> Best rule #328 for best value: >> intensional similarity = 3 >> extensional distance = 170 >> proper extension: 01l1b90; 01wmxfs; 01vrt_c; 01wxyx1; 01xcfy; 0993r; 02qwg; 05r5w; 03v3xp; 0c2ry; ... >> query: (?x9585, 03gh4) <- profession(?x9585, ?x1032), participant(?x65, ?x9585), participant(?x9585, ?x6008) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #262 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 170 *> proper extension: 01l1b90; 01wmxfs; 01vrt_c; 01wxyx1; 01xcfy; 0993r; 02qwg; 05r5w; 03v3xp; 0c2ry; ... *> query: (?x9585, 02_286) <- profession(?x9585, ?x1032), participant(?x65, ?x9585), participant(?x9585, ?x6008) *> conf = 0.03 ranks of expected_values: 10 EVAL 01k53x vacationer! 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 89.000 89.000 0.110 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #19402-01p4vl PRED entity: 01p4vl PRED relation: vacationer! PRED expected values: 0chghy => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 89): 03gh4 (0.21 #564, 0.20 #1048, 0.20 #80), 0b90_r (0.13 #971, 0.12 #487, 0.11 #366), 0jbs5 (0.12 #340, 0.05 #1187, 0.04 #1066), 0cv3w (0.11 #662, 0.11 #1025, 0.10 #783), 0f2v0 (0.10 #63, 0.09 #184, 0.09 #1031), 0160w (0.10 #2, 0.09 #123, 0.08 #970), 06c62 (0.10 #86, 0.09 #207, 0.07 #1054), 04jpl (0.10 #9, 0.07 #1098, 0.07 #493), 0h7h6 (0.09 #159, 0.06 #280, 0.03 #764), 0chghy (0.09 #131, 0.04 #978, 0.03 #1099) >> Best rule #564 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 0443y3; >> query: (?x7830, 03gh4) <- award_nominee(?x221, ?x7830), student(?x546, ?x7830), vacationer(?x4627, ?x7830) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #131 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 015t56; 08swgx; 01mqc_; *> query: (?x7830, 0chghy) <- award_nominee(?x5834, ?x7830), participant(?x4062, ?x7830), ?x5834 = 01z7s_ *> conf = 0.09 ranks of expected_values: 10 EVAL 01p4vl vacationer! 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 116.000 116.000 0.207 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #19401-0fbq2n PRED entity: 0fbq2n PRED relation: position_s PRED expected values: 05b3ts => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 35): 04nfpk (0.86 #583, 0.85 #637, 0.85 #526), 02g_6j (0.86 #1008, 0.85 #361, 0.82 #581), 05zm34 (0.74 #498, 0.73 #334, 0.73 #321), 05b3ts (0.72 #357, 0.71 #379, 0.69 #364), 0bgv8y (0.72 #357, 0.63 #535, 0.63 #317), 08ns5s (0.72 #357, 0.63 #535, 0.59 #875), 047g8h (0.71 #199, 0.67 #159, 0.63 #535), 0b13yt (0.68 #586, 0.67 #139, 0.65 #515), 03h42s4 (0.63 #535, 0.63 #317, 0.60 #577), 05fyy5 (0.63 #535, 0.59 #358, 0.55 #592) >> Best rule #583 for best value: >> intensional similarity = 16 >> extensional distance = 20 >> proper extension: 01jv_6; 01xvb; 07l24; 05g49; 06rpd; >> query: (?x179, 04nfpk) <- team(?x7749, ?x179), position_s(?x179, ?x1240), colors(?x179, ?x3621), position_s(?x5773, ?x1240), position_s(?x4546, ?x1240), position_s(?x4519, ?x1240), position_s(?x3658, ?x1240), position_s(?x2574, ?x1240), ?x3658 = 03b3j, ?x5773 = 06rny, position(?x387, ?x1240), ?x387 = 02896, ?x4546 = 05gg4, ?x2574 = 01y3v, position(?x706, ?x1240), ?x4519 = 084l5 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #357 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 11 *> proper extension: 01y49; *> query: (?x179, ?x7079) <- team(?x7749, ?x179), position_s(?x179, ?x3346), position_s(?x179, ?x1717), position_s(?x179, ?x1240), colors(?x179, ?x3621), ?x1240 = 023wyl, position(?x7079, ?x3346), ?x1717 = 02g_6x, position(?x11061, ?x3346), position(?x9172, ?x3346), position(?x7078, ?x3346), ?x9172 = 06rpd, position(?x729, ?x7079), ?x7078 = 0ws7, ?x11061 = 06x76 *> conf = 0.72 ranks of expected_values: 4 EVAL 0fbq2n position_s 05b3ts CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 107.000 0.864 http://example.org/sports/sports_team/roster./american_football/football_historical_roster_position/position_s #19400-01vxqyl PRED entity: 01vxqyl PRED relation: profession PRED expected values: 02hrh1q => 132 concepts (99 used for prediction) PRED predicted values (max 10 best out of 73): 02hrh1q (0.92 #754, 0.91 #6981, 0.90 #6833), 0nbcg (0.73 #179, 0.63 #2699, 0.59 #3291), 016z4k (0.56 #1039, 0.53 #891, 0.51 #2671), 0dxtg (0.50 #14255, 0.33 #12923, 0.31 #6536), 01d_h8 (0.43 #1932, 0.41 #6528, 0.40 #4749), 01c72t (0.41 #615, 0.37 #2543, 0.37 #2246), 039v1 (0.40 #3296, 0.34 #2111, 0.33 #184), 0fnpj (0.38 #652, 0.23 #1096, 0.15 #2283), 0n1h (0.35 #1195, 0.28 #899, 0.26 #3123), 02jknp (0.30 #1934, 0.24 #12917, 0.24 #14249) >> Best rule #754 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 01kwld; 016khd; 0blt6; 023v4_; >> query: (?x8782, 02hrh1q) <- profession(?x8782, ?x131), celebrity(?x8782, ?x2237), award(?x8782, ?x102), actor(?x7230, ?x8782) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vxqyl profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 99.000 0.923 http://example.org/people/person/profession #19399-01531 PRED entity: 01531 PRED relation: location! PRED expected values: 06mn7 03fqv5 => 98 concepts (77 used for prediction) PRED predicted values (max 10 best out of 2092): 0jmj (0.51 #59159, 0.51 #44368, 0.51 #4930), 06nd8c (0.51 #59159, 0.51 #44368, 0.51 #4930), 07myb2 (0.51 #59159, 0.51 #4930, 0.47 #128183), 02pb53 (0.51 #59159, 0.51 #4930, 0.47 #128183), 052h3 (0.51 #4930, 0.48 #113390, 0.46 #140508), 0bc71w (0.51 #4930, 0.46 #140508, 0.46 #182409), 0b7xl8 (0.51 #4930, 0.46 #140508, 0.46 #182409), 03fqv5 (0.51 #4930, 0.46 #140508, 0.46 #182409), 01wdqrx (0.45 #19718, 0.39 #61625, 0.28 #61624), 01twdk (0.25 #936, 0.12 #76418, 0.04 #13259) >> Best rule #59159 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 0dhdp; 0mnzd; 0r2l7; 0f04c; 0r5wt; 0r04p; 0ptj2; 0c5_3; 04cjn; 0bxbb; ... >> query: (?x3014, ?x8273) <- place_of_birth(?x8273, ?x3014), citytown(?x4016, ?x3014), film(?x8273, ?x626) >> conf = 0.51 => this is the best rule for 4 predicted values *> Best rule #4930 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0mb2b; 0jpy_; 01p726; 0tzt_; *> query: (?x3014, ?x434) <- place_of_birth(?x434, ?x3014), citytown(?x4016, ?x3014), currency(?x3014, ?x170) *> conf = 0.51 ranks of expected_values: 8, 845 EVAL 01531 location! 03fqv5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 98.000 77.000 0.506 http://example.org/people/person/places_lived./people/place_lived/location EVAL 01531 location! 06mn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 98.000 77.000 0.506 http://example.org/people/person/places_lived./people/place_lived/location #19398-07b2lv PRED entity: 07b2lv PRED relation: award_nominee PRED expected values: 0159h6 => 96 concepts (53 used for prediction) PRED predicted values (max 10 best out of 789): 0159h6 (0.81 #98405, 0.81 #77320, 0.81 #35147), 0bwgc_ (0.81 #98405, 0.81 #77320, 0.81 #35147), 0jfx1 (0.15 #124177, 0.15 #527, 0.02 #54421), 02qgqt (0.15 #124177, 0.07 #20, 0.05 #53914), 04g4n (0.15 #124177, 0.07 #1046, 0.01 #54940), 01kj0p (0.15 #124177, 0.07 #635, 0.01 #54529), 04y9dk (0.15 #124177, 0.07 #423, 0.01 #54317), 02p65p (0.15 #124177, 0.06 #4712, 0.06 #7055), 02qgyv (0.15 #124177, 0.06 #54395, 0.06 #5186), 0lpjn (0.15 #124177, 0.04 #54522, 0.04 #628) >> Best rule #98405 for best value: >> intensional similarity = 3 >> extensional distance = 1279 >> proper extension: 04glx0; >> query: (?x2279, ?x11983) <- award_winner(?x941, ?x2279), award_nominee(?x11983, ?x2279), award_winner(?x4756, ?x11983) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 07b2lv award_nominee 0159h6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 53.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19397-04q7r PRED entity: 04q7r PRED relation: role! PRED expected values: 09swkk => 67 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1249): 0lzkm (0.71 #4939, 0.71 #4458, 0.62 #5888), 050z2 (0.71 #4474, 0.70 #7335, 0.62 #5904), 023l9y (0.71 #4499, 0.64 #8314, 0.64 #7837), 04bpm6 (0.71 #4356, 0.62 #5309, 0.60 #7217), 016ntp (0.71 #4429, 0.62 #5382, 0.50 #5859), 01wxdn3 (0.71 #4701, 0.50 #5654, 0.50 #2321), 0137g1 (0.71 #4403, 0.50 #2023, 0.44 #15875), 082brv (0.57 #5037, 0.57 #4556, 0.50 #7417), 01vsnff (0.57 #4753, 0.57 #4376, 0.50 #6760), 02s6sh (0.57 #4727, 0.50 #7588, 0.50 #5680) >> Best rule #4939 for best value: >> intensional similarity = 23 >> extensional distance = 5 >> proper extension: 0342h; 02sgy; 0395lw; 01xqw; >> query: (?x960, 0lzkm) <- role(?x960, ?x4769), role(?x960, ?x2888), role(?x960, ?x2157), role(?x960, ?x1969), role(?x960, ?x716), role(?x960, ?x432), role(?x960, ?x212), ?x2888 = 02fsn, ?x212 = 026t6, group(?x960, ?x5303), role(?x5417, ?x960), ?x5417 = 02w3w, role(?x960, ?x315), ?x4769 = 0dwt5, ?x1969 = 04rzd, ?x432 = 042v_gx, role(?x2460, ?x2157), role(?x2459, ?x2157), role(?x3214, ?x960), ?x716 = 018vs, ?x2460 = 01wy6, role(?x2157, ?x885), ?x2459 = 021bmf >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4754 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 5 *> proper extension: 018vs; *> query: (?x960, ?x211) <- role(?x960, ?x5926), role(?x960, ?x2888), role(?x960, ?x2048), role(?x960, ?x1969), role(?x960, ?x212), ?x2888 = 02fsn, role(?x4913, ?x212), role(?x2309, ?x212), role(?x1647, ?x212), performance_role(?x10091, ?x212), performance_role(?x3375, ?x212), ?x2048 = 018j2, instrumentalists(?x212, ?x8341), instrumentalists(?x212, ?x8012), role(?x2187, ?x212), role(?x565, ?x212), role(?x211, ?x212), ?x1969 = 04rzd, ?x565 = 01wl38s, ?x2187 = 01vsnff, role(?x10091, ?x315), ?x2309 = 06ncr, ?x4913 = 03ndd, ?x8341 = 01wmjkb, ?x5926 = 0cfdd, award_winner(?x8423, ?x3375), ?x1647 = 05ljv7, artists(?x1000, ?x8012) *> conf = 0.24 ranks of expected_values: 345 EVAL 04q7r role! 09swkk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 37.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #19396-0cwfgz PRED entity: 0cwfgz PRED relation: honored_for PRED expected values: 07b1gq => 102 concepts (37 used for prediction) PRED predicted values (max 10 best out of 140): 02ny6g (0.89 #1081, 0.84 #1861, 0.83 #1236), 07sgdw (0.89 #1081, 0.83 #1236, 0.83 #1547), 0cwfgz (0.60 #2020, 0.56 #4186, 0.56 #4345), 07b1gq (0.60 #2020, 0.56 #4186, 0.56 #4345), 0cf08 (0.47 #2017, 0.45 #1703, 0.02 #5581), 08984j (0.47 #2017, 0.45 #1703, 0.02 #5581), 0dtfn (0.19 #30, 0.08 #339, 0.06 #801), 0dfw0 (0.19 #87, 0.08 #396, 0.05 #1168), 0ddt_ (0.19 #59, 0.08 #368, 0.05 #1140), 0f3m1 (0.15 #137, 0.08 #446, 0.04 #1218) >> Best rule #1081 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 0hmr4; >> query: (?x6206, ?x188) <- honored_for(?x188, ?x6206), genre(?x6206, ?x53), ?x53 = 07s9rl0, honored_for(?x6206, ?x3330) >> conf = 0.89 => this is the best rule for 2 predicted values *> Best rule #2020 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 113 *> proper extension: 0g60z; 0180mw; *> query: (?x6206, ?x3330) <- honored_for(?x2749, ?x6206), honored_for(?x2165, ?x6206), honored_for(?x188, ?x6206), honored_for(?x2749, ?x3330), nominated_for(?x574, ?x2165), nominated_for(?x7080, ?x188) *> conf = 0.60 ranks of expected_values: 4 EVAL 0cwfgz honored_for 07b1gq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 37.000 0.887 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #19395-04bd8y PRED entity: 04bd8y PRED relation: award_winner! PRED expected values: 092t4b => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 75): 092t4b (0.16 #1693, 0.05 #475, 0.04 #616), 0hr3c8y (0.16 #1693, 0.04 #433, 0.04 #574), 092_25 (0.16 #1693, 0.03 #213, 0.03 #1906), 0bxs_d (0.16 #1693, 0.02 #1243, 0.01 #115), 07z31v (0.16 #1693, 0.02 #31, 0.02 #1159), 0bx6zs (0.16 #1693, 0.02 #1255, 0.01 #127), 09qvms (0.05 #577, 0.05 #718, 0.05 #154), 092c5f (0.05 #437, 0.05 #296, 0.04 #578), 09g90vz (0.05 #688, 0.04 #265, 0.04 #829), 03gyp30 (0.04 #258, 0.04 #681, 0.04 #540) >> Best rule #1693 for best value: >> intensional similarity = 3 >> extensional distance = 901 >> proper extension: 0f721s; 0gsg7; 02f9wb; 0hm0k; 0283xx2; 037q1z; 01zcrv; >> query: (?x820, ?x3460) <- award_winner(?x820, ?x4165), award_winner(?x337, ?x820), award_winner(?x3460, ?x4165) >> conf = 0.16 => this is the best rule for 6 predicted values ranks of expected_values: 1 EVAL 04bd8y award_winner! 092t4b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.161 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19394-0345h PRED entity: 0345h PRED relation: language! PRED expected values: 03h3x5 => 205 concepts (150 used for prediction) PRED predicted values (max 10 best out of 585): 0dgq80b (0.14 #6821, 0.09 #10291, 0.05 #20707), 0gjc4d3 (0.14 #5720, 0.09 #9190, 0.05 #19606), 0c9k8 (0.07 #12612, 0.05 #16087, 0.05 #14348), 0g5qmbz (0.06 #192405, 0.06 #197613), 0n6ds (0.05 #15455, 0.01 #192470), 05_61y (0.05 #15039, 0.01 #192054), 0fjyzt (0.05 #14788, 0.01 #191803), 0jqj5 (0.05 #14735, 0.01 #191750), 02xtxw (0.05 #14444, 0.01 #191459), 0jzw (0.05 #13993, 0.01 #191008) >> Best rule #6821 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 022dp5; 012f86; >> query: (?x1264, 0dgq80b) <- split_to(?x5540, ?x1264), people(?x5540, ?x380) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0345h language! 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 205.000 150.000 0.143 http://example.org/film/film/language #19393-014dgf PRED entity: 014dgf PRED relation: contact_category! PRED expected values: 07y2s => 4 concepts (4 used for prediction) PRED predicted values (max 10 best out of 426): 045c7b (0.68 #100, 0.63 #95, 0.63 #91), 01c6k4 (0.68 #100, 0.63 #95, 0.63 #91), 01zpmq (0.68 #100, 0.63 #95, 0.63 #91), 029d_ (0.68 #100, 0.63 #95, 0.63 #91), 0z07 (0.68 #100, 0.63 #95, 0.63 #91), 0plw (0.68 #100, 0.63 #95, 0.63 #91), 01n073 (0.68 #100, 0.63 #95, 0.63 #91), 02brqp (0.68 #100, 0.63 #95, 0.63 #91), 069b85 (0.68 #100, 0.63 #95, 0.63 #91), 0cv9b (0.68 #100, 0.63 #95, 0.63 #91) >> Best rule #100 for best value: >> intensional similarity = 418 >> extensional distance = 1 >> proper extension: 03w5xm; >> query: (?x3231, ?x266) <- contact_category(?x13900, ?x3231), contact_category(?x13730, ?x3231), contact_category(?x12460, ?x3231), contact_category(?x12452, ?x3231), contact_category(?x11688, ?x3231), contact_category(?x11504, ?x3231), contact_category(?x11199, ?x3231), contact_category(?x11051, ?x3231), contact_category(?x10951, ?x3231), contact_category(?x10368, ?x3231), contact_category(?x8934, ?x3231), contact_category(?x8125, ?x3231), contact_category(?x7970, ?x3231), contact_category(?x7526, ?x3231), contact_category(?x6896, ?x3231), contact_category(?x6340, ?x3231), contact_category(?x5108, ?x3231), contact_category(?x4519, ?x3231), contact_category(?x3793, ?x3231), contact_category(?x3578, ?x3231), contact_category(?x3230, ?x3231), contact_category(?x502, ?x3231), contact_category(?x127, ?x3231), ?x7970 = 0py9b, ?x11051 = 07_dn, ?x3230 = 03mnk, ?x12460 = 013fn, colors(?x4519, ?x13863), ?x5108 = 01s73z, team(?x11624, ?x4519), team(?x5412, ?x4519), ?x6340 = 0j47s, child(?x10513, ?x10368), colors(?x7447, ?x13863), team(?x1792, ?x4519), team(?x1240, ?x4519), team(?x180, ?x4519), ?x3793 = 0k8z, ?x502 = 087c7, position(?x1792, ?x935), ?x6896 = 07l1c, currency(?x11688, ?x170), service_location(?x11688, ?x551), service_location(?x11688, ?x94), ?x8934 = 01b39j, category(?x11688, ?x134), service_language(?x4519, ?x254), organizations_founded(?x10499, ?x127), service_language(?x127, ?x6753), service_language(?x127, ?x5607), languages(?x419, ?x6753), language(?x11372, ?x6753), language(?x10029, ?x6753), language(?x8955, ?x6753), language(?x7722, ?x6753), language(?x6610, ?x6753), language(?x6095, ?x6753), language(?x6094, ?x6753), language(?x5271, ?x6753), language(?x4643, ?x6753), language(?x4596, ?x6753), language(?x4315, ?x6753), language(?x2323, ?x6753), language(?x2029, ?x6753), language(?x1224, ?x6753), language(?x1108, ?x6753), ?x7722 = 07kdkfj, contains(?x5658, ?x11688), official_language(?x87, ?x6753), ?x13730 = 026db_, ?x6094 = 0dnw1, ?x2029 = 020bv3, sport(?x4519, ?x1083), ?x12452 = 0vlf, ?x10029 = 02vzpb, countries_spoken_in(?x6753, ?x9459), countries_spoken_in(?x6753, ?x792), ?x10951 = 04fc6c, languages_spoken(?x3584, ?x6753), ?x1224 = 020fcn, company(?x346, ?x127), ?x6610 = 07ghv5, service_language(?x6717, ?x6753), service_language(?x5072, ?x6753), service_language(?x555, ?x6753), ?x1108 = 0jjy0, institution(?x865, ?x11688), position(?x706, ?x1240), ?x4315 = 0sxkh, ?x3578 = 08z129, ?x4643 = 080lkt7, ?x6717 = 064f29, company(?x8893, ?x13900), ?x11504 = 05njw, company(?x4682, ?x13900), ?x865 = 02h4rq6, ?x2323 = 05k2xy, citytown(?x13900, ?x4074), ?x792 = 0hzlz, origin(?x4819, ?x5658), county(?x4074, ?x8616), ?x5271 = 047vnkj, featured_film_locations(?x2788, ?x4074), ?x419 = 020qr4, ?x5072 = 045c7b, films(?x1083, ?x3081), contains(?x1755, ?x4074), ?x8125 = 06q07, athlete(?x1083, ?x445), ?x4682 = 0dq_5, location(?x13779, ?x4074), location(?x8746, ?x4074), language(?x12829, ?x5607), language(?x11385, ?x5607), language(?x11148, ?x5607), language(?x11073, ?x5607), language(?x10241, ?x5607), language(?x10157, ?x5607), language(?x10130, ?x5607), language(?x10060, ?x5607), language(?x9941, ?x5607), language(?x9642, ?x5607), language(?x9599, ?x5607), language(?x9379, ?x5607), language(?x8773, ?x5607), language(?x8769, ?x5607), language(?x8471, ?x5607), language(?x8457, ?x5607), language(?x8284, ?x5607), language(?x8217, ?x5607), language(?x8188, ?x5607), language(?x8119, ?x5607), language(?x7880, ?x5607), language(?x7741, ?x5607), language(?x7700, ?x5607), language(?x7590, ?x5607), language(?x7532, ?x5607), language(?x7514, ?x5607), language(?x7314, ?x5607), language(?x7305, ?x5607), language(?x7171, ?x5607), language(?x7149, ?x5607), language(?x6886, ?x5607), language(?x6773, ?x5607), language(?x6605, ?x5607), language(?x6445, ?x5607), language(?x6229, ?x5607), language(?x5992, ?x5607), language(?x5960, ?x5607), language(?x5849, ?x5607), language(?x5847, ?x5607), language(?x5687, ?x5607), language(?x5465, ?x5607), language(?x5429, ?x5607), language(?x5399, ?x5607), language(?x5365, ?x5607), language(?x5293, ?x5607), language(?x5129, ?x5607), language(?x5017, ?x5607), language(?x4971, ?x5607), language(?x4870, ?x5607), language(?x4844, ?x5607), language(?x4756, ?x5607), language(?x4626, ?x5607), language(?x4545, ?x5607), language(?x4541, ?x5607), language(?x4513, ?x5607), language(?x4375, ?x5607), language(?x4216, ?x5607), language(?x3992, ?x5607), language(?x3857, ?x5607), language(?x3599, ?x5607), language(?x3457, ?x5607), language(?x3441, ?x5607), language(?x3430, ?x5607), language(?x3201, ?x5607), language(?x2892, ?x5607), language(?x2814, ?x5607), language(?x2458, ?x5607), language(?x2380, ?x5607), language(?x2368, ?x5607), language(?x2189, ?x5607), language(?x2036, ?x5607), language(?x1808, ?x5607), language(?x1692, ?x5607), language(?x1688, ?x5607), language(?x1597, ?x5607), language(?x1385, ?x5607), language(?x1364, ?x5607), language(?x1046, ?x5607), language(?x1038, ?x5607), language(?x787, ?x5607), language(?x785, ?x5607), language(?x776, ?x5607), language(?x763, ?x5607), language(?x723, ?x5607), language(?x715, ?x5607), language(?x667, ?x5607), language(?x650, ?x5607), language(?x586, ?x5607), language(?x299, ?x5607), language(?x204, ?x5607), language(?x80, ?x5607), ?x5017 = 04nm0n0, languages_spoken(?x13305, ?x5607), languages_spoken(?x7139, ?x5607), languages_spoken(?x5606, ?x5607), countries_spoken_in(?x5607, ?x8033), countries_spoken_in(?x5607, ?x6974), countries_spoken_in(?x5607, ?x3016), countries_spoken_in(?x5607, ?x2291), languages(?x11985, ?x5607), languages(?x10520, ?x5607), languages(?x10224, ?x5607), languages(?x9253, ?x5607), languages(?x9095, ?x5607), languages(?x7893, ?x5607), languages(?x6440, ?x5607), languages(?x5330, ?x5607), languages(?x5283, ?x5607), languages(?x5043, ?x5607), languages(?x5040, ?x5607), languages(?x4345, ?x5607), languages(?x4119, ?x5607), languages(?x4005, ?x5607), languages(?x2580, ?x5607), languages(?x2538, ?x5607), languages(?x2531, ?x5607), languages(?x2461, ?x5607), languages(?x2382, ?x5607), languages(?x1991, ?x5607), languages(?x914, ?x5607), languages(?x413, ?x5607), ?x94 = 09c7w0, major_field_of_study(?x5607, ?x90), ?x2538 = 01x1cn2, ?x299 = 01gc7, languages(?x8644, ?x5607), ?x4844 = 02hfk5, ?x2368 = 075cph, major_field_of_study(?x2895, ?x5607), major_field_of_study(?x481, ?x5607), ?x204 = 028_yv, ?x6229 = 02q8ms8, ?x785 = 03hjv97, ?x5293 = 0cbv4g, administrative_parent(?x47, ?x551), ?x5283 = 01ps2h8, ?x10130 = 09wnnb, ?x13779 = 0443c, ?x9459 = 034m8, ?x3857 = 03z106, ?x667 = 0pc62, profession(?x11624, ?x1032), ?x13305 = 0fk3s, ?x9642 = 02_nsc, service_location(?x11188, ?x551), service_location(?x5956, ?x551), service_location(?x5055, ?x551), service_location(?x4211, ?x551), service_location(?x3795, ?x551), service_location(?x266, ?x551), ?x4005 = 01g23m, ?x254 = 02h40lc, ?x8033 = 04hhv, ?x9941 = 024lt6, ?x2189 = 02yvct, ?x7171 = 0fs9vc, ?x2892 = 05q54f5, service_language(?x14222, ?x5607), service_language(?x13476, ?x5607), service_language(?x13349, ?x5607), service_language(?x6945, ?x5607), service_language(?x610, ?x5607), ?x7741 = 01xq8v, ?x8746 = 03x16f, place_of_birth(?x4238, ?x4074), ?x6945 = 05w3y, ?x6773 = 05t54s, ?x7314 = 047vp1n, ?x3016 = 0697s, ?x481 = 052nd, ?x7526 = 03rwz3, official_language(?x6423, ?x5607), ?x10060 = 02jxrw, ?x6886 = 0gwjw0c, ?x11985 = 01vh3r, ?x5399 = 0fsw_7, ?x8471 = 0cp0t91, ?x4541 = 08nvyr, ?x763 = 061681, ?x1038 = 05q96q6, ?x9379 = 09y6pb, student(?x5607, ?x4265), ?x1692 = 03lrht, ?x2036 = 047qxs, ?x5687 = 07_k0c0, ?x2531 = 0kszw, ?x5847 = 0640y35, ?x8188 = 01qz5, ?x3430 = 0ctb4g, ?x5992 = 0g5q34q, ?x11199 = 069vt, ?x7305 = 031786, ?x7532 = 09gdh6k, ?x4375 = 01rxyb, ?x715 = 02py4c8, ?x555 = 01c6k4, ?x4596 = 02d49z, ?x7700 = 0cp08zg, ?x5365 = 05tgks, citytown(?x127, ?x108), ?x4211 = 0221g_, ?x2382 = 03wpmd, ?x787 = 08gsvw, languages(?x3200, ?x6753), ?x4216 = 0hfzr, ?x7149 = 01jr4j, ?x10224 = 02tc5y, ?x776 = 0p_sc, ?x7139 = 059_w, type_of_union(?x5412, ?x566), ?x14222 = 06rfy5, ?x8284 = 02p76f9, ?x650 = 026p_bs, ?x11073 = 01ry_x, ?x11188 = 0z07, ?x5465 = 0fjyzt, profession(?x8893, ?x987), ?x9599 = 07l450, ?x7514 = 06x43v, service_location(?x127, ?x455), ?x2895 = 0l2tk, ?x586 = 050r1z, ?x8217 = 04v89z, ?x5606 = 0g8_vp, ?x2458 = 021y7yw, ?x8119 = 0ft18, ?x5055 = 029d_, place_of_birth(?x236, ?x108), ?x723 = 04fzfj, ?x12829 = 0cbl95, ?x3992 = 0pd6l, place_of_birth(?x3018, ?x5658), ?x1385 = 044g_k, ?x5960 = 0f4_2k, location(?x2275, ?x108), ?x4971 = 01jwxx, ?x10157 = 0n6ds, ?x4545 = 05p09dd, ?x914 = 0htlr, ?x610 = 0p4wb, taxonomy(?x455, ?x939), ?x2580 = 0227tr, ?x2380 = 02q6gfp, ?x6605 = 012kyx, ?x413 = 0l8v5, ?x5849 = 02h22, ?x4870 = 015qqg, ?x2291 = 07z5n, ?x6440 = 0bdt8, ?x1364 = 047msdk, ?x8773 = 0cq806, ?x5043 = 015q43, ?x6095 = 0bq6ntw, place_of_death(?x767, ?x108), ?x3457 = 03x7hd, source(?x4074, ?x958), ?x3795 = 0178g, ?x10241 = 0bs5vty, ?x11148 = 01qdmh, ?x3441 = 07yvsn, ?x3599 = 0kxf1, ?x1991 = 02lf70, place_of_birth(?x11624, ?x3372), ?x7893 = 02n9k, ?x4756 = 0462hhb, ?x80 = 0b76d_m, ?x9253 = 01x2tm8, ?x7880 = 04jplwp, athlete(?x5063, ?x5412), ?x7590 = 08s6mr, school_type(?x11688, ?x3205), ?x11385 = 01c9d, ?x5040 = 06kb_, religion(?x8893, ?x1985), ?x3201 = 01ffx4, citytown(?x10368, ?x6960), ?x9095 = 0dqcm, ?x5956 = 01yfp7, ?x1597 = 0dr_4, ?x4345 = 073w14, ?x566 = 04ztj, ?x1808 = 01dyvs, ?x8769 = 0bj25, ?x3205 = 01rs41, ?x6445 = 05v38p, ?x2461 = 01cwhp, gender(?x5412, ?x231), ?x8457 = 034xyf, ?x1046 = 02qm_f, ?x5330 = 02f2p7, ?x10520 = 03crmd, ?x4119 = 01tj34, ?x13349 = 05b5c, ?x2814 = 078sj4, ?x13476 = 069b85, ?x5129 = 0jqj5, ?x170 = 09nqf, ?x5429 = 02psgq, time_zones(?x108, ?x2674), ?x939 = 04n6k, ?x11372 = 0419kt, ?x8955 = 0g4pl7z, ?x1688 = 024l2y, ?x4626 = 038bh3, ?x6974 = 01nln, ?x4513 = 05dmmc >> conf = 0.68 => this is the best rule for 96 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 56 EVAL 014dgf contact_category! 07y2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 4.000 4.000 0.676 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #19392-06hmd PRED entity: 06hmd PRED relation: influenced_by PRED expected values: 014nvr => 98 concepts (20 used for prediction) PRED predicted values (max 10 best out of 318): 03hnd (0.56 #1829, 0.43 #1398, 0.14 #2692), 09dt7 (0.33 #899, 0.22 #2624, 0.04 #3056), 06bng (0.33 #1146, 0.16 #2871, 0.14 #1577), 03rx9 (0.33 #1194, 0.14 #2919, 0.04 #3351), 0821j (0.33 #1160, 0.11 #2885, 0.04 #3317), 014ps4 (0.33 #1111, 0.08 #2836, 0.06 #6923), 018fq (0.33 #1027, 0.08 #2752, 0.04 #3184), 0g5ff (0.32 #2787, 0.22 #1924, 0.17 #1062), 04093 (0.29 #1589, 0.22 #2020, 0.12 #6924), 042q3 (0.28 #5986, 0.25 #362, 0.10 #4684) >> Best rule #1829 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01zwy; >> query: (?x5334, 03hnd) <- influenced_by(?x5334, ?x8210), influenced_by(?x5334, ?x6320), ?x8210 = 02mpb, student(?x3044, ?x6320) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2793 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 01k56k; *> query: (?x5334, 014nvr) <- influenced_by(?x5334, ?x8210), award(?x8210, ?x1375), ?x1375 = 0262zm *> conf = 0.03 ranks of expected_values: 215 EVAL 06hmd influenced_by 014nvr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 98.000 20.000 0.556 http://example.org/influence/influence_node/influenced_by #19391-0fpj4lx PRED entity: 0fpj4lx PRED relation: type_of_union PRED expected values: 04ztj => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.77 #13, 0.73 #45, 0.73 #9), 01g63y (0.16 #30, 0.15 #70, 0.15 #74), 01bl8s (0.05 #39) >> Best rule #13 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 01l1sq; 01bpc9; 03h_fk5; 01t110; 04mx7s; >> query: (?x3740, 04ztj) <- artists(?x302, ?x3740), instrumentalists(?x2798, ?x3740), instrumentalists(?x1969, ?x3740), ?x1969 = 04rzd, ?x2798 = 03qjg >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fpj4lx type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.769 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19390-02fqxm PRED entity: 02fqxm PRED relation: country PRED expected values: 0f8l9c => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 62): 09c7w0 (0.88 #369, 0.86 #925, 0.84 #1170), 07ssc (0.39 #2841, 0.37 #201, 0.29 #2365), 02jx1 (0.39 #2841, 0.05 #458, 0.05 #580), 0l35f (0.25 #2162), 0l2lk (0.25 #2162), 06pvr (0.25 #2162), 01n7q (0.25 #2162), 0345h (0.19 #640, 0.14 #889, 0.13 #1443), 0f8l9c (0.17 #82, 0.14 #449, 0.12 #2306), 01jfsb (0.13 #2411, 0.08 #2410, 0.07 #4252) >> Best rule #369 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 06znpjr; >> query: (?x12720, 09c7w0) <- genre(?x12720, ?x53), film_release_region(?x12720, ?x94), nominated_for(?x1312, ?x12720), ?x1312 = 07cbcy >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #82 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 05_5rjx; *> query: (?x12720, 0f8l9c) <- genre(?x12720, ?x714), genre(?x12720, ?x600), ?x714 = 0hn10, ?x600 = 02n4kr, language(?x12720, ?x254) *> conf = 0.17 ranks of expected_values: 9 EVAL 02fqxm country 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 94.000 94.000 0.885 http://example.org/film/film/country #19389-02r6c_ PRED entity: 02r6c_ PRED relation: film PRED expected values: 03lvwp => 158 concepts (70 used for prediction) PRED predicted values (max 10 best out of 337): 0bz3jx (0.25 #562, 0.07 #2218, 0.05 #3875), 011yhm (0.25 #566, 0.07 #2222, 0.05 #3879), 02704ff (0.25 #484, 0.07 #2140, 0.05 #3797), 01jzyf (0.25 #304, 0.07 #1960, 0.05 #3617), 02r1c18 (0.25 #118, 0.07 #1774, 0.05 #3431), 01vfqh (0.25 #99, 0.07 #1755, 0.05 #3412), 0b73_1d (0.25 #53, 0.07 #1709, 0.05 #3366), 07bwr (0.25 #426, 0.07 #2082, 0.03 #3739), 0jqkh (0.25 #645, 0.07 #2301, 0.02 #5614), 01rwyq (0.25 #276, 0.07 #1932, 0.02 #5245) >> Best rule #562 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02kxbwx; 06pjs; >> query: (?x8812, 0bz3jx) <- award(?x8812, ?x1862), student(?x2314, ?x8812), film(?x8812, ?x2121), ?x1862 = 0gr51 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02r6c_ film 03lvwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 70.000 0.250 http://example.org/film/director/film #19388-01x3g PRED entity: 01x3g PRED relation: major_field_of_study! PRED expected values: 04bbpm 01dq0z => 54 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1552): 01w5m (0.74 #4255, 0.67 #5440, 0.67 #2482), 03ksy (0.69 #13147, 0.68 #4256, 0.67 #2483), 06pwq (0.68 #3558, 0.67 #2967, 0.66 #8885), 08815 (0.68 #4138, 0.67 #2956, 0.63 #3547), 02zd460 (0.68 #4331, 0.54 #9067, 0.54 #13222), 07wrz (0.67 #3021, 0.67 #2430, 0.60 #1839), 07w0v (0.65 #4727, 0.40 #2976, 0.40 #1203), 01w3v (0.63 #4152, 0.60 #2970, 0.58 #3561), 07wjk (0.60 #3022, 0.60 #1249, 0.53 #3613), 09f2j (0.60 #3133, 0.54 #9051, 0.53 #4315) >> Best rule #4255 for best value: >> intensional similarity = 7 >> extensional distance = 17 >> proper extension: 03nfmq; >> query: (?x11566, 01w5m) <- student(?x11566, ?x12100), major_field_of_study(?x1200, ?x11566), company(?x12100, ?x13490), major_field_of_study(?x6132, ?x11566), ?x1200 = 016t_3, student(?x1011, ?x12100), institution(?x734, ?x6132) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #8281 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 31 *> proper extension: 06ntj; *> query: (?x11566, ?x388) <- major_field_of_study(?x11566, ?x1695), taxonomy(?x11566, ?x939), ?x939 = 04n6k, major_field_of_study(?x1668, ?x1695), ?x1668 = 01mkq, major_field_of_study(?x734, ?x1695), student(?x1695, ?x3806), major_field_of_study(?x388, ?x1695) *> conf = 0.16 ranks of expected_values: 350, 477 EVAL 01x3g major_field_of_study! 01dq0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 36.000 0.737 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01x3g major_field_of_study! 04bbpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 36.000 0.737 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19387-06m_5 PRED entity: 06m_5 PRED relation: participating_countries! PRED expected values: 0lgxj => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 41): 018ctl (0.66 #329, 0.62 #289, 0.62 #8), 0lgxj (0.63 #349, 0.60 #509, 0.59 #309), 09x3r (0.54 #12, 0.53 #132, 0.51 #333), 09n48 (0.52 #484, 0.50 #526, 0.49 #1048), 06sks6 (0.38 #24, 0.29 #644, 0.28 #522), 016r9z (0.37 #141, 0.35 #584, 0.31 #342), 0sx8l (0.31 #335, 0.31 #577, 0.31 #295), 0c_tl (0.31 #23, 0.29 #344, 0.28 #304), 0blfl (0.29 #592, 0.28 #1074, 0.26 #149), 0jdk_ (0.21 #523, 0.19 #161, 0.19 #1566) >> Best rule #329 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 05r4w; >> query: (?x8420, 018ctl) <- country(?x11762, ?x8420), location(?x4895, ?x8420), country(?x1352, ?x8420) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #349 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 05r4w; *> query: (?x8420, 0lgxj) <- country(?x11762, ?x8420), location(?x4895, ?x8420), country(?x1352, ?x8420) *> conf = 0.63 ranks of expected_values: 2 EVAL 06m_5 participating_countries! 0lgxj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.657 http://example.org/olympics/olympic_games/participating_countries #19386-02vzc PRED entity: 02vzc PRED relation: country! PRED expected values: 02_5h => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 28): 01cgz (0.84 #92, 0.73 #148, 0.72 #1661), 07jbh (0.73 #323, 0.68 #155, 0.68 #1023), 019tzd (0.70 #76, 0.64 #160, 0.63 #328), 02vx4 (0.70 #60, 0.59 #144, 0.52 #116), 096f8 (0.70 #61, 0.52 #117, 0.50 #397), 02y8z (0.67 #121, 0.65 #401, 0.63 #317), 07jjt (0.64 #151, 0.63 #319, 0.62 #403), 07rlg (0.64 #141, 0.60 #57, 0.59 #393), 02_5h (0.62 #119, 0.55 #147, 0.50 #63), 09_bl (0.60 #62, 0.59 #146, 0.57 #314) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 0160w; >> query: (?x1892, 01cgz) <- countries_within(?x455, ?x1892), olympics(?x1892, ?x391), vacationer(?x1892, ?x3421) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #119 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 05kr_; *> query: (?x1892, 02_5h) <- adjoins(?x1892, ?x304), film_release_region(?x1710, ?x1892), jurisdiction_of_office(?x182, ?x1892) *> conf = 0.62 ranks of expected_values: 9 EVAL 02vzc country! 02_5h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 166.000 166.000 0.842 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #19385-02bgmr PRED entity: 02bgmr PRED relation: artist! PRED expected values: 043g7l => 127 concepts (76 used for prediction) PRED predicted values (max 10 best out of 134): 015_1q (0.23 #446, 0.22 #2150, 0.21 #162), 011k1h (0.17 #436, 0.15 #294, 0.14 #152), 03rhqg (0.17 #442, 0.15 #2858, 0.15 #2146), 0181dw (0.17 #469, 0.15 #753, 0.14 #895), 01dtcb (0.17 #48, 0.08 #1184, 0.08 #1468), 01cl0d (0.17 #56, 0.07 #198, 0.07 #766), 01th4s (0.17 #40, 0.07 #182, 0.04 #324), 043g7l (0.15 #316, 0.14 #174, 0.09 #3158), 01trtc (0.15 #1068, 0.13 #1494, 0.09 #3200), 0mzkr (0.14 #168, 0.12 #310, 0.07 #2868) >> Best rule #446 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 03k0yw; >> query: (?x5768, 015_1q) <- award_nominee(?x5768, ?x1566), category(?x5768, ?x134), role(?x5768, ?x314), ?x314 = 02sgy >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #316 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0khth; 01k_yf; 0l8g0; 07hgm; 01s560x; 07n3s; *> query: (?x5768, 043g7l) <- award(?x5768, ?x884), artists(?x2996, ?x5768), ?x2996 = 01243b *> conf = 0.15 ranks of expected_values: 8 EVAL 02bgmr artist! 043g7l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 127.000 76.000 0.234 http://example.org/music/record_label/artist #19384-02p_04b PRED entity: 02p_04b PRED relation: award! PRED expected values: 05w6cw => 37 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2209): 045w_4 (0.33 #1346, 0.29 #8113, 0.29 #4730), 026dd2b (0.29 #9401, 0.24 #3384, 0.18 #12784), 02760sl (0.29 #9675, 0.24 #3384, 0.18 #13058), 025vw4t (0.29 #8569, 0.24 #3384, 0.18 #11952), 026n6cs (0.29 #7545, 0.24 #3384, 0.18 #10928), 02_2v2 (0.29 #7336, 0.24 #3384, 0.18 #10719), 025st2z (0.24 #3384, 0.17 #44003, 0.17 #2742), 063lqs (0.24 #3384, 0.17 #44003, 0.17 #1057), 070w7s (0.24 #3384, 0.17 #44003, 0.17 #770), 026n998 (0.24 #3384, 0.17 #44003, 0.17 #769) >> Best rule #1346 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02py_sj; >> query: (?x6853, 045w_4) <- ceremony(?x6853, ?x2751), ?x2751 = 0jt3qpk, award(?x3544, ?x6853), award(?x589, ?x6853), ?x3544 = 0phrl, tv_program(?x911, ?x589) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #23687 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 57 *> proper extension: 0gqng; 0bfvw2; 0bp_b2; 0gq_v; 0gkvb7; 0cqhk0; 0bdw1g; 09qvc0; 09qj50; 0fbvqf; ... *> query: (?x6853, ?x1700) <- ceremony(?x6853, ?x7721), award(?x1266, ?x6853), nominated_for(?x6853, ?x7175), award_winner(?x7721, ?x690), actor(?x7175, ?x1700) *> conf = 0.13 ranks of expected_values: 85 EVAL 02p_04b award! 05w6cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 37.000 14.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19383-03ywyk PRED entity: 03ywyk PRED relation: film PRED expected values: 02nt3d => 109 concepts (85 used for prediction) PRED predicted values (max 10 best out of 803): 01ft14 (0.65 #25079, 0.64 #30454, 0.63 #50160), 06_wqk4 (0.10 #1918, 0.07 #3709, 0.06 #12667), 03bx2lk (0.10 #1976, 0.06 #3767, 0.04 #10933), 035s95 (0.10 #2132, 0.04 #3923, 0.03 #12881), 01l_pn (0.10 #2759, 0.03 #17091, 0.03 #6341), 051zy_b (0.10 #2371, 0.03 #7745, 0.02 #13120), 08052t3 (0.10 #2186, 0.03 #7560, 0.02 #16518), 01shy7 (0.09 #4006, 0.05 #7589, 0.04 #23711), 01k1k4 (0.09 #58, 0.04 #3640, 0.03 #5431), 04hwbq (0.09 #192, 0.03 #130766, 0.01 #5565) >> Best rule #25079 for best value: >> intensional similarity = 4 >> extensional distance = 246 >> proper extension: 01vtqml; 063_t; 02m501; 0dzlk; 01f5q5; >> query: (?x9232, ?x10249) <- nationality(?x9232, ?x94), participant(?x9232, ?x9815), nominated_for(?x9232, ?x10249), award_winner(?x1112, ?x9815) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #4667 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 014zcr; 03zqc1; 0mdqp; 0pz7h; 0151w_; 01gq0b; 0320jz; 018grr; 0443y3; 0f4vbz; ... *> query: (?x9232, 02nt3d) <- nationality(?x9232, ?x94), award_nominee(?x9232, ?x5642), student(?x4916, ?x9232), vacationer(?x2623, ?x9232) *> conf = 0.02 ranks of expected_values: 351 EVAL 03ywyk film 02nt3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 109.000 85.000 0.646 http://example.org/film/actor/film./film/performance/film #19382-01d5z PRED entity: 01d5z PRED relation: draft PRED expected values: 02pq_x5 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 16): 02z6872 (0.85 #314, 0.81 #362, 0.78 #782), 02r6gw6 (0.83 #125, 0.81 #366, 0.79 #173), 02pq_x5 (0.78 #782, 0.75 #96, 0.74 #369), 02rl201 (0.78 #782, 0.74 #357, 0.73 #309), 0g3zpp (0.39 #420, 0.38 #783, 0.36 #616), 0f4vx0 (0.38 #783, 0.38 #73, 0.34 #565), 025tn92 (0.38 #783, 0.38 #75, 0.34 #565), 038c0q (0.38 #783, 0.38 #69, 0.34 #565), 09th87 (0.38 #783, 0.38 #77, 0.34 #565), 06439y (0.38 #783, 0.38 #80, 0.34 #565) >> Best rule #314 for best value: >> intensional similarity = 10 >> extensional distance = 24 >> proper extension: 03m1n; >> query: (?x1010, 02z6872) <- season(?x1010, ?x8517), ?x8517 = 0285r5d, school(?x1010, ?x4296), school(?x1010, ?x1884), school(?x1010, ?x1011), position(?x1010, ?x2010), organization(?x1011, ?x5487), student(?x1011, ?x400), institution(?x620, ?x1884), major_field_of_study(?x4296, ?x1154) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #782 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 91 *> proper extension: 01jvgt; *> query: (?x1010, ?x1633) <- team(?x4244, ?x1010), school(?x1010, ?x8479), school(?x3089, ?x8479), institution(?x865, ?x8479), team(?x4244, ?x12042), organization(?x346, ?x8479), draft(?x12042, ?x1633) *> conf = 0.78 ranks of expected_values: 3 EVAL 01d5z draft 02pq_x5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 91.000 0.846 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #19381-02qhqz4 PRED entity: 02qhqz4 PRED relation: film! PRED expected values: 02_j8x => 81 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1223): 073749 (0.36 #9004, 0.02 #53931, 0.02 #25598), 021bk (0.36 #8675, 0.02 #53931, 0.02 #27344), 051wwp (0.27 #9171, 0.06 #11245, 0.02 #53931), 07m77x (0.27 #9836, 0.03 #28505, 0.03 #30579), 07y8l9 (0.27 #9268, 0.02 #53931, 0.02 #63203), 01pcbg (0.27 #8878, 0.02 #53931, 0.01 #29621), 0pgjm (0.27 #8513, 0.02 #53931, 0.01 #39627), 0p8r1 (0.25 #2659, 0.22 #21326, 0.20 #25475), 01vs8ng (0.25 #4120, 0.12 #14491, 0.10 #18639), 02v92l (0.25 #3735, 0.12 #14106, 0.10 #18254) >> Best rule #9004 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 02x8fs; >> query: (?x2153, 073749) <- genre(?x2153, ?x811), film(?x9238, ?x2153), film(?x9085, ?x2153), film(?x4478, ?x2153), ?x4478 = 028k57, actor(?x7566, ?x9238), nominated_for(?x9085, ?x6984) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #36718 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 72 *> proper extension: 04n52p6; 05qbckf; 0ds2n; 0dzlbx; *> query: (?x2153, 02_j8x) <- genre(?x2153, ?x811), film_crew_role(?x2153, ?x7591), film(?x1735, ?x2153), ?x7591 = 0d2b38, genre(?x2084, ?x811), genre(?x1847, ?x811), nominated_for(?x350, ?x2084), ?x1847 = 02rb84n *> conf = 0.01 ranks of expected_values: 1052 EVAL 02qhqz4 film! 02_j8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 81.000 45.000 0.364 http://example.org/film/actor/film./film/performance/film #19380-06p0s1 PRED entity: 06p0s1 PRED relation: profession PRED expected values: 0dgd_ => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 67): 0dgd_ (0.89 #1834, 0.89 #1683, 0.88 #1984), 02hrh1q (0.78 #4518, 0.78 #6923, 0.78 #7374), 01d_h8 (0.50 #156, 0.47 #3758, 0.43 #456), 0dxtg (0.43 #464, 0.38 #3766, 0.34 #3916), 02jknp (0.38 #3760, 0.32 #3910, 0.31 #1059), 09jwl (0.29 #470, 0.25 #20, 0.23 #2572), 0cbd2 (0.29 #457, 0.25 #7, 0.22 #758), 01c72t (0.25 #175, 0.25 #25, 0.21 #3327), 03gjzk (0.25 #166, 0.22 #9331, 0.21 #5123), 02krf9 (0.25 #178, 0.20 #929, 0.19 #1079) >> Best rule #1834 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 087v17; >> query: (?x11915, 0dgd_) <- cinematography(?x1903, ?x11915), gender(?x11915, ?x231), nominated_for(?x294, ?x1903), award_winner(?x2192, ?x294) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06p0s1 profession 0dgd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.895 http://example.org/people/person/profession #19379-05pbl56 PRED entity: 05pbl56 PRED relation: nominated_for! PRED expected values: 02vx4c2 => 91 concepts (34 used for prediction) PRED predicted values (max 10 best out of 685): 02vx4c2 (0.44 #58461, 0.39 #7013, 0.01 #17955), 05vsxz (0.39 #35069, 0.30 #35068, 0.30 #9351), 0prfz (0.39 #35069, 0.30 #35068, 0.30 #9351), 0146pg (0.23 #7134, 0.10 #32851, 0.10 #37529), 0jfx1 (0.23 #28053, 0.14 #28052, 0.08 #503), 017s11 (0.15 #42087, 0.10 #25714, 0.07 #70157), 03rwz3 (0.15 #42087, 0.10 #25714, 0.07 #70157), 02pq9yv (0.14 #28052, 0.04 #5411, 0.02 #40485), 03h40_7 (0.14 #28052, 0.04 #6805, 0.01 #16157), 0b6mgp_ (0.13 #7974, 0.05 #10312, 0.05 #33691) >> Best rule #58461 for best value: >> intensional similarity = 3 >> extensional distance = 278 >> proper extension: 03lrqw; 03h3x5; 02q_4ph; 016ky6; 01f85k; 0296vv; 0g_zyp; 09qycb; >> query: (?x1595, ?x7384) <- film(?x100, ?x1595), nominated_for(?x500, ?x1595), cinematography(?x1595, ?x7384) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05pbl56 nominated_for! 02vx4c2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 34.000 0.441 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #19378-04f4z1k PRED entity: 04f4z1k PRED relation: draft! PRED expected values: 06x68 01yhm 0x2p 07l4z => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 171): 07l8x (0.79 #882, 0.78 #1173, 0.77 #956), 06x68 (0.79 #882, 0.78 #1173, 0.77 #956), 07l4z (0.79 #882, 0.78 #1173, 0.77 #956), 01yhm (0.79 #882, 0.78 #1173, 0.77 #956), 0x2p (0.79 #882, 0.78 #1173, 0.77 #956), 03lpp_ (0.79 #882, 0.78 #1173, 0.77 #956), 0jmj7 (0.64 #367, 0.54 #222, 0.50 #903), 0jmm4 (0.64 #367, 0.54 #222, 0.50 #511), 0487_ (0.64 #367, 0.54 #222, 0.50 #511), 02896 (0.64 #367, 0.54 #222, 0.50 #511) >> Best rule #882 for best value: >> intensional similarity = 64 >> extensional distance = 9 >> proper extension: 09l0x9; >> query: (?x10600, ?x700) <- draft(?x8995, ?x10600), draft(?x8894, ?x10600), draft(?x7060, ?x10600), draft(?x4208, ?x10600), draft(?x1010, ?x10600), draft(?x260, ?x10600), draft(?x8995, ?x11905), category(?x8995, ?x134), school(?x8995, ?x6814), school(?x8995, ?x5324), school(?x8995, ?x2711), school(?x8995, ?x466), ?x466 = 01pl14, school(?x7060, ?x7596), school(?x7060, ?x4257), sport(?x8894, ?x5063), school(?x2820, ?x6814), school(?x8894, ?x12736), school(?x8894, ?x4556), school(?x8894, ?x1884), school(?x8894, ?x1681), team(?x10434, ?x8995), school(?x1010, ?x2497), currency(?x4556, ?x170), colors(?x4208, ?x332), team(?x5412, ?x260), school_type(?x5324, ?x3205), time_zones(?x2711, ?x2674), major_field_of_study(?x7596, ?x8925), major_field_of_study(?x7596, ?x6870), ?x5412 = 03n69x, team(?x261, ?x260), school(?x260, ?x5621), school(?x260, ?x5426), institution(?x7636, ?x7596), institution(?x734, ?x7596), school(?x465, ?x6814), major_field_of_study(?x2711, ?x1668), state_province_region(?x4556, ?x1782), ?x2820 = 0jmj7, ?x8925 = 01zc2w, institution(?x1771, ?x2711), school_type(?x7596, ?x1044), draft(?x700, ?x11905), ?x6870 = 01540, colors(?x12736, ?x5325), citytown(?x12736, ?x10428), ?x734 = 04zx3q1, ?x1044 = 05pcjw, colors(?x5324, ?x663), team(?x7533, ?x1010), contains(?x94, ?x6814), ?x7636 = 01rr_d, ?x1681 = 07szy, ?x5621 = 01vs5c, teams(?x1860, ?x7060), ?x1884 = 0bx8pn, major_field_of_study(?x6814, ?x2981), student(?x4257, ?x846), ?x2497 = 0f1nl, contains(?x335, ?x5426), ?x94 = 09c7w0, major_field_of_study(?x4257, ?x373), ?x134 = 08mbj5d >> conf = 0.79 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3, 4, 5 EVAL 04f4z1k draft! 07l4z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 17.000 17.000 0.789 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 04f4z1k draft! 0x2p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 17.000 17.000 0.789 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 04f4z1k draft! 01yhm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 17.000 17.000 0.789 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 04f4z1k draft! 06x68 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 17.000 17.000 0.789 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #19377-01sg4_ PRED entity: 01sg4_ PRED relation: category PRED expected values: 08mbj5d => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.65 #34, 0.62 #38, 0.62 #42) >> Best rule #34 for best value: >> intensional similarity = 6 >> extensional distance = 1392 >> proper extension: 0fs54; >> query: (?x8838, 08mbj5d) <- contains(?x8420, ?x8838), jurisdiction_of_office(?x346, ?x8420), countries_spoken_in(?x254, ?x8420), organization(?x8420, ?x127), nationality(?x4895, ?x8420), taxonomy(?x8420, ?x939) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sg4_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.646 http://example.org/common/topic/webpage./common/webpage/category #19376-030s5g PRED entity: 030s5g PRED relation: gender PRED expected values: 05zppz => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #17, 0.85 #25, 0.84 #27), 02zsn (0.46 #163, 0.29 #52, 0.28 #44) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 0gry51; >> query: (?x11751, 05zppz) <- people(?x4322, ?x11751), profession(?x11751, ?x319), ?x319 = 01d_h8 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030s5g gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.910 http://example.org/people/person/gender #19375-01sbf2 PRED entity: 01sbf2 PRED relation: origin PRED expected values: 059rby => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 61): 0cc56 (0.30 #2598, 0.07 #23144, 0.07 #18652), 04jpl (0.12 #6, 0.06 #12519, 0.06 #4020), 0d6lp (0.12 #65, 0.02 #4079, 0.02 #13994), 0f2v0 (0.12 #71, 0.01 #8570, 0.01 #9278), 01b8w_ (0.12 #151, 0.01 #4165), 0fw2y (0.12 #54), 0162v (0.12 #41), 01n7q (0.12 #27), 02_286 (0.07 #252, 0.07 #724, 0.07 #488), 0cr3d (0.07 #1472, 0.03 #292, 0.02 #528) >> Best rule #2598 for best value: >> intensional similarity = 4 >> extensional distance = 160 >> proper extension: 01nqfh_; 0k4gf; 0285c; 0zjpz; 02jg92; 01tp5bj; 02ck1; 0lgm5; 03xl77; 01m65sp; ... >> query: (?x1613, ?x1131) <- instrumentalists(?x316, ?x1613), profession(?x1613, ?x131), ?x316 = 05r5c, place_of_birth(?x1613, ?x1131) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01sbf2 origin 059rby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 143.000 0.296 http://example.org/music/artist/origin #19374-040p_q PRED entity: 040p_q PRED relation: major_field_of_study! PRED expected values: 0jjw => 71 concepts (59 used for prediction) PRED predicted values (max 10 best out of 112): 02822 (0.67 #564, 0.50 #300, 0.36 #1281), 02j62 (0.50 #1807, 0.50 #912, 0.47 #1001), 062z7 (0.50 #553, 0.50 #289, 0.33 #110), 037mh8 (0.50 #586, 0.50 #322, 0.33 #143), 06ms6 (0.50 #369, 0.40 #457, 0.33 #1081), 0fdys (0.50 #298, 0.33 #562, 0.33 #119), 05qfh (0.36 #1812, 0.33 #1365, 0.33 #1187), 03g3w (0.34 #1985, 0.33 #1895, 0.33 #1357), 05qt0 (0.33 #576, 0.33 #133, 0.25 #667), 05r79 (0.33 #103, 0.30 #903, 0.25 #282) >> Best rule #564 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 06ms6; 01zc2w; >> query: (?x9093, 02822) <- major_field_of_study(?x9093, ?x2314), major_field_of_study(?x12728, ?x9093), major_field_of_study(?x9386, ?x9093), major_field_of_study(?x3948, ?x9093), major_field_of_study(?x581, ?x9093), major_field_of_study(?x1368, ?x9093), ?x581 = 06pwq, ?x2314 = 0h5k, citytown(?x12728, ?x9605), school(?x260, ?x3948), contains(?x94, ?x9386) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #558 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 06ms6; 01zc2w; *> query: (?x9093, 0jjw) <- major_field_of_study(?x9093, ?x2314), major_field_of_study(?x12728, ?x9093), major_field_of_study(?x9386, ?x9093), major_field_of_study(?x3948, ?x9093), major_field_of_study(?x581, ?x9093), major_field_of_study(?x1368, ?x9093), ?x581 = 06pwq, ?x2314 = 0h5k, citytown(?x12728, ?x9605), school(?x260, ?x3948), contains(?x94, ?x9386) *> conf = 0.33 ranks of expected_values: 22 EVAL 040p_q major_field_of_study! 0jjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 71.000 59.000 0.667 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #19373-0cfdd PRED entity: 0cfdd PRED relation: role! PRED expected values: 02snj9 => 76 concepts (58 used for prediction) PRED predicted values (max 10 best out of 118): 0342h (0.85 #1259, 0.84 #5972, 0.84 #1141), 05ljv7 (0.85 #1259, 0.84 #5972, 0.84 #1141), 02hnl (0.85 #1259, 0.84 #5972, 0.84 #1141), 01wy6 (0.85 #1259, 0.84 #1141, 0.84 #3799), 07y_7 (0.85 #1259, 0.84 #1141, 0.84 #3799), 03bx0bm (0.84 #4071, 0.82 #2564, 0.79 #3721), 042v_gx (0.82 #560, 0.76 #4048, 0.75 #1609), 03m5k (0.78 #2641, 0.75 #2289, 0.63 #453), 026t6 (0.78 #2641, 0.75 #2289, 0.63 #453), 0dwt5 (0.77 #2967, 0.75 #1576, 0.71 #1461) >> Best rule #1259 for best value: >> intensional similarity = 18 >> extensional distance = 5 >> proper extension: 02qjv; >> query: (?x5926, ?x75) <- role(?x5494, ?x5926), role(?x3215, ?x5926), role(?x716, ?x5926), role(?x315, ?x5926), role(?x228, ?x5926), ?x3215 = 0bxl5, role(?x5926, ?x1647), role(?x5926, ?x75), ?x5494 = 018x3, group(?x5926, ?x1945), ?x228 = 0l14qv, role(?x4162, ?x1647), role(?x868, ?x1647), ?x868 = 0dwvl, ?x716 = 018vs, group(?x315, ?x379), role(?x74, ?x315), instrumentalists(?x315, ?x226) >> conf = 0.85 => this is the best rule for 5 predicted values *> Best rule #1211 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 5 *> proper extension: 02qjv; *> query: (?x5926, 02snj9) <- role(?x5494, ?x5926), role(?x3215, ?x5926), role(?x716, ?x5926), role(?x315, ?x5926), role(?x228, ?x5926), ?x3215 = 0bxl5, role(?x5926, ?x1647), ?x5494 = 018x3, group(?x5926, ?x1945), ?x228 = 0l14qv, role(?x4162, ?x1647), role(?x868, ?x1647), ?x868 = 0dwvl, ?x716 = 018vs, group(?x315, ?x379), role(?x74, ?x315), instrumentalists(?x315, ?x226) *> conf = 0.71 ranks of expected_values: 21 EVAL 0cfdd role! 02snj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 76.000 58.000 0.852 http://example.org/music/performance_role/regular_performances./music/group_membership/role #19372-053vcrp PRED entity: 053vcrp PRED relation: award_winner! PRED expected values: 0c53zb 0c6vcj => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 125): 05hmp6 (0.29 #87, 0.17 #4655, 0.13 #4372), 0fy6bh (0.22 #188, 0.17 #4655, 0.14 #47), 0c53zb (0.22 #202, 0.17 #4655, 0.14 #61), 0d__c3 (0.22 #266, 0.17 #4655, 0.13 #4372), 0fz0c2 (0.18 #529, 0.17 #4655, 0.14 #106), 0dznvw (0.18 #558, 0.14 #135, 0.02 #11566), 0fk0xk (0.18 #501, 0.11 #219, 0.02 #11566), 0c6vcj (0.17 #4655, 0.14 #102, 0.13 #4372), 0fy59t (0.17 #4655, 0.14 #116, 0.13 #4372), 0fz2y7 (0.17 #4655, 0.13 #4372, 0.12 #483) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 072twv; 016ggh; >> query: (?x10609, 05hmp6) <- award_nominee(?x786, ?x10609), nominated_for(?x10609, ?x9572), ?x9572 = 025scjj >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #202 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 03mdw3c; *> query: (?x10609, 0c53zb) <- film_sets_designed(?x10609, ?x499), gender(?x10609, ?x231), award_winner(?x484, ?x10609) *> conf = 0.22 ranks of expected_values: 3, 8 EVAL 053vcrp award_winner! 0c6vcj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 90.000 90.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 053vcrp award_winner! 0c53zb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 90.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19371-0642xf3 PRED entity: 0642xf3 PRED relation: film_crew_role PRED expected values: 0215hd => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 27): 09vw2b7 (0.74 #306, 0.69 #458, 0.68 #397), 0dxtw (0.49 #400, 0.48 #461, 0.44 #491), 0ckd1 (0.40 #33, 0.12 #1368, 0.11 #63), 05smlt (0.40 #47, 0.12 #1368, 0.11 #1773), 02ynfr (0.33 #163, 0.32 #283, 0.31 #253), 0215hd (0.33 #75, 0.27 #135, 0.22 #165), 01pvkk (0.32 #704, 0.29 #1937, 0.28 #1844), 02rh1dz (0.27 #128, 0.24 #702, 0.23 #338), 02_n3z (0.22 #61, 0.21 #91, 0.20 #121), 089g0h (0.22 #76, 0.21 #256, 0.21 #286) >> Best rule #306 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 0cnztc4; >> query: (?x5081, 09vw2b7) <- genre(?x5081, ?x1509), category(?x5081, ?x134), ?x1509 = 060__y, film_crew_role(?x5081, ?x137), ?x137 = 09zzb8 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 0kvgtf; 03prz_; *> query: (?x5081, 0215hd) <- genre(?x5081, ?x1510), genre(?x5081, ?x1509), ?x1509 = 060__y, films(?x12759, ?x5081), nominated_for(?x2456, ?x5081), film(?x91, ?x5081), ?x1510 = 01hmnh *> conf = 0.33 ranks of expected_values: 6 EVAL 0642xf3 film_crew_role 0215hd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 92.000 92.000 0.743 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19370-0dthsy PRED entity: 0dthsy PRED relation: honored_for PRED expected values: 0cqnss => 33 concepts (19 used for prediction) PRED predicted values (max 10 best out of 495): 0dnw1 (0.33 #970, 0.25 #2165, 0.17 #8968), 0gzy02 (0.33 #616, 0.25 #1811, 0.06 #4800), 0ccd3x (0.33 #873, 0.25 #2068, 0.06 #5057), 0283_zv (0.33 #704, 0.25 #1899, 0.06 #4888), 0n0bp (0.33 #630, 0.25 #1825, 0.06 #4814), 0glbqt (0.33 #558, 0.20 #2989, 0.20 #2950), 0cq7tx (0.33 #262, 0.20 #2654, 0.18 #1794), 0fsw_7 (0.33 #329, 0.20 #2721, 0.17 #3319), 0168ls (0.33 #1287, 0.17 #8968, 0.17 #11366), 04tng0 (0.33 #1630, 0.08 #4022, 0.06 #5216) >> Best rule #970 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 0ftlkg; >> query: (?x5053, 0dnw1) <- award_winner(?x5053, ?x10412), award_winner(?x5053, ?x9170), award_winner(?x5053, ?x5660), honored_for(?x5053, ?x2168), ceremony(?x5409, ?x5053), instance_of_recurring_event(?x5053, ?x3459), ?x5409 = 0gr07, award(?x5660, ?x375), award(?x9170, ?x1232), nominated_for(?x9170, ?x10531), ?x10412 = 016jll, ?x3459 = 0g_w, people(?x1446, ?x5660), award_nominee(?x6011, ?x9170), award(?x10531, ?x601) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1794 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 1 *> proper extension: 0ftlxj; *> query: (?x5053, ?x4404) <- award_winner(?x5053, ?x9170), award_winner(?x5053, ?x8645), award_winner(?x5053, ?x6766), award_winner(?x5053, ?x5660), honored_for(?x5053, ?x11218), ceremony(?x5409, ?x5053), ceremony(?x500, ?x5053), instance_of_recurring_event(?x5053, ?x3459), ?x5409 = 0gr07, award(?x5660, ?x375), award(?x9170, ?x1232), nominated_for(?x9170, ?x278), ?x6766 = 07fzq3, ?x3459 = 0g_w, award_winner(?x4404, ?x9170), category(?x5660, ?x134), ?x500 = 0p9sw, award_winner(?x198, ?x8645), list(?x11218, ?x3004) *> conf = 0.18 ranks of expected_values: 27 EVAL 0dthsy honored_for 0cqnss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 33.000 19.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #19369-07ddz9 PRED entity: 07ddz9 PRED relation: nationality PRED expected values: 09c7w0 => 87 concepts (69 used for prediction) PRED predicted values (max 10 best out of 91): 09c7w0 (0.74 #504, 0.73 #1107, 0.73 #906), 02vzc (0.30 #403, 0.04 #3514, 0.04 #3515), 0jgd (0.30 #403, 0.02 #4316), 02jx1 (0.10 #3446, 0.10 #335, 0.10 #2644), 07ssc (0.09 #317, 0.09 #215, 0.09 #115), 0d060g (0.07 #107, 0.05 #2317, 0.04 #2116), 03rk0 (0.05 #4161, 0.04 #4563, 0.04 #750), 0chghy (0.04 #3514, 0.04 #3515, 0.03 #2712), 03rt9 (0.04 #3514, 0.04 #3515, 0.03 #2712), 0345h (0.04 #3514, 0.04 #3515, 0.03 #2712) >> Best rule #504 for best value: >> intensional similarity = 3 >> extensional distance = 760 >> proper extension: 01jb26; 04s430; 023n39; 02fybl; 01qn8k; 0ccqd7; 08k1lz; 03dn9v; 02d6n_; 054c1; ... >> query: (?x10167, 09c7w0) <- film(?x10167, ?x2816), location(?x10167, ?x12583), student(?x4916, ?x10167) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ddz9 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 69.000 0.740 http://example.org/people/person/nationality #19368-015rkw PRED entity: 015rkw PRED relation: film PRED expected values: 03hxsv 031hcx => 85 concepts (63 used for prediction) PRED predicted values (max 10 best out of 449): 043t8t (0.67 #784, 0.59 #32033, 0.41 #37372), 07nxvj (0.57 #21353, 0.34 #44493, 0.34 #58729), 01gkp1 (0.33 #811, 0.05 #39152, 0.04 #101446), 0cf8qb (0.23 #3113, 0.03 #87211), 0cmdwwg (0.17 #1123, 0.08 #2902, 0.05 #39152), 04pmnt (0.17 #1067, 0.08 #2846, 0.03 #88991), 033fqh (0.17 #836, 0.06 #53390, 0.05 #39152), 05dptj (0.17 #1322, 0.06 #53390, 0.05 #39152), 011yl_ (0.17 #582, 0.06 #53390, 0.03 #88991), 01shy7 (0.17 #420, 0.05 #39152, 0.04 #101446) >> Best rule #784 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 03hzl42; >> query: (?x1739, 043t8t) <- award_nominee(?x2263, ?x1739), ?x2263 = 01y_px, film(?x1739, ?x708) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #88991 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1678 *> proper extension: 02q6cv4; 057xn_m; *> query: (?x1739, ?x288) <- award_nominee(?x1739, ?x6916), film(?x6916, ?x288) *> conf = 0.03 ranks of expected_values: 214, 260 EVAL 015rkw film 031hcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 85.000 63.000 0.667 http://example.org/film/actor/film./film/performance/film EVAL 015rkw film 03hxsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 85.000 63.000 0.667 http://example.org/film/actor/film./film/performance/film #19367-017hnw PRED entity: 017hnw PRED relation: student PRED expected values: 04x1_w => 53 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1187): 01n1gc (0.33 #611, 0.12 #4783, 0.12 #2697), 05kfs (0.33 #98, 0.12 #4270, 0.12 #2184), 0432cd (0.33 #1316, 0.12 #5488, 0.12 #3402), 0638kv (0.33 #841, 0.12 #5013, 0.12 #2927), 07f7jp (0.33 #1974, 0.12 #6146, 0.12 #4060), 015qyf (0.33 #1324, 0.08 #52153, 0.07 #31291), 0d06m5 (0.33 #540, 0.06 #4712, 0.06 #2626), 05bnp0 (0.33 #11, 0.06 #4183, 0.06 #2097), 0bs8d (0.33 #930, 0.06 #5102, 0.06 #3016), 0d3qd0 (0.33 #783, 0.06 #4955, 0.06 #2869) >> Best rule #611 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 08815; >> query: (?x13219, 01n1gc) <- student(?x13219, ?x7540), student(?x13219, ?x4196), school_type(?x13219, ?x8834), ?x7540 = 034ls, ?x4196 = 09b6zr >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 017hnw student 04x1_w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 29.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #19366-0p9gg PRED entity: 0p9gg PRED relation: people! PRED expected values: 01tf_6 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 33): 0gk4g (0.24 #270, 0.23 #660, 0.22 #725), 0jdk0 (0.20 #5, 0.01 #265, 0.01 #330), 0j8hd (0.20 #46), 0dq9p (0.18 #82, 0.12 #732, 0.12 #147), 02y0js (0.18 #67, 0.08 #912, 0.07 #717), 0qcr0 (0.13 #261, 0.12 #651, 0.11 #391), 04p3w (0.09 #76, 0.08 #141, 0.08 #401), 01psyx (0.09 #109, 0.04 #694, 0.04 #434), 04psf (0.09 #72, 0.02 #917, 0.02 #722), 02knxx (0.07 #291, 0.05 #746, 0.05 #681) >> Best rule #270 for best value: >> intensional similarity = 3 >> extensional distance = 205 >> proper extension: 049tjg; 015wfg; 03q95r; 03wd5tk; 0c_drn; 0gm34; 0jvtp; 01bh6y; 05hjmd; 03f68r6; ... >> query: (?x13159, 0gk4g) <- nominated_for(?x13159, ?x1973), type_of_union(?x13159, ?x566), people(?x6821, ?x13159) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #1135 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 545 *> proper extension: 0qkj7; *> query: (?x13159, 01tf_6) <- people(?x6821, ?x13159), type_of_union(?x13159, ?x566) *> conf = 0.02 ranks of expected_values: 28 EVAL 0p9gg people! 01tf_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 69.000 69.000 0.242 http://example.org/people/cause_of_death/people #19365-01f7gh PRED entity: 01f7gh PRED relation: written_by PRED expected values: 01vz80y => 81 concepts (57 used for prediction) PRED predicted values (max 10 best out of 63): 0cv9fc (0.17 #3372, 0.15 #1686, 0.14 #8094), 07s93v (0.12 #47, 0.04 #1058, 0.03 #1733), 026dx (0.12 #152, 0.01 #2174), 0h96g (0.10 #15171, 0.09 #18544, 0.08 #12476), 086sj (0.08 #12476, 0.08 #12475, 0.08 #18881), 016tt2 (0.08 #12476, 0.08 #12475, 0.08 #18881), 03_gd (0.06 #358, 0.02 #3056, 0.02 #2719), 081lh (0.06 #367, 0.02 #7111, 0.02 #7449), 076_74 (0.06 #451, 0.02 #1463, 0.01 #3149), 012x2b (0.06 #628) >> Best rule #3372 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 01q2nx; 056xkh; >> query: (?x1430, ?x11580) <- film_crew_role(?x1430, ?x137), film_format(?x1430, ?x909), produced_by(?x1430, ?x11580) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #564 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 02qr3k8; *> query: (?x1430, 01vz80y) <- film(?x4771, ?x1430), genre(?x1430, ?x225), language(?x1430, ?x254), ?x4771 = 0h96g *> conf = 0.06 ranks of expected_values: 13 EVAL 01f7gh written_by 01vz80y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 81.000 57.000 0.172 http://example.org/film/film/written_by #19364-0315w4 PRED entity: 0315w4 PRED relation: genre PRED expected values: 06n90 => 78 concepts (75 used for prediction) PRED predicted values (max 10 best out of 117): 05p553 (0.77 #3143, 0.36 #366, 0.35 #847), 07s9rl0 (0.68 #1444, 0.68 #1565, 0.66 #2531), 09blyk (0.61 #5438, 0.61 #4348, 0.55 #602), 03k9fj (0.52 #493, 0.50 #854, 0.50 #1215), 02kdv5l (0.41 #3623, 0.35 #2292, 0.34 #1688), 02l7c8 (0.39 #16, 0.36 #137, 0.35 #257), 04xvlr (0.34 #2, 0.33 #123, 0.27 #243), 04xvh5 (0.27 #34, 0.24 #155, 0.22 #275), 0hcr (0.24 #1226, 0.22 #1106, 0.19 #745), 06n90 (0.23 #1216, 0.22 #855, 0.22 #1096) >> Best rule #3143 for best value: >> intensional similarity = 3 >> extensional distance = 751 >> proper extension: 04cf_l; 0hr41p6; >> query: (?x4799, 05p553) <- genre(?x4799, ?x1510), genre(?x7800, ?x1510), ?x7800 = 02wgbb >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1216 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 243 *> proper extension: 0dr1c2; *> query: (?x4799, 06n90) <- genre(?x4799, ?x1510), ?x1510 = 01hmnh, film(?x6618, ?x4799) *> conf = 0.23 ranks of expected_values: 10 EVAL 0315w4 genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 78.000 75.000 0.774 http://example.org/film/film/genre #19363-03tmr PRED entity: 03tmr PRED relation: country PRED expected values: 0d0vqn => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 369): 03rjj (0.92 #6196, 0.91 #5983, 0.90 #5557), 07t21 (0.90 #5591, 0.88 #6230, 0.87 #6017), 0f8l9c (0.86 #5787, 0.85 #6618, 0.85 #6212), 03_3d (0.81 #5772, 0.78 #6402, 0.76 #7633), 05qhw (0.81 #6389, 0.81 #6207, 0.78 #6185), 06qd3 (0.80 #5590, 0.80 #5160, 0.79 #5374), 07ylj (0.80 #5152, 0.71 #4126, 0.70 #5582), 0chghy (0.78 #5989, 0.76 #7219, 0.75 #5563), 0d0vqn (0.77 #6199, 0.74 #6179, 0.74 #5986), 07ssc (0.76 #7225, 0.76 #6413, 0.74 #7644) >> Best rule #6196 for best value: >> intensional similarity = 47 >> extensional distance = 24 >> proper extension: 01hp22; 0w0d; >> query: (?x453, 03rjj) <- olympics(?x453, ?x418), sports(?x7429, ?x453), country(?x453, ?x2188), country(?x453, ?x1603), country(?x453, ?x1264), sports(?x7429, ?x3309), country(?x3309, ?x3912), country(?x3309, ?x3277), country(?x3309, ?x3227), country(?x3309, ?x2236), country(?x3309, ?x2000), country(?x3309, ?x456), country(?x3309, ?x142), ?x2236 = 05sb1, ?x142 = 0jgd, ?x3912 = 04w58, ?x2188 = 0163v, ?x456 = 05qhw, ?x2000 = 0d0kn, ?x3277 = 06t8v, ?x1603 = 06bnz, film_release_region(?x7700, ?x1264), film_release_region(?x5576, ?x1264), film_release_region(?x4529, ?x1264), film_release_region(?x1370, ?x1264), film_release_region(?x1364, ?x1264), film_release_region(?x903, ?x1264), film_release_region(?x66, ?x1264), combatants(?x1264, ?x583), ?x4529 = 0gbtbm, contains(?x1264, ?x196), ?x1364 = 047msdk, ?x5576 = 0gbfn9, olympics(?x1264, ?x7688), country(?x6007, ?x1264), nationality(?x12841, ?x1264), nationality(?x9467, ?x1264), ?x7700 = 0cp08zg, ?x7688 = 0jkvj, medal(?x1264, ?x422), ?x6007 = 0dgq_kn, ?x903 = 04969y, ?x9467 = 04mby, ?x66 = 014lc_, ?x1370 = 0gmcwlb, influenced_by(?x12841, ?x3336), ?x3227 = 0bjv6 >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #6199 for first EXPECTED value: *> intensional similarity = 47 *> extensional distance = 24 *> proper extension: 01hp22; 0w0d; *> query: (?x453, 0d0vqn) <- olympics(?x453, ?x418), sports(?x7429, ?x453), country(?x453, ?x2188), country(?x453, ?x1603), country(?x453, ?x1264), sports(?x7429, ?x3309), country(?x3309, ?x3912), country(?x3309, ?x3277), country(?x3309, ?x3227), country(?x3309, ?x2236), country(?x3309, ?x2000), country(?x3309, ?x456), country(?x3309, ?x142), ?x2236 = 05sb1, ?x142 = 0jgd, ?x3912 = 04w58, ?x2188 = 0163v, ?x456 = 05qhw, ?x2000 = 0d0kn, ?x3277 = 06t8v, ?x1603 = 06bnz, film_release_region(?x7700, ?x1264), film_release_region(?x5576, ?x1264), film_release_region(?x4529, ?x1264), film_release_region(?x1370, ?x1264), film_release_region(?x1364, ?x1264), film_release_region(?x903, ?x1264), film_release_region(?x66, ?x1264), combatants(?x1264, ?x583), ?x4529 = 0gbtbm, contains(?x1264, ?x196), ?x1364 = 047msdk, ?x5576 = 0gbfn9, olympics(?x1264, ?x7688), country(?x6007, ?x1264), nationality(?x12841, ?x1264), nationality(?x9467, ?x1264), ?x7700 = 0cp08zg, ?x7688 = 0jkvj, medal(?x1264, ?x422), ?x6007 = 0dgq_kn, ?x903 = 04969y, ?x9467 = 04mby, ?x66 = 014lc_, ?x1370 = 0gmcwlb, influenced_by(?x12841, ?x3336), ?x3227 = 0bjv6 *> conf = 0.77 ranks of expected_values: 9 EVAL 03tmr country 0d0vqn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 39.000 39.000 0.923 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #19362-0169dl PRED entity: 0169dl PRED relation: award PRED expected values: 099jhq 09sb52 099tbz 03hl6lc => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 280): 0gr51 (0.70 #22126, 0.70 #22918, 0.69 #15802), 02x1dht (0.70 #22126, 0.70 #22918, 0.69 #15802), 027cyf7 (0.70 #22126, 0.70 #22918, 0.69 #15802), 09sb52 (0.47 #433, 0.39 #1618, 0.39 #1223), 02x73k6 (0.47 #452, 0.12 #35564, 0.11 #847), 099jhq (0.42 #413, 0.08 #1993, 0.08 #1598), 027dtxw (0.38 #399, 0.13 #794, 0.13 #4), 02w9sd7 (0.38 #559, 0.13 #2139, 0.09 #954), 0gr4k (0.30 #8326, 0.29 #5166, 0.27 #8721), 04kxsb (0.30 #517, 0.17 #2097, 0.14 #7232) >> Best rule #22126 for best value: >> intensional similarity = 2 >> extensional distance = 1294 >> proper extension: 01jq34; 018_q8; >> query: (?x2422, ?x746) <- award_winner(?x2422, ?x406), award_winner(?x746, ?x2422) >> conf = 0.70 => this is the best rule for 3 predicted values *> Best rule #433 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 013cr; 046qq; 015076; *> query: (?x2422, 09sb52) <- award(?x2422, ?x3066), participant(?x2422, ?x286), ?x3066 = 0gqy2 *> conf = 0.47 ranks of expected_values: 4, 6, 18, 64 EVAL 0169dl award 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 107.000 107.000 0.699 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0169dl award 099tbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 107.000 107.000 0.699 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0169dl award 09sb52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 107.000 0.699 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0169dl award 099jhq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 107.000 107.000 0.699 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19361-0dw6b PRED entity: 0dw6b PRED relation: influenced_by PRED expected values: 0465_ => 219 concepts (70 used for prediction) PRED predicted values (max 10 best out of 386): 03sbs (0.53 #4105, 0.44 #3674, 0.43 #1085), 0dw6b (0.50 #2018, 0.27 #3312, 0.18 #11237), 0448r (0.50 #2852, 0.12 #3714, 0.12 #4145), 0j3v (0.47 #3944, 0.43 #924, 0.38 #3513), 0113sg (0.43 #1296, 0.38 #2104, 0.24 #16858), 02wh0 (0.43 #1243, 0.25 #3832, 0.24 #4263), 015n8 (0.29 #4291, 0.29 #1271, 0.25 #3860), 0gz_ (0.29 #3987, 0.24 #12204, 0.19 #3556), 032l1 (0.29 #953, 0.25 #1817, 0.24 #3973), 06myp (0.29 #1235, 0.19 #3824, 0.18 #4255) >> Best rule #4105 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 0j3v; 06whf; 039n1; 02wh0; >> query: (?x8659, 03sbs) <- gender(?x8659, ?x231), influenced_by(?x8659, ?x10654), place_of_death(?x8659, ?x7184), ?x10654 = 042q3, influenced_by(?x3336, ?x8659) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #2789 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0dfrq; *> query: (?x8659, 0465_) <- gender(?x8659, ?x231), influenced_by(?x8659, ?x1279), ?x1279 = 028p0, profession(?x8659, ?x353), nationality(?x8659, ?x1603) *> conf = 0.08 ranks of expected_values: 80 EVAL 0dw6b influenced_by 0465_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 219.000 70.000 0.529 http://example.org/influence/influence_node/influenced_by #19360-038981 PRED entity: 038981 PRED relation: school PRED expected values: 01jsn5 => 16 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1475): 065y4w7 (0.67 #1488, 0.60 #1370, 0.57 #1246), 01pl14 (0.57 #1127, 0.53 #1484, 0.53 #1598), 07w0v (0.57 #1132, 0.50 #1116, 0.50 #886), 01vs5c (0.57 #1183, 0.33 #1540, 0.33 #1422), 0lyjf (0.57 #1175, 0.33 #1532, 0.33 #598), 015q1n (0.50 #1116, 0.50 #886, 0.50 #727), 06fq2 (0.50 #975, 0.40 #1446, 0.40 #859), 0j_sncb (0.50 #689, 0.38 #1004, 0.33 #576), 0bx8pn (0.50 #1003, 0.38 #1004, 0.33 #343), 0f1nl (0.50 #433, 0.33 #460, 0.33 #432) >> Best rule #1488 for best value: >> intensional similarity = 54 >> extensional distance = 13 >> proper extension: 05vsb7; 03nt7j; 09l0x9; 047dpm0; >> query: (?x8586, 065y4w7) <- draft(?x7136, ?x8586), draft(?x5756, ?x8586), draft(?x5483, ?x8586), school(?x8586, ?x7202), school(?x8586, ?x4296), school(?x8586, ?x581), school_type(?x7202, ?x1044), major_field_of_study(?x7202, ?x2981), school(?x5483, ?x2175), colors(?x7136, ?x4557), school(?x1160, ?x581), major_field_of_study(?x581, ?x11378), major_field_of_study(?x581, ?x10391), major_field_of_study(?x581, ?x9111), major_field_of_study(?x581, ?x9079), major_field_of_study(?x581, ?x8962), major_field_of_study(?x581, ?x7403), major_field_of_study(?x581, ?x1527), student(?x581, ?x1299), institution(?x620, ?x581), ?x9079 = 0l5mz, ?x8962 = 04g7x, currency(?x4296, ?x170), team(?x1348, ?x7136), ?x9111 = 04sh3, teams(?x3373, ?x5756), school(?x700, ?x4296), major_field_of_study(?x6814, ?x10391), team(?x11924, ?x7136), school_type(?x4296, ?x1507), ?x6814 = 03tw2s, major_field_of_study(?x12907, ?x10391), ?x1160 = 049n7, major_field_of_study(?x8095, ?x11378), student(?x4296, ?x11220), student(?x4296, ?x3927), major_field_of_study(?x1771, ?x7403), major_field_of_study(?x3212, ?x1527), team(?x6848, ?x5483), ?x3212 = 02bb47, fraternities_and_sororities(?x581, ?x4348), ?x4557 = 019sc, film(?x3927, ?x86), film(?x11220, ?x814), nationality(?x11220, ?x94), major_field_of_study(?x4296, ?x2606), award_nominee(?x3927, ?x4046), list(?x581, ?x2197), ?x8095 = 02mp0g, student(?x11378, ?x4882), ?x1044 = 05pcjw, team(?x13931, ?x5483), ?x2197 = 09g7thr, school(?x5756, ?x466) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #238 for first EXPECTED value: *> intensional similarity = 61 *> extensional distance = 1 *> proper extension: 09th87; *> query: (?x8586, 01jsn5) <- draft(?x10837, ?x8586), draft(?x10409, ?x8586), draft(?x9937, ?x8586), draft(?x9931, ?x8586), draft(?x9760, ?x8586), draft(?x9049, ?x8586), draft(?x7136, ?x8586), draft(?x5756, ?x8586), draft(?x5483, ?x8586), draft(?x4571, ?x8586), draft(?x2820, ?x8586), school(?x8586, ?x7202), school(?x8586, ?x2895), school_type(?x7202, ?x1044), ?x5483 = 0jml5, major_field_of_study(?x7202, ?x6756), ?x6756 = 0_jm, ?x5756 = 0jm4b, institution(?x865, ?x7202), currency(?x7202, ?x170), student(?x7202, ?x6659), ?x4571 = 0jm6n, school_type(?x6925, ?x1044), school_type(?x6541, ?x1044), school_type(?x3439, ?x1044), school_type(?x1665, ?x1044), school_type(?x1043, ?x1044), school_type(?x735, ?x1044), institution(?x1771, ?x2895), ?x735 = 065y4w7, contains(?x12088, ?x2895), ?x6925 = 01bm_, ?x865 = 02h4rq6, student(?x2895, ?x8863), ?x3439 = 03ksy, ?x7136 = 0jm74, county_seat(?x12087, ?x12088), contains(?x94, ?x7202), ?x9760 = 0bwjj, ?x1665 = 04rwx, award_winner(?x3486, ?x2895), ?x1771 = 019v9k, ?x2820 = 0jmj7, category(?x7202, ?x134), ?x9937 = 0jmjr, major_field_of_study(?x2895, ?x5607), ?x1043 = 0kz2w, major_field_of_study(?x90, ?x5607), team(?x9266, ?x10409), teams(?x1719, ?x10409), ?x94 = 09c7w0, position(?x10409, ?x1348), participant(?x6659, ?x5479), ?x9049 = 0jmm4, award_winner(?x4837, ?x6659), artist(?x12171, ?x6659), type_of_union(?x6659, ?x566), student(?x5607, ?x4265), ?x6541 = 02km0m, ?x10837 = 0jm7n, ?x9931 = 0jm3b *> conf = 0.33 ranks of expected_values: 27 EVAL 038981 school 01jsn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 16.000 16.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #19359-065dc4 PRED entity: 065dc4 PRED relation: language PRED expected values: 064_8sq => 68 concepts (66 used for prediction) PRED predicted values (max 10 best out of 28): 02bjrlw (0.64 #1066, 0.31 #169, 0.06 #897), 064_8sq (0.38 #188, 0.25 #20, 0.22 #76), 06b_j (0.25 #21, 0.22 #77, 0.20 #133), 01r2l (0.15 #191, 0.12 #23, 0.11 #79), 04306rv (0.15 #172, 0.11 #60, 0.10 #284), 03hkp (0.11 #69, 0.08 #181, 0.02 #741), 0jzc (0.11 #74, 0.03 #298, 0.03 #746), 0653m (0.08 #178, 0.04 #458, 0.04 #514), 03k50 (0.08 #176, 0.02 #1299, 0.02 #1582), 05zjd (0.08 #192, 0.02 #696, 0.02 #864) >> Best rule #1066 for best value: >> intensional similarity = 4 >> extensional distance = 811 >> proper extension: 04xbq3; >> query: (?x3953, ?x90) <- film(?x1958, ?x3953), nominated_for(?x666, ?x3953), people(?x743, ?x1958), languages(?x1958, ?x90) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #188 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 0cbl95; *> query: (?x3953, 064_8sq) <- written_by(?x3953, ?x8692), language(?x3953, ?x11038), ?x11038 = 04h9h *> conf = 0.38 ranks of expected_values: 2 EVAL 065dc4 language 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 66.000 0.640 http://example.org/film/film/language #19358-02zrv7 PRED entity: 02zrv7 PRED relation: film PRED expected values: 04cppj => 108 concepts (85 used for prediction) PRED predicted values (max 10 best out of 946): 04cppj (0.73 #3582, 0.71 #42973, 0.66 #7163), 09g8vhw (0.25 #325, 0.05 #5697, 0.04 #7489), 08rr3p (0.25 #442, 0.04 #7606, 0.03 #9396), 043n1r5 (0.25 #1620, 0.04 #8784, 0.03 #10574), 01gvsn (0.25 #1698, 0.04 #8862, 0.03 #10652), 03tps5 (0.25 #737, 0.04 #7901, 0.03 #9691), 09d3b7 (0.25 #1480, 0.04 #8644, 0.03 #10434), 02vnmc9 (0.25 #1348, 0.04 #8512, 0.03 #10302), 02x3lt7 (0.25 #84, 0.03 #9038, 0.02 #28734), 013q07 (0.15 #5728, 0.11 #12891, 0.06 #18263) >> Best rule #3582 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 04w391; 04fzk; 0fthdk; >> query: (?x6328, ?x6516) <- nominated_for(?x6328, ?x6516), participant(?x6328, ?x10777), language(?x6328, ?x254) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zrv7 film 04cppj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 85.000 0.731 http://example.org/film/actor/film./film/performance/film #19357-01v1ln PRED entity: 01v1ln PRED relation: language PRED expected values: 012w70 0295r => 92 concepts (84 used for prediction) PRED predicted values (max 10 best out of 37): 06nm1 (0.46 #65, 0.14 #234, 0.14 #515), 064_8sq (0.33 #244, 0.15 #75, 0.14 #862), 02bjrlw (0.25 #226, 0.23 #57, 0.11 #170), 06b_j (0.23 #76, 0.14 #245, 0.11 #582), 0jzc (0.15 #73, 0.08 #242, 0.05 #579), 0459q4 (0.12 #34, 0.03 #540, 0.03 #484), 03115z (0.12 #35, 0.02 #541, 0.02 #485), 03_9r (0.08 #514, 0.08 #233, 0.08 #458), 012w70 (0.08 #66, 0.07 #235, 0.06 #516), 01r2l (0.08 #78, 0.03 #696, 0.03 #528) >> Best rule #65 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 02qrv7; >> query: (?x6994, 06nm1) <- film(?x3692, ?x6994), film(?x2745, ?x6994), ?x3692 = 03kpvp, country(?x6994, ?x94), award_winner(?x4536, ?x2745) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #66 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 02qrv7; *> query: (?x6994, 012w70) <- film(?x3692, ?x6994), film(?x2745, ?x6994), ?x3692 = 03kpvp, country(?x6994, ?x94), award_winner(?x4536, ?x2745) *> conf = 0.08 ranks of expected_values: 9 EVAL 01v1ln language 0295r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 84.000 0.462 http://example.org/film/film/language EVAL 01v1ln language 012w70 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 92.000 84.000 0.462 http://example.org/film/film/language #19356-0k4f3 PRED entity: 0k4f3 PRED relation: film! PRED expected values: 033hqf => 54 concepts (37 used for prediction) PRED predicted values (max 10 best out of 787): 05x2t7 (0.48 #60384, 0.48 #12495, 0.47 #8332), 0c921 (0.48 #12495, 0.47 #8332, 0.44 #6249), 0fd6qb (0.48 #12495, 0.47 #8332, 0.44 #6249), 05728w1 (0.48 #12495, 0.47 #8332, 0.44 #6249), 09cdxn (0.48 #12495, 0.47 #8332, 0.44 #6249), 022p06 (0.35 #52052, 0.34 #52051, 0.32 #58301), 081lh (0.12 #161, 0.05 #12656, 0.05 #14737), 01f873 (0.11 #3979), 06hzsx (0.07 #4167, 0.04 #47886, 0.04 #58299), 087v17 (0.07 #4167, 0.04 #47886, 0.04 #58299) >> Best rule #60384 for best value: >> intensional similarity = 4 >> extensional distance = 1040 >> proper extension: 0gfzgl; 01f3p_; 02sqkh; 06qwh; 0sw0q; 07wqr6; 03g9xj; 0cskb; 023ny6; 015pnb; >> query: (?x2779, ?x509) <- nominated_for(?x509, ?x2779), titles(?x4150, ?x2779), location(?x509, ?x1523), profession(?x509, ?x319) >> conf = 0.48 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k4f3 film! 033hqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 37.000 0.480 http://example.org/film/actor/film./film/performance/film #19355-0sxfd PRED entity: 0sxfd PRED relation: honored_for! PRED expected values: 0bzm__ => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 112): 09gkdln (0.11 #228, 0.11 #350, 0.11 #106), 05zksls (0.09 #8910, 0.03 #28, 0.02 #150), 0fqpc7d (0.09 #8910, 0.02 #151, 0.02 #273), 0bzm__ (0.09 #8910, 0.02 #197, 0.02 #319), 026kq4q (0.09 #8910, 0.02 #1379, 0.01 #8421), 0bzmt8 (0.08 #84, 0.05 #206, 0.04 #328), 0418154 (0.06 #3417, 0.06 #459, 0.05 #215), 02wzl1d (0.06 #3417, 0.05 #7, 0.05 #129), 0bz6sb (0.06 #3417, 0.05 #53, 0.04 #419), 04n2r9h (0.06 #3417, 0.05 #1500, 0.04 #1378) >> Best rule #228 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0g4pl7z; >> query: (?x1402, 09gkdln) <- production_companies(?x1402, ?x902), film_festivals(?x1402, ?x5415), award_winner(?x1402, ?x3585), genre(?x1402, ?x53) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #8910 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1301 *> proper extension: 04bp0l; *> query: (?x1402, ?x8964) <- nominated_for(?x9391, ?x1402), nominated_for(?x879, ?x1402), award_winner(?x8964, ?x9391), award_winner(?x7511, ?x879) *> conf = 0.09 ranks of expected_values: 4 EVAL 0sxfd honored_for! 0bzm__ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 90.000 0.114 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #19354-026c0p PRED entity: 026c0p PRED relation: type_of_union PRED expected values: 04ztj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.81 #69, 0.81 #37, 0.81 #33), 01g63y (0.19 #383, 0.19 #374, 0.15 #199), 01bl8s (0.19 #383, 0.19 #374, 0.03 #27), 0jgjn (0.19 #383, 0.19 #374) >> Best rule #69 for best value: >> intensional similarity = 4 >> extensional distance = 175 >> proper extension: 04bgy; >> query: (?x12700, 04ztj) <- place_of_death(?x12700, ?x4698), film(?x12700, ?x7760), language(?x7760, ?x254), country(?x7760, ?x94) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026c0p type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.814 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19353-010z5n PRED entity: 010z5n PRED relation: contains! PRED expected values: 081mh => 72 concepts (30 used for prediction) PRED predicted values (max 10 best out of 77): 059rby (0.33 #19, 0.12 #24191, 0.07 #3600), 01n7q (0.19 #24249, 0.15 #8135, 0.14 #14402), 07ssc (0.15 #17937, 0.15 #7194, 0.14 #17041), 04_1l0v (0.12 #3135, 0.12 #9403, 0.10 #10298), 02jx1 (0.11 #7249, 0.10 #17992, 0.10 #17096), 03rk0 (0.06 #1031, 0.05 #7299, 0.04 #9984), 05fjf (0.06 #3954, 0.06 #4849, 0.05 #11117), 05k7sb (0.06 #12667, 0.05 #14457, 0.04 #21619), 07b_l (0.06 #3802, 0.06 #4697, 0.05 #5592), 02xry (0.05 #1952, 0.05 #12697, 0.05 #5533) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02_286; >> query: (?x12583, 059rby) <- source(?x12583, ?x958), location(?x8680, ?x12583), contains(?x94, ?x12583), ?x8680 = 035sc2 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1972 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 218 *> proper extension: 0p9nv; *> query: (?x12583, 081mh) <- source(?x12583, ?x958), location(?x8680, ?x12583), contains(?x94, ?x12583), ?x94 = 09c7w0 *> conf = 0.02 ranks of expected_values: 42 EVAL 010z5n contains! 081mh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 72.000 30.000 0.333 http://example.org/location/location/contains #19352-03b79 PRED entity: 03b79 PRED relation: capital PRED expected values: 0156q => 80 concepts (67 used for prediction) PRED predicted values (max 10 best out of 94): 0156q (0.50 #254, 0.30 #861, 0.21 #1103), 0ftjx (0.33 #60, 0.06 #1636, 0.05 #1757), 04jpl (0.25 #125, 0.20 #367, 0.14 #1095), 05qtj (0.25 #141, 0.20 #383, 0.06 #1596), 0dp90 (0.20 #445, 0.07 #1294, 0.06 #1536), 081m_ (0.17 #527, 0.14 #648, 0.12 #770), 095w_ (0.17 #494, 0.12 #1463, 0.10 #858), 04swd (0.14 #644, 0.12 #766, 0.08 #1008), 05ywg (0.14 #616, 0.12 #738, 0.06 #1343), 07mgr (0.14 #661, 0.12 #783, 0.06 #1631) >> Best rule #254 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 084n_; >> query: (?x3142, 0156q) <- nationality(?x11479, ?x3142), nationality(?x10654, ?x3142), nationality(?x9178, ?x3142), ?x9178 = 01kx1j, ?x11479 = 01llxp, influenced_by(?x2240, ?x10654), influenced_by(?x10654, ?x5004), student(?x12877, ?x10654) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03b79 capital 0156q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 67.000 0.500 http://example.org/location/country/capital #19351-018j2 PRED entity: 018j2 PRED relation: role PRED expected values: 02snj9 => 86 concepts (55 used for prediction) PRED predicted values (max 10 best out of 74): 028tv0 (0.87 #2231, 0.83 #1633, 0.82 #1933), 05148p4 (0.83 #1633, 0.82 #1933, 0.82 #3727), 0l14qv (0.83 #1633, 0.82 #1933, 0.82 #3727), 0g2dz (0.83 #1633, 0.82 #1933, 0.82 #3727), 01s0ps (0.83 #1633, 0.82 #1933, 0.82 #3727), 014zz1 (0.83 #1633, 0.82 #1933, 0.82 #3727), 0239kh (0.83 #1633, 0.82 #1933, 0.82 #3727), 02bxd (0.83 #1633, 0.82 #1933, 0.82 #3727), 018j2 (0.75 #1511, 0.67 #2255, 0.65 #366), 01wy6 (0.71 #1215, 0.70 #1889, 0.68 #2452) >> Best rule #2231 for best value: >> intensional similarity = 18 >> extensional distance = 10 >> proper extension: 0myk8; >> query: (?x2048, ?x645) <- role(?x2048, ?x4616), role(?x2048, ?x1574), role(?x2048, ?x228), ?x4616 = 01rhl, instrumentalists(?x2048, ?x7753), instrumentalists(?x2048, ?x2575), role(?x645, ?x2048), ?x1574 = 0l15bq, artists(?x1928, ?x2575), gender(?x7753, ?x514), group(?x645, ?x9999), group(?x645, ?x9228), group(?x645, ?x3420), ?x9999 = 01_wfj, ?x9228 = 0cbm64, role(?x679, ?x645), ?x228 = 0l14qv, artist(?x2149, ?x3420) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1598 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 7 *> proper extension: 0l14md; 05148p4; 0dwt5; *> query: (?x2048, 02snj9) <- role(?x2048, ?x4616), role(?x2048, ?x1969), role(?x2048, ?x1432), role(?x2048, ?x885), ?x4616 = 01rhl, role(?x211, ?x2048), role(?x2048, ?x74), instrumentalists(?x2048, ?x8799), ?x1432 = 0395lw, ?x885 = 0dwtp, ?x1969 = 04rzd, role(?x2253, ?x2048), award_winner(?x3375, ?x8799), artist(?x2931, ?x8799), award_winner(?x1088, ?x8799), role(?x645, ?x2048) *> conf = 0.67 ranks of expected_values: 13 EVAL 018j2 role 02snj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 86.000 55.000 0.870 http://example.org/music/performance_role/regular_performances./music/group_membership/role #19350-01t9qj_ PRED entity: 01t9qj_ PRED relation: award_winner! PRED expected values: 054ky1 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 280): 0bdwqv (0.45 #5609, 0.39 #3450, 0.37 #21573), 0f4x7 (0.21 #1756, 0.10 #9092, 0.04 #15564), 09sb52 (0.16 #17729, 0.10 #18160, 0.10 #18592), 03x3wf (0.15 #497, 0.14 #3083, 0.12 #65), 01by1l (0.15 #3131, 0.13 #545, 0.13 #976), 019bnn (0.13 #700, 0.12 #1131, 0.11 #2424), 0m7yy (0.12 #180, 0.07 #1905, 0.07 #3198), 05qck (0.11 #1918, 0.08 #625, 0.06 #1056), 0ck27z (0.11 #17781, 0.08 #19508, 0.08 #19939), 02v1m7 (0.10 #546, 0.06 #977, 0.05 #3132) >> Best rule #5609 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 05218gr; >> query: (?x8006, ?x3247) <- award(?x8006, ?x3247), place_of_death(?x8006, ?x13207), award_winner(?x8006, ?x6808) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1835 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 94 *> proper extension: 0d9kl; 057ph; 0dng4; *> query: (?x8006, 054ky1) <- celebrities_impersonated(?x3649, ?x8006), ?x3649 = 03m6t5 *> conf = 0.09 ranks of expected_values: 11 EVAL 01t9qj_ award_winner! 054ky1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 93.000 93.000 0.447 http://example.org/award/award_category/winners./award/award_honor/award_winner #19349-07yvsn PRED entity: 07yvsn PRED relation: film_crew_role PRED expected values: 09vw2b7 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 24): 09vw2b7 (0.79 #338, 0.71 #1236, 0.65 #1570), 0dxtw (0.43 #341, 0.38 #242, 0.38 #75), 02ynfr (0.38 #346, 0.38 #247, 0.29 #412), 01vx2h (0.38 #1240, 0.37 #939, 0.36 #1174), 02_n3z (0.29 #34, 0.25 #101, 0.19 #168), 089g0h (0.29 #50, 0.25 #117, 0.17 #349), 0d2b38 (0.29 #56, 0.25 #123, 0.17 #23), 02rh1dz (0.24 #241, 0.19 #340, 0.16 #208), 01xy5l_ (0.21 #443, 0.19 #344, 0.16 #1242), 015h31 (0.18 #936, 0.17 #7, 0.12 #438) >> Best rule #338 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 02x6dqb; >> query: (?x3441, 09vw2b7) <- film(?x4929, ?x3441), costume_design_by(?x3441, ?x4190), participant(?x4929, ?x2012), film_crew_role(?x3441, ?x137) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07yvsn film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.786 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19348-0hsn_ PRED entity: 0hsn_ PRED relation: profession PRED expected values: 0lgw7 => 125 concepts (102 used for prediction) PRED predicted values (max 10 best out of 63): 02jknp (0.47 #5594, 0.25 #1918, 0.25 #7211), 0dxtg (0.47 #5600, 0.33 #4865, 0.32 #9716), 03gjzk (0.32 #5601, 0.31 #3983, 0.24 #749), 0np9r (0.21 #314, 0.20 #6636, 0.15 #14134), 09jwl (0.20 #9868, 0.18 #10457, 0.18 #2076), 0cbd2 (0.17 #4563, 0.16 #7945, 0.15 #7357), 0kyk (0.16 #4586, 0.12 #7380, 0.12 #7968), 02krf9 (0.14 #5613, 0.12 #1790, 0.11 #4878), 018gz8 (0.14 #7808, 0.14 #2809, 0.13 #11631), 016z4k (0.14 #445, 0.12 #9854, 0.11 #10443) >> Best rule #5594 for best value: >> intensional similarity = 4 >> extensional distance = 611 >> proper extension: 02rchht; 0m2l9; 04rs03; 0168cl; 01g4zr; 052gzr; 086qd; 0144l1; 01wg982; 021lby; ... >> query: (?x8734, 02jknp) <- award(?x8734, ?x375), type_of_union(?x8734, ?x566), profession(?x8734, ?x319), ?x319 = 01d_h8 >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #194 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 046rfv; 024y6w; *> query: (?x8734, 0lgw7) <- profession(?x8734, ?x4773), ?x4773 = 0d1pc, student(?x3948, ?x8734) *> conf = 0.02 ranks of expected_values: 44 EVAL 0hsn_ profession 0lgw7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 125.000 102.000 0.473 http://example.org/people/person/profession #19347-033rq PRED entity: 033rq PRED relation: profession PRED expected values: 0dxtg => 119 concepts (95 used for prediction) PRED predicted values (max 10 best out of 85): 01d_h8 (0.81 #1049, 0.77 #155, 0.69 #900), 02hrh1q (0.75 #5229, 0.73 #4186, 0.72 #2994), 0dxtg (0.66 #907, 0.66 #1354, 0.66 #1503), 0cbd2 (0.57 #603, 0.47 #1348, 0.42 #1497), 03gjzk (0.46 #164, 0.33 #1058, 0.30 #1356), 05z96 (0.27 #12966, 0.26 #13414, 0.13 #14160), 0kyk (0.27 #30, 0.27 #1371, 0.25 #626), 02krf9 (0.27 #27, 0.20 #176, 0.17 #1070), 09jwl (0.22 #2999, 0.20 #764, 0.19 #5830), 01c72t (0.18 #769, 0.13 #14160, 0.10 #2110) >> Best rule #1049 for best value: >> intensional similarity = 3 >> extensional distance = 119 >> proper extension: 0q9kd; 054_mz; 0kr5_; 02ndbd; 05m883; 02r5w9; 022_lg; 01f7j9; 04y8r; 0184dt; ... >> query: (?x8573, 01d_h8) <- award(?x8573, ?x198), nominated_for(?x8573, ?x5429), ?x198 = 040njc >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #907 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 05bxwh; 09qc1; 05tjm3; *> query: (?x8573, 0dxtg) <- award(?x8573, ?x1313), nationality(?x8573, ?x205), ?x1313 = 0gs9p *> conf = 0.66 ranks of expected_values: 3 EVAL 033rq profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 95.000 0.810 http://example.org/people/person/profession #19346-02rxbmt PRED entity: 02rxbmt PRED relation: award PRED expected values: 0gqzz 02681xs => 106 concepts (81 used for prediction) PRED predicted values (max 10 best out of 381): 0fms83 (0.74 #23285, 0.71 #29315, 0.70 #28912), 01by1l (0.47 #6133, 0.11 #2116, 0.10 #12558), 026rsl9 (0.40 #335, 0.08 #737, 0.08 #30120), 02681_5 (0.40 #385, 0.08 #787, 0.08 #30120), 02w7fs (0.40 #352, 0.08 #754, 0.08 #30120), 02681xs (0.40 #189, 0.08 #591, 0.05 #24491), 01bgqh (0.36 #6064, 0.09 #12489, 0.09 #2047), 03qbh5 (0.23 #6227, 0.08 #2210, 0.07 #4217), 09sb52 (0.21 #12487, 0.21 #4052, 0.20 #13291), 02f6xy (0.20 #199, 0.11 #6222, 0.05 #24491) >> Best rule #23285 for best value: >> intensional similarity = 4 >> extensional distance = 1391 >> proper extension: 0785v8; 04107; >> query: (?x5342, ?x77) <- award_winner(?x77, ?x5342), nationality(?x5342, ?x2152), award(?x6957, ?x77), film(?x6957, ?x7307) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #189 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 040db; 03c602; 01s7ns; *> query: (?x5342, 02681xs) <- profession(?x5342, ?x319), award_winner(?x77, ?x5342), location(?x5342, ?x4698), ?x4698 = 056_y *> conf = 0.40 ranks of expected_values: 6, 303 EVAL 02rxbmt award 02681xs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 106.000 81.000 0.744 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02rxbmt award 0gqzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 106.000 81.000 0.744 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19345-023tp8 PRED entity: 023tp8 PRED relation: people! PRED expected values: 06v41q => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 51): 033tf_ (0.42 #82, 0.20 #158, 0.17 #2742), 065b6q (0.20 #3, 0.12 #79, 0.08 #611), 0x67 (0.18 #4189, 0.17 #3809, 0.16 #3885), 048z7l (0.14 #343, 0.07 #951, 0.07 #1407), 02w7gg (0.12 #154, 0.11 #3954, 0.10 #4106), 07bch9 (0.12 #174, 0.08 #630, 0.08 #98), 07hwkr (0.10 #923, 0.10 #999, 0.09 #1379), 0xnvg (0.10 #2900, 0.10 #2748, 0.09 #2216), 0g5y6 (0.09 #340, 0.03 #2544, 0.03 #1860), 01qhm_ (0.09 #2209, 0.08 #81, 0.07 #917) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 02js6_; 04cr6qv; 04f7c55; 030vnj; 01gkmx; 015076; 04zn7g; >> query: (?x376, 033tf_) <- participant(?x376, ?x4400), film(?x376, ?x377), sibling(?x7617, ?x376) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #636 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 104 *> proper extension: 094xh; *> query: (?x376, 06v41q) <- award(?x376, ?x154), people(?x1050, ?x376), celebrity(?x3056, ?x376) *> conf = 0.05 ranks of expected_values: 17 EVAL 023tp8 people! 06v41q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 127.000 127.000 0.417 http://example.org/people/ethnicity/people #19344-025hwq PRED entity: 025hwq PRED relation: award_nominee PRED expected values: 02mc79 => 114 concepts (45 used for prediction) PRED predicted values (max 10 best out of 726): 025hwq (0.33 #4104, 0.25 #6443, 0.22 #13461), 02mc79 (0.33 #4139, 0.12 #11156, 0.12 #8817), 02tf1y (0.26 #86571), 016tt2 (0.25 #9468, 0.25 #7129, 0.25 #4790), 03rwz3 (0.25 #6364, 0.22 #13382, 0.17 #4025), 03ktjq (0.24 #22415, 0.21 #37432, 0.17 #24755), 0151w_ (0.23 #84437), 086k8 (0.22 #70256, 0.21 #67916, 0.19 #18777), 07myb2 (0.21 #37432, 0.17 #4514, 0.01 #81728), 08hp53 (0.21 #37432, 0.12 #5060, 0.12 #21435) >> Best rule #4104 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 02mc79; 030g9z; 07myb2; >> query: (?x7935, 025hwq) <- award(?x7935, ?x1105), award_nominee(?x7935, ?x5064), ?x5064 = 02_l96 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4139 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 02mc79; 030g9z; 07myb2; *> query: (?x7935, 02mc79) <- award(?x7935, ?x1105), award_nominee(?x7935, ?x5064), ?x5064 = 02_l96 *> conf = 0.33 ranks of expected_values: 2 EVAL 025hwq award_nominee 02mc79 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 45.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19343-0d_skg PRED entity: 0d_skg PRED relation: produced_by! PRED expected values: 01sxly => 108 concepts (30 used for prediction) PRED predicted values (max 10 best out of 233): 01sxly (0.41 #5639, 0.40 #15042, 0.40 #23498), 0296rz (0.25 #864, 0.02 #3683, 0.02 #5563), 0gwjw0c (0.05 #6581, 0.05 #5640, 0.04 #3760), 02pw_n (0.05 #6581, 0.05 #5640, 0.04 #3760), 0f4_l (0.05 #6581, 0.05 #5640, 0.04 #3760), 04gcyg (0.05 #1678, 0.02 #2618), 03g90h (0.05 #958, 0.02 #1898), 03cp4cn (0.03 #5302, 0.03 #6243, 0.03 #3422), 0gm2_0 (0.03 #5540, 0.03 #6481, 0.02 #11182), 03wjm2 (0.03 #5622, 0.02 #6563, 0.02 #3742) >> Best rule #5639 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 03c9pqt; >> query: (?x6690, ?x582) <- nominated_for(?x6690, ?x582), executive_produced_by(?x2177, ?x6690), produced_by(?x9858, ?x6690), film_crew_role(?x9858, ?x468) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d_skg produced_by! 01sxly CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 30.000 0.415 http://example.org/film/film/produced_by #19342-0c12h PRED entity: 0c12h PRED relation: award_winner! PRED expected values: 03nqnk3 => 84 concepts (77 used for prediction) PRED predicted values (max 10 best out of 215): 027c924 (0.45 #439, 0.24 #868, 0.13 #4293), 02pqp12 (0.45 #498, 0.19 #1355, 0.14 #927), 02rdyk7 (0.38 #20982, 0.36 #857, 0.36 #26127), 019f4v (0.38 #20982, 0.36 #857, 0.36 #26127), 0gs9p (0.38 #20982, 0.36 #857, 0.36 #26127), 040njc (0.38 #20982, 0.36 #857, 0.36 #26127), 04dn09n (0.38 #20982, 0.36 #857, 0.36 #26127), 0gq9h (0.38 #20982, 0.36 #857, 0.36 #26127), 0gr51 (0.38 #20982, 0.36 #857, 0.36 #26127), 0gqy2 (0.38 #20982, 0.36 #857, 0.36 #26127) >> Best rule #439 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 02kxbwx; 06pk8; 081lh; 0bwh6; 0h1p; 06chf; 01q4qv; 02kxbx3; 0bzyh; 06mn7; ... >> query: (?x6239, 027c924) <- award(?x6239, ?x112), award_winner(?x5516, ?x6239), ?x5516 = 027b9ly >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #20982 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1625 *> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 012ljv; 02s2ft; 05vsxz; 0grwj; 05d7rk; ... *> query: (?x6239, ?x112) <- award(?x6239, ?x112), award_winner(?x5516, ?x6239), award(?x1230, ?x5516) *> conf = 0.38 ranks of expected_values: 13 EVAL 0c12h award_winner! 03nqnk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 84.000 77.000 0.450 http://example.org/award/award_category/winners./award/award_honor/award_winner #19341-02qgqt PRED entity: 02qgqt PRED relation: award_winner! PRED expected values: 027cyf7 => 118 concepts (116 used for prediction) PRED predicted values (max 10 best out of 205): 0gqy2 (0.39 #10130, 0.37 #20688, 0.37 #15619), 027dtxw (0.39 #10130, 0.37 #20688, 0.37 #15619), 09sdmz (0.39 #10130, 0.37 #20688, 0.37 #15619), 0bfvd4 (0.39 #10130, 0.37 #20688, 0.37 #15619), 02x8n1n (0.39 #10130, 0.37 #20688, 0.37 #15619), 02x73k6 (0.39 #10130, 0.37 #20688, 0.37 #15619), 027b9j5 (0.39 #10130, 0.37 #20688, 0.37 #15619), 0bs0bh (0.39 #10130, 0.37 #20688, 0.37 #15619), 0789_m (0.39 #10130, 0.37 #20688, 0.37 #15619), 09sb52 (0.20 #2571, 0.17 #3837, 0.17 #1305) >> Best rule #10130 for best value: >> intensional similarity = 3 >> extensional distance = 950 >> proper extension: 0khth; 014l4w; >> query: (?x157, ?x112) <- award_winner(?x157, ?x91), award_winner(?x1112, ?x157), award(?x157, ?x112) >> conf = 0.39 => this is the best rule for 9 predicted values *> Best rule #1464 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 392 *> proper extension: 037q1z; *> query: (?x157, 027cyf7) <- award_winner(?x1064, ?x157), award_winner(?x156, ?x157), people(?x1446, ?x157) *> conf = 0.03 ranks of expected_values: 118 EVAL 02qgqt award_winner! 027cyf7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 118.000 116.000 0.392 http://example.org/award/award_category/winners./award/award_honor/award_winner #19340-0p_pd PRED entity: 0p_pd PRED relation: influenced_by! PRED expected values: 0q9zc => 124 concepts (42 used for prediction) PRED predicted values (max 10 best out of 384): 0c00lh (0.10 #224, 0.07 #1243, 0.03 #8377), 016_mj (0.09 #1073, 0.07 #16814, 0.05 #19364), 040db (0.08 #11795, 0.07 #15361, 0.07 #10776), 01j7rd (0.07 #1090, 0.05 #19364, 0.05 #6697), 02633g (0.07 #16814, 0.06 #1335, 0.05 #19364), 0dzf_ (0.07 #16814, 0.06 #1199, 0.05 #19364), 014z8v (0.07 #16814, 0.06 #1176, 0.04 #5096), 0ph2w (0.07 #16814, 0.05 #19364, 0.05 #6781), 01wp_jm (0.07 #16814, 0.05 #19364, 0.04 #7030), 081lh (0.07 #16814, 0.05 #19364, 0.04 #6654) >> Best rule #224 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02ld6x; 020h2v; >> query: (?x397, 0c00lh) <- nominated_for(?x397, ?x4651), award_nominee(?x241, ?x397), ?x4651 = 043t8t >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #1350 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 041h0; 019z7q; 0gcs9; 01nrq5; 012gq6; 085pr; 01gn36; 03n6r; 0bs8d; 03s9b; ... *> query: (?x397, 0q9zc) <- nominated_for(?x397, ?x696), location(?x397, ?x335), influenced_by(?x1814, ?x397) *> conf = 0.01 ranks of expected_values: 276 EVAL 0p_pd influenced_by! 0q9zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 124.000 42.000 0.100 http://example.org/influence/influence_node/influenced_by #19339-0fy66 PRED entity: 0fy66 PRED relation: genre PRED expected values: 07s9rl0 01g6gs => 57 concepts (19 used for prediction) PRED predicted values (max 10 best out of 80): 01hmnh (0.90 #695, 0.57 #1034, 0.40 #17), 07s9rl0 (0.78 #114, 0.66 #1473, 0.66 #1588), 02kdv5l (0.50 #568, 0.47 #1247, 0.47 #1361), 03k9fj (0.49 #690, 0.40 #12, 0.34 #1029), 0lsxr (0.40 #348, 0.39 #235, 0.35 #461), 05p553 (0.40 #5, 0.32 #683, 0.29 #1135), 0hcr (0.40 #23, 0.21 #701, 0.13 #1040), 04t36 (0.40 #7, 0.08 #1024, 0.08 #2049), 02l7c8 (0.28 #1146, 0.27 #1944, 0.27 #2058), 03bxz7 (0.28 #1068, 0.09 #1523, 0.09 #1638) >> Best rule #695 for best value: >> intensional similarity = 4 >> extensional distance = 269 >> proper extension: 01f39b; 0dr1c2; 063y9fp; >> query: (?x3614, 01hmnh) <- genre(?x3614, ?x10849), film(?x3017, ?x3614), films(?x10849, ?x5465), film_format(?x5465, ?x909) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #114 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 03ffcz; *> query: (?x3614, 07s9rl0) <- genre(?x3614, ?x11108), film(?x3017, ?x3614), film_release_distribution_medium(?x3614, ?x81), ?x11108 = 02xh1 *> conf = 0.78 ranks of expected_values: 2, 14 EVAL 0fy66 genre 01g6gs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 57.000 19.000 0.900 http://example.org/film/film/genre EVAL 0fy66 genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 57.000 19.000 0.900 http://example.org/film/film/genre #19338-03p2xc PRED entity: 03p2xc PRED relation: film! PRED expected values: 0gthm 0301yj => 68 concepts (39 used for prediction) PRED predicted values (max 10 best out of 685): 01hkhq (0.25 #412, 0.22 #2485, 0.04 #6631), 059j1m (0.20 #5614, 0.02 #15974, 0.01 #11830), 0171cm (0.12 #424, 0.11 #2497, 0.07 #6643), 0l6px (0.12 #387, 0.11 #2460, 0.06 #4146), 015rkw (0.12 #281, 0.11 #2354, 0.06 #4146), 05cj4r (0.12 #48, 0.11 #2121, 0.06 #4146), 02l4rh (0.12 #1230, 0.11 #3303, 0.06 #4146), 016gr2 (0.12 #194, 0.11 #2267, 0.06 #4146), 0755wz (0.12 #1221, 0.11 #3294, 0.06 #4146), 02k6rq (0.12 #328, 0.11 #2401, 0.06 #4146) >> Best rule #412 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0b6m5fy; 043mk4y; 04gcyg; 01n30p; 011ywj; 05k4my; >> query: (?x7128, 01hkhq) <- film(?x4928, ?x7128), produced_by(?x7128, ?x3568), ?x4928 = 051wwp, titles(?x53, ?x7128) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #5941 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0320fn; 0g9yrw; 04j13sx; *> query: (?x7128, 0301yj) <- film(?x8491, ?x7128), ?x8491 = 01nr36, country(?x7128, ?x512), genre(?x7128, ?x53) *> conf = 0.10 ranks of expected_values: 83 EVAL 03p2xc film! 0301yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 68.000 39.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 03p2xc film! 0gthm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 39.000 0.250 http://example.org/film/actor/film./film/performance/film #19337-0f4k49 PRED entity: 0f4k49 PRED relation: nominated_for! PRED expected values: 03jvmp => 69 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1113): 02r5w9 (0.52 #39742, 0.02 #240, 0.01 #7254), 01nwwl (0.27 #9352, 0.24 #42081, 0.23 #16364), 014y6 (0.27 #9352, 0.24 #42081, 0.23 #16364), 030xr_ (0.27 #9352, 0.24 #42081, 0.23 #16364), 069nzr (0.27 #9352, 0.24 #42081, 0.23 #16364), 0pgjm (0.27 #9352, 0.24 #42081, 0.23 #16364), 0436kgz (0.27 #9352, 0.24 #42081, 0.23 #16364), 015grj (0.27 #9352, 0.24 #42081, 0.23 #16364), 0652ty (0.27 #9352, 0.24 #42081, 0.23 #16364), 03jvmp (0.10 #5130, 0.02 #9807, 0.02 #455) >> Best rule #39742 for best value: >> intensional similarity = 4 >> extensional distance = 415 >> proper extension: 05dy7p; 02n9bh; 027ct7c; >> query: (?x4811, ?x1197) <- film_crew_role(?x4811, ?x137), titles(?x53, ?x4811), film(?x1197, ?x4811), genre(?x4811, ?x3312) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #5130 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 01d8yn; *> query: (?x4811, 03jvmp) <- nominated_for(?x3247, ?x4811), award(?x2143, ?x3247), ceremony(?x3247, ?x1265), ?x2143 = 015pxr *> conf = 0.10 ranks of expected_values: 10 EVAL 0f4k49 nominated_for! 03jvmp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 69.000 19.000 0.521 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #19336-02k6hp PRED entity: 02k6hp PRED relation: people PRED expected values: 01v5h 021r6w 0c5vh => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 721): 0b22w (0.40 #5043, 0.29 #7653, 0.29 #7000), 03dbww (0.40 #5055, 0.29 #7665, 0.19 #26120), 06y7d (0.29 #7111, 0.22 #11026, 0.19 #26120), 02cvp8 (0.29 #7050, 0.22 #10965, 0.19 #26120), 08bqy9 (0.29 #6773, 0.22 #10688, 0.19 #26120), 01kws3 (0.29 #6733, 0.22 #10648, 0.19 #26120), 0136p1 (0.29 #6589, 0.22 #10504, 0.19 #26120), 09889g (0.29 #7363, 0.20 #4753, 0.19 #26120), 07pzc (0.27 #18030, 0.21 #15417, 0.20 #27827), 0blgl (0.25 #9684, 0.25 #3158, 0.20 #5117) >> Best rule #5043 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 0gg4h; >> query: (?x10199, 0b22w) <- people(?x10199, ?x9775), people(?x10199, ?x8473), people(?x10199, ?x3194), ?x3194 = 0jrny, profession(?x9775, ?x1032), film(?x9775, ?x240), award_winner(?x458, ?x8473), nominated_for(?x8473, ?x10362) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #26120 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 18 *> proper extension: 02vrr; *> query: (?x10199, ?x118) <- people(?x10199, ?x11676), people(?x10199, ?x4112), people(?x10199, ?x3194), gender(?x11676, ?x231), influenced_by(?x318, ?x4112), risk_factors(?x10199, ?x5802), people(?x4322, ?x3194), religion(?x4112, ?x1985), people(?x4322, ?x118) *> conf = 0.19 ranks of expected_values: 367, 476 EVAL 02k6hp people 0c5vh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 67.000 67.000 0.400 http://example.org/people/cause_of_death/people EVAL 02k6hp people 021r6w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.400 http://example.org/people/cause_of_death/people EVAL 02k6hp people 01v5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 67.000 0.400 http://example.org/people/cause_of_death/people #19335-02rdxsh PRED entity: 02rdxsh PRED relation: award! PRED expected values: 0h6r5 0404j37 => 40 concepts (28 used for prediction) PRED predicted values (max 10 best out of 657): 0hfzr (0.62 #10448, 0.57 #9443, 0.57 #8438), 03hmt9b (0.62 #10427, 0.50 #16455, 0.43 #8417), 0209hj (0.57 #9101, 0.55 #12116, 0.50 #10106), 05hjnw (0.57 #7519, 0.43 #8522, 0.29 #9527), 0c0zq (0.57 #9923, 0.40 #3898, 0.38 #10928), 0sxmx (0.57 #9506, 0.38 #14529, 0.31 #1001), 09cr8 (0.50 #5192, 0.50 #2178, 0.43 #8203), 0404j37 (0.50 #2664, 0.50 #1660, 0.43 #9694), 02yvct (0.50 #6027, 0.50 #5235, 0.31 #1001), 0gmcwlb (0.50 #10165, 0.44 #16193, 0.40 #3135) >> Best rule #10448 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 02qvyrt; >> query: (?x1063, 0hfzr) <- nominated_for(?x1063, ?x9533), nominated_for(?x1063, ?x6616), nominated_for(?x1063, ?x6121), ?x6121 = 064lsn, produced_by(?x9533, ?x9754), film(?x157, ?x9533), ?x6616 = 0yxf4 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2664 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 0gs9p; *> query: (?x1063, 0404j37) <- nominated_for(?x1063, ?x8277), nominated_for(?x1063, ?x7307), nominated_for(?x1063, ?x4009), nominated_for(?x1063, ?x2914), ?x7307 = 011yxy, ?x4009 = 0320fn, award(?x1490, ?x1063), genre(?x2914, ?x53), honored_for(?x6686, ?x2914), ?x8277 = 02r858_ *> conf = 0.50 ranks of expected_values: 8, 62 EVAL 02rdxsh award! 0404j37 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 40.000 28.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02rdxsh award! 0h6r5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 40.000 28.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19334-02z6l5f PRED entity: 02z6l5f PRED relation: people! PRED expected values: 0d7wh => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 46): 02w7gg (0.39 #695, 0.31 #2620, 0.29 #849), 041rx (0.16 #1390, 0.15 #466, 0.15 #1159), 033tf_ (0.14 #546, 0.09 #161, 0.09 #623), 07hwkr (0.11 #551, 0.09 #166, 0.07 #628), 0x67 (0.10 #5631, 0.10 #4553, 0.10 #6940), 02ctzb (0.10 #1709, 0.09 #477, 0.09 #169), 07bch9 (0.10 #1717, 0.07 #2795, 0.07 #2872), 0d7wh (0.09 #171, 0.09 #2635, 0.08 #1326), 0xnvg (0.09 #1399, 0.07 #1938, 0.06 #2246), 03lmx1 (0.08 #245, 0.06 #861, 0.04 #7240) >> Best rule #695 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 04rsd2; 0892sx; 0phx4; 01vw20h; 01v0fn1; 01vsyg9; 01vt5c_; 0140t7; 015cbq; >> query: (?x4857, 02w7gg) <- location(?x4857, ?x362), profession(?x4857, ?x319), award_nominee(?x2803, ?x4857), ?x362 = 04jpl >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 02vq8xn; *> query: (?x4857, 0d7wh) <- company(?x4857, ?x2776), program(?x2776, ?x1542), company(?x900, ?x2776) *> conf = 0.09 ranks of expected_values: 8 EVAL 02z6l5f people! 0d7wh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 138.000 138.000 0.390 http://example.org/people/ethnicity/people #19333-0jpmt PRED entity: 0jpmt PRED relation: risk_factors! PRED expected values: 0gk4g 06g7c => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 82): 0gk4g (0.67 #198, 0.57 #247, 0.50 #355), 0m32h (0.59 #347, 0.50 #208, 0.43 #257), 09969 (0.59 #347, 0.33 #120, 0.33 #764), 0h9dj (0.59 #347, 0.33 #94, 0.33 #764), 0c78m (0.59 #347, 0.33 #764, 0.29 #348), 0dcqh (0.59 #347, 0.33 #589, 0.29 #348), 01rt5h (0.59 #347, 0.29 #348, 0.29 #708), 01qqwn (0.50 #232, 0.43 #281, 0.38 #389), 09d11 (0.50 #617, 0.33 #987, 0.33 #104), 02vrr (0.43 #249, 0.38 #357, 0.33 #200) >> Best rule #198 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 0fltx; >> query: (?x8023, 0gk4g) <- risk_factors(?x14024, ?x8023), risk_factors(?x5118, ?x8023), risk_factors(?x4659, ?x8023), risk_factors(?x1158, ?x8023), ?x1158 = 02y0js, people(?x5118, ?x5119), symptom_of(?x3679, ?x5118), symptom_of(?x3679, ?x14096), people(?x4659, ?x1250), symptom_of(?x13487, ?x14024), symptom_of(?x10717, ?x14024), award_winner(?x2915, ?x1250), ?x13487 = 01cdt5, ?x14096 = 0h3bn, actor(?x7465, ?x1250), ?x2915 = 027c95y, ?x10717 = 0cjf0 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 25 EVAL 0jpmt risk_factors! 06g7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 30.000 30.000 0.667 http://example.org/medicine/disease/risk_factors EVAL 0jpmt risk_factors! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.667 http://example.org/medicine/disease/risk_factors #19332-06_6j3 PRED entity: 06_6j3 PRED relation: actor! PRED expected values: 026q3s3 07ghv5 => 123 concepts (115 used for prediction) PRED predicted values (max 10 best out of 30): 016ztl (0.33 #76, 0.31 #107, 0.29 #290), 06cgf (0.33 #28, 0.02 #424, 0.02 #454), 02q3fdr (0.23 #106, 0.20 #45, 0.19 #320), 02gs6r (0.21 #285, 0.19 #316, 0.18 #254), 0b60sq (0.18 #154, 0.15 #32, 0.14 #62), 031f_m (0.17 #450, 0.15 #54, 0.15 #420), 05pyrb (0.11 #470, 0.09 #440, 0.08 #410), 0564x (0.10 #57, 0.06 #423, 0.06 #453), 0dh8v4 (0.07 #438, 0.07 #164, 0.07 #468), 07ghv5 (0.07 #443, 0.07 #169, 0.07 #473) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 01bcq; >> query: (?x4632, 016ztl) <- actor(?x1419, ?x4632), film(?x4632, ?x6839), nationality(?x4632, ?x94), type_of_union(?x4632, ?x1873), place_of_birth(?x4632, ?x10261) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #443 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 52 *> proper extension: 066l3y; 0bn8fw; 08p1gp; 05q_mg; *> query: (?x4632, 07ghv5) <- actor(?x1419, ?x4632), profession(?x4632, ?x1183), profession(?x8784, ?x1183), profession(?x565, ?x1183), ?x565 = 01wl38s, ?x8784 = 01r4zfk *> conf = 0.07 ranks of expected_values: 10, 19 EVAL 06_6j3 actor! 07ghv5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 123.000 115.000 0.333 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor EVAL 06_6j3 actor! 026q3s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 123.000 115.000 0.333 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #19331-0n3g PRED entity: 0n3g PRED relation: exported_to PRED expected values: 05r7t => 153 concepts (128 used for prediction) PRED predicted values (max 10 best out of 144): 06tw8 (0.29 #346, 0.25 #704, 0.20 #524), 0h3y (0.29 #307, 0.17 #1865, 0.17 #665), 0j4b (0.25 #706, 0.20 #1906, 0.14 #348), 03_3d (0.20 #964, 0.20 #245, 0.17 #1147), 06f32 (0.20 #275, 0.13 #1177, 0.12 #2012), 0154j (0.20 #243, 0.12 #781, 0.11 #840), 0chghy (0.20 #249, 0.10 #968, 0.09 #1986), 0k6nt (0.20 #256, 0.04 #1694, 0.03 #2054), 06s9y (0.20 #297, 0.04 #1735, 0.03 #2095), 09pmkv (0.20 #258, 0.04 #1696, 0.03 #2056) >> Best rule #346 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0b90_r; 03rjj; 0f8l9c; 03rk0; 06q1r; >> query: (?x5411, 06tw8) <- location_of_ceremony(?x5951, ?x5411), award_winner(?x749, ?x5951), award_nominee(?x398, ?x5951), exported_to(?x5411, ?x94) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0n3g exported_to 05r7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 128.000 0.286 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #19330-01mpwj PRED entity: 01mpwj PRED relation: school_type PRED expected values: 01_srz => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.54 #1075, 0.51 #1654, 0.49 #350), 05pcjw (0.40 #47, 0.37 #231, 0.33 #1233), 07tf8 (0.40 #54, 0.33 #1233, 0.33 #77), 01_9fk (0.33 #1233, 0.31 #1720, 0.15 #1073), 06cs1 (0.33 #1233, 0.31 #1720, 0.06 #97), 0bwd5 (0.31 #1720, 0.07 #225, 0.06 #179), 0257h9 (0.25 #42, 0.03 #180, 0.03 #480), 01_srz (0.06 #1143, 0.05 #1074, 0.05 #1212), 02p0qmm (0.04 #401, 0.04 #332, 0.04 #424), 04399 (0.04 #128, 0.03 #779, 0.02 #1547) >> Best rule #1075 for best value: >> intensional similarity = 4 >> extensional distance = 243 >> proper extension: 02jyr8; 02zcz3; 05xb7q; 01nmgc; 05gm16l; 0xxc; 02rk23; >> query: (?x3485, 05jxkf) <- institution(?x865, ?x3485), ?x865 = 02h4rq6, contains(?x94, ?x3485), school_type(?x3485, ?x3205) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1143 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 259 *> proper extension: 02g839; 06xpp7; 05cwl_; 02zc7f; 02gn8s; 02h7qr; 0sxgh; 02lwv5; 043q2z; *> query: (?x3485, 01_srz) <- student(?x3485, ?x879), contains(?x94, ?x3485), ?x94 = 09c7w0 *> conf = 0.06 ranks of expected_values: 8 EVAL 01mpwj school_type 01_srz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 121.000 121.000 0.539 http://example.org/education/educational_institution/school_type #19329-0127xk PRED entity: 0127xk PRED relation: profession PRED expected values: 01d_h8 => 98 concepts (72 used for prediction) PRED predicted values (max 10 best out of 83): 01d_h8 (0.52 #1293, 0.33 #2580, 0.33 #7015), 0nbcg (0.41 #4745, 0.41 #4888, 0.39 #4459), 016z4k (0.37 #4437, 0.34 #4866, 0.33 #3722), 0dz3r (0.36 #4721, 0.34 #4864, 0.33 #4435), 02jknp (0.33 #1294, 0.23 #4154, 0.23 #7016), 0np9r (0.30 #2590, 0.16 #445, 0.13 #588), 0kyk (0.30 #2884, 0.29 #2312, 0.28 #3170), 01c8w0 (0.27 #723, 0.06 #151, 0.06 #3297), 039v1 (0.24 #4750, 0.23 #4893, 0.22 #3749), 02krf9 (0.18 #164, 0.14 #5886, 0.13 #2595) >> Best rule #1293 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 07c0j; >> query: (?x11334, 01d_h8) <- nominated_for(?x11334, ?x4223), influenced_by(?x1725, ?x11334), award(?x11334, ?x435) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0127xk profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 72.000 0.517 http://example.org/people/person/profession #19328-089j8p PRED entity: 089j8p PRED relation: genre PRED expected values: 05p553 04rlf => 99 concepts (96 used for prediction) PRED predicted values (max 10 best out of 118): 01z4y (0.61 #8680, 0.57 #3128, 0.56 #4337), 07ssc (0.61 #3129, 0.57 #3128, 0.56 #4337), 03k9fj (0.50 #11, 0.44 #371, 0.35 #611), 02l7c8 (0.50 #857, 0.38 #977, 0.38 #1218), 02kdv5l (0.42 #361, 0.36 #601, 0.35 #481), 05p553 (0.39 #1565, 0.38 #1685, 0.37 #2889), 01jfsb (0.39 #2777, 0.37 #2175, 0.34 #4349), 06n90 (0.33 #253, 0.29 #133, 0.25 #373), 0lsxr (0.33 #8, 0.20 #2171, 0.19 #2773), 060__y (0.26 #858, 0.25 #978, 0.23 #737) >> Best rule #8680 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 024rwx; 0ctzf1; >> query: (?x6446, ?x2480) <- titles(?x2480, ?x6446), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1565 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 226 *> proper extension: 0bz3jx; *> query: (?x6446, 05p553) <- film_release_distribution_medium(?x6446, ?x81), category(?x6446, ?x134), ?x134 = 08mbj5d, production_companies(?x6446, ?x9518) *> conf = 0.39 ranks of expected_values: 6, 48 EVAL 089j8p genre 04rlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 99.000 96.000 0.612 http://example.org/film/film/genre EVAL 089j8p genre 05p553 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 99.000 96.000 0.612 http://example.org/film/film/genre #19327-05t0_2v PRED entity: 05t0_2v PRED relation: country PRED expected values: 0d060g => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 42): 09c7w0 (0.84 #3573, 0.82 #4539, 0.82 #3936), 0d05w3 (0.28 #43, 0.08 #485, 0.08 #546), 03h64 (0.28 #46, 0.06 #168, 0.04 #107), 0345h (0.14 #391, 0.14 #694, 0.13 #1117), 0f8l9c (0.11 #1472, 0.10 #1894, 0.10 #1954), 03_3d (0.10 #3753, 0.08 #485, 0.08 #546), 06mkj (0.10 #3753, 0.08 #485, 0.08 #546), 05v8c (0.10 #3753), 05r4w (0.10 #3753), 04t2t (0.09 #122, 0.06 #2723, 0.06 #4116) >> Best rule #3573 for best value: >> intensional similarity = 4 >> extensional distance = 1374 >> proper extension: 02pb2bp; 0dh8v4; 0cks1m; 02r9p0c; 03gyvwg; >> query: (?x5945, 09c7w0) <- genre(?x5945, ?x225), film(?x1596, ?x5945), country(?x5945, ?x512), film(?x1104, ?x5945) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #485 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 99 *> proper extension: 02h2vv; 0m123; *> query: (?x5945, ?x94) <- award_winner(?x5945, ?x1104), film(?x1104, ?x11735), film(?x1104, ?x4179), country(?x11735, ?x94), award_winner(?x4179, ?x7615) *> conf = 0.08 ranks of expected_values: 12 EVAL 05t0_2v country 0d060g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 84.000 84.000 0.836 http://example.org/film/film/country #19326-05lf_ PRED entity: 05lf_ PRED relation: month! PRED expected values: 01914 0fhp9 052p7 02cft 08966 02z0j => 11 concepts (11 used for prediction) PRED predicted values (max 10 best out of 136): 02z0j (0.92 #87, 0.91 #47, 0.89 #106), 052p7 (0.92 #87, 0.91 #47, 0.89 #106), 08966 (0.92 #87, 0.91 #47, 0.89 #106), 0fhp9 (0.92 #87, 0.91 #47, 0.89 #106), 01914 (0.92 #87, 0.91 #47, 0.89 #106), 02cft (0.92 #87, 0.91 #47, 0.89 #106), 03czqs (0.89 #106, 0.88 #33, 0.33 #55), 0l0mk (0.89 #106, 0.88 #33, 0.25 #80), 06yxd (0.50 #97, 0.33 #56, 0.23 #67), 059rby (0.50 #97, 0.24 #107, 0.23 #67) >> Best rule #87 for best value: >> intensional similarity = 93 >> extensional distance = 2 >> proper extension: 03_ly; >> query: (?x3107, ?x206) <- month(?x11197, ?x3107), month(?x10610, ?x3107), month(?x8956, ?x3107), month(?x8602, ?x3107), month(?x8252, ?x3107), month(?x6960, ?x3107), month(?x6494, ?x3107), month(?x6054, ?x3107), month(?x4271, ?x3107), month(?x3373, ?x3107), month(?x3269, ?x3107), month(?x3125, ?x3107), month(?x3106, ?x3107), month(?x2985, ?x3107), month(?x2611, ?x3107), month(?x2316, ?x3107), month(?x1860, ?x3107), month(?x1658, ?x3107), month(?x1523, ?x3107), month(?x1458, ?x3107), month(?x659, ?x3107), month(?x108, ?x3107), ?x3373 = 0ply0, ?x659 = 02cl1, ?x1658 = 0h7h6, ?x3106 = 049d1, ?x4271 = 06wjf, ?x108 = 0rh6k, ?x11197 = 05l64, ?x3125 = 0d6lp, ?x1458 = 05ywg, ?x2611 = 02h6_6p, ?x10610 = 03902, seasonal_months(?x6303, ?x3107), seasonal_months(?x2255, ?x3107), ?x6494 = 02sn34, citytown(?x11273, ?x6960), location(?x4782, ?x6960), location(?x2786, ?x6960), place_of_death(?x7995, ?x6960), origin(?x5391, ?x6960), dog_breed(?x6960, ?x5194), seasonal_months(?x3107, ?x1459), contains(?x94, ?x6960), ?x2255 = 040fv, ?x6054 = 0fn2g, source(?x6960, ?x958), place_of_birth(?x1182, ?x6960), ?x94 = 09c7w0, month(?x8977, ?x6303), month(?x6357, ?x6303), month(?x206, ?x6303), friend(?x4782, ?x1896), participant(?x4782, ?x3865), place_of_birth(?x7995, ?x9907), type_of_union(?x7995, ?x566), ?x8602 = 0chgzm, ?x3269 = 0vzm, award_winner(?x1930, ?x7995), award_winner(?x2824, ?x2786), special_performance_type(?x4782, ?x4832), ?x8977 = 02z0j, profession(?x7995, ?x1183), gender(?x7995, ?x231), program_creator(?x8554, ?x1182), ?x1860 = 01_d4, industry(?x11273, ?x245), featured_film_locations(?x8302, ?x6960), nationality(?x7995, ?x2146), location(?x8476, ?x8956), participant(?x4782, ?x2221), citytown(?x5695, ?x8956), award_nominee(?x7995, ?x1089), nominated_for(?x4782, ?x1811), instrumentalists(?x227, ?x2786), award(?x4782, ?x1691), vacationer(?x8956, ?x5565), location_of_ceremony(?x3525, ?x6960), film(?x4782, ?x2128), ?x6357 = 02cft, ?x8252 = 0k3p, ?x2316 = 06t2t, ?x5194 = 01t032, ?x2985 = 03hrz, ?x5565 = 0mm1q, ?x1523 = 030qb3t, profession(?x1182, ?x987), award(?x7995, ?x1079), ?x1691 = 05zvj3m, profession(?x2786, ?x220), people(?x5855, ?x7995), award_nominee(?x794, ?x4782), languages(?x7995, ?x254) >> conf = 0.92 => this is the best rule for 6 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6 EVAL 05lf_ month! 02z0j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 11.000 11.000 0.919 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 05lf_ month! 08966 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 11.000 11.000 0.919 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 05lf_ month! 02cft CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 11.000 11.000 0.919 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 05lf_ month! 052p7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 11.000 11.000 0.919 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 05lf_ month! 0fhp9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 11.000 11.000 0.919 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 05lf_ month! 01914 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 11.000 11.000 0.919 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #19325-07bdd_ PRED entity: 07bdd_ PRED relation: award! PRED expected values: 01r97z 01qvz8 => 52 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1585): 01qvz8 (0.50 #4483, 0.33 #6492, 0.29 #3479), 0404j37 (0.50 #2662, 0.22 #5674, 0.18 #7684), 0gmcwlb (0.50 #2130, 0.12 #12173, 0.11 #5142), 09gq0x5 (0.50 #2177, 0.11 #5189, 0.11 #12220), 0h03fhx (0.50 #2462, 0.11 #5474, 0.09 #7484), 01r97z (0.38 #4084, 0.29 #3080, 0.22 #6093), 05h43ls (0.38 #4261, 0.25 #2008, 0.23 #7029), 017jd9 (0.33 #5475, 0.33 #2463, 0.27 #7485), 04v8x9 (0.33 #5056, 0.33 #2044, 0.27 #7066), 069q4f (0.33 #6142, 0.25 #4133, 0.20 #1121) >> Best rule #4483 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 03c7tr1; 05p1dby; 05p09zm; >> query: (?x1105, 01qvz8) <- award(?x5672, ?x1105), award(?x166, ?x1105), nominated_for(?x1105, ?x103), ?x5672 = 0ch3qr1 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 07bdd_ award! 01qvz8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 27.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 07bdd_ award! 01r97z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 52.000 27.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19324-0n2vl PRED entity: 0n2vl PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 81 concepts (46 used for prediction) PRED predicted values (max 10 best out of 3): 09c7w0 (0.88 #68, 0.87 #115, 0.87 #56), 05kkh (0.08 #337, 0.08 #323, 0.08 #285), 03rt9 (0.02 #222, 0.01 #399) >> Best rule #68 for best value: >> intensional similarity = 5 >> extensional distance = 178 >> proper extension: 0mw93; 0jgk3; 0jrxx; 0jrq9; 0mlyj; 0jrjb; 0nj3m; 0nv99; >> query: (?x12554, 09c7w0) <- adjoins(?x12554, ?x10235), adjoins(?x12554, ?x8086), adjoins(?x8086, ?x6689), currency(?x12554, ?x170), county(?x1629, ?x10235) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n2vl second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 46.000 0.878 http://example.org/location/country/second_level_divisions #19323-0243cq PRED entity: 0243cq PRED relation: film_distribution_medium PRED expected values: 0dq6p => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.68 #30, 0.67 #86, 0.65 #69), 029j_ (0.38 #66, 0.37 #94, 0.31 #83), 0dq6p (0.20 #67, 0.19 #95, 0.16 #84), 07z4p (0.02 #98, 0.01 #87, 0.01 #65), 07c52 (0.02 #24, 0.01 #96) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 0dr3sl; >> query: (?x4313, 0735l) <- currency(?x4313, ?x170), film_crew_role(?x4313, ?x2154), film_distribution_medium(?x4313, ?x627), ?x2154 = 01vx2h >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 91 *> proper extension: 053rxgm; 035w2k; 06fqlk; 0mbql; 07jnt; *> query: (?x4313, 0dq6p) <- music(?x4313, ?x3410), film_crew_role(?x4313, ?x281), genre(?x4313, ?x307), film_distribution_medium(?x4313, ?x627) *> conf = 0.20 ranks of expected_values: 3 EVAL 0243cq film_distribution_medium 0dq6p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 129.000 0.684 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #19322-01dtl PRED entity: 01dtl PRED relation: team! PRED expected values: 06sy4c => 105 concepts (94 used for prediction) PRED predicted values (max 10 best out of 71): 06sy4c (0.85 #1010, 0.84 #2561, 0.84 #1685), 0fw2d3 (0.41 #506, 0.40 #439, 0.38 #305), 0d3f83 (0.33 #46, 0.25 #385, 0.25 #181), 026y23w (0.33 #16, 0.25 #151, 0.25 #83), 0djvzd (0.29 #569, 0.25 #164, 0.25 #96), 0f1pyf (0.29 #555, 0.25 #354, 0.14 #2560), 02y0dd (0.27 #466, 0.25 #332, 0.24 #533), 0135nb (0.25 #286, 0.25 #149, 0.20 #420), 0g9zjp (0.25 #398, 0.24 #599, 0.14 #2560), 04bsx1 (0.25 #189, 0.20 #257, 0.14 #2560) >> Best rule #1010 for best value: >> intensional similarity = 9 >> extensional distance = 43 >> proper extension: 075q_; 049fbh; >> query: (?x8195, ?x8204) <- team(?x63, ?x8195), team(?x60, ?x8195), team(?x8598, ?x8195), place_of_birth(?x8598, ?x1406), ?x60 = 02nzb8, team(?x8598, ?x348), colors(?x8195, ?x663), team(?x8204, ?x8195), ?x63 = 02sdk9v >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dtl team! 06sy4c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 94.000 0.846 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #19321-0kft PRED entity: 0kft PRED relation: location PRED expected values: 07dfk => 126 concepts (122 used for prediction) PRED predicted values (max 10 best out of 138): 07dfk (0.50 #467, 0.04 #2075, 0.03 #20111), 018qt8 (0.17 #769), 02_286 (0.14 #8886, 0.13 #4863, 0.12 #841), 030qb3t (0.13 #33064, 0.12 #31456, 0.12 #29042), 04jpl (0.08 #821, 0.06 #13691, 0.06 #33803), 0cr3d (0.06 #8189, 0.06 #7384, 0.06 #12210), 01531 (0.05 #4180, 0.04 #962, 0.03 #7397), 0cc56 (0.05 #4883, 0.03 #23386, 0.03 #24190), 013yq (0.04 #923, 0.04 #1727, 0.02 #31492), 0fvvz (0.04 #870, 0.04 #1674) >> Best rule #467 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01r_t_; 01g4bk; >> query: (?x9149, 07dfk) <- award_winner(?x77, ?x9149), award(?x9149, ?x6147), award(?x9149, ?x198), ?x6147 = 0776drd, nominated_for(?x198, ?x144) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kft location 07dfk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 122.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #19320-049tjg PRED entity: 049tjg PRED relation: profession PRED expected values: 02hrh1q => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.89 #1215, 0.89 #6766, 0.89 #6016), 03gjzk (0.39 #5117, 0.32 #8418, 0.30 #316), 01d_h8 (0.38 #10059, 0.37 #11109, 0.37 #10659), 0dxtg (0.37 #5115, 0.31 #3165, 0.31 #4965), 02jknp (0.27 #10061, 0.27 #2259, 0.27 #11111), 0np9r (0.21 #7224, 0.21 #3773, 0.20 #8724), 02pjxr (0.20 #335, 0.17 #635, 0.02 #7537), 0cbd2 (0.20 #3608, 0.19 #2708, 0.17 #3458), 02krf9 (0.18 #5129, 0.17 #628, 0.14 #8430), 09jwl (0.18 #6921, 0.17 #15625, 0.17 #10823) >> Best rule #1215 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 044qx; 022g44; 01fwf1; 0bdt8; 01m42d0; 01vsy9_; 015076; >> query: (?x305, 02hrh1q) <- film(?x305, ?x306), people(?x9771, ?x305), people(?x1423, ?x305) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049tjg profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.894 http://example.org/people/person/profession #19319-0nbjq PRED entity: 0nbjq PRED relation: olympics! PRED expected values: 01sgl => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 54): 01sgl (0.88 #1494, 0.82 #1069, 0.82 #642), 03hr1p (0.71 #350, 0.71 #295, 0.67 #460), 06wrt (0.60 #232, 0.56 #400, 0.50 #269), 0crlz (0.57 #366, 0.57 #311, 0.56 #476), 01cgz (0.54 #772, 0.50 #269, 0.47 #1038), 02y8z (0.50 #269, 0.47 #1044, 0.46 #778), 02vx4 (0.50 #269, 0.46 #812, 0.44 #388), 01hp22 (0.50 #269, 0.46 #812, 0.44 #382), 096f8 (0.50 #269, 0.46 #812, 0.44 #382), 06f41 (0.50 #269, 0.46 #812, 0.44 #382) >> Best rule #1494 for best value: >> intensional similarity = 9 >> extensional distance = 23 >> proper extension: 018wrk; 0l6vl; 0l98s; 0l998; 0l6mp; 0lbbj; 016r9z; 0c_tl; 018qb4; 0jkvj; >> query: (?x2432, 01sgl) <- olympics(?x1003, ?x2432), olympics(?x792, ?x2432), capital(?x792, ?x8751), country(?x7108, ?x792), combatants(?x792, ?x151), ?x1003 = 03gj2, ?x7108 = 0194d, film_release_region(?x66, ?x792), locations(?x11802, ?x792) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nbjq olympics! 01sgl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.880 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #19318-01sn3 PRED entity: 01sn3 PRED relation: location! PRED expected values: 01vttb9 011s9r => 217 concepts (142 used for prediction) PRED predicted values (max 10 best out of 2305): 0k2mxq (0.51 #150030, 0.49 #5002, 0.48 #355068), 04qsdh (0.51 #150030, 0.49 #5002, 0.48 #355068), 05728w1 (0.49 #5002, 0.48 #5001, 0.48 #10003), 0gls4q_ (0.49 #5002, 0.48 #5001, 0.48 #10003), 047cqr (0.49 #5002, 0.48 #5001, 0.48 #10003), 02t__l (0.49 #5002, 0.48 #5001, 0.48 #10003), 06hzsx (0.48 #5001, 0.48 #10003, 0.48 #107521), 0d__g (0.48 #5001, 0.48 #10003, 0.48 #107521), 01sb5r (0.28 #222549, 0.28 #297556, 0.27 #297555), 0jg77 (0.28 #222549, 0.28 #297556, 0.27 #297555) >> Best rule #150030 for best value: >> intensional similarity = 5 >> extensional distance = 91 >> proper extension: 0jcg8; 0fw2y; 0m2rv; 0d04z6; 0d9y6; 0dyl9; 02_n7; 05r7t; 0r4qq; 04gxf; ... >> query: (?x4090, ?x1606) <- jurisdiction_of_office(?x1195, ?x4090), place_of_birth(?x1865, ?x4090), place_of_birth(?x1606, ?x4090), award(?x1865, ?x451), film(?x1606, ?x188) >> conf = 0.51 => this is the best rule for 2 predicted values *> Best rule #107523 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 02jx1; 0kpys; 0l2hf; 0hz35; *> query: (?x4090, ?x848) <- contains(?x177, ?x4090), place_of_birth(?x5995, ?x4090), award_winner(?x848, ?x5995), adjoins(?x13626, ?x4090) *> conf = 0.03 ranks of expected_values: 1327 EVAL 01sn3 location! 011s9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 217.000 142.000 0.513 http://example.org/people/person/places_lived./people/place_lived/location EVAL 01sn3 location! 01vttb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 217.000 142.000 0.513 http://example.org/people/person/places_lived./people/place_lived/location #19317-01wmjkb PRED entity: 01wmjkb PRED relation: instrumentalists! PRED expected values: 04rzd => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 118): 0342h (0.87 #1698, 0.87 #1622, 0.84 #2086), 05148p4 (0.47 #635, 0.47 #1020, 0.46 #789), 03bx0bm (0.44 #3098, 0.42 #4106, 0.40 #4028), 02dlh2 (0.34 #1697, 0.33 #2244, 0.32 #2708), 01vdm0 (0.34 #1697, 0.33 #2244, 0.32 #2708), 05842k (0.34 #1697, 0.32 #2708, 0.32 #2085), 03gvt (0.25 #57, 0.14 #135, 0.11 #3077), 0l14qv (0.24 #776, 0.22 #1161, 0.21 #1007), 04rzd (0.19 #1652, 0.18 #2663, 0.18 #2896), 018j2 (0.16 #1653, 0.16 #2897, 0.15 #2664) >> Best rule #1698 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 012zng; 01vv6_6; 01w8n89; 01vsy3q; 082brv; 0k1bs; 01tv3x2; 02l_7y; 0191h5; 01lz4tf; ... >> query: (?x8341, ?x227) <- instrumentalists(?x75, ?x8341), role(?x8341, ?x227), ?x227 = 0342h, location(?x8341, ?x1523) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1652 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 84 *> proper extension: 012zng; 01vv6_6; 01w8n89; 01vsy3q; 082brv; 0k1bs; 01tv3x2; 02l_7y; 0191h5; 01lz4tf; ... *> query: (?x8341, 04rzd) <- instrumentalists(?x75, ?x8341), role(?x8341, ?x227), ?x227 = 0342h, location(?x8341, ?x1523) *> conf = 0.19 ranks of expected_values: 9 EVAL 01wmjkb instrumentalists! 04rzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 175.000 175.000 0.872 http://example.org/music/instrument/instrumentalists #19316-0y2dl PRED entity: 0y2dl PRED relation: place_of_birth! PRED expected values: 07lp1 => 71 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1591): 0126y2 (0.09 #524, 0.03 #3136, 0.02 #5748), 01nd6v (0.09 #2608, 0.03 #5220, 0.02 #7832), 04zn7g (0.09 #2567, 0.03 #5179, 0.02 #7791), 01fxfk (0.09 #2510, 0.03 #5122, 0.02 #7734), 08141d (0.09 #2503, 0.03 #5115, 0.02 #7727), 02bc74 (0.09 #2495, 0.03 #5107, 0.02 #7719), 03j9ml (0.09 #2440, 0.03 #5052, 0.02 #7664), 044zvm (0.09 #2397, 0.03 #5009, 0.02 #7621), 02qzjj (0.09 #2372, 0.03 #4984, 0.02 #7596), 09g0h (0.09 #2351, 0.03 #4963, 0.02 #7575) >> Best rule #524 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 04jpl; 02m77; >> query: (?x3148, 0126y2) <- location(?x7553, ?x3148), film(?x7553, ?x8234), profession(?x7553, ?x131), ?x8234 = 06_sc3 >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0y2dl place_of_birth! 07lp1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 32.000 0.091 http://example.org/people/person/place_of_birth #19315-0pswc PRED entity: 0pswc PRED relation: location! PRED expected values: 01nkxvx => 113 concepts (63 used for prediction) PRED predicted values (max 10 best out of 2116): 03mp9s (0.33 #8958, 0.25 #6440, 0.18 #16516), 01m4yn (0.33 #8933, 0.25 #6415, 0.18 #16491), 01w02sy (0.33 #8150, 0.25 #5632, 0.18 #15708), 0k8y7 (0.33 #8398, 0.25 #5880, 0.18 #15956), 086sj (0.33 #8362, 0.25 #5844, 0.18 #15920), 0j5q3 (0.33 #8978, 0.25 #6460, 0.18 #16536), 02pjvc (0.33 #8735, 0.25 #6217, 0.18 #16293), 063b4k (0.33 #2436, 0.01 #80526, 0.01 #78008), 039crh (0.27 #10958, 0.25 #5921, 0.25 #3403), 04s430 (0.25 #6251, 0.25 #3733, 0.17 #8769) >> Best rule #8958 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 030qb3t; 0d6lp; 0d9jr; 071vr; >> query: (?x11561, 03mp9s) <- time_zones(?x11561, ?x2950), teams(?x11561, ?x5918), ?x2950 = 02lcqs, location_of_ceremony(?x2415, ?x11561), location_of_ceremony(?x566, ?x11561) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pswc location! 01nkxvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 63.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #19314-0dq630k PRED entity: 0dq630k PRED relation: role PRED expected values: 03qjg => 49 concepts (48 used for prediction) PRED predicted values (max 10 best out of 120): 01s0ps (0.84 #483, 0.84 #854, 0.83 #484), 0dwtp (0.84 #483, 0.84 #854, 0.83 #484), 01vj9c (0.84 #483, 0.84 #854, 0.83 #732), 028tv0 (0.80 #1232, 0.69 #1600, 0.65 #2092), 05148p4 (0.78 #4886, 0.75 #1945, 0.75 #1851), 018vs (0.77 #2577, 0.74 #2474, 0.72 #3207), 03qjg (0.70 #1338, 0.70 #1282, 0.63 #3986), 0bxl5 (0.70 #1339, 0.70 #1293, 0.56 #1051), 07xzm (0.70 #1245, 0.55 #1952, 0.55 #1852), 02k84w (0.70 #1264, 0.50 #894, 0.48 #1337) >> Best rule #483 for best value: >> intensional similarity = 28 >> extensional distance = 2 >> proper extension: 07y_7; >> query: (?x2205, ?x1495) <- role(?x1166, ?x2205), role(?x716, ?x2205), role(?x432, ?x2205), role(?x316, ?x2205), role(?x314, ?x2205), ?x1166 = 05148p4, role(?x2205, ?x8014), role(?x2205, ?x3991), role(?x2205, ?x885), ?x885 = 0dwtp, role(?x2764, ?x2205), role(?x1750, ?x2205), role(?x1495, ?x2205), role(?x6049, ?x2205), ?x716 = 018vs, ?x432 = 042v_gx, ?x1750 = 02hnl, ?x316 = 05r5c, ?x8014 = 0214km, ?x314 = 02sgy, ?x3991 = 05842k, role(?x158, ?x2764), role(?x75, ?x2764), artists(?x3108, ?x6049), instrumentalists(?x2764, ?x2765), role(?x3869, ?x2764), role(?x74, ?x2764), ?x3108 = 02w4v >> conf = 0.84 => this is the best rule for 3 predicted values *> Best rule #1338 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 8 *> proper extension: 02sgy; *> query: (?x2205, ?x2798) <- role(?x2923, ?x2205), role(?x1166, ?x2205), role(?x716, ?x2205), role(?x432, ?x2205), ?x1166 = 05148p4, role(?x2205, ?x885), ?x885 = 0dwtp, role(?x1495, ?x2205), role(?x1165, ?x2205), ?x716 = 018vs, role(?x6456, ?x432), role(?x2807, ?x432), role(?x1997, ?x432), role(?x1260, ?x432), role(?x432, ?x3215), role(?x432, ?x2798), role(?x1291, ?x432), ?x2798 = 03qjg, ?x1997 = 01wsl7c, instrumentalists(?x432, ?x133), artist(?x2190, ?x6456), group(?x2205, ?x4783), award_winner(?x2807, ?x2806), performance_role(?x1260, ?x1433), role(?x645, ?x432), ?x3215 = 0bxl5, ?x2923 = 02k856 *> conf = 0.70 ranks of expected_values: 7 EVAL 0dq630k role 03qjg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 49.000 48.000 0.843 http://example.org/music/performance_role/regular_performances./music/group_membership/role #19313-0pyg6 PRED entity: 0pyg6 PRED relation: gender PRED expected values: 02zsn => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.82 #51, 0.82 #47, 0.81 #31), 02zsn (0.49 #34, 0.49 #42, 0.47 #54) >> Best rule #51 for best value: >> intensional similarity = 2 >> extensional distance = 223 >> proper extension: 03ft8; 08xz51; 023jq1; 07f7jp; 08f3yq; >> query: (?x2194, 05zppz) <- producer_type(?x2194, ?x632), nationality(?x2194, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 168 *> proper extension: 06jzh; 04shbh; *> query: (?x2194, 02zsn) <- nominated_for(?x2194, ?x2078), participant(?x2387, ?x2194), award(?x2194, ?x724) *> conf = 0.49 ranks of expected_values: 2 EVAL 0pyg6 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 95.000 0.822 http://example.org/people/person/gender #19312-0qf43 PRED entity: 0qf43 PRED relation: people! PRED expected values: 02w7gg => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 34): 041rx (0.32 #312, 0.22 #158, 0.17 #543), 02w7gg (0.24 #1391, 0.23 #1545, 0.11 #1468), 01rv7x (0.17 #39, 0.04 #116, 0.02 #347), 03w9bjf (0.17 #54), 033tf_ (0.13 #1240, 0.12 #1162, 0.12 #1318), 0x67 (0.09 #87, 0.08 #3952, 0.07 #5646), 048z7l (0.09 #194, 0.07 #271, 0.05 #348), 0xnvg (0.08 #321, 0.07 #1246, 0.06 #1633), 0222qb (0.08 #352, 0.04 #198, 0.03 #583), 0d7wh (0.06 #1560, 0.06 #1406, 0.03 #1483) >> Best rule #312 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 032md; >> query: (?x276, 041rx) <- film(?x276, ?x4602), religion(?x276, ?x1985), nationality(?x276, ?x1310), nominated_for(?x384, ?x4602) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1391 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 346 *> proper extension: 07_3qd; 0fv6dr; 09lhln; 0bw7ly; 0djvzd; 0dv1hh; 09m465; 07zr66; *> query: (?x276, 02w7gg) <- gender(?x276, ?x231), nationality(?x276, ?x1310), ?x1310 = 02jx1 *> conf = 0.24 ranks of expected_values: 2 EVAL 0qf43 people! 02w7gg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.318 http://example.org/people/ethnicity/people #19311-01323p PRED entity: 01323p PRED relation: artist! PRED expected values: 02swsm => 82 concepts (46 used for prediction) PRED predicted values (max 10 best out of 125): 03rhqg (0.42 #554, 0.22 #1770, 0.21 #2040), 01cszh (0.33 #10, 0.11 #1226, 0.11 #955), 01clyr (0.29 #2460, 0.25 #569, 0.18 #434), 015_1q (0.29 #287, 0.28 #692, 0.26 #827), 041n43 (0.25 #242, 0.09 #512, 0.06 #1187), 02bh8z (0.21 #559, 0.17 #964, 0.17 #19), 01trtc (0.19 #1012, 0.19 #337, 0.15 #1283), 01w40h (0.18 #430, 0.14 #295, 0.12 #160), 03mp8k (0.18 #1547, 0.13 #871, 0.13 #2762), 0mzkr (0.17 #563, 0.16 #698, 0.10 #833) >> Best rule #554 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 03t9sp; 05k79; 0frsw; 016fmf; 03xhj6; 018gm9; 047cx; 07h76; 0178kd; 0gr69; ... >> query: (?x7682, 03rhqg) <- category(?x7682, ?x134), group(?x227, ?x7682), artists(?x3370, ?x7682), ?x3370 = 059kh >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1034 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 34 *> proper extension: 04cr6qv; *> query: (?x7682, 02swsm) <- category(?x7682, ?x134), artist(?x2149, ?x7682), artists(?x3243, ?x7682), artists(?x671, ?x7682), ?x3243 = 0y3_8, ?x671 = 064t9 *> conf = 0.08 ranks of expected_values: 32 EVAL 01323p artist! 02swsm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 82.000 46.000 0.417 http://example.org/music/record_label/artist #19310-01xr66 PRED entity: 01xr66 PRED relation: profession! PRED expected values: 08f3b1 01pj3h => 64 concepts (27 used for prediction) PRED predicted values (max 10 best out of 4124): 013cr (0.83 #21153, 0.82 #21154, 0.54 #46550), 062dn7 (0.83 #21153, 0.82 #21154, 0.54 #46550), 01mqh5 (0.83 #21153, 0.82 #21154, 0.54 #46550), 0bbf1f (0.83 #21153, 0.54 #46550, 0.50 #22005), 01qn8k (0.83 #21153, 0.54 #46550, 0.50 #24226), 03kxp7 (0.83 #21153, 0.54 #46550, 0.50 #23783), 0bdxs5 (0.83 #21153, 0.54 #46550, 0.50 #24015), 09nhvw (0.83 #21153, 0.54 #46550, 0.50 #24251), 06wm0z (0.83 #21153, 0.54 #46550, 0.43 #50782), 0794g (0.83 #21153, 0.54 #46550, 0.43 #50782) >> Best rule #21153 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 0fj9f; >> query: (?x7361, ?x286) <- profession(?x12525, ?x7361), profession(?x6059, ?x7361), profession(?x1898, ?x7361), participant(?x906, ?x6059), ?x12525 = 06c0j, participant(?x1898, ?x286), student(?x263, ?x6059), location(?x6059, ?x191) >> conf = 0.83 => this is the best rule for 19 predicted values *> Best rule #12206 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0dxtg; *> query: (?x7361, 01pj3h) <- profession(?x8018, ?x7361), profession(?x6059, ?x7361), profession(?x1898, ?x7361), ?x6059 = 01tnbn, team(?x1898, ?x1899), currency(?x1898, ?x170), artists(?x996, ?x8018) *> conf = 0.50 ranks of expected_values: 382, 710 EVAL 01xr66 profession! 01pj3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 27.000 0.829 http://example.org/people/person/profession EVAL 01xr66 profession! 08f3b1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 27.000 0.829 http://example.org/people/person/profession #19309-035zr0 PRED entity: 035zr0 PRED relation: nominated_for! PRED expected values: 0fq9zdv => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 201): 0fq9zdv (0.48 #1132, 0.12 #2332, 0.05 #17286), 0fq9zcx (0.40 #1193, 0.06 #2393, 0.03 #1673), 094qd5 (0.32 #997, 0.19 #2197, 0.11 #4357), 040njc (0.31 #2167, 0.15 #1687, 0.14 #4327), 099cng (0.28 #1029, 0.25 #309, 0.09 #11521), 02pqp12 (0.27 #2219, 0.20 #1019, 0.15 #4379), 02qyntr (0.26 #2342, 0.14 #4502, 0.12 #422), 027dtxw (0.25 #244, 0.24 #2164, 0.12 #1204), 0gqwc (0.25 #301, 0.20 #1021, 0.17 #2221), 0gqxm (0.25 #373, 0.08 #1573, 0.07 #2293) >> Best rule #1132 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 01tspc6; >> query: (?x7538, 0fq9zdv) <- nominated_for(?x3580, ?x7538), nominated_for(?x941, ?x7538), ?x941 = 0fq9zdn >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035zr0 nominated_for! 0fq9zdv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.480 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19308-0cc5qkt PRED entity: 0cc5qkt PRED relation: nominated_for! PRED expected values: 03q8ch => 73 concepts (29 used for prediction) PRED predicted values (max 10 best out of 747): 0b6mgp_ (0.33 #3288, 0.10 #23330, 0.08 #7954), 02l4pj (0.30 #18662, 0.01 #5387, 0.01 #12386), 0c_gcr (0.30 #18662), 0bxtg (0.25 #4749, 0.10 #23330, 0.07 #27997), 01gb54 (0.25 #4665, 0.14 #67666, 0.12 #60667), 04cygb3 (0.25 #4665, 0.14 #67666, 0.12 #60667), 030_1_ (0.25 #4665, 0.14 #67666, 0.12 #60667), 02qzjj (0.22 #2236, 0.07 #27997, 0.07 #20995), 0kk9v (0.22 #3165, 0.03 #7831, 0.02 #10164), 01q_ph (0.22 #64, 0.02 #16392, 0.02 #18726) >> Best rule #3288 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 07gp9; 01c22t; 0dr_4; 01jrbb; 0ddt_; 07024; 011wtv; >> query: (?x3596, 0b6mgp_) <- nominated_for(?x1933, ?x3596), production_companies(?x3596, ?x1686), film_crew_role(?x3596, ?x137), ?x1933 = 0c94fn >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3231 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 07gp9; 01c22t; 0dr_4; 01jrbb; 0ddt_; 07024; 011wtv; *> query: (?x3596, 03q8ch) <- nominated_for(?x1933, ?x3596), production_companies(?x3596, ?x1686), film_crew_role(?x3596, ?x137), ?x1933 = 0c94fn *> conf = 0.11 ranks of expected_values: 38 EVAL 0cc5qkt nominated_for! 03q8ch CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 73.000 29.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #19307-04k05 PRED entity: 04k05 PRED relation: group! PRED expected values: 01vj9c => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 70): 03bx0bm (0.53 #560, 0.40 #204, 0.31 #5197), 03qjg (0.53 #583, 0.31 #494, 0.28 #1475), 0l14md (0.53 #541, 0.28 #5178, 0.25 #7), 018vs (0.47 #548, 0.27 #5185, 0.25 #815), 05r5c (0.37 #542, 0.25 #8, 0.12 #453), 013y1f (0.37 #563, 0.12 #474, 0.12 #1455), 028tv0 (0.32 #547, 0.20 #191, 0.19 #5184), 01vj9c (0.26 #549, 0.16 #2600, 0.15 #816), 0l14qv (0.26 #539, 0.10 #1787, 0.10 #1698), 07gql (0.25 #38, 0.11 #572, 0.05 #839) >> Best rule #560 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 067mj; 05563d; >> query: (?x10671, 03bx0bm) <- artists(?x7083, ?x10671), ?x7083 = 02yv6b, group(?x5623, ?x10671), artist(?x5744, ?x10671) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #549 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 067mj; 05563d; *> query: (?x10671, 01vj9c) <- artists(?x7083, ?x10671), ?x7083 = 02yv6b, group(?x5623, ?x10671), artist(?x5744, ?x10671) *> conf = 0.26 ranks of expected_values: 8 EVAL 04k05 group! 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 135.000 135.000 0.526 http://example.org/music/performance_role/regular_performances./music/group_membership/group #19306-031sg0 PRED entity: 031sg0 PRED relation: film PRED expected values: 0kvf3b => 104 concepts (54 used for prediction) PRED predicted values (max 10 best out of 906): 02h2vv (0.65 #28661, 0.63 #30453, 0.62 #21495), 0bs5f0b (0.08 #3416, 0.02 #5207), 02qr3k8 (0.08 #4872, 0.03 #3081, 0.02 #22785), 0cfhfz (0.08 #4074, 0.03 #2283, 0.01 #11239), 03kxj2 (0.08 #358, 0.06 #3940, 0.03 #2149), 03bzjpm (0.08 #1316, 0.04 #8481, 0.03 #15645), 0f4_l (0.08 #349, 0.04 #3931, 0.02 #12887), 083shs (0.08 #19, 0.04 #3601, 0.01 #7184), 09cr8 (0.08 #284, 0.03 #9240, 0.02 #12822), 0n6ds (0.08 #1630, 0.03 #3421, 0.02 #8795) >> Best rule #28661 for best value: >> intensional similarity = 3 >> extensional distance = 358 >> proper extension: 06jzh; >> query: (?x9925, ?x6339) <- location(?x9925, ?x2474), nominated_for(?x9925, ?x6339), participant(?x7138, ?x9925) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 031sg0 film 0kvf3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 54.000 0.651 http://example.org/film/actor/film./film/performance/film #19305-0gd0c7x PRED entity: 0gd0c7x PRED relation: genre PRED expected values: 07s9rl0 => 123 concepts (99 used for prediction) PRED predicted values (max 10 best out of 110): 07s9rl0 (0.85 #3512, 0.78 #8089, 0.68 #11385), 05p553 (0.62 #5510, 0.55 #9741, 0.49 #6800), 01hmnh (0.61 #8572, 0.44 #1769, 0.44 #1652), 02l7c8 (0.40 #9749, 0.32 #1650, 0.29 #10453), 0lsxr (0.38 #7156, 0.38 #3518, 0.34 #10566), 082gq (0.33 #27, 0.20 #144, 0.16 #1314), 03g3w (0.33 #21, 0.12 #1308, 0.11 #957), 04xvlr (0.30 #11152, 0.21 #10443, 0.19 #3396), 04pbhw (0.28 #2511, 0.25 #2979, 0.23 #3096), 0gf28 (0.25 #997, 0.10 #1816, 0.09 #3455) >> Best rule #3512 for best value: >> intensional similarity = 10 >> extensional distance = 86 >> proper extension: 0yyg4; 04mzf8; 0yx7h; 0y_pg; 02ptczs; >> query: (?x1999, 07s9rl0) <- genre(?x1999, ?x811), genre(?x1999, ?x600), ?x600 = 02n4kr, currency(?x1999, ?x170), genre(?x5697, ?x811), genre(?x4087, ?x811), genre(?x810, ?x811), ?x5697 = 0bl06, ?x810 = 0jzw, ?x4087 = 01hw5kk >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gd0c7x genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 99.000 0.852 http://example.org/film/film/genre #19304-0151w_ PRED entity: 0151w_ PRED relation: student! PRED expected values: 05q2c => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 161): 0bwfn (0.20 #801, 0.11 #10269, 0.10 #10795), 0lyjf (0.14 #157, 0.10 #683, 0.04 #1209), 01w5m (0.14 #105, 0.05 #3261, 0.04 #47448), 03x33n (0.14 #129, 0.02 #3285, 0.02 #4337), 0217m9 (0.14 #171, 0.02 #3327, 0.02 #4905), 02qvvv (0.14 #99, 0.02 #4307, 0.01 #5359), 016w7b (0.14 #506), 02ldkf (0.14 #427), 015nl4 (0.10 #593, 0.07 #3223, 0.05 #44779), 04b_46 (0.10 #753, 0.06 #10747, 0.06 #10221) >> Best rule #801 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0prjs; 01z7s_; >> query: (?x989, 0bwfn) <- celebrity(?x989, ?x3553), participant(?x3308, ?x989), film(?x989, ?x813) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3469 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 53 *> proper extension: 0mj0c; 06hx2; 02x8mt; 0239zv; *> query: (?x989, 05q2c) <- student(?x8008, ?x989), sibling(?x875, ?x989) *> conf = 0.02 ranks of expected_values: 77 EVAL 0151w_ student! 05q2c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 143.000 143.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #19303-0c3zjn7 PRED entity: 0c3zjn7 PRED relation: film! PRED expected values: 01795t 01gb54 => 79 concepts (73 used for prediction) PRED predicted values (max 10 best out of 47): 01gb54 (0.58 #2757, 0.51 #435, 0.49 #2903), 04cygb3 (0.51 #435, 0.49 #2903, 0.49 #2756), 03xq0f (0.39 #729, 0.12 #222, 0.12 #585), 016tw3 (0.18 #372, 0.17 #2840, 0.17 #1166), 086k8 (0.18 #798, 0.17 #870, 0.17 #437), 016tt2 (0.15 #584, 0.13 #872, 0.13 #366), 017s11 (0.14 #655, 0.13 #2833, 0.13 #1015), 04f525m (0.09 #9, 0.08 #81, 0.07 #154), 032dg7 (0.08 #117, 0.07 #190, 0.02 #1274), 0jz9f (0.08 #1013, 0.07 #653, 0.06 #436) >> Best rule #2757 for best value: >> intensional similarity = 3 >> extensional distance = 893 >> proper extension: 053tj7; 016ztl; 02zk08; 0cbl95; >> query: (?x5553, ?x4564) <- production_companies(?x5553, ?x4564), language(?x5553, ?x254), award_nominee(?x4564, ?x574) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12 EVAL 0c3zjn7 film! 01gb54 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 73.000 0.576 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0c3zjn7 film! 01795t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 79.000 73.000 0.576 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #19302-0q59y PRED entity: 0q59y PRED relation: profession PRED expected values: 02jknp => 105 concepts (70 used for prediction) PRED predicted values (max 10 best out of 71): 02jknp (0.64 #864, 0.63 #4296, 0.59 #3581), 03gjzk (0.41 #4731, 0.39 #7449, 0.38 #4445), 0kyk (0.36 #1171, 0.35 #1314, 0.26 #598), 01c72t (0.33 #20, 0.21 #306, 0.15 #1880), 018gz8 (0.25 #156, 0.18 #872, 0.16 #1444), 0dz3r (0.25 #287, 0.14 #10015, 0.12 #8728), 025352 (0.21 #341, 0.19 #1200, 0.17 #55), 0nbcg (0.21 #314, 0.15 #1745, 0.14 #10015), 02krf9 (0.20 #4457, 0.18 #4314, 0.18 #882), 09jwl (0.17 #8742, 0.17 #4163, 0.17 #301) >> Best rule #864 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 03jldb; 032w8h; 04fcx7; 01wk51; 070yzk; 012x2b; 0405l; 0py5b; >> query: (?x3947, 02jknp) <- profession(?x3947, ?x106), type_of_union(?x3947, ?x566), languages(?x3947, ?x254), written_by(?x12829, ?x3947) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q59y profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 70.000 0.641 http://example.org/people/person/profession #19301-0b90_r PRED entity: 0b90_r PRED relation: combatants! PRED expected values: 0ctw_b => 222 concepts (132 used for prediction) PRED predicted values (max 10 best out of 321): 0ctw_b (0.81 #4366, 0.80 #1448, 0.75 #469), 01mk6 (0.81 #4366, 0.80 #1448, 0.64 #1227), 0b90_r (0.69 #461, 0.55 #2180, 0.55 #1184), 0bq0p9 (0.37 #726, 0.35 #1386, 0.31 #465), 059z0 (0.36 #1232, 0.34 #2693, 0.34 #4284), 07f1x (0.33 #5623, 0.30 #2245, 0.27 #4281), 01pj7 (0.33 #5623, 0.30 #2245, 0.26 #7014), 06c1y (0.33 #5623, 0.30 #2245, 0.26 #7014), 0193qj (0.33 #5623, 0.30 #2245, 0.26 #7014), 03gj2 (0.33 #5623, 0.30 #2245, 0.26 #7014) >> Best rule #4366 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 01z88t; 01z215; 05b7q; 016zwt; >> query: (?x151, ?x94) <- country(?x150, ?x151), combatants(?x151, ?x94), olympics(?x151, ?x391) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0b90_r combatants! 0ctw_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 222.000 132.000 0.809 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #19300-05nlx4 PRED entity: 05nlx4 PRED relation: film_crew_role PRED expected values: 09vw2b7 01xy5l_ => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 23): 09vw2b7 (0.75 #122, 0.70 #939, 0.68 #6), 01pvkk (0.39 #8, 0.30 #736, 0.30 #66), 02rh1dz (0.32 #7, 0.24 #182, 0.19 #501), 01xy5l_ (0.18 #68, 0.13 #10, 0.12 #243), 089fss (0.18 #121, 0.08 #938, 0.08 #1112), 0215hd (0.17 #72, 0.15 #742, 0.14 #1005), 033smt (0.16 #21, 0.09 #544, 0.07 #283), 04pyp5 (0.13 #12, 0.10 #128, 0.09 #740), 094hwz (0.13 #11, 0.09 #534, 0.08 #69), 02_n3z (0.10 #905, 0.10 #88, 0.10 #1) >> Best rule #122 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 09p0ct; 05p09dd; 01_0f7; 0cp0790; >> query: (?x7199, 09vw2b7) <- film_crew_role(?x7199, ?x3197), award_winner(?x7199, ?x2444), ?x3197 = 02ynfr >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 05nlx4 film_crew_role 01xy5l_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05nlx4 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19299-016fmf PRED entity: 016fmf PRED relation: artist! PRED expected values: 01dtcb => 97 concepts (46 used for prediction) PRED predicted values (max 10 best out of 137): 02bh8z (0.44 #1009, 0.26 #1573, 0.12 #2842), 03rhqg (0.39 #1567, 0.33 #1003, 0.24 #2977), 015kg1 (0.38 #726, 0.21 #1431, 0.19 #1713), 033hn8 (0.33 #14, 0.26 #1424, 0.22 #1706), 0g768 (0.33 #1025, 0.22 #1589, 0.20 #320), 03mp8k (0.30 #1196, 0.21 #1337, 0.15 #1901), 01clyr (0.29 #457, 0.26 #1585, 0.25 #175), 01dtcb (0.29 #471, 0.25 #189, 0.20 #330), 01cl0d (0.29 #479, 0.25 #197, 0.14 #1325), 015_1q (0.26 #1853, 0.25 #725, 0.22 #866) >> Best rule #1009 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 03xhj6; >> query: (?x2723, 02bh8z) <- group(?x227, ?x2723), origin(?x2723, ?x9417), artists(?x3370, ?x2723), artists(?x671, ?x2723), ?x3370 = 059kh, ?x671 = 064t9 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #471 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 03xl77; *> query: (?x2723, 01dtcb) <- category(?x2723, ?x134), artists(?x5934, ?x2723), artists(?x2722, ?x2723), origin(?x2723, ?x9417), ?x5934 = 05r6t, ?x2722 = 01g888 *> conf = 0.29 ranks of expected_values: 8 EVAL 016fmf artist! 01dtcb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 97.000 46.000 0.444 http://example.org/music/record_label/artist #19298-02dlfh PRED entity: 02dlfh PRED relation: nationality PRED expected values: 09c7w0 => 123 concepts (116 used for prediction) PRED predicted values (max 10 best out of 54): 09c7w0 (0.85 #8617, 0.84 #501, 0.81 #1905), 0m27n (0.27 #10322, 0.27 #10524), 0d35y (0.27 #10322, 0.27 #10524), 07ssc (0.14 #3221, 0.13 #1015, 0.13 #1617), 02jx1 (0.12 #133, 0.12 #1033, 0.11 #3239), 03_3d (0.06 #2210, 0.04 #7912, 0.03 #9520), 03rk0 (0.06 #11170, 0.06 #10970, 0.05 #2050), 0d060g (0.06 #2211, 0.06 #9025, 0.05 #8823), 0345h (0.06 #4438, 0.06 #3437, 0.06 #4238), 03rt9 (0.04 #213, 0.04 #7912, 0.03 #9520) >> Best rule #8617 for best value: >> intensional similarity = 3 >> extensional distance = 1314 >> proper extension: 04g9sq; >> query: (?x8160, 09c7w0) <- gender(?x8160, ?x231), location(?x8160, ?x9331), source(?x9331, ?x958) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02dlfh nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 116.000 0.847 http://example.org/people/person/nationality #19297-01k_r5b PRED entity: 01k_r5b PRED relation: film PRED expected values: 01jnc_ => 101 concepts (98 used for prediction) PRED predicted values (max 10 best out of 128): 033f8n (0.08 #826, 0.02 #6202, 0.02 #7994), 035s95 (0.08 #2133, 0.01 #3925, 0.01 #25429), 04yc76 (0.08 #2235), 0n1s0 (0.08 #1035), 01jnc_ (0.03 #10531, 0.02 #12323, 0.02 #23075), 03mnn0 (0.02 #4654), 03l6q0 (0.02 #25632, 0.02 #31008, 0.02 #36384), 0prrm (0.02 #25950, 0.02 #31326, 0.02 #36702), 07bzz7 (0.02 #9851, 0.02 #11643, 0.01 #13435), 03hfmm (0.02 #10440, 0.01 #15816, 0.01 #14024) >> Best rule #826 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01lmj3q; 0137n0; 0ggjt; 016srn; 03cfjg; 0x3b7; 02cx90; 01l47f5; 05sq20; 051m56; >> query: (?x5265, 033f8n) <- award_nominee(?x5265, ?x2300), award_winner(?x2518, ?x5265), ?x2300 = 01ww2fs >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #10531 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 189 *> proper extension: 01h5f8; *> query: (?x5265, 01jnc_) <- award_nominee(?x2300, ?x5265), profession(?x5265, ?x220), ?x220 = 016z4k *> conf = 0.03 ranks of expected_values: 5 EVAL 01k_r5b film 01jnc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 98.000 0.083 http://example.org/film/actor/film./film/performance/film #19296-03x82v PRED entity: 03x82v PRED relation: award_winner! PRED expected values: 02681xs => 119 concepts (90 used for prediction) PRED predicted values (max 10 best out of 238): 026rsl9 (0.71 #860, 0.46 #337, 0.44 #859), 02681xs (0.44 #859, 0.39 #6427, 0.38 #7285), 02v703 (0.39 #715, 0.08 #1145, 0.08 #285), 01by1l (0.18 #7827, 0.17 #6110, 0.14 #9972), 02f777 (0.18 #1166, 0.08 #306, 0.06 #736), 0l8z1 (0.15 #34706, 0.09 #35135, 0.08 #32560), 02qvyrt (0.15 #34706, 0.09 #35135, 0.08 #32560), 02x17c2 (0.15 #34706, 0.08 #217, 0.06 #647), 02f71y (0.14 #1040, 0.08 #180, 0.05 #3182), 02g3gj (0.14 #886, 0.08 #26, 0.04 #36849) >> Best rule #860 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 015882; >> query: (?x11182, ?x11010) <- award(?x11182, ?x11010), award_winner(?x11010, ?x9220), ceremony(?x11010, ?x139), ?x9220 = 0cc5tgk >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #859 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 015882; *> query: (?x11182, ?x3666) <- award(?x11182, ?x11010), award(?x11182, ?x3666), award_winner(?x11010, ?x9220), ceremony(?x11010, ?x139), ?x9220 = 0cc5tgk *> conf = 0.44 ranks of expected_values: 2 EVAL 03x82v award_winner! 02681xs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 90.000 0.708 http://example.org/award/award_category/winners./award/award_honor/award_winner #19295-033hqf PRED entity: 033hqf PRED relation: gender PRED expected values: 05zppz => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #11, 0.86 #9, 0.85 #13), 02zsn (0.45 #98, 0.45 #92, 0.45 #102) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0cg9y; >> query: (?x544, 05zppz) <- profession(?x544, ?x1032), celebrities_impersonated(?x3649, ?x544), ?x3649 = 03m6t5 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033hqf gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.889 http://example.org/people/person/gender #19294-0170yd PRED entity: 0170yd PRED relation: genre PRED expected values: 07s9rl0 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 90): 07s9rl0 (0.79 #243, 0.78 #485, 0.76 #122), 01jfsb (0.42 #5470, 0.37 #983, 0.36 #1468), 02kdv5l (0.37 #973, 0.35 #1458, 0.34 #1094), 05p553 (0.35 #1583, 0.35 #3765, 0.34 #4493), 02l7c8 (0.31 #1959, 0.30 #1351, 0.30 #622), 04xvlr (0.26 #486, 0.19 #244, 0.19 #607), 03k9fj (0.25 #5469, 0.24 #1103, 0.24 #1467), 02n4kr (0.25 #9, 0.16 #5466, 0.11 #6076), 017fp (0.25 #16, 0.14 #137, 0.13 #258), 03bxz7 (0.25 #298, 0.12 #56, 0.12 #540) >> Best rule #243 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 0yyg4; 0209xj; 0gjk1d; 05j82v; 0yyts; 05cvgl; 0f4vx; 0_816; 01rwyq; 0p_qr; ... >> query: (?x8410, 07s9rl0) <- nominated_for(?x3209, ?x8410), film(?x1244, ?x8410), nominated_for(?x6041, ?x8410), ?x3209 = 02w9sd7 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0170yd genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.792 http://example.org/film/film/genre #19293-0172jm PRED entity: 0172jm PRED relation: organization! PRED expected values: 060c4 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 16): 060c4 (0.75 #223, 0.73 #119, 0.73 #275), 07xl34 (0.21 #596, 0.20 #531, 0.20 #50), 0dq_5 (0.17 #646, 0.17 #711, 0.16 #685), 05k17c (0.16 #72, 0.07 #644, 0.07 #735), 0hm4q (0.06 #411, 0.05 #567, 0.05 #723), 05c0jwl (0.04 #447, 0.04 #109, 0.04 #57), 01t7n9 (0.02 #1237), 09n5b9 (0.02 #1237), 02079p (0.02 #1237), 0789n (0.02 #1237) >> Best rule #223 for best value: >> intensional similarity = 3 >> extensional distance = 289 >> proper extension: 03zw80; 0352gk; 01hnb; 02gn8s; 01qwb5; 04gd8j; 02lwv5; 03x1s8; >> query: (?x5868, 060c4) <- contains(?x94, ?x5868), colors(?x5868, ?x12067), ?x94 = 09c7w0 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0172jm organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.753 http://example.org/organization/role/leaders./organization/leadership/organization #19292-09lvl1 PRED entity: 09lvl1 PRED relation: award! PRED expected values: 0783m_ => 44 concepts (12 used for prediction) PRED predicted values (max 10 best out of 2428): 0gqrb (0.79 #23615, 0.75 #30363, 0.63 #20240), 014488 (0.79 #23615, 0.75 #30363, 0.63 #20239), 016k6x (0.43 #8199, 0.09 #18320, 0.09 #14946), 02qgyv (0.40 #611, 0.14 #16866, 0.14 #7356), 0jbp0 (0.40 #2906, 0.11 #9651, 0.05 #6279), 0170pk (0.38 #7189, 0.20 #444, 0.08 #13936), 02qgqt (0.35 #6766, 0.20 #21, 0.10 #3394), 03ym1 (0.32 #8424, 0.20 #1679, 0.08 #15171), 0237fw (0.32 #7390, 0.08 #14137, 0.08 #17511), 0bj9k (0.30 #7270, 0.20 #525, 0.10 #3898) >> Best rule #23615 for best value: >> intensional similarity = 5 >> extensional distance = 175 >> proper extension: 02wh75; 0l8z1; 01ckbq; 02rdyk7; 026mff; 01yz0x; 02gdjb; 0265wl; 056jm_; 0262x6; ... >> query: (?x7788, ?x3324) <- award(?x8835, ?x7788), award(?x1993, ?x7788), film(?x1993, ?x2640), notable_people_with_this_condition(?x6656, ?x8835), award_winner(?x7788, ?x3324) >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #7358 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 01kpt; *> query: (?x7788, 0783m_) <- award_winner(?x7788, ?x1034), award_winner(?x3066, ?x1034), award_winner(?x1033, ?x1034), ?x1033 = 02x73k6, nominated_for(?x3066, ?x144) *> conf = 0.03 ranks of expected_values: 1457 EVAL 09lvl1 award! 0783m_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 12.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19291-01xcqc PRED entity: 01xcqc PRED relation: profession PRED expected values: 01d_h8 => 121 concepts (68 used for prediction) PRED predicted values (max 10 best out of 65): 01d_h8 (0.69 #2652, 0.67 #6181, 0.66 #300), 03gjzk (0.43 #6335, 0.43 #2218, 0.40 #13), 018gz8 (0.34 #3690, 0.23 #15, 0.22 #2220), 0cbd2 (0.25 #1036, 0.25 #5888, 0.22 #8536), 0np9r (0.22 #166, 0.19 #3694, 0.15 #19), 02krf9 (0.21 #6200, 0.19 #6347, 0.19 #2230), 09jwl (0.19 #17, 0.18 #2957, 0.18 #3398), 0kyk (0.16 #1057, 0.15 #28, 0.14 #5909), 02hv44_ (0.15 #1085, 0.09 #2702, 0.08 #7556), 0d1pc (0.15 #5341, 0.14 #4018, 0.14 #5782) >> Best rule #2652 for best value: >> intensional similarity = 3 >> extensional distance = 329 >> proper extension: 086k8; 017s11; 0g1rw; 05qd_; 016tw3; 017jv5; 061dn_; 0k9ctht; 03m9c8; >> query: (?x1606, 01d_h8) <- award(?x1606, ?x5367), award(?x5366, ?x5367), ?x5366 = 0bs8d >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xcqc profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 68.000 0.686 http://example.org/people/person/profession #19290-03hjv97 PRED entity: 03hjv97 PRED relation: film_release_region PRED expected values: 01znc_ 03f2w => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 153): 0d0vqn (0.86 #2013, 0.85 #2180, 0.80 #509), 02vzc (0.82 #562, 0.78 #228, 0.75 #395), 03_3d (0.78 #173, 0.72 #2011, 0.71 #2178), 03rjj (0.75 #338, 0.72 #2009, 0.71 #2176), 07ssc (0.72 #2025, 0.72 #2192, 0.58 #354), 0chghy (0.72 #2018, 0.72 #2185, 0.56 #514), 0345h (0.70 #2045, 0.69 #2212, 0.42 #374), 03gj2 (0.65 #2036, 0.65 #2203, 0.34 #532), 035qy (0.61 #2047, 0.61 #2214, 0.50 #376), 015fr (0.61 #2027, 0.60 #2194, 0.33 #356) >> Best rule #2013 for best value: >> intensional similarity = 4 >> extensional distance = 453 >> proper extension: 014lc_; 0b76d_m; 0ds35l9; 0g56t9t; 0gtsx8c; 02vxq9m; 0c3ybss; 03g90h; 0ckr7s; 01gc7; ... >> query: (?x785, 0d0vqn) <- film_release_region(?x785, ?x2984), film_release_region(?x599, ?x2984), ?x599 = 04ddm4, film_release_region(?x6100, ?x2984) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2055 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 453 *> proper extension: 014lc_; 0b76d_m; 0ds35l9; 0g56t9t; 0gtsx8c; 02vxq9m; 0c3ybss; 03g90h; 0ckr7s; 01gc7; ... *> query: (?x785, 01znc_) <- film_release_region(?x785, ?x2984), film_release_region(?x599, ?x2984), ?x599 = 04ddm4, film_release_region(?x6100, ?x2984) *> conf = 0.59 ranks of expected_values: 14, 23 EVAL 03hjv97 film_release_region 03f2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 99.000 99.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03hjv97 film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 99.000 99.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19289-021l5s PRED entity: 021l5s PRED relation: school! PRED expected values: 01yjl => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 93): 0jmj7 (0.42 #1065, 0.42 #1442, 0.37 #406), 061xq (0.12 #35, 0.07 #412, 0.07 #223), 01yhm (0.12 #20, 0.07 #114, 0.06 #585), 01d6g (0.12 #73, 0.07 #167, 0.06 #450), 07147 (0.12 #68, 0.07 #162, 0.05 #256), 05m_8 (0.09 #380, 0.08 #944, 0.08 #662), 01yjl (0.07 #408, 0.07 #219, 0.06 #690), 04wmvz (0.07 #457, 0.07 #833, 0.06 #1021), 0cqt41 (0.07 #206, 0.06 #489, 0.06 #395), 0713r (0.07 #225, 0.06 #696, 0.06 #978) >> Best rule #1065 for best value: >> intensional similarity = 4 >> extensional distance = 133 >> proper extension: 01jssp; 02s62q; 0j_sncb; 0kw4j; 035wtd; 033x5p; 01jzyx; 02bqy; 025rcc; 01jq4b; ... >> query: (?x4340, 0jmj7) <- contains(?x449, ?x4340), currency(?x4340, ?x170), source(?x449, ?x958), category(?x4340, ?x134) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #408 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 96 *> proper extension: 024y8p; 02gr81; 09f2j; 02zkz7; 0c5x_; 0trv; 01p896; 0mbwf; *> query: (?x4340, 01yjl) <- school_type(?x4340, ?x3092), ?x3092 = 05jxkf, colors(?x4340, ?x663), state_province_region(?x4340, ?x448), currency(?x4340, ?x170) *> conf = 0.07 ranks of expected_values: 7 EVAL 021l5s school! 01yjl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 134.000 134.000 0.422 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #19288-02fjzt PRED entity: 02fjzt PRED relation: school_type PRED expected values: 01_9fk => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 18): 01rs41 (0.33 #579, 0.32 #809, 0.32 #602), 05pcjw (0.31 #576, 0.30 #806, 0.29 #599), 01_9fk (0.24 #163, 0.23 #186, 0.21 #209), 07tf8 (0.23 #31, 0.21 #1020, 0.20 #54), 01_srz (0.09 #601, 0.08 #578, 0.08 #785), 04399 (0.04 #220, 0.04 #174, 0.04 #289), 02p0qmm (0.04 #1182, 0.04 #906, 0.03 #377), 01jlsn (0.03 #1051, 0.03 #1352, 0.03 #1536), 04qbv (0.03 #268, 0.03 #820, 0.02 #337), 0bpgx (0.03 #388, 0.02 #710, 0.02 #871) >> Best rule #579 for best value: >> intensional similarity = 4 >> extensional distance = 249 >> proper extension: 02_2kg; >> query: (?x4341, 01rs41) <- currency(?x4341, ?x170), school_type(?x4341, ?x3092), ?x170 = 09nqf, state_province_region(?x4341, ?x2713) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #163 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 131 *> proper extension: 02jztz; *> query: (?x4341, 01_9fk) <- currency(?x4341, ?x170), school(?x2820, ?x4341), colors(?x4341, ?x663), major_field_of_study(?x4341, ?x2981) *> conf = 0.24 ranks of expected_values: 3 EVAL 02fjzt school_type 01_9fk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 152.000 152.000 0.327 http://example.org/education/educational_institution/school_type #19287-016sd3 PRED entity: 016sd3 PRED relation: institution! PRED expected values: 014mlp => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 24): 02h4rq6 (0.84 #203, 0.81 #228, 0.79 #1031), 014mlp (0.74 #206, 0.73 #282, 0.73 #131), 03bwzr4 (0.68 #216, 0.55 #417, 0.49 #894), 019v9k (0.65 #662, 0.65 #687, 0.65 #411), 02_xgp2 (0.55 #214, 0.43 #415, 0.38 #691), 016t_3 (0.52 #204, 0.44 #405, 0.40 #656), 0bkj86 (0.52 #209, 0.39 #410, 0.35 #661), 07s6fsf (0.50 #126, 0.44 #402, 0.42 #201), 04zx3q1 (0.42 #202, 0.33 #2, 0.22 #880), 022h5x (0.33 #22, 0.32 #222, 0.24 #423) >> Best rule #203 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0ks67; >> query: (?x10838, 02h4rq6) <- school(?x580, ?x10838), school_type(?x10838, ?x11936), school_type(?x1884, ?x11936), ?x1884 = 0bx8pn >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #206 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 0ks67; *> query: (?x10838, 014mlp) <- school(?x580, ?x10838), school_type(?x10838, ?x11936), school_type(?x1884, ?x11936), ?x1884 = 0bx8pn *> conf = 0.74 ranks of expected_values: 2 EVAL 016sd3 institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.839 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #19286-026lj PRED entity: 026lj PRED relation: interests PRED expected values: 0x0w => 124 concepts (113 used for prediction) PRED predicted values (max 10 best out of 12): 05qt0 (0.40 #42, 0.25 #30, 0.24 #78), 0x0w (0.33 #10, 0.22 #118, 0.20 #46), 09xq9d (0.24 #77, 0.16 #113, 0.14 #53), 097df (0.14 #59, 0.07 #71, 0.05 #119), 04rjg (0.08 #111, 0.08 #75, 0.07 #63), 05qfh (0.08 #112, 0.04 #76, 0.02 #244), 04g7x (0.07 #68, 0.04 #80, 0.03 #116), 06ms6 (0.05 #110, 0.04 #74, 0.01 #182), 06mq7 (0.04 #84, 0.03 #120), 04sh3 (0.04 #81, 0.03 #117) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0gz_; 043s3; 05qmj; >> query: (?x1857, 05qt0) <- influenced_by(?x2240, ?x1857), profession(?x1857, ?x7397), ?x2240 = 0j3v, interests(?x1857, ?x713) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 03sbs; *> query: (?x1857, 0x0w) <- influenced_by(?x11837, ?x1857), influenced_by(?x8233, ?x1857), interests(?x1857, ?x713), ?x11837 = 032r1, ?x8233 = 0399p *> conf = 0.33 ranks of expected_values: 2 EVAL 026lj interests 0x0w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 113.000 0.400 http://example.org/user/alexander/philosophy/philosopher/interests #19285-0jhn7 PRED entity: 0jhn7 PRED relation: olympics! PRED expected values: 05v8c 06mzp 0jgx => 51 concepts (47 used for prediction) PRED predicted values (max 10 best out of 168): 06mzp (0.85 #630, 0.82 #493, 0.80 #1819), 0h7x (0.78 #1252, 0.78 #275, 0.77 #1823), 0d060g (0.78 #275, 0.75 #1240, 0.73 #1811), 01pj7 (0.78 #275, 0.72 #69, 0.67 #294), 02k54 (0.78 #275, 0.72 #69, 0.56 #422), 047lj (0.78 #275, 0.72 #69, 0.33 #281), 01ls2 (0.67 #282, 0.62 #626, 0.45 #489), 04gzd (0.67 #280, 0.44 #418, 0.40 #211), 01mk6 (0.56 #879, 0.45 #535, 0.43 #138), 07twz (0.50 #385, 0.40 #246, 0.36 #522) >> Best rule #630 for best value: >> intensional similarity = 10 >> extensional distance = 11 >> proper extension: 0l98s; 06sks6; >> query: (?x3971, 06mzp) <- olympics(?x304, ?x3971), sports(?x3971, ?x6150), currency(?x304, ?x170), film_release_region(?x9565, ?x304), film_release_region(?x3276, ?x304), ?x6150 = 07_53, ?x3276 = 0gjc4d3, ?x9565 = 0hz6mv2, combatants(?x10176, ?x304), contains(?x304, ?x5168) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 18, 67 EVAL 0jhn7 olympics! 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 51.000 47.000 0.846 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jhn7 olympics! 06mzp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 47.000 0.846 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jhn7 olympics! 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 51.000 47.000 0.846 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #19284-03d_zl4 PRED entity: 03d_zl4 PRED relation: language PRED expected values: 02h40lc => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 5): 02h40lc (0.86 #45, 0.84 #35, 0.83 #52), 03_9r (0.06 #37, 0.04 #54, 0.01 #87), 03k50 (0.02 #53), 04306rv (0.02 #171, 0.02 #201, 0.02 #155), 064_8sq (0.01 #272) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 01bcq; 03k545; >> query: (?x6707, 02h40lc) <- location(?x6707, ?x1755), contains(?x1755, ?x503), actor(?x5286, ?x6707) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03d_zl4 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.865 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #19283-01vs5c PRED entity: 01vs5c PRED relation: school_type PRED expected values: 05jxkf => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.60 #580, 0.60 #604, 0.60 #124), 05pcjw (0.46 #217, 0.33 #1, 0.30 #193), 01_9fk (0.35 #578, 0.33 #530, 0.33 #602), 01rs41 (0.32 #221, 0.29 #1397, 0.27 #1277), 07tf8 (0.30 #129, 0.27 #345, 0.25 #321), 01_srz (0.11 #99, 0.10 #123, 0.07 #1467), 02p0qmm (0.05 #490, 0.04 #562, 0.04 #370), 01y64 (0.05 #492, 0.03 #780, 0.03 #564), 04399 (0.04 #926, 0.04 #1166, 0.04 #854), 04qbv (0.04 #208, 0.04 #1120, 0.03 #1216) >> Best rule #580 for best value: >> intensional similarity = 5 >> extensional distance = 66 >> proper extension: 01jssp; 05krk; 01j_9c; 01t8sr; 01j_cy; 07szy; 049dk; 02jyr8; 0bx8pn; 01jq34; ... >> query: (?x5621, 05jxkf) <- institution(?x865, ?x5621), school(?x2198, ?x5621), position(?x2198, ?x7079), ?x7079 = 08ns5s, sport(?x2198, ?x1083) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vs5c school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.603 http://example.org/education/educational_institution/school_type #19282-012x4t PRED entity: 012x4t PRED relation: award_winner! PRED expected values: 0gpjbt => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 111): 02rjjll (0.24 #549, 0.14 #2453, 0.14 #277), 0hhtgcw (0.21 #353, 0.10 #625, 0.04 #1713), 05pd94v (0.20 #546, 0.15 #2178, 0.14 #2858), 013b2h (0.18 #2251, 0.15 #2931, 0.14 #2523), 0466p0j (0.16 #615, 0.14 #343, 0.11 #2519), 0gpjbt (0.16 #571, 0.12 #2203, 0.11 #2883), 056878 (0.14 #574, 0.12 #30, 0.11 #2886), 01s695 (0.13 #2859, 0.12 #547, 0.11 #2179), 0jzphpx (0.12 #37, 0.12 #581, 0.11 #2213), 01mh_q (0.12 #220, 0.12 #628, 0.10 #12241) >> Best rule #549 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 02wb6yq; >> query: (?x1660, 02rjjll) <- instrumentalists(?x212, ?x1660), award_winner(?x1362, ?x1660), currency(?x1660, ?x170) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #571 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 02wb6yq; *> query: (?x1660, 0gpjbt) <- instrumentalists(?x212, ?x1660), award_winner(?x1362, ?x1660), currency(?x1660, ?x170) *> conf = 0.16 ranks of expected_values: 6 EVAL 012x4t award_winner! 0gpjbt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 116.000 116.000 0.240 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19281-0f42nz PRED entity: 0f42nz PRED relation: genre PRED expected values: 01jfsb => 70 concepts (58 used for prediction) PRED predicted values (max 10 best out of 89): 01jfsb (0.69 #1885, 0.53 #1298, 0.52 #1415), 03rk0 (0.52 #3634, 0.49 #4105, 0.49 #3869), 03k50 (0.52 #3634, 0.49 #4105, 0.49 #3869), 03q4nz (0.50 #16, 0.11 #133, 0.07 #1187), 0jtdp (0.44 #129, 0.32 #597, 0.28 #480), 03bxz7 (0.44 #169, 0.11 #637, 0.10 #286), 03k9fj (0.38 #1414, 0.38 #2821, 0.37 #1297), 04rlf (0.33 #179, 0.13 #647, 0.08 #530), 01j1n2 (0.33 #57, 0.04 #994, 0.03 #1111), 0219x_ (0.29 #258, 0.17 #609, 0.14 #492) >> Best rule #1885 for best value: >> intensional similarity = 4 >> extensional distance = 577 >> proper extension: 064n1pz; 09rfh9; 02pcq92; >> query: (?x5247, 01jfsb) <- film_release_distribution_medium(?x5247, ?x81), genre(?x5247, ?x225), genre(?x9199, ?x225), ?x9199 = 05nyqk >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f42nz genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 58.000 0.694 http://example.org/film/film/genre #19280-0f7hc PRED entity: 0f7hc PRED relation: award_nominee PRED expected values: 01wmxfs => 129 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1387): 01gb54 (0.80 #49058, 0.79 #72413, 0.79 #46722), 01wmxfs (0.80 #49058, 0.79 #72413, 0.78 #46721), 0147dk (0.25 #98, 0.03 #21121, 0.02 #32802), 05qd_ (0.23 #9526, 0.16 #44567, 0.15 #32886), 016tt2 (0.23 #9456, 0.13 #32816, 0.12 #44497), 016tw3 (0.23 #9569, 0.07 #44610, 0.07 #32929), 026dg51 (0.21 #28218, 0.15 #46908, 0.14 #51579), 05vsxz (0.20 #2346, 0.18 #7018, 0.03 #53737), 0blbxk (0.20 #2603, 0.09 #7275, 0.02 #138086), 06l9n8 (0.20 #4417, 0.09 #9089, 0.01 #32447) >> Best rule #49058 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 03mz9r; 0275_pj; 01gp_x; 03bx_5q; 04gtdnh; 03fykz; 04crrxr; 01my_c; 0grmhb; 0564mx; ... >> query: (?x4657, ?x400) <- profession(?x4657, ?x319), tv_program(?x4657, ?x6884), award_nominee(?x400, ?x4657) >> conf = 0.80 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0f7hc award_nominee 01wmxfs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 62.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19279-05t7c1 PRED entity: 05t7c1 PRED relation: major_field_of_study PRED expected values: 02j62 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 120): 062z7 (0.50 #525, 0.33 #1393, 0.30 #5740), 01mkq (0.47 #1380, 0.47 #512, 0.35 #5727), 04rjg (0.47 #517, 0.38 #1385, 0.35 #1261), 02j62 (0.44 #528, 0.44 #1396, 0.40 #5743), 03g3w (0.44 #524, 0.41 #1392, 0.37 #772), 02lp1 (0.38 #508, 0.35 #1376, 0.26 #1252), 0fdys (0.38 #537, 0.28 #1281, 0.26 #1405), 05qfh (0.35 #534, 0.29 #1402, 0.22 #162), 01lj9 (0.35 #538, 0.25 #1406, 0.23 #1282), 05qjt (0.32 #504, 0.28 #1372, 0.25 #5719) >> Best rule #525 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 06pwq; 01w5m; 09f2j; 0c5x_; >> query: (?x4002, 062z7) <- institution(?x1368, ?x4002), currency(?x4002, ?x5696), major_field_of_study(?x4002, ?x8221), ?x8221 = 037mh8 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #528 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 06pwq; 01w5m; 09f2j; 0c5x_; *> query: (?x4002, 02j62) <- institution(?x1368, ?x4002), currency(?x4002, ?x5696), major_field_of_study(?x4002, ?x8221), ?x8221 = 037mh8 *> conf = 0.44 ranks of expected_values: 4 EVAL 05t7c1 major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 135.000 135.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19278-0l14j_ PRED entity: 0l14j_ PRED relation: role! PRED expected values: 023l9y => 82 concepts (57 used for prediction) PRED predicted values (max 10 best out of 817): 050z2 (0.71 #5750, 0.62 #7139, 0.60 #2505), 023l9y (0.67 #3924, 0.60 #2530, 0.50 #1604), 01vsnff (0.67 #3802, 0.60 #2408, 0.33 #10287), 0161sp (0.67 #3839, 0.42 #10324, 0.40 #2910), 01wxdn3 (0.62 #7361, 0.57 #5509, 0.50 #9680), 01vs4ff (0.60 #3087, 0.60 #2622, 0.50 #10501), 05qhnq (0.60 #2628, 0.57 #5873, 0.50 #10507), 04bpm6 (0.60 #2389, 0.50 #3783, 0.50 #1926), 06x4l_ (0.60 #2441, 0.50 #1052, 0.43 #6149), 01w8n89 (0.60 #2487, 0.50 #3881, 0.33 #8510) >> Best rule #5750 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 0dwtp; >> query: (?x2944, 050z2) <- role(?x2764, ?x2944), role(?x1332, ?x2944), role(?x432, ?x2944), group(?x2944, ?x1751), ?x1332 = 03qlv7, instrumentalists(?x2944, ?x120), role(?x2944, ?x1831), ?x432 = 042v_gx, role(?x1831, ?x1432), role(?x1583, ?x2944), ?x2764 = 01s0ps >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3924 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 0214km; *> query: (?x2944, 023l9y) <- role(?x2944, ?x6938), role(?x2944, ?x5990), role(?x2944, ?x4311), role(?x2944, ?x2460), role(?x2944, ?x1574), ?x4311 = 01xqw, ?x6938 = 023r2x, ?x1574 = 0l15bq, role(?x120, ?x2944), role(?x5990, ?x1750), role(?x214, ?x2944), ?x2460 = 01wy6, ?x1750 = 02hnl *> conf = 0.67 ranks of expected_values: 2 EVAL 0l14j_ role! 023l9y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 57.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #19277-0fzrtf PRED entity: 0fzrtf PRED relation: ceremony! PRED expected values: 0l8z1 => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 374): 018wng (0.92 #5594, 0.92 #5109, 0.91 #6563), 0l8z1 (0.86 #5124, 0.84 #4399, 0.80 #7304), 018wdw (0.77 #4526, 0.76 #8714, 0.72 #5251), 0czp_ (0.76 #8714, 0.57 #921, 0.50 #2375), 0gqxm (0.76 #8714, 0.55 #4475, 0.50 #1087), 02x201b (0.76 #8714, 0.29 #658, 0.17 #1870), 0gqzz (0.76 #8714, 0.24 #5607, 0.20 #6576), 04dn09n (0.50 #242, 0.49 #2183, 0.44 #1698), 019f4v (0.50 #242, 0.41 #485, 0.33 #243), 040njc (0.41 #485, 0.33 #243, 0.23 #6538) >> Best rule #5594 for best value: >> intensional similarity = 16 >> extensional distance = 36 >> proper extension: 050yyb; 02pgky2; >> query: (?x4598, 018wng) <- award_winner(?x4598, ?x7130), award_winner(?x4598, ?x3771), award_winner(?x4598, ?x2027), honored_for(?x4598, ?x4457), nationality(?x7130, ?x94), ceremony(?x5409, ?x4598), ceremony(?x1307, ?x4598), ceremony(?x591, ?x4598), gender(?x7130, ?x231), ?x5409 = 0gr07, award(?x7130, ?x746), ?x591 = 0f4x7, award_nominee(?x3771, ?x3519), award_winner(?x1079, ?x2027), profession(?x2027, ?x563), ?x1307 = 0gq9h >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #5124 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 34 *> proper extension: 05qb8vx; *> query: (?x4598, 0l8z1) <- award_winner(?x4598, ?x7130), award_winner(?x4598, ?x2027), honored_for(?x4598, ?x4457), nationality(?x7130, ?x94), ceremony(?x5409, ?x4598), ceremony(?x2222, ?x4598), ceremony(?x1703, ?x4598), ceremony(?x591, ?x4598), gender(?x7130, ?x231), ?x5409 = 0gr07, award(?x7130, ?x746), ?x591 = 0f4x7, artists(?x3597, ?x2027), ?x1703 = 0k611, award(?x2027, ?x1079), award(?x8595, ?x2222), nominated_for(?x2222, ?x80), ?x8595 = 09tkzy *> conf = 0.86 ranks of expected_values: 2 EVAL 0fzrtf ceremony! 0l8z1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 43.000 43.000 0.921 http://example.org/award/award_category/winners./award/award_honor/ceremony #19276-02t_y3 PRED entity: 02t_y3 PRED relation: location PRED expected values: 030qb3t => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 82): 02_286 (0.43 #841, 0.33 #2447, 0.29 #37), 030qb3t (0.23 #27408, 0.17 #36247, 0.17 #34641), 04jpl (0.14 #17, 0.12 #1624, 0.11 #2427), 074r0 (0.14 #627, 0.12 #2234, 0.11 #3037), 02jx1 (0.14 #71, 0.12 #1678, 0.11 #2481), 0gyvgw (0.14 #796, 0.12 #2403, 0.11 #3206), 027l4q (0.14 #497, 0.12 #2104, 0.11 #2907), 059t8 (0.14 #455, 0.12 #2062, 0.11 #2865), 0zdkh (0.14 #1424), 050ks (0.14 #1142) >> Best rule #841 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 03qjlz; >> query: (?x9864, 02_286) <- student(?x4750, ?x9864), award(?x9864, ?x435), ?x4750 = 04hgpt >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #27408 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 958 *> proper extension: 06_6j3; *> query: (?x9864, 030qb3t) <- film(?x9864, ?x97), location(?x9864, ?x2254), featured_film_locations(?x69, ?x2254) *> conf = 0.23 ranks of expected_values: 2 EVAL 02t_y3 location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 123.000 123.000 0.429 http://example.org/people/person/places_lived./people/place_lived/location #19275-02xfj0 PRED entity: 02xfj0 PRED relation: influenced_by PRED expected values: 016_mj 0f7hc => 89 concepts (43 used for prediction) PRED predicted values (max 10 best out of 260): 014z8v (0.11 #6528, 0.11 #556, 0.09 #121), 01k9lpl (0.11 #6528, 0.06 #745, 0.06 #1615), 052hl (0.11 #6528, 0.05 #7401, 0.04 #644), 01gn36 (0.11 #6528, 0.05 #7401, 0.03 #571), 0l5yl (0.11 #6528, 0.05 #7401, 0.03 #704), 01wj9y9 (0.11 #6528, 0.02 #1366, 0.02 #2236), 03sbs (0.10 #1092, 0.07 #1962, 0.06 #4574), 03_87 (0.10 #2378, 0.10 #1508, 0.09 #3684), 081k8 (0.10 #156, 0.10 #1896, 0.10 #1026), 03f0324 (0.10 #1457, 0.10 #2327, 0.08 #3633) >> Best rule #6528 for best value: >> intensional similarity = 2 >> extensional distance = 464 >> proper extension: 0dw4g; 0b1zz; 0838y; 017mbb; 014_xj; 0chnf; 0716b6; 055yr; >> query: (?x7560, ?x2283) <- influenced_by(?x7560, ?x1145), influenced_by(?x1145, ?x2283) >> conf = 0.11 => this is the best rule for 6 predicted values *> Best rule #7401 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 515 *> proper extension: 02pb2bp; 0fpzzp; *> query: (?x7560, ?x397) <- influenced_by(?x7560, ?x1145), influenced_by(?x10560, ?x1145), influenced_by(?x10560, ?x397) *> conf = 0.05 ranks of expected_values: 35, 40 EVAL 02xfj0 influenced_by 0f7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 89.000 43.000 0.112 http://example.org/influence/influence_node/influenced_by EVAL 02xfj0 influenced_by 016_mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 89.000 43.000 0.112 http://example.org/influence/influence_node/influenced_by #19274-04pwg PRED entity: 04pwg PRED relation: entity_involved! PRED expected values: 0845v 01gqg3 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 19): 02kxjx (0.12 #245, 0.12 #179, 0.08 #311), 03jv8d (0.12 #249, 0.12 #183, 0.07 #513), 01hwkn (0.12 #248, 0.12 #182, 0.07 #512), 01_3rn (0.12 #229, 0.12 #163, 0.07 #493), 0cbvg (0.12 #227, 0.12 #161, 0.07 #491), 0py8j (0.12 #211, 0.12 #145, 0.07 #475), 05nqz (0.08 #274, 0.08 #340, 0.07 #406), 09x7p1 (0.08 #297, 0.08 #363, 0.07 #429), 0d06vc (0.08 #268, 0.08 #334, 0.07 #400), 01cpp0 (0.08 #324, 0.08 #390, 0.07 #456) >> Best rule #245 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 06cgy; 058vp; 0dj5q; 0j5b8; 01rgr; 01syr4; >> query: (?x11024, 02kxjx) <- people(?x6734, ?x11024), gender(?x11024, ?x231), ?x6734 = 03ts0c, ?x231 = 05zppz, religion(?x11024, ?x1985) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04pwg entity_involved! 01gqg3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 100.000 0.125 http://example.org/base/culturalevent/event/entity_involved EVAL 04pwg entity_involved! 0845v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 100.000 0.125 http://example.org/base/culturalevent/event/entity_involved #19273-0d3k14 PRED entity: 0d3k14 PRED relation: award_winner! PRED expected values: 05qck => 206 concepts (203 used for prediction) PRED predicted values (max 10 best out of 337): 05qck (0.50 #1917, 0.33 #193, 0.25 #3641), 0gq9h (0.25 #13439, 0.20 #940, 0.12 #20335), 02grdc (0.25 #1756, 0.12 #15548, 0.08 #9083), 0f4x7 (0.24 #5203, 0.22 #28477, 0.21 #30201), 05f3q (0.22 #2895, 0.14 #1602, 0.12 #2033), 054ky1 (0.20 #4851, 0.20 #3127, 0.18 #5282), 05p09zm (0.20 #987, 0.14 #1418, 0.11 #2280), 04dn09n (0.20 #906, 0.14 #3923, 0.07 #20301), 09sb52 (0.20 #903, 0.11 #29349, 0.09 #16850), 019f4v (0.20 #929, 0.10 #3084, 0.08 #13428) >> Best rule #1917 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0c_md_; >> query: (?x11088, 05qck) <- taxonomy(?x11088, ?x939), celebrities_impersonated(?x3649, ?x11088), profession(?x11088, ?x353) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d3k14 award_winner! 05qck CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 206.000 203.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #19272-01hmk9 PRED entity: 01hmk9 PRED relation: influenced_by! PRED expected values: 06crng 03g5_y => 115 concepts (73 used for prediction) PRED predicted values (max 10 best out of 377): 05ty4m (0.15 #2492, 0.13 #2989, 0.13 #1995), 040rjq (0.13 #2457, 0.12 #2954, 0.07 #3451), 05rx__ (0.13 #1789, 0.05 #1292, 0.05 #6766), 0c00lh (0.12 #2703, 0.09 #2206, 0.04 #3200), 0bqs56 (0.11 #3224, 0.09 #2230, 0.07 #7704), 03_87 (0.11 #749, 0.10 #1247, 0.09 #1744), 045bg (0.11 #532, 0.10 #1030, 0.09 #1527), 02kz_ (0.11 #711, 0.10 #1209, 0.09 #1706), 01hc9_ (0.11 #846, 0.10 #1344, 0.09 #1841), 0j0pf (0.11 #694, 0.10 #1192, 0.09 #1689) >> Best rule #2492 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0d608; 06d6y; >> query: (?x7183, 05ty4m) <- influenced_by(?x318, ?x7183), written_by(?x1210, ?x7183), award_winner(?x1088, ?x7183) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #799 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 02yy8; *> query: (?x7183, 03g5_y) <- influenced_by(?x318, ?x7183), notable_people_with_this_condition(?x12870, ?x7183), influenced_by(?x7183, ?x1145) *> conf = 0.05 ranks of expected_values: 85, 143 EVAL 01hmk9 influenced_by! 03g5_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 115.000 73.000 0.150 http://example.org/influence/influence_node/influenced_by EVAL 01hmk9 influenced_by! 06crng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 115.000 73.000 0.150 http://example.org/influence/influence_node/influenced_by #19271-097zcz PRED entity: 097zcz PRED relation: nominated_for! PRED expected values: 0gr4k => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 228): 019f4v (0.60 #1883, 0.59 #1654, 0.59 #2112), 0k611 (0.52 #1898, 0.51 #295, 0.49 #1669), 0gr4k (0.44 #252, 0.44 #23, 0.40 #1626), 02x73k6 (0.44 #275, 0.12 #10080, 0.11 #1878), 04dn09n (0.41 #261, 0.35 #2093, 0.33 #1864), 0gr51 (0.33 #2132, 0.32 #1903, 0.31 #3665), 027dtxw (0.32 #233, 0.31 #3665, 0.22 #15353), 02pqp12 (0.32 #284, 0.28 #1887, 0.28 #2116), 04kxsb (0.31 #3665, 0.25 #2148, 0.25 #1919), 02w9sd7 (0.31 #3665, 0.22 #15353, 0.20 #13977) >> Best rule #1883 for best value: >> intensional similarity = 3 >> extensional distance = 86 >> proper extension: 018f8; 0fy66; 0hv8w; >> query: (?x4280, 019f4v) <- list(?x4280, ?x3004), nominated_for(?x198, ?x4280), award_winner(?x4280, ?x199) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #252 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 083shs; 04v8x9; 0sxfd; 09cr8; 0jym0; 0bmpm; 015whm; 0jqj5; 01zfzb; 0bl5c; ... *> query: (?x4280, 0gr4k) <- award(?x4280, ?x3066), nominated_for(?x198, ?x4280), ?x3066 = 0gqy2 *> conf = 0.44 ranks of expected_values: 3 EVAL 097zcz nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.602 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19270-06t2t PRED entity: 06t2t PRED relation: organization PRED expected values: 02vk52z 0_2v 0j7v_ => 122 concepts (112 used for prediction) PRED predicted values (max 10 best out of 52): 02vk52z (0.89 #278, 0.88 #1861, 0.88 #116), 018cqq (0.70 #197, 0.64 #289, 0.62 #81), 0b6css (0.69 #126, 0.69 #80, 0.61 #196), 0_2v (0.69 #119, 0.62 #212, 0.62 #73), 01rz1 (0.64 #302, 0.61 #187, 0.56 #71), 0j7v_ (0.63 #375, 0.56 #1094, 0.51 #1142), 041288 (0.53 #386, 0.49 #1105, 0.47 #1575), 04k4l (0.46 #305, 0.46 #466, 0.44 #583), 02jxk (0.43 #188, 0.39 #303, 0.36 #280), 0gkjy (0.40 #377, 0.32 #2278, 0.30 #1566) >> Best rule #278 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 0chghy; ... >> query: (?x2316, 02vk52z) <- film_release_region(?x4352, ?x2316), film_release_region(?x1525, ?x2316), ?x1525 = 03qnvdl, ?x4352 = 09v71cj >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 6 EVAL 06t2t organization 0j7v_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 122.000 112.000 0.893 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 06t2t organization 0_2v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 122.000 112.000 0.893 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 06t2t organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 112.000 0.893 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #19269-013sg6 PRED entity: 013sg6 PRED relation: student! PRED expected values: 08815 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 200): 065y4w7 (0.33 #14, 0.14 #3176, 0.05 #17932), 025v3k (0.33 #120, 0.14 #3282, 0.04 #7498), 06kknt (0.33 #467, 0.14 #3629, 0.02 #21020), 02gnmp (0.33 #425, 0.14 #3587, 0.01 #13073), 06182p (0.25 #1352, 0.20 #2406, 0.20 #1879), 02xwzh (0.25 #1442, 0.20 #1969, 0.14 #3550), 017j69 (0.25 #1199, 0.20 #1726, 0.14 #3307), 02301 (0.25 #1128, 0.20 #1655, 0.14 #3236), 0g2jl (0.17 #3036, 0.11 #5144, 0.01 #12522), 01w5m (0.11 #4321, 0.08 #3794, 0.08 #18550) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01hbq0; >> query: (?x9587, 065y4w7) <- film(?x9587, ?x1746), ?x1746 = 0k4kk, type_of_union(?x9587, ?x566), actor(?x7904, ?x9587) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3691 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0m2l9; *> query: (?x9587, 08815) <- location(?x9587, ?x4356), award(?x9587, ?x2252), notable_people_with_this_condition(?x9933, ?x9587), influenced_by(?x7717, ?x9587) *> conf = 0.08 ranks of expected_values: 14 EVAL 013sg6 student! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 147.000 147.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #19268-05qtj PRED entity: 05qtj PRED relation: featured_film_locations! PRED expected values: 04dsnp => 258 concepts (216 used for prediction) PRED predicted values (max 10 best out of 885): 025rxjq (0.33 #569, 0.08 #9329, 0.05 #20285), 011yth (0.33 #133, 0.07 #14005, 0.05 #19849), 024mpp (0.33 #273, 0.04 #26560, 0.03 #110536), 07cdz (0.33 #249, 0.02 #110512, 0.01 #71082), 04kkz8 (0.33 #60, 0.02 #110323, 0.01 #70893), 02phtzk (0.33 #327, 0.01 #71160, 0.01 #73350), 061681 (0.23 #10269, 0.20 #13919, 0.15 #35096), 0pd64 (0.20 #3480, 0.14 #6400, 0.08 #10782), 0473rc (0.20 #2639, 0.11 #25276, 0.11 #40610), 0cwy47 (0.20 #2249, 0.08 #8819, 0.08 #10281) >> Best rule #569 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01jr6; >> query: (?x4627, 025rxjq) <- location(?x9585, ?x4627), contains(?x4627, ?x2593), ?x9585 = 01k53x >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #19782 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 05l64; *> query: (?x4627, 04dsnp) <- vacationer(?x4627, ?x436), place_of_death(?x598, ?x4627), mode_of_transportation(?x4627, ?x4272) *> conf = 0.18 ranks of expected_values: 13 EVAL 05qtj featured_film_locations! 04dsnp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 258.000 216.000 0.333 http://example.org/film/film/featured_film_locations #19267-049lr PRED entity: 049lr PRED relation: location! PRED expected values: 02wmbg 04ch23 => 193 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1323): 02xgdv (0.20 #13996, 0.17 #21544, 0.12 #19028), 02xfrd (0.20 #861, 0.13 #13442, 0.12 #5894), 047s_cr (0.20 #2419, 0.13 #15000, 0.12 #7452), 02qvhbb (0.20 #2437, 0.12 #7470, 0.12 #4954), 0tj9 (0.17 #25084, 0.17 #22568, 0.16 #30116), 084z0w (0.17 #23589, 0.17 #21073, 0.13 #13525), 01zt10 (0.17 #22586, 0.16 #30134, 0.13 #15038), 03nb5v (0.16 #26485, 0.15 #44097, 0.10 #46614), 0dfjb8 (0.13 #13623, 0.12 #6075, 0.12 #3559), 040wdl (0.13 #12924, 0.11 #22988, 0.11 #20472) >> Best rule #13996 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 019fbp; >> query: (?x9315, 02xgdv) <- contains(?x2146, ?x9315), location(?x12675, ?x9315), location(?x12189, ?x9315), ?x2146 = 03rk0, languages(?x12675, ?x254), people(?x5025, ?x12189) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #21858 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 020skc; 0c8tk; 09c6w; 0byh8j; 04vmp; 0cvw9; 0dlv0; 075_t2; 01j922; 019fm7; ... *> query: (?x9315, 02wmbg) <- contains(?x2146, ?x9315), location(?x12675, ?x9315), ?x2146 = 03rk0, award(?x12675, ?x10156), languages(?x12675, ?x254) *> conf = 0.11 ranks of expected_values: 21 EVAL 049lr location! 04ch23 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 193.000 50.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location EVAL 049lr location! 02wmbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 193.000 50.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location #19266-0_lr1 PRED entity: 0_lr1 PRED relation: category PRED expected values: 08mbj5d => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #15, 0.81 #14, 0.80 #25) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0235l; 0h1k6; >> query: (?x8853, 08mbj5d) <- contains(?x94, ?x8853), county_seat(?x12717, ?x8853), ?x94 = 09c7w0, source(?x12717, ?x958) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_lr1 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.821 http://example.org/common/topic/webpage./common/webpage/category #19265-03x31g PRED entity: 03x31g PRED relation: religion PRED expected values: 0flw86 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 17): 03j6c (0.56 #561, 0.53 #606, 0.41 #741), 0flw86 (0.25 #182, 0.20 #632, 0.18 #497), 0c8wxp (0.20 #1041, 0.19 #861, 0.19 #1087), 01lp8 (0.17 #316, 0.10 #406, 0.04 #676), 0kpl (0.17 #280, 0.07 #460, 0.06 #1728), 03_gx (0.08 #3221, 0.08 #1550, 0.07 #1049), 06yyp (0.06 #562, 0.05 #607, 0.03 #742), 042s9 (0.05 #674), 092bf5 (0.03 #1507, 0.03 #1143, 0.03 #1326), 0n2g (0.02 #1140, 0.02 #1232, 0.02 #1368) >> Best rule #561 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 0292l3; 040wdl; 02vmzp; 08d6bd; 02xgdv; 02n1p5; 03vrnh; 06gn7r; 06kl0k; 02wyc0; ... >> query: (?x11170, 03j6c) <- languages(?x11170, ?x1882), location(?x11170, ?x5384), ?x1882 = 03k50, award(?x11170, ?x10156), profession(?x11170, ?x1032) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #182 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0gcdzz; *> query: (?x11170, 0flw86) <- actor(?x12165, ?x11170), gender(?x11170, ?x514), people(?x5025, ?x11170), ?x5025 = 0dryh9k *> conf = 0.25 ranks of expected_values: 2 EVAL 03x31g religion 0flw86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.556 http://example.org/people/person/religion #19264-07c37 PRED entity: 07c37 PRED relation: nationality PRED expected values: 07ssc => 160 concepts (156 used for prediction) PRED predicted values (max 10 best out of 62): 02jx1 (0.84 #6845, 0.83 #6948, 0.83 #3016), 09c7w0 (0.83 #2916, 0.80 #10675, 0.79 #11281), 0f8l9c (0.77 #13605, 0.50 #1026, 0.49 #1727), 03_3d (0.42 #506, 0.02 #6749, 0.02 #6851), 07ssc (0.38 #717, 0.38 #918, 0.32 #6947), 0345h (0.31 #1235, 0.29 #1436, 0.27 #7954), 059j2 (0.27 #7954, 0.25 #7149, 0.24 #8759), 06mzp (0.27 #7954, 0.25 #7149, 0.24 #8759), 06q1r (0.25 #5737, 0.24 #3319, 0.24 #8759), 0h7x (0.17 #1841, 0.17 #1340, 0.14 #1640) >> Best rule #6845 for best value: >> intensional similarity = 4 >> extensional distance = 227 >> proper extension: 02pp_q_; 0dky9n; 02lkcc; 0dck27; 0b_fw; 0dqzkv; 07xr3w; 02q5xsx; 01ws9n6; 07hhnl; ... >> query: (?x5797, ?x1310) <- place_of_death(?x5797, ?x1976), country(?x1976, ?x1310), jurisdiction_of_office(?x14694, ?x1976), place_of_birth(?x1975, ?x1976) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #717 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 01wsl7c; 07fpm3; 04yt7; 011zwl; *> query: (?x5797, 07ssc) <- location(?x5797, ?x4627), student(?x11158, ?x5797), organization(?x11157, ?x11158), ?x11157 = 08jcfy *> conf = 0.38 ranks of expected_values: 5 EVAL 07c37 nationality 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 160.000 156.000 0.838 http://example.org/people/person/nationality #19263-01hb1t PRED entity: 01hb1t PRED relation: educational_institution PRED expected values: 01hb1t => 125 concepts (105 used for prediction) PRED predicted values (max 10 best out of 421): 017z88 (0.18 #31813, 0.09 #18331, 0.03 #48539), 01hb1t (0.18 #31813, 0.02 #52321, 0.02 #48540), 065y4w7 (0.18 #31813, 0.02 #1091, 0.02 #2169), 02lv2v (0.09 #18331, 0.03 #48539, 0.03 #832), 01n951 (0.09 #18331, 0.03 #48539, 0.03 #804), 02301 (0.09 #18331, 0.03 #48539, 0.03 #607), 01p7x7 (0.09 #18331, 0.03 #48539, 0.03 #960), 01t0dy (0.09 #18331, 0.03 #48539, 0.03 #741), 03bmmc (0.09 #18331, 0.03 #48539, 0.03 #721), 01vg13 (0.09 #18331, 0.03 #48539, 0.03 #744) >> Best rule #31813 for best value: >> intensional similarity = 4 >> extensional distance = 320 >> proper extension: 09wv__; 03gn1x; 0778_3; >> query: (?x3123, ?x735) <- student(?x3123, ?x65), state_province_region(?x3123, ?x335), award(?x65, ?x4921), student(?x735, ?x65) >> conf = 0.18 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01hb1t educational_institution 01hb1t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 105.000 0.182 http://example.org/education/educational_institution_campus/educational_institution #19262-04w_7 PRED entity: 04w_7 PRED relation: month! PRED expected values: 01914 06t2t 03hrz => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 233): 01914 (0.88 #47, 0.86 #68, 0.86 #27), 06t2t (0.88 #47, 0.86 #68, 0.86 #27), 03hrz (0.88 #47, 0.86 #68, 0.86 #27), 03czqs (0.88 #47, 0.86 #68, 0.86 #27), 0l0mk (0.88 #47, 0.86 #68, 0.86 #27), 0cr3d (0.52 #60, 0.17 #76), 01n43d (0.52 #60), 07mgr (0.52 #60), 0fvxz (0.52 #60), 01snm (0.33 #33, 0.17 #76) >> Best rule #47 for best value: >> intensional similarity = 83 >> extensional distance = 2 >> proper extension: 05lf_; >> query: (?x1459, ?x206) <- month(?x11197, ?x1459), month(?x8602, ?x1459), month(?x6703, ?x1459), month(?x5168, ?x1459), month(?x4698, ?x1459), month(?x3501, ?x1459), month(?x3373, ?x1459), month(?x3106, ?x1459), month(?x2611, ?x1459), month(?x2474, ?x1459), month(?x2277, ?x1459), month(?x1860, ?x1459), month(?x1658, ?x1459), month(?x739, ?x1459), month(?x659, ?x1459), ?x3106 = 049d1, ?x3501 = 0f2v0, ?x2611 = 02h6_6p, seasonal_months(?x7298, ?x1459), seasonal_months(?x4869, ?x1459), seasonal_months(?x4827, ?x1459), ?x3373 = 0ply0, featured_film_locations(?x12403, ?x2474), featured_film_locations(?x7393, ?x2474), featured_film_locations(?x5992, ?x2474), featured_film_locations(?x4479, ?x2474), featured_film_locations(?x2490, ?x2474), ?x8602 = 0chgzm, ?x1658 = 0h7h6, ?x659 = 02cl1, ?x4479 = 04gv3db, mode_of_transportation(?x2474, ?x4272), citytown(?x481, ?x2474), place_of_death(?x1984, ?x2474), ?x739 = 02_286, award_winner(?x12403, ?x5335), currency(?x12403, ?x170), teams(?x2474, ?x3073), film_crew_role(?x12403, ?x468), ?x4698 = 056_y, honored_for(?x7936, ?x2490), music(?x12403, ?x6251), nominated_for(?x143, ?x2490), executive_produced_by(?x12403, ?x4060), location(?x1410, ?x2474), nominated_for(?x846, ?x2490), film(?x1995, ?x2490), ?x4827 = 03_ly, ?x5168 = 06mxs, film_release_region(?x7393, ?x2346), film_release_region(?x7393, ?x1264), film_release_region(?x7393, ?x1003), film_release_region(?x7393, ?x252), film_release_region(?x7393, ?x142), film_release_region(?x7393, ?x94), state(?x2474, ?x6842), titles(?x53, ?x12403), ?x11197 = 05l64, ?x1003 = 03gj2, ?x1860 = 01_d4, titles(?x1510, ?x2490), ?x6703 = 0f04v, ?x252 = 03_3d, ?x468 = 02r96rf, film(?x3096, ?x7393), ?x2346 = 0d05w3, genre(?x2490, ?x1403), film_crew_role(?x7393, ?x1078), film_release_distribution_medium(?x5992, ?x81), month(?x206, ?x7298), ?x94 = 09c7w0, ?x143 = 02r0csl, film_release_region(?x5992, ?x1892), language(?x5992, ?x254), ?x1892 = 02vzc, place_of_birth(?x5351, ?x2474), film(?x382, ?x2490), ?x142 = 0jgd, ?x1264 = 0345h, ?x4869 = 02xx5, ?x2277 = 013yq, film_crew_role(?x2490, ?x1776), film(?x450, ?x12403) >> conf = 0.88 => this is the best rule for 5 predicted values ranks of expected_values: 1, 2, 3 EVAL 04w_7 month! 03hrz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.877 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 04w_7 month! 06t2t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.877 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 04w_7 month! 01914 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.877 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #19261-026wlxw PRED entity: 026wlxw PRED relation: nominated_for! PRED expected values: 05zvj3m 057xs89 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 212): 05zvj3m (0.38 #554, 0.25 #74, 0.19 #15363), 0gq9h (0.32 #1744, 0.22 #1024, 0.21 #13264), 0gq_v (0.26 #1700, 0.18 #980, 0.16 #12740), 019f4v (0.25 #1735, 0.17 #13255, 0.16 #12775), 03hl6lc (0.25 #133, 0.20 #14641, 0.19 #16804), 057xs89 (0.25 #602, 0.20 #14641, 0.19 #16804), 03hj5vf (0.25 #127, 0.17 #367, 0.12 #607), 063y_ky (0.25 #582, 0.06 #1302, 0.06 #3222), 0p9sw (0.23 #1701, 0.15 #981, 0.13 #7941), 0gr0m (0.23 #1741, 0.13 #7981, 0.13 #13981) >> Best rule #554 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 043tvp3; >> query: (?x8214, 05zvj3m) <- genre(?x8214, ?x225), film_crew_role(?x8214, ?x137), film(?x11624, ?x8214), ?x11624 = 063g7l >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 026wlxw nominated_for! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 82.000 82.000 0.375 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 026wlxw nominated_for! 05zvj3m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.375 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19260-02d_zc PRED entity: 02d_zc PRED relation: major_field_of_study PRED expected values: 05qfh => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 132): 04rjg (0.45 #20, 0.33 #1882, 0.32 #2379), 02j62 (0.40 #4632, 0.39 #1893, 0.39 #2390), 01mkq (0.39 #1257, 0.38 #263, 0.37 #1877), 0g26h (0.39 #1286, 0.38 #540, 0.38 #1782), 062z7 (0.39 #28, 0.34 #1270, 0.32 #2387), 02_7t (0.35 #67, 0.31 #1309, 0.26 #2054), 03g3w (0.35 #27, 0.30 #4628, 0.29 #4503), 06ms6 (0.35 #17, 0.18 #1259, 0.17 #1383), 03qsdpk (0.32 #49, 0.18 #2408, 0.17 #2533), 0_jm (0.30 #1674, 0.30 #680, 0.29 #1798) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 015nl4; >> query: (?x5357, 04rjg) <- institution(?x865, ?x5357), student(?x5357, ?x7732), place_of_birth(?x7732, ?x6253), athlete(?x1083, ?x7732) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2895 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 152 *> proper extension: 07tgn; 01_qgp; *> query: (?x5357, 05qfh) <- institution(?x865, ?x5357), student(?x5357, ?x8871), currency(?x5357, ?x170), actor(?x3303, ?x8871) *> conf = 0.24 ranks of expected_values: 18 EVAL 02d_zc major_field_of_study 05qfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 150.000 150.000 0.452 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19259-01wttr1 PRED entity: 01wttr1 PRED relation: diet PRED expected values: 07_jd => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 2): 07_jd (0.14 #3, 0.14 #17, 0.12 #13), 07_hy (0.02 #96, 0.02 #146, 0.02 #90) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 025p38; 02756j; 02c_wc; 050llt; 049468; >> query: (?x14044, 07_jd) <- award_winner(?x10156, ?x14044), type_of_union(?x14044, ?x566), student(?x11607, ?x14044), nationality(?x14044, ?x2146), ?x10156 = 03r8v_ >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wttr1 diet 07_jd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.143 http://example.org/base/eating/practicer_of_diet/diet #19258-0xqf3 PRED entity: 0xqf3 PRED relation: place_of_birth! PRED expected values: 0gd9k => 115 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1340): 01dw_f (0.41 #49644, 0.40 #2613, 0.38 #33966), 033cw (0.40 #2613, 0.38 #33966, 0.34 #41806), 01l47f5 (0.33 #1330, 0.03 #11781, 0.02 #22232), 0gcs9 (0.33 #569, 0.03 #11020, 0.02 #21471), 06w6_ (0.17 #3122, 0.04 #8348, 0.03 #13572), 0170vn (0.17 #2786, 0.04 #8012, 0.03 #13236), 05f0r8 (0.17 #5212, 0.04 #10438, 0.03 #15662), 05h7tk (0.17 #5184, 0.04 #10410, 0.03 #15634), 0745k7 (0.17 #5183, 0.04 #10409, 0.03 #15633), 058z1hb (0.17 #5181, 0.04 #10407, 0.03 #15631) >> Best rule #49644 for best value: >> intensional similarity = 4 >> extensional distance = 221 >> proper extension: 0h44w; >> query: (?x8944, ?x7570) <- location(?x7570, ?x8944), place_of_birth(?x7570, ?x3501), profession(?x7570, ?x1183), ?x1183 = 09jwl >> conf = 0.41 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0xqf3 place_of_birth! 0gd9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 28.000 0.405 http://example.org/people/person/place_of_birth #19257-0y_pg PRED entity: 0y_pg PRED relation: award PRED expected values: 02ppm4q => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 192): 02ppm4q (0.24 #700, 0.22 #10740, 0.22 #11908), 0gq9h (0.24 #700, 0.22 #10740, 0.22 #11908), 0l8z1 (0.24 #700, 0.22 #10740, 0.22 #11908), 02r0csl (0.24 #700, 0.22 #10740, 0.22 #11908), 02qyp19 (0.24 #700, 0.22 #10740, 0.22 #11908), 02hsq3m (0.24 #700, 0.22 #10740, 0.22 #11908), 054krc (0.18 #2568, 0.12 #12842, 0.12 #12375), 02qyntr (0.18 #2568, 0.12 #12842, 0.12 #12375), 02qvyrt (0.18 #2568, 0.12 #12842, 0.12 #12375), 05pcn59 (0.18 #2568, 0.12 #12842, 0.12 #12375) >> Best rule #700 for best value: >> intensional similarity = 5 >> extensional distance = 282 >> proper extension: 09tqkv2; 0bm2g; 0p_qr; 0ptxj; 0y_9q; 011ypx; 01mgw; >> query: (?x7922, ?x68) <- nominated_for(?x2763, ?x7922), genre(?x7922, ?x53), nominated_for(?x1307, ?x7922), nominated_for(?x68, ?x7922), ?x1307 = 0gq9h >> conf = 0.24 => this is the best rule for 6 predicted values ranks of expected_values: 1 EVAL 0y_pg award 02ppm4q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.242 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19256-034b6k PRED entity: 034b6k PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 30): 02r96rf (0.84 #78, 0.67 #795, 0.66 #191), 0ch6mp2 (0.82 #82, 0.75 #989, 0.75 #799), 0dxtw (0.51 #86, 0.47 #11, 0.41 #576), 01vx2h (0.47 #87, 0.45 #200, 0.44 #49), 02rh1dz (0.32 #10, 0.26 #85, 0.21 #198), 01pvkk (0.28 #1106, 0.28 #1329, 0.27 #1217), 01xy5l_ (0.21 #52, 0.16 #90, 0.15 #203), 0d2b38 (0.20 #102, 0.16 #215, 0.12 #554), 02ynfr (0.19 #582, 0.18 #92, 0.18 #620), 0215hd (0.19 #57, 0.16 #95, 0.16 #20) >> Best rule #78 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 0bvn25; 02hxhz; 08hmch; 0jjy0; 03sxd2; 01j8wk; 0g3zrd; 04g9gd; 065zlr; 03z20c; ... >> query: (?x10742, 02r96rf) <- executive_produced_by(?x10742, ?x2135), genre(?x10742, ?x225), produced_by(?x10742, ?x4574), crewmember(?x10742, ?x666) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #82 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 0bvn25; 02hxhz; 08hmch; 0jjy0; 03sxd2; 01j8wk; 0g3zrd; 04g9gd; 065zlr; 03z20c; ... *> query: (?x10742, 0ch6mp2) <- executive_produced_by(?x10742, ?x2135), genre(?x10742, ?x225), produced_by(?x10742, ?x4574), crewmember(?x10742, ?x666) *> conf = 0.82 ranks of expected_values: 2 EVAL 034b6k film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.838 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19255-04qw17 PRED entity: 04qw17 PRED relation: nominated_for! PRED expected values: 027dtxw => 109 concepts (86 used for prediction) PRED predicted values (max 10 best out of 202): 0fq9zdn (0.68 #12691, 0.68 #11554, 0.67 #12690), 0789r6 (0.68 #12691, 0.68 #11554, 0.67 #12690), 09cn0c (0.68 #12691, 0.68 #11554, 0.67 #12690), 0gq9h (0.40 #282, 0.40 #5045, 0.39 #7079), 019f4v (0.40 #275, 0.35 #5038, 0.33 #1865), 0gqyl (0.40 #296, 0.33 #750, 0.33 #523), 0gq_v (0.40 #243, 0.33 #470, 0.29 #5006), 0p9sw (0.40 #244, 0.33 #471, 0.24 #5007), 0gr4k (0.33 #704, 0.33 #477, 0.28 #5013), 0k611 (0.30 #7088, 0.30 #5054, 0.23 #4828) >> Best rule #12691 for best value: >> intensional similarity = 3 >> extensional distance = 975 >> proper extension: 02_1ky; 02rq7nd; >> query: (?x1863, ?x1008) <- award(?x1863, ?x1008), nominated_for(?x68, ?x1863), award(?x748, ?x1008) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #3862 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 134 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x1863, 027dtxw) <- titles(?x512, ?x1863), titles(?x53, ?x1863), ?x512 = 07ssc, titles(?x53, ?x6636), ?x6636 = 047bynf *> conf = 0.22 ranks of expected_values: 25 EVAL 04qw17 nominated_for! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 109.000 86.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19254-01wgr PRED entity: 01wgr PRED relation: official_language! PRED expected values: 012m_ => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 285): 01pj7 (0.62 #188, 0.44 #3055, 0.41 #4576), 05r7t (0.50 #303, 0.40 #491, 0.33 #115), 03rt9 (0.40 #390, 0.33 #14, 0.25 #579), 0d060g (0.33 #9, 0.25 #574, 0.25 #197), 06dfg (0.33 #123, 0.25 #688, 0.25 #311), 01nln (0.33 #122, 0.25 #687, 0.25 #310), 07z5n (0.33 #53, 0.25 #618, 0.25 #241), 0366c (0.33 #179, 0.25 #744, 0.25 #367), 03_xj (0.33 #101, 0.25 #666, 0.25 #289), 06tw8 (0.33 #100, 0.25 #288, 0.20 #476) >> Best rule #188 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 02h40lc; >> query: (?x10429, ?x1558) <- language(?x5008, ?x10429), language(?x2928, ?x10429), language(?x2550, ?x10429), language(?x1071, ?x10429), ?x1071 = 02d44q, ?x5008 = 035w2k, ?x2550 = 07j8r, countries_spoken_in(?x10429, ?x1558), languages_spoken(?x3584, ?x10429), ?x2928 = 07024, people(?x3584, ?x5884), people(?x3584, ?x669), ?x5884 = 0hwqz, award_winner(?x1386, ?x669), award_nominee(?x669, ?x2641), nominated_for(?x669, ?x670) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #906 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 9 *> proper extension: 04306rv; 02hxc3j; 03hkp; 064_8sq; 05qqm; 04h9h; *> query: (?x10429, 012m_) <- language(?x1071, ?x10429), film_release_region(?x1071, ?x1499), film_release_region(?x1071, ?x1264), film_release_region(?x1071, ?x1023), film_release_region(?x1071, ?x985), film_release_region(?x1071, ?x456), film_release_region(?x1071, ?x390), ?x985 = 0k6nt, film_crew_role(?x1071, ?x468), award(?x1071, ?x2252), produced_by(?x1071, ?x6356), nominated_for(?x704, ?x1071), ?x1264 = 0345h, ?x1023 = 0ctw_b, ?x456 = 05qhw, ?x390 = 0chghy, languages_spoken(?x3584, ?x10429), ?x1499 = 01znc_ *> conf = 0.27 ranks of expected_values: 72 EVAL 01wgr official_language! 012m_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 35.000 35.000 0.618 http://example.org/location/country/official_language #19253-0d4htf PRED entity: 0d4htf PRED relation: genre PRED expected values: 02l7c8 0hcr => 88 concepts (70 used for prediction) PRED predicted values (max 10 best out of 98): 02l7c8 (0.95 #3537, 0.83 #1653, 0.83 #834), 07s9rl0 (0.67 #7283, 0.67 #937, 0.64 #3523), 01z4y (0.61 #6931, 0.58 #2932, 0.54 #6930), 09b3v (0.54 #6930, 0.52 #4934, 0.52 #5990), 03k9fj (0.48 #2120, 0.44 #1180, 0.41 #1297), 02kdv5l (0.44 #3878, 0.44 #6110, 0.41 #588), 04xvlr (0.39 #938, 0.19 #1993, 0.18 #704), 01jfsb (0.38 #5532, 0.35 #2589, 0.34 #2707), 060__y (0.33 #133, 0.29 #952, 0.20 #250), 06n90 (0.28 #597, 0.26 #1416, 0.25 #12) >> Best rule #3537 for best value: >> intensional similarity = 5 >> extensional distance = 478 >> proper extension: 0ckr7s; 0dq626; 04zyhx; 05q4y12; 0crc2cp; 0gtvpkw; 0cp0ph6; 02w86hz; 0gy2y8r; 027pfb2; ... >> query: (?x5513, 02l7c8) <- genre(?x5513, ?x239), genre(?x5945, ?x239), genre(?x886, ?x239), ?x5945 = 05t0_2v, ?x886 = 0kv2hv >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14 EVAL 0d4htf genre 0hcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 88.000 70.000 0.954 http://example.org/film/film/genre EVAL 0d4htf genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 70.000 0.954 http://example.org/film/film/genre #19252-02bjrlw PRED entity: 02bjrlw PRED relation: language! PRED expected values: 08gsvw 0g5pv3 072x7s 09k56b7 0418wg 03q0r1 02xs6_ 05b_gq 06fqlk 043tvp3 0bl3nn 07kdkfj 0k419 => 68 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1706): 02psgq (0.75 #22990, 0.75 #24633, 0.67 #14777), 0k419 (0.65 #21348, 0.33 #3151, 0.33 #1510), 0dnw1 (0.65 #21348, 0.33 #958, 0.13 #28874), 0cq7kw (0.65 #21348, 0.33 #682, 0.10 #20388), 070fnm (0.65 #21348, 0.33 #284, 0.10 #19990), 02fqxm (0.65 #21348, 0.33 #1629, 0.10 #21335), 0kvf3b (0.65 #21348, 0.33 #1521, 0.10 #21227), 06bd5j (0.65 #21348, 0.33 #872, 0.10 #20578), 07qg8v (0.65 #21348, 0.17 #11687, 0.14 #14971), 0dl9_4 (0.65 #21348, 0.12 #48419, 0.12 #33646) >> Best rule #22990 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 03k50; 03_9r; 06mp7; 064_8sq; 05zjd; >> query: (?x90, ?x303) <- titles(?x90, ?x303), language(?x12214, ?x90), language(?x2160, ?x90), film(?x382, ?x12214), languages(?x914, ?x90), nominated_for(?x835, ?x2160), film(?x773, ?x12214) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #21348 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 0999q; *> query: (?x90, ?x4504) <- languages(?x9095, ?x90), languages(?x914, ?x90), religion(?x914, ?x1985), languages_spoken(?x3584, ?x90), countries_spoken_in(?x90, ?x142), award_winner(?x6331, ?x9095), award_winner(?x4504, ?x9095) *> conf = 0.65 ranks of expected_values: 2, 19, 26, 50, 56, 154, 278, 439, 451, 460, 461, 676, 1189 EVAL 02bjrlw language! 0k419 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 07kdkfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 0bl3nn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 043tvp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 06fqlk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 05b_gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 02xs6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 03q0r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 0418wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 09k56b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 072x7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 0g5pv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 42.000 0.755 http://example.org/film/film/language EVAL 02bjrlw language! 08gsvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 68.000 42.000 0.755 http://example.org/film/film/language #19251-02wrhj PRED entity: 02wrhj PRED relation: language PRED expected values: 02h40lc => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 2): 02h40lc (0.07 #40, 0.07 #10, 0.07 #138), 064_8sq (0.01 #192) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 162 >> proper extension: 01jrp0; >> query: (?x1765, 02h40lc) <- nominated_for(?x1765, ?x2555), type_of_union(?x1765, ?x566), genre(?x2555, ?x8534), ?x8534 = 0c4xc >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wrhj language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.073 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #19250-03bmmc PRED entity: 03bmmc PRED relation: student PRED expected values: 046_v => 140 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1612): 0ddkf (0.33 #1186, 0.02 #49189, 0.02 #7447), 02vntj (0.07 #4877, 0.06 #2790, 0.05 #9052), 0ff3y (0.06 #14587, 0.06 #4151, 0.04 #31283), 01hbq0 (0.06 #14576, 0.06 #4140, 0.03 #31272), 03ft8 (0.06 #12780, 0.04 #29476, 0.03 #2344), 0306ds (0.06 #2494, 0.04 #4581, 0.04 #17104), 015wc0 (0.06 #3778, 0.04 #5865, 0.03 #10040), 01l1rw (0.06 #3086, 0.04 #5173, 0.03 #9348), 03rs8y (0.06 #2133, 0.04 #4220, 0.03 #8395), 021bk (0.06 #2440, 0.04 #4527, 0.03 #8702) >> Best rule #1186 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03_fmr; >> query: (?x5981, 0ddkf) <- contains(?x2850, ?x5981), major_field_of_study(?x5981, ?x7017), student(?x5981, ?x2408), ?x2850 = 0cr3d >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03bmmc student 046_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 59.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #19249-0m123 PRED entity: 0m123 PRED relation: nominated_for! PRED expected values: 0fbtbt => 78 concepts (62 used for prediction) PRED predicted values (max 10 best out of 183): 0m7yy (0.68 #2612, 0.67 #10678, 0.67 #9254), 0fbtbt (0.61 #159, 0.40 #1111, 0.35 #872), 0gkts9 (0.57 #122, 0.26 #1074, 0.25 #835), 0bp_b2 (0.48 #17, 0.29 #730, 0.26 #969), 02x4x18 (0.43 #1051, 0.03 #12440, 0.03 #12678), 0gkr9q (0.39 #208, 0.25 #683, 0.25 #921), 0cqh6z (0.39 #54, 0.19 #1006, 0.16 #1243), 0ck27z (0.35 #71, 0.25 #546, 0.25 #784), 0cqhb3 (0.35 #198, 0.24 #673, 0.23 #911), 09v7wsg (0.35 #175, 0.18 #1364, 0.16 #1127) >> Best rule #2612 for best value: >> intensional similarity = 4 >> extensional distance = 115 >> proper extension: 0cwrr; 04glx0; 021gzd; 05sy0cv; >> query: (?x8627, ?x686) <- country_of_origin(?x8627, ?x94), ?x94 = 09c7w0, award(?x8627, ?x686), genre(?x8627, ?x53) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #159 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 0g60z; 039fgy; 0kfv9; 01xr2s; 01j67j; 02rzdcp; 01b_lz; 063ykwt; 01rf57; 02md2d; ... *> query: (?x8627, 0fbtbt) <- country_of_origin(?x8627, ?x94), ?x94 = 09c7w0, nominated_for(?x686, ?x8627), ?x686 = 0bdw1g *> conf = 0.61 ranks of expected_values: 2 EVAL 0m123 nominated_for! 0fbtbt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 62.000 0.681 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19248-06mp7 PRED entity: 06mp7 PRED relation: language! PRED expected values: 0g5838s => 46 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1855): 0f4_2k (0.33 #2701, 0.33 #980, 0.32 #14756), 0g5qmbz (0.33 #3214, 0.33 #1493, 0.32 #8378), 014kq6 (0.33 #2048, 0.33 #327, 0.25 #8935), 0gh6j94 (0.33 #2992, 0.33 #1271, 0.24 #15047), 034xyf (0.33 #3100, 0.33 #1379, 0.21 #9987), 025n07 (0.33 #2187, 0.33 #466, 0.18 #7351), 01sbv9 (0.33 #3284, 0.33 #1563, 0.18 #8448), 0f4yh (0.33 #2273, 0.33 #552, 0.17 #16049), 02n72k (0.33 #2822, 0.33 #1101, 0.17 #9709), 07nxvj (0.33 #2387, 0.33 #666, 0.17 #9274) >> Best rule #2701 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 04306rv; >> query: (?x4442, 0f4_2k) <- language(?x12641, ?x4442), language(?x4441, ?x4442), language(?x2098, ?x4442), country(?x2098, ?x304), genre(?x12641, ?x53), film_crew_role(?x12641, ?x468), nominated_for(?x749, ?x2098), film(?x609, ?x12641), ?x4441 = 0125xq, film_distribution_medium(?x2098, ?x2099) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13776 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 23 *> proper extension: 0999q; 09s02; *> query: (?x4442, ?x66) <- languages_spoken(?x913, ?x4442), countries_spoken_in(?x4442, ?x1892), languages(?x2715, ?x4442), film_release_region(?x4464, ?x1892), film_release_region(?x1202, ?x1892), film_release_region(?x504, ?x1892), film_release_region(?x66, ?x1892), ?x1202 = 0gj8t_b, country(?x453, ?x1892), ?x504 = 0g5qs2k, ?x4464 = 05pdh86 *> conf = 0.07 ranks of expected_values: 602 EVAL 06mp7 language! 0g5838s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 16.000 0.333 http://example.org/film/film/language #19247-02k5sc PRED entity: 02k5sc PRED relation: artist! PRED expected values: 03mp8k => 85 concepts (59 used for prediction) PRED predicted values (max 10 best out of 110): 033hn8 (0.25 #14, 0.14 #1783, 0.14 #422), 043g7l (0.25 #31, 0.10 #1255, 0.10 #1936), 04fcjt (0.25 #29, 0.09 #709, 0.07 #1525), 012b30 (0.25 #86, 0.03 #494, 0.03 #1038), 03rhqg (0.22 #968, 0.22 #1104, 0.21 #424), 01trtc (0.18 #749, 0.14 #1293, 0.13 #613), 011k1h (0.16 #554, 0.16 #1370, 0.12 #146), 01cf93 (0.16 #599, 0.08 #1824, 0.06 #3192), 0g768 (0.14 #1805, 0.12 #1941, 0.12 #6184), 073tm9 (0.14 #715, 0.08 #1531, 0.08 #1259) >> Best rule #14 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 0p3r8; >> query: (?x7865, 033hn8) <- artists(?x5406, ?x7865), ?x5406 = 07yklv >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #200 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02r3zy; 05crg7; 03fbc; 0hvbj; 0dw4g; 015bwt; *> query: (?x7865, 03mp8k) <- award_nominee(?x7865, ?x5906), award(?x7865, ?x4018), ?x4018 = 03qbh5, group(?x227, ?x7865) *> conf = 0.12 ranks of expected_values: 13 EVAL 02k5sc artist! 03mp8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 85.000 59.000 0.250 http://example.org/music/record_label/artist #19246-01bt59 PRED entity: 01bt59 PRED relation: major_field_of_study! PRED expected values: 03bwzr4 => 50 concepts (30 used for prediction) PRED predicted values (max 10 best out of 19): 014mlp (0.82 #492, 0.78 #345, 0.76 #301), 03bwzr4 (0.79 #266, 0.77 #244, 0.75 #148), 04zx3q1 (0.75 #139, 0.71 #120, 0.70 #158), 0bkj86 (0.68 #297, 0.61 #304, 0.60 #326), 02m4yg (0.50 #70, 0.46 #246, 0.43 #268), 01gkg3 (0.50 #69, 0.43 #130, 0.40 #188), 01ysy9 (0.50 #76, 0.40 #97, 0.39 #529), 07s6fsf (0.49 #255, 0.48 #98, 0.42 #118), 027f2w (0.49 #255, 0.48 #98, 0.42 #118), 013zdg (0.49 #255, 0.48 #98, 0.42 #118) >> Best rule #492 for best value: >> intensional similarity = 11 >> extensional distance = 78 >> proper extension: 01vrkm; >> query: (?x10264, 014mlp) <- major_field_of_study(?x7338, ?x10264), major_field_of_study(?x3437, ?x10264), organization(?x5510, ?x7338), institution(?x3437, ?x10659), school(?x729, ?x7338), major_field_of_study(?x3437, ?x12158), major_field_of_study(?x3437, ?x2921), student(?x3437, ?x1737), ?x12158 = 09s1f, ?x10659 = 01l8t8, ?x2921 = 06n6p >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #266 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 12 *> proper extension: 0hkf; *> query: (?x10264, 03bwzr4) <- major_field_of_study(?x4410, ?x10264), major_field_of_study(?x1667, ?x10264), major_field_of_study(?x1200, ?x10264), ?x1667 = 03v6t, institution(?x620, ?x4410), colors(?x4410, ?x663), student(?x4410, ?x856), ?x1200 = 016t_3, award_nominee(?x856, ?x382), currency(?x4410, ?x170) *> conf = 0.79 ranks of expected_values: 2 EVAL 01bt59 major_field_of_study! 03bwzr4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 50.000 30.000 0.825 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #19245-01w1sx PRED entity: 01w1sx PRED relation: films PRED expected values: 018js4 0q9sg 0286hyp => 90 concepts (50 used for prediction) PRED predicted values (max 10 best out of 678): 02yvct (0.40 #4227, 0.33 #5257, 0.31 #6288), 08hmch (0.33 #560, 0.33 #44, 0.29 #2621), 0y_9q (0.33 #263, 0.25 #2325, 0.15 #5931), 02jxbw (0.33 #305, 0.25 #2367, 0.14 #2882), 019vhk (0.33 #134, 0.25 #2196, 0.14 #2711), 02cbhg (0.33 #404, 0.25 #2466, 0.14 #2981), 0gmgwnv (0.33 #303, 0.25 #2365, 0.14 #2880), 09lcsj (0.33 #169, 0.25 #2231, 0.14 #2746), 0gj8nq2 (0.33 #158, 0.25 #2220, 0.14 #2735), 0jvt9 (0.33 #157, 0.25 #2219, 0.14 #2734) >> Best rule #4227 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0lrh; >> query: (?x10849, 02yvct) <- films(?x10849, ?x4551), executive_produced_by(?x4551, ?x846), film_distribution_medium(?x4551, ?x627), featured_film_locations(?x4551, ?x6930), language(?x4551, ?x254), titles(?x8581, ?x4551) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #12890 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 21 *> proper extension: 01hmnh; 07c52; 018h2; 0g1x2_; 03n93; 081k8; 018jz; 06c97; 0fzyg; 0fx2s; ... *> query: (?x10849, ?x66) <- films(?x10849, ?x4551), executive_produced_by(?x4551, ?x846), nominated_for(?x902, ?x4551), language(?x4551, ?x254), film(?x902, ?x103), production_companies(?x66, ?x902) *> conf = 0.02 ranks of expected_values: 534, 576 EVAL 01w1sx films 0286hyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 50.000 0.400 http://example.org/film/film_subject/films EVAL 01w1sx films 0q9sg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 50.000 0.400 http://example.org/film/film_subject/films EVAL 01w1sx films 018js4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 50.000 0.400 http://example.org/film/film_subject/films #19244-03s5lz PRED entity: 03s5lz PRED relation: film_crew_role PRED expected values: 09zzb8 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 23): 09zzb8 (0.77 #1479, 0.73 #1585, 0.72 #1055), 01vx2h (0.50 #10, 0.38 #1594, 0.35 #1488), 01pvkk (0.50 #11, 0.29 #1065, 0.29 #431), 094hwz (0.25 #14, 0.04 #927, 0.04 #892), 05smlt (0.25 #20, 0.04 #55, 0.03 #230), 014kbl (0.25 #32, 0.01 #627), 02ynfr (0.19 #717, 0.19 #1599, 0.18 #225), 089g0h (0.17 #89, 0.12 #579, 0.12 #1497), 0215hd (0.15 #368, 0.15 #578, 0.15 #1496), 02rh1dz (0.13 #1487, 0.13 #1593, 0.10 #1699) >> Best rule #1479 for best value: >> intensional similarity = 3 >> extensional distance = 729 >> proper extension: 0fq27fp; >> query: (?x1295, 09zzb8) <- genre(?x1295, ?x258), film_crew_role(?x1295, ?x1171), ?x1171 = 09vw2b7 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s5lz film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.773 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19243-03flwk PRED entity: 03flwk PRED relation: gender PRED expected values: 05zppz => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #9, 0.84 #5, 0.81 #29), 02zsn (0.31 #68, 0.31 #70, 0.30 #86) >> Best rule #9 for best value: >> intensional similarity = 7 >> extensional distance = 231 >> proper extension: 0gkydb; 021yw7; 02tf1y; 04rg6; 06cl2w; >> query: (?x5100, 05zppz) <- profession(?x5100, ?x1032), profession(?x5100, ?x987), profession(?x5100, ?x319), ?x1032 = 02hrh1q, award(?x5100, ?x198), ?x987 = 0dxtg, ?x319 = 01d_h8 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03flwk gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.845 http://example.org/people/person/gender #19242-05b1062 PRED entity: 05b1062 PRED relation: people! PRED expected values: 0dryh9k => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 38): 0dryh9k (0.43 #247, 0.39 #1171, 0.31 #170), 0bpjh3 (0.25 #102, 0.06 #333, 0.06 #487), 041rx (0.24 #928, 0.21 #1390, 0.18 #1852), 0222qb (0.12 #352, 0.11 #506, 0.10 #660), 02w7gg (0.12 #4162, 0.11 #4239, 0.07 #4547), 0x67 (0.09 #1858, 0.09 #1935, 0.09 #2012), 033tf_ (0.09 #931, 0.07 #1393, 0.07 #1855), 013xrm (0.08 #1098, 0.06 #790, 0.06 #482), 02sch9 (0.08 #1190, 0.01 #4195, 0.01 #4272), 01rv7x (0.07 #270, 0.05 #578, 0.03 #886) >> Best rule #247 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0g2mbn; 05nqq3; 06zmg7m; 071wvh; 08y7b9; 0894_x; 047s_cr; 0627zr; 09ld6g; 03chx58; >> query: (?x13492, 0dryh9k) <- profession(?x13492, ?x1146), nationality(?x13492, ?x2146), ?x1146 = 018gz8, ?x2146 = 03rk0, gender(?x13492, ?x231) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05b1062 people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.429 http://example.org/people/ethnicity/people #19241-0kpys PRED entity: 0kpys PRED relation: contains PRED expected values: 0284jb => 176 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2743): 0mzww (0.84 #122646, 0.83 #64240, 0.83 #78842), 06_kh (0.84 #122646, 0.83 #64240, 0.83 #78842), 0r04p (0.84 #122646, 0.83 #64240, 0.83 #78842), 0r172 (0.84 #122646, 0.83 #64240, 0.83 #78842), 0kpys (0.50 #344, 0.48 #99286, 0.31 #245285), 0gx1l (0.50 #1645, 0.48 #99286, 0.31 #245285), 05cwl_ (0.50 #732, 0.34 #43803, 0.29 #154770), 01bzw5 (0.50 #132, 0.34 #43803, 0.29 #154770), 03b8c4 (0.50 #2220, 0.34 #43803, 0.29 #154770), 06b7s9 (0.50 #2091, 0.34 #43803, 0.29 #154770) >> Best rule #122646 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 0jcgs; 0nvrd; 0m2gk; 0m7d0; 0nryt; 0mw7h; 0l34j; 0mwzv; 0f6_4; 0n5yh; ... >> query: (?x2949, ?x242) <- county(?x242, ?x2949), contains(?x2949, ?x682), location(?x794, ?x682) >> conf = 0.84 => this is the best rule for 4 predicted values *> Best rule #147 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09c7w0; 01n7q; *> query: (?x2949, 0284jb) <- location(?x495, ?x2949), contains(?x2949, ?x5895), place_of_birth(?x323, ?x2949), ?x5895 = 0k_p5 *> conf = 0.50 ranks of expected_values: 21 EVAL 0kpys contains 0284jb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 176.000 97.000 0.838 http://example.org/location/location/contains #19240-04mcw4 PRED entity: 04mcw4 PRED relation: film! PRED expected values: 03_2td => 124 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1304): 030_3z (0.49 #18722, 0.48 #93617, 0.46 #91536), 02q_cc (0.49 #18722, 0.48 #93617, 0.46 #91536), 0343h (0.49 #18722, 0.48 #93617, 0.46 #91536), 05qd_ (0.49 #18722, 0.48 #93617, 0.46 #91536), 0kx4m (0.49 #18722, 0.48 #93617, 0.46 #91536), 016k6x (0.25 #2968, 0.09 #9207, 0.07 #11289), 03cglm (0.25 #1044, 0.06 #9363, 0.05 #11445), 08x5c_ (0.25 #1947, 0.05 #12348, 0.03 #20669), 0jlv5 (0.25 #1178, 0.04 #13661, 0.03 #19900), 012ykt (0.25 #1090, 0.03 #9409, 0.02 #11491) >> Best rule #18722 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 053rxgm; 0416y94; 09p0ct; 075wx7_; 02yvct; 05c46y6; 05m_jsg; 043t8t; 0462hhb; 033f8n; ... >> query: (?x4551, ?x846) <- film_crew_role(?x4551, ?x2472), film(?x2938, ?x4551), award_winner(?x4551, ?x846), ?x2472 = 01xy5l_ >> conf = 0.49 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 04mcw4 film! 03_2td CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 62.000 0.489 http://example.org/film/actor/film./film/performance/film #19239-06rf7 PRED entity: 06rf7 PRED relation: category PRED expected values: 08mbj5d => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.74 #167, 0.72 #19, 0.71 #168) >> Best rule #167 for best value: >> intensional similarity = 5 >> extensional distance = 1055 >> proper extension: 0277jc; 04qdj; 01tpvt; 01dyk8; 0fydw; 0k424; 0d6nx; 0k6bt; 0194f5; >> query: (?x9664, 08mbj5d) <- contains(?x1264, ?x9664), film_release_region(?x4694, ?x1264), film_release_region(?x664, ?x1264), ?x664 = 0401sg, ?x4694 = 02j69w >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06rf7 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 189.000 189.000 0.739 http://example.org/common/topic/webpage./common/webpage/category #19238-01rc6f PRED entity: 01rc6f PRED relation: major_field_of_study PRED expected values: 04_tv => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 115): 01540 (0.64 #1029, 0.37 #2481, 0.34 #1634), 01mkq (0.60 #2073, 0.50 #984, 0.46 #6437), 02j62 (0.60 #273, 0.50 #757, 0.49 #2451), 02lp1 (0.57 #980, 0.51 #2069, 0.50 #738), 03g3w (0.50 #995, 0.45 #2447, 0.40 #2084), 037mh8 (0.50 #1035, 0.45 #1640, 0.40 #309), 05qfh (0.50 #1004, 0.40 #278, 0.35 #2093), 05qjt (0.50 #976, 0.39 #2428, 0.34 #1581), 04x_3 (0.47 #2083, 0.43 #994, 0.43 #510), 062z7 (0.45 #2448, 0.44 #2085, 0.43 #996) >> Best rule #1029 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 07y0n; >> query: (?x8120, 01540) <- company(?x1620, ?x8120), company(?x5510, ?x8120), category(?x8120, ?x134), politician(?x8714, ?x1620) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #741 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 01b1mj; 01ptt7; 01jsn5; 07t90; 09f2j; 0ks67; 01j_5k; 03tw2s; 0jkhr; 0dzst; *> query: (?x8120, 04_tv) <- school(?x8542, ?x8120), institution(?x620, ?x8120), organization(?x5510, ?x8120), ?x8542 = 09th87 *> conf = 0.17 ranks of expected_values: 49 EVAL 01rc6f major_field_of_study 04_tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 180.000 180.000 0.643 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19237-049dk PRED entity: 049dk PRED relation: service_location PRED expected values: 09c7w0 => 149 concepts (79 used for prediction) PRED predicted values (max 10 best out of 69): 09c7w0 (0.81 #2070, 0.80 #1378, 0.80 #1774), 0d060g (0.28 #1780, 0.25 #2570, 0.22 #1681), 07ssc (0.25 #16, 0.21 #1590, 0.18 #2183), 0345h (0.25 #28, 0.08 #2590, 0.08 #2788), 0f8l9c (0.25 #22, 0.08 #1794, 0.08 #3274), 03rjj (0.25 #6, 0.05 #2173, 0.04 #2074), 0b90_r (0.25 #4, 0.03 #1380, 0.03 #1578), 02j71 (0.25 #1790, 0.23 #2778, 0.23 #2185), 0chghy (0.12 #1784, 0.10 #2574, 0.10 #2080), 0488g (0.07 #6441, 0.05 #6644, 0.05 #6442) >> Best rule #2070 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 0p4wb; 018mxj; 011k1h; 03d6fyn; 0152x_; 064f29; 05w3y; 058j2; 06q07; 059wk; ... >> query: (?x1783, 09c7w0) <- citytown(?x1783, ?x2986), service_language(?x1783, ?x254), ?x254 = 02h40lc, category(?x1783, ?x134) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049dk service_location 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 79.000 0.808 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #19236-02ndbd PRED entity: 02ndbd PRED relation: film PRED expected values: 02ndy4 => 90 concepts (70 used for prediction) PRED predicted values (max 10 best out of 537): 014l6_ (0.57 #64425, 0.47 #85903, 0.43 #85902), 0fphf3v (0.57 #64425, 0.47 #85903, 0.43 #85902), 06_wqk4 (0.22 #7160, 0.02 #50236, 0.01 #41291), 051zy_b (0.07 #580, 0.04 #4160, 0.01 #18479), 0gl3hr (0.07 #1099, 0.02 #4679, 0.01 #8259), 0gcpc (0.07 #709, 0.02 #4289, 0.01 #7869), 033dbw (0.07 #1733, 0.02 #5313, 0.01 #10682), 08xvpn (0.07 #1594, 0.02 #5174, 0.01 #10543), 025ts_z (0.07 #1493, 0.02 #5073, 0.01 #10442), 08l0x2 (0.07 #1326, 0.02 #4906, 0.01 #10275) >> Best rule #64425 for best value: >> intensional similarity = 3 >> extensional distance = 816 >> proper extension: 0bz5v2; 04cf09; 01wjrn; 02lq10; 0c01c; 049_zz; 06vsbt; 05dtwm; 06fc0b; 05wqr1; ... >> query: (?x856, ?x3220) <- nominated_for(?x856, ?x3220), student(?x4016, ?x856), film(?x856, ?x5870) >> conf = 0.57 => this is the best rule for 2 predicted values *> Best rule #5278 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 02xp18; 01y0y6; *> query: (?x856, 02ndy4) <- program(?x856, ?x6248), type_of_union(?x856, ?x566), film(?x856, ?x5870) *> conf = 0.02 ranks of expected_values: 217 EVAL 02ndbd film 02ndy4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 90.000 70.000 0.568 http://example.org/film/actor/film./film/performance/film #19235-012j8z PRED entity: 012j8z PRED relation: nationality PRED expected values: 0d060g => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 20): 03rk0 (0.16 #642, 0.15 #343, 0.15 #442), 03_3d (0.13 #702, 0.12 #900, 0.12 #801), 02jx1 (0.10 #3424, 0.10 #7997, 0.10 #3226), 07ssc (0.09 #1809, 0.09 #8969, 0.09 #1509), 0d060g (0.05 #6779, 0.05 #4294, 0.05 #5688), 0345h (0.04 #1123, 0.04 #1924, 0.04 #1525), 06c1y (0.04 #38, 0.02 #237), 0f8l9c (0.03 #1516, 0.03 #1816, 0.03 #1915), 0h7x (0.02 #233, 0.02 #34, 0.02 #1928), 03rjj (0.02 #1799, 0.02 #1499, 0.02 #8464) >> Best rule #642 for best value: >> intensional similarity = 3 >> extensional distance = 122 >> proper extension: 07cjqy; 05vzql; 03fwln; 04j5fx; 02d6n_; >> query: (?x7155, 03rk0) <- location(?x7155, ?x739), special_performance_type(?x7155, ?x4832), profession(?x7155, ?x1032) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #6779 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2097 *> proper extension: 0f1pyf; 02x8kk; 02x8mt; 02784z; 03j90; 047g6; 015n8; 011zwl; 069d71; *> query: (?x7155, 0d060g) <- location(?x7155, ?x739), gender(?x7155, ?x231), nationality(?x7155, ?x94) *> conf = 0.05 ranks of expected_values: 5 EVAL 012j8z nationality 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 98.000 0.161 http://example.org/people/person/nationality #19234-01vrwfv PRED entity: 01vrwfv PRED relation: artists! PRED expected values: 05bt6j => 82 concepts (60 used for prediction) PRED predicted values (max 10 best out of 278): 064t9 (0.74 #3409, 0.73 #3101, 0.72 #3717), 05r6t (0.60 #1007, 0.27 #10267, 0.15 #6567), 05bt6j (0.60 #11460, 0.56 #3130, 0.53 #3746), 05w3f (0.50 #345, 0.36 #2813, 0.35 #2504), 059kh (0.50 #974, 0.19 #2207, 0.17 #4677), 03lty (0.50 #6203, 0.43 #4966, 0.38 #10212), 06j6l (0.48 #12697, 0.35 #13313, 0.35 #3443), 016clz (0.44 #4633, 0.39 #6490, 0.37 #5561), 01g888 (0.40 #960, 0.04 #4663, 0.04 #6520), 0glt670 (0.36 #12689, 0.26 #5908, 0.21 #8685) >> Best rule #3409 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 0168cl; 0136pk; 016h9b; 016ntp; 0qf11; 094xh; 07r4c; 0137hn; 019f9z; 01vs4ff; ... >> query: (?x2901, 064t9) <- artists(?x5300, ?x2901), artist(?x382, ?x2901), ?x5300 = 02k_kn, award(?x2901, ?x724) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #11460 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 315 *> proper extension: 01p45_v; 01wwnh2; 04d_mtq; 01vs8ng; *> query: (?x2901, 05bt6j) <- artists(?x1000, ?x2901), artists(?x1000, ?x10628), artists(?x1000, ?x115), ?x115 = 01pbxb, ?x10628 = 01w20rx *> conf = 0.60 ranks of expected_values: 3 EVAL 01vrwfv artists! 05bt6j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 60.000 0.739 http://example.org/music/genre/artists #19233-0pj9t PRED entity: 0pj9t PRED relation: profession PRED expected values: 016z4k => 133 concepts (89 used for prediction) PRED predicted values (max 10 best out of 75): 0nbcg (0.48 #6407, 0.48 #5221, 0.48 #2252), 01c72t (0.46 #467, 0.44 #23, 0.35 #2688), 016z4k (0.46 #3262, 0.45 #2225, 0.44 #3709), 0dz3r (0.44 #3112, 0.44 #2223, 0.42 #5192), 03gjzk (0.43 #311, 0.28 #1052, 0.23 #10702), 01d_h8 (0.39 #154, 0.34 #302, 0.30 #1043), 039v1 (0.39 #184, 0.30 #6412, 0.28 #7600), 0dxtg (0.34 #310, 0.28 #12777, 0.28 #10701), 0fnpj (0.26 #208, 0.16 #2281, 0.16 #800), 02jknp (0.26 #6228, 0.23 #1193, 0.21 #7124) >> Best rule #6407 for best value: >> intensional similarity = 3 >> extensional distance = 404 >> proper extension: 01yznp; 0p5mw; 01wk7b7; >> query: (?x3241, 0nbcg) <- instrumentalists(?x227, ?x3241), category(?x3241, ?x134), nationality(?x3241, ?x94) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #3262 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 234 *> proper extension: 016qtt; 03c7ln; 01vrx3g; 01pr_j6; 01w923; 019g40; 01zmpg; 0136pk; 01tp5bj; 0frsw; ... *> query: (?x3241, 016z4k) <- instrumentalists(?x227, ?x3241), artists(?x378, ?x3241), origin(?x3241, ?x3014) *> conf = 0.46 ranks of expected_values: 3 EVAL 0pj9t profession 016z4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 89.000 0.480 http://example.org/people/person/profession #19232-026v437 PRED entity: 026v437 PRED relation: film PRED expected values: 083shs => 115 concepts (64 used for prediction) PRED predicted values (max 10 best out of 457): 080dwhx (0.48 #32268, 0.47 #21510, 0.46 #43026), 0284b56 (0.31 #6362, 0.17 #986, 0.15 #4570), 011ywj (0.29 #8606, 0.06 #6814, 0.06 #100400), 0fpmrm3 (0.17 #426, 0.15 #4010, 0.15 #2218), 04ghz4m (0.17 #1244, 0.15 #4828, 0.15 #3036), 0gxtknx (0.15 #3832, 0.15 #2040, 0.06 #5624), 07bwr (0.12 #6246, 0.04 #8038, 0.03 #82472), 02cbhg (0.12 #6782, 0.04 #8574, 0.01 #31881), 092vkg (0.12 #5533, 0.04 #7325), 0dsvzh (0.08 #120, 0.08 #3704, 0.08 #1912) >> Best rule #32268 for best value: >> intensional similarity = 3 >> extensional distance = 554 >> proper extension: 04dyqk; >> query: (?x6359, ?x493) <- award_winner(?x493, ?x6359), location(?x6359, ?x739), place_of_death(?x340, ?x739) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #5395 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 07s8r0; *> query: (?x6359, 083shs) <- award_nominee(?x3842, ?x6359), location(?x6359, ?x739), ?x3842 = 0cjsxp *> conf = 0.06 ranks of expected_values: 120 EVAL 026v437 film 083shs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 115.000 64.000 0.475 http://example.org/film/actor/film./film/performance/film #19231-0cjdk PRED entity: 0cjdk PRED relation: award_winner! PRED expected values: 0gq9h => 223 concepts (223 used for prediction) PRED predicted values (max 10 best out of 311): 05p1dby (0.38 #8728, 0.31 #19073, 0.30 #22091), 07bdd_ (0.36 #9548, 0.33 #1359, 0.32 #21186), 02x1z2s (0.31 #8817, 0.29 #20886, 0.27 #15713), 0cqhk0 (0.30 #6071, 0.20 #29347, 0.19 #50899), 0ck27z (0.25 #58285, 0.23 #45352, 0.18 #28110), 0gq9h (0.20 #940, 0.18 #11284, 0.17 #2664), 08_vwq (0.18 #44666, 0.05 #95266, 0.02 #25269), 0cjyzs (0.17 #56036, 0.17 #57761, 0.13 #61747), 09qvf4 (0.17 #56036, 0.17 #57761, 0.11 #93109), 0cqhb3 (0.17 #56036, 0.17 #57761, 0.11 #93109) >> Best rule #8728 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 024rgt; >> query: (?x2554, 05p1dby) <- award_winner(?x2436, ?x2554), child(?x9077, ?x2554), organization(?x4682, ?x2554) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #940 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 01dtcb; *> query: (?x2554, 0gq9h) <- service_language(?x2554, ?x254), citytown(?x2554, ?x1523), ?x254 = 02h40lc, ?x1523 = 030qb3t *> conf = 0.20 ranks of expected_values: 6 EVAL 0cjdk award_winner! 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 223.000 223.000 0.385 http://example.org/award/award_category/winners./award/award_honor/award_winner #19230-09qs08 PRED entity: 09qs08 PRED relation: award! PRED expected values: 03q45x 01p85y 07k2p6 => 48 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2467): 01_j71 (0.72 #33513, 0.68 #36868, 0.66 #63685), 07m77x (0.72 #33513, 0.68 #36868, 0.66 #63685), 030hbp (0.50 #12960, 0.50 #9610, 0.33 #2910), 01z5tr (0.50 #8993, 0.33 #2293, 0.25 #12343), 01tj34 (0.50 #7846, 0.33 #1146, 0.25 #11196), 0m66w (0.50 #8417, 0.33 #1717, 0.21 #23458), 01p85y (0.50 #9202, 0.33 #2502, 0.21 #23458), 02_n5d (0.50 #7643, 0.33 #943, 0.21 #23458), 01bcq (0.50 #11468, 0.33 #4768, 0.17 #16751), 01dw4q (0.50 #6786, 0.33 #86, 0.16 #60331) >> Best rule #33513 for best value: >> intensional similarity = 4 >> extensional distance = 188 >> proper extension: 02g3v6; 02w_6xj; 09d28z; 02qrwjt; 03r8v_; 06196; 02wypbh; 09v478h; 07kfzsg; 04jhhng; >> query: (?x2603, ?x3446) <- award(?x631, ?x2603), award(?x12003, ?x2603), student(?x5149, ?x12003), award_winner(?x2603, ?x3446) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #9202 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0cqhmg; *> query: (?x2603, 01p85y) <- award(?x631, ?x2603), award(?x12003, ?x2603), award(?x4411, ?x2603), student(?x5149, ?x12003), ?x4411 = 033jkj *> conf = 0.50 ranks of expected_values: 7, 218, 871 EVAL 09qs08 award! 07k2p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 48.000 19.000 0.718 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qs08 award! 01p85y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 48.000 19.000 0.718 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qs08 award! 03q45x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 19.000 0.718 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19229-01pj5q PRED entity: 01pj5q PRED relation: film PRED expected values: 028_yv => 101 concepts (79 used for prediction) PRED predicted values (max 10 best out of 834): 0h6r5 (0.67 #1790, 0.62 #10739, 0.50 #7160), 06cm5 (0.67 #1790, 0.62 #10739, 0.50 #7160), 033g4d (0.67 #1790, 0.50 #7160, 0.49 #53680), 01xbxn (0.25 #1394, 0.02 #3184, 0.01 #8554), 0jsf6 (0.12 #1089, 0.11 #25052, 0.06 #73370), 033fqh (0.12 #840, 0.11 #25052, 0.06 #73370), 01b195 (0.12 #360, 0.11 #25052, 0.06 #73370), 07cw4 (0.12 #1024, 0.11 #25052, 0.06 #73370), 011ypx (0.12 #1022, 0.11 #25052, 0.06 #73370), 03s5lz (0.12 #197, 0.11 #25052, 0.06 #73370) >> Best rule #1790 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0f4vbz; >> query: (?x7733, ?x1185) <- nominated_for(?x7733, ?x1185), award_winner(?x1815, ?x7733), ?x1815 = 030hcs >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0f4vbz; *> query: (?x7733, 028_yv) <- nominated_for(?x7733, ?x1185), award_winner(?x1815, ?x7733), ?x1815 = 030hcs *> conf = 0.12 ranks of expected_values: 65 EVAL 01pj5q film 028_yv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 101.000 79.000 0.673 http://example.org/film/actor/film./film/performance/film #19228-03s9kp PRED entity: 03s9kp PRED relation: film_crew_role PRED expected values: 01pvkk => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 23): 01vx2h (0.37 #210, 0.37 #108, 0.34 #75), 01pvkk (0.30 #211, 0.28 #1219, 0.28 #646), 02ynfr (0.20 #80, 0.19 #248, 0.19 #182), 02rh1dz (0.19 #209, 0.17 #242, 0.17 #176), 0215hd (0.15 #50, 0.12 #218, 0.12 #1124), 0d2b38 (0.13 #57, 0.12 #258, 0.12 #90), 089g0h (0.12 #219, 0.11 #51, 0.10 #587), 01xy5l_ (0.11 #581, 0.09 #648, 0.09 #213), 02_n3z (0.09 #68, 0.09 #136, 0.09 #170), 089fss (0.09 #39, 0.07 #575, 0.07 #642) >> Best rule #210 for best value: >> intensional similarity = 4 >> extensional distance = 235 >> proper extension: 0gtsx8c; >> query: (?x11996, 01vx2h) <- film(?x496, ?x11996), language(?x11996, ?x254), film_release_distribution_medium(?x11996, ?x81), crewmember(?x11996, ?x6166) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #211 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 235 *> proper extension: 0gtsx8c; *> query: (?x11996, 01pvkk) <- film(?x496, ?x11996), language(?x11996, ?x254), film_release_distribution_medium(?x11996, ?x81), crewmember(?x11996, ?x6166) *> conf = 0.30 ranks of expected_values: 2 EVAL 03s9kp film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.371 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19227-01n44c PRED entity: 01n44c PRED relation: artist! PRED expected values: 03rhqg 01wdtv => 141 concepts (79 used for prediction) PRED predicted values (max 10 best out of 115): 03rhqg (0.62 #3076, 0.36 #4744, 0.15 #7666), 01w40h (0.38 #446, 0.33 #307, 0.18 #4757), 015_1q (0.35 #993, 0.29 #1410, 0.28 #1271), 033hn8 (0.34 #3908, 0.33 #153, 0.25 #431), 016ckq (0.33 #182, 0.17 #321, 0.13 #1016), 011k1h (0.33 #3904, 0.15 #705, 0.12 #3626), 0g768 (0.31 #4766, 0.18 #2264, 0.13 #3237), 01cl2y (0.25 #448, 0.17 #309, 0.13 #3091), 01clyr (0.25 #451, 0.17 #312, 0.09 #1146), 011k11 (0.25 #453, 0.17 #314, 0.08 #870) >> Best rule #3076 for best value: >> intensional similarity = 5 >> extensional distance = 189 >> proper extension: 0167_s; 0394y; 06gcn; 013rfk; 0qmny; 0167xy; 0cfgd; 0153nq; >> query: (?x5181, 03rhqg) <- artist(?x7089, ?x5181), artist(?x7089, ?x6042), artist(?x7089, ?x3539), ?x3539 = 01kstn9, film(?x6042, ?x8959) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 43 EVAL 01n44c artist! 01wdtv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 141.000 79.000 0.618 http://example.org/music/record_label/artist EVAL 01n44c artist! 03rhqg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 79.000 0.618 http://example.org/music/record_label/artist #19226-02gvwz PRED entity: 02gvwz PRED relation: film PRED expected values: 0642xf3 => 66 concepts (37 used for prediction) PRED predicted values (max 10 best out of 381): 0ndwt2w (0.44 #993, 0.33 #4559, 0.33 #2776), 05650n (0.12 #1005, 0.11 #2788, 0.10 #4571), 08k40m (0.11 #2264, 0.06 #481, 0.05 #4047), 01vksx (0.10 #3701, 0.06 #135, 0.06 #32101), 05nlx4 (0.10 #4815, 0.06 #1249, 0.06 #3032), 03y0pn (0.10 #4817, 0.06 #1251, 0.06 #3034), 03wh49y (0.10 #4511, 0.06 #945, 0.06 #2728), 0c34mt (0.10 #4140, 0.06 #574, 0.06 #2357), 05fcbk7 (0.10 #4024, 0.06 #458, 0.06 #2241), 04w7rn (0.10 #3802, 0.06 #236, 0.06 #2019) >> Best rule #993 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02bfmn; 09wj5; 01rh0w; 0241jw; 01v9l67; 015t56; 016ypb; 01846t; 0154qm; 0294fd; ... >> query: (?x1194, 0ndwt2w) <- award_nominee(?x1194, ?x3028), ?x3028 = 0f0kz, film(?x1194, ?x972) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02gvwz film 0642xf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 37.000 0.438 http://example.org/film/actor/film./film/performance/film #19225-02lf1j PRED entity: 02lf1j PRED relation: profession PRED expected values: 02hrh1q => 89 concepts (39 used for prediction) PRED predicted values (max 10 best out of 79): 02hrh1q (0.95 #4799, 0.93 #4944, 0.91 #5089), 02jknp (0.63 #4648, 0.50 #6, 0.45 #5519), 0np9r (0.33 #1177, 0.32 #1757, 0.30 #2901), 02krf9 (0.30 #2901, 0.27 #1183, 0.27 #3504), 09jwl (0.30 #2901, 0.27 #2916, 0.26 #1610), 0kyk (0.30 #2901, 0.25 #26, 0.23 #461), 015cjr (0.30 #2901, 0.25 #46, 0.16 #1206), 01c72t (0.30 #2901, 0.09 #2485, 0.07 #4372), 02hv44_ (0.30 #2901, 0.09 #489, 0.05 #2519), 0d8qb (0.30 #2901, 0.05 #1526, 0.05 #511) >> Best rule #4799 for best value: >> intensional similarity = 4 >> extensional distance = 582 >> proper extension: 06cv1; 01vvycq; 01vv7sc; 035gjq; 01ztgm; 01b9ck; 06w2sn5; 01pl9g; 015882; 01t07j; ... >> query: (?x2564, 02hrh1q) <- profession(?x2564, ?x1041), participant(?x5884, ?x2564), profession(?x6336, ?x1041), ?x6336 = 036jp8 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lf1j profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 39.000 0.949 http://example.org/people/person/profession #19224-03nm_fh PRED entity: 03nm_fh PRED relation: region PRED expected values: 07ssc => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 4): 07ssc (0.86 #52, 0.18 #147, 0.17 #123), 09c7w0 (0.03 #212, 0.02 #428, 0.01 #1802), 01hmnh (0.02 #379), 02jx1 (0.01 #1802, 0.01 #1996) >> Best rule #52 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 07kb7vh; 031hcx; 04nlb94; >> query: (?x4684, 07ssc) <- film(?x609, ?x4684), film_crew_role(?x4684, ?x137), nominated_for(?x298, ?x4684), ?x609 = 03xq0f >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03nm_fh region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.859 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #19223-02vnp2 PRED entity: 02vnp2 PRED relation: student PRED expected values: 05d7rk => 109 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1222): 0prfz (0.33 #43, 0.10 #8399, 0.10 #6310), 04myfb7 (0.33 #294, 0.02 #37907, 0.02 #44175), 06z8gn (0.33 #1518, 0.01 #57936), 01wgx4 (0.33 #2077), 02t901 (0.33 #2037), 026c0p (0.33 #2033), 03f4w4 (0.33 #2031), 0gpmp (0.33 #1988), 01tsbmv (0.33 #1933), 016ggh (0.33 #1888) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 015nl4; >> query: (?x9724, 0prfz) <- student(?x9724, ?x11861), student(?x9724, ?x4277), ?x11861 = 04d2yp, award(?x4277, ?x102) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 015nl4; *> query: (?x9724, 05d7rk) <- student(?x9724, ?x11861), student(?x9724, ?x4277), ?x11861 = 04d2yp, award(?x4277, ?x102) *> conf = 0.33 ranks of expected_values: 59 EVAL 02vnp2 student 05d7rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 109.000 93.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #19222-019cr PRED entity: 019cr PRED relation: religion! PRED expected values: 05kj_ 07z1m 0d0x8 => 39 concepts (34 used for prediction) PRED predicted values (max 10 best out of 409): 0d0x8 (0.80 #901, 0.78 #700, 0.73 #1035), 07z1m (0.73 #956, 0.70 #889, 0.67 #688), 05kj_ (0.71 #548, 0.71 #478, 0.70 #883), 05kr_ (0.51 #535, 0.50 #536, 0.45 #1029), 0d060g (0.51 #535, 0.50 #536, 0.42 #2033), 0694j (0.51 #535, 0.50 #536, 0.42 #2033), 04ych (0.51 #535, 0.50 #536, 0.42 #2033), 02_286 (0.51 #535, 0.50 #536, 0.42 #2033), 04kdn (0.51 #535, 0.50 #536, 0.42 #2033), 04kbn (0.51 #535, 0.50 #536, 0.42 #2033) >> Best rule #901 for best value: >> intensional similarity = 15 >> extensional distance = 8 >> proper extension: 021_0p; >> query: (?x2769, 0d0x8) <- religion(?x4776, ?x2769), religion(?x2623, ?x2769), religion(?x1274, ?x2769), religion(?x1138, ?x2769), religion(?x961, ?x2769), contains(?x4776, ?x2034), ?x2623 = 02xry, ?x1138 = 059_c, district_represented(?x5256, ?x4776), ?x5256 = 01grqd, religion(?x1545, ?x2769), contains(?x94, ?x4776), jurisdiction_of_office(?x900, ?x4776), ?x961 = 03s0w, location_of_ceremony(?x566, ?x1274) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 019cr religion! 0d0x8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 34.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 019cr religion! 07z1m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 34.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 019cr religion! 05kj_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 34.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #19221-0fqjks PRED entity: 0fqjks PRED relation: award_winner! PRED expected values: 0fy6bh => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 135): 0fy6bh (0.28 #3893, 0.18 #47, 0.14 #186), 0dznvw (0.18 #133, 0.14 #272, 0.10 #8899), 0c53zb (0.14 #199, 0.10 #8899, 0.10 #8759), 0dth6b (0.14 #163, 0.10 #8899, 0.10 #8759), 0fz0c2 (0.14 #243, 0.10 #8899, 0.10 #8759), 0d__c3 (0.10 #8899, 0.10 #262, 0.10 #8759), 0c4hx0 (0.10 #8899, 0.10 #8759, 0.05 #265), 0fz20l (0.10 #8899, 0.10 #8759, 0.05 #191), 0c6vcj (0.10 #239, 0.09 #100, 0.04 #378), 0ftlkg (0.09 #26, 0.03 #304, 0.02 #443) >> Best rule #3893 for best value: >> intensional similarity = 3 >> extensional distance = 943 >> proper extension: 03b78r; >> query: (?x7528, ?x3332) <- award_winner(?x10362, ?x7528), award_nominee(?x7528, ?x200), honored_for(?x3332, ?x10362) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fqjks award_winner! 0fy6bh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.275 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19220-01_vfy PRED entity: 01_vfy PRED relation: award_winner! PRED expected values: 027c924 => 85 concepts (62 used for prediction) PRED predicted values (max 10 best out of 242): 0gs9p (0.40 #2235, 0.33 #940, 0.30 #1804), 0gr4k (0.36 #5605, 0.34 #3452, 0.34 #3053), 02pqp12 (0.36 #5605, 0.34 #2587, 0.33 #1292), 040njc (0.36 #5605, 0.34 #2587, 0.33 #1292), 02rdyk7 (0.36 #5605, 0.34 #2587, 0.33 #1292), 09d28z (0.33 #1163, 0.24 #2458, 0.18 #3322), 04dn09n (0.33 #905, 0.20 #3064, 0.16 #2200), 027c924 (0.27 #2167, 0.26 #1736, 0.24 #872), 027c95y (0.25 #157, 0.06 #588, 0.06 #11786), 027986c (0.25 #49, 0.04 #20241, 0.04 #11678) >> Best rule #2235 for best value: >> intensional similarity = 5 >> extensional distance = 61 >> proper extension: 01p1z_; >> query: (?x2344, 0gs9p) <- award(?x2344, ?x1107), award(?x2344, ?x601), ?x1107 = 019f4v, student(?x9479, ?x2344), award(?x167, ?x601) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2167 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 61 *> proper extension: 01p1z_; *> query: (?x2344, 027c924) <- award(?x2344, ?x1107), award(?x2344, ?x601), ?x1107 = 019f4v, student(?x9479, ?x2344), award(?x167, ?x601) *> conf = 0.27 ranks of expected_values: 8 EVAL 01_vfy award_winner! 027c924 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 85.000 62.000 0.397 http://example.org/award/award_category/winners./award/award_honor/award_winner #19219-0f25y PRED entity: 0f25y PRED relation: location! PRED expected values: 05mkhs => 140 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1927): 03mcwq3 (0.49 #173660, 0.47 #113267, 0.47 #193792), 06fc0b (0.49 #173660, 0.47 #113267, 0.47 #193792), 09yhzs (0.20 #5612, 0.06 #15681, 0.06 #20717), 01t110 (0.20 #6343, 0.06 #16412, 0.06 #21448), 02byfd (0.17 #1809, 0.11 #4325, 0.05 #9358), 01p8r8 (0.17 #2051, 0.11 #4567, 0.03 #17152), 0347db (0.17 #1435, 0.11 #3951, 0.03 #21572), 0410cp (0.17 #807, 0.11 #3323, 0.03 #20944), 01zfmm (0.17 #528, 0.11 #3044, 0.03 #20665), 01vh3r (0.15 #14922, 0.09 #19958, 0.09 #24992) >> Best rule #173660 for best value: >> intensional similarity = 4 >> extensional distance = 309 >> proper extension: 0z843; 03pbf; 0gjcy; 0mmzt; 0tj4y; 0r2gj; 06cn5; 0ck6r; 0dzt9; 0150n; ... >> query: (?x9341, ?x2545) <- category(?x9341, ?x134), location(?x1287, ?x9341), profession(?x1287, ?x353), place_of_birth(?x2545, ?x9341) >> conf = 0.49 => this is the best rule for 2 predicted values *> Best rule #8284 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 09hzc; *> query: (?x9341, 05mkhs) <- administrative_division(?x9341, ?x5575), place_of_death(?x1287, ?x9341), influenced_by(?x576, ?x1287) *> conf = 0.05 ranks of expected_values: 404 EVAL 0f25y location! 05mkhs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 140.000 105.000 0.492 http://example.org/people/person/places_lived./people/place_lived/location #19218-0b_6q5 PRED entity: 0b_6q5 PRED relation: team PRED expected values: 091tgz 026wlnm => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 27): 026wlnm (0.81 #189, 0.81 #216, 0.80 #134), 091tgz (0.79 #242, 0.78 #88, 0.77 #215), 02pqcfz (0.67 #83, 0.62 #65, 0.60 #174), 027yf83 (0.66 #239, 0.62 #212, 0.60 #130), 04088s0 (0.50 #213, 0.50 #150, 0.50 #131), 02ptzz0 (0.50 #127, 0.48 #182, 0.46 #209), 03d555l (0.50 #129, 0.43 #57, 0.42 #148), 03d5m8w (0.42 #144, 0.40 #27, 0.38 #72), 02r2qt7 (0.40 #132, 0.38 #78, 0.33 #187), 0j86l (0.08 #145, 0.05 #317, 0.03 #381) >> Best rule #189 for best value: >> intensional similarity = 11 >> extensional distance = 19 >> proper extension: 0b_770; 0b_734; >> query: (?x11210, 026wlnm) <- team(?x11210, ?x9576), team(?x11210, ?x6003), ?x6003 = 02py8_w, team(?x6002, ?x9576), team(?x5897, ?x9576), team(?x4368, ?x9576), ?x4368 = 0b_6x2, position(?x9576, ?x6848), ?x5897 = 0b_6rk, ?x6002 = 0cc8q3, ?x6848 = 02_ssl >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0b_6q5 team 026wlnm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.810 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_6q5 team 091tgz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.810 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #19217-01vfqh PRED entity: 01vfqh PRED relation: genre PRED expected values: 02n4kr => 73 concepts (72 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.78 #5520, 0.77 #5755, 0.77 #1758), 02kdv5l (0.64 #2229, 0.50 #2, 0.47 #1173), 06n90 (0.50 #11, 0.22 #2238, 0.20 #1182), 0556j8 (0.50 #40, 0.15 #391, 0.15 #274), 03k9fj (0.45 #127, 0.31 #361, 0.31 #244), 0lsxr (0.44 #2118, 0.33 #1179, 0.27 #2235), 02l7c8 (0.35 #951, 0.34 #1302, 0.33 #834), 02n4kr (0.28 #2117, 0.23 #1178, 0.17 #2234), 0hcr (0.27 #139, 0.23 #373, 0.15 #256), 01hmnh (0.27 #133, 0.23 #1070, 0.16 #1421) >> Best rule #5520 for best value: >> intensional similarity = 3 >> extensional distance = 1296 >> proper extension: 0hz6mv2; >> query: (?x1331, 07s9rl0) <- genre(?x1331, ?x812), genre(?x1910, ?x812), ?x1910 = 011yth >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2117 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 650 *> proper extension: 07s3m4g; *> query: (?x1331, 02n4kr) <- genre(?x1331, ?x812), genre(?x12693, ?x812), ?x12693 = 04jn6y7 *> conf = 0.28 ranks of expected_values: 8 EVAL 01vfqh genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 73.000 72.000 0.775 http://example.org/film/film/genre #19216-0486tv PRED entity: 0486tv PRED relation: country PRED expected values: 0chghy 05qhw 01znc_ 06mkj 03shp 01nln 01xbgx 05bmq => 38 concepts (37 used for prediction) PRED predicted values (max 10 best out of 381): 05qhw (0.84 #745, 0.83 #3612, 0.81 #4066), 06mkj (0.84 #745, 0.80 #2590, 0.78 #3194), 01p1v (0.84 #745, 0.80 #2436, 0.78 #2284), 04wgh (0.84 #745, 0.80 #596, 0.77 #746), 0chghy (0.84 #745, 0.79 #4063, 0.78 #3162), 0ctw_b (0.84 #745, 0.78 #298, 0.77 #746), 0hzlz (0.84 #745, 0.78 #298, 0.77 #746), 0k6nt (0.84 #745, 0.78 #298, 0.77 #746), 04gzd (0.84 #745, 0.78 #298, 0.77 #746), 02vzc (0.84 #745, 0.78 #298, 0.77 #746) >> Best rule #745 for best value: >> intensional similarity = 34 >> extensional distance = 3 >> proper extension: 06z6r; >> query: (?x5396, ?x1174) <- country(?x5396, ?x8449), country(?x5396, ?x4121), country(?x5396, ?x1453), country(?x5396, ?x1203), country(?x5396, ?x789), country(?x5396, ?x512), country(?x5396, ?x304), ?x789 = 0f8l9c, ?x512 = 07ssc, sports(?x6464, ?x5396), sports(?x3729, ?x5396), sports(?x2233, ?x5396), olympics(?x5396, ?x2966), contains(?x2467, ?x4121), ?x8449 = 02k1b, ?x6464 = 0lbd9, organization(?x4121, ?x127), official_language(?x4121, ?x5359), ?x304 = 0d0vqn, ?x1453 = 06qd3, olympics(?x1499, ?x3729), olympics(?x1174, ?x3729), sports(?x3729, ?x150), olympics(?x6733, ?x2966), locations(?x3729, ?x5036), olympics(?x47, ?x2966), ?x1499 = 01znc_, administrative_area_type(?x4121, ?x2792), ?x2233 = 0l6mp, ?x6733 = 01sgl, film_release_region(?x66, ?x1174), medal(?x3729, ?x422), currency(?x1174, ?x170), ?x1203 = 07ylj >> conf = 0.84 => this is the best rule for 36 predicted values ranks of expected_values: 1, 2, 5, 21, 38, 56, 60, 105 EVAL 0486tv country 05bmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 38.000 37.000 0.845 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0486tv country 01xbgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 38.000 37.000 0.845 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0486tv country 01nln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 38.000 37.000 0.845 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0486tv country 03shp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 38.000 37.000 0.845 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0486tv country 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 37.000 0.845 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0486tv country 01znc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 38.000 37.000 0.845 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0486tv country 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 37.000 0.845 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0486tv country 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 38.000 37.000 0.845 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #19215-01wdj_ PRED entity: 01wdj_ PRED relation: student PRED expected values: 016vqk => 122 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1111): 09r9dp (0.11 #4797, 0.07 #613, 0.06 #6889), 0hwbd (0.11 #5204, 0.06 #7296, 0.05 #9388), 026m0 (0.11 #6007, 0.06 #8099, 0.03 #12283), 04hw4b (0.11 #5416, 0.06 #7508, 0.03 #11692), 013pp3 (0.11 #5107, 0.06 #7199, 0.02 #9291), 01f7j9 (0.11 #4512, 0.06 #6604, 0.02 #10788), 024y6w (0.07 #1452, 0.06 #3544, 0.05 #5636), 019vgs (0.07 #626, 0.06 #2718, 0.05 #4810), 033w9g (0.07 #770, 0.06 #2862, 0.05 #4954), 06pj8 (0.07 #321, 0.06 #2413, 0.05 #4505) >> Best rule #4797 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 07wrz; 01ky7c; 02yxjs; >> query: (?x2830, 09r9dp) <- student(?x2830, ?x672), major_field_of_study(?x2830, ?x3213), ?x3213 = 0g4gr, fraternities_and_sororities(?x2830, ?x3697) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wdj_ student 016vqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 67.000 0.105 http://example.org/education/educational_institution/students_graduates./education/education/student #19214-06whf PRED entity: 06whf PRED relation: type_of_union PRED expected values: 04ztj => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.82 #145, 0.81 #149, 0.81 #121), 01g63y (0.18 #74, 0.17 #58, 0.17 #38), 01bl8s (0.09 #35, 0.02 #87, 0.01 #103), 0jgjn (0.03 #60, 0.03 #64, 0.03 #68) >> Best rule #145 for best value: >> intensional similarity = 5 >> extensional distance = 116 >> proper extension: 0g5ff; >> query: (?x4265, 04ztj) <- influenced_by(?x13298, ?x4265), influenced_by(?x9173, ?x4265), award_winner(?x921, ?x4265), influenced_by(?x2608, ?x13298), student(?x1011, ?x9173) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06whf type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.822 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19213-03v0vd PRED entity: 03v0vd PRED relation: profession PRED expected values: 0dxtg => 71 concepts (51 used for prediction) PRED predicted values (max 10 best out of 40): 0dxtg (0.63 #308, 0.56 #161, 0.56 #896), 02jknp (0.52 #449, 0.52 #155, 0.49 #302), 01d_h8 (0.52 #300, 0.52 #153, 0.48 #888), 0np9r (0.40 #1343, 0.26 #5294, 0.25 #3823), 018gz8 (0.20 #163, 0.20 #898, 0.19 #1045), 09jwl (0.17 #1929, 0.16 #4429, 0.16 #3252), 0cbd2 (0.15 #301, 0.15 #1036, 0.15 #889), 0nbcg (0.11 #4441, 0.11 #1941, 0.11 #5765), 0dz3r (0.11 #4413, 0.10 #1913, 0.10 #5296), 016z4k (0.10 #1915, 0.10 #3238, 0.09 #1768) >> Best rule #308 for best value: >> intensional similarity = 4 >> extensional distance = 161 >> proper extension: 02hhtj; >> query: (?x9538, 0dxtg) <- profession(?x9538, ?x1943), profession(?x9538, ?x1041), ?x1041 = 03gjzk, ?x1943 = 02krf9 >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03v0vd profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 51.000 0.626 http://example.org/people/person/profession #19212-03g90h PRED entity: 03g90h PRED relation: film_distribution_medium PRED expected values: 0dq6p 07c52 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.86 #49, 0.84 #74, 0.83 #64), 02nxhr (0.38 #41, 0.27 #116, 0.27 #86), 0dq6p (0.23 #42, 0.14 #47, 0.13 #117), 07z4p (0.05 #312, 0.03 #90, 0.02 #65), 07c52 (0.02 #409) >> Best rule #49 for best value: >> intensional similarity = 9 >> extensional distance = 26 >> proper extension: 03h3x5; >> query: (?x280, 0735l) <- film(?x4655, ?x280), region(?x280, ?x94), film(?x5854, ?x280), film(?x5854, ?x5602), film(?x5854, ?x2772), film(?x5854, ?x2586), ?x2772 = 0gfsq9, film_crew_role(?x2586, ?x1171), ?x5602 = 02vyyl8 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 24 *> proper extension: 02r9p0c; *> query: (?x280, 0dq6p) <- film(?x4655, ?x280), genre(?x280, ?x53), film(?x5854, ?x280), country(?x280, ?x94), ?x5854 = 04mkft, language(?x280, ?x254) *> conf = 0.23 ranks of expected_values: 3, 5 EVAL 03g90h film_distribution_medium 07c52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 113.000 113.000 0.857 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 03g90h film_distribution_medium 0dq6p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 113.000 0.857 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #19211-034np8 PRED entity: 034np8 PRED relation: nationality PRED expected values: 09c7w0 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.73 #3418, 0.72 #3921, 0.70 #5326), 0cb4j (0.33 #7433, 0.02 #1509, 0.02 #1408), 01n7q (0.33 #7433, 0.01 #703), 07ssc (0.25 #115, 0.14 #617, 0.13 #1525), 02jx1 (0.14 #635, 0.14 #1139, 0.12 #533), 03rk0 (0.08 #3062, 0.08 #3263, 0.06 #7176), 0345h (0.07 #1338, 0.06 #2244, 0.06 #2546), 0d060g (0.06 #2422, 0.05 #2119, 0.05 #2019), 0h7x (0.03 #1342, 0.03 #1443, 0.03 #1946), 0f8l9c (0.03 #2537, 0.03 #2235, 0.03 #2637) >> Best rule #3418 for best value: >> intensional similarity = 3 >> extensional distance = 1142 >> proper extension: 01wgfp6; >> query: (?x1814, 09c7w0) <- award_nominee(?x1814, ?x6850), location(?x1814, ?x578), location(?x6850, ?x362) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034np8 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.733 http://example.org/people/person/nationality #19210-02vgh PRED entity: 02vgh PRED relation: group! PRED expected values: 01mxnvc => 96 concepts (39 used for prediction) PRED predicted values (max 10 best out of 200): 018x3 (0.25 #302, 0.10 #706, 0.08 #1106), 0qf3p (0.25 #244, 0.10 #648, 0.08 #1048), 023322 (0.20 #784, 0.08 #1184, 0.08 #1384), 02yygk (0.20 #580, 0.06 #1784, 0.06 #1985), 01vs4ff (0.10 #730, 0.08 #1130, 0.08 #1330), 01nn3m (0.10 #802, 0.08 #1202, 0.08 #1402), 021bk (0.10 #642, 0.08 #1042, 0.08 #1242), 0pgjm (0.10 #626, 0.08 #1026, 0.08 #1226), 03qd_ (0.10 #616, 0.08 #1016, 0.08 #1216), 0k1bs (0.10 #719, 0.08 #1119, 0.04 #2730) >> Best rule #302 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 02dw1_; 06gcn; >> query: (?x6986, 018x3) <- artist(?x2299, ?x6986), group(?x6039, ?x6986), group(?x1466, ?x6986), group(?x75, ?x6986), ?x6039 = 05kms, ?x75 = 07y_7, performance_role(?x248, ?x1466), role(?x115, ?x1466), group(?x1466, ?x12810), ?x12810 = 027kwc >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02vgh group! 01mxnvc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 39.000 0.250 http://example.org/music/group_member/membership./music/group_membership/group #19209-018j2 PRED entity: 018j2 PRED relation: role! PRED expected values: 03h_fqv 014cw2 => 87 concepts (43 used for prediction) PRED predicted values (max 10 best out of 730): 023l9y (0.75 #6106, 0.60 #2929, 0.57 #5198), 06x4l_ (0.75 #6025, 0.60 #2396, 0.50 #1943), 016ntp (0.71 #4680, 0.60 #2413, 0.50 #8770), 03j24kf (0.62 #6111, 0.62 #12475, 0.57 #5657), 01wxdn3 (0.62 #6300, 0.60 #3123, 0.50 #9482), 01vsnff (0.62 #5992, 0.60 #2815, 0.50 #1910), 014cw2 (0.62 #6349, 0.50 #2267, 0.40 #3172), 0326tc (0.62 #12603, 0.60 #2610, 0.57 #5785), 01vs4ff (0.60 #3020, 0.60 #2568, 0.57 #4835), 05qhnq (0.60 #3027, 0.60 #2575, 0.56 #16663) >> Best rule #6106 for best value: >> intensional similarity = 14 >> extensional distance = 6 >> proper extension: 06w7v; >> query: (?x2048, 023l9y) <- role(?x2048, ?x8014), role(?x2048, ?x1750), role(?x2048, ?x736), ?x8014 = 0214km, role(?x1166, ?x2048), role(?x211, ?x2048), performance_role(?x1750, ?x212), role(?x1750, ?x922), role(?x366, ?x1750), group(?x1750, ?x442), ?x736 = 06w87, instrumentalists(?x1166, ?x5623), ?x5623 = 01vsyg9, instrumentalists(?x2048, ?x367) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6349 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 6 *> proper extension: 06w7v; *> query: (?x2048, 014cw2) <- role(?x2048, ?x8014), role(?x2048, ?x1750), role(?x2048, ?x736), ?x8014 = 0214km, role(?x1166, ?x2048), role(?x211, ?x2048), performance_role(?x1750, ?x212), role(?x1750, ?x922), role(?x366, ?x1750), group(?x1750, ?x442), ?x736 = 06w87, instrumentalists(?x1166, ?x5623), ?x5623 = 01vsyg9, instrumentalists(?x2048, ?x367) *> conf = 0.62 ranks of expected_values: 7, 93 EVAL 018j2 role! 014cw2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 43.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 018j2 role! 03h_fqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 87.000 43.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role #19208-0296vv PRED entity: 0296vv PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #60, 0.80 #268, 0.80 #319), 02nxhr (0.04 #99, 0.03 #104, 0.03 #146), 07c52 (0.03 #40, 0.03 #46, 0.03 #77), 07z4p (0.02 #206, 0.02 #159, 0.02 #292) >> Best rule #60 for best value: >> intensional similarity = 3 >> extensional distance = 317 >> proper extension: 0fq27fp; >> query: (?x8039, 029j_) <- genre(?x8039, ?x1403), currency(?x8039, ?x170), ?x1403 = 02l7c8 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0296vv film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.821 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19207-03hfxx PRED entity: 03hfxx PRED relation: place_of_birth PRED expected values: 04vmp => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 149): 0fl2s (0.19 #1635, 0.04 #10801, 0.04 #5865), 0dlv0 (0.15 #2469, 0.14 #3173, 0.09 #5995), 02_286 (0.14 #19, 0.08 #23292, 0.08 #12710), 06cn5 (0.14 #295, 0.03 #3818, 0.01 #10166), 01sn04 (0.14 #40, 0.03 #3563), 04vmp (0.13 #6614, 0.12 #9434, 0.11 #5909), 05tbn (0.09 #9166, 0.08 #16922, 0.08 #21857), 029kpy (0.08 #3096, 0.06 #5918, 0.05 #2392), 0hj6h (0.06 #1897, 0.06 #6127, 0.06 #3305), 02c98m (0.06 #1935, 0.05 #2639, 0.03 #3343) >> Best rule #1635 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 03c_pqj; 02qvhbb; 0brddh; >> query: (?x8970, 0fl2s) <- people(?x5025, ?x8970), gender(?x8970, ?x231), profession(?x8970, ?x524), ?x5025 = 0dryh9k, ?x524 = 02jknp >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #6614 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 61 *> proper extension: 0241wg; 06wvfq; 0276g40; 07jmnh; *> query: (?x8970, 04vmp) <- people(?x5025, ?x8970), profession(?x8970, ?x1032), ?x5025 = 0dryh9k, nationality(?x8970, ?x2146), ?x1032 = 02hrh1q *> conf = 0.13 ranks of expected_values: 6 EVAL 03hfxx place_of_birth 04vmp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 111.000 111.000 0.188 http://example.org/people/person/place_of_birth #19206-05mrx8 PRED entity: 05mrx8 PRED relation: genre! PRED expected values: 05c5z8j => 34 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1785): 03pc89 (0.67 #8969, 0.60 #3368, 0.57 #12704), 03s6l2 (0.60 #3822, 0.56 #15019, 0.50 #20616), 02fwfb (0.60 #5042, 0.56 #16239, 0.50 #21836), 03c7twt (0.60 #5473, 0.56 #16670, 0.50 #22267), 0m313 (0.60 #3748, 0.56 #14945, 0.50 #20542), 027ct7c (0.60 #4718, 0.56 #15915, 0.50 #21512), 052_mn (0.60 #21973, 0.56 #16376, 0.40 #7045), 01p3ty (0.60 #20965, 0.56 #15368, 0.40 #6037), 047q2k1 (0.60 #20561, 0.56 #14964, 0.40 #5633), 02tcgh (0.60 #22306, 0.56 #16709, 0.40 #5512) >> Best rule #8969 for best value: >> intensional similarity = 16 >> extensional distance = 4 >> proper extension: 0vgkd; >> query: (?x13467, 03pc89) <- genre(?x6365, ?x13467), genre(?x5991, ?x13467), genre(?x5747, ?x13467), ?x6365 = 03n3gl, ?x5991 = 06__m6, film_crew_role(?x5747, ?x5136), film_crew_role(?x5747, ?x4305), film_crew_role(?x5747, ?x2472), ?x4305 = 0215hd, ?x2472 = 01xy5l_, film(?x9388, ?x5747), production_companies(?x5747, ?x9518), film_release_distribution_medium(?x5747, ?x81), award_nominee(?x3536, ?x9388), ?x5136 = 089g0h, ?x81 = 029j_ >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #8213 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 4 *> proper extension: 0vgkd; *> query: (?x13467, 05c5z8j) <- genre(?x6365, ?x13467), genre(?x5991, ?x13467), genre(?x5747, ?x13467), ?x6365 = 03n3gl, ?x5991 = 06__m6, film_crew_role(?x5747, ?x5136), film_crew_role(?x5747, ?x4305), film_crew_role(?x5747, ?x2472), ?x4305 = 0215hd, ?x2472 = 01xy5l_, film(?x9388, ?x5747), production_companies(?x5747, ?x9518), film_release_distribution_medium(?x5747, ?x81), award_nominee(?x3536, ?x9388), ?x5136 = 089g0h, ?x81 = 029j_ *> conf = 0.50 ranks of expected_values: 98 EVAL 05mrx8 genre! 05c5z8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 34.000 12.000 0.667 http://example.org/film/film/genre #19205-02wb6d PRED entity: 02wb6d PRED relation: nationality PRED expected values: 09c7w0 => 145 concepts (143 used for prediction) PRED predicted values (max 10 best out of 70): 09c7w0 (0.90 #13286, 0.84 #13584, 0.78 #4162), 07ssc (0.40 #15, 0.15 #213, 0.12 #1402), 0d060g (0.39 #4366, 0.19 #304, 0.17 #502), 0gx1l (0.32 #5947, 0.31 #6349), 0kpys (0.32 #5947, 0.31 #6349), 02jx1 (0.22 #527, 0.20 #3499, 0.20 #3301), 0chghy (0.20 #10, 0.13 #4369, 0.06 #604), 03rjj (0.17 #4364, 0.05 #1293, 0.05 #9024), 0h7x (0.15 #232, 0.10 #727, 0.08 #2313), 03rk0 (0.12 #342, 0.11 #540, 0.10 #738) >> Best rule #13286 for best value: >> intensional similarity = 3 >> extensional distance = 3039 >> proper extension: 07m69t; 01ly8d; 01nvdc; 03cxqp5; >> query: (?x6971, 09c7w0) <- nationality(?x6971, ?x1264), film_release_region(?x4778, ?x1264), films(?x6821, ?x4778) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wb6d nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 143.000 0.904 http://example.org/people/person/nationality #19204-0214km PRED entity: 0214km PRED relation: role PRED expected values: 05r5c => 61 concepts (44 used for prediction) PRED predicted values (max 10 best out of 86): 01vj9c (0.91 #1213, 0.88 #1220, 0.88 #2638), 0l14qv (0.91 #1213, 0.88 #1220, 0.85 #688), 0l14j_ (0.88 #1220, 0.84 #1835, 0.84 #685), 03qlv7 (0.85 #688, 0.84 #685, 0.84 #1122), 0l14v3 (0.84 #1835, 0.84 #685, 0.84 #1122), 0dq630k (0.84 #685, 0.84 #1122, 0.84 #682), 07brj (0.83 #1677, 0.80 #2569, 0.80 #2214), 0dwtp (0.83 #2030, 0.77 #2298, 0.77 #1211), 0dwt5 (0.82 #1546, 0.82 #1460, 0.78 #1190), 0l14md (0.82 #1489, 0.81 #1929, 0.80 #1395) >> Best rule #1213 for best value: >> intensional similarity = 21 >> extensional distance = 7 >> proper extension: 01vj9c; >> query: (?x8014, ?x3991) <- role(?x2747, ?x8014), role(?x2187, ?x8014), role(?x7869, ?x8014), role(?x3991, ?x8014), role(?x3239, ?x8014), role(?x1750, ?x8014), role(?x1495, ?x8014), ?x1750 = 02hnl, award_winner(?x342, ?x2747), role(?x3239, ?x3703), role(?x315, ?x3239), role(?x7869, ?x2157), role(?x3991, ?x5926), ?x5926 = 0cfdd, ?x3703 = 02dlh2, ?x2157 = 011_6p, role(?x1432, ?x3991), ?x1495 = 013y1f, ?x2187 = 01vsnff, role(?x1656, ?x3991), ?x1656 = 0l12d >> conf = 0.91 => this is the best rule for 2 predicted values *> Best rule #594 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 2 *> proper extension: 02sgy; *> query: (?x8014, ?x2764) <- role(?x11186, ?x8014), role(?x4206, ?x8014), role(?x2747, ?x8014), role(?x3991, ?x8014), role(?x3296, ?x8014), role(?x3239, ?x8014), role(?x2944, ?x8014), role(?x1750, ?x8014), ?x1750 = 02hnl, award_winner(?x342, ?x2747), role(?x2747, ?x2764), role(?x315, ?x3239), ?x3991 = 05842k, artist(?x3265, ?x2747), instrumentalists(?x3239, ?x5815), ?x11186 = 01304j, group(?x2944, ?x1751), award_nominee(?x4206, ?x1381), ?x3296 = 07_l6, profession(?x2747, ?x1614), place_of_birth(?x2747, ?x6253), role(?x1524, ?x2944) *> conf = 0.77 ranks of expected_values: 15 EVAL 0214km role 05r5c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 61.000 44.000 0.915 http://example.org/music/performance_role/track_performances./music/track_contribution/role #19203-04bdzg PRED entity: 04bdzg PRED relation: award_nominee! PRED expected values: 01ycbq => 113 concepts (60 used for prediction) PRED predicted values (max 10 best out of 717): 01ycbq (0.81 #37272, 0.81 #104809, 0.81 #11646), 04bdzg (0.57 #3769, 0.16 #139736, 0.15 #135079), 01wy5m (0.25 #1139, 0.16 #139736, 0.14 #3468), 02vntj (0.25 #980, 0.16 #139736, 0.14 #3309), 03zz8b (0.25 #1650, 0.16 #139736, 0.14 #3979), 05vsxz (0.25 #8, 0.16 #139736, 0.14 #2337), 016gr2 (0.25 #244, 0.16 #139736, 0.14 #2573), 024bbl (0.25 #1111, 0.16 #139736, 0.14 #3440), 048lv (0.25 #283, 0.16 #139736, 0.14 #2612), 031k24 (0.25 #1784, 0.16 #139736, 0.14 #4113) >> Best rule #37272 for best value: >> intensional similarity = 3 >> extensional distance = 395 >> proper extension: 01k5zk; 0hwqz; 01tnbn; 02ts3h; 01kgxf; 01933d; 036dyy; 0227vl; 01g969; >> query: (?x6242, ?x434) <- award(?x6242, ?x704), participant(?x1880, ?x6242), award_nominee(?x6242, ?x434) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bdzg award_nominee! 01ycbq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 60.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19202-0bxtg PRED entity: 0bxtg PRED relation: film PRED expected values: 04hwbq 0f4_2k => 146 concepts (127 used for prediction) PRED predicted values (max 10 best out of 1128): 0gmblvq (0.68 #54881, 0.65 #81439, 0.64 #97373), 015g28 (0.68 #54881, 0.65 #81439, 0.64 #97373), 02qjv1p (0.68 #54881, 0.65 #81439, 0.64 #97373), 03cv_gy (0.68 #54881, 0.65 #81439, 0.64 #97373), 0c3z0 (0.68 #54881, 0.65 #81439, 0.64 #97373), 016ky6 (0.68 #54881, 0.65 #81439, 0.64 #97373), 027qgy (0.68 #54881, 0.65 #81439, 0.64 #97373), 08bytj (0.68 #54881, 0.65 #81439, 0.64 #97373), 02kk_c (0.61 #72587, 0.57 #125699, 0.45 #118618), 03h3x5 (0.33 #23016, 0.15 #49570, 0.13 #44259) >> Best rule #54881 for best value: >> intensional similarity = 3 >> extensional distance = 247 >> proper extension: 02wb6yq; >> query: (?x496, ?x69) <- participant(?x496, ?x12041), participant(?x2963, ?x496), nominated_for(?x496, ?x69) >> conf = 0.68 => this is the best rule for 8 predicted values *> Best rule #5500 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 024bbl; 015pvh; *> query: (?x496, 04hwbq) <- award_nominee(?x8835, ?x496), ?x8835 = 01d0b1, award(?x496, ?x401) *> conf = 0.10 ranks of expected_values: 118, 353 EVAL 0bxtg film 0f4_2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 146.000 127.000 0.679 http://example.org/film/actor/film./film/performance/film EVAL 0bxtg film 04hwbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 146.000 127.000 0.679 http://example.org/film/actor/film./film/performance/film #19201-07s3vqk PRED entity: 07s3vqk PRED relation: profession PRED expected values: 01c72t => 141 concepts (123 used for prediction) PRED predicted values (max 10 best out of 79): 01c72t (0.75 #21, 0.63 #3360, 0.62 #3942), 0nbcg (0.56 #4095, 0.53 #1335, 0.47 #5401), 0dz3r (0.48 #4068, 0.45 #5084, 0.42 #437), 0dxtg (0.44 #3641, 0.39 #3061, 0.35 #3496), 0cbd2 (0.42 #5813, 0.39 #4217, 0.39 #4797), 039v1 (0.38 #4100, 0.28 #5406, 0.27 #1630), 01c8w0 (0.32 #1023, 0.29 #2331, 0.29 #1169), 01d_h8 (0.31 #3634, 0.30 #3054, 0.29 #8570), 0kyk (0.28 #13210, 0.27 #5834, 0.26 #4238), 05vyk (0.25 #17272, 0.25 #91, 0.17 #526) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 04k15; 01jrvr6; 082db; 03_f0; >> query: (?x215, 01c72t) <- music(?x6607, ?x215), influenced_by(?x1521, ?x215), place_of_death(?x215, ?x191) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07s3vqk profession 01c72t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 123.000 0.750 http://example.org/people/person/profession #19200-05br10 PRED entity: 05br10 PRED relation: award PRED expected values: 02x258x => 95 concepts (57 used for prediction) PRED predicted values (max 10 best out of 242): 0gq9h (0.38 #77, 0.16 #19455, 0.16 #10540), 02x258x (0.27 #2561, 0.21 #3371, 0.19 #1749), 0gqy2 (0.27 #4624, 0.16 #19455, 0.16 #10540), 09sb52 (0.26 #13821, 0.25 #6120, 0.25 #5310), 0gq_v (0.25 #22, 0.15 #19049, 0.14 #14591), 0gqyl (0.20 #4564, 0.15 #19049, 0.14 #14591), 0gr4k (0.18 #4492, 0.12 #33, 0.11 #4087), 02rdyk7 (0.18 #4145, 0.16 #3740, 0.15 #19049), 019f4v (0.18 #4526, 0.16 #19455, 0.16 #10540), 0gs9p (0.16 #19455, 0.16 #10540, 0.16 #15402) >> Best rule #77 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 051z6mv; >> query: (?x10704, 0gq9h) <- award(?x10704, ?x1243), nationality(?x10704, ?x1003), ?x1003 = 03gj2, gender(?x10704, ?x231) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2561 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 06t8b; *> query: (?x10704, 02x258x) <- cinematography(?x2914, ?x10704), nominated_for(?x2135, ?x2914), country(?x2914, ?x94), featured_film_locations(?x2914, ?x7412) *> conf = 0.27 ranks of expected_values: 2 EVAL 05br10 award 02x258x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 57.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19199-09c7w0 PRED entity: 09c7w0 PRED relation: country! PRED expected values: 01dq5z 01r3kd 09f2j 09v3hq_ 02htv6 01fkr_ => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 3): 02rr_z4 (0.33 #2, 0.04 #130, 0.04 #137), 01ygv2 (0.33 #1, 0.04 #129, 0.04 #136), 021q23 (0.07 #105, 0.05 #117, 0.03 #144) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 03h64; >> query: (?x94, 02rr_z4) <- nationality(?x51, ?x94), country(?x99, ?x94), country(?x54, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09c7w0 country! 01fkr_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 190.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/country EVAL 09c7w0 country! 02htv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 190.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/country EVAL 09c7w0 country! 09v3hq_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 190.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/country EVAL 09c7w0 country! 09f2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 190.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/country EVAL 09c7w0 country! 01r3kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 190.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/country EVAL 09c7w0 country! 01dq5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 190.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/country #19198-0l8z1 PRED entity: 0l8z1 PRED relation: ceremony PRED expected values: 059x66 0fzrtf 02glmx 0bzknt 0bvhz9 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 94): 0bvhz9 (0.89 #734, 0.75 #641, 0.38 #1013), 0fv89q (0.88 #636, 0.74 #729, 0.33 #357), 0fk0xk (0.84 #704, 0.69 #611, 0.34 #983), 0fzrtf (0.81 #600, 0.79 #693, 0.33 #321), 0c53vt (0.81 #629, 0.74 #722, 0.33 #350), 0fy6bh (0.81 #591, 0.74 #684, 0.33 #219), 0fz20l (0.81 #594, 0.68 #687, 0.33 #315), 0d__c3 (0.81 #638, 0.63 #731, 0.33 #359), 059x66 (0.79 #666, 0.75 #573, 0.34 #945), 02glmx (0.79 #706, 0.75 #613, 0.33 #334) >> Best rule #734 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 018wng; 0gr07; >> query: (?x1079, 0bvhz9) <- award_winner(?x1079, ?x84), award(?x925, ?x1079), ceremony(?x1079, ?x9899), ceremony(?x1079, ?x7589), award_winner(?x7589, ?x199), ?x9899 = 0c4hnm >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 9, 10, 14 EVAL 0l8z1 ceremony 0bvhz9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.895 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0l8z1 ceremony 0bzknt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 59.000 59.000 0.895 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0l8z1 ceremony 02glmx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 59.000 59.000 0.895 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0l8z1 ceremony 0fzrtf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 59.000 59.000 0.895 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0l8z1 ceremony 059x66 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 59.000 59.000 0.895 http://example.org/award/award_category/winners./award/award_honor/ceremony #19197-04bbv7 PRED entity: 04bbv7 PRED relation: actor! PRED expected values: 0dd6bf => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 31): 0dd6bf (0.57 #114, 0.19 #269, 0.18 #176), 016ztl (0.39 #295, 0.25 #357, 0.24 #171), 06cgf (0.33 #29, 0.06 #184, 0.04 #277), 02q3fdr (0.22 #201, 0.21 #294, 0.20 #232), 02gs6r (0.19 #445, 0.17 #73, 0.15 #352), 031f_m (0.17 #211, 0.17 #87, 0.15 #366), 0b60sq (0.17 #188, 0.16 #312, 0.15 #219), 05pyrb (0.15 #448, 0.15 #262, 0.14 #107), 0dh8v4 (0.14 #105, 0.12 #322, 0.11 #446), 07ng9k (0.14 #99, 0.12 #161, 0.11 #254) >> Best rule #114 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0814k3; 091n7z; 0678gl; >> query: (?x9269, 0dd6bf) <- actor(?x10187, ?x9269), profession(?x9269, ?x1032), category(?x9269, ?x134), ?x10187 = 05t0zfv >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bbv7 actor! 0dd6bf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.571 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #19196-03y0pn PRED entity: 03y0pn PRED relation: award PRED expected values: 0gr42 => 114 concepts (104 used for prediction) PRED predicted values (max 10 best out of 209): 0gr42 (0.67 #88, 0.31 #558, 0.18 #1262), 02hsq3m (0.50 #28, 0.36 #235, 0.27 #1409), 02g3ft (0.50 #67, 0.31 #537, 0.14 #2881), 0p9sw (0.36 #235, 0.33 #19, 0.27 #1409), 018wdw (0.36 #235, 0.33 #172, 0.27 #1409), 0gq_v (0.36 #235, 0.27 #1409, 0.25 #4688), 02r22gf (0.36 #235, 0.27 #1409, 0.25 #4688), 02r0csl (0.36 #235, 0.27 #1409, 0.25 #4688), 057xs89 (0.36 #235, 0.27 #1409, 0.25 #4688), 02g3v6 (0.36 #235, 0.27 #1409, 0.25 #4688) >> Best rule #88 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0bth54; 017gm7; 017jd9; 02dr9j; >> query: (?x7207, 0gr42) <- nominated_for(?x1983, ?x7207), nominated_for(?x484, ?x7207), ?x1983 = 04ktcgn, ?x484 = 0gq_v >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03y0pn award 0gr42 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 104.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19195-05f260 PRED entity: 05f260 PRED relation: film PRED expected values: 05dl1s => 108 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1750): 0gl3hr (0.71 #25534, 0.69 #11171, 0.61 #20745), 03mh_tp (0.50 #6833, 0.50 #5238, 0.40 #3643), 016fyc (0.50 #4834, 0.20 #6429, 0.20 #3239), 0299hs (0.40 #3690, 0.17 #5285, 0.10 #6880), 01rnly (0.40 #4591, 0.17 #6186, 0.10 #7781), 07ghq (0.40 #4472, 0.17 #6067, 0.10 #7662), 035s95 (0.35 #16262, 0.33 #13070, 0.30 #6686), 020fcn (0.33 #4955, 0.33 #167, 0.30 #6550), 05b6rdt (0.33 #5770, 0.30 #7365, 0.29 #8961), 01dvbd (0.33 #5232, 0.27 #21190, 0.24 #16403) >> Best rule #25534 for best value: >> intensional similarity = 6 >> extensional distance = 21 >> proper extension: 086k8; 030_1m; 017jv5; 03jvmp; 03mdt; 01gb54; 024rdh; 0gfmc_; >> query: (?x13497, ?x6243) <- film(?x13497, ?x10173), film(?x13497, ?x4300), child(?x9001, ?x13497), production_companies(?x6243, ?x13497), honored_for(?x4388, ?x4300), nominated_for(?x3879, ?x10173) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1521 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 09v3hq_; *> query: (?x13497, 05dl1s) <- industry(?x13497, ?x373), film(?x13497, ?x10173), film(?x13497, ?x6967), film(?x13497, ?x4865), ?x6967 = 0286vp, ?x4865 = 027rpym, ?x10173 = 01kqq7 *> conf = 0.33 ranks of expected_values: 19 EVAL 05f260 film 05dl1s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 108.000 40.000 0.709 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #19194-01sb5r PRED entity: 01sb5r PRED relation: profession PRED expected values: 04f2zj => 132 concepts (90 used for prediction) PRED predicted values (max 10 best out of 66): 0nbcg (0.63 #2495, 0.62 #1769, 0.60 #3950), 01c72t (0.46 #5837, 0.36 #4379, 0.35 #1326), 0fnpj (0.30 #782, 0.20 #3978, 0.20 #6018), 0dxtg (0.29 #12811, 0.27 #12086, 0.26 #12231), 01d_h8 (0.28 #12803, 0.28 #12078, 0.27 #11933), 0n1h (0.27 #3349, 0.27 #880, 0.25 #10), 09lbv (0.26 #1757, 0.16 #1322, 0.13 #162), 03gjzk (0.22 #2915, 0.21 #3497, 0.20 #12232), 0kyk (0.20 #172, 0.17 #607, 0.14 #317), 02jknp (0.20 #12805, 0.18 #12080, 0.16 #12660) >> Best rule #2495 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 02nfjp; >> query: (?x4140, 0nbcg) <- role(?x4140, ?x227), profession(?x4140, ?x2659), ?x2659 = 039v1 >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #963 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 05qhnq; 01693z; *> query: (?x4140, 04f2zj) <- role(?x4140, ?x227), profession(?x4140, ?x2659), origin(?x4140, ?x4090), ?x2659 = 039v1 *> conf = 0.14 ranks of expected_values: 12 EVAL 01sb5r profession 04f2zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 132.000 90.000 0.626 http://example.org/people/person/profession #19193-09qrn4 PRED entity: 09qrn4 PRED relation: award! PRED expected values: 02p65p 01hxs4 014zfs 0bg539 01pllx 018qql => 49 concepts (22 used for prediction) PRED predicted values (max 10 best out of 3011): 015pxr (0.81 #10033, 0.79 #30100, 0.78 #50172), 01nzz8 (0.81 #10033, 0.79 #30100, 0.78 #50172), 01vb403 (0.81 #10033, 0.79 #30100, 0.78 #50172), 02__7n (0.50 #2098, 0.38 #5441, 0.19 #70246), 025mb_ (0.50 #2593, 0.38 #5936, 0.16 #9279), 0pyww (0.50 #1391, 0.38 #4734, 0.15 #23410), 01rs5p (0.50 #2946, 0.25 #6289, 0.15 #23410), 01_j71 (0.38 #4287, 0.33 #944, 0.15 #23410), 0m66w (0.38 #5064, 0.33 #1721, 0.08 #8407), 05hrq4 (0.38 #5926, 0.17 #2583, 0.15 #23410) >> Best rule #10033 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 05q5t0b; 0h53c_5; 0262s1; >> query: (?x5235, ?x703) <- nominated_for(?x5235, ?x2293), program(?x201, ?x2293), award_winner(?x5235, ?x703) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #70246 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 203 *> proper extension: 0262zm; 0262yt; 040_9s0; 0265vt; *> query: (?x5235, ?x364) <- award(?x8163, ?x5235), award_nominee(?x364, ?x8163), influenced_by(?x2127, ?x8163) *> conf = 0.19 ranks of expected_values: 72, 101, 192, 198, 342, 1265 EVAL 09qrn4 award! 018qql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qrn4 award! 01pllx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qrn4 award! 0bg539 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qrn4 award! 014zfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qrn4 award! 01hxs4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qrn4 award! 02p65p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19192-0rvty PRED entity: 0rvty PRED relation: place PRED expected values: 0rvty => 77 concepts (43 used for prediction) PRED predicted values (max 10 best out of 77): 0nzw2 (0.05 #14986), 013yq (0.04 #45, 0.03 #561, 0.02 #1077), 0rt80 (0.04 #492), 0rw2x (0.04 #440), 0rwq6 (0.04 #439), 0rwgm (0.04 #421), 0rv97 (0.04 #233), 01ktz1 (0.04 #46), 094jv (0.03 #552, 0.02 #1068, 0.01 #1583), 02dtg (0.03 #525, 0.02 #1041, 0.01 #1556) >> Best rule #14986 for best value: >> intensional similarity = 3 >> extensional distance = 281 >> proper extension: 0mn0v; >> query: (?x6966, ?x12545) <- source(?x6966, ?x958), county(?x6966, ?x12545), ?x958 = 0jbk9 >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0rvty place 0rvty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 43.000 0.046 http://example.org/location/hud_county_place/place #19191-01qd_r PRED entity: 01qd_r PRED relation: major_field_of_study PRED expected values: 04rlf => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 104): 02lp1 (0.69 #451, 0.67 #561, 0.66 #1001), 05qjt (0.50 #557, 0.47 #777, 0.46 #447), 062z7 (0.49 #1013, 0.49 #793, 0.45 #133), 03g3w (0.47 #572, 0.44 #792, 0.43 #462), 0g26h (0.46 #1134, 0.43 #1024, 0.43 #34), 02_7t (0.45 #165, 0.41 #715, 0.39 #385), 037mh8 (0.44 #607, 0.37 #497, 0.35 #827), 041y2 (0.43 #67, 0.42 #397, 0.32 #727), 06ms6 (0.42 #566, 0.37 #786, 0.37 #456), 03qsdpk (0.39 #259, 0.36 #149, 0.26 #809) >> Best rule #451 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0d06m5; 0d05fv; >> query: (?x7660, 02lp1) <- list(?x7660, ?x2197), organization(?x7660, ?x5487), category(?x7660, ?x134) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2641 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 136 *> proper extension: 03bwzr4; 013719; *> query: (?x7660, ?x254) <- major_field_of_study(?x7660, ?x2314), major_field_of_study(?x7660, ?x1668), ?x1668 = 01mkq, major_field_of_study(?x254, ?x2314) *> conf = 0.20 ranks of expected_values: 35 EVAL 01qd_r major_field_of_study 04rlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 89.000 89.000 0.686 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19190-02002f PRED entity: 02002f PRED relation: languages_spoken! PRED expected values: 078ds => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 71): 07hwkr (0.52 #655, 0.52 #870, 0.50 #226), 03w9bjf (0.50 #120, 0.35 #548, 0.33 #49), 02vsw1 (0.38 #188, 0.29 #403, 0.26 #474), 04czx7 (0.33 #69, 0.31 #354, 0.25 #140), 078vc (0.33 #43, 0.25 #542, 0.25 #114), 059_w (0.33 #27, 0.25 #169, 0.25 #98), 0d2by (0.33 #30, 0.25 #101, 0.23 #315), 071x0k (0.33 #8, 0.25 #79, 0.21 #365), 0bbz66j (0.33 #45, 0.25 #116, 0.19 #616), 09zyn5 (0.33 #67, 0.25 #138, 0.17 #1072) >> Best rule #655 for best value: >> intensional similarity = 12 >> extensional distance = 19 >> proper extension: 06mp7; 01gp_d; >> query: (?x8419, 07hwkr) <- countries_spoken_in(?x8419, ?x8420), language(?x8107, ?x8419), contains(?x6304, ?x8420), country(?x1967, ?x8420), geographic_distribution(?x9347, ?x8420), contains(?x8420, ?x8838), currency(?x8420, ?x170), organization(?x8420, ?x127), participating_countries(?x1931, ?x8420), jurisdiction_of_office(?x346, ?x8420), administrative_area_type(?x8420, ?x2792), ?x1967 = 01cgz >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x8419, 078ds) <- countries_spoken_in(?x8419, ?x8420), language(?x8107, ?x8419), ?x8420 = 06m_5, ?x8107 = 0k_9j, languages_spoken(?x14336, ?x8419), ?x14336 = 0bhsnb *> conf = 0.33 ranks of expected_values: 23 EVAL 02002f languages_spoken! 078ds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 38.000 38.000 0.524 http://example.org/people/ethnicity/languages_spoken #19189-05vk_d PRED entity: 05vk_d PRED relation: location PRED expected values: 030qb3t => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 191): 030qb3t (0.44 #1691, 0.31 #2495, 0.30 #8123), 02_286 (0.21 #7273, 0.17 #28184, 0.17 #31401), 0d6lp (0.20 #168, 0.06 #1776, 0.03 #2580), 02jx1 (0.12 #1679, 0.10 #2483, 0.10 #71), 0dclg (0.12 #1725, 0.06 #3333, 0.04 #6549), 0cc56 (0.10 #57, 0.09 #3273, 0.08 #4881), 0c_m3 (0.10 #271, 0.06 #1879, 0.03 #2683), 02cft (0.10 #307, 0.06 #1915, 0.03 #2719), 0ctw_b (0.10 #51, 0.06 #1659, 0.03 #2463), 0c4kv (0.10 #645, 0.06 #2253, 0.03 #3057) >> Best rule #1691 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0147dk; 03lt8g; 014zfs; 03rl84; 0170s4; 0lx2l; 0161c2; 0484q; 0n839; >> query: (?x8638, 030qb3t) <- type_of_union(?x8638, ?x566), notable_people_with_this_condition(?x8318, ?x8638), ?x8318 = 0h99n >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05vk_d location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.438 http://example.org/people/person/places_lived./people/place_lived/location #19188-01699 PRED entity: 01699 PRED relation: adjoins! PRED expected values: 07f5x => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 323): 07f5x (0.82 #36664, 0.82 #48387, 0.82 #42136), 01699 (0.21 #49168, 0.21 #48388, 0.21 #49950), 01p1b (0.21 #49168, 0.21 #48388, 0.21 #49950), 03676 (0.21 #49168, 0.21 #48388, 0.21 #49950), 05cgv (0.21 #49168, 0.21 #48388, 0.21 #49950), 0h3y (0.21 #49168, 0.21 #48388, 0.21 #49950), 06srk (0.21 #49168, 0.21 #48388, 0.09 #1157), 04vjh (0.21 #49168, 0.21 #48388, 0.03 #500), 04gqr (0.21 #49950, 0.21 #39010, 0.11 #987), 04hzj (0.21 #49950, 0.21 #39010, 0.04 #1093) >> Best rule #36664 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 01914; 0f4y_; 0ldff; 017wh; 0n5_g; 0nm8n; 0drr3; 0vh3; 0n4z2; 01zlx; ... >> query: (?x6431, ?x8948) <- administrative_parent(?x6431, ?x551), adjoins(?x2051, ?x6431), adjoins(?x6431, ?x8948) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01699 adjoins! 07f5x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.822 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #19187-0244r8 PRED entity: 0244r8 PRED relation: award PRED expected values: 02qvyrt => 133 concepts (108 used for prediction) PRED predicted values (max 10 best out of 290): 0l8z1 (0.55 #4924, 0.45 #8569, 0.45 #9379), 02qvyrt (0.45 #8632, 0.43 #4177, 0.42 #4987), 01c99j (0.45 #5897, 0.13 #18859, 0.11 #19669), 025m8y (0.42 #4959, 0.40 #6984, 0.40 #4149), 0gqz2 (0.40 #8991, 0.40 #4131, 0.37 #13041), 01by1l (0.38 #5782, 0.36 #18744, 0.32 #17528), 09sb52 (0.37 #18268, 0.34 #32852, 0.33 #19078), 01bgqh (0.36 #5713, 0.28 #18675, 0.25 #2473), 054ks3 (0.36 #7432, 0.34 #9052, 0.34 #4192), 02f6ym (0.34 #5929, 0.11 #18891, 0.09 #22942) >> Best rule #4924 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 0c_mvb; >> query: (?x1489, 0l8z1) <- artists(?x4910, ?x1489), award_winner(?x1489, ?x3069), ?x4910 = 017_qw, gender(?x1489, ?x514) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #8632 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 0b6yp2; *> query: (?x1489, 02qvyrt) <- music(?x1077, ?x1489), executive_produced_by(?x1077, ?x1533), award(?x1489, ?x1443), award_winner(?x1077, ?x262) *> conf = 0.45 ranks of expected_values: 2 EVAL 0244r8 award 02qvyrt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 108.000 0.550 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19186-0143hl PRED entity: 0143hl PRED relation: contains! PRED expected values: 02jx1 => 196 concepts (90 used for prediction) PRED predicted values (max 10 best out of 282): 02jx1 (0.87 #60966, 0.74 #31461, 0.67 #2690), 09c7w0 (0.85 #38550, 0.81 #40342, 0.81 #44825), 0dmy0 (0.81 #40339, 0.80 #35855, 0.80 #36752), 0bdg5 (0.79 #52896, 0.78 #46617, 0.78 #51100), 0978r (0.47 #1999, 0.27 #206, 0.20 #2896), 04jpl (0.35 #11676, 0.15 #31397, 0.13 #6296), 01w0v (0.27 #2000, 0.13 #2897, 0.09 #207), 0d060g (0.24 #9873, 0.23 #7183, 0.18 #8976), 030qb3t (0.23 #8167, 0.03 #53891, 0.03 #44922), 05l5n (0.22 #10877, 0.19 #13567, 0.18 #121) >> Best rule #60966 for best value: >> intensional similarity = 5 >> extensional distance = 315 >> proper extension: 015zyd; 01rtm4; 01jssp; 05zl0; 01j_5k; 07x4c; 019pwv; 02mp0g; 02m0sc; 02f4s3; ... >> query: (?x4104, ?x1310) <- state_province_region(?x4104, ?x10272), institution(?x865, ?x4104), contains(?x512, ?x4104), contains(?x10272, ?x9502), administrative_parent(?x10272, ?x1310) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0143hl contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 90.000 0.868 http://example.org/location/location/contains #19185-07m77x PRED entity: 07m77x PRED relation: award PRED expected values: 09qvf4 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 267): 0cqhk0 (0.76 #849, 0.73 #812, 0.73 #443), 09qs08 (0.73 #812, 0.71 #406, 0.71 #17427), 09sb52 (0.36 #8957, 0.34 #14629, 0.33 #7337), 0ck27z (0.32 #6984, 0.31 #5768, 0.31 #6579), 05pcn59 (0.17 #1705, 0.11 #8188, 0.11 #7783), 01by1l (0.17 #4168, 0.10 #15918, 0.10 #16729), 09qj50 (0.15 #27964, 0.15 #16211, 0.15 #23102), 09qrn4 (0.15 #27964, 0.15 #16211, 0.15 #23102), 09qv3c (0.15 #27964, 0.15 #23102, 0.12 #38907), 02x4x18 (0.15 #16211, 0.13 #39719, 0.12 #38907) >> Best rule #849 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 0783m_; 07sgfsl; >> query: (?x8896, 0cqhk0) <- award_winner(?x1796, ?x8896), ?x1796 = 07sgfvl, award_winner(?x8896, ?x1825) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #38907 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2257 *> proper extension: 09mfvx; 0fvppk; *> query: (?x8896, ?x678) <- nominated_for(?x8896, ?x5060), nominated_for(?x678, ?x5060) *> conf = 0.12 ranks of expected_values: 30 EVAL 07m77x award 09qvf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 111.000 111.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19184-0gm2_0 PRED entity: 0gm2_0 PRED relation: film! PRED expected values: 05zbm4 => 103 concepts (59 used for prediction) PRED predicted values (max 10 best out of 687): 03h304l (0.50 #45814, 0.49 #122857, 0.48 #29154), 0d6484 (0.50 #45814, 0.49 #122857, 0.48 #29154), 02jxmr (0.50 #45814, 0.48 #29154, 0.47 #45813), 027z0pl (0.50 #45814, 0.48 #29154, 0.47 #45813), 0jz9f (0.50 #45814, 0.48 #29154, 0.47 #45813), 01nm3s (0.25 #689, 0.08 #87460, 0.03 #4852), 0fby2t (0.25 #754, 0.08 #87460, 0.03 #9082), 04t2l2 (0.25 #27, 0.08 #87460, 0.03 #8355), 055c8 (0.25 #542, 0.08 #87460, 0.03 #6787), 07y8l9 (0.25 #973, 0.08 #87460, 0.01 #17631) >> Best rule #45814 for best value: >> intensional similarity = 4 >> extensional distance = 523 >> proper extension: 0cw3yd; 07xvf; 011ywj; >> query: (?x9744, ?x9743) <- film_crew_role(?x9744, ?x1171), nominated_for(?x9743, ?x9744), ?x1171 = 09vw2b7, award_nominee(?x9743, ?x541) >> conf = 0.50 => this is the best rule for 5 predicted values *> Best rule #4314 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 0j43swk; *> query: (?x9744, 05zbm4) <- language(?x9744, ?x254), nominated_for(?x166, ?x9744), nominated_for(?x899, ?x9744), ?x899 = 02x1dht *> conf = 0.02 ranks of expected_values: 477 EVAL 0gm2_0 film! 05zbm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 103.000 59.000 0.498 http://example.org/film/actor/film./film/performance/film #19183-0gjk1d PRED entity: 0gjk1d PRED relation: language PRED expected values: 02h40lc => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 34): 02h40lc (0.93 #356, 0.92 #713, 0.91 #890), 064_8sq (0.22 #258, 0.18 #1029, 0.17 #1208), 03_9r (0.17 #128, 0.11 #246, 0.05 #602), 0349s (0.17 #163, 0.11 #281, 0.03 #399), 06nm1 (0.13 #663, 0.13 #544, 0.12 #188), 02bjrlw (0.12 #178, 0.10 #296, 0.09 #534), 06b_j (0.12 #200, 0.10 #318, 0.08 #734), 04306rv (0.12 #597, 0.11 #1369, 0.11 #1012), 05zjd (0.11 #262, 0.07 #380, 0.01 #914), 032f6 (0.11 #292, 0.02 #648, 0.01 #1597) >> Best rule #356 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 03mh_tp; 0b7l4x; 02x3y41; >> query: (?x1209, 02h40lc) <- film(?x1208, ?x1209), production_companies(?x1209, ?x6554), ?x6554 = 02j_j0 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gjk1d language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.925 http://example.org/film/film/language #19182-0jpdn PRED entity: 0jpdn PRED relation: location PRED expected values: 030qb3t => 128 concepts (94 used for prediction) PRED predicted values (max 10 best out of 291): 030qb3t (0.28 #61797, 0.27 #28129, 0.23 #11299), 059rby (0.26 #11233, 0.25 #4022, 0.21 #16040), 0r00l (0.25 #603, 0.14 #2206, 0.08 #6211), 0r0m6 (0.25 #216, 0.08 #5824, 0.07 #19445), 018lc_ (0.25 #797, 0.08 #6405, 0.03 #15218), 04jpl (0.20 #10432, 0.19 #59326, 0.17 #58525), 01btyw (0.17 #1219, 0.14 #3622, 0.12 #4423), 027l4q (0.17 #1297, 0.12 #4501, 0.03 #11712), 0rk71 (0.17 #1302, 0.12 #4506, 0.03 #12518), 0qpqn (0.17 #1252, 0.12 #4456, 0.03 #12468) >> Best rule #61797 for best value: >> intensional similarity = 4 >> extensional distance = 779 >> proper extension: 02wrhj; >> query: (?x8862, 030qb3t) <- location(?x8862, ?x1767), contains(?x1767, ?x14070), film(?x8862, ?x3093), time_zones(?x14070, ?x2674) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jpdn location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 94.000 0.280 http://example.org/people/person/places_lived./people/place_lived/location #19181-01f62 PRED entity: 01f62 PRED relation: category PRED expected values: 08mbj5d => 228 concepts (228 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.75 #175, 0.75 #132, 0.75 #177) >> Best rule #175 for best value: >> intensional similarity = 3 >> extensional distance = 935 >> proper extension: 0fcgd; >> query: (?x1649, 08mbj5d) <- contains(?x2152, ?x1649), partially_contains(?x1144, ?x2152), contains(?x6304, ?x2152) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f62 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 228.000 228.000 0.751 http://example.org/common/topic/webpage./common/webpage/category #19180-02ztjwg PRED entity: 02ztjwg PRED relation: language! PRED expected values: 01cmp9 => 33 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1874): 0gh6j94 (0.84 #1728, 0.50 #3004, 0.42 #8190), 0g5qmbz (0.84 #1728, 0.50 #6685, 0.42 #8413), 03twd6 (0.84 #1728, 0.50 #1943, 0.40 #3672), 01ffx4 (0.84 #1728, 0.50 #2225, 0.40 #3954), 02yvct (0.84 #1728, 0.50 #2064, 0.40 #3793), 026p_bs (0.84 #1728, 0.50 #1809, 0.40 #3538), 0sxkh (0.84 #1728, 0.50 #2419, 0.40 #4148), 011yrp (0.84 #1728, 0.50 #1762, 0.40 #3491), 0gwjw0c (0.84 #1728, 0.50 #2891, 0.40 #4620), 0jdr0 (0.84 #1728, 0.50 #3211, 0.40 #4940) >> Best rule #1728 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 02h40lc; >> query: (?x8650, ?x124) <- language(?x6806, ?x8650), language(?x5052, ?x8650), ?x6806 = 02q7yfq, countries_spoken_in(?x8650, ?x1471), ?x5052 = 04yg13l, country(?x1121, ?x1471), film_release_region(?x124, ?x1471), ?x1121 = 0bynt, olympics(?x1471, ?x418), nationality(?x558, ?x1471) >> conf = 0.84 => this is the best rule for 384 predicted values *> Best rule #1003 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x8650, 01cmp9) <- language(?x6806, ?x8650), language(?x5052, ?x8650), ?x6806 = 02q7yfq, countries_spoken_in(?x8650, ?x1471), ?x5052 = 04yg13l, country(?x1121, ?x1471), film_release_region(?x124, ?x1471), ?x1121 = 0bynt, olympics(?x1471, ?x418), nationality(?x558, ?x1471) *> conf = 0.33 ranks of expected_values: 1433 EVAL 02ztjwg language! 01cmp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 20.000 0.836 http://example.org/film/film/language #19179-0pqz3 PRED entity: 0pqz3 PRED relation: place PRED expected values: 0pqz3 => 141 concepts (59 used for prediction) PRED predicted values (max 10 best out of 13): 0d9y6 (0.25 #129, 0.20 #644, 0.17 #1159), 0tgcy (0.25 #277, 0.20 #792, 0.17 #1307), 0tct_ (0.20 #627, 0.10 #1657, 0.07 #2172), 0tj4y (0.17 #1167, 0.10 #1682, 0.07 #2197), 0th3k (0.17 #1473, 0.07 #2503, 0.01 #13929), 0fvvg (0.08 #6186, 0.07 #2456, 0.02 #14447), 0pqz3 (0.01 #13929, 0.01 #14446), 0225bv (0.01 #13929, 0.01 #14446), 0nn83 (0.01 #13929, 0.01 #14446), 038czx (0.01 #13929, 0.01 #14446) >> Best rule #129 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0d9y6; 0tgcy; >> query: (?x14081, 0d9y6) <- contains(?x4061, ?x14081), place_of_birth(?x2444, ?x14081), ?x4061 = 0498y, award_winner(?x485, ?x2444) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #13929 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 333 *> proper extension: 0wp9b; 0wq3z; 01m9f1; 0ws0h; 0qkyj; 0yx74; 043yj; 0wq36; 0wqwj; *> query: (?x14081, ?x2175) <- contains(?x4061, ?x14081), place_of_birth(?x2444, ?x14081), district_represented(?x176, ?x4061), contains(?x4061, ?x2175) *> conf = 0.01 ranks of expected_values: 7 EVAL 0pqz3 place 0pqz3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 141.000 59.000 0.250 http://example.org/location/hud_county_place/place #19178-01wmcbg PRED entity: 01wmcbg PRED relation: nominated_for PRED expected values: 0j_t1 => 102 concepts (60 used for prediction) PRED predicted values (max 10 best out of 357): 0pd57 (0.80 #27594, 0.79 #45448, 0.79 #35712), 05cj_j (0.47 #1623, 0.29 #9739, 0.29 #35711), 0j_t1 (0.47 #1623, 0.29 #9739, 0.29 #35711), 05z7c (0.33 #309, 0.06 #34087, 0.01 #3555), 0jwvf (0.33 #925, 0.06 #34087), 02r_pp (0.33 #807, 0.06 #34087), 02jr6k (0.33 #631, 0.06 #34087), 0k5g9 (0.33 #401, 0.06 #34087), 075cph (0.33 #350, 0.06 #34087), 0k7tq (0.33 #1065) >> Best rule #27594 for best value: >> intensional similarity = 3 >> extensional distance = 751 >> proper extension: 08wr3kg; 02cm2m; 0cj36c; >> query: (?x12809, ?x4179) <- award_nominee(?x8871, ?x12809), award_winner(?x4179, ?x12809), actor(?x3303, ?x8871) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1623 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0j_c; *> query: (?x12809, ?x2719) <- award_nominee(?x8871, ?x12809), film(?x12809, ?x2719), film(?x12809, ?x1708), ?x1708 = 05cj_j *> conf = 0.47 ranks of expected_values: 3 EVAL 01wmcbg nominated_for 0j_t1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 60.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #19177-0137g1 PRED entity: 0137g1 PRED relation: profession PRED expected values: 09jwl 0fnpj => 146 concepts (72 used for prediction) PRED predicted values (max 10 best out of 70): 09jwl (0.84 #1033, 0.82 #2485, 0.81 #743), 01c72t (0.60 #22, 0.45 #457, 0.43 #167), 01d_h8 (0.44 #294, 0.37 #4215, 0.32 #2907), 0n1h (0.43 #155, 0.40 #10, 0.36 #445), 0dxtg (0.32 #2915, 0.29 #7283, 0.24 #7428), 09lbv (0.27 #744, 0.11 #308, 0.09 #889), 0fnpj (0.24 #1218, 0.20 #57, 0.19 #783), 02jknp (0.22 #296, 0.22 #2909, 0.20 #6), 05vyk (0.22 #1252, 0.11 #3866, 0.10 #4593), 0cbd2 (0.22 #2908, 0.20 #5, 0.16 #7276) >> Best rule #1033 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 01rw116; >> query: (?x2784, 09jwl) <- people(?x913, ?x2784), nationality(?x2784, ?x94), profession(?x2784, ?x2659), ?x2659 = 039v1 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 0137g1 profession 0fnpj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 146.000 72.000 0.840 http://example.org/people/person/profession EVAL 0137g1 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 72.000 0.840 http://example.org/people/person/profession #19176-025v3k PRED entity: 025v3k PRED relation: company! PRED expected values: 04z0g => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 96): 03gkn5 (0.09 #306, 0.08 #1287, 0.07 #2263), 0x3r3 (0.06 #1093, 0.06 #2073, 0.06 #1341), 0nk72 (0.04 #408, 0.04 #2365, 0.04 #1141), 02sdx (0.04 #458, 0.04 #1439, 0.03 #1927), 01dvtx (0.04 #317, 0.04 #1298, 0.03 #1786), 04z0g (0.04 #359, 0.04 #1340, 0.03 #1828), 01bpn (0.04 #326, 0.04 #1307, 0.03 #1795), 06crk (0.04 #372, 0.04 #1353, 0.03 #2085), 0d05fv (0.04 #333, 0.04 #1314, 0.03 #2290), 0d4jl (0.04 #1035, 0.03 #2015, 0.03 #2259) >> Best rule #306 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 045c7b; 0cv_2; 02z_b; >> query: (?x3948, 03gkn5) <- state_province_region(?x3948, ?x1274), citytown(?x3948, ?x5771), organization(?x3948, ?x5487) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #359 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 045c7b; 0cv_2; 02z_b; *> query: (?x3948, 04z0g) <- state_province_region(?x3948, ?x1274), citytown(?x3948, ?x5771), organization(?x3948, ?x5487) *> conf = 0.04 ranks of expected_values: 6 EVAL 025v3k company! 04z0g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 111.000 111.000 0.089 http://example.org/people/person/employment_history./business/employment_tenure/company #19175-03tps5 PRED entity: 03tps5 PRED relation: prequel! PRED expected values: 09g8vhw => 90 concepts (51 used for prediction) PRED predicted values (max 10 best out of 50): 06ztvyx (0.08 #593), 033fqh (0.02 #1081, 0.02 #1262), 03nfnx (0.02 #1400, 0.02 #1580, 0.01 #858), 05nlx4 (0.01 #841, 0.01 #1021, 0.01 #1202), 013q07 (0.01 #767, 0.01 #947, 0.01 #1128), 09v8clw (0.01 #900, 0.01 #1080), 0315rp (0.01 #863), 02mc5v (0.01 #857), 031hcx (0.01 #844), 03y0pn (0.01 #842) >> Best rule #593 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 09fb5; >> query: (?x4409, 06ztvyx) <- nominated_for(?x4850, ?x4409), nominated_for(?x398, ?x4409), ?x398 = 0bl2g, award(?x4850, ?x1079) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03tps5 prequel! 09g8vhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 51.000 0.083 http://example.org/film/film/prequel #19174-0cqhmg PRED entity: 0cqhmg PRED relation: award! PRED expected values: 01dy7j 03q45x => 53 concepts (16 used for prediction) PRED predicted values (max 10 best out of 3050): 0pz7h (0.80 #33592, 0.79 #33591, 0.77 #13434), 030znt (0.50 #7042, 0.50 #326, 0.25 #10400), 01rs5p (0.50 #2956, 0.33 #9672, 0.20 #6314), 015c2f (0.50 #768, 0.20 #4126, 0.17 #10842), 01pcdn (0.50 #1381, 0.20 #4739, 0.17 #8097), 01qr1_ (0.50 #977, 0.20 #4335, 0.17 #7693), 01x6jd (0.50 #3181, 0.20 #6539, 0.17 #9897), 07z1_q (0.50 #895, 0.20 #4253, 0.17 #7611), 01dbgw (0.50 #9916, 0.08 #23354, 0.08 #26714), 01dy7j (0.40 #4174, 0.25 #816, 0.23 #17611) >> Best rule #33592 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 09v7wsg; >> query: (?x11179, ?x4816) <- nominated_for(?x11179, ?x631), ceremony(?x11179, ?x873), award_winner(?x11179, ?x4816), award(?x4816, ?x154) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #4174 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 05b4l5x; *> query: (?x11179, 01dy7j) <- nominated_for(?x11179, ?x4588), category(?x4588, ?x134), award(?x444, ?x11179), ?x444 = 01dw4q *> conf = 0.40 ranks of expected_values: 10, 278 EVAL 0cqhmg award! 03q45x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 53.000 16.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhmg award! 01dy7j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 53.000 16.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19173-09td7p PRED entity: 09td7p PRED relation: nominated_for PRED expected values: 09z2b7 09gq0x5 0g5879y 011ykb 04jplwp => 42 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1450): 05v38p (0.77 #7710, 0.74 #3083, 0.68 #23132), 09p0ct (0.77 #7710, 0.74 #3083, 0.68 #23132), 098s2w (0.77 #7710, 0.74 #3083, 0.68 #23132), 02q7fl9 (0.77 #7710, 0.74 #3083, 0.68 #23132), 09gq0x5 (0.64 #3327, 0.62 #1786, 0.31 #4869), 019vhk (0.58 #1936, 0.55 #3477, 0.23 #5019), 011yxg (0.55 #3120, 0.54 #1579, 0.20 #6204), 011yl_ (0.55 #3589, 0.42 #2048, 0.23 #5131), 017gl1 (0.54 #1668, 0.48 #3209, 0.30 #4751), 03hmt9b (0.54 #2113, 0.45 #3654, 0.25 #5196) >> Best rule #7710 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 06196; >> query: (?x2257, ?x385) <- award_winner(?x2257, ?x548), award(?x91, ?x2257), ceremony(?x2257, ?x873), award(?x385, ?x2257) >> conf = 0.77 => this is the best rule for 4 predicted values *> Best rule #3327 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 02qyp19; 027dtxw; 02r0csl; 040njc; 0gq_v; 0p9sw; 09qwmm; 02r22gf; 04dn09n; 02x1dht; ... *> query: (?x2257, 09gq0x5) <- award_winner(?x2257, ?x548), award(?x91, ?x2257), nominated_for(?x2257, ?x144), ?x144 = 0m313 *> conf = 0.64 ranks of expected_values: 5, 159, 160, 267, 388 EVAL 09td7p nominated_for 04jplwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 42.000 20.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09td7p nominated_for 011ykb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 20.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09td7p nominated_for 0g5879y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 20.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09td7p nominated_for 09gq0x5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 42.000 20.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09td7p nominated_for 09z2b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 42.000 20.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19172-027qgy PRED entity: 027qgy PRED relation: genre PRED expected values: 0219x_ 03p5xs => 83 concepts (67 used for prediction) PRED predicted values (max 10 best out of 90): 01jfsb (0.50 #130, 0.36 #1201, 0.36 #1082), 02kdv5l (0.39 #121, 0.32 #1192, 0.30 #1073), 06n90 (0.36 #131, 0.24 #250, 0.16 #7631), 01hmnh (0.28 #2873, 0.25 #135, 0.19 #730), 03k9fj (0.28 #7629, 0.25 #129, 0.25 #724), 01t_vv (0.25 #648, 0.13 #886, 0.11 #1005), 04xvlr (0.22 #953, 0.19 #834, 0.19 #3096), 0lsxr (0.21 #4888, 0.18 #2507, 0.18 #245), 060__y (0.21 #967, 0.20 #848, 0.18 #372), 03npn (0.18 #124, 0.07 #1791, 0.07 #2267) >> Best rule #130 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 09sh8k; 0gtv7pk; 0401sg; 01qb5d; 0cz8mkh; 02r79_h; 072x7s; 07f_7h; 05zy2cy; 0dr3sl; ... >> query: (?x238, 01jfsb) <- country(?x238, ?x279), production_companies(?x238, ?x2246), language(?x238, ?x254), film(?x2841, ?x238), ?x279 = 0d060g >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #382 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 0b1y_2; 03hkch7; 0284b56; 0h95927; 04b2qn; *> query: (?x238, 0219x_) <- nominated_for(?x12041, ?x238), nominated_for(?x995, ?x238), place_of_birth(?x12041, ?x1523), ?x995 = 099tbz *> conf = 0.14 ranks of expected_values: 15, 50 EVAL 027qgy genre 03p5xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 83.000 67.000 0.500 http://example.org/film/film/genre EVAL 027qgy genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 83.000 67.000 0.500 http://example.org/film/film/genre #19171-01sbv9 PRED entity: 01sbv9 PRED relation: language PRED expected values: 02h40lc => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 32): 02h40lc (0.96 #1215, 0.96 #3768, 0.95 #1105), 02bjrlw (0.16 #56, 0.10 #221, 0.08 #386), 06b_j (0.12 #240, 0.07 #20, 0.06 #790), 03_9r (0.11 #64, 0.07 #9, 0.06 #614), 0653m (0.07 #10, 0.06 #175, 0.05 #65), 012w70 (0.07 #11, 0.03 #231, 0.03 #1059), 02hwyss (0.07 #38, 0.01 #148, 0.01 #920), 04h9h (0.05 #259, 0.05 #94, 0.03 #424), 03k50 (0.05 #63, 0.04 #283, 0.02 #1166), 01jb8r (0.05 #105) >> Best rule #1215 for best value: >> intensional similarity = 4 >> extensional distance = 553 >> proper extension: 016kz1; 080dfr7; 0564x; 072hx4; >> query: (?x10192, 02h40lc) <- language(?x10192, ?x6753), award_winner(?x10192, ?x1835), production_companies(?x10192, ?x4564), service_language(?x127, ?x6753) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sbv9 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.962 http://example.org/film/film/language #19170-06ybb1 PRED entity: 06ybb1 PRED relation: nominated_for! PRED expected values: 04wp2p => 103 concepts (55 used for prediction) PRED predicted values (max 10 best out of 828): 04dyqk (0.60 #100468), 03gyh_z (0.42 #16353), 086k8 (0.24 #7066, 0.14 #11738, 0.11 #119162), 03_gd (0.21 #51398, 0.18 #95794, 0.17 #105141), 023361 (0.21 #51398, 0.12 #8777, 0.10 #4105), 03xmy1 (0.21 #51398, 0.09 #5050, 0.03 #16731), 0x3b7 (0.18 #95794, 0.17 #105141, 0.17 #49061), 0kftt (0.18 #95794, 0.17 #105141, 0.17 #49061), 02pzc4 (0.18 #95794, 0.17 #105141, 0.17 #49061), 015882 (0.18 #95794, 0.17 #105141, 0.17 #49061) >> Best rule #100468 for best value: >> intensional similarity = 3 >> extensional distance = 666 >> proper extension: 0cf8qb; >> query: (?x2165, ?x11573) <- language(?x2165, ?x254), nominated_for(?x574, ?x2165), film(?x11573, ?x2165) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #119162 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 912 *> proper extension: 027qgy; 02rrfzf; 03t95n; 04g73n; *> query: (?x2165, ?x133) <- award_winner(?x2165, ?x8799), profession(?x8799, ?x131), award_nominee(?x133, ?x8799) *> conf = 0.11 ranks of expected_values: 22 EVAL 06ybb1 nominated_for! 04wp2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 103.000 55.000 0.601 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #19169-0gw2w PRED entity: 0gw2w PRED relation: time_zones PRED expected values: 02llzg => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 10): 02llzg (0.65 #30, 0.40 #43, 0.38 #17), 02hcv8 (0.23 #107, 0.22 #120, 0.21 #628), 02lcqs (0.18 #161, 0.17 #83, 0.15 #148), 02fqwt (0.11 #105, 0.10 #118, 0.09 #209), 03bdv (0.07 #71, 0.06 #136, 0.05 #97), 02hczc (0.04 #210, 0.04 #275, 0.04 #288), 03plfd (0.03 #62, 0.02 #140, 0.01 #975), 052vwh (0.03 #64), 0gsrz4 (0.02 #60), 042g7t (0.02 #63) >> Best rule #30 for best value: >> intensional similarity = 6 >> extensional distance = 32 >> proper extension: 096g3; 04jr87; 02bbyw; 0bwfn; 0g7yx; 0c630; 0ggyr; 01tsq8; 01n43d; 01fxg8; ... >> query: (?x13757, 02llzg) <- category(?x13757, ?x134), contains(?x9274, ?x13757), contains(?x205, ?x13757), ?x134 = 08mbj5d, adjoins(?x9274, ?x6408), ?x205 = 03rjj >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gw2w time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.647 http://example.org/location/location/time_zones #19168-059lwy PRED entity: 059lwy PRED relation: written_by PRED expected values: 06kbb6 => 107 concepts (57 used for prediction) PRED predicted values (max 10 best out of 107): 0gn30 (0.50 #504, 0.40 #840, 0.33 #2185), 03_gd (0.25 #357, 0.20 #693, 0.06 #2374), 081_zm (0.16 #15155, 0.16 #15154, 0.15 #11784), 0f5mdz (0.16 #15155, 0.16 #15154, 0.15 #11784), 0237jb (0.11 #1915, 0.06 #2587, 0.04 #3933), 02vyw (0.07 #2794, 0.06 #5150, 0.05 #6159), 0kb3n (0.07 #4630, 0.07 #4966, 0.06 #5640), 03thw4 (0.05 #6870, 0.05 #6533, 0.04 #3168), 02sh8y (0.04 #2690, 0.01 #16838, 0.01 #7402), 02mt4k (0.04 #3519, 0.03 #3183, 0.03 #6548) >> Best rule #504 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0140g4; 02scbv; >> query: (?x6746, 0gn30) <- country(?x6746, ?x94), honored_for(?x6746, ?x3330), nominated_for(?x6746, ?x4538), ?x4538 = 0q9sg, ?x3330 = 0946bb >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 059lwy written_by 06kbb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 57.000 0.500 http://example.org/film/film/written_by #19167-040b5k PRED entity: 040b5k PRED relation: film_crew_role PRED expected values: 01pvkk => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 33): 09zzb8 (0.75 #1310, 0.74 #935, 0.74 #634), 09vw2b7 (0.68 #640, 0.68 #941, 0.67 #7), 01vx2h (0.44 #197, 0.43 #271, 0.41 #86), 0dxtw (0.38 #1320, 0.37 #644, 0.36 #945), 01pvkk (0.28 #873, 0.28 #1322, 0.28 #1842), 02rh1dz (0.26 #121, 0.21 #195, 0.20 #456), 02ynfr (0.26 #128, 0.21 #463, 0.20 #165), 0d2b38 (0.16 #101, 0.13 #622, 0.11 #961), 015h31 (0.14 #604, 0.14 #120, 0.12 #83), 01xy5l_ (0.13 #200, 0.11 #274, 0.11 #15) >> Best rule #1310 for best value: >> intensional similarity = 4 >> extensional distance = 812 >> proper extension: 02_1sj; 02z3r8t; 035xwd; 09p35z; 03ckwzc; 0963mq; 0c00zd0; 05p3738; 047qxs; 01j8wk; ... >> query: (?x2889, 09zzb8) <- film_crew_role(?x2889, ?x1284), country(?x2889, ?x2346), genre(?x2889, ?x53), ?x1284 = 0ch6mp2 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #873 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 524 *> proper extension: 0dnvn3; 03h_yy; 0k4d7; 05n6sq; 04xx9s; 047gpsd; 02bj22; 02x2jl_; *> query: (?x2889, 01pvkk) <- award_winner(?x2889, ?x10271), film_crew_role(?x2889, ?x468), language(?x2889, ?x2890), film(?x382, ?x2889) *> conf = 0.28 ranks of expected_values: 5 EVAL 040b5k film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 86.000 0.746 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #19166-06g4_ PRED entity: 06g4_ PRED relation: company PRED expected values: 02zd460 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 154): 07vsl (0.18 #570, 0.07 #2107, 0.04 #1531), 09c7w0 (0.15 #4229, 0.09 #385, 0.08 #5770), 01w5m (0.15 #1393, 0.09 #3122, 0.09 #3315), 04rwx (0.12 #22, 0.05 #1174, 0.04 #1751), 0c5x_ (0.12 #123, 0.05 #1275, 0.02 #3582), 07wrz (0.11 #1380, 0.09 #419, 0.07 #1572), 01w3v (0.11 #1360, 0.07 #1552, 0.05 #3089), 05zl0 (0.11 #3358, 0.07 #1436, 0.06 #4319), 03ksy (0.10 #241, 0.07 #1394, 0.06 #625), 02zd460 (0.10 #276, 0.06 #660, 0.05 #852) >> Best rule #570 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0pz7h; 08_hns; >> query: (?x11018, 07vsl) <- people(?x743, ?x11018), student(?x734, ?x11018), list(?x11018, ?x5160) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #276 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01kx_81; 024zq; *> query: (?x11018, 02zd460) <- category(?x11018, ?x134), peers(?x12147, ?x11018), religion(?x11018, ?x2694) *> conf = 0.10 ranks of expected_values: 10 EVAL 06g4_ company 02zd460 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 114.000 114.000 0.182 http://example.org/people/person/employment_history./business/employment_tenure/company #19165-03y5ky PRED entity: 03y5ky PRED relation: contains! PRED expected values: 03v1s => 108 concepts (62 used for prediction) PRED predicted values (max 10 best out of 96): 03v1s (0.79 #18800, 0.79 #21486, 0.77 #24172), 059rby (0.18 #1810, 0.12 #7180, 0.12 #4495), 02jx1 (0.14 #16200, 0.12 #26945, 0.11 #31421), 01n7q (0.12 #52018, 0.12 #52913, 0.12 #53809), 04ly1 (0.08 #1131, 0.05 #236, 0.03 #2027), 05tbn (0.08 #2014, 0.06 #7384, 0.06 #4699), 07ssc (0.07 #14355, 0.07 #16145, 0.07 #31366), 05k7sb (0.07 #48485, 0.05 #44902, 0.05 #45797), 02_286 (0.06 #1833, 0.06 #7203, 0.05 #4518), 0d060g (0.06 #8068, 0.05 #23288, 0.05 #10755) >> Best rule #18800 for best value: >> intensional similarity = 4 >> extensional distance = 361 >> proper extension: 0975t6; >> query: (?x6201, ?x448) <- category(?x6201, ?x134), contains(?x94, ?x6201), state_province_region(?x6201, ?x448), location(?x5346, ?x448) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03y5ky contains! 03v1s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 62.000 0.788 http://example.org/location/location/contains #19164-01j851 PRED entity: 01j851 PRED relation: producer_type PRED expected values: 0ckd1 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.18 #34, 0.13 #49, 0.12 #55) >> Best rule #34 for best value: >> intensional similarity = 4 >> extensional distance = 175 >> proper extension: 06tp4h; >> query: (?x9573, 0ckd1) <- profession(?x9573, ?x319), participant(?x9573, ?x7489), nationality(?x9573, ?x94), ?x319 = 01d_h8 >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j851 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.181 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #19163-06qgjh PRED entity: 06qgjh PRED relation: student! PRED expected values: 065y4w7 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 45): 09f2j (0.14 #158, 0.04 #13834, 0.04 #6470), 015zyd (0.14 #1, 0.01 #8417, 0.01 #6313), 0bwfn (0.09 #6586, 0.08 #7638, 0.08 #8164), 015nl4 (0.05 #8483, 0.05 #10587, 0.04 #4801), 08815 (0.05 #528, 0.03 #13678, 0.03 #8418), 065y4w7 (0.05 #7904, 0.05 #6326, 0.05 #13690), 03ksy (0.04 #13782, 0.04 #632, 0.04 #26407), 01w5m (0.04 #13781, 0.04 #4839, 0.03 #26406), 0fr9jp (0.04 #870, 0.02 #1396, 0.02 #1922), 017z88 (0.04 #7446, 0.04 #7972, 0.03 #6394) >> Best rule #158 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 056ws9; >> query: (?x8432, 09f2j) <- nominated_for(?x8432, ?x2709), nominated_for(?x8432, ?x1904), ?x1904 = 09146g, film_release_region(?x2709, ?x87) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #7904 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1046 *> proper extension: 0clvcx; 05qsxy; *> query: (?x8432, 065y4w7) <- nationality(?x8432, ?x94), award_nominee(?x5316, ?x8432), student(?x3948, ?x8432) *> conf = 0.05 ranks of expected_values: 6 EVAL 06qgjh student! 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 84.000 84.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #19162-01xqqp PRED entity: 01xqqp PRED relation: ceremony! PRED expected values: 026mg3 02nhxf 025m8y 01dpdh 01c9d1 0257__ => 35 concepts (29 used for prediction) PRED predicted values (max 10 best out of 233): 0gqy2 (0.87 #4889, 0.74 #5469, 0.36 #5082), 02nhxf (0.85 #3125, 0.85 #2934, 0.81 #1339), 026mg3 (0.85 #3071, 0.85 #2880, 0.81 #1339), 024fz9 (0.85 #3183, 0.85 #2992, 0.81 #1339), 025m8y (0.85 #3126, 0.81 #1339, 0.81 #1338), 03qbnj (0.81 #1339, 0.81 #1338, 0.80 #1724), 01c9dd (0.81 #1339, 0.81 #1338, 0.80 #1724), 03q27t (0.81 #1339, 0.81 #1338, 0.80 #1724), 03qbh5 (0.81 #1339, 0.81 #1338, 0.80 #1724), 01c9d1 (0.81 #1339, 0.81 #1338, 0.80 #1724) >> Best rule #4889 for best value: >> intensional similarity = 14 >> extensional distance = 68 >> proper extension: 073hkh; 0bzk8w; 02yw5r; 059x66; 073hmq; 0bzm81; 0dth6b; 02yv_b; 0ftlkg; 073h1t; ... >> query: (?x6869, 0gqy2) <- ceremony(?x6739, ?x6869), ceremony(?x3978, ?x6869), ceremony(?x3647, ?x6869), ceremony(?x3978, ?x3121), award_winner(?x6869, ?x1399), award_winner(?x3121, ?x6025), award_winner(?x6739, ?x9337), award_winner(?x6739, ?x4563), ?x6025 = 018gqj, type_of_union(?x9337, ?x1873), award(?x538, ?x3647), ?x4563 = 0dzf_, award_winner(?x158, ?x1399), person(?x424, ?x9337) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #3125 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 11 *> proper extension: 056878; *> query: (?x6869, 02nhxf) <- ceremony(?x8409, ?x6869), ceremony(?x5765, ?x6869), ceremony(?x3978, ?x6869), ?x3978 = 03t5b6, award_winner(?x6869, ?x3403), award_winner(?x6869, ?x1894), ceremony(?x8409, ?x5656), award(?x1894, ?x1323), award_winner(?x1089, ?x3403), award(?x2698, ?x8409), participant(?x3083, ?x3403), instrumentalists(?x227, ?x3403), music(?x188, ?x1894), type_of_union(?x1894, ?x566), ?x5765 = 024_fw, student(?x7545, ?x1894), ?x5656 = 0466p0j *> conf = 0.85 ranks of expected_values: 2, 3, 5, 10, 20, 26 EVAL 01xqqp ceremony! 0257__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 35.000 29.000 0.871 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01xqqp ceremony! 01c9d1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 35.000 29.000 0.871 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01xqqp ceremony! 01dpdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 35.000 29.000 0.871 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01xqqp ceremony! 025m8y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 29.000 0.871 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01xqqp ceremony! 02nhxf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 35.000 29.000 0.871 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01xqqp ceremony! 026mg3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 35.000 29.000 0.871 http://example.org/award/award_category/winners./award/award_honor/ceremony #19161-017gm7 PRED entity: 017gm7 PRED relation: nominated_for! PRED expected values: 05bm4sm => 80 concepts (31 used for prediction) PRED predicted values (max 10 best out of 660): 05bm4sm (0.50 #3562, 0.33 #1244, 0.13 #5881), 02gvwz (0.44 #4637, 0.33 #44051, 0.33 #2554), 015t7v (0.44 #4637, 0.33 #44051, 0.24 #34775), 02fgm7 (0.44 #4637, 0.33 #44051, 0.24 #34775), 024n3z (0.44 #4637, 0.33 #44051, 0.24 #34775), 016zp5 (0.33 #3516, 0.33 #1198, 0.07 #46370), 01tc9r (0.33 #818, 0.17 #3136, 0.13 #5455), 02fz3w (0.17 #4212), 0b6mgp_ (0.16 #12539, 0.05 #24129, 0.05 #14857), 0c94fn (0.16 #11976, 0.04 #7339, 0.04 #16612) >> Best rule #3562 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 017jd9; 0ndwt2w; 09hy79; >> query: (?x1392, 05bm4sm) <- written_by(?x1392, ?x3434), film_release_region(?x1392, ?x47), ?x3434 = 02bfxb, film(?x230, ?x1392) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017gm7 nominated_for! 05bm4sm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 31.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #19160-01vsgrn PRED entity: 01vsgrn PRED relation: participant PRED expected values: 01vrz41 => 112 concepts (80 used for prediction) PRED predicted values (max 10 best out of 318): 01vw20h (0.38 #12778, 0.07 #949, 0.06 #6700), 015f7 (0.16 #1510, 0.08 #8538, 0.08 #2149), 04xrx (0.14 #8945, 0.12 #4473, 0.12 #11500), 01vvyc_ (0.11 #1917, 0.07 #35785, 0.05 #1662), 0j1yf (0.11 #1394, 0.06 #3311, 0.05 #3950), 0bbf1f (0.10 #6587, 0.08 #2114, 0.07 #8503), 09889g (0.08 #2259, 0.08 #342, 0.06 #3537), 033wx9 (0.08 #2100, 0.07 #822, 0.05 #4017), 07r1h (0.08 #6801, 0.05 #8717, 0.05 #11272), 01vrz41 (0.08 #1998, 0.05 #1359, 0.05 #4554) >> Best rule #12778 for best value: >> intensional similarity = 2 >> extensional distance = 124 >> proper extension: 012_53; 04f7c55; 06tp4h; 06gb2q; 01qn8k; >> query: (?x5536, ?x4476) <- film(?x5536, ?x2695), friend(?x5536, ?x4476) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1998 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 03rl84; 02wb6yq; *> query: (?x5536, 01vrz41) <- participant(?x2614, ?x5536), origin(?x5536, ?x479), nominated_for(?x5536, ?x6298) *> conf = 0.08 ranks of expected_values: 10 EVAL 01vsgrn participant 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 112.000 80.000 0.378 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #19159-014x77 PRED entity: 014x77 PRED relation: location PRED expected values: 02jx1 => 150 concepts (144 used for prediction) PRED predicted values (max 10 best out of 248): 09bkv (0.52 #8047, 0.50 #61946, 0.49 #38614), 02_286 (0.33 #37, 0.23 #8889, 0.21 #3255), 04jpl (0.25 #5649, 0.25 #821, 0.23 #1625), 030qb3t (0.23 #10547, 0.22 #18588, 0.21 #16979), 0r0m6 (0.09 #7460, 0.09 #3436, 0.07 #14702), 0cc56 (0.09 #3275, 0.07 #7299, 0.06 #12933), 0cr3d (0.09 #31520, 0.08 #24280, 0.07 #10609), 02jx1 (0.07 #1679, 0.03 #7313, 0.03 #3289), 0fhp9 (0.06 #15331, 0.05 #21766, 0.03 #31418), 013yq (0.06 #3337, 0.04 #12995, 0.03 #21038) >> Best rule #8047 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 01n7qlf; 03h502k; 01z7s_; >> query: (?x548, ?x10042) <- celebrity(?x548, ?x1018), film(?x548, ?x278), place_of_birth(?x548, ?x10042) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1679 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 0xnc3; *> query: (?x548, 02jx1) <- student(?x2999, ?x548), ?x2999 = 07tg4, gender(?x548, ?x514) *> conf = 0.07 ranks of expected_values: 8 EVAL 014x77 location 02jx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 150.000 144.000 0.517 http://example.org/people/person/places_lived./people/place_lived/location #19158-05qm9f PRED entity: 05qm9f PRED relation: written_by PRED expected values: 06kbb6 => 101 concepts (61 used for prediction) PRED predicted values (max 10 best out of 138): 012rng (0.34 #4715, 0.29 #15844, 0.19 #2020), 079hvk (0.14 #2357, 0.13 #1347, 0.11 #2359), 0gm34 (0.14 #2357, 0.13 #1347, 0.11 #18550), 0cgzj (0.11 #2359, 0.11 #15845, 0.10 #2358), 06kbb6 (0.11 #2359, 0.11 #15845, 0.10 #2358), 039wsf (0.11 #2359, 0.11 #15845, 0.10 #2358), 01wd9lv (0.07 #673, 0.02 #7412, 0.01 #13823), 07s3vqk (0.07 #673, 0.02 #7412, 0.01 #13823), 02vyw (0.05 #440, 0.03 #2124, 0.03 #777), 03thw4 (0.05 #477, 0.03 #2161, 0.02 #1824) >> Best rule #4715 for best value: >> intensional similarity = 4 >> extensional distance = 203 >> proper extension: 0bs8hvm; >> query: (?x6607, ?x4307) <- genre(?x6607, ?x53), cinematography(?x6607, ?x2466), film(?x4307, ?x6607), language(?x6607, ?x254) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #2359 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 88 *> proper extension: 07bz5; *> query: (?x6607, ?x9866) <- award_winner(?x6607, ?x2466), nominated_for(?x9866, ?x6607), list(?x6607, ?x3004), profession(?x9866, ?x353) *> conf = 0.11 ranks of expected_values: 5 EVAL 05qm9f written_by 06kbb6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 61.000 0.344 http://example.org/film/film/written_by #19157-01y665 PRED entity: 01y665 PRED relation: people! PRED expected values: 0g6ff => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 36): 0x67 (0.16 #3733, 0.16 #4113, 0.14 #2213), 033tf_ (0.15 #82, 0.11 #234, 0.11 #6), 07bch9 (0.11 #22, 0.05 #2226, 0.05 #98), 07hwkr (0.11 #11, 0.05 #2215, 0.05 #3735), 048z7l (0.11 #39, 0.04 #343, 0.03 #799), 02w7gg (0.09 #2206, 0.09 #4106, 0.08 #3726), 0xnvg (0.07 #316, 0.06 #772, 0.06 #240), 0dryh9k (0.05 #2219, 0.05 #3739, 0.05 #4119), 02ctzb (0.04 #90, 0.04 #4118, 0.04 #3738), 01qhm_ (0.04 #81, 0.04 #461, 0.04 #233) >> Best rule #3733 for best value: >> intensional similarity = 2 >> extensional distance = 1568 >> proper extension: 015c1b; >> query: (?x3039, 0x67) <- gender(?x3039, ?x231), people(?x1050, ?x3039) >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01y665 people! 0g6ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 100.000 0.165 http://example.org/people/ethnicity/people #19156-0fs9jn PRED entity: 0fs9jn PRED relation: film PRED expected values: 076tw54 => 80 concepts (30 used for prediction) PRED predicted values (max 10 best out of 353): 078sj4 (0.20 #454, 0.01 #2242, 0.01 #4030), 011ysn (0.20 #566, 0.01 #5930, 0.01 #2354), 0b76t12 (0.20 #290), 0963mq (0.20 #138), 020bv3 (0.10 #318, 0.02 #5682, 0.02 #12834), 01chpn (0.10 #1109, 0.02 #2897, 0.01 #6473), 03lrht (0.10 #257, 0.02 #2045), 02_1sj (0.10 #79, 0.02 #5443, 0.01 #12595), 03mh_tp (0.10 #508, 0.02 #2296), 035s95 (0.10 #340, 0.02 #2128) >> Best rule #454 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0170s4; >> query: (?x10136, 078sj4) <- film(?x10136, ?x7672), film(?x10136, ?x5271), ?x5271 = 047vnkj, production_companies(?x7672, ?x382) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fs9jn film 076tw54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 30.000 0.200 http://example.org/film/actor/film./film/performance/film #19155-016l09 PRED entity: 016l09 PRED relation: category PRED expected values: 08mbj5d => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #8, 0.89 #7, 0.89 #13) >> Best rule #8 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 01vv7sc; 0l12d; 04qmr; 01w524f; 0180w8; 0czkbt; 02cpp; 07r4c; 0l8g0; 016lmg; ... >> query: (?x9791, 08mbj5d) <- award(?x9791, ?x4892), artists(?x302, ?x9791), ?x302 = 016clz, award(?x11700, ?x4892), ?x11700 = 017_hq >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016l09 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.900 http://example.org/common/topic/webpage./common/webpage/category #19154-04fc6c PRED entity: 04fc6c PRED relation: child PRED expected values: 073tm9 => 155 concepts (112 used for prediction) PRED predicted values (max 10 best out of 236): 04fc6c (0.47 #8720, 0.46 #14037, 0.25 #103), 073tm9 (0.47 #8720, 0.46 #14037, 0.25 #55), 05clg8 (0.47 #8720, 0.46 #14037, 0.25 #125), 03vtfp (0.47 #8720, 0.46 #14037, 0.25 #120), 01xjx6 (0.47 #8720, 0.46 #14037, 0.25 #114), 01xyqk (0.47 #8720, 0.46 #14037, 0.25 #109), 01t04r (0.47 #8720, 0.46 #14037, 0.25 #90), 0n85g (0.47 #8720, 0.46 #14037, 0.25 #89), 06x2ww (0.47 #8720, 0.46 #14037, 0.25 #75), 03d96s (0.47 #8720, 0.46 #14037, 0.25 #72) >> Best rule #8720 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 0136kr; 0f1r9; >> query: (?x10951, ?x4868) <- child(?x10951, ?x4483), category(?x10951, ?x134), ?x134 = 08mbj5d, child(?x7793, ?x4483), child(?x7793, ?x4868) >> conf = 0.47 => this is the best rule for 12 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 04fc6c child 073tm9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 155.000 112.000 0.475 http://example.org/organization/organization/child./organization/organization_relationship/child #19153-06ryl PRED entity: 06ryl PRED relation: organization PRED expected values: 07t65 => 109 concepts (97 used for prediction) PRED predicted values (max 10 best out of 17): 07t65 (0.91 #1011, 0.91 #717, 0.91 #1223), 01rz1 (0.73 #340, 0.71 #23, 0.34 #508), 0_2v (0.48 #215, 0.37 #68, 0.37 #110), 04k4l (0.47 #153, 0.40 #132, 0.33 #364), 0b6css (0.36 #579, 0.35 #747, 0.34 #978), 018cqq (0.33 #222, 0.32 #1545, 0.31 #1457), 0gkjy (0.32 #1545, 0.31 #1457, 0.31 #660), 085h1 (0.32 #1545, 0.31 #1457, 0.31 #1522), 02jxk (0.32 #1545, 0.31 #1457, 0.31 #1522), 059dn (0.32 #1545, 0.31 #1457, 0.31 #1522) >> Best rule #1011 for best value: >> intensional similarity = 5 >> extensional distance = 145 >> proper extension: 09c7w0; 0160w; 0j1z8; 04gzd; 0chghy; 05qhw; 02k54; 06npd; 06c1y; 0169t; ... >> query: (?x4402, 07t65) <- country(?x1121, ?x4402), participating_countries(?x1931, ?x4402), administrative_area_type(?x4402, ?x2792), organization(?x4402, ?x127), ?x2792 = 0hzc9wc >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06ryl organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 97.000 0.912 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #19152-01skmp PRED entity: 01skmp PRED relation: participant! PRED expected values: 01x6jd => 95 concepts (35 used for prediction) PRED predicted values (max 10 best out of 215): 03n_7k (0.09 #1277, 0.04 #5746, 0.04 #4469), 09889g (0.07 #349, 0.01 #13127, 0.01 #4179), 06rgq (0.07 #527, 0.01 #4357, 0.01 #5634), 015lhm (0.07 #379, 0.01 #1656, 0.01 #2294), 0c1j_ (0.07 #600), 01wz3cx (0.07 #135), 03zqc1 (0.07 #35), 0bxtg (0.07 #33), 01z7s_ (0.07 #10858, 0.07 #15971, 0.07 #8301), 02t__3 (0.07 #10858, 0.07 #15971, 0.07 #8301) >> Best rule #1277 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 0c4f4; 014x77; 0151ns; 01pcq3; 01j5x6; 0yfp; 0151w_; 0bwh6; 03ft8; 03xmy1; ... >> query: (?x6702, ?x2414) <- profession(?x6702, ?x1032), spouse(?x2414, ?x6702), location_of_ceremony(?x6702, ?x9341) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01skmp participant! 01x6jd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 35.000 0.087 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #19151-02khs PRED entity: 02khs PRED relation: form_of_government PRED expected values: 06cx9 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 5): 06cx9 (0.46 #36, 0.42 #171, 0.42 #66), 01fpfn (0.42 #68, 0.42 #58, 0.39 #173), 018wl5 (0.39 #22, 0.35 #57, 0.35 #7), 01q20 (0.32 #239, 0.30 #24, 0.30 #9), 026wp (0.08 #30, 0.07 #75, 0.07 #100) >> Best rule #36 for best value: >> intensional similarity = 2 >> extensional distance = 57 >> proper extension: 05g2v; >> query: (?x1756, 06cx9) <- contains(?x2467, ?x1756), ?x2467 = 0dg3n1 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02khs form_of_government 06cx9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.458 http://example.org/location/country/form_of_government #19150-030wkp PRED entity: 030wkp PRED relation: people! PRED expected values: 0x67 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 32): 041rx (0.20 #312, 0.20 #697, 0.19 #620), 07bch9 (0.20 #23, 0.18 #100, 0.17 #177), 0x67 (0.14 #472, 0.11 #703, 0.10 #2090), 033tf_ (0.10 #7, 0.09 #1548, 0.09 #84), 02ctzb (0.10 #15, 0.09 #92, 0.08 #169), 09vc4s (0.10 #9, 0.09 #86, 0.08 #163), 038723 (0.10 #69, 0.09 #146, 0.08 #223), 013xrm (0.09 #97, 0.08 #174, 0.05 #944), 019kn7 (0.09 #123, 0.08 #200), 07hwkr (0.07 #243, 0.06 #782, 0.06 #551) >> Best rule #312 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 0f87jy; >> query: (?x9692, 041rx) <- profession(?x9692, ?x1146), profession(?x9692, ?x353), ?x1146 = 018gz8, ?x353 = 0cbd2 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #472 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 155 *> proper extension: 03g5jw; 0d193h; 05xq9; 0lhn5; 014_lq; 07r1_; 01kcms4; 070b4; 0bk1p; 07hgm; ... *> query: (?x9692, 0x67) <- influenced_by(?x9692, ?x1145), award_nominee(?x1145, ?x2400), award(?x1145, ?x688) *> conf = 0.14 ranks of expected_values: 3 EVAL 030wkp people! 0x67 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 61.000 61.000 0.200 http://example.org/people/ethnicity/people #19149-0ddjy PRED entity: 0ddjy PRED relation: category PRED expected values: 08mbj5d => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.40 #1, 0.39 #11, 0.35 #47) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 06mmr; >> query: (?x2366, 08mbj5d) <- award_winner(?x2366, ?x2870), award_winner(?x2366, ?x1643), nationality(?x2870, ?x94), ?x1643 = 09pjnd >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ddjy category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.400 http://example.org/common/topic/webpage./common/webpage/category #19148-01rlz4 PRED entity: 01rlz4 PRED relation: teams! PRED expected values: 01zfrt => 117 concepts (102 used for prediction) PRED predicted values (max 10 best out of 189): 0fm2_ (0.33 #37, 0.25 #577, 0.25 #307), 013wf1 (0.25 #768, 0.20 #1308, 0.17 #2702), 04swd (0.25 #987, 0.11 #3691, 0.08 #5853), 0pfd9 (0.25 #519, 0.10 #4573, 0.06 #22718), 035yg (0.20 #1260, 0.17 #2341, 0.11 #3423), 0grd7 (0.17 #2633, 0.10 #4526, 0.10 #4256), 01t8gz (0.17 #2328, 0.08 #4761, 0.07 #6113), 01w2dq (0.17 #2939, 0.06 #22718, 0.03 #9158), 016wrq (0.14 #3204, 0.11 #3475, 0.08 #5097), 0619_ (0.10 #4031, 0.08 #5923, 0.08 #5383) >> Best rule #37 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 02b2np; >> query: (?x11337, 0fm2_) <- team(?x7234, ?x11337), ?x7234 = 0djvzd, position(?x11337, ?x203), position(?x11337, ?x60), ?x60 = 02nzb8, current_club(?x8511, ?x11337), ?x203 = 0dgrmp, team(?x8194, ?x11337) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01rlz4 teams! 01zfrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 102.000 0.333 http://example.org/sports/sports_team_location/teams #19147-033fqh PRED entity: 033fqh PRED relation: film! PRED expected values: 0gpprt => 58 concepts (30 used for prediction) PRED predicted values (max 10 best out of 583): 016szr (0.47 #60284, 0.41 #20785, 0.39 #29104), 0p8r1 (0.42 #584, 0.03 #2663, 0.02 #15132), 0bxtg (0.25 #76, 0.02 #14624, 0.02 #4233), 019vgs (0.25 #659, 0.02 #6894, 0.01 #4816), 01nm3s (0.25 #687, 0.01 #4844, 0.01 #15235), 015pvh (0.25 #1098, 0.01 #15646), 01h1b (0.17 #1203, 0.03 #47814, 0.01 #15751), 085q5 (0.17 #1717, 0.02 #3796, 0.01 #16265), 01mylz (0.17 #1944, 0.01 #6101, 0.01 #37284), 01rs5p (0.17 #1790, 0.01 #5947, 0.01 #8025) >> Best rule #60284 for best value: >> intensional similarity = 4 >> extensional distance = 1188 >> proper extension: 01f3p_; >> query: (?x4920, ?x4919) <- nominated_for(?x4919, ?x4920), nominated_for(?x4408, ?x4920), nationality(?x4408, ?x94), people(?x3584, ?x4919) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #18146 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 624 *> proper extension: 01br2w; 0dckvs; 0djb3vw; 04dsnp; 091z_p; 02phtzk; 0hv81; 064lsn; 0dkv90; 0gy0l_; ... *> query: (?x4920, 0gpprt) <- language(?x4920, ?x254), film_crew_role(?x4920, ?x137), produced_by(?x4920, ?x1554) *> conf = 0.01 ranks of expected_values: 477 EVAL 033fqh film! 0gpprt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 30.000 0.473 http://example.org/film/actor/film./film/performance/film #19146-02hhtj PRED entity: 02hhtj PRED relation: nationality PRED expected values: 07ylj => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 34): 07t21 (0.51 #7830, 0.03 #234, 0.01 #1323), 03rk0 (0.13 #3415, 0.13 #3118, 0.11 #9064), 02jx1 (0.12 #32, 0.11 #428, 0.11 #9843), 0d060g (0.12 #6, 0.08 #501, 0.08 #699), 07ssc (0.09 #6456, 0.08 #9924, 0.08 #10617), 03_3d (0.07 #5457, 0.06 #5754, 0.02 #9717), 03rjj (0.05 #103, 0.04 #598, 0.04 #2283), 0j5g9 (0.05 #160, 0.03 #1546, 0.02 #655), 0f8l9c (0.04 #615, 0.02 #2499, 0.02 #7058), 0345h (0.04 #4392, 0.03 #3995, 0.03 #4095) >> Best rule #7830 for best value: >> intensional similarity = 3 >> extensional distance = 509 >> proper extension: 0gm34; >> query: (?x5881, ?x94) <- participant(?x5881, ?x4956), film(?x5881, ?x1702), nationality(?x4956, ?x94) >> conf = 0.51 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02hhtj nationality 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 114.000 0.505 http://example.org/people/person/nationality #19145-0k4p0 PRED entity: 0k4p0 PRED relation: film! PRED expected values: 086k8 => 62 concepts (47 used for prediction) PRED predicted values (max 10 best out of 56): 016tw3 (0.26 #10, 0.16 #382, 0.15 #532), 086k8 (0.20 #152, 0.17 #300, 0.16 #3149), 05qd_ (0.16 #306, 0.13 #380, 0.12 #1806), 0jz9f (0.15 #225, 0.07 #597, 0.07 #823), 0g1rw (0.13 #157, 0.07 #82, 0.06 #3154), 017jv5 (0.13 #14, 0.11 #164, 0.11 #89), 03xq0f (0.13 #4, 0.10 #600, 0.10 #526), 016tt2 (0.12 #599, 0.12 #375, 0.11 #3150), 024rgt (0.09 #94, 0.04 #541, 0.04 #990), 025jfl (0.09 #229, 0.04 #5, 0.04 #601) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 035xwd; 038bh3; 02qr3k8; >> query: (?x5712, 016tw3) <- film(?x398, ?x5712), film(?x397, ?x5712), ?x398 = 0bl2g, award_nominee(?x241, ?x397) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #152 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 019kyn; *> query: (?x5712, 086k8) <- film(?x397, ?x5712), list(?x5712, ?x3004), titles(?x2480, ?x5712) *> conf = 0.20 ranks of expected_values: 2 EVAL 0k4p0 film! 086k8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 62.000 47.000 0.261 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #19144-0kb1g PRED entity: 0kb1g PRED relation: currency PRED expected values: 09nqf => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.79 #127, 0.76 #211, 0.75 #134), 01nv4h (0.04 #23, 0.03 #191, 0.02 #135), 02l6h (0.02 #11, 0.01 #109, 0.01 #116), 02gsvk (0.01 #20, 0.01 #27) >> Best rule #127 for best value: >> intensional similarity = 3 >> extensional distance = 389 >> proper extension: 0gtsx8c; >> query: (?x9993, 09nqf) <- film(?x7676, ?x9993), religion(?x7676, ?x1985), participant(?x5239, ?x7676) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kb1g currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.793 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #19143-01w9wwg PRED entity: 01w9wwg PRED relation: award_nominee! PRED expected values: 01wcp_g => 92 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1086): 01wcp_g (0.81 #46489, 0.80 #58113, 0.75 #6973), 026yqrr (0.39 #8416, 0.14 #69737, 0.09 #33985), 057176 (0.33 #1575, 0.31 #99960, 0.14 #69737), 08vr94 (0.33 #889, 0.31 #99960, 0.14 #69737), 01vw20h (0.33 #8024, 0.14 #69737, 0.10 #33593), 016kjs (0.33 #7194, 0.14 #69737, 0.08 #28113), 086nl7 (0.33 #1043), 07swvb (0.31 #99960, 0.22 #931, 0.14 #69737), 0794g (0.31 #99960, 0.14 #69737, 0.02 #37932), 01ws9n6 (0.30 #8023, 0.05 #33592, 0.04 #42890) >> Best rule #46489 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 011hdn; >> query: (?x6162, ?x827) <- award(?x6162, ?x528), award_nominee(?x6162, ?x827), instrumentalists(?x716, ?x6162) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w9wwg award_nominee! 01wcp_g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 44.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19142-02tjl3 PRED entity: 02tjl3 PRED relation: executive_produced_by PRED expected values: 07r1h => 106 concepts (76 used for prediction) PRED predicted values (max 10 best out of 66): 01qg7c (0.14 #213), 09yrh (0.14 #107), 05hj_k (0.08 #351, 0.05 #4396, 0.04 #855), 06q8hf (0.08 #420, 0.05 #1177, 0.05 #3958), 027z0pl (0.08 #473, 0.03 #725, 0.02 #977), 015pkc (0.08 #305, 0.03 #557, 0.01 #809), 052hl (0.08 #407, 0.03 #659), 0glyyw (0.04 #1704, 0.03 #4487, 0.03 #1957), 03x400 (0.03 #1768, 0.03 #2274, 0.03 #7831), 0fz27v (0.03 #723) >> Best rule #213 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 03mh_tp; >> query: (?x5520, 01qg7c) <- language(?x5520, ?x254), film(?x2317, ?x5520), ?x2317 = 04fhxp, film_crew_role(?x5520, ?x137) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02tjl3 executive_produced_by 07r1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 76.000 0.143 http://example.org/film/film/executive_produced_by #19141-02lv2v PRED entity: 02lv2v PRED relation: contact_category PRED expected values: 02zdwq => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 2): 02zdwq (0.37 #12, 0.33 #14, 0.30 #76), 014dgf (0.24 #75, 0.21 #29, 0.19 #89) >> Best rule #12 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 05w3y; >> query: (?x8434, 02zdwq) <- service_location(?x8434, ?x739), service_location(?x8434, ?x94), ?x94 = 09c7w0, location(?x163, ?x739), featured_film_locations(?x89, ?x739) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lv2v contact_category 02zdwq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.367 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #19140-01rzxl PRED entity: 01rzxl PRED relation: gender PRED expected values: 05zppz => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #21, 0.85 #11, 0.84 #7), 02zsn (0.39 #6, 0.34 #34, 0.32 #40) >> Best rule #21 for best value: >> intensional similarity = 5 >> extensional distance = 84 >> proper extension: 0fsm8c; 0dzc16; 01wwnh2; >> query: (?x11630, 05zppz) <- profession(?x11630, ?x1183), profession(?x11630, ?x319), nationality(?x11630, ?x94), ?x319 = 01d_h8, ?x1183 = 09jwl >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rzxl gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.860 http://example.org/people/person/gender #19139-0lx2l PRED entity: 0lx2l PRED relation: diet PRED expected values: 07_jd => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 07_jd (0.12 #3, 0.10 #5, 0.10 #13), 07_hy (0.04 #6, 0.04 #28, 0.03 #20) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 02g8h; 01q_ph; 0h5g_; 04bs3j; 01n5309; 0mdqp; 0pz7h; 081lh; 01vrncs; 02p21g; ... >> query: (?x2534, 07_jd) <- participant(?x1817, ?x2534), film(?x2534, ?x339), influenced_by(?x2534, ?x1145) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lx2l diet 07_jd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.122 http://example.org/base/eating/practicer_of_diet/diet #19138-01vsy3q PRED entity: 01vsy3q PRED relation: artists! PRED expected values: 0dl5d => 201 concepts (87 used for prediction) PRED predicted values (max 10 best out of 298): 016clz (0.65 #4605, 0.64 #3991, 0.50 #5), 064t9 (0.54 #15044, 0.50 #320, 0.48 #15657), 06j6l (0.43 #660, 0.42 #6489, 0.30 #4647), 0dl5d (0.43 #633, 0.29 #23632, 0.27 #20261), 0gywn (0.43 #670, 0.27 #4043, 0.26 #4657), 0cx7f (0.42 #3200, 0.33 #5348, 0.32 #23746), 05bt6j (0.38 #2186, 0.33 #348, 0.30 #17523), 02x8m (0.35 #4619, 0.32 #4005, 0.29 #632), 07sbbz2 (0.33 #6450, 0.26 #4301, 0.22 #11660), 0m0jc (0.33 #315, 0.18 #2460, 0.17 #4609) >> Best rule #4605 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 01yzl2; >> query: (?x4873, 016clz) <- artists(?x9013, ?x4873), artists(?x2249, ?x4873), instrumentalists(?x212, ?x4873), ?x9013 = 09nwwf, parent_genre(?x302, ?x2249) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #633 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 020_4z; *> query: (?x4873, 0dl5d) <- artists(?x9013, ?x4873), artists(?x2809, ?x4873), people(?x2510, ?x4873), ?x2809 = 05w3f, ?x9013 = 09nwwf *> conf = 0.43 ranks of expected_values: 4 EVAL 01vsy3q artists! 0dl5d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 201.000 87.000 0.652 http://example.org/music/genre/artists #19137-03s2dj PRED entity: 03s2dj PRED relation: film PRED expected values: 017z49 => 116 concepts (50 used for prediction) PRED predicted values (max 10 best out of 925): 02q5bx2 (0.50 #16118, 0.48 #17910, 0.33 #1791), 02gl58 (0.33 #1791, 0.30 #16117, 0.29 #17909), 07p62k (0.33 #354, 0.10 #2145, 0.03 #18264), 02825cv (0.33 #1144, 0.05 #2935, 0.04 #10096), 0b6m5fy (0.33 #1127, 0.05 #2918, 0.04 #15453), 02ht1k (0.33 #631, 0.05 #2422, 0.03 #6003), 02stbw (0.33 #384, 0.05 #2175, 0.03 #5756), 0888c3 (0.33 #1415, 0.05 #3206, 0.03 #10367), 047rkcm (0.33 #1199, 0.05 #2990, 0.03 #10151), 0cp0ph6 (0.33 #585, 0.05 #2376, 0.02 #13120) >> Best rule #16118 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 01kwh5j; >> query: (?x12185, ?x8597) <- actor(?x8597, ?x12185), country(?x8597, ?x94), genre(?x8597, ?x811), profession(?x12185, ?x1032) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7727 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 64 *> proper extension: 0dky9n; *> query: (?x12185, 017z49) <- place_of_birth(?x12185, ?x10753), nationality(?x12185, ?x279), ?x279 = 0d060g, citytown(?x5993, ?x10753) *> conf = 0.02 ranks of expected_values: 627 EVAL 03s2dj film 017z49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 116.000 50.000 0.496 http://example.org/film/actor/film./film/performance/film #19136-04r68 PRED entity: 04r68 PRED relation: gender PRED expected values: 02zsn => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #49, 0.79 #107, 0.79 #33), 02zsn (0.55 #153, 0.46 #184, 0.27 #76) >> Best rule #49 for best value: >> intensional similarity = 4 >> extensional distance = 408 >> proper extension: 079dy; >> query: (?x5049, 05zppz) <- nationality(?x5049, ?x94), profession(?x5049, ?x353), ?x353 = 0cbd2, film_release_region(?x54, ?x94) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2318 *> proper extension: 0bhtzw; *> query: (?x5049, ?x514) <- nationality(?x5049, ?x94), place_of_birth(?x5049, ?x6453), place_of_birth(?x9957, ?x6453), gender(?x9957, ?x514) *> conf = 0.55 ranks of expected_values: 2 EVAL 04r68 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 93.000 0.790 http://example.org/people/person/gender #19135-01dvbd PRED entity: 01dvbd PRED relation: nominated_for! PRED expected values: 0fq9zdv 05zx7xk => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 200): 05zx7xk (0.69 #1921, 0.68 #7684, 0.66 #7925), 040njc (0.35 #487, 0.29 #727, 0.21 #6488), 0gq9h (0.32 #6545, 0.28 #7507, 0.28 #7748), 02pqp12 (0.31 #540, 0.26 #780, 0.17 #6541), 0gs9p (0.28 #546, 0.28 #6547, 0.25 #7509), 027dtxw (0.26 #484, 0.22 #724, 0.14 #2165), 019f4v (0.26 #6536, 0.23 #7498, 0.23 #7739), 0gr4k (0.25 #507, 0.21 #27, 0.21 #747), 0k611 (0.24 #6556, 0.21 #7518, 0.21 #7759), 02qvyrt (0.24 #579, 0.20 #819, 0.16 #339) >> Best rule #1921 for best value: >> intensional similarity = 4 >> extensional distance = 385 >> proper extension: 04glx0; >> query: (?x3048, ?x13311) <- nominated_for(?x7870, ?x3048), nationality(?x7870, ?x512), category(?x7870, ?x134), award(?x3048, ?x13311) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 31 EVAL 01dvbd nominated_for! 05zx7xk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.694 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01dvbd nominated_for! 0fq9zdv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 73.000 73.000 0.694 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19134-03xx9l PRED entity: 03xx9l PRED relation: profession PRED expected values: 02hrh1q 03gjzk => 103 concepts (96 used for prediction) PRED predicted values (max 10 best out of 76): 02hrh1q (0.95 #1353, 0.94 #1204, 0.94 #1055), 01d_h8 (0.66 #8312, 0.52 #5054, 0.50 #4610), 02jknp (0.46 #5056, 0.44 #5204, 0.43 #8), 03gjzk (0.44 #609, 0.42 #1652, 0.42 #1205), 0nbcg (0.37 #4487, 0.20 #328, 0.14 #12155), 01c72t (0.34 #4479, 0.09 #9809, 0.09 #12474), 09jwl (0.34 #4474, 0.22 #910, 0.20 #315), 0dz3r (0.29 #447, 0.28 #4458, 0.20 #596), 016z4k (0.27 #4460, 0.21 #449, 0.20 #2237), 0cbd2 (0.26 #4611, 0.22 #5796, 0.21 #5055) >> Best rule #1353 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 018z_c; 01yg9y; 0b7t3p; 0261x8t; 02p68d; 01fkxr; 02_wxh; >> query: (?x7625, 02hrh1q) <- person(?x3480, ?x7625), nationality(?x7625, ?x94), type_of_union(?x7625, ?x566), award(?x7625, ?x1921) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 03xx9l profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 96.000 0.946 http://example.org/people/person/profession EVAL 03xx9l profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 96.000 0.946 http://example.org/people/person/profession #19133-0191n PRED entity: 0191n PRED relation: film! PRED expected values: 048hf => 89 concepts (28 used for prediction) PRED predicted values (max 10 best out of 622): 026dx (0.58 #33295, 0.43 #20809, 0.42 #43701), 09fb5 (0.05 #2137, 0.04 #14623, 0.02 #57), 0h0wc (0.05 #2503, 0.03 #31636, 0.03 #21232), 0l6px (0.05 #387, 0.02 #4547, 0.01 #31600), 06ltr (0.05 #946, 0.01 #32159), 0bxtg (0.05 #14642, 0.03 #8398, 0.03 #16722), 0gn30 (0.05 #15513, 0.01 #50891, 0.01 #30079), 04sry (0.04 #8322, 0.03 #7515, 0.02 #3355), 0bj9k (0.04 #6567, 0.04 #2407, 0.03 #14893), 06cgy (0.04 #2329, 0.03 #6489, 0.03 #16895) >> Best rule #33295 for best value: >> intensional similarity = 4 >> extensional distance = 673 >> proper extension: 09fb5; >> query: (?x5029, ?x4703) <- nominated_for(?x4703, ?x5029), award_winner(?x8364, ?x4703), award(?x3471, ?x8364), ?x3471 = 07cyl >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0191n film! 048hf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 28.000 0.575 http://example.org/film/actor/film./film/performance/film #19132-04wzr PRED entity: 04wzr PRED relation: month! PRED expected values: 01cx_ 0177z 0d9jr => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 522): 0177z (0.90 #62, 0.86 #53, 0.82 #13), 01cx_ (0.90 #62, 0.86 #53, 0.82 #13), 0d9jr (0.90 #62, 0.86 #53, 0.82 #13), 0l0mk (0.90 #62, 0.86 #53, 0.81 #25), 0fq8f (0.82 #13, 0.66 #31, 0.09 #7), 0jpkg (0.82 #13, 0.66 #31, 0.04 #5), 0mbf4 (0.82 #13, 0.66 #31, 0.04 #5), 0k9p4 (0.82 #13, 0.66 #31, 0.04 #5), 0171b8 (0.82 #13, 0.66 #31), 0853g (0.82 #13, 0.66 #31) >> Best rule #62 for best value: >> intensional similarity = 102 >> extensional distance = 1 >> proper extension: 03_ly; >> query: (?x7298, ?x3052) <- month(?x12674, ?x7298), month(?x11197, ?x7298), month(?x10610, ?x7298), month(?x10143, ?x7298), month(?x9605, ?x7298), month(?x9559, ?x7298), month(?x8977, ?x7298), month(?x8956, ?x7298), month(?x6959, ?x7298), month(?x6494, ?x7298), month(?x6458, ?x7298), month(?x5719, ?x7298), month(?x5168, ?x7298), month(?x4698, ?x7298), month(?x4627, ?x7298), month(?x3125, ?x7298), month(?x3026, ?x7298), month(?x2985, ?x7298), month(?x2645, ?x7298), month(?x2611, ?x7298), month(?x2474, ?x7298), month(?x2277, ?x7298), month(?x2254, ?x7298), month(?x1860, ?x7298), month(?x1658, ?x7298), month(?x1458, ?x7298), month(?x1036, ?x7298), month(?x863, ?x7298), month(?x739, ?x7298), month(?x659, ?x7298), ?x9605 = 02frhbc, ?x12674 = 0g6xq, ?x6458 = 08966, ?x10143 = 0h3tv, ?x10610 = 03902, seasonal_months(?x9905, ?x7298), seasonal_months(?x4869, ?x7298), seasonal_months(?x3107, ?x7298), seasonal_months(?x2255, ?x7298), seasonal_months(?x2140, ?x7298), seasonal_months(?x1459, ?x7298), ?x863 = 0fhp9, ?x2985 = 03hrz, ?x5168 = 06mxs, month(?x5267, ?x2140), month(?x4826, ?x2140), month(?x3052, ?x2140), ?x4869 = 02xx5, ?x2611 = 02h6_6p, ?x1458 = 05ywg, ?x9905 = 028kb, ?x5719 = 0f2rq, ?x2645 = 03h64, contains(?x8956, ?x8475), contains(?x8956, ?x5695), category(?x8956, ?x134), capital(?x10706, ?x8956), ?x2254 = 0dclg, ?x1459 = 04w_7, location(?x10520, ?x8956), location(?x8476, ?x8956), ?x4826 = 0177z, ?x3026 = 0cv3w, institution(?x3437, ?x5695), ?x739 = 02_286, seasonal_months(?x3270, ?x2140), ?x11197 = 05l64, ?x5267 = 0d9jr, ?x4698 = 056_y, ?x6959 = 06c62, ?x3437 = 02_xgp2, place_of_death(?x8920, ?x8956), major_field_of_study(?x5695, ?x1668), contains(?x205, ?x8475), ?x3125 = 0d6lp, ?x2277 = 013yq, ?x205 = 03rjj, currency(?x5695, ?x5696), ?x1658 = 0h7h6, ?x8977 = 02z0j, award(?x8476, ?x1232), time_zones(?x8956, ?x2864), ?x1036 = 080h2, ?x134 = 08mbj5d, ?x659 = 02cl1, nominated_for(?x8476, ?x5429), ?x2255 = 040fv, award_nominee(?x8476, ?x8532), mode_of_transportation(?x8956, ?x4272), adjoins(?x10706, ?x9230), ?x2474 = 052p7, award_winner(?x1079, ?x8476), award_winner(?x3173, ?x8476), ?x4627 = 05qtj, major_field_of_study(?x8475, ?x75), contains(?x10706, ?x1356), ?x3107 = 05lf_, ?x1860 = 01_d4, vacationer(?x8956, ?x1898), ?x6494 = 02sn34, languages(?x10520, ?x90), ?x9559 = 07dfk >> conf = 0.90 => this is the best rule for 4 predicted values ranks of expected_values: 1, 2, 3 EVAL 04wzr month! 0d9jr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 04wzr month! 0177z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 04wzr month! 01cx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #19131-0cpllql PRED entity: 0cpllql PRED relation: language PRED expected values: 02h40lc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 54): 02h40lc (0.92 #1070, 0.92 #1129, 0.90 #713), 03_9r (0.67 #69, 0.07 #188, 0.07 #2499), 02bjrlw (0.17 #60, 0.07 #535, 0.07 #2252), 064_8sq (0.16 #438, 0.16 #556, 0.14 #674), 06b_j (0.14 #201, 0.08 #380, 0.08 #2274), 01r2l (0.14 #203, 0.08 #143, 0.06 #262), 06nm1 (0.14 #368, 0.12 #427, 0.12 #1019), 04306rv (0.11 #480, 0.11 #598, 0.10 #539), 012w70 (0.08 #131, 0.04 #2086, 0.03 #2549), 0c_v2 (0.08 #135, 0.03 #2549, 0.01 #1854) >> Best rule #1070 for best value: >> intensional similarity = 5 >> extensional distance = 206 >> proper extension: 03g90h; 026mfbr; 03s5lz; 02vqhv0; 0gydcp7; 01hvjx; 04q00lw; 0cc846d; 014nq4; 0gjc4d3; ... >> query: (?x626, 02h40lc) <- film_crew_role(?x626, ?x137), genre(?x626, ?x225), film(?x9238, ?x626), story_by(?x626, ?x1387), actor(?x7566, ?x9238) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cpllql language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.918 http://example.org/film/film/language #19130-05jjl PRED entity: 05jjl PRED relation: type_of_union PRED expected values: 04ztj => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.79 #13, 0.77 #25, 0.76 #37), 01g63y (0.25 #357, 0.13 #66, 0.12 #30), 01bl8s (0.25 #357) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 09ftwr; 03flwk; >> query: (?x8683, 04ztj) <- award(?x8683, ?x1862), ?x1862 = 0gr51, profession(?x8683, ?x319) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05jjl type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.785 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19129-03whyr PRED entity: 03whyr PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.89 #41, 0.87 #16, 0.83 #101), 02nxhr (0.07 #47, 0.06 #37, 0.04 #62), 07c52 (0.04 #13, 0.03 #53, 0.03 #293), 07z4p (0.03 #125, 0.03 #155, 0.02 #265) >> Best rule #41 for best value: >> intensional similarity = 6 >> extensional distance = 208 >> proper extension: 05dy7p; 04lqvlr; 04lqvly; 03_wm6; 0gy0l_; >> query: (?x9524, 029j_) <- film_crew_role(?x9524, ?x2154), film_crew_role(?x9524, ?x1284), ?x1284 = 0ch6mp2, country(?x9524, ?x94), currency(?x9524, ?x170), ?x2154 = 01vx2h >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03whyr film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19128-04nm0n0 PRED entity: 04nm0n0 PRED relation: titles! PRED expected values: 03mqtr => 105 concepts (80 used for prediction) PRED predicted values (max 10 best out of 81): 01z4y (0.69 #3678, 0.68 #4795, 0.23 #4995), 01jfsb (0.58 #3562, 0.33 #219, 0.19 #2758), 0345h (0.50 #509, 0.39 #711, 0.38 #710), 03gj2 (0.50 #509, 0.39 #711, 0.38 #710), 01hmnh (0.47 #1651, 0.16 #3371, 0.13 #3873), 017fp (0.24 #732, 0.24 #631, 0.20 #21), 02n4kr (0.22 #3556, 0.08 #1728, 0.07 #3358), 0hn10 (0.20 #116, 0.05 #5063, 0.03 #5179), 0jtdp (0.20 #122, 0.01 #2760, 0.01 #2863), 0q00t (0.20 #196) >> Best rule #3678 for best value: >> intensional similarity = 6 >> extensional distance = 269 >> proper extension: 01zfzb; 07bxqz; >> query: (?x5017, 01z4y) <- film(?x4708, ?x5017), film_release_distribution_medium(?x5017, ?x81), genre(?x5017, ?x53), titles(?x512, ?x5017), titles(?x512, ?x6752), ?x6752 = 065_cjc >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1771 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 90 *> proper extension: 05p3738; *> query: (?x5017, 03mqtr) <- titles(?x512, ?x5017), titles(?x53, ?x5017), ?x512 = 07ssc, genre(?x11385, ?x53), genre(?x6094, ?x53), genre(?x273, ?x53), film_release_distribution_medium(?x6094, ?x81), film(?x8667, ?x11385) *> conf = 0.13 ranks of expected_values: 15 EVAL 04nm0n0 titles! 03mqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 105.000 80.000 0.686 http://example.org/media_common/netflix_genre/titles #19127-06mkj PRED entity: 06mkj PRED relation: form_of_government PRED expected values: 01fpfn => 269 concepts (269 used for prediction) PRED predicted values (max 10 best out of 4): 01fpfn (0.41 #558, 0.41 #338, 0.41 #594), 06cx9 (0.41 #557, 0.39 #809, 0.37 #593), 01d9r3 (0.37 #559, 0.34 #595, 0.33 #71), 026wp (0.18 #64, 0.18 #60, 0.14 #156) >> Best rule #558 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 0n3g; 035hm; 05rznz; >> query: (?x2152, 01fpfn) <- adjoins(?x2152, ?x87), currency(?x2152, ?x170), form_of_government(?x2152, ?x1926) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mkj form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 269.000 269.000 0.413 http://example.org/location/country/form_of_government #19126-0k5px PRED entity: 0k5px PRED relation: genre PRED expected values: 01g6gs 03bxz7 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 87): 03bxz7 (0.48 #295, 0.10 #775, 0.10 #1735), 05p553 (0.42 #724, 0.34 #5407, 0.34 #1444), 02l7c8 (0.38 #735, 0.33 #135, 0.33 #15), 04xvlr (0.35 #241, 0.19 #1201, 0.17 #3722), 082gq (0.33 #150, 0.15 #390, 0.12 #510), 01jfsb (0.30 #1452, 0.30 #2652, 0.29 #1092), 02kdv5l (0.27 #3002, 0.26 #7326, 0.26 #2642), 01g6gs (0.24 #620, 0.23 #380, 0.20 #500), 03k9fj (0.23 #1571, 0.23 #3011, 0.23 #2171), 0lsxr (0.19 #1088, 0.18 #1208, 0.18 #1328) >> Best rule #295 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 080lkt7; >> query: (?x11039, 03bxz7) <- music(?x11039, ?x9127), titles(?x1316, ?x11039), ?x1316 = 017fp >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 0k5px genre 03bxz7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.481 http://example.org/film/film/genre EVAL 0k5px genre 01g6gs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 65.000 65.000 0.481 http://example.org/film/film/genre #19125-05bcl PRED entity: 05bcl PRED relation: contains PRED expected values: 01vmv_ 02hgz => 174 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2901): 05bcl (0.52 #264637, 0.43 #226413, 0.42 #217593), 0j5g9 (0.52 #264637, 0.43 #226413, 0.42 #217593), 07ssc (0.52 #264637, 0.43 #226413, 0.42 #217593), 01vmv_ (0.50 #26464, 0.41 #67637, 0.35 #97038), 0hyxv (0.34 #235233, 0.33 #507, 0.31 #44108), 0m75g (0.34 #235233, 0.33 #985, 0.31 #44108), 0fm2_ (0.34 #235233, 0.33 #121, 0.31 #44108), 0ck6r (0.34 #235233, 0.33 #1473, 0.31 #44108), 0g133 (0.34 #235233, 0.33 #1329, 0.31 #44108), 02fvv (0.34 #235233, 0.33 #2557, 0.31 #44108) >> Best rule #264637 for best value: >> intensional similarity = 4 >> extensional distance = 178 >> proper extension: 0697s; >> query: (?x4071, ?x512) <- jurisdiction_of_office(?x182, ?x4071), contains(?x4071, ?x14206), contains(?x512, ?x14206), adjoins(?x4071, ?x429) >> conf = 0.52 => this is the best rule for 3 predicted values *> Best rule #26464 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 01d8l; *> query: (?x4071, ?x11459) <- jurisdiction_of_office(?x182, ?x4071), contains(?x4071, ?x4070), state_province_region(?x11459, ?x4070), contains(?x512, ?x4071) *> conf = 0.50 ranks of expected_values: 4, 239 EVAL 05bcl contains 02hgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 174.000 93.000 0.523 http://example.org/location/location/contains EVAL 05bcl contains 01vmv_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 174.000 93.000 0.523 http://example.org/location/location/contains #19124-011_3s PRED entity: 011_3s PRED relation: award PRED expected values: 0gqyl 03qgjwc => 109 concepts (107 used for prediction) PRED predicted values (max 10 best out of 277): 09sb52 (0.77 #436, 0.74 #833, 0.48 #8378), 019bnn (0.72 #34162, 0.71 #7147, 0.70 #21845), 02qkk9_ (0.71 #7147, 0.70 #21845, 0.70 #25024), 05pcn59 (0.27 #474, 0.26 #871, 0.15 #8416), 05zr6wv (0.27 #413, 0.26 #810, 0.15 #1207), 0cqhk0 (0.24 #2417, 0.19 #6387, 0.18 #9565), 01by1l (0.24 #5268, 0.15 #1298, 0.10 #8446), 01bgqh (0.20 #1232, 0.17 #5202, 0.08 #4408), 03qbh5 (0.20 #1390, 0.15 #5360, 0.06 #4963), 05zvj3m (0.20 #1279, 0.05 #3264, 0.05 #2470) >> Best rule #436 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 06151l; 0c4f4; 02qgyv; 015t56; 019pm_; 08swgx; 014488; 03_6y; 0391jz; 04w391; ... >> query: (?x3267, 09sb52) <- award_nominee(?x3267, ?x2352), ?x2352 = 01pgzn_, award(?x3267, ?x375) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #24229 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1577 *> proper extension: 06jntd; *> query: (?x3267, ?x746) <- award_winner(?x2719, ?x3267), nominated_for(?x746, ?x2719) *> conf = 0.14 ranks of expected_values: 25, 52 EVAL 011_3s award 03qgjwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 109.000 107.000 0.767 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 011_3s award 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 109.000 107.000 0.767 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19123-04jm_hq PRED entity: 04jm_hq PRED relation: film! PRED expected values: 01wbg84 => 70 concepts (31 used for prediction) PRED predicted values (max 10 best out of 795): 02qjpv5 (0.42 #58306, 0.37 #47891, 0.11 #39560), 02d42t (0.42 #58306, 0.37 #47891), 03h_9lg (0.20 #133, 0.05 #8461, 0.01 #43858), 049g_xj (0.20 #244, 0.04 #45808, 0.03 #4408), 02t_st (0.20 #1289, 0.04 #7535, 0.02 #11699), 07b2lv (0.20 #366, 0.04 #2448, 0.02 #12858), 06m6p7 (0.20 #1370, 0.03 #5534, 0.02 #11780), 044rvb (0.20 #102, 0.03 #4266, 0.02 #14676), 079vf (0.20 #8, 0.03 #8336, 0.01 #39568), 0jbp0 (0.20 #1759, 0.03 #10087, 0.01 #41319) >> Best rule #58306 for best value: >> intensional similarity = 3 >> extensional distance = 971 >> proper extension: 0cp08zg; >> query: (?x5169, ?x2415) <- titles(?x53, ?x5169), nominated_for(?x2415, ?x5169), film(?x609, ?x5169) >> conf = 0.42 => this is the best rule for 2 predicted values *> Best rule #39607 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 649 *> proper extension: 0401sg; 0crfwmx; 03bx2lk; 02qrv7; 08k40m; 03l6q0; 02rrfzf; 02_qt; 015g28; 027pfb2; ... *> query: (?x5169, 01wbg84) <- film(?x2415, ?x5169), genre(?x5169, ?x53), participant(?x2415, ?x3329), actor(?x4517, ?x2415) *> conf = 0.02 ranks of expected_values: 355 EVAL 04jm_hq film! 01wbg84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 70.000 31.000 0.419 http://example.org/film/actor/film./film/performance/film #19122-022g44 PRED entity: 022g44 PRED relation: gender PRED expected values: 05zppz => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 5): 05zppz (0.89 #20, 0.87 #5, 0.86 #37), 02zsn (0.29 #109, 0.28 #107, 0.27 #103), 0fltx (0.12 #36, 0.12 #55), 01hbgs (0.12 #36, 0.12 #55), 0c58k (0.12 #36, 0.12 #55) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 119 >> proper extension: 01c59k; 015rhv; 0674cw; 0cvbb9q; 0gv2r; 01p87y; 05z_p6; 0kc6; 04rg6; 04dyqk; ... >> query: (?x4961, 05zppz) <- profession(?x4961, ?x524), ?x524 = 02jknp, nationality(?x4961, ?x1310), place_of_death(?x4961, ?x4962) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022g44 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.893 http://example.org/people/person/gender #19121-0ds2n PRED entity: 0ds2n PRED relation: film! PRED expected values: 038nv6 => 93 concepts (55 used for prediction) PRED predicted values (max 10 best out of 889): 02ts3h (0.58 #113947, 0.49 #64226, 0.44 #105661), 016tt2 (0.44 #105661, 0.39 #47650, 0.37 #113946), 0lzb8 (0.43 #2167, 0.01 #14594), 026c1 (0.33 #356, 0.03 #6569, 0.03 #10712), 02js6_ (0.33 #445, 0.03 #31074, 0.03 #41434), 05txrz (0.33 #762, 0.03 #44269, 0.02 #11118), 023mdt (0.33 #1567, 0.01 #9851, 0.01 #26425), 01nms7 (0.33 #1403, 0.01 #17974, 0.01 #24190), 01t6xz (0.33 #1137, 0.01 #23924), 02h3tp (0.33 #1366) >> Best rule #113947 for best value: >> intensional similarity = 3 >> extensional distance = 1081 >> proper extension: 0gfzgl; 03y3bp7; 01f3p_; 02sqkh; 03g9xj; 0cskb; 03_b1g; >> query: (?x3218, ?x521) <- titles(?x8581, ?x3218), nominated_for(?x521, ?x3218), film(?x521, ?x1488) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ds2n film! 038nv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 55.000 0.583 http://example.org/film/actor/film./film/performance/film #19120-044zvm PRED entity: 044zvm PRED relation: student! PRED expected values: 053mhx => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 73): 02zcnq (0.12 #146, 0.10 #672), 065y4w7 (0.12 #14, 0.06 #4748, 0.05 #14216), 07wjk (0.12 #63, 0.01 #6901, 0.01 #7427), 0bwfn (0.10 #2904, 0.09 #3430, 0.09 #5008), 03ksy (0.10 #632, 0.04 #1158, 0.04 #38504), 033x5p (0.10 #668), 015nl4 (0.06 #2697, 0.05 #24263, 0.04 #15847), 09f2j (0.05 #2789, 0.05 #14361, 0.04 #6471), 04b_46 (0.04 #1804, 0.04 #1278, 0.04 #3382), 07tg4 (0.04 #1138, 0.04 #4294, 0.03 #5346) >> Best rule #146 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 03mdt; >> query: (?x12041, 02zcnq) <- award_nominee(?x798, ?x12041), award_winner(?x4881, ?x12041), ?x4881 = 02kk_c >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #2924 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 143 *> proper extension: 08h79x; *> query: (?x12041, 053mhx) <- student(?x5522, ?x12041), nominated_for(?x12041, ?x238), spouse(?x12041, ?x496) *> conf = 0.02 ranks of expected_values: 34 EVAL 044zvm student! 053mhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 101.000 101.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #19119-02wgln PRED entity: 02wgln PRED relation: film PRED expected values: 05pdd86 0symg => 65 concepts (59 used for prediction) PRED predicted values (max 10 best out of 595): 012s1d (0.62 #917, 0.03 #4481, 0.03 #2699), 0340hj (0.50 #235, 0.03 #3799, 0.01 #9145), 01hqhm (0.41 #53472, 0.38 #53471, 0.36 #67734), 0462hhb (0.41 #53472, 0.38 #53471, 0.36 #67734), 0h7t36 (0.16 #3459, 0.05 #30295, 0.05 #35642), 0g0x9c (0.12 #1360, 0.05 #3142, 0.03 #65951), 0298n7 (0.12 #1344, 0.03 #65951, 0.03 #3126), 01pvxl (0.12 #904, 0.03 #4468), 024mpp (0.12 #646), 04sntd (0.12 #488) >> Best rule #917 for best value: >> intensional similarity = 2 >> extensional distance = 14 >> proper extension: 079vf; 01h8f; 07nx9j; 015gsv; 01r9md; >> query: (?x1958, 012s1d) <- film(?x1958, ?x4502), ?x4502 = 02wgk1 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #40993 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1678 *> proper extension: 02v49c; *> query: (?x1958, ?x1209) <- award_nominee(?x1958, ?x6618), film(?x6618, ?x1209) *> conf = 0.03 ranks of expected_values: 413 EVAL 02wgln film 0symg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 59.000 0.625 http://example.org/film/actor/film./film/performance/film EVAL 02wgln film 05pdd86 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 59.000 0.625 http://example.org/film/actor/film./film/performance/film #19118-0yc84 PRED entity: 0yc84 PRED relation: place_of_birth! PRED expected values: 026r8q => 150 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1543): 0436f4 (0.40 #26095, 0.38 #167062, 0.34 #143566), 02lfcm (0.14 #55, 0.04 #5273, 0.02 #54811), 01zlh5 (0.14 #1707, 0.04 #6925, 0.02 #19974), 039x1k (0.14 #1574, 0.04 #6792, 0.02 #19841), 015qt5 (0.14 #847, 0.04 #6065, 0.02 #19114), 04x4s2 (0.14 #727, 0.04 #5945, 0.02 #18994), 0pmhf (0.14 #490, 0.04 #5708, 0.02 #18757), 06z9yh (0.14 #2561, 0.04 #7779, 0.02 #18219), 0b_7k (0.14 #530, 0.04 #5748, 0.02 #16188), 04yyhw (0.05 #5217, 0.02 #10435, 0.02 #13046) >> Best rule #26095 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 0f2tj; >> query: (?x1110, ?x446) <- country(?x1110, ?x94), category(?x1110, ?x134), location(?x446, ?x1110), state(?x1110, ?x335) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0yc84 place_of_birth! 026r8q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 71.000 0.402 http://example.org/people/person/place_of_birth #19117-03_f0 PRED entity: 03_f0 PRED relation: instrumentalists! PRED expected values: 07_l6 => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 93): 05r5c (0.74 #443, 0.67 #4099, 0.67 #95), 0342h (0.65 #9499, 0.63 #9848, 0.61 #4008), 05148p4 (0.35 #456, 0.34 #9865, 0.34 #4112), 07_l6 (0.33 #62, 0.22 #149, 0.12 #3917), 018vs (0.29 #9857, 0.29 #9508, 0.28 #11952), 03qjg (0.18 #2836, 0.17 #2923, 0.17 #4055), 0l14qv (0.17 #440, 0.13 #7324, 0.10 #9849), 02hnl (0.17 #2906, 0.17 #9529, 0.17 #9878), 0l14md (0.15 #2270, 0.14 #355, 0.13 #7326), 026t6 (0.15 #524, 0.14 #350, 0.14 #2874) >> Best rule #443 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 03bnv; >> query: (?x8177, 05r5c) <- location(?x8177, ?x4861), administrative_parent(?x4861, ?x10766), music(?x7307, ?x8177), contains(?x1264, ?x4861), instrumentalists(?x75, ?x8177) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #62 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 043d4; 0h336; *> query: (?x8177, 07_l6) <- nationality(?x8177, ?x1264), influenced_by(?x9600, ?x8177), influenced_by(?x3774, ?x8177), ?x3774 = 04k15, influenced_by(?x920, ?x9600), profession(?x9600, ?x353) *> conf = 0.33 ranks of expected_values: 4 EVAL 03_f0 instrumentalists! 07_l6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 199.000 199.000 0.739 http://example.org/music/instrument/instrumentalists #19116-04n6k PRED entity: 04n6k PRED relation: taxonomy! PRED expected values: 01lp8 0jgd 0d060g 015fr 06npd 05fkf 083p7 07ww5 0cd25 07t21 083pr 04rjg 05sb1 02rxj 0x67 03g3w 06btq 0b3wk 0jdd 012wgb 087vz 0jgx 0j5g9 06vbd 04tgp 07dzf 06tw8 01rxw 01pwz 02_7t 07t2k 022840 016zwt 07f5x 03_js 03_nq 05bmq 034ns 04s9n 03f2w 05fh2 0gzh 0gjm7 015c1b 07c1v 0jvq 01664_ 026y05 025x7g_ 0qb7t => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 04n6k taxonomy! 0qb7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 025x7g_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 026y05 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 01664_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0jvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 07c1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 015c1b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0gjm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0gzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 05fh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 03f2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 04s9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 034ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 05bmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 03_nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 03_js CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 07f5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 016zwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 022840 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 07t2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 02_7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 01pwz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 01rxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 06tw8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 07dzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 04tgp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 06vbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0j5g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 087vz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 012wgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0jdd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0b3wk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 06btq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 03g3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0x67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 02rxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 05sb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 04rjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 083pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 07t21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0cd25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 07ww5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 083p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 05fkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 06npd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 015fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0d060g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 0jgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy EVAL 04n6k taxonomy! 01lp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #19115-03wkwg PRED entity: 03wkwg PRED relation: colors! PRED expected values: 02h30z => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1204): 017z88 (0.71 #5602, 0.12 #6537, 0.09 #2332), 016sd3 (0.67 #3629, 0.60 #2695, 0.50 #1762), 0lyjf (0.60 #2464, 0.50 #1531, 0.38 #5266), 02qw_v (0.60 #2687, 0.50 #1754, 0.38 #5489), 01s7pm (0.50 #1804, 0.50 #871, 0.40 #2737), 02rv1w (0.50 #1745, 0.50 #812, 0.40 #2678), 03np_7 (0.50 #1834, 0.50 #901, 0.40 #2767), 01v3k2 (0.50 #3563, 0.50 #763, 0.40 #2629), 01tntf (0.50 #3606, 0.50 #806, 0.33 #340), 01b1mj (0.50 #3282, 0.50 #482, 0.33 #16) >> Best rule #5602 for best value: >> intensional similarity = 22 >> extensional distance = 6 >> proper extension: 01l849; 09ggk; >> query: (?x9464, ?x2909) <- colors(?x6925, ?x9464), colors(?x735, ?x9464), major_field_of_study(?x6925, ?x254), student(?x735, ?x3329), company(?x3484, ?x735), student(?x6925, ?x1051), school(?x3114, ?x735), school(?x1160, ?x735), state_province_region(?x735, ?x1227), institution(?x734, ?x6925), gender(?x1051, ?x231), award_nominee(?x286, ?x1051), ?x286 = 014zcr, position(?x3114, ?x3346), major_field_of_study(?x735, ?x373), ?x3346 = 02g_7z, category(?x1160, ?x134), student(?x2909, ?x1051), film(?x1051, ?x377), contains(?x94, ?x735), colors(?x705, ?x9464), award_winner(?x3329, ?x2100) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #404 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 1 *> proper extension: 083jv; *> query: (?x9464, 02h30z) <- colors(?x11387, ?x9464), colors(?x8016, ?x9464), colors(?x6925, ?x9464), colors(?x4187, ?x9464), colors(?x581, ?x9464), ?x6925 = 01bm_, ?x581 = 06pwq, school_type(?x8016, ?x3092), institution(?x865, ?x8016), colors(?x9576, ?x9464), colors(?x7078, ?x9464), colors(?x705, ?x9464), major_field_of_study(?x4187, ?x7979), ?x865 = 02h4rq6, fraternities_and_sororities(?x11387, ?x3697), organization(?x346, ?x4187), state_province_region(?x11387, ?x2831), major_field_of_study(?x8016, ?x1527), student(?x4187, ?x201), ?x7979 = 036nz, position(?x705, ?x935), ?x1527 = 04_tv, ?x7078 = 0ws7, sport(?x9576, ?x12913), award(?x201, ?x2016), team(?x1114, ?x705) *> conf = 0.33 ranks of expected_values: 179 EVAL 03wkwg colors! 02h30z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 19.000 19.000 0.708 http://example.org/education/educational_institution/colors #19114-02wzl1d PRED entity: 02wzl1d PRED relation: award_winner PRED expected values: 01wgcvn 02d42t 016yvw => 40 concepts (31 used for prediction) PRED predicted values (max 10 best out of 2534): 01nr36 (0.50 #4261, 0.27 #25925, 0.07 #11882), 01l9p (0.50 #3287, 0.07 #10908, 0.05 #17009), 0l6px (0.43 #6428, 0.33 #7952, 0.27 #9476), 04sry (0.39 #8697, 0.36 #10221, 0.22 #14797), 08hsww (0.37 #1526, 0.33 #737, 0.32 #1525), 0h3mrc (0.37 #1526, 0.33 #581, 0.32 #1525), 01kwsg (0.37 #1526, 0.33 #732, 0.26 #3053), 0170s4 (0.37 #1526, 0.33 #332, 0.26 #3053), 018ygt (0.37 #1526, 0.33 #952, 0.26 #3053), 0f4dx2 (0.37 #1526, 0.33 #486, 0.26 #3053) >> Best rule #4261 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 092c5f; 02glmx; >> query: (?x944, 01nr36) <- award_winner(?x944, ?x5043), award_winner(?x944, ?x3568), nominated_for(?x5043, ?x10531), award_winner(?x1245, ?x5043), ?x1245 = 0gqwc, award_winner(?x3012, ?x3568), nationality(?x3568, ?x1023), location(?x5043, ?x10242), produced_by(?x7128, ?x3568), ?x7128 = 03p2xc, profession(?x3568, ?x319), titles(?x162, ?x10531) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #822 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 03gyp30; *> query: (?x944, 016yvw) <- award_winner(?x944, ?x7543), award_winner(?x944, ?x5043), award_winner(?x944, ?x3789), award_winner(?x944, ?x906), ?x5043 = 015q43, participant(?x3789, ?x545), ?x906 = 0pz7h, award_nominee(?x3789, ?x1342), award_nominee(?x7543, ?x2912), award_winner(?x92, ?x3789), ?x2912 = 0bt4r4, film(?x3789, ?x1192), profession(?x7543, ?x353), honored_for(?x944, ?x485) *> conf = 0.33 ranks of expected_values: 64, 81, 337 EVAL 02wzl1d award_winner 016yvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 40.000 31.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02wzl1d award_winner 02d42t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 40.000 31.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02wzl1d award_winner 01wgcvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 40.000 31.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #19113-0x82 PRED entity: 0x82 PRED relation: language! PRED expected values: 0c38gj => 37 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1829): 0fr63l (0.65 #27737, 0.63 #27736, 0.33 #95), 02qmsr (0.65 #27737, 0.63 #27736, 0.33 #390), 02vrgnr (0.65 #27737, 0.63 #27736, 0.33 #752), 0466s8n (0.65 #27737, 0.63 #27736, 0.33 #1576), 0pvms (0.65 #27737, 0.63 #27736, 0.33 #397), 02qhqz4 (0.65 #27737, 0.63 #27736, 0.33 #330), 0gd0c7x (0.65 #27737, 0.33 #303, 0.25 #2035), 0c0zq (0.56 #3467, 0.56 #3466, 0.51 #3472), 01cmp9 (0.56 #3467, 0.56 #3466, 0.51 #3472), 04vr_f (0.56 #3467, 0.56 #3466, 0.51 #3472) >> Best rule #27737 for best value: >> intensional similarity = 16 >> extensional distance = 20 >> proper extension: 055qm; >> query: (?x13263, ?x721) <- languages(?x1735, ?x13263), award_winner(?x2478, ?x1735), award_winner(?x1245, ?x1735), gender(?x1735, ?x514), nominated_for(?x2478, ?x915), award(?x91, ?x2478), award(?x1735, ?x375), award(?x715, ?x375), nominated_for(?x375, ?x293), film(?x1735, ?x10225), film(?x1735, ?x721), location(?x1735, ?x1523), nationality(?x1735, ?x94), music(?x10225, ?x565), award(?x495, ?x1245), award(?x197, ?x1245) >> conf = 0.65 => this is the best rule for 7 predicted values *> Best rule #3467 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 2 *> proper extension: 02bv9; *> query: (?x13263, ?x167) <- countries_spoken_in(?x13263, ?x9458), language(?x2203, ?x13263), ?x9458 = 05bmq, nominated_for(?x2853, ?x2203), nominated_for(?x1162, ?x2203), award(?x253, ?x1162), nominated_for(?x1162, ?x7880), nominated_for(?x1162, ?x4159), nominated_for(?x1162, ?x3430), nominated_for(?x1162, ?x167), nominated_for(?x1162, ?x144), ?x4159 = 011yr9, ?x144 = 0m313, nominated_for(?x2422, ?x2203), ?x7880 = 04jplwp, ?x3430 = 0ctb4g, nominated_for(?x2853, ?x8063), ?x8063 = 01718w, award(?x6980, ?x2853), award(?x5661, ?x2853), ?x6980 = 0zcbl, film(?x382, ?x2203), ?x5661 = 03ym1 *> conf = 0.56 ranks of expected_values: 80 EVAL 0x82 language! 0c38gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 37.000 16.000 0.646 http://example.org/film/film/language #19112-03x16f PRED entity: 03x16f PRED relation: award_nominee PRED expected values: 05lb65 => 104 concepts (58 used for prediction) PRED predicted values (max 10 best out of 803): 03lt8g (0.82 #9316, 0.82 #4657, 0.81 #27945), 0443y3 (0.82 #9316, 0.82 #4657, 0.81 #27945), 05lb65 (0.76 #6210, 0.60 #3881, 0.44 #8540), 06b0d2 (0.65 #4881, 0.56 #7211, 0.40 #2552), 01rs5p (0.52 #9153, 0.40 #4494, 0.35 #6823), 05lb87 (0.52 #7265, 0.35 #4935, 0.20 #2606), 030znt (0.47 #4936, 0.40 #7266, 0.40 #2607), 01wb8bs (0.41 #5554, 0.36 #7884, 0.30 #3225), 03x16f (0.40 #8890, 0.40 #4231, 0.35 #6560), 0308kx (0.40 #3283, 0.35 #5612, 0.32 #7942) >> Best rule #9316 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 035gjq; 0443y3; 06_vpyq; 048q6x; 0bbvr84; >> query: (?x8746, ?x444) <- award_nominee(?x3051, ?x8746), award_nominee(?x444, ?x8746), award_nominee(?x8746, ?x516), ?x3051 = 0gd_b_ >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #6210 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 03zqc1; 0308kx; *> query: (?x8746, 05lb65) <- award_nominee(?x2129, ?x8746), film(?x8746, ?x7757), ?x2129 = 0443y3 *> conf = 0.76 ranks of expected_values: 3 EVAL 03x16f award_nominee 05lb65 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 58.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #19111-019l68 PRED entity: 019l68 PRED relation: award PRED expected values: 0bdwft => 135 concepts (117 used for prediction) PRED predicted values (max 10 best out of 258): 0gqyl (0.78 #38795, 0.70 #39604, 0.69 #32326), 0gqy2 (0.40 #164, 0.26 #1780, 0.21 #3800), 0f4x7 (0.34 #1647, 0.30 #31, 0.24 #839), 09sb52 (0.33 #6909, 0.30 #8930, 0.28 #17010), 0ck27z (0.31 #9385, 0.28 #9789, 0.26 #15849), 0bdw6t (0.30 #110, 0.07 #1322, 0.06 #514), 05pcn59 (0.23 #6949, 0.22 #7353, 0.21 #8162), 054ky1 (0.20 #109, 0.16 #917, 0.10 #1725), 0789_m (0.20 #1636, 0.12 #1232, 0.11 #3656), 09qvc0 (0.20 #40, 0.12 #1252, 0.06 #444) >> Best rule #38795 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 06lxn; >> query: (?x9055, ?x1972) <- award_winner(?x1972, ?x9055), award(?x91, ?x1972), ceremony(?x1972, ?x78) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #877 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 01kgg9; *> query: (?x9055, 0bdwft) <- award(?x9055, ?x1245), place_of_death(?x9055, ?x191), participant(?x6073, ?x9055) *> conf = 0.11 ranks of expected_values: 38 EVAL 019l68 award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 135.000 117.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19110-0h0jz PRED entity: 0h0jz PRED relation: actor! PRED expected values: 027tbrc => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 111): 026fs38 (0.12 #11063, 0.11 #9482, 0.10 #9746), 026gyn_ (0.12 #11063, 0.11 #9482, 0.10 #9746), 0bx0l (0.12 #11063, 0.11 #9482, 0.10 #9746), 0ywrc (0.10 #8162, 0.09 #13181, 0.09 #13180), 026bfsh (0.07 #358, 0.05 #4044, 0.05 #95), 02gjrc (0.05 #751, 0.03 #1014, 0.02 #1278), 02_1q9 (0.04 #3953, 0.03 #8168, 0.02 #10013), 0kfv9 (0.04 #3974, 0.02 #8189, 0.02 #6868), 05lfwd (0.03 #3260, 0.03 #3787, 0.02 #5630), 02zv4b (0.03 #549, 0.02 #3972, 0.01 #2392) >> Best rule #11063 for best value: >> intensional similarity = 2 >> extensional distance = 939 >> proper extension: 01wz01; 02xwq9; 04d2yp; >> query: (?x294, ?x1903) <- award_winner(?x1903, ?x294), film(?x294, ?x1763) >> conf = 0.12 => this is the best rule for 3 predicted values *> Best rule #3983 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 291 *> proper extension: 04bbv7; *> query: (?x294, 027tbrc) <- people(?x5056, ?x294), actor(?x293, ?x294), location(?x294, ?x4510) *> conf = 0.01 ranks of expected_values: 110 EVAL 0h0jz actor! 027tbrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 106.000 106.000 0.124 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #19109-02f9wb PRED entity: 02f9wb PRED relation: place_of_birth PRED expected values: 01n7q => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 67): 02_286 (0.08 #1427, 0.08 #18327, 0.08 #7763), 030qb3t (0.05 #5686, 0.04 #37379, 0.04 #9207), 0cr3d (0.04 #4318, 0.04 #1502, 0.04 #3614), 01_d4 (0.04 #4290, 0.04 #3586, 0.04 #6402), 09c7w0 (0.04 #2113, 0.03 #3521, 0.03 #4929), 0rh6k (0.03 #2, 0.03 #1410, 0.03 #706), 094jv (0.03 #61, 0.02 #4989, 0.01 #765), 04jpl (0.02 #9161, 0.02 #26771, 0.02 #41559), 01531 (0.02 #9962, 0.02 #10666, 0.02 #7145), 01jr6 (0.02 #1551, 0.02 #4367, 0.01 #3663) >> Best rule #1427 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 030_1_; >> query: (?x5958, 02_286) <- award_winner(?x8933, ?x5958), award_winner(?x1265, ?x8933), ?x1265 = 05c1t6z >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02f9wb place_of_birth 01n7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.081 http://example.org/people/person/place_of_birth #19108-02csf PRED entity: 02csf PRED relation: major_field_of_study PRED expected values: 05qdh => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 109): 05qdh (0.82 #2768, 0.81 #2767, 0.81 #2950), 02j62 (0.73 #1724, 0.67 #1904, 0.67 #1814), 02822 (0.62 #1103, 0.42 #568, 0.39 #1285), 02h40lc (0.50 #181, 0.44 #270, 0.40 #359), 01mkq (0.47 #902, 0.33 #1430, 0.21 #1443), 0_jm (0.41 #1206, 0.29 #758, 0.27 #848), 0fdys (0.40 #923, 0.38 #211, 0.33 #300), 037mh8 (0.40 #946, 0.33 #1430, 0.31 #1124), 03g3w (0.40 #2079, 0.33 #1430, 0.31 #2434), 05qfh (0.38 #209, 0.36 #475, 0.33 #1281) >> Best rule #2768 for best value: >> intensional similarity = 8 >> extensional distance = 79 >> proper extension: 03xks; >> query: (?x14034, ?x7017) <- major_field_of_study(?x10940, ?x14034), major_field_of_study(?x7017, ?x14034), major_field_of_study(?x5981, ?x7017), contains(?x94, ?x5981), student(?x5981, ?x2408), currency(?x5981, ?x170), ?x94 = 09c7w0, major_field_of_study(?x254, ?x7017) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02csf major_field_of_study 05qdh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.819 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #19107-0k5g9 PRED entity: 0k5g9 PRED relation: costume_design_by PRED expected values: 02cqbx => 103 concepts (94 used for prediction) PRED predicted values (max 10 best out of 26): 02cqbx (0.15 #212, 0.12 #241, 0.11 #156), 0c6g29 (0.08 #63, 0.07 #91, 0.03 #147), 0gl88b (0.08 #61, 0.04 #201, 0.04 #230), 04vzv4 (0.05 #209, 0.05 #238, 0.05 #153), 0dck27 (0.05 #204, 0.05 #233, 0.03 #148), 02w0dc0 (0.05 #113, 0.02 #339, 0.02 #650), 03mfqm (0.04 #1033, 0.04 #526, 0.03 #919), 09x8ms (0.04 #224, 0.04 #253, 0.03 #168), 05x2t7 (0.04 #202, 0.04 #231, 0.01 #287), 0bytfv (0.03 #462, 0.03 #490, 0.03 #1197) >> Best rule #212 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 0bl06; 029jt9; 06pyc2; >> query: (?x2717, 02cqbx) <- film(?x2465, ?x2717), film_art_direction_by(?x2717, ?x4168), music(?x2717, ?x9946), nominated_for(?x2716, ?x2717) >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k5g9 costume_design_by 02cqbx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 94.000 0.145 http://example.org/film/film/costume_design_by #19106-05r6t PRED entity: 05r6t PRED relation: artists PRED expected values: 01vv126 01w02sy 02t3ln 0143q0 016l09 016vn3 => 76 concepts (35 used for prediction) PRED predicted values (max 10 best out of 923): 014pg1 (0.62 #14768, 0.45 #17789, 0.44 #15774), 01w8n89 (0.60 #5325, 0.59 #21422, 0.53 #18404), 01518s (0.60 #16088, 0.57 #13039, 0.45 #17096), 01shhf (0.60 #5839, 0.57 #12879, 0.41 #18918), 0ycp3 (0.60 #16088, 0.50 #8617, 0.45 #17096), 0d193h (0.60 #16088, 0.50 #8393, 0.45 #17096), 0b1hw (0.60 #5920, 0.50 #8935, 0.34 #19112), 011_vz (0.60 #16088, 0.45 #17096, 0.43 #12854), 014_lq (0.60 #16088, 0.45 #17096, 0.40 #5478), 0fsyx (0.60 #16088, 0.45 #17096, 0.38 #15066) >> Best rule #14768 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 011j5x; 0xjl2; 0y3_8; 059kh; >> query: (?x5934, 014pg1) <- artists(?x5934, ?x11633), artists(?x5934, ?x5935), artists(?x5934, ?x3399), parent_genre(?x2407, ?x5934), place_of_birth(?x3399, ?x9300), ?x11633 = 01ww_vs, artist(?x8738, ?x5935) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3416 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0xhtw; *> query: (?x5934, 02t3ln) <- artists(?x5934, ?x10639), artists(?x5934, ?x3399), artists(?x5934, ?x1955), parent_genre(?x2407, ?x5934), place_of_birth(?x3399, ?x9300), ?x10639 = 03q_w5, ?x1955 = 0285c *> conf = 0.50 ranks of expected_values: 32, 69, 126, 150, 182, 204 EVAL 05r6t artists 016vn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 76.000 35.000 0.625 http://example.org/music/genre/artists EVAL 05r6t artists 016l09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 76.000 35.000 0.625 http://example.org/music/genre/artists EVAL 05r6t artists 0143q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 76.000 35.000 0.625 http://example.org/music/genre/artists EVAL 05r6t artists 02t3ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 76.000 35.000 0.625 http://example.org/music/genre/artists EVAL 05r6t artists 01w02sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 76.000 35.000 0.625 http://example.org/music/genre/artists EVAL 05r6t artists 01vv126 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 35.000 0.625 http://example.org/music/genre/artists #19105-01pctb PRED entity: 01pctb PRED relation: profession PRED expected values: 015cjr 0d1pc => 105 concepts (87 used for prediction) PRED predicted values (max 10 best out of 66): 0dxtg (0.66 #2807, 0.64 #896, 0.33 #308), 01d_h8 (0.58 #300, 0.55 #888, 0.50 #741), 09jwl (0.36 #6929, 0.35 #5458, 0.26 #2664), 015cjr (0.33 #637, 0.10 #931, 0.08 #343), 018gz8 (0.27 #604, 0.23 #898, 0.16 #3250), 02jknp (0.26 #2801, 0.26 #890, 0.25 #302), 0nbcg (0.26 #6942, 0.26 #5471, 0.20 #3853), 02krf9 (0.26 #2819, 0.23 #908, 0.14 #173), 0dz3r (0.25 #2648, 0.23 #5442, 0.23 #3824), 012t_z (0.25 #307, 0.08 #895, 0.08 #601) >> Best rule #2807 for best value: >> intensional similarity = 3 >> extensional distance = 207 >> proper extension: 0dbpyd; 06j0md; 02pp_q_; 0415svh; 02l840; 02773nt; 02773m2; 02778pf; 04l3_z; 04wvhz; ... >> query: (?x4884, 0dxtg) <- nationality(?x4884, ?x94), gender(?x4884, ?x514), producer_type(?x4884, ?x632) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #637 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 02h9_l; *> query: (?x4884, 015cjr) <- location(?x4884, ?x7934), program(?x4884, ?x7813), contains(?x7934, ?x9402) *> conf = 0.33 ranks of expected_values: 4, 13 EVAL 01pctb profession 0d1pc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 105.000 87.000 0.656 http://example.org/people/person/profession EVAL 01pctb profession 015cjr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 87.000 0.656 http://example.org/people/person/profession #19104-01crd5 PRED entity: 01crd5 PRED relation: film_release_region! PRED expected values: 0b76d_m 087wc7n 0bmhvpr 05c26ss 0gtxj2q 043sct5 0cc97st 047p798 => 162 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1291): 01fmys (0.87 #16962, 0.77 #23397, 0.66 #29832), 017jd9 (0.84 #17299, 0.84 #23734, 0.78 #30169), 08hmch (0.84 #16843, 0.82 #23278, 0.74 #29713), 087wc7n (0.84 #16816, 0.77 #23251, 0.66 #29686), 0btpm6 (0.84 #17678, 0.73 #24113, 0.70 #30548), 043tvp3 (0.82 #24054, 0.82 #17619, 0.78 #30489), 017gm7 (0.82 #16882, 0.77 #23317, 0.74 #29752), 05pdh86 (0.82 #17275, 0.75 #23710, 0.72 #30145), 05zlld0 (0.82 #17180, 0.75 #23615, 0.66 #30050), 0ch26b_ (0.80 #23385, 0.76 #16950, 0.60 #29820) >> Best rule #16962 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 05r4w; 09c7w0; 0jgd; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 04gzd; 03rt9; ... >> query: (?x8593, 01fmys) <- film_release_region(?x2350, ?x8593), country(?x10757, ?x8593), ?x2350 = 0661m4p >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #16816 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 05r4w; 09c7w0; 0jgd; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 04gzd; 03rt9; ... *> query: (?x8593, 087wc7n) <- film_release_region(?x2350, ?x8593), country(?x10757, ?x8593), ?x2350 = 0661m4p *> conf = 0.84 ranks of expected_values: 4, 32, 40, 51, 68, 117, 192, 259 EVAL 01crd5 film_release_region! 047p798 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 162.000 83.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01crd5 film_release_region! 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 162.000 83.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01crd5 film_release_region! 043sct5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 162.000 83.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01crd5 film_release_region! 0gtxj2q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 162.000 83.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01crd5 film_release_region! 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 162.000 83.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01crd5 film_release_region! 0bmhvpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 162.000 83.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01crd5 film_release_region! 087wc7n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 162.000 83.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01crd5 film_release_region! 0b76d_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 162.000 83.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19103-02bm1v PRED entity: 02bm1v PRED relation: company! PRED expected values: 060c4 09d6p2 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 36): 060c4 (0.74 #4433, 0.66 #4568, 0.64 #4658), 05_wyz (0.54 #830, 0.53 #1011, 0.50 #2008), 0dq3c (0.54 #815, 0.51 #1993, 0.50 #1222), 01yc02 (0.51 #1500, 0.50 #1228, 0.49 #1999), 09d6p2 (0.40 #1510, 0.39 #1465, 0.37 #2009), 02y6fz (0.33 #429, 0.33 #23, 0.25 #249), 02211by (0.33 #410, 0.24 #998, 0.20 #3078), 01kr6k (0.28 #1246, 0.27 #1337, 0.27 #1654), 0142rn (0.20 #3078, 0.19 #1154, 0.18 #1019), 01rk91 (0.20 #3078, 0.17 #407, 0.13 #3259) >> Best rule #4433 for best value: >> intensional similarity = 7 >> extensional distance = 176 >> proper extension: 09c7w0; 08815; 02vk52z; 017s11; 01j_9c; 016tt2; 06pwq; 025jfl; 0f8l9c; 07w0v; ... >> query: (?x9806, 060c4) <- company(?x4682, ?x9806), company(?x4682, ?x8237), company(?x4682, ?x5956), organization(?x4682, ?x1783), ?x1783 = 049dk, ?x8237 = 07xyn1, ?x5956 = 01yfp7 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 02bm1v company! 09d6p2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 141.000 141.000 0.742 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 02bm1v company! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.742 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #19102-05fm6m PRED entity: 05fm6m PRED relation: film! PRED expected values: 01nr63 => 64 concepts (30 used for prediction) PRED predicted values (max 10 best out of 795): 018grr (0.46 #43568, 0.44 #47720, 0.43 #47721), 017s11 (0.44 #47720, 0.43 #47721, 0.39 #10373), 01gzm2 (0.44 #47720, 0.43 #47721, 0.39 #10373), 04fcx7 (0.44 #47720, 0.39 #10373, 0.38 #45644), 02w29z (0.25 #1405, 0.07 #3479, 0.01 #22148), 0f5xn (0.25 #965, 0.03 #27932, 0.02 #36230), 01l9p (0.25 #278, 0.03 #37341, 0.03 #62242), 06cgy (0.25 #249, 0.03 #37591, 0.02 #35514), 028k57 (0.25 #789, 0.02 #31906, 0.02 #13236), 01vvb4m (0.25 #520, 0.02 #10893, 0.02 #4668) >> Best rule #43568 for best value: >> intensional similarity = 3 >> extensional distance = 723 >> proper extension: 06mmr; >> query: (?x7626, ?x2101) <- award_winner(?x7626, ?x2101), award_nominee(?x2101, ?x237), location(?x2101, ?x578) >> conf = 0.46 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05fm6m film! 01nr63 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 30.000 0.462 http://example.org/film/actor/film./film/performance/film #19101-07_pf PRED entity: 07_pf PRED relation: category PRED expected values: 08mbj5d => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #31, 0.75 #44, 0.70 #92) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0l_q9; 0d23k; 0fvwg; 0fvyz; 0fvvg; 0fvzz; 0fw1y; >> query: (?x10496, 08mbj5d) <- time_zones(?x10496, ?x2864), capital(?x10495, ?x10496), currency(?x10495, ?x5696) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07_pf category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.829 http://example.org/common/topic/webpage./common/webpage/category #19100-059lwy PRED entity: 059lwy PRED relation: currency PRED expected values: 09nqf => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.84 #204, 0.82 #325, 0.82 #218), 01nv4h (0.04 #58, 0.02 #149, 0.02 #163), 02l6h (0.01 #663, 0.01 #719, 0.01 #740) >> Best rule #204 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 02rqwhl; 05cvgl; 05zlld0; 0sxmx; 033f8n; 0dp7wt; >> query: (?x6746, 09nqf) <- nominated_for(?x2749, ?x6746), film_release_distribution_medium(?x6746, ?x81), award_winner(?x6746, ?x5338), film(?x382, ?x6746) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059lwy currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.841 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #19099-0dgst_d PRED entity: 0dgst_d PRED relation: award PRED expected values: 02x4x18 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 178): 0m7yy (0.29 #837, 0.14 #367, 0.07 #3423), 09qwmm (0.29 #262, 0.27 #4940, 0.22 #11769), 0gq_v (0.29 #254, 0.11 #2135, 0.09 #3310), 0gq9h (0.29 #298, 0.11 #3119, 0.10 #3354), 0gs96 (0.29 #325, 0.10 #2206, 0.10 #3146), 0gqyl (0.29 #316, 0.09 #6352, 0.07 #3137), 09sb52 (0.29 #269, 0.09 #6352, 0.06 #13183), 02x4x18 (0.27 #4940, 0.22 #11769, 0.22 #12947), 027dtxw (0.27 #4940, 0.22 #11769, 0.22 #12947), 0gqwc (0.27 #4940, 0.22 #11769, 0.22 #12947) >> Best rule #837 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 06hwzy; 025ljp; >> query: (?x1263, 0m7yy) <- honored_for(?x2515, ?x1263), award_winner(?x2515, ?x3571), ?x3571 = 07lwsz >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #4940 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 546 *> proper extension: 0g60z; 080dwhx; 0kfpm; 02k_4g; 0358x_; 019nnl; 0ddd0gc; 08jgk1; 0464pz; 0kfv9; ... *> query: (?x1263, ?x112) <- nominated_for(?x1371, ?x1263), honored_for(?x2515, ?x1263), nominated_for(?x112, ?x1263) *> conf = 0.27 ranks of expected_values: 8 EVAL 0dgst_d award 02x4x18 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 66.000 66.000 0.294 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19098-01vsn38 PRED entity: 01vsn38 PRED relation: role PRED expected values: 03bx0bm => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 111): 0342h (0.50 #135, 0.25 #331, 0.23 #527), 03bx0bm (0.36 #153, 0.19 #349, 0.17 #545), 05148p4 (0.26 #148, 0.14 #344, 0.12 #540), 05r5c (0.20 #139, 0.09 #531, 0.08 #335), 0l14md (0.15 #392, 0.15 #1509, 0.14 #1508), 018vs (0.14 #144, 0.10 #340, 0.09 #536), 02hnl (0.13 #160, 0.08 #356, 0.07 #552), 028tv0 (0.11 #274, 0.10 #339, 0.07 #143), 03qjg (0.10 #173, 0.04 #1086, 0.04 #1485), 01vj9c (0.07 #145, 0.05 #341, 0.03 #537) >> Best rule #135 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 037hgm; >> query: (?x11233, 0342h) <- award_nominee(?x11233, ?x2275), artists(?x1000, ?x11233), role(?x11233, ?x432) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 121 *> proper extension: 037hgm; *> query: (?x11233, 03bx0bm) <- award_nominee(?x11233, ?x2275), artists(?x1000, ?x11233), role(?x11233, ?x432) *> conf = 0.36 ranks of expected_values: 2 EVAL 01vsn38 role 03bx0bm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.496 http://example.org/music/group_member/membership./music/group_membership/role #19097-03b1sb PRED entity: 03b1sb PRED relation: nominated_for! PRED expected values: 02ppm4q => 74 concepts (67 used for prediction) PRED predicted values (max 10 best out of 186): 0gr51 (0.74 #1227, 0.40 #534, 0.38 #765), 027571b (0.66 #6479, 0.66 #3469, 0.66 #9258), 0gq9h (0.56 #521, 0.55 #752, 0.54 #290), 019f4v (0.52 #283, 0.51 #514, 0.49 #745), 04dn09n (0.45 #265, 0.43 #496, 0.41 #727), 040njc (0.38 #469, 0.37 #238, 0.36 #700), 02pqp12 (0.38 #519, 0.37 #288, 0.36 #750), 0k611 (0.37 #299, 0.37 #1223, 0.37 #530), 0gr0m (0.37 #520, 0.35 #751, 0.34 #289), 03hl6lc (0.36 #1278, 0.22 #585, 0.22 #816) >> Best rule #1227 for best value: >> intensional similarity = 4 >> extensional distance = 166 >> proper extension: 02d413; 0ds35l9; 0m313; 01gc7; 011yrp; 011yph; 08720; 0209hj; 0pv2t; 0344gc; ... >> query: (?x8890, 0gr51) <- titles(?x4205, ?x8890), nominated_for(?x1063, ?x8890), award(?x6740, ?x1063), ?x6740 = 0p_tz >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #7174 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 860 *> proper extension: 01j95; *> query: (?x8890, ?x618) <- titles(?x4205, ?x8890), award_winner(?x8890, ?x2028), award(?x2028, ?x618) *> conf = 0.24 ranks of expected_values: 36 EVAL 03b1sb nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 74.000 67.000 0.738 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19096-0ckrgs PRED entity: 0ckrgs PRED relation: film! PRED expected values: 02t1dv => 121 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1390): 0dt645q (0.50 #5932, 0.21 #18433, 0.20 #8015), 03fghg (0.42 #12730, 0.20 #6479, 0.15 #14814), 02t1dv (0.38 #60412, 0.36 #66664, 0.23 #16622), 01g4bk (0.33 #1668, 0.14 #10001, 0.04 #53747), 0p8r1 (0.27 #23504, 0.22 #40168, 0.21 #21421), 03q64h (0.25 #6205, 0.22 #12455, 0.14 #18706), 02h8hr (0.25 #13384, 0.20 #7133, 0.15 #15468), 04f62k (0.25 #14487, 0.20 #8236, 0.15 #16571), 03cz4j (0.25 #14471, 0.20 #8220, 0.15 #16555), 04j5fx (0.25 #14345, 0.20 #8094, 0.04 #70592) >> Best rule #5932 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0436yk; >> query: (?x3174, 0dt645q) <- film(?x296, ?x3174), actor(?x3174, ?x4134), production_companies(?x3174, ?x7003), genre(?x3174, ?x2540), film(?x10231, ?x3174), ?x2540 = 0hcr, actor(?x7953, ?x10231) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #60412 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 65 *> proper extension: 06z8s_; 04n52p6; *> query: (?x3174, ?x13175) <- film_release_region(?x3174, ?x94), language(?x3174, ?x2164), production_companies(?x3174, ?x7003), prequel(?x5633, ?x3174), genre(?x3174, ?x811), film(?x13175, ?x5633), film_crew_role(?x3174, ?x2095) *> conf = 0.38 ranks of expected_values: 3 EVAL 0ckrgs film! 02t1dv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 121.000 74.000 0.500 http://example.org/film/actor/film./film/performance/film #19095-09c7w0 PRED entity: 09c7w0 PRED relation: jurisdiction_of_office! PRED expected values: 03_js 042f1 => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 2737): 0d1_f (0.52 #981, 0.48 #715, 0.36 #655), 042f1 (0.33 #20, 0.03 #960, 0.03 #1019), 083q7 (0.25 #178, 0.07 #471, 0.03 #943), 0tc7 (0.20 #270, 0.20 #241, 0.07 #476), 0bwh6 (0.20 #267, 0.03 #1004, 0.02 #1654), 01k165 (0.14 #301, 0.11 #330, 0.10 #389), 063vn (0.14 #299, 0.11 #328, 0.10 #387), 0lzcs (0.14 #317, 0.11 #346, 0.07 #493), 0948xk (0.14 #311, 0.11 #340, 0.07 #487), 03f77 (0.14 #306, 0.11 #335, 0.07 #482) >> Best rule #981 for best value: >> intensional similarity = 2 >> extensional distance = 29 >> proper extension: 06s0l; 01n8qg; 0164b; 020p1; >> query: (?x94, 0d1_f) <- olympics(?x94, ?x358), jurisdiction_of_office(?x652, ?x94) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1 *> proper extension: 07h34; *> query: (?x94, 042f1) <- contains(?x94, ?x6824), ?x6824 = 02zc7f *> conf = 0.33 ranks of expected_values: 2 EVAL 09c7w0 jurisdiction_of_office! 042f1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 175.000 175.000 0.516 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office EVAL 09c7w0 jurisdiction_of_office! 03_js CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 175.000 175.000 0.516 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #19094-03_3d PRED entity: 03_3d PRED relation: country! PRED expected values: 0bynt 0dwxr 019w9j 019tzd 0194d => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 15): 0bynt (0.87 #573, 0.86 #198, 0.86 #1203), 0194d (0.80 #311, 0.78 #371, 0.74 #341), 019tzd (0.69 #308, 0.68 #248, 0.67 #368), 01sgl (0.66 #309, 0.65 #249, 0.64 #369), 0dwxr (0.55 #94, 0.54 #304, 0.53 #364), 01dys (0.46 #302, 0.45 #92, 0.44 #362), 019w9j (0.45 #95, 0.42 #230, 0.41 #260), 03krj (0.40 #1277, 0.39 #237, 0.38 #207), 018w8 (0.40 #1277, 0.38 #261, 0.36 #96), 01yfj (0.40 #1277, 0.36 #104, 0.33 #149) >> Best rule #573 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0j1z8; 0hzlz; 03gj2; 07t21; 06c1y; 0d0kn; 05sb1; 0697s; 06vbd; 09lxtg; ... >> query: (?x252, 0bynt) <- olympics(?x252, ?x418), country(?x536, ?x252), film_release_region(?x66, ?x252) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 7 EVAL 03_3d country! 0194d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_3d country! 019tzd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_3d country! 019w9j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 172.000 172.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_3d country! 0dwxr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 172.000 172.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_3d country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #19093-05r4w PRED entity: 05r4w PRED relation: form_of_government PRED expected values: 01fpfn => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 5): 01fpfn (0.50 #12, 0.43 #142, 0.41 #102), 06cx9 (0.39 #491, 0.35 #436, 0.33 #441), 01d9r3 (0.37 #179, 0.33 #204, 0.32 #134), 01q20 (0.36 #88, 0.33 #18, 0.33 #13), 026wp (0.17 #25, 0.17 #20, 0.14 #45) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 035hm; >> query: (?x87, 01fpfn) <- jurisdiction_of_office(?x182, ?x87), adjoins(?x2152, ?x87), ?x2152 = 06mkj >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05r4w form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.500 http://example.org/location/country/form_of_government #19092-02bjhv PRED entity: 02bjhv PRED relation: registering_agency PRED expected values: 03z19 => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.87 #24, 0.86 #8, 0.85 #14) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 03zw80; >> query: (?x2388, 03z19) <- organization(?x346, ?x2388), contains(?x2504, ?x2388), currency(?x2388, ?x170), location(?x4554, ?x2504) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bjhv registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.867 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #19091-0brddh PRED entity: 0brddh PRED relation: location PRED expected values: 0byh8j => 149 concepts (57 used for prediction) PRED predicted values (max 10 best out of 174): 0fl2s (0.56 #22549, 0.54 #33022, 0.51 #37855), 04vmp (0.29 #7603, 0.23 #10824, 0.20 #8410), 02_286 (0.28 #30643, 0.15 #5676, 0.14 #4872), 0c8tk (0.18 #3450, 0.07 #9087, 0.06 #6669), 0byh8j (0.14 #2419, 0.14 #1960, 0.08 #7249), 0fk98 (0.14 #2356, 0.05 #3163, 0.04 #13633), 030qb3t (0.14 #32300, 0.13 #40357, 0.12 #21023), 0cvw9 (0.11 #2817, 0.08 #11674, 0.07 #3622), 04jpl (0.10 #33845, 0.10 #4852, 0.09 #29815), 05jbn (0.06 #6696, 0.03 #15558, 0.03 #16362) >> Best rule #22549 for best value: >> intensional similarity = 6 >> extensional distance = 276 >> proper extension: 015882; >> query: (?x13446, ?x6250) <- category(?x13446, ?x134), place_of_birth(?x13446, ?x6250), profession(?x13446, ?x524), people(?x5025, ?x13446), location(?x8975, ?x6250), contains(?x2146, ?x6250) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2419 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 0738y5; 0cqcgj; 03dctt; *> query: (?x13446, ?x7297) <- gender(?x13446, ?x514), place_of_birth(?x13446, ?x6250), contains(?x7297, ?x6250), contains(?x2146, ?x6250), ?x2146 = 03rk0, ?x7297 = 0byh8j *> conf = 0.14 ranks of expected_values: 5 EVAL 0brddh location 0byh8j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 149.000 57.000 0.558 http://example.org/people/person/places_lived./people/place_lived/location #19090-02py_sj PRED entity: 02py_sj PRED relation: nominated_for PRED expected values: 02_1q9 01b65l => 35 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1189): 0358x_ (0.83 #7964, 0.83 #7965, 0.82 #1592), 0phrl (0.83 #7964, 0.83 #7965, 0.82 #1592), 02_1q9 (0.71 #8019, 0.71 #6426, 0.67 #9610), 01b65l (0.71 #8583, 0.71 #6990, 0.67 #10174), 05hjnw (0.54 #18290, 0.21 #23075, 0.21 #24669), 0gmgwnv (0.46 #18486, 0.25 #23271, 0.25 #24865), 09gq0x5 (0.44 #17779, 0.26 #22564, 0.26 #24158), 026p4q7 (0.42 #17882, 0.28 #22667, 0.28 #24261), 0gmcwlb (0.40 #17706, 0.23 #22491, 0.23 #24085), 03hmt9b (0.40 #18120, 0.21 #22905, 0.21 #24499) >> Best rule #7964 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 027qq9b; >> query: (?x9869, ?x3544) <- nominated_for(?x9869, ?x6080), ?x6080 = 01y6dz, award(?x3544, ?x9869), award(?x1280, ?x9869), ceremony(?x9869, ?x2751), nominated_for(?x438, ?x1280), nominated_for(?x588, ?x1280), country_of_origin(?x1280, ?x94) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #8019 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 5 *> proper extension: 02q1tc5; 02pz3j5; *> query: (?x9869, 02_1q9) <- nominated_for(?x9869, ?x7175), nominated_for(?x9869, ?x6080), nominated_for(?x9869, ?x4721), nominated_for(?x9869, ?x2829), nominated_for(?x415, ?x6080), award(?x6080, ?x588), ?x2829 = 01b64v, ?x4721 = 01b66t, ?x7175 = 02_1kl, actor(?x6080, ?x2129) *> conf = 0.71 ranks of expected_values: 3, 4 EVAL 02py_sj nominated_for 01b65l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 16.000 0.833 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02py_sj nominated_for 02_1q9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 16.000 0.833 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19089-09fqgj PRED entity: 09fqgj PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.86 #36, 0.83 #68, 0.83 #281), 02nxhr (0.19 #342, 0.06 #17, 0.05 #7), 07z4p (0.19 #342, 0.04 #15, 0.03 #35), 07c52 (0.19 #342, 0.03 #133, 0.03 #313), 0735l (0.19 #342) >> Best rule #36 for best value: >> intensional similarity = 6 >> extensional distance = 84 >> proper extension: 034qmv; 083shs; 095zlp; 0dqytn; 0fgpvf; 061681; 01r97z; 03ckwzc; 0dsvzh; 0b73_1d; ... >> query: (?x10509, 029j_) <- titles(?x811, ?x10509), genre(?x10509, ?x1509), film_crew_role(?x10509, ?x1171), film(?x294, ?x10509), ?x1509 = 060__y, ?x1171 = 09vw2b7 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09fqgj film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.860 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19088-05vtbl PRED entity: 05vtbl PRED relation: profession PRED expected values: 0196pc => 72 concepts (54 used for prediction) PRED predicted values (max 10 best out of 52): 02hrh1q (0.91 #2283, 0.88 #4839, 0.87 #6974), 018gz8 (0.36 #1149, 0.19 #2995, 0.19 #2711), 0nbcg (0.33 #3859, 0.12 #4711, 0.12 #3717), 09jwl (0.31 #3849, 0.17 #6694, 0.17 #3707), 0196pc (0.28 #5398, 0.06 #493, 0.05 #919), 0dz3r (0.25 #3836, 0.11 #3694, 0.11 #4688), 0kyk (0.21 #1443, 0.10 #1159, 0.10 #23), 016z4k (0.16 #3838, 0.09 #4690, 0.09 #7535), 01c72t (0.12 #3853, 0.10 #303, 0.10 #587), 0d1pc (0.12 #3878, 0.06 #7007, 0.06 #4872) >> Best rule #2283 for best value: >> intensional similarity = 3 >> extensional distance = 353 >> proper extension: 01vvydl; 01kwld; 064nh4k; 034x61; 016khd; 02gvwz; 04y79_n; 016ywr; 0170s4; 0738b8; ... >> query: (?x10152, 02hrh1q) <- award_winner(?x10152, ?x129), profession(?x10152, ?x319), actor(?x5852, ?x10152) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #5398 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1426 *> proper extension: 0c9l1; *> query: (?x10152, ?x319) <- award_winner(?x10152, ?x129), profession(?x129, ?x319), award_nominee(?x3145, ?x10152) *> conf = 0.28 ranks of expected_values: 5 EVAL 05vtbl profession 0196pc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 72.000 54.000 0.913 http://example.org/people/person/profession #19087-0277470 PRED entity: 0277470 PRED relation: gender PRED expected values: 05zppz => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #7, 0.80 #5, 0.78 #11), 02zsn (0.46 #203, 0.32 #4, 0.29 #16) >> Best rule #7 for best value: >> intensional similarity = 2 >> extensional distance = 249 >> proper extension: 03m_k0; 04snp2; 024swd; 0bbxd3; 03p01x; 07lz9l; 0b1s_q; 02k76g; >> query: (?x1266, 05zppz) <- profession(?x1266, ?x1032), program(?x1266, ?x2528) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0277470 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.801 http://example.org/people/person/gender #19086-03n0pv PRED entity: 03n0pv PRED relation: profession PRED expected values: 0nbcg => 93 concepts (90 used for prediction) PRED predicted values (max 10 best out of 73): 02hrh1q (0.76 #12077, 0.73 #12371, 0.72 #3104), 09jwl (0.67 #460, 0.56 #1931, 0.55 #4728), 0nbcg (0.67 #4740, 0.65 #4887, 0.43 #1943), 0dz3r (0.57 #4564, 0.41 #443, 0.37 #1914), 016z4k (0.42 #1916, 0.35 #1622, 0.35 #2946), 01d_h8 (0.33 #594, 0.32 #3096, 0.32 #2212), 0cbd2 (0.32 #595, 0.26 #301, 0.18 #4274), 0kyk (0.32 #323, 0.26 #617, 0.16 #4296), 03gjzk (0.24 #5608, 0.22 #1044, 0.21 #10019), 02jknp (0.24 #596, 0.21 #302, 0.21 #8248) >> Best rule #12077 for best value: >> intensional similarity = 3 >> extensional distance = 3305 >> proper extension: 08f3b1; 01q415; 01vhb0; 0bymv; 01h320; 085pr; 0chrwb; 0b78hw; 01s3kv; 0534v; ... >> query: (?x11729, 02hrh1q) <- profession(?x11729, ?x1614), profession(?x10547, ?x1614), ?x10547 = 01pbwwl >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #4740 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 636 *> proper extension: 0f0y8; 053y0s; 032t2z; 06y9c2; 01q7cb_; 09prnq; 025tdwc; 0cg9y; 01x1cn2; 0qf3p; ... *> query: (?x11729, 0nbcg) <- profession(?x11729, ?x1614), profession(?x5225, ?x1614), ?x5225 = 01pq5j7 *> conf = 0.67 ranks of expected_values: 3 EVAL 03n0pv profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 90.000 0.755 http://example.org/people/person/profession #19085-06q6jz PRED entity: 06q6jz PRED relation: artists PRED expected values: 0c73z => 38 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1063): 0bvzp (0.57 #4855, 0.56 #6998, 0.36 #9138), 09h_q (0.57 #5015, 0.50 #2875, 0.44 #7158), 0kvrb (0.57 #4456, 0.44 #6599, 0.40 #7668), 06449 (0.55 #8798, 0.50 #7727, 0.50 #2375), 02b25y (0.50 #10897, 0.50 #2335, 0.47 #9827), 0c73z (0.50 #4200, 0.50 #3130, 0.40 #6420), 04k15 (0.50 #2464, 0.43 #5348, 0.40 #6420), 06c44 (0.50 #2693, 0.43 #5348, 0.35 #16057), 03bxh (0.50 #2647, 0.43 #5348, 0.33 #1578), 03d6q (0.50 #2959, 0.33 #1890, 0.29 #5099) >> Best rule #4855 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 017_qw; 01wqlc; >> query: (?x11193, 0bvzp) <- artists(?x11193, ?x7386), artists(?x11193, ?x2693), artists(?x11193, ?x889), artists(?x888, ?x889), ?x888 = 05lls, religion(?x7386, ?x1985), influenced_by(?x7386, ?x8177), ?x2693 = 02ck1 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4200 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 0l8gh; *> query: (?x11193, 0c73z) <- artists(?x11193, ?x11638), artists(?x11193, ?x10682), artists(?x11193, ?x9728), artists(?x11193, ?x1211), artists(?x11193, ?x889), ?x889 = 0pcc0, type_of_union(?x11638, ?x566), ?x10682 = 0k1wz, ?x1211 = 0k4gf, ?x9728 = 0kn3g *> conf = 0.50 ranks of expected_values: 6 EVAL 06q6jz artists 0c73z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 38.000 16.000 0.571 http://example.org/music/genre/artists #19084-04n7gc6 PRED entity: 04n7gc6 PRED relation: religion PRED expected values: 0c8wxp => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 21): 03_gx (0.54 #239, 0.54 #149, 0.36 #59), 0flw86 (0.33 #2, 0.12 #182, 0.09 #47), 0kpl (0.17 #145, 0.13 #235, 0.09 #685), 06pq6 (0.17 #41, 0.02 #221), 0c8wxp (0.15 #2083, 0.14 #2129, 0.14 #1043), 03j6c (0.12 #201, 0.10 #1286, 0.08 #831), 04pk9 (0.05 #335, 0.04 #380, 0.01 #830), 01lp8 (0.04 #316, 0.03 #361, 0.03 #226), 092bf5 (0.03 #556, 0.03 #286, 0.03 #601), 0kq2 (0.03 #333, 0.02 #1598, 0.02 #378) >> Best rule #239 for best value: >> intensional similarity = 7 >> extensional distance = 66 >> proper extension: 01h2_6; >> query: (?x12073, 03_gx) <- nationality(?x12073, ?x6329), people(?x1050, ?x12073), ?x1050 = 041rx, official_language(?x6329, ?x11038), languages(?x3583, ?x11038), language(?x174, ?x11038), major_field_of_study(?x2605, ?x11038) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #2083 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 921 *> proper extension: 01k5t_3; 04y79_n; 01l2fn; 015pxr; 0pyg6; 0738b8; 01trhmt; 0hskw; 07cjqy; 062dn7; ... *> query: (?x12073, 0c8wxp) <- profession(?x12073, ?x13043), profession(?x8704, ?x13043), profession(?x532, ?x13043), people(?x1050, ?x12073), nationality(?x12073, ?x6329), ?x532 = 02nb2s, films(?x6329, ?x1077), ?x8704 = 0c0k1 *> conf = 0.15 ranks of expected_values: 5 EVAL 04n7gc6 religion 0c8wxp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 92.000 0.544 http://example.org/people/person/religion #19083-02bqvs PRED entity: 02bqvs PRED relation: film_release_region PRED expected values: 09c7w0 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 164): 09c7w0 (0.73 #3768, 0.73 #720, 0.73 #5203), 0345h (0.44 #898, 0.40 #1257, 0.25 #1124), 0f8l9c (0.29 #749, 0.28 #1109, 0.27 #8818), 0d0vqn (0.27 #8439, 0.27 #1089, 0.27 #8798), 03rjj (0.27 #1085, 0.25 #725, 0.23 #8794), 03_3d (0.26 #1087, 0.23 #727, 0.22 #8796), 02vzc (0.25 #1147, 0.24 #5808, 0.24 #8497), 0jgd (0.25 #1082, 0.23 #722, 0.22 #5743), 06mkj (0.25 #5814, 0.25 #8862, 0.25 #8503), 059j2 (0.24 #8472, 0.24 #8831, 0.24 #5783) >> Best rule #3768 for best value: >> intensional similarity = 3 >> extensional distance = 769 >> proper extension: 0dckvs; 0fq27fp; 0gh6j94; >> query: (?x8790, 09c7w0) <- film_crew_role(?x8790, ?x468), ?x468 = 02r96rf, genre(?x8790, ?x258) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bqvs film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.732 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19082-0c3ns PRED entity: 0c3ns PRED relation: place_of_birth PRED expected values: 06y57 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 31): 06y57 (0.30 #2113, 0.28 #19720, 0.28 #28875), 0dprg (0.25 #356), 02_286 (0.12 #723, 0.12 #2132, 0.12 #2836), 0cr3d (0.04 #7135, 0.04 #3615, 0.04 #19109), 01_d4 (0.04 #6403, 0.03 #18377, 0.03 #8516), 030qb3t (0.04 #6391, 0.04 #21887, 0.04 #36679), 05fly (0.03 #52820), 0chghy (0.03 #52820), 01531 (0.03 #2218, 0.02 #809, 0.02 #2922), 03l2n (0.02 #1577, 0.02 #873, 0.01 #2282) >> Best rule #2113 for best value: >> intensional similarity = 3 >> extensional distance = 136 >> proper extension: 016hvl; 03p01x; >> query: (?x2179, ?x5036) <- student(?x2013, ?x2179), location(?x2179, ?x5036), written_by(?x1224, ?x2179) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c3ns place_of_birth 06y57 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.300 http://example.org/people/person/place_of_birth #19081-02sn34 PRED entity: 02sn34 PRED relation: month PRED expected values: 040fv => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 1): 040fv (0.82 #11, 0.81 #10, 0.80 #14) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0g6xq; >> query: (?x6494, 040fv) <- country(?x6494, ?x1471), month(?x6494, ?x1459), contains(?x1603, ?x6494), ?x1459 = 04w_7 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02sn34 month 040fv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 182.000 0.816 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #19080-0bl1_ PRED entity: 0bl1_ PRED relation: genre PRED expected values: 07s9rl0 => 89 concepts (88 used for prediction) PRED predicted values (max 10 best out of 93): 05p553 (0.98 #3177, 0.56 #127, 0.50 #249), 07s9rl0 (0.81 #1099, 0.80 #977, 0.79 #1465), 02kdv5l (0.60 #3, 0.43 #1223, 0.41 #491), 0gf28 (0.44 #188, 0.20 #66, 0.10 #3238), 0l4h_ (0.44 #197, 0.01 #1417), 03k9fj (0.44 #501, 0.32 #1233, 0.23 #3917), 02l7c8 (0.35 #628, 0.33 #262, 0.33 #1360), 01jfsb (0.34 #868, 0.32 #3552, 0.32 #2088), 06n90 (0.28 #1235, 0.25 #503, 0.20 #15), 0lsxr (0.28 #986, 0.22 #864, 0.19 #2328) >> Best rule #3177 for best value: >> intensional similarity = 3 >> extensional distance = 593 >> proper extension: 0dtw1x; 0gj9qxr; 043sct5; 0g5q34q; 0gh6j94; 04svwx; 0d8w2n; >> query: (?x4706, 05p553) <- genre(?x4706, ?x5231), genre(?x11980, ?x5231), ?x11980 = 0640m69 >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #1099 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 0581vn8; *> query: (?x4706, 07s9rl0) <- nominated_for(?x6909, ?x4706), nominated_for(?x1703, ?x4706), ?x6909 = 02qyntr, ?x1703 = 0k611 *> conf = 0.81 ranks of expected_values: 2 EVAL 0bl1_ genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 88.000 0.980 http://example.org/film/film/genre #19079-03ys48 PRED entity: 03ys48 PRED relation: current_club PRED expected values: 0kq9l 03x73c => 107 concepts (71 used for prediction) PRED predicted values (max 10 best out of 832): 03j6_5 (0.50 #386, 0.17 #677, 0.17 #531), 04ltf (0.42 #1091, 0.29 #798, 0.26 #1817), 080_y (0.33 #687, 0.33 #541, 0.29 #980), 06l22 (0.33 #1077, 0.29 #1222, 0.17 #1803), 0xbm (0.33 #1040, 0.21 #1185, 0.21 #1475), 049f05 (0.33 #110, 0.21 #2001, 0.20 #2296), 02qhlm (0.33 #84, 0.17 #665, 0.14 #811), 03x6rj (0.33 #133, 0.17 #714, 0.14 #860), 049bp4 (0.33 #67, 0.17 #648, 0.14 #794), 0byq0v (0.33 #120, 0.17 #701, 0.14 #847) >> Best rule #386 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 033nzk; >> query: (?x9254, 03j6_5) <- team(?x60, ?x9254), current_club(?x9254, ?x9107), current_club(?x9254, ?x6705), sport(?x9254, ?x471), ?x60 = 02nzb8, ?x471 = 02vx4, teams(?x10519, ?x6705), ?x9107 = 0138mv >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1824 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 21 *> proper extension: 0cnk2q; 03yl2t; 01l3vx; 02s2lg; 02ltg3; 02rqxc; 03d8m4; 040whs; 0329r5; 03_qrp; ... *> query: (?x9254, 0kq9l) <- team(?x60, ?x9254), current_club(?x9254, ?x12792), current_club(?x9254, ?x9107), current_club(?x9254, ?x6705), sport(?x9254, ?x471), ?x60 = 02nzb8, ?x471 = 02vx4, teams(?x10519, ?x6705), team(?x9106, ?x9107), colors(?x12792, ?x663) *> conf = 0.04 ranks of expected_values: 183, 199 EVAL 03ys48 current_club 03x73c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 107.000 71.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club EVAL 03ys48 current_club 0kq9l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 107.000 71.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #19078-0mdqp PRED entity: 0mdqp PRED relation: award PRED expected values: 0c422z4 0hnf5vm => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 267): 09sb52 (0.54 #7585, 0.41 #9173, 0.35 #2024), 0gq9h (0.38 #4047, 0.35 #4445, 0.32 #4842), 040njc (0.30 #3979, 0.26 #4377, 0.24 #4774), 05p09zm (0.25 #1310, 0.19 #1707, 0.19 #2104), 0gs9p (0.21 #4048, 0.19 #4446, 0.17 #5240), 019f4v (0.20 #4036, 0.18 #4434, 0.17 #5228), 03c7tr1 (0.20 #851, 0.18 #2042, 0.17 #2439), 09cn0c (0.19 #3177, 0.17 #315, 0.16 #5958), 0hnf5vm (0.19 #3177, 0.17 #181, 0.16 #5958), 0gqy2 (0.19 #3177, 0.17 #159, 0.16 #5958) >> Best rule #7585 for best value: >> intensional similarity = 3 >> extensional distance = 477 >> proper extension: 07s3vqk; 047sxrj; 08cn4_; 01y0y6; 01vt9p3; 02633g; 059fjj; 03yrkt; 01ggc9; 0k6yt1; >> query: (?x794, 09sb52) <- award_nominee(?x794, ?x3553), film(?x794, ?x370), friend(?x2135, ?x3553) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #3177 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 138 *> proper extension: 03qcq; 01zkxv; 05jm7; 018fq; 0821j; 03rx9; 07d3x; 03hpr; 01g6bk; *> query: (?x794, ?x618) <- award_nominee(?x794, ?x3553), award_winner(?x618, ?x3553), influenced_by(?x794, ?x4112) *> conf = 0.19 ranks of expected_values: 9, 156 EVAL 0mdqp award 0hnf5vm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 82.000 82.000 0.537 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0mdqp award 0c422z4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 82.000 82.000 0.537 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19077-013w8y PRED entity: 013w8y PRED relation: group! PRED expected values: 018y2s => 61 concepts (20 used for prediction) PRED predicted values (max 10 best out of 14): 044mfr (0.17 #306, 0.12 #508, 0.10 #710), 01304j (0.17 #392, 0.10 #796), 01vsy95 (0.04 #1273, 0.03 #1477), 048tgl (0.02 #2409, 0.02 #1800, 0.02 #3223), 01wwvt2 (0.02 #1661, 0.02 #2068, 0.01 #3084), 06cc_1 (0.01 #1630, 0.01 #1833, 0.01 #2239), 01lz4tf (0.01 #1754, 0.01 #2161), 0191h5 (0.01 #1753, 0.01 #2160), 0k1bs (0.01 #1736, 0.01 #2143), 053y0s (0.01 #1622, 0.01 #2029) >> Best rule #306 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 05crg7; >> query: (?x8913, 044mfr) <- artists(?x9013, ?x8913), artists(?x3061, ?x8913), artists(?x284, ?x8913), parent_genre(?x283, ?x284), ?x3061 = 05bt6j, ?x9013 = 09nwwf, group(?x75, ?x8913) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 013w8y group! 018y2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 20.000 0.167 http://example.org/music/group_member/membership./music/group_membership/group #19076-0337vz PRED entity: 0337vz PRED relation: profession PRED expected values: 01d_h8 => 131 concepts (129 used for prediction) PRED predicted values (max 10 best out of 62): 01d_h8 (0.69 #3854, 0.50 #2522, 0.48 #1338), 0dxtg (0.62 #3861, 0.35 #2529, 0.30 #1345), 09jwl (0.40 #3570, 0.38 #5346, 0.37 #8898), 03gjzk (0.39 #2530, 0.34 #1346, 0.31 #3270), 0nbcg (0.29 #5359, 0.27 #8911, 0.26 #8319), 016z4k (0.27 #3556, 0.26 #5332, 0.23 #8884), 0dz3r (0.26 #5330, 0.23 #3554, 0.22 #8882), 0d1pc (0.23 #494, 0.21 #642, 0.21 #198), 018gz8 (0.22 #1348, 0.20 #3272, 0.19 #2532), 01c72t (0.20 #3575, 0.15 #2095, 0.15 #5351) >> Best rule #3854 for best value: >> intensional similarity = 3 >> extensional distance = 392 >> proper extension: 042rnl; 02_4fn; 04dz_y7; >> query: (?x193, 01d_h8) <- award_winner(?x704, ?x193), profession(?x193, ?x524), ?x524 = 02jknp >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0337vz profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 129.000 0.688 http://example.org/people/person/profession #19075-01prf3 PRED entity: 01prf3 PRED relation: organization! PRED expected values: 04411 01zwy => 140 concepts (83 used for prediction) PRED predicted values (max 10 best out of 332): 034rd (0.72 #5353, 0.71 #8196, 0.25 #1758), 0gzh (0.72 #5353, 0.71 #8196, 0.05 #3148), 06c0j (0.72 #5353, 0.71 #8196), 07ssc (0.67 #2543, 0.64 #7907, 0.55 #6641), 059j2 (0.67 #2567, 0.57 #7931, 0.50 #5088), 0345h (0.67 #2569, 0.50 #7933, 0.38 #5090), 015qh (0.67 #2577, 0.43 #7941, 0.42 #6992), 05qhw (0.67 #2541, 0.36 #7905, 0.27 #6639), 02vzc (0.57 #7958, 0.50 #5115, 0.50 #2594), 0k6nt (0.57 #7922, 0.50 #5079, 0.50 #2558) >> Best rule #5353 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0b6css; >> query: (?x10530, ?x12525) <- organization(?x5254, ?x10530), organization(?x1913, ?x10530), organizations_founded(?x5254, ?x1912), taxonomy(?x5254, ?x939), gender(?x1913, ?x231), organizations_founded(?x12525, ?x1912) >> conf = 0.72 => this is the best rule for 3 predicted values *> Best rule #3148 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 05g9h; *> query: (?x10530, ?x7251) <- organization(?x5796, ?x10530), organization(?x5254, ?x10530), organization(?x4480, ?x10530), student(?x1771, ?x4480), influenced_by(?x5254, ?x7251), student(?x6056, ?x5796), gender(?x5796, ?x231) *> conf = 0.05 ranks of expected_values: 325 EVAL 01prf3 organization! 01zwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 83.000 0.725 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 01prf3 organization! 04411 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 140.000 83.000 0.725 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #19074-026fmqm PRED entity: 026fmqm PRED relation: season! PRED expected values: 06x68 07l8x 051wf => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 339): 06x68 (0.83 #98, 0.82 #7, 0.80 #104), 07l8x (0.82 #7, 0.69 #61, 0.66 #54), 04wmvz (0.82 #7, 0.69 #61, 0.66 #54), 03lpp_ (0.82 #7, 0.69 #61, 0.66 #54), 051wf (0.82 #7, 0.69 #61, 0.60 #6), 0jmj7 (0.71 #35, 0.57 #68, 0.55 #10), 0jmk7 (0.71 #35, 0.57 #68, 0.55 #10), 0jmhr (0.71 #35, 0.55 #10, 0.53 #69), 05g3b (0.71 #35, 0.55 #10, 0.44 #16), 021f30 (0.66 #54, 0.66 #50, 0.61 #41) >> Best rule #98 for best value: >> intensional similarity = 101 >> extensional distance = 10 >> proper extension: 03c74_8; >> query: (?x10017, 06x68) <- season(?x12042, ?x10017), season(?x8894, ?x10017), season(?x8111, ?x10017), season(?x7060, ?x10017), season(?x6074, ?x10017), season(?x2405, ?x10017), season(?x2174, ?x10017), season(?x1823, ?x10017), season(?x1160, ?x10017), season(?x260, ?x10017), team(?x8520, ?x2405), school(?x8111, ?x5288), school(?x8111, ?x4955), school(?x8111, ?x3779), school(?x8111, ?x2522), season(?x8111, ?x11501), season(?x8111, ?x2406), ?x4955 = 09f2j, draft(?x2405, ?x11905), draft(?x2405, ?x8499), list(?x5288, ?x2197), company(?x3131, ?x5288), institution(?x1526, ?x5288), institution(?x1519, ?x5288), institution(?x1368, ?x5288), institution(?x620, ?x5288), ?x7060 = 01slc, team(?x11844, ?x2405), major_field_of_study(?x5288, ?x7134), major_field_of_study(?x5288, ?x5031), major_field_of_study(?x5288, ?x4268), major_field_of_study(?x5288, ?x2606), major_field_of_study(?x5288, ?x2014), major_field_of_study(?x5288, ?x1695), team(?x7533, ?x1160), ?x620 = 07s6fsf, ?x2014 = 04rjg, position(?x12042, ?x10822), draft(?x2174, ?x10600), ?x2522 = 022lly, ?x1368 = 014mlp, currency(?x5288, ?x170), ?x10600 = 04f4z1k, ?x1519 = 013zdg, student(?x5288, ?x460), ?x11501 = 027mvrc, school(?x12042, ?x8202), ?x8894 = 02d02, school(?x2174, ?x546), sport(?x1160, ?x5063), draft(?x12042, ?x1633), ?x260 = 01ypc, ?x8202 = 06fq2, ?x5031 = 0dc_v, teams(?x2277, ?x2405), ?x3779 = 01pq4w, school(?x2820, ?x5288), ?x11905 = 047dpm0, season(?x12042, ?x11834), season(?x12042, ?x9192), colors(?x12042, ?x3189), ?x9192 = 04110b0, school(?x1160, ?x10220), ?x7134 = 02_7t, citytown(?x2276, ?x2277), ?x8499 = 02r6gw6, location(?x624, ?x2277), ?x2406 = 03c6sl9, place_of_birth(?x3058, ?x2277), teams(?x2017, ?x1160), category(?x6074, ?x134), ?x1526 = 0bkj86, school(?x1823, ?x2895), jurisdiction_of_office(?x1195, ?x2277), colors(?x12019, ?x3189), colors(?x6548, ?x3189), ?x6548 = 0yls9, ?x1695 = 06ms6, ?x2820 = 0jmj7, colors(?x12780, ?x3189), colors(?x12039, ?x3189), colors(?x8537, ?x3189), colors(?x6803, ?x3189), colors(?x4511, ?x3189), ?x8520 = 01z9v6, ?x12039 = 026l1lq, ?x12019 = 0yl_w, colors(?x1160, ?x12067), county(?x2277, ?x13275), ?x6803 = 03by7wc, ?x4511 = 01xn7x1, origin(?x1206, ?x2277), organization(?x346, ?x2895), ?x4268 = 02822, ?x11834 = 02h7s73, ?x2606 = 062z7, student(?x10220, ?x1324), ?x8537 = 02029f, ?x170 = 09nqf, ?x12780 = 019mdt, contains(?x2256, ?x2895) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5 EVAL 026fmqm season! 051wf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 14.000 14.000 0.833 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 026fmqm season! 07l8x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.833 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 026fmqm season! 06x68 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.833 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #19073-0d060g PRED entity: 0d060g PRED relation: country! PRED expected values: 02rv_dz 0cw3yd 07sp4l 03f7xg 02tjl3 0415ggl 05t0_2v 014bpd 02r858_ => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 1707): 01m13b (0.45 #18204, 0.43 #9992, 0.38 #6706), 04z4j2 (0.43 #6417, 0.29 #11345, 0.22 #12987), 026qnh6 (0.43 #5681, 0.22 #8967, 0.14 #10609), 0gwlfnb (0.41 #8211, 0.40 #95254, 0.33 #4661), 05pdh86 (0.41 #8211, 0.40 #95254, 0.29 #157658), 0dscrwf (0.41 #8211, 0.40 #95254, 0.29 #9916), 04n52p6 (0.41 #8211, 0.40 #95254, 0.25 #6804), 03np63f (0.41 #8211, 0.40 #95254, 0.21 #11113), 0gtvpkw (0.41 #8211, 0.40 #95254, 0.17 #3794), 05q4y12 (0.41 #8211, 0.40 #95254, 0.17 #3690) >> Best rule #18204 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 05kr_; >> query: (?x279, 01m13b) <- contains(?x279, ?x481), adjoins(?x279, ?x94), film_release_region(?x1064, ?x279) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #95254 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 012m_; *> query: (?x279, ?x1444) <- contains(?x279, ?x481), nationality(?x4345, ?x279), film(?x4345, ?x1444) *> conf = 0.40 ranks of expected_values: 462, 492, 511, 512, 516, 535, 539, 548, 628 EVAL 0d060g country! 02r858_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 183.000 0.455 http://example.org/film/film/country EVAL 0d060g country! 014bpd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 183.000 0.455 http://example.org/film/film/country EVAL 0d060g country! 05t0_2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 183.000 0.455 http://example.org/film/film/country EVAL 0d060g country! 0415ggl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 183.000 0.455 http://example.org/film/film/country EVAL 0d060g country! 02tjl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 183.000 0.455 http://example.org/film/film/country EVAL 0d060g country! 03f7xg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 183.000 0.455 http://example.org/film/film/country EVAL 0d060g country! 07sp4l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 183.000 0.455 http://example.org/film/film/country EVAL 0d060g country! 0cw3yd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 183.000 0.455 http://example.org/film/film/country EVAL 0d060g country! 02rv_dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 183.000 0.455 http://example.org/film/film/country #19072-02822 PRED entity: 02822 PRED relation: major_field_of_study! PRED expected values: 065y4w7 0bx8pn 01q2sk 06kknt => 67 concepts (39 used for prediction) PRED predicted values (max 10 best out of 598): 03ksy (0.75 #8415, 0.67 #10629, 0.67 #5093), 01w5m (0.67 #13950, 0.67 #5645, 0.67 #5092), 06pwq (0.67 #9418, 0.67 #4435, 0.62 #8865), 01w3v (0.62 #8315, 0.56 #9421, 0.54 #13851), 07szy (0.62 #8341, 0.50 #13877, 0.50 #11109), 05zl0 (0.62 #8519, 0.50 #14055, 0.50 #5197), 07wrz (0.62 #8363, 0.50 #5041, 0.43 #13346), 01jt2w (0.60 #4159, 0.38 #8589, 0.33 #5267), 025v3k (0.53 #19379, 0.50 #5663, 0.50 #5110), 07w0v (0.53 #19379, 0.50 #8320, 0.44 #9426) >> Best rule #8415 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0dc_v; >> query: (?x4268, 03ksy) <- major_field_of_study(?x10497, ?x4268), major_field_of_study(?x5486, ?x4268), student(?x4268, ?x7093), student(?x10497, ?x968), ?x5486 = 0g8rj, major_field_of_study(?x4268, ?x254), award_nominee(?x624, ?x7093) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #19379 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 43 *> proper extension: 06nm1; 02csf; *> query: (?x4268, ?x8021) <- major_field_of_study(?x8363, ?x4268), student(?x4268, ?x7093), student(?x4268, ?x4735), student(?x8363, ?x2046), film(?x7093, ?x385), gender(?x7093, ?x514), student(?x8021, ?x4735), contains(?x94, ?x8363) *> conf = 0.53 ranks of expected_values: 11, 17, 24, 441 EVAL 02822 major_field_of_study! 06kknt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 67.000 39.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02822 major_field_of_study! 01q2sk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 67.000 39.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02822 major_field_of_study! 0bx8pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 67.000 39.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02822 major_field_of_study! 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 67.000 39.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #19071-02tjl3 PRED entity: 02tjl3 PRED relation: film! PRED expected values: 02_hj4 => 86 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1015): 03x400 (0.62 #54149, 0.62 #49980, 0.51 #45812), 01kb2j (0.20 #911, 0.07 #2993, 0.03 #5075), 0htlr (0.13 #147, 0.07 #2229, 0.02 #14722), 015p3p (0.13 #1096, 0.04 #3178, 0.03 #5260), 02pby8 (0.13 #1402, 0.04 #3484, 0.03 #5566), 01wbg84 (0.11 #2129, 0.05 #6293, 0.05 #14622), 020_95 (0.09 #5132, 0.04 #3050, 0.03 #7214), 081lh (0.09 #8490, 0.03 #14737, 0.02 #73059), 07b2lv (0.08 #49982, 0.08 #45813, 0.07 #54151), 0bwgc_ (0.08 #49982, 0.08 #45813, 0.07 #54151) >> Best rule #54149 for best value: >> intensional similarity = 4 >> extensional distance = 447 >> proper extension: 01h1bf; 05sy0cv; >> query: (?x5520, ?x6618) <- award_winner(?x5520, ?x6618), nominated_for(?x9781, ?x5520), award_nominee(?x488, ?x6618), participant(?x2626, ?x6618) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #54420 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 481 *> proper extension: 0dmn0x; *> query: (?x5520, 02_hj4) <- nominated_for(?x1033, ?x5520), film_crew_role(?x5520, ?x137), music(?x5520, ?x10700), film(?x1914, ?x5520) *> conf = 0.01 ranks of expected_values: 1015 EVAL 02tjl3 film! 02_hj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 48.000 0.620 http://example.org/film/actor/film./film/performance/film #19070-024l2y PRED entity: 024l2y PRED relation: production_companies PRED expected values: 03xsby => 127 concepts (119 used for prediction) PRED predicted values (max 10 best out of 65): 016tw3 (0.24 #11, 0.12 #1407, 0.12 #5855), 086k8 (0.16 #822, 0.14 #1314, 0.14 #2303), 05qd_ (0.16 #337, 0.14 #3134, 0.12 #1239), 01gb54 (0.12 #529, 0.11 #693, 0.09 #2256), 054lpb6 (0.12 #14, 0.10 #2479, 0.09 #5858), 05rrtf (0.12 #57, 0.06 #877, 0.04 #3511), 030_1_ (0.11 #180, 0.08 #2317, 0.06 #1412), 0kx4m (0.10 #254, 0.08 #90, 0.07 #910), 0c41qv (0.10 #301, 0.05 #957, 0.05 #711), 017s11 (0.09 #987, 0.09 #413, 0.08 #1151) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 02qrv7; 03mgx6z; >> query: (?x1688, 016tw3) <- language(?x1688, ?x5359), written_by(?x1688, ?x1689), film(?x2626, ?x1688), ?x5359 = 0jzc >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #511 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 0y_yw; *> query: (?x1688, 03xsby) <- honored_for(?x3322, ?x1688), genre(?x1688, ?x225), film_format(?x1688, ?x909), film_release_distribution_medium(?x1688, ?x81) *> conf = 0.03 ranks of expected_values: 39 EVAL 024l2y production_companies 03xsby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 127.000 119.000 0.235 http://example.org/film/film/production_companies #19069-02bc74 PRED entity: 02bc74 PRED relation: award PRED expected values: 03tk6z => 128 concepts (119 used for prediction) PRED predicted values (max 10 best out of 278): 01c427 (0.43 #488, 0.25 #1294, 0.23 #2906), 01bgqh (0.42 #1252, 0.33 #43, 0.30 #3670), 01by1l (0.40 #3739, 0.37 #18250, 0.34 #10591), 03qbh5 (0.35 #3832, 0.33 #205, 0.26 #10281), 03qbnj (0.33 #232, 0.30 #3859, 0.20 #10308), 02f705 (0.33 #152, 0.29 #555, 0.18 #10228), 03t5kl (0.33 #226, 0.21 #8286, 0.20 #10302), 02f71y (0.33 #182, 0.19 #3809, 0.17 #988), 02f716 (0.33 #176, 0.19 #3803, 0.15 #10252), 02f5qb (0.33 #155, 0.18 #8215, 0.17 #10231) >> Best rule #488 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 016qtt; 058s57; 03f1d47; 0mbw0; 01vvybv; >> query: (?x12743, 01c427) <- person(?x9723, ?x12743), profession(?x12743, ?x220), film(?x12743, ?x10769), ?x220 = 016z4k >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3841 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 01vvydl; 0lbj1; 01w02sy; 015f7; *> query: (?x12743, 03tk6z) <- participant(?x4740, ?x12743), artist(?x5634, ?x12743), award(?x12743, ?x1801), role(?x12743, ?x316) *> conf = 0.07 ranks of expected_values: 115 EVAL 02bc74 award 03tk6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 128.000 119.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19068-01jssp PRED entity: 01jssp PRED relation: school! PRED expected values: 03nt7j => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 16): 05vsb7 (0.25 #177, 0.18 #225, 0.17 #241), 092j54 (0.24 #183, 0.17 #23, 0.16 #215), 09l0x9 (0.22 #185, 0.17 #105, 0.17 #25), 03nt7j (0.20 #181, 0.15 #213, 0.15 #229), 025tn92 (0.18 #218, 0.17 #186, 0.16 #234), 02pq_x5 (0.16 #222, 0.15 #110, 0.15 #238), 02x2khw (0.13 #210, 0.12 #226, 0.12 #242), 038c0q (0.13 #228, 0.12 #244, 0.12 #212), 09th87 (0.12 #220, 0.12 #236, 0.12 #188), 02z6872 (0.12 #184, 0.11 #216, 0.11 #232) >> Best rule #177 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 0fht9f; >> query: (?x331, 05vsb7) <- school(?x5204, ?x331), position(?x5204, ?x180), position_s(?x5204, ?x935) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #181 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 0fht9f; *> query: (?x331, 03nt7j) <- school(?x5204, ?x331), position(?x5204, ?x180), position_s(?x5204, ?x935) *> conf = 0.20 ranks of expected_values: 4 EVAL 01jssp school! 03nt7j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 93.000 0.250 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #19067-017_hq PRED entity: 017_hq PRED relation: group! PRED expected values: 01vj9c => 73 concepts (39 used for prediction) PRED predicted values (max 10 best out of 119): 03bx0bm (0.79 #186, 0.72 #514, 0.71 #350), 028tv0 (0.43 #175, 0.42 #667, 0.40 #1489), 03qjg (0.40 #125, 0.32 #700, 0.26 #1194), 01vj9c (0.32 #668, 0.27 #1819, 0.26 #1736), 04rzd (0.30 #109, 0.16 #684, 0.14 #1178), 07y_7 (0.30 #84, 0.11 #1810, 0.11 #1153), 06ncr (0.24 #363, 0.24 #691, 0.21 #199), 0l14j_ (0.21 #704, 0.14 #868, 0.13 #786), 042v_gx (0.20 #88, 0.14 #335, 0.11 #1157), 018j2 (0.20 #110, 0.08 #165, 0.08 #1261) >> Best rule #186 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 016fmf; 0kr_t; 0dw4g; 03d9d6; 09lwrt; 01w5n51; 02k5sc; 046p9; 013w8y; 016l09; ... >> query: (?x11700, 03bx0bm) <- award(?x11700, ?x3647), ?x3647 = 01c9jp, artists(?x302, ?x11700), group(?x227, ?x11700), ?x302 = 016clz >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #668 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 07c0j; 05crg7; 0dtd6; 0frsw; 01vrwfv; 01rm8b; 0163m1; 0hvbj; 02jqjm; 015srx; ... *> query: (?x11700, 01vj9c) <- award(?x11700, ?x3647), ?x3647 = 01c9jp, artists(?x302, ?x11700), group(?x227, ?x11700) *> conf = 0.32 ranks of expected_values: 4 EVAL 017_hq group! 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 73.000 39.000 0.786 http://example.org/music/performance_role/regular_performances./music/group_membership/group #19066-02qk2d5 PRED entity: 02qk2d5 PRED relation: colors PRED expected values: 02rnmb => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.99 #941, 0.98 #811, 0.86 #420), 019sc (0.95 #693, 0.44 #478, 0.44 #466), 01g5v (0.95 #667, 0.40 #251, 0.40 #232), 01l849 (0.91 #560, 0.60 #191, 0.29 #324), 036k5h (0.67 #367, 0.15 #1084, 0.04 #485), 06fvc (0.39 #942, 0.39 #812, 0.36 #421), 0jc_p (0.33 #24, 0.33 #5, 0.29 #347), 01jnf1 (0.33 #50, 0.25 #107, 0.21 #767), 09ggk (0.25 #167, 0.25 #72, 0.20 #205), 067z2v (0.25 #105, 0.22 #917, 0.20 #219) >> Best rule #941 for best value: >> intensional similarity = 15 >> extensional distance = 185 >> proper extension: 075q_; 03x746; 0d_q40; 025txtg; 01yhm; 04913k; 0j2pg; 0266sb_; 0g701n; 0c41y70; ... >> query: (?x9576, 083jv) <- sport(?x9576, ?x12913), colors(?x9576, ?x9464), colors(?x6988, ?x9464), colors(?x4187, ?x9464), colors(?x3351, ?x9464), colors(?x1665, ?x9464), currency(?x3351, ?x170), institution(?x865, ?x3351), school_type(?x6988, ?x14682), country(?x4187, ?x94), colors(?x13083, ?x9464), ?x13083 = 0fw9n7, major_field_of_study(?x4187, ?x732), fraternities_and_sororities(?x1665, ?x4348), citytown(?x6988, ?x6987) >> conf = 0.99 => this is the best rule for 1 predicted values *> Best rule #432 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 12 *> proper extension: 0jm8l; 0jmcv; 0fw9vx; *> query: (?x9576, 02rnmb) <- sport(?x9576, ?x12913), colors(?x9576, ?x9464), position(?x9576, ?x6848), colors(?x6925, ?x9464), colors(?x6894, ?x9464), colors(?x6396, ?x9464), colors(?x581, ?x9464), ?x6925 = 01bm_, major_field_of_study(?x6894, ?x8221), student(?x6894, ?x1345), ?x8221 = 037mh8, ?x581 = 06pwq, institution(?x620, ?x6894), ?x6396 = 04s934, ?x6848 = 02_ssl *> conf = 0.21 ranks of expected_values: 11 EVAL 02qk2d5 colors 02rnmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 60.000 60.000 0.989 http://example.org/sports/sports_team/colors #19065-0cwy47 PRED entity: 0cwy47 PRED relation: film_release_region PRED expected values: 09c7w0 06mkj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 143): 09c7w0 (0.93 #5595, 0.93 #7076, 0.93 #10530), 06mkj (0.84 #2535, 0.82 #3192, 0.79 #1875), 05r4w (0.83 #2472, 0.77 #3129, 0.76 #1812), 059j2 (0.83 #3164, 0.81 #2507, 0.76 #1847), 03gj2 (0.77 #3156, 0.77 #2499, 0.63 #1839), 03h64 (0.77 #2545, 0.72 #3202, 0.67 #1227), 035qy (0.72 #2510, 0.69 #3167, 0.69 #1850), 05qhw (0.71 #3144, 0.71 #2487, 0.59 #1827), 015fr (0.70 #2490, 0.70 #3147, 0.66 #1830), 0154j (0.70 #2476, 0.69 #3133, 0.61 #1816) >> Best rule #5595 for best value: >> intensional similarity = 4 >> extensional distance = 587 >> proper extension: 01kf5lf; >> query: (?x951, 09c7w0) <- genre(?x951, ?x53), film(?x2378, ?x951), award(?x951, ?x484), film_release_region(?x951, ?x142) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0cwy47 film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cwy47 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19064-0brddh PRED entity: 0brddh PRED relation: profession PRED expected values: 02hrh1q => 150 concepts (66 used for prediction) PRED predicted values (max 10 best out of 64): 02hrh1q (0.93 #2997, 0.90 #2101, 0.89 #4192), 01d_h8 (0.70 #5973, 0.68 #6720, 0.67 #6122), 0dxtg (0.62 #5980, 0.62 #7771, 0.61 #7472), 09jwl (0.41 #4495, 0.37 #9715, 0.37 #8374), 016z4k (0.29 #3734, 0.28 #4629, 0.23 #7016), 03gjzk (0.27 #6131, 0.26 #7474, 0.26 #6729), 0nbcg (0.27 #9728, 0.26 #8387, 0.26 #8089), 02krf9 (0.23 #5994, 0.23 #6741, 0.22 #6143), 0dz3r (0.22 #9698, 0.21 #9549, 0.20 #8357), 0d1pc (0.18 #3781, 0.16 #4676, 0.13 #498) >> Best rule #2997 for best value: >> intensional similarity = 6 >> extensional distance = 71 >> proper extension: 0gcdzz; 02r99xw; >> query: (?x13446, 02hrh1q) <- people(?x5025, ?x13446), ?x5025 = 0dryh9k, gender(?x13446, ?x514), profession(?x13446, ?x524), profession(?x3827, ?x524), ?x3827 = 01vqrm >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0brddh profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 66.000 0.932 http://example.org/people/person/profession #19063-0d5_f PRED entity: 0d5_f PRED relation: influenced_by! PRED expected values: 03s9b => 124 concepts (44 used for prediction) PRED predicted values (max 10 best out of 434): 0683n (0.24 #846, 0.16 #3398, 0.15 #3908), 0dzkq (0.24 #635, 0.08 #11355, 0.07 #5228), 040db (0.19 #3648, 0.17 #5179, 0.16 #8752), 01vdrw (0.18 #951, 0.12 #11741, 0.12 #21443), 07h1q (0.18 #915, 0.12 #11741, 0.12 #21443), 0nk72 (0.18 #848, 0.12 #11741, 0.12 #21443), 05qzv (0.18 #910, 0.07 #2952, 0.06 #12652), 02yl42 (0.16 #1666, 0.14 #2176, 0.13 #2686), 0n6kf (0.12 #11741, 0.12 #3251, 0.12 #21443), 01hc9_ (0.12 #11741, 0.12 #1892, 0.12 #21443) >> Best rule #846 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 01d494; >> query: (?x4301, 0683n) <- student(?x9110, ?x4301), influenced_by(?x4301, ?x11097), nationality(?x4301, ?x304), ?x11097 = 02wh0 >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #4867 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 96 *> proper extension: 0p_pd; 04wqr; 081lh; 0gt_k; 086qd; 01w60_p; 01wj9y9; 046lt; 0407f; 012gq6; ... *> query: (?x4301, 03s9b) <- influenced_by(?x9610, ?x4301), location(?x4301, ?x4627), profession(?x4301, ?x353), languages(?x9610, ?x254) *> conf = 0.01 ranks of expected_values: 419 EVAL 0d5_f influenced_by! 03s9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 124.000 44.000 0.235 http://example.org/influence/influence_node/influenced_by #19062-0fz3b1 PRED entity: 0fz3b1 PRED relation: genre PRED expected values: 01j1n2 => 103 concepts (27 used for prediction) PRED predicted values (max 10 best out of 90): 01jfsb (0.89 #2614, 0.83 #2733, 0.57 #484), 03npn (0.77 #478, 0.24 #360, 0.20 #2727), 01hmnh (0.61 #1198, 0.18 #2501, 0.17 #2026), 01z4y (0.60 #2721, 0.43 #2364), 07s9rl0 (0.56 #3078, 0.52 #827, 0.52 #945), 02kdv5l (0.56 #3, 0.43 #2724, 0.34 #2605), 02l7c8 (0.44 #16, 0.42 #134, 0.34 #2855), 03k9fj (0.44 #11, 0.35 #1191, 0.26 #955), 06cvj (0.33 #122, 0.33 #4, 0.25 #240), 0lsxr (0.33 #2610, 0.29 #2729, 0.19 #1070) >> Best rule #2614 for best value: >> intensional similarity = 4 >> extensional distance = 388 >> proper extension: 0413cff; >> query: (?x4326, 01jfsb) <- genre(?x4326, ?x6452), titles(?x2480, ?x4326), genre(?x1456, ?x6452), ?x1456 = 0cz8mkh >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1003 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 277 *> proper extension: 0bmc4cm; *> query: (?x4326, 01j1n2) <- genre(?x4326, ?x258), category(?x4326, ?x134), country(?x4326, ?x94), nominated_for(?x298, ?x4326) *> conf = 0.04 ranks of expected_values: 60 EVAL 0fz3b1 genre 01j1n2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 103.000 27.000 0.892 http://example.org/film/film/genre #19061-0425gc PRED entity: 0425gc PRED relation: current_club! PRED expected values: 03zrhb => 86 concepts (71 used for prediction) PRED predicted values (max 10 best out of 43): 03zrhb (0.33 #113, 0.25 #31, 0.25 #17), 03z8bw (0.20 #86, 0.17 #150, 0.17 #117), 03yl2t (0.20 #36, 0.15 #198, 0.08 #164), 03_44z (0.20 #61, 0.09 #324, 0.09 #358), 02ltg3 (0.17 #167, 0.15 #201, 0.09 #336), 02s9vc (0.17 #182, 0.15 #216, 0.07 #351), 02bh_v (0.17 #180, 0.15 #214, 0.07 #315), 01_lhg (0.15 #237, 0.08 #168, 0.08 #202), 032jlh (0.15 #256, 0.08 #659, 0.08 #1105), 033nzk (0.10 #231, 0.07 #297, 0.05 #399) >> Best rule #113 for best value: >> intensional similarity = 18 >> extensional distance = 4 >> proper extension: 042rlf; >> query: (?x12834, 03zrhb) <- position(?x12834, ?x203), team(?x9910, ?x12834), team(?x6152, ?x12834), ?x203 = 0dgrmp, gender(?x9910, ?x231), team(?x63, ?x12834), team(?x9910, ?x7642), ?x6152 = 02y9ln, position(?x10635, ?x63), position(?x9107, ?x63), position(?x8585, ?x63), position(?x7829, ?x63), position(?x2427, ?x63), ?x9107 = 0138mv, ?x7829 = 0882r_, ?x2427 = 01l3vx, ?x8585 = 04ltf, ?x10635 = 03zbws >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0425gc current_club! 03zrhb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 71.000 0.333 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #19060-03_3d PRED entity: 03_3d PRED relation: country! PRED expected values: 02vw1w2 04sntd 016ztl => 136 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1675): 01m13b (0.50 #11622, 0.48 #14905, 0.44 #23110), 03rz2b (0.40 #6983, 0.25 #10263, 0.25 #5343), 01f8f7 (0.40 #7667, 0.25 #10947, 0.20 #20793), 01rxyb (0.37 #21327, 0.35 #21328, 0.25 #5587), 091xrc (0.37 #62338, 0.25 #6536, 0.11 #13096), 0d99k_ (0.37 #62338, 0.25 #6517, 0.11 #13077), 0m9p3 (0.37 #62338, 0.25 #5276, 0.09 #15119), 048yqf (0.37 #62338, 0.25 #6393, 0.09 #16236), 029v40 (0.37 #62338, 0.25 #6411, 0.06 #12971), 04x4nv (0.37 #62338, 0.25 #6306, 0.06 #12866) >> Best rule #11622 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0h7x; >> query: (?x252, 01m13b) <- medal(?x252, ?x422), film_release_region(?x9349, ?x252), ?x9349 = 0jdr0 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #62338 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 0bq0p9; *> query: (?x252, ?x869) <- nationality(?x11780, ?x252), nationality(?x9263, ?x252), gender(?x11780, ?x514), film(?x9263, ?x869) *> conf = 0.37 ranks of expected_values: 20, 34 EVAL 03_3d country! 016ztl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 59.000 0.500 http://example.org/film/film/country EVAL 03_3d country! 04sntd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 136.000 59.000 0.500 http://example.org/film/film/country EVAL 03_3d country! 02vw1w2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 136.000 59.000 0.500 http://example.org/film/film/country #19059-02m7r PRED entity: 02m7r PRED relation: place_of_death PRED expected values: 0978r => 167 concepts (146 used for prediction) PRED predicted values (max 10 best out of 102): 0f8j6 (0.33 #386, 0.25 #580, 0.17 #774), 0d9jr (0.33 #80, 0.17 #662, 0.04 #2809), 030qb3t (0.22 #7645, 0.21 #7449, 0.19 #10383), 04jpl (0.17 #589, 0.15 #5285, 0.10 #2541), 0k049 (0.15 #4302, 0.14 #7430, 0.13 #6258), 0ctw_b (0.14 #4299, 0.10 #4298, 0.09 #1557), 0r0m6 (0.12 #1030, 0.09 #1226, 0.05 #2399), 02_286 (0.12 #7245, 0.11 #3920, 0.10 #4115), 0f2wj (0.11 #6464, 0.11 #6855, 0.09 #10570), 02jx1 (0.10 #4298, 0.09 #1557, 0.08 #11933) >> Best rule #386 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 03s9v; >> query: (?x2397, 0f8j6) <- place_of_burial(?x2397, ?x4435), gender(?x2397, ?x231), profession(?x2397, ?x14162), profession(?x2397, ?x11056), ?x11056 = 05snw, ?x14162 = 01pxg >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5522 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 0424m; 02nygk; *> query: (?x2397, 0978r) <- nationality(?x2397, ?x512), profession(?x2397, ?x3802), student(?x2396, ?x2397), peers(?x10913, ?x2397) *> conf = 0.04 ranks of expected_values: 27 EVAL 02m7r place_of_death 0978r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 167.000 146.000 0.333 http://example.org/people/deceased_person/place_of_death #19058-060m4 PRED entity: 060m4 PRED relation: profession! PRED expected values: 01k165 => 43 concepts (43 used for prediction) No prediction ranks of expected_values: EVAL 060m4 profession! 01k165 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 43.000 0.000 http://example.org/people/person/profession #19057-02cx72 PRED entity: 02cx72 PRED relation: role PRED expected values: 05r5c => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 58): 05r5c (0.25 #9, 0.22 #327, 0.21 #115), 0342h (0.17 #535, 0.15 #2233, 0.15 #1384), 01vdm0 (0.13 #352, 0.12 #564, 0.11 #1413), 02sgy (0.10 #537, 0.09 #1386, 0.09 #2235), 042v_gx (0.10 #328, 0.09 #540, 0.09 #647), 0l14qv (0.09 #112, 0.08 #536, 0.07 #324), 05842k (0.09 #399, 0.09 #611, 0.07 #2309), 018vs (0.08 #545, 0.07 #2243, 0.06 #1288), 01vj9c (0.08 #547, 0.06 #2245, 0.06 #335), 026t6 (0.08 #533, 0.06 #2231, 0.06 #1276) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01gb54; >> query: (?x3732, 05r5c) <- nominated_for(?x3732, ?x3211), award(?x3732, ?x1443), ?x3211 = 02ctc6 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cx72 role 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.250 http://example.org/music/artist/track_contributions./music/track_contribution/role #19056-013q0p PRED entity: 013q0p PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.86 #86, 0.83 #26, 0.83 #76), 02nxhr (0.05 #92, 0.03 #82, 0.03 #97), 07z4p (0.03 #80, 0.03 #35, 0.03 #85), 07c52 (0.03 #78, 0.03 #83, 0.03 #292), 0735l (0.01 #39, 0.01 #44) >> Best rule #86 for best value: >> intensional similarity = 4 >> extensional distance = 408 >> proper extension: 02_1sj; 02z3r8t; 035xwd; 03ckwzc; 02sg5v; 0963mq; 04kkz8; 08hmch; 07g_0c; 02847m9; ... >> query: (?x4717, 029j_) <- film(?x1986, ?x4717), genre(?x4717, ?x225), music(?x4717, ?x6382), featured_film_locations(?x4717, ?x3026) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013q0p film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19055-02k13d PRED entity: 02k13d PRED relation: company PRED expected values: 0c0sl 02z_b => 38 concepts (35 used for prediction) PRED predicted values (max 10 best out of 814): 07k2d (0.84 #7439, 0.82 #8461, 0.80 #8119), 02r5dz (0.78 #6150, 0.75 #5477, 0.71 #4119), 060ppp (0.75 #5652, 0.71 #4294, 0.71 #3619), 019rl6 (0.75 #5564, 0.71 #4206, 0.71 #3531), 07xyn1 (0.75 #5589, 0.71 #4231, 0.67 #6262), 03s7h (0.75 #4991, 0.62 #4649, 0.59 #5404), 01yfp7 (0.71 #4172, 0.62 #5530, 0.59 #5404), 09b3v (0.71 #4136, 0.62 #5494, 0.59 #5404), 0sxdg (0.71 #4249, 0.62 #5607, 0.59 #5404), 0lwkh (0.71 #3659, 0.59 #5404, 0.57 #4334) >> Best rule #7439 for best value: >> intensional similarity = 12 >> extensional distance = 14 >> proper extension: 016fly; >> query: (?x3775, ?x12285) <- company(?x3775, ?x12930), company(?x3775, ?x6896), company(?x3775, ?x3776), company(?x3775, ?x2270), split_to(?x12285, ?x6896), company(?x1491, ?x2270), company(?x1491, ?x581), ?x581 = 06pwq, company(?x2761, ?x3776), organization(?x12930, ?x14299), nationality(?x2761, ?x94), profession(?x2761, ?x319) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #5404 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 6 *> proper extension: 05k17c; *> query: (?x3775, ?x234) <- company(?x3775, ?x12930), company(?x3775, ?x7457), company(?x3775, ?x6896), company(?x3775, ?x2270), company(?x3775, ?x1762), split_to(?x12285, ?x6896), service_location(?x12930, ?x94), list(?x1762, ?x8915), child(?x3920, ?x1762), company(?x8314, ?x1762), citytown(?x12930, ?x739), organization(?x4682, ?x2270), company(?x8314, ?x234), state_province_region(?x7457, ?x3038) *> conf = 0.59 ranks of expected_values: 158, 373 EVAL 02k13d company 02z_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 38.000 35.000 0.842 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 02k13d company 0c0sl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 38.000 35.000 0.842 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #19054-02ph9tm PRED entity: 02ph9tm PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #82, 0.83 #98, 0.82 #103), 02nxhr (0.11 #7, 0.04 #27, 0.04 #68), 07z4p (0.04 #61, 0.03 #202, 0.03 #117), 07c52 (0.03 #280, 0.03 #115, 0.03 #255) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 375 >> proper extension: 0d8w2n; >> query: (?x6245, 029j_) <- production_companies(?x6245, ?x541), featured_film_locations(?x6245, ?x739), titles(?x2480, ?x6245) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ph9tm film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.844 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #19053-0mmpz PRED entity: 0mmpz PRED relation: time_zones PRED expected values: 02lcqs => 204 concepts (204 used for prediction) PRED predicted values (max 10 best out of 12): 02lcqs (0.88 #616, 0.86 #1170, 0.86 #602), 02hcv8 (0.56 #1671, 0.56 #1658, 0.56 #1619), 02fqwt (0.35 #79, 0.34 #131, 0.29 #223), 02hczc (0.25 #67, 0.19 #106, 0.18 #145), 02llzg (0.17 #160, 0.11 #369, 0.11 #147), 03bdv (0.08 #1242, 0.05 #149, 0.05 #2184), 042g7t (0.05 #154, 0.05 #167, 0.04 #233), 03plfd (0.03 #427, 0.03 #1509, 0.03 #1548), 05jphn (0.03 #156, 0.02 #169, 0.02 #235), 02lcrv (0.03 #150, 0.02 #163, 0.02 #229) >> Best rule #616 for best value: >> intensional similarity = 5 >> extensional distance = 113 >> proper extension: 0m2gk; >> query: (?x11525, ?x2950) <- adjoins(?x11525, ?x12383), adjoins(?x11569, ?x11525), county(?x10213, ?x12383), time_zones(?x12383, ?x2950), contains(?x11525, ?x5267) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mmpz time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 204.000 204.000 0.878 http://example.org/location/location/time_zones #19052-015f7 PRED entity: 015f7 PRED relation: gender PRED expected values: 02zsn => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #111, 0.82 #93, 0.81 #127), 02zsn (0.85 #40, 0.67 #4, 0.65 #46) >> Best rule #111 for best value: >> intensional similarity = 2 >> extensional distance = 341 >> proper extension: 07c37; >> query: (?x3397, 05zppz) <- influenced_by(?x3397, ?x4960), location(?x3397, ?x4622) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 0151ns; 08f3b1; 086qd; 07swvb; 02t_99; 01gw4f; 0421st; 02633g; 029ghl; 0g476; ... *> query: (?x3397, 02zsn) <- award(?x3397, ?x3488), award(?x3397, ?x1007), award_winner(?x3488, ?x702), ?x1007 = 03c7tr1 *> conf = 0.85 ranks of expected_values: 2 EVAL 015f7 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 117.000 0.854 http://example.org/people/person/gender #19051-0146pg PRED entity: 0146pg PRED relation: gender PRED expected values: 05zppz => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.95 #13, 0.93 #23, 0.93 #7), 02zsn (0.31 #16, 0.29 #56, 0.28 #82) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 025vry; >> query: (?x669, 05zppz) <- award_winner(?x1386, ?x669), music(?x670, ?x669), award_winner(?x1079, ?x669) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0146pg gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.947 http://example.org/people/person/gender #19050-03cvvlg PRED entity: 03cvvlg PRED relation: currency PRED expected values: 09nqf => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.77 #85, 0.77 #99, 0.76 #15), 01nv4h (0.04 #2, 0.02 #93, 0.02 #107), 02gsvk (0.02 #69, 0.02 #83, 0.02 #48), 02l6h (0.02 #18, 0.01 #137, 0.01 #53) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 244 >> proper extension: 0bx_hnp; >> query: (?x8438, 09nqf) <- award_winner(?x8438, ?x2551), film_release_distribution_medium(?x8438, ?x81), film(?x4666, ?x8438), award_winner(?x1601, ?x4666) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cvvlg currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.768 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #19049-028p0 PRED entity: 028p0 PRED relation: influenced_by! PRED expected values: 01wd02c 05np2 => 105 concepts (41 used for prediction) PRED predicted values (max 10 best out of 783): 02wh0 (0.60 #4470, 0.33 #439, 0.28 #7496), 0mb5x (0.44 #7384, 0.33 #327, 0.18 #5366), 0j3v (0.40 #4108, 0.33 #77, 0.27 #5116), 039n1 (0.40 #4412, 0.18 #5420, 0.11 #7438), 03_hd (0.40 #4206, 0.09 #9251, 0.09 #5214), 014ps4 (0.36 #4838, 0.25 #808, 0.20 #1312), 05qzv (0.36 #5431, 0.22 #7449, 0.14 #10478), 06myp (0.33 #429, 0.30 #4460, 0.18 #5468), 01vh096 (0.33 #346, 0.30 #4377, 0.09 #14622), 040_t (0.33 #250, 0.28 #7307, 0.27 #5289) >> Best rule #4470 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0gz_; 043s3; 01lwx; >> query: (?x1279, 02wh0) <- profession(?x1279, ?x353), influenced_by(?x8085, ?x1279), influenced_by(?x7509, ?x1279), ?x7509 = 048cl, student(?x1369, ?x8085) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #271 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 081k8; *> query: (?x1279, 05np2) <- profession(?x1279, ?x5805), influenced_by(?x8085, ?x1279), ?x8085 = 0448r, profession(?x7295, ?x5805), company(?x7295, ?x11946) *> conf = 0.33 ranks of expected_values: 15, 101 EVAL 028p0 influenced_by! 05np2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 105.000 41.000 0.600 http://example.org/influence/influence_node/influenced_by EVAL 028p0 influenced_by! 01wd02c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 105.000 41.000 0.600 http://example.org/influence/influence_node/influenced_by #19048-0htcn PRED entity: 0htcn PRED relation: type_of_union PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.89 #9, 0.85 #5, 0.83 #21), 01g63y (0.15 #6, 0.14 #10, 0.13 #98) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 014zcr; 03_gd; 02kxbwx; 081lh; 0151w_; 05_k56; 0bwh6; 0h1p; 06pj8; 0127m7; ... >> query: (?x10226, 04ztj) <- award(?x10226, ?x198), award_winner(?x10226, ?x788), film(?x10226, ?x1804), ?x198 = 040njc >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0htcn type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.886 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19047-026kq4q PRED entity: 026kq4q PRED relation: honored_for PRED expected values: 0jzw 0p_tz => 31 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1147): 0d68qy (0.35 #6084, 0.33 #744, 0.25 #5488), 0d66j2 (0.33 #807, 0.25 #1994, 0.15 #10086), 0btpm6 (0.33 #1034, 0.25 #2221, 0.12 #6374), 03hmt9b (0.33 #829, 0.25 #2016, 0.12 #6169), 03cw411 (0.33 #811, 0.25 #1998, 0.12 #6151), 0d7vtk (0.33 #1118, 0.25 #2305, 0.12 #6458), 03hkch7 (0.33 #779, 0.25 #1966, 0.06 #6119), 0yx_w (0.33 #1704, 0.20 #2890, 0.15 #10086), 01sxly (0.33 #1215, 0.20 #2401, 0.14 #3587), 01qxc7 (0.33 #1451, 0.20 #2637, 0.14 #3823) >> Best rule #6084 for best value: >> intensional similarity = 16 >> extensional distance = 15 >> proper extension: 013b2h; >> query: (?x3001, 0d68qy) <- award_winner(?x3001, ?x4436), award_winner(?x3001, ?x2683), award_winner(?x3001, ?x398), ceremony(?x1107, ?x3001), award(?x2683, ?x749), award(?x2683, ?x537), gender(?x398, ?x231), award(?x4436, ?x704), film(?x398, ?x796), nominated_for(?x749, ?x2111), ?x2111 = 016z7s, ?x537 = 0gkvb7, award(?x197, ?x749), ?x704 = 09sb52, profession(?x2683, ?x220), nominated_for(?x4436, ?x810) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #10086 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 67 *> proper extension: 0c4hgj; *> query: (?x3001, ?x161) <- award_winner(?x3001, ?x4436), award_winner(?x3001, ?x2683), ceremony(?x1107, ?x3001), award(?x2683, ?x537), honored_for(?x3001, ?x1588), award_nominee(?x4436, ?x2518), award(?x4436, ?x102), artists(?x671, ?x2683), type_of_union(?x2683, ?x566), award_nominee(?x11469, ?x2683), award_winner(?x161, ?x4436), award_winner(?x112, ?x4436), gender(?x2683, ?x514), nominated_for(?x4436, ?x810) *> conf = 0.15 ranks of expected_values: 64, 586 EVAL 026kq4q honored_for 0p_tz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 21.000 0.353 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 026kq4q honored_for 0jzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 31.000 21.000 0.353 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #19046-065ym0c PRED entity: 065ym0c PRED relation: nominated_for! PRED expected values: 0pkr1 => 101 concepts (25 used for prediction) PRED predicted values (max 10 best out of 624): 0pksh (0.78 #53827, 0.77 #21057, 0.77 #23398), 0150t6 (0.25 #653, 0.20 #2992, 0.08 #14690), 07lt7b (0.25 #132, 0.20 #2471, 0.08 #7151), 05xf75 (0.25 #1811, 0.20 #4150, 0.08 #8830), 05zbm4 (0.25 #188, 0.20 #2527, 0.08 #7207), 0dvmd (0.25 #661, 0.20 #3000, 0.08 #7680), 049g_xj (0.25 #301, 0.20 #2640, 0.08 #7320), 092ys_y (0.25 #799, 0.06 #17175, 0.06 #12497), 04wp63 (0.25 #2065, 0.06 #13763, 0.03 #39503), 06rnl9 (0.25 #613, 0.06 #12311, 0.03 #38051) >> Best rule #53827 for best value: >> intensional similarity = 4 >> extensional distance = 245 >> proper extension: 05h95s; 05fgr_; >> query: (?x10080, ?x12529) <- award(?x10080, ?x12715), titles(?x2346, ?x10080), award_winner(?x10080, ?x12529), disciplines_or_subjects(?x12715, ?x373) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6807 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 05znbh7; *> query: (?x10080, 0pkr1) <- nominated_for(?x9377, ?x10080), nominated_for(?x9217, ?x10080), nominated_for(?x7215, ?x10080), nominated_for(?x5923, ?x10080), ?x9377 = 09v4bym, ?x5923 = 09v8db5, ?x9217 = 09v51c2, award(?x147, ?x7215) *> conf = 0.11 ranks of expected_values: 25 EVAL 065ym0c nominated_for! 0pkr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 101.000 25.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #19045-0bt7w PRED entity: 0bt7w PRED relation: artists PRED expected values: 01_wfj => 60 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1068): 01vtj38 (0.67 #1737, 0.56 #3890, 0.22 #18991), 01kd57 (0.67 #1579, 0.56 #3732, 0.16 #18833), 0lccn (0.67 #1253, 0.44 #3406, 0.08 #18507), 01vwyqp (0.56 #3505, 0.50 #1352, 0.14 #34810), 03t9sp (0.54 #18454, 0.33 #1200, 0.22 #3353), 0fpj4lx (0.50 #1400, 0.44 #3553, 0.22 #2476), 01w8n89 (0.50 #1393, 0.44 #3546, 0.21 #25127), 02z4b_8 (0.50 #1712, 0.44 #3865, 0.19 #18966), 0qf11 (0.50 #1457, 0.44 #3610, 0.16 #18711), 015882 (0.50 #1206, 0.44 #3359, 0.16 #18460) >> Best rule #1737 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 02w4v; >> query: (?x7577, 01vtj38) <- artists(?x7577, ?x12565), artists(?x7577, ?x11749), artists(?x7577, ?x10745), artist(?x2299, ?x10745), ?x12565 = 063t3j, parent_genre(?x5406, ?x7577), award(?x11749, ?x3631), category(?x11749, ?x134), ?x3631 = 02f73p >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #39933 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 132 *> proper extension: 06cqb; 03_d0; 06by7; 07lnk; 01lyv; 0glt670; 07gxw; 0gywn; 0m0fw; 04pcmw; ... *> query: (?x7577, ?x133) <- artists(?x7577, ?x1338), parent_genre(?x7577, ?x302), parent_genre(?x5406, ?x7577), artists(?x302, ?x7972), artists(?x302, ?x133), instrumentalists(?x227, ?x7972) *> conf = 0.08 ranks of expected_values: 715 EVAL 0bt7w artists 01_wfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 37.000 0.667 http://example.org/music/genre/artists #19044-027d5g5 PRED entity: 027d5g5 PRED relation: award PRED expected values: 0gr51 => 90 concepts (56 used for prediction) PRED predicted values (max 10 best out of 278): 0468g4r (0.71 #10162, 0.71 #11382, 0.71 #9348), 02g3v6 (0.33 #24, 0.03 #1244), 0gr51 (0.33 #2946, 0.31 #6097, 0.30 #4978), 0gr4k (0.33 #3691, 0.32 #4910, 0.30 #2878), 0gs9p (0.31 #6097, 0.25 #4957, 0.25 #3738), 02rdyk7 (0.31 #6097, 0.15 #3750, 0.15 #4969), 04dn09n (0.31 #2889, 0.30 #3702, 0.29 #4921), 040njc (0.30 #414, 0.20 #4885, 0.20 #3666), 0gq9h (0.30 #484, 0.19 #4549, 0.19 #3330), 09sb52 (0.25 #17117, 0.24 #8982, 0.23 #16303) >> Best rule #10162 for best value: >> intensional similarity = 3 >> extensional distance = 1208 >> proper extension: 04glx0; >> query: (?x8572, ?x12581) <- award_winner(?x8573, ?x8572), award_nominee(?x4732, ?x8572), award_winner(?x12581, ?x8572) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2946 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 122 *> proper extension: 027l0b; *> query: (?x8572, 0gr51) <- award_winner(?x8573, ?x8572), award_winner(?x12581, ?x8572), written_by(?x4355, ?x8572) *> conf = 0.33 ranks of expected_values: 3 EVAL 027d5g5 award 0gr51 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 56.000 0.709 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19043-028dcg PRED entity: 028dcg PRED relation: major_field_of_study PRED expected values: 05qfh 03qsdpk 011s0 03r8gp => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 133): 05qfh (0.82 #327, 0.78 #803, 0.77 #217), 062z7 (0.82 #327, 0.78 #796, 0.77 #217), 03g3w (0.82 #327, 0.77 #217, 0.77 #877), 01jzxy (0.82 #327, 0.77 #217, 0.77 #877), 02j62 (0.79 #1128, 0.78 #798, 0.75 #659), 05qjt (0.78 #774, 0.75 #659, 0.74 #438), 01mkq (0.78 #782, 0.75 #659, 0.74 #438), 02h40lc (0.78 #770, 0.75 #659, 0.74 #438), 03nfmq (0.78 #805, 0.75 #659, 0.74 #438), 0g4gr (0.78 #799, 0.67 #580, 0.60 #358) >> Best rule #327 for best value: >> intensional similarity = 24 >> extensional distance = 1 >> proper extension: 02_xgp2; >> query: (?x8398, ?x2605) <- major_field_of_study(?x8398, ?x12637), institution(?x8398, ?x6501), institution(?x8398, ?x4780), student(?x8398, ?x8665), student(?x8398, ?x3560), state_province_region(?x6501, ?x335), major_field_of_study(?x6501, ?x3490), major_field_of_study(?x6501, ?x2605), major_field_of_study(?x6501, ?x2172), ?x4780 = 017cy9, currency(?x6501, ?x170), student(?x6501, ?x12500), ?x3490 = 05qfh, ?x2172 = 01jzxy, ?x12637 = 026bk, nominated_for(?x12500, ?x1434), company(?x346, ?x6501), award_nominee(?x3560, ?x400), gender(?x12500, ?x514), award_winner(?x1707, ?x3560), country(?x6501, ?x94), profession(?x8665, ?x319), award(?x3560, ?x401), ?x170 = 09nqf >> conf = 0.82 => this is the best rule for 4 predicted values ranks of expected_values: 1, 20, 45, 72 EVAL 028dcg major_field_of_study 03r8gp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 20.000 20.000 0.821 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 028dcg major_field_of_study 011s0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 20.000 20.000 0.821 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 028dcg major_field_of_study 03qsdpk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 20.000 20.000 0.821 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 028dcg major_field_of_study 05qfh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 20.000 20.000 0.821 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #19042-034tl PRED entity: 034tl PRED relation: contains! PRED expected values: 05rgl => 178 concepts (85 used for prediction) PRED predicted values (max 10 best out of 250): 09c7w0 (0.85 #70750, 0.85 #49255, 0.84 #58215), 04_1l0v (0.69 #31790, 0.66 #27313, 0.60 #6721), 0chghy (0.57 #34948, 0.15 #17039, 0.04 #44799), 02qkt (0.50 #7513, 0.49 #67516, 0.47 #30791), 0j0k (0.43 #12917, 0.36 #12022, 0.27 #30822), 07ssc (0.35 #48391, 0.33 #51079, 0.23 #54663), 07c5l (0.31 #11144, 0.29 #12934, 0.29 #12039), 0dg3n1 (0.30 #35977, 0.27 #64639, 0.27 #70905), 04pnx (0.29 #12069, 0.21 #12964, 0.16 #22812), 02j9z (0.27 #8090, 0.25 #924, 0.23 #10777) >> Best rule #70750 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 01f62; >> query: (?x12929, ?x94) <- location(?x691, ?x12929), country(?x12929, ?x94), contains(?x10150, ?x12929), type_of_union(?x691, ?x566) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 03h64; 0h8d; *> query: (?x12929, 05rgl) <- location(?x691, ?x12929), country(?x12929, ?x94), official_language(?x12929, ?x254), country(?x1121, ?x12929) *> conf = 0.25 ranks of expected_values: 12 EVAL 034tl contains! 05rgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 178.000 85.000 0.855 http://example.org/location/location/contains #19041-0sbbq PRED entity: 0sbbq PRED relation: category PRED expected values: 08mbj5d => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #19, 0.85 #22, 0.84 #41) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 0s3y5; 02dtg; 0ydpd; 0r62v; 0r1jr; 0fw2y; 0fvzg; 0rp46; 0m2rv; 0ply0; ... >> query: (?x8553, 08mbj5d) <- location(?x1204, ?x8553), county_seat(?x8552, ?x8553), contains(?x94, ?x8553), source(?x8553, ?x958) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sbbq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.848 http://example.org/common/topic/webpage./common/webpage/category #19040-0bjv6 PRED entity: 0bjv6 PRED relation: film_release_region! PRED expected values: 0gffmn8 0gy2y8r => 100 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1310): 017jd9 (0.92 #3201, 0.70 #4508, 0.65 #8429), 01fmys (0.87 #2859, 0.65 #8087, 0.62 #9394), 08hmch (0.84 #2733, 0.68 #4040, 0.67 #9268), 02vxq9m (0.84 #2631, 0.66 #9166, 0.65 #3938), 043tvp3 (0.84 #3524, 0.65 #4831, 0.58 #10059), 0dtfn (0.84 #2775, 0.56 #9310, 0.56 #8003), 03nm_fh (0.82 #3214, 0.64 #9749, 0.60 #4521), 05zlld0 (0.82 #3081, 0.60 #4388, 0.59 #9616), 0872p_c (0.82 #2749, 0.56 #9284, 0.54 #7977), 0jjy0 (0.82 #2743, 0.53 #4050, 0.50 #9278) >> Best rule #3201 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 05r4w; 02k54; 0345h; 015qh; 06mkj; 03rj0; >> query: (?x3227, 017jd9) <- film_release_region(?x6520, ?x3227), participating_countries(?x418, ?x3227), ?x6520 = 02bg55 >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #3118 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 05r4w; 02k54; 0345h; 015qh; 06mkj; 03rj0; *> query: (?x3227, 0gy2y8r) <- film_release_region(?x6520, ?x3227), participating_countries(?x418, ?x3227), ?x6520 = 02bg55 *> conf = 0.71 ranks of expected_values: 62, 103 EVAL 0bjv6 film_release_region! 0gy2y8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 100.000 43.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bjv6 film_release_region! 0gffmn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 100.000 43.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #19039-027l4q PRED entity: 027l4q PRED relation: country PRED expected values: 09c7w0 => 92 concepts (72 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.80 #528, 0.75 #2634, 0.74 #2808), 030qb3t (0.39 #1486, 0.37 #1926, 0.25 #2362), 01n7q (0.37 #1926, 0.26 #350, 0.25 #2362), 03rjj (0.28 #4455, 0.26 #5153, 0.24 #5416), 0d05w3 (0.26 #5153, 0.24 #5416, 0.20 #2978), 0kpys (0.25 #2362, 0.24 #2014, 0.24 #1837), 0k_s5 (0.25 #2362, 0.24 #2014, 0.24 #1837), 07ssc (0.07 #1239, 0.07 #1327, 0.06 #1063), 0gx1l (0.06 #6306, 0.05 #4892, 0.05 #5946), 04_1l0v (0.06 #6306, 0.05 #5946) >> Best rule #528 for best value: >> intensional similarity = 2 >> extensional distance = 52 >> proper extension: 06pwq; >> query: (?x10298, 09c7w0) <- state(?x10298, ?x1227), ?x1227 = 01n7q >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027l4q country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 72.000 0.796 http://example.org/base/biblioness/bibs_location/country #19038-02g2yr PRED entity: 02g2yr PRED relation: award! PRED expected values: 01m4yn => 52 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2575): 01xcfy (0.81 #3375, 0.71 #13500, 0.71 #37137), 01xv77 (0.81 #3375, 0.71 #13500, 0.71 #37137), 05dbf (0.81 #3375, 0.71 #37137, 0.68 #30383), 0chw_ (0.81 #3375, 0.71 #37137, 0.68 #30383), 028knk (0.50 #7274, 0.33 #525, 0.15 #10124), 01kb2j (0.50 #8234, 0.33 #1485, 0.15 #10124), 046zh (0.50 #8278, 0.15 #10124, 0.14 #54018), 03mp9s (0.50 #8768, 0.10 #12144, 0.08 #15521), 0dvld (0.44 #8493, 0.33 #1744, 0.16 #11869), 086sj (0.44 #7905, 0.33 #1156, 0.15 #10124) >> Best rule #3375 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 05pcn59; >> query: (?x6463, ?x2275) <- nominated_for(?x6463, ?x408), award(?x4295, ?x6463), award(?x4252, ?x6463), ?x4252 = 05qg6g, ?x4295 = 09l3p, award_winner(?x6463, ?x2275) >> conf = 0.81 => this is the best rule for 4 predicted values *> Best rule #5353 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 05ztjjw; *> query: (?x6463, 01m4yn) <- nominated_for(?x6463, ?x11534), nominated_for(?x6463, ?x3507), award_winner(?x6463, ?x2258), ?x11534 = 0gy0n, language(?x3507, ?x254) *> conf = 0.25 ranks of expected_values: 172 EVAL 02g2yr award! 01m4yn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 16.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19037-0bs4r PRED entity: 0bs4r PRED relation: award PRED expected values: 0gs9p => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 168): 0gs9p (0.29 #736, 0.27 #2942, 0.27 #8146), 0gqy2 (0.27 #2942, 0.27 #8146, 0.27 #7693), 09d28z (0.18 #3395, 0.13 #13128, 0.13 #1543), 03nqnk3 (0.18 #3395, 0.13 #13128, 0.12 #10863), 0789_m (0.18 #3395, 0.13 #13128, 0.12 #10863), 027c924 (0.18 #3395, 0.13 #13128, 0.11 #1366), 05h5nb8 (0.18 #3395, 0.13 #13128, 0.05 #9280), 0p9sw (0.16 #697, 0.15 #1377, 0.12 #471), 0gq_v (0.16 #1376, 0.14 #696, 0.13 #922), 04dn09n (0.14 #1390, 0.10 #710, 0.10 #1162) >> Best rule #736 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 07bz5; >> query: (?x6069, 0gs9p) <- list(?x6069, ?x3004), award(?x6069, ?x591), award(?x123, ?x591) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bs4r award 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.293 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #19036-024dzn PRED entity: 024dzn PRED relation: award_winner PRED expected values: 02fgpf => 33 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1584): 02fgpf (0.60 #2465, 0.60 #390, 0.43 #9861), 018gqj (0.60 #1346, 0.50 #8741, 0.12 #6276), 012wg (0.60 #1010, 0.36 #8405, 0.07 #13336), 019x62 (0.60 #1565, 0.29 #8960, 0.08 #13891), 01jpmpv (0.60 #754, 0.21 #8149, 0.03 #13080), 02qwg (0.50 #5667, 0.17 #10598, 0.08 #15528), 0ddkf (0.50 #6444, 0.14 #8909, 0.13 #11375), 0178rl (0.43 #8580, 0.40 #1185, 0.14 #9860), 0kftt (0.42 #17255, 0.39 #24643, 0.37 #9858), 0140t7 (0.40 #1978, 0.38 #6908, 0.21 #9373) >> Best rule #2465 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0gqz2; 054krc; 054ks3; >> query: (?x9372, ?x1894) <- award(?x6544, ?x9372), award(?x6382, ?x9372), award(?x1894, ?x9372), award_winner(?x9372, ?x4563), ?x1894 = 02fgpf, ?x6382 = 01wd9lv, gender(?x6544, ?x514) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024dzn award_winner 02fgpf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 16.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #19035-0l14md PRED entity: 0l14md PRED relation: performance_role! PRED expected values: 027dpx => 80 concepts (61 used for prediction) PRED predicted values (max 10 best out of 721): 01r0t_j (0.50 #1974, 0.40 #606, 0.33 #2185), 0167v4 (0.40 #930, 0.40 #825, 0.40 #613), 09hnb (0.40 #762, 0.33 #1498, 0.33 #1286), 01vrncs (0.40 #851, 0.33 #1586, 0.25 #1902), 04mky3 (0.40 #1256, 0.29 #1782, 0.25 #310), 0m_v0 (0.40 #881, 0.20 #1195, 0.20 #776), 0140t7 (0.33 #1668, 0.25 #512, 0.25 #406), 02qwg (0.33 #1615, 0.25 #1931, 0.25 #459), 024dw0 (0.33 #1648, 0.25 #492, 0.25 #386), 0fpj9pm (0.33 #1645, 0.25 #489, 0.25 #383) >> Best rule #1974 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 028tv0; >> query: (?x315, 01r0t_j) <- role(?x460, ?x315), role(?x315, ?x2904), role(?x315, ?x2297), ?x2904 = 03_vpw, role(?x315, ?x736), group(?x315, ?x3875), performance_role(?x115, ?x315), ?x3875 = 0mgcr, group(?x2297, ?x2005), instrumentalists(?x736, ?x2987) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #268 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 0dwt5; *> query: (?x315, 027dpx) <- role(?x10237, ?x315), role(?x315, ?x74), group(?x315, ?x8060), role(?x214, ?x315), performance_role(?x1225, ?x315), role(?x2662, ?x315), role(?x569, ?x315), award(?x8060, ?x2139), ?x10237 = 023322 *> conf = 0.25 ranks of expected_values: 61 EVAL 0l14md performance_role! 027dpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 80.000 61.000 0.500 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #19034-0h7x PRED entity: 0h7x PRED relation: time_zones PRED expected values: 02llzg => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 12): 02llzg (0.68 #1797, 0.64 #209, 0.63 #1954), 02hcv8 (0.42 #107, 0.39 #198, 0.37 #1656), 02fqwt (0.39 #105, 0.23 #640, 0.23 #549), 042g7t (0.22 #11, 0.13 #37, 0.10 #76), 02hczc (0.22 #2, 0.11 #641, 0.10 #1187), 02lcqs (0.22 #200, 0.19 #748, 0.18 #553), 03plfd (0.17 #440, 0.13 #284, 0.13 #310), 03bdv (0.12 #345, 0.12 #384, 0.10 #45), 02lcrv (0.11 #7, 0.04 #33, 0.03 #46), 05jphn (0.11 #13, 0.04 #39, 0.03 #52) >> Best rule #1797 for best value: >> intensional similarity = 3 >> extensional distance = 276 >> proper extension: 05kkh; 0mw89; 0wh3; 0drsm; 06mz5; 015zxh; 0488g; 04rrx; 05k7sb; 07srw; ... >> query: (?x1355, ?x2864) <- contains(?x1355, ?x5368), time_zones(?x5368, ?x2864), adjoins(?x1355, ?x205) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h7x time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.683 http://example.org/location/location/time_zones #19033-07kb7vh PRED entity: 07kb7vh PRED relation: film! PRED expected values: 01twdk 01vtj38 => 89 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1008): 09y20 (0.26 #247, 0.06 #8558, 0.06 #2324), 0l6px (0.22 #386, 0.06 #2463, 0.06 #14928), 06ltr (0.22 #943, 0.06 #3020, 0.05 #9254), 0134w7 (0.22 #160, 0.06 #2237, 0.05 #8471), 065jlv (0.22 #311, 0.06 #2388, 0.05 #8622), 013_vh (0.17 #661, 0.04 #2738, 0.04 #8972), 03y_46 (0.17 #1013, 0.04 #3090, 0.04 #9324), 05sq84 (0.17 #234, 0.04 #2311, 0.04 #8545), 04w391 (0.13 #33242, 0.12 #33243, 0.09 #686), 034x61 (0.13 #33242, 0.12 #33243, 0.01 #2210) >> Best rule #247 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0dsvzh; 0p9lw; 0872p_c; 0dtfn; 031778; 034qzw; 03177r; 0dnqr; 05zlld0; 03176f; ... >> query: (?x4131, 09y20) <- film_release_distribution_medium(?x4131, ?x81), currency(?x4131, ?x170), film_distribution_medium(?x4131, ?x2099), nominated_for(?x4847, ?x4131) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #4998 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 053tj7; *> query: (?x4131, 01twdk) <- film_release_distribution_medium(?x4131, ?x81), produced_by(?x4131, ?x1335), film(?x609, ?x4131), film_distribution_medium(?x4131, ?x2099) *> conf = 0.01 ranks of expected_values: 749 EVAL 07kb7vh film! 01vtj38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 47.000 0.261 http://example.org/film/actor/film./film/performance/film EVAL 07kb7vh film! 01twdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 47.000 0.261 http://example.org/film/actor/film./film/performance/film #19032-016t_3 PRED entity: 016t_3 PRED relation: student PRED expected values: 063vn => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1272): 01zwy (0.40 #3019, 0.33 #3237, 0.33 #1704), 04z0g (0.33 #3409, 0.33 #3191, 0.33 #1658), 0b78hw (0.33 #3383, 0.33 #3165, 0.33 #1632), 06g4_ (0.33 #3492, 0.33 #3274, 0.33 #1085), 06y7d (0.33 #3283, 0.33 #1750, 0.33 #1094), 01tdnyh (0.33 #3176, 0.33 #1643, 0.33 #987), 0969fd (0.33 #3269, 0.33 #1736, 0.33 #1080), 059y0 (0.33 #1302, 0.33 #423, 0.27 #4155), 0tj9 (0.33 #1534, 0.33 #437, 0.25 #2193), 02r6c_ (0.33 #1488, 0.33 #391, 0.25 #2147) >> Best rule #3019 for best value: >> intensional similarity = 23 >> extensional distance = 3 >> proper extension: 027f2w; >> query: (?x1200, 01zwy) <- institution(?x1200, ?x12761), institution(?x1200, ?x12276), institution(?x1200, ?x10832), institution(?x1200, ?x8354), institution(?x1200, ?x7707), institution(?x1200, ?x5486), institution(?x1200, ?x2171), institution(?x1200, ?x1615), institution(?x1200, ?x122), ?x2171 = 01jq34, major_field_of_study(?x1200, ?x254), ?x8354 = 01hjy5, student(?x1200, ?x665), ?x10832 = 014jyk, ?x122 = 08815, category(?x12761, ?x134), contains(?x6401, ?x12276), currency(?x7707, ?x170), fraternities_and_sororities(?x1615, ?x3697), school(?x799, ?x7707), ?x170 = 09nqf, school_type(?x12276, ?x3092), ?x5486 = 0g8rj >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #702 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 1 *> proper extension: 01rr_d; *> query: (?x1200, 063vn) <- institution(?x1200, ?x9880), institution(?x1200, ?x9181), institution(?x1200, ?x8507), institution(?x1200, ?x6846), institution(?x1200, ?x5754), institution(?x1200, ?x4955), institution(?x1200, ?x2313), institution(?x1200, ?x2171), institution(?x1200, ?x741), institution(?x1200, ?x481), major_field_of_study(?x1200, ?x254), school(?x4779, ?x2171), student(?x1200, ?x5105), colors(?x5754, ?x1101), state_province_region(?x741, ?x335), ?x4955 = 09f2j, student(?x2171, ?x3338), student(?x741, ?x881), currency(?x2313, ?x170), time_zones(?x9880, ?x2674), fraternities_and_sororities(?x741, ?x4348), country(?x6846, ?x94), ?x481 = 052nd, ?x8507 = 04zwc, ?x9181 = 012lzr, people(?x11053, ?x5105) *> conf = 0.33 ranks of expected_values: 178 EVAL 016t_3 student 063vn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 25.000 25.000 0.400 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #19031-032xhg PRED entity: 032xhg PRED relation: film PRED expected values: 03clwtw 0g0x9c => 103 concepts (86 used for prediction) PRED predicted values (max 10 best out of 547): 02825cv (0.50 #4690, 0.02 #17136, 0.02 #61595), 02hct1 (0.39 #90693, 0.38 #65796, 0.35 #83579), 0bvn25 (0.38 #3606, 0.03 #14274, 0.03 #16052), 03m8y5 (0.29 #2183, 0.01 #30634, 0.01 #28855), 04f52jw (0.25 #3994, 0.01 #12884), 06_wqk4 (0.20 #126, 0.14 #1904, 0.05 #9016), 01flv_ (0.20 #1058, 0.14 #2836, 0.05 #6392), 03s6l2 (0.20 #82, 0.14 #1860, 0.03 #8972), 047vp1n (0.20 #1268, 0.14 #3046, 0.03 #104921), 05t0_2v (0.20 #1018, 0.14 #2796, 0.03 #104921) >> Best rule #4690 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0fby2t; 0h27vc; >> query: (?x436, 02825cv) <- film(?x436, ?x6984), film(?x436, ?x1531), award(?x1531, ?x68), ?x6984 = 02825kb >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3133 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 044rvb; 02bkdn; *> query: (?x436, 0g0x9c) <- film(?x436, ?x1531), ?x1531 = 02rv_dz, award_nominee(?x436, ?x665) *> conf = 0.14 ranks of expected_values: 25, 118 EVAL 032xhg film 0g0x9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 103.000 86.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 032xhg film 03clwtw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 103.000 86.000 0.500 http://example.org/film/actor/film./film/performance/film #19030-024cg8 PRED entity: 024cg8 PRED relation: colors PRED expected values: 083jv => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.79 #578, 0.38 #1028, 0.37 #1316), 01l849 (0.33 #1, 0.31 #289, 0.28 #307), 038hg (0.25 #442, 0.11 #586, 0.11 #118), 036k5h (0.24 #418, 0.10 #1333, 0.10 #742), 088fh (0.19 #347, 0.10 #1333, 0.10 #275), 09ggk (0.14 #86, 0.10 #1333, 0.10 #140), 04mkbj (0.10 #1333, 0.09 #1034, 0.09 #1088), 067z2v (0.10 #1333, 0.09 #223, 0.08 #241), 0jc_p (0.10 #1333, 0.07 #633, 0.07 #669), 03wkwg (0.10 #1333, 0.06 #1568, 0.06 #193) >> Best rule #578 for best value: >> intensional similarity = 5 >> extensional distance = 227 >> proper extension: 02lwv5; 0yl_w; 03np_7; 0p7tb; 01nhgd; >> query: (?x13707, 083jv) <- colors(?x13707, ?x1101), colors(?x11452, ?x1101), colors(?x11507, ?x1101), ?x11452 = 03k7dn, ?x11507 = 0175rc >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024cg8 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.790 http://example.org/education/educational_institution/colors #19029-03clwtw PRED entity: 03clwtw PRED relation: country PRED expected values: 0345h => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 127): 07ssc (0.33 #76, 0.31 #556, 0.28 #316), 0345h (0.20 #1708, 0.20 #1107, 0.18 #687), 0ctw_b (0.17 #83, 0.04 #323, 0.04 #383), 0f8l9c (0.14 #139, 0.14 #1039, 0.14 #919), 03h64 (0.11 #526, 0.05 #1126, 0.05 #1186), 0chghy (0.10 #1152, 0.07 #1513, 0.06 #192), 0d060g (0.09 #608, 0.08 #968, 0.07 #908), 0d05w3 (0.08 #523, 0.06 #223, 0.05 #1123), 01mjq (0.08 #95, 0.03 #1356, 0.03 #1416), 0d0vqn (0.08 #70, 0.02 #610, 0.02 #6397) >> Best rule #76 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 014kq6; 0639bg; 0ndwt2w; 063fh9; 0642ykh; 03b1l8; 0dc7hc; >> query: (?x7145, 07ssc) <- film(?x788, ?x7145), film(?x574, ?x7145), currency(?x7145, ?x170), country(?x7145, ?x94), ?x574 = 016tt2, ?x788 = 0g1rw >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1708 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 165 *> proper extension: 02psgq; *> query: (?x7145, 0345h) <- music(?x7145, ?x562), film(?x2125, ?x7145), country(?x7145, ?x94), film_format(?x7145, ?x909), language(?x7145, ?x254) *> conf = 0.20 ranks of expected_values: 2 EVAL 03clwtw country 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 126.000 0.333 http://example.org/film/film/country #19028-0pc56 PRED entity: 0pc56 PRED relation: place! PRED expected values: 0pc56 => 76 concepts (57 used for prediction) PRED predicted values (max 10 best out of 111): 0l2sr (0.06 #6185), 0pc56 (0.04 #23210), 01n7q (0.04 #23210), 0r3wm (0.01 #283, 0.01 #798), 0l0mk (0.01 #97, 0.01 #612), 0dc95 (0.01 #49, 0.01 #564), 0kcw2 (0.01 #445), 0y617 (0.01 #395), 0l39b (0.01 #358), 0fvyg (0.01 #318) >> Best rule #6185 for best value: >> intensional similarity = 3 >> extensional distance = 179 >> proper extension: 0mn0v; >> query: (?x11934, ?x9582) <- location(?x1398, ?x11934), county(?x11934, ?x9582), source(?x11934, ?x958) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #23210 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 895 *> proper extension: 0j3b; 02qkt; 06mx8; 07c5l; 06srk; 04wsz; 02j7k; 06s_2; 0lm0n; 02v3m7; ... *> query: (?x11934, ?x1227) <- contains(?x11934, ?x4363), contains(?x1227, ?x4363) *> conf = 0.04 ranks of expected_values: 2 EVAL 0pc56 place! 0pc56 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 57.000 0.055 http://example.org/location/hud_county_place/place #19027-02cft PRED entity: 02cft PRED relation: month PRED expected values: 06vkl 05lf_ 03_ly 02xx5 0ll3 => 225 concepts (225 used for prediction) PRED predicted values (max 10 best out of 5): 03_ly (0.93 #243, 0.93 #218, 0.92 #193), 0ll3 (0.93 #220, 0.91 #245, 0.90 #285), 02xx5 (0.92 #194, 0.89 #244, 0.88 #219), 05lf_ (0.88 #282, 0.88 #137, 0.87 #242), 06vkl (0.87 #241, 0.85 #216, 0.84 #136) >> Best rule #243 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 06t2t; >> query: (?x6357, 03_ly) <- mode_of_transportation(?x6357, ?x4272), contains(?x3699, ?x6357), month(?x6357, ?x3270), ?x3270 = 05cw8 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 02cft month 0ll3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 225.000 225.000 0.933 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 02cft month 02xx5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 225.000 225.000 0.933 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 02cft month 03_ly CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 225.000 225.000 0.933 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 02cft month 05lf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 225.000 225.000 0.933 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 02cft month 06vkl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 225.000 225.000 0.933 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #19026-0jn5l PRED entity: 0jn5l PRED relation: music! PRED expected values: 02z3r8t 043t8t => 122 concepts (28 used for prediction) PRED predicted values (max 10 best out of 903): 01hp5 (0.22 #2073, 0.08 #5094, 0.07 #6101), 01s7w3 (0.15 #5902, 0.15 #6909, 0.07 #16979), 0pdp8 (0.11 #6265, 0.08 #5258, 0.07 #8279), 02rrfzf (0.11 #2339, 0.06 #17444, 0.05 #19458), 035zr0 (0.11 #2754, 0.06 #3761, 0.04 #4768), 0dgpwnk (0.11 #2348, 0.06 #3355, 0.04 #4362), 0401sg (0.11 #2064, 0.05 #12134, 0.04 #5085), 0c9t0y (0.11 #2729, 0.04 #5750, 0.04 #6757), 058kh7 (0.11 #2906, 0.04 #5927, 0.04 #6934), 05dss7 (0.11 #2683, 0.04 #5704, 0.04 #6711) >> Best rule #2073 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02cpp; >> query: (?x5508, 01hp5) <- artists(?x3370, ?x5508), music(?x428, ?x5508), ?x3370 = 059kh >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #19193 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 120 *> proper extension: 0p5mw; 0dpqk; 02w670; 02mz_6; 020jqv; 07zhd7; *> query: (?x5508, 02z3r8t) <- music(?x10246, ?x5508), genre(?x10246, ?x258), ?x258 = 05p553, film(?x794, ?x10246) *> conf = 0.02 ranks of expected_values: 342 EVAL 0jn5l music! 043t8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 28.000 0.222 http://example.org/film/film/music EVAL 0jn5l music! 02z3r8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 122.000 28.000 0.222 http://example.org/film/film/music #19025-025mb9 PRED entity: 025mb9 PRED relation: category_of PRED expected values: 0c4ys => 45 concepts (42 used for prediction) PRED predicted values (max 10 best out of 3): 0c4ys (0.92 #148, 0.92 #106, 0.92 #170), 0gcf2r (0.14 #386, 0.14 #407, 0.13 #450), 0g_w (0.09 #473, 0.09 #429, 0.09 #451) >> Best rule #148 for best value: >> intensional similarity = 5 >> extensional distance = 77 >> proper extension: 02flqd; >> query: (?x4012, 0c4ys) <- ceremony(?x4012, ?x139), award(?x2698, ?x4012), award_winner(?x1079, ?x2698), ?x139 = 05pd94v, artists(?x505, ?x2698) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025mb9 category_of 0c4ys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 42.000 0.924 http://example.org/award/award_category/category_of #19024-02x258x PRED entity: 02x258x PRED relation: ceremony PRED expected values: 0275n3y => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 135): 0gpjbt (0.53 #838, 0.38 #3539, 0.38 #3810), 09n4nb (0.53 #855, 0.37 #3556, 0.36 #3827), 01c6qp (0.53 #828, 0.35 #3529, 0.35 #3800), 0466p0j (0.52 #883, 0.37 #3584, 0.36 #3855), 056878 (0.52 #841, 0.36 #3542, 0.35 #3813), 02rjjll (0.51 #815, 0.36 #3516, 0.36 #3787), 01mh_q (0.51 #895, 0.34 #3596, 0.34 #3867), 02yxh9 (0.50 #502, 0.29 #772, 0.26 #637), 0n8_m93 (0.50 #519, 0.29 #789, 0.26 #654), 0bvfqq (0.50 #437, 0.29 #707, 0.26 #572) >> Best rule #838 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 02wh75; 01d38g; 02grdc; 01bgqh; 086vfb; 0c4z8; 01c427; 01c4_6; 01c92g; 02nhxf; ... >> query: (?x2393, 0gpjbt) <- award(?x523, ?x2393), ceremony(?x2393, ?x762), location(?x523, ?x1719), music(?x814, ?x523) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #207 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 02x17c2; *> query: (?x2393, 0275n3y) <- award(?x523, ?x2393), ceremony(?x2393, ?x2245), ?x523 = 06cv1, ?x2245 = 0fqpc7d *> conf = 0.33 ranks of expected_values: 89 EVAL 02x258x ceremony 0275n3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 62.000 62.000 0.529 http://example.org/award/award_category/winners./award/award_honor/ceremony #19023-01yqqv PRED entity: 01yqqv PRED relation: school! PRED expected values: 09th87 => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 20): 0f4vx0 (0.14 #651, 0.14 #1291, 0.14 #1391), 038c0q (0.13 #46, 0.07 #646, 0.07 #1286), 02qw1zx (0.13 #1285, 0.12 #1385, 0.11 #645), 05vsb7 (0.12 #61, 0.10 #461, 0.10 #21), 092j54 (0.11 #9, 0.10 #249, 0.09 #1389), 0g3zpp (0.11 #2, 0.08 #62, 0.07 #1382), 038981 (0.11 #16, 0.05 #356, 0.04 #1396), 025tn92 (0.10 #1293, 0.10 #1393, 0.10 #253), 09l0x9 (0.10 #352, 0.09 #1392, 0.09 #652), 02pq_x5 (0.09 #1297, 0.09 #357, 0.09 #1397) >> Best rule #651 for best value: >> intensional similarity = 4 >> extensional distance = 112 >> proper extension: 0j_sncb; 02y9bj; 01nhgd; >> query: (?x9522, 0f4vx0) <- major_field_of_study(?x9522, ?x742), colors(?x9522, ?x663), institution(?x620, ?x9522), ?x620 = 07s6fsf >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1395 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 189 *> proper extension: 0fht9f; *> query: (?x9522, 09th87) <- school(?x2820, ?x9522), team(?x1579, ?x2820) *> conf = 0.07 ranks of expected_values: 13 EVAL 01yqqv school! 09th87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 173.000 173.000 0.140 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #19022-01qvgl PRED entity: 01qvgl PRED relation: nationality PRED expected values: 09c7w0 => 161 concepts (157 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.85 #12041, 0.78 #13043, 0.73 #8532), 01cx_ (0.25 #13444, 0.01 #11840), 05k7sb (0.25 #13444), 02jx1 (0.23 #635, 0.23 #2850, 0.22 #2148), 07ssc (0.20 #15, 0.14 #2130, 0.13 #818), 0d060g (0.08 #609, 0.08 #1920, 0.08 #910), 03rk0 (0.08 #8879, 0.05 #15396, 0.05 #14795), 0345h (0.07 #433, 0.03 #1743, 0.03 #2045), 0dzt9 (0.06 #301, 0.06 #2115, 0.05 #1609), 06q1r (0.05 #779, 0.04 #1182, 0.03 #880) >> Best rule #12041 for best value: >> intensional similarity = 3 >> extensional distance = 1205 >> proper extension: 01h2_6; >> query: (?x1322, ?x94) <- place_of_birth(?x1322, ?x9846), citytown(?x10104, ?x9846), country(?x9846, ?x94) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qvgl nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 157.000 0.848 http://example.org/people/person/nationality #19021-02rrfzf PRED entity: 02rrfzf PRED relation: executive_produced_by PRED expected values: 05hj_k => 72 concepts (54 used for prediction) PRED predicted values (max 10 best out of 77): 06q8hf (0.33 #167, 0.07 #420, 0.06 #2955), 05hj_k (0.33 #98, 0.07 #351, 0.05 #1365), 079vf (0.20 #255, 0.06 #1269, 0.06 #3044), 02xnjd (0.13 #429, 0.03 #3218, 0.02 #1190), 06pj8 (0.07 #308, 0.05 #1322, 0.04 #3097), 04hw4b (0.07 #415, 0.01 #1683, 0.01 #2444), 09zw90 (0.07 #484, 0.01 #2005, 0.01 #3019), 0glyyw (0.05 #1456, 0.05 #1963, 0.04 #2977), 0gg9_5q (0.05 #596, 0.05 #850, 0.04 #1611), 0343h (0.04 #2070, 0.04 #2576, 0.02 #2830) >> Best rule #167 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 025s1wg; >> query: (?x3344, 06q8hf) <- film(?x541, ?x3344), film(?x166, ?x3344), ?x541 = 017s11, music(?x3344, ?x523), ?x166 = 0jz9f >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #98 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 025s1wg; *> query: (?x3344, 05hj_k) <- film(?x541, ?x3344), film(?x166, ?x3344), ?x541 = 017s11, music(?x3344, ?x523), ?x166 = 0jz9f *> conf = 0.33 ranks of expected_values: 2 EVAL 02rrfzf executive_produced_by 05hj_k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 54.000 0.333 http://example.org/film/film/executive_produced_by #19020-02qvzf PRED entity: 02qvzf PRED relation: team PRED expected values: 0jnr_ => 26 concepts (11 used for prediction) PRED predicted values (max 10 best out of 966): 0jnq8 (0.88 #2821, 0.81 #9394, 0.77 #4702), 0jnpv (0.88 #2821, 0.81 #9394, 0.77 #4702), 0jnng (0.88 #2821, 0.81 #9394, 0.77 #4702), 04l57x (0.81 #9394, 0.77 #4702, 0.76 #4700), 05pcr (0.81 #9394, 0.77 #4702, 0.76 #4700), 02r7lqg (0.81 #9394, 0.77 #4702, 0.76 #4700), 0gx159f (0.81 #9394, 0.77 #4702, 0.76 #4700), 0gvt8sz (0.81 #9394, 0.77 #4702, 0.76 #4700), 0jnr_ (0.81 #9394, 0.77 #4702, 0.76 #4700), 04l58n (0.81 #9394, 0.77 #4702, 0.76 #4700) >> Best rule #2821 for best value: >> intensional similarity = 44 >> extensional distance = 1 >> proper extension: 02qvl7; >> query: (?x3724, ?x12757) <- position(?x14183, ?x3724), position(?x14035, ?x3724), position(?x13326, ?x3724), position(?x13166, ?x3724), position(?x12757, ?x3724), position(?x11995, ?x3724), position(?x11826, ?x3724), position(?x10755, ?x3724), position(?x10713, ?x3724), position(?x10644, ?x3724), position(?x10034, ?x3724), position(?x9835, ?x3724), position(?x9515, ?x3724), position(?x8892, ?x3724), position(?x7174, ?x3724), position(?x5380, ?x3724), position(?x4426, ?x3724), team(?x3724, ?x14124), team(?x3724, ?x10970), ?x10713 = 0gx159f, ?x10970 = 0hmt3, ?x8892 = 02fp3, team(?x5234, ?x12757), team(?x2918, ?x12757), ?x11826 = 0hn2q, teams(?x739, ?x12757), ?x14035 = 01tz_d, ?x7174 = 05pcr, ?x14124 = 04l590, ?x13326 = 0hm2b, position(?x5380, ?x3299), ?x9835 = 02hqt6, ?x10034 = 0jnq8, ?x4426 = 0c1gj5, ?x14183 = 0j8cb, ?x2918 = 02qvl7, ?x10644 = 0jnnx, ?x3299 = 02qvgy, ?x10755 = 0jbqf, ?x5234 = 02qvdc, ?x11995 = 048ldh, ?x13166 = 0j6tr, ?x9515 = 0j2zj, sport(?x5380, ?x453) >> conf = 0.88 => this is the best rule for 3 predicted values *> Best rule #9394 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 7 *> proper extension: 02sddg; *> query: (?x3724, ?x6640) <- team(?x3724, ?x13166), team(?x3724, ?x12757), team(?x3724, ?x8270), team(?x3724, ?x7197), team(?x3724, ?x3298), team(?x3724, ?x2919), colors(?x2919, ?x663), ?x663 = 083jv, team(?x5234, ?x8270), teams(?x3786, ?x7197), location(?x1852, ?x3786), sport(?x12757, ?x453), contains(?x3786, ?x2775), colors(?x13166, ?x12067), locations(?x4368, ?x3786), country(?x3786, ?x94), team(?x5234, ?x6640), team(?x13270, ?x2919), position(?x3299, ?x5234), source(?x3786, ?x958), teams(?x10718, ?x3298), ?x12067 = 06kqt3, company(?x4486, ?x3298), category(?x3786, ?x134), origin(?x9442, ?x10718), location(?x6217, ?x10718), time_zones(?x10718, ?x2088) *> conf = 0.81 ranks of expected_values: 9 EVAL 02qvzf team 0jnr_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 26.000 11.000 0.875 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #19019-0btmb PRED entity: 0btmb PRED relation: genre! PRED expected values: 01hr1 0bl3nn 042fgh => 36 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1858): 0bl3nn (0.71 #19794, 0.60 #10537, 0.50 #8689), 0b9rdk (0.67 #14014, 0.60 #12164, 0.57 #19570), 0bs8ndx (0.67 #14391, 0.60 #12541, 0.57 #18097), 01_mdl (0.62 #20355, 0.60 #11263, 0.50 #13113), 042fgh (0.62 #20355, 0.50 #8717, 0.43 #19822), 02nx2k (0.60 #12339, 0.60 #10488, 0.57 #19745), 06yykb (0.60 #12517, 0.60 #10666, 0.57 #19923), 07f_t4 (0.60 #12456, 0.60 #10605, 0.50 #14306), 012s1d (0.60 #12031, 0.60 #10180, 0.50 #13881), 048yqf (0.60 #10900, 0.57 #20157, 0.50 #9052) >> Best rule #19794 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 06n90; >> query: (?x11401, 0bl3nn) <- genre(?x4392, ?x11401), genre(?x3672, ?x11401), genre(?x1511, ?x11401), genre(?x936, ?x11401), film(?x773, ?x3672), ?x936 = 01qb5d, language(?x3672, ?x5607), film(?x382, ?x3672), ?x5607 = 064_8sq, produced_by(?x3672, ?x2724), ?x382 = 086k8, ?x1511 = 0340hj, ?x4392 = 06gb1w, honored_for(?x3672, ?x1072) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 80 EVAL 0btmb genre! 042fgh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 36.000 15.000 0.714 http://example.org/film/film/genre EVAL 0btmb genre! 0bl3nn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 15.000 0.714 http://example.org/film/film/genre EVAL 0btmb genre! 01hr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 36.000 15.000 0.714 http://example.org/film/film/genre #19018-065y4w7 PRED entity: 065y4w7 PRED relation: school! PRED expected values: 0ws7 0jm5b => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 69): 06rpd (0.20 #193, 0.20 #55, 0.12 #1711), 06rny (0.20 #174, 0.20 #36, 0.08 #1692), 01yhm (0.20 #16, 0.15 #1189, 0.14 #1396), 07l4z (0.20 #52, 0.14 #1708, 0.12 #1225), 0jmcb (0.20 #20, 0.10 #1193, 0.09 #1400), 01y3c (0.20 #10, 0.08 #1666, 0.06 #2495), 0jm9w (0.20 #58, 0.05 #1231, 0.05 #1438), 04mjl (0.18 #255, 0.11 #600, 0.09 #1083), 03wnh (0.18 #244, 0.10 #1210, 0.09 #1417), 01ypc (0.18 #208, 0.09 #2555, 0.08 #1657) >> Best rule #193 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01w_10; >> query: (?x735, 06rpd) <- award_winner(?x3486, ?x735), ?x3486 = 0m7yy, organization(?x735, ?x5487) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #251 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 9 *> proper extension: 05kj_; 06mkj; 0d05w3; *> query: (?x735, 0ws7) <- time_zones(?x735, ?x2950), school(?x465, ?x735) *> conf = 0.09 ranks of expected_values: 31, 55 EVAL 065y4w7 school! 0jm5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 125.000 125.000 0.200 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 065y4w7 school! 0ws7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 125.000 125.000 0.200 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #19017-05vk_d PRED entity: 05vk_d PRED relation: religion PRED expected values: 0c8wxp => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 25): 0c8wxp (0.38 #96, 0.33 #367, 0.28 #637), 03_gx (0.20 #14, 0.08 #1322, 0.08 #239), 01lp8 (0.11 #181, 0.05 #271, 0.04 #497), 0kpl (0.10 #506, 0.08 #280, 0.08 #822), 0kq2 (0.06 #153, 0.05 #198, 0.04 #514), 092bf5 (0.06 #151, 0.04 #377, 0.03 #286), 019cr (0.06 #146, 0.04 #236, 0.03 #372), 04pk9 (0.06 #155, 0.02 #290, 0.01 #336), 01y0s9 (0.06 #144, 0.01 #370, 0.01 #460), 06nzl (0.05 #195, 0.03 #376, 0.02 #466) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 05m63c; 02r3cn; >> query: (?x8638, 0c8wxp) <- participant(?x9482, ?x8638), participant(?x3999, ?x8638), location_of_ceremony(?x9482, ?x6226) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05vk_d religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.385 http://example.org/people/person/religion #19016-01vrz41 PRED entity: 01vrz41 PRED relation: type_of_union PRED expected values: 04ztj => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.94 #373, 0.94 #106, 0.93 #43), 01g63y (0.16 #176, 0.16 #56, 0.15 #311) >> Best rule #373 for best value: >> intensional similarity = 2 >> extensional distance = 2742 >> proper extension: 0cfywh; >> query: (?x1231, 04ztj) <- nationality(?x1231, ?x512), type_of_union(?x1231, ?x12360) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrz41 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.942 http://example.org/people/person/spouse_s./people/marriage/type_of_union #19015-01dk00 PRED entity: 01dk00 PRED relation: ceremony PRED expected values: 05pd94v 0gx1673 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 125): 05pd94v (0.88 #502, 0.87 #377, 0.82 #752), 0gx1673 (0.51 #480, 0.49 #855, 0.49 #605), 0n8_m93 (0.16 #1228, 0.11 #1978, 0.11 #1853), 0bzm81 (0.16 #1142, 0.11 #1892, 0.11 #1767), 02yvhx (0.15 #1191, 0.11 #1941, 0.11 #1816), 02yxh9 (0.15 #1211, 0.11 #1961, 0.11 #1836), 0bc773 (0.15 #1169, 0.11 #1919, 0.11 #1794), 02yw5r (0.15 #1134, 0.11 #1884, 0.11 #1759), 0bvfqq (0.15 #1151, 0.11 #1901, 0.11 #1776), 02hn5v (0.15 #1158, 0.11 #1908, 0.11 #1783) >> Best rule #502 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 02flqd; >> query: (?x2576, 05pd94v) <- ceremony(?x2576, ?x486), award_winner(?x2576, ?x4080), ?x486 = 02rjjll, award(?x4080, ?x724) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01dk00 ceremony 0gx1673 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.877 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01dk00 ceremony 05pd94v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.877 http://example.org/award/award_category/winners./award/award_honor/ceremony #19014-01sbhvd PRED entity: 01sbhvd PRED relation: profession PRED expected values: 01d_h8 => 94 concepts (45 used for prediction) PRED predicted values (max 10 best out of 66): 01d_h8 (0.56 #1601, 0.53 #3921, 0.48 #1456), 03gjzk (0.47 #158, 0.43 #303, 0.38 #1608), 09jwl (0.46 #5673, 0.46 #3061, 0.42 #5238), 0dz3r (0.31 #2, 0.27 #3047, 0.26 #4208), 016z4k (0.29 #4, 0.28 #4210, 0.27 #5371), 0nbcg (0.28 #5396, 0.28 #3074, 0.28 #4235), 0d1pc (0.24 #48, 0.24 #628, 0.21 #483), 0cbd2 (0.22 #1602, 0.19 #3922, 0.15 #3342), 01c72t (0.22 #3066, 0.17 #4227, 0.16 #2486), 0kyk (0.21 #1622, 0.14 #607, 0.13 #1477) >> Best rule #1601 for best value: >> intensional similarity = 4 >> extensional distance = 163 >> proper extension: 025p38; 0bz5v2; 04n7njg; 02_j7t; 02wr2r; 015lhm; 01sfmyk; 013zyw; 01my_c; 021r7r; ... >> query: (?x11200, 01d_h8) <- category(?x11200, ?x134), profession(?x11200, ?x987), gender(?x11200, ?x231), ?x987 = 0dxtg >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sbhvd profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 45.000 0.564 http://example.org/people/person/profession #19013-05hjmd PRED entity: 05hjmd PRED relation: award PRED expected values: 0gq9h => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 255): 0gq9h (0.52 #2108, 0.50 #2920, 0.50 #1296), 07bdd_ (0.48 #7374, 0.46 #6156, 0.46 #8186), 05p1dby (0.41 #7416, 0.39 #8228, 0.39 #6198), 0gqyl (0.33 #512, 0.12 #38983, 0.12 #38982), 040njc (0.30 #2038, 0.27 #2850, 0.26 #10158), 054ky1 (0.25 #922, 0.15 #4982, 0.14 #35327), 0gq_d (0.25 #1442, 0.15 #30860, 0.15 #28829), 02x1z2s (0.25 #1419, 0.15 #30860, 0.15 #28829), 018wng (0.25 #1260, 0.15 #30860, 0.15 #28829), 0gr42 (0.25 #1335, 0.15 #30860, 0.15 #28829) >> Best rule #2108 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 09gffmz; >> query: (?x11030, 0gq9h) <- award_winner(?x11030, ?x574), produced_by(?x9993, ?x11030), organizations_founded(?x11030, ?x11706) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05hjmd award 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.522 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19012-0j4b PRED entity: 0j4b PRED relation: adjoins PRED expected values: 01rxw => 112 concepts (102 used for prediction) PRED predicted values (max 10 best out of 396): 0j3b (0.82 #5396, 0.82 #29270, 0.82 #29269), 06tw8 (0.22 #47750, 0.21 #29271, 0.21 #57769), 07dzf (0.22 #47750, 0.21 #29271, 0.21 #57769), 01nyl (0.22 #47750, 0.21 #29271, 0.21 #57769), 07tp2 (0.22 #47750, 0.21 #29271, 0.21 #57769), 06dfg (0.22 #47750, 0.21 #29271, 0.21 #57769), 01rxw (0.22 #47750, 0.21 #29271, 0.21 #57769), 0j4b (0.22 #47750, 0.21 #29271, 0.21 #57769), 05rznz (0.22 #47750, 0.21 #29271, 0.21 #57769), 0169t (0.22 #47750, 0.21 #29271, 0.21 #57769) >> Best rule #5396 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 05sb1; 06tw8; 07t_x; >> query: (?x6428, ?x1144) <- exported_to(?x6428, ?x94), adjoins(?x1144, ?x6428), country(?x1121, ?x6428) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #47750 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 177 *> proper extension: 02j9z; 0dg3n1; 04s7y; 0j0k; 015jr; 059g4; 059z0; 06nrt; 059t8; 0j95; ... *> query: (?x6428, ?x1577) <- adjoins(?x2804, ?x6428), taxonomy(?x6428, ?x939), adjoins(?x2804, ?x1577) *> conf = 0.22 ranks of expected_values: 7 EVAL 0j4b adjoins 01rxw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 112.000 102.000 0.825 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #19011-0mkc3 PRED entity: 0mkc3 PRED relation: source PRED expected values: 0jbk9 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #16, 0.91 #23, 0.91 #14) >> Best rule #16 for best value: >> intensional similarity = 6 >> extensional distance = 280 >> proper extension: 0jcgs; 0jcjq; 0jc7g; 0jcky; 0jc6p; 0jcmj; 0jclr; >> query: (?x14474, 0jbk9) <- second_level_divisions(?x94, ?x14474), contains(?x4105, ?x14474), time_zones(?x14474, ?x1638), ?x94 = 09c7w0, adjoins(?x4105, ?x1274), district_represented(?x176, ?x4105) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mkc3 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.915 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #19010-03y3dk PRED entity: 03y3dk PRED relation: place_of_death PRED expected values: 03902 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 36): 030qb3t (0.29 #1192, 0.22 #2359, 0.20 #413), 0f2wj (0.20 #403, 0.09 #2349, 0.08 #792), 02_286 (0.17 #599, 0.14 #4298, 0.13 #4687), 0k049 (0.14 #198, 0.14 #3, 0.13 #2340), 06_kh (0.13 #1370, 0.09 #2733, 0.08 #591), 05qtj (0.10 #5515, 0.08 #844, 0.08 #650), 04jpl (0.10 #398, 0.03 #15205, 0.03 #2735), 06c62 (0.09 #390, 0.04 #5747, 0.03 #3023), 059rby (0.09 #390, 0.02 #4291, 0.02 #4680), 0fhsz (0.09 #390) >> Best rule #1192 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01tcf7; 0237fw; 0bmh4; 02_fj; 0lfbm; 03d1y3; 0gm34; 01933d; 025jbj; 02cj_f; ... >> query: (?x8480, 030qb3t) <- spouse(?x8480, ?x3931), nationality(?x8480, ?x205), award_winner(?x3029, ?x8480), people(?x14128, ?x8480) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #390 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0c9d9; *> query: (?x8480, ?x335) <- spouse(?x8480, ?x3931), nationality(?x8480, ?x789), ?x789 = 0f8l9c, location(?x3931, ?x335) *> conf = 0.09 ranks of expected_values: 11 EVAL 03y3dk place_of_death 03902 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 146.000 146.000 0.286 http://example.org/people/deceased_person/place_of_death #19009-05np2 PRED entity: 05np2 PRED relation: influenced_by PRED expected values: 028p0 02lt8 05qmj => 195 concepts (73 used for prediction) PRED predicted values (max 10 best out of 451): 05qmj (0.56 #4102, 0.33 #192, 0.22 #1060), 03_87 (0.44 #1069, 0.16 #8446, 0.16 #5411), 03sbs (0.33 #221, 0.30 #4131, 0.20 #17157), 02wh0 (0.33 #1249, 0.25 #816, 0.14 #17317), 02lt8 (0.33 #120, 0.22 #988, 0.14 #1858), 03f70xs (0.33 #938, 0.16 #7015, 0.12 #505), 039n1 (0.33 #324, 0.15 #4234, 0.10 #13026), 040_9 (0.33 #967, 0.11 #8344, 0.10 #4443), 084nh (0.33 #392, 0.10 #8204, 0.10 #13026), 015n8 (0.30 #4319, 0.11 #9956, 0.11 #13436) >> Best rule #4102 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 0jrg; >> query: (?x6975, 05qmj) <- influenced_by(?x6975, ?x3712), nationality(?x6975, ?x429), ?x3712 = 0gz_ >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 28 EVAL 05np2 influenced_by 05qmj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 73.000 0.556 http://example.org/influence/influence_node/influenced_by EVAL 05np2 influenced_by 02lt8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 195.000 73.000 0.556 http://example.org/influence/influence_node/influenced_by EVAL 05np2 influenced_by 028p0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 195.000 73.000 0.556 http://example.org/influence/influence_node/influenced_by #19008-01mt1fy PRED entity: 01mt1fy PRED relation: award PRED expected values: 0bdw6t => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 260): 09sb52 (0.42 #3290, 0.32 #14661, 0.31 #14255), 09sdmz (0.40 #2239, 0.10 #3457, 0.08 #15640), 02x73k6 (0.33 #61, 0.30 #2092, 0.13 #3310), 0gqy2 (0.33 #166, 0.20 #2197, 0.15 #3415), 07cbcy (0.33 #79, 0.11 #6170, 0.10 #2110), 0c422z4 (0.30 #2175, 0.25 #957, 0.25 #550), 05pcn59 (0.30 #2113, 0.25 #1301, 0.20 #1707), 09qvc0 (0.25 #853, 0.25 #446, 0.20 #1665), 09qv3c (0.25 #864, 0.25 #457, 0.20 #1676), 057xs89 (0.25 #568, 0.20 #2193, 0.20 #1787) >> Best rule #3290 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 01vvb4m; 046zh; 03h_fqv; 036qs_; 02l101; 01w0yrc; >> query: (?x4395, 09sb52) <- film(?x4395, ?x11686), language(?x11686, ?x90), film_distribution_medium(?x11686, ?x81), ?x90 = 02bjrlw >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #6202 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 182 *> proper extension: 06cv1; 033wx9; 01pcrw; 01vxlbm; 01ttg5; 033jkj; 01817f; 01j2xj; 01c6l; 0382m4; ... *> query: (?x4395, 0bdw6t) <- type_of_union(?x4395, ?x566), location(?x4395, ?x3521), participant(?x4395, ?x538), student(?x6271, ?x4395) *> conf = 0.05 ranks of expected_values: 91 EVAL 01mt1fy award 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 113.000 113.000 0.419 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #19007-06ntj PRED entity: 06ntj PRED relation: genre! PRED expected values: 098z9w => 68 concepts (55 used for prediction) PRED predicted values (max 10 best out of 313): 0kfpm (0.61 #3247, 0.24 #5021, 0.15 #2965), 05jyb2 (0.61 #3247, 0.20 #1241, 0.07 #3603), 0cpz4k (0.50 #1541, 0.38 #2131, 0.38 #1836), 06mr2s (0.50 #2152, 0.38 #1857, 0.33 #1562), 050kh5 (0.50 #2328, 0.38 #2033, 0.33 #1738), 026bfsh (0.50 #1476), 039cq4 (0.46 #3082, 0.38 #4264, 0.33 #4855), 0584r4 (0.44 #4753, 0.38 #2980, 0.29 #3571), 07gbf (0.43 #3747, 0.29 #6406, 0.26 #11426), 070ltt (0.40 #1407, 0.12 #15969, 0.09 #14483) >> Best rule #3247 for best value: >> intensional similarity = 11 >> extensional distance = 11 >> proper extension: 05p553; 01z4y; 06nbt; 0gf28; 0c4xc; 05jhg; 0dm00; 0q00t; >> query: (?x13313, ?x758) <- genre(?x2583, ?x13313), program(?x8196, ?x2583), program(?x8081, ?x2583), type_of_union(?x8081, ?x566), film(?x8081, ?x755), people(?x3591, ?x8196), program(?x1762, ?x2583), profession(?x8196, ?x987), student(?x1440, ?x8081), award_winner(?x537, ?x8081), actor(?x758, ?x8081) >> conf = 0.61 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 06ntj genre! 098z9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 55.000 0.608 http://example.org/tv/tv_program/genre #19006-01g1lp PRED entity: 01g1lp PRED relation: student! PRED expected values: 02s62q => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 90): 065y4w7 (0.13 #541, 0.06 #5285, 0.05 #4231), 0bwfn (0.10 #3437, 0.09 #4492, 0.08 #2910), 09f2j (0.07 #1213, 0.07 #159, 0.05 #3321), 08815 (0.07 #1056, 0.07 #2, 0.02 #11599), 0gdm1 (0.07 #230, 0.02 #1284), 01q0kg (0.07 #134, 0.01 #5405), 02mzg9 (0.07 #408), 0160nk (0.07 #400), 02rv1w (0.07 #385), 01w5m (0.05 #3267, 0.04 #7485, 0.03 #5904) >> Best rule #541 for best value: >> intensional similarity = 2 >> extensional distance = 28 >> proper extension: 04_m9gk; >> query: (?x7855, 065y4w7) <- edited_by(?x3781, ?x7855), genre(?x3781, ?x239) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #1106 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 0hm0k; *> query: (?x7855, 02s62q) <- award_winner(?x3124, ?x7855), person(?x3124, ?x11290), titles(?x53, ?x3124) *> conf = 0.02 ranks of expected_values: 59 EVAL 01g1lp student! 02s62q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 105.000 105.000 0.133 http://example.org/education/educational_institution/students_graduates./education/education/student #19005-03_js PRED entity: 03_js PRED relation: jurisdiction_of_office PRED expected values: 09c7w0 => 187 concepts (171 used for prediction) PRED predicted values (max 10 best out of 63): 09c7w0 (0.70 #1478, 0.67 #554, 0.67 #251), 05k7sb (0.35 #1320, 0.06 #960, 0.06 #2901), 07z1m (0.33 #70, 0.11 #522, 0.11 #1288), 059rby (0.22 #559, 0.12 #458, 0.10 #1533), 05kkh (0.16 #1271, 0.10 #2345, 0.07 #861), 07ssc (0.14 #360, 0.12 #1071, 0.12 #1173), 0d060g (0.12 #457, 0.12 #406, 0.11 #558), 0hptm (0.12 #440, 0.07 #897, 0.05 #1307), 0n5gq (0.12 #439, 0.07 #896, 0.05 #1306), 0t_gg (0.12 #452, 0.07 #909, 0.05 #1730) >> Best rule #1478 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 0rlz; >> query: (?x8991, 09c7w0) <- politician(?x13016, ?x8991), profession(?x8991, ?x5805), ?x5805 = 0fj9f, legislative_sessions(?x8991, ?x12714), gender(?x8991, ?x231) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_js jurisdiction_of_office 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 171.000 0.700 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #19004-04w4s PRED entity: 04w4s PRED relation: locations! PRED expected values: 05nqz => 97 concepts (92 used for prediction) PRED predicted values (max 10 best out of 117): 06k75 (0.22 #55, 0.19 #440, 0.19 #569), 05nqz (0.22 #41, 0.11 #426, 0.10 #555), 0b_6lb (0.16 #1365, 0.07 #5093, 0.06 #5994), 0b_6pv (0.16 #1367, 0.06 #5095, 0.06 #5996), 0b_75k (0.14 #1336, 0.07 #5064, 0.06 #5965), 01w1sx (0.12 #1504, 0.11 #475, 0.10 #604), 0b_6q5 (0.12 #1382, 0.07 #5110, 0.06 #6011), 0b_6rk (0.12 #1333, 0.06 #5061, 0.06 #5962), 0b_6x2 (0.12 #1320, 0.06 #5048, 0.05 #5949), 0b_6_l (0.12 #1392, 0.06 #5120, 0.05 #6021) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01rdm0; >> query: (?x3041, 06k75) <- contains(?x11687, ?x3041), ?x11687 = 09b69, organization(?x3041, ?x312) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #41 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 01rdm0; *> query: (?x3041, 05nqz) <- contains(?x11687, ?x3041), ?x11687 = 09b69, organization(?x3041, ?x312) *> conf = 0.22 ranks of expected_values: 2 EVAL 04w4s locations! 05nqz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 92.000 0.222 http://example.org/time/event/locations #19003-043t8t PRED entity: 043t8t PRED relation: language PRED expected values: 02h40lc => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 42): 02h40lc (0.96 #859, 0.95 #1883, 0.95 #2206), 0653m (0.12 #63, 0.04 #4352, 0.04 #1838), 071fb (0.12 #69, 0.04 #4352), 02bv9 (0.12 #77), 06nm1 (0.10 #274, 0.10 #1837, 0.09 #168), 06b_j (0.07 #179, 0.07 #285, 0.06 #878), 03_9r (0.07 #273, 0.05 #114, 0.05 #167), 04h9h (0.05 #144, 0.04 #4352, 0.03 #626), 0jzc (0.04 #4352, 0.04 #606, 0.03 #983), 012w70 (0.04 #4352, 0.02 #1839, 0.02 #2108) >> Best rule #859 for best value: >> intensional similarity = 4 >> extensional distance = 684 >> proper extension: 07kb7vh; >> query: (?x4651, 02h40lc) <- film_release_distribution_medium(?x4651, ?x81), language(?x4651, ?x90), produced_by(?x4651, ?x163), film(?x241, ?x4651) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043t8t language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.956 http://example.org/film/film/language #19002-01kcty PRED entity: 01kcty PRED relation: parent_genre PRED expected values: 03_d0 => 87 concepts (55 used for prediction) PRED predicted values (max 10 best out of 223): 06by7 (0.47 #8865, 0.44 #3675, 0.42 #3335), 05r6t (0.34 #8231, 0.21 #6894, 0.20 #8565), 0glt670 (0.33 #328, 0.33 #28, 0.32 #5364), 03_d0 (0.33 #328, 0.33 #9, 0.31 #5345), 02x8m (0.33 #328, 0.33 #14, 0.29 #1509), 08cyft (0.33 #328, 0.33 #40, 0.24 #1535), 06cqb (0.33 #328, 0.33 #1, 0.19 #1496), 07gxw (0.33 #328, 0.33 #39, 0.18 #870), 0fd3y (0.33 #328, 0.33 #8, 0.18 #9014), 0190y4 (0.33 #328, 0.33 #115, 0.18 #9014) >> Best rule #8865 for best value: >> intensional similarity = 8 >> extensional distance = 123 >> proper extension: 028cl7; 017ht; >> query: (?x12560, 06by7) <- parent_genre(?x12560, ?x14532), parent_genre(?x7280, ?x14532), parent_genre(?x3232, ?x7280), parent_genre(?x7280, ?x3915), ?x3232 = 01ym9b, ?x3915 = 07gxw, artists(?x7280, ?x1732), ?x1732 = 03t9sp >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #328 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 03_d0; *> query: (?x12560, ?x283) <- artists(?x12560, ?x1940), artists(?x12560, ?x1732), parent_genre(?x12560, ?x14532), parent_genre(?x3232, ?x12560), parent_genre(?x7280, ?x14532), ?x1732 = 03t9sp, ?x1940 = 04zwjd, parent_genre(?x7280, ?x283), artists(?x7280, ?x2946), ?x3232 = 01ym9b *> conf = 0.33 ranks of expected_values: 4 EVAL 01kcty parent_genre 03_d0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 55.000 0.472 http://example.org/music/genre/parent_genre #19001-01zb_g PRED entity: 01zb_g PRED relation: category PRED expected values: 08mbj5d => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #24, 0.79 #27, 0.79 #31) >> Best rule #24 for best value: >> intensional similarity = 11 >> extensional distance = 27 >> proper extension: 041n43; >> query: (?x14502, 08mbj5d) <- artist(?x14502, ?x5872), role(?x5872, ?x432), participant(?x2444, ?x5872), artists(?x302, ?x5872), nominated_for(?x2444, ?x224), award(?x5872, ?x247), award_nominee(?x398, ?x2444), gender(?x2444, ?x231), film(?x2444, ?x1481), type_of_union(?x2444, ?x566), profession(?x2444, ?x319) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01zb_g category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.828 http://example.org/common/topic/webpage./common/webpage/category #19000-015010 PRED entity: 015010 PRED relation: gender PRED expected values: 02zsn => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.94 #6, 0.90 #10, 0.76 #14), 05zppz (0.71 #128, 0.71 #131, 0.71 #126) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 030znt; 01hkhq; 05typm; 01bh6y; 0739z6; 01dbgw; >> query: (?x12347, 02zsn) <- award(?x12347, ?x375), nationality(?x12347, ?x1310), film(?x12347, ?x2345), ?x375 = 0bfvw2 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015010 gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.937 http://example.org/people/person/gender #18999-017y6l PRED entity: 017y6l PRED relation: major_field_of_study PRED expected values: 02j62 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 116): 01mkq (0.50 #1380, 0.42 #2870, 0.31 #6591), 02j62 (0.46 #1394, 0.38 #6605, 0.38 #2884), 02lp1 (0.44 #1376, 0.42 #2866, 0.31 #6587), 0g26h (0.39 #1407, 0.39 #2897, 0.33 #167), 062z7 (0.37 #1391, 0.31 #2881, 0.26 #5486), 02_7t (0.34 #1430, 0.31 #2920, 0.18 #686), 0_jm (0.30 #1423, 0.20 #2913, 0.18 #679), 05qfh (0.28 #1400, 0.26 #2890, 0.22 #5495), 04x_3 (0.28 #1390, 0.24 #2880, 0.17 #6601), 05qjt (0.26 #2862, 0.25 #5839, 0.24 #1372) >> Best rule #1380 for best value: >> intensional similarity = 5 >> extensional distance = 80 >> proper extension: 01jssp; 05krk; 01j_9c; 02w2bc; 07tgn; 07w0v; 01wdl3; 04rwx; 01j_cy; 07szy; ... >> query: (?x6816, 01mkq) <- major_field_of_study(?x6816, ?x2014), institution(?x1771, ?x6816), institution(?x620, ?x6816), ?x1771 = 019v9k, ?x620 = 07s6fsf >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1394 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 80 *> proper extension: 01jssp; 05krk; 01j_9c; 02w2bc; 07tgn; 07w0v; 01wdl3; 04rwx; 01j_cy; 07szy; ... *> query: (?x6816, 02j62) <- major_field_of_study(?x6816, ?x2014), institution(?x1771, ?x6816), institution(?x620, ?x6816), ?x1771 = 019v9k, ?x620 = 07s6fsf *> conf = 0.46 ranks of expected_values: 2 EVAL 017y6l major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 160.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18998-0408np PRED entity: 0408np PRED relation: spouse! PRED expected values: 023tp8 => 84 concepts (31 used for prediction) PRED predicted values (max 10 best out of 34): 023tp8 (0.81 #3216, 0.80 #3618, 0.80 #4020), 02lnhv (0.04 #1205, 0.03 #4422, 0.03 #2009), 033tln (0.03 #1377, 0.03 #573, 0.01 #3388), 030hbp (0.03 #763), 0kjrx (0.03 #695), 0kryqm (0.03 #653), 020trj (0.03 #620), 02kxbx3 (0.03 #538), 0bbf1f (0.03 #508), 0237fw (0.03 #483) >> Best rule #3216 for best value: >> intensional similarity = 3 >> extensional distance = 246 >> proper extension: 02vkvcz; >> query: (?x2692, ?x376) <- spouse(?x2692, ?x376), location(?x2692, ?x1705), award(?x2692, ?x704) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0408np spouse! 023tp8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 31.000 0.810 http://example.org/people/person/spouse_s./people/marriage/spouse #18997-0pvms PRED entity: 0pvms PRED relation: cinematography PRED expected values: 0f3zsq => 87 concepts (40 used for prediction) PRED predicted values (max 10 best out of 37): 03rqww (0.20 #42, 0.14 #168), 06r_by (0.17 #86, 0.05 #341, 0.02 #1165), 04g865 (0.17 #70), 04qvl7 (0.09 #190, 0.05 #256, 0.04 #572), 0f3zf_ (0.09 #192, 0.05 #258, 0.02 #448), 0854hr (0.09 #464, 0.02 #653, 0.01 #718), 0f3zsq (0.05 #305, 0.04 #558, 0.04 #621), 07mb57 (0.05 #267, 0.03 #520, 0.03 #583), 03cx282 (0.05 #271, 0.02 #461, 0.02 #524), 08t7nz (0.04 #484) >> Best rule #42 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 016fyc; 0sxgv; 035gnh; >> query: (?x2565, 03rqww) <- genre(?x2565, ?x162), film(?x2564, ?x2565), country(?x2565, ?x94), produced_by(?x2565, ?x798), ?x2564 = 02lf1j >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #305 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 03q0r1; *> query: (?x2565, 0f3zsq) <- genre(?x2565, ?x6674), film(?x496, ?x2565), ?x496 = 0bxtg, genre(?x3610, ?x6674), ?x3610 = 0d66j2 *> conf = 0.05 ranks of expected_values: 7 EVAL 0pvms cinematography 0f3zsq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 40.000 0.200 http://example.org/film/film/cinematography #18996-0265wl PRED entity: 0265wl PRED relation: award_winner PRED expected values: 05x8n => 61 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1322): 07w21 (0.73 #19836, 0.62 #22307, 0.57 #17366), 03rx9 (0.50 #9467, 0.43 #19349, 0.43 #14411), 05x8n (0.50 #6420, 0.43 #18771, 0.43 #13833), 04mhl (0.50 #8394, 0.43 #13338, 0.40 #3454), 03772 (0.46 #28317, 0.46 #25848, 0.45 #20905), 02y49 (0.46 #29082, 0.46 #26613, 0.43 #19200), 0fpzt5 (0.46 #26604, 0.43 #16723, 0.43 #14253), 04r68 (0.43 #15971, 0.43 #13501, 0.41 #22233), 07zl1 (0.43 #14824, 0.43 #14514, 0.41 #22233), 0c3kw (0.43 #17644, 0.39 #14823, 0.37 #7409) >> Best rule #19836 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 040_9s0; 0208wk; 039yzf; 045xh; >> query: (?x5050, 07w21) <- disciplines_or_subjects(?x5050, ?x1510), award(?x10438, ?x5050), award(?x4895, ?x5050), ?x1510 = 01hmnh, award_winner(?x3337, ?x10438), award_nominee(?x4353, ?x4895), ?x3337 = 01yz0x >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #6420 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 06196; *> query: (?x5050, 05x8n) <- disciplines_or_subjects(?x5050, ?x1510), award(?x10438, ?x5050), ?x10438 = 07zl1, genre(?x419, ?x1510), genre(?x97, ?x1510) *> conf = 0.50 ranks of expected_values: 3 EVAL 0265wl award_winner 05x8n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 61.000 44.000 0.727 http://example.org/award/award_category/winners./award/award_honor/award_winner #18995-01wd9lv PRED entity: 01wd9lv PRED relation: artists! PRED expected values: 064t9 01kcty => 131 concepts (75 used for prediction) PRED predicted values (max 10 best out of 205): 064t9 (0.58 #6703, 0.46 #11875, 0.46 #19482), 05bt6j (0.30 #6732, 0.25 #19511, 0.22 #15859), 025sc50 (0.25 #6738, 0.24 #11910, 0.21 #15257), 016clz (0.24 #15823, 0.23 #16736, 0.22 #19475), 01lyv (0.22 #11895, 0.21 #10982, 0.20 #12808), 0xhtw (0.20 #15834, 0.19 #16747, 0.17 #19486), 0155w (0.20 #5578, 0.16 #14095, 0.15 #1623), 02k_kn (0.20 #6752, 0.12 #1581, 0.12 #12533), 02lnbg (0.19 #1271, 0.18 #2183, 0.16 #6746), 02vjzr (0.18 #6822, 0.11 #12603, 0.11 #2259) >> Best rule #6703 for best value: >> intensional similarity = 2 >> extensional distance = 147 >> proper extension: 01bmlb; >> query: (?x6382, 064t9) <- award(?x6382, ?x724), ?x724 = 01bgqh >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1, 185 EVAL 01wd9lv artists! 01kcty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 131.000 75.000 0.577 http://example.org/music/genre/artists EVAL 01wd9lv artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 75.000 0.577 http://example.org/music/genre/artists #18994-07f1x PRED entity: 07f1x PRED relation: organization PRED expected values: 02vk52z => 142 concepts (130 used for prediction) PRED predicted values (max 10 best out of 95): 02vk52z (0.87 #1868, 0.86 #465, 0.86 #1357), 018cqq (0.54 #453, 0.52 #136, 0.52 #327), 0b6css (0.52 #135, 0.51 #473, 0.48 #961), 01rz1 (0.52 #319, 0.51 #445, 0.50 #658), 02jxk (0.35 #659, 0.34 #446, 0.33 #129), 0gkjy (0.32 #1362, 0.32 #2376, 0.28 #1574), 0j7v_ (0.32 #468, 0.32 #2376, 0.30 #342), 041288 (0.32 #1990, 0.32 #1583, 0.32 #2376), 059dn (0.32 #2376, 0.24 #140, 0.22 #183), 085h1 (0.32 #2376, 0.05 #475, 0.05 #963) >> Best rule #1868 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 02jxk; >> query: (?x7747, 02vk52z) <- member_states(?x7695, ?x7747), ?x7695 = 085h1, jurisdiction_of_office(?x182, ?x7747) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07f1x organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 130.000 0.866 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #18993-019fbp PRED entity: 019fbp PRED relation: category PRED expected values: 08mbj5d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.66 #33, 0.65 #34, 0.61 #46) >> Best rule #33 for best value: >> intensional similarity = 4 >> extensional distance = 295 >> proper extension: 0k3p; >> query: (?x12033, 08mbj5d) <- contains(?x9305, ?x12033), place_of_birth(?x9139, ?x12033), location(?x4645, ?x9305), capital(?x9305, ?x4335) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019fbp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.663 http://example.org/common/topic/webpage./common/webpage/category #18992-0bwjj PRED entity: 0bwjj PRED relation: colors PRED expected values: 01l849 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 17): 01l849 (0.67 #106, 0.41 #399, 0.33 #365), 06fvc (0.60 #72, 0.50 #19, 0.47 #625), 01g5v (0.54 #1395, 0.39 #1619, 0.35 #522), 02rnmb (0.33 #116, 0.28 #634, 0.27 #272), 03vtbc (0.26 #492, 0.26 #88, 0.25 #613), 036k5h (0.26 #88, 0.25 #22, 0.23 #35), 04mkbj (0.26 #88, 0.23 #35, 0.23 #175), 01jnf1 (0.26 #88, 0.23 #35, 0.23 #175), 03wkwg (0.26 #88, 0.23 #35, 0.23 #175), 09ggk (0.25 #205, 0.22 #222, 0.18 #411) >> Best rule #106 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 04cxw5b; >> query: (?x9760, 01l849) <- team(?x1348, ?x9760), colors(?x9760, ?x8047), draft(?x9760, ?x8133), colors(?x8046, ?x8047), colors(?x6083, ?x8047), colors(?x9358, ?x8047), ?x6083 = 09s5q8, team(?x208, ?x9358), ?x8046 = 02gnh0, ?x8133 = 025tn92 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bwjj colors 01l849 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.667 http://example.org/sports/sports_team/colors #18991-0h3k3f PRED entity: 0h3k3f PRED relation: award PRED expected values: 02z1nbg => 106 concepts (100 used for prediction) PRED predicted values (max 10 best out of 184): 0f4x7 (0.31 #3026, 0.31 #2817, 0.26 #3025), 0gq9h (0.31 #2853, 0.22 #4482, 0.21 #5182), 0gs9p (0.26 #3025, 0.26 #4654, 0.25 #3027), 0gq_v (0.26 #3025, 0.26 #4654, 0.25 #5355), 0gr0m (0.26 #3025, 0.26 #4654, 0.25 #5355), 0m7yy (0.24 #4420, 0.24 #4316, 0.23 #4888), 0gs96 (0.22 #88, 0.16 #1019, 0.14 #1949), 04kxsb (0.19 #2886, 0.16 #4515, 0.10 #5215), 019f4v (0.18 #5174, 0.18 #2845, 0.15 #3080), 02w9sd7 (0.18 #2914, 0.16 #4543, 0.09 #5243) >> Best rule #3026 for best value: >> intensional similarity = 5 >> extensional distance = 100 >> proper extension: 0sxg4; 0yyg4; 04v8x9; 0n0bp; 0c5dd; 04mzf8; 083skw; 019vhk; 0p4v_; 02vqsll; ... >> query: (?x8735, ?x591) <- nominated_for(?x2716, ?x8735), nominated_for(?x1313, ?x8735), nominated_for(?x591, ?x8735), ?x1313 = 0gs9p, ?x591 = 0f4x7 >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #137 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0jqb8; *> query: (?x8735, 02z1nbg) <- written_by(?x8735, ?x4180), costume_design_by(?x8735, ?x5611), ?x5611 = 02cqbx, film_art_direction_by(?x8735, ?x4896) *> conf = 0.11 ranks of expected_values: 23 EVAL 0h3k3f award 02z1nbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 106.000 100.000 0.314 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #18990-09gnn PRED entity: 09gnn PRED relation: student! PRED expected values: 013nky => 105 concepts (103 used for prediction) PRED predicted values (max 10 best out of 226): 04gd8j (0.33 #892, 0.14 #4042), 02kj7g (0.25 #3139, 0.20 #3664, 0.07 #8917), 05zl0 (0.25 #2826, 0.20 #3351, 0.07 #5451), 06fq2 (0.25 #1350, 0.14 #3975), 02sjgpq (0.25 #1313, 0.14 #3938), 07tds (0.25 #1723, 0.05 #6451, 0.03 #8026), 0bx8pn (0.25 #1620, 0.01 #37329, 0.01 #17375), 027kp3 (0.25 #1727), 03ksy (0.20 #3255, 0.16 #10083, 0.13 #8508), 014zws (0.20 #3480, 0.07 #8733, 0.04 #7683) >> Best rule #892 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0tc7; >> query: (?x10499, 04gd8j) <- profession(?x10499, ?x7841), profession(?x10499, ?x5805), ?x7841 = 025sppp, student(?x2999, ?x10499), ?x5805 = 0fj9f, gender(?x10499, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09gnn student! 013nky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 103.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #18989-053y4h PRED entity: 053y4h PRED relation: profession PRED expected values: 0dxtg => 88 concepts (78 used for prediction) PRED predicted values (max 10 best out of 47): 0dxtg (0.60 #310, 0.53 #458, 0.30 #5194), 03gjzk (0.59 #459, 0.59 #311, 0.24 #6083), 02jknp (0.54 #452, 0.53 #304, 0.21 #5188), 01d_h8 (0.50 #302, 0.47 #450, 0.33 #6), 0np9r (0.20 #1649, 0.19 #613, 0.16 #317), 018gz8 (0.20 #313, 0.15 #461, 0.15 #2681), 09jwl (0.18 #5495, 0.17 #19, 0.16 #4755), 0cbd2 (0.16 #303, 0.16 #1043, 0.16 #4891), 0nbcg (0.12 #5507, 0.11 #9209, 0.11 #9357), 0dz3r (0.12 #5478, 0.10 #8143, 0.10 #9328) >> Best rule #310 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 015zql; >> query: (?x5133, 0dxtg) <- student(?x2150, ?x5133), profession(?x5133, ?x1943), ?x1943 = 02krf9 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 053y4h profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 78.000 0.599 http://example.org/people/person/profession #18988-04nnpw PRED entity: 04nnpw PRED relation: genre PRED expected values: 07s9rl0 => 116 concepts (115 used for prediction) PRED predicted values (max 10 best out of 129): 07s9rl0 (0.81 #2511, 0.75 #1, 0.70 #4428), 01jfsb (0.77 #1198, 0.77 #1089, 0.73 #6588), 02n4kr (0.73 #6588, 0.72 #9219, 0.72 #8382), 03q4nz (0.50 #19, 0.07 #257, 0.06 #3966), 0lsxr (0.45 #1085, 0.32 #607, 0.31 #486), 05p553 (0.45 #843, 0.41 #242, 0.40 #2995), 02l7c8 (0.40 #2526, 0.34 #736, 0.32 #5398), 03k9fj (0.38 #850, 0.35 #2643, 0.33 #1329), 02kdv5l (0.33 #841, 0.33 #2634, 0.33 #1320), 01hmnh (0.29 #857, 0.27 #1336, 0.27 #2650) >> Best rule #2511 for best value: >> intensional similarity = 5 >> extensional distance = 185 >> proper extension: 02prwdh; >> query: (?x4696, 07s9rl0) <- titles(?x600, ?x4696), titles(?x162, ?x4696), film_crew_role(?x4696, ?x137), ?x162 = 04xvlr, genre(?x280, ?x600) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04nnpw genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 115.000 0.813 http://example.org/film/film/genre #18987-027r9t PRED entity: 027r9t PRED relation: film! PRED expected values: 02qgqt 083chw => 95 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1221): 01vb6z (0.72 #35306, 0.61 #49850, 0.54 #4153), 01cyjx (0.72 #35306, 0.61 #49850, 0.54 #4153), 034np8 (0.72 #35306, 0.61 #49850, 0.47 #35305), 05mvd62 (0.54 #4153, 0.47 #35305, 0.46 #33228), 094tsh6 (0.54 #4153, 0.47 #35305, 0.46 #33228), 01gb54 (0.47 #35305, 0.46 #33228, 0.44 #26993), 02lkcc (0.14 #240, 0.05 #10622, 0.04 #16851), 024bbl (0.14 #831, 0.03 #2907, 0.03 #21594), 04__f (0.14 #1377, 0.03 #15912, 0.03 #24217), 016_mj (0.14 #291, 0.02 #21054, 0.01 #8597) >> Best rule #35306 for best value: >> intensional similarity = 4 >> extensional distance = 237 >> proper extension: 015g28; 027j9wd; 02q8ms8; 05n6sq; 03t95n; 032clf; 03kx49; >> query: (?x7141, ?x4468) <- nominated_for(?x4468, ?x7141), country(?x7141, ?x94), film(?x4468, ?x370), category(?x7141, ?x134) >> conf = 0.72 => this is the best rule for 3 predicted values *> Best rule #27011 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 175 *> proper extension: 0b2v79; 01sxly; 0p_sc; 0jzw; 0jym0; 0m9p3; 0c9k8; 0dnqr; 0g68zt; 0f4yh; ... *> query: (?x7141, 02qgqt) <- nominated_for(?x704, ?x7141), award(?x7047, ?x704), award(?x851, ?x704), ?x7047 = 059xnf, film(?x851, ?x1038) *> conf = 0.05 ranks of expected_values: 117 EVAL 027r9t film! 083chw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 27.000 0.718 http://example.org/film/actor/film./film/performance/film EVAL 027r9t film! 02qgqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 95.000 27.000 0.718 http://example.org/film/actor/film./film/performance/film #18986-018_q8 PRED entity: 018_q8 PRED relation: citytown PRED expected values: 0h7h6 => 195 concepts (195 used for prediction) PRED predicted values (max 10 best out of 144): 030qb3t (0.76 #17600, 0.33 #8082, 0.33 #27), 05ksh (0.33 #1118), 0rj4g (0.32 #22699, 0.17 #3153, 0.17 #2055), 05qtj (0.20 #833, 0.18 #5592, 0.12 #12183), 0dclg (0.20 #773, 0.11 #3701, 0.10 #4434), 07dfk (0.19 #18150, 0.17 #2041, 0.14 #44885), 0r04p (0.18 #6694, 0.18 #5961, 0.17 #8158), 0k_q_ (0.18 #4027, 0.17 #18306, 0.14 #3340), 04jpl (0.18 #6598, 0.17 #8062, 0.15 #52370), 024bqj (0.18 #5688, 0.17 #2027, 0.14 #18136) >> Best rule #17600 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 01bzw5; 01w5gp; 02gnmp; 06kknt; 06b7s9; 03b8c4; >> query: (?x7326, 030qb3t) <- citytown(?x7326, ?x2474), organization(?x4682, ?x7326), featured_film_locations(?x603, ?x2474), ?x603 = 03s6l2 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #16139 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 027lf1; 03_c8p; *> query: (?x7326, 0h7h6) <- citytown(?x7326, ?x739), child(?x7326, ?x902), company(?x1491, ?x7326), origin(?x217, ?x739) *> conf = 0.05 ranks of expected_values: 54 EVAL 018_q8 citytown 0h7h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 195.000 195.000 0.762 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #18985-0pqp3 PRED entity: 0pqp3 PRED relation: group! PRED expected values: 042v_gx 01vj9c => 96 concepts (83 used for prediction) PRED predicted values (max 10 best out of 121): 03bx0bm (0.61 #2071, 0.60 #450, 0.59 #1985), 0l14qv (0.60 #431, 0.60 #176, 0.57 #516), 07y_7 (0.60 #173, 0.50 #428, 0.50 #88), 028tv0 (0.50 #268, 0.45 #1973, 0.44 #2059), 0mkg (0.38 #351, 0.32 #691, 0.30 #776), 03qjg (0.38 #302, 0.26 #727, 0.26 #897), 01vj9c (0.35 #864, 0.33 #1035, 0.28 #2401), 05r5c (0.30 #858, 0.30 #773, 0.29 #1029), 013y1f (0.30 #793, 0.26 #708, 0.26 #878), 02fsn (0.29 #558, 0.25 #133, 0.21 #728) >> Best rule #2071 for best value: >> intensional similarity = 7 >> extensional distance = 108 >> proper extension: 0b1zz; >> query: (?x11107, 03bx0bm) <- group(?x716, ?x11107), group(?x227, ?x11107), ?x716 = 018vs, artist(?x2190, ?x11107), artists(?x1380, ?x11107), role(?x219, ?x227), performance_role(?x75, ?x227) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #864 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 21 *> proper extension: 046p9; 01fchy; *> query: (?x11107, 01vj9c) <- group(?x2309, ?x11107), group(?x1750, ?x11107), group(?x1166, ?x11107), origin(?x11107, ?x362), ?x2309 = 06ncr, ?x1166 = 05148p4, instrumentalists(?x1750, ?x8560), ?x8560 = 02y7sr, role(?x1750, ?x74) *> conf = 0.35 ranks of expected_values: 7, 16 EVAL 0pqp3 group! 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 96.000 83.000 0.609 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0pqp3 group! 042v_gx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 96.000 83.000 0.609 http://example.org/music/performance_role/regular_performances./music/group_membership/group #18984-016vqk PRED entity: 016vqk PRED relation: profession PRED expected values: 0dz3r => 129 concepts (106 used for prediction) PRED predicted values (max 10 best out of 63): 01d_h8 (0.76 #11275, 0.33 #443, 0.30 #10398), 0dz3r (0.71 #294, 0.69 #878, 0.67 #732), 0nbcg (0.59 #2366, 0.52 #1197, 0.52 #5586), 01c72t (0.50 #751, 0.45 #605, 0.38 #897), 0dxtg (0.39 #11282, 0.30 #10405, 0.29 #11867), 02jknp (0.37 #11277, 0.20 #12154, 0.19 #4246), 039v1 (0.35 #1056, 0.30 #5591, 0.30 #1202), 02dsz (0.33 #200, 0.29 #346, 0.20 #54), 03gjzk (0.29 #5863, 0.25 #2789, 0.25 #11283), 0fnpj (0.25 #934, 0.21 #1226, 0.20 #58) >> Best rule #11275 for best value: >> intensional similarity = 2 >> extensional distance = 1273 >> proper extension: 0c8hct; 0652ty; >> query: (?x9008, 01d_h8) <- profession(?x9008, ?x955), film_crew_role(?x573, ?x955) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #294 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 023p29; *> query: (?x9008, 0dz3r) <- award_winner(?x1827, ?x9008), award_winner(?x1232, ?x9008), ?x1827 = 02nhxf, people(?x2510, ?x9008), award(?x248, ?x1232) *> conf = 0.71 ranks of expected_values: 2 EVAL 016vqk profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 106.000 0.765 http://example.org/people/person/profession #18983-0bykpk PRED entity: 0bykpk PRED relation: nominated_for! PRED expected values: 0k611 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 203): 0gs9p (0.67 #5457, 0.67 #5458, 0.66 #10442), 0gqyl (0.67 #5457, 0.67 #5458, 0.66 #10442), 019f4v (0.57 #1712, 0.53 #763, 0.39 #2423), 0gq_v (0.57 #20, 0.40 #731, 0.36 #4289), 040njc (0.49 #1667, 0.43 #718, 0.34 #4744), 04kxsb (0.45 #1754, 0.21 #805, 0.17 #5075), 04dn09n (0.43 #1693, 0.31 #744, 0.23 #5014), 0k611 (0.43 #782, 0.43 #71, 0.43 #1731), 0gqwc (0.43 #59, 0.27 #1719, 0.21 #770), 0gqy2 (0.42 #1781, 0.36 #832, 0.29 #121) >> Best rule #5457 for best value: >> intensional similarity = 4 >> extensional distance = 515 >> proper extension: 06mmr; >> query: (?x6100, ?x1972) <- award(?x6100, ?x1972), award(?x6100, ?x1313), honored_for(?x8259, ?x6100), award_winner(?x1313, ?x276) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #782 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 019kyn; *> query: (?x6100, 0k611) <- award(?x6100, ?x1313), production_companies(?x6100, ?x574), list(?x6100, ?x3004) *> conf = 0.43 ranks of expected_values: 8 EVAL 0bykpk nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 68.000 68.000 0.671 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18982-0137n0 PRED entity: 0137n0 PRED relation: location PRED expected values: 0vbk 05jbn => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 153): 0vzm (0.37 #4820, 0.03 #6599, 0.02 #9814), 02_286 (0.20 #9678, 0.18 #6463, 0.18 #42614), 030qb3t (0.19 #20165, 0.19 #12937, 0.18 #14543), 05fkf (0.12 #38, 0.06 #2447, 0.04 #841), 05jbn (0.12 #253, 0.06 #2662, 0.03 #5073), 05mph (0.12 #319, 0.04 #2728, 0.02 #5942), 0vbk (0.12 #246, 0.04 #2655, 0.02 #5869), 013kcv (0.12 #42, 0.02 #2451, 0.01 #3254), 0yj9v (0.12 #652), 0fwc0 (0.12 #506) >> Best rule #4820 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 02fybl; 0djc3s; >> query: (?x1270, ?x3269) <- student(?x6177, ?x1270), origin(?x1270, ?x3269), gender(?x1270, ?x231) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0fb2l; *> query: (?x1270, 05jbn) <- artist(?x12017, ?x1270), artists(?x283, ?x1270), ?x12017 = 01wsj0 *> conf = 0.12 ranks of expected_values: 5, 7 EVAL 0137n0 location 05jbn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 104.000 104.000 0.370 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0137n0 location 0vbk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 104.000 104.000 0.370 http://example.org/people/person/places_lived./people/place_lived/location #18981-02fsn PRED entity: 02fsn PRED relation: role! PRED expected values: 0bxl5 => 93 concepts (50 used for prediction) PRED predicted values (max 10 best out of 86): 0gkd1 (0.83 #4033, 0.83 #1962, 0.83 #3781), 013y1f (0.83 #4033, 0.83 #1962, 0.83 #3781), 0l14md (0.83 #4033, 0.83 #1962, 0.83 #3781), 02hnl (0.83 #4033, 0.83 #1962, 0.83 #3781), 0bxl5 (0.83 #4033, 0.83 #1962, 0.83 #3781), 02dlh2 (0.83 #4033, 0.83 #1962, 0.83 #3781), 01w4c9 (0.83 #4033, 0.83 #1962, 0.83 #3781), 05842k (0.83 #4033, 0.83 #1962, 0.83 #3781), 05148p4 (0.80 #2547, 0.76 #407, 0.70 #80), 03qjg (0.79 #3235, 0.78 #3568, 0.76 #407) >> Best rule #4033 for best value: >> intensional similarity = 18 >> extensional distance = 18 >> proper extension: 0979zs; >> query: (?x2888, ?x315) <- role(?x3239, ?x2888), role(?x2048, ?x2888), role(?x214, ?x2888), role(?x2888, ?x3991), role(?x2888, ?x315), ?x214 = 02pprs, ?x3991 = 05842k, role(?x569, ?x3239), group(?x2048, ?x11425), group(?x2048, ?x4791), group(?x2048, ?x4715), role(?x1662, ?x2048), instrumentalists(?x2048, ?x367), ?x1662 = 02bxd, ?x4715 = 0khth, role(?x3238, ?x3239), ?x11425 = 02vnpv, ?x4791 = 02t3ln >> conf = 0.83 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5 EVAL 02fsn role! 0bxl5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 50.000 0.829 http://example.org/music/performance_role/track_performances./music/track_contribution/role #18980-015njf PRED entity: 015njf PRED relation: place_of_birth PRED expected values: 0grd7 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 139): 0cr3d (0.33 #94, 0.06 #12768, 0.06 #9951), 02_286 (0.17 #6356, 0.13 #8468, 0.10 #19736), 01_d4 (0.14 #1474, 0.08 #12740, 0.06 #13444), 01531 (0.14 #2921, 0.05 #5033, 0.05 #15597), 0n9r8 (0.14 #1655, 0.04 #14329, 0.03 #7992), 0f2tj (0.14 #3064, 0.02 #11514, 0.02 #12218), 04jpl (0.11 #40150, 0.11 #40855, 0.10 #33107), 0d6lp (0.09 #9971, 0.04 #5746, 0.04 #12084), 04f_d (0.07 #3593, 0.07 #2185, 0.06 #4297), 09949m (0.07 #3841, 0.06 #4545, 0.03 #10178) >> Best rule #94 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03f0fnk; >> query: (?x4813, 0cr3d) <- profession(?x4813, ?x4354), spouse(?x4813, ?x12364), award_winner(?x289, ?x4813), ?x4354 = 0lgw7 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015njf place_of_birth 0grd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 111.000 0.333 http://example.org/people/person/place_of_birth #18979-09q23x PRED entity: 09q23x PRED relation: nominated_for! PRED expected values: 04dn09n 02x73k6 => 89 concepts (78 used for prediction) PRED predicted values (max 10 best out of 212): 0k611 (0.60 #73, 0.50 #311, 0.39 #1264), 0p9sw (0.60 #19, 0.50 #257, 0.31 #733), 0gq9h (0.57 #1253, 0.40 #62, 0.33 #300), 02qyntr (0.43 #1371, 0.23 #894, 0.20 #180), 099c8n (0.41 #1247, 0.40 #56, 0.33 #294), 0gs9p (0.41 #1255, 0.24 #1733, 0.21 #4352), 02pqp12 (0.41 #1249, 0.20 #58, 0.17 #296), 019f4v (0.40 #53, 0.39 #1244, 0.33 #291), 02n9nmz (0.40 #57, 0.33 #1430, 0.33 #1248), 04dn09n (0.40 #34, 0.33 #1225, 0.33 #272) >> Best rule #73 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0c0nhgv; 051zy_b; 07s846j; >> query: (?x5001, 0k611) <- nominated_for(?x112, ?x5001), nominated_for(?x3751, ?x5001), ?x3751 = 01d8yn, language(?x5001, ?x254) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0c0nhgv; 051zy_b; 07s846j; *> query: (?x5001, 04dn09n) <- nominated_for(?x112, ?x5001), nominated_for(?x3751, ?x5001), ?x3751 = 01d8yn, language(?x5001, ?x254) *> conf = 0.40 ranks of expected_values: 10, 55 EVAL 09q23x nominated_for! 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 89.000 78.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09q23x nominated_for! 04dn09n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 89.000 78.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18978-0l12d PRED entity: 0l12d PRED relation: student! PRED expected values: 02s62q => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 184): 019vv1 (0.20 #448, 0.02 #6246, 0.02 #6773), 07wrz (0.17 #589, 0.06 #2170, 0.02 #4805), 07szy (0.17 #1094, 0.04 #3202, 0.03 #3729), 0hsb3 (0.17 #1262, 0.04 #3370, 0.03 #3897), 03ksy (0.12 #11701, 0.11 #10647, 0.09 #6958), 02g839 (0.09 #1606, 0.08 #3187, 0.07 #3714), 0lyjf (0.09 #1738, 0.03 #4373, 0.02 #4900), 017z88 (0.08 #7988, 0.08 #4825, 0.07 #20109), 01qd_r (0.08 #3443, 0.04 #5024, 0.03 #8187), 09f2j (0.07 #13862, 0.07 #14389, 0.05 #4375) >> Best rule #448 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0f0y8; 01tp5bj; 0fhxv; >> query: (?x1656, 019vv1) <- performance_role(?x1656, ?x315), role(?x1656, ?x2944), ?x2944 = 0l14j_ >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0l12d student! 02s62q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 135.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #18977-03f1zdw PRED entity: 03f1zdw PRED relation: award_winner! PRED expected values: 0g55tzk => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 100): 09qvms (0.06 #708, 0.05 #13, 0.05 #1125), 0418154 (0.06 #384, 0.05 #106, 0.04 #245), 03nnm4t (0.05 #72, 0.04 #211, 0.03 #5632), 092c5f (0.05 #14, 0.04 #709, 0.04 #848), 09p3h7 (0.05 #69, 0.04 #347, 0.02 #903), 073h5b (0.05 #132, 0.03 #410, 0.01 #549), 0gx_st (0.05 #35, 0.02 #1147, 0.02 #1425), 05q7cj (0.05 #93, 0.01 #371), 07z31v (0.05 #30, 0.01 #1559, 0.01 #5590), 03gyp30 (0.05 #810, 0.04 #1227, 0.04 #1505) >> Best rule #708 for best value: >> intensional similarity = 3 >> extensional distance = 776 >> proper extension: 017j6; >> query: (?x1222, 09qvms) <- award_nominee(?x3553, ?x1222), award_winner(?x1222, ?x988), participant(?x2499, ?x3553) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #274 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 06_bq1; *> query: (?x1222, 0g55tzk) <- award_winner(?x1222, ?x989), ?x989 = 0151w_, nominated_for(?x1222, ?x144) *> conf = 0.04 ranks of expected_values: 16 EVAL 03f1zdw award_winner! 0g55tzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 84.000 84.000 0.059 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18976-042g97 PRED entity: 042g97 PRED relation: film_release_region PRED expected values: 09c7w0 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 192): 09c7w0 (0.74 #1619, 0.71 #10590, 0.71 #4132), 07ssc (0.52 #1797, 0.47 #7360, 0.46 #1077), 0f8l9c (0.26 #8288, 0.24 #7212, 0.23 #3083), 03gj2 (0.25 #3088, 0.24 #1653, 0.20 #216), 0d0vqn (0.24 #8268, 0.24 #3063, 0.23 #7192), 06qd3 (0.24 #1669, 0.24 #3104, 0.21 #949), 03h64 (0.24 #1704, 0.23 #8344, 0.23 #3139), 02vzc (0.24 #8326, 0.24 #3121, 0.22 #1686), 059j2 (0.24 #3096, 0.22 #8301, 0.22 #1661), 06mkj (0.24 #3127, 0.22 #8332, 0.21 #972) >> Best rule #1619 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 03mgx6z; 02825kb; 07ghq; >> query: (?x12214, 09c7w0) <- titles(?x811, ?x12214), prequel(?x3672, ?x12214), produced_by(?x3672, ?x2724), country(?x12214, ?x512) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042g97 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.741 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18975-06c1y PRED entity: 06c1y PRED relation: country! PRED expected values: 01sgl => 227 concepts (227 used for prediction) PRED predicted values (max 10 best out of 29): 06wrt (0.83 #355, 0.81 #384, 0.79 #529), 07gyv (0.77 #353, 0.71 #527, 0.68 #846), 0194d (0.71 #401, 0.71 #546, 0.70 #372), 019tzd (0.70 #366, 0.68 #395, 0.68 #540), 07jjt (0.67 #357, 0.65 #531, 0.65 #386), 01gqfm (0.65 #403, 0.63 #374, 0.59 #461), 01sgl (0.65 #398, 0.63 #369, 0.58 #514), 07rlg (0.63 #349, 0.62 #436, 0.61 #378), 096f8 (0.60 #354, 0.52 #383, 0.50 #528), 0d1t3 (0.57 #363, 0.53 #450, 0.52 #392) >> Best rule #355 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 05r4w; 05qx1; 015qh; 01pj7; >> query: (?x1536, 06wrt) <- contains(?x1536, ?x4962), film_release_region(?x1642, ?x1536), ?x1642 = 0bq8tmw, form_of_government(?x1536, ?x48) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #398 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 0chghy; 03rt9; 05qhw; ... *> query: (?x1536, 01sgl) <- contains(?x1536, ?x4962), film_release_region(?x8176, ?x1536), nationality(?x4379, ?x1536), ?x8176 = 0gvvm6l *> conf = 0.65 ranks of expected_values: 7 EVAL 06c1y country! 01sgl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 227.000 227.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #18974-071dcs PRED entity: 071dcs PRED relation: profession PRED expected values: 03gjzk => 93 concepts (71 used for prediction) PRED predicted values (max 10 best out of 50): 03gjzk (0.86 #599, 0.84 #745, 0.83 #307), 02hrh1q (0.80 #1037, 0.68 #10094, 0.68 #7464), 02jknp (0.71 #153, 0.61 #7, 0.45 #4244), 0dxtg (0.69 #159, 0.68 #597, 0.67 #305), 09jwl (0.38 #877, 0.34 #1480, 0.26 #9349), 018gz8 (0.38 #877, 0.27 #1040, 0.26 #9349), 0cbd2 (0.38 #877, 0.26 #9349, 0.23 #152), 0196pc (0.38 #877, 0.26 #9349, 0.23 #217), 01c72t (0.38 #877, 0.26 #9349, 0.14 #1484), 0kyk (0.38 #877, 0.26 #9349, 0.08 #10108) >> Best rule #599 for best value: >> intensional similarity = 3 >> extensional distance = 214 >> proper extension: 0grwj; 06j0md; 05ty4m; 02lf0c; 0d4fqn; 02773m2; 02778pf; 02q_cc; 0bg539; 03cs_z7; ... >> query: (?x2400, 03gjzk) <- award_nominee(?x2400, ?x1145), program(?x2400, ?x11035), profession(?x2400, ?x319) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 071dcs profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 71.000 0.856 http://example.org/people/person/profession #18973-0t_2 PRED entity: 0t_2 PRED relation: languages_spoken! PRED expected values: 09vc4s 059_w 02vsw1 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 49): 02vsw1 (0.50 #568, 0.50 #323, 0.50 #274), 059_w (0.33 #505, 0.33 #407, 0.33 #113), 03w9bjf (0.33 #766, 0.33 #129, 0.29 #1354), 0bbz66j (0.33 #126, 0.33 #28, 0.27 #665), 05l3g_ (0.33 #135, 0.33 #86, 0.25 #331), 09zyn5 (0.33 #584, 0.33 #143, 0.25 #486), 071x0k (0.33 #592, 0.33 #102, 0.25 #445), 0c41n (0.33 #441, 0.33 #147, 0.25 #294), 0fk3s (0.33 #435, 0.33 #141, 0.25 #288), 03x1x (0.33 #426, 0.33 #132, 0.25 #279) >> Best rule #568 for best value: >> intensional similarity = 10 >> extensional distance = 10 >> proper extension: 05f_3; 0295r; >> query: (?x3592, 02vsw1) <- languages_spoken(?x2510, ?x3592), languages(?x10086, ?x3592), award_winner(?x1784, ?x10086), people(?x2510, ?x10560), people(?x2510, ?x8143), people(?x2510, ?x7595), currency(?x10560, ?x170), award_nominee(?x100, ?x7595), role(?x8143, ?x227), film(?x10086, ?x1586) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0t_2 languages_spoken! 02vsw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.500 http://example.org/people/ethnicity/languages_spoken EVAL 0t_2 languages_spoken! 059_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.500 http://example.org/people/ethnicity/languages_spoken EVAL 0t_2 languages_spoken! 09vc4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 42.000 0.500 http://example.org/people/ethnicity/languages_spoken #18972-016ky6 PRED entity: 016ky6 PRED relation: language PRED expected values: 02h40lc => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.91 #834, 0.91 #1247, 0.90 #534), 064_8sq (0.18 #317, 0.16 #674, 0.16 #376), 06b_j (0.13 #82, 0.12 #141, 0.12 #200), 04306rv (0.12 #657, 0.12 #418, 0.10 #718), 06nm1 (0.10 #1315, 0.10 #543, 0.09 #1197), 02bjrlw (0.10 #533, 0.08 #653, 0.07 #414), 0jzc (0.07 #79, 0.06 #138, 0.06 #197), 03hkp (0.07 #74, 0.06 #133, 0.06 #192), 0349s (0.07 #104, 0.06 #163, 0.06 #222), 02hxcvy (0.07 #93, 0.06 #152, 0.06 #211) >> Best rule #834 for best value: >> intensional similarity = 3 >> extensional distance = 194 >> proper extension: 035xwd; 02sg5v; 04gknr; 0jjy0; 03qnvdl; 03lrqw; 05fgt1; 0p3_y; 0gj8nq2; 033srr; ... >> query: (?x5812, 02h40lc) <- cinematography(?x5812, ?x9681), produced_by(?x5812, ?x4385), genre(?x5812, ?x53) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016ky6 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.913 http://example.org/film/film/language #18971-0k_kr PRED entity: 0k_kr PRED relation: artist PRED expected values: 02cw1m => 61 concepts (38 used for prediction) PRED predicted values (max 10 best out of 4044): 01wg25j (0.60 #4696, 0.50 #3059, 0.43 #7153), 0knhk (0.60 #4644, 0.38 #7919, 0.33 #9555), 0qf11 (0.60 #4380, 0.33 #289, 0.29 #6837), 01q99h (0.50 #5339, 0.38 #13528, 0.33 #11069), 024qwq (0.50 #3125, 0.33 #671, 0.29 #7219), 01vw8mh (0.50 #5244, 0.25 #7700, 0.25 #1970), 01v0sxx (0.50 #2347, 0.25 #12172, 0.25 #3983), 0fsyx (0.50 #2446, 0.25 #12271, 0.25 #4082), 079kr (0.50 #2431, 0.25 #4067, 0.17 #12256), 02f1c (0.43 #7176, 0.40 #4719, 0.38 #8812) >> Best rule #4696 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0g768; >> query: (?x7681, 01wg25j) <- artist(?x7681, ?x6947), artist(?x7681, ?x4237), diet(?x4237, ?x3130), religion(?x4237, ?x1985), ?x6947 = 01vrnsk, profession(?x4237, ?x1183), artists(?x302, ?x4237) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4770 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 0g768; *> query: (?x7681, 02cw1m) <- artist(?x7681, ?x6947), artist(?x7681, ?x4237), diet(?x4237, ?x3130), religion(?x4237, ?x1985), ?x6947 = 01vrnsk, profession(?x4237, ?x1183), artists(?x302, ?x4237) *> conf = 0.20 ranks of expected_values: 312 EVAL 0k_kr artist 02cw1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 38.000 0.600 http://example.org/music/record_label/artist #18970-02vxq9m PRED entity: 02vxq9m PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.88 #31, 0.88 #51, 0.86 #81), 02nxhr (0.50 #7, 0.28 #17, 0.25 #22), 07c52 (0.14 #28, 0.09 #128, 0.07 #98), 07z4p (0.08 #45, 0.07 #30, 0.07 #130) >> Best rule #31 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 03l6q0; >> query: (?x186, 029j_) <- nominated_for(?x1582, ?x186), film(?x1018, ?x186), prequel(?x186, ?x787), participant(?x4295, ?x1582) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vxq9m film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.880 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #18969-02bp37 PRED entity: 02bp37 PRED relation: legislative_sessions PRED expected values: 02bn_p => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 38): 02bn_p (0.88 #568, 0.87 #644, 0.85 #486), 02glc4 (0.88 #568, 0.87 #644, 0.85 #486), 070mff (0.88 #568, 0.87 #644, 0.85 #486), 05l2z4 (0.88 #568, 0.87 #644, 0.85 #486), 02bp37 (0.79 #1214, 0.75 #856, 0.72 #1097), 043djx (0.50 #1134, 0.50 #1054, 0.50 #1012), 01gsvp (0.50 #1151, 0.50 #1029, 0.42 #567), 01gstn (0.50 #1146, 0.46 #944, 0.44 #1024), 01gtcc (0.50 #1061, 0.44 #1141, 0.44 #1019), 01gt99 (0.50 #1165, 0.44 #1043, 0.42 #567) >> Best rule #568 for best value: >> intensional similarity = 48 >> extensional distance = 5 >> proper extension: 06f0dc; >> query: (?x1829, ?x356) <- legislative_sessions(?x1829, ?x6933), legislative_sessions(?x1829, ?x4730), legislative_sessions(?x1829, ?x3765), legislative_sessions(?x1829, ?x3463), legislative_sessions(?x1829, ?x1137), legislative_sessions(?x1829, ?x952), legislative_sessions(?x1829, ?x606), legislative_sessions(?x1829, ?x355), legislative_sessions(?x652, ?x1829), legislative_sessions(?x356, ?x1829), ?x355 = 0495ys, district_represented(?x952, ?x13269), district_represented(?x952, ?x4754), district_represented(?x952, ?x4600), district_represented(?x952, ?x3818), district_represented(?x952, ?x3778), district_represented(?x952, ?x2977), district_represented(?x952, ?x2713), district_represented(?x952, ?x2049), district_represented(?x952, ?x2020), district_represented(?x952, ?x1782), district_represented(?x952, ?x1138), district_represented(?x952, ?x953), district_represented(?x952, ?x760), district_represented(?x952, ?x177), ?x2049 = 050l8, ?x4730 = 02cg7g, ?x1138 = 059_c, ?x3463 = 02bqmq, ?x953 = 0hjy, ?x4600 = 081yw, ?x3778 = 07h34, ?x606 = 03ww_x, legislative_sessions(?x2860, ?x952), ?x760 = 05fkf, ?x1782 = 0488g, ?x2977 = 081mh, ?x2020 = 05k7sb, legislative_sessions(?x2669, ?x952), ?x1137 = 02bqn1, ?x13269 = 0czr9_, ?x3765 = 04gp1d, ?x6933 = 024tkd, contains(?x177, ?x388), ?x3818 = 03v0t, ?x4754 = 0g0syc, state_province_region(?x1091, ?x177), ?x2713 = 06btq >> conf = 0.88 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 02bp37 legislative_sessions 02bn_p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.877 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #18968-09lxtg PRED entity: 09lxtg PRED relation: contains! PRED expected values: 07c5l => 90 concepts (67 used for prediction) PRED predicted values (max 10 best out of 105): 059g4 (0.73 #5375, 0.69 #11647, 0.07 #10319), 02qkt (0.65 #4826, 0.60 #9307, 0.57 #16476), 02j71 (0.61 #59139, 0.60 #15231, 0.33 #25984), 0dg3n1 (0.44 #155, 0.34 #21659, 0.28 #38683), 09c7w0 (0.36 #56450, 0.35 #33156, 0.31 #41217), 02j9z (0.33 #4507, 0.29 #8988, 0.29 #19739), 0j0k (0.29 #1273, 0.28 #13817, 0.25 #22777), 04_1l0v (0.27 #25538, 0.22 #33604, 0.22 #5827), 07c5l (0.22 #37132, 0.22 #10251, 0.20 #38028), 07ssc (0.21 #50207, 0.20 #27808, 0.20 #31393) >> Best rule #5375 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 07ytt; >> query: (?x4569, ?x8483) <- countries_within(?x8483, ?x4569), capital(?x4569, ?x11662), contains(?x8882, ?x4569) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #37132 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 137 *> proper extension: 06jnv; *> query: (?x4569, 07c5l) <- form_of_government(?x4569, ?x4763), participating_countries(?x418, ?x4569) *> conf = 0.22 ranks of expected_values: 9 EVAL 09lxtg contains! 07c5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 90.000 67.000 0.731 http://example.org/location/location/contains #18967-01vvyc_ PRED entity: 01vvyc_ PRED relation: award PRED expected values: 02x17c2 01c9dd => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 280): 02f716 (0.59 #573, 0.56 #3359, 0.37 #1767), 02f73b (0.49 #3466, 0.35 #680, 0.31 #1874), 02f72_ (0.48 #3410, 0.47 #624, 0.35 #1818), 01c9dd (0.47 #308, 0.18 #35825, 0.17 #21495), 01by1l (0.43 #3296, 0.35 #510, 0.33 #2102), 02f73p (0.43 #3370, 0.29 #1778, 0.24 #584), 01bgqh (0.41 #3227, 0.36 #2033, 0.28 #13975), 02v1m7 (0.41 #3297, 0.35 #511, 0.35 #113), 02f72n (0.41 #3330, 0.35 #544, 0.24 #1738), 03t5kl (0.41 #224, 0.24 #2214, 0.18 #622) >> Best rule #573 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 016fmf; 017959; 016vn3; 016t0h; >> query: (?x5798, 02f716) <- award(?x5798, ?x2877), award(?x5798, ?x1389), ?x2877 = 02f5qb, ?x1389 = 01c427 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #308 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 01wgfp6; *> query: (?x5798, 01c9dd) <- award(?x5798, ?x3978), award_winner(?x140, ?x5798), ?x3978 = 03t5b6 *> conf = 0.47 ranks of expected_values: 4, 39 EVAL 01vvyc_ award 01c9dd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 127.000 127.000 0.588 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vvyc_ award 02x17c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 127.000 127.000 0.588 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18966-02gpkt PRED entity: 02gpkt PRED relation: film! PRED expected values: 086k8 03xq0f => 86 concepts (54 used for prediction) PRED predicted values (max 10 best out of 48): 03xq0f (0.87 #295, 0.85 #728, 0.84 #873), 07k2x (0.33 #39), 05qd_ (0.31 #80, 0.24 #226, 0.19 #371), 086k8 (0.21 #726, 0.21 #220, 0.19 #1089), 016tt2 (0.15 #583, 0.15 #511, 0.14 #727), 020h2v (0.15 #187, 0.15 #114, 0.05 #1350), 03rwz3 (0.15 #186, 0.05 #621, 0.04 #477), 016tw3 (0.15 #1318, 0.14 #1827, 0.14 #1899), 01gb54 (0.11 #678, 0.11 #534, 0.09 #606), 04mkft (0.09 #396, 0.09 #251, 0.08 #757) >> Best rule #295 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 064n1pz; 04nlb94; >> query: (?x7541, 03xq0f) <- titles(?x7160, ?x7541), nominated_for(?x401, ?x7541), region(?x7541, ?x512), film_distribution_medium(?x7541, ?x2099) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 02gpkt film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 54.000 0.866 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 02gpkt film! 086k8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 54.000 0.866 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18965-041c4 PRED entity: 041c4 PRED relation: actor! PRED expected values: 0jq2r => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 113): 01s81 (0.35 #12425, 0.10 #19832, 0.09 #20627), 01sxly (0.35 #12425, 0.10 #19832, 0.09 #20627), 05pbsry (0.25 #521), 0ddd0gc (0.20 #548, 0.09 #1077, 0.02 #17736), 02q_x_l (0.20 #708, 0.08 #1502), 04sskp (0.20 #680), 0dl6fv (0.18 #1227), 0524b41 (0.10 #923, 0.02 #15462, 0.01 #16521), 02wyzmv (0.08 #1445), 01lv85 (0.08 #27779, 0.08 #24072, 0.08 #24337) >> Best rule #12425 for best value: >> intensional similarity = 3 >> extensional distance = 376 >> proper extension: 01sl1q; 044mz_; 0q9kd; 02s2ft; 0grwj; 01k7d9; 02p65p; 04t2l2; 0byfz; 083chw; ... >> query: (?x4988, ?x4517) <- film(?x4988, ?x148), actor(?x4339, ?x4988), award_winner(?x4517, ?x4988) >> conf = 0.35 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 041c4 actor! 0jq2r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 127.000 0.351 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #18964-02f73p PRED entity: 02f73p PRED relation: award! PRED expected values: 01wv9xn 01w7nww 03y82t6 0127s7 => 44 concepts (6 used for prediction) PRED predicted values (max 10 best out of 920): 0dvqq (0.82 #9963, 0.78 #9962, 0.67 #3939), 02z4b_8 (0.78 #9962, 0.50 #5351, 0.23 #15314), 07mvp (0.78 #9962, 0.25 #5169, 0.22 #1849), 01vt5c_ (0.78 #9962, 0.25 #5581, 0.06 #15544), 017959 (0.67 #6023, 0.22 #2703, 0.17 #12666), 02r3zy (0.58 #3568, 0.22 #248, 0.22 #10211), 03y82t6 (0.50 #4675, 0.22 #1355, 0.17 #7996), 0d193h (0.44 #1182, 0.42 #4502, 0.19 #7823), 04qmr (0.44 #995, 0.42 #4315, 0.15 #10958), 011_vz (0.44 #2580, 0.08 #9221, 0.08 #5900) >> Best rule #9963 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 05qck; >> query: (?x3631, ?x2395) <- award_winner(?x3631, ?x2395), artists(?x302, ?x2395), award(?x2395, ?x4912), ?x4912 = 01ckrr >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #4675 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 02f6yz; *> query: (?x3631, 03y82t6) <- award(?x4140, ?x3631), award(?x2521, ?x3631), ?x2521 = 0frsw, artist(?x2039, ?x4140), artists(?x1000, ?x4140) *> conf = 0.50 ranks of expected_values: 7, 66, 71, 82 EVAL 02f73p award! 0127s7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 44.000 6.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f73p award! 03y82t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 44.000 6.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f73p award! 01w7nww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 44.000 6.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f73p award! 01wv9xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 44.000 6.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18963-0kvgnq PRED entity: 0kvgnq PRED relation: language PRED expected values: 02h40lc => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.91 #298, 0.91 #1256, 0.91 #838), 06mp7 (0.29 #16, 0.03 #312, 0.02 #493), 064_8sq (0.21 #378, 0.18 #318, 0.17 #1756), 03mqtr (0.20 #60, 0.03 #1973, 0.03 #597), 07s9rl0 (0.20 #60, 0.03 #1973, 0.03 #597), 0880p (0.14 #46), 04306rv (0.13 #482, 0.13 #542, 0.12 #422), 02bjrlw (0.12 #61, 0.09 #418, 0.07 #538), 04h9h (0.12 #103, 0.04 #339, 0.04 #879), 03x42 (0.12 #110, 0.02 #346, 0.01 #886) >> Best rule #298 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 0d90m; 011yrp; 011yxg; 07gp9; 0ds3t5x; 07xtqq; 01k1k4; 0ds11z; 0ds33; 01cssf; ... >> query: (?x5752, 02h40lc) <- titles(?x53, ?x5752), film_crew_role(?x5752, ?x137), crewmember(?x5752, ?x1622), award(?x5752, ?x1063) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kvgnq language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.912 http://example.org/film/film/language #18962-02mzg9 PRED entity: 02mzg9 PRED relation: major_field_of_study PRED expected values: 01540 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 122): 01mkq (0.74 #135, 0.53 #254, 0.47 #1328), 02lp1 (0.63 #131, 0.58 #250, 0.53 #1324), 03g3w (0.63 #145, 0.39 #1698, 0.38 #1936), 02j62 (0.59 #147, 0.44 #1938, 0.43 #6944), 01540 (0.44 #177, 0.35 #296, 0.27 #1370), 01lj9 (0.44 #156, 0.32 #275, 0.29 #1947), 05qfh (0.41 #152, 0.33 #391, 0.30 #1345), 0fdys (0.41 #155, 0.33 #394, 0.25 #989), 04x_3 (0.41 #144, 0.28 #263, 0.23 #1337), 0193x (0.41 #151, 0.23 #270, 0.18 #390) >> Best rule #135 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 06pwq; 01w3v; 07tgn; 07w0v; 01j_cy; 0bx8pn; 07wjk; 0f1nl; 07wlf; 01y8zd; ... >> query: (?x10861, 01mkq) <- state_province_region(?x10861, ?x2982), institution(?x734, ?x10861), institution(?x620, ?x10861), ?x620 = 07s6fsf, ?x734 = 04zx3q1 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #177 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 06pwq; 01w3v; 07tgn; 07w0v; 01j_cy; 0bx8pn; 07wjk; 0f1nl; 07wlf; 01y8zd; ... *> query: (?x10861, 01540) <- state_province_region(?x10861, ?x2982), institution(?x734, ?x10861), institution(?x620, ?x10861), ?x620 = 07s6fsf, ?x734 = 04zx3q1 *> conf = 0.44 ranks of expected_values: 5 EVAL 02mzg9 major_field_of_study 01540 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 150.000 150.000 0.741 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18961-0fmqp6 PRED entity: 0fmqp6 PRED relation: award_nominee! PRED expected values: 051x52f => 107 concepts (43 used for prediction) PRED predicted values (max 10 best out of 723): 076lxv (0.81 #25648, 0.81 #83952, 0.81 #93281), 051x52f (0.81 #25648, 0.81 #83952, 0.81 #93281), 053vcrp (0.81 #25648, 0.81 #83952, 0.81 #93281), 057bc6m (0.20 #1850, 0.15 #4181, 0.06 #6512), 0fmqp6 (0.19 #3903, 0.18 #69958, 0.10 #1572), 03gyh_z (0.18 #69958, 0.11 #3130, 0.10 #799), 0cb77r (0.15 #29, 0.11 #2360, 0.06 #4691), 07h1tr (0.15 #596, 0.07 #2927, 0.06 #5258), 0579tg2 (0.15 #2298, 0.06 #6960, 0.06 #11622), 051ysmf (0.11 #4646, 0.10 #2315, 0.06 #6977) >> Best rule #25648 for best value: >> intensional similarity = 4 >> extensional distance = 528 >> proper extension: 02bwjv; 03g5_y; 019389; >> query: (?x6921, ?x786) <- award_nominee(?x6921, ?x2449), award_nominee(?x6921, ?x786), award_winner(?x785, ?x2449), location_of_ceremony(?x2449, ?x957) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0fmqp6 award_nominee! 051x52f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 43.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18960-0h03fhx PRED entity: 0h03fhx PRED relation: film! PRED expected values: 06hhrs => 81 concepts (39 used for prediction) PRED predicted values (max 10 best out of 814): 015grj (0.62 #66492, 0.56 #37398, 0.53 #45712), 014zcr (0.56 #37398, 0.47 #37397, 0.45 #64411), 01y665 (0.56 #37398, 0.47 #37397, 0.45 #64411), 0csdzz (0.56 #37398, 0.47 #37397, 0.45 #64411), 0bsb4j (0.47 #37397, 0.45 #64411, 0.44 #39476), 03pmty (0.47 #37397, 0.45 #64411, 0.44 #39476), 09fb5 (0.12 #2135, 0.03 #18754, 0.02 #45770), 02qgqt (0.08 #2095, 0.03 #33258, 0.03 #37416), 0f5xn (0.07 #964, 0.06 #3041, 0.03 #21738), 01wy5m (0.07 #854, 0.04 #2931, 0.02 #5009) >> Best rule #66492 for best value: >> intensional similarity = 3 >> extensional distance = 663 >> proper extension: 01f3p_; 05gnf; 03g9xj; 03_b1g; 0clpml; 06ys2; >> query: (?x4607, ?x968) <- nominated_for(?x968, ?x4607), languages(?x968, ?x254), film(?x968, ?x1120) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #12738 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 173 *> proper extension: 02z3r8t; 03ckwzc; 0gyy53; 03mh_tp; 02z2mr7; 03cyslc; 0dtzkt; 09rvwmy; *> query: (?x4607, 06hhrs) <- film_festivals(?x4607, ?x4903), language(?x4607, ?x254), country(?x4607, ?x94) *> conf = 0.01 ranks of expected_values: 639 EVAL 0h03fhx film! 06hhrs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 81.000 39.000 0.619 http://example.org/film/actor/film./film/performance/film #18959-02mpb PRED entity: 02mpb PRED relation: category PRED expected values: 08mbj5d => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.39 #34, 0.36 #31, 0.30 #68) >> Best rule #34 for best value: >> intensional similarity = 4 >> extensional distance = 235 >> proper extension: 01pbxb; 07c0j; 02pb53; 01dzz7; 040db; 0qdyf; 0d193h; 01900g; 03f0fnk; 0134tg; ... >> query: (?x8210, 08mbj5d) <- influenced_by(?x8433, ?x8210), influenced_by(?x8433, ?x5040), profession(?x5040, ?x353), award(?x8210, ?x1375) >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02mpb category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.388 http://example.org/common/topic/webpage./common/webpage/category #18958-037xlx PRED entity: 037xlx PRED relation: nominated_for! PRED expected values: 04ljl_l => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 212): 07cbcy (0.80 #766, 0.27 #5408, 0.27 #5407), 0k611 (0.58 #306, 0.50 #71, 0.31 #1246), 0gqy2 (0.58 #355, 0.50 #120, 0.31 #2470), 02qyntr (0.50 #412, 0.50 #177, 0.25 #1822), 0gs9p (0.50 #297, 0.33 #2412, 0.29 #2882), 02hsq3m (0.47 #1438, 0.37 #1673, 0.37 #1908), 02r22gf (0.42 #262, 0.40 #1202, 0.38 #1672), 0gq9h (0.42 #295, 0.40 #2410, 0.38 #2880), 019f4v (0.42 #286, 0.36 #2401, 0.35 #2871), 099c8n (0.42 #289, 0.33 #524, 0.32 #1699) >> Best rule #766 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 069q4f; 07b1gq; 01mszz; 0cwfgz; 06c0ns; 03phtz; >> query: (?x5731, 07cbcy) <- honored_for(?x5441, ?x5731), nominated_for(?x1585, ?x5731), award_winner(?x1585, ?x7648), ?x5441 = 04cbbz >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #708 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 069q4f; 07b1gq; 01mszz; 0cwfgz; 06c0ns; 03phtz; *> query: (?x5731, 04ljl_l) <- honored_for(?x5441, ?x5731), nominated_for(?x1585, ?x5731), award_winner(?x1585, ?x7648), ?x5441 = 04cbbz *> conf = 0.40 ranks of expected_values: 13 EVAL 037xlx nominated_for! 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 115.000 115.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18957-06b_0 PRED entity: 06b_0 PRED relation: profession PRED expected values: 0dxtg => 147 concepts (77 used for prediction) PRED predicted values (max 10 best out of 67): 0dxtg (0.84 #2364, 0.84 #3982, 0.84 #3393), 03gjzk (0.48 #1189, 0.44 #601, 0.44 #454), 0cbd2 (0.45 #6, 0.32 #1917, 0.29 #5446), 09jwl (0.38 #7075, 0.37 #11192, 0.37 #9722), 016z4k (0.28 #7062, 0.27 #7503, 0.23 #9562), 0nbcg (0.27 #11205, 0.27 #7088, 0.26 #9735), 02krf9 (0.26 #613, 0.23 #2965, 0.23 #172), 0dz3r (0.26 #7060, 0.24 #7501, 0.22 #11177), 018gz8 (0.22 #456, 0.22 #750, 0.20 #1926), 0kyk (0.18 #28, 0.15 #1939, 0.14 #3115) >> Best rule #2364 for best value: >> intensional similarity = 3 >> extensional distance = 183 >> proper extension: 03p01x; >> query: (?x7670, 0dxtg) <- written_by(?x5074, ?x7670), profession(?x7670, ?x524), ?x524 = 02jknp >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06b_0 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 77.000 0.843 http://example.org/people/person/profession #18956-0133ch PRED entity: 0133ch PRED relation: contains! PRED expected values: 07ssc => 133 concepts (66 used for prediction) PRED predicted values (max 10 best out of 256): 07ssc (0.97 #47474, 0.88 #11643, 0.86 #10779), 09c7w0 (0.94 #56435, 0.75 #11646, 0.73 #12541), 04_1l0v (0.72 #12092, 0.70 #12987, 0.52 #16571), 06q1r (0.56 #17368, 0.17 #15576, 0.07 #25431), 036wy (0.47 #8824, 0.43 #3449, 0.10 #11511), 059rby (0.39 #13453, 0.23 #22413, 0.23 #21517), 0345h (0.38 #7247, 0.37 #24267, 0.36 #17996), 02qkt (0.36 #40654, 0.14 #27218, 0.12 #50506), 05bcl (0.35 #8307, 0.02 #45929), 04jpl (0.29 #8082, 0.28 #39436, 0.16 #42123) >> Best rule #47474 for best value: >> intensional similarity = 5 >> extensional distance = 258 >> proper extension: 09lgt; >> query: (?x10385, ?x512) <- administrative_parent(?x10385, ?x1976), administrative_parent(?x11117, ?x1976), country(?x11117, ?x512), contains(?x1310, ?x10385), nationality(?x57, ?x1310) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0133ch contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 66.000 0.966 http://example.org/location/location/contains #18955-0p_47 PRED entity: 0p_47 PRED relation: profession PRED expected values: 0cbd2 => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 67): 0cbd2 (0.73 #6, 0.46 #2278, 0.42 #1710), 02jknp (0.47 #1853, 0.37 #433, 0.19 #575), 0nbcg (0.45 #2440, 0.13 #2866, 0.13 #168), 0dz3r (0.40 #2416, 0.14 #1422, 0.12 #2842), 016z4k (0.34 #2418, 0.13 #2844, 0.12 #3270), 01c72t (0.31 #2433, 0.11 #587, 0.10 #1723), 0np9r (0.28 #1578, 0.15 #3992, 0.14 #4134), 039v1 (0.26 #2445, 0.06 #173, 0.06 #315), 0d1pc (0.19 #329, 0.17 #187, 0.13 #3169), 0fnpj (0.14 #2468, 0.04 #1474, 0.03 #622) >> Best rule #6 for best value: >> intensional similarity = 2 >> extensional distance = 72 >> proper extension: 03j90; >> query: (?x3917, 0cbd2) <- story_by(?x10470, ?x3917), influenced_by(?x236, ?x3917) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p_47 profession 0cbd2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.730 http://example.org/people/person/profession #18954-036dyy PRED entity: 036dyy PRED relation: category PRED expected values: 08mbj5d => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.55 #7, 0.54 #8, 0.50 #34) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 02qjj7; 0lk90; 0162c8; 0285c; 0tc7; 01vv126; 03xl77; 01z0rcq; 039bpc; 05mkhs; ... >> query: (?x8274, 08mbj5d) <- participant(?x8274, ?x2221), currency(?x8274, ?x170), profession(?x8274, ?x319) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 036dyy category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.548 http://example.org/common/topic/webpage./common/webpage/category #18953-01fwf1 PRED entity: 01fwf1 PRED relation: gender PRED expected values: 02zsn => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 4): 02zsn (0.91 #20, 0.89 #22, 0.53 #241), 05zppz (0.85 #125, 0.85 #112, 0.85 #73), 0fltx (0.12 #116, 0.12 #95), 01hbgs (0.12 #116, 0.12 #95) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 04bdxl; 07fq1y; 01j5ts; 01csvq; 03d_w3h; 03knl; 0h1m9; 030znt; 01mqz0; 01gq0b; ... >> query: (?x4996, 02zsn) <- nationality(?x4996, ?x1310), award(?x4996, ?x1132), film(?x4996, ?x1072), ?x1132 = 0bdwft >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fwf1 gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.913 http://example.org/people/person/gender #18952-01y6dz PRED entity: 01y6dz PRED relation: category PRED expected values: 08mbj5d => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.50 #4, 0.47 #7, 0.46 #8) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01b66t; >> query: (?x6080, 08mbj5d) <- tv_program(?x2811, ?x6080), award(?x6080, ?x588), ?x2811 = 070w7s, genre(?x6080, ?x8805) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y6dz category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.500 http://example.org/common/topic/webpage./common/webpage/category #18951-02q0k7v PRED entity: 02q0k7v PRED relation: featured_film_locations PRED expected values: 0rh6k => 62 concepts (46 used for prediction) PRED predicted values (max 10 best out of 76): 02_286 (0.31 #1703, 0.17 #983, 0.17 #1463), 030qb3t (0.14 #279, 0.12 #2442, 0.12 #2684), 0rh6k (0.13 #481, 0.12 #723, 0.11 #964), 04jpl (0.12 #9, 0.09 #2654, 0.08 #2412), 094jv (0.10 #524, 0.09 #766, 0.09 #1007), 03rjj (0.06 #6, 0.06 #1209, 0.03 #246), 02nd_ (0.06 #116, 0.04 #2039, 0.04 #2279), 0h7h6 (0.06 #43, 0.03 #283, 0.03 #523), 03pzf (0.06 #176, 0.03 #416, 0.03 #1379), 0d6lp (0.06 #72, 0.03 #2475, 0.03 #552) >> Best rule #1703 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 02d44q; >> query: (?x7694, 02_286) <- film_crew_role(?x7694, ?x1078), film(?x3101, ?x7694), produced_by(?x7694, ?x2803), ?x1078 = 089fss >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #481 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 05cj_j; 075cph; 0yx7h; 03mgx6z; 0jwvf; 01jr4j; *> query: (?x7694, 0rh6k) <- production_companies(?x7694, ?x382), genre(?x7694, ?x4205), ?x4205 = 0c3351, country(?x7694, ?x94) *> conf = 0.13 ranks of expected_values: 3 EVAL 02q0k7v featured_film_locations 0rh6k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 46.000 0.306 http://example.org/film/film/featured_film_locations #18950-0yjvm PRED entity: 0yjvm PRED relation: time_zones PRED expected values: 02hcv8 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.56 #16, 0.47 #133, 0.43 #159), 02fqwt (0.31 #40, 0.29 #66, 0.22 #417), 02lcqs (0.22 #417, 0.18 #291, 0.18 #252), 02hczc (0.22 #417, 0.16 #704, 0.14 #67), 02lcrv (0.22 #417, 0.16 #704, 0.01 #46), 02llzg (0.06 #186, 0.06 #407, 0.06 #421), 03bdv (0.05 #175, 0.05 #110, 0.05 #149), 03plfd (0.02 #413, 0.02 #348, 0.02 #505), 042g7t (0.02 #76, 0.01 #115, 0.01 #128), 05jphn (0.01 #130) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0n4m5; 0n3ll; 0n474; >> query: (?x13620, 02hcv8) <- contains(?x13620, ?x4117), contains(?x760, ?x13620), ?x760 = 05fkf >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0yjvm time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.556 http://example.org/location/location/time_zones #18949-02wkmx PRED entity: 02wkmx PRED relation: award_winner PRED expected values: 01q4qv 02vyw 0gv40 01g1lp 0kft => 55 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1604): 0h1p (0.56 #7794, 0.50 #426, 0.33 #2882), 0js9s (0.56 #8815, 0.50 #1447, 0.18 #18643), 06pj8 (0.56 #7801, 0.43 #5345, 0.33 #2889), 0bwh6 (0.56 #7633, 0.33 #14738, 0.29 #46684), 081lh (0.50 #187, 0.44 #7555, 0.43 #5099), 01f8ld (0.50 #658, 0.44 #8026, 0.18 #10482), 0hskw (0.50 #580, 0.44 #7948, 0.17 #3036), 05kfs (0.50 #131, 0.44 #7499, 0.12 #9955), 0kr5_ (0.50 #120, 0.33 #7488, 0.14 #5032), 02vyw (0.50 #788, 0.33 #8156, 0.13 #17984) >> Best rule #7794 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 040njc; 019f4v; 02rdyk7; 02qt02v; 02w_6xj; >> query: (?x372, 0h1p) <- award_winner(?x372, ?x7670), award_winner(?x372, ?x3572), ?x7670 = 06b_0, award(?x3572, ?x2341), ?x2341 = 02x17s4, nationality(?x3572, ?x94) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #788 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 02pqp12; 0gs9p; *> query: (?x372, 02vyw) <- award_winner(?x372, ?x7670), award_winner(?x372, ?x4330), award_winner(?x372, ?x3572), ?x7670 = 06b_0, ?x3572 = 02kxbx3, award(?x810, ?x372), written_by(?x6148, ?x4330) *> conf = 0.50 ranks of expected_values: 10, 21, 54, 84, 85 EVAL 02wkmx award_winner 0kft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 55.000 24.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02wkmx award_winner 01g1lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 55.000 24.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02wkmx award_winner 0gv40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 55.000 24.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02wkmx award_winner 02vyw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 55.000 24.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02wkmx award_winner 01q4qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 55.000 24.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner #18948-02rjv2w PRED entity: 02rjv2w PRED relation: genre PRED expected values: 02l7c8 => 104 concepts (90 used for prediction) PRED predicted values (max 10 best out of 106): 02l7c8 (0.70 #3396, 0.59 #6185, 0.57 #1697), 01jfsb (0.51 #7048, 0.35 #4136, 0.35 #497), 03k9fj (0.39 #7047, 0.36 #133, 0.28 #1952), 02kdv5l (0.38 #7037, 0.32 #123, 0.31 #486), 05p553 (0.37 #730, 0.36 #1701, 0.36 #972), 04xvlr (0.36 #1211, 0.36 #1333, 0.23 #1576), 06n90 (0.27 #135, 0.23 #7049, 0.19 #498), 082gq (0.21 #1606, 0.19 #3427, 0.18 #3183), 01hmnh (0.21 #9123, 0.19 #7053, 0.15 #7541), 0lsxr (0.20 #3405, 0.20 #3283, 0.20 #2434) >> Best rule #3396 for best value: >> intensional similarity = 4 >> extensional distance = 434 >> proper extension: 07g_0c; 03wbqc4; 0gs973; 0415ggl; >> query: (?x2729, ?x1403) <- titles(?x1403, ?x2729), genre(?x2729, ?x53), featured_film_locations(?x2729, ?x739), genre(?x83, ?x1403) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rjv2w genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 90.000 0.703 http://example.org/film/film/genre #18947-078jnn PRED entity: 078jnn PRED relation: people! PRED expected values: 041rx => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 49): 041rx (0.50 #4, 0.29 #4130, 0.26 #4805), 01p7s6 (0.25 #57, 0.01 #582), 02ctzb (0.20 #163, 0.05 #313, 0.04 #4814), 0xnvg (0.12 #987, 0.11 #2787, 0.10 #3312), 02w7gg (0.12 #4728, 0.11 #5178, 0.11 #5703), 09vc4s (0.11 #533, 0.09 #909, 0.08 #158), 01qhm_ (0.10 #756, 0.10 #831, 0.08 #1282), 07bch9 (0.09 #246, 0.09 #1597, 0.08 #171), 07hwkr (0.08 #1586, 0.08 #1961, 0.08 #2186), 048z7l (0.08 #113, 0.08 #188, 0.08 #1989) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01d8yn; 057176; >> query: (?x8146, 041rx) <- people(?x1446, ?x8146), profession(?x8146, ?x987), film(?x8146, ?x4047), ?x4047 = 07s846j >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 078jnn people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.500 http://example.org/people/ethnicity/people #18946-050gkf PRED entity: 050gkf PRED relation: music PRED expected values: 01x6v6 => 108 concepts (92 used for prediction) PRED predicted values (max 10 best out of 81): 01l9v7n (0.15 #47, 0.02 #888, 0.01 #1310), 0244r8 (0.08 #24, 0.04 #631, 0.03 #444), 0bwh6 (0.08 #22, 0.03 #1074, 0.02 #863), 02fgpf (0.08 #30, 0.01 #4458, 0.01 #7622), 02w670 (0.08 #90, 0.01 #8523), 0fpjyd (0.08 #125), 0zcbl (0.07 #11602, 0.07 #3161, 0.07 #16666), 06dkzt (0.07 #11602, 0.07 #16666, 0.07 #10547), 06dv3 (0.07 #11602, 0.07 #16666, 0.07 #10547), 03h610 (0.07 #497, 0.05 #708, 0.05 #918) >> Best rule #47 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 01cgz; >> query: (?x1968, 01l9v7n) <- films(?x1967, ?x1968), ?x1967 = 01cgz >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #754 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 92 *> proper extension: 0j43swk; *> query: (?x1968, 01x6v6) <- production_companies(?x1968, ?x166), nominated_for(?x1162, ?x1968), ?x1162 = 099c8n, film(?x5636, ?x1968) *> conf = 0.06 ranks of expected_values: 12 EVAL 050gkf music 01x6v6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 108.000 92.000 0.154 http://example.org/film/film/music #18945-0432_5 PRED entity: 0432_5 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.88 #21, 0.88 #111, 0.87 #41), 07c52 (0.12 #53, 0.09 #168, 0.09 #178), 07z4p (0.09 #50, 0.08 #95, 0.08 #60), 02nxhr (0.07 #77, 0.07 #92, 0.06 #87), 0735l (0.01 #74) >> Best rule #21 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 0140g4; 01hr1; >> query: (?x4604, 029j_) <- nominated_for(?x5923, ?x4604), prequel(?x6014, ?x4604), titles(?x2645, ?x4604), prequel(?x4604, ?x7502), produced_by(?x4604, ?x7739) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0432_5 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #18944-0jmfv PRED entity: 0jmfv PRED relation: sport PRED expected values: 018w8 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 8): 018w8 (0.89 #31, 0.86 #76, 0.85 #103), 02vx4 (0.54 #660, 0.53 #859, 0.51 #850), 039yzs (0.31 #876, 0.23 #142, 0.19 #151), 0jm_ (0.31 #454, 0.26 #598, 0.26 #192), 018jz (0.30 #59, 0.26 #456, 0.24 #240), 03tmr (0.18 #272, 0.18 #263, 0.18 #416), 09xp_ (0.03 #529, 0.03 #547, 0.02 #502), 0z74 (0.02 #234, 0.01 #405, 0.01 #414) >> Best rule #31 for best value: >> intensional similarity = 7 >> extensional distance = 16 >> proper extension: 0jmbv; 0jm74; 0jm9w; 0jm7n; 0jm5b; >> query: (?x1347, 018w8) <- draft(?x1347, ?x12852), draft(?x1347, ?x8133), position(?x1347, ?x6848), ?x8133 = 025tn92, school(?x12852, ?x331), ?x6848 = 02_ssl, team(?x8996, ?x1347) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jmfv sport 018w8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.889 http://example.org/sports/sports_team/sport #18943-0chgzm PRED entity: 0chgzm PRED relation: origin! PRED expected values: 0244r8 => 204 concepts (172 used for prediction) PRED predicted values (max 10 best out of 477): 01wwvt2 (0.26 #18936, 0.17 #23030, 0.10 #4684), 016h9b (0.25 #108, 0.20 #620, 0.06 #8296), 0892sx (0.22 #3166, 0.20 #5724, 0.20 #5212), 06nv27 (0.20 #7380, 0.20 #4821, 0.17 #9428), 05crg7 (0.20 #4142, 0.17 #1074, 0.12 #2607), 02lfp4 (0.20 #4811, 0.14 #1743, 0.13 #7370), 0dm5l (0.20 #4711, 0.14 #1643, 0.13 #7270), 04n2vgk (0.17 #1434, 0.12 #2967, 0.11 #3479), 01wdqrx (0.17 #1059, 0.12 #2592, 0.11 #3104), 0g824 (0.17 #1299, 0.12 #2832, 0.11 #3344) >> Best rule #18936 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 02h6_6p; 03hrz; 0n2z; >> query: (?x8602, ?x649) <- place_of_birth(?x649, ?x8602), film_release_region(?x1861, ?x8602), artists(?x474, ?x649) >> conf = 0.26 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0chgzm origin! 0244r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 172.000 0.261 http://example.org/music/artist/origin #18942-03ydlnj PRED entity: 03ydlnj PRED relation: film! PRED expected values: 03m6pk => 64 concepts (48 used for prediction) PRED predicted values (max 10 best out of 864): 026rm_y (0.20 #1511, 0.12 #3591, 0.08 #5671), 07m9cm (0.20 #801, 0.06 #2881, 0.04 #9121), 09wj5 (0.20 #99, 0.06 #2179, 0.04 #4259), 041c4 (0.20 #893, 0.04 #5053, 0.03 #7133), 029k55 (0.20 #1822, 0.04 #5982, 0.03 #8062), 03xb2w (0.20 #878, 0.04 #5038, 0.03 #7118), 07jmnh (0.20 #1958, 0.04 #6118, 0.03 #8198), 087z12 (0.20 #1307, 0.04 #5467, 0.03 #7547), 03nb5v (0.20 #1146, 0.04 #5306, 0.03 #7386), 01tt43d (0.20 #1132, 0.04 #5292, 0.03 #7372) >> Best rule #1511 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 03rz2b; 02754c9; >> query: (?x8054, 026rm_y) <- country(?x8054, ?x1264), country(?x8054, ?x789), ?x789 = 0f8l9c, genre(?x8054, ?x258), ?x258 = 05p553, ?x1264 = 0345h >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #7405 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 07f_7h; *> query: (?x8054, 03m6pk) <- country(?x8054, ?x789), ?x789 = 0f8l9c, genre(?x8054, ?x258), genre(?x7678, ?x258), ?x7678 = 0gvvf4j *> conf = 0.03 ranks of expected_values: 267 EVAL 03ydlnj film! 03m6pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 64.000 48.000 0.200 http://example.org/film/actor/film./film/performance/film #18941-046lt PRED entity: 046lt PRED relation: influenced_by! PRED expected values: 05ty4m => 145 concepts (92 used for prediction) PRED predicted values (max 10 best out of 401): 01xwv7 (0.20 #1450, 0.19 #4526, 0.15 #936), 01xwqn (0.18 #442, 0.15 #1469, 0.14 #1982), 01s7qqw (0.18 #210, 0.14 #1750, 0.09 #4313), 05ty4m (0.18 #1547, 0.12 #7698, 0.05 #15898), 02p21g (0.18 #44, 0.10 #1071, 0.09 #1584), 02238b (0.18 #278, 0.07 #27182, 0.05 #40519), 01j7rd (0.16 #1540, 0.14 #1611, 0.12 #23586), 01xdf5 (0.16 #1540, 0.12 #23586, 0.11 #18972), 0pz7h (0.16 #1540, 0.12 #23586, 0.11 #18972), 04bs3j (0.16 #1540, 0.12 #23586, 0.11 #18972) >> Best rule #1450 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 07mvp; >> query: (?x2942, 01xwv7) <- influenced_by(?x13118, ?x2942), influenced_by(?x236, ?x13118), person(?x3404, ?x2942) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1547 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 022q4j; *> query: (?x2942, 05ty4m) <- influenced_by(?x692, ?x2942), participant(?x12255, ?x2942), profession(?x2942, ?x353) *> conf = 0.18 ranks of expected_values: 4 EVAL 046lt influenced_by! 05ty4m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 92.000 0.200 http://example.org/influence/influence_node/influenced_by #18940-01_0f7 PRED entity: 01_0f7 PRED relation: genre PRED expected values: 03k9fj => 87 concepts (56 used for prediction) PRED predicted values (max 10 best out of 95): 02l7c8 (0.56 #3270, 0.40 #481, 0.32 #6305), 03k9fj (0.48 #6173, 0.48 #5241, 0.46 #4655), 05p553 (0.47 #4073, 0.37 #1860, 0.37 #3257), 01jfsb (0.46 #825, 0.43 #4082, 0.35 #1173), 01hmnh (0.30 #3272, 0.19 #1179, 0.18 #17), 060__y (0.29 #482, 0.21 #16, 0.18 #132), 06n90 (0.25 #826, 0.18 #1174, 0.15 #4083), 0lsxr (0.22 #821, 0.19 #4078, 0.19 #2215), 03g3w (0.22 #373, 0.16 #722, 0.16 #256), 03bxz7 (0.18 #51, 0.16 #167, 0.12 #517) >> Best rule #3270 for best value: >> intensional similarity = 4 >> extensional distance = 740 >> proper extension: 04svwx; >> query: (?x6531, 02l7c8) <- genre(?x6531, ?x4088), country(?x6531, ?x94), genre(?x11073, ?x4088), ?x11073 = 01ry_x >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #6173 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1121 *> proper extension: 06qv_; *> query: (?x6531, ?x162) <- nominated_for(?x629, ?x6531), award_nominee(?x629, ?x230), titles(?x162, ?x6531) *> conf = 0.48 ranks of expected_values: 2 EVAL 01_0f7 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 56.000 0.562 http://example.org/film/film/genre #18939-03g3w PRED entity: 03g3w PRED relation: taxonomy PRED expected values: 04n6k => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.70 #33, 0.64 #70, 0.64 #69) >> Best rule #33 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 036hv; 02ky346; 04g51; >> query: (?x2605, 04n6k) <- student(?x2605, ?x445), major_field_of_study(?x11452, ?x2605), major_field_of_study(?x6127, ?x2605), major_field_of_study(?x2327, ?x2605), major_field_of_study(?x1043, ?x2605), ?x6127 = 0gjv_, currency(?x11452, ?x170), major_field_of_study(?x2605, ?x254), institution(?x620, ?x2327), school_type(?x1043, ?x1044) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03g3w taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.700 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #18938-01qqv5 PRED entity: 01qqv5 PRED relation: fraternities_and_sororities PRED expected values: 035tlh => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 3): 035tlh (0.34 #14, 0.25 #2, 0.25 #56), 0325pb (0.22 #82, 0.21 #55, 0.20 #13), 04m8fy (0.06 #209, 0.06 #15, 0.05 #21) >> Best rule #14 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 05krk; 06pwq; 01w3v; 04rwx; 07szy; 01jq34; 07wjk; 01wdj_; 01w5m; 03ksy; ... >> query: (?x9166, 035tlh) <- institution(?x865, ?x9166), major_field_of_study(?x9166, ?x1154), major_field_of_study(?x9166, ?x742), ?x742 = 05qjt, ?x1154 = 02lp1 >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qqv5 fraternities_and_sororities 035tlh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.343 http://example.org/education/university/fraternities_and_sororities #18937-03xp8d5 PRED entity: 03xp8d5 PRED relation: nationality PRED expected values: 09c7w0 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.85 #1302, 0.83 #5906, 0.83 #1803), 07ssc (0.34 #6908, 0.13 #2317, 0.12 #215), 02jx1 (0.15 #233, 0.11 #4238, 0.11 #2535), 03rk0 (0.06 #7654, 0.06 #9958, 0.05 #12662), 03rjj (0.05 #1006, 0.04 #305, 0.03 #1607), 0d060g (0.05 #808, 0.05 #2509, 0.04 #3009), 0cr3d (0.04 #1402, 0.02 #6006, 0.01 #3103), 03gj2 (0.03 #1227, 0.01 #3730), 0h7x (0.03 #3739, 0.01 #2937), 0345h (0.03 #3735, 0.02 #2533, 0.02 #2333) >> Best rule #1302 for best value: >> intensional similarity = 2 >> extensional distance = 114 >> proper extension: 02qnbs; 046_v; 02gnj2; 0p_r5; >> query: (?x4385, 09c7w0) <- place_of_birth(?x4385, ?x2850), tv_program(?x4385, ?x3180) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xp8d5 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.853 http://example.org/people/person/nationality #18936-0295sy PRED entity: 0295sy PRED relation: produced_by PRED expected values: 02q_cc => 72 concepts (56 used for prediction) PRED predicted values (max 10 best out of 188): 06pj8 (0.29 #6577, 0.06 #5869, 0.06 #5093), 02dbp7 (0.20 #938, 0.03 #1324), 027rwmr (0.16 #10450, 0.16 #9676, 0.16 #10064), 0bl2g (0.16 #10450, 0.16 #9676, 0.16 #10064), 03q8ch (0.12 #5416, 0.11 #6191), 046zh (0.11 #13538, 0.10 #8902, 0.09 #13149), 0bq2g (0.09 #2322, 0.07 #1934, 0.03 #8128), 02q_cc (0.07 #2741, 0.06 #5059, 0.06 #10063), 0c6qh (0.07 #1628, 0.06 #2016, 0.01 #4333), 02kxbwx (0.06 #5057, 0.06 #5833, 0.04 #6222) >> Best rule #6577 for best value: >> intensional similarity = 4 >> extensional distance = 193 >> proper extension: 0bs8hvm; >> query: (?x5570, ?x2135) <- genre(?x5570, ?x258), country(?x5570, ?x94), film(?x2135, ?x5570), cinematography(?x5570, ?x1075) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2741 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 86 *> proper extension: 07gp9; 0gzy02; 04v8x9; 0g5qs2k; 0ds33; 0bth54; 0pc62; 0fr63l; 01vksx; 0cwy47; ... *> query: (?x5570, 02q_cc) <- film(?x398, ?x5570), nominated_for(?x2209, ?x5570), ?x2209 = 0gr42 *> conf = 0.07 ranks of expected_values: 8 EVAL 0295sy produced_by 02q_cc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 72.000 56.000 0.292 http://example.org/film/film/produced_by #18935-03n69x PRED entity: 03n69x PRED relation: location PRED expected values: 013yq => 153 concepts (59 used for prediction) PRED predicted values (max 10 best out of 222): 030qb3t (0.33 #20984, 0.32 #14547, 0.32 #16960), 02_286 (0.31 #8873, 0.30 #15305, 0.29 #840), 0rh6k (0.22 #3216, 0.20 #4823, 0.07 #43431), 01_d4 (0.19 #8938, 0.14 #11351, 0.08 #15370), 013yq (0.19 #8955, 0.11 #33092, 0.09 #40332), 0498y (0.17 #213, 0.10 #5032, 0.07 #7442), 0ftvz (0.17 #134, 0.09 #28141, 0.03 #15402), 07b_l (0.17 #187, 0.06 #9023, 0.03 #15455), 0f__1 (0.17 #141, 0.03 #14605, 0.03 #17018), 0rnmy (0.17 #146, 0.03 #15414, 0.03 #17827) >> Best rule #20984 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 033wx9; 0bbf1f; 016fnb; 01jfrg; 03ywyk; >> query: (?x5412, 030qb3t) <- vacationer(?x9729, ?x5412), student(?x6953, ?x5412), location(?x5412, ?x10433), gender(?x5412, ?x231), time_zones(?x10433, ?x2674) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8955 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 06s6hs; *> query: (?x5412, 013yq) <- vacationer(?x9729, ?x5412), student(?x6953, ?x5412), location(?x5412, ?x10433), gender(?x5412, ?x231), county_seat(?x12385, ?x10433) *> conf = 0.19 ranks of expected_values: 5 EVAL 03n69x location 013yq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 153.000 59.000 0.326 http://example.org/people/person/places_lived./people/place_lived/location #18934-025tn92 PRED entity: 025tn92 PRED relation: draft! PRED expected values: 0jm3v 0jmfb 0jmnl => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 88): 0jm64 (0.74 #568, 0.60 #682, 0.60 #470), 0jmnl (0.74 #568, 0.60 #706, 0.60 #494), 0jm3v (0.74 #568, 0.60 #646, 0.50 #290), 0jmfb (0.74 #568, 0.40 #660, 0.40 #448), 0jmdb (0.74 #568, 0.40 #429, 0.36 #1066), 0jmcv (0.74 #568, 0.40 #694, 0.33 #765), 06rpd (0.50 #986, 0.43 #841, 0.39 #779), 043vc (0.50 #959, 0.43 #814, 0.36 #1140), 03lsq (0.50 #958, 0.43 #813, 0.36 #1066), 05l71 (0.50 #954, 0.43 #809, 0.35 #1069) >> Best rule #568 for best value: >> intensional similarity = 50 >> extensional distance = 3 >> proper extension: 02r6gw6; >> query: (?x8133, ?x660) <- school(?x8133, ?x6856), school(?x8133, ?x4955), school(?x8133, ?x4296), school(?x8133, ?x466), draft(?x5483, ?x8133), draft(?x4571, ?x8133), draft(?x2820, ?x8133), draft(?x1347, ?x8133), draft(?x4571, ?x12852), teams(?x5381, ?x1347), school(?x1823, ?x4296), major_field_of_study(?x4296, ?x3490), major_field_of_study(?x4296, ?x2314), student(?x4296, ?x3927), sport(?x4571, ?x4833), team(?x1348, ?x5483), school_type(?x466, ?x1507), student(?x4955, ?x11233), student(?x4955, ?x7762), student(?x4955, ?x6011), student(?x4955, ?x3841), student(?x466, ?x3134), major_field_of_study(?x4955, ?x373), ?x6856 = 0jkhr, ?x1823 = 01yhm, list(?x4955, ?x2197), school(?x2820, ?x10297), school(?x2820, ?x5288), school(?x2820, ?x331), major_field_of_study(?x254, ?x2314), ?x10297 = 02rv1w, state_province_region(?x331, ?x3670), institution(?x1200, ?x4955), institution(?x734, ?x4955), written_by(?x1488, ?x7762), award_nominee(?x6011, ?x1800), currency(?x466, ?x170), award(?x6011, ?x1232), draft(?x660, ?x12852), ?x734 = 04zx3q1, organization(?x5510, ?x5288), award_winner(?x873, ?x3841), student(?x331, ?x2993), organization(?x4296, ?x5487), award(?x7762, ?x384), ?x1200 = 016t_3, major_field_of_study(?x3424, ?x3490), ?x3424 = 01w5m, story_by(?x2490, ?x7762), film(?x11233, ?x1692) >> conf = 0.74 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3, 4 EVAL 025tn92 draft! 0jmnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 17.000 17.000 0.737 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 025tn92 draft! 0jmfb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 17.000 17.000 0.737 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 025tn92 draft! 0jm3v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 17.000 17.000 0.737 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #18933-03772 PRED entity: 03772 PRED relation: award PRED expected values: 0265vt => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 294): 0262x6 (0.72 #27086, 0.70 #18718, 0.70 #18717), 0262yt (0.72 #27086, 0.70 #18718, 0.70 #18717), 040_9s0 (0.72 #27086, 0.70 #18718, 0.70 #18717), 0262zm (0.72 #27086, 0.70 #18718, 0.70 #18717), 0265vt (0.68 #1913, 0.44 #719, 0.33 #321), 0cjyzs (0.38 #7670, 0.38 #6077, 0.38 #4086), 027x4ws (0.33 #316, 0.15 #9557, 0.11 #714), 01tgwv (0.27 #1950, 0.07 #27885, 0.04 #6728), 01bgqh (0.26 #2033, 0.15 #3227, 0.13 #5616), 045xh (0.24 #1963, 0.22 #769, 0.11 #371) >> Best rule #27086 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 099ks0; 06lxn; >> query: (?x5034, ?x1288) <- award_winner(?x1288, ?x5034), award(?x576, ?x1288) >> conf = 0.72 => this is the best rule for 4 predicted values *> Best rule #1913 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 0210f1; *> query: (?x5034, 0265vt) <- award(?x5034, ?x575), award_winner(?x1375, ?x5034), ?x575 = 040vk98 *> conf = 0.68 ranks of expected_values: 5 EVAL 03772 award 0265vt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 85.000 85.000 0.717 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18932-019ltg PRED entity: 019ltg PRED relation: teams! PRED expected values: 06gmr => 81 concepts (80 used for prediction) PRED predicted values (max 10 best out of 75): 04swd (0.10 #177, 0.09 #717, 0.09 #447), 01vc3y (0.10 #266, 0.09 #806, 0.09 #536), 03pbf (0.10 #104, 0.09 #644, 0.09 #374), 03hrz (0.10 #89, 0.09 #629, 0.09 #359), 0j7ng (0.10 #231, 0.09 #501, 0.08 #1041), 0htqt (0.10 #219, 0.09 #489, 0.08 #1029), 02fvv (0.09 #792, 0.08 #1062, 0.06 #1332), 025r_t (0.09 #758, 0.08 #1028, 0.06 #1298), 079yb (0.09 #764, 0.06 #1304, 0.05 #1574), 0h3tv (0.06 #1284, 0.05 #1554, 0.04 #1824) >> Best rule #177 for best value: >> intensional similarity = 14 >> extensional distance = 8 >> proper extension: 03qx63; 02mplj; 03c0vy; 019lvv; 0175tv; 0kwv2; 0fvly; 0h3c3g; >> query: (?x9247, 04swd) <- position(?x9247, ?x530), position(?x9247, ?x63), position(?x9247, ?x60), position(?x9247, ?x203), colors(?x9247, ?x4557), colors(?x9247, ?x663), ?x4557 = 019sc, ?x63 = 02sdk9v, ?x663 = 083jv, ?x530 = 02_j1w, ?x60 = 02nzb8, ?x203 = 0dgrmp, team(?x203, ?x9247), position(?x9247, ?x530) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 019ltg teams! 06gmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 80.000 0.100 http://example.org/sports/sports_team_location/teams #18931-07ylj PRED entity: 07ylj PRED relation: nationality! PRED expected values: 02hhtj => 106 concepts (101 used for prediction) PRED predicted values (max 10 best out of 4106): 01ps2h8 (0.22 #20342, 0.22 #4069, 0.20 #28480), 018_lb (0.22 #20342, 0.22 #4069, 0.20 #28480), 03h8_g (0.22 #20342, 0.22 #4069, 0.20 #28480), 059xvg (0.14 #1052, 0.10 #17325, 0.08 #5121), 0p__8 (0.10 #1849, 0.08 #5918, 0.08 #9986), 020hyj (0.10 #3207, 0.07 #19480, 0.06 #27618), 07m69t (0.10 #2706, 0.07 #18979, 0.05 #6775), 0202p_ (0.10 #3681, 0.07 #19954, 0.05 #7750), 03k545 (0.10 #3570, 0.07 #19843, 0.05 #7639), 03q5dr (0.10 #3107, 0.07 #19380, 0.05 #7176) >> Best rule #20342 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 03_xj; >> query: (?x1203, ?x5283) <- contains(?x7273, ?x1203), location(?x5283, ?x1203), countries_spoken_in(?x2502, ?x1203) >> conf = 0.22 => this is the best rule for 3 predicted values *> Best rule #1831 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 01k6y1; *> query: (?x1203, 02hhtj) <- form_of_government(?x1203, ?x6377), location(?x5283, ?x1203), capital(?x1203, ?x13229) *> conf = 0.03 ranks of expected_values: 2261 EVAL 07ylj nationality! 02hhtj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 101.000 0.221 http://example.org/people/person/nationality #18930-02g8h PRED entity: 02g8h PRED relation: influenced_by PRED expected values: 01wp_jm => 119 concepts (44 used for prediction) PRED predicted values (max 10 best out of 321): 03sbs (0.16 #5001, 0.07 #10644, 0.06 #14122), 0ph2w (0.15 #3595, 0.04 #5767, 0.04 #8806), 05qmj (0.15 #4972, 0.06 #18009, 0.05 #6274), 032l1 (0.13 #4869, 0.13 #6171, 0.08 #17906), 0gz_ (0.13 #4883, 0.06 #17920, 0.05 #10526), 013tjc (0.13 #3850, 0.04 #6022, 0.04 #9061), 012gq6 (0.12 #965, 0.10 #3572, 0.03 #7047), 052hl (0.12 #1077, 0.08 #3684, 0.06 #4119), 081k8 (0.12 #4935, 0.10 #6237, 0.08 #14056), 03_87 (0.11 #6284, 0.10 #4982, 0.07 #13233) >> Best rule #5001 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 01d494; >> query: (?x318, 03sbs) <- nationality(?x318, ?x94), influenced_by(?x318, ?x4112), company(?x318, ?x3922) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #6855 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 156 *> proper extension: 014_lq; 0b1zz; 0838y; 01kcms4; 01w5n51; 017mbb; 09jm8; 014_xj; 0chnf; 0716b6; *> query: (?x318, 01wp_jm) <- influenced_by(?x318, ?x4112), category(?x318, ?x134) *> conf = 0.07 ranks of expected_values: 34 EVAL 02g8h influenced_by 01wp_jm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 119.000 44.000 0.162 http://example.org/influence/influence_node/influenced_by #18929-01fszq PRED entity: 01fszq PRED relation: titles! PRED expected values: 07c52 => 72 concepts (46 used for prediction) PRED predicted values (max 10 best out of 54): 07c52 (0.81 #134, 0.81 #443, 0.64 #653), 07s9rl0 (0.30 #3778, 0.28 #4297, 0.26 #4400), 0hn10 (0.20 #1673, 0.20 #16, 0.06 #120), 01zcrv (0.20 #1673, 0.20 #84, 0.02 #1022), 015w9s (0.20 #1673, 0.06 #357, 0.05 #565), 01hmnh (0.20 #1673, 0.06 #4217, 0.05 #4531), 0d63kt (0.20 #1673, 0.03 #395, 0.02 #603), 04xvlr (0.18 #3990, 0.17 #3781, 0.17 #4508), 01z4y (0.16 #4226, 0.12 #4124, 0.10 #4435), 03mdt (0.14 #458, 0.12 #149, 0.10 #772) >> Best rule #134 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0d68qy; 01bv8b; >> query: (?x10618, 07c52) <- nominated_for(?x4225, ?x10618), actor(?x10618, ?x9140), ?x4225 = 09qvf4, award_winner(?x873, ?x9140) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fszq titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 46.000 0.812 http://example.org/media_common/netflix_genre/titles #18928-04smdd PRED entity: 04smdd PRED relation: genre PRED expected values: 01t_vv => 82 concepts (81 used for prediction) PRED predicted values (max 10 best out of 95): 01jfsb (0.56 #955, 0.33 #3081, 0.31 #1900), 02kdv5l (0.33 #947, 0.27 #3073, 0.27 #5079), 02l7c8 (0.31 #4737, 0.30 #2730, 0.29 #5681), 04xvlr (0.26 #3071, 0.26 #1, 0.26 #237), 02n4kr (0.26 #3071, 0.23 #952, 0.12 #6028), 01t_vv (0.26 #3071, 0.21 #642, 0.20 #288), 01hmnh (0.26 #3071, 0.16 #5093, 0.15 #3795), 06cvj (0.26 #3071, 0.13 #712, 0.12 #121), 01g6gs (0.26 #3071, 0.08 #137, 0.07 #2735), 0gsy3b (0.26 #3071, 0.07 #211, 0.05 #802) >> Best rule #955 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 0gx9rvq; 0cnztc4; 05p3738; 04g9gd; 03kg2v; 0crh5_f; 05_5rjx; 0glqh5_; 0b7l4x; 0bq6ntw; ... >> query: (?x4347, 01jfsb) <- country(?x4347, ?x94), genre(?x4347, ?x604), ?x604 = 0lsxr >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #3071 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 662 *> proper extension: 0170k0; *> query: (?x4347, ?x53) <- nominated_for(?x68, ?x4347), nominated_for(?x986, ?x4347), film(?x986, ?x5736), genre(?x5736, ?x53) *> conf = 0.26 ranks of expected_values: 6 EVAL 04smdd genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 82.000 81.000 0.564 http://example.org/film/film/genre #18927-01nrq5 PRED entity: 01nrq5 PRED relation: profession PRED expected values: 02hrh1q => 136 concepts (101 used for prediction) PRED predicted values (max 10 best out of 87): 02hrh1q (0.90 #12984, 0.90 #9108, 0.88 #11345), 01d_h8 (0.62 #1347, 0.59 #1198, 0.45 #2390), 0dxtg (0.60 #1355, 0.57 #1206, 0.54 #2398), 02jknp (0.49 #1200, 0.47 #1349, 0.29 #604), 03gjzk (0.46 #3145, 0.43 #761, 0.41 #5977), 0cbd2 (0.43 #7460, 0.43 #5073, 0.43 #7610), 0np9r (0.35 #3150, 0.30 #5982, 0.27 #7603), 0kyk (0.29 #3457, 0.28 #6736, 0.28 #7034), 09jwl (0.23 #5365, 0.22 #3595, 0.21 #4936), 02krf9 (0.23 #5365, 0.17 #27, 0.15 #6584) >> Best rule #12984 for best value: >> intensional similarity = 3 >> extensional distance = 1223 >> proper extension: 01vvydl; 023tp8; 04bs3j; 01j5x6; 01k5t_3; 01yb09; 058s57; 045bs6; 015pxr; 0738b8; ... >> query: (?x3261, 02hrh1q) <- profession(?x3261, ?x1146), place_of_birth(?x3261, ?x5775), film(?x3261, ?x1734) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nrq5 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 101.000 0.900 http://example.org/people/person/profession #18926-043c4j PRED entity: 043c4j PRED relation: artists! PRED expected values: 0fd3y 06by7 => 94 concepts (60 used for prediction) PRED predicted values (max 10 best out of 251): 06by7 (0.58 #2172, 0.55 #6789, 0.51 #7406), 064t9 (0.43 #15078, 0.41 #15692, 0.37 #17226), 0xhtw (0.40 #936, 0.36 #629, 0.35 #2168), 02yv6b (0.31 #2249, 0.18 #6866, 0.17 #6558), 06j6l (0.31 #5894, 0.22 #15113, 0.21 #15727), 0dl5d (0.31 #631, 0.30 #938, 0.22 #1554), 03lty (0.28 #333, 0.22 #6796, 0.21 #6488), 02w4v (0.27 #2195, 0.21 #15374, 0.14 #349), 05w3f (0.27 #35, 0.23 #956, 0.20 #2188), 05bt6j (0.25 #6811, 0.24 #2194, 0.23 #6503) >> Best rule #2172 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 0m19t; 07qnf; 067mj; 0249kn; 07yg2; 0394y; 01cblr; 0134tg; 01q99h; 01kcms4; ... >> query: (?x7683, 06by7) <- artists(?x7329, ?x7683), artist(?x1954, ?x7683), ?x7329 = 016jny >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1, 34 EVAL 043c4j artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 60.000 0.581 http://example.org/music/genre/artists EVAL 043c4j artists! 0fd3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 94.000 60.000 0.581 http://example.org/music/genre/artists #18925-03hhd3 PRED entity: 03hhd3 PRED relation: people! PRED expected values: 041rx => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 31): 041rx (0.23 #4, 0.13 #312, 0.13 #81), 033tf_ (0.19 #7, 0.09 #315, 0.08 #161), 0x67 (0.10 #1011, 0.10 #1627, 0.10 #2012), 01qhm_ (0.10 #6, 0.04 #314, 0.03 #160), 048z7l (0.10 #40, 0.04 #348, 0.03 #425), 07hwkr (0.07 #243, 0.06 #397, 0.06 #89), 0xnvg (0.06 #13, 0.05 #629, 0.05 #90), 09vc4s (0.06 #9, 0.02 #240, 0.02 #1703), 02w7gg (0.06 #1311, 0.06 #2235, 0.06 #79), 07bch9 (0.05 #408, 0.05 #254, 0.04 #177) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 01t6b4; 08m4c8; 012v1t; 03f1zhf; >> query: (?x8587, 041rx) <- student(?x5614, ?x8587), location(?x8587, ?x739), ?x739 = 02_286 >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hhd3 people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.226 http://example.org/people/ethnicity/people #18924-02qd04y PRED entity: 02qd04y PRED relation: film! PRED expected values: 01t2h2 => 90 concepts (46 used for prediction) PRED predicted values (max 10 best out of 774): 04dz_y7 (0.46 #72962, 0.45 #39604, 0.42 #72961), 0z4s (0.10 #2152, 0.07 #14658, 0.06 #16742), 0h0wc (0.08 #2509, 0.04 #15015, 0.03 #23351), 01f873 (0.07 #1900, 0.05 #6069, 0.04 #8338), 01vs8ng (0.07 #2055, 0.03 #6224), 02v92l (0.07 #1668, 0.03 #5837), 016ggh (0.07 #8124, 0.05 #12293, 0.03 #18545), 0dt645q (0.07 #5935, 0.02 #1766), 01nwwl (0.06 #6757, 0.04 #10926, 0.03 #15094), 02d4ct (0.06 #2475, 0.03 #17065, 0.03 #19149) >> Best rule #72962 for best value: >> intensional similarity = 4 >> extensional distance = 849 >> proper extension: 0gtvrv3; >> query: (?x9175, ?x7610) <- film_release_region(?x9175, ?x94), nominated_for(?x7610, ?x9175), gender(?x7610, ?x231), country(?x9175, ?x2346) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #297 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 026q3s3; 05dfy_; *> query: (?x9175, 01t2h2) <- genre(?x9175, ?x1626), film(?x7610, ?x9175), ?x1626 = 03q4nz, film(?x12671, ?x9175) *> conf = 0.05 ranks of expected_values: 29 EVAL 02qd04y film! 01t2h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 90.000 46.000 0.461 http://example.org/film/actor/film./film/performance/film #18923-07kb7vh PRED entity: 07kb7vh PRED relation: genre PRED expected values: 05p553 02l7c8 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 99): 05p553 (0.59 #1604, 0.56 #2344, 0.54 #2467), 07s9rl0 (0.57 #7294, 0.56 #4323, 0.56 #5563), 02kdv5l (0.54 #126, 0.50 #372, 0.50 #3), 01jfsb (0.43 #14, 0.39 #2105, 0.37 #3345), 03k9fj (0.38 #2104, 0.38 #628, 0.37 #136), 0bkbm (0.36 #42, 0.08 #1518, 0.07 #1764), 01hmnh (0.30 #512, 0.30 #758, 0.28 #1004), 0lsxr (0.29 #10, 0.21 #1978, 0.20 #1486), 02l7c8 (0.27 #1617, 0.27 #6077, 0.26 #6325), 06n90 (0.26 #2106, 0.24 #138, 0.20 #1122) >> Best rule #1604 for best value: >> intensional similarity = 5 >> extensional distance = 215 >> proper extension: 03wh49y; >> query: (?x4131, 05p553) <- film_crew_role(?x4131, ?x137), film(?x10905, ?x4131), film(?x1335, ?x4131), award_nominee(?x541, ?x1335), influenced_by(?x1593, ?x10905) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 07kb7vh genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 74.000 74.000 0.585 http://example.org/film/film/genre EVAL 07kb7vh genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.585 http://example.org/film/film/genre #18922-015882 PRED entity: 015882 PRED relation: award PRED expected values: 02lp0w => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 292): 03nl5k (0.78 #26012, 0.78 #54003, 0.78 #20888), 03qbh5 (0.50 #3351, 0.40 #4927, 0.40 #4139), 0c4z8 (0.40 #3222, 0.35 #4404, 0.33 #4010), 01ck6h (0.40 #2088, 0.34 #4846, 0.29 #1300), 02f73p (0.40 #2152, 0.29 #1364, 0.20 #3334), 02f6ym (0.40 #645, 0.23 #2615, 0.17 #12072), 03tcnt (0.40 #555, 0.14 #4889, 0.10 #3313), 054ks3 (0.38 #6047, 0.37 #4865, 0.35 #4471), 09sb52 (0.37 #13437, 0.30 #18166, 0.25 #32358), 01c92g (0.35 #3246, 0.32 #4428, 0.29 #4822) >> Best rule #26012 for best value: >> intensional similarity = 3 >> extensional distance = 463 >> proper extension: 01lcxbb; 01wz_ml; 01vsy3q; 0f6lx; 06lxn; >> query: (?x1817, ?x537) <- award_winner(?x537, ?x1817), artist(?x3240, ?x1817), artists(?x378, ?x1817) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3395 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 0lbj1; 0pkyh; 02qwg; 07zft; 03d2k; 0pk41; 0163kf; *> query: (?x1817, 02lp0w) <- award(?x1817, ?x724), performance_role(?x1817, ?x1225), ?x724 = 01bgqh, artists(?x378, ?x1817) *> conf = 0.10 ranks of expected_values: 87 EVAL 015882 award 02lp0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 155.000 155.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18921-02t901 PRED entity: 02t901 PRED relation: type_of_union PRED expected values: 04ztj => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.80 #29, 0.75 #57, 0.75 #65), 01g63y (0.19 #18, 0.18 #114, 0.18 #62), 01bl8s (0.01 #15), 0jgjn (0.01 #48) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 01rr9f; 062ftr; 0c8hct; >> query: (?x12765, 04ztj) <- profession(?x12765, ?x1943), location(?x12765, ?x2495), ?x1943 = 02krf9 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02t901 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.795 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18920-04s1zr PRED entity: 04s1zr PRED relation: film! PRED expected values: 044rvb => 85 concepts (56 used for prediction) PRED predicted values (max 10 best out of 627): 02js_6 (0.33 #1968, 0.25 #4043, 0.14 #8193), 0sw6g (0.33 #1400, 0.25 #3475, 0.05 #11777), 0mdqp (0.33 #118, 0.25 #2193, 0.03 #43707), 032xhg (0.33 #63, 0.25 #2138, 0.03 #43652), 07cjqy (0.33 #600, 0.25 #2675, 0.02 #44189), 0309lm (0.33 #1601, 0.25 #3676, 0.01 #43112), 0315q3 (0.33 #821, 0.25 #2896, 0.01 #50636), 04zqmj (0.33 #1865, 0.25 #3940), 015lhm (0.33 #1000, 0.25 #3075), 016z2j (0.29 #6614, 0.25 #8690, 0.03 #31521) >> Best rule #1968 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 034qrh; >> query: (?x11332, 02js_6) <- film(?x13847, ?x11332), titles(?x600, ?x11332), ?x13847 = 0378zn, genre(?x11332, ?x604), film_release_region(?x11332, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #43690 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 545 *> proper extension: 0gtvrv3; 0hgnl3t; *> query: (?x11332, 044rvb) <- film(?x2857, ?x11332), language(?x11332, ?x254), profession(?x2857, ?x1032), participant(?x521, ?x2857), vacationer(?x362, ?x2857) *> conf = 0.02 ranks of expected_values: 363 EVAL 04s1zr film! 044rvb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 85.000 56.000 0.333 http://example.org/film/actor/film./film/performance/film #18919-01d9r3 PRED entity: 01d9r3 PRED relation: form_of_government! PRED expected values: 05cgv 05v10 035dk 07t_x 03548 01p8s => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 277): 0k6nt (0.50 #532, 0.50 #413, 0.33 #17), 0d0vqn (0.50 #534, 0.50 #404, 0.33 #8), 07ssc (0.50 #535, 0.50 #409, 0.27 #537), 0154j (0.50 #535, 0.50 #400, 0.27 #537), 06v36 (0.50 #475, 0.33 #79, 0.28 #527), 07bxhl (0.50 #444, 0.33 #48, 0.28 #527), 03_3d (0.50 #402, 0.33 #6, 0.27 #131), 07f1x (0.50 #486, 0.33 #90, 0.21 #531), 01xbgx (0.50 #487, 0.33 #91, 0.21 #531), 04wgh (0.50 #419, 0.33 #23, 0.21 #531) >> Best rule #532 for best value: >> intensional similarity = 101 >> extensional distance = 2 >> proper extension: 018wl5; 01q20; >> query: (?x6377, ?x985) <- form_of_government(?x2346, ?x6377), form_of_government(?x583, ?x6377), form_of_government(?x550, ?x6377), combatants(?x3728, ?x583), combatants(?x2513, ?x583), combatants(?x172, ?x583), film_release_region(?x7651, ?x583), film_release_region(?x7493, ?x583), film_release_region(?x7016, ?x583), film_release_region(?x7009, ?x583), film_release_region(?x6216, ?x583), film_release_region(?x6078, ?x583), film_release_region(?x5825, ?x583), film_release_region(?x5713, ?x583), film_release_region(?x5644, ?x583), film_release_region(?x5220, ?x583), film_release_region(?x5162, ?x583), film_release_region(?x5017, ?x583), film_release_region(?x4290, ?x583), film_release_region(?x4047, ?x583), film_release_region(?x3981, ?x583), film_release_region(?x3565, ?x583), film_release_region(?x3392, ?x583), film_release_region(?x2896, ?x583), film_release_region(?x2893, ?x583), film_release_region(?x2709, ?x583), film_release_region(?x2628, ?x583), film_release_region(?x2340, ?x583), film_release_region(?x2189, ?x583), film_release_region(?x2050, ?x583), film_release_region(?x1701, ?x583), film_release_region(?x1642, ?x583), film_release_region(?x1490, ?x583), film_release_region(?x1456, ?x583), film_release_region(?x1364, ?x583), film_release_region(?x1022, ?x583), jurisdiction_of_office(?x182, ?x583), ?x2189 = 02yvct, ?x1022 = 0crfwmx, ?x1456 = 0cz8mkh, ?x3981 = 047tsx3, titles(?x583, ?x7081), participating_countries(?x418, ?x583), ?x7009 = 0bs8s1p, member_states(?x7695, ?x583), ?x2050 = 01fmys, ?x7016 = 07g1sm, ?x6078 = 04pk1f, ?x4047 = 07s846j, country(?x3127, ?x583), adjoins(?x583, ?x1592), ?x3127 = 03hr1p, ?x1364 = 047msdk, ?x5825 = 067ghz, ?x1642 = 0bq8tmw, nominated_for(?x1053, ?x6216), film_release_region(?x6216, ?x1790), film_release_region(?x6216, ?x985), film_release_region(?x6216, ?x774), film_release_region(?x6216, ?x304), ?x3728 = 087vz, ?x304 = 0d0vqn, contains(?x583, ?x1167), ?x2340 = 0fpv_3_, ?x7493 = 0btpm6, ?x2628 = 06wbm8q, ?x5017 = 04nm0n0, genre(?x6216, ?x258), ?x3392 = 0jwmp, ?x1790 = 01pj7, ?x2896 = 0645k5, contains(?x7273, ?x583), contains(?x2346, ?x1885), ?x5162 = 0j3d9tn, ?x7651 = 0h95927, country(?x7195, ?x550), ?x2709 = 06ztvyx, ?x2893 = 01jrbb, adjoins(?x2346, ?x2146), film_release_region(?x6480, ?x550), film_release_region(?x1625, ?x550), ?x4290 = 0gtxj2q, award_winner(?x5220, ?x200), ?x1625 = 01f8gz, country(?x453, ?x2346), ?x5713 = 0cc97st, ?x1490 = 0fpkhkz, ?x985 = 0k6nt, currency(?x2346, ?x170), ?x6480 = 02825cv, ?x5644 = 0dll_t2, geographic_distribution(?x9148, ?x583), olympics(?x2346, ?x452), country(?x206, ?x2346), ?x3565 = 0cp0ph6, ?x774 = 06mzp, country(?x1889, ?x2346), film_crew_role(?x1701, ?x137), country(?x2884, ?x172), organizations_founded(?x172, ?x1062), ?x2513 = 05b4w >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 98 *> extensional distance = 1 *> proper extension: 01fpfn; *> query: (?x6377, 05v10) <- form_of_government(?x8742, ?x6377), form_of_government(?x8449, ?x6377), form_of_government(?x7709, ?x6377), form_of_government(?x4752, ?x6377), form_of_government(?x583, ?x6377), form_of_government(?x94, ?x6377), combatants(?x172, ?x583), film_release_region(?x9501, ?x583), film_release_region(?x8646, ?x583), film_release_region(?x8471, ?x583), film_release_region(?x7016, ?x583), film_release_region(?x7009, ?x583), film_release_region(?x6078, ?x583), film_release_region(?x5425, ?x583), film_release_region(?x5109, ?x583), film_release_region(?x4707, ?x583), film_release_region(?x3981, ?x583), film_release_region(?x2889, ?x583), film_release_region(?x2714, ?x583), film_release_region(?x2512, ?x583), film_release_region(?x2189, ?x583), film_release_region(?x2050, ?x583), film_release_region(?x1456, ?x583), film_release_region(?x1293, ?x583), film_release_region(?x1035, ?x583), film_release_region(?x1022, ?x583), film_release_region(?x428, ?x583), film_release_region(?x409, ?x583), jurisdiction_of_office(?x182, ?x583), ?x2189 = 02yvct, ?x1022 = 0crfwmx, ?x1456 = 0cz8mkh, ?x3981 = 047tsx3, titles(?x583, ?x7081), participating_countries(?x418, ?x583), ?x7009 = 0bs8s1p, member_states(?x7695, ?x583), ?x2050 = 01fmys, ?x7016 = 07g1sm, ?x6078 = 04pk1f, ?x4752 = 04tr1, ?x2714 = 0kv238, ?x2889 = 040b5k, ?x4707 = 02xbyr, organization(?x8742, ?x127), nationality(?x6390, ?x583), ?x8646 = 05zvzf3, country(?x4503, ?x583), country(?x3015, ?x583), ?x4503 = 06z68, teams(?x8449, ?x11489), ?x409 = 0gtv7pk, ?x1035 = 08hmch, olympics(?x583, ?x778), countries_spoken_in(?x2502, ?x8449), ?x9501 = 0g5qmbz, film_release_region(?x280, ?x94), film_release_region(?x11296, ?x94), film_release_region(?x11192, ?x94), film_release_region(?x4203, ?x94), film_release_region(?x3137, ?x94), film_release_region(?x204, ?x94), nationality(?x6921, ?x94), nationality(?x6324, ?x94), nationality(?x4277, ?x94), nationality(?x839, ?x94), country(?x89, ?x94), country(?x4499, ?x94), country(?x4298, ?x94), contains(?x94, ?x95), second_level_divisions(?x94, ?x322), award_winner(?x6323, ?x6921), ?x5425 = 02prwdh, ?x1293 = 07g_0c, ?x5109 = 0b44shh, country_of_origin(?x50, ?x94), ?x204 = 028_yv, adjoins(?x1592, ?x583), participant(?x4277, ?x3366), ?x428 = 0h1cdwq, production_companies(?x8471, ?x10503), award(?x6324, ?x102), award_winner(?x873, ?x6324), ?x3137 = 0htww, production_companies(?x11192, ?x902), country(?x453, ?x94), time_zones(?x4298, ?x2950), location(?x13118, ?x4499), ?x3015 = 071t0, ?x2512 = 07x4qr, language(?x4203, ?x254), service_location(?x234, ?x94), ?x10503 = 02jd_7, ?x839 = 02lfl4, olympics(?x94, ?x358), administrative_parent(?x7709, ?x551), award_winner(?x484, ?x6921), film(?x2727, ?x11296) *> conf = 0.33 ranks of expected_values: 55, 79, 82, 96, 97, 164 EVAL 01d9r3 form_of_government! 01p8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 5.000 5.000 0.500 http://example.org/location/country/form_of_government EVAL 01d9r3 form_of_government! 03548 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 5.000 5.000 0.500 http://example.org/location/country/form_of_government EVAL 01d9r3 form_of_government! 07t_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 5.000 5.000 0.500 http://example.org/location/country/form_of_government EVAL 01d9r3 form_of_government! 035dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 5.000 5.000 0.500 http://example.org/location/country/form_of_government EVAL 01d9r3 form_of_government! 05v10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 5.000 5.000 0.500 http://example.org/location/country/form_of_government EVAL 01d9r3 form_of_government! 05cgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 5.000 5.000 0.500 http://example.org/location/country/form_of_government #18918-02qm_f PRED entity: 02qm_f PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #56, 0.84 #41, 0.81 #191), 07z4p (0.13 #15, 0.05 #25, 0.04 #30), 07c52 (0.08 #28, 0.05 #23, 0.04 #53), 02nxhr (0.07 #37, 0.06 #102, 0.05 #157) >> Best rule #56 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 04dsnp; 040rmy; 0g5qmbz; 04nlb94; >> query: (?x1046, 029j_) <- nominated_for(?x298, ?x1046), language(?x1046, ?x2502), ?x2502 = 06nm1, film_crew_role(?x1046, ?x1171) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qm_f film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.840 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #18917-01vvybv PRED entity: 01vvybv PRED relation: artist! PRED expected values: 0181hw => 175 concepts (156 used for prediction) PRED predicted values (max 10 best out of 100): 0mzkr (0.50 #2417, 0.24 #1752, 0.22 #1353), 015_1q (0.30 #1347, 0.22 #1746, 0.21 #4140), 03rhqg (0.22 #545, 0.22 #1343, 0.21 #1742), 0g768 (0.19 #1762, 0.19 #565, 0.18 #2427), 01trtc (0.19 #864, 0.16 #1529, 0.09 #2194), 0fb0v (0.19 #804, 0.14 #1469, 0.13 #2001), 011k1h (0.16 #1472, 0.15 #807, 0.12 #3201), 0181dw (0.16 #1368, 0.16 #1767, 0.14 #2432), 06x2ww (0.16 #2438, 0.12 #1773, 0.11 #576), 02p11jq (0.15 #809, 0.12 #410, 0.09 #2405) >> Best rule #2417 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 03xhj6; 047cx; 02dw1_; 06gcn; 01jkqfz; 0bk1p; 0p76z; 0mjn2; 016376; 017_hq; ... >> query: (?x10461, 0mzkr) <- artist(?x4868, ?x10461), artist(?x4868, ?x4957), ?x4957 = 0g_g2 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #578 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 0134wr; *> query: (?x10461, 0181hw) <- gender(?x10461, ?x231), artist(?x5744, ?x10461), award(?x10461, ?x7005), ?x5744 = 01clyr *> conf = 0.04 ranks of expected_values: 65 EVAL 01vvybv artist! 0181hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 175.000 156.000 0.495 http://example.org/music/record_label/artist #18916-02wk7b PRED entity: 02wk7b PRED relation: award PRED expected values: 02y_j8g => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 192): 0gr51 (0.27 #12674, 0.27 #12673, 0.27 #13597), 0gqwc (0.27 #12674, 0.27 #12673, 0.27 #13597), 03hl6lc (0.27 #12674, 0.27 #12673, 0.27 #13597), 0gq9h (0.27 #12674, 0.27 #12673, 0.27 #13597), 040njc (0.27 #12674, 0.27 #12673, 0.27 #13597), 09qwmm (0.27 #12674, 0.27 #12673, 0.27 #13597), 0gs9p (0.27 #12674, 0.27 #12673, 0.27 #13597), 0gqyl (0.27 #12674, 0.27 #12673, 0.27 #13597), 02ppm4q (0.27 #12674, 0.27 #12673, 0.27 #13597), 02pqp12 (0.27 #12674, 0.27 #12673, 0.27 #13597) >> Best rule #12674 for best value: >> intensional similarity = 4 >> extensional distance = 982 >> proper extension: 02gl58; >> query: (?x8247, ?x2880) <- nominated_for(?x2880, ?x8247), award(?x8247, ?x372), award_winner(?x372, ?x767), award(?x156, ?x2880) >> conf = 0.27 => this is the best rule for 12 predicted values *> Best rule #14059 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x8247, ?x704) <- award(?x8247, ?x68), award(?x6079, ?x68), award(?x6079, ?x704) *> conf = 0.05 ranks of expected_values: 84 EVAL 02wk7b award 02y_j8g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 95.000 95.000 0.273 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #18915-0lkr7 PRED entity: 0lkr7 PRED relation: award PRED expected values: 0f4x7 => 109 concepts (103 used for prediction) PRED predicted values (max 10 best out of 252): 09cm54 (0.71 #5578, 0.70 #26288, 0.70 #5577), 027986c (0.71 #5578, 0.70 #26288, 0.70 #5577), 0ck27z (0.32 #6466, 0.27 #5269, 0.27 #4074), 05pcn59 (0.31 #478, 0.20 #4461, 0.19 #4859), 0f4x7 (0.23 #7599, 0.23 #428, 0.16 #5178), 05zr6wv (0.23 #415, 0.15 #813, 0.13 #4398), 0gs9p (0.22 #78, 0.19 #7647, 0.16 #5178), 02rdyk7 (0.22 #90, 0.16 #5178, 0.13 #25888), 054ks3 (0.22 #140, 0.15 #538, 0.08 #2131), 040njc (0.22 #8, 0.15 #7577, 0.08 #18327) >> Best rule #5578 for best value: >> intensional similarity = 3 >> extensional distance = 431 >> proper extension: 026v437; >> query: (?x4992, ?x3209) <- actor(?x6415, ?x4992), award_winner(?x3209, ?x4992), nominated_for(?x3209, ?x288) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #7599 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 611 *> proper extension: 014l4w; 0gv2r; 04rg6; *> query: (?x4992, 0f4x7) <- award(?x4992, ?x4091), award(?x1371, ?x4091), ?x1371 = 0prjs *> conf = 0.23 ranks of expected_values: 5 EVAL 0lkr7 award 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 109.000 103.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18914-0988cp PRED entity: 0988cp PRED relation: gender PRED expected values: 05zppz => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #5, 0.84 #13, 0.84 #17), 02zsn (0.30 #26, 0.25 #64, 0.24 #82) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 03p01x; 01s7z0; >> query: (?x5677, 05zppz) <- nationality(?x5677, ?x94), profession(?x5677, ?x987), program_creator(?x4517, ?x5677) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0988cp gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.849 http://example.org/people/person/gender #18913-03tn9w PRED entity: 03tn9w PRED relation: award_winner PRED expected values: 01cbt3 => 37 concepts (17 used for prediction) PRED predicted values (max 10 best out of 657): 0693l (0.50 #1999, 0.33 #5079, 0.33 #3539), 02x7vq (0.33 #854, 0.25 #8552, 0.25 #2393), 03h26tm (0.33 #4741, 0.25 #1661, 0.19 #10900), 0bwh6 (0.33 #4798, 0.17 #3258, 0.14 #6338), 02h761 (0.33 #598, 0.17 #5217, 0.12 #11376), 07mb57 (0.33 #643, 0.14 #6802, 0.12 #11421), 0gnbw (0.33 #1079, 0.12 #8777, 0.12 #3079), 019x62 (0.33 #1051, 0.12 #8749, 0.11 #14908), 05kfs (0.33 #90, 0.12 #7788, 0.11 #13947), 03r1pr (0.33 #420, 0.12 #8118, 0.10 #17357) >> Best rule #1999 for best value: >> intensional similarity = 16 >> extensional distance = 2 >> proper extension: 059x66; 0n8_m93; >> query: (?x6686, 0693l) <- honored_for(?x6686, ?x718), award_winner(?x6686, ?x10704), award_winner(?x6686, ?x4398), ceremony(?x2222, ?x6686), ceremony(?x500, ?x6686), ?x2222 = 0gs96, ?x500 = 0p9sw, gender(?x10704, ?x231), cinematography(?x670, ?x10704), film(?x4398, ?x2933), award_winner(?x375, ?x4398), film_release_region(?x2933, ?x3277), film_release_region(?x2933, ?x404), ?x3277 = 06t8v, ?x404 = 047lj, participant(?x4398, ?x4397) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #11596 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 14 *> proper extension: 0dth6b; 0fk0xk; *> query: (?x6686, 01cbt3) <- honored_for(?x6686, ?x718), award_winner(?x6686, ?x10704), award_winner(?x6686, ?x4398), ceremony(?x6860, ?x6686), ceremony(?x2222, ?x6686), ceremony(?x500, ?x6686), ?x2222 = 0gs96, ?x500 = 0p9sw, gender(?x10704, ?x231), cinematography(?x670, ?x10704), film(?x4398, ?x2933), award_winner(?x1716, ?x4398), film_release_region(?x2933, ?x87), type_of_union(?x4398, ?x566), award(?x241, ?x1716), produced_by(?x670, ?x10522), ?x6860 = 018wdw *> conf = 0.06 ranks of expected_values: 353 EVAL 03tn9w award_winner 01cbt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18912-01w61th PRED entity: 01w61th PRED relation: award PRED expected values: 02f5qb 03qbh5 => 116 concepts (95 used for prediction) PRED predicted values (max 10 best out of 269): 02f77y (0.84 #1577, 0.77 #13402, 0.76 #8276), 01by1l (0.74 #4051, 0.55 #1292, 0.50 #5233), 0c4z8 (0.55 #1252, 0.34 #858, 0.34 #6769), 02f5qb (0.34 #942, 0.21 #6853, 0.19 #4095), 03qbh5 (0.34 #1383, 0.29 #5324, 0.28 #1778), 09sb52 (0.30 #26452, 0.23 #18565, 0.23 #26057), 054ks3 (0.30 #534, 0.28 #1322, 0.22 #4869), 01ck6h (0.30 #908, 0.28 #1302, 0.17 #514), 01c427 (0.28 #4812, 0.17 #5206, 0.17 #6782), 02f73b (0.28 #1070, 0.16 #6981, 0.16 #4223) >> Best rule #1577 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 01bmlb; >> query: (?x883, ?x6416) <- award(?x883, ?x1801), category(?x883, ?x134), ?x1801 = 01c92g, award_winner(?x6416, ?x883) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #942 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 0161sp; 01vw20_; 01wbz9; 01wzlxj; 01jfnvd; *> query: (?x883, 02f5qb) <- award(?x883, ?x3926), ?x3926 = 02f6xy, nationality(?x883, ?x94), artists(?x284, ?x883) *> conf = 0.34 ranks of expected_values: 4, 5 EVAL 01w61th award 03qbh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 95.000 0.845 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01w61th award 02f5qb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 95.000 0.845 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18911-02gjrc PRED entity: 02gjrc PRED relation: actor PRED expected values: 02lq10 => 55 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1217): 06t74h (0.33 #319, 0.09 #10073, 0.09 #10989), 012q4n (0.33 #505, 0.04 #40299), 05myd2 (0.33 #711), 0svqs (0.14 #7324, 0.12 #9157, 0.12 #9156), 02s5v5 (0.14 #7324, 0.12 #9157, 0.12 #9156), 03ym1 (0.14 #7324, 0.12 #9157, 0.12 #9156), 02ck7w (0.14 #7324, 0.12 #9157, 0.12 #9156), 018417 (0.14 #7324, 0.12 #9157, 0.12 #9156), 05kh_ (0.14 #7324, 0.12 #9157, 0.12 #9156), 0cj8x (0.14 #7324, 0.12 #9157, 0.12 #9156) >> Best rule #319 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 06dfz1; >> query: (?x11482, 06t74h) <- actor(?x11482, ?x5495), actor(?x11482, ?x4735), film(?x5495, ?x972), people(?x743, ?x5495), award_winner(?x5495, ?x628), ?x4735 = 02hsgn, award(?x5495, ?x112) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #40299 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 247 *> proper extension: 05hd32; 08cl7s; 017dtf; 06xkst; 051kd; *> query: (?x11482, ?x629) <- actor(?x11482, ?x5495), actor(?x11482, ?x3651), film(?x5495, ?x972), film(?x3651, ?x463), profession(?x3651, ?x1032), film(?x629, ?x972) *> conf = 0.04 ranks of expected_values: 662 EVAL 02gjrc actor 02lq10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 46.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #18910-01d1st PRED entity: 01d1st PRED relation: profession PRED expected values: 02hrh1q => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.89 #7037, 0.87 #4457, 0.86 #6894), 0dxtg (0.50 #441, 0.38 #298, 0.38 #155), 0nbcg (0.49 #1314, 0.43 #5189, 0.43 #2317), 0n1h (0.47 #725, 0.30 #1727, 0.29 #1297), 01d_h8 (0.47 #1149, 0.38 #577, 0.37 #4019), 016z4k (0.45 #1720, 0.42 #718, 0.42 #1434), 0d1pc (0.33 #45, 0.27 #903, 0.26 #760), 01c72t (0.28 #5182, 0.27 #4895, 0.24 #3602), 039v1 (0.26 #1319, 0.21 #747, 0.19 #5194), 02jknp (0.25 #435, 0.25 #292, 0.25 #149) >> Best rule #7037 for best value: >> intensional similarity = 2 >> extensional distance = 2012 >> proper extension: 0lzb8; 045bs6; 027l0b; 0241wg; 030x48; 02yplc; 02y_2y; 021yzs; 01h8f; 078g3l; ... >> query: (?x6935, 02hrh1q) <- profession(?x6935, ?x131), film(?x6935, ?x1477) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d1st profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.892 http://example.org/people/person/profession #18909-0lxg6 PRED entity: 0lxg6 PRED relation: time_zones PRED expected values: 03plfd => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 8): 03plfd (0.50 #23, 0.38 #53, 0.38 #49), 02hcv8 (0.36 #189, 0.26 #634, 0.25 #242), 02llzg (0.30 #57, 0.25 #30, 0.17 #164), 02lcqs (0.21 #191, 0.19 #244, 0.14 #283), 02fqwt (0.13 #279, 0.13 #174, 0.12 #240), 03bdv (0.12 #100, 0.10 #86, 0.09 #153), 02hczc (0.05 #241, 0.04 #280, 0.04 #306), 052vwh (0.03 #92, 0.03 #106, 0.02 #120) >> Best rule #23 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 018ym2; >> query: (?x14605, 03plfd) <- contains(?x1497, ?x14605), ?x1497 = 015qh >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lxg6 time_zones 03plfd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.500 http://example.org/location/location/time_zones #18908-0m2wm PRED entity: 0m2wm PRED relation: location PRED expected values: 09ksp => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 104): 030qb3t (0.29 #2492, 0.26 #4099, 0.25 #12934), 02_286 (0.20 #12888, 0.20 #25740, 0.18 #2446), 04jpl (0.14 #17, 0.07 #5640, 0.06 #14474), 0156q (0.08 #1694, 0.01 #16954), 059rby (0.07 #16, 0.06 #2425, 0.06 #819), 0cc56 (0.07 #57, 0.06 #860, 0.05 #8090), 07b_l (0.07 #186, 0.06 #989, 0.03 #2595), 01z645 (0.07 #803, 0.06 #1606, 0.02 #38555), 0126hc (0.07 #670, 0.06 #1473, 0.02 #38555), 0nbrp (0.07 #658, 0.06 #1461, 0.02 #38555) >> Best rule #2492 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 013v5j; 017b2p; >> query: (?x380, 030qb3t) <- profession(?x380, ?x4773), location(?x380, ?x2997), ?x4773 = 0d1pc >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0m2wm location 09ksp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 81.000 0.288 http://example.org/people/person/places_lived./people/place_lived/location #18907-01vzxld PRED entity: 01vzxld PRED relation: artist! PRED expected values: 04fcjt => 153 concepts (107 used for prediction) PRED predicted values (max 10 best out of 110): 015_1q (0.25 #574, 0.20 #4607, 0.20 #296), 01w40h (0.19 #165, 0.10 #721, 0.09 #304), 033hn8 (0.16 #2932, 0.13 #4601, 0.11 #4740), 0181dw (0.16 #318, 0.15 #1430, 0.14 #4629), 01clyr (0.14 #170, 0.10 #2951, 0.09 #309), 0g768 (0.14 #1843, 0.13 #2955, 0.12 #3094), 03rhqg (0.14 #570, 0.12 #5718, 0.12 #4603), 03mp8k (0.13 #2985, 0.12 #343, 0.10 #4237), 01trtc (0.11 #627, 0.09 #2991, 0.09 #3130), 043g7l (0.11 #2949, 0.11 #307, 0.10 #3088) >> Best rule #574 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 01q_ph; 01wmxfs; 03f1r6t; 020jqv; >> query: (?x10181, 015_1q) <- film(?x10181, ?x1262), award_winner(?x10180, ?x10181), artist(?x2149, ?x10181) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 0lbj1; 0b68vs; 01vrz41; 03kwtb; 01wp8w7; 01v_pj6; 01w923; 01wsl7c; 0144l1; 0kvrb; ... *> query: (?x10181, 04fcjt) <- artists(?x671, ?x10181), nationality(?x10181, ?x512), category(?x10181, ?x134), ?x512 = 07ssc *> conf = 0.05 ranks of expected_values: 29 EVAL 01vzxld artist! 04fcjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 153.000 107.000 0.253 http://example.org/music/record_label/artist #18906-0drtv8 PRED entity: 0drtv8 PRED relation: honored_for PRED expected values: 08nvyr => 48 concepts (47 used for prediction) PRED predicted values (max 10 best out of 810): 08jgk1 (0.50 #3020, 0.40 #1851, 0.33 #6536), 080dwhx (0.40 #12898, 0.28 #21702, 0.27 #22287), 01j7mr (0.40 #1973, 0.33 #6658, 0.33 #3142), 0431v3 (0.40 #2090, 0.33 #3259, 0.33 #330), 07c72 (0.40 #1947, 0.33 #3116, 0.33 #1360), 02rzdcp (0.40 #1956, 0.33 #3125, 0.26 #7228), 01vnbh (0.40 #2074, 0.33 #3243, 0.25 #6759), 01ft14 (0.40 #2882, 0.33 #4639, 0.22 #5223), 039cq4 (0.40 #2164, 0.33 #3333, 0.21 #7436), 07zhjj (0.40 #2250, 0.33 #3419, 0.20 #2834) >> Best rule #3020 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 027hjff; >> query: (?x4781, 08jgk1) <- award_winner(?x4781, ?x10160), award_winner(?x4781, ?x4397), tv_program(?x10160, ?x5808), student(?x735, ?x10160), honored_for(?x4781, ?x6439), honored_for(?x4781, ?x6023), ?x6439 = 04p5cr, profession(?x10160, ?x524), award_nominee(?x2028, ?x4397), participant(?x4397, ?x719), film(?x4397, ?x240), award(?x4397, ?x350), award_nominee(?x10160, ?x7604), award(?x6023, ?x880) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1440 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 1 *> proper extension: 0275n3y; *> query: (?x4781, 08nvyr) <- award_winner(?x4781, ?x10634), award_winner(?x4781, ?x10160), award_winner(?x4781, ?x2457), award_winner(?x4781, ?x1880), award_winner(?x4781, ?x1630), award_winner(?x4781, ?x361), nationality(?x10160, ?x94), music(?x5074, ?x10634), ?x2457 = 02cllz, ?x361 = 0h5f5n, ceremony(?x746, ?x4781), profession(?x10160, ?x524), people(?x2510, ?x10160), location_of_ceremony(?x1880, ?x4627), film(?x1880, ?x1372), award_winner(?x2143, ?x1630), titles(?x512, ?x5074) *> conf = 0.33 ranks of expected_values: 19 EVAL 0drtv8 honored_for 08nvyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 48.000 47.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18905-0bytkq PRED entity: 0bytkq PRED relation: film_production_design_by! PRED expected values: 015x74 => 115 concepts (62 used for prediction) PRED predicted values (max 10 best out of 161): 015x74 (0.81 #963, 0.81 #321, 0.80 #962), 0kt_4 (0.53 #1602, 0.04 #2402, 0.02 #481), 0glbqt (0.11 #311, 0.10 #631, 0.08 #792), 0cq806 (0.11 #296, 0.10 #616, 0.08 #777), 0cq86w (0.11 #255, 0.10 #575, 0.08 #736), 0pd6l (0.11 #225, 0.10 #545, 0.08 #706), 0bx0l (0.11 #198, 0.10 #518, 0.08 #679), 01c9d (0.11 #316, 0.10 #636, 0.08 #797), 0g5ptf (0.11 #312, 0.10 #632, 0.08 #793), 029v40 (0.11 #307, 0.10 #627, 0.08 #788) >> Best rule #963 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 07fzq3; 03cp7b3; >> query: (?x3080, ?x485) <- award_winner(?x485, ?x3080), film_production_design_by(?x2402, ?x3080), film_crew_role(?x485, ?x137), nominated_for(?x143, ?x485) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bytkq film_production_design_by! 015x74 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 62.000 0.810 http://example.org/film/film/film_production_design_by #18904-07t_x PRED entity: 07t_x PRED relation: teams PRED expected values: 03__77 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 101): 03lygq (0.05 #258, 0.03 #618, 0.03 #1338), 024nj1 (0.05 #354, 0.03 #1074, 0.03 #1434), 035l_9 (0.05 #315, 0.03 #1035, 0.03 #1395), 04gj8r (0.05 #339, 0.03 #1419, 0.03 #1779), 04h54p (0.05 #251, 0.03 #1331, 0.02 #3851), 086x3 (0.03 #1080, 0.03 #720, 0.02 #2160), 02fbb5 (0.03 #952, 0.03 #592, 0.02 #2032), 02ryyk (0.03 #1068, 0.03 #708, 0.02 #2148), 020wyp (0.03 #1053, 0.03 #693, 0.02 #2493), 0cnk2q (0.03 #721, 0.03 #361, 0.02 #2161) >> Best rule #258 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 02qkt; 04wsz; >> query: (?x6305, 03lygq) <- contains(?x6305, ?x13440), contains(?x6956, ?x13440), ?x6956 = 0j0k >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07t_x teams 03__77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 103.000 0.050 http://example.org/sports/sports_team_location/teams #18903-01wj18h PRED entity: 01wj18h PRED relation: profession PRED expected values: 0dz3r => 120 concepts (100 used for prediction) PRED predicted values (max 10 best out of 72): 02hrh1q (0.87 #1043, 0.84 #3692, 0.79 #3103), 0nbcg (0.54 #6951, 0.51 #766, 0.50 #4150), 0dz3r (0.50 #296, 0.50 #2, 0.44 #884), 01c72t (0.36 #464, 0.33 #7384, 0.30 #1199), 01d_h8 (0.31 #7660, 0.31 #6631, 0.31 #5599), 039v1 (0.28 #3272, 0.28 #6071, 0.27 #6366), 0dxtg (0.26 #5607, 0.26 #6639, 0.25 #14144), 0n1h (0.26 #13542, 0.26 #13984, 0.25 #11), 0fj9f (0.26 #13542, 0.26 #13984, 0.11 #1376), 012t_z (0.26 #13542, 0.26 #13984, 0.07 #747) >> Best rule #1043 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 01pnn3; 046rfv; 028pzq; 0mbhr; 04bdpf; 03t8v3; >> query: (?x3200, 02hrh1q) <- type_of_union(?x3200, ?x1873), profession(?x3200, ?x4773), ?x4773 = 0d1pc >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #296 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 01vxlbm; *> query: (?x3200, 0dz3r) <- artists(?x2936, ?x3200), ?x2936 = 029h7y, profession(?x3200, ?x220) *> conf = 0.50 ranks of expected_values: 3 EVAL 01wj18h profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 100.000 0.865 http://example.org/people/person/profession #18902-07y2b PRED entity: 07y2b PRED relation: program PRED expected values: 0d_rw => 81 concepts (70 used for prediction) PRED predicted values (max 10 best out of 332): 03g9xj (0.33 #409, 0.25 #887, 0.17 #1844), 03y317 (0.33 #394, 0.25 #872, 0.17 #1829), 05sy2k_ (0.33 #290, 0.25 #768, 0.17 #1725), 0gfzgl (0.33 #268, 0.25 #746, 0.17 #1703), 08cx5g (0.33 #1729, 0.14 #8436, 0.12 #9634), 0jq2r (0.33 #1807, 0.11 #4922, 0.10 #8514), 05397h (0.33 #1903, 0.11 #5018, 0.07 #8610), 02qfh (0.33 #1822, 0.11 #4937, 0.07 #8529), 03ffcz (0.33 #1779, 0.11 #4894, 0.07 #8486), 02wyzmv (0.33 #1781, 0.11 #4896, 0.07 #8488) >> Best rule #409 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0b275x; >> query: (?x13117, 03g9xj) <- program(?x13117, ?x8818), child(?x7326, ?x13117), organization(?x4682, ?x7326), ?x8818 = 01yb1y >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13899 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 54 *> proper extension: 01gl9g; 03sb38; 025tlyv; 01jygk; 02lw5z; *> query: (?x13117, ?x3626) <- child(?x7326, ?x13117), child(?x7326, ?x10068), nominated_for(?x10068, ?x5698), program(?x10068, ?x3626), state_province_region(?x7326, ?x335) *> conf = 0.11 ranks of expected_values: 83 EVAL 07y2b program 0d_rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 81.000 70.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #18901-01d494 PRED entity: 01d494 PRED relation: student! PRED expected values: 014mlp => 195 concepts (145 used for prediction) PRED predicted values (max 10 best out of 18): 014mlp (0.56 #672, 0.53 #1122, 0.53 #726), 01rr_d (0.25 #321, 0.18 #411, 0.13 #285), 019v9k (0.22 #369, 0.18 #405, 0.16 #801), 04zx3q1 (0.13 #272, 0.13 #560, 0.13 #542), 02h4rq6 (0.13 #543, 0.11 #363, 0.11 #111), 028dcg (0.13 #682, 0.12 #1132, 0.11 #862), 016t_3 (0.11 #112, 0.08 #976, 0.08 #220), 03mkk4 (0.10 #804, 0.08 #984, 0.08 #912), 013zdg (0.09 #566, 0.09 #548, 0.07 #278), 07s6fsf (0.06 #559, 0.06 #541, 0.03 #667) >> Best rule #672 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 058s57; 06w38l; >> query: (?x1737, 014mlp) <- student(?x1526, ?x1737), award_winner(?x12729, ?x1737), nationality(?x1737, ?x94), ?x94 = 09c7w0 >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d494 student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 145.000 0.558 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #18900-09gdm7q PRED entity: 09gdm7q PRED relation: film_crew_role PRED expected values: 02r96rf => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 27): 02r96rf (0.74 #636, 0.74 #143, 0.74 #389), 01vx2h (0.47 #185, 0.46 #396, 0.43 #468), 0dxtw (0.38 #642, 0.38 #395, 0.38 #782), 0215hd (0.35 #53, 0.23 #158, 0.15 #440), 01xy5l_ (0.35 #48, 0.19 #153, 0.15 #399), 01pvkk (0.30 #1241, 0.28 #1029, 0.28 #889), 02rh1dz (0.27 #183, 0.23 #8, 0.19 #288), 02ynfr (0.20 #190, 0.20 #225, 0.17 #858), 089g0h (0.20 #54, 0.19 #159, 0.12 #792), 0d2b38 (0.20 #60, 0.16 #165, 0.15 #270) >> Best rule #636 for best value: >> intensional similarity = 4 >> extensional distance = 143 >> proper extension: 0qm8b; >> query: (?x1170, 02r96rf) <- film_format(?x1170, ?x6392), film_crew_role(?x1170, ?x137), nominated_for(?x548, ?x1170), music(?x1170, ?x10700) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09gdm7q film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.745 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18899-01ydzx PRED entity: 01ydzx PRED relation: instrumentalists! PRED expected values: 05r5c => 104 concepts (101 used for prediction) PRED predicted values (max 10 best out of 125): 05r5c (0.53 #965, 0.53 #2100, 0.51 #1053), 05148p4 (0.50 #20, 0.45 #803, 0.43 #1239), 02hnl (0.35 #208, 0.25 #1253, 0.22 #1079), 02sgy (0.34 #3233, 0.31 #2881, 0.30 #2880), 0bxl5 (0.34 #3233, 0.31 #2881, 0.30 #2880), 03qjg (0.29 #225, 0.25 #486, 0.25 #51), 06ncr (0.25 #44, 0.18 #218, 0.13 #827), 07y_7 (0.25 #2, 0.12 #176, 0.11 #872), 013y1f (0.25 #31, 0.08 #901, 0.07 #379), 03f5mt (0.25 #83, 0.08 #866, 0.07 #170) >> Best rule #965 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 01m5m5b; >> query: (?x6774, 05r5c) <- nationality(?x6774, ?x1310), type_of_union(?x6774, ?x566), role(?x6774, ?x227), origin(?x6774, ?x8630) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ydzx instrumentalists! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 101.000 0.529 http://example.org/music/instrument/instrumentalists #18898-0167v4 PRED entity: 0167v4 PRED relation: category PRED expected values: 08mbj5d => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #73, 0.85 #82, 0.85 #80) >> Best rule #73 for best value: >> intensional similarity = 4 >> extensional distance = 313 >> proper extension: 07qnf; 04r1t; 02r1tx7; 07yg2; 03xhj6; 0394y; 01j59b0; 06nv27; 02mq_y; 02vgh; ... >> query: (?x9117, 08mbj5d) <- artists(?x2996, ?x9117), origin(?x9117, ?x11246), artists(?x2996, ?x2187), ?x2187 = 01vsnff >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0167v4 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.857 http://example.org/common/topic/webpage./common/webpage/category #18897-01gw8b PRED entity: 01gw8b PRED relation: nominated_for PRED expected values: 0ds35l9 => 99 concepts (39 used for prediction) PRED predicted values (max 10 best out of 340): 034fl9 (0.41 #21100, 0.37 #8116, 0.36 #27592), 08r4x3 (0.03 #144, 0.03 #29360, 0.03 #30983), 06_wqk4 (0.03 #119, 0.02 #1742, 0.01 #8235), 01b_lz (0.03 #502, 0.02 #6995, 0.02 #32964), 09k56b7 (0.03 #290, 0.01 #1913), 0gxsh4 (0.03 #1564, 0.01 #27533), 06zsk51 (0.03 #1386), 04gp58p (0.03 #1273), 01qbg5 (0.03 #1139), 0ctb4g (0.03 #513) >> Best rule #21100 for best value: >> intensional similarity = 3 >> extensional distance = 476 >> proper extension: 01nczg; 01bpc9; 015rhv; 0m32_; 01v3vp; 05typm; 0347db; 01s0l0; 01bmlb; 021b_; ... >> query: (?x10617, ?x9029) <- award(?x10617, ?x704), location(?x10617, ?x739), actor(?x9029, ?x10617) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1629 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 162 *> proper extension: 03qcq; 03m8lq; 04cf09; 049_zz; 01v3bn; 06s6hs; 02yygk; 025hzx; 03k48_; *> query: (?x10617, 0ds35l9) <- award_nominee(?x10617, ?x4490), student(?x263, ?x10617), type_of_union(?x10617, ?x566), participant(?x2551, ?x10617) *> conf = 0.01 ranks of expected_values: 257 EVAL 01gw8b nominated_for 0ds35l9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 99.000 39.000 0.406 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #18896-016n7b PRED entity: 016n7b PRED relation: administrative_parent PRED expected values: 09hrc => 97 concepts (29 used for prediction) PRED predicted values (max 10 best out of 20): 09hrc (0.53 #414, 0.27 #554, 0.26 #413), 02j71 (0.38 #2097, 0.13 #1402, 0.07 #3084), 09krp (0.26 #362, 0.07 #642, 0.06 #502), 0345h (0.26 #413, 0.15 #2366, 0.15 #694), 017v_ (0.21 #312, 0.06 #592, 0.04 #452), 09c7w0 (0.11 #1391, 0.07 #2229, 0.07 #2369), 036wy (0.08 #536, 0.03 #815, 0.01 #3192), 07nf6 (0.06 #658, 0.02 #518, 0.01 #2187), 070zc (0.03 #654), 09ksp (0.03 #641) >> Best rule #414 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 01cz_1; >> query: (?x14426, ?x10765) <- contains(?x10765, ?x14426), contains(?x10765, ?x12642), adjoins(?x10766, ?x10765), capital(?x10765, ?x12961), ?x10766 = 07nf6, location(?x5600, ?x12642) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016n7b administrative_parent 09hrc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 29.000 0.526 http://example.org/base/aareas/schema/administrative_area/administrative_parent #18895-01lf293 PRED entity: 01lf293 PRED relation: group! PRED expected values: 05r5c 018vs 03t22m 06ncr 07m2y => 85 concepts (66 used for prediction) PRED predicted values (max 10 best out of 115): 018vs (0.64 #1247, 0.62 #2328, 0.61 #2407), 03bx0bm (0.64 #1719, 0.58 #2337, 0.58 #2416), 06ncr (0.50 #879, 0.43 #262, 0.40 #416), 07y_7 (0.45 #850, 0.43 #310, 0.40 #387), 0l14qv (0.45 #852, 0.40 #389, 0.29 #312), 05r5c (0.43 #314, 0.30 #391, 0.25 #1473), 028tv0 (0.38 #1709, 0.36 #2406, 0.36 #1246), 02k84w (0.29 #333, 0.29 #256, 0.20 #410), 013y1f (0.29 #330, 0.20 #407, 0.18 #870), 042v_gx (0.17 #161, 0.14 #315, 0.14 #238) >> Best rule #1247 for best value: >> intensional similarity = 6 >> extensional distance = 62 >> proper extension: 02lbrd; 01yzl2; 01dwrc; 0167xy; 0ql36; >> query: (?x8429, 018vs) <- artist(?x2039, ?x8429), group(?x6996, ?x8429), artist(?x2039, ?x12427), artist(?x2039, ?x2169), ?x2169 = 01w60_p, artists(?x482, ?x12427) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 6, 22, 66 EVAL 01lf293 group! 07m2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 85.000 66.000 0.641 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01lf293 group! 06ncr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 66.000 0.641 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01lf293 group! 03t22m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 85.000 66.000 0.641 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01lf293 group! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 66.000 0.641 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01lf293 group! 05r5c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 66.000 0.641 http://example.org/music/performance_role/regular_performances./music/group_membership/group #18894-03v1xb PRED entity: 03v1xb PRED relation: profession PRED expected values: 0d8qb => 98 concepts (88 used for prediction) PRED predicted values (max 10 best out of 69): 02hrh1q (0.79 #5158, 0.79 #160, 0.76 #4717), 0dxtg (0.59 #1041, 0.56 #747, 0.52 #2070), 02jknp (0.52 #741, 0.50 #1476, 0.50 #1035), 03gjzk (0.48 #308, 0.46 #1043, 0.46 #455), 0cbd2 (0.26 #9999, 0.22 #1769, 0.20 #2210), 02krf9 (0.26 #9999, 0.17 #908, 0.17 #1055), 012t_z (0.26 #9999, 0.12 #11, 0.10 #305), 02hv44_ (0.24 #56, 0.09 #2408, 0.07 #1820), 01c72t (0.21 #23, 0.10 #2963, 0.10 #1787), 025352 (0.21 #58, 0.04 #1822, 0.03 #3145) >> Best rule #5158 for best value: >> intensional similarity = 3 >> extensional distance = 1226 >> proper extension: 01m7f5r; 0q1lp; 033071; >> query: (?x9044, 02hrh1q) <- profession(?x9044, ?x106), location(?x9044, ?x739), nominated_for(?x9044, ?x6215) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #2724 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 500 *> proper extension: 084w8; 0f0y8; 049tjg; 0h1_w; 01ty7ll; 01vvy; 019z7q; 0f0p0; 08433; 012cph; ... *> query: (?x9044, 0d8qb) <- gender(?x9044, ?x231), type_of_union(?x9044, ?x566), people(?x11563, ?x9044) *> conf = 0.03 ranks of expected_values: 34 EVAL 03v1xb profession 0d8qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 98.000 88.000 0.794 http://example.org/people/person/profession #18893-0f42nz PRED entity: 0f42nz PRED relation: category PRED expected values: 08mbj5d => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.31 #5, 0.31 #4, 0.29 #2) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 0fq27fp; >> query: (?x5247, 08mbj5d) <- genre(?x5247, ?x53), currency(?x5247, ?x10674), film_festivals(?x5247, ?x13003) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f42nz category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.307 http://example.org/common/topic/webpage./common/webpage/category #18892-0bs8ndx PRED entity: 0bs8ndx PRED relation: film! PRED expected values: 05d6q1 => 89 concepts (70 used for prediction) PRED predicted values (max 10 best out of 94): 0fvppk (0.25 #56, 0.18 #130, 0.13 #204), 03xq0f (0.22 #227, 0.20 #525, 0.18 #895), 017s11 (0.19 #299, 0.15 #1486, 0.14 #3340), 016tw3 (0.18 #605, 0.14 #3722, 0.14 #455), 054g1r (0.17 #257, 0.12 #479, 0.11 #1074), 086k8 (0.16 #446, 0.15 #3490, 0.14 #3639), 05qd_ (0.16 #1048, 0.13 #231, 0.13 #1640), 025jfl (0.14 #1267, 0.14 #1563, 0.13 #822), 061dn_ (0.13 #246, 0.08 #914, 0.08 #544), 016tt2 (0.12 #1339, 0.12 #448, 0.12 #1487) >> Best rule #56 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 03qnc6q; 017z49; 05ft32; 04z_3pm; 07ghq; 09v42sf; >> query: (?x8162, 0fvppk) <- film(?x275, ?x8162), genre(?x8162, ?x2753), ?x2753 = 0219x_, language(?x8162, ?x254), film_release_region(?x8162, ?x512), ?x512 = 07ssc >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #119 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 0ddfwj1; *> query: (?x8162, 05d6q1) <- film(?x275, ?x8162), genre(?x8162, ?x2753), ?x2753 = 0219x_, film_crew_role(?x8162, ?x137), film_release_region(?x8162, ?x1264), ?x1264 = 0345h *> conf = 0.09 ranks of expected_values: 21 EVAL 0bs8ndx film! 05d6q1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 89.000 70.000 0.250 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18891-024l2y PRED entity: 024l2y PRED relation: featured_film_locations PRED expected values: 030qb3t => 83 concepts (72 used for prediction) PRED predicted values (max 10 best out of 81): 04jpl (0.15 #1194, 0.13 #2380, 0.13 #2617), 030qb3t (0.15 #749, 0.15 #2409, 0.15 #2646), 0ctw_b (0.09 #21, 0.05 #258, 0.03 #733), 06y57 (0.08 #575, 0.04 #1523, 0.03 #1286), 0345h (0.05 #1217, 0.02 #1928, 0.02 #2165), 06c62 (0.05 #602, 0.03 #839, 0.02 #2499), 035p3 (0.05 #705, 0.02 #7351, 0.02 #2364), 05kj_ (0.05 #492, 0.02 #2151, 0.01 #2389), 080h2 (0.05 #734, 0.05 #7143, 0.04 #8333), 03gh4 (0.05 #824, 0.03 #587, 0.02 #1298) >> Best rule #1194 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 05y0cr; >> query: (?x1688, 04jpl) <- genre(?x1688, ?x225), featured_film_locations(?x1688, ?x108), language(?x1688, ?x5607), ?x5607 = 064_8sq >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #749 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 0d7vtk; *> query: (?x1688, 030qb3t) <- language(?x1688, ?x2164), ?x2164 = 03_9r, produced_by(?x1688, ?x1689) *> conf = 0.15 ranks of expected_values: 2 EVAL 024l2y featured_film_locations 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 72.000 0.152 http://example.org/film/film/featured_film_locations #18890-09r1j5 PRED entity: 09r1j5 PRED relation: profession PRED expected values: 0gl2ny2 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 145): 0gl2ny2 (0.73 #969, 0.72 #1119, 0.71 #2170), 02hrh1q (0.70 #4968, 0.64 #1515, 0.63 #5268), 01d_h8 (0.39 #1356, 0.39 #1206, 0.34 #4959), 02y5kn (0.33 #287, 0.21 #737, 0.07 #4040), 09jwl (0.32 #770, 0.24 #1220, 0.24 #1520), 0dxtg (0.28 #5717, 0.27 #5867, 0.27 #6167), 02jknp (0.27 #1208, 0.26 #1358, 0.22 #4961), 0cbd2 (0.22 #1958, 0.18 #5710, 0.18 #5860), 0dz3r (0.21 #752, 0.15 #1202, 0.14 #1502), 0n1h (0.21 #762, 0.12 #4365, 0.12 #1212) >> Best rule #969 for best value: >> intensional similarity = 7 >> extensional distance = 24 >> proper extension: 07h1h5; >> query: (?x7026, 0gl2ny2) <- team(?x7026, ?x13947), team(?x7026, ?x5708), team(?x7026, ?x2427), team(?x63, ?x5708), ?x63 = 02sdk9v, current_club(?x2427, ?x2428), colors(?x13947, ?x1101) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09r1j5 profession 0gl2ny2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.731 http://example.org/people/person/profession #18889-05fh2 PRED entity: 05fh2 PRED relation: taxonomy PRED expected values: 04n6k => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.71 #12, 0.64 #6, 0.64 #11) >> Best rule #12 for best value: >> intensional similarity = 11 >> extensional distance = 43 >> proper extension: 01ftz; >> query: (?x12206, 04n6k) <- major_field_of_study(?x4981, ?x12206), major_field_of_study(?x865, ?x12206), ?x4981 = 03bwzr4, institution(?x865, ?x10373), institution(?x865, ?x4599), institution(?x865, ?x4099), institution(?x865, ?x2175), ?x4099 = 01f1r4, ?x2175 = 01ptt7, ?x4599 = 07t90, ?x10373 = 01tzfz >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fh2 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.711 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #18888-0156q PRED entity: 0156q PRED relation: location! PRED expected values: 0h326 => 297 concepts (219 used for prediction) PRED predicted values (max 10 best out of 2316): 0277c3 (0.53 #168214, 0.52 #12551, 0.51 #105443), 0hskw (0.53 #168214, 0.52 #12551, 0.49 #22595), 04kj2v (0.53 #168214, 0.52 #12551, 0.49 #22595), 0bqytm (0.53 #168214, 0.52 #12551, 0.49 #22595), 018dyl (0.52 #12551, 0.51 #105443, 0.49 #22595), 01kwld (0.52 #12551, 0.49 #22595, 0.48 #276167), 0bpk2 (0.27 #544794, 0.16 #180769, 0.15 #65275), 04x1_w (0.25 #4003, 0.17 #19067, 0.17 #9023), 02mjmr (0.25 #3010, 0.17 #8030, 0.15 #45690), 0pyww (0.25 #3490, 0.17 #8510, 0.13 #63744) >> Best rule #168214 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 06cn5; >> query: (?x1646, ?x11305) <- capital(?x1264, ?x1646), place_of_birth(?x11305, ?x1646), people(?x1050, ?x11305) >> conf = 0.53 => this is the best rule for 4 predicted values *> Best rule #10037 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0rh6k; 01914; 0fhp9; 03khn; *> query: (?x1646, 0h326) <- capital(?x1264, ?x1646), month(?x1646, ?x1459), adjoins(?x6325, ?x1646) *> conf = 0.17 ranks of expected_values: 90 EVAL 0156q location! 0h326 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 297.000 219.000 0.533 http://example.org/people/person/places_lived./people/place_lived/location #18887-0194zl PRED entity: 0194zl PRED relation: nominated_for! PRED expected values: 04kxsb => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 207): 094qd5 (0.66 #6963, 0.66 #4642, 0.29 #3017), 02pqp12 (0.45 #288, 0.23 #1680, 0.19 #2608), 0gq9h (0.43 #1683, 0.40 #291, 0.37 #2843), 040njc (0.43 #238, 0.34 #1630, 0.26 #2790), 0gs9p (0.39 #293, 0.37 #1685, 0.33 #2845), 02qyntr (0.39 #406, 0.27 #1798, 0.21 #2958), 04dn09n (0.39 #265, 0.27 #1657, 0.24 #961), 019f4v (0.38 #1676, 0.36 #284, 0.33 #2836), 0gq_v (0.36 #1642, 0.27 #2802, 0.24 #3499), 099c8n (0.34 #287, 0.27 #1679, 0.22 #55) >> Best rule #6963 for best value: >> intensional similarity = 1 >> extensional distance = 1025 >> proper extension: 0lcdk; 0542n; 087z2; >> query: (?x4963, ?x749) <- award(?x4963, ?x749) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #324 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 99 *> proper extension: 0ds35l9; 0m313; 0sxg4; 02vxq9m; 01jc6q; 09m6kg; 0ds3t5x; 095zlp; 01sxly; 011yph; ... *> query: (?x4963, 04kxsb) <- nominated_for(?x2880, ?x4963), genre(?x4963, ?x53), film(?x2805, ?x4963), ?x2880 = 02ppm4q *> conf = 0.29 ranks of expected_values: 20 EVAL 0194zl nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 61.000 61.000 0.657 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18886-0125xq PRED entity: 0125xq PRED relation: language PRED expected values: 02h40lc => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 36): 02h40lc (0.96 #1742, 0.95 #585, 0.95 #2550), 064_8sq (0.25 #198, 0.16 #140, 0.15 #1354), 06nm1 (0.11 #995, 0.11 #130, 0.10 #881), 02bjrlw (0.10 #179, 0.08 #1, 0.08 #1160), 06b_j (0.08 #21, 0.08 #141, 0.07 #199), 03_9r (0.08 #418, 0.06 #592, 0.06 #304), 0jzc (0.06 #196, 0.03 #1409, 0.03 #138), 0653m (0.04 #131, 0.04 #652, 0.04 #996), 03hkp (0.04 #309, 0.03 #14, 0.02 #597), 04h9h (0.03 #161, 0.03 #336, 0.03 #41) >> Best rule #1742 for best value: >> intensional similarity = 4 >> extensional distance = 789 >> proper extension: 0d8w2n; >> query: (?x4441, 02h40lc) <- production_companies(?x4441, ?x541), language(?x4441, ?x732), nominated_for(?x541, ?x770), film(?x541, ?x80) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0125xq language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.956 http://example.org/film/film/language #18885-035qgm PRED entity: 035qgm PRED relation: current_club PRED expected values: 0j46b 0kz4w 0hqzm6r => 99 concepts (63 used for prediction) PRED predicted values (max 10 best out of 744): 0xbm (0.50 #1365, 0.43 #1516, 0.40 #1066), 06l22 (0.50 #1404, 0.43 #1555, 0.40 #1105), 01634x (0.43 #1576, 0.40 #2333, 0.33 #2631), 0138mv (0.40 #976, 0.33 #81, 0.25 #379), 0y54 (0.33 #1353, 0.33 #157, 0.29 #1504), 0cttx (0.33 #1476, 0.33 #280, 0.29 #1627), 0y9j (0.33 #1397, 0.31 #2902, 0.30 #2305), 0175rc (0.33 #1457, 0.29 #1608, 0.27 #2514), 049f05 (0.33 #111, 0.26 #3110, 0.25 #409), 01rly6 (0.33 #255, 0.25 #554, 0.23 #2956) >> Best rule #1365 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 02ltg3; >> query: (?x9542, 0xbm) <- current_club(?x9542, ?x8585), position(?x9542, ?x530), position(?x9542, ?x63), ?x8585 = 04ltf, team(?x8860, ?x9542), ?x530 = 02_j1w, team(?x63, ?x9319), ?x9319 = 0c02jh8 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2653 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 03yl2t; *> query: (?x9542, 0j46b) <- current_club(?x9542, ?x8585), position(?x9542, ?x60), team(?x7669, ?x8585), colors(?x8585, ?x1101), ?x7669 = 02rnns, sport(?x8585, ?x471) *> conf = 0.17 ranks of expected_values: 57, 107, 284 EVAL 035qgm current_club 0hqzm6r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 99.000 63.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club EVAL 035qgm current_club 0kz4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 99.000 63.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club EVAL 035qgm current_club 0j46b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 99.000 63.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #18884-0199wf PRED entity: 0199wf PRED relation: genre PRED expected values: 04t36 => 99 concepts (87 used for prediction) PRED predicted values (max 10 best out of 108): 02kdv5l (0.84 #7112, 0.56 #723, 0.54 #963), 07s9rl0 (0.70 #10233, 0.68 #10353, 0.64 #4579), 01hmnh (0.55 #858, 0.51 #3149, 0.50 #2907), 06n90 (0.50 #133, 0.45 #2044, 0.43 #1322), 0jxy (0.43 #525, 0.23 #3899, 0.16 #1125), 01jfsb (0.42 #7121, 0.38 #972, 0.31 #7601), 01zhp (0.41 #2966, 0.37 #1157, 0.33 #3208), 02l7c8 (0.30 #8327, 0.29 #496, 0.28 #8086), 070yc (0.25 #213, 0.22 #813, 0.20 #453), 082gq (0.25 #630, 0.19 #1352, 0.17 #1712) >> Best rule #7112 for best value: >> intensional similarity = 7 >> extensional distance = 544 >> proper extension: 0cnztc4; 0h95zbp; 02h22; 0dkv90; 072r5v; 0k2m6; 0581vn8; 0cbl95; >> query: (?x10492, 02kdv5l) <- genre(?x10492, ?x2540), genre(?x9698, ?x2540), genre(?x2155, ?x2540), genre(?x1847, ?x2540), ?x2155 = 0407yfx, ?x9698 = 031f_m, ?x1847 = 02rb84n >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #3137 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 49 *> proper extension: 0g56t9t; 02_fm2; 06w99h3; 0h1cdwq; 02qm_f; 0k2sk; 01c22t; 04hwbq; 0jnwx; 0407yfx; ... *> query: (?x10492, 04t36) <- film(?x3651, ?x10492), genre(?x10492, ?x2540), country(?x10492, ?x94), ?x94 = 09c7w0, music(?x10492, ?x669), ?x2540 = 0hcr *> conf = 0.22 ranks of expected_values: 13 EVAL 0199wf genre 04t36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 99.000 87.000 0.841 http://example.org/film/film/genre #18883-030znt PRED entity: 030znt PRED relation: award_nominee! PRED expected values: 05lb65 => 101 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1209): 05lb87 (0.82 #16200, 0.82 #16198, 0.81 #6941), 038g2x (0.82 #16200, 0.82 #16198, 0.81 #6941), 046m59 (0.82 #16200, 0.82 #16198, 0.81 #6941), 05lb65 (0.82 #16200, 0.82 #16198, 0.81 #6941), 01wb8bs (0.82 #16200, 0.82 #16198, 0.81 #6941), 026_w57 (0.82 #16200, 0.82 #16198, 0.81 #6941), 01dbk6 (0.82 #16200, 0.82 #16198, 0.81 #6941), 03lt8g (0.77 #30083, 0.77 #83308, 0.76 #83307), 05dxl5 (0.77 #30083, 0.77 #83308, 0.76 #83307), 03zqc1 (0.76 #71732, 0.75 #43969, 0.75 #94879) >> Best rule #16200 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 058ncz; 03zqc1; 09r9dp; 0308kx; 08pth9; 09btt1; 07s95_l; 03v1jf; 06vsbt; 0d810y; ... >> query: (?x1343, ?x3272) <- award_nominee(?x1343, ?x3272), award_nominee(?x1343, ?x1342), ?x1342 = 05lb87 >> conf = 0.82 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 030znt award_nominee! 05lb65 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 101.000 46.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18882-0plw PRED entity: 0plw PRED relation: company! PRED expected values: 0dq3c => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 37): 0dq_5 (0.90 #4926, 0.89 #4245, 0.86 #607), 0krdk (0.76 #2643, 0.73 #1687, 0.72 #2095), 060c4 (0.62 #457, 0.62 #5546, 0.62 #593), 05_wyz (0.57 #608, 0.50 #472, 0.41 #1789), 02211by (0.50 #4, 0.31 #5227, 0.18 #1684), 0dq3c (0.47 #2638, 0.45 #3272, 0.43 #592), 09d6p2 (0.44 #473, 0.43 #609, 0.31 #5227), 01kr6k (0.38 #481, 0.33 #617, 0.31 #5227), 014l7h (0.33 #73, 0.22 #527, 0.19 #663), 021q0l (0.33 #9, 0.13 #6135, 0.13 #5815) >> Best rule #4926 for best value: >> intensional similarity = 5 >> extensional distance = 97 >> proper extension: 034f0d; 0d6qjf; 01kcmr; >> query: (?x12850, 0dq_5) <- company(?x12865, ?x12850), company(?x12865, ?x13314), company(?x12865, ?x9873), ?x9873 = 01dfb6, ?x13314 = 06py2 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2638 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 53 *> proper extension: 0l8sx; *> query: (?x12850, 0dq3c) <- company(?x12865, ?x12850), service_language(?x12850, ?x254), company(?x12865, ?x9873), ?x9873 = 01dfb6, state_province_region(?x12850, ?x335) *> conf = 0.47 ranks of expected_values: 6 EVAL 0plw company! 0dq3c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 184.000 184.000 0.899 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #18881-0229rs PRED entity: 0229rs PRED relation: artist PRED expected values: 01vrwfv => 121 concepts (52 used for prediction) PRED predicted values (max 10 best out of 901): 01wg25j (0.60 #2229, 0.29 #3855, 0.25 #1416), 016szr (0.60 #1965, 0.24 #22775, 0.20 #2778), 0qf3p (0.50 #5028, 0.50 #963, 0.40 #1776), 0fhxv (0.50 #1134, 0.38 #5199, 0.24 #22775), 0150jk (0.50 #848, 0.25 #4913, 0.24 #22775), 01jfr3y (0.50 #1225, 0.25 #5290, 0.24 #22775), 01whg97 (0.50 #578, 0.24 #22775, 0.20 #2205), 08w4pm (0.50 #568, 0.20 #2195, 0.15 #23343), 01k23t (0.50 #548, 0.17 #23323, 0.17 #6240), 0g824 (0.50 #443, 0.17 #6135, 0.16 #11017) >> Best rule #2229 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 01cl2y; 01cf93; >> query: (?x3050, 01wg25j) <- artist(?x3050, ?x12357), artist(?x3050, ?x4239), artist(?x3050, ?x997), artists(?x3061, ?x997), ?x3061 = 05bt6j, ?x4239 = 0x3b7, group(?x227, ?x12357) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4244 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 09b3v; 04czhj; *> query: (?x3050, 01vrwfv) <- citytown(?x3050, ?x11930), state_province_region(?x3050, ?x1227), ?x11930 = 0r00l, category(?x3050, ?x134), ?x134 = 08mbj5d *> conf = 0.14 ranks of expected_values: 439 EVAL 0229rs artist 01vrwfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 121.000 52.000 0.600 http://example.org/music/record_label/artist #18880-0dvmd PRED entity: 0dvmd PRED relation: award PRED expected values: 0gqy2 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 292): 05p09zm (0.77 #3941, 0.70 #40194, 0.70 #48866), 027cyf7 (0.77 #3941, 0.70 #40194, 0.70 #48866), 0ck27z (0.33 #17820, 0.29 #19396, 0.26 #25306), 0gqyl (0.29 #100, 0.14 #9458, 0.12 #41771), 09qwmm (0.29 #32, 0.14 #9458, 0.12 #41771), 09td7p (0.29 #116, 0.14 #9458, 0.12 #41771), 099t8j (0.29 #135, 0.14 #9458, 0.12 #41771), 03qgjwc (0.29 #175, 0.14 #9458, 0.12 #41771), 0gq9h (0.25 #5985, 0.22 #11108, 0.18 #12684), 05b4l5x (0.22 #2370, 0.21 #4341, 0.21 #6311) >> Best rule #3941 for best value: >> intensional similarity = 2 >> extensional distance = 106 >> proper extension: 01xyt7; >> query: (?x3101, ?x2325) <- award_winner(?x2325, ?x3101), vacationer(?x362, ?x3101) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #2126 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 0g476; *> query: (?x3101, 0gqy2) <- participant(?x1017, ?x3101), student(?x4955, ?x3101), award_winner(?x638, ?x3101) *> conf = 0.15 ranks of expected_values: 21 EVAL 0dvmd award 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 141.000 141.000 0.766 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18879-0lgsq PRED entity: 0lgsq PRED relation: notable_people_with_this_condition! PRED expected values: 0j8hd => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 7): 0h99n (0.06 #10, 0.04 #208, 0.04 #76), 02vrr (0.04 #25, 0.02 #69, 0.02 #113), 068p_ (0.04 #42, 0.02 #86, 0.01 #416), 01g2q (0.02 #75, 0.01 #207, 0.01 #383), 029sk (0.01 #199, 0.01 #221, 0.01 #265), 03p41 (0.01 #270), 0j8hd (0.01 #499) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01qrbf; >> query: (?x1152, 0h99n) <- award_winner(?x5400, ?x1152), artist(?x4483, ?x1152), student(?x1151, ?x1152), profession(?x1152, ?x220) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #499 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 181 *> proper extension: 02zfdp; *> query: (?x1152, 0j8hd) <- award(?x1152, ?x247), profession(?x1152, ?x220), nominated_for(?x1152, ?x5400), artists(?x114, ?x1152) *> conf = 0.01 ranks of expected_values: 7 EVAL 0lgsq notable_people_with_this_condition! 0j8hd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 153.000 153.000 0.056 http://example.org/medicine/disease/notable_people_with_this_condition #18878-015xp4 PRED entity: 015xp4 PRED relation: artists! PRED expected values: 016cjb => 155 concepts (94 used for prediction) PRED predicted values (max 10 best out of 257): 064t9 (0.69 #15780, 0.62 #3412, 0.60 #4649), 025sc50 (0.51 #3449, 0.30 #4686, 0.29 #5922), 0glt670 (0.39 #3440, 0.30 #10242, 0.23 #5913), 02x8m (0.39 #945, 0.28 #3418, 0.23 #7127), 037n97 (0.33 #253, 0.07 #7362, 0.06 #1180), 05bt6j (0.33 #15811, 0.27 #11174, 0.27 #661), 016cjb (0.28 #1001, 0.20 #3474, 0.18 #383), 0m40d (0.27 #455, 0.17 #146, 0.08 #7255), 015y_n (0.27 #529, 0.06 #7329, 0.06 #3310), 0ggx5q (0.27 #4714, 0.26 #5950, 0.24 #5641) >> Best rule #15780 for best value: >> intensional similarity = 4 >> extensional distance = 390 >> proper extension: 01k5t_3; 05_pkf; 02bgmr; 018y81; 026yqrr; 024y6w; 01vs8ng; >> query: (?x5140, 064t9) <- artists(?x3928, ?x5140), nationality(?x5140, ?x94), artists(?x3928, ?x4960), ?x4960 = 09889g >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1001 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 01vrx3g; *> query: (?x5140, 016cjb) <- artists(?x3319, ?x5140), artist(?x4483, ?x5140), people(?x3799, ?x5140), ?x3319 = 06j6l *> conf = 0.28 ranks of expected_values: 7 EVAL 015xp4 artists! 016cjb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 155.000 94.000 0.691 http://example.org/music/genre/artists #18877-02sh8y PRED entity: 02sh8y PRED relation: actor! PRED expected values: 034fl9 => 136 concepts (114 used for prediction) PRED predicted values (max 10 best out of 102): 08cx5g (0.20 #65, 0.03 #863, 0.02 #1393), 05h95s (0.20 #144), 016ky6 (0.12 #14867, 0.12 #17259, 0.12 #3715), 072kp (0.08 #277, 0.06 #1603, 0.03 #808), 01ft14 (0.08 #471, 0.03 #1002, 0.02 #1267), 02_1q9 (0.08 #272, 0.03 #2129, 0.02 #2394), 07g9f (0.08 #468, 0.02 #2855, 0.02 #1794), 016zfm (0.08 #379, 0.02 #1705, 0.01 #2236), 02rcwq0 (0.08 #355, 0.02 #1681, 0.01 #3537), 0330r (0.08 #456, 0.02 #1782) >> Best rule #65 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01gy7r; 013vdl; 03kts; >> query: (?x5813, 08cx5g) <- languages(?x5813, ?x254), film(?x5813, ?x5812), film(?x5813, ?x3218), award(?x5812, ?x834), ?x3218 = 0ds2n >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2302 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 05ztm4r; 02tf1y; *> query: (?x5813, 034fl9) <- languages(?x5813, ?x254), location(?x5813, ?x739), nationality(?x5813, ?x94), ?x739 = 02_286 *> conf = 0.03 ranks of expected_values: 33 EVAL 02sh8y actor! 034fl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 136.000 114.000 0.200 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #18876-04bbb8 PRED entity: 04bbb8 PRED relation: combatants! PRED expected values: 0jnh => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 69): 0845v (0.78 #671, 0.78 #608, 0.71 #271), 0jnh (0.73 #710, 0.71 #804, 0.61 #1684), 0cm2xh (0.70 #1154, 0.59 #1358, 0.47 #1629), 0k4y6 (0.67 #898, 0.60 #1100, 0.50 #1142), 081pw (0.65 #1212, 0.56 #2166, 0.36 #2919), 01hwkn (0.62 #520, 0.60 #1056, 0.60 #248), 0dr7s (0.60 #247, 0.50 #181, 0.45 #1055), 06k75 (0.59 #1293, 0.40 #1909, 0.38 #2045), 03gqgt3 (0.58 #1741, 0.29 #1469, 0.27 #1267), 086m1 (0.50 #1142, 0.50 #265, 0.44 #198) >> Best rule #671 for best value: >> intensional similarity = 14 >> extensional distance = 7 >> proper extension: 0285m87; 0cn_tpv; >> query: (?x13821, ?x1777) <- combatants(?x11910, ?x13821), combatants(?x13256, ?x13821), entity_involved(?x11910, ?x5609), combatants(?x11910, ?x13859), combatants(?x1777, ?x13256), entity_involved(?x9798, ?x13256), combatants(?x13256, ?x9328), combatants(?x13256, ?x8949), ?x8949 = 0dv0z, ?x1777 = 0845v, ?x13859 = 043870, ?x9328 = 024pcx, locations(?x11910, ?x8483), combatants(?x9798, ?x1611) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #710 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 9 *> proper extension: 03gk2; 01m3dv; 05pq3_; *> query: (?x13821, 0jnh) <- combatants(?x11910, ?x13821), combatants(?x11910, ?x13662), combatants(?x11910, ?x12118), combatants(?x11910, ?x6371), entity_involved(?x11910, ?x5609), locations(?x11910, ?x8483), people(?x13662, ?x1128), ?x6371 = 014tss, films(?x11910, ?x586), ?x8483 = 059g4, ?x12118 = 03x1x, language(?x586, ?x254), genre(?x586, ?x53), nominated_for(?x143, ?x586), nominated_for(?x5363, ?x586) *> conf = 0.73 ranks of expected_values: 2 EVAL 04bbb8 combatants! 0jnh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 58.000 0.778 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #18875-0ws7 PRED entity: 0ws7 PRED relation: draft PRED expected values: 02qw1zx 03nt7j => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 16): 03nt7j (0.81 #836, 0.81 #1050, 0.80 #1067), 02qw1zx (0.81 #836, 0.81 #1050, 0.80 #1067), 02pq_x5 (0.54 #193, 0.37 #930, 0.31 #979), 0f4vx0 (0.45 #860, 0.44 #827, 0.32 #714), 02pq_rp (0.41 #923, 0.34 #972, 0.30 #1005), 02r6gw6 (0.38 #927, 0.32 #976, 0.27 #1009), 04f4z1k (0.38 #931, 0.31 #980, 0.31 #1064), 02z6872 (0.38 #924, 0.31 #973, 0.31 #1040), 047dpm0 (0.38 #932, 0.31 #981, 0.27 #1014), 025tn92 (0.38 #861, 0.37 #828, 0.31 #1059) >> Best rule #836 for best value: >> intensional similarity = 16 >> extensional distance = 50 >> proper extension: 0jmfv; 0jm2v; 0jmmn; 0jml5; 0jmbv; 0jm64; 0jm4v; 0jmjr; 0jmhr; 0jm5b; ... >> query: (?x7078, ?x1883) <- draft(?x7078, ?x6462), team(?x1240, ?x7078), school(?x6462, ?x7338), school(?x6462, ?x726), school(?x6462, ?x388), location(?x5217, ?x726), team(?x1240, ?x6645), school(?x7078, ?x331), school(?x1160, ?x7338), position(?x706, ?x1240), adjoins(?x726, ?x1138), institution(?x620, ?x7338), ?x388 = 05krk, draft(?x6645, ?x1883), ?x1160 = 049n7, jurisdiction_of_office(?x900, ?x726) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2 EVAL 0ws7 draft 03nt7j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.815 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 0ws7 draft 02qw1zx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.815 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #18874-01g4yw PRED entity: 01g4yw PRED relation: school_type PRED expected values: 07tf8 => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 18): 05pcjw (0.36 #946, 0.29 #714, 0.26 #1550), 01rs41 (0.31 #949, 0.29 #1765, 0.29 #1600), 02p0qmm (0.27 #170, 0.17 #285, 0.15 #377), 07tf8 (0.20 #583, 0.19 #744, 0.18 #537), 01_9fk (0.19 #807, 0.16 #508, 0.16 #531), 01y64 (0.11 #103, 0.11 #80, 0.09 #126), 01jlsn (0.11 #85, 0.09 #131, 0.05 #568), 01_srz (0.08 #948, 0.07 #1320, 0.06 #1552), 04399 (0.04 #772, 0.04 #726, 0.04 #634), 0bwd5 (0.03 #616, 0.03 #754, 0.03 #3990) >> Best rule #946 for best value: >> intensional similarity = 5 >> extensional distance = 209 >> proper extension: 02_2kg; 026036; >> query: (?x13052, 05pcjw) <- state_province_region(?x13052, ?x12774), school_type(?x13052, ?x3092), contains(?x12774, ?x4049), category(?x12774, ?x134), time_zones(?x12774, ?x5327) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #583 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 92 *> proper extension: 08815; *> query: (?x13052, 07tf8) <- institution(?x1368, ?x13052), institution(?x1200, ?x13052), major_field_of_study(?x13052, ?x3440), school_type(?x13052, ?x3092), ?x1200 = 016t_3, ?x1368 = 014mlp *> conf = 0.20 ranks of expected_values: 4 EVAL 01g4yw school_type 07tf8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 194.000 194.000 0.355 http://example.org/education/educational_institution/school_type #18873-0h1cdwq PRED entity: 0h1cdwq PRED relation: film! PRED expected values: 01pk3z => 76 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1197): 04pqqb (0.16 #12472, 0.13 #8315, 0.13 #6236), 0f0kz (0.12 #2593, 0.08 #12987, 0.07 #4672), 0bxtg (0.12 #14627, 0.11 #27097, 0.04 #6313), 086nl7 (0.12 #15335, 0.06 #7021, 0.05 #27805), 0p8r1 (0.09 #2663, 0.05 #4742, 0.05 #8900), 01r93l (0.08 #747, 0.05 #6983, 0.04 #4904), 016z2j (0.08 #389, 0.05 #12861, 0.04 #2467), 0b_dy (0.08 #534, 0.04 #8849, 0.03 #17163), 055c8 (0.08 #542, 0.04 #13014, 0.04 #2620), 01chc7 (0.08 #559, 0.04 #13031, 0.03 #25501) >> Best rule #12472 for best value: >> intensional similarity = 6 >> extensional distance = 81 >> proper extension: 01gglm; >> query: (?x428, ?x4854) <- film_crew_role(?x428, ?x137), currency(?x428, ?x170), executive_produced_by(?x428, ?x4854), film(?x8365, ?x428), country(?x428, ?x94), artists(?x671, ?x8365) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #15537 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 83 *> proper extension: 014zwb; 03bzyn4; *> query: (?x428, 01pk3z) <- film_crew_role(?x428, ?x137), film(?x7795, ?x428), award_nominee(?x3082, ?x7795), genre(?x428, ?x258), program(?x7795, ?x3630), ?x258 = 05p553 *> conf = 0.02 ranks of expected_values: 460 EVAL 0h1cdwq film! 01pk3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 44.000 0.163 http://example.org/film/actor/film./film/performance/film #18872-01_6dw PRED entity: 01_6dw PRED relation: people! PRED expected values: 041rx => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 32): 041rx (0.25 #4, 0.17 #389, 0.17 #466), 02w7gg (0.25 #2, 0.07 #156, 0.06 #2312), 013xrm (0.25 #20, 0.05 #713, 0.05 #790), 0x67 (0.10 #5708, 0.10 #2936, 0.10 #3244), 07bch9 (0.09 #562, 0.08 #639, 0.03 #5721), 02ctzb (0.08 #554, 0.07 #631, 0.03 #785), 07hwkr (0.08 #89, 0.06 #628, 0.05 #859), 013b6_ (0.08 #130, 0.04 #592, 0.03 #669), 022dp5 (0.08 #127, 0.01 #281, 0.01 #743), 033tf_ (0.07 #2163, 0.07 #546, 0.07 #2856) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 07h1q; >> query: (?x6534, 041rx) <- place_of_birth(?x6534, ?x1131), influenced_by(?x6534, ?x1645), ?x1645 = 017r2 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_6dw people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.250 http://example.org/people/ethnicity/people #18871-0d060g PRED entity: 0d060g PRED relation: taxonomy PRED expected values: 04n6k => 197 concepts (197 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.82 #40, 0.82 #93, 0.81 #73) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0160w; >> query: (?x279, 04n6k) <- contains(?x279, ?x481), country(?x136, ?x279), country(?x1036, ?x279) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d060g taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 197.000 197.000 0.822 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #18870-01vrncs PRED entity: 01vrncs PRED relation: type_of_union PRED expected values: 04ztj => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #81, 0.83 #105, 0.79 #25), 01g63y (0.20 #278, 0.19 #449, 0.16 #30), 0jgjn (0.19 #449, 0.02 #52, 0.02 #56), 01bl8s (0.19 #449, 0.01 #111, 0.01 #87) >> Best rule #81 for best value: >> intensional similarity = 2 >> extensional distance = 84 >> proper extension: 01l3j; >> query: (?x1089, 04ztj) <- celebrities_impersonated(?x8145, ?x1089), film(?x1089, ?x10931) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrncs type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.884 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18869-03hfxx PRED entity: 03hfxx PRED relation: people! PRED expected values: 019dmc => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 38): 0qcr0 (0.17 #1, 0.07 #331, 0.07 #859), 0gk4g (0.13 #1858, 0.13 #1990, 0.13 #1924), 0dq9p (0.09 #875, 0.08 #1337, 0.07 #1997), 04p3w (0.08 #275, 0.07 #77, 0.07 #539), 02y0js (0.07 #68, 0.05 #332, 0.05 #1982), 01psyx (0.07 #111, 0.04 #771, 0.03 #507), 02k6hp (0.05 #103, 0.05 #367, 0.05 #301), 01_qc_ (0.05 #292, 0.04 #490, 0.04 #94), 01l2m3 (0.04 #280, 0.04 #1006, 0.04 #82), 02knxx (0.04 #1022, 0.03 #1946, 0.03 #2012) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 075wq; >> query: (?x8970, 0qcr0) <- place_of_death(?x8970, ?x3670), contains(?x3670, ?x331), first_level_division_of(?x3670, ?x94), state_province_region(?x3351, ?x3670) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #314 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 96 *> proper extension: 07c37; *> query: (?x8970, 019dmc) <- place_of_death(?x8970, ?x3670), contains(?x3670, ?x10767), jurisdiction_of_office(?x900, ?x3670), adjoins(?x10767, ?x12846) *> conf = 0.02 ranks of expected_values: 27 EVAL 03hfxx people! 019dmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 117.000 117.000 0.167 http://example.org/people/cause_of_death/people #18868-049nq PRED entity: 049nq PRED relation: contains PRED expected values: 07g0_ 051ls => 110 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2886): 0k3p (0.83 #108060, 0.81 #131426, 0.80 #122665), 07g0_ (0.75 #75932, 0.61 #64248, 0.50 #16180), 051ls (0.75 #75932, 0.61 #64248, 0.40 #268712), 06hdk (0.75 #75932, 0.50 #15936, 0.40 #268712), 01lvrm (0.75 #75932, 0.40 #268712, 0.37 #181082), 07w6r (0.75 #75932, 0.40 #268712, 0.37 #181082), 0q19t (0.75 #75932, 0.40 #268712, 0.37 #181082), 02nq10 (0.75 #75932, 0.40 #268712, 0.35 #265791), 0345h (0.64 #172320, 0.62 #96378, 0.61 #184004), 059qw (0.64 #172320, 0.62 #96378, 0.61 #184004) >> Best rule #108060 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 07f1x; >> query: (?x10382, ?x8252) <- form_of_government(?x10382, ?x1926), capital(?x10382, ?x8252), currency(?x10382, ?x170) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #75932 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 0d0vqn; 05v8c; 0k6nt; 01znc_; 035dk; 03shp; 03spz; 05vz3zq; 0166b; 04hqz; ... *> query: (?x10382, ?x11172) <- contains(?x10382, ?x11173), official_language(?x10382, ?x254), contains(?x11173, ?x11172) *> conf = 0.75 ranks of expected_values: 2, 3 EVAL 049nq contains 051ls CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 92.000 0.831 http://example.org/location/location/contains EVAL 049nq contains 07g0_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 92.000 0.831 http://example.org/location/location/contains #18867-0cc5mcj PRED entity: 0cc5mcj PRED relation: film_release_region PRED expected values: 0jgd 0154j 06t8v => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 177): 0154j (0.81 #406, 0.78 #2288, 0.64 #809), 0jgd (0.80 #2287, 0.79 #405, 0.74 #1210), 06qd3 (0.67 #430, 0.58 #833, 0.50 #2312), 0ctw_b (0.66 #420, 0.55 #2302, 0.45 #1360), 06t8v (0.59 #461, 0.43 #2343, 0.35 #864), 016wzw (0.57 #450, 0.49 #2332, 0.40 #1390), 06f32 (0.57 #449, 0.42 #2331, 0.39 #852), 0h7x (0.53 #427, 0.45 #1367, 0.43 #1232), 06npd (0.41 #416, 0.28 #3895, 0.25 #2298), 06c1y (0.40 #433, 0.35 #2315, 0.28 #3895) >> Best rule #406 for best value: >> intensional similarity = 5 >> extensional distance = 56 >> proper extension: 0bhwhj; 0g5qmbz; >> query: (?x2441, 0154j) <- film_release_region(?x2441, ?x4743), film_release_region(?x2441, ?x1892), ?x1892 = 02vzc, written_by(?x2441, ?x2442), ?x4743 = 03spz >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5 EVAL 0cc5mcj film_release_region 06t8v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.810 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc5mcj film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.810 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc5mcj film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.810 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18866-0223g8 PRED entity: 0223g8 PRED relation: type_of_union PRED expected values: 04ztj => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.84 #9, 0.83 #1, 0.80 #37), 01g63y (0.20 #333, 0.16 #42, 0.14 #46), 01bl8s (0.20 #333, 0.02 #19, 0.02 #27), 0jgjn (0.20 #333) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 01hkck; >> query: (?x10998, 04ztj) <- profession(?x10998, ?x353), actor(?x5946, ?x10998), people(?x4322, ?x10998) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0223g8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.839 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18865-01l1rw PRED entity: 01l1rw PRED relation: nationality PRED expected values: 09c7w0 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 59): 09c7w0 (0.73 #1803, 0.71 #701, 0.71 #4806), 02jx1 (0.34 #7107, 0.16 #333, 0.15 #233), 0d060g (0.25 #6506, 0.11 #8510, 0.06 #607), 03rjj (0.25 #6506, 0.11 #8510, 0.05 #105), 02_286 (0.25 #6506, 0.04 #2303, 0.04 #1601), 059rby (0.25 #6506), 07ssc (0.11 #8510, 0.11 #315, 0.11 #1215), 0345h (0.11 #8510, 0.11 #131, 0.06 #531), 0cdbq (0.11 #8510, 0.08 #63, 0.03 #463), 03rk0 (0.11 #8510, 0.06 #5051, 0.06 #4951) >> Best rule #1803 for best value: >> intensional similarity = 3 >> extensional distance = 170 >> proper extension: 025xt8y; 016ksk; 012xdf; 02zfdp; >> query: (?x5720, 09c7w0) <- artists(?x4910, ?x5720), award_nominee(?x6011, ?x5720), student(?x2909, ?x5720) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l1rw nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.727 http://example.org/people/person/nationality #18864-049d_ PRED entity: 049d_ PRED relation: teams! PRED expected values: 04f_d => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 108): 035yg (0.17 #180, 0.08 #11353, 0.08 #9187), 0126hc (0.17 #234, 0.08 #11353, 0.08 #9187), 01fbb3 (0.17 #214, 0.08 #11353, 0.08 #9187), 0m75g (0.17 #159, 0.08 #699, 0.07 #4053), 02m77 (0.08 #11353, 0.08 #9187, 0.07 #4053), 0k33p (0.07 #469, 0.01 #1010, 0.01 #1281), 0dhdp (0.07 #304, 0.01 #845, 0.01 #1116), 02jx1 (0.05 #9729, 0.05 #4325, 0.04 #811), 013wf1 (0.04 #768, 0.01 #1039, 0.01 #1310), 0ck6r (0.04 #738, 0.01 #1009, 0.01 #1280) >> Best rule #180 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0182r9; 01x4wq; 023fb; 046f25; >> query: (?x8504, 035yg) <- position(?x8504, ?x203), position(?x8504, ?x63), position(?x8504, ?x60), ?x60 = 02nzb8, position(?x8504, ?x530), team(?x6523, ?x8504), sport(?x8504, ?x471), ?x203 = 0dgrmp, ?x471 = 02vx4, ?x63 = 02sdk9v, ?x6523 = 0d1swh >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 049d_ teams! 04f_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 64.000 0.167 http://example.org/sports/sports_team_location/teams #18863-05gpy PRED entity: 05gpy PRED relation: nationality PRED expected values: 09c7w0 => 166 concepts (137 used for prediction) PRED predicted values (max 10 best out of 54): 09c7w0 (0.90 #8659, 0.89 #9977, 0.89 #8150), 05k7sb (0.39 #12107, 0.38 #12310, 0.35 #10786), 0k3hn (0.38 #12310, 0.35 #10786, 0.34 #11494), 0f8l9c (0.33 #322, 0.33 #22, 0.20 #222), 059g4 (0.32 #12716, 0.29 #13855, 0.27 #9976), 04_1l0v (0.32 #12716, 0.29 #13855, 0.27 #9976), 029jpy (0.32 #12716, 0.29 #13855, 0.27 #9976), 0k3k1 (0.29 #13855, 0.27 #11393, 0.27 #9976), 050ks (0.25 #10481, 0.24 #12511, 0.02 #1104), 07ssc (0.22 #1220, 0.20 #1927, 0.20 #215) >> Best rule #8659 for best value: >> intensional similarity = 4 >> extensional distance = 677 >> proper extension: 0337vz; 0cb77r; 06gp3f; 01dw4q; 01rrwf6; 041ly3; 012c6x; 0415svh; 057d89; 02lkcc; ... >> query: (?x6320, 09c7w0) <- gender(?x6320, ?x231), place_of_birth(?x6320, ?x12012), county(?x12012, ?x6905), time_zones(?x12012, ?x2674) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05gpy nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 137.000 0.900 http://example.org/people/person/nationality #18862-06cgy PRED entity: 06cgy PRED relation: award_winner PRED expected values: 01wz01 => 127 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1038): 016khd (0.82 #49804, 0.82 #56229, 0.82 #27309), 01wz01 (0.82 #49804, 0.82 #56229, 0.82 #27309), 0mdqp (0.52 #86756, 0.52 #101214, 0.45 #112463), 01q_ph (0.52 #86756, 0.52 #101214, 0.45 #112463), 0f7hc (0.52 #86756, 0.52 #101214, 0.45 #112463), 01kt17 (0.52 #86756, 0.52 #101214, 0.45 #112463), 0h0wc (0.52 #86756, 0.52 #101214, 0.45 #112463), 0b13g7 (0.52 #86756, 0.52 #101214, 0.45 #112463), 02x0dzw (0.52 #86756, 0.52 #101214, 0.45 #112463), 05cl2w (0.52 #86756, 0.52 #101214, 0.45 #112463) >> Best rule #49804 for best value: >> intensional similarity = 3 >> extensional distance = 490 >> proper extension: 04nw9; 05jjl; >> query: (?x1554, ?x163) <- award_winner(?x591, ?x1554), award_winner(?x163, ?x1554), people(?x743, ?x1554) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 06cgy award_winner 01wz01 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 72.000 0.817 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #18861-03dm7 PRED entity: 03dm7 PRED relation: location! PRED expected values: 04bs3j => 73 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1365): 06cgy (0.71 #27699, 0.70 #47845, 0.70 #32736), 021mlp (0.71 #27699, 0.70 #47845, 0.70 #32736), 0136pk (0.30 #12588, 0.28 #20144, 0.28 #10070), 01vrncs (0.30 #12588, 0.28 #10070, 0.27 #20143), 011vx3 (0.30 #12588, 0.28 #10070, 0.27 #20143), 0ddkf (0.12 #6417, 0.04 #47846, 0.03 #8934), 0sx5w (0.12 #7175, 0.04 #47846, 0.02 #12211), 013w7j (0.12 #6283, 0.04 #47846, 0.02 #8800), 01kkx2 (0.12 #7381, 0.04 #47846, 0.02 #9898), 0d0l91 (0.12 #7313, 0.04 #47846, 0.02 #9830) >> Best rule #27699 for best value: >> intensional similarity = 4 >> extensional distance = 359 >> proper extension: 01914; >> query: (?x11527, ?x12822) <- place_of_birth(?x12822, ?x11527), place_of_birth(?x1554, ?x11527), location(?x12822, ?x739), nominated_for(?x1554, ?x887) >> conf = 0.71 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 03dm7 location! 04bs3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 27.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location #18860-03nx8mj PRED entity: 03nx8mj PRED relation: genre PRED expected values: 0556j8 => 86 concepts (85 used for prediction) PRED predicted values (max 10 best out of 117): 07s9rl0 (0.70 #8392, 0.60 #2913, 0.60 #1941), 01z4y (0.62 #7296, 0.62 #7539, 0.61 #7782), 02kdv5l (0.45 #1821, 0.36 #2185, 0.33 #1458), 01jfsb (0.38 #1831, 0.37 #2195, 0.32 #2318), 02l7c8 (0.36 #502, 0.36 #2564, 0.33 #138), 03k9fj (0.34 #1830, 0.33 #1588, 0.30 #740), 06cvj (0.30 #489, 0.25 #4, 0.21 #2430), 02n4kr (0.28 #129, 0.16 #978, 0.14 #1221), 04xvlr (0.28 #2549, 0.18 #5351, 0.17 #2914), 01hmnh (0.26 #1837, 0.25 #1595, 0.25 #19) >> Best rule #8392 for best value: >> intensional similarity = 4 >> extensional distance = 1349 >> proper extension: 07ng9k; 0cks1m; 05pyrb; 02v5xg; >> query: (?x4176, 07s9rl0) <- genre(?x4176, ?x258), film(?x1986, ?x4176), genre(?x2788, ?x258), ?x2788 = 05q4y12 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #529 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 05cj_j; *> query: (?x4176, 0556j8) <- nominated_for(?x4176, ?x3619), genre(?x4176, ?x258), film(?x5636, ?x4176), ?x258 = 05p553 *> conf = 0.08 ranks of expected_values: 32 EVAL 03nx8mj genre 0556j8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 86.000 85.000 0.697 http://example.org/film/film/genre #18859-09hnb PRED entity: 09hnb PRED relation: award PRED expected values: 02v1m7 => 125 concepts (112 used for prediction) PRED predicted values (max 10 best out of 252): 01by1l (0.82 #1565, 0.79 #7431, 0.78 #35997), 02hgm4 (0.82 #1565, 0.79 #7431, 0.78 #35997), 01bgqh (0.56 #825, 0.32 #2390, 0.30 #1608), 02f716 (0.53 #955, 0.17 #1346, 0.12 #39129), 0c4z8 (0.38 #853, 0.24 #8284, 0.24 #6719), 02f73p (0.38 #966, 0.17 #1357, 0.12 #2140), 02v1m7 (0.38 #894, 0.11 #10281, 0.11 #112), 01ckcd (0.34 #1497, 0.16 #31299, 0.15 #34038), 09sb52 (0.33 #23899, 0.25 #21552, 0.21 #30557), 01c99j (0.31 #1001, 0.18 #33646, 0.16 #31299) >> Best rule #1565 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 02lbrd; 0134tg; 0l8g0; 015cxv; 0b_xm; 0134pk; 0jg77; >> query: (?x2698, ?x1079) <- award_winner(?x1079, ?x2698), award(?x2698, ?x4912), ?x4912 = 01ckrr >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #894 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 0152cw; 0frsw; *> query: (?x2698, 02v1m7) <- instrumentalists(?x228, ?x2698), award(?x2698, ?x2877), ?x2877 = 02f5qb *> conf = 0.38 ranks of expected_values: 7 EVAL 09hnb award 02v1m7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 125.000 112.000 0.819 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18858-01qhm_ PRED entity: 01qhm_ PRED relation: people PRED expected values: 0jfx1 0227tr 0f_y9 02qhm3 => 31 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2192): 01vwllw (0.50 #5476, 0.38 #8843, 0.33 #3792), 01rrd4 (0.50 #5939, 0.38 #9306, 0.33 #4255), 06qgvf (0.50 #5060, 0.38 #8427, 0.33 #3376), 08f3b1 (0.40 #6828, 0.25 #8511, 0.14 #18610), 0261x8t (0.40 #7681, 0.14 #19463, 0.13 #22830), 0g824 (0.38 #9293, 0.33 #4242, 0.25 #5926), 07r1h (0.38 #9264, 0.33 #844, 0.25 #5897), 0f7fy (0.38 #9328, 0.33 #908, 0.25 #5961), 01pk3z (0.38 #9183, 0.25 #5816, 0.20 #14232), 08vr94 (0.38 #8944, 0.25 #5577, 0.20 #7261) >> Best rule #5476 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 09vc4s; >> query: (?x1423, 01vwllw) <- people(?x1423, ?x6771), people(?x1423, ?x5625), people(?x1423, ?x5000), people(?x1423, ?x4106), ?x5625 = 0bx_q, executive_produced_by(?x3116, ?x6771), award_nominee(?x5000, ?x1384), award_winner(?x693, ?x6771), participant(?x1733, ?x4106), nominated_for(?x6771, ?x1210) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8745 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 6 *> proper extension: 013xrm; 07bch9; *> query: (?x1423, 0227tr) <- people(?x1423, ?x9105), people(?x1423, ?x5625), people(?x1423, ?x4106), participant(?x4106, ?x629), religion(?x5625, ?x1985), nominated_for(?x4106, ?x1490), type_of_union(?x9105, ?x566), diet(?x9105, ?x3130), participant(?x5625, ?x489), executive_produced_by(?x3053, ?x9105) *> conf = 0.25 ranks of expected_values: 194, 200, 232, 305 EVAL 01qhm_ people 02qhm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 31.000 23.000 0.500 http://example.org/people/ethnicity/people EVAL 01qhm_ people 0f_y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 23.000 0.500 http://example.org/people/ethnicity/people EVAL 01qhm_ people 0227tr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 31.000 23.000 0.500 http://example.org/people/ethnicity/people EVAL 01qhm_ people 0jfx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 31.000 23.000 0.500 http://example.org/people/ethnicity/people #18857-0gy7bj4 PRED entity: 0gy7bj4 PRED relation: film! PRED expected values: 02tr7d => 85 concepts (36 used for prediction) PRED predicted values (max 10 best out of 844): 02q42j_ (0.48 #16659, 0.45 #24989, 0.44 #70813), 07h07 (0.48 #16659, 0.45 #24989, 0.44 #70813), 0b13g7 (0.11 #45820, 0.05 #43737, 0.04 #39569), 0p8r1 (0.06 #2667, 0.06 #4749, 0.05 #585), 01nm3s (0.06 #689, 0.03 #2771, 0.03 #4853), 0c_gcr (0.06 #1646, 0.03 #14140, 0.03 #16222), 08qxx9 (0.06 #1520, 0.03 #14014, 0.02 #16096), 016ypb (0.06 #498, 0.03 #12992, 0.02 #25487), 0bxtg (0.05 #2159, 0.05 #4241, 0.04 #77), 0h0wc (0.05 #2505, 0.05 #4587, 0.03 #6670) >> Best rule #16659 for best value: >> intensional similarity = 5 >> extensional distance = 176 >> proper extension: 014lc_; 04969y; 0h3xztt; 0fq7dv_; 01fmys; 0407yfx; 0407yj_; 0j43swk; 011ycb; 01sby_; ... >> query: (?x9839, ?x1634) <- film_release_region(?x9839, ?x1003), film_release_region(?x9839, ?x789), nominated_for(?x1634, ?x9839), ?x789 = 0f8l9c, ?x1003 = 03gj2 >> conf = 0.48 => this is the best rule for 2 predicted values *> Best rule #10679 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 142 *> proper extension: 016z43; *> query: (?x9839, 02tr7d) <- film(?x1634, ?x9839), nominated_for(?x484, ?x9839), ?x484 = 0gq_v, honored_for(?x8407, ?x9839) *> conf = 0.02 ranks of expected_values: 273 EVAL 0gy7bj4 film! 02tr7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 85.000 36.000 0.479 http://example.org/film/actor/film./film/performance/film #18856-0dx84s PRED entity: 0dx84s PRED relation: season! PRED expected values: 051vz 0x0d => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 337): 051vz (0.85 #7, 0.82 #51, 0.68 #21), 0x0d (0.85 #7, 0.82 #51, 0.68 #21), 051wf (0.85 #7, 0.82 #51, 0.57 #20), 021f30 (0.68 #21, 0.14 #27, 0.09 #32), 04b5l3 (0.68 #21, 0.14 #27, 0.09 #32), 04c9bn (0.68 #21, 0.14 #27, 0.09 #32), 03qrh9 (0.68 #21, 0.09 #32, 0.09 #14), 04913k (0.68 #21, 0.09 #32, 0.09 #14), 02hfgl (0.68 #21, 0.08 #15, 0.08 #4), 02h8p8 (0.68 #21, 0.08 #15, 0.08 #4) >> Best rule #7 for best value: >> intensional similarity = 117 >> extensional distance = 1 >> proper extension: 03c6sl9; >> query: (?x9267, ?x2174) <- season(?x12042, ?x9267), season(?x11361, ?x9267), season(?x10279, ?x9267), season(?x8995, ?x9267), season(?x8901, ?x9267), season(?x8894, ?x9267), season(?x8111, ?x9267), season(?x7725, ?x9267), season(?x7399, ?x9267), season(?x7357, ?x9267), season(?x7060, ?x9267), season(?x6823, ?x9267), season(?x6074, ?x9267), season(?x4487, ?x9267), season(?x4243, ?x9267), season(?x4208, ?x9267), season(?x3333, ?x9267), season(?x2405, ?x9267), season(?x2067, ?x9267), season(?x1823, ?x9267), season(?x1632, ?x9267), season(?x1438, ?x9267), season(?x1160, ?x9267), season(?x1010, ?x9267), season(?x700, ?x9267), season(?x662, ?x9267), season(?x580, ?x9267), season(?x260, ?x9267), ?x1160 = 049n7, ?x4243 = 0713r, ?x11361 = 03m1n, ?x662 = 03lpp_, ?x260 = 01ypc, ?x2405 = 0x2p, ?x4487 = 01ync, ?x1632 = 0cqt41, ?x3333 = 01yjl, ?x580 = 05m_8, ?x6074 = 02__x, ?x7060 = 01slc, ?x7357 = 04mjl, ?x8111 = 07147, ?x8901 = 07l4z, ?x6823 = 07l8f, ?x8894 = 02d02, ?x7399 = 06wpc, ?x10279 = 04wmvz, ?x1010 = 01d5z, ?x2067 = 05g76, ?x700 = 06x68, ?x7725 = 07l8x, ?x12042 = 05xvj, ?x4208 = 061xq, ?x8995 = 01d6g, ?x1823 = 01yhm, season(?x1438, ?x8529), season(?x1438, ?x3431), season(?x1438, ?x701), school(?x1438, ?x9131), school(?x1438, ?x8706), school(?x1438, ?x5621), school(?x1438, ?x3948), school(?x1438, ?x3779), school(?x1438, ?x1011), school(?x1438, ?x466), ?x466 = 01pl14, colors(?x1438, ?x8271), colors(?x1438, ?x1101), draft(?x1438, ?x10600), draft(?x1438, ?x8786), draft(?x1438, ?x4779), draft(?x1438, ?x3334), draft(?x1438, ?x1633), ?x3431 = 025ygqm, team(?x12238, ?x1438), team(?x261, ?x1438), ?x12238 = 02dwpf, ?x1101 = 06fvc, ?x1633 = 02rl201, ?x10600 = 04f4z1k, season(?x12956, ?x8529), season(?x2174, ?x8529), ?x1011 = 07w0v, ?x4779 = 02z6872, colors(?x11919, ?x8271), colors(?x9995, ?x8271), colors(?x7247, ?x8271), colors(?x11559, ?x8271), colors(?x9620, ?x8271), colors(?x8833, ?x8271), ?x12956 = 051wf, ?x9995 = 0jm9w, ?x9620 = 02l424, ?x7247 = 04991x, ?x3334 = 02pq_rp, major_field_of_study(?x8706, ?x947), ?x701 = 05kcgsf, ?x8786 = 02pq_x5, student(?x8706, ?x1817), ?x11559 = 02bpy_, ?x5621 = 01vs5c, currency(?x8706, ?x170), ?x8833 = 0173s9, institution(?x1526, ?x8706), institution(?x1200, ?x8706), school(?x2820, ?x8706), ?x261 = 02dwn9, organization(?x346, ?x8706), ?x3948 = 025v3k, ?x1526 = 0bkj86, ?x1200 = 016t_3, contains(?x94, ?x9131), school(?x4171, ?x8706), institution(?x620, ?x9131), ?x11919 = 04b5l3, ?x2820 = 0jmj7, state_province_region(?x3779, ?x3778) >> conf = 0.85 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2 EVAL 0dx84s season! 0x0d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.852 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 0dx84s season! 051vz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.852 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #18855-0mn6 PRED entity: 0mn6 PRED relation: profession! PRED expected values: 08304 04lg6 => 36 concepts (19 used for prediction) PRED predicted values (max 10 best out of 4283): 01tdnyh (0.60 #10131, 0.13 #35553, 0.12 #48265), 0m93 (0.60 #10869, 0.07 #49003, 0.06 #74428), 0372p (0.60 #9702, 0.05 #73261, 0.05 #35124), 034ks (0.60 #11745, 0.05 #49879, 0.04 #75304), 01t_z (0.60 #11616, 0.05 #49750, 0.04 #75175), 0gz_ (0.60 #9598, 0.03 #47732, 0.03 #73157), 051ysmf (0.42 #50840, 0.02 #50625, 0.01 #76050), 03s9v (0.40 #10841, 0.17 #8471, 0.03 #65925), 01zwy (0.40 #11274, 0.09 #49408, 0.07 #40931), 05d1y (0.40 #11202, 0.07 #49336, 0.05 #40859) >> Best rule #10131 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 06q2q; 04s2z; 05snw; >> query: (?x8368, 01tdnyh) <- profession(?x11077, ?x8368), profession(?x5249, ?x8368), nationality(?x11077, ?x94), location(?x11077, ?x739), ?x94 = 09c7w0, ?x5249 = 0dx97, student(?x3439, ?x11077) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6353 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 0n1h; *> query: (?x8368, 08304) <- profession(?x14143, ?x8368), profession(?x11077, ?x8368), profession(?x7296, ?x8368), profession(?x5249, ?x8368), ?x11077 = 0d__g, award_winner(?x484, ?x14143), influenced_by(?x7296, ?x2240), location(?x7296, ?x863), gender(?x5249, ?x231), student(?x892, ?x5249) *> conf = 0.33 ranks of expected_values: 17, 44 EVAL 0mn6 profession! 04lg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 36.000 19.000 0.600 http://example.org/people/person/profession EVAL 0mn6 profession! 08304 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 36.000 19.000 0.600 http://example.org/people/person/profession #18854-01243b PRED entity: 01243b PRED relation: parent_genre! PRED expected values: 0175zz => 84 concepts (50 used for prediction) PRED predicted values (max 10 best out of 303): 01ym9b (0.50 #281, 0.40 #770, 0.33 #1261), 0xjl2 (0.50 #1505, 0.33 #1751, 0.21 #2490), 059kh (0.40 #773, 0.38 #1509, 0.36 #2494), 01h0kx (0.40 #850, 0.33 #1341, 0.33 #116), 06cp5 (0.40 #801, 0.33 #1292, 0.29 #2522), 01738f (0.40 #819, 0.33 #1310, 0.25 #330), 0xv2x (0.38 #1584, 0.22 #1830, 0.21 #2569), 0y2tr (0.38 #1688, 0.22 #1934, 0.20 #952), 08cg36 (0.38 #1692, 0.20 #956, 0.17 #1447), 01b4p4 (0.38 #1617, 0.20 #881, 0.17 #1372) >> Best rule #281 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 0glt670; >> query: (?x2996, 01ym9b) <- artists(?x2996, ?x2187), artists(?x2996, ?x1826), parent_genre(?x2996, ?x302), ?x1826 = 09mq4m, person(?x1619, ?x2187), role(?x2187, ?x432), role(?x2187, ?x1166), role(?x74, ?x432) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2593 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 12 *> proper extension: 016clz; 02x8m; 06by7; 01g888; 02qdgx; 05bt6j; 07gxw; 01fh36; 09nwwf; 01f9y_; *> query: (?x2996, 0175zz) <- artists(?x2996, ?x7865), artists(?x2996, ?x1826), parent_genre(?x2996, ?x302), instrumentalists(?x212, ?x1826), ?x7865 = 02k5sc, artist(?x3265, ?x1826), award_winner(?x8407, ?x1826) *> conf = 0.14 ranks of expected_values: 97 EVAL 01243b parent_genre! 0175zz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 84.000 50.000 0.500 http://example.org/music/genre/parent_genre #18853-0gn30 PRED entity: 0gn30 PRED relation: location PRED expected values: 02_286 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 173): 02_286 (0.70 #64253, 0.61 #29717, 0.41 #87544), 0r0m6 (0.33 #217, 0.25 #1020, 0.14 #1823), 030qb3t (0.29 #8916, 0.26 #7310, 0.22 #20964), 01n7q (0.29 #1669, 0.08 #4078, 0.05 #8896), 018lc_ (0.25 #1602, 0.02 #4011), 0vzm (0.15 #2581, 0.05 #4990, 0.04 #3384), 059rby (0.15 #2425, 0.05 #25717, 0.04 #6440), 01qh7 (0.15 #2565, 0.04 #3368, 0.03 #4974), 0k049 (0.14 #1614, 0.08 #4023, 0.07 #16062), 0f2wj (0.14 #1640, 0.08 #2443, 0.02 #28947) >> Best rule #64253 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 0f1pyf; 07m69t; 02x8kk; 0459z; >> query: (?x5338, ?x739) <- location(?x5338, ?x2254), place_of_birth(?x5338, ?x739) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gn30 location 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #18852-08fbnx PRED entity: 08fbnx PRED relation: genre PRED expected values: 0jxy => 108 concepts (56 used for prediction) PRED predicted values (max 10 best out of 134): 07s9rl0 (0.91 #3026, 0.78 #1979, 0.67 #6407), 01jfsb (0.86 #6068, 0.55 #2223, 0.49 #6185), 05p553 (0.85 #3612, 0.77 #4194, 0.67 #5943), 0jxy (0.82 #1319, 0.75 #1554, 0.75 #970), 04rlf (0.40 #757, 0.06 #6056, 0.06 #5705), 02l7c8 (0.39 #4322, 0.31 #4205, 0.31 #5954), 04t2t (0.33 #286, 0.25 #402, 0.20 #1214), 095bb (0.33 #58, 0.20 #638, 0.20 #522), 02n4kr (0.31 #6181, 0.30 #6297, 0.26 #6064), 0lsxr (0.31 #6065, 0.25 #6182, 0.24 #6298) >> Best rule #3026 for best value: >> intensional similarity = 11 >> extensional distance = 86 >> proper extension: 0g5qs2k; >> query: (?x4770, 07s9rl0) <- genre(?x4770, ?x1626), genre(?x4770, ?x1510), ?x1510 = 01hmnh, genre(?x9893, ?x1626), genre(?x9279, ?x1626), genre(?x1283, ?x1626), genre(?x534, ?x1626), ?x9279 = 05y0cr, ?x1283 = 0cnztc4, ?x9893 = 0dmn0x, ?x534 = 04nl83 >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #1319 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 9 *> proper extension: 02pb2bp; *> query: (?x4770, 0jxy) <- country(?x4770, ?x252), genre(?x4770, ?x1013), genre(?x4770, ?x811), ?x1013 = 06n90, actor(?x4770, ?x6414), language(?x4770, ?x254), genre(?x6510, ?x811), film_crew_role(?x6510, ?x137) *> conf = 0.82 ranks of expected_values: 4 EVAL 08fbnx genre 0jxy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 56.000 0.909 http://example.org/film/film/genre #18851-0fh694 PRED entity: 0fh694 PRED relation: honored_for! PRED expected values: 03gwpw2 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 116): 0g5b0q5 (0.29 #14, 0.12 #135, 0.07 #256), 0hr6lkl (0.18 #133, 0.04 #254, 0.03 #2916), 0fqpc7d (0.14 #29, 0.06 #150, 0.04 #271), 0g55tzk (0.14 #119, 0.06 #240, 0.04 #361), 058m5m4 (0.14 #45, 0.06 #166, 0.02 #1255), 04110lv (0.14 #94, 0.06 #215, 0.02 #1304), 02glmx (0.14 #68, 0.04 #310, 0.02 #1278), 092c5f (0.14 #10, 0.03 #2067, 0.02 #2551), 09gkdln (0.12 #226, 0.05 #2162, 0.04 #2283), 09pj68 (0.12 #210, 0.03 #2146, 0.03 #2267) >> Best rule #14 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 02c638; 03hkch7; 0c38gj; >> query: (?x964, 0g5b0q5) <- nominated_for(?x591, ?x964), nominated_for(?x451, ?x964), production_companies(?x964, ?x382), ?x591 = 0f4x7, ?x451 = 099jhq >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2062 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 387 *> proper extension: 07s8z_l; *> query: (?x964, 03gwpw2) <- honored_for(?x6594, ?x964), titles(?x812, ?x964), award_winner(?x964, ?x2646) *> conf = 0.05 ranks of expected_values: 30 EVAL 0fh694 honored_for! 03gwpw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 82.000 82.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18850-047n8xt PRED entity: 047n8xt PRED relation: film_crew_role PRED expected values: 09vw2b7 01xy5l_ => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 29): 09vw2b7 (0.68 #1664, 0.65 #730, 0.65 #1630), 0dxtw (0.38 #1668, 0.36 #1739, 0.35 #1808), 01pvkk (0.31 #1530, 0.29 #2260, 0.28 #1982), 0215hd (0.24 #411, 0.16 #17, 0.15 #1815), 089g0h (0.24 #411, 0.13 #1816, 0.12 #742), 0d2b38 (0.24 #411, 0.13 #748, 0.12 #609), 02_n3z (0.24 #411, 0.12 #308, 0.11 #3046), 01xy5l_ (0.24 #411, 0.12 #563, 0.12 #737), 02rh1dz (0.24 #411, 0.11 #3046, 0.10 #733), 02vs3x5 (0.24 #411, 0.11 #3046, 0.09 #90) >> Best rule #1664 for best value: >> intensional similarity = 4 >> extensional distance = 723 >> proper extension: 01jnc_; >> query: (?x2121, 09vw2b7) <- film_release_distribution_medium(?x2121, ?x81), genre(?x2121, ?x53), film_crew_role(?x2121, ?x1284), ?x1284 = 0ch6mp2 >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 047n8xt film_crew_role 01xy5l_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 121.000 121.000 0.676 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 047n8xt film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.676 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18849-01hcvm PRED entity: 01hcvm PRED relation: parent_genre PRED expected values: 05r6t => 61 concepts (41 used for prediction) PRED predicted values (max 10 best out of 177): 05r6t (0.55 #1519, 0.51 #2829, 0.43 #1030), 06by7 (0.50 #666, 0.45 #1808, 0.44 #2625), 0xhtw (0.50 #1805, 0.43 #1151, 0.33 #13), 03lty (0.46 #2793, 0.38 #1647, 0.33 #507), 01243b (0.40 #1329, 0.33 #679, 0.33 #517), 05bt6j (0.33 #680, 0.29 #1005, 0.25 #193), 017371 (0.33 #106, 0.25 #269, 0.20 #432), 011j5x (0.33 #510, 0.20 #1322, 0.20 #348), 0jrv_ (0.26 #1628, 0.19 #1957, 0.17 #4412), 01_bkd (0.26 #1628, 0.19 #1957, 0.17 #4412) >> Best rule #1519 for best value: >> intensional similarity = 10 >> extensional distance = 9 >> proper extension: 03gt7s; >> query: (?x7124, 05r6t) <- parent_genre(?x7124, ?x13938), parent_genre(?x7124, ?x7808), parent_genre(?x13938, ?x2249), ?x7808 = 0jmwg, artists(?x2249, ?x12506), artists(?x2249, ?x6241), artists(?x2249, ?x2073), ?x2073 = 01czx, ?x12506 = 01518s, ?x6241 = 07bzp >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hcvm parent_genre 05r6t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 41.000 0.545 http://example.org/music/genre/parent_genre #18848-01xbxn PRED entity: 01xbxn PRED relation: film! PRED expected values: 03xq0f => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 55): 03xq0f (0.83 #598, 0.82 #449, 0.77 #523), 056ws9 (0.51 #3655, 0.48 #3730, 0.46 #1565), 05qd_ (0.33 #157, 0.31 #231, 0.23 #305), 016tw3 (0.33 #11, 0.17 #1950, 0.16 #3591), 025tlyv (0.33 #58, 0.07 #206, 0.06 #428), 0fqy4p (0.33 #102, 0.02 #398, 0.02 #472), 086k8 (0.21 #372, 0.19 #595, 0.19 #224), 016tt2 (0.16 #522, 0.14 #448, 0.13 #2167), 0jz9f (0.15 #297, 0.07 #967, 0.07 #1641), 017s11 (0.14 #3063, 0.13 #151, 0.13 #1569) >> Best rule #598 for best value: >> intensional similarity = 2 >> extensional distance = 115 >> proper extension: 04nlb94; >> query: (?x8028, 03xq0f) <- film_crew_role(?x8028, ?x468), region(?x8028, ?x512) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xbxn film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.829 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18847-0fx02 PRED entity: 0fx02 PRED relation: written_by! PRED expected values: 014kq6 => 156 concepts (125 used for prediction) PRED predicted values (max 10 best out of 385): 01v1ln (0.25 #34970, 0.23 #29691, 0.23 #36292), 03r0g9 (0.25 #34970, 0.23 #29691, 0.22 #40919), 02sg5v (0.23 #29691, 0.22 #11216, 0.22 #40919), 0g5pv3 (0.23 #29691, 0.22 #11216, 0.22 #47520), 0g5pvv (0.23 #29691, 0.22 #47520, 0.21 #11217), 01kf5lf (0.23 #36292, 0.22 #47520, 0.21 #40920), 0fsw_7 (0.23 #36292, 0.22 #47520, 0.21 #40920), 02n72k (0.22 #11216, 0.22 #40919, 0.22 #47520), 0fxmbn (0.22 #11216, 0.22 #47520, 0.21 #40920), 02gqm3 (0.22 #11216, 0.22 #47520, 0.21 #40920) >> Best rule #34970 for best value: >> intensional similarity = 8 >> extensional distance = 129 >> proper extension: 012t1; 05gpy; 0p50v; >> query: (?x3686, ?x3693) <- story_by(?x7713, ?x3686), story_by(?x5870, ?x3686), story_by(?x5598, ?x3686), story_by(?x3693, ?x3686), language(?x5598, ?x254), film_release_distribution_medium(?x7713, ?x81), award_winner(?x3693, ?x1018), genre(?x5870, ?x604) >> conf = 0.25 => this is the best rule for 2 predicted values *> Best rule #13868 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 40 *> proper extension: 05np2; *> query: (?x3686, ?x835) <- story_by(?x5598, ?x3686), story_by(?x2506, ?x3686), nominated_for(?x1261, ?x2506), nominated_for(?x835, ?x2506), film(?x1194, ?x5598), profession(?x3686, ?x353), genre(?x1261, ?x812) *> conf = 0.12 ranks of expected_values: 18 EVAL 0fx02 written_by! 014kq6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 156.000 125.000 0.246 http://example.org/film/film/written_by #18846-02ptczs PRED entity: 02ptczs PRED relation: genre PRED expected values: 03npn => 115 concepts (113 used for prediction) PRED predicted values (max 10 best out of 93): 01jfsb (0.69 #12, 0.66 #252, 0.59 #612), 05p553 (0.41 #1444, 0.39 #1204, 0.39 #1684), 0lsxr (0.40 #608, 0.34 #8, 0.32 #248), 02kdv5l (0.35 #722, 0.32 #1202, 0.30 #1082), 02l7c8 (0.32 #3376, 0.32 #2776, 0.31 #976), 03npn (0.29 #7, 0.26 #247, 0.10 #607), 03k9fj (0.28 #731, 0.27 #851, 0.25 #1091), 0c3351 (0.23 #277, 0.21 #37, 0.14 #637), 04xvlr (0.21 #1321, 0.20 #8522, 0.19 #9242), 060__y (0.18 #617, 0.17 #3377, 0.16 #2777) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 04sh80; >> query: (?x9772, 01jfsb) <- titles(?x3613, ?x9772), genre(?x9772, ?x53), ?x3613 = 09blyk, film_release_distribution_medium(?x9772, ?x81) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 04sh80; *> query: (?x9772, 03npn) <- titles(?x3613, ?x9772), genre(?x9772, ?x53), ?x3613 = 09blyk, film_release_distribution_medium(?x9772, ?x81) *> conf = 0.29 ranks of expected_values: 6 EVAL 02ptczs genre 03npn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 115.000 113.000 0.690 http://example.org/film/film/genre #18845-05fjf PRED entity: 05fjf PRED relation: category PRED expected values: 08mbj5d => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.68 #40, 0.68 #17, 0.67 #39) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 0z1vw; >> query: (?x6895, 08mbj5d) <- location(?x8535, ?x6895), place_of_birth(?x4509, ?x6895), celebrity(?x8122, ?x8535) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fjf category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.680 http://example.org/common/topic/webpage./common/webpage/category #18844-064lsn PRED entity: 064lsn PRED relation: nominated_for! PRED expected values: 099c8n 04kxsb => 93 concepts (87 used for prediction) PRED predicted values (max 10 best out of 232): 0f4x7 (0.68 #12905, 0.68 #680, 0.68 #1359), 02rdyk7 (0.68 #12905, 0.68 #680, 0.68 #1359), 02wkmx (0.68 #12905, 0.68 #680, 0.68 #1359), 02qt02v (0.68 #680, 0.68 #1359, 0.67 #13133), 04kxsb (0.49 #766, 0.24 #9732, 0.24 #1671), 019f4v (0.46 #731, 0.42 #1636, 0.33 #4123), 04dn09n (0.41 #713, 0.39 #1618, 0.32 #1812), 0gqy2 (0.34 #791, 0.30 #1696, 0.24 #9843), 0gqyl (0.34 #752, 0.30 #1657, 0.21 #4371), 02x4wr9 (0.32 #1812, 0.30 #4299, 0.27 #11996) >> Best rule #12905 for best value: >> intensional similarity = 3 >> extensional distance = 978 >> proper extension: 07bz5; >> query: (?x6121, ?x3209) <- award(?x6121, ?x3209), award(?x4360, ?x3209), award_winner(?x2177, ?x4360) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #766 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 09qycb; *> query: (?x6121, 04kxsb) <- genre(?x6121, ?x53), nominated_for(?x3209, ?x6121), film(?x1104, ?x6121), ?x3209 = 02w9sd7 *> conf = 0.49 ranks of expected_values: 5, 17 EVAL 064lsn nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 87.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 064lsn nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 93.000 87.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18843-0lkm PRED entity: 0lkm PRED relation: month! PRED expected values: 02_286 0fhp9 080h2 030qb3t 056_y 03khn => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 162): 056_y (0.90 #18, 0.88 #90, 0.86 #48), 030qb3t (0.90 #18, 0.88 #90, 0.86 #48), 02_286 (0.90 #18, 0.88 #90, 0.86 #48), 03khn (0.90 #18, 0.88 #90, 0.86 #48), 0fhp9 (0.90 #18, 0.88 #90, 0.86 #48), 080h2 (0.90 #18, 0.88 #90, 0.86 #48), 0l0mk (0.90 #18, 0.88 #90, 0.86 #48), 03czqs (0.90 #18, 0.88 #90, 0.86 #48), 059rby (0.58 #88, 0.58 #67, 0.24 #76), 042tq (0.58 #88, 0.58 #67) >> Best rule #18 for best value: >> intensional similarity = 95 >> extensional distance = 1 >> proper extension: 03_ly; >> query: (?x6303, ?x739) <- month(?x10143, ?x6303), month(?x8602, ?x6303), month(?x8252, ?x6303), month(?x6494, ?x6303), month(?x6357, ?x6303), month(?x5036, ?x6303), month(?x3501, ?x6303), month(?x3373, ?x6303), month(?x3269, ?x6303), month(?x3106, ?x6303), month(?x3052, ?x6303), month(?x2985, ?x6303), month(?x2645, ?x6303), month(?x2316, ?x6303), month(?x2277, ?x6303), month(?x1860, ?x6303), month(?x659, ?x6303), month(?x108, ?x6303), ?x3052 = 01cx_, ?x5036 = 06y57, seasonal_months(?x9905, ?x6303), seasonal_months(?x7298, ?x6303), seasonal_months(?x4827, ?x6303), seasonal_months(?x1459, ?x6303), ?x2645 = 03h64, ?x10143 = 0h3tv, ?x108 = 0rh6k, ?x6494 = 02sn34, state_province_region(?x8694, ?x6357), place_of_birth(?x585, ?x6357), seasonal_months(?x6303, ?x3107), featured_film_locations(?x1002, ?x6357), location(?x11396, ?x6357), location(?x6356, ?x6357), location(?x3651, ?x6357), location(?x3118, ?x6357), location(?x489, ?x6357), ?x3501 = 0f2v0, participant(?x989, ?x3118), participant(?x3118, ?x2221), award(?x3651, ?x2071), profession(?x3118, ?x131), award_nominee(?x9236, ?x489), award_nominee(?x7186, ?x489), award_nominee(?x6122, ?x489), award_nominee(?x5743, ?x489), award_nominee(?x5454, ?x489), award_nominee(?x2284, ?x489), award_nominee(?x1410, ?x489), award_nominee(?x1222, ?x489), award_nominee(?x380, ?x489), ?x2071 = 0bdw6t, film(?x489, ?x2029), profession(?x489, ?x1383), nationality(?x489, ?x94), ?x7298 = 04wzr, ?x2985 = 03hrz, ?x3106 = 049d1, award_winner(?x594, ?x3651), ?x7186 = 01qrbf, film(?x3118, ?x7299), ?x380 = 0m2wm, film(?x3651, ?x463), ?x3269 = 0vzm, ?x1410 = 01yhvv, award_winner(?x2757, ?x3118), ?x659 = 02cl1, vacationer(?x6357, ?x1656), award(?x3118, ?x247), ?x6122 = 016xh5, ?x2277 = 013yq, ?x2284 = 07hbxm, film_release_region(?x6394, ?x6357), friend(?x7571, ?x3118), participant(?x513, ?x489), ?x9905 = 028kb, ?x1860 = 01_d4, ?x3107 = 05lf_, ?x2316 = 06t2t, ?x5454 = 020_95, student(?x11459, ?x11396), month(?x739, ?x4827), ?x8602 = 0chgzm, written_by(?x2151, ?x6356), ?x9236 = 02fz3w, ?x5743 = 0175wg, ?x8252 = 0k3p, film(?x6356, ?x1071), award_nominee(?x3651, ?x3002), ?x1222 = 03f1zdw, ?x3373 = 0ply0, ?x1459 = 04w_7, people(?x7322, ?x6356), award(?x6356, ?x198), ?x2029 = 020bv3 >> conf = 0.90 => this is the best rule for 8 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6 EVAL 0lkm month! 03khn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0lkm month! 056_y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0lkm month! 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0lkm month! 080h2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0lkm month! 0fhp9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0lkm month! 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #18842-01vv6xv PRED entity: 01vv6xv PRED relation: category PRED expected values: 08mbj5d => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.87 #19, 0.87 #36, 0.85 #59) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 011_vz; >> query: (?x11443, 08mbj5d) <- role(?x11443, ?x1466), artist(?x1954, ?x11443), ?x1466 = 03bx0bm, origin(?x11443, ?x1523) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vv6xv category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 194.000 0.867 http://example.org/common/topic/webpage./common/webpage/category #18841-025g__ PRED entity: 025g__ PRED relation: artists PRED expected values: 012d40 => 39 concepts (14 used for prediction) PRED predicted values (max 10 best out of 3192): 06mt91 (0.67 #3846, 0.57 #8157, 0.50 #9235), 049qx (0.67 #3618, 0.57 #7929, 0.38 #9007), 018n6m (0.67 #3643, 0.57 #7954, 0.33 #5799), 02wb6yq (0.62 #8897, 0.57 #6741, 0.50 #5664), 03t9sp (0.57 #7668, 0.50 #8746, 0.50 #5513), 01dwrc (0.57 #8070, 0.50 #9148, 0.50 #5915), 01v_pj6 (0.57 #7663, 0.50 #3352, 0.33 #1196), 03y82t6 (0.50 #9046, 0.50 #5813, 0.50 #3657), 01vtj38 (0.50 #6053, 0.50 #3897, 0.43 #8208), 0127s7 (0.50 #5931, 0.50 #3775, 0.43 #8086) >> Best rule #3846 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 06j6l; 0ggx5q; >> query: (?x9401, 06mt91) <- artists(?x9401, ?x8947), artists(?x9401, ?x2306), ?x8947 = 017b2p, award_winner(?x1079, ?x2306), performance_role(?x2306, ?x228), instrumentalists(?x316, ?x2306), role(?x2306, ?x615), gender(?x2306, ?x231) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #12938 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 56 *> proper extension: 01lxd4; 01fbr2; 0gt_0v; 0p9xd; 04z1v0; 0cx6f; *> query: (?x9401, ?x51) <- artists(?x9401, ?x8947), profession(?x8947, ?x6476), profession(?x8947, ?x1032), ?x6476 = 025352, location(?x8947, ?x4163), category(?x8947, ?x134), profession(?x2359, ?x1032), profession(?x51, ?x1032), ?x2359 = 0783m_ *> conf = 0.02 ranks of expected_values: 3167 EVAL 025g__ artists 012d40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 14.000 0.667 http://example.org/music/genre/artists #18840-02h22 PRED entity: 02h22 PRED relation: executive_produced_by PRED expected values: 04w1j9 => 119 concepts (64 used for prediction) PRED predicted values (max 10 best out of 129): 05hj_k (0.20 #2359, 0.16 #6881, 0.15 #11155), 06q8hf (0.17 #2428, 0.14 #6950, 0.13 #11224), 01twdk (0.17 #364, 0.09 #1368, 0.08 #3629), 03c9pqt (0.14 #1501, 0.07 #10549, 0.05 #14820), 079vf (0.12 #7036, 0.07 #13571, 0.06 #14074), 0glyyw (0.12 #1948, 0.07 #11748, 0.07 #13758), 06pj8 (0.10 #3571, 0.09 #1310, 0.09 #9854), 02z6l5f (0.09 #1624, 0.07 #3132, 0.06 #11175), 02z2xdf (0.09 #1664, 0.07 #3172, 0.05 #911), 04jspq (0.09 #1406, 0.06 #4421, 0.05 #9950) >> Best rule #2359 for best value: >> intensional similarity = 6 >> extensional distance = 28 >> proper extension: 0bmpm; >> query: (?x5849, 05hj_k) <- executive_produced_by(?x5849, ?x8563), nominated_for(?x1243, ?x5849), ?x1243 = 0gr0m, genre(?x5849, ?x225), genre(?x4269, ?x225), ?x4269 = 05sns6 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02h22 executive_produced_by 04w1j9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 64.000 0.200 http://example.org/film/film/executive_produced_by #18839-02rxbmt PRED entity: 02rxbmt PRED relation: award_winner! PRED expected values: 05pd94v => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 134): 01s695 (0.85 #1099, 0.17 #551, 0.16 #825), 0gx1673 (0.35 #1625, 0.09 #940, 0.09 #666), 01bx35 (0.29 #281, 0.25 #1103, 0.15 #1514), 01c6qp (0.28 #1115, 0.24 #293, 0.15 #1526), 02cg41 (0.24 #398, 0.21 #1220, 0.14 #1631), 0466p0j (0.20 #622, 0.18 #485, 0.16 #896), 056878 (0.19 #990, 0.18 #716, 0.14 #442), 02rjjll (0.18 #690, 0.18 #1512, 0.17 #553), 05pd94v (0.18 #276, 0.18 #1509, 0.16 #687), 013b2h (0.17 #78, 0.16 #900, 0.15 #1037) >> Best rule #1099 for best value: >> intensional similarity = 5 >> extensional distance = 59 >> proper extension: 01czx; 0flpy; >> query: (?x5342, 01s695) <- award(?x5342, ?x77), award_winner(?x12139, ?x5342), ceremony(?x3666, ?x12139), award_winner(?x12139, ?x11186), ?x11186 = 01304j >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #276 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 01wbl_r; *> query: (?x5342, 05pd94v) <- award(?x5342, ?x77), award_winner(?x12139, ?x5342), ceremony(?x3666, ?x12139), ?x3666 = 02681xs *> conf = 0.18 ranks of expected_values: 9 EVAL 02rxbmt award_winner! 05pd94v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 117.000 117.000 0.852 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18838-09n4nb PRED entity: 09n4nb PRED relation: award_winner PRED expected values: 03fbc 02mjmr 02x_h0 01hmk9 016l09 => 39 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1532): 0fpjd_g (0.60 #6182, 0.57 #13650, 0.55 #18128), 06fmdb (0.60 #6757, 0.57 #11236, 0.50 #15718), 02qwg (0.60 #4976, 0.50 #15430, 0.43 #10948), 016srn (0.60 #3442, 0.45 #18374, 0.40 #6428), 06rgq (0.60 #5665, 0.43 #11637, 0.40 #7158), 02cx90 (0.57 #14087, 0.57 #12592, 0.55 #18565), 01m3b1t (0.57 #12987, 0.40 #4028, 0.36 #18960), 01lmj3q (0.55 #17955, 0.46 #19453, 0.43 #13477), 016szr (0.50 #8204, 0.33 #9697, 0.33 #734), 0lbj1 (0.50 #7493, 0.33 #8986, 0.29 #13468) >> Best rule #6182 for best value: >> intensional similarity = 23 >> extensional distance = 3 >> proper extension: 01s695; 01c6qp; >> query: (?x3121, 0fpjd_g) <- award_winner(?x3121, ?x6144), award_winner(?x3121, ?x5760), ceremony(?x12833, ?x3121), ceremony(?x11010, ?x3121), ceremony(?x6652, ?x3121), ceremony(?x4796, ?x3121), ceremony(?x3033, ?x3121), ceremony(?x2962, ?x3121), ceremony(?x1584, ?x3121), ceremony(?x1232, ?x3121), ceremony(?x567, ?x3121), award_winner(?x3391, ?x5760), ?x567 = 01d38g, ?x6652 = 01cw7s, ?x1584 = 02gx2k, ?x2962 = 02ddqh, ?x4796 = 01c99j, ?x3033 = 0257yf, ?x11010 = 02w7fs, award(?x5760, ?x884), ?x1232 = 0c4z8, ?x12833 = 0257pw, participant(?x1896, ?x6144) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #8963 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 4 *> proper extension: 01bx35; *> query: (?x3121, ?x3234) <- award_winner(?x3121, ?x6144), award_winner(?x3121, ?x6025), award_winner(?x3121, ?x5760), award_winner(?x3121, ?x2169), award_winner(?x3121, ?x1835), ceremony(?x6652, ?x3121), ceremony(?x4796, ?x3121), ceremony(?x3033, ?x3121), ceremony(?x2962, ?x3121), ceremony(?x1584, ?x3121), ceremony(?x567, ?x3121), award_winner(?x3391, ?x5760), ?x567 = 01d38g, ?x6652 = 01cw7s, ?x1584 = 02gx2k, ?x2962 = 02ddqh, ?x4796 = 01c99j, artists(?x474, ?x5760), award(?x6144, ?x2855), category_of(?x3033, ?x2421), award_winner(?x6025, ?x3234), company(?x1835, ?x1836), ?x2169 = 01w60_p *> conf = 0.22 ranks of expected_values: 194, 230, 352, 450, 688 EVAL 09n4nb award_winner 016l09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 39.000 19.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09n4nb award_winner 01hmk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 19.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09n4nb award_winner 02x_h0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 39.000 19.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09n4nb award_winner 02mjmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 19.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09n4nb award_winner 03fbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 19.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18837-022q32 PRED entity: 022q32 PRED relation: people! PRED expected values: 0x67 => 171 concepts (120 used for prediction) PRED predicted values (max 10 best out of 55): 041rx (0.27 #4, 0.23 #2623, 0.22 #81), 033tf_ (0.20 #7, 0.19 #3242, 0.19 #1547), 0x67 (0.20 #10, 0.18 #5247, 0.17 #5093), 0xnvg (0.18 #706, 0.17 #167, 0.16 #1014), 09vc4s (0.11 #1472, 0.11 #86, 0.11 #1857), 01qhm_ (0.11 #699, 0.11 #1854, 0.10 #1007), 07bch9 (0.10 #562, 0.10 #1101, 0.09 #716), 02w7gg (0.09 #772, 0.07 #2, 0.06 #4854), 059_w (0.08 #5392, 0.08 #7395, 0.08 #8321), 065b6q (0.08 #5392, 0.07 #1158, 0.06 #1466) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 01w7nwm; >> query: (?x10777, 041rx) <- celebrity(?x6035, ?x10777), profession(?x10777, ?x2225), gender(?x10777, ?x514), ?x2225 = 0kyk >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 01w7nwm; *> query: (?x10777, 0x67) <- celebrity(?x6035, ?x10777), profession(?x10777, ?x2225), gender(?x10777, ?x514), ?x2225 = 0kyk *> conf = 0.20 ranks of expected_values: 3 EVAL 022q32 people! 0x67 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 171.000 120.000 0.267 http://example.org/people/ethnicity/people #18836-01r47h PRED entity: 01r47h PRED relation: student PRED expected values: 0lrh => 126 concepts (109 used for prediction) PRED predicted values (max 10 best out of 1287): 02vyw (0.20 #582, 0.17 #2675, 0.11 #4768), 0c8br (0.20 #1388, 0.17 #3481, 0.11 #5574), 02hg53 (0.20 #1969), 03txms (0.20 #1377), 0n00 (0.18 #6826, 0.17 #2640, 0.11 #4733), 0d3k14 (0.17 #3948, 0.11 #6041, 0.09 #8134), 05p92jn (0.17 #3237, 0.11 #5330, 0.09 #7423), 013pp3 (0.17 #3018, 0.11 #5111, 0.09 #7204), 01my4f (0.17 #3290, 0.11 #5383, 0.09 #7476), 0hnjt (0.17 #2916, 0.11 #5009, 0.09 #7102) >> Best rule #582 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03t4nx; >> query: (?x11480, 02vyw) <- institution(?x1368, ?x11480), contains(?x6895, ?x11480), organization(?x346, ?x11480), ?x6895 = 05fjf >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #8829 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 0dplh; 0ylvj; 0677j; 0ymf1; *> query: (?x11480, 0lrh) <- student(?x11480, ?x8898), major_field_of_study(?x11480, ?x1668), organization(?x346, ?x11480), friend(?x917, ?x8898) *> conf = 0.02 ranks of expected_values: 344 EVAL 01r47h student 0lrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 126.000 109.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #18835-0ylzs PRED entity: 0ylzs PRED relation: colors PRED expected values: 06fvc => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.45 #1010, 0.44 #1637, 0.44 #1827), 038hg (0.36 #239, 0.22 #1189, 0.17 #942), 019sc (0.35 #1451, 0.31 #311, 0.30 #444), 06fvc (0.33 #1313, 0.33 #2, 0.32 #1484), 088fh (0.33 #25, 0.20 #443, 0.15 #253), 01l849 (0.29 #419, 0.28 #1027, 0.28 #1369), 0jc_p (0.16 #935, 0.14 #384, 0.10 #1030), 036k5h (0.14 #423, 0.11 #157, 0.10 #1354), 09ggk (0.13 #452, 0.11 #186, 0.09 #1839), 03wkwg (0.12 #337, 0.12 #147, 0.12 #128) >> Best rule #1010 for best value: >> intensional similarity = 6 >> extensional distance = 140 >> proper extension: 02d9nr; 02xwzh; 0dbns; >> query: (?x11158, 01g5v) <- student(?x11158, ?x5797), colors(?x11158, ?x663), state_province_region(?x11158, ?x2235), colors(?x8822, ?x663), ?x8822 = 020ddc, colors(?x260, ?x663) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1313 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 201 *> proper extension: 06b19; *> query: (?x11158, 06fvc) <- colors(?x11158, ?x663), colors(?x8826, ?x663), colors(?x1297, ?x663), institution(?x1368, ?x11158), ?x1297 = 03x746, ?x8826 = 03x6w8 *> conf = 0.33 ranks of expected_values: 4 EVAL 0ylzs colors 06fvc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 160.000 160.000 0.451 http://example.org/education/educational_institution/colors #18834-017v_ PRED entity: 017v_ PRED relation: state_province_region! PRED expected values: 01z3bz => 210 concepts (138 used for prediction) PRED predicted values (max 10 best out of 742): 05bkf (0.28 #62304, 0.28 #78087, 0.27 #86355), 05x30m (0.28 #62304, 0.28 #78087, 0.27 #86355), 0d34_ (0.28 #62304, 0.28 #78087, 0.27 #86355), 01cz_1 (0.28 #62304, 0.28 #78087, 0.27 #86355), 0d58_ (0.24 #21008, 0.23 #43535, 0.22 #52540), 02h6_6p (0.24 #21008, 0.23 #43535, 0.22 #52540), 09f8q (0.24 #21008, 0.23 #43535, 0.22 #52540), 035yzw (0.20 #2846, 0.17 #4346, 0.10 #8847), 0m7yh (0.17 #5614, 0.08 #10868, 0.08 #10116), 01z3bz (0.09 #56295, 0.05 #98388, 0.03 #27015) >> Best rule #62304 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 04sqj; 0f485; >> query: (?x1679, ?x12638) <- contains(?x1679, ?x12638), country(?x1679, ?x1264), adjoins(?x8264, ?x1679), category(?x12638, ?x134) >> conf = 0.28 => this is the best rule for 4 predicted values *> Best rule #56295 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 04jpl; 03fb3t; 0d6br; 01zst8; 014wxc; *> query: (?x1679, ?x11717) <- contains(?x1679, ?x2611), place_of_death(?x7572, ?x2611), contains(?x2611, ?x11717), contains(?x1264, ?x1679) *> conf = 0.09 ranks of expected_values: 10 EVAL 017v_ state_province_region! 01z3bz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 210.000 138.000 0.285 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #18833-01h8rk PRED entity: 01h8rk PRED relation: major_field_of_study PRED expected values: 05qjt 0g4gr => 211 concepts (181 used for prediction) PRED predicted values (max 10 best out of 119): 03g3w (0.67 #25, 0.65 #625, 0.41 #1585), 0h5k (0.67 #21, 0.35 #621, 0.18 #3846), 04rjg (0.61 #618, 0.56 #18, 0.44 #4227), 01lj9 (0.56 #38, 0.48 #638, 0.33 #1598), 05qfh (0.56 #34, 0.48 #634, 0.31 #1594), 037mh8 (0.56 #65, 0.48 #665, 0.26 #1625), 0fdys (0.56 #37, 0.48 #637, 0.23 #1597), 05qjt (0.52 #608, 0.44 #8, 0.39 #968), 01tbp (0.50 #1137, 0.31 #1737, 0.28 #3058), 02j62 (0.49 #1588, 0.48 #628, 0.47 #2308) >> Best rule #25 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 01nnsv; >> query: (?x5068, 03g3w) <- major_field_of_study(?x5068, ?x4321), major_field_of_study(?x5068, ?x1695), major_field_of_study(?x5068, ?x1668), school_type(?x5068, ?x1507), ?x1695 = 06ms6, ?x1668 = 01mkq, ?x4321 = 0g26h >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #608 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 0kz2w; *> query: (?x5068, 05qjt) <- major_field_of_study(?x5068, ?x1695), major_field_of_study(?x5068, ?x1668), school_type(?x5068, ?x1507), ?x1695 = 06ms6, ?x1668 = 01mkq *> conf = 0.52 ranks of expected_values: 8, 39 EVAL 01h8rk major_field_of_study 0g4gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 211.000 181.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01h8rk major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 211.000 181.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18832-0161h5 PRED entity: 0161h5 PRED relation: film PRED expected values: 0_7w6 029jt9 => 118 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1229): 0ckrnn (0.71 #53617, 0.67 #57192, 0.67 #66131), 0bvn25 (0.21 #50, 0.11 #7199, 0.11 #10774), 056xkh (0.21 #1597, 0.05 #17683, 0.05 #24832), 026wlxw (0.14 #1415, 0.07 #8564, 0.07 #12139), 02qydsh (0.14 #1496, 0.07 #17582, 0.07 #12220), 04hwbq (0.14 #192, 0.06 #14490, 0.06 #1979), 0661m4p (0.14 #374, 0.04 #5735, 0.04 #9311), 07bzz7 (0.12 #4461, 0.06 #22335, 0.03 #36631), 01jnc_ (0.10 #23014, 0.06 #33736, 0.06 #3353), 0b3n61 (0.09 #15654, 0.08 #6717, 0.07 #17442) >> Best rule #53617 for best value: >> intensional similarity = 4 >> extensional distance = 266 >> proper extension: 07c0j; 04qmr; >> query: (?x10929, ?x3614) <- award_winner(?x3989, ?x10929), participant(?x3628, ?x10929), nominated_for(?x10929, ?x3614), genre(?x3614, ?x600) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #34260 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 06_6j3; *> query: (?x10929, 0_7w6) <- film(?x10929, ?x5243), gender(?x10929, ?x514), genre(?x5243, ?x53), language(?x10929, ?x254) *> conf = 0.03 ranks of expected_values: 319, 902 EVAL 0161h5 film 029jt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 118.000 61.000 0.712 http://example.org/film/actor/film./film/performance/film EVAL 0161h5 film 0_7w6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 118.000 61.000 0.712 http://example.org/film/actor/film./film/performance/film #18831-04_tv PRED entity: 04_tv PRED relation: major_field_of_study! PRED expected values: 02h4rq6 03bwzr4 01ysy9 => 54 concepts (52 used for prediction) PRED predicted values (max 10 best out of 14): 02h4rq6 (0.78 #358, 0.77 #243, 0.76 #328), 03bwzr4 (0.77 #248, 0.71 #134, 0.71 #333), 04zx3q1 (0.62 #158, 0.60 #200, 0.57 #447), 01ysy9 (0.57 #447, 0.44 #508, 0.38 #29), 01rr_d (0.44 #508, 0.38 #29, 0.37 #430), 013zdg (0.44 #508, 0.38 #29, 0.37 #430), 027f2w (0.44 #508, 0.38 #29, 0.37 #430), 03mkk4 (0.38 #29, 0.37 #430, 0.33 #583), 028dcg (0.38 #29, 0.37 #430, 0.33 #583), 02m4yg (0.38 #29, 0.37 #430, 0.33 #583) >> Best rule #358 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 011s0; >> query: (?x1527, 02h4rq6) <- major_field_of_study(?x1526, ?x1527), major_field_of_study(?x1675, ?x1527), ?x1675 = 01j_cy, institution(?x1526, ?x7920), ?x7920 = 01p79b >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 04_tv major_field_of_study! 01ysy9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 54.000 52.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 04_tv major_field_of_study! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 52.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 04_tv major_field_of_study! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 52.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #18830-0h1x5f PRED entity: 0h1x5f PRED relation: honored_for! PRED expected values: 027n06w => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 102): 09gkdln (0.15 #345, 0.14 #465, 0.09 #825), 0hr6lkl (0.15 #732, 0.11 #372, 0.09 #252), 03gwpw2 (0.12 #245, 0.11 #365, 0.07 #845), 0n8_m93 (0.11 #102, 0.11 #822, 0.09 #342), 09k5jh7 (0.11 #790, 0.11 #550, 0.09 #310), 05qb8vx (0.11 #767, 0.09 #287, 0.08 #407), 05zksls (0.11 #508, 0.09 #748, 0.09 #268), 09g90vz (0.09 #347, 0.08 #467, 0.08 #587), 0275n3y (0.09 #303, 0.08 #423, 0.07 #783), 0g5b0q5 (0.09 #734, 0.08 #374, 0.06 #254) >> Best rule #345 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0ds35l9; 095zlp; 0ds11z; 0dsvzh; 0b6tzs; 04vr_f; 0gmcwlb; 02rv_dz; 02c638; 02yvct; ... >> query: (?x9701, 09gkdln) <- honored_for(?x3624, ?x9701), nominated_for(?x995, ?x9701), ?x995 = 099tbz, language(?x9701, ?x254) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #2762 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 597 *> proper extension: 09v38qj; *> query: (?x9701, ?x2707) <- honored_for(?x9921, ?x9701), award_winner(?x9921, ?x1585), award(?x1585, ?x500), award_winner(?x2707, ?x1585) *> conf = 0.05 ranks of expected_values: 41 EVAL 0h1x5f honored_for! 027n06w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 79.000 79.000 0.152 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18829-04cw0n4 PRED entity: 04cw0n4 PRED relation: place_of_birth PRED expected values: 09pxc => 53 concepts (49 used for prediction) PRED predicted values (max 10 best out of 51): 0cr3d (0.14 #2208, 0.08 #5731, 0.07 #94), 02_286 (0.13 #724, 0.10 #1428, 0.09 #3543), 06c62 (0.08 #5894, 0.07 #257, 0.05 #2371), 03dm7 (0.07 #459, 0.05 #2573, 0.02 #6096), 01lfy (0.07 #293, 0.05 #2407, 0.02 #5930), 0s5cg (0.07 #181, 0.05 #2295, 0.02 #5818), 030qb3t (0.07 #4282, 0.07 #4986, 0.06 #3578), 01_d4 (0.07 #771, 0.05 #1475, 0.04 #2885), 012fzm (0.07 #1383, 0.05 #2087, 0.04 #3497), 0sf9_ (0.07 #847, 0.05 #1551, 0.04 #2961) >> Best rule #2208 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 058ncz; 02lfl4; 01j4ls; 02lgj6; 01rrd4; 0c1jh; 0bn3jg; 062yh9; >> query: (?x13048, 0cr3d) <- nationality(?x13048, ?x205), nationality(?x13048, ?x94), ?x205 = 03rjj, ?x94 = 09c7w0, gender(?x13048, ?x231) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04cw0n4 place_of_birth 09pxc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 49.000 0.136 http://example.org/people/person/place_of_birth #18828-0hgxh PRED entity: 0hgxh PRED relation: risk_factors PRED expected values: 09jg8 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 102): 0c58k (0.86 #2590, 0.82 #2234, 0.68 #2838), 0d19y2 (0.71 #1732, 0.40 #1292, 0.33 #1682), 0jpmt (0.67 #960, 0.65 #2956, 0.60 #900), 01hbgs (0.67 #960, 0.60 #900, 0.60 #898), 05zppz (0.67 #960, 0.60 #900, 0.60 #898), 097ns (0.60 #900, 0.60 #898, 0.60 #897), 012jc (0.60 #900, 0.60 #898, 0.60 #897), 0x67 (0.60 #900, 0.60 #898, 0.60 #897), 02zsn (0.60 #900, 0.60 #898, 0.60 #897), 0167bx (0.60 #900, 0.60 #898, 0.60 #897) >> Best rule #2590 for best value: >> intensional similarity = 19 >> extensional distance = 12 >> proper extension: 01_qc_; 07x16; >> query: (?x9510, 0c58k) <- symptom_of(?x3679, ?x9510), risk_factors(?x9510, ?x7260), people(?x7260, ?x12891), people(?x7260, ?x11011), people(?x7260, ?x9074), people(?x7260, ?x8286), people(?x7260, ?x6934), location(?x6934, ?x1025), nationality(?x12891, ?x94), participant(?x4240, ?x6934), nominated_for(?x6934, ?x5509), award_winner(?x1245, ?x6934), celebrities_impersonated(?x3649, ?x11011), symptom_of(?x9118, ?x7260), award(?x11011, ?x458), type_of_union(?x8286, ?x566), award(?x8286, ?x5235), place_of_birth(?x12891, ?x1860), artist(?x2149, ?x9074) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2503 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 10 *> proper extension: 0hg11; *> query: (?x9510, ?x6710) <- symptom_of(?x13605, ?x9510), symptom_of(?x9509, ?x9510), symptom_of(?x4905, ?x9510), symptom_of(?x3679, ?x9510), risk_factors(?x9510, ?x7260), symptom_of(?x13605, ?x7007), risk_factors(?x7260, ?x2510), ?x7007 = 097ns, symptom_of(?x9509, ?x11739), symptom_of(?x9509, ?x11659), symptom_of(?x9509, ?x11126), symptom_of(?x9509, ?x3799), symptom_of(?x9118, ?x7260), symptom_of(?x3679, ?x9898), symptom_of(?x3679, ?x4959), ?x11739 = 0167bx, ?x11126 = 0hg45, ?x4959 = 01dcqj, ?x4905 = 01j6t0, ?x3799 = 04psf, ?x11659 = 072hv, risk_factors(?x6710, ?x2510), ?x9898 = 09jg8 *> conf = 0.09 ranks of expected_values: 78 EVAL 0hgxh risk_factors 09jg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 62.000 62.000 0.857 http://example.org/medicine/disease/risk_factors #18827-0gl3hr PRED entity: 0gl3hr PRED relation: nominated_for! PRED expected values: 0gs96 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 173): 0gq9h (0.60 #62, 0.56 #1018, 0.52 #2213), 019f4v (0.50 #292, 0.42 #2921, 0.40 #53), 0gs96 (0.50 #329, 0.40 #2958, 0.40 #90), 0f4x7 (0.40 #24, 0.39 #980, 0.33 #263), 0gr0m (0.40 #59, 0.37 #1015, 0.36 #2927), 0p9sw (0.40 #19, 0.33 #2887, 0.30 #1453), 0gs9p (0.39 #2932, 0.36 #1737, 0.34 #1020), 0gqwc (0.38 #538, 0.36 #777, 0.34 #1016), 0gr4k (0.36 #1698, 0.35 #503, 0.34 #2176), 0k611 (0.36 #2941, 0.26 #2463, 0.25 #1507) >> Best rule #62 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0cwy47; 0bj25; 025scjj; >> query: (?x6243, 0gq9h) <- film(?x3017, ?x6243), written_by(?x6243, ?x8225), nominated_for(?x2304, ?x6243), ?x8225 = 027vps >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #329 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 097zcz; 02vnmc9; *> query: (?x6243, 0gs96) <- film(?x3017, ?x6243), film_sets_designed(?x12725, ?x6243), cinematography(?x6243, ?x10741), ?x10741 = 07djnx *> conf = 0.50 ranks of expected_values: 3 EVAL 0gl3hr nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18826-03hy3g PRED entity: 03hy3g PRED relation: award PRED expected values: 02x4wr9 => 134 concepts (98 used for prediction) PRED predicted values (max 10 best out of 364): 05h5nb8 (0.71 #5155, 0.68 #32497, 0.68 #32496), 09d28z (0.71 #5155, 0.68 #32497, 0.68 #32496), 02x17s4 (0.53 #4875, 0.47 #6857, 0.37 #7649), 0f4x7 (0.52 #16671, 0.25 #21028, 0.17 #822), 02rdyk7 (0.48 #3652, 0.36 #2463, 0.33 #84), 02pqp12 (0.43 #3633, 0.36 #2444, 0.33 #65), 0f_nbyh (0.39 #3577, 0.24 #6749, 0.22 #4767), 02qyp19 (0.35 #9909, 0.35 #3569, 0.33 #1), 02x4wr9 (0.35 #3696, 0.27 #2507, 0.19 #4886), 0gkr9q (0.33 #325, 0.09 #2704, 0.09 #3893) >> Best rule #5155 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 030tjk; >> query: (?x6356, ?x8364) <- award(?x6356, ?x1180), award(?x6356, ?x384), award_winner(?x8364, ?x6356), ?x1180 = 02n9nmz, ?x384 = 03hkv_r >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #3696 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 01ycck; *> query: (?x6356, 02x4wr9) <- award(?x6356, ?x1180), nominated_for(?x6356, ?x1071), film(?x6356, ?x1002), ?x1180 = 02n9nmz *> conf = 0.35 ranks of expected_values: 9 EVAL 03hy3g award 02x4wr9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 134.000 98.000 0.711 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18825-06mzp PRED entity: 06mzp PRED relation: medal PRED expected values: 02lq67 02lq5w => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 2): 02lq67 (0.85 #59, 0.85 #25, 0.85 #37), 02lq5w (0.79 #40, 0.79 #38, 0.78 #26) >> Best rule #59 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 034cm; >> query: (?x774, 02lq67) <- service_location(?x896, ?x774), medal(?x774, ?x2132), country(?x359, ?x774) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 06mzp medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.854 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 06mzp medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.854 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #18824-02dsz PRED entity: 02dsz PRED relation: profession! PRED expected values: 01l1sq 084x96 => 56 concepts (21 used for prediction) PRED predicted values (max 10 best out of 4183): 0ffgh (0.71 #36095, 0.67 #27653, 0.60 #19210), 028qyn (0.71 #37218, 0.67 #28776, 0.60 #20333), 0dpqk (0.71 #31151, 0.50 #22709, 0.50 #10045), 015pxr (0.71 #30148, 0.36 #47033, 0.35 #51257), 052hl (0.71 #31737, 0.33 #44400, 0.33 #2190), 0mbw0 (0.71 #32282, 0.33 #23840, 0.33 #2735), 083chw (0.71 #29604, 0.33 #57, 0.28 #46489), 0q9t7 (0.71 #32300, 0.33 #2753, 0.25 #11194), 01pjr7 (0.71 #32030, 0.33 #2483, 0.25 #10924), 02mz_6 (0.71 #31891, 0.33 #2344, 0.25 #10785) >> Best rule #36095 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 047rgpy; >> query: (?x6183, 0ffgh) <- profession(?x12054, ?x6183), profession(?x5715, ?x6183), profession(?x3183, ?x6183), profession(?x140, ?x6183), type_of_union(?x5715, ?x566), ?x140 = 01vvydl, actor(?x2555, ?x12054), people(?x1050, ?x3183), participant(?x5604, ?x3183), award_winner(?x1972, ?x3183) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #25773 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 0dz3r; *> query: (?x6183, 01l1sq) <- profession(?x12054, ?x6183), profession(?x5715, ?x6183), profession(?x140, ?x6183), type_of_union(?x5715, ?x566), ?x140 = 01vvydl, actor(?x2555, ?x12054), category(?x2555, ?x134), film(?x12054, ?x1965), genre(?x2555, ?x258) *> conf = 0.67 ranks of expected_values: 36, 3129 EVAL 02dsz profession! 084x96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 21.000 0.714 http://example.org/people/person/profession EVAL 02dsz profession! 01l1sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 56.000 21.000 0.714 http://example.org/people/person/profession #18823-0gs96 PRED entity: 0gs96 PRED relation: award! PRED expected values: 06w33f8 03gt0c5 => 61 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2583): 03qhyn8 (0.81 #13456, 0.79 #80770, 0.78 #43747), 0c12h (0.50 #8541, 0.28 #28728, 0.22 #25364), 014zcr (0.50 #6779, 0.23 #30332, 0.23 #33700), 02vyw (0.50 #7732, 0.22 #27919, 0.20 #17827), 02bfxb (0.50 #7667, 0.22 #27854, 0.17 #47113), 02kxbwx (0.50 #6906, 0.22 #27093, 0.17 #30459), 03hy3g (0.50 #8573, 0.22 #28760, 0.17 #25396), 02hfp_ (0.50 #9049, 0.20 #15780, 0.17 #47113), 04y8r (0.50 #7328, 0.20 #14059, 0.17 #47113), 0c921 (0.50 #9391, 0.17 #47113, 0.17 #29578) >> Best rule #13456 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 027h4yd; >> query: (?x2222, ?x771) <- award_winner(?x2222, ?x4190), award_winner(?x2222, ?x771), award(?x2068, ?x2222), ?x2068 = 0gl88b, costume_design_by(?x240, ?x4190) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #6673 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 02g3v6; *> query: (?x2222, 03gt0c5) <- award(?x7978, ?x2222), award(?x12364, ?x2222), nominated_for(?x2222, ?x80), nominated_for(?x143, ?x7978), ?x12364 = 02vkvcz *> conf = 0.33 ranks of expected_values: 84, 85 EVAL 0gs96 award! 03gt0c5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 61.000 25.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gs96 award! 06w33f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 61.000 25.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18822-02wb6yq PRED entity: 02wb6yq PRED relation: artists! PRED expected values: 06by7 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 201): 06by7 (0.67 #22, 0.60 #19788, 0.45 #17354), 0glt670 (0.53 #2473, 0.51 #5514, 0.40 #5818), 0gywn (0.45 #2487, 0.41 #5528, 0.33 #15561), 0xhtw (0.39 #17, 0.17 #19783, 0.16 #24955), 03_d0 (0.36 #15518, 0.18 #5485, 0.17 #12), 016clz (0.28 #5, 0.26 #2437, 0.23 #1525), 05r6t (0.28 #78, 0.08 #7071, 0.08 #10112), 01cbwl (0.28 #42, 0.05 #7948, 0.05 #650), 02x8m (0.23 #2451, 0.22 #5492, 0.22 #15525), 0cx7f (0.22 #132, 0.06 #19289, 0.06 #25070) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01cv3n; 0144l1; 016ntp; 0kxbc; 0167km; 04bgy; 0135xb; 01dw_f; 01wphh2; 095x_; ... >> query: (?x3244, 06by7) <- artists(?x8386, ?x3244), profession(?x3244, ?x220), ?x8386 = 016ybr >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wb6yq artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.667 http://example.org/music/genre/artists #18821-016k62 PRED entity: 016k62 PRED relation: notable_people_with_this_condition! PRED expected values: 068p_ => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 10): 0g02vk (0.03 #56, 0.02 #210, 0.01 #122), 01g2q (0.03 #53, 0.02 #119, 0.01 #251), 0h99n (0.03 #54, 0.02 #604, 0.02 #428), 0brgy (0.03 #55, 0.01 #121), 06vr2 (0.02 #215), 029sk (0.02 #111, 0.02 #309, 0.01 #287), 02vrr (0.02 #69), 0m32h (0.02 #205), 068p_ (0.01 #130), 03p41 (0.01 #116) >> Best rule #56 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 01p45_v; 0hgqq; 01mr2g6; 0g7k2g; >> query: (?x5151, 0g02vk) <- artists(?x505, ?x5151), profession(?x5151, ?x563), company(?x5151, ?x2909) >> conf = 0.03 => this is the best rule for 1 predicted values *> Best rule #130 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 92 *> proper extension: 02lk1s; 06rq2l; *> query: (?x5151, 068p_) <- award_nominee(?x5125, ?x5151), company(?x5151, ?x2909), profession(?x5151, ?x563) *> conf = 0.01 ranks of expected_values: 9 EVAL 016k62 notable_people_with_this_condition! 068p_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 112.000 112.000 0.033 http://example.org/medicine/disease/notable_people_with_this_condition #18820-07yg2 PRED entity: 07yg2 PRED relation: group! PRED expected values: 01vsqvs => 78 concepts (31 used for prediction) PRED predicted values (max 10 best out of 119): 01vsyjy (0.14 #537, 0.05 #1337, 0.04 #1537), 01vsyg9 (0.14 #506, 0.05 #1306, 0.04 #1506), 01sb5r (0.14 #481, 0.05 #1281, 0.02 #2086), 01jfnvd (0.14 #561, 0.05 #1361, 0.02 #2166), 01xzb6 (0.14 #498, 0.05 #1298, 0.02 #2103), 025xt8y (0.08 #614, 0.07 #815, 0.06 #1014), 01vtqml (0.07 #878, 0.06 #1077, 0.04 #1477), 01w724 (0.07 #848, 0.06 #1047, 0.03 #3460), 053y0s (0.07 #803, 0.06 #1002, 0.02 #5029), 01p95y0 (0.07 #982, 0.06 #1181, 0.01 #2989) >> Best rule #537 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0kj34; 01t8399; >> query: (?x4182, 01vsyjy) <- artists(?x7329, ?x4182), ?x7329 = 016jny, artist(?x5744, ?x4182), ?x5744 = 01clyr >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07yg2 group! 01vsqvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 31.000 0.143 http://example.org/music/group_member/membership./music/group_membership/group #18819-023p33 PRED entity: 023p33 PRED relation: films! PRED expected values: 0bxg3 => 116 concepts (33 used for prediction) PRED predicted values (max 10 best out of 61): 0bxg3 (0.29 #80, 0.27 #238, 0.25 #714), 0cm2xh (0.14 #47, 0.09 #205, 0.06 #681), 0fzyg (0.12 #845, 0.06 #1481, 0.05 #1640), 07c52 (0.08 #336, 0.06 #1447, 0.05 #1606), 04jjy (0.08 #323, 0.03 #2227, 0.03 #2388), 015j7 (0.06 #774, 0.06 #616, 0.06 #931), 01vq3 (0.06 #675, 0.06 #517, 0.04 #4810), 0ddct (0.06 #1515, 0.06 #2150, 0.05 #1674), 0g0vx (0.06 #903, 0.03 #1539, 0.03 #1698), 0fx2s (0.05 #4525, 0.04 #1817, 0.03 #4211) >> Best rule #80 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0d4htf; >> query: (?x2097, 0bxg3) <- production_companies(?x2097, ?x10685), genre(?x2097, ?x1510), film_release_distribution_medium(?x2097, ?x81), ?x10685 = 04rcl7, ?x1510 = 01hmnh, nominated_for(?x500, ?x2097) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023p33 films! 0bxg3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 33.000 0.286 http://example.org/film/film_subject/films #18818-05szp PRED entity: 05szp PRED relation: artist! PRED expected values: 01w40h => 126 concepts (86 used for prediction) PRED predicted values (max 10 best out of 107): 015_1q (0.27 #1290, 0.25 #1572, 0.25 #725), 03rhqg (0.20 #1568, 0.20 #298, 0.16 #1286), 0g768 (0.18 #602, 0.16 #2578, 0.15 #1590), 01w40h (0.17 #311, 0.15 #452, 0.12 #1157), 017l96 (0.15 #301, 0.13 #865, 0.12 #442), 011k1h (0.15 #1421, 0.15 #856, 0.14 #1280), 033hn8 (0.15 #1425, 0.15 #860, 0.12 #578), 03mp8k (0.15 #1478, 0.12 #631, 0.10 #913), 043g7l (0.14 #32, 0.13 #1443, 0.12 #596), 01q940 (0.14 #53, 0.04 #617, 0.04 #758) >> Best rule #1290 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 01wz_ml; 06lxn; >> query: (?x6666, 015_1q) <- inductee(?x11145, ?x6666), award_winner(?x2180, ?x6666), artists(?x1952, ?x6666) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #311 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 01vtg4q; 01wg25j; *> query: (?x6666, 01w40h) <- inductee(?x11145, ?x6666), type_of_union(?x6666, ?x566), artists(?x1952, ?x6666) *> conf = 0.17 ranks of expected_values: 4 EVAL 05szp artist! 01w40h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 126.000 86.000 0.271 http://example.org/music/record_label/artist #18817-021r7r PRED entity: 021r7r PRED relation: nationality PRED expected values: 09c7w0 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 128): 09c7w0 (0.86 #1502, 0.84 #9319, 0.81 #10928), 02jx1 (0.27 #934, 0.22 #3138, 0.22 #1936), 07ssc (0.21 #916, 0.17 #315, 0.15 #2018), 03rk0 (0.09 #6857, 0.09 #9063, 0.09 #10068), 0345h (0.08 #5639, 0.06 #4037, 0.05 #7845), 0d060g (0.07 #1308, 0.05 #3212, 0.05 #6919), 0hzlz (0.06 #123, 0.06 #323, 0.03 #924), 035qy (0.06 #134, 0.06 #234, 0.02 #634), 0d0vqn (0.06 #109, 0.02 #609, 0.02 #13738), 0jgx (0.06 #258, 0.03 #358, 0.02 #13738) >> Best rule #1502 for best value: >> intensional similarity = 2 >> extensional distance = 100 >> proper extension: 05218gr; >> query: (?x7437, 09c7w0) <- place_of_birth(?x7437, ?x1860), ?x1860 = 01_d4 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021r7r nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.863 http://example.org/people/person/nationality #18816-018wdw PRED entity: 018wdw PRED relation: award! PRED expected values: 04ktcgn => 55 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2673): 04wp63 (0.78 #64311, 0.77 #67696, 0.73 #20303), 01vrz41 (0.61 #23688, 0.09 #47682, 0.08 #61221), 0178rl (0.61 #23688, 0.04 #67695, 0.03 #18451), 09rp4r_ (0.60 #16918, 0.11 #54156, 0.07 #13942), 092ys_y (0.60 #16918, 0.11 #54156, 0.07 #14584), 021yc7p (0.60 #16918, 0.11 #54156, 0.07 #13932), 0b6mgp_ (0.60 #16918, 0.11 #54156, 0.07 #8020), 09dvgb8 (0.60 #16918, 0.07 #8994, 0.06 #23689), 09pjnd (0.40 #415, 0.33 #3798, 0.11 #54156), 027rwmr (0.40 #215, 0.33 #3598, 0.11 #54156) >> Best rule #64311 for best value: >> intensional similarity = 4 >> extensional distance = 144 >> proper extension: 0j6j8; 01ppdy; 02tzwd; 01tgwv; >> query: (?x6860, ?x1933) <- category_of(?x6860, ?x3459), award_winner(?x6860, ?x1933), category_of(?x1307, ?x3459), award(?x71, ?x1307) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #54156 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 130 *> proper extension: 026mg3; *> query: (?x6860, ?x1933) <- ceremony(?x6860, ?x78), nominated_for(?x6860, ?x1080), nominated_for(?x1933, ?x1080) *> conf = 0.11 ranks of expected_values: 348 EVAL 018wdw award! 04ktcgn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 22.000 0.783 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18815-04ddm4 PRED entity: 04ddm4 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 33): 0ch6mp2 (0.71 #2345, 0.70 #1696, 0.70 #1848), 09zzb8 (0.71 #1688, 0.71 #153, 0.71 #2337), 02r96rf (0.67 #767, 0.66 #385, 0.63 #461), 09vw2b7 (0.59 #2344, 0.59 #771, 0.58 #1695), 0dxtw (0.43 #776, 0.39 #891, 0.37 #165), 01vx2h (0.38 #892, 0.35 #777, 0.34 #585), 02ynfr (0.19 #781, 0.19 #896, 0.19 #437), 02rh1dz (0.17 #775, 0.16 #317, 0.15 #240), 0215hd (0.12 #1860, 0.12 #1708, 0.12 #784), 015h31 (0.12 #774, 0.10 #316, 0.10 #239) >> Best rule #2345 for best value: >> intensional similarity = 4 >> extensional distance = 926 >> proper extension: 0963mq; 0d_2fb; 03mnn0; 0fsd9t; 0dtzkt; >> query: (?x599, 0ch6mp2) <- film_release_distribution_medium(?x599, ?x81), country(?x599, ?x94), genre(?x599, ?x225), film_crew_role(?x599, ?x2178) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ddm4 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.712 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18814-0451j PRED entity: 0451j PRED relation: award PRED expected values: 0dgr5xp => 109 concepts (98 used for prediction) PRED predicted values (max 10 best out of 282): 09sb52 (0.56 #15759, 0.33 #14147, 0.31 #6489), 0f4x7 (0.41 #6479, 0.25 #31, 0.20 #15749), 09v92_x (0.33 #1890, 0.18 #1487, 0.16 #22167), 04kxsb (0.28 #6575, 0.25 #127, 0.17 #933), 0gqy2 (0.25 #6613, 0.16 #15883, 0.13 #2583), 04ljl_l (0.25 #6451, 0.25 #3, 0.17 #809), 05p09zm (0.25 #125, 0.17 #931, 0.13 #5767), 07bdd_ (0.25 #66, 0.17 #872, 0.08 #6111), 05f4m9q (0.25 #13, 0.17 #819, 0.07 #27006), 0gr51 (0.25 #101, 0.17 #907, 0.07 #18237) >> Best rule #15759 for best value: >> intensional similarity = 4 >> extensional distance = 714 >> proper extension: 09fqtq; 022g44; 01v90t; 078mgh; 014g9y; 0cj2w; 01tsbmv; >> query: (?x7610, 09sb52) <- award(?x7610, ?x3019), award_winner(?x3019, ?x406), award(?x3101, ?x3019), ?x3101 = 0dvmd >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #24587 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1365 *> proper extension: 06jntd; *> query: (?x7610, ?x5039) <- award_winner(?x9175, ?x7610), titles(?x2346, ?x9175), nominated_for(?x5039, ?x9175) *> conf = 0.14 ranks of expected_values: 36 EVAL 0451j award 0dgr5xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 109.000 98.000 0.561 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18813-01wqg8 PRED entity: 01wqg8 PRED relation: student PRED expected values: 056wb => 130 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1100): 01my4f (0.20 #1196, 0.10 #3289, 0.06 #11661), 02x8z_ (0.10 #2865, 0.10 #772, 0.09 #4958), 01cv3n (0.10 #2182, 0.10 #89, 0.09 #4275), 01wd9lv (0.10 #3207, 0.10 #1114, 0.09 #5300), 01_x6v (0.10 #2457, 0.10 #364, 0.09 #4550), 01qvgl (0.10 #2286, 0.10 #193, 0.09 #4379), 02p2zq (0.10 #3399, 0.10 #1306, 0.09 #5492), 03cd1q (0.10 #4000, 0.10 #1907, 0.09 #6093), 01kvqc (0.10 #2336, 0.10 #243, 0.09 #4429), 01dhjz (0.10 #3668, 0.10 #1575, 0.09 #5761) >> Best rule #1196 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02kj7g; >> query: (?x8903, 01my4f) <- citytown(?x8903, ?x3052), school_type(?x8903, ?x3205), ?x3052 = 01cx_ >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #60713 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 269 *> proper extension: 01xcgf; *> query: (?x8903, ?x1322) <- citytown(?x8903, ?x3052), school_type(?x8903, ?x3205), location(?x1322, ?x3052), place_of_birth(?x1871, ?x3052) *> conf = 0.02 ranks of expected_values: 346 EVAL 01wqg8 student 056wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 130.000 63.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #18812-0415ggl PRED entity: 0415ggl PRED relation: country PRED expected values: 0d060g => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 38): 04hqz (0.41 #5388, 0.40 #5449), 07ssc (0.29 #320, 0.27 #440, 0.26 #2423), 0345h (0.26 #2423, 0.18 #148, 0.18 #632), 0f8l9c (0.26 #2423, 0.13 #262, 0.11 #19), 03_3d (0.26 #2423, 0.07 #250, 0.06 #7), 03rjj (0.26 #2423, 0.05 #490, 0.04 #249), 03rk0 (0.26 #2423, 0.03 #343, 0.03 #39), 03spz (0.26 #2423, 0.01 #538), 0d060g (0.08 #69, 0.06 #2003, 0.06 #2064), 017fp (0.08 #1332, 0.08 #1211, 0.06 #1090) >> Best rule #5388 for best value: >> intensional similarity = 4 >> extensional distance = 1520 >> proper extension: 0fkwzs; 04mx8h4; >> query: (?x5724, ?x7413) <- nominated_for(?x84, ?x5724), nationality(?x84, ?x7413), nominated_for(?x84, ?x1199), nominated_for(?x143, ?x1199) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #69 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 02vqhv0; 05hjnw; 03h0byn; *> query: (?x5724, 0d060g) <- film_crew_role(?x5724, ?x5136), film(?x3078, ?x5724), ?x5136 = 089g0h, written_by(?x5724, ?x11705) *> conf = 0.08 ranks of expected_values: 9 EVAL 0415ggl country 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 101.000 101.000 0.406 http://example.org/film/film/country #18811-027m67 PRED entity: 027m67 PRED relation: film_release_region PRED expected values: 0f8l9c 0h7x => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 149): 0f8l9c (0.89 #4068, 0.88 #3900, 0.84 #3564), 059j2 (0.85 #4080, 0.80 #1722, 0.79 #3576), 06mkj (0.84 #4108, 0.81 #3604, 0.80 #3940), 05r4w (0.82 #4045, 0.82 #3877, 0.80 #3541), 03rjj (0.82 #4050, 0.77 #3546, 0.76 #3882), 0chghy (0.82 #4056, 0.78 #1698, 0.77 #3552), 0k6nt (0.82 #365, 0.80 #1714, 0.80 #3568), 03h64 (0.76 #1762, 0.76 #4120, 0.70 #3952), 015fr (0.76 #4062, 0.67 #1704, 0.64 #3894), 0154j (0.76 #4049, 0.62 #3545, 0.61 #3881) >> Best rule #4068 for best value: >> intensional similarity = 3 >> extensional distance = 272 >> proper extension: 0h95zbp; 07s3m4g; >> query: (?x7293, 0f8l9c) <- genre(?x7293, ?x53), film_release_region(?x7293, ?x1353), ?x1353 = 035qy >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 23 EVAL 027m67 film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 87.000 87.000 0.887 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 027m67 film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.887 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18810-0dxmyh PRED entity: 0dxmyh PRED relation: language PRED expected values: 02h40lc => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 2): 02h40lc (0.12 #182, 0.09 #107, 0.07 #296), 03_9r (0.01 #184) >> Best rule #182 for best value: >> intensional similarity = 4 >> extensional distance = 193 >> proper extension: 0lzb8; 01y9xg; 03rwng; 01kwh5j; 01r4bps; 084x96; >> query: (?x10277, 02h40lc) <- category(?x10277, ?x134), ?x134 = 08mbj5d, actor(?x8775, ?x10277), nationality(?x10277, ?x94) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dxmyh language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.123 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #18809-01tx9m PRED entity: 01tx9m PRED relation: educational_institution PRED expected values: 01tx9m => 171 concepts (114 used for prediction) PRED predicted values (max 10 best out of 306): 0221g_ (0.10 #115, 0.07 #654, 0.07 #1732), 01pq4w (0.10 #99, 0.07 #638, 0.07 #1716), 01fsv9 (0.10 #405, 0.07 #944, 0.07 #2022), 037njl (0.10 #139, 0.02 #15094, 0.02 #9842), 033x5p (0.10 #128, 0.02 #7674, 0.02 #9831), 01pl14 (0.10 #8, 0.02 #10250, 0.01 #11328), 0bwfn (0.07 #793, 0.07 #1871, 0.06 #2949), 029d_ (0.07 #686, 0.07 #1764, 0.06 #2842), 07wrz (0.07 #596, 0.07 #1674, 0.02 #7064), 02rky4 (0.07 #925, 0.05 #4159, 0.02 #10089) >> Best rule #115 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0l2tk; >> query: (?x6177, 0221g_) <- registering_agency(?x6177, ?x1982), citytown(?x6177, ?x10364), school(?x3333, ?x6177), season(?x3333, ?x701) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #15094 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 0frm7n; *> query: (?x6177, ?x581) <- school(?x1161, ?x6177), school(?x7060, ?x6177), school(?x7060, ?x581), position(?x7060, ?x261) *> conf = 0.02 ranks of expected_values: 96 EVAL 01tx9m educational_institution 01tx9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 171.000 114.000 0.100 http://example.org/education/educational_institution_campus/educational_institution #18808-01w5gg6 PRED entity: 01w5gg6 PRED relation: artists! PRED expected values: 06rqw => 96 concepts (74 used for prediction) PRED predicted values (max 10 best out of 246): 064t9 (0.52 #934, 0.50 #2169, 0.46 #6799), 06j6l (0.31 #2205, 0.30 #970, 0.29 #6835), 0glt670 (0.30 #2197, 0.28 #962, 0.25 #6827), 025sc50 (0.28 #2206, 0.25 #971, 0.24 #6836), 05bt6j (0.27 #1583, 0.25 #657, 0.23 #965), 0xhtw (0.26 #1247, 0.26 #1864, 0.21 #1556), 01lyv (0.26 #2807, 0.24 #2498, 0.22 #4043), 05w3f (0.25 #651, 0.18 #1268, 0.17 #1885), 0ggx5q (0.22 #1000, 0.18 #2235, 0.14 #6865), 0gywn (0.21 #6844, 0.20 #2214, 0.19 #7152) >> Best rule #934 for best value: >> intensional similarity = 4 >> extensional distance = 174 >> proper extension: 012d40; 06688p; 01l1b90; 0147dk; 01vrt_c; 01k5t_3; 05mt_q; 01j4ls; 0pyg6; 07ss8_; ... >> query: (?x9241, 064t9) <- category(?x9241, ?x134), profession(?x9241, ?x1032), artists(?x283, ?x9241), film(?x9241, ?x4828) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1623 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 187 *> proper extension: 05563d; 05xq9; 02cpp; 014pg1; 07rnh; 016l09; 01s560x; *> query: (?x9241, 06rqw) <- artists(?x302, ?x9241), artist(?x3265, ?x9241), ?x302 = 016clz *> conf = 0.03 ranks of expected_values: 162 EVAL 01w5gg6 artists! 06rqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 96.000 74.000 0.517 http://example.org/music/genre/artists #18807-02qvvv PRED entity: 02qvvv PRED relation: institution! PRED expected values: 019v9k => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.81 #292, 0.80 #509, 0.80 #340), 014mlp (0.73 #343, 0.69 #488, 0.68 #271), 03bwzr4 (0.71 #185, 0.59 #233, 0.55 #329), 019v9k (0.68 #347, 0.68 #227, 0.67 #275), 07s6fsf (0.61 #170, 0.50 #218, 0.43 #410), 016t_3 (0.54 #293, 0.54 #173, 0.53 #341), 02_xgp2 (0.54 #303, 0.53 #327, 0.48 #351), 0bkj86 (0.46 #178, 0.42 #274, 0.41 #81), 013zdg (0.32 #177, 0.29 #273, 0.27 #417), 04zx3q1 (0.30 #291, 0.28 #315, 0.26 #411) >> Best rule #292 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 0frm7n; >> query: (?x3314, 02h4rq6) <- category(?x3314, ?x134), school(?x4856, ?x3314), position(?x4856, ?x180) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #347 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 01jssp; 02w2bc; 01j_06; 07szy; 01ptt7; 01jsn5; 0f1nl; 01jswq; 01wdj_; 0j_sncb; ... *> query: (?x3314, 019v9k) <- institution(?x9054, ?x3314), contains(?x2623, ?x3314), student(?x3314, ?x3175), school(?x685, ?x3314) *> conf = 0.68 ranks of expected_values: 4 EVAL 02qvvv institution! 019v9k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 162.000 162.000 0.806 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18806-0cf8qb PRED entity: 0cf8qb PRED relation: film_crew_role PRED expected values: 0dxtw => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 24): 0dxtw (0.57 #140, 0.50 #8, 0.44 #173), 01vx2h (0.45 #141, 0.38 #772, 0.38 #174), 02ynfr (0.27 #13, 0.24 #79, 0.21 #46), 0d2b38 (0.21 #155, 0.16 #188, 0.14 #56), 0215hd (0.17 #148, 0.14 #779, 0.13 #181), 01xy5l_ (0.15 #143, 0.14 #44, 0.13 #1673), 04pyp5 (0.14 #47, 0.13 #1673, 0.12 #14), 015h31 (0.13 #1673, 0.11 #139, 0.11 #73), 089g0h (0.13 #1673, 0.11 #149, 0.11 #780), 02vs3x5 (0.13 #1673, 0.10 #54, 0.07 #318) >> Best rule #140 for best value: >> intensional similarity = 5 >> extensional distance = 51 >> proper extension: 03hxsv; >> query: (?x7726, 0dxtw) <- language(?x7726, ?x254), country(?x7726, ?x94), titles(?x8581, ?x7726), film_crew_role(?x7726, ?x137), ?x8581 = 024qqx >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cf8qb film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.566 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18805-016ynj PRED entity: 016ynj PRED relation: type_of_union PRED expected values: 04ztj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.81 #37, 0.79 #65, 0.78 #13), 01g63y (0.24 #10, 0.20 #6, 0.17 #14), 0jgjn (0.02 #28, 0.01 #48) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 166 >> proper extension: 033hqf; 02knnd; 0ly5n; 0cf_h9; 03llf8; 04bgy; 029cpw; 013qvn; 022q4j; 069z_5; ... >> query: (?x8301, 04ztj) <- film(?x8301, ?x6013), nationality(?x8301, ?x390), place_of_death(?x8301, ?x191) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016ynj type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.810 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18804-01yjl PRED entity: 01yjl PRED relation: school PRED expected values: 021l5s 0ks67 => 96 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1011): 06pwq (0.89 #900, 0.77 #898, 0.73 #899), 0bx8pn (0.89 #900, 0.77 #898, 0.73 #899), 02pptm (0.89 #900, 0.77 #898, 0.73 #899), 01q0kg (0.89 #900, 0.77 #898, 0.73 #899), 01ptt7 (0.89 #900, 0.77 #898, 0.73 #899), 022lly (0.89 #900, 0.77 #898, 0.73 #899), 01jq34 (0.89 #900, 0.77 #898, 0.73 #899), 05krk (0.89 #900, 0.77 #898, 0.73 #899), 01jswq (0.89 #900, 0.77 #898, 0.73 #899), 017cy9 (0.89 #900, 0.77 #898, 0.73 #899) >> Best rule #900 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 0jmj7; >> query: (?x3333, ?x466) <- colors(?x3333, ?x663), draft(?x3333, ?x11905), draft(?x3333, ?x4779), team(?x2010, ?x3333), school(?x3333, ?x4672), school(?x3333, ?x4296), draft(?x8894, ?x11905), draft(?x7399, ?x11905), ?x4296 = 07vyf, school(?x11905, ?x6953), school(?x8894, ?x466), school(?x4779, ?x2175), ?x2175 = 01ptt7, ?x4672 = 07tds, team(?x8206, ?x7399) >> conf = 0.89 => this is the best rule for 100 predicted values *> Best rule #538 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 06wpc; *> query: (?x3333, ?x481) <- colors(?x3333, ?x1101), draft(?x3333, ?x1161), position(?x3333, ?x2010), school(?x3333, ?x10297), school(?x3333, ?x2497), school(?x3333, ?x735), ?x735 = 065y4w7, currency(?x10297, ?x170), contains(?x94, ?x10297), school(?x11361, ?x10297), ?x2497 = 0f1nl, ?x11361 = 03m1n, colors(?x481, ?x1101) *> conf = 0.06 ranks of expected_values: 369, 508 EVAL 01yjl school 0ks67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 96.000 65.000 0.887 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01yjl school 021l5s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 96.000 65.000 0.887 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #18803-011wtv PRED entity: 011wtv PRED relation: genre PRED expected values: 0lsxr => 91 concepts (63 used for prediction) PRED predicted values (max 10 best out of 91): 024qqx (0.52 #4266, 0.50 #3458, 0.49 #6112), 05p553 (0.39 #6462, 0.39 #578, 0.35 #2191), 02l7c8 (0.39 #6587, 0.39 #4857, 0.36 #13), 0lsxr (0.33 #122, 0.28 #2079, 0.26 #813), 03k9fj (0.33 #931, 0.32 #1852, 0.31 #1277), 04xvlr (0.26 #346, 0.21 #1038, 0.20 #5882), 082gq (0.25 #947, 0.25 #832, 0.18 #26), 0219x_ (0.25 #137, 0.13 #1404, 0.11 #3364), 01hmnh (0.25 #1281, 0.23 #1856, 0.22 #2086), 03mqtr (0.18 #25, 0.15 #7269, 0.08 #140) >> Best rule #4266 for best value: >> intensional similarity = 4 >> extensional distance = 877 >> proper extension: 02d44q; 047svrl; 07k2mq; 01gglm; >> query: (?x4565, ?x8581) <- titles(?x8581, ?x4565), film_crew_role(?x4565, ?x468), film(?x3687, ?x4565), award_nominee(?x3687, ?x91) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #122 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 03ffcz; *> query: (?x4565, 0lsxr) <- genre(?x4565, ?x11108), award_winner(?x4565, ?x669), film(?x1051, ?x4565), ?x11108 = 02xh1 *> conf = 0.33 ranks of expected_values: 4 EVAL 011wtv genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 91.000 63.000 0.516 http://example.org/film/film/genre #18802-06sy4c PRED entity: 06sy4c PRED relation: team PRED expected values: 01dtl => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 577): 01dtl (0.79 #5260, 0.77 #2624, 0.72 #1050), 0j2jr (0.79 #5260, 0.77 #2624, 0.72 #1050), 0182r9 (0.50 #278, 0.12 #1853, 0.11 #1591), 02b17t (0.40 #770, 0.06 #2083, 0.03 #6838), 0j46b (0.33 #1492, 0.25 #2017, 0.20 #967), 02b0_6 (0.25 #338, 0.25 #76, 0.20 #863), 01kj5h (0.25 #351, 0.25 #89, 0.20 #876), 0272vm (0.25 #1988, 0.25 #413, 0.20 #938), 01xn7x1 (0.25 #309, 0.20 #834, 0.17 #1359), 01tqfs (0.25 #95, 0.20 #882, 0.17 #1407) >> Best rule #5260 for best value: >> intensional similarity = 7 >> extensional distance = 67 >> proper extension: 02y8bn; 01x2_q; >> query: (?x8204, ?x3363) <- athlete(?x471, ?x8204), team(?x8204, ?x3363), sport(?x9542, ?x471), sports(?x358, ?x471), country(?x471, ?x774), ?x774 = 06mzp, team(?x60, ?x9542) >> conf = 0.79 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 06sy4c team 01dtl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.793 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #18801-0xhtw PRED entity: 0xhtw PRED relation: artists PRED expected values: 032t2z 0274ck 02r3zy 03g5jw 0134s5 0fcsd 0fhxv 03j24kf 047cx 01mwsnc 094xh 0167km 01q99h 081wh1 0187x8 0232lm 01m7pwq 01wkmgb 020_4z => 71 concepts (39 used for prediction) PRED predicted values (max 10 best out of 950): 01gx5f (0.73 #13100, 0.50 #4845, 0.48 #10087), 01kcms4 (0.71 #7907, 0.62 #9739, 0.57 #6991), 020_4z (0.71 #7227, 0.58 #14562, 0.57 #8143), 03h502k (0.67 #4988, 0.48 #10087, 0.40 #11408), 01vwyqp (0.62 #9413, 0.57 #7581, 0.50 #3911), 0394y (0.62 #8612, 0.50 #4026, 0.50 #3110), 0191h5 (0.57 #6989, 0.50 #3319, 0.48 #10087), 01kx_81 (0.57 #7414, 0.50 #9246, 0.48 #10087), 01vsksr (0.57 #7839, 0.50 #9671, 0.43 #6923), 0k1bs (0.50 #8763, 0.50 #4177, 0.48 #10087) >> Best rule #13100 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 03339m; >> query: (?x1000, 01gx5f) <- artists(?x1000, ?x9463), artists(?x1000, ?x8004), artists(?x1000, ?x3403), instrumentalists(?x227, ?x3403), profession(?x3403, ?x131), ?x9463 = 01shhf, influenced_by(?x8004, ?x916) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #7227 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 5 *> proper extension: 016clz; 0dl5d; *> query: (?x1000, 020_4z) <- artists(?x1000, ?x3403), artists(?x1000, ?x2242), artists(?x1000, ?x1412), artists(?x1000, ?x565), ?x2242 = 09prnq, profession(?x565, ?x987), ?x1412 = 067mj, role(?x565, ?x212), award_winner(?x247, ?x3403) *> conf = 0.71 ranks of expected_values: 3, 11, 22, 27, 47, 49, 78, 90, 115, 127, 131, 147, 148, 149, 168, 185, 307, 405, 419 EVAL 0xhtw artists 020_4z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 01wkmgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 01m7pwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 0232lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 0187x8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 081wh1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 01q99h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 0167km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 094xh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 01mwsnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 047cx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 03j24kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 0fhxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 0fcsd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 0134s5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 03g5jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 02r3zy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 0274ck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 71.000 39.000 0.727 http://example.org/music/genre/artists EVAL 0xhtw artists 032t2z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 71.000 39.000 0.727 http://example.org/music/genre/artists #18800-027kp3 PRED entity: 027kp3 PRED relation: colors PRED expected values: 01g5v => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 19): 01l849 (0.30 #305, 0.30 #381, 0.30 #191), 01g5v (0.29 #117, 0.28 #1296, 0.28 #1048), 019sc (0.29 #45, 0.18 #1052, 0.18 #64), 06fvc (0.29 #40, 0.18 #59, 0.17 #534), 038hg (0.14 #49, 0.11 #391, 0.11 #334), 036k5h (0.11 #461, 0.11 #518, 0.10 #480), 0jc_p (0.11 #327, 0.10 #384, 0.08 #1274), 03wkwg (0.09 #71, 0.08 #90, 0.08 #1274), 01jnf1 (0.08 #86, 0.08 #200, 0.08 #1274), 09ggk (0.08 #1274, 0.07 #129, 0.06 #1155) >> Best rule #305 for best value: >> intensional similarity = 5 >> extensional distance = 51 >> proper extension: 0gy3w; 026ssfj; >> query: (?x4794, 01l849) <- major_field_of_study(?x4794, ?x6756), major_field_of_study(?x4794, ?x3490), ?x6756 = 0_jm, school_type(?x4794, ?x1044), student(?x3490, ?x1125) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #117 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 01mpwj; 01s7j5; 034q81; *> query: (?x4794, 01g5v) <- major_field_of_study(?x4794, ?x6756), major_field_of_study(?x4794, ?x5864), major_field_of_study(?x865, ?x6756), contains(?x335, ?x4794), ?x5864 = 04g51 *> conf = 0.29 ranks of expected_values: 2 EVAL 027kp3 colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 127.000 0.302 http://example.org/education/educational_institution/colors #18799-042v2 PRED entity: 042v2 PRED relation: influenced_by PRED expected values: 0hcvy => 151 concepts (49 used for prediction) PRED predicted values (max 10 best out of 391): 0l99s (0.33 #224, 0.25 #1090, 0.13 #3255), 07dnx (0.33 #296, 0.25 #1162, 0.07 #3327), 014dq7 (0.33 #48, 0.25 #914, 0.05 #21238), 03_87 (0.31 #3231, 0.17 #7129, 0.12 #11027), 032l1 (0.31 #3121, 0.16 #7019, 0.14 #10917), 03f0324 (0.27 #3184, 0.11 #7082, 0.11 #9681), 058vp (0.18 #1482, 0.14 #3214, 0.08 #3648), 081k8 (0.18 #7086, 0.17 #10984, 0.16 #3188), 02lt8 (0.16 #3152, 0.11 #10948, 0.11 #7484), 02kz_ (0.16 #3202, 0.11 #2336, 0.09 #3636) >> Best rule #224 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0gthm; >> query: (?x8656, 0l99s) <- student(?x2064, ?x8656), influenced_by(?x8656, ?x5091), profession(?x8656, ?x353), ?x2064 = 01cyd5 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #19501 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 467 *> proper extension: 070b4; 0chnf; 0fpzzp; 0716b6; 055yr; *> query: (?x8656, ?x916) <- influenced_by(?x8656, ?x6320), influenced_by(?x7334, ?x6320), influenced_by(?x916, ?x7334) *> conf = 0.02 ranks of expected_values: 247 EVAL 042v2 influenced_by 0hcvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 151.000 49.000 0.333 http://example.org/influence/influence_node/influenced_by #18798-0gx1l PRED entity: 0gx1l PRED relation: contains PRED expected values: 07vjm => 110 concepts (70 used for prediction) PRED predicted values (max 10 best out of 2678): 0l1pj (0.60 #1095, 0.30 #141261, 0.07 #6981), 06b7s9 (0.60 #2111, 0.30 #141261, 0.07 #7997), 06kknt (0.60 #2043, 0.30 #141261, 0.07 #7929), 02gnmp (0.60 #1808, 0.30 #141261, 0.07 #7694), 05q2c (0.60 #1206, 0.30 #141261, 0.07 #7092), 03b8c4 (0.60 #2241, 0.30 #141261, 0.07 #8127), 0r04p (0.60 #614, 0.30 #141261, 0.07 #6500), 0284jb (0.60 #148, 0.30 #141261, 0.07 #6034), 0gx1l (0.48 #58849, 0.46 #102993, 0.46 #91218), 0kpys (0.48 #58849, 0.46 #102993, 0.46 #91218) >> Best rule #1095 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 09c7w0; 01n7q; 030qb3t; >> query: (?x10687, 0l1pj) <- location(?x10372, ?x10687), contains(?x10687, ?x5702), ?x5702 = 05cwl_, profession(?x10372, ?x319) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #885 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 09c7w0; 01n7q; 030qb3t; *> query: (?x10687, 07vjm) <- location(?x10372, ?x10687), contains(?x10687, ?x5702), ?x5702 = 05cwl_, profession(?x10372, ?x319) *> conf = 0.20 ranks of expected_values: 122 EVAL 0gx1l contains 07vjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 110.000 70.000 0.600 http://example.org/location/location/contains #18797-015ppk PRED entity: 015ppk PRED relation: nominated_for! PRED expected values: 0g69lg => 83 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1272): 0g69lg (0.68 #30290, 0.66 #48932, 0.64 #46601), 0f721s (0.68 #30290, 0.64 #46601, 0.61 #48931), 0347xz (0.62 #16310, 0.54 #39610, 0.51 #51265), 03wbzp (0.33 #1553, 0.21 #48933, 0.15 #18640), 0jmj (0.33 #943, 0.15 #18640, 0.08 #44270), 056wb (0.33 #1327, 0.08 #44270, 0.04 #37280), 01jw4r (0.33 #1811, 0.07 #4140, 0.04 #13461), 02p65p (0.33 #24, 0.07 #2353, 0.04 #37280), 01z_g6 (0.33 #1132, 0.07 #3461, 0.04 #37280), 03c6vl (0.33 #1906, 0.07 #4235, 0.04 #37280) >> Best rule #30290 for best value: >> intensional similarity = 4 >> extensional distance = 106 >> proper extension: 016zfm; 01fs__; 0d7vtk; >> query: (?x7116, ?x1394) <- nominated_for(?x7043, ?x7116), nominated_for(?x783, ?x7116), award_winner(?x7043, ?x6071), program(?x1394, ?x7116) >> conf = 0.68 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 015ppk nominated_for! 0g69lg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 47.000 0.678 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #18796-07wg3 PRED entity: 07wg3 PRED relation: company! PRED expected values: 015czt => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 43): 060c4 (0.74 #3277, 0.51 #3325, 0.47 #697), 01cpkt (0.50 #226, 0.43 #134, 0.28 #3321), 0130xz (0.43 #136, 0.38 #228, 0.28 #3321), 0dq_5 (0.36 #3292, 0.35 #3340, 0.32 #712), 0krdk (0.36 #3281, 0.29 #3329, 0.26 #701), 0dq3c (0.30 #3276, 0.21 #696, 0.21 #3324), 01cpjx (0.29 #130, 0.28 #3321, 0.25 #222), 01c83m (0.28 #3321, 0.25 #229, 0.14 #137), 015czt (0.25 #224, 0.14 #132, 0.13 #3417), 05_wyz (0.22 #3293, 0.20 #66, 0.18 #3341) >> Best rule #3277 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 059j2; >> query: (?x13773, 060c4) <- company(?x13489, ?x13773), company(?x13489, ?x10370), entity_involved(?x10369, ?x10370) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #224 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 02wf01; *> query: (?x13773, 015czt) <- company(?x13489, ?x13773), company(?x13489, ?x13490), company(?x13489, ?x13471), company(?x13489, ?x10370), ?x10370 = 07wj1, ?x13471 = 01j_x, company(?x187, ?x13490) *> conf = 0.25 ranks of expected_values: 9 EVAL 07wg3 company! 015czt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 76.000 76.000 0.742 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #18795-091z_p PRED entity: 091z_p PRED relation: nominated_for! PRED expected values: 02r22gf 0fms83 => 118 concepts (107 used for prediction) PRED predicted values (max 10 best out of 221): 02r0csl (0.77 #11307, 0.77 #11535, 0.68 #11534), 0gq_v (0.77 #11307, 0.77 #11535, 0.68 #11534), 099c8n (0.70 #1634, 0.55 #2086, 0.50 #2538), 03hl6lc (0.64 #802, 0.59 #3288, 0.55 #4870), 02pqp12 (0.63 #1410, 0.48 #2540, 0.47 #2088), 0gq9h (0.57 #735, 0.51 #2091, 0.48 #14538), 0gs9p (0.57 #737, 0.47 #1415, 0.47 #2093), 040njc (0.50 #683, 0.47 #1361, 0.47 #2039), 09sb52 (0.50 #1613, 0.41 #2065, 0.39 #2517), 02qyntr (0.49 #4917, 0.49 #4013, 0.48 #3335) >> Best rule #11307 for best value: >> intensional similarity = 5 >> extensional distance = 471 >> proper extension: 07bz5; >> query: (?x1786, ?x1243) <- honored_for(?x5592, ?x1786), award(?x1786, ?x1243), award_winner(?x5592, ?x361), award(?x185, ?x1243), ceremony(?x1243, ?x78) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #2060 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 47 *> proper extension: 09k56b7; 03hkch7; 02rn00y; 01gkp1; 0404j37; *> query: (?x1786, 02r22gf) <- nominated_for(?x68, ?x1786), ?x68 = 02qyp19, titles(?x1510, ?x1786), film_crew_role(?x1786, ?x468), honored_for(?x5592, ?x1786) *> conf = 0.31 ranks of expected_values: 25, 50 EVAL 091z_p nominated_for! 0fms83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 118.000 107.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 091z_p nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 118.000 107.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18794-07tlg PRED entity: 07tlg PRED relation: colors PRED expected values: 01g5v => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 20): 01g5v (0.38 #943, 0.29 #643, 0.28 #1303), 019sc (0.35 #687, 0.25 #947, 0.22 #207), 06fvc (0.33 #2, 0.31 #642, 0.19 #202), 036k5h (0.33 #5, 0.17 #45, 0.13 #1321), 01l849 (0.25 #601, 0.25 #901, 0.25 #841), 038hg (0.14 #312, 0.13 #1321, 0.13 #332), 0jc_p (0.13 #1321, 0.09 #404, 0.08 #904), 088fh (0.13 #1321, 0.09 #646, 0.08 #106), 04mkbj (0.13 #1321, 0.09 #850, 0.09 #210), 09ggk (0.13 #1321, 0.08 #216, 0.06 #356) >> Best rule #943 for best value: >> intensional similarity = 5 >> extensional distance = 320 >> proper extension: 01v3ht; 0k9wp; 0ylsr; >> query: (?x12396, 01g5v) <- contains(?x1310, ?x12396), colors(?x12396, ?x663), colors(?x260, ?x663), colors(?x1520, ?x663), ?x1520 = 07lx1s >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tlg colors 01g5v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.379 http://example.org/education/educational_institution/colors #18793-02q6gfp PRED entity: 02q6gfp PRED relation: nominated_for! PRED expected values: 09qwmm 02r22gf => 104 concepts (102 used for prediction) PRED predicted values (max 10 best out of 208): 09qwmm (0.69 #256, 0.32 #27, 0.22 #14199), 0gq_v (0.64 #1164, 0.47 #706, 0.33 #8492), 0gq9h (0.55 #747, 0.43 #3953, 0.43 #1205), 0gs9p (0.44 #749, 0.41 #5100, 0.40 #3955), 019f4v (0.42 #739, 0.41 #52, 0.40 #3945), 099c8n (0.42 #284, 0.30 #3032, 0.29 #3490), 0k611 (0.40 #757, 0.34 #1215, 0.33 #3963), 0gr0m (0.39 #1203, 0.32 #745, 0.27 #3951), 04dn09n (0.35 #35, 0.29 #3928, 0.27 #5073), 0f4x7 (0.35 #712, 0.30 #25, 0.29 #1170) >> Best rule #256 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 0j8f09z; >> query: (?x2380, 09qwmm) <- genre(?x2380, ?x53), language(?x2380, ?x254), nominated_for(?x1441, ?x2380), ?x1441 = 099cng >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 30 EVAL 02q6gfp nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 104.000 102.000 0.688 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02q6gfp nominated_for! 09qwmm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 102.000 0.688 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18792-0bz6sb PRED entity: 0bz6sb PRED relation: honored_for PRED expected values: 01fx4k => 39 concepts (26 used for prediction) PRED predicted values (max 10 best out of 743): 05y0cr (0.20 #513, 0.14 #1701, 0.12 #2295), 035_2h (0.20 #321, 0.14 #1509, 0.12 #2103), 0y_9q (0.20 #322, 0.14 #1510, 0.12 #2104), 0y_pg (0.20 #469, 0.14 #1657, 0.12 #2251), 016yxn (0.20 #582, 0.14 #1770, 0.12 #2364), 0h6r5 (0.20 #247, 0.14 #1435, 0.12 #2029), 0c0zq (0.20 #518, 0.12 #2300, 0.09 #2893), 083shs (0.20 #5, 0.12 #1787, 0.09 #2380), 02nczh (0.20 #392, 0.12 #2174, 0.09 #2767), 041td_ (0.20 #383, 0.12 #2165, 0.09 #2758) >> Best rule #513 for best value: >> intensional similarity = 19 >> extensional distance = 3 >> proper extension: 02yxh9; >> query: (?x4700, 05y0cr) <- ceremony(?x2222, ?x4700), ceremony(?x1307, ?x4700), ceremony(?x1243, ?x4700), ceremony(?x720, ?x4700), ceremony(?x591, ?x4700), award_winner(?x4700, ?x6947), award_winner(?x4700, ?x4701), ?x1307 = 0gq9h, ?x591 = 0f4x7, ?x2222 = 0gs96, ?x720 = 018wng, ?x1243 = 0gr0m, profession(?x4701, ?x967), profession(?x4701, ?x655), award(?x4701, ?x567), ?x967 = 012t_z, profession(?x7272, ?x655), ?x7272 = 01vsyjy, award_winner(?x6947, ?x1089) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #8320 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 30 *> proper extension: 0dth6b; 050yyb; 073h9x; 02pgky2; 05q7cj; 073hgx; 0c4hnm; *> query: (?x4700, ?x2729) <- ceremony(?x2222, ?x4700), ceremony(?x1307, ?x4700), ceremony(?x1243, ?x4700), ceremony(?x720, ?x4700), ceremony(?x591, ?x4700), award_winner(?x4700, ?x6947), award_winner(?x4700, ?x4701), ?x1307 = 0gq9h, ?x591 = 0f4x7, ?x2222 = 0gs96, ?x720 = 018wng, ?x1243 = 0gr0m, profession(?x4701, ?x967), award(?x4701, ?x567), profession(?x5647, ?x967), award_winner(?x2729, ?x6947), ?x5647 = 027kmrb *> conf = 0.16 ranks of expected_values: 55 EVAL 0bz6sb honored_for 01fx4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 39.000 26.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18791-035gnh PRED entity: 035gnh PRED relation: executive_produced_by PRED expected values: 0ksf29 => 73 concepts (14 used for prediction) PRED predicted values (max 10 best out of 55): 079vf (0.12 #2, 0.04 #1017, 0.04 #1269), 04fyhv (0.12 #182, 0.01 #689, 0.01 #1449), 08gf93 (0.12 #229, 0.01 #736), 06q8hf (0.05 #420, 0.05 #1686, 0.05 #926), 05hj_k (0.05 #351, 0.05 #1617, 0.04 #1113), 06pj8 (0.04 #1830, 0.04 #1322, 0.04 #562), 0343h (0.04 #549, 0.03 #1309, 0.03 #1817), 027z0pl (0.04 #727, 0.03 #473, 0.01 #1235), 03h304l (0.04 #631, 0.01 #1139), 02q_cc (0.04 #535, 0.01 #1043) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02ljhg; >> query: (?x7428, 079vf) <- genre(?x7428, ?x225), currency(?x7428, ?x170), film(?x10317, ?x7428), ?x10317 = 0341n5 >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 035gnh executive_produced_by 0ksf29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 14.000 0.125 http://example.org/film/film/executive_produced_by #18790-01j5ws PRED entity: 01j5ws PRED relation: religion PRED expected values: 0c8wxp => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 19): 0c8wxp (0.41 #322, 0.38 #232, 0.20 #186), 03_gx (0.08 #194, 0.08 #1682, 0.08 #1590), 0kpl (0.08 #461, 0.08 #596, 0.07 #641), 019cr (0.08 #56, 0.03 #687, 0.03 #642), 092bf5 (0.07 #16, 0.05 #196, 0.05 #512), 0kq2 (0.07 #18, 0.04 #63, 0.03 #649), 0v53x (0.05 #74, 0.02 #660, 0.02 #705), 03j6c (0.05 #1552, 0.04 #427, 0.03 #2143), 05sfs (0.04 #48, 0.02 #634, 0.02 #679), 01hng3 (0.04 #84, 0.02 #490, 0.02 #625) >> Best rule #322 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 01438g; >> query: (?x3025, 0c8wxp) <- people(?x1446, ?x3025), ?x1446 = 033tf_, award(?x3025, ?x880) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j5ws religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.409 http://example.org/people/person/religion #18789-0466k4 PRED entity: 0466k4 PRED relation: gender PRED expected values: 05zppz => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #81, 0.84 #85, 0.83 #75), 02zsn (0.67 #16, 0.50 #10, 0.46 #122) >> Best rule #81 for best value: >> intensional similarity = 8 >> extensional distance = 85 >> proper extension: 03nbbv; 0gs5q; >> query: (?x12279, 05zppz) <- location(?x12279, ?x6437), profession(?x12279, ?x5805), profession(?x7961, ?x5805), profession(?x3864, ?x5805), legislative_sessions(?x7961, ?x6728), jurisdiction_of_office(?x7961, ?x6252), ?x6728 = 070mff, ?x3864 = 03f5vvx >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0466k4 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.851 http://example.org/people/person/gender #18788-0d6qjf PRED entity: 0d6qjf PRED relation: films PRED expected values: 05pbl56 03k8th => 181 concepts (143 used for prediction) PRED predicted values (max 10 best out of 752): 03k8th (0.50 #7388, 0.33 #6859, 0.25 #17968), 0jqp3 (0.33 #1107, 0.25 #7985, 0.25 #7456), 04q01mn (0.33 #6880, 0.25 #7409, 0.15 #25926), 02p86pb (0.33 #6801, 0.25 #7330, 0.12 #17910), 0jqj5 (0.33 #6611, 0.25 #7140, 0.12 #17720), 0p_qr (0.33 #6518, 0.25 #7047, 0.12 #17627), 03h0byn (0.33 #6854, 0.25 #7383, 0.12 #17963), 03qnvdl (0.33 #43386, 0.25 #30159, 0.12 #17990), 03m5y9p (0.33 #2010, 0.17 #13649, 0.10 #21586), 0sxgv (0.33 #301, 0.10 #19879, 0.08 #24111) >> Best rule #7388 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 02vnz; >> query: (?x11817, 03k8th) <- films(?x11817, ?x1470), films(?x11817, ?x763), ?x763 = 061681, film_release_region(?x1470, ?x87), film(?x92, ?x1470), executive_produced_by(?x1470, ?x4552) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 641 EVAL 0d6qjf films 03k8th CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 143.000 0.500 http://example.org/film/film_subject/films EVAL 0d6qjf films 05pbl56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 181.000 143.000 0.500 http://example.org/film/film_subject/films #18787-01r9fv PRED entity: 01r9fv PRED relation: category PRED expected values: 08mbj5d => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #3, 0.87 #6, 0.87 #14) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 01v_pj6; 09hnb; 03h_fk5; 0161sp; 01wj18h; 03bnv; 0qf11; 016fnb; 018n6m; 03f1d47; ... >> query: (?x1544, 08mbj5d) <- gender(?x1544, ?x514), award(?x1544, ?x4892), award(?x1544, ?x3365), award_winner(?x3365, ?x379), ?x4892 = 02f72_ >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r9fv category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.897 http://example.org/common/topic/webpage./common/webpage/category #18786-04yc76 PRED entity: 04yc76 PRED relation: film! PRED expected values: 0ck91 => 74 concepts (38 used for prediction) PRED predicted values (max 10 best out of 541): 0315q3 (0.57 #60283, 0.46 #51964, 0.44 #45727), 02_p8v (0.14 #921, 0.01 #15468, 0.01 #17546), 02m501 (0.14 #1686), 06cgy (0.07 #250, 0.06 #51965, 0.05 #54046), 015rkw (0.07 #282, 0.06 #51965, 0.05 #54046), 01f7dd (0.07 #1206, 0.06 #51965, 0.05 #54046), 0h0wc (0.07 #422, 0.04 #21204, 0.03 #8734), 0f5xn (0.07 #966, 0.04 #3044, 0.03 #5122), 0fby2t (0.07 #750, 0.03 #13219, 0.03 #19453), 05dbf (0.07 #364, 0.03 #2442, 0.03 #16989) >> Best rule #60283 for best value: >> intensional similarity = 3 >> extensional distance = 837 >> proper extension: 02xhpl; 0h3mh3q; 0ph24; >> query: (?x2754, ?x4631) <- award_winner(?x2754, ?x4631), nominated_for(?x401, ?x2754), film(?x4631, ?x437) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04yc76 film! 0ck91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 38.000 0.574 http://example.org/film/actor/film./film/performance/film #18785-056xkh PRED entity: 056xkh PRED relation: music PRED expected values: 06fxnf => 113 concepts (79 used for prediction) PRED predicted values (max 10 best out of 124): 02jxkw (0.33 #142, 0.07 #984, 0.05 #3940), 02g1jh (0.14 #2446, 0.14 #2235, 0.08 #758), 02bh9 (0.13 #1314, 0.10 #471, 0.07 #2789), 023361 (0.13 #1413, 0.07 #992, 0.06 #2468), 01wmjkb (0.12 #842, 0.10 #1685, 0.04 #841), 01q7cb_ (0.12 #842, 0.10 #1685, 0.04 #841), 0gps0z (0.12 #842, 0.04 #841, 0.03 #3161), 01s1zk (0.12 #842, 0.04 #841, 0.03 #3161), 04pf4r (0.10 #488, 0.08 #698, 0.06 #2386), 01d4cb (0.10 #582, 0.07 #1214, 0.03 #1848) >> Best rule #142 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 09w6br; >> query: (?x9858, 02jxkw) <- film(?x788, ?x9858), film_release_distribution_medium(?x9858, ?x81), film(?x8341, ?x9858), ?x8341 = 01wmjkb, ?x81 = 029j_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1542 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 09qljs; *> query: (?x9858, 06fxnf) <- film(?x788, ?x9858), film(?x8341, ?x9858), film_format(?x9858, ?x909), language(?x9858, ?x254), group(?x8341, ?x9868) *> conf = 0.04 ranks of expected_values: 36 EVAL 056xkh music 06fxnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 113.000 79.000 0.333 http://example.org/film/film/music #18784-01cszh PRED entity: 01cszh PRED relation: artist PRED expected values: 086qd 05sq20 0133x7 01304j 02ktrs => 90 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1004): 0161c2 (0.54 #6376, 0.50 #6377, 0.47 #7174), 03d2k (0.50 #2238, 0.22 #7023, 0.22 #6225), 07s3vqk (0.50 #1598, 0.21 #11164, 0.13 #19951), 03j0br4 (0.50 #1733, 0.08 #16098, 0.07 #20086), 0277c3 (0.40 #2803, 0.33 #5196, 0.25 #2007), 03j1p2n (0.40 #2928, 0.33 #5321, 0.25 #2132), 0pj9t (0.40 #2594, 0.33 #4987, 0.20 #3392), 01wgcvn (0.40 #3435, 0.27 #9019, 0.23 #11413), 01w724 (0.40 #3346, 0.20 #2548, 0.18 #8930), 01wbsdz (0.36 #9174, 0.33 #6781, 0.33 #5983) >> Best rule #6376 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 01rc6f; >> query: (?x2190, ?x3126) <- category(?x2190, ?x134), company(?x6383, ?x2190), company(?x3126, ?x2190), instrumentalists(?x227, ?x6383), profession(?x6383, ?x131), award_winner(?x567, ?x6383) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1530 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 0181dw; *> query: (?x2190, 01304j) <- artist(?x2190, ?x11455), artist(?x2190, ?x5059), artist(?x2190, ?x2562), award(?x5059, ?x567), student(?x11654, ?x5059), origin(?x11455, ?x94), ?x2562 = 01trhmt *> conf = 0.33 ranks of expected_values: 46, 68, 331, 818, 876 EVAL 01cszh artist 02ktrs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 90.000 56.000 0.538 http://example.org/music/record_label/artist EVAL 01cszh artist 01304j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 90.000 56.000 0.538 http://example.org/music/record_label/artist EVAL 01cszh artist 0133x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 90.000 56.000 0.538 http://example.org/music/record_label/artist EVAL 01cszh artist 05sq20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 90.000 56.000 0.538 http://example.org/music/record_label/artist EVAL 01cszh artist 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 90.000 56.000 0.538 http://example.org/music/record_label/artist #18783-0c7f7 PRED entity: 0c7f7 PRED relation: time_zones PRED expected values: 02llzg => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 10): 02llzg (0.65 #30, 0.45 #320, 0.43 #4), 02hcv8 (0.25 #282, 0.24 #226, 0.24 #456), 02lcqs (0.21 #137, 0.16 #176, 0.15 #150), 03bdv (0.09 #125, 0.04 #111, 0.04 #177), 02fqwt (0.09 #280, 0.09 #454, 0.08 #266), 02hczc (0.04 #199, 0.03 #239, 0.03 #225), 03plfd (0.03 #115, 0.01 #75, 0.01 #129), 052vwh (0.03 #117), 0gsrz4 (0.02 #113), 042g7t (0.01 #116) >> Best rule #30 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 01n1pp; 096g3; 04jr87; 0cht6; 02bbyw; 06c62; 0bwfn; 0g7yx; 02bd_f; 031y2; ... >> query: (?x13840, 02llzg) <- contains(?x10706, ?x13840), contains(?x205, ?x13840), ?x205 = 03rjj, category(?x13840, ?x134), administrative_parent(?x9792, ?x10706) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c7f7 time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.647 http://example.org/location/location/time_zones #18782-02_j1w PRED entity: 02_j1w PRED relation: position! PRED expected values: 04d817 071rlr 05p8bf9 => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 173): 0cj_v7 (0.83 #698, 0.83 #1051, 0.83 #1049), 01rl_3 (0.83 #698, 0.83 #1051, 0.83 #1049), 01dtl (0.83 #698, 0.83 #1051, 0.83 #1049), 01xn7x1 (0.83 #698, 0.83 #1051, 0.83 #1049), 0dwz3t (0.83 #698, 0.83 #1051, 0.83 #1049), 0230rx (0.83 #698, 0.83 #1051, 0.83 #1049), 02b190 (0.83 #698, 0.83 #1051, 0.83 #1049), 0177gl (0.83 #698, 0.83 #1051, 0.83 #1049), 050fh (0.83 #698, 0.83 #1051, 0.83 #1049), 01634x (0.83 #698, 0.83 #1051, 0.83 #1049) >> Best rule #698 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 03f0fp; >> query: (?x530, ?x10847) <- position(?x9068, ?x530), position(?x8673, ?x530), position(?x6155, ?x530), position(?x5546, ?x530), position(?x4511, ?x530), team(?x530, ?x10847), team(?x60, ?x5546), team(?x208, ?x4511), team(?x7669, ?x10847), sport(?x9068, ?x471), position(?x1123, ?x530), team(?x3047, ?x8673), colors(?x6155, ?x663) >> conf = 0.83 => this is the best rule for 51 predicted values *> Best rule #697 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 03f0fp; *> query: (?x530, ?x852) <- position(?x9068, ?x530), position(?x8673, ?x530), position(?x6155, ?x530), position(?x5546, ?x530), position(?x4511, ?x530), team(?x530, ?x10847), team(?x530, ?x852), team(?x60, ?x5546), team(?x208, ?x4511), team(?x7669, ?x10847), sport(?x9068, ?x471), position(?x1123, ?x530), team(?x3047, ?x8673), colors(?x6155, ?x663) *> conf = 0.82 ranks of expected_values: 136, 138, 155 EVAL 02_j1w position! 05p8bf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 13.000 13.000 0.832 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position EVAL 02_j1w position! 071rlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 13.000 13.000 0.832 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position EVAL 02_j1w position! 04d817 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 13.000 13.000 0.832 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #18781-01wdqrx PRED entity: 01wdqrx PRED relation: artists! PRED expected values: 02x8m => 145 concepts (87 used for prediction) PRED predicted values (max 10 best out of 292): 064t9 (0.71 #4665, 0.69 #1871, 0.67 #1562), 01lyv (0.52 #652, 0.25 #342, 0.23 #9948), 025sc50 (0.52 #1599, 0.51 #1908, 0.50 #4702), 0glt670 (0.35 #4693, 0.34 #1899, 0.34 #9646), 02x8m (0.34 #1877, 0.28 #4671, 0.26 #5290), 05bt6j (0.31 #1282, 0.25 #352, 0.25 #5624), 016clz (0.31 #2173, 0.30 #2484, 0.30 #12395), 0xhtw (0.29 #8692, 0.28 #2184, 0.27 #4978), 03lty (0.29 #27, 0.23 #2818, 0.19 #336), 02w4v (0.26 #663, 0.15 #5935, 0.14 #9340) >> Best rule #4665 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 01rm8b; 0163m1; 01dwrc; 011z3g; 046p9; 016376; 016ppr; 015bwt; 012x03; >> query: (?x1282, 064t9) <- artists(?x3928, ?x1282), ?x3928 = 0gywn, category(?x1282, ?x134) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1877 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 015srx; 0qmny; *> query: (?x1282, 02x8m) <- artists(?x3928, ?x1282), ?x3928 = 0gywn, origin(?x1282, ?x739) *> conf = 0.34 ranks of expected_values: 5 EVAL 01wdqrx artists! 02x8m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 145.000 87.000 0.706 http://example.org/music/genre/artists #18780-035qy PRED entity: 035qy PRED relation: member_states! PRED expected values: 085h1 059dn => 234 concepts (234 used for prediction) PRED predicted values (max 10 best out of 51): 085h1 (0.91 #22, 0.90 #13, 0.90 #64), 059dn (0.50 #16, 0.48 #25, 0.43 #21), 07t65 (0.14 #6, 0.14 #5, 0.14 #78), 02vk52z (0.14 #6, 0.14 #5, 0.14 #78), 04k4l (0.14 #6, 0.14 #5, 0.14 #78), 01rz1 (0.14 #6, 0.14 #5, 0.14 #78), 02y_9cf (0.02 #95, 0.01 #17), 0v74 (0.02 #95, 0.01 #17), 03m7d (0.02 #95, 0.01 #17), 07jqh (0.02 #95, 0.01 #17) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 05r4w; 0jgd; 03_3d; 0d0vqn; 03rt9; 01znc_; 06mkj; 03h64; >> query: (?x1353, 085h1) <- film_release_region(?x5588, ?x1353), film_release_region(?x3000, ?x1353), ?x5588 = 0gtt5fb, ?x3000 = 045j3w >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 035qy member_states! 059dn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 234.000 234.000 0.913 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 035qy member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 234.000 234.000 0.913 http://example.org/user/ktrueman/default_domain/international_organization/member_states #18779-06mvyf PRED entity: 06mvyf PRED relation: school_type PRED expected values: 01rs41 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 16): 01rs41 (0.95 #580, 0.64 #166, 0.62 #74), 05pcjw (0.54 #70, 0.53 #47, 0.52 #254), 05jxkf (0.48 #1546, 0.47 #1269, 0.47 #1730), 01_9fk (0.13 #899, 0.13 #784, 0.12 #945), 01_srz (0.13 #210, 0.11 #187, 0.10 #256), 07tf8 (0.12 #1274, 0.12 #1735, 0.11 #1551), 04qbv (0.08 #61, 0.07 #84, 0.06 #107), 02p0qmm (0.04 #999, 0.04 #1275, 0.04 #1068), 06cs1 (0.04 #98, 0.04 #190, 0.03 #213), 04399 (0.04 #128, 0.03 #59, 0.03 #381) >> Best rule #580 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 020yvh; 022r38; >> query: (?x10686, 01rs41) <- school_type(?x10686, ?x9240), school_type(?x2909, ?x9240), ?x2909 = 017z88 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mvyf school_type 01rs41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.945 http://example.org/education/educational_institution/school_type #18778-02kk_c PRED entity: 02kk_c PRED relation: genre PRED expected values: 01z77k => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 74): 0hcr (0.58 #342, 0.46 #423, 0.33 #99), 05p553 (0.50 #328, 0.49 #2110, 0.49 #1786), 01z4y (0.33 #908, 0.33 #1718, 0.32 #665), 0pr6f (0.33 #130, 0.27 #373, 0.25 #454), 025s89p (0.33 #131, 0.27 #374, 0.21 #455), 095bb (0.33 #117, 0.21 #441, 0.21 #360), 01htzx (0.33 #97, 0.18 #421, 0.17 #2365), 04rlf (0.33 #204, 0.01 #609), 0c4xc (0.25 #932, 0.24 #1742, 0.23 #851), 06n90 (0.25 #337, 0.21 #418, 0.19 #3658) >> Best rule #342 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 05x72k; 02v5xg; 03d3ht; 017dtf; 0gxr1c; >> query: (?x4881, 0hcr) <- actor(?x4881, ?x6962), actor(?x4881, ?x3051), film(?x3051, ?x796), actor(?x10826, ?x6962) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #270 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 0cp08zg; *> query: (?x4881, 01z77k) <- nominated_for(?x12041, ?x4881), nominated_for(?x12041, ?x238), ?x238 = 027qgy *> conf = 0.14 ranks of expected_values: 22 EVAL 02kk_c genre 01z77k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 80.000 80.000 0.577 http://example.org/tv/tv_program/genre #18777-01pny5 PRED entity: 01pny5 PRED relation: artists! PRED expected values: 04qftx => 115 concepts (54 used for prediction) PRED predicted values (max 10 best out of 297): 03_d0 (0.62 #8595, 0.46 #5833, 0.45 #4915), 016clz (0.57 #8895, 0.35 #2456, 0.34 #10122), 064t9 (0.51 #9517, 0.51 #11358, 0.49 #4303), 0cx7f (0.33 #1972, 0.27 #3198, 0.23 #440), 08jyyk (0.31 #371, 0.27 #1597, 0.26 #2210), 0126t5 (0.31 #389, 0.27 #1615, 0.26 #2228), 02x8m (0.28 #11363, 0.27 #3389, 0.24 #4614), 06j6l (0.28 #11389, 0.24 #3415, 0.24 #6172), 025sc50 (0.26 #967, 0.25 #1273, 0.22 #4336), 0gywn (0.26 #975, 0.25 #1281, 0.20 #668) >> Best rule #8595 for best value: >> intensional similarity = 6 >> extensional distance = 196 >> proper extension: 01nqfh_; 01jrz5j; 037lyl; 01pbs9w; 028qyn; 01y_rz; >> query: (?x12791, 03_d0) <- profession(?x12791, ?x131), artists(?x1380, ?x12791), artists(?x1380, ?x11916), artists(?x1380, ?x3399), ?x11916 = 023slg, ?x3399 = 01gx5f >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #4289 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 56 *> proper extension: 02rgz4; 04m2zj; *> query: (?x12791, ?x671) <- profession(?x12791, ?x131), artists(?x1380, ?x12791), ?x1380 = 0dl5d, profession(?x9407, ?x131), artists(?x671, ?x9407) *> conf = 0.03 ranks of expected_values: 227 EVAL 01pny5 artists! 04qftx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 115.000 54.000 0.621 http://example.org/music/genre/artists #18776-0yx1m PRED entity: 0yx1m PRED relation: music PRED expected values: 01l9v7n => 99 concepts (47 used for prediction) PRED predicted values (max 10 best out of 95): 0146pg (0.10 #1701, 0.09 #853, 0.08 #2545), 01kp66 (0.07 #4018, 0.07 #1479, 0.07 #3168), 03xp8d5 (0.07 #4018, 0.07 #1479, 0.07 #3168), 016khd (0.07 #4018, 0.07 #1479, 0.07 #3168), 013cr (0.07 #4018, 0.07 #3168, 0.07 #5499), 0bqytm (0.07 #4018, 0.07 #3168, 0.07 #5499), 03h610 (0.07 #288, 0.05 #3246, 0.04 #1344), 09fb5 (0.06 #7405, 0.06 #6134, 0.06 #8041), 04ls53 (0.05 #79, 0.03 #2403, 0.02 #1133), 04pf4r (0.05 #68, 0.02 #5567, 0.02 #489) >> Best rule #1701 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 0d_wms; 042fgh; >> query: (?x8330, 0146pg) <- honored_for(?x670, ?x8330), film(?x851, ?x8330), film(?x574, ?x8330) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #468 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 03m8y5; 0gffmn8; 0gtvpkw; 03clwtw; 01lbcqx; 0199wf; 07p12s; *> query: (?x8330, 01l9v7n) <- written_by(?x8330, ?x4385), award_nominee(?x635, ?x4385), currency(?x4385, ?x170) *> conf = 0.03 ranks of expected_values: 24 EVAL 0yx1m music 01l9v7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 99.000 47.000 0.103 http://example.org/film/film/music #18775-0821j PRED entity: 0821j PRED relation: influenced_by PRED expected values: 08433 012cph => 113 concepts (40 used for prediction) PRED predicted values (max 10 best out of 357): 05qzv (0.40 #333, 0.13 #6903, 0.12 #10361), 041xl (0.40 #223, 0.12 #1517, 0.07 #17285), 014ps4 (0.40 #243, 0.07 #17285, 0.07 #16849), 081lh (0.37 #1744, 0.35 #881, 0.18 #2606), 081k8 (0.22 #6625, 0.13 #6903, 0.12 #9650), 0g5ff (0.20 #192, 0.18 #1486, 0.13 #6903), 09dt7 (0.20 #31, 0.13 #6903, 0.12 #10361), 03rx9 (0.20 #326, 0.09 #1620, 0.07 #17285), 0821j (0.20 #293, 0.07 #17285, 0.07 #16849), 018fq (0.20 #158, 0.07 #17285, 0.07 #16849) >> Best rule #333 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01dzz7; >> query: (?x8718, 05qzv) <- nationality(?x8718, ?x94), influenced_by(?x8718, ?x9284), influenced_by(?x8718, ?x4712), ?x9284 = 0gd_s, people(?x1050, ?x4712) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #6903 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 150 *> proper extension: 01c58j; 0177s6; 0453t; 03f70xs; 04cbtrw; 0379s; 0d4jl; 032l1; 0lcx; 0zm1; ... *> query: (?x8718, ?x1287) <- nationality(?x8718, ?x94), influenced_by(?x8718, ?x9284), influenced_by(?x8718, ?x4712), languages(?x4712, ?x254), influenced_by(?x9284, ?x1287) *> conf = 0.13 ranks of expected_values: 40, 329 EVAL 0821j influenced_by 012cph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 113.000 40.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 0821j influenced_by 08433 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 113.000 40.000 0.400 http://example.org/influence/influence_node/influenced_by #18774-01vs_v8 PRED entity: 01vs_v8 PRED relation: artist! PRED expected values: 01trtc => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 90): 015_1q (0.21 #853, 0.18 #1826, 0.17 #5025), 0fb0v (0.19 #7, 0.09 #1814, 0.07 #841), 01trtc (0.16 #906, 0.10 #1879, 0.09 #211), 0g768 (0.15 #1844, 0.13 #871, 0.10 #6851), 0181dw (0.13 #1849, 0.09 #876, 0.08 #5466), 03mp8k (0.12 #900, 0.10 #66, 0.07 #1873), 01dtcb (0.12 #881, 0.07 #1854, 0.05 #4079), 0n85g (0.11 #1869, 0.10 #62, 0.09 #896), 033hn8 (0.10 #14, 0.09 #848, 0.09 #6828), 011k1h (0.10 #10, 0.09 #844, 0.09 #1817) >> Best rule #853 for best value: >> intensional similarity = 2 >> extensional distance = 73 >> proper extension: 02r3zy; 01v0sx2; 0dtd6; 0dvqq; 0frsw; 016fmf; 017j6; 04qmr; 0d193h; 0hvbj; ... >> query: (?x2237, 015_1q) <- award(?x2237, ?x2877), ?x2877 = 02f5qb >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #906 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 73 *> proper extension: 02r3zy; 01v0sx2; 0dtd6; 0dvqq; 0frsw; 016fmf; 017j6; 04qmr; 0d193h; 0hvbj; ... *> query: (?x2237, 01trtc) <- award(?x2237, ?x2877), ?x2877 = 02f5qb *> conf = 0.16 ranks of expected_values: 3 EVAL 01vs_v8 artist! 01trtc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.213 http://example.org/music/record_label/artist #18773-0770cd PRED entity: 0770cd PRED relation: award PRED expected values: 025m8l => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 258): 01bgqh (0.46 #851, 0.26 #2467, 0.23 #4083), 09sb52 (0.42 #9333, 0.40 #8929, 0.32 #12969), 0c4z8 (0.29 #880, 0.24 #1688, 0.24 #2092), 03t5kl (0.23 #631, 0.13 #22629, 0.13 #27882), 03qbh5 (0.22 #1013, 0.19 #6669, 0.18 #7073), 023vrq (0.20 #730, 0.15 #19799, 0.13 #27882), 054ks3 (0.19 #950, 0.17 #1758, 0.17 #2162), 02f716 (0.18 #985, 0.18 #18181, 0.13 #22629), 03qbnj (0.18 #1041, 0.14 #2657, 0.13 #22629), 02f5qb (0.18 #964, 0.13 #22629, 0.13 #27882) >> Best rule #851 for best value: >> intensional similarity = 3 >> extensional distance = 175 >> proper extension: 01pfr3; 01v0sx2; 01wv9xn; 0frsw; 01vrwfv; 02jqjm; 0178_w; 07r1_; 01lf293; 033s6; ... >> query: (?x1818, 01bgqh) <- award(?x1818, ?x2139), artists(?x671, ?x1818), ?x2139 = 01by1l >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #19799 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1559 *> proper extension: 035_2h; 0hm0k; 03yxwq; 01zcrv; 04rqd; 03lpbx; *> query: (?x1818, ?x704) <- award_winner(?x3176, ?x1818), award(?x3176, ?x704) *> conf = 0.15 ranks of expected_values: 24 EVAL 0770cd award 025m8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 88.000 88.000 0.463 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18772-038w8 PRED entity: 038w8 PRED relation: place_of_death PRED expected values: 0ljsz => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 58): 0dq16 (0.33 #69, 0.08 #656, 0.06 #851), 02_286 (0.18 #209, 0.14 #2550, 0.12 #1575), 0rh6k (0.14 #1175, 0.12 #1953, 0.09 #3125), 030qb3t (0.13 #9582, 0.13 #10753, 0.13 #10167), 0mp3l (0.09 #231, 0.06 #817, 0.05 #1208), 04f_d (0.09 #228, 0.05 #1010, 0.05 #1205), 0f2rq (0.09 #282, 0.05 #1259, 0.04 #1648), 0f2wj (0.08 #6654, 0.06 #2744, 0.04 #9572), 019fh (0.08 #7809, 0.08 #8588, 0.06 #5858), 0k049 (0.08 #1371, 0.08 #590, 0.06 #8591) >> Best rule #69 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0fd_1; >> query: (?x11869, 0dq16) <- profession(?x11869, ?x3342), nationality(?x11869, ?x94), jurisdiction_of_office(?x11869, ?x335), gender(?x11869, ?x231), ?x335 = 059rby >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 038w8 place_of_death 0ljsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 117.000 0.333 http://example.org/people/deceased_person/place_of_death #18771-099pks PRED entity: 099pks PRED relation: actor PRED expected values: 0168dy => 84 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1067): 02gf_l (0.40 #5199, 0.40 #3346, 0.25 #2419), 0sw6y (0.40 #5487, 0.40 #3634, 0.25 #2707), 029cpw (0.25 #2401, 0.20 #5181, 0.20 #3328), 02wrhj (0.25 #1064, 0.20 #4770, 0.20 #2917), 01rrwf6 (0.25 #963, 0.20 #4669, 0.20 #2816), 031c2r (0.25 #2716, 0.20 #5496, 0.20 #3643), 024my5 (0.25 #2458, 0.20 #5238, 0.20 #3385), 02k4b2 (0.25 #2282, 0.20 #3209, 0.13 #24103), 021yw7 (0.25 #2145, 0.20 #3072, 0.12 #6778), 01nd6v (0.25 #1851, 0.20 #3704, 0.12 #7410) >> Best rule #5199 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 05f7w84; 025x1t; >> query: (?x5583, 02gf_l) <- program(?x2554, ?x5583), genre(?x5583, ?x10159), genre(?x5583, ?x258), actor(?x5583, ?x2352), ?x258 = 05p553, ?x10159 = 025s89p >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #24099 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 86 *> proper extension: 02zv4b; 026bfsh; *> query: (?x5583, ?x525) <- actor(?x5583, ?x6550), actor(?x5583, ?x2352), location(?x6550, ?x1227), film(?x6550, ?x6425), award_nominee(?x6550, ?x1875), award_nominee(?x450, ?x2352), award_nominee(?x525, ?x450), participant(?x3307, ?x6550) *> conf = 0.03 ranks of expected_values: 381 EVAL 099pks actor 0168dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 38.000 0.400 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #18770-01mqc_ PRED entity: 01mqc_ PRED relation: award_nominee PRED expected values: 015t56 03_6y => 102 concepts (42 used for prediction) PRED predicted values (max 10 best out of 960): 01mqc_ (0.88 #1669, 0.27 #11616, 0.25 #46474), 03_6y (0.88 #773, 0.27 #11616, 0.16 #10064), 04w391 (0.81 #906, 0.81 #69708, 0.80 #39502), 015t56 (0.75 #606, 0.15 #9897, 0.13 #39503), 0184jc (0.27 #11616, 0.16 #97574, 0.14 #9296), 03_wj_ (0.27 #11616, 0.16 #97574, 0.14 #9743), 06bzwt (0.27 #11616, 0.16 #97574, 0.13 #39503), 0blq0z (0.27 #11616, 0.13 #39503, 0.12 #37177), 0c35b1 (0.27 #11616, 0.13 #39503, 0.12 #37177), 02p65p (0.25 #27, 0.16 #97574, 0.13 #39503) >> Best rule #1669 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 06151l; 0c4f4; 0hvb2; 01pgzn_; 019pm_; 014488; 016vg8; 0278x6s; 07h565; 01z7s_; ... >> query: (?x7525, 01mqc_) <- film(?x7525, ?x1045), award_nominee(?x7525, ?x2844), ?x2844 = 08swgx >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #773 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 06151l; 0c4f4; 0hvb2; 01pgzn_; 019pm_; 014488; 016vg8; 0278x6s; 07h565; 01z7s_; ... *> query: (?x7525, 03_6y) <- film(?x7525, ?x1045), award_nominee(?x7525, ?x2844), ?x2844 = 08swgx *> conf = 0.88 ranks of expected_values: 2, 4 EVAL 01mqc_ award_nominee 03_6y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 42.000 0.875 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 01mqc_ award_nominee 015t56 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 42.000 0.875 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18769-071pf2 PRED entity: 071pf2 PRED relation: team PRED expected values: 0j46b => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 526): 027ffq (0.50 #297, 0.06 #8373, 0.06 #8724), 049fbh (0.25 #1211, 0.20 #509, 0.11 #1562), 02279c (0.25 #14, 0.12 #1067, 0.05 #8090), 01kj5h (0.25 #1187, 0.12 #5399, 0.09 #1889), 01zhs3 (0.25 #138, 0.08 #8214, 0.07 #9267), 01cwm1 (0.25 #165, 0.08 #8592, 0.07 #8943), 029q3k (0.25 #212, 0.07 #3371, 0.06 #8288), 01z1r (0.25 #145, 0.07 #3304, 0.06 #8221), 0223bl (0.25 #7, 0.06 #6677, 0.05 #8083), 01s0t3 (0.25 #127, 0.05 #4339, 0.04 #4690) >> Best rule #297 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0457w0; 0d9v9q; >> query: (?x3031, 027ffq) <- athlete(?x471, ?x3031), team(?x3031, ?x6503), team(?x3031, ?x59), ?x471 = 02vx4, ?x6503 = 0k_l4, teams(?x390, ?x59) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8335 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 61 *> proper extension: 02y9ln; 0gtgp6; 0b7l1f; 02y0dd; *> query: (?x3031, 0j46b) <- team(?x3031, ?x59), nationality(?x3031, ?x390), gender(?x3031, ?x231), position(?x59, ?x60), sport(?x59, ?x471) *> conf = 0.06 ranks of expected_values: 112 EVAL 071pf2 team 0j46b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 88.000 88.000 0.500 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #18768-01p7b6b PRED entity: 01p7b6b PRED relation: student! PRED expected values: 031n8c => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 116): 02301 (0.17 #74, 0.02 #5871, 0.02 #7452), 06rkfs (0.17 #375), 0ks67 (0.17 #189), 017z88 (0.16 #2717, 0.10 #5352, 0.09 #6406), 033x5p (0.14 #669, 0.10 #1196, 0.05 #3831), 0288zy (0.14 #543, 0.10 #1070, 0.05 #3705), 01t0dy (0.12 #2325, 0.10 #3906, 0.10 #3379), 0bwfn (0.11 #2910, 0.09 #1856, 0.08 #7653), 02cw8s (0.11 #2705, 0.04 #4813, 0.03 #6921), 01d34b (0.09 #1837, 0.06 #2364, 0.05 #2891) >> Best rule #74 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 015wfg; 02q9kqf; >> query: (?x10146, 02301) <- place_of_birth(?x10146, ?x8171), nominated_for(?x10146, ?x1255), ?x1255 = 0hv1t >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01p7b6b student! 031n8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 99.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #18767-04gv3db PRED entity: 04gv3db PRED relation: film_crew_role PRED expected values: 02r96rf => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 25): 02r96rf (0.75 #268, 0.74 #334, 0.71 #69), 0dxtw (0.41 #1010, 0.41 #274, 0.40 #373), 01pvkk (0.38 #10, 0.29 #109, 0.29 #1709), 02ynfr (0.19 #914, 0.19 #378, 0.18 #1116), 015h31 (0.16 #107, 0.14 #140, 0.12 #8), 01xy5l_ (0.16 #111, 0.12 #277, 0.12 #78), 0215hd (0.16 #282, 0.15 #83, 0.15 #1551), 089g0h (0.15 #283, 0.13 #349, 0.12 #1552), 0d2b38 (0.14 #288, 0.14 #155, 0.12 #354), 0ckd1 (0.12 #4, 0.04 #103, 0.03 #269) >> Best rule #268 for best value: >> intensional similarity = 4 >> extensional distance = 218 >> proper extension: 0dkv90; >> query: (?x4479, 02r96rf) <- film_release_distribution_medium(?x4479, ?x81), film_crew_role(?x4479, ?x137), currency(?x4479, ?x170), film_format(?x4479, ?x10390) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gv3db film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.755 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18766-0hz35 PRED entity: 0hz35 PRED relation: citytown! PRED expected values: 0kz2w => 109 concepts (63 used for prediction) PRED predicted values (max 10 best out of 291): 043q2z (0.11 #2337, 0.10 #3956, 0.05 #6384), 01_f90 (0.11 #2255, 0.10 #3874, 0.05 #6302), 014zws (0.11 #2055, 0.10 #3674, 0.05 #6102), 01hr11 (0.11 #2036, 0.10 #3655, 0.05 #6083), 01k7xz (0.11 #1715, 0.10 #3334, 0.05 #5762), 04rwx (0.11 #1676, 0.10 #3295, 0.05 #5723), 03ksy (0.11 #1757, 0.10 #3376, 0.05 #5804), 0473m9 (0.06 #4089, 0.05 #5708, 0.04 #9753), 04kqk (0.06 #4803, 0.05 #6422, 0.02 #9658), 013807 (0.06 #4597, 0.05 #6216, 0.02 #9452) >> Best rule #2337 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 043q2z; >> query: (?x11730, 043q2z) <- category(?x11730, ?x134), contains(?x9065, ?x11730), ?x9065 = 0k3k1 >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hz35 citytown! 0kz2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 63.000 0.111 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #18765-025ldg PRED entity: 025ldg PRED relation: instrumentalists! PRED expected values: 0342h => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 95): 0342h (0.86 #184, 0.82 #274, 0.80 #453), 018vs (0.65 #283, 0.48 #1176, 0.45 #462), 05r5c (0.55 #457, 0.53 #278, 0.52 #1171), 03qjg (0.50 #232, 0.41 #411, 0.40 #501), 05148p4 (0.40 #1184, 0.38 #22, 0.36 #201), 02hnl (0.30 #484, 0.29 #305, 0.25 #36), 04rzd (0.20 #487, 0.14 #1201, 0.13 #1111), 06w7v (0.19 #1236, 0.15 #1146, 0.14 #253), 018j2 (0.19 #1202, 0.13 #1112, 0.13 #1561), 03gvt (0.18 #156, 0.14 #246, 0.12 #425) >> Best rule #184 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 016s_5; 0fq117k; >> query: (?x4200, 0342h) <- artists(?x7083, ?x4200), artists(?x2664, ?x4200), ?x7083 = 02yv6b, ?x2664 = 01lyv, profession(?x4200, ?x131) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025ldg instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.857 http://example.org/music/instrument/instrumentalists #18764-083pr PRED entity: 083pr PRED relation: student! PRED expected values: 01rr_d => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 17): 014mlp (0.19 #446, 0.18 #686, 0.17 #106), 02_xgp2 (0.17 #14, 0.14 #694, 0.11 #74), 02h4rq6 (0.17 #3, 0.11 #63, 0.10 #83), 0bkj86 (0.12 #689, 0.08 #109, 0.06 #1131), 04zx3q1 (0.11 #682, 0.06 #1124, 0.06 #1104), 019v9k (0.08 #450, 0.07 #731, 0.07 #812), 013zdg (0.08 #448, 0.07 #701, 0.07 #810), 01rr_d (0.07 #257, 0.07 #277, 0.07 #577), 02mjs7 (0.07 #701, 0.02 #545, 0.02 #605), 02cq61 (0.07 #701) >> Best rule #446 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 03kdl; 01lct6; >> query: (?x1913, 014mlp) <- profession(?x1913, ?x3342), jurisdiction_of_office(?x1913, ?x94), people(?x5741, ?x1913), student(?x122, ?x1913) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #257 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 07_m9_; 0fb7c; *> query: (?x1913, 01rr_d) <- profession(?x1913, ?x3342), place_of_death(?x1913, ?x108), type_of_union(?x1913, ?x566), politician(?x1912, ?x1913) *> conf = 0.07 ranks of expected_values: 8 EVAL 083pr student! 01rr_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 183.000 183.000 0.189 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #18763-04192r PRED entity: 04192r PRED relation: company PRED expected values: 04f0xq => 36 concepts (18 used for prediction) PRED predicted values (max 10 best out of 752): 0300cp (0.67 #3746, 0.64 #4085, 0.62 #5098), 019rl6 (0.64 #4196, 0.60 #1836, 0.57 #3185), 0z90c (0.64 #4208, 0.60 #1848, 0.57 #2185), 060ppp (0.64 #4282, 0.60 #1922, 0.57 #2259), 0537b (0.60 #1821, 0.60 #1485, 0.57 #3170), 04sv4 (0.60 #1885, 0.60 #1549, 0.57 #3234), 01qygl (0.60 #1873, 0.57 #3222, 0.57 #2887), 087c7 (0.60 #1681, 0.57 #2018, 0.56 #3702), 0vlf (0.60 #1964, 0.57 #2301, 0.56 #3985), 01s73z (0.60 #1785, 0.57 #2122, 0.56 #3806) >> Best rule #3746 for best value: >> intensional similarity = 16 >> extensional distance = 7 >> proper extension: 02k13d; >> query: (?x12865, 0300cp) <- company(?x12865, ?x12850), company(?x12865, ?x11636), company(?x12865, ?x9873), company(?x554, ?x11636), category(?x12850, ?x134), child(?x1908, ?x12850), state_province_region(?x12850, ?x335), list(?x9873, ?x7472), ?x7472 = 01ptsx, service_location(?x11636, ?x252), service_language(?x11636, ?x254), company(?x554, ?x555), ?x335 = 059rby, ?x555 = 01c6k4, contact_category(?x11636, ?x897), service_location(?x9873, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2182 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 5 *> proper extension: 014l7h; *> query: (?x12865, 04f0xq) <- company(?x12865, ?x12850), company(?x12865, ?x11636), company(?x12865, ?x9873), category(?x12850, ?x134), child(?x1908, ?x12850), state_province_region(?x12850, ?x335), list(?x9873, ?x7472), ?x7472 = 01ptsx, service_location(?x11636, ?x2146), service_language(?x11636, ?x254), contact_category(?x12850, ?x897), exported_to(?x2146, ?x3352), film_release_region(?x80, ?x2146), contains(?x2146, ?x1391) *> conf = 0.43 ranks of expected_values: 58 EVAL 04192r company 04f0xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 36.000 18.000 0.667 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #18762-0m68w PRED entity: 0m68w PRED relation: profession PRED expected values: 02hrh1q => 135 concepts (103 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.94 #3863, 0.92 #4011, 0.91 #9489), 0dxtg (0.70 #12597, 0.53 #4306, 0.51 #3714), 03gjzk (0.62 #1052, 0.42 #3124, 0.42 #3716), 01d_h8 (0.55 #1042, 0.43 #8739, 0.42 #450), 0cbd2 (0.45 #1931, 0.35 #12590, 0.17 #3707), 0d1pc (0.40 #50, 0.33 #346, 0.25 #1678), 09jwl (0.37 #10381, 0.37 #11566, 0.37 #8160), 02jknp (0.33 #8741, 0.32 #1044, 0.32 #12591), 0np9r (0.30 #3129, 0.29 #3721, 0.27 #4313), 0nbcg (0.27 #11578, 0.26 #10393, 0.26 #10837) >> Best rule #3863 for best value: >> intensional similarity = 4 >> extensional distance = 240 >> proper extension: 024dgj; 0c3jz; 01yg9y; 01bczm; 06hgym; 03j3pg9; 0dxmyh; 063t3j; >> query: (?x12255, 02hrh1q) <- participant(?x12255, ?x2237), profession(?x12255, ?x2225), profession(?x4831, ?x2225), ?x4831 = 0hgqq >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m68w profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 103.000 0.942 http://example.org/people/person/profession #18761-01nnsv PRED entity: 01nnsv PRED relation: institution! PRED expected values: 016t_3 02mjs7 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 14): 016t_3 (0.62 #44, 0.52 #188, 0.50 #203), 04zx3q1 (0.42 #43, 0.30 #187, 0.30 #202), 03mkk4 (0.28 #48, 0.25 #754, 0.18 #690), 022h5x (0.25 #754, 0.23 #53, 0.19 #124), 0bjrnt (0.25 #754, 0.19 #60, 0.18 #4), 02mjs7 (0.25 #754, 0.18 #690, 0.12 #59), 028dcg (0.18 #690, 0.13 #52, 0.13 #123), 02m4yg (0.18 #690, 0.07 #65, 0.06 #51), 071tyz (0.18 #690, 0.06 #61, 0.06 #146), 01ysy9 (0.18 #690, 0.06 #214, 0.06 #199) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 02xpy5; >> query: (?x5750, 016t_3) <- institution(?x4981, ?x5750), ?x4981 = 03bwzr4, fraternities_and_sororities(?x5750, ?x3697) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 01nnsv institution! 02mjs7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 58.000 58.000 0.623 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01nnsv institution! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.623 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18760-03flwk PRED entity: 03flwk PRED relation: place_of_death PRED expected values: 0n2z => 72 concepts (24 used for prediction) PRED predicted values (max 10 best out of 22): 02_286 (0.20 #13, 0.17 #401, 0.13 #790), 0rnmy (0.20 #42, 0.06 #430, 0.05 #236), 030qb3t (0.05 #216, 0.04 #3722, 0.04 #3136), 0cc56 (0.05 #211, 0.03 #405, 0.02 #599), 0r15k (0.05 #326, 0.03 #520, 0.01 #909), 0k_p5 (0.03 #670, 0.01 #1059), 0r3w7 (0.03 #565, 0.01 #954), 01x73 (0.03 #4090, 0.02 #777, 0.02 #4286), 0f2wj (0.02 #1177, 0.02 #1371, 0.01 #2151), 0k049 (0.02 #974, 0.02 #2922, 0.02 #3117) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 05pq9; 0q59y; 06z4wj; >> query: (?x5100, 02_286) <- profession(?x5100, ?x11804), award(?x5100, ?x3105), ?x3105 = 01l29r, ?x11804 = 0q04f >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03flwk place_of_death 0n2z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 24.000 0.200 http://example.org/people/deceased_person/place_of_death #18759-0jzphpx PRED entity: 0jzphpx PRED relation: instance_of_recurring_event PRED expected values: 0c4ys => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 7): 0c4ys (0.87 #41, 0.86 #33, 0.86 #25), 02jp2w (0.57 #79, 0.43 #119, 0.39 #191), 0g_w (0.41 #388, 0.30 #372), 015hr (0.17 #68, 0.13 #84, 0.12 #100), 07wcy (0.17 #14, 0.05 #62, 0.04 #70), 0gcf2r (0.12 #371, 0.09 #387), 018cvf (0.10 #85, 0.09 #93, 0.09 #101) >> Best rule #41 for best value: >> intensional similarity = 8 >> extensional distance = 13 >> proper extension: 01s695; >> query: (?x2431, 0c4ys) <- ceremony(?x7691, ?x2431), ceremony(?x3835, ?x2431), ceremony(?x3045, ?x2431), ?x3045 = 02sp_v, ?x7691 = 026m9w, award_winner(?x2431, ?x11621), award_winner(?x3835, ?x702), artists(?x671, ?x11621) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jzphpx instance_of_recurring_event 0c4ys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.867 http://example.org/time/event/instance_of_recurring_event #18758-0f2nf PRED entity: 0f2nf PRED relation: source PRED expected values: 0jbk9 => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #70, 0.94 #76, 0.93 #68) >> Best rule #70 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 0xqf3; 0b_cr; >> query: (?x9336, 0jbk9) <- county(?x9336, ?x13940), place_of_birth(?x8100, ?x9336), gender(?x8100, ?x231), ?x231 = 05zppz >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2nf source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 181.000 0.935 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #18757-0jhd PRED entity: 0jhd PRED relation: country! PRED expected values: 03hr1p 03fyrh => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 43): 01lb14 (0.74 #526, 0.69 #483, 0.69 #354), 03hr1p (0.64 #532, 0.62 #489, 0.60 #1435), 01z27 (0.64 #528, 0.46 #270, 0.46 #356), 07jbh (0.60 #625, 0.55 #539, 0.53 #1744), 0w0d (0.59 #481, 0.57 #352, 0.53 #1083), 06wrt (0.59 #484, 0.54 #355, 0.54 #441), 0194d (0.58 #122, 0.57 #380, 0.56 #509), 03fyrh (0.55 #536, 0.54 #278, 0.53 #106), 07jjt (0.54 #488, 0.51 #359, 0.45 #617), 03rbzn (0.52 #535, 0.51 #363, 0.51 #492) >> Best rule #526 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 077qn; >> query: (?x8588, 01lb14) <- olympics(?x8588, ?x784), adjoins(?x1499, ?x8588), ?x784 = 018ctl >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #532 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 077qn; *> query: (?x8588, 03hr1p) <- olympics(?x8588, ?x784), adjoins(?x1499, ?x8588), ?x784 = 018ctl *> conf = 0.64 ranks of expected_values: 2, 8 EVAL 0jhd country! 03fyrh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 147.000 147.000 0.738 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0jhd country! 03hr1p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 147.000 0.738 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #18756-01n4f8 PRED entity: 01n4f8 PRED relation: profession PRED expected values: 0dxtg 02hrh1q => 139 concepts (116 used for prediction) PRED predicted values (max 10 best out of 91): 02hrh1q (0.90 #12632, 0.90 #4220, 0.90 #7845), 0dxtg (0.70 #4074, 0.68 #5669, 0.67 #1318), 0np9r (0.50 #454, 0.45 #309, 0.32 #8267), 0fj9f (0.50 #197, 0.33 #52, 0.21 #2662), 02jknp (0.46 #11030, 0.35 #2182, 0.31 #1312), 0cbd2 (0.45 #7402, 0.45 #8273, 0.45 #7112), 04gc2 (0.38 #184, 0.10 #2504, 0.09 #2649), 09jwl (0.37 #12925, 0.37 #12200, 0.36 #10170), 015cjr (0.36 #337, 0.33 #482, 0.33 #47), 08z956 (0.33 #75, 0.06 #1960, 0.03 #2830) >> Best rule #12632 for best value: >> intensional similarity = 3 >> extensional distance = 1066 >> proper extension: 025p38; 02lq10; 07hbxm; 01v3bn; 02wycg2; 073749; 0dh73w; 01gy7r; 0175wg; 01d1yr; ... >> query: (?x1725, 02hrh1q) <- profession(?x1725, ?x319), student(?x741, ?x1725), film(?x1725, ?x590) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01n4f8 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 116.000 0.904 http://example.org/people/person/profession EVAL 01n4f8 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 116.000 0.904 http://example.org/people/person/profession #18755-051zy_b PRED entity: 051zy_b PRED relation: film! PRED expected values: 024rgt => 92 concepts (73 used for prediction) PRED predicted values (max 10 best out of 67): 031rq5 (0.48 #2516, 0.45 #666, 0.43 #1108), 016tw3 (0.33 #9, 0.29 #158, 0.19 #601), 05qd_ (0.21 #524, 0.17 #673, 0.16 #376), 086k8 (0.20 #445, 0.20 #371, 0.18 #594), 0jz9f (0.17 #74, 0.14 #223, 0.07 #667), 016tt2 (0.14 #372, 0.14 #225, 0.14 #520), 03xq0f (0.14 #153, 0.12 #521, 0.10 #373), 03sb38 (0.14 #190, 0.02 #633, 0.02 #928), 0g1rw (0.09 #302, 0.08 #746, 0.06 #1115), 054g1r (0.08 #402, 0.07 #329, 0.07 #2475) >> Best rule #2516 for best value: >> intensional similarity = 4 >> extensional distance = 657 >> proper extension: 0dtw1x; 0crh5_f; 0h95zbp; >> query: (?x3534, ?x5908) <- film_release_region(?x3534, ?x94), film(?x541, ?x3534), production_companies(?x3534, ?x5908), ?x94 = 09c7w0 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2607 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 688 *> proper extension: 0gj9qxr; 07s3m4g; *> query: (?x3534, 024rgt) <- film_release_region(?x3534, ?x94), currency(?x3534, ?x170), film_release_distribution_medium(?x3534, ?x81), film(?x541, ?x3534) *> conf = 0.04 ranks of expected_values: 31 EVAL 051zy_b film! 024rgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 92.000 73.000 0.483 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18754-045931 PRED entity: 045931 PRED relation: gender PRED expected values: 05zppz => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #5, 0.71 #132, 0.71 #130), 02zsn (0.36 #8, 0.30 #12, 0.29 #32) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0kt64b; 0flj39; >> query: (?x11741, 05zppz) <- profession(?x11741, ?x5716), profession(?x11741, ?x1032), ?x1032 = 02hrh1q, ?x5716 = 021wpb >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045931 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.786 http://example.org/people/person/gender #18753-042xh PRED entity: 042xh PRED relation: nationality PRED expected values: 02jx1 => 180 concepts (179 used for prediction) PRED predicted values (max 10 best out of 72): 09c7w0 (0.87 #9750, 0.84 #8854, 0.83 #3476), 02jx1 (0.76 #12239, 0.59 #3907, 0.48 #1322), 05r4w (0.76 #11143, 0.28 #15026), 0345h (0.50 #894, 0.09 #1817, 0.08 #8983), 04jpl (0.28 #15026, 0.03 #4669, 0.02 #7260), 036wy (0.28 #15026), 03rk0 (0.20 #6311, 0.11 #11389, 0.11 #1931), 03_3d (0.20 #105, 0.09 #6272, 0.08 #701), 05b4w (0.20 #149, 0.08 #745, 0.03 #1638), 0d060g (0.12 #901, 0.10 #304, 0.09 #603) >> Best rule #9750 for best value: >> intensional similarity = 4 >> extensional distance = 433 >> proper extension: 02vptk_; >> query: (?x13644, 09c7w0) <- currency(?x13644, ?x170), nationality(?x13644, ?x512), ?x170 = 09nqf, film_release_region(?x66, ?x512) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #12239 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 813 *> proper extension: 02k6rq; 0443c; *> query: (?x13644, ?x1310) <- award_winner(?x3337, ?x13644), location(?x13644, ?x14442), place_of_birth(?x3849, ?x14442), country(?x14442, ?x1310) *> conf = 0.76 ranks of expected_values: 2 EVAL 042xh nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 180.000 179.000 0.871 http://example.org/people/person/nationality #18752-0f7hc PRED entity: 0f7hc PRED relation: gender PRED expected values: 05zppz => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #77, 0.86 #15, 0.86 #65), 02zsn (0.44 #50, 0.44 #100, 0.40 #80) >> Best rule #77 for best value: >> intensional similarity = 2 >> extensional distance = 385 >> proper extension: 07kb5; 0177s6; 03f70xs; 0379s; 032l1; 05wh0sh; 040_9; 0fx02; 052h3; 0gz_; ... >> query: (?x4657, 05zppz) <- profession(?x4657, ?x319), influenced_by(?x1835, ?x4657) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f7hc gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.866 http://example.org/people/person/gender #18751-0jswp PRED entity: 0jswp PRED relation: nominated_for! PRED expected values: 0gq9h 0gs9p => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 201): 0f4x7 (0.77 #6142, 0.66 #11578, 0.66 #8032), 019f4v (0.66 #1706, 0.46 #1942, 0.46 #761), 0gq9h (0.65 #1715, 0.64 #1951, 0.60 #770), 0gs9p (0.61 #1717, 0.55 #1953, 0.50 #772), 0gq_v (0.50 #20, 0.47 #728, 0.40 #1200), 0p9sw (0.50 #21, 0.29 #1674, 0.28 #6635), 02qyntr (0.43 #1831, 0.25 #6792, 0.24 #6082), 040njc (0.43 #1660, 0.37 #1896, 0.35 #715), 02pqp12 (0.42 #1711, 0.25 #1947, 0.23 #6672), 0gqy2 (0.37 #1773, 0.35 #2009, 0.35 #828) >> Best rule #6142 for best value: >> intensional similarity = 4 >> extensional distance = 545 >> proper extension: 075cph; 019kyn; 0fsw_7; 01kf5lf; >> query: (?x3369, ?x591) <- film(?x5913, ?x3369), award(?x3369, ?x591), ceremony(?x591, ?x78), award(?x123, ?x591) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1715 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 176 *> proper extension: 0p_sc; *> query: (?x3369, 0gq9h) <- nominated_for(?x746, ?x3369), film(?x5913, ?x3369), genre(?x3369, ?x53), ?x746 = 04dn09n *> conf = 0.65 ranks of expected_values: 3, 4 EVAL 0jswp nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.769 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0jswp nominated_for! 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.769 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18750-03_9r PRED entity: 03_9r PRED relation: language! PRED expected values: 0pc62 02z9hqn 0407yj_ 08fbnx 0bh72t 01f8f7 076xkdz 05vc35 => 84 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1812): 0f4_2k (0.71 #15805, 0.60 #20762, 0.56 #19109), 034xyf (0.71 #16192, 0.56 #19496, 0.42 #22800), 0dr_4 (0.57 #15093, 0.50 #20050, 0.44 #18397), 0g5qmbz (0.57 #16299, 0.44 #19603, 0.44 #29513), 03twd6 (0.57 #15071, 0.44 #18375, 0.42 #21679), 01ffx4 (0.57 #15339, 0.44 #18643, 0.40 #20296), 03z9585 (0.57 #16162, 0.44 #19466, 0.40 #21119), 0c_j9x (0.57 #15203, 0.44 #18507, 0.40 #20160), 0ft18 (0.57 #16154, 0.44 #19458, 0.40 #21111), 02yvct (0.57 #15186, 0.44 #18490, 0.40 #20143) >> Best rule #15805 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 02bjrlw; 04306rv; 06nm1; 064_8sq; >> query: (?x2164, 0f4_2k) <- languages(?x5314, ?x2164), language(?x6069, ?x2164), language(?x5255, ?x2164), language(?x4621, ?x2164), language(?x3496, ?x2164), genre(?x5255, ?x53), major_field_of_study(?x7660, ?x2164), nominated_for(?x574, ?x3496), award(?x6069, ?x198), film(?x436, ?x4621) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #15306 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 5 *> proper extension: 02bjrlw; 04306rv; 06nm1; 064_8sq; *> query: (?x2164, 0407yj_) <- languages(?x5314, ?x2164), language(?x6069, ?x2164), language(?x5255, ?x2164), language(?x4621, ?x2164), language(?x3496, ?x2164), genre(?x5255, ?x53), major_field_of_study(?x7660, ?x2164), nominated_for(?x574, ?x3496), award(?x6069, ?x198), film(?x436, ?x4621) *> conf = 0.43 ranks of expected_values: 35, 259, 473, 754, 1269, 1568, 1665, 1738 EVAL 03_9r language! 05vc35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 28.000 0.714 http://example.org/film/film/language EVAL 03_9r language! 076xkdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 28.000 0.714 http://example.org/film/film/language EVAL 03_9r language! 01f8f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 28.000 0.714 http://example.org/film/film/language EVAL 03_9r language! 0bh72t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 28.000 0.714 http://example.org/film/film/language EVAL 03_9r language! 08fbnx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 84.000 28.000 0.714 http://example.org/film/film/language EVAL 03_9r language! 0407yj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 84.000 28.000 0.714 http://example.org/film/film/language EVAL 03_9r language! 02z9hqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 28.000 0.714 http://example.org/film/film/language EVAL 03_9r language! 0pc62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 84.000 28.000 0.714 http://example.org/film/film/language #18749-02vw1w2 PRED entity: 02vw1w2 PRED relation: film! PRED expected values: 0392kz => 87 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1064): 07rzf (0.33 #3964, 0.09 #22684, 0.09 #20604), 01vwllw (0.33 #2630, 0.09 #21350, 0.09 #19270), 0dt645q (0.29 #32968, 0.28 #28808, 0.22 #26727), 01rmnp (0.25 #16152, 0.25 #7832, 0.25 #5751), 0392kz (0.25 #5905, 0.20 #12147, 0.12 #16306), 01kym3 (0.25 #6214, 0.20 #12456, 0.12 #16615), 02t1dv (0.22 #26995, 0.20 #31156, 0.20 #10357), 03q64h (0.20 #12438, 0.20 #10358, 0.17 #29077), 042gr4 (0.20 #12447, 0.20 #10367, 0.17 #29086), 0151ns (0.20 #12578, 0.09 #37536, 0.05 #56248) >> Best rule #3964 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 06cgf; >> query: (?x1419, 07rzf) <- genre(?x1419, ?x5937), genre(?x1419, ?x571), actor(?x1419, ?x51), ?x571 = 03npn, film(?x1418, ?x1419), film_release_distribution_medium(?x1419, ?x81), genre(?x419, ?x5937) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5905 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 0ckrgs; *> query: (?x1419, 0392kz) <- genre(?x1419, ?x225), actor(?x1419, ?x4632), prequel(?x1419, ?x7029), language(?x1419, ?x2164), film_release_region(?x7029, ?x94), country(?x7029, ?x252), film(?x1418, ?x1419), student(?x11559, ?x4632) *> conf = 0.25 ranks of expected_values: 5 EVAL 02vw1w2 film! 0392kz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 54.000 0.333 http://example.org/film/actor/film./film/performance/film #18748-0r5wt PRED entity: 0r5wt PRED relation: place_of_birth! PRED expected values: 02qggqc => 109 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1590): 05f0r8 (0.33 #2599, 0.03 #13047, 0.02 #18274), 05h7tk (0.33 #2571, 0.03 #13019, 0.02 #18246), 0745k7 (0.33 #2570, 0.03 #13018, 0.02 #18245), 058z1hb (0.33 #2568, 0.03 #13016, 0.02 #18243), 02nygk (0.33 #2559, 0.03 #13007, 0.02 #18234), 01g04k (0.33 #2545, 0.03 #12993, 0.02 #18220), 089z0z (0.33 #2538, 0.03 #12986, 0.02 #18213), 01hbq0 (0.33 #2532, 0.03 #12980, 0.02 #18207), 0f3nn (0.33 #2527, 0.03 #12975, 0.02 #18202), 02k76g (0.33 #2524, 0.03 #12972, 0.02 #18199) >> Best rule #2599 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02_286; >> query: (?x4578, 05f0r8) <- contains(?x94, ?x4578), citytown(?x11504, ?x4578), citytown(?x5970, ?x4578), ?x11504 = 05njw, award_winner(?x1904, ?x5970) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r5wt place_of_birth! 02qggqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 90.000 0.333 http://example.org/people/person/place_of_birth #18747-01sl1q PRED entity: 01sl1q PRED relation: location PRED expected values: 027rn => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 187): 0f2w0 (0.42 #72099, 0.42 #73702, 0.41 #76107), 030qb3t (0.25 #83, 0.23 #8093, 0.23 #6491), 01rwf_ (0.25 #567, 0.08 #3771, 0.07 #4572), 05v8c (0.25 #28, 0.07 #4033, 0.01 #4834), 02jx1 (0.22 #4877, 0.18 #5678, 0.02 #6479), 02_286 (0.18 #2440, 0.17 #11251, 0.17 #16858), 04jpl (0.12 #818, 0.09 #1619, 0.08 #3221), 0cr3d (0.12 #946, 0.09 #1747, 0.08 #3349), 05tbn (0.12 #988, 0.09 #1789, 0.08 #3391), 0846v (0.12 #966, 0.09 #1767, 0.08 #3369) >> Best rule #72099 for best value: >> intensional similarity = 2 >> extensional distance = 2373 >> proper extension: 037mh8; 01cqz5; >> query: (?x56, ?x1719) <- gender(?x56, ?x514), place_of_birth(?x56, ?x1719) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #4807 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 90 *> proper extension: 0m77m; 04xjp; 041mt; 02m7r; 0n00; 043s3; 034bs; 0b78hw; 01tdnyh; 0dx97; ... *> query: (?x56, 027rn) <- location(?x56, ?x6559), countries_spoken_in(?x254, ?x6559) *> conf = 0.01 ranks of expected_values: 172 EVAL 01sl1q location 027rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 109.000 109.000 0.419 http://example.org/people/person/places_lived./people/place_lived/location #18746-0mwh1 PRED entity: 0mwh1 PRED relation: adjoins PRED expected values: 0mwl2 => 93 concepts (38 used for prediction) PRED predicted values (max 10 best out of 398): 0n57k (0.80 #24712, 0.79 #2315, 0.79 #2314), 0mwl2 (0.80 #24712, 0.79 #2315, 0.79 #2314), 0n5c9 (0.33 #1912, 0.23 #2316, 0.02 #10408), 059rby (0.33 #17, 0.20 #788, 0.05 #17778), 05fjf (0.33 #300, 0.20 #1071, 0.04 #8022), 05kkh (0.33 #7, 0.20 #778, 0.03 #17768), 04rrd (0.33 #95, 0.20 #866, 0.03 #17856), 081mh (0.33 #146, 0.20 #917, 0.02 #7868), 02_286 (0.33 #35, 0.20 #806, 0.02 #17796), 026mj (0.33 #338, 0.20 #1109, 0.01 #18099) >> Best rule #24712 for best value: >> intensional similarity = 4 >> extensional distance = 168 >> proper extension: 0rh6k; 02dtg; 02_286; 0wh3; 01_d4; 0dclg; 0dc95; 0m2gk; 0m2gz; 01sn3; ... >> query: (?x2744, ?x855) <- contains(?x2744, ?x1494), adjoins(?x855, ?x2744), source(?x2744, ?x958), ?x958 = 0jbk9 >> conf = 0.80 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0mwh1 adjoins 0mwl2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 38.000 0.799 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #18745-05cgy8 PRED entity: 05cgy8 PRED relation: profession PRED expected values: 01d_h8 0cbd2 02jknp 09jwl 0kyk => 96 concepts (61 used for prediction) PRED predicted values (max 10 best out of 142): 02hrh1q (0.90 #6443, 0.82 #3081, 0.81 #7173), 02jknp (0.90 #738, 0.88 #2490, 0.87 #446), 01d_h8 (0.81 #444, 0.81 #3804, 0.78 #2488), 09jwl (0.54 #165, 0.47 #3378, 0.40 #1333), 03gjzk (0.38 #2935, 0.36 #4544, 0.36 #2497), 0nbcg (0.37 #2073, 0.36 #2219, 0.33 #2365), 01c8w0 (0.30 #1615, 0.29 #1323, 0.27 #1907), 0dz3r (0.30 #3361, 0.24 #148, 0.22 #2046), 0cbd2 (0.26 #2927, 0.22 #4536, 0.15 #2489), 025352 (0.20 #203, 0.15 #3416, 0.13 #2101) >> Best rule #6443 for best value: >> intensional similarity = 4 >> extensional distance = 1656 >> proper extension: 06688p; 05bp8g; 01rrwf6; 01ty7ll; 018dnt; 0c7ct; 041ly3; 01pw2f1; 01pl9g; 01qkqwg; ... >> query: (?x6643, 02hrh1q) <- profession(?x6643, ?x2265), place_of_birth(?x6643, ?x12464), profession(?x7740, ?x2265), ?x7740 = 02404v >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #738 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 03bw6; *> query: (?x6643, 02jknp) <- place_of_birth(?x6643, ?x12464), film(?x6643, ?x2840), award(?x6643, ?x1313), ?x1313 = 0gs9p *> conf = 0.90 ranks of expected_values: 2, 3, 4, 9, 14 EVAL 05cgy8 profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 96.000 61.000 0.900 http://example.org/people/person/profession EVAL 05cgy8 profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 61.000 0.900 http://example.org/people/person/profession EVAL 05cgy8 profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 61.000 0.900 http://example.org/people/person/profession EVAL 05cgy8 profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 61.000 0.900 http://example.org/people/person/profession EVAL 05cgy8 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 61.000 0.900 http://example.org/people/person/profession #18744-0fc9js PRED entity: 0fc9js PRED relation: nominated_for PRED expected values: 039cq4 => 60 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1601): 039cq4 (0.77 #14402, 0.77 #27216, 0.77 #28815), 01j7mr (0.77 #14402, 0.77 #27216, 0.77 #28815), 0b005 (0.77 #14402, 0.77 #27216, 0.77 #28815), 01bv8b (0.38 #5188, 0.35 #3589, 0.35 #6787), 0ddd0gc (0.35 #3397, 0.33 #4996, 0.31 #6595), 0124k9 (0.35 #3418, 0.33 #5017, 0.31 #6616), 01g03q (0.35 #4580, 0.29 #6179, 0.27 #7778), 030cx (0.35 #3897, 0.29 #5496, 0.27 #7095), 0q9jk (0.35 #4444, 0.29 #6043, 0.27 #7642), 02hct1 (0.35 #3552, 0.25 #5151, 0.23 #6750) >> Best rule #14402 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 054knh; >> query: (?x4386, ?x3626) <- award(?x3626, ?x4386), ceremony(?x4386, ?x9450), award_winner(?x9450, ?x8596), ?x8596 = 0hz_1 >> conf = 0.77 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0fc9js nominated_for 039cq4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 25.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18743-04v048 PRED entity: 04v048 PRED relation: nationality PRED expected values: 09c7w0 => 102 concepts (87 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.88 #1104, 0.86 #703, 0.83 #1204), 0d060g (0.40 #8446, 0.05 #1513, 0.05 #4028), 03rjj (0.40 #8446, 0.05 #205, 0.03 #1713), 02jx1 (0.12 #1036, 0.11 #1741, 0.10 #2647), 07ssc (0.10 #1018, 0.08 #7858, 0.08 #8461), 03rk0 (0.07 #3766, 0.07 #3061, 0.07 #4370), 0345h (0.04 #2039, 0.03 #3650, 0.03 #3448), 0f8l9c (0.04 #522, 0.04 #2030, 0.03 #3641), 0d05w3 (0.03 #2564, 0.02 #1758, 0.02 #2764), 03_3d (0.03 #506, 0.02 #5535, 0.02 #2520) >> Best rule #1104 for best value: >> intensional similarity = 4 >> extensional distance = 202 >> proper extension: 01nqfh_; >> query: (?x8526, 09c7w0) <- place_of_birth(?x8526, ?x2850), profession(?x8526, ?x319), location(?x968, ?x2850), ?x968 = 015grj >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04v048 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 87.000 0.877 http://example.org/people/person/nationality #18742-05zvq6g PRED entity: 05zvq6g PRED relation: award! PRED expected values: 0159h6 => 38 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1877): 01hkhq (0.71 #4033, 0.57 #7403, 0.45 #10773), 03bxsw (0.71 #4286, 0.43 #7656, 0.33 #916), 0154qm (0.57 #7641, 0.57 #4271, 0.36 #11011), 043kzcr (0.57 #7407, 0.57 #4037, 0.36 #10777), 028knk (0.57 #7264, 0.43 #3894, 0.36 #10634), 0lpjn (0.57 #7505, 0.43 #4135, 0.36 #10875), 0kszw (0.57 #4042, 0.43 #7412, 0.36 #10782), 02jsgf (0.57 #4520, 0.43 #7890, 0.33 #1150), 03mp9s (0.57 #5390, 0.43 #8760, 0.33 #2020), 0gx_p (0.57 #5203, 0.43 #8573, 0.33 #1833) >> Best rule #4033 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0fq9zdn; 0gqyl; 09td7p; 02ppm4q; >> query: (?x1008, 01hkhq) <- award(?x6997, ?x1008), nominated_for(?x1008, ?x2380), nominated_for(?x2379, ?x2380), ?x6997 = 016nff, ?x2379 = 02qvyrt >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6839 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 094qd5; 099cng; 02y_rq5; 027b9k6; *> query: (?x1008, 0159h6) <- award(?x5951, ?x1008), nominated_for(?x1008, ?x4359), nominated_for(?x1008, ?x2380), ?x2380 = 02q6gfp, ?x4359 = 0g9lm2, ?x5951 = 0dvld *> conf = 0.43 ranks of expected_values: 29 EVAL 05zvq6g award! 0159h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 38.000 12.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18741-016tt2 PRED entity: 016tt2 PRED relation: film PRED expected values: 02v8kmz 0dtfn 02847m9 0pvms 016kv6 0gjcrrw 0gyh2wm 02q_4ph 06gb1w 038bh3 0ptxj 04zl8 0ddj0x 0gmgwnv 072r5v 0h2zvzr 07phbc => 155 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1443): 0dr_4 (0.59 #29890, 0.59 #28465, 0.59 #25618), 027rpym (0.59 #29890, 0.59 #28465, 0.59 #25618), 0cq8qq (0.59 #29890, 0.59 #28465, 0.59 #25618), 0419kt (0.59 #29890, 0.59 #28465, 0.59 #25618), 0fpv_3_ (0.59 #29890, 0.59 #28465, 0.59 #25618), 01y9r2 (0.59 #29890, 0.59 #28465, 0.59 #25618), 057__d (0.59 #29890, 0.59 #28465, 0.59 #25618), 03pc89 (0.59 #29890, 0.59 #28465, 0.59 #25618), 03yvf2 (0.59 #29890, 0.59 #28465, 0.59 #25618), 01s3vk (0.59 #29890, 0.59 #28465, 0.59 #25618) >> Best rule #29890 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 03yxwq; >> query: (?x574, ?x6680) <- production_companies(?x6680, ?x574), award_winner(?x902, ?x574), film(?x7232, ?x6680) >> conf = 0.59 => this is the best rule for 10 predicted values *> Best rule #6424 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0g1rw; 03rwz3; *> query: (?x574, 04zl8) <- film(?x574, ?x4810), film(?x574, ?x2160), ?x2160 = 014kq6, genre(?x4810, ?x53) *> conf = 0.40 ranks of expected_values: 37, 149, 166, 731, 807, 846, 943, 1303, 1332, 1345, 1364 EVAL 016tt2 film 07phbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 0h2zvzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 072r5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 0gmgwnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 0ddj0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 04zl8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 0ptxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 038bh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 06gb1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 02q_4ph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 0gyh2wm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 0gjcrrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 016kv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 0pvms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 02847m9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 0dtfn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tt2 film 02v8kmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 107.000 0.591 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18740-05683cn PRED entity: 05683cn PRED relation: nominated_for PRED expected values: 0k419 => 95 concepts (40 used for prediction) PRED predicted values (max 10 best out of 338): 0k419 (0.67 #3240, 0.56 #8100, 0.55 #11341), 0bl06 (0.67 #3240, 0.56 #8100, 0.55 #11341), 072192 (0.67 #3240, 0.56 #8100, 0.55 #11341), 0k4bc (0.67 #3240, 0.56 #8100, 0.55 #11341), 0bcndz (0.50 #3488, 0.19 #11589, 0.13 #6727), 0gndh (0.33 #1189, 0.25 #12962, 0.25 #2809), 0k4fz (0.33 #757, 0.25 #12962, 0.17 #11340), 0kb07 (0.33 #817, 0.25 #12962, 0.17 #11340), 0cwy47 (0.33 #3371, 0.25 #12962, 0.16 #14582), 0kvb6p (0.33 #4570, 0.14 #12671, 0.07 #9430) >> Best rule #3240 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0c4qzm; >> query: (?x9875, ?x1746) <- award_nominee(?x9875, ?x4896), film_art_direction_by(?x1746, ?x9875), ?x4896 = 07hhnl >> conf = 0.67 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 05683cn nominated_for 0k419 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 40.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #18739-012fvq PRED entity: 012fvq PRED relation: major_field_of_study PRED expected values: 06ms6 03g3w => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 120): 01mkq (0.66 #11980, 0.60 #1845, 0.60 #991), 03g3w (0.60 #1002, 0.59 #148, 0.57 #1490), 02lp1 (0.53 #133, 0.48 #1109, 0.47 #377), 04x_3 (0.53 #391, 0.45 #1123, 0.42 #269), 0g26h (0.50 #12738, 0.45 #41, 0.31 #1139), 05qjt (0.48 #983, 0.45 #1105, 0.42 #1349), 062z7 (0.47 #271, 0.47 #149, 0.43 #14068), 037mh8 (0.45 #67, 0.44 #1043, 0.42 #311), 01lj9 (0.45 #1136, 0.40 #1868, 0.37 #404), 05qfh (0.42 #279, 0.38 #9556, 0.35 #157) >> Best rule #11980 for best value: >> intensional similarity = 6 >> extensional distance = 176 >> proper extension: 0f102; 027xx3; 04chyn; 025v3k; 01bcwk; 057bxr; 080z7; 01p5xy; 01g6l8; 011xy1; ... >> query: (?x3576, 01mkq) <- major_field_of_study(?x3576, ?x2014), contains(?x3670, ?x3576), major_field_of_study(?x11397, ?x2014), major_field_of_study(?x2196, ?x2014), ?x2196 = 07w4j, ?x11397 = 02hp70 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #1002 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 23 *> proper extension: 0373qt; *> query: (?x3576, 03g3w) <- major_field_of_study(?x3576, ?x3995), major_field_of_study(?x3576, ?x2981), ?x3995 = 0fdys, currency(?x3576, ?x170), ?x2981 = 02j62, institution(?x1368, ?x3576) *> conf = 0.60 ranks of expected_values: 2, 23 EVAL 012fvq major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 201.000 201.000 0.663 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 012fvq major_field_of_study 06ms6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 201.000 201.000 0.663 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18738-02r0csl PRED entity: 02r0csl PRED relation: nominated_for PRED expected values: 095zlp 09q5w2 0ch26b_ 02s4l6 0hfzr 03cfkrw 0k2cb 047wh1 0k4p0 0ndwt2w 02lxrv 0yxf4 017180 0btpm6 01mgw 04165w 05567m => 44 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1336): 01vksx (0.82 #4528, 0.78 #6037, 0.74 #3019), 0d87hc (0.82 #4528, 0.78 #6037, 0.74 #3019), 015x74 (0.82 #4528, 0.78 #6037, 0.74 #3019), 091z_p (0.82 #4528, 0.78 #6037, 0.74 #3019), 041td_ (0.82 #4528, 0.78 #6037, 0.74 #3019), 04jpg2p (0.82 #4528, 0.78 #6037, 0.74 #3019), 016z7s (0.82 #4528, 0.78 #6037, 0.74 #3019), 02vp1f_ (0.82 #4528, 0.78 #6037, 0.74 #3019), 011yr9 (0.82 #4528, 0.78 #6037, 0.74 #3018), 0btpm6 (0.70 #2584, 0.62 #1075, 0.29 #5603) >> Best rule #4528 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 02x2gy0; >> query: (?x143, ?x251) <- award(?x251, ?x143), nominated_for(?x143, ?x7207), nominated_for(?x143, ?x4197), film_format(?x7207, ?x909), ?x4197 = 01242_ >> conf = 0.82 => this is the best rule for 9 predicted values *> Best rule #2584 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 057xs89; 018wdw; *> query: (?x143, 0btpm6) <- award(?x10274, ?x143), nominated_for(?x143, ?x7207), ?x7207 = 03y0pn, film_crew_role(?x10274, ?x137) *> conf = 0.70 ranks of expected_values: 10, 11, 13, 47, 49, 51, 54, 74, 78, 125, 174, 176, 177, 262, 338, 350, 1157 EVAL 02r0csl nominated_for 05567m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 04165w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 01mgw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 017180 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 0yxf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 02lxrv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 0ndwt2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 0k4p0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 047wh1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 0k2cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 03cfkrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 0hfzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 02s4l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 0ch26b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 09q5w2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r0csl nominated_for 095zlp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 44.000 14.000 0.822 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18737-06gmr PRED entity: 06gmr PRED relation: teams PRED expected values: 019lty 019ltg 019mcm => 192 concepts (113 used for prediction) PRED predicted values (max 10 best out of 287): 020wyp (0.33 #332, 0.04 #6448, 0.04 #5730), 0cnk2q (0.33 #1, 0.04 #6117, 0.04 #5399), 0cqt41 (0.08 #389, 0.07 #1109, 0.07 #749), 0hmtk (0.08 #675, 0.07 #1395, 0.07 #1035), 05g76 (0.08 #394, 0.07 #1114, 0.07 #754), 0jm3v (0.08 #372, 0.07 #1092, 0.07 #732), 0jnlm (0.08 #710, 0.07 #1430, 0.07 #1070), 0jm74 (0.08 #506, 0.07 #1226, 0.07 #866), 01slc (0.08 #502, 0.07 #1222, 0.07 #862), 01yjl (0.08 #416, 0.07 #1136, 0.07 #776) >> Best rule #332 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0chghy; >> query: (?x1167, 020wyp) <- contains(?x583, ?x1167), vacationer(?x1167, ?x5514), ?x5514 = 04cr6qv, teams(?x1167, ?x8703), position(?x8703, ?x60) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06gmr teams 019mcm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 192.000 113.000 0.333 http://example.org/sports/sports_team_location/teams EVAL 06gmr teams 019ltg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 192.000 113.000 0.333 http://example.org/sports/sports_team_location/teams EVAL 06gmr teams 019lty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 192.000 113.000 0.333 http://example.org/sports/sports_team_location/teams #18736-092kgw PRED entity: 092kgw PRED relation: award_winner! PRED expected values: 0drtv8 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 133): 013b2h (0.33 #80, 0.10 #8326, 0.03 #3184), 0bvhz9 (0.22 #271, 0.22 #2963, 0.21 #2680), 0drtv8 (0.22 #207, 0.22 #2963, 0.21 #2680), 0275n3y (0.22 #216, 0.22 #2963, 0.21 #2680), 04n2r9h (0.22 #186, 0.01 #2583, 0.01 #1878), 050yyb (0.22 #2963, 0.21 #2680, 0.20 #3810), 027hjff (0.22 #2963, 0.21 #2680, 0.20 #3810), 027n06w (0.22 #2963, 0.21 #2680, 0.20 #3810), 0hndn2q (0.11 #181, 0.10 #322, 0.05 #604), 02wzl1d (0.11 #152, 0.10 #293, 0.04 #575) >> Best rule #80 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01v_pj6; 0xsk8; >> query: (?x5527, 013b2h) <- award_nominee(?x2689, ?x5527), award_nominee(?x976, ?x5527), ?x976 = 06pk8, nationality(?x2689, ?x6401) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #207 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0jz9f; 0h5f5n; *> query: (?x5527, 0drtv8) <- award_nominee(?x976, ?x5527), nominated_for(?x5527, ?x4359), ?x4359 = 0g9lm2 *> conf = 0.22 ranks of expected_values: 3 EVAL 092kgw award_winner! 0drtv8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 94.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18735-06g2d1 PRED entity: 06g2d1 PRED relation: type_of_union PRED expected values: 04ztj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #65, 0.72 #101, 0.72 #13), 01g63y (0.24 #30, 0.23 #42, 0.23 #26) >> Best rule #65 for best value: >> intensional similarity = 3 >> extensional distance = 728 >> proper extension: 034bs; 0443c; >> query: (?x6085, 04ztj) <- location(?x6085, ?x739), award_winner(?x704, ?x6085), student(?x5981, ?x6085) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06g2d1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.736 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18734-01rxw PRED entity: 01rxw PRED relation: taxonomy PRED expected values: 04n6k => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.72 #43, 0.72 #2, 0.72 #4) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 208 >> proper extension: 0rh6k; 05kkh; 059rby; 03v1s; 05kj_; 02_286; 05fkf; 0vmt; 03s0w; 05fhy; ... >> query: (?x6863, 04n6k) <- adjoins(?x6572, ?x6863), jurisdiction_of_office(?x346, ?x6863), currency(?x6572, ?x170) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rxw taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.724 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #18733-02h9_l PRED entity: 02h9_l PRED relation: artists! PRED expected values: 03_d0 => 154 concepts (91 used for prediction) PRED predicted values (max 10 best out of 233): 06by7 (0.58 #1865, 0.56 #5549, 0.55 #24600), 0ggx5q (0.52 #8676, 0.28 #7447, 0.22 #16358), 06j6l (0.48 #8648, 0.41 #3733, 0.38 #16022), 03_d0 (0.36 #15986, 0.29 #1855, 0.27 #320), 01lyv (0.33 #34, 0.23 #9248, 0.22 #11706), 05bt6j (0.32 #16325, 0.29 #8643, 0.28 #6186), 016clz (0.30 #24583, 0.29 #3690, 0.25 #6148), 0xhtw (0.22 #17, 0.19 #7695, 0.18 #24902), 0mhfr (0.22 #25, 0.13 #5552, 0.13 #333), 06924p (0.22 #174, 0.07 #7545, 0.07 #11846) >> Best rule #1865 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 05qw5; 01vt9p3; 012vd6; 01k23t; >> query: (?x10148, 06by7) <- artist(?x2190, ?x10148), ?x2190 = 01cszh, gender(?x10148, ?x514), type_of_union(?x10148, ?x566) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #15986 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 377 *> proper extension: 01nqfh_; 0h08p; *> query: (?x10148, 03_d0) <- artists(?x3562, ?x10148), artists(?x3562, ?x7536), artists(?x3562, ?x3176), ?x3176 = 01w7nww, award_nominee(?x6104, ?x7536) *> conf = 0.36 ranks of expected_values: 4 EVAL 02h9_l artists! 03_d0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 154.000 91.000 0.583 http://example.org/music/genre/artists #18732-0gmcwlb PRED entity: 0gmcwlb PRED relation: nominated_for! PRED expected values: 02x2gy0 099t8j => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 177): 054krc (0.71 #2084, 0.71 #836, 0.71 #835), 02qvyrt (0.71 #2084, 0.71 #836, 0.71 #835), 09qv_s (0.71 #2084, 0.71 #836, 0.71 #835), 02z13jg (0.71 #2084, 0.71 #836, 0.71 #835), 09d28z (0.71 #2084, 0.71 #836, 0.71 #835), 0p9sw (0.57 #226, 0.54 #643, 0.43 #16), 02ppm4q (0.50 #1967, 0.36 #2589, 0.17 #7888), 099t8j (0.48 #1955, 0.21 #2577, 0.17 #7888), 02z0dfh (0.48 #1922, 0.19 #2544, 0.11 #2958), 02hsq3m (0.43 #233, 0.35 #650, 0.29 #1481) >> Best rule #2084 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 02chhq; >> query: (?x1370, ?x850) <- nominated_for(?x2257, ?x1370), film(?x4004, ?x1370), award(?x1370, ?x850), ?x2257 = 09td7p >> conf = 0.71 => this is the best rule for 5 predicted values *> Best rule #1955 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 02chhq; *> query: (?x1370, 099t8j) <- nominated_for(?x2257, ?x1370), film(?x4004, ?x1370), award(?x1370, ?x850), ?x2257 = 09td7p *> conf = 0.48 ranks of expected_values: 8, 64 EVAL 0gmcwlb nominated_for! 099t8j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 86.000 86.000 0.713 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gmcwlb nominated_for! 02x2gy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 86.000 86.000 0.713 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18731-0q9sg PRED entity: 0q9sg PRED relation: genre PRED expected values: 03mqtr => 139 concepts (137 used for prediction) PRED predicted values (max 10 best out of 102): 03mqtr (0.72 #12503, 0.64 #7404, 0.64 #7403), 01cgz (0.64 #7404, 0.64 #7403, 0.59 #12504), 024qqx (0.64 #7404, 0.64 #7403, 0.59 #12504), 01hmnh (0.61 #5715, 0.61 #5838, 0.59 #4500), 01jfsb (0.54 #6440, 0.45 #254, 0.45 #5101), 0lsxr (0.50 #129, 0.35 #1219, 0.34 #735), 05p553 (0.45 #730, 0.40 #487, 0.38 #5213), 03k9fj (0.44 #5100, 0.41 #5831, 0.41 #5708), 02l7c8 (0.44 #500, 0.33 #5226, 0.32 #9121), 04xvlr (0.29 #7282, 0.19 #9106, 0.19 #8012) >> Best rule #12503 for best value: >> intensional similarity = 3 >> extensional distance = 1096 >> proper extension: 0dtw1x; 03t97y; 07sc6nw; 04zyhx; 0cz8mkh; 03twd6; 05p3738; 0c8tkt; 028cg00; 08052t3; ... >> query: (?x4538, ?x53) <- titles(?x53, ?x4538), genre(?x4538, ?x225), genre(?x54, ?x53) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q9sg genre 03mqtr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 137.000 0.717 http://example.org/film/film/genre #18730-0fvzg PRED entity: 0fvzg PRED relation: location! PRED expected values: 02knnd 033jkj => 260 concepts (145 used for prediction) PRED predicted values (max 10 best out of 2130): 011xjd (0.51 #75555, 0.51 #113328, 0.49 #128441), 0l8g0 (0.32 #284589, 0.31 #259403, 0.31 #329922), 01kv4mb (0.25 #381, 0.22 #2899, 0.17 #5417), 01q_ph (0.20 #7604, 0.13 #17676, 0.13 #15158), 023kzp (0.17 #23880, 0.15 #33955, 0.12 #59141), 01s21dg (0.13 #8518, 0.12 #23628, 0.11 #33703), 0151ns (0.13 #7638, 0.12 #22748, 0.11 #32823), 03h_0_z (0.13 #8802, 0.12 #11320, 0.08 #23912), 05m63c (0.13 #7585, 0.10 #12621, 0.09 #17657), 05lb30 (0.13 #8889, 0.10 #13925, 0.09 #16443) >> Best rule #75555 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0b2lw; >> query: (?x2941, ?x3338) <- state(?x2941, ?x6521), county_seat(?x10256, ?x2941), citytown(?x4145, ?x2941), place_of_birth(?x3338, ?x2941) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #23550 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 02_286; 030qb3t; 094jv; 0f2w0; 01_d4; 04f_d; 013yq; 0f__1; 0vzm; 0f2v0; ... *> query: (?x2941, 033jkj) <- dog_breed(?x2941, ?x3095), jurisdiction_of_office(?x1195, ?x2941), locations(?x6583, ?x2941), ?x3095 = 01_gx_ *> conf = 0.08 ranks of expected_values: 97 EVAL 0fvzg location! 033jkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 260.000 145.000 0.513 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0fvzg location! 02knnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 260.000 145.000 0.513 http://example.org/people/person/places_lived./people/place_lived/location #18729-014635 PRED entity: 014635 PRED relation: influenced_by PRED expected values: 04xjp => 124 concepts (31 used for prediction) PRED predicted values (max 10 best out of 340): 032l1 (0.56 #1841, 0.33 #966, 0.33 #90), 0379s (0.44 #1830, 0.33 #955, 0.33 #79), 02lt8 (0.44 #1872, 0.17 #997, 0.09 #2311), 02wh0 (0.33 #2135, 0.33 #1260, 0.33 #384), 081k8 (0.33 #1908, 0.33 #157, 0.17 #1033), 04xjp (0.33 #57, 0.22 #1808, 0.17 #933), 03pm9 (0.33 #69, 0.22 #1820, 0.17 #945), 05np2 (0.33 #216, 0.18 #2406, 0.17 #1092), 03_dj (0.33 #414, 0.17 #1290, 0.11 #2165), 07kb5 (0.33 #15, 0.17 #891, 0.11 #1766) >> Best rule #1841 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 032l1; 040_9; 0zm1; 058vp; 07dnx; >> query: (?x3969, 032l1) <- influenced_by(?x3325, ?x3969), influenced_by(?x1865, ?x3969), ?x3325 = 073v6, influenced_by(?x3969, ?x5435), award(?x1865, ?x451) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #57 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 03_87; *> query: (?x3969, 04xjp) <- influenced_by(?x5346, ?x3969), influenced_by(?x3325, ?x3969), ?x3325 = 073v6, location(?x3969, ?x1025), ?x5346 = 049gc *> conf = 0.33 ranks of expected_values: 6 EVAL 014635 influenced_by 04xjp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 124.000 31.000 0.556 http://example.org/influence/influence_node/influenced_by #18728-0mwk9 PRED entity: 0mwk9 PRED relation: adjoins PRED expected values: 0frf6 => 160 concepts (56 used for prediction) PRED predicted values (max 10 best out of 406): 0frf6 (0.81 #34085, 0.81 #34859, 0.81 #40287), 0mwx6 (0.33 #471, 0.15 #1244, 0.08 #4343), 0mwyq (0.33 #686, 0.07 #2234, 0.06 #3008), 0mww2 (0.25 #34861, 0.24 #38736, 0.23 #34086), 0mwk9 (0.23 #34086, 0.04 #4510, 0.03 #5284), 0mwxz (0.23 #1111, 0.17 #2660, 0.15 #3434), 0mwvq (0.23 #1214, 0.17 #2763, 0.15 #3537), 0mw89 (0.19 #3921, 0.17 #4695, 0.13 #5468), 0mwht (0.17 #2896, 0.15 #1347, 0.15 #3670), 0mwl2 (0.15 #3914, 0.15 #815, 0.14 #1590) >> Best rule #34085 for best value: >> intensional similarity = 4 >> extensional distance = 278 >> proper extension: 0mx4_; 0mw93; 0m7fm; 0n5fl; 0mx6c; 0f04c; 0l2l_; 013m43; 0r679; 0r5wt; ... >> query: (?x12296, ?x10767) <- contains(?x3670, ?x12296), time_zones(?x12296, ?x2674), source(?x12296, ?x958), adjoins(?x10767, ?x12296) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwk9 adjoins 0frf6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 56.000 0.815 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #18727-06rzwx PRED entity: 06rzwx PRED relation: film_release_region PRED expected values: 05r4w => 120 concepts (100 used for prediction) PRED predicted values (max 10 best out of 290): 06mkj (0.90 #1229, 0.87 #897, 0.86 #3227), 0345h (0.88 #1370, 0.84 #3202, 0.83 #1204), 03rjj (0.87 #1170, 0.85 #3168, 0.81 #1336), 05r4w (0.86 #3163, 0.85 #1165, 0.84 #3495), 07ssc (0.86 #520, 0.83 #1184, 0.81 #1350), 03h64 (0.84 #1406, 0.83 #576, 0.82 #1240), 03gj2 (0.83 #1195, 0.81 #1361, 0.81 #3193), 03_3d (0.83 #1172, 0.76 #3170, 0.75 #5501), 015fr (0.82 #3184, 0.75 #1352, 0.72 #5681), 05qhw (0.81 #1348, 0.78 #3180, 0.77 #518) >> Best rule #1229 for best value: >> intensional similarity = 7 >> extensional distance = 69 >> proper extension: 0hgnl3t; >> query: (?x7114, 06mkj) <- film_release_region(?x7114, ?x2513), film_release_region(?x7114, ?x1229), film(?x788, ?x7114), ?x1229 = 059j2, ?x2513 = 05b4w, film(?x2587, ?x7114), film(?x2724, ?x7114) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #3163 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 180 *> proper extension: 0b76d_m; 0ds35l9; 02vxq9m; 0c3ybss; 03g90h; 01gc7; 0gtv7pk; 0h1cdwq; 0dscrwf; 0djb3vw; ... *> query: (?x7114, 05r4w) <- film_release_region(?x7114, ?x1353), film_release_region(?x7114, ?x1229), film(?x788, ?x7114), ?x1229 = 059j2, genre(?x7114, ?x53), language(?x7114, ?x254), ?x1353 = 035qy *> conf = 0.86 ranks of expected_values: 4 EVAL 06rzwx film_release_region 05r4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 120.000 100.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18726-03t8v3 PRED entity: 03t8v3 PRED relation: profession PRED expected values: 02hrh1q => 88 concepts (45 used for prediction) PRED predicted values (max 10 best out of 90): 02hrh1q (0.95 #6198, 0.93 #6345, 0.93 #2812), 0np9r (0.83 #2228, 0.33 #4146, 0.26 #1933), 018gz8 (0.82 #1929, 0.33 #17, 0.33 #3995), 0dxtg (0.63 #5461, 0.57 #13, 0.53 #3697), 09jwl (0.61 #4438, 0.21 #2965, 0.20 #3113), 03gjzk (0.50 #162, 0.47 #604, 0.44 #751), 02jknp (0.47 #3397, 0.47 #3544, 0.46 #3838), 0nbcg (0.42 #4451, 0.13 #3126, 0.13 #2978), 01c72t (0.30 #4443, 0.05 #3242, 0.04 #2526), 0kyk (0.29 #30, 0.25 #766, 0.25 #472) >> Best rule #6198 for best value: >> intensional similarity = 6 >> extensional distance = 1894 >> proper extension: 05vsxz; 0cnl80; 02zq43; 03w1v2; 07lmxq; 027dtv3; 018dnt; 01gvr1; 05b__vr; 064nh4k; ... >> query: (?x13784, 02hrh1q) <- profession(?x13784, ?x319), film(?x13784, ?x5247), profession(?x5571, ?x319), profession(?x237, ?x319), ?x237 = 04t2l2, ?x5571 = 03cxsvl >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t8v3 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 45.000 0.948 http://example.org/people/person/profession #18725-06r_by PRED entity: 06r_by PRED relation: nominated_for PRED expected values: 016kv6 => 94 concepts (43 used for prediction) PRED predicted values (max 10 best out of 315): 09sr0 (0.81 #27441, 0.81 #51646, 0.80 #41965), 01rxyb (0.50 #6457, 0.45 #9686, 0.44 #4842), 0g22z (0.50 #6457, 0.45 #9686, 0.44 #4842), 0gyv0b4 (0.50 #6457, 0.45 #9686, 0.44 #4842), 051zy_b (0.50 #6457, 0.45 #9686, 0.44 #4842), 016z9n (0.50 #6457, 0.45 #9686, 0.42 #6456), 059rc (0.45 #9686, 0.44 #4842, 0.42 #6456), 0n1s0 (0.45 #9686, 0.42 #6456, 0.41 #11302), 07kdkfj (0.44 #4842, 0.42 #6456, 0.41 #11302), 02847m9 (0.44 #4842, 0.42 #6456, 0.41 #11302) >> Best rule #27441 for best value: >> intensional similarity = 3 >> extensional distance = 1028 >> proper extension: 06w33f8; 02dbn2; 06_bq1; 01hkck; >> query: (?x6062, ?x1916) <- award_winner(?x1916, ?x6062), nominated_for(?x6062, ?x4174), type_of_union(?x6062, ?x566) >> conf = 0.81 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06r_by nominated_for 016kv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 43.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #18724-0kbhf PRED entity: 0kbhf PRED relation: nominated_for! PRED expected values: 0gqwc => 83 concepts (73 used for prediction) PRED predicted values (max 10 best out of 168): 0gs9p (0.61 #531, 0.59 #999, 0.55 #63), 019f4v (0.55 #990, 0.54 #522, 0.52 #54), 0p9sw (0.45 #956, 0.45 #488, 0.40 #1424), 040njc (0.45 #943, 0.45 #7, 0.43 #475), 04dn09n (0.44 #971, 0.43 #35, 0.41 #503), 02qyntr (0.43 #1112, 0.43 #644, 0.31 #1580), 0gr4k (0.40 #2366, 0.40 #26, 0.33 #962), 0f4x7 (0.40 #25, 0.37 #2365, 0.31 #961), 02pqp12 (0.37 #995, 0.37 #527, 0.31 #1463), 099c8n (0.37 #525, 0.34 #993, 0.26 #5205) >> Best rule #531 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 0m313; 018js4; 09m6kg; 0yyg4; 011yxg; 07xtqq; 095zlp; 04v8x9; 0bth54; 0n0bp; ... >> query: (?x5843, 0gs9p) <- nominated_for(?x1703, ?x5843), produced_by(?x5843, ?x4785), ?x1703 = 0k611, country(?x5843, ?x94) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #60 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 0gcrg; *> query: (?x5843, 0gqwc) <- nominated_for(?x1307, ?x5843), cinematography(?x5843, ?x6549), music(?x5843, ?x3811), ?x1307 = 0gq9h *> conf = 0.31 ranks of expected_values: 15 EVAL 0kbhf nominated_for! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 83.000 73.000 0.607 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18723-03f2_rc PRED entity: 03f2_rc PRED relation: nationality PRED expected values: 09c7w0 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 31): 09c7w0 (0.79 #603, 0.78 #1906, 0.77 #905), 0d060g (0.40 #12444, 0.06 #207, 0.05 #811), 03rjj (0.40 #12444, 0.03 #105, 0.03 #406), 02jx1 (0.23 #1037, 0.21 #133, 0.19 #434), 07ssc (0.16 #416, 0.12 #315, 0.11 #3526), 0345h (0.09 #432, 0.05 #1636, 0.04 #1736), 03rk0 (0.08 #3255, 0.06 #9378, 0.06 #9779), 0h7x (0.04 #436, 0.02 #1640, 0.02 #1740), 06q1r (0.03 #177, 0.03 #478, 0.03 #1081), 0d05w3 (0.03 #1554, 0.03 #2556, 0.02 #2956) >> Best rule #603 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 03c9pqt; >> query: (?x538, 09c7w0) <- place_of_birth(?x538, ?x2850), executive_produced_by(?x8677, ?x538), student(?x10621, ?x538) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f2_rc nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.786 http://example.org/people/person/nationality #18722-01qwb5 PRED entity: 01qwb5 PRED relation: category PRED expected values: 08mbj5d => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #48, 0.90 #46, 0.90 #39) >> Best rule #48 for best value: >> intensional similarity = 3 >> extensional distance = 306 >> proper extension: 01b1pf; 0jksm; >> query: (?x7939, 08mbj5d) <- colors(?x7939, ?x5845), organization(?x346, ?x7939), ?x346 = 060c4 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qwb5 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.899 http://example.org/common/topic/webpage./common/webpage/category #18721-01q415 PRED entity: 01q415 PRED relation: story_by! PRED expected values: 0sxfd => 98 concepts (59 used for prediction) PRED predicted values (max 10 best out of 180): 02wgk1 (0.14 #497, 0.02 #1526, 0.02 #2896), 03ntbmw (0.14 #682, 0.01 #1711), 02q56mk (0.14 #426, 0.01 #1455), 0gtvrv3 (0.14 #388, 0.01 #1417), 0dqytn (0.14 #366, 0.01 #1395), 0466s8n (0.14 #656), 0b6tzs (0.14 #376), 05hjnw (0.14 #3427, 0.14 #2057, 0.12 #1371), 015gm8 (0.12 #1371, 0.11 #5833, 0.11 #1370), 02q_4ph (0.03 #2887, 0.02 #4260, 0.02 #3574) >> Best rule #497 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0n6kf; 013pp3; 040_t; 01v_0b; >> query: (?x2248, 02wgk1) <- award(?x2248, ?x7111), story_by(?x2057, ?x2248), ?x7111 = 0c_dx >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01q415 story_by! 0sxfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 59.000 0.143 http://example.org/film/film/story_by #18720-011xy1 PRED entity: 011xy1 PRED relation: institution! PRED expected values: 02h4rq6 0bkj86 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 17): 02_xgp2 (0.87 #144, 0.80 #125, 0.76 #105), 02h4rq6 (0.83 #97, 0.80 #117, 0.77 #136), 0bkj86 (0.72 #101, 0.71 #121, 0.62 #140), 016t_3 (0.69 #118, 0.66 #137, 0.66 #98), 04zx3q1 (0.55 #96, 0.53 #135, 0.51 #116), 027f2w (0.45 #141, 0.41 #122, 0.38 #102), 013zdg (0.38 #100, 0.32 #139, 0.29 #120), 0bjrnt (0.31 #99, 0.21 #215, 0.21 #138), 03mkk4 (0.24 #124, 0.21 #104, 0.19 #143), 028dcg (0.18 #130, 0.17 #264, 0.15 #149) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 019q50; >> query: (?x8694, 02_xgp2) <- institution(?x620, ?x8694), list(?x8694, ?x2197), state_province_region(?x8694, ?x6357) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 017j69; 0ks67; 0trv; *> query: (?x8694, 02h4rq6) <- major_field_of_study(?x8694, ?x3489), student(?x8694, ?x1191), ?x3489 = 0193x *> conf = 0.83 ranks of expected_values: 2, 3 EVAL 011xy1 institution! 0bkj86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.868 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 011xy1 institution! 02h4rq6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.868 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18719-043kzcr PRED entity: 043kzcr PRED relation: gender PRED expected values: 02zsn => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.88 #12, 0.86 #10, 0.83 #8), 05zppz (0.72 #61, 0.71 #67, 0.71 #131) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 0hwqz; >> query: (?x2516, 02zsn) <- award(?x2516, ?x2880), award_nominee(?x2516, ?x624), ?x2880 = 02ppm4q >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043kzcr gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.883 http://example.org/people/person/gender #18718-02z0f6l PRED entity: 02z0f6l PRED relation: film_crew_role PRED expected values: 02_n3z => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 30): 09zzb8 (0.76 #1233, 0.76 #2055, 0.75 #1953), 0dxtw (0.42 #901, 0.40 #1381, 0.40 #489), 01vx2h (0.38 #147, 0.35 #1243, 0.34 #2065), 01pvkk (0.34 #491, 0.32 #525, 0.32 #422), 02ynfr (0.24 #495, 0.20 #1248, 0.19 #634), 089g0h (0.19 #326, 0.17 #429, 0.15 #394), 04pyp5 (0.18 #17, 0.12 #496, 0.09 #530), 01xy5l_ (0.15 #116, 0.14 #48, 0.13 #2068), 0d2b38 (0.14 #161, 0.13 #127, 0.12 #504), 0263ycg (0.14 #52, 0.10 #120, 0.06 #291) >> Best rule #1233 for best value: >> intensional similarity = 4 >> extensional distance = 427 >> proper extension: 014_x2; 0d90m; 03qcfvw; 034qmv; 06w99h3; 05p1tzf; 03s6l2; 08720; 02z3r8t; 08gsvw; ... >> query: (?x6900, 09zzb8) <- currency(?x6900, ?x170), featured_film_locations(?x6900, ?x362), genre(?x6900, ?x1316), film_crew_role(?x6900, ?x468) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 88 *> proper extension: 04nm0n0; 0crs0b8; 09rfpk; 02rtqvb; *> query: (?x6900, 02_n3z) <- titles(?x512, ?x6900), ?x512 = 07ssc, country(?x6900, ?x94), film_crew_role(?x6900, ?x468) *> conf = 0.12 ranks of expected_values: 12 EVAL 02z0f6l film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 129.000 129.000 0.762 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18717-039xcr PRED entity: 039xcr PRED relation: award PRED expected values: 0bdwqv => 115 concepts (99 used for prediction) PRED predicted values (max 10 best out of 255): 08_vwq (0.78 #35281, 0.73 #38529, 0.72 #39342), 02x73k6 (0.46 #873, 0.10 #4926, 0.09 #1279), 0f4x7 (0.40 #31, 0.29 #843, 0.28 #1249), 09sb52 (0.40 #14226, 0.39 #853, 0.35 #17874), 09sdmz (0.32 #1019, 0.08 #14392, 0.07 #18040), 027dtxw (0.27 #816, 0.10 #14189, 0.10 #1627), 0789_m (0.24 #832, 0.14 #1238, 0.13 #2048), 04kxsb (0.24 #939, 0.12 #14312, 0.11 #1750), 099jhq (0.24 #831, 0.06 #14204, 0.05 #18665), 0bdwqv (0.22 #985, 0.12 #5038, 0.11 #2606) >> Best rule #35281 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 04k05; 06lxn; >> query: (?x10058, ?x3066) <- award_winner(?x3066, ?x10058), award(?x92, ?x3066), ceremony(?x3066, ?x78) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #985 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 014v1q; *> query: (?x10058, 0bdwqv) <- gender(?x10058, ?x231), award_winner(?x3066, ?x10058), profession(?x10058, ?x353), ?x3066 = 0gqy2 *> conf = 0.22 ranks of expected_values: 10 EVAL 039xcr award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 115.000 99.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18716-06wvj PRED entity: 06wvj PRED relation: artists! PRED expected values: 05lls 01wqlc => 244 concepts (119 used for prediction) PRED predicted values (max 10 best out of 252): 06by7 (0.96 #31695, 0.48 #24864, 0.48 #18031), 03_d0 (0.77 #25165, 0.72 #32928, 0.40 #322), 05lls (0.77 #5599, 0.67 #3428, 0.62 #9010), 064t9 (0.71 #36656, 0.54 #25479, 0.54 #23612), 0l8gh (0.60 #4212, 0.54 #5762, 0.44 #3591), 01wqlc (0.60 #696, 0.40 #4109, 0.38 #9070), 0dl5d (0.53 #22068, 0.16 #31693, 0.15 #25173), 0ggx5q (0.48 #23678, 0.25 #25545, 0.22 #26787), 01wtlq (0.40 #328, 0.22 #3120, 0.12 #9634), 025sc50 (0.35 #23651, 0.29 #25518, 0.25 #26760) >> Best rule #31695 for best value: >> intensional similarity = 3 >> extensional distance = 182 >> proper extension: 01pbxb; 01vsxdm; 0bkg4; 028qdb; 01bczm; 0kxbc; 02bgmr; 018y81; 01ydzx; 0191h5; >> query: (?x2536, 06by7) <- role(?x2536, ?x316), artists(?x9137, ?x2536), films(?x9137, ?x4276) >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #5599 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 043d4; *> query: (?x2536, 05lls) <- artists(?x11193, ?x2536), type_of_union(?x2536, ?x566), ?x11193 = 06q6jz, profession(?x2536, ?x563), instrumentalists(?x316, ?x2536) *> conf = 0.77 ranks of expected_values: 3, 6 EVAL 06wvj artists! 01wqlc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 244.000 119.000 0.962 http://example.org/music/genre/artists EVAL 06wvj artists! 05lls CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 244.000 119.000 0.962 http://example.org/music/genre/artists #18715-06r2_ PRED entity: 06r2_ PRED relation: film_release_region PRED expected values: 0jgd 0154j 0k6nt 059j2 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 96): 059j2 (0.81 #872, 0.29 #1540, 0.26 #7723), 0k6nt (0.79 #863, 0.31 #1531, 0.28 #696), 03rjj (0.77 #841, 0.27 #1509, 0.25 #7692), 0jgd (0.75 #838, 0.29 #1506, 0.24 #671), 0345h (0.75 #874, 0.27 #1542, 0.25 #7725), 03gj2 (0.74 #864, 0.26 #1532, 0.23 #7715), 03h64 (0.72 #912, 0.25 #1580, 0.23 #578), 0154j (0.68 #840, 0.22 #1508, 0.21 #7691), 035qy (0.68 #876, 0.23 #1544, 0.21 #7727), 01znc_ (0.67 #885, 0.24 #1553, 0.22 #718) >> Best rule #872 for best value: >> intensional similarity = 4 >> extensional distance = 275 >> proper extension: 0gtsx8c; 0c3ybss; 03g90h; 0dtw1x; 0h1cdwq; 0fq27fp; 0c40vxk; 0gx9rvq; 0401sg; 0crfwmx; ... >> query: (?x3524, 059j2) <- film_release_region(?x3524, ?x94), film_release_region(?x3524, ?x87), ?x94 = 09c7w0, ?x87 = 05r4w >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 8 EVAL 06r2_ film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.809 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06r2_ film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.809 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06r2_ film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 68.000 68.000 0.809 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06r2_ film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.809 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18714-0t0n5 PRED entity: 0t0n5 PRED relation: contains! PRED expected values: 03s0w => 95 concepts (67 used for prediction) PRED predicted values (max 10 best out of 250): 03rk0 (0.41 #45601, 0.41 #45600, 0.06 #35003), 03s0w (0.37 #49180, 0.07 #4527, 0.05 #1845), 07c5l (0.37 #49180, 0.03 #393, 0.02 #8439), 07b_l (0.18 #4691, 0.14 #8267, 0.12 #2009), 01n7q (0.17 #54626, 0.17 #17063, 0.16 #10805), 07ssc (0.15 #34004, 0.15 #26851, 0.15 #42051), 04_1l0v (0.13 #6707, 0.12 #9389, 0.12 #449), 02jx1 (0.12 #34059, 0.11 #39424, 0.11 #41212), 02xry (0.12 #10890, 0.06 #2844, 0.05 #3738), 059rby (0.10 #19, 0.09 #17005, 0.08 #42933) >> Best rule #45601 for best value: >> intensional similarity = 2 >> extensional distance = 760 >> proper extension: 01k6y1; 01z645; >> query: (?x5972, ?x2146) <- location(?x10783, ?x5972), nationality(?x10783, ?x2146) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #49180 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 891 *> proper extension: 08815; 01fpvz; 01j_06; 02hft3; 0f1nl; 02_2kg; 0bqxw; 02dq8f; 06xpp7; 02zd2b; ... *> query: (?x5972, ?x961) <- contains(?x4213, ?x5972), contains(?x94, ?x5972), ?x94 = 09c7w0, contains(?x961, ?x4213) *> conf = 0.37 ranks of expected_values: 2 EVAL 0t0n5 contains! 03s0w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 67.000 0.408 http://example.org/location/location/contains #18713-0432cd PRED entity: 0432cd PRED relation: people! PRED expected values: 033tf_ => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 51): 041rx (0.23 #235, 0.22 #4, 0.19 #697), 033tf_ (0.15 #84, 0.12 #1779, 0.12 #1393), 0x67 (0.10 #4403, 0.10 #1396, 0.10 #3787), 07hwkr (0.10 #1398, 0.08 #1013, 0.08 #320), 0xnvg (0.09 #1091, 0.09 #90, 0.09 #13), 02ctzb (0.09 #400, 0.08 #631, 0.05 #92), 07bch9 (0.09 #100, 0.08 #639, 0.06 #1409), 048z7l (0.09 #40, 0.06 #271, 0.05 #117), 02w7gg (0.06 #926, 0.06 #1852, 0.06 #2701), 0d7wh (0.05 #171, 0.03 #402, 0.02 #1018) >> Best rule #235 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 01n8_g; 015vq_; 05kh_; 015pvh; 06b_0; 028pzq; 01syr4; 01bbwp; 0cgzj; 0652ty; >> query: (?x7607, 041rx) <- film(?x7607, ?x638), profession(?x7607, ?x524), ?x524 = 02jknp, religion(?x7607, ?x1985) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #84 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 01hkhq; *> query: (?x7607, 033tf_) <- film(?x7607, ?x638), type_of_union(?x7607, ?x566), award(?x7607, ?x458), company(?x7607, ?x13490) *> conf = 0.15 ranks of expected_values: 2 EVAL 0432cd people! 033tf_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.233 http://example.org/people/ethnicity/people #18712-07j8r PRED entity: 07j8r PRED relation: nominated_for! PRED expected values: 0gqy2 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 200): 0gqyl (0.66 #965, 0.63 #1189, 0.34 #5672), 019f4v (0.62 #2961, 0.61 #3185, 0.60 #2289), 04dn09n (0.50 #32, 0.46 #2272, 0.43 #2944), 0gr4k (0.46 #3159, 0.46 #2935, 0.45 #2263), 09td7p (0.41 #978, 0.37 #1202, 0.17 #754), 0gr0m (0.38 #2294, 0.35 #3190, 0.35 #2966), 0gqy2 (0.38 #3022, 0.37 #3246, 0.33 #2350), 02qyntr (0.38 #166, 0.36 #614, 0.34 #2406), 0gq_v (0.37 #2929, 0.37 #3153, 0.34 #2257), 099c8n (0.36 #948, 0.34 #1172, 0.33 #500) >> Best rule #965 for best value: >> intensional similarity = 5 >> extensional distance = 57 >> proper extension: 02nczh; >> query: (?x2550, 0gqyl) <- nominated_for(?x3435, ?x2550), nominated_for(?x1254, ?x2550), ?x1254 = 02z0dfh, nominated_for(?x3435, ?x573), ?x573 = 0bth54 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #3022 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 226 *> proper extension: 0sxg4; 083shs; 0yyg4; 0gzy02; 04v8x9; 01sxly; 0n0bp; 0c5dd; 020fcn; 04mzf8; ... *> query: (?x2550, 0gqy2) <- nominated_for(?x1313, ?x2550), country(?x2550, ?x252), ?x1313 = 0gs9p, nominated_for(?x7068, ?x2550) *> conf = 0.38 ranks of expected_values: 7 EVAL 07j8r nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 83.000 83.000 0.661 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18711-01bpnd PRED entity: 01bpnd PRED relation: type_of_union PRED expected values: 04ztj => 223 concepts (223 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #98, 0.87 #182, 0.87 #218), 01g63y (0.61 #65, 0.37 #250, 0.32 #38), 0jgjn (0.61 #65, 0.37 #250, 0.03 #125), 01bl8s (0.02 #116, 0.01 #168) >> Best rule #98 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 0l8v5; 04wqr; 0151ns; 01pcq3; 03xmy1; 018swb; 012_53; 02v406; 01x72k; 015q43; ... >> query: (?x5872, 04ztj) <- location_of_ceremony(?x5872, ?x3026), location(?x5872, ?x362), languages(?x5872, ?x254), gender(?x5872, ?x231) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bpnd type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 223.000 223.000 0.875 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18710-047gn4y PRED entity: 047gn4y PRED relation: film_crew_role PRED expected values: 0ch6mp2 015h31 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.88 #37, 0.86 #191, 0.84 #67), 0dxtw (0.50 #349, 0.49 #71, 0.43 #133), 02_n3z (0.47 #93, 0.42 #186, 0.36 #62), 033smt (0.32 #53, 0.23 #83, 0.21 #114), 01pvkk (0.31 #350, 0.29 #72, 0.28 #1934), 015h31 (0.27 #69, 0.25 #39, 0.18 #347), 02ynfr (0.21 #353, 0.19 #628, 0.18 #477), 02rh1dz (0.20 #40, 0.19 #348, 0.18 #132), 0263ycg (0.19 #46, 0.19 #76, 0.16 #200), 089fss (0.16 #66, 0.14 #36, 0.12 #2289) >> Best rule #37 for best value: >> intensional similarity = 5 >> extensional distance = 57 >> proper extension: 053rxgm; 07s846j; 03ydlnj; 04jpg2p; >> query: (?x363, 0ch6mp2) <- film_crew_role(?x363, ?x4305), film_crew_role(?x363, ?x2154), ?x4305 = 0215hd, currency(?x363, ?x170), ?x2154 = 01vx2h >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 047gn4y film_crew_role 015h31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 92.000 0.881 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 047gn4y film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.881 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18709-017gm7 PRED entity: 017gm7 PRED relation: genre PRED expected values: 02xlf => 96 concepts (76 used for prediction) PRED predicted values (max 10 best out of 90): 01jfsb (0.58 #479, 0.47 #830, 0.42 #7625), 05p553 (0.47 #5975, 0.40 #2699, 0.38 #120), 02l7c8 (0.40 #5987, 0.38 #1187, 0.32 #2594), 06n90 (0.35 #480, 0.23 #129, 0.23 #949), 04xvlr (0.29 #1055, 0.28 #1173, 0.26 #2580), 0lsxr (0.25 #7622, 0.20 #476, 0.20 #1180), 0hcr (0.24 #2131, 0.21 #372, 0.18 #606), 02n4kr (0.20 #7, 0.16 #7621, 0.16 #1179), 04xvh5 (0.20 #32, 0.12 #1204, 0.11 #2260), 082gq (0.17 #2373, 0.15 #2256, 0.12 #2607) >> Best rule #479 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 0g5pv3; 02vw1w2; 018nnz; 0d1qmz; 0cks1m; 042fgh; 031f_m; 025twgt; >> query: (?x1392, 01jfsb) <- film(?x230, ?x1392), prequel(?x1392, ?x972), genre(?x1392, ?x225), ?x225 = 02kdv5l >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0kt_4; *> query: (?x1392, 02xlf) <- nominated_for(?x143, ?x1392), film(?x3028, ?x1392), award(?x1392, ?x298), ?x3028 = 0f0kz *> conf = 0.10 ranks of expected_values: 21 EVAL 017gm7 genre 02xlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 96.000 76.000 0.582 http://example.org/film/film/genre #18708-02q4ntp PRED entity: 02q4ntp PRED relation: team! PRED expected values: 05g_nr => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 6): 0b_6_l (0.80 #139, 0.73 #151, 0.70 #145), 0br1x_ (0.73 #206, 0.71 #94, 0.70 #143), 0b_6x2 (0.70 #135, 0.67 #116, 0.67 #80), 05g_nr (0.60 #144, 0.60 #138, 0.55 #207), 0b_734 (0.50 #73, 0.50 #31, 0.50 #25), 0br1xn (0.33 #69, 0.33 #2, 0.30 #136) >> Best rule #139 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 02pqcfz; 027yf83; >> query: (?x9983, 0b_6_l) <- team(?x12451, ?x9983), team(?x9908, ?x9983), team(?x8527, ?x9983), team(?x4747, ?x9983), ?x8527 = 0b_6v_, ?x12451 = 0b_6xf, locations(?x9908, ?x2254), ?x2254 = 0dclg >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 8 *> proper extension: 02pjzvh; 02r2qt7; 02pyyld; *> query: (?x9983, 05g_nr) <- team(?x10736, ?x9983), team(?x8527, ?x9983), team(?x4747, ?x9983), locations(?x8527, ?x6683), locations(?x8527, ?x2087), colors(?x9983, ?x3189), ?x10736 = 0f9rw9, ?x2087 = 099ty, location(?x2789, ?x6683), contains(?x94, ?x6683) *> conf = 0.60 ranks of expected_values: 4 EVAL 02q4ntp team! 05g_nr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 176.000 176.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #18707-01n1gc PRED entity: 01n1gc PRED relation: company PRED expected values: 07vhb => 92 concepts (77 used for prediction) PRED predicted values (max 10 best out of 10): 09c7w0 (0.02 #2714, 0.01 #2327, 0.01 #4068), 032j_n (0.01 #152), 061dn_ (0.01 #51), 030_1_ (0.01 #25), 0jz9f (0.01 #3), 07wh1 (0.01 #1343), 032r4n (0.01 #1356), 02bq1j (0.01 #1356), 01w5m (0.01 #1356), 08815 (0.01 #1356) >> Best rule #2714 for best value: >> intensional similarity = 3 >> extensional distance = 745 >> proper extension: 01xyt7; 07h1q; 01cqz5; >> query: (?x3768, 09c7w0) <- gender(?x3768, ?x231), religion(?x3768, ?x7131), ?x231 = 05zppz >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01n1gc company 07vhb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 77.000 0.016 http://example.org/people/person/employment_history./business/employment_tenure/company #18706-092c5f PRED entity: 092c5f PRED relation: award_winner PRED expected values: 083chw 024n3z 016zp5 03ym1 0m66w => 30 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2437): 01j7rd (0.50 #7859, 0.40 #4831, 0.36 #10887), 02xs0q (0.40 #5070, 0.33 #8098, 0.25 #3554), 06msq2 (0.40 #5204, 0.33 #8232, 0.25 #3688), 03ym1 (0.35 #4540, 0.33 #862, 0.17 #18155), 0m_v0 (0.35 #4540, 0.33 #2011, 0.17 #6555), 0bwh6 (0.35 #4540, 0.19 #7569, 0.17 #6234), 0js9s (0.35 #4540, 0.19 #7569, 0.14 #4541), 02bfxb (0.35 #4540, 0.19 #7569, 0.11 #9084), 024n3z (0.35 #4540, 0.17 #18155, 0.14 #4541), 0lbj1 (0.35 #4540, 0.17 #6078, 0.11 #9084) >> Best rule #7859 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 07y_p6; >> query: (?x1193, 01j7rd) <- award_winner(?x1193, ?x8022), award_winner(?x1193, ?x2762), award_winner(?x1193, ?x2444), ?x8022 = 02661h, ceremony(?x618, ?x1193), honored_for(?x1193, ?x8084), film(?x2762, ?x972), award_nominee(?x5925, ?x2762), film(?x5925, ?x1734), award(?x8084, ?x1972), nominated_for(?x84, ?x8084), award(?x2762, ?x401), award_nominee(?x92, ?x5925), award_nominee(?x2444, ?x398) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4540 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 0gx_st; 09p30_; *> query: (?x1193, ?x248) <- award_winner(?x1193, ?x8022), award_winner(?x1193, ?x2762), ?x8022 = 02661h, ceremony(?x618, ?x1193), honored_for(?x1193, ?x8084), film(?x2762, ?x972), award_nominee(?x2844, ?x2762), genre(?x8084, ?x53), film(?x157, ?x8084), languages(?x2844, ?x254), nominated_for(?x143, ?x8084), award_winner(?x8084, ?x248) *> conf = 0.35 ranks of expected_values: 4, 9, 43, 146, 147 EVAL 092c5f award_winner 0m66w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 30.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 092c5f award_winner 03ym1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 30.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 092c5f award_winner 016zp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 30.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 092c5f award_winner 024n3z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 30.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 092c5f award_winner 083chw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 30.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18705-027s4dn PRED entity: 027s4dn PRED relation: ceremony PRED expected values: 09p2r9 09pj68 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 130): 02wzl1d (0.67 #140, 0.12 #660, 0.04 #1440), 09pj68 (0.67 #226, 0.12 #746, 0.04 #876), 09p2r9 (0.67 #215, 0.12 #735, 0.04 #865), 0g5b0q5 (0.67 #149, 0.12 #669, 0.04 #799), 0hndn2q (0.67 #168, 0.12 #688, 0.03 #1468), 0n8_m93 (0.55 #757, 0.20 #107, 0.11 #2187), 0bzm81 (0.55 #671, 0.20 #21, 0.11 #2101), 02yxh9 (0.55 #743, 0.20 #93, 0.11 #613), 0bc773 (0.55 #701, 0.20 #51, 0.11 #571), 02yw5r (0.55 #661, 0.20 #11, 0.11 #531) >> Best rule #140 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 054knh; >> query: (?x7451, 02wzl1d) <- ceremony(?x7451, ?x7767), ceremony(?x7451, ?x6595), ceremony(?x7451, ?x6238), ceremony(?x7451, ?x747), ?x7767 = 0418154, ?x747 = 09q_6t, ?x6238 = 09p30_, ?x6595 = 026kqs9 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #226 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 054knh; *> query: (?x7451, 09pj68) <- ceremony(?x7451, ?x7767), ceremony(?x7451, ?x6595), ceremony(?x7451, ?x6238), ceremony(?x7451, ?x747), ?x7767 = 0418154, ?x747 = 09q_6t, ?x6238 = 09p30_, ?x6595 = 026kqs9 *> conf = 0.67 ranks of expected_values: 2, 3 EVAL 027s4dn ceremony 09pj68 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 47.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 027s4dn ceremony 09p2r9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 47.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony #18704-01f_mw PRED entity: 01f_mw PRED relation: film PRED expected values: 05jf85 => 111 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1667): 03mh_tp (0.33 #449, 0.31 #6797, 0.26 #9971), 0dgq_kn (0.33 #925, 0.31 #7273, 0.21 #10447), 014kq6 (0.33 #308, 0.31 #6656, 0.21 #9830), 05b6rdt (0.33 #976, 0.23 #7324, 0.21 #10498), 03clwtw (0.33 #1109, 0.23 #7457, 0.21 #10631), 01hw5kk (0.33 #608, 0.23 #6956, 0.17 #2195), 0dc7hc (0.33 #1410, 0.23 #7758, 0.17 #2997), 08984j (0.33 #1101, 0.23 #7449, 0.17 #2688), 0g83dv (0.33 #620, 0.23 #6968, 0.16 #10142), 0ndwt2w (0.33 #887, 0.22 #4061, 0.18 #8822) >> Best rule #449 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0g1rw; >> query: (?x9001, 03mh_tp) <- film(?x9001, ?x11417), film(?x9001, ?x9527), nominated_for(?x3435, ?x11417), ?x3435 = 03hl6lc, film(?x986, ?x11417), ?x9527 = 01rnly >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #22223 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 34 *> proper extension: 024rdh; *> query: (?x9001, ?x253) <- film(?x9001, ?x11417), nominated_for(?x3435, ?x11417), nominated_for(?x2880, ?x11417), ?x3435 = 03hl6lc, film(?x719, ?x11417), award(?x253, ?x2880) *> conf = 0.05 ranks of expected_values: 1389 EVAL 01f_mw film 05jf85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 111.000 24.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18703-0x0d PRED entity: 0x0d PRED relation: season PRED expected values: 0dx84s => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 8): 0dx84s (0.88 #332, 0.88 #316, 0.87 #300), 027pwzc (0.75 #317, 0.75 #69, 0.74 #301), 05kcgsf (0.61 #249, 0.54 #313, 0.54 #145), 02h7s73 (0.44 #78, 0.35 #302, 0.33 #254), 03c6s24 (0.44 #79, 0.30 #303, 0.29 #319), 04110b0 (0.39 #251, 0.38 #147, 0.33 #315), 03c74_8 (0.33 #74, 0.30 #298, 0.26 #258), 04n36qk (0.20 #24, 0.12 #240, 0.11 #88) >> Best rule #332 for best value: >> intensional similarity = 18 >> extensional distance = 24 >> proper extension: 04wmvz; >> query: (?x10939, 0dx84s) <- season(?x10939, ?x8517), season(?x10939, ?x3431), ?x8517 = 0285r5d, season(?x12956, ?x3431), season(?x11361, ?x3431), season(?x1010, ?x3431), season(?x580, ?x3431), season(?x260, ?x3431), ?x580 = 05m_8, ?x11361 = 03m1n, ?x1010 = 01d5z, position(?x10939, ?x2010), draft(?x12956, ?x1161), team(?x12826, ?x12956), ?x2010 = 02lyr4, school(?x12956, ?x1681), ?x260 = 01ypc, draft(?x10939, ?x1633) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0x0d season 0dx84s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.885 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #18702-0lkr7 PRED entity: 0lkr7 PRED relation: profession PRED expected values: 0d1pc => 91 concepts (54 used for prediction) PRED predicted values (max 10 best out of 62): 0dxtg (0.53 #602, 0.37 #161, 0.35 #749), 01d_h8 (0.50 #741, 0.44 #447, 0.40 #888), 03gjzk (0.41 #603, 0.36 #750, 0.32 #1780), 09jwl (0.25 #1341, 0.24 #1194, 0.22 #900), 02jknp (0.25 #743, 0.25 #3685, 0.24 #4126), 0cbd2 (0.23 #7, 0.17 #595, 0.12 #2655), 0dz3r (0.21 #1325, 0.20 #1178, 0.17 #884), 0nbcg (0.20 #1353, 0.20 #1206, 0.19 #471), 0d1pc (0.15 #1078, 0.14 #1667, 0.14 #1961), 016z4k (0.15 #886, 0.14 #1327, 0.13 #1180) >> Best rule #602 for best value: >> intensional similarity = 3 >> extensional distance = 251 >> proper extension: 02hblj; >> query: (?x4992, 0dxtg) <- profession(?x4992, ?x1146), film(?x4992, ?x2886), ?x1146 = 018gz8 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1078 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 378 *> proper extension: 05m63c; 031zkw; 015882; 07cjqy; 0k8y7; 04cr6qv; 02w5q6; 04r7p; 015076; *> query: (?x4992, 0d1pc) <- profession(?x4992, ?x1032), people(?x3584, ?x4992), participant(?x3585, ?x4992) *> conf = 0.15 ranks of expected_values: 9 EVAL 0lkr7 profession 0d1pc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 91.000 54.000 0.526 http://example.org/people/person/profession #18701-0cct7p PRED entity: 0cct7p PRED relation: type_of_union PRED expected values: 04ztj => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.86 #9, 0.82 #49, 0.80 #33), 01g63y (0.43 #113, 0.41 #118, 0.33 #215), 01bl8s (0.43 #113, 0.41 #118, 0.02 #23) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 02vmzp; 03bnv; 0hky; 02xgdv; 03fw4y; 0pj8m; 07n39; 0239zv; 071xj; 01k6nm; ... >> query: (?x13096, 04ztj) <- religion(?x13096, ?x8967), place_of_death(?x13096, ?x7412), ?x8967 = 03j6c, gender(?x13096, ?x231) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cct7p type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.857 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18700-02yy8 PRED entity: 02yy8 PRED relation: type_of_union PRED expected values: 04ztj => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.91 #233, 0.90 #189, 0.90 #185), 01g63y (0.42 #202, 0.31 #414, 0.31 #398), 01bl8s (0.10 #43, 0.06 #131, 0.05 #147), 0jgjn (0.02 #324, 0.02 #340) >> Best rule #233 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0159h6; 0htlr; 0157m; 05r5w; 0bq2g; 01ft2l; 01vtqml; 0gbwp; 02t_99; 015njf; ... >> query: (?x12571, 04ztj) <- spouse(?x12571, ?x7893), profession(?x12571, ?x353), profession(?x3963, ?x353), ?x3963 = 02g75 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02yy8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.911 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18699-07hn5 PRED entity: 07hn5 PRED relation: instance_of_recurring_event! PRED expected values: 027yjnv 02rxd26 => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 07hn5 instance_of_recurring_event! 02rxd26 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/time/event/instance_of_recurring_event EVAL 07hn5 instance_of_recurring_event! 027yjnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/time/event/instance_of_recurring_event #18698-068p2 PRED entity: 068p2 PRED relation: teams PRED expected values: 02pyyld => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 235): 086x3 (0.06 #2142, 0.01 #18217, 0.01 #20717), 02d02 (0.04 #541, 0.03 #898, 0.03 #1255), 02fp3 (0.04 #540, 0.03 #897, 0.03 #1254), 02c_4 (0.04 #515, 0.03 #872, 0.03 #1229), 06rpd (0.04 #555, 0.03 #912, 0.03 #1269), 0j86l (0.04 #707, 0.03 #1064, 0.03 #1421), 02wvfxz (0.04 #690, 0.03 #1047, 0.03 #1404), 0wsr (0.04 #483, 0.03 #840, 0.03 #1197), 0x2p (0.04 #399, 0.03 #756, 0.03 #1113), 0jnlm (0.04 #706, 0.03 #1063, 0.03 #1420) >> Best rule #2142 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 05kr_; >> query: (?x4499, 086x3) <- contains(?x4499, ?x6894), currency(?x6894, ?x170), time_zones(?x6894, ?x2674) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 068p2 teams 02pyyld CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 169.000 169.000 0.057 http://example.org/sports/sports_team_location/teams #18697-0gfhg1y PRED entity: 0gfhg1y PRED relation: entity_involved PRED expected values: 01k165 060d2 => 68 concepts (61 used for prediction) PRED predicted values (max 10 best out of 959): 02lmk (0.63 #465, 0.50 #221, 0.40 #534), 028rk (0.63 #465, 0.40 #498, 0.39 #6608), 02psqkz (0.63 #465, 0.39 #6608, 0.38 #6770), 0bq0p9 (0.63 #465, 0.39 #6608, 0.38 #6770), 059z0 (0.63 #465, 0.39 #6608, 0.38 #6770), 018q7 (0.63 #465, 0.39 #6608, 0.38 #6770), 09b6zr (0.50 #348, 0.44 #3136, 0.38 #1289), 079dy (0.44 #3136, 0.36 #7413, 0.34 #9351), 08_hns (0.44 #3136, 0.36 #7413, 0.34 #9351), 0948xk (0.44 #3136, 0.36 #7413, 0.34 #9351) >> Best rule #465 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 09x7p1; >> query: (?x11216, ?x613) <- locations(?x11216, ?x4120), entity_involved(?x11216, ?x2669), locations(?x12789, ?x4120), administrative_parent(?x4120, ?x551), entity_involved(?x12789, ?x613), ?x2669 = 02mjmr, contains(?x4120, ?x13844) >> conf = 0.63 => this is the best rule for 6 predicted values *> Best rule #309 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 03w6sj; *> query: (?x11216, ?x117) <- locations(?x11216, ?x4120), entity_involved(?x11216, ?x2669), ?x4120 = 04gqr, student(?x3424, ?x2669), profession(?x2669, ?x2225), gender(?x2669, ?x231), profession(?x117, ?x2225) *> conf = 0.02 ranks of expected_values: 279 EVAL 0gfhg1y entity_involved 060d2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 61.000 0.632 http://example.org/base/culturalevent/event/entity_involved EVAL 0gfhg1y entity_involved 01k165 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 68.000 61.000 0.632 http://example.org/base/culturalevent/event/entity_involved #18696-0gghm PRED entity: 0gghm PRED relation: instrumentalists PRED expected values: 016s_5 => 84 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1094): 01gg59 (0.57 #4545, 0.50 #8891, 0.49 #1853), 016s_5 (0.57 #4642, 0.49 #1853, 0.42 #8988), 01sb5r (0.50 #9529, 0.50 #8910, 0.50 #8292), 04m2zj (0.50 #8510, 0.50 #2311, 0.50 #1691), 01vvycq (0.50 #8705, 0.50 #8087, 0.49 #1853), 01gf5h (0.50 #1851, 0.50 #1279, 0.49 #1853), 03gr7w (0.50 #1946, 0.50 #1326, 0.33 #8145), 018y81 (0.50 #8402, 0.49 #1853, 0.43 #9639), 032t2z (0.50 #8698, 0.49 #1853, 0.43 #4352), 0473q (0.50 #9693, 0.49 #1853, 0.43 #4728) >> Best rule #4545 for best value: >> intensional similarity = 19 >> extensional distance = 5 >> proper extension: 02k84w; 06ncr; >> query: (?x2310, 01gg59) <- group(?x2310, ?x6854), role(?x2310, ?x2048), role(?x2310, ?x228), ?x2048 = 018j2, role(?x212, ?x2310), instrumentalists(?x228, ?x1338), instrumentalists(?x228, ?x654), role(?x6626, ?x228), role(?x4428, ?x228), role(?x2690, ?x228), role(?x74, ?x228), ?x6626 = 0b_j2, music(?x218, ?x4428), role(?x1260, ?x2310), category(?x1338, ?x134), participant(?x8146, ?x2690), gender(?x654, ?x231), award_nominee(?x3069, ?x4428), ?x6854 = 0178_w >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4642 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 5 *> proper extension: 02k84w; 06ncr; *> query: (?x2310, 016s_5) <- group(?x2310, ?x6854), role(?x2310, ?x2048), role(?x2310, ?x228), ?x2048 = 018j2, role(?x212, ?x2310), instrumentalists(?x228, ?x1338), instrumentalists(?x228, ?x654), role(?x6626, ?x228), role(?x4428, ?x228), role(?x2690, ?x228), role(?x74, ?x228), ?x6626 = 0b_j2, music(?x218, ?x4428), role(?x1260, ?x2310), category(?x1338, ?x134), participant(?x8146, ?x2690), gender(?x654, ?x231), award_nominee(?x3069, ?x4428), ?x6854 = 0178_w *> conf = 0.57 ranks of expected_values: 2 EVAL 0gghm instrumentalists 016s_5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 21.000 0.571 http://example.org/music/instrument/instrumentalists #18695-04nfpk PRED entity: 04nfpk PRED relation: team PRED expected values: 026l1lq => 34 concepts (25 used for prediction) PRED predicted values (max 10 best out of 999): 05l71 (0.87 #7345, 0.85 #11017, 0.85 #7346), 0wsr (0.87 #7345, 0.85 #11017, 0.85 #7346), 084l5 (0.87 #7345, 0.85 #11017, 0.85 #7346), 0fsb_6 (0.87 #7345, 0.85 #11017, 0.85 #7346), 0g0z58 (0.87 #7345, 0.85 #11017, 0.85 #7346), 02vklm3 (0.87 #7345, 0.85 #11017, 0.85 #7346), 01ct6 (0.85 #11017, 0.85 #7346, 0.84 #8262), 043tz8m (0.85 #11017, 0.85 #7346, 0.83 #3669), 07l24 (0.85 #7346, 0.83 #3669, 0.82 #20225), 05g3v (0.85 #7346, 0.83 #3669, 0.82 #20225) >> Best rule #7345 for best value: >> intensional similarity = 27 >> extensional distance = 2 >> proper extension: 05zm34; >> query: (?x2147, ?x7450) <- position(?x7539, ?x2147), position(?x7450, ?x2147), position(?x7078, ?x2147), position(?x6976, ?x2147), position(?x4256, ?x2147), position(?x4170, ?x2147), position(?x1576, ?x2147), position(?x1516, ?x2147), position_s(?x5204, ?x2147), position_s(?x4546, ?x2147), position_s(?x1639, ?x2147), position(?x1517, ?x2147), team(?x1792, ?x7450), ?x7539 = 02px_23, ?x1576 = 05tfm, ?x1516 = 0ft5vs, ?x7078 = 0ws7, ?x5204 = 05g49, school(?x6976, ?x388), colors(?x6976, ?x663), ?x1639 = 07l24, ?x4170 = 05l71, ?x1792 = 05zm34, position_s(?x6976, ?x3113), ?x4546 = 05gg4, draft(?x6976, ?x465), ?x4256 = 03lsq >> conf = 0.87 => this is the best rule for 6 predicted values *> Best rule #7346 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 2 *> proper extension: 05zm34; *> query: (?x2147, ?x729) <- position(?x7539, ?x2147), position(?x7450, ?x2147), position(?x7078, ?x2147), position(?x6976, ?x2147), position(?x4256, ?x2147), position(?x4170, ?x2147), position(?x1576, ?x2147), position(?x1516, ?x2147), position_s(?x5204, ?x2147), position_s(?x4546, ?x2147), position_s(?x1639, ?x2147), position_s(?x729, ?x2147), position(?x1517, ?x2147), team(?x1792, ?x7450), ?x7539 = 02px_23, ?x1576 = 05tfm, ?x1516 = 0ft5vs, ?x7078 = 0ws7, ?x5204 = 05g49, school(?x6976, ?x388), colors(?x6976, ?x663), ?x1639 = 07l24, ?x4170 = 05l71, ?x1792 = 05zm34, position_s(?x6976, ?x3113), ?x4546 = 05gg4, draft(?x6976, ?x465), ?x4256 = 03lsq *> conf = 0.85 ranks of expected_values: 12 EVAL 04nfpk team 026l1lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 34.000 25.000 0.870 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #18694-05z775 PRED entity: 05z775 PRED relation: profession PRED expected values: 0np9r => 95 concepts (47 used for prediction) PRED predicted values (max 10 best out of 47): 0np9r (0.81 #1213, 0.75 #1959, 0.74 #1362), 09jwl (0.37 #5682, 0.36 #6278, 0.33 #6129), 01d_h8 (0.34 #2987, 0.31 #4328, 0.30 #4775), 016z4k (0.29 #3581, 0.28 #3134, 0.28 #3879), 0nbcg (0.26 #5695, 0.25 #6291, 0.24 #3609), 0dxtg (0.25 #14, 0.23 #2995, 0.22 #1654), 0d1pc (0.25 #51, 0.17 #3628, 0.16 #3926), 0dz3r (0.22 #5665, 0.22 #6261, 0.18 #6112), 018gz8 (0.22 #2998, 0.21 #1955, 0.19 #2402), 03gjzk (0.21 #2996, 0.19 #2847, 0.19 #4337) >> Best rule #1213 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 06sn8m; >> query: (?x11435, 0np9r) <- language(?x11435, ?x254), actor(?x1366, ?x11435), actor(?x8628, ?x11435), genre(?x8628, ?x811) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05z775 profession 0np9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 47.000 0.806 http://example.org/people/person/profession #18693-03y3bp7 PRED entity: 03y3bp7 PRED relation: actor PRED expected values: 06cddt => 70 concepts (61 used for prediction) PRED predicted values (max 10 best out of 740): 0gz5hs (0.33 #151, 0.05 #2007, 0.05 #2935), 0582cf (0.20 #1628, 0.15 #2556, 0.11 #3484), 027xbpw (0.20 #1197, 0.07 #21348, 0.07 #19491), 03jldb (0.20 #1050, 0.07 #21348, 0.07 #19491), 02tqkf (0.20 #1166, 0.05 #2094, 0.05 #3022), 023v4_ (0.20 #1334, 0.05 #2262, 0.02 #3190), 01pm0_ (0.20 #1435, 0.05 #2363, 0.02 #3291), 02k4b2 (0.20 #1359, 0.05 #2287, 0.02 #3215), 0sw6y (0.14 #3640, 0.12 #4568, 0.11 #5496), 02gf_l (0.14 #3353, 0.12 #4281, 0.11 #5209) >> Best rule #151 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0584r4; >> query: (?x3102, 0gz5hs) <- genre(?x3102, ?x2540), ?x2540 = 0hcr, actor(?x3102, ?x10593), ?x10593 = 0f87jy >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03y3bp7 actor 06cddt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 61.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #18692-03j0d PRED entity: 03j0d PRED relation: influenced_by! PRED expected values: 01zkxv 03ftmg 014ps4 06jcc => 152 concepts (63 used for prediction) PRED predicted values (max 10 best out of 445): 0683n (0.46 #1848, 0.45 #1343, 0.31 #3361), 0n6kf (0.46 #1701, 0.45 #1196, 0.21 #4228), 013pp3 (0.46 #1731, 0.45 #1226, 0.19 #3244), 019z7q (0.36 #1034, 0.31 #1539, 0.17 #4572), 0g72r (0.36 #1499, 0.31 #2004, 0.09 #5543), 01v_0b (0.31 #1988, 0.27 #1483, 0.12 #3501), 014ps4 (0.29 #8395, 0.27 #2325, 0.23 #1820), 03vrp (0.27 #1201, 0.23 #1706, 0.17 #4739), 01x53m (0.27 #1374, 0.23 #1879, 0.13 #4912), 03f0324 (0.27 #1202, 0.23 #1707, 0.11 #697) >> Best rule #1848 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 0m77m; 03_87; >> query: (?x10000, 0683n) <- influenced_by(?x10000, ?x4072), influenced_by(?x10974, ?x10000), influenced_by(?x2343, ?x10000), ?x10974 = 01vdrw, profession(?x10000, ?x353), location(?x2343, ?x7058) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #8395 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 50 *> proper extension: 014nvr; *> query: (?x10000, 014ps4) <- influenced_by(?x3858, ?x10000), influenced_by(?x3858, ?x6810), influenced_by(?x3858, ?x477), ?x477 = 041h0, influenced_by(?x6810, ?x712) *> conf = 0.29 ranks of expected_values: 7, 21, 65, 146 EVAL 03j0d influenced_by! 06jcc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 152.000 63.000 0.462 http://example.org/influence/influence_node/influenced_by EVAL 03j0d influenced_by! 014ps4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 152.000 63.000 0.462 http://example.org/influence/influence_node/influenced_by EVAL 03j0d influenced_by! 03ftmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 152.000 63.000 0.462 http://example.org/influence/influence_node/influenced_by EVAL 03j0d influenced_by! 01zkxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 152.000 63.000 0.462 http://example.org/influence/influence_node/influenced_by #18691-01kgxf PRED entity: 01kgxf PRED relation: film PRED expected values: 047qxs => 105 concepts (58 used for prediction) PRED predicted values (max 10 best out of 492): 05sy_5 (0.33 #2841, 0.01 #15350, 0.01 #35009), 04g73n (0.33 #1405), 0k5fg (0.33 #1088), 02ryz24 (0.25 #4042, 0.20 #5829, 0.02 #7616), 03tbg6 (0.25 #5227, 0.20 #7014, 0.02 #10588), 0340hj (0.25 #3811, 0.20 #5598, 0.01 #10959), 02q56mk (0.25 #3991, 0.20 #5778, 0.01 #11139), 06lpmt (0.25 #4258, 0.20 #6045, 0.01 #11406), 0kvgxk (0.25 #3902, 0.20 #5689, 0.01 #18199), 03h0byn (0.25 #5273, 0.20 #7060) >> Best rule #2841 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01ksr1; >> query: (?x7569, 05sy_5) <- film(?x7569, ?x4167), participant(?x7569, ?x8716), ?x4167 = 08fn5b >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kgxf film 047qxs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 58.000 0.333 http://example.org/film/actor/film./film/performance/film #18690-0g476 PRED entity: 0g476 PRED relation: award PRED expected values: 0gqwc => 143 concepts (123 used for prediction) PRED predicted values (max 10 best out of 310): 0gqwc (0.87 #71, 0.74 #1268, 0.72 #869), 09qwmm (0.53 #32, 0.41 #830, 0.36 #1229), 0cqgl9 (0.47 #189, 0.33 #987, 0.30 #1386), 0bdwft (0.43 #1262, 0.43 #863, 0.33 #65), 07bdd_ (0.41 #3653, 0.09 #8840, 0.06 #2057), 02z0dfh (0.40 #72, 0.30 #870, 0.26 #1269), 09sb52 (0.39 #837, 0.36 #1236, 0.36 #3231), 0gqyl (0.37 #899, 0.33 #101, 0.33 #1298), 01by1l (0.37 #10084, 0.32 #10483, 0.31 #12877), 02x4x18 (0.33 #927, 0.31 #1326, 0.20 #129) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 057hz; >> query: (?x9963, 0gqwc) <- award_winner(?x749, ?x9963), award(?x9963, ?x1716), ?x1716 = 02y_rq5, religion(?x9963, ?x9362) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g476 award 0gqwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 123.000 0.867 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18689-016m5c PRED entity: 016m5c PRED relation: artists! PRED expected values: 0jrv_ => 116 concepts (73 used for prediction) PRED predicted values (max 10 best out of 266): 06by7 (0.81 #18895, 0.81 #19204, 0.76 #15184), 0jrv_ (0.70 #179, 0.11 #16399, 0.07 #10511), 064t9 (0.62 #16414, 0.56 #7125, 0.54 #17033), 016clz (0.44 #5260, 0.41 #12374, 0.40 #11445), 05bt6j (0.37 #353, 0.35 #17990, 0.34 #20773), 02yv6b (0.36 #3091, 0.33 #10301, 0.29 #12780), 04qftx (0.36 #3091, 0.03 #21348, 0.03 #3921), 0155w (0.33 #1035, 0.29 #2889, 0.28 #1962), 0hdf8 (0.30 #72, 0.07 #10511, 0.05 #12752), 06j6l (0.30 #17068, 0.30 #16449, 0.29 #7160) >> Best rule #18895 for best value: >> intensional similarity = 5 >> extensional distance = 479 >> proper extension: 053y0s; 01nqfh_; 01cv3n; 01pr_j6; 01vs14j; 01p45_v; 01m65sp; 01vswwx; 02bgmr; 0326tc; ... >> query: (?x12228, 06by7) <- artists(?x1000, ?x12228), artists(?x1000, ?x11186), artists(?x1000, ?x8335), ?x8335 = 015cqh, ?x11186 = 01304j >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #179 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 020hh3; *> query: (?x12228, 0jrv_) <- artists(?x8639, ?x12228), artists(?x2249, ?x12228), artists(?x1000, ?x12228), ?x1000 = 0xhtw, ?x8639 = 07bbw, ?x2249 = 03lty *> conf = 0.70 ranks of expected_values: 2 EVAL 016m5c artists! 0jrv_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 73.000 0.809 http://example.org/music/genre/artists #18688-0yx74 PRED entity: 0yx74 PRED relation: contains! PRED expected values: 09c7w0 => 167 concepts (96 used for prediction) PRED predicted values (max 10 best out of 286): 09c7w0 (0.75 #5374, 0.73 #33131, 0.73 #21490), 0yx74 (0.40 #69844, 0.20 #32233, 0.07 #3461), 01n7q (0.23 #5449, 0.17 #35892, 0.15 #33206), 07b_l (0.22 #3803, 0.20 #2013, 0.20 #28872), 04_1l0v (0.20 #2241, 0.19 #11191, 0.18 #13877), 07ssc (0.19 #55546, 0.18 #50173, 0.16 #73460), 059rby (0.14 #915, 0.11 #60010, 0.09 #24194), 04jpl (0.14 #917, 0.10 #9868, 0.08 #14345), 02_286 (0.14 #938, 0.07 #9889, 0.07 #7204), 0k3k1 (0.14 #1392, 0.07 #2288, 0.02 #4973) >> Best rule #5374 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 0fsb8; 0rmby; >> query: (?x12883, 09c7w0) <- source(?x12883, ?x958), ?x958 = 0jbk9, location(?x7064, ?x12883), athlete(?x1083, ?x7064) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0yx74 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 96.000 0.755 http://example.org/location/location/contains #18687-09tcg4 PRED entity: 09tcg4 PRED relation: nominated_for! PRED expected values: 0l8z1 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 225): 0l8z1 (0.37 #1008, 0.20 #16974, 0.19 #530), 0p9sw (0.33 #21, 0.30 #977, 0.17 #8149), 0k611 (0.33 #73, 0.29 #1029, 0.25 #551), 019f4v (0.33 #54, 0.29 #1010, 0.27 #532), 04dn09n (0.33 #36, 0.24 #514, 0.21 #992), 0f4x7 (0.32 #504, 0.18 #6481, 0.17 #7676), 0gq9h (0.29 #1019, 0.29 #6518, 0.28 #541), 0gq_v (0.29 #976, 0.26 #498, 0.20 #6475), 0gr0m (0.26 #1016, 0.18 #6515, 0.17 #538), 02qvyrt (0.26 #1053, 0.18 #575, 0.17 #97) >> Best rule #1008 for best value: >> intensional similarity = 4 >> extensional distance = 195 >> proper extension: 01vrwfv; >> query: (?x10048, 0l8z1) <- nominated_for(?x669, ?x10048), award_winner(?x669, ?x4850), music(?x670, ?x669), artists(?x505, ?x669) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09tcg4 nominated_for! 0l8z1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.365 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18686-0642xf3 PRED entity: 0642xf3 PRED relation: language PRED expected values: 02hwyss => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 34): 064_8sq (0.20 #21, 0.13 #1191, 0.13 #3428), 06nm1 (0.14 #68, 0.11 #126, 0.10 #887), 04306rv (0.11 #1233, 0.10 #120, 0.09 #822), 03_9r (0.10 #9, 0.06 #827, 0.06 #2001), 06b_j (0.08 #80, 0.07 #840, 0.06 #1251), 02bjrlw (0.08 #1230, 0.07 #1112, 0.06 #1053), 0653m (0.04 #829, 0.04 #1004, 0.04 #1181), 04h9h (0.04 #158, 0.04 #565, 0.04 #506), 0jzc (0.04 #1248, 0.04 #837, 0.04 #77), 02hwhyv (0.04 #87) >> Best rule #21 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0bscw; 015x74; 0f4_l; 07tw_b; 01hqk; 01rxyb; 09v9mks; 056xkh; >> query: (?x5081, 064_8sq) <- film(?x8134, ?x5081), film_crew_role(?x5081, ?x137), ?x8134 = 0kjrx, genre(?x5081, ?x225), language(?x5081, ?x254) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #681 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 262 *> proper extension: 01_1pv; 05znbh7; 0gnkb; 0gndh; 02k1pr; 029jt9; 02q_x_l; 02q_ncg; *> query: (?x5081, 02hwyss) <- film(?x91, ?x5081), genre(?x5081, ?x811), nominated_for(?x2456, ?x5081), ?x811 = 03k9fj *> conf = 0.01 ranks of expected_values: 32 EVAL 0642xf3 language 02hwyss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 82.000 82.000 0.200 http://example.org/film/film/language #18685-024vjd PRED entity: 024vjd PRED relation: award! PRED expected values: 015rmq => 41 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2680): 0149xx (0.81 #43993, 0.80 #33841, 0.80 #20301), 011zf2 (0.81 #43993, 0.80 #33841, 0.80 #20301), 015rmq (0.81 #43993, 0.80 #20301, 0.79 #57533), 0bvzp (0.38 #3385, 0.27 #60919, 0.22 #50765), 0127gn (0.38 #3385, 0.22 #50765, 0.20 #77845), 02cx90 (0.38 #3385, 0.22 #50765, 0.20 #77845), 07z542 (0.38 #3385, 0.22 #50765, 0.20 #77845), 03xgm3 (0.38 #3385, 0.22 #50765, 0.20 #77845), 028q6 (0.38 #3385, 0.22 #50765, 0.20 #77845), 01vd7hn (0.38 #3385, 0.22 #50765, 0.20 #77845) >> Best rule #43993 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 09v1lrz; >> query: (?x3903, ?x5125) <- award_winner(?x3903, ?x5125), role(?x5125, ?x4311), award(?x5150, ?x3903), award_winner(?x5132, ?x5150) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 024vjd award! 015rmq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 24.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18684-0ccck7 PRED entity: 0ccck7 PRED relation: films! PRED expected values: 04gb7 => 87 concepts (30 used for prediction) PRED predicted values (max 10 best out of 76): 0fx2s (0.09 #1473, 0.06 #848, 0.05 #227), 081pw (0.08 #158, 0.08 #2653, 0.07 #4376), 0g1x2_ (0.08 #27, 0.05 #648, 0.05 #337), 048n7 (0.08 #230, 0.05 #385, 0.04 #851), 07_nf (0.08 #221, 0.04 #842, 0.03 #3811), 02_h0 (0.08 #1500, 0.07 #875, 0.05 #99), 03r8gp (0.07 #865, 0.06 #1490, 0.05 #244), 05489 (0.06 #2701, 0.05 #206, 0.05 #51), 0fzyg (0.05 #3798, 0.05 #208, 0.05 #2703), 0kbq (0.05 #259, 0.05 #104, 0.05 #725) >> Best rule #1473 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 011yrp; 0g9wdmc; 0kb57; 08vd2q; 0pd6l; 06nr2h; 0kb07; 0hv27; 047myg9; 04lhc4; ... >> query: (?x11218, 0fx2s) <- nominated_for(?x1972, ?x11218), films(?x5069, ?x11218), award(?x6282, ?x1972), ?x6282 = 01fx5l >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #820 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 65 *> proper extension: 0jyx6; 0kbhf; 011ypx; 063hp4; 01gvts; 0p_rk; 03cvvlg; 0kt_4; 01q7h2; 08xvpn; ... *> query: (?x11218, 04gb7) <- nominated_for(?x1972, ?x11218), nominated_for(?x1107, ?x11218), films(?x5069, ?x11218), ?x1972 = 0gqyl, award(?x276, ?x1107) *> conf = 0.04 ranks of expected_values: 15 EVAL 0ccck7 films! 04gb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 87.000 30.000 0.087 http://example.org/film/film_subject/films #18683-04g5k PRED entity: 04g5k PRED relation: country! PRED expected values: 0dwxr => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 37): 06f41 (0.81 #233, 0.75 #381, 0.75 #196), 07bs0 (0.81 #231, 0.67 #379, 0.65 #157), 01cgz (0.79 #1527, 0.78 #1416, 0.67 #2193), 06wrt (0.78 #382, 0.70 #160, 0.69 #234), 03fyrh (0.77 #240, 0.64 #388, 0.55 #1424), 02y8z (0.73 #236, 0.62 #199, 0.58 #384), 0194d (0.72 #402, 0.69 #254, 0.60 #920), 07gyv (0.69 #227, 0.65 #153, 0.65 #1078), 09_b4 (0.69 #250, 0.39 #398, 0.35 #176), 0w0d (0.67 #193, 0.65 #896, 0.65 #230) >> Best rule #233 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 01mjq; >> query: (?x5482, 06f41) <- country(?x520, ?x5482), film_release_region(?x559, ?x5482), ?x520 = 01dys >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 01mjq; *> query: (?x5482, 0dwxr) <- country(?x520, ?x5482), film_release_region(?x559, ?x5482), ?x520 = 01dys *> conf = 0.58 ranks of expected_values: 16 EVAL 04g5k country! 0dwxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 137.000 137.000 0.808 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #18682-03v3xp PRED entity: 03v3xp PRED relation: award_winner! PRED expected values: 02tr7d => 90 concepts (44 used for prediction) PRED predicted values (max 10 best out of 555): 046zh (0.82 #70200, 0.81 #39888, 0.81 #59033), 0l6px (0.82 #70200, 0.81 #39888, 0.81 #59033), 02k6rq (0.82 #70200, 0.81 #39888, 0.81 #59033), 051wwp (0.82 #70200, 0.81 #39888, 0.81 #59033), 02tr7d (0.82 #70200, 0.81 #39888, 0.81 #59033), 09fqtq (0.34 #46271, 0.34 #44675, 0.33 #55842), 02_hj4 (0.34 #46271, 0.34 #44675, 0.33 #17549), 03v3xp (0.33 #55842, 0.28 #52652, 0.16 #54247), 02x7vq (0.33 #55842, 0.28 #52652, 0.16 #54247), 01z7_f (0.33 #55842, 0.28 #52652, 0.16 #54247) >> Best rule #70200 for best value: >> intensional similarity = 3 >> extensional distance = 1487 >> proper extension: 0f721s; 0gsg7; 0c01c; 02wr2r; 035_2h; 02f9wb; 0hm0k; 05gnf; 039cq4; 06_bq1; ... >> query: (?x3604, ?x5246) <- award_winner(?x1951, ?x3604), award_nominee(?x1951, ?x1669), award_winner(?x3604, ?x5246) >> conf = 0.82 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5 EVAL 03v3xp award_winner! 02tr7d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 44.000 0.819 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #18681-03h64 PRED entity: 03h64 PRED relation: country! PRED expected values: 0w0d 0194d => 207 concepts (207 used for prediction) PRED predicted values (max 10 best out of 56): 01lb14 (0.88 #373, 0.84 #598, 0.76 #463), 064vjs (0.88 #384, 0.77 #744, 0.76 #474), 0194d (0.79 #623, 0.73 #758, 0.72 #1073), 01cgz (0.77 #327, 0.73 #2894, 0.73 #732), 07gyv (0.77 #322, 0.71 #457, 0.69 #367), 09w1n (0.75 #377, 0.65 #467, 0.65 #917), 01sgl (0.75 #395, 0.63 #620, 0.62 #350), 0w0d (0.74 #595, 0.69 #1765, 0.69 #1045), 07bs0 (0.71 #461, 0.69 #326, 0.69 #371), 03rbzn (0.71 #470, 0.69 #380, 0.63 #1595) >> Best rule #373 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0b90_r; 0hzlz; >> query: (?x2645, 01lb14) <- film_release_region(?x6270, ?x2645), location_of_ceremony(?x566, ?x2645), ?x6270 = 0g9zljd >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #623 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 06t8v; *> query: (?x2645, 0194d) <- film_release_region(?x7538, ?x2645), film_release_region(?x6556, ?x2645), ?x6556 = 05dss7, ?x7538 = 035zr0 *> conf = 0.79 ranks of expected_values: 3, 8 EVAL 03h64 country! 0194d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 207.000 207.000 0.875 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03h64 country! 0w0d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 207.000 207.000 0.875 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #18680-01wgfp6 PRED entity: 01wgfp6 PRED relation: gender PRED expected values: 05zppz => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #215, 0.76 #53, 0.76 #25), 02zsn (0.66 #18, 0.60 #22, 0.57 #2) >> Best rule #215 for best value: >> intensional similarity = 3 >> extensional distance = 2341 >> proper extension: 07kb5; 0454s1; 0ct9_; 0652ty; 081t6; >> query: (?x5901, 05zppz) <- profession(?x5901, ?x1183), profession(?x2761, ?x1183), ?x2761 = 04g865 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wgfp6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.792 http://example.org/people/person/gender #18679-0xwj PRED entity: 0xwj PRED relation: state_province_region PRED expected values: 059rby => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 167): 059rby (0.91 #2476, 0.90 #2106, 0.78 #5453), 01n7q (0.50 #636, 0.50 #513, 0.46 #6193), 07z1m (0.49 #3711, 0.09 #1009, 0.06 #1625), 081yw (0.25 #307, 0.12 #925, 0.12 #1791), 09c7w0 (0.25 #17355, 0.23 #11644, 0.22 #14874), 05fjf (0.22 #4954, 0.07 #1429, 0.06 #494), 07h34 (0.22 #4954, 0.07 #1285, 0.05 #2277), 05kkh (0.22 #4954, 0.05 #3464, 0.04 #10281), 05fly (0.22 #4954, 0.04 #2556, 0.02 #4044), 01vsb_ (0.17 #472, 0.12 #966, 0.06 #1832) >> Best rule #2476 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 01p7x7; >> query: (?x5077, 059rby) <- currency(?x5077, ?x170), ?x170 = 09nqf, citytown(?x5077, ?x739), ?x739 = 02_286, currency(?x5077, ?x170) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xwj state_province_region 059rby CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.913 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #18678-0284b56 PRED entity: 0284b56 PRED relation: genre PRED expected values: 01jfsb => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 87): 05p553 (0.40 #124, 0.36 #244, 0.35 #604), 01t_vv (0.40 #174, 0.14 #294, 0.13 #54), 02l7c8 (0.39 #2538, 0.31 #1936, 0.31 #9630), 03k9fj (0.36 #1211, 0.33 #611, 0.31 #491), 01jfsb (0.36 #3254, 0.34 #1692, 0.33 #9626), 02kdv5l (0.34 #1202, 0.34 #3244, 0.32 #1682), 0vgkd (0.32 #250, 0.10 #130, 0.06 #3252), 01hmnh (0.29 #618, 0.27 #1218, 0.20 #2298), 0hn10 (0.25 #129, 0.09 #249, 0.08 #2531), 04xvlr (0.23 #241, 0.21 #2523, 0.20 #1) >> Best rule #124 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 04gp58p; >> query: (?x5706, 05p553) <- nominated_for(?x9343, ?x5706), film(?x156, ?x5706), ?x9343 = 02xj3rw >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3254 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 543 *> proper extension: 0gtsx8c; *> query: (?x5706, 01jfsb) <- film_crew_role(?x5706, ?x1171), ?x1171 = 09vw2b7, film_release_region(?x5706, ?x94) *> conf = 0.36 ranks of expected_values: 5 EVAL 0284b56 genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 91.000 0.400 http://example.org/film/film/genre #18677-08mg_b PRED entity: 08mg_b PRED relation: music PRED expected values: 02jxmr => 70 concepts (33 used for prediction) PRED predicted values (max 10 best out of 84): 01x6v6 (0.33 #123, 0.09 #967, 0.02 #3504), 02bh9 (0.27 #895, 0.03 #1531, 0.03 #5761), 01cbt3 (0.20 #301, 0.06 #1359, 0.03 #2204), 0127m7 (0.18 #1056, 0.08 #1903, 0.06 #4444), 0739z6 (0.18 #1056, 0.08 #1903, 0.06 #4444), 0410cp (0.18 #1056, 0.08 #1903, 0.06 #4444), 047q2wc (0.18 #1056, 0.08 #1903, 0.06 #4444), 02fcs2 (0.18 #1056, 0.08 #1903, 0.06 #4444), 086k8 (0.18 #1056, 0.08 #1903, 0.06 #4444), 02jxkw (0.15 #1198, 0.14 #565, 0.02 #4373) >> Best rule #123 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 04k9y6; >> query: (?x6352, 01x6v6) <- nominated_for(?x350, ?x6352), film(?x2383, ?x6352), ?x2383 = 028d4v, costume_design_by(?x6352, ?x6327) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2187 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 191 *> proper extension: 0dnvn3; 03s6l2; 02sg5v; 02qrv7; 0g5pv3; 05cj_j; 018nnz; 01b195; 075cph; 0d1qmz; ... *> query: (?x6352, 02jxmr) <- genre(?x6352, ?x8280), nominated_for(?x6334, ?x6352), award_winner(?x6334, ?x382), titles(?x8280, ?x531) *> conf = 0.04 ranks of expected_values: 33 EVAL 08mg_b music 02jxmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 70.000 33.000 0.333 http://example.org/film/film/music #18676-013crh PRED entity: 013crh PRED relation: category PRED expected values: 08mbj5d => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #25, 0.78 #1, 0.78 #32) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 347 >> proper extension: 013jz2; 0tj4y; 07l5z; 0txhf; >> query: (?x12097, 08mbj5d) <- contains(?x94, ?x12097), ?x94 = 09c7w0, place(?x12097, ?x12097) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013crh category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.782 http://example.org/common/topic/webpage./common/webpage/category #18675-026l37 PRED entity: 026l37 PRED relation: people! PRED expected values: 0xnvg => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 23): 048z7l (0.17 #40, 0.03 #194, 0.02 #733), 022dp5 (0.17 #50), 02w7gg (0.14 #79, 0.06 #387, 0.06 #772), 07hwkr (0.14 #89, 0.03 #166, 0.03 #628), 041rx (0.13 #158, 0.11 #697, 0.11 #466), 0x67 (0.12 #164, 0.09 #626, 0.09 #87), 033tf_ (0.12 #161, 0.09 #84, 0.07 #546), 01qhm_ (0.09 #83, 0.04 #160, 0.02 #237), 06v41q (0.09 #106, 0.02 #183, 0.01 #799), 0xnvg (0.08 #167, 0.05 #244, 0.04 #552) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0kcdl; >> query: (?x4580, 048z7l) <- nominated_for(?x4580, ?x4581), nominated_for(?x4580, ?x2973), ?x4581 = 02ppg1r, film(?x539, ?x2973) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #167 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 219 *> proper extension: 01gw4f; 01c65z; *> query: (?x4580, 0xnvg) <- nominated_for(?x4580, ?x2973), currency(?x4580, ?x170), film(?x4580, ?x1965) *> conf = 0.08 ranks of expected_values: 10 EVAL 026l37 people! 0xnvg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 60.000 60.000 0.167 http://example.org/people/ethnicity/people #18674-047rkcm PRED entity: 047rkcm PRED relation: film_crew_role PRED expected values: 09zzb8 09vw2b7 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 26): 09zzb8 (0.80 #301, 0.78 #400, 0.78 #732), 09vw2b7 (0.75 #105, 0.72 #737, 0.71 #803), 0dxtw (0.46 #109, 0.43 #277, 0.42 #575), 0d2b38 (0.46 #123, 0.38 #57, 0.30 #90), 015h31 (0.43 #107, 0.25 #41, 0.20 #74), 01xy5l_ (0.43 #112, 0.17 #13, 0.12 #46), 089g0h (0.39 #117, 0.33 #18, 0.12 #51), 01pvkk (0.28 #410, 0.28 #1311, 0.28 #278), 02ynfr (0.25 #48, 0.22 #414, 0.21 #746), 02vs3x5 (0.20 #88, 0.12 #55, 0.06 #421) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 362 >> proper extension: 0b76d_m; 0ds35l9; 0m313; 02y_lrp; 028_yv; 09m6kg; 011yxg; 07gp9; 09xbpt; 047gn4y; ... >> query: (?x6762, 09zzb8) <- produced_by(?x6762, ?x9316), production_companies(?x6762, ?x1478), titles(?x2480, ?x6762), film_crew_role(?x6762, ?x281) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 047rkcm film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.799 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 047rkcm film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.799 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18673-02n4kr PRED entity: 02n4kr PRED relation: genre! PRED expected values: 03cf9ly => 63 concepts (44 used for prediction) PRED predicted values (max 10 best out of 474): 06r1k (0.67 #2212, 0.50 #2777, 0.33 #226), 0fkwzs (0.62 #2715, 0.50 #2150, 0.24 #7264), 025x1t (0.62 #2783, 0.50 #2218, 0.21 #7332), 0gvsh7l (0.60 #1858, 0.38 #2992, 0.33 #441), 0ctzf1 (0.57 #2404, 0.50 #2122, 0.38 #2687), 0gxr1c (0.57 #2532, 0.40 #1965, 0.33 #2250), 020qr4 (0.50 #2557, 0.50 #1992, 0.43 #2274), 09g_31 (0.50 #2719, 0.50 #2154, 0.29 #2436), 043qqt5 (0.50 #2787, 0.50 #2222, 0.17 #7336), 024rwx (0.50 #2658, 0.50 #2093, 0.17 #7207) >> Best rule #2212 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0pr6f; >> query: (?x600, 06r1k) <- genre(?x9514, ?x600), genre(?x9340, ?x600), genre(?x8846, ?x600), ?x9340 = 05nlzq, ?x8846 = 0170k0, nominated_for(?x4155, ?x9514), actor(?x9514, ?x2654), nominated_for(?x1111, ?x9514) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #245 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 07s9rl0; *> query: (?x600, 03cf9ly) <- titles(?x600, ?x394), genre(?x9901, ?x600), genre(?x3251, ?x600), genre(?x1625, ?x600), genre(?x1184, ?x600), genre(?x377, ?x600), film(?x166, ?x1184), ?x3251 = 0571m, ?x377 = 0dq626, ?x9901 = 0fh2v5, ?x1625 = 01f8gz *> conf = 0.33 ranks of expected_values: 66 EVAL 02n4kr genre! 03cf9ly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 63.000 44.000 0.667 http://example.org/tv/tv_program/genre #18672-0bq2g PRED entity: 0bq2g PRED relation: award_nominee PRED expected values: 039bp => 121 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1214): 03t0k1 (0.81 #7000, 0.81 #151593, 0.81 #100289), 0151w_ (0.81 #7000, 0.81 #151593, 0.81 #100289), 01r93l (0.81 #7000, 0.81 #151593, 0.81 #100289), 039bp (0.81 #7000, 0.81 #151593, 0.81 #100289), 01j5ts (0.81 #7000, 0.81 #151593, 0.81 #100289), 0p_pd (0.81 #7000, 0.81 #151593, 0.81 #100289), 030vnj (0.81 #7000, 0.81 #151593, 0.81 #100289), 0bq2g (0.32 #160923, 0.28 #107283, 0.26 #130605), 06q8hf (0.32 #160923, 0.28 #107283, 0.22 #95624), 05hj_k (0.32 #160923, 0.28 #107283, 0.22 #95624) >> Best rule #7000 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 01pq5j7; >> query: (?x3553, ?x241) <- award_winner(?x3553, ?x989), award_nominee(?x241, ?x3553), vacationer(?x3454, ?x3553) >> conf = 0.81 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 0bq2g award_nominee 039bp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 121.000 71.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18671-02_1q9 PRED entity: 02_1q9 PRED relation: award PRED expected values: 02py_sj => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 163): 0m7yy (0.47 #3148, 0.45 #2916, 0.45 #3845), 02p_7cr (0.46 #3483, 0.40 #4412, 0.39 #4877), 02p_04b (0.46 #3483, 0.40 #4412, 0.39 #4877), 02pzxlw (0.46 #3483, 0.40 #4412, 0.39 #4877), 02pzz3p (0.46 #3483, 0.40 #4412, 0.39 #4877), 0ck27z (0.33 #305, 0.20 #537, 0.11 #1465), 09qj50 (0.33 #37, 0.19 #734, 0.14 #1198), 027gs1_ (0.33 #178, 0.16 #875, 0.15 #3428), 0cqhmg (0.33 #211, 0.16 #908, 0.14 #1372), 0cqh6z (0.33 #288, 0.12 #752, 0.11 #1216) >> Best rule #3148 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 05h95s; 097h2; 019g8j; >> query: (?x416, 0m7yy) <- award(?x416, ?x2720), actor(?x416, ?x1918), people(?x1423, ?x1918) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1130 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 03y317; 047m_w; *> query: (?x416, 02py_sj) <- program(?x6678, ?x416), genre(?x416, ?x8805), ?x8805 = 06q7n *> conf = 0.22 ranks of expected_values: 17 EVAL 02_1q9 award 02py_sj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 71.000 71.000 0.473 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #18670-0d8lm PRED entity: 0d8lm PRED relation: performance_role PRED expected values: 085jw => 86 concepts (58 used for prediction) PRED predicted values (max 10 best out of 123): 02snj9 (0.86 #2568, 0.84 #2647, 0.83 #3290), 05r5c (0.83 #3061, 0.82 #2413, 0.82 #3528), 04rzd (0.62 #1014, 0.62 #933, 0.60 #487), 02sgy (0.60 #410, 0.60 #328, 0.50 #989), 0dwt5 (0.60 #466, 0.43 #801, 0.40 #1367), 03m5k (0.57 #484, 0.53 #1060, 0.50 #399), 018vs (0.57 #484, 0.53 #481, 0.52 #2990), 0395lw (0.57 #844, 0.40 #343, 0.38 #3547), 01qbl (0.57 #841, 0.40 #340, 0.37 #3294), 011k_j (0.57 #881, 0.40 #380, 0.36 #2652) >> Best rule #2568 for best value: >> intensional similarity = 16 >> extensional distance = 16 >> proper extension: 042v_gx; 03qjg; >> query: (?x10811, ?x3214) <- role(?x1466, ?x10811), role(?x315, ?x10811), instrumentalists(?x10811, ?x562), performance_role(?x10811, ?x212), performance_role(?x4343, ?x10811), ?x1466 = 03bx0bm, performance_role(?x3214, ?x10811), group(?x3214, ?x498), performance_role(?x3214, ?x1495), role(?x3214, ?x2888), role(?x3214, ?x885), ?x885 = 0dwtp, ?x2888 = 02fsn, role(?x3834, ?x1495), ?x3834 = 01wzlxj, ?x315 = 0l14md >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #370 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 3 *> proper extension: 026t6; *> query: (?x10811, 085jw) <- role(?x227, ?x10811), family(?x894, ?x10811), role(?x894, ?x2309), role(?x894, ?x1969), role(?x894, ?x1212), performance_role(?x10811, ?x212), role(?x4913, ?x894), role(?x6838, ?x894), ?x2309 = 06ncr, instrumentalists(?x894, ?x3890), role(?x366, ?x1969), ?x4913 = 03ndd, ?x3890 = 01gg59, role(?x2865, ?x1969), ?x1212 = 07xzm *> conf = 0.40 ranks of expected_values: 41 EVAL 0d8lm performance_role 085jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 86.000 58.000 0.857 http://example.org/music/performance_role/guest_performances./music/recording_contribution/performance_role #18669-03mh_tp PRED entity: 03mh_tp PRED relation: music PRED expected values: 01m5m5b => 85 concepts (70 used for prediction) PRED predicted values (max 10 best out of 89): 02cyfz (0.33 #34, 0.08 #244, 0.07 #878), 03h610 (0.15 #709, 0.08 #3668, 0.08 #498), 0csdzz (0.15 #608, 0.08 #1667, 0.07 #2088), 0146pg (0.15 #4026, 0.14 #4237, 0.14 #3601), 02bh9 (0.09 #1320, 0.06 #3642, 0.06 #4067), 01tc9r (0.08 #697, 0.08 #486, 0.04 #1334), 01m5m5b (0.08 #820, 0.08 #609, 0.04 #1457), 04pf4r (0.08 #278, 0.07 #912, 0.06 #1124), 06fxnf (0.08 #279, 0.07 #913, 0.06 #1125), 04bpm6 (0.08 #236, 0.07 #870, 0.06 #1082) >> Best rule #34 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 08sk8l; >> query: (?x3084, 02cyfz) <- film(?x4371, ?x3084), film(?x4039, ?x3084), production_companies(?x3084, ?x788), ?x4371 = 05txrz, edited_by(?x3084, ?x707), nominated_for(?x4039, ?x2052) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #820 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 0cq7tx; *> query: (?x3084, 01m5m5b) <- produced_by(?x3084, ?x3568), film_festivals(?x3084, ?x9189), executive_produced_by(?x3084, ?x4536), award_winner(?x198, ?x3568), production_companies(?x3084, ?x788) *> conf = 0.08 ranks of expected_values: 7 EVAL 03mh_tp music 01m5m5b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 70.000 0.333 http://example.org/film/film/music #18668-02cbg0 PRED entity: 02cbg0 PRED relation: nominated_for! PRED expected values: 0f4x7 0l8z1 09qv_s => 78 concepts (69 used for prediction) PRED predicted values (max 10 best out of 166): 0gq9h (0.52 #63, 0.25 #2414, 0.24 #6645), 0gs9p (0.49 #65, 0.23 #2416, 0.22 #6647), 019f4v (0.46 #55, 0.22 #2171, 0.21 #2406), 0gr4k (0.40 #27, 0.17 #2378, 0.16 #6609), 0f4x7 (0.37 #26, 0.21 #6818, 0.21 #7760), 0gqy2 (0.36 #121, 0.21 #6818, 0.21 #7760), 04dn09n (0.35 #36, 0.18 #2387, 0.18 #2152), 040njc (0.35 #7, 0.17 #5883, 0.16 #5178), 02ppm4q (0.35 #115, 0.13 #1290, 0.09 #5286), 0k611 (0.35 #73, 0.23 #2424, 0.21 #2894) >> Best rule #63 for best value: >> intensional similarity = 5 >> extensional distance = 160 >> proper extension: 02wk7b; >> query: (?x8436, 0gq9h) <- genre(?x8436, ?x1509), nominated_for(?x3499, ?x8436), nominated_for(?x1972, ?x8436), ?x1972 = 0gqyl, award(?x241, ?x3499) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 160 *> proper extension: 02wk7b; *> query: (?x8436, 0f4x7) <- genre(?x8436, ?x1509), nominated_for(?x3499, ?x8436), nominated_for(?x1972, ?x8436), ?x1972 = 0gqyl, award(?x241, ?x3499) *> conf = 0.37 ranks of expected_values: 5, 19, 31 EVAL 02cbg0 nominated_for! 09qv_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 78.000 69.000 0.525 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02cbg0 nominated_for! 0l8z1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 78.000 69.000 0.525 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02cbg0 nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 69.000 0.525 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18667-080r3 PRED entity: 080r3 PRED relation: influenced_by PRED expected values: 01tz6vs => 163 concepts (54 used for prediction) PRED predicted values (max 10 best out of 388): 03f0324 (0.36 #1451, 0.35 #1886, 0.30 #583), 03f47xl (0.31 #1068, 0.20 #633, 0.15 #1936), 0j3v (0.26 #4830, 0.20 #493, 0.19 #2660), 03sbs (0.26 #8021, 0.22 #8890, 0.21 #1519), 0379s (0.25 #1814, 0.24 #4848, 0.23 #2678), 040db (0.25 #1791, 0.23 #923, 0.20 #488), 02kz_ (0.25 #1904, 0.20 #601, 0.16 #4938), 03jxw (0.25 #2072, 0.20 #769, 0.15 #1204), 01tz6vs (0.24 #4944, 0.20 #1910, 0.15 #2774), 01v9724 (0.23 #2775, 0.21 #4945, 0.20 #1911) >> Best rule #1451 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 014dq7; 04135; 07h1q; 01h2_6; >> query: (?x5262, 03f0324) <- influenced_by(?x2485, ?x5262), influenced_by(?x5262, ?x5612), ?x5612 = 058vp, type_of_union(?x2485, ?x566) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #4944 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 02m4t; *> query: (?x5262, 01tz6vs) <- influenced_by(?x5262, ?x5612), influenced_by(?x5262, ?x3336), ?x3336 = 032l1, influenced_by(?x5612, ?x2080) *> conf = 0.24 ranks of expected_values: 9 EVAL 080r3 influenced_by 01tz6vs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 163.000 54.000 0.357 http://example.org/influence/influence_node/influenced_by #18666-0gs1_ PRED entity: 0gs1_ PRED relation: location PRED expected values: 030qb3t => 150 concepts (148 used for prediction) PRED predicted values (max 10 best out of 222): 06y57 (0.33 #251, 0.17 #1847, 0.02 #24196), 0h6l4 (0.33 #1441, 0.17 #2239, 0.01 #9423), 030qb3t (0.27 #24823, 0.27 #20832, 0.25 #43979), 04jpl (0.22 #11987, 0.18 #19970, 0.08 #20768), 0hyxv (0.17 #1802, 0.03 #15370, 0.02 #16168), 0r00l (0.11 #2996, 0.03 #3794, 0.03 #4592), 0xmp9 (0.11 #3075), 0cc56 (0.09 #29587, 0.09 #4044, 0.05 #8833), 0rh6k (0.09 #8784, 0.07 #7982, 0.07 #15168), 0cr3d (0.08 #8920, 0.07 #20893, 0.07 #75173) >> Best rule #251 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 02r6c_; >> query: (?x6558, 06y57) <- student(?x7017, ?x6558), ?x7017 = 05qdh, award_winner(?x289, ?x6558) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #24823 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 190 *> proper extension: 01wkmgb; *> query: (?x6558, 030qb3t) <- languages(?x6558, ?x254), participant(?x1126, ?x6558) *> conf = 0.27 ranks of expected_values: 3 EVAL 0gs1_ location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 150.000 148.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #18665-01ycbq PRED entity: 01ycbq PRED relation: award_nominee! PRED expected values: 01chc7 => 110 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1046): 06dv3 (0.81 #58283, 0.15 #88592, 0.03 #23355), 0fgg4 (0.81 #58283, 0.15 #88592), 017149 (0.81 #58283, 0.05 #93255, 0.03 #4764), 01chc7 (0.81 #58283, 0.03 #86260, 0.02 #12396), 03jqw5 (0.81 #58283), 03yj_0n (0.44 #5479, 0.04 #12472, 0.03 #24128), 02tr7d (0.41 #5011, 0.06 #12004, 0.04 #23660), 01wbg84 (0.41 #4721, 0.05 #11714, 0.05 #93255), 08w7vj (0.41 #4837, 0.04 #11830, 0.02 #23486), 0bx0lc (0.41 #6034, 0.03 #13027, 0.02 #24683) >> Best rule #58283 for best value: >> intensional similarity = 3 >> extensional distance = 1093 >> proper extension: 07s6tbm; 08wr3kg; 03bx_5q; 0b478; 03ys2f; 03ysmg; 01mkn_d; 0bc71w; 09dv0sz; 01hrqc; ... >> query: (?x2033, ?x262) <- gender(?x2033, ?x231), award_nominee(?x2033, ?x262), award_winner(?x1910, ?x2033) >> conf = 0.81 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 01ycbq award_nominee! 01chc7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 40.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18664-01ypc PRED entity: 01ypc PRED relation: teams! PRED expected values: 01snm => 95 concepts (74 used for prediction) PRED predicted values (max 10 best out of 140): 06wxw (0.33 #661, 0.25 #2283, 0.14 #3093), 0fpzwf (0.33 #948, 0.10 #4190, 0.09 #4461), 0dc95 (0.33 #78, 0.10 #4130, 0.09 #4401), 01_d4 (0.25 #1952, 0.25 #1140, 0.20 #2762), 0dclg (0.25 #2234, 0.25 #1424, 0.10 #4124), 0nqph (0.25 #2151, 0.20 #2961, 0.11 #7565), 0d35y (0.25 #2554, 0.14 #3094, 0.12 #3634), 02cl1 (0.25 #2452, 0.14 #2992, 0.12 #3532), 01cx_ (0.25 #1446, 0.10 #4146, 0.07 #5501), 094jv (0.20 #2758, 0.06 #6822, 0.06 #6549) >> Best rule #661 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 06x68; >> query: (?x260, 06wxw) <- team(?x11844, ?x260), school(?x260, ?x466), colors(?x260, ?x663), team(?x7724, ?x260), team(?x5727, ?x260), team(?x4244, ?x260), draft(?x260, ?x8786), ?x4244 = 028c_8, ?x8786 = 02pq_x5, season(?x260, ?x8923), season(?x260, ?x3431), ?x5727 = 02wszf, ?x8923 = 03c74_8, ?x3431 = 025ygqm, position(?x580, ?x7724) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5283 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 10 *> proper extension: 0182r9; 01rl_3; 01kj5h; 0cj_v7; *> query: (?x260, 01snm) <- team(?x11844, ?x260), team(?x261, ?x260), team(?x9180, ?x260), gender(?x9180, ?x231), location(?x9180, ?x3670), athlete(?x1083, ?x9180), contains(?x3670, ?x331), district_represented(?x176, ?x3670) *> conf = 0.08 ranks of expected_values: 27 EVAL 01ypc teams! 01snm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 95.000 74.000 0.333 http://example.org/sports/sports_team_location/teams #18663-0kzy0 PRED entity: 0kzy0 PRED relation: type_of_union PRED expected values: 04ztj => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.70 #177, 0.70 #221, 0.70 #109), 01g63y (0.37 #406, 0.33 #389, 0.22 #10), 01bl8s (0.05 #19, 0.01 #35, 0.01 #39) >> Best rule #177 for best value: >> intensional similarity = 3 >> extensional distance = 370 >> proper extension: 03h40_7; >> query: (?x654, 04ztj) <- languages(?x654, ?x254), gender(?x654, ?x231), ?x231 = 05zppz >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kzy0 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.704 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18662-01crd5 PRED entity: 01crd5 PRED relation: featured_film_locations! PRED expected values: 05_61y => 137 concepts (55 used for prediction) PRED predicted values (max 10 best out of 471): 02vz6dn (0.10 #2013, 0.04 #2751, 0.04 #4962), 03k8th (0.08 #5865, 0.06 #15449, 0.06 #8813), 061681 (0.07 #16266, 0.06 #22901, 0.05 #1521), 03q8xj (0.05 #1996, 0.04 #2734, 0.04 #3471), 02x0fs9 (0.05 #2159, 0.04 #2897, 0.04 #5108), 048yqf (0.05 #2138, 0.04 #2876, 0.04 #5087), 0cp0t91 (0.05 #2082, 0.04 #2820, 0.04 #5031), 0bw20 (0.05 #1999, 0.04 #2737, 0.04 #4948), 05b6rdt (0.05 #1940, 0.04 #2678, 0.04 #4889), 047tsx3 (0.05 #1751, 0.04 #2489, 0.04 #4700) >> Best rule #2013 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 02_286; 07dfk; >> query: (?x8593, 02vz6dn) <- administrative_parent(?x10757, ?x8593), film_release_region(?x124, ?x8593), locations(?x7455, ?x8593) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #16724 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 04wsz; *> query: (?x8593, 05_61y) <- locations(?x7455, ?x8593), contains(?x8593, ?x10757), taxonomy(?x8593, ?x939) *> conf = 0.02 ranks of expected_values: 311 EVAL 01crd5 featured_film_locations! 05_61y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 137.000 55.000 0.095 http://example.org/film/film/featured_film_locations #18661-092_25 PRED entity: 092_25 PRED relation: ceremony! PRED expected values: 0cqhb3 => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 258): 0cqhb3 (0.80 #1453, 0.29 #953, 0.25 #4257), 018wng (0.63 #1776, 0.53 #2027, 0.44 #2277), 0gqz2 (0.63 #1803, 0.47 #2054, 0.44 #2304), 0k611 (0.61 #1812, 0.53 #2063, 0.45 #2313), 0gqy2 (0.61 #1861, 0.52 #2112, 0.48 #2500), 0gq_d (0.61 #1897, 0.52 #2148, 0.45 #2398), 0gq9h (0.61 #1801, 0.51 #2052, 0.44 #2302), 0gs96 (0.61 #1830, 0.51 #2081, 0.43 #2331), 0gvx_ (0.60 #1876, 0.52 #2127, 0.44 #2377), 0gr4k (0.60 #1771, 0.49 #2022, 0.42 #2272) >> Best rule #1453 for best value: >> intensional similarity = 14 >> extensional distance = 8 >> proper extension: 0hr3c8y; 09qvms; 092c5f; 092t4b; 027hjff; 03gyp30; 09g90vz; 0g55tzk; >> query: (?x5459, 0cqhb3) <- award_winner(?x5459, ?x8596), award_winner(?x5459, ?x2028), award_winner(?x5459, ?x1384), ceremony(?x2257, ?x5459), type_of_union(?x2028, ?x566), award_nominee(?x1384, ?x72), film(?x2028, ?x2052), award_winner(?x451, ?x1384), film(?x1384, ?x394), ?x2257 = 09td7p, award(?x2028, ?x749), nationality(?x1384, ?x94), nominated_for(?x8596, ?x3169), award_nominee(?x2661, ?x8596) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 092_25 ceremony! 0cqhb3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony #18660-042fk PRED entity: 042fk PRED relation: gender PRED expected values: 05zppz => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.93 #129, 0.91 #109, 0.88 #117), 02zsn (0.45 #397, 0.35 #160, 0.35 #212) >> Best rule #129 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 01c58j; 0d4jl; 0bzyh; 0hgqq; 016gkf; 0ct9_; >> query: (?x13098, 05zppz) <- company(?x13098, ?x94), profession(?x13098, ?x3342), people(?x12624, ?x13098) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042fk gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 200.000 200.000 0.927 http://example.org/people/person/gender #18659-015n8 PRED entity: 015n8 PRED relation: influenced_by PRED expected values: 0m93 => 177 concepts (71 used for prediction) PRED predicted values (max 10 best out of 293): 03sbs (0.57 #3700, 0.47 #9777, 0.47 #5873), 081k8 (0.57 #3635, 0.28 #6242, 0.16 #15785), 042q3 (0.43 #3843, 0.28 #6450, 0.20 #10788), 0j3v (0.43 #3540, 0.22 #6147, 0.21 #9184), 02lt8 (0.43 #3599, 0.11 #6206, 0.10 #27927), 043s3 (0.40 #5768, 0.26 #10540, 0.24 #9239), 015n8 (0.38 #5191, 0.33 #6495, 0.33 #3017), 0m93 (0.33 #2399, 0.25 #5008, 0.08 #26936), 02wh0 (0.31 #9505, 0.30 #9938, 0.26 #10806), 039n1 (0.29 #3804, 0.28 #9448, 0.27 #9881) >> Best rule #3700 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 07dnx; >> query: (?x12259, 03sbs) <- influenced_by(?x9308, ?x12259), influenced_by(?x4309, ?x12259), ?x9308 = 03jht, award_winner(?x921, ?x4309), people(?x13231, ?x4309) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2399 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0gz_; *> query: (?x12259, 0m93) <- influenced_by(?x7250, ?x12259), interests(?x12259, ?x713), influenced_by(?x12259, ?x3712), ?x7250 = 03sbs *> conf = 0.33 ranks of expected_values: 8 EVAL 015n8 influenced_by 0m93 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 177.000 71.000 0.571 http://example.org/influence/influence_node/influenced_by #18658-0hh2s PRED entity: 0hh2s PRED relation: parent_genre! PRED expected values: 01y3_q => 78 concepts (37 used for prediction) PRED predicted values (max 10 best out of 281): 01ym9b (0.40 #830, 0.33 #1097, 0.33 #40), 0kz10 (0.40 #1024, 0.33 #1291, 0.33 #234), 01y3_q (0.40 #875, 0.33 #1142, 0.29 #1674), 0y3_8 (0.40 #568, 0.25 #305, 0.23 #5372), 0g_bh (0.33 #107, 0.25 #371, 0.20 #3839), 01hydr (0.33 #240, 0.25 #504, 0.20 #1030), 01vw77 (0.33 #237, 0.25 #501, 0.20 #1027), 0lc1r (0.33 #206, 0.25 #470, 0.20 #996), 0mmp3 (0.25 #347, 0.20 #873, 0.20 #610), 07lnk (0.25 #290, 0.20 #553, 0.14 #5357) >> Best rule #830 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 0283d; 01z9l_; >> query: (?x9012, 01ym9b) <- parent_genre(?x9012, ?x12818), parent_genre(?x9012, ?x3915), ?x3915 = 07gxw, artists(?x9012, ?x8332), artists(?x9012, ?x8199), parent_genre(?x7280, ?x9012), ?x8199 = 016lmg, award_winner(?x528, ?x8332), artists(?x12818, ?x677) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #875 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 0283d; 01z9l_; *> query: (?x9012, 01y3_q) <- parent_genre(?x9012, ?x12818), parent_genre(?x9012, ?x3915), ?x3915 = 07gxw, artists(?x9012, ?x8332), artists(?x9012, ?x8199), parent_genre(?x7280, ?x9012), ?x8199 = 016lmg, award_winner(?x528, ?x8332), artists(?x12818, ?x677) *> conf = 0.40 ranks of expected_values: 3 EVAL 0hh2s parent_genre! 01y3_q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 37.000 0.400 http://example.org/music/genre/parent_genre #18657-04yc76 PRED entity: 04yc76 PRED relation: currency PRED expected values: 09nqf => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.80 #127, 0.79 #36, 0.79 #43), 01nv4h (0.04 #191, 0.03 #100, 0.03 #107), 02gsvk (0.03 #76, 0.02 #174, 0.02 #55), 02l6h (0.02 #95, 0.01 #39, 0.01 #81) >> Best rule #127 for best value: >> intensional similarity = 4 >> extensional distance = 295 >> proper extension: 04dsnp; 02phtzk; 0hv81; >> query: (?x2754, 09nqf) <- film_crew_role(?x2754, ?x1284), featured_film_locations(?x2754, ?x1767), nominated_for(?x401, ?x2754), ?x1284 = 0ch6mp2 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04yc76 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.805 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #18656-0mwxz PRED entity: 0mwxz PRED relation: currency PRED expected values: 09nqf => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.88 #13, 0.88 #12, 0.86 #15) >> Best rule #13 for best value: >> intensional similarity = 6 >> extensional distance = 229 >> proper extension: 0mkdm; >> query: (?x7500, ?x170) <- adjoins(?x7500, ?x9090), adjoins(?x7500, ?x2744), contains(?x3670, ?x7500), second_level_divisions(?x94, ?x7500), currency(?x9090, ?x170), source(?x2744, ?x958) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwxz currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.879 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #18655-01vv6_6 PRED entity: 01vv6_6 PRED relation: artists! PRED expected values: 0155w => 159 concepts (68 used for prediction) PRED predicted values (max 10 best out of 271): 05w3f (0.80 #4386, 0.25 #5008, 0.24 #2523), 06by7 (0.66 #19918, 0.57 #7143, 0.57 #6853), 064t9 (0.59 #5604, 0.51 #2810, 0.50 #1257), 016clz (0.50 #5, 0.34 #6215, 0.33 #4042), 09jw2 (0.44 #475, 0.14 #6064, 0.12 #786), 03_d0 (0.44 #4982, 0.33 #19597, 0.21 #15550), 05bt6j (0.40 #976, 0.34 #3150, 0.32 #2529), 0dl5d (0.39 #4990, 0.26 #5920, 0.24 #4368), 06j6l (0.37 #19945, 0.35 #2845, 0.32 #3155), 02t8gf (0.33 #454, 0.33 #143, 0.18 #765) >> Best rule #4386 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 06br6t; >> query: (?x3472, 05w3f) <- role(?x3472, ?x227), artists(?x11040, ?x3472), artists(?x11040, ?x3569), ?x3569 = 011hdn >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1039 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 02fybl; *> query: (?x3472, 0155w) <- profession(?x3472, ?x319), role(?x3472, ?x645), ?x319 = 01d_h8, origin(?x3472, ?x1523) *> conf = 0.25 ranks of expected_values: 18 EVAL 01vv6_6 artists! 0155w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 159.000 68.000 0.796 http://example.org/music/genre/artists #18654-0chsq PRED entity: 0chsq PRED relation: politician! PRED expected values: 07wbk => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 6): 0d075m (0.14 #27, 0.06 #219, 0.04 #147), 0_00 (0.03 #84, 0.02 #108, 0.02 #156), 07wbk (0.03 #217, 0.03 #361, 0.02 #625), 024qk1 (0.02 #189), 0135dr (0.02 #186), 07wf9 (0.01 #366) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 03h_fk5; 016z1t; 06c0j; >> query: (?x510, 0d075m) <- award_winner(?x102, ?x510), notable_people_with_this_condition(?x5801, ?x510), celebrities_impersonated(?x3649, ?x510) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #217 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 124 *> proper extension: 0d9kl; 057ph; 0dng4; *> query: (?x510, 07wbk) <- celebrities_impersonated(?x6707, ?x510), profession(?x6707, ?x987) *> conf = 0.03 ranks of expected_values: 3 EVAL 0chsq politician! 07wbk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 129.000 0.143 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #18653-02lpp7 PRED entity: 02lpp7 PRED relation: medal! PRED expected values: 01f1jy 0c_tl 018ljb 0jkvj 01f1kd => 3 concepts (3 used for prediction) PRED predicted values (max 10 best out of 5): 01f1kd (0.86 #13, 0.85 #12, 0.82 #6), 0jkvj (0.86 #13, 0.85 #12, 0.82 #6), 018ljb (0.86 #13, 0.85 #12, 0.82 #6), 01f1jy (0.86 #13, 0.82 #6, 0.33 #7), 0c_tl (0.82 #6, 0.33 #8, 0.33 #2) >> Best rule #13 for best value: >> intensional similarity = 403 >> extensional distance = 1 >> proper extension: 02lq67; >> query: (?x2132, ?x1617) <- medal(?x9730, ?x2132), medal(?x9051, ?x2132), medal(?x8588, ?x2132), medal(?x7430, ?x2132), medal(?x7287, ?x2132), medal(?x6974, ?x2132), medal(?x6305, ?x2132), medal(?x5114, ?x2132), medal(?x4521, ?x2132), medal(?x3749, ?x2132), medal(?x3730, ?x2132), medal(?x3728, ?x2132), medal(?x3432, ?x2132), medal(?x2629, ?x2132), medal(?x2152, ?x2132), medal(?x1917, ?x2132), medal(?x1892, ?x2132), medal(?x1790, ?x2132), medal(?x1603, ?x2132), medal(?x1577, ?x2132), medal(?x1558, ?x2132), medal(?x1497, ?x2132), medal(?x1475, ?x2132), medal(?x1355, ?x2132), medal(?x1353, ?x2132), medal(?x1229, ?x2132), medal(?x1023, ?x2132), medal(?x910, ?x2132), medal(?x792, ?x2132), medal(?x583, ?x2132), medal(?x429, ?x2132), medal(?x390, ?x2132), medal(?x344, ?x2132), medal(?x291, ?x2132), medal(?x252, ?x2132), medal(?x142, ?x2132), medal(?x94, ?x2132), medal(?x47, ?x2132), ?x1353 = 035qy, ?x1558 = 01mjq, ?x910 = 019rg5, ?x6305 = 07t_x, ?x252 = 03_3d, combatants(?x3141, ?x7430), combatants(?x279, ?x7430), combatants(?x151, ?x7430), film_release_region(?x3471, ?x7430), film_release_region(?x1386, ?x7430), olympics(?x1577, ?x3971), ?x6974 = 01nln, ?x3471 = 07cyl, country(?x4045, ?x8588), country(?x3309, ?x8588), country(?x2867, ?x8588), country(?x2044, ?x8588), country(?x668, ?x8588), medal(?x7429, ?x2132), medal(?x3729, ?x2132), medal(?x2966, ?x2132), medal(?x2630, ?x2132), medal(?x2553, ?x2132), medal(?x2233, ?x2132), medal(?x2131, ?x2132), medal(?x1931, ?x2132), medal(?x1608, ?x2132), medal(?x1277, ?x2132), medal(?x584, ?x2132), medal(?x418, ?x2132), religion(?x8588, ?x492), medal(?x9730, ?x1242), film_release_region(?x9194, ?x9730), film_release_region(?x7126, ?x9730), film_release_region(?x2512, ?x9730), film_release_region(?x2340, ?x9730), film_release_region(?x280, ?x9730), ?x2152 = 06mkj, ?x1277 = 0swbd, contains(?x7430, ?x1458), ?x3730 = 03shp, ?x418 = 09n48, ?x2966 = 06sks6, ?x151 = 0b90_r, ?x1475 = 05qx1, organization(?x9730, ?x312), ?x1023 = 0ctw_b, ?x1608 = 09x3r, ?x2512 = 07x4qr, countries_within(?x8483, ?x9730), ?x2131 = 0lk8j, combatants(?x4815, ?x3141), month(?x1458, ?x9905), month(?x1458, ?x7298), ?x3728 = 087vz, ?x2629 = 06f32, adjoins(?x7430, ?x2517), ?x5114 = 05vz3zq, form_of_government(?x7430, ?x48), locations(?x7241, ?x8588), organization(?x1577, ?x9102), organization(?x1577, ?x4753), organization(?x1577, ?x127), ?x1931 = 0kbws, vacationer(?x9730, ?x4247), vacationer(?x9730, ?x3397), contains(?x7273, ?x9730), ?x429 = 03rt9, nationality(?x1825, ?x9730), place_of_birth(?x4915, ?x1458), taxonomy(?x9730, ?x939), currency(?x1577, ?x170), ?x1603 = 06bnz, ?x7298 = 04wzr, ?x1355 = 0h7x, ?x1917 = 01p1v, film_release_region(?x9961, ?x1790), film_release_region(?x9002, ?x1790), film_release_region(?x7651, ?x1790), film_release_region(?x6882, ?x1790), film_release_region(?x6516, ?x1790), film_release_region(?x6480, ?x1790), film_release_region(?x5827, ?x1790), film_release_region(?x5791, ?x1790), film_release_region(?x5564, ?x1790), film_release_region(?x5271, ?x1790), film_release_region(?x5162, ?x1790), film_release_region(?x4998, ?x1790), film_release_region(?x4684, ?x1790), film_release_region(?x4047, ?x1790), film_release_region(?x3757, ?x1790), film_release_region(?x3453, ?x1790), film_release_region(?x3292, ?x1790), film_release_region(?x3217, ?x1790), film_release_region(?x2655, ?x1790), film_release_region(?x2471, ?x1790), film_release_region(?x2189, ?x1790), film_release_region(?x1803, ?x1790), film_release_region(?x1707, ?x1790), film_release_region(?x1642, ?x1790), film_release_region(?x1602, ?x1790), film_release_region(?x1463, ?x1790), film_release_region(?x1392, ?x1790), film_release_region(?x1364, ?x1790), film_release_region(?x1035, ?x1790), film_release_region(?x791, ?x1790), film_release_region(?x504, ?x1790), ?x1242 = 02lq5w, jurisdiction_of_office(?x265, ?x1577), contains(?x1790, ?x1791), administrative_parent(?x9730, ?x551), ?x4047 = 07s846j, ?x3292 = 0gvs1kt, film_release_region(?x3377, ?x8588), sports(?x2233, ?x5182), sports(?x2233, ?x1557), sports(?x2233, ?x471), ?x668 = 07gyv, combatants(?x326, ?x7430), ?x2630 = 0swff, ?x1602 = 0gxtknx, capital(?x291, ?x292), ?x7651 = 0h95927, award_winner(?x262, ?x4247), profession(?x4247, ?x955), award_nominee(?x4247, ?x123), adjoins(?x4120, ?x291), ?x6480 = 02825cv, ?x955 = 0n1h, ?x4521 = 07fj_, ?x3217 = 0gffmn8, contains(?x8588, ?x11419), ?x265 = 0dq3c, adjoins(?x1577, ?x2804), ?x5564 = 03yvf2, ?x5182 = 0crlz, student(?x6501, ?x4247), combatants(?x4373, ?x1790), film_release_region(?x11839, ?x1497), film_release_region(?x10475, ?x1497), film_release_region(?x10029, ?x1497), film_release_region(?x6095, ?x1497), film_release_region(?x5270, ?x1497), film_release_region(?x5092, ?x1497), film_release_region(?x4040, ?x1497), film_release_region(?x3748, ?x1497), film_release_region(?x3498, ?x1497), film_release_region(?x3482, ?x1497), film_release_region(?x3088, ?x1497), film_release_region(?x3081, ?x1497), film_release_region(?x3000, ?x1497), film_release_region(?x2709, ?x1497), film_release_region(?x2644, ?x1497), film_release_region(?x2501, ?x1497), film_release_region(?x1919, ?x1497), film_release_region(?x1370, ?x1497), film_release_region(?x1173, ?x1497), film_release_region(?x1108, ?x1497), film_release_region(?x1080, ?x1497), film_release_region(?x622, ?x1497), film_release_region(?x607, ?x1497), state_province_region(?x5281, ?x1458), ?x10029 = 02vzpb, ?x1386 = 0dtfn, adjoins(?x2804, ?x7871), ?x3748 = 05zlld0, ?x5827 = 0ggbfwf, ?x2655 = 0fpmrm3, organization(?x1497, ?x1062), ?x3749 = 03ryn, ?x9905 = 028kb, ?x7126 = 0ds1glg, ?x1919 = 0_7w6, award_nominee(?x2443, ?x4247), olympics(?x7430, ?x1617), ?x3000 = 045j3w, official_language(?x1790, ?x5814), country(?x3127, ?x9730), profession(?x3397, ?x131), ?x131 = 0dz3r, award(?x3397, ?x4958), contains(?x1497, ?x11012), people(?x4195, ?x4247), ?x1803 = 0g9wdmc, participant(?x3397, ?x556), ?x7429 = 0124ld, category(?x1458, ?x134), award(?x4247, ?x704), ?x1108 = 0jjy0, adjoins(?x3227, ?x1497), ?x1642 = 0bq8tmw, location(?x2162, ?x291), olympics(?x7430, ?x7775), ?x5092 = 0gg5qcw, locations(?x11216, ?x4120), ?x583 = 015fr, ?x326 = 081pw, ?x7871 = 01nyl, award_winner(?x2245, ?x4247), adjoins(?x1790, ?x3277), role(?x3397, ?x316), ?x3377 = 0gj8nq2, ?x7287 = 05b7q, ?x4958 = 03qbnj, ?x1463 = 0gtvrv3, ?x11839 = 072hx4, ?x3127 = 03hr1p, ?x3432 = 088q4, ?x504 = 0g5qs2k, adjoins(?x2517, ?x5482), ?x5791 = 03mgx6z, country(?x2911, ?x142), ?x9102 = 041288, artist(?x5666, ?x3397), participant(?x1896, ?x3397), combatants(?x4908, ?x142), olympics(?x6733, ?x2233), partially_contains(?x142, ?x12972), country(?x2631, ?x1790), ?x1392 = 017gm7, film_release_region(?x9832, ?x142), film_release_region(?x9565, ?x142), film_release_region(?x9349, ?x142), film_release_region(?x9216, ?x142), film_release_region(?x8770, ?x142), film_release_region(?x8137, ?x142), film_release_region(?x7897, ?x142), film_release_region(?x7628, ?x142), film_release_region(?x7393, ?x142), film_release_region(?x7379, ?x142), film_release_region(?x6536, ?x142), film_release_region(?x6394, ?x142), film_release_region(?x5849, ?x142), film_release_region(?x5721, ?x142), film_release_region(?x5706, ?x142), film_release_region(?x5576, ?x142), film_release_region(?x3619, ?x142), film_release_region(?x3392, ?x142), film_release_region(?x2746, ?x142), film_release_region(?x1916, ?x142), film_release_region(?x1859, ?x142), film_release_region(?x1118, ?x142), film_release_region(?x1071, ?x142), film_release_region(?x430, ?x142), ?x2709 = 06ztvyx, ?x2501 = 040rmy, contains(?x2467, ?x291), ?x4040 = 02mt51, ?x9961 = 0bx_hnp, award_nominee(?x2083, ?x3397), ?x2471 = 08052t3, adjoins(?x9485, ?x3227), mode_of_transportation(?x1458, ?x4272), ?x6095 = 0bq6ntw, ?x430 = 0m2kd, artists(?x671, ?x3397), contains(?x455, ?x1497), ?x3309 = 09w1n, ?x279 = 0d060g, ?x3453 = 0dgpwnk, ?x9349 = 0jdr0, adjoins(?x9730, ?x7709), film_crew_role(?x3498, ?x137), participant(?x4247, ?x1017), ?x1370 = 0gmcwlb, ?x2189 = 02yvct, featured_film_locations(?x10475, ?x3634), ?x1859 = 0m491, ?x5162 = 0j3d9tn, ?x9216 = 08j7lh, ?x792 = 0hzlz, award_winner(?x2186, ?x3397), film(?x72, ?x5270), ?x1892 = 02vzc, ?x280 = 03g90h, ?x4684 = 03nm_fh, ?x1173 = 0872p_c, ?x4753 = 0gkjy, ?x4272 = 07jdr, combatants(?x6465, ?x1497), film(?x3186, ?x3498), ?x1364 = 047msdk, film(?x617, ?x10475), ?x6394 = 0cmdwwg, genre(?x3498, ?x53), ?x3729 = 0jdk_, ?x2553 = 016r9z, countries_spoken_in(?x5003, ?x1577), ?x2746 = 04f52jw, ?x7628 = 0bcp9b, ?x1557 = 07bs0, ?x584 = 0l98s, featured_film_locations(?x3498, ?x1860), participant(?x3397, ?x2108), ?x6516 = 04cppj, ?x7241 = 06k75, ?x7273 = 07c5l, nationality(?x1940, ?x142), ?x4998 = 0dzlbx, ?x7897 = 03np63f, ?x622 = 0fq27fp, country(?x1037, ?x1497), film(?x9388, ?x3088), ?x9051 = 06nnj, story_by(?x5270, ?x1233), film(?x692, ?x10475), ?x5721 = 01d259, ?x1916 = 0ch26b_, ?x791 = 087wc7n, ?x9832 = 01xlqd, ?x7393 = 02vz6dn, countries_spoken_in(?x90, ?x3277), ?x1118 = 0_92w, film_release_region(?x2644, ?x11889), ?x2631 = 01z27, ?x127 = 02vk52z, ?x2044 = 06f41, teams(?x142, ?x7667), ?x3619 = 0fphgb, ?x3757 = 02vr3gz, film(?x380, ?x2644), ?x5271 = 047vnkj, contains(?x2804, ?x14187), ?x9194 = 0fpgp26, ?x5706 = 0284b56, language(?x3498, ?x254), olympics(?x2236, ?x2233), religion(?x2517, ?x962), ?x3482 = 017z49, ?x2867 = 02y8z, ?x6536 = 09gmmt6, film(?x3175, ?x6882), film_release_distribution_medium(?x8770, ?x81), featured_film_locations(?x2644, ?x739), ?x4045 = 06z6r, ?x94 = 09c7w0, production_companies(?x5270, ?x902), ?x9388 = 0309lm, ?x3186 = 055c8, ?x344 = 04gzd, ?x3392 = 0jwmp, ?x47 = 027rn, ?x471 = 02vx4, ?x1071 = 02d44q, ?x53 = 07s9rl0, ?x1707 = 04n52p6, ?x8137 = 0gtx63s, ?x1080 = 01c22t, film_release_region(?x886, ?x142), ?x81 = 029j_, ?x390 = 0chghy, ?x1229 = 059j2, ?x2340 = 0fpv_3_, ?x9565 = 0hz6mv2, ?x3081 = 023gxx, ?x11889 = 0gp5l6, ?x9002 = 0ndsl1x, adjoins(?x8588, ?x3855), ?x5849 = 02h22, ?x607 = 02x3lt7, produced_by(?x2644, ?x163), ?x5576 = 0gbfn9, location(?x8299, ?x1458), ?x1035 = 08hmch, ?x7379 = 032clf >> conf = 0.86 => this is the best rule for 4 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 02lpp7 medal! 01f1kd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.860 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 0jkvj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.860 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 018ljb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.860 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 0c_tl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.860 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 01f1jy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.860 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #18652-0_92w PRED entity: 0_92w PRED relation: film_release_region PRED expected values: 03rjj 03gj2 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 130): 03rjj (0.85 #793, 0.84 #1899, 0.84 #1583), 05qhw (0.82 #1593, 0.76 #1909, 0.71 #803), 035qy (0.82 #824, 0.79 #1614, 0.78 #1930), 03gj2 (0.81 #1604, 0.75 #814, 0.74 #1920), 05b4w (0.79 #1645, 0.72 #855, 0.71 #1961), 0d060g (0.74 #795, 0.74 #1585, 0.71 #1901), 06bnz (0.70 #1941, 0.69 #1625, 0.68 #835), 03spz (0.69 #1678, 0.68 #888, 0.63 #1994), 06t2t (0.69 #1642, 0.65 #1958, 0.64 #852), 03rj0 (0.64 #1640, 0.59 #1956, 0.57 #850) >> Best rule #793 for best value: >> intensional similarity = 4 >> extensional distance = 158 >> proper extension: 0bh8yn3; 047svrl; 0ndsl1x; >> query: (?x1118, 03rjj) <- nominated_for(?x1119, ?x1118), film_release_region(?x1118, ?x151), film(?x3186, ?x1118), ?x151 = 0b90_r >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0_92w film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0_92w film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18651-015g1w PRED entity: 015g1w PRED relation: citytown PRED expected values: 04jpl => 134 concepts (66 used for prediction) PRED predicted values (max 10 best out of 127): 02_286 (0.40 #1123, 0.38 #2965, 0.18 #4070), 02jx1 (0.29 #1477, 0.27 #5163, 0.27 #5162), 0f485 (0.29 #1477, 0.27 #5163, 0.27 #5162), 01qh7 (0.20 #1170, 0.12 #3012, 0.12 #2643), 0rh6k (0.18 #4056, 0.17 #1478, 0.08 #5902), 0dclg (0.17 #1887, 0.14 #2255, 0.12 #3360), 0978r (0.15 #4499, 0.13 #9661, 0.13 #10029), 04jpl (0.12 #2588, 0.12 #8480, 0.12 #9593), 02h6_6p (0.12 #2632, 0.04 #6689, 0.03 #7426), 01531 (0.12 #3013, 0.03 #7438, 0.02 #12600) >> Best rule #1123 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 03ksy; 06thjt; 032r4n; >> query: (?x8052, 02_286) <- student(?x8052, ?x2625), influenced_by(?x2845, ?x2625), type_of_union(?x2625, ?x566), ?x2845 = 0lrh, contains(?x1310, ?x8052) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2588 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 01hb1t; *> query: (?x8052, 04jpl) <- student(?x8052, ?x6211), student(?x8052, ?x2625), influenced_by(?x8753, ?x2625), ?x8753 = 0yxl, place_of_birth(?x2625, ?x362), film(?x6211, ?x1889) *> conf = 0.12 ranks of expected_values: 8 EVAL 015g1w citytown 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 134.000 66.000 0.400 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #18650-035p3 PRED entity: 035p3 PRED relation: featured_film_locations! PRED expected values: 0k5g9 => 138 concepts (116 used for prediction) PRED predicted values (max 10 best out of 923): 0ddjy (0.33 #166, 0.24 #47220, 0.20 #1618), 02fqxm (0.24 #47220, 0.20 #4354, 0.13 #5807), 032zq6 (0.24 #47220, 0.20 #3922, 0.13 #5375), 0192hw (0.24 #47220, 0.13 #5315, 0.12 #6041), 0c0nhgv (0.24 #47220, 0.10 #3706, 0.07 #5159), 0x25q (0.24 #47220, 0.10 #3850, 0.07 #5303), 0k2sk (0.24 #47220, 0.10 #3703, 0.07 #5156), 0g9yrw (0.24 #47220, 0.10 #3911, 0.07 #5364), 018js4 (0.24 #47220, 0.10 #3641, 0.07 #5094), 04j14qc (0.20 #4222, 0.14 #9308, 0.13 #5675) >> Best rule #166 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0l2hf; >> query: (?x14186, 0ddjy) <- featured_film_locations(?x836, ?x14186), adjoins(?x3125, ?x14186), adjoins(?x1879, ?x14186), ?x3125 = 0d6lp, adjoins(?x1227, ?x1879), contains(?x1227, ?x191) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3825 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 0dc95; 0d6lp; 01jr6; 0135g; 01d26y; *> query: (?x14186, 0k5g9) <- featured_film_locations(?x836, ?x14186), adjoins(?x3125, ?x14186), place_of_birth(?x399, ?x3125), state(?x3125, ?x1227), origin(?x1751, ?x3125) *> conf = 0.10 ranks of expected_values: 66 EVAL 035p3 featured_film_locations! 0k5g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 138.000 116.000 0.333 http://example.org/film/film/featured_film_locations #18649-065y4w7 PRED entity: 065y4w7 PRED relation: company! PRED expected values: 0hnlx => 143 concepts (137 used for prediction) PRED predicted values (max 10 best out of 298): 03gkn5 (0.30 #1758, 0.16 #1030, 0.14 #2244), 06pj8 (0.13 #763, 0.04 #14800, 0.03 #6093), 04jspq (0.13 #859, 0.03 #6189, 0.03 #2799), 02q_cc (0.13 #738, 0.03 #6068, 0.02 #8007), 0x3r3 (0.11 #1811, 0.10 #2297, 0.08 #4960), 0nk72 (0.11 #1859, 0.10 #2345, 0.06 #5734), 01w_10 (0.10 #2581, 0.09 #2823, 0.07 #883), 06y3r (0.09 #3083, 0.04 #7927, 0.03 #12542), 02sdx (0.08 #5300, 0.07 #697, 0.07 #2395), 07n39 (0.07 #1884, 0.07 #672, 0.07 #2370) >> Best rule #1758 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 02zc7f; >> query: (?x735, 03gkn5) <- school(?x580, ?x735), student(?x735, ?x65), company(?x5309, ?x735) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 065y4w7 company! 0hnlx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 137.000 0.296 http://example.org/people/person/employment_history./business/employment_tenure/company #18648-0cc5qkt PRED entity: 0cc5qkt PRED relation: nominated_for! PRED expected values: 0gr0m 054krc => 114 concepts (91 used for prediction) PRED predicted values (max 10 best out of 200): 0k611 (0.75 #523, 0.61 #1663, 0.57 #295), 02pqp12 (0.68 #509, 0.43 #965, 0.40 #281), 02qyntr (0.62 #626, 0.50 #398, 0.46 #1082), 040njc (0.57 #463, 0.57 #235, 0.52 #919), 0gs9p (0.56 #970, 0.50 #514, 0.47 #286), 019f4v (0.53 #505, 0.49 #1645, 0.47 #277), 0gr0m (0.53 #510, 0.43 #282, 0.43 #1650), 04dn09n (0.50 #259, 0.48 #943, 0.41 #2311), 03hkv_r (0.50 #242, 0.39 #926, 0.26 #2294), 09sb52 (0.48 #942, 0.47 #258, 0.40 #486) >> Best rule #523 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 05jzt3; 0b6tzs; 0gmcwlb; 017gm7; 09k56b7; 08nvyr; 02dr9j; 01mgw; >> query: (?x3596, 0k611) <- nominated_for(?x1162, ?x3596), nominated_for(?x637, ?x3596), ?x1162 = 099c8n, nominated_for(?x669, ?x3596), ?x637 = 02r22gf >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #510 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 05jzt3; 0b6tzs; 0gmcwlb; 017gm7; 09k56b7; 08nvyr; 02dr9j; 01mgw; *> query: (?x3596, 0gr0m) <- nominated_for(?x1162, ?x3596), nominated_for(?x637, ?x3596), ?x1162 = 099c8n, nominated_for(?x669, ?x3596), ?x637 = 02r22gf *> conf = 0.53 ranks of expected_values: 7, 17 EVAL 0cc5qkt nominated_for! 054krc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 114.000 91.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0cc5qkt nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 114.000 91.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18647-0x0w PRED entity: 0x0w PRED relation: interests! PRED expected values: 026lj 043tg => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1068): 01bpn (0.75 #529, 0.50 #474, 0.50 #267), 0b78hw (0.67 #436, 0.50 #155, 0.44 #413), 07kb5 (0.60 #408, 0.60 #372, 0.56 #409), 043s3 (0.60 #408, 0.60 #385, 0.56 #409), 0nk72 (0.60 #396, 0.57 #352, 0.44 #413), 01dvtx (0.60 #383, 0.57 #352, 0.37 #596), 026lj (0.60 #376, 0.56 #409, 0.50 #463), 04411 (0.57 #352, 0.44 #413, 0.40 #83), 015n8 (0.56 #409, 0.50 #489, 0.50 #226), 05qmj (0.56 #409, 0.50 #75, 0.34 #133) >> Best rule #529 for best value: >> intensional similarity = 62 >> extensional distance = 6 >> proper extension: 04rjg; 097df; >> query: (?x10606, 01bpn) <- interests(?x10605, ?x10606), influenced_by(?x3336, ?x10605), profession(?x10605, ?x2225), nationality(?x10605, ?x1264), peers(?x10605, ?x10654), profession(?x11492, ?x2225), profession(?x4308, ?x2225), ?x4308 = 0b78hw, film_release_region(?x10623, ?x1264), film_release_region(?x10246, ?x1264), film_release_region(?x6931, ?x1264), film_release_region(?x6422, ?x1264), film_release_region(?x6168, ?x1264), film_release_region(?x5825, ?x1264), film_release_region(?x5588, ?x1264), film_release_region(?x4707, ?x1264), film_release_region(?x4615, ?x1264), film_release_region(?x3812, ?x1264), film_release_region(?x2655, ?x1264), film_release_region(?x1490, ?x1264), film_release_region(?x1463, ?x1264), film_release_region(?x1022, ?x1264), film_release_region(?x559, ?x1264), film_release_region(?x66, ?x1264), olympics(?x1264, ?x452), country(?x1121, ?x1264), ?x1490 = 0fpkhkz, ?x1121 = 0bynt, ?x5825 = 067ghz, ?x5588 = 0gtt5fb, ?x4707 = 02xbyr, ?x10623 = 0dgq80b, contains(?x1264, ?x196), country(?x9996, ?x1264), country(?x6387, ?x1264), country(?x3322, ?x1264), country(?x1644, ?x1264), country(?x146, ?x1264), ?x6168 = 0gj96ln, ?x6387 = 047myg9, combatants(?x94, ?x1264), ?x11492 = 082xp, ?x1644 = 09txzv, first_level_division_of(?x7049, ?x1264), ?x3812 = 0c3xw46, ?x66 = 014lc_, ?x4615 = 0dlngsd, ?x2655 = 0fpmrm3, ?x6422 = 02qk3fk, ?x9996 = 03cwwl, ?x6931 = 09v3jyg, currency(?x1264, ?x170), administrative_area_type(?x1264, ?x2792), influenced_by(?x2994, ?x10654), ?x146 = 02y_lrp, medal(?x1264, ?x422), ?x559 = 05p1tzf, ?x3322 = 03n785, type_of_union(?x10654, ?x566), ?x10246 = 023vcd, ?x1463 = 0gtvrv3, ?x1022 = 0crfwmx >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #376 for first EXPECTED value: *> intensional similarity = 54 *> extensional distance = 3 *> proper extension: 05r79; *> query: (?x10606, 026lj) <- interests(?x12592, ?x10606), interests(?x10605, ?x10606), interests(?x9600, ?x10606), interests(?x7250, ?x10606), influenced_by(?x3336, ?x10605), profession(?x10605, ?x2225), nationality(?x10605, ?x1264), taxonomy(?x10606, ?x939), influenced_by(?x12592, ?x9595), influenced_by(?x12592, ?x4915), influenced_by(?x11499, ?x7250), influenced_by(?x10111, ?x7250), influenced_by(?x8404, ?x7250), influenced_by(?x4308, ?x7250), influenced_by(?x4265, ?x7250), place_of_death(?x10605, ?x4861), people(?x5540, ?x12592), ?x4308 = 0b78hw, peers(?x12592, ?x1645), ?x9600 = 039n1, influenced_by(?x7250, ?x6015), influenced_by(?x7250, ?x712), people(?x1158, ?x9595), student(?x3439, ?x11499), people(?x6734, ?x9595), ?x5540 = 013xrm, influenced_by(?x8389, ?x9595), religion(?x12592, ?x7131), gender(?x11499, ?x231), religion(?x9595, ?x1985), location(?x8404, ?x3622), ?x939 = 04n6k, ?x231 = 05zppz, place_of_birth(?x12592, ?x1646), influenced_by(?x11286, ?x6015), ?x8389 = 0683n, ?x4915 = 03f0324, interests(?x8404, ?x713), influenced_by(?x1029, ?x4265), company(?x10111, ?x4096), location(?x4265, ?x1591), influenced_by(?x3711, ?x712), place_of_death(?x12592, ?x2152), student(?x8694, ?x4265), ?x3711 = 052h3, location(?x10111, ?x13410), profession(?x712, ?x353), ?x11286 = 030dr, film_release_region(?x66, ?x2152), contains(?x2152, ?x1649), profession(?x6015, ?x11056), type_of_union(?x10111, ?x566), nationality(?x4265, ?x429), interests(?x712, ?x3561) *> conf = 0.60 ranks of expected_values: 7, 31 EVAL 0x0w interests! 043tg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 14.000 14.000 0.750 http://example.org/user/alexander/philosophy/philosopher/interests EVAL 0x0w interests! 026lj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 14.000 14.000 0.750 http://example.org/user/alexander/philosophy/philosopher/interests #18646-0l2q3 PRED entity: 0l2q3 PRED relation: source PRED expected values: 0jbk9 => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.95 #24, 0.94 #10, 0.93 #31) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 0fc_p; >> query: (?x10399, 0jbk9) <- county_seat(?x10399, ?x10400), contains(?x94, ?x10399), adjoins(?x9472, ?x10399), place_of_birth(?x9461, ?x10400) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l2q3 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.947 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #18645-02rsz0 PRED entity: 02rsz0 PRED relation: place_of_birth PRED expected values: 02ly_ => 111 concepts (107 used for prediction) PRED predicted values (max 10 best out of 126): 0r3tq (0.33 #430), 04jpl (0.14 #9170, 0.14 #712, 0.14 #5647), 02_286 (0.07 #24690, 0.07 #43717, 0.07 #7068), 01_d4 (0.05 #7819, 0.05 #8524, 0.05 #10639), 030qb3t (0.05 #2875, 0.03 #19084, 0.03 #69840), 0b_yz (0.05 #1137, 0.02 #1843, 0.02 #2548), 0n96z (0.05 #1378, 0.02 #2084, 0.02 #2789), 0q34g (0.05 #1262, 0.02 #1968, 0.02 #2673), 05l5n (0.05 #769, 0.02 #2180, 0.02 #2886), 0206v5 (0.05 #1091, 0.02 #6026, 0.02 #3208) >> Best rule #430 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01vb6z; >> query: (?x12474, 0r3tq) <- influenced_by(?x12474, ?x117), profession(?x12474, ?x353), category(?x12474, ?x134), gender(?x12474, ?x231), ?x117 = 03qcq >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02rsz0 place_of_birth 02ly_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 107.000 0.333 http://example.org/people/person/place_of_birth #18644-018p5f PRED entity: 018p5f PRED relation: state_province_region PRED expected values: 07b_l => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 80): 01n7q (0.67 #757, 0.60 #2359, 0.57 #3223), 07b_l (0.55 #6541, 0.24 #15814, 0.23 #15319), 059rby (0.44 #3578, 0.33 #4444, 0.29 #1729), 07z1m (0.33 #145, 0.25 #392, 0.14 #1747), 02jx1 (0.25 #636, 0.14 #1375, 0.12 #1991), 06btq (0.25 #409, 0.14 #1764, 0.09 #2873), 09c7w0 (0.24 #15814, 0.23 #15319, 0.23 #15939), 0mskq (0.24 #15814, 0.23 #15319, 0.23 #15939), 05kkh (0.17 #3083, 0.11 #3823, 0.09 #4812), 059_c (0.14 #1619, 0.10 #2481, 0.05 #4209) >> Best rule #757 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 046b0s; 0kk9v; 056ws9; >> query: (?x7390, 01n7q) <- award_winner(?x3105, ?x7390), industry(?x7390, ?x3368), category(?x7390, ?x134), company(?x346, ?x7390) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #6541 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 025tlyv; *> query: (?x7390, ?x3634) <- place_founded(?x7390, ?x1719), category(?x7390, ?x134), location(?x523, ?x1719), state(?x1719, ?x3634) *> conf = 0.55 ranks of expected_values: 2 EVAL 018p5f state_province_region 07b_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 161.000 161.000 0.667 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #18643-011wtv PRED entity: 011wtv PRED relation: film! PRED expected values: 01wxyx1 => 89 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1077): 06pj8 (0.59 #16603, 0.47 #53977, 0.45 #103806), 0146pg (0.47 #53977, 0.45 #103806, 0.42 #26983), 0c94fn (0.47 #53977, 0.42 #72663, 0.40 #95501), 027rwmr (0.47 #53977, 0.42 #72663, 0.40 #95501), 016fjj (0.33 #634, 0.08 #24905, 0.06 #15160), 05np4c (0.33 #577, 0.08 #24905, 0.06 #15103), 0bxtg (0.33 #77, 0.08 #24905, 0.05 #26984), 0dvmd (0.33 #528, 0.08 #24905, 0.03 #35288), 0hvb2 (0.33 #299, 0.08 #24905, 0.03 #23128), 0716t2 (0.33 #1903, 0.08 #24905, 0.03 #16429) >> Best rule #16603 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 0g60z; 02k_4g; 0q9jk; >> query: (?x4565, ?x2135) <- honored_for(?x2006, ?x4565), award_winner(?x4565, ?x2135), special_performance_type(?x2135, ?x4832) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #14867 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 0g60z; 02k_4g; 0q9jk; *> query: (?x4565, 01wxyx1) <- honored_for(?x2006, ?x4565), award_winner(?x4565, ?x2135), special_performance_type(?x2135, ?x4832) *> conf = 0.03 ranks of expected_values: 499 EVAL 011wtv film! 01wxyx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 89.000 53.000 0.595 http://example.org/film/actor/film./film/performance/film #18642-0509bl PRED entity: 0509bl PRED relation: award_nominee! PRED expected values: 073w14 => 89 concepts (34 used for prediction) PRED predicted values (max 10 best out of 717): 073w14 (0.81 #11655, 0.81 #69932, 0.81 #55942), 044lyq (0.81 #11655, 0.81 #69932, 0.81 #55942), 0509bl (0.57 #422, 0.14 #60607), 02p65p (0.29 #26, 0.16 #53610, 0.14 #60607), 0p_pd (0.29 #65, 0.14 #60607, 0.02 #11720), 015t56 (0.16 #53610, 0.14 #60607, 0.14 #606), 05vsxz (0.16 #53610, 0.14 #60607, 0.14 #8), 016zp5 (0.16 #53610, 0.14 #60607, 0.14 #1291), 015t7v (0.16 #53610, 0.14 #60607, 0.14 #1187), 02gvwz (0.16 #53610, 0.14 #60607, 0.14 #239) >> Best rule #11655 for best value: >> intensional similarity = 3 >> extensional distance = 166 >> proper extension: 0grwj; 05bnp0; 06dv3; 014zcr; 0m2wm; 01qscs; 01q_ph; 09fb5; 01dw4q; 06jzh; ... >> query: (?x1995, ?x1289) <- award_nominee(?x1995, ?x1289), film(?x1995, ?x1263), participant(?x2499, ?x1995) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0509bl award_nominee! 073w14 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 34.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18641-0glt670 PRED entity: 0glt670 PRED relation: parent_genre! PRED expected values: 016_v3 06kcjr 05lwjc 01f9y_ 04n7jdv => 60 concepts (32 used for prediction) PRED predicted values (max 10 best out of 276): 07ym47 (0.38 #2241, 0.38 #1754, 0.30 #2728), 059kh (0.38 #1739, 0.27 #2956, 0.25 #2226), 01h0kx (0.38 #2059, 0.25 #1815, 0.18 #3032), 06kcjr (0.33 #149, 0.25 #633, 0.20 #876), 03mb9 (0.25 #2263, 0.25 #1776, 0.20 #2750), 01fm07 (0.25 #2278, 0.25 #1791, 0.20 #2765), 0133_p (0.25 #2303, 0.25 #1816, 0.20 #2790), 0y3_8 (0.25 #1981, 0.25 #1737, 0.18 #2954), 0grjmv (0.25 #2050, 0.25 #1806, 0.18 #3023), 0g_bh (0.25 #1796, 0.18 #3013, 0.17 #3257) >> Best rule #2241 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 064t9; 02x8m; 06j6l; 025sc50; 0gywn; 02lnbg; >> query: (?x2937, 07ym47) <- artists(?x2937, ?x9167), artists(?x2937, ?x6289), artists(?x2937, ?x5760), ?x6289 = 0x3n, ?x5760 = 01dwrc, currency(?x9167, ?x170), people(?x9888, ?x9167) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 016_rm; *> query: (?x2937, 06kcjr) <- artists(?x2937, ?x9167), artists(?x2937, ?x7909), artists(?x2937, ?x1989), ?x9167 = 07pzc, parent_genre(?x1952, ?x2937), profession(?x7909, ?x131), location(?x1989, ?x2673) *> conf = 0.33 ranks of expected_values: 4, 107, 114, 120, 128 EVAL 0glt670 parent_genre! 04n7jdv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 60.000 32.000 0.375 http://example.org/music/genre/parent_genre EVAL 0glt670 parent_genre! 01f9y_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 60.000 32.000 0.375 http://example.org/music/genre/parent_genre EVAL 0glt670 parent_genre! 05lwjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 60.000 32.000 0.375 http://example.org/music/genre/parent_genre EVAL 0glt670 parent_genre! 06kcjr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 60.000 32.000 0.375 http://example.org/music/genre/parent_genre EVAL 0glt670 parent_genre! 016_v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 60.000 32.000 0.375 http://example.org/music/genre/parent_genre #18640-0dr31 PRED entity: 0dr31 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.50 #13, 0.50 #5, 0.42 #17), 01g63y (0.03 #34, 0.03 #46, 0.02 #58) >> Best rule #13 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 09f8q; 0d34_; >> query: (?x11045, 04ztj) <- administrative_parent(?x11045, ?x8264), category(?x8264, ?x134), adjoins(?x8264, ?x1679), contains(?x8264, ?x8977), combatants(?x6371, ?x8264) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dr31 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.500 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #18639-033q4k PRED entity: 033q4k PRED relation: institution! PRED expected values: 014mlp => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 21): 02h4rq6 (0.79 #325, 0.70 #118, 0.67 #1085), 014mlp (0.76 #327, 0.74 #258, 0.74 #350), 03bwzr4 (0.57 #336, 0.50 #60, 0.44 #83), 02_xgp2 (0.54 #334, 0.41 #104, 0.39 #1002), 07s6fsf (0.50 #47, 0.40 #1, 0.39 #323), 0bkj86 (0.43 #330, 0.43 #146, 0.38 #169), 04zx3q1 (0.33 #324, 0.29 #1593, 0.19 #1200), 0bjrnt (0.29 #1593, 0.24 #98, 0.14 #328), 027f2w (0.28 #331, 0.24 #101, 0.15 #1091), 013zdg (0.23 #329, 0.20 #7, 0.18 #1066) >> Best rule #325 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 06pwq; 01w3v; 01w5m; 09f2j; 0b1xl; 01nnsv; 0ks67; 08qnnv; 0gl5_; 0trv; ... >> query: (?x1615, 02h4rq6) <- student(?x1615, ?x1616), school_type(?x1615, ?x3092), fraternities_and_sororities(?x1615, ?x3697) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #327 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 06pwq; 01w3v; 01w5m; 09f2j; 0b1xl; 01nnsv; 0ks67; 08qnnv; 0gl5_; 0trv; ... *> query: (?x1615, 014mlp) <- student(?x1615, ?x1616), school_type(?x1615, ?x3092), fraternities_and_sororities(?x1615, ?x3697) *> conf = 0.76 ranks of expected_values: 2 EVAL 033q4k institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.789 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18638-07pzc PRED entity: 07pzc PRED relation: award PRED expected values: 02f75t => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 392): 02f5qb (0.55 #4579, 0.54 #3373, 0.30 #5785), 01by1l (0.54 #3329, 0.45 #5741, 0.41 #11369), 02f75t (0.53 #3878, 0.31 #3476, 0.27 #5888), 02f72n (0.50 #4569, 0.23 #3363, 0.19 #4971), 01d38t (0.50 #4750, 0.18 #11584, 0.17 #12790), 02f76h (0.48 #5807, 0.25 #1787, 0.23 #3395), 03t5kl (0.47 #3846, 0.42 #5856, 0.38 #3444), 02f73p (0.45 #4611, 0.26 #11445, 0.24 #12651), 02v1m7 (0.41 #4536, 0.27 #5742, 0.25 #1722), 01ckcd (0.41 #4756, 0.27 #11590, 0.26 #12796) >> Best rule #4579 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 01jcxwp; 07hgm; 0c9l1; >> query: (?x9167, 02f5qb) <- artists(?x12988, ?x9167), artist(?x8738, ?x9167), ?x8738 = 01fjfv, parent_genre(?x12988, ?x283) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #3878 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 01vw917; *> query: (?x9167, 02f75t) <- artists(?x11545, ?x9167), category(?x9167, ?x134), ?x11545 = 036jv, award_nominee(?x140, ?x9167) *> conf = 0.53 ranks of expected_values: 3 EVAL 07pzc award 02f75t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 177.000 177.000 0.545 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18637-04b7xr PRED entity: 04b7xr PRED relation: award PRED expected values: 05q8pss => 142 concepts (112 used for prediction) PRED predicted values (max 10 best out of 270): 01by1l (0.50 #908, 0.47 #3308, 0.44 #2508), 02g3gj (0.50 #25, 0.18 #39205, 0.18 #34402), 01c99j (0.38 #622, 0.25 #222, 0.22 #4222), 03qbh5 (0.26 #2601, 0.25 #3401, 0.23 #12201), 02f6xy (0.26 #2596, 0.19 #2996, 0.16 #3396), 01ckbq (0.25 #486, 0.25 #86, 0.15 #40006), 01ck6h (0.25 #918, 0.24 #2518, 0.19 #2918), 02f6ym (0.25 #254, 0.16 #3454, 0.13 #4254), 02f71y (0.25 #178, 0.14 #3378, 0.12 #578), 01cw7s (0.25 #261, 0.12 #661, 0.06 #1861) >> Best rule #908 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 019f9z; >> query: (?x6942, 01by1l) <- place_of_birth(?x6942, ?x2254), artists(?x378, ?x6942), award(?x6942, ?x724), ?x378 = 07sbbz2 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #38804 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1504 *> proper extension: 0c1pj; 049gc; 02__94; *> query: (?x6942, ?x1237) <- award_winner(?x7088, ?x6942), award_winner(?x5298, ?x6942), profession(?x7088, ?x131), award(?x5298, ?x1237) *> conf = 0.15 ranks of expected_values: 41 EVAL 04b7xr award 05q8pss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 142.000 112.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18636-04rzd PRED entity: 04rzd PRED relation: role PRED expected values: 03gvt 011k_j => 92 concepts (70 used for prediction) PRED predicted values (max 10 best out of 61): 07y_7 (0.89 #1562, 0.87 #1976, 0.86 #1038), 04rzd (0.87 #1993, 0.84 #2465, 0.81 #2111), 011k_j (0.86 #1038, 0.84 #398, 0.83 #1270), 02sgy (0.84 #398, 0.83 #1270, 0.83 #1914), 03qjg (0.84 #398, 0.83 #1270, 0.83 #1914), 0dwt5 (0.84 #398, 0.83 #1270, 0.83 #1914), 01dnws (0.84 #398, 0.83 #1270, 0.83 #1914), 03t22m (0.84 #398, 0.83 #1270, 0.83 #1914), 01679d (0.84 #398, 0.83 #1270, 0.83 #1914), 07m2y (0.84 #398, 0.83 #1270, 0.83 #1914) >> Best rule #1562 for best value: >> intensional similarity = 15 >> extensional distance = 8 >> proper extension: 0l14qv; >> query: (?x1969, ?x1495) <- role(?x2923, ?x1969), role(?x1495, ?x1969), role(?x1473, ?x1969), role(?x1212, ?x1969), role(?x1969, ?x228), role(?x366, ?x1969), ?x2923 = 02k856, ?x1212 = 07xzm, performance_role(?x212, ?x1969), group(?x1969, ?x1929), instrumentalists(?x1969, ?x1001), ?x1473 = 0g2dz, role(?x5676, ?x1495), ?x5676 = 0151b0, performance_role(?x130, ?x1495) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1038 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 6 *> proper extension: 0dwt5; *> query: (?x1969, ?x1495) <- role(?x2923, ?x1969), role(?x1495, ?x1969), role(?x1473, ?x1969), role(?x1212, ?x1969), role(?x1969, ?x228), role(?x6949, ?x1969), ?x2923 = 02k856, ?x1212 = 07xzm, performance_role(?x212, ?x1969), group(?x1969, ?x1929), instrumentalists(?x1969, ?x1001), ?x1473 = 0g2dz, role(?x5676, ?x1495), ?x5676 = 0151b0, ?x6949 = 03ryks *> conf = 0.86 ranks of expected_values: 3, 39 EVAL 04rzd role 011k_j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 70.000 0.885 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 04rzd role 03gvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 92.000 70.000 0.885 http://example.org/music/performance_role/track_performances./music/track_contribution/role #18635-0bxsk PRED entity: 0bxsk PRED relation: film! PRED expected values: 025j1t => 63 concepts (36 used for prediction) PRED predicted values (max 10 best out of 912): 01vvb4m (0.35 #10875, 0.25 #519, 0.17 #6733), 029k55 (0.25 #8027, 0.25 #1813, 0.01 #30814), 0h0wc (0.25 #16991, 0.09 #4563, 0.08 #8706), 03pmzt (0.20 #2565, 0.01 #19135, 0.01 #23279), 02tv80 (0.18 #5267, 0.15 #9410, 0.07 #17695), 014v6f (0.18 #5104, 0.15 #9247, 0.04 #17532), 059_gf (0.18 #5135, 0.14 #17563, 0.08 #9278), 01vsn38 (0.17 #8058, 0.12 #1844, 0.09 #5986), 07mz77 (0.17 #7624, 0.12 #1410), 01m4yn (0.17 #7407, 0.12 #1193) >> Best rule #10875 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 01fx1l; >> query: (?x6855, 01vvb4m) <- nominated_for(?x338, ?x6855), film(?x338, ?x7107), film(?x338, ?x4087), ?x7107 = 04ghz4m, genre(?x4087, ?x53) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1069 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 084qpk; 0cwfgz; 0ds5_72; *> query: (?x6855, 025j1t) <- film(?x10126, ?x6855), film(?x4277, ?x6855), ?x10126 = 01xllf, award(?x4277, ?x102), music(?x6855, ?x5556) *> conf = 0.12 ranks of expected_values: 18 EVAL 0bxsk film! 025j1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 63.000 36.000 0.346 http://example.org/film/actor/film./film/performance/film #18634-022p06 PRED entity: 022p06 PRED relation: people! PRED expected values: 01p7s6 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 38): 02ctzb (0.23 #92, 0.20 #169, 0.09 #554), 07bch9 (0.20 #177, 0.20 #100, 0.15 #23), 041rx (0.17 #928, 0.17 #1390, 0.16 #2083), 01qhm_ (0.11 #6, 0.10 #160, 0.10 #83), 0x67 (0.11 #3783, 0.10 #3706, 0.09 #6940), 033tf_ (0.10 #392, 0.07 #7, 0.07 #1162), 0xnvg (0.07 #13, 0.07 #167, 0.07 #90), 063k3h (0.07 #31, 0.03 #185, 0.03 #108), 07mqps (0.07 #96, 0.04 #19, 0.03 #173), 02w7gg (0.07 #4468, 0.06 #6393, 0.05 #5007) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 083q7; 028rk; 09bg4l; 07_m9_; 07cbs; 034rd; 0f7fy; 03y2kr; 06y3r; 04jvt; ... >> query: (?x4943, 02ctzb) <- profession(?x4943, ?x319), people(?x5855, ?x4943), organizations_founded(?x4943, ?x788) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #59 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 03kdl; 0dx97; 042f1; 07bty; 01hdht; 019fz; *> query: (?x4943, 01p7s6) <- profession(?x4943, ?x319), place_of_death(?x4943, ?x1523), organizations_founded(?x4943, ?x788) *> conf = 0.04 ranks of expected_values: 12 EVAL 022p06 people! 01p7s6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 113.000 113.000 0.233 http://example.org/people/ethnicity/people #18633-0f_nbyh PRED entity: 0f_nbyh PRED relation: award! PRED expected values: 0bwh6 02pq9yv 03kpvp 0d6484 => 50 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2865): 0151w_ (0.63 #56896, 0.62 #56897, 0.51 #40161), 0c12h (0.50 #5144, 0.33 #1798, 0.29 #11836), 01nr36 (0.50 #5775, 0.24 #19161, 0.20 #9121), 0jmj (0.50 #4560, 0.10 #58113, 0.10 #64808), 07r1h (0.44 #28553, 0.24 #18513, 0.19 #50201), 0693l (0.38 #14224, 0.38 #10877, 0.27 #7531), 02f93t (0.38 #12710, 0.33 #16057, 0.33 #2672), 01ts_3 (0.38 #12067, 0.33 #15414, 0.27 #8721), 016k6x (0.38 #18165, 0.25 #4779, 0.20 #8125), 0f502 (0.34 #27989, 0.17 #34683, 0.14 #17949) >> Best rule #56896 for best value: >> intensional similarity = 4 >> extensional distance = 160 >> proper extension: 02qkk9_; 04qy5; 0bqsk5; 01pqx6; >> query: (?x277, ?x163) <- award_winner(?x277, ?x3572), award_winner(?x277, ?x163), award_nominee(?x826, ?x3572), written_by(?x392, ?x3572) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #953 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0gq9h; *> query: (?x277, 02pq9yv) <- award(?x163, ?x277), nominated_for(?x277, ?x964), ?x964 = 0fh694, ?x163 = 0fvf9q *> conf = 0.33 ranks of expected_values: 62, 64, 141, 369 EVAL 0f_nbyh award! 0d6484 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 50.000 26.000 0.634 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f_nbyh award! 03kpvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 50.000 26.000 0.634 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f_nbyh award! 02pq9yv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 50.000 26.000 0.634 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f_nbyh award! 0bwh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 50.000 26.000 0.634 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18632-02wwmhc PRED entity: 02wwmhc PRED relation: film! PRED expected values: 03fbb6 => 79 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1038): 02pq9yv (0.12 #62498, 0.11 #72917), 0h0wc (0.10 #424, 0.09 #2507, 0.08 #12923), 04t2l2 (0.09 #2111, 0.08 #6277, 0.06 #28), 0mdqp (0.08 #6368, 0.08 #119, 0.07 #2202), 0b_dy (0.08 #8867, 0.05 #10950, 0.03 #13034), 0863x_ (0.08 #842, 0.07 #2925, 0.07 #7091), 0bj9k (0.08 #10744, 0.05 #8661, 0.04 #329), 0lpjn (0.08 #12978, 0.03 #10894, 0.03 #40058), 01yf85 (0.07 #3594, 0.07 #5677, 0.06 #1511), 0pz91 (0.07 #2295, 0.07 #6461, 0.06 #212) >> Best rule #62498 for best value: >> intensional similarity = 4 >> extensional distance = 570 >> proper extension: 05f67hw; >> query: (?x10778, ?x3528) <- film_release_region(?x10778, ?x94), produced_by(?x10778, ?x3528), nominated_for(?x3528, ?x224), award_nominee(?x2135, ?x3528) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #9311 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 0g60z; 0180mw; *> query: (?x10778, 03fbb6) <- nominated_for(?x10778, ?x12423), titles(?x53, ?x10778), honored_for(?x6297, ?x10778), award_winner(?x10778, ?x2444) *> conf = 0.03 ranks of expected_values: 176 EVAL 02wwmhc film! 03fbb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 79.000 47.000 0.116 http://example.org/film/actor/film./film/performance/film #18631-03rl1g PRED entity: 03rl1g PRED relation: district_represented PRED expected values: 059rby 05fkf => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 392): 059rby (0.95 #564, 0.94 #643, 0.92 #524), 05fkf (0.85 #222, 0.79 #259, 0.68 #526), 050l8 (0.79 #259, 0.75 #498, 0.75 #388), 05fjy (0.79 #259, 0.75 #398, 0.69 #508), 05fky (0.79 #259, 0.75 #396, 0.67 #433), 059_c (0.79 #259, 0.75 #383, 0.67 #420), 081mh (0.79 #259, 0.69 #501, 0.64 #465), 07srw (0.79 #259, 0.69 #499, 0.64 #463), 05mph (0.79 #259, 0.62 #400, 0.62 #363), 0gj4fx (0.79 #259, 0.62 #371, 0.60 #215) >> Best rule #564 for best value: >> intensional similarity = 46 >> extensional distance = 42 >> proper extension: 01grr2; 01grrf; 01gsry; >> query: (?x176, 059rby) <- district_represented(?x176, ?x2623), district_represented(?x176, ?x1755), district_represented(?x176, ?x961), religion(?x961, ?x10107), religion(?x961, ?x2769), religion(?x961, ?x962), contains(?x961, ?x10175), contains(?x961, ?x9911), district_represented(?x6728, ?x961), district_represented(?x4821, ?x961), district_represented(?x3540, ?x961), district_represented(?x653, ?x961), ?x653 = 070m6c, ?x2769 = 019cr, state(?x2624, ?x2623), major_field_of_study(?x9911, ?x742), ?x4821 = 02bqm0, first_level_division_of(?x2623, ?x94), legislative_sessions(?x176, ?x3669), location(?x2444, ?x2623), location(?x237, ?x2623), religion(?x2623, ?x7131), contains(?x2623, ?x95), award_winner(?x237, ?x2602), award_winner(?x237, ?x679), ?x679 = 08wq0g, location(?x1987, ?x961), currency(?x2623, ?x170), award_nominee(?x364, ?x237), award_nominee(?x2444, ?x398), award(?x2444, ?x401), award(?x237, ?x678), nominated_for(?x2444, ?x224), category(?x9911, ?x134), ?x2602 = 072bb1, award_nominee(?x4440, ?x2444), place_of_birth(?x237, ?x11331), ?x3540 = 024tcq, ?x10107 = 05w5d, ?x6728 = 070mff, participant(?x2444, ?x117), film(?x2444, ?x1481), student(?x10175, ?x1188), major_field_of_study(?x10175, ?x1668), ?x1755 = 01x73, ?x962 = 05sfs >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03rl1g district_represented 05fkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 20.000 20.000 0.955 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 03rl1g district_represented 059rby CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 20.000 20.000 0.955 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #18630-011yg9 PRED entity: 011yg9 PRED relation: honored_for! PRED expected values: 05q7cj => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 78): 073hgx (0.08 #326, 0.04 #448, 0.03 #570), 02yvhx (0.08 #309, 0.04 #431, 0.03 #553), 0bzn6_ (0.08 #290, 0.04 #412, 0.03 #534), 0clfdj (0.08 #246, 0.04 #368, 0.03 #490), 02glmx (0.08 #312, 0.04 #434, 0.03 #678), 092c5f (0.08 #254, 0.04 #376, 0.03 #620), 0n8_m93 (0.08 #347, 0.04 #469, 0.03 #713), 09306z (0.08 #338, 0.02 #826, 0.02 #948), 073h1t (0.08 #265, 0.02 #753, 0.02 #875), 04110lv (0.08 #339, 0.02 #461, 0.02 #583) >> Best rule #326 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 017jd9; 0gmgwnv; >> query: (?x5950, 073hgx) <- nominated_for(?x2222, ?x5950), nominated_for(?x384, ?x5950), ?x384 = 03hkv_r, production_companies(?x5950, ?x541), ?x2222 = 0gs96 >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #5491 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1341 *> proper extension: 047gn4y; 07g_0c; 0d_2fb; 03kg2v; 0gs973; 0ndsl1x; 0353tm; 0dc7hc; 02gqm3; 06cgf; *> query: (?x5950, ?x342) <- film(?x5951, ?x5950), country(?x5950, ?x94), award_winner(?x342, ?x5951), award(?x5951, ?x401) *> conf = 0.03 ranks of expected_values: 27 EVAL 011yg9 honored_for! 05q7cj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 69.000 69.000 0.077 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18629-03qjg PRED entity: 03qjg PRED relation: role! PRED expected values: 06w87 0dq630k 01w4c9 06rvn => 79 concepts (74 used for prediction) PRED predicted values (max 10 best out of 74): 07y_7 (0.83 #1789, 0.80 #1185, 0.79 #1918), 01s0ps (0.82 #2115, 0.82 #2449, 0.81 #3651), 01wy6 (0.82 #2115, 0.82 #2449, 0.81 #3651), 01dnws (0.82 #2115, 0.82 #2449, 0.81 #3651), 04q7r (0.82 #2115, 0.82 #2449, 0.81 #3651), 011_6p (0.82 #2115, 0.82 #2449, 0.81 #3651), 0bmnm (0.82 #2115, 0.82 #2449, 0.81 #3651), 01w4c9 (0.82 #2115, 0.82 #2449, 0.81 #3651), 06rvn (0.82 #2115, 0.82 #2449, 0.81 #3651), 05kms (0.71 #198, 0.70 #464, 0.69 #725) >> Best rule #1789 for best value: >> intensional similarity = 13 >> extensional distance = 16 >> proper extension: 042v_gx; 0395lw; 01hww_; 0jtg0; >> query: (?x2798, 07y_7) <- role(?x1433, ?x2798), role(?x615, ?x2798), role(?x212, ?x2798), group(?x2798, ?x997), role(?x300, ?x2798), instrumentalists(?x2798, ?x4473), instrumentalists(?x2798, ?x3503), profession(?x3503, ?x131), artist(?x3240, ?x3503), ?x615 = 0dwsp, role(?x1260, ?x1433), nationality(?x3503, ?x512), award_nominee(?x4473, ?x3146) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #2115 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 21 *> proper extension: 05ljv7; *> query: (?x2798, ?x2157) <- role(?x1473, ?x2798), role(?x1166, ?x2798), role(?x316, ?x2798), role(?x1437, ?x2798), instrumentalists(?x2798, ?x211), ?x1166 = 05148p4, role(?x366, ?x2798), ?x1437 = 01vdm0, ?x316 = 05r5c, group(?x1473, ?x2635), instrumentalists(?x1473, ?x2584), role(?x1152, ?x2798), role(?x2798, ?x2157) *> conf = 0.82 ranks of expected_values: 8, 9, 48, 57 EVAL 03qjg role! 06rvn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 79.000 74.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 03qjg role! 01w4c9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 79.000 74.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 03qjg role! 0dq630k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 79.000 74.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 03qjg role! 06w87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 79.000 74.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/role #18628-0drrw PRED entity: 0drrw PRED relation: currency PRED expected values: 09nqf => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.88 #2, 0.87 #41, 0.87 #40) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 04n3l; >> query: (?x13694, 09nqf) <- contains(?x335, ?x13694), ?x335 = 059rby, adjoins(?x13694, ?x334) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0drrw currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.881 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #18627-013bd1 PRED entity: 013bd1 PRED relation: type_of_union PRED expected values: 04ztj => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.81 #29, 0.79 #53, 0.78 #69), 01g63y (0.19 #182, 0.18 #214, 0.18 #190), 0jgjn (0.01 #148, 0.01 #156, 0.01 #68) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 04ns3gy; 0c_md_; >> query: (?x9495, 04ztj) <- company(?x9495, ?x8021), profession(?x9495, ?x1032), student(?x6760, ?x9495), gender(?x9495, ?x231) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013bd1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.806 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18626-026lgs PRED entity: 026lgs PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.71 #424, 0.71 #328, 0.70 #264), 01pvkk (0.29 #266, 0.28 #426, 0.27 #330), 04pyp5 (0.25 #13, 0.06 #173, 0.06 #335), 02ynfr (0.18 #270, 0.18 #334, 0.17 #430), 01xy5l_ (0.16 #74, 0.09 #170, 0.09 #428), 0215hd (0.13 #79, 0.12 #433, 0.11 #273), 089g0h (0.12 #434, 0.09 #274, 0.09 #338), 0d2b38 (0.11 #440, 0.11 #86, 0.11 #280), 089fss (0.11 #37, 0.07 #263, 0.06 #327), 02_n3z (0.09 #323, 0.08 #451, 0.08 #161) >> Best rule #424 for best value: >> intensional similarity = 3 >> extensional distance = 290 >> proper extension: 01q2nx; 04y9mm8; 0gy30w; 016017; >> query: (?x5418, 0ch6mp2) <- crewmember(?x5418, ?x3782), film(?x1700, ?x5418), film(?x382, ?x5418) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026lgs film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.709 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18625-01x2tm8 PRED entity: 01x2tm8 PRED relation: people! PRED expected values: 01rv7x => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 68): 0bpjh3 (0.50 #102, 0.05 #1026, 0.03 #1104), 0dryh9k (0.38 #1017, 0.36 #1095, 0.35 #1483), 04mvp8 (0.31 #375, 0.17 #67, 0.04 #1919), 041rx (0.29 #158, 0.27 #389, 0.24 #697), 033tf_ (0.22 #238, 0.14 #161, 0.11 #1396), 02w7gg (0.20 #387, 0.16 #541, 0.11 #233), 01rv7x (0.17 #39, 0.08 #347, 0.08 #1040), 07hwkr (0.12 #474, 0.11 #243, 0.08 #628), 02ctzb (0.12 #477, 0.05 #554, 0.04 #631), 0x67 (0.11 #241, 0.09 #3404, 0.08 #3560) >> Best rule #102 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0241wg; 0d0mbj; 06kl0k; 05g3ss; >> query: (?x9253, 0bpjh3) <- languages(?x9253, ?x11341), languages(?x9253, ?x254), ?x11341 = 01c7y, profession(?x9253, ?x319), ?x254 = 02h40lc, nationality(?x9253, ?x2146) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #39 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 02r99xw; *> query: (?x9253, 01rv7x) <- languages(?x9253, ?x8098), profession(?x9253, ?x319), ?x8098 = 0999q, ?x319 = 01d_h8 *> conf = 0.17 ranks of expected_values: 7 EVAL 01x2tm8 people! 01rv7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 100.000 100.000 0.500 http://example.org/people/ethnicity/people #18624-02hyt PRED entity: 02hyt PRED relation: location! PRED expected values: 048_p => 160 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1632): 099d4 (0.22 #7404, 0.12 #4885, 0.11 #9923), 0kvnn (0.16 #23548, 0.08 #46220, 0.06 #18510), 0bwh6 (0.15 #12829, 0.12 #2753, 0.11 #17867), 0c6g1l (0.15 #13048, 0.12 #2972, 0.11 #8010), 0c5vh (0.12 #4872, 0.11 #19986, 0.11 #9910), 018fmr (0.12 #3551, 0.11 #18665, 0.11 #8589), 02t__3 (0.12 #3743, 0.11 #8781, 0.11 #6262), 01wp8w7 (0.12 #2779, 0.11 #7817, 0.11 #5298), 0k8y7 (0.12 #3363, 0.11 #8401, 0.11 #5882), 086sj (0.12 #3327, 0.11 #8365, 0.11 #5846) >> Best rule #7404 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0135g; 0jpkg; >> query: (?x9758, 099d4) <- jurisdiction_of_office(?x10525, ?x9758), ?x10525 = 01q24l, adjoins(?x9758, ?x2621), contains(?x94, ?x2621) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #161234 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 266 *> proper extension: 071zb; *> query: (?x9758, ?x397) <- state(?x9758, ?x1227), location(?x397, ?x1227), contains(?x1227, ?x191) *> conf = 0.01 ranks of expected_values: 1411 EVAL 02hyt location! 048_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 160.000 90.000 0.222 http://example.org/people/person/places_lived./people/place_lived/location #18623-0gl88b PRED entity: 0gl88b PRED relation: student! PRED expected values: 09wv__ => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 114): 065y4w7 (0.20 #4222, 0.14 #14, 0.12 #1066), 02301 (0.14 #74, 0.12 #1126, 0.11 #2178), 06pwq (0.12 #538, 0.11 #2116, 0.11 #1590), 02zd460 (0.12 #696, 0.11 #2274, 0.11 #1748), 03ksy (0.12 #1158, 0.06 #9574, 0.06 #13256), 05cwl_ (0.11 #2287, 0.11 #1761, 0.10 #3865), 0bwfn (0.11 #2379, 0.10 #2905, 0.10 #19737), 015nl4 (0.11 #2171, 0.10 #2697, 0.05 #4801), 0cwx_ (0.11 #2345, 0.10 #2871, 0.05 #4975), 02237m (0.11 #2501, 0.10 #3027, 0.05 #5131) >> Best rule #4222 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06cv1; 09pjnd; 06rnl9; 03r1pr; 03y_46; 02lp3c; 019fnv; 02vxyl5; >> query: (?x2068, 065y4w7) <- student(?x12869, ?x2068), nominated_for(?x2068, ?x951), crewmember(?x4513, ?x2068) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #14896 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 302 *> proper extension: 01d494; 0j3v; 0dzkq; 04107; 02ln1; 0cl_m; 03j90; 047g6; 01h2_6; 011zwl; ... *> query: (?x2068, 09wv__) <- student(?x12869, ?x2068), place_of_death(?x2068, ?x739), nationality(?x2068, ?x94) *> conf = 0.01 ranks of expected_values: 103 EVAL 0gl88b student! 09wv__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 123.000 123.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #18622-0jvq PRED entity: 0jvq PRED relation: taxonomy PRED expected values: 04n6k => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.03 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14802, 04n6k) <- >> conf = 0.03 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jvq taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.030 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #18621-049xgc PRED entity: 049xgc PRED relation: film! PRED expected values: 021vwt 01f7dd => 91 concepts (42 used for prediction) PRED predicted values (max 10 best out of 976): 0bytkq (0.50 #16612, 0.43 #39465, 0.43 #72699), 08h79x (0.50 #16612, 0.43 #39465, 0.43 #72699), 02pq9yv (0.50 #16612, 0.43 #39465, 0.43 #72699), 04y8r (0.50 #16612, 0.43 #39465, 0.43 #72699), 0dvmd (0.50 #16612, 0.43 #39465, 0.42 #58161), 016zp5 (0.50 #16612, 0.43 #39465, 0.42 #58161), 018ygt (0.50 #16612, 0.43 #39465, 0.42 #58161), 06r_by (0.50 #16612, 0.43 #39465, 0.42 #58161), 01tc9r (0.50 #16612, 0.43 #39465, 0.42 #58161), 095zvfg (0.50 #16612, 0.43 #39465, 0.42 #58161) >> Best rule #16612 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 02vxq9m; 0dgst_d; 0j_t1; 04t9c0; 01gvts; >> query: (?x5648, ?x166) <- nominated_for(?x2880, ?x5648), honored_for(?x2294, ?x5648), ?x2880 = 02ppm4q, nominated_for(?x166, ?x5648) >> conf = 0.50 => this is the best rule for 12 predicted values *> Best rule #3281 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0blpg; *> query: (?x5648, 01f7dd) <- nominated_for(?x166, ?x5648), film(?x3281, ?x5648), ?x166 = 0jz9f *> conf = 0.06 ranks of expected_values: 108, 363 EVAL 049xgc film! 01f7dd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 91.000 42.000 0.499 http://example.org/film/actor/film./film/performance/film EVAL 049xgc film! 021vwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 91.000 42.000 0.499 http://example.org/film/actor/film./film/performance/film #18620-04dn09n PRED entity: 04dn09n PRED relation: disciplines_or_subjects PRED expected values: 02vxn => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 17): 02jknp (0.25 #2, 0.12 #41, 0.06 #119), 02vxn (0.17 #470, 0.15 #352, 0.14 #313), 0w7c (0.10 #495, 0.08 #299, 0.08 #924), 04g51 (0.09 #1547, 0.09 #1742, 0.09 #726), 02xlf (0.06 #728, 0.05 #455, 0.05 #1549), 01hmnh (0.05 #440, 0.04 #401, 0.04 #713), 06n90 (0.04 #1687, 0.04 #437, 0.04 #1373), 05hgj (0.04 #454, 0.03 #727, 0.03 #415), 0dwly (0.01 #730, 0.01 #1746, 0.01 #652), 0jtdp (0.01 #359, 0.01 #203, 0.01 #438) >> Best rule #2 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 019f4v; >> query: (?x746, 02jknp) <- award(?x9452, ?x746), ?x9452 = 0c0zq, award(?x276, ?x746), nominated_for(?x746, ?x3283), ?x3283 = 06gjk9 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #470 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 186 *> proper extension: 02v1m7; 054ks3; 0g_w; 0dgshf6; 099vwn; 01c99j; 025m98; 04zx08r; 0dgr5xp; 09v1lrz; *> query: (?x746, 02vxn) <- nominated_for(?x746, ?x2029), award(?x276, ?x746), film(?x100, ?x2029) *> conf = 0.17 ranks of expected_values: 2 EVAL 04dn09n disciplines_or_subjects 02vxn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 49.000 49.000 0.250 http://example.org/award/award_category/disciplines_or_subjects #18619-03x73c PRED entity: 03x73c PRED relation: current_club! PRED expected values: 03ys48 => 68 concepts (54 used for prediction) PRED predicted values (max 10 best out of 33): 02ltg3 (0.38 #280, 0.28 #369, 0.26 #679), 03y_f8 (0.33 #3, 0.26 #679, 0.25 #32), 01l3wr (0.33 #24, 0.25 #53, 0.23 #297), 02bh_v (0.31 #293, 0.25 #423, 0.21 #453), 03_qj1 (0.29 #350, 0.18 #117, 0.18 #305), 03d8m4 (0.29 #349, 0.18 #305, 0.17 #433), 01l3vx (0.28 #369, 0.26 #679, 0.26 #553), 02w64f (0.28 #369, 0.26 #679, 0.26 #553), 02s2lg (0.28 #369, 0.26 #679, 0.26 #553), 03dj48 (0.28 #369, 0.26 #679, 0.26 #553) >> Best rule #280 for best value: >> intensional similarity = 17 >> extensional distance = 11 >> proper extension: 02mplj; 0y9j; >> query: (?x14018, 02ltg3) <- position(?x14018, ?x530), position(?x14018, ?x203), position(?x14018, ?x63), position(?x14018, ?x60), ?x530 = 02_j1w, ?x203 = 0dgrmp, ?x63 = 02sdk9v, current_club(?x11564, ?x14018), ?x60 = 02nzb8, current_club(?x11564, ?x9860), current_club(?x11564, ?x7423), current_club(?x11564, ?x3158), ?x3158 = 0xbm, current_club(?x8102, ?x9860), ?x8102 = 03_qrp, team(?x12564, ?x7423), teams(?x774, ?x11564) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #679 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 41 *> proper extension: 03x746; 01nd2c; 019lty; 03j7cf; 06khkb; 070tng; 048gd_; 032c7m; 057pq5; 03fnqj; ... *> query: (?x14018, ?x2427) <- position(?x14018, ?x530), position(?x14018, ?x203), position(?x14018, ?x63), position(?x14018, ?x60), ?x530 = 02_j1w, ?x203 = 0dgrmp, ?x63 = 02sdk9v, current_club(?x11564, ?x14018), ?x60 = 02nzb8, current_club(?x11564, ?x3158), current_club(?x2427, ?x3158), team(?x9106, ?x3158), sport(?x11564, ?x471), ?x9106 = 09j028, ?x471 = 02vx4, teams(?x774, ?x11564) *> conf = 0.26 ranks of expected_values: 12 EVAL 03x73c current_club! 03ys48 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 68.000 54.000 0.385 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #18618-01_2n PRED entity: 01_2n PRED relation: program! PRED expected values: 018kcp => 64 concepts (59 used for prediction) PRED predicted values (max 10 best out of 74): 0gsg7 (0.40 #58, 0.36 #283, 0.33 #453), 0g5lhl7 (0.33 #6, 0.31 #345, 0.18 #682), 01f2w0 (0.33 #23, 0.25 #362, 0.17 #418), 0cjdk (0.24 #624, 0.24 #568, 0.23 #229), 05gnf (0.22 #974, 0.22 #1202, 0.21 #1088), 01fsyp (0.20 #105, 0.17 #161, 0.08 #273), 018kcp (0.19 #2786, 0.02 #729, 0.01 #1241), 01w92 (0.19 #347, 0.06 #684, 0.06 #403), 0187wh (0.17 #138, 0.09 #533, 0.08 #250), 03mdt (0.14 #853, 0.14 #796, 0.13 #1025) >> Best rule #58 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 03ln8b; 01b9w3; 0cs134; >> query: (?x9749, 0gsg7) <- languages(?x9749, ?x254), genre(?x9749, ?x8805), genre(?x9749, ?x2480), program(?x8545, ?x9749), country_of_origin(?x9749, ?x512), actor(?x9749, ?x5661), ?x8805 = 06q7n, ?x2480 = 01z4y, award_winner(?x8545, ?x9793), country(?x362, ?x512) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2786 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 179 *> proper extension: 0bx_hnp; *> query: (?x9749, ?x14550) <- languages(?x9749, ?x254), program(?x14163, ?x9749), ?x254 = 02h40lc, category(?x14163, ?x134), program(?x14163, ?x7433), ?x134 = 08mbj5d, actor(?x7433, ?x649), program(?x14550, ?x7433) *> conf = 0.19 ranks of expected_values: 7 EVAL 01_2n program! 018kcp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 64.000 59.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #18617-02x4w6g PRED entity: 02x4w6g PRED relation: award! PRED expected values: 092vkg => 48 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1385): 0m313 (0.40 #6, 0.23 #2030, 0.14 #3045), 0hfzr (0.35 #2434, 0.18 #3449, 0.11 #13576), 09gq0x5 (0.32 #2195, 0.20 #171, 0.16 #3210), 03hmt9b (0.32 #2412, 0.20 #388, 0.14 #3427), 0pv3x (0.32 #2132, 0.10 #3147, 0.09 #8207), 0c0zq (0.29 #2919, 0.20 #895, 0.18 #3934), 0b6tzs (0.29 #2112, 0.20 #88, 0.16 #3127), 05hjnw (0.27 #23293, 0.26 #12154, 0.25 #9112), 092vkg (0.27 #23293, 0.26 #12154, 0.25 #9112), 02qpt1w (0.27 #23293, 0.26 #12154, 0.25 #9112) >> Best rule #6 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 09sb52; 05pcn59; 099ck7; >> query: (?x2183, 0m313) <- nominated_for(?x2183, ?x4939), nominated_for(?x2183, ?x4788), award(?x92, ?x2183), ?x4939 = 05hjnw, ?x4788 = 06t6dz >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #23293 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 226 *> proper extension: 0bp_b2; 0gkvb7; 02p_7cr; 0cqhk0; 0bdw1g; 09qvc0; 09qj50; 0fbvqf; 09qv3c; 047byns; ... *> query: (?x2183, ?x4939) <- nominated_for(?x2183, ?x4939), award(?x92, ?x2183), award(?x4939, ?x112), nominated_for(?x112, ?x144) *> conf = 0.27 ranks of expected_values: 9 EVAL 02x4w6g award! 092vkg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 48.000 23.000 0.400 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #18616-01qd_r PRED entity: 01qd_r PRED relation: company! PRED expected values: 09d6p2 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 36): 0dq_5 (0.44 #441, 0.38 #1053, 0.34 #535), 060c4 (0.44 #565, 0.43 #426, 0.36 #1038), 0krdk (0.41 #430, 0.35 #1042, 0.32 #524), 0dq3c (0.29 #425, 0.24 #1037, 0.24 #519), 05_wyz (0.29 #442, 0.24 #536, 0.23 #1054), 021q1c (0.27 #58, 0.19 #105, 0.17 #293), 09d6p2 (0.22 #443, 0.18 #537, 0.16 #1055), 01yc02 (0.19 #1044, 0.18 #432, 0.17 #1326), 01kr6k (0.17 #451, 0.14 #545, 0.12 #1063), 05k17c (0.12 #60, 0.09 #248, 0.08 #107) >> Best rule #441 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 02zs4; 087c7; 0cv9b; 0gsg7; 0hpt3; 09d5h; 01xdn1; 0gvbw; 03mnk; 02r5dz; ... >> query: (?x7660, 0dq_5) <- citytown(?x7660, ?x3987), state_province_region(?x7660, ?x2982), list(?x7660, ?x2197) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #443 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 93 *> proper extension: 02zs4; 087c7; 0cv9b; 0gsg7; 0hpt3; 09d5h; 01xdn1; 0gvbw; 03mnk; 02r5dz; ... *> query: (?x7660, 09d6p2) <- citytown(?x7660, ?x3987), state_province_region(?x7660, ?x2982), list(?x7660, ?x2197) *> conf = 0.22 ranks of expected_values: 7 EVAL 01qd_r company! 09d6p2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 114.000 114.000 0.442 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #18615-02dth1 PRED entity: 02dth1 PRED relation: people! PRED expected values: 033tf_ => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 46): 0xnvg (0.17 #244, 0.13 #475, 0.11 #1399), 041rx (0.16 #2701, 0.16 #2546, 0.14 #4319), 033tf_ (0.16 #7, 0.14 #315, 0.13 #854), 0x67 (0.15 #472, 0.12 #857, 0.12 #1704), 09vc4s (0.14 #240, 0.12 #86, 0.09 #471), 07hwkr (0.11 #12, 0.05 #320, 0.04 #89), 01qhm_ (0.08 #237, 0.08 #83, 0.08 #776), 02w7gg (0.08 #1311, 0.07 #3547, 0.07 #4009), 063k3h (0.08 #108, 0.06 #493, 0.05 #31), 07bch9 (0.07 #177, 0.07 #870, 0.06 #793) >> Best rule #244 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 0dxmyh; >> query: (?x4204, 0xnvg) <- actor(?x2710, ?x4204), nationality(?x4204, ?x94), friend(?x4204, ?x2799) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0hcvy; 02hg53; *> query: (?x4204, 033tf_) <- actor(?x2710, ?x4204), people(?x268, ?x4204), religion(?x4204, ?x1985) *> conf = 0.16 ranks of expected_values: 3 EVAL 02dth1 people! 033tf_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 115.000 0.167 http://example.org/people/ethnicity/people #18614-0ywqc PRED entity: 0ywqc PRED relation: film PRED expected values: 02qr3k8 => 163 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1149): 02bqxb (0.25 #1752, 0.05 #7083), 02mpyh (0.21 #5008, 0.02 #61874, 0.02 #63651), 03bx2lk (0.14 #3738, 0.05 #10846, 0.04 #62381), 04cv9m (0.14 #4251, 0.03 #9582, 0.03 #62894), 07nxnw (0.14 #4758, 0.03 #10089, 0.03 #63401), 04cj79 (0.14 #4146, 0.02 #11254, 0.02 #57458), 011xg5 (0.14 #4978, 0.02 #36964, 0.01 #49403), 095zlp (0.14 #3614, 0.01 #78252, 0.01 #48039), 0_9wr (0.14 #4780, 0.01 #61646), 02rx2m5 (0.12 #2068, 0.07 #3845, 0.01 #21615) >> Best rule #1752 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0p_2r; >> query: (?x10626, 02bqxb) <- student(?x2327, ?x10626), people(?x412, ?x10626), location(?x10626, ?x1310), ?x2327 = 07wjk >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3058 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 09r9dp; 024bbl; 022411; *> query: (?x10626, 02qr3k8) <- student(?x2327, ?x10626), film(?x10626, ?x6167), ?x6167 = 05r3qc, nationality(?x10626, ?x279) *> conf = 0.12 ranks of expected_values: 12 EVAL 0ywqc film 02qr3k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 163.000 89.000 0.250 http://example.org/film/actor/film./film/performance/film #18613-05pt0l PRED entity: 05pt0l PRED relation: nominated_for! PRED expected values: 0j298t8 => 88 concepts (70 used for prediction) PRED predicted values (max 10 best out of 219): 0j298t8 (0.78 #4571, 0.77 #5773, 0.68 #10349), 0fbtbt (0.41 #403, 0.05 #4491, 0.05 #5693), 0gq9h (0.38 #4393, 0.35 #5595, 0.30 #4152), 0ck27z (0.38 #314, 0.29 #483, 0.27 #9145), 0bdw6t (0.34 #482, 0.34 #327, 0.29 #483), 0fbvqf (0.34 #280, 0.07 #7458, 0.06 #15396), 0gs9p (0.34 #4395, 0.32 #5597, 0.31 #549), 09qvc0 (0.33 #34, 0.29 #483, 0.27 #9145), 09qs08 (0.33 #111, 0.16 #352, 0.04 #4440), 09qrn4 (0.33 #166, 0.16 #407, 0.03 #4495) >> Best rule #4571 for best value: >> intensional similarity = 4 >> extensional distance = 593 >> proper extension: 07bz5; 06mmr; >> query: (?x7481, ?x13664) <- award_winner(?x7481, ?x10701), award(?x7481, ?x13664), award(?x1197, ?x13664), ceremony(?x13664, ?x13189) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05pt0l nominated_for! 0j298t8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 70.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18612-03v0t PRED entity: 03v0t PRED relation: district_represented! PRED expected values: 043djx 07p__7 => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 38): 07p__7 (0.84 #727, 0.83 #499, 0.75 #955), 043djx (0.55 #498, 0.53 #726, 0.53 #308), 02bqm0 (0.53 #743, 0.50 #515, 0.49 #971), 02bqmq (0.48 #506, 0.47 #962, 0.47 #316), 02bqn1 (0.44 #1635, 0.43 #729, 0.41 #311), 02cg7g (0.44 #1635, 0.41 #740, 0.38 #512), 02gkzs (0.44 #1635, 0.41 #737, 0.38 #509), 01gssz (0.44 #1635, 0.36 #526, 0.35 #336), 01gssm (0.44 #1635, 0.36 #510, 0.35 #320), 01gsrl (0.44 #1635, 0.33 #511, 0.33 #739) >> Best rule #727 for best value: >> intensional similarity = 2 >> extensional distance = 47 >> proper extension: 0g0syc; >> query: (?x3818, 07p__7) <- district_represented(?x653, ?x3818), ?x653 = 070m6c >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03v0t district_represented! 07p__7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.837 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 03v0t district_represented! 043djx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.837 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #18611-015t7v PRED entity: 015t7v PRED relation: film PRED expected values: 0gtsxr4 => 69 concepts (43 used for prediction) PRED predicted values (max 10 best out of 307): 017gl1 (0.62 #1929, 0.04 #71457, 0.04 #76817), 0ndwt2w (0.38 #2784, 0.03 #23220, 0.03 #69670), 031hcx (0.29 #1271, 0.01 #8415, 0.01 #10201), 03177r (0.29 #463, 0.01 #7607, 0.01 #11179), 03176f (0.29 #705), 0ch26b_ (0.14 #300, 0.06 #2086, 0.03 #23220), 0fg04 (0.14 #101, 0.06 #1887, 0.03 #23220), 04x4gw (0.14 #1744, 0.03 #23220, 0.03 #69670), 01qz5 (0.14 #1413, 0.03 #23220, 0.03 #69670), 034qmv (0.14 #15, 0.03 #23220, 0.03 #69670) >> Best rule #1929 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02bfmn; >> query: (?x4999, 017gl1) <- award_nominee(?x4999, ?x2728), ?x2728 = 01v9l67, award(?x4999, ?x704) >> conf = 0.62 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015t7v film 0gtsxr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 43.000 0.625 http://example.org/film/actor/film./film/performance/film #18610-041y2 PRED entity: 041y2 PRED relation: student PRED expected values: 039bp 0dn3n 02sh8y => 51 concepts (23 used for prediction) PRED predicted values (max 10 best out of 434): 01mqh5 (0.33 #920, 0.25 #452, 0.22 #1391), 0br1w (0.25 #314, 0.20 #548, 0.17 #782), 06hgj (0.25 #401, 0.20 #635, 0.17 #869), 09l3p (0.25 #331, 0.20 #565, 0.17 #799), 01j7rd (0.25 #270, 0.20 #504, 0.17 #738), 01t6b4 (0.25 #256, 0.20 #490, 0.17 #724), 016kjs (0.25 #252, 0.20 #486, 0.17 #720), 04z0g (0.25 #123, 0.15 #1534, 0.14 #1063), 06c0j (0.25 #229, 0.15 #1640, 0.07 #3057), 012x2b (0.25 #183, 0.10 #3011, 0.09 #2539) >> Best rule #920 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 01zc2w; >> query: (?x10046, 01mqh5) <- major_field_of_study(?x7545, ?x10046), major_field_of_study(?x4794, ?x10046), major_field_of_study(?x3416, ?x10046), ?x3416 = 02183k, major_field_of_study(?x10046, ?x254), major_field_of_study(?x1200, ?x10046), student(?x4794, ?x1485), student(?x10046, ?x690), organization(?x346, ?x4794), student(?x7545, ?x157), institution(?x620, ?x7545) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #237 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 02822; *> query: (?x10046, ?x2639) <- major_field_of_study(?x3416, ?x10046), major_field_of_study(?x735, ?x10046), major_field_of_study(?x388, ?x10046), student(?x3416, ?x5030), student(?x3416, ?x2639), institution(?x3386, ?x3416), ?x3386 = 03mkk4, major_field_of_study(?x10046, ?x5614), award_nominee(?x5030, ?x968), ?x388 = 05krk, student(?x735, ?x65), school(?x580, ?x735), ?x5614 = 03qsdpk *> conf = 0.02 ranks of expected_values: 338 EVAL 041y2 student 02sh8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 23.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 041y2 student 0dn3n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 23.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 041y2 student 039bp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 51.000 23.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #18609-01vsl PRED entity: 01vsl PRED relation: place_of_birth! PRED expected values: 0226cw => 152 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1672): 02bfmn (0.46 #138543, 0.39 #122855, 0.35 #7841), 0226cw (0.35 #7841, 0.33 #188206, 0.33 #188205), 05d1y (0.35 #7841, 0.33 #188206, 0.33 #188205), 01pbxb (0.33 #9, 0.08 #5236, 0.03 #13079), 01rh0w (0.08 #5466, 0.04 #8081, 0.03 #13309), 05f0r8 (0.08 #7827, 0.04 #10442, 0.03 #15670), 05h7tk (0.08 #7799, 0.04 #10414, 0.03 #15642), 0745k7 (0.08 #7798, 0.04 #10413, 0.03 #15641), 058z1hb (0.08 #7796, 0.04 #10411, 0.03 #15639), 02nygk (0.08 #7787, 0.04 #10402, 0.03 #15630) >> Best rule #138543 for best value: >> intensional similarity = 4 >> extensional distance = 195 >> proper extension: 0fvxz; 0_jq4; 0xkyn; 0sbv7; >> query: (?x7770, ?x230) <- location(?x230, ?x7770), source(?x7770, ?x958), award_nominee(?x628, ?x230), place_of_birth(?x230, ?x3097) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #7841 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 0hptm; 01m9f1; 0ftvg; *> query: (?x7770, ?x230) <- location(?x8607, ?x7770), location(?x230, ?x7770), source(?x7770, ?x958), student(?x3228, ?x8607), legislative_sessions(?x8607, ?x355) *> conf = 0.35 ranks of expected_values: 2 EVAL 01vsl place_of_birth! 0226cw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 152.000 76.000 0.464 http://example.org/people/person/place_of_birth #18608-05qsxy PRED entity: 05qsxy PRED relation: award_nominee! PRED expected values: 050023 => 92 concepts (31 used for prediction) PRED predicted values (max 10 best out of 750): 070w7s (0.86 #4649, 0.84 #4648, 0.81 #48803), 0265vcb (0.86 #4649, 0.84 #4648, 0.81 #48803), 050023 (0.86 #4649, 0.81 #48803, 0.81 #72045), 045w_4 (0.86 #4649, 0.81 #48803, 0.81 #72045), 01rgcg (0.84 #4648, 0.77 #6973, 0.77 #39507), 0646qh (0.70 #1551, 0.67 #6200, 0.53 #3875), 03cl8lb (0.50 #1497, 0.47 #6146, 0.40 #3821), 026dcvf (0.50 #76, 0.40 #4725, 0.40 #2400), 026n3rs (0.50 #956, 0.33 #18592, 0.33 #5605), 08chdb (0.47 #4451, 0.40 #2127, 0.33 #18592) >> Best rule #4649 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 02_2v2; 0265vcb; 01rgcg; 04gtdnh; 025vw4t; 026n9h3; 08chdb; >> query: (?x2543, ?x438) <- award_winner(?x2543, ?x415), award_nominee(?x2543, ?x2544), award_nominee(?x2543, ?x438), ?x2544 = 02rghbp >> conf = 0.86 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 05qsxy award_nominee! 050023 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 31.000 0.860 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18607-0bvzp PRED entity: 0bvzp PRED relation: people! PRED expected values: 0dq9p => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 32): 0gk4g (0.13 #1925, 0.12 #2519, 0.11 #2189), 0dq9p (0.08 #1932, 0.07 #2526, 0.07 #149), 0qcr0 (0.07 #1916, 0.06 #2180, 0.06 #2510), 0gg4h (0.06 #36, 0.05 #102, 0.03 #432), 051_y (0.06 #48, 0.03 #378, 0.03 #510), 01l2m3 (0.06 #16, 0.03 #1931, 0.02 #2525), 019dmc (0.06 #50, 0.02 #182, 0.02 #248), 032s66 (0.06 #49, 0.02 #181, 0.02 #247), 0x2fg (0.06 #38, 0.02 #170, 0.02 #236), 06z5s (0.06 #25, 0.02 #2204, 0.02 #2534) >> Best rule #1925 for best value: >> intensional similarity = 2 >> extensional distance = 422 >> proper extension: 01cqz5; >> query: (?x6399, 0gk4g) <- place_of_death(?x6399, ?x739), place_of_birth(?x6399, ?x12919) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #1932 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 422 *> proper extension: 01cqz5; *> query: (?x6399, 0dq9p) <- place_of_death(?x6399, ?x739), place_of_birth(?x6399, ?x12919) *> conf = 0.08 ranks of expected_values: 2 EVAL 0bvzp people! 0dq9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.130 http://example.org/people/cause_of_death/people #18606-01yf85 PRED entity: 01yf85 PRED relation: award PRED expected values: 05zr6wv => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 250): 05pcn59 (0.35 #80, 0.29 #7334, 0.26 #1692), 09sb52 (0.33 #18175, 0.32 #21802, 0.32 #1249), 05zr6wv (0.26 #16, 0.23 #1628, 0.22 #2031), 05ztrmj (0.20 #183, 0.17 #1392, 0.17 #3004), 07cbcy (0.19 #883, 0.18 #2898, 0.17 #1286), 01bgqh (0.16 #2057, 0.16 #445, 0.15 #3669), 0f4x7 (0.16 #2851, 0.15 #836, 0.14 #4060), 0gqwc (0.16 #476, 0.15 #7730, 0.14 #10954), 01by1l (0.15 #2126, 0.14 #514, 0.13 #3738), 04kxsb (0.15 #930, 0.14 #1736, 0.14 #2139) >> Best rule #80 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 04d_mtq; >> query: (?x8716, 05pcn59) <- friend(?x8716, ?x5940), gender(?x8716, ?x514), vacationer(?x151, ?x8716) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 04d_mtq; *> query: (?x8716, 05zr6wv) <- friend(?x8716, ?x5940), gender(?x8716, ?x514), vacationer(?x151, ?x8716) *> conf = 0.26 ranks of expected_values: 3 EVAL 01yf85 award 05zr6wv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.348 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18605-05dbf PRED entity: 05dbf PRED relation: award_nominee PRED expected values: 04yj5z => 139 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1249): 0h0wc (0.81 #197702, 0.81 #111651, 0.81 #116304), 01nwwl (0.81 #197702, 0.81 #111651, 0.81 #116304), 07r1h (0.81 #197702, 0.81 #111651, 0.81 #116304), 0525b (0.81 #197702, 0.81 #111651, 0.81 #116304), 01vsn38 (0.81 #197702, 0.81 #111651, 0.81 #116304), 04yj5z (0.81 #197702, 0.81 #111651, 0.81 #116304), 013tcv (0.21 #174445, 0.17 #76759, 0.16 #60473), 03lvyj (0.21 #174445, 0.14 #1877, 0.02 #18156), 0pz91 (0.21 #174445, 0.07 #11902, 0.06 #16554), 02cx90 (0.21 #174445, 0.04 #124287, 0.03 #138242) >> Best rule #197702 for best value: >> intensional similarity = 2 >> extensional distance = 1236 >> proper extension: 02lymt; 06cl2w; >> query: (?x2275, ?x748) <- film(?x2275, ?x308), award_nominee(?x748, ?x2275) >> conf = 0.81 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 05dbf award_nominee 04yj5z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 139.000 90.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18604-0f7hc PRED entity: 0f7hc PRED relation: type_of_union PRED expected values: 04ztj 01g63y => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.78 #53, 0.75 #49, 0.75 #145), 01g63y (0.33 #2, 0.25 #6, 0.19 #126), 01bl8s (0.01 #39, 0.01 #43), 0jgjn (0.01 #76, 0.01 #48) >> Best rule #53 for best value: >> intensional similarity = 2 >> extensional distance = 103 >> proper extension: 0p_jc; >> query: (?x4657, 04ztj) <- film(?x4657, ?x886), influenced_by(?x1835, ?x4657) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0f7hc type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.781 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 0f7hc type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.781 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18603-0694j PRED entity: 0694j PRED relation: state_province_region! PRED expected values: 0bsnm => 275 concepts (138 used for prediction) PRED predicted values (max 10 best out of 753): 052p7 (0.34 #65833, 0.32 #41130, 0.32 #72566), 01dbxr (0.34 #65833, 0.32 #41130, 0.32 #72566), 0pml7 (0.34 #65833, 0.32 #41130, 0.32 #72566), 01fd26 (0.34 #65833, 0.32 #41130, 0.32 #72566), 0pmp2 (0.34 #65833, 0.32 #41130, 0.32 #72566), 019vsw (0.20 #1966, 0.12 #6451, 0.03 #18415), 0h6rm (0.20 #1681, 0.10 #8408, 0.08 #9905), 04jr87 (0.20 #1770, 0.02 #38409, 0.01 #61611), 05frqx (0.17 #3739, 0.11 #7476, 0.07 #11963), 01dq0z (0.17 #3695, 0.11 #7432, 0.07 #11919) >> Best rule #65833 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 09d4_; >> query: (?x6842, ?x481) <- country(?x6842, ?x279), contains(?x6842, ?x481), state_province_region(?x6091, ?x6842), category(?x481, ?x134) >> conf = 0.34 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 0694j state_province_region! 0bsnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 275.000 138.000 0.345 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #18602-01trhmt PRED entity: 01trhmt PRED relation: location PRED expected values: 013yq => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 242): 0f2rq (0.51 #14460, 0.51 #36947, 0.47 #73903), 030qb3t (0.32 #63543, 0.30 #8116, 0.24 #6509), 02_286 (0.19 #1643, 0.17 #8070, 0.17 #37), 059rby (0.17 #16, 0.05 #52229, 0.04 #10459), 07_fl (0.17 #566, 0.01 #2975), 0t0n5 (0.17 #293, 0.01 #4309, 0.01 #6719), 0cr3d (0.12 #1750, 0.08 #13800, 0.08 #29863), 01n7q (0.10 #1669, 0.07 #8096, 0.07 #4079), 01531 (0.09 #960, 0.06 #4977, 0.05 #20238), 013yq (0.06 #5741, 0.05 #3331, 0.05 #4938) >> Best rule #14460 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 079vf; 03ldxq; 03qd_; 0p_2r; 0738b8; 03y9ccy; 01n1gc; 04w391; 024bbl; 056wb; ... >> query: (?x2562, ?x5719) <- currency(?x2562, ?x170), award_winner(?x6264, ?x2562), place_of_birth(?x2562, ?x5719) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #5741 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 93 *> proper extension: 0gps0z; 01vzz1c; *> query: (?x2562, 013yq) <- currency(?x2562, ?x170), artists(?x671, ?x2562), ?x671 = 064t9 *> conf = 0.06 ranks of expected_values: 10 EVAL 01trhmt location 013yq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 135.000 135.000 0.510 http://example.org/people/person/places_lived./people/place_lived/location #18601-07t_l23 PRED entity: 07t_l23 PRED relation: award_winner PRED expected values: 03p9hl => 38 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1419): 0l6px (0.67 #5442, 0.33 #2965, 0.09 #7919), 05cj4r (0.33 #7430, 0.33 #5006, 0.31 #4951), 011_3s (0.33 #3182, 0.33 #706, 0.17 #5659), 02jsgf (0.33 #3374, 0.33 #898, 0.17 #5851), 02mqc4 (0.33 #5865, 0.33 #3388, 0.04 #8342), 01dbk6 (0.33 #6179, 0.33 #3702, 0.04 #13608), 0gjvqm (0.33 #5199, 0.33 #2722, 0.03 #12628), 01hkhq (0.33 #521, 0.17 #5474, 0.12 #12903), 0154qm (0.33 #709, 0.17 #5662, 0.07 #13091), 015c2f (0.33 #3084, 0.17 #5561, 0.07 #8038) >> Best rule #5442 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0bfvw2; 09sb52; 02ppm4q; >> query: (?x13235, 0l6px) <- award(?x8307, ?x13235), award(?x2185, ?x13235), award(?x374, ?x13235), profession(?x2185, ?x1383), ?x374 = 05cj4r, ?x1383 = 0np9r, film(?x8307, ?x3803) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #12381 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 02py_sj; *> query: (?x13235, ?x65) <- nominated_for(?x13235, ?x7254), genre(?x7254, ?x53), nominated_for(?x4921, ?x7254), award(?x65, ?x4921) *> conf = 0.03 ranks of expected_values: 455 EVAL 07t_l23 award_winner 03p9hl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 38.000 12.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #18600-0bh8drv PRED entity: 0bh8drv PRED relation: film_festivals PRED expected values: 0bmj62v => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 15): 04_m9gk (0.17 #76, 0.05 #517, 0.05 #328), 059_y8d (0.12 #44, 0.04 #65, 0.02 #737), 0g57ws5 (0.12 #49, 0.03 #217, 0.02 #154), 0gg7gsl (0.09 #64, 0.04 #106, 0.02 #1219), 0bmj62v (0.09 #75, 0.04 #159, 0.03 #369), 04grdgy (0.04 #72, 0.03 #93, 0.02 #366), 0fpkxfd (0.04 #69, 0.02 #90, 0.01 #762), 03nn7l2 (0.04 #80, 0.01 #605, 0.01 #1004), 0kfhjq0 (0.03 #467, 0.03 #698, 0.03 #530), 09rwjly (0.03 #365, 0.03 #407, 0.02 #155) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0g83dv; >> query: (?x7516, 04_m9gk) <- nominated_for(?x941, ?x7516), film_crew_role(?x7516, ?x137), genre(?x7516, ?x53), ?x941 = 0fq9zdn >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 0g83dv; *> query: (?x7516, 0bmj62v) <- nominated_for(?x941, ?x7516), film_crew_role(?x7516, ?x137), genre(?x7516, ?x53), ?x941 = 0fq9zdn *> conf = 0.09 ranks of expected_values: 5 EVAL 0bh8drv film_festivals 0bmj62v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 113.000 113.000 0.174 http://example.org/film/film/film_festivals #18599-0281rb PRED entity: 0281rb PRED relation: contains! PRED expected values: 030qb3t => 77 concepts (58 used for prediction) PRED predicted values (max 10 best out of 203): 0kpys (0.28 #3757, 0.27 #6439, 0.21 #10909), 07ssc (0.21 #34904, 0.21 #47424, 0.21 #35798), 06pvr (0.18 #10894, 0.16 #6424, 0.16 #3742), 030qb3t (0.16 #3677, 0.13 #3578, 0.12 #99), 02jx1 (0.16 #47478, 0.15 #44796, 0.15 #34958), 059rby (0.14 #2701, 0.09 #24159, 0.09 #19689), 04_1l0v (0.12 #449, 0.10 #1343, 0.06 #2237), 0l2lk (0.12 #366, 0.10 #1260, 0.06 #2154), 0cv5l (0.12 #858, 0.10 #1752), 0f8l9c (0.12 #46, 0.03 #12564, 0.03 #14352) >> Best rule #3757 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 0r89d; >> query: (?x5670, 0kpys) <- place_of_birth(?x7046, ?x5670), contains(?x1227, ?x5670), ?x1227 = 01n7q, actor(?x2191, ?x7046) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #3677 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0r89d; *> query: (?x5670, 030qb3t) <- place_of_birth(?x7046, ?x5670), contains(?x1227, ?x5670), ?x1227 = 01n7q, actor(?x2191, ?x7046) *> conf = 0.16 ranks of expected_values: 4 EVAL 0281rb contains! 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 58.000 0.280 http://example.org/location/location/contains #18598-0d0vqn PRED entity: 0d0vqn PRED relation: film_release_region! PRED expected values: 0crfwmx 047msdk 07qg8v 04zyhx 0jqn5 0ch26b_ 05z7c 085ccd 0f4m2z 0879bpq 0dyb1 023gxx 0gyfp9c 0c8qq 09gkx35 0c3xw46 06zn2v2 0dlngsd 02xbyr 0hv8w 031ldd 0gmd3k7 01f85k 02qk3fk 07pd_j 078mm1 0ds5_72 08j7lh 0m3gy 0ddbjy4 024lt6 02vzpb 023vcd 0dw4b0 => 145 concepts (84 used for prediction) PRED predicted values (max 10 best out of 948): 0ch26b_ (0.84 #9536, 0.68 #10475, 0.67 #2962), 0gmd3k7 (0.76 #9962, 0.67 #3388, 0.65 #10901), 0gj96ln (0.74 #9949, 0.70 #10888, 0.67 #3375), 02xbyr (0.74 #9794, 0.65 #13552, 0.62 #10733), 0dlngsd (0.74 #9781, 0.65 #10720, 0.63 #13539), 0879bpq (0.73 #3034, 0.72 #10547, 0.71 #9608), 0c3xw46 (0.73 #3130, 0.71 #9704, 0.65 #10643), 047msdk (0.73 #2914, 0.66 #9488, 0.65 #10427), 0ddbjy4 (0.68 #10205, 0.67 #3631, 0.65 #11144), 0jqn5 (0.67 #2923, 0.65 #10436, 0.63 #9497) >> Best rule #9536 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 01ls2; 05v10; >> query: (?x304, 0ch26b_) <- film_release_region(?x141, ?x304), countries_spoken_in(?x4442, ?x304), ?x141 = 0gtsx8c >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 29, 42, 44, 47, 50, 52, 59, 60, 61, 65, 71 EVAL 0d0vqn film_release_region! 0dw4b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 023vcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 02vzpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 024lt6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0ddbjy4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0m3gy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 08j7lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0ds5_72 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 078mm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 07pd_j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 02qk3fk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 01f85k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0gmd3k7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 031ldd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0hv8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 02xbyr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0dlngsd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 06zn2v2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0c3xw46 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 09gkx35 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0c8qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0gyfp9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 023gxx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0dyb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0879bpq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0f4m2z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 085ccd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 05z7c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0ch26b_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0jqn5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 04zyhx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 07qg8v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 047msdk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0vqn film_release_region! 0crfwmx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 84.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18597-04v3q PRED entity: 04v3q PRED relation: organization PRED expected values: 02vk52z 02jxk => 176 concepts (152 used for prediction) PRED predicted values (max 10 best out of 20): 02vk52z (0.89 #2055, 0.88 #1735, 0.88 #930), 0b6css (0.69 #72, 0.62 #51, 0.57 #284), 018cqq (0.65 #158, 0.62 #73, 0.62 #52), 0_2v (0.59 #151, 0.55 #193, 0.54 #66), 04k4l (0.54 #67, 0.53 #152, 0.50 #427), 02jxk (0.54 #44, 0.53 #150, 0.46 #65), 041288 (0.40 #2825, 0.39 #2090, 0.36 #1157), 059dn (0.40 #2825, 0.33 #2501, 0.32 #2933), 085h1 (0.40 #2825, 0.33 #2501, 0.32 #2933), 0gkjy (0.34 #1487, 0.32 #1148, 0.32 #2933) >> Best rule #2055 for best value: >> intensional similarity = 3 >> extensional distance = 138 >> proper extension: 06s6l; 07z5n; 0jdd; 07bxhl; 07fb6; 06s9y; 03188; >> query: (?x1061, 02vk52z) <- contains(?x455, ?x1061), currency(?x1061, ?x170), jurisdiction_of_office(?x182, ?x1061) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 04v3q organization 02jxk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 176.000 152.000 0.886 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04v3q organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 152.000 0.886 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #18596-016lv3 PRED entity: 016lv3 PRED relation: profession PRED expected values: 0dxtg 018gz8 => 87 concepts (35 used for prediction) PRED predicted values (max 10 best out of 68): 01d_h8 (0.78 #1190, 0.76 #1338, 0.76 #598), 0dxtg (0.66 #901, 0.66 #1641, 0.60 #2086), 03gjzk (0.46 #606, 0.45 #1198, 0.44 #1346), 0kyk (0.36 #29, 0.29 #177, 0.14 #4474), 0cbd2 (0.32 #155, 0.32 #7, 0.18 #2526), 02krf9 (0.26 #914, 0.24 #1654, 0.23 #1506), 09jwl (0.23 #1794, 0.20 #1942, 0.18 #1054), 018gz8 (0.20 #16, 0.18 #2535, 0.16 #312), 01c72t (0.15 #1799, 0.13 #1947, 0.10 #763), 0nbcg (0.14 #1807, 0.14 #1955, 0.12 #1067) >> Best rule #1190 for best value: >> intensional similarity = 4 >> extensional distance = 236 >> proper extension: 02hy9p; >> query: (?x12252, 01d_h8) <- profession(?x12252, ?x524), executive_produced_by(?x8555, ?x12252), genre(?x8555, ?x53), film(?x374, ?x8555) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #901 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 199 *> proper extension: 03f2_rc; 03qd_; 03gm48; 015grj; 0sz28; 01vs_v8; 0127m7; 09ftwr; 03nk3t; 0gn30; ... *> query: (?x12252, 0dxtg) <- gender(?x12252, ?x231), profession(?x12252, ?x524), student(?x2999, ?x12252), place_of_birth(?x12252, ?x9502), ?x524 = 02jknp *> conf = 0.66 ranks of expected_values: 2, 8 EVAL 016lv3 profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 35.000 0.782 http://example.org/people/person/profession EVAL 016lv3 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 35.000 0.782 http://example.org/people/person/profession #18595-08jtv5 PRED entity: 08jtv5 PRED relation: film PRED expected values: 02xbyr => 88 concepts (74 used for prediction) PRED predicted values (max 10 best out of 406): 0_7w6 (0.50 #303, 0.20 #2096, 0.14 #9261), 05sw5b (0.43 #4400, 0.25 #816, 0.20 #2609), 099bhp (0.29 #5204, 0.25 #1620, 0.21 #10578), 050f0s (0.29 #3894, 0.20 #2103, 0.07 #9268), 047csmy (0.25 #915, 0.22 #6290, 0.21 #9873), 0g56t9t (0.25 #10, 0.21 #8968, 0.20 #10759), 01ry_x (0.25 #1708, 0.20 #3501, 0.14 #5292), 0872p_c (0.25 #175, 0.14 #9133, 0.13 #10924), 023p7l (0.25 #619, 0.14 #9577, 0.13 #11368), 03h3x5 (0.25 #423, 0.07 #9381, 0.07 #14757) >> Best rule #303 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02gf_l; 024my5; >> query: (?x9867, 0_7w6) <- actor(?x5955, ?x9867), actor(?x9340, ?x9867), nationality(?x9867, ?x94), ?x9340 = 05nlzq >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6181 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01lly5; 0219q; 0725ny; 01rcmg; 0582cf; 01x0sy; 031c2r; *> query: (?x9867, 02xbyr) <- actor(?x5955, ?x9867), actor(?x9340, ?x9867), type_of_union(?x9867, ?x566), ?x5955 = 016ztl *> conf = 0.11 ranks of expected_values: 34 EVAL 08jtv5 film 02xbyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 88.000 74.000 0.500 http://example.org/film/actor/film./film/performance/film #18594-0c7t58 PRED entity: 0c7t58 PRED relation: award_winner! PRED expected values: 03gt46z => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 110): 0fqpc7d (0.33 #34, 0.04 #170, 0.02 #2890), 0275n3y (0.19 #342, 0.16 #206, 0.11 #9523), 0g55tzk (0.19 #132, 0.12 #268, 0.08 #404), 0gvstc3 (0.19 #440, 0.11 #6666, 0.10 #576), 05c1t6z (0.17 #423, 0.11 #6666, 0.11 #1239), 0bq_mx (0.13 #536, 0.07 #1624, 0.06 #1352), 09qftb (0.12 #244, 0.12 #380, 0.04 #1332), 03nnm4t (0.11 #6666, 0.11 #477, 0.10 #613), 09pnw5 (0.11 #6666, 0.11 #9523, 0.06 #1322), 0418154 (0.11 #6666, 0.10 #103, 0.06 #1327) >> Best rule #34 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 0fvf9q; 03f1zdw; 01271h; 01wy5m; 07k51gd; 02g40r; 027zz; 02zj61; >> query: (?x3763, 0fqpc7d) <- award_winner(?x1553, ?x3763), award_winner(?x3763, ?x3762), ?x1553 = 0g5b0q5 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6666 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1438 *> proper extension: 0f721s; 01p5yn; 0hm0k; 039cq4; 0283xx2; 01j53q; 01zcrv; 0kc8y; 07k2d; 04rqd; ... *> query: (?x3763, ?x2126) <- award_winner(?x3763, ?x5557), award_winner(?x5557, ?x3880), award_winner(?x2126, ?x3880) *> conf = 0.11 ranks of expected_values: 12 EVAL 0c7t58 award_winner! 03gt46z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 102.000 102.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18593-047gpsd PRED entity: 047gpsd PRED relation: production_companies PRED expected values: 054lpb6 => 108 concepts (61 used for prediction) PRED predicted values (max 10 best out of 64): 024rgt (0.56 #332, 0.56 #272, 0.38 #331), 086k8 (0.40 #1162, 0.40 #1079, 0.38 #331), 017s11 (0.38 #331, 0.38 #330, 0.36 #4150), 016tw3 (0.38 #331, 0.38 #330, 0.36 #4150), 01795t (0.29 #104, 0.04 #1017, 0.03 #769), 020h2v (0.20 #59, 0.12 #224, 0.06 #1054), 0338lq (0.20 #7, 0.02 #919, 0.02 #1084), 056ws9 (0.20 #46, 0.02 #793, 0.02 #4030), 05qd_ (0.14 #92, 0.12 #591, 0.11 #2495), 09b3v (0.14 #115, 0.06 #198, 0.04 #614) >> Best rule #332 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 091z_p; >> query: (?x6719, ?x2549) <- film_crew_role(?x6719, ?x137), film(?x2549, ?x6719), ?x2549 = 024rgt >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #430 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 02gpkt; 0yx_w; *> query: (?x6719, 054lpb6) <- featured_film_locations(?x6719, ?x1523), film(?x382, ?x6719), ?x1523 = 030qb3t, film(?x3477, ?x6719) *> conf = 0.11 ranks of expected_values: 13 EVAL 047gpsd production_companies 054lpb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 108.000 61.000 0.561 http://example.org/film/film/production_companies #18592-05g3b PRED entity: 05g3b PRED relation: colors PRED expected values: 083jv 06fvc => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.59 #282, 0.58 #543, 0.57 #643), 019sc (0.36 #368, 0.33 #549, 0.33 #248), 01l849 (0.32 #361, 0.31 #261, 0.29 #462), 06fvc (0.22 #644, 0.20 #1204, 0.19 #1224), 02rnmb (0.18 #1235, 0.17 #1215, 0.17 #1256), 03vtbc (0.18 #550, 0.18 #369, 0.17 #650), 01g5v (0.17 #1507, 0.16 #1526, 0.16 #1525), 0jc_p (0.16 #1526, 0.16 #1525, 0.16 #1524), 09ggk (0.16 #1526, 0.16 #1525, 0.16 #1524), 04d18d (0.16 #1526, 0.16 #1525, 0.16 #1524) >> Best rule #282 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 043vc; 05gg4; >> query: (?x729, 083jv) <- position_s(?x729, ?x2573), position_s(?x729, ?x1792), draft(?x729, ?x465), school(?x729, ?x1681), ?x2573 = 05b3ts, ?x1792 = 05zm34 >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 05g3b colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.588 http://example.org/sports/sports_team/colors EVAL 05g3b colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.588 http://example.org/sports/sports_team/colors #18591-0dzst PRED entity: 0dzst PRED relation: school! PRED expected values: 0f4vx0 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 18): 0f4vx0 (0.31 #64, 0.30 #82, 0.27 #226), 02qw1zx (0.25 #221, 0.24 #59, 0.24 #77), 025tn92 (0.18 #264, 0.18 #228, 0.17 #318), 05vsb7 (0.18 #253, 0.17 #19, 0.17 #307), 09l0x9 (0.17 #29, 0.16 #227, 0.16 #65), 02pq_rp (0.17 #25, 0.11 #313, 0.10 #259), 02pq_x5 (0.17 #267, 0.15 #321, 0.11 #69), 092j54 (0.16 #314, 0.16 #260, 0.15 #224), 06439y (0.15 #234, 0.14 #469, 0.13 #324), 03nt7j (0.15 #258, 0.14 #24, 0.14 #312) >> Best rule #64 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 03h64; 059ss; >> query: (?x9200, 0f4vx0) <- organization(?x9200, ?x5487), contains(?x94, ?x9200), country(?x54, ?x94) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dzst school! 0f4vx0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.311 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #18590-0kw4j PRED entity: 0kw4j PRED relation: colors PRED expected values: 01g5v => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.44 #322, 0.39 #422, 0.38 #1862), 01l849 (0.31 #101, 0.26 #681, 0.25 #2081), 01g5v (0.30 #323, 0.29 #1823, 0.29 #43), 019sc (0.18 #1827, 0.18 #2087, 0.18 #1687), 036k5h (0.14 #45, 0.12 #65, 0.11 #325), 04mkbj (0.11 #330, 0.11 #310, 0.11 #250), 03wkwg (0.11 #195, 0.07 #395, 0.07 #715), 0jc_p (0.11 #544, 0.10 #324, 0.09 #684), 038hg (0.09 #1872, 0.09 #2092, 0.09 #1832), 088fh (0.07 #386, 0.05 #686, 0.05 #206) >> Best rule #322 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 03zw80; 016sd3; >> query: (?x3821, 083jv) <- state_province_region(?x3821, ?x108), category(?x3821, ?x134), currency(?x3821, ?x170), colors(?x3821, ?x1101) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #323 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 69 *> proper extension: 03zw80; 016sd3; *> query: (?x3821, 01g5v) <- state_province_region(?x3821, ?x108), category(?x3821, ?x134), currency(?x3821, ?x170), colors(?x3821, ?x1101) *> conf = 0.30 ranks of expected_values: 3 EVAL 0kw4j colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 176.000 176.000 0.437 http://example.org/education/educational_institution/colors #18589-02vqhv0 PRED entity: 02vqhv0 PRED relation: film! PRED expected values: 015vq_ => 99 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1052): 02qgqt (0.33 #18, 0.17 #4179, 0.05 #24990), 01s7zw (0.33 #424, 0.17 #4585, 0.04 #8745), 078mgh (0.33 #1421, 0.17 #5582), 098n_m (0.33 #951, 0.17 #5112), 026_w57 (0.33 #630, 0.17 #4791), 0z4s (0.25 #2149, 0.17 #4229, 0.09 #6309), 086sj (0.25 #2793, 0.17 #4873, 0.03 #11115), 01sp81 (0.25 #2229, 0.17 #4309, 0.03 #27202), 03h_fqv (0.25 #3034, 0.17 #5114, 0.02 #19683), 0479b (0.25 #3289, 0.17 #5369, 0.01 #49073) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01s7w3; >> query: (?x2024, 02qgqt) <- film_crew_role(?x2024, ?x137), film(?x4214, ?x2024), ?x4214 = 0219q, music(?x2024, ?x7701) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #96438 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 533 *> proper extension: 05dl1s; *> query: (?x2024, 015vq_) <- film(?x2182, ?x2024), genre(?x2024, ?x53), student(?x735, ?x2182), profession(?x2182, ?x319) *> conf = 0.02 ranks of expected_values: 703 EVAL 02vqhv0 film! 015vq_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 99.000 80.000 0.333 http://example.org/film/actor/film./film/performance/film #18588-051q39 PRED entity: 051q39 PRED relation: people! PRED expected values: 0fk1z => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 25): 0x67 (0.65 #550, 0.64 #860, 0.64 #627), 033tf_ (0.14 #316, 0.06 #857, 0.06 #1165), 041rx (0.09 #467, 0.08 #3319, 0.08 #4244), 0xnvg (0.09 #476, 0.04 #2249, 0.04 #2403), 09vc4s (0.09 #472, 0.03 #936, 0.02 #1013), 02ctzb (0.07 #1404, 0.06 #1173, 0.05 #1250), 06v41q (0.05 #646, 0.04 #724, 0.04 #801), 0fqz6 (0.05 #659, 0.04 #737, 0.04 #814), 02w7gg (0.04 #3009, 0.04 #2932, 0.04 #3317), 07hwkr (0.03 #2402, 0.03 #2248, 0.03 #2556) >> Best rule #550 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 01ztgm; 03n69x; 03l295; 01kmd4; 01f492; 01sg7_; 015cbq; 0cymln; 054c1; 02hg53; ... >> query: (?x13558, 0x67) <- athlete(?x1557, ?x13558), profession(?x13558, ?x1581), ?x1581 = 01445t, sports(?x358, ?x1557), country(?x1557, ?x1917), film_release_region(?x80, ?x1917) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 051q39 people! 0fk1z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.650 http://example.org/people/ethnicity/people #18587-0clvcx PRED entity: 0clvcx PRED relation: gender PRED expected values: 02zsn => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #156, 0.72 #49, 0.72 #55), 02zsn (0.52 #151, 0.51 #138, 0.50 #117) >> Best rule #156 for best value: >> intensional similarity = 3 >> extensional distance = 2567 >> proper extension: 014dq7; 05yvfd; 01cqz5; 0bhtzw; >> query: (?x1435, 05zppz) <- place_of_birth(?x1435, ?x14415), place_of_birth(?x2320, ?x14415), gender(?x2320, ?x231) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #151 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2243 *> proper extension: 05qhnq; *> query: (?x1435, ?x514) <- award_nominee(?x5144, ?x1435), gender(?x5144, ?x514) *> conf = 0.52 ranks of expected_values: 2 EVAL 0clvcx gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.724 http://example.org/people/person/gender #18586-0bv8h2 PRED entity: 0bv8h2 PRED relation: film! PRED expected values: 02xv8m => 90 concepts (53 used for prediction) PRED predicted values (max 10 best out of 788): 02x7vq (0.22 #3061, 0.04 #108186, 0.03 #104025), 01r2c7 (0.21 #6241, 0.15 #31214, 0.15 #58261), 0f5xn (0.20 #971, 0.08 #5131, 0.04 #11374), 0klh7 (0.20 #490, 0.08 #4650, 0.03 #6732), 06j8wx (0.20 #962, 0.08 #5122, 0.03 #7204), 0h0wc (0.20 #425, 0.05 #6667, 0.03 #17072), 01kp66 (0.20 #733, 0.03 #6975, 0.01 #11136), 0kr5_ (0.20 #106, 0.02 #14673, 0.02 #10509), 01y_px (0.20 #365, 0.02 #14932, 0.01 #10768), 01pcq3 (0.20 #132, 0.02 #16779, 0.01 #27184) >> Best rule #3061 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 024hbv; >> query: (?x3595, 02x7vq) <- nominated_for(?x4563, ?x3595), nominated_for(?x3458, ?x3595), ?x4563 = 0dzf_ >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #6913 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 02v8kmz; 09m6kg; 026mfbr; 01_mdl; 0416y94; 0pb33; 02r79_h; 09txzv; 04n52p6; 0kvgxk; ... *> query: (?x3595, 02xv8m) <- film_crew_role(?x3595, ?x281), film(?x9354, ?x3595), ?x281 = 02_n3z, film(?x4563, ?x3595) *> conf = 0.03 ranks of expected_values: 252 EVAL 0bv8h2 film! 02xv8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 90.000 53.000 0.222 http://example.org/film/actor/film./film/performance/film #18585-016nvh PRED entity: 016nvh PRED relation: profession PRED expected values: 0dz3r => 79 concepts (30 used for prediction) PRED predicted values (max 10 best out of 69): 09jwl (0.76 #3699, 0.70 #3111, 0.69 #1637), 0nbcg (0.55 #1650, 0.52 #3712, 0.49 #915), 016z4k (0.52 #1622, 0.46 #2359, 0.44 #2801), 0dz3r (0.52 #738, 0.51 #885, 0.43 #2357), 039v1 (0.35 #1214, 0.26 #3570, 0.26 #1655), 01c72t (0.29 #1201, 0.29 #3557, 0.28 #3410), 0dxtg (0.29 #308, 0.23 #1780, 0.23 #1927), 01d_h8 (0.27 #1330, 0.23 #1477, 0.21 #300), 09lbv (0.25 #20, 0.06 #1197, 0.06 #461), 0n1h (0.23 #748, 0.22 #1630, 0.20 #895) >> Best rule #3699 for best value: >> intensional similarity = 4 >> extensional distance = 613 >> proper extension: 015grj; 0blbxk; 03xmy1; 0127m7; 02bfxb; 0cjsxp; 04d2yp; 02h48; 02qx5h; >> query: (?x10624, 09jwl) <- award(?x10624, ?x528), profession(?x10624, ?x6183), profession(?x3422, ?x6183), ?x3422 = 07g2v >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #738 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 01vv7sc; 0ftps; 01v_pj6; 01vs_v8; 06k02; 03bxwtd; 01w806h; 0qdyf; 0840vq; 07g2v; ... *> query: (?x10624, 0dz3r) <- artists(?x3916, ?x10624), ?x3916 = 08cyft, profession(?x10624, ?x1032), award(?x10624, ?x528) *> conf = 0.52 ranks of expected_values: 4 EVAL 016nvh profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 79.000 30.000 0.758 http://example.org/people/person/profession #18584-03s5lz PRED entity: 03s5lz PRED relation: featured_film_locations PRED expected values: 02_286 => 91 concepts (85 used for prediction) PRED predicted values (max 10 best out of 78): 02_286 (0.27 #2906, 0.22 #981, 0.21 #20), 030qb3t (0.12 #2925, 0.10 #4859, 0.10 #1962), 04jpl (0.10 #1210, 0.08 #970, 0.08 #489), 0rh6k (0.08 #962, 0.07 #1683, 0.07 #2165), 0f94t (0.07 #22, 0.04 #262, 0.04 #502), 0d6lp (0.07 #72, 0.04 #312, 0.03 #1033), 0qpqn (0.07 #160, 0.04 #400, 0.02 #1601), 0qpn9 (0.07 #136, 0.04 #376, 0.02 #1577), 052p7 (0.06 #1981, 0.05 #2462, 0.02 #5120), 0cv3w (0.05 #1031, 0.04 #310, 0.04 #550) >> Best rule #2906 for best value: >> intensional similarity = 3 >> extensional distance = 120 >> proper extension: 02_qt; >> query: (?x1295, 02_286) <- film(?x6883, ?x1295), student(?x6611, ?x6883), ?x6611 = 04b_46 >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s5lz featured_film_locations 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 85.000 0.270 http://example.org/film/film/featured_film_locations #18583-0gt_k PRED entity: 0gt_k PRED relation: type_of_union PRED expected values: 04ztj => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.95 #320, 0.94 #70, 0.93 #173), 0jgjn (0.02 #27, 0.01 #42, 0.01 #45), 01bl8s (0.01 #53) >> Best rule #320 for best value: >> intensional similarity = 3 >> extensional distance = 2153 >> proper extension: 0bk4s; 0frnff; 07zr66; 05p606; 0jrg; 04cw0n4; >> query: (?x1930, 04ztj) <- gender(?x1930, ?x231), ?x231 = 05zppz, type_of_union(?x1930, ?x1873) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gt_k type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.952 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18582-0330r PRED entity: 0330r PRED relation: award_winner PRED expected values: 06j0md => 66 concepts (45 used for prediction) PRED predicted values (max 10 best out of 848): 0170s4 (0.53 #24512, 0.50 #3268, 0.47 #65368), 02wk_43 (0.53 #24512, 0.50 #3268, 0.47 #65368), 0crx5w (0.53 #24512, 0.50 #3268, 0.47 #65368), 0f6_x (0.53 #24512, 0.50 #3268, 0.46 #9803), 02bvt (0.50 #3268, 0.48 #19606, 0.47 #65368), 01nxzv (0.50 #3268, 0.47 #65368, 0.46 #9803), 02xs5v (0.50 #3268, 0.47 #65368, 0.46 #9803), 02__7n (0.50 #3268, 0.47 #65368, 0.46 #9803), 01y665 (0.50 #3268, 0.47 #65368, 0.46 #9803), 06j0md (0.50 #3268, 0.47 #65368, 0.46 #9803) >> Best rule #24512 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 06cs95; 019nnl; 0n2bh; 01h72l; 01h1bf; 03y3bp7; 0557yqh; 05jyb2; 02pqs8l; 08cx5g; ... >> query: (?x9541, ?x5586) <- nominated_for(?x5586, ?x9541), genre(?x9541, ?x258), award_winner(?x5585, ?x5586) >> conf = 0.53 => this is the best rule for 4 predicted values *> Best rule #3268 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0124k9; 08jgk1; 03ln8b; 02hct1; 01s81; 0l76z; 02h2vv; 01rp13; 02r1ysd; 014gjp; ... *> query: (?x9541, ?x201) <- nominated_for(?x201, ?x9541), award(?x9541, ?x7510), ?x7510 = 027gs1_ *> conf = 0.50 ranks of expected_values: 10 EVAL 0330r award_winner 06j0md CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 66.000 45.000 0.534 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #18581-014zws PRED entity: 014zws PRED relation: student PRED expected values: 02mjmr => 116 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1499): 06jkm (0.33 #1905, 0.23 #6077, 0.10 #8163), 06hx2 (0.31 #5241, 0.17 #3155, 0.17 #1069), 0194xc (0.31 #5809, 0.17 #3723, 0.17 #1637), 0gs7x (0.17 #1935, 0.15 #6107, 0.10 #10279), 0gd5z (0.17 #382, 0.15 #4554, 0.05 #8726), 0pk41 (0.17 #1587, 0.15 #5759, 0.05 #9931), 02mqc4 (0.17 #692, 0.15 #4864, 0.05 #9036), 0d3k14 (0.17 #1849, 0.15 #6021, 0.05 #10193), 02yy8 (0.17 #2016, 0.15 #6188, 0.05 #10360), 01ty4 (0.17 #1970, 0.15 #6142, 0.05 #10314) >> Best rule #1905 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 043q2z; >> query: (?x9045, 06jkm) <- contains(?x3007, ?x9045), contains(?x94, ?x9045), student(?x9045, ?x4771), ?x94 = 09c7w0, ?x3007 = 01qh7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #37977 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 153 *> proper extension: 05nrkb; 01w3vc; *> query: (?x9045, 02mjmr) <- student(?x9045, ?x8991), place_of_death(?x8991, ?x4989), citytown(?x9045, ?x3007) *> conf = 0.01 ranks of expected_values: 1148 EVAL 014zws student 02mjmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 69.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #18580-026fs38 PRED entity: 026fs38 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #26, 0.83 #1, 0.82 #16), 02nxhr (0.03 #225, 0.03 #77, 0.03 #161), 07c52 (0.03 #210, 0.03 #351, 0.02 #416), 07z4p (0.03 #30, 0.02 #307, 0.02 #269) >> Best rule #26 for best value: >> intensional similarity = 4 >> extensional distance = 112 >> proper extension: 02v63m; 01_1hw; 02bj22; >> query: (?x7434, 029j_) <- titles(?x162, ?x7434), prequel(?x1903, ?x7434), film(?x11364, ?x7434), award_nominee(?x11364, ?x1738) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026fs38 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #18579-0h1x5f PRED entity: 0h1x5f PRED relation: film! PRED expected values: 03_1pg => 101 concepts (40 used for prediction) PRED predicted values (max 10 best out of 869): 06pj8 (0.57 #16632, 0.44 #20792, 0.41 #72766), 02z2xdf (0.44 #20792, 0.41 #72766, 0.40 #16631), 03qmx_f (0.44 #20792, 0.41 #72766, 0.40 #16631), 02pq9yv (0.44 #20792, 0.41 #72766, 0.40 #16631), 092kgw (0.44 #20792, 0.41 #72766, 0.40 #16631), 0bxtg (0.40 #76, 0.06 #6310, 0.05 #10468), 044zvm (0.40 #1939), 02hhtj (0.20 #1041, 0.14 #5197), 01rh0w (0.20 #229, 0.05 #8543, 0.02 #25178), 03j149k (0.20 #1418, 0.05 #9732) >> Best rule #16632 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 03s6l2; 01b195; 05m_jsg; 0bbw2z6; 02wwmhc; >> query: (?x9701, ?x2135) <- award_winner(?x9701, ?x237), nominated_for(?x2135, ?x9701), nominated_for(?x1135, ?x9701), currency(?x2135, ?x170) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0h1x5f film! 03_1pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 40.000 0.571 http://example.org/film/actor/film./film/performance/film #18578-01mvjl0 PRED entity: 01mvjl0 PRED relation: profession PRED expected values: 09jwl 0nbcg => 143 concepts (97 used for prediction) PRED predicted values (max 10 best out of 58): 09jwl (0.84 #1503, 0.81 #2244, 0.81 #1207), 02hrh1q (0.69 #11740, 0.67 #9365, 0.66 #13221), 0nbcg (0.59 #775, 0.59 #3446, 0.58 #1516), 016z4k (0.58 #151, 0.54 #1043, 0.53 #1191), 01c72t (0.44 #24, 0.39 #915, 0.38 #3885), 01d_h8 (0.31 #10989, 0.30 #11731, 0.30 #9356), 0dxtg (0.31 #9364, 0.31 #7583, 0.30 #11739), 0fnpj (0.29 #10539, 0.25 #60, 0.17 #208), 0n1h (0.25 #1199, 0.25 #159, 0.23 #5502), 03gjzk (0.24 #11741, 0.24 #9366, 0.23 #10999) >> Best rule #1503 for best value: >> intensional similarity = 3 >> extensional distance = 157 >> proper extension: 0dt645q; 01rw116; >> query: (?x6027, 09jwl) <- profession(?x6027, ?x2659), ?x2659 = 039v1, gender(?x6027, ?x231) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01mvjl0 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 97.000 0.836 http://example.org/people/person/profession EVAL 01mvjl0 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 97.000 0.836 http://example.org/people/person/profession #18577-04jn6y7 PRED entity: 04jn6y7 PRED relation: produced_by PRED expected values: 02pq9yv => 170 concepts (87 used for prediction) PRED predicted values (max 10 best out of 215): 02tn0_ (0.33 #328, 0.10 #1881, 0.06 #5379), 01nr36 (0.25 #678, 0.11 #1455, 0.07 #4174), 030_3z (0.20 #939, 0.10 #1716, 0.09 #6378), 029m83 (0.20 #1051, 0.07 #3768, 0.06 #4548), 02q42j_ (0.20 #1763, 0.06 #6425, 0.04 #18846), 0b13g7 (0.20 #1671, 0.05 #18754, 0.05 #17199), 02xnjd (0.20 #1826, 0.05 #9980, 0.04 #13472), 01r2c7 (0.20 #1091, 0.02 #22447, 0.01 #23610), 02lf0c (0.17 #5074, 0.10 #1576, 0.07 #3516), 0j_c (0.14 #3573, 0.11 #1244, 0.09 #6295) >> Best rule #328 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 04f6df0; >> query: (?x12693, 02tn0_) <- films(?x12333, ?x12693), titles(?x600, ?x12693), film(?x1286, ?x12693), executive_produced_by(?x12693, ?x4857), genre(?x12693, ?x604), ?x4857 = 02z6l5f >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2057 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 0418wg; 043t8t; 04k9y6; 0drnwh; 0292qb; *> query: (?x12693, 02pq9yv) <- film(?x1286, ?x12693), film_crew_role(?x12693, ?x2472), film_crew_role(?x12693, ?x137), country(?x12693, ?x94), costume_design_by(?x12693, ?x1500), ?x137 = 09zzb8, ?x2472 = 01xy5l_ *> conf = 0.10 ranks of expected_values: 25 EVAL 04jn6y7 produced_by 02pq9yv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 170.000 87.000 0.333 http://example.org/film/film/produced_by #18576-01qn8k PRED entity: 01qn8k PRED relation: gender PRED expected values: 02zsn => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #104, 0.72 #102, 0.71 #136), 02zsn (0.57 #79, 0.49 #16, 0.48 #14) >> Best rule #104 for best value: >> intensional similarity = 2 >> extensional distance = 1294 >> proper extension: 09bx1k; >> query: (?x9323, 05zppz) <- student(?x2486, ?x9323), place_of_birth(?x9323, ?x362) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #79 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 562 *> proper extension: 0cbm64; *> query: (?x9323, ?x514) <- participant(?x9323, ?x10777), participant(?x9323, ?x6613), gender(?x10777, ?x514), nationality(?x6613, ?x94) *> conf = 0.57 ranks of expected_values: 2 EVAL 01qn8k gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.722 http://example.org/people/person/gender #18575-02mpyh PRED entity: 02mpyh PRED relation: language PRED expected values: 02h40lc => 84 concepts (80 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.90 #416, 0.90 #534, 0.90 #1010), 064_8sq (0.33 #81, 0.23 #199, 0.18 #22), 04306rv (0.18 #5, 0.17 #182, 0.16 #123), 02bjrlw (0.18 #1, 0.10 #178, 0.08 #119), 06b_j (0.18 #23, 0.09 #732, 0.07 #792), 012w70 (0.18 #13, 0.04 #663, 0.03 #722), 03_9r (0.12 #128, 0.05 #1077, 0.05 #660), 0653m (0.12 #130, 0.04 #662, 0.04 #1079), 06nm1 (0.12 #661, 0.11 #1019, 0.10 #840), 0jzc (0.09 #20, 0.04 #729, 0.04 #789) >> Best rule #416 for best value: >> intensional similarity = 5 >> extensional distance = 78 >> proper extension: 09sh8k; 09m6kg; 0bth54; 06_wqk4; 053rxgm; 0pb33; 02r79_h; 05sxzwc; 05pbl56; 09txzv; ... >> query: (?x8574, 02h40lc) <- film_crew_role(?x8574, ?x4305), film_crew_role(?x8574, ?x1171), nominated_for(?x112, ?x8574), ?x4305 = 0215hd, ?x1171 = 09vw2b7 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02mpyh language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 80.000 0.900 http://example.org/film/film/language #18574-01zmpg PRED entity: 01zmpg PRED relation: gender PRED expected values: 05zppz => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #19, 0.78 #73, 0.78 #63), 02zsn (0.62 #27, 0.55 #37, 0.54 #39) >> Best rule #19 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 0d5_f; 081l_; 03j2gxx; >> query: (?x2273, 05zppz) <- profession(?x2273, ?x2225), profession(?x2273, ?x131), ?x2225 = 0kyk, profession(?x7951, ?x131), ?x7951 = 01vt5c_ >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01zmpg gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.805 http://example.org/people/person/gender #18573-01xlqd PRED entity: 01xlqd PRED relation: film_release_region PRED expected values: 05v8c => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 138): 09c7w0 (0.94 #4008, 0.92 #6095, 0.92 #6255), 05r4w (0.84 #962, 0.83 #2404, 0.76 #322), 03gj2 (0.82 #186, 0.80 #986, 0.77 #2428), 05qhw (0.81 #974, 0.74 #2416, 0.63 #174), 015fr (0.81 #977, 0.73 #177, 0.73 #2419), 0345h (0.79 #995, 0.79 #2437, 0.73 #195), 035qy (0.78 #997, 0.74 #2439, 0.67 #197), 0154j (0.77 #965, 0.72 #2407, 0.58 #2086), 03spz (0.75 #1060, 0.60 #2502, 0.57 #260), 03rj0 (0.72 #1024, 0.57 #2466, 0.49 #224) >> Best rule #4008 for best value: >> intensional similarity = 3 >> extensional distance = 941 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; 018js4; ... >> query: (?x9832, 09c7w0) <- film_release_region(?x9832, ?x789), currency(?x9832, ?x170), combatants(?x94, ?x789) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #176 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 0ds35l9; 0c3ybss; 03g90h; 01gc7; 0401sg; 01vksx; 03cvwkr; 0bwfwpj; 08hmch; 0872p_c; ... *> query: (?x9832, 05v8c) <- film_release_region(?x9832, ?x789), currency(?x9832, ?x170), ?x789 = 0f8l9c, film_distribution_medium(?x9832, ?x81) *> conf = 0.67 ranks of expected_values: 13 EVAL 01xlqd film_release_region 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 55.000 55.000 0.936 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18572-0167xy PRED entity: 0167xy PRED relation: artist! PRED expected values: 011k1h 03vtrv => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 111): 015_1q (0.55 #7645, 0.45 #2470, 0.44 #2061), 043g7l (0.51 #1390, 0.47 #2480, 0.47 #2071), 033hn8 (0.38 #1375, 0.33 #7640, 0.30 #2465), 011k1h (0.25 #8996, 0.17 #283, 0.15 #4230), 017l96 (0.23 #155, 0.22 #9004, 0.18 #18), 01clyr (0.23 #168, 0.20 #440, 0.15 #1120), 0181dw (0.20 #1672, 0.18 #1945, 0.17 #312), 0229rs (0.19 #698, 0.08 #6416, 0.08 #4918), 01t04r (0.17 #1832, 0.10 #743, 0.09 #6461), 0n85g (0.17 #333, 0.11 #3736, 0.11 #1966) >> Best rule #7645 for best value: >> intensional similarity = 3 >> extensional distance = 270 >> proper extension: 016kjs; 01vrz41; 016pns; 039bpc; 016fnb; 0277c3; 013w7j; 0x3n; 0677ng; 016l09; ... >> query: (?x10670, 015_1q) <- artist(?x9492, ?x10670), artist(?x9492, ?x8156), ?x8156 = 046p9 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #8996 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 349 *> proper extension: 01vvydl; 05cljf; 0c9d9; 01vrx3g; 01lmj3q; 089tm; 01pfr3; 0147dk; 03f2_rc; 032t2z; ... *> query: (?x10670, 011k1h) <- artist(?x9492, ?x10670), artist(?x9492, ?x6228), ?x6228 = 01q99h *> conf = 0.25 ranks of expected_values: 4, 25 EVAL 0167xy artist! 03vtrv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 88.000 88.000 0.548 http://example.org/music/record_label/artist EVAL 0167xy artist! 011k1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 88.000 0.548 http://example.org/music/record_label/artist #18571-02z0j PRED entity: 02z0j PRED relation: place_founded! PRED expected values: 027jw0c => 213 concepts (177 used for prediction) PRED predicted values (max 10 best out of 93): 0p4wb (0.13 #1111, 0.09 #7238, 0.08 #4676), 02m_41 (0.13 #1111, 0.08 #3448, 0.08 #3447), 04fv0k (0.10 #1164, 0.10 #1052, 0.07 #1498), 06nfl (0.10 #1222, 0.10 #1110, 0.07 #1556), 07rfp (0.10 #1215, 0.10 #1103, 0.07 #1549), 0260p2 (0.10 #1211, 0.10 #1099, 0.07 #1545), 06zl7g (0.10 #1210, 0.10 #1098, 0.07 #1544), 05b0f7 (0.10 #1198, 0.10 #1086, 0.07 #1532), 01bvx1 (0.10 #1194, 0.10 #1082, 0.07 #1528), 01qckn (0.10 #1172, 0.10 #1060, 0.07 #1506) >> Best rule #1111 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0hn4h; >> query: (?x8977, ?x610) <- administrative_parent(?x8977, ?x8264), country(?x8977, ?x1264), citytown(?x610, ?x8977), capital(?x1778, ?x8977) >> conf = 0.13 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02z0j place_founded! 027jw0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 213.000 177.000 0.131 http://example.org/organization/organization/place_founded #18570-02rjjll PRED entity: 02rjjll PRED relation: ceremony! PRED expected values: 01bgqh 0c4z8 026mfs 026mff 026mmy 01c9d1 0257__ 02gm9n => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 234): 01bgqh (0.88 #2538, 0.83 #2717, 0.83 #2358), 01c9d1 (0.88 #2685, 0.78 #2864, 0.78 #2325), 0c4z8 (0.78 #2200, 0.71 #1841, 0.69 #2560), 026mfs (0.75 #2590, 0.75 #2410, 0.75 #2050), 026mff (0.75 #2427, 0.71 #1888, 0.71 #1708), 026mmy (0.75 #2138, 0.71 #1599, 0.69 #2678), 0257__ (0.75 #2686, 0.67 #2865, 0.67 #2326), 02gm9n (0.71 #1968, 0.69 #2687, 0.67 #2866), 0l8z1 (0.71 #5427, 0.71 #5246, 0.69 #5066), 0gqy2 (0.69 #5300, 0.68 #5481, 0.67 #4940) >> Best rule #2538 for best value: >> intensional similarity = 19 >> extensional distance = 14 >> proper extension: 0gx1673; >> query: (?x486, 01bgqh) <- ceremony(?x8705, ?x486), ceremony(?x7691, ?x486), ceremony(?x7005, ?x486), ceremony(?x3903, ?x486), ceremony(?x2212, ?x486), award(?x5150, ?x3903), award_winner(?x486, ?x9791), award_winner(?x486, ?x7201), award_winner(?x7691, ?x4080), award_winner(?x3390, ?x7201), award_winner(?x3903, ?x1373), instrumentalists(?x227, ?x7201), ?x2212 = 02nbqh, group(?x316, ?x9791), ?x4080 = 0dl567, ?x8705 = 01c9dd, award(?x4741, ?x7005), type_of_union(?x7201, ?x566), ?x4741 = 01s21dg >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8 EVAL 02rjjll ceremony! 02gm9n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02rjjll ceremony! 0257__ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02rjjll ceremony! 01c9d1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02rjjll ceremony! 026mmy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02rjjll ceremony! 026mff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02rjjll ceremony! 026mfs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02rjjll ceremony! 0c4z8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02rjjll ceremony! 01bgqh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony #18569-03hkv_r PRED entity: 03hkv_r PRED relation: award! PRED expected values: 07s846j => 49 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1001): 017jd9 (0.75 #5039, 0.75 #4484, 0.53 #5493), 0ywrc (0.67 #4335, 0.62 #3325, 0.53 #5344), 0cq806 (0.62 #3867, 0.57 #2860, 0.42 #4877), 0pv3x (0.58 #4138, 0.47 #5147, 0.45 #6157), 0mcl0 (0.58 #4407, 0.43 #2390, 0.42 #5416), 0209hj (0.57 #2076, 0.50 #4093, 0.50 #3083), 0bs4r (0.57 #2619, 0.50 #3626, 0.42 #4636), 05sbv3 (0.57 #2979, 0.50 #4996, 0.38 #3986), 04v8x9 (0.50 #4068, 0.50 #3058, 0.32 #5077), 0bx0l (0.50 #4244, 0.50 #3234, 0.32 #5253) >> Best rule #5039 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 0gq_v; 0p9sw; 0l8z1; 019f4v; 02n9nmz; 0gs9p; >> query: (?x384, ?x4610) <- nominated_for(?x384, ?x5519), nominated_for(?x384, ?x4610), ?x5519 = 09p3_s, ?x4610 = 017jd9, award(?x164, ?x384), ceremony(?x384, ?x4617) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3415 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 040njc; *> query: (?x384, 07s846j) <- nominated_for(?x384, ?x5519), nominated_for(?x384, ?x1064), award(?x8645, ?x384), ?x8645 = 0jgwf, honored_for(?x3618, ?x5519), nominated_for(?x2323, ?x1064) *> conf = 0.50 ranks of expected_values: 13 EVAL 03hkv_r award! 07s846j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 49.000 22.000 0.750 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #18568-04hxyv PRED entity: 04hxyv PRED relation: place_of_birth PRED expected values: 0xrzh => 110 concepts (105 used for prediction) PRED predicted values (max 10 best out of 74): 01cx_ (0.38 #3523, 0.38 #2927, 0.34 #47927), 0xl08 (0.33 #241, 0.25 #945, 0.10 #1649), 030qb3t (0.25 #758, 0.08 #2167, 0.06 #7805), 02_286 (0.12 #28208, 0.12 #26092, 0.10 #29619), 01531 (0.10 #1513, 0.04 #5038, 0.03 #2218), 01_d4 (0.10 #1474, 0.03 #62090, 0.03 #16274), 02cl1 (0.10 #1424, 0.03 #2129, 0.03 #2834), 071cn (0.10 #1543, 0.03 #2248, 0.02 #3659), 052p7 (0.10 #1490), 02s838 (0.08 #3223) >> Best rule #3523 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 0gl88b; >> query: (?x13239, ?x3052) <- profession(?x13239, ?x1383), location(?x13239, ?x3052), nationality(?x13239, ?x94), ?x3052 = 01cx_, ?x94 = 09c7w0 >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04hxyv place_of_birth 0xrzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 105.000 0.378 http://example.org/people/person/place_of_birth #18567-081m_ PRED entity: 081m_ PRED relation: film_regional_debut_venue! PRED expected values: 01jwxx => 226 concepts (130 used for prediction) PRED predicted values (max 10 best out of 23): 0gffmn8 (0.13 #2293, 0.12 #2479, 0.10 #3038), 0btpm6 (0.13 #2377, 0.12 #2563, 0.10 #3122), 0hv81 (0.11 #671, 0.10 #1043, 0.07 #2159), 0crh5_f (0.10 #5642, 0.06 #7877, 0.06 #9926), 02v_r7d (0.07 #1793, 0.07 #1607, 0.07 #2352), 01jrbb (0.07 #1728, 0.07 #1914, 0.05 #3032), 01sby_ (0.07 #5686, 0.06 #2706, 0.04 #7921), 0b44shh (0.07 #5683, 0.06 #2703, 0.04 #7918), 0blpg (0.07 #5660, 0.06 #2680, 0.04 #7895), 01s9vc (0.07 #2411, 0.07 #2038, 0.06 #2597) >> Best rule #2293 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 06t2t; >> query: (?x8989, 0gffmn8) <- teams(?x8989, ?x10463), contains(?x8989, ?x9018), contains(?x456, ?x8989), capital(?x2517, ?x8989) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #5678 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 0s987; *> query: (?x8989, 01jwxx) <- place_of_birth(?x4943, ?x8989), place_of_burial(?x4943, ?x1227), location_of_ceremony(?x566, ?x8989) *> conf = 0.03 ranks of expected_values: 17 EVAL 081m_ film_regional_debut_venue! 01jwxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 226.000 130.000 0.133 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #18566-026c1 PRED entity: 026c1 PRED relation: award_winner! PRED expected values: 09gkdln => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 122): 09gkdln (0.28 #10287, 0.17 #12929, 0.04 #10129), 05c1t6z (0.28 #10287, 0.17 #12929, 0.04 #9467), 09k5jh7 (0.28 #10287, 0.17 #12929, 0.03 #361), 013b2h (0.10 #5917, 0.06 #774, 0.05 #8558), 05pd94v (0.09 #5840, 0.06 #2643, 0.04 #2), 02rjjll (0.09 #5843, 0.07 #144, 0.06 #2646), 0466p0j (0.09 #5913, 0.06 #214, 0.05 #631), 01s695 (0.09 #5841, 0.05 #2644, 0.04 #698), 01c6qp (0.09 #5857, 0.06 #2660, 0.04 #8498), 02cg41 (0.08 #5962, 0.06 #2765, 0.05 #8603) >> Best rule #10287 for best value: >> intensional similarity = 3 >> extensional distance = 1147 >> proper extension: 06jntd; >> query: (?x2221, ?x1265) <- award_winner(?x5682, ?x2221), nominated_for(?x375, ?x5682), honored_for(?x1265, ?x5682) >> conf = 0.28 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 026c1 award_winner! 09gkdln CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.278 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18565-03gwpw2 PRED entity: 03gwpw2 PRED relation: honored_for PRED expected values: 0fh694 02rzdcp 0dt8xq 03_gz8 => 32 concepts (21 used for prediction) PRED predicted values (max 10 best out of 982): 08zrbl (0.50 #2751, 0.40 #3324, 0.33 #1603), 02rqwhl (0.46 #4019, 0.04 #9763, 0.03 #11491), 04vr_f (0.44 #3503, 0.12 #5798, 0.10 #8095), 0cw3yd (0.40 #3030, 0.33 #1309, 0.25 #2457), 0fh694 (0.33 #626, 0.25 #2348, 0.09 #1151), 03_gz8 (0.33 #953, 0.25 #2675, 0.08 #4395), 04w7rn (0.33 #659, 0.25 #2381, 0.08 #4101), 0hz55 (0.33 #1434, 0.20 #3155, 0.19 #6598), 08jgk1 (0.33 #1238, 0.20 #2959, 0.15 #4106), 0kfv9 (0.33 #1252, 0.20 #2973, 0.11 #3546) >> Best rule #2751 for best value: >> intensional similarity = 19 >> extensional distance = 2 >> proper extension: 02wzl1d; >> query: (?x762, 08zrbl) <- honored_for(?x762, ?x2380), honored_for(?x762, ?x1877), honored_for(?x762, ?x1531), honored_for(?x762, ?x945), honored_for(?x762, ?x763), ?x945 = 0b6tzs, award_winner(?x762, ?x647), award_winner(?x762, ?x236), award_winner(?x647, ?x6518), language(?x763, ?x254), ?x2380 = 02q6gfp, award_nominee(?x647, ?x2609), award_nominee(?x236, ?x1040), films(?x11817, ?x763), award(?x647, ?x384), film(?x100, ?x1877), nominated_for(?x2422, ?x763), location(?x236, ?x108), award_winner(?x1531, ?x6279) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #626 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 02pgky2; *> query: (?x762, 0fh694) <- honored_for(?x762, ?x10425), honored_for(?x762, ?x945), honored_for(?x762, ?x763), ?x945 = 0b6tzs, award_winner(?x762, ?x2556), award_winner(?x762, ?x647), award_winner(?x762, ?x236), ?x763 = 061681, profession(?x236, ?x353), award(?x236, ?x537), location(?x236, ?x108), profession(?x647, ?x6421), award_winner(?x1869, ?x647), award(?x6534, ?x1869), language(?x10425, ?x254), award_nominee(?x1040, ?x236), ?x6534 = 01_6dw, nominated_for(?x2556, ?x144) *> conf = 0.33 ranks of expected_values: 5, 6, 21, 107 EVAL 03gwpw2 honored_for 03_gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 32.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 03gwpw2 honored_for 0dt8xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 32.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 03gwpw2 honored_for 02rzdcp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 32.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 03gwpw2 honored_for 0fh694 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 32.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18564-0g8fs PRED entity: 0g8fs PRED relation: major_field_of_study PRED expected values: 05qjt => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 116): 02j62 (0.54 #755, 0.50 #29, 0.48 #2209), 01tbp (0.50 #2602, 0.42 #3086, 0.23 #2844), 04rjg (0.47 #2561, 0.44 #3045, 0.43 #6812), 062z7 (0.42 #3784, 0.41 #2206, 0.39 #4026), 03g3w (0.41 #2205, 0.41 #2810, 0.38 #4389), 01540 (0.41 #2603, 0.29 #3087, 0.26 #3818), 05qjt (0.38 #2793, 0.38 #2551, 0.35 #3035), 037mh8 (0.38 #793, 0.28 #2852, 0.28 #2610), 04x_3 (0.38 #3051, 0.38 #2567, 0.28 #2809), 0g26h (0.38 #2584, 0.34 #2221, 0.33 #3799) >> Best rule #755 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 015zyd; 07wrz; 02bb47; 0g8rj; 02bqy; 0hsb3; 06thjt; 01hc1j; 05qgd9; >> query: (?x9691, 02j62) <- institution(?x865, ?x9691), major_field_of_study(?x9691, ?x1154), organizations_founded(?x9765, ?x9691) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #2793 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 37 *> proper extension: 026036; 02sdwt; 08tyb_; 017hnw; *> query: (?x9691, 05qjt) <- student(?x9691, ?x8433), student(?x9691, ?x2357), profession(?x2357, ?x353), celebrities_impersonated(?x5915, ?x2357), influenced_by(?x8433, ?x3542) *> conf = 0.38 ranks of expected_values: 7 EVAL 0g8fs major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 137.000 137.000 0.538 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18563-046qq PRED entity: 046qq PRED relation: film PRED expected values: 032sl_ => 96 concepts (55 used for prediction) PRED predicted values (max 10 best out of 949): 0p_qr (0.64 #23090, 0.63 #24867, 0.62 #23089), 09wnnb (0.64 #23090, 0.63 #24867, 0.40 #72837), 02ctc6 (0.17 #2293, 0.12 #517, 0.09 #4069), 0kv2hv (0.12 #130, 0.08 #1906, 0.06 #3682), 031hcx (0.12 #1264, 0.08 #3040, 0.06 #4816), 028kj0 (0.12 #1653, 0.08 #3429, 0.04 #10533), 051zy_b (0.12 #573, 0.08 #2349, 0.04 #7677), 0pv3x (0.12 #178, 0.08 #1954, 0.04 #7282), 011ykb (0.12 #1132, 0.08 #2908, 0.04 #8236), 0f7hw (0.12 #1547, 0.08 #3323, 0.04 #8651) >> Best rule #23090 for best value: >> intensional similarity = 3 >> extensional distance = 298 >> proper extension: 02wb6yq; >> query: (?x4277, ?x2262) <- award_winner(?x1488, ?x4277), participant(?x4277, ?x3366), nominated_for(?x4277, ?x2262) >> conf = 0.64 => this is the best rule for 2 predicted values *> Best rule #10428 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 70 *> proper extension: 022769; 06crng; 02h3tp; 029pnn; 017yxq; 03kxdw; 0427y; 02pzck; 04gr35; 0hqly; ... *> query: (?x4277, 032sl_) <- award(?x4277, ?x1312), award(?x4277, ?x880), ?x1312 = 07cbcy, award(?x9866, ?x880), award_nominee(?x538, ?x9866) *> conf = 0.01 ranks of expected_values: 477 EVAL 046qq film 032sl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 96.000 55.000 0.641 http://example.org/film/actor/film./film/performance/film #18562-09zw90 PRED entity: 09zw90 PRED relation: produced_by! PRED expected values: 024mpp => 159 concepts (125 used for prediction) PRED predicted values (max 10 best out of 680): 0435vm (0.33 #345, 0.07 #4115, 0.03 #34285), 0900j5 (0.33 #317, 0.07 #4087, 0.01 #20110), 0pc62 (0.33 #57, 0.07 #3827, 0.01 #19850), 0cc5mcj (0.22 #2096, 0.13 #3981, 0.11 #5866), 027qgy (0.13 #3784, 0.11 #1899, 0.03 #19807), 060__7 (0.12 #1718, 0.07 #3603, 0.07 #4545), 0bmhvpr (0.12 #1279, 0.05 #11646, 0.05 #6934), 03wjm2 (0.12 #1869, 0.04 #35809, 0.03 #17891), 03h4fq7 (0.12 #1421, 0.03 #17443, 0.02 #24986), 033fqh (0.12 #1395, 0.03 #17417, 0.02 #24960) >> Best rule #345 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02qzjj; >> query: (?x11526, 0435vm) <- profession(?x11526, ?x319), executive_produced_by(?x324, ?x11526), produced_by(?x508, ?x11526), nationality(?x11526, ?x94), ?x508 = 0ds33 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #34286 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 106 *> proper extension: 0q9kd; 0fvf9q; 02lfcm; 054_mz; 0bxtg; 07f8wg; 0147dk; 06cv1; 02lf0c; 05kfs; ... *> query: (?x11526, 024mpp) <- profession(?x11526, ?x319), executive_produced_by(?x324, ?x11526), produced_by(?x508, ?x11526), nationality(?x11526, ?x94), featured_film_locations(?x508, ?x108) *> conf = 0.02 ranks of expected_values: 342 EVAL 09zw90 produced_by! 024mpp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 159.000 125.000 0.333 http://example.org/film/film/produced_by #18561-0cw3yd PRED entity: 0cw3yd PRED relation: nominated_for! PRED expected values: 02qwzkm => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 200): 099c8n (0.55 #1215, 0.36 #1911, 0.30 #4464), 0gq9h (0.53 #292, 0.45 #1220, 0.41 #5165), 02n9nmz (0.45 #1216, 0.26 #520, 0.20 #288), 0gs9p (0.40 #294, 0.36 #7023, 0.36 #5167), 019f4v (0.39 #5157, 0.36 #1908, 0.35 #7013), 0k611 (0.39 #1926, 0.34 #5175, 0.33 #1230), 03hkv_r (0.38 #1175, 0.21 #1871, 0.17 #479), 04dn09n (0.33 #1194, 0.30 #962, 0.29 #1890), 040njc (0.33 #2559, 0.32 #5112, 0.30 #1863), 09sb52 (0.32 #1193, 0.24 #17174, 0.24 #17175) >> Best rule #1215 for best value: >> intensional similarity = 5 >> extensional distance = 64 >> proper extension: 0170xl; >> query: (?x2812, 099c8n) <- currency(?x2812, ?x2244), nominated_for(?x2341, ?x2812), nominated_for(?x1441, ?x2812), ?x2341 = 02x17s4, award(?x396, ?x1441) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #916 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 0cp08zg; *> query: (?x2812, 02qwzkm) <- nominated_for(?x5043, ?x2812), genre(?x2812, ?x53), written_by(?x2812, ?x3096), film_festivals(?x2812, ?x9080) *> conf = 0.02 ranks of expected_values: 173 EVAL 0cw3yd nominated_for! 02qwzkm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 103.000 103.000 0.545 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18560-0cp6w PRED entity: 0cp6w PRED relation: vacationer PRED expected values: 0456xp => 179 concepts (85 used for prediction) PRED predicted values (max 10 best out of 183): 04fzk (0.50 #271, 0.37 #3032, 0.36 #4102), 016fnb (0.50 #284, 0.30 #1710, 0.29 #1175), 0bksh (0.40 #466, 0.25 #288, 0.21 #2249), 01pllx (0.37 #3032, 0.36 #4102, 0.35 #2139), 015z4j (0.37 #3032, 0.36 #4102, 0.35 #2139), 02d9k (0.33 #926, 0.25 #213, 0.22 #1461), 01kgv4 (0.33 #141, 0.07 #5315, 0.07 #2280), 01pgk0 (0.33 #176, 0.07 #2315, 0.04 #4813), 03lt8g (0.29 #1092, 0.25 #201, 0.22 #1449), 01xyt7 (0.29 #1199, 0.25 #308, 0.22 #1556) >> Best rule #271 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02_286; >> query: (?x12542, 04fzk) <- contains(?x12778, ?x12542), location(?x8080, ?x12542), ?x8080 = 09h_q, vacationer(?x12542, ?x7025), adjoins(?x12778, ?x3912) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #912 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 01f62; 07mgr; *> query: (?x12542, 0456xp) <- time_zones(?x12542, ?x2864), administrative_division(?x12542, ?x12778), second_level_divisions(?x789, ?x12778), ?x2864 = 02llzg, location_of_ceremony(?x566, ?x12542) *> conf = 0.17 ranks of expected_values: 64 EVAL 0cp6w vacationer 0456xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 179.000 85.000 0.500 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #18559-05d1y PRED entity: 05d1y PRED relation: location PRED expected values: 05qtj => 170 concepts (72 used for prediction) PRED predicted values (max 10 best out of 312): 09c7w0 (0.60 #1595, 0.47 #8753, 0.29 #3185), 0cr3d (0.40 #1732, 0.29 #3322, 0.17 #6505), 04jpl (0.37 #42998, 0.19 #9568, 0.08 #26289), 02jx1 (0.36 #12805, 0.31 #8026, 0.22 #13602), 07ssc (0.29 #2413, 0.17 #6391, 0.15 #7983), 0rh6k (0.29 #23093, 0.11 #42985, 0.11 #39004), 03s0w (0.25 #844, 0.12 #4025, 0.02 #11191), 05qtj (0.20 #10584, 0.19 #9787, 0.17 #15361), 03v_5 (0.20 #1683, 0.14 #3273, 0.08 #6456), 0ccvx (0.20 #1809, 0.08 #6582, 0.08 #8174) >> Best rule #1595 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 04z0g; 06crk; 01zwy; >> query: (?x8299, 09c7w0) <- place_of_death(?x8299, ?x1131), location(?x8299, ?x9006), location(?x8299, ?x1374), nationality(?x13735, ?x9006), place_of_birth(?x1373, ?x1374), ?x13735 = 01l3j >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #10584 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 075wq; *> query: (?x8299, 05qtj) <- place_of_death(?x8299, ?x1131), gender(?x8299, ?x231), ?x231 = 05zppz, second_level_divisions(?x94, ?x1131) *> conf = 0.20 ranks of expected_values: 8 EVAL 05d1y location 05qtj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 170.000 72.000 0.600 http://example.org/people/person/places_lived./people/place_lived/location #18558-0412f5y PRED entity: 0412f5y PRED relation: award_nominee PRED expected values: 02zmh5 => 92 concepts (31 used for prediction) PRED predicted values (max 10 best out of 659): 05vzw3 (0.83 #9340, 0.81 #65364, 0.81 #53689), 067nsm (0.81 #65364, 0.81 #53689, 0.81 #65362), 01vw8mh (0.81 #65364, 0.81 #53689, 0.81 #65362), 02cyfz (0.81 #65364, 0.81 #53689, 0.81 #65362), 04lgymt (0.81 #65364, 0.81 #53689, 0.81 #65362), 01vvyvk (0.74 #44351, 0.74 #32679, 0.73 #44350), 02l840 (0.53 #2495, 0.44 #160, 0.36 #4829), 0412f5y (0.44 #814, 0.17 #44352, 0.16 #3149), 0478__m (0.22 #1086, 0.22 #8091, 0.14 #60695), 016kjs (0.22 #230, 0.21 #2565, 0.17 #4899) >> Best rule #9340 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 01j7z7; >> query: (?x3607, ?x4594) <- award_nominee(?x4594, ?x3607), award_nominee(?x4594, ?x7908), ?x7908 = 01vs73g >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #444 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 047sxrj; 01vw20h; 05vzw3; 03y82t6; 06mt91; 02wwwv5; 01wwnh2; *> query: (?x3607, 02zmh5) <- artists(?x671, ?x3607), award_nominee(?x6573, ?x3607), ?x6573 = 067nsm, award_nominee(?x3607, ?x1388) *> conf = 0.22 ranks of expected_values: 13 EVAL 0412f5y award_nominee 02zmh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 92.000 31.000 0.825 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18557-03cdg PRED entity: 03cdg PRED relation: influenced_by PRED expected values: 01v9724 => 163 concepts (80 used for prediction) PRED predicted values (max 10 best out of 388): 01v9724 (0.43 #1899, 0.33 #2760, 0.12 #20648), 03_87 (0.41 #19126, 0.22 #2784, 0.17 #4502), 03f0324 (0.37 #19076, 0.25 #582, 0.17 #1444), 032l1 (0.33 #88, 0.31 #19013, 0.29 #1810), 03sbs (0.33 #219, 0.25 #24306, 0.25 #1081), 05qmj (0.33 #192, 0.25 #1054, 0.25 #623), 039n1 (0.33 #322, 0.25 #1184, 0.25 #753), 07ym0 (0.33 #274, 0.25 #1136, 0.25 #705), 06jkm (0.33 #390, 0.25 #1252, 0.25 #821), 0gz_ (0.33 #102, 0.25 #964, 0.24 #24189) >> Best rule #1899 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 014635; 034bs; 03_87; 03rx9; 0ff3y; >> query: (?x11554, 01v9724) <- location(?x11554, ?x1591), influenced_by(?x5346, ?x11554), influenced_by(?x11554, ?x2240), ?x5346 = 049gc, profession(?x11554, ?x353) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cdg influenced_by 01v9724 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 80.000 0.429 http://example.org/influence/influence_node/influenced_by #18556-012x4t PRED entity: 012x4t PRED relation: award PRED expected values: 0gkvb7 054ks3 02f764 => 133 concepts (130 used for prediction) PRED predicted values (max 10 best out of 297): 0ck27z (0.69 #17248, 0.11 #30508, 0.11 #30898), 0gqy2 (0.57 #15367, 0.23 #27847, 0.18 #5227), 09sb52 (0.52 #27729, 0.33 #12909, 0.30 #15249), 05zr6wv (0.48 #12887, 0.20 #13277, 0.19 #5477), 0bdwqv (0.38 #15375, 0.17 #5235, 0.15 #27855), 01ck6h (0.33 #2065, 0.20 #115, 0.17 #5965), 02wh75 (0.33 #1959, 0.13 #1179, 0.12 #7029), 0f4x7 (0.31 #2760, 0.28 #8220, 0.26 #9000), 054ks3 (0.30 #1304, 0.28 #7154, 0.26 #9884), 02f716 (0.30 #169, 0.25 #1729, 0.21 #3679) >> Best rule #17248 for best value: >> intensional similarity = 3 >> extensional distance = 317 >> proper extension: 04hpck; 02fb1n; 036jp8; 06sn8m; 03jg5t; 01j5sd; 02j4sk; 033db3; >> query: (?x1660, 0ck27z) <- award(?x1660, ?x693), award(?x3583, ?x693), ?x3583 = 0blt6 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1304 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 013qvn; *> query: (?x1660, 054ks3) <- profession(?x1660, ?x220), celebrities_impersonated(?x4657, ?x1660), artist(?x7448, ?x1660) *> conf = 0.30 ranks of expected_values: 9, 26, 48 EVAL 012x4t award 02f764 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 133.000 130.000 0.687 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 012x4t award 054ks3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 133.000 130.000 0.687 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 012x4t award 0gkvb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 133.000 130.000 0.687 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18555-099t8j PRED entity: 099t8j PRED relation: award! PRED expected values: 014x77 0blbxk 01g257 02d42t 01kb2j 0b25vg => 46 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2740): 01hkhq (0.70 #10699, 0.69 #20746, 0.60 #7350), 04wx2v (0.67 #50238, 0.66 #50239, 0.66 #50237), 0fgg4 (0.67 #50238, 0.66 #50239, 0.66 #50237), 02ktrs (0.67 #50238, 0.66 #50239, 0.66 #50237), 0b25vg (0.67 #50238, 0.66 #50239, 0.66 #50237), 0l6px (0.64 #17355, 0.44 #20705, 0.40 #7309), 01j5ts (0.64 #16784, 0.44 #20134, 0.40 #6738), 028knk (0.62 #20610, 0.60 #10563, 0.60 #7214), 01kb2j (0.62 #21560, 0.60 #11513, 0.60 #8164), 02d42t (0.60 #11443, 0.60 #8094, 0.56 #21490) >> Best rule #10699 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 09qwmm; 09sb52; 094qd5; 0fq9zdn; 0gqwc; 099cng; >> query: (?x2577, 01hkhq) <- award(?x11983, ?x2577), ?x11983 = 0bwgc_, nominated_for(?x2577, ?x8084), film_release_distribution_medium(?x8084, ?x81) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #50238 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 180 *> proper extension: 0m7yy; 02wwsh8; 0468g4r; *> query: (?x2577, ?x4949) <- award_winner(?x2577, ?x4949), award_winner(?x2577, ?x4247), award(?x253, ?x2577), film(?x4247, ?x857), profession(?x4949, ?x1032) *> conf = 0.67 ranks of expected_values: 5, 9, 10, 48, 137, 417 EVAL 099t8j award! 0b25vg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 46.000 21.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099t8j award! 01kb2j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 46.000 21.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099t8j award! 02d42t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 46.000 21.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099t8j award! 01g257 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 46.000 21.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099t8j award! 0blbxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 46.000 21.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099t8j award! 014x77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 21.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18554-04yg13l PRED entity: 04yg13l PRED relation: titles! PRED expected values: 04xvlr => 76 concepts (46 used for prediction) PRED predicted values (max 10 best out of 135): 07s9rl0 (0.88 #2608, 0.40 #4268, 0.36 #4687), 04xvlr (0.35 #2611, 0.26 #4271, 0.24 #4690), 024qqx (0.20 #707, 0.15 #1543, 0.14 #916), 01hmnh (0.20 #1904, 0.19 #653, 0.18 #2529), 01z4y (0.19 #3054, 0.17 #4198, 0.16 #3158), 01jfsb (0.17 #4286, 0.16 #4705, 0.15 #4391), 02p0szs (0.17 #4682, 0.17 #3537, 0.17 #3743), 02kdv5l (0.17 #4682, 0.17 #3537, 0.17 #3743), 017fp (0.14 #2630, 0.09 #4709, 0.08 #4290), 07ssc (0.10 #2617, 0.09 #216, 0.09 #4277) >> Best rule #2608 for best value: >> intensional similarity = 7 >> extensional distance = 455 >> proper extension: 03kq98; >> query: (?x5052, 07s9rl0) <- titles(?x811, ?x5052), genre(?x5561, ?x811), genre(?x5936, ?x811), genre(?x5724, ?x811), ?x5724 = 0415ggl, ?x5561 = 0431v3, ?x5936 = 02q3fdr >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #2611 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 455 *> proper extension: 03kq98; *> query: (?x5052, 04xvlr) <- titles(?x811, ?x5052), genre(?x5561, ?x811), genre(?x5936, ?x811), genre(?x5724, ?x811), ?x5724 = 0415ggl, ?x5561 = 0431v3, ?x5936 = 02q3fdr *> conf = 0.35 ranks of expected_values: 2 EVAL 04yg13l titles! 04xvlr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 46.000 0.880 http://example.org/media_common/netflix_genre/titles #18553-07nf6 PRED entity: 07nf6 PRED relation: adjoins PRED expected values: 070zc => 222 concepts (112 used for prediction) PRED predicted values (max 10 best out of 604): 04p0c (0.87 #5406, 0.87 #24714, 0.86 #47900), 07nf6 (0.40 #2839, 0.33 #1294, 0.30 #4386), 09ksp (0.33 #1130, 0.25 #1902, 0.25 #27035), 05qhw (0.33 #26, 0.25 #1570, 0.16 #54852), 01mjq (0.33 #85, 0.25 #1629, 0.16 #54852), 0f8l9c (0.33 #39, 0.12 #37892, 0.09 #63395), 06mzp (0.33 #38, 0.10 #9313, 0.08 #11634), 04g61 (0.33 #239, 0.07 #6421, 0.06 #9514), 0fhnf (0.33 #454, 0.07 #6636, 0.04 #8183), 059j2 (0.33 #64, 0.07 #6246, 0.04 #7793) >> Best rule #5406 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 017v_; 03hrz; 017wh; 06jtd; 09ksp; 06rf7; 09hrc; >> query: (?x10766, ?x3623) <- adjoins(?x3623, ?x10766), adjoins(?x1679, ?x10766), contains(?x10766, ?x4861), country(?x10766, ?x1264), ?x1264 = 0345h, contains(?x1679, ?x1680) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #5139 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 017v_; 03hrz; 017wh; 06jtd; 09ksp; 06rf7; 09hrc; *> query: (?x10766, 070zc) <- adjoins(?x1679, ?x10766), contains(?x10766, ?x4861), country(?x10766, ?x1264), ?x1264 = 0345h, contains(?x1679, ?x1680) *> conf = 0.27 ranks of expected_values: 12 EVAL 07nf6 adjoins 070zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 222.000 112.000 0.871 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #18552-05bnx3j PRED entity: 05bnx3j PRED relation: award_winner! PRED expected values: 05c1t6z 0gvstc3 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 109): 0gvstc3 (0.26 #1694, 0.20 #988, 0.17 #10439), 0hndn2q (0.26 #1694, 0.20 #988, 0.17 #10439), 04n2r9h (0.26 #1694, 0.20 #988, 0.17 #10439), 02yvhx (0.26 #1694, 0.17 #8181, 0.09 #9451), 09gkdln (0.26 #1694, 0.03 #6610, 0.03 #5200), 0275n3y (0.26 #1694, 0.03 #6281, 0.03 #5153), 03gwpw2 (0.26 #1694, 0.02 #6497, 0.02 #6638), 03nnm4t (0.20 #988, 0.17 #10439, 0.10 #920), 0hn821n (0.20 #988, 0.17 #10439, 0.05 #272), 0lp_cd3 (0.20 #988, 0.17 #10439, 0.02 #1575) >> Best rule #1694 for best value: >> intensional similarity = 3 >> extensional distance = 199 >> proper extension: 025504; >> query: (?x12500, ?x2213) <- program(?x12500, ?x1434), program(?x6673, ?x1434), award_winner(?x2213, ?x6673) >> conf = 0.26 => this is the best rule for 7 predicted values ranks of expected_values: 1, 14 EVAL 05bnx3j award_winner! 0gvstc3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.260 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05bnx3j award_winner! 05c1t6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 120.000 120.000 0.260 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18551-07rd7 PRED entity: 07rd7 PRED relation: film PRED expected values: 09g7vfw => 116 concepts (109 used for prediction) PRED predicted values (max 10 best out of 453): 0g56t9t (0.48 #4911, 0.47 #13918, 0.41 #13919), 0243cq (0.41 #13919, 0.39 #4092, 0.22 #31934), 0gj8nq2 (0.41 #13919, 0.39 #4092, 0.22 #31934), 01hr1 (0.41 #13919, 0.39 #4092, 0.22 #31934), 050xxm (0.27 #32754, 0.26 #26201, 0.25 #31933), 07p12s (0.17 #1596, 0.01 #8147), 01xvjb (0.17 #1526, 0.01 #8077), 07ghq (0.17 #1501, 0.01 #8052), 0hwpz (0.17 #1438, 0.01 #7989), 0bxsk (0.17 #1402, 0.01 #7953) >> Best rule #4911 for best value: >> intensional similarity = 2 >> extensional distance = 49 >> proper extension: 0fx02; >> query: (?x4314, ?x124) <- written_by(?x124, ?x4314), influenced_by(?x12556, ?x4314) >> conf = 0.48 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07rd7 film 09g7vfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 109.000 0.476 http://example.org/film/director/film #18550-07w0v PRED entity: 07w0v PRED relation: school! PRED expected values: 025tn92 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 11): 02qw1zx (0.37 #212, 0.33 #135, 0.32 #91), 092j54 (0.29 #213, 0.26 #158, 0.23 #114), 06439y (0.29 #22, 0.23 #55, 0.23 #99), 02z6872 (0.29 #16, 0.18 #507, 0.16 #159), 025tn92 (0.23 #50, 0.21 #160, 0.20 #28), 02pq_x5 (0.23 #53, 0.20 #31, 0.18 #507), 02x2khw (0.23 #34, 0.18 #507, 0.15 #45), 02rl201 (0.18 #507, 0.15 #35, 0.14 #13), 02r6gw6 (0.18 #507, 0.15 #40, 0.11 #216), 038981 (0.18 #507, 0.15 #52, 0.10 #129) >> Best rule #212 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 05kj_; >> query: (?x1011, 02qw1zx) <- school(?x3089, ?x1011), draft(?x11061, ?x3089), ?x11061 = 06x76 >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 01jssp; 05krk; 06pwq; 065y4w7; 01w3v; 01w5m; 03ksy; 07vyf; 07t90; 0cwx_; ... *> query: (?x1011, 025tn92) <- major_field_of_study(?x1011, ?x4321), major_field_of_study(?x1011, ?x1154), ?x4321 = 0g26h, organization(?x1011, ?x5487), ?x1154 = 02lp1 *> conf = 0.23 ranks of expected_values: 5 EVAL 07w0v school! 025tn92 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 146.000 146.000 0.365 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #18549-05cj4r PRED entity: 05cj4r PRED relation: film PRED expected values: 02wyzmv => 92 concepts (55 used for prediction) PRED predicted values (max 10 best out of 527): 03ctqqf (0.69 #5350, 0.54 #12485, 0.48 #33882), 049xgc (0.25 #969, 0.07 #2752, 0.01 #9887), 078sj4 (0.25 #451, 0.01 #52169, 0.01 #16502), 05sy_5 (0.24 #4618, 0.03 #58852, 0.03 #49933), 02mpyh (0.21 #3240, 0.01 #15725), 04cj79 (0.14 #2374, 0.04 #4157, 0.03 #58852), 04cv9m (0.14 #2481, 0.01 #14966), 011xg5 (0.14 #3212), 0_9wr (0.14 #3013), 07nxnw (0.14 #2991) >> Best rule #5350 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 02k6rq; >> query: (?x374, ?x161) <- award_winner(?x3307, ?x374), ?x3307 = 01ksr1, nominated_for(?x374, ?x161) >> conf = 0.69 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05cj4r film 02wyzmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 55.000 0.694 http://example.org/film/actor/film./film/performance/film #18548-02_1sj PRED entity: 02_1sj PRED relation: genre PRED expected values: 06cvj => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.78 #485, 0.73 #5867, 0.67 #1342), 0l4h_ (0.72 #5375, 0.62 #7329, 0.62 #7206), 01z4y (0.62 #7329, 0.62 #7206, 0.61 #7575), 03k9fj (0.50 #375, 0.25 #254, 0.24 #4162), 01jfsb (0.39 #6367, 0.36 #1599, 0.32 #2455), 02l7c8 (0.38 #3191, 0.37 #1848, 0.33 #138), 02kdv5l (0.37 #6357, 0.30 #4274, 0.29 #4153), 06cvj (0.33 #125, 0.25 #246, 0.21 #1835), 0hcr (0.33 #388, 0.25 #267, 0.11 #631), 0lsxr (0.33 #9, 0.23 #6363, 0.22 #1104) >> Best rule #485 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 01cgz; >> query: (?x590, 07s9rl0) <- films(?x2008, ?x590), titles(?x2008, ?x273), films(?x2008, ?x3133), country(?x3133, ?x94) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #125 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 01633c; *> query: (?x590, 06cvj) <- film(?x2942, ?x590), ?x2942 = 046lt, film_release_distribution_medium(?x590, ?x81), film_crew_role(?x590, ?x137), produced_by(?x590, ?x1039) *> conf = 0.33 ranks of expected_values: 8 EVAL 02_1sj genre 06cvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 83.000 83.000 0.784 http://example.org/film/film/genre #18547-01vv126 PRED entity: 01vv126 PRED relation: artists! PRED expected values: 05r6t => 178 concepts (131 used for prediction) PRED predicted values (max 10 best out of 264): 064t9 (0.82 #36625, 0.76 #7772, 0.71 #2183), 06by7 (0.68 #10573, 0.66 #7161, 0.66 #23909), 02vjzr (0.50 #754, 0.26 #7893, 0.24 #2304), 02x8m (0.50 #639, 0.14 #10259, 0.14 #32285), 026z9 (0.50 #697, 0.10 #8146, 0.10 #10317), 0glt670 (0.46 #6868, 0.42 #5626, 0.40 #16484), 02lnbg (0.45 #2538, 0.43 #4090, 0.43 #3780), 0ggx5q (0.45 #2558, 0.43 #4110, 0.41 #5353), 025sc50 (0.43 #3771, 0.42 #6876, 0.41 #5324), 05r6t (0.43 #1012, 0.40 #82, 0.22 #1322) >> Best rule #36625 for best value: >> intensional similarity = 5 >> extensional distance = 425 >> proper extension: 01pr_j6; 01qkqwg; 0770cd; 02fgpf; 02qlg7s; 01tp5bj; 06x4l_; 0412f5y; 0dzc16; 05vzw3; ... >> query: (?x2658, 064t9) <- artists(?x3061, ?x2658), artists(?x3061, ?x10989), artists(?x3061, ?x6639), ?x10989 = 02s6sh, ?x6639 = 0137hn >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1012 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 03xl77; *> query: (?x2658, 05r6t) <- participant(?x4740, ?x2658), artists(?x2995, ?x2658), ?x2995 = 01cbwl *> conf = 0.43 ranks of expected_values: 10 EVAL 01vv126 artists! 05r6t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 178.000 131.000 0.824 http://example.org/music/genre/artists #18546-02_pft PRED entity: 02_pft PRED relation: film PRED expected values: 0jvt9 => 128 concepts (91 used for prediction) PRED predicted values (max 10 best out of 424): 02qr3k8 (0.33 #1288, 0.04 #13804, 0.04 #10228), 01wb95 (0.33 #622, 0.03 #13138, 0.03 #9562), 07b1gq (0.33 #602, 0.02 #7754), 0sxns (0.33 #1077, 0.02 #26109, 0.01 #2865), 0y_hb (0.33 #1113, 0.01 #15417, 0.01 #27933), 02gqm3 (0.33 #1614), 06x43v (0.33 #1307), 0b9rdk (0.33 #1045), 011wtv (0.33 #771), 032zq6 (0.33 #690) >> Best rule #1288 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0k525; >> query: (?x11057, 02qr3k8) <- film(?x11057, ?x1744), profession(?x11057, ?x1032), people(?x1050, ?x11057), ?x1744 = 035yn8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13055 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 171 *> proper extension: 01vrx3g; *> query: (?x11057, 0jvt9) <- film(?x11057, ?x1744), profession(?x11057, ?x1032), award(?x11057, ?x3066), people(?x4322, ?x11057) *> conf = 0.05 ranks of expected_values: 15 EVAL 02_pft film 0jvt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 128.000 91.000 0.333 http://example.org/film/actor/film./film/performance/film #18545-0gywn PRED entity: 0gywn PRED relation: parent_genre! PRED expected values: 021_z5 => 60 concepts (49 used for prediction) PRED predicted values (max 10 best out of 287): 09jw2 (0.50 #886, 0.20 #1900, 0.20 #1139), 059kh (0.40 #1813, 0.40 #1052, 0.33 #39), 0y3_8 (0.40 #1051, 0.33 #38, 0.30 #1812), 0dn16 (0.40 #1025, 0.33 #12, 0.25 #772), 01cbwl (0.40 #1047, 0.33 #34, 0.25 #794), 01fbr2 (0.40 #1825, 0.33 #1570, 0.25 #558), 01h0kx (0.33 #120, 0.25 #627, 0.25 #373), 0grjmv (0.33 #111, 0.25 #618, 0.25 #364), 03xnwz (0.33 #27, 0.25 #534, 0.25 #280), 016ybr (0.33 #97, 0.25 #604, 0.25 #350) >> Best rule #886 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 016jny; >> query: (?x3928, 09jw2) <- artists(?x3928, ?x8253), artists(?x3928, ?x6124), artists(?x3928, ?x3256), artists(?x3928, ?x2824), ?x2824 = 02w4fkq, artist(?x2931, ?x6124), nationality(?x8253, ?x94), ?x3256 = 01vwyqp >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1540 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 0m0jc; 03_d0; 0glt670; 05bt6j; 0155w; *> query: (?x3928, 021_z5) <- artists(?x3928, ?x12102), artists(?x3928, ?x5906), artists(?x3928, ?x4640), ?x5906 = 0127s7, award_winner(?x2186, ?x12102), artist(?x6474, ?x4640), parent_genre(?x1127, ?x3928) *> conf = 0.11 ranks of expected_values: 103 EVAL 0gywn parent_genre! 021_z5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 60.000 49.000 0.500 http://example.org/music/genre/parent_genre #18544-030hcs PRED entity: 030hcs PRED relation: award_winner PRED expected values: 01swck => 125 concepts (77 used for prediction) PRED predicted values (max 10 best out of 656): 015rkw (0.82 #90073, 0.82 #77206, 0.82 #93289), 01swck (0.82 #90073, 0.82 #77206, 0.82 #93289), 01g23m (0.51 #91681, 0.51 #93290, 0.42 #107766), 01pcq3 (0.51 #91681, 0.51 #93290, 0.42 #107766), 034np8 (0.51 #91681, 0.51 #93290, 0.42 #107766), 01p7yb (0.51 #91681, 0.51 #93290, 0.42 #107766), 03hzl42 (0.51 #91681, 0.51 #93290, 0.36 #107765), 02qgqt (0.42 #107766, 0.36 #107765, 0.36 #123849), 01cyjx (0.42 #107766, 0.36 #107765, 0.36 #123849), 0151w_ (0.27 #98114, 0.27 #96506, 0.15 #94898) >> Best rule #90073 for best value: >> intensional similarity = 3 >> extensional distance = 772 >> proper extension: 01wp8w7; 0g51l1; 02645b; 01wn718; 036px; 02__94; 01wgfp6; 039x1k; 01dhpj; 01y8d4; ... >> query: (?x1815, ?x1554) <- location(?x1815, ?x335), award_winner(?x1554, ?x1815), nationality(?x1815, ?x94) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 030hcs award_winner 01swck CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 77.000 0.816 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #18543-07gbf PRED entity: 07gbf PRED relation: genre PRED expected values: 01hmnh => 79 concepts (73 used for prediction) PRED predicted values (max 10 best out of 73): 01hmnh (0.58 #9, 0.33 #83, 0.30 #305), 03k9fj (0.50 #302, 0.49 #377, 0.25 #154), 01htzx (0.48 #306, 0.48 #381, 0.42 #84), 01z4y (0.37 #679, 0.36 #531, 0.34 #1424), 0hcr (0.28 #308, 0.26 #383, 0.22 #2837), 0c4xc (0.25 #701, 0.25 #627, 0.24 #1222), 01t_vv (0.25 #24, 0.19 #990, 0.18 #1437), 01z77k (0.17 #242, 0.14 #466, 0.13 #1507), 09n3wz (0.17 #58, 0.08 #132, 0.07 #3496), 01tz3c (0.17 #36, 0.07 #3496, 0.05 #332) >> Best rule #9 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0dk0dj; 0d_rw; >> query: (?x9633, 01hmnh) <- program(?x1762, ?x9633), genre(?x9633, ?x4205), genre(?x9633, ?x571), ?x571 = 03npn, titles(?x4205, ?x599) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07gbf genre 01hmnh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 73.000 0.583 http://example.org/tv/tv_program/genre #18542-0h96g PRED entity: 0h96g PRED relation: student! PRED expected values: 08815 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 94): 0bwfn (0.20 #798, 0.20 #274, 0.08 #7610), 04rkkv (0.20 #830, 0.05 #1878, 0.03 #2926), 026gvfj (0.20 #634, 0.02 #1158, 0.02 #1682), 0fr9jp (0.20 #867, 0.02 #7679, 0.02 #6631), 01hc1j (0.20 #971, 0.02 #2019), 01_qgp (0.20 #799, 0.02 #1847), 01mpwj (0.20 #630, 0.02 #1678), 026036 (0.20 #390), 01p896 (0.20 #369), 05nrkb (0.11 #1395, 0.07 #2443, 0.03 #1919) >> Best rule #798 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0f4vbz; >> query: (?x4771, 0bwfn) <- award(?x4771, ?x6463), award(?x4771, ?x1972), student(?x581, ?x4771), ?x1972 = 0gqyl, ?x6463 = 02g2yr >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1050 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 01wz01; *> query: (?x4771, 08815) <- location(?x4771, ?x1131), ?x1131 = 0cc56, film(?x4771, ?x377) *> conf = 0.07 ranks of expected_values: 11 EVAL 0h96g student! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 101.000 101.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #18541-0c4y8 PRED entity: 0c4y8 PRED relation: profession PRED expected values: 0cbd2 0dxtg 05z96 => 175 concepts (140 used for prediction) PRED predicted values (max 10 best out of 104): 0dxtg (0.87 #10378, 0.83 #10674, 0.82 #11266), 02hrh1q (0.83 #14083, 0.81 #15712, 0.81 #16748), 0cbd2 (0.78 #4893, 0.73 #7, 0.71 #5929), 0nbcg (0.75 #16025, 0.38 #13655, 0.22 #4473), 01d_h8 (0.67 #10666, 0.65 #11702, 0.65 #11258), 02jknp (0.59 #11704, 0.56 #11260, 0.54 #10372), 09jwl (0.49 #16013, 0.39 #13643, 0.31 #4461), 03gjzk (0.46 #10676, 0.45 #904, 0.43 #10380), 018gz8 (0.45 #906, 0.38 #314, 0.31 #9789), 0dz3r (0.32 #15995, 0.32 #13625, 0.17 #15253) >> Best rule #10378 for best value: >> intensional similarity = 4 >> extensional distance = 205 >> proper extension: 058kqy; 05183k; 07s93v; 01gzm2; 01f7j9; 01q415; 081_zm; 07_s4b; 02l5rm; 021yw7; ... >> query: (?x9610, 0dxtg) <- profession(?x9610, ?x2225), nationality(?x9610, ?x94), student(?x546, ?x9610), written_by(?x5725, ?x9610) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 12 EVAL 0c4y8 profession 05z96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 175.000 140.000 0.870 http://example.org/people/person/profession EVAL 0c4y8 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 140.000 0.870 http://example.org/people/person/profession EVAL 0c4y8 profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 175.000 140.000 0.870 http://example.org/people/person/profession #18540-05fjy PRED entity: 05fjy PRED relation: time_zones PRED expected values: 02hczc => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 13): 02hczc (0.78 #1160, 0.71 #612, 0.69 #1681), 02fqwt (0.59 #1734, 0.50 #14, 0.38 #66), 02hcv8 (0.47 #133, 0.47 #29, 0.46 #562), 02llzg (0.30 #56, 0.20 #746, 0.18 #798), 02lcqs (0.22 #408, 0.20 #981, 0.19 #1217), 042g7t (0.13 #63, 0.05 #323, 0.05 #375), 03bdv (0.11 #318, 0.10 #1022, 0.08 #1805), 03plfd (0.08 #1039, 0.06 #1143, 0.06 #1313), 02lcrv (0.06 #46, 0.04 #59, 0.04 #319), 0gsrz4 (0.05 #1337, 0.05 #659, 0.05 #685) >> Best rule #1160 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 0ml25; >> query: (?x5575, ?x2088) <- administrative_division(?x9341, ?x5575), category(?x9341, ?x134), time_zones(?x9341, ?x2088) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fjy time_zones 02hczc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.777 http://example.org/location/location/time_zones #18539-03r8gp PRED entity: 03r8gp PRED relation: major_field_of_study! PRED expected values: 01rtm4 => 106 concepts (67 used for prediction) PRED predicted values (max 10 best out of 670): 08815 (0.67 #4723, 0.59 #22451, 0.52 #21269), 03ksy (0.65 #21387, 0.60 #3661, 0.56 #26116), 017j69 (0.65 #21430, 0.40 #18478, 0.40 #3704), 09f2j (0.61 #21447, 0.60 #18495, 0.60 #15534), 01w5m (0.61 #21386, 0.60 #3660, 0.50 #4840), 01k2wn (0.60 #3566, 0.33 #4746, 0.26 #21292), 06pwq (0.57 #21280, 0.56 #22462, 0.55 #18328), 02zd460 (0.57 #21463, 0.50 #4917, 0.48 #22645), 07szy (0.57 #21311, 0.45 #18359, 0.44 #22493), 07w0v (0.52 #21289, 0.38 #7105, 0.35 #18337) >> Best rule #4723 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 03qsdpk; >> query: (?x10705, 08815) <- major_field_of_study(?x1200, ?x10705), films(?x10705, ?x6288), films(?x10705, ?x5648), films(?x10705, ?x2501), titles(?x812, ?x2501), genre(?x2501, ?x53), student(?x10705, ?x123), nominated_for(?x112, ?x6288), cinematography(?x5648, ?x6062) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #23044 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 25 *> proper extension: 02h40lc; 05qjt; 01mkq; 0pf2; 01lj9; 037mh8; 01zc2w; 04g7x; *> query: (?x10705, ?x3416) <- major_field_of_study(?x1200, ?x10705), major_field_of_study(?x2606, ?x10705), major_field_of_study(?x3416, ?x2606), major_field_of_study(?x3178, ?x2606), major_field_of_study(?x1043, ?x2606), major_field_of_study(?x734, ?x2606), ?x3178 = 01vc5m, ?x1043 = 0kz2w, student(?x3416, ?x2639) *> conf = 0.18 ranks of expected_values: 270 EVAL 03r8gp major_field_of_study! 01rtm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 106.000 67.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18538-038981 PRED entity: 038981 PRED relation: draft! PRED expected values: 0jmmn => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 284): 0jmgb (0.82 #453, 0.72 #305, 0.72 #304), 0jmfv (0.82 #453, 0.72 #305, 0.72 #304), 0jm8l (0.82 #453, 0.72 #305, 0.72 #304), 0jmmn (0.82 #453, 0.72 #305, 0.72 #304), 0jmbv (0.82 #453, 0.72 #305, 0.72 #304), 04cxw5b (0.82 #453, 0.72 #305, 0.72 #304), 06rpd (0.82 #453, 0.72 #305, 0.72 #304), 05g3b (0.82 #453, 0.72 #305, 0.72 #304), 05l71 (0.82 #453, 0.72 #305, 0.72 #304), 043vc (0.82 #453, 0.72 #305, 0.72 #304) >> Best rule #453 for best value: >> intensional similarity = 77 >> extensional distance = 2 >> proper extension: 06439y; >> query: (?x8586, ?x260) <- draft(?x13777, ?x8586), draft(?x11805, ?x8586), draft(?x11420, ?x8586), draft(?x9049, ?x8586), draft(?x6128, ?x8586), draft(?x5483, ?x8586), draft(?x4571, ?x8586), draft(?x2568, ?x8586), ?x6128 = 0jm64, school(?x8586, ?x581), ?x9049 = 0jmm4, position(?x11805, ?x6848), position(?x11805, ?x5755), position(?x11805, ?x1348), school(?x11805, ?x9131), ?x9131 = 02pptm, ?x1348 = 01pv51, ?x5755 = 0355dz, ?x2568 = 0jmcb, ?x5483 = 0jml5, fraternities_and_sororities(?x581, ?x4348), major_field_of_study(?x581, ?x10391), major_field_of_study(?x581, ?x8681), major_field_of_study(?x581, ?x2601), major_field_of_study(?x581, ?x2014), major_field_of_study(?x581, ?x1527), school(?x8786, ?x581), student(?x581, ?x6369), ?x11420 = 0jmhr, company(?x346, ?x581), ?x4571 = 0jm6n, major_field_of_study(?x10197, ?x2014), major_field_of_study(?x9620, ?x2014), major_field_of_study(?x7278, ?x2014), major_field_of_study(?x5486, ?x2014), major_field_of_study(?x4582, ?x2014), major_field_of_study(?x1675, ?x2014), major_field_of_study(?x1103, ?x2014), ?x10197 = 013nky, student(?x2014, ?x1328), draft(?x260, ?x8786), ?x7278 = 02sjgpq, ?x346 = 060c4, industry(?x648, ?x8681), ?x2601 = 04x_3, institution(?x4981, ?x581), institution(?x2636, ?x581), major_field_of_study(?x732, ?x2014), position(?x3798, ?x6848), ?x1527 = 04_tv, major_field_of_study(?x5750, ?x10391), major_field_of_study(?x1513, ?x10391), institution(?x4981, ?x10666), institution(?x4981, ?x9399), institution(?x4981, ?x5754), institution(?x4981, ?x4209), institution(?x4981, ?x1783), institution(?x4981, ?x621), ?x1783 = 049dk, ?x5486 = 0g8rj, ?x5750 = 01nnsv, ?x10666 = 01dzg0, ?x1103 = 01k2wn, ?x4582 = 02897w, ?x9620 = 02l424, ?x3798 = 02ptzz0, ?x9399 = 02z6fs, major_field_of_study(?x8681, ?x2164), ?x5754 = 02ln0f, institution(?x2636, ?x11717), ?x13777 = 0jmnl, ?x1675 = 01j_cy, ?x11717 = 01z3bz, ?x4209 = 02gr81, ?x621 = 02w2bc, place_of_birth(?x6369, ?x1523), ?x1513 = 017d77 >> conf = 0.82 => this is the best rule for 65 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 038981 draft! 0jmmn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 13.000 13.000 0.821 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #18537-047csmy PRED entity: 047csmy PRED relation: film_crew_role PRED expected values: 02_n3z 015h31 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 23): 0d2b38 (0.40 #96, 0.18 #330, 0.15 #418), 02_n3z (0.37 #80, 0.15 #418, 0.12 #314), 01pvkk (0.32 #1417, 0.32 #1208, 0.32 #1365), 02ynfr (0.23 #322, 0.19 #1184, 0.19 #584), 015h31 (0.18 #111, 0.17 #319, 0.15 #418), 0263ycg (0.15 #418, 0.11 #90, 0.09 #64), 089fss (0.15 #418, 0.11 #84, 0.08 #1206), 0ckd1 (0.15 #418, 0.11 #82, 0.06 #108), 02vs3x5 (0.15 #418, 0.09 #198, 0.09 #68), 04pyp5 (0.15 #418, 0.08 #1185, 0.08 #1211) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 03whyr; >> query: (?x5277, 0d2b38) <- category(?x5277, ?x134), film_crew_role(?x5277, ?x2472), ?x2472 = 01xy5l_ >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #80 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 03whyr; *> query: (?x5277, 02_n3z) <- category(?x5277, ?x134), film_crew_role(?x5277, ?x2472), ?x2472 = 01xy5l_ *> conf = 0.37 ranks of expected_values: 2, 5 EVAL 047csmy film_crew_role 015h31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 125.000 125.000 0.400 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 047csmy film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.400 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18536-01npcx PRED entity: 01npcx PRED relation: prequel PRED expected values: 029v40 => 70 concepts (42 used for prediction) PRED predicted values (max 10 best out of 29): 017gm7 (0.20 #25, 0.10 #206), 017gl1 (0.20 #17, 0.10 #198), 01v1ln (0.05 #485, 0.04 #725, 0.04 #666), 014kq6 (0.05 #401, 0.04 #582, 0.03 #765), 01npcx (0.05 #464, 0.04 #645, 0.01 #726), 03r0g9 (0.05 #422, 0.04 #603), 0164qt (0.05 #375), 01kf3_9 (0.04 #578, 0.03 #761), 08gsvw (0.04 #555), 025twgt (0.03 #905) >> Best rule #25 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 017jd9; >> query: (?x5598, 017gm7) <- film(?x1194, ?x5598), country(?x5598, ?x512), ?x1194 = 02gvwz, costume_design_by(?x5598, ?x1500) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01npcx prequel 029v40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 42.000 0.200 http://example.org/film/film/prequel #18535-0f__1 PRED entity: 0f__1 PRED relation: contains! PRED expected values: 0498y => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 317): 0498y (0.77 #40299, 0.69 #60892, 0.68 #62684), 0f2pf9 (0.77 #40299, 0.69 #60892), 0nn83 (0.73 #35819, 0.72 #60891, 0.71 #25964), 01n7q (0.36 #9031, 0.30 #11716, 0.26 #20670), 02qkt (0.35 #16462, 0.23 #36165, 0.23 #28102), 03rjj (0.27 #9, 0.06 #4486, 0.06 #14334), 0f__1 (0.24 #117304, 0.09 #94920, 0.09 #96711), 07ssc (0.19 #34059, 0.17 #118231, 0.17 #119126), 02j9z (0.18 #16143, 0.14 #27783, 0.14 #35846), 04_1l0v (0.17 #61342, 0.17 #45228, 0.16 #54178) >> Best rule #40299 for best value: >> intensional similarity = 3 >> extensional distance = 171 >> proper extension: 0t_gg; >> query: (?x2740, ?x4061) <- place_of_birth(?x117, ?x2740), county(?x2740, ?x10845), contains(?x4061, ?x10845) >> conf = 0.77 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0f__1 contains! 0498y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.768 http://example.org/location/location/contains #18534-01f8f7 PRED entity: 01f8f7 PRED relation: language PRED expected values: 03_9r => 81 concepts (80 used for prediction) PRED predicted values (max 10 best out of 32): 02h40lc (0.96 #405, 0.95 #1210, 0.95 #3236), 0459q4 (0.38 #149, 0.02 #725, 0.02 #954), 03_9r (0.33 #10, 0.24 #3756, 0.12 #124), 03115z (0.33 #36, 0.24 #3756, 0.12 #150), 03k50 (0.33 #9, 0.24 #3756, 0.08 #123), 064_8sq (0.24 #3756, 0.15 #363, 0.14 #1463), 0c_v2 (0.24 #3756, 0.08 #129, 0.01 #648), 01jb8r (0.24 #3756, 0.04 #166, 0.02 #223), 07qv_ (0.24 #3756, 0.04 #145, 0.01 #434), 07c9s (0.24 #3756, 0.04 #131) >> Best rule #405 for best value: >> intensional similarity = 4 >> extensional distance = 189 >> proper extension: 02x8fs; 0bx_hnp; 0ckt6; 04hk0w; >> query: (?x6788, 02h40lc) <- country(?x6788, ?x205), award_winner(?x6788, ?x4169), cinematography(?x6788, ?x7740), language(?x6788, ?x2890) >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 01f8gz; *> query: (?x6788, 03_9r) <- costume_design_by(?x6788, ?x9086), nominated_for(?x4169, ?x6788), nominated_for(?x7215, ?x6788), ?x7215 = 09v92_x *> conf = 0.33 ranks of expected_values: 3 EVAL 01f8f7 language 03_9r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 81.000 80.000 0.963 http://example.org/film/film/language #18533-0dcp_ PRED entity: 0dcp_ PRED relation: symptom_of! PRED expected values: 01cdt5 => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 86): 01j6t0 (0.88 #1005, 0.86 #1194, 0.83 #900), 0cjf0 (0.88 #1014, 0.84 #1089, 0.84 #1177), 02y0js (0.71 #974, 0.69 #749, 0.62 #773), 02k6hp (0.71 #974, 0.69 #749, 0.62 #773), 0d19y2 (0.71 #974, 0.69 #749, 0.62 #773), 019dmc (0.71 #974, 0.69 #749, 0.62 #773), 01psyx (0.71 #974, 0.69 #749, 0.62 #773), 01tf_6 (0.71 #974, 0.69 #749, 0.62 #773), 0brgy (0.71 #492, 0.60 #357, 0.54 #697), 012qjw (0.67 #675, 0.57 #493, 0.55 #736) >> Best rule #1005 for best value: >> intensional similarity = 16 >> extensional distance = 14 >> proper extension: 04psf; 07jwr; 01gkcc; 07s4l; 04nz3; >> query: (?x12536, 01j6t0) <- symptom_of(?x6260, ?x12536), symptom_of(?x3679, ?x12536), symptom_of(?x3679, ?x9898), symptom_of(?x3679, ?x3680), ?x3680 = 025hl8, ?x9898 = 09jg8, people(?x6260, ?x11097), people(?x6260, ?x8006), people(?x6260, ?x2001), nationality(?x11097, ?x1264), religion(?x11097, ?x2694), influenced_by(?x11097, ?x3336), location(?x11097, ?x5952), type_of_union(?x2001, ?x566), nationality(?x8006, ?x94), profession(?x8006, ?x987) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #697 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 7 *> proper extension: 0gk4g; *> query: (?x12536, ?x4905) <- symptom_of(?x3679, ?x12536), risk_factors(?x11659, ?x12536), symptom_of(?x3679, ?x14096), symptom_of(?x3679, ?x11064), symptom_of(?x3679, ?x10480), symptom_of(?x3679, ?x5118), symptom_of(?x3679, ?x4959), people(?x5118, ?x5119), ?x10480 = 0h1n9, ?x14096 = 0h3bn, ?x11064 = 01n3bm, symptom_of(?x4905, ?x11659), risk_factors(?x5118, ?x231), people(?x4959, ?x598), symptom_of(?x13487, ?x4959), ?x13487 = 01cdt5 *> conf = 0.54 ranks of expected_values: 37 EVAL 0dcp_ symptom_of! 01cdt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 52.000 52.000 0.875 http://example.org/medicine/symptom/symptom_of #18532-016zwt PRED entity: 016zwt PRED relation: organization PRED expected values: 07t65 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 49): 07t65 (0.92 #295, 0.90 #169, 0.90 #1606), 01rz1 (0.63 #296, 0.55 #170, 0.46 #758), 0b6css (0.57 #73, 0.52 #178, 0.50 #157), 04k4l (0.48 #508, 0.47 #319, 0.45 #109), 041288 (0.45 #856, 0.33 #1429, 0.33 #1725), 018cqq (0.45 #200, 0.45 #179, 0.41 #116), 0gkjy (0.38 #848, 0.31 #1314, 0.31 #154), 0j7v_ (0.36 #47, 0.31 #152, 0.27 #89), 02jxk (0.32 #297, 0.29 #171, 0.29 #402), 034h1h (0.21 #1869, 0.18 #1995, 0.02 #1593) >> Best rule #295 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 05r4w; >> query: (?x8620, 07t65) <- country(?x2631, ?x8620), country(?x1121, ?x8620), ?x2631 = 01z27, ?x1121 = 0bynt >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016zwt organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.921 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #18531-03fbc PRED entity: 03fbc PRED relation: artists! PRED expected values: 03xnwz 05w3f 0glt670 0190y4 => 72 concepts (35 used for prediction) PRED predicted values (max 10 best out of 284): 0glt670 (0.76 #3632, 0.75 #3034, 0.62 #1834), 025sc50 (0.62 #1842, 0.33 #346, 0.31 #3640), 0gywn (0.62 #1849, 0.33 #353, 0.21 #6946), 01fm07 (0.50 #1913, 0.33 #417, 0.11 #3113), 06j6l (0.44 #1840, 0.33 #3040, 0.33 #344), 03_d0 (0.44 #1805, 0.33 #309, 0.33 #5405), 0ggx5q (0.44 #3368, 0.19 #8761, 0.17 #6966), 0xhtw (0.40 #4508, 0.39 #4809, 0.39 #3907), 02x8m (0.38 #1812, 0.33 #316, 0.19 #3012), 05bt6j (0.36 #6933, 0.33 #340, 0.30 #5436) >> Best rule #3632 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 04lgymt; >> query: (?x2635, 0glt670) <- award(?x2635, ?x9295), ceremony(?x9295, ?x139), award(?x8169, ?x9295), ?x8169 = 01vz0g4 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 17, 29, 101 EVAL 03fbc artists! 0190y4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 72.000 35.000 0.759 http://example.org/music/genre/artists EVAL 03fbc artists! 0glt670 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 35.000 0.759 http://example.org/music/genre/artists EVAL 03fbc artists! 05w3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 72.000 35.000 0.759 http://example.org/music/genre/artists EVAL 03fbc artists! 03xnwz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 72.000 35.000 0.759 http://example.org/music/genre/artists #18530-06q8qh PRED entity: 06q8qh PRED relation: executive_produced_by PRED expected values: 02_340 => 107 concepts (80 used for prediction) PRED predicted values (max 10 best out of 95): 03c9pqt (0.30 #752, 0.02 #7336, 0.02 #7588), 0z4s (0.25 #15), 04q5zw (0.10 #586, 0.02 #1090, 0.02 #838), 0343h (0.08 #1051, 0.08 #799, 0.04 #2059), 05hj_k (0.07 #603, 0.06 #1863, 0.05 #1611), 02z6l5f (0.06 #1379, 0.05 #1631, 0.03 #4423), 06q8hf (0.06 #1932, 0.05 #672, 0.05 #1428), 0glyyw (0.05 #442, 0.03 #6263, 0.03 #7278), 02z2xdf (0.05 #1419, 0.03 #4463, 0.03 #1671), 0bzyh (0.05 #253, 0.04 #7594, 0.03 #8603) >> Best rule #752 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 0gj8t_b; >> query: (?x3684, 03c9pqt) <- film_crew_role(?x3684, ?x137), film(?x2549, ?x3684), language(?x3684, ?x7599), ?x2549 = 024rgt >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06q8qh executive_produced_by 02_340 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 80.000 0.300 http://example.org/film/film/executive_produced_by #18529-01ycbq PRED entity: 01ycbq PRED relation: award_winner! PRED expected values: 0hr6lkl => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 132): 0gkxgfq (0.48 #240, 0.02 #2432, 0.02 #788), 0jt3qpk (0.41 #176, 0.02 #2368, 0.02 #4423), 0jzphpx (0.11 #173, 0.04 #1680, 0.03 #5242), 01xqqp (0.11 #229, 0.03 #1736, 0.03 #5298), 013b2h (0.07 #213, 0.06 #1720, 0.05 #3638), 09n4nb (0.07 #181, 0.05 #1688, 0.04 #5250), 09qvms (0.06 #1245, 0.06 #971, 0.05 #2341), 01c6qp (0.06 #1662, 0.04 #5224, 0.04 #5361), 01s695 (0.05 #1647, 0.04 #277, 0.04 #5209), 02rjjll (0.05 #1649, 0.04 #5211, 0.04 #5348) >> Best rule #240 for best value: >> intensional similarity = 2 >> extensional distance = 25 >> proper extension: 0_b3d; >> query: (?x2033, 0gkxgfq) <- nominated_for(?x2033, ?x6590), award_winner(?x3486, ?x6590) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #12742 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2499 *> proper extension: 01cv3n; 01g4zr; 0ftps; 016ntp; 06gd4; 0180w8; 0h005; 04kjrv; 0fpj9pm; 011lvx; ... *> query: (?x2033, ?x78) <- nationality(?x2033, ?x279), award(?x2033, ?x3066), ceremony(?x3066, ?x78) *> conf = 0.02 ranks of expected_values: 91 EVAL 01ycbq award_winner! 0hr6lkl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 110.000 110.000 0.481 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18528-09qr6 PRED entity: 09qr6 PRED relation: artists! PRED expected values: 06by7 => 110 concepts (72 used for prediction) PRED predicted values (max 10 best out of 222): 06j6l (0.66 #7625, 0.46 #1260, 0.41 #957), 06by7 (0.55 #2750, 0.48 #324, 0.46 #6992), 0gywn (0.44 #1269, 0.39 #7634, 0.29 #1517), 016clz (0.41 #308, 0.27 #2128, 0.27 #3037), 03_d0 (0.29 #1517, 0.22 #1225, 0.20 #9713), 016_nr (0.29 #1517, 0.19 #70, 0.17 #677), 01flzq (0.29 #1517, 0.15 #113, 0.14 #720), 036jv (0.29 #1517, 0.12 #186, 0.10 #793), 06cp5 (0.29 #1517, 0.11 #391, 0.08 #88), 012yc (0.29 #1517, 0.09 #2569, 0.08 #2266) >> Best rule #7625 for best value: >> intensional similarity = 3 >> extensional distance = 271 >> proper extension: 01v27pl; >> query: (?x1338, 06j6l) <- artists(?x3996, ?x1338), artists(?x3996, ?x6666), ?x6666 = 05szp >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #2750 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 03xhj6; 06gcn; *> query: (?x1338, 06by7) <- artists(?x671, ?x1338), artist(?x9224, ?x1338), ?x9224 = 0n85g *> conf = 0.55 ranks of expected_values: 2 EVAL 09qr6 artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 72.000 0.659 http://example.org/music/genre/artists #18527-01738w PRED entity: 01738w PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.87 #76, 0.86 #156, 0.85 #136), 02nxhr (0.08 #52, 0.07 #82, 0.05 #162), 07c52 (0.08 #23, 0.03 #123, 0.03 #193), 07z4p (0.05 #25, 0.03 #110, 0.03 #125) >> Best rule #76 for best value: >> intensional similarity = 6 >> extensional distance = 171 >> proper extension: 0bh8yn3; 0d_2fb; 02rmd_2; 0gs973; 0298n7; >> query: (?x6411, 029j_) <- film_crew_role(?x6411, ?x2154), film_crew_role(?x6411, ?x2095), ?x2154 = 01vx2h, ?x2095 = 0dxtw, film(?x1345, ?x6411), award_nominee(?x2383, ?x1345) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01738w film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #18526-09146g PRED entity: 09146g PRED relation: genre PRED expected values: 04t2t => 92 concepts (81 used for prediction) PRED predicted values (max 10 best out of 94): 07s9rl0 (0.66 #3544, 0.66 #9334, 0.62 #4252), 01jfsb (0.56 #6037, 0.54 #3081, 0.52 #3790), 01hmnh (0.36 #3914, 0.34 #1551, 0.33 #607), 02l7c8 (0.33 #15, 0.29 #1195, 0.29 #251), 06n90 (0.31 #3082, 0.30 #4735, 0.28 #4027), 0lsxr (0.23 #3787, 0.23 #6034, 0.22 #3078), 082gq (0.22 #501, 0.16 #1091, 0.12 #3572), 04xvlr (0.18 #3545, 0.14 #4253, 0.14 #8509), 060__y (0.17 #1078, 0.16 #3559, 0.15 #9349), 03mqtr (0.17 #28, 0.14 #264, 0.13 #382) >> Best rule #3544 for best value: >> intensional similarity = 4 >> extensional distance = 360 >> proper extension: 0cvkv5; >> query: (?x1904, 07s9rl0) <- nominated_for(?x1053, ?x1904), genre(?x1904, ?x225), nominated_for(?x3069, ?x1904), music(?x224, ?x3069) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #1591 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 04y9mm8; 06_sc3; 02bj22; *> query: (?x1904, 04t2t) <- film(?x147, ?x1904), film_crew_role(?x1904, ?x468), production_companies(?x1904, ?x902), prequel(?x2709, ?x1904) *> conf = 0.04 ranks of expected_values: 58 EVAL 09146g genre 04t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 92.000 81.000 0.660 http://example.org/film/film/genre #18525-018h2 PRED entity: 018h2 PRED relation: titles PRED expected values: 03prz_ => 68 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1576): 01qbg5 (0.60 #5748, 0.50 #7302, 0.40 #9329), 03hkch7 (0.60 #5102, 0.50 #6656, 0.33 #3548), 03hfmm (0.50 #7477, 0.40 #9329, 0.40 #5923), 02prwdh (0.50 #7017, 0.40 #5463, 0.33 #3909), 03h_yy (0.50 #6283, 0.33 #3175, 0.20 #4729), 0353xq (0.50 #7005, 0.33 #3897, 0.20 #5451), 05hjnw (0.40 #9329, 0.40 #5385, 0.33 #6939), 0d8w2n (0.40 #9329, 0.40 #6195, 0.33 #7749), 02nczh (0.40 #5618, 0.33 #7172, 0.33 #4064), 05_5rjx (0.40 #5209, 0.33 #6763, 0.33 #3655) >> Best rule #5748 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 017fp; >> query: (?x2286, 01qbg5) <- titles(?x2286, ?x7664), titles(?x2286, ?x2287), music(?x2287, ?x4644), featured_film_locations(?x2287, ?x362), ?x7664 = 046f3p, currency(?x2287, ?x170), film(?x2125, ?x2287), genre(?x2287, ?x53) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7064 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 04xvlr; 04t36; *> query: (?x2286, 03prz_) <- titles(?x2286, ?x3803), titles(?x2286, ?x2287), titles(?x2286, ?x414), ?x2287 = 02s4l6, costume_design_by(?x414, ?x3685), award_winner(?x414, ?x2275), country(?x3803, ?x512), nominated_for(?x112, ?x414), ?x3685 = 0bytfv *> conf = 0.33 ranks of expected_values: 84 EVAL 018h2 titles 03prz_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 68.000 37.000 0.600 http://example.org/media_common/netflix_genre/titles #18524-021bk PRED entity: 021bk PRED relation: award PRED expected values: 03hj5vf 03hl6lc => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 295): 054krc (0.37 #4900, 0.36 #890, 0.24 #5703), 02qvyrt (0.35 #928, 0.29 #4938, 0.19 #5741), 0gqz2 (0.35 #883, 0.28 #4893, 0.22 #5696), 09sb52 (0.33 #10067, 0.33 #11270, 0.32 #18890), 054ks3 (0.30 #942, 0.20 #4952, 0.17 #1744), 0l8z1 (0.29 #4876, 0.27 #866, 0.19 #5679), 0gr4k (0.29 #3642, 0.28 #6851, 0.15 #2038), 04dn09n (0.28 #3653, 0.24 #6862, 0.18 #2049), 01by1l (0.27 #1316, 0.27 #1717, 0.25 #2519), 0gr51 (0.25 #3710, 0.25 #6919, 0.12 #2106) >> Best rule #4900 for best value: >> intensional similarity = 2 >> extensional distance = 190 >> proper extension: 07m4c; >> query: (?x2328, 054krc) <- music(?x8182, ?x2328), nominated_for(?x2478, ?x8182) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #3785 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 152 *> proper extension: 01r216; *> query: (?x2328, 03hl6lc) <- place_of_birth(?x2328, ?x739), written_by(?x2329, ?x2328), award_nominee(?x2328, ?x2383) *> conf = 0.21 ranks of expected_values: 15, 113 EVAL 021bk award 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 104.000 104.000 0.370 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 021bk award 03hj5vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 104.000 104.000 0.370 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18523-0678gl PRED entity: 0678gl PRED relation: language PRED expected values: 02h40lc => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 3): 02h40lc (0.95 #16, 0.92 #28, 0.89 #22), 03_9r (0.03 #33), 03k50 (0.02 #32) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 05dxl5; 081jbk; 01x_d8; 01x9_8; 0ccqd7; 05q_mg; 09ykwk; >> query: (?x13758, 02h40lc) <- actor(?x10187, ?x13758), film(?x296, ?x10187), profession(?x13758, ?x1032), country(?x10187, ?x252) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0678gl language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.947 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #18522-0d1_f PRED entity: 0d1_f PRED relation: jurisdiction_of_office PRED expected values: 0d060g => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.75 #371, 0.75 #337, 0.73 #405), 07ssc (0.64 #100, 0.62 #302, 0.29 #72), 059rby (0.22 #38, 0.09 #206, 0.06 #375), 05nrg (0.12 #540), 07c5l (0.12 #540), 0d060g (0.11 #37, 0.10 #70, 0.06 #205), 01n7q (0.11 #46, 0.07 #13, 0.05 #113), 0d04z6 (0.10 #93, 0.06 #228, 0.04 #295), 05vz3zq (0.10 #92, 0.03 #396, 0.03 #498), 05kkh (0.09 #203, 0.05 #372, 0.05 #338) >> Best rule #371 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 083q7; 042f1; >> query: (?x3444, 09c7w0) <- jurisdiction_of_office(?x3444, ?x6401), taxonomy(?x6401, ?x939), contains(?x6401, ?x4030) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0bwh6; *> query: (?x3444, 0d060g) <- jurisdiction_of_office(?x3444, ?x7096), jurisdiction_of_office(?x3444, ?x6401), vacationer(?x6401, ?x848), jurisdiction_of_office(?x182, ?x7096) *> conf = 0.11 ranks of expected_values: 6 EVAL 0d1_f jurisdiction_of_office 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 49.000 49.000 0.746 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #18521-0m2kw PRED entity: 0m2kw PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 108 concepts (47 used for prediction) PRED predicted values (max 10 best out of 6): 09c7w0 (0.89 #190, 0.88 #217, 0.87 #229), 026mj (0.28 #45, 0.15 #108, 0.15 #69), 07ssc (0.04 #75), 05fjf (0.04 #148), 03rt9 (0.02 #85, 0.02 #503, 0.02 #530), 02jx1 (0.01 #330, 0.01 #78, 0.01 #104) >> Best rule #190 for best value: >> intensional similarity = 4 >> extensional distance = 226 >> proper extension: 0n5j_; 0fm9_; 0jcgs; 0mx4_; 0mwl2; 0mw89; 0mw93; 0cc56; 0m7fm; 0drsm; ... >> query: (?x12023, 09c7w0) <- source(?x12023, ?x958), contains(?x7518, ?x12023), adjoins(?x2832, ?x12023), currency(?x12023, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m2kw second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 47.000 0.886 http://example.org/location/country/second_level_divisions #18520-0bwfn PRED entity: 0bwfn PRED relation: contact_category PRED expected values: 03w5xm => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 3): 03w5xm (0.75 #71, 0.72 #101, 0.71 #117), 02zdwq (0.30 #55, 0.27 #73, 0.26 #103), 014dgf (0.21 #69, 0.21 #60, 0.21 #102) >> Best rule #71 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 016tt2; 07l1c; 0z90c; 01b39j; 03_c8p; 013fn; 0gy1_; >> query: (?x7545, 03w5xm) <- company(?x3484, ?x7545), organization(?x346, ?x7545), service_location(?x7545, ?x94) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bwfn contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.750 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #18519-0cbm64 PRED entity: 0cbm64 PRED relation: artists! PRED expected values: 016clz 05bt6j => 115 concepts (81 used for prediction) PRED predicted values (max 10 best out of 255): 02lnbg (0.64 #1590, 0.36 #5583, 0.29 #9580), 05bt6j (0.64 #1574, 0.30 #8028, 0.29 #3724), 016clz (0.60 #1842, 0.55 #2762, 0.47 #3378), 0ggx5q (0.57 #1609, 0.31 #5602, 0.27 #6217), 0glt670 (0.50 #346, 0.49 #4336, 0.48 #3105), 0xhtw (0.44 #4005, 0.40 #9228, 0.40 #8614), 025sc50 (0.40 #5574, 0.40 #4346, 0.39 #5881), 06j6l (0.39 #5572, 0.36 #3113, 0.36 #1579), 0gywn (0.36 #977, 0.34 #5276, 0.29 #1283), 0y3_8 (0.36 #1578, 0.26 #3373, 0.24 #4604) >> Best rule #1590 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02twdq; >> query: (?x9228, 02lnbg) <- artists(?x996, ?x9228), artist(?x3265, ?x9228), ?x996 = 0dn16 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #1574 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 02twdq; *> query: (?x9228, 05bt6j) <- artists(?x996, ?x9228), artist(?x3265, ?x9228), ?x996 = 0dn16 *> conf = 0.64 ranks of expected_values: 2, 3 EVAL 0cbm64 artists! 05bt6j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 81.000 0.643 http://example.org/music/genre/artists EVAL 0cbm64 artists! 016clz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 81.000 0.643 http://example.org/music/genre/artists #18518-04tr1 PRED entity: 04tr1 PRED relation: organization PRED expected values: 0b6css => 89 concepts (85 used for prediction) PRED predicted values (max 10 best out of 47): 0b6css (0.67 #46, 0.65 #27, 0.58 #707), 0_2v (0.31 #1128, 0.30 #213, 0.29 #233), 04k4l (0.31 #1128, 0.29 #118, 0.27 #214), 01rz1 (0.31 #1128, 0.26 #804, 0.26 #211), 018cqq (0.31 #1128, 0.26 #804, 0.20 #104), 085h1 (0.31 #1128, 0.26 #804, 0.18 #191), 02jxk (0.31 #1128, 0.26 #804, 0.15 #116), 059dn (0.31 #1128, 0.26 #804, 0.05 #108), 034h1h (0.21 #1001, 0.18 #1115, 0.03 #1445), 02_l9 (0.07 #1120, 0.02 #1239) >> Best rule #46 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 07p7g; >> query: (?x4752, 0b6css) <- contains(?x2467, ?x4752), ?x2467 = 0dg3n1, administrative_parent(?x4752, ?x551) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04tr1 organization 0b6css CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 85.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #18517-049dyj PRED entity: 049dyj PRED relation: location PRED expected values: 0167q3 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 204): 06wxw (0.55 #14440, 0.54 #4011, 0.50 #29681), 02_286 (0.24 #36135, 0.22 #40947, 0.18 #6453), 04ykg (0.18 #869, 0.02 #7286, 0.01 #4078), 01_d4 (0.12 #100, 0.09 #902, 0.06 #4111), 0ccvx (0.12 #220, 0.09 #1022, 0.05 #12253), 080h2 (0.12 #53, 0.09 #855, 0.01 #36152), 0r0m6 (0.12 #216, 0.06 #2622, 0.04 #1820), 06yxd (0.12 #245, 0.04 #1849, 0.03 #7464), 0chrx (0.12 #403), 0d35y (0.12 #229) >> Best rule #14440 for best value: >> intensional similarity = 2 >> extensional distance = 145 >> proper extension: 063vn; 075npt; >> query: (?x1065, ?x4356) <- place_of_birth(?x1065, ?x4356), student(?x3213, ?x1065) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #10760 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 0jf1b; 0qdyf; *> query: (?x1065, 0167q3) <- award_nominee(?x1065, ?x1379), languages(?x1065, ?x254), category(?x1065, ?x134) *> conf = 0.02 ranks of expected_values: 95 EVAL 049dyj location 0167q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 97.000 97.000 0.551 http://example.org/people/person/places_lived./people/place_lived/location #18516-0jqd3 PRED entity: 0jqd3 PRED relation: film! PRED expected values: 0jvtp => 74 concepts (53 used for prediction) PRED predicted values (max 10 best out of 864): 03thw4 (0.45 #81230, 0.43 #49990, 0.42 #24996), 058vfp4 (0.45 #81230, 0.43 #49990, 0.42 #24996), 04gmp_z (0.45 #81230, 0.43 #49990, 0.42 #24996), 03gyh_z (0.45 #81230, 0.43 #49990, 0.42 #24996), 0584j4n (0.45 #81230, 0.43 #49990, 0.42 #24996), 044qx (0.19 #732, 0.03 #11146, 0.02 #9064), 02cj_f (0.12 #1623), 0jfx1 (0.07 #6655, 0.05 #2489, 0.03 #8739), 01q6bg (0.06 #804, 0.05 #2886, 0.01 #21632), 015c4g (0.06 #4944, 0.04 #11194, 0.02 #7028) >> Best rule #81230 for best value: >> intensional similarity = 3 >> extensional distance = 942 >> proper extension: 0gh8zks; >> query: (?x6309, ?x2801) <- film(?x2465, ?x6309), film_crew_role(?x6309, ?x137), nominated_for(?x2801, ?x6309) >> conf = 0.45 => this is the best rule for 5 predicted values *> Best rule #41018 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 478 *> proper extension: 04969y; 05dy7p; 02n9bh; 027ct7c; 02q3fdr; 0hv81; 012jfb; 064lsn; 02wk7b; 0gy0l_; ... *> query: (?x6309, 0jvtp) <- genre(?x6309, ?x53), country(?x6309, ?x94), award_winner(?x6309, ?x3627), film(?x2465, ?x6309) *> conf = 0.01 ranks of expected_values: 830 EVAL 0jqd3 film! 0jvtp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 74.000 53.000 0.451 http://example.org/film/actor/film./film/performance/film #18515-02b1hb PRED entity: 02b1hb PRED relation: colors PRED expected values: 06fvc => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 17): 083jv (0.96 #638, 0.96 #655, 0.95 #415), 01l849 (0.59 #362, 0.27 #292, 0.25 #120), 06fvc (0.49 #518, 0.42 #569, 0.40 #586), 088fh (0.48 #350, 0.47 #177, 0.33 #108), 0jc_p (0.25 #120, 0.21 #344, 0.20 #223), 09ggk (0.25 #120, 0.21 #344, 0.13 #927), 03vtbc (0.25 #120, 0.21 #344, 0.13 #927), 036k5h (0.25 #120, 0.20 #223, 0.13 #927), 067z2v (0.25 #120, 0.13 #927, 0.12 #773), 03wkwg (0.25 #120, 0.13 #927, 0.12 #773) >> Best rule #638 for best value: >> intensional similarity = 14 >> extensional distance = 222 >> proper extension: 01ypc; 02896; 03lpp_; 01ct6; 06x68; 01jv_6; 01d5z; 01y3c; 01xvb; 0512p; ... >> query: (?x10830, 083jv) <- team(?x60, ?x10830), colors(?x10830, ?x8047), colors(?x10994, ?x8047), colors(?x7154, ?x8047), colors(?x5651, ?x8047), colors(?x4980, ?x8047), colors(?x4904, ?x8047), colors(?x216, ?x8047), ?x4904 = 0lyjf, ?x7154 = 01lhdt, country(?x5651, ?x94), ?x216 = 05zjtn4, featured_film_locations(?x2104, ?x4980), ?x10994 = 02bvc5 >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #518 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 161 *> proper extension: 0223bl; 0xbm; 01fjz9; 019lty; 01vqc7; 011v3; 01xn5th; 01x4wq; 0cgwt8; 0k_l4; ... *> query: (?x10830, 06fvc) <- position(?x10830, ?x63), colors(?x10830, ?x8047), position(?x10830, ?x530), colors(?x12301, ?x8047), colors(?x5204, ?x8047), colors(?x10838, ?x8047), colors(?x4904, ?x8047), ?x10838 = 016sd3, team(?x1717, ?x5204), ?x1717 = 02g_6x, ?x12301 = 0498yf, institution(?x620, ?x4904) *> conf = 0.49 ranks of expected_values: 3 EVAL 02b1hb colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 70.000 70.000 0.964 http://example.org/sports/sports_team/colors #18514-0fqxw PRED entity: 0fqxw PRED relation: location! PRED expected values: 08gwzt => 156 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1061): 02pk6x (0.25 #3670, 0.14 #8708, 0.10 #11227), 07_m2 (0.17 #7224, 0.14 #9743, 0.10 #12262), 0227tr (0.14 #8037, 0.10 #10556, 0.08 #20632), 043s3 (0.14 #8342, 0.10 #10861, 0.08 #13380), 09yrh (0.12 #21066, 0.06 #28624, 0.05 #46260), 0prfz (0.12 #20201, 0.06 #27759, 0.04 #45395), 02lt8 (0.09 #28507, 0.08 #20949, 0.07 #31026), 01yzhn (0.09 #29843, 0.08 #22285, 0.06 #34881), 0gs1_ (0.08 #21477, 0.06 #29035, 0.05 #31554), 02fybl (0.08 #21598, 0.06 #29156, 0.05 #31675) >> Best rule #3670 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0fqyc; 07371; >> query: (?x12932, 02pk6x) <- adjoins(?x14134, ?x12932), contains(?x10382, ?x12932), ?x10382 = 049nq, ?x14134 = 0d9rp, administrative_parent(?x13925, ?x12932) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #35268 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 51 *> proper extension: 0ck1d; 0c7hq; 01kk32; 0nr2v; 018q42; 018jcq; 07dfk; 0gqm3; 06w92; 0fxrk; ... *> query: (?x12932, ?x731) <- contains(?x1229, ?x12932), nationality(?x731, ?x1229), administrative_parent(?x13925, ?x12932), film_release_region(?x3000, ?x1229), ?x3000 = 045j3w *> conf = 0.01 ranks of expected_values: 1041 EVAL 0fqxw location! 08gwzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 156.000 101.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #18513-019z7q PRED entity: 019z7q PRED relation: profession PRED expected values: 0d8qb => 110 concepts (87 used for prediction) PRED predicted values (max 10 best out of 73): 018gz8 (0.48 #157, 0.33 #445, 0.30 #877), 03gjzk (0.47 #11, 0.40 #443, 0.38 #7217), 0kyk (0.40 #1322, 0.36 #602, 0.35 #2330), 09jwl (0.31 #303, 0.26 #1743, 0.26 #4322), 0nbcg (0.30 #5908, 0.26 #4322, 0.25 #4899), 0d8qb (0.30 #5908, 0.26 #4322, 0.25 #4899), 0n1h (0.30 #5908, 0.23 #297, 0.12 #729), 01c72t (0.30 #5908, 0.19 #308, 0.11 #2036), 016wtf (0.29 #4033, 0.26 #4322, 0.25 #4899), 05z96 (0.26 #4322, 0.25 #4899, 0.19 #1335) >> Best rule #157 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 02yy8; >> query: (?x916, 018gz8) <- profession(?x916, ?x319), influenced_by(?x1946, ?x916), person(?x1315, ?x916) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #5908 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 483 *> proper extension: 0lhn5; 02m4t; *> query: (?x916, ?x955) <- influenced_by(?x916, ?x7495), gender(?x7495, ?x231), profession(?x7495, ?x955) *> conf = 0.30 ranks of expected_values: 6 EVAL 019z7q profession 0d8qb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 110.000 87.000 0.480 http://example.org/people/person/profession #18512-0_jm PRED entity: 0_jm PRED relation: major_field_of_study! PRED expected values: 065y4w7 078bz 02gr81 04hgpt 01rgdw 0trv 01y06y => 59 concepts (31 used for prediction) PRED predicted values (max 10 best out of 612): 01bm_ (0.75 #7578, 0.45 #9151, 0.44 #10728), 0cwx_ (0.75 #7574, 0.36 #9147, 0.33 #10724), 01w5m (0.73 #9533, 0.73 #9009, 0.70 #8484), 0bwfn (0.71 #6023, 0.67 #4450, 0.64 #9169), 017j69 (0.71 #5903, 0.67 #8000, 0.55 #9049), 03ksy (0.70 #8485, 0.67 #9534, 0.67 #4291), 02zd460 (0.70 #8550, 0.57 #5405, 0.53 #9599), 07t90 (0.67 #8004, 0.64 #9053, 0.50 #4859), 06pwq (0.67 #9447, 0.62 #7350, 0.56 #7874), 0lfgr (0.64 #8951, 0.62 #7378, 0.50 #10528) >> Best rule #7578 for best value: >> intensional similarity = 15 >> extensional distance = 6 >> proper extension: 02lp1; 04rjg; 04x_3; 037mh8; >> query: (?x6756, 01bm_) <- major_field_of_study(?x10945, ?x6756), major_field_of_study(?x5324, ?x6756), major_field_of_study(?x5167, ?x6756), major_field_of_study(?x1675, ?x6756), major_field_of_study(?x1428, ?x6756), student(?x10945, ?x7795), ?x1675 = 01j_cy, ?x5167 = 015cz0, program(?x7795, ?x6884), cast_members(?x905, ?x7795), major_field_of_study(?x6756, ?x2606), major_field_of_study(?x1200, ?x6756), school(?x260, ?x1428), ?x1200 = 016t_3, state_province_region(?x5324, ?x2713) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6303 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 02ky346; *> query: (?x6756, 065y4w7) <- major_field_of_study(?x11278, ?x6756), major_field_of_study(?x10945, ?x6756), student(?x10945, ?x1177), organization(?x346, ?x10945), institution(?x620, ?x10945), major_field_of_study(?x6756, ?x2606), school(?x2820, ?x10945), ?x11278 = 037q2p, ?x2820 = 0jmj7 *> conf = 0.62 ranks of expected_values: 13, 25, 54, 62, 70, 203, 529 EVAL 0_jm major_field_of_study! 01y06y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 31.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0_jm major_field_of_study! 0trv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 59.000 31.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0_jm major_field_of_study! 01rgdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 59.000 31.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0_jm major_field_of_study! 04hgpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 59.000 31.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0_jm major_field_of_study! 02gr81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 59.000 31.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0_jm major_field_of_study! 078bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 59.000 31.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0_jm major_field_of_study! 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 59.000 31.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18511-03m2fg PRED entity: 03m2fg PRED relation: place_of_birth PRED expected values: 04vmp => 108 concepts (104 used for prediction) PRED predicted values (max 10 best out of 104): 029kpy (0.25 #277, 0.22 #981, 0.06 #3093), 04vmp (0.14 #6607, 0.13 #4492, 0.11 #5902), 0dlv0 (0.12 #3170, 0.12 #354, 0.11 #1058), 0hj6h (0.12 #3302, 0.06 #6120, 0.04 #4710), 020skc (0.12 #81, 0.02 #4305), 01_yvy (0.11 #1094, 0.02 #4614), 02_286 (0.09 #11298, 0.08 #10594, 0.07 #12709), 0d6lp (0.08 #1522, 0.06 #2226, 0.05 #3634), 01km6_ (0.08 #1851, 0.06 #2555, 0.03 #3963), 0c8tk (0.07 #6494, 0.04 #13549, 0.04 #5789) >> Best rule #277 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 025p38; >> query: (?x7778, 029kpy) <- profession(?x7778, ?x1032), profession(?x7778, ?x319), film(?x7778, ?x5247), ?x1032 = 02hrh1q, ?x5247 = 0f42nz, ?x319 = 01d_h8 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #6607 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 70 *> proper extension: 05d7rk; 04rs03; 0241wg; 0jrqq; 01gg59; 0288crq; 02wxvtv; 02xfrd; 061zc_; 0cc63l; ... *> query: (?x7778, 04vmp) <- people(?x5025, ?x7778), nationality(?x7778, ?x2146), profession(?x7778, ?x319), type_of_union(?x7778, ?x566), ?x2146 = 03rk0 *> conf = 0.14 ranks of expected_values: 2 EVAL 03m2fg place_of_birth 04vmp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 104.000 0.250 http://example.org/people/person/place_of_birth #18510-05d1y PRED entity: 05d1y PRED relation: people! PRED expected values: 0ffjqy => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 32): 07bch9 (0.33 #23, 0.20 #408, 0.04 #2179), 07mqps (0.33 #19, 0.10 #404, 0.02 #635), 01qhm_ (0.25 #83, 0.17 #160, 0.11 #314), 013xrm (0.23 #482, 0.14 #251, 0.12 #559), 041rx (0.20 #543, 0.18 #466, 0.17 #928), 0222qb (0.17 #198, 0.11 #352, 0.10 #429), 02ctzb (0.14 #246, 0.05 #477, 0.03 #2556), 0d7wh (0.14 #248, 0.05 #479, 0.01 #1557), 0x67 (0.10 #1319, 0.10 #4014, 0.10 #3629), 013b6_ (0.09 #515, 0.05 #746, 0.05 #900) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 07bty; >> query: (?x8299, 07bch9) <- award_winner(?x13868, ?x8299), location(?x8299, ?x739), ?x13868 = 03dkh6, contains(?x739, ?x1005) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05d1y people! 0ffjqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 91.000 0.333 http://example.org/people/ethnicity/people #18509-01trxd PRED entity: 01trxd PRED relation: major_field_of_study PRED expected values: 05qjt => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 123): 04rjg (0.70 #758, 0.58 #1004, 0.46 #635), 02j62 (0.61 #3479, 0.51 #1631, 0.51 #1754), 02lp1 (0.60 #1242, 0.59 #1612, 0.57 #1735), 03g3w (0.55 #765, 0.46 #1011, 0.38 #1997), 02_7t (0.50 #803, 0.38 #680, 0.36 #434), 0fdys (0.45 #777, 0.33 #1023, 0.26 #2009), 041y2 (0.45 #817, 0.23 #1063, 0.19 #1679), 062z7 (0.44 #1628, 0.41 #2244, 0.41 #1751), 01tbp (0.43 #1290, 0.40 #1660, 0.36 #1783), 01lj9 (0.40 #1024, 0.40 #778, 0.39 #1640) >> Best rule #758 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 06pwq; 01w3v; 01w5m; 08qnnv; >> query: (?x13080, 04rjg) <- institution(?x865, ?x13080), company(?x5652, ?x13080), major_field_of_study(?x13080, ?x4321), major_field_of_study(?x13080, ?x1668), ?x1668 = 01mkq, ?x4321 = 0g26h >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #992 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 0kz2w; 09f2j; 01d650; *> query: (?x13080, 05qjt) <- institution(?x865, ?x13080), company(?x5652, ?x13080), major_field_of_study(?x13080, ?x1668), ?x1668 = 01mkq *> conf = 0.38 ranks of expected_values: 11 EVAL 01trxd major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 141.000 141.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18508-02f8lw PRED entity: 02f8lw PRED relation: people! PRED expected values: 033tf_ => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 47): 0x67 (0.25 #238, 0.20 #86, 0.20 #10), 041rx (0.23 #3576, 0.22 #2208, 0.21 #3728), 0xnvg (0.20 #89, 0.10 #2217, 0.09 #545), 01qhm_ (0.20 #82, 0.08 #690, 0.07 #2210), 033tf_ (0.18 #539, 0.17 #2211, 0.13 #1755), 02w7gg (0.18 #382, 0.17 #610, 0.16 #458), 07hwkr (0.12 #240, 0.12 #164, 0.09 #1228), 02ctzb (0.12 #243, 0.12 #167, 0.08 #699), 07bch9 (0.12 #251, 0.09 #403, 0.08 #479), 09vc4s (0.12 #161, 0.06 #693, 0.06 #2213) >> Best rule #238 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 06c0j; >> query: (?x3329, 0x67) <- participant(?x3329, ?x1970), student(?x4268, ?x3329), person(?x5929, ?x3329) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #539 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 04bs3j; 01ft2l; 02jq1; 0bx_q; 01wc7p; 06hx2; 02zrv7; 0gs6vr; 02w5q6; 0cgbf; ... *> query: (?x3329, 033tf_) <- participant(?x3329, ?x1970), nationality(?x3329, ?x94), person(?x5929, ?x3329) *> conf = 0.18 ranks of expected_values: 5 EVAL 02f8lw people! 033tf_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 98.000 0.250 http://example.org/people/ethnicity/people #18507-0132k4 PRED entity: 0132k4 PRED relation: profession PRED expected values: 016fly => 108 concepts (59 used for prediction) PRED predicted values (max 10 best out of 84): 02hrh1q (0.60 #158, 0.60 #5824, 0.56 #3060), 016z4k (0.54 #1744, 0.52 #3050, 0.51 #2034), 01c72t (0.54 #457, 0.43 #1327, 0.40 #892), 039v1 (0.41 #2210, 0.40 #2791, 0.37 #1194), 01d_h8 (0.33 #5, 0.23 #440, 0.22 #8144), 012t_z (0.33 #11, 0.15 #446, 0.08 #7121), 03gjzk (0.33 #14, 0.14 #6115, 0.12 #3788), 0dxtg (0.25 #3641, 0.24 #4803, 0.23 #7715), 09lbv (0.23 #2340, 0.23 #2049, 0.21 #1178), 0fnpj (0.23 #493, 0.23 #1509, 0.22 #638) >> Best rule #158 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01wwvt2; 0163r3; 03kts; >> query: (?x6996, 02hrh1q) <- profession(?x6996, ?x131), instrumentalists(?x716, ?x6996), origin(?x6996, ?x4201), artists(?x5424, ?x6996), ?x5424 = 07ym47 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #5229 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 452 *> proper extension: 02qx69; 016yzz; 016k62; 01vz80y; 0djywgn; *> query: (?x6996, ?x220) <- profession(?x6996, ?x655), profession(?x2945, ?x655), artists(?x302, ?x2945), role(?x6996, ?x212), profession(?x2945, ?x220) *> conf = 0.06 ranks of expected_values: 46 EVAL 0132k4 profession 016fly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 108.000 59.000 0.600 http://example.org/people/person/profession #18506-02g2yr PRED entity: 02g2yr PRED relation: nominated_for PRED expected values: 014_x2 03fts => 44 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1437): 09gq0x5 (0.57 #3417, 0.36 #1834, 0.32 #6586), 0b6tzs (0.57 #3292, 0.27 #1709, 0.25 #126), 011yxg (0.55 #1622, 0.40 #3205, 0.21 #6374), 0m313 (0.53 #3178, 0.45 #1595, 0.30 #6347), 0gmcwlb (0.53 #3346, 0.45 #1763, 0.29 #6515), 07w8fz (0.53 #3619, 0.45 #2036, 0.22 #6788), 0gmgwnv (0.53 #4123, 0.30 #7292, 0.27 #2540), 011yqc (0.50 #3370, 0.45 #1787, 0.25 #6539), 0dr_4 (0.50 #219, 0.43 #3385, 0.36 #1802), 0btpm6 (0.50 #1137, 0.37 #4303, 0.27 #2720) >> Best rule #3417 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 02qyp19; 027dtxw; 0f_nbyh; 0p9sw; 09sb52; 04dn09n; 02z13jg; 099tbz; 02x73k6; 02rdxsh; ... >> query: (?x6463, 09gq0x5) <- nominated_for(?x6463, ?x2488), nominated_for(?x6463, ?x2189), ?x2189 = 02yvct, nominated_for(?x1365, ?x2488), genre(?x2488, ?x53) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1781 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 02wkmx; 02g3v6; 099c8n; 02pqp12; 0gr0m; 02g3ft; 02x258x; 02qyntr; 02g3gw; *> query: (?x6463, 03fts) <- nominated_for(?x6463, ?x11534), nominated_for(?x6463, ?x2488), nominated_for(?x6463, ?x2189), ?x2189 = 02yvct, ?x2488 = 02qr69m, language(?x11534, ?x254) *> conf = 0.09 ranks of expected_values: 664, 1089 EVAL 02g2yr nominated_for 03fts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 12.000 0.567 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02g2yr nominated_for 014_x2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 12.000 0.567 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18505-0h_cssd PRED entity: 0h_cssd PRED relation: honored_for PRED expected values: 0g4pl7z => 20 concepts (11 used for prediction) PRED predicted values (max 10 best out of 650): 0bs5vty (0.50 #542, 0.12 #1737, 0.04 #1140), 0bmhvpr (0.50 #217, 0.12 #1412, 0.04 #815), 0dgq_kn (0.25 #352, 0.08 #1547, 0.04 #950), 0g9zljd (0.25 #378, 0.08 #1573, 0.04 #976), 05zr0xl (0.25 #483, 0.08 #1678, 0.03 #5858), 0524b41 (0.25 #417, 0.08 #1612, 0.02 #5792), 02rb84n (0.25 #101, 0.04 #1296, 0.02 #1194), 0dgst_d (0.25 #67, 0.04 #1262, 0.02 #1194), 0gvsh7l (0.25 #476, 0.04 #1671, 0.01 #4655), 0ddd0gc (0.25 #77, 0.04 #1272, 0.01 #4256) >> Best rule #542 for best value: >> intensional similarity = 18 >> extensional distance = 2 >> proper extension: 0hndn2q; >> query: (?x2032, 0bs5vty) <- ceremony(?x640, ?x2032), award_winner(?x2032, ?x1559), honored_for(?x2032, ?x1370), nominated_for(?x640, ?x8570), nominated_for(?x640, ?x4489), nominated_for(?x640, ?x2947), nominated_for(?x640, ?x1385), film(?x382, ?x4489), nominated_for(?x3672, ?x1385), ?x8570 = 04jpg2p, award_winner(?x640, ?x929), award(?x2947, ?x6860), titles(?x1510, ?x4489), country(?x2947, ?x94), film(?x2383, ?x4489), honored_for(?x1385, ?x1072), award_winner(?x1385, ?x65), ?x1370 = 0gmcwlb >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0h_cssd honored_for 0g4pl7z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 20.000 11.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18504-064lsn PRED entity: 064lsn PRED relation: language PRED expected values: 04306rv => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 41): 064_8sq (0.25 #425, 0.17 #543, 0.14 #136), 04306rv (0.11 #1167, 0.11 #1512, 0.11 #408), 02bjrlw (0.10 #405, 0.09 #116, 0.08 #1164), 06nm1 (0.10 #1114, 0.09 #2152, 0.09 #2095), 0jzc (0.06 #598, 0.06 #423, 0.05 #307), 0653m (0.06 #299, 0.04 #1233, 0.04 #1115), 03_9r (0.06 #240, 0.05 #67, 0.05 #1113), 04h9h (0.05 #272, 0.04 #99, 0.04 #387), 07ssc (0.04 #231, 0.03 #986, 0.03 #2376), 04xvlr (0.04 #231, 0.03 #986, 0.03 #2376) >> Best rule #425 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 02vl9ln; >> query: (?x6121, 064_8sq) <- country(?x6121, ?x789), country(?x6121, ?x512), ?x789 = 0f8l9c, olympics(?x512, ?x358) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1167 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 479 *> proper extension: 099bhp; 0gfzfj; *> query: (?x6121, 04306rv) <- genre(?x6121, ?x53), country(?x6121, ?x456), films(?x326, ?x6121) *> conf = 0.11 ranks of expected_values: 2 EVAL 064lsn language 04306rv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.246 http://example.org/film/film/language #18503-0150jk PRED entity: 0150jk PRED relation: award PRED expected values: 01bgqh 01d38t => 64 concepts (53 used for prediction) PRED predicted values (max 10 best out of 207): 01by1l (0.64 #8980, 0.54 #9383, 0.46 #10593), 02wh75 (0.50 #9, 0.38 #1621, 0.33 #3233), 054ks3 (0.50 #143, 0.27 #546, 0.19 #1755), 01bgqh (0.43 #9313, 0.36 #10523, 0.34 #8910), 02f77l (0.40 #3479, 0.25 #255, 0.21 #1061), 02f716 (0.38 #1789, 0.25 #177, 0.24 #3401), 01c9jp (0.36 #1399, 0.27 #7041, 0.27 #4220), 09sb52 (0.36 #20603, 0.27 #21008, 0.05 #7658), 02f5qb (0.31 #1769, 0.29 #3381, 0.25 #157), 02f6yz (0.31 #3543, 0.25 #1931, 0.25 #319) >> Best rule #8980 for best value: >> intensional similarity = 8 >> extensional distance = 293 >> proper extension: 04lgymt; 06pk8; 0jdhp; 0ggl02; 01x15dc; 01vd7hn; 03k0yw; 02_jkc; 01wyq0w; 010xjr; ... >> query: (?x717, 01by1l) <- award(?x717, ?x3103), award(?x8913, ?x3103), award(?x5493, ?x3103), award(?x1060, ?x3103), ?x5493 = 0kr_t, ?x1060 = 02r3zy, ?x8913 = 013w8y, ceremony(?x3103, ?x139) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #9313 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 345 *> proper extension: 0h1nt; 01h5f8; *> query: (?x717, 01bgqh) <- award(?x717, ?x3103), award(?x8490, ?x3103), ceremony(?x3103, ?x725), ?x725 = 01bx35, ?x8490 = 06rgq *> conf = 0.43 ranks of expected_values: 4, 17 EVAL 0150jk award 01d38t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 64.000 53.000 0.641 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0150jk award 01bgqh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 64.000 53.000 0.641 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18502-04g61 PRED entity: 04g61 PRED relation: religion PRED expected values: 0c8wxp => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 34): 0c8wxp (0.41 #2349, 0.41 #2313, 0.31 #3430), 01lp8 (0.37 #2308, 0.36 #2344, 0.32 #3281), 051kv (0.35 #2312, 0.33 #2348, 0.27 #3285), 019cr (0.34 #2318, 0.33 #2354, 0.27 #3291), 0631_ (0.33 #2315, 0.33 #2351, 0.27 #3288), 05sfs (0.32 #2346, 0.31 #2310, 0.27 #3283), 0flw86 (0.32 #470, 0.27 #615, 0.26 #1011), 04pk9 (0.31 #2362, 0.30 #2326, 0.25 #3299), 05w5d (0.30 #2330, 0.29 #2366, 0.24 #3303), 021_0p (0.24 #2361, 0.23 #2325, 0.19 #3298) >> Best rule #2349 for best value: >> intensional similarity = 2 >> extensional distance = 105 >> proper extension: 04ykz; >> query: (?x5274, 0c8wxp) <- taxonomy(?x5274, ?x939), time_zones(?x5274, ?x2864) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04g61 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.411 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #18501-0ctw_b PRED entity: 0ctw_b PRED relation: geographic_distribution! PRED expected values: 0d29z => 217 concepts (217 used for prediction) PRED predicted values (max 10 best out of 39): 0d29z (0.61 #683, 0.55 #293, 0.50 #332), 04mvp8 (0.29 #696, 0.27 #462, 0.26 #501), 0g48m4 (0.25 #118, 0.13 #3244, 0.13 #1796), 01xhh5 (0.18 #682, 0.12 #1423, 0.12 #1501), 0g6ff (0.17 #984, 0.14 #1570, 0.12 #1452), 01rv7x (0.15 #333, 0.14 #684, 0.14 #450), 013b6_ (0.14 #455, 0.10 #338, 0.10 #299), 0j6x8 (0.12 #112, 0.08 #151, 0.08 #190), 09vc4s (0.12 #82, 0.08 #121, 0.08 #160), 06mvq (0.12 #251, 0.10 #290, 0.09 #446) >> Best rule #683 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 07t_x; >> query: (?x1023, 0d29z) <- country(?x6291, ?x1023), exported_to(?x1023, ?x94), organization(?x1023, ?x312) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ctw_b geographic_distribution! 0d29z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 217.000 217.000 0.607 http://example.org/people/ethnicity/geographic_distribution #18500-0dyl9 PRED entity: 0dyl9 PRED relation: locations! PRED expected values: 0b_6zk => 191 concepts (172 used for prediction) PRED predicted values (max 10 best out of 118): 0b_6s7 (0.26 #447, 0.24 #829, 0.19 #193), 0b_6pv (0.26 #461, 0.24 #207, 0.24 #970), 0b_6qj (0.24 #831, 0.24 #195, 0.19 #449), 0b_6rk (0.24 #174, 0.22 #428, 0.22 #1191), 0b_6x2 (0.22 #415, 0.19 #161, 0.18 #797), 0b_75k (0.22 #50, 0.18 #2337, 0.18 #11589), 0bzrsh (0.21 #842, 0.19 #460, 0.19 #333), 0b_6zk (0.21 #794, 0.18 #11589, 0.16 #1810), 0b_6mr (0.21 #851, 0.18 #11589, 0.16 #596), 0b_6q5 (0.19 #222, 0.19 #1874, 0.18 #11589) >> Best rule #447 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 02mf7; >> query: (?x6088, 0b_6s7) <- teams(?x6088, ?x2174), contains(?x94, ?x6088), administrative_division(?x6088, ?x7568), source(?x6088, ?x958) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #794 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 010016; *> query: (?x6088, 0b_6zk) <- locations(?x8527, ?x6088), source(?x6088, ?x958), administrative_division(?x6088, ?x7568) *> conf = 0.21 ranks of expected_values: 8 EVAL 0dyl9 locations! 0b_6zk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 191.000 172.000 0.259 http://example.org/time/event/locations #18499-0btbyn PRED entity: 0btbyn PRED relation: production_companies PRED expected values: 054lpb6 => 89 concepts (64 used for prediction) PRED predicted values (max 10 best out of 58): 086k8 (0.25 #167, 0.12 #1823, 0.12 #2236), 04f525m (0.25 #11, 0.02 #340, 0.01 #671), 0g1rw (0.20 #91, 0.08 #173, 0.08 #255), 05rrtf (0.17 #222, 0.05 #799, 0.04 #1630), 05qd_ (0.11 #752, 0.11 #3905, 0.11 #339), 017s11 (0.11 #332, 0.08 #168, 0.07 #3898), 016tt2 (0.10 #251, 0.09 #912, 0.09 #746), 02w29z (0.10 #2734, 0.08 #83, 0.04 #2733), 0b6yp2 (0.10 #2734, 0.08 #83, 0.04 #2733), 016tw3 (0.09 #3907, 0.08 #2994, 0.08 #1667) >> Best rule #167 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 09lcsj; >> query: (?x4021, 086k8) <- genre(?x4021, ?x8280), featured_film_locations(?x4021, ?x3983), ?x8280 = 0hfjk, production_companies(?x4021, ?x1914) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2002 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 361 *> proper extension: 0522wp; *> query: (?x4021, 054lpb6) <- category(?x4021, ?x134), ?x134 = 08mbj5d, film(?x1914, ?x4021) *> conf = 0.08 ranks of expected_values: 16 EVAL 0btbyn production_companies 054lpb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 89.000 64.000 0.250 http://example.org/film/film/production_companies #18498-01grr2 PRED entity: 01grr2 PRED relation: district_represented PRED expected values: 05k7sb => 27 concepts (27 used for prediction) PRED predicted values (max 10 best out of 410): 05k7sb (0.85 #59, 0.85 #52, 0.85 #51), 0g0syc (0.85 #59, 0.85 #52, 0.85 #51), 0gyh (0.85 #59, 0.85 #52, 0.85 #51), 04ly1 (0.85 #59, 0.85 #52, 0.85 #51), 04tgp (0.85 #59, 0.85 #52, 0.85 #51), 03v0t (0.85 #59, 0.85 #52, 0.85 #51), 050ks (0.85 #59, 0.85 #52, 0.85 #51), 03v1s (0.85 #59, 0.85 #52, 0.85 #51), 04ych (0.85 #59, 0.85 #52, 0.85 #51), 081mh (0.80 #1300, 0.71 #610, 0.66 #1279) >> Best rule #59 for best value: >> intensional similarity = 46 >> extensional distance = 1 >> proper extension: 01grpc; >> query: (?x7714, ?x1025) <- legislative_sessions(?x7714, ?x9702), legislative_sessions(?x7714, ?x7914), legislative_sessions(?x7714, ?x5256), legislative_sessions(?x7714, ?x2712), legislative_sessions(?x7714, ?x1754), ?x5256 = 01grqd, district_represented(?x7714, ?x3778), district_represented(?x7714, ?x1755), district_represented(?x7714, ?x760), legislative_sessions(?x9046, ?x7714), ?x1755 = 01x73, legislative_sessions(?x2860, ?x9702), district_represented(?x2712, ?x4622), district_represented(?x2712, ?x3908), district_represented(?x2712, ?x3818), district_represented(?x2712, ?x1025), ?x760 = 05fkf, legislative_sessions(?x2712, ?x759), state_province_region(?x2821, ?x4622), contains(?x94, ?x4622), capital(?x4622, ?x12941), religion(?x4622, ?x10107), religion(?x4622, ?x2591), religion(?x4622, ?x1624), ?x1754 = 01grnp, ?x94 = 09c7w0, district_represented(?x1829, ?x3908), state(?x1248, ?x3908), ?x1624 = 051kv, location(?x118, ?x4622), ?x1829 = 02bp37, adjoins(?x3908, ?x3634), ?x10107 = 05w5d, category(?x4622, ?x134), profession(?x9046, ?x5805), religion(?x9046, ?x14017), ?x5805 = 0fj9f, religion(?x3818, ?x492), ?x3778 = 07h34, ?x7914 = 01grrf, state_province_region(?x1440, ?x3818), contains(?x3908, ?x466), people(?x10900, ?x9046), ?x2591 = 0631_, contains(?x3818, ?x405), jurisdiction_of_office(?x900, ?x3818) >> conf = 0.85 => this is the best rule for 9 predicted values ranks of expected_values: 1 EVAL 01grr2 district_represented 05k7sb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 27.000 27.000 0.850 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #18497-0512p PRED entity: 0512p PRED relation: school PRED expected values: 01q0kg => 92 concepts (58 used for prediction) PRED predicted values (max 10 best out of 670): 06fq2 (0.60 #1227, 0.56 #2740, 0.56 #2686), 012vwb (0.50 #2791, 0.50 #1877, 0.50 #965), 01dzg0 (0.50 #892, 0.44 #2715, 0.40 #3084), 09f2j (0.50 #253, 0.42 #3735, 0.36 #4649), 0bx8pn (0.50 #573, 0.40 #3134, 0.33 #2578), 03tw2s (0.50 #287, 0.38 #2298, 0.33 #2662), 01qgr3 (0.50 #298, 0.38 #1761, 0.33 #3780), 037njl (0.50 #250, 0.27 #3368, 0.25 #1896), 01jt2w (0.50 #675, 0.25 #2316, 0.25 #305), 01jssp (0.50 #184, 0.24 #2742, 0.22 #3112) >> Best rule #1227 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 02d02; >> query: (?x1438, 06fq2) <- position(?x1438, ?x5727), school(?x1438, ?x1011), school(?x1438, ?x466), ?x5727 = 02wszf, draft(?x1438, ?x11905), draft(?x1438, ?x8499), teams(?x5771, ?x1438), ?x466 = 01pl14, ?x11905 = 047dpm0, ?x8499 = 02r6gw6, season(?x1438, ?x701), team(?x261, ?x1438), major_field_of_study(?x1011, ?x254), citytown(?x1011, ?x3269) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2612 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 7 *> proper extension: 0jm5b; *> query: (?x1438, 01q0kg) <- draft(?x1438, ?x1633), team(?x261, ?x1438), school(?x1438, ?x9131), school(?x1438, ?x4556), school(?x1633, ?x8202), state_province_region(?x4556, ?x1782), currency(?x4556, ?x170), colors(?x4556, ?x3621), ?x9131 = 02pptm, ?x8202 = 06fq2 *> conf = 0.44 ranks of expected_values: 12 EVAL 0512p school 01q0kg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 92.000 58.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #18496-06xkst PRED entity: 06xkst PRED relation: genre PRED expected values: 0jxy => 59 concepts (46 used for prediction) PRED predicted values (max 10 best out of 196): 05p553 (0.96 #2208, 0.84 #1149, 0.79 #2289), 02kdv5l (0.67 #492, 0.50 #245, 0.50 #3273), 0jxy (0.64 #846, 0.55 #928, 0.52 #1255), 03k9fj (0.64 #993, 0.59 #1237, 0.54 #1972), 01z4y (0.62 #2220, 0.60 #2301, 0.54 #751), 01hmnh (0.50 #3273, 0.49 #2056, 0.35 #1322), 01htzx (0.43 #1976, 0.42 #2138, 0.37 #1568), 01t_vv (0.43 #602, 0.39 #1911, 0.39 #1584), 0c4xc (0.41 #2244, 0.38 #775, 0.38 #693), 025s89p (0.40 #1195, 0.30 #1358, 0.29 #1439) >> Best rule #2208 for best value: >> intensional similarity = 15 >> extensional distance = 89 >> proper extension: 01bv8b; 01j7mr; 05_z42; 0fpxp; 0q9jk; 016tvq; 0sw0q; 0330r; 01ft14; 095sx6; >> query: (?x10555, 05p553) <- actor(?x10555, ?x10231), genre(?x10555, ?x2540), genre(?x10555, ?x1403), genre(?x10105, ?x1403), genre(?x9174, ?x1403), genre(?x5247, ?x1403), genre(?x4000, ?x1403), genre(?x2586, ?x1403), genre(?x10327, ?x2540), ?x4000 = 011yfd, ?x9174 = 087pfc, ?x2586 = 05h43ls, ?x10327 = 03vfr_, ?x5247 = 0f42nz, ?x10105 = 0bs5f0b >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #846 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 12 *> proper extension: 07ng9k; 099pks; 0dr1c2; 01lk02; 06r1k; 0q6g3; 0gxr1c; 02rhwjr; 051kd; *> query: (?x10555, 0jxy) <- actor(?x10555, ?x10231), genre(?x10555, ?x2540), genre(?x10555, ?x1403), ?x2540 = 0hcr, genre(?x8595, ?x1403), genre(?x7336, ?x1403), genre(?x5152, ?x1403), ?x8595 = 09tkzy, ?x5152 = 08sfxj, nominated_for(?x2456, ?x7336), ?x2456 = 063y_ky, titles(?x1403, ?x308) *> conf = 0.64 ranks of expected_values: 3 EVAL 06xkst genre 0jxy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 59.000 46.000 0.956 http://example.org/tv/tv_program/genre #18495-034qt_ PRED entity: 034qt_ PRED relation: place_of_birth PRED expected values: 0v9qg => 111 concepts (106 used for prediction) PRED predicted values (max 10 best out of 47): 0v9qg (0.33 #54997, 0.33 #20462, 0.32 #23991), 030qb3t (0.14 #1464, 0.12 #4285, 0.11 #2169), 04swd (0.11 #2431, 0.07 #1726, 0.04 #3842), 0k_p5 (0.09 #4937, 0.06 #18348, 0.06 #17642), 02_286 (0.09 #13425, 0.09 #8484, 0.08 #6365), 01_d4 (0.09 #2887, 0.08 #6412, 0.07 #7118), 0cr3d (0.08 #94, 0.08 #3620, 0.08 #800), 0t_3w (0.08 #397, 0.08 #1103, 0.07 #1807), 01cx_ (0.08 #109, 0.08 #815, 0.06 #4340), 01sn3 (0.08 #149, 0.08 #855, 0.06 #2264) >> Best rule #54997 for best value: >> intensional similarity = 3 >> extensional distance = 2248 >> proper extension: 026lj; 03j0br4; 0m32_; 01jbx1; 01wz_ml; 07h1h5; 0mj0c; 0854hr; 01wbsdz; 05gpy; ... >> query: (?x14143, ?x4025) <- location(?x14143, ?x4025), place_of_birth(?x3934, ?x4025), contains(?x94, ?x4025) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034qt_ place_of_birth 0v9qg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 106.000 0.330 http://example.org/people/person/place_of_birth #18494-02dr9j PRED entity: 02dr9j PRED relation: film_format PRED expected values: 07fb8_ => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 4): 07fb8_ (0.33 #1, 0.20 #27, 0.18 #85), 0cj16 (0.32 #13, 0.29 #23, 0.17 #8), 017fx5 (0.09 #66, 0.08 #71, 0.08 #77), 01dc60 (0.02 #41, 0.01 #99) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 08k40m; >> query: (?x7214, 07fb8_) <- film(?x6589, ?x7214), film(?x10958, ?x7214), ?x6589 = 0js9s, genre(?x7214, ?x53) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02dr9j film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.333 http://example.org/film/film/film_format #18493-07s3m4g PRED entity: 07s3m4g PRED relation: film! PRED expected values: 01gb54 => 88 concepts (71 used for prediction) PRED predicted values (max 10 best out of 114): 017s11 (0.52 #1591, 0.16 #507, 0.15 #435), 086k8 (0.26 #867, 0.23 #939, 0.23 #74), 03xq0f (0.23 #436, 0.17 #508, 0.16 #292), 0fqy4p (0.22 #26, 0.08 #98, 0.06 #602), 01795t (0.16 #304, 0.14 #1241, 0.10 #520), 016tw3 (0.16 #874, 0.14 #4070, 0.14 #946), 01gb54 (0.15 #99, 0.09 #243, 0.07 #2198), 024rgt (0.14 #234, 0.09 #450, 0.08 #1243), 025jfl (0.13 #726, 0.10 #798, 0.07 #1883), 0g1rw (0.12 #151, 0.10 #656, 0.10 #728) >> Best rule #1591 for best value: >> intensional similarity = 7 >> extensional distance = 248 >> proper extension: 0pd6l; >> query: (?x6587, 017s11) <- genre(?x6587, ?x600), titles(?x571, ?x6587), film(?x574, ?x6587), film(?x574, ?x6773), film_crew_role(?x6773, ?x137), award_nominee(?x574, ?x9743), ?x9743 = 0d6484 >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #99 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 11 *> proper extension: 032xky; *> query: (?x6587, 01gb54) <- genre(?x6587, ?x6277), genre(?x6587, ?x600), country(?x6587, ?x94), ?x6277 = 0fdjb, ?x600 = 02n4kr, language(?x6587, ?x254), ?x254 = 02h40lc, film_release_distribution_medium(?x6587, ?x81) *> conf = 0.15 ranks of expected_values: 7 EVAL 07s3m4g film! 01gb54 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 88.000 71.000 0.524 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18492-032wdd PRED entity: 032wdd PRED relation: award PRED expected values: 05p09zm => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 252): 09sb52 (0.39 #1252, 0.38 #848, 0.38 #17816), 0bfvd4 (0.33 #518, 0.06 #9406, 0.06 #34860), 05pcn59 (0.32 #888, 0.28 #1292, 0.27 #3716), 05zr6wv (0.25 #420, 0.20 #16, 0.20 #824), 05p09zm (0.24 #2951, 0.24 #931, 0.24 #2547), 0hnf5vm (0.20 #188, 0.18 #33534, 0.15 #33129), 04ljl_l (0.20 #3, 0.17 #407, 0.08 #10103), 05zvj3m (0.20 #92, 0.15 #33129, 0.13 #39999), 02x4w6g (0.20 #113, 0.08 #517, 0.08 #1729), 03hl6lc (0.20 #178, 0.05 #21186, 0.04 #986) >> Best rule #1252 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 044k8; >> query: (?x8691, 09sb52) <- award_winner(?x8691, ?x2559), participant(?x8691, ?x8341), location(?x2559, ?x335) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #2951 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 138 *> proper extension: 0n6f8; 01gbbz; 015076; *> query: (?x8691, 05p09zm) <- film(?x8691, ?x141), participant(?x8691, ?x4106), participant(?x8691, ?x1460) *> conf = 0.24 ranks of expected_values: 5 EVAL 032wdd award 05p09zm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 110.000 110.000 0.388 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18491-02ndy4 PRED entity: 02ndy4 PRED relation: film! PRED expected values: 02ndbd => 71 concepts (45 used for prediction) PRED predicted values (max 10 best out of 957): 0c1j_ (0.57 #76946, 0.48 #93586, 0.43 #41592), 0f6_x (0.20 #8941, 0.10 #624), 02zyy4 (0.20 #271, 0.15 #8588, 0.03 #6509), 02ldv0 (0.20 #3222, 0.08 #31195, 0.06 #72785), 017149 (0.20 #2161, 0.08 #31195, 0.06 #72785), 02sh8y (0.20 #1028, 0.03 #9345), 01pk3z (0.15 #9302, 0.10 #985, 0.01 #11381), 02yplc (0.14 #9054, 0.10 #737, 0.01 #46488), 02l3_5 (0.14 #9725, 0.10 #1408), 0grrq8 (0.11 #64465, 0.10 #29115, 0.09 #20792) >> Best rule #76946 for best value: >> intensional similarity = 4 >> extensional distance = 964 >> proper extension: 0clpml; >> query: (?x10960, ?x10754) <- nominated_for(?x10754, ?x10960), film(?x10754, ?x7800), type_of_union(?x10754, ?x566), student(?x6912, ?x10754) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02ndy4 film! 02ndbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 45.000 0.574 http://example.org/film/actor/film./film/performance/film #18490-03xkps PRED entity: 03xkps PRED relation: nominated_for PRED expected values: 0d66j2 => 111 concepts (47 used for prediction) PRED predicted values (max 10 best out of 485): 09kn9 (0.53 #16175, 0.52 #11321, 0.51 #19412), 06cs95 (0.53 #16175, 0.52 #11321, 0.51 #19412), 011ykb (0.29 #50157, 0.29 #38830, 0.28 #42066), 01f39b (0.29 #50157, 0.29 #38830, 0.28 #42066), 0180mw (0.11 #4272, 0.07 #5889, 0.03 #10741), 0g60z (0.08 #3275, 0.03 #9744, 0.03 #16216), 02k_4g (0.08 #3342, 0.02 #9811, 0.02 #17902), 01fx1l (0.07 #4113, 0.02 #5730, 0.02 #10582), 01gkp1 (0.06 #2362, 0.03 #745, 0.03 #7213), 05z43v (0.06 #2827, 0.03 #1210, 0.03 #7678) >> Best rule #16175 for best value: >> intensional similarity = 3 >> extensional distance = 364 >> proper extension: 0q9kd; 0grwj; 01k7d9; 0byfz; 0h0jz; 0p_pd; 01tvz5j; 025h4z; 0h5g_; 027dtv3; ... >> query: (?x3808, ?x531) <- award_winner(?x7941, ?x3808), award_nominee(?x3808, ?x286), actor(?x531, ?x3808) >> conf = 0.53 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 03xkps nominated_for 0d66j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 47.000 0.527 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #18489-03q43g PRED entity: 03q43g PRED relation: film PRED expected values: 03x7hd => 87 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1102): 031t2d (0.31 #252, 0.03 #3824, 0.03 #5610), 016dj8 (0.15 #1112, 0.06 #4684, 0.03 #6470), 0gfsq9 (0.15 #444, 0.01 #29022, 0.01 #30809), 020bv3 (0.13 #2102, 0.08 #316, 0.04 #11033), 027r9t (0.13 #3031, 0.05 #8390, 0.04 #11962), 0kvgtf (0.13 #2404, 0.04 #11335, 0.03 #29196), 02hct1 (0.11 #30365, 0.10 #32152, 0.09 #80384), 099bhp (0.10 #17692, 0.07 #28409, 0.06 #33769), 035s95 (0.09 #3910, 0.07 #2124, 0.05 #5696), 0872p_c (0.09 #16249, 0.06 #32326, 0.06 #26966) >> Best rule #252 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0147dk; 086nl7; 0478__m; 01qg7c; 0cw67g; >> query: (?x6569, 031t2d) <- film(?x6569, ?x2350), ?x2350 = 0661m4p, award(?x6569, ?x678) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #7703 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 07cn2c; 030_3z; 06sn8m; *> query: (?x6569, 03x7hd) <- language(?x6569, ?x254), student(?x8191, ?x6569), award(?x6569, ?x678) *> conf = 0.05 ranks of expected_values: 170 EVAL 03q43g film 03x7hd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 87.000 66.000 0.308 http://example.org/film/actor/film./film/performance/film #18488-02pgky2 PRED entity: 02pgky2 PRED relation: honored_for PRED expected values: 0ds11z => 41 concepts (14 used for prediction) PRED predicted values (max 10 best out of 652): 04q827 (0.14 #557, 0.12 #1145, 0.11 #1733), 02jxrw (0.14 #535, 0.12 #1123, 0.11 #1711), 04b2qn (0.14 #459, 0.12 #1047, 0.11 #1635), 01y9r2 (0.14 #449, 0.12 #1037, 0.11 #1625), 06823p (0.14 #395, 0.12 #983, 0.11 #1571), 03xf_m (0.14 #379, 0.12 #967, 0.11 #1555), 064lsn (0.14 #367, 0.12 #955, 0.11 #1543), 01cmp9 (0.14 #357, 0.12 #945, 0.11 #1533), 012jfb (0.14 #356, 0.12 #944, 0.11 #1532), 049xgc (0.14 #332, 0.12 #920, 0.11 #1508) >> Best rule #557 for best value: >> intensional similarity = 22 >> extensional distance = 5 >> proper extension: 0gmdkyy; >> query: (?x6594, 04q827) <- award_winner(?x6594, ?x163), ceremony(?x5409, ?x6594), ceremony(?x4573, ?x6594), ceremony(?x2209, ?x6594), ceremony(?x1307, ?x6594), ceremony(?x1053, ?x6594), ceremony(?x720, ?x6594), ?x4573 = 0gq_d, ?x720 = 018wng, ?x2209 = 0gr42, honored_for(?x6594, ?x3430), honored_for(?x6594, ?x763), nominated_for(?x2880, ?x3430), nominated_for(?x647, ?x3430), ?x1307 = 0gq9h, film_crew_role(?x3430, ?x137), titles(?x812, ?x763), language(?x763, ?x254), ?x5409 = 0gr07, production_companies(?x3430, ?x1478), ?x2880 = 02ppm4q, ?x1053 = 0gqzz >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #8245 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 34 *> proper extension: 0c4hnm; 0fzrhn; *> query: (?x6594, ?x253) <- award_winner(?x6594, ?x163), ceremony(?x4573, ?x6594), ceremony(?x2209, ?x6594), ceremony(?x1862, ?x6594), ceremony(?x1307, ?x6594), ceremony(?x720, ?x6594), ?x4573 = 0gq_d, ?x720 = 018wng, ?x2209 = 0gr42, honored_for(?x6594, ?x3430), nominated_for(?x2577, ?x3430), nominated_for(?x647, ?x3430), ?x1307 = 0gq9h, film(?x1104, ?x3430), award(?x91, ?x2577), nominated_for(?x1862, ?x5473), award(?x361, ?x1862), award(?x253, ?x2577), ?x5473 = 0hv8w *> conf = 0.01 ranks of expected_values: 639 EVAL 02pgky2 honored_for 0ds11z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 14.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18487-0cf08 PRED entity: 0cf08 PRED relation: award PRED expected values: 0k611 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 171): 0gq9h (0.29 #1865, 0.28 #529, 0.27 #1461), 0k611 (0.29 #1865, 0.28 #539, 0.27 #17966), 0gqwc (0.29 #1865, 0.27 #17966, 0.27 #18667), 019f4v (0.29 #1865, 0.27 #17966, 0.27 #18667), 0p9sw (0.29 #1865, 0.27 #17966, 0.27 #18667), 0f4x7 (0.29 #1865, 0.27 #17966, 0.27 #18667), 040njc (0.29 #1865, 0.27 #17966, 0.27 #18667), 0gqy2 (0.29 #1865, 0.27 #17966, 0.27 #18667), 0gr51 (0.29 #1865, 0.27 #17966, 0.27 #18667), 04dn09n (0.29 #1865, 0.27 #17966, 0.27 #18667) >> Best rule #1865 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 0cbn7c; 01bjbk; 0jz71; >> query: (?x7370, ?x198) <- film(?x187, ?x7370), nominated_for(?x198, ?x7370), list(?x7370, ?x3004) >> conf = 0.29 => this is the best rule for 15 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0cf08 award 0k611 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 113.000 0.290 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #18486-03_vpw PRED entity: 03_vpw PRED relation: role! PRED expected values: 01xqw => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 112): 07brj (0.67 #437, 0.67 #228, 0.60 #124), 0g2dz (0.67 #445, 0.67 #236, 0.60 #132), 0l14j_ (0.67 #267, 0.60 #163, 0.57 #371), 01wy6 (0.67 #466, 0.57 #361, 0.51 #523), 0l14qv (0.66 #3655, 0.62 #4188, 0.61 #3869), 0mkg (0.64 #3658, 0.60 #112, 0.59 #3872), 013y1f (0.62 #3680, 0.60 #134, 0.58 #3894), 07y_7 (0.62 #3652, 0.60 #106, 0.58 #3866), 0bxl5 (0.60 #169, 0.56 #482, 0.51 #523), 02w3w (0.60 #190, 0.56 #503, 0.51 #523) >> Best rule #437 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0l14md; 06ncr; >> query: (?x2904, 07brj) <- role(?x5676, ?x2904), role(?x2309, ?x2904), ?x5676 = 0151b0, instrumentalists(?x2309, ?x1832), ?x1832 = 01ky2h, role(?x74, ?x2309) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #523 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 0l14md; 06ncr; *> query: (?x2904, ?x75) <- role(?x5676, ?x2904), role(?x2309, ?x2904), ?x5676 = 0151b0, instrumentalists(?x2309, ?x1832), ?x1832 = 01ky2h, role(?x2309, ?x75), role(?x74, ?x2309) *> conf = 0.51 ranks of expected_values: 30 EVAL 03_vpw role! 01xqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 61.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/role #18485-0k0rf PRED entity: 0k0rf PRED relation: film_sets_designed! PRED expected values: 051x52f => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 17): 07h1tr (0.44 #50, 0.40 #27, 0.30 #73), 057bc6m (0.20 #34, 0.11 #57, 0.10 #80), 051x52f (0.11 #56, 0.10 #79, 0.04 #194), 0584j4n (0.11 #53, 0.04 #191, 0.02 #444), 0fd6qb (0.08 #177), 076psv (0.08 #190, 0.07 #213, 0.06 #305), 0579tg2 (0.08 #204, 0.07 #227, 0.03 #319), 053j4w4 (0.06 #101, 0.04 #170, 0.04 #193), 0c0tzp (0.06 #111, 0.03 #272, 0.03 #686), 0cb77r (0.06 #93, 0.02 #369, 0.01 #1592) >> Best rule #50 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0k5g9; >> query: (?x5134, 07h1tr) <- language(?x5134, ?x254), titles(?x600, ?x5134), film(?x382, ?x5134), nominated_for(?x1708, ?x5134), nominated_for(?x5134, ?x10404), ?x1708 = 05cj_j >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 0k5g9; *> query: (?x5134, 051x52f) <- language(?x5134, ?x254), titles(?x600, ?x5134), film(?x382, ?x5134), nominated_for(?x1708, ?x5134), nominated_for(?x5134, ?x10404), ?x1708 = 05cj_j *> conf = 0.11 ranks of expected_values: 3 EVAL 0k0rf film_sets_designed! 051x52f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 131.000 131.000 0.444 http://example.org/film/film_set_designer/film_sets_designed #18484-05fg2 PRED entity: 05fg2 PRED relation: gender PRED expected values: 05zppz => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #31, 0.80 #79, 0.79 #81), 02zsn (0.51 #133, 0.49 #124, 0.48 #111) >> Best rule #31 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 07nv3_; 07h1h5; 0dhrqx; 02qny_; >> query: (?x1309, 05zppz) <- profession(?x1309, ?x8368), profession(?x11282, ?x8368), ?x11282 = 03vrv9, specialization_of(?x8368, ?x9674) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fg2 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.859 http://example.org/people/person/gender #18483-02wk4d PRED entity: 02wk4d PRED relation: type_of_union PRED expected values: 04ztj => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.76 #9, 0.75 #317, 0.75 #33), 01g63y (0.22 #98, 0.22 #122, 0.21 #10), 01bl8s (0.02 #23), 0jgjn (0.01 #44, 0.01 #48) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 03xmy1; 015q43; >> query: (?x5922, 04ztj) <- award_winner(?x5923, ?x5922), languages(?x5922, ?x3271), award_winner(?x6219, ?x5922), titles(?x3271, ?x467) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wk4d type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.762 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18482-0345h PRED entity: 0345h PRED relation: country! PRED expected values: 014_x2 0645k5 049xgc 05rfst 02q7fl9 0bz3jx 01y9jr 03clwtw 06x43v 0h14ln 0ct2tf5 0d6_s 03ntbmw => 212 concepts (148 used for prediction) PRED predicted values (max 10 best out of 1600): 085ccd (0.78 #13972, 0.76 #13971, 0.25 #18633), 03xj05 (0.78 #13972, 0.76 #13971, 0.25 #18633), 01vw8k (0.46 #24849, 0.22 #6773, 0.21 #20187), 02z0f6l (0.46 #24849, 0.22 #7258, 0.13 #11914), 047p7fr (0.46 #24849, 0.21 #20187, 0.19 #17079), 09gkx35 (0.46 #24849, 0.21 #20187, 0.19 #17079), 0cwy47 (0.46 #24849, 0.21 #20187, 0.19 #17079), 0f4k49 (0.46 #24849, 0.21 #20187, 0.19 #17078), 05z7c (0.46 #24849, 0.19 #17079, 0.19 #17078), 02psgq (0.46 #24849, 0.13 #13231, 0.13 #11679) >> Best rule #13972 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 06mx8; >> query: (?x1264, ?x6387) <- contains(?x1264, ?x196), titles(?x1264, ?x6387), film_crew_role(?x6387, ?x137) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #6614 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 7 *> proper extension: 041rx; *> query: (?x1264, 0645k5) <- combatants(?x13022, ?x1264), split_to(?x5540, ?x1264) *> conf = 0.22 ranks of expected_values: 43, 94, 362, 477, 512, 515, 588, 942, 979, 1049, 1418, 1507 EVAL 0345h country! 03ntbmw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 0d6_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 0ct2tf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 0h14ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 06x43v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 03clwtw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 01y9jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 0bz3jx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 02q7fl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 05rfst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 049xgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 0645k5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 212.000 148.000 0.782 http://example.org/film/film/country EVAL 0345h country! 014_x2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 212.000 148.000 0.782 http://example.org/film/film/country #18481-0167q3 PRED entity: 0167q3 PRED relation: location! PRED expected values: 049dyj => 187 concepts (127 used for prediction) PRED predicted values (max 10 best out of 2142): 0m0hw (0.57 #2516, 0.51 #62890, 0.51 #75471), 08cn_n (0.57 #2516, 0.51 #62890, 0.51 #75471), 01tcf7 (0.22 #10062, 0.12 #35215, 0.11 #173581), 0sx5w (0.12 #9685, 0.12 #14716, 0.10 #19746), 012v1t (0.12 #8762, 0.08 #13793, 0.07 #46494), 0x3r3 (0.12 #8726, 0.08 #13757, 0.07 #18787), 06jkm (0.12 #9815, 0.04 #14846, 0.04 #57611), 01vsy3q (0.11 #33689, 0.08 #68911, 0.08 #36205), 01wb8bs (0.11 #3283, 0.11 #767, 0.08 #5798), 0443c (0.11 #5015, 0.11 #2499, 0.08 #7530) >> Best rule #2516 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01m23s; >> query: (?x6930, ?x6668) <- contains(?x1755, ?x6930), ?x1755 = 01x73, location(?x3417, ?x6930), place_of_birth(?x6668, ?x6930) >> conf = 0.57 => this is the best rule for 2 predicted values *> Best rule #20300 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 02dtg; 0f2r6; 0r62v; 0r1yc; 030qb3t; 01_d4; 0k_q_; 0f__1; 0cv3w; 01sn3; ... *> query: (?x6930, 049dyj) <- featured_film_locations(?x4551, ?x6930), country(?x6930, ?x94), source(?x6930, ?x958), county(?x6930, ?x8616) *> conf = 0.03 ranks of expected_values: 685 EVAL 0167q3 location! 049dyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 187.000 127.000 0.571 http://example.org/people/person/places_lived./people/place_lived/location #18480-041_y PRED entity: 041_y PRED relation: gender PRED expected values: 05zppz => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.93 #55, 0.91 #25, 0.91 #37), 02zsn (0.75 #133, 0.55 #255, 0.50 #244) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 143 >> proper extension: 04xm_; 03j90; >> query: (?x7039, 05zppz) <- influenced_by(?x6319, ?x7039), student(?x3424, ?x7039), influenced_by(?x7039, ?x2994), religion(?x6319, ?x4641) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 041_y gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.931 http://example.org/people/person/gender #18479-09zf_q PRED entity: 09zf_q PRED relation: film! PRED expected values: 0175wg => 95 concepts (29 used for prediction) PRED predicted values (max 10 best out of 914): 01nc3rh (0.27 #58319, 0.27 #60402, 0.17 #35409), 015t56 (0.12 #469, 0.06 #4634, 0.02 #21297), 0f0kz (0.12 #4680, 0.04 #15093, 0.03 #19260), 0n8bn (0.12 #1219, 0.03 #5384, 0.03 #54154), 024bbl (0.12 #837, 0.03 #5002, 0.03 #13332), 0jbp0 (0.12 #1759, 0.03 #54154, 0.02 #10088), 0p_pd (0.12 #53, 0.03 #54154, 0.02 #10465), 021yzs (0.12 #850, 0.03 #54154, 0.01 #25844), 016vg8 (0.12 #832, 0.03 #54154, 0.01 #13327), 0hz_1 (0.12 #1493, 0.03 #54154, 0.01 #16071) >> Best rule #58319 for best value: >> intensional similarity = 4 >> extensional distance = 546 >> proper extension: 0g60z; 080dwhx; 0kfpm; 02k_4g; 0358x_; 019nnl; 0ddd0gc; 08jgk1; 0464pz; 0kfv9; ... >> query: (?x5054, ?x10295) <- nominated_for(?x10295, ?x5054), nominated_for(?x3458, ?x5054), honored_for(?x5369, ?x5054), award(?x251, ?x3458) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #3103 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 06g77c; 0m63c; 01_1hw; *> query: (?x5054, 0175wg) <- film(?x1867, ?x5054), music(?x5054, ?x10295), production_companies(?x5054, ?x382), ?x1867 = 016ywr *> conf = 0.11 ranks of expected_values: 73 EVAL 09zf_q film! 0175wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 95.000 29.000 0.272 http://example.org/film/actor/film./film/performance/film #18478-027rpym PRED entity: 027rpym PRED relation: list PRED expected values: 05glt => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.47 #51, 0.40 #65, 0.39 #58) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 0147sh; 01wb95; 048rn; 0gt1k; 0fxmbn; 0jqb8; 02gqm3; 0g5ptf; 06pyc2; 0bbgly; >> query: (?x4865, 05glt) <- film(?x1666, ?x4865), film_art_direction_by(?x4865, ?x2304), currency(?x4865, ?x170), ?x170 = 09nqf >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027rpym list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.473 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #18477-017v_ PRED entity: 017v_ PRED relation: combatants! PRED expected values: 0845v => 303 concepts (303 used for prediction) PRED predicted values (max 10 best out of 75): 0845v (0.67 #135, 0.33 #1371, 0.18 #10745), 081pw (0.60 #1173, 0.59 #1630, 0.53 #1108), 048n7 (0.50 #349, 0.43 #219, 0.40 #1195), 07j9n (0.48 #1396, 0.29 #2048, 0.22 #1266), 03gqgt3 (0.47 #1163, 0.41 #1685, 0.40 #1228), 01gjd0 (0.43 #199, 0.40 #69, 0.38 #329), 07_nf (0.40 #83, 0.38 #343, 0.33 #473), 0cm2xh (0.40 #1119, 0.29 #208, 0.27 #1641), 01hwkn (0.33 #181, 0.24 #1417, 0.18 #10745), 0dr7s (0.33 #180, 0.19 #1416, 0.14 #7943) >> Best rule #135 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 014tss; 03f4n1; 0cn_tpv; 040vgd; >> query: (?x1679, 0845v) <- combatants(?x9602, ?x1679), combatants(?x9328, ?x1679), ?x9602 = 0285m87, entity_involved(?x6982, ?x1679), ?x9328 = 024pcx >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017v_ combatants! 0845v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 303.000 303.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #18476-0n6kf PRED entity: 0n6kf PRED relation: nationality PRED expected values: 09c7w0 => 152 concepts (131 used for prediction) PRED predicted values (max 10 best out of 108): 09c7w0 (0.90 #1206, 0.90 #805, 0.89 #303), 059rby (0.34 #9940), 02jx1 (0.25 #6419, 0.24 #6520, 0.22 #5516), 06bnz (0.25 #6419, 0.24 #6520, 0.22 #5516), 01mk6 (0.25 #6419, 0.24 #6520, 0.22 #5516), 0d060g (0.25 #6419, 0.24 #6520, 0.22 #5516), 03rt9 (0.25 #6419, 0.24 #6520, 0.22 #5516), 012m_ (0.25 #6419, 0.24 #6520, 0.22 #5516), 07ssc (0.25 #6419, 0.24 #6520, 0.22 #5516), 03rjj (0.25 #6419, 0.24 #6520, 0.22 #5516) >> Best rule #1206 for best value: >> intensional similarity = 3 >> extensional distance = 92 >> proper extension: 04cr6qv; 02jyhv; >> query: (?x4795, 09c7w0) <- people(?x3591, ?x4795), profession(?x4795, ?x353), ?x3591 = 0xnvg >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n6kf nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 131.000 0.904 http://example.org/people/person/nationality #18475-04fzk PRED entity: 04fzk PRED relation: participant PRED expected values: 06mfvc => 127 concepts (94 used for prediction) PRED predicted values (max 10 best out of 436): 07cjqy (0.81 #31066, 0.81 #31065, 0.81 #12936), 01kgv4 (0.81 #31066, 0.81 #31065, 0.81 #12936), 015pkc (0.81 #31066, 0.81 #31065, 0.81 #12936), 06fc0b (0.29 #16175, 0.24 #18762, 0.24 #21351), 0j1yf (0.29 #16175, 0.24 #21351, 0.24 #5823), 019pm_ (0.17 #190, 0.05 #3423, 0.05 #2775), 04fzk (0.17 #288, 0.04 #12577, 0.04 #14520), 0127s7 (0.17 #398, 0.04 #5573, 0.04 #15924), 01phtd (0.17 #536, 0.02 #1828), 022q32 (0.17 #606, 0.02 #2544, 0.02 #3839) >> Best rule #31066 for best value: >> intensional similarity = 2 >> extensional distance = 500 >> proper extension: 06c0j; >> query: (?x4106, ?x8274) <- participant(?x8274, ?x4106), award_nominee(?x8274, ?x4295) >> conf = 0.81 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 04fzk participant 06mfvc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 94.000 0.813 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #18474-06924p PRED entity: 06924p PRED relation: artists PRED expected values: 01dpsv => 59 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1516): 0lk90 (0.64 #6440, 0.43 #6373, 0.33 #9564), 01_ztw (0.64 #6870, 0.27 #5808, 0.21 #8999), 01jfr3y (0.64 #6894, 0.25 #9023, 0.18 #5832), 02z4b_8 (0.55 #5936, 0.50 #4873, 0.50 #1686), 0259r0 (0.55 #5528, 0.50 #4465, 0.36 #6590), 03y82t6 (0.55 #6785, 0.46 #8914, 0.20 #15295), 01vtj38 (0.55 #7022, 0.45 #5960, 0.39 #9151), 0127s7 (0.55 #6901, 0.32 #9030, 0.27 #11155), 01wsl7c (0.50 #4391, 0.50 #3329, 0.50 #1204), 01kph_c (0.50 #4664, 0.50 #3602, 0.50 #1477) >> Best rule #6440 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 02lnbg; 0ggx5q; 02ny8t; >> query: (?x10833, 0lk90) <- artists(?x10833, ?x8693), artists(?x10833, ?x6461), award(?x6461, ?x1389), ?x8693 = 0bdxs5, instrumentalists(?x227, ?x6461), award_nominee(?x6461, ?x1751), ?x1389 = 01c427 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #4219 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 0mhfr; 017510; *> query: (?x10833, 01dpsv) <- parent_genre(?x10833, ?x2664), artists(?x10833, ?x217), artists(?x10833, ?x133), ?x133 = 016qtt, parent_genre(?x9007, ?x10833), origin(?x217, ?x739), award_nominee(?x215, ?x217) *> conf = 0.33 ranks of expected_values: 127 EVAL 06924p artists 01dpsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 59.000 19.000 0.636 http://example.org/music/genre/artists #18473-01yf92 PRED entity: 01yf92 PRED relation: child! PRED expected values: 05q78ky => 103 concepts (100 used for prediction) PRED predicted values (max 10 best out of 58): 049ql1 (0.20 #69, 0.12 #733, 0.12 #650), 03d6fyn (0.20 #30, 0.08 #694, 0.08 #611), 06q07 (0.20 #45, 0.07 #294, 0.07 #211), 06p8m (0.07 #233, 0.05 #399, 0.05 #482), 01qckn (0.07 #305, 0.03 #3109, 0.02 #1052), 01bfjy (0.07 #330, 0.03 #3109, 0.02 #1077), 011k1h (0.07 #265), 01dycg (0.05 #385, 0.05 #468, 0.04 #717), 07733f (0.05 #410, 0.05 #493, 0.04 #742), 0kx4m (0.05 #340, 0.05 #423, 0.04 #672) >> Best rule #69 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 03_c8p; 025txrl; 026wmz6; >> query: (?x14420, 049ql1) <- industry(?x14420, ?x10022), industry(?x14420, ?x245), citytown(?x14420, ?x11227), ?x245 = 01mw1, ?x10022 = 020mfr, place_of_birth(?x12375, ?x11227), administrative_division(?x11227, ?x11226) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3109 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 178 *> proper extension: 02y7t7; 02d6ph; 0537b; *> query: (?x14420, ?x847) <- industry(?x14420, ?x10022), industry(?x14118, ?x10022), industry(?x13750, ?x10022), industry(?x9309, ?x10022), place_founded(?x14118, ?x362), company(?x10979, ?x9309), state_province_region(?x9309, ?x4600), child(?x847, ?x13750) *> conf = 0.03 ranks of expected_values: 24 EVAL 01yf92 child! 05q78ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 103.000 100.000 0.200 http://example.org/organization/organization/child./organization/organization_relationship/child #18472-016h4r PRED entity: 016h4r PRED relation: student! PRED expected values: 0ym17 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 157): 025v3k (0.33 #119, 0.25 #1169, 0.02 #9044), 0gl5_ (0.33 #768, 0.02 #7068, 0.02 #2343), 01tx9m (0.14 #1783), 02g839 (0.09 #7899, 0.07 #11049, 0.06 #6324), 0bwfn (0.08 #37553, 0.08 #39128, 0.08 #40703), 017z88 (0.08 #6381, 0.06 #11106, 0.05 #2706), 08815 (0.07 #8927, 0.06 #3152, 0.06 #3677), 04b_46 (0.06 #3376, 0.06 #3901, 0.04 #7051), 03ksy (0.05 #37384, 0.05 #41059, 0.04 #33708), 065y4w7 (0.05 #3164, 0.05 #24690, 0.05 #37293) >> Best rule #119 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 03hhd3; >> query: (?x3495, 025v3k) <- film(?x3495, ?x7463), ?x7463 = 02fj8n, award_nominee(?x3495, ?x1270) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016h4r student! 0ym17 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 121.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #18471-03qgjwc PRED entity: 03qgjwc PRED relation: nominated_for PRED expected values: 01qncf 0fpmrm3 => 44 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1475): 092vkg (0.71 #1719, 0.40 #3291, 0.20 #8008), 03b1sb (0.57 #2878, 0.30 #4450, 0.21 #18879), 07j8r (0.57 #1941, 0.21 #18879, 0.20 #3513), 03qnc6q (0.57 #1954, 0.21 #18879, 0.10 #3526), 011ywj (0.57 #2812, 0.20 #5957, 0.20 #4384), 0p_th (0.57 #1794, 0.20 #4939, 0.20 #3366), 03pc89 (0.57 #2832, 0.20 #4404, 0.13 #5977), 0sxmx (0.57 #2300, 0.20 #5445, 0.10 #3872), 0j_t1 (0.57 #1962, 0.20 #3534, 0.10 #8251), 0191n (0.57 #2346, 0.20 #3918, 0.10 #7063) >> Best rule #1719 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 02rdxsh; >> query: (?x3499, 092vkg) <- award(?x2742, ?x3499), nominated_for(?x3499, ?x6427), nominated_for(?x3499, ?x4009), featured_film_locations(?x6427, ?x3269), ?x4009 = 0320fn >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1901 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 02rdxsh; *> query: (?x3499, 01qncf) <- award(?x2742, ?x3499), nominated_for(?x3499, ?x6427), nominated_for(?x3499, ?x4009), featured_film_locations(?x6427, ?x3269), ?x4009 = 0320fn *> conf = 0.43 ranks of expected_values: 29, 173 EVAL 03qgjwc nominated_for 0fpmrm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 23.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03qgjwc nominated_for 01qncf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 44.000 23.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18470-0dz3r PRED entity: 0dz3r PRED relation: profession! PRED expected values: 01nqfh_ 01vvycq 02r4qs 012x4t 0j1yf 019g40 01vs_v8 018pj3 02b25y 04xrx 016h9b 01wmgrf 01wj18h 01gx5f 050z2 049qx 01vw20h 03j24kf 03f0fnk 0837ql 01mwsnc 09swkk 02nfjp 03h502k 02rxbmt 01kd57 01jfr3y 01wbsdz 018gqj 0163r3 017g21 01k3qj 0dw3l 032nl2 01vn0t_ 016vqk 02wwwv5 02yygk 01rwcgb 016nvh 0k6yt1 0ql36 => 43 concepts (23 used for prediction) PRED predicted values (max 10 best out of 3875): 0144l1 (0.71 #31913, 0.71 #27994, 0.67 #43670), 03j24kf (0.71 #32717, 0.67 #44474, 0.67 #24880), 01vtqml (0.71 #28528, 0.56 #44204, 0.50 #20692), 013v5j (0.71 #28018, 0.50 #20182, 0.50 #16262), 01k47c (0.71 #37996, 0.43 #41915, 0.33 #2730), 03y82t6 (0.67 #44491, 0.57 #40572, 0.57 #28815), 02_t2t (0.67 #45593, 0.57 #41674, 0.57 #29917), 0jsg0m (0.67 #45316, 0.57 #41397, 0.57 #29640), 0f_y9 (0.67 #45289, 0.57 #41370, 0.57 #29613), 0163r3 (0.57 #41170, 0.57 #37251, 0.57 #29413) >> Best rule #31913 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 01d_h8; >> query: (?x131, 0144l1) <- profession(?x3316, ?x131), profession(?x2731, ?x131), profession(?x1715, ?x131), ?x2731 = 01wwvc5, music(?x750, ?x1715), award(?x3316, ?x567) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #32717 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 01d_h8; *> query: (?x131, 03j24kf) <- profession(?x3316, ?x131), profession(?x2731, ?x131), profession(?x1715, ?x131), ?x2731 = 01wwvc5, music(?x750, ?x1715), award(?x3316, ?x567) *> conf = 0.71 ranks of expected_values: 2, 10, 25, 26, 29, 33, 39, 41, 67, 72, 79, 90, 107, 118, 123, 130, 136, 146, 148, 152, 265, 297, 317, 322, 343, 358, 371, 475, 483, 495, 505, 521, 592, 597, 621, 674, 701, 1059, 1817, 1849, 2693, 2709 EVAL 0dz3r profession! 0ql36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 0k6yt1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 016nvh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01rwcgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 02yygk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 02wwwv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 016vqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01vn0t_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 032nl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 0dw3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01k3qj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 017g21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 0163r3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 018gqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01wbsdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01jfr3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01kd57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 02rxbmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 03h502k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 02nfjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 09swkk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01mwsnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 0837ql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 03f0fnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 03j24kf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01vw20h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 049qx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 050z2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01gx5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01wj18h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01wmgrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 016h9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 04xrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 02b25y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 018pj3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01vs_v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 019g40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 0j1yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 02r4qs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01vvycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 43.000 23.000 0.714 http://example.org/people/person/profession EVAL 0dz3r profession! 01nqfh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 23.000 0.714 http://example.org/people/person/profession #18469-099tbz PRED entity: 099tbz PRED relation: award! PRED expected values: 0169dl 048s0r => 44 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2422): 01vw37m (0.88 #6721, 0.81 #3359, 0.81 #10084), 02cpb7 (0.88 #6721, 0.81 #3359, 0.81 #10084), 0794g (0.88 #6721, 0.81 #3359, 0.81 #10084), 03v3xp (0.88 #6721, 0.81 #3359, 0.81 #10084), 01rh0w (0.88 #6721, 0.81 #3359, 0.81 #10084), 0227tr (0.81 #3359, 0.81 #10084, 0.80 #6720), 0337vz (0.81 #3359, 0.81 #10084, 0.80 #6720), 01sp81 (0.81 #3359, 0.81 #10084, 0.80 #6720), 01v9l67 (0.81 #3359, 0.81 #10084, 0.80 #6720), 0h0yt (0.81 #3359, 0.81 #10084, 0.80 #6720) >> Best rule #6721 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01c9dd; >> query: (?x995, ?x2499) <- award_winner(?x995, ?x6264), award_winner(?x995, ?x2499), ?x6264 = 01vw37m, nominated_for(?x995, ?x86), participant(?x1995, ?x2499) >> conf = 0.88 => this is the best rule for 5 predicted values *> Best rule #13446 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 02f75t; *> query: (?x995, ?x230) <- award_winner(?x995, ?x6264), award_winner(?x995, ?x4923), award_winner(?x995, ?x1846), ?x6264 = 01vw37m, profession(?x4923, ?x319), award_nominee(?x230, ?x1846) *> conf = 0.40 ranks of expected_values: 171, 195 EVAL 099tbz award! 048s0r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 19.000 0.875 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099tbz award! 0169dl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 44.000 19.000 0.875 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18468-03bxh PRED entity: 03bxh PRED relation: artists! PRED expected values: 01fsz => 199 concepts (146 used for prediction) PRED predicted values (max 10 best out of 249): 017_qw (0.79 #18852, 0.64 #8517, 0.61 #20104), 064t9 (0.54 #43239, 0.42 #40418, 0.40 #37599), 06by7 (0.51 #43247, 0.48 #17556, 0.37 #45441), 06q6jz (0.42 #3633, 0.41 #7703, 0.33 #4259), 0l8gh (0.33 #7693, 0.33 #3623, 0.33 #180), 07sbbz2 (0.33 #2825, 0.33 #1886, 0.25 #634), 021dvj (0.33 #4121, 0.30 #7565, 0.24 #14767), 01lyv (0.33 #1912, 0.27 #4416, 0.25 #660), 01wqlc (0.33 #76, 0.26 #7589, 0.25 #4145), 05bt6j (0.33 #1922, 0.24 #43270, 0.18 #45464) >> Best rule #18852 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 015cxv; >> query: (?x5600, 017_qw) <- music(?x2111, ?x5600), artists(?x597, ?x5600), category(?x2111, ?x134), film(?x1975, ?x2111) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #45734 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 696 *> proper extension: 07qnf; 01yzl2; *> query: (?x5600, ?x671) <- nationality(?x5600, ?x1310), artists(?x597, ?x5600), parent_genre(?x671, ?x597) *> conf = 0.05 ranks of expected_values: 106 EVAL 03bxh artists! 01fsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 199.000 146.000 0.790 http://example.org/music/genre/artists #18467-0k1wz PRED entity: 0k1wz PRED relation: artists! PRED expected values: 05lls => 113 concepts (59 used for prediction) PRED predicted values (max 10 best out of 222): 016clz (0.78 #9995, 0.25 #13745, 0.21 #17183), 06by7 (0.69 #13762, 0.50 #17200, 0.47 #13136), 03_d0 (0.61 #7501, 0.29 #4381, 0.27 #3756), 017_qw (0.53 #2873, 0.52 #2249, 0.49 #4122), 05lls (0.53 #1263, 0.38 #5008, 0.29 #3759), 064t9 (0.49 #13127, 0.46 #17191, 0.39 #7816), 05bt6j (0.33 #13785, 0.24 #17223, 0.23 #13159), 0dl5d (0.33 #20, 0.30 #332, 0.23 #956), 0cx7f (0.33 #142, 0.30 #454, 0.23 #1078), 01wqlc (0.33 #1325, 0.19 #1949, 0.16 #2885) >> Best rule #9995 for best value: >> intensional similarity = 6 >> extensional distance = 265 >> proper extension: 05k79; 0dvqq; 05xq9; 02cpp; 0l8g0; 0143q0; 0838y; 01w5n51; 016lmg; 070b4; ... >> query: (?x10682, 016clz) <- artists(?x597, ?x10682), artists(?x597, ?x6910), artists(?x597, ?x6626), parent_genre(?x671, ?x597), ?x6910 = 05y7hc, artist(?x3265, ?x6626) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1263 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 03d6q; *> query: (?x10682, 05lls) <- artists(?x597, ?x10682), ?x597 = 0ggq0m, nationality(?x10682, ?x1603), place_of_death(?x10682, ?x7184), gender(?x10682, ?x231) *> conf = 0.53 ranks of expected_values: 5 EVAL 0k1wz artists! 05lls CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 113.000 59.000 0.783 http://example.org/music/genre/artists #18466-059j2 PRED entity: 059j2 PRED relation: combatants PRED expected values: 015fr => 240 concepts (183 used for prediction) PRED predicted values (max 10 best out of 321): 06f32 (0.83 #4689, 0.83 #4688, 0.82 #3618), 05b4w (0.83 #4689, 0.83 #4688, 0.82 #3618), 015qh (0.83 #4689, 0.83 #4688, 0.82 #3618), 015fr (0.52 #2211, 0.50 #1804, 0.48 #1735), 059j2 (0.48 #2217, 0.46 #1810, 0.42 #2015), 05v8c (0.40 #403, 0.33 #1803, 0.31 #2008), 07f1x (0.31 #1443, 0.29 #4690, 0.26 #4758), 03b79 (0.30 #960, 0.29 #4690, 0.29 #494), 027qpc (0.30 #973, 0.29 #4690, 0.26 #6849), 06qd3 (0.29 #4690, 0.29 #478, 0.26 #4758) >> Best rule #4689 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 017v_; 0cdbq; 09krp; 012m_; 01d8l; 01mzwp; >> query: (?x1229, ?x3918) <- combatants(?x3918, ?x1229), contains(?x1229, ?x2351), combatants(?x279, ?x3918) >> conf = 0.83 => this is the best rule for 3 predicted values *> Best rule #2211 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 014tss; *> query: (?x1229, 015fr) <- combatants(?x151, ?x1229), country(?x1009, ?x1229), jurisdiction_of_office(?x182, ?x1229) *> conf = 0.52 ranks of expected_values: 4 EVAL 059j2 combatants 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 240.000 183.000 0.826 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #18465-012m_ PRED entity: 012m_ PRED relation: capital PRED expected values: 0fhp9 => 133 concepts (95 used for prediction) PRED predicted values (max 10 best out of 126): 04swd (0.29 #37, 0.08 #3177, 0.08 #3539), 04jpl (0.14 #4, 0.10 #485, 0.10 #1330), 081m_ (0.14 #161, 0.08 #1729, 0.08 #884), 0rh6k (0.14 #1, 0.06 #1085, 0.04 #1569), 06pr6 (0.14 #29, 0.03 #2805, 0.03 #3169), 0fhp9 (0.13 #969, 0.10 #608, 0.09 #728), 0156q (0.12 #251, 0.12 #1699, 0.09 #733), 096gm (0.12 #261, 0.04 #1709, 0.03 #5453), 06mxs (0.11 #383, 0.05 #1348, 0.05 #1469), 0fhzy (0.10 #615, 0.09 #735, 0.08 #856) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 03b79; >> query: (?x9006, 04swd) <- nationality(?x4724, ?x9006), combatants(?x3654, ?x9006), ?x3654 = 0gfq9, combatants(?x3141, ?x9006) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #969 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0fhzf; *> query: (?x9006, 0fhp9) <- contains(?x455, ?x9006), ?x455 = 02j9z, location(?x8299, ?x9006) *> conf = 0.13 ranks of expected_values: 6 EVAL 012m_ capital 0fhp9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 133.000 95.000 0.286 http://example.org/location/country/capital #18464-03f0324 PRED entity: 03f0324 PRED relation: influenced_by PRED expected values: 07dnx => 183 concepts (64 used for prediction) PRED predicted values (max 10 best out of 361): 03_87 (0.60 #2352, 0.35 #15247, 0.33 #4072), 03sbs (0.56 #10965, 0.34 #9671, 0.33 #217), 03_dj (0.50 #1696, 0.20 #2128, 0.17 #4280), 081k8 (0.42 #4026, 0.33 #152, 0.25 #15201), 0448r (0.40 #1979, 0.33 #3698, 0.25 #4131), 04xjp (0.33 #57, 0.25 #3931, 0.25 #1347), 05qmj (0.33 #188, 0.25 #10936, 0.25 #1047), 039n1 (0.33 #321, 0.25 #1180, 0.20 #2043), 01vh096 (0.33 #289, 0.25 #1148, 0.17 #4163), 0113sg (0.33 #374, 0.25 #1233, 0.17 #4248) >> Best rule #2352 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0h0yt; >> query: (?x4915, 03_87) <- influenced_by(?x4915, ?x2240), influenced_by(?x4265, ?x2240), ?x4265 = 06whf, religion(?x2240, ?x2694), student(?x5179, ?x4915) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #21499 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 161 *> proper extension: 0b_c7; *> query: (?x4915, ?x1279) <- influenced_by(?x4915, ?x2240), influenced_by(?x4265, ?x2240), profession(?x4915, ?x353), company(?x2240, ?x13316), influenced_by(?x4265, ?x1279) *> conf = 0.11 ranks of expected_values: 87 EVAL 03f0324 influenced_by 07dnx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 183.000 64.000 0.600 http://example.org/influence/influence_node/influenced_by #18463-095zvfg PRED entity: 095zvfg PRED relation: profession PRED expected values: 02tx6q => 104 concepts (99 used for prediction) PRED predicted values (max 10 best out of 58): 02hrh1q (0.88 #165, 0.68 #1966, 0.68 #2716), 02tx6q (0.38 #353, 0.23 #803, 0.23 #653), 01d_h8 (0.36 #6, 0.34 #5260, 0.33 #1207), 03gjzk (0.36 #1817, 0.35 #1967, 0.25 #1217), 0dxtg (0.32 #1815, 0.31 #1965, 0.29 #1215), 02jknp (0.24 #2259, 0.24 #4060, 0.23 #5562), 09jwl (0.19 #4222, 0.19 #3322, 0.18 #3622), 02krf9 (0.15 #1829, 0.14 #1979, 0.12 #178), 0cbd2 (0.14 #1358, 0.12 #1508, 0.12 #9312), 026sdt1 (0.13 #971, 0.11 #520, 0.10 #670) >> Best rule #165 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 03jvmp; >> query: (?x9151, 02hrh1q) <- nominated_for(?x9151, ?x493), award_winner(?x500, ?x9151), ?x493 = 080dwhx >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #353 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 22 *> proper extension: 05f260; *> query: (?x9151, 02tx6q) <- award(?x9151, ?x500), ?x500 = 0p9sw *> conf = 0.38 ranks of expected_values: 2 EVAL 095zvfg profession 02tx6q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 99.000 0.875 http://example.org/people/person/profession #18462-02_hj4 PRED entity: 02_hj4 PRED relation: location PRED expected values: 0cc56 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 139): 0cc56 (0.10 #1660, 0.09 #2462, 0.08 #6472), 0cr3d (0.08 #11371, 0.06 #40246, 0.06 #12975), 0k049 (0.08 #6424, 0.07 #7226, 0.07 #1612), 013yq (0.07 #3325, 0.07 #117, 0.06 #11345), 01n7q (0.07 #62, 0.05 #4072, 0.05 #4874), 0ccvx (0.07 #3428, 0.05 #4230, 0.03 #11448), 059rby (0.07 #16, 0.05 #13650, 0.04 #18462), 0dclg (0.07 #115, 0.03 #11343, 0.02 #9739), 0f2wj (0.07 #34, 0.03 #15272, 0.03 #17678), 01x73 (0.07 #94, 0.03 #896, 0.02 #3302) >> Best rule #1660 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 01ztgm; 015882; 01s21dg; >> query: (?x1672, 0cc56) <- participant(?x3466, ?x1672), people(?x2510, ?x1672), award_winner(?x2373, ?x1672) >> conf = 0.10 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_hj4 location 0cc56 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.098 http://example.org/people/person/places_lived./people/place_lived/location #18461-01l849 PRED entity: 01l849 PRED relation: colors! PRED expected values: 084l5 07l8f 0bwjj 04l5b4 01jvgt => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 944): 01y3c (0.50 #4234, 0.50 #3586, 0.34 #2594), 07l4z (0.50 #4386, 0.50 #3738, 0.34 #2594), 02mplj (0.50 #4274, 0.50 #3626, 0.34 #2594), 05gg4 (0.50 #3638, 0.34 #2594, 0.33 #5263), 01yjl (0.50 #3618, 0.34 #2594, 0.33 #4266), 0jnm_ (0.50 #4360, 0.34 #2594, 0.33 #5010), 05xvj (0.50 #3831, 0.34 #2594, 0.33 #4479), 07l8x (0.50 #3709, 0.34 #2594, 0.33 #4357), 01_1kk (0.50 #3876, 0.34 #2594, 0.33 #4524), 02w64f (0.50 #3868, 0.34 #2594, 0.33 #4516) >> Best rule #4234 for best value: >> intensional similarity = 35 >> extensional distance = 4 >> proper extension: 0jc_p; >> query: (?x332, 01y3c) <- colors(?x11318, ?x332), colors(?x10220, ?x332), colors(?x9803, ?x332), colors(?x8008, ?x332), colors(?x6177, ?x332), colors(?x12370, ?x332), colors(?x11789, ?x332), colors(?x5773, ?x332), colors(?x2148, ?x332), major_field_of_study(?x8008, ?x5614), major_field_of_study(?x8008, ?x2014), contains(?x94, ?x8008), team(?x8824, ?x11789), ?x2014 = 04rjg, position(?x11789, ?x1348), institution(?x865, ?x9803), student(?x8008, ?x838), teams(?x1248, ?x2148), ?x5614 = 03qsdpk, position(?x2148, ?x180), position_s(?x2148, ?x1240), ?x8824 = 05g_nr, school_type(?x11318, ?x3092), currency(?x10220, ?x170), ?x3092 = 05jxkf, organization(?x346, ?x10220), team(?x7042, ?x12370), category(?x10220, ?x134), contains(?x760, ?x11318), ?x7042 = 0b_72t, ?x1240 = 023wyl, draft(?x5773, ?x685), position(?x12370, ?x4570), ?x865 = 02h4rq6, citytown(?x6177, ?x10364) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2594 for first EXPECTED value: *> intensional similarity = 45 *> extensional distance = 1 *> proper extension: 01g5v; *> query: (?x332, ?x202) <- colors(?x12726, ?x332), colors(?x10220, ?x332), colors(?x8008, ?x332), colors(?x6908, ?x332), colors(?x6177, ?x332), colors(?x2621, ?x332), colors(?x1667, ?x332), colors(?x13601, ?x332), colors(?x11948, ?x332), colors(?x11789, ?x332), colors(?x9319, ?x332), colors(?x684, ?x332), ?x11789 = 02pyyld, currency(?x8008, ?x170), ?x9319 = 0c02jh8, institution(?x1526, ?x6177), citytown(?x6177, ?x10364), position(?x684, ?x1517), position(?x684, ?x1114), organization(?x346, ?x6177), colors(?x684, ?x9778), student(?x10220, ?x1324), ?x13601 = 03k2hn, school(?x7060, ?x6177), school(?x3333, ?x6177), major_field_of_study(?x1667, ?x1154), contains(?x94, ?x10220), state_province_region(?x6177, ?x3634), ?x2621 = 07vht, ?x1114 = 047g8h, ?x1517 = 02g_6j, ?x3333 = 01yjl, sport(?x684, ?x1083), position(?x11948, ?x60), ?x6908 = 01dthg, season(?x7060, ?x11501), teams(?x1860, ?x7060), major_field_of_study(?x6177, ?x3995), fraternities_and_sororities(?x1667, ?x3697), ?x11501 = 027mvrc, colors(?x202, ?x9778), ?x1526 = 0bkj86, student(?x6177, ?x1270), category(?x12726, ?x134), school(?x1160, ?x10220) *> conf = 0.34 ranks of expected_values: 66, 95, 179, 256, 332 EVAL 01l849 colors! 01jvgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01l849 colors! 04l5b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01l849 colors! 0bwjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01l849 colors! 07l8f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01l849 colors! 084l5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/sports/sports_team/colors #18460-01c427 PRED entity: 01c427 PRED relation: award_winner PRED expected values: 07c0j 02h9_l => 45 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1214): 011z3g (0.60 #8864, 0.57 #16247, 0.33 #1481), 0gcs9 (0.57 #12949, 0.27 #20334, 0.20 #10488), 02l840 (0.53 #7383, 0.53 #7382, 0.53 #7381), 01s21dg (0.53 #7383, 0.53 #7382, 0.53 #7381), 0dl567 (0.53 #7383, 0.53 #7382, 0.53 #7381), 0b68vs (0.53 #7383, 0.53 #7382, 0.53 #7381), 026spg (0.53 #7383, 0.53 #7382, 0.53 #7381), 02vr7 (0.53 #7383, 0.53 #7382, 0.53 #7381), 0197tq (0.53 #7383, 0.53 #7382, 0.53 #7381), 01vvyc_ (0.53 #7383, 0.53 #7382, 0.53 #7381) >> Best rule #8864 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01c9dd; 023vrq; >> query: (?x1389, 011z3g) <- award(?x3109, ?x1389), award(?x2334, ?x1389), ?x2334 = 047sxrj, ceremony(?x1389, ?x139), category(?x3109, ?x134) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #218 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 01by1l; *> query: (?x1389, 07c0j) <- award(?x3109, ?x1389), award(?x2334, ?x1389), ?x2334 = 047sxrj, ?x3109 = 018ndc, ceremony(?x1389, ?x139) *> conf = 0.33 ranks of expected_values: 94, 340 EVAL 01c427 award_winner 02h9_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 45.000 28.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01c427 award_winner 07c0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 45.000 28.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #18459-0889x PRED entity: 0889x PRED relation: artists! PRED expected values: 0173b0 => 154 concepts (69 used for prediction) PRED predicted values (max 10 best out of 288): 06by7 (0.71 #10789, 0.68 #12327, 0.67 #22), 064t9 (0.56 #6780, 0.55 #14473, 0.51 #5550), 0glt670 (0.42 #6807, 0.35 #3420, 0.33 #4344), 0155w (0.39 #1336, 0.27 #10874, 0.23 #3179), 01fh36 (0.39 #1316, 0.23 #8615, 0.22 #3687), 0dl5d (0.35 #12017, 0.29 #1249, 0.28 #2478), 025sc50 (0.34 #6817, 0.29 #3430, 0.27 #5587), 03_d0 (0.32 #1241, 0.24 #1548, 0.23 #627), 05bt6j (0.32 #2195, 0.32 #2502, 0.29 #966), 06j6l (0.32 #6815, 0.31 #664, 0.30 #2814) >> Best rule #10789 for best value: >> intensional similarity = 4 >> extensional distance = 249 >> proper extension: 05crg7; >> query: (?x12266, 06by7) <- role(?x12266, ?x314), artists(?x6107, ?x12266), artists(?x6107, ?x7987), ?x7987 = 0j6cj >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #8489 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 169 *> proper extension: 02t3ln; *> query: (?x12266, 0173b0) <- artists(?x12618, ?x12266), artists(?x1000, ?x12266), ?x1000 = 0xhtw, parent_genre(?x12618, ?x5138) *> conf = 0.09 ranks of expected_values: 53 EVAL 0889x artists! 0173b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 154.000 69.000 0.705 http://example.org/music/genre/artists #18458-0298n7 PRED entity: 0298n7 PRED relation: nominated_for! PRED expected values: 0gq_v 0p9sw => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 189): 0gq_v (0.63 #1851, 0.28 #5515, 0.19 #6889), 02n9nmz (0.54 #1429, 0.52 #1658, 0.43 #742), 057xs89 (0.43 #111, 0.10 #2401, 0.09 #5378), 019f4v (0.42 #5548, 0.42 #1884, 0.37 #1655), 0gs9p (0.41 #5556, 0.39 #1663, 0.38 #1892), 04dn09n (0.35 #1636, 0.34 #1407, 0.33 #5529), 0l8z1 (0.34 #1882, 0.29 #1653, 0.26 #1424), 0f4x7 (0.33 #5521, 0.29 #1628, 0.27 #1399), 04kxsb (0.32 #1692, 0.32 #1463, 0.24 #5585), 040njc (0.31 #5503, 0.30 #1381, 0.30 #1839) >> Best rule #1851 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 01c9d; >> query: (?x7755, 0gq_v) <- film(?x92, ?x7755), film(?x1104, ?x7755), nominated_for(?x2222, ?x7755), ?x2222 = 0gs96 >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1, 16 EVAL 0298n7 nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 77.000 77.000 0.626 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0298n7 nominated_for! 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.626 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18457-03z9585 PRED entity: 03z9585 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 26): 09vw2b7 (0.74 #1308, 0.72 #1065, 0.67 #311), 01vx2h (0.71 #146, 0.67 #180, 0.54 #248), 0dxtw (0.50 #1312, 0.50 #77, 0.45 #1277), 01pvkk (0.50 #113, 0.35 #385, 0.33 #181), 02rh1dz (0.50 #76, 0.33 #8, 0.29 #144), 0215hd (0.33 #17, 0.15 #255, 0.15 #2050), 01xy5l_ (0.33 #13, 0.15 #251, 0.14 #523), 05smlt (0.33 #53, 0.06 #597, 0.04 #871), 04pyp5 (0.25 #83, 0.18 #219, 0.17 #117), 0d2b38 (0.18 #500, 0.16 #568, 0.16 #602) >> Best rule #1308 for best value: >> intensional similarity = 7 >> extensional distance = 149 >> proper extension: 03_wm6; >> query: (?x8193, 09vw2b7) <- film_crew_role(?x8193, ?x137), genre(?x8193, ?x812), genre(?x8193, ?x225), ?x812 = 01jfsb, country(?x8193, ?x94), ?x225 = 02kdv5l, language(?x8193, ?x90) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03z9585 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.735 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18456-07t90 PRED entity: 07t90 PRED relation: school! PRED expected values: 0f4vx0 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 16): 0f4vx0 (0.31 #169, 0.26 #313, 0.26 #377), 02qw1zx (0.30 #309, 0.25 #5, 0.22 #373), 05vsb7 (0.25 #305, 0.25 #65, 0.17 #369), 025tn92 (0.25 #11, 0.17 #315, 0.15 #379), 038c0q (0.25 #6, 0.12 #374, 0.11 #310), 092j54 (0.24 #311, 0.15 #375, 0.15 #87), 09l0x9 (0.22 #314, 0.21 #58, 0.20 #74), 02rl201 (0.21 #52, 0.10 #372, 0.08 #308), 02pq_x5 (0.21 #174, 0.15 #94, 0.14 #318), 047dpm0 (0.20 #80, 0.10 #176, 0.08 #320) >> Best rule #169 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 0cv_2; 02z_b; >> query: (?x4599, 0f4vx0) <- organization(?x4599, ?x5487), category(?x4599, ?x134), company(?x2998, ?x4599) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07t90 school! 0f4vx0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.310 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #18455-0jfx1 PRED entity: 0jfx1 PRED relation: participant PRED expected values: 01mwsnc => 120 concepts (109 used for prediction) PRED predicted values (max 10 best out of 333): 01pcvn (0.83 #40553, 0.80 #39285, 0.11 #12038), 01t6b4 (0.83 #40553, 0.80 #39285), 027jq2 (0.34 #14574, 0.32 #1900), 01fx2g (0.34 #14574, 0.32 #1900), 019pm_ (0.14 #1450, 0.07 #817, 0.07 #3351), 0jfx1 (0.14 #159, 0.04 #9027, 0.03 #13465), 01rh0w (0.14 #93, 0.02 #31046, 0.01 #38017), 086sj (0.11 #12038, 0.10 #13940, 0.09 #9502), 01vrz41 (0.10 #3247, 0.07 #1346, 0.04 #5783), 01p4vl (0.07 #1756, 0.07 #1123, 0.06 #2390) >> Best rule #40553 for best value: >> intensional similarity = 2 >> extensional distance = 588 >> proper extension: 01xyt7; >> query: (?x2444, ?x1285) <- participant(?x2444, ?x117), participant(?x1285, ?x2444) >> conf = 0.83 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0jfx1 participant 01mwsnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 109.000 0.828 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #18454-01kwsg PRED entity: 01kwsg PRED relation: athlete! PRED expected values: 0jm_ => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 3): 02vx4 (0.02 #852, 0.02 #882, 0.02 #892), 018w8 (0.02 #16), 0jm_ (0.01 #593, 0.01 #313, 0.01 #693) >> Best rule #852 for best value: >> intensional similarity = 2 >> extensional distance = 3218 >> proper extension: 0g4gr; 01k6y1; 075wq; 03mz5b; 0jvs0; 08lpkq; 037mh8; 06q83; 07h1q; 01cqz5; ... >> query: (?x4702, 02vx4) <- gender(?x4702, ?x231), ?x231 = 05zppz >> conf = 0.02 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1874 *> proper extension: 0cm03; 0frmb1; 02ln1; 0cl_m; 019g65; 02vptk_; 09jrf; 03c_8t; 02cg2v; *> query: (?x4702, 0jm_) <- student(?x3485, ?x4702), major_field_of_study(?x3485, ?x254) *> conf = 0.01 ranks of expected_values: 3 EVAL 01kwsg athlete! 0jm_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 95.000 0.019 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #18453-02vy5j PRED entity: 02vy5j PRED relation: location PRED expected values: 0f2v0 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 60): 0d6hn (0.49 #16865, 0.44 #48194, 0.44 #51408), 02_286 (0.25 #37, 0.18 #840, 0.16 #9674), 06_kh (0.17 #11, 0.02 #814, 0.02 #9648), 0r0m6 (0.08 #217, 0.05 #1020, 0.04 #1823), 01n7q (0.08 #63, 0.05 #2472, 0.04 #866), 01531 (0.08 #157, 0.04 #960, 0.04 #8991), 0d6lp (0.08 #167, 0.03 #970, 0.02 #4182), 0r00l (0.08 #605, 0.03 #1408, 0.02 #2211), 0165b (0.08 #357), 0xpp5 (0.08 #298) >> Best rule #16865 for best value: >> intensional similarity = 3 >> extensional distance = 938 >> proper extension: 02wrhj; >> query: (?x2282, ?x10708) <- type_of_union(?x2282, ?x566), film(?x2282, ?x1820), place_of_birth(?x2282, ?x10708) >> conf = 0.49 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02vy5j location 0f2v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 99.000 0.488 http://example.org/people/person/places_lived./people/place_lived/location #18452-0d05q4 PRED entity: 0d05q4 PRED relation: form_of_government PRED expected values: 018wl5 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 5): 01q20 (0.55 #58, 0.34 #163, 0.34 #103), 018wl5 (0.52 #56, 0.40 #6, 0.40 #1), 01d9r3 (0.48 #234, 0.36 #324, 0.35 #299), 01fpfn (0.45 #42, 0.43 #162, 0.43 #147), 026wp (0.13 #55, 0.12 #75, 0.12 #65) >> Best rule #58 for best value: >> intensional similarity = 2 >> extensional distance = 31 >> proper extension: 06ryl; 07fsv; 0604m; 020p1; >> query: (?x4092, 01q20) <- country(?x1121, ?x4092), jurisdiction_of_office(?x12920, ?x4092) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 31 *> proper extension: 06ryl; 07fsv; 0604m; 020p1; *> query: (?x4092, 018wl5) <- country(?x1121, ?x4092), jurisdiction_of_office(?x12920, ?x4092) *> conf = 0.52 ranks of expected_values: 2 EVAL 0d05q4 form_of_government 018wl5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 160.000 0.545 http://example.org/location/country/form_of_government #18451-03hfmm PRED entity: 03hfmm PRED relation: film! PRED expected values: 02vr7 => 99 concepts (64 used for prediction) PRED predicted values (max 10 best out of 911): 04t38b (0.14 #39437, 0.14 #22832, 0.13 #41513), 0jfx1 (0.09 #4554, 0.04 #14933, 0.03 #23236), 081lh (0.06 #4310, 0.02 #6385, 0.02 #68661), 0170pk (0.06 #279, 0.04 #8580, 0.03 #10655), 01nwwl (0.06 #500, 0.03 #10876, 0.03 #15029), 0h7pj (0.06 #5691, 0.02 #13993, 0.02 #18145), 01vsn38 (0.06 #5999, 0.02 #22605, 0.01 #116009), 03kpvp (0.05 #2705, 0.02 #27612, 0.01 #85737), 0h0wc (0.05 #122461, 0.04 #47741, 0.04 #51893), 02qgyv (0.05 #122461, 0.04 #47741, 0.04 #51893) >> Best rule #39437 for best value: >> intensional similarity = 4 >> extensional distance = 456 >> proper extension: 072r5v; 06zn1c; >> query: (?x8664, ?x4495) <- titles(?x53, ?x8664), currency(?x8664, ?x170), film(?x788, ?x8664), film(?x4495, ?x8664) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03hfmm film! 02vr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 64.000 0.142 http://example.org/film/actor/film./film/performance/film #18450-0f2zc PRED entity: 0f2zc PRED relation: athlete! PRED expected values: 018jz => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 56): 018jz (0.71 #37, 0.71 #29, 0.33 #5), 02vx4 (0.60 #122, 0.58 #227, 0.58 #218), 037hz (0.25 #24, 0.18 #64, 0.08 #88), 03tmr (0.02 #97, 0.02 #153, 0.02 #169), 01yfj (0.01 #225), 09f6b (0.01 #225), 01gqfm (0.01 #225), 09_9n (0.01 #225), 03krj (0.01 #225), 0194d (0.01 #225) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01g0jn; >> query: (?x9180, 018jz) <- team(?x9180, ?x260), location(?x9180, ?x2949), athlete(?x1083, ?x9180), season(?x260, ?x701) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2zc athlete! 018jz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.714 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #18449-03r0g9 PRED entity: 03r0g9 PRED relation: nominated_for! PRED expected values: 02n9nmz => 98 concepts (85 used for prediction) PRED predicted values (max 10 best out of 189): 0fbtbt (0.70 #391, 0.04 #4818, 0.03 #5983), 0bdw6t (0.48 #317, 0.03 #4744, 0.03 #12669), 0bdx29 (0.46 #316, 0.03 #4743, 0.03 #13368), 0fbvqf (0.46 #270, 0.03 #4697, 0.03 #13322), 0bp_b2 (0.39 #250, 0.03 #4677, 0.03 #10969), 0ck27z (0.39 #304, 0.03 #4731, 0.03 #5896), 0gkts9 (0.37 #354, 0.03 #4781, 0.02 #11073), 0bdw1g (0.37 #263, 0.02 #12615, 0.02 #4690), 0gs96 (0.36 #90, 0.23 #1022, 0.18 #3585), 027dtxw (0.36 #4, 0.15 #1635, 0.15 #19349) >> Best rule #391 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 0g60z; 080dwhx; 02k_4g; 0ddd0gc; 0kfv9; 03d34x8; 01j67j; 030k94; 02rzdcp; 063ykwt; ... >> query: (?x3693, 0fbtbt) <- award_winner(?x3693, ?x1018), nominated_for(?x7862, ?x3693), award(?x4299, ?x7862), ?x4299 = 04511f >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #57 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 09tkzy; *> query: (?x3693, 02n9nmz) <- film(?x2805, ?x3693), film(?x1018, ?x3693), genre(?x3693, ?x604), celebrity(?x1018, ?x548), ?x2805 = 0lpjn *> conf = 0.18 ranks of expected_values: 45 EVAL 03r0g9 nominated_for! 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 98.000 85.000 0.696 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18448-07qv_ PRED entity: 07qv_ PRED relation: languages_spoken! PRED expected values: 03295l => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 70): 07hwkr (0.68 #2566, 0.67 #1600, 0.67 #1462), 059_w (0.50 #580, 0.50 #442, 0.43 #994), 0x67 (0.50 #563, 0.40 #632, 0.33 #839), 09zyn5 (0.50 #1378, 0.33 #65, 0.29 #1240), 0c41n (0.50 #485, 0.33 #69, 0.29 #1037), 0fk3s (0.50 #479, 0.33 #63, 0.29 #1031), 03x1x (0.50 #466, 0.33 #50, 0.29 #1018), 0g8_vp (0.50 #434, 0.33 #18, 0.29 #986), 02vsw1 (0.45 #1702, 0.44 #1633, 0.44 #1495), 03w9bjf (0.38 #2119, 0.33 #47, 0.27 #2533) >> Best rule #2566 for best value: >> intensional similarity = 19 >> extensional distance = 20 >> proper extension: 01bkv; >> query: (?x9057, 07hwkr) <- countries_spoken_in(?x9057, ?x550), language(?x4651, ?x9057), languages_spoken(?x1571, ?x9057), film_release_region(?x8474, ?x550), film_release_region(?x6684, ?x550), film_release_region(?x6216, ?x550), film_release_region(?x5644, ?x550), film_release_region(?x299, ?x550), film_release_region(?x86, ?x550), ?x6216 = 06fcqw, film_release_region(?x86, ?x2645), ?x5644 = 0dll_t2, honored_for(?x1442, ?x86), ?x2645 = 03h64, nationality(?x1408, ?x550), ?x299 = 01gc7, film_release_distribution_medium(?x6684, ?x81), nominated_for(?x1384, ?x8474), jurisdiction_of_office(?x265, ?x550) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #227 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 1 *> proper extension: 01jb8r; *> query: (?x9057, 03295l) <- languages_spoken(?x13941, ?x9057), languages_spoken(?x9648, ?x9057), languages_spoken(?x1571, ?x9057), language(?x12964, ?x9057), language(?x4651, ?x9057), ?x1571 = 071x0k, ?x12964 = 04hk0w, ?x13941 = 04czx7, ?x4651 = 043t8t, people(?x9648, ?x8432), people(?x9648, ?x1408), category(?x8432, ?x134), film(?x8432, ?x1904), profession(?x8432, ?x524), nominated_for(?x8432, ?x2709), ?x1408 = 031zkw *> conf = 0.33 ranks of expected_values: 15 EVAL 07qv_ languages_spoken! 03295l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 60.000 60.000 0.682 http://example.org/people/ethnicity/languages_spoken #18447-018vbf PRED entity: 018vbf PRED relation: combatants PRED expected values: 03spz => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 253): 03m7d (0.67 #2526, 0.67 #1461, 0.64 #2126), 07ssc (0.64 #6581, 0.57 #4146, 0.57 #7255), 03spz (0.58 #2260, 0.54 #1463, 0.50 #190), 0604m (0.58 #2260, 0.47 #4268, 0.42 #3598), 0d05q4 (0.49 #1064, 0.48 #4265, 0.48 #4264), 02k54 (0.49 #1064, 0.48 #4265, 0.48 #4264), 03__y (0.49 #1064, 0.48 #4265, 0.48 #4264), 03m6j (0.49 #1064, 0.48 #4265, 0.48 #4264), 08849 (0.47 #5089, 0.46 #4404, 0.45 #2523), 012bk (0.47 #5089, 0.46 #4404, 0.45 #2523) >> Best rule #2526 for best value: >> intensional similarity = 13 >> extensional distance = 13 >> proper extension: 01gjd0; 0dl4z; 07j9n; 0flry; 02cnqk; 02rwmk; >> query: (?x14182, ?x14077) <- entity_involved(?x14182, ?x14077), entity_involved(?x14182, ?x11617), entity_involved(?x14182, ?x4302), entity_involved(?x13684, ?x11617), locations(?x13684, ?x7413), films(?x13684, ?x7114), combatants(?x13022, ?x14077), combatants(?x13684, ?x4743), contains(?x6304, ?x4302), adjoins(?x4302, ?x1499), film_release_region(?x559, ?x4302), time_zones(?x4302, ?x10735), combatants(?x4302, ?x608) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2260 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 12 *> proper extension: 07_nf; *> query: (?x14182, ?x10569) <- entity_involved(?x14182, ?x11617), entity_involved(?x14182, ?x8437), profession(?x11617, ?x8498), jurisdiction_of_office(?x11617, ?x10569), specialization_of(?x1682, ?x8498), basic_title(?x11617, ?x346), religion(?x8437, ?x7131), type_of_union(?x11617, ?x566), currency(?x10569, ?x170), people(?x1050, ?x8437) *> conf = 0.58 ranks of expected_values: 3 EVAL 018vbf combatants 03spz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.673 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #18446-047svrl PRED entity: 047svrl PRED relation: genre PRED expected values: 07s9rl0 05p553 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.63 #2217, 0.62 #617, 0.62 #863), 01z4y (0.61 #5552, 0.49 #2340, 0.48 #7398), 05p553 (0.56 #1482, 0.43 #2221, 0.38 #744), 03k9fj (0.48 #136, 0.48 #13, 0.25 #506), 02kdv5l (0.38 #126, 0.33 #372, 0.33 #249), 01jfsb (0.35 #1861, 0.35 #383, 0.35 #137), 02l7c8 (0.32 #2234, 0.27 #2111, 0.27 #3963), 01hmnh (0.23 #20, 0.21 #143, 0.20 #389), 06n90 (0.21 #138, 0.20 #508, 0.20 #15), 060__y (0.20 #388, 0.18 #265, 0.16 #2235) >> Best rule #2217 for best value: >> intensional similarity = 3 >> extensional distance = 649 >> proper extension: 03kq98; 02qjv1p; >> query: (?x2695, 07s9rl0) <- titles(?x2480, ?x2695), titles(?x2480, ?x2755), ?x2755 = 08rr3p >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 047svrl genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.628 http://example.org/film/film/genre EVAL 047svrl genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.628 http://example.org/film/film/genre #18445-01w8n89 PRED entity: 01w8n89 PRED relation: profession PRED expected values: 09lbv => 110 concepts (66 used for prediction) PRED predicted values (max 10 best out of 58): 02hrh1q (0.73 #9233, 0.72 #7912, 0.67 #5717), 016z4k (0.73 #150, 0.59 #734, 0.58 #1172), 0dz3r (0.50 #586, 0.49 #1609, 0.49 #1902), 0lgw7 (0.33 #45, 0.05 #483, 0.02 #1213), 01c72t (0.33 #4262, 0.31 #5286, 0.31 #4116), 01d_h8 (0.29 #8494, 0.26 #9225, 0.25 #8640), 0n1h (0.27 #157, 0.27 #1179, 0.24 #2204), 0dxtg (0.26 #8501, 0.23 #8647, 0.23 #9086), 0fnpj (0.22 #496, 0.18 #350, 0.18 #2397), 02hv44_ (0.20 #6636, 0.04 #6050, 0.03 #8543) >> Best rule #9233 for best value: >> intensional similarity = 4 >> extensional distance = 1024 >> proper extension: 01hkck; >> query: (?x3657, 02hrh1q) <- profession(?x3657, ?x655), location(?x3657, ?x6047), county(?x6047, ?x578), source(?x6047, ?x958) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #4532 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 352 *> proper extension: 05pq9; 02zft0; 01d1yr; 03lmzl; 095p3z; 01p7b6b; 01pbwwl; 07f7jp; 02rf51g; *> query: (?x3657, ?x220) <- profession(?x3657, ?x2348), profession(?x3657, ?x1183), ?x2348 = 0nbcg, specialization_of(?x220, ?x1183) *> conf = 0.13 ranks of expected_values: 17 EVAL 01w8n89 profession 09lbv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 110.000 66.000 0.730 http://example.org/people/person/profession #18444-0qcr0 PRED entity: 0qcr0 PRED relation: films PRED expected values: 0cmdwwg => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 20): 09fc83 (0.20 #3453, 0.19 #7972, 0.14 #5577), 01pv91 (0.19 #7972, 0.12 #5973, 0.08 #7566), 02rlj20 (0.08 #7849, 0.06 #14229, 0.06 #13698), 06_x996 (0.08 #7638), 012kyx (0.06 #13097, 0.05 #19485, 0.03 #25882), 0gfsq9 (0.06 #12891, 0.05 #19279, 0.03 #25676), 09ps01 (0.05 #19917, 0.04 #20452, 0.04 #22583), 0qm98 (0.05 #19741, 0.04 #20276, 0.04 #22407), 04jn6y7 (0.04 #22336, 0.04 #23939, 0.03 #25005), 03s9kp (0.02 #34046) >> Best rule #3453 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0cycc; >> query: (?x268, 09fc83) <- people(?x268, ?x3868), people(?x268, ?x2916), location(?x3868, ?x739), award(?x3868, ?x757), risk_factors(?x6655, ?x268), award_winner(?x458, ?x2916), ?x6655 = 09d11 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qcr0 films 0cmdwwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 70.000 0.200 http://example.org/film/film_subject/films #18443-05g49 PRED entity: 05g49 PRED relation: draft PRED expected values: 02qw1zx => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 16): 05vsb7 (0.79 #178, 0.73 #162, 0.60 #17), 02qw1zx (0.61 #181, 0.52 #165, 0.40 #33), 02pq_rp (0.40 #33, 0.34 #546, 0.28 #535), 0f4vx0 (0.40 #33, 0.34 #546, 0.26 #586), 047dpm0 (0.40 #33, 0.34 #546, 0.26 #593), 025tn92 (0.40 #33, 0.34 #546, 0.26 #538), 02z6872 (0.40 #33, 0.34 #546, 0.26 #536), 02pq_x5 (0.40 #33, 0.34 #546, 0.25 #542), 09th87 (0.40 #33, 0.34 #546, 0.25 #92), 06439y (0.40 #33, 0.34 #546, 0.25 #97) >> Best rule #178 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 02896; 01ct6; 05g3b; 01y3c; 01xvb; 07l24; 01y49; 05g3v; 01y3v; 070xg; ... >> query: (?x5204, 05vsb7) <- school(?x5204, ?x5486), position_s(?x5204, ?x180), position(?x5204, ?x1114), major_field_of_study(?x5486, ?x254) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #181 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 02896; 01ct6; 05g3b; 01y3c; 01xvb; 07l24; 01y49; 05g3v; 01y3v; 070xg; ... *> query: (?x5204, 02qw1zx) <- school(?x5204, ?x5486), position_s(?x5204, ?x180), position(?x5204, ?x1114), major_field_of_study(?x5486, ?x254) *> conf = 0.61 ranks of expected_values: 2 EVAL 05g49 draft 02qw1zx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 58.000 0.788 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #18442-0g824 PRED entity: 0g824 PRED relation: artist! PRED expected values: 03gfvsz => 135 concepts (121 used for prediction) PRED predicted values (max 10 best out of 4): 03gfvsz (0.18 #13, 0.15 #86, 0.14 #74), 01fjfv (0.12 #75, 0.06 #32, 0.06 #81), 04rqd (0.09 #78, 0.06 #84, 0.05 #41), 04y652m (0.02 #170, 0.02 #510, 0.02 #471) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 01q_ph; 03f2_rc; 01wmxfs; 0pyg6; 016pns; 01svw8n; 0f7hc; 0c7xjb; 01vw37m; 06rgq; >> query: (?x6383, 03gfvsz) <- artist(?x2190, ?x6383), participant(?x6383, ?x827), nominated_for(?x6383, ?x787) >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g824 artist! 03gfvsz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 121.000 0.182 http://example.org/broadcast/content/artist #18441-0243cq PRED entity: 0243cq PRED relation: language PRED expected values: 02h40lc => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 46): 02h40lc (0.90 #179, 0.88 #1379, 0.88 #1499), 064_8sq (0.16 #199, 0.14 #498, 0.13 #1399), 04306rv (0.12 #1320, 0.11 #1442, 0.11 #242), 06b_j (0.12 #260, 0.06 #1460, 0.06 #1338), 06nm1 (0.11 #1027, 0.11 #248, 0.10 #1690), 02bjrlw (0.09 #1316, 0.08 #1438, 0.07 #477), 02hxcvy (0.08 #330, 0.04 #271, 0.02 #1593), 03_9r (0.08 #247, 0.06 #366, 0.06 #128), 03k50 (0.07 #305, 0.03 #1568, 0.03 #1688), 0653m (0.07 #12, 0.06 #71, 0.04 #3726) >> Best rule #179 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 0pvms; 01jnc_; >> query: (?x4313, 02h40lc) <- genre(?x4313, ?x307), ?x307 = 04t36, country(?x4313, ?x94), film_crew_role(?x4313, ?x281) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0243cq language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.905 http://example.org/film/film/language #18440-0n6ds PRED entity: 0n6ds PRED relation: genre PRED expected values: 02l7c8 => 97 concepts (78 used for prediction) PRED predicted values (max 10 best out of 84): 05p553 (0.92 #2991, 0.50 #123, 0.39 #1796), 018h2 (0.52 #6576, 0.52 #6217, 0.51 #9205), 0glj9q (0.52 #6576, 0.52 #6217, 0.51 #9205), 02kdv5l (0.49 #601, 0.33 #1317, 0.32 #1794), 02l7c8 (0.38 #254, 0.37 #8146, 0.32 #3003), 04xvlr (0.38 #239, 0.18 #4062, 0.17 #6098), 01hmnh (0.36 #855, 0.35 #496, 0.24 #375), 0lsxr (0.32 #608, 0.18 #1443, 0.18 #2279), 03k9fj (0.26 #1565, 0.25 #1804, 0.25 #849), 06cvj (0.25 #122, 0.19 #2990, 0.12 #241) >> Best rule #2991 for best value: >> intensional similarity = 3 >> extensional distance = 630 >> proper extension: 04svwx; >> query: (?x10157, 05p553) <- genre(?x10157, ?x6452), genre(?x4326, ?x6452), ?x4326 = 0fz3b1 >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #254 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0140g4; 08rr3p; 04jwly; 0drnwh; 01fwzk; 0yx_w; *> query: (?x10157, 02l7c8) <- film(?x1299, ?x10157), genre(?x10157, ?x6530), produced_by(?x10157, ?x1417), ?x6530 = 01lrrt *> conf = 0.38 ranks of expected_values: 5 EVAL 0n6ds genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 78.000 0.922 http://example.org/film/film/genre #18439-02kxg_ PRED entity: 02kxg_ PRED relation: combatants PRED expected values: 02vzc 05vz3zq => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 215): 0chghy (0.62 #649, 0.53 #776, 0.45 #1033), 088q1s (0.60 #92, 0.50 #347, 0.50 #220), 06bnz (0.59 #896, 0.43 #1024, 0.26 #127), 03rjj (0.59 #896, 0.30 #1023, 0.26 #127), 06v9sf (0.58 #1670, 0.52 #1280, 0.40 #3338), 03bxbql (0.58 #1670, 0.52 #1280, 0.40 #3338), 0bxjv (0.52 #1280, 0.40 #3338, 0.40 #3080), 0ctw_b (0.44 #534, 0.31 #1412, 0.29 #917), 0bq0p9 (0.40 #15, 0.33 #270, 0.33 #143), 01h3dj (0.40 #72, 0.33 #327, 0.33 #200) >> Best rule #649 for best value: >> intensional similarity = 8 >> extensional distance = 11 >> proper extension: 0d06vc; 07_nf; 01y998; 048n7; 018w0j; 03w6sj; 02h2z_; 01cpp0; 0j5ym; >> query: (?x10764, 0chghy) <- combatants(?x10764, ?x13069), combatants(?x10764, ?x94), entity_involved(?x10764, ?x10986), combatants(?x13069, ?x456), ?x94 = 09c7w0, organization(?x13069, ?x4230), profession(?x10986, ?x5805), ?x4230 = 04k4l >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #292 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 025rzfc; *> query: (?x10764, 02vzc) <- combatants(?x10764, ?x13069), combatants(?x10764, ?x3918), ?x13069 = 01rdm0, combatants(?x1536, ?x3918), combatants(?x1229, ?x3918), ?x1536 = 06c1y, capital(?x3918, ?x9660), ?x1229 = 059j2 *> conf = 0.33 ranks of expected_values: 18, 21 EVAL 02kxg_ combatants 05vz3zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 30.000 30.000 0.615 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 02kxg_ combatants 02vzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 30.000 30.000 0.615 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #18438-01k3s2 PRED entity: 01k3s2 PRED relation: institution! PRED expected values: 014mlp => 202 concepts (125 used for prediction) PRED predicted values (max 10 best out of 21): 02h4rq6 (0.82 #70, 0.75 #47, 0.75 #1101), 014mlp (0.81 #322, 0.79 #831, 0.78 #966), 019v9k (0.76 #834, 0.73 #76, 0.72 #969), 016t_3 (0.56 #320, 0.52 #297, 0.48 #142), 03bwzr4 (0.55 #1112, 0.50 #330, 0.48 #1224), 07s6fsf (0.45 #68, 0.44 #317, 0.40 #23), 04zx3q1 (0.44 #318, 0.38 #184, 0.36 #69), 02mjs7 (0.40 #27, 0.23 #321, 0.20 #298), 027f2w (0.38 #326, 0.36 #77, 0.32 #1108), 013zdg (0.31 #2639, 0.30 #2783, 0.29 #324) >> Best rule #70 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 017cy9; 0bwfn; 01dq0z; >> query: (?x4342, 02h4rq6) <- major_field_of_study(?x4342, ?x2605), contains(?x279, ?x4342), student(?x4342, ?x8256), ?x279 = 0d060g, organization(?x346, ?x4342) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #322 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 50 *> proper extension: 017j69; *> query: (?x4342, 014mlp) <- major_field_of_study(?x4342, ?x3995), student(?x4342, ?x13591), profession(?x13591, ?x319), place_of_birth(?x13591, ?x1411), ?x3995 = 0fdys *> conf = 0.81 ranks of expected_values: 2 EVAL 01k3s2 institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 202.000 125.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18437-09889g PRED entity: 09889g PRED relation: people! PRED expected values: 0x67 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 60): 0x67 (0.35 #3013, 0.35 #1011, 0.33 #1319), 041rx (0.19 #1390, 0.15 #3084, 0.15 #1929), 033tf_ (0.15 #1624, 0.14 #84, 0.13 #2240), 06v41q (0.14 #106, 0.14 #29, 0.04 #1030), 02g7sp (0.14 #95, 0.13 #172, 0.11 #249), 09vc4s (0.14 #86, 0.07 #2704, 0.06 #2396), 065b6q (0.14 #3, 0.04 #1620, 0.03 #2236), 038723 (0.14 #69, 0.03 #1609, 0.02 #993), 0xnvg (0.13 #706, 0.11 #2708, 0.11 #629), 02ctzb (0.11 #862, 0.05 #708, 0.04 #477) >> Best rule #3013 for best value: >> intensional similarity = 2 >> extensional distance = 179 >> proper extension: 01v27pl; >> query: (?x4960, 0x67) <- artists(?x3319, ?x4960), ?x3319 = 06j6l >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09889g people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.354 http://example.org/people/ethnicity/people #18436-01wv9xn PRED entity: 01wv9xn PRED relation: category PRED expected values: 08mbj5d => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.89 #37, 0.88 #41, 0.86 #58) >> Best rule #37 for best value: >> intensional similarity = 5 >> extensional distance = 178 >> proper extension: 016jll; >> query: (?x1684, 08mbj5d) <- origin(?x1684, ?x3301), artist(?x2149, ?x1684), award_winner(?x4912, ?x1684), contains(?x3301, ?x1369), citytown(?x1098, ?x3301) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wv9xn category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.889 http://example.org/common/topic/webpage./common/webpage/category #18435-03_wm6 PRED entity: 03_wm6 PRED relation: film_crew_role PRED expected values: 09vw2b7 01pvkk => 101 concepts (99 used for prediction) PRED predicted values (max 10 best out of 33): 09vw2b7 (0.76 #1594, 0.75 #418, 0.71 #1952), 02rh1dz (0.60 #146, 0.50 #43, 0.40 #352), 01pvkk (0.50 #353, 0.44 #318, 0.34 #867), 02ynfr (0.26 #871, 0.25 #426, 0.25 #48), 01xy5l_ (0.24 #1339, 0.22 #320, 0.20 #355), 0215hd (0.24 #1339, 0.15 #2580, 0.15 #1605), 089g0h (0.24 #1339, 0.14 #291, 0.13 #3286), 0d2b38 (0.24 #1339, 0.13 #983, 0.13 #3286), 02vs3x5 (0.24 #1339, 0.13 #3286, 0.12 #2999), 015h31 (0.20 #351, 0.20 #145, 0.13 #1383) >> Best rule #1594 for best value: >> intensional similarity = 10 >> extensional distance = 387 >> proper extension: 0gtsx8c; 07kb7vh; >> query: (?x6540, 09vw2b7) <- language(?x6540, ?x732), production_companies(?x6540, ?x1104), film_crew_role(?x6540, ?x1284), film_crew_role(?x6540, ?x468), ?x1284 = 0ch6mp2, language(?x6984, ?x732), language(?x6267, ?x732), ?x6984 = 02825kb, ?x468 = 02r96rf, ?x6267 = 03cp4cn >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 03_wm6 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 99.000 0.763 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 03_wm6 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 99.000 0.763 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18434-0h6sv PRED entity: 0h6sv PRED relation: music! PRED expected values: 0gkz3nz => 133 concepts (101 used for prediction) PRED predicted values (max 10 best out of 681): 0n6ds (0.25 #929), 01y9jr (0.25 #676), 026hxwx (0.25 #668), 01l_pn (0.25 #567), 0cfhfz (0.25 #300), 03kg2v (0.25 #291), 09z2b7 (0.25 #147), 034b6k (0.20 #1971, 0.02 #10100, 0.01 #13148), 064ndc (0.20 #1927, 0.02 #10056, 0.01 #13104), 0f61tk (0.20 #1854, 0.02 #9983, 0.01 #13031) >> Best rule #929 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01m7f5r; >> query: (?x13167, 0n6ds) <- nationality(?x13167, ?x1310), student(?x9239, ?x13167), profession(?x13167, ?x563), ?x9239 = 017rbx, role(?x13167, ?x316) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0h6sv music! 0gkz3nz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 101.000 0.250 http://example.org/film/film/music #18433-042fgh PRED entity: 042fgh PRED relation: honored_for! PRED expected values: 0275n3y => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 102): 0275n3y (0.60 #430, 0.34 #3418, 0.33 #3051), 09gkdln (0.12 #594, 0.07 #838, 0.05 #3524), 02yxh9 (0.12 #574, 0.07 #818, 0.03 #2038), 03tn9w (0.12 #568, 0.06 #1056, 0.06 #1422), 026kq4q (0.12 #525, 0.05 #3332, 0.04 #2599), 0fy6bh (0.11 #1380, 0.06 #1014, 0.03 #1868), 04n2r9h (0.10 #1500, 0.08 #2842, 0.06 #1012), 0bc773 (0.09 #654, 0.07 #898, 0.06 #1142), 09pnw5 (0.09 #698, 0.07 #942, 0.02 #2528), 09bymc (0.09 #715, 0.06 #1203, 0.05 #1569) >> Best rule #430 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0d_wms; >> query: (?x7425, 0275n3y) <- honored_for(?x1072, ?x7425), language(?x7425, ?x254), ?x254 = 02h40lc, genre(?x7425, ?x225), ?x1072 = 01_mdl >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042fgh honored_for! 0275n3y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18432-036nz PRED entity: 036nz PRED relation: major_field_of_study! PRED expected values: 01mpwj => 57 concepts (31 used for prediction) PRED predicted values (max 10 best out of 625): 02zd460 (0.78 #9017, 0.66 #10787, 0.64 #11962), 06pwq (0.68 #7070, 0.62 #8839, 0.59 #11784), 01w3v (0.61 #7073, 0.59 #8842, 0.51 #11787), 07szy (0.60 #2984, 0.57 #7101, 0.53 #8280), 01j_cy (0.60 #2983, 0.50 #2395, 0.50 #1806), 07w0v (0.60 #2962, 0.44 #588, 0.44 #8258), 01r3y2 (0.60 #3036, 0.35 #2351, 0.33 #1273), 0fnmz (0.60 #3054, 0.33 #1291, 0.33 #114), 0l2tk (0.60 #3027, 0.33 #1264, 0.33 #87), 09f2j (0.57 #7233, 0.54 #6643, 0.53 #8412) >> Best rule #9017 for best value: >> intensional similarity = 13 >> extensional distance = 30 >> proper extension: 03nfmq; 01tbp; 01bt59; 02cm61; >> query: (?x7979, 02zd460) <- major_field_of_study(?x5638, ?x7979), major_field_of_study(?x5035, ?x7979), taxonomy(?x7979, ?x939), contains(?x94, ?x5638), major_field_of_study(?x5638, ?x8221), major_field_of_study(?x5638, ?x6364), school_type(?x5035, ?x3092), ?x8221 = 037mh8, currency(?x5638, ?x170), contains(?x390, ?x5035), ?x6364 = 05qt0, institution(?x620, ?x5638), student(?x5638, ?x2239) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1882 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 01mkq; *> query: (?x7979, 01mpwj) <- major_field_of_study(?x5035, ?x7979), major_field_of_study(?x7979, ?x12158), major_field_of_study(?x7979, ?x2605), major_field_of_study(?x1368, ?x7979), major_field_of_study(?x4296, ?x12158), ?x5035 = 01bcwk, ?x1368 = 014mlp, major_field_of_study(?x865, ?x2605), major_field_of_study(?x122, ?x2605), ?x4296 = 07vyf, taxonomy(?x7979, ?x939) *> conf = 0.50 ranks of expected_values: 20 EVAL 036nz major_field_of_study! 01mpwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 57.000 31.000 0.781 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18431-017c87 PRED entity: 017c87 PRED relation: student! PRED expected values: 0hcr => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 25): 05qfh (0.33 #27, 0.01 #709, 0.01 #771), 02j62 (0.09 #148, 0.05 #334, 0.02 #768), 0fdys (0.07 #773, 0.01 #2138), 03g3w (0.06 #765, 0.01 #2068, 0.01 #1882), 02822 (0.05 #775, 0.03 #2140, 0.03 #713), 03qsdpk (0.04 #780, 0.02 #2145, 0.01 #1028), 0w7c (0.04 #786, 0.02 #538, 0.02 #600), 01zc2w (0.03 #668, 0.02 #792, 0.02 #482), 02vxn (0.02 #748, 0.02 #500, 0.01 #686), 037mh8 (0.02 #790) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09l3p; >> query: (?x8665, 05qfh) <- award_winner(?x1988, ?x8665), ?x1988 = 09k56b7, location(?x8665, ?x335) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 017c87 student! 0hcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 97.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #18430-05v1sb PRED entity: 05v1sb PRED relation: award PRED expected values: 0gq_v => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 238): 0gq_v (0.76 #2458, 0.73 #1646, 0.72 #29248), 0gr0m (0.33 #480, 0.20 #886, 0.17 #1292), 0gq9h (0.33 #1296, 0.20 #890, 0.15 #4138), 0gs9p (0.33 #1298, 0.20 #892, 0.13 #20717), 019f4v (0.33 #1285, 0.20 #879, 0.13 #20717), 040njc (0.33 #1226, 0.20 #820, 0.13 #20717), 09sb52 (0.30 #8162, 0.27 #6944, 0.26 #15882), 0gqy2 (0.20 #978, 0.17 #1384, 0.13 #20717), 0gr4k (0.20 #845, 0.17 #1251, 0.13 #20717), 0gr51 (0.20 #913, 0.17 #1319, 0.08 #2131) >> Best rule #2458 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 0520r2x; 05218gr; 072twv; 04gmp_z; 0dh73w; 07hhnl; 0cdf37; 07fzq3; 0fmqp6; 0fqjks; ... >> query: (?x4251, 0gq_v) <- award_winner(?x4251, ?x4423), award_nominee(?x4251, ?x200), film_art_direction_by(?x1308, ?x4251) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v1sb award 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18429-019pm_ PRED entity: 019pm_ PRED relation: participant PRED expected values: 02f8lw => 111 concepts (76 used for prediction) PRED predicted values (max 10 best out of 389): 01vs_v8 (0.82 #10847, 0.81 #23608, 0.81 #18501), 02f8lw (0.82 #10847, 0.81 #23608, 0.81 #18501), 0bksh (0.82 #10847, 0.81 #18501, 0.81 #20417), 01z7s_ (0.21 #3193, 0.19 #1277, 0.16 #1916), 0dvmd (0.21 #3193, 0.19 #1277, 0.16 #1916), 015pkc (0.21 #3193, 0.19 #1277, 0.16 #1916), 01wxyx1 (0.21 #3193, 0.19 #1277, 0.16 #1916), 01wk7b7 (0.21 #3193, 0.19 #1277, 0.16 #1916), 0170s4 (0.10 #639, 0.07 #10846, 0.05 #4469), 048lv (0.10 #639, 0.07 #10846, 0.05 #4469) >> Best rule #10847 for best value: >> intensional similarity = 3 >> extensional distance = 234 >> proper extension: 04nw9; 0c01c; 0hwbd; 016kkx; 02ts3h; 01kgxf; 01pj5q; 01933d; 05g7q; >> query: (?x2763, ?x248) <- film(?x2763, ?x351), award_winner(?x2763, ?x1384), participant(?x248, ?x2763) >> conf = 0.82 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 019pm_ participant 02f8lw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 76.000 0.816 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #18428-03dbds PRED entity: 03dbds PRED relation: profession PRED expected values: 02jknp => 85 concepts (66 used for prediction) PRED predicted values (max 10 best out of 69): 02jknp (0.90 #742, 0.88 #889, 0.86 #1626), 0cbd2 (0.48 #2360, 0.47 #2507, 0.45 #3389), 03gjzk (0.46 #1337, 0.45 #1190, 0.44 #1926), 0kyk (0.44 #1472, 0.34 #2382, 0.33 #28), 016z4k (0.44 #1472, 0.26 #4269, 0.26 #3975), 09jwl (0.38 #4282, 0.38 #3988, 0.37 #4429), 0nbcg (0.29 #4295, 0.29 #4001, 0.27 #4442), 0dz3r (0.26 #4267, 0.25 #3973, 0.25 #4414), 02krf9 (0.26 #907, 0.26 #760, 0.22 #1644), 018gz8 (0.25 #456, 0.25 #162, 0.23 #603) >> Best rule #742 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 03ys2f; 03ysmg; >> query: (?x7621, 02jknp) <- award_nominee(?x7621, ?x3138), film(?x7621, ?x5576), film_release_region(?x5576, ?x87) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03dbds profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 66.000 0.903 http://example.org/people/person/profession #18427-01t032 PRED entity: 01t032 PRED relation: dog_breed! PRED expected values: 02dtg 0f2r6 0f2w0 01sn3 0d35y 07bcn 0f2s6 => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 493): 06mxs (0.57 #21, 0.27 #32, 0.24 #19), 01n7q (0.57 #21, 0.19 #42, 0.04 #9), 035qy (0.57 #21, 0.15 #43), 0f8l9c (0.57 #21, 0.15 #43), 01qcx_ (0.57 #21), 0281s1 (0.57 #21), 0f2w0 (0.54 #31, 0.50 #35, 0.33 #25), 07bcn (0.54 #31, 0.50 #38, 0.33 #28), 0ygbf (0.54 #31, 0.19 #42), 0d9y6 (0.54 #31, 0.19 #42) >> Best rule #21 for best value: >> intensional similarity = 144 >> extensional distance = 1 >> proper extension: 0km5c; >> query: (?x5194, ?x1227) <- dog_breed(?x11246, ?x5194), dog_breed(?x9605, ?x5194), dog_breed(?x8468, ?x5194), dog_breed(?x8451, ?x5194), dog_breed(?x6960, ?x5194), dog_breed(?x6703, ?x5194), dog_breed(?x6555, ?x5194), dog_breed(?x6088, ?x5194), dog_breed(?x5719, ?x5194), dog_breed(?x5381, ?x5194), dog_breed(?x5193, ?x5194), dog_breed(?x4978, ?x5194), dog_breed(?x4499, ?x5194), dog_breed(?x4362, ?x5194), dog_breed(?x4356, ?x5194), dog_breed(?x4350, ?x5194), dog_breed(?x3983, ?x5194), dog_breed(?x3689, ?x5194), dog_breed(?x3501, ?x5194), dog_breed(?x3269, ?x5194), dog_breed(?x3125, ?x5194), dog_breed(?x3026, ?x5194), dog_breed(?x2879, ?x5194), dog_breed(?x2254, ?x5194), dog_breed(?x2017, ?x5194), dog_breed(?x1860, ?x5194), dog_breed(?x1523, ?x5194), ?x5193 = 02hrh0_, ?x4499 = 068p2, ?x1523 = 030qb3t, ?x6703 = 0f04v, ?x1860 = 01_d4, ?x5719 = 0f2rq, ?x3269 = 0vzm, ?x2017 = 04f_d, ?x2254 = 0dclg, ?x5381 = 0c_m3, ?x8468 = 0nbwf, place_of_birth(?x4387, ?x8451), country(?x8451, ?x94), ?x9605 = 02frhbc, state(?x3501, ?x2623), vacationer(?x3501, ?x8716), vacationer(?x3501, ?x7025), vacationer(?x3501, ?x4420), vacationer(?x3501, ?x3126), vacationer(?x3501, ?x1093), vacationer(?x3501, ?x521), source(?x8451, ?x958), place_of_birth(?x10371, ?x3501), place_of_birth(?x5925, ?x3501), place_of_birth(?x2367, ?x3501), place_of_birth(?x1881, ?x3501), ?x4978 = 05jbn, ?x4362 = 02j3w, ?x4356 = 06wxw, ?x4350 = 0_vn7, adjoins(?x11644, ?x8451), month(?x3501, ?x9905), month(?x3501, ?x6303), month(?x3501, ?x4925), month(?x3501, ?x4827), month(?x3501, ?x3270), month(?x3501, ?x2140), location(?x4476, ?x3501), ?x6088 = 0dyl9, award_nominee(?x527, ?x4420), jurisdiction_of_office(?x1195, ?x8451), ?x3983 = 0fr0t, ?x2879 = 0ftxw, origin(?x8185, ?x3501), nominated_for(?x4420, ?x3317), ?x6555 = 01snm, ?x94 = 09c7w0, time_zones(?x3501, ?x2674), ?x4925 = 0ll3, program(?x4420, ?x3905), award_nominee(?x382, ?x2367), award(?x4420, ?x724), ?x4827 = 03_ly, profession(?x10371, ?x1032), award_winner(?x462, ?x521), participant(?x1735, ?x521), ?x3270 = 05cw8, ?x1093 = 0lk90, ?x3026 = 0cv3w, ?x9905 = 028kb, contains(?x3501, ?x4904), film(?x10371, ?x365), award_nominee(?x1660, ?x521), award_winner(?x1372, ?x521), award(?x521, ?x401), participant(?x10371, ?x2422), ?x2140 = 040fb, ?x6960 = 071vr, languages(?x10371, ?x254), ?x3689 = 019fh, student(?x1681, ?x2367), profession(?x1881, ?x353), artist(?x2149, ?x4420), producer_type(?x4420, ?x632), vacationer(?x6959, ?x10371), type_of_union(?x8716, ?x566), produced_by(?x5644, ?x521), award_nominee(?x8716, ?x5788), award_nominee(?x8185, ?x4593), location(?x8716, ?x1227), award(?x8185, ?x2139), month(?x6494, ?x6303), month(?x3106, ?x6303), award_nominee(?x6772, ?x5925), award_nominee(?x5834, ?x5925), artists(?x3996, ?x8185), award(?x2367, ?x1105), ?x3125 = 0d6lp, ?x1195 = 0pqc5, nationality(?x4420, ?x512), ?x6772 = 073x6y, sibling(?x7025, ?x9374), nominated_for(?x5925, ?x2090), ?x3106 = 049d1, award(?x3126, ?x1232), participant(?x8716, ?x1117), category(?x8185, ?x134), gender(?x8185, ?x231), award_winner(?x2075, ?x8716), artists(?x302, ?x3126), ?x6494 = 02sn34, gender(?x8716, ?x514), ?x3996 = 02lnbg, nationality(?x3126, ?x279), artist(?x3265, ?x521), film(?x8716, ?x136), award_winner(?x5925, ?x157), ?x1105 = 07bdd_, award_winner(?x6297, ?x7025), award_nominee(?x1881, ?x1880), ?x11246 = 0fvyg, participant(?x1206, ?x3126), religion(?x5925, ?x8613), award_winner(?x10731, ?x1881), artist(?x6474, ?x8185), ?x5834 = 01z7s_, award(?x5925, ?x435) >> conf = 0.57 => this is the best rule for 6 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 159 *> extensional distance = 1 *> proper extension: 0km3f; *> query: (?x5194, ?x2087) <- dog_breed(?x8993, ?x5194), dog_breed(?x8468, ?x5194), dog_breed(?x6703, ?x5194), dog_breed(?x6555, ?x5194), dog_breed(?x6088, ?x5194), dog_breed(?x6084, ?x5194), dog_breed(?x5719, ?x5194), dog_breed(?x5381, ?x5194), dog_breed(?x5267, ?x5194), dog_breed(?x5193, ?x5194), dog_breed(?x4978, ?x5194), dog_breed(?x4499, ?x5194), dog_breed(?x4362, ?x5194), dog_breed(?x4350, ?x5194), dog_breed(?x3983, ?x5194), dog_breed(?x3501, ?x5194), dog_breed(?x3373, ?x5194), dog_breed(?x3269, ?x5194), dog_breed(?x3052, ?x5194), dog_breed(?x3026, ?x5194), dog_breed(?x2879, ?x5194), dog_breed(?x2740, ?x5194), dog_breed(?x2254, ?x5194), dog_breed(?x2017, ?x5194), dog_breed(?x1705, ?x5194), dog_breed(?x1523, ?x5194), dog_breed(?x659, ?x5194), dog_breed(?x108, ?x5194), ?x5193 = 02hrh0_, ?x4499 = 068p2, ?x1523 = 030qb3t, month(?x6703, ?x9905), month(?x6703, ?x4925), month(?x6703, ?x3107), month(?x6703, ?x2255), month(?x6703, ?x2140), location(?x5809, ?x6703), ?x1705 = 094jv, mode_of_transportation(?x6703, ?x6665), mode_of_transportation(?x6703, ?x4272), ?x3107 = 05lf_, ?x4272 = 07jdr, ?x2254 = 0dclg, ?x4978 = 05jbn, ?x8993 = 0fsb8, ?x3026 = 0cv3w, citytown(?x12452, ?x6703), citytown(?x7633, ?x6703), citytown(?x6404, ?x6703), ?x2255 = 040fv, profession(?x5809, ?x1032), origin(?x6854, ?x6703), award_winner(?x5592, ?x5809), award_winner(?x5809, ?x2061), award_winner(?x5809, ?x1918), award_winner(?x5809, ?x848), ?x1918 = 08m4c8, ?x2740 = 0f__1, adjoins(?x3794, ?x6703), film(?x5809, ?x7626), ?x1032 = 02hrh1q, ?x2061 = 09f0bj, ?x3501 = 0f2v0, award(?x6854, ?x724), participant(?x5809, ?x5485), ?x108 = 0rh6k, source(?x6703, ?x958), ?x5267 = 0d9jr, artist(?x2190, ?x6854), ?x3052 = 01cx_, ?x659 = 02cl1, ?x6084 = 0n1rj, dog_breed(?x6703, ?x1706), locations(?x12162, ?x6703), contact_category(?x7633, ?x3231), ?x2879 = 0ftxw, mode_of_transportation(?x13190, ?x6665), mode_of_transportation(?x10610, ?x6665), mode_of_transportation(?x9559, ?x6665), mode_of_transportation(?x8174, ?x6665), mode_of_transportation(?x6494, ?x6665), mode_of_transportation(?x6357, ?x6665), mode_of_transportation(?x5168, ?x6665), mode_of_transportation(?x4627, ?x6665), mode_of_transportation(?x2474, ?x6665), mode_of_transportation(?x2316, ?x6665), mode_of_transportation(?x1649, ?x6665), mode_of_transportation(?x863, ?x6665), company(?x265, ?x7633), artists(?x1572, ?x6854), ?x5719 = 0f2rq, ?x3269 = 0vzm, ?x9905 = 028kb, teams(?x6703, ?x7766), ?x8174 = 01lfy, ?x3983 = 0fr0t, ?x2474 = 052p7, ?x3373 = 0ply0, ?x13190 = 0mbf4, ?x4362 = 02j3w, ?x6088 = 0dyl9, religion(?x5809, ?x1985), gender(?x5809, ?x514), actor(?x5808, ?x5809), ?x2316 = 06t2t, ?x2140 = 040fb, ?x6494 = 02sn34, place_of_birth(?x9269, ?x2017), place_of_birth(?x8050, ?x2017), place_of_birth(?x6025, ?x2017), place_of_birth(?x192, ?x2017), nationality(?x5809, ?x94), ?x4350 = 0_vn7, locations(?x8527, ?x2017), ?x4925 = 0ll3, ?x5381 = 0c_m3, ?x5168 = 06mxs, citytown(?x7177, ?x2017), ?x9559 = 07dfk, ?x8468 = 0nbwf, ?x6357 = 02cft, ?x4627 = 05qtj, gender(?x9269, ?x231), actor(?x1419, ?x9269), ?x1419 = 02vw1w2, ?x1572 = 06by7, ?x1706 = 0km5c, team(?x8527, ?x10846), team(?x8527, ?x9576), team(?x8527, ?x3798), ?x6555 = 01snm, list(?x12452, ?x7472), locations(?x8527, ?x5259), locations(?x8527, ?x2087), ?x3798 = 02ptzz0, ?x1649 = 01f62, award_winner(?x8635, ?x8050), ?x9576 = 02qk2d5, ?x10610 = 03902, ?x5259 = 0d9y6, ?x10846 = 02pzy52, place_of_death(?x3563, ?x2017), nominated_for(?x192, ?x876), ?x848 = 034x61, award_nominee(?x192, ?x286), film(?x192, ?x240), service_location(?x6404, ?x279), award_winner(?x5996, ?x5809), service_language(?x6404, ?x254), people(?x9428, ?x8050), award(?x6025, ?x1079), ?x286 = 014zcr, category(?x6854, ?x134), ?x3231 = 014dgf, award_winner(?x747, ?x192), award_winner(?x193, ?x192), jurisdiction_of_office(?x1195, ?x2017), award_nominee(?x1324, ?x192), ?x863 = 0fhp9 *> conf = 0.54 ranks of expected_values: 7, 8, 22, 23, 24, 25, 28 EVAL 01t032 dog_breed! 0f2s6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 5.000 5.000 0.571 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01t032 dog_breed! 07bcn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 5.000 5.000 0.571 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01t032 dog_breed! 0d35y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 5.000 5.000 0.571 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01t032 dog_breed! 01sn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 5.000 5.000 0.571 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01t032 dog_breed! 0f2w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 5.000 5.000 0.571 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01t032 dog_breed! 0f2r6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 5.000 5.000 0.571 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01t032 dog_breed! 02dtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 5.000 5.000 0.571 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #18426-01cwkq PRED entity: 01cwkq PRED relation: award_nominee PRED expected values: 02bkdn => 115 concepts (64 used for prediction) PRED predicted values (max 10 best out of 920): 0gx_p (0.82 #39718, 0.82 #9345, 0.82 #149503), 02bkdn (0.82 #39718, 0.82 #9345, 0.81 #44389), 01cwkq (0.57 #4554, 0.50 #6890, 0.50 #2217), 09h4b5 (0.25 #1783, 0.24 #149504, 0.14 #4120), 01j5ws (0.25 #679, 0.24 #149504, 0.04 #123804), 04w391 (0.25 #2337, 0.11 #14017, 0.11 #21029), 02qgyv (0.24 #149504, 0.20 #5175, 0.14 #2839), 0q9kd (0.24 #149504, 0.14 #2341, 0.14 #98111), 011zd3 (0.24 #149504, 0.14 #2830, 0.14 #98111), 030xr_ (0.24 #149504, 0.14 #4328, 0.14 #98111) >> Best rule #39718 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 016qtt; 05ty4m; 01vrx3g; 05tk7y; 0157m; 01dw9z; 073749; 01wbsdz; >> query: (?x11198, ?x1871) <- award_nominee(?x1871, ?x11198), film(?x11198, ?x463), category(?x11198, ?x134) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01cwkq award_nominee 02bkdn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 64.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18425-0gqwc PRED entity: 0gqwc PRED relation: nominated_for PRED expected values: 0416y94 02rv_dz 0dr_4 09tqkv2 0bm2g 021y7yw 0qm9n 05vxdh 0kbhf 02nczh 01gvpz 04vq33 => 61 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1417): 0_816 (0.79 #10554, 0.47 #12001, 0.41 #14895), 02nczh (0.77 #23142, 0.77 #28928, 0.74 #13015), 0yxm1 (0.77 #23142, 0.77 #28928, 0.74 #13015), 0h95927 (0.77 #23142, 0.77 #28928, 0.74 #13015), 0qm9n (0.77 #23142, 0.77 #28928, 0.74 #13015), 0294mx (0.77 #23142, 0.77 #28928, 0.74 #13015), 0cy__l (0.77 #23142, 0.77 #28928, 0.74 #13015), 03cw411 (0.77 #23142, 0.77 #28928, 0.74 #13015), 0kcn7 (0.77 #23142, 0.77 #28928, 0.74 #13015), 0c5dd (0.77 #23142, 0.77 #28928, 0.74 #13015) >> Best rule #10554 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 02rdxsh; >> query: (?x1245, 0_816) <- nominated_for(?x1245, ?x7765), nominated_for(?x1245, ?x3471), ?x7765 = 0hvvf, film(?x450, ?x3471), films(?x3530, ?x3471) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #23142 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 119 *> proper extension: 02qyp19; 0gqng; 027dtxw; 02r0csl; 040njc; 03hkv_r; 0bp_b2; 099jhq; 0gkvb7; 02p_7cr; ... *> query: (?x1245, ?x1133) <- award(?x241, ?x1245), ceremony(?x1245, ?x78), nominated_for(?x1245, ?x144), award(?x1133, ?x1245) *> conf = 0.77 ranks of expected_values: 2, 5, 39, 63, 78, 109, 132, 207, 220, 299, 374, 391 EVAL 0gqwc nominated_for 04vq33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 01gvpz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 02nczh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 0kbhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 05vxdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 0qm9n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 021y7yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 0bm2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 09tqkv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 0dr_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 02rv_dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqwc nominated_for 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 32.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18424-05sb1 PRED entity: 05sb1 PRED relation: country! PRED expected values: 0xnt5 => 146 concepts (92 used for prediction) PRED predicted values (max 10 best out of 689): 075mb (0.49 #613, 0.38 #8575, 0.37 #3675), 01hf6 (0.49 #613, 0.38 #8575, 0.37 #3675), 065zr (0.49 #613, 0.38 #8575, 0.37 #3675), 05hwn (0.49 #613, 0.38 #8575, 0.37 #3675), 0xnt5 (0.49 #613, 0.27 #31240, 0.25 #3063), 0n84k (0.27 #31240, 0.25 #3063, 0.22 #17761), 023vwt (0.27 #31240, 0.25 #3063, 0.22 #17761), 03x83_ (0.25 #3063, 0.22 #17762, 0.22 #17761), 05ftw3 (0.25 #3063, 0.22 #17762, 0.22 #17761), 01yf40 (0.25 #3063, 0.22 #17762, 0.22 #17761) >> Best rule #613 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 06mx8; >> query: (?x2236, ?x5967) <- contains(?x2236, ?x5967), contains(?x5967, ?x5968), region(?x2627, ?x2236) >> conf = 0.49 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5 EVAL 05sb1 country! 0xnt5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 146.000 92.000 0.494 http://example.org/base/biblioness/bibs_location/country #18423-04d5v9 PRED entity: 04d5v9 PRED relation: school_type PRED expected values: 05pcjw => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 16): 05jxkf (0.61 #211, 0.52 #165, 0.52 #349), 05pcjw (0.58 #47, 0.49 #93, 0.44 #70), 07tf8 (0.18 #215, 0.13 #169, 0.13 #353), 01_9fk (0.15 #163, 0.15 #209, 0.12 #255), 0bwd5 (0.08 #64, 0.08 #87, 0.06 #133), 01jlsn (0.08 #62, 0.02 #706, 0.02 #729), 01_srz (0.06 #256, 0.06 #187, 0.06 #302), 04qbv (0.05 #199, 0.02 #107, 0.02 #268), 01y64 (0.04 #195, 0.04 #103, 0.04 #149), 02p0qmm (0.04 #239, 0.03 #699, 0.03 #722) >> Best rule #211 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 08qnnv; 0gl5_; >> query: (?x3665, 05jxkf) <- institution(?x3437, ?x3665), ?x3437 = 02_xgp2, school_type(?x3665, ?x3205) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 02cttt; 027kp3; 0bwfn; 01bk1y; 04_j5s; *> query: (?x3665, 05pcjw) <- contains(?x335, ?x3665), institution(?x3437, ?x3665), ?x335 = 059rby, ?x3437 = 02_xgp2 *> conf = 0.58 ranks of expected_values: 2 EVAL 04d5v9 school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 93.000 0.606 http://example.org/education/educational_institution/school_type #18422-04dsnp PRED entity: 04dsnp PRED relation: featured_film_locations PRED expected values: 05qtj => 112 concepts (104 used for prediction) PRED predicted values (max 10 best out of 115): 0f94t (0.14 #248, 0.09 #1165, 0.09 #936), 01jr6 (0.13 #1910, 0.08 #2597, 0.08 #2827), 0h7h6 (0.12 #3018, 0.10 #727, 0.10 #2330), 0rh6k (0.10 #2064, 0.09 #5274, 0.08 #1377), 0r0m6 (0.10 #771, 0.03 #3062, 0.02 #3520), 081m_ (0.10 #837, 0.03 #3128, 0.02 #3586), 0d6lp (0.09 #983, 0.07 #1670, 0.03 #13379), 080h2 (0.09 #5293, 0.07 #6672, 0.07 #1854), 01_d4 (0.08 #1418, 0.05 #12438, 0.04 #13356), 02jx1 (0.07 #1865, 0.05 #2094, 0.04 #2552) >> Best rule #248 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0dzlbx; 01chpn; 043mk4y; >> query: (?x1015, 0f94t) <- honored_for(?x762, ?x1015), titles(?x1014, ?x1015), person(?x1015, ?x1620), executive_produced_by(?x1015, ?x4060) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #3298 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 03y3bp7; *> query: (?x1015, 05qtj) <- nominated_for(?x3593, ?x1015), film(?x3593, ?x6043), student(?x10046, ?x3593), gender(?x3593, ?x231) *> conf = 0.04 ranks of expected_values: 33 EVAL 04dsnp featured_film_locations 05qtj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 112.000 104.000 0.143 http://example.org/film/film/featured_film_locations #18421-0300ml PRED entity: 0300ml PRED relation: genre PRED expected values: 07s9rl0 => 92 concepts (87 used for prediction) PRED predicted values (max 10 best out of 127): 07s9rl0 (0.95 #669, 0.95 #2844, 0.91 #919), 05p553 (0.84 #1763, 0.77 #2263, 0.74 #1257), 01z4y (0.54 #1776, 0.52 #1270, 0.49 #2276), 01hmnh (0.41 #434, 0.16 #2274, 0.15 #2859), 0c4xc (0.38 #1295, 0.37 #1801, 0.34 #2301), 01jfsb (0.37 #430, 0.25 #262, 0.25 #12), 02n4kr (0.33 #427, 0.30 #92, 0.25 #259), 01htzx (0.33 #435, 0.28 #935, 0.21 #351), 06n90 (0.33 #431, 0.23 #2856, 0.22 #931), 0djd22 (0.29 #189, 0.21 #356, 0.12 #690) >> Best rule #669 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 0n2bh; 099pks; >> query: (?x12324, 07s9rl0) <- producer_type(?x12324, ?x632), genre(?x12324, ?x604), genre(?x7204, ?x604), ?x7204 = 0280061 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0300ml genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 87.000 0.950 http://example.org/tv/tv_program/genre #18420-04k25 PRED entity: 04k25 PRED relation: award PRED expected values: 0gqz2 => 118 concepts (99 used for prediction) PRED predicted values (max 10 best out of 371): 02qt02v (0.81 #3232, 0.81 #3231, 0.81 #2827), 02wwsh8 (0.81 #3232, 0.81 #3231, 0.81 #2827), 0gr0m (0.54 #4111, 0.54 #3708, 0.52 #3305), 0gs9p (0.47 #6939, 0.41 #7343, 0.37 #7746), 054ks3 (0.44 #2968, 0.42 #2564, 0.20 #11435), 019f4v (0.43 #6926, 0.39 #7330, 0.35 #7733), 025m8l (0.42 #2541, 0.42 #2945, 0.11 #11412), 0gqz2 (0.39 #2907, 0.38 #2503, 0.19 #11374), 040njc (0.39 #6868, 0.35 #7272, 0.34 #7675), 01by1l (0.37 #2938, 0.37 #2534, 0.33 #11405) >> Best rule #3232 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 081wh1; >> query: (?x2671, ?x8313) <- award(?x2671, ?x4481), award_winner(?x8313, ?x2671), ?x4481 = 02x17c2 >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #2907 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 081wh1; *> query: (?x2671, 0gqz2) <- award(?x2671, ?x4481), award_winner(?x3233, ?x2671), ?x4481 = 02x17c2 *> conf = 0.39 ranks of expected_values: 8 EVAL 04k25 award 0gqz2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 118.000 99.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18419-049fgvm PRED entity: 049fgvm PRED relation: people! PRED expected values: 01g7zj => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 47): 041rx (0.24 #1853, 0.23 #4, 0.19 #2238), 0x67 (0.20 #395, 0.20 #241, 0.19 #780), 033tf_ (0.17 #3167, 0.12 #1625, 0.10 #469), 0xnvg (0.11 #783, 0.10 #475, 0.09 #3173), 02w7gg (0.10 #2466, 0.09 #464, 0.09 #927), 07hwkr (0.10 #2466, 0.06 #937, 0.06 #628), 013xrm (0.08 #1869, 0.06 #2794, 0.06 #2408), 02ctzb (0.08 #400, 0.05 #1248, 0.05 #1017), 013b6_ (0.08 #53, 0.07 #1902, 0.05 #1286), 048z7l (0.08 #40, 0.07 #348, 0.05 #1273) >> Best rule #1853 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 0j3v; 0dzkq; 099bk; 07c37; 02ln1; 07h1q; 02wh0; 047g6; 015n8; 01h2_6; >> query: (?x6693, 041rx) <- influenced_by(?x6693, ?x986), influenced_by(?x2143, ?x6693), religion(?x6693, ?x1985) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #3212 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 360 *> proper extension: 0j5b8; 0cl_m; 03d6q; 04pwg; 01l3j; *> query: (?x6693, 01g7zj) <- religion(?x6693, ?x1985), ?x1985 = 0c8wxp *> conf = 0.01 ranks of expected_values: 37 EVAL 049fgvm people! 01g7zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 101.000 101.000 0.241 http://example.org/people/ethnicity/people #18418-01ptt7 PRED entity: 01ptt7 PRED relation: fraternities_and_sororities PRED expected values: 0325pb => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 0325pb (0.67 #7, 0.54 #11, 0.41 #19), 04m8fy (0.10 #6, 0.05 #10, 0.04 #28) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 049dk; 03tw2s; 01rc6f; >> query: (?x2175, 0325pb) <- major_field_of_study(?x2175, ?x2014), school(?x4171, ?x2175), fraternities_and_sororities(?x2175, ?x4348), currency(?x2175, ?x170) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ptt7 fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.673 http://example.org/education/university/fraternities_and_sororities #18417-016ypb PRED entity: 016ypb PRED relation: award_winner! PRED expected values: 099tbz => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 214): 05ztrmj (0.37 #21556, 0.36 #35792, 0.31 #31044), 02z13jg (0.33 #480, 0.02 #5221, 0.02 #9101), 027cyf7 (0.17 #637, 0.08 #7759, 0.08 #28457), 0f4x7 (0.17 #462, 0.08 #7759, 0.05 #5203), 02w9sd7 (0.17 #598, 0.08 #7759, 0.03 #9219), 09qvc0 (0.17 #471, 0.08 #7759, 0.01 #2626), 027986c (0.17 #479, 0.08 #28457, 0.03 #9100), 09cm54 (0.17 #527, 0.03 #5268, 0.03 #9148), 0ck27z (0.11 #6557, 0.10 #3540, 0.10 #9144), 099tbz (0.09 #2212, 0.09 #2643, 0.08 #7759) >> Best rule #21556 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 04f525m; 01czx; 016fmf; 0b79gfg; 0134s5; 02lbrd; 03q8ch; 0d193h; 0g_g2; 0134tg; ... >> query: (?x2922, ?x704) <- award_winner(?x1193, ?x2922), award(?x2922, ?x704) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #2212 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 423 *> proper extension: 01nrgq; 024jwt; 02js_6; *> query: (?x2922, 099tbz) <- student(?x8357, ?x2922), film(?x2922, ?x1173), award_winner(?x2922, ?x628) *> conf = 0.09 ranks of expected_values: 10 EVAL 016ypb award_winner! 099tbz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 100.000 100.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner #18416-065y4w7 PRED entity: 065y4w7 PRED relation: institution! PRED expected values: 07s6fsf 022h5x => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 11): 07s6fsf (0.50 #230, 0.50 #181, 0.46 #446), 022h5x (0.23 #418, 0.23 #454, 0.22 #430), 0bjrnt (0.21 #268, 0.19 #292, 0.18 #256), 02cq61 (0.18 #32, 0.14 #870, 0.10 #273), 02mjs7 (0.18 #267, 0.15 #182, 0.14 #870), 02m4yg (0.14 #870, 0.09 #248, 0.08 #586), 01ysy9 (0.14 #870, 0.09 #590, 0.05 #252), 071tyz (0.14 #870, 0.08 #269, 0.07 #184), 01gkg3 (0.05 #102, 0.03 #174, 0.01 #658), 01kxxq (0.03 #880, 0.02 #589, 0.02 #1152) >> Best rule #230 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 02t4yc; >> query: (?x735, 07s6fsf) <- school(?x580, ?x735), student(?x735, ?x1387), story_by(?x626, ?x1387) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 065y4w7 institution! 022h5x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 065y4w7 institution! 07s6fsf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18415-0163zw PRED entity: 0163zw PRED relation: artists PRED expected values: 0m19t => 74 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1249): 06br6t (0.61 #15013, 0.21 #19362, 0.15 #30229), 01k3qj (0.50 #1773, 0.43 #3942, 0.33 #689), 01ww_vs (0.50 #2093, 0.43 #4262, 0.33 #1009), 016t0h (0.50 #3190, 0.43 #4273, 0.33 #1020), 01323p (0.50 #2870, 0.43 #3953, 0.33 #700), 01x1cn2 (0.50 #2367, 0.33 #4536, 0.33 #197), 048xh (0.50 #1760, 0.33 #676, 0.29 #3929), 01dq9q (0.50 #2841, 0.33 #671, 0.25 #1755), 01w5n51 (0.48 #14812, 0.43 #3947, 0.33 #694), 01w8n89 (0.47 #30737, 0.36 #7913, 0.33 #5742) >> Best rule #15013 for best value: >> intensional similarity = 9 >> extensional distance = 21 >> proper extension: 025tm81; 03ckfl9; >> query: (?x12407, 06br6t) <- artists(?x12407, ?x9706), parent_genre(?x9248, ?x12407), group(?x2798, ?x9706), group(?x1148, ?x9706), group(?x745, ?x9706), ?x1148 = 02qjv, instrumentalists(?x2798, ?x211), role(?x745, ?x75), role(?x2798, ?x212) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #14146 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 21 *> proper extension: 025tm81; 03ckfl9; *> query: (?x12407, 0m19t) <- artists(?x12407, ?x9706), parent_genre(?x9248, ?x12407), group(?x2798, ?x9706), group(?x1148, ?x9706), group(?x745, ?x9706), ?x1148 = 02qjv, instrumentalists(?x2798, ?x211), role(?x745, ?x75), role(?x2798, ?x212) *> conf = 0.35 ranks of expected_values: 25 EVAL 0163zw artists 0m19t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 74.000 29.000 0.609 http://example.org/music/genre/artists #18414-04gknr PRED entity: 04gknr PRED relation: film_crew_role PRED expected values: 02r96rf 09vw2b7 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 29): 09vw2b7 (0.77 #358, 0.68 #534, 0.67 #781), 02r96rf (0.73 #3, 0.71 #214, 0.70 #354), 01vx2h (0.49 #362, 0.41 #538, 0.39 #222), 0215hd (0.22 #88, 0.18 #18, 0.17 #369), 02rh1dz (0.22 #221, 0.18 #361, 0.16 #537), 02ynfr (0.19 #366, 0.18 #15, 0.18 #1530), 0d2b38 (0.18 #25, 0.18 #95, 0.14 #376), 01xy5l_ (0.18 #83, 0.12 #1246, 0.11 #2198), 02_n3z (0.18 #71, 0.11 #282, 0.09 #2186), 089g0h (0.14 #54, 0.14 #1252, 0.13 #2204) >> Best rule #358 for best value: >> intensional similarity = 4 >> extensional distance = 270 >> proper extension: 09sh8k; 0czyxs; 0bth54; 0164qt; 0bwfwpj; 09q5w2; 04vr_f; 0c0nhgv; 044g_k; 0340hj; ... >> query: (?x924, 09vw2b7) <- film_release_distribution_medium(?x924, ?x81), film_crew_role(?x924, ?x2095), currency(?x924, ?x170), ?x2095 = 0dxtw >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04gknr film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.768 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 04gknr film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.768 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18413-09v38qj PRED entity: 09v38qj PRED relation: actor PRED expected values: 029q_y => 65 concepts (35 used for prediction) PRED predicted values (max 10 best out of 786): 02p7_k (0.20 #292, 0.10 #16768, 0.10 #4019), 01vyv9 (0.20 #368, 0.10 #16768, 0.10 #4095), 0219q (0.20 #335, 0.10 #4062, 0.03 #4997), 04qt29 (0.20 #1626, 0.08 #2557, 0.04 #8147), 044mvs (0.15 #2635, 0.10 #1704, 0.06 #5432), 048hf (0.15 #2479, 0.06 #5276, 0.03 #8069), 016ks_ (0.10 #360, 0.10 #16768, 0.09 #10249), 021vwt (0.10 #132, 0.10 #16768, 0.09 #10249), 0hvb2 (0.10 #142, 0.10 #3869, 0.02 #5735), 02zfdp (0.10 #694, 0.10 #4421, 0.02 #6287) >> Best rule #292 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0147w8; >> query: (?x11547, 02p7_k) <- actor(?x11547, ?x4137), award_nominee(?x4137, ?x525), award_nominee(?x4137, ?x450), ?x525 = 017149, profession(?x4137, ?x1032), ?x450 = 0z4s >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #8048 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 67 *> proper extension: 05z43v; 0jq2r; 06qxh; 03cf9ly; 0300ml; *> query: (?x11547, 029q_y) <- titles(?x2008, ?x11547), country_of_origin(?x11547, ?x94), genre(?x11547, ?x600), program(?x13395, ?x11547), genre(?x2345, ?x600), ?x2345 = 0c_j9x *> conf = 0.01 ranks of expected_values: 623 EVAL 09v38qj actor 029q_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 35.000 0.200 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #18412-01nn79 PRED entity: 01nn79 PRED relation: service_language PRED expected values: 064_8sq => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 18): 064_8sq (0.41 #209, 0.35 #127, 0.25 #27), 03_9r (0.40 #40, 0.35 #127, 0.25 #22), 04306rv (0.35 #127, 0.32 #202, 0.25 #20), 06nm1 (0.35 #127, 0.26 #205, 0.25 #23), 06b_j (0.35 #127, 0.25 #28, 0.25 #744), 02hwhyv (0.35 #127, 0.25 #32, 0.25 #744), 097kp (0.35 #127, 0.25 #36, 0.25 #744), 0459q4 (0.35 #127, 0.25 #34, 0.25 #744), 03k50 (0.35 #127, 0.25 #21, 0.25 #744), 05zjd (0.35 #127, 0.25 #744, 0.17 #1592) >> Best rule #209 for best value: >> intensional similarity = 6 >> extensional distance = 32 >> proper extension: 02vk52z; 0p4wb; 07y2s; 02slt7; 01zpmq; 0dmtp; 05w3y; 01_qgp; 013fn; 0226k3; >> query: (?x8121, 064_8sq) <- service_language(?x8121, ?x254), service_location(?x8121, ?x550), country(?x7195, ?x550), film_release_region(?x2394, ?x550), administrative_parent(?x550, ?x551), ?x2394 = 0661ql3 >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nn79 service_language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.412 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #18411-01trtc PRED entity: 01trtc PRED relation: artist PRED expected values: 0147dk 01vrz41 01vs_v8 03xl77 01w9wwg 0838y 01q_wyj => 81 concepts (63 used for prediction) PRED predicted values (max 10 best out of 942): 0g824 (0.40 #1204, 0.33 #419, 0.29 #3556), 01q99h (0.40 #1191, 0.26 #4327, 0.25 #1975), 01wbsdz (0.40 #1174, 0.25 #1958, 0.24 #3526), 017g21 (0.40 #1270, 0.25 #2054, 0.21 #4406), 01vs4ff (0.40 #1247, 0.25 #2031, 0.21 #4383), 01vrkdt (0.40 #1036, 0.25 #1820, 0.18 #3388), 01wj18h (0.40 #981, 0.18 #3333, 0.17 #1765), 01s560x (0.40 #1492, 0.18 #3844, 0.17 #2276), 046p9 (0.40 #1342, 0.18 #3694, 0.17 #2126), 03g5jw (0.40 #864, 0.18 #3216, 0.17 #1648) >> Best rule #1204 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 015_1q; 043g7l; 02p4jf0; >> query: (?x10426, 0g824) <- artist(?x10426, ?x9070), artist(?x10426, ?x6576), artist(?x10426, ?x1001), ?x6576 = 01vw917, award_nominee(?x7027, ?x1001), profession(?x1001, ?x220), location(?x9070, ?x1719) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1187 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 015_1q; 043g7l; 02p4jf0; *> query: (?x10426, 01w9wwg) <- artist(?x10426, ?x9070), artist(?x10426, ?x6576), artist(?x10426, ?x1001), ?x6576 = 01vw917, award_nominee(?x7027, ?x1001), profession(?x1001, ?x220), location(?x9070, ?x1719) *> conf = 0.20 ranks of expected_values: 77, 162, 286, 370, 602, 630, 904 EVAL 01trtc artist 01q_wyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 81.000 63.000 0.400 http://example.org/music/record_label/artist EVAL 01trtc artist 0838y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 81.000 63.000 0.400 http://example.org/music/record_label/artist EVAL 01trtc artist 01w9wwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 81.000 63.000 0.400 http://example.org/music/record_label/artist EVAL 01trtc artist 03xl77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 81.000 63.000 0.400 http://example.org/music/record_label/artist EVAL 01trtc artist 01vs_v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 81.000 63.000 0.400 http://example.org/music/record_label/artist EVAL 01trtc artist 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 81.000 63.000 0.400 http://example.org/music/record_label/artist EVAL 01trtc artist 0147dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 81.000 63.000 0.400 http://example.org/music/record_label/artist #18410-036jp8 PRED entity: 036jp8 PRED relation: place_of_burial PRED expected values: 018mmj => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 12): 0lbp_ (0.50 #109, 0.50 #16, 0.12 #141), 018mm4 (0.21 #512, 0.16 #918, 0.16 #481), 018mmw (0.12 #142, 0.09 #204, 0.08 #237), 018mmj (0.11 #167, 0.09 #1170, 0.06 #951), 01n7q (0.11 #160, 0.06 #257, 0.05 #288), 0r04p (0.06 #266, 0.03 #547, 0.03 #640), 018mlg (0.04 #371, 0.03 #433, 0.03 #464), 0nb1s (0.04 #377, 0.03 #502, 0.03 #564), 0bvqq (0.02 #827, 0.02 #1582, 0.01 #1900), 01f38z (0.02 #1723, 0.01 #2045, 0.01 #2173) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02j4sk; >> query: (?x6336, 0lbp_) <- celebrities_impersonated(?x3649, ?x6336), profession(?x6336, ?x319), sibling(?x6336, ?x10219), nationality(?x6336, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #167 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01t9qj_; *> query: (?x6336, 018mmj) <- celebrities_impersonated(?x3649, ?x6336), gender(?x6336, ?x231), people(?x9771, ?x6336), inductee(?x11145, ?x6336) *> conf = 0.11 ranks of expected_values: 4 EVAL 036jp8 place_of_burial 018mmj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 178.000 178.000 0.500 http://example.org/people/deceased_person/place_of_burial #18409-09prnq PRED entity: 09prnq PRED relation: artists! PRED expected values: 08jyyk 05rwpb => 89 concepts (38 used for prediction) PRED predicted values (max 10 best out of 286): 08jyyk (0.67 #66, 0.25 #5331, 0.24 #1302), 03lty (0.56 #2503, 0.55 #644, 0.54 #3125), 0cx7f (0.50 #137, 0.34 #4782, 0.31 #5402), 064t9 (0.48 #940, 0.47 #5588, 0.44 #9616), 016jny (0.45 #721, 0.33 #103, 0.28 #3820), 0155w (0.40 #4441, 0.34 #3822, 0.28 #5989), 01lyv (0.36 #6847, 0.27 #2197, 0.25 #2819), 02qm5j (0.33 #153, 0.12 #462, 0.10 #1080), 029fbr (0.33 #182, 0.12 #491, 0.10 #1109), 052smk (0.33 #257, 0.12 #566, 0.10 #1184) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 07yg2; >> query: (?x2242, 08jyyk) <- artists(?x2809, ?x2242), artist(?x12666, ?x2242), ?x12666 = 02y21l, ?x2809 = 05w3f >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 268 EVAL 09prnq artists! 05rwpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 89.000 38.000 0.667 http://example.org/music/genre/artists EVAL 09prnq artists! 08jyyk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 38.000 0.667 http://example.org/music/genre/artists #18408-04g61 PRED entity: 04g61 PRED relation: country! PRED expected values: 06gjk9 => 138 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1768): 01m13b (0.36 #5267, 0.33 #27458, 0.33 #1853), 023g6w (0.36 #6524, 0.33 #3110, 0.21 #28715), 02pjc1h (0.27 #5336, 0.22 #1922, 0.14 #10457), 04jkpgv (0.22 #1935, 0.20 #3642, 0.18 #5349), 0fpkhkz (0.22 #1932, 0.18 #5346, 0.10 #27537), 064n1pz (0.22 #2025, 0.18 #5439, 0.10 #27630), 0cnztc4 (0.22 #1898, 0.18 #5312, 0.10 #8726), 07l4zhn (0.20 #4335, 0.09 #21405, 0.08 #26526), 049mql (0.18 #7471, 0.18 #5764, 0.17 #12592), 03prz_ (0.18 #7774, 0.18 #6067, 0.13 #28258) >> Best rule #5267 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 09gnn; >> query: (?x5274, 01m13b) <- organizations_founded(?x5274, ?x1062), organization(?x6435, ?x1062), ?x6435 = 0166b >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04g61 country! 06gjk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 93.000 0.364 http://example.org/film/film/country #18407-01jmyj PRED entity: 01jmyj PRED relation: film! PRED expected values: 020h2v => 78 concepts (59 used for prediction) PRED predicted values (max 10 best out of 51): 025jfl (0.40 #5, 0.04 #958, 0.04 #1621), 05qd_ (0.23 #81, 0.20 #154, 0.14 #813), 086k8 (0.21 #294, 0.16 #367, 0.16 #2729), 03xsby (0.20 #15, 0.08 #4359, 0.03 #88), 016tw3 (0.15 #2365, 0.14 #815, 0.14 #229), 016tt2 (0.12 #2730, 0.12 #3325, 0.12 #660), 01gb54 (0.10 #101, 0.09 #174, 0.08 #4359), 03xq0f (0.10 #1400, 0.10 #957, 0.10 #1620), 0g1rw (0.09 #226, 0.09 #372, 0.08 #4359), 017jv5 (0.08 #4359, 0.07 #233, 0.07 #379) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 04dsnp; 02rqwhl; 03hj5lq; >> query: (?x8605, 025jfl) <- genre(?x8605, ?x10928), currency(?x8605, ?x170), ?x10928 = 02hmvc >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #921 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 681 *> proper extension: 058kh7; *> query: (?x8605, 020h2v) <- genre(?x8605, ?x225), film(?x6314, ?x8605), featured_film_locations(?x8605, ?x108), award_nominee(?x6314, ?x539) *> conf = 0.04 ranks of expected_values: 30 EVAL 01jmyj film! 020h2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 78.000 59.000 0.400 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18406-015rkw PRED entity: 015rkw PRED relation: people! PRED expected values: 03bkbh => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 36): 02w7gg (0.44 #156, 0.22 #2, 0.18 #79), 0xnvg (0.18 #90, 0.11 #13, 0.06 #631), 041rx (0.15 #1008, 0.15 #313, 0.15 #235), 033tf_ (0.12 #625, 0.10 #316, 0.10 #238), 03bkbh (0.11 #32, 0.09 #109, 0.04 #186), 0222qb (0.11 #44, 0.09 #121, 0.03 #275), 02ctzb (0.11 #15, 0.09 #92, 0.03 #324), 013xrm (0.11 #20, 0.09 #97, 0.03 #1024), 07mqps (0.11 #19, 0.09 #96, 0.02 #250), 03ts0c (0.11 #26, 0.09 #103, 0.01 #799) >> Best rule #156 for best value: >> intensional similarity = 2 >> extensional distance = 23 >> proper extension: 010xjr; 02d45s; 01wskg; >> query: (?x1739, 02w7gg) <- award_nominee(?x1223, ?x1739), ?x1223 = 016gr2 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #32 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 016khd; 06cgy; 030hcs; 0c6qh; 01wz01; 01swck; 0h10vt; *> query: (?x1739, 03bkbh) <- award_winner(?x2258, ?x1739), ?x2258 = 0f4vbz, award_nominee(?x1739, ?x397) *> conf = 0.11 ranks of expected_values: 5 EVAL 015rkw people! 03bkbh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 84.000 84.000 0.440 http://example.org/people/ethnicity/people #18405-015l4k PRED entity: 015l4k PRED relation: medal PRED expected values: 02lq67 => 26 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.88 #34, 0.88 #29, 0.88 #33) >> Best rule #34 for best value: >> intensional similarity = 49 >> extensional distance = 39 >> proper extension: 018qb4; 018ljb; >> query: (?x8189, ?x1242) <- olympics(?x1175, ?x8189), olympics(?x7430, ?x8189), olympics(?x1229, ?x8189), olympics(?x774, ?x8189), combatants(?x3728, ?x7430), sports(?x8189, ?x453), participating_countries(?x784, ?x774), film_release_region(?x9652, ?x774), film_release_region(?x8025, ?x774), film_release_region(?x2889, ?x774), film_release_region(?x2746, ?x774), film_release_region(?x2656, ?x774), film_release_region(?x2655, ?x774), film_release_region(?x1490, ?x774), film_release_region(?x1386, ?x774), nationality(?x1221, ?x774), ?x9652 = 0ddbjy4, taxonomy(?x774, ?x939), olympics(?x7430, ?x2432), ?x2432 = 0nbjq, ?x2889 = 040b5k, medal(?x7430, ?x1242), ?x2656 = 03qnc6q, contains(?x1229, ?x2351), film_release_region(?x9900, ?x1229), film_release_region(?x3276, ?x1229), film_release_region(?x2155, ?x1229), film_release_region(?x1785, ?x1229), ?x2655 = 0fpmrm3, ?x2746 = 04f52jw, first_level_division_of(?x5535, ?x774), ?x1386 = 0dtfn, ?x2155 = 0407yfx, ?x3276 = 0gjc4d3, adjustment_currency(?x774, ?x170), contains(?x774, ?x1220), location(?x2580, ?x1229), adjoins(?x1264, ?x774), geographic_distribution(?x1571, ?x1229), ?x9900 = 0qmfk, ?x1785 = 0gj9tn5, member_states(?x2106, ?x1229), ?x1490 = 0fpkhkz, ?x8025 = 03nsm5x, countries_spoken_in(?x90, ?x774), jurisdiction_of_office(?x182, ?x1229), country(?x150, ?x1229), ?x3728 = 087vz, nationality(?x731, ?x1229) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015l4k medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 26.000 26.000 0.882 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #18404-0dwly PRED entity: 0dwly PRED relation: disciplines_or_subjects! PRED expected values: 01cdjp => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 106): 0265vt (0.96 #643, 0.96 #211, 0.69 #537), 0208wk (0.96 #643, 0.96 #211, 0.69 #537), 01yz0x (0.96 #643, 0.96 #211, 0.69 #537), 02664f (0.96 #643, 0.96 #211, 0.69 #537), 039yzf (0.96 #643, 0.96 #211, 0.69 #537), 0262yt (0.96 #643, 0.96 #211, 0.69 #537), 045xh (0.96 #643, 0.96 #211, 0.69 #537), 02662b (0.96 #643, 0.96 #211, 0.69 #537), 0262zm (0.96 #643, 0.96 #211, 0.69 #537), 040_9s0 (0.96 #643, 0.96 #211, 0.69 #537) >> Best rule #643 for best value: >> intensional similarity = 18 >> extensional distance = 4 >> proper extension: 03nfmq; 0dc_v; >> query: (?x7879, ?x921) <- disciplines_or_subjects(?x9629, ?x7879), award_winner(?x9629, ?x10536), award_winner(?x9629, ?x6796), profession(?x6796, ?x353), influenced_by(?x6796, ?x477), disciplines_or_subjects(?x9629, ?x5864), influenced_by(?x3858, ?x6796), major_field_of_study(?x5288, ?x5864), major_field_of_study(?x3439, ?x5864), major_field_of_study(?x3437, ?x5864), religion(?x6796, ?x109), ?x5288 = 02zd460, disciplines_or_subjects(?x921, ?x5864), ?x3439 = 03ksy, ?x353 = 0cbd2, taxonomy(?x7879, ?x939), ?x3437 = 02_xgp2, type_of_union(?x10536, ?x566) >> conf = 0.96 => this is the best rule for 38 predicted values *> Best rule #3418 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 19 *> proper extension: 01lj9; *> query: (?x7879, ?x3676) <- disciplines_or_subjects(?x11084, ?x7879), disciplines_or_subjects(?x9629, ?x7879), award_winner(?x9629, ?x6796), profession(?x6796, ?x353), influenced_by(?x3858, ?x6796), student(?x892, ?x6796), award_winner(?x11084, ?x9519), nationality(?x6796, ?x512), student(?x5238, ?x9519), location(?x9519, ?x2850), type_of_union(?x9519, ?x1873), profession(?x9519, ?x319), religion(?x9519, ?x2694), ?x2694 = 0kpl, award_winner(?x3676, ?x9519) *> conf = 0.40 ranks of expected_values: 40 EVAL 0dwly disciplines_or_subjects! 01cdjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 35.000 35.000 0.960 http://example.org/award/award_category/disciplines_or_subjects #18403-01_1pv PRED entity: 01_1pv PRED relation: language PRED expected values: 02h40lc => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.93 #5069, 0.92 #352, 0.90 #2), 064_8sq (0.18 #897, 0.17 #837, 0.16 #21), 06nm1 (0.11 #826, 0.10 #768, 0.10 #886), 02hxcvy (0.10 #441, 0.07 #558, 0.02 #1791), 02bjrlw (0.10 #1, 0.08 #1231, 0.08 #995), 03k50 (0.09 #416, 0.06 #533, 0.02 #1531), 0653m (0.09 #302, 0.07 #243, 0.03 #769), 06b_j (0.07 #1016, 0.07 #898, 0.06 #838), 0jzc (0.06 #251, 0.04 #310, 0.04 #953), 03_9r (0.05 #767, 0.05 #5076, 0.05 #241) >> Best rule #5069 for best value: >> intensional similarity = 2 >> extensional distance = 1726 >> proper extension: 05f67hw; >> query: (?x2223, 02h40lc) <- language(?x2223, ?x732), official_language(?x774, ?x732) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_1pv language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.932 http://example.org/film/film/language #18402-016wyn PRED entity: 016wyn PRED relation: student PRED expected values: 044mrh => 120 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1296): 016lh0 (0.14 #911, 0.11 #3002, 0.10 #5093), 0p8jf (0.11 #2568, 0.10 #6750, 0.10 #4659), 036jb (0.11 #2860, 0.10 #4951, 0.09 #9133), 0kc6 (0.10 #7989, 0.09 #10080, 0.08 #14262), 04fcx7 (0.09 #9234, 0.08 #13416, 0.06 #2961), 013pp3 (0.07 #924, 0.06 #3015, 0.05 #7197), 042v2 (0.07 #1502, 0.06 #3593, 0.05 #7775), 03ktjq (0.07 #1005, 0.06 #3096, 0.05 #7278), 017yfz (0.07 #688, 0.06 #2779, 0.05 #6961), 024t0y (0.07 #1994, 0.06 #4085, 0.05 #8267) >> Best rule #911 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 02yr3z; >> query: (?x6787, 016lh0) <- institution(?x865, ?x6787), contains(?x3670, ?x6787), ?x3670 = 05tbn, student(?x6787, ?x2100), currency(?x6787, ?x170) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #862 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 02yr3z; *> query: (?x6787, 044mrh) <- institution(?x865, ?x6787), contains(?x3670, ?x6787), ?x3670 = 05tbn, student(?x6787, ?x2100), currency(?x6787, ?x170) *> conf = 0.07 ranks of expected_values: 14 EVAL 016wyn student 044mrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 120.000 30.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #18401-01hmb_ PRED entity: 01hmb_ PRED relation: location PRED expected values: 02_286 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 76): 030qb3t (0.21 #4101, 0.18 #3298, 0.18 #4904), 02_286 (0.20 #4055, 0.19 #4858, 0.17 #15303), 0dclg (0.17 #117, 0.02 #4135, 0.02 #3332), 06_kh (0.17 #11, 0.01 #10456, 0.01 #18492), 06mxs (0.17 #260), 0r0m6 (0.11 #1824, 0.11 #1020, 0.05 #2628), 059rby (0.11 #819, 0.07 #2427, 0.04 #12871), 04jpl (0.11 #820, 0.07 #4035, 0.06 #3232), 01n7q (0.11 #866, 0.06 #1670, 0.05 #2474), 0cc56 (0.11 #860, 0.06 #1664, 0.04 #28180) >> Best rule #4101 for best value: >> intensional similarity = 3 >> extensional distance = 251 >> proper extension: 01lz4tf; >> query: (?x10050, 030qb3t) <- spouse(?x10520, ?x10050), location(?x10050, ?x2850), gender(?x10050, ?x231) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #4055 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 251 *> proper extension: 01lz4tf; *> query: (?x10050, 02_286) <- spouse(?x10520, ?x10050), location(?x10050, ?x2850), gender(?x10050, ?x231) *> conf = 0.20 ranks of expected_values: 2 EVAL 01hmb_ location 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.213 http://example.org/people/person/places_lived./people/place_lived/location #18400-0d_w7 PRED entity: 0d_w7 PRED relation: people! PRED expected values: 041rx => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 50): 041rx (0.63 #1467, 0.61 #1159, 0.61 #466), 033tf_ (0.50 #7, 0.33 #84, 0.12 #623), 07bch9 (0.25 #23, 0.20 #639, 0.17 #100), 065b6q (0.25 #3, 0.17 #80, 0.11 #157), 01qhm_ (0.25 #6, 0.17 #83, 0.06 #776), 09vc4s (0.25 #9, 0.17 #86, 0.05 #394), 013xrm (0.18 #482, 0.08 #1637, 0.07 #1483), 03x1x (0.17 #133), 013b6_ (0.15 #515, 0.06 #823, 0.06 #1208), 0x67 (0.15 #626, 0.11 #549, 0.11 #395) >> Best rule #1467 for best value: >> intensional similarity = 2 >> extensional distance = 164 >> proper extension: 01w3v; 0mcf4; >> query: (?x11596, 041rx) <- religion(?x11596, ?x7131), ?x7131 = 03_gx >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d_w7 people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.627 http://example.org/people/ethnicity/people #18399-018ty9 PRED entity: 018ty9 PRED relation: profession PRED expected values: 0dxtg => 78 concepts (21 used for prediction) PRED predicted values (max 10 best out of 73): 0dxtg (0.71 #302, 0.61 #447, 0.57 #2042), 03gjzk (0.24 #2043, 0.19 #158, 0.18 #303), 02krf9 (0.21 #2055, 0.18 #1475, 0.18 #315), 0cbd2 (0.17 #2619, 0.17 #2327, 0.16 #2911), 0fj9f (0.17 #51, 0.10 #631, 0.10 #921), 01xr66 (0.17 #61, 0.06 #206, 0.06 #2467), 01c72t (0.13 #1037, 0.13 #457, 0.12 #1182), 09jwl (0.13 #452, 0.12 #307, 0.10 #2047), 0kyk (0.12 #1623, 0.12 #608, 0.12 #1333), 018gz8 (0.11 #2045, 0.11 #1900, 0.10 #740) >> Best rule #302 for best value: >> intensional similarity = 7 >> extensional distance = 15 >> proper extension: 01xv77; >> query: (?x6928, 0dxtg) <- nationality(?x6928, ?x94), profession(?x6928, ?x2265), profession(?x6928, ?x1032), profession(?x6928, ?x319), ?x1032 = 02hrh1q, ?x2265 = 0dgd_, ?x319 = 01d_h8 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018ty9 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 21.000 0.706 http://example.org/people/person/profession #18398-07srw PRED entity: 07srw PRED relation: category PRED expected values: 08mbj5d => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.76 #176, 0.74 #97, 0.73 #8) >> Best rule #176 for best value: >> intensional similarity = 2 >> extensional distance = 891 >> proper extension: 0rs6x; 015zyd; 08815; 05zjtn4; 01fq7; 01rtm4; 04wlz2; 05krk; 01pl14; 01j_9c; ... >> query: (?x2256, 08mbj5d) <- contains(?x94, ?x2256), ?x94 = 09c7w0 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07srw category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.760 http://example.org/common/topic/webpage./common/webpage/category #18397-0dd6bf PRED entity: 0dd6bf PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #126, 0.83 #161, 0.83 #171), 07c52 (0.29 #33, 0.22 #43, 0.21 #58), 07z4p (0.25 #15, 0.07 #65, 0.06 #85), 02nxhr (0.06 #117, 0.05 #122, 0.04 #227) >> Best rule #126 for best value: >> intensional similarity = 7 >> extensional distance = 53 >> proper extension: 03qcfvw; 0bvn25; 04jwjq; 0jzw; 044g_k; 0m491; 02yvct; 0661m4p; 0170th; 03z20c; ... >> query: (?x7029, 029j_) <- film(?x12484, ?x7029), language(?x7029, ?x2164), profession(?x12484, ?x1383), genre(?x7029, ?x1510), film(?x296, ?x7029), film_release_region(?x7029, ?x94), location(?x12484, ?x8889) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dd6bf film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.836 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #18396-01rnxn PRED entity: 01rnxn PRED relation: place_of_birth PRED expected values: 01_d4 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 31): 02_286 (0.08 #11989, 0.08 #14101, 0.07 #3539), 01_d4 (0.05 #2178, 0.03 #52176, 0.03 #52881), 030qb3t (0.04 #4982, 0.04 #50050, 0.04 #52164), 0cr3d (0.04 #2206, 0.04 #12064, 0.03 #14176), 0d6lp (0.02 #2930, 0.02 #3634, 0.02 #4338), 01531 (0.02 #809, 0.02 #12779, 0.02 #7850), 0rh6k (0.02 #706, 0.02 #1410, 0.02 #2818), 05qtj (0.02 #2279, 0.02 #7208, 0.01 #6503), 04jpl (0.02 #2120, 0.01 #12682, 0.01 #712), 06_kh (0.02 #2117, 0.01 #709, 0.01 #1413) >> Best rule #11989 for best value: >> intensional similarity = 4 >> extensional distance = 909 >> proper extension: 034bs; >> query: (?x2991, 02_286) <- profession(?x2991, ?x1032), student(?x4672, ?x2991), award_winner(?x1033, ?x2991), school_type(?x4672, ?x1044) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #2178 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 149 *> proper extension: 024rbz; 031rx9; *> query: (?x2991, 01_d4) <- nominated_for(?x2991, ?x3505), award(?x3505, ?x1770), ?x1770 = 09cm54 *> conf = 0.05 ranks of expected_values: 2 EVAL 01rnxn place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.080 http://example.org/people/person/place_of_birth #18395-01z7_f PRED entity: 01z7_f PRED relation: profession PRED expected values: 02hrh1q => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 83): 02hrh1q (0.89 #1066, 0.88 #9767, 0.88 #3766), 03gjzk (0.83 #1967, 0.82 #2417, 0.82 #1817), 0dxtg (0.66 #1815, 0.65 #2265, 0.65 #1965), 01d_h8 (0.59 #6, 0.57 #457, 0.50 #1957), 018gz8 (0.36 #469, 0.33 #18, 0.31 #301), 02jknp (0.33 #459, 0.30 #8, 0.28 #1809), 02krf9 (0.31 #1829, 0.31 #301, 0.29 #2279), 09jwl (0.31 #301, 0.28 #11104, 0.27 #9002), 0kyk (0.31 #301, 0.28 #11104, 0.27 #9002), 0cbd2 (0.31 #301, 0.28 #11104, 0.27 #9002) >> Best rule #1066 for best value: >> intensional similarity = 2 >> extensional distance = 118 >> proper extension: 05bnp0; 01p7yb; 0prfz; 02r_d4; 05ml_s; 01yk13; 0yfp; 049dyj; 02r34n; 0n6f8; ... >> query: (?x4328, 02hrh1q) <- student(?x1368, ?x4328), film(?x4328, ?x1644) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01z7_f profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.892 http://example.org/people/person/profession #18394-0465_ PRED entity: 0465_ PRED relation: influenced_by! PRED expected values: 0dw6b => 151 concepts (57 used for prediction) PRED predicted values (max 10 best out of 420): 032l1 (0.50 #1128, 0.36 #5178, 0.19 #6192), 0d4jl (0.50 #2137, 0.25 #7710, 0.25 #1631), 0hcvy (0.50 #2472, 0.25 #1966, 0.21 #5510), 07g2b (0.50 #2040, 0.25 #1534, 0.19 #7613), 0683n (0.43 #6409, 0.43 #5395, 0.33 #6916), 0dzkq (0.40 #4171, 0.33 #2652, 0.25 #628), 073bb (0.33 #2591, 0.30 #4110, 0.25 #567), 013pp3 (0.33 #2749, 0.25 #2243, 0.25 #1737), 03f47xl (0.33 #2787, 0.25 #763, 0.22 #3294), 05qzv (0.33 #2926, 0.25 #902, 0.20 #4445) >> Best rule #1128 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 081k8; 01vh096; >> query: (?x6370, 032l1) <- influenced_by(?x6975, ?x6370), influenced_by(?x2161, ?x6370), ?x2161 = 040db, location(?x6370, ?x205), ?x6975 = 05np2, place_of_death(?x6370, ?x6959) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1360 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 081k8; 01vh096; *> query: (?x6370, 0dw6b) <- influenced_by(?x6975, ?x6370), influenced_by(?x2161, ?x6370), ?x2161 = 040db, location(?x6370, ?x205), ?x6975 = 05np2, place_of_death(?x6370, ?x6959) *> conf = 0.25 ranks of expected_values: 83 EVAL 0465_ influenced_by! 0dw6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 151.000 57.000 0.500 http://example.org/influence/influence_node/influenced_by #18393-03h_fk5 PRED entity: 03h_fk5 PRED relation: award_winner! PRED expected values: 01c6qp => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 133): 013b2h (0.20 #3639, 0.16 #5283, 0.13 #6379), 01mhwk (0.20 #38, 0.16 #312, 0.12 #723), 09n4nb (0.16 #1826, 0.13 #1141, 0.11 #2922), 01mh_q (0.14 #3648, 0.13 #5292, 0.12 #3374), 05pd94v (0.13 #1098, 0.13 #10688, 0.12 #687), 056878 (0.13 #1125, 0.12 #10715, 0.12 #1810), 0466p0j (0.12 #10759, 0.12 #758, 0.12 #3224), 01s695 (0.12 #10689, 0.12 #5209, 0.12 #1784), 01c6qp (0.12 #10702, 0.10 #10839, 0.10 #1112), 0jzphpx (0.12 #1817, 0.10 #3187, 0.10 #3324) >> Best rule #3639 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 0lzkm; >> query: (?x2807, 013b2h) <- role(?x2807, ?x227), ?x227 = 0342h, award_winner(?x2806, ?x2807) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #10702 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 334 *> proper extension: 013w8y; 016ppr; *> query: (?x2807, 01c6qp) <- artists(?x302, ?x2807), artist(?x3265, ?x2807), award_winner(?x486, ?x2807) *> conf = 0.12 ranks of expected_values: 9 EVAL 03h_fk5 award_winner! 01c6qp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 162.000 162.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18392-0fhp9 PRED entity: 0fhp9 PRED relation: category PRED expected values: 08mbj5d => 215 concepts (215 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.76 #108, 0.73 #54, 0.71 #58) >> Best rule #108 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 0fnx1; 0fjsl; >> query: (?x863, 08mbj5d) <- administrative_division(?x863, ?x1355), time_zones(?x863, ?x2864), adjoins(?x1355, ?x205) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fhp9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 215.000 215.000 0.765 http://example.org/common/topic/webpage./common/webpage/category #18391-06gn7r PRED entity: 06gn7r PRED relation: profession PRED expected values: 02hrh1q => 181 concepts (98 used for prediction) PRED predicted values (max 10 best out of 116): 02hrh1q (0.92 #5345, 0.90 #4752, 0.88 #6384), 01d_h8 (0.78 #3264, 0.76 #13497, 0.75 #9045), 0cbd2 (0.50 #13794, 0.48 #2969, 0.47 #4005), 03gjzk (0.41 #14245, 0.37 #1938, 0.35 #1790), 0kyk (0.38 #2843, 0.33 #1065, 0.32 #4027), 028kk_ (0.38 #667, 0.25 #75, 0.08 #1111), 0n1h (0.33 #307, 0.19 #1639, 0.17 #159), 06q2q (0.27 #1228, 0.18 #2265, 0.17 #2858), 0d1pc (0.27 #1382, 0.17 #3604, 0.16 #5233), 018gz8 (0.25 #16, 0.21 #14247, 0.20 #12912) >> Best rule #5345 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 07yw6t; >> query: (?x8296, 02hrh1q) <- award(?x8296, ?x4687), ?x4687 = 03rbj2, profession(?x8296, ?x524) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06gn7r profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 98.000 0.920 http://example.org/people/person/profession #18390-058s44 PRED entity: 058s44 PRED relation: film PRED expected values: 057lbk => 112 concepts (77 used for prediction) PRED predicted values (max 10 best out of 862): 07w8fz (0.43 #513, 0.02 #62989, 0.01 #23718), 06z8s_ (0.10 #129, 0.04 #3699, 0.04 #12624), 07vn_9 (0.10 #1680, 0.03 #108890, 0.03 #133883), 05qbckf (0.10 #308, 0.03 #108890, 0.03 #133883), 07gghl (0.10 #1173, 0.03 #2958, 0.02 #11883), 03cp4cn (0.10 #1101, 0.03 #133883, 0.03 #132097), 04cv9m (0.10 #701, 0.03 #133883, 0.03 #132097), 078sj4 (0.10 #453, 0.02 #4023, 0.02 #23658), 09xbpt (0.10 #46, 0.02 #5401, 0.02 #8971), 04g9gd (0.10 #386, 0.02 #18236, 0.02 #9311) >> Best rule #513 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 02p65p; 014zcr; 03pmty; 03jldb; 02wcx8c; 03mg35; 011zd3; 02d4ct; 0bsb4j; 01qr1_; ... >> query: (?x5788, 07w8fz) <- award_nominee(?x2373, ?x5788), ?x2373 = 016z2j, award_nominee(?x5788, ?x4039) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #108890 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1446 *> proper extension: 01wg982; 0280mv7; *> query: (?x5788, ?x557) <- award_nominee(?x2373, ?x5788), place_of_birth(?x2373, ?x1131), film(?x2373, ?x557) *> conf = 0.03 ranks of expected_values: 175 EVAL 058s44 film 057lbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 112.000 77.000 0.429 http://example.org/film/actor/film./film/performance/film #18389-0hcvy PRED entity: 0hcvy PRED relation: story_by! PRED expected values: 0fjyzt => 132 concepts (120 used for prediction) PRED predicted values (max 10 best out of 309): 03ntbmw (0.25 #680, 0.12 #2046, 0.02 #8185), 033dbw (0.25 #675, 0.02 #10228, 0.02 #10910), 03prz_ (0.20 #887, 0.14 #1570, 0.09 #3617), 08ct6 (0.20 #852, 0.14 #1535, 0.05 #7333), 0bv8h2 (0.20 #803, 0.14 #1486, 0.05 #7967), 04gcyg (0.20 #943, 0.14 #1626, 0.03 #7424), 09d3b7 (0.17 #1303, 0.11 #2327, 0.04 #6760), 083shs (0.14 #1372, 0.02 #9901, 0.02 #10583), 01ry_x (0.14 #1693, 0.01 #13973, 0.01 #14314), 02q_4ph (0.12 #5605, 0.08 #5946, 0.04 #6287) >> Best rule #680 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0bv7t; >> query: (?x11271, 03ntbmw) <- influenced_by(?x11271, ?x2162), story_by(?x4458, ?x11271), award(?x11271, ?x11388), ?x11388 = 04hddx >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hcvy story_by! 0fjyzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 120.000 0.250 http://example.org/film/film/story_by #18388-0gkr9q PRED entity: 0gkr9q PRED relation: award! PRED expected values: 064jjy 04h68j => 53 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2896): 02f93t (0.64 #12794, 0.15 #22895, 0.11 #32995), 02kxbx3 (0.64 #11086, 0.15 #27920, 0.14 #31287), 02kxbwx (0.64 #10278, 0.15 #27112, 0.13 #30479), 05ldnp (0.55 #10995, 0.33 #896, 0.18 #27829), 014zcr (0.55 #10151, 0.30 #20252, 0.25 #6784), 0151w_ (0.55 #10333, 0.17 #20434, 0.16 #27167), 02hfp_ (0.55 #12423, 0.12 #22524, 0.12 #32624), 081lh (0.55 #10331, 0.12 #20432, 0.11 #30532), 0184dt (0.55 #10773, 0.10 #27607, 0.10 #20874), 01vb6z (0.55 #12043, 0.10 #28877, 0.07 #15410) >> Best rule #12794 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 02qyp19; 040njc; 04dn09n; 02x1dht; 019f4v; 02pqp12; 0gs9p; 0gr51; 026mmy; >> query: (?x9640, 02f93t) <- award(?x9214, ?x9640), award(?x3117, ?x9640), award_winner(?x635, ?x9214), ceremony(?x9640, ?x1265), ?x3117 = 0693l >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #2384 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 0fbtbt; *> query: (?x9640, 064jjy) <- award(?x9214, ?x9640), award(?x7604, ?x9640), ?x9214 = 03c6vl, ?x7604 = 079kdz, award(?x337, ?x9640) *> conf = 0.33 ranks of expected_values: 77, 114 EVAL 0gkr9q award! 04h68j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 53.000 22.000 0.636 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gkr9q award! 064jjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 53.000 22.000 0.636 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18387-052hl PRED entity: 052hl PRED relation: profession PRED expected values: 01c72t => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 84): 01c72t (0.46 #3797, 0.23 #1136, 0.22 #1416), 0kyk (0.45 #1982, 0.34 #9684, 0.31 #9264), 0nbcg (0.41 #3805, 0.24 #1424, 0.20 #2965), 09jwl (0.37 #3793, 0.33 #6162, 0.33 #1412), 02krf9 (0.33 #999, 0.33 #6162, 0.26 #5900), 0dgd_ (0.33 #23, 0.17 #163, 0.15 #1003), 0lgw7 (0.33 #40, 0.17 #180, 0.06 #460), 015cjr (0.33 #6162, 0.32 #2661, 0.11 #462), 0dz3r (0.24 #3782, 0.18 #1401, 0.17 #4202), 016z4k (0.21 #3784, 0.15 #4204, 0.14 #11485) >> Best rule #3797 for best value: >> intensional similarity = 2 >> extensional distance = 105 >> proper extension: 01vs14j; 01vswx5; 01vswwx; 08c7cz; >> query: (?x6771, 01c72t) <- award(?x6771, ?x1323), ?x1323 = 0gqz2 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 052hl profession 01c72t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.458 http://example.org/people/person/profession #18386-0rjg8 PRED entity: 0rjg8 PRED relation: place PRED expected values: 0rjg8 => 86 concepts (42 used for prediction) PRED predicted values (max 10 best out of 72): 0rj0z (0.50 #1547, 0.33 #1031, 0.26 #4653), 0rql_ (0.50 #1547, 0.33 #1031, 0.26 #4653), 0rqf1 (0.26 #4653, 0.17 #837, 0.10 #1353), 0c5v2 (0.26 #4653, 0.10 #1459, 0.02 #7666), 0rjg8 (0.26 #4653), 0rj4g (0.25 #266, 0.17 #781, 0.10 #1297), 0rk71 (0.17 #797, 0.10 #1313, 0.03 #6487), 0ggh3 (0.17 #719, 0.03 #6409, 0.02 #7442), 0rqyx (0.10 #1162, 0.05 #2713, 0.03 #5818), 0rn0z (0.10 #1379, 0.03 #6553, 0.02 #7586) >> Best rule #1547 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0ply0; 0rn0z; 0c5v2; >> query: (?x6194, ?x3892) <- contains(?x8219, ?x6194), contains(?x2623, ?x6194), ?x2623 = 02xry, county_seat(?x8219, ?x3892), adjoins(?x8219, ?x10379) >> conf = 0.50 => this is the best rule for 2 predicted values *> Best rule #4653 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 01sn3; 04pry; 0yvjx; *> query: (?x6194, ?x8127) <- adjoins(?x3892, ?x6194), source(?x6194, ?x958), contains(?x9290, ?x6194), county(?x8127, ?x9290), time_zones(?x6194, ?x2674) *> conf = 0.26 ranks of expected_values: 5 EVAL 0rjg8 place 0rjg8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 42.000 0.500 http://example.org/location/hud_county_place/place #18385-03k7bd PRED entity: 03k7bd PRED relation: award_nominee PRED expected values: 016khd => 100 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1022): 0315q3 (0.81 #25684, 0.81 #25683, 0.81 #56034), 043js (0.81 #25684, 0.81 #25683, 0.81 #56034), 0154qm (0.18 #24087, 0.04 #739, 0.04 #33427), 02qgyv (0.15 #25685, 0.14 #91048, 0.09 #23848), 0h0wc (0.15 #25685, 0.14 #91048, 0.07 #23902), 053y4h (0.15 #25685, 0.14 #91048, 0.07 #24563), 011_3s (0.15 #25685, 0.14 #91048, 0.06 #24082), 0fthdk (0.15 #25685, 0.14 #91048, 0.05 #25338), 02x7vq (0.15 #25685, 0.14 #91048, 0.05 #5968), 024bbl (0.15 #25685, 0.14 #91048, 0.05 #24460) >> Best rule #25684 for best value: >> intensional similarity = 4 >> extensional distance = 187 >> proper extension: 09bx1k; >> query: (?x1865, ?x4046) <- award_nominee(?x8099, ?x1865), award_nominee(?x4046, ?x1865), award_nominee(?x851, ?x4046), actor(?x596, ?x8099) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #25685 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 187 *> proper extension: 09bx1k; *> query: (?x1865, ?x851) <- award_nominee(?x8099, ?x1865), award_nominee(?x4046, ?x1865), award_nominee(?x851, ?x4046), actor(?x596, ?x8099) *> conf = 0.15 ranks of expected_values: 21 EVAL 03k7bd award_nominee 016khd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 100.000 39.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18384-01x73 PRED entity: 01x73 PRED relation: location! PRED expected values: 04snp2 => 172 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2180): 01cv3n (0.28 #195035, 0.28 #142525, 0.27 #222541), 01yzhn (0.22 #4615, 0.19 #7116, 0.12 #12117), 03nb5v (0.22 #3810, 0.19 #6311, 0.11 #31314), 023s8 (0.22 #4594, 0.12 #12096, 0.12 #7095), 0p_pd (0.22 #2548, 0.12 #5049, 0.09 #7549), 01wp8w7 (0.22 #2760, 0.12 #5261, 0.09 #7761), 01vtmw6 (0.22 #3847, 0.12 #6348, 0.09 #8848), 02yl42 (0.22 #3202, 0.12 #5703, 0.09 #8203), 06dn58 (0.22 #4039, 0.12 #6540, 0.05 #19042), 030hcs (0.22 #2819, 0.12 #5320, 0.05 #17822) >> Best rule #195035 for best value: >> intensional similarity = 3 >> extensional distance = 136 >> proper extension: 0_3cs; 0kygv; 0r3tb; 01vx3m; 0fg6k; 05l64; 014kj2; 0r172; >> query: (?x1755, ?x680) <- contains(?x94, ?x1755), origin(?x680, ?x1755), time_zones(?x1755, ?x2674) >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01x73 location! 04snp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 105.000 0.280 http://example.org/people/person/places_lived./people/place_lived/location #18383-02pgky2 PRED entity: 02pgky2 PRED relation: ceremony! PRED expected values: 0gs9p 0gqz2 0k611 018wdw => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 349): 0k611 (0.93 #6087, 0.90 #5847, 0.90 #4403), 0gs9p (0.91 #5354, 0.90 #5836, 0.88 #4150), 018wdw (0.90 #4511, 0.88 #4269, 0.86 #3061), 0gqz2 (0.88 #5355, 0.86 #4393, 0.82 #4151), 099c8n (0.45 #967, 0.39 #3621, 0.36 #1931), 02pqp12 (0.45 #967, 0.39 #3621, 0.36 #1931), 040njc (0.45 #967, 0.39 #3621, 0.36 #1931), 027dtxw (0.45 #967, 0.39 #3621, 0.36 #1931), 09qv_s (0.45 #967, 0.39 #3621, 0.36 #1931), 04kxsb (0.45 #967, 0.39 #3621, 0.36 #1931) >> Best rule #6087 for best value: >> intensional similarity = 14 >> extensional distance = 41 >> proper extension: 073hmq; 0fy6bh; 0bc773; 0bzm__; 073hgx; 0bzmt8; 09306z; 0c4hx0; 0fzrhn; >> query: (?x6594, 0k611) <- ceremony(?x720, ?x6594), ?x720 = 018wng, award_winner(?x6594, ?x3080), award_winner(?x6594, ?x2646), honored_for(?x6594, ?x1531), titles(?x2480, ?x1531), award(?x1531, ?x3190), nominated_for(?x277, ?x1531), film(?x609, ?x1531), nationality(?x3080, ?x205), produced_by(?x1531, ?x6279), film(?x436, ?x1531), nominated_for(?x3190, ?x86), award_nominee(?x92, ?x2646) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 02pgky2 ceremony! 018wdw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.930 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02pgky2 ceremony! 0k611 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.930 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02pgky2 ceremony! 0gqz2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.930 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02pgky2 ceremony! 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.930 http://example.org/award/award_category/winners./award/award_honor/ceremony #18382-03qnc6q PRED entity: 03qnc6q PRED relation: nominated_for! PRED expected values: 099c8n => 91 concepts (83 used for prediction) PRED predicted values (max 10 best out of 226): 03hl6lc (0.76 #354, 0.56 #816, 0.19 #585), 09cm54 (0.69 #8556, 0.68 #3235, 0.68 #13649), 099c8n (0.60 #286, 0.52 #748, 0.29 #979), 0gr51 (0.56 #303, 0.53 #765, 0.40 #534), 019f4v (0.54 #514, 0.51 #976, 0.47 #1207), 02qyp19 (0.48 #694, 0.48 #232, 0.27 #463), 04dn09n (0.45 #959, 0.41 #1190, 0.40 #266), 02qyntr (0.44 #404, 0.35 #866, 0.35 #635), 09sb52 (0.44 #265, 0.32 #727, 0.26 #5546), 0gqy2 (0.42 #1040, 0.39 #1271, 0.37 #1502) >> Best rule #354 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 046f3p; 04b_jc; >> query: (?x2656, 03hl6lc) <- genre(?x2656, ?x53), nominated_for(?x899, ?x2656), written_by(?x2656, ?x4330), ?x899 = 02x1dht >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #286 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 046f3p; 04b_jc; *> query: (?x2656, 099c8n) <- genre(?x2656, ?x53), nominated_for(?x899, ?x2656), written_by(?x2656, ?x4330), ?x899 = 02x1dht *> conf = 0.60 ranks of expected_values: 3 EVAL 03qnc6q nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 83.000 0.760 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18381-01mr2g6 PRED entity: 01mr2g6 PRED relation: artist! PRED expected values: 015kg1 => 158 concepts (151 used for prediction) PRED predicted values (max 10 best out of 130): 09f2j (0.43 #1963, 0.40 #1682, 0.18 #2385), 015_1q (0.33 #1561, 0.27 #1842, 0.27 #1281), 043g7l (0.28 #1572, 0.15 #1853, 0.12 #1713), 01trtc (0.27 #1894, 0.17 #1613, 0.13 #1333), 04fcjt (0.25 #29, 0.09 #590, 0.09 #450), 03d96s (0.25 #47, 0.09 #608, 0.09 #468), 06wcbk7 (0.25 #4, 0.09 #565, 0.09 #425), 03mp8k (0.22 #1607, 0.15 #1888, 0.13 #1467), 06x2ww (0.20 #1309, 0.09 #469, 0.04 #1730), 017l96 (0.19 #3804, 0.17 #6184, 0.17 #1701) >> Best rule #1963 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 01cwhp; 01vw20h; 0c7xjb; 01vw8mh; 016k62; 01vvyc_; 013w7j; 03f0qd7; >> query: (?x8272, ?x4955) <- artists(?x302, ?x8272), company(?x8272, ?x4955), nationality(?x8272, ?x94), parent_genre(?x301, ?x302) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1142 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 03sww; 03lgg; 01w03jv; *> query: (?x8272, 015kg1) <- artists(?x7808, ?x8272), artists(?x7329, ?x8272), profession(?x8272, ?x131), ?x7808 = 0jmwg, parent_genre(?x837, ?x7329) *> conf = 0.07 ranks of expected_values: 57 EVAL 01mr2g6 artist! 015kg1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 158.000 151.000 0.433 http://example.org/music/record_label/artist #18380-02t__3 PRED entity: 02t__3 PRED relation: languages PRED expected values: 02h40lc => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 7): 02h40lc (0.40 #587, 0.31 #1172, 0.31 #1250), 064_8sq (0.06 #951, 0.05 #990, 0.05 #795), 0t_2 (0.04 #282, 0.03 #399, 0.03 #555), 02bjrlw (0.02 #898, 0.02 #1210, 0.02 #820), 04306rv (0.02 #783, 0.01 #939, 0.01 #1056), 03k50 (0.01 #1915, 0.01 #3943, 0.01 #3046), 06nm1 (0.01 #1956, 0.01 #1176, 0.01 #1566) >> Best rule #587 for best value: >> intensional similarity = 3 >> extensional distance = 119 >> proper extension: 012_53; 01v3bn; 02t_99; 037gjc; 0bdxs5; 02rrsz; 05myd2; 01f5q5; >> query: (?x5979, 02h40lc) <- participant(?x5979, ?x2258), location(?x5979, ?x1523), ?x1523 = 030qb3t >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02t__3 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.405 http://example.org/people/person/languages #18379-04yt7 PRED entity: 04yt7 PRED relation: award_nominee PRED expected values: 03dq9 0dn44 => 104 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1086): 03dq9 (0.83 #11716, 0.81 #39836, 0.81 #126550), 07h5d (0.83 #11716, 0.81 #39836, 0.81 #126550), 0dn44 (0.83 #11716, 0.81 #39836, 0.81 #126550), 0dpqk (0.83 #11716, 0.81 #39836, 0.81 #126550), 03f1zdw (0.17 #4937, 0.11 #7280, 0.05 #11968), 0151w_ (0.17 #4893, 0.11 #7236, 0.05 #11924), 0grrq8 (0.14 #1082, 0.09 #3425, 0.02 #17486), 01kv4mb (0.14 #452, 0.09 #9824, 0.03 #33258), 02zft0 (0.14 #1410, 0.07 #17814, 0.03 #10782), 01cwhp (0.14 #534, 0.03 #16938, 0.02 #54429) >> Best rule #11716 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 03qd_; 025xt8y; 01kx_81; 01wwvt2; 01w806h; 014488; 0phx4; 01ttg5; 016fnb; 0fhxv; ... >> query: (?x4297, ?x4987) <- student(?x892, ?x4297), award_nominee(?x4987, ?x4297), group(?x4297, ?x12459) >> conf = 0.83 => this is the best rule for 4 predicted values ranks of expected_values: 1, 3 EVAL 04yt7 award_nominee 0dn44 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 67.000 0.834 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 04yt7 award_nominee 03dq9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 67.000 0.834 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18378-0683n PRED entity: 0683n PRED relation: profession PRED expected values: 0kyk => 113 concepts (91 used for prediction) PRED predicted values (max 10 best out of 117): 0kyk (0.73 #2658, 0.67 #321, 0.64 #174), 02hrh1q (0.70 #7758, 0.68 #12579, 0.66 #9950), 01d_h8 (0.57 #11112, 0.41 #9066, 0.38 #9212), 02hv44_ (0.43 #494, 0.31 #4677, 0.27 #2685), 09jwl (0.38 #7471, 0.37 #5571, 0.37 #8347), 03gjzk (0.31 #9073, 0.29 #9219, 0.26 #3666), 0d8qb (0.31 #4677, 0.17 #293, 0.17 #732), 015btn (0.31 #4677, 0.17 #293, 0.17 #732), 016wtf (0.31 #4677, 0.17 #293, 0.17 #732), 025352 (0.31 #4677, 0.17 #293, 0.17 #732) >> Best rule #2658 for best value: >> intensional similarity = 5 >> extensional distance = 201 >> proper extension: 0gv5c; 039xcr; >> query: (?x8389, 0kyk) <- profession(?x8389, ?x3746), profession(?x8389, ?x353), ?x353 = 0cbd2, profession(?x2162, ?x3746), ?x2162 = 04xjp >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0683n profession 0kyk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 91.000 0.729 http://example.org/people/person/profession #18377-047tsx3 PRED entity: 047tsx3 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.76 #476, 0.74 #1918, 0.70 #908), 0dxtw (0.38 #47, 0.35 #1922, 0.35 #480), 01vx2h (0.35 #481, 0.35 #11, 0.33 #553), 01pvkk (0.29 #49, 0.29 #914, 0.28 #1024), 02ynfr (0.23 #16, 0.19 #486, 0.16 #1928), 02rh1dz (0.19 #9, 0.12 #82, 0.12 #479), 0215hd (0.14 #1931, 0.13 #1031, 0.13 #921), 089g0h (0.12 #1932, 0.10 #922, 0.10 #1032), 01xy5l_ (0.11 #1926, 0.10 #484, 0.10 #51), 0d2b38 (0.11 #928, 0.10 #1938, 0.10 #1038) >> Best rule #476 for best value: >> intensional similarity = 3 >> extensional distance = 228 >> proper extension: 0d_2fb; >> query: (?x3981, 0ch6mp2) <- category(?x3981, ?x134), film_crew_role(?x3981, ?x137), production_companies(?x3981, ?x2549) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047tsx3 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.757 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18376-0dgrmp PRED entity: 0dgrmp PRED relation: position! PRED expected values: 0j2pg 02b0y3 049n3s 031zm1 => 11 concepts (8 used for prediction) PRED predicted values (max 10 best out of 263): 02b0_m (0.82 #520, 0.82 #260, 0.82 #1305), 01x4wq (0.82 #520, 0.82 #260, 0.82 #1305), 01rl_3 (0.82 #520, 0.82 #260, 0.82 #1305), 037mp6 (0.82 #520, 0.82 #260, 0.82 #1305), 01z1r (0.82 #520, 0.82 #260, 0.82 #1305), 01fwqn (0.82 #520, 0.82 #260, 0.82 #1305), 0fvly (0.82 #520, 0.82 #260, 0.82 #1305), 03c0vy (0.82 #520, 0.82 #260, 0.82 #1305), 01xn5th (0.82 #520, 0.82 #260, 0.82 #1305), 01634x (0.82 #520, 0.82 #260, 0.82 #1305) >> Best rule #520 for best value: >> intensional similarity = 21 >> extensional distance = 1 >> proper extension: 02sdk9v; >> query: (?x203, ?x348) <- position(?x12612, ?x203), position(?x11991, ?x203), position(?x9412, ?x203), position(?x7820, ?x203), position(?x7110, ?x203), position(?x6566, ?x203), position(?x5953, ?x203), position(?x5027, ?x203), position(?x4524, ?x203), position(?x2096, ?x203), ?x7110 = 049f88, ?x4524 = 03j722, position(?x348, ?x203), ?x7820 = 021mkg, ?x11991 = 01dwyd, ?x5027 = 048xg8, ?x12612 = 04j689, ?x2096 = 0371rb, ?x9412 = 01r5xw, ?x6566 = 0329t7, ?x5953 = 04255q >> conf = 0.82 => this is the best rule for 114 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 18, 21, 52, 184 EVAL 0dgrmp position! 031zm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 11.000 8.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position EVAL 0dgrmp position! 049n3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 11.000 8.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position EVAL 0dgrmp position! 02b0y3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 11.000 8.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position EVAL 0dgrmp position! 0j2pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 11.000 8.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #18375-048xyn PRED entity: 048xyn PRED relation: film! PRED expected values: 0bl2g => 91 concepts (47 used for prediction) PRED predicted values (max 10 best out of 704): 0b478 (0.48 #29128, 0.47 #83228, 0.46 #76983), 0170pk (0.08 #280, 0.02 #58537, 0.02 #4441), 016ggh (0.06 #1867, 0.02 #66366, 0.02 #41398), 0h5g_ (0.05 #4235, 0.04 #8395, 0.03 #2155), 0lpjn (0.05 #19203, 0.04 #27525, 0.03 #33767), 0dvld (0.05 #1060, 0.03 #19785, 0.03 #13543), 024bbl (0.05 #837, 0.02 #7078, 0.02 #15400), 01nwwl (0.05 #502, 0.02 #71242, 0.02 #19227), 01ycbq (0.05 #326, 0.02 #12809, 0.02 #4487), 0zcbl (0.05 #1220, 0.02 #5381, 0.02 #9541) >> Best rule #29128 for best value: >> intensional similarity = 5 >> extensional distance = 357 >> proper extension: 0dl6fv; >> query: (?x6273, ?x4685) <- production_companies(?x6273, ?x541), nominated_for(?x4685, ?x6273), nominated_for(?x558, ?x6273), film(?x558, ?x409), languages(?x558, ?x254) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #18780 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 311 *> proper extension: 0crfwmx; 091xrc; *> query: (?x6273, 0bl2g) <- production_companies(?x6273, ?x541), film(?x5821, ?x6273), award(?x5821, ?x749), ?x749 = 094qd5 *> conf = 0.02 ranks of expected_values: 119 EVAL 048xyn film! 0bl2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 91.000 47.000 0.477 http://example.org/film/actor/film./film/performance/film #18374-027dpx PRED entity: 027dpx PRED relation: performance_role PRED expected values: 0l14md => 123 concepts (82 used for prediction) PRED predicted values (max 10 best out of 42): 03bx0bm (0.42 #1535, 0.38 #1361, 0.20 #278), 0342h (0.27 #175, 0.09 #1346, 0.08 #1520), 0l14md (0.19 #1349, 0.18 #178, 0.18 #352), 0l14qv (0.17 #89, 0.15 #1347, 0.14 #1521), 03gvt (0.17 #123, 0.07 #298, 0.07 #253), 018vs (0.14 #138, 0.09 #182, 0.07 #270), 013y1f (0.12 #1536, 0.11 #1362, 0.05 #797), 0d8lm (0.09 #214, 0.06 #388, 0.03 #474), 05r5c (0.08 #1524, 0.07 #1350, 0.02 #2563), 02hnl (0.06 #367, 0.04 #2341, 0.04 #2210) >> Best rule #1535 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 01gv_f; 01ccr8; >> query: (?x5437, 03bx0bm) <- gender(?x5437, ?x231), profession(?x5437, ?x131), performance_role(?x5437, ?x212), nationality(?x5437, ?x94) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1349 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 79 *> proper extension: 012j5h; 02zrv7; 016ggh; *> query: (?x5437, 0l14md) <- gender(?x5437, ?x231), profession(?x5437, ?x131), performance_role(?x5437, ?x212), nationality(?x5437, ?x94), ?x231 = 05zppz *> conf = 0.19 ranks of expected_values: 3 EVAL 027dpx performance_role 0l14md CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 82.000 0.419 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #18373-026w_gk PRED entity: 026w_gk PRED relation: award_winner! PRED expected values: 03nnm4t => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 107): 0418154 (0.20 #1808, 0.17 #7371, 0.17 #6813), 09g90vz (0.20 #1808, 0.17 #7371, 0.17 #6813), 03nnm4t (0.20 #1808, 0.17 #7371, 0.17 #6813), 02wzl1d (0.20 #1808, 0.17 #7371, 0.17 #6813), 058m5m4 (0.20 #1808, 0.17 #7371, 0.17 #6813), 05zksls (0.20 #1808, 0.17 #312, 0.05 #451), 0drtv8 (0.20 #1808, 0.11 #342, 0.04 #898), 027hjff (0.20 #1808, 0.06 #334, 0.05 #2837), 0g55tzk (0.20 #1808, 0.06 #413, 0.04 #691), 0hr3c8y (0.20 #1808, 0.06 #288, 0.03 #3069) >> Best rule #1808 for best value: >> intensional similarity = 2 >> extensional distance = 186 >> proper extension: 04rtpt; >> query: (?x5263, ?x873) <- program(?x5263, ?x2528), honored_for(?x873, ?x2528) >> conf = 0.20 => this is the best rule for 11 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 026w_gk award_winner! 03nnm4t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 104.000 0.204 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18372-0lzkm PRED entity: 0lzkm PRED relation: artists! PRED expected values: 0g_bh => 164 concepts (79 used for prediction) PRED predicted values (max 10 best out of 267): 016clz (0.82 #2180, 0.79 #4352, 0.78 #1870), 06by7 (0.67 #1888, 0.64 #2198, 0.60 #644), 064t9 (0.51 #4051, 0.50 #14, 0.46 #10274), 0cx7f (0.50 #140, 0.44 #1384, 0.43 #2936), 02qm5j (0.50 #156, 0.25 #2641, 0.11 #1400), 05r6t (0.44 #1638, 0.29 #2879, 0.27 #2258), 0grjmv (0.40 #767, 0.22 #1390, 0.17 #1078), 0xhtw (0.34 #7475, 0.27 #7164, 0.26 #9344), 011j5x (0.33 #1589, 0.25 #34, 0.21 #2830), 08cyft (0.33 #2543, 0.14 #2854, 0.11 #1613) >> Best rule #2180 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0b1zz; 09jm8; >> query: (?x3735, 016clz) <- award_winner(?x1565, ?x3735), ?x1565 = 01c4_6, award_winner(?x2186, ?x3735), artists(?x2996, ?x3735) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #24574 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 581 *> proper extension: 0k4gf; 02wb6yq; 04gycf; 011hdn; 04k15; 05_pkf; 01v40wd; 04pf4r; 0hgqq; 0kvjrw; ... *> query: (?x3735, ?x302) <- artists(?x10307, ?x3735), instrumentalists(?x227, ?x3735), artists(?x10307, ?x3657), artists(?x302, ?x3657) *> conf = 0.03 ranks of expected_values: 188 EVAL 0lzkm artists! 0g_bh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 164.000 79.000 0.818 http://example.org/music/genre/artists #18371-03v0t PRED entity: 03v0t PRED relation: religion PRED expected values: 05sfs 01y0s9 03_gx => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 22): 05sfs (0.73 #145, 0.71 #97, 0.69 #194), 01y0s9 (0.56 #100, 0.56 #148, 0.53 #197), 03_gx (0.40 #201, 0.39 #104, 0.38 #370), 058x5 (0.35 #146, 0.33 #195, 0.32 #98), 03j6c (0.25 #35, 0.12 #59, 0.09 #808), 0kpl (0.25 #29, 0.06 #1233, 0.04 #53), 07w8f (0.25 #42, 0.06 #1233, 0.04 #66), 072w0 (0.24 #110, 0.22 #182, 0.21 #207), 0n2g (0.04 #54, 0.03 #683, 0.03 #78), 01spm (0.04 #69, 0.03 #93, 0.02 #189) >> Best rule #145 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0hjy; 06nrt; >> query: (?x3818, 05sfs) <- contains(?x94, ?x3818), location(?x1897, ?x3818), district_represented(?x176, ?x3818) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 03v0t religion 03_gx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.731 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 03v0t religion 01y0s9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.731 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 03v0t religion 05sfs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.731 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #18370-015p3p PRED entity: 015p3p PRED relation: award_nominee PRED expected values: 0bxtg => 93 concepts (48 used for prediction) PRED predicted values (max 10 best out of 862): 04m064 (0.81 #74775, 0.81 #88795, 0.81 #91132), 015p3p (0.26 #39722, 0.16 #112160, 0.03 #60754), 0bxtg (0.26 #39722, 0.16 #112160, 0.03 #79539), 053y4h (0.26 #39722, 0.02 #15236, 0.02 #78329), 0h1nt (0.26 #39722, 0.02 #35304, 0.02 #77367), 01s7zw (0.26 #39722, 0.01 #14575, 0.01 #56635), 01rnxn (0.26 #39722), 019f2f (0.26 #39722), 03jvmp (0.26 #39722), 0415svh (0.26 #39722) >> Best rule #74775 for best value: >> intensional similarity = 3 >> extensional distance = 1115 >> proper extension: 03x22w; >> query: (?x6221, ?x1522) <- film(?x6221, ?x224), gender(?x6221, ?x231), award_nominee(?x1522, ?x6221) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #39722 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 436 *> proper extension: 04n7njg; 02dh86; 01507p; 02hg53; 0dszr0; *> query: (?x6221, ?x496) <- student(?x2175, ?x6221), actor(?x7756, ?x6221), nominated_for(?x496, ?x7756) *> conf = 0.26 ranks of expected_values: 3 EVAL 015p3p award_nominee 0bxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 48.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18369-02784z PRED entity: 02784z PRED relation: award PRED expected values: 04ljl_l => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 305): 0gr51 (0.33 #101, 0.05 #913, 0.04 #7003), 04dn09n (0.33 #44, 0.04 #1668, 0.04 #2074), 02n9nmz (0.33 #70, 0.03 #2100, 0.03 #1694), 01l78d (0.33 #291, 0.03 #2727, 0.03 #3945), 02x17s4 (0.33 #126, 0.03 #1750, 0.02 #9058), 02qyp19 (0.33 #1, 0.02 #25989, 0.02 #15430), 02x1dht (0.33 #55, 0.02 #25989, 0.02 #14672), 09sb52 (0.25 #17906, 0.25 #19937, 0.25 #24810), 0f4x7 (0.22 #1249, 0.21 #437, 0.10 #19927), 0gqy2 (0.22 #1384, 0.18 #572, 0.11 #18031) >> Best rule #101 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 07h07; >> query: (?x10454, 0gr51) <- nationality(?x10454, ?x1558), nationality(?x10454, ?x512), ?x1558 = 01mjq, contains(?x512, ?x362), film_release_region(?x2893, ?x512), ?x2893 = 01jrbb >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #409 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 0f2c8g; 01wskg; *> query: (?x10454, 04ljl_l) <- place_of_death(?x10454, ?x362), gender(?x10454, ?x231), film(?x10454, ?x1547), people(?x1050, ?x10454) *> conf = 0.07 ranks of expected_values: 30 EVAL 02784z award 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 105.000 105.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18368-0227tr PRED entity: 0227tr PRED relation: film PRED expected values: 03h3x5 => 91 concepts (73 used for prediction) PRED predicted values (max 10 best out of 257): 0ds3t5x (0.26 #7185, 0.11 #53, 0.02 #23235), 03xf_m (0.20 #2885, 0.17 #6451, 0.06 #8234), 06t6dz (0.20 #2601, 0.08 #6167, 0.06 #7950), 07kh6f3 (0.11 #7753), 0bxsk (0.11 #1203, 0.08 #6552, 0.03 #42796), 02_1sj (0.11 #78, 0.03 #7210, 0.01 #16127), 05k4my (0.11 #1646, 0.03 #8778), 09gdh6k (0.11 #1305, 0.03 #8437), 043tvp3 (0.11 #1206, 0.03 #8338), 02q87z6 (0.11 #1026, 0.03 #8158) >> Best rule #7185 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 018swb; 06_bq1; >> query: (?x2580, 0ds3t5x) <- award_winner(?x4043, ?x2580), award_nominee(?x9152, ?x4043), ?x9152 = 02zfdp >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #3985 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 9 *> proper extension: 01lcxbb; 01qklj; 02x8s9; *> query: (?x2580, 03h3x5) <- location(?x2580, ?x2879), ?x2879 = 0ftxw *> conf = 0.09 ranks of expected_values: 68 EVAL 0227tr film 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 91.000 73.000 0.257 http://example.org/film/actor/film./film/performance/film #18367-06c62 PRED entity: 06c62 PRED relation: featured_film_locations! PRED expected values: 0cwy47 => 220 concepts (206 used for prediction) PRED predicted values (max 10 best out of 742): 09q5w2 (0.33 #2192, 0.01 #24101, 0.01 #25564), 061681 (0.27 #3700, 0.25 #4430, 0.14 #11004), 072x7s (0.25 #4496, 0.18 #3766, 0.14 #16910), 047csmy (0.25 #1854, 0.14 #15731, 0.14 #17921), 03k8th (0.19 #7273, 0.10 #18956, 0.05 #45248), 0413cff (0.18 #17167, 0.12 #7676, 0.09 #4023), 02yvct (0.17 #4538, 0.12 #1615, 0.12 #7461), 02f6g5 (0.17 #853, 0.12 #1583, 0.08 #5236), 0192hw (0.17 #4615, 0.11 #14109, 0.11 #16299), 05mrf_p (0.17 #4758, 0.09 #11332, 0.09 #10602) >> Best rule #2192 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01xbgx; >> query: (?x6959, ?x1077) <- locations(?x6464, ?x6959), film_release_region(?x204, ?x6959), films(?x6959, ?x1077) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3712 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 07ytt; *> query: (?x6959, 0cwy47) <- capital(?x205, ?x6959), contains(?x11886, ?x6959), taxonomy(?x6959, ?x939) *> conf = 0.09 ranks of expected_values: 183 EVAL 06c62 featured_film_locations! 0cwy47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 220.000 206.000 0.333 http://example.org/film/film/featured_film_locations #18366-0161sp PRED entity: 0161sp PRED relation: award PRED expected values: 02wh75 09sb52 => 98 concepts (80 used for prediction) PRED predicted values (max 10 best out of 274): 01by1l (0.43 #3313, 0.38 #513, 0.36 #5313), 01bgqh (0.40 #3243, 0.31 #5243, 0.29 #443), 01d38g (0.40 #3228, 0.18 #428, 0.13 #5228), 02f73p (0.38 #187, 0.12 #587, 0.12 #1387), 09sb52 (0.34 #19242, 0.33 #16041, 0.26 #10441), 01ckrr (0.33 #1429, 0.17 #3829, 0.15 #1829), 0c4z8 (0.32 #472, 0.23 #3272, 0.22 #2872), 03qbh5 (0.32 #3404, 0.29 #604, 0.26 #5404), 054ks3 (0.28 #4142, 0.18 #6942, 0.16 #5342), 01c427 (0.28 #3285, 0.23 #885, 0.13 #5285) >> Best rule #3313 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 011z3g; 012x03; >> query: (?x2908, 01by1l) <- artists(?x3928, ?x2908), award_winner(?x342, ?x2908), ?x3928 = 0gywn >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #19242 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1166 *> proper extension: 03zqc1; 0785v8; 04sx9_; 019_1h; 02lg9w; 06lgq8; 0f6_dy; 02xb2bt; 02j9lm; 050t68; ... *> query: (?x2908, 09sb52) <- film(?x2908, ?x781), award(?x2908, ?x2322), award_nominee(?x2908, ?x2415) *> conf = 0.34 ranks of expected_values: 5, 44 EVAL 0161sp award 09sb52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 80.000 0.427 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0161sp award 02wh75 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 98.000 80.000 0.427 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18365-02sjp PRED entity: 02sjp PRED relation: artists! PRED expected values: 0161rf => 172 concepts (77 used for prediction) PRED predicted values (max 10 best out of 261): 016clz (0.56 #23682, 0.50 #316, 0.50 #5), 064t9 (0.55 #17146, 0.54 #18391, 0.54 #7796), 06j6l (0.54 #17183, 0.45 #4098, 0.44 #18428), 08jyyk (0.50 #379, 0.25 #68, 0.12 #4428), 09nwwf (0.50 #138, 0.12 #449, 0.08 #4498), 06by7 (0.49 #18400, 0.47 #18712, 0.47 #6869), 0gywn (0.47 #4108, 0.43 #5663, 0.38 #18438), 0dl5d (0.39 #19022, 0.25 #330, 0.18 #4379), 05r6t (0.38 #83, 0.25 #394, 0.16 #23760), 03ckfl9 (0.38 #475, 0.15 #787, 0.12 #4524) >> Best rule #23682 for best value: >> intensional similarity = 3 >> extensional distance = 373 >> proper extension: 0m19t; 03xhj6; 018gm9; 05xq9; 01j59b0; 02mq_y; 06gcn; 013rfk; 02hzz; 01516r; ... >> query: (?x9163, 016clz) <- artists(?x4910, ?x9163), artists(?x4910, ?x1656), ?x1656 = 0l12d >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #13199 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 130 *> proper extension: 01q8wk7; *> query: (?x9163, 0161rf) <- artists(?x505, ?x9163), type_of_union(?x9163, ?x566), artists(?x505, ?x7556), ?x7556 = 01vttb9 *> conf = 0.08 ranks of expected_values: 94 EVAL 02sjp artists! 0161rf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 172.000 77.000 0.557 http://example.org/music/genre/artists #18364-09k56b7 PRED entity: 09k56b7 PRED relation: nominated_for! PRED expected values: 05ztjjw 0c422z4 02ppm4q 03hl6lc 0fhpv4 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 184): 099cng (0.78 #7426, 0.78 #7206, 0.71 #272), 094qd5 (0.78 #7426, 0.78 #7206, 0.67 #5895), 02x4wr9 (0.67 #5895, 0.67 #5894, 0.67 #7425), 04dn09n (0.42 #1337, 0.28 #2647, 0.25 #4610), 0gqy2 (0.33 #1413, 0.28 #2723, 0.27 #4686), 02x17s4 (0.32 #1388, 0.21 #297, 0.13 #2479), 0gq_v (0.31 #2635, 0.31 #3943, 0.31 #4598), 09sdmz (0.31 #1434, 0.17 #6986, 0.14 #343), 09qv_s (0.31 #1405, 0.17 #6986, 0.15 #2715), 0gr4k (0.30 #1332, 0.24 #8324, 0.24 #7009) >> Best rule #7426 for best value: >> intensional similarity = 4 >> extensional distance = 596 >> proper extension: 06k176; >> query: (?x1988, ?x1245) <- award_winner(?x1988, ?x4295), award(?x1988, ?x1245), ceremony(?x1245, ?x78), nominated_for(?x1245, ?x144) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #1420 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 132 *> proper extension: 048scx; 01719t; 07w8fz; 071nw5; *> query: (?x1988, 03hl6lc) <- award_winner(?x1988, ?x4295), genre(?x1988, ?x53), nominated_for(?x1162, ?x1988), ?x1162 = 099c8n *> conf = 0.26 ranks of expected_values: 22, 30, 34, 41, 61 EVAL 09k56b7 nominated_for! 0fhpv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 85.000 85.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09k56b7 nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 85.000 85.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09k56b7 nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 85.000 85.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09k56b7 nominated_for! 0c422z4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 85.000 85.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09k56b7 nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 85.000 85.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18363-01l2m3 PRED entity: 01l2m3 PRED relation: people PRED expected values: 09h_q => 68 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1082): 014zn0 (0.40 #3883, 0.25 #3222, 0.22 #7190), 0gyy0 (0.33 #6981, 0.25 #3013, 0.20 #8967), 02wr6r (0.33 #444, 0.25 #3087, 0.20 #3748), 016dgz (0.33 #1154, 0.20 #5120, 0.18 #11241), 02nrdp (0.33 #449, 0.17 #5736, 0.15 #13674), 0byfz (0.33 #6, 0.17 #5293, 0.15 #13231), 019z7q (0.33 #22, 0.17 #5309, 0.08 #11264), 03cdg (0.33 #559, 0.17 #5846, 0.08 #11801), 01w724 (0.33 #90, 0.17 #5377, 0.08 #11332), 06gg5c (0.33 #580, 0.17 #5867, 0.08 #11822) >> Best rule #3883 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0dcsx; >> query: (?x5855, 014zn0) <- people(?x5855, ?x12896), people(?x5855, ?x10913), people(?x5855, ?x10439), people(?x5855, ?x9738), nationality(?x10439, ?x94), award_winner(?x11301, ?x10913), people(?x1050, ?x9738), award_nominee(?x10275, ?x9738), risk_factors(?x5855, ?x5802), film(?x12896, ?x3643), ?x5802 = 0k95h >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #14550 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 12 *> proper extension: 0m32h; 0c58k; 0j8hd; *> query: (?x5855, ?x65) <- people(?x5855, ?x12896), people(?x5855, ?x10913), people(?x5855, ?x10439), people(?x5855, ?x9738), people(?x5855, ?x5484), nationality(?x10439, ?x94), award_winner(?x11301, ?x10913), people(?x1050, ?x9738), award_nominee(?x10275, ?x9738), risk_factors(?x5855, ?x5802), film(?x12896, ?x3643), people(?x1050, ?x65), participant(?x5484, ?x6433) *> conf = 0.02 ranks of expected_values: 1016 EVAL 01l2m3 people 09h_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 43.000 0.400 http://example.org/people/cause_of_death/people #18362-026gyn_ PRED entity: 026gyn_ PRED relation: award_winner PRED expected values: 09r9m7 => 97 concepts (29 used for prediction) PRED predicted values (max 10 best out of 637): 0z4s (0.22 #27918, 0.21 #13136, 0.21 #29562), 0143wl (0.22 #27918, 0.21 #13136, 0.21 #29562), 06p0s1 (0.22 #21349, 0.13 #22991), 0gn30 (0.08 #17304, 0.08 #18947, 0.07 #7450), 0146pg (0.07 #97, 0.06 #9949, 0.06 #11591), 02vyw (0.07 #594, 0.04 #7161, 0.03 #15372), 0h0wc (0.07 #409, 0.04 #8619, 0.03 #16830), 0159h6 (0.07 #65, 0.03 #8275, 0.02 #3348), 05kfs (0.07 #109, 0.02 #9961, 0.02 #8319), 07s93v (0.07 #247, 0.02 #8457, 0.01 #19706) >> Best rule #27918 for best value: >> intensional similarity = 4 >> extensional distance = 312 >> proper extension: 015g28; 042g97; >> query: (?x1903, ?x294) <- film(?x294, ?x1903), film(?x13579, ?x1903), honored_for(?x8150, ?x1903), titles(?x512, ?x1903) >> conf = 0.22 => this is the best rule for 2 predicted values *> Best rule #19706 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 153 *> proper extension: 02xhpl; *> query: (?x1903, ?x395) <- nominated_for(?x112, ?x1903), honored_for(?x5873, ?x1903), award_winner(?x112, ?x395) *> conf = 0.01 ranks of expected_values: 380 EVAL 026gyn_ award_winner 09r9m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 97.000 29.000 0.217 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #18361-016732 PRED entity: 016732 PRED relation: award_winner! PRED expected values: 031b3h => 117 concepts (95 used for prediction) PRED predicted values (max 10 best out of 313): 01bgqh (0.47 #5150, 0.42 #3004, 0.37 #32194), 01l29r (0.47 #5150, 0.42 #3004, 0.37 #32194), 0gr51 (0.33 #101, 0.03 #9973, 0.02 #2246), 023vrq (0.29 #749, 0.23 #1178, 0.16 #1607), 01cky2 (0.25 #1906, 0.15 #28758, 0.14 #619), 01c9dd (0.25 #2025, 0.14 #738, 0.11 #33053), 0gqz2 (0.24 #10382, 0.16 #11240, 0.12 #3085), 0gq9h (0.21 #2223, 0.16 #1365, 0.15 #936), 054ks3 (0.20 #10442, 0.19 #11300, 0.12 #3574), 0c4z8 (0.19 #11231, 0.14 #3076, 0.12 #4792) >> Best rule #5150 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 02jqjm; 01lf293; >> query: (?x6792, ?x724) <- award_winner(?x2139, ?x6792), ?x2139 = 01by1l, award(?x6792, ?x724) >> conf = 0.47 => this is the best rule for 2 predicted values *> Best rule #28758 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1319 *> proper extension: 035_2h; 0hm0k; 01j53q; *> query: (?x6792, ?x2139) <- award_winner(?x6792, ?x6819), award_winner(?x2139, ?x6819), award_winner(?x342, ?x6819) *> conf = 0.15 ranks of expected_values: 40 EVAL 016732 award_winner! 031b3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 117.000 95.000 0.471 http://example.org/award/award_category/winners./award/award_honor/award_winner #18360-015qsq PRED entity: 015qsq PRED relation: genre PRED expected values: 07s9rl0 06lbpz => 85 concepts (83 used for prediction) PRED predicted values (max 10 best out of 121): 07s9rl0 (0.79 #715, 0.76 #596, 0.73 #120), 02kdv5l (0.61 #3336, 0.59 #3574, 0.36 #955), 05p553 (0.39 #1552, 0.35 #4410, 0.34 #5839), 060__y (0.38 #135, 0.23 #3214, 0.19 #611), 02l7c8 (0.33 #253, 0.32 #491, 0.29 #1562), 03k9fj (0.29 #3583, 0.28 #1202, 0.27 #3345), 06n90 (0.27 #3584, 0.23 #3214, 0.22 #3346), 04xvlr (0.24 #597, 0.23 #3214, 0.23 #716), 02n4kr (0.24 #961, 0.23 #3214, 0.17 #3342), 03npn (0.23 #3214, 0.16 #3341, 0.12 #3579) >> Best rule #715 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 01c9d; >> query: (?x89, 07s9rl0) <- nominated_for(?x1198, ?x89), genre(?x89, ?x604), ?x1198 = 02pqp12 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 80 EVAL 015qsq genre 06lbpz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 85.000 83.000 0.788 http://example.org/film/film/genre EVAL 015qsq genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 83.000 0.788 http://example.org/film/film/genre #18359-04ykg PRED entity: 04ykg PRED relation: state_province_region! PRED expected values: 01b39j => 208 concepts (133 used for prediction) PRED predicted values (max 10 best out of 736): 01pdgp (0.55 #33608, 0.48 #51546, 0.32 #47808), 0w9hk (0.30 #39586, 0.20 #32859, 0.20 #85177), 0h1k6 (0.30 #39586, 0.20 #32859, 0.20 #85177), 013f9v (0.30 #39586, 0.20 #32859, 0.20 #85177), 0fpzwf (0.30 #39586, 0.20 #32859, 0.20 #85177), 0nhr5 (0.20 #32859, 0.20 #39585, 0.19 #53790), 0nhmw (0.20 #32859, 0.20 #39585, 0.19 #53790), 0ngy8 (0.20 #32859, 0.20 #39585, 0.19 #53790), 0nh1v (0.20 #32859, 0.20 #39585, 0.19 #53790), 0nht0 (0.20 #32859, 0.20 #39585, 0.19 #53790) >> Best rule #33608 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 02fvv; >> query: (?x1274, ?x3204) <- contains(?x1274, ?x3204), administrative_parent(?x1274, ?x94), currency(?x3204, ?x170) >> conf = 0.55 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04ykg state_province_region! 01b39j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 208.000 133.000 0.551 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #18358-0164v PRED entity: 0164v PRED relation: member_states! PRED expected values: 085h1 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 11): 085h1 (0.79 #23, 0.76 #43, 0.74 #132), 018cqq (0.38 #22, 0.30 #42, 0.23 #70), 02jxk (0.22 #21, 0.20 #41, 0.16 #85), 059dn (0.22 #24, 0.18 #44, 0.14 #48), 041288 (0.07 #134, 0.06 #117, 0.06 #188), 0b6css (0.07 #134, 0.06 #117, 0.06 #188), 0gkjy (0.07 #134, 0.06 #117, 0.06 #188), 02vk52z (0.07 #134, 0.06 #117, 0.06 #188), 0j7v_ (0.03 #297), 04k4l (0.03 #297) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 0cdbq; >> query: (?x8857, 085h1) <- participating_countries(?x1931, ?x8857), nationality(?x10866, ?x8857), award_nominee(?x10866, ?x262) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0164v member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.793 http://example.org/user/ktrueman/default_domain/international_organization/member_states #18357-0rxyk PRED entity: 0rxyk PRED relation: place PRED expected values: 0rxyk => 177 concepts (104 used for prediction) PRED predicted values (max 10 best out of 182): 013yq (0.33 #45, 0.17 #560, 0.10 #3606), 0rw2x (0.17 #955, 0.07 #1985, 0.06 #2500), 01ktz1 (0.17 #561, 0.07 #1591, 0.04 #2621), 0rxyk (0.10 #3606, 0.09 #14436, 0.08 #12889), 0rvty (0.10 #3606, 0.09 #14436, 0.08 #12889), 030qb3t (0.10 #3606, 0.09 #14436, 0.08 #12889), 0rt80 (0.09 #1522, 0.07 #2037, 0.06 #2552), 0rv97 (0.09 #1263, 0.07 #1778, 0.06 #2293), 0rwq6 (0.09 #1469, 0.07 #1984, 0.06 #2499), 0rwgm (0.09 #1451, 0.07 #1966, 0.06 #2481) >> Best rule #45 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 013yq; >> query: (?x11376, 013yq) <- location(?x10101, ?x11376), contains(?x3038, ?x11376), ?x3038 = 0d0x8, featured_film_locations(?x2362, ?x11376), time_zones(?x11376, ?x2674) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3606 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 0160w; *> query: (?x11376, ?x1523) <- location(?x10101, ?x11376), time_zones(?x11376, ?x2674), featured_film_locations(?x2362, ?x11376), ?x2674 = 02hcv8, featured_film_locations(?x2362, ?x1523) *> conf = 0.10 ranks of expected_values: 4 EVAL 0rxyk place 0rxyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 177.000 104.000 0.333 http://example.org/location/hud_county_place/place #18356-01vq3nl PRED entity: 01vq3nl PRED relation: artist! PRED expected values: 043ljr => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 38): 015_1q (0.04 #9822, 0.04 #1724, 0.04 #9964), 017l96 (0.03 #303, 0.03 #445, 0.03 #1013), 03rhqg (0.03 #9818, 0.03 #9960, 0.02 #8965), 0g768 (0.02 #9982, 0.02 #9840, 0.02 #8987), 0181dw (0.02 #9987, 0.02 #9845, 0.02 #7287), 033hn8 (0.02 #9816, 0.02 #9958, 0.02 #8963), 011k1h (0.02 #9812, 0.02 #9954, 0.02 #8959), 016ckq (0.02 #470, 0.01 #1180, 0.01 #328), 086k8 (0.02 #427, 0.01 #285), 0n85g (0.02 #9866, 0.02 #10008, 0.01 #9013) >> Best rule #9822 for best value: >> intensional similarity = 5 >> extensional distance = 2871 >> proper extension: 053y0s; 01nqfh_; 025xt8y; 07q1v4; 01kwlwp; 01x66d; 01vrt_c; 05pdbs; 01vs14j; 01l4zqz; ... >> query: (?x11676, 015_1q) <- profession(?x11676, ?x1032), profession(?x4140, ?x1032), profession(?x1672, ?x1032), ?x4140 = 01sb5r, film(?x1672, ?x814) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01vq3nl artist! 043ljr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 84.000 0.038 http://example.org/music/record_label/artist #18355-02b61v PRED entity: 02b61v PRED relation: prequel! PRED expected values: 06fqlk => 100 concepts (48 used for prediction) PRED predicted values (max 10 best out of 99): 09q5w2 (0.06 #2349, 0.05 #1264, 0.05 #3435), 014kq6 (0.05 #45, 0.04 #406, 0.03 #767), 03qnvdl (0.05 #31, 0.03 #753, 0.02 #1476), 061681 (0.05 #14, 0.03 #736, 0.02 #1459), 02b61v (0.05 #101, 0.03 #823, 0.01 #1807), 02vxq9m (0.05 #4, 0.03 #726), 01v1ln (0.05 #119), 0fdv3 (0.04 #218, 0.04 #399, 0.03 #940), 075wx7_ (0.04 #214, 0.01 #2383), 03r0g9 (0.04 #428, 0.03 #969, 0.02 #1150) >> Best rule #2349 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 0ptx_; >> query: (?x5871, ?x1077) <- nominated_for(?x154, ?x5871), nominated_for(?x5871, ?x1077), film_crew_role(?x5871, ?x137), film(?x10186, ?x5871) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02b61v prequel! 06fqlk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 48.000 0.060 http://example.org/film/film/prequel #18354-01dq9q PRED entity: 01dq9q PRED relation: artists! PRED expected values: 059kh => 90 concepts (39 used for prediction) PRED predicted values (max 10 best out of 232): 025sc50 (0.61 #668, 0.42 #3144, 0.42 #2215), 016clz (0.56 #5, 0.43 #1862, 0.42 #315), 059kh (0.56 #47, 0.25 #357, 0.22 #3716), 02lnbg (0.49 #677, 0.26 #3153, 0.25 #2224), 0gywn (0.46 #2223, 0.41 #985, 0.39 #676), 0glt670 (0.44 #661, 0.31 #4376, 0.30 #2208), 0ggx5q (0.44 #697, 0.29 #3173, 0.24 #8121), 0m0jc (0.44 #9, 0.20 #629, 0.18 #1247), 0xhtw (0.40 #2493, 0.39 #3422, 0.38 #1874), 02x8m (0.33 #948, 0.29 #329, 0.26 #2186) >> Best rule #668 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 01vvydl; 0lbj1; 01vvycq; 01w61th; 01vrt_c; 01vrz41; 01v_pj6; 0136p1; 07ss8_; 01vs_v8; ... >> query: (?x7407, 025sc50) <- artists(?x671, ?x7407), category(?x7407, ?x134), award(?x7407, ?x3488), ?x3488 = 02f71y >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 03t9sp; 05k79; 03fbc; 03xhj6; 03d9d6; 02vgh; 048xh; 01w5n51; 01323p; 02hzz; ... *> query: (?x7407, 059kh) <- artists(?x3243, ?x7407), category(?x7407, ?x134), group(?x227, ?x7407), ?x3243 = 0y3_8 *> conf = 0.56 ranks of expected_values: 3 EVAL 01dq9q artists! 059kh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 39.000 0.610 http://example.org/music/genre/artists #18353-065d1h PRED entity: 065d1h PRED relation: profession PRED expected values: 02hrh1q 04j5jl => 80 concepts (41 used for prediction) PRED predicted values (max 10 best out of 101): 02hrh1q (0.95 #5126, 0.95 #888, 0.90 #5858), 0cbd2 (0.85 #3512, 0.45 #4681, 0.40 #2636), 0kyk (0.81 #2658, 0.31 #4703, 0.29 #3534), 03gjzk (0.53 #451, 0.39 #3373, 0.36 #743), 018gz8 (0.53 #453, 0.33 #891, 0.32 #745), 0np9r (0.36 #4694, 0.31 #457, 0.25 #603), 02krf9 (0.24 #1486, 0.24 #1340, 0.24 #1632), 0d8qb (0.16 #515, 0.15 #1099, 0.13 #1246), 09jwl (0.13 #2793, 0.13 #2500, 0.13 #3377), 05z96 (0.12 #187, 0.09 #3547, 0.08 #2671) >> Best rule #5126 for best value: >> intensional similarity = 5 >> extensional distance = 1157 >> proper extension: 01sl1q; 044mz_; 04bdxl; 02s2ft; 079vf; 06qgvf; 01vvydl; 04yywz; 01k7d9; 02p65p; ... >> query: (?x10573, 02hrh1q) <- profession(?x10573, ?x524), film(?x10573, ?x4604), place_of_birth(?x10573, ?x2645), profession(?x2794, ?x524), ?x2794 = 027l0b >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 64 EVAL 065d1h profession 04j5jl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 80.000 41.000 0.951 http://example.org/people/person/profession EVAL 065d1h profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 41.000 0.951 http://example.org/people/person/profession #18352-0lbd9 PRED entity: 0lbd9 PRED relation: olympics! PRED expected values: 06mzp 01mk6 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 221): 03rjj (0.85 #3689, 0.82 #4839, 0.80 #4524), 06mzp (0.80 #4850, 0.78 #4535, 0.74 #3700), 0d04z6 (0.78 #1112, 0.75 #797, 0.50 #902), 06qd3 (0.75 #753, 0.67 #1150, 0.67 #1068), 019rg5 (0.75 #746, 0.67 #1061, 0.60 #642), 0d060g (0.74 #3690, 0.73 #4525, 0.73 #4840), 01mk6 (0.67 #1232, 0.62 #916, 0.56 #1972), 06mkj (0.67 #1081, 0.50 #766, 0.50 #558), 05b7q (0.67 #1124, 0.50 #809, 0.50 #601), 05b4w (0.64 #4875, 0.61 #4560, 0.59 #3725) >> Best rule #3689 for best value: >> intensional similarity = 12 >> extensional distance = 32 >> proper extension: 09n48; 0l6ny; 0swbd; 0lv1x; 0lk8j; 0blg2; 0nbjq; 0swff; 0kbvv; 018qb4; ... >> query: (?x6464, 03rjj) <- olympics(?x5114, ?x6464), olympics(?x3635, ?x6464), olympics(?x2316, ?x6464), olympics(?x304, ?x6464), sports(?x6464, ?x171), countries_spoken_in(?x254, ?x3635), organization(?x3635, ?x127), ?x304 = 0d0vqn, time_zones(?x3635, ?x6582), film_release_region(?x124, ?x2316), participating_countries(?x1931, ?x3635), medal(?x5114, ?x422) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #4850 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 42 *> proper extension: 018ctl; *> query: (?x6464, 06mzp) <- olympics(?x1203, ?x6464), olympics(?x142, ?x6464), country(?x359, ?x1203), film_release_region(?x1259, ?x1203), film_release_region(?x1202, ?x1203), ?x1202 = 0gj8t_b, administrative_area_type(?x1203, ?x2792), ?x1259 = 04hwbq, film_release_region(?x3886, ?x142), ?x3886 = 0198b6, medal(?x6464, ?x422) *> conf = 0.80 ranks of expected_values: 2, 7 EVAL 0lbd9 olympics! 01mk6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 64.000 64.000 0.853 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0lbd9 olympics! 06mzp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.853 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #18351-0x2p PRED entity: 0x2p PRED relation: season PRED expected values: 027mvrc => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 7): 027mvrc (0.79 #116, 0.76 #109, 0.76 #102), 05kcgsf (0.57 #29, 0.56 #99, 0.55 #92), 04110b0 (0.37 #87, 0.36 #101, 0.35 #94), 02h7s73 (0.32 #103, 0.32 #89, 0.30 #96), 03c6s24 (0.26 #90, 0.25 #97, 0.24 #111), 03c74_8 (0.26 #86, 0.25 #93, 0.20 #107), 04n36qk (0.08 #112, 0.08 #105, 0.07 #119) >> Best rule #116 for best value: >> intensional similarity = 11 >> extensional distance = 26 >> proper extension: 01yhm; >> query: (?x2405, 027mvrc) <- draft(?x2405, ?x3334), position(?x2405, ?x2010), ?x2010 = 02lyr4, draft(?x11361, ?x3334), draft(?x7399, ?x3334), draft(?x700, ?x3334), school(?x3334, ?x735), ?x700 = 06x68, ?x7399 = 06wpc, ?x11361 = 03m1n, ?x735 = 065y4w7 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0x2p season 027mvrc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.786 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #18350-026n9h3 PRED entity: 026n9h3 PRED relation: award_winner! PRED expected values: 02_1kl => 74 concepts (34 used for prediction) PRED predicted values (max 10 best out of 75): 02_1kl (0.46 #7943, 0.45 #5674, 0.04 #38600), 0gj50 (0.33 #434, 0.24 #12481, 0.16 #21565), 01b66t (0.33 #525, 0.24 #12481, 0.16 #21565), 0358x_ (0.28 #130, 0.24 #12481, 0.16 #21565), 02_1rq (0.24 #12481, 0.16 #21565, 0.15 #27249), 0phrl (0.24 #12481, 0.16 #21565, 0.15 #27249), 01b65l (0.24 #12481, 0.16 #21565, 0.15 #27249), 01y6dz (0.24 #12481, 0.16 #21565, 0.15 #27249), 034fl9 (0.16 #21565, 0.15 #27249, 0.15 #22701), 08jgk1 (0.10 #11517, 0.04 #12654, 0.03 #1306) >> Best rule #7943 for best value: >> intensional similarity = 3 >> extensional distance = 150 >> proper extension: 04n7njg; 03yf4d; 02pbp9; >> query: (?x6970, ?x7175) <- tv_program(?x6970, ?x2829), profession(?x6970, ?x987), nominated_for(?x6970, ?x7175) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026n9h3 award_winner! 02_1kl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 34.000 0.462 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #18349-020_95 PRED entity: 020_95 PRED relation: nominated_for PRED expected values: 02c638 => 110 concepts (47 used for prediction) PRED predicted values (max 10 best out of 723): 05q_dw (0.78 #64666, 0.77 #71131, 0.77 #74365), 04xbq3 (0.57 #12929, 0.57 #22630, 0.53 #17781), 03np63f (0.29 #35562, 0.22 #21014, 0.17 #43650), 02rqwhl (0.29 #35562, 0.22 #21014, 0.17 #43650), 0879bpq (0.29 #35562, 0.22 #21014, 0.17 #43650), 0422v0 (0.29 #35562, 0.22 #21014, 0.17 #43650), 03tn80 (0.29 #35562, 0.22 #21014, 0.17 #43650), 02k_4g (0.13 #6572, 0.03 #11420, 0.03 #21122), 011yg9 (0.12 #2548, 0.11 #5779, 0.10 #932), 095zlp (0.12 #3285, 0.06 #8133, 0.02 #17834) >> Best rule #64666 for best value: >> intensional similarity = 2 >> extensional distance = 848 >> proper extension: 01nqfh_; 0gv07g; 01m7f5r; 01b0k1; >> query: (?x5454, ?x5157) <- student(?x2909, ?x5454), award_winner(?x5157, ?x5454) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #63049 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 843 *> proper extension: 0b478; 09dv0sz; 01hrqc; 013ybx; *> query: (?x5454, ?x1330) <- award_nominee(?x5454, ?x489), spouse(?x9807, ?x489), film(?x489, ?x1330) *> conf = 0.04 ranks of expected_values: 158 EVAL 020_95 nominated_for 02c638 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 110.000 47.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #18348-028_yv PRED entity: 028_yv PRED relation: nominated_for! PRED expected values: 02pqp12 02ppm4q => 74 concepts (66 used for prediction) PRED predicted values (max 10 best out of 187): 02qt02v (0.67 #3811, 0.66 #8579, 0.66 #7148), 0l8z1 (0.45 #291, 0.33 #767, 0.22 #5955), 0gq9h (0.40 #301, 0.31 #3158, 0.31 #2682), 0k611 (0.40 #311, 0.28 #787, 0.27 #2692), 04dn09n (0.37 #274, 0.22 #3131, 0.21 #3608), 0gs9p (0.34 #303, 0.27 #3160, 0.27 #6974), 0gq_v (0.34 #258, 0.26 #734, 0.25 #3115), 0gr0m (0.31 #298, 0.25 #774, 0.21 #3155), 040njc (0.31 #245, 0.21 #3102, 0.20 #6916), 02qyntr (0.30 #418, 0.21 #894, 0.19 #3275) >> Best rule #3811 for best value: >> intensional similarity = 3 >> extensional distance = 466 >> proper extension: 05sy0cv; >> query: (?x204, ?x3233) <- nominated_for(?x9163, ?x204), award(?x204, ?x3233), languages(?x9163, ?x90) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #297 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 132 *> proper extension: 0c5qvw; *> query: (?x204, 02pqp12) <- nominated_for(?x9163, ?x204), nominated_for(?x1443, ?x204), ?x1443 = 054krc, language(?x204, ?x90) *> conf = 0.28 ranks of expected_values: 13, 22 EVAL 028_yv nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 74.000 66.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 028_yv nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 74.000 66.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18347-02j416 PRED entity: 02j416 PRED relation: contains! PRED expected values: 09c7w0 => 192 concepts (132 used for prediction) PRED predicted values (max 10 best out of 262): 0d0x8 (0.80 #80588, 0.78 #111038, 0.78 #57300), 09c7w0 (0.79 #10749, 0.78 #899, 0.75 #25968), 0nzny (0.58 #68044, 0.57 #45660, 0.26 #115519), 02jx1 (0.36 #60072, 0.19 #31424, 0.19 #33214), 04jpl (0.28 #31359, 0.28 #33149, 0.28 #34044), 059rby (0.25 #60900, 0.19 #5395, 0.18 #25985), 0d060g (0.22 #59998, 0.11 #12550, 0.11 #11654), 07ssc (0.18 #60017, 0.12 #9883, 0.11 #31369), 02_286 (0.18 #21532, 0.18 #31380, 0.18 #33170), 01cx_ (0.18 #12733, 0.18 #11837, 0.12 #4675) >> Best rule #80588 for best value: >> intensional similarity = 4 >> extensional distance = 296 >> proper extension: 01ngz1; 06jk5_; 01bvw5; 02bjhv; 02zccd; 01y17m; 018m5q; 0bqxw; 01lnyf; 02897w; ... >> query: (?x11415, ?x3038) <- state_province_region(?x11415, ?x3038), major_field_of_study(?x11415, ?x3995), colors(?x11415, ?x663), contains(?x2277, ?x11415) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #10749 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 02cttt; 07szy; 0pspl; 019dwp; 0217m9; 02xpy5; 0c5x_; 01hjy5; 0qlnr; 016w7b; *> query: (?x11415, 09c7w0) <- student(?x11415, ?x5246), currency(?x11415, ?x170), colors(?x11415, ?x3189), ?x3189 = 01g5v *> conf = 0.79 ranks of expected_values: 2 EVAL 02j416 contains! 09c7w0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 132.000 0.799 http://example.org/location/location/contains #18346-0c921 PRED entity: 0c921 PRED relation: award_winner! PRED expected values: 0ftlxj => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 138): 0c53zb (0.17 #11705, 0.06 #2740, 0.05 #61), 02rjjll (0.14 #992, 0.14 #1274, 0.03 #9735), 09n4nb (0.13 #1035, 0.12 #1317, 0.03 #9778), 0466p0j (0.13 #1063, 0.12 #1345, 0.03 #9806), 0jzphpx (0.10 #1308, 0.09 #1026, 0.04 #1590), 01s695 (0.10 #1272, 0.09 #990, 0.03 #11143), 019bk0 (0.10 #1003, 0.09 #1285, 0.03 #1567), 02wzl1d (0.10 #11, 0.06 #2267, 0.06 #1139), 0n8_m93 (0.10 #118, 0.05 #823, 0.04 #15654), 02jp5r (0.10 #69, 0.04 #15654, 0.04 #9307) >> Best rule #11705 for best value: >> intensional similarity = 3 >> extensional distance = 1364 >> proper extension: 03f5spx; 0jdhp; 02knnd; 0770cd; 07sgfsl; 014hr0; 01w7nwm; 063472; 02vyh; 062hgx; ... >> query: (?x9320, ?x4445) <- award_winner(?x10964, ?x9320), award_winner(?x1300, ?x10964), award_winner(?x4445, ?x10964) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #211 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 01c58j; 0177s6; 013qvn; 0btj0; 04rfq; *> query: (?x9320, 0ftlxj) <- place_of_burial(?x9320, ?x3153), profession(?x9320, ?x319), ?x319 = 01d_h8 *> conf = 0.03 ranks of expected_values: 118 EVAL 0c921 award_winner! 0ftlxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 143.000 143.000 0.170 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18345-01v0sx2 PRED entity: 01v0sx2 PRED relation: group! PRED expected values: 012x4t 01wrcxr => 77 concepts (34 used for prediction) PRED predicted values (max 10 best out of 251): 01wwvt2 (0.20 #214, 0.06 #393, 0.05 #573), 01vrnsk (0.17 #179, 0.13 #717, 0.13 #718), 01w724 (0.17 #179, 0.13 #717, 0.13 #718), 0fq117k (0.17 #179, 0.13 #717, 0.13 #718), 018dyl (0.17 #179, 0.13 #717, 0.13 #718), 02qwg (0.17 #179, 0.13 #717, 0.13 #718), 03bnv (0.17 #179, 0.13 #717, 0.13 #718), 06cc_1 (0.17 #179, 0.13 #717, 0.13 #718), 0163kf (0.17 #179, 0.13 #717, 0.13 #718), 02qtywd (0.17 #179, 0.13 #717, 0.13 #718) >> Best rule #214 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 02_5x9; 01qqwp9; 02mq_y; 0123r4; >> query: (?x1271, 01wwvt2) <- group(?x2273, ?x1271), role(?x2273, ?x745), artists(?x1127, ?x2273), ?x745 = 01vj9c >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #179 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 015srx; *> query: (?x1271, ?x5297) <- group(?x8049, ?x1271), group(?x7522, ?x1271), group(?x2273, ?x1271), ?x2273 = 01zmpg, nationality(?x7522, ?x279), award_nominee(?x8049, ?x5297), profession(?x8049, ?x220) *> conf = 0.17 ranks of expected_values: 71 EVAL 01v0sx2 group! 01wrcxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 34.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group EVAL 01v0sx2 group! 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 77.000 34.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group #18344-01nkxvx PRED entity: 01nkxvx PRED relation: role PRED expected values: 0bxl5 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 119): 0342h (0.66 #1928, 0.60 #1350, 0.56 #3471), 05148p4 (0.36 #2234, 0.13 #4265, 0.13 #3486), 0l14md (0.33 #199, 0.12 #2221, 0.08 #1834), 013y1f (0.33 #1377, 0.25 #224, 0.22 #1955), 018vs (0.32 #1934, 0.25 #203, 0.24 #3477), 0l14qv (0.31 #2220, 0.25 #198, 0.23 #3472), 01vj9c (0.27 #1840, 0.22 #2227, 0.19 #3479), 03qjg (0.26 #1017, 0.20 #1402, 0.11 #1980), 026t6 (0.25 #195, 0.24 #1830, 0.19 #4248), 04rzd (0.25 #232, 0.13 #1963, 0.08 #3506) >> Best rule #1928 for best value: >> intensional similarity = 5 >> extensional distance = 77 >> proper extension: 0p5mw; 0jfx1; 03k0yw; >> query: (?x8599, 0342h) <- award(?x8599, ?x884), role(?x8599, ?x614), role(?x8599, ?x314), role(?x614, ?x74), ?x314 = 02sgy >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #1987 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 77 *> proper extension: 0p5mw; 0jfx1; 03k0yw; *> query: (?x8599, 0bxl5) <- award(?x8599, ?x884), role(?x8599, ?x614), role(?x8599, ?x314), role(?x614, ?x74), ?x314 = 02sgy *> conf = 0.13 ranks of expected_values: 20 EVAL 01nkxvx role 0bxl5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 122.000 122.000 0.658 http://example.org/music/artist/track_contributions./music/track_contribution/role #18343-02hxhz PRED entity: 02hxhz PRED relation: currency PRED expected values: 09nqf => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.84 #85, 0.84 #78, 0.83 #36), 01nv4h (0.05 #23, 0.04 #30, 0.04 #44), 02l6h (0.01 #410, 0.01 #368, 0.01 #312), 088n7 (0.01 #112) >> Best rule #85 for best value: >> intensional similarity = 3 >> extensional distance = 153 >> proper extension: 02vrgnr; 09dv8h; 032clf; 03kx49; 017kz7; 042zrm; 032sl_; 01xlqd; >> query: (?x821, 09nqf) <- film(?x1335, ?x821), nominated_for(?x541, ?x821), film_distribution_medium(?x821, ?x2099) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hxhz currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.839 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #18342-01vvb4m PRED entity: 01vvb4m PRED relation: people! PRED expected values: 0xnvg => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 56): 041rx (0.26 #3816, 0.25 #4, 0.24 #4814), 033tf_ (0.24 #1835, 0.22 #1911, 0.20 #538), 0x67 (0.19 #1914, 0.19 #3132, 0.16 #6651), 0xnvg (0.19 #164, 0.13 #88, 0.12 #1841), 02w7gg (0.12 #230, 0.12 #914, 0.10 #5807), 07bch9 (0.10 #326, 0.09 #174, 0.09 #98), 03bkbh (0.09 #563, 0.08 #259, 0.08 #1096), 09vc4s (0.09 #540, 0.08 #1073, 0.08 #388), 07hwkr (0.08 #999, 0.08 #1229, 0.08 #1306), 02ctzb (0.08 #242, 0.07 #166, 0.07 #698) >> Best rule #3816 for best value: >> intensional similarity = 3 >> extensional distance = 368 >> proper extension: 07h1q; >> query: (?x3056, 041rx) <- religion(?x3056, ?x1985), people(?x1423, ?x3056), place_of_birth(?x3056, ?x8468) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #164 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 01rr9f; 06w2sn5; 0169dl; 0jfx1; 0kszw; 0q5hw; 04cr6qv; 02pjvc; 01wrcxr; 0gs6vr; *> query: (?x3056, 0xnvg) <- celebrity(?x376, ?x3056), profession(?x3056, ?x319), friend(?x5391, ?x3056) *> conf = 0.19 ranks of expected_values: 4 EVAL 01vvb4m people! 0xnvg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 117.000 0.265 http://example.org/people/ethnicity/people #18341-01tp5bj PRED entity: 01tp5bj PRED relation: religion PRED expected values: 0c8wxp => 119 concepts (81 used for prediction) PRED predicted values (max 10 best out of 19): 0c8wxp (0.34 #3488, 0.26 #270, 0.25 #930), 0flw86 (0.25 #2, 0.07 #266, 0.05 #222), 03_gx (0.15 #3495, 0.13 #937, 0.07 #277), 092bf5 (0.15 #279, 0.11 #147, 0.06 #323), 0kq2 (0.12 #941, 0.07 #281, 0.04 #3499), 0n2g (0.10 #936, 0.04 #1464, 0.04 #2171), 03j6c (0.07 #3192, 0.07 #3502, 0.07 #3369), 01lp8 (0.05 #177, 0.04 #3483, 0.04 #2072), 05tgm (0.05 #202, 0.01 #1611), 01spm (0.04 #960, 0.04 #300, 0.02 #2195) >> Best rule #3488 for best value: >> intensional similarity = 5 >> extensional distance = 1069 >> proper extension: 01w3v; 07c37; 01xyt7; 0mcf4; 036hf4; 07h1q; 01cqz5; 015c1b; >> query: (?x2492, 0c8wxp) <- religion(?x2492, ?x2694), religion(?x4512, ?x2694), religion(?x1593, ?x2694), gender(?x4512, ?x231), profession(?x1593, ?x987) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tp5bj religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 81.000 0.337 http://example.org/people/person/religion #18340-03x1s8 PRED entity: 03x1s8 PRED relation: school! PRED expected values: 0jmj7 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 88): 0jmj7 (0.42 #593, 0.39 #499, 0.38 #1439), 05m_8 (0.08 #1037, 0.07 #473, 0.07 #1413), 049n7 (0.08 #12, 0.05 #670, 0.05 #1422), 07147 (0.08 #68, 0.05 #538, 0.04 #2042), 06x68 (0.06 #477, 0.06 #1041, 0.06 #665), 07l4z (0.06 #541, 0.05 #1387, 0.05 #2233), 05xvj (0.06 #559, 0.04 #1687, 0.04 #1123), 0713r (0.05 #601, 0.05 #507, 0.04 #1917), 01yjl (0.05 #501, 0.05 #1911, 0.05 #2193), 02d02 (0.05 #540, 0.04 #1104, 0.04 #634) >> Best rule #593 for best value: >> intensional similarity = 4 >> extensional distance = 147 >> proper extension: 01nkcn; 02s62q; 04sylm; 017z88; 02bb47; 01f1r4; 0m9_5; 020923; 02zd460; 02bqy; ... >> query: (?x12126, 0jmj7) <- currency(?x12126, ?x170), contains(?x12384, ?x12126), ?x170 = 09nqf, source(?x12384, ?x958) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x1s8 school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.416 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #18339-02tz9z PRED entity: 02tz9z PRED relation: registering_agency PRED expected values: 03z19 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.89 #6, 0.84 #22, 0.83 #18) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 017j69; 027mdh; >> query: (?x12127, 03z19) <- institution(?x4981, ?x12127), ?x4981 = 03bwzr4, currency(?x12127, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tz9z registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.889 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #18338-0m7d0 PRED entity: 0m7d0 PRED relation: time_zones PRED expected values: 02hcv8 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.85 #495, 0.85 #68, 0.79 #42), 02fqwt (0.22 #417, 0.21 #170, 0.21 #313), 02lcqs (0.21 #265, 0.21 #395, 0.21 #486), 02hczc (0.12 #171, 0.10 #314, 0.10 #340), 02llzg (0.07 #277, 0.07 #459, 0.07 #720), 03bdv (0.05 #110, 0.05 #279, 0.05 #618), 03plfd (0.03 #361, 0.03 #609, 0.03 #713), 042g7t (0.03 #167, 0.02 #115, 0.02 #284), 052vwh (0.02 #116, 0.01 #415, 0.01 #350), 0gsrz4 (0.02 #802, 0.02 #856, 0.01 #882) >> Best rule #495 for best value: >> intensional similarity = 3 >> extensional distance = 278 >> proper extension: 0ntwb; >> query: (?x3350, ?x2674) <- adjoins(?x3350, ?x13653), currency(?x13653, ?x170), time_zones(?x13653, ?x2674) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m7d0 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.854 http://example.org/location/location/time_zones #18337-01s7qqw PRED entity: 01s7qqw PRED relation: artists! PRED expected values: 02yw1c => 130 concepts (105 used for prediction) PRED predicted values (max 10 best out of 222): 06by7 (0.69 #4368, 0.68 #3124, 0.65 #8098), 0155w (0.47 #6937, 0.38 #8183, 0.37 #3209), 02yv6b (0.44 #1649, 0.35 #4445, 0.33 #2270), 016clz (0.41 #4975, 0.39 #9013, 0.35 #15546), 064t9 (0.35 #27359, 0.34 #30467, 0.33 #9957), 05w3f (0.33 #4693, 0.33 #2208, 0.32 #3139), 0dl5d (0.33 #1571, 0.30 #4969, 0.24 #13054), 03_d0 (0.33 #25803, 0.32 #3426, 0.29 #6843), 01lyv (0.32 #3136, 0.27 #3757, 0.25 #2205), 07sbbz2 (0.32 #3111, 0.27 #3732, 0.20 #6839) >> Best rule #4368 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 04r1t; 0qdyf; 0d193h; 0134tg; 07mvp; 02vr7; 033s6; 016vn3; >> query: (?x5208, 06by7) <- influenced_by(?x986, ?x5208), artists(?x2249, ?x5208), artists(?x2249, ?x8819), ?x8819 = 01j590z >> conf = 0.69 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01s7qqw artists! 02yw1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 105.000 0.692 http://example.org/music/genre/artists #18336-0glmv PRED entity: 0glmv PRED relation: film PRED expected values: 06rhz7 => 101 concepts (59 used for prediction) PRED predicted values (max 10 best out of 981): 0prrm (0.39 #20516, 0.05 #38386, 0.03 #29451), 07c72 (0.35 #80428, 0.34 #87580, 0.33 #82217), 02_fz3 (0.33 #1381, 0.06 #105455, 0.02 #21039), 03k8th (0.33 #1718, 0.02 #21376), 061681 (0.33 #109, 0.02 #19767), 02yxbc (0.33 #1297, 0.01 #31677), 04x4nv (0.33 #1511), 055td_ (0.33 #734), 047tsx3 (0.33 #652), 01shy7 (0.25 #3996, 0.25 #2209, 0.08 #16506) >> Best rule #20516 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 022769; >> query: (?x3242, 0prrm) <- film(?x3242, ?x1219), film(?x9656, ?x1219), film_release_region(?x1219, ?x87), ?x9656 = 04tnqn >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0glmv film 06rhz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 59.000 0.392 http://example.org/film/actor/film./film/performance/film #18335-0trv PRED entity: 0trv PRED relation: colors PRED expected values: 04mkbj => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.50 #1, 0.42 #644, 0.40 #21), 019sc (0.44 #550, 0.22 #309, 0.21 #1970), 01g5v (0.37 #164, 0.36 #144, 0.33 #83), 03wkwg (0.25 #15, 0.20 #35, 0.12 #483), 06fvc (0.25 #82, 0.17 #2306, 0.17 #2305), 038hg (0.23 #474, 0.17 #2306, 0.17 #2305), 0jc_p (0.20 #104, 0.20 #44, 0.14 #306), 09ggk (0.20 #116, 0.17 #2306, 0.17 #2305), 01jnf1 (0.20 #51, 0.12 #483, 0.08 #2284), 03vtbc (0.17 #2306, 0.17 #2305, 0.16 #470) >> Best rule #1 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 06pwq; 0bx8pn; >> query: (?x8706, 083jv) <- school(?x8901, ?x8706), major_field_of_study(?x8706, ?x4321), ?x8901 = 07l4z, ?x4321 = 0g26h, state_province_region(?x8706, ?x938), colors(?x8706, ?x332) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #483 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 59 *> proper extension: 016sd3; *> query: (?x8706, ?x9778) <- school(?x580, ?x8706), colors(?x8706, ?x332), colors(?x7418, ?x332), colors(?x6919, ?x332), colors(?x7418, ?x9778), ?x6919 = 017v3q, colors(?x662, ?x332) *> conf = 0.12 ranks of expected_values: 14 EVAL 0trv colors 04mkbj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 200.000 200.000 0.500 http://example.org/education/educational_institution/colors #18334-0xmp9 PRED entity: 0xmp9 PRED relation: location! PRED expected values: 01sb5r 03f6fl0 => 113 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1697): 03l3ln (0.62 #15102, 0.62 #42791, 0.59 #22654), 0pyww (0.33 #981, 0.12 #11048, 0.10 #8531), 0jsg0m (0.33 #1495, 0.12 #11562, 0.10 #9045), 02mjmr (0.33 #501, 0.10 #8051, 0.08 #3018), 04x1_w (0.33 #1493, 0.08 #11560, 0.06 #14078), 01gvxv (0.33 #2281, 0.08 #12348, 0.06 #14866), 02g8h (0.33 #34, 0.06 #12619, 0.04 #10101), 05ry0p (0.17 #2159, 0.12 #12226, 0.09 #14744), 023mdt (0.17 #1863, 0.12 #11930, 0.09 #14448), 022yb4 (0.17 #1708, 0.12 #11775, 0.09 #14293) >> Best rule #15102 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0nbrp; 01qcx_; >> query: (?x12867, ?x6677) <- place_of_birth(?x6677, ?x12867), film(?x6677, ?x1385), company(?x6677, ?x4672), award_winner(?x1385, ?x65) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3326 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0tbql; 04gxf; *> query: (?x12867, 01sb5r) <- place_of_birth(?x6677, ?x12867), time_zones(?x12867, ?x2674), politician(?x8714, ?x6677), student(?x373, ?x6677) *> conf = 0.08 ranks of expected_values: 501, 1042 EVAL 0xmp9 location! 03f6fl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 113.000 52.000 0.625 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0xmp9 location! 01sb5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 113.000 52.000 0.625 http://example.org/people/person/places_lived./people/place_lived/location #18333-01j6mff PRED entity: 01j6mff PRED relation: student! PRED expected values: 01qdhx => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 190): 0fr9jp (0.12 #872, 0.06 #6669, 0.03 #12993), 02q253 (0.12 #1032), 09f2j (0.12 #1213, 0.08 #3321, 0.06 #2794), 01w5m (0.06 #4848, 0.06 #14862, 0.06 #12753), 03ksy (0.06 #1160, 0.06 #19606, 0.05 #4322), 01rtm4 (0.06 #1058, 0.04 #1585, 0.03 #3693), 07szy (0.06 #1094, 0.04 #1621, 0.02 #2675), 01g0p5 (0.06 #1261, 0.04 #1788, 0.02 #3369), 06thjt (0.06 #1452, 0.04 #1979, 0.02 #3560), 019n9w (0.06 #1367, 0.04 #1894, 0.02 #3475) >> Best rule #872 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01j59b0; >> query: (?x9493, 0fr9jp) <- artist(?x2149, ?x9493), artists(?x12241, ?x9493), ?x12241 = 0m8vm >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01j6mff student! 01qdhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #18332-018m5q PRED entity: 018m5q PRED relation: campuses! PRED expected values: 018m5q => 137 concepts (74 used for prediction) PRED predicted values (max 10 best out of 225): 02hmw9 (0.09 #229, 0.07 #26247, 0.07 #14221), 07tl0 (0.09 #26, 0.07 #26247, 0.07 #14221), 013nky (0.09 #377, 0.07 #26247, 0.07 #14221), 01nn7r (0.09 #503, 0.07 #26247, 0.07 #14221), 0c_zj (0.09 #133, 0.07 #26247, 0.07 #14221), 07tlg (0.09 #486, 0.05 #1032, 0.03 #1578), 0k2h6 (0.07 #26247, 0.07 #14221, 0.05 #932), 01f2xy (0.07 #26247, 0.07 #14221, 0.05 #801), 01s753 (0.07 #26247, 0.07 #14221, 0.05 #1061), 01clyb (0.07 #26247, 0.07 #14221, 0.05 #28433) >> Best rule #229 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 05zhg; 029rmn; >> query: (?x3671, 02hmw9) <- contains(?x3302, ?x3671), contains(?x3301, ?x3671), contains(?x1310, ?x3671), ?x1310 = 02jx1, place_of_birth(?x698, ?x3301), ?x3302 = 01w0v, location(?x5345, ?x3301) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #26247 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 353 *> proper extension: 01rs59; 043ttv; *> query: (?x3671, ?x1098) <- citytown(?x3671, ?x3301), citytown(?x1098, ?x3301), state_province_region(?x3671, ?x3302), time_zones(?x3301, ?x5327) *> conf = 0.07 ranks of expected_values: 11 EVAL 018m5q campuses! 018m5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 137.000 74.000 0.091 http://example.org/education/educational_institution/campuses #18331-01fxck PRED entity: 01fxck PRED relation: category PRED expected values: 08mbj5d => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.76 #70, 0.70 #94, 0.68 #95) >> Best rule #70 for best value: >> intensional similarity = 3 >> extensional distance = 247 >> proper extension: 0f0y8; 053y0s; 05cljf; 06cc_1; 0168cl; 01kwlwp; 03f5spx; 01jrz5j; 02whj; 016kjs; ... >> query: (?x7814, 08mbj5d) <- profession(?x7814, ?x2348), location(?x7814, ?x739), ?x2348 = 0nbcg >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fxck category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.755 http://example.org/common/topic/webpage./common/webpage/category #18330-0gps0z PRED entity: 0gps0z PRED relation: artists! PRED expected values: 05bt6j 02lnbg => 181 concepts (131 used for prediction) PRED predicted values (max 10 best out of 282): 02lnbg (0.57 #6238, 0.56 #7165, 0.54 #1911), 06j6l (0.45 #2520, 0.44 #8394, 0.42 #7156), 05bt6j (0.39 #7461, 0.35 #18904, 0.34 #18594), 03mb9 (0.33 #100, 0.15 #7208, 0.14 #1954), 016clz (0.30 #33102, 0.24 #32483, 0.23 #30938), 0y3_8 (0.30 #1901, 0.23 #7155, 0.21 #6228), 0xhtw (0.25 #32495, 0.23 #33732, 0.22 #34351), 026z9 (0.24 #1930, 0.18 #6257, 0.17 #76), 08cyft (0.22 #1910, 0.21 #7164, 0.21 #6237), 0m0jc (0.22 #1863, 0.18 #6190, 0.17 #7117) >> Best rule #6238 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 01yzl2; >> query: (?x9639, 02lnbg) <- artists(?x5876, ?x9639), artists(?x671, ?x9639), ?x671 = 064t9, nationality(?x9639, ?x94), ?x5876 = 0ggx5q >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0gps0z artists! 02lnbg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 131.000 0.567 http://example.org/music/genre/artists EVAL 0gps0z artists! 05bt6j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 181.000 131.000 0.567 http://example.org/music/genre/artists #18329-01dbns PRED entity: 01dbns PRED relation: country PRED expected values: 06t2t => 145 concepts (117 used for prediction) PRED predicted values (max 10 best out of 11): 09c7w0 (0.28 #421, 0.25 #4, 0.24 #291), 0chghy (0.23 #386, 0.22 #112, 0.21 #81), 03h64 (0.13 #37, 0.12 #43, 0.11 #34), 06mtq (0.09 #219, 0.08 #315, 0.08 #448), 05nrg (0.08 #315, 0.08 #180, 0.08 #179), 05rgl (0.04 #467), 01b8jj (0.04 #164), 0mgp (0.04 #164), 062qg (0.04 #164), 0chgzm (0.04 #164) >> Best rule #421 for best value: >> intensional similarity = 10 >> extensional distance = 408 >> proper extension: 0ljl8; >> query: (?x7950, 09c7w0) <- organization(?x4095, ?x7950), organization(?x4095, ?x12086), organization(?x4095, ?x11822), organization(?x4095, ?x11488), organization(?x4095, ?x6034), citytown(?x11822, ?x8745), company(?x4095, ?x4390), student(?x6034, ?x164), ?x12086 = 07w6r, category(?x11488, ?x134) >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01dbns country 06t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 145.000 117.000 0.276 http://example.org/organization/organization/headquarters./location/mailing_address/country #18328-01h5f8 PRED entity: 01h5f8 PRED relation: artists! PRED expected values: 01lyv => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 188): 06by7 (0.45 #12642, 0.42 #13273, 0.42 #16425), 03_d0 (0.38 #1908, 0.31 #5069, 0.30 #1276), 017_qw (0.37 #382, 0.30 #66, 0.23 #3858), 064t9 (0.36 #13264, 0.34 #19255, 0.34 #18939), 021dvj (0.30 #54, 0.26 #370, 0.12 #3846), 0ggq0m (0.30 #13, 0.21 #329, 0.14 #3173), 06q6jz (0.30 #192, 0.21 #508, 0.11 #3352), 06j6l (0.23 #4159, 0.23 #12039, 0.22 #1315), 0gywn (0.23 #4169, 0.22 #1325, 0.18 #2273), 01lyv (0.22 #13286, 0.18 #18961, 0.18 #19277) >> Best rule #12642 for best value: >> intensional similarity = 3 >> extensional distance = 258 >> proper extension: 04mx7s; >> query: (?x11509, 06by7) <- nationality(?x11509, ?x94), role(?x11509, ?x1655), type_of_union(?x11509, ?x566) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #13286 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 271 *> proper extension: 011zf2; 01vd7hn; 01l03w2; 01yzl2; *> query: (?x11509, 01lyv) <- nationality(?x11509, ?x94), award_nominee(?x2807, ?x11509), instrumentalists(?x227, ?x11509) *> conf = 0.22 ranks of expected_values: 10 EVAL 01h5f8 artists! 01lyv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 136.000 136.000 0.446 http://example.org/music/genre/artists #18327-01pcrw PRED entity: 01pcrw PRED relation: film PRED expected values: 01mszz => 131 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1055): 03nqnnk (0.18 #1025, 0.15 #4609, 0.08 #9986), 0m9p3 (0.15 #3972, 0.05 #7557, 0.04 #11142), 04zl8 (0.11 #8094, 0.03 #34976, 0.02 #45729), 01shy7 (0.10 #23723, 0.09 #41643, 0.07 #57773), 04tc1g (0.09 #133, 0.08 #9094, 0.08 #1925), 0prrm (0.09 #862, 0.08 #9823, 0.08 #11616), 0sxns (0.09 #1079, 0.08 #10040, 0.08 #11833), 04smdd (0.09 #726, 0.08 #9687, 0.08 #11480), 02qr3k8 (0.09 #1291, 0.08 #3083, 0.08 #4875), 03hj3b3 (0.09 #307, 0.08 #2099, 0.08 #3891) >> Best rule #1025 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 01dw4q; 06cv1; 0pz7h; 0h1mt; 0693l; 0f7hc; 01tnbn; 01xv77; 036dyy; >> query: (?x3083, 03nqnnk) <- participant(?x380, ?x3083), profession(?x3083, ?x1032), profession(?x3083, ?x353), participant(?x3083, ?x2451), ?x1032 = 02hrh1q, ?x353 = 0cbd2 >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #8257 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 02j8nx; 0178rl; *> query: (?x3083, 01mszz) <- profession(?x3083, ?x1032), profession(?x3083, ?x353), ?x353 = 0cbd2, ?x1032 = 02hrh1q, nationality(?x3083, ?x1310), ?x1310 = 02jx1 *> conf = 0.05 ranks of expected_values: 136 EVAL 01pcrw film 01mszz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 131.000 85.000 0.182 http://example.org/film/actor/film./film/performance/film #18326-0dsvzh PRED entity: 0dsvzh PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 25): 0ch6mp2 (0.74 #745, 0.72 #356, 0.72 #1980), 0dxtw (0.40 #115, 0.40 #325, 0.34 #749), 01vx2h (0.35 #750, 0.33 #326, 0.31 #396), 02ynfr (0.17 #754, 0.16 #330, 0.15 #400), 089g0h (0.13 #334, 0.11 #1993, 0.10 #2063), 0215hd (0.12 #1992, 0.12 #2062, 0.12 #970), 0d2b38 (0.12 #340, 0.10 #270, 0.10 #305), 02rh1dz (0.12 #394, 0.11 #324, 0.10 #748), 015h31 (0.11 #393, 0.08 #747, 0.08 #253), 01xy5l_ (0.11 #363, 0.11 #328, 0.10 #752) >> Best rule #745 for best value: >> intensional similarity = 3 >> extensional distance = 331 >> proper extension: 0170z3; 0g56t9t; 09sh8k; 09xbpt; 0bvn25; 0czyxs; 0gtv7pk; 0dtw1x; 01ln5z; 0cpllql; ... >> query: (?x813, 0ch6mp2) <- category(?x813, ?x134), genre(?x813, ?x53), film_crew_role(?x813, ?x137) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dsvzh film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.745 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18325-0133sq PRED entity: 0133sq PRED relation: nationality PRED expected values: 02jx1 => 98 concepts (96 used for prediction) PRED predicted values (max 10 best out of 146): 09c7w0 (0.79 #1, 0.77 #5945, 0.77 #694), 02jx1 (0.55 #230, 0.39 #2012, 0.36 #1022), 03rjj (0.15 #104, 0.05 #401, 0.03 #3371), 03rk0 (0.14 #639, 0.09 #3906, 0.08 #2817), 06q1r (0.08 #571, 0.04 #2056, 0.04 #1066), 0j5g9 (0.06 #1051, 0.03 #2041, 0.01 #3328), 012m_ (0.05 #90, 0.02 #387, 0.02 #684), 03_3d (0.05 #6, 0.02 #1095, 0.02 #2382), 02k1b (0.05 #83, 0.01 #5845), 0d060g (0.05 #403, 0.05 #1888, 0.04 #2779) >> Best rule #1 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 01p8r8; 0b57p6; 0jnb0; 01c5d5; 05h7tk; >> query: (?x10854, 09c7w0) <- profession(?x10854, ?x1966), profession(?x10854, ?x987), profession(?x10854, ?x524), ?x987 = 0dxtg, ?x524 = 02jknp, ?x1966 = 015h31 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #230 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 0h5f5n; 05cj4r; 09fb5; 0159h6; 08f3b1; 01sp81; 0136g9; 02_j7t; 015pxr; 0184dt; ... *> query: (?x10854, 02jx1) <- profession(?x10854, ?x987), ?x987 = 0dxtg, people(?x743, ?x10854), ?x743 = 02w7gg *> conf = 0.55 ranks of expected_values: 2 EVAL 0133sq nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 96.000 0.789 http://example.org/people/person/nationality #18324-01wyz92 PRED entity: 01wyz92 PRED relation: award PRED expected values: 03t5kl 02f72_ => 115 concepts (97 used for prediction) PRED predicted values (max 10 best out of 283): 03qbh5 (0.78 #18006, 0.77 #24411, 0.76 #24410), 03t5n3 (0.60 #249, 0.18 #20808, 0.18 #20007), 02f716 (0.56 #4577, 0.54 #4177, 0.49 #2177), 02f73b (0.51 #2286, 0.49 #4686, 0.48 #4286), 01by1l (0.49 #2113, 0.45 #16118, 0.43 #4513), 02f72_ (0.48 #4629, 0.48 #4229, 0.40 #2229), 01bgqh (0.47 #12046, 0.43 #2043, 0.41 #4443), 02f73p (0.43 #4187, 0.43 #4587, 0.37 #2187), 02f6ym (0.43 #2258, 0.22 #4258, 0.22 #2658), 02v1m7 (0.42 #4114, 0.41 #4514, 0.40 #114) >> Best rule #18006 for best value: >> intensional similarity = 3 >> extensional distance = 463 >> proper extension: 01ky2h; 01lcxbb; 01wz_ml; 01vsy3q; 0lsw9; 0f6lx; 013rds; 06lxn; >> query: (?x3481, ?x2877) <- artist(?x6474, ?x3481), award_winner(?x2877, ?x3481), artists(?x671, ?x3481) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4629 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 07hgm; 017959; 016l09; *> query: (?x3481, 02f72_) <- award(?x3481, ?x2877), ?x2877 = 02f5qb, artists(?x671, ?x3481) *> conf = 0.48 ranks of expected_values: 6, 15 EVAL 01wyz92 award 02f72_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 115.000 97.000 0.783 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01wyz92 award 03t5kl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 115.000 97.000 0.783 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18323-0kv238 PRED entity: 0kv238 PRED relation: film_crew_role PRED expected values: 09zzb8 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.72 #100, 0.72 #1162, 0.69 #1461), 01pvkk (0.31 #174, 0.29 #573, 0.29 #9), 02ynfr (0.29 #13, 0.18 #112, 0.17 #543), 015h31 (0.20 #41, 0.11 #107, 0.09 #1992), 0d2b38 (0.16 #56, 0.10 #288, 0.09 #1184), 033smt (0.16 #58, 0.09 #1992, 0.05 #124), 0215hd (0.16 #115, 0.13 #413, 0.12 #546), 089fss (0.14 #6, 0.09 #171, 0.09 #1992), 04pyp5 (0.14 #14, 0.09 #1992, 0.07 #279), 02vs3x5 (0.14 #21, 0.09 #1992, 0.06 #319) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 04b_jc; >> query: (?x2714, 09zzb8) <- film(?x4968, ?x2714), film(?x4968, ?x5001), film_crew_role(?x2714, ?x468), ?x5001 = 09q23x >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kv238 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.719 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18322-032jlh PRED entity: 032jlh PRED relation: current_club PRED expected values: 021mkg 03d0d7 => 111 concepts (98 used for prediction) PRED predicted values (max 10 best out of 721): 03x6m (0.50 #504, 0.42 #1937, 0.40 #933), 0y54 (0.50 #294, 0.33 #7, 0.25 #2300), 0371rb (0.50 #158, 0.33 #15, 0.25 #445), 06l22 (0.42 #287, 0.42 #1777, 0.25 #1920), 03yfh3 (0.42 #287, 0.33 #1287, 0.29 #1431), 02qhlm (0.42 #287, 0.33 #1230, 0.29 #1374), 080_y (0.42 #287, 0.33 #104, 0.25 #1824), 03b04g (0.42 #287, 0.33 #101, 0.25 #674), 03j6_5 (0.42 #287, 0.33 #94, 0.25 #667), 03m10r (0.42 #287, 0.33 #20, 0.25 #593) >> Best rule #504 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 03ys48; >> query: (?x11564, 03x6m) <- position(?x11564, ?x203), current_club(?x11564, ?x11421), current_club(?x11564, ?x9107), current_club(?x11564, ?x979), team(?x5471, ?x979), sport(?x11421, ?x471), ?x203 = 0dgrmp, teams(?x774, ?x11564), ?x9107 = 0138mv >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1289 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 03_qj1; 03xh50; *> query: (?x11564, ?x529) <- position(?x11564, ?x530), position(?x11564, ?x60), current_club(?x11564, ?x11421), current_club(?x11564, ?x979), current_club(?x978, ?x979), ?x11421 = 049f05, ?x60 = 02nzb8, ?x530 = 02_j1w, sport(?x11564, ?x471), current_club(?x978, ?x529) *> conf = 0.11 ranks of expected_values: 100, 118 EVAL 032jlh current_club 03d0d7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 111.000 98.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club EVAL 032jlh current_club 021mkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 111.000 98.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #18321-04pk9 PRED entity: 04pk9 PRED relation: religion! PRED expected values: 05fhy 01n7q 06mz5 07b_l => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 384): 01n7q (0.82 #651, 0.80 #368, 0.80 #297), 07b_l (0.80 #385, 0.75 #173, 0.75 #103), 05fhy (0.80 #437, 0.75 #155, 0.75 #85), 06btq (0.75 #169, 0.71 #28, 0.70 #451), 06mz5 (0.70 #370, 0.70 #299, 0.64 #795), 0l3h (0.57 #44, 0.50 #467, 0.50 #397), 07_f2 (0.55 #707, 0.50 #1064, 0.50 #827), 05kr_ (0.55 #707, 0.50 #1064, 0.50 #587), 0d060g (0.55 #707, 0.50 #1064, 0.47 #991), 0694j (0.55 #707, 0.50 #1064, 0.47 #991) >> Best rule #651 for best value: >> intensional similarity = 9 >> extensional distance = 9 >> proper extension: 03_gx; 092bf5; >> query: (?x8613, 01n7q) <- religion(?x2317, ?x8613), religion(?x3778, ?x8613), religion(?x2020, ?x8613), religion(?x1906, ?x8613), ?x1906 = 04rrx, contains(?x3778, ?x1506), adjoins(?x1025, ?x3778), ?x2020 = 05k7sb, district_represented(?x176, ?x3778) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5 EVAL 04pk9 religion! 07b_l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.818 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 04pk9 religion! 06mz5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 36.000 36.000 0.818 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 04pk9 religion! 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.818 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 04pk9 religion! 05fhy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.818 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #18320-01lj9 PRED entity: 01lj9 PRED relation: major_field_of_study! PRED expected values: 0bkj86 => 67 concepts (46 used for prediction) PRED predicted values (max 10 best out of 16): 0bkj86 (0.71 #437, 0.67 #501, 0.65 #554), 0bjrnt (0.61 #50, 0.60 #122, 0.53 #515), 02m4yg (0.61 #50, 0.53 #515, 0.52 #169), 071tyz (0.61 #50, 0.53 #515, 0.52 #169), 03mkk4 (0.61 #50, 0.52 #169, 0.50 #136), 01rr_d (0.61 #50, 0.52 #169, 0.50 #136), 013zdg (0.61 #50, 0.52 #169, 0.50 #136), 027f2w (0.61 #50, 0.52 #169, 0.50 #136), 01ysy9 (0.53 #515, 0.50 #136, 0.38 #334), 022h5x (0.50 #136, 0.43 #68, 0.41 #102) >> Best rule #437 for best value: >> intensional similarity = 11 >> extensional distance = 22 >> proper extension: 06ntj; >> query: (?x4100, 0bkj86) <- major_field_of_study(?x742, ?x4100), taxonomy(?x4100, ?x939), major_field_of_study(?x4100, ?x2981), major_field_of_study(?x12260, ?x2981), major_field_of_study(?x8565, ?x2981), major_field_of_study(?x5486, ?x2981), major_field_of_study(?x1809, ?x2981), ?x5486 = 0g8rj, ?x1809 = 0lfgr, organization(?x346, ?x8565), currency(?x12260, ?x170) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lj9 major_field_of_study! 0bkj86 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 46.000 0.708 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #18319-07kb7vh PRED entity: 07kb7vh PRED relation: film! PRED expected values: 017s11 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 45): 054lpb6 (0.59 #2418, 0.58 #3304, 0.53 #1025), 06rq1k (0.53 #1025, 0.51 #3303, 0.50 #659), 086k8 (0.31 #2, 0.24 #222, 0.22 #3526), 016tw3 (0.21 #3533, 0.20 #2278, 0.20 #2352), 05qd_ (0.20 #2350, 0.20 #154, 0.19 #80), 017s11 (0.19 #3527, 0.16 #1321, 0.15 #1468), 016tt2 (0.17 #1102, 0.16 #3528, 0.16 #1980), 01gb54 (0.13 #174, 0.10 #2370, 0.09 #539), 017jv5 (0.10 #1551, 0.09 #891, 0.08 #745), 054g1r (0.09 #2819, 0.09 #2451, 0.09 #106) >> Best rule #2418 for best value: >> intensional similarity = 4 >> extensional distance = 443 >> proper extension: 0cbl95; >> query: (?x4131, ?x1478) <- production_companies(?x4131, ?x1478), language(?x4131, ?x254), nominated_for(?x102, ?x4131), organization(?x346, ?x1478) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #3527 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 751 *> proper extension: 0gpx6; *> query: (?x4131, 017s11) <- film(?x788, ?x4131), film_crew_role(?x4131, ?x137), production_companies(?x5080, ?x788), list(?x5080, ?x3004) *> conf = 0.19 ranks of expected_values: 6 EVAL 07kb7vh film! 017s11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 94.000 94.000 0.594 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18318-0f2df PRED entity: 0f2df PRED relation: award_winner! PRED expected values: 0fz2y7 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 135): 026kqs9 (0.17 #91, 0.06 #373, 0.05 #655), 0c53zb (0.08 #61, 0.04 #202, 0.04 #12835), 01mhwk (0.08 #41, 0.04 #182, 0.04 #1451), 0bzm__ (0.08 #88, 0.04 #12835, 0.04 #11001), 0bz6sb (0.08 #64, 0.04 #12835, 0.04 #11001), 0fy6bh (0.08 #188, 0.04 #12835, 0.04 #11001), 092c5f (0.07 #719, 0.05 #1001, 0.03 #4527), 09qvms (0.07 #718, 0.03 #1705, 0.03 #10872), 02rjjll (0.07 #710, 0.03 #1838, 0.03 #992), 0clfdj (0.05 #427, 0.04 #2824, 0.04 #2965) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01t9qj_; 0141kz; 0btj0; 021mlp; 0h326; >> query: (?x1567, 026kqs9) <- nationality(?x1567, ?x512), location(?x1567, ?x362), film(?x1567, ?x3755), ?x3755 = 04954r >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #12835 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1897 *> proper extension: 06lxn; *> query: (?x1567, ?x78) <- award_winner(?x591, ?x1567), ceremony(?x591, ?x78), award(?x123, ?x591) *> conf = 0.04 ranks of expected_values: 73 EVAL 0f2df award_winner! 0fz2y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 120.000 120.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18317-04sry PRED entity: 04sry PRED relation: currency PRED expected values: 09nqf => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.56 #16, 0.44 #25, 0.44 #10), 01nv4h (0.02 #101, 0.01 #104) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0sx5w; >> query: (?x7310, 09nqf) <- student(?x373, ?x7310), profession(?x7310, ?x319), producer_type(?x7310, ?x632) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sry currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.556 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #18316-03mgx6z PRED entity: 03mgx6z PRED relation: film! PRED expected values: 03_wpf => 90 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1277): 016z68 (0.33 #1872, 0.07 #12284, 0.04 #8119), 053xw6 (0.33 #1253, 0.03 #11665, 0.03 #72049), 0qf3p (0.33 #443, 0.03 #10855), 01pcq3 (0.20 #16791, 0.07 #18874, 0.05 #4295), 079vf (0.16 #4172, 0.11 #18751, 0.08 #24998), 0b478 (0.16 #93701, 0.13 #77043, 0.12 #29155), 0tc7 (0.14 #2476, 0.08 #12889, 0.06 #27466), 07r1h (0.14 #3172, 0.08 #9420, 0.05 #5254), 01rzqj (0.14 #2661, 0.07 #17239, 0.03 #18743), 01q_ph (0.14 #2139, 0.05 #4221, 0.04 #27129) >> Best rule #1872 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 02prwdh; >> query: (?x5791, 016z68) <- film_release_region(?x5791, ?x279), film(?x489, ?x5791), ?x279 = 0d060g, titles(?x789, ?x5791), ?x489 = 0h5g_, written_by(?x5791, ?x4685) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #9516 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 23 *> proper extension: 05p1tzf; 08hmch; 0gj8t_b; 05z_kps; 047msdk; 04n52p6; 01fmys; 02yvct; 08052t3; 03qnc6q; ... *> query: (?x5791, 03_wpf) <- film_release_region(?x5791, ?x1892), film_release_region(?x5791, ?x1790), film(?x6701, ?x5791), featured_film_locations(?x5791, ?x1523), ?x1892 = 02vzc, ?x1790 = 01pj7, place_of_birth(?x6701, ?x2850) *> conf = 0.04 ranks of expected_values: 421 EVAL 03mgx6z film! 03_wpf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 64.000 0.333 http://example.org/film/actor/film./film/performance/film #18315-0f13b PRED entity: 0f13b PRED relation: film PRED expected values: 0872p_c 0gc_c_ => 114 concepts (70 used for prediction) PRED predicted values (max 10 best out of 730): 06qwh (0.51 #57158, 0.50 #62517, 0.47 #98243), 0bvn25 (0.21 #1836, 0.15 #5409, 0.05 #3623), 03h4fq7 (0.16 #2668, 0.11 #6241, 0.03 #18744), 07w8fz (0.16 #2301, 0.11 #5874, 0.03 #18377), 0888c3 (0.14 #4986, 0.02 #37136, 0.02 #42495), 02ht1k (0.14 #4203, 0.02 #18492, 0.01 #50642), 02stbw (0.14 #3957, 0.02 #18246), 01hvjx (0.11 #5734, 0.11 #2161, 0.09 #3948), 014kq6 (0.11 #346, 0.05 #2132, 0.04 #5705), 02pg45 (0.11 #931, 0.05 #4504, 0.02 #22365) >> Best rule #57158 for best value: >> intensional similarity = 4 >> extensional distance = 398 >> proper extension: 057d89; 01t07j; 01cwhp; 0gcs9; 02wb6yq; 02sj1x; 01pr6q7; 0fqyzz; 01gg59; 01xcr4; ... >> query: (?x8485, ?x7488) <- religion(?x8485, ?x7131), nationality(?x8485, ?x94), nominated_for(?x8485, ?x7488), gender(?x8485, ?x231) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #75195 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 549 *> proper extension: 076df9; *> query: (?x8485, 0872p_c) <- gender(?x8485, ?x231), actor(?x7488, ?x8485), ?x231 = 05zppz *> conf = 0.01 ranks of expected_values: 529 EVAL 0f13b film 0gc_c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 70.000 0.507 http://example.org/film/actor/film./film/performance/film EVAL 0f13b film 0872p_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 114.000 70.000 0.507 http://example.org/film/actor/film./film/performance/film #18314-016tt2 PRED entity: 016tt2 PRED relation: contact_category PRED expected values: 03w5xm => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 3): 03w5xm (0.68 #141, 0.67 #162, 0.64 #165), 02zdwq (0.24 #128, 0.24 #143, 0.23 #140), 014dgf (0.19 #142, 0.19 #127, 0.18 #139) >> Best rule #141 for best value: >> intensional similarity = 2 >> extensional distance = 108 >> proper extension: 07y2s; 084l5; 0l0wv; 01r2lw; >> query: (?x574, 03w5xm) <- service_location(?x574, ?x94), ?x94 = 09c7w0 >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016tt2 contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.682 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #18313-0131kb PRED entity: 0131kb PRED relation: film PRED expected values: 01wb95 => 124 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1085): 014kq6 (0.19 #14314, 0.06 #12870, 0.04 #21817), 0fztbq (0.19 #14314, 0.03 #14233, 0.03 #17812), 0g5pv3 (0.19 #14314, 0.03 #34194), 0164qt (0.19 #14314, 0.02 #21596, 0.02 #53808), 02n72k (0.19 #14314, 0.02 #22628, 0.01 #89473), 01kf4tt (0.19 #14314, 0.02 #27244, 0.02 #29034), 025twgt (0.19 #14314, 0.02 #55412, 0.01 #64357), 0g5pvv (0.19 #14314, 0.01 #89473), 01kf3_9 (0.19 #14314, 0.01 #89473), 02qrv7 (0.19 #14314, 0.01 #89473) >> Best rule #14314 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 01xdf5; 05ty4m; 0p_pd; 02lk1s; 081lh; 0pz91; 0343h; 02pb53; 016_mj; 01vs_v8; ... >> query: (?x12896, ?x835) <- influenced_by(?x7717, ?x12896), award(?x12896, ?x458), film(?x12896, ?x3643), nominated_for(?x3643, ?x835) >> conf = 0.19 => this is the best rule for 12 predicted values *> Best rule #23883 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 015njf; 01mv_n; 01m4kpp; *> query: (?x12896, 01wb95) <- actor(?x11482, ?x12896), people(?x5855, ?x12896), award(?x12896, ?x458), nationality(?x12896, ?x512) *> conf = 0.06 ranks of expected_values: 171 EVAL 0131kb film 01wb95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 124.000 67.000 0.194 http://example.org/film/actor/film./film/performance/film #18312-085wqm PRED entity: 085wqm PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 22): 0ch6mp2 (0.74 #71, 0.74 #103, 0.74 #620), 09zzb8 (0.73 #97, 0.72 #65, 0.71 #614), 0215hd (0.15 #336, 0.14 #628, 0.13 #304), 089g0h (0.12 #112, 0.12 #337, 0.11 #629), 015h31 (0.11 #105, 0.11 #73, 0.11 #9), 0d2b38 (0.11 #343, 0.11 #118, 0.11 #86), 01xy5l_ (0.11 #332, 0.11 #624, 0.10 #300), 02_n3z (0.10 #323, 0.09 #291, 0.09 #66), 089fss (0.06 #619, 0.06 #295, 0.06 #749), 033smt (0.06 #88, 0.06 #120, 0.05 #313) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 282 >> proper extension: 0bx_hnp; >> query: (?x10397, 0ch6mp2) <- film_crew_role(?x10397, ?x468), language(?x10397, ?x254), ?x254 = 02h40lc, crewmember(?x10397, ?x1585) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 085wqm film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.739 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18311-015pxr PRED entity: 015pxr PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #41, 0.87 #47, 0.87 #51), 02zsn (0.38 #2, 0.36 #50, 0.29 #106) >> Best rule #41 for best value: >> intensional similarity = 2 >> extensional distance = 224 >> proper extension: 07kb5; 032l1; 040_9; 01v9724; 058vp; 067xw; 0m93; 043d4; 0ct9_; 04jvt; ... >> query: (?x2143, 05zppz) <- influenced_by(?x2143, ?x986), religion(?x2143, ?x2694) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015pxr gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.872 http://example.org/people/person/gender #18310-0fcsd PRED entity: 0fcsd PRED relation: artist! PRED expected values: 01w565 => 123 concepts (110 used for prediction) PRED predicted values (max 10 best out of 131): 011k1h (0.83 #279, 0.35 #8112, 0.25 #1629), 015_1q (0.63 #4476, 0.54 #1909, 0.52 #2044), 03rhqg (0.55 #8523, 0.40 #2715, 0.38 #3120), 03mp8k (0.54 #1955, 0.48 #2090, 0.23 #4522), 0g768 (0.43 #1520, 0.33 #170, 0.32 #980), 033hn8 (0.41 #4470, 0.33 #283, 0.32 #1903), 0181dw (0.36 #8143, 0.15 #8278, 0.15 #12873), 017l96 (0.33 #1638, 0.33 #288, 0.24 #2313), 0n85g (0.26 #1006, 0.25 #331, 0.25 #61), 02p11jq (0.26 #8520, 0.11 #957, 0.09 #3794) >> Best rule #279 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 01pfkw; >> query: (?x4461, 011k1h) <- artist(?x5666, ?x4461), artist(?x1954, ?x4461), artist(?x1954, ?x3608), artist(?x1954, ?x3472), ?x5666 = 043g7l, ?x3608 = 02lbrd, ?x3472 = 01vv6_6 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1799 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 26 *> proper extension: 04m2zj; *> query: (?x4461, 01w565) <- category(?x4461, ?x134), origin(?x4461, ?x1310), artists(?x1380, ?x4461), contains(?x1310, ?x892), nationality(?x57, ?x1310) *> conf = 0.04 ranks of expected_values: 89 EVAL 0fcsd artist! 01w565 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 123.000 110.000 0.833 http://example.org/music/record_label/artist #18309-04xg2f PRED entity: 04xg2f PRED relation: country PRED expected values: 07ssc => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 72): 07ssc (0.35 #248, 0.27 #365, 0.26 #482), 0345h (0.16 #258, 0.11 #3293, 0.11 #2827), 03_3d (0.11 #65, 0.04 #5203, 0.04 #533), 0d05w3 (0.11 #99, 0.03 #567, 0.03 #625), 0f8l9c (0.10 #251, 0.09 #3402, 0.09 #3928), 04xvlr (0.06 #1169, 0.06 #4904, 0.06 #1287), 07s9rl0 (0.06 #1169, 0.06 #4904, 0.06 #1287), 0ctw_b (0.04 #137, 0.04 #312, 0.03 #488), 0chghy (0.04 #244, 0.04 #478, 0.04 #3686), 03rjj (0.04 #239, 0.04 #297, 0.04 #1175) >> Best rule #248 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 072r5v; 0dmn0x; >> query: (?x9364, 07ssc) <- genre(?x9364, ?x162), film_crew_role(?x9364, ?x137), ?x162 = 04xvlr, ?x137 = 09zzb8 >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04xg2f country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.350 http://example.org/film/film/country #18308-013f1h PRED entity: 013f1h PRED relation: category PRED expected values: 08mbj5d => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.77 #38, 0.77 #14, 0.75 #27) >> Best rule #38 for best value: >> intensional similarity = 3 >> extensional distance = 203 >> proper extension: 0s3y5; 05k7sb; 05fjy; 0f67f; 0s6jm; 0k39j; 0105y2; 0r02m; 0fsv2; >> query: (?x12446, 08mbj5d) <- country(?x12446, ?x94), ?x94 = 09c7w0, location(?x3593, ?x12446) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013f1h category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.771 http://example.org/common/topic/webpage./common/webpage/category #18307-01n7q PRED entity: 01n7q PRED relation: state! PRED expected values: 071vr 01zqy6t => 210 concepts (192 used for prediction) PRED predicted values (max 10 best out of 402): 0mzy7 (0.41 #20898, 0.26 #20386, 0.20 #29073), 0r2dp (0.41 #20898, 0.26 #20386, 0.20 #29073), 06_kh (0.41 #20898, 0.26 #20386, 0.20 #29073), 0k049 (0.41 #20898, 0.26 #20386, 0.20 #22686), 0pc56 (0.41 #20898, 0.26 #20386, 0.19 #28052), 0r00l (0.41 #20898, 0.26 #20386, 0.19 #28052), 0r0m6 (0.41 #20898, 0.26 #20386, 0.19 #28052), 0r6cx (0.41 #20898, 0.26 #20386, 0.19 #28052), 0r04p (0.41 #20898, 0.20 #29073, 0.19 #28052), 01zqy6t (0.41 #20898, 0.20 #29073, 0.19 #28052) >> Best rule #20898 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 01vsb_; >> query: (?x1227, ?x3794) <- state_province_region(?x6019, ?x1227), state_province_region(?x6016, ?x1227), category(?x6019, ?x134), citytown(?x6016, ?x3794) >> conf = 0.41 => this is the best rule for 22 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 10, 23 EVAL 01n7q state! 01zqy6t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 210.000 192.000 0.409 http://example.org/base/biblioness/bibs_location/state EVAL 01n7q state! 071vr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 210.000 192.000 0.409 http://example.org/base/biblioness/bibs_location/state #18306-06w7v PRED entity: 06w7v PRED relation: instrumentalists PRED expected values: 04kjrv 02l_7y 02pt27 => 55 concepts (33 used for prediction) PRED predicted values (max 10 best out of 3379): 018y81 (0.67 #6388, 0.60 #1554, 0.44 #4574), 01kx_81 (0.60 #1208, 0.54 #6042, 0.51 #13298), 01vsyjy (0.60 #1208, 0.54 #6042, 0.51 #13298), 01vvycq (0.60 #1245, 0.58 #6079, 0.44 #4265), 01vrncs (0.60 #1260, 0.50 #6094, 0.42 #5488), 04m2zj (0.60 #1660, 0.42 #6494, 0.33 #5888), 016qtt (0.60 #1214, 0.33 #6048, 0.29 #1819), 01vw20_ (0.56 #4393, 0.50 #6207, 0.50 #768), 01vtg4q (0.56 #4687, 0.50 #1062, 0.33 #7104), 09prnq (0.55 #7252, 0.50 #1210, 0.50 #722) >> Best rule #6388 for best value: >> intensional similarity = 13 >> extensional distance = 10 >> proper extension: 0dwvl; >> query: (?x4917, 018y81) <- role(?x4917, ?x2459), role(?x4917, ?x1574), role(?x4917, ?x922), role(?x4917, ?x745), ?x745 = 01vj9c, role(?x4917, ?x75), ?x922 = 050rj, ?x1574 = 0l15bq, role(?x2459, ?x894), role(?x4550, ?x4917), instrumentalists(?x4917, ?x1656), type_of_union(?x4550, ?x566), role(?x74, ?x2459) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5825 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 10 *> proper extension: 0j871; *> query: (?x4917, 04kjrv) <- group(?x4917, ?x5385), role(?x1291, ?x4917), role(?x2309, ?x4917), role(?x615, ?x4917), role(?x614, ?x4917), instrumentalists(?x614, ?x317), role(?x74, ?x614), artists(?x482, ?x5385), ?x2309 = 06ncr, group(?x614, ?x2567), ?x615 = 0dwsp, role(?x1147, ?x614) *> conf = 0.25 ranks of expected_values: 161, 273, 462 EVAL 06w7v instrumentalists 02pt27 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 33.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 06w7v instrumentalists 02l_7y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 33.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 06w7v instrumentalists 04kjrv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 33.000 0.667 http://example.org/music/instrument/instrumentalists #18305-03tn80 PRED entity: 03tn80 PRED relation: film_production_design_by PRED expected values: 02x2t07 => 87 concepts (47 used for prediction) PRED predicted values (max 10 best out of 23): 02x2t07 (0.03 #117, 0.03 #55, 0.02 #344), 03mdw3c (0.03 #85, 0.01 #572, 0.01 #508), 04_1nk (0.02 #563, 0.01 #952, 0.01 #334), 0bytkq (0.02 #68, 0.02 #422, 0.02 #456), 0d5wn3 (0.02 #460, 0.02 #426, 0.01 #41), 0499lc (0.02 #254, 0.02 #256, 0.02 #482), 05qd_ (0.02 #254, 0.02 #256, 0.02 #482), 02q_cc (0.02 #254, 0.02 #256, 0.02 #482), 030_3z (0.02 #254, 0.02 #256, 0.02 #482), 023361 (0.02 #254, 0.02 #482, 0.01 #709) >> Best rule #117 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 0dnw1; >> query: (?x5002, 02x2t07) <- nominated_for(?x4666, ?x5002), film(?x1515, ?x5002), award(?x4666, ?x688), ?x688 = 05b1610 >> conf = 0.03 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03tn80 film_production_design_by 02x2t07 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 47.000 0.032 http://example.org/film/film/film_production_design_by #18304-04fv5b PRED entity: 04fv5b PRED relation: film_crew_role PRED expected values: 09vw2b7 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 24): 09vw2b7 (0.71 #505, 0.66 #182, 0.64 #577), 0dxtw (0.44 #186, 0.38 #509, 0.36 #581), 01vx2h (0.40 #510, 0.38 #187, 0.37 #223), 02ynfr (0.20 #514, 0.19 #191, 0.16 #586), 02rh1dz (0.20 #185, 0.18 #44, 0.16 #221), 0215hd (0.16 #517, 0.15 #730, 0.14 #1121), 01xy5l_ (0.13 #512, 0.12 #48, 0.12 #725), 089g0h (0.13 #518, 0.13 #195, 0.12 #731), 0d2b38 (0.12 #524, 0.12 #201, 0.12 #737), 015h31 (0.12 #184, 0.10 #507, 0.10 #43) >> Best rule #505 for best value: >> intensional similarity = 4 >> extensional distance = 587 >> proper extension: 0gs973; >> query: (?x5361, 09vw2b7) <- genre(?x5361, ?x571), currency(?x5361, ?x170), film_crew_role(?x5361, ?x468), ?x468 = 02r96rf >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04fv5b film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.711 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18303-077qn PRED entity: 077qn PRED relation: organization PRED expected values: 02vk52z 07t65 01rz1 => 146 concepts (115 used for prediction) PRED predicted values (max 10 best out of 13): 07t65 (0.92 #410, 0.91 #362, 0.90 #700), 02vk52z (0.92 #409, 0.88 #289, 0.88 #1307), 01rz1 (0.73 #339, 0.71 #147, 0.59 #1836), 02jxk (0.59 #1836, 0.50 #148, 0.42 #76), 04k4l (0.59 #1836, 0.47 #366, 0.43 #150), 059dn (0.59 #1836, 0.25 #89, 0.21 #161), 018cqq (0.56 #373, 0.56 #397, 0.50 #85), 0_2v (0.54 #293, 0.47 #173, 0.45 #437), 0b6css (0.50 #84, 0.44 #324, 0.42 #300), 041288 (0.33 #1300, 0.32 #1565, 0.32 #1589) >> Best rule #410 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 05r4w; 09c7w0; 0jgd; 0154j; 0d060g; 04gzd; 047lj; 05qhw; 07ssc; 02k54; ... >> query: (?x4059, 07t65) <- film_release_region(?x124, ?x4059), locations(?x9939, ?x4059), adjoins(?x1003, ?x4059), medal(?x4059, ?x422) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 077qn organization 01rz1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 115.000 0.917 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 077qn organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 115.000 0.917 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 077qn organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 115.000 0.917 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #18302-01cf5 PRED entity: 01cf5 PRED relation: colors PRED expected values: 083jv => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.45 #22, 0.44 #302, 0.43 #102), 036k5h (0.27 #25, 0.14 #145, 0.13 #125), 01l849 (0.26 #1461, 0.25 #1381, 0.25 #921), 06fvc (0.19 #923, 0.18 #303, 0.16 #843), 019sc (0.19 #1387, 0.18 #927, 0.18 #1467), 04mkbj (0.18 #30, 0.12 #310, 0.11 #70), 038hg (0.15 #112, 0.12 #192, 0.09 #972), 0jc_p (0.11 #124, 0.10 #144, 0.09 #304), 03vtbc (0.10 #8, 0.06 #108, 0.05 #188), 03wkwg (0.09 #115, 0.08 #195, 0.06 #55) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0288zy; 04rwx; 07wrz; 086xm; 0pspl; 012fvq; 0kw4j; 01_s9q; 01ljpm; 01g7_r; ... >> query: (?x12302, 083jv) <- major_field_of_study(?x12302, ?x254), currency(?x12302, ?x170), company(?x346, ?x12302) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cf5 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.455 http://example.org/education/educational_institution/colors #18301-04qzm PRED entity: 04qzm PRED relation: group! PRED expected values: 05148p4 02snj9 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 120): 05148p4 (0.69 #804, 0.69 #978, 0.69 #891), 0l14md (0.68 #529, 0.62 #791, 0.60 #878), 018vs (0.65 #536, 0.62 #798, 0.61 #885), 028tv0 (0.50 #535, 0.46 #797, 0.39 #884), 05r5c (0.29 #356, 0.29 #443, 0.28 #269), 03qjg (0.24 #745, 0.24 #396, 0.23 #1180), 0l14qv (0.23 #1225, 0.22 #1137, 0.22 #876), 0192l (0.20 #78, 0.07 #1220, 0.06 #610), 06ncr (0.14 #1259, 0.14 #736, 0.13 #1171), 04rzd (0.14 #729, 0.12 #380, 0.12 #1164) >> Best rule #804 for best value: >> intensional similarity = 4 >> extensional distance = 106 >> proper extension: 04r1t; 0167_s; 05563d; 018gm9; 01j59b0; 06nv27; 02vgh; 01kcms4; 013rfk; 01516r; ... >> query: (?x10427, 05148p4) <- group(?x1466, ?x10427), artist(?x1954, ?x10427), ?x1466 = 03bx0bm, artists(?x302, ?x10427) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 93 EVAL 04qzm group! 02snj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 74.000 74.000 0.694 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 04qzm group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.694 http://example.org/music/performance_role/regular_performances./music/group_membership/group #18300-03cw411 PRED entity: 03cw411 PRED relation: executive_produced_by PRED expected values: 06q8hf => 128 concepts (63 used for prediction) PRED predicted values (max 10 best out of 89): 04jspq (0.12 #1156, 0.08 #1909, 0.04 #7454), 02q42j_ (0.12 #388, 0.02 #9200, 0.02 #10206), 0fvf9q (0.10 #12838, 0.10 #761, 0.08 #6), 02hfp_ (0.10 #12838, 0.04 #755, 0.03 #12837), 029m83 (0.10 #12838, 0.04 #755, 0.03 #12837), 06pj8 (0.10 #1061, 0.06 #1814, 0.05 #558), 06q8hf (0.09 #10236, 0.09 #9230, 0.08 #166), 05fyss (0.08 #138, 0.02 #1395, 0.01 #2148), 027kmrb (0.08 #131), 0343h (0.08 #1299, 0.03 #4320, 0.03 #4825) >> Best rule #1156 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 0g56t9t; 0gkz15s; 0bwfwpj; 08hmch; 01c22t; 0872p_c; 053rxgm; 0gj9tn5; 05qbckf; 0661m4p; ... >> query: (?x3745, 04jspq) <- film_crew_role(?x3745, ?x137), film_release_region(?x3745, ?x2629), film_release_region(?x3745, ?x1122), ?x1122 = 09pmkv, ?x2629 = 06f32 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #10236 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 342 *> proper extension: 04y9mm8; 0fsd9t; 0dtzkt; 0fzm0g; *> query: (?x3745, 06q8hf) <- film_crew_role(?x3745, ?x137), country(?x3745, ?x94), film(?x3462, ?x3745), executive_produced_by(?x3745, ?x4060) *> conf = 0.09 ranks of expected_values: 7 EVAL 03cw411 executive_produced_by 06q8hf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 128.000 63.000 0.122 http://example.org/film/film/executive_produced_by #18299-02ndj5 PRED entity: 02ndj5 PRED relation: award PRED expected values: 02f72n => 97 concepts (63 used for prediction) PRED predicted values (max 10 best out of 239): 03tcnt (0.72 #3813, 0.19 #6243, 0.18 #5433), 01ckcd (0.41 #3982, 0.40 #6412, 0.36 #6007), 01ckrr (0.40 #1448, 0.40 #1043, 0.30 #2258), 01by1l (0.36 #3758, 0.35 #8618, 0.33 #9834), 02f77l (0.36 #3902, 0.24 #5522, 0.23 #6332), 02f5qb (0.36 #3802, 0.23 #5827, 0.22 #6232), 01bgqh (0.33 #3688, 0.31 #10169, 0.30 #6523), 01c9jp (0.33 #6266, 0.31 #5861, 0.28 #3431), 02f716 (0.31 #3823, 0.25 #6253, 0.24 #5443), 02wh75 (0.31 #3654, 0.12 #8109, 0.12 #4059) >> Best rule #3813 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 016732; >> query: (?x9841, 03tcnt) <- award(?x9841, ?x9462), award_winner(?x5656, ?x9841), ceremony(?x9462, ?x139), award(?x10737, ?x9462), ?x10737 = 0b1hw >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #6222 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 101 *> proper extension: 089tm; 01t_xp_; 01pfr3; 04rcr; 0150jk; 02r3zy; 07c0j; 01vsxdm; 03g5jw; 01wv9xn; ... *> query: (?x9841, 02f72n) <- artists(?x837, ?x9841), award(?x9841, ?x9462), artists(?x837, ?x5385), ?x5385 = 0134tg, group(?x227, ?x9841) *> conf = 0.25 ranks of expected_values: 15 EVAL 02ndj5 award 02f72n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 97.000 63.000 0.718 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18298-06cgy PRED entity: 06cgy PRED relation: award PRED expected values: 05zr6wv => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 282): 027986c (0.71 #31925, 0.70 #11034, 0.70 #28771), 027c95y (0.71 #31925, 0.70 #11034, 0.70 #28771), 09cm54 (0.71 #31925, 0.70 #11034, 0.70 #28771), 0789r6 (0.71 #31925, 0.70 #11034, 0.70 #28771), 05zr6wv (0.50 #17, 0.22 #805, 0.18 #4351), 0gq9h (0.36 #7561, 0.36 #8349, 0.35 #13472), 0fbtbt (0.36 #3374, 0.32 #6133, 0.31 #5739), 05zvj3m (0.33 #89, 0.18 #39413, 0.13 #50448), 040njc (0.31 #7495, 0.29 #8283, 0.27 #12618), 0cjyzs (0.29 #3254, 0.29 #6013, 0.28 #6801) >> Best rule #31925 for best value: >> intensional similarity = 3 >> extensional distance = 1196 >> proper extension: 04cy8rb; 03jvmp; 0g5lhl7; 01w92; 03kpvp; 05dxl5; 02cm2m; 04gtdnh; 05xbx; 06vsbt; ... >> query: (?x1554, ?x591) <- award_nominee(?x400, ?x1554), award_winner(?x591, ?x1554), award_winner(?x1554, ?x163) >> conf = 0.71 => this is the best rule for 4 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 01900g; 02x0dzw; *> query: (?x1554, 05zr6wv) <- award_nominee(?x1445, ?x1554), award(?x1554, ?x451), ?x1445 = 0292l3 *> conf = 0.50 ranks of expected_values: 5 EVAL 06cgy award 05zr6wv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 146.000 146.000 0.711 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18297-02ln1 PRED entity: 02ln1 PRED relation: student! PRED expected values: 01stzp => 141 concepts (121 used for prediction) PRED predicted values (max 10 best out of 206): 01stzp (0.50 #1559, 0.31 #5760, 0.29 #3134), 01zzy3 (0.25 #1524, 0.15 #5725, 0.14 #3099), 0m7yh (0.25 #1323, 0.14 #2898, 0.14 #2373), 07tgn (0.16 #11569, 0.13 #13144, 0.12 #5793), 01lhdt (0.15 #5510, 0.11 #7085, 0.08 #9186), 0dplh (0.14 #2154, 0.12 #3204, 0.06 #5830), 0301dp (0.14 #2625, 0.12 #3675, 0.01 #15228), 01w5m (0.14 #14282, 0.12 #16383, 0.11 #12706), 03ksy (0.12 #5881, 0.11 #9557, 0.10 #7456), 07tk7 (0.12 #4116, 0.10 #4641, 0.10 #7792) >> Best rule #1559 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0j3v; 048cl; >> query: (?x8418, 01stzp) <- influenced_by(?x1236, ?x8418), people(?x1050, ?x8418), religion(?x8418, ?x109), ?x1236 = 045bg, student(?x2637, ?x8418) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ln1 student! 01stzp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 121.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/student #18296-011x_4 PRED entity: 011x_4 PRED relation: genre PRED expected values: 07s9rl0 => 95 concepts (62 used for prediction) PRED predicted values (max 10 best out of 98): 07s9rl0 (0.75 #2229, 0.71 #469, 0.70 #352), 03k9fj (0.74 #2592, 0.50 #1769, 0.48 #1183), 01z4y (0.59 #7043, 0.52 #6223, 0.51 #7161), 01jfsb (0.49 #5882, 0.42 #246, 0.35 #2945), 02kdv5l (0.47 #5873, 0.42 #2584, 0.33 #1761), 082gq (0.36 #849, 0.20 #1552, 0.19 #2727), 0vgkd (0.36 #127, 0.09 #3412, 0.07 #5285), 0lsxr (0.30 #8, 0.27 #359, 0.27 #476), 04xvlr (0.25 #353, 0.24 #470, 0.24 #822), 06n90 (0.25 #2594, 0.23 #1771, 0.22 #5883) >> Best rule #2229 for best value: >> intensional similarity = 5 >> extensional distance = 414 >> proper extension: 06mmr; >> query: (?x7656, 07s9rl0) <- award(?x7656, ?x68), nominated_for(?x68, ?x5648), nominated_for(?x68, ?x1425), ?x5648 = 049xgc, award(?x1425, ?x77) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011x_4 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 62.000 0.752 http://example.org/film/film/genre #18295-016ywb PRED entity: 016ywb PRED relation: film! PRED expected values: 06ltr => 69 concepts (31 used for prediction) PRED predicted values (max 10 best out of 914): 081k8 (0.12 #22875, 0.10 #18714, 0.09 #29114), 014zcr (0.10 #8354, 0.03 #2117, 0.02 #33311), 016ggh (0.09 #1866, 0.05 #16421, 0.04 #12263), 01f6zc (0.09 #941, 0.03 #25895, 0.03 #3021), 0c3p7 (0.09 #1114, 0.01 #21909, 0.01 #17748), 05nzw6 (0.09 #5348, 0.07 #11586, 0.06 #15744), 01ycbq (0.09 #4484, 0.04 #10722, 0.04 #14880), 0169dl (0.08 #2479, 0.06 #8716, 0.03 #23274), 0f5xn (0.08 #3047, 0.05 #9284, 0.04 #19681), 02ck7w (0.08 #3017, 0.04 #13413, 0.03 #937) >> Best rule #22875 for best value: >> intensional similarity = 4 >> extensional distance = 199 >> proper extension: 0267wwv; >> query: (?x7073, ?x5004) <- genre(?x7073, ?x53), story_by(?x7073, ?x5004), production_companies(?x7073, ?x13497), award(?x13497, ?x500) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #944 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 02vqhv0; 047qxs; 03kg2v; 039zft; 0fsd9t; *> query: (?x7073, 06ltr) <- genre(?x7073, ?x4088), story_by(?x7073, ?x5004), film(?x488, ?x7073), ?x4088 = 04xvh5 *> conf = 0.03 ranks of expected_values: 133 EVAL 016ywb film! 06ltr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 69.000 31.000 0.123 http://example.org/film/actor/film./film/performance/film #18294-018gqj PRED entity: 018gqj PRED relation: profession PRED expected values: 0dz3r 05vyk => 141 concepts (140 used for prediction) PRED predicted values (max 10 best out of 72): 02hrh1q (0.86 #1645, 0.82 #757, 0.82 #3276), 09jwl (0.70 #5209, 0.69 #3725, 0.68 #6099), 016z4k (0.46 #301, 0.44 #4450, 0.44 #4599), 0dz3r (0.44 #2966, 0.42 #6229, 0.41 #6081), 01d_h8 (0.35 #9199, 0.34 #7124, 0.34 #2228), 0dxtg (0.32 #7132, 0.31 #7428, 0.31 #1792), 02jknp (0.32 #1046, 0.27 #1786, 0.26 #2230), 039v1 (0.29 #3741, 0.28 #5225, 0.27 #3445), 03gjzk (0.28 #1794, 0.26 #2238, 0.26 #7134), 01c8w0 (0.27 #603, 0.27 #2083, 0.25 #1195) >> Best rule #1645 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 04cr6qv; 0gs6vr; >> query: (?x6025, 02hrh1q) <- participant(?x6025, ?x3235), profession(?x6025, ?x1614), film(?x6025, ?x4717) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2966 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 175 *> proper extension: 05qhnq; *> query: (?x6025, 0dz3r) <- award_nominee(?x3358, ?x6025), artists(?x671, ?x6025), group(?x3358, ?x1271) *> conf = 0.44 ranks of expected_values: 4, 14 EVAL 018gqj profession 05vyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 141.000 140.000 0.856 http://example.org/people/person/profession EVAL 018gqj profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 141.000 140.000 0.856 http://example.org/people/person/profession #18293-0d__g PRED entity: 0d__g PRED relation: award_winner! PRED expected values: 05kjlr => 113 concepts (107 used for prediction) PRED predicted values (max 10 best out of 340): 01by1l (0.19 #1841, 0.15 #545, 0.11 #3139), 025m8y (0.15 #532, 0.07 #1828, 0.07 #3126), 0cqhk0 (0.15 #37, 0.05 #4360, 0.03 #24669), 05kjlr (0.14 #3459, 0.14 #3026, 0.05 #2593), 0blst_ (0.14 #3459, 0.14 #3026, 0.05 #2593), 019bnn (0.14 #6320, 0.07 #8480, 0.05 #11936), 03x3wf (0.11 #1793, 0.08 #497, 0.07 #3091), 01bgqh (0.11 #1771, 0.06 #5231, 0.06 #9119), 054ks3 (0.11 #3168, 0.09 #2735, 0.08 #13539), 0c_dx (0.11 #1571, 0.07 #2435, 0.04 #3734) >> Best rule #1841 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 07rd7; >> query: (?x11077, 01by1l) <- location(?x11077, ?x739), influenced_by(?x11596, ?x11077), profession(?x11077, ?x955), ?x955 = 0n1h >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #3459 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 0dw4g; 03d9d6; *> query: (?x11077, ?x13257) <- peers(?x12622, ?x11077), award_winner(?x13257, ?x12622), influenced_by(?x12622, ?x5811) *> conf = 0.14 ranks of expected_values: 4 EVAL 0d__g award_winner! 05kjlr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 113.000 107.000 0.185 http://example.org/award/award_category/winners./award/award_honor/award_winner #18292-018s6c PRED entity: 018s6c PRED relation: languages_spoken PRED expected values: 06b_j => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 53): 02h40lc (0.78 #956, 0.76 #1009, 0.67 #1062), 0t_2 (0.51 #1231, 0.50 #860, 0.50 #754), 032f6 (0.50 #154, 0.33 #260, 0.12 #1055), 0h407 (0.40 #100, 0.33 #365, 0.33 #312), 06b_j (0.33 #230, 0.33 #124, 0.24 #1131), 064_8sq (0.23 #1236, 0.20 #1289, 0.20 #1183), 06nm1 (0.23 #1175, 0.17 #221, 0.17 #115), 03x42 (0.22 #415, 0.22 #309, 0.20 #97), 02bv9 (0.20 #659, 0.19 #765, 0.18 #818), 04306rv (0.19 #747, 0.17 #217, 0.17 #111) >> Best rule #956 for best value: >> intensional similarity = 10 >> extensional distance = 21 >> proper extension: 09v5bdn; 0g8_vp; 059_w; 03bkbh; 0d2by; 0dbxy; 0bbz66j; 03w9bjf; 0ffjqy; 09zyn5; ... >> query: (?x13001, 02h40lc) <- people(?x13001, ?x8437), languages_spoken(?x13001, ?x3966), nationality(?x8437, ?x4743), language(?x6798, ?x3966), language(?x5066, ?x3966), language(?x3453, ?x3966), religion(?x8437, ?x7131), ?x6798 = 0g7pm1, ?x5066 = 07bwr, ?x3453 = 0dgpwnk >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #230 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 0222qb; *> query: (?x13001, 06b_j) <- people(?x13001, ?x8437), languages_spoken(?x13001, ?x3966), nationality(?x8437, ?x4743), language(?x6798, ?x3966), language(?x3965, ?x3966), language(?x174, ?x3966), religion(?x8437, ?x7131), film(?x123, ?x6798), honored_for(?x7767, ?x3965), ?x174 = 01br2w *> conf = 0.33 ranks of expected_values: 5 EVAL 018s6c languages_spoken 06b_j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 37.000 37.000 0.783 http://example.org/people/ethnicity/languages_spoken #18291-01rc6f PRED entity: 01rc6f PRED relation: contains! PRED expected values: 09c7w0 => 183 concepts (104 used for prediction) PRED predicted values (max 10 best out of 268): 09c7w0 (0.82 #88577, 0.78 #23254, 0.77 #63519), 07c5l (0.33 #394, 0.09 #3970, 0.06 #6652), 01n7q (0.26 #87758, 0.20 #59120, 0.16 #7231), 02jx1 (0.23 #43021, 0.17 #46603, 0.17 #53758), 059rby (0.21 #59062, 0.18 #13432, 0.18 #87700), 02_286 (0.21 #59085, 0.06 #10772, 0.06 #87723), 07ssc (0.20 #87712, 0.14 #42966, 0.12 #46548), 0p2rj (0.17 #1546, 0.12 #2440), 07h34 (0.15 #4701, 0.09 #8278, 0.05 #14537), 01x73 (0.15 #4585, 0.06 #87795, 0.05 #7268) >> Best rule #88577 for best value: >> intensional similarity = 4 >> extensional distance = 362 >> proper extension: 02_2kg; >> query: (?x8120, 09c7w0) <- contains(?x4758, ?x8120), school_type(?x8120, ?x1507), partially_contains(?x4758, ?x4540), religion(?x4758, ?x109) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rc6f contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 104.000 0.816 http://example.org/location/location/contains #18290-03gm48 PRED entity: 03gm48 PRED relation: student! PRED expected values: 06pwq => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 144): 02m0sc (0.25 #346), 08815 (0.12 #528, 0.06 #3158, 0.05 #6314), 033gn8 (0.12 #903, 0.04 #6689, 0.03 #8793), 017j69 (0.12 #670, 0.03 #1196, 0.03 #16450), 0lyjf (0.12 #682, 0.02 #3312, 0.02 #7520), 01bm_ (0.12 #771, 0.01 #16025, 0.01 #16551), 01j_9c (0.12 #536, 0.01 #3166), 01ky7c (0.12 #749), 02w2bc (0.12 #539), 015nl4 (0.12 #6379, 0.06 #11113, 0.05 #16373) >> Best rule #346 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 05gnf; >> query: (?x965, 02m0sc) <- award_winner(?x2071, ?x965), award_winner(?x12105, ?x965), ?x12105 = 024hbv >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3168 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 0d0vj4; 06hx2; 06bss; 037s5h; *> query: (?x965, 06pwq) <- person(?x6125, ?x965), student(?x2909, ?x965), genre(?x6125, ?x1014) *> conf = 0.02 ranks of expected_values: 39 EVAL 03gm48 student! 06pwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 123.000 123.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #18289-059yj PRED entity: 059yj PRED relation: service_location PRED expected values: 09c7w0 => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 223): 09c7w0 (0.88 #1986, 0.87 #1588, 0.80 #1886), 0d060g (0.47 #1594, 0.40 #304, 0.33 #1195), 05kkh (0.36 #1288, 0.31 #2084, 0.30 #593), 02j71 (0.33 #1902, 0.33 #1205, 0.25 #2002), 07ssc (0.20 #1602, 0.18 #4304, 0.16 #4705), 01n7q (0.20 #322, 0.17 #520, 0.08 #1213), 0z1vw (0.20 #296, 0.17 #493, 0.06 #1983), 04_1l0v (0.15 #3777, 0.08 #4488), 0chghy (0.13 #1598, 0.12 #4300, 0.10 #4502), 0f8l9c (0.12 #2106, 0.08 #1209, 0.08 #4310) >> Best rule #1986 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 01xdn1; 0178g; 03d6fyn; 0dmtp; 059wk; 01dycg; 01pf21; 05njw; 01hlwv; 02l48d; ... >> query: (?x11323, 09c7w0) <- service_language(?x11323, ?x254), organization(?x4682, ?x11323), ?x254 = 02h40lc, ?x4682 = 0dq_5, place_founded(?x11323, ?x11595), location(?x1157, ?x11595), contains(?x94, ?x11595) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059yj service_location 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.875 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #18288-078bz PRED entity: 078bz PRED relation: student PRED expected values: 06lj1m 05xpv => 95 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1397): 02hsgn (0.33 #813, 0.04 #13239, 0.04 #15310), 05qsxy (0.33 #386, 0.04 #4528, 0.03 #6599), 05typm (0.33 #787, 0.04 #4929, 0.03 #7000), 031v3p (0.33 #1984, 0.02 #10268, 0.02 #14410), 0ywqc (0.33 #1779, 0.02 #10063, 0.02 #14205), 0219q (0.33 #690, 0.02 #11045, 0.01 #19329), 06z8gn (0.33 #1505, 0.02 #11860, 0.01 #20144), 030h95 (0.33 #272, 0.01 #27198, 0.01 #31340), 01yk13 (0.33 #114, 0.01 #33253), 01c65z (0.33 #1989) >> Best rule #813 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 053mhx; >> query: (?x2775, 02hsgn) <- student(?x2775, ?x7157), student(?x2775, ?x6538), nationality(?x7157, ?x94), ?x6538 = 03w4sh >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #11865 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 036921; *> query: (?x2775, 05xpv) <- state_province_region(?x2775, ?x335), ?x335 = 059rby, contains(?x94, ?x2775) *> conf = 0.02 ranks of expected_values: 934 EVAL 078bz student 05xpv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 95.000 83.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 078bz student 06lj1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 83.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #18287-01bpc9 PRED entity: 01bpc9 PRED relation: award PRED expected values: 026mfs => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 282): 01by1l (0.31 #1717, 0.28 #915, 0.28 #4524), 01bgqh (0.22 #845, 0.22 #4454, 0.21 #1246), 0ck27z (0.21 #11321, 0.09 #13326, 0.08 #14529), 0c4z8 (0.20 #1676, 0.18 #4483, 0.18 #2879), 09sb52 (0.18 #2447, 0.16 #2046, 0.15 #11269), 01l78d (0.18 #688, 0.02 #16328, 0.02 #3896), 03qbh5 (0.18 #4615, 0.18 #1808, 0.15 #1006), 054ks3 (0.17 #4551, 0.17 #2947, 0.16 #1744), 023vrq (0.17 #1126, 0.15 #1527, 0.07 #2730), 0gqz2 (0.16 #1685, 0.12 #4492, 0.12 #2888) >> Best rule #1717 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 01m5m5b; >> query: (?x1654, 01by1l) <- nationality(?x1654, ?x94), role(?x1654, ?x227), award_winner(?x139, ?x1654) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #129 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 01nqfh_; 0zjpz; 0565cz; *> query: (?x1654, 026mfs) <- instrumentalists(?x2158, ?x1654), instrumentalists(?x227, ?x1654), ?x2158 = 01dnws, ?x227 = 0342h *> conf = 0.14 ranks of expected_values: 16 EVAL 01bpc9 award 026mfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 62.000 62.000 0.309 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18286-09r8l PRED entity: 09r8l PRED relation: place_of_death PRED expected values: 0qpjt => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 22): 030qb3t (0.15 #1769, 0.14 #3904, 0.13 #8763), 02_286 (0.10 #3895, 0.09 #1760, 0.07 #8754), 0k049 (0.08 #3885, 0.06 #1750, 0.05 #8744), 07b_l (0.07 #5633, 0.07 #4660, 0.02 #3494), 05jbn (0.05 #265, 0.03 #653, 0.02 #2012), 06_kh (0.04 #4470, 0.04 #5443, 0.03 #8746), 0f2wj (0.04 #1759, 0.03 #8753, 0.03 #5450), 04jpl (0.03 #3889, 0.03 #8748, 0.02 #5445), 05qtj (0.03 #5502, 0.02 #8805, 0.02 #4529), 0k_p5 (0.02 #5526, 0.02 #4553, 0.02 #8829) >> Best rule #1769 for best value: >> intensional similarity = 3 >> extensional distance = 174 >> proper extension: 01933d; 03dbww; 03csqj4; >> query: (?x3957, 030qb3t) <- award_nominee(?x3957, ?x1270), profession(?x3957, ?x131), people(?x8523, ?x3957) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09r8l place_of_death 0qpjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 113.000 0.153 http://example.org/people/deceased_person/place_of_death #18285-0dl567 PRED entity: 0dl567 PRED relation: award_winner! PRED expected values: 02f6ym => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 238): 01bgqh (0.37 #14557, 0.36 #1714, 0.33 #1285), 01c99j (0.37 #14557, 0.36 #1714, 0.33 #1285), 0c4z8 (0.37 #14557, 0.36 #1714, 0.33 #1285), 054ks3 (0.37 #14557, 0.36 #1714, 0.33 #1285), 025m8l (0.37 #14557, 0.36 #1714, 0.33 #1285), 02f705 (0.37 #14557, 0.36 #1714, 0.33 #1285), 01c427 (0.37 #14557, 0.36 #1714, 0.33 #1285), 05pcn59 (0.37 #14557, 0.36 #1714, 0.33 #1285), 03qbh5 (0.37 #14557, 0.36 #1714, 0.33 #1285), 03qbnj (0.12 #1514, 0.04 #229, 0.04 #6225) >> Best rule #14557 for best value: >> intensional similarity = 2 >> extensional distance = 1462 >> proper extension: 0cb77r; 05ty4m; 05cj4r; 086k8; 03ckxdg; 04lgymt; 017s11; 06cc_1; 016tt2; 01gvr1; ... >> query: (?x4080, ?x724) <- award(?x4080, ?x724), award_winner(?x2824, ?x4080) >> conf = 0.37 => this is the best rule for 9 predicted values *> Best rule #683 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 83 *> proper extension: 02twdq; *> query: (?x4080, 02f6ym) <- artists(?x3996, ?x4080), ?x3996 = 02lnbg *> conf = 0.08 ranks of expected_values: 36 EVAL 0dl567 award_winner! 02f6ym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 71.000 71.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #18284-01v3s2_ PRED entity: 01v3s2_ PRED relation: location PRED expected values: 02_286 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 144): 0yx74 (0.51 #31369, 0.51 #33782, 0.50 #51485), 030qb3t (0.50 #83, 0.38 #1691, 0.33 #3299), 059rby (0.25 #16, 0.14 #820, 0.12 #1624), 0c_m3 (0.25 #271, 0.11 #2683, 0.08 #5095), 02_286 (0.22 #6469, 0.21 #7273, 0.21 #10492), 0mnz0 (0.14 #1481, 0.12 #2285, 0.11 #3893), 0y1rf (0.14 #1355, 0.12 #2159, 0.11 #3767), 013kcv (0.14 #846, 0.12 #1650, 0.11 #3258), 059f4 (0.14 #840, 0.11 #2448, 0.08 #4860), 01n7q (0.12 #1671, 0.11 #3279, 0.08 #4887) >> Best rule #31369 for best value: >> intensional similarity = 3 >> extensional distance = 562 >> proper extension: 01wbgdv; 015882; 01qdjm; 0191h5; 0l5yl; 01w9k25; >> query: (?x905, ?x12883) <- people(?x5540, ?x905), place_of_birth(?x905, ?x12883), award_nominee(?x1736, ?x905) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #6469 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 026ps1; 052gzr; 010hn; 02v3yy; 0cj2w; *> query: (?x905, 02_286) <- student(?x3123, ?x905), spouse(?x905, ?x4507), award_winner(?x1342, ?x4507) *> conf = 0.22 ranks of expected_values: 5 EVAL 01v3s2_ location 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 120.000 120.000 0.511 http://example.org/people/person/places_lived./people/place_lived/location #18283-01vw20h PRED entity: 01vw20h PRED relation: profession PRED expected values: 0dz3r 01d_h8 02hrh1q 0nbcg => 131 concepts (130 used for prediction) PRED predicted values (max 10 best out of 82): 02hrh1q (0.89 #3984, 0.87 #1043, 0.87 #4278), 0dz3r (0.77 #148, 0.53 #295, 0.49 #1765), 01d_h8 (0.65 #2505, 0.43 #593, 0.42 #3093), 0nbcg (0.60 #765, 0.54 #177, 0.51 #1794), 016z4k (0.47 #297, 0.46 #1767, 0.43 #1914), 0n1h (0.46 #158, 0.31 #1775, 0.31 #305), 0dxtg (0.34 #2513, 0.28 #10159, 0.28 #1189), 039v1 (0.29 #770, 0.26 #6475, 0.25 #14276), 0d1pc (0.28 #10159, 0.26 #6475, 0.25 #14276), 012t_z (0.28 #10159, 0.26 #6475, 0.25 #14276) >> Best rule #3984 for best value: >> intensional similarity = 3 >> extensional distance = 282 >> proper extension: 039crh; 01jb26; >> query: (?x4476, 02hrh1q) <- location(?x4476, ?x362), participant(?x1672, ?x4476), participant(?x2562, ?x4476) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 01vw20h profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 130.000 0.894 http://example.org/people/person/profession EVAL 01vw20h profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 130.000 0.894 http://example.org/people/person/profession EVAL 01vw20h profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 130.000 0.894 http://example.org/people/person/profession EVAL 01vw20h profession 0dz3r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 130.000 0.894 http://example.org/people/person/profession #18282-031t2d PRED entity: 031t2d PRED relation: country PRED expected values: 09c7w0 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 29): 09c7w0 (0.84 #492, 0.83 #1169, 0.83 #370), 07ssc (0.26 #1000, 0.24 #1247, 0.23 #753), 0345h (0.17 #457, 0.16 #1258, 0.15 #335), 0f8l9c (0.10 #1744, 0.09 #2675, 0.09 #4153), 0chghy (0.09 #442, 0.07 #564, 0.06 #626), 0d060g (0.08 #254, 0.07 #2167, 0.07 #1053), 0ctw_b (0.05 #331, 0.02 #1684, 0.02 #1994), 03_3d (0.04 #4449, 0.04 #437, 0.04 #4571), 03h64 (0.04 #906, 0.04 #1030, 0.03 #1463), 01mjq (0.04 #281, 0.03 #772, 0.03 #1019) >> Best rule #492 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 01zfzb; 01f69m; >> query: (?x1673, 09c7w0) <- featured_film_locations(?x1673, ?x739), ?x739 = 02_286, film(?x521, ?x1673), music(?x1673, ?x3410) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031t2d country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.839 http://example.org/film/film/country #18281-07z4fy PRED entity: 07z4fy PRED relation: place_of_death PRED expected values: 02hrh0_ => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 19): 0fhp9 (0.19 #404, 0.06 #988, 0.05 #1182), 0f2wj (0.17 #12, 0.04 #3906, 0.03 #2350), 06_kh (0.17 #5, 0.03 #1561, 0.03 #1757), 0r540 (0.17 #31), 0r62v (0.17 #15), 030qb3t (0.12 #3916, 0.08 #217, 0.07 #3721), 02_286 (0.11 #4089, 0.09 #793, 0.06 #403), 04jpl (0.06 #1948, 0.05 #1752, 0.05 #4484), 04swd (0.04 #1094, 0.03 #1288), 0k049 (0.03 #1559, 0.03 #1755, 0.03 #3897) >> Best rule #404 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0bvzp; >> query: (?x13232, 0fhp9) <- nationality(?x13232, ?x1310), profession(?x13232, ?x11127), ?x11127 = 05vyk, music(?x11682, ?x13232) >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07z4fy place_of_death 02hrh0_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.188 http://example.org/people/deceased_person/place_of_death #18280-043n0v_ PRED entity: 043n0v_ PRED relation: titles! PRED expected values: 0653m => 105 concepts (42 used for prediction) PRED predicted values (max 10 best out of 84): 07c52 (0.81 #1433, 0.19 #331, 0.14 #1533), 07s9rl0 (0.67 #400, 0.56 #102, 0.49 #902), 04xvlr (0.56 #105, 0.51 #403, 0.41 #905), 07ssc (0.56 #111, 0.19 #311, 0.18 #409), 0653m (0.38 #799, 0.20 #901, 0.20 #900), 0459q4 (0.38 #799, 0.20 #901, 0.20 #900), 017fp (0.35 #4114, 0.34 #2602, 0.31 #125), 03bxz7 (0.35 #4114, 0.27 #499, 0.26 #201), 024qqx (0.28 #1279, 0.15 #278, 0.12 #3186), 03q4nz (0.28 #3514, 0.27 #499, 0.26 #201) >> Best rule #1433 for best value: >> intensional similarity = 2 >> extensional distance = 167 >> proper extension: 01qn7n; 09kn9; 01p4wv; 099pks; 05r1_t; 03y317; 06r4f; 025ljp; 06qxh; 09v38qj; >> query: (?x5038, 07c52) <- titles(?x2164, ?x5038), major_field_of_study(?x7660, ?x2164) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #799 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 011yfd; 0bz3jx; 05y0cr; *> query: (?x5038, ?x2164) <- language(?x5038, ?x2164), titles(?x3271, ?x5038), languages(?x147, ?x3271) *> conf = 0.38 ranks of expected_values: 5 EVAL 043n0v_ titles! 0653m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 105.000 42.000 0.811 http://example.org/media_common/netflix_genre/titles #18279-0167v4 PRED entity: 0167v4 PRED relation: artists! PRED expected values: 06by7 => 138 concepts (93 used for prediction) PRED predicted values (max 10 best out of 309): 06by7 (0.82 #25895, 0.73 #17790, 0.63 #2828), 0xhtw (0.81 #4694, 0.75 #9989, 0.75 #953), 064t9 (0.64 #7497, 0.63 #5939, 0.55 #1261), 01_bkd (0.48 #2861, 0.25 #991, 0.21 #10027), 06j6l (0.45 #1296, 0.43 #5974, 0.41 #7532), 0glt670 (0.37 #5967, 0.35 #5030, 0.34 #7525), 05bt6j (0.35 #5969, 0.34 #7527, 0.33 #5032), 025sc50 (0.33 #5976, 0.33 #7534, 0.30 #5039), 02lnbg (0.33 #5985, 0.33 #7543, 0.27 #6920), 016jny (0.33 #106, 0.25 #2290, 0.19 #2912) >> Best rule #25895 for best value: >> intensional similarity = 4 >> extensional distance = 423 >> proper extension: 05563d; 018gm9; >> query: (?x9117, 06by7) <- artists(?x2249, ?x9117), artist(?x8027, ?x9117), artists(?x2249, ?x10565), ?x10565 = 0c9l1 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0167v4 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 93.000 0.816 http://example.org/music/genre/artists #18278-01_k1z PRED entity: 01_k1z PRED relation: nationality PRED expected values: 09c7w0 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.85 #1103, 0.84 #3008, 0.83 #2706), 07ssc (0.40 #8745, 0.27 #3709, 0.13 #1817), 02jx1 (0.40 #8745, 0.27 #3709, 0.12 #5553), 0d060g (0.40 #8745, 0.06 #2712, 0.05 #5930), 0345h (0.40 #8745, 0.06 #3639, 0.06 #3940), 0f8l9c (0.40 #8745, 0.05 #823, 0.03 #3530), 03rjj (0.40 #8745, 0.05 #405, 0.03 #506), 03_3d (0.40 #8745, 0.04 #406, 0.02 #7742), 03rt9 (0.40 #8745, 0.03 #2618, 0.03 #3120), 03gj2 (0.27 #3709) >> Best rule #1103 for best value: >> intensional similarity = 4 >> extensional distance = 162 >> proper extension: 01d494; 04n_g; 03q8ch; 07_grx; 0grrq8; 03h610; 025cn2; 02q9kqf; 0c_drn; 03bw6; ... >> query: (?x5668, 09c7w0) <- gender(?x5668, ?x231), ?x231 = 05zppz, place_of_birth(?x5668, ?x739), ?x739 = 02_286 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_k1z nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.854 http://example.org/people/person/nationality #18277-03pm9 PRED entity: 03pm9 PRED relation: influenced_by! PRED expected values: 0l99s => 103 concepts (21 used for prediction) PRED predicted values (max 10 best out of 363): 03f0324 (0.50 #2240, 0.50 #1727, 0.40 #1216), 040db (0.50 #585, 0.38 #4161, 0.38 #2119), 034bs (0.50 #2198, 0.38 #1685, 0.29 #3219), 084w8 (0.50 #512, 0.33 #2, 0.25 #2046), 0d5_f (0.50 #675, 0.33 #165, 0.25 #2209), 0bk5r (0.40 #1228, 0.33 #208, 0.27 #1531), 07dnx (0.40 #1380, 0.33 #360, 0.25 #2404), 04hcw (0.40 #1307, 0.33 #287, 0.25 #2331), 03jht (0.40 #1399, 0.33 #379, 0.25 #2423), 0h25 (0.40 #1438, 0.33 #418, 0.25 #2462) >> Best rule #2240 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0379s; 01v9724; 03_dj; >> query: (?x2610, 03f0324) <- influenced_by(?x10895, ?x2610), influenced_by(?x6457, ?x2610), ?x6457 = 03_87, peers(?x10895, ?x5148), place_of_burial(?x10895, ?x14321), type_of_union(?x10895, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2333 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 0379s; 01v9724; 03_dj; *> query: (?x2610, 0l99s) <- influenced_by(?x10895, ?x2610), influenced_by(?x6457, ?x2610), ?x6457 = 03_87, peers(?x10895, ?x5148), place_of_burial(?x10895, ?x14321), type_of_union(?x10895, ?x566) *> conf = 0.25 ranks of expected_values: 56 EVAL 03pm9 influenced_by! 0l99s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 103.000 21.000 0.500 http://example.org/influence/influence_node/influenced_by #18276-06r1k PRED entity: 06r1k PRED relation: program! PRED expected values: 05gnf => 84 concepts (64 used for prediction) PRED predicted values (max 10 best out of 54): 05gnf (0.33 #14, 0.25 #71, 0.25 #472), 07y2b (0.25 #97, 0.18 #212, 0.12 #269), 0gsg7 (0.21 #1104, 0.20 #1741, 0.19 #870), 0cjdk (0.18 #177, 0.17 #348, 0.16 #405), 0187wh (0.18 #198, 0.12 #255, 0.11 #312), 03mdt (0.13 #407, 0.13 #638, 0.12 #875), 09d5h (0.13 #1105, 0.12 #1970, 0.11 #871), 0b275x (0.12 #191, 0.08 #248, 0.07 #305), 0215n (0.12 #693, 0.12 #692, 0.12 #926), 07c52 (0.12 #693, 0.12 #692, 0.12 #926) >> Best rule #14 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 06qwh; >> query: (?x11035, 05gnf) <- actor(?x11035, ?x8485), actor(?x11035, ?x3395), ?x8485 = 0f13b, ?x3395 = 01_rh4, genre(?x11035, ?x53) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06r1k program! 05gnf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 64.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #18275-07y_p6 PRED entity: 07y_p6 PRED relation: honored_for PRED expected values: 0266s9 => 31 concepts (22 used for prediction) PRED predicted values (max 10 best out of 663): 0266s9 (0.67 #5318, 0.33 #2352, 0.33 #1170), 039cq4 (0.46 #6337, 0.45 #5744, 0.36 #6931), 07s8z_l (0.45 #5887, 0.38 #6480, 0.33 #2327), 0d68qy (0.41 #7265, 0.38 #7859, 0.36 #8454), 0330r (0.40 #4076, 0.33 #4669, 0.33 #1706), 07zhjj (0.38 #6423, 0.36 #5830, 0.36 #7017), 06mr2s (0.38 #6210, 0.36 #5617, 0.36 #6804), 01vnbh (0.38 #6244, 0.36 #5651, 0.29 #6838), 06hwzy (0.38 #6081, 0.36 #5488, 0.29 #6675), 01b_lz (0.36 #5533, 0.33 #4939, 0.33 #1973) >> Best rule #5318 for best value: >> intensional similarity = 16 >> extensional distance = 4 >> proper extension: 09p30_; >> query: (?x7085, 0266s9) <- ceremony(?x2041, ?x7085), ceremony(?x686, ?x7085), award_winner(?x7085, ?x8022), award_winner(?x7085, ?x2127), award_winner(?x7085, ?x1343), award_winner(?x686, ?x376), award(?x337, ?x686), nominated_for(?x2041, ?x493), award(?x931, ?x2041), award_winner(?x3956, ?x1343), profession(?x2127, ?x1041), nominated_for(?x368, ?x493), award_nominee(?x236, ?x2127), ?x8022 = 02661h, nominated_for(?x3956, ?x2078), ?x1041 = 03gjzk >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07y_p6 honored_for 0266s9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18274-02cx90 PRED entity: 02cx90 PRED relation: award PRED expected values: 02ddq4 => 121 concepts (89 used for prediction) PRED predicted values (max 10 best out of 273): 01by1l (0.78 #5559, 0.77 #9530, 0.77 #9531), 01c99j (0.34 #1809, 0.15 #221, 0.14 #2206), 054krc (0.33 #1275, 0.14 #28209, 0.13 #35365), 01dpdh (0.31 #126, 0.18 #26615, 0.16 #33377), 0l8z1 (0.29 #1252, 0.14 #28209, 0.12 #29402), 02f6ym (0.26 #1841, 0.12 #650, 0.11 #2238), 02qvyrt (0.24 #1315, 0.18 #26615, 0.16 #33377), 0c4z8 (0.24 #3245, 0.22 #5628, 0.21 #2848), 025m8y (0.24 #1287, 0.09 #5655, 0.09 #3272), 026mff (0.23 #161, 0.18 #26615, 0.16 #33377) >> Best rule #5559 for best value: >> intensional similarity = 3 >> extensional distance = 330 >> proper extension: 04rcr; 02r3zy; 07c0j; 011zf2; 03g5jw; 0dvqq; 03fbc; 03yf3z; 0249kn; 018ndc; ... >> query: (?x4343, ?x159) <- award_nominee(?x158, ?x4343), award_winner(?x159, ?x4343), artist(?x3006, ?x4343) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #26615 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1473 *> proper extension: 0f721s; 01_8w2; 01p5yn; 035_2h; 0hm0k; 06z4wj; 03yxwq; 0gsgr; 0283xx2; 01j53q; ... *> query: (?x4343, ?x2420) <- award_winner(?x6467, ?x4343), award_winner(?x4343, ?x367), award_winner(?x2420, ?x6467) *> conf = 0.18 ranks of expected_values: 24 EVAL 02cx90 award 02ddq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 121.000 89.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18273-02rv_dz PRED entity: 02rv_dz PRED relation: award PRED expected values: 02x1dht => 96 concepts (89 used for prediction) PRED predicted values (max 10 best out of 273): 0p9sw (0.33 #19, 0.15 #3710, 0.14 #2095), 0gr0m (0.33 #59, 0.14 #2135, 0.14 #3750), 018wdw (0.33 #169, 0.11 #1319, 0.10 #1784), 0gr42 (0.33 #87, 0.11 #1237, 0.08 #1702), 02hsq3m (0.33 #28, 0.09 #1178, 0.08 #1643), 02r22gf (0.33 #27, 0.08 #2103, 0.07 #1177), 02x258x (0.33 #96, 0.05 #14989, 0.04 #326), 03m73lj (0.33 #110, 0.05 #14989, 0.04 #340), 05zrvfd (0.33 #84, 0.05 #14989, 0.01 #1467), 0gq9h (0.29 #1383, 0.28 #1381, 0.28 #13605) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0661ql3; >> query: (?x1531, 0p9sw) <- award_winner(?x1531, ?x1530), ?x1530 = 049g_xj, genre(?x1531, ?x53), country(?x1531, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #14989 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x1531, ?x834) <- award(?x1531, ?x3190), award(?x5070, ?x3190), award(?x5070, ?x834) *> conf = 0.05 ranks of expected_values: 95 EVAL 02rv_dz award 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 96.000 89.000 0.333 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #18272-01k5zk PRED entity: 01k5zk PRED relation: type_of_union PRED expected values: 04ztj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.86 #5, 0.84 #65, 0.83 #17), 01g63y (0.59 #133, 0.35 #6, 0.35 #66) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 01ztgm; 01wvxw1; >> query: (?x3585, 04ztj) <- award_winner(?x1716, ?x3585), vacationer(?x291, ?x3585), spouse(?x3585, ?x5979) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k5zk type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.865 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18271-09b6zr PRED entity: 09b6zr PRED relation: student! PRED expected values: 0kqj1 => 222 concepts (222 used for prediction) PRED predicted values (max 10 best out of 374): 02bq1j (0.29 #1216, 0.25 #1741, 0.13 #7517), 07tgn (0.25 #16, 0.12 #2116, 0.08 #14193), 05qgd9 (0.25 #462, 0.08 #4662, 0.08 #4137), 09kvv (0.25 #40, 0.08 #4240, 0.08 #3715), 02pdhz (0.25 #481, 0.08 #4681, 0.08 #4156), 0f11p (0.25 #513, 0.08 #4713, 0.04 #14690), 033gn8 (0.24 #28733, 0.04 #36609, 0.03 #34508), 065y4w7 (0.21 #6313, 0.09 #21541, 0.07 #36245), 03ksy (0.20 #7456, 0.15 #27411, 0.14 #1155), 07x4c (0.19 #10760, 0.19 #8660, 0.12 #8135) >> Best rule #1216 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 036jb; >> query: (?x4196, 02bq1j) <- student(?x620, ?x4196), student(?x122, ?x4196), celebrities_impersonated(?x2101, ?x4196) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #22713 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 01pfkw; 01wwvd2; 0n839; 07f7jp; *> query: (?x4196, 0kqj1) <- currency(?x4196, ?x170), company(?x4196, ?x7725), nationality(?x4196, ?x94) *> conf = 0.03 ranks of expected_values: 164 EVAL 09b6zr student! 0kqj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 222.000 222.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #18270-0jcx PRED entity: 0jcx PRED relation: location PRED expected values: 0d6nx => 195 concepts (182 used for prediction) PRED predicted values (max 10 best out of 368): 019xz9 (0.61 #36893, 0.59 #20050, 0.51 #100256), 0fhp9 (0.33 #1646, 0.11 #13673, 0.09 #28112), 030qb3t (0.26 #30556, 0.21 #34567, 0.19 #7297), 02_286 (0.24 #7252, 0.24 #4847, 0.19 #9658), 02jx1 (0.23 #4079, 0.09 #8889, 0.07 #10493), 04jpl (0.17 #1620, 0.08 #4026, 0.07 #29689), 0mp3l (0.17 #2525, 0.08 #4128, 0.06 #4929), 04ych (0.17 #2459, 0.06 #11278, 0.05 #6466), 013gxt (0.17 #2747, 0.05 #6754, 0.05 #5952), 0mnz0 (0.17 #3080) >> Best rule #36893 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 05fyss; 07qcbw; >> query: (?x3335, ?x14568) <- award_winner(?x11301, ?x3335), nationality(?x3335, ?x94), place_of_birth(?x3335, ?x14568), company(?x3335, ?x5281) >> conf = 0.61 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jcx location 0d6nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 195.000 182.000 0.608 http://example.org/people/person/places_lived./people/place_lived/location #18269-0l8v5 PRED entity: 0l8v5 PRED relation: type_of_union PRED expected values: 01g63y => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 01g63y (0.25 #1, 0.21 #73, 0.21 #70), 0jgjn (0.01 #12, 0.01 #15) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01rr9f; 01pcq3; 042xrr; 04kr63w; 02js9p; 04qt29; >> query: (?x413, 01g63y) <- nominated_for(?x413, ?x1184), film(?x413, ?x8130), ?x8130 = 0bwhdbl >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l8v5 type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.250 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18268-03q8ch PRED entity: 03q8ch PRED relation: edited_by! PRED expected values: 0cc5qkt 08xvpn => 97 concepts (26 used for prediction) PRED predicted values (max 10 best out of 145): 02704ff (0.13 #92, 0.12 #522, 0.12 #379), 07bwr (0.13 #85, 0.12 #515, 0.12 #372), 05fgt1 (0.13 #47, 0.12 #477, 0.12 #334), 02r1c18 (0.13 #31, 0.12 #461, 0.12 #318), 01vfqh (0.13 #28, 0.12 #458, 0.12 #315), 0b6tzs (0.13 #21, 0.12 #451, 0.12 #308), 0hwpz (0.12 #546, 0.08 #689, 0.08 #832), 0dfw0 (0.08 #653, 0.08 #796, 0.07 #939), 03mh_tp (0.08 #627, 0.07 #913, 0.06 #197), 03wy8t (0.07 #137, 0.06 #280, 0.06 #567) >> Best rule #92 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 03_gd; 02kxbwx; 052gzr; 06pj8; 027pdrh; 02kxbx3; 03crcpt; 02lp3c; 08h79x; 06t8b; ... >> query: (?x4215, 02704ff) <- award_winner(?x1747, ?x4215), edited_by(?x10260, ?x4215), award(?x4215, ?x1703), film_crew_role(?x10260, ?x137) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03q8ch edited_by! 08xvpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 26.000 0.133 http://example.org/film/film/edited_by EVAL 03q8ch edited_by! 0cc5qkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 26.000 0.133 http://example.org/film/film/edited_by #18267-01w5n51 PRED entity: 01w5n51 PRED relation: artists! PRED expected values: 01756d 08jyyk 05c6073 => 95 concepts (63 used for prediction) PRED predicted values (max 10 best out of 275): 0glt670 (0.75 #938, 0.35 #10861, 0.34 #10561), 025sc50 (0.75 #945, 0.32 #11468, 0.31 #10868), 06by7 (0.72 #4229, 0.57 #619, 0.57 #9938), 064t9 (0.62 #8127, 0.60 #8427, 0.58 #10536), 0133_p (0.57 #747, 0.24 #1502, 0.22 #6613), 011j5x (0.52 #2735, 0.50 #3636, 0.33 #330), 0xhtw (0.51 #7229, 0.42 #4225, 0.40 #1216), 05bt6j (0.50 #3645, 0.48 #2744, 0.33 #339), 05r6t (0.50 #376, 0.36 #3682, 0.30 #2781), 0gywn (0.50 #953, 0.34 #8167, 0.33 #8467) >> Best rule #938 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 015mrk; >> query: (?x7612, 0glt670) <- award(?x7612, ?x3391), award(?x7612, ?x1389), artists(?x302, ?x7612), ?x1389 = 01c427, ?x3391 = 02f76h >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1263 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 01vng3b; *> query: (?x7612, 08jyyk) <- artists(?x13553, ?x7612), artists(?x7960, ?x7612), parent_genre(?x7960, ?x5934), category(?x7612, ?x134), ?x13553 = 0b_6yv *> conf = 0.50 ranks of expected_values: 12, 18, 177 EVAL 01w5n51 artists! 05c6073 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 95.000 63.000 0.750 http://example.org/music/genre/artists EVAL 01w5n51 artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 95.000 63.000 0.750 http://example.org/music/genre/artists EVAL 01w5n51 artists! 01756d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 95.000 63.000 0.750 http://example.org/music/genre/artists #18266-026hxwx PRED entity: 026hxwx PRED relation: film_distribution_medium PRED expected values: 0735l => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.85 #64, 0.81 #52, 0.76 #19), 029j_ (0.21 #22, 0.19 #48, 0.12 #60), 0dq6p (0.14 #50, 0.11 #3, 0.08 #24), 02nxhr (0.12 #49, 0.12 #61, 0.08 #16), 07z4p (0.03 #13, 0.02 #33, 0.02 #40) >> Best rule #64 for best value: >> intensional similarity = 5 >> extensional distance = 135 >> proper extension: 0522wp; >> query: (?x6500, 0735l) <- film(?x609, ?x6500), film(?x574, ?x6500), ?x609 = 03xq0f, film(?x574, ?x6680), award_winner(?x6680, ?x4251) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026hxwx film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.847 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #18265-04wf_b PRED entity: 04wf_b PRED relation: nationality PRED expected values: 09c7w0 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 65): 09c7w0 (0.87 #201, 0.78 #1002, 0.76 #1602), 0l2vz (0.33 #2807, 0.33 #7219), 06pvr (0.33 #2807, 0.33 #7219), 0345h (0.27 #4109, 0.04 #301, 0.03 #1332), 0f8l9c (0.27 #4109, 0.02 #3329, 0.02 #5132), 0d060g (0.17 #107, 0.09 #7, 0.05 #808), 02jx1 (0.11 #334, 0.10 #3240, 0.10 #3340), 07ssc (0.09 #1917, 0.08 #4023, 0.08 #3222), 03shp (0.09 #56), 09pmkv (0.08 #128, 0.04 #301) >> Best rule #201 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 01xllf; >> query: (?x9218, 09c7w0) <- film(?x9218, ?x6855), film(?x9218, ?x2709), ?x6855 = 0bxsk, film_release_region(?x2709, ?x87) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wf_b nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.867 http://example.org/people/person/nationality #18264-05dss7 PRED entity: 05dss7 PRED relation: film! PRED expected values: 0gfmc_ => 122 concepts (107 used for prediction) PRED predicted values (max 10 best out of 64): 016tt2 (0.33 #4, 0.16 #2816, 0.15 #2527), 020h2v (0.33 #42, 0.07 #1050, 0.07 #3142), 03xq0f (0.22 #221, 0.20 #77, 0.18 #149), 05qd_ (0.22 #224, 0.18 #2243, 0.17 #1304), 086k8 (0.21 #2814, 0.20 #74, 0.18 #146), 017s11 (0.17 #3, 0.16 #1083, 0.16 #507), 024rgt (0.17 #17, 0.10 #89, 0.09 #161), 0jz9f (0.17 #1, 0.09 #2452, 0.07 #865), 030_1m (0.17 #11, 0.05 #2823, 0.04 #2534), 019v67 (0.17 #66, 0.02 #7076, 0.02 #570) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01gglm; >> query: (?x6556, 016tt2) <- film_crew_role(?x6556, ?x468), film(?x4782, ?x6556), ?x4782 = 0bksh, film(?x8235, ?x6556) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7076 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1267 *> proper extension: 06wzvr; 0gcrg; *> query: (?x6556, ?x574) <- film_crew_role(?x6556, ?x1171), genre(?x6556, ?x258), film_crew_role(?x5201, ?x1171), film_crew_role(?x1803, ?x1171), film(?x585, ?x5201), film(?x574, ?x1803) *> conf = 0.02 ranks of expected_values: 48 EVAL 05dss7 film! 0gfmc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 122.000 107.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18263-02x4wb PRED entity: 02x4wb PRED relation: award! PRED expected values: 01wg982 => 52 concepts (11 used for prediction) PRED predicted values (max 10 best out of 2372): 0134pk (0.82 #9542, 0.50 #6173, 0.33 #2804), 0dvqq (0.73 #7369, 0.50 #4000, 0.33 #631), 07r1_ (0.73 #8802, 0.50 #5433, 0.33 #2064), 06mj4 (0.73 #9075, 0.50 #5706, 0.33 #2337), 0kr_t (0.73 #8358, 0.25 #4989, 0.25 #18468), 01vs_v8 (0.64 #7322, 0.38 #20801, 0.36 #17432), 0gbwp (0.64 #7853, 0.30 #21332, 0.28 #17963), 09889g (0.64 #8189, 0.26 #18299, 0.25 #21668), 0478__m (0.64 #8066, 0.25 #4697, 0.23 #18176), 016l09 (0.64 #9521, 0.16 #19631, 0.11 #23000) >> Best rule #9542 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 02f72n; 02f5qb; 02f716; 02f73p; 02f72_; 02f77l; 02f73b; >> query: (?x11068, 0134pk) <- award(?x10565, ?x11068), award(?x9706, ?x11068), award(?x3933, ?x11068), ?x10565 = 0c9l1, film(?x3933, ?x2907), group(?x227, ?x9706) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #634 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 01d38t; *> query: (?x11068, 01wg982) <- award(?x12449, ?x11068), award(?x3933, ?x11068), ?x3933 = 01vtqml, group(?x227, ?x12449), category_of(?x11068, ?x2421) *> conf = 0.33 ranks of expected_values: 61 EVAL 02x4wb award! 01wg982 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 52.000 11.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18262-06r_by PRED entity: 06r_by PRED relation: award_nominee PRED expected values: 026rm_y => 114 concepts (49 used for prediction) PRED predicted values (max 10 best out of 596): 08h79x (0.27 #63112, 0.26 #79478, 0.25 #95842), 06r_by (0.27 #63112, 0.26 #79478, 0.25 #95842), 02mxbd (0.27 #63112, 0.26 #79478, 0.25 #95842), 0bytkq (0.27 #63112, 0.26 #79478, 0.25 #95842), 0dvmd (0.27 #63112, 0.26 #79478, 0.25 #95842), 018ygt (0.27 #63112, 0.26 #79478, 0.25 #95842), 021npv (0.27 #63112, 0.26 #79478, 0.25 #95842), 026rm_y (0.27 #63112, 0.26 #79478, 0.25 #95842), 04sry (0.27 #63112, 0.26 #79478, 0.25 #95842), 02pq9yv (0.27 #63112, 0.26 #79478, 0.25 #95842) >> Best rule #63112 for best value: >> intensional similarity = 3 >> extensional distance = 1064 >> proper extension: 02wrhj; >> query: (?x6062, ?x2332) <- award_winner(?x2294, ?x6062), nominated_for(?x6062, ?x5648), award_winner(?x5648, ?x2332) >> conf = 0.27 => this is the best rule for 25 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 8 EVAL 06r_by award_nominee 026rm_y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 114.000 49.000 0.267 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18261-0fr63l PRED entity: 0fr63l PRED relation: film! PRED expected values: 044mz_ => 86 concepts (54 used for prediction) PRED predicted values (max 10 best out of 995): 0cw67g (0.57 #81219, 0.44 #62477, 0.42 #45815), 04vq3h (0.25 #3785), 0170qf (0.14 #367, 0.12 #2449, 0.03 #19110), 018swb (0.14 #342, 0.12 #2424, 0.02 #29498), 03q45x (0.14 #1356, 0.04 #5520, 0.01 #26347), 0h0wc (0.14 #424, 0.04 #8753, 0.02 #29580), 0h5g_ (0.14 #74, 0.04 #22983, 0.03 #31313), 01_xtx (0.14 #663, 0.03 #4827), 02_p8v (0.14 #925, 0.03 #19668, 0.03 #17584), 015t7v (0.14 #898, 0.03 #33322, 0.02 #23807) >> Best rule #81219 for best value: >> intensional similarity = 4 >> extensional distance = 971 >> proper extension: 0clpml; >> query: (?x721, ?x10416) <- nominated_for(?x10416, ?x721), place_of_birth(?x10416, ?x12820), film(?x10416, ?x1673), type_of_union(?x10416, ?x566) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #8331 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 0c3xpwy; *> query: (?x721, 044mz_) <- nominated_for(?x10416, ?x721), place_of_birth(?x10416, ?x12820), profession(?x10416, ?x1032), crewmember(?x1372, ?x10416) *> conf = 0.01 ranks of expected_values: 911 EVAL 0fr63l film! 044mz_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 54.000 0.575 http://example.org/film/actor/film./film/performance/film #18260-06mvq PRED entity: 06mvq PRED relation: people PRED expected values: 01kgv4 03s9b => 27 concepts (18 used for prediction) PRED predicted values (max 10 best out of 3865): 08f3b1 (0.50 #5264, 0.40 #6986, 0.33 #8709), 0jfx1 (0.40 #7207, 0.33 #8930, 0.25 #5485), 04f7c55 (0.40 #7707, 0.33 #9430, 0.25 #5985), 0693l (0.40 #7314, 0.33 #9037, 0.25 #5592), 0127m7 (0.40 #7209, 0.33 #8932, 0.25 #5487), 01_ztw (0.40 #7682, 0.33 #9405, 0.25 #5960), 06cgy (0.38 #15700, 0.33 #8814, 0.33 #197), 01fwj8 (0.33 #8832, 0.33 #215, 0.31 #15718), 032_jg (0.33 #110, 0.30 #13890, 0.25 #5282), 02184q (0.33 #1381, 0.30 #15161, 0.25 #6553) >> Best rule #5264 for best value: >> intensional similarity = 15 >> extensional distance = 2 >> proper extension: 033tf_; >> query: (?x7790, 08f3b1) <- people(?x7790, ?x7799), people(?x7790, ?x4440), award_nominee(?x4440, ?x4254), award_nominee(?x4440, ?x2805), profession(?x4440, ?x1032), nominated_for(?x4440, ?x5752), ?x4254 = 0fbx6, languages_spoken(?x7790, ?x4442), film(?x4440, ?x8690), artists(?x7329, ?x7799), instrumentalists(?x227, ?x7799), type_of_union(?x4440, ?x566), ?x2805 = 0lpjn, film_crew_role(?x8690, ?x137), ?x7329 = 016jny >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #14709 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 8 *> proper extension: 041rx; 03lmx1; 07bch9; 03bkbh; 0bbz66j; *> query: (?x7790, 01kgv4) <- people(?x7790, ?x4440), award_nominee(?x4440, ?x4254), award_nominee(?x4440, ?x2805), profession(?x4440, ?x1032), nominated_for(?x4440, ?x5752), nationality(?x4254, ?x789), award(?x4254, ?x618), ?x2805 = 0lpjn, award(?x4440, ?x1254), people(?x6734, ?x4254), student(?x2593, ?x4254), profession(?x4254, ?x2225), ?x2225 = 0kyk *> conf = 0.10 ranks of expected_values: 557, 2413 EVAL 06mvq people 03s9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 27.000 18.000 0.500 http://example.org/people/ethnicity/people EVAL 06mvq people 01kgv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 18.000 0.500 http://example.org/people/ethnicity/people #18259-02v63m PRED entity: 02v63m PRED relation: country PRED expected values: 09c7w0 => 74 concepts (63 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.82 #1290, 0.82 #307, 0.82 #1351), 07ssc (0.40 #3872, 0.35 #1535, 0.35 #1490), 0345h (0.17 #517, 0.17 #456, 0.17 #333), 0d060g (0.11 #131, 0.08 #70, 0.07 #253), 03npn (0.10 #1473, 0.06 #3687, 0.05 #3564), 0f8l9c (0.10 #2602, 0.10 #3217, 0.09 #3155), 0chghy (0.08 #74, 0.05 #1548, 0.05 #1301), 03rjj (0.08 #68, 0.04 #1603, 0.03 #2589), 01mjq (0.08 #97, 0.03 #830, 0.03 #219), 03_3d (0.05 #2776, 0.04 #1049, 0.04 #863) >> Best rule #1290 for best value: >> intensional similarity = 4 >> extensional distance = 237 >> proper extension: 03q8xj; >> query: (?x1184, 09c7w0) <- genre(?x1184, ?x258), executive_produced_by(?x1184, ?x7324), film_crew_role(?x1184, ?x137), music(?x1184, ?x3414) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v63m country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 63.000 0.824 http://example.org/film/film/country #18258-01y665 PRED entity: 01y665 PRED relation: religion PRED expected values: 03_gx => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 16): 03_gx (0.29 #330, 0.26 #194, 0.13 #59), 0c8wxp (0.23 #276, 0.21 #413, 0.21 #231), 03j6c (0.09 #111, 0.04 #66, 0.02 #1740), 0kpl (0.07 #326, 0.07 #190, 0.05 #1413), 01lp8 (0.05 #46, 0.04 #91, 0.03 #362), 06nzl (0.04 #60, 0.02 #105, 0.02 #240), 092bf5 (0.03 #196, 0.03 #286, 0.03 #241), 0flw86 (0.02 #92, 0.02 #1405, 0.02 #2532), 0kq2 (0.02 #243, 0.02 #63, 0.02 #198), 019cr (0.02 #281, 0.02 #464, 0.01 #418) >> Best rule #330 for best value: >> intensional similarity = 3 >> extensional distance = 330 >> proper extension: 067xw; 032md; 030dx5; 0q1lp; 01h2_6; >> query: (?x3039, 03_gx) <- people(?x1050, ?x3039), nationality(?x3039, ?x279), ?x1050 = 041rx >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y665 religion 03_gx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.292 http://example.org/people/person/religion #18257-03cw411 PRED entity: 03cw411 PRED relation: film_release_region PRED expected values: 06qd3 01pj7 06mkj 03rj0 03spz => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 116): 06mkj (0.85 #1296, 0.82 #1852, 0.82 #1435), 0d060g (0.80 #1256, 0.77 #1395, 0.76 #1812), 06bnz (0.76 #1423, 0.76 #1284, 0.75 #1840), 03spz (0.76 #1333, 0.75 #1472, 0.75 #1889), 03rj0 (0.68 #1300, 0.67 #1856, 0.67 #187), 01p1v (0.64 #1291, 0.59 #1430, 0.58 #1847), 06qd3 (0.61 #1417, 0.60 #1834, 0.56 #1278), 04gzd (0.60 #1259, 0.58 #1398, 0.55 #1815), 047yc (0.53 #1409, 0.53 #1826, 0.52 #1270), 03rk0 (0.53 #1295, 0.51 #1434, 0.49 #1851) >> Best rule #1296 for best value: >> intensional similarity = 6 >> extensional distance = 85 >> proper extension: 0b76d_m; 0ds35l9; 0g56t9t; 0c3ybss; 0gtv7pk; 0g5qs2k; 087wc7n; 02d44q; 017gm7; 07qg8v; ... >> query: (?x3745, 06mkj) <- film_release_region(?x3745, ?x7747), film_release_region(?x3745, ?x739), film_release_region(?x3745, ?x87), ?x7747 = 07f1x, ?x87 = 05r4w, featured_film_locations(?x89, ?x739) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 5, 7, 14 EVAL 03cw411 film_release_region 03spz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03cw411 film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03cw411 film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03cw411 film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 108.000 108.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03cw411 film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 108.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18256-0hwbd PRED entity: 0hwbd PRED relation: location PRED expected values: 059rby => 134 concepts (131 used for prediction) PRED predicted values (max 10 best out of 248): 030qb3t (0.25 #4103, 0.25 #18580, 0.25 #5712), 02_286 (0.20 #32207, 0.20 #5666, 0.20 #14511), 04jpl (0.18 #17, 0.08 #27362, 0.06 #11274), 01_d4 (0.16 #906, 0.10 #4122, 0.07 #3318), 01n7q (0.11 #1671, 0.08 #4083, 0.06 #8908), 0cc56 (0.09 #6490, 0.09 #5686, 0.09 #57), 0r0m6 (0.09 #218, 0.08 #5847, 0.07 #9867), 0cr3d (0.09 #145, 0.08 #7382, 0.07 #2557), 0498y (0.09 #213, 0.05 #1017, 0.02 #2625), 0f__1 (0.09 #141, 0.04 #1749, 0.01 #24267) >> Best rule #4103 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 01xcr4; >> query: (?x5821, 030qb3t) <- student(?x5614, ?x5821), award(?x5821, ?x154), participant(?x2444, ?x5821) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #5645 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 06cgy; 01g257; 01vsnff; 03jqw5; 01w7nwm; 01w7nww; 07swvb; 0315q3; 01w_10; *> query: (?x5821, 059rby) <- film(?x5821, ?x1451), award_winner(?x5821, ?x9301), celebrity(?x5821, ?x4397) *> conf = 0.08 ranks of expected_values: 18 EVAL 0hwbd location 059rby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 134.000 131.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #18255-030hbp PRED entity: 030hbp PRED relation: award PRED expected values: 0bfvw2 0cqgl9 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 269): 09sb52 (0.52 #2434, 0.47 #16799, 0.44 #23184), 0cqh6z (0.40 #465, 0.22 #1263, 0.13 #44297), 0bfvw2 (0.40 #414, 0.22 #1212, 0.13 #44297), 0gkts9 (0.40 #564, 0.22 #1362, 0.13 #44297), 09qs08 (0.33 #939, 0.14 #38708, 0.13 #44297), 05pcn59 (0.33 #6463, 0.31 #6862, 0.31 #3670), 05p09zm (0.31 #3711, 0.29 #6504, 0.28 #6903), 02x4x18 (0.25 #129, 0.20 #528, 0.14 #3321), 05zr6wv (0.25 #17, 0.17 #4406, 0.17 #4007), 04kxsb (0.25 #122, 0.14 #3713, 0.14 #1718) >> Best rule #2434 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 02s2ft; 017149; 04bd8y; 0h1nt; 0gjvqm; 048lv; 027f7dj; 02qgyv; 043kzcr; 0h0wc; ... >> query: (?x10491, 09sb52) <- award_nominee(?x1871, ?x10491), film(?x10491, ?x4643), ?x1871 = 02bkdn >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #414 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 011_3s; 040t74; 02mqc4; *> query: (?x10491, 0bfvw2) <- award_nominee(?x10161, ?x10491), award_nominee(?x1870, ?x10491), ?x10161 = 01ggc9, ?x1870 = 0hvb2 *> conf = 0.40 ranks of expected_values: 3, 26 EVAL 030hbp award 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 127.000 127.000 0.523 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 030hbp award 0bfvw2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 127.000 127.000 0.523 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18254-015zql PRED entity: 015zql PRED relation: place_of_birth PRED expected values: 0167q3 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 94): 02_286 (0.13 #19, 0.12 #7064, 0.10 #4245), 0cr3d (0.09 #94, 0.07 #799, 0.06 #2207), 0hptm (0.09 #225, 0.05 #930, 0.02 #12682), 01_d4 (0.06 #2179, 0.05 #5701, 0.05 #1475), 030qb3t (0.05 #2167, 0.04 #24709, 0.04 #5689), 01531 (0.04 #105, 0.02 #810, 0.02 #14196), 0_24q (0.04 #358, 0.02 #1063, 0.02 #12682), 0n5bk (0.04 #270, 0.02 #975, 0.02 #12682), 0xn5b (0.04 #199, 0.02 #904, 0.02 #12682), 02s838 (0.04 #405, 0.02 #1110) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0pyg6; >> query: (?x8591, 02_286) <- profession(?x8591, ?x319), student(?x2775, ?x8591), ?x2775 = 078bz, nationality(?x8591, ?x94) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015zql place_of_birth 0167q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 103.000 0.130 http://example.org/people/person/place_of_birth #18253-022qqh PRED entity: 022qqh PRED relation: specialization_of PRED expected values: 04_tv => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 022qqh specialization_of 04_tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/people/profession/specialization_of #18252-0161rf PRED entity: 0161rf PRED relation: artists PRED expected values: 01m42d0 02sjp => 61 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2542): 0b_j2 (0.60 #1667, 0.40 #2741, 0.33 #4890), 01gg59 (0.60 #1412, 0.40 #2486, 0.33 #4635), 0178_w (0.60 #2759, 0.40 #1685, 0.26 #8134), 094xh (0.60 #2628, 0.40 #1554, 0.26 #8003), 02dbp7 (0.60 #1471, 0.33 #4694, 0.22 #7522), 03j24kf (0.60 #1490, 0.33 #4713, 0.20 #2564), 01sbf2 (0.60 #1187, 0.33 #4410, 0.20 #2261), 01x0yrt (0.60 #2958, 0.20 #1884, 0.17 #8333), 0pj9t (0.57 #5643, 0.50 #6719, 0.50 #3493), 01wd9lv (0.57 #5946, 0.50 #7022, 0.50 #3796) >> Best rule #1667 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 05bt6j; >> query: (?x8138, 0b_j2) <- artists(?x8138, ?x12743), artists(?x8138, ?x7414), artists(?x8138, ?x672), artist(?x3240, ?x7414), film(?x7414, ?x4179), student(?x2605, ?x672), ?x12743 = 02bc74, influenced_by(?x236, ?x7414) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1913 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 05bt6j; *> query: (?x8138, 02sjp) <- artists(?x8138, ?x12743), artists(?x8138, ?x7414), artists(?x8138, ?x672), artist(?x3240, ?x7414), film(?x7414, ?x4179), student(?x2605, ?x672), ?x12743 = 02bc74, influenced_by(?x236, ?x7414) *> conf = 0.20 ranks of expected_values: 529, 871 EVAL 0161rf artists 02sjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 27.000 0.600 http://example.org/music/genre/artists EVAL 0161rf artists 01m42d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 27.000 0.600 http://example.org/music/genre/artists #18251-01ycfv PRED entity: 01ycfv PRED relation: place_of_birth PRED expected values: 02_286 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 146): 02_286 (0.11 #12695, 0.10 #16215, 0.10 #13399), 0cr3d (0.10 #94, 0.05 #9953, 0.04 #7137), 0p7vt (0.10 #406, 0.02 #6040, 0.01 #8857), 049kw (0.10 #427), 030qb3t (0.07 #9209, 0.07 #10617, 0.06 #3576), 0cc56 (0.07 #2146, 0.04 #2850, 0.03 #3555), 01_d4 (0.06 #14854, 0.05 #6405, 0.03 #30344), 04jpl (0.06 #33095, 0.04 #9163, 0.04 #3522), 094jv (0.05 #766, 0.05 #1470, 0.03 #3583), 0r00l (0.05 #1192, 0.05 #1896, 0.02 #5417) >> Best rule #12695 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 07zhd7; >> query: (?x9408, 02_286) <- music(?x1842, ?x9408), award_winner(?x725, ?x9408), ceremony(?x159, ?x725) >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ycfv place_of_birth 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.113 http://example.org/people/person/place_of_birth #18250-019fnv PRED entity: 019fnv PRED relation: award_winner! PRED expected values: 0fy59t => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 138): 0bzm81 (0.33 #442, 0.33 #22, 0.14 #1982), 02jp5r (0.33 #69, 0.31 #1889, 0.29 #2170), 02yxh9 (0.33 #101, 0.17 #521, 0.15 #1921), 059x66 (0.33 #18, 0.17 #438, 0.14 #1978), 02yv_b (0.33 #25, 0.17 #445, 0.07 #1985), 073hmq (0.33 #21, 0.17 #441, 0.07 #1981), 04n2r9h (0.33 #45, 0.17 #465, 0.07 #2005), 0fy6bh (0.25 #327, 0.17 #467, 0.14 #2568), 0fk0xk (0.25 #358, 0.17 #498, 0.09 #1758), 0ftlkg (0.25 #306, 0.17 #446, 0.09 #1706) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 09pjnd; >> query: (?x10164, 0bzm81) <- location(?x10164, ?x739), award_winner(?x9400, ?x10164), crewmember(?x3599, ?x10164) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2076 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 054g1r; *> query: (?x10164, 0fy59t) <- award(?x10164, ?x2209), award(?x10164, ?x500), ?x2209 = 0gr42, ceremony(?x500, ?x78) *> conf = 0.07 ranks of expected_values: 59 EVAL 019fnv award_winner! 0fy59t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 170.000 170.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18249-01fsyp PRED entity: 01fsyp PRED relation: category PRED expected values: 08mbj5d => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #64, 0.83 #105, 0.83 #104) >> Best rule #64 for best value: >> intensional similarity = 4 >> extensional distance = 380 >> proper extension: 07vht; >> query: (?x14290, 08mbj5d) <- citytown(?x14290, ?x739), citytown(?x6611, ?x739), major_field_of_study(?x6611, ?x373), state(?x739, ?x335) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fsyp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.830 http://example.org/common/topic/webpage./common/webpage/category #18248-0flw6 PRED entity: 0flw6 PRED relation: award_winner! PRED expected values: 099ck7 => 97 concepts (95 used for prediction) PRED predicted values (max 10 best out of 252): 0bdwqv (0.30 #37600, 0.30 #31187, 0.30 #31186), 09sb52 (0.30 #37600, 0.30 #31187, 0.30 #31186), 04kxsb (0.30 #37600, 0.30 #31187, 0.30 #31186), 0gqy2 (0.30 #37600, 0.30 #31187, 0.30 #31186), 09sdmz (0.30 #37600, 0.30 #31187, 0.30 #31186), 099ck7 (0.30 #37600, 0.30 #31187, 0.30 #31186), 027c95y (0.26 #1866, 0.12 #1437, 0.10 #1008), 09cm54 (0.15 #1807, 0.12 #949, 0.10 #1378), 02w9sd7 (0.15 #1876, 0.10 #1018, 0.10 #1447), 0gq9h (0.15 #76, 0.09 #503, 0.05 #8547) >> Best rule #37600 for best value: >> intensional similarity = 2 >> extensional distance = 3377 >> proper extension: 084w8; 0411q; 02rchht; 0hl3d; 01vrx3g; 01lmj3q; 089tm; 01yznp; 07w21; 041h0; ... >> query: (?x4324, ?x3247) <- award(?x4324, ?x3247), award_winner(?x3247, ?x269) >> conf = 0.30 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 0flw6 award_winner! 099ck7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 97.000 95.000 0.304 http://example.org/award/award_category/winners./award/award_honor/award_winner #18247-02hp70 PRED entity: 02hp70 PRED relation: fraternities_and_sororities PRED expected values: 035tlh => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 3): 035tlh (0.25 #2, 0.21 #20, 0.18 #17), 0325pb (0.19 #16, 0.19 #37, 0.18 #31), 04m8fy (0.03 #24, 0.02 #21, 0.02 #66) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01cyd5; >> query: (?x11397, 035tlh) <- state_province_region(?x11397, ?x728), school_type(?x11397, ?x1507), student(?x11397, ?x8656), ?x8656 = 042v2 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hp70 fraternities_and_sororities 035tlh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.250 http://example.org/education/university/fraternities_and_sororities #18246-0dz3r PRED entity: 0dz3r PRED relation: specialization_of PRED expected values: 047rgpy => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 65): 09jwl (0.50 #265, 0.33 #39, 0.33 #7), 0n1h (0.33 #68, 0.25 #132, 0.20 #230), 01c8w0 (0.25 #164, 0.03 #161, 0.02 #326), 0cbd2 (0.08 #421, 0.08 #552, 0.08 #584), 06q2q (0.08 #891, 0.07 #826, 0.07 #958), 02hrh1q (0.05 #524, 0.05 #460, 0.04 #654), 01c979 (0.04 #771, 0.04 #803, 0.04 #869), 04_tv (0.04 #1022, 0.03 #429, 0.03 #592), 04gc2 (0.03 #890, 0.03 #1026, 0.03 #825), 015cjr (0.03 #372, 0.03 #404, 0.03 #534) >> Best rule #265 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 01c72t; 01b30l; 025352; >> query: (?x131, 09jwl) <- profession(?x6626, ?x131), profession(?x4620, ?x131), profession(?x4062, ?x131), profession(?x3503, ?x131), ?x4620 = 01vsy7t, artists(?x284, ?x4062), participant(?x3503, ?x4106), ?x6626 = 0b_j2 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1046 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 137 *> proper extension: 018rn4; *> query: (?x131, ?x955) <- profession(?x5494, ?x131), profession(?x4646, ?x131), profession(?x5494, ?x955), type_of_union(?x5494, ?x566), nationality(?x4646, ?x792) *> conf = 0.02 ranks of expected_values: 43 EVAL 0dz3r specialization_of 047rgpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 33.000 33.000 0.500 http://example.org/people/profession/specialization_of #18245-0345h PRED entity: 0345h PRED relation: contains PRED expected values: 0m7yh 0ps1q => 202 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2852): 017wh (0.86 #20124, 0.85 #135127, 0.83 #207014), 051ls (0.64 #60370, 0.62 #227140, 0.61 #301893), 01mjq (0.64 #60370, 0.62 #227140, 0.61 #301893), 05qhw (0.64 #60370, 0.62 #227140, 0.61 #301893), 018f94 (0.64 #60370, 0.62 #227140, 0.61 #301893), 01cz_1 (0.62 #227140, 0.61 #301893, 0.02 #134370), 0k6bt (0.25 #5338, 0.06 #25462, 0.05 #28338), 0k424 (0.25 #4919, 0.06 #25043, 0.05 #27919), 0fydw (0.25 #4375, 0.06 #24499, 0.05 #27375), 0177z (0.25 #3483, 0.06 #23607, 0.05 #26483) >> Best rule #20124 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 09c7w0; 0jgd; 03rjj; 03_3d; 0d060g; 0chghy; 07ssc; 0f8l9c; 06qd3; 06mkj; ... >> query: (?x1264, ?x1646) <- film_release_region(?x951, ?x1264), olympics(?x1264, ?x452), administrative_parent(?x1646, ?x1264) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #178107 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 62 *> proper extension: 0jhwd; *> query: (?x1264, 0ps1q) <- contains(?x1264, ?x12866), contains(?x1264, ?x2611), film_release_region(?x903, ?x2611), place_of_birth(?x1645, ?x12866) *> conf = 0.02 ranks of expected_values: 2786 EVAL 0345h contains 0ps1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 202.000 108.000 0.856 http://example.org/location/location/contains EVAL 0345h contains 0m7yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 202.000 108.000 0.856 http://example.org/location/location/contains #18244-0cx6f PRED entity: 0cx6f PRED relation: artists PRED expected values: 0fpjd_g => 61 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1088): 03t9sp (0.60 #2273, 0.40 #4429, 0.33 #6580), 011z3g (0.50 #4905, 0.40 #2749, 0.37 #7056), 0fpjd_g (0.44 #3337, 0.33 #1185, 0.30 #4416), 01vrt_c (0.41 #6535, 0.26 #9692, 0.23 #20457), 012vd6 (0.40 #4782, 0.40 #2626, 0.33 #1551), 032nwy (0.40 #4333, 0.40 #2177, 0.33 #1102), 0163m1 (0.40 #4652, 0.40 #2496, 0.33 #1421), 02mslq (0.40 #4339, 0.40 #2183, 0.33 #1108), 0140t7 (0.40 #5167, 0.40 #3011, 0.33 #1936), 03j0br4 (0.40 #4503, 0.40 #2347, 0.33 #1272) >> Best rule #2273 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01kcty; >> query: (?x11023, 03t9sp) <- parent_genre(?x5355, ?x11023), artists(?x11023, ?x2698), artists(?x11023, ?x1940), instrumentalists(?x228, ?x2698), ?x1940 = 04zwjd >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3337 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 01lxd4; 07ym47; 01ppfv; *> query: (?x11023, 0fpjd_g) <- artists(?x11023, ?x2835), artists(?x11023, ?x1524), ?x2835 = 053yx, award_nominee(?x4568, ?x1524), award_winner(?x4568, ?x4044), award_nominee(?x5172, ?x4568) *> conf = 0.44 ranks of expected_values: 3 EVAL 0cx6f artists 0fpjd_g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 61.000 33.000 0.600 http://example.org/music/genre/artists #18243-02z0f6l PRED entity: 02z0f6l PRED relation: nominated_for! PRED expected values: 0gq_v 0gqxm => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 206): 0gs96 (0.67 #8538, 0.67 #3794, 0.66 #6877), 0gq9h (0.48 #1010, 0.48 #6701, 0.45 #7177), 0gq_v (0.45 #967, 0.43 #256, 0.37 #3575), 0gs9p (0.42 #6703, 0.40 #7179, 0.40 #6941), 019f4v (0.42 #6692, 0.42 #1001, 0.40 #7168), 0k611 (0.38 #1020, 0.36 #6711, 0.34 #7187), 040njc (0.37 #954, 0.33 #6645, 0.32 #7121), 0gqy2 (0.36 #358, 0.30 #1069, 0.30 #6760), 02hsq3m (0.33 #503, 0.16 #1451, 0.16 #1688), 0gr0m (0.32 #1007, 0.29 #296, 0.28 #6698) >> Best rule #8538 for best value: >> intensional similarity = 3 >> extensional distance = 510 >> proper extension: 02gl58; 02rq7nd; >> query: (?x6900, ?x2222) <- honored_for(?x4224, ?x6900), nominated_for(?x143, ?x6900), award(?x6900, ?x2222) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #967 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 0b2v79; 07j8r; 07cyl; 03cw411; 0pd57; 0cq7tx; 017jd9; 027rpym; 049xgc; 02lxrv; ... *> query: (?x6900, 0gq_v) <- costume_design_by(?x6900, ?x5613), currency(?x6900, ?x170), genre(?x6900, ?x1316), honored_for(?x4224, ?x6900) *> conf = 0.45 ranks of expected_values: 3, 57 EVAL 02z0f6l nominated_for! 0gqxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 97.000 97.000 0.674 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02z0f6l nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 97.000 0.674 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18242-03__77 PRED entity: 03__77 PRED relation: teams! PRED expected values: 07t_x => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 78): 0947l (0.02 #452, 0.02 #722, 0.02 #992), 0d0vqn (0.02 #278, 0.02 #548, 0.02 #818), 04jpl (0.02 #549, 0.02 #819, 0.02 #5679), 02m77 (0.01 #5285, 0.01 #6095, 0.01 #6365), 030qb3t (0.01 #7881, 0.01 #8153, 0.01 #8426), 04sqj (0.01 #5840, 0.01 #6110, 0.01 #6380), 02fvv (0.01 #5922, 0.01 #6462, 0.01 #252), 0jp26 (0.01 #5769, 0.01 #369), 0h3tv (0.01 #5874), 04swd (0.01 #6657, 0.01 #6117, 0.01 #6387) >> Best rule #452 for best value: >> intensional similarity = 11 >> extensional distance = 98 >> proper extension: 01k2yr; 01j95f; 02ltg3; 0f5hyg; 0k_l4; 023fb; 03xh50; 01jdxj; 01cwm1; 049n2l; ... >> query: (?x10030, 0947l) <- position(?x10030, ?x530), position(?x10030, ?x203), position(?x10030, ?x63), position(?x10030, ?x60), ?x63 = 02sdk9v, ?x203 = 0dgrmp, sport(?x10030, ?x471), ?x471 = 02vx4, ?x530 = 02_j1w, ?x60 = 02nzb8, position(?x10030, ?x203) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03__77 teams! 07t_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 44.000 0.020 http://example.org/sports/sports_team_location/teams #18241-01p8r8 PRED entity: 01p8r8 PRED relation: profession PRED expected values: 01d_h8 01c72t => 107 concepts (62 used for prediction) PRED predicted values (max 10 best out of 51): 01d_h8 (0.85 #3983, 0.83 #1426, 0.71 #716), 03gjzk (0.83 #1148, 0.78 #864, 0.74 #1574), 018gz8 (0.56 #866, 0.36 #2144, 0.31 #1434), 09jwl (0.25 #158, 0.16 #5129, 0.16 #7262), 0kyk (0.25 #166, 0.16 #5705, 0.14 #7128), 0n1h (0.25 #294, 0.12 #720, 0.11 #3987), 0nbcg (0.25 #310, 0.10 #7272, 0.10 #5139), 05z96 (0.25 #321, 0.06 #747, 0.05 #5292), 0d1pc (0.20 #471, 0.12 #613, 0.10 #3028), 01c72t (0.12 #1156, 0.12 #730, 0.11 #872) >> Best rule #3983 for best value: >> intensional similarity = 3 >> extensional distance = 588 >> proper extension: 0dbpyd; 0197tq; 02rchht; 05g8ky; 02qjj7; 06cv1; 0168cl; 02w0dc0; 02q_cc; 019z7q; ... >> query: (?x10124, 01d_h8) <- profession(?x10124, ?x1966), student(?x6637, ?x10124), film_crew_role(?x83, ?x1966) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 01p8r8 profession 01c72t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 107.000 62.000 0.853 http://example.org/people/person/profession EVAL 01p8r8 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 62.000 0.853 http://example.org/people/person/profession #18240-012x4t PRED entity: 012x4t PRED relation: gender PRED expected values: 05zppz => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #17, 0.87 #25, 0.84 #9), 02zsn (0.31 #58, 0.29 #96, 0.27 #108) >> Best rule #17 for best value: >> intensional similarity = 2 >> extensional distance = 85 >> proper extension: 0xnc3; >> query: (?x1660, 05zppz) <- location(?x1660, ?x12873), celebrities_impersonated(?x4657, ?x1660) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012x4t gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.874 http://example.org/people/person/gender #18239-01bbwp PRED entity: 01bbwp PRED relation: people! PRED expected values: 06gbnc => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 43): 06gbnc (0.48 #258, 0.02 #1413), 0x67 (0.40 #87, 0.21 #318, 0.14 #626), 02w7gg (0.33 #2, 0.17 #1388, 0.10 #79), 041rx (0.26 #697, 0.19 #543, 0.17 #1621), 033tf_ (0.20 #84, 0.09 #1701, 0.09 #1624), 0xnvg (0.11 #398, 0.10 #90, 0.06 #475), 07bch9 (0.10 #100, 0.07 #408, 0.06 #870), 013b6_ (0.09 #746, 0.06 #592, 0.05 #1054), 07hwkr (0.08 #551, 0.06 #936, 0.04 #1244), 0dryh9k (0.05 #1402, 0.04 #1864, 0.04 #1787) >> Best rule #258 for best value: >> intensional similarity = 2 >> extensional distance = 19 >> proper extension: 07m69t; >> query: (?x9685, 06gbnc) <- nationality(?x9685, ?x4221), ?x4221 = 0j5g9 >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bbwp people! 06gbnc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.476 http://example.org/people/ethnicity/people #18238-01nn6c PRED entity: 01nn6c PRED relation: nationality PRED expected values: 07ssc => 126 concepts (51 used for prediction) PRED predicted values (max 10 best out of 37): 09c7w0 (0.77 #4162, 0.77 #4460, 0.76 #4959), 07ssc (0.40 #1203, 0.38 #1699, 0.35 #2592), 021y1s (0.25 #4562, 0.25 #4561, 0.24 #5061), 0dyjz (0.25 #4562, 0.25 #4561, 0.24 #5061), 03rjj (0.20 #5, 0.10 #104, 0.02 #3473), 0jgx (0.10 #255, 0.05 #453, 0.03 #552), 0b90_r (0.10 #102), 0d060g (0.08 #4367, 0.05 #3277, 0.04 #1592), 03rt9 (0.07 #310, 0.03 #2292, 0.02 #3085), 03spz (0.07 #363, 0.02 #957, 0.02 #4426) >> Best rule #4162 for best value: >> intensional similarity = 4 >> extensional distance = 264 >> proper extension: 06jzh; 0d1_f; 01z7_f; 03ym1; 02_nkp; >> query: (?x3266, 09c7w0) <- currency(?x3266, ?x1099), location(?x3266, ?x9878), contains(?x9878, ?x6908), origin(?x6986, ?x9878) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1203 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 63 *> proper extension: 0131kb; *> query: (?x3266, 07ssc) <- gender(?x3266, ?x231), religion(?x3266, ?x2694), nationality(?x3266, ?x1310), ?x1310 = 02jx1, ?x231 = 05zppz *> conf = 0.40 ranks of expected_values: 2 EVAL 01nn6c nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 51.000 0.774 http://example.org/people/person/nationality #18237-013knm PRED entity: 013knm PRED relation: film PRED expected values: 0322yj => 95 concepts (68 used for prediction) PRED predicted values (max 10 best out of 504): 0418wg (0.57 #7133, 0.44 #65990, 0.42 #107016), 08phg9 (0.23 #4448, 0.22 #2665, 0.07 #882), 02rrh1w (0.13 #1352, 0.11 #3135, 0.09 #4918), 06z8s_ (0.11 #1912, 0.09 #3695, 0.07 #129), 017jd9 (0.07 #777, 0.06 #2560, 0.05 #6126), 01vksx (0.07 #134, 0.06 #1917, 0.05 #5483), 027pfg (0.07 #1219, 0.06 #3002, 0.05 #4785), 02fqxm (0.07 #1772, 0.06 #3555, 0.05 #5338), 09rvwmy (0.07 #1688, 0.06 #3471, 0.05 #5254), 043n1r5 (0.07 #1615, 0.06 #3398, 0.05 #5181) >> Best rule #7133 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 01vn0t_; >> query: (?x3708, ?x1820) <- award_nominee(?x192, ?x3708), nominated_for(?x3708, ?x1820), notable_people_with_this_condition(?x7374, ?x3708) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1767 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 02p65p; 01qscs; 04sx9_; 030h95; 04smkr; 024n3z; 0b_dy; 01zg98; 05qg6g; 07h565; ... *> query: (?x3708, 0322yj) <- award_winner(?x1596, ?x3708), ?x1596 = 0dlglj, award_winner(?x704, ?x3708) *> conf = 0.07 ranks of expected_values: 59 EVAL 013knm film 0322yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 95.000 68.000 0.574 http://example.org/film/actor/film./film/performance/film #18236-03zyvw PRED entity: 03zyvw PRED relation: people! PRED expected values: 04p3w => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 7): 0gk4g (0.04 #6346, 0.04 #4168, 0.03 #6214), 0dq9p (0.02 #809, 0.02 #4307, 0.02 #6353), 0qcr0 (0.02 #4159, 0.02 #793, 0.02 #1453), 02k6hp (0.02 #1027, 0.02 #1489, 0.02 #2017), 04p3w (0.01 #4169, 0.01 #2783, 0.01 #539), 02y0js (0.01 #4292, 0.01 #6338, 0.01 #1454), 02knxx (0.01 #1352, 0.01 #692, 0.01 #758) >> Best rule #6346 for best value: >> intensional similarity = 2 >> extensional distance = 2892 >> proper extension: 01ty7ll; 0f1vrl; 01wj9y9; 07nv3_; 017yfz; 01wy61y; 01mt1fy; 03_hd; 0dfjb8; 01_k1z; ... >> query: (?x3688, 0gk4g) <- profession(?x3688, ?x1032), type_of_union(?x3688, ?x566) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #4169 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1623 *> proper extension: 0cmpn; 02wlk; *> query: (?x3688, 04p3w) <- award_winner(?x1670, ?x3688), type_of_union(?x3688, ?x566) *> conf = 0.01 ranks of expected_values: 5 EVAL 03zyvw people! 04p3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 108.000 0.037 http://example.org/people/cause_of_death/people #18235-03cw411 PRED entity: 03cw411 PRED relation: nominated_for! PRED expected values: 0gr4k => 126 concepts (120 used for prediction) PRED predicted values (max 10 best out of 229): 0gqwc (0.73 #1131, 0.70 #13122, 0.70 #11989), 094qd5 (0.73 #1131, 0.70 #13122, 0.70 #11989), 0k611 (0.57 #2326, 0.57 #291, 0.50 #4588), 04kxsb (0.52 #2347, 0.49 #3704, 0.43 #4609), 0gr4k (0.48 #2287, 0.43 #478, 0.43 #252), 0gqy2 (0.46 #4635, 0.43 #338, 0.41 #5088), 0gq_v (0.46 #4542, 0.43 #245, 0.40 #19), 0l8z1 (0.43 #276, 0.40 #50, 0.38 #1633), 02qvyrt (0.43 #313, 0.40 #87, 0.35 #4610), 02rdyk7 (0.43 #288, 0.40 #62, 0.25 #1645) >> Best rule #1131 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 04jkpgv; 03hmt9b; >> query: (?x3745, ?x749) <- honored_for(?x1753, ?x3745), film_festivals(?x3745, ?x3831), currency(?x3745, ?x170), award(?x3745, ?x749) >> conf = 0.73 => this is the best rule for 2 predicted values *> Best rule #2287 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 09cr8; 02q56mk; *> query: (?x3745, 0gr4k) <- honored_for(?x1753, ?x3745), nominated_for(?x746, ?x3745), ?x746 = 04dn09n, film_crew_role(?x3745, ?x137) *> conf = 0.48 ranks of expected_values: 5 EVAL 03cw411 nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 126.000 120.000 0.733 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18234-0kxbc PRED entity: 0kxbc PRED relation: participant! PRED expected values: 02k4gv => 108 concepts (48 used for prediction) PRED predicted values (max 10 best out of 266): 02k4gv (0.82 #24904, 0.81 #7663, 0.81 #17240), 09889g (0.17 #349, 0.08 #2263, 0.06 #3540), 01pcrw (0.17 #221, 0.08 #2135, 0.02 #7245), 0f4vbz (0.09 #3344, 0.05 #791, 0.04 #4623), 01w02sy (0.06 #3415, 0.05 #862, 0.03 #4056), 0484q (0.06 #3662, 0.05 #1109, 0.03 #4941), 026c1 (0.06 #3341, 0.05 #788, 0.03 #4620), 0693l (0.06 #3414, 0.05 #861, 0.03 #4693), 03bnv (0.06 #3429, 0.04 #2152, 0.03 #4708), 019pm_ (0.05 #833, 0.04 #2109, 0.03 #3386) >> Best rule #24904 for best value: >> intensional similarity = 4 >> extensional distance = 359 >> proper extension: 03zqc1; 02nwxc; >> query: (?x5635, ?x5507) <- participant(?x5635, ?x5507), award(?x5635, ?x247), type_of_union(?x5635, ?x566), nationality(?x5635, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kxbc participant! 02k4gv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 48.000 0.815 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #18233-0f2tj PRED entity: 0f2tj PRED relation: featured_film_locations! PRED expected values: 083skw 02p76f9 01kqq7 => 202 concepts (184 used for prediction) PRED predicted values (max 10 best out of 750): 02z2mr7 (0.25 #422, 0.05 #1152, 0.05 #15022), 04dsnp (0.16 #13936, 0.16 #9556, 0.14 #2986), 061681 (0.14 #2967, 0.13 #10997, 0.12 #12457), 047csmy (0.14 #1853, 0.12 #6233, 0.10 #3313), 03s6l2 (0.14 #1499, 0.12 #68623, 0.09 #5879), 0473rc (0.12 #68623, 0.11 #15050, 0.11 #9210), 09fc83 (0.12 #68623, 0.11 #14978, 0.10 #12058), 0872p_c (0.12 #68623, 0.11 #9566, 0.09 #5916), 0g3zrd (0.12 #68623, 0.11 #892, 0.09 #7462), 0m491 (0.12 #68623, 0.09 #5964, 0.09 #1584) >> Best rule #422 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 04ly1; >> query: (?x6769, 02z2mr7) <- contains(?x6769, ?x10572), ?x10572 = 0160nk, location(?x1125, ?x6769) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #52562 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 91 *> proper extension: 0hyyq; *> query: (?x6769, ?x141) <- locations(?x5897, ?x6769), location(?x4107, ?x6769), film(?x4107, ?x141) *> conf = 0.06 ranks of expected_values: 150, 203 EVAL 0f2tj featured_film_locations! 01kqq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 202.000 184.000 0.250 http://example.org/film/film/featured_film_locations EVAL 0f2tj featured_film_locations! 02p76f9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 202.000 184.000 0.250 http://example.org/film/film/featured_film_locations EVAL 0f2tj featured_film_locations! 083skw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 202.000 184.000 0.250 http://example.org/film/film/featured_film_locations #18232-0lzkm PRED entity: 0lzkm PRED relation: student! PRED expected values: 02mj7c => 153 concepts (145 used for prediction) PRED predicted values (max 10 best out of 100): 080z7 (0.11 #2823, 0.01 #7039, 0.01 #8620), 065y4w7 (0.09 #3703, 0.03 #24783, 0.03 #38486), 02g839 (0.07 #10038, 0.06 #3714, 0.06 #7930), 0bwfn (0.06 #25044, 0.05 #38747, 0.05 #34531), 03ksy (0.06 #3795, 0.03 #24875, 0.03 #48067), 017j69 (0.06 #3834, 0.02 #45471, 0.01 #24914), 01w5m (0.06 #1686, 0.03 #24874, 0.02 #7483), 01g0p5 (0.06 #1788, 0.02 #6004, 0.02 #5477), 025v3k (0.06 #2228, 0.02 #5917, 0.02 #5390), 02cw8s (0.06 #1651, 0.01 #9029, 0.01 #27475) >> Best rule #2823 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0bg539; >> query: (?x3735, 080z7) <- role(?x3735, ?x228), nationality(?x3735, ?x429), profession(?x3735, ?x131), ?x228 = 0l14qv >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #4381 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 06y9c2; 04_jsg; 02rn_bj; *> query: (?x3735, 02mj7c) <- role(?x3735, ?x716), nationality(?x3735, ?x429), artists(?x2996, ?x3735), ?x716 = 018vs *> conf = 0.03 ranks of expected_values: 34 EVAL 0lzkm student! 02mj7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 153.000 145.000 0.105 http://example.org/education/educational_institution/students_graduates./education/education/student #18231-01q0kg PRED entity: 01q0kg PRED relation: major_field_of_study PRED expected values: 05qfh => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 110): 03g3w (0.53 #3299, 0.47 #4353, 0.46 #4471), 02lp1 (0.53 #6680, 0.47 #3285, 0.46 #1413), 01mkq (0.52 #6684, 0.43 #5748, 0.41 #4343), 02j62 (0.51 #3302, 0.49 #3419, 0.46 #2366), 0_jm (0.50 #1222, 0.47 #1690, 0.46 #1456), 04rjg (0.47 #3410, 0.46 #2357, 0.42 #4347), 037mh8 (0.43 #3221, 0.40 #3338, 0.38 #3806), 01lj9 (0.42 #3312, 0.40 #3429, 0.38 #3195), 01540 (0.42 #3331, 0.35 #4385, 0.34 #4503), 05qfh (0.40 #3308, 0.33 #4362, 0.33 #3191) >> Best rule #3299 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 02zc7f; >> query: (?x4257, 03g3w) <- company(?x7749, ?x4257), student(?x4257, ?x2053), colors(?x4257, ?x332), place_of_birth(?x2053, ?x1860) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #3308 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 02zc7f; *> query: (?x4257, 05qfh) <- company(?x7749, ?x4257), student(?x4257, ?x2053), colors(?x4257, ?x332), place_of_birth(?x2053, ?x1860) *> conf = 0.40 ranks of expected_values: 10 EVAL 01q0kg major_field_of_study 05qfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 183.000 183.000 0.535 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18230-017j69 PRED entity: 017j69 PRED relation: student PRED expected values: 01xdf5 03rl84 02l3_5 0405l => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1439): 015wc0 (0.25 #1670, 0.10 #14050, 0.04 #20241), 024_vw (0.25 #1898, 0.07 #10151, 0.04 #18406), 0405l (0.25 #1827, 0.05 #14207, 0.04 #16271), 05jjl (0.25 #1486, 0.05 #13866, 0.04 #15930), 01g42 (0.25 #1475, 0.05 #13855, 0.04 #15919), 017yfz (0.25 #676, 0.02 #101776, 0.01 #52258), 02pb53 (0.25 #253, 0.02 #39455, 0.01 #51835), 017_pb (0.25 #1275, 0.01 #61110, 0.01 #63173), 02nygk (0.25 #2042, 0.01 #63940), 04tnqn (0.25 #1631) >> Best rule #1670 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 09r4xx; >> query: (?x4410, 015wc0) <- student(?x4410, ?x12809), student(?x4410, ?x856), ?x856 = 02ndbd, award_nominee(?x8871, ?x12809) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1827 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09r4xx; *> query: (?x4410, 0405l) <- student(?x4410, ?x12809), student(?x4410, ?x856), ?x856 = 02ndbd, award_nominee(?x8871, ?x12809) *> conf = 0.25 ranks of expected_values: 3 EVAL 017j69 student 0405l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 101.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 017j69 student 02l3_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 017j69 student 03rl84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 017j69 student 01xdf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #18229-01kwld PRED entity: 01kwld PRED relation: religion PRED expected values: 0c8wxp => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 13): 0c8wxp (0.24 #141, 0.20 #186, 0.19 #96), 03_gx (0.15 #59, 0.08 #329, 0.06 #509), 0kpl (0.11 #55, 0.08 #10, 0.05 #280), 092bf5 (0.08 #16, 0.04 #106, 0.04 #151), 05sfs (0.04 #3, 0.03 #48), 01lp8 (0.04 #181, 0.04 #136, 0.03 #46), 03j6c (0.03 #336, 0.02 #4162, 0.02 #3262), 0kq2 (0.03 #153, 0.03 #63, 0.02 #108), 04pk9 (0.03 #65, 0.01 #470), 0flw86 (0.02 #317, 0.02 #587, 0.01 #407) >> Best rule #141 for best value: >> intensional similarity = 2 >> extensional distance = 137 >> proper extension: 02qjj7; 01pw2f1; 02d9k; 01vv126; 02mjmr; 047hpm; 015z4j; 0161c2; 0p3r8; 02v60l; ... >> query: (?x628, 0c8wxp) <- location(?x628, ?x1523), vacationer(?x7035, ?x628) >> conf = 0.24 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kwld religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.245 http://example.org/people/person/religion #18228-017j6 PRED entity: 017j6 PRED relation: award_winner! PRED expected values: 02rjjll => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 97): 09n4nb (0.20 #47, 0.17 #187, 0.15 #607), 0466p0j (0.20 #75, 0.10 #1755, 0.10 #2315), 02rjjll (0.17 #705, 0.16 #985, 0.15 #425), 05pd94v (0.15 #422, 0.11 #562, 0.11 #842), 02cg41 (0.15 #685, 0.11 #1525, 0.10 #1805), 013b2h (0.14 #1759, 0.13 #1479, 0.11 #1899), 019bk0 (0.14 #996, 0.10 #1416, 0.10 #1696), 01s695 (0.12 #843, 0.10 #1403, 0.10 #2243), 01bx35 (0.11 #1407, 0.11 #1687, 0.09 #2247), 056878 (0.10 #1711, 0.10 #451, 0.09 #1431) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 016kjs; 04bpm6; 01f2q5; >> query: (?x3390, 09n4nb) <- artists(?x1572, ?x3390), award(?x3390, ?x8705), ?x8705 = 01c9dd, ?x1572 = 06by7 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #705 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 33 *> proper extension: 0phx4; *> query: (?x3390, 02rjjll) <- artists(?x9630, ?x3390), artists(?x378, ?x3390), ?x9630 = 012yc, artists(?x378, ?x4840), award(?x4840, ?x594) *> conf = 0.17 ranks of expected_values: 3 EVAL 017j6 award_winner! 02rjjll CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 81.000 81.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18227-02zfdp PRED entity: 02zfdp PRED relation: film PRED expected values: 04w7rn => 73 concepts (60 used for prediction) PRED predicted values (max 10 best out of 576): 0cfhfz (0.82 #3571, 0.82 #2276, 0.80 #491), 039c26 (0.72 #3570, 0.64 #1785, 0.58 #53532), 0cz_ym (0.20 #3865, 0.03 #91010, 0.03 #60672), 0qm8b (0.13 #243, 0.12 #2028, 0.05 #3814), 0bxsk (0.13 #1205, 0.12 #2990, 0.05 #4776), 0407yj_ (0.13 #482, 0.12 #2267, 0.03 #21895), 0prhz (0.13 #795, 0.06 #2580, 0.03 #60672), 0fpmrm3 (0.12 #2209, 0.03 #91010, 0.03 #60672), 020bv3 (0.11 #5673, 0.05 #12809, 0.05 #18162), 0466s8n (0.10 #5202, 0.07 #1631, 0.06 #3416) >> Best rule #3571 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 07lmxq; 06jzh; 07s8r0; 0306ds; 0fthdk; >> query: (?x9152, ?x2973) <- award_nominee(?x230, ?x9152), nominated_for(?x9152, ?x2973), ?x2973 = 0cfhfz >> conf = 0.82 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02zfdp film 04w7rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 60.000 0.824 http://example.org/film/actor/film./film/performance/film #18226-01q0kg PRED entity: 01q0kg PRED relation: school! PRED expected values: 0512p => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 88): 07147 (0.18 #948, 0.12 #3943, 0.09 #1653), 07l4z (0.14 #3946, 0.09 #4211, 0.08 #3682), 051vz (0.13 #3902, 0.08 #3638, 0.08 #4167), 01yhm (0.13 #3899, 0.08 #4164, 0.08 #4429), 07l8x (0.13 #3942, 0.08 #4472, 0.07 #3678), 01yjl (0.13 #3909, 0.07 #4439, 0.07 #5585), 049n7 (0.12 #896, 0.11 #3891, 0.08 #3627), 02d02 (0.12 #950, 0.11 #3945, 0.08 #1302), 0cqt41 (0.12 #902, 0.10 #990, 0.09 #3897), 05l71 (0.12 #917, 0.10 #1005, 0.05 #3912) >> Best rule #948 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 09c7w0; >> query: (?x4257, 07147) <- company(?x10438, ?x4257), contains(?x1227, ?x4257), category(?x10438, ?x134), ?x134 = 08mbj5d >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #3894 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 109 *> proper extension: 0frm7n; *> query: (?x4257, 0512p) <- school(?x7357, ?x4257), position(?x7357, ?x2010), colors(?x7357, ?x663), draft(?x7357, ?x1633) *> conf = 0.09 ranks of expected_values: 20 EVAL 01q0kg school! 0512p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 187.000 187.000 0.176 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #18225-0cks1m PRED entity: 0cks1m PRED relation: category PRED expected values: 08mbj5d => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.60 #6, 0.60 #5, 0.33 #1) >> Best rule #6 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 0ckr7s; 0b60sq; 076xkdz; >> query: (?x5633, ?x134) <- genre(?x5633, ?x1403), ?x1403 = 02l7c8, film(?x902, ?x5633), actor(?x5633, ?x4134), film(?x902, ?x10192), film(?x902, ?x2746), citytown(?x902, ?x9938), award_nominee(?x163, ?x902), film_release_region(?x2746, ?x87), category(?x10192, ?x134) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cks1m category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.600 http://example.org/common/topic/webpage./common/webpage/category #18224-0191h5 PRED entity: 0191h5 PRED relation: gender PRED expected values: 05zppz => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #13, 0.83 #15, 0.82 #51), 02zsn (0.29 #40, 0.28 #76, 0.28 #94) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 0jfx1; 03k0yw; 05mxw33; 02qtywd; >> query: (?x7221, 05zppz) <- award_winner(?x342, ?x7221), role(?x7221, ?x227), ?x227 = 0342h >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0191h5 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.827 http://example.org/people/person/gender #18223-082brv PRED entity: 082brv PRED relation: role PRED expected values: 0dwtp => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 107): 05r5c (0.44 #1475, 0.44 #1302, 0.40 #3118), 01qzyz (0.33 #2848, 0.33 #2937, 0.32 #2847), 026t6 (0.23 #90, 0.21 #177, 0.20 #1472), 03gvt (0.17 #63, 0.15 #150, 0.14 #237), 01s0ps (0.17 #48, 0.15 #135, 0.11 #222), 0l15bq (0.15 #116, 0.11 #203, 0.08 #1325), 0214km (0.15 #169, 0.11 #256, 0.08 #429), 02qjv (0.15 #104, 0.07 #191, 0.05 #364), 013y1f (0.14 #3313, 0.14 #3140, 0.14 #3227), 06w7v (0.13 #418, 0.13 #591, 0.11 #245) >> Best rule #1475 for best value: >> intensional similarity = 4 >> extensional distance = 169 >> proper extension: 01k5t_3; 01d_h; >> query: (?x6049, 05r5c) <- profession(?x6049, ?x1614), origin(?x6049, ?x8451), role(?x6049, ?x1432), role(?x1432, ?x74) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #3372 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 435 *> proper extension: 01vsxdm; 05crg7; 0dm5l; 014hr0; 02pt7h_; 0gr69; 06br6t; 016lj_; 01m5m5b; *> query: (?x6049, ?x315) <- role(?x6049, ?x1212), artists(?x2996, ?x6049), role(?x1212, ?x75), role(?x1212, ?x315) *> conf = 0.04 ranks of expected_values: 30 EVAL 082brv role 0dwtp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 94.000 94.000 0.444 http://example.org/music/artist/track_contributions./music/track_contribution/role #18222-01dvtx PRED entity: 01dvtx PRED relation: profession PRED expected values: 016fly => 133 concepts (112 used for prediction) PRED predicted values (max 10 best out of 110): 0dxtg (0.78 #15416, 0.78 #15712, 0.65 #4752), 02hrh1q (0.71 #10826, 0.70 #15269, 0.70 #16454), 0kyk (0.57 #770, 0.49 #2399, 0.48 #10250), 01d_h8 (0.46 #5486, 0.42 #3708, 0.41 #15705), 03gjzk (0.39 #3717, 0.31 #15418, 0.31 #15714), 0frz0 (0.35 #10220, 0.25 #86, 0.21 #1270), 02jknp (0.34 #15706, 0.34 #15410, 0.30 #5487), 0fj9f (0.34 #1830, 0.34 #3460, 0.27 #2719), 05z96 (0.25 #6413, 0.22 #3893, 0.19 #7006), 016fly (0.24 #1850, 0.22 #4665, 0.21 #1999) >> Best rule #15416 for best value: >> intensional similarity = 4 >> extensional distance = 1102 >> proper extension: 0dbpyd; 06j0md; 02rchht; 050023; 026dcvf; 0bxtg; 017149; 02nb2s; 01wl38s; 02pp_q_; ... >> query: (?x4003, 0dxtg) <- profession(?x4003, ?x353), nationality(?x4003, ?x94), profession(?x7861, ?x353), ?x7861 = 06jcc >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1850 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 0d0vj4; 05fg2; 02mjmr; 0klh7; 03gkn5; 03l3ln; 04ns3gy; 0c_md_; 02p5hf; *> query: (?x4003, 016fly) <- profession(?x4003, ?x353), company(?x4003, ?x2313), student(?x3437, ?x4003), student(?x8221, ?x4003) *> conf = 0.24 ranks of expected_values: 10 EVAL 01dvtx profession 016fly CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 133.000 112.000 0.780 http://example.org/people/person/profession #18221-083p7 PRED entity: 083p7 PRED relation: people! PRED expected values: 034qg => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 43): 0gk4g (0.21 #6770, 0.21 #5600, 0.20 #6120), 02k6hp (0.20 #37, 0.14 #947, 0.14 #297), 0dq9p (0.18 #407, 0.15 #537, 0.14 #927), 07jwr (0.12 #464, 0.10 #204, 0.09 #399), 02y0js (0.11 #132, 0.10 #717, 0.10 #2), 0qcr0 (0.10 #4226, 0.10 #6761, 0.09 #5591), 0148xv (0.10 #65, 0.06 #195, 0.03 #975), 04p3w (0.10 #2936, 0.09 #2351, 0.09 #3001), 01l2m3 (0.09 #406, 0.09 #926, 0.07 #536), 08g5q7 (0.09 #432, 0.07 #692, 0.06 #107) >> Best rule #6770 for best value: >> intensional similarity = 3 >> extensional distance = 682 >> proper extension: 0c_drn; 01kx1j; 0qkj7; >> query: (?x1157, 0gk4g) <- people(?x12781, ?x1157), people(?x12781, ?x8383), location(?x8383, ?x362) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #98 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 01_4z; 03_js; *> query: (?x1157, 034qg) <- politician(?x1912, ?x1157), basic_title(?x1157, ?x346), place_of_death(?x1157, ?x3689), location(?x1157, ?x11595) *> conf = 0.06 ranks of expected_values: 14 EVAL 083p7 people! 034qg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 164.000 164.000 0.208 http://example.org/people/cause_of_death/people #18220-04954 PRED entity: 04954 PRED relation: award PRED expected values: 02x4w6g => 95 concepts (77 used for prediction) PRED predicted values (max 10 best out of 275): 0ck27z (0.34 #6491, 0.32 #6091, 0.32 #2891), 01by1l (0.27 #511, 0.20 #5311, 0.11 #3711), 0cqhk0 (0.24 #2837, 0.19 #6437, 0.18 #7237), 0c4z8 (0.23 #470, 0.13 #5270, 0.12 #2470), 01bgqh (0.18 #5242, 0.17 #442, 0.12 #2442), 054ks3 (0.17 #541, 0.14 #2541, 0.12 #5341), 02wh75 (0.16 #409, 0.10 #3609, 0.05 #5209), 0gqy2 (0.16 #21205, 0.15 #23606, 0.15 #25209), 0f4x7 (0.16 #21205, 0.15 #23606, 0.15 #25209), 02w9sd7 (0.16 #21205, 0.15 #23606, 0.15 #25209) >> Best rule #6491 for best value: >> intensional similarity = 3 >> extensional distance = 559 >> proper extension: 06gp3f; 066m4g; 01hxs4; 02qflgv; 021_rm; 06b0d2; 016kjs; 014zfs; 02w9895; 02pkpfs; ... >> query: (?x7530, 0ck27z) <- actor(?x9029, ?x7530), award(?x7530, ?x704), award_nominee(?x7530, ?x496) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #21205 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1426 *> proper extension: 0c9l1; *> query: (?x7530, ?x591) <- award_winner(?x7530, ?x496), award_nominee(?x1208, ?x7530), award_winner(?x591, ?x1208) *> conf = 0.16 ranks of expected_values: 21 EVAL 04954 award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 95.000 77.000 0.337 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18219-07hpv3 PRED entity: 07hpv3 PRED relation: genre PRED expected values: 05p553 => 103 concepts (79 used for prediction) PRED predicted values (max 10 best out of 124): 05p553 (0.96 #3423, 0.96 #4779, 0.95 #4542), 07s9rl0 (0.91 #5015, 0.89 #2213, 0.85 #1973), 06n90 (0.60 #954, 0.35 #3512, 0.23 #1985), 0lsxr (0.55 #2381, 0.16 #1903, 0.15 #1982), 01t_vv (0.50 #499, 0.44 #2163, 0.43 #2323), 03k9fj (0.50 #952, 0.43 #639, 0.33 #1745), 0jxy (0.40 #3527, 0.11 #4963, 0.10 #5199), 01hmnh (0.38 #4632, 0.31 #3515, 0.23 #1513), 025s89p (0.33 #282, 0.25 #3546, 0.23 #1544), 0215n (0.33 #34, 0.25 #347, 0.20 #425) >> Best rule #3423 for best value: >> intensional similarity = 10 >> extensional distance = 52 >> proper extension: 01b9w3; 014gjp; 07zhjj; >> query: (?x808, 05p553) <- genre(?x808, ?x2540), genre(?x808, ?x2480), genre(?x808, ?x1844), ?x2480 = 01z4y, titles(?x2008, ?x808), genre(?x8870, ?x1844), honored_for(?x762, ?x8870), genre(?x1628, ?x2540), nominated_for(?x435, ?x8870), ?x1628 = 0436yk >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07hpv3 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 79.000 0.963 http://example.org/tv/tv_program/genre #18218-044mz_ PRED entity: 044mz_ PRED relation: film PRED expected values: 0fr63l => 103 concepts (58 used for prediction) PRED predicted values (max 10 best out of 544): 0828jw (0.57 #37599, 0.57 #30437, 0.50 #23275), 020bv3 (0.52 #2108, 0.02 #7478, 0.02 #9268), 031hcx (0.24 #3065, 0.07 #8435, 0.03 #39390), 011yg9 (0.24 #2819, 0.02 #8189, 0.01 #9979), 03177r (0.14 #2254, 0.06 #7624, 0.01 #32690), 04jpg2p (0.14 #3254, 0.02 #8624, 0.02 #6834), 0m313 (0.14 #1804, 0.02 #5384, 0.02 #17915), 031778 (0.10 #2105, 0.06 #7475, 0.01 #21798), 011ywj (0.10 #3227, 0.05 #8597, 0.03 #19338), 031786 (0.10 #3066, 0.04 #8436, 0.01 #22759) >> Best rule #37599 for best value: >> intensional similarity = 2 >> extensional distance = 939 >> proper extension: 02dbn2; 01hkck; >> query: (?x57, ?x5810) <- award_winner(?x5810, ?x57), film(?x57, ?x814) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 044mz_ film 0fr63l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 58.000 0.574 http://example.org/film/actor/film./film/performance/film #18217-0f6rc PRED entity: 0f6rc PRED relation: entity_involved PRED expected values: 02189 => 99 concepts (66 used for prediction) PRED predicted values (max 10 best out of 315): 0193qj (0.33 #214, 0.33 #59, 0.29 #1143), 01h3dj (0.33 #69, 0.28 #3002, 0.26 #3785), 02psqkz (0.33 #36, 0.20 #4060, 0.17 #5769), 07_m9_ (0.33 #819, 0.18 #1738, 0.11 #1281), 01hnp (0.33 #72, 0.17 #849, 0.17 #694), 011zwl (0.33 #589, 0.17 #745, 0.11 #3369), 012bk (0.33 #1312, 0.16 #3474, 0.16 #3319), 0cdbq (0.33 #39, 0.16 #3755, 0.15 #4063), 03b79 (0.33 #498, 0.14 #961, 0.10 #154), 0gfq9 (0.33 #34, 0.14 #1118, 0.08 #1239) >> Best rule #214 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 0j5ym; >> query: (?x9351, 0193qj) <- locations(?x9351, ?x2346), ?x2346 = 0d05w3, entity_involved(?x9351, ?x10154), entity_involved(?x9351, ?x2629), films(?x9351, ?x2402), ?x10154 = 04xzm, combatants(?x1353, ?x2629), organization(?x1353, ?x127) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f6rc entity_involved 02189 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 66.000 0.333 http://example.org/base/culturalevent/event/entity_involved #18216-0794g PRED entity: 0794g PRED relation: award_winner! PRED expected values: 09k5jh7 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 106): 092_25 (0.20 #69, 0.04 #206, 0.03 #3631), 09p30_ (0.20 #82, 0.03 #493, 0.03 #219), 0bx6zs (0.10 #123, 0.02 #260, 0.02 #2178), 02hn5v (0.10 #40, 0.02 #451, 0.02 #588), 05q7cj (0.10 #92, 0.02 #366, 0.02 #777), 01c6qp (0.08 #155, 0.04 #4539, 0.04 #5224), 01s695 (0.06 #140, 0.05 #4524, 0.04 #277), 092c5f (0.06 #424, 0.06 #561, 0.05 #3575), 013b2h (0.06 #4598, 0.05 #351, 0.04 #5283), 027hjff (0.06 #740, 0.04 #1836, 0.04 #4439) >> Best rule #69 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 01ry0f; >> query: (?x3308, 092_25) <- film(?x3308, ?x4853), ?x4853 = 09p4w8 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1040 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 347 *> proper extension: 024c1b; *> query: (?x3308, 09k5jh7) <- produced_by(?x7968, ?x3308), nominated_for(?x574, ?x7968) *> conf = 0.02 ranks of expected_values: 65 EVAL 0794g award_winner! 09k5jh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 103.000 103.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18215-01z3bz PRED entity: 01z3bz PRED relation: state_province_region PRED expected values: 017v_ => 152 concepts (111 used for prediction) PRED predicted values (max 10 best out of 78): 017v_ (0.73 #4460, 0.43 #4708, 0.33 #24), 059rby (0.32 #4587, 0.21 #6959, 0.21 #6835), 0345h (0.31 #7329, 0.29 #6078, 0.28 #5204), 02h6_6p (0.28 #5204, 0.28 #5578, 0.26 #7328), 01n7q (0.20 #388, 0.19 #883, 0.17 #1254), 05kr_ (0.20 #399, 0.11 #1513, 0.11 #894), 070zc (0.20 #341, 0.08 #588, 0.06 #712), 02qkt (0.12 #6204, 0.12 #11683), 02j9z (0.12 #6204, 0.12 #11683), 05tbn (0.11 #916, 0.11 #1040, 0.10 #1287) >> Best rule #4460 for best value: >> intensional similarity = 4 >> extensional distance = 205 >> proper extension: 0lbp_; >> query: (?x11717, ?x1679) <- contains(?x2611, ?x11717), location(?x2610, ?x2611), state(?x2611, ?x1679), adjoins(?x1679, ?x8264) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01z3bz state_province_region 017v_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 111.000 0.729 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #18214-0f4vbz PRED entity: 0f4vbz PRED relation: award PRED expected values: 094qd5 0c422z4 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 258): 09sb52 (0.57 #39, 0.34 #430, 0.34 #1212), 02z0dfh (0.57 #71, 0.22 #462, 0.07 #18448), 02ppm4q (0.50 #147, 0.41 #538, 0.09 #9140), 094qd5 (0.44 #434, 0.14 #43, 0.14 #2389), 02y_rq5 (0.25 #480, 0.14 #89, 0.09 #5563), 0bb57s (0.22 #623, 0.14 #232, 0.05 #19391), 03qgjwc (0.21 #173, 0.09 #564, 0.05 #18550), 0bsjcw (0.19 #582, 0.07 #191, 0.04 #18568), 05p09zm (0.18 #34409, 0.18 #4026, 0.18 #1289), 0f4x7 (0.18 #34409, 0.17 #2376, 0.15 #7068) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 01p7yb; 01fwpt; 02kxwk; 0fgg4; 01phtd; 04wx2v; >> query: (?x2258, 09sb52) <- nationality(?x2258, ?x7748), award_winner(?x2577, ?x2258), ?x2577 = 099t8j >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #434 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 04bdxl; 0h1m9; 01mqz0; 01wc7p; 0hwqz; 0bw6y; 01z5tr; 01j851; 01gw8b; 0161h5; *> query: (?x2258, 094qd5) <- participant(?x286, ?x2258), award(?x2258, ?x1132), ?x1132 = 0bdwft *> conf = 0.44 ranks of expected_values: 4, 61 EVAL 0f4vbz award 0c422z4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 131.000 131.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f4vbz award 094qd5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 131.000 131.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18213-03v_5 PRED entity: 03v_5 PRED relation: place PRED expected values: 03v_5 => 159 concepts (78 used for prediction) PRED predicted values (max 10 best out of 244): 03v_5 (0.31 #18072, 0.30 #14457, 0.30 #19623), 059rby (0.31 #18072, 0.30 #14457, 0.30 #19623), 09c7w0 (0.31 #18072, 0.30 #14457, 0.30 #19623), 0k_q_ (0.17 #1079, 0.08 #2110, 0.05 #3657), 0vzm (0.17 #1103, 0.03 #7300, 0.02 #9364), 02_286 (0.11 #1561, 0.05 #3108, 0.05 #2592), 0fplg (0.06 #25818, 0.06 #22206, 0.06 #26334), 0y617 (0.05 #3489, 0.05 #2973, 0.04 #4520), 071cn (0.05 #3174, 0.05 #2658, 0.04 #4205), 02zp1t (0.05 #3519, 0.05 #3003, 0.04 #5067) >> Best rule #18072 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 02_286; 01mc11; 0mnzd; 0r2l7; 013yq; 013ksx; 0ncj8; 0r0m6; 06wxw; 0r5lz; ... >> query: (?x1730, ?x94) <- county(?x1730, ?x13866), contains(?x1730, ?x13963), contains(?x94, ?x13963), institution(?x1519, ?x13963) >> conf = 0.31 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 03v_5 place 03v_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 78.000 0.312 http://example.org/location/hud_county_place/place #18212-01wdj_ PRED entity: 01wdj_ PRED relation: school! PRED expected values: 084l5 01d6g => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 88): 03wnh (0.43 #222, 0.09 #1092, 0.08 #918), 05g3b (0.43 #181, 0.07 #1225, 0.05 #877), 01yjl (0.33 #115, 0.15 #1507, 0.15 #1594), 07147 (0.33 #149, 0.15 #1541, 0.14 #323), 01y3v (0.33 #113, 0.14 #287, 0.14 #200), 01ypc (0.33 #88, 0.14 #262, 0.14 #175), 0jmmn (0.33 #131, 0.03 #914, 0.03 #1001), 07l8x (0.29 #322, 0.29 #235, 0.17 #148), 01yhm (0.29 #279, 0.17 #105, 0.14 #888), 0512p (0.29 #275, 0.17 #101, 0.12 #1232) >> Best rule #222 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 01qgr3; >> query: (?x2830, 03wnh) <- institution(?x620, ?x2830), school(?x6976, ?x2830), school(?x1639, ?x2830), ?x6976 = 04vn5, major_field_of_study(?x2830, ?x742), position_s(?x1639, ?x180) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 012vwb; 07t90; 01vs5c; 015q1n; *> query: (?x2830, 01d6g) <- institution(?x620, ?x2830), school(?x1639, ?x2830), category(?x2830, ?x134), ?x1639 = 07l24, currency(?x2830, ?x170) *> conf = 0.17 ranks of expected_values: 22, 69 EVAL 01wdj_ school! 01d6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 174.000 174.000 0.429 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01wdj_ school! 084l5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 174.000 174.000 0.429 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #18211-01693z PRED entity: 01693z PRED relation: artists! PRED expected values: 03x2qp => 115 concepts (46 used for prediction) PRED predicted values (max 10 best out of 249): 0xhtw (0.68 #1244, 0.64 #937, 0.64 #630), 0dl5d (0.50 #940, 0.47 #1247, 0.38 #326), 064t9 (0.49 #12012, 0.48 #10473, 0.48 #4936), 05bt6j (0.40 #12043, 0.34 #4967, 0.31 #10504), 05w3f (0.40 #37, 0.29 #958, 0.26 #1265), 016clz (0.40 #2774, 0.36 #2158, 0.36 #4620), 03lty (0.36 #948, 0.32 #1255, 0.27 #641), 01lyv (0.28 #3110, 0.22 #2494, 0.20 #4957), 016jny (0.27 #717, 0.20 #103, 0.18 #1640), 06j6l (0.26 #7126, 0.25 #4972, 0.24 #10509) >> Best rule #1244 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 01wg3q; >> query: (?x8391, 0xhtw) <- artists(?x6107, ?x8391), artists(?x1572, ?x8391), ?x6107 = 0126t5, artist(?x2299, ?x8391), parent_genre(?x1572, ?x3108) >> conf = 0.68 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01693z artists! 03x2qp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 46.000 0.684 http://example.org/music/genre/artists #18210-07hwkr PRED entity: 07hwkr PRED relation: people PRED expected values: 04411 01q415 01mmslz 05hj_k 01s21dg 03q45x 02wlk => 26 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2399): 03rx9 (0.50 #7887, 0.12 #16108, 0.09 #21041), 01rrd4 (0.50 #2514, 0.12 #15671, 0.09 #20604), 04z0g (0.50 #7368, 0.09 #15589, 0.07 #39467), 0lrh (0.50 #6953, 0.09 #15174, 0.07 #39467), 01vrt_c (0.33 #6726, 0.25 #3436, 0.20 #5081), 052hl (0.33 #7471, 0.25 #4181, 0.20 #5826), 01vtj38 (0.33 #969, 0.25 #2613, 0.13 #9194), 01twdk (0.33 #7223, 0.15 #15444, 0.13 #8868), 04bs3j (0.33 #6647, 0.13 #13156, 0.10 #21380), 05xpv (0.33 #7762, 0.12 #15983, 0.09 #20916) >> Best rule #7887 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 041rx; >> query: (?x3584, 03rx9) <- people(?x3584, ?x7022), languages_spoken(?x3584, ?x12272), languages_spoken(?x3584, ?x732), languages(?x147, ?x732), language(?x5122, ?x732), type_of_union(?x7022, ?x566), official_language(?x774, ?x732), film(?x3705, ?x5122), ?x12272 = 0880p >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7226 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 041rx; *> query: (?x3584, 01s21dg) <- people(?x3584, ?x7022), languages_spoken(?x3584, ?x12272), languages_spoken(?x3584, ?x732), languages(?x147, ?x732), language(?x5122, ?x732), type_of_union(?x7022, ?x566), official_language(?x774, ?x732), film(?x3705, ?x5122), ?x12272 = 0880p *> conf = 0.17 ranks of expected_values: 255, 2189 EVAL 07hwkr people 02wlk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 26.000 0.500 http://example.org/people/ethnicity/people EVAL 07hwkr people 03q45x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 26.000 0.500 http://example.org/people/ethnicity/people EVAL 07hwkr people 01s21dg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 26.000 0.500 http://example.org/people/ethnicity/people EVAL 07hwkr people 05hj_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 26.000 0.500 http://example.org/people/ethnicity/people EVAL 07hwkr people 01mmslz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 26.000 0.500 http://example.org/people/ethnicity/people EVAL 07hwkr people 01q415 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 26.000 0.500 http://example.org/people/ethnicity/people EVAL 07hwkr people 04411 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 26.000 0.500 http://example.org/people/ethnicity/people #18209-01b_lz PRED entity: 01b_lz PRED relation: nominated_for! PRED expected values: 0bdw6t => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 188): 0gq9h (0.37 #4565, 0.36 #4802, 0.34 #5039), 0gs9p (0.33 #4567, 0.33 #4804, 0.30 #5041), 019f4v (0.32 #4557, 0.32 #4794, 0.29 #5031), 0fbtbt (0.29 #396, 0.29 #159, 0.25 #1581), 0bdx29 (0.29 #82, 0.21 #1504, 0.17 #5216), 0bdw6t (0.29 #83, 0.20 #1505, 0.18 #320), 0gkr9q (0.29 #208, 0.19 #919, 0.18 #1156), 0k611 (0.28 #4576, 0.27 #4813, 0.26 #5050), 040njc (0.27 #4511, 0.26 #4748, 0.25 #4985), 0gq_v (0.26 #4523, 0.26 #4760, 0.25 #4997) >> Best rule #4565 for best value: >> intensional similarity = 3 >> extensional distance = 503 >> proper extension: 0bshwmp; 0ch3qr1; >> query: (?x3326, 0gq9h) <- nominated_for(?x2589, ?x3326), honored_for(?x1265, ?x3326), award(?x3326, ?x686) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #83 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 01cvtf; 0qmk5; *> query: (?x3326, 0bdw6t) <- nominated_for(?x2589, ?x3326), genre(?x3326, ?x12176), ?x12176 = 02fgmn *> conf = 0.29 ranks of expected_values: 6 EVAL 01b_lz nominated_for! 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 53.000 53.000 0.370 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18208-0qf2t PRED entity: 0qf2t PRED relation: film_crew_role PRED expected values: 04pyp5 02vs3x5 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 36): 0ch6mp2 (0.77 #1086, 0.74 #389, 0.74 #351), 02r96rf (0.70 #308, 0.69 #1081, 0.64 #384), 09vw2b7 (0.68 #1085, 0.68 #350, 0.68 #312), 0dxtw (0.40 #1090, 0.39 #317, 0.38 #126), 01pvkk (0.35 #128, 0.34 #242, 0.29 #319), 01vx2h (0.35 #1091, 0.32 #318, 0.30 #2055), 02ynfr (0.19 #285, 0.19 #399, 0.19 #361), 089g0h (0.16 #289, 0.15 #403, 0.14 #365), 02_n3z (0.16 #268, 0.14 #382, 0.14 #344), 01xy5l_ (0.15 #283, 0.14 #397, 0.14 #359) >> Best rule #1086 for best value: >> intensional similarity = 4 >> extensional distance = 483 >> proper extension: 0gtv7pk; 0c40vxk; 035xwd; 04gknr; 03t97y; 0jjy0; 07sc6nw; 0cz8mkh; 03qnvdl; 0gj9qxr; ... >> query: (?x4864, 0ch6mp2) <- film_crew_role(?x4864, ?x137), genre(?x4864, ?x1509), genre(?x9786, ?x1509), ?x9786 = 06bc59 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2812 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1267 *> proper extension: 05q96q6; 02847m9; 04yg13l; 03mnn0; 037q31; 09v3jyg; 0g5qmbz; 065ym0c; 0dtzkt; 0d99k_; *> query: (?x4864, ?x281) <- film_crew_role(?x4864, ?x137), genre(?x4864, ?x1509), genre(?x3595, ?x1509), film_crew_role(?x3595, ?x281) *> conf = 0.13 ranks of expected_values: 14, 17 EVAL 0qf2t film_crew_role 02vs3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 101.000 101.000 0.767 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0qf2t film_crew_role 04pyp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 101.000 101.000 0.767 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18207-04xhwn PRED entity: 04xhwn PRED relation: location PRED expected values: 0r00l => 108 concepts (87 used for prediction) PRED predicted values (max 10 best out of 139): 0f2wj (0.33 #15280, 0.33 #12061, 0.31 #15281), 0rd6b (0.33 #15280, 0.33 #12061, 0.31 #15281), 05mph (0.33 #15280, 0.33 #12061, 0.31 #15281), 030qb3t (0.27 #4907, 0.25 #20995, 0.25 #5711), 02_286 (0.22 #13707, 0.22 #12099, 0.21 #12903), 06_kh (0.20 #815, 0.12 #7247, 0.09 #8855), 01531 (0.20 #962, 0.07 #4178, 0.03 #24287), 0k33p (0.20 #1286), 0k3p (0.17 #2002), 03s5t (0.17 #1750) >> Best rule #15280 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 02x8mt; >> query: (?x12566, ?x682) <- gender(?x12566, ?x231), sibling(?x12566, ?x7837), location(?x7837, ?x682), country(?x682, ?x94) >> conf = 0.33 => this is the best rule for 3 predicted values *> Best rule #7842 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 021j72; 0b5x23; *> query: (?x12566, 0r00l) <- gender(?x12566, ?x231), languages(?x12566, ?x254), sibling(?x7837, ?x12566), people(?x1423, ?x12566) *> conf = 0.06 ranks of expected_values: 62 EVAL 04xhwn location 0r00l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 108.000 87.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #18206-0gh6j94 PRED entity: 0gh6j94 PRED relation: language PRED expected values: 064_8sq => 107 concepts (96 used for prediction) PRED predicted values (max 10 best out of 61): 064_8sq (0.39 #942, 0.30 #181, 0.29 #346), 06b_j (0.26 #1921, 0.20 #943, 0.12 #126), 0653m (0.25 #1203, 0.09 #227, 0.07 #61), 03k50 (0.13 #59, 0.06 #113, 0.03 #1526), 0688f (0.13 #88, 0.06 #142, 0.03 #4103), 03_9r (0.12 #226, 0.05 #1202, 0.05 #4600), 012w70 (0.09 #1204, 0.06 #172, 0.05 #717), 0jzc (0.09 #179, 0.07 #1643, 0.07 #69), 01r2l (0.08 #21, 0.06 #128, 0.03 #240), 0t_2 (0.07 #447, 0.05 #284, 0.04 #664) >> Best rule #942 for best value: >> intensional similarity = 9 >> extensional distance = 144 >> proper extension: 0cfhfz; 0d1qmz; 0pd6l; >> query: (?x7680, 064_8sq) <- films(?x14173, ?x7680), language(?x7680, ?x10486), language(?x7680, ?x254), language(?x7680, ?x90), official_language(?x2517, ?x10486), ?x254 = 02h40lc, language(?x9501, ?x90), ?x9501 = 0g5qmbz, languages(?x914, ?x90) >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gh6j94 language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 96.000 0.390 http://example.org/film/film/language #18205-047n8xt PRED entity: 047n8xt PRED relation: language PRED expected values: 064_8sq => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 39): 064_8sq (0.25 #21, 0.25 #1249, 0.20 #604), 04306rv (0.17 #1411, 0.13 #704, 0.12 #529), 06nm1 (0.17 #652, 0.12 #2417, 0.12 #2238), 0t_2 (0.12 #129, 0.04 #538, 0.03 #713), 02bjrlw (0.11 #1408, 0.10 #994, 0.10 #1229), 07ssc (0.10 #465, 0.03 #4780, 0.03 #2527), 04xvlr (0.10 #465, 0.03 #4780, 0.03 #2527), 03_9r (0.09 #415, 0.08 #4670, 0.07 #1120), 0653m (0.09 #417, 0.06 #2063, 0.05 #2478), 012w70 (0.09 #418, 0.04 #2064, 0.04 #2599) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0sxfd; 09qycb; >> query: (?x2121, 064_8sq) <- film_festivals(?x2121, ?x11147), nominated_for(?x1033, ?x2121), film(?x381, ?x2121), ?x1033 = 02x73k6 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047n8xt language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.250 http://example.org/film/film/language #18204-0hz55 PRED entity: 0hz55 PRED relation: genre PRED expected values: 07s9rl0 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 74): 07s9rl0 (0.90 #1582, 0.77 #80, 0.60 #396), 06n90 (0.59 #405, 0.23 #1591, 0.19 #3095), 05p553 (0.51 #637, 0.48 #1032, 0.48 #953), 01z4y (0.39 #172, 0.35 #489, 0.35 #647), 0hcr (0.35 #410, 0.23 #2862, 0.22 #3100), 01hmnh (0.30 #408, 0.17 #1594, 0.15 #3019), 0c4xc (0.25 #672, 0.25 #276, 0.24 #514), 01t_vv (0.22 #505, 0.22 #346, 0.20 #742), 0jxy (0.21 #423, 0.10 #1609, 0.09 #3113), 06nbt (0.19 #175, 0.16 #254, 0.13 #333) >> Best rule #1582 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 0jq2r; 047m_w; 070ltt; 07qht4; 05397h; >> query: (?x4932, 07s9rl0) <- genre(?x4932, ?x812), genre(?x5230, ?x812), ?x5230 = 0mb8c >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hz55 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.897 http://example.org/tv/tv_program/genre #18203-01sbf2 PRED entity: 01sbf2 PRED relation: profession PRED expected values: 01c8w0 0nbcg => 128 concepts (72 used for prediction) PRED predicted values (max 10 best out of 58): 02hrh1q (0.78 #4988, 0.75 #10263, 0.72 #5870), 0nbcg (0.54 #321, 0.51 #1052, 0.51 #3686), 039v1 (0.33 #1934, 0.31 #3837, 0.30 #3253), 01d_h8 (0.31 #7767, 0.30 #9230, 0.28 #7473), 0n1h (0.30 #1179, 0.28 #1325, 0.26 #887), 0dxtg (0.25 #9970, 0.25 #9384, 0.25 #7481), 03gjzk (0.23 #9386, 0.21 #7777, 0.21 #10118), 0fnpj (0.22 #350, 0.17 #204, 0.16 #3277), 05vyk (0.21 #238, 0.18 #823, 0.13 #92), 01c8w0 (0.21 #153, 0.17 #1614, 0.13 #738) >> Best rule #4988 for best value: >> intensional similarity = 3 >> extensional distance = 543 >> proper extension: 039cq4; >> query: (?x1613, 02hrh1q) <- award_winner(?x1613, ?x406), participant(?x406, ?x241), award_winner(?x670, ?x406) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #321 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 020jqv; *> query: (?x1613, 0nbcg) <- award(?x1613, ?x1232), instrumentalists(?x316, ?x1613), gender(?x1613, ?x231), ?x1232 = 0c4z8 *> conf = 0.54 ranks of expected_values: 2, 10 EVAL 01sbf2 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 72.000 0.780 http://example.org/people/person/profession EVAL 01sbf2 profession 01c8w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 128.000 72.000 0.780 http://example.org/people/person/profession #18202-0cymln PRED entity: 0cymln PRED relation: type_of_union PRED expected values: 04ztj => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.84 #33, 0.78 #37, 0.76 #41), 01g63y (0.25 #338, 0.25 #333, 0.14 #82), 01bl8s (0.25 #338, 0.25 #333) >> Best rule #33 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 05cv94; 06yj20; >> query: (?x10097, 04ztj) <- nationality(?x10097, ?x94), ?x94 = 09c7w0, student(?x5907, ?x10097), athlete(?x4833, ?x10097) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cymln type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.837 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18201-04b19t PRED entity: 04b19t PRED relation: nationality PRED expected values: 03rk0 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 32): 03rk0 (0.90 #446, 0.84 #8243, 0.80 #546), 09c7w0 (0.80 #801, 0.79 #1103, 0.78 #1), 07ssc (0.44 #4820, 0.11 #2123, 0.10 #3028), 02jx1 (0.44 #4820, 0.11 #3046, 0.10 #2844), 055vr (0.32 #9149, 0.01 #3915), 0d060g (0.10 #3821, 0.08 #2818, 0.05 #5929), 05sb1 (0.08 #148, 0.02 #548), 0345h (0.07 #932, 0.03 #4951, 0.03 #5051), 0f8l9c (0.07 #3836, 0.06 #222, 0.03 #4942), 03spz (0.06 #667, 0.06 #267, 0.02 #968) >> Best rule #446 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0276g40; >> query: (?x2618, 03rk0) <- place_of_birth(?x2618, ?x7412), profession(?x2618, ?x319), ?x7412 = 04vmp >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04b19t nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.900 http://example.org/people/person/nationality #18200-07w0v PRED entity: 07w0v PRED relation: student PRED expected values: 05m63c 0443xn => 102 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2034): 0d3k14 (0.14 #5980, 0.05 #8052, 0.04 #16341), 0h0wc (0.11 #6605, 0.05 #4533, 0.02 #25256), 06hx2 (0.10 #5206, 0.05 #7278, 0.03 #15567), 0194xc (0.10 #5769, 0.05 #7841, 0.03 #16130), 07f7jp (0.10 #6105, 0.05 #8177, 0.02 #22683), 0gs7x (0.10 #6067, 0.03 #33006, 0.03 #16428), 0683n (0.10 #5588, 0.03 #15949, 0.03 #7660), 0hnjt (0.10 #4958, 0.03 #15319, 0.03 #7030), 02779r4 (0.10 #5298, 0.03 #15659, 0.03 #19804), 03n93 (0.10 #4811, 0.03 #15172, 0.02 #21389) >> Best rule #5980 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 0f8l9c; >> query: (?x1011, 0d3k14) <- company(?x346, ?x1011), ?x346 = 060c4, organization(?x1011, ?x5487) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07w0v student 0443xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 80.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07w0v student 05m63c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 80.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #18199-01_gx_ PRED entity: 01_gx_ PRED relation: dog_breed! PRED expected values: 0rh6k 02cl1 0ftxw 0cv3w 05jbn => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1100): 05jbn (0.36 #9, 0.33 #39, 0.33 #29), 0ftxw (0.36 #9, 0.33 #36, 0.33 #26), 02cl1 (0.36 #9, 0.33 #35, 0.33 #25), 04gxf (0.36 #9, 0.06 #23, 0.02 #22), 0lphb (0.36 #9, 0.06 #23, 0.02 #22), 0fvvz (0.36 #9, 0.06 #23, 0.02 #22), 013kcv (0.36 #9, 0.06 #23), 010h9y (0.36 #9, 0.02 #22), 0f1sm (0.36 #9, 0.02 #22), 0vm39 (0.36 #9, 0.02 #22) >> Best rule #9 for best value: >> intensional similarity = 124 >> extensional distance = 1 >> proper extension: 0km5c; >> query: (?x3095, ?x2879) <- dog_breed(?x11246, ?x3095), dog_breed(?x9605, ?x3095), dog_breed(?x8468, ?x3095), dog_breed(?x6769, ?x3095), dog_breed(?x6703, ?x3095), dog_breed(?x6453, ?x3095), dog_breed(?x5381, ?x3095), dog_breed(?x5267, ?x3095), dog_breed(?x4356, ?x3095), dog_breed(?x4350, ?x3095), dog_breed(?x4090, ?x3095), dog_breed(?x3983, ?x3095), dog_breed(?x3373, ?x3095), dog_breed(?x2740, ?x3095), dog_breed(?x2277, ?x3095), dog_breed(?x2017, ?x3095), dog_breed(?x1719, ?x3095), dog_breed(?x1705, ?x3095), dog_breed(?x1523, ?x3095), dog_breed(?x739, ?x3095), dog_breed(?x674, ?x3095), ?x6769 = 0f2tj, ?x4350 = 0_vn7, ?x5381 = 0c_m3, ?x3983 = 0fr0t, ?x9605 = 02frhbc, ?x4356 = 06wxw, ?x4090 = 01sn3, ?x11246 = 0fvyg, month(?x2277, ?x1650), month(?x2277, ?x1459), ?x2017 = 04f_d, citytown(?x10217, ?x2277), citytown(?x9968, ?x2277), citytown(?x7169, ?x2277), ?x1459 = 04w_7, ?x1719 = 0f2w0, administrative_division(?x2277, ?x3038), location(?x10482, ?x2277), location(?x5405, ?x2277), origin(?x9623, ?x2277), ?x6453 = 01smm, locations(?x10673, ?x2277), locations(?x9974, ?x2277), locations(?x9908, ?x2277), ?x10482 = 0b25vg, jurisdiction_of_office(?x1195, ?x2277), team(?x9974, ?x9147), ?x3373 = 0ply0, ?x9147 = 0263cyj, contains(?x3038, ?x2410), mode_of_transportation(?x2277, ?x4272), ?x1523 = 030qb3t, instance_of_recurring_event(?x10673, ?x10863), ?x2740 = 0f__1, ?x7169 = 01w5gp, ?x1195 = 0pqc5, ?x1650 = 06vkl, ?x4272 = 07jdr, ?x9908 = 0b_6lb, ?x5267 = 0d9jr, location(?x2560, ?x3038), award_winner(?x1232, ?x9623), team(?x10673, ?x5551), profession(?x9623, ?x12718), ?x1705 = 094jv, locations(?x9974, ?x2879), ?x12718 = 047rgpy, location_of_ceremony(?x566, ?x2277), featured_film_locations(?x10276, ?x739), featured_film_locations(?x8959, ?x739), featured_film_locations(?x4093, ?x739), place_of_death(?x7454, ?x739), origin(?x6164, ?x739), origin(?x1800, ?x739), place_of_birth(?x3692, ?x739), place_of_birth(?x3465, ?x739), location(?x10701, ?x739), location(?x8311, ?x739), location(?x396, ?x739), citytown(?x8056, ?x739), citytown(?x5647, ?x739), citytown(?x4483, ?x739), citytown(?x2909, ?x739), nominated_for(?x6164, ?x4648), vacationer(?x739, ?x5565), ?x674 = 0f2r6, location_of_ceremony(?x548, ?x739), ?x8056 = 06182p, award(?x4093, ?x198), category(?x9623, ?x134), featured_film_locations(?x2362, ?x2277), award(?x396, ?x2257), award(?x396, ?x618), ?x6703 = 0f04v, award_nominee(?x157, ?x396), ?x618 = 09qwmm, artists(?x1000, ?x8311), contains(?x739, ?x1005), award(?x7454, ?x3066), award(?x1800, ?x724), award_nominee(?x5405, ?x2698), award_nominee(?x828, ?x9623), ?x8468 = 0nbwf, institution(?x1368, ?x10217), crewmember(?x10276, ?x4774), nominated_for(?x5565, ?x3048), religion(?x548, ?x7131), award_winner(?x382, ?x5647), music(?x8959, ?x4139), currency(?x9968, ?x170), institution(?x8398, ?x2909), film(?x446, ?x4093), company(?x3687, ?x2909), nationality(?x548, ?x94), award_winner(?x3692, ?x1532), artist(?x4483, ?x300), student(?x2909, ?x338), ?x2257 = 09td7p, gender(?x3465, ?x231), ?x1532 = 05183k, nominated_for(?x384, ?x4093), film(?x548, ?x278), nationality(?x10701, ?x390) >> conf = 0.36 => this is the best rule for 20 predicted values ranks of expected_values: 1, 2, 3, 23, 24 EVAL 01_gx_ dog_breed! 05jbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 5.000 5.000 0.357 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01_gx_ dog_breed! 0cv3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 5.000 5.000 0.357 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01_gx_ dog_breed! 0ftxw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 5.000 5.000 0.357 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01_gx_ dog_breed! 02cl1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 5.000 5.000 0.357 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01_gx_ dog_breed! 0rh6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 5.000 5.000 0.357 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #18198-0g60z PRED entity: 0g60z PRED relation: nominated_for! PRED expected values: 011_3s 04yqlk 05l4yg 04qz6n 03swmf => 76 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1538): 01ggc9 (0.83 #71858, 0.80 #71857, 0.80 #23178), 09fb5 (0.27 #4699, 0.16 #50995, 0.11 #64904), 02p65p (0.17 #2342, 0.16 #50995, 0.15 #90400), 05vsxz (0.17 #2327, 0.16 #50995, 0.11 #64904), 02lhm2 (0.17 #3508, 0.16 #50995, 0.11 #64904), 0blbxk (0.17 #2571, 0.16 #50995, 0.11 #64904), 05yh_t (0.17 #3580, 0.11 #41722, 0.11 #64903), 070j61 (0.17 #3932, 0.05 #113585, 0.02 #31746), 02vyw (0.17 #3080, 0.05 #12352, 0.04 #16987), 02kxwk (0.17 #3258, 0.03 #35707, 0.02 #40342) >> Best rule #71858 for best value: >> intensional similarity = 3 >> extensional distance = 194 >> proper extension: 02rjv2w; 072hx4; >> query: (?x337, ?x10161) <- honored_for(?x2126, ?x337), award_winner(?x337, ?x10161), actor(?x3610, ?x10161) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #90400 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 240 *> proper extension: 02sg5v; 018nnz; 0fztbq; 025twgt; *> query: (?x337, ?x192) <- nominated_for(?x337, ?x6482), nominated_for(?x192, ?x6482) *> conf = 0.15 ranks of expected_values: 44, 69, 83, 84 EVAL 0g60z nominated_for! 03swmf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 54.000 0.828 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0g60z nominated_for! 04qz6n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 76.000 54.000 0.828 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0g60z nominated_for! 05l4yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 76.000 54.000 0.828 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0g60z nominated_for! 04yqlk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 76.000 54.000 0.828 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0g60z nominated_for! 011_3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 76.000 54.000 0.828 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #18197-06q83 PRED entity: 06q83 PRED relation: major_field_of_study! PRED expected values: 0bsnm => 62 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1366): 07t90 (0.85 #7800, 0.50 #5451, 0.50 #754), 03ksy (0.70 #5404, 0.55 #13626, 0.51 #14801), 08815 (0.70 #5286, 0.50 #1176, 0.46 #7635), 07tgn (0.70 #5302, 0.50 #1192, 0.35 #7651), 01w5m (0.69 #7752, 0.60 #5403, 0.50 #1293), 07szy (0.60 #5328, 0.50 #7677, 0.50 #1218), 07wjk (0.60 #5352, 0.50 #1242, 0.46 #7701), 025v3k (0.60 #5420, 0.50 #1310, 0.42 #7769), 07tds (0.60 #5453, 0.50 #1343, 0.35 #7802), 02bqy (0.60 #5489, 0.50 #1379, 0.31 #7838) >> Best rule #7800 for best value: >> intensional similarity = 9 >> extensional distance = 24 >> proper extension: 01r2l; >> query: (?x9444, 07t90) <- major_field_of_study(?x7546, ?x9444), major_field_of_study(?x3543, ?x9444), major_field_of_study(?x865, ?x9444), colors(?x3543, ?x663), currency(?x3543, ?x170), contains(?x4600, ?x3543), ?x4600 = 081yw, category(?x3543, ?x134), institution(?x1305, ?x7546) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #587 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 04x_3; *> query: (?x9444, ?x5807) <- major_field_of_study(?x13670, ?x9444), major_field_of_study(?x7546, ?x9444), major_field_of_study(?x5280, ?x9444), ?x13670 = 01dq0z, institution(?x9742, ?x7546), school_type(?x7546, ?x3092), ?x9742 = 01kxxq, major_field_of_study(?x5280, ?x13318), major_field_of_study(?x5807, ?x13318) *> conf = 0.11 ranks of expected_values: 489 EVAL 06q83 major_field_of_study! 0bsnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 39.000 0.846 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18196-01r3w7 PRED entity: 01r3w7 PRED relation: institution! PRED expected values: 07s6fsf => 161 concepts (98 used for prediction) PRED predicted values (max 10 best out of 21): 03bwzr4 (0.82 #194, 0.82 #103, 0.79 #148), 014mlp (0.77 #162, 0.77 #510, 0.76 #1185), 02_xgp2 (0.74 #146, 0.73 #192, 0.73 #101), 0bkj86 (0.55 #97, 0.52 #377, 0.51 #880), 07s6fsf (0.54 #205, 0.47 #136, 0.46 #552), 022h5x (0.51 #880, 0.43 #809, 0.39 #739), 04zx3q1 (0.45 #92, 0.45 #1301, 0.42 #713), 02m4yg (0.45 #1301, 0.42 #713, 0.40 #1507), 01ysy9 (0.45 #1301, 0.42 #713, 0.40 #1507), 01gkg3 (0.45 #1301, 0.42 #713, 0.39 #739) >> Best rule #194 for best value: >> intensional similarity = 7 >> extensional distance = 20 >> proper extension: 04hgpt; >> query: (?x7447, 03bwzr4) <- major_field_of_study(?x7447, ?x1682), currency(?x7447, ?x170), student(?x7447, ?x4586), institution(?x865, ?x7447), ?x170 = 09nqf, school_type(?x7447, ?x3205), ?x1682 = 02ky346 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #205 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 26 *> proper extension: 015cz0; *> query: (?x7447, 07s6fsf) <- major_field_of_study(?x7447, ?x1154), major_field_of_study(?x7447, ?x742), organization(?x346, ?x7447), ?x742 = 05qjt, institution(?x865, ?x7447), school_type(?x7447, ?x3205), ?x865 = 02h4rq6, ?x1154 = 02lp1 *> conf = 0.54 ranks of expected_values: 5 EVAL 01r3w7 institution! 07s6fsf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 161.000 98.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18195-025xt8y PRED entity: 025xt8y PRED relation: artists! PRED expected values: 0126t5 => 168 concepts (54 used for prediction) PRED predicted values (max 10 best out of 295): 064t9 (0.55 #321, 0.53 #9599, 0.50 #630), 0xhtw (0.48 #1250, 0.34 #8059, 0.33 #1870), 06j6l (0.40 #664, 0.40 #355, 0.30 #13037), 025sc50 (0.40 #357, 0.35 #666, 0.30 #10871), 05w3f (0.37 #1270, 0.21 #35, 0.21 #962), 08jyyk (0.37 #1301, 0.21 #66, 0.15 #6870), 0glt670 (0.36 #10861, 0.29 #2821, 0.28 #13029), 05bt6j (0.36 #41, 0.30 #9628, 0.26 #5917), 0cx7f (0.36 #137, 0.22 #1372, 0.12 #1064), 0gywn (0.35 #674, 0.35 #365, 0.24 #13047) >> Best rule #321 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 02j3d4; 01vwbts; >> query: (?x838, 064t9) <- artist(?x5666, ?x838), instrumentalists(?x227, ?x838), ?x5666 = 043g7l, award_nominee(?x838, ?x2698) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1319 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 0qmpd; *> query: (?x838, 0126t5) <- artists(?x6210, ?x838), artists(?x1380, ?x838), ?x1380 = 0dl5d, ?x6210 = 01fh36 *> conf = 0.26 ranks of expected_values: 16 EVAL 025xt8y artists! 0126t5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 168.000 54.000 0.550 http://example.org/music/genre/artists #18194-078tg PRED entity: 078tg PRED relation: religion! PRED expected values: 08849 => 40 concepts (37 used for prediction) PRED predicted values (max 10 best out of 3524): 019f2f (0.85 #7497, 0.56 #34262, 0.37 #9641), 019_1h (0.85 #7497, 0.56 #34262, 0.37 #9641), 02g9z1 (0.85 #7497, 0.56 #34262, 0.37 #9641), 0m93 (0.85 #7497, 0.56 #34262, 0.37 #9641), 08849 (0.85 #7497, 0.56 #34262, 0.37 #9641), 023sng (0.85 #7497, 0.56 #34262, 0.37 #9641), 02n1p5 (0.85 #7497, 0.56 #34262, 0.37 #9641), 0m77m (0.85 #7497, 0.56 #34262, 0.37 #9641), 04rs03 (0.85 #7497, 0.56 #34262, 0.37 #9641), 09r1j5 (0.85 #7497, 0.56 #34262, 0.37 #9641) >> Best rule #7497 for best value: >> intensional similarity = 16 >> extensional distance = 4 >> proper extension: 0kq2; >> query: (?x13970, ?x111) <- religion(?x12920, ?x13970), people(?x12672, ?x12920), profession(?x12920, ?x5805), profession(?x12920, ?x353), ?x5805 = 0fj9f, type_of_union(?x12920, ?x566), ?x566 = 04ztj, ?x353 = 0cbd2, religion(?x12920, ?x492), religion(?x111, ?x492), nationality(?x12920, ?x4092), country(?x1121, ?x4092), film_release_region(?x186, ?x4092), combatants(?x4092, ?x94), locations(?x2391, ?x4092), capital(?x4092, ?x13482) >> conf = 0.85 => this is the best rule for 48 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5 EVAL 078tg religion! 08849 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 40.000 37.000 0.850 http://example.org/people/person/religion #18193-05p3738 PRED entity: 05p3738 PRED relation: film! PRED expected values: 05d6kv => 106 concepts (84 used for prediction) PRED predicted values (max 10 best out of 104): 086k8 (0.65 #3554, 0.53 #1204, 0.52 #1053), 05mgj0 (0.53 #1204, 0.52 #1053, 0.52 #5074), 02slt7 (0.53 #1204, 0.52 #1053, 0.52 #5074), 016tw3 (0.33 #236, 0.25 #86, 0.22 #311), 025jfl (0.33 #6, 0.06 #2497, 0.05 #2800), 016tt2 (0.25 #1132, 0.25 #529, 0.21 #981), 024rbz (0.25 #87, 0.20 #162, 0.17 #237), 03sb38 (0.25 #118, 0.20 #193, 0.17 #268), 017s11 (0.25 #829, 0.18 #980, 0.18 #603), 03xsby (0.25 #1296, 0.11 #316, 0.10 #391) >> Best rule #3554 for best value: >> intensional similarity = 6 >> extensional distance = 488 >> proper extension: 034qbx; >> query: (?x1710, ?x382) <- language(?x1710, ?x254), film_crew_role(?x1710, ?x137), genre(?x1710, ?x53), currency(?x1710, ?x170), production_companies(?x1710, ?x382), film(?x382, ?x83) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #6364 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1058 *> proper extension: 09qljs; *> query: (?x1710, ?x902) <- language(?x1710, ?x254), genre(?x1710, ?x3515), production_companies(?x1710, ?x382), genre(?x8668, ?x3515), genre(?x3857, ?x3515), film(?x556, ?x3857), film(?x902, ?x3857), nominated_for(?x496, ?x8668) *> conf = 0.02 ranks of expected_values: 73 EVAL 05p3738 film! 05d6kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 106.000 84.000 0.654 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18192-0rql_ PRED entity: 0rql_ PRED relation: contains! PRED expected values: 02xry => 64 concepts (36 used for prediction) PRED predicted values (max 10 best out of 177): 02xry (0.69 #11629, 0.69 #11628, 0.36 #29520), 07c5l (0.36 #29520, 0.34 #30415), 01n7q (0.25 #971, 0.23 #1866, 0.22 #5444), 07ssc (0.16 #13450, 0.15 #19711, 0.14 #23288), 0kpys (0.14 #1074, 0.13 #1969, 0.08 #6442), 02jx1 (0.12 #13505, 0.11 #19766, 0.11 #23343), 059rby (0.11 #20593, 0.10 #7175, 0.09 #15226), 05fjf (0.10 #7529, 0.08 #1267, 0.08 #20947), 05k7sb (0.09 #1026, 0.08 #5499, 0.08 #20706), 04_1l0v (0.08 #19235, 0.05 #8500, 0.05 #21918) >> Best rule #11629 for best value: >> intensional similarity = 2 >> extensional distance = 259 >> proper extension: 0pbhz; 0jq27; 09hzc; >> query: (?x8127, ?x94) <- administrative_division(?x8127, ?x9290), contains(?x94, ?x9290) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rql_ contains! 02xry CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 36.000 0.689 http://example.org/location/location/contains #18191-0hwqg PRED entity: 0hwqg PRED relation: people! PRED expected values: 013b6_ => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 50): 0x67 (0.21 #3390, 0.21 #4666, 0.20 #234), 033tf_ (0.14 #757, 0.14 #1511, 0.13 #5188), 02w7gg (0.13 #980, 0.12 #1732, 0.10 #4659), 07hwkr (0.11 #611, 0.11 #762, 0.10 #1516), 0xnvg (0.08 #763, 0.08 #237, 0.08 #1517), 048z7l (0.08 #263, 0.08 #1993, 0.06 #488), 02g7sp (0.08 #17, 0.06 #92, 0.04 #995), 07bch9 (0.07 #698, 0.07 #1452, 0.06 #1377), 013xrm (0.07 #1974, 0.03 #2725, 0.03 #7377), 03bkbh (0.07 #782, 0.06 #631, 0.06 #933) >> Best rule #3390 for best value: >> intensional similarity = 3 >> extensional distance = 571 >> proper extension: 01vvydl; 034x61; 01k5t_3; 012x4t; 06t61y; 01trhmt; 015f7; 0gbwp; 014g22; 0k8y7; ... >> query: (?x10795, 0x67) <- award_winner(?x6331, ?x10795), profession(?x10795, ?x1032), people(?x1050, ?x10795) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2006 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 164 *> proper extension: 01w3v; 0mdyn; 045m1_; 0mcf4; 0168ql; *> query: (?x10795, 013b6_) <- religion(?x10795, ?x7131), ?x7131 = 03_gx *> conf = 0.05 ranks of expected_values: 16 EVAL 0hwqg people! 013b6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 142.000 142.000 0.208 http://example.org/people/ethnicity/people #18190-028k57 PRED entity: 028k57 PRED relation: award_nominee! PRED expected values: 07m77x => 102 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1051): 0p_47 (0.11 #51220, 0.10 #62864, 0.10 #60535), 039bp (0.09 #7208, 0.05 #11864, 0.05 #9536), 02zft0 (0.09 #24678, 0.08 #20022, 0.08 #22350), 0154qm (0.08 #44971, 0.04 #63601, 0.03 #737), 015t56 (0.08 #44841, 0.04 #63471, 0.03 #65799), 02l840 (0.08 #4813, 0.03 #51377, 0.03 #11797), 016fjj (0.08 #5487, 0.02 #7815, 0.01 #49722), 01jz6x (0.08 #6803, 0.02 #9131, 0.01 #13787), 01cwkq (0.08 #6867, 0.02 #9195, 0.01 #13851), 042ly5 (0.08 #6290, 0.01 #66826, 0.01 #50525) >> Best rule #51220 for best value: >> intensional similarity = 3 >> extensional distance = 438 >> proper extension: 01l1b90; 01vw87c; 03ds3; 02lnhv; 01j4ls; 031zkw; 01nczg; 046lt; 01jbx1; 05r5w; ... >> query: (?x4478, ?x3917) <- award(?x4478, ?x102), film(?x4478, ?x2153), participant(?x4478, ?x3917) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #6589 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 018ndc; *> query: (?x4478, 07m77x) <- award_nominee(?x1345, ?x4478), award(?x4478, ?x4416), ?x4416 = 099vwn *> conf = 0.05 ranks of expected_values: 46 EVAL 028k57 award_nominee! 07m77x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 102.000 29.000 0.108 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18189-02xh1 PRED entity: 02xh1 PRED relation: titles PRED expected values: 016ks5 => 61 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1715): 0k0rf (0.50 #10119, 0.32 #15591, 0.32 #1557), 02q0v8n (0.50 #10657, 0.27 #12213, 0.24 #13774), 02xs6_ (0.50 #10087, 0.27 #11643, 0.24 #13204), 02tktw (0.50 #10294, 0.27 #11850, 0.24 #13411), 04mzf8 (0.50 #9531, 0.27 #11087, 0.24 #12648), 0qf2t (0.40 #6951, 0.33 #711, 0.29 #8511), 07z6xs (0.38 #10117, 0.34 #1558, 0.33 #757), 06gjk9 (0.38 #9816, 0.33 #456, 0.27 #11372), 02c638 (0.38 #9648, 0.33 #288, 0.27 #11204), 01q7h2 (0.38 #10702, 0.33 #1342, 0.25 #4463) >> Best rule #10119 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 03h64; 012w70; >> query: (?x11108, 0k0rf) <- titles(?x11108, ?x3986), film_format(?x3986, ?x6392), film_release_region(?x3986, ?x2645), cinematography(?x3986, ?x7537), nominated_for(?x484, ?x3986), ?x2645 = 03h64, award_nominee(?x7537, ?x6116) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #904 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 07s9rl0; *> query: (?x11108, 016ks5) <- titles(?x11108, ?x5044), genre(?x9209, ?x11108), genre(?x3614, ?x11108), genre(?x10089, ?x11108), ?x9209 = 0crs0b8, ?x5044 = 0413cff, featured_film_locations(?x3614, ?x108), nominated_for(?x198, ?x3614) *> conf = 0.33 ranks of expected_values: 44 EVAL 02xh1 titles 016ks5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 61.000 12.000 0.500 http://example.org/media_common/netflix_genre/titles #18188-07sp4l PRED entity: 07sp4l PRED relation: nominated_for! PRED expected values: 04ljl_l => 87 concepts (75 used for prediction) PRED predicted values (max 10 best out of 201): 0gq9h (0.33 #4801, 0.29 #7882, 0.27 #6934), 019f4v (0.30 #4792, 0.25 #5029, 0.24 #7873), 0gs9p (0.29 #4803, 0.26 #7884, 0.24 #6462), 09v51c2 (0.27 #205, 0.16 #442, 0.13 #679), 09v4bym (0.27 #207, 0.13 #444, 0.11 #681), 04ljl_l (0.25 #9246, 0.24 #10432, 0.19 #3556), 05p09zm (0.25 #9246, 0.24 #10432, 0.19 #3556), 0k611 (0.24 #4812, 0.21 #7893, 0.21 #6945), 0gqy2 (0.22 #4862, 0.19 #3556, 0.19 #10670), 0gr4k (0.22 #4766, 0.19 #7847, 0.18 #6899) >> Best rule #4801 for best value: >> intensional similarity = 4 >> extensional distance = 421 >> proper extension: 016ztl; >> query: (?x3063, 0gq9h) <- film(?x574, ?x3063), genre(?x3063, ?x53), film(?x7619, ?x3063), ?x53 = 07s9rl0 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #9246 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1028 *> proper extension: 0cwrr; 01h1bf; 02kk_c; 0c3xpwy; 05sy0cv; 07bz5; 03d17dg; 0gxsh4; *> query: (?x3063, ?x102) <- award_winner(?x3063, ?x7619), nominated_for(?x703, ?x3063), profession(?x7619, ?x319), award(?x7619, ?x102) *> conf = 0.25 ranks of expected_values: 6 EVAL 07sp4l nominated_for! 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 87.000 75.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18187-0f61tk PRED entity: 0f61tk PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #54, 0.81 #135, 0.81 #125), 02nxhr (0.09 #13, 0.09 #50, 0.07 #55), 07c52 (0.09 #14, 0.08 #8, 0.02 #353), 07z4p (0.02 #284, 0.02 #85, 0.02 #90) >> Best rule #54 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 076xkdz; >> query: (?x8615, 029j_) <- genre(?x8615, ?x225), country(?x8615, ?x94), ?x225 = 02kdv5l, award(?x8615, ?x507) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f61tk film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.816 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #18186-0p8r1 PRED entity: 0p8r1 PRED relation: film PRED expected values: 0f3m1 => 125 concepts (53 used for prediction) PRED predicted values (max 10 best out of 691): 01s81 (0.26 #10664, 0.09 #40881, 0.09 #23105), 09146g (0.20 #295, 0.07 #26955, 0.04 #35842), 0gjcrrw (0.20 #619, 0.03 #7728, 0.02 #13060), 09sr0 (0.20 #1508, 0.03 #22835, 0.03 #24613), 016017 (0.20 #1700, 0.03 #23027, 0.02 #28360), 06gb1w (0.20 #722, 0.02 #93152, 0.02 #48712), 0992d9 (0.20 #980, 0.02 #15198, 0.02 #16975), 01pvxl (0.20 #896, 0.02 #22223, 0.02 #24001), 0hx4y (0.20 #460, 0.02 #23565, 0.01 #39563), 05sxr_ (0.20 #1660, 0.01 #28320, 0.01 #33653) >> Best rule #10664 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 02dh86; 018z_c; 05f7snc; 04crrxr; 0b7t3p; 01wf86y; >> query: (?x3417, ?x4517) <- profession(?x3417, ?x1032), nominated_for(?x3417, ?x4517), person(?x3480, ?x3417) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #40546 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 257 *> proper extension: 01sl1q; 044mz_; 0q9kd; 01xdf5; 04t2l2; 014zcr; 0h0jz; 01vw87c; 049tjg; 01wbg84; ... *> query: (?x3417, 0f3m1) <- film(?x3417, ?x9487), actor(?x4517, ?x3417), award(?x9487, ?x1723), crewmember(?x9487, ?x1933) *> conf = 0.03 ranks of expected_values: 213 EVAL 0p8r1 film 0f3m1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 125.000 53.000 0.258 http://example.org/film/actor/film./film/performance/film #18185-04dqdk PRED entity: 04dqdk PRED relation: artist! PRED expected values: 015_1q => 104 concepts (64 used for prediction) PRED predicted values (max 10 best out of 107): 03rhqg (0.22 #567, 0.20 #705, 0.17 #291), 015_1q (0.21 #2504, 0.21 #433, 0.20 #1123), 0g768 (0.19 #312, 0.18 #588, 0.16 #726), 011k1h (0.18 #562, 0.17 #286, 0.16 #700), 0n85g (0.14 #337, 0.11 #613, 0.10 #751), 0181dw (0.14 #2250, 0.12 #1559, 0.11 #1145), 017l96 (0.11 #570, 0.10 #708, 0.09 #2227), 01dtcb (0.11 #460, 0.08 #1426, 0.07 #1564), 01w40h (0.10 #1408, 0.09 #442, 0.09 #580), 02p11jq (0.09 #2498, 0.09 #2222, 0.08 #1393) >> Best rule #567 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 01t_xp_; 0fp_v1x; 032t2z; 025xt8y; 02whj; 0ftps; 067mj; 01wv9xn; 0dtd6; 01czx; ... >> query: (?x1381, 03rhqg) <- artists(?x1380, ?x1381), ?x1380 = 0dl5d, artist(?x2299, ?x1381) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #2504 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 363 *> proper extension: 01pfkw; 0h7pj; *> query: (?x1381, 015_1q) <- award_nominee(?x1381, ?x2461), profession(?x1381, ?x220), artist(?x2299, ?x1381) *> conf = 0.21 ranks of expected_values: 2 EVAL 04dqdk artist! 015_1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 64.000 0.222 http://example.org/music/record_label/artist #18184-06_bq1 PRED entity: 06_bq1 PRED relation: film PRED expected values: 02z3r8t => 156 concepts (134 used for prediction) PRED predicted values (max 10 best out of 1099): 0gfzgl (0.69 #60832, 0.63 #139556, 0.47 #220076), 0m313 (0.46 #1802, 0.25 #13, 0.06 #10747), 03bzjpm (0.25 #1314, 0.09 #8470, 0.09 #10259), 02qzh2 (0.25 #692, 0.08 #22162, 0.08 #13215), 07_k0c0 (0.25 #979, 0.08 #2768, 0.05 #4557), 0h03fhx (0.25 #778, 0.08 #2567, 0.04 #114508), 02qydsh (0.25 #1498, 0.08 #3287, 0.04 #114508), 07sc6nw (0.25 #179, 0.08 #1968, 0.04 #114508), 07l50_1 (0.25 #1745, 0.08 #3534, 0.04 #114508), 05r3qc (0.25 #1074, 0.08 #2863, 0.04 #114508) >> Best rule #60832 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 02jtjz; >> query: (?x7046, ?x2191) <- participant(?x7046, ?x10139), film(?x7046, ?x1701), nominated_for(?x7046, ?x2191) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #62729 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 81 *> proper extension: 07jrjb; *> query: (?x7046, 02z3r8t) <- celebrity(?x7046, ?x10139), award_winner(?x3790, ?x7046) *> conf = 0.04 ranks of expected_values: 302 EVAL 06_bq1 film 02z3r8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 156.000 134.000 0.688 http://example.org/film/actor/film./film/performance/film #18183-0fhxv PRED entity: 0fhxv PRED relation: role PRED expected values: 0l14qv 05r5c 013y1f => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 104): 0342h (0.42 #307, 0.39 #2434, 0.39 #2637), 05r5c (0.41 #2438, 0.40 #2641, 0.40 #3757), 018vs (0.32 #3852, 0.31 #2532, 0.25 #1215), 05148p4 (0.32 #3852, 0.31 #2532, 0.25 #1215), 03qjg (0.32 #3852, 0.31 #2532, 0.25 #1215), 02hnl (0.32 #3852, 0.31 #2532, 0.25 #1215), 05kms (0.32 #3852, 0.31 #2532, 0.25 #1215), 042v_gx (0.32 #312, 0.22 #2642, 0.21 #2439), 03bx0bm (0.31 #405, 0.25 #2430, 0.24 #608), 0l14md (0.31 #405, 0.25 #2430, 0.12 #209) >> Best rule #307 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 02lvtb; >> query: (?x4646, 0342h) <- award(?x4646, ?x2139), ?x2139 = 01by1l, role(?x4646, ?x315) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #2438 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 264 *> proper extension: 09g0h; *> query: (?x4646, 05r5c) <- type_of_union(?x4646, ?x566), role(?x4646, ?x212), instrumentalists(?x227, ?x4646) *> conf = 0.41 ranks of expected_values: 2, 12, 14 EVAL 0fhxv role 013y1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 138.000 138.000 0.421 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0fhxv role 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 138.000 0.421 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0fhxv role 0l14qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 138.000 138.000 0.421 http://example.org/music/artist/track_contributions./music/track_contribution/role #18182-05kr_ PRED entity: 05kr_ PRED relation: district_represented! PRED expected values: 04fhps => 241 concepts (241 used for prediction) PRED predicted values (max 10 best out of 55): 077g7n (0.88 #2644, 0.81 #499, 0.80 #2919), 070m6c (0.85 #2646, 0.80 #721, 0.78 #2921), 06f0dc (0.83 #2649, 0.76 #2924, 0.76 #504), 07p__7 (0.83 #2648, 0.75 #2923, 0.71 #3693), 070mff (0.81 #2680, 0.72 #755, 0.72 #2295), 024tcq (0.79 #2661, 0.71 #2936, 0.67 #2276), 04fhps (0.75 #384, 0.55 #4181, 0.23 #824), 02bn_p (0.67 #2650, 0.62 #505, 0.57 #2925), 02bqm0 (0.67 #525, 0.53 #1075, 0.52 #2670), 02bqmq (0.67 #515, 0.53 #1065, 0.52 #1010) >> Best rule #2644 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05kkh; 05kj_; 059f4; 03s0w; 05fhy; 059_c; 01x73; 04rrd; 0488g; 05k7sb; ... >> query: (?x1905, 077g7n) <- religion(?x1905, ?x109), adjoins(?x1905, ?x177), state(?x1196, ?x1905) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #384 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0h5qxv; *> query: (?x1905, 04fhps) <- district_represented(?x8777, ?x1905), adjoins(?x1905, ?x177), ?x8777 = 01gvxh *> conf = 0.75 ranks of expected_values: 7 EVAL 05kr_ district_represented! 04fhps CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 241.000 241.000 0.875 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #18181-01d2v1 PRED entity: 01d2v1 PRED relation: film_production_design_by PRED expected values: 0fqjks => 79 concepts (50 used for prediction) PRED predicted values (max 10 best out of 11): 03wd5tk (0.12 #15, 0.01 #772), 04_1nk (0.06 #107, 0.02 #518, 0.01 #899), 02x2t07 (0.03 #148, 0.02 #368, 0.02 #463), 03mdw3c (0.03 #147, 0.01 #655, 0.01 #813), 09pjnd (0.02 #790, 0.01 #1422, 0.01 #1166), 0p_pd (0.02 #790, 0.01 #1422, 0.01 #1166), 0d5wn3 (0.02 #134, 0.01 #767, 0.01 #926), 05b2gsm (0.02 #585, 0.02 #553, 0.01 #712), 0dh73w (0.01 #703, 0.01 #195, 0.01 #893), 0bytkq (0.01 #477, 0.01 #763, 0.01 #1140) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0ds33; 024lff; 02_sr1; 035_2h; 0jqd3; 0p9tm; >> query: (?x11174, 03wd5tk) <- genre(?x11174, ?x225), film_crew_role(?x11174, ?x2154), ?x225 = 02kdv5l, film_sets_designed(?x2230, ?x11174) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01d2v1 film_production_design_by 0fqjks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 50.000 0.125 http://example.org/film/film/film_production_design_by #18180-029cr PRED entity: 029cr PRED relation: locations! PRED expected values: 0b_6v_ => 130 concepts (103 used for prediction) PRED predicted values (max 10 best out of 103): 0b_6q5 (0.19 #1678, 0.15 #2533, 0.14 #3757), 0b_6rk (0.19 #1633, 0.15 #2488, 0.13 #2122), 0b_6x2 (0.19 #1622, 0.15 #2477, 0.13 #2355), 0b_6zk (0.17 #1619, 0.16 #2108, 0.14 #2474), 0b_6qj (0.17 #1653, 0.15 #2142, 0.14 #2508), 0b_6_l (0.17 #1688, 0.14 #2543, 0.12 #3523), 0b_6pv (0.16 #198, 0.15 #1664, 0.15 #2153), 0b_6v_ (0.15 #1651, 0.12 #2506, 0.12 #3730), 0b_6xf (0.15 #1689, 0.12 #2544, 0.11 #3768), 0b_75k (0.13 #3715, 0.13 #1636, 0.12 #4203) >> Best rule #1678 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 0pc6x; 0sq2v; 010016; 0qpsn; >> query: (?x2504, 0b_6q5) <- category(?x2504, ?x134), ?x134 = 08mbj5d, locations(?x10673, ?x2504), team(?x10673, ?x5551) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1651 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 0pc6x; 0sq2v; 010016; 0qpsn; *> query: (?x2504, 0b_6v_) <- category(?x2504, ?x134), ?x134 = 08mbj5d, locations(?x10673, ?x2504), team(?x10673, ?x5551) *> conf = 0.15 ranks of expected_values: 8 EVAL 029cr locations! 0b_6v_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 130.000 103.000 0.185 http://example.org/time/event/locations #18179-051ys82 PRED entity: 051ys82 PRED relation: film_crew_role PRED expected values: 09zzb8 0dxtw => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 26): 09zzb8 (0.85 #552, 0.75 #33, 0.70 #1717), 02r96rf (0.78 #555, 0.72 #262, 0.72 #198), 0215hd (0.62 #49, 0.38 #17, 0.17 #503), 0dxtw (0.50 #43, 0.40 #562, 0.37 #269), 0d2b38 (0.50 #55, 0.13 #281, 0.12 #23), 01pvkk (0.32 #76, 0.28 #1243, 0.27 #2053), 015h31 (0.25 #41, 0.13 #203, 0.13 #267), 089fss (0.24 #558, 0.12 #39, 0.07 #687), 02rh1dz (0.18 #74, 0.15 #561, 0.14 #204), 020xn5 (0.12 #40, 0.12 #8, 0.06 #72) >> Best rule #552 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 03twd6; 05q4y12; 025n07; 03z9585; 08c6k9; 0b2km_; >> query: (?x6005, 09zzb8) <- film(?x875, ?x6005), film_crew_role(?x6005, ?x3197), ?x3197 = 02ynfr >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 051ys82 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 94.000 0.848 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 051ys82 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.848 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18178-02f8lw PRED entity: 02f8lw PRED relation: participant! PRED expected values: 019pm_ => 140 concepts (116 used for prediction) PRED predicted values (max 10 best out of 339): 019pm_ (0.81 #14697, 0.80 #44108, 0.80 #38999), 0zjpz (0.10 #10224, 0.09 #32599, 0.08 #24289), 0f502 (0.10 #942, 0.06 #304, 0.04 #1581), 033tln (0.09 #32599, 0.08 #24289, 0.08 #10223), 0170s4 (0.09 #32599, 0.08 #24289, 0.08 #10223), 018ygt (0.09 #32599, 0.06 #42193, 0.06 #12143), 09yrh (0.08 #2238, 0.03 #32281, 0.03 #9267), 0gx_p (0.07 #3615, 0.05 #1059, 0.04 #1698), 01pcvn (0.07 #3578, 0.04 #9968, 0.03 #6774), 0237fw (0.06 #2084, 0.05 #806, 0.04 #1445) >> Best rule #14697 for best value: >> intensional similarity = 3 >> extensional distance = 152 >> proper extension: 02jyhv; >> query: (?x3329, ?x2763) <- languages(?x3329, ?x254), film(?x3329, ?x641), participant(?x3329, ?x2763) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02f8lw participant! 019pm_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 116.000 0.810 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #18177-0178kd PRED entity: 0178kd PRED relation: artist! PRED expected values: 026s90 => 81 concepts (59 used for prediction) PRED predicted values (max 10 best out of 109): 017l96 (0.69 #827, 0.62 #152, 0.60 #557), 015_1q (0.59 #693, 0.57 #4744, 0.42 #5149), 0n85g (0.46 #3434, 0.38 #194, 0.33 #59), 02p3cr5 (0.38 #160, 0.33 #565, 0.33 #295), 0k_kr (0.38 #175, 0.33 #580, 0.33 #310), 03rhqg (0.33 #14, 0.27 #689, 0.22 #2579), 01cl2y (0.33 #27, 0.25 #4348, 0.12 #162), 01clyr (0.33 #30, 0.14 #705, 0.10 #3405), 01txts (0.33 #79, 0.08 #484, 0.07 #619), 0mzkr (0.23 #428, 0.18 #698, 0.13 #563) >> Best rule #827 for best value: >> intensional similarity = 6 >> extensional distance = 30 >> proper extension: 02r3zy; 0249kn; 0178_w; 01kcms4; 07m4c; 01jkqfz; 09jm8; 02vnpv; 0560w; >> query: (?x6368, 017l96) <- artist(?x2149, ?x6368), artist(?x2149, ?x3321), artist(?x2149, ?x642), ?x642 = 032t2z, ?x3321 = 03bnv, group(?x227, ?x6368) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #712 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 20 *> proper extension: 0136p1; 01wg25j; 01vvybv; *> query: (?x6368, 026s90) <- artist(?x4868, ?x6368), artist(?x2149, ?x6368), artist(?x2149, ?x5340), artist(?x2149, ?x1953), artists(?x2937, ?x5340), ?x1953 = 019g40, ?x4868 = 01w40h *> conf = 0.05 ranks of expected_values: 65 EVAL 0178kd artist! 026s90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 81.000 59.000 0.688 http://example.org/music/record_label/artist #18176-04lhc4 PRED entity: 04lhc4 PRED relation: currency PRED expected values: 09nqf => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.83 #36, 0.82 #29, 0.82 #22), 01nv4h (0.06 #2, 0.04 #16, 0.03 #198), 02gsvk (0.06 #6, 0.03 #20), 02l6h (0.01 #18, 0.01 #396, 0.01 #340) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 176 >> proper extension: 04dsnp; 053tj7; 091z_p; 02phtzk; 02q3fdr; 012jfb; 0g4pl7z; 0gy0l_; 0g5qmbz; >> query: (?x6899, 09nqf) <- produced_by(?x6899, ?x71), category(?x6899, ?x134), language(?x6899, ?x254), award(?x71, ?x198) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04lhc4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.826 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #18175-0jmj7 PRED entity: 0jmj7 PRED relation: sport PRED expected values: 018w8 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 8): 018w8 (0.76 #252, 0.73 #421, 0.71 #195), 018jz (0.60 #68, 0.50 #41, 0.47 #187), 039yzs (0.57 #458, 0.50 #312, 0.50 #154), 02vx4 (0.57 #559, 0.50 #769, 0.50 #705), 03tmr (0.45 #449, 0.15 #145, 0.13 #165), 0jm_ (0.45 #232, 0.40 #396, 0.40 #369), 09xp_ (0.11 #786, 0.04 #390, 0.02 #381), 06f3l (0.11 #786, 0.02 #384, 0.02 #393) >> Best rule #252 for best value: >> intensional similarity = 12 >> extensional distance = 19 >> proper extension: 04cxw5b; >> query: (?x2820, 018w8) <- draft(?x2820, ?x2569), team(?x5755, ?x2820), ?x5755 = 0355dz, draft(?x9760, ?x2569), draft(?x5756, ?x2569), draft(?x2568, ?x2569), draft(?x1347, ?x2569), ?x1347 = 0jmfv, ?x2568 = 0jmcb, ?x9760 = 0bwjj, ?x5756 = 0jm4b, school(?x2569, ?x621) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jmj7 sport 018w8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.762 http://example.org/sports/sports_team/sport #18174-0kvgtf PRED entity: 0kvgtf PRED relation: genre PRED expected values: 07s9rl0 01hwc6 => 77 concepts (47 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.84 #4048, 0.69 #1387, 0.66 #2199), 01z4y (0.61 #5322, 0.54 #2545, 0.52 #4627), 03k9fj (0.61 #2672, 0.50 #819, 0.42 #241), 02kdv5l (0.44 #2665, 0.34 #4398, 0.33 #812), 01jfsb (0.43 #4406, 0.33 #3135, 0.31 #2556), 06n90 (0.35 #2674, 0.23 #821, 0.23 #243), 0lsxr (0.25 #4403, 0.22 #4055, 0.20 #469), 0hcr (0.24 #829, 0.21 #366, 0.20 #20), 082gq (0.21 #604, 0.19 #2108, 0.19 #1991), 04xvlr (0.21 #1388, 0.20 #463, 0.20 #1966) >> Best rule #4048 for best value: >> intensional similarity = 4 >> extensional distance = 977 >> proper extension: 05jyb2; 09rfh9; >> query: (?x3781, 07s9rl0) <- nominated_for(?x154, ?x3781), genre(?x3781, ?x1509), genre(?x2116, ?x1509), ?x2116 = 02c638 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 46 EVAL 0kvgtf genre 01hwc6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 77.000 47.000 0.843 http://example.org/film/film/genre EVAL 0kvgtf genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 47.000 0.843 http://example.org/film/film/genre #18173-031x_3 PRED entity: 031x_3 PRED relation: gender PRED expected values: 02zsn => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.76 #37, 0.74 #27, 0.73 #145), 02zsn (0.55 #6, 0.47 #8, 0.31 #102) >> Best rule #37 for best value: >> intensional similarity = 2 >> extensional distance = 115 >> proper extension: 053y0s; 0274ck; 0326tc; 02mx98; 03wjb7; >> query: (?x8583, 05zppz) <- performance_role(?x8583, ?x14713), artists(?x888, ?x8583) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 05yjhm; 0716t2; *> query: (?x8583, 02zsn) <- award_nominee(?x8583, ?x352), award_nominee(?x9727, ?x8583), organization(?x8583, ?x4542) *> conf = 0.55 ranks of expected_values: 2 EVAL 031x_3 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 146.000 0.761 http://example.org/people/person/gender #18172-0ksrf8 PRED entity: 0ksrf8 PRED relation: award_winner! PRED expected values: 0blq0z => 97 concepts (52 used for prediction) PRED predicted values (max 10 best out of 641): 01713c (0.82 #35179, 0.81 #68771, 0.52 #68770), 0blq0z (0.82 #35179, 0.81 #68771, 0.29 #78368), 03mg35 (0.52 #68770, 0.52 #47975, 0.45 #65572), 01pkhw (0.52 #68770, 0.52 #47975, 0.45 #65572), 04954 (0.52 #68770, 0.52 #47975, 0.45 #65572), 04wp3s (0.52 #68770, 0.52 #47975, 0.45 #54374), 071ywj (0.45 #65572, 0.37 #57574, 0.34 #63971), 0ksrf8 (0.29 #78368, 0.28 #70371, 0.28 #83166), 0169dl (0.29 #78368, 0.28 #70371, 0.28 #83166), 03f1zdw (0.29 #78368, 0.28 #70371, 0.28 #83166) >> Best rule #35179 for best value: >> intensional similarity = 3 >> extensional distance = 807 >> proper extension: 01vvydl; 0l6qt; 07s3vqk; 0197tq; 02rchht; 05ty4m; 01r42_g; 03rs8y; 02lfcm; 026ps1; ... >> query: (?x5563, ?x1582) <- award_winner(?x91, ?x5563), location(?x5563, ?x362), award_winner(?x5563, ?x1582) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0ksrf8 award_winner! 0blq0z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 52.000 0.817 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #18171-03zrp PRED entity: 03zrp PRED relation: music! PRED expected values: 05dmmc => 129 concepts (99 used for prediction) PRED predicted values (max 10 best out of 833): 01_1pv (0.25 #1232, 0.07 #2246, 0.06 #8330), 08l0x2 (0.12 #1769, 0.06 #3797, 0.04 #15965), 03s9kp (0.12 #2013, 0.06 #4041, 0.04 #7083), 03h0byn (0.12 #1984, 0.06 #4012, 0.04 #7054), 0c0zq (0.12 #1904, 0.06 #3932, 0.04 #6974), 0bnzd (0.12 #1729, 0.06 #3757, 0.04 #6799), 0_9wr (0.12 #1725, 0.06 #3753, 0.04 #6795), 04lhc4 (0.12 #1717, 0.06 #3745, 0.04 #6787), 07jnt (0.12 #1709, 0.06 #3737, 0.04 #6779), 071nw5 (0.12 #1648, 0.06 #3676, 0.04 #6718) >> Best rule #1232 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 07qy0b; >> query: (?x11034, 01_1pv) <- award_nominee(?x7168, ?x11034), music(?x8735, ?x11034), sibling(?x9593, ?x11034), profession(?x7168, ?x1614) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2476 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 03wdsbz; *> query: (?x11034, 05dmmc) <- sibling(?x9593, ?x11034), place_of_birth(?x11034, ?x739), place_of_death(?x9593, ?x682) *> conf = 0.07 ranks of expected_values: 40 EVAL 03zrp music! 05dmmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 129.000 99.000 0.250 http://example.org/film/film/music #18170-05bnp0 PRED entity: 05bnp0 PRED relation: student! PRED expected values: 04b_46 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 115): 065y4w7 (0.16 #3667, 0.08 #2101, 0.05 #15674), 015nl4 (0.12 #65, 0.05 #6851, 0.05 #2675), 015fs3 (0.12 #418, 0.02 #1462), 01pj48 (0.12 #468), 02x9cv (0.12 #317), 03gn1x (0.12 #311), 07tds (0.12 #146), 05krk (0.12 #6), 04gd8j (0.10 #885, 0.02 #2451, 0.01 #4539), 0bwfn (0.09 #9666, 0.08 #14365, 0.08 #13842) >> Best rule #3667 for best value: >> intensional similarity = 3 >> extensional distance = 364 >> proper extension: 03c_8t; >> query: (?x123, 065y4w7) <- student(?x4209, ?x123), place_of_birth(?x123, ?x2935), school(?x1161, ?x4209) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #2310 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 255 *> proper extension: 0456xp; 02lnhv; 0n6f8; 01j4ls; 0j582; 045bs6; 01gbbz; 01z0rcq; 0cqt90; 029_3; ... *> query: (?x123, 04b_46) <- film(?x123, ?x1219), student(?x122, ?x123), participant(?x123, ?x1017) *> conf = 0.04 ranks of expected_values: 16 EVAL 05bnp0 student! 04b_46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 94.000 94.000 0.156 http://example.org/education/educational_institution/students_graduates./education/education/student #18169-02ntb8 PRED entity: 02ntb8 PRED relation: film! PRED expected values: 018grr 03vrv9 => 93 concepts (50 used for prediction) PRED predicted values (max 10 best out of 949): 01q_ph (0.70 #16608, 0.65 #70587, 0.64 #64357), 0gv07g (0.45 #62280, 0.45 #60203, 0.42 #87191), 02qgqt (0.31 #2093, 0.12 #24915, 0.06 #6243), 018ygt (0.23 #3188, 0.12 #24915, 0.12 #1113), 01vsn38 (0.18 #1850, 0.06 #3925, 0.04 #47524), 04yqlk (0.18 #776, 0.06 #2851, 0.02 #13230), 0sw6g (0.12 #3475, 0.12 #24915, 0.12 #1400), 01z5tr (0.12 #24915, 0.06 #1378, 0.02 #3453), 07cjqy (0.12 #24915, 0.03 #4750, 0.03 #91344), 0309lm (0.12 #24915, 0.03 #5752, 0.03 #91344) >> Best rule #16608 for best value: >> intensional similarity = 4 >> extensional distance = 178 >> proper extension: 0gfzgl; >> query: (?x4888, ?x932) <- titles(?x2480, ?x4888), nominated_for(?x932, ?x4888), participant(?x932, ?x3754), participant(?x1213, ?x932) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #337 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 0bmssv; 025rvx0; *> query: (?x4888, 018grr) <- film(?x794, ?x4888), production_companies(?x4888, ?x382), ?x794 = 0mdqp, genre(?x4888, ?x225) *> conf = 0.06 ranks of expected_values: 91 EVAL 02ntb8 film! 03vrv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 50.000 0.704 http://example.org/film/actor/film./film/performance/film EVAL 02ntb8 film! 018grr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 93.000 50.000 0.704 http://example.org/film/actor/film./film/performance/film #18168-03z_g7 PRED entity: 03z_g7 PRED relation: gender PRED expected values: 05zppz => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #29, 0.86 #17, 0.86 #19), 02zsn (0.46 #134, 0.36 #46, 0.35 #50) >> Best rule #29 for best value: >> intensional similarity = 5 >> extensional distance = 83 >> proper extension: 02x2097; >> query: (?x13459, 05zppz) <- award(?x13459, ?x4687), award(?x9136, ?x4687), award_winner(?x704, ?x9136), nominated_for(?x4687, ?x5247), ?x5247 = 0f42nz >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03z_g7 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.871 http://example.org/people/person/gender #18167-059rby PRED entity: 059rby PRED relation: contains PRED expected values: 0y1rf 0f4zv => 184 concepts (130 used for prediction) PRED predicted values (max 10 best out of 2843): 0f4y_ (0.85 #101587, 0.84 #160850, 0.84 #158027), 0drr3 (0.85 #101587, 0.84 #160850, 0.84 #158027), 0cr3d (0.80 #42325, 0.59 #64901, 0.50 #273728), 06thjt (0.76 #228578, 0.72 #228580, 0.71 #237046), 03qdm (0.76 #228578, 0.72 #228580, 0.71 #237046), 08htt0 (0.76 #228578, 0.72 #228580, 0.71 #237046), 01rtm4 (0.76 #228578, 0.72 #228580, 0.71 #237046), 02301 (0.76 #228578, 0.72 #228580, 0.71 #237046), 032r4n (0.76 #228578, 0.72 #228580, 0.71 #237046), 023zl (0.76 #228578, 0.72 #228580, 0.71 #237046) >> Best rule #101587 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 023sm8; >> query: (?x335, ?x334) <- administrative_parent(?x334, ?x335), contains(?x335, ?x3813), student(?x3813, ?x2587) >> conf = 0.85 => this is the best rule for 2 predicted values *> Best rule #64901 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 0d05q4; 0604m; *> query: (?x335, ?x94) <- contains(?x335, ?x1005), jurisdiction_of_office(?x7891, ?x335), contains(?x94, ?x1005) *> conf = 0.59 ranks of expected_values: 30, 873 EVAL 059rby contains 0f4zv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 184.000 130.000 0.846 http://example.org/location/location/contains EVAL 059rby contains 0y1rf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 184.000 130.000 0.846 http://example.org/location/location/contains #18166-01vs4f3 PRED entity: 01vs4f3 PRED relation: artists! PRED expected values: 0190_q 0grjmv => 166 concepts (107 used for prediction) PRED predicted values (max 10 best out of 260): 016clz (0.76 #19837, 0.50 #5, 0.45 #13331), 06by7 (0.71 #3118, 0.62 #6213, 0.60 #4354), 0xhtw (0.61 #18612, 0.50 #16131, 0.45 #4658), 0cx7f (0.56 #8189, 0.50 #1996, 0.45 #4779), 01243b (0.50 #44, 0.29 #2520, 0.25 #354), 064t9 (0.48 #12722, 0.47 #5583, 0.46 #9310), 0dl5d (0.45 #7120, 0.44 #8071, 0.39 #13347), 05bt6j (0.45 #7120, 0.44 #24832, 0.43 #3140), 03_d0 (0.45 #7120, 0.43 #2798, 0.40 #5581), 0ggq0m (0.45 #7120, 0.28 #8684, 0.21 #7753) >> Best rule #19837 for best value: >> intensional similarity = 4 >> extensional distance = 159 >> proper extension: 0892sx; 06x4l_; 01vxlbm; 01wn718; 03xnq9_; 01hrqc; 01nkxvx; 02g40r; 01tpl1p; >> query: (?x8579, 016clz) <- artists(?x2809, ?x8579), nationality(?x8579, ?x1310), artists(?x2809, ?x5872), ?x5872 = 01bpnd >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #2621 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 02dw1_; *> query: (?x8579, 0grjmv) <- artists(?x10721, ?x8579), artists(?x2809, ?x8579), ?x10721 = 04z1v0, artists(?x2809, ?x4620), profession(?x4620, ?x131) *> conf = 0.29 ranks of expected_values: 29, 102 EVAL 01vs4f3 artists! 0grjmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 166.000 107.000 0.758 http://example.org/music/genre/artists EVAL 01vs4f3 artists! 0190_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 166.000 107.000 0.758 http://example.org/music/genre/artists #18165-0j871 PRED entity: 0j871 PRED relation: role! PRED expected values: 0dwt5 => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 150): 0l14j_ (0.85 #1192, 0.75 #738, 0.75 #621), 0dwt5 (0.83 #786, 0.83 #553, 0.83 #442), 0mkg (0.83 #786, 0.83 #553, 0.83 #442), 01s0ps (0.83 #1074, 0.80 #1414, 0.80 #962), 013y1f (0.80 #1840, 0.80 #1392, 0.79 #2170), 0j862 (0.78 #561, 0.78 #446, 0.76 #680), 0l14qv (0.78 #2370, 0.77 #1139, 0.75 #1027), 07xzm (0.78 #816, 0.75 #1041, 0.75 #699), 042v_gx (0.78 #803, 0.75 #1028, 0.75 #569), 018j2 (0.76 #2518, 0.74 #2404, 0.67 #1061) >> Best rule #1192 for best value: >> intensional similarity = 22 >> extensional distance = 11 >> proper extension: 07gql; 02fsn; >> query: (?x2592, 0l14j_) <- role(?x2592, ?x2460), role(?x2592, ?x2309), role(?x2592, ?x2206), role(?x2592, ?x716), role(?x2592, ?x316), ?x316 = 05r5c, role(?x3161, ?x2206), role(?x2206, ?x3239), role(?x2835, ?x2206), ?x3161 = 01v1d8, ?x716 = 018vs, role(?x6104, ?x2206), instrumentalists(?x2206, ?x669), place_of_burial(?x2835, ?x14112), role(?x2206, ?x214), award_winner(?x1504, ?x6104), ?x2460 = 01wy6, role(?x3546, ?x2592), artists(?x119, ?x2835), group(?x2206, ?x1751), ?x2309 = 06ncr, award(?x6104, ?x1079) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #786 for first EXPECTED value: *> intensional similarity = 32 *> extensional distance = 6 *> proper extension: 042v_gx; *> query: (?x2592, ?x227) <- role(?x2592, ?x4769), role(?x2592, ?x2206), role(?x2592, ?x716), role(?x2592, ?x645), role(?x2592, ?x614), role(?x2592, ?x316), role(?x2592, ?x315), role(?x2592, ?x227), ?x316 = 05r5c, ?x2206 = 07gql, performance_role(?x2592, ?x7772), ?x645 = 028tv0, ?x614 = 0mkg, ?x716 = 018vs, ?x315 = 0l14md, role(?x3546, ?x2592), role(?x4769, ?x3703), role(?x4769, ?x2310), role(?x4769, ?x1886), role(?x4769, ?x1495), role(?x4769, ?x1432), role(?x4769, ?x1148), ?x1886 = 02k84w, ?x1148 = 02qjv, role(?x6351, ?x4769), ?x1495 = 013y1f, ?x2310 = 0gghm, role(?x922, ?x4769), ?x3703 = 02dlh2, group(?x4769, ?x3516), ?x1432 = 0395lw, ?x6351 = 01vsksr *> conf = 0.83 ranks of expected_values: 2 EVAL 0j871 role! 0dwt5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 40.000 40.000 0.846 http://example.org/music/performance_role/regular_performances./music/group_membership/role #18164-023zl PRED entity: 023zl PRED relation: major_field_of_study PRED expected values: 09s1f => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 116): 03g3w (0.75 #514, 0.50 #636, 0.44 #3076), 01mkq (0.62 #624, 0.62 #502, 0.57 #6846), 02j62 (0.62 #640, 0.62 #518, 0.48 #6862), 04rjg (0.62 #507, 0.50 #629, 0.44 #6851), 02lp1 (0.55 #6842, 0.52 #7331, 0.48 #8558), 0193x (0.50 #645, 0.50 #523, 0.29 #157), 01540 (0.50 #548, 0.38 #670, 0.33 #7381), 037mh8 (0.50 #555, 0.38 #677, 0.32 #2263), 04sh3 (0.50 #563, 0.38 #685, 0.29 #197), 01lhy (0.50 #499, 0.38 #621, 0.29 #133) >> Best rule #514 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 07tgn; 07tg4; 03ksy; 0gjv_; 08qnnv; 0bwfn; >> query: (?x10759, 03g3w) <- institution(?x4981, ?x10759), institution(?x1368, ?x10759), ?x4981 = 03bwzr4, child(?x10759, ?x2730), ?x1368 = 014mlp >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6078 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 02jztz; *> query: (?x10759, 09s1f) <- major_field_of_study(?x10759, ?x4321), organization(?x5510, ?x10759), ?x4321 = 0g26h, category(?x10759, ?x134) *> conf = 0.14 ranks of expected_values: 43 EVAL 023zl major_field_of_study 09s1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 172.000 172.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18163-0147dk PRED entity: 0147dk PRED relation: artists! PRED expected values: 0glt670 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 188): 0glt670 (0.64 #665, 0.21 #11273, 0.18 #9401), 025sc50 (0.59 #675, 0.21 #363, 0.20 #11283), 06by7 (0.42 #14374, 0.38 #13750, 0.36 #19057), 06j6l (0.36 #673, 0.25 #11281, 0.22 #10033), 017_qw (0.32 #6304, 0.13 #10984, 0.12 #15352), 0ggx5q (0.23 #703, 0.21 #391, 0.20 #1951), 016clz (0.23 #14357, 0.19 #19040, 0.19 #13733), 05bt6j (0.21 #14396, 0.20 #1916, 0.19 #13772), 02lnbg (0.21 #372, 0.20 #1932, 0.14 #3804), 0xhtw (0.19 #14369, 0.16 #19052, 0.14 #13745) >> Best rule #665 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 02l840; 016kjs; 04mn81; 01wgxtl; 0412f5y; 04qmr; 01q32bd; 01ws9n6; 0837ql; 01yzl2; ... >> query: (?x521, 0glt670) <- award_nominee(?x7609, ?x521), award_nominee(?x4476, ?x521), participant(?x7609, ?x4777), ?x4476 = 01vw20h >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0147dk artists! 0glt670 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.636 http://example.org/music/genre/artists #18162-06btq PRED entity: 06btq PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 20): 0fkvn (0.82 #109, 0.78 #172, 0.76 #151), 0pqc5 (0.53 #1392, 0.41 #1140, 0.36 #2212), 060c4 (0.50 #1327, 0.49 #1537, 0.49 #1516), 060bp (0.44 #1325, 0.43 #1535, 0.43 #1514), 0fkzq (0.38 #379, 0.37 #1766, 0.24 #183), 02079p (0.38 #379, 0.37 #1766, 0.07 #52), 0789n (0.38 #379, 0.18 #30, 0.17 #9), 01t7n9 (0.38 #379, 0.17 #17, 0.13 #185), 01gkgk (0.38 #379, 0.13 #27, 0.08 #6), 0p5vf (0.15 #32, 0.11 #537, 0.09 #1209) >> Best rule #109 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 0ny1p; >> query: (?x2713, 0fkvn) <- location(?x4806, ?x2713), capital(?x2713, ?x8263), jurisdiction_of_office(?x3959, ?x2713) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06btq jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.820 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #18161-02h9_l PRED entity: 02h9_l PRED relation: award PRED expected values: 01c427 02f6ym 01cw7s => 126 concepts (98 used for prediction) PRED predicted values (max 10 best out of 290): 01cw7s (0.50 #262, 0.22 #1064, 0.19 #4673), 01by1l (0.38 #2518, 0.38 #4523, 0.36 #8533), 01bgqh (0.36 #1246, 0.35 #4454, 0.32 #6459), 01d38g (0.34 #6444, 0.32 #4439, 0.25 #28), 09sb52 (0.33 #843, 0.29 #2848, 0.25 #41), 02f705 (0.33 #955, 0.28 #1757, 0.22 #2158), 02f6ym (0.33 #1057, 0.26 #3864, 0.25 #255), 0c4z8 (0.32 #1275, 0.25 #72, 0.25 #8493), 01c427 (0.31 #3694, 0.25 #85, 0.25 #4496), 03qbh5 (0.31 #2611, 0.29 #4616, 0.26 #6621) >> Best rule #262 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0x3n; >> query: (?x10148, 01cw7s) <- gender(?x10148, ?x514), religion(?x10148, ?x1624), artists(?x671, ?x10148), award(?x10148, ?x4532), ?x4532 = 02f764 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 9 EVAL 02h9_l award 01cw7s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 98.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02h9_l award 02f6ym CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 126.000 98.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02h9_l award 01c427 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 126.000 98.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18160-0hgnl3t PRED entity: 0hgnl3t PRED relation: film_release_region PRED expected values: 06bnz 01pj7 02vzc => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 101): 035qy (0.89 #462, 0.89 #607, 0.82 #1622), 06bnz (0.85 #617, 0.82 #472, 0.77 #1632), 02vzc (0.78 #624, 0.78 #479, 0.76 #1639), 05v8c (0.72 #591, 0.72 #446, 0.61 #1606), 016wzw (0.66 #492, 0.65 #637, 0.50 #1652), 03rk0 (0.66 #483, 0.65 #628, 0.48 #1643), 03rj0 (0.66 #632, 0.65 #487, 0.64 #1647), 01ls2 (0.65 #443, 0.63 #588, 0.47 #1603), 06t8v (0.54 #648, 0.53 #503, 0.47 #1663), 05qx1 (0.49 #469, 0.48 #614, 0.41 #1629) >> Best rule #462 for best value: >> intensional similarity = 6 >> extensional distance = 93 >> proper extension: 0g56t9t; 0gtsx8c; 0c3ybss; 0gx1bnj; 0ds3t5x; 0g5qs2k; 0dscrwf; 02x3lt7; 0gkz15s; 087wc7n; ... >> query: (?x4518, 035qy) <- film_release_region(?x4518, ?x2316), film_release_region(?x4518, ?x1917), film_release_region(?x4518, ?x512), ?x2316 = 06t2t, ?x1917 = 01p1v, ?x512 = 07ssc >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #617 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 112 *> proper extension: 02vxq9m; 03g90h; 0gtv7pk; 0h3xztt; 0gj8t_b; 0dgst_d; 0gxtknx; 0bh8yn3; 0dr3sl; 0gtsxr4; ... *> query: (?x4518, 06bnz) <- film_release_region(?x4518, ?x2316), film_release_region(?x4518, ?x1917), film_release_region(?x4518, ?x512), ?x2316 = 06t2t, ?x1917 = 01p1v, country(?x124, ?x512) *> conf = 0.85 ranks of expected_values: 2, 3, 16 EVAL 0hgnl3t film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 47.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hgnl3t film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 47.000 47.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hgnl3t film_release_region 06bnz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 47.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18159-0dc95 PRED entity: 0dc95 PRED relation: locations! PRED expected values: 0bzrxn => 233 concepts (233 used for prediction) PRED predicted values (max 10 best out of 116): 0b_6lb (0.20 #590, 0.16 #3534, 0.14 #2638), 0b_6jz (0.20 #547, 0.12 #10164, 0.12 #2595), 0b_6v_ (0.20 #577, 0.11 #3777, 0.11 #1985), 0b_6pv (0.19 #2001, 0.18 #3537, 0.16 #2641), 0b_6x2 (0.19 #3746, 0.16 #3490, 0.12 #7977), 0b_6mr (0.18 #3545, 0.17 #3801, 0.14 #2009), 0bzrsh (0.17 #3792, 0.14 #2640, 0.14 #2000), 0bzrxn (0.17 #3767, 0.14 #1975, 0.13 #2103), 0b_6_l (0.17 #3818, 0.11 #490, 0.10 #8049), 0b_6q5 (0.16 #2016, 0.14 #2656, 0.13 #2144) >> Best rule #590 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01lxw6; >> query: (?x2552, 0b_6lb) <- adjoins(?x2552, ?x3125), state(?x2552, ?x1227), locations(?x5897, ?x2552) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3767 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 0pc6x; 03khn; 010016; 0qpsn; *> query: (?x2552, 0bzrxn) <- citytown(?x580, ?x2552), locations(?x5897, ?x2552), category(?x2552, ?x134) *> conf = 0.17 ranks of expected_values: 8 EVAL 0dc95 locations! 0bzrxn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 233.000 233.000 0.200 http://example.org/time/event/locations #18158-03ckfl9 PRED entity: 03ckfl9 PRED relation: artists PRED expected values: 01qqwp9 0326tc 01516r 04mky3 => 61 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1088): 0191h5 (0.70 #4849, 0.64 #6957, 0.57 #10119), 01w5n51 (0.69 #9102, 0.64 #10156, 0.64 #6994), 016ntp (0.56 #3418, 0.50 #4472, 0.50 #2364), 01w8n89 (0.50 #5576, 0.50 #2414, 0.47 #12955), 02ndj5 (0.50 #5089, 0.50 #1928, 0.46 #9305), 067mj (0.50 #2203, 0.50 #1150, 0.40 #4311), 0l8g0 (0.50 #1603, 0.46 #8980, 0.43 #10034), 03fbc (0.50 #4413, 0.45 #6521, 0.38 #8629), 01kd57 (0.50 #2597, 0.44 #3651, 0.40 #4705), 01m65sp (0.50 #2370, 0.44 #3424, 0.36 #6586) >> Best rule #4849 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 016clz; 0m0jc; 016jhr; 01243b; 0cx7f; >> query: (?x10290, 0191h5) <- artists(?x10290, ?x9757), artists(?x10290, ?x9631), artists(?x10290, ?x4052), artists(?x10290, ?x3321), ?x9757 = 06br6t, parent_genre(?x301, ?x10290), role(?x4052, ?x212), award(?x9631, ?x884), religion(?x3321, ?x8967), award_nominee(?x483, ?x3321) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #3106 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 016jny; *> query: (?x10290, 04mky3) <- artists(?x10290, ?x7683), artists(?x10290, ?x6406), artists(?x10290, ?x1656), ?x7683 = 043c4j, category(?x1656, ?x134), role(?x1656, ?x227), instrumentalists(?x716, ?x1656), ?x6406 = 01386_ *> conf = 0.50 ranks of expected_values: 16, 21, 45, 621 EVAL 03ckfl9 artists 04mky3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 61.000 26.000 0.700 http://example.org/music/genre/artists EVAL 03ckfl9 artists 01516r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 61.000 26.000 0.700 http://example.org/music/genre/artists EVAL 03ckfl9 artists 0326tc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 61.000 26.000 0.700 http://example.org/music/genre/artists EVAL 03ckfl9 artists 01qqwp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 26.000 0.700 http://example.org/music/genre/artists #18157-02pptm PRED entity: 02pptm PRED relation: institution! PRED expected values: 03bwzr4 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 22): 014mlp (0.89 #1868, 0.70 #447, 0.69 #284), 019v9k (0.70 #451, 0.67 #545, 0.64 #614), 03bwzr4 (0.58 #457, 0.55 #481, 0.55 #271), 016t_3 (0.53 #352, 0.50 #469, 0.48 #445), 02_xgp2 (0.50 #479, 0.49 #2042, 0.49 #455), 0bkj86 (0.48 #474, 0.40 #450, 0.40 #30), 027f2w (0.33 #9, 0.23 #452, 0.21 #546), 02m4yg (0.33 #16, 0.17 #2631, 0.16 #2557), 013zdg (0.31 #170, 0.26 #356, 0.25 #473), 04zx3q1 (0.28 #468, 0.27 #351, 0.24 #444) >> Best rule #1868 for best value: >> intensional similarity = 4 >> extensional distance = 375 >> proper extension: 015nl4; >> query: (?x9131, 014mlp) <- institution(?x620, ?x9131), major_field_of_study(?x9131, ?x2601), institution(?x620, ?x13705), ?x13705 = 01h8sf >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #457 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 017j69; 01jt2w; *> query: (?x9131, 03bwzr4) <- school(?x7357, ?x9131), position(?x7357, ?x2010), major_field_of_study(?x9131, ?x2601) *> conf = 0.58 ranks of expected_values: 3 EVAL 02pptm institution! 03bwzr4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 165.000 165.000 0.891 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18156-01whg97 PRED entity: 01whg97 PRED relation: people! PRED expected values: 033tf_ => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 51): 0x67 (0.48 #85, 0.40 #2973, 0.37 #1073), 07bch9 (0.35 #1846, 0.11 #3193, 0.08 #2378), 041rx (0.25 #5401, 0.25 #1296, 0.25 #5249), 013xrm (0.25 #1843, 0.08 #1311, 0.06 #703), 06v41q (0.20 #28, 0.13 #1852, 0.08 #180), 038723 (0.20 #68, 0.04 #372, 0.04 #448), 03bkbh (0.19 #1855, 0.05 #2387, 0.04 #3224), 033tf_ (0.17 #1831, 0.15 #2363, 0.13 #5860), 02w7gg (0.11 #2358, 0.10 #6083, 0.10 #5855), 07hwkr (0.11 #2367, 0.09 #1683, 0.08 #1303) >> Best rule #85 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 01wmxfs; >> query: (?x8149, 0x67) <- artist(?x7089, ?x8149), ?x7089 = 0181dw, people(?x1816, ?x8149) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1831 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 221 *> proper extension: 071jv5; *> query: (?x8149, 033tf_) <- people(?x1816, ?x8149), people(?x1816, ?x5541), ?x5541 = 01pk3z *> conf = 0.17 ranks of expected_values: 8 EVAL 01whg97 people! 033tf_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 146.000 146.000 0.485 http://example.org/people/ethnicity/people #18155-09gffmz PRED entity: 09gffmz PRED relation: profession PRED expected values: 01d_h8 => 87 concepts (49 used for prediction) PRED predicted values (max 10 best out of 67): 01d_h8 (0.86 #1329, 0.85 #3828, 0.85 #3534), 02hrh1q (0.75 #3688, 0.73 #4718, 0.72 #4571), 02krf9 (0.48 #2671, 0.46 #613, 0.33 #3112), 018gz8 (0.35 #897, 0.16 #3984, 0.13 #4720), 012t_z (0.29 #453, 0.23 #159, 0.16 #1041), 0cbd2 (0.27 #7206, 0.21 #3241, 0.20 #3976), 0fj9f (0.19 #1082, 0.03 #347, 0.03 #494), 0dz3r (0.18 #149, 0.14 #443, 0.11 #6324), 01c72t (0.18 #169, 0.08 #6638, 0.08 #6344), 09jwl (0.17 #6339, 0.17 #6780, 0.17 #6633) >> Best rule #1329 for best value: >> intensional similarity = 4 >> extensional distance = 125 >> proper extension: 0q9kd; 0fvf9q; 0qf43; 014zcr; 042l3v; 054_mz; 03f2_rc; 0c1pj; 05kfs; 03_gd; ... >> query: (?x1712, 01d_h8) <- award(?x1712, ?x1307), nominated_for(?x1712, ?x430), ?x1307 = 0gq9h, profession(?x1712, ?x524) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09gffmz profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 49.000 0.858 http://example.org/people/person/profession #18154-0l30v PRED entity: 0l30v PRED relation: source PRED expected values: 0jbk9 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #11, 0.93 #38, 0.92 #12) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 0l2lk; >> query: (?x9275, 0jbk9) <- adjoins(?x9521, ?x9275), adjoins(?x1939, ?x9521), county_seat(?x9275, ?x7020), currency(?x9521, ?x170) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l30v source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.929 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #18153-0jn38 PRED entity: 0jn38 PRED relation: artist! PRED expected values: 03qy3l => 100 concepts (89 used for prediction) PRED predicted values (max 10 best out of 128): 015_1q (0.60 #155, 0.56 #1114, 0.44 #5913), 03rhqg (0.59 #2618, 0.59 #2207, 0.33 #288), 0mzkr (0.54 #4412, 0.33 #24, 0.29 #2217), 01cszh (0.49 #5630, 0.38 #2887, 0.16 #8640), 02bh8z (0.38 #979, 0.33 #1802, 0.29 #2076), 043g7l (0.34 #5925, 0.14 #578, 0.13 #8532), 0181dw (0.33 #40, 0.29 #451, 0.27 #2644), 0n85g (0.33 #60, 0.29 #471, 0.26 #3624), 025t8bv (0.33 #58, 0.20 #195, 0.17 #332), 011k1h (0.30 #7002, 0.30 #4533, 0.27 #1790) >> Best rule #155 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 07mvp; >> query: (?x8614, 015_1q) <- artist(?x8721, ?x8614), artist(?x6474, ?x8614), artist(?x8721, ?x13145), artist(?x8721, ?x12825), artist(?x8721, ?x11827), ?x11827 = 01wx756, ?x6474 = 0g768, group(?x227, ?x8614), ?x13145 = 0p8h0, award_winner(?x139, ?x12825) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2117 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 15 *> proper extension: 0150jk; 0dtd6; *> query: (?x8614, 03qy3l) <- artist(?x8721, ?x8614), artist(?x6474, ?x8614), artist(?x8721, ?x13145), artist(?x8721, ?x11827), artist(?x8721, ?x4675), ?x11827 = 01wx756, ?x6474 = 0g768, group(?x227, ?x8614), artists(?x671, ?x4675), origin(?x13145, ?x1523) *> conf = 0.24 ranks of expected_values: 16 EVAL 0jn38 artist! 03qy3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 100.000 89.000 0.600 http://example.org/music/record_label/artist #18152-03m8y5 PRED entity: 03m8y5 PRED relation: film! PRED expected values: 01l9p => 67 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1042): 01h8f (0.33 #11268, 0.02 #25756, 0.02 #27826), 081lh (0.32 #16554, 0.20 #22762, 0.20 #26902), 0mdqp (0.25 #12530, 0.25 #4254, 0.23 #18740), 0716t2 (0.25 #1896, 0.22 #12240, 0.20 #3964), 06m6p7 (0.25 #1358, 0.22 #11702, 0.20 #3426), 0gy6z9 (0.25 #562, 0.20 #2630, 0.11 #6767), 030hcs (0.25 #289, 0.20 #2357, 0.05 #14481), 06mfvc (0.25 #315, 0.20 #2383, 0.05 #14483), 0210hf (0.25 #842, 0.20 #2910, 0.03 #14482), 02t_st (0.25 #1278, 0.20 #3346, 0.02 #19900) >> Best rule #11268 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 06__m6; >> query: (?x2529, 01h8f) <- genre(?x2529, ?x53), film(?x643, ?x2529), film(?x986, ?x2529), film_crew_role(?x2529, ?x468), ?x643 = 044rvb, currency(?x2529, ?x170) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #56143 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 635 *> proper extension: 0gtsx8c; 07kb7vh; 07k2mq; 01gglm; *> query: (?x2529, 01l9p) <- language(?x2529, ?x254), film(?x3101, ?x2529), participant(?x709, ?x3101), ?x254 = 02h40lc, participant(?x1017, ?x3101) *> conf = 0.01 ranks of expected_values: 1019 EVAL 03m8y5 film! 01l9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 67.000 33.000 0.333 http://example.org/film/actor/film./film/performance/film #18151-031sg0 PRED entity: 031sg0 PRED relation: award PRED expected values: 0bdwft => 119 concepts (89 used for prediction) PRED predicted values (max 10 best out of 259): 09qvf4 (0.79 #1211, 0.75 #2419, 0.74 #2823), 0bfvw2 (0.79 #1211, 0.75 #2419, 0.74 #2823), 0ck27z (0.61 #1304, 0.26 #14587, 0.24 #6938), 09sb52 (0.51 #10105, 0.33 #2057, 0.31 #8094), 05pcn59 (0.40 #890, 0.38 #2502, 0.36 #2098), 05b4l5x (0.40 #814, 0.20 #2426, 0.20 #2022), 0cqhk0 (0.36 #5274, 0.19 #441, 0.17 #37), 09qj50 (0.33 #46, 0.31 #450, 0.17 #16506), 03c7tr1 (0.33 #867, 0.19 #5698, 0.18 #3285), 05p09zm (0.30 #932, 0.30 #2140, 0.28 #3350) >> Best rule #1211 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 01dw4q; 01pcq3; 01lbp; 01rh0w; 01gq0b; 026c1; 01vs_v8; 05dbf; 01vhb0; 04xrx; ... >> query: (?x9925, ?x375) <- award_winner(?x375, ?x9925), participant(?x7138, ?x9925), gender(?x9925, ?x514), ?x514 = 02zsn >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #1280 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 027r8p; *> query: (?x9925, 0bdwft) <- award(?x9925, ?x2041), ?x2041 = 0bdx29, nationality(?x9925, ?x94) *> conf = 0.22 ranks of expected_values: 21 EVAL 031sg0 award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 119.000 89.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18150-014635 PRED entity: 014635 PRED relation: influenced_by! PRED expected values: 03hnd 0gthm => 144 concepts (70 used for prediction) PRED predicted values (max 10 best out of 486): 01hb6v (0.50 #2626, 0.14 #22426, 0.11 #33012), 040db (0.36 #5146, 0.24 #5655, 0.21 #14282), 0683n (0.33 #4897, 0.21 #5405, 0.16 #17081), 0d_w7 (0.33 #460, 0.05 #6549, 0.02 #13654), 0n6kf (0.29 #5261, 0.17 #4753, 0.13 #29448), 05jm7 (0.27 #14855, 0.13 #30468, 0.13 #25519), 014ps4 (0.25 #2839, 0.24 #5886, 0.20 #1825), 07dnx (0.25 #4919, 0.20 #1875, 0.14 #5427), 07lp1 (0.25 #2945, 0.20 #1931, 0.13 #29448), 0d4jl (0.25 #2649, 0.18 #5696, 0.14 #5187) >> Best rule #2626 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0hky; >> query: (?x3969, 01hb6v) <- influenced_by(?x5346, ?x3969), profession(?x3969, ?x353), ?x5346 = 049gc >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #32501 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 200 *> proper extension: 0fpzzp; *> query: (?x3969, ?x2485) <- influenced_by(?x5336, ?x3969), peers(?x6163, ?x5336), influenced_by(?x5336, ?x5434), influenced_by(?x2485, ?x5434) *> conf = 0.08 ranks of expected_values: 187, 222 EVAL 014635 influenced_by! 0gthm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 144.000 70.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 014635 influenced_by! 03hnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 144.000 70.000 0.500 http://example.org/influence/influence_node/influenced_by #18149-07sp4l PRED entity: 07sp4l PRED relation: language PRED expected values: 02h40lc => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 45): 02h40lc (0.88 #1850, 0.88 #1611, 0.88 #3701), 04306rv (0.14 #5, 0.11 #418, 0.09 #597), 02bjrlw (0.14 #1, 0.06 #414, 0.06 #1610), 064_8sq (0.14 #972, 0.14 #1392, 0.13 #1213), 06nm1 (0.12 #663, 0.11 #722, 0.10 #781), 04h9h (0.10 #102, 0.08 #161, 0.05 #3282), 097kp (0.10 #112, 0.08 #171, 0.02 #230), 0t_2 (0.10 #73, 0.08 #132, 0.01 #606), 06b_j (0.08 #200, 0.08 #141, 0.06 #436), 012w70 (0.08 #131, 0.05 #3282, 0.03 #665) >> Best rule #1850 for best value: >> intensional similarity = 4 >> extensional distance = 1145 >> proper extension: 07kb7vh; >> query: (?x3063, 02h40lc) <- nominated_for(?x154, ?x3063), film(?x3181, ?x3063), award_winner(?x629, ?x3181), award_nominee(?x3181, ?x230) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07sp4l language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.885 http://example.org/film/film/language #18148-015t56 PRED entity: 015t56 PRED relation: award_nominee PRED expected values: 01mqc_ => 72 concepts (32 used for prediction) PRED predicted values (max 10 best out of 513): 0z4s (0.81 #43823, 0.80 #66893, 0.03 #25451), 08swgx (0.81 #43823, 0.80 #66893, 0.01 #26002), 02fgm7 (0.76 #66894, 0.76 #73816, 0.75 #62279), 015t56 (0.53 #601, 0.18 #73817, 0.16 #32291), 0dvmd (0.18 #73817, 0.16 #32291, 0.16 #29984), 01yfm8 (0.18 #73817, 0.16 #32291, 0.16 #29984), 0dlglj (0.18 #73817, 0.16 #32291, 0.16 #29984), 018ygt (0.18 #73817, 0.16 #32291, 0.16 #29984), 03_wj_ (0.18 #73817, 0.16 #32291, 0.16 #29984), 0blbxk (0.18 #73817, 0.16 #32291, 0.16 #29984) >> Best rule #43823 for best value: >> intensional similarity = 2 >> extensional distance = 1236 >> proper extension: 02_hj4; 029_3; 0k8y7; 02lymt; 01wbsdz; 04gr35; 06cl2w; >> query: (?x2762, ?x221) <- award_nominee(?x221, ?x2762), film(?x2762, ?x972) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #24717 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1049 *> proper extension: 07nznf; 0l8v5; 05kfs; 02ndbd; 0yfp; 05_k56; 0bg539; 08hp53; 01t2h2; 01vb403; ... *> query: (?x2762, 01mqc_) <- award_nominee(?x2762, ?x221), film(?x2762, ?x972), nominated_for(?x2762, ?x4610) *> conf = 0.02 ranks of expected_values: 193 EVAL 015t56 award_nominee 01mqc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 72.000 32.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18147-087qxp PRED entity: 087qxp PRED relation: profession PRED expected values: 0dxtg => 87 concepts (66 used for prediction) PRED predicted values (max 10 best out of 58): 0dxtg (0.87 #1345, 0.84 #1641, 0.84 #1789), 03gjzk (0.74 #1347, 0.71 #1051, 0.68 #1791), 02hrh1q (0.70 #2530, 0.69 #1198, 0.68 #4159), 01d_h8 (0.40 #1486, 0.34 #1042, 0.33 #1338), 02jknp (0.38 #1487, 0.22 #155, 0.22 #5929), 0n1h (0.34 #8738, 0.30 #603, 0.22 #899), 0kyk (0.30 #2398, 0.20 #1510, 0.18 #326), 018gz8 (0.29 #17, 0.28 #6367, 0.21 #1349), 025352 (0.28 #6367, 0.14 #60, 0.06 #504), 015cjr (0.28 #6367, 0.11 #198, 0.09 #346) >> Best rule #1345 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 05g8ky; 04bs3j; 04n7njg; 013cr; 03ft8; 02jm0n; 01wyzyl; 0jt90f5; 03m_k0; 0glmv; ... >> query: (?x7583, 0dxtg) <- student(?x5638, ?x7583), tv_program(?x7583, ?x3626), profession(?x7583, ?x353) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087qxp profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 66.000 0.867 http://example.org/people/person/profession #18146-02r1c18 PRED entity: 02r1c18 PRED relation: film_release_region PRED expected values: 0154j 0chghy 015fr 0k6nt 0345h 03h64 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 137): 0chghy (0.86 #605, 0.85 #2247, 0.81 #1949), 0k6nt (0.84 #618, 0.83 #1962, 0.83 #469), 0345h (0.83 #627, 0.83 #2269, 0.79 #3764), 03h64 (0.83 #657, 0.78 #2299, 0.78 #508), 015fr (0.82 #610, 0.79 #2252, 0.73 #2103), 0154j (0.80 #2241, 0.78 #599, 0.71 #2092), 06bnz (0.74 #2279, 0.73 #637, 0.69 #2130), 05v8c (0.73 #609, 0.59 #2251, 0.58 #1953), 0ctw_b (0.64 #619, 0.54 #1068, 0.54 #2261), 016wzw (0.56 #658, 0.53 #509, 0.47 #1107) >> Best rule #605 for best value: >> intensional similarity = 5 >> extensional distance = 92 >> proper extension: 0h1cdwq; 0gx9rvq; 0401sg; 0jjy0; 0gj8t_b; 03bx2lk; 045j3w; 06w839_; 0gj8nq2; 024mpp; ... >> query: (?x1535, 0chghy) <- currency(?x1535, ?x170), film_release_region(?x1535, ?x2513), film_release_region(?x1535, ?x429), ?x2513 = 05b4w, ?x429 = 03rt9 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6 EVAL 02r1c18 film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02r1c18 film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02r1c18 film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02r1c18 film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02r1c18 film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02r1c18 film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18145-03c5f7l PRED entity: 03c5f7l PRED relation: type_of_union PRED expected values: 04ztj => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #41, 0.74 #53, 0.71 #141), 01g63y (0.14 #62, 0.14 #78, 0.14 #74) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 785 >> proper extension: 04rs03; 01g4zr; 03pvt; 02xfrd; 0674cw; 0h005; 0fr7nt; 01bzr4; 036jp8; 03ftmg; ... >> query: (?x9796, 04ztj) <- award(?x9796, ?x2252), profession(?x9796, ?x319), ?x319 = 01d_h8 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03c5f7l type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.742 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18144-0347db PRED entity: 0347db PRED relation: type_of_union PRED expected values: 01g63y => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.80 #17, 0.80 #13, 0.79 #21), 01g63y (0.20 #54, 0.20 #50, 0.16 #42) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 03q1vd; >> query: (?x7117, 04ztj) <- nominated_for(?x7117, ?x5060), award(?x7117, ?x693), special_performance_type(?x7117, ?x4832) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #54 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 347 *> proper extension: 01r42_g; 0m2wm; 02zq43; 03m8lq; 01j5x6; 08m4c8; 04smkr; 05wjnt; 04b19t; 02xbw2; ... *> query: (?x7117, 01g63y) <- nominated_for(?x7117, ?x5060), profession(?x7117, ?x319), languages(?x7117, ?x254) *> conf = 0.20 ranks of expected_values: 2 EVAL 0347db type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.798 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18143-0mlzk PRED entity: 0mlzk PRED relation: contains! PRED expected values: 081yw => 137 concepts (82 used for prediction) PRED predicted values (max 10 best out of 166): 081yw (0.69 #64705, 0.67 #67400, 0.61 #14370), 01n7q (0.66 #1875, 0.60 #8163, 0.59 #42306), 0mlzk (0.48 #73690, 0.41 #28745, 0.41 #22455), 09c7w0 (0.47 #7190, 0.47 #52117, 0.47 #42231), 04_1l0v (0.22 #39082, 0.22 #48068, 0.22 #48969), 06pvr (0.22 #1963, 0.21 #8251, 0.18 #13637), 05kj_ (0.17 #8126, 0.15 #13512, 0.14 #1838), 059rby (0.15 #55731, 0.14 #64725, 0.14 #63825), 07ssc (0.12 #65634, 0.12 #3625, 0.12 #54844), 05kkh (0.11 #4500, 0.10 #10788, 0.09 #16178) >> Best rule #64705 for best value: >> intensional similarity = 5 >> extensional distance = 241 >> proper extension: 0d6lp; 0fr61; 0mwxl; 0fkhz; 0ms6_; 0mww2; 0j_1v; 0l2nd; 0mw_q; >> query: (?x11569, ?x4600) <- second_level_divisions(?x94, ?x11569), adjoins(?x11569, ?x11525), ?x94 = 09c7w0, currency(?x11525, ?x170), contains(?x4600, ?x11525) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mlzk contains! 081yw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 82.000 0.686 http://example.org/location/location/contains #18142-01c7y PRED entity: 01c7y PRED relation: countries_spoken_in PRED expected values: 03rk0 => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 224): 03rk0 (0.80 #3118, 0.80 #2992, 0.75 #2932), 0162b (0.73 #5139, 0.60 #3669, 0.58 #732), 0bq0p9 (0.73 #3117, 0.56 #916, 0.55 #2933), 07ytt (0.60 #1811, 0.57 #2543, 0.50 #3464), 01ppq (0.60 #1806, 0.50 #1988, 0.50 #1073), 0697s (0.50 #2089, 0.50 #992, 0.43 #2457), 0162v (0.50 #2620, 0.50 #972, 0.43 #2437), 0hzlz (0.50 #2774, 0.43 #2407, 0.40 #2959), 04hhv (0.50 #1062, 0.40 #1795, 0.33 #1977), 01nln (0.50 #1048, 0.40 #1781, 0.33 #1963) >> Best rule #3118 for best value: >> intensional similarity = 12 >> extensional distance = 8 >> proper extension: 09bnf; >> query: (?x11341, ?x2146) <- languages(?x6961, ?x11341), gender(?x6961, ?x231), people(?x6725, ?x6961), type_of_union(?x6961, ?x566), nationality(?x6961, ?x2146), nationality(?x6961, ?x613), combatants(?x612, ?x613), capital(?x613, ?x8297), combatants(?x613, ?x94), official_language(?x613, ?x254), religion(?x6961, ?x8967), ?x2146 = 03rk0 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c7y countries_spoken_in 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.800 http://example.org/language/human_language/countries_spoken_in #18141-08_vwq PRED entity: 08_vwq PRED relation: award! PRED expected values: 02wgln 01bmlb => 42 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2040): 0170pk (0.71 #13816, 0.70 #20505, 0.60 #10472), 017149 (0.71 #13491, 0.60 #20180, 0.60 #10147), 03ym1 (0.71 #15046, 0.60 #11702, 0.50 #21735), 01v6480 (0.68 #43490, 0.68 #36795, 0.68 #43489), 0121rx (0.68 #43490, 0.68 #36795, 0.68 #43489), 039xcr (0.68 #36795, 0.68 #43489, 0.67 #30103), 018ygt (0.60 #21906, 0.57 #15217, 0.50 #5183), 016ggh (0.60 #13104, 0.57 #16448, 0.50 #23137), 048lv (0.60 #10366, 0.57 #13710, 0.50 #20399), 09y20 (0.60 #10413, 0.57 #13757, 0.50 #20446) >> Best rule #13816 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 027dtxw; 09sdmz; >> query: (?x6878, 0170pk) <- award(?x3701, ?x6878), award(?x2033, ?x6878), ?x3701 = 016fjj, ceremony(?x6878, ?x4141), award_nominee(?x2033, ?x262) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #13876 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 027dtxw; 09sdmz; *> query: (?x6878, 02wgln) <- award(?x3701, ?x6878), award(?x2033, ?x6878), ?x3701 = 016fjj, ceremony(?x6878, ?x4141), award_nominee(?x2033, ?x262) *> conf = 0.57 ranks of expected_values: 20, 281 EVAL 08_vwq award! 01bmlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 17.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 08_vwq award! 02wgln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 42.000 17.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18140-06mt91 PRED entity: 06mt91 PRED relation: award PRED expected values: 01by1l => 110 concepts (94 used for prediction) PRED predicted values (max 10 best out of 269): 03t5n3 (0.74 #21400, 0.71 #26461, 0.70 #29966), 02g3gj (0.74 #21400, 0.71 #26461, 0.70 #29966), 01by1l (0.41 #4778, 0.35 #7112, 0.34 #4000), 01c99j (0.39 #605, 0.29 #1772, 0.25 #994), 09sb52 (0.33 #11321, 0.33 #5485, 0.32 #13657), 01c427 (0.25 #860, 0.24 #1638, 0.24 #2027), 02f72_ (0.25 #996, 0.23 #218, 0.18 #1774), 05pcn59 (0.23 #11361, 0.22 #9026, 0.22 #13697), 05p09zm (0.23 #121, 0.21 #899, 0.20 #6734), 02f777 (0.23 #295, 0.21 #1073, 0.18 #1851) >> Best rule #21400 for best value: >> intensional similarity = 2 >> extensional distance = 745 >> proper extension: 0kc6x; 065y4w7; 01y67v; 0c8br; 03mv0b; 099ks0; 01j7pt; 02p10m; 0kctd; 01fkr_; ... >> query: (?x6835, ?x154) <- award_winner(?x154, ?x6835), category(?x6835, ?x134) >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #4778 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 126 *> proper extension: 07s3vqk; 01vrx3g; 032nwy; 06cc_1; 03f5spx; 01gf5h; 0b68vs; 01k5t_3; 01wdqrx; 01wcp_g; ... *> query: (?x6835, 01by1l) <- artists(?x3319, ?x6835), ?x3319 = 06j6l, award_nominee(?x6835, ?x140) *> conf = 0.41 ranks of expected_values: 3 EVAL 06mt91 award 01by1l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 94.000 0.739 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18139-06fqlk PRED entity: 06fqlk PRED relation: prequel PRED expected values: 02b61v => 105 concepts (61 used for prediction) PRED predicted values (max 10 best out of 57): 035w2k (0.17 #97, 0.10 #822), 0d6_s (0.12 #532, 0.03 #1620, 0.03 #1802), 06z8s_ (0.12 #376, 0.03 #1464, 0.03 #1646), 08fn5b (0.07 #981), 0233bn (0.06 #1771, 0.06 #1953, 0.05 #2135), 01vksx (0.04 #1102, 0.03 #1465, 0.03 #1283), 01qb5d (0.04 #1103, 0.03 #1284, 0.02 #2195), 042g97 (0.04 #1267, 0.03 #1448, 0.02 #2359), 042fgh (0.04 #1221, 0.03 #1402, 0.02 #2313), 02d478 (0.04 #1160, 0.03 #1341, 0.02 #2252) >> Best rule #97 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0f2sx4; >> query: (?x6489, 035w2k) <- film_crew_role(?x6489, ?x137), film(?x4667, ?x6489), ?x4667 = 032zg9, featured_film_locations(?x6489, ?x1646), country(?x6489, ?x94) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06fqlk prequel 02b61v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 61.000 0.167 http://example.org/film/film/prequel #18138-06dfg PRED entity: 06dfg PRED relation: administrative_parent PRED expected values: 02j71 => 80 concepts (73 used for prediction) PRED predicted values (max 10 best out of 29): 02j71 (0.86 #148, 0.86 #4113, 0.86 #3838), 0dg3n1 (0.22 #7409, 0.18 #7551, 0.16 #8519), 09c7w0 (0.15 #6581, 0.14 #818, 0.14 #6719), 03rjj (0.07 #4794, 0.02 #6168, 0.02 #7968), 0345h (0.05 #6190, 0.03 #7438, 0.03 #7298), 07ssc (0.04 #967, 0.04 #1103, 0.03 #691), 059rby (0.03 #7279, 0.03 #7419, 0.03 #7832), 04gqr (0.03 #9635, 0.03 #9775, 0.02 #607), 06s_2 (0.03 #9635), 06srk (0.03 #9635) >> Best rule #148 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 05g2v; >> query: (?x7035, 02j71) <- contains(?x2467, ?x7035), ?x2467 = 0dg3n1, taxonomy(?x7035, ?x939) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06dfg administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 73.000 0.865 http://example.org/base/aareas/schema/administrative_area/administrative_parent #18137-02bxjp PRED entity: 02bxjp PRED relation: type_of_union PRED expected values: 04ztj => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.84 #13, 0.78 #49, 0.78 #57), 01g63y (0.33 #266, 0.25 #323, 0.23 #6) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 0dky9n; 01d494; 0hwd8; 04n_g; 04107; 03bw6; 0gm34; 012gbb; 0jvtp; 014g91; ... >> query: (?x5322, 04ztj) <- nationality(?x5322, ?x252), people(?x11064, ?x5322), award_winner(?x372, ?x5322) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bxjp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.836 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18136-04h4c9 PRED entity: 04h4c9 PRED relation: film_crew_role PRED expected values: 0dxtw => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 28): 0ch6mp2 (0.83 #401, 0.82 #586, 0.81 #36), 0215hd (0.62 #107, 0.58 #16, 0.22 #1660), 0dxtw (0.50 #40, 0.49 #312, 0.43 #711), 015h31 (0.45 #7, 0.19 #310, 0.16 #709), 01xy5l_ (0.39 #11, 0.32 #102, 0.22 #1660), 02ynfr (0.32 #715, 0.22 #1660, 0.21 #594), 02rh1dz (0.22 #1660, 0.22 #39, 0.17 #311), 089fss (0.22 #1660, 0.10 #706, 0.09 #1535), 04pyp5 (0.22 #1660, 0.09 #1535, 0.09 #1993), 06qc5 (0.22 #1660, 0.09 #1535, 0.09 #1993) >> Best rule #401 for best value: >> intensional similarity = 4 >> extensional distance = 389 >> proper extension: 0hgnl3t; >> query: (?x8670, 0ch6mp2) <- film_crew_role(?x8670, ?x1171), ?x1171 = 09vw2b7, country(?x8670, ?x94), music(?x8670, ?x9891) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 0gmgwnv; *> query: (?x8670, 0dxtw) <- genre(?x8670, ?x53), nominated_for(?x2393, ?x8670), films(?x5503, ?x8670), ?x2393 = 02x258x *> conf = 0.50 ranks of expected_values: 3 EVAL 04h4c9 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 66.000 0.834 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18135-0k4gf PRED entity: 0k4gf PRED relation: profession PRED expected values: 01c72t 05vyk => 226 concepts (189 used for prediction) PRED predicted values (max 10 best out of 108): 09jwl (0.90 #19878, 0.72 #2704, 0.72 #9867), 01c72t (0.90 #14650, 0.89 #2559, 0.89 #1366), 02hrh1q (0.81 #26294, 0.72 #20621, 0.72 #20023), 0cbd2 (0.57 #5676, 0.51 #7915, 0.50 #1946), 0nbcg (0.57 #21236, 0.53 #7640, 0.52 #2269), 016z4k (0.48 #5076, 0.45 #10150, 0.43 #16124), 0dxtg (0.41 #15984, 0.40 #17032, 0.40 #908), 0dz3r (0.41 #7610, 0.40 #19861, 0.40 #5074), 0kyk (0.40 #6891, 0.38 #6742, 0.38 #5699), 05vyk (0.38 #840, 0.36 #2630, 0.33 #1437) >> Best rule #19878 for best value: >> intensional similarity = 4 >> extensional distance = 529 >> proper extension: 02z4b_8; >> query: (?x1211, 09jwl) <- profession(?x1211, ?x563), artists(?x597, ?x1211), profession(?x5125, ?x563), ?x5125 = 0149xx >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #14650 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 303 *> proper extension: 05mxw33; *> query: (?x1211, 01c72t) <- profession(?x1211, ?x563), gender(?x1211, ?x231), profession(?x10203, ?x563), ?x10203 = 02r38 *> conf = 0.90 ranks of expected_values: 2, 10 EVAL 0k4gf profession 05vyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 226.000 189.000 0.902 http://example.org/people/person/profession EVAL 0k4gf profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 226.000 189.000 0.902 http://example.org/people/person/profession #18134-03b12 PRED entity: 03b12 PRED relation: contains! PRED expected values: 09c7w0 => 136 concepts (101 used for prediction) PRED predicted values (max 10 best out of 290): 09c7w0 (0.82 #42938, 0.76 #60825, 0.73 #73348), 03rk0 (0.50 #4606, 0.49 #8183, 0.46 #14446), 02qkt (0.41 #10182, 0.41 #13760, 0.40 #6604), 07h34 (0.28 #90352, 0.26 #87664, 0.25 #1124), 0msyb (0.28 #90352, 0.26 #87664, 0.25 #1556), 0kpys (0.28 #90352, 0.26 #87664, 0.20 #1968), 0gx1l (0.28 #90352, 0.26 #87664, 0.20 #2392), 04_1l0v (0.28 #90352, 0.26 #87664, 0.14 #35332), 04tgp (0.28 #90352, 0.25 #1173, 0.06 #26219), 0j0k (0.28 #90352, 0.16 #13791, 0.15 #10213) >> Best rule #42938 for best value: >> intensional similarity = 4 >> extensional distance = 323 >> proper extension: 0345_; >> query: (?x10584, 09c7w0) <- contains(?x11129, ?x10584), place_of_birth(?x2274, ?x10584), time_zones(?x11129, ?x1638), ?x1638 = 02fqwt >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03b12 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 101.000 0.825 http://example.org/location/location/contains #18133-026xxv_ PRED entity: 026xxv_ PRED relation: team! PRED expected values: 05g_nr 0b_6qj 0bzrsh 0b_6_l => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 7): 0b_6_l (0.86 #83, 0.83 #69, 0.80 #62), 0b_77q (0.78 #50, 0.75 #71, 0.75 #64), 0f9rw9 (0.59 #96, 0.57 #82, 0.56 #54), 0bzrsh (0.58 #74, 0.57 #88, 0.57 #81), 05g_nr (0.57 #114, 0.53 #93, 0.50 #86), 0b_6qj (0.52 #115, 0.43 #87, 0.42 #73), 0b_734 (0.50 #91, 0.50 #77, 0.50 #42) >> Best rule #83 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 027yf83; >> query: (?x8728, 0b_6_l) <- team(?x6802, ?x8728), team(?x4803, ?x8728), ?x6802 = 0br1x_, locations(?x4803, ?x2254), ?x2254 = 0dclg >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 5, 6 EVAL 026xxv_ team! 0b_6_l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.857 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 026xxv_ team! 0bzrsh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 52.000 52.000 0.857 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 026xxv_ team! 0b_6qj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 52.000 52.000 0.857 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 026xxv_ team! 05g_nr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 52.000 52.000 0.857 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #18132-01s7w3 PRED entity: 01s7w3 PRED relation: nominated_for! PRED expected values: 05b4l5x => 99 concepts (96 used for prediction) PRED predicted values (max 10 best out of 209): 09qv_s (0.43 #114, 0.12 #14520, 0.11 #21662), 0gq9h (0.30 #8154, 0.30 #9106, 0.29 #11487), 0gq_v (0.29 #2400, 0.22 #1210, 0.22 #8112), 099c8n (0.29 #56, 0.24 #3150, 0.23 #3388), 0f4x7 (0.29 #25, 0.19 #6689, 0.18 #9784), 099ck7 (0.29 #177, 0.11 #21662, 0.09 #3509), 0k611 (0.28 #2453, 0.24 #3405, 0.22 #8165), 019f4v (0.28 #1243, 0.26 #1957, 0.26 #3385), 0l8z1 (0.28 #1241, 0.22 #1479, 0.22 #1955), 0gs9p (0.27 #9823, 0.26 #8156, 0.26 #11489) >> Best rule #114 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02czd5; >> query: (?x9154, 09qv_s) <- nominated_for(?x495, ?x9154), nominated_for(?x500, ?x9154), ?x495 = 0c4f4 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1910 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 134 *> proper extension: 02pg45; *> query: (?x9154, 05b4l5x) <- film(?x2557, ?x9154), nominated_for(?x500, ?x9154), edited_by(?x9154, ?x4215), type_of_union(?x2557, ?x566) *> conf = 0.11 ranks of expected_values: 107 EVAL 01s7w3 nominated_for! 05b4l5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 99.000 96.000 0.429 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18131-026l37 PRED entity: 026l37 PRED relation: film PRED expected values: 06_wqk4 0_816 => 88 concepts (44 used for prediction) PRED predicted values (max 10 best out of 692): 0prhz (0.17 #2578, 0.13 #4364, 0.10 #6150), 0fpmrm3 (0.17 #2210, 0.13 #3996, 0.10 #5782), 0qm8b (0.17 #2030, 0.10 #5602, 0.08 #7388), 06lpmt (0.17 #682, 0.01 #11398, 0.01 #16756), 0dw4b0 (0.17 #1641, 0.01 #12357), 01srq2 (0.17 #1261, 0.01 #11977), 02nt3d (0.17 #1079, 0.01 #11795), 09p3_s (0.17 #949, 0.01 #11665), 0gtvpkw (0.17 #562, 0.01 #11278), 07yvsn (0.17 #555, 0.01 #11271) >> Best rule #2578 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 06jzh; >> query: (?x4580, 0prhz) <- award_nominee(?x5133, ?x4580), award_nominee(?x2616, ?x4580), ?x2616 = 05th8t, ?x5133 = 053y4h >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #16201 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 238 *> proper extension: 0gcs9; 0kvqv; 037hgm; 07g7h2; 036dyy; 0227vl; 01g969; *> query: (?x4580, 06_wqk4) <- currency(?x4580, ?x170), nominated_for(?x4580, ?x2973), award_nominee(?x4580, ?x539) *> conf = 0.02 ranks of expected_values: 249 EVAL 026l37 film 0_816 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 44.000 0.167 http://example.org/film/actor/film./film/performance/film EVAL 026l37 film 06_wqk4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 88.000 44.000 0.167 http://example.org/film/actor/film./film/performance/film #18130-02bqvs PRED entity: 02bqvs PRED relation: country PRED expected values: 09c7w0 => 60 concepts (49 used for prediction) PRED predicted values (max 10 best out of 21): 09c7w0 (0.82 #248, 0.81 #431, 0.81 #1592), 0345h (0.39 #62, 0.38 #124, 0.11 #213), 07ssc (0.21 #1179, 0.21 #2156, 0.21 #2341), 0f8l9c (0.11 #20, 0.09 #1304, 0.09 #2220), 03rjj (0.05 #69, 0.05 #7, 0.03 #680), 0d060g (0.05 #9, 0.04 #1293, 0.04 #2209), 0chghy (0.04 #198, 0.04 #381, 0.04 #75), 03_3d (0.04 #315, 0.03 #1964, 0.03 #2822), 0d05w3 (0.03 #44, 0.02 #106, 0.02 #2244), 03h64 (0.02 #1514, 0.02 #1209, 0.02 #47) >> Best rule #248 for best value: >> intensional similarity = 3 >> extensional distance = 312 >> proper extension: 03bx2lk; 04tz52; 08k40m; 033pf1; 01lbcqx; 058kh7; 03wy8t; 0199wf; >> query: (?x8790, 09c7w0) <- genre(?x8790, ?x258), music(?x8790, ?x13700), ?x258 = 05p553 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bqvs country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 49.000 0.815 http://example.org/film/film/country #18129-02rh1dz PRED entity: 02rh1dz PRED relation: film_crew_role! PRED expected values: 07gp9 04fzfj 044g_k 0fpv_3_ 0bv8h2 05c9zr 07_fj54 03cd0x 0j8f09z => 31 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1747): 0bth54 (0.86 #17596, 0.78 #15256, 0.73 #19937), 04jpg2p (0.79 #19698, 0.75 #13848, 0.75 #12678), 05qbckf (0.78 #15422, 0.71 #18933, 0.71 #9573), 05pdd86 (0.78 #15928, 0.62 #11249, 0.60 #20609), 01f6x7 (0.78 #14664, 0.62 #12324, 0.60 #6476), 0p9lw (0.78 #14132, 0.60 #5944, 0.60 #4774), 09gmmt6 (0.78 #15984, 0.60 #6627, 0.57 #10135), 028cg00 (0.75 #13073, 0.75 #11903, 0.60 #7224), 05pbl56 (0.75 #10696, 0.71 #9526, 0.67 #20056), 0fdv3 (0.75 #13062, 0.71 #8382, 0.67 #14232) >> Best rule #17596 for best value: >> intensional similarity = 21 >> extensional distance = 12 >> proper extension: 0n1h; >> query: (?x2091, 0bth54) <- film_crew_role(?x7672, ?x2091), film_crew_role(?x7480, ?x2091), film_crew_role(?x5277, ?x2091), film_crew_role(?x4810, ?x2091), film_crew_role(?x4684, ?x2091), film_crew_role(?x1386, ?x2091), film_crew_role(?x508, ?x2091), nominated_for(?x844, ?x4810), films(?x326, ?x4810), film(?x250, ?x5277), film_release_region(?x4684, ?x87), ?x7672 = 07f_t4, featured_film_locations(?x4684, ?x726), film_release_region(?x1386, ?x421), featured_film_locations(?x4810, ?x5036), film(?x5636, ?x508), category(?x7480, ?x134), film(?x1207, ?x7480), titles(?x1403, ?x4684), featured_film_locations(?x5277, ?x739), nominated_for(?x669, ?x1386) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #12934 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 6 *> proper extension: 02ynfr; *> query: (?x2091, 04fzfj) <- film_crew_role(?x7480, ?x2091), film_crew_role(?x5704, ?x2091), film_crew_role(?x4810, ?x2091), film_crew_role(?x2471, ?x2091), film_crew_role(?x708, ?x2091), film_crew_role(?x508, ?x2091), ?x7480 = 02vjp3, nominated_for(?x989, ?x508), nominated_for(?x3410, ?x708), titles(?x162, ?x4810), nominated_for(?x154, ?x508), language(?x4810, ?x254), film_distribution_medium(?x4810, ?x2099), country(?x4810, ?x390), titles(?x571, ?x708), film(?x844, ?x4810), ?x3410 = 02bh9, ?x5704 = 0h95zbp, film_release_region(?x2471, ?x151), ?x844 = 03h_9lg, ?x151 = 0b90_r *> conf = 0.75 ranks of expected_values: 31, 83, 195, 197, 284, 398, 399, 515, 949 EVAL 02rh1dz film_crew_role! 0j8f09z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02rh1dz film_crew_role! 03cd0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 31.000 19.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02rh1dz film_crew_role! 07_fj54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 31.000 19.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02rh1dz film_crew_role! 05c9zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 31.000 19.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02rh1dz film_crew_role! 0bv8h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 19.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02rh1dz film_crew_role! 0fpv_3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02rh1dz film_crew_role! 044g_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 31.000 19.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02rh1dz film_crew_role! 04fzfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 31.000 19.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02rh1dz film_crew_role! 07gp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 19.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18128-0993r PRED entity: 0993r PRED relation: location PRED expected values: 0r540 0s9b_ => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 240): 030qb3t (0.40 #2495, 0.34 #13751, 0.30 #6515), 01n7q (0.33 #63, 0.20 #2475, 0.17 #4887), 05kkh (0.33 #7, 0.06 #4831, 0.05 #5635), 02_286 (0.25 #16117, 0.25 #14509, 0.24 #13705), 04jpl (0.22 #8057, 0.17 #821, 0.14 #3233), 06y9v (0.17 #960, 0.14 #3372, 0.12 #4176), 0nbrp (0.17 #1463, 0.10 #2267, 0.07 #3875), 06q1r (0.17 #1114, 0.10 #1918, 0.07 #3526), 02cft (0.17 #1111, 0.10 #1915, 0.07 #3523), 01dzq6 (0.17 #1390, 0.04 #8626, 0.04 #9430) >> Best rule #2495 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02_wxh; >> query: (?x3034, 030qb3t) <- person(?x3480, ?x3034), languages(?x3034, ?x3592), program(?x3034, ?x2583) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0993r location 0s9b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 138.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0993r location 0r540 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 138.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #18127-02725hs PRED entity: 02725hs PRED relation: films! PRED expected values: 081pw => 130 concepts (34 used for prediction) PRED predicted values (max 10 best out of 96): 081pw (0.40 #3, 0.22 #3152, 0.19 #2206), 01w1sx (0.20 #90, 0.09 #878, 0.08 #404), 0fzyg (0.20 #53, 0.07 #524, 0.06 #2098), 07jq_ (0.14 #552, 0.11 #236, 0.04 #869), 07yjb (0.11 #219, 0.07 #695, 0.07 #535), 0ddct (0.11 #242, 0.07 #558, 0.02 #1659), 05489 (0.11 #1309, 0.05 #3516, 0.05 #4620), 07_nf (0.09 #2269, 0.07 #3215, 0.02 #3846), 02_h0 (0.08 #1044, 0.07 #1357, 0.06 #1988), 0jrg (0.08 #420, 0.07 #737, 0.04 #1207) >> Best rule #3 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 02scbv; >> query: (?x2289, 081pw) <- titles(?x162, ?x2289), nominated_for(?x2288, ?x2289), production_companies(?x2289, ?x1104), genre(?x2289, ?x3515), ?x3515 = 082gq, nominated_for(?x1007, ?x2288) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02725hs films! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 34.000 0.400 http://example.org/film/film_subject/films #18126-05prs8 PRED entity: 05prs8 PRED relation: executive_produced_by! PRED expected values: 034b6k => 100 concepts (70 used for prediction) PRED predicted values (max 10 best out of 193): 02phtzk (0.10 #6308, 0.05 #13152, 0.04 #10515), 08720 (0.10 #6308, 0.05 #13152, 0.04 #10515), 0ds11z (0.10 #6308, 0.04 #10515, 0.04 #11043), 0y_hb (0.10 #6308, 0.04 #10515, 0.04 #11043), 016dj8 (0.10 #6308, 0.04 #10514, 0.04 #11042), 0322yj (0.03 #3157, 0.03 #3156, 0.03 #2105), 0gm2_0 (0.03 #3157, 0.03 #3156, 0.03 #2105), 01flv_ (0.03 #3157, 0.03 #3156, 0.03 #2105), 011wtv (0.03 #3157, 0.03 #3156, 0.03 #2105), 03h4fq7 (0.03 #3157, 0.03 #3156, 0.03 #2105) >> Best rule #6308 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 05fyss; 01rrd4; 01my_c; 03mstc; >> query: (?x1533, ?x485) <- nominated_for(?x1533, ?x485), award_nominee(?x1532, ?x1533), executive_produced_by(?x1077, ?x1533) >> conf = 0.10 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 05prs8 executive_produced_by! 034b6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 70.000 0.097 http://example.org/film/film/executive_produced_by #18125-02ccqg PRED entity: 02ccqg PRED relation: institution! PRED expected values: 019v9k => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 21): 019v9k (0.68 #318, 0.64 #409, 0.62 #230), 02_xgp2 (0.55 #413, 0.54 #234, 0.51 #167), 0bkj86 (0.52 #229, 0.51 #251, 0.46 #162), 016t_3 (0.50 #3, 0.46 #225, 0.46 #247), 03bwzr4 (0.49 #415, 0.44 #236, 0.43 #169), 07s6fsf (0.36 #402, 0.34 #223, 0.34 #156), 04zx3q1 (0.35 #224, 0.33 #68, 0.29 #335), 013zdg (0.34 #161, 0.33 #72, 0.29 #1865), 01rr_d (0.30 #82, 0.29 #1865, 0.25 #171), 027f2w (0.29 #164, 0.29 #1865, 0.25 #231) >> Best rule #318 for best value: >> intensional similarity = 4 >> extensional distance = 135 >> proper extension: 01b1pf; >> query: (?x3182, 019v9k) <- major_field_of_study(?x3182, ?x2981), organization(?x346, ?x3182), institution(?x865, ?x3182), ?x2981 = 02j62 >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ccqg institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.679 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18124-08z0wx PRED entity: 08z0wx PRED relation: parent_genre PRED expected values: 0172rj => 43 concepts (37 used for prediction) PRED predicted values (max 10 best out of 202): 06by7 (0.60 #2102, 0.55 #2745, 0.55 #2906), 0190xp (0.50 #256, 0.06 #5796, 0.06 #5794), 0hdf8 (0.40 #526, 0.29 #686, 0.25 #204), 0xhtw (0.33 #13, 0.25 #334, 0.24 #1457), 02yv6b (0.33 #62, 0.25 #383, 0.16 #1283), 0pm85 (0.33 #95, 0.25 #416, 0.16 #1283), 05r6t (0.30 #1657, 0.26 #2138, 0.25 #2781), 01dqhq (0.25 #208, 0.11 #1010, 0.06 #5796), 02w4v (0.25 #190, 0.07 #832, 0.06 #5796), 0ggq0m (0.25 #170, 0.06 #5796, 0.06 #5794) >> Best rule #2102 for best value: >> intensional similarity = 5 >> extensional distance = 96 >> proper extension: 01gbcf; 018ysx; 088vmr; 017ht; >> query: (?x6349, 06by7) <- parent_genre(?x6349, ?x2249), artists(?x2249, ?x9463), artists(?x2249, ?x3867), ?x9463 = 01shhf, ?x3867 = 0bkg4 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #5796 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 265 *> proper extension: 0145m; *> query: (?x6349, ?x13686) <- parent_genre(?x6349, ?x2249), parent_genre(?x8011, ?x2249), parent_genre(?x8011, ?x13686) *> conf = 0.06 ranks of expected_values: 61 EVAL 08z0wx parent_genre 0172rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 43.000 37.000 0.602 http://example.org/music/genre/parent_genre #18123-0sw62 PRED entity: 0sw62 PRED relation: religion PRED expected values: 06nzl => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 21): 03_gx (0.18 #464, 0.10 #1596, 0.09 #2453), 0c8wxp (0.17 #547, 0.16 #1132, 0.15 #997), 0kpl (0.10 #460, 0.08 #866, 0.07 #1182), 01lp8 (0.04 #722, 0.03 #1492, 0.03 #316), 03j6c (0.04 #1375, 0.03 #967, 0.03 #922), 092bf5 (0.03 #962, 0.03 #557, 0.03 #466), 04pk9 (0.03 #470, 0.03 #290, 0.03 #335), 05sfs (0.03 #453, 0.02 #408, 0.01 #1220), 06nzl (0.03 #285, 0.03 #330, 0.02 #420), 07w8f (0.03 #305, 0.03 #350, 0.01 #485) >> Best rule #464 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 0qdwr; >> query: (?x10109, 03_gx) <- people(?x3584, ?x10109), type_of_union(?x10109, ?x1873), people(?x3584, ?x5884), ?x5884 = 0hwqz >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #285 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 06jcc; *> query: (?x10109, 06nzl) <- people(?x3584, ?x10109), languages(?x10109, ?x254), ?x3584 = 07hwkr, location(?x10109, ?x1523) *> conf = 0.03 ranks of expected_values: 9 EVAL 0sw62 religion 06nzl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 145.000 145.000 0.178 http://example.org/people/person/religion #18122-0n08r PRED entity: 0n08r PRED relation: film! PRED expected values: 033071 012g92 => 81 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1138): 016h4r (0.63 #70538, 0.34 #68461, 0.33 #68463), 0p5mw (0.41 #70540, 0.33 #68463, 0.33 #76764), 02l5rm (0.36 #78840, 0.36 #82991, 0.34 #89219), 0127m7 (0.23 #4557, 0.05 #8708, 0.05 #12857), 043gj (0.13 #824, 0.08 #7051, 0.02 #17423), 0652ty (0.13 #1829, 0.08 #8056, 0.02 #18428), 02x08c (0.13 #1567, 0.08 #7794, 0.02 #18166), 02h0f3 (0.13 #1307, 0.08 #7534, 0.02 #17906), 0gn30 (0.11 #13395, 0.03 #44512, 0.02 #69407), 01q_ph (0.10 #8358, 0.03 #12507, 0.03 #12451) >> Best rule #70538 for best value: >> intensional similarity = 5 >> extensional distance = 777 >> proper extension: 0123qq; >> query: (?x11065, ?x3495) <- nominated_for(?x3495, ?x11065), nominated_for(?x1887, ?x11065), place_of_birth(?x1887, ?x4499), profession(?x1887, ?x1183), participant(?x3495, ?x538) >> conf = 0.63 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0n08r film! 012g92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 43.000 0.631 http://example.org/film/actor/film./film/performance/film EVAL 0n08r film! 033071 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 43.000 0.631 http://example.org/film/actor/film./film/performance/film #18121-037ls6 PRED entity: 037ls6 PRED relation: nutrient PRED expected values: 0h1wg 0h1zw 09pbb 02kd0rh 07q0m => 22 concepts (21 used for prediction) PRED predicted values (max 10 best out of 42): 06jry (0.80 #506, 0.80 #484, 0.80 #24), 09pbb (0.80 #24, 0.78 #118, 0.77 #145), 02p0tjr (0.80 #24, 0.78 #118, 0.77 #145), 07q0m (0.80 #24, 0.78 #118, 0.77 #145), 05v_8y (0.80 #24, 0.78 #118, 0.77 #145), 0h1zw (0.80 #24, 0.78 #118, 0.77 #145), 02kd0rh (0.80 #24, 0.78 #118, 0.77 #145), 0h1wg (0.80 #24, 0.78 #118, 0.77 #145), 02y_3rf (0.80 #24, 0.78 #118, 0.77 #145), 08lb68 (0.80 #24, 0.78 #118, 0.77 #145) >> Best rule #506 for best value: >> intensional similarity = 79 >> extensional distance = 8 >> proper extension: 0dcfv; >> query: (?x8298, ?x6192) <- nutrient(?x8298, ?x12454), nutrient(?x8298, ?x9915), nutrient(?x8298, ?x9490), nutrient(?x8298, ?x9365), nutrient(?x8298, ?x5549), nutrient(?x8298, ?x5337), nutrient(?x8298, ?x2018), ?x5549 = 025s7j4, nutrient(?x9732, ?x9365), nutrient(?x9005, ?x9365), nutrient(?x7719, ?x9365), nutrient(?x7057, ?x9365), nutrient(?x6285, ?x9365), nutrient(?x6191, ?x9365), nutrient(?x6159, ?x9365), nutrient(?x6032, ?x9365), nutrient(?x5373, ?x9365), nutrient(?x5009, ?x9365), nutrient(?x4068, ?x9365), nutrient(?x3900, ?x9365), nutrient(?x3468, ?x9365), nutrient(?x2701, ?x9365), nutrient(?x1303, ?x9365), nutrient(?x1257, ?x9365), nutrient(?x9489, ?x9490), ?x9005 = 04zpv, taxonomy(?x9365, ?x939), ?x3900 = 061_f, ?x939 = 04n6k, ?x4068 = 0fbw6, ?x7057 = 0fbdb, ?x6191 = 014j1m, ?x2018 = 01sh2, ?x1303 = 0fj52s, ?x7719 = 0dj75, ?x6159 = 033cnk, ?x5009 = 0fjfh, ?x9915 = 025tkqy, ?x9489 = 07j87, ?x12454 = 025rw19, ?x2701 = 0hkxq, nutrient(?x5373, ?x14210), nutrient(?x5373, ?x13545), nutrient(?x5373, ?x13498), nutrient(?x5373, ?x8243), nutrient(?x5373, ?x6192), nutrient(?x5373, ?x5526), nutrient(?x5373, ?x3469), nutrient(?x5373, ?x1304), nutrient(?x5373, ?x1258), ?x13545 = 01w_3, ?x1258 = 0h1wg, ?x13498 = 07q0m, ?x6192 = 06jry, ?x1304 = 08lb68, ?x3468 = 0cxn2, nutrient(?x6285, ?x12868), nutrient(?x6285, ?x12481), nutrient(?x6285, ?x9949), nutrient(?x6285, ?x9855), nutrient(?x6285, ?x9840), nutrient(?x6285, ?x6286), nutrient(?x6285, ?x3901), ?x3901 = 0466p20, ?x8243 = 014d7f, ?x9855 = 0d9t0, ?x9840 = 02p0tjr, ?x5526 = 09pbb, ?x3469 = 0h1zw, ?x5337 = 06x4c, nutrient(?x9732, ?x12336), ?x6032 = 01nkt, ?x12336 = 0f4l5, ?x1257 = 09728, ?x9949 = 02kd0rh, ?x12481 = 027g6p7, ?x6286 = 02y_3rf, ?x14210 = 0f4k5, ?x12868 = 03d49 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 128 *> extensional distance = 1 *> proper extension: 0cxn2; *> query: (?x8298, ?x1304) <- nutrient(?x8298, ?x13944), nutrient(?x8298, ?x13126), nutrient(?x8298, ?x12902), nutrient(?x8298, ?x12454), nutrient(?x8298, ?x12083), nutrient(?x8298, ?x11758), nutrient(?x8298, ?x11409), nutrient(?x8298, ?x11270), nutrient(?x8298, ?x10891), nutrient(?x8298, ?x10709), nutrient(?x8298, ?x10453), nutrient(?x8298, ?x10195), nutrient(?x8298, ?x10098), nutrient(?x8298, ?x9915), nutrient(?x8298, ?x9708), nutrient(?x8298, ?x9619), nutrient(?x8298, ?x9436), nutrient(?x8298, ?x9426), nutrient(?x8298, ?x9365), nutrient(?x8298, ?x8442), nutrient(?x8298, ?x8413), nutrient(?x8298, ?x7894), nutrient(?x8298, ?x7720), nutrient(?x8298, ?x7652), nutrient(?x8298, ?x7431), nutrient(?x8298, ?x7362), nutrient(?x8298, ?x7219), nutrient(?x8298, ?x7135), nutrient(?x8298, ?x6160), nutrient(?x8298, ?x6033), nutrient(?x8298, ?x6026), nutrient(?x8298, ?x5549), nutrient(?x8298, ?x5451), nutrient(?x8298, ?x5337), nutrient(?x8298, ?x5010), nutrient(?x8298, ?x4069), nutrient(?x8298, ?x3203), nutrient(?x8298, ?x2702), nutrient(?x8298, ?x2018), nutrient(?x8298, ?x1960), ?x7362 = 02kc5rj, ?x9436 = 025sqz8, ?x7219 = 0h1vg, ?x7720 = 025s7x6, ?x2018 = 01sh2, ?x9915 = 025tkqy, nutrient(?x9732, ?x9708), nutrient(?x9489, ?x9708), nutrient(?x7719, ?x9708), nutrient(?x7057, ?x9708), nutrient(?x6191, ?x9708), nutrient(?x4068, ?x9708), nutrient(?x3900, ?x9708), ?x6160 = 041r51, ?x10195 = 0hkwr, ?x10891 = 0g5gq, ?x6191 = 014j1m, ?x6026 = 025sf8g, ?x10098 = 0h1_c, ?x4069 = 0hqw8p_, ?x7135 = 025rsfk, ?x10453 = 075pwf, ?x12902 = 0fzjh, ?x5451 = 05wvs, ?x6033 = 04zjxcz, ?x3900 = 061_f, ?x13126 = 02kc_w5, ?x7719 = 0dj75, nutrient(?x10612, ?x12454), nutrient(?x9005, ?x12454), nutrient(?x6285, ?x12454), nutrient(?x6159, ?x12454), nutrient(?x6032, ?x12454), nutrient(?x5373, ?x12454), nutrient(?x3264, ?x12454), nutrient(?x2701, ?x12454), nutrient(?x1959, ?x12454), nutrient(?x1303, ?x12454), nutrient(?x1257, ?x12454), ?x7431 = 09gwd, ?x6159 = 033cnk, ?x5337 = 06x4c, ?x1303 = 0fj52s, ?x6032 = 01nkt, ?x5010 = 0h1vz, ?x10612 = 0frq6, ?x9619 = 0h1tg, ?x3264 = 0dcfv, ?x11758 = 0q01m, ?x10709 = 0h1sz, ?x2702 = 0838f, ?x12083 = 01n78x, ?x4068 = 0fbw6, ?x9005 = 04zpv, ?x9365 = 04k8n, ?x3203 = 04kl74p, ?x13944 = 0f4kp, ?x1959 = 0f25w9, ?x5373 = 0971v, ?x8413 = 02kc4sf, ?x2701 = 0hkxq, ?x7652 = 025s0s0, ?x1257 = 09728, ?x8442 = 02kcv4x, ?x1960 = 07hnp, ?x7894 = 0f4hc, ?x9489 = 07j87, ?x5549 = 025s7j4, ?x11409 = 0h1yf, ?x11270 = 02kc008, nutrient(?x6285, ?x12868), nutrient(?x6285, ?x9949), nutrient(?x6285, ?x9840), nutrient(?x6285, ?x9795), nutrient(?x6285, ?x3901), nutrient(?x6285, ?x3469), nutrient(?x6285, ?x1304), nutrient(?x6285, ?x1258), ?x12868 = 03d49, ?x1258 = 0h1wg, ?x9426 = 0h1yy, ?x9840 = 02p0tjr, ?x3469 = 0h1zw, ?x3901 = 0466p20, ?x9795 = 05v_8y, ?x7057 = 0fbdb, ?x9732 = 05z55, ?x9949 = 02kd0rh *> conf = 0.80 ranks of expected_values: 2, 4, 6, 7, 8 EVAL 037ls6 nutrient 07q0m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 21.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 037ls6 nutrient 02kd0rh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 21.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 037ls6 nutrient 09pbb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 22.000 21.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 037ls6 nutrient 0h1zw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 21.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 037ls6 nutrient 0h1wg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 21.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #18120-059j4x PRED entity: 059j4x PRED relation: profession PRED expected values: 01d_h8 02jknp => 113 concepts (112 used for prediction) PRED predicted values (max 10 best out of 50): 01d_h8 (0.84 #2064, 0.82 #1770, 0.81 #1623), 02hrh1q (0.78 #14, 0.73 #10158, 0.73 #4571), 02jknp (0.50 #890, 0.42 #5741, 0.38 #2948), 0cbd2 (0.26 #448, 0.24 #595, 0.23 #301), 09jwl (0.20 #5163, 0.19 #7809, 0.19 #8544), 018gz8 (0.18 #2368, 0.18 #898, 0.17 #2515), 0np9r (0.15 #902, 0.15 #1049, 0.14 #2372), 0dz3r (0.14 #6764, 0.13 #5147, 0.13 #6617), 0nbcg (0.13 #5175, 0.13 #6645, 0.13 #6939), 0kyk (0.12 #5761, 0.09 #14293, 0.09 #14440) >> Best rule #2064 for best value: >> intensional similarity = 3 >> extensional distance = 138 >> proper extension: 079vf; >> query: (?x12138, 01d_h8) <- award_winner(?x1039, ?x12138), executive_produced_by(?x8770, ?x12138), profession(?x12138, ?x987) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 059j4x profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 112.000 0.836 http://example.org/people/person/profession EVAL 059j4x profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 112.000 0.836 http://example.org/people/person/profession #18119-01_wfj PRED entity: 01_wfj PRED relation: group! PRED expected values: 02hnl => 110 concepts (87 used for prediction) PRED predicted values (max 10 best out of 120): 0342h (0.92 #4620, 0.91 #1240, 0.91 #1733), 02hnl (0.81 #1755, 0.80 #685, 0.80 #4559), 03bx0bm (0.62 #3810, 0.58 #4554, 0.57 #4637), 03qjg (0.37 #1280, 0.37 #1197, 0.36 #3005), 042v_gx (0.33 #171, 0.25 #336, 0.20 #1571), 06ncr (0.32 #940, 0.28 #1271, 0.28 #1188), 07y_7 (0.30 #906, 0.27 #3292, 0.26 #1237), 013y1f (0.29 #2985, 0.28 #1260, 0.28 #1177), 01vj9c (0.28 #4543, 0.28 #4626, 0.27 #3717), 0l14j_ (0.24 #952, 0.23 #1283, 0.23 #1200) >> Best rule #4620 for best value: >> intensional similarity = 6 >> extensional distance = 179 >> proper extension: 04rcr; 0150jk; 01rm8b; 018gm9; 0143q0; 015cxv; 01323p; 01516r; >> query: (?x9999, 0342h) <- group(?x228, ?x9999), instrumentalists(?x228, ?x140), performance_role(?x130, ?x228), role(?x1147, ?x228), role(?x642, ?x228), ?x1147 = 07kc_ >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1755 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 51 *> proper extension: 013rfk; 012x1l; *> query: (?x9999, 02hnl) <- origin(?x9999, ?x12268), origin(?x9999, ?x362), artists(?x1000, ?x9999), ?x1000 = 0xhtw, contains(?x512, ?x12268), place_of_birth(?x361, ?x362), group(?x228, ?x9999) *> conf = 0.81 ranks of expected_values: 2 EVAL 01_wfj group! 02hnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 87.000 0.923 http://example.org/music/performance_role/regular_performances./music/group_membership/group #18118-0b_6v_ PRED entity: 0b_6v_ PRED relation: team PRED expected values: 04088s0 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 22): 026w398 (0.81 #161, 0.80 #170, 0.75 #188), 02py8_w (0.71 #148, 0.69 #302, 0.67 #130), 04088s0 (0.67 #65, 0.60 #38, 0.56 #92), 02pjzvh (0.62 #75, 0.60 #183, 0.59 #301), 03d555l (0.50 #64, 0.45 #181, 0.42 #136), 0263cyj (0.50 #150, 0.44 #87, 0.41 #304), 03d5m8w (0.44 #160, 0.40 #187, 0.40 #25), 02pyyld (0.41 #307, 0.40 #36, 0.38 #316), 02r2qt7 (0.33 #140, 0.33 #68, 0.33 #50), 0j86l (0.05 #573, 0.04 #217, 0.04 #481) >> Best rule #161 for best value: >> intensional similarity = 10 >> extensional distance = 14 >> proper extension: 0b_6_l; >> query: (?x8527, 026w398) <- team(?x8527, ?x10846), team(?x8527, ?x9983), team(?x10594, ?x9983), team(?x7378, ?x9983), position(?x9983, ?x4747), ?x7378 = 0bzrxn, ?x4747 = 02sf_r, ?x10594 = 0b_756, locations(?x8527, ?x2017), ?x10846 = 02pzy52 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #65 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 4 *> proper extension: 0cc8q3; *> query: (?x8527, 04088s0) <- team(?x8527, ?x9983), team(?x8527, ?x9909), team(?x8527, ?x8528), team(?x8527, ?x6803), team(?x8527, ?x4938), team(?x8527, ?x4369), team(?x8527, ?x2303), ?x9983 = 02q4ntp, ?x2303 = 02plv57, ?x8528 = 091tgz, ?x4369 = 02pqcfz, ?x4938 = 027yf83, ?x9909 = 026wlnm, team(?x6583, ?x6803), ?x6583 = 0b_75k *> conf = 0.67 ranks of expected_values: 3 EVAL 0b_6v_ team 04088s0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 72.000 0.812 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #18117-0fpzwf PRED entity: 0fpzwf PRED relation: place_of_birth! PRED expected values: 06x4l_ => 201 concepts (164 used for prediction) PRED predicted values (max 10 best out of 2197): 01vvycq (0.44 #221412, 0.41 #65116, 0.36 #41672), 06vsbt (0.41 #65116, 0.36 #41672, 0.35 #62511), 0f7h2g (0.41 #65116, 0.36 #41672, 0.35 #62511), 01kgv4 (0.41 #65116, 0.36 #41672, 0.35 #62511), 06qgjh (0.41 #65116, 0.36 #41672, 0.35 #62511), 03fvqg (0.41 #65116, 0.36 #41672, 0.35 #62511), 0bxfmk (0.41 #65116, 0.36 #41672, 0.35 #62511), 02j4sk (0.41 #65116, 0.36 #41672, 0.35 #62511), 01lct6 (0.41 #65116, 0.36 #41672, 0.35 #62511), 016zdd (0.03 #174523, 0.03 #2224, 0.03 #4829) >> Best rule #221412 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 0_3cs; 01c40n; 036k0s; 0_75d; 02m__; 0hpyv; 0rng; 020d8d; 0f2nf; 02s838; ... >> query: (?x5771, ?x702) <- origin(?x702, ?x5771), place_of_birth(?x849, ?x5771), instrumentalists(?x227, ?x702) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fpzwf place_of_birth! 06x4l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 201.000 164.000 0.441 http://example.org/people/person/place_of_birth #18116-012x4t PRED entity: 012x4t PRED relation: group PRED expected values: 01v0sx2 => 117 concepts (74 used for prediction) PRED predicted values (max 10 best out of 58): 02r1tx7 (0.08 #124, 0.04 #1966, 0.03 #2074), 01qqwp9 (0.08 #237, 0.06 #129, 0.03 #1429), 0123r4 (0.08 #585, 0.05 #260, 0.05 #477), 014_lq (0.06 #143, 0.02 #2093, 0.01 #1985), 0qmpd (0.06 #184), 01v0sx2 (0.05 #221, 0.05 #980, 0.05 #1630), 07c0j (0.03 #112, 0.03 #220, 0.03 #328), 09lwrt (0.03 #155, 0.03 #371), 081wh1 (0.03 #160, 0.02 #2002, 0.01 #4388), 0cbm64 (0.03 #185, 0.02 #1376, 0.01 #2895) >> Best rule #124 for best value: >> intensional similarity = 2 >> extensional distance = 34 >> proper extension: 08959; >> query: (?x1660, 02r1tx7) <- profession(?x1660, ?x1359), ?x1359 = 09lbv >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #221 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 0d06m5; 057hz; 015qt5; 045cq; 01t94_1; 01m4kpp; 02l0xc; *> query: (?x1660, 01v0sx2) <- people(?x2510, ?x1660), award_winner(?x1362, ?x1660), inductee(?x1091, ?x1660) *> conf = 0.05 ranks of expected_values: 6 EVAL 012x4t group 01v0sx2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 117.000 74.000 0.083 http://example.org/music/group_member/membership./music/group_membership/group #18115-06fvc PRED entity: 06fvc PRED relation: genre PRED expected values: 07s9rl0 => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 22): 07s9rl0 (0.07 #2638, 0.02 #2514), 01jfsb (0.05 #2527, 0.03 #2651), 02kdv5l (0.05 #2516, 0.03 #2640), 03k9fj (0.04 #2526, 0.02 #2650), 05p553 (0.04 #2642, 0.01 #2518), 02l7c8 (0.03 #2655, 0.01 #2531), 01hmnh (0.02 #2533, 0.02 #2657), 02n4kr (0.02 #2522, 0.01 #2646), 0lsxr (0.02 #2647, 0.01 #2523), 04xvlr (0.01 #2639) >> Best rule #2638 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x1101, 07s9rl0) <- >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06fvc genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.068 http://example.org/film/film/genre #18114-011yxy PRED entity: 011yxy PRED relation: titles! PRED expected values: 06mx8 => 97 concepts (47 used for prediction) PRED predicted values (max 10 best out of 61): 07s9rl0 (0.47 #1, 0.46 #813, 0.42 #712), 04xvlr (0.27 #2967, 0.26 #4514, 0.24 #1018), 02l7c8 (0.25 #3066, 0.24 #4612, 0.23 #2247), 03q4nz (0.25 #3066, 0.24 #4612, 0.23 #2247), 01z4y (0.21 #1870, 0.20 #1974, 0.20 #2077), 03mqtr (0.20 #46, 0.08 #3009, 0.08 #4556), 07ssc (0.18 #111, 0.13 #10, 0.12 #214), 01jfsb (0.18 #731, 0.17 #20, 0.14 #4530), 07c52 (0.15 #2481, 0.15 #2277, 0.14 #1657), 017fp (0.13 #24, 0.11 #1548, 0.11 #2987) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 0k20s; >> query: (?x7307, 07s9rl0) <- nominated_for(?x1063, ?x7307), award(?x7307, ?x8364), film(?x6957, ?x7307), ?x8364 = 09d28z >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #577 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 07k2mq; *> query: (?x7307, 06mx8) <- award_winner(?x7307, ?x2530), film_festivals(?x7307, ?x7988), award(?x7307, ?x289), nominated_for(?x1063, ?x7307) *> conf = 0.03 ranks of expected_values: 36 EVAL 011yxy titles! 06mx8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 97.000 47.000 0.467 http://example.org/media_common/netflix_genre/titles #18113-089tm PRED entity: 089tm PRED relation: inductee! PRED expected values: 0g2c8 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 3): 0g2c8 (0.38 #37, 0.37 #46, 0.23 #73), 06szd3 (0.02 #480, 0.02 #491, 0.02 #502), 0qjfl (0.01 #165, 0.01 #84) >> Best rule #37 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 05crg7; >> query: (?x379, 0g2c8) <- artists(?x7083, ?x379), artists(?x1000, ?x379), ?x7083 = 02yv6b, group(?x227, ?x379), ?x1000 = 0xhtw >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 089tm inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.381 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #18112-03x23q PRED entity: 03x23q PRED relation: educational_institution PRED expected values: 03x23q => 117 concepts (58 used for prediction) PRED predicted values (max 10 best out of 124): 03k7dn (0.10 #426), 0bwfn (0.10 #254), 04b_46 (0.10 #212), 02zd460 (0.10 #158), 065y4w7 (0.10 #13), 015zyd (0.10 #1), 03x33n (0.07 #652, 0.02 #1191, 0.02 #24811), 01pl14 (0.07 #547, 0.02 #1086, 0.02 #24811), 019tfm (0.07 #1078, 0.02 #24811), 02vkzcx (0.07 #1074, 0.02 #24811) >> Best rule #426 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02l9wl; >> query: (?x12732, 03k7dn) <- student(?x12732, ?x3176), award_nominee(?x3176, ?x1736), ?x1736 = 032w8h >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #24811 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 419 *> proper extension: 01xcgf; *> query: (?x12732, ?x466) <- state_province_region(?x12732, ?x3908), school_type(?x12732, ?x3092), state_province_region(?x466, ?x3908) *> conf = 0.02 ranks of expected_values: 76 EVAL 03x23q educational_institution 03x23q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 117.000 58.000 0.100 http://example.org/education/educational_institution_campus/educational_institution #18111-050zr4 PRED entity: 050zr4 PRED relation: film PRED expected values: 0jdgr 03t79f => 123 concepts (100 used for prediction) PRED predicted values (max 10 best out of 611): 04b2qn (0.58 #114581, 0.58 #51915, 0.48 #66237), 06q8qh (0.58 #114581, 0.48 #66237, 0.41 #82351), 06_wqk4 (0.09 #127, 0.04 #1917, 0.04 #3707), 0_9l_ (0.09 #1736, 0.04 #3526, 0.04 #7106), 0b76d_m (0.09 #4, 0.04 #1794, 0.04 #5374), 03s6l2 (0.09 #83, 0.04 #1873, 0.04 #7243), 031hcx (0.05 #1274, 0.04 #3064, 0.04 #4854), 07kdkfj (0.05 #1341, 0.04 #3131, 0.04 #4921), 0418wg (0.05 #401, 0.04 #2191, 0.04 #3981), 01cz7r (0.05 #1324, 0.04 #3114, 0.04 #4904) >> Best rule #114581 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 0q1lp; >> query: (?x8346, ?x3684) <- film(?x8346, ?x2262), nominated_for(?x8346, ?x3684) >> conf = 0.58 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 050zr4 film 03t79f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 100.000 0.585 http://example.org/film/actor/film./film/performance/film EVAL 050zr4 film 0jdgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 100.000 0.585 http://example.org/film/actor/film./film/performance/film #18110-0fgg4 PRED entity: 0fgg4 PRED relation: award PRED expected values: 03qgjwc => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 259): 0gqyl (0.72 #35400, 0.71 #15685, 0.70 #22523), 099t8j (0.72 #35400, 0.71 #15685, 0.70 #22523), 0gqy2 (0.31 #966, 0.24 #1368, 0.15 #564), 0bdwqv (0.31 #974, 0.24 #1376, 0.15 #572), 0789_m (0.31 #824, 0.24 #1226, 0.15 #32179), 05pcn59 (0.26 #2091, 0.23 #1688, 0.18 #2493), 0f4x7 (0.23 #835, 0.18 #1237, 0.17 #1639), 04kxsb (0.23 #928, 0.18 #1330, 0.15 #526), 09sdmz (0.23 #1008, 0.18 #1410, 0.15 #32179), 0bfvd4 (0.23 #917, 0.18 #1319, 0.14 #113) >> Best rule #35400 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x4949, ?x2880) <- award_winner(?x2880, ?x4949), award(?x156, ?x2880) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #32582 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2257 *> proper extension: 0kcdl; *> query: (?x4949, ?x1245) <- nominated_for(?x4949, ?x4950), nominated_for(?x1245, ?x4950) *> conf = 0.12 ranks of expected_values: 51 EVAL 0fgg4 award 03qgjwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 104.000 104.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18109-01j67j PRED entity: 01j67j PRED relation: nominated_for! PRED expected values: 0bp_b2 0gkts9 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 172): 0bdx29 (0.77 #8736, 0.70 #1181, 0.67 #11574), 0m7yy (0.70 #1181, 0.67 #8498, 0.66 #8735), 0bp_b2 (0.52 #17, 0.19 #15118, 0.19 #961), 0cqhb3 (0.48 #197, 0.16 #433, 0.16 #1141), 0gkr9q (0.45 #207, 0.19 #15118, 0.18 #1151), 0ck27z (0.41 #70, 0.19 #15118, 0.18 #306), 0gkts9 (0.41 #122, 0.18 #1066, 0.18 #358), 0gq9h (0.36 #8559, 0.29 #10924, 0.29 #9269), 0gs9p (0.33 #8561, 0.25 #10926, 0.25 #9271), 019f4v (0.29 #8550, 0.25 #9260, 0.25 #8313) >> Best rule #8736 for best value: >> intensional similarity = 3 >> extensional distance = 670 >> proper extension: 05_61y; 0j8f09z; >> query: (?x2660, ?x4921) <- award(?x2660, ?x4921), award_winner(?x4921, ?x1039), ceremony(?x4921, ?x2126) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 03_8kz; *> query: (?x2660, 0bp_b2) <- award_winner(?x2660, ?x2661), nominated_for(?x2071, ?x2660), ?x2071 = 0bdw6t *> conf = 0.52 ranks of expected_values: 3, 7 EVAL 01j67j nominated_for! 0gkts9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 77.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01j67j nominated_for! 0bp_b2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18108-036jp8 PRED entity: 036jp8 PRED relation: type_of_union PRED expected values: 04ztj => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.87 #131, 0.87 #203, 0.86 #171), 01g63y (0.20 #6, 0.14 #108, 0.13 #392), 01bl8s (0.01 #129, 0.01 #141, 0.01 #253), 0jgjn (0.01 #146) >> Best rule #131 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 0gnbw; 0h1q6; >> query: (?x6336, 04ztj) <- nationality(?x6336, ?x94), celebrities_impersonated(?x3649, ?x6336), location(?x6336, ?x1274) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 036jp8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.873 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18107-02qr3k8 PRED entity: 02qr3k8 PRED relation: genre PRED expected values: 0jtdp => 52 concepts (50 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.71 #3752, 0.54 #5935, 0.54 #5329), 05p553 (0.60 #610, 0.56 #1699, 0.56 #1820), 06n90 (0.50 #618, 0.22 #497, 0.20 #1586), 02kdv5l (0.47 #1455, 0.47 #1576, 0.44 #487), 0jtdp (0.43 #982, 0.41 #861, 0.39 #1103), 01hmnh (0.40 #623, 0.17 #3043, 0.16 #3164), 0lsxr (0.34 #1461, 0.34 #1582, 0.26 #735), 02l7c8 (0.33 #500, 0.32 #3767, 0.25 #4130), 0bkbm (0.33 #282, 0.08 #1613, 0.08 #1492), 0vgkd (0.33 #11, 0.05 #1705, 0.05 #1826) >> Best rule #3752 for best value: >> intensional similarity = 4 >> extensional distance = 1411 >> proper extension: 0fq27fp; 05jyb2; 072r5v; 0cvkv5; 076xkdz; 06zn1c; >> query: (?x7415, 07s9rl0) <- genre(?x7415, ?x812), titles(?x812, ?x80), genre(?x4786, ?x812), ?x4786 = 0bbw2z6 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #982 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 0hz6mv2; *> query: (?x7415, 0jtdp) <- person(?x7415, ?x406), film(?x1104, ?x7415), award_nominee(?x1870, ?x406), location(?x406, ?x191) *> conf = 0.43 ranks of expected_values: 5 EVAL 02qr3k8 genre 0jtdp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 52.000 50.000 0.712 http://example.org/film/film/genre #18106-0jbp0 PRED entity: 0jbp0 PRED relation: award PRED expected values: 02g2wv => 99 concepts (92 used for prediction) PRED predicted values (max 10 best out of 310): 09sb52 (0.33 #2434, 0.31 #6026, 0.30 #13609), 0ck27z (0.27 #10067, 0.25 #9269, 0.21 #11264), 05pcn59 (0.25 #79, 0.18 #5667, 0.13 #2474), 05zr6wv (0.25 #17, 0.14 #5605, 0.12 #2412), 03c7tr1 (0.25 #56, 0.11 #5644, 0.05 #11630), 0gqwc (0.25 #72, 0.09 #5660, 0.09 #2467), 0gq9h (0.25 #75, 0.09 #15242, 0.08 #24424), 0gqyl (0.25 #103, 0.09 #6489, 0.08 #8484), 05b4l5x (0.25 #6, 0.09 #5594, 0.06 #6392), 094qd5 (0.25 #43, 0.08 #5631, 0.07 #2438) >> Best rule #2434 for best value: >> intensional similarity = 4 >> extensional distance = 221 >> proper extension: 079vf; 05bnp0; 02p65p; 0h0jz; 02g8h; 0p_pd; 0z4s; 0159h6; 0h5g_; 04bs3j; ... >> query: (?x10398, 09sb52) <- film(?x10398, ?x5948), film_production_design_by(?x5948, ?x12933), film_crew_role(?x5948, ?x2095), ?x2095 = 0dxtw >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #11574 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 814 *> proper extension: 0kcdl; *> query: (?x10398, ?x1429) <- nominated_for(?x10398, ?x10661), nominated_for(?x10398, ?x9017), nominated_for(?x1429, ?x9017), country_of_origin(?x10661, ?x94) *> conf = 0.13 ranks of expected_values: 32 EVAL 0jbp0 award 02g2wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 99.000 92.000 0.327 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18105-01vsy95 PRED entity: 01vsy95 PRED relation: artists! PRED expected values: 01p_2p => 151 concepts (74 used for prediction) PRED predicted values (max 10 best out of 230): 06by7 (0.69 #19, 0.59 #1869, 0.57 #943), 0xhtw (0.49 #1865, 0.35 #939, 0.32 #4025), 0cx7f (0.46 #137, 0.34 #1987, 0.24 #1061), 05bt6j (0.46 #42, 0.30 #1892, 0.29 #966), 05w3f (0.30 #1886, 0.25 #960, 0.18 #4046), 06j6l (0.29 #13319, 0.28 #13936, 0.27 #14863), 03lty (0.28 #1876, 0.27 #950, 0.21 #4036), 01lyv (0.27 #4350, 0.23 #8057, 0.22 #6512), 016clz (0.27 #1854, 0.25 #928, 0.23 #10804), 0glt670 (0.26 #13928, 0.25 #14855, 0.25 #13311) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0187x8; >> query: (?x3374, 06by7) <- artists(?x1380, ?x3374), award_winner(?x487, ?x3374), ?x1380 = 0dl5d >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #609 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 01qq_lp; *> query: (?x3374, 01p_2p) <- type_of_union(?x3374, ?x566), award_winner(?x9431, ?x3374), ?x9431 = 02cg41 *> conf = 0.03 ranks of expected_values: 123 EVAL 01vsy95 artists! 01p_2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 151.000 74.000 0.692 http://example.org/music/genre/artists #18104-0cqh46 PRED entity: 0cqh46 PRED relation: award_winner PRED expected values: 016ywr => 49 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1524): 09fb5 (0.57 #2531, 0.29 #4999, 0.17 #63), 03f1zdw (0.57 #2706, 0.17 #238, 0.14 #5174), 02qgqt (0.57 #2485, 0.14 #4953, 0.14 #22206), 016yvw (0.57 #3684, 0.14 #6152, 0.12 #8619), 06dv3 (0.57 #2505, 0.14 #4973, 0.06 #51830), 0171cm (0.50 #537, 0.36 #41953, 0.34 #51829), 01qscs (0.50 #57, 0.14 #2525, 0.12 #7460), 026rm_y (0.50 #1860, 0.12 #9263, 0.06 #7403), 01wmxfs (0.43 #2616, 0.36 #41953, 0.34 #51829), 0cgzj (0.43 #7012, 0.36 #41953, 0.34 #51829) >> Best rule #2531 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0bp_b2; 0f4x7; 04kxsb; 09qv_s; 099ck7; >> query: (?x880, 09fb5) <- award_winner(?x880, ?x1549), award(?x4470, ?x880), ceremony(?x880, ?x873), ?x4470 = 02y_2y >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #41953 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 227 *> proper extension: 0fm3kw; *> query: (?x880, ?x8147) <- award(?x8147, ?x880), award(?x3808, ?x880), award_winner(?x395, ?x8147), actor(?x531, ?x3808) *> conf = 0.36 ranks of expected_values: 24 EVAL 0cqh46 award_winner 016ywr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 49.000 22.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner #18103-07lp1 PRED entity: 07lp1 PRED relation: influenced_by PRED expected values: 03f47xl 0hcvy => 130 concepts (47 used for prediction) PRED predicted values (max 10 best out of 354): 0zm1 (0.50 #120, 0.25 #1821, 0.22 #2246), 032l1 (0.37 #3915, 0.36 #4342, 0.36 #2636), 03f0324 (0.37 #3976, 0.32 #4403, 0.25 #1846), 058vp (0.32 #4435, 0.27 #3156, 0.16 #4008), 081k8 (0.29 #4407, 0.29 #2701, 0.21 #3980), 0379s (0.29 #2626, 0.27 #3053, 0.18 #4332), 02lt8 (0.29 #2666, 0.26 #3945, 0.20 #3093), 07lp1 (0.29 #1616, 0.25 #340, 0.12 #2041), 01v9724 (0.29 #2722, 0.20 #595, 0.14 #4428), 04xjp (0.29 #2606, 0.13 #3033, 0.13 #16642) >> Best rule #120 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01hc9_; >> query: (?x10313, 0zm1) <- influenced_by(?x10313, ?x2208), student(?x741, ?x10313), ?x2208 = 041mt, award_winner(?x12729, ?x10313) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3175 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0399p; *> query: (?x10313, 03f47xl) <- influenced_by(?x10313, ?x10090), influenced_by(?x10313, ?x2161), ?x2161 = 040db, peers(?x6320, ?x10090) *> conf = 0.27 ranks of expected_values: 13 EVAL 07lp1 influenced_by 0hcvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 47.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 07lp1 influenced_by 03f47xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 130.000 47.000 0.500 http://example.org/influence/influence_node/influenced_by #18102-02lq10 PRED entity: 02lq10 PRED relation: languages PRED expected values: 02h40lc => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 15): 02h40lc (0.40 #197, 0.35 #314, 0.35 #548), 04306rv (0.06 #2539, 0.04 #1328, 0.04 #276), 02bjrlw (0.06 #2539, 0.04 #235, 0.02 #898), 064_8sq (0.06 #2539, 0.04 #483, 0.04 #600), 02hwyss (0.06 #2539), 06mp7 (0.06 #2539), 03hkp (0.06 #2539), 03_9r (0.06 #2539), 03k50 (0.04 #1328, 0.02 #394, 0.02 #1292), 09s02 (0.04 #1328, 0.02 #426) >> Best rule #197 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 02fb1n; 01ggc9; >> query: (?x2217, 02h40lc) <- location(?x2217, ?x1227), film(?x2217, ?x2345), film(?x2217, ?x2218), ?x2218 = 013q07, nominated_for(?x198, ?x2345) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lq10 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.400 http://example.org/people/person/languages #18101-07c0j PRED entity: 07c0j PRED relation: artist! PRED expected values: 01wdtv => 127 concepts (109 used for prediction) PRED predicted values (max 10 best out of 107): 015_1q (0.34 #834, 0.32 #698, 0.29 #290), 01cszh (0.25 #10, 0.11 #282, 0.10 #554), 03rhqg (0.23 #695, 0.21 #287, 0.20 #831), 043g7l (0.20 #574, 0.13 #4659, 0.11 #5204), 033hn8 (0.18 #285, 0.16 #5187, 0.13 #6963), 0181dw (0.17 #2080, 0.17 #584, 0.14 #6990), 0n85g (0.16 #740, 0.14 #876, 0.10 #604), 0mzkr (0.13 #568, 0.11 #840, 0.10 #432), 03mp8k (0.13 #4692, 0.13 #2103, 0.13 #5237), 01w40h (0.13 #707, 0.11 #843, 0.10 #1115) >> Best rule #834 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 01pbxb; 07s3vqk; 0m2l9; 04rcr; 081lh; 01vrncs; 014zfs; 01wp8w7; 0gt_k; 086qd; ... >> query: (?x1136, 015_1q) <- artists(?x671, ?x1136), influenced_by(?x483, ?x1136), award_nominee(?x1136, ?x538) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #785 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 04wqr; *> query: (?x1136, 01wdtv) <- influenced_by(?x4942, ?x1136), award_nominee(?x1136, ?x538), origin(?x4942, ?x3052) *> conf = 0.06 ranks of expected_values: 32 EVAL 07c0j artist! 01wdtv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 127.000 109.000 0.343 http://example.org/music/record_label/artist #18100-02pxst PRED entity: 02pxst PRED relation: film_release_region PRED expected values: 06mkj => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 164): 06mkj (0.86 #1528, 0.86 #1690, 0.85 #2177), 03rjj (0.85 #1471, 0.85 #1633, 0.80 #2772), 03gj2 (0.85 #1656, 0.83 #1494, 0.74 #2795), 0k6nt (0.85 #188, 0.83 #2142, 0.82 #2794), 05qhw (0.85 #1481, 0.83 #1643, 0.68 #4253), 0345h (0.83 #1665, 0.83 #1503, 0.79 #524), 015fr (0.81 #1646, 0.77 #1484, 0.69 #2785), 0b90_r (0.80 #1632, 0.72 #1470, 0.65 #2771), 03h64 (0.78 #1539, 0.77 #1701, 0.76 #2840), 0d060g (0.78 #1635, 0.78 #1473, 0.69 #2774) >> Best rule #1528 for best value: >> intensional similarity = 6 >> extensional distance = 128 >> proper extension: 014lc_; 053tj7; 0407yfx; 0j43swk; 0gwjw0c; 0hz6mv2; >> query: (?x7170, 06mkj) <- film_release_region(?x7170, ?x2267), film_release_region(?x7170, ?x512), film_release_region(?x7170, ?x172), ?x512 = 07ssc, ?x2267 = 03rj0, ?x172 = 0154j >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pxst film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18099-03shp PRED entity: 03shp PRED relation: exported_to PRED expected values: 04sj3 => 155 concepts (115 used for prediction) PRED predicted values (max 10 best out of 100): 09c7w0 (0.44 #800, 0.36 #986, 0.29 #924), 01f08r (0.32 #861, 0.28 #1047, 0.06 #716), 0j4b (0.26 #171, 0.18 #232, 0.15 #848), 06tw8 (0.25 #108, 0.16 #169, 0.16 #1032), 0h3y (0.21 #129, 0.14 #930, 0.14 #1117), 06s_2 (0.16 #182, 0.14 #243, 0.12 #304), 07ssc (0.13 #996, 0.13 #810, 0.09 #194), 027jk (0.12 #115, 0.06 #606, 0.05 #853), 0d05w3 (0.11 #1020, 0.08 #834, 0.05 #157), 04hhv (0.11 #419, 0.08 #791, 0.08 #297) >> Best rule #800 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 01f08r; >> query: (?x3730, 09c7w0) <- exported_to(?x3838, ?x3730), jurisdiction_of_office(?x3119, ?x3838), location_of_ceremony(?x566, ?x3838) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #181 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 17 *> proper extension: 02y_9cf; *> query: (?x3730, 04sj3) <- combatants(?x9203, ?x3730), ?x9203 = 048n7 *> conf = 0.11 ranks of expected_values: 13 EVAL 03shp exported_to 04sj3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 155.000 115.000 0.436 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #18098-06z8gn PRED entity: 06z8gn PRED relation: film PRED expected values: 078sj4 011ycb 01svry 023vcd => 148 concepts (69 used for prediction) PRED predicted values (max 10 best out of 977): 02yvct (0.40 #351, 0.25 #2140, 0.01 #23609), 01vw8k (0.20 #653, 0.15 #6020, 0.12 #2442), 0_816 (0.20 #533, 0.12 #2322, 0.11 #98402), 033qdy (0.20 #1175, 0.12 #2964, 0.07 #6542), 09g8vhw (0.20 #325, 0.12 #2114, 0.04 #11059), 03p2xc (0.20 #1245, 0.12 #3034, 0.04 #6612), 035bcl (0.20 #1010, 0.12 #2799, 0.04 #6377), 02__34 (0.20 #341, 0.12 #2130, 0.04 #5708), 07cw4 (0.20 #1024, 0.12 #2813, 0.03 #8180), 09xbpt (0.20 #47, 0.12 #1836, 0.03 #7203) >> Best rule #351 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0klh7; >> query: (?x8743, 02yvct) <- location(?x8743, ?x739), type_of_union(?x8743, ?x566), film(?x8743, ?x1230), ?x1230 = 026390q >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #6223 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 04d2yp; *> query: (?x8743, 011ycb) <- location(?x8743, ?x739), student(?x2486, ?x8743), profession(?x8743, ?x1032), ?x2486 = 015nl4 *> conf = 0.04 ranks of expected_values: 218, 335, 883 EVAL 06z8gn film 023vcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 69.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 06z8gn film 01svry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 148.000 69.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 06z8gn film 011ycb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 148.000 69.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 06z8gn film 078sj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 148.000 69.000 0.400 http://example.org/film/actor/film./film/performance/film #18097-02lymt PRED entity: 02lymt PRED relation: student! PRED expected values: 01d34b 0234_c => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 82): 0bwfn (0.09 #3438, 0.08 #9766, 0.08 #8185), 09r4xx (0.08 #123, 0.02 #6451, 0.01 #4341), 02301 (0.08 #74, 0.01 #18527, 0.01 #20635), 065y4w7 (0.07 #2650, 0.07 #3177, 0.06 #541), 017j69 (0.05 #145, 0.02 #5418, 0.02 #3835), 06182p (0.05 #298, 0.02 #16643, 0.02 #33516), 017z88 (0.05 #1664, 0.04 #6410, 0.03 #18008), 04b_46 (0.04 #754, 0.04 #5500, 0.04 #2863), 09f2j (0.04 #3849, 0.03 #18085, 0.03 #5432), 0217m9 (0.04 #1753, 0.03 #4389, 0.03 #6499) >> Best rule #3438 for best value: >> intensional similarity = 3 >> extensional distance = 167 >> proper extension: 0gg9_5q; >> query: (?x4777, 0bwfn) <- nationality(?x4777, ?x94), produced_by(?x8068, ?x4777), executive_produced_by(?x8068, ?x96) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #4474 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 199 *> proper extension: 03lh3v; 040j2_; 02_nkp; 0443c; 04g9sq; *> query: (?x4777, 01d34b) <- people(?x2510, ?x4777), ?x2510 = 0x67, location(?x4777, ?x1523) *> conf = 0.03 ranks of expected_values: 17 EVAL 02lymt student! 0234_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 122.000 0.089 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 02lymt student! 01d34b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 122.000 122.000 0.089 http://example.org/education/educational_institution/students_graduates./education/education/student #18096-0478__m PRED entity: 0478__m PRED relation: award_winner! PRED expected values: 03qbnj => 137 concepts (124 used for prediction) PRED predicted values (max 10 best out of 332): 02f72_ (0.40 #38509, 0.39 #1693, 0.37 #49942), 02f73p (0.40 #38509, 0.39 #1693, 0.37 #49942), 02f73b (0.40 #38509, 0.39 #1693, 0.37 #49942), 02f6ym (0.40 #38509, 0.39 #1693, 0.37 #49942), 02f71y (0.40 #38509, 0.39 #1693, 0.37 #49942), 01by1l (0.40 #38509, 0.39 #1693, 0.37 #49942), 03qbnj (0.40 #38509, 0.39 #1693, 0.37 #49942), 099vwn (0.40 #38509, 0.39 #1693, 0.37 #49942), 01bgqh (0.40 #38509, 0.39 #1693, 0.37 #49942), 056jm_ (0.40 #38509, 0.39 #1693, 0.37 #49942) >> Best rule #38509 for best value: >> intensional similarity = 3 >> extensional distance = 1229 >> proper extension: 012ljv; 0411q; 015rmq; 0244r8; 030_1_; 01dw9z; 027l0b; 094wz7q; 0khth; 02tkzn; ... >> query: (?x4593, ?x724) <- award_winner(?x528, ?x4593), award(?x4593, ?x724), award_winner(?x5544, ?x4593) >> conf = 0.40 => this is the best rule for 11 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7 EVAL 0478__m award_winner! 03qbnj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 137.000 124.000 0.397 http://example.org/award/award_category/winners./award/award_honor/award_winner #18095-060c4 PRED entity: 060c4 PRED relation: organization PRED expected values: 01pl14 065y4w7 02cttt 01nkcn 037s9x 04sylm 027xx3 01r3y2 01y8zd 01hb1t 023znp 07tds 01rr31 019dwp 01y9st 01vs5c 0172jm 0123j6 01tpvt 02yr3z 0gl5_ 038czx 02y9bj 01g7_r 02j04_ 015y3j 02lv2v 01q8hj 015fsv 02l424 019q50 01p896 06thjt 02m0b0 02lwv5 02hp6p 03205_ 06nvzg 0225bv 03x23q 03np_7 0p7tb 01nhgd => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 560): 0hpt3 (0.59 #3305, 0.58 #3306, 0.56 #6622), 05njw (0.59 #3305, 0.58 #3306, 0.56 #6622), 03mdt (0.59 #3305, 0.58 #3306, 0.56 #6622), 09d5h (0.59 #3305, 0.58 #3306, 0.56 #6622), 01pf21 (0.59 #3305, 0.58 #3306, 0.56 #6622), 03s7h (0.59 #3305, 0.58 #3306, 0.56 #6622), 08z129 (0.59 #3305, 0.58 #3306, 0.56 #6622), 0gvbw (0.59 #3305, 0.58 #3306, 0.56 #6622), 0cv9b (0.59 #3305, 0.58 #3306, 0.56 #6622), 02zs4 (0.59 #3305, 0.58 #3306, 0.56 #6622) >> Best rule #3305 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 04192r; >> query: (?x346, ?x94) <- company(?x346, ?x13314), company(?x346, ?x12452), company(?x346, ?x11051), company(?x346, ?x3920), company(?x346, ?x94), ?x11051 = 07_dn, ?x13314 = 06py2, currency(?x3920, ?x170), citytown(?x12452, ?x6084), contact_category(?x12452, ?x897), child(?x3920, ?x166) >> conf = 0.59 => this is the best rule for 90 predicted values *> Best rule #3017 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 04192r; *> query: (?x346, 0123j6) <- company(?x346, ?x13314), company(?x346, ?x12452), company(?x346, ?x11051), company(?x346, ?x3920), ?x11051 = 07_dn, ?x13314 = 06py2, currency(?x3920, ?x170), citytown(?x12452, ?x6084), contact_category(?x12452, ?x897), child(?x3920, ?x166) *> conf = 0.25 ranks of expected_values: 223, 389, 548, 559 EVAL 060c4 organization 01nhgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 0p7tb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 03np_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 03x23q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 0225bv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 06nvzg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 03205_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 02hp6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 02lwv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 02m0b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 06thjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01p896 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 019q50 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 02l424 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 015fsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01q8hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 02lv2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 015y3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 02j04_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01g7_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 02y9bj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 038czx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 0gl5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 02yr3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01tpvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 0123j6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 0172jm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01vs5c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01y9st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 019dwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01rr31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 07tds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 023znp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01hb1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01y8zd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01r3y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 027xx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 04sylm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 037s9x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01nkcn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 02cttt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 060c4 organization 01pl14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.590 http://example.org/organization/role/leaders./organization/leadership/organization #18094-06182p PRED entity: 06182p PRED relation: student PRED expected values: 03nkts 0gs1_ 01p1z_ 01yfm8 0sw6g 01p85y 01gvxv => 130 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1538): 02sb1w (0.29 #1094, 0.04 #115883, 0.03 #15580), 01vhrz (0.29 #1594, 0.04 #115883, 0.02 #11941), 01_xtx (0.14 #623, 0.05 #13039, 0.04 #115883), 022411 (0.14 #1667, 0.05 #16153, 0.04 #9945), 05bnp0 (0.14 #10, 0.05 #4150, 0.05 #2081), 024y6w (0.14 #1436, 0.05 #5576, 0.05 #3507), 021r7r (0.14 #1271, 0.05 #5411, 0.05 #3342), 09r9dp (0.14 #608, 0.04 #115883, 0.03 #15094), 0cjsxp (0.14 #618, 0.04 #115883, 0.03 #14486), 02lfns (0.14 #159, 0.04 #115883, 0.03 #14486) >> Best rule #1094 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 07w0v; 01ymvk; 02mj7c; 01vg13; 01j_5k; >> query: (?x8056, 02sb1w) <- contains(?x94, ?x8056), student(?x8056, ?x494), award_nominee(?x3841, ?x494), ?x3841 = 07s8hms >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #115883 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 308 *> proper extension: 022xml; 0ymgk; 0jkhr; 02qw_v; *> query: (?x8056, ?x826) <- contains(?x94, ?x8056), student(?x8056, ?x494), award_nominee(?x3841, ?x494), award_winner(?x3841, ?x826) *> conf = 0.04 ranks of expected_values: 403, 406, 534, 1163 EVAL 06182p student 01gvxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 80.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06182p student 01p85y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 80.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06182p student 0sw6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 130.000 80.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06182p student 01yfm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 130.000 80.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06182p student 01p1z_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 130.000 80.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06182p student 0gs1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 130.000 80.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06182p student 03nkts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 80.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #18093-04pk1f PRED entity: 04pk1f PRED relation: nominated_for! PRED expected values: 02g3v6 => 103 concepts (87 used for prediction) PRED predicted values (max 10 best out of 194): 0gr42 (0.44 #326, 0.21 #1511, 0.20 #2222), 0gq_v (0.31 #256, 0.23 #3337, 0.19 #9973), 02x1z2s (0.31 #379, 0.18 #20152, 0.17 #20627), 0l8z1 (0.26 #3369, 0.24 #15881, 0.21 #2184), 0gq9h (0.26 #10016, 0.25 #299, 0.24 #1721), 057xs89 (0.25 #355, 0.24 #15881, 0.18 #20152), 0p9sw (0.25 #257, 0.23 #3338, 0.23 #2153), 0gr0m (0.25 #296, 0.21 #3377, 0.19 #6458), 02g3v6 (0.25 #258, 0.18 #1443, 0.17 #969), 02x2gy0 (0.25 #337, 0.09 #19677, 0.08 #1522) >> Best rule #326 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0kt_4; >> query: (?x6078, 0gr42) <- nominated_for(?x707, ?x6078), film(?x3028, ?x6078), ?x3028 = 0f0kz, nominated_for(?x143, ?x6078) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #258 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0kt_4; *> query: (?x6078, 02g3v6) <- nominated_for(?x707, ?x6078), film(?x3028, ?x6078), ?x3028 = 0f0kz, nominated_for(?x143, ?x6078) *> conf = 0.25 ranks of expected_values: 9 EVAL 04pk1f nominated_for! 02g3v6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 103.000 87.000 0.438 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18092-01j5ts PRED entity: 01j5ts PRED relation: type_of_union PRED expected values: 04ztj => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.77 #13, 0.76 #21, 0.75 #29), 01g63y (0.24 #22, 0.24 #34, 0.24 #38) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 03n0q5; 03wd5tk; 02tn0_; 0356dp; 03zrp; 03n0pv; >> query: (?x241, 04ztj) <- nominated_for(?x241, ?x2231), award(?x241, ?x995), sibling(?x1286, ?x241) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j5ts type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.771 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18091-0h0yt PRED entity: 0h0yt PRED relation: profession PRED expected values: 0cbd2 0dxtg => 92 concepts (91 used for prediction) PRED predicted values (max 10 best out of 71): 0dxtg (0.65 #448, 0.53 #1171, 0.45 #1315), 01d_h8 (0.56 #440, 0.39 #584, 0.32 #4476), 0cbd2 (0.45 #2028, 0.45 #2316, 0.44 #1596), 03gjzk (0.45 #449, 0.39 #1172, 0.26 #593), 02jknp (0.39 #442, 0.27 #10234, 0.27 #10089), 09jwl (0.38 #1895, 0.37 #2471, 0.36 #3480), 05z96 (0.30 #2742, 0.12 #2206, 0.12 #2062), 015btn (0.30 #2742, 0.03 #242, 0.02 #2119), 0747nrk (0.30 #2742, 0.02 #1212, 0.02 #1788), 0np9r (0.28 #1177, 0.27 #10234, 0.27 #10089) >> Best rule #448 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 0p_pd; 0h5g_; 0yfp; 0b_c7; 034np8; 0184dt; 0693l; 019vgs; 029_3; 06mn7; ... >> query: (?x7746, 0dxtg) <- award_nominee(?x374, ?x7746), award_winner(?x8367, ?x7746), influenced_by(?x7746, ?x2608) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0h0yt profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 91.000 0.650 http://example.org/people/person/profession EVAL 0h0yt profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 91.000 0.650 http://example.org/people/person/profession #18090-0lrh PRED entity: 0lrh PRED relation: people! PRED expected values: 03m3vr6 => 202 concepts (202 used for prediction) PRED predicted values (max 10 best out of 49): 0gk4g (0.40 #75, 0.23 #8461, 0.22 #5990), 02y0js (0.40 #262, 0.20 #977, 0.18 #1172), 012hw (0.29 #506, 0.25 #831, 0.20 #961), 0dq9p (0.18 #1187, 0.17 #407, 0.15 #1772), 034qg (0.17 #358, 0.14 #553, 0.14 #488), 01tf_6 (0.17 #421, 0.14 #1396, 0.12 #1526), 06z5s (0.14 #610, 0.12 #805, 0.11 #870), 051_y (0.14 #567, 0.12 #827, 0.10 #957), 0qcr0 (0.14 #1366, 0.12 #5981, 0.12 #1496), 04psf (0.14 #592, 0.03 #7418, 0.03 #8523) >> Best rule #75 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0br1w; 0qkj7; >> query: (?x2845, 0gk4g) <- location(?x2845, ?x3807), ?x3807 = 0xrzh, gender(?x2845, ?x231) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1930 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 01c58j; 01c1px; *> query: (?x2845, 03m3vr6) <- peers(?x2845, ?x1029), profession(?x2845, ?x1032), profession(?x4512, ?x1032), ?x4512 = 036jb *> conf = 0.04 ranks of expected_values: 27 EVAL 0lrh people! 03m3vr6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 202.000 202.000 0.400 http://example.org/people/cause_of_death/people #18089-06bc59 PRED entity: 06bc59 PRED relation: nominated_for! PRED expected values: 05p1dby => 79 concepts (68 used for prediction) PRED predicted values (max 10 best out of 190): 018wdw (0.33 #664, 0.28 #905, 0.15 #1628), 0p9sw (0.32 #2652, 0.30 #744, 0.29 #503), 019f4v (0.32 #2652, 0.27 #4394, 0.21 #6804), 0gq9h (0.32 #2652, 0.26 #4403, 0.24 #6090), 05ztjjw (0.32 #2652, 0.26 #733, 0.25 #492), 0gr4k (0.32 #2652, 0.26 #4366, 0.15 #6776), 02g3v6 (0.32 #2652, 0.23 #1468, 0.23 #2432), 02n9nmz (0.32 #2652, 0.22 #4398, 0.15 #15675), 0k611 (0.32 #2652, 0.21 #6824, 0.17 #6342), 02r22gf (0.32 #2652, 0.21 #511, 0.20 #1957) >> Best rule #664 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 03twd6; 0p3_y; 01gwk3; 02n72k; 01g3gq; >> query: (?x9786, 018wdw) <- prequel(?x3471, ?x9786), genre(?x9786, ?x812), films(?x3530, ?x9786), ?x812 = 01jfsb >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #16400 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1577 *> proper extension: 01tspc6; *> query: (?x9786, ?x2022) <- nominated_for(?x3462, ?x9786), nominated_for(?x3462, ?x1813), award(?x3462, ?x2022), nominated_for(?x68, ?x1813) *> conf = 0.19 ranks of expected_values: 34 EVAL 06bc59 nominated_for! 05p1dby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 79.000 68.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18088-01p3ty PRED entity: 01p3ty PRED relation: film_release_region PRED expected values: 03_3d 05qhw => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 135): 09c7w0 (0.94 #3350, 0.93 #3174, 0.93 #5115), 0d0vqn (0.91 #364, 0.90 #716, 0.86 #1246), 03h64 (0.90 #437, 0.77 #789, 0.67 #1319), 03rjj (0.89 #360, 0.82 #712, 0.73 #1242), 05qhw (0.89 #374, 0.76 #726, 0.58 #1256), 05r4w (0.87 #354, 0.85 #706, 0.76 #1236), 06mkj (0.87 #425, 0.84 #777, 0.80 #1307), 03gj2 (0.87 #388, 0.80 #740, 0.67 #1270), 059j2 (0.87 #748, 0.82 #396, 0.75 #1278), 0345h (0.85 #398, 0.80 #750, 0.65 #1280) >> Best rule #3350 for best value: >> intensional similarity = 3 >> extensional distance = 578 >> proper extension: 0dtw1x; 0crh5_f; 016kz1; 0564x; >> query: (?x2617, 09c7w0) <- film_release_region(?x2617, ?x789), production_companies(?x2617, ?x9690), titles(?x1882, ?x2617) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #374 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 77 *> proper extension: 0h95zbp; *> query: (?x2617, 05qhw) <- film_release_region(?x2617, ?x2146), production_companies(?x2617, ?x9690), ?x2146 = 03rk0 *> conf = 0.89 ranks of expected_values: 5, 28 EVAL 01p3ty film_release_region 05qhw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 89.000 89.000 0.936 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p3ty film_release_region 03_3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 89.000 89.000 0.936 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18087-02_286 PRED entity: 02_286 PRED relation: location! PRED expected values: 01gvr1 01v3s2_ 016z2j 015c2f 01d8yn 01nm3s 01sb5r 022p06 03f1r6t 0gn30 01cwcr 04zkj5 0252fh 02h3tp 01g42 04qt29 05hrq4 01ycfv 01l_yg 01hmb_ 016tbr 06yj20 => 214 concepts (178 used for prediction) PRED predicted values (max 10 best out of 2455): 0gn30 (0.50 #256014, 0.48 #143191, 0.46 #375337), 01817f (0.50 #256014, 0.48 #143191, 0.46 #375337), 01p1z_ (0.50 #256014, 0.48 #143191, 0.46 #375337), 06w6_ (0.50 #256014, 0.48 #143191, 0.46 #375337), 021bk (0.50 #256014, 0.48 #143191, 0.46 #375337), 03m8lq (0.50 #256014, 0.48 #143191, 0.46 #375337), 0677ng (0.50 #256014, 0.48 #143191, 0.46 #375337), 01wv9p (0.50 #256014, 0.48 #143191, 0.46 #375337), 02n9k (0.50 #256014, 0.48 #143191, 0.46 #375337), 012wg (0.48 #143191, 0.46 #375337, 0.46 #364488) >> Best rule #256014 for best value: >> intensional similarity = 2 >> extensional distance = 143 >> proper extension: 01rmjw; 0v1xg; 017j4q; 0135p7; 01mgsn; 0pfd9; 02gw_w; 01z26v; 0sc6p; 01j4rs; >> query: (?x739, ?x6275) <- place_of_birth(?x6275, ?x739), spouse(?x6275, ?x976) >> conf = 0.50 => this is the best rule for 9 predicted values ranks of expected_values: 1, 37, 154, 300, 528, 568, 677, 897, 1042, 1227, 1309, 1454, 1500, 1506, 1958, 1981, 2185, 2340 EVAL 02_286 location! 06yj20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 016tbr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 01hmb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 01l_yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 01ycfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 05hrq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 04qt29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 01g42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 02h3tp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 0252fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 04zkj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 01cwcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 0gn30 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 03f1r6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 022p06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 01sb5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 01nm3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 01d8yn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 015c2f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 016z2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 01v3s2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02_286 location! 01gvr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 214.000 178.000 0.495 http://example.org/people/person/places_lived./people/place_lived/location #18086-01vw26l PRED entity: 01vw26l PRED relation: category PRED expected values: 08mbj5d => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #4, 0.84 #18, 0.82 #3) >> Best rule #4 for best value: >> intensional similarity = 2 >> extensional distance = 86 >> proper extension: 0150jk; 0dtd6; 01czx; 01vrwfv; 0134s5; 01rm8b; 0mgcr; 0d193h; 013w2r; 0b1zz; ... >> query: (?x3494, 08mbj5d) <- artists(?x6513, ?x3494), artist(?x8738, ?x3494) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vw26l category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.864 http://example.org/common/topic/webpage./common/webpage/category #18085-01cgz PRED entity: 01cgz PRED relation: sports! PRED expected values: 0lbd9 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 22): 0jkvj (0.84 #910, 0.82 #909, 0.77 #45), 0lbd9 (0.84 #910, 0.82 #909, 0.77 #45), 0kbws (0.81 #113, 0.81 #112, 0.79 #180), 018ljb (0.50 #374, 0.50 #151, 0.47 #418), 0c_tl (0.50 #368, 0.50 #145, 0.40 #412), 0swff (0.40 #166, 0.33 #211, 0.25 #98), 0swbd (0.40 #161, 0.33 #206, 0.25 #93), 09n48 (0.40 #158, 0.33 #203, 0.25 #90), 019n8z (0.40 #172, 0.33 #217, 0.25 #104), 0sx7r (0.40 #159, 0.33 #204, 0.25 #91) >> Best rule #910 for best value: >> intensional similarity = 8 >> extensional distance = 50 >> proper extension: 04lgq; >> query: (?x1967, ?x3971) <- sports(?x3971, ?x1967), olympics(?x1453, ?x3971), olympics(?x94, ?x3971), ?x94 = 09c7w0, film_release_region(?x7864, ?x1453), film_release_region(?x204, ?x1453), ?x7864 = 0cbn7c, ?x204 = 028_yv >> conf = 0.84 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01cgz sports! 0lbd9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 78.000 0.841 http://example.org/olympics/olympic_games/sports #18084-05cvgl PRED entity: 05cvgl PRED relation: nominated_for! PRED expected values: 02n9nmz 02pqp12 => 99 concepts (83 used for prediction) PRED predicted values (max 10 best out of 210): 02n9nmz (0.52 #276, 0.32 #50, 0.25 #3618), 0k611 (0.52 #3681, 0.47 #289, 0.36 #1871), 02pqp12 (0.44 #51, 0.32 #3669, 0.29 #503), 0gr0m (0.39 #1860, 0.36 #3670, 0.34 #956), 02qvyrt (0.39 #85, 0.26 #311, 0.25 #1893), 0gqy2 (0.37 #3729, 0.26 #563, 0.26 #1919), 09qwmm (0.37 #23, 0.35 #475, 0.18 #17409), 099c8n (0.36 #275, 0.25 #3667, 0.22 #1857), 02qyntr (0.32 #168, 0.32 #394, 0.31 #3786), 0gqyl (0.31 #3688, 0.30 #522, 0.27 #296) >> Best rule #276 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 0qm8b; 02xtxw; >> query: (?x2734, 02n9nmz) <- country(?x2734, ?x512), nominated_for(?x384, ?x2734), currency(?x2734, ?x170), ?x384 = 03hkv_r >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 05cvgl nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 83.000 0.521 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05cvgl nominated_for! 02n9nmz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 83.000 0.521 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18083-02d_zc PRED entity: 02d_zc PRED relation: institution! PRED expected values: 02_xgp2 03bwzr4 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 20): 02_xgp2 (0.64 #53, 0.49 #74, 0.44 #137), 03bwzr4 (0.57 #55, 0.51 #139, 0.51 #287), 07s6fsf (0.54 #44, 0.44 #65, 0.43 #276), 0bkj86 (0.50 #49, 0.43 #6, 0.38 #197), 013zdg (0.32 #48, 0.29 #5, 0.26 #69), 04zx3q1 (0.30 #1628, 0.28 #1843, 0.26 #66), 01ysy9 (0.30 #1628, 0.28 #1843, 0.18 #191), 02m4yg (0.30 #1628, 0.28 #1843, 0.18 #191), 01gkg3 (0.30 #1628, 0.28 #1843, 0.18 #191), 027f2w (0.29 #50, 0.24 #71, 0.22 #198) >> Best rule #53 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 06mkj; 0d05w3; >> query: (?x5357, 02_xgp2) <- school(?x4979, ?x5357), ?x4979 = 0f4vx0, contains(?x94, ?x5357) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02d_zc institution! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.643 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02d_zc institution! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.643 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18082-01l_pn PRED entity: 01l_pn PRED relation: genre PRED expected values: 03k9fj 04t2t => 76 concepts (75 used for prediction) PRED predicted values (max 10 best out of 84): 07s9rl0 (0.62 #121, 0.62 #2773, 0.61 #3734), 03k9fj (0.47 #2661, 0.46 #250, 0.38 #490), 01jfsb (0.43 #371, 0.38 #131, 0.36 #1334), 01hmnh (0.32 #2668, 0.31 #377, 0.17 #497), 02l7c8 (0.29 #976, 0.29 #2787, 0.29 #615), 06n90 (0.27 #2663, 0.27 #252, 0.17 #492), 0gf28 (0.25 #64, 0.16 #544, 0.05 #1509), 04xvlr (0.25 #122, 0.15 #1447, 0.15 #2774), 0556j8 (0.25 #42, 0.15 #522, 0.04 #282), 060__y (0.25 #136, 0.15 #616, 0.14 #1943) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 08lr6s; 051zy_b; 0y_pg; >> query: (?x5608, 07s9rl0) <- nominated_for(?x2763, ?x5608), nominated_for(?x154, ?x5608), ?x2763 = 019pm_ >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2661 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 784 *> proper extension: 06n90; *> query: (?x5608, 03k9fj) <- genre(?x5608, ?x225), genre(?x12214, ?x225), ?x12214 = 042g97 *> conf = 0.47 ranks of expected_values: 2, 13 EVAL 01l_pn genre 04t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 76.000 75.000 0.625 http://example.org/film/film/genre EVAL 01l_pn genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 75.000 0.625 http://example.org/film/film/genre #18081-02c7k4 PRED entity: 02c7k4 PRED relation: film! PRED expected values: 01795t => 79 concepts (43 used for prediction) PRED predicted values (max 10 best out of 42): 01795t (0.50 #18, 0.40 #166, 0.21 #240), 0kk9v (0.48 #1710, 0.46 #1635, 0.46 #2385), 017s11 (0.25 #77, 0.12 #2462, 0.12 #670), 024rgt (0.25 #94, 0.05 #316, 0.04 #1132), 031rx9 (0.25 #100, 0.01 #248, 0.01 #2635), 086k8 (0.18 #594, 0.18 #446, 0.16 #1937), 05qd_ (0.17 #379, 0.16 #601, 0.15 #899), 03xq0f (0.16 #301, 0.14 #597, 0.12 #895), 016tt2 (0.15 #300, 0.13 #596, 0.13 #2463), 016tw3 (0.15 #2320, 0.14 #1646, 0.14 #1571) >> Best rule #18 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01jrbb; >> query: (?x6256, 01795t) <- award(?x6256, ?x1723), film(?x8439, ?x6256), film(?x2825, ?x6256), ?x8439 = 01rcmg, profession(?x2825, ?x987) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02c7k4 film! 01795t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 43.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #18080-053j4w4 PRED entity: 053j4w4 PRED relation: film_sets_designed PRED expected values: 07cdz => 111 concepts (90 used for prediction) PRED predicted values (max 10 best out of 100): 097zcz (0.70 #96, 0.70 #95, 0.35 #191), 0cwy47 (0.12 #7, 0.08 #103, 0.03 #199), 048rn (0.12 #47, 0.05 #143, 0.02 #239), 0h0wd9 (0.08 #87, 0.05 #183, 0.02 #279), 0kvb6p (0.08 #75, 0.05 #171, 0.02 #267), 02q_4ph (0.08 #36, 0.05 #132, 0.02 #228), 0bcndz (0.08 #8, 0.05 #104, 0.02 #200), 014knw (0.08 #84, 0.05 #180, 0.02 #276), 04wddl (0.08 #82, 0.05 #178, 0.02 #274), 029jt9 (0.08 #79, 0.05 #175, 0.02 #271) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 05b4rcb; >> query: (?x7438, ?x4280) <- nominated_for(?x7438, ?x4280), film_sets_designed(?x7438, ?x3614), nominated_for(?x198, ?x4280) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 053j4w4 film_sets_designed 07cdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 90.000 0.695 http://example.org/film/film_set_designer/film_sets_designed #18079-0fnmz PRED entity: 0fnmz PRED relation: major_field_of_study PRED expected values: 0g4gr => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 137): 03g3w (0.75 #146, 0.56 #266, 0.36 #3026), 05qjt (0.62 #127, 0.56 #247, 0.26 #3007), 04rjg (0.62 #139, 0.44 #259, 0.38 #3019), 037mh8 (0.50 #185, 0.44 #305, 0.25 #3065), 04sh3 (0.50 #193, 0.44 #313, 0.19 #3073), 01lhy (0.50 #132, 0.44 #252, 0.07 #3732), 01540 (0.50 #179, 0.33 #299, 0.22 #1019), 0g26h (0.41 #641, 0.39 #2921, 0.39 #2561), 02lp1 (0.39 #3011, 0.38 #2891, 0.38 #3131), 062z7 (0.38 #147, 0.33 #3027, 0.33 #267) >> Best rule #146 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 08qnnv; >> query: (?x3360, 03g3w) <- child(?x3360, ?x1615), student(?x3360, ?x800), institution(?x620, ?x3360) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2070 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 02zkz7; *> query: (?x3360, 0g4gr) <- colors(?x3360, ?x663), citytown(?x3360, ?x8468), school(?x1161, ?x3360), organization(?x5510, ?x3360) *> conf = 0.19 ranks of expected_values: 31 EVAL 0fnmz major_field_of_study 0g4gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 133.000 133.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #18078-016376 PRED entity: 016376 PRED relation: artists! PRED expected values: 09nwwf => 108 concepts (52 used for prediction) PRED predicted values (max 10 best out of 272): 05bt6j (0.67 #4597, 0.38 #7328, 0.37 #11883), 0ggx5q (0.60 #679, 0.56 #376, 0.42 #6146), 03_d0 (0.55 #9423, 0.40 #1223, 0.37 #3046), 0glt670 (0.54 #6112, 0.53 #9755, 0.44 #10968), 016clz (0.53 #7291, 0.43 #5470, 0.41 #6381), 0xhtw (0.52 #1836, 0.40 #6392, 0.40 #7606), 02lnbg (0.47 #6128, 0.36 #2787, 0.33 #1267), 09nwwf (0.40 #132, 0.18 #1041, 0.16 #3167), 03lty (0.37 #1846, 0.28 #7616, 0.28 #6402), 02k_kn (0.33 #365, 0.30 #668, 0.28 #3097) >> Best rule #4597 for best value: >> intensional similarity = 5 >> extensional distance = 67 >> proper extension: 05crg7; >> query: (?x10712, 05bt6j) <- category(?x10712, ?x134), artists(?x3928, ?x10712), artists(?x3928, ?x2925), ?x2925 = 01vx5w7, group(?x227, ?x10712) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #132 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0xsk8; 0ql36; *> query: (?x10712, 09nwwf) <- category(?x10712, ?x134), artists(?x13401, ?x10712), artist(?x2299, ?x10712), ?x134 = 08mbj5d, ?x13401 = 0509cr *> conf = 0.40 ranks of expected_values: 8 EVAL 016376 artists! 09nwwf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 108.000 52.000 0.667 http://example.org/music/genre/artists #18077-01w_10 PRED entity: 01w_10 PRED relation: student! PRED expected values: 019v9k => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 12): 014mlp (0.30 #406, 0.17 #6, 0.15 #286), 02_xgp2 (0.25 #14, 0.11 #294, 0.09 #74), 019v9k (0.17 #10, 0.08 #30, 0.06 #90), 013zdg (0.17 #8, 0.08 #28, 0.06 #68), 02h4rq6 (0.08 #3, 0.08 #23, 0.06 #63), 016t_3 (0.08 #4, 0.05 #404, 0.02 #84), 04zx3q1 (0.08 #2, 0.04 #282, 0.04 #322), 0bkj86 (0.08 #29, 0.05 #449, 0.05 #329), 028dcg (0.07 #418, 0.04 #178, 0.03 #78), 03mkk4 (0.05 #413, 0.05 #53, 0.02 #333) >> Best rule #406 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 0n6f8; 04l19_; 01zh29; 03c6v3; 01hbq0; >> query: (?x8122, 014mlp) <- profession(?x8122, ?x1041), film(?x8122, ?x2218), student(?x254, ?x8122) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 099bk; *> query: (?x8122, 019v9k) <- organization(?x8122, ?x4542), student(?x254, ?x8122), major_field_of_study(?x122, ?x254) *> conf = 0.17 ranks of expected_values: 3 EVAL 01w_10 student! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 93.000 0.299 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #18076-01_f_5 PRED entity: 01_f_5 PRED relation: film PRED expected values: 07g1sm => 161 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1002): 09ps01 (0.63 #101987, 0.50 #148495, 0.49 #153860), 07yvsn (0.63 #101987, 0.50 #148495, 0.49 #153860), 0209xj (0.47 #35781, 0.44 #48305, 0.43 #121665), 03wy8t (0.47 #35781, 0.44 #48305, 0.42 #14309), 013q07 (0.22 #3934, 0.14 #5723, 0.12 #14666), 0f40w (0.22 #3940, 0.14 #5729, 0.08 #14672), 02b61v (0.22 #4596, 0.07 #6385, 0.04 #15328), 0gjc4d3 (0.22 #536, 0.04 #14845, 0.02 #20212), 0sxgv (0.13 #8201), 0fdv3 (0.12 #141341, 0.08 #116300, 0.02 #34275) >> Best rule #101987 for best value: >> intensional similarity = 3 >> extensional distance = 328 >> proper extension: 0m2wm; 05hdf; 02wb6yq; 01pctb; 02zrv7; 01gc7h; >> query: (?x6275, ?x2899) <- participant(?x2705, ?x6275), nominated_for(?x6275, ?x2899), location(?x6275, ?x739) >> conf = 0.63 => this is the best rule for 2 predicted values *> Best rule #141341 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 590 *> proper extension: 0584j4n; *> query: (?x6275, ?x6213) <- nominated_for(?x6275, ?x6097), nominated_for(?x6097, ?x6213) *> conf = 0.12 ranks of expected_values: 11 EVAL 01_f_5 film 07g1sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 161.000 98.000 0.631 http://example.org/film/actor/film./film/performance/film #18075-01kmd4 PRED entity: 01kmd4 PRED relation: currency PRED expected values: 09nqf => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.73 #16, 0.55 #19, 0.47 #34) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 02qjj7; >> query: (?x7555, 09nqf) <- participant(?x7555, ?x3291), people(?x2510, ?x7555), athlete(?x4833, ?x7555), sport(?x660, ?x4833) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kmd4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.727 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #18074-02f1c PRED entity: 02f1c PRED relation: type_of_union PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.70 #5, 0.70 #185, 0.69 #241), 01g63y (0.15 #10, 0.14 #2, 0.13 #130) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 012wg; 01v6480; 09xx0m; >> query: (?x8799, 04ztj) <- profession(?x8799, ?x131), award(?x8799, ?x2585), ?x2585 = 054ks3 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02f1c type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.698 http://example.org/people/person/spouse_s./people/marriage/type_of_union #18073-0kvgnq PRED entity: 0kvgnq PRED relation: film! PRED expected values: 0fbx6 => 92 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1013): 01y64_ (0.61 #91726, 0.58 #43782, 0.49 #64626), 02qgqt (0.20 #18, 0.06 #6272, 0.06 #10441), 04fhn_ (0.20 #684, 0.04 #6938, 0.03 #11107), 01r93l (0.20 #750, 0.03 #4918, 0.03 #21598), 0lpjn (0.11 #2564, 0.04 #8819, 0.02 #63020), 0c0k1 (0.10 #1511, 0.08 #5679, 0.05 #20274), 0169dl (0.10 #402, 0.05 #14995, 0.05 #19165), 02yxwd (0.10 #746, 0.05 #15339, 0.04 #2830), 0h0wc (0.10 #425, 0.04 #42121, 0.03 #33783), 09l3p (0.10 #751, 0.04 #15344, 0.03 #27854) >> Best rule #91726 for best value: >> intensional similarity = 4 >> extensional distance = 938 >> proper extension: 03_b1g; >> query: (?x5752, ?x4440) <- nominated_for(?x4440, ?x5752), people(?x7790, ?x4440), type_of_union(?x4440, ?x566), film(?x4440, ?x3745) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #2824 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 0ckrgs; *> query: (?x5752, 0fbx6) <- film(?x9001, ?x5752), film_crew_role(?x5752, ?x137), film(?x9001, ?x9527), ?x9527 = 01rnly *> conf = 0.02 ranks of expected_values: 368 EVAL 0kvgnq film! 0fbx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 92.000 47.000 0.613 http://example.org/film/actor/film./film/performance/film #18072-0f4vbz PRED entity: 0f4vbz PRED relation: participant PRED expected values: 014zcr => 160 concepts (98 used for prediction) PRED predicted values (max 10 best out of 393): 01fh9 (0.81 #24251, 0.81 #17232, 0.80 #36373), 014zcr (0.81 #24251, 0.81 #17232, 0.80 #36373), 06cgy (0.17 #2552, 0.10 #2551, 0.07 #20423), 015rkw (0.17 #2552, 0.10 #2551, 0.07 #20423), 02t__3 (0.10 #2551, 0.08 #25526, 0.07 #20423), 01swck (0.10 #2551, 0.07 #20423, 0.07 #24250), 030hcs (0.10 #2551, 0.07 #20423, 0.07 #24250), 0h10vt (0.10 #2551, 0.07 #20423, 0.07 #24250), 01wz01 (0.10 #2551, 0.07 #20423, 0.07 #24250), 016khd (0.10 #2551, 0.07 #20423, 0.07 #24250) >> Best rule #24251 for best value: >> intensional similarity = 3 >> extensional distance = 271 >> proper extension: 01sl1q; 0184jc; 04bdxl; 01vvydl; 0411q; 01xdf5; 04t2l2; 0lbj1; 0h0jz; 05ty4m; ... >> query: (?x2258, ?x286) <- award_winner(?x2258, ?x851), award(?x2258, ?x375), participant(?x286, ?x2258) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0f4vbz participant 014zcr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 98.000 0.807 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #18071-0640m69 PRED entity: 0640m69 PRED relation: produced_by PRED expected values: 02r251z => 91 concepts (58 used for prediction) PRED predicted values (max 10 best out of 104): 02r251z (0.50 #631, 0.06 #1018, 0.04 #1405), 06rq2l (0.21 #697, 0.04 #3105, 0.04 #4659), 01fyzy (0.21 #777, 0.14 #7373, 0.12 #7760), 092kgw (0.17 #195, 0.07 #585, 0.02 #972), 016_mj (0.08 #389, 0.03 #388, 0.03 #7761), 02k21g (0.08 #389, 0.03 #388, 0.02 #4270), 01tvz5j (0.08 #389, 0.03 #388, 0.02 #4270), 01wbg84 (0.08 #389, 0.03 #388, 0.02 #4270), 05ty4m (0.07 #402, 0.06 #789, 0.02 #5060), 0fvf9q (0.04 #783, 0.03 #8542, 0.03 #3111) >> Best rule #631 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0ch3qr1; 0gwgn1k; >> query: (?x11980, 02r251z) <- nominated_for(?x1335, ?x11980), production_companies(?x11980, ?x1478), ?x1335 = 0pz91, film_crew_role(?x11980, ?x137) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0640m69 produced_by 02r251z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 58.000 0.500 http://example.org/film/film/produced_by #18070-0ftqr PRED entity: 0ftqr PRED relation: music! PRED expected values: 031f_m => 102 concepts (67 used for prediction) PRED predicted values (max 10 best out of 42): 03_gz8 (0.10 #10816, 0.01 #16912), 017n9 (0.07 #6077, 0.06 #8109, 0.06 #7093), 01qz5 (0.07 #5891, 0.06 #7923, 0.06 #6907), 0qmhk (0.07 #5641, 0.06 #7673, 0.06 #6657), 04j4tx (0.07 #5499, 0.06 #7531, 0.06 #6515), 049mql (0.07 #5488, 0.06 #7520, 0.06 #6504), 035yn8 (0.07 #5249, 0.06 #7281, 0.06 #6265), 0564x (0.06 #9091, 0.06 #8075, 0.06 #7059), 016ztl (0.06 #8731, 0.06 #7715, 0.06 #6699), 02q3fdr (0.06 #8728, 0.06 #7712, 0.06 #6696) >> Best rule #10816 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 03_0p; >> query: (?x10039, 03_gz8) <- instrumentalists(?x75, ?x10039), category(?x10039, ?x134), gender(?x10039, ?x231), ?x75 = 07y_7, ?x231 = 05zppz >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ftqr music! 031f_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 67.000 0.095 http://example.org/film/film/music #18069-02x4wb PRED entity: 02x4wb PRED relation: ceremony PRED expected values: 0jzphpx => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 125): 0jzphpx (0.68 #656, 0.49 #156, 0.46 #531), 0gx1673 (0.56 #230, 0.50 #730, 0.44 #355), 092868 (0.38 #124, 0.06 #374, 0.04 #499), 08pc1x (0.38 #123, 0.06 #373, 0.04 #498), 05c1t6z (0.17 #2011, 0.12 #4261, 0.12 #4011), 02q690_ (0.16 #2055, 0.11 #4055, 0.11 #3930), 0gvstc3 (0.16 #2026, 0.10 #4276, 0.10 #4401), 0bzm81 (0.16 #1516, 0.13 #2016, 0.10 #3891), 0n8_m93 (0.16 #1603, 0.13 #2103, 0.10 #3978), 03nnm4t (0.15 #2064, 0.11 #4064, 0.10 #3939) >> Best rule #656 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 02581c; 0257w4; 02flpc; 024vjd; 024fz9; 019bnn; 024_41; 02flqd; 0257wh; 024_dt; ... >> query: (?x11068, 0jzphpx) <- award_winner(?x11068, ?x3933), ceremony(?x11068, ?x2054), award(?x646, ?x11068), ?x2054 = 0gpjbt >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02x4wb ceremony 0jzphpx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.679 http://example.org/award/award_category/winners./award/award_honor/ceremony #18068-03ft8 PRED entity: 03ft8 PRED relation: story_by! PRED expected values: 06r2h => 235 concepts (231 used for prediction) PRED predicted values (max 10 best out of 252): 012gk9 (0.23 #9237, 0.17 #23955, 0.14 #17111), 0mbql (0.20 #1598, 0.11 #4678, 0.11 #4334), 06r2h (0.18 #12604, 0.14 #11235, 0.11 #17739), 014lc_ (0.17 #4448, 0.15 #20533, 0.12 #26696), 0bv8h2 (0.14 #2856, 0.11 #4910, 0.08 #9357), 063y9fp (0.14 #3024, 0.08 #9525, 0.06 #12606), 04jpg2p (0.14 #3013, 0.08 #9514, 0.06 #13623), 01f39b (0.14 #2935, 0.08 #9436, 0.06 #13545), 039zft (0.14 #2932, 0.08 #9433, 0.06 #13542), 085bd1 (0.14 #2828, 0.08 #9329, 0.06 #13438) >> Best rule #9237 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0fx02; >> query: (?x1683, ?x8971) <- people(?x10199, ?x1683), student(?x735, ?x1683), written_by(?x8971, ?x1683), story_by(?x3221, ?x1683) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #12604 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 0721cy; 04snp2; 046mxj; *> query: (?x1683, 06r2h) <- program(?x1683, ?x7488), tv_program(?x1683, ?x11035), nationality(?x1683, ?x94), story_by(?x3221, ?x1683) *> conf = 0.18 ranks of expected_values: 3 EVAL 03ft8 story_by! 06r2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 235.000 231.000 0.226 http://example.org/film/film/story_by #18067-0267wwv PRED entity: 0267wwv PRED relation: film_release_region PRED expected values: 03gj2 0h7x => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 150): 0f8l9c (0.93 #521, 0.90 #1181, 0.90 #2833), 03rjj (0.87 #996, 0.85 #2813, 0.83 #3309), 03h64 (0.84 #2884, 0.81 #2224, 0.81 #1067), 0345h (0.83 #2846, 0.80 #3839, 0.79 #2186), 0154j (0.83 #2812, 0.78 #3805, 0.78 #3308), 035qy (0.83 #2188, 0.82 #2848, 0.82 #1031), 015fr (0.82 #2827, 0.78 #2167, 0.78 #3820), 05qhw (0.81 #2824, 0.78 #3817, 0.77 #2164), 07ssc (0.81 #1009, 0.80 #2826, 0.79 #2166), 03gj2 (0.81 #1020, 0.80 #2837, 0.78 #2177) >> Best rule #521 for best value: >> intensional similarity = 6 >> extensional distance = 41 >> proper extension: 04zl8; 0m3gy; 01xlqd; >> query: (?x10095, 0f8l9c) <- film_release_region(?x10095, ?x2984), film_release_region(?x10095, ?x1229), film_release_region(?x10095, ?x985), ?x2984 = 082fr, ?x985 = 0k6nt, ?x1229 = 059j2 >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1020 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 0dckvs; 0djb3vw; 0c40vxk; 0bwfwpj; 0jjy0; 0872p_c; 0gtvrv3; 04w7rn; 0gxtknx; 0gj9tn5; ... *> query: (?x10095, 03gj2) <- film_release_region(?x10095, ?x1603), film_crew_role(?x10095, ?x137), ?x1603 = 06bnz, film(?x4307, ?x10095) *> conf = 0.81 ranks of expected_values: 10, 30 EVAL 0267wwv film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 108.000 108.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0267wwv film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 108.000 108.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18066-01m3x5p PRED entity: 01m3x5p PRED relation: role PRED expected values: 0214km => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 88): 05r5c (0.39 #2320, 0.39 #114, 0.36 #1899), 0342h (0.38 #2316, 0.36 #1895, 0.36 #1790), 01vdm0 (0.26 #2345, 0.25 #1924, 0.25 #1819), 02sgy (0.23 #2318, 0.22 #1897, 0.21 #1792), 042v_gx (0.21 #1795, 0.21 #1900, 0.21 #2321), 05842k (0.17 #2391, 0.13 #1970, 0.13 #1865), 018vs (0.16 #2326, 0.14 #1905, 0.13 #1800), 01vj9c (0.16 #2328, 0.15 #1802, 0.14 #1907), 0l14qv (0.15 #2317, 0.13 #1896, 0.12 #1791), 026t6 (0.15 #2314, 0.13 #1893, 0.12 #1788) >> Best rule #2320 for best value: >> intensional similarity = 3 >> extensional distance = 383 >> proper extension: 053y0s; 03c7ln; 02rgz4; 0274ck; 07_3qd; 01w923; 012zng; 0zjpz; 09prnq; 02jg92; ... >> query: (?x4184, 05r5c) <- nationality(?x4184, ?x94), artists(?x505, ?x4184), role(?x4184, ?x2206) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1886 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 223 *> proper extension: 01pbxb; 01vvydl; 07s3vqk; 01lmj3q; 0m2l9; 01wl38s; 02l840; 03f5spx; 01gf5h; 018y2s; ... *> query: (?x4184, 0214km) <- award_nominee(?x4184, ?x5172), role(?x4184, ?x2206), nationality(?x4184, ?x94) *> conf = 0.05 ranks of expected_values: 25 EVAL 01m3x5p role 0214km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 120.000 120.000 0.390 http://example.org/music/artist/track_contributions./music/track_contribution/role #18065-01hkck PRED entity: 01hkck PRED relation: nationality PRED expected values: 09c7w0 => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 54): 09c7w0 (0.88 #4430, 0.87 #3220, 0.87 #3019), 02jx1 (0.39 #1036, 0.27 #935, 0.25 #433), 01n7q (0.29 #13494, 0.27 #16205, 0.27 #16507), 0l2k7 (0.29 #13494, 0.27 #16205, 0.27 #16507), 0pc56 (0.25 #14097, 0.25 #14399), 07ssc (0.20 #515, 0.16 #3637, 0.15 #2024), 06bnz (0.11 #2716, 0.10 #541, 0.09 #6143), 0d060g (0.11 #2716, 0.09 #6143, 0.07 #8262), 0345h (0.11 #2716, 0.09 #6143, 0.07 #8262), 0f8l9c (0.11 #2716, 0.09 #6143, 0.07 #8262) >> Best rule #4430 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 01lcxbb; 01bzr4; 01y8d4; 05d1y; 06hgj; 01934k; 03f4k; 0gppg; 03jxw; 02s6sh; ... >> query: (?x11311, 09c7w0) <- location(?x11311, ?x13692), county(?x13692, ?x14029), place_of_death(?x11311, ?x1523) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hkck nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.885 http://example.org/people/person/nationality #18064-0p4wb PRED entity: 0p4wb PRED relation: industry PRED expected values: 0vg8 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 46): 08mh3kd (0.25 #60, 0.15 #204, 0.13 #156), 0vg8 (0.23 #99, 0.15 #195, 0.09 #435), 029g_vk (0.20 #155, 0.12 #683, 0.11 #491), 019z7b (0.20 #153, 0.06 #921, 0.06 #681), 020mfr (0.17 #737, 0.15 #113, 0.12 #1265), 01mw1 (0.13 #145, 0.13 #2210, 0.12 #721), 01mf0 (0.13 #175, 0.12 #367, 0.10 #415), 07c1v (0.13 #187, 0.07 #427, 0.06 #379), 06xw2 (0.13 #180, 0.05 #420, 0.03 #708), 02h400t (0.10 #410, 0.09 #362, 0.08 #698) >> Best rule #60 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0cv9b; 01zpmq; >> query: (?x610, 08mh3kd) <- category(?x610, ?x134), citytown(?x610, ?x8977), service_location(?x610, ?x1603), contact_category(?x610, ?x897), ?x1603 = 06bnz, service_language(?x610, ?x254) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #99 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 011k1h; 0hsb3; 05w3y; 04fv0k; 06p8m; *> query: (?x610, 0vg8) <- category(?x610, ?x134), citytown(?x610, ?x8977), service_location(?x610, ?x512), ?x512 = 07ssc, organization(?x4682, ?x610), place_of_birth(?x3069, ?x8977) *> conf = 0.23 ranks of expected_values: 2 EVAL 0p4wb industry 0vg8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.250 http://example.org/business/business_operation/industry #18063-05bnp0 PRED entity: 05bnp0 PRED relation: film PRED expected values: 0bvn25 => 94 concepts (50 used for prediction) PRED predicted values (max 10 best out of 904): 02825nf (0.55 #17791, 0.41 #87183, 0.38 #55153), 0298n7 (0.12 #1338, 0.03 #85402), 083shs (0.12 #19, 0.03 #85402), 04sntd (0.12 #487, 0.02 #2266), 0g0x9c (0.12 #1354, 0.01 #4912), 01pvxl (0.12 #899), 024mpp (0.12 #644), 0dnvn3 (0.12 #55), 03m5y9p (0.06 #1409, 0.04 #3188, 0.03 #85402), 01y9r2 (0.06 #1335, 0.04 #3114, 0.01 #15567) >> Best rule #17791 for best value: >> intensional similarity = 2 >> extensional distance = 355 >> proper extension: 04b19t; 0bkq_8; >> query: (?x123, ?x3124) <- languages(?x123, ?x254), nominated_for(?x123, ?x3124) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #12503 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 266 *> proper extension: 03qcq; 06cv1; 066m4g; 035gjq; 016kjs; 06w2sn5; 02lxj_; 027cxsm; 033wx9; 02ld6x; ... *> query: (?x123, 0bvn25) <- award_nominee(?x1733, ?x123), place_of_birth(?x123, ?x2935), participant(?x123, ?x1017) *> conf = 0.04 ranks of expected_values: 114 EVAL 05bnp0 film 0bvn25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 94.000 50.000 0.554 http://example.org/film/actor/film./film/performance/film #18062-03rj0 PRED entity: 03rj0 PRED relation: country! PRED expected values: 09w1n => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 51): 06wrt (0.90 #322, 0.85 #424, 0.83 #169), 06f41 (0.83 #168, 0.81 #321, 0.78 #423), 07gyv (0.83 #160, 0.76 #313, 0.71 #262), 03hr1p (0.82 #277, 0.80 #941, 0.79 #584), 01gqfm (0.82 #302, 0.67 #200, 0.63 #455), 07jbh (0.81 #338, 0.76 #287, 0.75 #185), 07bs0 (0.76 #268, 0.75 #166, 0.62 #319), 064vjs (0.76 #285, 0.71 #336, 0.67 #592), 0w0d (0.75 #165, 0.71 #267, 0.70 #420), 019tzd (0.75 #192, 0.71 #294, 0.63 #447) >> Best rule #322 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0f8l9c; 06bnz; 06t2t; 016wzw; >> query: (?x2267, 06wrt) <- film_release_region(?x5255, ?x2267), film_release_region(?x3784, ?x2267), ?x5255 = 01sby_, ?x3784 = 0bmhvpr >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #176 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 09c7w0; *> query: (?x2267, 09w1n) <- film_release_region(?x5255, ?x2267), film_release_region(?x5052, ?x2267), ?x5255 = 01sby_, ?x5052 = 04yg13l *> conf = 0.75 ranks of expected_values: 15 EVAL 03rj0 country! 09w1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 94.000 94.000 0.905 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #18061-0bjrnt PRED entity: 0bjrnt PRED relation: institution PRED expected values: 07wrz 0ymcz => 25 concepts (22 used for prediction) PRED predicted values (max 10 best out of 974): 07szy (0.86 #11351, 0.85 #10156, 0.83 #9558), 065y4w7 (0.85 #10127, 0.83 #9529, 0.79 #11322), 07wjk (0.79 #11373, 0.78 #6005, 0.77 #10775), 0bwfn (0.78 #6230, 0.75 #9211, 0.75 #5042), 0g8rj (0.78 #6129, 0.75 #9110, 0.75 #4941), 025v3k (0.78 #6065, 0.75 #4877, 0.71 #11433), 0gl5_ (0.78 #6203, 0.75 #5015, 0.71 #11571), 08qnnv (0.78 #6172, 0.75 #4984, 0.71 #4391), 01jssp (0.78 #5946, 0.75 #4758, 0.71 #4165), 07wlf (0.78 #6017, 0.71 #4236, 0.69 #10787) >> Best rule #11351 for best value: >> intensional similarity = 24 >> extensional distance = 12 >> proper extension: 022h5x; >> query: (?x1390, 07szy) <- major_field_of_study(?x1390, ?x732), institution(?x1390, ?x13707), institution(?x1390, ?x11614), institution(?x1390, ?x3513), currency(?x3513, ?x170), school(?x2569, ?x3513), student(?x3513, ?x2015), colors(?x13707, ?x1101), currency(?x13707, ?x1099), major_field_of_study(?x3513, ?x7134), contains(?x512, ?x13707), ?x7134 = 02_7t, institution(?x1771, ?x3513), major_field_of_study(?x4187, ?x732), school_type(?x11614, ?x5931), registering_agency(?x3513, ?x1982), ?x4187 = 05mv4, colors(?x3513, ?x3364), category(?x11614, ?x134), organization(?x2361, ?x11614), institution(?x1771, ?x8095), institution(?x1771, ?x4794), ?x4794 = 027kp3, ?x8095 = 02mp0g >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #8985 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 10 *> proper extension: 013zdg; *> query: (?x1390, 07wrz) <- major_field_of_study(?x1390, ?x254), institution(?x1390, ?x13707), institution(?x1390, ?x4199), institution(?x1390, ?x3513), ?x3513 = 0pspl, category(?x13707, ?x134), currency(?x13707, ?x1099), contains(?x512, ?x13707), school_type(?x13707, ?x3092), country(?x124, ?x512), film_release_region(?x499, ?x512), nationality(?x111, ?x512), olympics(?x512, ?x358), titles(?x512, ?x582), combatants(?x512, ?x94), film_release_region(?x9294, ?x512), film_release_region(?x8373, ?x512), film_release_region(?x7629, ?x512), film_release_region(?x6527, ?x512), ?x9294 = 0m3gy, ?x7629 = 02825nf, major_field_of_study(?x4199, ?x1154), ?x6527 = 0gfh84d, ?x8373 = 0bs8hvm *> conf = 0.58 ranks of expected_values: 78, 222 EVAL 0bjrnt institution 0ymcz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 22.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bjrnt institution 07wrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 25.000 22.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #18060-0jmfb PRED entity: 0jmfb PRED relation: draft PRED expected values: 025tn92 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 17): 025tn92 (0.88 #457, 0.86 #337, 0.82 #234), 038c0q (0.80 #365, 0.79 #1121, 0.75 #263), 038981 (0.79 #1121, 0.75 #792, 0.74 #494), 02pq_rp (0.47 #505, 0.38 #573, 0.37 #886), 092j54 (0.41 #730, 0.34 #921, 0.34 #887), 05vsb7 (0.41 #722, 0.33 #913, 0.32 #879), 0g3zpp (0.38 #723, 0.36 #914, 0.35 #880), 09l0x9 (0.38 #732, 0.34 #923, 0.34 #889), 02r6gw6 (0.37 #510, 0.35 #891, 0.34 #925), 047dpm0 (0.37 #514, 0.34 #895, 0.33 #929) >> Best rule #457 for best value: >> intensional similarity = 20 >> extensional distance = 15 >> proper extension: 0jmbv; 0jm5b; >> query: (?x2398, 025tn92) <- position(?x2398, ?x6848), position(?x2398, ?x5755), position(?x2398, ?x4570), position(?x9760, ?x4570), position(?x9049, ?x4570), position(?x8079, ?x4570), position(?x7158, ?x4570), position(?x5419, ?x4570), position(?x2568, ?x4570), ?x8079 = 04cxw5b, team(?x4570, ?x9931), ?x6848 = 02_ssl, ?x9049 = 0jmm4, ?x9931 = 0jm3b, ?x7158 = 0jm4v, ?x5419 = 0jmmn, ?x9760 = 0bwjj, draft(?x2398, ?x4979), ?x5755 = 0355dz, ?x2568 = 0jmcb >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jmfb draft 025tn92 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.882 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #18059-037xlx PRED entity: 037xlx PRED relation: film! PRED expected values: 015qq1 => 100 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1012): 034hck (0.74 #8332, 0.62 #70829, 0.62 #54156), 030_1m (0.45 #70828, 0.45 #58323, 0.45 #54155), 021yc7p (0.45 #70828, 0.45 #58323, 0.45 #54155), 02v406 (0.25 #2810, 0.01 #11143, 0.01 #13225), 04yywz (0.17 #2101, 0.08 #19, 0.03 #14599), 016z51 (0.17 #963, 0.08 #3045, 0.03 #11378), 0tc7 (0.17 #2476, 0.02 #12891, 0.02 #4559), 0234pg (0.17 #3834, 0.02 #14249, 0.02 #8000), 0451j (0.17 #3407, 0.01 #38821), 07f8wg (0.11 #72913, 0.11 #87499, 0.11 #77082) >> Best rule #8332 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0ds33; 03rtz1; 01kff7; 05h43ls; 0cn_b8; 0kvgtf; 01hqk; 01qvz8; 02ntb8; 02wgbb; ... >> query: (?x5731, ?x9403) <- nominated_for(?x9403, ?x5731), spouse(?x9403, ?x2715), nominated_for(?x154, ?x5731), ?x154 = 05b4l5x >> conf = 0.74 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 037xlx film! 015qq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 59.000 0.738 http://example.org/film/actor/film./film/performance/film #18058-02w9k1c PRED entity: 02w9k1c PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.83 #91, 0.82 #221, 0.82 #295), 0735l (0.17 #6, 0.16 #44, 0.14 #38), 02nxhr (0.05 #2, 0.05 #29, 0.04 #40), 07c52 (0.05 #123, 0.03 #57, 0.03 #35), 07z4p (0.04 #125, 0.03 #110, 0.03 #141) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 0cbl95; >> query: (?x5819, 029j_) <- genre(?x5819, ?x1509), language(?x5819, ?x254), ?x1509 = 060__y, film_release_region(?x5819, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02w9k1c film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.831 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #18057-017fp PRED entity: 017fp PRED relation: titles PRED expected values: 047d21r 03vyw8 0n_hp => 43 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1526): 02rcdc2 (0.70 #1467, 0.47 #16152, 0.45 #16151), 0170xl (0.70 #1467, 0.47 #16152, 0.40 #11665), 016kv6 (0.70 #1467, 0.47 #16152, 0.36 #13210), 092vkg (0.70 #1467, 0.47 #16152, 0.36 #16150), 01gc7 (0.70 #1467, 0.45 #16151, 0.40 #10307), 015qqg (0.70 #1467, 0.45 #16151, 0.36 #16150), 016z9n (0.70 #1467, 0.40 #10575, 0.36 #13210), 0294mx (0.70 #1467, 0.40 #11285, 0.36 #13210), 01flv_ (0.70 #1467, 0.40 #11119, 0.36 #13210), 025rvx0 (0.70 #1467, 0.40 #11072, 0.36 #13210) >> Best rule #1467 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 04xvlr; >> query: (?x1316, ?x195) <- titles(?x1316, ?x8664), titles(?x1316, ?x4159), titles(?x1316, ?x1813), titles(?x1316, ?x675), genre(?x195, ?x1316), film(?x1104, ?x675), ?x1813 = 09gq0x5, ?x8664 = 03hfmm, ?x4159 = 011yr9, film(?x399, ?x675) >> conf = 0.70 => this is the best rule for 51 predicted values *> Best rule #11496 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 0345h; *> query: (?x1316, 0n_hp) <- titles(?x1316, ?x6387), titles(?x1316, ?x2386), titles(?x1316, ?x861), ?x6387 = 047myg9, award(?x861, ?x618), film(?x2156, ?x2386), film_crew_role(?x2386, ?x137) *> conf = 0.40 ranks of expected_values: 93, 117, 304 EVAL 017fp titles 0n_hp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 43.000 16.000 0.697 http://example.org/media_common/netflix_genre/titles EVAL 017fp titles 03vyw8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 43.000 16.000 0.697 http://example.org/media_common/netflix_genre/titles EVAL 017fp titles 047d21r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 16.000 0.697 http://example.org/media_common/netflix_genre/titles #18056-024mpp PRED entity: 024mpp PRED relation: film_release_region PRED expected values: 05r4w 0d0kn => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 131): 05r4w (0.90 #971, 0.88 #1109, 0.84 #1800), 0d060g (0.86 #974, 0.82 #1112, 0.79 #1803), 03rk0 (0.71 #1012, 0.67 #1150, 0.55 #874), 01p1v (0.68 #1146, 0.68 #1008, 0.50 #1837), 06f32 (0.55 #1158, 0.55 #1020, 0.49 #1849), 047lj (0.55 #978, 0.51 #1116, 0.39 #1807), 09pmkv (0.54 #989, 0.53 #1127, 0.39 #1818), 03ryn (0.48 #1037, 0.47 #1175, 0.35 #1866), 06c1y (0.46 #1138, 0.45 #1000, 0.37 #724), 07ylj (0.46 #1129, 0.45 #991, 0.32 #1820) >> Best rule #971 for best value: >> intensional similarity = 8 >> extensional distance = 82 >> proper extension: 087wc7n; 0h3xztt; 03bx2lk; 0407yfx; 0j43swk; >> query: (?x3938, 05r4w) <- film_release_region(?x3938, ?x4743), film_release_region(?x3938, ?x1603), film_release_region(?x3938, ?x344), film_release_region(?x3938, ?x252), ?x4743 = 03spz, ?x252 = 03_3d, ?x344 = 04gzd, ?x1603 = 06bnz >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 23 EVAL 024mpp film_release_region 0d0kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 87.000 87.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 024mpp film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #18055-0j46b PRED entity: 0j46b PRED relation: current_club! PRED expected values: 035qgm => 110 concepts (75 used for prediction) PRED predicted values (max 10 best out of 51): 02s9vc (0.50 #77, 0.25 #49, 0.20 #106), 0cnk2q (0.33 #1, 0.25 #57, 0.25 #29), 02s2lg (0.33 #5, 0.25 #33, 0.20 #90), 03z8bw (0.33 #133, 0.14 #363, 0.10 #419), 02pp1 (0.25 #53, 0.20 #110, 0.17 #197), 03dj48 (0.25 #50, 0.20 #107, 0.08 #651), 03zrc_ (0.25 #71, 0.14 #358, 0.07 #414), 02rqxc (0.24 #293, 0.20 #236, 0.20 #551), 03y_f8 (0.24 #288, 0.20 #402, 0.19 #374), 032jlh (0.24 #311, 0.15 #397, 0.13 #425) >> Best rule #77 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 02b2np; 02_lt; >> query: (?x10847, 02s9vc) <- colors(?x10847, ?x663), position(?x10847, ?x60), team(?x3031, ?x10847), current_club(?x1598, ?x10847), team(?x3031, ?x3871), ?x3871 = 02b0xq >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #303 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 15 *> proper extension: 07s8qm7; *> query: (?x10847, 035qgm) <- colors(?x10847, ?x663), position(?x10847, ?x530), position(?x10847, ?x60), ?x663 = 083jv, team(?x7669, ?x10847), ?x530 = 02_j1w, current_club(?x1598, ?x10847), ?x60 = 02nzb8 *> conf = 0.12 ranks of expected_values: 24 EVAL 0j46b current_club! 035qgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 110.000 75.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #18054-01vsy3q PRED entity: 01vsy3q PRED relation: instrumentalists! PRED expected values: 03q5t => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 114): 05148p4 (0.48 #787, 0.44 #1043, 0.42 #1553), 02sgy (0.44 #2985, 0.41 #3328, 0.40 #3585), 03bx0bm (0.44 #2985, 0.41 #3328, 0.40 #3585), 03qjg (0.33 #219, 0.32 #305, 0.31 #1584), 02hnl (0.33 #32, 0.22 #3530, 0.21 #2931), 0l14md (0.33 #6, 0.16 #775, 0.15 #2905), 03q5t (0.31 #769, 0.29 #4612, 0.29 #4013), 0l14qv (0.22 #4, 0.12 #3245, 0.11 #773), 06w7v (0.18 #1605, 0.16 #752, 0.12 #2287), 018j2 (0.18 #1571, 0.14 #3106, 0.13 #718) >> Best rule #787 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 067mj; 03fbc; 014_lq; 01k_yf; 0l8g0; 0b_xm; 01516r; 01shhf; 02ndj5; 0p76z; ... >> query: (?x4873, 05148p4) <- category(?x4873, ?x134), artists(?x5379, ?x4873), ?x5379 = 08jyyk, artist(?x2241, ?x4873) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #769 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 05crg7; *> query: (?x4873, ?x74) <- category(?x4873, ?x134), artists(?x7083, ?x4873), role(?x4873, ?x74), ?x7083 = 02yv6b *> conf = 0.31 ranks of expected_values: 7 EVAL 01vsy3q instrumentalists! 03q5t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 151.000 151.000 0.477 http://example.org/music/instrument/instrumentalists #18053-01pt5w PRED entity: 01pt5w PRED relation: citytown! PRED expected values: 06v99d => 147 concepts (38 used for prediction) PRED predicted values (max 10 best out of 819): 07w5rq (0.33 #805, 0.33 #72, 0.25 #806), 0356lc (0.33 #847, 0.04 #8891, 0.03 #10501), 02hwww (0.25 #806, 0.20 #807, 0.02 #13474), 036hnm (0.25 #806, 0.20 #807, 0.01 #13683), 01nmgc (0.25 #806, 0.20 #807, 0.01 #13683), 01pt5w (0.25 #806, 0.20 #807), 0_2v (0.25 #1760, 0.10 #6585), 01rr31 (0.20 #807, 0.02 #28172), 082pc (0.20 #807), 0fsmy (0.20 #807) >> Best rule #805 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0fnb4; >> query: (?x12585, ?x1961) <- contains(?x6956, ?x12585), citytown(?x3913, ?x12585), contains(?x6956, ?x12175), contains(?x6956, ?x1961), ?x1961 = 07w5rq, category(?x12175, ?x134) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pt5w citytown! 06v99d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 38.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #18052-06s6l PRED entity: 06s6l PRED relation: organization PRED expected values: 02vk52z 041288 => 62 concepts (60 used for prediction) PRED predicted values (max 10 best out of 50): 02vk52z (0.89 #287, 0.89 #442, 0.88 #265), 041288 (0.45 #236, 0.39 #524, 0.39 #502), 04k4l (0.41 #27, 0.39 #5, 0.33 #115), 0b6css (0.34 #451, 0.33 #274, 0.32 #518), 0_2v (0.30 #70, 0.30 #48, 0.29 #335), 0gkjy (0.28 #249, 0.26 #493, 0.26 #515), 01rz1 (0.27 #310, 0.25 #244, 0.25 #443), 018cqq (0.23 #55, 0.18 #143, 0.16 #452), 034h1h (0.21 #781, 0.18 #826, 0.03 #1098), 059dn (0.16 #1134, 0.05 #279, 0.05 #59) >> Best rule #287 for best value: >> intensional similarity = 3 >> extensional distance = 119 >> proper extension: 04thp; >> query: (?x1925, 02vk52z) <- currency(?x1925, ?x170), administrative_parent(?x1925, ?x551), contains(?x7273, ?x1925) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 06s6l organization 041288 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 60.000 0.893 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 06s6l organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 60.000 0.893 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #18051-051ls PRED entity: 051ls PRED relation: contains! PRED expected values: 049nq => 139 concepts (42 used for prediction) PRED predicted values (max 10 best out of 323): 049nq (0.86 #1474, 0.82 #577, 0.75 #8957), 09c7w0 (0.78 #19707, 0.76 #21499, 0.74 #24190), 0345h (0.71 #34945, 0.67 #2689, 0.67 #1874), 09ksp (0.68 #32256, 0.62 #8062, 0.47 #26876), 04_1l0v (0.57 #7616, 0.36 #10304, 0.36 #11199), 02qkt (0.55 #6617, 0.33 #16468, 0.27 #11095), 02j9z (0.52 #6299, 0.20 #10777, 0.17 #17046), 06q1r (0.49 #8413, 0.05 #11996, 0.04 #14682), 07ssc (0.39 #8094, 0.22 #14363, 0.22 #15258), 0f8l9c (0.37 #3630, 0.09 #17065, 0.09 #9005) >> Best rule #1474 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 059j2; 0fqyc; 02_vs; >> query: (?x11172, 049nq) <- contains(?x1229, ?x11172), adjoins(?x11172, ?x11274), contains(?x1229, ?x14121), contains(?x1229, ?x13675), ?x14121 = 0d9s5, ?x13675 = 0cl8c >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051ls contains! 049nq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 42.000 0.857 http://example.org/location/location/contains #18050-01c9f2 PRED entity: 01c9f2 PRED relation: ceremony PRED expected values: 01s695 01bx35 => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 124): 01s695 (0.88 #129, 0.88 #510, 0.88 #383), 01bx35 (0.86 #513, 0.86 #386, 0.85 #259), 01xqqp (0.81 #592, 0.80 #465, 0.76 #211), 0jzphpx (0.77 #414, 0.76 #287, 0.74 #541), 0hhtgcw (0.51 #1780, 0.31 #1652, 0.21 #3051), 04n2r9h (0.51 #1780, 0.31 #1652, 0.03 #2453), 09pj68 (0.31 #1652, 0.21 #3051, 0.05 #1109), 0n8_m93 (0.31 #1652, 0.16 #741, 0.16 #1249), 05qb8vx (0.31 #1652, 0.16 #686, 0.13 #1194), 02yvhx (0.31 #1652, 0.15 #1211, 0.14 #703) >> Best rule #129 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 01d38g; 02grdc; 01bgqh; 0c4z8; 01c427; 01ckbq; 01c4_6; 025m8y; 01by1l; 02v1m7; ... >> query: (?x1361, 01s695) <- award_winner(?x1361, ?x2187), ceremony(?x1361, ?x2704), ?x2704 = 01mhwk, award(?x226, ?x1361), person(?x1619, ?x2187) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01c9f2 ceremony 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.881 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c9f2 ceremony 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.881 http://example.org/award/award_category/winners./award/award_honor/ceremony #18049-03mp9s PRED entity: 03mp9s PRED relation: nationality PRED expected values: 09c7w0 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 51): 09c7w0 (0.75 #602, 0.73 #3211, 0.72 #1), 07ssc (0.37 #2610, 0.10 #1017, 0.09 #2023), 02jx1 (0.12 #233, 0.12 #333, 0.12 #534), 03rk0 (0.06 #7060, 0.05 #7362, 0.05 #7562), 0d060g (0.06 #7, 0.05 #1914, 0.05 #107), 0chghy (0.05 #1403, 0.05 #2509, 0.03 #1212), 0f8l9c (0.05 #1403, 0.05 #2509, 0.03 #222), 03rjj (0.05 #1403, 0.05 #2509, 0.02 #2514), 03spz (0.05 #1403, 0.05 #2509, 0.02 #67), 0345h (0.05 #1403, 0.05 #2509, 0.02 #2540) >> Best rule #602 for best value: >> intensional similarity = 2 >> extensional distance = 271 >> proper extension: 01386_; 0jsg0m; 010p3; 02rn_bj; 0cymln; 01tpl1p; 0dq9wx; 02jm9c; >> query: (?x6977, 09c7w0) <- location(?x6977, ?x1523), ?x1523 = 030qb3t >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mp9s nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.755 http://example.org/people/person/nationality #18048-02qzh2 PRED entity: 02qzh2 PRED relation: titles! PRED expected values: 01z4y => 65 concepts (45 used for prediction) PRED predicted values (max 10 best out of 56): 07s9rl0 (0.37 #723, 0.37 #619, 0.34 #2074), 01z4y (0.36 #140, 0.32 #1070, 0.25 #243), 02l7c8 (0.35 #1656, 0.32 #3534, 0.30 #3638), 04xvlr (0.31 #1556, 0.31 #622, 0.27 #4), 06cvj (0.22 #103, 0.22 #2073, 0.22 #1655), 01jfsb (0.18 #20, 0.13 #1467, 0.13 #846), 024qqx (0.16 #493, 0.15 #390, 0.15 #596), 07ssc (0.15 #628, 0.12 #939, 0.11 #1457), 017fp (0.12 #1576, 0.09 #24, 0.09 #1994), 01hmnh (0.09 #131, 0.09 #2310, 0.09 #2519) >> Best rule #723 for best value: >> intensional similarity = 5 >> extensional distance = 204 >> proper extension: 09z2b7; 0c00zd0; 021y7yw; 0dx8gj; 01242_; 0bs5k8r; 027ct7c; 02z2mr7; 04pmnt; 02pw_n; ... >> query: (?x4160, ?x53) <- genre(?x4160, ?x1403), genre(?x4160, ?x53), film_crew_role(?x4160, ?x137), ?x53 = 07s9rl0, ?x1403 = 02l7c8 >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #140 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 06zn1c; *> query: (?x4160, 01z4y) <- genre(?x4160, ?x53), nominated_for(?x1312, ?x4160), currency(?x4160, ?x170), ?x1312 = 07cbcy *> conf = 0.36 ranks of expected_values: 2 EVAL 02qzh2 titles! 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 45.000 0.369 http://example.org/media_common/netflix_genre/titles #18047-03l6q0 PRED entity: 03l6q0 PRED relation: films! PRED expected values: 07c52 => 77 concepts (43 used for prediction) PRED predicted values (max 10 best out of 44): 06d4h (0.33 #43, 0.03 #669, 0.03 #4461), 01d5g (0.08 #579, 0.05 #892, 0.03 #1050), 0g1x2_ (0.08 #496, 0.03 #653, 0.01 #3496), 081pw (0.08 #472, 0.03 #2203, 0.03 #4899), 05489 (0.08 #521, 0.02 #3679, 0.02 #3994), 0jm_ (0.08 #477, 0.02 #2048, 0.01 #3317), 018w8 (0.08 #626), 07s2s (0.07 #881, 0.05 #1039, 0.04 #1665), 0l8bg (0.04 #1213, 0.02 #1683, 0.01 #2317), 0fzyg (0.04 #836, 0.03 #994, 0.03 #680) >> Best rule #43 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0dt8xq; >> query: (?x3317, 06d4h) <- film(?x3707, ?x3317), film(?x3421, ?x3317), religion(?x3707, ?x1985), genre(?x3317, ?x571), ?x3421 = 05r5w >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2220 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 215 *> proper extension: 029jt9; 0k5px; *> query: (?x3317, 07c52) <- film(?x3756, ?x3317), award_winner(?x3317, ?x4420), cinematography(?x3317, ?x3318), award_winner(?x3722, ?x3756) *> conf = 0.02 ranks of expected_values: 25 EVAL 03l6q0 films! 07c52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 77.000 43.000 0.333 http://example.org/film/film_subject/films #18046-033pf1 PRED entity: 033pf1 PRED relation: film! PRED expected values: 01rcmg => 89 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1412): 03lgg (0.33 #878, 0.09 #5032), 0d_84 (0.33 #44, 0.08 #6276, 0.06 #8353), 03cglm (0.33 #1042, 0.08 #7274, 0.03 #69604), 09l3p (0.33 #746, 0.06 #17365, 0.05 #27756), 0sw6g (0.33 #1400, 0.06 #9709, 0.03 #18019), 06t74h (0.33 #694, 0.05 #23547, 0.04 #13157), 06cgy (0.33 #249, 0.04 #72967, 0.03 #52185), 0bj9k (0.33 #327, 0.03 #70967, 0.03 #21102), 046qq (0.33 #739, 0.02 #29826, 0.02 #69301), 01xllf (0.33 #1718, 0.02 #74436, 0.02 #37036) >> Best rule #878 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0bxsk; >> query: (?x8148, 03lgg) <- titles(?x1510, ?x8148), genre(?x8148, ?x258), cinematography(?x8148, ?x1075), film(?x3289, ?x8148), ?x3289 = 0347xl >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #90809 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 456 *> proper extension: 0ds35l9; 02y_lrp; 034qmv; 02v8kmz; 047q2k1; 06wzvr; 011yrp; 09xbpt; 0bvn25; 0ddfwj1; ... *> query: (?x8148, 01rcmg) <- titles(?x1510, ?x8148), genre(?x8148, ?x6674), genre(?x12401, ?x6674), genre(?x3084, ?x6674), ?x3084 = 03mh_tp, ?x12401 = 016z43 *> conf = 0.01 ranks of expected_values: 1235 EVAL 033pf1 film! 01rcmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 57.000 0.333 http://example.org/film/actor/film./film/performance/film #18045-0zjpz PRED entity: 0zjpz PRED relation: instrumentalists! PRED expected values: 02hnl => 126 concepts (90 used for prediction) PRED predicted values (max 10 best out of 117): 02sgy (0.42 #382, 0.31 #3975, 0.31 #688), 0214km (0.42 #382, 0.31 #688, 0.29 #2292), 028tv0 (0.40 #2673, 0.39 #459, 0.37 #1148), 02hnl (0.33 #871, 0.32 #1024, 0.26 #1101), 03qjg (0.29 #1647, 0.28 #882, 0.27 #959), 026t6 (0.21 #308, 0.19 #1074, 0.19 #1609), 0l14md (0.19 #848, 0.17 #1001, 0.15 #618), 013y1f (0.17 #792, 0.16 #945, 0.16 #332), 06w7v (0.16 #366, 0.14 #826, 0.14 #979), 02w3w (0.16 #367, 0.11 #980, 0.10 #827) >> Best rule #382 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 01vd7hn; >> query: (?x1970, ?x314) <- instrumentalists(?x2048, ?x1970), instrumentalists(?x1969, ?x1970), ?x1969 = 04rzd, ?x2048 = 018j2, role(?x1970, ?x314) >> conf = 0.42 => this is the best rule for 2 predicted values *> Best rule #871 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 0892sx; *> query: (?x1970, 02hnl) <- profession(?x1970, ?x131), role(?x1970, ?x227), participant(?x3034, ?x1970), artist(?x4868, ?x1970) *> conf = 0.33 ranks of expected_values: 4 EVAL 0zjpz instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 126.000 90.000 0.419 http://example.org/music/instrument/instrumentalists #18044-01mkn_d PRED entity: 01mkn_d PRED relation: student! PRED expected values: 015zyd => 80 concepts (66 used for prediction) PRED predicted values (max 10 best out of 67): 017z88 (0.15 #609, 0.10 #1136, 0.07 #1663), 02_gzx (0.07 #911, 0.01 #3019, 0.01 #1438), 09f2j (0.07 #1213, 0.07 #2267, 0.06 #1740), 065y4w7 (0.06 #1068, 0.04 #541, 0.04 #3703), 04sylm (0.06 #1130, 0.04 #603, 0.04 #1657), 0bwfn (0.05 #20828, 0.05 #13977, 0.05 #26625), 02g839 (0.04 #552, 0.03 #10565, 0.03 #2133), 015nl4 (0.04 #12188, 0.03 #12715, 0.03 #21147), 01w5m (0.04 #1686, 0.03 #22766, 0.03 #19077), 03ksy (0.03 #2741, 0.03 #1160, 0.03 #5903) >> Best rule #609 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 0hnlx; 0pcc0; 02whj; 06wvj; 02ck1; 0kvjrw; 06c44; 05n19y; 03kts; 09h_q; ... >> query: (?x6664, 017z88) <- profession(?x6664, ?x563), gender(?x6664, ?x231), ?x563 = 01c8w0 >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #12649 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 631 *> proper extension: 04f525m; 03jvmp; 05xbx; *> query: (?x6664, 015zyd) <- award_winner(?x3344, ?x6664), film(?x7621, ?x3344), produced_by(?x4453, ?x7621) *> conf = 0.01 ranks of expected_values: 63 EVAL 01mkn_d student! 015zyd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 80.000 66.000 0.149 http://example.org/education/educational_institution/students_graduates./education/education/student #18043-0bt4g PRED entity: 0bt4g PRED relation: category PRED expected values: 08mbj5d => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.50 #32, 0.50 #31, 0.46 #5) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 299 >> proper extension: 08815; 02zs4; 014zcr; 01jssp; 05krk; 052nd; 0bxtg; 087c7; 06pwq; 0p4wb; ... >> query: (?x7692, ?x134) <- list(?x7692, ?x3004), list(?x1804, ?x3004), category(?x1804, ?x134) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bt4g category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.505 http://example.org/common/topic/webpage./common/webpage/category #18042-0h1x5f PRED entity: 0h1x5f PRED relation: nominated_for! PRED expected values: 040njc 0gq9h => 94 concepts (86 used for prediction) PRED predicted values (max 10 best out of 200): 0gr51 (0.81 #510, 0.72 #730, 0.69 #5948), 02x1dht (0.62 #479, 0.61 #699, 0.34 #1580), 02z0dfh (0.56 #713, 0.22 #6831, 0.19 #493), 019f4v (0.50 #487, 0.42 #927, 0.35 #8201), 0gs9p (0.50 #496, 0.38 #3137, 0.38 #8210), 0gq9h (0.44 #494, 0.44 #8208, 0.39 #2915), 04dn09n (0.44 #470, 0.42 #1571, 0.33 #690), 040njc (0.44 #445, 0.32 #1546, 0.29 #3086), 02qyntr (0.44 #603, 0.30 #1704, 0.29 #1043), 0l8z1 (0.42 #925, 0.31 #485, 0.29 #2906) >> Best rule #510 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0m313; 0209xj; 026390q; 01hqhm; 0661ql3; 07024; 01bb9r; 0_816; 016kv6; 0g9lm2; ... >> query: (?x9701, 0gr51) <- nominated_for(?x3435, ?x9701), ?x3435 = 03hl6lc, nominated_for(?x968, ?x9701), nominated_for(?x9701, ?x1135) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #494 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0m313; 0209xj; 026390q; 01hqhm; 0661ql3; 07024; 01bb9r; 0_816; 016kv6; 0g9lm2; ... *> query: (?x9701, 0gq9h) <- nominated_for(?x3435, ?x9701), ?x3435 = 03hl6lc, nominated_for(?x968, ?x9701), nominated_for(?x9701, ?x1135) *> conf = 0.44 ranks of expected_values: 6, 8 EVAL 0h1x5f nominated_for! 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 94.000 86.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0h1x5f nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 94.000 86.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #18041-01lqnff PRED entity: 01lqnff PRED relation: profession PRED expected values: 02hrh1q 01c72t 0gl2ny2 => 117 concepts (80 used for prediction) PRED predicted values (max 10 best out of 74): 02hrh1q (0.93 #3888, 0.92 #7465, 0.91 #9551), 0gl2ny2 (0.64 #962, 0.40 #2154, 0.36 #2452), 0dxtg (0.51 #8209, 0.50 #6719, 0.48 #5974), 02jknp (0.48 #8203, 0.48 #5968, 0.45 #6862), 0np9r (0.46 #170, 0.20 #21, 0.16 #1660), 09jwl (0.44 #6129, 0.42 #8066, 0.37 #5831), 03gjzk (0.38 #164, 0.38 #5976, 0.35 #7913), 018gz8 (0.38 #166, 0.21 #2550, 0.20 #17), 01445t (0.30 #619, 0.30 #470, 0.24 #1066), 0nbcg (0.27 #8079, 0.27 #9867, 0.27 #6142) >> Best rule #3888 for best value: >> intensional similarity = 4 >> extensional distance = 279 >> proper extension: 05nzw6; 0d608; 08jfkw; 05myd2; 033jj1; >> query: (?x7870, 02hrh1q) <- languages(?x7870, ?x254), film(?x7870, ?x3839), place_of_birth(?x7870, ?x12597), profession(?x7870, ?x319) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 15 EVAL 01lqnff profession 0gl2ny2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 80.000 0.929 http://example.org/people/person/profession EVAL 01lqnff profession 01c72t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 117.000 80.000 0.929 http://example.org/people/person/profession EVAL 01lqnff profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 80.000 0.929 http://example.org/people/person/profession #18040-088vb PRED entity: 088vb PRED relation: organization PRED expected values: 02vk52z => 118 concepts (114 used for prediction) PRED predicted values (max 10 best out of 47): 02vk52z (0.90 #39, 0.89 #514, 0.88 #857), 0_2v (0.64 #4, 0.46 #80, 0.34 #23), 01rz1 (0.41 #21, 0.30 #401, 0.30 #230), 04k4l (0.39 #214, 0.37 #157, 0.37 #176), 018cqq (0.28 #28, 0.27 #9, 0.26 #85), 02jxk (0.24 #22, 0.20 #231, 0.18 #212), 034h1h (0.22 #1631, 0.21 #1532, 0.18 #1726), 059dn (0.18 #13, 0.10 #32, 0.07 #89), 02_l9 (0.10 #1517, 0.07 #1730, 0.02 #1791), 085h1 (0.09 #10, 0.05 #86, 0.04 #48) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05tr7; >> query: (?x5453, 02vk52z) <- currency(?x5453, ?x170), organization(?x5453, ?x9102), ?x9102 = 041288 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 088vb organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 114.000 0.896 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #18039-04m2zj PRED entity: 04m2zj PRED relation: instrumentalists! PRED expected values: 018j2 => 161 concepts (92 used for prediction) PRED predicted values (max 10 best out of 122): 05r5c (0.78 #252, 0.64 #4143, 0.64 #577), 03gvt (0.67 #985, 0.42 #984, 0.33 #1151), 05148p4 (0.60 #99, 0.50 #588, 0.48 #1086), 026t6 (0.41 #905, 0.33 #249, 0.32 #4955), 06ncr (0.40 #122, 0.32 #611, 0.11 #1193), 018j2 (0.40 #116, 0.25 #34, 0.12 #442), 01wy6 (0.40 #125, 0.18 #614, 0.12 #451), 013y1f (0.40 #110, 0.14 #191, 0.12 #436), 02hnl (0.33 #277, 0.32 #602, 0.32 #933), 0l14md (0.33 #251, 0.20 #87, 0.18 #576) >> Best rule #252 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0161c2; >> query: (?x8152, 05r5c) <- instrumentalists(?x432, ?x8152), place_of_birth(?x8152, ?x13437), profession(?x8152, ?x5917), artists(?x1380, ?x8152), ?x432 = 042v_gx >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #116 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 09prnq; *> query: (?x8152, 018j2) <- instrumentalists(?x2944, ?x8152), instrumentalists(?x1969, ?x8152), profession(?x8152, ?x5917), artists(?x1380, ?x8152), ?x2944 = 0l14j_, ?x1969 = 04rzd *> conf = 0.40 ranks of expected_values: 6 EVAL 04m2zj instrumentalists! 018j2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 161.000 92.000 0.778 http://example.org/music/instrument/instrumentalists #18038-02f6g5 PRED entity: 02f6g5 PRED relation: genre PRED expected values: 05p553 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 94): 05p553 (0.71 #851, 0.53 #1214, 0.44 #4), 07s9rl0 (0.66 #1696, 0.62 #4977, 0.61 #3764), 02kdv5l (0.64 #124, 0.56 #3, 0.41 #1455), 01z4y (0.61 #6553, 0.52 #3033, 0.52 #7282), 01jfsb (0.44 #13, 0.36 #134, 0.34 #2071), 03k9fj (0.38 #738, 0.36 #496, 0.35 #254), 0lsxr (0.36 #130, 0.33 #9, 0.20 #372), 01hmnh (0.30 #502, 0.26 #744, 0.25 #623), 0556j8 (0.22 #43, 0.18 #164, 0.06 #406), 01hwc6 (0.22 #20, 0.09 #141, 0.03 #383) >> Best rule #851 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 09d38d; >> query: (?x1810, 05p553) <- film(?x5975, ?x1810), nominated_for(?x298, ?x1810), country(?x1810, ?x94), tv_program(?x5975, ?x6884) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02f6g5 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.713 http://example.org/film/film/genre #18037-05pxnmb PRED entity: 05pxnmb PRED relation: film_crew_role PRED expected values: 09zzb8 09vw2b7 0d2b38 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.79 #688, 0.76 #145, 0.75 #399), 02r96rf (0.73 #147, 0.71 #257, 0.71 #401), 09vw2b7 (0.71 #261, 0.69 #405, 0.68 #1168), 0dxtw (0.58 #83, 0.49 #155, 0.45 #1208), 01vx2h (0.49 #156, 0.47 #84, 0.39 #1173), 02rh1dz (0.26 #82, 0.24 #154, 0.20 #1171), 0215hd (0.26 #91, 0.20 #55, 0.14 #1180), 02ynfr (0.22 #160, 0.21 #88, 0.20 #414), 0d2b38 (0.21 #98, 0.20 #62, 0.13 #1187), 089g0h (0.21 #92, 0.16 #164, 0.16 #274) >> Best rule #688 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 011xg5; >> query: (?x7729, 09zzb8) <- nominated_for(?x7729, ?x886), film(?x820, ?x7729), language(?x7729, ?x254), film_crew_role(?x7729, ?x281) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 9 EVAL 05pxnmb film_crew_role 0d2b38 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 109.000 109.000 0.790 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05pxnmb film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.790 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05pxnmb film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.790 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18036-03lmzl PRED entity: 03lmzl PRED relation: location_of_ceremony PRED expected values: 07mgr => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 15): 059rby (0.04 #127, 0.04 #246, 0.03 #484), 0cv3w (0.03 #1108, 0.03 #1227, 0.03 #35), 03gh4 (0.03 #63, 0.02 #1136, 0.02 #1971), 07fr_ (0.02 #192, 0.02 #311, 0.02 #430), 0lhn5 (0.02 #179, 0.02 #298, 0.02 #417), 06y57 (0.02 #176, 0.02 #295, 0.02 #414), 01x73 (0.02 #142, 0.02 #261, 0.02 #380), 01n7q (0.02 #137, 0.02 #256, 0.02 #375), 027rn (0.02 #120, 0.02 #239, 0.02 #358), 0k049 (0.02 #1196, 0.01 #1674, 0.01 #1555) >> Best rule #127 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 01j5ts; 023tp8; 0p_pd; 032_jg; 0151w_; 0h1mt; 02js6_; 01fwpt; 01fdc0; 02vyw; ... >> query: (?x8871, 059rby) <- sibling(?x10445, ?x8871), nationality(?x8871, ?x94), ?x94 = 09c7w0, film(?x8871, ?x2441) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03lmzl location_of_ceremony 07mgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 99.000 0.043 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #18035-094xh PRED entity: 094xh PRED relation: artist! PRED expected values: 03rhqg 0g768 => 232 concepts (226 used for prediction) PRED predicted values (max 10 best out of 129): 015_1q (0.50 #13364, 0.28 #2243, 0.27 #1826), 03rhqg (0.42 #1405, 0.28 #2100, 0.21 #4463), 0g768 (0.31 #13380, 0.19 #2954, 0.19 #1147), 043g7l (0.27 #1003, 0.12 #1142, 0.12 #4896), 02swsm (0.23 #1900, 0.18 #927, 0.13 #3151), 03qx_f (0.22 #1323, 0.19 #4520, 0.16 #5494), 02p11jq (0.21 #1402, 0.15 #5434, 0.13 #4460), 0n85g (0.20 #1729, 0.18 #895, 0.18 #2563), 0181dw (0.20 #179, 0.17 #1291, 0.17 #318), 01f_3w (0.20 #171, 0.17 #310, 0.14 #727) >> Best rule #13364 for best value: >> intensional similarity = 4 >> extensional distance = 219 >> proper extension: 01dw9z; 0163m1; 01wv9p; 01lw3kh; 01vw917; 02pt7h_; 01d1st; 0bs1g5r; 01w5jwb; 01hgwkr; ... >> query: (?x5312, 015_1q) <- gender(?x5312, ?x514), artist(?x5744, ?x5312), artist(?x5744, ?x6838), ?x6838 = 0130sy >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1405 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 011_vz; *> query: (?x5312, 03rhqg) <- artists(?x671, ?x5312), artist(?x5634, ?x5312), role(?x5312, ?x1166), ?x5634 = 01cl2y *> conf = 0.42 ranks of expected_values: 2, 3 EVAL 094xh artist! 0g768 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 232.000 226.000 0.498 http://example.org/music/record_label/artist EVAL 094xh artist! 03rhqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 232.000 226.000 0.498 http://example.org/music/record_label/artist #18034-03rjj PRED entity: 03rjj PRED relation: teams PRED expected values: 01_lhg => 260 concepts (260 used for prediction) PRED predicted values (max 10 best out of 230): 033g0y (0.20 #1371, 0.06 #4972, 0.04 #8573), 02_t6d (0.20 #1315, 0.02 #20760, 0.02 #28320), 03lygq (0.17 #1698, 0.11 #2419, 0.08 #2779), 086x3 (0.17 #1800, 0.11 #2161, 0.08 #3961), 01l3vx (0.11 #1845, 0.08 #2565, 0.08 #3645), 02w64f (0.11 #2128, 0.07 #4288, 0.06 #5008), 06l7jj (0.11 #2013, 0.06 #5613, 0.05 #8134), 02rytm (0.11 #1850, 0.06 #5450, 0.05 #7971), 02rqxc (0.11 #1890, 0.04 #8371, 0.04 #10531), 038zh6 (0.11 #6111, 0.08 #3231, 0.07 #4671) >> Best rule #1371 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0mhhw; >> query: (?x205, 033g0y) <- adjoins(?x1355, ?x205), adjoins(?x774, ?x205), film_release_region(?x66, ?x1355), ?x774 = 06mzp >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03rjj teams 01_lhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 260.000 260.000 0.200 http://example.org/sports/sports_team_location/teams #18033-04sx9_ PRED entity: 04sx9_ PRED relation: award PRED expected values: 0bfvd4 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 266): 0bdwqv (0.33 #173, 0.17 #21418, 0.16 #22631), 01by1l (0.20 #516, 0.17 #921, 0.09 #21530), 03c7tr1 (0.19 #1271, 0.18 #462, 0.16 #22631), 02x73k6 (0.19 #23036, 0.17 #21418, 0.17 #60), 0gqyl (0.19 #23036, 0.17 #21418, 0.16 #9297), 05b4l5x (0.19 #23036, 0.17 #21418, 0.16 #9297), 09td7p (0.19 #23036, 0.17 #21418, 0.16 #9297), 099t8j (0.19 #23036, 0.17 #21418, 0.16 #9297), 099tbz (0.19 #23036, 0.17 #21418, 0.16 #9297), 02lp0w (0.19 #23036, 0.17 #21418, 0.16 #9297) >> Best rule #173 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 036c_0; 0ywqc; >> query: (?x919, 0bdwqv) <- film(?x919, ?x2104), ?x2104 = 0j_tw, nationality(?x919, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #115 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 036c_0; 0ywqc; *> query: (?x919, 0bfvd4) <- film(?x919, ?x2104), ?x2104 = 0j_tw, nationality(?x919, ?x94) *> conf = 0.17 ranks of expected_values: 29 EVAL 04sx9_ award 0bfvd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 109.000 109.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18032-01f9y_ PRED entity: 01f9y_ PRED relation: parent_genre PRED expected values: 03_d0 0glt670 => 44 concepts (27 used for prediction) PRED predicted values (max 10 best out of 230): 016clz (0.88 #821, 0.37 #1311, 0.25 #1639), 0glt670 (0.75 #515, 0.23 #650, 0.15 #2949), 05r6t (0.68 #1689, 0.35 #871, 0.24 #1361), 016_rm (0.67 #618, 0.23 #650, 0.11 #294), 01243b (0.53 #846, 0.29 #1336, 0.27 #1664), 06by7 (0.39 #1634, 0.39 #1487, 0.38 #1651), 02x8m (0.25 #501, 0.10 #651, 0.10 #1157), 016jny (0.24 #1377, 0.10 #651, 0.05 #2032), 0gywn (0.20 #366, 0.20 #41, 0.16 #1020), 011j5x (0.20 #22, 0.18 #839, 0.15 #1657) >> Best rule #821 for best value: >> intensional similarity = 9 >> extensional distance = 15 >> proper extension: 01gbcf; >> query: (?x12070, 016clz) <- parent_genre(?x12070, ?x9630), artists(?x9630, ?x11897), artists(?x9630, ?x6035), artists(?x9630, ?x3494), artists(?x9630, ?x3390), ?x6035 = 02r3cn, ?x11897 = 01f2q5, ?x3390 = 017j6, nominated_for(?x3494, ?x1642) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 025tjk_; *> query: (?x12070, 0glt670) <- parent_genre(?x12070, ?x9630), artists(?x9630, ?x6035), artists(?x9630, ?x3494), ?x3494 = 01vw26l, parent_genre(?x9630, ?x2937), profession(?x6035, ?x1183) *> conf = 0.75 ranks of expected_values: 2, 35 EVAL 01f9y_ parent_genre 0glt670 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 27.000 0.882 http://example.org/music/genre/parent_genre EVAL 01f9y_ parent_genre 03_d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 44.000 27.000 0.882 http://example.org/music/genre/parent_genre #18031-0149xx PRED entity: 0149xx PRED relation: role PRED expected values: 05r5c => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 119): 05r5c (0.54 #841, 0.53 #945, 0.52 #529), 0342h (0.39 #5311, 0.38 #6663, 0.37 #6559), 01vdm0 (0.28 #6692, 0.27 #6068, 0.27 #6276), 02sgy (0.25 #5313, 0.25 #319, 0.24 #3231), 042v_gx (0.23 #3234, 0.22 #4275, 0.21 #6044), 03q5t (0.20 #1, 0.08 #313, 0.04 #6869), 01vj9c (0.19 #1681, 0.17 #4074, 0.16 #4386), 05842k (0.19 #4450, 0.18 #4138, 0.17 #2369), 018vs (0.18 #5321, 0.17 #4072, 0.17 #4384), 026t6 (0.18 #5309, 0.16 #6661, 0.16 #4372) >> Best rule #841 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0f0y8; 03c7ln; 0p3sf; 04bgy; 0484q; 02r38; 01tw31; 02s6sh; >> query: (?x5125, 05r5c) <- role(?x5125, ?x4311), place_of_death(?x5125, ?x8745), gender(?x5125, ?x231) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0149xx role 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.538 http://example.org/music/artist/track_contributions./music/track_contribution/role #18030-04n52p6 PRED entity: 04n52p6 PRED relation: film_crew_role PRED expected values: 02r96rf 089fss => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 23): 02r96rf (0.69 #52, 0.66 #129, 0.65 #734), 01pvkk (0.30 #359, 0.29 #989, 0.28 #738), 02ynfr (0.18 #613, 0.18 #741, 0.18 #664), 089g0h (0.12 #666, 0.10 #86, 0.10 #994), 0ckd1 (0.10 #3, 0.09 #2100, 0.02 #78), 089fss (0.09 #55, 0.09 #2100, 0.08 #30), 04pyp5 (0.09 #2100, 0.08 #742, 0.08 #85), 094hwz (0.09 #2100, 0.06 #58, 0.05 #83), 02vs3x5 (0.09 #2100, 0.05 #670, 0.05 #747), 06qc5 (0.09 #2100, 0.05 #43, 0.05 #196) >> Best rule #52 for best value: >> intensional similarity = 5 >> extensional distance = 104 >> proper extension: 0gtsx8c; 02vxq9m; 0gx1bnj; 0ds3t5x; 0dscrwf; 02x3lt7; 0gkz15s; 01vksx; 017gl1; 08hmch; ... >> query: (?x1707, 02r96rf) <- film_release_region(?x1707, ?x2843), film_release_region(?x1707, ?x151), film_crew_role(?x1707, ?x137), ?x151 = 0b90_r, ?x2843 = 016wzw >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 04n52p6 film_crew_role 089fss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 84.000 84.000 0.689 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 04n52p6 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.689 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18029-0fn5bx PRED entity: 0fn5bx PRED relation: gender PRED expected values: 02zsn => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #136, 0.73 #142, 0.72 #110), 02zsn (0.52 #87, 0.44 #10, 0.38 #6) >> Best rule #136 for best value: >> intensional similarity = 2 >> extensional distance = 1701 >> proper extension: 0pcc0; 01s7qqw; 027dpx; 0h0p_; 018d6l; 03wjb7; 01dhjz; 063tn; >> query: (?x5200, 05zppz) <- student(?x2711, ?x5200), school_type(?x2711, ?x3092) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1249 *> proper extension: 07_grx; 076df9; 0bm9xk; *> query: (?x5200, ?x231) <- location(?x5200, ?x1426), award_nominee(?x5200, ?x822), gender(?x822, ?x231) *> conf = 0.52 ranks of expected_values: 2 EVAL 0fn5bx gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 108.000 0.729 http://example.org/people/person/gender #18028-02plv57 PRED entity: 02plv57 PRED relation: position PRED expected values: 02sf_r 02_ssl => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 6): 02_ssl (0.86 #59, 0.84 #95, 0.80 #108), 02sf_r (0.80 #106, 0.79 #57, 0.75 #110), 03558l (0.75 #110, 0.72 #105, 0.70 #98), 0355dz (0.75 #110, 0.68 #94, 0.66 #103), 0ctt4z (0.75 #110, 0.66 #103, 0.58 #178), 0619m3 (0.52 #177, 0.12 #72, 0.06 #122) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0jm8l; 0jmcv; >> query: (?x2303, 02_ssl) <- position(?x2303, ?x1348), sport(?x2303, ?x12913), ?x1348 = 01pv51, colors(?x2303, ?x1101) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02plv57 position 02_ssl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.857 http://example.org/sports/sports_team/roster./basketball/basketball_roster_position/position EVAL 02plv57 position 02sf_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.857 http://example.org/sports/sports_team/roster./basketball/basketball_roster_position/position #18027-0c7xjb PRED entity: 0c7xjb PRED relation: currency PRED expected values: 09nqf => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.62 #4, 0.59 #43, 0.58 #52), 01nv4h (0.03 #116, 0.03 #137, 0.03 #140) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 026_dq6; >> query: (?x4819, 09nqf) <- sibling(?x4819, ?x2697), participant(?x5589, ?x4819), category(?x4819, ?x134) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c7xjb currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.625 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #18026-0jt90f5 PRED entity: 0jt90f5 PRED relation: profession PRED expected values: 01d_h8 => 197 concepts (166 used for prediction) PRED predicted values (max 10 best out of 99): 01d_h8 (0.78 #14616, 0.78 #14324, 0.76 #11256), 0kyk (0.70 #465, 0.67 #173, 0.55 #2655), 02jknp (0.61 #1905, 0.56 #11843, 0.54 #16369), 02hv44_ (0.42 #931, 0.35 #9056, 0.33 #201), 018gz8 (0.40 #3080, 0.40 #2204, 0.36 #598), 0nbcg (0.38 #21801, 0.26 #20341, 0.26 #21509), 09jwl (0.38 #20328, 0.38 #19011, 0.37 #21350), 05z96 (0.35 #9056, 0.30 #7301, 0.30 #19289), 02krf9 (0.34 #10541, 0.33 #2506, 0.33 #6740), 0196pc (0.33 #71, 0.30 #7301, 0.14 #2553) >> Best rule #14616 for best value: >> intensional similarity = 3 >> extensional distance = 208 >> proper extension: 0cj2k3; 03qncl3; 027z0pl; >> query: (?x2343, 01d_h8) <- award(?x2343, ?x350), executive_produced_by(?x5116, ?x2343), profession(?x2343, ?x353) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jt90f5 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 197.000 166.000 0.776 http://example.org/people/person/profession #18025-0bn3jg PRED entity: 0bn3jg PRED relation: edited_by! PRED expected values: 0gd0c7x 05f4_n0 02xs6_ 06bc59 => 153 concepts (78 used for prediction) PRED predicted values (max 10 best out of 169): 09q5w2 (0.29 #839, 0.11 #168, 0.01 #4034), 0mbql (0.25 #120, 0.20 #456, 0.15 #791), 05z43v (0.25 #136, 0.20 #472, 0.07 #975), 02ll45 (0.25 #91, 0.20 #427, 0.07 #930), 04j4tx (0.25 #77, 0.20 #413, 0.07 #916), 04sntd (0.25 #57, 0.20 #393, 0.07 #896), 035s95 (0.25 #42, 0.20 #378, 0.07 #881), 04qw17 (0.25 #37, 0.20 #373, 0.07 #876), 06_wqk4 (0.25 #20, 0.20 #356, 0.07 #859), 0164qt (0.25 #19, 0.20 #355, 0.07 #858) >> Best rule #839 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 03cp7b3; >> query: (?x11314, ?x1077) <- award_winner(?x1077, ?x11314), place_of_birth(?x11314, ?x10311), edited_by(?x1038, ?x11314) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bn3jg edited_by! 06bc59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 78.000 0.286 http://example.org/film/film/edited_by EVAL 0bn3jg edited_by! 02xs6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 78.000 0.286 http://example.org/film/film/edited_by EVAL 0bn3jg edited_by! 05f4_n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 78.000 0.286 http://example.org/film/film/edited_by EVAL 0bn3jg edited_by! 0gd0c7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 78.000 0.286 http://example.org/film/film/edited_by #18024-01vs_v8 PRED entity: 01vs_v8 PRED relation: nationality PRED expected values: 09c7w0 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 50): 09c7w0 (0.84 #601, 0.80 #901, 0.79 #3704), 02jx1 (0.28 #533, 0.20 #33, 0.16 #6241), 07ssc (0.16 #1516, 0.15 #1917, 0.13 #3818), 0d060g (0.09 #107, 0.08 #707, 0.06 #307), 0f8l9c (0.08 #1924, 0.07 #1523, 0.05 #22), 03rk0 (0.08 #7155, 0.05 #13767, 0.05 #13868), 0h7x (0.08 #1536, 0.07 #1937, 0.05 #535), 0345h (0.07 #4134, 0.07 #4736, 0.07 #1933), 05vz3zq (0.06 #770, 0.03 #2272), 0hzlz (0.05 #23, 0.03 #323, 0.03 #423) >> Best rule #601 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 02qjj7; 02zrv7; >> query: (?x2237, 09c7w0) <- profession(?x2237, ?x987), participant(?x2237, ?x702), ?x987 = 0dxtg >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vs_v8 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.844 http://example.org/people/person/nationality #18023-042tq PRED entity: 042tq PRED relation: place! PRED expected values: 042tq => 103 concepts (40 used for prediction) PRED predicted values (max 10 best out of 76): 02cft (0.09 #8258, 0.09 #16522, 0.09 #13938), 01n4w (0.09 #8258, 0.09 #16522, 0.09 #13938), 059rby (0.09 #8258, 0.09 #16522, 0.09 #13938), 042tq (0.09 #8258, 0.09 #16522, 0.09 #13938), 0vm39 (0.06 #238, 0.01 #754), 02dtg (0.06 #9, 0.01 #525), 0vm5t (0.06 #493), 013d_f (0.06 #442), 04pry (0.06 #385), 0xckc (0.06 #188) >> Best rule #8258 for best value: >> intensional similarity = 4 >> extensional distance = 368 >> proper extension: 05r4w; 02j9z; 06pvr; 05fly; 03902; >> query: (?x8911, ?x335) <- location(?x3651, ?x8911), location(?x3651, ?x335), award_winner(?x458, ?x3651), award_winner(?x3651, ?x3002) >> conf = 0.09 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 042tq place! 042tq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 40.000 0.095 http://example.org/location/hud_county_place/place #18022-031zkw PRED entity: 031zkw PRED relation: gender PRED expected values: 05zppz => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #23, 0.84 #65, 0.84 #77), 02zsn (0.46 #52, 0.46 #167, 0.45 #44) >> Best rule #23 for best value: >> intensional similarity = 5 >> extensional distance = 76 >> proper extension: 022_lg; 03nk3t; 0627sn; >> query: (?x1408, 05zppz) <- award_winner(?x2252, ?x1408), profession(?x1408, ?x1943), profession(?x1408, ?x524), ?x524 = 02jknp, ?x1943 = 02krf9 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031zkw gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.885 http://example.org/people/person/gender #18021-09hy79 PRED entity: 09hy79 PRED relation: honored_for! PRED expected values: 09k5jh7 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 112): 058m5m4 (0.12 #167, 0.10 #533, 0.04 #777), 0418154 (0.12 #215, 0.10 #581, 0.02 #825), 09pj68 (0.12 #334, 0.07 #456, 0.04 #822), 09qvms (0.12 #253, 0.07 #375, 0.02 #741), 0bvfqq (0.12 #270, 0.07 #392, 0.02 #758), 04110lv (0.12 #217, 0.05 #583, 0.02 #827), 0hr6lkl (0.10 #500, 0.05 #744, 0.02 #866), 0n8_m93 (0.05 #835, 0.05 #591, 0.01 #6226), 0g5b0q5 (0.05 #746, 0.02 #1112, 0.01 #1357), 04n2r9h (0.05 #524, 0.04 #768, 0.03 #1012) >> Best rule #167 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0466s8n; >> query: (?x7012, 058m5m4) <- nominated_for(?x11466, ?x7012), ?x11466 = 099flj, executive_produced_by(?x7012, ?x2135), produced_by(?x1012, ?x2135) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #681 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 043sct5; *> query: (?x7012, 09k5jh7) <- country(?x7012, ?x94), film_crew_role(?x7012, ?x8411), genre(?x7012, ?x812), ?x8411 = 033smt *> conf = 0.05 ranks of expected_values: 18 EVAL 09hy79 honored_for! 09k5jh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 67.000 67.000 0.125 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #18020-0hx4y PRED entity: 0hx4y PRED relation: film_crew_role PRED expected values: 02r96rf => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 34): 09zzb8 (0.76 #1027, 0.73 #685, 0.73 #1331), 02r96rf (0.76 #308, 0.72 #1334, 0.69 #384), 09vw2b7 (0.70 #616, 0.68 #312, 0.67 #1034), 0dxtw (0.46 #316, 0.45 #1342, 0.44 #772), 01vx2h (0.40 #431, 0.39 #393, 0.38 #1343), 01pvkk (0.35 #660, 0.34 #1306, 0.33 #1078), 0d2b38 (0.31 #180, 0.22 #332, 0.19 #218), 02rh1dz (0.24 #315, 0.23 #277, 0.23 #201), 02ynfr (0.21 #436, 0.20 #626, 0.20 #740), 0215hd (0.20 #325, 0.14 #249, 0.14 #705) >> Best rule #1027 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 020fcn; 05dptj; 03hp2y1; 02rtqvb; >> query: (?x2878, 09zzb8) <- genre(?x2878, ?x1509), ?x1509 = 060__y, film_crew_role(?x2878, ?x1284), film(?x539, ?x2878) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #308 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 0dnvn3; 05dy7p; 07bwr; 03t79f; 02704ff; 0bw20; 0292qb; *> query: (?x2878, 02r96rf) <- film_release_distribution_medium(?x2878, ?x81), crewmember(?x2878, ?x2870), edited_by(?x2878, ?x4215), award_nominee(?x2870, ?x929) *> conf = 0.76 ranks of expected_values: 2 EVAL 0hx4y film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 116.000 0.764 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #18019-05vsxz PRED entity: 05vsxz PRED relation: award_nominee PRED expected values: 03f1zdw 09y20 06mmb => 68 concepts (27 used for prediction) PRED predicted values (max 10 best out of 654): 014v6f (0.85 #6938, 0.84 #6937, 0.84 #4625), 02p65p (0.85 #6938, 0.84 #6937, 0.84 #4625), 09y20 (0.85 #6938, 0.84 #6937, 0.84 #4625), 01qrbf (0.85 #6938, 0.84 #6937, 0.84 #4625), 06mmb (0.81 #2856, 0.80 #5168, 0.28 #13874), 05vsxz (0.75 #2322, 0.70 #4634, 0.55 #9), 03f1zdw (0.70 #4869, 0.69 #2557, 0.28 #13874), 02d45s (0.28 #13874, 0.23 #37004, 0.21 #23126), 018db8 (0.28 #13874, 0.23 #37004, 0.18 #53190), 03zz8b (0.28 #13874, 0.23 #37004, 0.16 #18500) >> Best rule #6938 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0h5g_; 07hbxm; 02cllz; 0dvld; >> query: (?x100, ?x101) <- award_nominee(?x6122, ?x100), award_nominee(?x101, ?x100), ?x6122 = 016xh5, type_of_union(?x101, ?x1873) >> conf = 0.85 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 5, 7 EVAL 05vsxz award_nominee 06mmb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 27.000 0.850 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 05vsxz award_nominee 09y20 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 27.000 0.850 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 05vsxz award_nominee 03f1zdw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 68.000 27.000 0.850 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #18018-03fnyk PRED entity: 03fnyk PRED relation: profession PRED expected values: 01d_h8 => 107 concepts (91 used for prediction) PRED predicted values (max 10 best out of 74): 0kyk (0.71 #326, 0.20 #1806, 0.18 #2694), 01d_h8 (0.60 #2374, 0.57 #2522, 0.52 #3262), 0dz3r (0.43 #1482, 0.42 #1630, 0.29 #1038), 0dxtg (0.43 #310, 0.38 #4010, 0.38 #1790), 0cbd2 (0.43 #303, 0.19 #895, 0.17 #1783), 03gjzk (0.42 #2383, 0.41 #4159, 0.41 #1939), 02jknp (0.36 #2376, 0.34 #2524, 0.29 #3264), 09jwl (0.31 #1647, 0.30 #1499, 0.29 #315), 0nbcg (0.30 #1660, 0.29 #1512, 0.24 #1068), 0d1pc (0.29 #8885, 0.25 #199, 0.18 #1087) >> Best rule #326 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01bpc9; >> query: (?x11899, 0kyk) <- currency(?x11899, ?x170), gender(?x11899, ?x231), profession(?x11899, ?x6421), ?x6421 = 02hv44_ >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2374 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 124 *> proper extension: 0170s4; *> query: (?x11899, 01d_h8) <- currency(?x11899, ?x170), type_of_union(?x11899, ?x566), award_winner(?x11336, ?x11899), ?x566 = 04ztj *> conf = 0.60 ranks of expected_values: 2 EVAL 03fnyk profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 91.000 0.714 http://example.org/people/person/profession #18017-094jv PRED entity: 094jv PRED relation: teams PRED expected values: 01ct6 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 170): 06rpd (0.25 #200, 0.02 #2354, 0.02 #2714), 0jmk7 (0.17 #661, 0.02 #2456, 0.02 #2816), 0jnq8 (0.17 #587, 0.02 #2382, 0.02 #2742), 0jmjr (0.17 #580, 0.02 #2375, 0.02 #2735), 04mjl (0.17 #515, 0.02 #2310, 0.02 #2670), 02pqcfz (0.17 #441, 0.02 #2236, 0.02 #2596), 04112r (0.17 #410, 0.02 #2205, 0.02 #2565), 07k53y (0.17 #371, 0.02 #2166, 0.02 #2526), 0ckf6 (0.17 #677, 0.02 #2472, 0.02 #3551), 01z1r (0.17 #510, 0.02 #2305, 0.02 #3384) >> Best rule #200 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0r02m; >> query: (?x1705, 06rpd) <- location(?x1092, ?x1705), citytown(?x1768, ?x1705), ?x1092 = 02whj >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 094jv teams 01ct6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 118.000 0.250 http://example.org/sports/sports_team_location/teams #18016-01tszq PRED entity: 01tszq PRED relation: actor! PRED expected values: 04mx8h4 => 153 concepts (102 used for prediction) PRED predicted values (max 10 best out of 163): 0jwl2 (0.25 #73, 0.04 #2163, 0.04 #3471), 05h95s (0.25 #143, 0.02 #3541, 0.01 #2233), 07vqnc (0.25 #222), 026bfsh (0.16 #620, 0.10 #3495, 0.09 #4800), 0fkwzs (0.12 #160, 0.02 #4863, 0.02 #3558), 04mx8h4 (0.12 #174, 0.02 #5661, 0.01 #6706), 03y3bp7 (0.12 #44, 0.02 #4747, 0.01 #2134), 03nymk (0.12 #155, 0.01 #4858), 05631 (0.11 #776, 0.02 #3389, 0.01 #2604), 0180mw (0.09 #380, 0.05 #642, 0.04 #7436) >> Best rule #73 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02hblj; >> query: (?x2594, 0jwl2) <- actor(?x8017, ?x2594), film(?x2594, ?x2163), ?x8017 = 01hvv0 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #174 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 02hblj; *> query: (?x2594, 04mx8h4) <- actor(?x8017, ?x2594), film(?x2594, ?x2163), ?x8017 = 01hvv0 *> conf = 0.12 ranks of expected_values: 6 EVAL 01tszq actor! 04mx8h4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 153.000 102.000 0.250 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #18015-02bn_p PRED entity: 02bn_p PRED relation: district_represented PRED expected values: 06btq 0824r 081yw 03gh4 050ks => 34 concepts (31 used for prediction) PRED predicted values (max 10 best out of 174): 03gh4 (0.91 #34, 0.85 #96, 0.84 #224), 06yxd (0.91 #34, 0.85 #96, 0.84 #224), 06btq (0.91 #34, 0.85 #96, 0.84 #224), 0d0x8 (0.91 #34, 0.85 #96, 0.84 #224), 026mj (0.91 #34, 0.85 #96, 0.84 #224), 0824r (0.91 #34, 0.85 #96, 0.84 #224), 05fkf (0.91 #34, 0.85 #96, 0.84 #224), 081yw (0.91 #34, 0.85 #96, 0.84 #224), 07h34 (0.91 #34, 0.85 #96, 0.84 #224), 07z1m (0.91 #34, 0.85 #96, 0.84 #224) >> Best rule #34 for best value: >> intensional similarity = 36 >> extensional distance = 1 >> proper extension: 077g7n; >> query: (?x1027, ?x1024) <- legislative_sessions(?x3540, ?x1027), legislative_sessions(?x1028, ?x1027), legislative_sessions(?x605, ?x1027), district_represented(?x1027, ?x6521), district_represented(?x1027, ?x4754), district_represented(?x1027, ?x4622), district_represented(?x1027, ?x4061), district_represented(?x1027, ?x3086), district_represented(?x1027, ?x2768), district_represented(?x1027, ?x1767), district_represented(?x1027, ?x1138), district_represented(?x1027, ?x961), district_represented(?x1027, ?x726), legislative_sessions(?x1027, ?x355), ?x6521 = 05mph, ?x1028 = 032ft5, ?x3086 = 0846v, legislative_sessions(?x11605, ?x1027), ?x1138 = 059_c, ?x1767 = 04rrd, ?x3540 = 024tcq, ?x11605 = 024_vw, legislative_sessions(?x2860, ?x1027), ?x4622 = 04tgp, district_represented(?x605, ?x3038), district_represented(?x605, ?x1024), district_represented(?x605, ?x953), district_represented(?x605, ?x760), ?x760 = 05fkf, ?x4754 = 0g0syc, ?x726 = 05kj_, state_province_region(?x546, ?x961), ?x3038 = 0d0x8, ?x4061 = 0498y, ?x2768 = 03s5t, ?x953 = 0hjy >> conf = 0.91 => this is the best rule for 17 predicted values ranks of expected_values: 1, 3, 6, 8, 14 EVAL 02bn_p district_represented 050ks CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 34.000 31.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 02bn_p district_represented 03gh4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 31.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 02bn_p district_represented 081yw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 34.000 31.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 02bn_p district_represented 0824r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 34.000 31.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 02bn_p district_represented 06btq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 34.000 31.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #18014-01svq8 PRED entity: 01svq8 PRED relation: influenced_by PRED expected values: 013qvn => 116 concepts (45 used for prediction) PRED predicted values (max 10 best out of 329): 01hmk9 (0.30 #1525, 0.25 #2396, 0.20 #1090), 01j7rd (0.30 #1795, 0.24 #3539, 0.16 #5281), 0p_47 (0.20 #977, 0.20 #542, 0.18 #4897), 081lh (0.20 #890, 0.20 #455, 0.18 #4810), 014zfs (0.20 #1766, 0.20 #1330, 0.17 #2201), 013tjc (0.20 #2117, 0.20 #1681, 0.17 #2552), 01k9lpl (0.20 #1180, 0.20 #745, 0.14 #5100), 029_3 (0.20 #988, 0.20 #553, 0.14 #4908), 01svq8 (0.20 #1295, 0.20 #860, 0.10 #10448), 049gc (0.20 #1042, 0.20 #607, 0.05 #4962) >> Best rule #1525 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 09qh1; >> query: (?x13118, 01hmk9) <- profession(?x13118, ?x987), celebrities_impersonated(?x8145, ?x13118), participant(?x13118, ?x496), influenced_by(?x13118, ?x2942) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #1972 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01wc7p; 04zkj5; *> query: (?x13118, 013qvn) <- profession(?x13118, ?x987), ?x987 = 0dxtg, person(?x8144, ?x13118), award_winner(?x4386, ?x13118) *> conf = 0.10 ranks of expected_values: 61 EVAL 01svq8 influenced_by 013qvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 116.000 45.000 0.300 http://example.org/influence/influence_node/influenced_by #18013-01dw9z PRED entity: 01dw9z PRED relation: award PRED expected values: 03c7tr1 => 108 concepts (97 used for prediction) PRED predicted values (max 10 best out of 311): 02qkk9_ (0.80 #9047, 0.80 #787, 0.80 #5507), 0c4z8 (0.48 #461, 0.30 #5181, 0.28 #4395), 03qbh5 (0.44 #591, 0.30 #1379, 0.26 #3345), 054ks3 (0.40 #528, 0.25 #1316, 0.21 #3282), 01c92g (0.36 #484, 0.20 #1272, 0.19 #4418), 025m8l (0.36 #505, 0.16 #1293, 0.14 #3259), 09sb52 (0.35 #21279, 0.32 #22856, 0.30 #3974), 02f5qb (0.32 #542, 0.21 #4476, 0.19 #5262), 03qbnj (0.28 #616, 0.21 #3370, 0.20 #4550), 0gqz2 (0.28 #469, 0.15 #11484, 0.14 #1257) >> Best rule #9047 for best value: >> intensional similarity = 4 >> extensional distance = 244 >> proper extension: 01q_ph; 03f1r6t; 0m0hw; 013pk3; 0h7pj; 016jll; 020jqv; >> query: (?x2683, ?x724) <- artist(?x2931, ?x2683), type_of_union(?x2683, ?x566), award_winner(?x724, ?x2683), award(?x2683, ?x537) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5563 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 174 *> proper extension: 01j5x6; 05hdf; 01pnn3; 02v406; 02v60l; 01pctb; 04mlmx; *> query: (?x2683, 03c7tr1) <- spouse(?x919, ?x2683), film(?x2683, ?x2128), people(?x9428, ?x2683) *> conf = 0.15 ranks of expected_values: 38 EVAL 01dw9z award 03c7tr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 108.000 97.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #18012-0c4y8 PRED entity: 0c4y8 PRED relation: student! PRED expected values: 0187nd => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 286): 0bwfn (0.29 #274, 0.25 #2370, 0.25 #798), 03ksy (0.18 #3773, 0.16 #4822, 0.09 #6394), 01w5m (0.15 #1676, 0.14 #104, 0.12 #628), 065y4w7 (0.15 #1585, 0.13 #7874, 0.12 #2109), 02301 (0.14 #73, 0.12 #597, 0.10 #1121), 017j69 (0.14 #144, 0.12 #668, 0.10 #1192), 07wrz (0.14 #61, 0.12 #585, 0.10 #1109), 025v3k (0.14 #119, 0.12 #643, 0.10 #1167), 0373qt (0.14 #325, 0.10 #1373, 0.02 #11330), 08815 (0.14 #5767, 0.06 #12579, 0.05 #3670) >> Best rule #274 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 03_87; >> query: (?x9610, 0bwfn) <- influenced_by(?x9610, ?x4292), student(?x546, ?x9610), ?x4292 = 0zm1, religion(?x9610, ?x1985) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3509 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 01ty7ll; 03jm6c; *> query: (?x9610, 0187nd) <- gender(?x9610, ?x231), location(?x9610, ?x4356), profession(?x9610, ?x2225), ?x4356 = 06wxw *> conf = 0.05 ranks of expected_values: 48 EVAL 0c4y8 student! 0187nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 170.000 170.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #18011-0gd5z PRED entity: 0gd5z PRED relation: influenced_by PRED expected values: 0m77m 03_dj => 143 concepts (59 used for prediction) PRED predicted values (max 10 best out of 331): 03f0324 (0.40 #1007, 0.29 #2720, 0.26 #5290), 084w8 (0.30 #859, 0.21 #5142, 0.21 #2572), 02kz_ (0.30 #1024, 0.21 #2737, 0.16 #5307), 0l99s (0.30 #1077, 0.17 #2790, 0.13 #5360), 03_87 (0.29 #5336, 0.21 #2766, 0.21 #3622), 0j3v (0.26 #5200, 0.21 #2630, 0.15 #5568), 03rx9 (0.25 #751, 0.20 #323, 0.10 #1608), 0gd_s (0.25 #735, 0.08 #2448, 0.07 #4161), 0klw (0.25 #577, 0.07 #4003, 0.07 #3574), 0g5ff (0.24 #3614, 0.21 #2330, 0.20 #4472) >> Best rule #1007 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01x53m; 0dfrq; >> query: (?x2485, 03f0324) <- student(?x2327, ?x2485), award(?x2485, ?x11388), influenced_by(?x2485, ?x3336), ?x11388 = 04hddx >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #5568 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 058vp; 02m4t; *> query: (?x2485, ?x2994) <- influenced_by(?x2485, ?x4055), influenced_by(?x2485, ?x3336), ?x3336 = 032l1, influenced_by(?x4055, ?x2994) *> conf = 0.15 ranks of expected_values: 29, 84 EVAL 0gd5z influenced_by 03_dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 143.000 59.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 0gd5z influenced_by 0m77m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 143.000 59.000 0.400 http://example.org/influence/influence_node/influenced_by #18010-03d9v8 PRED entity: 03d9v8 PRED relation: people! PRED expected values: 05l3g_ => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 51): 041rx (0.87 #2332, 0.37 #378, 0.25 #4813), 0x67 (0.34 #834, 0.33 #9, 0.27 #3090), 0dryh9k (0.30 #90, 0.17 #240, 0.11 #540), 07bch9 (0.23 #471, 0.20 #546, 0.17 #246), 02ctzb (0.20 #539, 0.20 #464, 0.18 #164), 033tf_ (0.19 #306, 0.18 #156, 0.17 #1056), 063k3h (0.18 #179, 0.09 #554, 0.09 #479), 07mqps (0.17 #243, 0.10 #93, 0.09 #168), 0xnvg (0.15 #312, 0.11 #2116, 0.10 #1137), 02sch9 (0.10 #108, 0.04 #333, 0.03 #558) >> Best rule #2332 for best value: >> intensional similarity = 3 >> extensional distance = 403 >> proper extension: 015c1b; >> query: (?x9162, 041rx) <- people(?x5540, ?x9162), people(?x5540, ?x1211), ?x1211 = 0k4gf >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #434 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 07h1q; *> query: (?x9162, 05l3g_) <- people(?x5540, ?x9162), ?x5540 = 013xrm, religion(?x9162, ?x1624) *> conf = 0.07 ranks of expected_values: 18 EVAL 03d9v8 people! 05l3g_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 127.000 127.000 0.872 http://example.org/people/ethnicity/people #18009-02wk4d PRED entity: 02wk4d PRED relation: profession PRED expected values: 016z4k => 124 concepts (86 used for prediction) PRED predicted values (max 10 best out of 71): 01d_h8 (0.68 #444, 0.52 #298, 0.43 #152), 0dxtg (0.68 #451, 0.51 #9652, 0.48 #305), 0nbcg (0.59 #321, 0.48 #3825, 0.47 #6454), 016z4k (0.53 #734, 0.49 #1026, 0.48 #3800), 0dz3r (0.45 #1316, 0.43 #1754, 0.40 #1170), 03gjzk (0.34 #598, 0.24 #10968, 0.24 #4832), 0d1pc (0.33 #48, 0.19 #778, 0.15 #1946), 0n1h (0.30 #303, 0.28 #1325, 0.28 #741), 039v1 (0.27 #6459, 0.27 #3830, 0.27 #2224), 0cbd2 (0.25 #9646, 0.25 #5701, 0.22 #299) >> Best rule #444 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 029k55; >> query: (?x5922, 01d_h8) <- place_of_birth(?x5922, ?x2645), profession(?x5922, ?x1183), profession(?x5922, ?x524), ?x1183 = 09jwl, ?x524 = 02jknp >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #734 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 04f7c55; 01wkmgb; *> query: (?x5922, 016z4k) <- instrumentalists(?x227, ?x5922), languages(?x5922, ?x254), profession(?x5922, ?x1032), ?x1032 = 02hrh1q *> conf = 0.53 ranks of expected_values: 4 EVAL 02wk4d profession 016z4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 124.000 86.000 0.676 http://example.org/people/person/profession #18008-0d6lp PRED entity: 0d6lp PRED relation: county_seat! PRED expected values: 0d6lp => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 94): 06pvr (0.10 #8385, 0.10 #14219, 0.08 #6017), 09c7w0 (0.10 #8385, 0.10 #14219, 0.08 #6017), 0k3l5 (0.07 #239, 0.05 #421, 0.05 #603), 0mpbx (0.07 #306, 0.05 #670, 0.04 #1217), 0cc56 (0.07 #192, 0.04 #1103, 0.03 #2380), 02cl1 (0.07 #188, 0.04 #1099, 0.03 #4016), 0mnrb (0.07 #361, 0.04 #1272, 0.02 #5282), 0fw4v (0.07 #258, 0.02 #5179, 0.02 #7365), 0nvt9 (0.05 #411, 0.05 #593, 0.04 #1504), 0ms1n (0.05 #501, 0.05 #683, 0.04 #1594) >> Best rule #8385 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 0t6hk; >> query: (?x3125, ?x94) <- teams(?x3125, ?x4243), state(?x3125, ?x1227), contains(?x94, ?x3125) >> conf = 0.10 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0d6lp county_seat! 0d6lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 220.000 220.000 0.104 http://example.org/location/us_county/county_seat #18007-01k0vq PRED entity: 01k0vq PRED relation: genre PRED expected values: 05p553 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 91): 07s9rl0 (0.67 #8005, 0.67 #715, 0.66 #1670), 05p553 (0.62 #124, 0.50 #5, 0.45 #1435), 01z4y (0.61 #7527, 0.51 #2028, 0.50 #4422), 02kdv5l (0.54 #837, 0.38 #241, 0.31 #4185), 01jfsb (0.44 #848, 0.35 #4196, 0.32 #8018), 03k9fj (0.41 #847, 0.25 #251, 0.23 #132), 01hmnh (0.29 #853, 0.19 #257, 0.15 #2406), 06cvj (0.28 #1434, 0.25 #2032, 0.24 #2271), 0lsxr (0.25 #248, 0.22 #1082, 0.20 #605), 06n90 (0.23 #849, 0.19 #253, 0.15 #2521) >> Best rule #8005 for best value: >> intensional similarity = 3 >> extensional distance = 1494 >> proper extension: 01br2w; 0dckvs; 0djb3vw; 0cnztc4; 053tj7; 0d6b7; 091z_p; 064n1pz; 040rmy; 0crh5_f; ... >> query: (?x7579, 07s9rl0) <- genre(?x7579, ?x1403), genre(?x4007, ?x1403), ?x4007 = 03hmt9b >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 03b_fm5; 027j9wd; 01g3gq; 0fpgp26; 09rx7tx; *> query: (?x7579, 05p553) <- film(?x1461, ?x7579), genre(?x7579, ?x1403), ?x1461 = 03n08b *> conf = 0.62 ranks of expected_values: 2 EVAL 01k0vq genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.672 http://example.org/film/film/genre #18006-073hmq PRED entity: 073hmq PRED relation: award_winner PRED expected values: 0kjgl => 51 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1552): 0133sq (0.25 #4526, 0.20 #2983, 0.17 #13772), 012rng (0.24 #9246, 0.23 #3080, 0.22 #30823), 05kfs (0.24 #9246, 0.10 #15508, 0.08 #6258), 0b6mgp_ (0.23 #6844, 0.22 #678, 0.21 #8385), 0c94fn (0.23 #6435, 0.22 #269, 0.21 #7976), 03r1pr (0.23 #6589, 0.21 #8130, 0.18 #11212), 01vvb4m (0.23 #3080, 0.22 #30823, 0.22 #21575), 01xv77 (0.23 #3080, 0.22 #30823, 0.22 #21575), 0h0jz (0.23 #3080, 0.22 #30823, 0.22 #21575), 06rnl9 (0.22 #422, 0.21 #8129, 0.19 #15838) >> Best rule #4526 for best value: >> intensional similarity = 14 >> extensional distance = 10 >> proper extension: 0bzkgg; >> query: (?x1601, 0133sq) <- ceremony(?x6860, ?x1601), ceremony(?x484, ?x1601), honored_for(?x1601, ?x4460), award_winner(?x1601, ?x7331), film(?x3056, ?x4460), production_companies(?x4460, ?x788), award(?x4460, ?x1862), film_release_region(?x4460, ?x94), film_release_distribution_medium(?x4460, ?x81), featured_film_locations(?x4460, ?x739), nominated_for(?x4307, ?x4460), ?x484 = 0gq_v, celebrity(?x7331, ?x6187), nominated_for(?x6860, ?x155) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #46243 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 59 *> proper extension: 092c5f; 027hjff; *> query: (?x1601, ?x4748) <- ceremony(?x1972, ?x1601), ceremony(?x1245, ?x1601), honored_for(?x1601, ?x4460), honored_for(?x1601, ?x3433), award_winner(?x1601, ?x1656), film(?x3056, ?x4460), production_companies(?x4460, ?x788), award(?x4460, ?x1862), film_release_region(?x4460, ?x94), film(?x4748, ?x3433), featured_film_locations(?x4460, ?x739), genre(?x3433, ?x225), award(?x91, ?x1972), nominated_for(?x1972, ?x86), award(?x197, ?x1245) *> conf = 0.10 ranks of expected_values: 185 EVAL 073hmq award_winner 0kjgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 51.000 30.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #18005-0280mv7 PRED entity: 0280mv7 PRED relation: profession PRED expected values: 02jknp => 73 concepts (55 used for prediction) PRED predicted values (max 10 best out of 59): 02jknp (0.93 #2097, 0.50 #157, 0.33 #8), 02hrh1q (0.67 #3893, 0.66 #3148, 0.66 #6578), 01d_h8 (0.62 #2095, 0.44 #453, 0.36 #155), 0dxtg (0.57 #2103, 0.36 #461, 0.31 #2700), 09jwl (0.47 #1212, 0.45 #1510, 0.43 #1810), 0dz3r (0.34 #1194, 0.32 #1492, 0.32 #1792), 0nbcg (0.34 #1522, 0.33 #1224, 0.32 #1822), 02krf9 (0.33 #28, 0.21 #177, 0.20 #2117), 03gjzk (0.30 #1357, 0.29 #463, 0.25 #2702), 016z4k (0.29 #1196, 0.29 #1494, 0.28 #1794) >> Best rule #2097 for best value: >> intensional similarity = 3 >> extensional distance = 710 >> proper extension: 022_lg; 0c8hct; >> query: (?x4974, 02jknp) <- profession(?x4974, ?x2265), profession(?x11297, ?x2265), ?x11297 = 0l15n >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0280mv7 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 55.000 0.930 http://example.org/people/person/profession #18004-02m3sd PRED entity: 02m3sd PRED relation: place_of_birth PRED expected values: 0zrlp => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 104): 05tbn (0.29 #25359, 0.28 #54233, 0.27 #38035), 02_286 (0.12 #3546, 0.10 #4954, 0.09 #7068), 030qb3t (0.07 #14148, 0.07 #2875, 0.07 #15556), 02hrh0_ (0.05 #895, 0.04 #2305, 0.03 #3011), 01_d4 (0.05 #19792, 0.04 #11344, 0.04 #8524), 0cr3d (0.04 #19116, 0.04 #8552, 0.04 #37424), 0rd5k (0.04 #1536, 0.02 #830, 0.02 #2240), 02dtg (0.04 #4945, 0.03 #7059, 0.02 #4241), 0106dv (0.04 #2510, 0.03 #3216, 0.02 #1100), 0rh6k (0.03 #8460, 0.02 #4233, 0.02 #14800) >> Best rule #25359 for best value: >> intensional similarity = 3 >> extensional distance = 516 >> proper extension: 042rnl; 045bg; 017r2; 064p92m; 014dq7; 0g51l1; 01q415; 04k25; 04g865; 0lrh; ... >> query: (?x10841, ?x3670) <- profession(?x10841, ?x987), location(?x10841, ?x3670), ?x987 = 0dxtg >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02m3sd place_of_birth 0zrlp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 112.000 0.289 http://example.org/people/person/place_of_birth #18003-01flzb PRED entity: 01flzb PRED relation: artists PRED expected values: 01kwlwp => 45 concepts (25 used for prediction) PRED predicted values (max 10 best out of 959): 017j6 (0.60 #293, 0.44 #5726, 0.40 #6811), 01vw20_ (0.50 #4594, 0.44 #5681, 0.40 #6766), 0fpj4lx (0.50 #4673, 0.44 #5760, 0.40 #6845), 011_vz (0.50 #5195, 0.44 #6282, 0.40 #7367), 01gf5h (0.50 #4409, 0.44 #5496, 0.40 #6581), 0161sp (0.50 #4585, 0.40 #6757, 0.33 #5672), 01w8n89 (0.44 #5752, 0.40 #6837, 0.40 #319), 013w8y (0.44 #6261, 0.40 #7346, 0.40 #828), 02k5sc (0.44 #6147, 0.40 #7232, 0.40 #714), 05crg7 (0.44 #5559, 0.40 #6644, 0.38 #4472) >> Best rule #293 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 016clz; 02x8m; 06cp5; >> query: (?x14516, 017j6) <- artists(?x14516, ?x10628), ?x10628 = 01w20rx, parent_genre(?x14516, ?x13245), parent_genre(?x13245, ?x5717), artists(?x13245, ?x954), artists(?x5717, ?x3256), ?x3256 = 01vwyqp >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7604 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 07nf_4; *> query: (?x14516, ?x954) <- artists(?x14516, ?x10628), ?x10628 = 01w20rx, parent_genre(?x14516, ?x13245), parent_genre(?x13245, ?x5717), artists(?x13245, ?x2055), artists(?x13245, ?x954), artists(?x5717, ?x215), award_winner(?x342, ?x2055) *> conf = 0.16 ranks of expected_values: 413 EVAL 01flzb artists 01kwlwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 25.000 0.600 http://example.org/music/genre/artists #18002-0dzlk PRED entity: 0dzlk PRED relation: artists! PRED expected values: 02k_kn => 186 concepts (124 used for prediction) PRED predicted values (max 10 best out of 251): 06by7 (0.62 #2830, 0.60 #3454, 0.56 #3767), 06j6l (0.54 #2857, 0.47 #5354, 0.43 #7227), 0ggx5q (0.53 #5385, 0.37 #7258, 0.37 #11626), 02lnbg (0.53 #5365, 0.35 #7238, 0.32 #11606), 025sc50 (0.50 #5356, 0.46 #2859, 0.43 #7229), 0mhfr (0.50 #1897, 0.33 #337, 0.13 #5018), 0glt670 (0.42 #9715, 0.41 #16581, 0.37 #18141), 0155w (0.40 #1045, 0.29 #6039, 0.26 #6975), 05bt6j (0.39 #3789, 0.38 #5349, 0.33 #3476), 0gywn (0.35 #7861, 0.33 #3804, 0.33 #7237) >> Best rule #2830 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0m2l9; 02mslq; 01vvycq; 03h_fk5; 01vsykc; 01vtqml; 049qx; 0478__m; 044mfr; 06tp4h; ... >> query: (?x11475, 06by7) <- origin(?x11475, ?x8602), artist(?x2241, ?x11475), category(?x11475, ?x134), notable_people_with_this_condition(?x11990, ?x11475) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0134wr; *> query: (?x11475, 02k_kn) <- artist(?x13729, ?x11475), ?x13729 = 098cpg, artists(?x671, ?x11475), award_winner(?x724, ?x11475) *> conf = 0.33 ranks of expected_values: 20 EVAL 0dzlk artists! 02k_kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 186.000 124.000 0.615 http://example.org/music/genre/artists #18001-02jq1 PRED entity: 02jq1 PRED relation: artist! PRED expected values: 03gfvsz => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 4): 03gfvsz (0.17 #142, 0.13 #105, 0.12 #87), 01fjfv (0.09 #69, 0.07 #14, 0.05 #82), 04rqd (0.07 #72, 0.03 #443, 0.03 #97), 04y652m (0.02 #71, 0.02 #176, 0.02 #467) >> Best rule #142 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 07c0j; 03t9sp; 05crg7; 0frsw; 016fmf; 0fcsd; 03xhj6; 01cblr; 0g_g2; 06nv27; ... >> query: (?x5442, 03gfvsz) <- artists(?x3061, ?x5442), origin(?x5442, ?x5381), ?x3061 = 05bt6j >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jq1 artist! 03gfvsz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.173 http://example.org/broadcast/content/artist #18000-07yk1xz PRED entity: 07yk1xz PRED relation: executive_produced_by PRED expected values: 03h304l => 110 concepts (81 used for prediction) PRED predicted values (max 10 best out of 120): 04jspq (0.15 #1653, 0.05 #2655, 0.03 #3907), 05hj_k (0.12 #5858, 0.12 #2351, 0.11 #4356), 06q8hf (0.12 #5927, 0.11 #2420, 0.11 #4425), 0bwh6 (0.12 #4510, 0.11 #4008, 0.08 #2505), 02z6l5f (0.11 #367, 0.05 #5878, 0.04 #6882), 02q_cc (0.11 #278, 0.05 #2282, 0.04 #3785), 02z2xdf (0.11 #407, 0.02 #1408, 0.02 #5918), 0438pz (0.11 #446, 0.01 #1699), 06pj8 (0.08 #2309, 0.08 #3812, 0.06 #4314), 0glyyw (0.06 #4698, 0.06 #6953, 0.06 #3695) >> Best rule #1653 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 018nnz; 063y9fp; 0hz6mv2; >> query: (?x2203, 04jspq) <- genre(?x2203, ?x2605), executive_produced_by(?x2203, ?x3186), student(?x2605, ?x445), major_field_of_study(?x122, ?x2605) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #4884 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 237 *> proper extension: 0g5pv3; 09fc83; *> query: (?x2203, 03h304l) <- genre(?x2203, ?x1316), executive_produced_by(?x2203, ?x3186), featured_film_locations(?x2203, ?x2204) *> conf = 0.02 ranks of expected_values: 48 EVAL 07yk1xz executive_produced_by 03h304l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 110.000 81.000 0.147 http://example.org/film/film/executive_produced_by #17999-0gd9k PRED entity: 0gd9k PRED relation: place_of_birth PRED expected values: 0xqf3 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 144): 02_286 (0.13 #7771, 0.12 #8476, 0.11 #19), 0cr3d (0.11 #5029, 0.09 #4324, 0.04 #9960), 01_d4 (0.06 #4296, 0.05 #5001, 0.05 #7818), 0s5cg (0.06 #4411, 0.03 #7229, 0.02 #12160), 030qb3t (0.06 #29641, 0.05 #5693, 0.05 #38801), 02dtg (0.05 #4945, 0.05 #1420, 0.02 #43690), 0chrx (0.05 #5240, 0.02 #16509, 0.01 #19326), 013yq (0.05 #79, 0.05 #784, 0.04 #2194), 0r7fy (0.05 #49, 0.05 #754, 0.04 #2164), 01snm (0.05 #239, 0.05 #944, 0.04 #2354) >> Best rule #7771 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 02j8nx; 02wr2r; >> query: (?x7984, 02_286) <- nationality(?x7984, ?x94), film(?x7984, ?x994), producer_type(?x7984, ?x632) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gd9k place_of_birth 0xqf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 127.000 0.128 http://example.org/people/person/place_of_birth #17998-019nnl PRED entity: 019nnl PRED relation: genre PRED expected values: 095bb => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 78): 07s9rl0 (0.53 #1405, 0.50 #1015, 0.50 #937), 01t_vv (0.33 #108, 0.28 #342, 0.19 #498), 06n90 (0.23 #246, 0.19 #1416, 0.16 #1573), 01htzx (0.19 #250, 0.17 #562, 0.16 #172), 01hmnh (0.17 #249, 0.15 #327, 0.15 #171), 03k9fj (0.17 #1414, 0.16 #244, 0.14 #1336), 06q7n (0.16 #196, 0.15 #274, 0.13 #898), 0lsxr (0.13 #555, 0.13 #633, 0.12 #789), 01jfsb (0.12 #479, 0.12 #401, 0.11 #557), 01z77k (0.11 #961, 0.11 #1039, 0.10 #1508) >> Best rule #1405 for best value: >> intensional similarity = 2 >> extensional distance = 244 >> proper extension: 017dcd; 01qn7n; 07hpv3; 07ng9k; 05hd32; 02648p; 027pfb2; 0jwl2; 01p4wv; 0283ph; ... >> query: (?x1395, 07s9rl0) <- genre(?x1395, ?x258), genre(?x86, ?x258) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #190 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 0gxsh4; *> query: (?x1395, 095bb) <- nominated_for(?x1537, ?x1395), genre(?x1395, ?x258), tv_program(?x8713, ?x1395) *> conf = 0.08 ranks of expected_values: 17 EVAL 019nnl genre 095bb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 45.000 45.000 0.533 http://example.org/tv/tv_program/genre #17997-0grd7 PRED entity: 0grd7 PRED relation: place_of_birth! PRED expected values: 059xvg 015njf => 137 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1445): 066yfh (0.20 #2439, 0.11 #5051, 0.05 #10275), 02g40r (0.20 #2181, 0.11 #4793, 0.05 #10017), 02q42j_ (0.20 #1228, 0.11 #3840, 0.05 #9064), 04_1nk (0.20 #1134, 0.11 #3746, 0.05 #8970), 02pq9yv (0.20 #679, 0.11 #3291, 0.05 #8515), 04v7kt (0.20 #2465, 0.04 #18137, 0.01 #46874), 0ct_yc (0.20 #2033, 0.04 #17705, 0.01 #46442), 0mb5x (0.20 #1754, 0.04 #17426, 0.01 #46163), 0135xb (0.20 #1493), 026g801 (0.11 #3672, 0.02 #37633, 0.02 #40245) >> Best rule #2439 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01gln9; 0n96z; >> query: (?x9976, 066yfh) <- contains(?x512, ?x9976), ?x512 = 07ssc, place_of_birth(?x4858, ?x9976), adjoins(?x13212, ?x9976) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0grd7 place_of_birth! 015njf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 56.000 0.200 http://example.org/people/person/place_of_birth EVAL 0grd7 place_of_birth! 059xvg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 56.000 0.200 http://example.org/people/person/place_of_birth #17996-0pc_l PRED entity: 0pc_l PRED relation: actor PRED expected values: 02zfdp => 78 concepts (52 used for prediction) PRED predicted values (max 10 best out of 970): 0d07j8 (0.54 #931, 0.51 #1863, 0.40 #9306), 04cf09 (0.15 #1030, 0.04 #4752, 0.04 #6613), 0f6_dy (0.14 #164, 0.10 #1095, 0.03 #11330), 02pkpfs (0.14 #96, 0.10 #1027, 0.02 #8471), 07fpm3 (0.14 #298, 0.10 #1229, 0.02 #12395), 02p7_k (0.14 #292, 0.07 #16754, 0.05 #1223), 022yb4 (0.14 #651, 0.05 #1582, 0.04 #9026), 0hvb2 (0.14 #142, 0.05 #1073, 0.04 #3865), 05wqr1 (0.14 #619, 0.05 #1550, 0.04 #5272), 01ggc9 (0.14 #768, 0.05 #1699, 0.04 #2631) >> Best rule #931 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0g60z; 0ddd0gc; 039c26; 02rzdcp; 01dvry; >> query: (?x12173, ?x2452) <- nominated_for(?x6724, ?x12173), nominated_for(?x2071, ?x12173), nominated_for(?x2452, ?x12173), ?x6724 = 09v7wsg, ?x2071 = 0bdw6t >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #693 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0g60z; 0ddd0gc; 039c26; 02rzdcp; 01dvry; *> query: (?x12173, 02zfdp) <- nominated_for(?x6724, ?x12173), nominated_for(?x2071, ?x12173), nominated_for(?x2452, ?x12173), ?x6724 = 09v7wsg, ?x2071 = 0bdw6t *> conf = 0.14 ranks of expected_values: 21 EVAL 0pc_l actor 02zfdp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 78.000 52.000 0.541 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #17995-02p76f9 PRED entity: 02p76f9 PRED relation: genre PRED expected values: 07s9rl0 => 82 concepts (58 used for prediction) PRED predicted values (max 10 best out of 98): 03k9fj (0.74 #4005, 0.64 #496, 0.55 #859), 07s9rl0 (0.70 #1574, 0.64 #4841, 0.63 #6173), 05p553 (0.67 #6055, 0.65 #6299, 0.48 #368), 01jfsb (0.57 #2070, 0.57 #5579, 0.49 #6793), 02kdv5l (0.48 #487, 0.46 #6783, 0.42 #3996), 02l7c8 (0.42 #1590, 0.29 #5099, 0.29 #5704), 0lsxr (0.34 #5575, 0.27 #2066, 0.21 #3034), 06n90 (0.28 #2555, 0.27 #861, 0.26 #740), 04pbhw (0.25 #904, 0.21 #541, 0.15 #1751), 0hcr (0.22 #2686, 0.22 #1718, 0.22 #2928) >> Best rule #4005 for best value: >> intensional similarity = 4 >> extensional distance = 493 >> proper extension: 014lc_; 034qmv; 02_fm2; 03g90h; 0dq626; 0gtv7pk; 0m2kd; 03mh94; 0401sg; 0gkz15s; ... >> query: (?x8284, 03k9fj) <- genre(?x8284, ?x1510), titles(?x1510, ?x83), genre(?x1385, ?x1510), ?x1385 = 044g_k >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1574 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 123 *> proper extension: 06wzvr; 06krf3; 0p3_y; 0kv238; 02x6dqb; 0glnm; 07yvsn; 03cw411; 02q_4ph; 03cfkrw; ... *> query: (?x8284, 07s9rl0) <- genre(?x8284, ?x571), award(?x8284, ?x507), nominated_for(?x143, ?x8284), costume_design_by(?x8284, ?x5613) *> conf = 0.70 ranks of expected_values: 2 EVAL 02p76f9 genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 58.000 0.743 http://example.org/film/film/genre #17994-019pm_ PRED entity: 019pm_ PRED relation: type_of_union PRED expected values: 01g63y => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1): 01g63y (0.43 #7, 0.37 #61, 0.34 #34) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 06y9c2; >> query: (?x2763, 01g63y) <- participant(?x2763, ?x400), type_of_union(?x2763, ?x566), spouse(?x5834, ?x2763) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019pm_ type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.435 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17993-0bw20 PRED entity: 0bw20 PRED relation: film! PRED expected values: 0h5g_ => 146 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1331): 0h5g_ (0.23 #2156, 0.10 #16724, 0.09 #18805), 0pmhf (0.16 #4606, 0.11 #10850, 0.11 #8769), 01f8ld (0.16 #81183, 0.15 #197763, 0.15 #160288), 0170qf (0.15 #2450, 0.14 #12856, 0.13 #14937), 0bxtg (0.12 #6321, 0.09 #20889, 0.05 #39624), 05vsxz (0.11 #4172, 0.08 #2091, 0.07 #8335), 05nzw6 (0.11 #5356, 0.07 #9519, 0.06 #17843), 02s2ft (0.11 #4170, 0.07 #8333, 0.06 #22901), 09l3p (0.10 #29888, 0.08 #2833, 0.08 #40298), 0f5xn (0.10 #30108, 0.08 #40518, 0.06 #84237) >> Best rule #2156 for best value: >> intensional similarity = 7 >> extensional distance = 11 >> proper extension: 0fy66; 02rq8k8; >> query: (?x7161, 0h5g_) <- genre(?x7161, ?x3515), featured_film_locations(?x7161, ?x279), ?x3515 = 082gq, language(?x7161, ?x254), country(?x7161, ?x94), film(?x5184, ?x7161), currency(?x279, ?x170) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bw20 film! 0h5g_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 107.000 0.231 http://example.org/film/actor/film./film/performance/film #17992-09cm54 PRED entity: 09cm54 PRED relation: award_winner PRED expected values: 014zcr 03f1zdw 0170pk 0p_47 => 44 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2050): 0bj9k (0.56 #7754, 0.50 #5305, 0.50 #409), 040z9 (0.56 #8959, 0.33 #6510, 0.25 #1614), 02qgqt (0.50 #4913, 0.50 #17, 0.44 #7362), 016khd (0.50 #5050, 0.50 #154, 0.33 #7499), 0kjgl (0.50 #1706, 0.33 #9051, 0.33 #6602), 0170pk (0.50 #348, 0.33 #7693, 0.33 #5244), 06dv3 (0.50 #37, 0.33 #4933, 0.22 #7382), 01520h (0.50 #1479, 0.33 #6375, 0.22 #8824), 0flw6 (0.50 #943, 0.33 #5839, 0.11 #8288), 01wmxfs (0.44 #7488, 0.33 #5039, 0.25 #143) >> Best rule #7754 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 054ky1; >> query: (?x1770, 0bj9k) <- award_winner(?x1770, ?x4992), award_winner(?x1770, ?x2499), award_winner(?x1770, ?x1119), ?x1119 = 039bp, people(?x3584, ?x4992), friend(?x2499, ?x286) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #348 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0f4x7; 027986c; *> query: (?x1770, 0170pk) <- award(?x407, ?x1770), award_winner(?x1770, ?x4277), award_winner(?x1770, ?x450), ?x4277 = 046qq, ?x450 = 0z4s *> conf = 0.50 ranks of expected_values: 6, 20, 22, 113 EVAL 09cm54 award_winner 0p_47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 44.000 21.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 09cm54 award_winner 0170pk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 44.000 21.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 09cm54 award_winner 03f1zdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 44.000 21.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 09cm54 award_winner 014zcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 44.000 21.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner #17991-085q5 PRED entity: 085q5 PRED relation: nationality PRED expected values: 09c7w0 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 75): 09c7w0 (0.87 #802, 0.87 #1202, 0.86 #902), 059rby (0.33 #7836, 0.32 #6324), 01znc_ (0.31 #2409, 0.30 #2106, 0.03 #5622), 0d060g (0.30 #7228, 0.28 #2207, 0.12 #308), 0f8l9c (0.30 #7228, 0.28 #2207, 0.05 #723), 07ssc (0.21 #716, 0.10 #2020, 0.09 #2323), 02jx1 (0.17 #134, 0.11 #2038, 0.10 #2944), 03rk0 (0.10 #2455, 0.09 #2756, 0.08 #1848), 0345h (0.07 #1132, 0.03 #3747, 0.03 #5622), 0h7x (0.06 #1136, 0.03 #1636, 0.03 #5622) >> Best rule #802 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 07_grx; 0grrq8; 025cn2; >> query: (?x10121, 09c7w0) <- place_of_birth(?x10121, ?x739), student(?x3439, ?x10121), ?x739 = 02_286, gender(?x10121, ?x231) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 085q5 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.870 http://example.org/people/person/nationality #17990-04psf PRED entity: 04psf PRED relation: symptom_of! PRED expected values: 0j5fv 0f3kl => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 56): 01cdt5 (0.71 #106, 0.67 #87, 0.67 #84), 012qjw (0.67 #87, 0.67 #84, 0.53 #60), 0j5fv (0.67 #87, 0.67 #84, 0.53 #60), 0brgy (0.50 #71, 0.33 #423, 0.33 #48), 0f3kl (0.50 #79, 0.33 #56, 0.21 #412), 02tfl8 (0.43 #398, 0.33 #456, 0.33 #417), 02y0js (0.33 #41, 0.25 #64, 0.04 #741), 0hgxh (0.25 #73, 0.20 #1152, 0.18 #750), 01pf6 (0.20 #1152, 0.14 #104, 0.10 #587), 0hg45 (0.20 #1152, 0.14 #102, 0.09 #256) >> Best rule #106 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 0dcqh; >> query: (?x3799, 01cdt5) <- people(?x3799, ?x3800), people(?x3799, ?x1774), place_of_birth(?x3800, ?x11979), award(?x1774, ?x350), written_by(?x1318, ?x1774), symptom_of(?x4905, ?x3799), nominated_for(?x350, ?x4032), nominated_for(?x350, ?x188), ?x4032 = 0g9yrw, award_winner(?x350, ?x916), ?x188 = 0140g4 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 0167bx; *> query: (?x3799, ?x9438) <- symptom_of(?x10717, ?x3799), symptom_of(?x9509, ?x3799), symptom_of(?x4905, ?x3799), ?x4905 = 01j6t0, risk_factors(?x3799, ?x13738), ?x10717 = 0cjf0, risk_factors(?x8675, ?x13738), symptom_of(?x9438, ?x8675), ?x9509 = 0gxb2 *> conf = 0.67 ranks of expected_values: 3, 5 EVAL 04psf symptom_of! 0f3kl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 60.000 60.000 0.714 http://example.org/medicine/symptom/symptom_of EVAL 04psf symptom_of! 0j5fv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 60.000 0.714 http://example.org/medicine/symptom/symptom_of #17989-03h_fqv PRED entity: 03h_fqv PRED relation: artists! PRED expected values: 03_d0 => 147 concepts (103 used for prediction) PRED predicted values (max 10 best out of 212): 064t9 (0.79 #322, 0.77 #2798, 0.70 #3106), 05bt6j (0.57 #353, 0.52 #1902, 0.50 #1282), 02lnbg (0.50 #368, 0.43 #2844, 0.41 #3771), 06j6l (0.38 #1286, 0.37 #2833, 0.35 #3141), 0ggx5q (0.36 #387, 0.34 #2863, 0.33 #1936), 0glt670 (0.34 #2826, 0.33 #3134, 0.30 #8997), 02k_kn (0.33 #66, 0.21 #375, 0.19 #994), 025sc50 (0.31 #2835, 0.30 #3143, 0.29 #3762), 02ny8t (0.30 #1990, 0.29 #441, 0.22 #3844), 0y3_8 (0.25 #1285, 0.22 #1905, 0.21 #356) >> Best rule #322 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0gs6vr; >> query: (?x5391, 064t9) <- friend(?x5391, ?x3056), artists(?x1572, ?x5391), ?x1572 = 06by7 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #11128 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 246 *> proper extension: 01vsxdm; *> query: (?x5391, 03_d0) <- award_winner(?x1565, ?x5391), artists(?x302, ?x5391), role(?x5391, ?x212) *> conf = 0.23 ranks of expected_values: 15 EVAL 03h_fqv artists! 03_d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 147.000 103.000 0.786 http://example.org/music/genre/artists #17988-0g4pl7z PRED entity: 0g4pl7z PRED relation: honored_for! PRED expected values: 0h_cssd => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 102): 0hr6lkl (0.11 #1110, 0.11 #1232, 0.09 #866), 0bvhz9 (0.10 #114, 0.05 #2310, 0.04 #846), 0418154 (0.10 #93, 0.05 #1069, 0.04 #3021), 0drtv8 (0.10 #55, 0.04 #787, 0.03 #421), 0275n3y (0.10 #64, 0.03 #2260, 0.03 #3358), 027hjff (0.10 #47, 0.03 #2243, 0.02 #5171), 0gmdkyy (0.09 #878, 0.08 #1122, 0.08 #1244), 09gkdln (0.08 #1692, 0.07 #1814, 0.06 #1448), 02pgky2 (0.07 #320, 0.07 #198, 0.03 #442), 0h_cssd (0.07 #876, 0.06 #5979, 0.06 #1120) >> Best rule #1110 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 0372j5; >> query: (?x8955, 0hr6lkl) <- film_festivals(?x8955, ?x6828), nominated_for(?x5886, ?x8955), film_release_region(?x8955, ?x1264), ?x1264 = 0345h >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #876 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 0gh8zks; 07k2mq; *> query: (?x8955, 0h_cssd) <- film_festivals(?x8955, ?x6828), nominated_for(?x5886, ?x8955), film_release_region(?x8955, ?x279), ?x279 = 0d060g *> conf = 0.07 ranks of expected_values: 10 EVAL 0g4pl7z honored_for! 0h_cssd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 132.000 132.000 0.111 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #17987-044mvs PRED entity: 044mvs PRED relation: currency PRED expected values: 09nqf => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.23 #64, 0.22 #34, 0.22 #28) >> Best rule #64 for best value: >> intensional similarity = 2 >> extensional distance = 895 >> proper extension: 019f9z; 05cx7x; 01nhkxp; 010xjr; 03j3pg9; 02tc5y; 01wskg; 0py5b; >> query: (?x10188, 09nqf) <- award_nominee(?x56, ?x10188), people(?x1446, ?x10188) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044mvs currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.227 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #17986-0ksf29 PRED entity: 0ksf29 PRED relation: produced_by! PRED expected values: 0gtv7pk => 147 concepts (30 used for prediction) PRED predicted values (max 10 best out of 295): 03cwwl (0.42 #22667, 0.40 #7557, 0.07 #24556), 0408m53 (0.10 #872, 0.05 #1818, 0.03 #2763), 0gwgn1k (0.10 #821, 0.05 #1767, 0.03 #2712), 02hxhz (0.10 #72, 0.05 #1018, 0.03 #1963), 0b6f8pf (0.10 #858, 0.05 #1804, 0.03 #2749), 0gzlb9 (0.06 #1891, 0.05 #7558, 0.05 #2836), 05nyqk (0.06 #1891, 0.05 #7558, 0.05 #2836), 033dbw (0.05 #917, 0.05 #1863, 0.03 #2808), 087pfc (0.05 #814, 0.05 #1760, 0.03 #2705), 02ph9tm (0.05 #602, 0.05 #1548, 0.03 #2493) >> Best rule #22667 for best value: >> intensional similarity = 4 >> extensional distance = 251 >> proper extension: 04b19t; >> query: (?x1714, ?x9996) <- produced_by(?x6375, ?x1714), nominated_for(?x1714, ?x9996), type_of_union(?x1714, ?x566), nominated_for(?x375, ?x6375) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ksf29 produced_by! 0gtv7pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 30.000 0.421 http://example.org/film/film/produced_by #17985-0ctw_b PRED entity: 0ctw_b PRED relation: olympics PRED expected values: 09n48 => 240 concepts (240 used for prediction) PRED predicted values (max 10 best out of 34): 0jdk_ (0.65 #597, 0.62 #661, 0.61 #309), 09n48 (0.61 #963, 0.55 #387, 0.53 #163), 018ctl (0.56 #231, 0.56 #839, 0.56 #295), 0l6m5 (0.53 #169, 0.41 #553, 0.41 #873), 09x3r (0.52 #1121, 0.46 #1251, 0.46 #1510), 0sx8l (0.52 #1121, 0.46 #1510, 0.36 #5297), 0l6mp (0.47 #175, 0.46 #1251, 0.45 #559), 0swff (0.47 #179, 0.44 #243, 0.40 #403), 0ldqf (0.46 #1251, 0.45 #1154, 0.44 #285), 0l998 (0.46 #1251, 0.45 #1154, 0.44 #262) >> Best rule #597 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 047yc; 015qh; 02vzc; >> query: (?x1023, 0jdk_) <- country(?x150, ?x1023), film_release_region(?x9652, ?x1023), combatants(?x1023, ?x94), ?x9652 = 0ddbjy4 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #963 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 03rj0; *> query: (?x1023, 09n48) <- country(?x7108, ?x1023), film_release_region(?x124, ?x1023), ?x7108 = 0194d, ?x124 = 0g56t9t *> conf = 0.61 ranks of expected_values: 2 EVAL 0ctw_b olympics 09n48 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 240.000 240.000 0.652 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #17984-0bl2g PRED entity: 0bl2g PRED relation: people! PRED expected values: 041rx => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 40): 041rx (0.63 #389, 0.38 #4, 0.33 #235), 0xnvg (0.17 #167, 0.07 #860, 0.07 #706), 033tf_ (0.12 #777, 0.12 #700, 0.11 #1085), 0x67 (0.11 #934, 0.10 #857, 0.10 #1397), 07hwkr (0.09 #89, 0.05 #551, 0.05 #859), 01qhm_ (0.09 #83, 0.04 #776, 0.03 #1084), 065b6q (0.09 #80, 0.03 #773, 0.02 #696), 0g5y6 (0.08 #422, 0.02 #499, 0.02 #1192), 03bkbh (0.08 #263, 0.07 #340, 0.03 #802), 03x1x (0.08 #287, 0.07 #364) >> Best rule #389 for best value: >> intensional similarity = 2 >> extensional distance = 164 >> proper extension: 01w3v; 0mcf4; >> query: (?x398, 041rx) <- religion(?x398, ?x7131), ?x7131 = 03_gx >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bl2g people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.627 http://example.org/people/ethnicity/people #17983-056xkh PRED entity: 056xkh PRED relation: film! PRED expected values: 0315q3 01w9wwg => 73 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1092): 0blbxk (0.33 #203, 0.04 #2073, 0.03 #26942), 0418ft (0.33 #1429, 0.03 #15936, 0.02 #18009), 0bxtg (0.33 #76, 0.03 #35308, 0.03 #37382), 01_rh4 (0.33 #576, 0.03 #35808, 0.03 #37882), 046zh (0.33 #931, 0.01 #36163, 0.01 #75544), 044zvm (0.33 #1933, 0.01 #37165, 0.01 #39239), 02hhtj (0.33 #1038, 0.01 #36270, 0.01 #38344), 05fnl9 (0.33 #268, 0.01 #35500, 0.01 #37574), 02ts3h (0.25 #3320, 0.17 #11610), 015v3r (0.25 #2603, 0.11 #6748, 0.09 #8821) >> Best rule #203 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0c00zd0; >> query: (?x9858, 0blbxk) <- film(?x5450, ?x9858), film(?x4360, ?x9858), film(?x3558, ?x9858), award_nominee(?x382, ?x4360), ?x5450 = 02lhm2 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 056xkh film! 01w9wwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 49.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 056xkh film! 0315q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 49.000 0.333 http://example.org/film/actor/film./film/performance/film #17982-01d8yn PRED entity: 01d8yn PRED relation: location PRED expected values: 02_286 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 139): 059rby (0.55 #42624, 0.51 #38603, 0.50 #55495), 0k049 (0.33 #1616, 0.29 #812, 0.04 #22527), 030qb3t (0.29 #887, 0.22 #1691, 0.22 #21798), 02_286 (0.23 #3253, 0.20 #6469, 0.20 #5665), 04jpl (0.16 #14493, 0.14 #821, 0.08 #2429), 0cc56 (0.14 #861, 0.11 #1665, 0.05 #8098), 01531 (0.14 #962, 0.11 #1766, 0.04 #19460), 01n7q (0.14 #867, 0.11 #1671, 0.04 #33036), 0r0m6 (0.14 #1022, 0.11 #1826, 0.04 #8259), 05fjf (0.14 #1136, 0.11 #1940, 0.02 #8373) >> Best rule #42624 for best value: >> intensional similarity = 3 >> extensional distance = 697 >> proper extension: 012v1t; 040j2_; 07h1q; 047g6; 01h2_6; 011zwl; 02cg2v; 0cfywh; >> query: (?x3751, ?x335) <- people(?x1050, ?x3751), place_of_birth(?x3751, ?x335), contains(?x335, ?x322) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #3253 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 021yw7; 01vz80y; 02r6c_; 0bq4j6; *> query: (?x3751, 02_286) <- written_by(?x1163, ?x3751), student(?x8398, ?x3751), award_winner(?x1442, ?x3751) *> conf = 0.23 ranks of expected_values: 4 EVAL 01d8yn location 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 134.000 134.000 0.546 http://example.org/people/person/places_lived./people/place_lived/location #17981-0z4s PRED entity: 0z4s PRED relation: award PRED expected values: 02w9sd7 => 93 concepts (85 used for prediction) PRED predicted values (max 10 best out of 268): 0bdwqv (0.71 #17299, 0.70 #14153, 0.70 #32241), 054ky1 (0.71 #17299, 0.70 #14153, 0.70 #32241), 03nqnk3 (0.71 #17299, 0.70 #14153, 0.70 #32241), 0279c15 (0.71 #17299, 0.70 #14153, 0.70 #32241), 027b9j5 (0.71 #17299, 0.70 #14153, 0.70 #32241), 027c95y (0.71 #17299, 0.70 #14153, 0.70 #32241), 09cm54 (0.71 #17299, 0.70 #14153, 0.70 #32241), 027986c (0.71 #17299, 0.70 #14153, 0.70 #32241), 0gq9h (0.35 #3219, 0.35 #4399, 0.33 #4006), 0ck27z (0.31 #4806, 0.19 #9130, 0.18 #11488) >> Best rule #17299 for best value: >> intensional similarity = 3 >> extensional distance = 1571 >> proper extension: 04glx0; >> query: (?x450, ?x591) <- award_nominee(?x4482, ?x450), award_winner(?x591, ?x450), award_nominee(?x3815, ?x4482) >> conf = 0.71 => this is the best rule for 8 predicted values *> Best rule #17693 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1577 *> proper extension: 06jntd; *> query: (?x450, ?x198) <- award_winner(?x3471, ?x450), nominated_for(?x198, ?x3471) *> conf = 0.14 ranks of expected_values: 35 EVAL 0z4s award 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 93.000 85.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17980-08c4yn PRED entity: 08c4yn PRED relation: country PRED expected values: 07ssc => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 41): 09c7w0 (0.84 #3218, 0.83 #2743, 0.82 #4702), 0f8l9c (0.36 #138, 0.25 #19, 0.21 #197), 07ssc (0.33 #16, 0.25 #490, 0.23 #1147), 0345h (0.24 #145, 0.19 #3691, 0.19 #323), 03rjj (0.24 #126, 0.19 #3691, 0.10 #185), 03_3d (0.19 #3691, 0.16 #186, 0.04 #4885), 06mkj (0.19 #3691, 0.15 #158, 0.05 #217), 0d05w3 (0.19 #3691, 0.15 #220, 0.06 #161), 03h64 (0.19 #3691, 0.13 #223, 0.06 #164), 0d060g (0.19 #3691, 0.08 #9, 0.06 #69) >> Best rule #3218 for best value: >> intensional similarity = 4 >> extensional distance = 1234 >> proper extension: 015bpl; 0m5s5; 023cjg; >> query: (?x11544, 09c7w0) <- nominated_for(?x7291, ?x11544), country(?x11544, ?x390), film(?x1286, ?x11544), genre(?x11544, ?x53) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 02fwfb; *> query: (?x11544, 07ssc) <- nominated_for(?x7291, ?x11544), country(?x11544, ?x390), titles(?x53, ?x11544), ?x7291 = 0274v0r *> conf = 0.33 ranks of expected_values: 3 EVAL 08c4yn country 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.840 http://example.org/film/film/country #17979-01m3b1t PRED entity: 01m3b1t PRED relation: music! PRED expected values: 09rsjpv 01qbg5 => 178 concepts (104 used for prediction) PRED predicted values (max 10 best out of 923): 01s7w3 (0.10 #10968, 0.06 #9958, 0.06 #11978), 04jn6y7 (0.08 #4035, 0.04 #7065, 0.02 #16155), 02bqxb (0.08 #4026, 0.04 #7056, 0.02 #16146), 01dc0c (0.08 #3853, 0.04 #6883, 0.02 #15973), 025rxjq (0.08 #3802, 0.04 #6832, 0.02 #15922), 01srq2 (0.08 #3755, 0.04 #6785, 0.02 #15875), 02lxrv (0.08 #3622, 0.04 #6652, 0.02 #15742), 0ndwt2w (0.08 #3609, 0.04 #6639, 0.02 #15729), 05rfst (0.08 #3598, 0.04 #6628, 0.02 #15718), 049xgc (0.08 #3596, 0.04 #6626, 0.02 #15716) >> Best rule #10968 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 020jqv; >> query: (?x7240, 01s7w3) <- award_winner(?x139, ?x7240), instrumentalists(?x2206, ?x7240), music(?x805, ?x7240) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01m3b1t music! 01qbg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 178.000 104.000 0.102 http://example.org/film/film/music EVAL 01m3b1t music! 09rsjpv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 178.000 104.000 0.102 http://example.org/film/film/music #17978-0509cr PRED entity: 0509cr PRED relation: artists PRED expected values: 012x4t 012z8_ => 84 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1304): 011z3g (0.64 #6010, 0.64 #4929, 0.60 #3848), 0127s7 (0.64 #5946, 0.60 #2703, 0.57 #4865), 0407f (0.60 #3524, 0.60 #2443, 0.50 #5686), 012vd6 (0.60 #3724, 0.60 #2643, 0.50 #5886), 0bs1g5r (0.60 #3996, 0.60 #2915, 0.50 #6158), 019f9z (0.60 #3843, 0.60 #2762, 0.50 #6005), 07s3vqk (0.60 #3254, 0.60 #2173, 0.50 #1092), 01pq5j7 (0.60 #3714, 0.60 #2633, 0.50 #1552), 012z8_ (0.60 #3643, 0.60 #2562, 0.50 #1481), 01wd9lv (0.60 #3821, 0.60 #2740, 0.50 #1659) >> Best rule #6010 for best value: >> intensional similarity = 10 >> extensional distance = 12 >> proper extension: 0ggx5q; >> query: (?x13401, 011z3g) <- artists(?x13401, ?x12670), artists(?x13401, ?x5048), artists(?x13401, ?x702), ?x702 = 01vvycq, artists(?x3928, ?x5048), instrumentalists(?x227, ?x5048), award(?x5048, ?x2563), ?x3928 = 0gywn, profession(?x12670, ?x1032), nationality(?x5048, ?x94) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #3643 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 06by7; *> query: (?x13401, 012z8_) <- artists(?x13401, ?x12670), artists(?x13401, ?x10712), artists(?x13401, ?x5048), artists(?x13401, ?x702), ?x702 = 01vvycq, instrumentalists(?x227, ?x5048), award(?x5048, ?x2563), ?x12670 = 0ql36, location(?x5048, ?x1860), ?x10712 = 016376 *> conf = 0.60 ranks of expected_values: 9, 41 EVAL 0509cr artists 012z8_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 84.000 29.000 0.643 http://example.org/music/genre/artists EVAL 0509cr artists 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 84.000 29.000 0.643 http://example.org/music/genre/artists #17977-0g_92 PRED entity: 0g_92 PRED relation: film PRED expected values: 01lbcqx => 89 concepts (47 used for prediction) PRED predicted values (max 10 best out of 220): 017jd9 (0.09 #778, 0.02 #20446, 0.02 #27598), 011ywj (0.09 #1435, 0.02 #24679, 0.02 #28255), 017gl1 (0.09 #142, 0.02 #19810, 0.02 #26962), 017gm7 (0.09 #210, 0.02 #27030, 0.02 #19878), 03d8jd1 (0.09 #1723), 03hfmm (0.09 #1477), 04165w (0.09 #1316), 0qf2t (0.09 #831), 011yr9 (0.09 #690), 07tw_b (0.09 #679) >> Best rule #778 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01_k0d; >> query: (?x9020, 017jd9) <- profession(?x9020, ?x1032), place_of_birth(?x9020, ?x4030), ?x4030 = 0hyxv >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #5025 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 201 *> proper extension: 01ty7ll; 01d5vk; 014zn0; *> query: (?x9020, 01lbcqx) <- film(?x9020, ?x2112), people(?x9933, ?x9020), nominated_for(?x198, ?x2112) *> conf = 0.02 ranks of expected_values: 50 EVAL 0g_92 film 01lbcqx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 89.000 47.000 0.091 http://example.org/film/actor/film./film/performance/film #17976-01v0sxx PRED entity: 01v0sxx PRED relation: category PRED expected values: 08mbj5d => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #27, 0.85 #43, 0.85 #47) >> Best rule #27 for best value: >> intensional similarity = 6 >> extensional distance = 113 >> proper extension: 05crg7; 01qqwp9; 02t3ln; 0qmpd; >> query: (?x10257, 08mbj5d) <- artists(?x1000, ?x10257), group(?x1466, ?x10257), group(?x716, ?x10257), ?x716 = 018vs, performance_role(?x248, ?x1466), role(?x115, ?x1466) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v0sxx category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.878 http://example.org/common/topic/webpage./common/webpage/category #17975-0m7yh PRED entity: 0m7yh PRED relation: student PRED expected values: 02wh0 => 176 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1653): 01h2_6 (0.10 #4120, 0.07 #8306, 0.05 #14652), 02ln1 (0.10 #3556, 0.05 #14652, 0.05 #31405), 0372p (0.10 #2752, 0.05 #14652, 0.05 #31405), 04xm_ (0.10 #3814, 0.05 #14652, 0.05 #31405), 06c44 (0.10 #3173, 0.05 #14652, 0.05 #31405), 0j3v (0.10 #2432, 0.05 #14652, 0.05 #31405), 0nk72 (0.10 #3554, 0.04 #14654, 0.04 #14653), 042q3 (0.10 #3897, 0.03 #8083, 0.03 #50249), 0k4gf (0.10 #2267, 0.03 #6453, 0.01 #12732), 0ff3y (0.07 #14628, 0.05 #14652, 0.05 #31381) >> Best rule #4120 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 04kf4; >> query: (?x7508, 01h2_6) <- contains(?x10334, ?x7508), country(?x10334, ?x1264), location_of_ceremony(?x566, ?x10334), ?x1264 = 0345h, contains(?x7934, ?x10334) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #14652 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 69 *> proper extension: 020yvh; *> query: (?x7508, ?x4915) <- school_type(?x7508, ?x3092), student(?x7508, ?x7509), influenced_by(?x12592, ?x7509), religion(?x7509, ?x962), influenced_by(?x12592, ?x4915) *> conf = 0.05 ranks of expected_values: 67 EVAL 0m7yh student 02wh0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 176.000 76.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student #17974-03wpmd PRED entity: 03wpmd PRED relation: film PRED expected values: 01p3ty => 124 concepts (73 used for prediction) PRED predicted values (max 10 best out of 883): 030z4z (0.62 #19722, 0.52 #50198, 0.52 #50197), 052_mn (0.62 #19722, 0.52 #50198, 0.52 #50197), 0f42nz (0.25 #910, 0.20 #12552, 0.19 #23309), 02tcgh (0.21 #34066, 0.13 #25102, 0.11 #21516), 04q00lw (0.13 #25102, 0.11 #21516, 0.09 #28688), 0h1fktn (0.10 #11730, 0.07 #22487, 0.04 #31451), 013q07 (0.08 #12909, 0.08 #9322, 0.08 #14701), 016dj8 (0.08 #8288, 0.08 #10081, 0.05 #15460), 0k_9j (0.08 #8580, 0.04 #10373, 0.02 #19337), 02825cv (0.08 #8316, 0.03 #13696, 0.03 #15488) >> Best rule #19722 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 018fmr; 029pnn; 0cyhq; >> query: (?x2382, ?x8074) <- nominated_for(?x2382, ?x8074), special_performance_type(?x2382, ?x4832), profession(?x2382, ?x319) >> conf = 0.62 => this is the best rule for 2 predicted values *> Best rule #18348 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 018fmr; 029pnn; 0cyhq; *> query: (?x2382, 01p3ty) <- nominated_for(?x2382, ?x8074), special_performance_type(?x2382, ?x4832), profession(?x2382, ?x319) *> conf = 0.02 ranks of expected_values: 296 EVAL 03wpmd film 01p3ty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 124.000 73.000 0.619 http://example.org/film/actor/film./film/performance/film #17973-0g48m4 PRED entity: 0g48m4 PRED relation: geographic_distribution PRED expected values: 059rby 02xry => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 99): 09c7w0 (0.84 #880, 0.80 #948, 0.70 #1016), 07ssc (0.45 #551, 0.22 #416, 0.18 #618), 09pmkv (0.36 #558, 0.22 #423, 0.18 #625), 03gh4 (0.33 #47, 0.20 #181, 0.20 #114), 0d060g (0.27 #546, 0.22 #411, 0.18 #613), 06t2t (0.27 #573, 0.22 #438, 0.18 #640), 06m_5 (0.22 #464, 0.18 #666, 0.18 #599), 03rk0 (0.22 #434, 0.18 #636, 0.18 #569), 0345h (0.22 #427, 0.18 #629, 0.18 #562), 04ly1 (0.20 #177, 0.20 #110, 0.17 #245) >> Best rule #880 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 01qhm_; 0x67; 07hwkr; 0xnvg; 0g6ff; 07bch9; 03295l; 059_w; 01xhh5; 01336l; ... >> query: (?x1176, 09c7w0) <- languages_spoken(?x1176, ?x3592), people(?x1176, ?x1177), geographic_distribution(?x1176, ?x3634), contains(?x3634, ?x11688), time_zones(?x3634, ?x1638), ?x11688 = 0558_1 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 0432mrk; *> query: (?x1176, ?x3908) <- people(?x1176, ?x4258), geographic_distribution(?x1176, ?x3634), award_winner(?x4258, ?x9220), artists(?x3996, ?x4258), ?x3996 = 02lnbg, adjoins(?x3908, ?x3634) *> conf = 0.11 ranks of expected_values: 51 EVAL 0g48m4 geographic_distribution 02xry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 30.000 0.842 http://example.org/people/ethnicity/geographic_distribution EVAL 0g48m4 geographic_distribution 059rby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 30.000 30.000 0.842 http://example.org/people/ethnicity/geographic_distribution #17972-01304j PRED entity: 01304j PRED relation: artist! PRED expected values: 01cszh 026s90 => 152 concepts (111 used for prediction) PRED predicted values (max 10 best out of 118): 0g768 (0.50 #36, 0.17 #731, 0.14 #1009), 0mcf4 (0.50 #57, 0.04 #6036, 0.03 #3256), 03rhqg (0.28 #1128, 0.23 #711, 0.21 #989), 01w40h (0.25 #28, 0.12 #306, 0.12 #723), 041p3y (0.25 #73, 0.05 #1185, 0.04 #1046), 011k1h (0.17 #1122, 0.13 #705, 0.13 #1261), 017l96 (0.17 #1131, 0.13 #1409, 0.12 #1687), 01dtcb (0.17 #323, 0.12 #1157, 0.08 #3661), 0n85g (0.13 #895, 0.12 #1173, 0.12 #756), 033hn8 (0.12 #987, 0.12 #292, 0.11 #5854) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0f0y8; 024zq; >> query: (?x11186, 0g768) <- type_of_union(?x11186, ?x566), artists(?x10207, ?x11186), role(?x11186, ?x227), ?x10207 = 0p9xd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #289 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 09qr6; 012x4t; 01qkqwg; 09hnb; 016h9b; 0407f; 01wy61y; 012z8_; 012vd6; 0163r3; ... *> query: (?x11186, 01cszh) <- type_of_union(?x11186, ?x566), artists(?x1127, ?x11186), role(?x11186, ?x227), ?x1127 = 02x8m *> conf = 0.08 ranks of expected_values: 24, 50 EVAL 01304j artist! 026s90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 152.000 111.000 0.500 http://example.org/music/record_label/artist EVAL 01304j artist! 01cszh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 152.000 111.000 0.500 http://example.org/music/record_label/artist #17971-051cc PRED entity: 051cc PRED relation: student! PRED expected values: 014mlp => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 19): 014mlp (0.58 #1070, 0.54 #784, 0.54 #632), 019v9k (0.26 #199, 0.24 #218, 0.23 #256), 0bkj86 (0.20 #217, 0.19 #255, 0.13 #198), 02_xgp2 (0.17 #203, 0.16 #241, 0.15 #260), 013zdg (0.14 #159, 0.09 #178, 0.09 #197), 02h4rq6 (0.13 #515, 0.11 #553, 0.11 #1067), 03mkk4 (0.13 #430, 0.12 #145, 0.11 #639), 028dcg (0.12 #644, 0.11 #796, 0.11 #530), 016t_3 (0.11 #421, 0.10 #858, 0.10 #155), 01rr_d (0.06 #149, 0.06 #320, 0.05 #168) >> Best rule #1070 for best value: >> intensional similarity = 3 >> extensional distance = 195 >> proper extension: 0frmb1; >> query: (?x8494, 014mlp) <- student(?x734, ?x8494), major_field_of_study(?x734, ?x1668), ?x1668 = 01mkq >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051cc student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 194.000 0.579 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #17970-0n5_t PRED entity: 0n5_t PRED relation: contains! PRED expected values: 0czr9_ => 136 concepts (45 used for prediction) PRED predicted values (max 10 best out of 125): 0gj4fx (0.69 #11683, 0.64 #11684, 0.62 #18880), 050ks (0.67 #16181, 0.62 #18880, 0.57 #29659), 09c7w0 (0.56 #8086, 0.50 #7187, 0.50 #3597), 04_1l0v (0.51 #8533, 0.18 #13933, 0.17 #4939), 0n5yh (0.25 #3893, 0.17 #5688, 0.06 #6583), 07_f2 (0.25 #1302, 0.08 #4893, 0.03 #7588), 02qkt (0.22 #26411, 0.18 #21925, 0.17 #13829), 059g4 (0.20 #2258, 0.17 #4951, 0.14 #3157), 029jpy (0.20 #2011, 0.17 #4704, 0.14 #2910), 059rby (0.18 #7204, 0.14 #14403, 0.14 #12604) >> Best rule #11683 for best value: >> intensional similarity = 5 >> extensional distance = 215 >> proper extension: 0ntwb; >> query: (?x12433, ?x12828) <- adjoins(?x12433, ?x7565), contains(?x12828, ?x7565), county(?x7564, ?x7565), source(?x7565, ?x958), district_represented(?x605, ?x12828) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #4399 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01x96; 01m94f; 0xhmb; 0xhj2; *> query: (?x12433, 0czr9_) <- source(?x12433, ?x958), contains(?x728, ?x12433), ?x958 = 0jbk9, ?x728 = 059f4 *> conf = 0.12 ranks of expected_values: 14 EVAL 0n5_t contains! 0czr9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 136.000 45.000 0.685 http://example.org/location/location/contains #17969-0ddbjy4 PRED entity: 0ddbjy4 PRED relation: film_release_region PRED expected values: 0d0vqn 03gj2 03ryn => 94 concepts (92 used for prediction) PRED predicted values (max 10 best out of 204): 03rjj (0.92 #699, 0.91 #421, 0.90 #282), 03gj2 (0.91 #21, 0.89 #160, 0.85 #578), 0d0vqn (0.89 #5, 0.89 #562, 0.89 #144), 035qy (0.86 #167, 0.86 #28, 0.85 #724), 07ssc (0.86 #570, 0.86 #431, 0.85 #848), 04gzd (0.72 #147, 0.71 #8, 0.62 #565), 016wzw (0.65 #193, 0.61 #54, 0.55 #333), 06t8v (0.62 #65, 0.62 #204, 0.55 #344), 01mjq (0.59 #37, 0.58 #455, 0.56 #594), 06qd3 (0.54 #589, 0.54 #867, 0.52 #311) >> Best rule #699 for best value: >> intensional similarity = 11 >> extensional distance = 94 >> proper extension: 0bmc4cm; 07l50vn; >> query: (?x9652, 03rjj) <- film_release_region(?x9652, ?x2152), film_release_region(?x9652, ?x1497), film_release_region(?x9652, ?x1023), film_release_region(?x9652, ?x252), film_release_region(?x9652, ?x172), ?x252 = 03_3d, ?x2152 = 06mkj, ?x1023 = 0ctw_b, ?x172 = 0154j, film_release_region(?x4684, ?x1497), ?x4684 = 03nm_fh >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 54 *> proper extension: 0g56t9t; 02vxq9m; 05p1tzf; 087wc7n; 08hmch; 01c22t; 0jjy0; 0c0nhgv; 0gj8t_b; 03bx2lk; ... *> query: (?x9652, 03gj2) <- film_release_region(?x9652, ?x2152), film_release_region(?x9652, ?x1497), film_release_region(?x9652, ?x1264), film_release_region(?x9652, ?x1023), film_release_region(?x9652, ?x252), ?x252 = 03_3d, ?x2152 = 06mkj, ?x1023 = 0ctw_b, film(?x2727, ?x9652), ?x1264 = 0345h, ?x1497 = 015qh *> conf = 0.91 ranks of expected_values: 2, 3, 17 EVAL 0ddbjy4 film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 94.000 92.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ddbjy4 film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 92.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ddbjy4 film_release_region 0d0vqn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 92.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17968-037bm2 PRED entity: 037bm2 PRED relation: film PRED expected values: 03cvwkr => 62 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1794): 0466s8n (0.33 #6248, 0.25 #7843, 0.21 #9438), 07h9gp (0.33 #5021, 0.25 #6616, 0.21 #8211), 07s3m4g (0.33 #5827, 0.21 #9017, 0.20 #10612), 02rrfzf (0.33 #5269, 0.21 #8459, 0.17 #6864), 025s1wg (0.33 #6309, 0.21 #9499, 0.17 #7904), 0g7pm1 (0.33 #5862, 0.21 #9052, 0.13 #10647), 0jzw (0.29 #8080, 0.25 #6485, 0.22 #4890), 02yxbc (0.25 #7539, 0.22 #5944, 0.21 #9134), 0bh8x1y (0.25 #7092, 0.22 #5497, 0.21 #8687), 04z257 (0.25 #6914, 0.22 #5319, 0.21 #8509) >> Best rule #6248 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0jz9f; 017s11; 016tt2; 05qd_; 061dn_; 0fvppk; 032j_n; >> query: (?x8763, 0466s8n) <- industry(?x8763, ?x373), ?x373 = 02vxn, film(?x8763, ?x2826), company(?x2724, ?x8763), music(?x2826, ?x5720) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #17553 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 23 *> proper extension: 017z88; 01_8w2; *> query: (?x8763, ?x915) <- company(?x7036, ?x8763), company(?x2724, ?x8763), award_nominee(?x7036, ?x1742), nationality(?x7036, ?x4743), type_of_union(?x7036, ?x566), produced_by(?x915, ?x2724) *> conf = 0.07 ranks of expected_values: 959 EVAL 037bm2 film 03cvwkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 31.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #17967-0d3f83 PRED entity: 0d3f83 PRED relation: religion PRED expected values: 0flw86 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 14): 0c8wxp (0.25 #186, 0.22 #1536, 0.20 #276), 03_gx (0.10 #914, 0.08 #1004, 0.07 #1139), 03j6c (0.08 #1596, 0.08 #1686, 0.06 #1776), 0kpl (0.08 #910, 0.08 #1720, 0.07 #1630), 0flw86 (0.04 #1262, 0.04 #1352, 0.04 #1307), 01lp8 (0.04 #676, 0.03 #856, 0.03 #811), 0n2g (0.02 #1633, 0.02 #1723, 0.02 #1678), 0kq2 (0.02 #1638, 0.02 #1413, 0.02 #918), 0631_ (0.02 #1403), 02vxy_ (0.02 #934, 0.02 #1024, 0.01 #1159) >> Best rule #186 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 05_6_y; >> query: (?x9231, 0c8wxp) <- team(?x9231, ?x9922), team(?x9231, ?x6477), team(?x60, ?x9922), team(?x9779, ?x9922), team(?x9410, ?x9922), ?x6477 = 02_lt, ?x9410 = 0dv1hh, athlete(?x471, ?x9779) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1262 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 94 *> proper extension: 01pj3h; *> query: (?x9231, 0flw86) <- nationality(?x9231, ?x789), athlete(?x471, ?x9231), sport(?x59, ?x471), country(?x471, ?x3720), sports(?x358, ?x471), countries_spoken_in(?x254, ?x3720) *> conf = 0.04 ranks of expected_values: 5 EVAL 0d3f83 religion 0flw86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 81.000 81.000 0.250 http://example.org/people/person/religion #17966-04qvl7 PRED entity: 04qvl7 PRED relation: cinematography! PRED expected values: 02cbg0 => 97 concepts (36 used for prediction) PRED predicted values (max 10 best out of 489): 02yy9r (0.33 #329, 0.06 #1977, 0.02 #1318), 0pv54 (0.33 #176, 0.06 #1977, 0.02 #1165), 02ll45 (0.33 #156, 0.06 #1977, 0.02 #1145), 0gvs1kt (0.33 #99, 0.06 #1977, 0.02 #1088), 021y7yw (0.33 #66, 0.06 #1977, 0.02 #1055), 02q5g1z (0.06 #1977, 0.03 #11547, 0.03 #3294), 05dy7p (0.06 #1977, 0.03 #11547, 0.03 #3294), 02r1c18 (0.06 #1977, 0.03 #3294, 0.03 #11546), 0gyv0b4 (0.06 #1977, 0.02 #1298, 0.02 #1627), 05k4my (0.06 #1977, 0.02 #1297, 0.02 #1626) >> Best rule #329 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 07mb57; >> query: (?x185, 02yy9r) <- cinematography(?x3745, ?x185), cinematography(?x945, ?x185), ?x3745 = 03cw411, nominated_for(?x166, ?x945) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04qvl7 cinematography! 02cbg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 36.000 0.333 http://example.org/film/film/cinematography #17965-02v60l PRED entity: 02v60l PRED relation: sibling! PRED expected values: 023tp8 => 144 concepts (79 used for prediction) PRED predicted values (max 10 best out of 118): 01zmpg (0.25 #246, 0.14 #704, 0.05 #818), 0gbwp (0.25 #265, 0.07 #723, 0.05 #837), 09889g (0.25 #275, 0.07 #733, 0.03 #2797), 026_dq6 (0.25 #311, 0.05 #1112, 0.04 #1456), 02v60l (0.22 #4020, 0.05 #843, 0.05 #1187), 023tp8 (0.22 #4020), 02tf1y (0.10 #989, 0.09 #1219, 0.08 #1448), 030g9z (0.09 #1222, 0.05 #992, 0.05 #1795), 0194xc (0.08 #1344, 0.03 #2491, 0.03 #2836), 01z7s_ (0.07 #738, 0.05 #852, 0.05 #966) >> Best rule #246 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 013v5j; >> query: (?x4611, 01zmpg) <- sibling(?x7617, ?x4611), location(?x4611, ?x4612), profession(?x4611, ?x319), person(?x3480, ?x4611) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4020 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 96 *> proper extension: 0dv1hh; 09m465; *> query: (?x4611, ?x376) <- sibling(?x7617, ?x4611), gender(?x4611, ?x231), nationality(?x4611, ?x94), sibling(?x7617, ?x376) *> conf = 0.22 ranks of expected_values: 6 EVAL 02v60l sibling! 023tp8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 144.000 79.000 0.250 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #17964-04bsx1 PRED entity: 04bsx1 PRED relation: team PRED expected values: 0bl8l => 85 concepts (26 used for prediction) PRED predicted values (max 10 best out of 890): 01zhs3 (0.38 #488, 0.25 #835, 0.18 #693), 01cwm1 (0.38 #514, 0.25 #861, 0.11 #3810), 085v7 (0.25 #444, 0.25 #98, 0.18 #693), 01x4wq (0.25 #454, 0.25 #108, 0.17 #801), 02_lt (0.25 #127, 0.18 #693, 0.13 #6235), 03jb2n (0.25 #294, 0.18 #693, 0.13 #6235), 03yfh3 (0.25 #343, 0.18 #693, 0.13 #6235), 02b2np (0.25 #412, 0.17 #4914, 0.17 #2489), 01rly6 (0.25 #609, 0.17 #956, 0.13 #6235), 01rlz4 (0.25 #616, 0.17 #963, 0.11 #3810) >> Best rule #488 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0f1pyf; 0457w0; 07zr66; >> query: (?x10129, 01zhs3) <- team(?x10129, ?x10996), athlete(?x471, ?x10129), team(?x7703, ?x10996), team(?x2666, ?x10996), ?x2666 = 083qy7, colors(?x10996, ?x1101), team(?x7703, ?x1085), ?x1101 = 06fvc >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #436 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0f1pyf; 0457w0; 07zr66; *> query: (?x10129, 0bl8l) <- team(?x10129, ?x10996), athlete(?x471, ?x10129), team(?x7703, ?x10996), team(?x2666, ?x10996), ?x2666 = 083qy7, colors(?x10996, ?x1101), team(?x7703, ?x1085), ?x1101 = 06fvc *> conf = 0.12 ranks of expected_values: 71 EVAL 04bsx1 team 0bl8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 85.000 26.000 0.375 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #17963-0dbb3 PRED entity: 0dbb3 PRED relation: place_of_birth PRED expected values: 0dclg => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 204): 094jv (0.40 #705, 0.37 #45084, 0.35 #35221), 02_286 (0.20 #19, 0.18 #1428, 0.16 #4226), 0tz14 (0.20 #467), 0h1k6 (0.10 #1150, 0.07 #3262, 0.05 #6079), 0n90z (0.10 #1390, 0.07 #3502, 0.05 #6319), 0nbrp (0.10 #1237, 0.07 #3349, 0.05 #6166), 0yz30 (0.10 #1384, 0.07 #3496, 0.04 #7017), 0v1xg (0.10 #1024, 0.07 #3136, 0.03 #8066), 0xq63 (0.10 #941, 0.07 #3053, 0.03 #7983), 052bw (0.10 #1025, 0.07 #3137, 0.01 #20747) >> Best rule #705 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 012wg; >> query: (?x10559, ?x1705) <- origin(?x10559, ?x1705), profession(?x10559, ?x1032), written_by(?x8959, ?x10559) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #5712 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 01qdjm; *> query: (?x10559, 0dclg) <- religion(?x10559, ?x1985), artist(?x3265, ?x10559), award_nominee(?x6207, ?x10559), ?x3265 = 015_1q *> conf = 0.09 ranks of expected_values: 11 EVAL 0dbb3 place_of_birth 0dclg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 132.000 132.000 0.400 http://example.org/people/person/place_of_birth #17962-0h21v2 PRED entity: 0h21v2 PRED relation: film_release_region PRED expected values: 05r4w 03h64 082fr => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 137): 03gj2 (0.91 #2121, 0.91 #186, 0.82 #669), 03h64 (0.89 #2167, 0.86 #232, 0.82 #1683), 035qy (0.88 #2132, 0.82 #680, 0.73 #5036), 0345h (0.88 #678, 0.81 #2130, 0.79 #5034), 03spz (0.86 #262, 0.77 #2197, 0.72 #745), 05r4w (0.84 #3227, 0.84 #4196, 0.82 #2098), 05qhw (0.83 #2111, 0.78 #659, 0.77 #176), 0b90_r (0.82 #165, 0.78 #2100, 0.69 #5004), 0154j (0.79 #2101, 0.74 #5005, 0.72 #3230), 0d060g (0.79 #2103, 0.69 #3232, 0.68 #5007) >> Best rule #2121 for best value: >> intensional similarity = 5 >> extensional distance = 88 >> proper extension: 02vxq9m; 0dscrwf; 0872p_c; 0dgst_d; 0gmcwlb; 07qg8v; 04jkpgv; 03qnvdl; 04n52p6; 05qbckf; ... >> query: (?x5735, 03gj2) <- nominated_for(?x640, ?x5735), film_release_region(?x5735, ?x2843), film_release_region(?x5735, ?x789), ?x789 = 0f8l9c, ?x2843 = 016wzw >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #2167 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 88 *> proper extension: 02vxq9m; 0dscrwf; 0872p_c; 0dgst_d; 0gmcwlb; 07qg8v; 04jkpgv; 03qnvdl; 04n52p6; 05qbckf; ... *> query: (?x5735, 03h64) <- nominated_for(?x640, ?x5735), film_release_region(?x5735, ?x2843), film_release_region(?x5735, ?x789), ?x789 = 0f8l9c, ?x2843 = 016wzw *> conf = 0.89 ranks of expected_values: 2, 6, 48 EVAL 0h21v2 film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 105.000 105.000 0.911 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h21v2 film_release_region 03h64 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 105.000 0.911 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h21v2 film_release_region 05r4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 105.000 105.000 0.911 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17961-01f7kl PRED entity: 01f7kl PRED relation: film! PRED expected values: 016tw3 => 110 concepts (80 used for prediction) PRED predicted values (max 10 best out of 57): 016tw3 (0.80 #458, 0.40 #85, 0.40 #11), 030_1_ (0.61 #3946, 0.51 #4022, 0.50 #4321), 03xq0f (0.60 #2237, 0.56 #1865, 0.50 #1269), 086k8 (0.23 #524, 0.22 #1788, 0.22 #598), 05qd_ (0.20 #83, 0.19 #1273, 0.19 #3880), 017jv5 (0.20 #15, 0.13 #981, 0.11 #611), 04mkft (0.20 #36, 0.11 #1896, 0.08 #706), 016tt2 (0.20 #1044, 0.18 #1193, 0.17 #1938), 061dn_ (0.17 #322, 0.17 #247, 0.07 #2553), 032j_n (0.17 #356, 0.17 #281, 0.07 #2587) >> Best rule #458 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 053tj7; >> query: (?x2470, 016tw3) <- production_companies(?x2470, ?x1686), film_release_distribution_medium(?x2470, ?x81), film(?x10958, ?x2470), genre(?x2470, ?x258), ?x10958 = 025tlyv >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f7kl film! 016tw3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 80.000 0.800 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #17960-08w7vj PRED entity: 08w7vj PRED relation: film PRED expected values: 0g3zrd => 97 concepts (63 used for prediction) PRED predicted values (max 10 best out of 405): 080dwhx (0.57 #33940, 0.50 #23221, 0.46 #25008), 026p4q7 (0.29 #2185), 07bzz7 (0.27 #4461), 05f4_n0 (0.17 #713, 0.14 #2499), 02q0k7v (0.17 #1335, 0.07 #6693, 0.03 #28581), 0symg (0.17 #1700, 0.03 #28581, 0.03 #58949), 078sj4 (0.17 #455, 0.02 #7599, 0.01 #14745), 03176f (0.17 #706, 0.02 #9637, 0.01 #7850), 02z3r8t (0.17 #108, 0.02 #51911, 0.02 #17970), 035s95 (0.17 #341, 0.02 #36067, 0.01 #23562) >> Best rule #33940 for best value: >> intensional similarity = 3 >> extensional distance = 746 >> proper extension: 01k7d9; 07b2lv; 011_3s; 050t68; 02jsgf; 01d1yr; 03x400; 02zfdp; 016kft; 035kl6; ... >> query: (?x874, ?x493) <- award_nominee(?x874, ?x368), award_winner(?x493, ?x874), film(?x874, ?x2852) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08w7vj film 0g3zrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 63.000 0.568 http://example.org/film/actor/film./film/performance/film #17959-02xhpl PRED entity: 02xhpl PRED relation: award_winner PRED expected values: 0q9kd => 73 concepts (43 used for prediction) PRED predicted values (max 10 best out of 813): 021b_ (0.26 #31241, 0.23 #44402, 0.21 #42756), 0q9kd (0.26 #31241, 0.23 #44402, 0.21 #42756), 09d5h (0.25 #16444, 0.23 #41111, 0.04 #10188), 06g4l (0.23 #44402, 0.21 #42756, 0.20 #44403), 025h4z (0.23 #44402, 0.21 #42756, 0.20 #44403), 01l_yg (0.23 #44402, 0.21 #42756, 0.20 #44403), 06j0md (0.21 #42756, 0.20 #44403, 0.19 #31242), 03xp8d5 (0.21 #42756, 0.20 #44403, 0.19 #31242), 02qlkc3 (0.21 #42756, 0.20 #44403, 0.19 #31242), 0cjdk (0.18 #5343, 0.17 #8631, 0.14 #2055) >> Best rule #31241 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 09v8clw; >> query: (?x2063, ?x10588) <- honored_for(?x2063, ?x8132), award_winner(?x8132, ?x10588), people(?x1050, ?x10588), film(?x10588, ?x718) >> conf = 0.26 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02xhpl award_winner 0q9kd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 43.000 0.263 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #17958-01_r9k PRED entity: 01_r9k PRED relation: institution! PRED expected values: 02h4rq6 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.73 #341, 0.70 #316, 0.67 #366), 019v9k (0.67 #322, 0.62 #347, 0.56 #372), 02_xgp2 (0.56 #326, 0.53 #376, 0.53 #351), 03bwzr4 (0.53 #353, 0.51 #378, 0.50 #328), 016t_3 (0.49 #367, 0.49 #317, 0.49 #268), 0bkj86 (0.48 #321, 0.45 #614, 0.40 #346), 07s6fsf (0.39 #314, 0.32 #943, 0.30 #364), 04zx3q1 (0.34 #315, 0.34 #365, 0.33 #266), 013zdg (0.26 #320, 0.24 #468, 0.23 #127), 027f2w (0.26 #274, 0.25 #323, 0.25 #373) >> Best rule #341 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 01mpwj; >> query: (?x10170, 02h4rq6) <- major_field_of_study(?x10170, ?x742), contains(?x94, ?x10170), ?x742 = 05qjt >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_r9k institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.730 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #17957-01wzlxj PRED entity: 01wzlxj PRED relation: instrumentalists! PRED expected values: 02hnl => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 123): 05148p4 (0.45 #103, 0.41 #439, 0.40 #1455), 013y1f (0.29 #1522, 0.26 #2882, 0.26 #2034), 01vdm0 (0.29 #1522, 0.26 #2882, 0.26 #2034), 0l15bq (0.29 #1522, 0.26 #2882, 0.26 #2034), 02hnl (0.20 #1469, 0.20 #453, 0.18 #1130), 03qjg (0.18 #1147, 0.17 #2338, 0.17 #2169), 0l14md (0.15 #1443, 0.13 #1104, 0.13 #1954), 0l14qv (0.14 #425, 0.11 #510, 0.10 #1441), 026t6 (0.13 #1439, 0.12 #3227, 0.12 #1950), 06ncr (0.12 #43, 0.10 #295, 0.09 #463) >> Best rule #103 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 01wmxfs; >> query: (?x3834, 05148p4) <- award(?x3834, ?x3835), artist(?x2149, ?x3834), award_winner(?x725, ?x3834), ?x3835 = 01cky2 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1469 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 305 *> proper extension: 01pbxb; 032t2z; 01w923; 01w02sy; 0bkg4; 023l9y; 01l4g5; 01ydzx; 0130sy; 01w9mnm; ... *> query: (?x3834, 02hnl) <- artists(?x1127, ?x3834), artist(?x2149, ?x3834), role(?x3834, ?x1166), instrumentalists(?x227, ?x3834) *> conf = 0.20 ranks of expected_values: 5 EVAL 01wzlxj instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 118.000 118.000 0.452 http://example.org/music/instrument/instrumentalists #17956-01s21dg PRED entity: 01s21dg PRED relation: people! PRED expected values: 07hwkr => 122 concepts (119 used for prediction) PRED predicted values (max 10 best out of 53): 041rx (0.28 #3652, 0.23 #4944, 0.21 #5400), 0x67 (0.24 #3050, 0.24 #162, 0.23 #998), 033tf_ (0.19 #691, 0.17 #235, 0.14 #7), 0xnvg (0.19 #241, 0.19 #317, 0.16 #393), 09vc4s (0.15 #541, 0.14 #9, 0.11 #693), 0222qb (0.14 #43, 0.10 #119, 0.02 #347), 07bch9 (0.14 #23, 0.09 #707, 0.07 #631), 03bkbh (0.14 #32, 0.03 #2920, 0.03 #5200), 02w7gg (0.10 #78, 0.08 #6994, 0.08 #5398), 01336l (0.10 #116) >> Best rule #3652 for best value: >> intensional similarity = 3 >> extensional distance = 557 >> proper extension: 01h2_6; >> query: (?x4741, 041rx) <- place_of_birth(?x4741, ?x6930), people(?x9428, ?x4741), featured_film_locations(?x4551, ?x6930) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #1760 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 194 *> proper extension: 01j5ts; 05m63c; 0m2wm; 0l8v5; 02qjj7; 0bxtg; 04wqr; 04bs3j; 09wj5; 05gml8; ... *> query: (?x4741, 07hwkr) <- profession(?x4741, ?x131), participant(?x4741, ?x4536), languages(?x4741, ?x254) *> conf = 0.09 ranks of expected_values: 13 EVAL 01s21dg people! 07hwkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 122.000 119.000 0.283 http://example.org/people/ethnicity/people #17955-0ql36 PRED entity: 0ql36 PRED relation: profession PRED expected values: 0dz3r 0nbcg => 93 concepts (61 used for prediction) PRED predicted values (max 10 best out of 116): 09jwl (0.85 #8195, 0.83 #8343, 0.83 #1946), 01c72t (0.80 #4631, 0.62 #320, 0.34 #468), 016z4k (0.59 #448, 0.57 #744, 0.50 #1188), 0dz3r (0.59 #446, 0.50 #1929, 0.47 #2527), 0nbcg (0.58 #2557, 0.56 #1959, 0.56 #2706), 039v1 (0.50 #185, 0.43 #629, 0.41 #481), 01d_h8 (0.36 #2081, 0.35 #1784, 0.34 #2379), 0np9r (0.29 #2394, 0.27 #2096, 0.22 #1799), 0fnpj (0.28 #504, 0.19 #4667, 0.18 #1987), 0dxtg (0.25 #2089, 0.24 #1792, 0.23 #2387) >> Best rule #8195 for best value: >> intensional similarity = 5 >> extensional distance = 731 >> proper extension: 01fwj8; 0fb1q; 022g44; 01x4sb; 01tt43d; 02l0sf; 01j7z7; 0mbw0; 02cj_f; 010xjr; ... >> query: (?x12670, 09jwl) <- profession(?x12670, ?x4654), profession(?x2765, ?x4654), profession(?x367, ?x4654), ?x367 = 01lmj3q, ?x2765 = 01w724 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #446 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 028qdb; *> query: (?x12670, 0dz3r) <- instrumentalists(?x228, ?x12670), role(?x12670, ?x1166), ?x228 = 0l14qv, artists(?x1127, ?x12670) *> conf = 0.59 ranks of expected_values: 4, 5 EVAL 0ql36 profession 0nbcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 61.000 0.849 http://example.org/people/person/profession EVAL 0ql36 profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 61.000 0.849 http://example.org/people/person/profession #17954-01mk6 PRED entity: 01mk6 PRED relation: film_release_region! PRED expected values: 0jyb4 => 183 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1813): 017jd9 (0.90 #11144, 0.84 #7186, 0.83 #29611), 0jjy0 (0.90 #10681, 0.84 #6723, 0.69 #29148), 0ds3t5x (0.90 #10593, 0.79 #6635, 0.67 #29060), 03nm_fh (0.89 #7199, 0.86 #11157, 0.83 #37539), 05p1tzf (0.89 #6653, 0.81 #10611, 0.75 #29078), 08hmch (0.86 #10671, 0.84 #6713, 0.83 #29138), 01c22t (0.86 #10680, 0.84 #6722, 0.68 #37062), 02vr3gz (0.86 #11025, 0.84 #7067, 0.67 #29492), 01vksx (0.86 #10654, 0.84 #6696, 0.64 #29121), 0bpm4yw (0.86 #11097, 0.81 #29564, 0.79 #7139) >> Best rule #11144 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 05r4w; 0jgd; 0d0vqn; 01ls2; 06mzp; 01znc_; 06mkj; >> query: (?x7430, 017jd9) <- adjoins(?x2517, ?x7430), film_release_region(?x8370, ?x7430), participating_countries(?x1608, ?x7430), ?x8370 = 07ghq >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #11384 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 05r4w; 0jgd; 0d0vqn; 01ls2; 06mzp; 01znc_; 06mkj; *> query: (?x7430, 0jyb4) <- adjoins(?x2517, ?x7430), film_release_region(?x8370, ?x7430), participating_countries(?x1608, ?x7430), ?x8370 = 07ghq *> conf = 0.62 ranks of expected_values: 269 EVAL 01mk6 film_release_region! 0jyb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 83.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17953-0byfz PRED entity: 0byfz PRED relation: influenced_by! PRED expected values: 0jbp0 => 155 concepts (112 used for prediction) PRED predicted values (max 10 best out of 237): 05rx__ (0.17 #826, 0.11 #1858, 0.06 #7541), 07lp1 (0.17 #935, 0.04 #7134, 0.02 #21092), 0n6kf (0.17 #708, 0.04 #6907, 0.02 #20865), 018zvb (0.17 #957, 0.02 #6640, 0.02 #21114), 01w9ph_ (0.17 #836, 0.02 #11684, 0.02 #20993), 014dq7 (0.17 #580, 0.01 #11428), 09889g (0.14 #1233, 0.11 #1749, 0.04 #6400), 0ph2w (0.14 #1189, 0.11 #1705, 0.03 #14624), 0djywgn (0.14 #1383, 0.04 #6550, 0.03 #4483), 01c58j (0.14 #1088, 0.04 #6255, 0.03 #4188) >> Best rule #826 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 030pr; >> query: (?x269, 05rx__) <- award_winner(?x3514, ?x269), people(?x268, ?x269), person(?x7480, ?x269) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0byfz influenced_by! 0jbp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 112.000 0.167 http://example.org/influence/influence_node/influenced_by #17952-02rdxsh PRED entity: 02rdxsh PRED relation: nominated_for PRED expected values: 02rcdc2 093dqjy 0h6r5 0sxlb => 39 concepts (18 used for prediction) PRED predicted values (max 10 best out of 897): 026p4q7 (0.83 #20128, 0.75 #23171, 0.73 #18606), 01mgw (0.82 #19367, 0.75 #20889, 0.71 #13279), 07s846j (0.82 #6087, 0.78 #16743, 0.77 #10654), 0k20s (0.82 #6087, 0.78 #16743, 0.77 #10654), 0571m (0.82 #6087, 0.78 #16743, 0.77 #10654), 0fpkhkz (0.82 #6087, 0.78 #16743, 0.77 #10654), 03hmt9b (0.80 #17313, 0.73 #18834, 0.71 #12746), 07cyl (0.80 #17229, 0.67 #21793, 0.64 #18750), 0pv3x (0.75 #22985, 0.73 #18420, 0.70 #16899), 0gmgwnv (0.75 #23754, 0.71 #13101, 0.70 #16145) >> Best rule #20128 for best value: >> intensional similarity = 9 >> extensional distance = 10 >> proper extension: 0gq_v; 02hsq3m; 0k611; >> query: (?x1063, 026p4q7) <- nominated_for(?x1063, ?x7765), nominated_for(?x1063, ?x3219), nominated_for(?x1063, ?x2116), ?x3219 = 011ydl, nominated_for(?x1107, ?x7765), ?x1107 = 019f4v, award(?x2116, ?x451), genre(?x7765, ?x225), production_companies(?x2116, ?x2548) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #12763 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 03hkv_r; 0gr4k; 02n9nmz; *> query: (?x1063, 0h6r5) <- nominated_for(?x1063, ?x7765), nominated_for(?x1063, ?x7307), nominated_for(?x1063, ?x3219), nominated_for(?x1063, ?x2116), nominated_for(?x1063, ?x696), ?x3219 = 011ydl, genre(?x7307, ?x1626), ?x2116 = 02c638, ?x1626 = 03q4nz, nominated_for(?x695, ?x696), produced_by(?x7765, ?x4397) *> conf = 0.71 ranks of expected_values: 14, 16, 68, 315 EVAL 02rdxsh nominated_for 0sxlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 39.000 18.000 0.833 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02rdxsh nominated_for 0h6r5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 39.000 18.000 0.833 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02rdxsh nominated_for 093dqjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 39.000 18.000 0.833 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02rdxsh nominated_for 02rcdc2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 39.000 18.000 0.833 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17951-071cn PRED entity: 071cn PRED relation: place_of_birth! PRED expected values: 07nx9j 03kxdw => 126 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1945): 034g2b (0.36 #167102, 0.35 #20887, 0.33 #78326), 02lz1s (0.36 #167102, 0.35 #20887, 0.33 #78326), 0bv7t (0.36 #167102, 0.35 #20887, 0.33 #78326), 01x1cn2 (0.36 #167102, 0.35 #20887, 0.33 #78326), 04mby (0.36 #167102, 0.35 #20887, 0.33 #78326), 02x0dzw (0.25 #1816, 0.04 #12259, 0.03 #14869), 01g1lp (0.25 #1631, 0.04 #12074, 0.03 #14684), 07q0g5 (0.25 #1618, 0.04 #12061, 0.03 #14671), 01ws9n6 (0.25 #910, 0.04 #11353, 0.03 #13963), 02mxw0 (0.25 #519, 0.04 #10962, 0.03 #13572) >> Best rule #167102 for best value: >> intensional similarity = 3 >> extensional distance = 246 >> proper extension: 0mnsf; 0fw4v; 0104lr; >> query: (?x3786, ?x1852) <- location(?x1852, ?x3786), source(?x3786, ?x958), category(?x3786, ?x134) >> conf = 0.36 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 071cn place_of_birth! 03kxdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 76.000 0.363 http://example.org/people/person/place_of_birth EVAL 071cn place_of_birth! 07nx9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 76.000 0.363 http://example.org/people/person/place_of_birth #17950-05d6kv PRED entity: 05d6kv PRED relation: citytown PRED expected values: 02_286 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 68): 0r00l (0.28 #2858, 0.26 #5066, 0.25 #650), 02_286 (0.24 #3696, 0.21 #20637, 0.21 #2960), 0r04p (0.22 #2682, 0.22 #1210, 0.22 #4890), 030qb3t (0.22 #1133, 0.17 #2605, 0.13 #4813), 04jpl (0.14 #4424, 0.12 #6633, 0.11 #1112), 07dfk (0.14 #21204, 0.14 #21573, 0.13 #21941), 0k049 (0.11 #1107, 0.07 #20991, 0.06 #20254), 0d6lp (0.09 #1542, 0.08 #5958, 0.08 #1910), 06_kh (0.09 #1477, 0.08 #1845, 0.07 #6998), 0f2w0 (0.09 #1508, 0.08 #1876, 0.07 #2244) >> Best rule #2858 for best value: >> intensional similarity = 8 >> extensional distance = 16 >> proper extension: 05qd_; 030_1m; 01795t; 024rgt; 01gb54; 06jntd; 054g1r; 04mkft; 093h7p; >> query: (?x2972, 0r00l) <- film(?x2972, ?x12641), film(?x2972, ?x2973), region(?x12641, ?x512), genre(?x12641, ?x1510), genre(?x12641, ?x571), ?x1510 = 01hmnh, film(?x539, ?x2973), genre(?x3413, ?x571) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #3696 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 19 *> proper extension: 05gnf; *> query: (?x2972, 02_286) <- film(?x2972, ?x12641), film(?x2972, ?x2973), genre(?x12641, ?x53), award_winner(?x2988, ?x2972), film(?x539, ?x2973), genre(?x273, ?x53) *> conf = 0.24 ranks of expected_values: 2 EVAL 05d6kv citytown 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 73.000 0.278 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #17949-02sfnv PRED entity: 02sfnv PRED relation: genre PRED expected values: 03k9fj 0ltv => 68 concepts (67 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.70 #3725, 0.60 #2643, 0.58 #4326), 01z4y (0.61 #4446, 0.53 #2763, 0.48 #3724), 01jfsb (0.60 #2174, 0.49 #492, 0.47 #1092), 02l7c8 (0.42 #136, 0.41 #16, 0.36 #256), 03k9fj (0.40 #491, 0.38 #371, 0.38 #1091), 06n90 (0.39 #1093, 0.23 #493, 0.19 #2175), 0lsxr (0.29 #368, 0.24 #2170, 0.23 #488), 01hmnh (0.20 #138, 0.20 #1098, 0.18 #18), 082gq (0.19 #2193, 0.16 #1952, 0.13 #511), 04xvlr (0.17 #2644, 0.16 #4327, 0.16 #3605) >> Best rule #3725 for best value: >> intensional similarity = 4 >> extensional distance = 1176 >> proper extension: 0h2zvzr; >> query: (?x5187, 07s9rl0) <- nominated_for(?x703, ?x5187), genre(?x5187, ?x225), genre(?x7225, ?x225), ?x7225 = 02mmwk >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #491 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 305 *> proper extension: 04hk0w; *> query: (?x5187, 03k9fj) <- nominated_for(?x703, ?x5187), genre(?x5187, ?x225), ?x225 = 02kdv5l, film(?x147, ?x5187) *> conf = 0.40 ranks of expected_values: 5 EVAL 02sfnv genre 0ltv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 67.000 0.695 http://example.org/film/film/genre EVAL 02sfnv genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 68.000 67.000 0.695 http://example.org/film/film/genre #17948-02kxbwx PRED entity: 02kxbwx PRED relation: student! PRED expected values: 05zl0 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 164): 053mhx (0.20 #822, 0.05 #4511, 0.04 #11363), 017j69 (0.20 #145, 0.04 #2253, 0.03 #4361), 07wjk (0.20 #590, 0.02 #10604, 0.02 #11131), 0pz6q (0.20 #900), 0277jc (0.20 #555), 065y4w7 (0.15 #1595, 0.13 #1068, 0.11 #2649), 0bwfn (0.10 #3437, 0.09 #9762, 0.09 #10289), 04b_46 (0.10 #3389, 0.08 #4443, 0.06 #4970), 08815 (0.08 #6853, 0.08 #8434, 0.08 #4218), 03ksy (0.08 #2214, 0.06 #4849, 0.06 #3795) >> Best rule #822 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 04jzj; 099bk; >> query: (?x826, 053mhx) <- gender(?x826, ?x231), student(?x8221, ?x826), ?x8221 = 037mh8 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3364 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 0969fd; *> query: (?x826, 05zl0) <- award_winner(?x372, ?x826), student(?x1368, ?x826), award_winner(?x826, ?x163) *> conf = 0.04 ranks of expected_values: 27 EVAL 02kxbwx student! 05zl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 125.000 125.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #17947-05q5t0b PRED entity: 05q5t0b PRED relation: award! PRED expected values: 03xmy1 0127m7 => 58 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2574): 03xmy1 (0.81 #20155, 0.64 #40318, 0.63 #50404), 026c1 (0.60 #3935, 0.38 #17372, 0.33 #20732), 019pm_ (0.60 #4104, 0.33 #30983, 0.33 #746), 02t_99 (0.57 #14771, 0.44 #21490, 0.40 #8053), 02_l96 (0.57 #14912, 0.44 #21631, 0.40 #8194), 030g9z (0.57 #16059, 0.40 #9341, 0.38 #19418), 0f7hc (0.56 #21499, 0.43 #14780, 0.40 #8062), 0gn30 (0.44 #21711, 0.43 #14992, 0.40 #8274), 0pyww (0.44 #24909, 0.40 #28270, 0.33 #1394), 025mb_ (0.44 #26121, 0.40 #29482, 0.33 #32843) >> Best rule #20155 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 05ztjjw; >> query: (?x3064, ?x1213) <- nominated_for(?x3064, ?x7354), nominated_for(?x3064, ?x3268), nominated_for(?x3064, ?x3059), ?x7354 = 0258dh, award_winner(?x3064, ?x1213), nominated_for(?x541, ?x3059), film_release_region(?x3268, ?x94) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 165 EVAL 05q5t0b award! 0127m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 58.000 18.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05q5t0b award! 03xmy1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 18.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17946-06151l PRED entity: 06151l PRED relation: profession PRED expected values: 02hrh1q => 76 concepts (75 used for prediction) PRED predicted values (max 10 best out of 62): 02hrh1q (0.90 #1802, 0.90 #908, 0.89 #7764), 0dxtg (0.48 #3142, 0.47 #1205, 0.32 #1950), 02jknp (0.46 #1199, 0.45 #3136, 0.23 #752), 03gjzk (0.35 #1952, 0.31 #3144, 0.30 #1207), 0d1pc (0.28 #349, 0.27 #498, 0.19 #647), 09jwl (0.17 #5087, 0.17 #5236, 0.16 #6130), 02krf9 (0.17 #325, 0.15 #1964, 0.14 #474), 0cbd2 (0.16 #3284, 0.16 #2986, 0.15 #3583), 0np9r (0.15 #7771, 0.13 #6430, 0.11 #1809), 018gz8 (0.14 #1805, 0.13 #6426, 0.13 #7767) >> Best rule #1802 for best value: >> intensional similarity = 3 >> extensional distance = 522 >> proper extension: 06tp4h; >> query: (?x221, 02hrh1q) <- religion(?x221, ?x1985), profession(?x221, ?x319), film(?x221, ?x1045) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06151l profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 75.000 0.905 http://example.org/people/person/profession #17945-09v82c0 PRED entity: 09v82c0 PRED relation: ceremony PRED expected values: 03nnm4t => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 136): 03nnm4t (0.57 #886, 0.50 #342, 0.46 #1569), 0gpjbt (0.50 #2617, 0.48 #2753, 0.37 #3845), 09n4nb (0.49 #2634, 0.47 #2770, 0.35 #3862), 0466p0j (0.48 #2662, 0.46 #2798, 0.35 #3890), 05pd94v (0.48 #2592, 0.46 #2728, 0.34 #3820), 02q690_ (0.48 #1696, 0.48 #1560, 0.48 #1423), 02rjjll (0.48 #2595, 0.46 #2731, 0.35 #3823), 02cg41 (0.48 #2711, 0.46 #2847, 0.34 #3939), 056878 (0.48 #2620, 0.46 #2756, 0.34 #3848), 01c6qp (0.47 #2608, 0.45 #2744, 0.33 #3836) >> Best rule #886 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 0bfvd4; 0bdwqv; 04g2jz2; >> query: (?x7498, 03nnm4t) <- nominated_for(?x7498, ?x8597), nominated_for(?x7498, ?x8030), nominated_for(?x7498, ?x4396), film(?x879, ?x4396), award(?x3685, ?x7498), ?x8030 = 04f6df0, production_companies(?x4396, ?x2246), honored_for(?x5585, ?x8597) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09v82c0 ceremony 03nnm4t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.571 http://example.org/award/award_category/winners./award/award_honor/ceremony #17944-02t_tp PRED entity: 02t_tp PRED relation: award_nominee! PRED expected values: 03nkts => 81 concepts (42 used for prediction) PRED predicted values (max 10 best out of 765): 03xkps (0.81 #72348, 0.81 #86354, 0.81 #72347), 07s8r0 (0.34 #339, 0.04 #9674, 0.03 #16674), 02pv_d (0.28 #95690), 05m883 (0.28 #95690), 025jfl (0.28 #95690), 015t56 (0.27 #608, 0.08 #5275, 0.03 #30944), 0svqs (0.27 #1164, 0.04 #5831, 0.02 #31500), 0154qm (0.25 #739, 0.09 #5406, 0.03 #31075), 015t7v (0.25 #1190, 0.06 #5857, 0.02 #10525), 01846t (0.25 #713, 0.04 #5380, 0.02 #31049) >> Best rule #72348 for best value: >> intensional similarity = 3 >> extensional distance = 1495 >> proper extension: 0m2wm; 04wqr; 02pp_q_; 03ldxq; 03m8lq; 02knnd; 0162c8; 02zyy4; 049k07; 04smkr; ... >> query: (?x2587, ?x2588) <- award_nominee(?x2587, ?x2588), film(?x2588, ?x2955), gender(?x2587, ?x231) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #98026 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1718 *> proper extension: 09d5h; 03jvmp; 0g5lhl7; 0kk9v; 031rx9; 0fqy4p; 05xbx; *> query: (?x2587, ?x6397) <- award_nominee(?x2587, ?x2588), award_nominee(?x6397, ?x2588), nominated_for(?x2587, ?x3784) *> conf = 0.16 ranks of expected_values: 46 EVAL 02t_tp award_nominee! 03nkts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 81.000 42.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17943-03rk0 PRED entity: 03rk0 PRED relation: place_of_birth! PRED expected values: 0cfz_z => 221 concepts (184 used for prediction) PRED predicted values (max 10 best out of 1745): 05yvfd (0.33 #7188, 0.02 #163822, 0.01 #161854), 02qvhbb (0.31 #62654, 0.25 #480325, 0.24 #394183), 026g801 (0.17 #11500, 0.14 #14110, 0.11 #16720), 01f873 (0.11 #18004, 0.06 #23225, 0.03 #49333), 0pkr1 (0.11 #17871, 0.06 #23092, 0.03 #49200), 065d1h (0.11 #17839, 0.06 #23060, 0.03 #49168), 04h68j (0.11 #17782, 0.06 #23003, 0.03 #49111), 0139q5 (0.11 #17700, 0.06 #22921, 0.03 #49029), 03cp7b3 (0.11 #17548, 0.06 #22769, 0.03 #48877), 0342vg (0.11 #17385, 0.06 #22606, 0.03 #48714) >> Best rule #7188 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09f07; >> query: (?x2146, 05yvfd) <- contains(?x2146, ?x11800), ?x11800 = 058z2d, administrative_parent(?x2146, ?x551) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03rk0 place_of_birth! 0cfz_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 221.000 184.000 0.333 http://example.org/people/person/place_of_birth #17942-0ch3qr1 PRED entity: 0ch3qr1 PRED relation: nominated_for! PRED expected values: 04ljl_l => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 180): 0gq9h (0.41 #2157, 0.36 #2390, 0.35 #3089), 0gs9p (0.36 #2158, 0.32 #2391, 0.31 #3090), 019f4v (0.36 #2148, 0.32 #2381, 0.30 #3080), 0k611 (0.31 #2167, 0.27 #2400, 0.26 #3099), 0gq_v (0.31 #2116, 0.26 #2349, 0.26 #3048), 040njc (0.30 #2104, 0.26 #1638, 0.26 #2337), 0gqy2 (0.27 #2215, 0.24 #2448, 0.22 #3147), 0f4x7 (0.26 #2122, 0.23 #2355, 0.22 #3054), 04dn09n (0.25 #2131, 0.23 #2364, 0.21 #3063), 04ljl_l (0.25 #1399, 0.22 #12587, 0.20 #11419) >> Best rule #2157 for best value: >> intensional similarity = 2 >> extensional distance = 462 >> proper extension: 0d_wms; >> query: (?x5672, 0gq9h) <- honored_for(?x6297, ?x5672), film(?x1335, ?x5672) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1399 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 245 *> proper extension: 02fn5r; *> query: (?x5672, ?x102) <- nominated_for(?x1066, ?x5672), nominated_for(?x102, ?x1066) *> conf = 0.25 ranks of expected_values: 10 EVAL 0ch3qr1 nominated_for! 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 61.000 61.000 0.407 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17941-05kwx2 PRED entity: 05kwx2 PRED relation: student! PRED expected values: 015nl4 => 92 concepts (88 used for prediction) PRED predicted values (max 10 best out of 86): 0m4yg (0.12 #365, 0.10 #892, 0.03 #2473), 01bm_ (0.12 #246, 0.10 #773, 0.02 #1300), 027xq5 (0.12 #521, 0.10 #1048), 0ymgk (0.12 #155, 0.10 #682), 0h6rm (0.12 #144, 0.10 #671), 053mhx (0.10 #822, 0.02 #41645, 0.02 #3457), 015nl4 (0.07 #2175, 0.03 #5864, 0.03 #3229), 07tg4 (0.06 #2194, 0.02 #41645, 0.01 #3248), 0bwfn (0.05 #9234, 0.05 #7126, 0.05 #9761), 07tgn (0.04 #2125, 0.02 #41645, 0.01 #38497) >> Best rule #365 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0134w7; 065jlv; 0l6px; 013_vh; 06ltr; 03jj93; >> query: (?x6227, 0m4yg) <- film(?x6227, ?x6332), film(?x6227, ?x2869), ?x6332 = 03hxsv, ?x2869 = 03177r >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #2175 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 394 *> proper extension: 0784v1; *> query: (?x6227, 015nl4) <- nationality(?x6227, ?x1310), ?x1310 = 02jx1 *> conf = 0.07 ranks of expected_values: 7 EVAL 05kwx2 student! 015nl4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 92.000 88.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #17940-0ckh4k PRED entity: 0ckh4k PRED relation: program! PRED expected values: 02h758 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 55): 09d5h (0.79 #508, 0.78 #621, 0.55 #1187), 0g5lhl7 (0.55 #343, 0.23 #399, 0.20 #1131), 0gsg7 (0.53 #1298, 0.34 #1638, 0.33 #451), 025snf (0.50 #204, 0.44 #260, 0.36 #2368), 0cjdk (0.43 #961, 0.40 #1017, 0.39 #905), 05gnf (0.40 #1535, 0.32 #1649, 0.26 #1593), 03mdt (0.38 #568, 0.35 #737, 0.33 #851), 01bfjy (0.38 #219, 0.36 #2368, 0.33 #275), 01f2w0 (0.36 #359, 0.23 #415, 0.12 #696), 03lpbx (0.36 #2368, 0.33 #32, 0.25 #201) >> Best rule #508 for best value: >> intensional similarity = 9 >> extensional distance = 27 >> proper extension: 01b66d; 01kt_j; 03k99c; >> query: (?x6793, 09d5h) <- program(?x5919, ?x6793), actor(?x6793, ?x9317), service_language(?x5919, ?x254), program(?x5919, ?x8444), service_location(?x5919, ?x8963), location(?x10701, ?x8963), category(?x5919, ?x134), genre(?x8444, ?x225), contains(?x390, ?x8963) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 051kd; *> query: (?x6793, 02h758) <- program(?x5919, ?x6793), actor(?x6793, ?x9317), gender(?x9317, ?x231), film(?x9317, ?x66), program(?x5919, ?x8444), country_of_origin(?x6793, ?x390), ?x8444 = 045qmr, nationality(?x9317, ?x1023) *> conf = 0.10 ranks of expected_values: 27 EVAL 0ckh4k program! 02h758 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 79.000 79.000 0.793 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #17939-0sxgh PRED entity: 0sxgh PRED relation: colors PRED expected values: 01g5v 0jc_p => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 21): 019sc (0.50 #50, 0.40 #29, 0.31 #92), 01l849 (0.38 #43, 0.24 #127, 0.24 #589), 083jv (0.33 #401, 0.33 #422, 0.32 #464), 067z2v (0.25 #10, 0.20 #31, 0.12 #52), 01g5v (0.23 #130, 0.23 #466, 0.23 #403), 0jc_p (0.20 #26, 0.15 #131, 0.12 #47), 036k5h (0.14 #153, 0.09 #300, 0.09 #384), 06fvc (0.13 #633, 0.12 #150, 0.12 #45), 02rnmb (0.12 #56, 0.08 #98, 0.07 #1534), 038hg (0.09 #412, 0.09 #475, 0.09 #433) >> Best rule #50 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0g8fs; >> query: (?x9212, 019sc) <- state_province_region(?x9212, ?x1767), contains(?x94, ?x9212), ?x94 = 09c7w0, category(?x9212, ?x134), ?x1767 = 04rrd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #130 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 73 *> proper extension: 02yr3z; *> query: (?x9212, 01g5v) <- state_province_region(?x9212, ?x1767), contains(?x94, ?x9212), currency(?x9212, ?x170), featured_film_locations(?x2754, ?x1767), district_represented(?x176, ?x1767) *> conf = 0.23 ranks of expected_values: 5, 6 EVAL 0sxgh colors 0jc_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 126.000 126.000 0.500 http://example.org/education/educational_institution/colors EVAL 0sxgh colors 01g5v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 126.000 126.000 0.500 http://example.org/education/educational_institution/colors #17938-03g3w PRED entity: 03g3w PRED relation: major_field_of_study! PRED expected values: 01fpvz 0lfgr 0yjf0 0l2tk 01c333 012fvq 01f1r4 05mv4 0bqxw 07t90 02zd460 01g7_r 05hf_5 => 86 concepts (74 used for prediction) PRED predicted values (max 10 best out of 567): 02zd460 (0.75 #12981, 0.67 #14882, 0.65 #21067), 01j_cy (0.60 #8112, 0.58 #15240, 0.50 #14765), 01qd_r (0.60 #8316, 0.50 #15920, 0.50 #4515), 0cwx_ (0.60 #8288, 0.50 #15416, 0.50 #4011), 06fq2 (0.60 #8337, 0.50 #4060, 0.42 #15465), 04rwx (0.58 #15238, 0.55 #14287, 0.50 #4309), 0lfgr (0.55 #14293, 0.50 #3839, 0.50 #3363), 05mv4 (0.55 #14367, 0.50 #3913, 0.50 #3437), 07t90 (0.55 #14385, 0.50 #2980, 0.42 #15812), 012mzw (0.55 #14491, 0.33 #1659, 0.33 #1183) >> Best rule #12981 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 04g7x; >> query: (?x2605, 02zd460) <- major_field_of_study(?x5324, ?x2605), major_field_of_study(?x4750, ?x2605), major_field_of_study(?x3485, ?x2605), ?x3485 = 01mpwj, major_field_of_study(?x8221, ?x2605), ?x8221 = 037mh8, colors(?x5324, ?x663), organization(?x346, ?x4750) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 8, 9, 18, 46, 55, 62, 66, 188, 245, 317, 352 EVAL 03g3w major_field_of_study! 05hf_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 01g7_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 02zd460 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 07t90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 0bqxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 05mv4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 01f1r4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 012fvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 01c333 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 0l2tk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 0yjf0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 0lfgr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03g3w major_field_of_study! 01fpvz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 86.000 74.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17937-0jmj7 PRED entity: 0jmj7 PRED relation: school PRED expected values: 0kz2w 017zq0 0l2tk 02183k 035wtd 017j69 0g8rj 035ktt 01vs5c 02yr3z 01jq0j 0k__z 01hx2t 02l424 03x1s8 02pdhz => 93 concepts (75 used for prediction) PRED predicted values (max 10 best out of 140): 07w0v (0.50 #472, 0.40 #606, 0.33 #271), 012vwb (0.50 #489, 0.40 #623, 0.25 #422), 022lly (0.50 #478, 0.40 #612, 0.25 #411), 01j_06 (0.38 #810, 0.33 #273, 0.27 #1011), 01vs5c (0.33 #233, 0.27 #1038, 0.25 #837), 01jq0j (0.33 #247, 0.26 #1595, 0.20 #1119), 0l2tk (0.33 #11, 0.25 #480, 0.25 #413), 01jq4b (0.33 #103, 0.25 #505, 0.20 #639), 01n_g9 (0.33 #250, 0.25 #384, 0.18 #1055), 0frm7n (0.33 #293, 0.12 #830, 0.11 #897) >> Best rule #472 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 01yhm; 07147; >> query: (?x2820, 07w0v) <- school(?x2820, ?x10572), school(?x2820, ?x8937), school(?x2820, ?x7338), school(?x2820, ?x2760), draft(?x2820, ?x2569), colors(?x8937, ?x5325), organization(?x346, ?x8937), major_field_of_study(?x2760, ?x254), student(?x7338, ?x4976), ?x10572 = 0160nk, state_province_region(?x7338, ?x3778), ?x254 = 02h40lc >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 07l8x; *> query: (?x2820, 01vs5c) <- school(?x2820, ?x7338), school(?x2820, ?x7133), school(?x2820, ?x1675), school(?x2820, ?x331), draft(?x2820, ?x2569), ?x7338 = 01qgr3, ?x331 = 01jssp, major_field_of_study(?x1675, ?x2601), state_province_region(?x1675, ?x1906), ?x2601 = 04x_3, organization(?x346, ?x7133) *> conf = 0.33 ranks of expected_values: 5, 6, 7, 24, 30, 31, 32, 33, 36, 122, 124, 135 EVAL 0jmj7 school 02pdhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 03x1s8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 02l424 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 01hx2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 0k__z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 01jq0j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 02yr3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 01vs5c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 035ktt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 0g8rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 017j69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 035wtd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 02183k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 0l2tk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 017zq0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmj7 school 0kz2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 75.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #17936-099cng PRED entity: 099cng PRED relation: nominated_for PRED expected values: 093l8p 02cbhg 0gvvm6l => 53 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1517): 011yg9 (0.73 #8637, 0.71 #7089, 0.58 #5540), 0gmgwnv (0.71 #10229, 0.53 #8682, 0.50 #7134), 05jzt3 (0.68 #34074, 0.65 #27875, 0.64 #24775), 09k56b7 (0.68 #34074, 0.65 #27875, 0.63 #27874), 02nczh (0.68 #34074, 0.65 #27875, 0.63 #27874), 011yr9 (0.68 #34074, 0.65 #27875, 0.63 #27874), 01cmp9 (0.65 #10202, 0.60 #8655, 0.58 #5558), 0b6tzs (0.65 #9418, 0.53 #7871, 0.50 #6323), 03hmt9b (0.65 #9868, 0.47 #8321, 0.43 #6773), 04vr_f (0.65 #9445, 0.47 #7898, 0.43 #6350) >> Best rule #8637 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 099c8n; >> query: (?x1441, 011yg9) <- nominated_for(?x1441, ?x4756), nominated_for(?x1441, ?x1803), ceremony(?x1441, ?x472), ?x4756 = 0462hhb, film_release_region(?x1803, ?x87) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #10488 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 03hkv_r; 0f4x7; 09sb52; 019f4v; 0gs9p; 054krc; 0k611; 0gqy2; 09sdmz; *> query: (?x1441, 02cbhg) <- nominated_for(?x1441, ?x10806), nominated_for(?x1441, ?x4756), ceremony(?x1441, ?x472), film_crew_role(?x4756, ?x137), ?x10806 = 04q827 *> conf = 0.35 ranks of expected_values: 127, 163, 208 EVAL 099cng nominated_for 0gvvm6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 53.000 22.000 0.733 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099cng nominated_for 02cbhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 53.000 22.000 0.733 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099cng nominated_for 093l8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 22.000 0.733 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17935-02ll45 PRED entity: 02ll45 PRED relation: genre PRED expected values: 04xvh5 => 99 concepts (98 used for prediction) PRED predicted values (max 10 best out of 158): 07ssc (0.53 #8066, 0.52 #5539, 0.52 #5903), 02kdv5l (0.44 #241, 0.40 #1, 0.34 #601), 02l7c8 (0.38 #855, 0.34 #1817, 0.30 #6638), 05p553 (0.38 #3973, 0.37 #4938, 0.37 #5421), 01jfsb (0.35 #3377, 0.33 #251, 0.33 #11), 060__y (0.30 #376, 0.23 #856, 0.20 #2300), 04xvh5 (0.30 #394, 0.20 #874, 0.18 #154), 0lsxr (0.27 #8, 0.26 #248, 0.24 #969), 01hmnh (0.21 #1940, 0.18 #3142, 0.18 #3263), 06n90 (0.20 #492, 0.15 #3378, 0.15 #1935) >> Best rule #8066 for best value: >> intensional similarity = 3 >> extensional distance = 1156 >> proper extension: 026p_bs; 03bx2lk; 04kzqz; 015g28; 0k0rf; 02gs6r; 0fsw_7; 0dh8v4; 0h1fktn; 05css_; ... >> query: (?x5028, ?x53) <- film(?x489, ?x5028), film(?x382, ?x5028), titles(?x53, ?x5028) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #394 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 01c9d; *> query: (?x5028, 04xvh5) <- film(?x489, ?x5028), nominated_for(?x1198, ?x5028), nominated_for(?x484, ?x5028), ?x1198 = 02pqp12, ?x484 = 0gq_v *> conf = 0.30 ranks of expected_values: 7 EVAL 02ll45 genre 04xvh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 99.000 98.000 0.526 http://example.org/film/film/genre #17934-01gpkz PRED entity: 01gpkz PRED relation: organization! PRED expected values: 07xl34 => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 12): 060c4 (0.65 #887, 0.65 #834, 0.63 #860), 0dq_5 (0.61 #165, 0.59 #217, 0.56 #178), 07xl34 (0.56 #128, 0.41 #206, 0.40 #193), 08jcfy (0.33 #64, 0.33 #38, 0.17 #51), 05c0jwl (0.29 #135, 0.25 #343, 0.23 #187), 0dq3c (0.20 #14, 0.03 #144, 0.02 #235), 05k17c (0.15 #1383, 0.15 #1382, 0.15 #1410), 0hm4q (0.15 #1383, 0.15 #1382, 0.15 #1410), 04n1q6 (0.15 #1383, 0.15 #1382, 0.15 #1410), 0p5vf (0.07 #899, 0.07 #1082, 0.03 #1933) >> Best rule #887 for best value: >> intensional similarity = 5 >> extensional distance = 307 >> proper extension: 08815; 05zjtn4; 04wlz2; 05krk; 01pl14; 052nd; 01j_9c; 02w2bc; 065y4w7; 0288zy; ... >> query: (?x13543, 060c4) <- school_type(?x13543, ?x3092), currency(?x13543, ?x1099), institution(?x1200, ?x13543), contains(?x6401, ?x13543), jurisdiction_of_office(?x3119, ?x6401) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 01v3k2; 015wy_; *> query: (?x13543, 07xl34) <- school_type(?x13543, ?x3092), currency(?x13543, ?x1099), major_field_of_study(?x13543, ?x1668), ?x1099 = 01nv4h, ?x3092 = 05jxkf *> conf = 0.56 ranks of expected_values: 3 EVAL 01gpkz organization! 07xl34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 167.000 167.000 0.650 http://example.org/organization/role/leaders./organization/leadership/organization #17933-06_bq1 PRED entity: 06_bq1 PRED relation: currency PRED expected values: 09nqf => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.44 #122, 0.42 #65, 0.42 #62), 01nv4h (0.01 #187, 0.01 #54, 0.01 #166) >> Best rule #122 for best value: >> intensional similarity = 3 >> extensional distance = 156 >> proper extension: 03xnq9_; 06tp4h; 04d_mtq; >> query: (?x7046, 09nqf) <- profession(?x7046, ?x4773), gender(?x7046, ?x514), vacationer(?x2146, ?x7046) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06_bq1 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.437 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #17932-0gyfp9c PRED entity: 0gyfp9c PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.84 #91, 0.83 #131, 0.83 #161), 02nxhr (0.22 #2, 0.10 #47, 0.07 #27), 07z4p (0.11 #5, 0.07 #110, 0.07 #40), 07c52 (0.10 #143, 0.10 #23, 0.09 #13), 0735l (0.03 #14, 0.02 #24, 0.01 #39) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 186 >> proper extension: 02bj22; >> query: (?x3226, 029j_) <- award_winner(?x3226, ?x495), genre(?x3226, ?x258), film_crew_role(?x3226, ?x137), ?x258 = 05p553 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gyfp9c film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.835 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17931-09c7w0 PRED entity: 09c7w0 PRED relation: exported_to PRED expected values: 06mzp => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 59): 09c7w0 (0.41 #1520, 0.40 #1868, 0.38 #2345), 04sj3 (0.36 #1911, 0.32 #2476, 0.31 #952), 047t_ (0.36 #1911, 0.32 #2476, 0.31 #952), 016zwt (0.36 #1911, 0.32 #2476, 0.31 #952), 03rjj (0.36 #1911, 0.32 #2476, 0.31 #952), 01z215 (0.36 #1911, 0.32 #2476, 0.31 #952), 0f8l9c (0.36 #1911, 0.32 #2476, 0.31 #952), 0d060g (0.36 #1911, 0.32 #2476, 0.31 #952), 0ctw_b (0.36 #1911, 0.32 #2476, 0.31 #952), 03rt9 (0.36 #1911, 0.32 #2476, 0.31 #952) >> Best rule #1520 for best value: >> intensional similarity = 2 >> extensional distance = 42 >> proper extension: 0853g; >> query: (?x94, 09c7w0) <- contains(?x94, ?x95), exported_to(?x94, ?x151) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1397 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 32 *> proper extension: 05kkh; 0hjy; 07b_l; 07h34; 03v0t; 0n5gq; *> query: (?x94, 06mzp) <- contains(?x94, ?x95), jurisdiction_of_office(?x652, ?x94) *> conf = 0.03 ranks of expected_values: 43 EVAL 09c7w0 exported_to 06mzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 156.000 156.000 0.409 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #17930-03pvt PRED entity: 03pvt PRED relation: type_of_union PRED expected values: 01g63y => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 3): 01g63y (0.20 #127, 0.19 #318, 0.19 #314), 0jgjn (0.19 #318, 0.19 #314, 0.19 #271), 01bl8s (0.19 #318, 0.19 #314, 0.19 #271) >> Best rule #127 for best value: >> intensional similarity = 4 >> extensional distance = 268 >> proper extension: 01vvydl; 01kwld; 01g257; 012x4t; 058s57; 01zfmm; 01vsykc; 0gbwp; 03xp8d5; 01gw4f; ... >> query: (?x3710, 01g63y) <- currency(?x3710, ?x170), profession(?x3710, ?x319), type_of_union(?x3710, ?x566), award(?x3710, ?x102) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03pvt type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.196 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17929-0fh694 PRED entity: 0fh694 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #36, 0.82 #21, 0.82 #98), 02nxhr (0.04 #99, 0.04 #94, 0.04 #104), 07c52 (0.04 #120, 0.04 #171, 0.04 #176), 07z4p (0.03 #178, 0.03 #173, 0.03 #112) >> Best rule #36 for best value: >> intensional similarity = 3 >> extensional distance = 159 >> proper extension: 03lrqw; >> query: (?x964, 029j_) <- nominated_for(?x68, ?x964), produced_by(?x964, ?x8041), cinematography(?x964, ?x7384) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fh694 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.820 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17928-0z4s PRED entity: 0z4s PRED relation: film PRED expected values: 0kv238 => 98 concepts (67 used for prediction) PRED predicted values (max 10 best out of 823): 08gg47 (0.77 #5311, 0.57 #40713, 0.45 #46026), 05cvgl (0.77 #5311, 0.57 #40713, 0.41 #46025), 09rfpk (0.77 #5311, 0.36 #58422, 0.36 #99140), 03bx2lk (0.25 #183, 0.06 #3723, 0.04 #16114), 0fphf3v (0.25 #1345, 0.06 #4885, 0.04 #19046), 05m_jsg (0.25 #637, 0.06 #4177, 0.04 #83205), 06_wqk4 (0.25 #126, 0.06 #3666, 0.04 #83205), 02bg55 (0.25 #1137, 0.06 #4677, 0.03 #90286), 029zqn (0.25 #264, 0.05 #5575, 0.04 #83205), 04x4vj (0.25 #762, 0.05 #6073, 0.03 #11383) >> Best rule #5311 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0c4f4; 01pgzn_; 019pm_; 08swgx; 014488; 0278x6s; 014gf8; 07h565; 073x6y; 01d1st; ... >> query: (?x450, ?x1903) <- gender(?x450, ?x231), nominated_for(?x450, ?x1903), nominated_for(?x450, ?x1045), ?x1045 = 08r4x3 >> conf = 0.77 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0z4s film 0kv238 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 67.000 0.767 http://example.org/film/actor/film./film/performance/film #17927-044n3h PRED entity: 044n3h PRED relation: student! PRED expected values: 0gl5_ => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 68): 02zd460 (0.17 #696, 0.17 #170, 0.01 #26476), 0bwfn (0.12 #2378, 0.09 #6060, 0.09 #6586), 01w5m (0.07 #1157, 0.06 #2209, 0.06 #1683), 0gl5_ (0.07 #1296, 0.06 #2348, 0.06 #1822), 0m4yg (0.07 #1416, 0.06 #2468, 0.06 #1942), 014xf6 (0.07 #1355, 0.06 #2407, 0.06 #1881), 0gjv_ (0.07 #1258, 0.06 #2310, 0.06 #1784), 017cy9 (0.07 #1204, 0.06 #2256, 0.06 #1730), 01g7_r (0.07 #1308, 0.06 #1834), 02w2bc (0.07 #1065, 0.06 #1591) >> Best rule #696 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 02zbjhq; >> query: (?x10401, 02zd460) <- nationality(?x10401, ?x1453), ?x1453 = 06qd3, profession(?x10401, ?x1032) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1296 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 01sl1q; 044mz_; 01kwld; 044mm6; 07f3xb; 03_wj_; 031ydm; 033w9g; 044mrh; 02k4gv; ... *> query: (?x10401, 0gl5_) <- award_nominee(?x5769, ?x10401), nationality(?x10401, ?x94), ?x5769 = 03_wvl *> conf = 0.07 ranks of expected_values: 4 EVAL 044n3h student! 0gl5_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 116.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #17926-0k7pf PRED entity: 0k7pf PRED relation: award_winner! PRED expected values: 0jzphpx => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 134): 01s695 (0.43 #3, 0.13 #4063, 0.12 #3923), 0gpjbt (0.29 #28, 0.12 #4088, 0.11 #3948), 013b2h (0.15 #3999, 0.14 #219, 0.14 #79), 05pd94v (0.14 #142, 0.14 #2, 0.14 #3922), 02cg41 (0.14 #125, 0.12 #4185, 0.12 #4045), 01bx35 (0.14 #6, 0.12 #4066, 0.11 #3926), 019bk0 (0.14 #15, 0.11 #4075, 0.10 #5755), 0bz6sb (0.14 #203, 0.06 #483, 0.05 #763), 09pnw5 (0.14 #102, 0.03 #4442, 0.02 #3742), 0418154 (0.14 #107, 0.02 #6827, 0.02 #527) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02ht0ln; >> query: (?x3030, 01s695) <- artist(?x2149, ?x3030), award(?x3030, ?x10881), ?x10881 = 026mmy >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3958 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 248 *> proper extension: 03qmj9; 02wb6yq; 0flpy; 01wqflx; *> query: (?x3030, 0jzphpx) <- profession(?x3030, ?x131), instrumentalists(?x74, ?x3030), award_winner(?x486, ?x3030) *> conf = 0.10 ranks of expected_values: 27 EVAL 0k7pf award_winner! 0jzphpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 109.000 109.000 0.429 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17925-05wvs PRED entity: 05wvs PRED relation: nutrient! PRED expected values: 0dj75 => 59 concepts (55 used for prediction) PRED predicted values (max 10 best out of 71): 0dj75 (0.89 #41, 0.89 #186, 0.89 #132), 0dcfv (0.89 #41, 0.89 #186, 0.89 #132), 06x4c (0.89 #41, 0.89 #186, 0.89 #132), 04k8n (0.03 #6, 0.02 #9, 0.02 #8), 05wvs (0.03 #6, 0.02 #9, 0.02 #8), 01sh2 (0.03 #6, 0.02 #9, 0.02 #8), 07q0m (0.03 #6, 0.02 #9, 0.02 #8), 0fzjh (0.03 #6, 0.02 #9, 0.02 #8), 025rw19 (0.03 #6, 0.02 #9, 0.02 #8), 01n78x (0.03 #6, 0.02 #9, 0.02 #8) >> Best rule #41 for best value: >> intensional similarity = 123 >> extensional distance = 9 >> proper extension: 02kc008; >> query: (?x5451, ?x7719) <- nutrient(?x10612, ?x5451), nutrient(?x9732, ?x5451), nutrient(?x9489, ?x5451), nutrient(?x9005, ?x5451), nutrient(?x8298, ?x5451), nutrient(?x7057, ?x5451), nutrient(?x6285, ?x5451), nutrient(?x6159, ?x5451), nutrient(?x6032, ?x5451), nutrient(?x5373, ?x5451), nutrient(?x5009, ?x5451), nutrient(?x4068, ?x5451), nutrient(?x3468, ?x5451), nutrient(?x2701, ?x5451), nutrient(?x1959, ?x5451), nutrient(?x1303, ?x5451), nutrient(?x1257, ?x5451), ?x5009 = 0fjfh, ?x2701 = 0hkxq, ?x9489 = 07j87, ?x10612 = 0frq6, ?x4068 = 0fbw6, ?x1959 = 0f25w9, ?x3468 = 0cxn2, nutrient(?x6159, ?x14210), nutrient(?x6159, ?x13545), nutrient(?x6159, ?x13498), nutrient(?x6159, ?x13126), nutrient(?x6159, ?x12902), nutrient(?x6159, ?x12868), nutrient(?x6159, ?x12454), nutrient(?x6159, ?x12083), nutrient(?x6159, ?x11758), nutrient(?x6159, ?x11592), nutrient(?x6159, ?x10891), nutrient(?x6159, ?x10098), nutrient(?x6159, ?x9949), nutrient(?x6159, ?x9915), nutrient(?x6159, ?x9733), nutrient(?x6159, ?x9619), nutrient(?x6159, ?x9436), nutrient(?x6159, ?x9426), nutrient(?x6159, ?x9365), nutrient(?x6159, ?x8487), nutrient(?x6159, ?x8413), nutrient(?x6159, ?x7720), nutrient(?x6159, ?x7652), nutrient(?x6159, ?x7431), nutrient(?x6159, ?x7364), nutrient(?x6159, ?x7362), nutrient(?x6159, ?x7219), nutrient(?x6159, ?x7135), nutrient(?x6159, ?x6586), nutrient(?x6159, ?x6192), nutrient(?x6159, ?x6160), nutrient(?x6159, ?x6033), nutrient(?x6159, ?x6026), nutrient(?x6159, ?x5549), nutrient(?x6159, ?x5526), nutrient(?x6159, ?x5337), nutrient(?x6159, ?x5010), nutrient(?x6159, ?x4069), nutrient(?x6159, ?x3469), nutrient(?x6159, ?x3264), nutrient(?x6159, ?x3203), nutrient(?x6159, ?x2702), nutrient(?x6159, ?x2018), nutrient(?x6159, ?x1960), nutrient(?x6159, ?x1304), ?x9915 = 025tkqy, ?x10098 = 0h1_c, ?x6192 = 06jry, ?x4069 = 0hqw8p_, ?x9005 = 04zpv, ?x7362 = 02kc5rj, ?x9949 = 02kd0rh, ?x1257 = 09728, ?x13545 = 01w_3, ?x9619 = 0h1tg, ?x1304 = 08lb68, ?x6026 = 025sf8g, ?x8487 = 014yzm, ?x12083 = 01n78x, ?x2702 = 0838f, ?x11758 = 0q01m, ?x5010 = 0h1vz, ?x7364 = 09gvd, ?x5337 = 06x4c, ?x7652 = 025s0s0, ?x1960 = 07hnp, ?x7431 = 09gwd, ?x12454 = 025rw19, ?x9365 = 04k8n, ?x9733 = 0h1tz, ?x13126 = 02kc_w5, ?x6285 = 01645p, ?x6160 = 041r51, ?x8413 = 02kc4sf, ?x8298 = 037ls6, ?x7219 = 0h1vg, ?x14210 = 0f4k5, ?x6032 = 01nkt, ?x9732 = 05z55, ?x1303 = 0fj52s, ?x6586 = 05gh50, ?x9436 = 025sqz8, ?x10891 = 0g5gq, ?x7720 = 025s7x6, ?x5373 = 0971v, ?x3264 = 0dcfv, ?x3203 = 04kl74p, ?x7135 = 025rsfk, ?x5549 = 025s7j4, ?x13498 = 07q0m, ?x7057 = 0fbdb, ?x12902 = 0fzjh, ?x11592 = 025sf0_, ?x3469 = 0h1zw, ?x5526 = 09pbb, ?x9426 = 0h1yy, taxonomy(?x2018, ?x939), nutrient(?x7719, ?x12868), ?x6033 = 04zjxcz >> conf = 0.89 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 05wvs nutrient! 0dj75 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 55.000 0.890 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #17924-0136p1 PRED entity: 0136p1 PRED relation: artist! PRED expected values: 0g768 => 81 concepts (51 used for prediction) PRED predicted values (max 10 best out of 124): 011k1h (0.40 #9, 0.12 #145, 0.12 #1777), 03rhqg (0.28 #150, 0.18 #1782, 0.17 #1510), 015_1q (0.22 #698, 0.20 #834, 0.20 #1786), 02p3cr5 (0.20 #25, 0.15 #161, 0.05 #705), 017l96 (0.20 #17, 0.12 #153, 0.12 #1785), 0n85g (0.20 #60, 0.12 #196, 0.10 #1828), 01clyr (0.20 #166, 0.11 #1798, 0.10 #710), 0181dw (0.20 #39, 0.10 #3169, 0.10 #175), 0k_kr (0.20 #41, 0.05 #857, 0.05 #721), 016ckq (0.20 #40, 0.04 #856, 0.04 #720) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 049qx; 04b7xr; 019x62; >> query: (?x1974, 011k1h) <- artists(?x10474, ?x1974), artists(?x1572, ?x1974), ?x1572 = 06by7, ?x10474 = 02cqny >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #170 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 01x1cn2; 0qf3p; *> query: (?x1974, 0g768) <- artists(?x3370, ?x1974), artists(?x1572, ?x1974), ?x1572 = 06by7, ?x3370 = 059kh *> conf = 0.17 ranks of expected_values: 12 EVAL 0136p1 artist! 0g768 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 81.000 51.000 0.400 http://example.org/music/record_label/artist #17923-0vbk PRED entity: 0vbk PRED relation: district_represented! PRED expected values: 01gtc0 01gtcq => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 46): 01gt99 (0.70 #42, 0.55 #1060, 0.55 #737), 01gtbb (0.70 #9, 0.55 #1060, 0.55 #737), 01gst_ (0.70 #10, 0.55 #1060, 0.55 #737), 01gsvb (0.70 #36, 0.55 #1060, 0.55 #737), 01gtc0 (0.60 #24, 0.55 #1060, 0.55 #737), 01gsvp (0.60 #29, 0.55 #1060, 0.55 #737), 02bp37 (0.59 #697, 0.57 #513, 0.56 #467), 02bqm0 (0.55 #1060, 0.55 #737, 0.53 #482), 02bqmq (0.55 #1060, 0.55 #737, 0.51 #473), 01gstn (0.55 #1060, 0.55 #737, 0.50 #23) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0f8x_r; >> query: (?x4758, 01gt99) <- adjoins(?x3908, ?x4758), adjoins(?x3778, ?x4758), ?x3778 = 07h34, religion(?x3908, ?x109) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0f8x_r; *> query: (?x4758, 01gtc0) <- adjoins(?x3908, ?x4758), adjoins(?x3778, ?x4758), ?x3778 = 07h34, religion(?x3908, ?x109) *> conf = 0.60 ranks of expected_values: 5, 14 EVAL 0vbk district_represented! 01gtcq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 187.000 187.000 0.700 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 0vbk district_represented! 01gtc0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 187.000 187.000 0.700 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #17922-0dy04 PRED entity: 0dy04 PRED relation: major_field_of_study PRED expected values: 062z7 => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 116): 01mkq (0.71 #138, 0.70 #261, 0.64 #2107), 03g3w (0.71 #149, 0.59 #887, 0.57 #2118), 037mh8 (0.71 #192, 0.44 #561, 0.41 #930), 02lp1 (0.70 #257, 0.63 #2720, 0.57 #134), 02j62 (0.62 #2122, 0.60 #276, 0.55 #891), 041y2 (0.60 #326, 0.32 #2172, 0.29 #203), 0fdys (0.57 #162, 0.45 #2131, 0.36 #1270), 02ky346 (0.57 #139, 0.26 #2725, 0.26 #2108), 01zc2w (0.57 #196, 0.20 #1304, 0.20 #319), 062z7 (0.50 #273, 0.48 #2736, 0.42 #2982) >> Best rule #138 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 06pwq; 03ksy; 09f2j; 01bm_; 07tk7; >> query: (?x2637, 01mkq) <- student(?x2637, ?x2800), major_field_of_study(?x2637, ?x11206), major_field_of_study(?x2637, ?x2014), institution(?x734, ?x2637), ?x2014 = 04rjg, ?x11206 = 05b6c >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #273 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 09wv__; *> query: (?x2637, 062z7) <- student(?x2637, ?x12216), student(?x2637, ?x11500), student(?x2637, ?x4974), cinematography(?x915, ?x4974), influenced_by(?x3711, ?x11500), influenced_by(?x12216, ?x1857), religion(?x12216, ?x7131) *> conf = 0.50 ranks of expected_values: 10 EVAL 0dy04 major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 189.000 189.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17921-02r251z PRED entity: 02r251z PRED relation: profession PRED expected values: 03gjzk => 96 concepts (95 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.77 #4039, 0.71 #4337, 0.69 #4635), 03gjzk (0.62 #462, 0.51 #1207, 0.50 #1356), 0dxtg (0.57 #2844, 0.53 #1652, 0.51 #3740), 02jknp (0.50 #2838, 0.48 #3436, 0.47 #3734), 09jwl (0.33 #19, 0.25 #168, 0.17 #7026), 0nbcg (0.33 #32, 0.25 #181, 0.14 #777), 0cbd2 (0.25 #155, 0.20 #3137, 0.20 #2390), 018gz8 (0.25 #166, 0.15 #464, 0.12 #613), 0d1pc (0.25 #349, 0.08 #3331, 0.07 #4076), 02krf9 (0.24 #474, 0.17 #1666, 0.16 #2858) >> Best rule #4039 for best value: >> intensional similarity = 3 >> extensional distance = 714 >> proper extension: 08849; >> query: (?x7090, 02hrh1q) <- award_winner(?x1335, ?x7090), profession(?x7090, ?x319), participant(?x364, ?x1335) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #462 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 039cq4; *> query: (?x7090, 03gjzk) <- award_winner(?x1335, ?x7090), tv_program(?x1335, ?x6884), participant(?x364, ?x1335) *> conf = 0.62 ranks of expected_values: 2 EVAL 02r251z profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 95.000 0.765 http://example.org/people/person/profession #17920-06w7mlh PRED entity: 06w7mlh PRED relation: nominated_for! PRED expected values: 0bp_b2 => 82 concepts (80 used for prediction) PRED predicted values (max 10 best out of 198): 0gkts9 (0.78 #2655, 0.76 #2413, 0.68 #15442), 0gkr9q (0.38 #212, 0.24 #1660, 0.21 #1419), 027gs1_ (0.33 #3086, 0.27 #2845, 0.26 #2120), 0fbtbt (0.32 #1611, 0.31 #1370, 0.27 #2818), 0gq9h (0.32 #11881, 0.29 #11398, 0.28 #12848), 0cjyzs (0.32 #2979, 0.25 #2738, 0.24 #3461), 0ck27z (0.30 #1521, 0.28 #1280, 0.25 #73), 0gqyl (0.29 #2253, 0.18 #10692, 0.17 #11899), 0gs9p (0.28 #11883, 0.25 #11400, 0.25 #10676), 019f4v (0.28 #11872, 0.24 #10665, 0.24 #11389) >> Best rule #2655 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0147w8; >> query: (?x9082, ?x3184) <- program(?x1648, ?x9082), award(?x9082, ?x3184), nominated_for(?x3184, ?x687), award_winner(?x3184, ?x1343) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02gl58; *> query: (?x9082, 0bp_b2) <- genre(?x9082, ?x1013), award_winner(?x9082, ?x4380), ?x1013 = 06n90, award(?x9082, ?x3184) *> conf = 0.25 ranks of expected_values: 13 EVAL 06w7mlh nominated_for! 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 82.000 80.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17919-03q1vd PRED entity: 03q1vd PRED relation: profession PRED expected values: 02hrh1q => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.89 #5119, 0.89 #1369, 0.88 #2719), 0np9r (0.52 #472, 0.26 #8105, 0.25 #12308), 01d_h8 (0.49 #607, 0.35 #908, 0.34 #1209), 0dxtg (0.33 #615, 0.26 #11421, 0.26 #11721), 03gjzk (0.29 #617, 0.26 #8105, 0.26 #768), 02jknp (0.29 #8, 0.26 #8105, 0.25 #12308), 09jwl (0.26 #8105, 0.25 #12308, 0.17 #6024), 018gz8 (0.26 #8105, 0.25 #12308, 0.16 #619), 02krf9 (0.26 #8105, 0.25 #12308, 0.15 #178), 021wpb (0.26 #8105, 0.25 #12308, 0.08 #204) >> Best rule #5119 for best value: >> intensional similarity = 3 >> extensional distance = 968 >> proper extension: 0q1lp; >> query: (?x2726, 02hrh1q) <- location(?x2726, ?x2850), nominated_for(?x2726, ?x2336), film(?x2726, ?x240) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q1vd profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.890 http://example.org/people/person/profession #17918-02d02 PRED entity: 02d02 PRED relation: colors PRED expected values: 0jc_p => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 18): 06fvc (0.50 #38, 0.39 #1748, 0.37 #1784), 01g5v (0.39 #1803, 0.38 #39, 0.29 #1839), 019sc (0.38 #133, 0.37 #1789, 0.35 #115), 03vtbc (0.33 #8, 0.25 #26, 0.22 #62), 01l849 (0.24 #1045, 0.23 #703, 0.22 #595), 0jc_p (0.14 #94, 0.12 #40, 0.12 #544), 038hg (0.13 #1776, 0.11 #1794, 0.10 #1758), 088fh (0.07 #1842, 0.07 #204, 0.06 #1104), 09ggk (0.07 #410, 0.05 #608, 0.05 #1004), 06kqt3 (0.06 #69, 0.05 #1059, 0.05 #915) >> Best rule #38 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0jmj7; >> query: (?x8894, 06fvc) <- school(?x8894, ?x4556), team(?x2010, ?x8894), draft(?x8894, ?x1161), ?x4556 = 01lnyf >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #94 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 20 *> proper extension: 05m_8; 03lpp_; 06x68; 01d5z; 049n7; 0512p; 05g76; 0x2p; 0713r; 01ync; ... *> query: (?x8894, 0jc_p) <- teams(?x479, ?x8894), draft(?x8894, ?x1161), season(?x8894, ?x701), team(?x4244, ?x8894), school(?x8894, ?x466), ?x4244 = 028c_8 *> conf = 0.14 ranks of expected_values: 6 EVAL 02d02 colors 0jc_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 107.000 107.000 0.500 http://example.org/sports/sports_team/colors #17917-0g51l1 PRED entity: 0g51l1 PRED relation: type_of_union PRED expected values: 04ztj => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.77 #105, 0.77 #25, 0.76 #57), 01g63y (0.12 #26, 0.11 #42, 0.11 #34) >> Best rule #105 for best value: >> intensional similarity = 3 >> extensional distance = 562 >> proper extension: 0q9kd; 032xhg; 02qjj7; 0m2l9; 02nb2s; 025p38; 0168cl; 09byk; 042rnl; 04yj5z; ... >> query: (?x1996, 04ztj) <- profession(?x1996, ?x319), ?x319 = 01d_h8, location(?x1996, ?x739) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g51l1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.771 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17916-0g5q34q PRED entity: 0g5q34q PRED relation: nominated_for! PRED expected values: 02qrbbx => 109 concepts (91 used for prediction) PRED predicted values (max 10 best out of 205): 0gr51 (0.44 #1044, 0.42 #562, 0.40 #1285), 03hl6lc (0.42 #615, 0.33 #1097, 0.30 #1338), 02qyp19 (0.33 #965, 0.33 #483, 0.30 #1206), 027dtxw (0.33 #968, 0.30 #1209, 0.24 #3619), 07cbcy (0.33 #65, 0.18 #6513, 0.14 #6512), 05b1610 (0.33 #33, 0.14 #6512, 0.09 #1961), 03c7tr1 (0.33 #48, 0.14 #6512, 0.06 #6318), 05f4m9q (0.33 #12, 0.09 #1940, 0.08 #8453), 05ztrmj (0.29 #1583, 0.27 #2065, 0.14 #6650), 02x4sn8 (0.29 #1807, 0.25 #361, 0.19 #5185) >> Best rule #1044 for best value: >> intensional similarity = 12 >> extensional distance = 16 >> proper extension: 02704ff; >> query: (?x5992, 0gr51) <- genre(?x5992, ?x809), genre(?x5992, ?x258), genre(?x5992, ?x53), featured_film_locations(?x5992, ?x2474), ?x809 = 0vgkd, ?x53 = 07s9rl0, genre(?x9379, ?x258), genre(?x5109, ?x258), genre(?x770, ?x258), ?x9379 = 09y6pb, ?x770 = 01r97z, ?x5109 = 0b44shh >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #17373 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 688 *> proper extension: 04cf_l; *> query: (?x5992, ?x3233) <- genre(?x5992, ?x1626), genre(?x5992, ?x53), genre(?x9175, ?x1626), genre(?x9133, ?x1626), ?x9175 = 02qd04y, genre(?x10089, ?x53), award(?x9133, ?x3233), ?x10089 = 07g9f *> conf = 0.04 ranks of expected_values: 151 EVAL 0g5q34q nominated_for! 02qrbbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 109.000 91.000 0.444 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17915-06w87 PRED entity: 06w87 PRED relation: group PRED expected values: 0khth 01jkqfz => 64 concepts (38 used for prediction) PRED predicted values (max 10 best out of 377): 02vnpv (0.83 #3405, 0.65 #5127, 0.64 #3214), 05563d (0.67 #1178, 0.62 #1942, 0.60 #793), 0134tg (0.67 #1201, 0.60 #816, 0.59 #191), 0134wr (0.67 #3353, 0.60 #866, 0.59 #191), 0khth (0.67 #3291, 0.60 #804, 0.59 #191), 06nv27 (0.67 #2728, 0.59 #191, 0.55 #948), 07bzp (0.60 #1025, 0.60 #833, 0.59 #191), 07mvp (0.60 #837, 0.59 #191, 0.58 #3324), 017_hq (0.60 #922, 0.59 #191, 0.58 #3409), 01qqwp9 (0.60 #790, 0.59 #191, 0.55 #948) >> Best rule #3405 for best value: >> intensional similarity = 21 >> extensional distance = 10 >> proper extension: 05148p4; >> query: (?x736, 02vnpv) <- role(?x8014, ?x736), role(?x5676, ?x736), role(?x314, ?x736), role(?x736, ?x2310), role(?x736, ?x1166), ?x314 = 02sgy, role(?x885, ?x5676), role(?x316, ?x5676), instrumentalists(?x736, ?x2987), ?x885 = 0dwtp, role(?x5883, ?x5676), ?x316 = 05r5c, role(?x8014, ?x2798), role(?x8014, ?x1574), ?x1574 = 0l15bq, role(?x2310, ?x1436), role(?x487, ?x8014), ?x2987 = 01vw20_, role(?x565, ?x1166), ?x2798 = 03qjg, role(?x248, ?x1166) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #3291 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 10 *> proper extension: 05148p4; *> query: (?x736, 0khth) <- role(?x8014, ?x736), role(?x5676, ?x736), role(?x314, ?x736), role(?x736, ?x2310), role(?x736, ?x1166), ?x314 = 02sgy, role(?x885, ?x5676), role(?x316, ?x5676), instrumentalists(?x736, ?x2987), ?x885 = 0dwtp, role(?x5883, ?x5676), ?x316 = 05r5c, role(?x8014, ?x2798), role(?x8014, ?x1574), ?x1574 = 0l15bq, role(?x2310, ?x1436), role(?x487, ?x8014), ?x2987 = 01vw20_, role(?x565, ?x1166), ?x2798 = 03qjg, role(?x248, ?x1166) *> conf = 0.67 ranks of expected_values: 5, 178 EVAL 06w87 group 01jkqfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 64.000 38.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 06w87 group 0khth CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 38.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17914-0301yj PRED entity: 0301yj PRED relation: film PRED expected values: 03vyw8 03p2xc => 116 concepts (93 used for prediction) PRED predicted values (max 10 best out of 296): 09txzv (0.25 #254), 01chpn (0.12 #1109, 0.06 #85790, 0.05 #84002), 0ds5_72 (0.12 #1456, 0.02 #6817), 02z3r8t (0.12 #108, 0.02 #14404, 0.01 #53727), 078sj4 (0.12 #455, 0.02 #16538, 0.01 #27262), 095zlp (0.12 #60, 0.02 #16143, 0.01 #5421), 062zm5h (0.12 #856, 0.01 #6217, 0.01 #18727), 07vn_9 (0.12 #1681, 0.01 #7042), 0gtsx8c (0.12 #12, 0.01 #39332), 0g7pm1 (0.12 #1202, 0.01 #20860, 0.01 #11924) >> Best rule #254 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 035rnz; 0gyx4; 01x_d8; >> query: (?x10743, 09txzv) <- type_of_union(?x10743, ?x566), award_nominee(?x10743, ?x2028), ?x2028 = 028knk >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4623 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 207 *> proper extension: 04g865; 0fpj4lx; 03hbzj; 087yty; 023n39; 02qnbs; 03h2p5; 022q4j; 0ccqd7; 0mbs8; ... *> query: (?x10743, 03vyw8) <- profession(?x10743, ?x353), place_of_birth(?x10743, ?x739), ?x739 = 02_286 *> conf = 0.01 ranks of expected_values: 126 EVAL 0301yj film 03p2xc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 93.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 0301yj film 03vyw8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 116.000 93.000 0.250 http://example.org/film/actor/film./film/performance/film #17913-034fl9 PRED entity: 034fl9 PRED relation: actor PRED expected values: 02sh8y => 75 concepts (53 used for prediction) PRED predicted values (max 10 best out of 738): 01vhb0 (0.40 #178, 0.03 #2018, 0.03 #2938), 026dg51 (0.36 #12892, 0.36 #11051, 0.36 #8286), 023s8 (0.20 #778, 0.11 #19335, 0.07 #28539), 0pz7h (0.20 #74, 0.11 #19335, 0.07 #28539), 02g5h5 (0.20 #303, 0.11 #19335, 0.07 #28539), 0gcdzz (0.20 #108, 0.11 #19335, 0.07 #28539), 018ygt (0.20 #502, 0.11 #19335, 0.07 #28539), 0h27vc (0.20 #465, 0.11 #19335, 0.07 #28539), 0863x_ (0.20 #384, 0.11 #19335, 0.07 #28539), 0gkydb (0.20 #230, 0.11 #19335, 0.07 #28539) >> Best rule #178 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0d68qy; >> query: (?x9029, 01vhb0) <- actor(?x9029, ?x3557), actor(?x9029, ?x3261), ?x3557 = 01qr1_, nominated_for(?x588, ?x9029), film(?x3261, ?x1734) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 034fl9 actor 02sh8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 53.000 0.400 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #17912-0h21v2 PRED entity: 0h21v2 PRED relation: film_crew_role PRED expected values: 09zzb8 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 30): 09zzb8 (0.74 #1166, 0.70 #618, 0.70 #2004), 09vw2b7 (0.67 #1172, 0.66 #624, 0.62 #152), 0dxtw (0.43 #628, 0.38 #1176, 0.38 #556), 01vx2h (0.39 #230, 0.38 #629, 0.38 #84), 01pvkk (0.30 #341, 0.29 #630, 0.28 #1178), 02rh1dz (0.22 #46, 0.19 #627, 0.19 #82), 02ynfr (0.19 #634, 0.18 #1182, 0.17 #162), 0215hd (0.14 #1184, 0.14 #91, 0.13 #636), 089g0h (0.14 #565, 0.13 #637, 0.11 #1185), 0d2b38 (0.14 #134, 0.12 #643, 0.12 #171) >> Best rule #1166 for best value: >> intensional similarity = 4 >> extensional distance = 776 >> proper extension: 03_wm6; >> query: (?x5735, 09zzb8) <- genre(?x5735, ?x571), film(?x382, ?x5735), film_crew_role(?x5735, ?x1284), ?x1284 = 0ch6mp2 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h21v2 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.744 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17911-01ypc PRED entity: 01ypc PRED relation: season PRED expected values: 025ygws => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 3): 025ygws (0.81 #85, 0.81 #103, 0.80 #55), 04110b0 (0.40 #80, 0.40 #44, 0.38 #83), 04n36qk (0.17 #30, 0.12 #42, 0.10 #45) >> Best rule #85 for best value: >> intensional similarity = 17 >> extensional distance = 19 >> proper extension: 03lpp_; >> query: (?x260, 025ygws) <- team(?x2010, ?x260), team(?x261, ?x260), draft(?x260, ?x10600), draft(?x260, ?x8786), draft(?x260, ?x3334), school(?x260, ?x4209), team(?x261, ?x1438), ?x8786 = 02pq_x5, team(?x2010, ?x4487), draft(?x12956, ?x10600), ?x4487 = 01ync, ?x3334 = 02pq_rp, school(?x12956, ?x3360), team(?x11844, ?x12956), student(?x4209, ?x123), institution(?x620, ?x4209), ?x1438 = 0512p >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ypc season 025ygws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.810 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #17910-07l8x PRED entity: 07l8x PRED relation: school PRED expected values: 0225v9 => 161 concepts (158 used for prediction) PRED predicted values (max 10 best out of 384): 01lnyf (0.60 #778, 0.33 #3272, 0.23 #13623), 065y4w7 (0.50 #12494, 0.50 #1255, 0.44 #3215), 012vwb (0.43 #1831, 0.39 #9501, 0.33 #9143), 0bx8pn (0.43 #1803, 0.33 #9651, 0.28 #9473), 01dzg0 (0.40 #870, 0.39 #9250, 0.29 #10856), 01tx9m (0.40 #810, 0.33 #1344, 0.28 #9726), 025v3k (0.40 #765, 0.33 #50, 0.22 #9681), 06fq2 (0.38 #7616, 0.38 #2977, 0.35 #13685), 07szy (0.33 #1265, 0.33 #16, 0.23 #20009), 0lyjf (0.33 #3279, 0.33 #70, 0.23 #23096) >> Best rule #778 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0512p; 01yjl; 05xvj; >> query: (?x7725, 01lnyf) <- category(?x7725, ?x134), team(?x2010, ?x7725), sport(?x7725, ?x5063), school(?x7725, ?x5621), ?x5621 = 01vs5c, ?x2010 = 02lyr4, ?x5063 = 018jz >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #23744 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 69 *> proper extension: 04cxw5b; *> query: (?x7725, ?x2497) <- team(?x261, ?x7725), draft(?x7725, ?x8499), colors(?x7725, ?x663), draft(?x4208, ?x8499), school(?x8499, ?x2497), school(?x4208, ?x2522) *> conf = 0.12 ranks of expected_values: 83 EVAL 07l8x school 0225v9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 161.000 158.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #17909-023g6w PRED entity: 023g6w PRED relation: film_release_region PRED expected values: 03rjj => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 17): 09c7w0 (0.06 #723, 0.06 #76, 0.06 #200), 0345h (0.05 #59, 0.04 #148, 0.04 #746), 0k6nt (0.04 #1268, 0.04 #148, 0.04 #746), 0d0vqn (0.04 #1268, 0.04 #148, 0.04 #746), 05b4w (0.04 #1268, 0.04 #148, 0.04 #746), 059j2 (0.04 #1268, 0.04 #148, 0.04 #746), 02vzc (0.04 #1268, 0.04 #148, 0.04 #746), 07ssc (0.04 #148, 0.04 #746, 0.03 #921), 0f8l9c (0.04 #148, 0.04 #746, 0.03 #921), 0jgd (0.03 #51, 0.02 #201, 0.02 #101) >> Best rule #723 for best value: >> intensional similarity = 5 >> extensional distance = 828 >> proper extension: 0170z3; 0b76d_m; 014_x2; 0ds35l9; 0d90m; 03qcfvw; 0g56t9t; 09sh8k; 034qmv; 02vxq9m; ... >> query: (?x8679, 09c7w0) <- film_crew_role(?x8679, ?x137), film(?x1324, ?x8679), genre(?x8679, ?x53), country(?x8679, ?x304), film_release_region(?x8679, ?x94) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 023g6w film_release_region 03rjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 66.000 0.060 http://example.org/film/film/runtime./film/film_cut/film_release_region #17908-01t8399 PRED entity: 01t8399 PRED relation: artists! PRED expected values: 05w3f => 114 concepts (55 used for prediction) PRED predicted values (max 10 best out of 284): 05bt6j (0.45 #348, 0.38 #1575, 0.30 #1882), 064t9 (0.42 #14132, 0.40 #16588, 0.38 #12291), 05w3f (0.37 #1876, 0.27 #4024, 0.25 #36), 016clz (0.36 #8600, 0.36 #13816, 0.36 #3993), 0mhfr (0.32 #942, 0.15 #1248, 0.13 #9542), 01fh36 (0.31 #1620, 0.22 #1927, 0.20 #2233), 0cx7f (0.31 #1670, 0.22 #1977, 0.14 #13948), 0155w (0.30 #1330, 0.27 #2558, 0.23 #3172), 059kh (0.30 #1888, 0.19 #1581, 0.17 #660), 09nwwf (0.29 #6884, 0.25 #1360, 0.18 #2588) >> Best rule #348 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0150jk; 0bk1p; 017mbb; 011xhx; >> query: (?x10744, 05bt6j) <- artists(?x2249, ?x10744), ?x2249 = 03lty, category(?x10744, ?x134), artist(?x7793, ?x10744), ?x7793 = 01dtcb >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1876 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 079kr; *> query: (?x10744, 05w3f) <- artists(?x2249, ?x10744), artists(?x2249, ?x8012), artist(?x5744, ?x10744), ?x8012 = 01wt4wc, ?x5744 = 01clyr *> conf = 0.37 ranks of expected_values: 3 EVAL 01t8399 artists! 05w3f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 55.000 0.455 http://example.org/music/genre/artists #17907-07c0j PRED entity: 07c0j PRED relation: group! PRED expected values: 03bx0bm => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 68): 03bx0bm (0.82 #286, 0.64 #2984, 0.60 #4551), 05148p4 (0.71 #2368, 0.71 #4371, 0.69 #2978), 0l14md (0.64 #2356, 0.60 #2617, 0.59 #2966), 018vs (0.64 #4365, 0.61 #2972, 0.61 #4539), 03qjg (0.41 #396, 0.37 #657, 0.32 #831), 05r5c (0.32 #878, 0.30 #2357, 0.29 #443), 04rzd (0.32 #641, 0.24 #380, 0.18 #815), 0l14qv (0.26 #614, 0.25 #2354, 0.24 #4357), 013y1f (0.25 #724, 0.24 #376, 0.21 #637), 06w7v (0.20 #769, 0.06 #2422, 0.06 #421) >> Best rule #286 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 02cpp; 017mbb; >> query: (?x1136, 03bx0bm) <- influenced_by(?x1136, ?x5442), award(?x1136, ?x724), group(?x227, ?x1136) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07c0j group! 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.824 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17906-01dpsv PRED entity: 01dpsv PRED relation: artists! PRED expected values: 06924p => 113 concepts (84 used for prediction) PRED predicted values (max 10 best out of 171): 06by7 (0.78 #5346, 0.62 #9104, 0.48 #23), 064t9 (0.56 #9095, 0.51 #640, 0.48 #1893), 06j6l (0.29 #988, 0.28 #1928, 0.28 #675), 025sc50 (0.29 #677, 0.26 #1930, 0.24 #990), 0glt670 (0.29 #980, 0.26 #1920, 0.25 #1606), 05bt6j (0.27 #5367, 0.26 #9125, 0.26 #670), 0gywn (0.26 #685, 0.22 #1938, 0.21 #2251), 02w4v (0.25 #45, 0.13 #671, 0.12 #2237), 0xhtw (0.24 #5341, 0.19 #12231, 0.18 #10038), 016clz (0.24 #5328, 0.23 #12218, 0.22 #10025) >> Best rule #5346 for best value: >> intensional similarity = 3 >> extensional distance = 498 >> proper extension: 02mq_y; 03_gx; >> query: (?x12659, 06by7) <- artists(?x2664, ?x12659), artists(?x2664, ?x5452), ?x5452 = 016s_5 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #179 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 112 *> proper extension: 04r1t; 07m4c; *> query: (?x12659, 06924p) <- artists(?x2664, ?x12659), artist(?x4868, ?x12659), ?x2664 = 01lyv *> conf = 0.18 ranks of expected_values: 14 EVAL 01dpsv artists! 06924p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 113.000 84.000 0.778 http://example.org/music/genre/artists #17905-0419kt PRED entity: 0419kt PRED relation: crewmember PRED expected values: 04ktcgn => 71 concepts (42 used for prediction) PRED predicted values (max 10 best out of 31): 0284n42 (0.07 #4, 0.04 #195, 0.03 #630), 04ktcgn (0.05 #12, 0.04 #203, 0.03 #398), 0b79gfg (0.04 #209, 0.03 #404, 0.03 #644), 051z6rz (0.04 #220, 0.02 #171, 0.02 #655), 0bbxx9b (0.04 #68, 0.03 #163, 0.03 #115), 04wp63 (0.04 #89, 0.02 #184, 0.02 #330), 0b6mgp_ (0.04 #69, 0.02 #164), 027y151 (0.03 #134, 0.03 #231, 0.02 #279), 095zvfg (0.03 #229, 0.02 #1381, 0.02 #664), 0794g (0.03 #1392, 0.03 #1875, 0.03 #1391) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 06krf3; >> query: (?x11372, 0284n42) <- nominated_for(?x350, ?x11372), film_release_distribution_medium(?x11372, ?x81), language(?x11372, ?x254), ?x350 = 05f4m9q >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 06krf3; *> query: (?x11372, 04ktcgn) <- nominated_for(?x350, ?x11372), film_release_distribution_medium(?x11372, ?x81), language(?x11372, ?x254), ?x350 = 05f4m9q *> conf = 0.05 ranks of expected_values: 2 EVAL 0419kt crewmember 04ktcgn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 42.000 0.066 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #17904-0bqdvt PRED entity: 0bqdvt PRED relation: award_nominee! PRED expected values: 01kb2j => 100 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1090): 016vg8 (0.84 #2327, 0.83 #27927, 0.82 #2328), 0187y5 (0.84 #2327, 0.83 #27927, 0.82 #2328), 01kb2j (0.37 #3529, 0.29 #46540, 0.29 #62835), 0h0wc (0.32 #2876, 0.21 #5203, 0.16 #6985), 0bqdvt (0.29 #46540, 0.25 #74470, 0.25 #76799), 07r1h (0.29 #46540, 0.25 #74470, 0.25 #76799), 01tcf7 (0.29 #46540, 0.25 #74470, 0.25 #76799), 01pgzn_ (0.29 #62835, 0.25 #74470, 0.25 #76799), 0hvb2 (0.29 #62835, 0.25 #74470, 0.25 #76799), 015t56 (0.29 #62835, 0.25 #74470, 0.25 #76799) >> Best rule #2327 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 02x0bdb; >> query: (?x4509, ?x192) <- award_nominee(?x4509, ?x4662), award_nominee(?x4509, ?x192), profession(?x4509, ?x1032), participant(?x262, ?x4662), people(?x268, ?x4509) >> conf = 0.84 => this is the best rule for 2 predicted values *> Best rule #3529 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 05ty4m; 017149; 0187y5; 01yb09; 07vc_9; 04t7ts; 02bkdn; 01gq0b; 02wgln; 05dbf; ... *> query: (?x4509, 01kb2j) <- award_nominee(?x4509, ?x2353), profession(?x4509, ?x1032), award(?x4509, ?x704), ?x2353 = 02qgyv *> conf = 0.37 ranks of expected_values: 3 EVAL 0bqdvt award_nominee! 01kb2j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 100.000 33.000 0.840 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17903-044k8 PRED entity: 044k8 PRED relation: influenced_by PRED expected values: 012vd6 => 174 concepts (97 used for prediction) PRED predicted values (max 10 best out of 237): 01vsy3q (0.33 #5256, 0.27 #5255, 0.21 #9204), 01w9ph_ (0.33 #5256, 0.27 #5255, 0.21 #9204), 03sbs (0.19 #5041, 0.13 #5919, 0.11 #8989), 0lrh (0.17 #9205, 0.08 #2262, 0.08 #950), 01rgr (0.17 #9205, 0.08 #5142, 0.08 #1200), 01w60_p (0.17 #9205, 0.08 #933, 0.07 #2684), 03f70xs (0.17 #9205, 0.07 #4012, 0.06 #4887), 041mt (0.17 #9205, 0.04 #4439, 0.04 #4876), 0hky (0.17 #9205, 0.04 #5012, 0.04 #5890), 019z7q (0.17 #9205, 0.03 #2644, 0.02 #4398) >> Best rule #5256 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 0399p; >> query: (?x4608, ?x4873) <- peers(?x4608, ?x4873), place_of_death(?x4608, ?x1523), influenced_by(?x2835, ?x4873) >> conf = 0.33 => this is the best rule for 2 predicted values *> Best rule #11565 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 115 *> proper extension: 01q_ph; *> query: (?x4608, 012vd6) <- people(?x3584, ?x4608), artist(?x3265, ?x4608), award_winner(?x4608, ?x4609) *> conf = 0.05 ranks of expected_values: 65 EVAL 044k8 influenced_by 012vd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 174.000 97.000 0.333 http://example.org/influence/influence_node/influenced_by #17902-011yd2 PRED entity: 011yd2 PRED relation: production_companies PRED expected values: 04rtpt => 84 concepts (49 used for prediction) PRED predicted values (max 10 best out of 69): 016tt2 (0.33 #85, 0.17 #166, 0.14 #331), 024rgt (0.33 #24, 0.11 #270, 0.04 #2636), 01gb54 (0.17 #363, 0.11 #198, 0.10 #690), 0kx4m (0.17 #336, 0.08 #663, 0.08 #745), 05qd_ (0.14 #746, 0.14 #1477, 0.13 #664), 086k8 (0.12 #2614, 0.12 #3594, 0.11 #3675), 030_1_ (0.10 #343, 0.06 #507, 0.06 #262), 054lpb6 (0.09 #1894, 0.08 #1237, 0.08 #2301), 017s11 (0.08 #3595, 0.08 #1470, 0.08 #3676), 0kk9v (0.07 #360, 0.06 #195, 0.03 #1256) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01f8hf; >> query: (?x2215, 016tt2) <- nominated_for(?x2215, ?x394), nominated_for(?x2214, ?x2215), ?x2214 = 02cyfz, honored_for(?x6861, ?x2215) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1189 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 152 *> proper extension: 01q2nx; *> query: (?x2215, 04rtpt) <- film_format(?x2215, ?x909), film(?x496, ?x2215), film_crew_role(?x2215, ?x137), ?x909 = 07fb8_ *> conf = 0.03 ranks of expected_values: 49 EVAL 011yd2 production_companies 04rtpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 84.000 49.000 0.333 http://example.org/film/film/production_companies #17901-01y8cr PRED entity: 01y8cr PRED relation: place_of_birth PRED expected values: 01_d4 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 110): 03b12 (0.29 #32417, 0.28 #38052, 0.27 #76087), 06n8j (0.25 #599), 02_286 (0.10 #10591, 0.10 #4952, 0.10 #8477), 0cr3d (0.09 #7142, 0.08 #4323, 0.07 #5732), 02dtg (0.07 #715, 0.04 #7058, 0.03 #9877), 0100mt (0.07 #994, 0.02 #3813), 015zxh (0.07 #764, 0.02 #12040, 0.02 #12745), 0dyl9 (0.07 #927, 0.01 #10794), 0tz1x (0.07 #818), 0rh6k (0.05 #2116, 0.04 #4935, 0.04 #5640) >> Best rule #32417 for best value: >> intensional similarity = 3 >> extensional distance = 690 >> proper extension: 026lj; 05yvfd; 01xwqn; >> query: (?x4279, ?x10584) <- people(?x4195, ?x4279), location(?x4279, ?x10584), category(?x10584, ?x134) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #11342 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 0436zq; *> query: (?x4279, 01_d4) <- film(?x4279, ?x4280), nationality(?x4279, ?x94), people(?x9771, ?x4279), film_art_direction_by(?x4280, ?x199) *> conf = 0.04 ranks of expected_values: 12 EVAL 01y8cr place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 126.000 126.000 0.295 http://example.org/people/person/place_of_birth #17900-07x4qr PRED entity: 07x4qr PRED relation: film! PRED expected values: 08hsww => 58 concepts (25 used for prediction) PRED predicted values (max 10 best out of 920): 086nl7 (0.26 #7001, 0.23 #9073, 0.21 #4928), 019803 (0.25 #3999, 0.03 #12288), 02k21g (0.24 #4936, 0.19 #7009, 0.18 #9081), 0fby2t (0.21 #4896, 0.17 #6969, 0.16 #9041), 08vr94 (0.18 #8963, 0.15 #4818, 0.14 #6891), 05txrz (0.18 #4908, 0.14 #6981, 0.11 #9053), 02_p5w (0.17 #2715, 0.09 #11004, 0.02 #21359), 016ypb (0.17 #2568, 0.03 #12928, 0.03 #14999), 03dn9v (0.17 #3899, 0.03 #18401, 0.03 #14259), 083wr9 (0.17 #4116, 0.03 #14476, 0.02 #18618) >> Best rule #7001 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0bscw; 02xtxw; 02_sr1; 03cyslc; 02825kb; 0gldyz; >> query: (?x2512, 086nl7) <- film(?x905, ?x2512), cast_members(?x906, ?x905), genre(?x2512, ?x258), film_crew_role(?x2512, ?x468) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #4984 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 07kb7vh; *> query: (?x2512, 08hsww) <- film(?x7598, ?x2512), film(?x905, ?x2512), cast_members(?x906, ?x905), production_companies(?x2512, ?x541), award_nominee(?x7598, ?x3224) *> conf = 0.06 ranks of expected_values: 140 EVAL 07x4qr film! 08hsww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 58.000 25.000 0.262 http://example.org/film/actor/film./film/performance/film #17899-0bx6zs PRED entity: 0bx6zs PRED relation: ceremony! PRED expected values: 09qvf4 07kjk7c => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 315): 09qvf4 (0.75 #1877, 0.70 #2373, 0.64 #2621), 0gqy2 (0.69 #5069, 0.65 #5565, 0.59 #5813), 0k611 (0.64 #5022, 0.60 #5518, 0.57 #5766), 0gqwc (0.64 #5009, 0.60 #5505, 0.57 #5753), 0gq_d (0.64 #5105, 0.60 #5601, 0.57 #5849), 07kjk7c (0.62 #1925, 0.60 #2421, 0.60 #2174), 0gvx_ (0.62 #5083, 0.59 #5579, 0.56 #5827), 018wng (0.61 #4984, 0.57 #5480, 0.55 #5728), 0gqyl (0.61 #5030, 0.57 #5526, 0.55 #5774), 0f4x7 (0.61 #4975, 0.57 #5471, 0.55 #5719) >> Best rule #1877 for best value: >> intensional similarity = 15 >> extensional distance = 6 >> proper extension: 0gvstc3; 02q690_; 07y9ts; >> query: (?x9450, 09qvf4) <- award_winner(?x9450, ?x8596), award_winner(?x9450, ?x7189), award_winner(?x9450, ?x832), ceremony(?x7510, ?x9450), ceremony(?x3184, ?x9450), ceremony(?x435, ?x9450), ?x435 = 0bp_b2, ?x3184 = 0gkts9, ?x7510 = 027gs1_, place_of_birth(?x8596, ?x10718), nationality(?x832, ?x94), award_winner(?x829, ?x832), award(?x8596, ?x594), award_nominee(?x7189, ?x2135), honored_for(?x9450, ?x337) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 0bx6zs ceremony! 07kjk7c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 28.000 28.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bx6zs ceremony! 09qvf4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 28.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony #17898-016yzz PRED entity: 016yzz PRED relation: profession PRED expected values: 02hrh1q => 98 concepts (69 used for prediction) PRED predicted values (max 10 best out of 64): 02hrh1q (0.89 #9812, 0.89 #8660, 0.88 #7794), 09jwl (0.70 #1457, 0.68 #3475, 0.68 #3763), 01d_h8 (0.51 #438, 0.50 #294, 0.50 #150), 0nbcg (0.50 #1468, 0.47 #3774, 0.46 #3486), 018gz8 (0.49 #159, 0.47 #591, 0.46 #303), 03gjzk (0.41 #301, 0.40 #589, 0.40 #13), 0dz3r (0.41 #3460, 0.40 #3748, 0.40 #4325), 016z4k (0.38 #1444, 0.34 #3750, 0.34 #3462), 05z96 (0.34 #2017, 0.31 #3314, 0.30 #4613), 0q04f (0.34 #2017, 0.31 #3314, 0.30 #4613) >> Best rule #9812 for best value: >> intensional similarity = 2 >> extensional distance = 2012 >> proper extension: 064nh4k; 01k5t_3; 04y79_n; 01l2fn; 0pyg6; 04kj2v; 043kzcr; 0347xl; 07cjqy; 01y9xg; ... >> query: (?x3980, 02hrh1q) <- film(?x3980, ?x1586), profession(?x3980, ?x353) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016yzz profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 69.000 0.892 http://example.org/people/person/profession #17897-0f2r6 PRED entity: 0f2r6 PRED relation: locations! PRED expected values: 0bzrxn => 206 concepts (188 used for prediction) PRED predicted values (max 10 best out of 111): 0b_6qj (0.33 #1026, 0.28 #545, 0.24 #1746), 0b_6zk (0.29 #991, 0.28 #510, 0.24 #1711), 0bzrsh (0.25 #1037, 0.24 #1757, 0.23 #4161), 0b_6_l (0.24 #2621, 0.21 #1060, 0.20 #3343), 0b_75k (0.24 #2569, 0.21 #1008, 0.20 #2449), 0b_6jz (0.23 #5799, 0.20 #14333, 0.19 #1955), 0b_6v_ (0.23 #1985, 0.20 #14333, 0.19 #663), 0b_6xf (0.22 #2622, 0.20 #2502, 0.20 #1181), 0bzrxn (0.20 #3297, 0.20 #1134, 0.20 #14333), 0b_6h7 (0.20 #14333, 0.18 #14578, 0.17 #13610) >> Best rule #1026 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 04gxf; >> query: (?x674, 0b_6qj) <- locations(?x4368, ?x674), teams(?x674, ?x11420), team(?x4368, ?x11789), ?x11789 = 02pyyld >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3297 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 0pc6x; 0qpsn; *> query: (?x674, 0bzrxn) <- locations(?x5897, ?x674), citytown(?x2760, ?x674), category(?x674, ?x134), instance_of_recurring_event(?x5897, ?x10863) *> conf = 0.20 ranks of expected_values: 9 EVAL 0f2r6 locations! 0bzrxn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 206.000 188.000 0.333 http://example.org/time/event/locations #17896-01n44c PRED entity: 01n44c PRED relation: artists! PRED expected values: 064t9 => 151 concepts (87 used for prediction) PRED predicted values (max 10 best out of 233): 064t9 (0.62 #13120, 0.59 #2197, 0.57 #6565), 06by7 (0.53 #18123, 0.48 #3142, 0.47 #2206), 0gywn (0.51 #10044, 0.33 #15975, 0.31 #13167), 06j6l (0.48 #10034, 0.35 #2234, 0.34 #11908), 0glt670 (0.43 #10026, 0.34 #9090, 0.32 #16581), 025sc50 (0.37 #10036, 0.35 #2236, 0.34 #13159), 016clz (0.30 #629, 0.28 #3125, 0.25 #1565), 0xhtw (0.30 #641, 0.26 #18118, 0.20 #21865), 05r6t (0.30 #708, 0.12 #1644, 0.10 #18185), 0ggx5q (0.27 #13188, 0.25 #81, 0.25 #11939) >> Best rule #13120 for best value: >> intensional similarity = 4 >> extensional distance = 144 >> proper extension: 03yf3z; 094xh; 01l79yc; 0134wr; 012x1l; >> query: (?x5181, 064t9) <- artists(?x505, ?x5181), award(?x5181, ?x4796), gender(?x5181, ?x514), ?x514 = 02zsn >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n44c artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 87.000 0.623 http://example.org/music/genre/artists #17895-0pkr1 PRED entity: 0pkr1 PRED relation: award PRED expected values: 09v92_x => 96 concepts (83 used for prediction) PRED predicted values (max 10 best out of 290): 09v51c2 (0.33 #325, 0.16 #2431, 0.13 #22682), 09v92_x (0.33 #279, 0.13 #22682, 0.13 #26330), 09v8db5 (0.33 #253, 0.13 #22682, 0.13 #26330), 09sb52 (0.31 #10977, 0.30 #8952, 0.27 #11382), 0gs9p (0.24 #4131, 0.19 #3726, 0.16 #5751), 019f4v (0.22 #4118, 0.18 #3713, 0.17 #7358), 040njc (0.20 #4059, 0.16 #7299, 0.15 #3654), 01by1l (0.18 #9429, 0.15 #13074, 0.11 #13884), 0gq9h (0.18 #4129, 0.15 #3724, 0.12 #7369), 0gr51 (0.17 #4152, 0.11 #8202, 0.10 #8607) >> Best rule #325 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0pksh; >> query: (?x10695, 09v51c2) <- type_of_union(?x10695, ?x566), nationality(?x10695, ?x2346), award(?x10695, ?x5039), ?x2346 = 0d05w3 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #279 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0pksh; *> query: (?x10695, 09v92_x) <- type_of_union(?x10695, ?x566), nationality(?x10695, ?x2346), award(?x10695, ?x5039), ?x2346 = 0d05w3 *> conf = 0.33 ranks of expected_values: 2 EVAL 0pkr1 award 09v92_x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 83.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17894-01w724 PRED entity: 01w724 PRED relation: people! PRED expected values: 0x67 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 35): 0x67 (0.28 #1781, 0.25 #10, 0.25 #2551), 041rx (0.15 #543, 0.14 #697, 0.14 #620), 02g7sp (0.08 #172, 0.03 #249, 0.03 #480), 013b6_ (0.08 #207, 0.01 #361, 0.01 #1362), 033tf_ (0.07 #3318, 0.06 #4165, 0.06 #5089), 02w7gg (0.06 #3159, 0.05 #4160, 0.05 #2466), 07bch9 (0.04 #408, 0.03 #562, 0.03 #3642), 07hwkr (0.04 #1552, 0.03 #3554, 0.03 #2168), 0xnvg (0.04 #3324, 0.04 #1091, 0.04 #783), 013xrm (0.04 #328, 0.03 #3639, 0.02 #3562) >> Best rule #1781 for best value: >> intensional similarity = 3 >> extensional distance = 335 >> proper extension: 01vw917; >> query: (?x2765, 0x67) <- award_nominee(?x1089, ?x2765), artist(?x7448, ?x2765), nationality(?x2765, ?x94) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w724 people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.279 http://example.org/people/ethnicity/people #17893-041bnw PRED entity: 041bnw PRED relation: state_province_region PRED expected values: 01n7q => 90 concepts (80 used for prediction) PRED predicted values (max 10 best out of 35): 01n7q (0.69 #1135, 0.67 #390, 0.42 #4839), 059rby (0.47 #996, 0.42 #5689, 0.42 #499), 09c7w0 (0.33 #9789, 0.26 #9790, 0.24 #7547), 0kpys (0.26 #9790, 0.24 #7547, 0.23 #7298), 03v0t (0.09 #4874, 0.06 #5738, 0.05 #6481), 0d0x8 (0.09 #4865, 0.05 #5481, 0.05 #5976), 081yw (0.07 #804, 0.04 #1547, 0.04 #1672), 0r00l (0.06 #4821, 0.04 #5685, 0.03 #7548), 05k7sb (0.05 #4852, 0.05 #8200, 0.04 #6708), 0rh6k (0.05 #4698, 0.03 #5562, 0.02 #5809) >> Best rule #1135 for best value: >> intensional similarity = 2 >> extensional distance = 14 >> proper extension: 054g1r; 04mkft; 032dg7; 093h7p; 02w_l9; 07733f; 0d8c4; >> query: (?x9671, 01n7q) <- citytown(?x9671, ?x11930), ?x11930 = 0r00l >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 041bnw state_province_region 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 80.000 0.688 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #17892-05sxg2 PRED entity: 05sxg2 PRED relation: profession! PRED expected values: 0b13g7 026g4l_ 0b7xl8 => 33 concepts (15 used for prediction) PRED predicted values (max 10 best out of 4174): 04g3p5 (0.71 #14144, 0.50 #9921, 0.50 #5697), 01_x6v (0.71 #13344, 0.50 #4897, 0.45 #21118), 09px1w (0.71 #15329, 0.50 #6882, 0.33 #19554), 026dx (0.71 #14172, 0.50 #5725, 0.33 #18397), 05wm88 (0.71 #16471, 0.50 #8024, 0.33 #3801), 021yw7 (0.71 #13770, 0.50 #5323, 0.33 #1100), 01xndd (0.71 #13910, 0.50 #5463, 0.33 #1240), 02b29 (0.71 #14900, 0.50 #6453, 0.33 #2230), 015pxr (0.71 #13268, 0.50 #4821, 0.33 #598), 015njf (0.71 #14208, 0.50 #5761, 0.33 #1538) >> Best rule #14144 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02jknp; 0dxtg; 02krf9; >> query: (?x106, 04g3p5) <- profession(?x3170, ?x106), award_winner(?x3105, ?x3170), award_nominee(?x164, ?x3170), ?x164 = 0l6qt >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5285 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 03gjzk; *> query: (?x106, 0b13g7) <- profession(?x3170, ?x106), award_winner(?x3105, ?x3170), award_nominee(?x164, ?x3170), ?x164 = 0l6qt, award_winner(?x10337, ?x3170) *> conf = 0.50 ranks of expected_values: 343, 364, 365 EVAL 05sxg2 profession! 0b7xl8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 33.000 15.000 0.714 http://example.org/people/person/profession EVAL 05sxg2 profession! 026g4l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 33.000 15.000 0.714 http://example.org/people/person/profession EVAL 05sxg2 profession! 0b13g7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 33.000 15.000 0.714 http://example.org/people/person/profession #17891-0gdqy PRED entity: 0gdqy PRED relation: nationality PRED expected values: 06mzp => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 40): 09c7w0 (0.82 #3179, 0.79 #4575, 0.79 #12436), 02jx1 (0.56 #9248, 0.27 #1718, 0.16 #2114), 07ssc (0.56 #9248, 0.20 #15, 0.18 #1701), 05qhw (0.56 #9248, 0.03 #1204), 0345h (0.22 #626, 0.20 #1022, 0.20 #725), 0d05w3 (0.22 #545, 0.14 #1140, 0.12 #445), 03_3d (0.17 #105, 0.11 #602, 0.10 #701), 05b4w (0.17 #149, 0.11 #646, 0.10 #745), 0chghy (0.12 #406, 0.11 #506, 0.06 #1597), 03h64 (0.12 #448, 0.11 #548, 0.05 #1143) >> Best rule #3179 for best value: >> intensional similarity = 4 >> extensional distance = 356 >> proper extension: 0288fyj; >> query: (?x10354, 09c7w0) <- place_of_birth(?x10354, ?x4627), award_winner(?x11779, ?x10354), award_winner(?x10354, ?x1365), jurisdiction_of_office(?x1195, ?x4627) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1013 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 01h2_6; *> query: (?x10354, 06mzp) <- place_of_birth(?x10354, ?x4627), peers(?x10354, ?x8043), people(?x6734, ?x10354), student(?x12823, ?x10354) *> conf = 0.05 ranks of expected_values: 19 EVAL 0gdqy nationality 06mzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 133.000 133.000 0.824 http://example.org/people/person/nationality #17890-03f22dp PRED entity: 03f22dp PRED relation: nationality PRED expected values: 03rk0 => 132 concepts (128 used for prediction) PRED predicted values (max 10 best out of 34): 03rk0 (0.89 #346, 0.84 #746, 0.83 #646), 09c7w0 (0.78 #3806, 0.77 #4312, 0.76 #401), 0cvw9 (0.29 #7622, 0.27 #1301, 0.25 #6420), 086g2 (0.27 #1301, 0.25 #6420, 0.25 #8723), 02jx1 (0.11 #1435, 0.11 #9225, 0.11 #2737), 07ssc (0.11 #9225, 0.10 #2719, 0.10 #9426), 05sb1 (0.10 #9426, 0.06 #48, 0.02 #348), 0f8l9c (0.06 #1022, 0.04 #1624, 0.04 #3626), 0345h (0.05 #1633, 0.04 #1031, 0.04 #2334), 0d060g (0.05 #1107, 0.04 #6226, 0.04 #6727) >> Best rule #346 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 07yw6t; >> query: (?x12200, 03rk0) <- profession(?x12200, ?x319), gender(?x12200, ?x231), award(?x12200, ?x4687), ?x4687 = 03rbj2 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f22dp nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 128.000 0.886 http://example.org/people/person/nationality #17889-06sw9 PRED entity: 06sw9 PRED relation: official_language PRED expected values: 02h40lc => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 43): 02h40lc (0.50 #88, 0.47 #1206, 0.43 #647), 06nm1 (0.22 #8, 0.19 #94, 0.16 #739), 0jzc (0.14 #143, 0.14 #315, 0.13 #186), 04306rv (0.09 #48, 0.09 #478, 0.07 #521), 05zjd (0.09 #148, 0.08 #363, 0.07 #449), 0349s (0.07 #33, 0.06 #119, 0.04 #248), 071fb (0.07 #141, 0.07 #184, 0.05 #313), 02bjrlw (0.06 #44, 0.04 #388, 0.04 #474), 0653m (0.04 #396, 0.04 #482, 0.04 #525), 02bv9 (0.04 #493, 0.03 #63, 0.03 #407) >> Best rule #88 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 07fr_; >> query: (?x5680, 02h40lc) <- location_of_ceremony(?x566, ?x5680), official_language(?x5680, ?x5607), ?x566 = 04ztj >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06sw9 official_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.500 http://example.org/location/country/official_language #17888-0prjs PRED entity: 0prjs PRED relation: people! PRED expected values: 0d7wh => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 61): 041rx (0.25 #3429, 0.25 #80, 0.23 #2895), 033tf_ (0.17 #83, 0.15 #463, 0.14 #3279), 07bch9 (0.17 #327, 0.14 #251, 0.08 #479), 01qhm_ (0.17 #82, 0.07 #310, 0.06 #158), 0x67 (0.16 #6705, 0.16 #4423, 0.15 #3282), 02w7gg (0.16 #382, 0.12 #2206, 0.11 #1826), 0dryh9k (0.15 #2220, 0.07 #2907, 0.05 #4734), 0xnvg (0.12 #317, 0.09 #3285, 0.08 #89), 07hwkr (0.12 #848, 0.11 #240, 0.10 #696), 09vc4s (0.10 #313, 0.08 #85, 0.05 #465) >> Best rule #3429 for best value: >> intensional similarity = 3 >> extensional distance = 806 >> proper extension: 0j3v; 02ln1; >> query: (?x1371, 041rx) <- nationality(?x1371, ?x4071), people(?x7322, ?x1371), student(?x2486, ?x1371) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #397 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 60 *> proper extension: 01k7d9; 0337vz; 01j5ts; 0159h6; 0134w7; 019_1h; 0f0p0; 01tcf7; 030znt; 045c66; ... *> query: (?x1371, 0d7wh) <- film(?x1371, ?x9142), film(?x1371, ?x1372), place_of_birth(?x1371, ?x12190), film(?x9681, ?x1372), genre(?x9142, ?x53) *> conf = 0.05 ranks of expected_values: 21 EVAL 0prjs people! 0d7wh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 131.000 131.000 0.254 http://example.org/people/ethnicity/people #17887-0khth PRED entity: 0khth PRED relation: group! PRED expected values: 05r5c 06w87 03bx0bm => 135 concepts (122 used for prediction) PRED predicted values (max 10 best out of 105): 05r5c (0.74 #396, 0.27 #942, 0.26 #864), 018vs (0.69 #89, 0.69 #1261, 0.67 #1183), 03bx0bm (0.66 #1194, 0.64 #959, 0.64 #1428), 028tv0 (0.41 #1103, 0.40 #1260, 0.39 #1182), 042v_gx (0.38 #84, 0.21 #397, 0.21 #157), 07y_7 (0.23 #79, 0.21 #157, 0.14 #1877), 02k84w (0.23 #105, 0.21 #157, 0.14 #1877), 0mkg (0.23 #86, 0.16 #399, 0.11 #477), 02sgy (0.21 #395, 0.21 #157, 0.15 #82), 06ncr (0.21 #424, 0.17 #1126, 0.16 #502) >> Best rule #396 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 01qqwp9; 02t3ln; >> query: (?x4715, 05r5c) <- category(?x4715, ?x134), group(?x1495, ?x4715), artists(?x302, ?x4715), ?x1495 = 013y1f >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 45 EVAL 0khth group! 03bx0bm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 122.000 0.737 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0khth group! 06w87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 135.000 122.000 0.737 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0khth group! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 122.000 0.737 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17886-043zg PRED entity: 043zg PRED relation: nationality PRED expected values: 09c7w0 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 32): 09c7w0 (0.79 #501, 0.79 #901, 0.78 #3504), 03rk0 (0.15 #1947, 0.08 #6156, 0.07 #4149), 03rjj (0.12 #5, 0.05 #3208, 0.04 #1206), 0chghy (0.12 #10, 0.04 #110, 0.02 #4715), 03_3d (0.12 #1907, 0.03 #2307, 0.02 #2007), 02jx1 (0.12 #2334, 0.12 #5239, 0.11 #2034), 07ssc (0.09 #6125, 0.09 #2617, 0.09 #7226), 0d060g (0.05 #1107, 0.05 #4210, 0.05 #607), 01531 (0.05 #3203, 0.01 #1201), 0345h (0.04 #331, 0.04 #431, 0.03 #4134) >> Best rule #501 for best value: >> intensional similarity = 2 >> extensional distance = 56 >> proper extension: 0fwy0h; >> query: (?x5364, 09c7w0) <- program(?x5364, ?x9788), award_nominee(?x5364, ?x286) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043zg nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.793 http://example.org/people/person/nationality #17885-051q39 PRED entity: 051q39 PRED relation: nationality PRED expected values: 06mkj => 70 concepts (60 used for prediction) PRED predicted values (max 10 best out of 110): 09c7w0 (0.79 #1013, 0.74 #913, 0.70 #810), 07ssc (0.36 #502, 0.29 #416, 0.18 #721), 05qhw (0.36 #502, 0.08 #911, 0.06 #503), 0hzlz (0.33 #23, 0.25 #223, 0.25 #123), 06mzp (0.33 #21, 0.25 #221, 0.25 #121), 03rk0 (0.28 #809, 0.14 #549, 0.10 #1834), 0d060g (0.28 #809, 0.14 #510, 0.10 #1834), 02jx1 (0.25 #1146, 0.24 #1248, 0.18 #1662), 077qn (0.25 #259, 0.17 #359, 0.14 #562), 06m_5 (0.14 #484, 0.09 #789, 0.01 #1627) >> Best rule #1013 for best value: >> intensional similarity = 12 >> extensional distance = 45 >> proper extension: 01pj3h; >> query: (?x13558, 09c7w0) <- athlete(?x1557, ?x13558), profession(?x13558, ?x1581), profession(?x13842, ?x1581), profession(?x8206, ?x1581), profession(?x7732, ?x1581), profession(?x2715, ?x1581), specialization_of(?x63, ?x1581), ?x2715 = 01fwk3, ?x13842 = 095nx, people(?x2510, ?x7732), film(?x7732, ?x6206), nationality(?x8206, ?x94) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #911 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 18 *> proper extension: 0cymln; *> query: (?x13558, ?x2146) <- athlete(?x1557, ?x13558), profession(?x13558, ?x1581), ?x1581 = 01445t, sports(?x358, ?x1557), country(?x1557, ?x2146), film_release_region(?x80, ?x2146), nationality(?x111, ?x2146), country(?x2446, ?x2146), geographic_distribution(?x9347, ?x2146) *> conf = 0.08 ranks of expected_values: 18 EVAL 051q39 nationality 06mkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 70.000 60.000 0.787 http://example.org/people/person/nationality #17884-022fj_ PRED entity: 022fj_ PRED relation: state_province_region PRED expected values: 02xry => 165 concepts (140 used for prediction) PRED predicted values (max 10 best out of 88): 02xry (0.69 #3580, 0.46 #12496, 0.30 #8163), 01n7q (0.51 #1254, 0.34 #4835, 0.28 #3474), 059rby (0.32 #5441, 0.31 #8907, 0.29 #9654), 09c7w0 (0.30 #8163, 0.30 #7668, 0.30 #7667), 07h34 (0.17 #298, 0.12 #916, 0.04 #4126), 05k7sb (0.15 #895, 0.11 #3240, 0.10 #771), 03v0t (0.12 #2030, 0.11 #176, 0.10 #1783), 0d0x8 (0.11 #167, 0.09 #3500, 0.07 #4367), 04ly1 (0.11 #178, 0.05 #2772, 0.05 #1167), 04ykg (0.11 #142, 0.04 #389, 0.04 #1378) >> Best rule #3580 for best value: >> intensional similarity = 6 >> extensional distance = 108 >> proper extension: 04qhdf; 01s73z; 0z90c; 01prf3; 0ljc_; 012b30; 0gmf0nj; 02np2n; 07733f; >> query: (?x9022, ?x2623) <- citytown(?x9022, ?x3501), origin(?x4995, ?x3501), time_zones(?x3501, ?x2674), location(?x4476, ?x3501), month(?x3501, ?x1459), state(?x3501, ?x2623) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022fj_ state_province_region 02xry CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 140.000 0.688 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #17883-01twmp PRED entity: 01twmp PRED relation: profession PRED expected values: 018gz8 => 58 concepts (54 used for prediction) PRED predicted values (max 10 best out of 70): 0nbcg (0.61 #177, 0.31 #324, 0.22 #1794), 016z4k (0.56 #151, 0.26 #298, 0.13 #592), 01d_h8 (0.52 #2358, 0.52 #2211, 0.49 #2946), 02jknp (0.46 #2360, 0.44 #2213, 0.42 #3389), 0cbd2 (0.40 #301, 0.22 #2800, 0.22 #2947), 0kyk (0.40 #322, 0.21 #910, 0.19 #616), 03gjzk (0.38 #2219, 0.37 #2807, 0.37 #2954), 039v1 (0.33 #182, 0.17 #329, 0.09 #1799), 0n1h (0.28 #159, 0.17 #306, 0.08 #1188), 018gz8 (0.23 #16, 0.18 #2368, 0.17 #3397) >> Best rule #177 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01vvycq; >> query: (?x9662, 0nbcg) <- notable_people_with_this_condition(?x6720, ?x9662), profession(?x9662, ?x1183), ?x1183 = 09jwl, location(?x9662, ?x10718) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0p_2r; 028d4v; 0227tr; 03_6y; 02xv8m; 01trf3; 0315q3; 070yzk; 03dn9v; *> query: (?x9662, 018gz8) <- people(?x5606, ?x9662), nationality(?x9662, ?x94), place_of_birth(?x9662, ?x10718), ?x5606 = 0g8_vp *> conf = 0.23 ranks of expected_values: 10 EVAL 01twmp profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 58.000 54.000 0.611 http://example.org/people/person/profession #17882-01pcq3 PRED entity: 01pcq3 PRED relation: award_winner! PRED expected values: 073h1t => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 118): 09qvms (0.41 #289, 0.08 #3049, 0.06 #1945), 0fqpc7d (0.14 #174, 0.08 #450, 0.07 #588), 02glmx (0.10 #80, 0.01 #4496), 03nnm4t (0.09 #211, 0.08 #487, 0.07 #625), 09g90vz (0.09 #259, 0.05 #3985, 0.05 #535), 05c1t6z (0.09 #153, 0.05 #429, 0.05 #567), 0g55tzk (0.09 #272, 0.05 #548, 0.05 #686), 058m5m4 (0.09 #192, 0.05 #606, 0.04 #3090), 09qftb (0.06 #939, 0.05 #801, 0.05 #249), 09p3h7 (0.06 #898, 0.05 #70, 0.03 #2002) >> Best rule #289 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 035gjq; 04mz10g; 04myfb7; 01541z; 0443y3; 05lb65; 06w2yp9; 06hgym; >> query: (?x843, 09qvms) <- award_nominee(?x1116, ?x843), award(?x843, ?x375), ?x1116 = 06b0d2 >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 19 *> proper extension: 07zr66; *> query: (?x843, 073h1t) <- nationality(?x843, ?x1023), ?x1023 = 0ctw_b *> conf = 0.05 ranks of expected_values: 22 EVAL 01pcq3 award_winner! 073h1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 96.000 96.000 0.407 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17881-02vr3gz PRED entity: 02vr3gz PRED relation: titles! PRED expected values: 06nm1 => 83 concepts (23 used for prediction) PRED predicted values (max 10 best out of 60): 07ssc (0.83 #1336, 0.17 #10, 0.14 #417), 06nm1 (0.60 #136, 0.17 #35, 0.03 #442), 07s9rl0 (0.43 #1427, 0.37 #2143, 0.37 #2042), 04xvlr (0.33 #1330, 0.23 #2045, 0.21 #718), 02n4kr (0.31 #816, 0.31 #728, 0.28 #815), 03npn (0.28 #815, 0.22 #2142, 0.20 #508), 01hmnh (0.24 #229, 0.22 #331, 0.20 #535), 024qqx (0.24 #282, 0.14 #1304, 0.13 #588), 09blyk (0.23 #759, 0.12 #248, 0.11 #1881), 01z4y (0.18 #2177, 0.14 #2279, 0.12 #1157) >> Best rule #1336 for best value: >> intensional similarity = 6 >> extensional distance = 160 >> proper extension: 01cjhz; 08cx5g; 03j63k; 0jq2r; 02qr46y; 06f0k; >> query: (?x3757, 07ssc) <- titles(?x2152, ?x3757), film_release_region(?x5496, ?x2152), film_release_region(?x1642, ?x2152), ?x1642 = 0bq8tmw, ?x5496 = 07l50vn, form_of_government(?x2152, ?x1926) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #136 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 0cvkv5; *> query: (?x3757, 06nm1) <- genre(?x3757, ?x53), ?x53 = 07s9rl0, nominated_for(?x4695, ?x3757), film_crew_role(?x3757, ?x137), ?x4695 = 0fm3b5 *> conf = 0.60 ranks of expected_values: 2 EVAL 02vr3gz titles! 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 23.000 0.833 http://example.org/media_common/netflix_genre/titles #17880-0pz6q PRED entity: 0pz6q PRED relation: currency PRED expected values: 02l6h => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.67 #91, 0.66 #205, 0.66 #229), 02l6h (0.27 #16, 0.18 #385, 0.14 #10), 01nv4h (0.22 #20, 0.20 #50, 0.18 #385), 0ptk_ (0.18 #385, 0.09 #21, 0.07 #33), 0kz1h (0.18 #385, 0.01 #131, 0.01 #480) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 02kth6; 04rwx; 01wdj_; 035wtd; 0885n; 01f2xy; 019pwv; 02mp0g; 02z6fs; 027ybp; ... >> query: (?x9988, 09nqf) <- student(?x9988, ?x3993), student(?x3437, ?x3993), gender(?x3993, ?x231), colors(?x9988, ?x663) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 01trxd; *> query: (?x9988, 02l6h) <- contains(?x1264, ?x9988), organization(?x4095, ?x9988), ?x1264 = 0345h *> conf = 0.27 ranks of expected_values: 2 EVAL 0pz6q currency 02l6h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 129.000 0.667 http://example.org/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency #17879-02j490 PRED entity: 02j490 PRED relation: award PRED expected values: 0cqh46 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 246): 09sb52 (0.38 #40, 0.30 #2854, 0.29 #9688), 0ck27z (0.27 #6121, 0.25 #91, 0.25 #2101), 09qv_s (0.25 #150, 0.09 #1758, 0.07 #2964), 099ck7 (0.25 #265, 0.07 #1873, 0.06 #3079), 0cqhk0 (0.19 #2046, 0.15 #6066, 0.13 #4056), 05pcn59 (0.17 #2492, 0.17 #3296, 0.15 #4502), 0f4x7 (0.16 #1638, 0.13 #2844, 0.12 #30), 0gqy2 (0.13 #1770, 0.12 #162, 0.12 #2976), 0gqwc (0.13 #1681, 0.12 #475, 0.11 #4495), 01by1l (0.13 #915, 0.10 #4935, 0.09 #22623) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0bl60p; >> query: (?x10897, 09sb52) <- film(?x10897, ?x6175), ?x6175 = 0gg5kmg, nominated_for(?x10897, ?x6482) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #51 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0bl60p; *> query: (?x10897, 0cqh46) <- film(?x10897, ?x6175), ?x6175 = 0gg5kmg, nominated_for(?x10897, ?x6482) *> conf = 0.12 ranks of expected_values: 24 EVAL 02j490 award 0cqh46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 101.000 101.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17878-0dvqq PRED entity: 0dvqq PRED relation: artist! PRED expected values: 015kg1 01cl2y => 93 concepts (55 used for prediction) PRED predicted values (max 10 best out of 118): 0g768 (0.33 #38, 0.20 #743, 0.14 #179), 01f_3w (0.33 #35, 0.04 #1445, 0.04 #740), 033hn8 (0.29 #155, 0.19 #860, 0.18 #1001), 011k1h (0.29 #151, 0.15 #2266, 0.13 #2407), 03rhqg (0.27 #298, 0.22 #1990, 0.22 #2272), 0229rs (0.27 #300, 0.10 #864, 0.09 #1005), 015_1q (0.25 #2699, 0.23 #866, 0.22 #2981), 01trtc (0.20 #778, 0.17 #637, 0.14 #214), 01dtcb (0.18 #470, 0.16 #893, 0.16 #1598), 017l96 (0.17 #1288, 0.14 #2416, 0.14 #2557) >> Best rule #38 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01vw20_; >> query: (?x2395, 0g768) <- award(?x2395, ?x9462), award(?x2395, ?x1389), award_nominee(?x2395, ?x1060), ?x9462 = 01d38t, ?x1389 = 01c427 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #162 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0178kd; 0838y; 016l09; 0c9l1; 0ycfj; *> query: (?x2395, 015kg1) <- award_winner(?x7535, ?x2395), award(?x2395, ?x462), group(?x227, ?x2395), ?x7535 = 02f73b *> conf = 0.14 ranks of expected_values: 19, 27 EVAL 0dvqq artist! 01cl2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 93.000 55.000 0.333 http://example.org/music/record_label/artist EVAL 0dvqq artist! 015kg1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 93.000 55.000 0.333 http://example.org/music/record_label/artist #17877-05fky PRED entity: 05fky PRED relation: category PRED expected values: 08mbj5d => 254 concepts (254 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.76 #268, 0.70 #18, 0.69 #11) >> Best rule #268 for best value: >> intensional similarity = 3 >> extensional distance = 897 >> proper extension: 0rs6x; 015zyd; 08815; 05zjtn4; 01rtm4; 04wlz2; 05krk; 01j_9c; 01fpvz; 02w2bc; ... >> query: (?x4198, 08mbj5d) <- contains(?x8260, ?x4198), contains(?x8260, ?x2623), ?x2623 = 02xry >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fky category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 254.000 254.000 0.761 http://example.org/common/topic/webpage./common/webpage/category #17876-03k7bd PRED entity: 03k7bd PRED relation: film PRED expected values: 0g22z => 116 concepts (62 used for prediction) PRED predicted values (max 10 best out of 947): 06q8qh (0.24 #606, 0.03 #33916, 0.02 #18456), 0b6tzs (0.18 #140, 0.03 #33916, 0.01 #3710), 01k60v (0.12 #745, 0.03 #33916, 0.02 #7885), 03p2xc (0.12 #1243, 0.03 #33916, 0.02 #19093), 028kj0 (0.12 #1663, 0.03 #5233, 0.02 #10588), 0bcp9b (0.12 #1319, 0.01 #4889), 03mh94 (0.07 #3634, 0.04 #8989, 0.02 #12559), 02qr3k8 (0.06 #10212, 0.06 #4857, 0.02 #24492), 09sr0 (0.06 #5087, 0.04 #10442, 0.03 #33916), 011ywj (0.06 #1433, 0.04 #5003, 0.03 #10358) >> Best rule #606 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 0147dk; 01713c; 02tr7d; 01pgzn_; 02qgyv; 043js; 011_3s; 0f4dx2; 02k21g; 031k24; ... >> query: (?x1865, 06q8qh) <- award(?x1865, ?x451), award_nominee(?x4702, ?x1865), ?x4702 = 01kwsg >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0147dk; 01713c; 02tr7d; 01pgzn_; 02qgyv; 043js; 011_3s; 0f4dx2; 02k21g; 031k24; ... *> query: (?x1865, 0g22z) <- award(?x1865, ?x451), award_nominee(?x4702, ?x1865), ?x4702 = 01kwsg *> conf = 0.06 ranks of expected_values: 89 EVAL 03k7bd film 0g22z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 116.000 62.000 0.235 http://example.org/film/actor/film./film/performance/film #17875-0g10g PRED entity: 0g10g PRED relation: people! PRED expected values: 01tf_6 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 24): 0dq9p (0.17 #83, 0.13 #215, 0.12 #149), 0gk4g (0.17 #76, 0.13 #340, 0.05 #208), 07jwr (0.09 #141), 04p3w (0.07 #341, 0.05 #209, 0.03 #143), 01l2m3 (0.05 #214, 0.03 #148, 0.01 #2854), 0dcsx (0.05 #213, 0.02 #345), 02k6hp (0.04 #367, 0.03 #169, 0.01 #4855), 02knxx (0.04 #362, 0.02 #296, 0.01 #560), 02y0js (0.03 #134, 0.03 #332, 0.03 #200), 0c58k (0.03 #162) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01n9d9; >> query: (?x10973, 0dq9p) <- nominated_for(?x10973, ?x592), ?x592 = 0n0bp, award(?x10973, ?x537) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #361 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 186 *> proper extension: 012dtf; 06f_qn; 0436zq; 02drd3; 05v45k; 01l3j; *> query: (?x10973, 01tf_6) <- film(?x10973, ?x1804), nationality(?x10973, ?x94), film_art_direction_by(?x1804, ?x2449) *> conf = 0.02 ranks of expected_values: 20 EVAL 0g10g people! 01tf_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 116.000 116.000 0.167 http://example.org/people/cause_of_death/people #17874-04z_3pm PRED entity: 04z_3pm PRED relation: film_release_region PRED expected values: 0154j 0h7x => 118 concepts (113 used for prediction) PRED predicted values (max 10 best out of 221): 03rjj (0.87 #3634, 0.82 #3028, 0.80 #3785), 03h64 (0.82 #2027, 0.81 #3084, 0.78 #4294), 0154j (0.80 #3784, 0.77 #3027, 0.75 #4237), 01znc_ (0.72 #3667, 0.69 #4271, 0.68 #5177), 03spz (0.70 #3721, 0.67 #3115, 0.65 #696), 03rj0 (0.64 #3684, 0.62 #3078, 0.59 #2021), 03rt9 (0.62 #3643, 0.62 #1980, 0.62 #3037), 04gzd (0.58 #8, 0.49 #3032, 0.47 #3789), 0h7x (0.54 #3660, 0.46 #2601, 0.42 #30), 047yc (0.52 #3048, 0.42 #5164, 0.41 #3654) >> Best rule #3634 for best value: >> intensional similarity = 6 >> extensional distance = 155 >> proper extension: 07s846j; >> query: (?x7887, 03rjj) <- film_release_region(?x7887, ?x774), genre(?x7887, ?x53), genre(?x7194, ?x53), genre(?x273, ?x53), nominated_for(?x198, ?x7194), ?x774 = 06mzp >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #3784 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 161 *> proper extension: 0gtsx8c; 047svrl; 07k2mq; 0372j5; *> query: (?x7887, 0154j) <- film_release_region(?x7887, ?x1353), film_release_region(?x7887, ?x94), ?x94 = 09c7w0, film_release_distribution_medium(?x7887, ?x81), ?x81 = 029j_, ?x1353 = 035qy *> conf = 0.80 ranks of expected_values: 3, 9 EVAL 04z_3pm film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 118.000 113.000 0.866 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04z_3pm film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 118.000 113.000 0.866 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17873-020y73 PRED entity: 020y73 PRED relation: language PRED expected values: 02h40lc => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 33): 02h40lc (0.96 #2541, 0.96 #1787, 0.96 #2425), 064_8sq (0.14 #939, 0.14 #765, 0.14 #1056), 06nm1 (0.14 #239, 0.11 #697, 0.10 #1737), 0jzc (0.10 #76, 0.06 #191, 0.06 #134), 02bjrlw (0.08 #1095, 0.08 #344, 0.08 #1383), 03_9r (0.06 #870, 0.06 #466, 0.06 #238), 0653m (0.05 #240, 0.05 #698, 0.05 #872), 04h9h (0.04 #213, 0.04 #98, 0.04 #1365), 012w70 (0.04 #184, 0.04 #873, 0.03 #241), 03k50 (0.03 #1043, 0.03 #926, 0.02 #3350) >> Best rule #2541 for best value: >> intensional similarity = 4 >> extensional distance = 871 >> proper extension: 02r2j8; >> query: (?x2326, 02h40lc) <- music(?x2326, ?x2214), film(?x2654, ?x2326), language(?x2326, ?x732), place_of_birth(?x2654, ?x14110) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 020y73 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.959 http://example.org/film/film/language #17872-02jr26 PRED entity: 02jr26 PRED relation: film PRED expected values: 0gy2y8r 06lpmt => 128 concepts (78 used for prediction) PRED predicted values (max 10 best out of 1016): 01kt_j (0.08 #17871, 0.07 #39316), 03nqnnk (0.08 #2809, 0.05 #9957, 0.02 #42125), 02qr3k8 (0.06 #10222, 0.03 #83492, 0.03 #45964), 02z3r8t (0.06 #1894, 0.03 #5468, 0.03 #9042), 01chpn (0.06 #2896, 0.03 #10044, 0.03 #11831), 0h7t36 (0.06 #3470, 0.03 #10618), 05hjnw (0.06 #2628, 0.01 #9776), 0c57yj (0.06 #2424), 031hcx (0.05 #10207, 0.03 #17355, 0.02 #56671), 0ds3t5x (0.05 #8988, 0.02 #1840, 0.01 #42943) >> Best rule #17871 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 01r42_g; >> query: (?x7023, ?x10595) <- award_winner(?x1716, ?x7023), languages(?x7023, ?x3592), actor(?x10595, ?x7023) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #4241 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 53 *> proper extension: 0mj0c; *> query: (?x7023, 0gy2y8r) <- location(?x7023, ?x10211), student(?x3439, ?x7023), ?x3439 = 03ksy *> conf = 0.02 ranks of expected_values: 547, 606 EVAL 02jr26 film 06lpmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 128.000 78.000 0.078 http://example.org/film/actor/film./film/performance/film EVAL 02jr26 film 0gy2y8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 128.000 78.000 0.078 http://example.org/film/actor/film./film/performance/film #17871-09vc4s PRED entity: 09vc4s PRED relation: people PRED expected values: 0bwh6 04nw9 025mb_ 03yk8z 03j0d 04xhwn => 19 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2782): 032_jg (0.50 #5172, 0.33 #107, 0.08 #10237), 06cgy (0.33 #5259, 0.33 #194, 0.15 #10324), 0lkr7 (0.33 #5759, 0.33 #694, 0.09 #9136), 06gh0t (0.33 #5606, 0.33 #541, 0.07 #18577), 0132k4 (0.33 #6004, 0.33 #939, 0.07 #18577), 0311wg (0.33 #5348, 0.20 #3659, 0.10 #12104), 0227tr (0.33 #5389, 0.20 #3700, 0.08 #10454), 04nw9 (0.33 #5257, 0.20 #3568, 0.07 #18577), 0dzlk (0.33 #1530, 0.17 #6595, 0.10 #8283), 07bsj (0.33 #1454, 0.17 #6519, 0.10 #8207) >> Best rule #5172 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 07bch9; 06v41q; 03bkbh; >> query: (?x1816, 032_jg) <- people(?x1816, ?x5541), people(?x1816, ?x4228), ?x5541 = 01pk3z, film(?x4228, ?x1728), profession(?x4228, ?x1032) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5257 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 07bch9; 06v41q; 03bkbh; *> query: (?x1816, 04nw9) <- people(?x1816, ?x5541), people(?x1816, ?x4228), ?x5541 = 01pk3z, film(?x4228, ?x1728), profession(?x4228, ?x1032) *> conf = 0.33 ranks of expected_values: 8, 130, 266, 840, 1990 EVAL 09vc4s people 04xhwn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 19.000 16.000 0.500 http://example.org/people/ethnicity/people EVAL 09vc4s people 03j0d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 19.000 16.000 0.500 http://example.org/people/ethnicity/people EVAL 09vc4s people 03yk8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 19.000 16.000 0.500 http://example.org/people/ethnicity/people EVAL 09vc4s people 025mb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 19.000 16.000 0.500 http://example.org/people/ethnicity/people EVAL 09vc4s people 04nw9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 19.000 16.000 0.500 http://example.org/people/ethnicity/people EVAL 09vc4s people 0bwh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 19.000 16.000 0.500 http://example.org/people/ethnicity/people #17870-0170_p PRED entity: 0170_p PRED relation: film! PRED expected values: 02t_vx => 97 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1046): 012rng (0.64 #87355, 0.63 #76953, 0.43 #101912), 02pzck (0.25 #1734, 0.01 #20450), 02v60l (0.25 #817), 01rr9f (0.25 #78), 0l8v5 (0.25 #58), 024bbl (0.12 #836, 0.07 #2915, 0.03 #9153), 0h0wc (0.12 #423, 0.06 #12900, 0.03 #21218), 03cglm (0.12 #1046, 0.04 #128948, 0.03 #3125), 05vsxz (0.12 #9, 0.04 #128948, 0.03 #2088), 05nzw6 (0.12 #1192, 0.03 #3271, 0.03 #7430) >> Best rule #87355 for best value: >> intensional similarity = 3 >> extensional distance = 794 >> proper extension: 09fb5; 0clpml; >> query: (?x675, ?x4307) <- nominated_for(?x4307, ?x675), award_winner(?x5533, ?x4307), participant(?x7331, ?x4307) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #3455 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 0yyg4; 01719t; 05sy_5; 05qm9f; 05ft32; 0280061; 0jqkh; 01gvpz; 0ccck7; 01f69m; ... *> query: (?x675, 02t_vx) <- film(?x399, ?x675), films(?x5069, ?x675), nominated_for(?x591, ?x675), ?x5069 = 06d4h *> conf = 0.03 ranks of expected_values: 145 EVAL 0170_p film! 02t_vx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 97.000 71.000 0.640 http://example.org/film/actor/film./film/performance/film #17869-02nygk PRED entity: 02nygk PRED relation: place_of_burial PRED expected values: 018mmw => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 18): 01f38z (0.20 #60, 0.09 #122, 0.07 #185), 018mmw (0.12 #204, 0.11 #267, 0.08 #486), 018mmj (0.07 #1170, 0.07 #2185, 0.06 #2471), 0bvqq (0.06 #199, 0.05 #262, 0.02 #609), 018mm4 (0.06 #1168, 0.04 #1647, 0.04 #2342), 0nb1s (0.05 #280, 0.04 #342, 0.03 #815), 0lbp_ (0.05 #485, 0.04 #644, 0.02 #518), 08966 (0.05 #296), 0r22d (0.04 #330, 0.03 #424, 0.02 #552), 02_286 (0.04 #344) >> Best rule #60 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0d__g; >> query: (?x13339, 01f38z) <- peers(?x8209, ?x13339), profession(?x13339, ?x955), ?x955 = 0n1h, student(?x4016, ?x13339) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #204 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 02wh0; *> query: (?x13339, 018mmw) <- peers(?x8209, ?x13339), gender(?x13339, ?x231), place_of_birth(?x8209, ?x4090), company(?x13339, ?x4585) *> conf = 0.12 ranks of expected_values: 2 EVAL 02nygk place_of_burial 018mmw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 144.000 0.200 http://example.org/people/deceased_person/place_of_burial #17868-0blpg PRED entity: 0blpg PRED relation: film_release_region PRED expected values: 015fr 02vzc => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 143): 03rjj (0.85 #2326, 0.85 #2946, 0.84 #3100), 035qy (0.84 #2975, 0.83 #2355, 0.83 #3129), 02vzc (0.83 #822, 0.80 #1906, 0.80 #4536), 0d060g (0.83 #2328, 0.79 #2948, 0.79 #3102), 015fr (0.80 #2338, 0.79 #2958, 0.77 #3112), 0b90_r (0.74 #2325, 0.72 #2945, 0.71 #3099), 03spz (0.73 #3036, 0.72 #2416, 0.72 #1485), 05b4w (0.71 #2384, 0.70 #3004, 0.70 #1918), 03rt9 (0.67 #2335, 0.65 #2955, 0.63 #3109), 05v8c (0.60 #2957, 0.58 #3111, 0.57 #2337) >> Best rule #2326 for best value: >> intensional similarity = 5 >> extensional distance = 130 >> proper extension: 0g56t9t; 0g5qs2k; 0dscrwf; 0gkz15s; 0dgst_d; 0bq8tmw; 0gj9tn5; 0ch26b_; 0gvrws1; 0407yfx; ... >> query: (?x3988, 03rjj) <- nominated_for(?x166, ?x3988), film_release_region(?x3988, ?x1603), film_release_region(?x3988, ?x87), ?x1603 = 06bnz, ?x87 = 05r4w >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #822 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 02d44q; *> query: (?x3988, 02vzc) <- nominated_for(?x166, ?x3988), featured_film_locations(?x3988, ?x739), film_release_region(?x3988, ?x1264), ?x1264 = 0345h *> conf = 0.83 ranks of expected_values: 3, 5 EVAL 0blpg film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 85.000 0.848 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0blpg film_release_region 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 85.000 0.848 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17867-03fmfs PRED entity: 03fmfs PRED relation: registering_agency PRED expected values: 03z19 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.83 #15, 0.83 #11, 0.82 #12) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 03_fmr; >> query: (?x3509, 03z19) <- category(?x3509, ?x134), currency(?x3509, ?x170), contains(?x94, ?x3509), film_release_region(?x54, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03fmfs registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.833 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #17866-061681 PRED entity: 061681 PRED relation: film! PRED expected values: 02jtjz => 130 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1298): 030_3z (0.47 #62379, 0.32 #62378, 0.32 #110207), 032_jg (0.33 #140, 0.25 #6377, 0.08 #12614), 0151w_ (0.33 #164, 0.25 #6401, 0.07 #33268), 0gd9k (0.33 #1387, 0.25 #7624, 0.03 #32574), 034np8 (0.33 #292, 0.25 #6529, 0.02 #31479), 079vf (0.33 #2087, 0.12 #10403, 0.12 #14562), 0f5xn (0.33 #3048, 0.10 #21761, 0.08 #11364), 0gnbw (0.29 #5428, 0.04 #34538, 0.04 #11665), 0c6qh (0.25 #6651, 0.06 #60712, 0.06 #21206), 02s2ft (0.25 #6244, 0.05 #24957, 0.04 #22878) >> Best rule #62379 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 0275kr; >> query: (?x763, ?x4552) <- category(?x763, ?x134), nominated_for(?x4552, ?x763), spouse(?x4552, ?x846) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #6901 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 03k8th; *> query: (?x763, 02jtjz) <- prequel(?x1525, ?x763), film(?x2422, ?x763), ?x2422 = 0169dl, film_crew_role(?x763, ?x468) *> conf = 0.25 ranks of expected_values: 16 EVAL 061681 film! 02jtjz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 130.000 64.000 0.475 http://example.org/film/actor/film./film/performance/film #17865-09qgm PRED entity: 09qgm PRED relation: country PRED expected values: 015fr 0ctw_b => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 448): 03rt9 (0.89 #1741, 0.77 #389, 0.75 #1935), 07t21 (0.86 #1933, 0.84 #392, 0.83 #2539), 04g5k (0.86 #1933, 0.84 #392, 0.79 #1158), 01p1v (0.86 #1933, 0.83 #2549, 0.82 #2360), 0jgd (0.86 #1933, 0.83 #198, 0.77 #389), 047lj (0.86 #1933, 0.82 #395, 0.79 #1158), 06c1y (0.86 #1933, 0.79 #1158, 0.77 #389), 04gzd (0.86 #1933, 0.79 #1158, 0.77 #389), 01pj7 (0.86 #1933, 0.79 #1158, 0.77 #389), 07ylj (0.86 #1933, 0.79 #1158, 0.77 #389) >> Best rule #1741 for best value: >> intensional similarity = 58 >> extensional distance = 6 >> proper extension: 01hp22; 06wrt; 0dwxr; >> query: (?x3345, ?x429) <- country(?x3345, ?x2756), country(?x3345, ?x2513), country(?x3345, ?x1892), country(?x3345, ?x1558), country(?x3345, ?x789), country(?x3345, ?x512), country(?x3345, ?x94), ?x1892 = 02vzc, sports(?x418, ?x3345), countries_spoken_in(?x2502, ?x2756), participating_countries(?x1741, ?x2756), currency(?x2756, ?x170), ?x1558 = 01mjq, religion(?x2756, ?x1985), olympics(?x3345, ?x784), medal(?x418, ?x422), participating_countries(?x418, ?x6305), participating_countries(?x418, ?x5482), participating_countries(?x418, ?x429), ?x2502 = 06nm1, ?x512 = 07ssc, contains(?x6304, ?x6305), ?x94 = 09c7w0, film_release_region(?x10475, ?x2513), film_release_region(?x9194, ?x2513), film_release_region(?x7693, ?x2513), film_release_region(?x6235, ?x2513), film_release_region(?x5092, ?x2513), film_release_region(?x4518, ?x2513), film_release_region(?x3423, ?x2513), film_release_region(?x2512, ?x2513), film_release_region(?x2168, ?x2513), film_release_region(?x1451, ?x2513), film_release_region(?x1364, ?x2513), film_release_region(?x385, ?x2513), ?x2512 = 07x4qr, ?x4518 = 0hgnl3t, ?x1451 = 04zyhx, country(?x1009, ?x2513), religion(?x6305, ?x492), olympics(?x2513, ?x452), ?x789 = 0f8l9c, contains(?x6305, ?x13440), ?x5482 = 04g5k, jurisdiction_of_office(?x182, ?x2756), ?x3423 = 09g7vfw, ?x9194 = 0fpgp26, organization(?x2513, ?x5701), ?x10475 = 047p798, ?x7693 = 0m63c, ?x5701 = 0b6css, ?x1364 = 047msdk, ?x5092 = 0gg5qcw, country(?x11197, ?x2513), ?x385 = 0ds3t5x, ?x2168 = 0bx0l, ?x6235 = 05b6rdt, second_level_divisions(?x429, ?x1788) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1933 for first EXPECTED value: *> intensional similarity = 53 *> extensional distance = 6 *> proper extension: 01gqfm; *> query: (?x3345, ?x6305) <- country(?x3345, ?x2756), country(?x3345, ?x2513), country(?x3345, ?x1892), country(?x3345, ?x1558), country(?x3345, ?x1497), country(?x3345, ?x1264), country(?x3345, ?x512), country(?x3345, ?x94), ?x1892 = 02vzc, sports(?x418, ?x3345), countries_spoken_in(?x2502, ?x2756), participating_countries(?x1931, ?x2756), currency(?x2756, ?x170), ?x1558 = 01mjq, religion(?x2756, ?x1985), olympics(?x3345, ?x784), medal(?x418, ?x422), participating_countries(?x418, ?x6305), participating_countries(?x418, ?x5482), ?x2502 = 06nm1, ?x512 = 07ssc, contains(?x6304, ?x6305), ?x94 = 09c7w0, ?x2513 = 05b4w, film_release_region(?x5271, ?x1497), film_release_region(?x4615, ?x1497), film_release_region(?x3784, ?x1497), film_release_region(?x3565, ?x1497), film_release_region(?x3276, ?x1497), film_release_region(?x1370, ?x1497), film_release_region(?x622, ?x1497), film_release_region(?x343, ?x1497), ?x4615 = 0dlngsd, combatants(?x326, ?x1497), ?x3276 = 0gjc4d3, ?x3565 = 0cp0ph6, taxonomy(?x1497, ?x939), ?x622 = 0fq27fp, olympics(?x1497, ?x584), olympics(?x1003, ?x418), ?x5271 = 047vnkj, ?x3784 = 0bmhvpr, adjoins(?x3352, ?x6305), country(?x766, ?x6305), contains(?x455, ?x5482), ?x1370 = 0gmcwlb, organization(?x2756, ?x312), ?x343 = 0gx1bnj, member_states(?x2106, ?x5482), ?x1931 = 0kbws, ?x1264 = 0345h, ?x766 = 01hp22, ?x1003 = 03gj2 *> conf = 0.86 ranks of expected_values: 11, 22 EVAL 09qgm country 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 28.000 28.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09qgm country 015fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 28.000 28.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #17864-02zk08 PRED entity: 02zk08 PRED relation: nominated_for! PRED expected values: 0p9sw 0bdw1g => 100 concepts (80 used for prediction) PRED predicted values (max 10 best out of 214): 0l8z1 (0.76 #6294, 0.67 #4894, 0.66 #12824), 0gq9h (0.70 #7288, 0.62 #761, 0.56 #6121), 0gr4k (0.64 #5387, 0.41 #7253, 0.38 #9351), 0gs9p (0.61 #7290, 0.54 #763, 0.50 #6123), 019f4v (0.57 #753, 0.53 #520, 0.52 #2151), 0gr0m (0.57 #758, 0.41 #1224, 0.41 #2156), 040njc (0.56 #706, 0.49 #2104, 0.47 #473), 0p9sw (0.54 #720, 0.47 #2817, 0.46 #2584), 0gq_v (0.51 #719, 0.46 #1185, 0.42 #2816), 0gr42 (0.48 #3581, 0.24 #1251, 0.22 #1484) >> Best rule #6294 for best value: >> intensional similarity = 4 >> extensional distance = 330 >> proper extension: 06mmr; >> query: (?x8701, ?x1079) <- award_winner(?x8701, ?x6232), award(?x8701, ?x1079), nominated_for(?x1079, ?x7016), ?x7016 = 07g1sm >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #720 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 59 *> proper extension: 09q5w2; 09gq0x5; 0y_9q; 01mgw; *> query: (?x8701, 0p9sw) <- nominated_for(?x1703, ?x8701), nominated_for(?x637, ?x8701), nominated_for(?x4056, ?x8701), ?x637 = 02r22gf, film(?x788, ?x8701), ?x1703 = 0k611 *> conf = 0.54 ranks of expected_values: 8, 187 EVAL 02zk08 nominated_for! 0bdw1g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 100.000 80.000 0.760 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02zk08 nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 100.000 80.000 0.760 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17863-0j1yf PRED entity: 0j1yf PRED relation: role PRED expected values: 0j862 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 43): 0342h (0.62 #137, 0.35 #270, 0.33 #865), 03bx0bm (0.46 #156, 0.24 #1149, 0.23 #289), 0l14md (0.31 #140, 0.12 #273, 0.08 #206), 05148p4 (0.23 #151, 0.16 #3377, 0.15 #1192), 02hnl (0.19 #296, 0.15 #163, 0.12 #229), 03f5mt (0.16 #3377, 0.15 #1192, 0.15 #4040), 05r5c (0.15 #141, 0.13 #869, 0.12 #274), 018vs (0.13 #875, 0.12 #280, 0.08 #3325), 028tv0 (0.12 #874, 0.09 #1139, 0.08 #212), 0l15bq (0.09 #332, 0.08 #3112) >> Best rule #137 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 04f7c55; >> query: (?x1896, 0342h) <- celebrity(?x1896, ?x1897), group(?x1896, ?x4842) >> conf = 0.62 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0j1yf role 0j862 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 126.000 0.615 http://example.org/music/group_member/membership./music/group_membership/role #17862-04wgh PRED entity: 04wgh PRED relation: olympics PRED expected values: 0l6ny => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 36): 0l6ny (0.55 #184, 0.45 #1062, 0.42 #219), 0lgxj (0.55 #199, 0.39 #656, 0.36 #1007), 0l998 (0.40 #182, 0.38 #217, 0.33 #990), 0jkvj (0.40 #207, 0.32 #1085, 0.31 #277), 09x3r (0.40 #186, 0.31 #221, 0.28 #994), 0l98s (0.35 #181, 0.33 #1059, 0.31 #216), 0l6vl (0.35 #178, 0.31 #213, 0.28 #1056), 0lv1x (0.35 #224, 0.31 #997, 0.30 #189), 0nbjq (0.35 #228, 0.25 #193, 0.24 #123), 0lbbj (0.33 #1000, 0.33 #1105, 0.31 #1525) >> Best rule #184 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 01f08r; >> query: (?x1273, 0l6ny) <- contains(?x2467, ?x1273), vacationer(?x1273, ?x2626), exported_to(?x87, ?x1273) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wgh olympics 0l6ny CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.550 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #17861-01ly5m PRED entity: 01ly5m PRED relation: film_release_region! PRED expected values: 0cz8mkh 0bh8tgs => 220 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1323): 0gffmn8 (1.00 #95287, 1.00 #103227, 0.96 #35735), 0gvrws1 (1.00 #95287, 1.00 #103227, 0.96 #35735), 0j8f09z (1.00 #95287, 1.00 #103227, 0.96 #35735), 043tvp3 (1.00 #95287, 1.00 #103227, 0.96 #35735), 017jd9 (1.00 #95287, 1.00 #103227, 0.96 #35735), 03nm_fh (1.00 #95287, 1.00 #103227, 0.96 #35735), 0fpgp26 (1.00 #95287, 1.00 #103227, 0.96 #35735), 047vnkj (1.00 #95287, 1.00 #103227, 0.96 #35735), 04f52jw (1.00 #95287, 1.00 #103227, 0.96 #35735), 0gd0c7x (1.00 #95287, 1.00 #103227, 0.96 #35735) >> Best rule #95287 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 0fhzy; >> query: (?x2911, ?x80) <- teams(?x2911, ?x7667), teams(?x142, ?x7667), film_release_region(?x80, ?x142), position(?x7667, ?x60) >> conf = 1.00 => this is the best rule for 310 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 50, 157 EVAL 01ly5m film_release_region! 0bh8tgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 220.000 107.000 1.000 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ly5m film_release_region! 0cz8mkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 220.000 107.000 1.000 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17860-06msq2 PRED entity: 06msq2 PRED relation: producer_type PRED expected values: 0ckd1 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.70 #5, 0.62 #10, 0.58 #1) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 0jt90f5; 01jbx1; >> query: (?x4415, 0ckd1) <- award(?x4415, ?x882), tv_program(?x4415, ?x3626), program(?x4415, ?x5698) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06msq2 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.698 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #17859-01kgg9 PRED entity: 01kgg9 PRED relation: gender PRED expected values: 02zsn => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.91 #16, 0.67 #6, 0.60 #2), 05zppz (0.87 #23, 0.85 #151, 0.85 #163) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 06r3p2; >> query: (?x9777, 02zsn) <- award(?x9777, ?x3989), profession(?x9777, ?x1032), ?x3989 = 0bsjcw >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kgg9 gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.914 http://example.org/people/person/gender #17858-03y5ky PRED entity: 03y5ky PRED relation: major_field_of_study PRED expected values: 02jfc => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 84): 02j62 (0.31 #2034, 0.31 #3911, 0.30 #157), 01mkq (0.25 #5521, 0.25 #3895, 0.25 #2018), 03g3w (0.25 #4032, 0.24 #2531, 0.24 #3907), 04rjg (0.25 #146, 0.24 #271, 0.24 #4025), 062z7 (0.25 #904, 0.25 #1531, 0.24 #2156), 02lp1 (0.24 #887, 0.24 #2139, 0.24 #1263), 0g26h (0.21 #2172, 0.21 #1296, 0.21 #170), 05qjt (0.20 #2887, 0.20 #5513, 0.20 #4638), 05qfh (0.18 #163, 0.17 #288, 0.16 #913), 01540 (0.17 #939, 0.17 #189, 0.16 #439) >> Best rule #2034 for best value: >> intensional similarity = 4 >> extensional distance = 301 >> proper extension: 0bqxw; 02pptm; 01zh3_; >> query: (?x6201, 02j62) <- contains(?x94, ?x6201), school_type(?x6201, ?x1044), major_field_of_study(?x6201, ?x12907), currency(?x6201, ?x170) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #6757 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 555 *> proper extension: 015nl4; *> query: (?x6201, ?x2981) <- major_field_of_study(?x6201, ?x12907), institution(?x1771, ?x6201), major_field_of_study(?x2981, ?x12907) *> conf = 0.13 ranks of expected_values: 18 EVAL 03y5ky major_field_of_study 02jfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 121.000 121.000 0.307 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17857-02sch9 PRED entity: 02sch9 PRED relation: languages_spoken PRED expected values: 03k50 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 54): 02h40lc (0.86 #546, 0.50 #222, 0.38 #438), 03k50 (0.50 #227, 0.50 #116, 0.33 #7), 0t_2 (0.43 #448, 0.41 #665, 0.40 #719), 07c9s (0.29 #164, 0.28 #817, 0.26 #1311), 09s02 (0.26 #1311, 0.25 #1368, 0.25 #1367), 032f6 (0.25 #269, 0.25 #104, 0.10 #599), 0swlx (0.25 #270, 0.25 #105, 0.10 #599), 01c7y (0.25 #260, 0.25 #149, 0.10 #599), 0121sr (0.25 #264, 0.25 #153, 0.10 #599), 06nm1 (0.25 #64, 0.18 #499, 0.16 #608) >> Best rule #546 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 078ds; 0fk3s; 04czx7; >> query: (?x7838, 02h40lc) <- languages_spoken(?x7838, ?x10323), languages(?x7517, ?x10323), languages_spoken(?x10322, ?x10323), ?x10322 = 078vc, ?x7517 = 03vrnh, language(?x657, ?x10323) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #227 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 078vc; *> query: (?x7838, 03k50) <- languages_spoken(?x7838, ?x10323), languages_spoken(?x7838, ?x9113), ?x10323 = 0688f, ?x9113 = 02hxcvy *> conf = 0.50 ranks of expected_values: 2 EVAL 02sch9 languages_spoken 03k50 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 33.000 33.000 0.862 http://example.org/people/ethnicity/languages_spoken #17856-024jwt PRED entity: 024jwt PRED relation: student! PRED expected values: 033gn8 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 201): 0bwfn (0.11 #273, 0.11 #798, 0.09 #13400), 065y4w7 (0.11 #13, 0.11 #538, 0.06 #2638), 09f2j (0.11 #157, 0.05 #682, 0.04 #17484), 07szy (0.11 #39, 0.05 #564, 0.04 #4239), 01k2wn (0.11 #23, 0.05 #548, 0.02 #2123), 03ksy (0.07 #13232, 0.05 #7981, 0.04 #10082), 01w5m (0.06 #2204, 0.06 #6404, 0.06 #5879), 04b_46 (0.06 #2850, 0.05 #6000, 0.04 #6525), 07tg4 (0.06 #2185, 0.05 #610, 0.04 #6385), 02cttt (0.05 #543, 0.01 #1593, 0.01 #2118) >> Best rule #273 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 016tw3; 043q6n_; >> query: (?x10694, 0bwfn) <- award_nominee(?x10694, ?x4946), ?x4946 = 03h304l, nominated_for(?x10694, ?x782) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #3001 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 96 *> proper extension: 03yf3z; *> query: (?x10694, 033gn8) <- award_nominee(?x10694, ?x4946), nominated_for(?x4946, ?x2207), student(?x1771, ?x10694) *> conf = 0.03 ranks of expected_values: 19 EVAL 024jwt student! 033gn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 105.000 105.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #17855-0btpm6 PRED entity: 0btpm6 PRED relation: category PRED expected values: 08mbj5d => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.46 #17, 0.42 #10, 0.37 #25) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 0d8w2n; >> query: (?x7493, 08mbj5d) <- films(?x9677, ?x7493), genre(?x7493, ?x53), film_distribution_medium(?x7493, ?x2099) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0btpm6 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.463 http://example.org/common/topic/webpage./common/webpage/category #17854-01xd9 PRED entity: 01xd9 PRED relation: location! PRED expected values: 015rkw 01wxyx1 => 95 concepts (49 used for prediction) PRED predicted values (max 10 best out of 2096): 01vh3r (0.33 #4826, 0.14 #7328, 0.12 #9830), 044mvs (0.33 #4555, 0.14 #7057, 0.12 #9559), 0dx97 (0.33 #3562, 0.14 #6064, 0.12 #8566), 014g9y (0.33 #4636, 0.14 #7138, 0.12 #9640), 01k53x (0.33 #4437, 0.14 #6939, 0.12 #9441), 07ym0 (0.33 #4194, 0.14 #6696, 0.12 #9198), 01_f_5 (0.33 #3768, 0.14 #6270, 0.12 #8772), 09h_q (0.33 #4119, 0.14 #6621, 0.12 #9123), 01vsqvs (0.33 #4351, 0.14 #6853, 0.12 #9355), 019fz (0.33 #4860, 0.12 #14868, 0.09 #17370) >> Best rule #4826 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 05qtj; >> query: (?x1591, 01vh3r) <- location(?x8452, ?x1591), location(?x7147, ?x1591), location(?x4265, ?x1591), ?x4265 = 06whf, profession(?x7147, ?x1032), languages(?x7147, ?x254), influenced_by(?x6723, ?x8452) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10009 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 04jpl; 02_286; 0f__1; 0cr3d; 01531; 0l0mk; 056_y; 0xq63; 05fjf; 07_f2; ... *> query: (?x1591, ?x2108) <- location(?x7147, ?x1591), location(?x4265, ?x1591), participant(?x7147, ?x2108), profession(?x4265, ?x6421), ?x6421 = 02hv44_, influenced_by(?x1029, ?x4265) *> conf = 0.10 ranks of expected_values: 144, 615 EVAL 01xd9 location! 01wxyx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 95.000 49.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location EVAL 01xd9 location! 015rkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 95.000 49.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #17853-05vxdh PRED entity: 05vxdh PRED relation: nominated_for! PRED expected values: 0gqwc 05pcn59 => 88 concepts (68 used for prediction) PRED predicted values (max 10 best out of 180): 040njc (0.29 #241, 0.18 #4707, 0.16 #5647), 0gq9h (0.27 #4760, 0.27 #2879, 0.25 #3114), 02pqp12 (0.26 #290, 0.14 #4756, 0.13 #1700), 0gs9p (0.24 #296, 0.23 #4762, 0.22 #2881), 02qyntr (0.24 #412, 0.17 #1352, 0.16 #4878), 019f4v (0.23 #4752, 0.22 #2871, 0.21 #5692), 0f4x7 (0.23 #5877, 0.17 #4725, 0.16 #5665), 04kxsb (0.23 #5877, 0.15 #327, 0.13 #4793), 09sb52 (0.23 #5877, 0.12 #15986, 0.09 #1207), 09qv_s (0.23 #5877, 0.12 #15986, 0.08 #1991) >> Best rule #241 for best value: >> intensional similarity = 2 >> extensional distance = 134 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x4592, 040njc) <- titles(?x512, ?x4592), ?x512 = 07ssc >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #292 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 134 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x4592, 0gqwc) <- titles(?x512, ?x4592), ?x512 = 07ssc *> conf = 0.16 ranks of expected_values: 24, 66 EVAL 05vxdh nominated_for! 05pcn59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 88.000 68.000 0.294 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05vxdh nominated_for! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 88.000 68.000 0.294 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17852-0mrf1 PRED entity: 0mrf1 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 83 concepts (45 used for prediction) PRED predicted values (max 10 best out of 4): 09c7w0 (0.88 #481, 0.88 #479, 0.88 #468), 0msck (0.19 #280, 0.17 #317, 0.16 #367), 07b_l (0.19 #280, 0.17 #317, 0.16 #367), 03rt9 (0.02 #271, 0.02 #445, 0.02 #458) >> Best rule #481 for best value: >> intensional similarity = 6 >> extensional distance = 228 >> proper extension: 0nht0; 0ff0x; 0n2kw; >> query: (?x13388, 09c7w0) <- source(?x13388, ?x958), ?x958 = 0jbk9, adjoins(?x14231, ?x13388), currency(?x13388, ?x170), ?x170 = 09nqf, time_zones(?x14231, ?x1638) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mrf1 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 45.000 0.878 http://example.org/location/country/second_level_divisions #17851-048rn PRED entity: 048rn PRED relation: genre PRED expected values: 02kdv5l 087lqx => 75 concepts (40 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.80 #1630, 0.79 #1049, 0.70 #1746), 01hmnh (0.56 #949, 0.56 #483, 0.41 #716), 02l7c8 (0.56 #365, 0.55 #598, 0.50 #16), 05p553 (0.56 #470, 0.50 #936, 0.47 #703), 0h9qh (0.51 #4418, 0.42 #582, 0.38 #1048), 06n90 (0.50 #246, 0.40 #129, 0.35 #711), 02kdv5l (0.50 #236, 0.40 #119, 0.33 #468), 01jfsb (0.40 #128, 0.36 #3499, 0.33 #3267), 04xvlr (0.34 #1631, 0.31 #1050, 0.22 #817), 060__y (0.32 #1646, 0.25 #17, 0.24 #1065) >> Best rule #1630 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0fg04; 0jjy0; 09z2b7; 02q5g1z; 02s4l6; 04jwly; 0bmpm; 07cyl; 04cj79; 03cw411; ... >> query: (?x5198, 07s9rl0) <- executive_produced_by(?x5198, ?x4785), film(?x382, ?x5198), genre(?x5198, ?x571), costume_design_by(?x5198, ?x4526) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #236 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0299hs; 04z_3pm; 07ghq; 032xky; *> query: (?x5198, 02kdv5l) <- genre(?x5198, ?x12344), film(?x12439, ?x5198), ?x12344 = 06qln, featured_film_locations(?x5198, ?x739) *> conf = 0.50 ranks of expected_values: 7, 74 EVAL 048rn genre 087lqx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 75.000 40.000 0.795 http://example.org/film/film/genre EVAL 048rn genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 75.000 40.000 0.795 http://example.org/film/film/genre #17850-017z88 PRED entity: 017z88 PRED relation: student PRED expected values: 01sxq9 06449 06czyr 0gv07g 035wq7 => 142 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1579): 01cj6y (0.33 #704, 0.11 #8859, 0.08 #14978), 044ntk (0.33 #217, 0.11 #8372, 0.05 #10411), 01lvcs1 (0.33 #527, 0.11 #8682, 0.04 #14801), 02nrdp (0.33 #1632, 0.11 #9787, 0.03 #22024), 02z1yj (0.33 #1643, 0.11 #9798, 0.02 #32236), 06cgy (0.33 #223, 0.11 #8378, 0.02 #134565), 02_l96 (0.33 #848, 0.11 #9003, 0.02 #37560), 01mqc_ (0.33 #1261, 0.11 #9416, 0.01 #48166), 06w6_ (0.33 #405, 0.11 #8560, 0.01 #47310), 044n3h (0.33 #1709, 0.11 #9864) >> Best rule #704 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 01d34b; >> query: (?x2909, 01cj6y) <- student(?x2909, ?x2910), student(?x2909, ?x2129), student(?x2909, ?x1538), ?x2910 = 03pmzt, award_nominee(?x286, ?x1538), award_winner(?x444, ?x2129) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7328 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 01bm_; *> query: (?x2909, 0gv07g) <- student(?x2909, ?x11865), student(?x2909, ?x1538), ?x1538 = 02wcx8c, school_type(?x2909, ?x3205), student(?x4268, ?x11865) *> conf = 0.33 ranks of expected_values: 37, 529, 1525 EVAL 017z88 student 035wq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 142.000 119.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 017z88 student 0gv07g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 142.000 119.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 017z88 student 06czyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 119.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 017z88 student 06449 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 142.000 119.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 017z88 student 01sxq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 119.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #17849-0lmm3 PRED entity: 0lmm3 PRED relation: sport PRED expected values: 02vx4 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 9): 02vx4 (0.90 #424, 0.89 #406, 0.89 #469), 0z74 (0.49 #1023, 0.27 #1105, 0.11 #816), 0jm_ (0.17 #799, 0.15 #847, 0.15 #884), 03tmr (0.13 #314, 0.11 #816, 0.11 #587), 018w8 (0.11 #816, 0.11 #399, 0.11 #727), 018jz (0.11 #816, 0.10 #983, 0.10 #765), 09xp_ (0.11 #816, 0.10 #983, 0.03 #887), 039yzs (0.11 #816, 0.10 #983, 0.03 #694), 06f3l (0.11 #816, 0.10 #983) >> Best rule #424 for best value: >> intensional similarity = 12 >> extensional distance = 65 >> proper extension: 03j7cf; >> query: (?x13090, 02vx4) <- position(?x13090, ?x530), ?x530 = 02_j1w, colors(?x13090, ?x3189), colors(?x13753, ?x3189), colors(?x2959, ?x3189), colors(?x2621, ?x3189), colors(?x10935, ?x3189), ?x13753 = 02zkdz, currency(?x2959, ?x170), category(?x2621, ?x134), list(?x2621, ?x2197), current_club(?x676, ?x10935) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lmm3 sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.896 http://example.org/sports/sports_team/sport #17848-0j6b5 PRED entity: 0j6b5 PRED relation: film_release_region PRED expected values: 0d060g 0345h 035qy 03h64 => 89 concepts (79 used for prediction) PRED predicted values (max 10 best out of 102): 03h64 (0.88 #591, 0.77 #1672, 0.76 #456), 035qy (0.86 #565, 0.82 #430, 0.72 #1916), 0345h (0.81 #1914, 0.80 #563, 0.80 #428), 0d060g (0.79 #544, 0.70 #409, 0.68 #1895), 05v8c (0.76 #416, 0.76 #551, 0.60 #956), 0ctw_b (0.73 #558, 0.60 #423, 0.49 #1909), 03rk0 (0.70 #582, 0.42 #447, 0.38 #1933), 015qh (0.68 #570, 0.56 #435, 0.47 #975), 06f32 (0.59 #590, 0.54 #455, 0.44 #995), 06c1y (0.59 #572, 0.36 #437, 0.31 #1923) >> Best rule #591 for best value: >> intensional similarity = 5 >> extensional distance = 64 >> proper extension: 02vxq9m; 0ds3t5x; 0g5qs2k; 05p1tzf; 02x3lt7; 0c40vxk; 0gkz15s; 01vksx; 017gl1; 08hmch; ... >> query: (?x2163, 03h64) <- film(?x709, ?x2163), film_release_region(?x2163, ?x1203), film_release_region(?x2163, ?x1003), ?x1003 = 03gj2, ?x1203 = 07ylj >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0j6b5 film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 79.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j6b5 film_release_region 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 79.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j6b5 film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 79.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j6b5 film_release_region 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 79.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17847-0n6nl PRED entity: 0n6nl PRED relation: currency PRED expected values: 09nqf => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.86 #24, 0.86 #23, 0.85 #22), 0ptk_ (0.19 #67) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 264 >> proper extension: 0mrf1; >> query: (?x13139, ?x170) <- source(?x13139, ?x958), ?x958 = 0jbk9, adjoins(?x9896, ?x13139), currency(?x9896, ?x170) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n6nl currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.857 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #17846-01qqtr PRED entity: 01qqtr PRED relation: gender PRED expected values: 02zsn => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.71 #119, 0.71 #147, 0.71 #157), 02zsn (0.56 #2, 0.52 #4, 0.45 #42) >> Best rule #119 for best value: >> intensional similarity = 2 >> extensional distance = 1757 >> proper extension: 01d494; 0c11mj; 01qx13; 071pf2; 0457w0; 02rnns; 04mx7s; 03xyp_; 02y0dd; 0ngg; >> query: (?x8966, 05zppz) <- type_of_union(?x8966, ?x566), place_of_birth(?x8966, ?x682) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 02l4pj; 02x7vq; 02d45s; *> query: (?x8966, 02zsn) <- award_nominee(?x8966, ?x11233), award_nominee(?x8966, ?x4872), ?x4872 = 02d42t, film(?x11233, ?x1692) *> conf = 0.56 ranks of expected_values: 2 EVAL 01qqtr gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.714 http://example.org/people/person/gender #17845-0hkq4 PRED entity: 0hkq4 PRED relation: adjoins! PRED expected values: 0121h7 => 156 concepts (80 used for prediction) PRED predicted values (max 10 best out of 464): 01279v (0.83 #36861, 0.82 #35293, 0.82 #36078), 0clzr (0.50 #5853, 0.33 #6637, 0.33 #1154), 0hkq4 (0.33 #1666, 0.33 #883, 0.25 #6265), 0clz7 (0.33 #947, 0.25 #6265, 0.25 #5646), 0m_w6 (0.22 #7013, 0.12 #6229, 0.08 #10931), 0mwxz (0.22 #11312, 0.03 #47410, 0.03 #54465), 0kpzy (0.14 #11268, 0.11 #9700, 0.03 #34023), 0fxyd (0.14 #11182, 0.02 #47280, 0.02 #51200), 0p54z (0.12 #5955, 0.11 #6739, 0.08 #7523), 0m__z (0.12 #6179, 0.11 #6963, 0.08 #7747) >> Best rule #36861 for best value: >> intensional similarity = 3 >> extensional distance = 167 >> proper extension: 01zlx; >> query: (?x1788, ?x13878) <- adjoins(?x7986, ?x1788), adjoins(?x1788, ?x13878), country(?x13878, ?x429) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #52556 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 263 *> proper extension: 07p7g; *> query: (?x1788, ?x1591) <- country(?x1788, ?x429), country(?x1591, ?x429), taxonomy(?x429, ?x939), organization(?x429, ?x127) *> conf = 0.04 ranks of expected_values: 134 EVAL 0hkq4 adjoins! 0121h7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 156.000 80.000 0.832 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #17844-01grpc PRED entity: 01grpc PRED relation: legislative_sessions! PRED expected values: 01gsrl => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 52): 01grpq (0.86 #838, 0.85 #1340, 0.85 #1339), 01grrf (0.86 #838, 0.85 #1340, 0.85 #1339), 01gstn (0.76 #1821, 0.76 #1820, 0.76 #1819), 01gst_ (0.76 #1821, 0.76 #1820, 0.76 #1819), 01gsvp (0.76 #1821, 0.76 #1820, 0.76 #1819), 01gsrl (0.76 #1821, 0.76 #1820, 0.76 #1819), 01gsvb (0.76 #1821, 0.76 #1820, 0.76 #1819), 01grpc (0.76 #1821, 0.76 #1820, 0.76 #1819), 01gst9 (0.76 #1821, 0.76 #1820, 0.76 #1819), 01gssm (0.76 #1821, 0.76 #1820, 0.76 #1819) >> Best rule #838 for best value: >> intensional similarity = 44 >> extensional distance = 4 >> proper extension: 077g7n; >> query: (?x4812, ?x1754) <- legislative_sessions(?x4812, ?x7714), legislative_sessions(?x4812, ?x1754), district_represented(?x4812, ?x7405), district_represented(?x4812, ?x6895), district_represented(?x4812, ?x4061), district_represented(?x4812, ?x3778), district_represented(?x4812, ?x3670), district_represented(?x4812, ?x3038), district_represented(?x4812, ?x2713), district_represented(?x4812, ?x2020), district_represented(?x4812, ?x1767), district_represented(?x4812, ?x1755), district_represented(?x4812, ?x1426), district_represented(?x4812, ?x728), legislative_sessions(?x7714, ?x6712), legislative_sessions(?x7714, ?x2712), legislative_sessions(?x4665, ?x7714), district_represented(?x6712, ?x4622), district_represented(?x6712, ?x3818), ?x7405 = 07_f2, ?x2020 = 05k7sb, ?x4665 = 07t58, ?x2713 = 06btq, ?x4622 = 04tgp, ?x3818 = 03v0t, ?x3778 = 07h34, ?x3038 = 0d0x8, legislative_sessions(?x9046, ?x7714), ?x1767 = 04rrd, legislative_sessions(?x5978, ?x1754), ?x1426 = 07z1m, legislative_sessions(?x5742, ?x4812), district_represented(?x2712, ?x3908), legislative_sessions(?x2712, ?x759), ?x728 = 059f4, ?x1755 = 01x73, ?x3908 = 04ly1, jurisdiction_of_office(?x9046, ?x94), ?x4061 = 0498y, gender(?x5742, ?x231), student(?x3439, ?x9046), ?x6895 = 05fjf, ?x3670 = 05tbn, ?x94 = 09c7w0 >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #1821 for first EXPECTED value: *> intensional similarity = 39 *> extensional distance = 22 *> proper extension: 0495ys; 03ww_x; *> query: (?x4812, ?x2712) <- legislative_sessions(?x4812, ?x7714), district_represented(?x4812, ?x7405), district_represented(?x4812, ?x4754), district_represented(?x4812, ?x4061), legislative_sessions(?x7714, ?x6712), legislative_sessions(?x7714, ?x2712), legislative_sessions(?x2860, ?x7714), district_represented(?x6712, ?x177), district_represented(?x3766, ?x7405), district_represented(?x2019, ?x7405), district_represented(?x1137, ?x7405), district_represented(?x952, ?x7405), district_represented(?x759, ?x7405), location_of_ceremony(?x566, ?x7405), location(?x6914, ?x7405), location(?x5040, ?x7405), legislative_sessions(?x7891, ?x7714), ?x1137 = 02bqn1, contains(?x7405, ?x1476), ?x2019 = 01gtbb, influenced_by(?x6914, ?x4265), ?x759 = 043djx, jurisdiction_of_office(?x900, ?x7405), award(?x6914, ?x384), religion(?x7405, ?x2769), religion(?x7405, ?x1624), ?x1624 = 051kv, spouse(?x6914, ?x9483), ?x952 = 06f0dc, ?x2769 = 019cr, legislative_sessions(?x5742, ?x4812), award_nominee(?x6914, ?x6866), profession(?x6914, ?x319), languages(?x5040, ?x254), ?x566 = 04ztj, state_province_region(?x2175, ?x4061), ?x4754 = 0g0syc, contains(?x4061, ?x2970), ?x3766 = 02gkzs *> conf = 0.76 ranks of expected_values: 6 EVAL 01grpc legislative_sessions! 01gsrl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 39.000 39.000 0.865 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #17843-06qxh PRED entity: 06qxh PRED relation: languages PRED expected values: 02h40lc => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 58): 02h40lc (0.91 #322, 0.90 #489, 0.90 #388), 06nm1 (0.26 #836, 0.14 #148, 0.12 #71), 03_9r (0.26 #836, 0.12 #224, 0.11 #257), 0t_2 (0.26 #836, 0.11 #83, 0.07 #215), 064_8sq (0.26 #836, 0.10 #106, 0.07 #150), 02bv9 (0.26 #836, 0.10 #108, 0.07 #152), 04306rv (0.26 #836, 0.10 #102, 0.07 #146), 02bjrlw (0.26 #836, 0.10 #100, 0.07 #144), 05zjd (0.10 #107, 0.07 #151, 0.03 #298), 01lqm (0.03 #298, 0.02 #802, 0.02 #632) >> Best rule #322 for best value: >> intensional similarity = 6 >> extensional distance = 54 >> proper extension: 03_8kz; >> query: (?x10140, 02h40lc) <- country_of_origin(?x10140, ?x94), program(?x12138, ?x10140), program_creator(?x10140, ?x4299), award_winner(?x4932, ?x12138), producer_type(?x12138, ?x632), award_nominee(?x12138, ?x1039) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06qxh languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.911 http://example.org/tv/tv_program/languages #17842-011ypx PRED entity: 011ypx PRED relation: nominated_for! PRED expected values: 0f4x7 099c8n => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 208): 04dn09n (0.70 #1353, 0.67 #6984, 0.67 #8111), 0f4x7 (0.68 #925, 0.37 #2728, 0.32 #3403), 099c8n (0.62 #954, 0.30 #1856, 0.29 #3432), 019f4v (0.58 #3429, 0.56 #275, 0.46 #2754), 04kxsb (0.53 #987, 0.44 #311, 0.30 #3465), 099ck7 (0.53 #1065, 0.19 #17804, 0.19 #17803), 0p9sw (0.45 #3398, 0.31 #244, 0.27 #920), 040njc (0.45 #3386, 0.43 #908, 0.38 #232), 0gr4k (0.44 #250, 0.40 #2729, 0.38 #3404), 0gr0m (0.44 #3435, 0.38 #281, 0.28 #2760) >> Best rule #1353 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 01hqhm; 0ddjy; 0qf2t; 011xg5; 02mpyh; >> query: (?x5927, ?x746) <- film_crew_role(?x5927, ?x1078), nominated_for(?x5927, ?x2107), award(?x5927, ?x746) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #925 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 011yfd; *> query: (?x5927, 0f4x7) <- award(?x5927, ?x746), nominated_for(?x2853, ?x5927), ?x2853 = 09qv_s *> conf = 0.68 ranks of expected_values: 2, 3 EVAL 011ypx nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.700 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 011ypx nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.700 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17841-0c3zjn7 PRED entity: 0c3zjn7 PRED relation: genre PRED expected values: 07s9rl0 => 86 concepts (81 used for prediction) PRED predicted values (max 10 best out of 89): 07s9rl0 (0.75 #7644, 0.70 #1945, 0.68 #1094), 01jfsb (0.50 #1592, 0.49 #1227, 0.41 #740), 03k9fj (0.43 #4502, 0.40 #1226, 0.39 #1591), 05p553 (0.43 #4, 0.39 #610, 0.38 #125), 02l7c8 (0.32 #1960, 0.31 #258, 0.30 #987), 01hmnh (0.30 #4508, 0.20 #139, 0.19 #745), 04xvlr (0.22 #244, 0.18 #973, 0.18 #1095), 0lsxr (0.21 #1588, 0.20 #1223, 0.20 #736), 0hcr (0.20 #145, 0.14 #4514, 0.08 #266), 060__y (0.16 #501, 0.16 #866, 0.15 #1110) >> Best rule #7644 for best value: >> intensional similarity = 3 >> extensional distance = 1345 >> proper extension: 0c0wvx; >> query: (?x5553, 07s9rl0) <- genre(?x5553, ?x225), genre(?x3857, ?x225), ?x3857 = 03z106 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c3zjn7 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 81.000 0.747 http://example.org/film/film/genre #17840-045zr PRED entity: 045zr PRED relation: role PRED expected values: 042v_gx => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 84): 042v_gx (0.55 #297, 0.24 #782, 0.23 #2339), 0l14qv (0.35 #102, 0.17 #5, 0.16 #2338), 018vs (0.32 #302, 0.21 #108, 0.19 #787), 02g9p4 (0.27 #1165, 0.24 #2041, 0.24 #3699), 026t6 (0.25 #100, 0.21 #3, 0.20 #779), 01vj9c (0.21 #110, 0.20 #789, 0.18 #304), 0bxl5 (0.17 #163, 0.11 #357, 0.10 #1263), 03gvt (0.15 #170, 0.08 #73, 0.07 #849), 01s0ps (0.13 #154, 0.10 #1263, 0.10 #348), 03qjg (0.13 #350, 0.10 #156, 0.09 #835) >> Best rule #297 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 07_3qd; >> query: (?x2662, 042v_gx) <- artist(?x1954, ?x2662), role(?x2662, ?x314), ?x314 = 02sgy >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045zr role 042v_gx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.552 http://example.org/music/artist/track_contributions./music/track_contribution/role #17839-03tk6z PRED entity: 03tk6z PRED relation: award! PRED expected values: 03f2_rc 01817f 03f7m4h 02bc74 => 29 concepts (13 used for prediction) PRED predicted values (max 10 best out of 2117): 02qtywd (0.79 #20169, 0.74 #33619, 0.19 #13446), 05pdbs (0.79 #20169, 0.74 #33619, 0.13 #43706), 0m_v0 (0.79 #20169, 0.74 #33619, 0.07 #11027), 01vs_v8 (0.27 #10668, 0.18 #17391, 0.15 #20753), 0gbwp (0.23 #11192, 0.15 #17915, 0.14 #1108), 01vvycq (0.23 #10233, 0.14 #16956, 0.13 #6871), 0fhxv (0.20 #11424, 0.13 #18147, 0.11 #8062), 01wwvc5 (0.19 #13446, 0.18 #23531, 0.15 #10819), 0197tq (0.19 #13446, 0.18 #23531, 0.15 #30255), 01cwhp (0.19 #13446, 0.18 #23531, 0.15 #30255) >> Best rule #20169 for best value: >> intensional similarity = 5 >> extensional distance = 168 >> proper extension: 09v8db5; 09v1lrz; >> query: (?x4382, ?x1238) <- award(?x7906, ?x4382), award(?x2662, ?x4382), languages(?x7906, ?x254), award_winner(?x4382, ?x1238), instrumentalists(?x227, ?x2662) >> conf = 0.79 => this is the best rule for 3 predicted values *> Best rule #23531 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 199 *> proper extension: 09v0wy2; 0dgr5xp; 09v51c2; *> query: (?x4382, ?x217) <- award(?x2662, ?x4382), artists(?x284, ?x2662), award_nominee(?x2662, ?x217), category(?x2662, ?x134) *> conf = 0.18 ranks of expected_values: 45, 231, 699, 1128 EVAL 03tk6z award! 02bc74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 29.000 13.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03tk6z award! 03f7m4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 29.000 13.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03tk6z award! 01817f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 29.000 13.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03tk6z award! 03f2_rc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 29.000 13.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17838-025cn2 PRED entity: 025cn2 PRED relation: award PRED expected values: 054ks3 => 114 concepts (85 used for prediction) PRED predicted values (max 10 best out of 264): 01by1l (0.39 #2125, 0.37 #2528, 0.35 #1319), 01bgqh (0.32 #446, 0.29 #849, 0.29 #2058), 054ks3 (0.32 #543, 0.23 #3767, 0.23 #2961), 023vrq (0.26 #728, 0.12 #1534, 0.12 #1131), 02gdjb (0.26 #622, 0.12 #1025, 0.08 #1428), 09sb52 (0.24 #17373, 0.24 #10522, 0.24 #27456), 03qbh5 (0.22 #1413, 0.22 #2219, 0.21 #2622), 01c427 (0.21 #485, 0.17 #888, 0.12 #1291), 0gs9p (0.20 #78, 0.09 #4512, 0.09 #7736), 019f4v (0.20 #66, 0.09 #4500, 0.09 #7724) >> Best rule #2125 for best value: >> intensional similarity = 3 >> extensional distance = 157 >> proper extension: 01vvydl; 0146pg; 07c0j; 01k5t_3; 012x4t; 05crg7; 01trhmt; 0249kn; 01vsykc; 017j6; ... >> query: (?x6164, 01by1l) <- award_winner(?x6164, ?x4013), origin(?x6164, ?x739), award_nominee(?x3483, ?x6164) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #543 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 06lxn; *> query: (?x6164, 054ks3) <- award_winner(?x6164, ?x4013), origin(?x6164, ?x739), ?x739 = 02_286 *> conf = 0.32 ranks of expected_values: 3 EVAL 025cn2 award 054ks3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 85.000 0.390 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17837-0lzkm PRED entity: 0lzkm PRED relation: nationality PRED expected values: 09c7w0 => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 86): 09c7w0 (0.85 #2683, 0.78 #14012, 0.78 #13414), 02jx1 (0.31 #428, 0.29 #1717, 0.29 #1518), 07ssc (0.30 #212, 0.27 #311, 0.19 #1302), 0ctw_b (0.18 #323, 0.02 #1413, 0.02 #2807), 0345h (0.10 #228, 0.07 #525, 0.03 #4403), 0d060g (0.09 #1990, 0.08 #403, 0.07 #1096), 0chghy (0.08 #406, 0.05 #1199, 0.02 #1298), 03_3d (0.08 #402, 0.05 #1195, 0.02 #9747), 0hzlz (0.08 #418, 0.02 #1211, 0.01 #1707), 0j5g9 (0.07 #754, 0.01 #4038) >> Best rule #2683 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 086qd; >> query: (?x3735, 09c7w0) <- origin(?x3735, ?x1860), artists(?x2996, ?x3735), county(?x1860, ?x6410), profession(?x3735, ?x131) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lzkm nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.855 http://example.org/people/person/nationality #17836-02qw2xb PRED entity: 02qw2xb PRED relation: film PRED expected values: 0h1fktn => 86 concepts (77 used for prediction) PRED predicted values (max 10 best out of 438): 0h1fktn (0.54 #2762, 0.47 #6345, 0.43 #4553), 05f4vxd (0.47 #73455, 0.45 #46573, 0.37 #53740), 0888c3 (0.10 #1416, 0.08 #3207, 0.07 #4998), 02ht1k (0.10 #631, 0.08 #2422, 0.07 #4213), 02stbw (0.10 #384, 0.08 #2175, 0.07 #3966), 02825cv (0.10 #1144, 0.08 #2935, 0.07 #4726), 06fpsx (0.10 #1340, 0.08 #3131, 0.07 #4922), 0cc97st (0.10 #989, 0.08 #2780, 0.07 #4571), 065_cjc (0.10 #1198, 0.08 #2989, 0.07 #4780), 05znxx (0.10 #880, 0.08 #2671, 0.07 #4462) >> Best rule #2762 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 05ztm4r; 0783m_; 077yk0; >> query: (?x7797, 0h1fktn) <- award_winner(?x6804, ?x7797), award_winner(?x5571, ?x7797), ?x6804 = 022q4l9, ?x5571 = 03cxsvl >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qw2xb film 0h1fktn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 77.000 0.538 http://example.org/film/actor/film./film/performance/film #17835-06j2v PRED entity: 06j2v PRED relation: geographic_distribution PRED expected values: 02j9z => 42 concepts (40 used for prediction) PRED predicted values (max 10 best out of 82): 09c7w0 (0.68 #918, 0.62 #1071, 0.58 #1909), 02jx1 (0.41 #764, 0.40 #459, 0.26 #917), 0345h (0.36 #482, 0.33 #634, 0.33 #251), 07ssc (0.36 #470, 0.33 #622, 0.33 #239), 09pmkv (0.36 #477, 0.33 #629, 0.33 #246), 0d060g (0.36 #465, 0.33 #617, 0.33 #234), 016wzw (0.33 #269, 0.29 #1986, 0.12 #1987), 06t2t (0.33 #264, 0.27 #495, 0.25 #647), 0chghy (0.33 #237, 0.27 #468, 0.25 #620), 07f1x (0.33 #292, 0.25 #675, 0.18 #523) >> Best rule #918 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 01336l; >> query: (?x13372, 09c7w0) <- people(?x13372, ?x4918), geographic_distribution(?x13372, ?x583), artists(?x283, ?x4918), combatants(?x583, ?x94), film_release_region(?x7126, ?x583), ?x7126 = 0ds1glg >> conf = 0.68 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06j2v geographic_distribution 02j9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 40.000 0.684 http://example.org/people/ethnicity/geographic_distribution #17834-05vk_d PRED entity: 05vk_d PRED relation: profession PRED expected values: 0d1pc => 131 concepts (127 used for prediction) PRED predicted values (max 10 best out of 68): 018gz8 (0.54 #464, 0.20 #8661, 0.15 #9555), 0dxtg (0.46 #461, 0.30 #8658, 0.30 #759), 01d_h8 (0.45 #8650, 0.43 #8799, 0.41 #3135), 03gjzk (0.38 #1654, 0.33 #164, 0.32 #1207), 0nbcg (0.35 #1671, 0.30 #1969, 0.29 #628), 02jknp (0.31 #8048, 0.23 #8205, 0.22 #1051), 09jwl (0.31 #1360, 0.30 #1956, 0.28 #1658), 0cbd2 (0.31 #454, 0.17 #305, 0.11 #18786), 0d1pc (0.31 #2584, 0.29 #2733, 0.25 #3627), 016z4k (0.25 #749, 0.25 #302, 0.21 #600) >> Best rule #464 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0pgm3; >> query: (?x8638, 018gz8) <- gender(?x8638, ?x514), film(?x8638, ?x7311), ?x7311 = 0g9z_32 >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #2584 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 01trhmt; *> query: (?x8638, 0d1pc) <- vacationer(?x9191, ?x8638), award(?x8638, ?x1007), participant(?x3999, ?x8638) *> conf = 0.31 ranks of expected_values: 9 EVAL 05vk_d profession 0d1pc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 131.000 127.000 0.538 http://example.org/people/person/profession #17833-01d_4t PRED entity: 01d_4t PRED relation: people! PRED expected values: 0g48m4 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 31): 07hwkr (0.33 #89, 0.22 #166, 0.12 #397), 0x67 (0.31 #241, 0.30 #472, 0.21 #318), 0xnvg (0.25 #13, 0.17 #90, 0.06 #398), 033tf_ (0.22 #161, 0.12 #392, 0.09 #2472), 041rx (0.19 #389, 0.15 #235, 0.15 #466), 03bkbh (0.17 #109, 0.06 #417, 0.02 #1187), 01g7zj (0.08 #283, 0.07 #360, 0.05 #514), 02ctzb (0.07 #708, 0.07 #785, 0.03 #1632), 013b6_ (0.07 #361, 0.05 #515, 0.04 #746), 038723 (0.07 #377, 0.05 #531) >> Best rule #89 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0gd9k; >> query: (?x8876, 07hwkr) <- profession(?x8876, ?x8709), profession(?x8876, ?x319), student(?x2228, ?x8876), ?x8709 = 08z956, ?x319 = 01d_h8 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01d_4t people! 0g48m4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 83.000 0.333 http://example.org/people/ethnicity/people #17832-025scjj PRED entity: 025scjj PRED relation: nominated_for! PRED expected values: 0gq_v => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 199): 027c95y (0.66 #5489, 0.66 #2383, 0.66 #7159), 0gs9p (0.53 #301, 0.50 #63, 0.30 #1729), 0gq_v (0.50 #257, 0.46 #19, 0.28 #8113), 019f4v (0.45 #767, 0.32 #53, 0.29 #291), 0gr4k (0.44 #263, 0.39 #25, 0.30 #977), 0gqyl (0.41 #317, 0.39 #79, 0.25 #793), 0gr0m (0.39 #59, 0.35 #297, 0.20 #1725), 0gqy2 (0.36 #122, 0.35 #360, 0.23 #3576), 04dn09n (0.34 #748, 0.22 #2178, 0.20 #1700), 0k611 (0.29 #786, 0.24 #1738, 0.24 #1978) >> Best rule #5489 for best value: >> intensional similarity = 3 >> extensional distance = 890 >> proper extension: 0fpxp; >> query: (?x9572, ?x2915) <- nominated_for(?x6921, ?x9572), award(?x9572, ?x2915), award_nominee(?x6921, ?x786) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #257 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 0jyx6; 0c5dd; 02q52q; 0283_zv; 070fnm; 0jym0; 0gxfz; 0k4f3; 0hfzr; 097zcz; ... *> query: (?x9572, 0gq_v) <- award_winner(?x9572, ?x786), genre(?x9572, ?x1805), ?x1805 = 01g6gs, film(?x4597, ?x9572) *> conf = 0.50 ranks of expected_values: 3 EVAL 025scjj nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 64.000 0.663 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17831-09p0q PRED entity: 09p0q PRED relation: profession PRED expected values: 02krf9 => 109 concepts (76 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.81 #9784, 0.79 #10228, 0.78 #4159), 0dxtg (0.62 #9191, 0.61 #3417, 0.54 #1048), 03gjzk (0.43 #2975, 0.40 #4456, 0.38 #4604), 0cbd2 (0.34 #2078, 0.31 #2523, 0.29 #1634), 0kyk (0.28 #2101, 0.26 #1657, 0.26 #325), 02krf9 (0.23 #3431, 0.17 #1062, 0.17 #1802), 018gz8 (0.21 #312, 0.16 #2088, 0.15 #1644), 09jwl (0.19 #10233, 0.17 #1646, 0.17 #8605), 0n1h (0.16 #158, 0.09 #306, 0.08 #1934), 0nbcg (0.15 #2399, 0.14 #1955, 0.13 #2251) >> Best rule #9784 for best value: >> intensional similarity = 4 >> extensional distance = 1705 >> proper extension: 01gp_x; 027hnjh; 027d5g5; 0b4rf3; >> query: (?x8662, 02hrh1q) <- award_nominee(?x4383, ?x8662), profession(?x8662, ?x319), profession(?x731, ?x319), ?x731 = 09byk >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #3431 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 398 *> proper extension: 0c8hct; *> query: (?x8662, 02krf9) <- place_of_birth(?x8662, ?x1860), profession(?x8662, ?x524), ?x524 = 02jknp *> conf = 0.23 ranks of expected_values: 6 EVAL 09p0q profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 109.000 76.000 0.811 http://example.org/people/person/profession #17830-04zkj5 PRED entity: 04zkj5 PRED relation: influenced_by PRED expected values: 01xdf5 => 93 concepts (47 used for prediction) PRED predicted values (max 10 best out of 328): 01hmk9 (0.18 #2398, 0.14 #3268, 0.12 #4139), 014z8v (0.16 #2299, 0.14 #4040, 0.11 #3169), 014zfs (0.14 #2203, 0.11 #3073, 0.09 #4379), 081lh (0.13 #2198, 0.10 #3068, 0.09 #3939), 0p_47 (0.12 #3155, 0.10 #2285, 0.10 #4026), 08433 (0.10 #3940, 0.05 #1327, 0.04 #4811), 032l1 (0.10 #5314, 0.07 #6624, 0.07 #7059), 01k9lpl (0.10 #2488, 0.09 #4229, 0.08 #3358), 081k8 (0.09 #1461, 0.08 #6690, 0.08 #5380), 041h0 (0.09 #2188, 0.02 #5235, 0.02 #6545) >> Best rule #2398 for best value: >> intensional similarity = 2 >> extensional distance = 103 >> proper extension: 02yl42; 017_pb; 0739y; 014ps4; 06hgj; 01vs4f3; 07lp1; 0167xy; 02ghq; 01d5g; ... >> query: (?x7663, 01hmk9) <- influenced_by(?x7663, ?x4657), award_winner(?x886, ?x4657) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #7406 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 515 *> proper extension: 02pb2bp; 040_9; 02cpp; 017mbb; 014_xj; 04sd0; 0chnf; 0fpzzp; 0w6w; 0716b6; ... *> query: (?x7663, ?x236) <- influenced_by(?x7663, ?x2127), influenced_by(?x236, ?x2127) *> conf = 0.05 ranks of expected_values: 41 EVAL 04zkj5 influenced_by 01xdf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 93.000 47.000 0.181 http://example.org/influence/influence_node/influenced_by #17829-0jm9w PRED entity: 0jm9w PRED relation: team! PRED expected values: 02_ssl => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 49): 02_ssl (0.84 #1020, 0.84 #1169, 0.84 #2873), 0ctt4z (0.81 #5054, 0.79 #1070, 0.74 #3266), 02sdk9v (0.77 #5007, 0.63 #6234, 0.63 #6380), 02nzb8 (0.71 #5006, 0.60 #6379, 0.58 #6233), 02_j1w (0.71 #5011, 0.66 #6238, 0.64 #6033), 0dgrmp (0.70 #5009, 0.56 #3900, 0.55 #4044), 0619m3 (0.60 #4715, 0.59 #2335, 0.58 #1557), 01z9v6 (0.50 #184, 0.43 #476, 0.42 #1206), 02wszf (0.50 #177, 0.43 #469, 0.42 #1199), 02lyr4 (0.50 #162, 0.42 #1184, 0.41 #3136) >> Best rule #1020 for best value: >> intensional similarity = 9 >> extensional distance = 8 >> proper extension: 0jmcv; >> query: (?x9995, ?x1348) <- sport(?x9995, ?x4833), team(?x13926, ?x9995), colors(?x9995, ?x332), position(?x9995, ?x1348), draft(?x9995, ?x8586), draft(?x9937, ?x8586), school(?x9995, ?x4296), nationality(?x13926, ?x94), ?x9937 = 0jmjr >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jm9w team! 02_ssl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.842 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #17828-04pz5c PRED entity: 04pz5c PRED relation: profession PRED expected values: 0dxtg => 85 concepts (82 used for prediction) PRED predicted values (max 10 best out of 68): 0dxtg (0.65 #301, 0.45 #589, 0.44 #157), 01d_h8 (0.49 #294, 0.35 #1734, 0.34 #150), 0cbd2 (0.47 #439, 0.45 #1015, 0.45 #1303), 018gz8 (0.28 #15, 0.21 #591, 0.21 #735), 02krf9 (0.27 #312, 0.09 #3769, 0.09 #2184), 09jwl (0.19 #1889, 0.18 #3186, 0.18 #5490), 0d8qb (0.13 #75, 0.07 #507, 0.07 #219), 0nbcg (0.13 #2765, 0.13 #3197, 0.12 #5501), 0dz3r (0.13 #3171, 0.13 #2739, 0.12 #5475), 05z96 (0.13 #1191, 0.12 #1047, 0.12 #1335) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 241 >> proper extension: 0lzb8; 04l3_z; 04n7njg; 02p21g; 03ft8; 0126rp; 0jt90f5; 01gbbz; 03m_k0; 01jbx1; ... >> query: (?x5642, 0dxtg) <- profession(?x5642, ?x524), producer_type(?x5642, ?x632), ?x632 = 0ckd1 >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04pz5c profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 82.000 0.654 http://example.org/people/person/profession #17827-02t3ln PRED entity: 02t3ln PRED relation: artists! PRED expected values: 05r6t 04qftx => 95 concepts (58 used for prediction) PRED predicted values (max 10 best out of 266): 06by7 (0.90 #9644, 0.86 #10267, 0.85 #14925), 05r6t (0.71 #3497, 0.67 #1331, 0.60 #704), 064t9 (0.67 #17401, 0.46 #16780, 0.45 #16157), 01lyv (0.66 #13387, 0.40 #967, 0.33 #35), 016clz (0.60 #624, 0.56 #13048, 0.55 #14288), 03lty (0.60 #9962, 0.52 #4996, 0.44 #5931), 05bt6j (0.50 #1909, 0.37 #16811, 0.33 #1600), 05w3f (0.47 #3762, 0.45 #4695, 0.42 #4075), 02yv6b (0.41 #3825, 0.40 #4758, 0.37 #6004), 0mhfr (0.40 #3128, 0.40 #957, 0.35 #3748) >> Best rule #9644 for best value: >> intensional similarity = 10 >> extensional distance = 94 >> proper extension: 089tm; 01t_xp_; 01pfr3; 02r3zy; 07c0j; 03t9sp; 05crg7; 04r1t; 0frsw; 02r1tx7; ... >> query: (?x4791, 06by7) <- origin(?x4791, ?x3052), artists(?x7329, ?x4791), group(?x227, ?x4791), artists(?x7329, ?x8143), artists(?x7329, ?x4642), artists(?x7329, ?x4381), ?x8143 = 01wvxw1, ?x4381 = 0qf11, parent_genre(?x837, ?x7329), ?x4642 = 0394y >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #3497 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 12 *> proper extension: 0285c; 02y7sr; *> query: (?x4791, 05r6t) <- origin(?x4791, ?x3052), artists(?x12179, ?x4791), artists(?x7808, ?x4791), ?x7808 = 0jmwg, parent_genre(?x2664, ?x12179), parent_genre(?x12179, ?x284), artists(?x284, ?x9631), ?x9631 = 09z1lg *> conf = 0.71 ranks of expected_values: 2, 132 EVAL 02t3ln artists! 04qftx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 95.000 58.000 0.896 http://example.org/music/genre/artists EVAL 02t3ln artists! 05r6t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 58.000 0.896 http://example.org/music/genre/artists #17826-05ys0ws PRED entity: 05ys0ws PRED relation: film_festivals! PRED expected values: 0gcpc => 74 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1520): 0ddfwj1 (0.40 #7, 0.22 #705, 0.21 #2108), 0cp08zg (0.40 #182, 0.22 #880, 0.16 #6787), 09gq0x5 (0.40 #37, 0.22 #735, 0.16 #6787), 0cw3yd (0.22 #760, 0.20 #62, 0.16 #6787), 02rb607 (0.22 #749, 0.18 #1216, 0.16 #6787), 047vp1n (0.22 #871, 0.18 #1338, 0.16 #6787), 047p798 (0.22 #917, 0.18 #1384, 0.14 #2320), 0462hhb (0.21 #1977, 0.20 #2680, 0.18 #1277), 0b76d_m (0.20 #1, 0.18 #1166, 0.17 #234), 0gyfp9c (0.20 #2640, 0.18 #1237, 0.14 #2173) >> Best rule #7 for best value: >> intensional similarity = 13 >> extensional distance = 3 >> proper extension: 0g57ws5; 0bmj62v; 0hrcs29; >> query: (?x13969, 0ddfwj1) <- film_festivals(?x5849, ?x13969), film_festivals(?x4024, ?x13969), film_festivals(?x1308, ?x13969), nominated_for(?x484, ?x1308), film_release_region(?x5849, ?x1264), production_companies(?x5849, ?x541), nominated_for(?x3637, ?x4024), language(?x5849, ?x5607), film_crew_role(?x4024, ?x137), ?x5607 = 064_8sq, genre(?x5849, ?x53), cinematography(?x1308, ?x6549), ?x1264 = 0345h >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #932 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 7 *> proper extension: 0kfhjq0; 09rwjly; 04_m9gk; 0bx_f_t; *> query: (?x13969, ?x66) <- film_festivals(?x5849, ?x13969), film_festivals(?x4024, ?x13969), film_festivals(?x1308, ?x13969), nominated_for(?x484, ?x1308), film_release_region(?x5849, ?x1264), production_companies(?x5849, ?x541), nominated_for(?x3637, ?x4024), language(?x5849, ?x5607), film_crew_role(?x4024, ?x137), ?x5607 = 064_8sq, genre(?x5849, ?x53), cinematography(?x1308, ?x6549), film_release_region(?x66, ?x1264) *> conf = 0.01 ranks of expected_values: 1084 EVAL 05ys0ws film_festivals! 0gcpc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 74.000 62.000 0.400 http://example.org/film/film/film_festivals #17825-0dbxy PRED entity: 0dbxy PRED relation: people PRED expected values: 04xhwn => 72 concepts (38 used for prediction) PRED predicted values (max 10 best out of 4128): 01pk3z (0.50 #5949, 0.40 #14559, 0.33 #785), 0484q (0.50 #6180, 0.40 #14790, 0.33 #1016), 018ygt (0.50 #6052, 0.40 #14662, 0.33 #888), 044mvs (0.50 #6585, 0.40 #15195, 0.33 #1421), 01hb6v (0.33 #2062, 0.33 #340, 0.25 #8947), 01twdk (0.33 #2395, 0.25 #9280, 0.25 #7558), 03_vx9 (0.33 #1844, 0.25 #8729, 0.25 #7007), 016z2j (0.33 #2027, 0.25 #8912, 0.25 #7190), 01vrt_c (0.33 #1876, 0.25 #8761, 0.25 #7039), 03rx9 (0.33 #3089, 0.25 #9974, 0.25 #8252) >> Best rule #5949 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 06v41q; >> query: (?x11053, 01pk3z) <- people(?x11053, ?x7331), people(?x11053, ?x2444), ?x7331 = 01vtj38, award_nominee(?x398, ?x2444), role(?x2444, ?x227), film(?x2444, ?x1642), film_release_region(?x1642, ?x2513), ?x2513 = 05b4w >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #17218 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 0xff; *> query: (?x11053, ?x71) <- combatants(?x8416, ?x11053), people(?x11053, ?x5691), people(?x11053, ?x5604), people(?x11053, ?x4930), place_of_birth(?x4930, ?x3037), people(?x3591, ?x5691), profession(?x4930, ?x1032), type_of_union(?x5604, ?x566), people(?x3591, ?x71) *> conf = 0.20 ranks of expected_values: 460 EVAL 0dbxy people 04xhwn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 38.000 0.500 http://example.org/people/ethnicity/people #17824-01h0kx PRED entity: 01h0kx PRED relation: parent_genre PRED expected values: 0133_p => 84 concepts (72 used for prediction) PRED predicted values (max 10 best out of 308): 059kh (0.60 #345, 0.43 #2402, 0.38 #473), 03lty (0.56 #8272, 0.50 #6837, 0.48 #6043), 0glt670 (0.53 #7322, 0.44 #7960, 0.33 #653), 02x8m (0.50 #1435, 0.50 #642, 0.33 #485), 016clz (0.50 #1745, 0.40 #319, 0.38 #473), 05bt6j (0.47 #2557, 0.36 #2239, 0.33 #25), 08cyft (0.42 #5109, 0.38 #4001, 0.37 #5904), 01243b (0.42 #4305, 0.40 #1765, 0.40 #339), 09jw2 (0.40 #410, 0.38 #473, 0.36 #788), 01h0kx (0.38 #473, 0.36 #788, 0.26 #2532) >> Best rule #345 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 01b4p4; >> query: (?x9881, 059kh) <- parent_genre(?x9881, ?x5934), parent_genre(?x9881, ?x5909), parent_genre(?x7577, ?x9881), artists(?x5909, ?x2854), artists(?x5909, ?x2005), ?x2854 = 0dm5l, ?x5934 = 05r6t, ?x2005 = 05k79 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4844 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 22 *> proper extension: 02l96k; *> query: (?x9881, 0133_p) <- parent_genre(?x9881, ?x5934), parent_genre(?x7577, ?x9881), artists(?x7577, ?x2635), artists(?x5934, ?x9497), parent_genre(?x13882, ?x5934), award_nominee(?x4548, ?x2635), ?x13882 = 08s6r6, award(?x9497, ?x2634) *> conf = 0.17 ranks of expected_values: 30 EVAL 01h0kx parent_genre 0133_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 84.000 72.000 0.600 http://example.org/music/genre/parent_genre #17823-0bc773 PRED entity: 0bc773 PRED relation: award_winner PRED expected values: 0d6d2 => 36 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1436): 06rnl9 (0.25 #421, 0.25 #18467, 0.11 #6578), 04ktcgn (0.25 #3355, 0.17 #9509, 0.15 #14126), 0pmhf (0.25 #375, 0.17 #3455, 0.11 #9609), 09rp4r_ (0.25 #220, 0.12 #4839, 0.11 #6377), 02lp3c (0.25 #951, 0.12 #5570, 0.11 #7108), 016yvw (0.25 #832, 0.11 #27702, 0.09 #13850), 0l6px (0.25 #331, 0.09 #1871, 0.08 #3411), 02l5rm (0.25 #442, 0.09 #1982, 0.06 #23085), 0h1mt (0.25 #149, 0.09 #1689, 0.06 #4768), 03wdsbz (0.25 #1531, 0.09 #3071, 0.06 #6150) >> Best rule #421 for best value: >> intensional similarity = 16 >> extensional distance = 2 >> proper extension: 073hgx; 0bzlrh; >> query: (?x3579, 06rnl9) <- ceremony(?x2222, ?x3579), ceremony(?x601, ?x3579), ?x601 = 0gr4k, award_winner(?x3579, ?x3954), award_winner(?x3579, ?x986), award_winner(?x3579, ?x777), ?x777 = 05kfs, gender(?x3954, ?x514), award_winner(?x68, ?x986), award_winner(?x5816, ?x3954), participant(?x719, ?x986), film(?x986, ?x718), nominated_for(?x986, ?x306), profession(?x3954, ?x353), award_nominee(?x4562, ?x986), ?x2222 = 0gs96 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #25807 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 38 *> proper extension: 0hhtgcw; *> query: (?x3579, 0d6d2) <- award_winner(?x3579, ?x986), honored_for(?x3579, ?x12108), profession(?x986, ?x353), student(?x2730, ?x986), nominated_for(?x986, ?x10241), nominated_for(?x986, ?x5020), influenced_by(?x986, ?x2283), honored_for(?x1442, ?x10241), award_winner(?x68, ?x986), genre(?x5020, ?x2700), film(?x382, ?x12108), film_release_region(?x12108, ?x94) *> conf = 0.03 ranks of expected_values: 580 EVAL 0bc773 award_winner 0d6d2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 18.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17822-0z20d PRED entity: 0z20d PRED relation: location! PRED expected values: 0bkmf => 131 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1957): 03vhvp (0.33 #75401, 0.33 #60320, 0.32 #57806), 02hzz (0.33 #75401, 0.33 #60320, 0.32 #57806), 01s21dg (0.11 #8504, 0.08 #21070, 0.06 #5990), 03d_w3h (0.11 #2663, 0.08 #10203, 0.08 #22770), 01vvzb1 (0.10 #6112, 0.04 #51351, 0.04 #36272), 06pj8 (0.09 #384, 0.06 #7924, 0.04 #12951), 01wz01 (0.09 #815, 0.04 #3328, 0.03 #43540), 03h502k (0.09 #1042, 0.04 #3555, 0.02 #18635), 083p7 (0.09 #193, 0.04 #2706, 0.02 #17786), 04pxcx (0.09 #893, 0.04 #36079, 0.03 #5919) >> Best rule #75401 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 059rby; 04jpl; 05ksh; 0fvvz; 0pmq2; 0pzpz; 052p7; 01ly5m; 0cv3w; 019k6n; ... >> query: (?x7919, ?x8131) <- place_of_birth(?x9176, ?x7919), origin(?x8131, ?x7919), location(?x2308, ?x7919), category(?x9176, ?x134) >> conf = 0.33 => this is the best rule for 2 predicted values *> Best rule #37079 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 07b_l; 05tbn; 0161c; 0vg8x; 08809; *> query: (?x7919, 0bkmf) <- time_zones(?x7919, ?x2674), location(?x5508, ?x7919), music(?x428, ?x5508) *> conf = 0.02 ranks of expected_values: 1730 EVAL 0z20d location! 0bkmf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 131.000 71.000 0.329 http://example.org/people/person/places_lived./people/place_lived/location #17821-03d63lb PRED entity: 03d63lb PRED relation: gender PRED expected values: 05zppz => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #23, 0.74 #27, 0.73 #21), 02zsn (0.46 #127, 0.45 #132, 0.30 #6) >> Best rule #23 for best value: >> intensional similarity = 2 >> extensional distance = 205 >> proper extension: 01qx13; 015k7; 0dhqyw; 0cmpn; 0b5x23; 0frpd5; 0cfywh; 02qfk4j; >> query: (?x14192, 05zppz) <- nationality(?x14192, ?x2146), ?x2146 = 03rk0 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03d63lb gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.739 http://example.org/people/person/gender #17820-0fy6bh PRED entity: 0fy6bh PRED relation: ceremony! PRED expected values: 0gs9p => 42 concepts (41 used for prediction) PRED predicted values (max 10 best out of 364): 0gs9p (0.93 #2708, 0.92 #2466, 0.92 #3675), 0gr07 (0.82 #2086, 0.81 #1845, 0.78 #3296), 0l8z1 (0.78 #2698, 0.77 #2214, 0.76 #1972), 018wdw (0.69 #3070, 0.67 #1618, 0.67 #1375), 0gqxm (0.60 #1566, 0.60 #1323, 0.59 #3018), 0gqzz (0.24 #2939, 0.24 #1970, 0.21 #4145), 054krc (0.21 #6332, 0.19 #7779, 0.15 #8502), 04dn09n (0.21 #6304, 0.18 #7751, 0.15 #8474), 054knh (0.21 #6466, 0.18 #7913, 0.15 #8636), 019f4v (0.19 #6319, 0.18 #7766, 0.14 #8489) >> Best rule #2708 for best value: >> intensional similarity = 19 >> extensional distance = 25 >> proper extension: 02yw5r; >> query: (?x3029, 0gs9p) <- ceremony(?x3617, ?x3029), ceremony(?x1862, ?x3029), ceremony(?x1245, ?x3029), ceremony(?x484, ?x3029), award_winner(?x3029, ?x12398), award_winner(?x3029, ?x9363), award_winner(?x3029, ?x6440), award_winner(?x3029, ?x3348), ?x3617 = 0gvx_, ?x1245 = 0gqwc, ?x484 = 0gq_v, honored_for(?x3029, ?x984), ?x1862 = 0gr51, people(?x1050, ?x3348), participant(?x1607, ?x6440), award(?x6440, ?x1716), award_nominee(?x9363, ?x788), award_nominee(?x199, ?x12398), nominated_for(?x1716, ?x718) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fy6bh ceremony! 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 41.000 0.926 http://example.org/award/award_category/winners./award/award_honor/ceremony #17819-06t8v PRED entity: 06t8v PRED relation: adjoins PRED expected values: 01pj7 => 143 concepts (73 used for prediction) PRED predicted values (max 10 best out of 575): 01pj7 (0.84 #30813, 0.83 #26958, 0.82 #28501), 0345h (0.25 #64, 0.24 #833, 0.17 #1602), 077qn (0.22 #24646, 0.21 #51605, 0.21 #30814), 0f8l9c (0.22 #24646, 0.21 #51605, 0.21 #30814), 06npd (0.22 #24646, 0.21 #51605, 0.21 #30814), 06t8v (0.22 #24646, 0.21 #51605, 0.21 #30814), 07t21 (0.22 #24646, 0.21 #51605, 0.21 #30814), 06c1y (0.22 #24646, 0.21 #51605, 0.21 #30814), 06mzp (0.22 #24646, 0.21 #51605, 0.21 #30814), 01mjq (0.21 #51605, 0.21 #46215, 0.09 #2389) >> Best rule #30813 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 01531; >> query: (?x3277, ?x1790) <- teams(?x3277, ?x9254), adjoins(?x1790, ?x3277), adjoins(?x2979, ?x1790) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06t8v adjoins 01pj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 73.000 0.836 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #17818-02_01w PRED entity: 02_01w PRED relation: category PRED expected values: 08mbj5d => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.44 #12, 0.42 #37, 0.41 #32) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01sb5r; >> query: (?x12004, 08mbj5d) <- celebrity(?x3628, ?x12004), location(?x12004, ?x2453), nationality(?x12004, ?x94), capital(?x6842, ?x2453) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_01w category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.444 http://example.org/common/topic/webpage./common/webpage/category #17817-0fv6dr PRED entity: 0fv6dr PRED relation: place_of_birth PRED expected values: 0dhdp => 47 concepts (33 used for prediction) PRED predicted values (max 10 best out of 87): 01s3v (0.33 #1079, 0.25 #1783, 0.20 #2487), 01lfy (0.25 #1701, 0.20 #2405, 0.01 #6638), 06y57 (0.20 #2996, 0.02 #4405, 0.01 #6525), 04jpl (0.11 #16226, 0.10 #14816, 0.10 #15521), 01llj3 (0.06 #4150, 0.02 #4855, 0.02 #5561), 0m75g (0.06 #3785, 0.02 #4490, 0.02 #5196), 01ngx6 (0.06 #4179, 0.02 #4884, 0.02 #5590), 013wf1 (0.06 #4035, 0.02 #4740, 0.02 #5446), 0195j0 (0.06 #4022, 0.02 #4727, 0.02 #5433), 01vx3m (0.06 #3832, 0.02 #4537, 0.02 #5243) >> Best rule #1079 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0fp_xp; >> query: (?x3047, 01s3v) <- team(?x3047, ?x10294), team(?x3047, ?x10112), ?x10112 = 01kc4s, team(?x63, ?x10294), athlete(?x471, ?x3047), gender(?x3047, ?x231), ?x63 = 02sdk9v >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fv6dr place_of_birth 0dhdp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 33.000 0.333 http://example.org/people/person/place_of_birth #17816-01lnyf PRED entity: 01lnyf PRED relation: contains! PRED expected values: 09c7w0 => 232 concepts (139 used for prediction) PRED predicted values (max 10 best out of 247): 09c7w0 (0.83 #12539, 0.82 #3583, 0.82 #2688), 0tbql (0.79 #51957, 0.79 #58231, 0.78 #70768), 0np52 (0.60 #96750, 0.57 #86893, 0.57 #26873), 04_1l0v (0.32 #90477, 0.01 #12986), 01n7q (0.25 #78, 0.20 #1868, 0.18 #3658), 02jx1 (0.20 #51148, 0.18 #67272, 0.15 #95940), 05fkf (0.20 #1835, 0.12 #45, 0.04 #11686), 06yxd (0.18 #3868, 0.18 #2973, 0.03 #20885), 059rby (0.16 #23307, 0.13 #40329, 0.13 #27788), 02_286 (0.14 #23330, 0.11 #40352, 0.10 #24226) >> Best rule #12539 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 05kj_; >> query: (?x4556, 09c7w0) <- school(?x8786, ?x4556), category(?x4556, ?x134), contains(?x1782, ?x4556), ?x134 = 08mbj5d >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lnyf contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 232.000 139.000 0.833 http://example.org/location/location/contains #17815-01z27 PRED entity: 01z27 PRED relation: country PRED expected values: 04gzd => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 444): 01znc_ (0.92 #1027, 0.83 #515, 0.80 #510), 0jhd (0.92 #1027, 0.83 #515, 0.80 #1035), 0d0kn (0.92 #1027, 0.83 #515, 0.75 #688), 07ssc (0.87 #5148, 0.85 #170, 0.84 #3428), 0b90_r (0.86 #3262, 0.85 #3095, 0.81 #3776), 015qh (0.85 #170, 0.83 #515, 0.81 #517), 059j2 (0.85 #170, 0.83 #515, 0.81 #517), 04j53 (0.85 #170, 0.83 #515, 0.81 #517), 087vz (0.85 #170, 0.81 #517, 0.80 #510), 01mk6 (0.85 #170, 0.81 #517, 0.80 #510) >> Best rule #1027 for best value: >> intensional similarity = 55 >> extensional distance = 2 >> proper extension: 071t0; >> query: (?x2631, ?x2000) <- country(?x2631, ?x3855), country(?x2631, ?x3635), country(?x2631, ?x3227), country(?x2631, ?x2843), country(?x2631, ?x2513), country(?x2631, ?x2188), country(?x2631, ?x985), country(?x2631, ?x792), country(?x2631, ?x304), country(?x2631, ?x205), country(?x2631, ?x142), country(?x2631, ?x87), sports(?x418, ?x2631), olympics(?x3635, ?x784), ?x304 = 0d0vqn, ?x2513 = 05b4w, ?x792 = 0hzlz, countries_within(?x2467, ?x3635), country(?x5044, ?x3635), ?x142 = 0jgd, ?x205 = 03rjj, administrative_parent(?x3635, ?x551), film_release_region(?x7126, ?x3855), film_release_region(?x4615, ?x3855), film_release_region(?x3377, ?x3855), film_release_region(?x1035, ?x3855), adjoins(?x1756, ?x3635), ?x1035 = 08hmch, ?x784 = 018ctl, adjoins(?x3855, ?x2000), olympics(?x172, ?x418), ?x2188 = 0163v, olympics(?x453, ?x418), ?x3377 = 0gj8nq2, organization(?x3635, ?x127), participating_countries(?x418, ?x7413), participating_countries(?x418, ?x6435), participating_countries(?x418, ?x5186), locations(?x7241, ?x3855), currency(?x3855, ?x170), ?x3227 = 0bjv6, ?x6435 = 0166b, ?x2843 = 016wzw, ?x5186 = 06sff, ?x7413 = 04hqz, jurisdiction_of_office(?x182, ?x3855), ?x7126 = 0ds1glg, ?x4615 = 0dlngsd, ?x985 = 0k6nt, medal(?x3855, ?x422), film_release_region(?x1178, ?x2000), contains(?x2000, ?x13996), countries_within(?x455, ?x2000), ?x172 = 0154j, ?x87 = 05r4w >> conf = 0.92 => this is the best rule for 3 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 63 *> extensional distance = 1 *> proper extension: 0bynt; *> query: (?x2631, ?x172) <- country(?x2631, ?x8620), country(?x2631, ?x3855), country(?x2631, ?x3635), country(?x2631, ?x3277), country(?x2631, ?x2513), country(?x2631, ?x2152), country(?x2631, ?x1558), country(?x2631, ?x1471), country(?x2631, ?x1264), country(?x2631, ?x1003), country(?x2631, ?x985), country(?x2631, ?x789), country(?x2631, ?x774), country(?x2631, ?x291), country(?x2631, ?x205), sports(?x6893, ?x2631), sports(?x4424, ?x2631), sports(?x3110, ?x2631), sports(?x1277, ?x2631), ?x3635 = 019pcs, ?x774 = 06mzp, ?x8620 = 016zwt, ?x2513 = 05b4w, sports(?x6893, ?x5177), sports(?x6893, ?x1175), ?x3855 = 0jgx, ?x1003 = 03gj2, olympics(?x2631, ?x784), olympics(?x1037, ?x1277), ?x789 = 0f8l9c, ?x985 = 0k6nt, olympics(?x11872, ?x6893), olympics(?x2629, ?x3110), olympics(?x2000, ?x3110), olympics(?x1499, ?x3110), olympics(?x404, ?x3110), olympics(?x172, ?x3110), ?x2629 = 06f32, ?x291 = 0h3y, ?x1558 = 01mjq, medal(?x3110, ?x2132), medal(?x3110, ?x1242), ?x205 = 03rjj, ?x3277 = 06t8v, ?x1264 = 0345h, ?x2152 = 06mkj, ?x404 = 047lj, ?x1471 = 07t21, olympics(?x5177, ?x7775), ?x7775 = 01f1kd, participating_countries(?x4424, ?x1917), ?x1242 = 02lq5w, ?x1917 = 01p1v, ?x2000 = 0d0kn, olympics(?x11872, ?x775), olympics(?x11872, ?x584), ?x584 = 0l98s, film_release_region(?x204, ?x11872), olympics(?x11872, ?x391), ?x775 = 0l998, ?x2132 = 02lpp7, ?x1175 = 02_5h, ?x1499 = 01znc_ *> conf = 0.83 ranks of expected_values: 19 EVAL 01z27 country 04gzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 38.000 38.000 0.923 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #17814-030b93 PRED entity: 030b93 PRED relation: award PRED expected values: 0bfvw2 0cqh6z 0gqyl => 94 concepts (79 used for prediction) PRED predicted values (max 10 best out of 260): 027571b (0.72 #30171, 0.71 #14477, 0.70 #19306), 0cqh6z (0.40 #469, 0.14 #9651, 0.13 #12466), 0gqyl (0.37 #505, 0.14 #7742, 0.13 #2515), 0bfvw2 (0.27 #417, 0.09 #7654, 0.08 #1221), 09sb52 (0.26 #11299, 0.26 #6473, 0.25 #6875), 0bdw6t (0.25 #108, 0.16 #17294, 0.16 #17293), 0bp_b2 (0.25 #18, 0.14 #9651, 0.13 #12466), 0gqwc (0.23 #475, 0.15 #7712, 0.12 #2485), 0gkts9 (0.23 #569, 0.14 #9651, 0.13 #12466), 0gs9p (0.21 #3696, 0.19 #6109, 0.17 #2892) >> Best rule #30171 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 01h320; 06whf; 0d0mbj; 0d3k14; >> query: (?x7132, ?x686) <- award_winner(?x686, ?x7132), award(?x1343, ?x686) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #469 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 01mqz0; 0bw87; 0bw6y; 0dx_q; 02l3_5; 015nhn; 01l1ls; 01kgg9; 0161h5; 02c7lt; ... *> query: (?x7132, 0cqh6z) <- award(?x7132, ?x686), type_of_union(?x7132, ?x566), ?x686 = 0bdw1g *> conf = 0.40 ranks of expected_values: 2, 3, 4 EVAL 030b93 award 0gqyl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 79.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 030b93 award 0cqh6z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 79.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 030b93 award 0bfvw2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 79.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17813-027rpym PRED entity: 027rpym PRED relation: film! PRED expected values: 0k9ctht => 93 concepts (32 used for prediction) PRED predicted values (max 10 best out of 59): 016tt2 (0.56 #882, 0.22 #150, 0.19 #664), 086k8 (0.23 #516, 0.18 #1691, 0.18 #1398), 05s_k6 (0.22 #209, 0.12 #282, 0.12 #136), 05qd_ (0.21 #743, 0.19 #228, 0.17 #817), 0g1rw (0.21 #300, 0.12 #81, 0.11 #595), 016tw3 (0.19 #819, 0.19 #230, 0.17 #525), 017jv5 (0.17 #15, 0.12 #234, 0.12 #88), 01795t (0.17 #18, 0.06 #1707, 0.06 #1267), 024rgt (0.17 #20, 0.04 #902, 0.04 #680), 030_1m (0.17 #14, 0.04 #528, 0.03 #970) >> Best rule #882 for best value: >> intensional similarity = 4 >> extensional distance = 125 >> proper extension: 07kb7vh; >> query: (?x4865, ?x574) <- nominated_for(?x1745, ?x4865), film(?x11867, ?x4865), country(?x4865, ?x94), production_companies(?x4865, ?x574) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0k5g9; 0glnm; 0bm2x; 067ghz; 0gnjh; *> query: (?x4865, 0k9ctht) <- nominated_for(?x1745, ?x4865), genre(?x4865, ?x307), film_art_direction_by(?x4865, ?x2304), film(?x1666, ?x4865) *> conf = 0.12 ranks of expected_values: 13 EVAL 027rpym film! 0k9ctht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 93.000 32.000 0.556 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #17812-04ty8 PRED entity: 04ty8 PRED relation: vacationer PRED expected values: 07r1h => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 186): 0261x8t (0.12 #1917, 0.10 #497, 0.09 #674), 01yf85 (0.11 #1574, 0.11 #1042, 0.10 #1397), 0lk90 (0.11 #1439, 0.11 #907, 0.10 #1262), 03lt8g (0.11 #908, 0.10 #1086, 0.10 #377), 016fnb (0.11 #990, 0.10 #3296, 0.10 #1345), 0bksh (0.11 #1526, 0.10 #1883, 0.08 #3300), 026c1 (0.11 #1456, 0.08 #1813, 0.07 #3230), 01pgzn_ (0.11 #1462, 0.07 #3236, 0.06 #1819), 024dgj (0.10 #433, 0.09 #610, 0.09 #787), 06mt91 (0.09 #672, 0.07 #1558, 0.06 #849) >> Best rule #1917 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 054rw; >> query: (?x11052, 0261x8t) <- jurisdiction_of_office(?x265, ?x11052), vacationer(?x11052, ?x2857), participant(?x6730, ?x2857) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #1551 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 04swx; *> query: (?x11052, 07r1h) <- vacationer(?x11052, ?x7046), participant(?x7046, ?x10139), award_winner(?x7046, ?x989) *> conf = 0.09 ranks of expected_values: 19 EVAL 04ty8 vacationer 07r1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 84.000 84.000 0.118 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #17811-01_9fk PRED entity: 01_9fk PRED relation: school_type! PRED expected values: 01jswq 0j_sncb 02fjzt 01qd_r 01qwb5 01q7q2 => 23 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1607): 02km0m (0.50 #2885, 0.50 #1817, 0.43 #3421), 06fq2 (0.50 #1881, 0.40 #2415, 0.33 #2949), 065y4w7 (0.50 #1612, 0.40 #2146, 0.33 #2680), 06pwq (0.50 #1609, 0.40 #2143, 0.33 #2677), 01j_06 (0.50 #1629, 0.40 #2163, 0.33 #2697), 03ksy (0.50 #1699, 0.40 #2233, 0.33 #2767), 01bm_ (0.50 #1839, 0.40 #2373, 0.33 #2907), 0kz2w (0.50 #1617, 0.40 #2151, 0.33 #2685), 0cwx_ (0.50 #1834, 0.40 #2368, 0.33 #2902), 03fgm (0.43 #3578, 0.33 #5185, 0.33 #3042) >> Best rule #2885 for best value: >> intensional similarity = 25 >> extensional distance = 4 >> proper extension: 01rs41; >> query: (?x1507, 02km0m) <- school_type(?x11318, ?x1507), school_type(?x9768, ?x1507), school_type(?x6856, ?x1507), school_type(?x2175, ?x1507), school_type(?x1665, ?x1507), school_type(?x946, ?x1507), state_province_region(?x11318, ?x760), school(?x11168, ?x2175), service_location(?x1665, ?x94), contains(?x4061, ?x2175), colors(?x2175, ?x663), category(?x9768, ?x134), ?x11168 = 01k8vh, organization(?x5510, ?x946), major_field_of_study(?x1665, ?x8221), major_field_of_study(?x1665, ?x1154), currency(?x6856, ?x170), institution(?x1368, ?x2175), colors(?x1665, ?x3364), major_field_of_study(?x10910, ?x8221), school(?x1632, ?x6856), student(?x1665, ?x4463), ?x1368 = 014mlp, ?x10910 = 013807, ?x1154 = 02lp1 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1197 for first EXPECTED value: *> intensional similarity = 32 *> extensional distance = 1 *> proper extension: 05jxkf; *> query: (?x1507, 02fjzt) <- school_type(?x11516, ?x1507), school_type(?x11397, ?x1507), school_type(?x11318, ?x1507), school_type(?x6973, ?x1507), school_type(?x5280, ?x1507), school_type(?x4750, ?x1507), school_type(?x4555, ?x1507), school_type(?x2621, ?x1507), school_type(?x1699, ?x1507), school_type(?x1011, ?x1507), school_type(?x946, ?x1507), school_type(?x466, ?x1507), ?x11318 = 02ldkf, ?x466 = 01pl14, student(?x4555, ?x496), contains(?x94, ?x5280), organization(?x346, ?x4555), student(?x5280, ?x9585), student(?x5280, ?x5958), ?x1011 = 07w0v, spouse(?x2435, ?x9585), ?x4750 = 04hgpt, ?x11516 = 01xysf, film(?x9585, ?x97), ?x1699 = 01nkcn, list(?x5280, ?x2197), award_winner(?x5958, ?x4948), ?x11397 = 02hp70, ?x946 = 01hhvg, ?x2621 = 07vht, award(?x9585, ?x154), ?x6973 = 05x_5 *> conf = 0.33 ranks of expected_values: 140, 178, 201, 490, 492, 564 EVAL 01_9fk school_type! 01q7q2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 23.000 16.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01_9fk school_type! 01qwb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 23.000 16.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01_9fk school_type! 01qd_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 23.000 16.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01_9fk school_type! 02fjzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 23.000 16.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01_9fk school_type! 0j_sncb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 23.000 16.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01_9fk school_type! 01jswq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 23.000 16.000 0.500 http://example.org/education/educational_institution/school_type #17810-027pdrh PRED entity: 027pdrh PRED relation: nominated_for PRED expected values: 04lhc4 => 102 concepts (33 used for prediction) PRED predicted values (max 10 best out of 650): 016z5x (0.39 #11347, 0.34 #8105, 0.33 #8104), 03tn80 (0.39 #11347, 0.34 #8105, 0.33 #8104), 02jkkv (0.39 #11347, 0.34 #8105, 0.32 #11346), 026fs38 (0.39 #11347, 0.34 #8105, 0.32 #11346), 01f69m (0.39 #11347, 0.34 #8105, 0.32 #11346), 01r97z (0.39 #11347, 0.34 #8105, 0.32 #11346), 04x4gw (0.39 #11347, 0.34 #8105, 0.32 #11346), 048htn (0.39 #11347, 0.34 #8105, 0.32 #11346), 04w7rn (0.39 #11347, 0.34 #8105, 0.32 #11346), 0glbqt (0.38 #3117, 0.05 #4736, 0.04 #11223) >> Best rule #11347 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 06cv1; 0343h; 0bs1yy; 03nqbvz; 01g1lp; 0gd9k; >> query: (?x2572, ?x11483) <- award_winner(?x1703, ?x2572), edited_by(?x11483, ?x2572), nominated_for(?x4154, ?x11483), nationality(?x2572, ?x1310) >> conf = 0.39 => this is the best rule for 9 predicted values *> Best rule #4325 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 02kxbwx; 052gzr; 06pj8; 02kxbx3; 0534v; 02lp3c; 06t8b; 0jgwf; 0kft; 04wp63; ... *> query: (?x2572, 04lhc4) <- award_winner(?x1703, ?x2572), edited_by(?x518, ?x2572), award_winner(?x5053, ?x2572), award(?x197, ?x1703) *> conf = 0.05 ranks of expected_values: 80 EVAL 027pdrh nominated_for 04lhc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 102.000 33.000 0.390 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17809-07j8kh PRED entity: 07j8kh PRED relation: profession PRED expected values: 02hrh1q => 103 concepts (66 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.74 #1597, 0.69 #5779, 0.69 #9099), 0nbcg (0.52 #3488, 0.48 #4641, 0.46 #4353), 016z4k (0.41 #4614, 0.38 #3605, 0.37 #3461), 0n1h (0.38 #4334, 0.19 #4622, 0.18 #3469), 01d_h8 (0.37 #1588, 0.37 #3031, 0.37 #5626), 0dxtg (0.33 #5778, 0.31 #444, 0.30 #7655), 01c8w0 (0.29 #1303, 0.29 #1015, 0.27 #1735), 039v1 (0.26 #322, 0.26 #3926, 0.25 #898), 03gjzk (0.25 #5780, 0.23 #9388, 0.22 #7657), 02jknp (0.24 #5772, 0.24 #6061, 0.24 #6783) >> Best rule #1597 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 016qtt; 0lbj1; 01nqfh_; 01wmxfs; 0lgsq; 04n7njg; 03kwtb; 0pgjm; 0244r8; 0fsm8c; ... >> query: (?x5556, 02hrh1q) <- profession(?x5556, ?x1183), award_winner(?x10060, ?x5556), ?x1183 = 09jwl >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07j8kh profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 66.000 0.739 http://example.org/people/person/profession #17808-07s8r0 PRED entity: 07s8r0 PRED relation: award_nominee! PRED expected values: 0fthdk => 78 concepts (34 used for prediction) PRED predicted values (max 10 best out of 862): 0h1nt (0.81 #39178, 0.81 #78354, 0.81 #36872), 0fthdk (0.81 #39178, 0.81 #78354, 0.81 #36872), 018z_c (0.81 #39178, 0.81 #78354, 0.81 #36872), 035kl6 (0.81 #39178, 0.81 #78354, 0.81 #36872), 01l9p (0.81 #39178, 0.81 #78354, 0.81 #36872), 07s8r0 (0.38 #334, 0.35 #13825, 0.16 #29957), 02p7_k (0.38 #811, 0.16 #29957, 0.15 #78355), 03mcwq3 (0.38 #541, 0.16 #29957, 0.15 #78355), 02bfmn (0.31 #34, 0.16 #29957, 0.15 #78355), 0785v8 (0.31 #146, 0.16 #29957, 0.15 #78355) >> Best rule #39178 for best value: >> intensional similarity = 3 >> extensional distance = 1251 >> proper extension: 01w806h; 0407f; 01t110; 07db6x; >> query: (?x1641, ?x1244) <- award_nominee(?x190, ?x1641), location(?x1641, ?x8470), award_nominee(?x1641, ?x1244) >> conf = 0.81 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07s8r0 award_nominee! 0fthdk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 34.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17807-05fg2 PRED entity: 05fg2 PRED relation: profession PRED expected values: 01c979 => 87 concepts (84 used for prediction) PRED predicted values (max 10 best out of 111): 02hrh1q (0.83 #9579, 0.64 #8084, 0.62 #8233), 0cbd2 (0.69 #5836, 0.50 #1201, 0.50 #455), 0kyk (0.50 #479, 0.46 #5860, 0.44 #927), 01c979 (0.49 #6278, 0.45 #2541, 0.40 #4334), 09jwl (0.49 #6147, 0.30 #2710, 0.28 #2859), 0nbcg (0.45 #6160, 0.19 #2723, 0.18 #2872), 026sdt1 (0.44 #3207, 0.16 #9563, 0.03 #6197), 0dxtg (0.42 #1208, 0.39 #5843, 0.37 #9426), 02pjxr (0.41 #3172, 0.16 #9563, 0.03 #6162), 0fj9f (0.40 #1101, 0.35 #1698, 0.29 #1399) >> Best rule #9579 for best value: >> intensional similarity = 5 >> extensional distance = 3003 >> proper extension: 02zq43; 01rr9f; 01j5x6; 05tk7y; 02_hj4; 02778qt; 035rnz; 026g801; 01wbsdz; 0lh0c; ... >> query: (?x1309, 02hrh1q) <- profession(?x1309, ?x8368), profession(?x12622, ?x8368), profession(?x11282, ?x8368), ?x11282 = 03vrv9, location(?x12622, ?x938) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #6278 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 953 *> proper extension: 03j0br4; 01n7qlf; 01wy61y; 023l9y; 0c8hct; 024zq; 019f9z; 05qhnq; 01lz4tf; 017l4; ... *> query: (?x1309, ?x9674) <- profession(?x1309, ?x8368), specialization_of(?x8368, ?x9674), specialization_of(?x9674, ?x955) *> conf = 0.49 ranks of expected_values: 4 EVAL 05fg2 profession 01c979 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 84.000 0.831 http://example.org/people/person/profession #17806-0g_wn2 PRED entity: 0g_wn2 PRED relation: currency PRED expected values: 09nqf => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.90 #8, 0.88 #4, 0.87 #12) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 0m24v; 0kwmc; >> query: (?x6497, 09nqf) <- second_level_divisions(?x94, ?x6497), ?x94 = 09c7w0, time_zones(?x6497, ?x6498), administrative_parent(?x6497, ?x953) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g_wn2 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.897 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #17805-030w19 PRED entity: 030w19 PRED relation: colors PRED expected values: 019sc => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.43 #302, 0.40 #82, 0.38 #342), 01g5v (0.30 #344, 0.30 #204, 0.30 #424), 06fvc (0.29 #3, 0.19 #303, 0.15 #423), 01l849 (0.26 #1062, 0.25 #1402, 0.25 #1082), 019sc (0.18 #1409, 0.17 #1489, 0.17 #208), 036k5h (0.17 #86, 0.17 #46, 0.14 #6), 04mkbj (0.12 #50, 0.12 #310, 0.12 #230), 02rnmb (0.10 #33, 0.07 #93, 0.05 #994), 038hg (0.09 #1093, 0.09 #1493, 0.09 #1413), 0jc_p (0.09 #305, 0.08 #485, 0.08 #205) >> Best rule #302 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 02d9nr; >> query: (?x12260, 083jv) <- contains(?x94, ?x12260), currency(?x12260, ?x170), ?x170 = 09nqf, colors(?x12260, ?x5845) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1409 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 378 *> proper extension: 0pz6q; *> query: (?x12260, 019sc) <- organization(?x346, ?x12260), colors(?x12260, ?x5845), institution(?x1368, ?x12260), company(?x346, ?x94) *> conf = 0.18 ranks of expected_values: 5 EVAL 030w19 colors 019sc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 142.000 142.000 0.433 http://example.org/education/educational_institution/colors #17804-03hr1p PRED entity: 03hr1p PRED relation: sports! PRED expected values: 0l6mp => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 26): 018qb4 (0.83 #533, 0.75 #431, 0.70 #508), 0lbbj (0.80 #501, 0.78 #940, 0.75 #526), 0l6m5 (0.78 #721, 0.78 #940, 0.75 #570), 018ljb (0.78 #940, 0.75 #1020, 0.75 #816), 0sxrz (0.78 #940, 0.75 #1020, 0.75 #816), 0l6mp (0.75 #525, 0.75 #423, 0.70 #500), 06sks6 (0.70 #169, 0.68 #316, 0.66 #517), 0kbws (0.70 #169, 0.68 #316, 0.66 #517), 016r9z (0.67 #528, 0.60 #156, 0.60 #132), 018wrk (0.62 #416, 0.50 #518, 0.50 #493) >> Best rule #533 for best value: >> intensional similarity = 39 >> extensional distance = 10 >> proper extension: 02vx4; 0crlz; >> query: (?x3127, 018qb4) <- sports(?x2043, ?x3127), country(?x3127, ?x8449), country(?x3127, ?x7747), country(?x3127, ?x5482), country(?x3127, ?x2513), country(?x3127, ?x2152), country(?x3127, ?x429), country(?x3127, ?x344), ?x2043 = 0lv1x, film_release_region(?x9839, ?x2513), film_release_region(?x8370, ?x2513), film_release_region(?x6932, ?x2513), film_release_region(?x3599, ?x2513), film_release_region(?x3392, ?x2513), film_release_region(?x3287, ?x2513), film_release_region(?x1386, ?x2513), country(?x2266, ?x344), film_release_region(?x11701, ?x344), member_states(?x2106, ?x344), ?x3287 = 026njb5, currency(?x429, ?x170), film_release_region(?x622, ?x429), olympics(?x7747, ?x1931), ?x2152 = 06mkj, ?x8370 = 07ghq, ?x6932 = 027pfg, ?x3599 = 0kxf1, ?x3392 = 0jwmp, country(?x1591, ?x429), jurisdiction_of_office(?x182, ?x5482), ?x2266 = 01lb14, ?x9839 = 0gy7bj4, organization(?x429, ?x127), olympics(?x2513, ?x418), ?x1386 = 0dtfn, ?x11701 = 0gys2jp, administrative_parent(?x8449, ?x551), nationality(?x294, ?x429), second_level_divisions(?x429, ?x9696) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #525 for first EXPECTED value: *> intensional similarity = 39 *> extensional distance = 10 *> proper extension: 02vx4; 0crlz; *> query: (?x3127, 0l6mp) <- sports(?x2043, ?x3127), country(?x3127, ?x8449), country(?x3127, ?x7747), country(?x3127, ?x5482), country(?x3127, ?x2513), country(?x3127, ?x2152), country(?x3127, ?x429), country(?x3127, ?x344), ?x2043 = 0lv1x, film_release_region(?x9839, ?x2513), film_release_region(?x8370, ?x2513), film_release_region(?x6932, ?x2513), film_release_region(?x3599, ?x2513), film_release_region(?x3392, ?x2513), film_release_region(?x3287, ?x2513), film_release_region(?x1386, ?x2513), country(?x2266, ?x344), film_release_region(?x11701, ?x344), member_states(?x2106, ?x344), ?x3287 = 026njb5, currency(?x429, ?x170), film_release_region(?x622, ?x429), olympics(?x7747, ?x1931), ?x2152 = 06mkj, ?x8370 = 07ghq, ?x6932 = 027pfg, ?x3599 = 0kxf1, ?x3392 = 0jwmp, country(?x1591, ?x429), jurisdiction_of_office(?x182, ?x5482), ?x2266 = 01lb14, ?x9839 = 0gy7bj4, organization(?x429, ?x127), olympics(?x2513, ?x418), ?x1386 = 0dtfn, ?x11701 = 0gys2jp, administrative_parent(?x8449, ?x551), nationality(?x294, ?x429), second_level_divisions(?x429, ?x9696) *> conf = 0.75 ranks of expected_values: 6 EVAL 03hr1p sports! 0l6mp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 43.000 43.000 0.833 http://example.org/user/jg/default_domain/olympic_games/sports #17803-03gj2 PRED entity: 03gj2 PRED relation: country! PRED expected values: 07rlg 01hp22 071t0 03hr1p 06z6r => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 35): 071t0 (0.92 #607, 0.89 #1238, 0.89 #572), 06z6r (0.88 #473, 0.88 #403, 0.87 #648), 03hr1p (0.84 #643, 0.83 #398, 0.79 #468), 07gyv (0.79 #460, 0.69 #740, 0.65 #600), 0194d (0.72 #834, 0.71 #659, 0.70 #484), 01hp22 (0.71 #391, 0.63 #636, 0.63 #811), 07rlg (0.67 #386, 0.64 #456, 0.58 #631), 09w1n (0.67 #434, 0.61 #469, 0.58 #399), 01sgl (0.64 #481, 0.61 #656, 0.58 #516), 07jjt (0.63 #641, 0.63 #571, 0.58 #396) >> Best rule #607 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 05b7q; >> query: (?x1003, 071t0) <- participating_countries(?x784, ?x1003), administrative_area_type(?x1003, ?x2792), combatants(?x1003, ?x756) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 6, 7 EVAL 03gj2 country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.919 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03gj2 country! 03hr1p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.919 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03gj2 country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.919 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03gj2 country! 01hp22 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 140.000 0.919 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03gj2 country! 07rlg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 140.000 0.919 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #17802-05b3ts PRED entity: 05b3ts PRED relation: team PRED expected values: 0fbq2n => 37 concepts (26 used for prediction) PRED predicted values (max 10 best out of 954): 05tfm (0.87 #1857, 0.86 #1856, 0.85 #1858), 084l5 (0.87 #1857, 0.86 #1856, 0.85 #1858), 04vn5 (0.87 #1857, 0.86 #1856, 0.85 #1858), 01ct6 (0.87 #1857, 0.86 #1856, 0.84 #8367), 04ls81 (0.87 #1857, 0.86 #1856, 0.84 #8367), 0ft5vs (0.86 #1856, 0.84 #8367, 0.83 #10226), 026ldz7 (0.86 #1856, 0.84 #8367, 0.83 #10226), 07kcvl (0.82 #10233, 0.81 #12104, 0.81 #21416), 0fjzsy (0.76 #3719, 0.75 #20752, 0.75 #14249), 057xlyq (0.76 #3719, 0.71 #10492, 0.71 #9560) >> Best rule #1857 for best value: >> intensional similarity = 28 >> extensional distance = 1 >> proper extension: 047g8h; >> query: (?x2573, ?x4856) <- position(?x11061, ?x2573), position(?x5229, ?x2573), position(?x4856, ?x2573), position(?x4546, ?x2573), position(?x4189, ?x2573), position(?x3674, ?x2573), position(?x2574, ?x2573), position(?x1576, ?x2573), position(?x1115, ?x2573), ?x11061 = 06x76, position(?x1718, ?x2573), team(?x2573, ?x1239), ?x1576 = 05tfm, ?x4546 = 05gg4, position(?x706, ?x2573), ?x4189 = 026lg0s, position(?x5229, ?x2312), position(?x5229, ?x2147), ?x2574 = 01y3v, ?x3674 = 05tg3, ?x1115 = 01y3c, ?x2147 = 04nfpk, ?x1718 = 0fgg8c, ?x1239 = 01xvb, team(?x11323, ?x5229), ?x2312 = 02qpbqj, colors(?x4856, ?x3189), colors(?x5229, ?x663) >> conf = 0.87 => this is the best rule for 5 predicted values *> Best rule #24201 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 19 *> proper extension: 02nzb8; 02sdk9v; 0dgrmp; 02_j1w; *> query: (?x2573, ?x684) <- position(?x706, ?x2573), team(?x2573, ?x5229), team(?x2573, ?x2574), team(?x2573, ?x387), team(?x7079, ?x5229), teams(?x1860, ?x2574), team(?x11323, ?x2574), location(?x396, ?x1860), featured_film_locations(?x195, ?x1860), sport(?x2574, ?x1083), origin(?x1945, ?x1860), place_of_death(?x8619, ?x1860), place_of_birth(?x193, ?x1860), team(?x5412, ?x387), month(?x1860, ?x1459), location_of_ceremony(?x4703, ?x1860), team(?x7079, ?x684) *> conf = 0.70 ranks of expected_values: 27 EVAL 05b3ts team 0fbq2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 37.000 26.000 0.870 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #17801-0cpyv PRED entity: 0cpyv PRED relation: location_of_ceremony! PRED expected values: 04ztj => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.83 #49, 0.82 #45, 0.78 #21), 0jgjn (0.12 #20, 0.11 #24, 0.06 #44), 01g63y (0.12 #18, 0.11 #22, 0.06 #42), 01bl8s (0.03 #71, 0.02 #84, 0.01 #178) >> Best rule #49 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 01914; >> query: (?x4861, 04ztj) <- place_of_death(?x11097, ?x4861), place_of_death(?x10605, ?x4861), administrative_parent(?x4861, ?x10766), profession(?x10605, ?x2225), location(?x11097, ?x5952) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cpyv location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.833 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #17800-0sg4x PRED entity: 0sg4x PRED relation: place PRED expected values: 0sg4x => 84 concepts (31 used for prediction) PRED predicted values (max 10 best out of 23): 0sd7v (0.06 #411, 0.04 #926, 0.04 #1441), 0sf9_ (0.06 #87, 0.04 #602, 0.04 #1117), 0s3y5 (0.06 #7, 0.04 #522, 0.04 #1037), 0sgxg (0.06 #462, 0.04 #977, 0.04 #1492), 0s9b_ (0.06 #436, 0.04 #951, 0.04 #1466), 0s2z0 (0.06 #375, 0.04 #890, 0.04 #1405), 0s9z_ (0.06 #332, 0.04 #847, 0.04 #1362), 0sjqm (0.06 #273, 0.04 #788, 0.04 #1303), 0s4sj (0.06 #511, 0.03 #2058, 0.03 #2574), 0s6g4 (0.06 #382, 0.03 #1929, 0.03 #2445) >> Best rule #411 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 0s5cg; 0sbbq; 0s987; 0s6g4; 0sbv7; 0sc6p; 0s4sj; >> query: (?x14549, 0sd7v) <- source(?x14549, ?x958), ?x958 = 0jbk9, contains(?x3818, ?x14549), contains(?x94, ?x14549), ?x3818 = 03v0t, ?x94 = 09c7w0 >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0sg4x place 0sg4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 31.000 0.056 http://example.org/location/hud_county_place/place #17799-05q4y12 PRED entity: 05q4y12 PRED relation: language PRED expected values: 02h40lc => 85 concepts (82 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.91 #2590, 0.90 #2287, 0.90 #2653), 064_8sq (0.28 #201, 0.19 #381, 0.16 #1221), 06nm1 (0.20 #669, 0.13 #70, 0.13 #907), 02bjrlw (0.19 #360, 0.14 #239, 0.13 #120), 04306rv (0.17 #184, 0.15 #364, 0.14 #243), 0jzc (0.12 #20, 0.12 #4782, 0.11 #199), 04h9h (0.12 #43, 0.12 #4782, 0.04 #1842), 05zjd (0.12 #4782, 0.11 #205, 0.07 #385), 0653m (0.12 #4782, 0.09 #490, 0.07 #1392), 032f6 (0.12 #4782, 0.07 #115, 0.05 #355) >> Best rule #2590 for best value: >> intensional similarity = 6 >> extensional distance = 504 >> proper extension: 0jym0; 016z9n; 083skw; 03l6q0; 0kvgtf; 015whm; 015g28; 0p_tz; 02mc5v; 06zsk51; >> query: (?x2788, 02h40lc) <- film(?x9207, ?x2788), titles(?x2480, ?x2788), featured_film_locations(?x2788, ?x4419), location(?x11918, ?x4419), award(?x11918, ?x375), location(?x9207, ?x739) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05q4y12 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 82.000 0.907 http://example.org/film/film/language #17798-0d9xq PRED entity: 0d9xq PRED relation: place_of_death PRED expected values: 0k049 => 136 concepts (117 used for prediction) PRED predicted values (max 10 best out of 38): 030qb3t (0.22 #1770, 0.15 #1964, 0.14 #8575), 0mndw (0.14 #1554, 0.07 #8942, 0.07 #5830), 0n6dc (0.14 #1554, 0.07 #8942, 0.07 #5830), 02_286 (0.12 #789, 0.11 #401, 0.11 #207), 0k049 (0.12 #1751, 0.09 #7972, 0.08 #8556), 06_kh (0.11 #199, 0.05 #8168, 0.04 #8752), 05jbn (0.11 #71, 0.05 #2013, 0.05 #1819), 04vmp (0.11 #108, 0.02 #5742, 0.02 #14683), 0c8tk (0.11 #62), 04swd (0.06 #1479, 0.06 #508, 0.01 #2840) >> Best rule #1770 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0443c; >> query: (?x5101, 030qb3t) <- inductee(?x13697, ?x5101), people(?x8523, ?x5101), inductee(?x13697, ?x1545), profession(?x1545, ?x1032) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1751 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 0443c; *> query: (?x5101, 0k049) <- inductee(?x13697, ?x5101), people(?x8523, ?x5101), inductee(?x13697, ?x1545), profession(?x1545, ?x1032) *> conf = 0.12 ranks of expected_values: 5 EVAL 0d9xq place_of_death 0k049 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 136.000 117.000 0.220 http://example.org/people/deceased_person/place_of_death #17797-01w9mnm PRED entity: 01w9mnm PRED relation: performance_role PRED expected values: 03bx0bm => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 66): 03bx0bm (0.50 #60, 0.44 #906, 0.42 #865), 026t6 (0.34 #643, 0.23 #977, 0.20 #1065), 0l15bq (0.25 #62, 0.09 #233, 0.07 #908), 01qbl (0.25 #56, 0.09 #227, 0.05 #932), 0l14jd (0.25 #84, 0.09 #255, 0.03 #598), 0j210 (0.25 #77, 0.09 #248, 0.03 #591), 013y1f (0.20 #658, 0.17 #992, 0.13 #907), 0342h (0.15 #683, 0.13 #1017, 0.12 #644), 05148p4 (0.15 #683, 0.05 #932, 0.04 #1528), 05r5c (0.13 #1017, 0.10 #980, 0.09 #854) >> Best rule #60 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 015882; >> query: (?x8539, 03bx0bm) <- artists(?x3370, ?x8539), artists(?x505, ?x8539), ?x3370 = 059kh, performance_role(?x8539, ?x228), ?x505 = 03_d0, profession(?x8539, ?x6565) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w9mnm performance_role 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.500 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #17796-09_gdc PRED entity: 09_gdc PRED relation: special_performance_type! PRED expected values: 0139q5 013ybx => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 4353): 0f502 (0.33 #49, 0.25 #563, 0.25 #395), 01pllx (0.33 #101, 0.25 #615, 0.25 #447), 0fb1q (0.33 #37, 0.25 #551, 0.25 #383), 0q9kd (0.33 #1, 0.25 #515, 0.25 #347), 012d40 (0.33 #3, 0.25 #517, 0.25 #349), 0n839 (0.33 #140, 0.25 #654, 0.25 #486), 01vsn38 (0.33 #132, 0.25 #646, 0.25 #478), 01h1b (0.33 #78, 0.25 #592, 0.25 #424), 060j8b (0.33 #73, 0.25 #587, 0.25 #419), 03y82t6 (0.33 #52, 0.25 #566, 0.25 #398) >> Best rule #49 for best value: >> intensional similarity = 76 >> extensional distance = 1 >> proper extension: 01pb34; >> query: (?x3558, 0f502) <- film(?x3558, ?x7141), film(?x3558, ?x4050), film(?x3558, ?x4038), film(?x3558, ?x3986), film(?x3558, ?x66), film_release_region(?x66, ?x4743), film_release_region(?x66, ?x2267), film_release_region(?x66, ?x2152), film_release_region(?x66, ?x1264), film_release_region(?x66, ?x1229), film_release_region(?x66, ?x774), film_release_region(?x66, ?x304), film_release_region(?x66, ?x279), film_release_region(?x66, ?x172), special_performance_type(?x8134, ?x3558), special_performance_type(?x4229, ?x3558), special_performance_type(?x3557, ?x3558), special_performance_type(?x1733, ?x3558), participant(?x123, ?x1733), language(?x66, ?x254), nominated_for(?x1733, ?x4502), ?x4743 = 03spz, vacationer(?x6226, ?x1733), participant(?x2763, ?x1733), film(?x541, ?x4502), ?x172 = 0154j, genre(?x4050, ?x1403), film_release_distribution_medium(?x4502, ?x81), music(?x4050, ?x10634), story_by(?x4502, ?x96), film_crew_role(?x4502, ?x137), award(?x4050, ?x1443), ?x2267 = 03rj0, nominated_for(?x2183, ?x4050), film(?x1733, ?x1734), gender(?x1733, ?x231), award_nominee(?x1733, ?x1735), participant(?x395, ?x4229), story_by(?x66, ?x5431), film(?x399, ?x4050), location(?x1733, ?x242), award_nominee(?x1735, ?x5240), featured_film_locations(?x4050, ?x362), award(?x1735, ?x2478), award_winner(?x8134, ?x6236), ?x2152 = 06mkj, profession(?x4229, ?x1032), film(?x147, ?x4038), nominated_for(?x1691, ?x4038), titles(?x53, ?x7141), religion(?x8134, ?x7422), spouse(?x4229, ?x10004), ?x774 = 06mzp, category(?x4050, ?x134), award(?x8134, ?x154), award_winner(?x472, ?x123), ?x1264 = 0345h, ?x1691 = 05zvj3m, film(?x1561, ?x4038), ?x1229 = 059j2, film(?x8134, ?x1444), ?x304 = 0d0vqn, award_nominee(?x123, ?x1208), ?x279 = 0d060g, profession(?x123, ?x319), nominated_for(?x123, ?x3124), produced_by(?x7141, ?x6698), award_nominee(?x906, ?x3557), award_winner(?x2183, ?x157), ?x2478 = 02x4x18, genre(?x12108, ?x1403), award_winner(?x4038, ?x1940), participant(?x4229, ?x794), nominated_for(?x200, ?x3986), ?x12108 = 02bqxb, genre(?x4502, ?x225) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #139 for first EXPECTED value: *> intensional similarity = 76 *> extensional distance = 1 *> proper extension: 01pb34; *> query: (?x3558, 013ybx) <- film(?x3558, ?x7141), film(?x3558, ?x4050), film(?x3558, ?x4038), film(?x3558, ?x3986), film(?x3558, ?x66), film_release_region(?x66, ?x4743), film_release_region(?x66, ?x2267), film_release_region(?x66, ?x2152), film_release_region(?x66, ?x1264), film_release_region(?x66, ?x1229), film_release_region(?x66, ?x774), film_release_region(?x66, ?x304), film_release_region(?x66, ?x279), film_release_region(?x66, ?x172), special_performance_type(?x8134, ?x3558), special_performance_type(?x4229, ?x3558), special_performance_type(?x3557, ?x3558), special_performance_type(?x1733, ?x3558), participant(?x123, ?x1733), language(?x66, ?x254), nominated_for(?x1733, ?x4502), ?x4743 = 03spz, vacationer(?x6226, ?x1733), participant(?x2763, ?x1733), film(?x541, ?x4502), ?x172 = 0154j, genre(?x4050, ?x1403), film_release_distribution_medium(?x4502, ?x81), music(?x4050, ?x10634), story_by(?x4502, ?x96), film_crew_role(?x4502, ?x137), award(?x4050, ?x1443), ?x2267 = 03rj0, nominated_for(?x2183, ?x4050), film(?x1733, ?x1734), gender(?x1733, ?x231), award_nominee(?x1733, ?x1735), participant(?x395, ?x4229), story_by(?x66, ?x5431), film(?x399, ?x4050), location(?x1733, ?x242), award_nominee(?x1735, ?x5240), featured_film_locations(?x4050, ?x362), award(?x1735, ?x2478), award_winner(?x8134, ?x6236), ?x2152 = 06mkj, profession(?x4229, ?x1032), film(?x147, ?x4038), nominated_for(?x1691, ?x4038), titles(?x53, ?x7141), religion(?x8134, ?x7422), spouse(?x4229, ?x10004), ?x774 = 06mzp, category(?x4050, ?x134), award(?x8134, ?x154), award_winner(?x472, ?x123), ?x1264 = 0345h, ?x1691 = 05zvj3m, film(?x1561, ?x4038), ?x1229 = 059j2, film(?x8134, ?x1444), ?x304 = 0d0vqn, award_nominee(?x123, ?x1208), ?x279 = 0d060g, profession(?x123, ?x319), nominated_for(?x123, ?x3124), produced_by(?x7141, ?x6698), award_nominee(?x906, ?x3557), award_winner(?x2183, ?x157), ?x2478 = 02x4x18, genre(?x12108, ?x1403), award_winner(?x4038, ?x1940), participant(?x4229, ?x794), nominated_for(?x200, ?x3986), ?x12108 = 02bqxb, genre(?x4502, ?x225) *> conf = 0.33 ranks of expected_values: 59, 302 EVAL 09_gdc special_performance_type! 013ybx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 5.000 5.000 0.333 http://example.org/film/actor/film./film/performance/special_performance_type EVAL 09_gdc special_performance_type! 0139q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/film/actor/film./film/performance/special_performance_type #17795-0p7tb PRED entity: 0p7tb PRED relation: organization! PRED expected values: 060c4 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 17): 060c4 (0.75 #275, 0.75 #288, 0.74 #262), 0dq_5 (0.33 #165, 0.31 #152, 0.28 #87), 07xl34 (0.30 #24, 0.23 #63, 0.22 #11), 05k17c (0.23 #111, 0.20 #98, 0.14 #534), 08jcfy (0.14 #534, 0.08 #64, 0.03 #181), 0hm4q (0.14 #534, 0.07 #216, 0.06 #372), 05c0jwl (0.14 #534, 0.03 #382, 0.03 #447), 04n1q6 (0.14 #534, 0.02 #175), 0dq3c (0.02 #1121, 0.01 #183), 01t7n9 (0.02 #1121) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 267 >> proper extension: 01xk7r; >> query: (?x12937, 060c4) <- currency(?x12937, ?x170), ?x170 = 09nqf, category(?x12937, ?x134) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p7tb organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.755 http://example.org/organization/role/leaders./organization/leadership/organization #17794-05sfs PRED entity: 05sfs PRED relation: religion! PRED expected values: 0d060g 05fkf 04rrd 0488g 03v0t 0vbk => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1394): 04rrd (0.78 #962, 0.75 #899, 0.73 #1088), 03v0t (0.78 #971, 0.75 #908, 0.73 #1097), 05fkf (0.78 #953, 0.75 #890, 0.67 #700), 0488g (0.75 #900, 0.73 #1089, 0.67 #963), 04ly1 (0.75 #845, 0.67 #719, 0.62 #909), 0l3h (0.67 #728, 0.62 #918, 0.62 #854), 09c7w0 (0.67 #631, 0.60 #378, 0.50 #566), 026mj (0.62 #922, 0.62 #858, 0.60 #541), 0vbk (0.62 #912, 0.60 #469, 0.56 #975), 07ssc (0.60 #508, 0.50 #637, 0.50 #572) >> Best rule #962 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 01s5nb; >> query: (?x962, 04rrd) <- religion(?x7058, ?x962), religion(?x5575, ?x962), religion(?x4198, ?x962), religion(?x1024, ?x962), religion(?x728, ?x962), ?x1024 = 05fhy, ?x7058 = 050ks, ?x728 = 059f4, contains(?x4198, ?x7067), administrative_parent(?x11575, ?x5575), location(?x338, ?x5575) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 9, 13 EVAL 05sfs religion! 0vbk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 45.000 45.000 0.778 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05sfs religion! 03v0t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.778 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05sfs religion! 0488g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.778 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05sfs religion! 04rrd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.778 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05sfs religion! 05fkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.778 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05sfs religion! 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 45.000 45.000 0.778 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #17793-04nnpw PRED entity: 04nnpw PRED relation: executive_produced_by PRED expected values: 05hj_k => 82 concepts (53 used for prediction) PRED predicted values (max 10 best out of 77): 05hj_k (0.22 #348, 0.12 #599, 0.12 #98), 0glyyw (0.22 #437, 0.12 #688, 0.12 #939), 05wm88 (0.12 #232, 0.01 #1235, 0.01 #1736), 0grwj (0.12 #3, 0.01 #1006, 0.01 #1507), 03c9pqt (0.11 #495, 0.06 #746, 0.06 #997), 029m83 (0.11 #425, 0.06 #676, 0.06 #927), 02hfp_ (0.11 #426, 0.06 #677, 0.06 #928), 06rq2l (0.11 #451, 0.06 #702, 0.06 #953), 06pj8 (0.07 #1559, 0.07 #1309, 0.05 #3816), 0343h (0.07 #1546, 0.07 #1296, 0.04 #2296) >> Best rule #348 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05k2xy; 026hh0m; 03ntbmw; >> query: (?x4696, 05hj_k) <- film(?x2275, ?x4696), ?x2275 = 05dbf, executive_produced_by(?x4696, ?x6187) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04nnpw executive_produced_by 05hj_k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 53.000 0.222 http://example.org/film/film/executive_produced_by #17792-03f1zdw PRED entity: 03f1zdw PRED relation: profession PRED expected values: 02hrh1q => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 77): 02hrh1q (0.88 #3917, 0.88 #8720, 0.87 #6468), 01d_h8 (0.34 #4358, 0.32 #2257, 0.31 #6008), 0dxtg (0.29 #1951, 0.28 #6754, 0.28 #6905), 03gjzk (0.29 #1951, 0.28 #6754, 0.28 #6905), 02jknp (0.29 #1951, 0.28 #6754, 0.28 #6905), 09jwl (0.29 #1951, 0.28 #6754, 0.28 #6905), 018gz8 (0.29 #1951, 0.28 #6754, 0.28 #6905), 0np9r (0.29 #1951, 0.28 #6754, 0.28 #6905), 0cbd2 (0.29 #1951, 0.28 #6754, 0.28 #6905), 0kyk (0.29 #1951, 0.28 #6754, 0.28 #6905) >> Best rule #3917 for best value: >> intensional similarity = 2 >> extensional distance = 1155 >> proper extension: 05d7rk; 01vw87c; 014x77; 0lzb8; 025p38; 0kr5_; 012c6x; 03ds3; 0152cw; 0htlr; ... >> query: (?x1222, 02hrh1q) <- award_winner(?x591, ?x1222), film(?x1222, ?x695) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f1zdw profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.876 http://example.org/people/person/profession #17791-016srn PRED entity: 016srn PRED relation: film PRED expected values: 01b195 => 122 concepts (80 used for prediction) PRED predicted values (max 10 best out of 143): 035s95 (0.08 #3925, 0.07 #5717, 0.04 #11093), 04yc76 (0.08 #4027, 0.07 #5819, 0.04 #11195), 07bzz7 (0.08 #11643, 0.02 #18811, 0.02 #20603), 08r4x3 (0.06 #7322, 0.03 #79002, 0.02 #130980), 0ds5_72 (0.06 #8627, 0.02 #14003), 01shy7 (0.06 #7592, 0.02 #25512, 0.02 #39848), 03bx2lk (0.06 #7353, 0.01 #89786, 0.01 #100538), 056xkh (0.06 #8770, 0.01 #37442), 03ynwqj (0.06 #8643), 0cmf0m0 (0.06 #8599) >> Best rule #3925 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01lmj3q; 01ww2fs; 016sp_; 0ggjt; 03cfjg; 0x3b7; 02cx90; 01k_r5b; 01l47f5; 051m56; >> query: (?x3159, 035s95) <- award_winner(?x3159, ?x6562), artist(?x2931, ?x3159), ?x6562 = 05sq20 >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016srn film 01b195 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 80.000 0.083 http://example.org/film/actor/film./film/performance/film #17790-06qgvf PRED entity: 06qgvf PRED relation: film PRED expected values: 03h_yy 04tqtl => 109 concepts (76 used for prediction) PRED predicted values (max 10 best out of 431): 0cz_ym (0.49 #25008, 0.49 #37514, 0.43 #53593), 05rfst (0.33 #974, 0.04 #6332), 047vnkj (0.33 #909, 0.03 #48234, 0.03 #112542), 07f_t4 (0.33 #1330), 0bz3jx (0.22 #2923, 0.15 #4709, 0.01 #24358), 01svry (0.22 #2976, 0.15 #4762), 02jxrw (0.22 #3401, 0.08 #5187), 05nyqk (0.22 #3322, 0.08 #5108), 0cmf0m0 (0.22 #3213, 0.08 #4999), 02pg45 (0.22 #2715, 0.08 #4501) >> Best rule #25008 for best value: >> intensional similarity = 3 >> extensional distance = 800 >> proper extension: 03y1mlp; 04cbtrw; 07xr3w; 0b6yp2; 03cx282; 0bqytm; 02mxbd; 01l79yc; 05km8z; 02wb6d; ... >> query: (?x101, ?x1877) <- gender(?x101, ?x514), people(?x1446, ?x101), nominated_for(?x101, ?x1877) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #2295 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 01cwcr; *> query: (?x101, 04tqtl) <- award_nominee(?x100, ?x101), film(?x101, ?x11066), ?x11066 = 025s1wg *> conf = 0.11 ranks of expected_values: 60 EVAL 06qgvf film 04tqtl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 109.000 76.000 0.494 http://example.org/film/actor/film./film/performance/film EVAL 06qgvf film 03h_yy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 76.000 0.494 http://example.org/film/actor/film./film/performance/film #17789-05nyqk PRED entity: 05nyqk PRED relation: film! PRED expected values: 0gnbw 02m501 => 72 concepts (39 used for prediction) PRED predicted values (max 10 best out of 591): 04zwtdy (0.41 #66546, 0.38 #45748, 0.38 #81105), 086k8 (0.41 #66546, 0.38 #45748, 0.38 #24951), 04yywz (0.18 #2098, 0.16 #19, 0.04 #60307), 02v406 (0.14 #2807, 0.11 #728, 0.04 #60307), 03swmf (0.11 #20792), 04h6mm (0.11 #20792), 0ksf29 (0.11 #20792), 06cgy (0.11 #12725, 0.02 #29360, 0.02 #37678), 01ry0f (0.11 #850, 0.09 #2929, 0.04 #60307), 023nlj (0.11 #1516, 0.09 #3595, 0.04 #60307) >> Best rule #66546 for best value: >> intensional similarity = 4 >> extensional distance = 1070 >> proper extension: 0hr41p6; >> query: (?x9199, ?x382) <- genre(?x9199, ?x225), country(?x9199, ?x94), ?x94 = 09c7w0, nominated_for(?x382, ?x9199) >> conf = 0.41 => this is the best rule for 2 predicted values *> Best rule #1687 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 06ybb1; *> query: (?x9199, 02m501) <- genre(?x9199, ?x225), film(?x5338, ?x9199), nominated_for(?x1312, ?x9199), ?x5338 = 0gn30 *> conf = 0.05 ranks of expected_values: 58, 155 EVAL 05nyqk film! 02m501 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 72.000 39.000 0.414 http://example.org/film/actor/film./film/performance/film EVAL 05nyqk film! 0gnbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 72.000 39.000 0.414 http://example.org/film/actor/film./film/performance/film #17788-0g83dv PRED entity: 0g83dv PRED relation: film_crew_role PRED expected values: 09vw2b7 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 34): 09vw2b7 (0.73 #1976, 0.70 #2152, 0.70 #534), 0dxtw (0.43 #2156, 0.43 #1629, 0.42 #292), 01vx2h (0.41 #152, 0.38 #504, 0.36 #293), 02ynfr (0.34 #212, 0.21 #1985, 0.20 #2161), 01xy5l_ (0.34 #212, 0.17 #48, 0.15 #154), 0215hd (0.34 #212, 0.16 #53, 0.14 #546), 02rh1dz (0.34 #212, 0.14 #256, 0.13 #1628), 089g0h (0.34 #212, 0.12 #3449, 0.12 #547), 0d2b38 (0.34 #212, 0.12 #3449, 0.12 #553), 02_n3z (0.34 #212, 0.12 #3449, 0.11 #142) >> Best rule #1976 for best value: >> intensional similarity = 5 >> extensional distance = 580 >> proper extension: 0h95zbp; 0j8f09z; >> query: (?x4158, 09vw2b7) <- film_crew_role(?x4158, ?x1284), film_crew_role(?x4158, ?x137), ?x137 = 09zzb8, ?x1284 = 0ch6mp2, film(?x166, ?x4158) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g83dv film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.727 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17787-04_by PRED entity: 04_by PRED relation: story_by! PRED expected values: 0jqzt => 173 concepts (68 used for prediction) PRED predicted values (max 10 best out of 285): 06znpjr (0.20 #260, 0.04 #4706, 0.03 #5390), 090s_0 (0.20 #12, 0.04 #4458, 0.03 #5142), 04954r (0.20 #128, 0.02 #8337, 0.01 #10047), 034qmv (0.20 #5, 0.02 #8214, 0.01 #9924), 0642ykh (0.09 #920, 0.07 #1946, 0.04 #4682), 063fh9 (0.09 #917, 0.07 #1943, 0.04 #4679), 0639bg (0.09 #813, 0.07 #1839, 0.04 #4575), 0dp7wt (0.09 #941, 0.06 #2651, 0.04 #4019), 02mmwk (0.09 #927, 0.06 #2637, 0.04 #4005), 0ccd3x (0.09 #844, 0.04 #6658, 0.04 #4264) >> Best rule #260 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 05qmj; >> query: (?x10716, 06znpjr) <- influenced_by(?x7861, ?x10716), influenced_by(?x3542, ?x10716), ?x3542 = 03hnd, influenced_by(?x7861, ?x5435), influenced_by(?x2343, ?x7861), ?x5435 = 01v9724 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04_by story_by! 0jqzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 68.000 0.200 http://example.org/film/film/story_by #17786-01rtm4 PRED entity: 01rtm4 PRED relation: major_field_of_study PRED expected values: 01zc2w 03r8gp => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 115): 04rjg (0.45 #266, 0.44 #1744, 0.39 #882), 03g3w (0.45 #273, 0.44 #1751, 0.39 #889), 02j62 (0.45 #276, 0.44 #1754, 0.39 #2493), 01mkq (0.43 #1739, 0.34 #6047, 0.33 #138), 05qjt (0.41 #1731, 0.38 #499, 0.33 #130), 01lj9 (0.37 #1763, 0.36 #285, 0.33 #162), 037mh8 (0.37 #1792, 0.36 #314, 0.33 #930), 0fdys (0.36 #284, 0.33 #1762, 0.33 #900), 0g26h (0.36 #288, 0.33 #42, 0.28 #780), 06ms6 (0.36 #263, 0.31 #509, 0.24 #1741) >> Best rule #266 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01g7_r; >> query: (?x263, 04rjg) <- currency(?x263, ?x170), major_field_of_study(?x263, ?x254), student(?x263, ?x264), company(?x2239, ?x263) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #934 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 016ckq; 02jd_7; *> query: (?x263, 01zc2w) <- company(?x2239, ?x263), profession(?x2239, ?x353), peers(?x2239, ?x4808) *> conf = 0.24 ranks of expected_values: 41, 52 EVAL 01rtm4 major_field_of_study 03r8gp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 128.000 128.000 0.455 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01rtm4 major_field_of_study 01zc2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 128.000 128.000 0.455 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17785-04kjrv PRED entity: 04kjrv PRED relation: profession PRED expected values: 01c72t => 166 concepts (81 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.92 #6701, 0.86 #9175, 0.85 #8738), 01c72t (0.53 #894, 0.53 #1184, 0.38 #10494), 0dz3r (0.53 #1163, 0.47 #873, 0.46 #4506), 01d_h8 (0.39 #4364, 0.34 #9166, 0.33 #5382), 0dxtg (0.31 #4372, 0.26 #4082, 0.26 #8008), 0n1h (0.29 #301, 0.26 #3643, 0.25 #737), 0gbbt (0.29 #445, 0.25 #9, 0.20 #880), 03gjzk (0.26 #9176, 0.25 #4374, 0.24 #9321), 0d1pc (0.21 #3825, 0.16 #2370, 0.12 #2951), 02jknp (0.20 #4366, 0.20 #4221, 0.20 #8440) >> Best rule #6701 for best value: >> intensional similarity = 4 >> extensional distance = 342 >> proper extension: 044mz_; 04bdxl; 0byfz; 05cj4r; 023tp8; 0prfz; 0159h6; 0h5g_; 0c4f4; 04wqr; ... >> query: (?x7121, 02hrh1q) <- profession(?x7121, ?x6565), spouse(?x7121, ?x4005), profession(?x4609, ?x6565), ?x4609 = 0p7h7 >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #894 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0gr69; *> query: (?x7121, 01c72t) <- role(?x7121, ?x7033), artist(?x382, ?x7121), artists(?x302, ?x7121), ?x7033 = 0gkd1 *> conf = 0.53 ranks of expected_values: 2 EVAL 04kjrv profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 166.000 81.000 0.916 http://example.org/people/person/profession #17784-07gql PRED entity: 07gql PRED relation: role PRED expected values: 023r2x => 89 concepts (49 used for prediction) PRED predicted values (max 10 best out of 114): 042v_gx (0.89 #4241, 0.79 #5047, 0.78 #5395), 05842k (0.87 #5000, 0.85 #5616, 0.85 #5581), 06ncr (0.87 #3817, 0.83 #3251, 0.82 #1590), 01vdm0 (0.85 #4496, 0.81 #5535, 0.79 #5070), 05148p4 (0.82 #1590, 0.82 #1248, 0.82 #3537), 0l15bq (0.82 #1590, 0.82 #1248, 0.82 #3537), 023r2x (0.82 #1590, 0.82 #1248, 0.82 #3537), 013y1f (0.81 #5539, 0.80 #2309, 0.79 #5074), 02sgy (0.80 #4471, 0.80 #2512, 0.72 #902), 04rzd (0.80 #2550, 0.74 #5548, 0.73 #5315) >> Best rule #4241 for best value: >> intensional similarity = 14 >> extensional distance = 17 >> proper extension: 0979zs; >> query: (?x2206, 042v_gx) <- role(?x2206, ?x1472), role(?x2206, ?x745), role(?x2206, ?x569), role(?x925, ?x2206), role(?x4917, ?x569), ?x745 = 01vj9c, role(?x1166, ?x2206), role(?x8014, ?x569), ?x8014 = 0214km, ?x1472 = 0319l, ?x4917 = 06w7v, artists(?x505, ?x925), group(?x569, ?x1751), music(?x924, ?x925) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1590 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 5 *> proper extension: 0l14qv; 06ncr; *> query: (?x2206, ?x75) <- role(?x2206, ?x7869), role(?x2206, ?x2048), role(?x2206, ?x1166), role(?x75, ?x2206), group(?x2206, ?x8429), group(?x2206, ?x4010), ?x4010 = 0163m1, instrumentalists(?x2206, ?x2492), origin(?x8429, ?x739), artists(?x2809, ?x8429), nationality(?x2492, ?x512), ?x1166 = 05148p4, role(?x7869, ?x74), role(?x925, ?x2206), role(?x1831, ?x2206), ?x2048 = 018j2 *> conf = 0.82 ranks of expected_values: 7 EVAL 07gql role 023r2x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 89.000 49.000 0.895 http://example.org/music/performance_role/track_performances./music/track_contribution/role #17783-06c62 PRED entity: 06c62 PRED relation: mode_of_transportation PRED expected values: 07jdr => 327 concepts (327 used for prediction) PRED predicted values (max 10 best out of 4): 01bjv (0.91 #230, 0.90 #126, 0.90 #122), 07jdr (0.80 #373, 0.80 #161, 0.80 #357), 0k4j (0.05 #119, 0.04 #199, 0.03 #487), 06d_3 (0.03 #296, 0.02 #412, 0.02 #496) >> Best rule #230 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0fhp9; 08966; 02z0j; 07dfk; 03902; >> query: (?x6959, 01bjv) <- contains(?x6959, ?x5994), month(?x6959, ?x1459), location(?x914, ?x6959), place_of_death(?x4732, ?x6959) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #373 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 049d1; 02sn34; *> query: (?x6959, 07jdr) <- month(?x6959, ?x1459), location(?x10965, ?x6959), place_of_birth(?x9099, ?x6959), gender(?x10965, ?x514) *> conf = 0.80 ranks of expected_values: 2 EVAL 06c62 mode_of_transportation 07jdr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 327.000 327.000 0.906 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #17782-01x6jd PRED entity: 01x6jd PRED relation: participant! PRED expected values: 01skmp => 87 concepts (29 used for prediction) PRED predicted values (max 10 best out of 76): 01tnbn (0.10 #1042, 0.08 #2320, 0.01 #2958), 056rgc (0.09 #1506, 0.01 #2783), 01rr9f (0.08 #1950, 0.02 #2588, 0.01 #5144), 0237fw (0.08 #2084, 0.02 #3999, 0.01 #8473), 09yrh (0.08 #2238, 0.01 #2876, 0.01 #5432), 01s21dg (0.08 #2255), 014zcr (0.03 #2572, 0.02 #3211, 0.01 #10882), 046zh (0.03 #2914, 0.01 #3553), 04fzk (0.03 #2842, 0.01 #3481), 031296 (0.03 #8304, 0.02 #16625, 0.01 #15343) >> Best rule #1042 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 013cr; 0p8r1; >> query: (?x12003, 01tnbn) <- film(?x12003, ?x10455), film(?x12003, ?x2893), ?x2893 = 01jrbb, produced_by(?x10455, ?x2803) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01x6jd participant! 01skmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 29.000 0.100 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #17781-04knvh PRED entity: 04knvh PRED relation: category PRED expected values: 08mbj5d => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #71, 0.12 #44, 0.11 #70) >> Best rule #71 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x9824, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04knvh category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #17780-034qrh PRED entity: 034qrh PRED relation: nominated_for! PRED expected values: 07cjqy => 95 concepts (57 used for prediction) PRED predicted values (max 10 best out of 456): 0gv07g (0.80 #119156, 0.79 #86447, 0.79 #107473), 07cjqy (0.29 #130842, 0.28 #102801, 0.28 #35042), 015lhm (0.29 #130842, 0.28 #102801, 0.28 #35042), 032xhg (0.29 #130842, 0.28 #35042, 0.27 #128505), 0378zn (0.28 #102801, 0.28 #35042, 0.27 #11680), 0sw6g (0.28 #102801, 0.28 #35042, 0.27 #11680), 0309lm (0.28 #35042, 0.27 #128505, 0.27 #11680), 02js_6 (0.28 #35042, 0.27 #128505, 0.27 #11680), 01q_ph (0.20 #7072, 0.20 #2400, 0.10 #77099), 018grr (0.20 #2760, 0.13 #7432, 0.02 #35466) >> Best rule #119156 for best value: >> intensional similarity = 2 >> extensional distance = 1120 >> proper extension: 06mmr; >> query: (?x437, ?x7205) <- award_winner(?x437, ?x7205), nominated_for(?x7205, ?x4083) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #130842 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1184 *> proper extension: 0gx9rvq; 0gydcp7; 0k54q; 06_sc3; 0h63q6t; *> query: (?x437, ?x4631) <- language(?x437, ?x254), film(?x4631, ?x437), profession(?x4631, ?x319), participant(?x4631, ?x400) *> conf = 0.29 ranks of expected_values: 2 EVAL 034qrh nominated_for! 07cjqy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 57.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17779-016z2j PRED entity: 016z2j PRED relation: award_nominee PRED expected values: 0blq0z => 113 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1153): 0blq0z (0.82 #20930, 0.81 #151132, 0.80 #109281), 031k24 (0.82 #20930, 0.81 #151132, 0.80 #109281), 0gpprt (0.82 #20930, 0.81 #151132, 0.80 #109281), 04w391 (0.75 #137181, 0.75 #118581, 0.75 #162759), 02x7vq (0.75 #137181, 0.75 #118581, 0.75 #162759), 06lvlf (0.75 #137181, 0.75 #118581, 0.75 #162759), 02t_v1 (0.75 #137181, 0.75 #118581, 0.75 #162759), 02_hj4 (0.75 #137181, 0.75 #118581, 0.75 #162759), 016z2j (0.37 #83710, 0.36 #90685, 0.36 #88360), 05sdxx (0.37 #83710, 0.36 #90685, 0.36 #88360) >> Best rule #20930 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 03m8lq; 04cf09; 01pctb; >> query: (?x2373, ?x969) <- celebrity(?x2373, ?x1564), film(?x2373, ?x557), award_nominee(?x969, ?x2373) >> conf = 0.82 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 016z2j award_nominee 0blq0z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 70.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17778-07tgn PRED entity: 07tgn PRED relation: student PRED expected values: 0f8pz 04ch23 => 91 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1250): 04cbtrw (0.25 #464, 0.06 #8672, 0.03 #18932), 031y07 (0.25 #982), 06g4_ (0.10 #55414, 0.08 #61571, 0.08 #49255), 05m0h (0.10 #55414, 0.08 #61571, 0.08 #49255), 07nx9j (0.08 #11551, 0.07 #15655, 0.06 #21811), 01d494 (0.08 #10520, 0.07 #14624, 0.06 #20780), 0d3k14 (0.08 #12085, 0.07 #16189, 0.06 #10033), 07f7jp (0.08 #12204, 0.07 #16308, 0.06 #10152), 0683n (0.08 #11696, 0.07 #15800, 0.06 #9644), 03qd_ (0.08 #10359, 0.07 #14463, 0.06 #8307) >> Best rule #464 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0d5fb; >> query: (?x892, 04cbtrw) <- student(?x892, ?x11271), student(?x892, ?x11104), profession(?x11271, ?x319), ?x11104 = 03j2gxx >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07tgn student 04ch23 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 74.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07tgn student 0f8pz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 74.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #17777-0d_wms PRED entity: 0d_wms PRED relation: written_by PRED expected values: 011s9r => 94 concepts (50 used for prediction) PRED predicted values (max 10 best out of 125): 02vyw (0.33 #1110, 0.29 #1445, 0.20 #1780), 05drq5 (0.33 #39, 0.25 #374, 0.20 #710), 021lby (0.25 #3692), 01wyy_ (0.17 #1101, 0.14 #1436, 0.10 #1771), 0jw67 (0.15 #2450, 0.12 #3459, 0.10 #4463), 02rk45 (0.08 #3631, 0.07 #4635, 0.06 #2957), 03thw4 (0.08 #6515, 0.04 #7519, 0.03 #7184), 02lk1s (0.08 #2371, 0.07 #4384, 0.05 #4719), 02mt4k (0.08 #2503, 0.06 #2838, 0.04 #3175), 07s93v (0.08 #2394, 0.04 #6421, 0.04 #3403) >> Best rule #1110 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0dp7wt; >> query: (?x3847, 02vyw) <- currency(?x3847, ?x170), film(?x7958, ?x3847), ?x7958 = 04__f, nominated_for(?x1072, ?x3847), ?x170 = 09nqf >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d_wms written_by 011s9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 50.000 0.333 http://example.org/film/film/written_by #17776-026wlnm PRED entity: 026wlnm PRED relation: team! PRED expected values: 0b_6h7 0b_6qj 0b_6q5 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 7): 0bzrxn (0.71 #114, 0.58 #74, 0.57 #31), 0b_6q5 (0.58 #74, 0.57 #110, 0.57 #31), 0b_6qj (0.58 #74, 0.57 #31, 0.56 #133), 0b_6jz (0.58 #74, 0.57 #31, 0.50 #75), 0b_6h7 (0.58 #74, 0.57 #31, 0.50 #77), 0br1xn (0.58 #74, 0.57 #31, 0.50 #63), 0jhn7 (0.57 #31) >> Best rule #114 for best value: >> intensional similarity = 26 >> extensional distance = 5 >> proper extension: 04088s0; >> query: (?x9909, 0bzrxn) <- team(?x13209, ?x9909), team(?x10736, ?x9909), team(?x9908, ?x9909), team(?x8527, ?x9909), team(?x7042, ?x9909), team(?x4368, ?x9909), team(?x2302, ?x9909), locations(?x4368, ?x3786), ?x7042 = 0b_72t, team(?x4368, ?x10171), team(?x4368, ?x9576), ?x3786 = 071cn, ?x2302 = 0b_77q, ?x13209 = 0b_734, locations(?x8527, ?x13387), locations(?x8527, ?x6088), ?x10171 = 026w398, ?x9576 = 02qk2d5, instance_of_recurring_event(?x10736, ?x10863), place_of_birth(?x2794, ?x6088), locations(?x9908, ?x5381), category(?x6088, ?x134), dog_breed(?x6088, ?x1706), ?x5381 = 0c_m3, location(?x105, ?x6088), ?x13387 = 0kcw2 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #74 for first EXPECTED value: *> intensional similarity = 29 *> extensional distance = 2 *> proper extension: 02qk2d5; *> query: (?x9909, ?x5258) <- team(?x12162, ?x9909), team(?x9908, ?x9909), team(?x8992, ?x9909), team(?x8824, ?x9909), team(?x8527, ?x9909), team(?x6002, ?x9909), team(?x4368, ?x9909), team(?x3797, ?x9909), team(?x2302, ?x9909), ?x4368 = 0b_6x2, ?x8824 = 05g_nr, ?x6002 = 0cc8q3, ?x8527 = 0b_6v_, team(?x5755, ?x9909), ?x2302 = 0b_77q, ?x8992 = 0b_6s7, locations(?x9908, ?x5381), ?x3797 = 0b_6zk, team(?x9908, ?x12370), team(?x9908, ?x10171), team(?x9908, ?x3798), ?x12162 = 0b_6_l, ?x5381 = 0c_m3, ?x3798 = 02ptzz0, sport(?x12370, ?x12913), instance_of_recurring_event(?x9908, ?x10863), team(?x5258, ?x12370), ?x10171 = 026w398, team(?x4570, ?x12370) *> conf = 0.58 ranks of expected_values: 2, 3, 5 EVAL 026wlnm team! 0b_6q5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 56.000 0.714 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 026wlnm team! 0b_6qj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 56.000 0.714 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 026wlnm team! 0b_6h7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 56.000 56.000 0.714 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #17775-03w5xm PRED entity: 03w5xm PRED relation: contact_category! PRED expected values: 02vk52z 016tt2 0168nq 0dmtp 05w3y 058j2 0bwfn 04fv0k 01yx7f 06p8m 05b5c 0gy1_ 0dn_w => 3 concepts (3 used for prediction) PRED predicted values (max 10 best out of 1110): 05b5c (0.33 #41, 0.22 #28, 0.17 #27), 02qdyj (0.33 #35, 0.22 #28, 0.17 #27), 07zl6m (0.33 #43, 0.22 #28, 0.17 #27), 02bm1v (0.33 #37, 0.22 #28, 0.17 #27), 01qvcr (0.33 #46, 0.22 #28, 0.16 #47), 02vk52z (0.33 #1, 0.17 #27, 0.11 #19), 025v3k (0.33 #31, 0.17 #27, 0.03 #24), 06pwq (0.33 #30, 0.17 #27, 0.03 #24), 0558_1 (0.33 #12, 0.17 #27, 0.03 #24), 02rky4 (0.33 #10, 0.17 #27, 0.03 #24) >> Best rule #41 for best value: >> intensional similarity = 278 >> extensional distance = 1 >> proper extension: 02zdwq; >> query: (?x897, 05b5c) <- contact_category(?x12452, ?x897), contact_category(?x12350, ?x897), contact_category(?x11303, ?x897), contact_category(?x11070, ?x897), contact_category(?x10951, ?x897), contact_category(?x9968, ?x897), contact_category(?x9476, ?x897), contact_category(?x9469, ?x897), contact_category(?x8125, ?x897), contact_category(?x7970, ?x897), contact_category(?x7546, ?x897), contact_category(?x6333, ?x897), contact_category(?x6092, ?x897), contact_category(?x5108, ?x897), contact_category(?x5007, ?x897), contact_category(?x3793, ?x897), contact_category(?x3779, ?x897), contact_category(?x3387, ?x897), contact_category(?x3253, ?x897), contact_category(?x2554, ?x897), contact_category(?x2021, ?x897), contact_category(?x1492, ?x897), contact_category(?x610, ?x897), contact_category(?x555, ?x897), ?x11070 = 02brqp, ?x3387 = 02fgdx, ?x3253 = 01n073, award_winner(?x5060, ?x2554), award_winner(?x2436, ?x2554), service_location(?x2021, ?x94), award_winner(?x6091, ?x6092), award_nominee(?x2021, ?x902), service_language(?x1492, ?x1882), service_language(?x1492, ?x254), organization(?x4682, ?x7546), citytown(?x12350, ?x4600), nominated_for(?x2021, ?x5277), award_winner(?x3487, ?x5007), currency(?x12350, ?x170), ?x254 = 02h40lc, list(?x1492, ?x8915), list(?x1492, ?x7472), list(?x1492, ?x5997), award_nominee(?x4564, ?x2021), category(?x1492, ?x134), industry(?x9469, ?x245), company(?x346, ?x1492), film(?x5007, ?x8733), nominated_for(?x2554, ?x8770), service_language(?x9469, ?x5607), ?x12452 = 0vlf, major_field_of_study(?x6333, ?x11820), major_field_of_study(?x6333, ?x7403), major_field_of_study(?x6333, ?x2601), service_location(?x1492, ?x3749), service_location(?x1492, ?x2645), service_location(?x1492, ?x2146), service_location(?x1492, ?x390), service_location(?x1492, ?x252), institution(?x4981, ?x7546), citytown(?x2021, ?x13019), ?x2146 = 03rk0, ?x2601 = 04x_3, state_province_region(?x2554, ?x1227), citytown(?x6333, ?x1569), child(?x9469, ?x12217), ?x5997 = 04k4rt, ?x11303 = 03_c8p, nominated_for(?x5007, ?x1434), ?x3793 = 0k8z, ?x7403 = 06mnr, award_winner(?x3486, ?x5007), ?x2645 = 03h64, school(?x2174, ?x6333), nominated_for(?x436, ?x2436), film(?x4128, ?x8733), service_location(?x9968, ?x1355), service_location(?x9968, ?x205), nominated_for(?x678, ?x2436), company(?x6403, ?x9968), industry(?x9968, ?x1605), ?x610 = 0p4wb, languages(?x12914, ?x1882), languages(?x12309, ?x1882), languages(?x9253, ?x1882), languages(?x8296, ?x1882), languages(?x8097, ?x1882), languages(?x7517, ?x1882), languages(?x3129, ?x1882), languages(?x2385, ?x1882), ?x2385 = 01n8_g, film(?x4564, ?x253), state_province_region(?x5007, ?x1426), ?x8915 = 01pd60, ?x3129 = 0241wg, languages_spoken(?x5025, ?x1882), language(?x5271, ?x1882), language(?x4089, ?x1882), language(?x4007, ?x1882), language(?x2617, ?x1882), language(?x2381, ?x1882), language(?x1150, ?x1882), ?x4089 = 02kfzz, ?x5108 = 01s73z, currency(?x7546, ?x7888), nominated_for(?x154, ?x5277), major_field_of_study(?x734, ?x11820), child(?x2021, ?x12095), language(?x8073, ?x1882), ?x1150 = 0h3xztt, award_winner(?x2554, ?x1394), written_by(?x5277, ?x2442), ?x6403 = 0142rn, ?x8296 = 06gn7r, ?x4981 = 03bwzr4, film(?x250, ?x5277), nominated_for(?x822, ?x5060), genre(?x5277, ?x225), film_release_region(?x7832, ?x3749), film_release_region(?x5791, ?x3749), film_release_region(?x4707, ?x3749), film_release_region(?x4336, ?x3749), film_release_region(?x3757, ?x3749), film_release_region(?x1525, ?x3749), film_release_region(?x1170, ?x3749), film_release_region(?x249, ?x3749), ?x7970 = 0py9b, countries_spoken_in(?x1882, ?x792), ?x3757 = 02vr3gz, citytown(?x9476, ?x479), school(?x4171, ?x6333), olympics(?x3749, ?x1931), film_crew_role(?x5277, ?x137), featured_film_locations(?x5277, ?x1523), ?x12914 = 02qvhbb, ?x7517 = 03vrnh, state_province_region(?x9476, ?x1906), ?x5791 = 03mgx6z, country(?x11611, ?x3749), film_release_region(?x11395, ?x205), film_release_region(?x11209, ?x205), film_release_region(?x10475, ?x205), film_release_region(?x8162, ?x205), film_release_region(?x7336, ?x205), film_release_region(?x6321, ?x205), film_release_region(?x6247, ?x205), film_release_region(?x5735, ?x205), film_release_region(?x5704, ?x205), film_release_region(?x5230, ?x205), film_release_region(?x4441, ?x205), film_release_region(?x4041, ?x205), film_release_region(?x3745, ?x205), film_release_region(?x3599, ?x205), film_release_region(?x3276, ?x205), film_release_region(?x3000, ?x205), film_release_region(?x2628, ?x205), film_release_region(?x2558, ?x205), film_release_region(?x2441, ?x205), film_release_region(?x1919, ?x205), film_release_region(?x1915, ?x205), film_release_region(?x1535, ?x205), film_release_region(?x1498, ?x205), film_release_region(?x1069, ?x205), film_release_region(?x984, ?x205), film_release_region(?x204, ?x205), olympics(?x205, ?x775), olympics(?x205, ?x584), olympics(?x205, ?x452), olympics(?x205, ?x418), ?x134 = 08mbj5d, ?x775 = 0l998, ?x1525 = 03qnvdl, country(?x7191, ?x205), country(?x10585, ?x205), country(?x3659, ?x205), country(?x3015, ?x205), country(?x2315, ?x205), country(?x1175, ?x205), country(?x171, ?x205), ?x249 = 0c3ybss, ?x4707 = 02xbyr, ?x2381 = 04q00lw, ?x5735 = 0h21v2, ?x3015 = 071t0, ?x2441 = 0cc5mcj, adjoins(?x205, ?x774), contains(?x205, ?x1356), artist(?x10951, ?x4476), ?x11395 = 05ypj5, ?x8162 = 0bs8ndx, ?x8097 = 046rfv, ?x5607 = 064_8sq, ?x2558 = 0bby9p5, ?x1915 = 0fq7dv_, ?x3659 = 0dwxr, ?x1170 = 09gdm7q, ?x12309 = 0894_x, country(?x9213, ?x205), ?x10475 = 047p798, ?x7832 = 0fphf3v, film_release_distribution_medium(?x8733, ?x81), ?x204 = 028_yv, award_nominee(?x521, ?x4476), jurisdiction_of_office(?x265, ?x3749), production_companies(?x2075, ?x4564), nationality(?x7782, ?x205), ?x1498 = 04jkpgv, award_winner(?x6093, ?x6092), ?x252 = 03_3d, ?x7336 = 0bdjd, contains(?x9494, ?x7546), award_winner(?x7589, ?x6091), ?x9253 = 01x2tm8, ?x555 = 01c6k4, ?x4007 = 03hmt9b, ?x3276 = 0gjc4d3, ?x418 = 09n48, ?x5271 = 047vnkj, ?x8125 = 06q07, ?x7472 = 01ptsx, ?x171 = 0d1tm, ?x6321 = 0gg8z1f, ?x1175 = 02_5h, ?x11209 = 04fjzv, location(?x4587, ?x205), ?x5230 = 0mb8c, ?x2315 = 06wrt, ?x584 = 0l98s, artists(?x2937, ?x4476), ?x734 = 04zx3q1, ?x3599 = 0kxf1, school(?x1438, ?x3779), country_of_origin(?x6793, ?x390), olympics(?x205, ?x2432), ?x4441 = 0125xq, entity_involved(?x3278, ?x390), contains(?x390, ?x901), combatants(?x326, ?x390), ?x4336 = 0bpm4yw, nationality(?x72, ?x390), company(?x3131, ?x6333), ?x6247 = 09v9mks, ?x5704 = 0h95zbp, film_release_region(?x280, ?x1355), organization(?x205, ?x312), ?x3278 = 0dl4z, ?x9213 = 0353tm, country(?x308, ?x390), place_founded(?x4564, ?x2495), second_level_divisions(?x1355, ?x863), ?x2174 = 051vz, ?x2432 = 0nbjq, ?x3000 = 045j3w, ?x2628 = 06wbm8q, ?x1535 = 02r1c18, ?x4041 = 0gy2y8r, olympics(?x1355, ?x2748), ?x2617 = 01p3ty, combatants(?x151, ?x390), nationality(?x681, ?x1355), list(?x3779, ?x2197), ?x1931 = 0kbws, location_of_ceremony(?x566, ?x390), administrative_parent(?x11642, ?x205), award(?x5060, ?x693), adjoins(?x1355, ?x756), award(?x4476, ?x3631), ?x984 = 0m_mm, ?x452 = 0sx7r, ?x10585 = 01gqfm, origin(?x4476, ?x2850), ?x7782 = 09bxq9, ?x1919 = 0_7w6, ?x3745 = 03cw411, ?x3631 = 02f73p, ?x4128 = 015vq_, ?x1069 = 0jqp3, school_type(?x7546, ?x3092) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 11, 12, 13, 22, 84, 86, 89, 103, 107, 108, 115 EVAL 03w5xm contact_category! 0dn_w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 0gy1_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 05b5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 06p8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 01yx7f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 04fv0k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 0bwfn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 058j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 05w3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 0dmtp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 0168nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 03w5xm contact_category! 02vk52z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 3.000 3.000 0.333 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #17774-07yk1xz PRED entity: 07yk1xz PRED relation: nominated_for! PRED expected values: 0f_nbyh 099jhq 0gqy2 => 134 concepts (132 used for prediction) PRED predicted values (max 10 best out of 222): 04kxsb (0.69 #3385, 0.54 #330, 0.49 #1270), 027c95y (0.66 #21393, 0.66 #16688, 0.66 #23981), 027c924 (0.66 #21393, 0.66 #16688, 0.66 #23981), 0gq9h (0.63 #4526, 0.59 #1941, 0.58 #296), 0gs9p (0.62 #298, 0.58 #4528, 0.57 #1943), 04dn09n (0.54 #1915, 0.54 #270, 0.47 #3325), 040njc (0.54 #242, 0.48 #3297, 0.47 #1887), 0k611 (0.51 #1952, 0.51 #3362, 0.46 #307), 0gqy2 (0.51 #2001, 0.46 #356, 0.45 #4116), 02pqp12 (0.47 #3347, 0.46 #292, 0.38 #1937) >> Best rule #3385 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 0hmr4; 05y0cr; >> query: (?x2203, 04kxsb) <- nominated_for(?x4091, ?x2203), featured_film_locations(?x2203, ?x2204), award(?x11835, ?x4091), ?x11835 = 01l7qw >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2001 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 01jc6q; 0c0yh4; 0yyg4; 0n0bp; 0jzw; 0sxfd; 0_816; 011yr9; 0gcpc; 0j80w; ... *> query: (?x2203, 0gqy2) <- nominated_for(?x1107, ?x2203), produced_by(?x2203, ?x1365), films(?x10552, ?x2203), ?x1107 = 019f4v *> conf = 0.51 ranks of expected_values: 9, 25, 43 EVAL 07yk1xz nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 134.000 132.000 0.695 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07yk1xz nominated_for! 099jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 134.000 132.000 0.695 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07yk1xz nominated_for! 0f_nbyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 134.000 132.000 0.695 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17773-0gbfn9 PRED entity: 0gbfn9 PRED relation: film! PRED expected values: 02t_st => 90 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1151): 03dbds (0.26 #16621, 0.23 #2078, 0.20 #31163), 09d5d5 (0.13 #64405, 0.13 #54018, 0.12 #41550), 0p8r1 (0.13 #583, 0.07 #4739, 0.06 #15126), 012x2b (0.10 #1635, 0.07 #16178, 0.03 #41107), 057_yx (0.10 #1836, 0.06 #16379, 0.04 #39231), 083wr9 (0.10 #2051, 0.04 #16594, 0.02 #39446), 01vy_v8 (0.10 #730, 0.04 #15273, 0.02 #4886), 05txrz (0.08 #15306, 0.06 #763, 0.05 #38158), 016xh5 (0.08 #3157, 0.03 #30163, 0.03 #34318), 0151w_ (0.08 #2242, 0.02 #58335, 0.02 #37558) >> Best rule #16621 for best value: >> intensional similarity = 5 >> extensional distance = 69 >> proper extension: 0cp08zg; >> query: (?x5576, ?x7621) <- film_release_region(?x5576, ?x87), production_companies(?x5576, ?x617), category(?x5576, ?x134), written_by(?x5576, ?x7621), genre(?x5576, ?x53) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #7519 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 02rqwhl; 0kvgxk; 021y7yw; 03hmt9b; 0415ggl; 03hj5lq; 02nczh; 02pw_n; 08s6mr; 046f3p; ... *> query: (?x5576, 02t_st) <- film(?x376, ?x5576), film(?x617, ?x5576), genre(?x5576, ?x53), ?x617 = 025jfl *> conf = 0.02 ranks of expected_values: 593 EVAL 0gbfn9 film! 02t_st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 34.000 0.258 http://example.org/film/actor/film./film/performance/film #17772-01hr1 PRED entity: 01hr1 PRED relation: genre PRED expected values: 02kdv5l 0btmb => 96 concepts (94 used for prediction) PRED predicted values (max 10 best out of 93): 02kdv5l (0.77 #239, 0.59 #712, 0.58 #948), 07s9rl0 (0.70 #8993, 0.66 #5798, 0.65 #5679), 03k9fj (0.55 #1075, 0.49 #1193, 0.49 #2259), 024qqx (0.51 #2129, 0.51 #2010, 0.49 #3195), 06n90 (0.46 #249, 0.38 #131, 0.38 #722), 05p553 (0.41 #8997, 0.39 #3081, 0.38 #832), 02l7c8 (0.32 #9008, 0.32 #1788, 0.29 #6996), 02n4kr (0.24 #1427, 0.11 #5687, 0.11 #5806), 0btmb (0.23 #204, 0.15 #322, 0.12 #677), 04xvlr (0.19 #5680, 0.19 #5799, 0.16 #1774) >> Best rule #239 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0f3m1; >> query: (?x339, 02kdv5l) <- category(?x339, ?x134), titles(?x8581, ?x339), story_by(?x339, ?x13339), ?x8581 = 024qqx >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 01hr1 genre 0btmb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 96.000 94.000 0.769 http://example.org/film/film/genre EVAL 01hr1 genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 94.000 0.769 http://example.org/film/film/genre #17771-08n__5 PRED entity: 08n__5 PRED relation: profession PRED expected values: 0dxtg => 146 concepts (119 used for prediction) PRED predicted values (max 10 best out of 69): 0dxtg (0.84 #2643, 0.83 #2789, 0.83 #2351), 0nbcg (0.62 #1635, 0.62 #29, 0.58 #759), 01d_h8 (0.56 #3512, 0.54 #3950, 0.52 #4681), 0dz3r (0.53 #878, 0.48 #4384, 0.45 #5846), 016z4k (0.52 #4240, 0.50 #880, 0.50 #734), 039v1 (0.41 #4416, 0.38 #5878, 0.38 #5585), 01c72t (0.35 #1920, 0.35 #3235, 0.35 #3381), 02krf9 (0.34 #3822, 0.32 #4114, 0.30 #5283), 02jknp (0.31 #3514, 0.31 #14909, 0.29 #3952), 0fnpj (0.31 #1664, 0.24 #496, 0.17 #5609) >> Best rule #2643 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 03p01x; >> query: (?x5820, 0dxtg) <- program_creator(?x12535, ?x5820), profession(?x5820, ?x1032), nationality(?x5820, ?x1023) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08n__5 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 119.000 0.839 http://example.org/people/person/profession #17770-0169t PRED entity: 0169t PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 19): 060bp (0.62 #358, 0.62 #148, 0.62 #1), 0pqc5 (0.36 #928, 0.13 #633, 0.13 #654), 0f6c3 (0.24 #552, 0.22 #594, 0.20 #636), 0fkvn (0.22 #548, 0.22 #611, 0.22 #590), 09n5b9 (0.20 #556, 0.18 #598, 0.18 #640), 04syw (0.18 #257, 0.17 #5, 0.16 #799), 0fj45 (0.16 #799, 0.14 #18, 0.11 #270), 0p5vf (0.16 #799, 0.13 #74, 0.10 #95), 01zq91 (0.16 #799, 0.11 #76, 0.09 #97), 0377k9 (0.16 #799, 0.08 #77, 0.05 #161) >> Best rule #358 for best value: >> intensional similarity = 2 >> extensional distance = 166 >> proper extension: 05r4w; 0b90_r; 0154j; 03rjj; 03_3d; 0h3y; 0d0vqn; 0j1z8; 04gzd; 0chghy; ... >> query: (?x1577, 060bp) <- administrative_parent(?x1577, ?x551), organization(?x1577, ?x127) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0169t jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.625 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #17769-0m491 PRED entity: 0m491 PRED relation: film! PRED expected values: 01k8rb => 75 concepts (51 used for prediction) PRED predicted values (max 10 best out of 652): 0lx2l (0.08 #33266, 0.07 #45743, 0.06 #60299), 04x4s2 (0.08 #33266, 0.07 #45743, 0.06 #60299), 01trf3 (0.05 #47824, 0.01 #27757, 0.01 #11125), 0jfx1 (0.05 #6643, 0.03 #2485, 0.03 #31591), 0h0wc (0.05 #6661, 0.02 #46167, 0.02 #42007), 0m66w (0.05 #39504, 0.01 #17679, 0.01 #7284), 0f0kz (0.05 #2595, 0.03 #6753, 0.03 #8832), 0169dl (0.04 #27428, 0.04 #4559, 0.03 #50304), 0c6qh (0.04 #4572, 0.03 #25362, 0.03 #19125), 01q_ph (0.04 #27084, 0.03 #31242, 0.03 #16689) >> Best rule #33266 for best value: >> intensional similarity = 4 >> extensional distance = 579 >> proper extension: 06dfz1; >> query: (?x1859, ?x2534) <- nominated_for(?x722, ?x1859), award_nominee(?x722, ?x2534), people(?x1050, ?x722), participant(?x722, ?x495) >> conf = 0.08 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0m491 film! 01k8rb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 51.000 0.077 http://example.org/film/actor/film./film/performance/film #17768-0r3wm PRED entity: 0r3wm PRED relation: place_of_birth! PRED expected values: 0g9zcgx => 248 concepts (174 used for prediction) PRED predicted values (max 10 best out of 2120): 03xl77 (0.40 #242970, 0.40 #237745, 0.37 #250807), 015xp4 (0.06 #269096, 0.06 #266483, 0.06 #41800), 02y7sr (0.06 #4391, 0.06 #1778, 0.04 #9618), 01pm0_ (0.06 #3917, 0.06 #1304, 0.04 #9144), 03rwng (0.06 #3765, 0.06 #1152, 0.04 #8992), 01vsy3q (0.06 #3615, 0.06 #1002, 0.04 #8842), 04smkr (0.06 #3029, 0.06 #416, 0.04 #8256), 05m883 (0.06 #2805, 0.06 #192, 0.04 #8032), 01gf5h (0.06 #2770, 0.06 #157, 0.04 #7997), 083chw (0.06 #2648, 0.06 #35, 0.04 #7875) >> Best rule #242970 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 01xd9; >> query: (?x10400, ?x2946) <- location(?x2946, ?x10400), place_of_birth(?x2946, ?x12025), role(?x2946, ?x315) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r3wm place_of_birth! 0g9zcgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 248.000 174.000 0.401 http://example.org/people/person/place_of_birth #17767-01tt43d PRED entity: 01tt43d PRED relation: award PRED expected values: 02x4w6g => 92 concepts (88 used for prediction) PRED predicted values (max 10 best out of 310): 09qv_s (0.71 #21970, 0.71 #16475, 0.70 #18832), 040njc (0.57 #5889, 0.40 #4319, 0.36 #5104), 019f4v (0.50 #60, 0.43 #4373, 0.40 #5943), 02w9sd7 (0.42 #2118, 0.06 #8001, 0.05 #25115), 0gq9h (0.39 #5954, 0.38 #71, 0.30 #4384), 04dn09n (0.38 #37, 0.32 #821, 0.30 #1213), 02pqp12 (0.38 #64, 0.29 #4377, 0.26 #2809), 03hl6lc (0.38 #166, 0.21 #4087, 0.20 #2911), 0gqy2 (0.36 #2112, 0.11 #12311, 0.10 #7995), 07bdd_ (0.32 #843, 0.30 #1627, 0.30 #1235) >> Best rule #21970 for best value: >> intensional similarity = 3 >> extensional distance = 1536 >> proper extension: 0grwj; 05bnp0; 016qtt; 04qvl7; 01k7d9; 05cljf; 01j5ts; 0h5f5n; 01vrx3g; 023tp8; ... >> query: (?x6426, ?x2853) <- award(?x6426, ?x68), award_nominee(?x4854, ?x6426), award_winner(?x2853, ?x6426) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2065 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 0bkmf; *> query: (?x6426, 02x4w6g) <- nominated_for(?x6426, ?x303), award_winner(?x591, ?x6426), ?x591 = 0f4x7 *> conf = 0.13 ranks of expected_values: 47 EVAL 01tt43d award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 92.000 88.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17766-0kbvb PRED entity: 0kbvb PRED relation: olympics! PRED expected values: 07jjt => 27 concepts (27 used for prediction) PRED predicted values (max 10 best out of 84): 01hp22 (0.81 #272, 0.78 #242, 0.77 #29), 07_53 (0.81 #272, 0.78 #242, 0.77 #29), 07jjt (0.81 #272, 0.78 #242, 0.77 #29), 0d1tm (0.81 #272, 0.78 #242, 0.77 #29), 07gyv (0.81 #272, 0.78 #242, 0.77 #29), 0dwxr (0.81 #272, 0.78 #242, 0.77 #29), 01gqfm (0.81 #272, 0.78 #242, 0.77 #29), 071t0 (0.81 #272, 0.78 #242, 0.77 #29), 06z6r (0.81 #272, 0.78 #242, 0.77 #29), 0194d (0.81 #272, 0.78 #242, 0.77 #29) >> Best rule #272 for best value: >> intensional similarity = 54 >> extensional distance = 4 >> proper extension: 09n48; >> query: (?x778, ?x171) <- olympics(?x1003, ?x778), olympics(?x87, ?x778), olympics(?x47, ?x778), olympics(?x3277, ?x778), film_release_region(?x9345, ?x87), film_release_region(?x7379, ?x87), film_release_region(?x6886, ?x87), film_release_region(?x6661, ?x87), film_release_region(?x6095, ?x87), film_release_region(?x5827, ?x87), film_release_region(?x5347, ?x87), film_release_region(?x5270, ?x87), film_release_region(?x4684, ?x87), film_release_region(?x2878, ?x87), film_release_region(?x2717, ?x87), film_release_region(?x2441, ?x87), film_release_region(?x1744, ?x87), film_release_region(?x1252, ?x87), film_release_region(?x1173, ?x87), film_release_region(?x1071, ?x87), film_release_region(?x511, ?x87), film_release_region(?x430, ?x87), film_release_region(?x409, ?x87), ?x3277 = 06t8v, ?x409 = 0gtv7pk, ?x5347 = 02ylg6, country(?x5989, ?x87), exported_to(?x87, ?x291), ?x2441 = 0cc5mcj, ?x1744 = 035yn8, sports(?x778, ?x171), ?x7379 = 032clf, ?x5989 = 019tzd, film_release_region(?x66, ?x1003), ?x5827 = 0ggbfwf, ?x1252 = 02c6d, ?x5270 = 0bc1yhb, olympics(?x359, ?x778), ?x6095 = 0bq6ntw, jurisdiction_of_office(?x182, ?x87), ?x2717 = 0k5g9, ?x1173 = 0872p_c, organization(?x87, ?x127), ?x4684 = 03nm_fh, ?x511 = 0dscrwf, ?x6886 = 0gwjw0c, contains(?x7273, ?x47), award_winner(?x2878, ?x1933), ?x9345 = 014knw, ?x430 = 0m2kd, administrative_area_type(?x1003, ?x2792), film_crew_role(?x6661, ?x137), medal(?x778, ?x422), ?x1071 = 02d44q >> conf = 0.81 => this is the best rule for 10 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0kbvb olympics! 07jjt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 27.000 27.000 0.810 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #17765-0d0x8 PRED entity: 0d0x8 PRED relation: origin! PRED expected values: 02wwwv5 => 195 concepts (195 used for prediction) PRED predicted values (max 10 best out of 293): 06nv27 (0.25 #219, 0.07 #3316, 0.06 #4864), 015bwt (0.25 #476, 0.03 #20609, 0.02 #3573), 01wv9p (0.25 #169, 0.02 #3266, 0.02 #4814), 0d193h (0.25 #173, 0.02 #3270, 0.02 #4818), 0837ql (0.25 #203, 0.02 #3300, 0.02 #4848), 03j0br4 (0.25 #90, 0.02 #3187, 0.02 #4735), 0153nq (0.25 #516, 0.02 #3613, 0.02 #5161), 01tpl1p (0.25 #457, 0.02 #3554, 0.02 #5102), 0cbm64 (0.25 #411, 0.02 #3508, 0.02 #5056), 01jgkj2 (0.25 #407, 0.02 #3504, 0.02 #5052) >> Best rule #219 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0nzny; >> query: (?x3038, 06nv27) <- contains(?x3038, ?x2277), adjoins(?x760, ?x3038), ?x2277 = 013yq >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1974 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 01vsb_; *> query: (?x3038, 02wwwv5) <- state_province_region(?x2276, ?x3038), industry(?x2276, ?x3368), company(?x1907, ?x2276) *> conf = 0.04 ranks of expected_values: 31 EVAL 0d0x8 origin! 02wwwv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 195.000 195.000 0.250 http://example.org/music/artist/origin #17764-018phr PRED entity: 018phr PRED relation: artists! PRED expected values: 016zgj => 108 concepts (51 used for prediction) PRED predicted values (max 10 best out of 222): 06by7 (0.86 #9663, 0.61 #953, 0.60 #13394), 064t9 (0.51 #13695, 0.45 #11514, 0.44 #10274), 016clz (0.35 #4041, 0.32 #13376, 0.29 #5907), 0155w (0.31 #1968, 0.29 #1658, 0.22 #9747), 0xhtw (0.31 #4678, 0.28 #948, 0.27 #9658), 05bt6j (0.31 #1284, 0.30 #973, 0.30 #2524), 016jny (0.30 #1966, 0.30 #1035, 0.29 #1656), 06j6l (0.28 #13418, 0.26 #13728, 0.26 #4396), 02yv6b (0.26 #1029, 0.20 #99, 0.20 #9739), 015pdg (0.26 #10, 0.19 #940, 0.18 #13371) >> Best rule #9663 for best value: >> intensional similarity = 3 >> extensional distance = 449 >> proper extension: 07yg2; 03q_w5; 0cfgd; >> query: (?x7937, 06by7) <- artists(?x1928, ?x7937), artists(?x1928, ?x4029), ?x4029 = 01c8v0 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #151 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 04r1t; 07m4c; *> query: (?x7937, 016zgj) <- artists(?x2664, ?x7937), artists(?x1928, ?x7937), ?x1928 = 0mhfr, ?x2664 = 01lyv *> conf = 0.23 ranks of expected_values: 12 EVAL 018phr artists! 016zgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 108.000 51.000 0.863 http://example.org/music/genre/artists #17763-07_l6 PRED entity: 07_l6 PRED relation: group PRED expected values: 02mq_y => 72 concepts (39 used for prediction) PRED predicted values (max 10 best out of 398): 07mvp (0.75 #3058, 0.70 #3430, 0.67 #1930), 0khth (0.67 #1527, 0.60 #5466, 0.60 #3399), 048xh (0.67 #1385, 0.60 #1014, 0.57 #2128), 02t3ln (0.67 #1528, 0.50 #6594, 0.50 #3586), 03k3b (0.62 #2323, 0.50 #1769, 0.50 #1396), 0134wr (0.60 #3459, 0.60 #845, 0.58 #4399), 05563d (0.60 #3202, 0.60 #959, 0.57 #2073), 014pg1 (0.60 #3643, 0.50 #2326, 0.50 #1772), 07m4c (0.56 #6638, 0.50 #3258, 0.50 #1572), 0mjn2 (0.53 #5564, 0.50 #6504, 0.50 #3497) >> Best rule #3058 for best value: >> intensional similarity = 22 >> extensional distance = 6 >> proper extension: 02k84w; >> query: (?x3296, 07mvp) <- role(?x3296, ?x6039), role(?x3296, ?x5417), role(?x3296, ?x2158), role(?x3296, ?x645), role(?x3296, ?x75), role(?x4769, ?x3296), role(?x1574, ?x3296), role(?x885, ?x3296), role(?x2158, ?x212), role(?x6039, ?x1750), ?x885 = 0dwtp, ?x4769 = 0dwt5, instrumentalists(?x3296, ?x1399), role(?x2662, ?x6039), ?x75 = 07y_7, ?x212 = 026t6, ?x645 = 028tv0, role(?x3214, ?x3296), instrumentalists(?x5417, ?x9298), ?x9298 = 016j2t, family(?x2158, ?x7256), ?x1574 = 0l15bq >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1908 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 4 *> proper extension: 042v_gx; 03qjg; *> query: (?x3296, 02mq_y) <- role(?x3296, ?x6039), role(?x3296, ?x2956), role(?x3296, ?x2158), role(?x3296, ?x1267), role(?x3296, ?x1166), role(?x3296, ?x716), role(?x2253, ?x3296), role(?x1437, ?x3296), role(?x314, ?x3296), ?x2158 = 01dnws, role(?x1332, ?x6039), role(?x3296, ?x212), ?x1437 = 01vdm0, instrumentalists(?x3296, ?x7386), ?x716 = 018vs, ?x1267 = 07brj, ?x2253 = 01679d, ?x314 = 02sgy, profession(?x7386, ?x1183), location(?x7386, ?x863), ?x1166 = 05148p4, role(?x1225, ?x2956), family(?x2956, ?x7256), group(?x6039, ?x5838) *> conf = 0.50 ranks of expected_values: 23 EVAL 07_l6 group 02mq_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 72.000 39.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17762-0ds11z PRED entity: 0ds11z PRED relation: film_crew_role PRED expected values: 09vw2b7 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 23): 09vw2b7 (0.68 #1066, 0.66 #1298, 0.65 #967), 01vx2h (0.57 #75, 0.50 #9, 0.43 #176), 0d2b38 (0.40 #56, 0.29 #89, 0.24 #156), 02ynfr (0.29 #79, 0.20 #46, 0.18 #444), 0215hd (0.29 #82, 0.20 #49, 0.18 #149), 089g0h (0.29 #83, 0.20 #50, 0.18 #150), 015h31 (0.20 #41, 0.18 #141, 0.18 #108), 01xy5l_ (0.20 #44, 0.16 #178, 0.14 #77), 033smt (0.20 #58, 0.14 #91, 0.12 #158), 089fss (0.20 #38, 0.07 #966, 0.07 #1297) >> Best rule #1066 for best value: >> intensional similarity = 3 >> extensional distance = 769 >> proper extension: 0fq27fp; 0gj9qxr; 0h95zbp; 03_wm6; 0gh6j94; >> query: (?x485, 09vw2b7) <- film_crew_role(?x485, ?x468), genre(?x485, ?x53), ?x468 = 02r96rf >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ds11z film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.681 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17761-0b66qd PRED entity: 0b66qd PRED relation: spouse PRED expected values: 01ggbx => 114 concepts (34 used for prediction) PRED predicted values (max 10 best out of 203): 01ggbx (0.81 #13297, 0.81 #1562, 0.80 #11341), 07r1h (0.11 #609, 0.03 #1390, 0.03 #8822), 05r5w (0.04 #2859, 0.04 #3250, 0.04 #514), 01lbp (0.04 #3548, 0.03 #5502, 0.02 #7068), 033tln (0.04 #550, 0.03 #7589, 0.02 #6805), 01ft2l (0.04 #522, 0.03 #1303, 0.02 #2085), 01qqtr (0.04 #699, 0.03 #1480, 0.02 #2262), 01yd8v (0.04 #513, 0.03 #1294, 0.02 #2076), 02f8lw (0.04 #512, 0.03 #1293, 0.02 #2075), 05cljf (0.04 #393, 0.03 #1174, 0.02 #1956) >> Best rule #13297 for best value: >> intensional similarity = 5 >> extensional distance = 293 >> proper extension: 03lq43; 02jxmr; 07hgkd; 018gqj; 06_bq1; >> query: (?x14141, ?x13441) <- spouse(?x13441, ?x14141), profession(?x14141, ?x1032), gender(?x14141, ?x514), profession(?x6835, ?x1032), ?x6835 = 06mt91 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b66qd spouse 01ggbx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 34.000 0.808 http://example.org/people/person/spouse_s./people/marriage/spouse #17760-0h32q PRED entity: 0h32q PRED relation: award_winner! PRED expected values: 0cqgl9 => 120 concepts (96 used for prediction) PRED predicted values (max 10 best out of 244): 0gqwc (0.36 #34877, 0.35 #34878, 0.35 #23808), 0gqyl (0.36 #34877, 0.35 #34878, 0.35 #23808), 02ppm4q (0.36 #34877, 0.35 #34878, 0.35 #23808), 099t8j (0.36 #34877, 0.35 #34878, 0.35 #23808), 03qgjwc (0.36 #34877, 0.35 #34878, 0.34 #23807), 0bb57s (0.36 #34877, 0.35 #23808, 0.34 #23807), 09cn0c (0.30 #2014, 0.14 #2440, 0.11 #33600), 027571b (0.30 #1972, 0.11 #33600, 0.08 #1122), 094qd5 (0.26 #1744, 0.16 #25085, 0.15 #2170), 02z1nbg (0.25 #1891, 0.18 #2317, 0.11 #33600) >> Best rule #34877 for best value: >> intensional similarity = 4 >> extensional distance = 1828 >> proper extension: 01sl1q; 07nznf; 012ljv; 05vsxz; 06j0md; 06gp3f; 0qf43; 086k8; 01qscs; 0p_pd; ... >> query: (?x4398, ?x375) <- award(?x4398, ?x1716), award(?x4398, ?x375), award_winner(?x7521, ?x4398), award(?x718, ?x1716) >> conf = 0.36 => this is the best rule for 6 predicted values *> Best rule #2312 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 02rmxx; 0bw6y; 01j851; 01gw8b; *> query: (?x4398, 0cqgl9) <- award(?x4398, ?x1132), type_of_union(?x4398, ?x566), ?x1132 = 0bdwft *> conf = 0.17 ranks of expected_values: 11 EVAL 0h32q award_winner! 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 120.000 96.000 0.362 http://example.org/award/award_category/winners./award/award_honor/award_winner #17759-010h9y PRED entity: 010h9y PRED relation: locations! PRED expected values: 02z6gky => 159 concepts (126 used for prediction) PRED predicted values (max 10 best out of 102): 0b_6v_ (0.33 #314, 0.33 #189, 0.24 #1564), 0b_6rk (0.33 #295, 0.28 #1045, 0.24 #1295), 0b_6pv (0.33 #1079, 0.24 #1329, 0.21 #1579), 0b_6mr (0.33 #337, 0.24 #1337, 0.21 #1587), 0b_75k (0.33 #298, 0.22 #1048, 0.21 #1548), 0b_6qj (0.33 #317, 0.20 #1317, 0.19 #9412), 0b_6zk (0.32 #1281, 0.23 #1656, 0.19 #9412), 0b_6lb (0.28 #1077, 0.21 #1577, 0.20 #1327), 0b_6q5 (0.22 #1094, 0.21 #1594, 0.20 #94), 0b_6_l (0.22 #1104, 0.19 #9412, 0.18 #9540) >> Best rule #314 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0kcw2; >> query: (?x11669, 0b_6v_) <- county(?x11669, ?x12599), time_zones(?x11669, ?x2088), locations(?x9956, ?x11669), ?x9956 = 0bzrsh >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 010dft; *> query: (?x11669, 02z6gky) <- county(?x11669, ?x12599), time_zones(?x11669, ?x2088), contains(?x2256, ?x11669), ?x2256 = 07srw *> conf = 0.20 ranks of expected_values: 13 EVAL 010h9y locations! 02z6gky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 159.000 126.000 0.333 http://example.org/time/event/locations #17758-0h0wc PRED entity: 0h0wc PRED relation: award_winner! PRED expected values: 027571b => 113 concepts (76 used for prediction) PRED predicted values (max 10 best out of 231): 094qd5 (0.37 #25275, 0.37 #24431, 0.37 #11795), 0gqyl (0.37 #25275, 0.37 #24431, 0.37 #11795), 0bdwft (0.37 #25275, 0.37 #24431, 0.37 #11795), 09sb52 (0.37 #25275, 0.37 #24431, 0.37 #11795), 02ppm4q (0.37 #25275, 0.37 #24431, 0.37 #11795), 099t8j (0.37 #25275, 0.37 #24431, 0.37 #11795), 057xs89 (0.37 #25275, 0.37 #24431, 0.37 #11795), 026mmy (0.37 #25275, 0.37 #24431, 0.37 #11795), 0bsjcw (0.37 #25275, 0.37 #24431, 0.37 #11795), 05ztrmj (0.20 #598, 0.03 #3968, 0.03 #4810) >> Best rule #25275 for best value: >> intensional similarity = 2 >> extensional distance = 1462 >> proper extension: 014hr0; 04f9r2; >> query: (?x2551, ?x704) <- award_winner(?x2275, ?x2551), award(?x2551, ?x704) >> conf = 0.37 => this is the best rule for 9 predicted values *> Best rule #24853 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1462 *> proper extension: 0hm0k; *> query: (?x2551, ?x375) <- award_winner(?x715, ?x2551), award(?x715, ?x375) *> conf = 0.16 ranks of expected_values: 23 EVAL 0h0wc award_winner! 027571b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 113.000 76.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #17757-0py9b PRED entity: 0py9b PRED relation: list PRED expected values: 01pd60 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 4): 01pd60 (0.81 #660, 0.81 #654, 0.73 #551), 09g7thr (0.53 #496, 0.50 #436, 0.49 #546), 05glt (0.38 #650, 0.38 #656), 026cl_m (0.09 #651, 0.09 #657) >> Best rule #660 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 07bz5; >> query: (?x7970, ?x8915) <- list(?x7970, ?x5997), list(?x14051, ?x5997), list(?x14051, ?x8915) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0py9b list 01pd60 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.814 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #17756-01f6zc PRED entity: 01f6zc PRED relation: location_of_ceremony PRED expected values: 0r62v => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 6): 0cv3w (0.20 #35, 0.03 #630, 0.03 #749), 0k049 (0.02 #837, 0.02 #1075, 0.01 #1789), 02_286 (0.02 #489, 0.01 #727, 0.01 #1560), 0r0m6 (0.01 #764, 0.01 #883, 0.01 #1954), 030qb3t (0.01 #1090, 0.01 #495), 07fr_ (0.01 #549) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01rnpy; >> query: (?x5316, 0cv3w) <- film(?x5316, ?x351), award(?x5316, ?x68), ?x351 = 08lr6s >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01f6zc location_of_ceremony 0r62v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.200 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #17755-076689 PRED entity: 076689 PRED relation: religion PRED expected values: 03_gx => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 18): 0kpl (0.25 #55, 0.08 #1090, 0.08 #1135), 0c8wxp (0.20 #96, 0.17 #816, 0.17 #186), 092bf5 (0.14 #16, 0.03 #151, 0.03 #556), 01hng3 (0.14 #39, 0.02 #4637), 03_gx (0.13 #104, 0.09 #1274, 0.09 #1139), 0kq2 (0.12 #63, 0.05 #513, 0.04 #558), 0n2g (0.04 #1048, 0.04 #958, 0.03 #1138), 03j6c (0.03 #1326, 0.03 #1461, 0.03 #1641), 0flw86 (0.03 #137, 0.03 #182, 0.03 #227), 06nzl (0.03 #150, 0.02 #285, 0.02 #4637) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0bc71w; 03g62; >> query: (?x11606, 0kpl) <- place_of_death(?x11606, ?x5895), type_of_union(?x11606, ?x566), student(?x741, ?x11606), ?x741 = 01w3v >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 01kt17; *> query: (?x11606, 03_gx) <- place_of_death(?x11606, ?x5895), type_of_union(?x11606, ?x566), award(?x11606, ?x2192), ?x2192 = 0bfvd4 *> conf = 0.13 ranks of expected_values: 5 EVAL 076689 religion 03_gx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 113.000 113.000 0.250 http://example.org/people/person/religion #17754-026b33f PRED entity: 026b33f PRED relation: languages PRED expected values: 02h40lc => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.90 #123, 0.89 #156, 0.88 #200), 0t_2 (0.26 #431, 0.11 #419, 0.10 #72), 06nm1 (0.14 #49, 0.07 #93, 0.04 #104), 03_9r (0.05 #147, 0.05 #312, 0.04 #224), 064_8sq (0.02 #106, 0.02 #238, 0.02 #117), 02bv9 (0.02 #108, 0.01 #240), 04306rv (0.02 #102, 0.01 #234), 02bjrlw (0.02 #100, 0.01 #232) >> Best rule #123 for best value: >> intensional similarity = 4 >> extensional distance = 133 >> proper extension: 0bx_hnp; >> query: (?x2787, 02h40lc) <- program(?x12871, ?x2787), state_province_region(?x12871, ?x335), category(?x12871, ?x134), ?x335 = 059rby >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026b33f languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.896 http://example.org/tv/tv_program/languages #17753-01w923 PRED entity: 01w923 PRED relation: role PRED expected values: 042v_gx => 117 concepts (68 used for prediction) PRED predicted values (max 10 best out of 122): 05842k (0.60 #274, 0.52 #574, 0.45 #874), 0342h (0.59 #403, 0.58 #1309, 0.58 #1909), 042v_gx (0.53 #406, 0.45 #1912, 0.42 #1312), 0dwr4 (0.52 #299, 0.39 #2499, 0.37 #1904), 01vdm0 (0.46 #1733, 0.40 #229, 0.39 #529), 05148p4 (0.40 #3205, 0.40 #220, 0.39 #3204), 0l14md (0.40 #3205, 0.39 #3204, 0.39 #3307), 07y_7 (0.40 #3205, 0.39 #3204, 0.39 #3307), 0l14qv (0.40 #205, 0.35 #505, 0.33 #106), 013y1f (0.40 #234, 0.30 #534, 0.29 #433) >> Best rule #274 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 01gx5f; >> query: (?x1694, 05842k) <- artists(?x10290, ?x1694), role(?x1694, ?x314), ?x10290 = 03ckfl9, performance_role(?x1694, ?x2059), nationality(?x1694, ?x512) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 018y81; *> query: (?x1694, 042v_gx) <- instrumentalists(?x315, ?x1694), role(?x1694, ?x314), artists(?x4910, ?x1694), ?x315 = 0l14md, ?x314 = 02sgy *> conf = 0.53 ranks of expected_values: 3 EVAL 01w923 role 042v_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 68.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role #17752-024pcx PRED entity: 024pcx PRED relation: combatants! PRED expected values: 0f8l9c => 183 concepts (120 used for prediction) PRED predicted values (max 10 best out of 251): 03f4n1 (0.87 #435, 0.87 #434, 0.85 #73), 0b90_r (0.76 #2041, 0.50 #1896, 0.47 #1531), 087vz (0.76 #2080, 0.50 #1935, 0.46 #3831), 0d060g (0.76 #2044, 0.50 #2194, 0.46 #3795), 0154j (0.71 #2042, 0.60 #1897, 0.49 #3793), 09c7w0 (0.71 #2040, 0.55 #2701, 0.55 #1895), 07ssc (0.70 #1902, 0.67 #2197, 0.62 #2708), 0f8l9c (0.67 #2053, 0.62 #2203, 0.55 #2714), 059j2 (0.67 #2059, 0.50 #2209, 0.42 #1549), 0ctw_b (0.62 #2057, 0.55 #1912, 0.49 #3808) >> Best rule #435 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 05kyr; >> query: (?x9328, ?x1612) <- combatants(?x9328, ?x10120), combatants(?x9328, ?x1612), entity_involved(?x6982, ?x9328), ?x10120 = 01fvhp, nationality(?x5249, ?x9328) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #2053 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 0b90_r; 0154j; 05qhw; 0hzlz; 0ctw_b; 0345h; 015qh; 06bnz; 05b4w; 087vz; ... *> query: (?x9328, 0f8l9c) <- combatants(?x9328, ?x1679), combatants(?x1777, ?x9328), jurisdiction_of_office(?x3119, ?x9328), first_level_division_of(?x1679, ?x1264), adjoins(?x1679, ?x8264) *> conf = 0.67 ranks of expected_values: 8 EVAL 024pcx combatants! 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 183.000 120.000 0.868 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #17751-0k4gf PRED entity: 0k4gf PRED relation: place_of_death PRED expected values: 04kf4 => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 80): 0fhp9 (0.33 #209, 0.12 #598, 0.10 #1181), 06mkj (0.33 #33), 030qb3t (0.26 #1577, 0.15 #995, 0.15 #1965), 0156q (0.17 #218, 0.12 #3885, 0.08 #12638), 0cpyv (0.17 #263, 0.04 #3176, 0.02 #5311), 02_286 (0.16 #791, 0.14 #1568, 0.10 #12651), 02h6_6p (0.14 #426, 0.06 #3145, 0.02 #5280), 05qtj (0.14 #453, 0.05 #6861, 0.04 #6279), 04swd (0.08 #1093, 0.07 #1481, 0.04 #3034), 0k049 (0.08 #976, 0.06 #5635, 0.06 #587) >> Best rule #209 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 039n1; >> query: (?x1211, 0fhp9) <- gender(?x1211, ?x231), influenced_by(?x1211, ?x8177), ?x8177 = 03_f0, nationality(?x1211, ?x1264) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1639 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 02w670; 06zd1c; 02rf51g; *> query: (?x1211, 04kf4) <- gender(?x1211, ?x231), profession(?x1211, ?x563), music(?x2738, ?x1211), people(?x1158, ?x1211) *> conf = 0.03 ranks of expected_values: 29 EVAL 0k4gf place_of_death 04kf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 186.000 186.000 0.333 http://example.org/people/deceased_person/place_of_death #17750-04nm0n0 PRED entity: 04nm0n0 PRED relation: genre PRED expected values: 02l7c8 082gq => 139 concepts (138 used for prediction) PRED predicted values (max 10 best out of 171): 04xvlr (0.74 #10792, 0.73 #13253, 0.68 #11286), 07ssc (0.65 #5396, 0.64 #4658, 0.64 #11409), 02l7c8 (0.56 #997, 0.56 #875, 0.56 #753), 01jfsb (0.54 #3687, 0.46 #6265, 0.41 #7123), 05p553 (0.50 #248, 0.42 #15346, 0.41 #6501), 060__y (0.50 #629, 0.35 #1242, 0.28 #3447), 02kdv5l (0.45 #1104, 0.43 #6254, 0.40 #7112), 03k9fj (0.35 #1114, 0.33 #134, 0.33 #5897), 06n90 (0.35 #1116, 0.32 #1974, 0.23 #6266), 06cvj (0.33 #492, 0.33 #3, 0.22 #861) >> Best rule #10792 for best value: >> intensional similarity = 5 >> extensional distance = 401 >> proper extension: 03kq98; >> query: (?x5017, ?x162) <- titles(?x162, ?x5017), titles(?x53, ?x5017), ?x53 = 07s9rl0, genre(?x6531, ?x162), ?x6531 = 01_0f7 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #997 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 16 *> proper extension: 02z3r8t; 04lqvly; 03hmt9b; 02z2mr7; 03cyslc; 04gp58p; *> query: (?x5017, 02l7c8) <- film_festivals(?x5017, ?x9189), ?x9189 = 04grdgy, film_crew_role(?x5017, ?x2095), film_crew_role(?x10684, ?x2095), film_crew_role(?x4602, ?x2095), ?x10684 = 05sxr_, ?x4602 = 09gb_4p *> conf = 0.56 ranks of expected_values: 3, 13 EVAL 04nm0n0 genre 082gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 139.000 138.000 0.738 http://example.org/film/film/genre EVAL 04nm0n0 genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 139.000 138.000 0.738 http://example.org/film/film/genre #17749-07g2v PRED entity: 07g2v PRED relation: artist! PRED expected values: 02p11jq => 185 concepts (183 used for prediction) PRED predicted values (max 10 best out of 113): 011k1h (0.43 #574, 0.20 #292, 0.15 #2548), 01f_3w (0.29 #599, 0.25 #1022, 0.20 #1163), 0n85g (0.29 #627, 0.25 #63, 0.19 #1332), 0g768 (0.29 #602, 0.25 #38, 0.14 #7794), 03rhqg (0.29 #580, 0.20 #298, 0.16 #3259), 0fb0v (0.29 #571, 0.19 #1276, 0.14 #712), 015_1q (0.28 #4673, 0.28 #4391, 0.25 #161), 0181dw (0.25 #1030, 0.20 #1171, 0.19 #1312), 02bh8z (0.25 #22, 0.20 #304, 0.14 #586), 03mp8k (0.25 #208, 0.17 #3592, 0.14 #772) >> Best rule #574 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01sxd1; >> query: (?x3422, 011k1h) <- spouse(?x3421, ?x3422), profession(?x3422, ?x1183), artists(?x474, ?x3422), ?x474 = 0m0jc >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #577 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 01sxd1; *> query: (?x3422, 02p11jq) <- spouse(?x3421, ?x3422), profession(?x3422, ?x1183), artists(?x474, ?x3422), ?x474 = 0m0jc *> conf = 0.14 ranks of expected_values: 31 EVAL 07g2v artist! 02p11jq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 185.000 183.000 0.429 http://example.org/music/record_label/artist #17748-04g51 PRED entity: 04g51 PRED relation: disciplines_or_subjects! PRED expected values: 040vk98 0223xd => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 84): 0262s1 (0.50 #513, 0.22 #1588, 0.20 #915), 04zngls (0.50 #503, 0.22 #1578, 0.20 #905), 0fq9zcx (0.40 #993, 0.40 #792, 0.29 #1531), 07kfzsg (0.40 #991, 0.40 #790, 0.29 #1529), 0fm3h2 (0.40 #990, 0.40 #789, 0.29 #1528), 09v1lrz (0.40 #987, 0.40 #786, 0.29 #1525), 09v478h (0.40 #985, 0.40 #784, 0.29 #1523), 0dgr5xp (0.40 #975, 0.40 #774, 0.29 #1513), 02y_j8g (0.40 #971, 0.40 #770, 0.29 #1509), 09v8db5 (0.40 #966, 0.40 #765, 0.29 #1504) >> Best rule #513 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 06n90; >> query: (?x5864, 0262s1) <- disciplines_or_subjects(?x12418, ?x5864), disciplines_or_subjects(?x11471, ?x5864), disciplines_or_subjects(?x9285, ?x5864), ?x12418 = 045xh, award(?x13298, ?x11471), award(?x9854, ?x11471), ?x9285 = 0265vt, ?x9854 = 0gthm, nationality(?x13298, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #875 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 05hgj; *> query: (?x5864, 040vk98) <- disciplines_or_subjects(?x12418, ?x5864), disciplines_or_subjects(?x11471, ?x5864), disciplines_or_subjects(?x10505, ?x5864), disciplines_or_subjects(?x10222, ?x5864), award(?x5336, ?x11471), nationality(?x5336, ?x94), award(?x3663, ?x12418), ?x3663 = 02yl42, award_winner(?x10222, ?x2343), ?x10505 = 0208wk *> conf = 0.40 ranks of expected_values: 16, 53 EVAL 04g51 disciplines_or_subjects! 0223xd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 90.000 90.000 0.500 http://example.org/award/award_category/disciplines_or_subjects EVAL 04g51 disciplines_or_subjects! 040vk98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 90.000 90.000 0.500 http://example.org/award/award_category/disciplines_or_subjects #17747-01ysy9 PRED entity: 01ysy9 PRED relation: institution PRED expected values: 01y888 => 23 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1318): 01w5m (0.82 #9486, 0.80 #8236, 0.77 #3118), 09f2j (0.82 #9551, 0.80 #8301, 0.77 #3118), 02zd460 (0.82 #9566, 0.80 #8316, 0.77 #3118), 03ksy (0.82 #9487, 0.80 #8237, 0.77 #3118), 07szy (0.82 #9412, 0.77 #11911, 0.77 #3118), 025v3k (0.82 #9504, 0.77 #3118, 0.70 #3744), 07tg4 (0.82 #9460, 0.77 #3118, 0.70 #3744), 0gl5_ (0.82 #9649, 0.77 #3118, 0.70 #3744), 0bwfn (0.82 #9678, 0.77 #3118, 0.70 #3744), 07wjk (0.82 #9435, 0.77 #3118, 0.70 #3744) >> Best rule #9486 for best value: >> intensional similarity = 28 >> extensional distance = 9 >> proper extension: 07s6fsf; >> query: (?x11690, 01w5m) <- institution(?x11690, ?x13080), institution(?x11690, ?x12257), institution(?x11690, ?x8903), institution(?x11690, ?x5621), institution(?x11690, ?x2013), institution(?x11690, ?x892), category(?x13080, ?x134), contains(?x2020, ?x8903), contains(?x94, ?x8903), organization(?x346, ?x13080), organization(?x3484, ?x8903), citytown(?x12257, ?x4627), ?x94 = 09c7w0, student(?x2013, ?x1197), major_field_of_study(?x11690, ?x6760), institution(?x7817, ?x2013), school(?x2574, ?x5621), major_field_of_study(?x2013, ?x1327), major_field_of_study(?x10910, ?x6760), major_field_of_study(?x8220, ?x6760), student(?x5621, ?x525), ?x10910 = 013807, ?x7817 = 02cq61, ?x892 = 07tgn, ?x2020 = 05k7sb, citytown(?x8220, ?x5783), ?x2574 = 01y3v, student(?x8220, ?x1787) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #4994 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 4 *> proper extension: 03mkk4; 028dcg; *> query: (?x11690, ?x122) <- institution(?x11690, ?x13080), institution(?x11690, ?x12257), institution(?x11690, ?x8903), institution(?x11690, ?x2013), category(?x13080, ?x134), contains(?x2020, ?x8903), contains(?x94, ?x8903), organization(?x346, ?x13080), organization(?x3484, ?x8903), citytown(?x12257, ?x4627), ?x94 = 09c7w0, student(?x2013, ?x2179), major_field_of_study(?x11690, ?x6760), major_field_of_study(?x11690, ?x3878), institution(?x2759, ?x2013), ?x6760 = 0w7c, contains(?x789, ?x12257), major_field_of_study(?x2013, ?x1327), major_field_of_study(?x122, ?x3878), student(?x3878, ?x1309), type_of_union(?x2179, ?x566), ?x346 = 060c4, award(?x2179, ?x68), profession(?x2179, ?x319), ?x2020 = 05k7sb, ?x2759 = 071tyz, taxonomy(?x3878, ?x939) *> conf = 0.54 ranks of expected_values: 330 EVAL 01ysy9 institution 01y888 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 23.000 22.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #17746-01crd5 PRED entity: 01crd5 PRED relation: jurisdiction_of_office! PRED expected values: 060c4 => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.78 #442, 0.78 #486, 0.77 #552), 0f6c3 (0.51 #1196, 0.31 #1570, 0.31 #843), 0fkvn (0.46 #1192, 0.29 #839, 0.29 #1566), 09n5b9 (0.46 #1200, 0.27 #1574, 0.24 #2146), 0pqc5 (0.43 #1435, 0.36 #2734, 0.29 #2513), 0p5vf (0.35 #166, 0.26 #276, 0.26 #320), 0dq3c (0.31 #1167, 0.21 #397, 0.17 #595), 09d6p2 (0.31 #1167, 0.16 #2664, 0.08 #140), 01zq91 (0.28 #146, 0.24 #80, 0.24 #256), 02079p (0.24 #76, 0.16 #142, 0.16 #2664) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 05v8c; 0d05q4; >> query: (?x8593, 060c4) <- locations(?x7455, ?x8593), medal(?x8593, ?x1242), film_release_region(?x124, ?x8593) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01crd5 jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.780 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #17745-01drsx PRED entity: 01drsx PRED relation: genre! PRED expected values: 04fzfj 078mm1 => 55 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1848): 01242_ (0.71 #10010, 0.33 #11868, 0.25 #13724), 07nt8p (0.60 #5935, 0.50 #13365, 0.43 #9651), 011wtv (0.60 #4507, 0.50 #2651, 0.42 #13793), 06__m6 (0.60 #6632, 0.43 #8489, 0.27 #25204), 09z2b7 (0.60 #5814, 0.43 #9530, 0.27 #11144), 02jkkv (0.60 #7172, 0.40 #5316, 0.33 #20172), 03cvwkr (0.60 #5712, 0.40 #3856, 0.25 #13142), 03hmt9b (0.60 #6253, 0.33 #13683, 0.29 #15540), 0f4vx (0.60 #6042, 0.29 #9758, 0.20 #4186), 05c46y6 (0.60 #6023, 0.27 #11144, 0.20 #4167) >> Best rule #10010 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 02p0szs; >> query: (?x5276, 01242_) <- genre(?x5212, ?x5276), genre(?x5198, ?x5276), genre(?x1597, ?x5276), cinematography(?x5212, ?x5862), nominated_for(?x1703, ?x5212), nominated_for(?x1243, ?x5212), film(?x772, ?x5212), ?x1243 = 0gr0m, film_sets_designed(?x200, ?x5198), ?x1597 = 0dr_4, award(?x707, ?x1703), production_companies(?x5198, ?x5537) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3352 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 01jfsb; *> query: (?x5276, 078mm1) <- genre(?x8605, ?x5276), genre(?x5212, ?x5276), genre(?x1688, ?x5276), cinematography(?x5212, ?x5862), nominated_for(?x1243, ?x5212), film(?x772, ?x5212), ?x1243 = 0gr0m, featured_film_locations(?x5212, ?x3125), award_winner(?x5212, ?x669), company(?x772, ?x13490), ?x8605 = 01jmyj, ?x1688 = 024l2y *> conf = 0.50 ranks of expected_values: 102, 225 EVAL 01drsx genre! 078mm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 55.000 15.000 0.714 http://example.org/film/film/genre EVAL 01drsx genre! 04fzfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 15.000 0.714 http://example.org/film/film/genre #17744-019803 PRED entity: 019803 PRED relation: actor! PRED expected values: 03k99c => 114 concepts (53 used for prediction) PRED predicted values (max 10 best out of 133): 01h1bf (0.50 #43, 0.29 #833, 0.18 #2154), 063zky (0.40 #371, 0.22 #1691, 0.22 #1426), 019g8j (0.40 #755, 0.22 #1546, 0.02 #4182), 0ctzf1 (0.33 #1454, 0.20 #663, 0.20 #399), 015w8_ (0.33 #1364, 0.20 #573, 0.20 #309), 0jwl2 (0.20 #336, 0.12 #1126, 0.11 #1656), 07c72 (0.20 #311, 0.12 #1101, 0.11 #1631), 03k99c (0.20 #505, 0.12 #1295, 0.11 #1825), 01h72l (0.20 #301, 0.11 #1621, 0.11 #1356), 025x1t (0.20 #485, 0.11 #1805, 0.11 #1540) >> Best rule #43 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 049_zz; 02v0ff; >> query: (?x12005, 01h1bf) <- film(?x12005, ?x2097), award_winner(?x2751, ?x12005), ?x2751 = 0jt3qpk, actor(?x9340, ?x12005), genre(?x2097, ?x307) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #505 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 02gf_l; 06b4wb; 0c5vh; *> query: (?x12005, 03k99c) <- film(?x12005, ?x1076), nationality(?x12005, ?x94), ?x1076 = 0k2sk, actor(?x9340, ?x12005), profession(?x12005, ?x1032) *> conf = 0.20 ranks of expected_values: 8 EVAL 019803 actor! 03k99c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 114.000 53.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #17743-011xg5 PRED entity: 011xg5 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.88 #66, 0.85 #71, 0.85 #76), 07c52 (0.06 #8, 0.05 #13, 0.04 #23), 02nxhr (0.05 #72, 0.04 #107, 0.04 #188), 07z4p (0.04 #5, 0.03 #10, 0.03 #25) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 187 >> proper extension: 0d1qmz; 025twgt; >> query: (?x8349, 029j_) <- film(?x1835, ?x8349), language(?x8349, ?x254), nominated_for(?x8349, ?x4235), participant(?x1835, ?x2817) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011xg5 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17742-0456zg PRED entity: 0456zg PRED relation: nominated_for! PRED expected values: 03kwtb => 67 concepts (37 used for prediction) PRED predicted values (max 10 best out of 859): 0146pg (0.29 #2458, 0.18 #4795, 0.14 #30507), 01r93l (0.26 #70130, 0.26 #77145, 0.25 #72468), 01g257 (0.26 #70130, 0.26 #77145, 0.25 #72468), 01vwllw (0.26 #70130, 0.26 #77145, 0.25 #72468), 05dbf (0.20 #458, 0.04 #12143, 0.04 #7469), 07m77x (0.20 #1869, 0.04 #8880, 0.04 #6543), 0lx2l (0.20 #524, 0.03 #2861, 0.02 #23896), 0178rl (0.20 #1157, 0.03 #3494, 0.02 #8168), 016ywr (0.20 #371, 0.03 #2708, 0.02 #7382), 01vrz41 (0.20 #246, 0.03 #2583, 0.02 #7257) >> Best rule #2458 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 019nnl; 08jgk1; 0d68qy; 0266s9; >> query: (?x8358, 0146pg) <- nominated_for(?x1291, ?x8358), category(?x8358, ?x134), instrumentalists(?x227, ?x1291), student(?x6132, ?x1291) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #51429 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 313 *> proper extension: 0g60z; 080dwhx; 06cs95; 0cwrr; 0124k9; 03d34x8; 03ln8b; 01q_y0; 01b64v; 01vrwfv; ... *> query: (?x8358, ?x1292) <- nominated_for(?x1291, ?x8358), category(?x8358, ?x134), award_nominee(?x1292, ?x1291), nationality(?x1291, ?x1310) *> conf = 0.11 ranks of expected_values: 37 EVAL 0456zg nominated_for! 03kwtb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 67.000 37.000 0.286 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17741-07z1m PRED entity: 07z1m PRED relation: district_represented! PRED expected values: 01gtcq 070mff 01grrf 01grmk => 218 concepts (218 used for prediction) PRED predicted values (max 10 best out of 31): 07p__7 (0.85 #593, 0.83 #624, 0.83 #686), 070mff (0.83 #643, 0.83 #612, 0.81 #705), 024tcq (0.81 #634, 0.81 #603, 0.79 #696), 024tkd (0.69 #645, 0.68 #707, 0.67 #614), 02bn_p (0.69 #594, 0.67 #625, 0.64 #687), 02bp37 (0.60 #628, 0.58 #690, 0.58 #597), 02bqm0 (0.56 #421, 0.55 #700, 0.54 #638), 02bqmq (0.54 #415, 0.53 #694, 0.52 #632), 01gtcq (0.46 #422, 0.42 #608, 0.38 #701), 02bqn1 (0.46 #627, 0.44 #596, 0.44 #410) >> Best rule #593 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05kr_; >> query: (?x1426, 07p__7) <- contains(?x1426, ?x347), religion(?x1426, ?x109), district_represented(?x176, ?x1426) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #643 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 46 *> proper extension: 0g0syc; *> query: (?x1426, 070mff) <- district_represented(?x952, ?x1426), ?x952 = 06f0dc *> conf = 0.83 ranks of expected_values: 2, 9, 13, 15 EVAL 07z1m district_represented! 01grmk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 218.000 218.000 0.854 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07z1m district_represented! 01grrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 218.000 218.000 0.854 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07z1m district_represented! 070mff CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 218.000 218.000 0.854 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07z1m district_represented! 01gtcq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 218.000 218.000 0.854 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #17740-02sgy PRED entity: 02sgy PRED relation: role! PRED expected values: 02pprs => 79 concepts (67 used for prediction) PRED predicted values (max 10 best out of 57): 0342h (0.85 #101, 0.85 #1465, 0.84 #883), 04rzd (0.85 #101, 0.85 #1465, 0.84 #883), 042v_gx (0.85 #101, 0.85 #1465, 0.84 #883), 0jtg0 (0.85 #101, 0.85 #1465, 0.84 #883), 03m5k (0.85 #101, 0.85 #1465, 0.84 #883), 018j2 (0.85 #101, 0.85 #1465, 0.84 #883), 01s0ps (0.85 #101, 0.85 #1465, 0.84 #883), 02k84w (0.85 #101, 0.85 #1465, 0.84 #883), 02pprs (0.85 #101, 0.85 #1465, 0.84 #883), 0319l (0.85 #101, 0.85 #1465, 0.84 #883) >> Best rule #101 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 01vdm0; >> query: (?x314, ?x74) <- role(?x314, ?x214), role(?x314, ?x74), role(?x4873, ?x314), role(?x2187, ?x314), role(?x1292, ?x314), profession(?x2187, ?x1183), ?x1292 = 03kwtb, ?x214 = 02pprs, role(?x614, ?x314), role(?x314, ?x780), award(?x2187, ?x247), instrumentalists(?x314, ?x133), artists(?x1000, ?x4873), influenced_by(?x4873, ?x2169), ?x1183 = 09jwl >> conf = 0.85 => this is the best rule for 19 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 9 EVAL 02sgy role! 02pprs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 79.000 67.000 0.852 http://example.org/music/performance_role/track_performances./music/track_contribution/role #17739-0bzyh PRED entity: 0bzyh PRED relation: profession PRED expected values: 01d_h8 => 108 concepts (93 used for prediction) PRED predicted values (max 10 best out of 77): 01d_h8 (0.89 #1601, 0.87 #2181, 0.85 #2616), 02hrh1q (0.76 #157, 0.73 #882, 0.71 #12), 0kyk (0.23 #8148, 0.22 #606, 0.16 #10760), 01c72t (0.21 #8143, 0.13 #12475, 0.11 #1471), 018gz8 (0.20 #884, 0.19 #3059, 0.19 #2479), 09jwl (0.18 #7993, 0.18 #7558, 0.18 #6543), 0np9r (0.18 #10752, 0.17 #1323, 0.16 #888), 0dgd_ (0.17 #317, 0.13 #12475, 0.08 #1622), 0dz3r (0.13 #6529, 0.13 #12475, 0.12 #7544), 0fj9f (0.13 #631, 0.13 #12475, 0.04 #1501) >> Best rule #1601 for best value: >> intensional similarity = 3 >> extensional distance = 164 >> proper extension: 0cm89v; >> query: (?x3960, 01d_h8) <- produced_by(?x2719, ?x3960), profession(?x3960, ?x353), film(?x3960, ?x2816) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bzyh profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 93.000 0.886 http://example.org/people/person/profession #17738-01wv9xn PRED entity: 01wv9xn PRED relation: group! PRED expected values: 018vs 05148p4 => 90 concepts (63 used for prediction) PRED predicted values (max 10 best out of 92): 05148p4 (0.73 #591, 0.72 #919, 0.71 #509), 018vs (0.67 #912, 0.65 #1816, 0.64 #995), 06ncr (0.44 #1068, 0.44 #985, 0.17 #363), 04rzd (0.44 #1068, 0.44 #985, 0.14 #520), 06w7v (0.44 #1068, 0.44 #985, 0.14 #560), 026t6 (0.44 #1068, 0.44 #985, 0.08 #331), 018j2 (0.44 #1068, 0.44 #985, 0.08 #1014), 048j4l (0.44 #1068, 0.44 #985, 0.03 #4202), 07xzm (0.44 #1068, 0.44 #985, 0.03 #4202), 07y_7 (0.25 #330, 0.13 #987, 0.13 #904) >> Best rule #591 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 05crg7; >> query: (?x1684, 05148p4) <- artists(?x1000, ?x1684), ?x1000 = 0xhtw, group(?x3266, ?x1684), inductee(?x1091, ?x1684), group(?x227, ?x1684) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01wv9xn group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 63.000 0.733 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01wv9xn group! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 63.000 0.733 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17737-027xbpw PRED entity: 027xbpw PRED relation: profession PRED expected values: 03gjzk => 89 concepts (87 used for prediction) PRED predicted values (max 10 best out of 59): 02hrh1q (0.92 #2827, 0.92 #5643, 0.90 #3271), 03gjzk (0.85 #1495, 0.83 #1199, 0.82 #1051), 09jwl (0.50 #315, 0.45 #1795, 0.41 #463), 01d_h8 (0.49 #4450, 0.48 #4302, 0.48 #1190), 018gz8 (0.44 #609, 0.33 #165, 0.33 #17), 02jknp (0.42 #4452, 0.42 #4304, 0.31 #600), 016z4k (0.40 #1780, 0.35 #300, 0.23 #2520), 0nbcg (0.38 #1807, 0.35 #327, 0.29 #475), 0dz3r (0.34 #1778, 0.31 #298, 0.27 #2518), 02krf9 (0.31 #174, 0.31 #914, 0.29 #1506) >> Best rule #2827 for best value: >> intensional similarity = 3 >> extensional distance = 687 >> proper extension: 01vw87c; 02nb2s; 0lzb8; 03ds3; 03gm48; 04hpck; 01sxq9; 0prjs; 01j4ls; 05sq84; ... >> query: (?x3340, 02hrh1q) <- award(?x3340, ?x2016), actor(?x1395, ?x3340), profession(?x3340, ?x987) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1495 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 249 *> proper extension: 0f1vrl; *> query: (?x3340, 03gjzk) <- program(?x3340, ?x1395), profession(?x3340, ?x987) *> conf = 0.85 ranks of expected_values: 2 EVAL 027xbpw profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 87.000 0.922 http://example.org/people/person/profession #17736-01qbjg PRED entity: 01qbjg PRED relation: place_of_death PRED expected values: 0mzww => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 17): 02_286 (0.08 #1373, 0.06 #984, 0.06 #1178), 030qb3t (0.05 #10916, 0.05 #13834, 0.04 #14223), 0k049 (0.03 #780, 0.03 #3, 0.02 #2142), 04jpl (0.03 #1367, 0.02 #978, 0.02 #1172), 0f2wj (0.02 #3513, 0.02 #2151, 0.01 #10906), 06_kh (0.02 #587, 0.01 #976, 0.01 #1170), 05jbn (0.01 #71, 0.01 #265, 0.01 #1821), 0fn7r (0.01 #159, 0.01 #547), 0l1pj (0.01 #112), 05l5n (0.01 #1388, 0.01 #1193) >> Best rule #1373 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 04107; 09jd9; >> query: (?x7932, 02_286) <- nationality(?x7932, ?x94), story_by(?x1642, ?x7932), award(?x7932, ?x3906) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01qbjg place_of_death 0mzww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 96.000 0.081 http://example.org/people/deceased_person/place_of_death #17735-0fj9f PRED entity: 0fj9f PRED relation: profession! PRED expected values: 083p7 0b_c7 061zc_ 012v1t 04xfb 042d1 0d3k14 042fk 0ngg => 50 concepts (26 used for prediction) PRED predicted values (max 10 best out of 4047): 0161c2 (0.75 #29909, 0.60 #13337, 0.33 #909), 02cx90 (0.75 #30346, 0.60 #13774, 0.33 #1346), 01w02sy (0.75 #29907, 0.60 #13335, 0.33 #907), 03f2_rc (0.67 #20714, 0.50 #20842, 0.50 #4270), 016732 (0.67 #20714, 0.47 #8285, 0.38 #24857), 02pt7h_ (0.67 #20714, 0.47 #8285, 0.38 #24857), 05crg7 (0.67 #20714, 0.47 #8285, 0.38 #24857), 055c8 (0.67 #20714, 0.33 #21644, 0.33 #930), 0mz73 (0.67 #20714, 0.33 #23227, 0.33 #2513), 01vvyvk (0.67 #20714, 0.33 #1402, 0.27 #103578) >> Best rule #29909 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0dz3r; 016z4k; 0nbcg; >> query: (?x5805, 0161c2) <- profession(?x7614, ?x5805), profession(?x5401, ?x5805), profession(?x4196, ?x5805), ?x7614 = 01s1zk, type_of_union(?x5401, ?x566), nationality(?x4196, ?x94) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #22506 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 01d_h8; 02jknp; *> query: (?x5805, 061zc_) <- profession(?x10452, ?x5805), profession(?x7614, ?x5805), award_winner(?x7614, ?x1751), ?x10452 = 023sng, instrumentalists(?x227, ?x7614), artists(?x2937, ?x7614) *> conf = 0.33 ranks of expected_values: 1331, 2813, 2927, 2997, 3278, 3283, 3284 EVAL 0fj9f profession! 0ngg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 26.000 0.750 http://example.org/people/person/profession EVAL 0fj9f profession! 042fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 26.000 0.750 http://example.org/people/person/profession EVAL 0fj9f profession! 0d3k14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 26.000 0.750 http://example.org/people/person/profession EVAL 0fj9f profession! 042d1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 26.000 0.750 http://example.org/people/person/profession EVAL 0fj9f profession! 04xfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 26.000 0.750 http://example.org/people/person/profession EVAL 0fj9f profession! 012v1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 26.000 0.750 http://example.org/people/person/profession EVAL 0fj9f profession! 061zc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 26.000 0.750 http://example.org/people/person/profession EVAL 0fj9f profession! 0b_c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 26.000 0.750 http://example.org/people/person/profession EVAL 0fj9f profession! 083p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 26.000 0.750 http://example.org/people/person/profession #17734-03s0w PRED entity: 03s0w PRED relation: location! PRED expected values: 01wt4wc => 160 concepts (132 used for prediction) PRED predicted values (max 10 best out of 2076): 051ysmf (0.46 #118029, 0.46 #27624, 0.45 #223498), 02t901 (0.33 #2423, 0.02 #12468, 0.01 #110405), 01lc5 (0.33 #2227, 0.02 #12272, 0.01 #110209), 03nb5v (0.18 #3831, 0.10 #23921, 0.10 #33967), 0p_pd (0.18 #2559, 0.07 #10093, 0.06 #22649), 0cgbf (0.18 #3902, 0.06 #23992, 0.06 #26503), 06jw0s (0.12 #6166, 0.12 #8677, 0.12 #3654), 09fb5 (0.12 #2562, 0.10 #10096, 0.08 #108033), 01yzhn (0.12 #4637, 0.10 #12171, 0.06 #24727), 0x3b7 (0.12 #3339, 0.07 #5851, 0.07 #13385) >> Best rule #118029 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 02_n7; 0727_; 0qymv; 0r4h3; 0r066; 0qxzd; 0q_0z; >> query: (?x961, ?x12725) <- category(?x961, ?x134), place_of_birth(?x12725, ?x961), jurisdiction_of_office(?x900, ?x961) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #185830 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 174 *> proper extension: 0ml25; *> query: (?x961, ?x8012) <- administrative_division(?x4362, ?x961), place_of_birth(?x8012, ?x4362) *> conf = 0.03 ranks of expected_values: 459 EVAL 03s0w location! 01wt4wc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 160.000 132.000 0.460 http://example.org/people/person/places_lived./people/place_lived/location #17733-01vv6_6 PRED entity: 01vv6_6 PRED relation: award PRED expected values: 01ckrr => 138 concepts (127 used for prediction) PRED predicted values (max 10 best out of 381): 01ckrr (0.29 #639, 0.27 #1857, 0.21 #4699), 01by1l (0.28 #925, 0.26 #14323, 0.23 #9857), 0ck27z (0.22 #19581, 0.22 #21611, 0.21 #22017), 09sb52 (0.20 #2883, 0.19 #19529, 0.18 #32116), 02wh75 (0.20 #9, 0.13 #5693, 0.12 #6911), 02f73p (0.20 #189, 0.12 #3437, 0.11 #3843), 026mfs (0.20 #130, 0.10 #9874, 0.09 #18400), 02qvyrt (0.20 #128, 0.08 #6218, 0.08 #17586), 026mg3 (0.20 #12, 0.04 #12192, 0.04 #18282), 01bgqh (0.19 #3291, 0.18 #3697, 0.18 #22373) >> Best rule #639 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 018y2s; 067mj; 05crg7; 07bzp; 018d6l; 021r7r; 03c3yf; 01693z; 01l_w0; 01k47c; ... >> query: (?x3472, 01ckrr) <- artists(?x7083, ?x3472), artists(?x6210, ?x3472), ?x6210 = 01fh36, ?x7083 = 02yv6b, category(?x3472, ?x134) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vv6_6 award 01ckrr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 127.000 0.294 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17732-027pfg PRED entity: 027pfg PRED relation: crewmember PRED expected values: 0284n42 => 101 concepts (77 used for prediction) PRED predicted values (max 10 best out of 34): 0284n42 (0.07 #96, 0.03 #331, 0.03 #238), 0c94fn (0.06 #103, 0.06 #245, 0.05 #386), 04ktcgn (0.06 #152, 0.05 #12, 0.04 #529), 02xc1w4 (0.06 #167, 0.04 #73, 0.04 #308), 092ys_y (0.05 #112, 0.04 #537, 0.03 #629), 051z6rz (0.05 #121, 0.03 #404, 0.03 #263), 027rwmr (0.05 #98, 0.03 #615, 0.02 #1040), 095zvfg (0.04 #223, 0.04 #459, 0.04 #271), 0bbxx9b (0.04 #396, 0.03 #21, 0.03 #67), 04wp63 (0.04 #133, 0.03 #227, 0.03 #275) >> Best rule #96 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 0260bz; 04t6fk; 03mh_tp; 01hq1; 011xg5; 09lxv9; >> query: (?x6932, 0284n42) <- film_crew_role(?x6932, ?x468), ?x468 = 02r96rf, film(?x71, ?x6932), edited_by(?x6932, ?x707) >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027pfg crewmember 0284n42 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 77.000 0.071 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #17731-03qh03g PRED entity: 03qh03g PRED relation: industry! PRED expected values: 02630g 07vj4v => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 647): 04sv4 (0.78 #4907, 0.60 #7317, 0.60 #4349), 01tlrp (0.60 #7317, 0.60 #4349, 0.47 #6862), 05b5c (0.60 #7317, 0.60 #4349, 0.47 #6862), 058j2 (0.60 #7317, 0.60 #4349, 0.47 #6862), 03pnvq (0.60 #7317, 0.60 #4349, 0.47 #6862), 03y7ml (0.60 #7317, 0.60 #4349, 0.47 #6862), 02hvd (0.60 #7317, 0.60 #4349, 0.47 #6862), 0g5lhl7 (0.60 #7317, 0.60 #4349, 0.47 #6862), 0206k5 (0.60 #7317, 0.60 #4349, 0.47 #6862), 0hm0k (0.60 #7317, 0.60 #4349, 0.47 #6862) >> Best rule #4907 for best value: >> intensional similarity = 19 >> extensional distance = 7 >> proper extension: 01mf0; 06xw2; 01mfj; 07c1v; >> query: (?x2271, 04sv4) <- industry(?x13890, ?x2271), industry(?x12752, ?x2271), industry(?x11641, ?x2271), industry(?x2270, ?x2271), industry(?x1908, ?x2271), industry(?x2270, ?x12816), category(?x12752, ?x134), organization(?x4682, ?x12752), citytown(?x11641, ?x8951), ?x134 = 08mbj5d, company(?x265, ?x2270), ?x12816 = 0hz28, list(?x1908, ?x8915), child(?x1908, ?x382), industry(?x13890, ?x245), ?x8915 = 01pd60, child(?x12752, ?x8336), ?x4682 = 0dq_5, ?x245 = 01mw1 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6169 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 11 *> proper extension: 05jnl; *> query: (?x2271, ?x502) <- industry(?x12752, ?x2271), industry(?x11641, ?x2271), industry(?x2270, ?x2271), industry(?x1908, ?x2271), industry(?x2270, ?x12816), category(?x12752, ?x134), organization(?x4682, ?x12752), citytown(?x11641, ?x8951), ?x134 = 08mbj5d, company(?x265, ?x2270), ?x12816 = 0hz28, list(?x1908, ?x8915), place_founded(?x11641, ?x11227), list(?x9968, ?x8915), list(?x7008, ?x8915), list(?x502, ?x8915), ?x9968 = 0k9ts, ?x7008 = 03phgz *> conf = 0.05 ranks of expected_values: 463 EVAL 03qh03g industry! 07vj4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 54.000 0.778 http://example.org/business/business_operation/industry EVAL 03qh03g industry! 02630g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 54.000 0.778 http://example.org/business/business_operation/industry #17730-03swmf PRED entity: 03swmf PRED relation: nominated_for PRED expected values: 0g60z => 154 concepts (89 used for prediction) PRED predicted values (max 10 best out of 742): 01k60v (0.38 #1623, 0.33 #19478, 0.33 #21103), 016yxn (0.38 #1623, 0.33 #19478, 0.33 #21103), 01719t (0.38 #1623, 0.33 #19478, 0.33 #21103), 05nyqk (0.31 #8118, 0.15 #58422, 0.15 #19477), 01chpn (0.20 #1008, 0.17 #2632, 0.10 #7503), 0gmgwnv (0.20 #980, 0.17 #2604, 0.04 #12343), 02py4c8 (0.20 #99, 0.17 #1723, 0.04 #11462), 03shpq (0.20 #1292, 0.17 #2916, 0.03 #24017), 06q8qh (0.20 #557, 0.17 #2181, 0.02 #15164), 0b76kw1 (0.20 #291, 0.17 #1915, 0.02 #16521) >> Best rule #1623 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 045931; >> query: (?x9156, ?x1488) <- award_winner(?x458, ?x9156), film(?x9156, ?x4448), film(?x9156, ?x1488), ?x4448 = 01k60v >> conf = 0.38 => this is the best rule for 3 predicted values *> Best rule #30878 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 101 *> proper extension: 03zqc1; 06t74h; 02jsgf; 036jb; 024bbl; 02t_w8; 07m77x; 012x2b; 01rs5p; 07k2p6; *> query: (?x9156, 0g60z) <- student(?x918, ?x9156), student(?x1200, ?x9156), film(?x9156, ?x4448), nominated_for(?x1774, ?x4448) *> conf = 0.03 ranks of expected_values: 214 EVAL 03swmf nominated_for 0g60z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 154.000 89.000 0.378 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17729-03np63f PRED entity: 03np63f PRED relation: film_release_region PRED expected values: 05r4w 03spz => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 180): 05r4w (0.85 #940, 0.83 #1877, 0.82 #1408), 03_3d (0.84 #162, 0.82 #631, 0.74 #2037), 03h64 (0.81 #690, 0.80 #1003, 0.76 #1940), 01znc_ (0.76 #977, 0.73 #195, 0.71 #1445), 06bnz (0.76 #982, 0.70 #1919, 0.70 #1450), 03spz (0.76 #721, 0.75 #1034, 0.73 #252), 06t2t (0.72 #998, 0.69 #685, 0.65 #1466), 0b90_r (0.72 #942, 0.67 #629, 0.66 #1879), 06qd3 (0.70 #659, 0.69 #190, 0.52 #972), 05v8c (0.63 #953, 0.60 #171, 0.59 #1421) >> Best rule #940 for best value: >> intensional similarity = 5 >> extensional distance = 189 >> proper extension: 014lc_; 0b76d_m; 0ds35l9; 0gtsx8c; 02vxq9m; 0c3ybss; 011yrp; 0gx1bnj; 0h1cdwq; 0dscrwf; ... >> query: (?x7897, 05r4w) <- film_release_region(?x7897, ?x456), film_release_region(?x7897, ?x172), ?x456 = 05qhw, language(?x7897, ?x254), ?x172 = 0154j >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 03np63f film_release_region 03spz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 72.000 72.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03np63f film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17728-01yznp PRED entity: 01yznp PRED relation: location PRED expected values: 0cv3w => 148 concepts (145 used for prediction) PRED predicted values (max 10 best out of 245): 030qb3t (0.50 #888, 0.38 #2496, 0.33 #3300), 0vzm (0.25 #173, 0.08 #4194, 0.05 #8214), 01sn3 (0.25 #215, 0.08 #4236, 0.05 #8256), 0f2w0 (0.25 #94, 0.08 #4115, 0.05 #8135), 02_286 (0.24 #10492, 0.24 #6470, 0.19 #23360), 01n7q (0.22 #3280, 0.14 #6496, 0.07 #10518), 05k7sb (0.20 #4934, 0.12 #2522, 0.09 #11369), 0cymp (0.17 #1053, 0.13 #5073, 0.12 #2661), 0ftxw (0.17 #952, 0.12 #2560, 0.07 #4972), 013yq (0.17 #924, 0.12 #2532, 0.07 #4944) >> Best rule #888 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01jbx1; 029_3; 01yg9y; 02b9g4; >> query: (?x425, 030qb3t) <- person(?x9723, ?x425), profession(?x425, ?x1041), ?x1041 = 03gjzk, ?x9723 = 026h21_, program(?x425, ?x2583) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #37958 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 210 *> proper extension: 01f9mq; *> query: (?x425, 0cv3w) <- film(?x425, ?x3053), profession(?x425, ?x1183), ?x1183 = 09jwl, film_release_distribution_medium(?x3053, ?x81) *> conf = 0.01 ranks of expected_values: 163 EVAL 01yznp location 0cv3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 148.000 145.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #17727-01vs8ng PRED entity: 01vs8ng PRED relation: gender PRED expected values: 02zsn => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #151, 0.79 #145, 0.77 #175), 02zsn (0.50 #10, 0.50 #6, 0.48 #46) >> Best rule #151 for best value: >> intensional similarity = 7 >> extensional distance = 1224 >> proper extension: 06j0md; 050023; 026dcvf; 04r7jc; 02773nt; 0265v21; 012t1; 0207wx; 05m883; 045bg; ... >> query: (?x13457, 05zppz) <- profession(?x13457, ?x1383), profession(?x8196, ?x1383), profession(?x4463, ?x1383), profession(?x2295, ?x1383), ?x2295 = 04gcd1, ?x8196 = 010p3, award_nominee(?x450, ?x4463) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01pgzn_; 01vx5w7; 02jyhv; *> query: (?x13457, 02zsn) <- film(?x13457, ?x1334), artists(?x3061, ?x13457), actor(?x10018, ?x13457), ?x3061 = 05bt6j *> conf = 0.50 ranks of expected_values: 2 EVAL 01vs8ng gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.788 http://example.org/people/person/gender #17726-0nbjq PRED entity: 0nbjq PRED relation: medal PRED expected values: 02lq5w => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1): 02lq5w (0.90 #26, 0.88 #21, 0.85 #32) >> Best rule #26 for best value: >> intensional similarity = 9 >> extensional distance = 18 >> proper extension: 0ldqf; 0jkvj; >> query: (?x2432, 02lq5w) <- sports(?x2432, ?x2315), sports(?x2432, ?x359), sports(?x2432, ?x4045), country(?x2315, ?x583), country(?x2315, ?x390), ?x359 = 02bkg, ?x583 = 015fr, olympics(?x2315, ?x778), ?x390 = 0chghy >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nbjq medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.900 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #17725-0443c PRED entity: 0443c PRED relation: athlete! PRED expected values: 0jm_ => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.62 #173, 0.55 #209, 0.54 #219), 018w8 (0.60 #24, 0.50 #60, 0.41 #87), 0jm_ (0.56 #75, 0.39 #84, 0.38 #39), 07bs0 (0.10 #139, 0.10 #112, 0.07 #175), 037hz (0.05 #117, 0.04 #144, 0.04 #153), 09xp_ (0.03 #116, 0.03 #125, 0.03 #143), 03tmr (0.02 #172, 0.02 #109, 0.02 #118), 01cgz (0.02 #113, 0.01 #140) >> Best rule #173 for best value: >> intensional similarity = 2 >> extensional distance = 111 >> proper extension: 051q39; >> query: (?x13779, 02vx4) <- athlete(?x5063, ?x13779), sports(?x778, ?x5063) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 019y64; 05cv94; 03l295; 01gct2; 0cg39k; 019g65; 03vrv9; 04bdpf; 06yj20; 02cg2v; *> query: (?x13779, 0jm_) <- nationality(?x13779, ?x94), ?x94 = 09c7w0, student(?x4955, ?x13779), athlete(?x5063, ?x13779) *> conf = 0.56 ranks of expected_values: 3 EVAL 0443c athlete! 0jm_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 110.000 0.619 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #17724-02v8kmz PRED entity: 02v8kmz PRED relation: film! PRED expected values: 0svqs 018yj6 => 69 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1140): 021vwt (0.33 #266, 0.25 #2338, 0.06 #4145), 039bp (0.33 #177, 0.25 #2249, 0.06 #4145), 045931 (0.33 #1892, 0.25 #3964, 0.02 #16398), 09fb5 (0.33 #56, 0.12 #2128, 0.04 #22853), 01csvq (0.33 #107, 0.12 #2179, 0.03 #53888), 017149 (0.25 #2153, 0.06 #4145, 0.04 #10362), 0652ty (0.25 #3898, 0.01 #57788, 0.01 #24623), 03tdlh (0.22 #5762), 03k7bd (0.12 #2365, 0.11 #4438, 0.01 #35526), 015vq_ (0.12 #2781, 0.06 #4145, 0.04 #15215) >> Best rule #266 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0c9k8; >> query: (?x240, 021vwt) <- film(?x2726, ?x240), titles(?x2480, ?x240), ?x2726 = 03q1vd, costume_design_by(?x240, ?x4190) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7741 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 07ng9k; *> query: (?x240, 018yj6) <- film(?x192, ?x240), genre(?x240, ?x53), ?x53 = 07s9rl0, film(?x4832, ?x240) *> conf = 0.03 ranks of expected_values: 399, 560 EVAL 02v8kmz film! 018yj6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 37.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 02v8kmz film! 0svqs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 69.000 37.000 0.333 http://example.org/film/actor/film./film/performance/film #17723-0frf6 PRED entity: 0frf6 PRED relation: adjoins! PRED expected values: 0mwk9 => 120 concepts (54 used for prediction) PRED predicted values (max 10 best out of 475): 0mwk9 (0.83 #27518, 0.82 #25944, 0.81 #33804), 0mwx6 (0.33 #479, 0.27 #3142, 0.25 #19646), 0mwyq (0.27 #3142, 0.27 #1573, 0.25 #19646), 0frf6 (0.27 #3142, 0.27 #1573, 0.25 #19646), 0mw89 (0.25 #1622, 0.23 #2406, 0.23 #29871), 0mwcz (0.23 #29871, 0.11 #1299, 0.11 #2086), 0l3n4 (0.15 #1196, 0.14 #1983, 0.13 #3552), 0m7d0 (0.13 #2526, 0.11 #955, 0.11 #1742), 0fxyd (0.11 #1001, 0.11 #1788, 0.10 #3357), 0mwkp (0.11 #1370, 0.11 #2157, 0.10 #3726) >> Best rule #27518 for best value: >> intensional similarity = 4 >> extensional distance = 287 >> proper extension: 0f4y_; 0f04v; 0mm0p; 0nvd8; 0n5_g; 0k3ll; 0mws3; 0n5y4; 0nh57; 0cc1v; ... >> query: (?x10767, ?x12296) <- time_zones(?x10767, ?x2674), adjoins(?x12846, ?x10767), source(?x10767, ?x958), adjoins(?x10767, ?x12296) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0frf6 adjoins! 0mwk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 54.000 0.831 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #17722-01hv3t PRED entity: 01hv3t PRED relation: nominated_for! PRED expected values: 040njc 05ztjjw 02g2wv => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 190): 02qyp19 (0.67 #6562, 0.66 #2488, 0.66 #9058), 0gq9h (0.44 #2319, 0.41 #1188, 0.38 #1414), 0k611 (0.36 #1197, 0.33 #2328, 0.31 #1423), 040njc (0.32 #2267, 0.28 #1136, 0.24 #1362), 0gq_v (0.32 #2280, 0.25 #3185, 0.24 #4543), 0f4x7 (0.30 #1155, 0.27 #2286, 0.26 #1381), 0p9sw (0.29 #1150, 0.26 #2281, 0.25 #1376), 0gr0m (0.27 #1185, 0.26 #2316, 0.22 #281), 0gr4k (0.26 #1156, 0.24 #2287, 0.24 #252), 0gqyl (0.25 #2334, 0.22 #1203, 0.20 #299) >> Best rule #6562 for best value: >> intensional similarity = 3 >> extensional distance = 773 >> proper extension: 06w7mlh; >> query: (?x7432, ?x5516) <- award(?x7432, ?x5516), titles(?x2480, ?x7432), award(?x2179, ?x5516) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2267 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 422 *> proper extension: 075cph; 015g28; 019kyn; 0fsw_7; 01kf5lf; *> query: (?x7432, 040njc) <- award(?x7432, ?x68), film(?x525, ?x7432), honored_for(?x747, ?x7432) *> conf = 0.32 ranks of expected_values: 4, 30, 75 EVAL 01hv3t nominated_for! 02g2wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 78.000 78.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01hv3t nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 78.000 78.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01hv3t nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 78.000 78.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17721-0196bp PRED entity: 0196bp PRED relation: service_location PRED expected values: 07ssc => 161 concepts (134 used for prediction) PRED predicted values (max 10 best out of 101): 09c7w0 (0.86 #7130, 0.86 #6342, 0.85 #5172), 07ssc (0.82 #5169, 0.50 #600, 0.50 #308), 0127c4 (0.36 #5170, 0.30 #5070, 0.29 #4383), 0d060g (0.27 #6056, 0.27 #4882, 0.27 #6251), 0chghy (0.14 #4886, 0.14 #6060, 0.14 #6255), 0345h (0.12 #4901, 0.11 #4116, 0.11 #6367), 06mkj (0.11 #2373, 0.10 #2669, 0.10 #2960), 0b90_r (0.11 #1657, 0.04 #4878, 0.03 #5663), 02jx1 (0.10 #7524, 0.08 #779), 02qkt (0.10 #7524) >> Best rule #7130 for best value: >> intensional similarity = 5 >> extensional distance = 116 >> proper extension: 03ksy; 0168nq; >> query: (?x5993, 09c7w0) <- service_language(?x5993, ?x254), service_location(?x5993, ?x551), ?x254 = 02h40lc, administrative_parent(?x1499, ?x551), film_release_region(?x86, ?x1499) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #5169 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 77 *> proper extension: 07tds; *> query: (?x5993, ?x512) <- service_location(?x5993, ?x551), citytown(?x5993, ?x10753), contains(?x512, ?x10753), place_of_birth(?x1292, ?x10753), nationality(?x111, ?x512), member_states(?x2106, ?x512) *> conf = 0.82 ranks of expected_values: 2 EVAL 0196bp service_location 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 161.000 134.000 0.864 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #17720-045bg PRED entity: 045bg PRED relation: influenced_by PRED expected values: 02wh0 0420y => 137 concepts (42 used for prediction) PRED predicted values (max 10 best out of 350): 043s3 (0.67 #1384, 0.39 #3080, 0.33 #4770), 0gz_ (0.56 #3069, 0.54 #3913, 0.47 #5185), 015n8 (0.53 #9752, 0.53 #9750, 0.45 #2521), 02wh0 (0.53 #9752, 0.53 #9750, 0.44 #1218), 03_87 (0.53 #9752, 0.53 #9750, 0.40 #621), 0420y (0.53 #9752, 0.53 #9750, 0.33 #1664), 0ct9_ (0.53 #9752, 0.53 #9750, 0.33 #2968), 0372p (0.53 #9752, 0.53 #9750, 0.33 #2654), 026lj (0.53 #9752, 0.53 #9750, 0.30 #4703), 040db (0.53 #9752, 0.53 #9750, 0.25 #2601) >> Best rule #1384 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 04411; 026lj; 01bpn; 03sbs; 04hcw; 048cl; 0tfc; >> query: (?x1236, 043s3) <- interests(?x1236, ?x713), influenced_by(?x1029, ?x1236), gender(?x1236, ?x231), influenced_by(?x5494, ?x1029), ?x5494 = 018x3 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #9752 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 91 *> proper extension: 03d9d6; *> query: (?x1236, ?x3993) <- peers(?x8430, ?x1236), peers(?x3428, ?x8430), influenced_by(?x3428, ?x3993) *> conf = 0.53 ranks of expected_values: 4, 6 EVAL 045bg influenced_by 0420y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 137.000 42.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 045bg influenced_by 02wh0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 137.000 42.000 0.667 http://example.org/influence/influence_node/influenced_by #17719-01wz3cx PRED entity: 01wz3cx PRED relation: artists! PRED expected values: 09qxq7 => 156 concepts (83 used for prediction) PRED predicted values (max 10 best out of 281): 0glt670 (0.56 #1889, 0.55 #2814, 0.47 #1581), 025sc50 (0.50 #1898, 0.47 #1590, 0.45 #2823), 0gywn (0.46 #3447, 0.32 #5912, 0.30 #17633), 06j6l (0.44 #1896, 0.41 #2821, 0.40 #1588), 02lnbg (0.44 #1907, 0.36 #2523, 0.36 #4064), 0mhfr (0.40 #1257, 0.21 #948, 0.21 #2181), 0155w (0.38 #3495, 0.35 #3187, 0.33 #5036), 016clz (0.38 #3703, 0.33 #1238, 0.33 #5), 0ggx5q (0.38 #1926, 0.36 #2542, 0.36 #4083), 05bt6j (0.36 #967, 0.35 #3741, 0.33 #43) >> Best rule #1889 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01q7cb_; 09qr6; 0j1yf; 07ss8_; 04xrx; 0161sp; 07g2v; 0c7xjb; 043zg; 013w7j; ... >> query: (?x1992, 0glt670) <- artists(?x671, ?x1992), celebrity(?x1992, ?x1089), award_winner(?x1930, ?x1089), participant(?x1992, ?x6236) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2388 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 0249kn; *> query: (?x1992, 09qxq7) <- artists(?x7329, ?x1992), artists(?x3108, ?x1992), ?x3108 = 02w4v, award(?x1992, ?x4796), ?x7329 = 016jny *> conf = 0.21 ranks of expected_values: 25 EVAL 01wz3cx artists! 09qxq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 156.000 83.000 0.562 http://example.org/music/genre/artists #17718-0ply0 PRED entity: 0ply0 PRED relation: time_zones PRED expected values: 02hcv8 => 252 concepts (252 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.80 #2512, 0.60 #29, 0.57 #42), 02fqwt (0.50 #14, 0.31 #339, 0.31 #612), 02lcqs (0.44 #57, 0.41 #148, 0.41 #200), 02llzg (0.31 #212, 0.27 #719, 0.26 #537), 02hczc (0.25 #15, 0.18 #184, 0.17 #379), 042g7t (0.25 #24, 0.04 #883, 0.04 #961), 02lcrv (0.25 #20, 0.02 #879, 0.02 #892), 03bdv (0.12 #760, 0.12 #1359, 0.10 #2075), 03plfd (0.11 #309, 0.08 #530, 0.07 #960), 052vwh (0.06 #441, 0.05 #610, 0.05 #597) >> Best rule #2512 for best value: >> intensional similarity = 3 >> extensional distance = 240 >> proper extension: 0t_hx; >> query: (?x3373, ?x2674) <- county(?x3373, ?x7460), adjoins(?x7460, ?x11257), time_zones(?x11257, ?x2674) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ply0 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 252.000 252.000 0.799 http://example.org/location/location/time_zones #17717-08hmch PRED entity: 08hmch PRED relation: nominated_for! PRED expected values: 05ztrmj => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 233): 02hsq3m (0.28 #1958, 0.26 #512, 0.26 #753), 018wdw (0.26 #905, 0.26 #1628, 0.21 #2110), 0gr42 (0.26 #814, 0.23 #1537, 0.21 #573), 0gq9h (0.26 #4884, 0.26 #8499, 0.25 #8017), 02g3v6 (0.26 #1709, 0.23 #1950, 0.22 #745), 05ztjjw (0.26 #1938, 0.23 #1697, 0.21 #492), 0p9sw (0.26 #1949, 0.22 #744, 0.22 #2672), 0k611 (0.23 #8510, 0.22 #8028, 0.22 #4895), 057xs89 (0.23 #2050, 0.20 #1809, 0.17 #122), 0gs9p (0.23 #10430, 0.23 #8501, 0.22 #4886) >> Best rule #1958 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 02bj22; >> query: (?x1035, 02hsq3m) <- film(?x399, ?x1035), films(?x7455, ?x1035), film(?x574, ?x1035), region(?x1035, ?x512) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #19768 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1288 *> proper extension: 05jyb2; 0c5qvw; *> query: (?x1035, ?x401) <- country(?x1035, ?x94), nominated_for(?x844, ?x1035), award(?x844, ?x401) *> conf = 0.19 ranks of expected_values: 24 EVAL 08hmch nominated_for! 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 99.000 99.000 0.282 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17716-0_b3d PRED entity: 0_b3d PRED relation: nominated_for! PRED expected values: 0f4x7 => 108 concepts (90 used for prediction) PRED predicted values (max 10 best out of 197): 0f4x7 (0.80 #2114, 0.58 #5584, 0.44 #3967), 040njc (0.68 #5566, 0.60 #2096, 0.57 #8570), 02pqp12 (0.68 #8620, 0.65 #5616, 0.51 #4461), 05h5nb8 (0.68 #17114, 0.66 #17346, 0.66 #18503), 02qyntr (0.60 #5732, 0.51 #8736, 0.40 #4577), 019f4v (0.58 #4457, 0.58 #5612, 0.56 #3995), 04dn09n (0.57 #4438, 0.57 #5593, 0.53 #3976), 02x17s4 (0.56 #2874, 0.52 #2643, 0.47 #1716), 02ppm4q (0.52 #2895, 0.52 #2664, 0.52 #3360), 0l8z1 (0.52 #2604, 0.48 #3300, 0.47 #1677) >> Best rule #2114 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 0k4p0; 07cw4; >> query: (?x1002, 0f4x7) <- nominated_for(?x3209, ?x1002), nominated_for(?x1307, ?x1002), featured_film_locations(?x1002, ?x362), film_crew_role(?x1002, ?x137), ?x1307 = 0gq9h, ?x3209 = 02w9sd7 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_b3d nominated_for! 0f4x7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 90.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17715-031x_3 PRED entity: 031x_3 PRED relation: artist! PRED expected values: 011k11 => 137 concepts (104 used for prediction) PRED predicted values (max 10 best out of 110): 011k1h (0.53 #1410, 0.11 #3511, 0.11 #3651), 017l96 (0.25 #1419, 0.12 #159, 0.11 #1979), 015_1q (0.24 #1980, 0.23 #4221, 0.22 #5764), 03rhqg (0.20 #16, 0.19 #1836, 0.18 #2676), 0g768 (0.20 #1997, 0.19 #1437, 0.14 #2697), 0n85g (0.20 #63, 0.12 #1463, 0.12 #203), 01cf93 (0.20 #58, 0.12 #198, 0.11 #618), 01cszh (0.20 #11, 0.12 #151, 0.11 #571), 041bnw (0.20 #69, 0.12 #209, 0.11 #629), 0229rs (0.20 #18, 0.12 #158, 0.11 #578) >> Best rule #1410 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0150jk; 0dtd6; 0cg9y; 0dm5l; 01wbz9; 023l9y; 03f0vvr; 01jfr3y; 01wgjj5; 02cpp; ... >> query: (?x8583, 011k1h) <- artist(?x4797, ?x8583), artists(?x888, ?x8583), ?x4797 = 02p3cr5 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1435 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 0150jk; 0dtd6; 0cg9y; 0dm5l; 01wbz9; 023l9y; 03f0vvr; 01jfr3y; 01wgjj5; 02cpp; ... *> query: (?x8583, 011k11) <- artist(?x4797, ?x8583), artists(?x888, ?x8583), ?x4797 = 02p3cr5 *> conf = 0.12 ranks of expected_values: 15 EVAL 031x_3 artist! 011k11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 137.000 104.000 0.531 http://example.org/music/record_label/artist #17714-03f19q4 PRED entity: 03f19q4 PRED relation: award_nominee PRED expected values: 01wgxtl => 102 concepts (42 used for prediction) PRED predicted values (max 10 best out of 795): 02l840 (0.83 #7015, 0.83 #4837, 0.80 #11692), 01q32bd (0.78 #11691, 0.77 #9352, 0.77 #4676), 0837ql (0.78 #11691, 0.77 #9352, 0.77 #4676), 01wgxtl (0.50 #605, 0.47 #2943, 0.33 #5282), 03f19q4 (0.42 #1230, 0.40 #3568, 0.29 #5907), 06mt91 (0.29 #10912, 0.27 #3896, 0.25 #6235), 01vsgrn (0.27 #3646, 0.25 #1308, 0.20 #10662), 067nsm (0.26 #10860, 0.25 #6183, 0.24 #8521), 0288fyj (0.26 #9853, 0.25 #5176, 0.21 #7514), 05mt_q (0.25 #293, 0.20 #2631, 0.19 #4677) >> Best rule #7015 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 04lgymt; 04bpm6; 0770cd; 026yqrr; 05mxw33; >> query: (?x5203, ?x827) <- award_nominee(?x827, ?x5203), ?x827 = 02l840, award(?x5203, ?x2139), ?x2139 = 01by1l >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #605 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 02l840; 02qwg; 01q32bd; 02dbp7; *> query: (?x5203, 01wgxtl) <- award_nominee(?x2731, ?x5203), award_nominee(?x827, ?x5203), ?x2731 = 01wwvc5, artist(?x6230, ?x5203), profession(?x827, ?x131) *> conf = 0.50 ranks of expected_values: 4 EVAL 03f19q4 award_nominee 01wgxtl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 42.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17713-01s7w3 PRED entity: 01s7w3 PRED relation: film_crew_role PRED expected values: 09zzb8 0ch6mp2 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.77 #1098, 0.76 #824, 0.76 #654), 09zzb8 (0.75 #409, 0.74 #580, 0.73 #1092), 01pvkk (0.33 #78, 0.28 #1989, 0.27 #2125), 02ynfr (0.18 #661, 0.18 #1105, 0.18 #831), 0215hd (0.14 #834, 0.14 #1108, 0.13 #664), 0d2b38 (0.12 #126, 0.11 #841, 0.11 #92), 015h31 (0.12 #111, 0.10 #656, 0.10 #826), 01xy5l_ (0.11 #80, 0.11 #829, 0.11 #1103), 089g0h (0.11 #86, 0.11 #835, 0.11 #1109), 094hwz (0.11 #81, 0.06 #115, 0.05 #319) >> Best rule #1098 for best value: >> intensional similarity = 2 >> extensional distance = 777 >> proper extension: 0fq27fp; 0gh6j94; >> query: (?x9154, 0ch6mp2) <- film_crew_role(?x9154, ?x468), ?x468 = 02r96rf >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01s7w3 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01s7w3 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17712-02mf7 PRED entity: 02mf7 PRED relation: citytown! PRED expected values: 01n6r0 => 185 concepts (100 used for prediction) PRED predicted values (max 10 best out of 646): 01n6r0 (0.74 #12955, 0.68 #10526, 0.63 #4856), 01gwck (0.17 #1498, 0.17 #689, 0.04 #77721), 025rcc (0.17 #1072, 0.17 #263, 0.04 #77721), 06bw5 (0.17 #1061, 0.17 #252, 0.03 #78532), 02897w (0.17 #193, 0.08 #1811, 0.04 #77721), 017z88 (0.06 #39673, 0.03 #2539, 0.02 #5779), 027lf1 (0.06 #3001, 0.05 #5431, 0.04 #8671), 01dtcb (0.06 #2812, 0.04 #6052, 0.04 #6862), 01pf21 (0.06 #2908, 0.04 #6148, 0.04 #6958), 0146mv (0.06 #3013, 0.04 #6253, 0.04 #7063) >> Best rule #12955 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 07l5z; 0l39b; >> query: (?x13303, ?x4980) <- county(?x13303, ?x12499), location(?x4109, ?x13303), contains(?x13303, ?x4980), institution(?x865, ?x4980) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02mf7 citytown! 01n6r0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 185.000 100.000 0.742 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #17711-03t79f PRED entity: 03t79f PRED relation: film_crew_role PRED expected values: 09vw2b7 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 27): 09vw2b7 (0.79 #40, 0.71 #142, 0.68 #176), 01vx2h (0.50 #43, 0.42 #145, 0.40 #179), 01pvkk (0.42 #146, 0.40 #10, 0.37 #78), 02rh1dz (0.32 #110, 0.32 #42, 0.21 #144), 02ynfr (0.20 #184, 0.20 #14, 0.19 #627), 0215hd (0.20 #85, 0.20 #17, 0.16 #153), 089g0h (0.20 #86, 0.18 #52, 0.17 #188), 0d2b38 (0.20 #24, 0.17 #194, 0.17 #92), 01xy5l_ (0.20 #12, 0.14 #46, 0.13 #80), 033smt (0.20 #26, 0.13 #2498, 0.11 #60) >> Best rule #40 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 0ch3qr1; >> query: (?x5372, 09vw2b7) <- nominated_for(?x2325, ?x5372), nominated_for(?x688, ?x5372), ?x688 = 05b1610, film_crew_role(?x5372, ?x137), ?x2325 = 05p09zm >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t79f film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.786 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17710-09xbpt PRED entity: 09xbpt PRED relation: film! PRED expected values: 0126rp 02z1yj => 90 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1032): 0252fh (0.33 #1350, 0.10 #5502, 0.03 #7578), 015c4g (0.33 #776, 0.10 #4928, 0.02 #46435), 06cgy (0.33 #248, 0.07 #6228, 0.05 #105853), 046zh (0.20 #5083, 0.11 #3008, 0.07 #6228), 028r4y (0.20 #5118, 0.11 #3043, 0.03 #7194), 02lyx4 (0.20 #5905, 0.01 #10057), 06t8b (0.19 #8304, 0.17 #2076, 0.16 #83019), 0glyyw (0.17 #2077, 0.12 #18680), 03cglm (0.17 #1042, 0.11 #3119, 0.02 #7270), 015p3p (0.17 #1090, 0.10 #5242, 0.02 #7318) >> Best rule #1350 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0bxsk; >> query: (?x349, 0252fh) <- film(?x2035, ?x349), film(?x7903, ?x349), ?x2035 = 0bj9k, executive_produced_by(?x349, ?x8503) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2418 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0dsvzh; 051ys82; 0gd92; *> query: (?x349, 0126rp) <- film(?x875, ?x349), country(?x349, ?x94), film_crew_role(?x349, ?x137), ?x875 = 032_jg *> conf = 0.11 ranks of expected_values: 46, 347 EVAL 09xbpt film! 02z1yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 90.000 61.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 09xbpt film! 0126rp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 90.000 61.000 0.333 http://example.org/film/actor/film./film/performance/film #17709-01mwsnc PRED entity: 01mwsnc PRED relation: participant! PRED expected values: 0jfx1 => 140 concepts (28 used for prediction) PRED predicted values (max 10 best out of 213): 0484q (0.13 #1110, 0.06 #1748, 0.05 #3025), 0f4vbz (0.12 #1430, 0.10 #2707, 0.09 #3347), 09889g (0.07 #988, 0.06 #6097, 0.05 #7375), 049qx (0.07 #948, 0.05 #2863, 0.05 #3503), 0237fw (0.07 #807, 0.05 #3362, 0.04 #4638), 0693l (0.07 #862, 0.04 #8529, 0.03 #11085), 026c1 (0.07 #789, 0.04 #8456, 0.03 #11012), 0fq117k (0.07 #1105, 0.03 #6852, 0.02 #8131), 02qwg (0.07 #883, 0.03 #6630, 0.02 #7909), 0227vl (0.07 #1176, 0.02 #12675, 0.02 #15871) >> Best rule #1110 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 01rw116; >> query: (?x4918, 0484q) <- profession(?x4918, ?x2659), people(?x743, ?x4918), type_of_union(?x4918, ?x566), film(?x4918, ?x1619), ?x2659 = 039v1 >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #2085 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 01vw20_; *> query: (?x4918, 0jfx1) <- instrumentalists(?x716, ?x4918), gender(?x4918, ?x231), artists(?x2664, ?x4918), ?x2664 = 01lyv, ?x716 = 018vs *> conf = 0.05 ranks of expected_values: 31 EVAL 01mwsnc participant! 0jfx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 140.000 28.000 0.133 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #17708-0828jw PRED entity: 0828jw PRED relation: honored_for! PRED expected values: 05c1t6z => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 88): 0bq_mx (0.27 #469, 0.09 #5736, 0.08 #6088), 05c1t6z (0.26 #479, 0.24 #127, 0.18 #361), 0gvstc3 (0.26 #143, 0.22 #495, 0.16 #26), 02q690_ (0.25 #522, 0.23 #170, 0.20 #404), 0lp_cd3 (0.17 #133, 0.15 #485, 0.15 #16), 0gx_st (0.12 #498, 0.12 #380, 0.10 #146), 0jt3qpk (0.10 #150, 0.08 #502, 0.08 #33), 0gkxgfq (0.10 #205, 0.07 #557, 0.06 #322), 0hn821n (0.10 #227, 0.06 #461, 0.06 #579), 0bxs_d (0.09 #565, 0.07 #213, 0.07 #447) >> Best rule #469 for best value: >> intensional similarity = 2 >> extensional distance = 100 >> proper extension: 06qxh; 02pvqmz; >> query: (?x5810, ?x5296) <- program(?x10340, ?x5810), award_winner(?x5296, ?x10340) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #479 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 103 *> proper extension: 0gbtbm; *> query: (?x5810, 05c1t6z) <- honored_for(?x1112, ?x5810), actor(?x5810, ?x56) *> conf = 0.26 ranks of expected_values: 2 EVAL 0828jw honored_for! 05c1t6z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 65.000 0.271 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #17707-01vrwfv PRED entity: 01vrwfv PRED relation: artist! PRED expected values: 0229rs => 77 concepts (50 used for prediction) PRED predicted values (max 10 best out of 116): 03rhqg (0.45 #575, 0.25 #855, 0.22 #1417), 011k1h (0.41 #429, 0.18 #1411, 0.17 #709), 017l96 (0.33 #18, 0.18 #438, 0.14 #298), 033hn8 (0.33 #13, 0.17 #1836, 0.16 #2257), 06x2ww (0.33 #48, 0.05 #608, 0.04 #5893), 04gmlt (0.33 #52, 0.04 #5893, 0.02 #2296), 0g768 (0.25 #736, 0.21 #876, 0.18 #1438), 043g7l (0.24 #450, 0.11 #2696, 0.10 #2976), 03mp8k (0.24 #486, 0.08 #2170, 0.08 #2310), 01t04r (0.21 #764, 0.12 #1887, 0.12 #484) >> Best rule #575 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 013pk3; >> query: (?x2901, 03rhqg) <- award(?x2901, ?x724), artist(?x382, ?x2901), ?x382 = 086k8 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #297 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 05563d; 07m4c; 02cw1m; *> query: (?x2901, 0229rs) <- artists(?x1000, ?x2901), group(?x3156, ?x2901), ?x3156 = 085jw *> conf = 0.14 ranks of expected_values: 17 EVAL 01vrwfv artist! 0229rs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 77.000 50.000 0.455 http://example.org/music/record_label/artist #17706-01chpn PRED entity: 01chpn PRED relation: film! PRED expected values: 0184jw => 82 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1122): 06pk8 (0.58 #130644, 0.57 #105756, 0.45 #97461), 02mt4k (0.45 #97461, 0.44 #91240, 0.43 #51839), 017s11 (0.45 #97461, 0.44 #91240, 0.43 #51839), 04353 (0.45 #97461, 0.44 #91240, 0.43 #51839), 0f5xn (0.12 #7186, 0.11 #11333, 0.09 #9260), 02mxw0 (0.12 #2531, 0.10 #458, 0.02 #35706), 046qq (0.12 #2811, 0.03 #25617, 0.02 #23544), 0f502 (0.10 #758, 0.09 #9052, 0.07 #13198), 0bj9k (0.10 #327, 0.08 #18987, 0.03 #31429), 03fbb6 (0.10 #975, 0.06 #5121, 0.06 #7195) >> Best rule #130644 for best value: >> intensional similarity = 3 >> extensional distance = 1368 >> proper extension: 0gfzgl; 03y3bp7; 01f3p_; 02sqkh; 03g9xj; 0cskb; 03_b1g; >> query: (?x6288, ?x976) <- nominated_for(?x976, ?x6288), film(?x976, ?x1163), profession(?x976, ?x319) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01chpn film! 0184jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 66.000 0.585 http://example.org/film/actor/film./film/performance/film #17705-09q5w2 PRED entity: 09q5w2 PRED relation: language PRED expected values: 02h40lc => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.92 #299, 0.92 #180, 0.91 #834), 064_8sq (0.23 #319, 0.17 #1746, 0.17 #22), 04306rv (0.17 #5, 0.12 #183, 0.12 #1251), 012w70 (0.17 #13, 0.07 #191, 0.04 #1081), 03_9r (0.17 #10, 0.06 #604, 0.06 #1078), 06nm1 (0.15 #189, 0.15 #70, 0.12 #605), 02bjrlw (0.10 #60, 0.09 #1247, 0.09 #238), 06b_j (0.10 #82, 0.08 #558, 0.07 #1091), 04h9h (0.09 #161, 0.08 #280, 0.05 #102), 0jzc (0.07 #79, 0.05 #1266, 0.04 #555) >> Best rule #299 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 0hmr4; 01c9d; >> query: (?x1077, 02h40lc) <- nominated_for(?x1198, ?x1077), ?x1198 = 02pqp12, award(?x1077, ?x591), award_winner(?x1077, ?x262) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09q5w2 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.923 http://example.org/film/film/language #17704-05pcn59 PRED entity: 05pcn59 PRED relation: award! PRED expected values: 05bnp0 03n08b 05dbf 015v3r 036hf4 => 30 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2036): 03ds83 (0.68 #58670, 0.68 #29330, 0.67 #58672), 01f5q5 (0.68 #58670, 0.68 #29330, 0.67 #58672), 01g257 (0.68 #58670, 0.68 #29330, 0.67 #58672), 02kxwk (0.50 #7681, 0.33 #4423, 0.25 #10939), 0btpx (0.50 #12115, 0.33 #5599, 0.25 #8857), 0f7hc (0.50 #11050, 0.33 #4534, 0.25 #7792), 01rh0w (0.50 #10106, 0.33 #3590, 0.25 #6848), 0c1j_ (0.50 #12658, 0.33 #6142, 0.25 #9400), 01vs_v8 (0.50 #10322, 0.33 #3806, 0.13 #26619), 015f7 (0.50 #10650, 0.33 #4134, 0.12 #42373) >> Best rule #58670 for best value: >> intensional similarity = 5 >> extensional distance = 332 >> proper extension: 06szd3; >> query: (?x1336, ?x400) <- award_winner(?x1336, ?x2415), award_winner(?x1336, ?x2101), award_winner(?x1336, ?x400), nominated_for(?x2101, ?x2102), location(?x2415, ?x6226) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #3810 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 05b4l5x; *> query: (?x1336, 05dbf) <- award(?x2857, ?x1336), award(?x2221, ?x1336), ?x2857 = 0bbf1f, ?x2221 = 026c1 *> conf = 0.33 ranks of expected_values: 22, 245, 594, 1487, 1605 EVAL 05pcn59 award! 036hf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 20.000 0.678 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05pcn59 award! 015v3r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 20.000 0.678 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05pcn59 award! 05dbf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 30.000 20.000 0.678 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05pcn59 award! 03n08b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 20.000 0.678 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05pcn59 award! 05bnp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 30.000 20.000 0.678 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17703-0c031k6 PRED entity: 0c031k6 PRED relation: genre! PRED expected values: 0b005 015ppk => 63 concepts (24 used for prediction) PRED predicted values (max 10 best out of 346): 0584r4 (0.71 #612, 0.62 #907, 0.38 #2085), 07gbf (0.62 #1671, 0.60 #495, 0.50 #1378), 039cq4 (0.62 #1008, 0.57 #713, 0.29 #2186), 03d34x8 (0.60 #321, 0.50 #1497, 0.50 #1204), 04f6hhm (0.60 #453, 0.50 #161, 0.38 #1629), 06dfz1 (0.60 #462, 0.50 #170, 0.38 #1638), 01h72l (0.57 #623, 0.50 #918, 0.35 #6770), 019nnl (0.57 #604, 0.50 #899, 0.29 #2077), 06y_n (0.57 #792, 0.50 #1087, 0.29 #2265), 07c72 (0.57 #634, 0.50 #929, 0.26 #1172) >> Best rule #612 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 0dm00; >> query: (?x10647, 0584r4) <- genre(?x10595, ?x10647), genre(?x9668, ?x10647), actor(?x10595, ?x2296), genre(?x10595, ?x600), nominated_for(?x1285, ?x10595), titles(?x600, ?x394), genre(?x2869, ?x600), ?x2869 = 03177r, ?x9668 = 025ljp >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1002 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 01w613; *> query: (?x10647, 0b005) <- genre(?x10595, ?x10647), genre(?x9668, ?x10647), actor(?x10595, ?x2296), genre(?x10595, ?x600), nominated_for(?x1285, ?x10595), titles(?x600, ?x394), nominated_for(?x4921, ?x10595), award(?x65, ?x4921), ?x9668 = 025ljp *> conf = 0.50 ranks of expected_values: 18, 24 EVAL 0c031k6 genre! 015ppk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 63.000 24.000 0.714 http://example.org/tv/tv_program/genre EVAL 0c031k6 genre! 0b005 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 63.000 24.000 0.714 http://example.org/tv/tv_program/genre #17702-07jq_ PRED entity: 07jq_ PRED relation: people PRED expected values: 02bvt => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 25): 079dy (0.03 #4097, 0.03 #4790, 0.02 #7555), 0cbgl (0.02 #8291, 0.02 #8984, 0.02 #9674), 0p9gg (0.02 #8262, 0.02 #8955, 0.02 #9645), 08gyz_ (0.02 #8241, 0.02 #8934, 0.02 #9624), 01h2_6 (0.02 #8235, 0.02 #8928, 0.02 #9618), 07_m2 (0.02 #8133, 0.02 #8826, 0.02 #9516), 06myp (0.02 #8127, 0.02 #8820, 0.02 #9510), 02tn0_ (0.02 #8069, 0.02 #8762, 0.02 #9452), 02cj_f (0.02 #8051, 0.02 #8744, 0.02 #9434), 01kx1j (0.02 #8034, 0.02 #8727, 0.02 #9417) >> Best rule #4097 for best value: >> intensional similarity = 8 >> extensional distance = 32 >> proper extension: 07wh1; >> query: (?x9677, 079dy) <- films(?x9677, ?x7694), films(?x9677, ?x7462), film_release_distribution_medium(?x7694, ?x81), film(?x3101, ?x7694), genre(?x7462, ?x53), nominated_for(?x12453, ?x7462), genre(?x7694, ?x225), list(?x12453, ?x5160) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07jq_ people 02bvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 49.000 0.029 http://example.org/people/cause_of_death/people #17701-01w4dy PRED entity: 01w4dy PRED relation: role PRED expected values: 01dnws => 82 concepts (54 used for prediction) PRED predicted values (max 10 best out of 116): 0l14j_ (0.84 #953, 0.84 #3862, 0.83 #353), 0859_ (0.84 #953, 0.84 #3862, 0.83 #353), 01dnws (0.84 #953, 0.84 #3862, 0.83 #353), 01w4c9 (0.84 #953, 0.84 #3862, 0.83 #353), 018vs (0.82 #2794, 0.80 #3757, 0.80 #6044), 013y1f (0.80 #2699, 0.79 #3542, 0.73 #2817), 07gql (0.80 #2711, 0.73 #2829, 0.71 #3554), 06ncr (0.80 #2714, 0.71 #3557, 0.71 #1862), 0dwtp (0.80 #2681, 0.71 #4847, 0.71 #3524), 02k84w (0.80 #2706, 0.71 #3549, 0.67 #2049) >> Best rule #953 for best value: >> intensional similarity = 22 >> extensional distance = 2 >> proper extension: 07y_7; >> query: (?x1663, ?x4975) <- role(?x1663, ?x3967), role(?x1663, ?x1212), role(?x1663, ?x3703), role(?x1663, ?x1432), role(?x1212, ?x894), role(?x1212, ?x614), role(?x1212, ?x316), role(?x7869, ?x1212), role(?x4975, ?x1663), split_to(?x1212, ?x7256), performance_role(?x1432, ?x1433), ?x614 = 0mkg, role(?x5949, ?x1432), ?x5949 = 02ryx0, ?x3967 = 01p970, role(?x2253, ?x1212), ?x3703 = 02dlh2, group(?x1432, ?x3516), ?x316 = 05r5c, ?x7869 = 0l14v3, role(?x1432, ?x74), ?x894 = 03m5k >> conf = 0.84 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 01w4dy role 01dnws CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 54.000 0.840 http://example.org/music/performance_role/regular_performances./music/group_membership/role #17700-04f525m PRED entity: 04f525m PRED relation: production_companies! PRED expected values: 08nvyr => 157 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1172): 03459x (0.50 #1533, 0.25 #8400, 0.20 #11832), 05mrf_p (0.48 #33193, 0.40 #32048, 0.37 #21748), 075wx7_ (0.48 #33193, 0.40 #32048, 0.37 #21748), 0fh694 (0.37 #21748, 0.37 #19457, 0.37 #21747), 07jqjx (0.37 #21748, 0.37 #19457, 0.37 #21747), 02qk3fk (0.37 #21748, 0.37 #19457, 0.37 #21747), 02qlp4 (0.37 #21748, 0.37 #19457, 0.37 #21747), 0ch26b_ (0.37 #21748, 0.37 #19457, 0.37 #21747), 03clwtw (0.37 #21748, 0.37 #19457, 0.37 #21747), 0cmdwwg (0.37 #21748, 0.37 #19457, 0.37 #21747) >> Best rule #1533 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 030_1m; >> query: (?x963, 03459x) <- country(?x963, ?x94), award(?x963, ?x1105), film(?x963, ?x964) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04f525m production_companies! 08nvyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 157.000 133.000 0.500 http://example.org/film/film/production_companies #17699-01cgz PRED entity: 01cgz PRED relation: country PRED expected values: 027nb 0d060g 03rt9 035dk 077qn 0l3h 06sw9 0j4b 05cc1 01xbgx 05br2 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 135): 0d060g (0.92 #1973, 0.84 #3548, 0.84 #2956), 02vzc (0.71 #810, 0.71 #1498, 0.67 #1991), 059j2 (0.71 #800, 0.67 #997, 0.67 #701), 06f32 (0.71 #817, 0.67 #718, 0.60 #619), 0154j (0.71 #790, 0.62 #1971, 0.60 #493), 0h7x (0.71 #1984, 0.67 #704, 0.65 #1491), 01pj7 (0.67 #1300, 0.67 #710, 0.60 #414), 06t8v (0.67 #724, 0.60 #625, 0.60 #526), 03spz (0.67 #735, 0.60 #636, 0.60 #341), 0jt3tjf (0.67 #772, 0.60 #476, 0.60 #378) >> Best rule #1973 for best value: >> intensional similarity = 9 >> extensional distance = 22 >> proper extension: 02bkg; 07bs0; 06wrt; 01z27; 07jjt; 064vjs; 0d1t3; 019tzd; 01sgl; 01yfj; >> query: (?x1967, 0d060g) <- country(?x1967, ?x9072), country(?x1967, ?x2979), country(?x1967, ?x985), country(?x1967, ?x550), participating_countries(?x1931, ?x9072), ?x1931 = 0kbws, ?x985 = 0k6nt, film_release_region(?x66, ?x550), organization(?x2979, ?x127) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 13, 23, 36, 37, 40, 75, 82, 90, 116, 117 EVAL 01cgz country 05br2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01cgz country 01xbgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01cgz country 05cc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01cgz country 0j4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01cgz country 06sw9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01cgz country 0l3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01cgz country 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01cgz country 035dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01cgz country 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01cgz country 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01cgz country 027nb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 85.000 85.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #17698-01mskc3 PRED entity: 01mskc3 PRED relation: category PRED expected values: 08mbj5d => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #18, 0.84 #8, 0.83 #44) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 01vvydl; 01vvycq; 02l840; 03f5spx; 01wbgdv; 09qr6; 01wcp_g; 05mt_q; 0j1yf; 04mn81; ... >> query: (?x11953, 08mbj5d) <- award_nominee(?x2335, ?x11953), artists(?x2937, ?x11953), location(?x11953, ?x3249), ?x2937 = 0glt670 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mskc3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.849 http://example.org/common/topic/webpage./common/webpage/category #17697-08z129 PRED entity: 08z129 PRED relation: list PRED expected values: 04k4rt => 195 concepts (195 used for prediction) PRED predicted values (max 10 best out of 4): 04k4rt (0.82 #84, 0.81 #739, 0.80 #653), 05glt (0.53 #649, 0.38 #735), 09g7thr (0.50 #451, 0.50 #11, 0.49 #281), 026cl_m (0.26 #474, 0.12 #650, 0.09 #736) >> Best rule #84 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 0l8sx; >> query: (?x3578, 04k4rt) <- list(?x3578, ?x8915), company(?x346, ?x3578), ?x8915 = 01pd60, service_language(?x3578, ?x254), industry(?x3578, ?x6575) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08z129 list 04k4rt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 195.000 0.821 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #17696-04g61 PRED entity: 04g61 PRED relation: member_states! PRED expected values: 085h1 => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 11): 085h1 (0.98 #183, 0.76 #69, 0.74 #23), 02jxk (0.55 #7, 0.30 #36, 0.29 #14), 0_2v (0.15 #45, 0.14 #67, 0.14 #66), 07t65 (0.15 #45, 0.14 #67, 0.14 #66), 02vk52z (0.15 #45, 0.14 #67, 0.14 #66), 04k4l (0.15 #45, 0.14 #67, 0.14 #66), 0b6css (0.15 #45, 0.13 #19, 0.07 #84), 01rz1 (0.15 #45, 0.13 #19, 0.07 #84), 0j7v_ (0.15 #45, 0.13 #19, 0.07 #322), 041288 (0.07 #322) >> Best rule #183 for best value: >> intensional similarity = 3 >> extensional distance = 129 >> proper extension: 02jxk; >> query: (?x5274, 085h1) <- member_states(?x7416, ?x5274), member_states(?x7416, ?x2513), ?x2513 = 05b4w >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04g61 member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.977 http://example.org/user/ktrueman/default_domain/international_organization/member_states #17695-01f99l PRED entity: 01f99l PRED relation: state_province_region PRED expected values: 050ks => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 16): 07z1m (0.43 #4721, 0.43 #7458, 0.37 #2231), 01x73 (0.40 #1238, 0.39 #4472, 0.37 #2603), 01n7q (0.39 #4615, 0.38 #4490, 0.38 #4989), 059rby (0.29 #4725, 0.25 #375, 0.23 #4476), 07b_l (0.20 #1040, 0.04 #4146, 0.04 #4396), 081yw (0.17 #1299, 0.07 #2044, 0.05 #2916), 05tbn (0.12 #1661), 04rrx (0.12 #1640), 05kkh (0.12 #1612), 0jt5zcn (0.08 #4380, 0.02 #7741, 0.01 #7864) >> Best rule #4721 for best value: >> intensional similarity = 18 >> extensional distance = 26 >> proper extension: 04mkft; 03yxwq; >> query: (?x14669, ?x1426) <- child(?x12938, ?x14669), category(?x12938, ?x134), ?x134 = 08mbj5d, citytown(?x12938, ?x13182), place_founded(?x12938, ?x739), place_of_birth(?x6086, ?x13182), location(?x5097, ?x13182), state_province_region(?x12938, ?x1426), source(?x13182, ?x958), ?x958 = 0jbk9, featured_film_locations(?x89, ?x739), place_of_birth(?x65, ?x739), place_of_death(?x340, ?x739), contains(?x739, ?x1005), location_of_ceremony(?x548, ?x739), location(?x305, ?x739), film_release_region(?x467, ?x739), citytown(?x166, ?x739) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01f99l state_province_region 050ks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 65.000 0.429 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #17694-052_mn PRED entity: 052_mn PRED relation: music PRED expected values: 01mz9lt => 56 concepts (33 used for prediction) PRED predicted values (max 10 best out of 58): 01m5m5b (0.33 #398, 0.01 #4200, 0.01 #3566), 01zh29 (0.07 #4224, 0.06 #5070, 0.06 #6130), 044prt (0.07 #4224, 0.06 #5070, 0.06 #6130), 03wpmd (0.06 #5070, 0.06 #6130, 0.06 #4646), 0cbxl0 (0.06 #5070, 0.06 #6130, 0.06 #4646), 02bn75 (0.06 #564), 0146pg (0.05 #2545, 0.05 #851, 0.04 #1061), 0150t6 (0.04 #887, 0.03 #2158, 0.03 #4270), 06fxnf (0.04 #910, 0.03 #2816, 0.02 #2181), 02jxkw (0.04 #983, 0.03 #2254, 0.02 #2889) >> Best rule #398 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03rz2b; >> query: (?x8074, 01m5m5b) <- genre(?x8074, ?x53), film(?x7531, ?x8074), ?x53 = 07s9rl0, ?x7531 = 087z12 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 052_mn music 01mz9lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 33.000 0.333 http://example.org/film/film/music #17693-0dwr4 PRED entity: 0dwr4 PRED relation: group PRED expected values: 05563d => 91 concepts (67 used for prediction) PRED predicted values (max 10 best out of 597): 02vnpv (0.71 #2241, 0.67 #1294, 0.67 #1103), 01qqwp9 (0.70 #3637, 0.50 #1356, 0.50 #1166), 017_hq (0.67 #1298, 0.67 #1107, 0.60 #3769), 0134wr (0.67 #1242, 0.67 #1051, 0.57 #2189), 0gr69 (0.67 #1223, 0.67 #1032, 0.57 #2170), 01fchy (0.67 #1270, 0.67 #1079, 0.53 #5832), 0187x8 (0.67 #1234, 0.67 #1043, 0.43 #2181), 0123r4 (0.67 #1206, 0.67 #1015, 0.43 #2153), 03qkcn9 (0.67 #1315, 0.62 #2452, 0.57 #2262), 047cx (0.67 #1181, 0.62 #2697, 0.56 #3080) >> Best rule #2241 for best value: >> intensional similarity = 19 >> extensional distance = 5 >> proper extension: 018j2; >> query: (?x2059, 02vnpv) <- performance_role(?x2059, ?x212), role(?x3409, ?x2059), role(?x3161, ?x2059), role(?x1437, ?x2059), role(?x569, ?x2059), ?x569 = 07c6l, role(?x2460, ?x3409), ?x2460 = 01wy6, role(?x1294, ?x2059), role(?x1715, ?x3409), role(?x1997, ?x2059), type_of_union(?x1294, ?x566), artists(?x302, ?x1997), role(?x2059, ?x1647), ?x1437 = 01vdm0, award_nominee(?x5820, ?x1294), ?x1647 = 05ljv7, profession(?x1997, ?x220), ?x3161 = 01v1d8 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4781 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 12 *> proper extension: 0dwt5; *> query: (?x2059, 05563d) <- performance_role(?x2059, ?x212), role(?x3409, ?x2059), role(?x3215, ?x2059), role(?x2253, ?x2059), role(?x569, ?x2059), ?x569 = 07c6l, role(?x2460, ?x3409), role(?x432, ?x3409), ?x2460 = 01wy6, role(?x1294, ?x2059), role(?x1715, ?x3409), role(?x1997, ?x2059), artists(?x671, ?x1997), role(?x2059, ?x1647), ?x671 = 064t9, ?x432 = 042v_gx, nationality(?x1294, ?x1023), ?x3215 = 0bxl5, ?x2253 = 01679d *> conf = 0.64 ranks of expected_values: 29 EVAL 0dwr4 group 05563d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 91.000 67.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17692-02zd460 PRED entity: 02zd460 PRED relation: institution! PRED expected values: 02m4yg => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 10): 027f2w (0.58 #57, 0.57 #34, 0.52 #157), 01rr_d (0.43 #39, 0.42 #62, 0.33 #51), 028dcg (0.29 #41, 0.25 #64, 0.22 #164), 02cq61 (0.29 #40, 0.25 #63, 0.18 #96), 02m4yg (0.25 #50, 0.24 #127, 0.24 #83), 071tyz (0.19 #236, 0.17 #58, 0.17 #47), 01ysy9 (0.16 #132, 0.14 #43, 0.07 #166), 01kxxq (0.04 #131, 0.02 #1029, 0.02 #1041), 0g26h (0.04 #159, 0.03 #248, 0.02 #270), 01gkg3 (0.01 #847, 0.01 #969, 0.01 #1002) >> Best rule #57 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 06y3r; 023p29; 0n839; >> query: (?x5288, 027f2w) <- organizations_founded(?x5288, ?x5487), list(?x5288, ?x2197) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 10 *> proper extension: 05qd_; *> query: (?x5288, 02m4yg) <- organizations_founded(?x5288, ?x5487), organization(?x5510, ?x5288) *> conf = 0.25 ranks of expected_values: 5 EVAL 02zd460 institution! 02m4yg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 124.000 0.583 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #17691-07bcn PRED entity: 07bcn PRED relation: location! PRED expected values: 02hblj => 165 concepts (120 used for prediction) PRED predicted values (max 10 best out of 2276): 05bht9 (0.52 #2514, 0.51 #12568, 0.49 #163371), 09yrh (0.33 #913, 0.21 #160857, 0.16 #65350), 030h95 (0.33 #315, 0.21 #160857, 0.04 #35503), 0gs1_ (0.33 #1322, 0.21 #160857, 0.03 #54105), 018grr (0.33 #377, 0.21 #160857, 0.03 #20484), 013cr (0.33 #246, 0.21 #160857, 0.03 #20353), 01_f_5 (0.33 #1271, 0.16 #65350, 0.05 #70378), 0jw67 (0.33 #692, 0.16 #65350, 0.05 #70378), 02jt1k (0.33 #298, 0.16 #65350, 0.05 #70378), 01t6b4 (0.33 #217, 0.12 #2731, 0.08 #168398) >> Best rule #2514 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02_286; >> query: (?x5893, ?x4624) <- place_of_birth(?x4624, ?x5893), location(?x10696, ?x5893), ?x10696 = 06j8q_, jurisdiction_of_office(?x1195, ?x5893) >> conf = 0.52 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07bcn location! 02hblj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 120.000 0.525 http://example.org/people/person/places_lived./people/place_lived/location #17690-0393g PRED entity: 0393g PRED relation: category PRED expected values: 08mbj5d => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #49, 0.79 #56, 0.77 #63) >> Best rule #49 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 0r22d; >> query: (?x12917, 08mbj5d) <- place_of_birth(?x10186, ?x12917), administrative_division(?x12917, ?x14612), award_winner(?x1429, ?x10186), location(?x10186, ?x94) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0393g category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 171.000 0.798 http://example.org/common/topic/webpage./common/webpage/category #17689-02pxmgz PRED entity: 02pxmgz PRED relation: production_companies PRED expected values: 05rrtf => 89 concepts (77 used for prediction) PRED predicted values (max 10 best out of 60): 016tt2 (0.38 #494, 0.38 #415, 0.37 #3456), 0c41qv (0.19 #467, 0.07 #220, 0.04 #2687), 086k8 (0.17 #1730, 0.17 #2633, 0.14 #2469), 05qd_ (0.17 #2641, 0.17 #1738, 0.14 #2066), 01gb54 (0.12 #38, 0.10 #1766, 0.09 #2012), 024rgt (0.12 #25, 0.06 #1012, 0.05 #2574), 09b3v (0.12 #33, 0.04 #2007, 0.03 #1679), 025jfl (0.12 #5, 0.03 #1074, 0.03 #2472), 02rr_z4 (0.12 #77), 016tw3 (0.12 #1658, 0.12 #2479, 0.12 #3220) >> Best rule #494 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 040rmy; >> query: (?x1246, ?x574) <- film_crew_role(?x1246, ?x137), film(?x574, ?x1246), ?x137 = 09zzb8, ?x574 = 016tt2 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #140 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 02c6d; 02qmsr; 0btbyn; 02kfzz; 016y_f; 04nnpw; 07z6xs; 01kjr0; 0gm2_0; 02yy9r; *> query: (?x1246, 05rrtf) <- executive_produced_by(?x1246, ?x4946), nominated_for(?x350, ?x1246), genre(?x1246, ?x4205), ?x4205 = 0c3351 *> conf = 0.08 ranks of expected_values: 15 EVAL 02pxmgz production_companies 05rrtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 89.000 77.000 0.378 http://example.org/film/film/production_companies #17688-0cd2vh9 PRED entity: 0cd2vh9 PRED relation: film! PRED expected values: 03v1jf => 63 concepts (33 used for prediction) PRED predicted values (max 10 best out of 875): 031k24 (0.33 #3478, 0.01 #9697, 0.01 #28355), 08vr94 (0.25 #4820, 0.03 #22804, 0.01 #11039), 05p92jn (0.25 #5304, 0.03 #22804), 09l3p (0.25 #4893, 0.02 #21477, 0.01 #23551), 015p3p (0.20 #1092, 0.17 #3165, 0.01 #52918), 063g7l (0.20 #1888, 0.12 #6034, 0.03 #10180), 0h96g (0.20 #850, 0.10 #7069, 0.03 #25727), 0kszw (0.20 #418, 0.03 #22804, 0.03 #12856), 03m8lq (0.20 #6328, 0.03 #22804, 0.01 #8401), 01q6bg (0.20 #802, 0.03 #22804, 0.01 #11167) >> Best rule #3478 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 07w8fz; 0299hs; 03p2xc; >> query: (?x1640, 031k24) <- film(?x12551, ?x1640), film(?x3295, ?x1640), genre(?x1640, ?x53), ?x12551 = 0736qr, student(?x13639, ?x3295) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cd2vh9 film! 03v1jf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 33.000 0.333 http://example.org/film/actor/film./film/performance/film #17687-0b80__ PRED entity: 0b80__ PRED relation: place_of_death PRED expected values: 0r3tq => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 950): 02_286 (0.33 #208, 0.20 #403, 0.06 #2157), 05qtj (0.33 #64, 0.15 #6499, 0.06 #2339), 0f2wj (0.20 #402, 0.07 #1961, 0.05 #2742), 0r3tq (0.14 #2098, 0.12 #2293, 0.11 #2879), 0k049 (0.12 #977, 0.03 #5654, 0.02 #13830), 030qb3t (0.12 #2166, 0.07 #1971, 0.05 #2948), 0d35y (0.10 #1426, 0.08 #1817, 0.03 #5714), 01j2_7 (0.08 #1940, 0.04 #4089, 0.03 #6621), 0t_07 (0.08 #1907, 0.01 #6588), 0r62v (0.07 #1964, 0.06 #2159, 0.05 #2745) >> Best rule #208 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0gl88b; >> query: (?x4774, 02_286) <- crewmember(?x10276, ?x4774), costume_design_by(?x11356, ?x4774), ?x11356 = 09d38d, nationality(?x4774, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2098 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 03y1mlp; 0bytfv; 03qhyn8; *> query: (?x4774, 0r3tq) <- award_winner(?x2222, ?x4774), nationality(?x4774, ?x94), award_winner(?x3029, ?x4774), ?x2222 = 0gs96 *> conf = 0.14 ranks of expected_values: 4 EVAL 0b80__ place_of_death 0r3tq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 142.000 142.000 0.333 http://example.org/people/deceased_person/place_of_death #17686-04zkj5 PRED entity: 04zkj5 PRED relation: location PRED expected values: 02_286 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 157): 013yq (0.47 #32971, 0.47 #60316, 0.45 #49055), 02_286 (0.17 #21750, 0.15 #4862, 0.15 #1646), 030qb3t (0.14 #18580, 0.14 #17776, 0.13 #3300), 0qkcb (0.12 #387, 0.11 #1192, 0.05 #5630), 05k7sb (0.08 #3326, 0.08 #1718, 0.06 #109), 01n7q (0.08 #3280, 0.08 #1672, 0.06 #5693), 0cr3d (0.08 #1754, 0.07 #2558, 0.07 #7383), 0cc56 (0.06 #4078, 0.05 #6491, 0.05 #4882), 059rby (0.06 #16, 0.06 #3233, 0.04 #6450), 04jpl (0.06 #17, 0.06 #822, 0.05 #20926) >> Best rule #32971 for best value: >> intensional similarity = 2 >> extensional distance = 1265 >> proper extension: 04yywz; 05bp8g; 049tjg; 0d_84; 0h1_w; 01ty7ll; 04bs3j; 014x77; 0151ns; 018dnt; ... >> query: (?x7663, ?x2277) <- film(?x7663, ?x559), place_of_birth(?x7663, ?x2277) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #21750 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 859 *> proper extension: 01cqz5; *> query: (?x7663, 02_286) <- place_of_birth(?x7663, ?x2277), month(?x2277, ?x1459) *> conf = 0.17 ranks of expected_values: 2 EVAL 04zkj5 location 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.472 http://example.org/people/person/places_lived./people/place_lived/location #17685-03gt0c5 PRED entity: 03gt0c5 PRED relation: nominated_for PRED expected values: 027pfb2 => 130 concepts (39 used for prediction) PRED predicted values (max 10 best out of 765): 05hjnw (0.46 #4862, 0.45 #3240, 0.37 #12963), 02rq8k8 (0.46 #4862, 0.45 #3240, 0.37 #12963), 0symg (0.37 #12963, 0.36 #3239, 0.33 #8102), 0bw20 (0.37 #8103, 0.36 #3239, 0.33 #8102), 04cv9m (0.37 #8103, 0.36 #3239, 0.33 #8102), 024mpp (0.37 #8103, 0.36 #3239, 0.33 #8102), 05gnf (0.16 #10789), 01h1bf (0.16 #10186), 029jt9 (0.12 #2968, 0.11 #4590, 0.09 #7831), 0k4kk (0.12 #1868, 0.11 #3490, 0.09 #6731) >> Best rule #4862 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0b80__; 026lyl4; 0cbxl0; >> query: (?x13091, ?x3904) <- costume_design_by(?x3904, ?x13091), featured_film_locations(?x3904, ?x362), nominated_for(?x350, ?x3904), award_winner(?x3904, ?x574) >> conf = 0.46 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 03gt0c5 nominated_for 027pfb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 39.000 0.460 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17684-014z8v PRED entity: 014z8v PRED relation: award_winner! PRED expected values: 01bx35 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 133): 05pd94v (0.20 #2, 0.06 #554, 0.06 #416), 03nnm4t (0.19 #487, 0.18 #349, 0.14 #763), 0gx_st (0.18 #1555, 0.11 #2797, 0.09 #313), 02q690_ (0.18 #340, 0.14 #2824, 0.12 #1168), 05c1t6z (0.18 #291, 0.12 #1119, 0.10 #2775), 09q_6t (0.18 #284, 0.06 #1664, 0.06 #1802), 0lp_cd3 (0.18 #299, 0.06 #1127, 0.06 #575), 09p3h7 (0.18 #346, 0.06 #622, 0.06 #2830), 0hndn2q (0.18 #315, 0.06 #591, 0.05 #1419), 073h1t (0.18 #303, 0.06 #579, 0.03 #1131) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0p_47; 01hmk9; 01l7qw; >> query: (?x4112, 05pd94v) <- award(?x4112, ?x537), influenced_by(?x2125, ?x4112), ?x2125 = 0126rp, profession(?x4112, ?x353) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #6909 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 184 *> proper extension: 0bz5v2; 07ymr5; 049_zz; 03d9v8; 05xpms; 013rds; *> query: (?x4112, 01bx35) <- film(?x4112, ?x994), category(?x4112, ?x134), award_winner(?x2431, ?x4112) *> conf = 0.06 ranks of expected_values: 48 EVAL 014z8v award_winner! 01bx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 153.000 153.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17683-0f7hw PRED entity: 0f7hw PRED relation: nominated_for! PRED expected values: 0gs96 => 71 concepts (64 used for prediction) PRED predicted values (max 10 best out of 183): 0p9sw (0.37 #741, 0.22 #4821, 0.15 #7943), 0gq9h (0.35 #4864, 0.28 #4624, 0.23 #7986), 0gs9p (0.30 #4866, 0.26 #4626, 0.22 #2946), 02x1z2s (0.30 #625, 0.29 #1105, 0.23 #385), 02r0csl (0.29 #10328, 0.22 #8403, 0.20 #12491), 0gr42 (0.28 #811, 0.19 #14416, 0.14 #91), 0gq_v (0.28 #4820, 0.20 #4580, 0.19 #7942), 019f4v (0.27 #4855, 0.24 #775, 0.23 #4615), 0k611 (0.26 #4875, 0.21 #4635, 0.21 #795), 02hsq3m (0.25 #750, 0.19 #14416, 0.12 #990) >> Best rule #741 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 0c3xpwy; >> query: (?x9424, 0p9sw) <- nominated_for(?x10416, ?x9424), crewmember(?x6528, ?x10416), type_of_union(?x10416, ?x566), film_release_region(?x6528, ?x87) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #4892 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 821 *> proper extension: 04z_x4v; *> query: (?x9424, 0gs96) <- nominated_for(?x3458, ?x9424), ceremony(?x3458, ?x3579), award(?x2871, ?x3458), ?x3579 = 0bc773 *> conf = 0.18 ranks of expected_values: 26 EVAL 0f7hw nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 71.000 64.000 0.373 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17682-03y317 PRED entity: 03y317 PRED relation: actor PRED expected values: 04vcdj => 67 concepts (36 used for prediction) PRED predicted values (max 10 best out of 780): 044mvs (0.29 #1703, 0.25 #771, 0.15 #3569), 04qt29 (0.29 #1625, 0.15 #3491, 0.04 #14683), 0q1lp (0.25 #733, 0.14 #1665, 0.08 #2598), 01f9mq (0.25 #844, 0.14 #1776, 0.08 #2709), 03pmty (0.25 #80, 0.08 #1945, 0.03 #10337), 0301yj (0.25 #802, 0.08 #2667, 0.03 #11059), 01qn8k (0.25 #710, 0.08 #2575, 0.03 #10967), 047hpm (0.25 #229, 0.08 #2094, 0.03 #10486), 0sw6y (0.15 #3658, 0.08 #12051, 0.07 #12984), 02wrhj (0.15 #2935, 0.05 #11328, 0.05 #12261) >> Best rule #1703 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0gfzgl; 05sy2k_; 03g9xj; 02xhwm; 03_b1g; >> query: (?x9032, 044mvs) <- languages(?x9032, ?x254), titles(?x2008, ?x9032), program(?x8817, ?x9032), program(?x2389, ?x9032), ?x254 = 02h40lc, country_of_origin(?x9032, ?x94), ?x8817 = 0b275x >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #11170 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 32 *> proper extension: 02r2j8; *> query: (?x9032, 04vcdj) <- languages(?x9032, ?x254), titles(?x11671, ?x9032), titles(?x11671, ?x9649), genre(?x5060, ?x11671), country_of_origin(?x9649, ?x94), nominated_for(?x822, ?x5060), actor(?x9649, ?x9650) *> conf = 0.03 ranks of expected_values: 267 EVAL 03y317 actor 04vcdj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 67.000 36.000 0.286 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #17681-01tz6vs PRED entity: 01tz6vs PRED relation: influenced_by PRED expected values: 0bwx3 => 154 concepts (51 used for prediction) PRED predicted values (max 10 best out of 370): 03sbs (0.55 #5786, 0.33 #6213, 0.33 #1077), 02lt8 (0.40 #3975, 0.33 #976, 0.33 #118), 039n1 (0.36 #5887, 0.33 #1178, 0.27 #6314), 081k8 (0.33 #1012, 0.33 #154, 0.27 #4865), 015n8 (0.33 #6397, 0.30 #9397, 0.27 #5970), 04xjp (0.33 #57, 0.27 #4768, 0.26 #8620), 042q3 (0.33 #1217, 0.27 #5926, 0.25 #2930), 0372p (0.33 #968, 0.27 #5677, 0.20 #6104), 02wh0 (0.33 #376, 0.27 #5087, 0.17 #8939), 01tz6vs (0.33 #175, 0.25 #1887, 0.17 #8738) >> Best rule #5786 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 0j3v; 099bk; 03sbs; 047g6; >> query: (?x5434, 03sbs) <- nationality(?x5434, ?x1603), influenced_by(?x5434, ?x6015), influenced_by(?x5434, ?x3712), influenced_by(?x5434, ?x3336), ?x3712 = 0gz_, ?x6015 = 05qmj, people(?x5590, ?x3336) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1897 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0bwx3; 04xfb; *> query: (?x5434, 0bwx3) <- nationality(?x5434, ?x1603), profession(?x5434, ?x353), influenced_by(?x5434, ?x2240), influenced_by(?x8494, ?x5434), ?x8494 = 051cc *> conf = 0.25 ranks of expected_values: 32 EVAL 01tz6vs influenced_by 0bwx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 154.000 51.000 0.545 http://example.org/influence/influence_node/influenced_by #17680-01f1jf PRED entity: 01f1jf PRED relation: olympics! PRED expected values: 05b4w => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 383): 03rjj (0.81 #1601, 0.79 #1470, 0.77 #1339), 06mzp (0.79 #1617, 0.77 #1355, 0.77 #1221), 05b4w (0.79 #1522, 0.77 #1391, 0.73 #1127), 07ssc (0.71 #1612, 0.47 #1732, 0.40 #414), 03_3d (0.64 #1602, 0.63 #533, 0.54 #1206), 05qhw (0.63 #533, 0.57 #1611, 0.47 #1732), 01mjq (0.63 #533, 0.40 #531, 0.40 #438), 06bnz (0.63 #533, 0.40 #440, 0.31 #1730), 06t8v (0.63 #533, 0.31 #930, 0.31 #1730), 06npd (0.63 #533, 0.31 #930, 0.29 #262) >> Best rule #1601 for best value: >> intensional similarity = 39 >> extensional distance = 40 >> proper extension: 018wrk; 0l6vl; 0l98s; 0l998; 0kbvb; 0l6ny; 0l6m5; 09x3r; 0kbws; 0lv1x; ... >> query: (?x8584, 03rjj) <- sports(?x8584, ?x11927), sports(?x8584, ?x3309), sports(?x3110, ?x3309), sports(?x1617, ?x3309), sports(?x452, ?x3309), country(?x3309, ?x9874), country(?x3309, ?x8197), country(?x3309, ?x3912), country(?x3309, ?x2513), country(?x3309, ?x2152), country(?x3309, ?x1603), country(?x3309, ?x1264), country(?x3309, ?x789), country(?x3309, ?x756), country(?x3309, ?x304), ?x8197 = 06srk, ?x756 = 06npd, ?x2513 = 05b4w, medal(?x452, ?x422), olympics(?x5114, ?x1617), olympics(?x2984, ?x1617), ?x1264 = 0345h, olympics(?x2346, ?x452), ?x5114 = 05vz3zq, ?x2346 = 0d05w3, ?x2984 = 082fr, ?x1603 = 06bnz, olympics(?x2267, ?x3110), olympics(?x1497, ?x3110), ?x789 = 0f8l9c, ?x422 = 02lq67, ?x304 = 0d0vqn, ?x9874 = 01nty, country(?x11927, ?x1471), sports(?x1741, ?x11927), ?x3912 = 04w58, ?x2267 = 03rj0, ?x1497 = 015qh, ?x2152 = 06mkj >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1522 for first EXPECTED value: *> intensional similarity = 34 *> extensional distance = 12 *> proper extension: 0sx92; *> query: (?x8584, 05b4w) <- sports(?x8584, ?x3309), ?x3309 = 09w1n, olympics(?x94, ?x8584), film_release_region(?x7844, ?x94), film_release_region(?x7293, ?x94), film_release_region(?x5992, ?x94), film_release_region(?x5098, ?x94), film_release_region(?x4751, ?x94), film_release_region(?x1822, ?x94), film_release_region(?x1192, ?x94), film_release_region(?x1002, ?x94), first_level_division_of(?x177, ?x94), nationality(?x9403, ?x94), nationality(?x3664, ?x94), second_level_divisions(?x94, ?x321), country(?x9565, ?x94), country(?x4158, ?x94), country(?x557, ?x94), contains(?x94, ?x1681), ?x557 = 03h_yy, ?x1002 = 0_b3d, school_type(?x1681, ?x3092), ?x4158 = 0g83dv, ?x5992 = 0g5q34q, nominated_for(?x384, ?x4751), country_of_origin(?x50, ?x94), titles(?x53, ?x7844), award(?x9403, ?x350), ?x3664 = 059xvg, nominated_for(?x7265, ?x1822), honored_for(?x1998, ?x5098), ?x7293 = 027m67, ?x9565 = 0hz6mv2, ?x1192 = 07sc6nw *> conf = 0.79 ranks of expected_values: 3 EVAL 01f1jf olympics! 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 14.000 14.000 0.810 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #17679-0cdbq PRED entity: 0cdbq PRED relation: contains PRED expected values: 02sn34 => 161 concepts (52 used for prediction) PRED predicted values (max 10 best out of 2174): 06pr6 (0.79 #114922, 0.79 #144406, 0.75 #111975), 04swd (0.79 #114922, 0.79 #144406, 0.75 #111975), 020vx9 (0.61 #50082, 0.61 #67762, 0.55 #38296), 02_jjm (0.61 #50082, 0.61 #67762, 0.55 #38296), 0277jc (0.61 #50082, 0.61 #67762, 0.55 #38296), 02_gzx (0.61 #50082, 0.61 #67762, 0.52 #58922), 02cgp8 (0.33 #1667, 0.20 #16392, 0.17 #31125), 0rt80 (0.33 #2708, 0.20 #17433, 0.17 #32166), 03tw2s (0.33 #905, 0.20 #15630, 0.17 #30363), 050ks (0.33 #958, 0.20 #15683, 0.17 #30416) >> Best rule #114922 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 04gzd; >> query: (?x4492, ?x7184) <- capital(?x4492, ?x7184), contains(?x4492, ?x10734), time_zones(?x10734, ?x10735), location_of_ceremony(?x566, ?x10734), locations(?x7241, ?x4492) >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #135558 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0fv_t; *> query: (?x4492, ?x790) <- partially_contains(?x455, ?x4492), partially_contains(?x455, ?x789), contains(?x4492, ?x10734), contains(?x789, ?x790) *> conf = 0.30 ranks of expected_values: 904 EVAL 0cdbq contains 02sn34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 161.000 52.000 0.789 http://example.org/location/location/contains #17678-04fcjt PRED entity: 04fcjt PRED relation: artist PRED expected values: 016ksk 01vzxld 015bwt => 39 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1290): 03j1p2n (0.50 #5467, 0.38 #6284, 0.18 #7106), 01t110 (0.50 #5360, 0.38 #6177, 0.18 #6999), 04qzm (0.50 #4816, 0.33 #2362, 0.25 #3998), 019g40 (0.50 #4194, 0.33 #1740, 0.25 #3376), 07mvp (0.50 #3727, 0.33 #2091, 0.25 #4545), 01vw8mh (0.50 #3613, 0.33 #1977, 0.25 #4431), 0gy6z9 (0.50 #3489, 0.33 #1853, 0.25 #4307), 0jg77 (0.50 #4089, 0.33 #2453, 0.25 #4907), 01wbsdz (0.50 #2871, 0.33 #412, 0.18 #6965), 05mt_q (0.50 #2525, 0.33 #66, 0.12 #6551) >> Best rule #5467 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 03rhqg; 01cl2y; 011k11; 01sqd7; >> query: (?x5021, 03j1p2n) <- artist(?x5021, ?x9791), artist(?x5021, ?x6124), ?x6124 = 0277c3, category(?x5021, ?x134), award(?x9791, ?x3631), group(?x227, ?x9791), ?x3631 = 02f73p, origin(?x9791, ?x479) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5674 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 03rhqg; 01cl2y; 011k11; 01sqd7; *> query: (?x5021, 015bwt) <- artist(?x5021, ?x9791), artist(?x5021, ?x6124), ?x6124 = 0277c3, category(?x5021, ?x134), award(?x9791, ?x3631), group(?x227, ?x9791), ?x3631 = 02f73p, origin(?x9791, ?x479) *> conf = 0.33 ranks of expected_values: 76, 419, 1232 EVAL 04fcjt artist 015bwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 39.000 21.000 0.500 http://example.org/music/record_label/artist EVAL 04fcjt artist 01vzxld CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 21.000 0.500 http://example.org/music/record_label/artist EVAL 04fcjt artist 016ksk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 21.000 0.500 http://example.org/music/record_label/artist #17677-07ghv5 PRED entity: 07ghv5 PRED relation: actor PRED expected values: 06_6j3 => 86 concepts (63 used for prediction) PRED predicted values (max 10 best out of 71): 09ykwk (0.33 #131, 0.25 #265, 0.20 #399), 081jbk (0.33 #88, 0.25 #222, 0.20 #356), 066l3y (0.33 #156, 0.23 #828, 0.17 #897), 09wlpl (0.33 #29, 0.20 #431, 0.17 #904), 0bn8fw (0.33 #40, 0.20 #442, 0.11 #915), 084x96 (0.33 #200, 0.08 #872, 0.06 #941), 05j0wc (0.27 #721, 0.25 #788, 0.25 #520), 044_7j (0.23 #843, 0.22 #912, 0.17 #981), 091n7z (0.23 #868, 0.22 #937, 0.17 #1006), 08141d (0.22 #598, 0.18 #732, 0.12 #531) >> Best rule #131 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 02r9p0c; >> query: (?x6610, 09ykwk) <- genre(?x6610, ?x225), language(?x6610, ?x2164), language(?x6610, ?x254), ?x2164 = 03_9r, film(?x296, ?x6610), ?x254 = 02h40lc, actor(?x6610, ?x12353), ?x225 = 02kdv5l, film(?x7764, ?x6610), location(?x12353, ?x3634) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #893 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 16 *> proper extension: 02gs6r; *> query: (?x6610, 06_6j3) <- genre(?x6610, ?x5937), language(?x6610, ?x2164), ?x2164 = 03_9r, ?x5937 = 0jxy, film(?x10418, ?x6610), gender(?x10418, ?x231), nationality(?x10418, ?x252), ?x231 = 05zppz *> conf = 0.17 ranks of expected_values: 18 EVAL 07ghv5 actor 06_6j3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 86.000 63.000 0.333 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #17676-0qmpd PRED entity: 0qmpd PRED relation: origin PRED expected values: 0f8l9c => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 90): 02jx1 (0.50 #504, 0.40 #741, 0.33 #977), 04jpl (0.25 #478, 0.22 #1895, 0.20 #715), 09c7w0 (0.25 #473, 0.20 #710, 0.17 #946), 07ssc (0.25 #483, 0.20 #720, 0.17 #956), 0d6lp (0.22 #1954, 0.18 #2190, 0.17 #1246), 0k33p (0.20 #872, 0.17 #1108, 0.11 #2052), 02_286 (0.18 #2141, 0.17 #1197, 0.14 #2849), 0mp3l (0.17 #1227, 0.14 #1463, 0.12 #1699), 02frhbc (0.14 #1578, 0.12 #1814, 0.08 #2758), 030qb3t (0.13 #6644, 0.12 #7116, 0.12 #6407) >> Best rule #504 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 06nv27; >> query: (?x9196, 02jx1) <- group(?x1166, ?x9196), group(?x716, ?x9196), group(?x227, ?x9196), group(?x75, ?x9196), ?x75 = 07y_7, ?x227 = 0342h, artists(?x1380, ?x9196), group(?x7506, ?x9196), ?x716 = 018vs, role(?x7506, ?x316), ?x316 = 05r5c, ?x1166 = 05148p4, place_of_birth(?x7506, ?x9929) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qmpd origin 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 73.000 0.500 http://example.org/music/artist/origin #17675-05hjnw PRED entity: 05hjnw PRED relation: nominated_for! PRED expected values: 0gr4k => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 178): 019f4v (0.75 #677, 0.69 #1307, 0.68 #11349), 0k611 (0.68 #689, 0.55 #1319, 0.53 #1739), 04dn09n (0.68 #11349, 0.68 #12826, 0.67 #11348), 0gr4k (0.68 #11349, 0.68 #12826, 0.67 #11348), 099t8j (0.68 #11349, 0.68 #12826, 0.67 #11348), 02x4wr9 (0.68 #11349, 0.68 #12826, 0.67 #11348), 027b9ly (0.68 #11349, 0.68 #12826, 0.67 #11348), 0789r6 (0.68 #11349, 0.68 #12826, 0.67 #11348), 027c924 (0.68 #11349, 0.68 #12826, 0.67 #11348), 09d28z (0.68 #11349, 0.68 #12826, 0.67 #11348) >> Best rule #677 for best value: >> intensional similarity = 5 >> extensional distance = 67 >> proper extension: 0gmcwlb; 03cw411; >> query: (?x4939, 019f4v) <- nominated_for(?x1313, ?x4939), nominated_for(?x1243, ?x4939), honored_for(?x2210, ?x4939), ?x1313 = 0gs9p, ?x1243 = 0gr0m >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #11349 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 938 *> proper extension: 06w7mlh; 07bz5; *> query: (?x4939, ?x384) <- award(?x4939, ?x384), nominated_for(?x2248, ?x4939), award(?x164, ?x384) *> conf = 0.68 ranks of expected_values: 4 EVAL 05hjnw nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 103.000 0.754 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17674-02bft PRED entity: 02bft PRED relation: risk_factors PRED expected values: 02vrr => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 99): 01hbgs (0.85 #1357, 0.81 #1093, 0.70 #1691), 0jpmt (0.67 #982, 0.58 #828, 0.58 #1300), 05zppz (0.65 #1501, 0.56 #1076, 0.51 #1124), 0217g (0.57 #503, 0.30 #1176, 0.29 #460), 012jc (0.51 #1124, 0.50 #1155, 0.50 #390), 0fltx (0.51 #1124, 0.48 #1012, 0.44 #697), 0k95h (0.51 #1124, 0.48 #1012, 0.44 #622), 0g9pc (0.51 #1124, 0.48 #1012, 0.42 #1549), 097ns (0.50 #238, 0.47 #1720, 0.30 #1016), 098s1 (0.50 #293, 0.21 #716, 0.20 #349) >> Best rule #1357 for best value: >> intensional similarity = 17 >> extensional distance = 18 >> proper extension: 025hl8; 0dq9p; 09969; 0fltx; 0cycc; 06g7c; 0146bp; 0h3bn; >> query: (?x6483, 01hbgs) <- risk_factors(?x6483, ?x8523), risk_factors(?x6483, ?x1158), risk_factors(?x6483, ?x268), symptom_of(?x4905, ?x6483), risk_factors(?x6655, ?x268), risk_factors(?x11126, ?x8523), risk_factors(?x11064, ?x8523), risk_factors(?x10199, ?x8523), ?x11126 = 0hg45, ?x10199 = 02k6hp, people(?x11064, ?x120), symptom_of(?x3679, ?x11064), risk_factors(?x11064, ?x8023), ?x6655 = 09d11, risk_factors(?x5784, ?x1158), ?x8023 = 0jpmt, ?x5784 = 02vrr >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #721 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 7 *> proper extension: 0hg11; *> query: (?x6483, ?x5118) <- risk_factors(?x6483, ?x8523), risk_factors(?x6483, ?x6197), risk_factors(?x6483, ?x4322), symptom_of(?x4905, ?x6483), risk_factors(?x4322, ?x231), risk_factors(?x6197, ?x4195), symptom_of(?x9438, ?x4322), risk_factors(?x11307, ?x4195), risk_factors(?x5118, ?x4195), risk_factors(?x3984, ?x4195), ?x11307 = 09969, risk_factors(?x7260, ?x8523), ?x3984 = 0h9dj, risk_factors(?x8523, ?x9648), people(?x7260, ?x1737), ?x4905 = 01j6t0 *> conf = 0.06 ranks of expected_values: 74 EVAL 02bft risk_factors 02vrr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 42.000 42.000 0.850 http://example.org/medicine/disease/risk_factors #17673-026rm_y PRED entity: 026rm_y PRED relation: award_nominee! PRED expected values: 06r_by => 110 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1003): 01q6bg (0.81 #83857, 0.80 #111812, 0.78 #30280), 0154qm (0.25 #5397, 0.24 #104821, 0.09 #739), 0f4vbz (0.25 #51245, 0.24 #104821, 0.19 #6988), 02p65p (0.25 #51245, 0.24 #104821, 0.11 #7014), 046zh (0.25 #51245, 0.24 #104821, 0.07 #104822), 07r1h (0.25 #51245, 0.24 #104821, 0.07 #8419), 0blbxk (0.25 #51245, 0.24 #104821, 0.06 #4919), 032_jg (0.25 #51245, 0.24 #104821, 0.04 #4831), 0pnf3 (0.25 #51245, 0.24 #104821, 0.04 #6783), 013knm (0.25 #51245, 0.24 #104821, 0.03 #40434) >> Best rule #83857 for best value: >> intensional similarity = 3 >> extensional distance = 1130 >> proper extension: 0jz9f; 032xhg; 01t6b4; 01wbl_r; 0pyg6; 02cllz; 021lby; 03np3w; 0blt6; 0253b6; ... >> query: (?x8740, ?x815) <- award(?x8740, ?x112), award_winner(?x2220, ?x8740), award_nominee(?x8740, ?x815) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #104821 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1275 *> proper extension: 02knnd; 01wbsdz; 03k48_; *> query: (?x8740, ?x192) <- award_nominee(?x2499, ?x8740), participant(?x2499, ?x91), award_nominee(?x192, ?x2499) *> conf = 0.24 ranks of expected_values: 25 EVAL 026rm_y award_nominee! 06r_by CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 110.000 52.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17672-040_lv PRED entity: 040_lv PRED relation: film! PRED expected values: 06lht1 => 64 concepts (38 used for prediction) PRED predicted values (max 10 best out of 822): 06m6z6 (0.41 #62183, 0.37 #16580, 0.34 #29021), 02qgqt (0.40 #2090, 0.08 #8308, 0.03 #18653), 016ypb (0.33 #6716, 0.25 #4643, 0.02 #27446), 083wr9 (0.33 #8264, 0.25 #6191, 0.01 #22774), 04hxyv (0.33 #8249, 0.25 #6176, 0.01 #22759), 03dn9v (0.33 #8049, 0.25 #5976, 0.01 #22559), 08vr94 (0.31 #8964, 0.01 #31768, 0.01 #21402), 01l1b90 (0.25 #4176, 0.22 #6249, 0.02 #12465), 02d4ct (0.25 #4535, 0.22 #6608, 0.01 #12824), 01kb2j (0.25 #904, 0.20 #2976, 0.03 #18653) >> Best rule #62183 for best value: >> intensional similarity = 4 >> extensional distance = 1177 >> proper extension: 01fs__; >> query: (?x6036, ?x3961) <- language(?x6036, ?x254), ?x254 = 02h40lc, nominated_for(?x3961, ?x6036), award_winner(?x1180, ?x3961) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #2955 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 01hqhm; *> query: (?x6036, 06lht1) <- film(?x398, ?x6036), music(?x6036, ?x1715), ?x1715 = 04bpm6, film_crew_role(?x6036, ?x137), film(?x3961, ?x6036) *> conf = 0.20 ranks of expected_values: 44 EVAL 040_lv film! 06lht1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 64.000 38.000 0.408 http://example.org/film/actor/film./film/performance/film #17671-0gr51 PRED entity: 0gr51 PRED relation: ceremony PRED expected values: 073hmq 02yv_b 03tn9w 0c4hnm 0bvhz9 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 86): 0bvhz9 (0.89 #1108, 0.85 #764, 0.85 #678), 02yv_b (0.86 #793, 0.83 #1051, 0.77 #707), 0c4hnm (0.79 #849, 0.69 #763, 0.69 #677), 073hmq (0.78 #1049, 0.77 #705, 0.77 #619), 03tn9w (0.78 #1091, 0.77 #661, 0.71 #833), 0fzrhn (0.76 #1029, 0.69 #771, 0.69 #685), 0dznvw (0.71 #854, 0.71 #1026, 0.69 #768), 0dthsy (0.69 #645, 0.67 #1075, 0.64 #817), 0ftlxj (0.64 #819, 0.54 #647, 0.53 #991), 0ftlkg (0.64 #794, 0.54 #622, 0.50 #1052) >> Best rule #1108 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 0gr42; 0gs96; 0gqxm; 0gvx_; 018wdw; >> query: (?x1862, 0bvhz9) <- nominated_for(?x1862, ?x69), ceremony(?x1862, ?x3173), ceremony(?x1862, ?x1449), award(?x361, ?x1862), ?x1449 = 059x66, honored_for(?x3173, ?x2345) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 0gr51 ceremony 0bvhz9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr51 ceremony 0c4hnm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr51 ceremony 03tn9w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr51 ceremony 02yv_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr51 ceremony 073hmq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony #17670-062z7 PRED entity: 062z7 PRED relation: major_field_of_study! PRED expected values: 0bjrnt => 106 concepts (73 used for prediction) PRED predicted values (max 10 best out of 14): 0bjrnt (0.60 #118, 0.53 #415, 0.50 #431), 01ysy9 (0.48 #130, 0.48 #131, 0.45 #115), 02m4yg (0.48 #130, 0.48 #131, 0.45 #115), 022h5x (0.48 #130, 0.48 #131, 0.45 #115), 028dcg (0.48 #130, 0.48 #131, 0.45 #115), 027f2w (0.48 #130, 0.48 #131, 0.45 #115), 01rr_d (0.48 #130, 0.48 #131, 0.45 #115), 013zdg (0.48 #130, 0.48 #131, 0.45 #115), 07s6fsf (0.48 #130, 0.48 #131, 0.45 #115), 03mkk4 (0.48 #130, 0.48 #131, 0.45 #115) >> Best rule #118 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 037mh8; 0l5mz; >> query: (?x2606, 0bjrnt) <- major_field_of_study(?x7575, ?x2606), major_field_of_study(?x6132, ?x2606), major_field_of_study(?x4672, ?x2606), ?x4672 = 07tds, state_province_region(?x7575, ?x11096), ?x6132 = 0hsb3, major_field_of_study(?x947, ?x2606), institution(?x1519, ?x7575) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 062z7 major_field_of_study! 0bjrnt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 73.000 0.600 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #17669-02lnbg PRED entity: 02lnbg PRED relation: artists PRED expected values: 01vrt_c 01cwhp 0840vq 01vvyfh 019f9z 01k3qj 01wf86y 01lqf49 0gps0z => 46 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1677): 01vvycq (0.71 #5061, 0.62 #6063, 0.60 #3055), 01yzl2 (0.71 #5468, 0.62 #6470, 0.60 #3462), 06s7rd (0.71 #5708, 0.62 #6710, 0.60 #3702), 01wwvc5 (0.62 #6214, 0.60 #3206, 0.57 #5212), 01vvyfh (0.60 #3324, 0.60 #2320, 0.57 #5330), 019f9z (0.60 #3553, 0.57 #5559, 0.50 #8568), 0415mzy (0.60 #3470, 0.57 #5476, 0.50 #6478), 0gps0z (0.60 #3823, 0.57 #5829, 0.50 #6831), 04xrx (0.60 #3194, 0.57 #5200, 0.50 #6202), 01vs73g (0.60 #3665, 0.57 #5671, 0.50 #6673) >> Best rule #5061 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 0glt670; 06j6l; >> query: (?x3996, 01vvycq) <- artists(?x3996, ?x6835), artists(?x3996, ?x2562), artists(?x3996, ?x2227), artists(?x3996, ?x883), ?x6835 = 06mt91, ?x2227 = 07ss8_, ?x2562 = 01trhmt, languages(?x883, ?x254), origin(?x883, ?x8428) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3324 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 025sc50; *> query: (?x3996, 01vvyfh) <- artists(?x3996, ?x8020), artists(?x3996, ?x6835), artists(?x3996, ?x2274), artists(?x3996, ?x2227), ?x6835 = 06mt91, ?x2227 = 07ss8_, ?x2274 = 013v5j, diet(?x8020, ?x3130) *> conf = 0.60 ranks of expected_values: 5, 6, 8, 13, 27, 31, 41, 154, 346 EVAL 02lnbg artists 0gps0z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 46.000 28.000 0.714 http://example.org/music/genre/artists EVAL 02lnbg artists 01lqf49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 28.000 0.714 http://example.org/music/genre/artists EVAL 02lnbg artists 01wf86y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 46.000 28.000 0.714 http://example.org/music/genre/artists EVAL 02lnbg artists 01k3qj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 46.000 28.000 0.714 http://example.org/music/genre/artists EVAL 02lnbg artists 019f9z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 46.000 28.000 0.714 http://example.org/music/genre/artists EVAL 02lnbg artists 01vvyfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 46.000 28.000 0.714 http://example.org/music/genre/artists EVAL 02lnbg artists 0840vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 46.000 28.000 0.714 http://example.org/music/genre/artists EVAL 02lnbg artists 01cwhp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 46.000 28.000 0.714 http://example.org/music/genre/artists EVAL 02lnbg artists 01vrt_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 46.000 28.000 0.714 http://example.org/music/genre/artists #17668-016ks5 PRED entity: 016ks5 PRED relation: nominated_for! PRED expected values: 02qyp19 => 74 concepts (47 used for prediction) PRED predicted values (max 10 best out of 207): 02w9sd7 (0.72 #3266, 0.69 #10043, 0.68 #10278), 027b9j5 (0.69 #10043, 0.68 #10278, 0.67 #9336), 02z13jg (0.69 #10043, 0.68 #10278, 0.67 #9336), 09cm54 (0.69 #10043, 0.68 #10278, 0.67 #9336), 027986c (0.69 #10043, 0.68 #10278, 0.67 #9336), 0gqwc (0.61 #1457, 0.53 #3789, 0.25 #3088), 0gq9h (0.61 #3090, 0.48 #991, 0.48 #4957), 0gs9p (0.59 #3092, 0.51 #4959, 0.38 #6591), 019f4v (0.51 #3082, 0.40 #4949, 0.37 #1451), 027dtxw (0.48 #936, 0.28 #1171, 0.23 #3035) >> Best rule #3266 for best value: >> intensional similarity = 4 >> extensional distance = 221 >> proper extension: 019kyn; >> query: (?x6149, ?x3209) <- award(?x6149, ?x3209), nominated_for(?x3209, ?x7846), ?x7846 = 0p9tm, titles(?x53, ?x6149) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #933 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 0b2v79; 016z5x; 0209hj; 09q5w2; 04m1bm; 02c638; 0yzvw; 0f4_l; 02yvct; 011yl_; ... *> query: (?x6149, 02qyp19) <- award(?x6149, ?x4894), nominated_for(?x198, ?x6149), genre(?x6149, ?x53), ?x4894 = 027b9j5 *> conf = 0.26 ranks of expected_values: 30 EVAL 016ks5 nominated_for! 02qyp19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 74.000 47.000 0.723 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17667-01tbp PRED entity: 01tbp PRED relation: major_field_of_study! PRED expected values: 06pwq 02bjhv 02301 0j_sncb 02txdf 011kn2 => 52 concepts (23 used for prediction) PRED predicted values (max 10 best out of 629): 01w5m (0.80 #10882, 0.80 #8184, 0.80 #6029), 06pwq (0.80 #5399, 0.77 #9711, 0.75 #4322), 03ksy (0.73 #6030, 0.73 #9804, 0.67 #10344), 07tds (0.70 #5536, 0.62 #4459, 0.56 #4997), 08815 (0.62 #4314, 0.60 #5929, 0.60 #5391), 0dzst (0.62 #4650, 0.60 #3034, 0.57 #4111), 02bqy (0.62 #4490, 0.57 #3951, 0.56 #5028), 01bm_ (0.62 #4557, 0.53 #6172, 0.50 #2401), 07vhb (0.62 #4478, 0.50 #5555, 0.38 #9867), 015cz0 (0.62 #4476, 0.47 #6091, 0.43 #3937) >> Best rule #10882 for best value: >> intensional similarity = 7 >> extensional distance = 43 >> proper extension: 02vxn; 088tb; 05r79; 01jzxy; 03qsdpk; 02_7t; 036nz; 01zc2w; 040p_q; 041y2; ... >> query: (?x6859, 01w5m) <- major_field_of_study(?x3779, ?x6859), major_field_of_study(?x741, ?x6859), student(?x741, ?x7044), state_province_region(?x741, ?x335), ?x7044 = 0crqcc, company(?x4988, ?x741), school(?x1633, ?x3779) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5399 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 8 *> proper extension: 0h5k; 05qfh; 01540; *> query: (?x6859, 06pwq) <- major_field_of_study(?x8797, ?x6859), major_field_of_study(?x7660, ?x6859), major_field_of_study(?x7618, ?x6859), major_field_of_study(?x7338, ?x6859), major_field_of_study(?x2327, ?x6859), major_field_of_study(?x1011, ?x6859), major_field_of_study(?x741, ?x6859), ?x741 = 01w3v, ?x7660 = 01qd_r, student(?x2327, ?x1422), organization(?x346, ?x7618), category(?x8797, ?x134), school(?x1883, ?x7338), fraternities_and_sororities(?x1011, ?x3697), school_type(?x8797, ?x3205) *> conf = 0.80 ranks of expected_values: 2, 21, 139, 158, 227, 368 EVAL 01tbp major_field_of_study! 011kn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 52.000 23.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01tbp major_field_of_study! 02txdf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 23.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01tbp major_field_of_study! 0j_sncb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 52.000 23.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01tbp major_field_of_study! 02301 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 52.000 23.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01tbp major_field_of_study! 02bjhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 23.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01tbp major_field_of_study! 06pwq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 23.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17666-033f8n PRED entity: 033f8n PRED relation: nominated_for! PRED expected values: 03n08b 046b0s => 85 concepts (23 used for prediction) PRED predicted values (max 10 best out of 458): 0187y5 (0.60 #2338, 0.60 #125, 0.52 #2337), 03n08b (0.52 #2337, 0.29 #18702, 0.29 #53764), 0137n0 (0.52 #2337, 0.29 #18702, 0.29 #53764), 06b3g4 (0.52 #2337, 0.29 #18702, 0.29 #53764), 059j1m (0.52 #2337, 0.29 #18702, 0.29 #53764), 017s11 (0.20 #99, 0.09 #14126, 0.04 #9449), 024rgt (0.20 #530, 0.07 #16364, 0.05 #14557), 02p65p (0.20 #24, 0.02 #16389, 0.02 #23401), 01kb2j (0.20 #1136, 0.02 #15163, 0.01 #26851), 02qgqt (0.20 #17, 0.02 #2355, 0.02 #35080) >> Best rule #2338 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01r97z; 02sfnv; 02b6n9; >> query: (?x4820, ?x703) <- film(?x703, ?x4820), film(?x382, ?x4820), film_release_distribution_medium(?x4820, ?x81), ?x703 = 0187y5 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2337 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 01r97z; 02sfnv; 02b6n9; *> query: (?x4820, ?x1270) <- film(?x1270, ?x4820), film(?x703, ?x4820), film(?x382, ?x4820), film_release_distribution_medium(?x4820, ?x81), ?x703 = 0187y5 *> conf = 0.52 ranks of expected_values: 2, 41 EVAL 033f8n nominated_for! 046b0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 85.000 23.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 033f8n nominated_for! 03n08b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 23.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17665-0bpk2 PRED entity: 0bpk2 PRED relation: group! PRED expected values: 0l14qv => 104 concepts (81 used for prediction) PRED predicted values (max 10 best out of 121): 0l14md (0.71 #1283, 0.64 #1624, 0.62 #773), 05r5c (0.68 #1710, 0.32 #1625, 0.25 #1199), 018vs (0.65 #3505, 0.64 #1631, 0.63 #3593), 03bx0bm (0.64 #3691, 0.63 #3605, 0.62 #3517), 03qjg (0.56 #1749, 0.27 #1153, 0.27 #3970), 0l14qv (0.50 #1281, 0.50 #771, 0.48 #1707), 028tv0 (0.42 #3504, 0.39 #3592, 0.39 #3678), 01vj9c (0.40 #1717, 0.32 #1632, 0.29 #3938), 07y_7 (0.38 #768, 0.36 #1278, 0.27 #1619), 07c6l (0.38 #776, 0.36 #1286, 0.27 #1627) >> Best rule #1283 for best value: >> intensional similarity = 8 >> extensional distance = 12 >> proper extension: 0dtd6; 01czx; >> query: (?x5751, 0l14md) <- group(?x2944, ?x5751), group(?x2309, ?x5751), group(?x227, ?x5751), award(?x5751, ?x3045), ?x227 = 0342h, ?x2944 = 0l14j_, artists(?x474, ?x5751), role(?x74, ?x2309) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1281 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 12 *> proper extension: 0dtd6; 01czx; *> query: (?x5751, 0l14qv) <- group(?x2944, ?x5751), group(?x2309, ?x5751), group(?x227, ?x5751), award(?x5751, ?x3045), ?x227 = 0342h, ?x2944 = 0l14j_, artists(?x474, ?x5751), role(?x74, ?x2309) *> conf = 0.50 ranks of expected_values: 6 EVAL 0bpk2 group! 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 104.000 81.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17664-0168dy PRED entity: 0168dy PRED relation: actor! PRED expected values: 099pks => 107 concepts (60 used for prediction) PRED predicted values (max 10 best out of 78): 080dwhx (0.25 #6, 0.01 #11677, 0.01 #13803), 0524b41 (0.17 #396, 0.14 #661), 01j8wk (0.13 #11937, 0.12 #14063, 0.07 #15659), 026bfsh (0.04 #892, 0.03 #2217, 0.02 #1952), 02gjrc (0.04 #1022, 0.02 #3938, 0.01 #7119), 03ln8b (0.03 #1621, 0.02 #1091, 0.01 #11702), 05lfwd (0.03 #1694, 0.01 #5671, 0.01 #4345), 0vjr (0.02 #1155, 0.02 #2215, 0.02 #2745), 0d66j2 (0.02 #1114, 0.01 #4825), 02rcwq0 (0.02 #1148) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0cjsxp; 0c35b1; >> query: (?x10770, 080dwhx) <- film(?x10770, ?x6298), film(?x10770, ?x2081), crewmember(?x2081, ?x1585), ?x6298 = 017d93 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2484 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 191 *> proper extension: 07h1q; 01cqz5; 047g6; 015n8; *> query: (?x10770, 099pks) <- place_of_birth(?x10770, ?x2277), religion(?x10770, ?x2672), month(?x2277, ?x1459) *> conf = 0.01 ranks of expected_values: 59 EVAL 0168dy actor! 099pks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 107.000 60.000 0.250 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #17663-07gp9 PRED entity: 07gp9 PRED relation: award PRED expected values: 02r22gf => 150 concepts (141 used for prediction) PRED predicted values (max 10 best out of 193): 0k611 (0.28 #15992, 0.27 #25693, 0.27 #26594), 02r22gf (0.28 #15992, 0.27 #25693, 0.27 #26594), 0gr0m (0.28 #15992, 0.27 #25693, 0.27 #26594), 057xs89 (0.28 #15992, 0.27 #25693, 0.27 #26594), 0gq9h (0.22 #60, 0.22 #27271, 0.15 #13122), 0gs9p (0.22 #62, 0.22 #27271, 0.15 #4792), 054krc (0.22 #66, 0.14 #1642, 0.10 #2318), 0f4x7 (0.22 #23, 0.12 #13085, 0.10 #7906), 02qvyrt (0.22 #92, 0.09 #7975, 0.09 #317), 0gs96 (0.22 #1661, 0.19 #2337, 0.12 #7968) >> Best rule #15992 for best value: >> intensional similarity = 3 >> extensional distance = 510 >> proper extension: 02rq7nd; >> query: (?x324, ?x637) <- award(?x324, ?x298), nominated_for(?x637, ?x324), honored_for(?x3254, ?x324) >> conf = 0.28 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07gp9 award 02r22gf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 150.000 141.000 0.282 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #17662-0hb37 PRED entity: 0hb37 PRED relation: contains! PRED expected values: 03rjj => 121 concepts (42 used for prediction) PRED predicted values (max 10 best out of 309): 03rjj (0.97 #10776, 0.94 #3595, 0.87 #8074), 0f8l9c (0.72 #2732, 0.23 #35028, 0.19 #35926), 09c7w0 (0.67 #27843, 0.67 #24246, 0.65 #29642), 0d060g (0.56 #22458, 0.24 #26054, 0.22 #26954), 01n7q (0.40 #24321, 0.36 #25220, 0.23 #27918), 0345h (0.35 #7260, 0.33 #23426, 0.26 #11746), 02j9z (0.25 #17084, 0.25 #16187, 0.19 #18881), 02qkt (0.24 #16506, 0.22 #17403, 0.21 #19200), 059j2 (0.23 #35028, 0.19 #35926, 0.18 #37722), 0d0vqn (0.23 #35028, 0.19 #35926, 0.18 #37722) >> Best rule #10776 for best value: >> intensional similarity = 9 >> extensional distance = 64 >> proper extension: 0dlw0; 0jfvs; 07kg3; 0c7hq; 04_xrs; 0nr2v; 05p7tx; 05314s; 068cn; 015m08; ... >> query: (?x14115, 03rjj) <- contains(?x10706, ?x14115), contains(?x205, ?x10706), contains(?x10706, ?x14229), contains(?x10706, ?x14190), contains(?x10706, ?x13840), ?x14229 = 01jp4s, ?x13840 = 0c7f7, ?x14190 = 0d8zt, administrative_parent(?x9792, ?x10706) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hb37 contains! 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 42.000 0.970 http://example.org/location/location/contains #17661-03lfd_ PRED entity: 03lfd_ PRED relation: film_release_distribution_medium PRED expected values: 07c52 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 3): 02nxhr (0.13 #4, 0.08 #13, 0.08 #40), 07c52 (0.11 #14, 0.08 #26, 0.07 #66), 0735l (0.02 #18) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 07s3m4g; >> query: (?x8867, 02nxhr) <- film_release_region(?x8867, ?x1023), ?x1023 = 0ctw_b, film(?x902, ?x8867), ?x902 = 05qd_ >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 0bmc4cm; *> query: (?x8867, 07c52) <- film_release_region(?x8867, ?x1892), film_release_region(?x8867, ?x1023), ?x1023 = 0ctw_b, film_festivals(?x8867, ?x6557), ?x1892 = 02vzc *> conf = 0.11 ranks of expected_values: 2 EVAL 03lfd_ film_release_distribution_medium 07c52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.133 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17660-02hwhyv PRED entity: 02hwhyv PRED relation: language! PRED expected values: 05pbl56 02825nf => 59 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1863): 043sct5 (0.76 #20741, 0.58 #5183, 0.50 #17285), 03bx2lk (0.68 #6912, 0.58 #5183, 0.54 #5184), 05c26ss (0.68 #6912, 0.58 #5183, 0.54 #5184), 04cppj (0.68 #6912, 0.58 #5183, 0.54 #5184), 02bg55 (0.68 #6912, 0.58 #5183, 0.54 #5184), 0ddfwj1 (0.68 #6912, 0.58 #5183, 0.54 #5184), 034qrh (0.68 #6912, 0.54 #5184, 0.51 #15555), 02nt3d (0.68 #6912, 0.54 #5184, 0.51 #15555), 02ryz24 (0.68 #6912, 0.54 #5184, 0.50 #3903), 025rvx0 (0.68 #6912, 0.54 #5184, 0.50 #4411) >> Best rule #20741 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 03_9r; >> query: (?x7926, ?x4430) <- service_language(?x555, ?x7926), titles(?x7926, ?x4430), official_language(?x1453, ?x7926), language(?x5602, ?x7926), currency(?x5602, ?x170), ?x170 = 09nqf, nominated_for(?x1691, ?x5602), category(?x5602, ?x134), produced_by(?x5602, ?x6369), film_release_distribution_medium(?x5602, ?x81), film_crew_role(?x5602, ?x137) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #5183 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 0653m; *> query: (?x7926, ?x186) <- countries_spoken_in(?x7926, ?x2346), language(?x4888, ?x7926), genre(?x4888, ?x8467), film(?x11624, ?x4888), film(?x436, ?x4888), film_crew_role(?x4888, ?x137), ?x8467 = 0gf28, ?x436 = 032xhg, ?x137 = 09zzb8, profession(?x11624, ?x1032), nationality(?x754, ?x2346), film_release_region(?x186, ?x2346) *> conf = 0.58 ranks of expected_values: 402, 519 EVAL 02hwhyv language! 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 21.000 0.757 http://example.org/film/film/language EVAL 02hwhyv language! 05pbl56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 21.000 0.757 http://example.org/film/film/language #17659-028qdb PRED entity: 028qdb PRED relation: profession PRED expected values: 09jwl 0nbcg => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 60): 09jwl (0.79 #3327, 0.79 #3479, 0.74 #923), 02hrh1q (0.67 #7097, 0.66 #12951, 0.66 #13251), 0dz3r (0.63 #302, 0.58 #2, 0.46 #603), 0nbcg (0.57 #3340, 0.54 #333, 0.54 #3492), 016z4k (0.42 #5577, 0.42 #2559, 0.42 #154), 01c72t (0.42 #1229, 0.36 #325, 0.34 #4241), 039v1 (0.37 #3345, 0.35 #3497, 0.32 #4254), 0fnpj (0.36 #362, 0.22 #62, 0.21 #1266), 01d_h8 (0.31 #7388, 0.30 #7538, 0.28 #11585), 03gjzk (0.28 #11585, 0.27 #4819, 0.26 #14137) >> Best rule #3327 for best value: >> intensional similarity = 2 >> extensional distance = 268 >> proper extension: 06y9c2; 01cv3n; 01p45_v; 019g40; 03rl84; 01vv126; 02lvtb; 04cr6qv; 044mfr; 04f7c55; ... >> query: (?x4206, 09jwl) <- artists(?x378, ?x4206), role(?x4206, ?x316) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 028qdb profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 105.000 0.793 http://example.org/people/person/profession EVAL 028qdb profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.793 http://example.org/people/person/profession #17658-0173s9 PRED entity: 0173s9 PRED relation: category PRED expected values: 08mbj5d => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.89 #23, 0.89 #12, 0.89 #32) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 288 >> proper extension: 0q19t; 03zw80; 01rgdw; 0k9wp; 039d4; 02bvc5; 0325dj; 0yl_w; 01gwck; 0p7tb; ... >> query: (?x8833, 08mbj5d) <- state_province_region(?x8833, ?x14064), country(?x14064, ?x512), contains(?x14064, ?x14110), colors(?x8833, ?x663) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0173s9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.890 http://example.org/common/topic/webpage./common/webpage/category #17657-0443xn PRED entity: 0443xn PRED relation: award_nominee PRED expected values: 031v3p => 94 concepts (50 used for prediction) PRED predicted values (max 10 best out of 921): 03d_w3h (0.81 #30453, 0.81 #25767, 0.81 #98395), 031v3p (0.81 #30453, 0.81 #25767, 0.81 #98395), 066m4g (0.69 #173, 0.21 #112454, 0.18 #70282), 01_njt (0.62 #1824, 0.21 #112454, 0.18 #70282), 08yx9q (0.62 #1030, 0.21 #112454, 0.18 #70282), 06s6hs (0.62 #1370, 0.18 #70282, 0.02 #22449), 08m4c8 (0.54 #412, 0.21 #112454, 0.18 #70282), 095b70 (0.54 #1405, 0.21 #112454, 0.18 #70282), 09f0bj (0.54 #440, 0.21 #112454, 0.18 #70282), 034x61 (0.54 #172, 0.21 #112454, 0.18 #70282) >> Best rule #30453 for best value: >> intensional similarity = 3 >> extensional distance = 590 >> proper extension: 01mvth; 03qd_; 0gcdzz; 05tk7y; 05fnl9; 080knyg; 0m31m; 0gd_b_; 07z1_q; 03f4xvm; ... >> query: (?x13236, ?x940) <- award_nominee(?x13236, ?x3583), award_nominee(?x940, ?x13236), actor(?x4108, ?x13236) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0443xn award_nominee 031v3p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 50.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17656-01jpmpv PRED entity: 01jpmpv PRED relation: award_winner! PRED expected values: 0fz2y7 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 134): 0c53zb (0.31 #617, 0.27 #756, 0.10 #12791), 0fy6bh (0.31 #603, 0.27 #742, 0.07 #1993), 0ftlxj (0.28 #8759, 0.10 #12791, 0.03 #1182), 0ftlkg (0.28 #8759, 0.04 #860, 0.03 #1138), 0fz0c2 (0.27 #800, 0.23 #661, 0.05 #2051), 0fv89q (0.23 #677, 0.20 #816, 0.10 #12791), 0fy59t (0.23 #670, 0.20 #809, 0.05 #2060), 0d__c3 (0.23 #679, 0.13 #818, 0.10 #2069), 0fzrhn (0.15 #692, 0.11 #414, 0.11 #275), 0fz20l (0.15 #609, 0.07 #748, 0.05 #1999) >> Best rule #617 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0h005; >> query: (?x3483, 0c53zb) <- award_winner(?x6323, ?x3483), nationality(?x3483, ?x94), ?x6323 = 05hmp6 >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #2225 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 132 *> proper extension: 01ky2h; *> query: (?x3483, ?x78) <- music(?x327, ?x3483), award_winner(?x1323, ?x3483), ceremony(?x1323, ?x78) *> conf = 0.04 ranks of expected_values: 49 EVAL 01jpmpv award_winner! 0fz2y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 122.000 122.000 0.308 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17655-05zvzf3 PRED entity: 05zvzf3 PRED relation: film_format PRED expected values: 0cj16 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 3): 0cj16 (0.29 #28, 0.26 #33, 0.24 #118), 07fb8_ (0.17 #138, 0.16 #148, 0.16 #101), 017fx5 (0.11 #39, 0.11 #44, 0.10 #24) >> Best rule #28 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 0sxfd; >> query: (?x8646, 0cj16) <- films(?x4450, ?x8646), film_festivals(?x8646, ?x11147), language(?x8646, ?x5359) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05zvzf3 film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.293 http://example.org/film/film/film_format #17654-06c62 PRED entity: 06c62 PRED relation: place_of_death! PRED expected values: 033rq => 274 concepts (239 used for prediction) PRED predicted values (max 10 best out of 809): 016ghw (0.25 #2237, 0.20 #3739, 0.14 #8242), 011zwl (0.25 #2220, 0.20 #3722, 0.14 #8225), 01b0k1 (0.25 #2190, 0.20 #3692, 0.14 #8195), 02vkvcz (0.25 #2174, 0.20 #3676, 0.14 #8179), 047g6 (0.25 #2168, 0.20 #3670, 0.14 #8173), 01tw31 (0.25 #2076, 0.20 #3578, 0.14 #8081), 06myp (0.25 #2074, 0.20 #3576, 0.14 #8079), 02784z (0.25 #2047, 0.20 #3549, 0.14 #8052), 0239zv (0.25 #2023, 0.20 #3525, 0.14 #8028), 0kn3g (0.25 #2001, 0.20 #3503, 0.14 #8006) >> Best rule #2237 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04jpl; 03pbf; >> query: (?x6959, 016ghw) <- location(?x5600, ?x6959), place_of_death(?x4732, ?x6959), place_of_birth(?x9099, ?x6959), ?x5600 = 03bxh >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #10509 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0d6nx; *> query: (?x6959, ?x101) <- capital(?x205, ?x6959), capital(?x11886, ?x6959), nationality(?x101, ?x205) *> conf = 0.08 ranks of expected_values: 246 EVAL 06c62 place_of_death! 033rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 274.000 239.000 0.250 http://example.org/people/deceased_person/place_of_death #17653-06rrzn PRED entity: 06rrzn PRED relation: award PRED expected values: 0c4z8 04njml => 91 concepts (48 used for prediction) PRED predicted values (max 10 best out of 248): 04njml (0.81 #3633, 0.78 #1613, 0.77 #3632), 0c4z8 (0.50 #878, 0.33 #72, 0.26 #1281), 01l29r (0.50 #972, 0.33 #166, 0.06 #1779), 01by1l (0.37 #3340, 0.34 #1321, 0.27 #2129), 01bgqh (0.37 #1252, 0.30 #3271, 0.21 #1656), 02f5qb (0.37 #1363, 0.13 #3382, 0.08 #2171), 02x201b (0.33 #274, 0.25 #1080, 0.21 #2017), 054krc (0.33 #87, 0.25 #893, 0.21 #2017), 05q8pss (0.33 #212, 0.25 #1018, 0.21 #2017), 04dn09n (0.33 #44, 0.25 #850, 0.18 #3228) >> Best rule #3633 for best value: >> intensional similarity = 3 >> extensional distance = 333 >> proper extension: 089tm; 01vrwfv; 050z2; 02jqjm; 0kxbc; 0178_w; 01lf293; 0mjn2; >> query: (?x6518, ?x2585) <- award_winner(?x2585, ?x6518), award(?x1231, ?x2585), ?x1231 = 01vrz41 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 06rrzn award 04njml CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 48.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 06rrzn award 0c4z8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 48.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17652-034hck PRED entity: 034hck PRED relation: profession PRED expected values: 01d_h8 => 87 concepts (62 used for prediction) PRED predicted values (max 10 best out of 68): 01d_h8 (0.82 #450, 0.79 #1338, 0.79 #1782), 0dxtg (0.71 #1345, 0.69 #2381, 0.69 #1789), 03gjzk (0.41 #1346, 0.40 #458, 0.36 #2678), 09jwl (0.39 #4014, 0.37 #3718, 0.37 #4754), 0nbcg (0.28 #3287, 0.28 #4027, 0.28 #623), 016z4k (0.27 #4000, 0.27 #596, 0.26 #3704), 02krf9 (0.26 #2690, 0.25 #1358, 0.24 #2394), 012t_z (0.26 #8141, 0.17 #308, 0.15 #12), 0cbd2 (0.26 #7111, 0.22 #451, 0.16 #2227), 0dz3r (0.25 #3258, 0.24 #3998, 0.24 #3554) >> Best rule #450 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 04dyqk; >> query: (?x9403, 01d_h8) <- profession(?x9403, ?x524), award(?x9403, ?x350), ?x350 = 05f4m9q >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034hck profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 62.000 0.820 http://example.org/people/person/profession #17651-0hjy PRED entity: 0hjy PRED relation: contains PRED expected values: 0l_v1 => 166 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2650): 0g_wn2 (0.85 #50000, 0.84 #197062, 0.83 #32353), 0hjy (0.59 #214713, 0.54 #141176, 0.51 #197061), 09c7w0 (0.59 #214713, 0.54 #141176, 0.51 #197061), 02lwv5 (0.33 #1744, 0.11 #19394, 0.10 #10567), 04ftdq (0.33 #1245, 0.11 #18895, 0.10 #10068), 021q2j (0.33 #1261, 0.11 #18911, 0.10 #10084), 03bmmc (0.33 #777, 0.11 #18427, 0.10 #9600), 09k9d0 (0.33 #1971, 0.11 #19621, 0.10 #10794), 01p7x7 (0.33 #1835, 0.11 #19485, 0.10 #10658), 026ssfj (0.33 #1203, 0.11 #18853, 0.10 #10026) >> Best rule #50000 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 04w58; >> query: (?x953, ?x5244) <- adjoins(?x7468, ?x953), administrative_parent(?x5244, ?x953), taxonomy(?x953, ?x939) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #2768 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 09c7w0; *> query: (?x953, 0l_v1) <- contains(?x953, ?x13425), ?x13425 = 0l_n1, taxonomy(?x953, ?x939) *> conf = 0.33 ranks of expected_values: 811 EVAL 0hjy contains 0l_v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 166.000 97.000 0.852 http://example.org/location/location/contains #17650-0d__c3 PRED entity: 0d__c3 PRED relation: award_winner PRED expected values: 072twv => 54 concepts (37 used for prediction) PRED predicted values (max 10 best out of 3790): 07zhd7 (0.50 #6087, 0.40 #7616, 0.33 #16794), 0bs8d (0.50 #1531, 0.25 #5420, 0.21 #21418), 086k8 (0.50 #1531, 0.21 #21457, 0.14 #7687), 0g10g (0.50 #1531, 0.20 #1530, 0.11 #4591), 0jvtp (0.50 #1531, 0.20 #1530, 0.11 #4591), 0m9c1 (0.50 #1531, 0.11 #4591, 0.07 #22871), 0c921 (0.50 #1531, 0.11 #4591, 0.05 #39785), 03thw4 (0.50 #1531, 0.02 #22949, 0.02 #27543), 0g5lhl7 (0.43 #8044, 0.38 #12634, 0.29 #21814), 02_fj (0.43 #9622, 0.30 #17270, 0.25 #20331) >> Best rule #6087 for best value: >> intensional similarity = 15 >> extensional distance = 2 >> proper extension: 0c53vt; >> query: (?x9400, 07zhd7) <- award_winner(?x9400, ?x7232), award_winner(?x9400, ?x6971), award_winner(?x9400, ?x4279), award_winner(?x9400, ?x2426), nominated_for(?x7232, ?x9100), award(?x7232, ?x198), ?x2426 = 081nh, profession(?x7232, ?x319), nominated_for(?x6971, ?x4841), film(?x4279, ?x3294), ?x9100 = 072192, ceremony(?x3066, ?x9400), award_winner(?x1443, ?x6971), people(?x1050, ?x6971), ?x3066 = 0gqy2 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #17169 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 8 *> proper extension: 0fz0c2; 0fy59t; *> query: (?x9400, 072twv) <- award_winner(?x9400, ?x7232), award_winner(?x9400, ?x4505), award_winner(?x9400, ?x2426), nominated_for(?x7232, ?x984), award(?x7232, ?x198), award_winner(?x720, ?x2426), place_of_death(?x2426, ?x11930), award(?x4505, ?x601), award_winner(?x4445, ?x2426), organizations_founded(?x2426, ?x99), ?x4445 = 0c53zb, origin(?x4505, ?x739), people(?x1423, ?x2426) *> conf = 0.40 ranks of expected_values: 14 EVAL 0d__c3 award_winner 072twv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 54.000 37.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17649-01fh36 PRED entity: 01fh36 PRED relation: artists PRED expected values: 05563d 06mj4 01fh0q 023slg => 57 concepts (29 used for prediction) PRED predicted values (max 10 best out of 988): 01vsy3q (0.62 #8553, 0.60 #4482, 0.57 #6518), 01tw31 (0.62 #9053, 0.60 #4982, 0.43 #7018), 07mvp (0.62 #8697, 0.60 #4626, 0.43 #6662), 044k8 (0.62 #8518, 0.60 #4447, 0.43 #6483), 01vw20_ (0.60 #4299, 0.57 #6335, 0.50 #8370), 033s6 (0.60 #4878, 0.57 #6914, 0.50 #8949), 0zjpz (0.60 #4199, 0.57 #6235, 0.50 #8270), 01kx_81 (0.60 #4149, 0.50 #8220, 0.44 #10256), 02qwg (0.60 #4345, 0.50 #8416, 0.43 #6381), 04r1t (0.60 #4193, 0.50 #8264, 0.43 #6229) >> Best rule #8553 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0xhtw; >> query: (?x6210, 01vsy3q) <- artists(?x6210, ?x8978), artists(?x6210, ?x7437), artists(?x6210, ?x7221), award_winner(?x9034, ?x8978), ?x7437 = 021r7r, role(?x8978, ?x228), people(?x3591, ?x7221), profession(?x8978, ?x131) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #8428 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0xhtw; *> query: (?x6210, 05563d) <- artists(?x6210, ?x8978), artists(?x6210, ?x7437), artists(?x6210, ?x7221), award_winner(?x9034, ?x8978), ?x7437 = 021r7r, role(?x8978, ?x228), people(?x3591, ?x7221), profession(?x8978, ?x131) *> conf = 0.50 ranks of expected_values: 39, 51, 77, 221 EVAL 01fh36 artists 023slg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 57.000 29.000 0.625 http://example.org/music/genre/artists EVAL 01fh36 artists 01fh0q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 57.000 29.000 0.625 http://example.org/music/genre/artists EVAL 01fh36 artists 06mj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 57.000 29.000 0.625 http://example.org/music/genre/artists EVAL 01fh36 artists 05563d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 57.000 29.000 0.625 http://example.org/music/genre/artists #17648-01cz7r PRED entity: 01cz7r PRED relation: production_companies PRED expected values: 02j_j0 => 94 concepts (66 used for prediction) PRED predicted values (max 10 best out of 69): 016tw3 (0.43 #920, 0.31 #584, 0.31 #1585), 0g1rw (0.18 #90, 0.17 #7, 0.06 #173), 017s11 (0.17 #169, 0.09 #86, 0.09 #336), 02j_j0 (0.17 #47, 0.15 #213, 0.09 #130), 054lpb6 (0.17 #14, 0.11 #1017, 0.08 #2840), 032j_n (0.17 #73, 0.09 #156, 0.03 #1574), 08wjc1 (0.17 #27, 0.03 #528, 0.02 #2853), 017jv5 (0.17 #18, 0.02 #184, 0.02 #2179), 03sb38 (0.15 #220, 0.10 #303, 0.09 #137), 086k8 (0.13 #3159, 0.12 #3656, 0.12 #1005) >> Best rule #920 for best value: >> intensional similarity = 4 >> extensional distance = 225 >> proper extension: 0cq8nx; >> query: (?x7645, ?x1104) <- nominated_for(?x1104, ?x7645), film_release_distribution_medium(?x7645, ?x81), country(?x7645, ?x94), production_companies(?x144, ?x1104) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 03mh_tp; *> query: (?x7645, 02j_j0) <- executive_produced_by(?x7645, ?x5973), film(?x11470, ?x7645), ?x11470 = 03k545, award_nominee(?x1039, ?x5973) *> conf = 0.17 ranks of expected_values: 4 EVAL 01cz7r production_companies 02j_j0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 66.000 0.434 http://example.org/film/film/production_companies #17647-06ybb1 PRED entity: 06ybb1 PRED relation: written_by PRED expected values: 04wp2p => 95 concepts (58 used for prediction) PRED predicted values (max 10 best out of 72): 04dyqk (0.33 #14429, 0.27 #672, 0.10 #336), 02f1c (0.17 #12749, 0.11 #5031, 0.09 #15105), 016tt2 (0.17 #12749, 0.07 #19480, 0.07 #7714), 0237jb (0.10 #233, 0.09 #569, 0.07 #1240), 0kb3n (0.08 #2603, 0.06 #3610, 0.06 #3945), 06cv1 (0.07 #1019, 0.06 #1354, 0.02 #1689), 02vyw (0.06 #1781, 0.04 #4128, 0.04 #2787), 0343h (0.06 #1721, 0.04 #2391, 0.03 #2727), 02mt4k (0.04 #1833, 0.03 #3174, 0.03 #3510), 02fcs2 (0.04 #1745, 0.03 #2415, 0.02 #2751) >> Best rule #14429 for best value: >> intensional similarity = 3 >> extensional distance = 699 >> proper extension: 0dckvs; 0djb3vw; 04969y; 05dy7p; 02n9bh; 0gcrg; 027ct7c; 064lsn; 08j7lh; 0cq8nx; ... >> query: (?x2165, ?x11573) <- nominated_for(?x350, ?x2165), award(?x770, ?x350), film(?x11573, ?x2165) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06ybb1 written_by 04wp2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 58.000 0.333 http://example.org/film/film/written_by #17646-01srq2 PRED entity: 01srq2 PRED relation: titles! PRED expected values: 09blyk => 104 concepts (51 used for prediction) PRED predicted values (max 10 best out of 66): 04xvlr (0.44 #207, 0.44 #3, 0.25 #2468), 01hmnh (0.43 #1947, 0.43 #1870, 0.22 #3288), 017fp (0.22 #227, 0.11 #125, 0.11 #23), 01z4y (0.16 #3841, 0.16 #4048, 0.15 #4358), 024qqx (0.15 #795, 0.14 #692, 0.12 #1103), 01jfsb (0.14 #428, 0.12 #2175, 0.12 #938), 04t36 (0.11 #824, 0.08 #2472, 0.06 #3813), 07ssc (0.11 #3297, 0.11 #213, 0.11 #111), 03mqtr (0.11 #249, 0.11 #45, 0.07 #2510), 06l3bl (0.11 #156, 0.08 #871, 0.05 #666) >> Best rule #207 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 03m8y5; >> query: (?x7246, 04xvlr) <- film(?x8674, ?x7246), ?x8674 = 01jmv8, film(?x574, ?x7246) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #556 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 053tj7; 0g5q34q; *> query: (?x7246, 09blyk) <- film_release_region(?x7246, ?x1003), film_format(?x7246, ?x6392), ?x1003 = 03gj2 *> conf = 0.08 ranks of expected_values: 17 EVAL 01srq2 titles! 09blyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 104.000 51.000 0.444 http://example.org/media_common/netflix_genre/titles #17645-05jf85 PRED entity: 05jf85 PRED relation: genre PRED expected values: 06cvj 05p553 06nbt 0219x_ => 79 concepts (78 used for prediction) PRED predicted values (max 10 best out of 100): 01hmnh (0.61 #5416, 0.54 #5415, 0.52 #4449), 01z4y (0.61 #5416, 0.54 #5415, 0.52 #4449), 05p553 (0.60 #4, 0.44 #125, 0.42 #606), 0219x_ (0.40 #27, 0.11 #268, 0.10 #3272), 0lsxr (0.33 #130, 0.18 #1813, 0.18 #1332), 01jfsb (0.32 #2658, 0.32 #2177, 0.31 #2417), 04xvlr (0.31 #242, 0.19 #482, 0.17 #4329), 06cvj (0.30 #3, 0.24 #605, 0.24 #484), 02kdv5l (0.30 #123, 0.27 #2647, 0.26 #2166), 060__y (0.25 #258, 0.18 #498, 0.17 #619) >> Best rule #5416 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 024rwx; 0ctzf1; 09g_31; >> query: (?x306, ?x1510) <- titles(?x1510, ?x306), genre(?x419, ?x1510) >> conf = 0.61 => this is the best rule for 2 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0blpg; *> query: (?x306, 05p553) <- film(?x305, ?x306), nominated_for(?x986, ?x306), titles(?x1510, ?x306), ?x986 = 081lh *> conf = 0.60 ranks of expected_values: 3, 4, 8, 21 EVAL 05jf85 genre 0219x_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 78.000 0.612 http://example.org/film/film/genre EVAL 05jf85 genre 06nbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 79.000 78.000 0.612 http://example.org/film/film/genre EVAL 05jf85 genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 78.000 0.612 http://example.org/film/film/genre EVAL 05jf85 genre 06cvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 78.000 0.612 http://example.org/film/film/genre #17644-09d11 PRED entity: 09d11 PRED relation: symptom_of! PRED expected values: 0j5fv => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 72): 0j5fv (0.62 #589, 0.50 #1210, 0.45 #869), 0gxb2 (0.62 #518, 0.43 #407, 0.39 #1213), 01cdt5 (0.60 #266, 0.50 #505, 0.50 #354), 0brgy (0.50 #120, 0.45 #869, 0.44 #1212), 02tfl8 (0.45 #869, 0.38 #534, 0.30 #1227), 0f3kl (0.45 #869, 0.38 #534, 0.30 #1227), 0hgxh (0.30 #1227, 0.30 #662, 0.29 #2091), 01pf6 (0.30 #1227, 0.29 #2091, 0.25 #918), 0hg45 (0.30 #1227, 0.29 #2091, 0.25 #918), 04kllm9 (0.30 #1227, 0.25 #918, 0.22 #1954) >> Best rule #589 for best value: >> intensional similarity = 12 >> extensional distance = 6 >> proper extension: 07s4l; >> query: (?x6655, 0j5fv) <- symptom_of(?x10717, ?x6655), symptom_of(?x9438, ?x6655), symptom_of(?x4905, ?x6655), people(?x6655, ?x6975), ?x4905 = 01j6t0, ?x10717 = 0cjf0, symptom_of(?x9438, ?x10480), symptom_of(?x9438, ?x8675), symptom_of(?x9438, ?x6781), ?x6781 = 035482, risk_factors(?x8675, ?x8523), ?x10480 = 0h1n9 >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09d11 symptom_of! 0j5fv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.625 http://example.org/medicine/symptom/symptom_of #17643-01jpqb PRED entity: 01jpqb PRED relation: major_field_of_study PRED expected values: 05qfh => 218 concepts (218 used for prediction) PRED predicted values (max 10 best out of 114): 02lp1 (0.65 #632, 0.54 #880, 0.40 #2246), 01mkq (0.59 #1505, 0.59 #636, 0.57 #1629), 02j62 (0.59 #1521, 0.57 #13694, 0.57 #2017), 04rjg (0.59 #641, 0.54 #889, 0.53 #393), 03g3w (0.57 #2013, 0.53 #400, 0.53 #1517), 062z7 (0.54 #897, 0.54 #277, 0.50 #2014), 04x_3 (0.47 #771, 0.47 #399, 0.38 #275), 02h40lc (0.47 #624, 0.42 #872, 0.33 #376), 05qfh (0.47 #410, 0.42 #906, 0.41 #1527), 0fdys (0.47 #413, 0.42 #909, 0.39 #1530) >> Best rule #632 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 07w3r; 05zl0; 01q8hj; >> query: (?x9745, 02lp1) <- major_field_of_study(?x9745, ?x8221), ?x8221 = 037mh8, school(?x700, ?x9745), state_province_region(?x9745, ?x1138) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #410 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 027xx3; 02fy0z; *> query: (?x9745, 05qfh) <- major_field_of_study(?x9745, ?x8221), ?x8221 = 037mh8, school(?x700, ?x9745), currency(?x9745, ?x170) *> conf = 0.47 ranks of expected_values: 9 EVAL 01jpqb major_field_of_study 05qfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 218.000 218.000 0.647 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17642-0f6_dy PRED entity: 0f6_dy PRED relation: profession PRED expected values: 02hrh1q => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 45): 02hrh1q (0.89 #1215, 0.88 #7366, 0.88 #765), 01d_h8 (0.34 #4206, 0.34 #3906, 0.31 #5257), 0dxtg (0.28 #6915, 0.28 #5265, 0.27 #4801), 03gjzk (0.27 #4801, 0.24 #3166, 0.24 #3316), 02jknp (0.27 #4801, 0.24 #4208, 0.24 #3908), 09jwl (0.27 #4801, 0.17 #2570, 0.16 #4370), 0d1pc (0.27 #4801, 0.10 #202, 0.07 #2002), 021wpb (0.27 #4801, 0.10 #204), 016z4k (0.27 #4801, 0.09 #8855, 0.08 #10805), 0np9r (0.20 #2272, 0.20 #2872, 0.18 #1372) >> Best rule #1215 for best value: >> intensional similarity = 3 >> extensional distance = 499 >> proper extension: 01zmpg; 02t_99; 01tnbn; 036dyy; 03ywyk; 01vh18t; >> query: (?x2123, 02hrh1q) <- actor(?x3787, ?x2123), award_nominee(?x2123, ?x230), program(?x2062, ?x3787) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f6_dy profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.890 http://example.org/people/person/profession #17641-012c6x PRED entity: 012c6x PRED relation: people! PRED expected values: 02knxx => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 35): 0gk4g (0.21 #274, 0.18 #604, 0.18 #538), 0qcr0 (0.21 #199, 0.16 #265, 0.07 #727), 0gg4h (0.17 #36, 0.14 #102, 0.11 #168), 02k6hp (0.17 #37, 0.14 #103, 0.11 #169), 01qqwn (0.17 #61, 0.14 #127, 0.11 #193), 014w_8 (0.17 #39, 0.14 #105, 0.11 #171), 0cycc (0.11 #188), 0dq9p (0.11 #215, 0.10 #677, 0.09 #545), 04p3w (0.10 #407, 0.08 #539, 0.08 #1001), 0dcsx (0.07 #411, 0.05 #543, 0.05 #675) >> Best rule #274 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 0177s6; 03mv0b; >> query: (?x773, 0gk4g) <- profession(?x773, ?x1032), profession(?x773, ?x524), place_of_burial(?x773, ?x7496), ?x1032 = 02hrh1q, ?x524 = 02jknp >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #428 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 0py5b; 070px; *> query: (?x773, 02knxx) <- film(?x773, ?x1072), profession(?x773, ?x524), place_of_burial(?x773, ?x7496) *> conf = 0.05 ranks of expected_values: 16 EVAL 012c6x people! 02knxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 116.000 116.000 0.211 http://example.org/people/cause_of_death/people #17640-01vwllw PRED entity: 01vwllw PRED relation: film PRED expected values: 09hy79 => 127 concepts (99 used for prediction) PRED predicted values (max 10 best out of 878): 01rp13 (0.68 #19575, 0.65 #35590, 0.63 #33810), 030cx (0.68 #19575, 0.65 #35590, 0.63 #33810), 0gjk1d (0.60 #90756, 0.59 #96095, 0.58 #88976), 0n0bp (0.25 #1860, 0.02 #14317), 03mh94 (0.25 #5404, 0.02 #19639, 0.02 #46328), 05vxdh (0.25 #6113), 078mm1 (0.25 #3227), 0pd57 (0.25 #2477), 0bm2g (0.25 #2115), 03b1sb (0.17 #5057, 0.15 #8616, 0.14 #10395) >> Best rule #19575 for best value: >> intensional similarity = 3 >> extensional distance = 152 >> proper extension: 06hgym; >> query: (?x3210, ?x1230) <- nominated_for(?x3210, ?x1230), award_nominee(?x3210, ?x1335), participant(?x1301, ?x3210) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #22582 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 197 *> proper extension: 035gjq; 04bpm6; 01271h; 02wb6yq; 01wv9p; 033jkj; 018z_c; 01817f; 0gv40; 01j2xj; ... *> query: (?x3210, 09hy79) <- nominated_for(?x3210, ?x1230), award_winner(?x6861, ?x3210), participant(?x1208, ?x3210) *> conf = 0.01 ranks of expected_values: 856 EVAL 01vwllw film 09hy79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 127.000 99.000 0.684 http://example.org/film/actor/film./film/performance/film #17639-03zrhb PRED entity: 03zrhb PRED relation: current_club PRED expected values: 0425hg 0425gc => 85 concepts (63 used for prediction) PRED predicted values (max 10 best out of 722): 023fb (0.50 #194, 0.12 #1781, 0.10 #2646), 01rly6 (0.43 #680, 0.20 #1113, 0.19 #1834), 04ltf (0.40 #936, 0.38 #1801, 0.30 #1080), 0xbm (0.40 #886, 0.31 #1751, 0.27 #1319), 06l22 (0.40 #922, 0.25 #1787, 0.20 #1355), 03j6_5 (0.33 #95, 0.25 #239, 0.14 #672), 03m10r (0.33 #22, 0.25 #166, 0.07 #2330), 02gys2 (0.33 #6, 0.20 #872, 0.13 #1449), 049f05 (0.33 #106, 0.19 #2847, 0.18 #2414), 03yfh3 (0.33 #142, 0.14 #719, 0.10 #1152) >> Best rule #194 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 03y_f8; >> query: (?x8511, 023fb) <- current_club(?x8511, ?x11337), current_club(?x8511, ?x5710), current_club(?x8511, ?x4006), position(?x4006, ?x203), team(?x5685, ?x11337), position(?x4006, ?x60), teams(?x9310, ?x4006), colors(?x11337, ?x663), ?x5710 = 050fh >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2886 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 29 *> proper extension: 03xh50; *> query: (?x8511, ?x6153) <- current_club(?x8511, ?x12496), current_club(?x8511, ?x4006), position(?x4006, ?x203), position(?x4006, ?x60), team(?x6152, ?x12496), team(?x6152, ?x6153), nationality(?x6152, ?x1453) *> conf = 0.08 ranks of expected_values: 102, 424 EVAL 03zrhb current_club 0425gc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 85.000 63.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club EVAL 03zrhb current_club 0425hg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 85.000 63.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #17638-0d2b38 PRED entity: 0d2b38 PRED relation: film_crew_role! PRED expected values: 050r1z 05sxzwc 02vqhv0 02725hs 035bcl 027j9wd 05pxnmb 06znpjr 0bs5f0b 0640m69 => 72 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1178): 0g0x9c (0.71 #14919, 0.71 #13752, 0.67 #11418), 04jplwp (0.71 #14926, 0.71 #13759, 0.60 #7921), 047csmy (0.71 #14633, 0.67 #12299, 0.60 #7628), 076zy_g (0.71 #14623, 0.67 #12289, 0.60 #7618), 04ydr95 (0.71 #14411, 0.67 #10910, 0.60 #7406), 0gy30w (0.71 #14977, 0.67 #11476, 0.60 #7972), 07p12s (0.71 #15118, 0.67 #12784, 0.60 #8113), 09lcsj (0.71 #14412, 0.67 #10911, 0.60 #7407), 05q7874 (0.71 #14727, 0.60 #7722, 0.57 #13560), 051ys82 (0.71 #13546, 0.60 #7708, 0.57 #14713) >> Best rule #14919 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 0215hd; >> query: (?x7591, 0g0x9c) <- film_crew_role(?x6798, ?x7591), film_crew_role(?x6081, ?x7591), film_crew_role(?x5135, ?x7591), film_crew_role(?x4176, ?x7591), film_crew_role(?x1956, ?x7591), ?x6798 = 0g7pm1, ?x1956 = 05qbckf, ?x4176 = 03nx8mj, currency(?x5135, ?x170), ?x6081 = 027m5wv, nominated_for(?x10455, ?x5135) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #11901 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 0dxtw; *> query: (?x7591, 02vqhv0) <- film_crew_role(?x6798, ?x7591), film_crew_role(?x6429, ?x7591), film_crew_role(?x6306, ?x7591), film_crew_role(?x6053, ?x7591), film_crew_role(?x5746, ?x7591), film_crew_role(?x1956, ?x7591), ?x6798 = 0g7pm1, ?x1956 = 05qbckf, ?x6429 = 01gwk3, ?x6053 = 05qbbfb, film_release_distribution_medium(?x5746, ?x81), ?x6306 = 016dj8 *> conf = 0.67 ranks of expected_values: 14, 16, 118, 120, 150, 439, 618, 712, 973 EVAL 0d2b38 film_crew_role! 0640m69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 30.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0d2b38 film_crew_role! 0bs5f0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 72.000 30.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0d2b38 film_crew_role! 06znpjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 72.000 30.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0d2b38 film_crew_role! 05pxnmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 30.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0d2b38 film_crew_role! 027j9wd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 30.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0d2b38 film_crew_role! 035bcl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 30.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0d2b38 film_crew_role! 02725hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 72.000 30.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0d2b38 film_crew_role! 02vqhv0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 72.000 30.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0d2b38 film_crew_role! 05sxzwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 72.000 30.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0d2b38 film_crew_role! 050r1z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 30.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17637-0lbj1 PRED entity: 0lbj1 PRED relation: film PRED expected values: 01dvbd => 126 concepts (109 used for prediction) PRED predicted values (max 10 best out of 735): 02cbhg (0.58 #168182, 0.55 #25047, 0.51 #55463), 01jnc_ (0.12 #15880, 0.11 #23036, 0.09 #21247), 017jd9 (0.10 #2568, 0.02 #4357, 0.02 #52663), 017gm7 (0.10 #2000, 0.02 #52095, 0.02 #53884), 07bzz7 (0.07 #11623, 0.06 #15201, 0.05 #22357), 04jpk2 (0.07 #584, 0.07 #20263, 0.06 #11318), 01shy7 (0.07 #422, 0.05 #20101, 0.05 #23679), 032016 (0.07 #502, 0.03 #2291, 0.02 #4080), 084302 (0.07 #519, 0.03 #2308, 0.02 #4097), 02pxst (0.07 #1251, 0.03 #15563, 0.02 #22719) >> Best rule #168182 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 049tjg; >> query: (?x248, ?x8084) <- nominated_for(?x248, ?x8084), film(?x248, ?x1842) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0lbj1 film 01dvbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 109.000 0.585 http://example.org/film/actor/film./film/performance/film #17636-02y49 PRED entity: 02y49 PRED relation: award_winner! PRED expected values: 047xyn => 125 concepts (103 used for prediction) PRED predicted values (max 10 best out of 293): 0265vt (0.50 #2867, 0.43 #4574, 0.41 #3294), 040vk98 (0.41 #3007, 0.33 #4287, 0.32 #3433), 040_9s0 (0.40 #735, 0.38 #3403, 0.38 #2860), 02664f (0.38 #3403, 0.36 #2976, 0.36 #4684), 0265wl (0.38 #3403, 0.36 #2976, 0.36 #4684), 039yzf (0.38 #3403, 0.36 #2976, 0.36 #4684), 01bb1c (0.29 #3386, 0.27 #4666, 0.26 #4239), 05qck (0.25 #190, 0.05 #7002, 0.04 #10402), 011w54 (0.25 #398, 0.02 #5082, 0.01 #7210), 03x3wf (0.21 #7302, 0.08 #10277, 0.07 #8152) >> Best rule #2867 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 0c3kw; 01dzz7; 048_p; >> query: (?x8908, 0265vt) <- award(?x8908, ?x4418), award(?x8908, ?x3337), ?x3337 = 01yz0x, award_winner(?x10222, ?x8908), ?x4418 = 02664f >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1924 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 014nvr; *> query: (?x8908, 047xyn) <- type_of_union(?x8908, ?x566), influenced_by(?x2343, ?x8908), ?x566 = 04ztj, ?x2343 = 0jt90f5 *> conf = 0.18 ranks of expected_values: 12 EVAL 02y49 award_winner! 047xyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 125.000 103.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #17635-034qzw PRED entity: 034qzw PRED relation: language PRED expected values: 02h40lc 06nm1 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 40): 02h40lc (0.94 #2801, 0.91 #1747, 0.91 #1339), 05f_3 (0.25 #26, 0.01 #548, 0.01 #490), 06nm1 (0.21 #185, 0.12 #1756, 0.11 #1057), 04306rv (0.10 #179, 0.10 #527, 0.10 #469), 03_9r (0.10 #126, 0.06 #1755, 0.05 #1637), 0880p (0.10 #161, 0.01 #1731), 06b_j (0.10 #370, 0.06 #1126, 0.06 #1533), 02bjrlw (0.08 #349, 0.08 #175, 0.06 #1105), 0653m (0.05 #186, 0.04 #1757, 0.04 #3105), 012w70 (0.05 #187, 0.03 #1758, 0.02 #245) >> Best rule #2801 for best value: >> intensional similarity = 3 >> extensional distance = 626 >> proper extension: 05f67hw; >> query: (?x2102, 02h40lc) <- produced_by(?x2102, ?x364), film_release_region(?x2102, ?x94), language(?x2102, ?x5607) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 034qzw language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.943 http://example.org/film/film/language EVAL 034qzw language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.943 http://example.org/film/film/language #17634-03m6_z PRED entity: 03m6_z PRED relation: profession PRED expected values: 09jwl => 123 concepts (122 used for prediction) PRED predicted values (max 10 best out of 88): 09jwl (0.81 #907, 0.79 #611, 0.79 #3278), 0nbcg (0.62 #624, 0.57 #2550, 0.57 #1069), 01d_h8 (0.52 #2673, 0.47 #450, 0.45 #3117), 0dz3r (0.48 #150, 0.46 #2520, 0.44 #3261), 016z4k (0.46 #596, 0.43 #152, 0.41 #1041), 039v1 (0.43 #185, 0.38 #2555, 0.35 #3296), 03gjzk (0.35 #2682, 0.31 #3571, 0.30 #3126), 0dxtg (0.30 #3125, 0.30 #2681, 0.29 #3570), 01c72t (0.29 #912, 0.24 #3283, 0.21 #2542), 02jknp (0.28 #452, 0.25 #2675, 0.22 #12297) >> Best rule #907 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 01mwsnc; 04f7c55; 04bgy; 01r4zfk; 01k_0fp; 09g0h; >> query: (?x7156, 09jwl) <- film(?x7156, ?x3081), gender(?x7156, ?x231), role(?x7156, ?x227) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03m6_z profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 122.000 0.810 http://example.org/people/person/profession #17633-09p35z PRED entity: 09p35z PRED relation: film_crew_role PRED expected values: 02r96rf => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 30): 02r96rf (0.77 #883, 0.73 #1465, 0.73 #660), 09vw2b7 (0.69 #1469, 0.69 #887, 0.68 #1799), 01vx2h (0.38 #891, 0.37 #1072, 0.36 #1036), 0d2b38 (0.33 #26, 0.25 #98, 0.25 #62), 0215hd (0.33 #19, 0.25 #55, 0.15 #1389), 089g0h (0.33 #20, 0.25 #56, 0.15 #1389), 02_n3z (0.33 #1, 0.25 #37, 0.15 #1389), 015h31 (0.33 #9, 0.25 #45, 0.15 #1389), 05smlt (0.33 #21, 0.25 #57, 0.13 #1134), 094hwz (0.33 #15, 0.25 #51, 0.13 #1134) >> Best rule #883 for best value: >> intensional similarity = 6 >> extensional distance = 185 >> proper extension: 0bq8tmw; >> query: (?x797, 02r96rf) <- film_crew_role(?x797, ?x1284), film_crew_role(?x797, ?x137), category(?x797, ?x134), ?x134 = 08mbj5d, ?x137 = 09zzb8, ?x1284 = 0ch6mp2 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09p35z film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.770 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17632-088tp3 PRED entity: 088tp3 PRED relation: genre PRED expected values: 03k9fj => 43 concepts (31 used for prediction) PRED predicted values (max 10 best out of 178): 07s9rl0 (0.71 #2154, 0.70 #1984, 0.64 #495), 03k9fj (0.69 #421, 0.62 #258, 0.57 #92), 05p553 (0.67 #1820, 0.65 #1734, 0.62 #743), 06n90 (0.64 #507, 0.58 #342, 0.57 #94), 01z4y (0.44 #1747, 0.43 #1833, 0.39 #1917), 01htzx (0.43 #1332, 0.42 #1581, 0.33 #1414), 0pr6f (0.35 #868, 0.34 #787, 0.34 #1031), 025s89p (0.31 #789, 0.28 #1033, 0.28 #870), 0c4xc (0.31 #1770, 0.29 #1856, 0.28 #1940), 02l7c8 (0.30 #1482, 0.24 #1729, 0.24 #1316) >> Best rule #2154 for best value: >> intensional similarity = 23 >> extensional distance = 182 >> proper extension: 01qn7n; 02py4c8; 02k_4g; 0ddd0gc; 03j63k; 08l0x2; 07wqr6; 045r_9; 09rfpk; 070ltt; ... >> query: (?x8869, 07s9rl0) <- genre(?x8869, ?x5937), genre(?x13050, ?x5937), genre(?x12093, ?x5937), genre(?x9523, ?x5937), genre(?x6551, ?x5937), genre(?x1366, ?x5937), genre(?x11333, ?x5937), genre(?x10826, ?x5937), genre(?x1628, ?x5937), ?x1366 = 07ng9k, actor(?x10826, ?x6962), country_of_origin(?x6551, ?x252), award_winner(?x10826, ?x5287), language(?x10826, ?x2164), film(?x296, ?x1628), ?x252 = 03_3d, genre(?x12093, ?x258), prequel(?x1628, ?x4770), film(?x1382, ?x11333), film(?x382, ?x11333), actor(?x9523, ?x4944), ?x13050 = 0gxr1c, production_companies(?x11333, ?x4585) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 11 *> proper extension: 08cl7s; 045qmr; 03d3ht; *> query: (?x8869, 03k9fj) <- country_of_origin(?x8869, ?x252), genre(?x8869, ?x5937), genre(?x8869, ?x1510), ?x5937 = 0jxy, genre(?x10192, ?x1510), genre(?x9800, ?x1510), genre(?x7801, ?x1510), genre(?x6610, ?x1510), genre(?x5458, ?x1510), genre(?x4313, ?x1510), genre(?x3457, ?x1510), genre(?x2628, ?x1510), genre(?x293, ?x1510), titles(?x1510, ?x83), ?x10192 = 01sbv9, ?x9800 = 027fwmt, ?x6610 = 07ghv5, nominated_for(?x500, ?x3457), film_release_distribution_medium(?x5458, ?x81), ?x2628 = 06wbm8q, executive_produced_by(?x7801, ?x846), story_by(?x3457, ?x2295), films(?x5011, ?x4313), ?x293 = 090s_0 *> conf = 0.69 ranks of expected_values: 2 EVAL 088tp3 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 43.000 31.000 0.712 http://example.org/tv/tv_program/genre #17631-01kb2j PRED entity: 01kb2j PRED relation: award_nominee! PRED expected values: 0bqdvt => 105 concepts (47 used for prediction) PRED predicted values (max 10 best out of 819): 02qgqt (0.83 #4627, 0.81 #106435, 0.81 #94866), 016vg8 (0.83 #4627, 0.81 #106435, 0.81 #94866), 017149 (0.83 #4627, 0.81 #106435, 0.81 #94866), 0z4s (0.83 #4627, 0.81 #106435, 0.81 #94866), 01kb2j (0.60 #3506, 0.19 #104121, 0.04 #85610), 01rh0w (0.20 #294, 0.19 #104121, 0.02 #25747), 0bqdvt (0.20 #3366, 0.19 #104121), 05qd_ (0.20 #178, 0.10 #2491, 0.02 #48766), 03hzl42 (0.20 #1040, 0.10 #3353, 0.02 #56568), 0dvmd (0.20 #685, 0.04 #85610, 0.04 #9939) >> Best rule #4627 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0408np; >> query: (?x5097, ?x157) <- award_nominee(?x5097, ?x157), film(?x5097, ?x2090), ?x2090 = 01hqhm >> conf = 0.83 => this is the best rule for 4 predicted values *> Best rule #3366 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0408np; *> query: (?x5097, 0bqdvt) <- award_nominee(?x5097, ?x157), film(?x5097, ?x2090), ?x2090 = 01hqhm *> conf = 0.20 ranks of expected_values: 7 EVAL 01kb2j award_nominee! 0bqdvt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 105.000 47.000 0.835 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17630-09gq0x5 PRED entity: 09gq0x5 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.74 #696, 0.73 #186, 0.71 #1708), 0dxtw (0.33 #1712, 0.32 #1784, 0.31 #700), 0215hd (0.30 #55, 0.20 #490, 0.18 #527), 01vx2h (0.30 #701, 0.29 #919, 0.29 #737), 01pvkk (0.28 #1714, 0.27 #520, 0.26 #1786), 02ynfr (0.19 #88, 0.18 #16, 0.18 #706), 01xy5l_ (0.18 #50, 0.15 #704, 0.14 #14), 089g0h (0.18 #56, 0.14 #491, 0.13 #528), 0d2b38 (0.15 #62, 0.13 #3041, 0.12 #279), 02_n3z (0.13 #3041, 0.12 #37, 0.10 #691) >> Best rule #696 for best value: >> intensional similarity = 4 >> extensional distance = 260 >> proper extension: 09xbpt; 03s6l2; 02v63m; 03s5lz; 01j8wk; 01hvjx; 025n07; 0cp0ph6; 07bwr; 03h4fq7; ... >> query: (?x1813, 0ch6mp2) <- executive_produced_by(?x1813, ?x4060), titles(?x162, ?x1813), nominated_for(?x72, ?x1813), film_crew_role(?x1813, ?x137) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09gq0x5 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.737 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17629-016zwt PRED entity: 016zwt PRED relation: taxonomy PRED expected values: 04n6k => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.82 #50, 0.81 #8, 0.81 #33) >> Best rule #50 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 0rh6k; 05kkh; 059rby; 03v1s; 05kj_; 0vmt; 01n7q; 04ykg; 06mz5; 07z1m; ... >> query: (?x8620, 04n6k) <- religion(?x8620, ?x492), administrative_parent(?x8620, ?x551), adjoins(?x8620, ?x2146) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016zwt taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.816 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #17628-095w_ PRED entity: 095w_ PRED relation: place_of_birth! PRED expected values: 0gry51 => 153 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2001): 05d1y (0.37 #75617, 0.36 #75616, 0.35 #237280), 03dq9 (0.33 #2148, 0.02 #80372, 0.01 #85587), 01k47c (0.33 #1884, 0.02 #80108, 0.01 #85323), 016dmx (0.33 #1741, 0.02 #79965, 0.01 #85180), 03nk3t (0.33 #908, 0.02 #79132, 0.01 #84347), 07663r (0.17 #5125, 0.11 #7733, 0.01 #93779), 01kkx2 (0.17 #4997, 0.11 #7605, 0.01 #93651), 01f5q5 (0.17 #4971, 0.11 #7579, 0.01 #93625), 01r7t9 (0.17 #4915, 0.11 #7523, 0.01 #93569), 0d500h (0.17 #4884, 0.11 #7492, 0.01 #93538) >> Best rule #75617 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 05hcy; 0dp90; >> query: (?x1374, ?x8299) <- location(?x8299, ?x1374), nationality(?x8299, ?x94), capital(?x1003, ?x1374) >> conf = 0.37 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 095w_ place_of_birth! 0gry51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 102.000 0.366 http://example.org/people/person/place_of_birth #17627-04k4l PRED entity: 04k4l PRED relation: organization! PRED expected values: 0154j 0d060g 07ssc 03gj2 07ylj 03h2c 0193qj 04hzj 01p8s 0165v => 68 concepts (15 used for prediction) PRED predicted values (max 10 best out of 298): 07ssc (0.82 #1362, 0.75 #824, 0.69 #2174), 0345h (0.64 #1381, 0.63 #2156, 0.63 #1886), 0f8l9c (0.64 #1369, 0.63 #2156, 0.63 #1886), 0154j (0.64 #1350, 0.63 #2156, 0.63 #1886), 05r4w (0.64 #1346, 0.63 #2156, 0.63 #1886), 09c7w0 (0.63 #2156, 0.63 #1886, 0.55 #1347), 01n8qg (0.63 #2156, 0.63 #1886, 0.50 #1308), 019rg5 (0.63 #2156, 0.63 #1886, 0.50 #1102), 03rk0 (0.63 #2156, 0.63 #1886, 0.50 #867), 09pmkv (0.63 #2156, 0.63 #1886, 0.50 #839) >> Best rule #1362 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 01rz1; 0b6css; >> query: (?x4230, 07ssc) <- organization(?x390, ?x4230), film_release_region(?x3217, ?x390), country(?x901, ?x390), nationality(?x72, ?x390), country(?x308, ?x390), ?x3217 = 0gffmn8 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 24, 35, 40, 43, 47, 90, 99 EVAL 04k4l organization! 0165v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 68.000 15.000 0.818 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04k4l organization! 01p8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 68.000 15.000 0.818 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04k4l organization! 04hzj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 68.000 15.000 0.818 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04k4l organization! 0193qj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 15.000 0.818 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04k4l organization! 03h2c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 68.000 15.000 0.818 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04k4l organization! 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 68.000 15.000 0.818 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04k4l organization! 03gj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 68.000 15.000 0.818 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04k4l organization! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 15.000 0.818 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04k4l organization! 0d060g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 68.000 15.000 0.818 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 04k4l organization! 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 15.000 0.818 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #17626-04bsx1 PRED entity: 04bsx1 PRED relation: team PRED expected values: 0bl8l => 105 concepts (80 used for prediction) PRED predicted values (max 10 best out of 262): 014nzp (0.82 #7876, 0.82 #9983, 0.81 #10511), 02b190 (0.82 #7876, 0.82 #9983, 0.81 #10511), 04ltf (0.82 #7876, 0.82 #9983, 0.81 #10511), 05z01 (0.50 #684, 0.08 #5407, 0.06 #7772), 02b10g (0.33 #54, 0.25 #579, 0.14 #3726), 0182r9 (0.33 #16, 0.25 #541, 0.11 #7629), 0cnk2q (0.33 #1, 0.12 #5249, 0.11 #1838), 01kj5h (0.33 #91, 0.09 #7704, 0.07 #9022), 03fhml (0.33 #244, 0.05 #4178, 0.05 #3916), 0j46b (0.25 #968, 0.22 #2016, 0.20 #1492) >> Best rule #7876 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 0dhrqx; >> query: (?x10129, ?x8585) <- team(?x10129, ?x9971), gender(?x10129, ?x231), athlete(?x471, ?x10129), team(?x10129, ?x8585), team(?x8594, ?x9971) >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #5300 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 24 *> proper extension: 0c11mj; 071pf2; 0457w0; 02y0dd; *> query: (?x10129, 0bl8l) <- type_of_union(?x10129, ?x566), team(?x10129, ?x10996), team(?x10129, ?x8678), team(?x1696, ?x8678), position(?x8678, ?x63), team(?x10129, ?x8585), teams(?x8809, ?x10996) *> conf = 0.04 ranks of expected_values: 203 EVAL 04bsx1 team 0bl8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 105.000 80.000 0.821 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #17625-0jt90f5 PRED entity: 0jt90f5 PRED relation: people! PRED expected values: 063k3h => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 43): 02w7gg (0.30 #156, 0.08 #387, 0.07 #1003), 041rx (0.22 #928, 0.21 #543, 0.18 #1236), 0x67 (0.20 #4168, 0.17 #5092, 0.16 #3090), 048z7l (0.10 #887, 0.10 #194, 0.10 #733), 033tf_ (0.09 #1393, 0.09 #4165, 0.08 #1932), 013xrm (0.08 #405, 0.07 #2253, 0.07 #2330), 03ts0c (0.08 #411, 0.03 #642, 0.02 #719), 07hwkr (0.08 #1398, 0.06 #859, 0.05 #628), 013b6_ (0.08 #284, 0.04 #1670, 0.04 #438), 0g6ff (0.08 #252, 0.04 #406, 0.02 #791) >> Best rule #156 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0pqzh; >> query: (?x2343, 02w7gg) <- influenced_by(?x8841, ?x2343), influenced_by(?x2343, ?x10000), ?x10000 = 03j0d, profession(?x8841, ?x987) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #1417 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 139 *> proper extension: 012v1t; *> query: (?x2343, 063k3h) <- location(?x2343, ?x7058), type_of_union(?x2343, ?x566), student(?x1368, ?x2343) *> conf = 0.04 ranks of expected_values: 21 EVAL 0jt90f5 people! 063k3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 120.000 120.000 0.300 http://example.org/people/ethnicity/people #17624-0dzlbx PRED entity: 0dzlbx PRED relation: film! PRED expected values: 0fpj4lx 06g2d1 => 109 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1161): 016z2j (0.65 #124726, 0.48 #62356, 0.46 #47802), 02x2t07 (0.65 #124726, 0.40 #106016, 0.38 #124725), 04ktcgn (0.40 #106016, 0.38 #124725, 0.31 #54041), 02xnjd (0.14 #10391, 0.13 #16627, 0.12 #18706), 0kszw (0.09 #6653, 0.04 #25359, 0.03 #21203), 0h5g_ (0.09 #4229, 0.04 #6307, 0.04 #14621), 04zd4m (0.08 #45723), 0f502 (0.08 #2838, 0.08 #760, 0.04 #11151), 01ggc9 (0.08 #3804, 0.08 #1726, 0.04 #12117), 048lv (0.08 #2298, 0.08 #220, 0.03 #133042) >> Best rule #124726 for best value: >> intensional similarity = 3 >> extensional distance = 843 >> proper extension: 0clpml; >> query: (?x4998, ?x9062) <- nominated_for(?x9062, ?x4998), award(?x9062, ?x484), participant(?x9062, ?x5884) >> conf = 0.65 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0dzlbx film! 06g2d1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 65.000 0.645 http://example.org/film/actor/film./film/performance/film EVAL 0dzlbx film! 0fpj4lx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 65.000 0.645 http://example.org/film/actor/film./film/performance/film #17623-01q99h PRED entity: 01q99h PRED relation: artists! PRED expected values: 0xhtw => 98 concepts (34 used for prediction) PRED predicted values (max 10 best out of 302): 0xhtw (0.75 #4662, 0.72 #3116, 0.60 #1256), 016clz (0.50 #624, 0.46 #3723, 0.44 #9927), 0dl5d (0.45 #2188, 0.38 #2499, 0.30 #1259), 0m0jc (0.40 #9, 0.38 #628, 0.20 #1248), 059kh (0.40 #48, 0.23 #3766, 0.20 #1287), 07v64s (0.40 #52, 0.20 #1291, 0.08 #2220), 06j6l (0.38 #4383, 0.32 #1906, 0.31 #5622), 0ggx5q (0.38 #697, 0.29 #4414, 0.20 #5653), 025sc50 (0.37 #1908, 0.35 #4385, 0.26 #5624), 0glt670 (0.32 #1899, 0.23 #7789, 0.22 #6237) >> Best rule #4662 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 01s7qqw; 018y81; 020hh3; 015196; >> query: (?x6228, 0xhtw) <- category(?x6228, ?x134), ?x134 = 08mbj5d, artists(?x2249, ?x6228), ?x2249 = 03lty >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q99h artists! 0xhtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 34.000 0.753 http://example.org/music/genre/artists #17622-0h7x PRED entity: 0h7x PRED relation: adjoins PRED expected values: 0345h => 189 concepts (115 used for prediction) PRED predicted values (max 10 best out of 582): 06mzp (0.86 #44501, 0.85 #44500, 0.83 #53718), 0345h (0.27 #1596, 0.22 #4662, 0.17 #8498), 05rgl (0.25 #98, 0.20 #1631, 0.19 #6997), 059_c (0.25 #56, 0.12 #6955, 0.06 #13862), 05kj_ (0.25 #31, 0.08 #13837, 0.05 #27647), 05qhw (0.22 #50646, 0.17 #8460, 0.16 #27616), 077qn (0.22 #50646, 0.17 #13225, 0.16 #27616), 07t21 (0.22 #50646, 0.16 #27616, 0.14 #2377), 0h7x (0.22 #50646, 0.16 #27616, 0.12 #49106), 01pj7 (0.22 #50646, 0.16 #27616, 0.12 #49106) >> Best rule #44501 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 09hzw; >> query: (?x1355, ?x3277) <- administrative_parent(?x863, ?x1355), adjoins(?x3277, ?x1355), contains(?x455, ?x3277) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1596 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0b90_r; 0d060g; 0k6nt; 06mkj; 05b4w; *> query: (?x1355, 0345h) <- film_release_region(?x5067, ?x1355), ?x5067 = 01rwpj, country(?x863, ?x1355) *> conf = 0.27 ranks of expected_values: 2 EVAL 0h7x adjoins 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 189.000 115.000 0.859 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #17621-06wpc PRED entity: 06wpc PRED relation: teams! PRED expected values: 0d9jr => 78 concepts (68 used for prediction) PRED predicted values (max 10 best out of 92): 094jv (0.33 #56, 0.17 #866, 0.12 #1946), 0d6lp (0.25 #635, 0.25 #365, 0.17 #1175), 013yq (0.25 #613, 0.17 #1423, 0.17 #1153), 0fpzwf (0.25 #408, 0.12 #2298, 0.11 #2568), 0nqph (0.17 #1609, 0.17 #1339, 0.14 #4582), 02_286 (0.17 #3263, 0.17 #1102, 0.13 #5429), 0h7h6 (0.17 #1405, 0.12 #1945, 0.11 #2756), 0f2v0 (0.17 #1453, 0.12 #1993, 0.11 #2804), 030qb3t (0.17 #860, 0.11 #2751, 0.11 #2480), 01_d4 (0.13 #5196, 0.12 #2220, 0.12 #1680) >> Best rule #56 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 01d6g; >> query: (?x7399, 094jv) <- team(?x261, ?x7399), season(?x7399, ?x9267), season(?x7399, ?x8529), school(?x7399, ?x5288), school(?x7399, ?x735), team(?x8206, ?x7399), ?x8529 = 025ygws, ?x9267 = 0dx84s, draft(?x7399, ?x10600), draft(?x7399, ?x8499), draft(?x7399, ?x4779), ?x4779 = 02z6872, ?x10600 = 04f4z1k, ?x8499 = 02r6gw6, ?x735 = 065y4w7, major_field_of_study(?x5288, ?x254), company(?x3131, ?x5288) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1753 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 6 *> proper extension: 070xg; 05gg4; *> query: (?x7399, 0d9jr) <- team(?x261, ?x7399), school(?x7399, ?x5288), school(?x7399, ?x1011), school(?x7399, ?x735), ?x735 = 065y4w7, ?x1011 = 07w0v, draft(?x7399, ?x8786), draft(?x6074, ?x8786), major_field_of_study(?x5288, ?x254), company(?x3131, ?x5288), student(?x5288, ?x460), organization(?x5288, ?x5487), colors(?x6074, ?x663), institution(?x620, ?x5288) *> conf = 0.12 ranks of expected_values: 16 EVAL 06wpc teams! 0d9jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 78.000 68.000 0.333 http://example.org/sports/sports_team_location/teams #17620-01zhp PRED entity: 01zhp PRED relation: genre! PRED expected values: 0bth54 02_qt 027s39y => 60 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1641): 0436yk (0.71 #31577, 0.62 #37101, 0.60 #20521), 0407yfx (0.71 #31670, 0.60 #20614, 0.60 #18772), 027j9wd (0.71 #33162, 0.60 #19475, 0.57 #32373), 0fpgp26 (0.71 #33162, 0.60 #19990, 0.50 #16308), 0g9yrw (0.67 #22782, 0.57 #30155, 0.50 #37520), 03nfnx (0.60 #21693, 0.60 #19851, 0.57 #32749), 015ynm (0.60 #21724, 0.60 #19882, 0.57 #32780), 0cmf0m0 (0.60 #21718, 0.60 #19876, 0.57 #32774), 01pvxl (0.60 #21182, 0.60 #19340, 0.57 #32238), 07bzz7 (0.60 #21164, 0.60 #19322, 0.57 #32220) >> Best rule #31577 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 02kdv5l; >> query: (?x10185, 0436yk) <- genre(?x8794, ?x10185), genre(?x3088, ?x10185), genre(?x2893, ?x10185), genre(?x2153, ?x10185), ?x8794 = 02qydsh, music(?x2153, ?x3042), film_release_distribution_medium(?x2153, ?x81), film_release_region(?x3088, ?x2146), prequel(?x6000, ?x3088), award(?x2893, ?x1053), ?x2146 = 03rk0 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #20904 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 3 *> proper extension: 01hmnh; *> query: (?x10185, 02_qt) <- genre(?x12232, ?x10185), genre(?x10192, ?x10185), genre(?x8794, ?x10185), genre(?x3088, ?x10185), genre(?x2153, ?x10185), genre(?x1847, ?x10185), ?x8794 = 02qydsh, ?x3088 = 06w839_, film(?x9238, ?x2153), ?x10192 = 01sbv9, ?x9238 = 0582cf, ?x12232 = 091xrc, film_crew_role(?x1847, ?x137) *> conf = 0.60 ranks of expected_values: 17, 150, 154 EVAL 01zhp genre! 027s39y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 60.000 21.000 0.714 http://example.org/film/film/genre EVAL 01zhp genre! 02_qt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 60.000 21.000 0.714 http://example.org/film/film/genre EVAL 01zhp genre! 0bth54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 60.000 21.000 0.714 http://example.org/film/film/genre #17619-0jm74 PRED entity: 0jm74 PRED relation: draft PRED expected values: 038c0q => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 16): 038c0q (0.80 #295, 0.77 #322, 0.76 #504), 06439y (0.77 #322, 0.75 #225, 0.72 #514), 02pq_rp (0.53 #442, 0.38 #749, 0.36 #313), 02x2khw (0.50 #260, 0.50 #132, 0.47 #389), 092j54 (0.50 #122, 0.43 #170, 0.39 #881), 0g3zpp (0.50 #115, 0.43 #163, 0.39 #874), 09l0x9 (0.50 #124, 0.43 #172, 0.39 #883), 047dpm0 (0.50 #144, 0.42 #756, 0.41 #449), 04f4z1k (0.42 #755, 0.37 #658, 0.37 #722), 02z6872 (0.41 #444, 0.40 #654, 0.40 #396) >> Best rule #295 for best value: >> intensional similarity = 11 >> extensional distance = 8 >> proper extension: 0jml5; 0jm5b; >> query: (?x7136, 038c0q) <- school(?x7136, ?x2948), team(?x4747, ?x7136), draft(?x7136, ?x8133), ?x8133 = 025tn92, major_field_of_study(?x2948, ?x8925), major_field_of_study(?x2948, ?x5615), currency(?x2948, ?x170), ?x4747 = 02sf_r, fraternities_and_sororities(?x2948, ?x3697), ?x8925 = 01zc2w, major_field_of_study(?x3213, ?x5615) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jm74 draft 038c0q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.800 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #17618-016kv6 PRED entity: 016kv6 PRED relation: nominated_for! PRED expected values: 06r_by => 73 concepts (21 used for prediction) PRED predicted values (max 10 best out of 670): 03np3w (0.34 #4675, 0.28 #21042, 0.27 #18704), 0146pg (0.20 #121, 0.07 #18825, 0.07 #16487), 06pj8 (0.20 #433, 0.04 #19137, 0.04 #16799), 0z4s (0.20 #76, 0.04 #4751, 0.02 #14104), 024bbl (0.20 #1042, 0.02 #3379), 02p65p (0.20 #24, 0.02 #7038, 0.02 #32755), 015vq_ (0.20 #883, 0.02 #7897, 0.01 #14911), 0410cp (0.20 #886, 0.02 #7900), 014g22 (0.20 #892, 0.02 #5567, 0.01 #7906), 02jsgf (0.20 #875, 0.02 #5550, 0.01 #7889) >> Best rule #4675 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 04h41v; 06__m6; >> query: (?x3523, ?x3522) <- film(?x3522, ?x3523), genre(?x3523, ?x53), nominated_for(?x2902, ?x3523), ?x2902 = 02x4sn8 >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #6006 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 04cv9m; 08tq4x; 01j5ql; 03p2xc; 0gtx63s; 03m5y9p; *> query: (?x3523, 06r_by) <- film(?x3522, ?x3523), genre(?x3523, ?x3506), ?x3506 = 03mqtr, titles(?x11405, ?x3523) *> conf = 0.04 ranks of expected_values: 72 EVAL 016kv6 nominated_for! 06r_by CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 73.000 21.000 0.343 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17617-02xs6_ PRED entity: 02xs6_ PRED relation: film_crew_role PRED expected values: 02r96rf 09vw2b7 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.82 #74, 0.72 #253, 0.70 #501), 09vw2b7 (0.72 #77, 0.72 #504, 0.71 #256), 01vx2h (0.46 #82, 0.41 #11, 0.35 #261), 0dxtw (0.43 #10, 0.42 #508, 0.40 #81), 01pvkk (0.31 #83, 0.30 #510, 0.28 #1472), 0215hd (0.25 #18, 0.14 #660, 0.14 #624), 0d2b38 (0.20 #25, 0.11 #96, 0.11 #275), 02rh1dz (0.19 #80, 0.17 #44, 0.12 #259), 089g0h (0.18 #19, 0.12 #661, 0.12 #625), 01xy5l_ (0.14 #14, 0.12 #264, 0.11 #656) >> Best rule #74 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 09sh8k; 07gp9; 09xbpt; 01k1k4; 0gtv7pk; 0h1cdwq; 02z3r8t; 061681; 08gsvw; 0164qt; ... >> query: (?x4991, 02r96rf) <- film_crew_role(?x4991, ?x1284), ?x1284 = 0ch6mp2, genre(?x4991, ?x53), prequel(?x4991, ?x3471) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02xs6_ film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.825 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02xs6_ film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.825 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17616-0jdx PRED entity: 0jdx PRED relation: form_of_government PRED expected values: 018wl5 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 6): 01fpfn (0.42 #111, 0.39 #87, 0.38 #219), 06cx9 (0.40 #319, 0.34 #325, 0.34 #487), 018wl5 (0.38 #128, 0.33 #8, 0.32 #38), 01d9r3 (0.36 #323, 0.32 #359, 0.32 #407), 01q20 (0.33 #10, 0.32 #130, 0.30 #82), 026wp (0.09 #30, 0.09 #36, 0.08 #90) >> Best rule #111 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 020d5; >> query: (?x7833, 01fpfn) <- jurisdiction_of_office(?x182, ?x7833), taxonomy(?x7833, ?x939), nationality(?x2259, ?x7833) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 0167v; 0h44w; 07ytt; *> query: (?x7833, 018wl5) <- countries_spoken_in(?x11590, ?x7833), language(?x4355, ?x11590), ?x4355 = 08tq4x *> conf = 0.38 ranks of expected_values: 3 EVAL 0jdx form_of_government 018wl5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 133.000 0.422 http://example.org/location/country/form_of_government #17615-05zy3sc PRED entity: 05zy3sc PRED relation: nominated_for! PRED expected values: 099flj => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 219): 094qd5 (0.54 #744, 0.50 #272, 0.50 #36), 019f4v (0.52 #1234, 0.51 #998, 0.38 #1942), 0gs9p (0.51 #1243, 0.49 #1007, 0.38 #4311), 099c8n (0.50 #293, 0.50 #57, 0.46 #765), 0gr4k (0.40 #1915, 0.39 #1207, 0.39 #971), 0k611 (0.38 #1015, 0.37 #1251, 0.34 #4319), 0gqy2 (0.38 #1064, 0.35 #1300, 0.28 #2008), 0gs96 (0.38 #88, 0.33 #324, 0.31 #1032), 02qyp19 (0.38 #1, 0.33 #237, 0.25 #709), 02qvyrt (0.38 #95, 0.33 #331, 0.23 #1039) >> Best rule #744 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 0gjk1d; 0j43swk; 011yr9; 011yg9; 011yhm; >> query: (?x6438, 094qd5) <- film(?x382, ?x6438), nominated_for(?x618, ?x6438), ?x618 = 09qwmm, film(?x3308, ?x6438) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #7554 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 418 *> proper extension: 04xbq3; *> query: (?x6438, ?x77) <- film(?x9796, ?x6438), honored_for(?x4224, ?x6438), award_nominee(?x1641, ?x9796), ceremony(?x77, ?x4224) *> conf = 0.19 ranks of expected_values: 60 EVAL 05zy3sc nominated_for! 099flj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 93.000 93.000 0.542 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17614-016tbr PRED entity: 016tbr PRED relation: location PRED expected values: 02_286 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 144): 01nl79 (0.70 #69747, 0.68 #32861, 0.65 #16032), 02_286 (0.18 #64970, 0.18 #11260, 0.18 #15267), 059rby (0.13 #16, 0.06 #1620, 0.04 #28870), 0cc56 (0.10 #1661, 0.06 #12082, 0.05 #16089), 0cr3d (0.08 #1747, 0.07 #143, 0.07 #60264), 04jpl (0.07 #31275, 0.06 #10439, 0.06 #54525), 0f2wj (0.07 #34, 0.06 #1638, 0.02 #31292), 0ccvx (0.07 #220, 0.03 #35485, 0.03 #54728), 094jv (0.07 #92, 0.02 #1696, 0.02 #5704), 0b1t1 (0.07 #471, 0.02 #2075, 0.01 #9291) >> Best rule #69747 for best value: >> intensional similarity = 3 >> extensional distance = 1387 >> proper extension: 0f1pyf; 0g5ff; 0399p; 07m69t; 02x8kk; 02x8mt; 03j90; 0459z; 015n8; 02vkvcz; >> query: (?x10259, ?x12563) <- nationality(?x10259, ?x94), location(?x10259, ?x1523), place_of_birth(?x10259, ?x12563) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #64970 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1290 *> proper extension: 07c37; *> query: (?x10259, 02_286) <- location(?x10259, ?x1523), student(?x4410, ?x10259) *> conf = 0.18 ranks of expected_values: 2 EVAL 016tbr location 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 144.000 0.703 http://example.org/people/person/places_lived./people/place_lived/location #17613-0gvx_ PRED entity: 0gvx_ PRED relation: ceremony PRED expected values: 073hkh 02hn5v 0bzk2h 0ftlxj 0c4hnm => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 82): 02hn5v (0.91 #850, 0.90 #686, 0.88 #604), 073hkh (0.86 #821, 0.85 #657, 0.83 #83), 0bzk2h (0.85 #363, 0.85 #281, 0.83 #199), 0c4hnm (0.80 #730, 0.73 #894, 0.71 #566), 0fy59t (0.69 #394, 0.69 #312, 0.64 #558), 0gpjbt (0.61 #1497, 0.51 #2155, 0.36 #3385), 0ftlxj (0.60 #700, 0.59 #864, 0.58 #208), 09n4nb (0.60 #1511, 0.49 #2169, 0.35 #3399), 0466p0j (0.59 #1527, 0.49 #2185, 0.35 #3415), 05pd94v (0.59 #1479, 0.49 #2137, 0.33 #3367) >> Best rule #850 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0l8z1; 0gr0m; 0gqxm; >> query: (?x3617, 02hn5v) <- ceremony(?x3617, ?x4445), ceremony(?x3617, ?x3254), award(?x574, ?x3617), ?x3254 = 073h9x, award_winner(?x4445, ?x786) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 7 EVAL 0gvx_ ceremony 0c4hnm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gvx_ ceremony 0ftlxj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 66.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gvx_ ceremony 0bzk2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gvx_ ceremony 02hn5v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gvx_ ceremony 073hkh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony #17612-01q8hj PRED entity: 01q8hj PRED relation: organization! PRED expected values: 060c4 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 22): 060c4 (0.78 #223, 0.77 #119, 0.76 #236), 0dq_5 (0.24 #178, 0.15 #737, 0.13 #724), 07xl34 (0.22 #414, 0.21 #531, 0.20 #674), 05k17c (0.21 #33, 0.18 #46, 0.12 #98), 0hm4q (0.05 #710, 0.05 #684, 0.05 #541), 05c0jwl (0.04 #447, 0.04 #512, 0.03 #551), 0fkzq (0.02 #1029, 0.02 #1069), 09n5b9 (0.02 #1029, 0.02 #1069), 02079p (0.02 #1029, 0.02 #1069), 0789n (0.02 #1029, 0.02 #1069) >> Best rule #223 for best value: >> intensional similarity = 3 >> extensional distance = 175 >> proper extension: 01b1mj; 01t8sr; 02jyr8; 02zkz7; 04bfg; 02zc7f; 029qzx; >> query: (?x8589, 060c4) <- colors(?x8589, ?x663), school(?x2820, ?x8589), draft(?x2820, ?x2569) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q8hj organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.780 http://example.org/organization/role/leaders./organization/leadership/organization #17611-01x3g PRED entity: 01x3g PRED relation: major_field_of_study! PRED expected values: 014mlp => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 20): 014mlp (0.89 #417, 0.88 #398, 0.88 #535), 02h4rq6 (0.83 #313, 0.80 #333, 0.80 #638), 0bkj86 (0.78 #123, 0.77 #162, 0.71 #85), 04zx3q1 (0.63 #312, 0.57 #79, 0.54 #332), 03bwzr4 (0.62 #383, 0.58 #602, 0.57 #810), 0bjrnt (0.57 #83, 0.56 #121, 0.50 #25), 028dcg (0.54 #451, 0.50 #799, 0.41 #135), 07s6fsf (0.51 #372, 0.49 #717, 0.48 #904), 02mjs7 (0.41 #135, 0.39 #820, 0.38 #988), 01rr_d (0.41 #135, 0.37 #330, 0.36 #291) >> Best rule #417 for best value: >> intensional similarity = 12 >> extensional distance = 42 >> proper extension: 0w7c; >> query: (?x11566, 014mlp) <- major_field_of_study(?x11566, ?x1695), major_field_of_study(?x2999, ?x11566), major_field_of_study(?x1200, ?x11566), student(?x11566, ?x7522), institution(?x1200, ?x10832), institution(?x1200, ?x9227), institution(?x1200, ?x6856), institution(?x1200, ?x4980), ?x9227 = 01dyk8, ?x10832 = 014jyk, school(?x1632, ?x6856), organization(?x4980, ?x5487) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x3g major_field_of_study! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.886 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #17610-06c1y PRED entity: 06c1y PRED relation: film_release_region! PRED expected values: 0ds35l9 0gmcwlb 04jkpgv 04n52p6 0gj9tn5 02yvct 0g5838s 026njb5 0dlngsd 062zm5h 047vnkj 0cc97st 0gwjw0c 0cmf0m0 => 215 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1235): 05p1tzf (0.81 #23312, 0.79 #49024, 0.79 #22088), 047vnkj (0.80 #28791, 0.78 #66744, 0.78 #23893), 053rxgm (0.80 #28279, 0.78 #23381, 0.77 #3794), 06fcqw (0.80 #28916, 0.78 #24018, 0.77 #49730), 0872p_c (0.79 #49092, 0.75 #66231, 0.71 #22156), 03q0r1 (0.79 #22473, 0.74 #23697, 0.72 #49409), 017z49 (0.79 #22421, 0.74 #23645, 0.69 #10178), 0879bpq (0.77 #3975, 0.77 #28460, 0.71 #22338), 0kv238 (0.77 #3960, 0.73 #28445, 0.69 #49259), 04w7rn (0.77 #49131, 0.70 #28317, 0.69 #66270) >> Best rule #23312 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 03_3d; 0d0vqn; 019rg5; 0h7x; 07t21; 06mkj; >> query: (?x1536, 05p1tzf) <- olympics(?x1536, ?x3729), country(?x471, ?x1536), ?x3729 = 0jdk_, nationality(?x4379, ?x1536) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #28791 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 052p7; *> query: (?x1536, 047vnkj) <- film_release_region(?x8646, ?x1536), film_release_region(?x3748, ?x1536), film(?x609, ?x3748), ?x8646 = 05zvzf3 *> conf = 0.80 ranks of expected_values: 2, 21, 24, 42, 50, 51, 60, 62, 69, 73, 75, 98, 99, 126 EVAL 06c1y film_release_region! 0cmf0m0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 0gwjw0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 047vnkj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 062zm5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 0dlngsd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 026njb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 0g5838s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 0gj9tn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 04n52p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 04jkpgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 0gmcwlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06c1y film_release_region! 0ds35l9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 215.000 97.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17609-01cwhp PRED entity: 01cwhp PRED relation: profession PRED expected values: 0n1h => 145 concepts (141 used for prediction) PRED predicted values (max 10 best out of 94): 09jwl (0.62 #7432, 0.62 #7730, 0.59 #2389), 0dz3r (0.54 #2372, 0.44 #4300, 0.42 #4003), 016z4k (0.53 #597, 0.48 #2225, 0.43 #4302), 01d_h8 (0.45 #895, 0.44 #1043, 0.42 #3415), 0n1h (0.38 #605, 0.27 #14524, 0.26 #160), 03gjzk (0.35 #756, 0.31 #904, 0.30 #3424), 01c72t (0.34 #2543, 0.31 #172, 0.29 #5657), 0dxtg (0.33 #755, 0.29 #14686, 0.28 #1495), 02jknp (0.28 #15117, 0.27 #14524, 0.23 #2081), 0cbd2 (0.28 #15117, 0.27 #14524, 0.22 #1192) >> Best rule #7432 for best value: >> intensional similarity = 3 >> extensional distance = 589 >> proper extension: 03j0br4; 017yfz; 01wy61y; 024zq; 018d6l; 01lz4tf; 021r7r; 03h_yfh; 02jyhv; 01wxdn3; ... >> query: (?x2461, 09jwl) <- gender(?x2461, ?x514), artist(?x3265, ?x2461), artists(?x671, ?x2461) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #605 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 012x1l; *> query: (?x2461, 0n1h) <- gender(?x2461, ?x514), artist(?x3265, ?x2461), artist(?x6672, ?x2461) *> conf = 0.38 ranks of expected_values: 5 EVAL 01cwhp profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 145.000 141.000 0.621 http://example.org/people/person/profession #17608-0cq7kw PRED entity: 0cq7kw PRED relation: nominated_for! PRED expected values: 0gqyl => 112 concepts (104 used for prediction) PRED predicted values (max 10 best out of 218): 0p9sw (0.70 #2553, 0.68 #14860, 0.67 #9519), 0k611 (0.45 #764, 0.45 #300, 0.39 #532), 0gr0m (0.45 #288, 0.43 #520, 0.40 #984), 0l8z1 (0.41 #281, 0.36 #513, 0.32 #977), 03hkv_r (0.41 #710, 0.19 #246, 0.16 #6511), 02n9nmz (0.40 #750, 0.22 #286, 0.19 #982), 04dn09n (0.36 #728, 0.30 #6529, 0.27 #2585), 02x2gy0 (0.34 #1256, 0.24 #560, 0.20 #1024), 0gqyl (0.31 #771, 0.27 #1003, 0.27 #1699), 02pqp12 (0.31 #751, 0.24 #983, 0.24 #6552) >> Best rule #2553 for best value: >> intensional similarity = 4 >> extensional distance = 198 >> proper extension: 0g60z; 080dwhx; 0124k9; 08jgk1; 03d34x8; 03ln8b; 01q_y0; 0d68qy; 01b64v; 01b66d; ... >> query: (?x4504, ?x500) <- nominated_for(?x198, ?x4504), nominated_for(?x4505, ?x4504), category(?x4504, ?x134), award(?x4504, ?x500) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #771 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 116 *> proper extension: 01c9d; *> query: (?x4504, 0gqyl) <- currency(?x4504, ?x170), nominated_for(?x601, ?x4504), ?x601 = 0gr4k, film(?x382, ?x4504) *> conf = 0.31 ranks of expected_values: 9 EVAL 0cq7kw nominated_for! 0gqyl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 112.000 104.000 0.699 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17607-07fj_ PRED entity: 07fj_ PRED relation: country! PRED expected values: 07gyv 071t0 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 41): 071t0 (0.85 #303, 0.82 #836, 0.81 #57), 03hr1p (0.75 #304, 0.69 #58, 0.66 #837), 0w0d (0.67 #298, 0.61 #52, 0.58 #831), 07gyv (0.65 #294, 0.57 #950, 0.56 #786), 0194d (0.62 #322, 0.61 #76, 0.55 #855), 07jbh (0.62 #311, 0.57 #844, 0.57 #967), 019tzd (0.54 #316, 0.50 #70, 0.45 #972), 07rlg (0.53 #42, 0.48 #288, 0.42 #493), 07jjt (0.52 #302, 0.47 #56, 0.44 #1477), 09w1n (0.50 #59, 0.49 #838, 0.48 #305) >> Best rule #303 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 0165b; >> query: (?x4521, 071t0) <- country(?x2315, ?x4521), currency(?x4521, ?x170), ?x2315 = 06wrt >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 07fj_ country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.854 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07fj_ country! 07gyv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 145.000 0.854 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #17606-03f2w PRED entity: 03f2w PRED relation: film_release_region! PRED expected values: 03hjv97 0k7tq 0cbn7c 0b85mm => 73 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1356): 017gm7 (0.86 #10713, 0.86 #6756, 0.86 #5437), 017jd9 (0.86 #7187, 0.86 #5868, 0.81 #11144), 08hmch (0.86 #6713, 0.86 #5394, 0.81 #10670), 02vr3gz (0.86 #7068, 0.86 #5749, 0.78 #11872), 02vxq9m (0.86 #6612, 0.86 #5293, 0.78 #21113), 09k56b7 (0.86 #6834, 0.86 #5515, 0.78 #1558), 0jjy0 (0.86 #6723, 0.86 #5404, 0.78 #1447), 0fpgp26 (0.86 #7735, 0.86 #6416, 0.78 #2459), 0ds3t5x (0.86 #6635, 0.86 #5316, 0.78 #1359), 06fcqw (0.86 #7428, 0.86 #6109, 0.78 #2152) >> Best rule #10713 for best value: >> intensional similarity = 8 >> extensional distance = 19 >> proper extension: 084n_; >> query: (?x11872, 017gm7) <- country(?x3757, ?x11872), organization(?x11872, ?x312), film_release_region(?x3757, ?x985), film_release_region(?x3757, ?x429), ?x985 = 0k6nt, film_crew_role(?x3757, ?x137), ?x429 = 03rt9, nominated_for(?x533, ?x3757) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2344 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 7 *> proper extension: 0b90_r; *> query: (?x11872, 0cbn7c) <- film_release_region(?x11218, ?x11872), film_release_region(?x7711, ?x11872), film_release_region(?x1547, ?x11872), olympics(?x11872, ?x584), olympics(?x11872, ?x391), ?x7711 = 0pd64, medal(?x11872, ?x422), ?x11218 = 0ccck7, award(?x1547, ?x484) *> conf = 0.78 ranks of expected_values: 138, 156, 294, 417 EVAL 03f2w film_release_region! 0b85mm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 73.000 16.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03f2w film_release_region! 0cbn7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 73.000 16.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03f2w film_release_region! 0k7tq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 73.000 16.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03f2w film_release_region! 03hjv97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 16.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17605-01w5gg6 PRED entity: 01w5gg6 PRED relation: instrumentalists! PRED expected values: 018vs => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 126): 03qjg (0.67 #176, 0.67 #138, 0.44 #702), 018vs (0.50 #99, 0.48 #623, 0.44 #888), 05148p4 (0.45 #1070, 0.43 #983, 0.42 #544), 03bx0bm (0.42 #1313, 0.42 #2273, 0.42 #2888), 02hnl (0.29 #910, 0.25 #823, 0.25 #121), 026t6 (0.29 #879, 0.23 #527, 0.23 #792), 0l14md (0.27 #270, 0.25 #444, 0.22 #883), 06ncr (0.21 #220, 0.17 #394, 0.17 #131), 0l14qv (0.20 #794, 0.20 #881, 0.13 #355), 04rzd (0.18 #300, 0.18 #739, 0.17 #474) >> Best rule #176 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 021bk; >> query: (?x9241, ?x2798) <- role(?x9241, ?x2798), role(?x9241, ?x1466), ?x2798 = 03qjg, profession(?x9241, ?x1032), ?x1466 = 03bx0bm >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #99 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 021bk; *> query: (?x9241, 018vs) <- role(?x9241, ?x2798), role(?x9241, ?x1466), ?x2798 = 03qjg, profession(?x9241, ?x1032), ?x1466 = 03bx0bm *> conf = 0.50 ranks of expected_values: 2 EVAL 01w5gg6 instrumentalists! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.667 http://example.org/music/instrument/instrumentalists #17604-0319l PRED entity: 0319l PRED relation: role! PRED expected values: 018x3 => 77 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1097): 050z2 (0.70 #9125, 0.67 #9597, 0.64 #14803), 082brv (0.67 #9678, 0.60 #4498, 0.50 #6380), 023l9y (0.60 #9150, 0.60 #4442, 0.59 #12468), 05qhnq (0.60 #9249, 0.60 #4068, 0.57 #7836), 06x4l_ (0.60 #4353, 0.60 #3880, 0.50 #3409), 018y81 (0.60 #4223, 0.60 #4026, 0.50 #3555), 01wgjj5 (0.60 #5909, 0.60 #4495, 0.43 #6848), 04bpm6 (0.60 #4300, 0.58 #9480, 0.57 #7595), 01l4g5 (0.60 #3982, 0.50 #8692, 0.50 #3511), 02s6sh (0.60 #4195, 0.50 #9848, 0.50 #3724) >> Best rule #9125 for best value: >> intensional similarity = 29 >> extensional distance = 8 >> proper extension: 0dwtp; 0395lw; >> query: (?x1472, 050z2) <- role(?x7033, ?x1472), role(?x3703, ?x1472), role(?x2206, ?x1472), role(?x1969, ?x1472), role(?x1495, ?x1472), ?x1969 = 04rzd, role(?x1472, ?x316), ?x1495 = 013y1f, role(?x2206, ?x7869), role(?x2206, ?x2048), role(?x2206, ?x1473), role(?x2206, ?x1466), role(?x2206, ?x1267), role(?x2206, ?x645), ?x2048 = 018j2, ?x1267 = 07brj, group(?x1472, ?x997), ?x645 = 028tv0, role(?x487, ?x1472), ?x1466 = 03bx0bm, instrumentalists(?x2206, ?x8341), ?x7869 = 0l14v3, ?x1473 = 0g2dz, ?x7033 = 0gkd1, ?x8341 = 01wmjkb, group(?x2206, ?x1751), instrumentalists(?x1472, ?x11947), ?x3703 = 02dlh2, role(?x4184, ?x2206) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #17706 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 24 *> proper extension: 02bxd; 0dwt5; *> query: (?x1472, 018x3) <- role(?x2206, ?x1472), role(?x1969, ?x1472), role(?x569, ?x1472), role(?x314, ?x1472), role(?x1969, ?x4917), role(?x1969, ?x2459), role(?x1969, ?x645), role(?x316, ?x1969), role(?x366, ?x1969), ?x645 = 028tv0, ?x4917 = 06w7v, role(?x2392, ?x2206), role(?x487, ?x1472), group(?x1969, ?x8429), group(?x1969, ?x4715), group(?x1969, ?x3109), ?x2459 = 021bmf, instrumentalists(?x2206, ?x8341), ?x4715 = 0khth, ?x314 = 02sgy, instrumentalists(?x1969, ?x4568), ?x8341 = 01wmjkb, ?x4568 = 02j3d4, ?x569 = 07c6l, award_nominee(?x3109, ?x8393), ?x8429 = 01lf293 *> conf = 0.35 ranks of expected_values: 135 EVAL 0319l role! 018x3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 77.000 51.000 0.700 http://example.org/music/artist/track_contributions./music/track_contribution/role #17603-05bnq3j PRED entity: 05bnq3j PRED relation: award_winner! PRED expected values: 0gvstc3 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 126): 0gvstc3 (0.55 #32, 0.26 #2177, 0.20 #168), 09v0p2c (0.27 #79, 0.15 #487, 0.10 #351), 0418154 (0.20 #376, 0.05 #648, 0.04 #784), 0gx_st (0.18 #5309, 0.18 #7488, 0.17 #7625), 07y_p6 (0.18 #5309, 0.18 #7488, 0.17 #7625), 07z31v (0.18 #5309, 0.17 #5854, 0.13 #165), 07y9ts (0.18 #5309, 0.17 #5854, 0.13 #201), 05pd94v (0.18 #5309, 0.17 #5854, 0.13 #5991), 056878 (0.18 #5309, 0.17 #5854, 0.13 #5991), 027n06w (0.17 #478, 0.07 #750, 0.07 #1022) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 05txrz; >> query: (?x4629, 0gvstc3) <- award_nominee(?x4629, ?x4415), profession(?x4629, ?x987), ?x4415 = 06msq2 >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05bnq3j award_winner! 0gvstc3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.545 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17602-01mjq PRED entity: 01mjq PRED relation: jurisdiction_of_office! PRED expected values: 060c4 => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.75 #68, 0.75 #1411, 0.74 #1125), 0f6c3 (0.55 #976, 0.37 #1725, 0.36 #1703), 0pqc5 (0.51 #2669, 0.41 #2229, 0.36 #3441), 09n5b9 (0.49 #980, 0.33 #1707, 0.32 #1729), 0fkvn (0.47 #972, 0.32 #1699, 0.32 #1721), 0p5vf (0.39 #2093, 0.39 #1696, 0.38 #2886), 0377k9 (0.39 #2093, 0.39 #1696, 0.38 #2886), 01zq91 (0.39 #1696, 0.38 #2886, 0.36 #2975), 04syw (0.25 #116, 0.19 #315, 0.18 #204), 0789n (0.25 #31, 0.17 #53, 0.12 #207) >> Best rule #68 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 04w4s; >> query: (?x1558, 060c4) <- country(?x453, ?x1558), countries_spoken_in(?x403, ?x1558), ?x403 = 0cjk9, adjoins(?x1558, ?x456) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mjq jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 190.000 190.000 0.750 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #17601-07dzf PRED entity: 07dzf PRED relation: adjoins! PRED expected values: 07tp2 => 118 concepts (102 used for prediction) PRED predicted values (max 10 best out of 409): 07tp2 (0.82 #21814, 0.82 #21813, 0.82 #54534), 06dfg (0.82 #21814, 0.82 #21813, 0.82 #54534), 019pcs (0.36 #956, 0.31 #1735, 0.22 #77172), 02k54 (0.25 #31, 0.07 #809, 0.06 #1588), 06vbd (0.25 #217, 0.05 #18137, 0.04 #18915), 06tw8 (0.22 #79512, 0.22 #77172, 0.22 #21815), 07dzf (0.22 #79512, 0.22 #77172, 0.22 #21815), 0hzlz (0.22 #79512, 0.22 #77172, 0.22 #21815), 088q4 (0.22 #79512, 0.22 #77172, 0.22 #21815), 04sj3 (0.22 #79512, 0.22 #77172, 0.22 #21815) >> Best rule #21814 for best value: >> intensional similarity = 2 >> extensional distance = 98 >> proper extension: 06btq; >> query: (?x5360, ?x2804) <- adjoins(?x5360, ?x2804), religion(?x5360, ?x109) >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 07dzf adjoins! 07tp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 102.000 0.820 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #17600-0315rp PRED entity: 0315rp PRED relation: region PRED expected values: 07ssc => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 8): 07ssc (0.87 #351, 0.24 #98, 0.20 #75), 09nm_ (0.04 #138, 0.01 #391, 0.01 #485), 09c7w0 (0.03 #392, 0.02 #439, 0.02 #300), 059j2 (0.01 #170), 02jx1 (0.01 #1644), 0ctw_b (0.01 #1644), 0f8l9c (0.01 #1644), 0d060g (0.01 #1644) >> Best rule #351 for best value: >> intensional similarity = 5 >> extensional distance = 135 >> proper extension: 0522wp; >> query: (?x8397, 07ssc) <- film(?x1104, ?x8397), film(?x609, ?x8397), ?x609 = 03xq0f, film(?x1104, ?x12108), film(?x2263, ?x12108) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0315rp region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.869 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #17599-0bxg3 PRED entity: 0bxg3 PRED relation: films PRED expected values: 023p33 => 84 concepts (27 used for prediction) PRED predicted values (max 10 best out of 746): 04w7rn (0.33 #69, 0.25 #2178, 0.25 #1650), 0fs9vc (0.33 #891, 0.25 #1416, 0.05 #7765), 017kct (0.33 #701, 0.25 #1226, 0.05 #7575), 026p4q7 (0.33 #649, 0.25 #1174, 0.05 #7523), 0cc5qkt (0.33 #703, 0.25 #1228, 0.05 #7577), 0cbl95 (0.33 #1052, 0.25 #1577, 0.05 #7926), 03bdkd (0.33 #1016, 0.25 #1541, 0.05 #7890), 0kb1g (0.33 #999, 0.25 #1524, 0.05 #7873), 04h4c9 (0.33 #965, 0.25 #1490, 0.05 #7839), 0hv27 (0.33 #838, 0.25 #1363, 0.05 #7712) >> Best rule #69 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 01hmnh; >> query: (?x9516, 04w7rn) <- genre(?x1786, ?x9516), ?x1786 = 091z_p, films(?x9516, ?x1893), film(?x3758, ?x1893), film(?x2156, ?x1893), genre(?x1893, ?x4088), currency(?x1893, ?x170), genre(?x9993, ?x4088), ?x9993 = 0kb1g >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10031 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 26 *> proper extension: 018h2; *> query: (?x9516, ?x218) <- films(?x9516, ?x11073), films(?x9516, ?x4707), film(?x2156, ?x11073), production_companies(?x11073, ?x10685), production_companies(?x218, ?x10685), award_winner(?x2209, ?x10685), film_release_region(?x4707, ?x4743), ?x4743 = 03spz *> conf = 0.02 ranks of expected_values: 466 EVAL 0bxg3 films 023p33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 84.000 27.000 0.333 http://example.org/film/film_subject/films #17598-047gn4y PRED entity: 047gn4y PRED relation: language PRED expected values: 02h40lc => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 36): 02h40lc (0.92 #417, 0.92 #298, 0.91 #358), 06nm1 (0.33 #70, 0.29 #11, 0.20 #129), 0jzc (0.22 #79, 0.14 #20, 0.10 #138), 064_8sq (0.20 #140, 0.15 #199, 0.14 #318), 06b_j (0.20 #141, 0.08 #438, 0.06 #497), 04h9h (0.14 #43, 0.11 #102, 0.10 #161), 03_9r (0.11 #69, 0.05 #780, 0.05 #720), 04306rv (0.11 #182, 0.10 #123, 0.09 #895), 03hkp (0.10 #133, 0.02 #192, 0.02 #964), 01r2l (0.10 #143, 0.01 #261) >> Best rule #417 for best value: >> intensional similarity = 4 >> extensional distance = 224 >> proper extension: 0c40vxk; >> query: (?x363, 02h40lc) <- genre(?x363, ?x811), film(?x1958, ?x363), cinematography(?x363, ?x7327), production_companies(?x363, ?x2156) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047gn4y language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.925 http://example.org/film/film/language #17597-03qncl3 PRED entity: 03qncl3 PRED relation: gender PRED expected values: 05zppz => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #13, 0.88 #101, 0.88 #99), 02zsn (0.46 #197, 0.24 #170, 0.24 #134) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 02lk1s; >> query: (?x10281, 05zppz) <- profession(?x10281, ?x319), company(?x10281, ?x11695), award_nominee(?x382, ?x10281), production_companies(?x586, ?x11695) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qncl3 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.900 http://example.org/people/person/gender #17596-01kqq7 PRED entity: 01kqq7 PRED relation: featured_film_locations PRED expected values: 0f2tj => 79 concepts (61 used for prediction) PRED predicted values (max 10 best out of 43): 02_286 (0.29 #502, 0.16 #2668, 0.16 #3874), 0fr0t (0.25 #84, 0.01 #1048), 030qb3t (0.12 #1003, 0.08 #2207, 0.07 #521), 03gh4 (0.10 #355, 0.02 #1319), 05kj_ (0.10 #258, 0.01 #741, 0.01 #3389), 0r0m6 (0.07 #571, 0.02 #13027), 0b90_r (0.07 #486, 0.02 #968, 0.02 #1208), 0cv3w (0.07 #552, 0.01 #3924, 0.01 #1034), 081m_ (0.07 #637), 0rh6k (0.07 #2169, 0.05 #965, 0.05 #1205) >> Best rule #502 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0dgpwnk; 02qr3k8; >> query: (?x10173, 02_286) <- film(?x752, ?x10173), film(?x6221, ?x10173), country(?x10173, ?x94), ?x6221 = 015p3p >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2291 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 398 *> proper extension: 03t97y; 02vw1w2; 03twd6; 05p3738; 04sntd; 01d259; 03ffcz; 025twgt; *> query: (?x10173, 0f2tj) <- film_release_distribution_medium(?x10173, ?x81), ?x81 = 029j_, genre(?x10173, ?x812), ?x812 = 01jfsb *> conf = 0.01 ranks of expected_values: 42 EVAL 01kqq7 featured_film_locations 0f2tj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 79.000 61.000 0.286 http://example.org/film/film/featured_film_locations #17595-03bnv PRED entity: 03bnv PRED relation: person! PRED expected values: 0bx_hnp => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 29): 0bx_hnp (0.11 #274, 0.10 #695, 0.10 #625), 02847m9 (0.07 #640, 0.07 #570, 0.06 #219), 053tj7 (0.06 #218, 0.05 #639, 0.05 #569), 037q31 (0.05 #536, 0.05 #816, 0.04 #956), 05c46y6 (0.04 #156, 0.03 #296, 0.02 #717), 05_61y (0.04 #884, 0.03 #394, 0.03 #464), 05_5_22 (0.04 #1850, 0.04 #1008, 0.04 #1148), 04dsnp (0.03 #215, 0.03 #356, 0.02 #636), 0g9lm2 (0.03 #233, 0.02 #514, 0.02 #654), 012jfb (0.03 #246, 0.02 #667, 0.02 #597) >> Best rule #274 for best value: >> intensional similarity = 2 >> extensional distance = 34 >> proper extension: 03pvt; 0gthm; >> query: (?x3321, 0bx_hnp) <- type_of_appearance(?x3321, ?x3429), award(?x3321, ?x724) >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bnv person! 0bx_hnp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.111 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #17594-0llcx PRED entity: 0llcx PRED relation: award PRED expected values: 0k611 => 121 concepts (94 used for prediction) PRED predicted values (max 10 best out of 215): 04kxsb (0.28 #1173, 0.28 #1032, 0.28 #1408), 0gq9h (0.28 #1408, 0.27 #935, 0.26 #5851), 0l8z1 (0.28 #1408, 0.27 #935, 0.26 #5851), 04dn09n (0.28 #1408, 0.27 #935, 0.26 #5851), 0gs9p (0.28 #1408, 0.27 #935, 0.26 #5851), 019f4v (0.28 #1408, 0.27 #935, 0.26 #5851), 094qd5 (0.28 #1408, 0.27 #935, 0.26 #5851), 0gr0m (0.28 #1408, 0.27 #935, 0.26 #5851), 054krc (0.28 #1408, 0.27 #935, 0.26 #5851), 040njc (0.28 #1408, 0.27 #935, 0.26 #5851) >> Best rule #1173 for best value: >> intensional similarity = 7 >> extensional distance = 65 >> proper extension: 09q5w2; >> query: (?x7883, ?x2375) <- genre(?x7883, ?x53), nominated_for(?x2375, ?x7883), nominated_for(?x1313, ?x7883), nominated_for(?x749, ?x7883), ?x1313 = 0gs9p, ?x2375 = 04kxsb, award_winner(?x749, ?x488) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #775 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 62 *> proper extension: 011yfd; *> query: (?x7883, 0k611) <- titles(?x812, ?x7883), nominated_for(?x1307, ?x7883), nominated_for(?x1079, ?x7883), ?x1307 = 0gq9h, ?x1079 = 0l8z1, language(?x7883, ?x254) *> conf = 0.20 ranks of expected_values: 21 EVAL 0llcx award 0k611 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 121.000 94.000 0.284 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #17593-01yx7f PRED entity: 01yx7f PRED relation: list PRED expected values: 01ptsx => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.86 #59, 0.85 #154, 0.84 #79), 09g7thr (0.53 #444, 0.49 #318, 0.49 #517), 05glt (0.53 #603, 0.38 #670, 0.09 #507), 026cl_m (0.26 #335, 0.12 #604, 0.09 #671) >> Best rule #59 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 01yfp7; 04f0xq; >> query: (?x10637, 01ptsx) <- company(?x265, ?x10637), service_location(?x10637, ?x335), list(?x10637, ?x8915), industry(?x10637, ?x12014), ?x8915 = 01pd60 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01yx7f list 01ptsx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.857 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #17592-0cymln PRED entity: 0cymln PRED relation: team PRED expected values: 02ptzz0 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 331): 0jmk7 (0.27 #664, 0.19 #1376, 0.17 #2800), 0cqt41 (0.22 #2167, 0.17 #4303, 0.17 #1455), 0jm2v (0.18 #385, 0.17 #2521, 0.16 #1809), 0jmdb (0.18 #366, 0.11 #1434, 0.09 #2146), 0jm3v (0.18 #370, 0.08 #2506, 0.08 #3218), 0jmcv (0.18 #535, 0.08 #2671, 0.08 #3383), 0bwjj (0.16 #2011, 0.13 #2367, 0.12 #2723), 01lpx8 (0.15 #3054, 0.09 #4122, 0.09 #5546), 085v7 (0.14 #4727, 0.12 #5083, 0.10 #3659), 0fbtm7 (0.12 #1256, 0.11 #1612, 0.08 #3036) >> Best rule #664 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01sg7_; >> query: (?x10097, 0jmk7) <- team(?x10097, ?x4571), position(?x4571, ?x1348), student(?x5907, ?x10097) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #423 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 01sg7_; *> query: (?x10097, 02ptzz0) <- team(?x10097, ?x4571), position(?x4571, ?x1348), student(?x5907, ?x10097) *> conf = 0.09 ranks of expected_values: 27 EVAL 0cymln team 02ptzz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 79.000 79.000 0.273 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #17591-027_sn PRED entity: 027_sn PRED relation: award PRED expected values: 0bdwqv => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 287): 0f4x7 (0.38 #31, 0.10 #3271, 0.10 #4081), 09sb52 (0.29 #7736, 0.25 #21506, 0.23 #29606), 0bdwqv (0.25 #173, 0.11 #2603, 0.09 #7868), 01by1l (0.25 #1733, 0.24 #1328, 0.06 #9833), 0ck27z (0.23 #5763, 0.22 #3738, 0.22 #6978), 01bgqh (0.22 #1258, 0.17 #1663, 0.05 #31229), 03qbh5 (0.19 #1421, 0.17 #1826, 0.08 #1016), 01c427 (0.19 #1300, 0.12 #1705, 0.03 #9805), 09qrn4 (0.18 #645, 0.06 #1050, 0.05 #2670), 0cqhk0 (0.15 #5707, 0.15 #3682, 0.14 #442) >> Best rule #31 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0gr36; 01vy_v8; 0c0k1; >> query: (?x7076, 0f4x7) <- type_of_union(?x7076, ?x566), nationality(?x7076, ?x94), film(?x7076, ?x8486), ?x8486 = 0f3m1 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #173 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0gr36; 01vy_v8; 0c0k1; *> query: (?x7076, 0bdwqv) <- type_of_union(?x7076, ?x566), nationality(?x7076, ?x94), film(?x7076, ?x8486), ?x8486 = 0f3m1 *> conf = 0.25 ranks of expected_values: 3 EVAL 027_sn award 0bdwqv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 121.000 121.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17590-01n8_g PRED entity: 01n8_g PRED relation: type_of_union PRED expected values: 04ztj => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #9, 0.80 #157, 0.80 #161), 01bl8s (0.59 #209), 01g63y (0.20 #166, 0.19 #70, 0.18 #190) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 016jfw; 03lmzl; 0d3k14; 02g5bf; >> query: (?x2385, 04ztj) <- profession(?x2385, ?x319), sibling(?x10750, ?x2385), award_winner(?x4687, ?x2385), people(?x5855, ?x10750) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n8_g type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.867 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17589-039crh PRED entity: 039crh PRED relation: languages PRED expected values: 06nm1 064_8sq => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 24): 064_8sq (0.23 #470, 0.19 #546, 0.17 #622), 0t_2 (0.11 #388, 0.11 #122, 0.11 #426), 02bjrlw (0.09 #191, 0.07 #1027, 0.06 #1559), 071fb (0.09 #201), 03k50 (0.09 #1865, 0.08 #3045, 0.08 #3273), 06nm1 (0.06 #1449, 0.05 #1563, 0.05 #1829), 03hkp (0.06 #389, 0.05 #465, 0.04 #541), 03_9r (0.06 #384, 0.03 #1106, 0.02 #1866), 02ztjwg (0.06 #404, 0.02 #1088), 07c9s (0.05 #1874, 0.04 #3054, 0.04 #3282) >> Best rule #470 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 01wc7p; >> query: (?x4407, 064_8sq) <- actor(?x2137, ?x4407), languages(?x4407, ?x254), participant(?x4407, ?x3293), type_of_union(?x4407, ?x566) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 039crh languages 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.227 http://example.org/people/person/languages EVAL 039crh languages 06nm1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 183.000 183.000 0.227 http://example.org/people/person/languages #17588-0f3zf_ PRED entity: 0f3zf_ PRED relation: cinematography! PRED expected values: 0m3gy => 55 concepts (33 used for prediction) PRED predicted values (max 10 best out of 338): 0bshwmp (0.19 #673), 016fyc (0.19 #673), 03wy8t (0.05 #974, 0.05 #1310, 0.04 #1646), 03cw411 (0.05 #789, 0.05 #1125, 0.04 #1461), 0kbhf (0.05 #1202, 0.04 #2210, 0.04 #529), 0jvt9 (0.05 #1113, 0.04 #2121, 0.03 #2457), 083skw (0.05 #1089, 0.04 #2097, 0.03 #2433), 0jymd (0.04 #1471, 0.04 #1807, 0.03 #2479), 0422v0 (0.04 #671, 0.02 #1008, 0.02 #1344), 04hk0w (0.04 #670, 0.02 #1007, 0.02 #1343) >> Best rule #673 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 04flrx; >> query: (?x1075, ?x1066) <- cinematography(?x5672, ?x1075), nominated_for(?x5672, ?x1066), film(?x1335, ?x5672) >> conf = 0.19 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0f3zf_ cinematography! 0m3gy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 33.000 0.194 http://example.org/film/film/cinematography #17587-0h3vhfb PRED entity: 0h3vhfb PRED relation: award! PRED expected values: 04n7njg => 77 concepts (31 used for prediction) PRED predicted values (max 10 best out of 3869): 06pj8 (0.37 #10711, 0.27 #17481, 0.21 #24254), 02kxbwx (0.37 #10333, 0.22 #17103, 0.21 #23876), 03h_fk5 (0.34 #17692, 0.26 #24465, 0.08 #78643), 02kxbx3 (0.33 #11142, 0.20 #17912, 0.19 #24685), 05ldnp (0.33 #11051, 0.20 #17821, 0.19 #24594), 0jmj (0.30 #8007, 0.22 #4623, 0.22 #14777), 0gyx4 (0.30 #11410, 0.22 #18180, 0.17 #24953), 0c12h (0.30 #11982, 0.22 #18752, 0.17 #25525), 0jrqq (0.30 #11234, 0.22 #18004, 0.17 #24777), 05kfs (0.30 #10317, 0.20 #17087, 0.17 #23860) >> Best rule #10711 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 040vk98; 0g9wd99; 058bzgm; >> query: (?x11338, 06pj8) <- award(?x6682, ?x11338), profession(?x6682, ?x319), company(?x6682, ?x3945), story_by(?x1259, ?x6682), nominated_for(?x6682, ?x3457) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #30468 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 02rdxsh; 054knh; 02py_sj; *> query: (?x11338, ?x1182) <- nominated_for(?x11338, ?x8554), program_creator(?x8554, ?x1182), actor(?x8554, ?x381), honored_for(?x2213, ?x8554) *> conf = 0.19 ranks of expected_values: 128 EVAL 0h3vhfb award! 04n7njg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 77.000 31.000 0.367 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17586-0gvvf4j PRED entity: 0gvvf4j PRED relation: genre PRED expected values: 01jfsb => 71 concepts (69 used for prediction) PRED predicted values (max 10 best out of 94): 01jfsb (0.69 #1090, 0.65 #1575, 0.49 #738), 01q03 (0.69 #1090, 0.65 #1575, 0.17 #5), 0jdm8 (0.69 #1090, 0.65 #1575, 0.06 #2908), 07s9rl0 (0.65 #6185, 0.60 #7881, 0.58 #2181), 02kdv5l (0.62 #124, 0.56 #1093, 0.55 #971), 03k9fj (0.55 #253, 0.43 #1101, 0.42 #737), 02l7c8 (0.38 #3289, 0.34 #3167, 0.28 #1954), 0lsxr (0.38 #129, 0.26 #734, 0.23 #1098), 06n90 (0.31 #981, 0.25 #1588, 0.21 #1224), 01hmnh (0.29 #1350, 0.28 #1471, 0.28 #1593) >> Best rule #1090 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 018nnz; 01kf3_9; 01f7kl; 08984j; 02mc5v; 01_1hw; >> query: (?x7678, ?x258) <- prequel(?x7678, ?x6681), film(?x815, ?x6681), executive_produced_by(?x7678, ?x4060), genre(?x6681, ?x258) >> conf = 0.69 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0gvvf4j genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 69.000 0.686 http://example.org/film/film/genre #17585-0mpbx PRED entity: 0mpbx PRED relation: place_of_birth! PRED expected values: 01w_10 => 142 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1775): 01w_10 (0.37 #47002, 0.32 #211524, 0.28 #151461), 02rn_bj (0.25 #1735, 0.04 #17403, 0.03 #27848), 05zh9c (0.25 #980, 0.04 #16648, 0.03 #27093), 01d8yn (0.25 #723, 0.04 #16391, 0.03 #26836), 02cx72 (0.25 #716, 0.04 #16384, 0.03 #26829), 0146pg (0.25 #97, 0.04 #15765, 0.03 #26210), 0mfc0 (0.25 #1962, 0.03 #30686, 0.02 #41130), 05lb30 (0.25 #1362, 0.03 #30086, 0.02 #40530), 02sb1w (0.25 #1308, 0.03 #30032, 0.02 #40476), 03h_0_z (0.25 #1257, 0.03 #29981, 0.02 #40425) >> Best rule #47002 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0mnsf; 0tr3p; 0mnwd; 0p4gy; >> query: (?x11240, ?x3308) <- source(?x11240, ?x958), currency(?x11240, ?x170), location(?x3308, ?x11240) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mpbx place_of_birth! 01w_10 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 98.000 0.371 http://example.org/people/person/place_of_birth #17584-02ddq4 PRED entity: 02ddq4 PRED relation: award! PRED expected values: 0565cz 02cx90 0180w8 => 33 concepts (13 used for prediction) PRED predicted values (max 10 best out of 2091): 0pkyh (0.80 #3373, 0.79 #37105, 0.79 #30357), 07r1_ (0.50 #2057, 0.08 #25667, 0.07 #29040), 02qwg (0.25 #931, 0.19 #26983, 0.17 #13493), 015882 (0.25 #464, 0.19 #26983, 0.17 #13493), 02cx90 (0.25 #1229, 0.19 #26983, 0.17 #13493), 01kd57 (0.25 #1629, 0.19 #26983, 0.17 #13493), 06rgq (0.25 #2455, 0.19 #26983, 0.17 #33731), 0137n0 (0.25 #304, 0.19 #26983, 0.17 #33731), 03bnv (0.25 #911, 0.19 #26983, 0.17 #33731), 0m2l9 (0.25 #97, 0.19 #26983, 0.17 #33731) >> Best rule #3373 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01bgqh; 01d38t; >> query: (?x10316, ?x1089) <- award_winner(?x10316, ?x2930), award_winner(?x10316, ?x1089), award(?x1992, ?x10316), ceremony(?x10316, ?x139), ?x2930 = 0pkyh >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1229 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01bgqh; 01d38t; *> query: (?x10316, 02cx90) <- award_winner(?x10316, ?x2930), award(?x1992, ?x10316), ceremony(?x10316, ?x139), ?x2930 = 0pkyh *> conf = 0.25 ranks of expected_values: 5, 210, 839 EVAL 02ddq4 award! 0180w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 13.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02ddq4 award! 02cx90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 33.000 13.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02ddq4 award! 0565cz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 33.000 13.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17583-0nvt9 PRED entity: 0nvt9 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 197 concepts (110 used for prediction) PRED predicted values (max 10 best out of 11): 09c7w0 (0.89 #291, 0.89 #279, 0.89 #405), 03v0t (0.26 #645, 0.26 #495, 0.17 #138), 0nvt9 (0.26 #645, 0.26 #495, 0.08 #749), 01w65s (0.26 #645, 0.26 #495), 0psxp (0.08 #956, 0.04 #996, 0.03 #1036), 02jx1 (0.07 #552, 0.06 #850, 0.06 #771), 03v1s (0.05 #1148, 0.02 #661, 0.02 #381), 0d060g (0.04 #81, 0.04 #191, 0.03 #116), 03rt9 (0.02 #1041, 0.02 #1394, 0.02 #1165), 07ssc (0.02 #1247, 0.01 #755, 0.01 #859) >> Best rule #291 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 0mxcf; 0nr_q; 0dc3_; 0p01x; 0fkhl; 0n5kc; 0mwvq; 0kwgs; 0cc1v; 0f6zs; ... >> query: (?x6410, 09c7w0) <- source(?x6410, ?x958), county(?x1860, ?x6410), adjoins(?x1963, ?x6410), currency(?x6410, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nvt9 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 197.000 110.000 0.894 http://example.org/location/country/second_level_divisions #17582-07wtc PRED entity: 07wtc PRED relation: contains! PRED expected values: 0hx5f => 74 concepts (61 used for prediction) PRED predicted values (max 10 best out of 188): 0hx5f (0.78 #27700, 0.77 #29488, 0.77 #26806), 07ssc (0.76 #27731, 0.64 #17900, 0.47 #7176), 09c7w0 (0.65 #16979, 0.65 #39321, 0.64 #33959), 04xn_ (0.44 #37530), 0978r (0.17 #5564, 0.15 #7350, 0.14 #1098), 04jpl (0.16 #17891, 0.15 #7167, 0.14 #915), 09ctj (0.14 #1739, 0.02 #18715, 0.02 #6205), 088cp (0.14 #1643, 0.02 #6109, 0.01 #7895), 01llj3 (0.14 #1722, 0.02 #6188, 0.01 #7974), 0nccd (0.14 #1136) >> Best rule #27700 for best value: >> intensional similarity = 3 >> extensional distance = 332 >> proper extension: 02_gzx; >> query: (?x11740, ?x12491) <- contains(?x455, ?x11740), citytown(?x11740, ?x12491), school_type(?x11740, ?x3092) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07wtc contains! 0hx5f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 61.000 0.775 http://example.org/location/location/contains #17581-04tnqn PRED entity: 04tnqn PRED relation: currency PRED expected values: 09nqf => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.50 #1, 0.41 #19, 0.38 #16), 01nv4h (0.04 #17, 0.03 #14, 0.03 #23) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 016_mj; 01900g; >> query: (?x9656, 09nqf) <- award_nominee(?x906, ?x9656), influenced_by(?x9656, ?x8065), ?x8065 = 02633g >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04tnqn currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.500 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #17580-042gr4 PRED entity: 042gr4 PRED relation: nationality PRED expected values: 03_3d => 82 concepts (76 used for prediction) PRED predicted values (max 10 best out of 26): 03_3d (0.88 #706, 0.83 #606, 0.75 #506), 09c7w0 (0.72 #3002, 0.72 #2802, 0.72 #3303), 03rk0 (0.15 #1747, 0.15 #1446, 0.13 #1646), 02jx1 (0.12 #333, 0.12 #2734, 0.11 #2134), 07ssc (0.12 #315, 0.09 #2716, 0.08 #2316), 03h64 (0.12 #353, 0.08 #953, 0.01 #1353), 0d060g (0.11 #1007, 0.10 #1107, 0.09 #1207), 0d05w3 (0.09 #950, 0.02 #1650, 0.02 #1751), 0h7x (0.06 #335, 0.01 #935), 03rt9 (0.03 #913, 0.02 #1013, 0.02 #1113) >> Best rule #706 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 01kwh5j; >> query: (?x13336, 03_3d) <- special_performance_type(?x13336, ?x296), profession(?x13336, ?x1383), ?x1383 = 0np9r, ?x296 = 01kyvx, actor(?x3721, ?x13336) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042gr4 nationality 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 76.000 0.880 http://example.org/people/person/nationality #17579-0180mw PRED entity: 0180mw PRED relation: award_winner PRED expected values: 030_1_ => 55 concepts (48 used for prediction) PRED predicted values (max 10 best out of 845): 02vqpx8 (0.59 #11465, 0.54 #19652, 0.51 #55674), 056wb (0.59 #11465, 0.54 #19652, 0.51 #55674), 06pj8 (0.59 #11465, 0.54 #19652, 0.51 #55674), 02p65p (0.59 #11465, 0.54 #19652, 0.51 #55674), 03c6vl (0.59 #11465, 0.54 #19652, 0.51 #55674), 03rs8y (0.59 #11465, 0.54 #19652, 0.51 #55674), 016ks_ (0.59 #11465, 0.54 #19652, 0.51 #55674), 03kcyd (0.59 #11465, 0.54 #19652, 0.51 #55674), 02js9p (0.59 #11465, 0.54 #19652, 0.51 #55674), 024bbl (0.54 #19652, 0.49 #11464, 0.49 #4910) >> Best rule #11465 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 0cskb; >> query: (?x6482, ?x2135) <- nominated_for(?x2135, ?x6482), award_winner(?x2135, ?x798), tv_program(?x10301, ?x6482) >> conf = 0.59 => this is the best rule for 9 predicted values *> Best rule #3538 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 015g28; *> query: (?x6482, 030_1_) <- nominated_for(?x2135, ?x6482), ?x2135 = 06pj8, award(?x6482, ?x435) *> conf = 0.06 ranks of expected_values: 250 EVAL 0180mw award_winner 030_1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 48.000 0.593 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #17578-0f1sm PRED entity: 0f1sm PRED relation: location_of_ceremony! PRED expected values: 03n6r => 136 concepts (49 used for prediction) PRED predicted values (max 10 best out of 131): 01bmlb (0.07 #479, 0.03 #733, 0.03 #987), 0bymv (0.07 #307, 0.03 #561, 0.03 #815), 01t265 (0.07 #417, 0.02 #5266), 0dvld (0.04 #2438, 0.04 #2949, 0.04 #3973), 02m30v (0.04 #2800, 0.04 #3311, 0.04 #3822), 048hf (0.03 #692, 0.03 #946, 0.03 #1457), 02fn5 (0.03 #611, 0.03 #1120, 0.03 #1630), 054k_8 (0.03 #644, 0.03 #1153, 0.02 #2682), 02mjf2 (0.03 #1124, 0.03 #869, 0.03 #1380), 0fpj9pm (0.03 #1187, 0.03 #932, 0.03 #1443) >> Best rule #479 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 029cr; >> query: (?x9445, 01bmlb) <- locations(?x11210, ?x9445), locations(?x4803, ?x9445), ?x4803 = 0b_6jz, team(?x11210, ?x2303) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f1sm location_of_ceremony! 03n6r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 49.000 0.067 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #17577-0bmch_x PRED entity: 0bmch_x PRED relation: language PRED expected values: 02hwyss => 126 concepts (111 used for prediction) PRED predicted values (max 10 best out of 53): 064_8sq (0.47 #911, 0.36 #133, 0.25 #1439), 03_9r (0.47 #911, 0.14 #180, 0.10 #977), 06b_j (0.21 #248, 0.15 #305, 0.13 #418), 02bjrlw (0.20 #286, 0.14 #229, 0.13 #399), 06nm1 (0.18 #519, 0.14 #237, 0.14 #1372), 0t_2 (0.14 #184, 0.05 #466, 0.04 #4686), 05zjd (0.09 #137, 0.05 #704, 0.04 #4686), 012w70 (0.09 #125, 0.05 #748, 0.04 #4686), 02bv9 (0.07 #253, 0.05 #310, 0.04 #4686), 01wgr (0.07 #209, 0.04 #4686, 0.04 #2736) >> Best rule #911 for best value: >> intensional similarity = 6 >> extensional distance = 68 >> proper extension: 018nnz; 0k54q; 03kx49; 01k5y0; >> query: (?x4860, ?x254) <- genre(?x4860, ?x53), country(?x4860, ?x279), film_distribution_medium(?x4860, ?x2099), country(?x150, ?x279), official_language(?x279, ?x254), film_release_region(?x66, ?x279) >> conf = 0.47 => this is the best rule for 2 predicted values *> Best rule #4686 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 907 *> proper extension: 0413cff; 05_61y; *> query: (?x4860, ?x254) <- genre(?x4860, ?x600), currency(?x4860, ?x170), genre(?x5829, ?x600), genre(?x3430, ?x600), ?x3430 = 0ctb4g, language(?x5829, ?x254), featured_film_locations(?x5829, ?x739) *> conf = 0.04 ranks of expected_values: 24 EVAL 0bmch_x language 02hwyss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 126.000 111.000 0.470 http://example.org/film/film/language #17576-0gl88b PRED entity: 0gl88b PRED relation: award_winner! PRED expected values: 0dth6b => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 132): 0dth6b (0.38 #435, 0.10 #9592, 0.09 #1120), 0dznvw (0.33 #816, 0.33 #679, 0.15 #1364), 0c6vcj (0.27 #1195, 0.02 #3387, 0.02 #2154), 0bzm81 (0.22 #844, 0.20 #981, 0.14 #22), 0fzrtf (0.18 #1156, 0.14 #60, 0.12 #197), 0c53zb (0.18 #1155, 0.14 #59, 0.12 #196), 0bz6l9 (0.18 #1145, 0.12 #460, 0.10 #9592), 02jp5r (0.16 #1574, 0.11 #889, 0.10 #1026), 073h1t (0.16 #1533, 0.03 #2903, 0.02 #4821), 0ftlxj (0.14 #68, 0.12 #479, 0.12 #205) >> Best rule #435 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 079hvk; 0cg9f; >> query: (?x2068, 0dth6b) <- award_winner(?x6269, ?x2068), ?x6269 = 0286gm1 >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gl88b award_winner! 0dth6b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.375 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17575-02lk95 PRED entity: 02lk95 PRED relation: award PRED expected values: 099vwn => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 267): 09sb52 (0.32 #13309, 0.26 #5267, 0.26 #11297), 01by1l (0.30 #5740, 0.30 #514, 0.29 #3730), 01bgqh (0.27 #5671, 0.26 #847, 0.22 #445), 054ks3 (0.22 #2149, 0.20 #1747, 0.18 #3355), 0c4z8 (0.20 #3690, 0.20 #3288, 0.19 #2082), 03qbh5 (0.20 #3419, 0.20 #1811, 0.19 #1007), 026mg3 (0.19 #816, 0.06 #3228, 0.05 #12865), 026mff (0.19 #163, 0.16 #967, 0.05 #12865), 05pcn59 (0.19 #5308, 0.17 #6112, 0.14 #7318), 01c92g (0.18 #2107, 0.16 #2509, 0.14 #3715) >> Best rule #13309 for best value: >> intensional similarity = 2 >> extensional distance = 1236 >> proper extension: 02zq43; 073749; 01wz01; >> query: (?x4560, 09sb52) <- award_nominee(?x2639, ?x4560), film(?x4560, ?x4967) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1822 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 121 *> proper extension: 01tszq; 02qfhb; 03llf8; 04bgy; 01vsqvs; 01k_0fp; 09g0h; *> query: (?x4560, 099vwn) <- nationality(?x4560, ?x94), instrumentalists(?x227, ?x4560), film(?x4560, ?x4967) *> conf = 0.10 ranks of expected_values: 44 EVAL 02lk95 award 099vwn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 92.000 92.000 0.321 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17574-04gp1d PRED entity: 04gp1d PRED relation: legislative_sessions! PRED expected values: 07t58 => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 6): 07t58 (0.91 #241, 0.90 #220, 0.90 #262), 0x2sv (0.13 #193, 0.10 #264), 0h6dy (0.10 #194, 0.08 #265), 0l_j_ (0.07 #195, 0.06 #266), 030p4s (0.03 #197, 0.02 #268), 0162kb (0.02 #267) >> Best rule #241 for best value: >> intensional similarity = 36 >> extensional distance = 34 >> proper extension: 01gst_; >> query: (?x3765, ?x4665) <- legislative_sessions(?x5977, ?x3765), legislative_sessions(?x2861, ?x3765), district_represented(?x3765, ?x2020), legislative_sessions(?x4665, ?x2861), legislative_sessions(?x2860, ?x2861), legislative_sessions(?x5266, ?x2861), ?x2860 = 0b3wk, legislative_sessions(?x6933, ?x5977), district_represented(?x2861, ?x7405), district_represented(?x2861, ?x6895), district_represented(?x2861, ?x4754), district_represented(?x2861, ?x3670), district_represented(?x2861, ?x2049), district_represented(?x2861, ?x961), district_represented(?x2861, ?x448), legislative_sessions(?x652, ?x5977), religion(?x2049, ?x109), location(?x2295, ?x2049), country(?x2049, ?x94), district_represented(?x11142, ?x4754), district_represented(?x5256, ?x4754), district_represented(?x4812, ?x4754), adjoins(?x1351, ?x2049), ?x6895 = 05fjf, district_represented(?x6933, ?x2831), ?x5256 = 01grqd, ?x3670 = 05tbn, ?x448 = 03v1s, ?x2831 = 0gyh, contains(?x2049, ?x5554), ?x4812 = 01grpc, ?x961 = 03s0w, ?x2020 = 05k7sb, ?x11142 = 01grq1, ?x7405 = 07_f2, jurisdiction_of_office(?x900, ?x2049) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gp1d legislative_sessions! 07t58 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.914 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #17573-0gn30 PRED entity: 0gn30 PRED relation: nationality PRED expected values: 09c7w0 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 28): 09c7w0 (0.80 #702, 0.79 #802, 0.76 #2705), 02jx1 (0.14 #2136, 0.10 #5245, 0.10 #1936), 0d060g (0.12 #508, 0.07 #608, 0.05 #1610), 07ssc (0.11 #2118, 0.11 #215, 0.10 #2820), 03rk0 (0.10 #3652, 0.08 #4356, 0.06 #3853), 05bcl (0.05 #60, 0.05 #160, 0.04 #260), 03spz (0.05 #167, 0.04 #267, 0.03 #367), 03rt9 (0.05 #113, 0.03 #313, 0.02 #414), 0f8l9c (0.05 #122, 0.02 #423, 0.02 #3027), 05qhw (0.05 #114, 0.02 #415) >> Best rule #702 for best value: >> intensional similarity = 2 >> extensional distance = 80 >> proper extension: 0n6f8; 02dh86; 0klh7; 01ft2l; 02t_w8; 013pp3; 0f5zj6; 02jr26; 01zh29; 07jrjb; ... >> query: (?x5338, 09c7w0) <- student(?x8398, ?x5338), award_winner(?x1311, ?x5338) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gn30 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.805 http://example.org/people/person/nationality #17572-01d2v1 PRED entity: 01d2v1 PRED relation: language PRED expected values: 02h40lc => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 57): 02h40lc (0.89 #1369, 0.89 #1666, 0.89 #357), 064_8sq (0.16 #2978, 0.14 #81, 0.13 #1566), 02bjrlw (0.16 #2978, 0.11 #60, 0.07 #416), 04306rv (0.16 #2978, 0.09 #656, 0.09 #1013), 03_9r (0.16 #2978, 0.08 #543, 0.06 #780), 06b_j (0.16 #2978, 0.08 #260, 0.08 #674), 012w70 (0.16 #2978, 0.04 #664, 0.04 #724), 05zjd (0.16 #2978, 0.03 #1842, 0.02 #855), 01jb8r (0.16 #2978, 0.03 #1842, 0.01 #54), 07qv_ (0.16 #2978, 0.03 #1842, 0.01 #33) >> Best rule #1369 for best value: >> intensional similarity = 4 >> extensional distance = 704 >> proper extension: 03g90h; 09xbpt; 047gn4y; 0dnvn3; 03h_yy; 02_1sj; 026mfbr; 09p35z; 02sg5v; 04gknr; ... >> query: (?x11174, 02h40lc) <- produced_by(?x11174, ?x595), genre(?x11174, ?x225), film(?x397, ?x11174), currency(?x11174, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d2v1 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.894 http://example.org/film/film/language #17571-01kp_1t PRED entity: 01kp_1t PRED relation: currency PRED expected values: 09nqf => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.50 #1, 0.39 #22, 0.36 #25), 01nv4h (0.03 #83, 0.03 #23, 0.03 #95) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 019g40; >> query: (?x9528, 09nqf) <- artists(?x1952, ?x9528), artist(?x2299, ?x9528), award(?x9528, ?x4018), ?x1952 = 021_z5 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kp_1t currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.500 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #17570-03nymk PRED entity: 03nymk PRED relation: actor PRED expected values: 0jbp0 => 87 concepts (68 used for prediction) PRED predicted values (max 10 best out of 685): 027xbpw (0.25 #269, 0.09 #17686, 0.06 #1199), 03jldb (0.25 #122, 0.09 #17686, 0.06 #1052), 02tqkf (0.25 #238, 0.06 #1168, 0.05 #3029), 023v4_ (0.25 #406, 0.06 #1336, 0.03 #2266), 01pm0_ (0.25 #507, 0.06 #1437, 0.03 #2367), 02k4b2 (0.25 #431, 0.06 #1361, 0.03 #2291), 02q6cv4 (0.24 #3722, 0.14 #2791, 0.13 #9309), 0f721s (0.13 #9309, 0.11 #13033, 0.10 #13964), 0725ny (0.12 #1573, 0.04 #19259, 0.03 #11815), 01lly5 (0.12 #1233, 0.04 #4025, 0.04 #4956) >> Best rule #269 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0gj9tn5; >> query: (?x8396, 027xbpw) <- nominated_for(?x11272, ?x8396), nominated_for(?x3673, ?x8396), ?x3673 = 021yw7, award(?x9787, ?x11272) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #12879 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 97 *> proper extension: 03_8kz; *> query: (?x8396, 0jbp0) <- languages(?x8396, ?x254), program(?x1394, ?x8396), ?x254 = 02h40lc, producer_type(?x8396, ?x632) *> conf = 0.01 ranks of expected_values: 643 EVAL 03nymk actor 0jbp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 68.000 0.250 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #17569-020_95 PRED entity: 020_95 PRED relation: film PRED expected values: 02c638 => 96 concepts (64 used for prediction) PRED predicted values (max 10 best out of 804): 03cv_gy (0.73 #3565, 0.47 #81996, 0.44 #23174), 046f3p (0.73 #3565, 0.47 #81996, 0.42 #99820), 0330r (0.73 #3565, 0.47 #81996, 0.41 #23173), 04xbq3 (0.07 #28521, 0.07 #14259, 0.07 #7130), 031hcx (0.07 #3051, 0.03 #105167, 0.01 #58313), 0ds3t5x (0.05 #1836, 0.05 #3619, 0.04 #54), 02qr3k8 (0.05 #3066, 0.04 #1284, 0.02 #28022), 03nqnnk (0.05 #2801, 0.04 #1019, 0.02 #8149), 0_9l_ (0.05 #3511, 0.03 #105167, 0.02 #1729), 0ctb4g (0.05 #2335, 0.03 #105167, 0.02 #553) >> Best rule #3565 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 06lj1m; 019f2f; 01dw9z; 01xcfy; 03bxsw; 02f2p7; 0bdt8; 0lfbm; 0421st; 01jw4r; ... >> query: (?x5454, ?x2029) <- award(?x5454, ?x1716), nominated_for(?x5454, ?x2029), ?x1716 = 02y_rq5 >> conf = 0.73 => this is the best rule for 3 predicted values *> Best rule #105167 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1570 *> proper extension: 02v49c; *> query: (?x5454, ?x224) <- award_nominee(?x5454, ?x2457), profession(?x5454, ?x1032), film(?x2457, ?x224) *> conf = 0.03 ranks of expected_values: 112 EVAL 020_95 film 02c638 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 96.000 64.000 0.735 http://example.org/film/actor/film./film/performance/film #17568-06cgf PRED entity: 06cgf PRED relation: language PRED expected values: 02h40lc => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.89 #772, 0.89 #713, 0.88 #593), 03_9r (0.74 #246, 0.71 #128, 0.64 #482), 064_8sq (0.16 #317, 0.14 #1507, 0.14 #553), 04306rv (0.14 #775, 0.13 #596, 0.12 #1550), 06nm1 (0.13 #781, 0.11 #1437, 0.11 #306), 05zjd (0.11 #262, 0.08 #380, 0.07 #498), 04h9h (0.11 #338, 0.07 #220, 0.04 #1230), 02bjrlw (0.10 #1367, 0.10 #1605, 0.10 #1486), 06b_j (0.08 #793, 0.08 #614, 0.07 #1210), 012w70 (0.07 #190, 0.05 #308, 0.04 #426) >> Best rule #772 for best value: >> intensional similarity = 3 >> extensional distance = 117 >> proper extension: 011xg5; 02bj22; >> query: (?x10873, 02h40lc) <- films(?x2286, ?x10873), genre(?x10873, ?x258), category(?x10873, ?x134) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06cgf language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.891 http://example.org/film/film/language #17567-07tg4 PRED entity: 07tg4 PRED relation: institution! PRED expected values: 02cq61 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 11): 0bjrnt (0.40 #92, 0.33 #66, 0.33 #27), 013zdg (0.36 #197, 0.28 #757, 0.24 #119), 027f2w (0.33 #172, 0.33 #68, 0.28 #757), 02cq61 (0.33 #73, 0.28 #757, 0.20 #60), 022h5x (0.28 #757, 0.13 #179, 0.11 #455), 01ysy9 (0.28 #757, 0.11 #77, 0.08 #103), 02m4yg (0.28 #757, 0.11 #72, 0.07 #176), 01gkg3 (0.28 #757, 0.01 #542, 0.01 #568), 028dcg (0.22 #74, 0.20 #61, 0.11 #376), 03mkk4 (0.20 #121, 0.18 #147, 0.17 #173) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 01zn4y; 030nwm; >> query: (?x2999, 0bjrnt) <- company(?x2998, ?x2999), currency(?x2999, ?x1099), ?x1099 = 01nv4h >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #73 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 7 *> proper extension: 039cpd; *> query: (?x2999, 02cq61) <- contains(?x455, ?x2999), child(?x2999, ?x7306) *> conf = 0.33 ranks of expected_values: 4 EVAL 07tg4 institution! 02cq61 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 66.000 66.000 0.400 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #17566-0kv238 PRED entity: 0kv238 PRED relation: executive_produced_by PRED expected values: 0gg9_5q => 100 concepts (71 used for prediction) PRED predicted values (max 10 best out of 74): 05hj_k (0.12 #605, 0.07 #858, 0.06 #3894), 0343h (0.06 #42, 0.06 #295, 0.05 #1055), 06pj8 (0.06 #55, 0.05 #1322, 0.04 #3851), 079vf (0.06 #1269, 0.03 #2, 0.02 #2283), 02q_cc (0.06 #1801, 0.04 #1041, 0.03 #1547), 06q8hf (0.06 #420, 0.05 #3963, 0.05 #674), 02lp3c (0.05 #2027, 0.03 #1267, 0.03 #1773), 04jspq (0.05 #658, 0.04 #2432, 0.04 #911), 0glyyw (0.05 #696, 0.04 #949, 0.03 #1456), 04pqqb (0.05 #624, 0.04 #877, 0.02 #2145) >> Best rule #605 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 03bx2lk; 01fmys; 03mgx6z; 02qk3fk; >> query: (?x2714, 05hj_k) <- film_release_region(?x2714, ?x3749), film(?x2922, ?x2714), ?x3749 = 03ryn, titles(?x571, ?x2714) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #90 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 01ln5z; 065dc4; 02nx2k; 02vjp3; 03whyr; *> query: (?x2714, 0gg9_5q) <- film(?x2922, ?x2714), film_crew_role(?x2714, ?x2091), category(?x2714, ?x134), ?x2091 = 02rh1dz *> conf = 0.03 ranks of expected_values: 17 EVAL 0kv238 executive_produced_by 0gg9_5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 100.000 71.000 0.119 http://example.org/film/film/executive_produced_by #17565-05cj_j PRED entity: 05cj_j PRED relation: titles! PRED expected values: 01z4y => 99 concepts (84 used for prediction) PRED predicted values (max 10 best out of 122): 01z4y (0.51 #1434, 0.49 #1334, 0.39 #4857), 028v3 (0.34 #299, 0.26 #7764, 0.25 #7866), 0vjs6 (0.34 #299, 0.25 #7866, 0.22 #599), 05p553 (0.34 #299, 0.23 #2506, 0.22 #599), 0vgkd (0.34 #299, 0.22 #599, 0.22 #100), 07s9rl0 (0.32 #5128, 0.30 #6340, 0.29 #6544), 04xvlr (0.25 #2510, 0.25 #4, 0.24 #4024), 09blyk (0.25 #244, 0.25 #45, 0.24 #1044), 024qqx (0.22 #677, 0.17 #777, 0.16 #3595), 04t36 (0.18 #1809, 0.12 #2111, 0.10 #4028) >> Best rule #1434 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 034qrh; 03s6l2; 05m_jsg; 0dln8jk; 0bxxzb; 0g7pm1; 05dptj; 0ddf2bm; 02wwmhc; >> query: (?x1708, 01z4y) <- genre(?x1708, ?x258), film(?x1104, ?x1708), ?x258 = 05p553, nominated_for(?x1708, ?x2094) >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05cj_j titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 84.000 0.508 http://example.org/media_common/netflix_genre/titles #17564-0dgrmp PRED entity: 0dgrmp PRED relation: team PRED expected values: 02b0y3 02b14q 02b1mr => 9 concepts (9 used for prediction) PRED predicted values (max 10 best out of 486): 0j46b (0.88 #2418, 0.87 #2904, 0.84 #482), 03v9yw (0.88 #2418, 0.87 #2904, 0.84 #482), 014nzp (0.88 #2418, 0.87 #2904, 0.84 #482), 01_8n9 (0.88 #2418, 0.87 #2904, 0.84 #482), 046f25 (0.88 #2418, 0.87 #2904, 0.84 #482), 04gkp3 (0.88 #2418, 0.87 #2904, 0.84 #482), 03_9hm (0.88 #2418, 0.87 #2904, 0.84 #482), 04b4yg (0.88 #2418, 0.87 #2904, 0.84 #482), 032498 (0.88 #2418, 0.87 #2904, 0.84 #482), 01n_2f (0.88 #2418, 0.87 #2904, 0.84 #482) >> Best rule #2418 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 02qvgy; >> query: (?x203, ?x9511) <- position(?x9511, ?x203), position(?x9319, ?x203), position(?x6871, ?x203), position(?x4281, ?x203), team(?x530, ?x9511), ?x530 = 02_j1w, team(?x203, ?x852), colors(?x9319, ?x332), teams(?x2863, ?x6871), teams(?x8958, ?x4281) >> conf = 0.88 => this is the best rule for 84 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 62, 173, 230 EVAL 0dgrmp team 02b1mr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 9.000 9.000 0.878 http://example.org/sports/sports_position/players./sports/sports_team_roster/team EVAL 0dgrmp team 02b14q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 9.000 9.000 0.878 http://example.org/sports/sports_position/players./sports/sports_team_roster/team EVAL 0dgrmp team 02b0y3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 9.000 9.000 0.878 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #17563-05f7snc PRED entity: 05f7snc PRED relation: nationality PRED expected values: 09c7w0 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 17): 09c7w0 (0.80 #301, 0.79 #401, 0.73 #501), 0f8l9c (0.37 #2203, 0.36 #2806, 0.36 #2505), 06f32 (0.37 #2203, 0.36 #2806, 0.36 #2505), 02jx1 (0.18 #233, 0.09 #6042, 0.08 #5841), 07ssc (0.17 #215, 0.08 #815, 0.08 #2218), 0d060g (0.08 #107, 0.04 #4714, 0.04 #1608), 03rk0 (0.05 #5854, 0.05 #5954, 0.05 #6055), 03rjj (0.04 #105, 0.02 #1706, 0.02 #1105), 0chghy (0.02 #1711, 0.02 #1311, 0.02 #1411), 06q1r (0.02 #677, 0.02 #577, 0.02 #277) >> Best rule #301 for best value: >> intensional similarity = 2 >> extensional distance = 84 >> proper extension: 0frmb1; >> query: (?x4762, 09c7w0) <- gender(?x4762, ?x514), person(?x3775, ?x4762) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05f7snc nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.802 http://example.org/people/person/nationality #17562-0167v PRED entity: 0167v PRED relation: olympics PRED expected values: 06sks6 => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 41): 06sks6 (0.88 #929, 0.88 #764, 0.88 #395), 0kbws (0.46 #96, 0.43 #589, 0.41 #466), 0kbvv (0.40 #108, 0.28 #601, 0.27 #273), 09n48 (0.37 #85, 0.25 #250, 0.25 #578), 018ctl (0.32 #90, 0.25 #583, 0.24 #542), 0kbvb (0.28 #459, 0.28 #89, 0.27 #582), 0jdk_ (0.25 #109, 0.20 #232, 0.20 #1014), 0swbd (0.22 #93, 0.18 #586, 0.17 #258), 0jhn7 (0.15 #480, 0.15 #1015, 0.14 #521), 0swff (0.13 #105, 0.11 #598, 0.11 #228) >> Best rule #929 for best value: >> intensional similarity = 2 >> extensional distance = 168 >> proper extension: 09lxtg; 01p8s; >> query: (?x5445, 06sks6) <- organization(?x5445, ?x127), administrative_area_type(?x5445, ?x2792) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0167v olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.882 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #17561-0sxfd PRED entity: 0sxfd PRED relation: story_by PRED expected values: 01q415 => 66 concepts (29 used for prediction) PRED predicted values (max 10 best out of 18): 03xp8d5 (0.10 #2384, 0.02 #2820, 0.02 #5871), 02l5rm (0.08 #478, 0.04 #694, 0.01 #910), 0ff3y (0.08 #641, 0.01 #1073, 0.01 #1291), 01v_0b (0.04 #849, 0.01 #1065, 0.01 #1283), 01yk13 (0.02 #5871, 0.02 #1732, 0.01 #3256), 01w1kyf (0.02 #1732, 0.02 #6090, 0.02 #6308), 01k5zk (0.02 #1732, 0.02 #6090, 0.02 #6308), 094tsh6 (0.02 #1732, 0.01 #3256, 0.01 #5870), 0284n42 (0.02 #1732, 0.01 #3256, 0.01 #5870), 081k8 (0.01 #1602, 0.01 #3564, 0.01 #3781) >> Best rule #2384 for best value: >> intensional similarity = 3 >> extensional distance = 633 >> proper extension: 03g90h; 026p_bs; 026mfbr; 02z3r8t; 035xwd; 02sg5v; 0gj8t_b; 02qrv7; 0436yk; 0c8tkt; ... >> query: (?x1402, ?x4385) <- film(?x71, ?x1402), genre(?x1402, ?x53), written_by(?x1402, ?x4385) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0sxfd story_by 01q415 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 29.000 0.104 http://example.org/film/film/story_by #17560-03lrls PRED entity: 03lrls PRED relation: major_field_of_study! PRED expected values: 0bjrnt => 47 concepts (34 used for prediction) PRED predicted values (max 10 best out of 21): 02_xgp2 (0.82 #75, 0.80 #143, 0.76 #164), 02h4rq6 (0.75 #89, 0.73 #112, 0.73 #225), 016t_3 (0.75 #136, 0.73 #113, 0.73 #202), 04zx3q1 (0.73 #111, 0.67 #88, 0.65 #134), 03bwzr4 (0.67 #98, 0.60 #121, 0.59 #220), 07s6fsf (0.53 #43, 0.51 #198, 0.50 #64), 0bjrnt (0.53 #43, 0.51 #198, 0.50 #64), 071tyz (0.53 #43, 0.51 #198, 0.50 #64), 01ysy9 (0.53 #43, 0.51 #198, 0.50 #64), 013zdg (0.53 #43, 0.51 #198, 0.50 #64) >> Best rule #75 for best value: >> intensional similarity = 16 >> extensional distance = 9 >> proper extension: 01lhy; 04rjg; >> query: (?x13501, 02_xgp2) <- major_field_of_study(?x14209, ?x13501), major_field_of_study(?x14116, ?x13501), major_field_of_study(?x3898, ?x13501), major_field_of_study(?x892, ?x13501), ?x892 = 07tgn, citytown(?x14209, ?x1841), currency(?x3898, ?x1099), student(?x14209, ?x5131), student(?x14209, ?x2217), organization(?x346, ?x3898), nationality(?x2217, ?x94), ?x5131 = 01tdnyh, profession(?x2217, ?x2225), film(?x2217, ?x197), institution(?x1368, ?x14116), major_field_of_study(?x1526, ?x13501) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 03g3w; *> query: (?x13501, ?x1200) <- major_field_of_study(?x14209, ?x13501), major_field_of_study(?x14116, ?x13501), major_field_of_study(?x3898, ?x13501), major_field_of_study(?x892, ?x13501), ?x892 = 07tgn, ?x3898 = 0ymdn, student(?x14209, ?x6796), institution(?x1200, ?x14209), organization(?x2361, ?x14209), ?x6796 = 01wd02c, major_field_of_study(?x1526, ?x13501), category(?x14116, ?x134), citytown(?x14116, ?x1841), currency(?x14209, ?x1099) *> conf = 0.53 ranks of expected_values: 7 EVAL 03lrls major_field_of_study! 0bjrnt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 47.000 34.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #17559-02704ff PRED entity: 02704ff PRED relation: film! PRED expected values: 074tb5 => 69 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1077): 02kxbx3 (0.43 #78955, 0.42 #81033, 0.42 #70643), 02kxbwx (0.43 #78955, 0.42 #81033, 0.42 #70643), 041c4 (0.33 #891, 0.08 #2968, 0.07 #5045), 04yt7 (0.33 #747, 0.08 #2824, 0.07 #4901), 01gbn6 (0.33 #1623, 0.08 #3700, 0.07 #5777), 0h0yt (0.33 #1342, 0.08 #3419, 0.07 #5496), 01_xtx (0.23 #2737, 0.20 #4814, 0.02 #21433), 01q_ph (0.15 #2132, 0.13 #4209, 0.06 #6286), 0mdqp (0.15 #2193, 0.13 #4270, 0.04 #10503), 02r_d4 (0.15 #2178, 0.13 #4255, 0.02 #39578) >> Best rule #78955 for best value: >> intensional similarity = 4 >> extensional distance = 1103 >> proper extension: 0170z3; 014lc_; 02d413; 0b76d_m; 014_x2; 0ds35l9; 015qsq; 03qcfvw; 0g56t9t; 09sh8k; ... >> query: (?x5694, ?x396) <- genre(?x5694, ?x53), film_release_distribution_medium(?x5694, ?x81), nominated_for(?x396, ?x5694), film(?x286, ?x5694) >> conf = 0.43 => this is the best rule for 2 predicted values *> Best rule #7270 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 032_wv; 0f4_l; *> query: (?x5694, 074tb5) <- genre(?x5694, ?x809), film(?x826, ?x5694), ?x809 = 0vgkd, film_crew_role(?x5694, ?x137) *> conf = 0.03 ranks of expected_values: 288 EVAL 02704ff film! 074tb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 46.000 0.430 http://example.org/film/actor/film./film/performance/film #17558-02x1dht PRED entity: 02x1dht PRED relation: nominated_for PRED expected values: 02rv_dz 017z49 02d478 043t8t 01rwpj 0404j37 0gvt53w 0c0zq => 63 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1576): 09gq0x5 (0.77 #27678, 0.77 #27677, 0.71 #26140), 02_06s (0.77 #27677, 0.65 #44614, 0.65 #44611), 0yzbg (0.67 #7208, 0.67 #5671, 0.62 #11819), 0_92w (0.67 #6296, 0.67 #4759, 0.50 #10907), 019vhk (0.67 #6547, 0.62 #11158, 0.60 #1934), 0yxf4 (0.67 #5600, 0.62 #11748, 0.50 #7137), 0llcx (0.67 #5769, 0.62 #11917, 0.50 #7306), 0yx_w (0.67 #5942, 0.62 #12090, 0.40 #2866), 0pv3x (0.67 #9381, 0.62 #15530, 0.61 #17067), 01cmp9 (0.67 #10118, 0.56 #16267, 0.56 #17804) >> Best rule #27678 for best value: >> intensional similarity = 5 >> extensional distance = 107 >> proper extension: 0fqnzts; >> query: (?x899, ?x1364) <- award(?x1364, ?x899), award(?x2705, ?x899), honored_for(?x8964, ?x1364), location(?x2705, ?x3269), ceremony(?x899, ?x762) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #7120 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 040njc; 02pqp12; *> query: (?x899, 0404j37) <- nominated_for(?x899, ?x1259), nominated_for(?x899, ?x144), ?x144 = 0m313, award(?x9281, ?x899), film_release_region(?x1259, ?x87), ?x9281 = 013tcv *> conf = 0.67 ranks of expected_values: 13, 27, 34, 136, 163, 224, 380, 1202 EVAL 02x1dht nominated_for 0c0zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 63.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x1dht nominated_for 0gvt53w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 63.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x1dht nominated_for 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 63.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x1dht nominated_for 01rwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 63.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x1dht nominated_for 043t8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 63.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x1dht nominated_for 02d478 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 63.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x1dht nominated_for 017z49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 63.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x1dht nominated_for 02rv_dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 63.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17557-0yldt PRED entity: 0yldt PRED relation: organization! PRED expected values: 05c0jwl => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 12): 060c4 (0.61 #320, 0.60 #1555, 0.60 #1424), 05c0jwl (0.52 #359, 0.39 #278, 0.25 #457), 0dq_5 (0.48 #353, 0.37 #368, 0.33 #662), 07xl34 (0.38 #50, 0.36 #76, 0.34 #276), 08jcfy (0.18 #25, 0.17 #38, 0.08 #464), 0hm4q (0.17 #190, 0.17 #34, 0.15 #486), 05k17c (0.13 #485, 0.12 #85, 0.12 #111), 04n1q6 (0.12 #97, 0.08 #32, 0.07 #1305), 01___w (0.11 #358, 0.08 #373, 0.07 #209), 021q0l (0.10 #574, 0.07 #1305, 0.05 #1462) >> Best rule #320 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 01jssp; 01fpvz; 06pwq; 01j_06; 07szy; 09kvv; 0bx8pn; 02hft3; 01s0_f; 07wjk; ... >> query: (?x13424, 060c4) <- major_field_of_study(?x13424, ?x866), student(?x13424, ?x11411), jurisdiction_of_office(?x11411, ?x512), institution(?x1368, ?x13424), ?x1368 = 014mlp >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #359 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 44 *> proper extension: 02qdyj; 02wbnv; *> query: (?x13424, ?x2361) <- child(?x892, ?x13424), child(?x892, ?x893), company(?x3970, ?x892), organization(?x2361, ?x893), citytown(?x13424, ?x1841) *> conf = 0.52 ranks of expected_values: 2 EVAL 0yldt organization! 05c0jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 160.000 0.614 http://example.org/organization/role/leaders./organization/leadership/organization #17556-01vvyvk PRED entity: 01vvyvk PRED relation: award PRED expected values: 099vwn => 134 concepts (117 used for prediction) PRED predicted values (max 10 best out of 312): 02f73b (0.54 #670, 0.19 #1456, 0.15 #31836), 02f777 (0.51 #693, 0.21 #300, 0.15 #1479), 01by1l (0.49 #503, 0.34 #15437, 0.34 #4433), 0gqz2 (0.46 #6366, 0.17 #19651, 0.16 #25547), 02f716 (0.46 #567, 0.15 #31836, 0.15 #1353), 02f5qb (0.44 #546, 0.18 #1332, 0.17 #2904), 02f6ym (0.41 #642, 0.22 #1428, 0.18 #4572), 03qbnj (0.38 #617, 0.21 #1403, 0.17 #19651), 02v1m7 (0.36 #504, 0.29 #111, 0.15 #31836), 02f73p (0.36 #576, 0.17 #19651, 0.16 #1362) >> Best rule #670 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 01wgfp6; 01dq9q; >> query: (?x4474, 02f73b) <- award_nominee(?x4474, ?x248), award(?x4474, ?x3488), ?x3488 = 02f71y >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #6497 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 119 *> proper extension: 09xx0m; *> query: (?x4474, 099vwn) <- award(?x4474, ?x2585), ?x2585 = 054ks3 *> conf = 0.19 ranks of expected_values: 32 EVAL 01vvyvk award 099vwn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 134.000 117.000 0.538 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17555-03rk0 PRED entity: 03rk0 PRED relation: countries_spoken_in! PRED expected values: 055qm 01c7y => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 48): 0jzc (0.23 #1801, 0.21 #2506, 0.19 #907), 064_8sq (0.21 #6504, 0.19 #7257, 0.18 #1003), 02hwhyv (0.20 #494, 0.14 #447, 0.12 #588), 06nm1 (0.18 #5555, 0.18 #4661, 0.18 #994), 02ztjwg (0.17 #684, 0.11 #3411, 0.11 #2236), 04306rv (0.16 #3249, 0.16 #1180, 0.14 #522), 05qqm (0.14 #456, 0.12 #644, 0.10 #926), 06b_j (0.14 #440, 0.12 #1804, 0.09 #2556), 01r2l (0.14 #442, 0.10 #489, 0.08 #583), 032f6 (0.14 #465, 0.09 #3615, 0.08 #2863) >> Best rule #1801 for best value: >> intensional similarity = 2 >> extensional distance = 46 >> proper extension: 035hm; >> query: (?x2146, 0jzc) <- locations(?x9532, ?x2146), countries_spoken_in(?x254, ?x2146) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #7007 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 161 *> proper extension: 02qkt; 028n3; 0bzjf; 0nt4s; 021y1s; 048kw; 05hwn; 0htx8; 01mzwp; 0np52; ... *> query: (?x2146, ?x254) <- contains(?x2146, ?x9466), place_of_birth(?x12204, ?x9466), languages(?x12204, ?x254) *> conf = 0.05 ranks of expected_values: 36, 37 EVAL 03rk0 countries_spoken_in! 01c7y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 220.000 220.000 0.229 http://example.org/language/human_language/countries_spoken_in EVAL 03rk0 countries_spoken_in! 055qm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 220.000 220.000 0.229 http://example.org/language/human_language/countries_spoken_in #17554-07c37 PRED entity: 07c37 PRED relation: people! PRED expected values: 02w7gg => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 43): 013xrm (0.37 #405, 0.27 #944, 0.23 #1175), 041rx (0.32 #466, 0.30 #928, 0.26 #389), 013b6_ (0.20 #53, 0.16 #361, 0.15 #669), 03ts0c (0.19 #796, 0.11 #257, 0.09 #1258), 02w7gg (0.16 #310, 0.15 #618, 0.14 #3623), 0x67 (0.14 #1011, 0.11 #1319, 0.09 #12036), 0222qb (0.11 #2465, 0.03 #6360, 0.02 #1276), 07bch9 (0.11 #177, 0.09 #1255, 0.08 #2179), 03bkbh (0.11 #571, 0.08 #2111, 0.08 #879), 033tf_ (0.10 #7866, 0.10 #7710, 0.10 #8098) >> Best rule #405 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0nk72; >> query: (?x5797, 013xrm) <- gender(?x5797, ?x231), influenced_by(?x5797, ?x3712), interests(?x5797, ?x6978), ?x3712 = 0gz_ >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #310 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 01w8sf; 04cbtrw; 073v6; 0b78hw; 01bpn; 07cbs; 0hky; 03_87; 03f47xl; 07ym0; ... *> query: (?x5797, 02w7gg) <- gender(?x5797, ?x231), influenced_by(?x5797, ?x3712), influenced_by(?x2608, ?x5797), ?x2608 = 01hb6v *> conf = 0.16 ranks of expected_values: 5 EVAL 07c37 people! 02w7gg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 181.000 181.000 0.368 http://example.org/people/ethnicity/people #17553-07kjk7c PRED entity: 07kjk7c PRED relation: nominated_for PRED expected values: 015g28 02kk_c 043mk4y => 55 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1477): 045r_9 (0.67 #4544, 0.29 #6122, 0.25 #2966), 06zsk51 (0.67 #4508, 0.29 #6086, 0.25 #2930), 05jyb2 (0.67 #3712, 0.29 #5290, 0.25 #2134), 02ppg1r (0.50 #3858, 0.50 #2280, 0.43 #5436), 02qr46y (0.50 #4702, 0.14 #6280, 0.14 #7859), 0h3mh3q (0.43 #6113, 0.33 #4535, 0.25 #2957), 026p4q7 (0.39 #17729, 0.38 #19310, 0.37 #20889), 01bv8b (0.38 #8280, 0.36 #6700, 0.30 #9859), 05f4vxd (0.36 #7101, 0.33 #10260, 0.33 #8681), 01q_y0 (0.36 #6645, 0.33 #8225, 0.30 #9804) >> Best rule #4544 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0bfvw2; 0bdwft; 0bfvd4; 0bdwqv; >> query: (?x7850, 045r_9) <- ceremony(?x7850, ?x2292), ?x2292 = 0gx_st, nominated_for(?x7850, ?x4083), ?x4083 = 0gmblvq, award(?x361, ?x7850) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4338 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0bfvw2; 0bdwft; 0bfvd4; 0bdwqv; *> query: (?x7850, 043mk4y) <- ceremony(?x7850, ?x2292), ?x2292 = 0gx_st, nominated_for(?x7850, ?x4083), ?x4083 = 0gmblvq, award(?x361, ?x7850) *> conf = 0.33 ranks of expected_values: 19 EVAL 07kjk7c nominated_for 043mk4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 55.000 21.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07kjk7c nominated_for 02kk_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 21.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07kjk7c nominated_for 015g28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 21.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17552-09qljs PRED entity: 09qljs PRED relation: country PRED expected values: 09c7w0 => 102 concepts (80 used for prediction) PRED predicted values (max 10 best out of 108): 09c7w0 (0.90 #612, 0.86 #1043, 0.81 #3809), 07ssc (0.38 #261, 0.28 #935, 0.27 #2841), 03rjj (0.25 #129, 0.25 #68, 0.04 #3507), 0345h (0.25 #150, 0.20 #333, 0.19 #577), 0f8l9c (0.25 #142, 0.11 #1246, 0.10 #3520), 06mkj (0.25 #163, 0.07 #468, 0.05 #834), 059j2 (0.25 #149, 0.01 #883, 0.01 #4607), 0d0vqn (0.25 #133, 0.01 #4607, 0.01 #2333), 0jgd (0.25 #126, 0.01 #4607, 0.01 #2333), 05b4w (0.25 #167, 0.01 #4607, 0.01 #2333) >> Best rule #612 for best value: >> intensional similarity = 6 >> extensional distance = 50 >> proper extension: 02zk08; >> query: (?x10651, 09c7w0) <- production_companies(?x10651, ?x3462), production_companies(?x10651, ?x788), genre(?x10651, ?x271), language(?x10651, ?x254), ?x788 = 0g1rw, award_winner(?x3486, ?x3462) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09qljs country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 80.000 0.904 http://example.org/film/film/country #17551-03jldb PRED entity: 03jldb PRED relation: location PRED expected values: 01n7q => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 130): 02_286 (0.31 #32965, 0.18 #31359, 0.17 #36178), 030qb3t (0.24 #33011, 0.24 #4901, 0.18 #19359), 01x73 (0.22 #96, 0.02 #2505, 0.02 #1702), 0c1d0 (0.11 #395, 0.02 #3607, 0.02 #2001), 0cr3d (0.07 #17815, 0.07 #1751, 0.05 #22634), 01qh7 (0.06 #960, 0.04 #1763), 04jpl (0.06 #4835, 0.06 #31339, 0.06 #19293), 0cc56 (0.06 #4875, 0.03 #17727, 0.03 #32985), 0k049 (0.05 #2417, 0.03 #4023, 0.03 #16072), 059rby (0.04 #4834, 0.04 #31338, 0.04 #23308) >> Best rule #32965 for best value: >> intensional similarity = 2 >> extensional distance = 1113 >> proper extension: 04107; 03lh3v; 094xh; 012v1t; 019fnv; 014g91; 03f68r6; 02y8bn; 069d71; 0443c; >> query: (?x1537, 02_286) <- location(?x1537, ?x11639), county(?x11639, ?x8552) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #19339 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 416 *> proper extension: 05m63c; 04rs03; 04bs3j; 0151ns; 09byk; 0pcc0; 0htlr; 0prjs; 031zkw; 0f2df; ... *> query: (?x1537, 01n7q) <- profession(?x1537, ?x319), people(?x1050, ?x1537), languages(?x1537, ?x254) *> conf = 0.03 ranks of expected_values: 22 EVAL 03jldb location 01n7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 90.000 90.000 0.314 http://example.org/people/person/places_lived./people/place_lived/location #17550-022h5x PRED entity: 022h5x PRED relation: institution PRED expected values: 065y4w7 07t90 04hgpt 0lyjf 01h8rk 02w6bq => 23 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1736): 09f2j (0.83 #7915, 0.82 #7321, 0.75 #6131), 07wjk (0.83 #7809, 0.75 #6025, 0.75 #5424), 0bwfn (0.83 #8034, 0.75 #6250, 0.75 #5649), 0g8rj (0.83 #7933, 0.75 #6149, 0.75 #5548), 0gl5_ (0.83 #8004, 0.75 #6220, 0.75 #5619), 06pwq (0.83 #7755, 0.73 #7161, 0.64 #6566), 01w3v (0.83 #7759, 0.64 #6570, 0.62 #5975), 01w5m (0.82 #7261, 0.75 #7855, 0.75 #6071), 03ksy (0.82 #7262, 0.75 #7856, 0.75 #6072), 07tgn (0.75 #7761, 0.75 #5977, 0.75 #5376) >> Best rule #7915 for best value: >> intensional similarity = 29 >> extensional distance = 10 >> proper extension: 027f2w; >> query: (?x9054, 09f2j) <- institution(?x9054, ?x11975), institution(?x9054, ?x3149), institution(?x9054, ?x1884), institution(?x9054, ?x1011), institution(?x9054, ?x388), service_language(?x388, ?x254), contains(?x2146, ?x11975), school(?x8902, ?x388), institution(?x1368, ?x11975), major_field_of_study(?x388, ?x1154), ?x1154 = 02lp1, currency(?x388, ?x170), ?x254 = 02h40lc, institution(?x4981, ?x388), major_field_of_study(?x9054, ?x1527), ?x1368 = 014mlp, ?x4981 = 03bwzr4, team(?x1517, ?x8902), team(?x935, ?x8902), team(?x11323, ?x8902), ?x935 = 06b1q, student(?x3149, ?x287), ?x1884 = 0bx8pn, fraternities_and_sororities(?x3149, ?x3697), ?x1517 = 02g_6j, citytown(?x388, ?x6453), school_type(?x1011, ?x1507), school(?x465, ?x1011), major_field_of_study(?x1011, ?x866) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #7758 for first EXPECTED value: *> intensional similarity = 29 *> extensional distance = 10 *> proper extension: 027f2w; *> query: (?x9054, 065y4w7) <- institution(?x9054, ?x11975), institution(?x9054, ?x3149), institution(?x9054, ?x1884), institution(?x9054, ?x1011), institution(?x9054, ?x388), service_language(?x388, ?x254), contains(?x2146, ?x11975), school(?x8902, ?x388), institution(?x1368, ?x11975), major_field_of_study(?x388, ?x1154), ?x1154 = 02lp1, currency(?x388, ?x170), ?x254 = 02h40lc, institution(?x4981, ?x388), major_field_of_study(?x9054, ?x1527), ?x1368 = 014mlp, ?x4981 = 03bwzr4, team(?x1517, ?x8902), team(?x935, ?x8902), team(?x11323, ?x8902), ?x935 = 06b1q, student(?x3149, ?x287), ?x1884 = 0bx8pn, fraternities_and_sororities(?x3149, ?x3697), ?x1517 = 02g_6j, citytown(?x388, ?x6453), school_type(?x1011, ?x1507), school(?x465, ?x1011), major_field_of_study(?x1011, ?x866) *> conf = 0.75 ranks of expected_values: 20, 38, 66, 88, 93, 128 EVAL 022h5x institution 02w6bq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 23.000 15.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 022h5x institution 01h8rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 23.000 15.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 022h5x institution 0lyjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 23.000 15.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 022h5x institution 04hgpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 23.000 15.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 022h5x institution 07t90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 23.000 15.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 022h5x institution 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 23.000 15.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #17549-06gb2q PRED entity: 06gb2q PRED relation: profession PRED expected values: 0np9r => 76 concepts (25 used for prediction) PRED predicted values (max 10 best out of 61): 03gjzk (0.52 #1620, 0.43 #2058, 0.42 #1912), 0kyk (0.50 #466, 0.46 #320, 0.31 #612), 015cjr (0.45 #194, 0.25 #48, 0.23 #340), 01d_h8 (0.42 #1612, 0.40 #882, 0.39 #1028), 0cbd2 (0.37 #445, 0.36 #3655, 0.31 #591), 0np9r (0.30 #2209, 0.29 #457, 0.29 #1625), 02hv44_ (0.27 #202, 0.15 #348, 0.10 #640), 02jknp (0.25 #1614, 0.24 #2344, 0.21 #3515), 0d1pc (0.25 #49, 0.18 #1071, 0.17 #1217), 02dsz (0.25 #55, 0.03 #785, 0.03 #1661) >> Best rule #1620 for best value: >> intensional similarity = 5 >> extensional distance = 151 >> proper extension: 01wj9y9; 05bnq3j; 0b7t3p; 0bz60q; 0739y; 03g5_y; 010p3; 02m92h; 01t94_1; 02tf1y; ... >> query: (?x7318, 03gjzk) <- profession(?x7318, ?x1146), profession(?x7318, ?x987), ?x1146 = 018gz8, gender(?x7318, ?x231), ?x987 = 0dxtg >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #2209 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 247 *> proper extension: 012d40; 01xdf5; 01yznp; 01rrwf6; 01n5309; 03jldb; 04nw9; 02p21g; 034np8; 0gz5hs; ... *> query: (?x7318, 0np9r) <- profession(?x7318, ?x1146), film(?x7318, ?x8859), ?x1146 = 018gz8, country(?x8859, ?x94) *> conf = 0.30 ranks of expected_values: 6 EVAL 06gb2q profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 76.000 25.000 0.516 http://example.org/people/person/profession #17548-0chghy PRED entity: 0chghy PRED relation: olympics PRED expected values: 0ldqf => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 29): 0swbd (0.56 #223, 0.56 #115, 0.53 #711), 016r9z (0.51 #433, 0.47 #596, 0.44 #1164), 09x3r (0.51 #433, 0.47 #596, 0.44 #1164), 0sx8l (0.51 #433, 0.42 #1977, 0.41 #2222), 0c_tl (0.51 #433, 0.42 #1977, 0.41 #2222), 0l6vl (0.47 #596, 0.44 #1164, 0.42 #1597), 0l998 (0.47 #596, 0.44 #1164, 0.42 #1597), 0lv1x (0.47 #596, 0.44 #1164, 0.42 #1597), 0blg2 (0.47 #596, 0.44 #1164, 0.42 #1597), 0l6ny (0.47 #596, 0.44 #1164, 0.42 #1597) >> Best rule #223 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 05kyr; 016zwt; >> query: (?x390, 0swbd) <- nationality(?x72, ?x390), religion(?x390, ?x492), combatants(?x390, ?x94) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #131 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0l3h; *> query: (?x390, 0ldqf) <- nationality(?x72, ?x390), religion(?x390, ?x492), jurisdiction_of_office(?x3444, ?x390) *> conf = 0.44 ranks of expected_values: 15 EVAL 0chghy olympics 0ldqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 193.000 193.000 0.562 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #17547-01hp5 PRED entity: 01hp5 PRED relation: honored_for! PRED expected values: 073hgx => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 77): 02jp5r (0.20 #302, 0.09 #5857, 0.09 #6102), 09qftb (0.20 #342, 0.09 #5857, 0.09 #6102), 0drtv8 (0.17 #421, 0.04 #543, 0.02 #1397), 0fqpc7d (0.17 #395, 0.04 #517, 0.02 #1127), 0g5b0q5 (0.17 #380, 0.02 #1112, 0.02 #1356), 0gpjbt (0.17 #389), 09p30_ (0.09 #5857, 0.09 #6102, 0.04 #560), 0275n3y (0.09 #5857, 0.09 #6102, 0.02 #552), 0bzm__ (0.09 #5857, 0.09 #6102), 02q690_ (0.09 #542, 0.03 #1396, 0.03 #1152) >> Best rule #302 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 02gqm3; >> query: (?x751, 02jp5r) <- film(?x7076, ?x751), film(?x4930, ?x751), award(?x7076, ?x2252), place_of_birth(?x7076, ?x739), ?x4930 = 01d0fp >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #570 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 43 *> proper extension: 03hy3g; 0gls4q_; *> query: (?x751, 073hgx) <- nominated_for(?x406, ?x751), nominated_for(?x398, ?x406) *> conf = 0.02 ranks of expected_values: 33 EVAL 01hp5 honored_for! 073hgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 75.000 75.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #17546-04rrx PRED entity: 04rrx PRED relation: contains PRED expected values: 043z0 08809 0vp5f 0njcw => 173 concepts (106 used for prediction) PRED predicted values (max 10 best out of 2728): 0nj1c (0.79 #23277, 0.78 #8728, 0.60 #98945), 01s0_f (0.74 #104767, 0.56 #186265, 0.51 #110589), 015fsv (0.74 #104767, 0.51 #110589, 0.51 #66932), 08809 (0.67 #130964, 0.64 #96035, 0.60 #98945), 043z0 (0.64 #96035, 0.60 #98945, 0.60 #232845), 05kkh (0.64 #96035, 0.60 #98945, 0.60 #232845), 01y9pk (0.64 #96035, 0.60 #98945, 0.60 #232845), 0843m (0.64 #96035, 0.60 #98945, 0.60 #232845), 0d060g (0.64 #96035, 0.60 #98945, 0.60 #232845), 05kr_ (0.64 #96035, 0.60 #98945, 0.60 #232845) >> Best rule #23277 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0lwkz; >> query: (?x1906, ?x8003) <- country(?x1906, ?x94), administrative_parent(?x8003, ?x1906), capital(?x1906, ?x12488) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #130964 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 018jcq; *> query: (?x1906, ?x13337) <- state_province_region(?x1675, ?x1906), contains(?x94, ?x1906), citytown(?x1675, ?x13337) *> conf = 0.67 ranks of expected_values: 4, 5, 786 EVAL 04rrx contains 0njcw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 106.000 0.795 http://example.org/location/location/contains EVAL 04rrx contains 0vp5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 173.000 106.000 0.795 http://example.org/location/location/contains EVAL 04rrx contains 08809 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 173.000 106.000 0.795 http://example.org/location/location/contains EVAL 04rrx contains 043z0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 173.000 106.000 0.795 http://example.org/location/location/contains #17545-04kjrv PRED entity: 04kjrv PRED relation: instrumentalists! PRED expected values: 06w7v => 146 concepts (109 used for prediction) PRED predicted values (max 10 best out of 123): 0342h (0.90 #5817, 0.78 #3279, 0.77 #3197), 05r5c (0.73 #5738, 0.67 #174, 0.66 #1558), 0l14qv (0.45 #2295, 0.45 #2378, 0.42 #1557), 03bx0bm (0.45 #2295, 0.45 #2378, 0.42 #1557), 0l14md (0.40 #254, 0.22 #5492, 0.19 #498), 03qjg (0.35 #1193, 0.33 #213, 0.32 #1111), 03gvt (0.34 #1888, 0.32 #1312, 0.31 #3356), 01s0ps (0.34 #1888, 0.32 #1312, 0.31 #3356), 01v1d8 (0.34 #1888, 0.32 #1312, 0.31 #3356), 01rhl (0.34 #1888, 0.32 #1312, 0.31 #3356) >> Best rule #5817 for best value: >> intensional similarity = 5 >> extensional distance = 401 >> proper extension: 01pbxb; 016qtt; 0197tq; 0411q; 01vw87c; 01lmj3q; 0fp_v1x; 0m2l9; 01nqfh_; 01wl38s; ... >> query: (?x7121, 0342h) <- instrumentalists(?x716, ?x7121), role(?x74, ?x716), instrumentalists(?x716, ?x5442), ?x5442 = 02jq1, family(?x716, ?x7256) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #805 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 02fybl; *> query: (?x7121, 06w7v) <- profession(?x7121, ?x220), role(?x7121, ?x1466), participant(?x7121, ?x3503), ?x1466 = 03bx0bm, role(?x7121, ?x1495) *> conf = 0.16 ranks of expected_values: 26 EVAL 04kjrv instrumentalists! 06w7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 146.000 109.000 0.903 http://example.org/music/instrument/instrumentalists #17544-014kg4 PRED entity: 014kg4 PRED relation: nationality PRED expected values: 09c7w0 => 114 concepts (110 used for prediction) PRED predicted values (max 10 best out of 40): 09c7w0 (0.88 #2311, 0.84 #6235, 0.83 #3618), 05k7sb (0.33 #6134, 0.32 #7643), 068p2 (0.25 #7138, 0.25 #5629), 0m7d0 (0.25 #7138, 0.25 #5629), 03rt9 (0.20 #13, 0.03 #113, 0.02 #315), 02jx1 (0.13 #435, 0.11 #1139, 0.11 #938), 07ssc (0.11 #417, 0.11 #1222, 0.11 #1020), 03rk0 (0.06 #9198, 0.06 #10301, 0.06 #10501), 0d060g (0.05 #4628, 0.05 #6642, 0.05 #3516), 0f8l9c (0.05 #3516, 0.04 #927, 0.04 #7541) >> Best rule #2311 for best value: >> intensional similarity = 3 >> extensional distance = 771 >> proper extension: 020hyj; 09hd6f; >> query: (?x13113, 09c7w0) <- award_winner(?x1921, ?x13113), place_of_birth(?x13113, ?x3046), source(?x3046, ?x958) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014kg4 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 110.000 0.881 http://example.org/people/person/nationality #17543-02_286 PRED entity: 02_286 PRED relation: origin! PRED expected values: 05w6cw 06br6t => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 1051): 0m2l9 (0.33 #12, 0.10 #1442, 0.06 #2874), 01m5m5b (0.33 #427, 0.10 #1857, 0.06 #3289), 07m4c (0.33 #303, 0.10 #1733, 0.06 #3165), 01t_xp_ (0.33 #10, 0.10 #1440, 0.06 #2872), 01s7ns (0.25 #1388, 0.10 #1864, 0.04 #11401), 03f0qd7 (0.25 #1402, 0.04 #3339, 0.03 #6172), 01w58n3 (0.25 #1341, 0.04 #3339, 0.03 #8494), 02vwckw (0.25 #1284, 0.03 #8437, 0.02 #10342), 01dw_f (0.25 #1260, 0.03 #8413, 0.02 #10318), 01fmz6 (0.25 #1153, 0.03 #8306, 0.02 #10211) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0h7h6; >> query: (?x739, 0m2l9) <- featured_film_locations(?x89, ?x739), location(?x1657, ?x739), ?x1657 = 07csf4 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1769 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 8 *> proper extension: 07751; 02fzs; *> query: (?x739, 05w6cw) <- film_regional_debut_venue(?x2047, ?x739), vacationer(?x739, ?x444) *> conf = 0.10 ranks of expected_values: 104 EVAL 02_286 origin! 06br6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 172.000 0.333 http://example.org/music/artist/origin EVAL 02_286 origin! 05w6cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 172.000 172.000 0.333 http://example.org/music/artist/origin #17542-0b73_1d PRED entity: 0b73_1d PRED relation: nominated_for! PRED expected values: 0gq_v 099c8n 0gs9p => 117 concepts (108 used for prediction) PRED predicted values (max 10 best out of 199): 099c8n (0.65 #1364, 0.58 #707, 0.42 #926), 0k611 (0.62 #2473, 0.58 #2254, 0.58 #940), 02qyntr (0.54 #2571, 0.53 #2352, 0.52 #1914), 0gq_v (0.53 #893, 0.45 #1769, 0.42 #2207), 02qvyrt (0.48 #1836, 0.48 #2493, 0.47 #2274), 02pqp12 (0.47 #2460, 0.47 #927, 0.46 #2241), 02ppm4q (0.47 #758, 0.25 #1415, 0.22 #977), 02hsq3m (0.45 #2213, 0.44 #1775, 0.42 #899), 02z0dfh (0.42 #710, 0.16 #1367, 0.11 #2900), 019f4v (0.40 #923, 0.39 #1361, 0.38 #2237) >> Best rule #1364 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 0209xj; 0gmgwnv; 0170xl; >> query: (?x825, 099c8n) <- award_winner(?x825, ?x185), nominated_for(?x6729, ?x825), genre(?x825, ?x53), ?x6729 = 099ck7 >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 11 EVAL 0b73_1d nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 117.000 108.000 0.647 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0b73_1d nominated_for! 099c8n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 108.000 0.647 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0b73_1d nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 108.000 0.647 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17541-0d608 PRED entity: 0d608 PRED relation: people! PRED expected values: 0g8_vp => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 61): 0x67 (0.33 #549, 0.20 #626, 0.19 #3860), 041rx (0.25 #4, 0.22 #1082, 0.21 #1467), 033tf_ (0.25 #84, 0.15 #1162, 0.14 #1008), 048z7l (0.25 #40, 0.14 #964, 0.11 #579), 02w7gg (0.20 #156, 0.08 #6085, 0.06 #8703), 07hwkr (0.15 #628, 0.07 #1013, 0.07 #936), 03bkbh (0.12 #417, 0.11 #494, 0.07 #956), 0xnvg (0.12 #1399, 0.11 #2169, 0.11 #937), 07bch9 (0.11 #947, 0.10 #1024, 0.10 #716), 0g8_vp (0.09 #3102, 0.04 #869, 0.02 #2101) >> Best rule #549 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 07s3vqk; 0407f; 012vd6; 0mfj2; 04d2yp; >> query: (?x7522, 0x67) <- film(?x7522, ?x8788), film(?x7522, ?x1743), ?x1743 = 0c8tkt, genre(?x8788, ?x225) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3102 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 148 *> proper extension: 02y8bn; *> query: (?x7522, 0g8_vp) <- nationality(?x7522, ?x279), ?x279 = 0d060g *> conf = 0.09 ranks of expected_values: 10 EVAL 0d608 people! 0g8_vp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 156.000 156.000 0.333 http://example.org/people/ethnicity/people #17540-0cymp PRED entity: 0cymp PRED relation: time_zones PRED expected values: 02hcv8 => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.84 #209, 0.82 #1473, 0.80 #407), 02fqwt (0.57 #79, 0.29 #66, 0.22 #1226), 02lcqs (0.51 #1073, 0.45 #135, 0.40 #122), 02hczc (0.29 #67, 0.12 #328, 0.11 #575), 042g7t (0.29 #76, 0.11 #272, 0.05 #441), 02llzg (0.26 #265, 0.15 #434, 0.13 #356), 03bdv (0.14 #71, 0.13 #293, 0.13 #358), 05jphn (0.14 #78, 0.04 #274, 0.04 #287), 02lcrv (0.14 #72, 0.04 #268, 0.02 #372), 052vwh (0.04 #273, 0.03 #442, 0.02 #572) >> Best rule #209 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0mmzt; 0mndw; >> query: (?x4789, ?x2674) <- location(?x4065, ?x4789), county(?x13924, ?x4789), time_zones(?x13924, ?x2674), contains(?x335, ?x13924) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cymp time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.842 http://example.org/location/location/time_zones #17539-0kvgtf PRED entity: 0kvgtf PRED relation: film! PRED expected values: 016vg8 => 68 concepts (47 used for prediction) PRED predicted values (max 10 best out of 916): 02xwgr (0.40 #934, 0.01 #36169), 0169dl (0.20 #401, 0.04 #10761, 0.04 #12834), 0c6qh (0.20 #414, 0.04 #29429, 0.04 #41868), 013qvn (0.20 #1292, 0.04 #12433), 014zcr (0.20 #37, 0.04 #29052, 0.02 #12470), 046zh (0.20 #935, 0.03 #29950, 0.03 #35235), 016zp5 (0.20 #976, 0.03 #9264, 0.03 #3048), 0bl2g (0.20 #55, 0.03 #24924, 0.02 #45654), 0hwbd (0.20 #1030, 0.03 #35235), 019f2f (0.20 #438, 0.03 #35235) >> Best rule #934 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 06z8s_; 02c6d; 02qr3k8; >> query: (?x3781, 02xwgr) <- film(?x10410, ?x3781), genre(?x3781, ?x239), language(?x3781, ?x732), ?x10410 = 02lyx4 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #29846 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 301 *> proper extension: 01b7h8; *> query: (?x3781, 016vg8) <- nominated_for(?x8134, ?x3781), vacationer(?x126, ?x8134), film(?x8134, ?x1444), participant(?x8134, ?x2373) *> conf = 0.02 ranks of expected_values: 502 EVAL 0kvgtf film! 016vg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 47.000 0.400 http://example.org/film/actor/film./film/performance/film #17538-0319l PRED entity: 0319l PRED relation: group PRED expected values: 027kwc => 84 concepts (65 used for prediction) PRED predicted values (max 10 best out of 518): 01qqwp9 (0.74 #914, 0.73 #3536, 0.60 #1311), 02vnpv (0.74 #914, 0.67 #5516, 0.67 #4403), 02dw1_ (0.74 #914, 0.67 #5426, 0.67 #4128), 05563d (0.74 #914, 0.67 #4097, 0.64 #5544), 07mvp (0.74 #914, 0.67 #2097, 0.64 #3877), 07m4c (0.74 #914, 0.67 #2109, 0.57 #2479), 02t3ln (0.74 #914, 0.67 #2067, 0.57 #2437), 01cblr (0.74 #914, 0.67 #2069, 0.57 #2439), 013w2r (0.74 #914, 0.67 #2086, 0.57 #2456), 0b_xm (0.74 #914, 0.64 #5544, 0.64 #3598) >> Best rule #914 for best value: >> intensional similarity = 29 >> extensional distance = 2 >> proper extension: 0l14md; >> query: (?x1472, ?x379) <- role(?x3703, ?x1472), role(?x2309, ?x1472), role(?x1495, ?x1472), role(?x1437, ?x1472), role(?x745, ?x1472), role(?x716, ?x1472), ?x2309 = 06ncr, role(?x4769, ?x1472), role(?x3161, ?x1472), role(?x2798, ?x1472), role(?x315, ?x1472), ?x4769 = 0dwt5, ?x2798 = 03qjg, ?x1495 = 013y1f, group(?x1472, ?x997), ?x1437 = 01vdm0, ?x745 = 01vj9c, ?x716 = 018vs, ?x3703 = 02dlh2, role(?x925, ?x1472), artists(?x505, ?x925), music(?x924, ?x925), ?x3161 = 01v1d8, profession(?x925, ?x1183), category(?x925, ?x134), role(?x74, ?x315), role(?x460, ?x315), role(?x214, ?x315), group(?x315, ?x379) >> conf = 0.74 => this is the best rule for 177 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 41 EVAL 0319l group 027kwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 84.000 65.000 0.743 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17537-0lrh PRED entity: 0lrh PRED relation: student! PRED expected values: 01r47h => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 178): 03ksy (0.20 #2730, 0.14 #1680, 0.11 #2205), 025v3k (0.20 #119, 0.10 #3794, 0.10 #3269), 0gjv_ (0.20 #205, 0.10 #3355, 0.09 #4405), 01vg0s (0.20 #328, 0.10 #3478, 0.09 #4528), 08815 (0.20 #2, 0.10 #3677, 0.09 #4202), 017hnw (0.20 #507, 0.10 #4182, 0.09 #4707), 0dplh (0.17 #579, 0.10 #3204, 0.09 #4254), 05nrkb (0.17 #1398, 0.05 #21349, 0.02 #29749), 01stzp (0.17 #1034, 0.05 #12059, 0.04 #18884), 01r3w7 (0.17 #1322) >> Best rule #2730 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 02jq1; 011vx3; 01wg25j; >> query: (?x2845, 03ksy) <- influenced_by(?x4701, ?x2845), influenced_by(?x1089, ?x2845), ?x1089 = 01vrncs, profession(?x2845, ?x353), profession(?x4701, ?x220) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0lrh student! 01r47h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 135.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #17536-0f2rq PRED entity: 0f2rq PRED relation: location! PRED expected values: 01p45_v => 230 concepts (168 used for prediction) PRED predicted values (max 10 best out of 3466): 01trhmt (0.78 #238602, 0.52 #110503, 0.51 #384282), 01k70_ (0.78 #238602, 0.52 #110503, 0.49 #168274), 01pcmd (0.78 #238602, 0.52 #110503, 0.49 #168274), 04f7c55 (0.52 #110503, 0.51 #384282, 0.49 #168274), 01vvybv (0.52 #110503, 0.49 #168274, 0.48 #389306), 05w1vf (0.52 #110503, 0.49 #168274, 0.48 #389306), 01w7nww (0.52 #110503, 0.49 #168274, 0.48 #389306), 02g5h5 (0.52 #110503, 0.49 #168274, 0.48 #389306), 01693z (0.52 #110503, 0.49 #168274, 0.48 #389306), 033wx9 (0.38 #278785, 0.31 #359162, 0.30 #376747) >> Best rule #238602 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 01t8gz; 017w_; >> query: (?x5719, ?x10738) <- place_of_birth(?x10738, ?x5719), teams(?x5719, ?x11473), location(?x10738, ?x3908) >> conf = 0.78 => this is the best rule for 3 predicted values *> Best rule #37934 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 0q_0z; *> query: (?x5719, 01p45_v) <- county_seat(?x11836, ?x5719), state(?x5719, ?x3634), origin(?x2697, ?x5719) *> conf = 0.04 ranks of expected_values: 1363 EVAL 0f2rq location! 01p45_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 230.000 168.000 0.778 http://example.org/people/person/places_lived./people/place_lived/location #17535-0jmcv PRED entity: 0jmcv PRED relation: teams! PRED expected values: 0f2rq => 87 concepts (75 used for prediction) PRED predicted values (max 10 best out of 111): 0cr3d (0.33 #354, 0.16 #14341, 0.11 #2245), 02dtg (0.33 #16, 0.06 #4070, 0.05 #5691), 0ftxw (0.25 #896, 0.11 #1977, 0.10 #3058), 01sn3 (0.25 #656, 0.10 #3088, 0.07 #3899), 0f2tj (0.20 #1233, 0.11 #2314, 0.11 #1774), 0qplq (0.20 #1311, 0.01 #13757, 0.01 #14841), 030qb3t (0.16 #14341, 0.12 #4645, 0.12 #5455), 02_286 (0.16 #14341, 0.11 #1644, 0.10 #18956), 0d6lp (0.14 #1447, 0.11 #1987, 0.06 #9830), 0nqph (0.14 #1611, 0.11 #2151, 0.06 #9994) >> Best rule #354 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 0jm3b; >> query: (?x8228, 0cr3d) <- school(?x8228, ?x9745), sport(?x8228, ?x4833), team(?x13931, ?x8228), ?x4833 = 018w8, position(?x8228, ?x1348), colors(?x8228, ?x663), ?x13931 = 02cg2v, fraternities_and_sororities(?x9745, ?x3697), currency(?x9745, ?x170), state_province_region(?x9745, ?x1138), ?x170 = 09nqf >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #15019 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 75 *> proper extension: 0cnk2q; 033nzk; 0266shh; 01cwm1; 02s2ys; 01352_; 0d3fdn; 02ryyk; *> query: (?x8228, 0f2rq) <- team(?x13931, ?x8228), gender(?x13931, ?x231), type_of_union(?x13931, ?x566), ?x566 = 04ztj, team(?x4570, ?x8228), place_of_birth(?x13931, ?x3125), sport(?x8228, ?x4833) *> conf = 0.01 ranks of expected_values: 103 EVAL 0jmcv teams! 0f2rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 87.000 75.000 0.333 http://example.org/sports/sports_team_location/teams #17534-016ntp PRED entity: 016ntp PRED relation: origin PRED expected values: 09cpb => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 125): 04jpl (0.13 #1186, 0.09 #478, 0.07 #3546), 01sn3 (0.12 #78, 0.06 #786, 0.02 #2910), 0r3tb (0.12 #1083, 0.08 #1791, 0.08 #1555), 02_286 (0.09 #488, 0.09 #252, 0.08 #2612), 09c7w0 (0.09 #473, 0.06 #709, 0.04 #2833), 01531 (0.09 #297, 0.04 #2185, 0.04 #2421), 0mm_4 (0.09 #414), 0978r (0.08 #1720, 0.08 #1484, 0.08 #1956), 01l63 (0.06 #908, 0.04 #1380, 0.02 #3504), 071vr (0.06 #831, 0.04 #1775, 0.03 #2719) >> Best rule #1186 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 0hnlx; >> query: (?x3168, 04jpl) <- gender(?x3168, ?x514), profession(?x3168, ?x1614), profession(?x3168, ?x955), ?x1614 = 01c72t, ?x955 = 0n1h >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016ntp origin 09cpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 153.000 0.130 http://example.org/music/artist/origin #17533-0hz55 PRED entity: 0hz55 PRED relation: nominated_for! PRED expected values: 01yhvv => 81 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1159): 059j4x (0.81 #27960, 0.81 #34951, 0.81 #23301), 0f721s (0.69 #30291, 0.67 #46600, 0.66 #25630), 04511f (0.69 #30291, 0.67 #46600, 0.66 #25630), 04rtpt (0.69 #30291, 0.67 #46600, 0.66 #25630), 015p37 (0.62 #16309, 0.60 #23300, 0.60 #23299), 03j367r (0.62 #16309, 0.60 #23300, 0.60 #23299), 05dbyt (0.62 #16309, 0.60 #23300, 0.60 #23299), 02g5h5 (0.62 #16309, 0.60 #23299, 0.59 #18639), 0cjdk (0.35 #20970, 0.24 #48931, 0.19 #72232), 02d4ct (0.33 #483, 0.03 #79220, 0.02 #7474) >> Best rule #27960 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 039fgy; 016tvq; 03czz87; >> query: (?x4932, ?x3571) <- actor(?x4932, ?x3366), award_winner(?x4932, ?x3571), titles(?x2008, ?x4932), languages(?x4932, ?x254) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #132806 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 793 *> proper extension: 04gknr; 03twd6; 02x8fs; 047gpsd; 07ghq; *> query: (?x4932, ?x3726) <- award_winner(?x4932, ?x3571), award_nominee(?x3571, ?x3726), award_winner(?x3571, ?x1039) *> conf = 0.10 ranks of expected_values: 39 EVAL 0hz55 nominated_for! 01yhvv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 81.000 57.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17532-01vvycq PRED entity: 01vvycq PRED relation: artists! PRED expected values: 02x8m => 144 concepts (86 used for prediction) PRED predicted values (max 10 best out of 241): 017_qw (0.47 #3671, 0.44 #7890, 0.15 #11202), 01lyv (0.43 #32, 0.23 #5455, 0.22 #11782), 0mhfr (0.43 #22, 0.19 #2132, 0.14 #5445), 02lnbg (0.38 #4871, 0.32 #1258, 0.30 #956), 0xhtw (0.35 #6644, 0.31 #1824, 0.28 #13578), 06924p (0.29 #168, 0.06 #1375, 0.06 #4085), 015pdg (0.29 #9, 0.05 #311, 0.05 #13572), 017510 (0.29 #132, 0.05 #735, 0.04 #3447), 02k_kn (0.27 #6687, 0.17 #23817, 0.16 #5179), 02x8m (0.27 #9960, 0.17 #23817, 0.13 #3332) >> Best rule #3671 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0csdzz; >> query: (?x702, 017_qw) <- award(?x702, ?x1854), ?x1854 = 025m8y, profession(?x702, ?x220) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #9960 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 159 *> proper extension: 02mslq; 019g40; 0cg9y; 01x1cn2; 03j0br4; 01rm8b; 049qx; 012z8_; 0czkbt; 015x1f; ... *> query: (?x702, 02x8m) <- artists(?x3319, ?x702), award(?x702, ?x350), ?x3319 = 06j6l *> conf = 0.27 ranks of expected_values: 10 EVAL 01vvycq artists! 02x8m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 144.000 86.000 0.471 http://example.org/music/genre/artists #17531-05g76 PRED entity: 05g76 PRED relation: draft PRED expected values: 02pq_x5 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 15): 02r6gw6 (0.88 #282, 0.79 #312, 0.74 #297), 02pq_rp (0.83 #307, 0.81 #292, 0.80 #277), 02pq_x5 (0.80 #285, 0.76 #315, 0.70 #300), 0g3zpp (0.55 #378, 0.50 #92, 0.37 #514), 09l0x9 (0.53 #385, 0.50 #99, 0.37 #521), 092j54 (0.53 #383, 0.50 #97, 0.37 #519), 05vsb7 (0.50 #377, 0.34 #422, 0.34 #513), 03nt7j (0.45 #381, 0.36 #140, 0.32 #517), 02qw1zx (0.40 #379, 0.38 #93, 0.28 #515), 025tn92 (0.33 #10, 0.29 #537, 0.29 #175) >> Best rule #282 for best value: >> intensional similarity = 9 >> extensional distance = 23 >> proper extension: 01ypc; 05m_8; 0512p; 01yhm; 0x2p; 01yjl; 0713r; 02__x; 07l8f; 01slc; ... >> query: (?x2067, 02r6gw6) <- team(?x2010, ?x2067), season(?x2067, ?x8517), school(?x2067, ?x6333), position(?x2067, ?x7724), colors(?x2067, ?x3189), draft(?x2067, ?x1161), major_field_of_study(?x6333, ?x742), ?x2010 = 02lyr4, ?x8517 = 0285r5d >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #285 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 23 *> proper extension: 01ypc; 05m_8; 0512p; 01yhm; 0x2p; 01yjl; 0713r; 02__x; 07l8f; 01slc; ... *> query: (?x2067, 02pq_x5) <- team(?x2010, ?x2067), season(?x2067, ?x8517), school(?x2067, ?x6333), position(?x2067, ?x7724), colors(?x2067, ?x3189), draft(?x2067, ?x1161), major_field_of_study(?x6333, ?x742), ?x2010 = 02lyr4, ?x8517 = 0285r5d *> conf = 0.80 ranks of expected_values: 3 EVAL 05g76 draft 02pq_x5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 102.000 0.880 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #17530-0j6cj PRED entity: 0j6cj PRED relation: artists! PRED expected values: 01756d => 138 concepts (94 used for prediction) PRED predicted values (max 10 best out of 238): 064t9 (0.60 #938, 0.56 #15124, 0.55 #11734), 05bt6j (0.44 #351, 0.43 #7443, 0.41 #4052), 03_d0 (0.44 #6179, 0.32 #4329, 0.31 #4946), 0glt670 (0.42 #9600, 0.41 #15150, 0.40 #12684), 025sc50 (0.40 #974, 0.38 #2209, 0.34 #15160), 0gywn (0.38 #2217, 0.31 #6842, 0.30 #7150), 0dl5d (0.38 #6187, 0.21 #4646, 0.19 #3720), 06j6l (0.38 #6832, 0.35 #11768, 0.33 #4982), 05w3f (0.33 #345, 0.30 #2504, 0.27 #1270), 0ggx5q (0.33 #79, 0.30 #1003, 0.29 #2238) >> Best rule #938 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 03f1zhf; >> query: (?x7987, 064t9) <- currency(?x7987, ?x170), artists(?x1000, ?x7987), gender(?x7987, ?x231), student(?x8681, ?x7987) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2182 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 021r7r; 0dr5y; *> query: (?x7987, 01756d) <- currency(?x7987, ?x170), artists(?x1000, ?x7987), influenced_by(?x7987, ?x4873), category(?x7987, ?x134) *> conf = 0.05 ranks of expected_values: 124 EVAL 0j6cj artists! 01756d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 138.000 94.000 0.600 http://example.org/music/genre/artists #17529-01pcdn PRED entity: 01pcdn PRED relation: award_winner! PRED expected values: 092t4b => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 111): 0466p0j (0.17 #9943, 0.07 #4557, 0.07 #2737), 092t4b (0.17 #9943, 0.05 #7893, 0.05 #2242), 03nnm4t (0.17 #9943, 0.05 #1121, 0.05 #2242), 09pnw5 (0.17 #9943, 0.05 #2242, 0.04 #102), 09p3h7 (0.17 #9943, 0.05 #2242, 0.04 #1331), 09p30_ (0.17 #9943, 0.04 #84, 0.04 #224), 01s695 (0.10 #283, 0.08 #4485, 0.08 #4625), 013b2h (0.10 #2741, 0.10 #4561, 0.09 #4701), 02rjjll (0.09 #425, 0.09 #285, 0.09 #2667), 09n4nb (0.09 #327, 0.08 #2709, 0.08 #467) >> Best rule #9943 for best value: >> intensional similarity = 3 >> extensional distance = 784 >> proper extension: 01ws9n6; 01l7cxq; >> query: (?x4775, ?x3460) <- award_winner(?x4775, ?x5889), place_of_birth(?x4775, ?x3987), award_winner(?x3460, ?x5889) >> conf = 0.17 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01pcdn award_winner! 092t4b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.174 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17528-04mby PRED entity: 04mby PRED relation: profession PRED expected values: 0kyk => 58 concepts (30 used for prediction) PRED predicted values (max 10 best out of 61): 02jknp (0.59 #2636, 0.47 #1906, 0.27 #591), 0cbd2 (0.51 #6, 0.51 #1320, 0.49 #1174), 018gz8 (0.40 #161, 0.39 #307, 0.27 #1914), 03gjzk (0.37 #159, 0.37 #1912, 0.35 #305), 0kyk (0.34 #1342, 0.33 #28, 0.33 #1196), 0np9r (0.21 #311, 0.19 #165, 0.14 #1918), 09jwl (0.20 #163, 0.19 #309, 0.15 #3084), 05z96 (0.18 #41, 0.16 #1209, 0.15 #771), 02krf9 (0.17 #2654, 0.17 #1924, 0.10 #317), 02hv44_ (0.17 #56, 0.14 #786, 0.13 #1370) >> Best rule #2636 for best value: >> intensional similarity = 5 >> extensional distance = 713 >> proper extension: 0hskw; 0347xl; 021yzs; 01xv77; 0d608; 02404v; 01wj5hp; 04kwbt; 01g5kv; >> query: (?x9467, 02jknp) <- profession(?x9467, ?x9081), profession(?x9467, ?x1032), ?x1032 = 02hrh1q, profession(?x9296, ?x9081), ?x9296 = 04vt98 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #1342 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 357 *> proper extension: 09jm8; *> query: (?x9467, 0kyk) <- influenced_by(?x9467, ?x11104), people(?x6260, ?x11104) *> conf = 0.34 ranks of expected_values: 5 EVAL 04mby profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 58.000 30.000 0.594 http://example.org/people/person/profession #17527-04n_g PRED entity: 04n_g PRED relation: place_of_burial PRED expected values: 0lbp_ => 142 concepts (124 used for prediction) PRED predicted values (max 10 best out of 11): 018mmj (0.12 #74, 0.09 #11, 0.08 #137), 018mmw (0.07 #80, 0.06 #17, 0.03 #206), 018mm4 (0.07 #231, 0.07 #198, 0.06 #263), 018mlg (0.05 #55, 0.02 #246, 0.02 #278), 01n7q (0.05 #67, 0.03 #4, 0.02 #130), 0lbp_ (0.05 #79, 0.03 #16, 0.01 #658), 018mrd (0.02 #86, 0.02 #149, 0.01 #1183), 0nb1s (0.02 #156, 0.02 #219, 0.02 #252), 0bvqq (0.02 #138), 05rgl (0.02 #133) >> Best rule #74 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 01ty7ll; >> query: (?x3891, 018mmj) <- participant(?x3891, ?x7632), people(?x4322, ?x3891), location(?x3891, ?x739) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #79 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 01ty7ll; *> query: (?x3891, 0lbp_) <- participant(?x3891, ?x7632), people(?x4322, ?x3891), location(?x3891, ?x739) *> conf = 0.05 ranks of expected_values: 6 EVAL 04n_g place_of_burial 0lbp_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 142.000 124.000 0.122 http://example.org/people/deceased_person/place_of_burial #17526-0kw4j PRED entity: 0kw4j PRED relation: school_type PRED expected values: 05pcjw => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 19): 05pcjw (0.53 #121, 0.53 #241, 0.47 #385), 01rs41 (0.52 #485, 0.50 #893, 0.50 #725), 05jxkf (0.51 #1084, 0.50 #1205, 0.46 #820), 07tf8 (0.32 #225, 0.27 #825, 0.24 #1210), 01_srz (0.20 #75, 0.14 #315, 0.13 #387), 01_9fk (0.18 #1203, 0.17 #818, 0.16 #554), 04399 (0.10 #86, 0.07 #2479, 0.04 #326), 0bpgx (0.10 #69, 0.03 #1077, 0.03 #621), 0m4mb (0.10 #59, 0.03 #2222, 0.02 #1740), 03ss47 (0.10 #61, 0.02 #349, 0.01 #445) >> Best rule #121 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 01ljpm; 02grjf; 01cf5; >> query: (?x3821, 05pcjw) <- institution(?x620, ?x3821), company(?x346, ?x3821), currency(?x3821, ?x170), student(?x3821, ?x6742) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kw4j school_type 05pcjw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.526 http://example.org/education/educational_institution/school_type #17525-0sgtz PRED entity: 0sgtz PRED relation: place! PRED expected values: 0sgtz => 68 concepts (23 used for prediction) PRED predicted values (max 10 best out of 22): 0sgxg (0.43 #1549, 0.33 #462, 0.03 #977), 0sgtz (0.43 #1549), 0ntxg (0.08 #2065), 0s4sj (0.03 #1026, 0.01 #4128), 0sd7v (0.03 #926, 0.01 #4128), 0s6g4 (0.03 #897, 0.01 #4128), 0sf9_ (0.03 #602, 0.01 #4128), 0s69k (0.03 #553, 0.01 #4128), 0s3y5 (0.03 #522, 0.01 #4128), 0sc6p (0.03 #1004) >> Best rule #1549 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 0f04v; >> query: (?x10876, ?x13584) <- source(?x10876, ?x958), ?x958 = 0jbk9, county_seat(?x10877, ?x10876), contains(?x10877, ?x13584) >> conf = 0.43 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0sgtz place! 0sgtz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 23.000 0.428 http://example.org/location/hud_county_place/place #17524-01bmlb PRED entity: 01bmlb PRED relation: type_of_union PRED expected values: 04ztj => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #81, 0.87 #41, 0.87 #37), 01g63y (0.25 #489, 0.16 #238, 0.15 #62), 01bl8s (0.25 #489, 0.06 #15, 0.01 #51), 0jgjn (0.02 #32, 0.02 #48, 0.01 #96) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 03ds83; 0djywgn; >> query: (?x10411, 04ztj) <- film(?x10411, ?x2506), location_of_ceremony(?x10411, ?x2254), nominated_for(?x10411, ?x6080) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bmlb type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.883 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17523-06npd PRED entity: 06npd PRED relation: contains! PRED expected values: 02qkt => 169 concepts (55 used for prediction) PRED predicted values (max 10 best out of 115): 02qkt (0.74 #8423, 0.74 #7527, 0.70 #9319), 02j9z (0.71 #1824, 0.55 #22447, 0.53 #3593), 09b69 (0.55 #22447, 0.29 #1562, 0.19 #46693), 09c7w0 (0.46 #40408, 0.36 #25144, 0.05 #41306), 04_1l0v (0.44 #40856, 0.11 #25592, 0.03 #41754), 073q1 (0.29 #1306, 0.07 #7590, 0.07 #22859), 0j0k (0.27 #38091, 0.27 #18330, 0.26 #37194), 07c5l (0.26 #17451, 0.24 #27333, 0.19 #22844), 03rjj (0.25 #32335, 0.13 #27845, 0.06 #41313), 06mx8 (0.19 #46693, 0.17 #40405, 0.14 #2166) >> Best rule #8423 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 01ls2; 06f32; >> query: (?x756, 02qkt) <- country(?x3127, ?x756), film_release_region(?x7693, ?x756), ?x7693 = 0m63c, ?x3127 = 03hr1p >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06npd contains! 02qkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 55.000 0.741 http://example.org/location/location/contains #17522-02nzb8 PRED entity: 02nzb8 PRED relation: position! PRED expected values: 037css => 11 concepts (9 used for prediction) PRED predicted values (max 10 best out of 304): 02w64f (0.82 #147, 0.82 #146, 0.80 #736), 0272vm (0.82 #147, 0.82 #146, 0.80 #736), 03b04g (0.82 #147, 0.82 #146, 0.80 #736), 02rh_0 (0.82 #147, 0.82 #146, 0.80 #736), 01j95f (0.82 #147, 0.82 #146, 0.80 #736), 01z1r (0.82 #147, 0.82 #146, 0.80 #736), 01_gv (0.82 #147, 0.82 #146, 0.80 #736), 01njml (0.82 #147, 0.82 #146, 0.80 #736), 0hvgt (0.82 #147, 0.82 #146, 0.80 #736), 01453 (0.82 #147, 0.82 #146, 0.80 #736) >> Best rule #147 for best value: >> intensional similarity = 18 >> extensional distance = 1 >> proper extension: 02sdk9v; >> query: (?x60, ?x676) <- position(?x12807, ?x60), position(?x12269, ?x60), position(?x11507, ?x60), position(?x10719, ?x60), position(?x10472, ?x60), position(?x9389, ?x60), position(?x5341, ?x60), position(?x2355, ?x60), position(?x202, ?x60), ?x12269 = 0gfnqh, ?x12807 = 0ckf6, team(?x60, ?x676), ?x9389 = 04d817, ?x2355 = 02b1mc, ?x10719 = 01fcmh, ?x10472 = 01vcnl, ?x11507 = 0175rc, ?x5341 = 05hywl >> conf = 0.82 => this is the best rule for 106 predicted values *> Best rule #1039 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 8 *> proper extension: 02qvl7; 02qvzf; 02qvdc; *> query: (?x60, ?x1100) <- team(?x60, ?x11421), team(?x60, ?x10996), team(?x60, ?x10194), team(?x60, ?x4523), team(?x60, ?x1759), team(?x63, ?x10194), sport(?x4523, ?x471), colors(?x11421, ?x663), position(?x1100, ?x63), teams(?x6453, ?x1759), team(?x2666, ?x10996) *> conf = 0.56 ranks of expected_values: 129 EVAL 02nzb8 position! 037css CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 11.000 9.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #17521-077q8x PRED entity: 077q8x PRED relation: film_crew_role PRED expected values: 09zzb8 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 30): 09zzb8 (0.82 #773, 0.80 #808, 0.77 #1405), 02r96rf (0.74 #1058, 0.72 #1408, 0.66 #811), 01vx2h (0.41 #818, 0.35 #1065, 0.35 #783), 02vs3x5 (0.20 #23, 0.13 #3804, 0.08 #654), 02ynfr (0.18 #1419, 0.18 #1069, 0.17 #822), 0215hd (0.17 #1072, 0.15 #1422, 0.15 #825), 02rh1dz (0.16 #817, 0.15 #1064, 0.14 #1414), 089g0h (0.15 #1073, 0.13 #3804, 0.12 #1423), 01xy5l_ (0.13 #1067, 0.13 #3804, 0.12 #1417), 0d2b38 (0.13 #1079, 0.13 #3804, 0.12 #832) >> Best rule #773 for best value: >> intensional similarity = 4 >> extensional distance = 293 >> proper extension: 03_wm6; >> query: (?x6169, 09zzb8) <- film_crew_role(?x6169, ?x2095), titles(?x53, ?x6169), ?x2095 = 0dxtw, country(?x6169, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 077q8x film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.824 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17520-01nn3m PRED entity: 01nn3m PRED relation: category PRED expected values: 08mbj5d => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #1, 0.83 #55, 0.82 #20) >> Best rule #1 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 02twdq; >> query: (?x12623, 08mbj5d) <- artist(?x11171, ?x12623), artists(?x8386, ?x12623), artists(?x671, ?x12623), ?x8386 = 016ybr, ?x671 = 064t9 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nn3m category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.833 http://example.org/common/topic/webpage./common/webpage/category #17519-0436kgz PRED entity: 0436kgz PRED relation: profession PRED expected values: 0np9r => 123 concepts (122 used for prediction) PRED predicted values (max 10 best out of 80): 01d_h8 (0.42 #1049, 0.40 #1943, 0.37 #900), 0dxtg (0.35 #1057, 0.33 #610, 0.33 #3143), 09jwl (0.33 #615, 0.31 #2552, 0.29 #2850), 03gjzk (0.33 #1058, 0.28 #3144, 0.28 #909), 02jknp (0.31 #8793, 0.26 #5521, 0.26 #902), 0d1pc (0.31 #8793, 0.23 #3925, 0.21 #796), 02krf9 (0.29 #325, 0.25 #176, 0.22 #623), 01c72t (0.29 #322, 0.22 #620, 0.14 #471), 0cbd2 (0.28 #2242, 0.27 #1050, 0.16 #3434), 018gz8 (0.27 #1060, 0.25 #3146, 0.25 #3593) >> Best rule #1049 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 079vf; 0bymv; 01_rh4; 0d05fv; 05g7q; 0l_dv; >> query: (?x6658, 01d_h8) <- person(?x5929, ?x6658), religion(?x6658, ?x1985), film(?x6658, ?x69) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1064 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 079vf; 0bymv; 01_rh4; 0d05fv; 05g7q; 0l_dv; *> query: (?x6658, 0np9r) <- person(?x5929, ?x6658), religion(?x6658, ?x1985), film(?x6658, ?x69) *> conf = 0.17 ranks of expected_values: 15 EVAL 0436kgz profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 123.000 122.000 0.423 http://example.org/people/person/profession #17518-07r4c PRED entity: 07r4c PRED relation: location PRED expected values: 0rh6k => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 256): 02_286 (0.42 #80747, 0.24 #56767, 0.23 #8026), 0k33p (0.17 #479, 0.10 #2077, 0.02 #10866), 0cc56 (0.15 #67977, 0.12 #3252, 0.07 #6448), 05qtj (0.15 #42586, 0.08 #68159, 0.06 #16218), 0rh6k (0.13 #42352, 0.04 #80715, 0.03 #67125), 01531 (0.12 #68076, 0.05 #14537, 0.05 #4150), 0r0m6 (0.11 #1014, 0.10 #2612, 0.04 #5009), 0h1k6 (0.11 #1358, 0.10 #2956, 0.04 #5353), 0dj0x (0.11 #1569, 0.10 #3167, 0.04 #5564), 0mnz0 (0.11 #1472, 0.10 #3070, 0.04 #5467) >> Best rule #80747 for best value: >> intensional similarity = 5 >> extensional distance = 650 >> proper extension: 0bl60p; >> query: (?x6208, 02_286) <- award(?x6208, ?x1479), location(?x6208, ?x11319), location(?x6208, ?x1523), country(?x11319, ?x512), place_of_birth(?x338, ?x1523) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #42352 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 279 *> proper extension: 01h2_6; *> query: (?x6208, 0rh6k) <- location(?x6208, ?x362), capital(?x512, ?x362), featured_film_locations(?x136, ?x362) *> conf = 0.13 ranks of expected_values: 5 EVAL 07r4c location 0rh6k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 148.000 148.000 0.422 http://example.org/people/person/places_lived./people/place_lived/location #17517-01lhf PRED entity: 01lhf PRED relation: major_field_of_study! PRED expected values: 01swxv 0jkhr => 59 concepts (24 used for prediction) PRED predicted values (max 10 best out of 650): 08815 (0.79 #3495, 0.78 #2912, 0.75 #1166), 07szy (0.78 #2370, 0.75 #1206, 0.71 #3535), 025v3k (0.78 #2464, 0.62 #1300, 0.60 #718), 01w5m (0.75 #1282, 0.71 #3611, 0.69 #7690), 07wrz (0.75 #1229, 0.67 #2975, 0.67 #1811), 03ksy (0.71 #3612, 0.67 #3029, 0.67 #2447), 01w3v (0.71 #3507, 0.65 #4090, 0.62 #1178), 02zd460 (0.71 #3683, 0.62 #7762, 0.57 #9515), 0bwfn (0.70 #6120, 0.68 #4952, 0.67 #2040), 05zl0 (0.67 #2555, 0.64 #3720, 0.54 #7799) >> Best rule #3495 for best value: >> intensional similarity = 11 >> extensional distance = 12 >> proper extension: 02ky346; 04rjg; 04x_3; 02j62; 03nfmq; 0fdys; 037mh8; >> query: (?x11378, 08815) <- major_field_of_study(?x7991, ?x11378), major_field_of_study(?x6637, ?x11378), major_field_of_study(?x4672, ?x11378), ?x4672 = 07tds, major_field_of_study(?x1200, ?x11378), ?x1200 = 016t_3, student(?x11378, ?x4882), organization(?x346, ?x7991), institution(?x734, ?x6637), student(?x6637, ?x395), colors(?x7991, ?x3315) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #2011 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 7 *> proper extension: 04_tv; *> query: (?x11378, 0jkhr) <- major_field_of_study(?x7991, ?x11378), major_field_of_study(?x6637, ?x11378), major_field_of_study(?x4672, ?x11378), major_field_of_study(?x4410, ?x11378), ?x4672 = 07tds, major_field_of_study(?x1200, ?x11378), ?x1200 = 016t_3, citytown(?x6637, ?x1659), major_field_of_study(?x2606, ?x11378), contains(?x94, ?x7991), school(?x1883, ?x7991), ?x4410 = 017j69 *> conf = 0.44 ranks of expected_values: 55, 119 EVAL 01lhf major_field_of_study! 0jkhr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 59.000 24.000 0.786 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01lhf major_field_of_study! 01swxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 59.000 24.000 0.786 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17516-02b71x PRED entity: 02b71x PRED relation: parent_genre PRED expected values: 025sc50 => 51 concepts (40 used for prediction) PRED predicted values (max 10 best out of 170): 0glt670 (0.69 #514, 0.33 #676, 0.14 #1490), 06j6l (0.54 #358, 0.29 #195, 0.25 #33), 064t9 (0.38 #336, 0.29 #498, 0.14 #660), 06by7 (0.34 #828, 0.31 #341, 0.29 #178), 03_d0 (0.32 #658, 0.16 #1146, 0.15 #1309), 02x8m (0.26 #501, 0.19 #339, 0.16 #663), 05r6t (0.23 #378, 0.17 #540, 0.14 #4772), 016_rm (0.20 #618, 0.10 #780, 0.06 #1106), 05bt6j (0.15 #354, 0.14 #191, 0.11 #516), 011j5x (0.15 #346, 0.11 #508, 0.07 #670) >> Best rule #514 for best value: >> intensional similarity = 7 >> extensional distance = 33 >> proper extension: 025tjk_; >> query: (?x9789, 0glt670) <- parent_genre(?x9789, ?x2030), artists(?x2030, ?x5059), artists(?x2030, ?x3187), artists(?x2030, ?x1953), award_nominee(?x5059, ?x1231), ?x1953 = 019g40, ?x3187 = 0840vq >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1300 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 99 *> proper extension: 02rvwt; 0d6n1; 0ck9l7; *> query: (?x9789, ?x505) <- artists(?x9789, ?x4675), artists(?x9789, ?x4576), award_winner(?x724, ?x4675), award(?x4675, ?x537), artists(?x505, ?x4576), people(?x9888, ?x4576), award_nominee(?x4675, ?x506) *> conf = 0.07 ranks of expected_values: 33 EVAL 02b71x parent_genre 025sc50 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 51.000 40.000 0.686 http://example.org/music/genre/parent_genre #17515-06fq2 PRED entity: 06fq2 PRED relation: major_field_of_study PRED expected values: 05qjt 01540 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 115): 03g3w (0.57 #262, 0.52 #143, 0.50 #738), 01lj9 (0.57 #155, 0.50 #274, 0.44 #988), 05qjt (0.52 #127, 0.46 #246, 0.45 #603), 0fdys (0.52 #154, 0.40 #987, 0.37 #749), 06ms6 (0.52 #135, 0.39 #254, 0.32 #611), 01tbp (0.48 #413, 0.45 #532, 0.40 #1008), 037mh8 (0.48 #183, 0.43 #302, 0.43 #64), 05qfh (0.48 #151, 0.42 #984, 0.41 #389), 0g26h (0.46 #753, 0.45 #396, 0.43 #158), 041y2 (0.45 #432, 0.42 #551, 0.35 #789) >> Best rule #262 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 0cv_2; 02z_b; >> query: (?x8202, 03g3w) <- organization(?x8202, ?x5487), company(?x346, ?x8202), state_province_region(?x8202, ?x3634) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #127 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 045c7b; *> query: (?x8202, 05qjt) <- organization(?x8202, ?x5487), company(?x346, ?x8202), list(?x8202, ?x2197) *> conf = 0.52 ranks of expected_values: 3, 15 EVAL 06fq2 major_field_of_study 01540 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 143.000 143.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 06fq2 major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 143.000 143.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17514-02yvct PRED entity: 02yvct PRED relation: film_release_region PRED expected values: 0154j 03rjj 0chghy 01ls2 06npd 06mzp 06qd3 06c1y 01mjq => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 98): 0chghy (0.83 #3250, 0.82 #549, 0.78 #1360), 03rjj (0.82 #1355, 0.81 #3245, 0.80 #2570), 0154j (0.71 #1894, 0.69 #3244, 0.68 #3514), 06qd3 (0.59 #567, 0.54 #1378, 0.49 #2593), 01mjq (0.52 #1382, 0.49 #2597, 0.49 #1922), 06mzp (0.52 #1367, 0.49 #556, 0.43 #3257), 01ls2 (0.49 #550, 0.39 #1361, 0.39 #3251), 06f32 (0.49 #589, 0.39 #3290, 0.38 #3560), 01p1v (0.42 #3279, 0.41 #1929, 0.41 #578), 07f1x (0.38 #638, 0.30 #1449, 0.30 #3339) >> Best rule #3250 for best value: >> intensional similarity = 4 >> extensional distance = 268 >> proper extension: 07s3m4g; >> query: (?x2189, 0chghy) <- film_release_region(?x2189, ?x4737), film_release_region(?x2189, ?x1499), ?x1499 = 01znc_, official_language(?x4737, ?x2502) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 11, 12 EVAL 02yvct film_release_region 01mjq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.830 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02yvct film_release_region 06c1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 111.000 0.830 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02yvct film_release_region 06qd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.830 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02yvct film_release_region 06mzp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.830 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02yvct film_release_region 06npd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 111.000 0.830 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02yvct film_release_region 01ls2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.830 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02yvct film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.830 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02yvct film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.830 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02yvct film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.830 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17513-0gs96 PRED entity: 0gs96 PRED relation: nominated_for PRED expected values: 095zlp 01vfqh 0qmd5 0jvt9 0_b9f 0cqnss 04k9y6 0gl3hr 072zl1 04165w 0f7hw 0sxlb 027fwmt 08xvpn 01k5y0 01gvsn => 46 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1299): 042y1c (0.78 #4350, 0.77 #15951, 0.76 #11600), 0ywrc (0.78 #4350, 0.77 #15951, 0.76 #11600), 0k2cb (0.78 #4350, 0.77 #15951, 0.76 #11600), 01qz5 (0.78 #4350, 0.77 #15951, 0.76 #11600), 0glbqt (0.78 #4350, 0.77 #15951, 0.76 #11600), 0168ls (0.78 #4350, 0.77 #15951, 0.76 #11600), 01xq8v (0.78 #4350, 0.77 #15951, 0.76 #11600), 0dtfn (0.78 #4350, 0.77 #15951, 0.76 #11600), 0hv1t (0.78 #4350, 0.77 #15951, 0.76 #11600), 0kt_4 (0.78 #4350, 0.77 #15951, 0.76 #11600) >> Best rule #4350 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 040njc; 0gq_v; 0gq9h; 0k611; 0gqxm; >> query: (?x2222, ?x1255) <- nominated_for(?x2222, ?x2370), nominated_for(?x2222, ?x1224), ?x1224 = 020fcn, award(?x1255, ?x2222), ?x2370 = 0yyts >> conf = 0.78 => this is the best rule for 17 predicted values *> Best rule #1280 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 03hkv_r; *> query: (?x2222, 08xvpn) <- nominated_for(?x2222, ?x6199), award(?x771, ?x2222), ?x6199 = 03bxp5 *> conf = 0.67 ranks of expected_values: 21, 23, 24, 73, 93, 99, 145, 173, 184, 185, 189, 205, 265, 386, 395, 539 EVAL 0gs96 nominated_for 01gvsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 01k5y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 08xvpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 027fwmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 0sxlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 0f7hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 04165w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 072zl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 0gl3hr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 04k9y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 0cqnss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 0_b9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 0jvt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 0qmd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 01vfqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs96 nominated_for 095zlp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 46.000 21.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17512-04gzd PRED entity: 04gzd PRED relation: time_zones PRED expected values: 03plfd => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 12): 03plfd (0.68 #940, 0.64 #1150, 0.58 #1058), 02llzg (0.64 #1150, 0.41 #548, 0.40 #601), 02hcv8 (0.35 #1139, 0.33 #929, 0.28 #1192), 02lcqs (0.15 #1141, 0.12 #123, 0.12 #1207), 02fqwt (0.13 #588, 0.13 #1045, 0.13 #914), 02hczc (0.10 #589, 0.09 #928, 0.08 #120), 042g7t (0.09 #11, 0.06 #116, 0.06 #63), 03bdv (0.08 #280, 0.07 #763, 0.07 #228), 0gsrz4 (0.06 #321, 0.06 #269, 0.06 #622), 052vwh (0.03 #808, 0.03 #117, 0.03 #25) >> Best rule #940 for best value: >> intensional similarity = 3 >> extensional distance = 276 >> proper extension: 0msck; >> query: (?x344, ?x10735) <- adjoins(?x344, ?x456), contains(?x344, ?x1249), time_zones(?x1249, ?x10735) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gzd time_zones 03plfd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.683 http://example.org/location/location/time_zones #17511-0f721s PRED entity: 0f721s PRED relation: child! PRED expected values: 0sxdg => 95 concepts (67 used for prediction) PRED predicted values (max 10 best out of 20): 05th69 (0.25 #138, 0.17 #305, 0.17 #221), 02_l39 (0.25 #146, 0.17 #313, 0.10 #732), 06gst (0.25 #159, 0.17 #326, 0.10 #745), 03bnb (0.25 #144, 0.17 #311, 0.10 #730), 018_q8 (0.17 #207, 0.16 #878, 0.14 #459), 09b3v (0.17 #194, 0.14 #446, 0.14 #361), 0l8sx (0.17 #263, 0.11 #1358, 0.11 #850), 04qhdf (0.17 #267, 0.10 #686, 0.09 #769), 0sxdg (0.07 #1944, 0.07 #1176, 0.05 #969), 086k8 (0.06 #1347, 0.03 #1178, 0.03 #1859) >> Best rule #138 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05gnf; >> query: (?x1394, 05th69) <- award_winner(?x3486, ?x1394), award_winner(?x1762, ?x1394), ?x1762 = 0gsg7, award_winner(?x3169, ?x1394) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1944 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 0fvf9q; 07f8wg; 030pr; 0pz91; 032v0v; 0j1yf; 026c1; 046b0s; 0b13g7; 05nn4k; ... *> query: (?x1394, ?x3920) <- award_winner(?x3486, ?x1394), award_winner(?x1762, ?x1394), child(?x3920, ?x1762) *> conf = 0.07 ranks of expected_values: 9 EVAL 0f721s child! 0sxdg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 95.000 67.000 0.250 http://example.org/organization/organization/child./organization/organization_relationship/child #17510-091rc5 PRED entity: 091rc5 PRED relation: film_format PRED expected values: 07fb8_ => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.19 #6, 0.13 #123, 0.12 #57), 0cj16 (0.10 #315, 0.10 #325, 0.10 #13), 017fx5 (0.07 #9, 0.03 #85, 0.02 #251) >> Best rule #6 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 0872p_c; 06ybb1; 05zlld0; 095z4q; 05nlx4; 085wqm; >> query: (?x5012, 07fb8_) <- genre(?x5012, ?x258), titles(?x2480, ?x5012), nominated_for(?x102, ?x5012), film(?x2156, ?x5012), ?x102 = 04ljl_l >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 091rc5 film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.186 http://example.org/film/film/film_format #17509-01q7q2 PRED entity: 01q7q2 PRED relation: major_field_of_study PRED expected values: 0g26h => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 111): 02j62 (0.57 #607, 0.43 #1884, 0.43 #1419), 01tbp (0.46 #636, 0.25 #1564, 0.23 #172), 03g3w (0.44 #1416, 0.43 #604, 0.41 #952), 05qfh (0.43 #613, 0.38 #149, 0.28 #497), 0g26h (0.41 #619, 0.40 #1547, 0.31 #155), 01lj9 (0.38 #152, 0.30 #616, 0.28 #500), 037mh8 (0.38 #643, 0.29 #1455, 0.29 #991), 02_7t (0.38 #640, 0.27 #292, 0.25 #756), 05qjt (0.35 #588, 0.31 #124, 0.29 #1400), 02ky346 (0.31 #131, 0.27 #595, 0.21 #1523) >> Best rule #607 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 01j_cy; 07wjk; 027xx3; 03ksy; 07vfj; 025v3k; 04hgpt; 017cy9; 037fqp; 01nnsv; ... >> query: (?x8008, 02j62) <- student(?x8008, ?x838), major_field_of_study(?x8008, ?x2606), major_field_of_study(?x8008, ?x1154), ?x2606 = 062z7, ?x1154 = 02lp1 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #619 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 01j_cy; 07wjk; 027xx3; 03ksy; 07vfj; 025v3k; 04hgpt; 017cy9; 037fqp; 01nnsv; ... *> query: (?x8008, 0g26h) <- student(?x8008, ?x838), major_field_of_study(?x8008, ?x2606), major_field_of_study(?x8008, ?x1154), ?x2606 = 062z7, ?x1154 = 02lp1 *> conf = 0.41 ranks of expected_values: 5 EVAL 01q7q2 major_field_of_study 0g26h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 113.000 113.000 0.568 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17508-0xgpv PRED entity: 0xgpv PRED relation: contains! PRED expected values: 09c7w0 => 69 concepts (54 used for prediction) PRED predicted values (max 10 best out of 100): 09c7w0 (0.77 #1793, 0.70 #7164, 0.69 #8954), 04_1l0v (0.42 #42081, 0.27 #45666, 0.25 #47459), 07h34 (0.25 #231, 0.08 #2021, 0.02 #3811), 03v1s (0.25 #26, 0.08 #1816, 0.02 #39390), 0msyb (0.25 #663, 0.08 #2453), 0nt6b (0.25 #630, 0.08 #2420), 07ssc (0.17 #39423, 0.12 #42114, 0.12 #43010), 01n7q (0.15 #1868, 0.13 #37676, 0.13 #10819), 0kpys (0.15 #1971, 0.04 #5551, 0.04 #6446), 02jx1 (0.11 #41271, 0.10 #43065, 0.10 #44857) >> Best rule #1793 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01n7q; 04rrd; 04ly1; >> query: (?x13861, 09c7w0) <- contains(?x4622, ?x13861), location(?x3186, ?x13861), film(?x3186, ?x8474), ?x8474 = 01dc0c >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xgpv contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 54.000 0.769 http://example.org/location/location/contains #17507-0190xp PRED entity: 0190xp PRED relation: parent_genre PRED expected values: 0dl5d => 97 concepts (71 used for prediction) PRED predicted values (max 10 best out of 221): 06by7 (0.65 #5836, 0.57 #7135, 0.51 #3085), 05r6t (0.55 #5544, 0.52 #4415, 0.50 #3933), 0xhtw (0.40 #499, 0.33 #980, 0.33 #820), 0jmwg (0.33 #75, 0.25 #398, 0.18 #2658), 01qzt1 (0.29 #5652, 0.28 #3878, 0.28 #2744), 02yv6b (0.25 #1517, 0.25 #225, 0.20 #550), 0pm85 (0.25 #257, 0.20 #582, 0.17 #1063), 09jw2 (0.25 #423, 0.20 #2356, 0.17 #907), 05bt6j (0.25 #352, 0.18 #1642, 0.17 #836), 05w3f (0.25 #1478, 0.17 #672, 0.13 #3254) >> Best rule #5836 for best value: >> intensional similarity = 8 >> extensional distance = 75 >> proper extension: 061fhg; 0xjl2; 01pfpt; 0g_bh; 06hzq3; 0xv2x; 0pm85; 07s7gk6; 0621cs; 0m8vm; >> query: (?x10128, 06by7) <- parent_genre(?x10128, ?x2249), artists(?x10128, ?x12228), artist(?x2299, ?x12228), group(?x227, ?x12228), artists(?x2249, ?x8999), artists(?x2249, ?x1970), ?x8999 = 0bk1p, ?x1970 = 0zjpz >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #1790 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 11 *> proper extension: 01jwt; 04b675; *> query: (?x10128, 0dl5d) <- parent_genre(?x10128, ?x2249), artists(?x10128, ?x12228), artist(?x7089, ?x12228), group(?x1166, ?x12228), artist(?x7089, ?x2964), ?x2249 = 03lty, ?x2964 = 0565cz, ?x1166 = 05148p4 *> conf = 0.15 ranks of expected_values: 26 EVAL 0190xp parent_genre 0dl5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 97.000 71.000 0.649 http://example.org/music/genre/parent_genre #17506-058kh7 PRED entity: 058kh7 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.88 #16, 0.83 #111, 0.83 #6), 02nxhr (0.06 #22, 0.05 #77, 0.05 #92), 07c52 (0.05 #93, 0.04 #43, 0.04 #48), 07z4p (0.02 #105, 0.02 #155, 0.02 #130) >> Best rule #16 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 06wbm8q; 01gkp1; 02nt3d; 0b3n61; >> query: (?x9646, 029j_) <- film(?x6324, ?x9646), film(?x3025, ?x9646), genre(?x9646, ?x53), ?x6324 = 018ygt, participant(?x513, ?x3025), type_of_union(?x3025, ?x566) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 058kh7 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17505-05bm4sm PRED entity: 05bm4sm PRED relation: nominated_for PRED expected values: 017gm7 02c638 027s39y 0dgq_kn => 97 concepts (49 used for prediction) PRED predicted values (max 10 best out of 484): 02rn00y (0.28 #4850, 0.25 #11317, 0.25 #3232), 08zrbl (0.28 #4850, 0.25 #11317, 0.25 #3232), 01cssf (0.28 #4850, 0.25 #11317, 0.25 #3232), 024lt6 (0.28 #4850, 0.25 #11317, 0.25 #3232), 04jpg2p (0.28 #4850, 0.25 #11317, 0.25 #3232), 0dc_ms (0.28 #4850, 0.25 #11317, 0.25 #3232), 02ylg6 (0.28 #4850, 0.25 #11317, 0.25 #3232), 024mpp (0.28 #4850, 0.25 #11317, 0.25 #3232), 0jjy0 (0.28 #4850, 0.25 #11317, 0.25 #3232), 0m313 (0.21 #12945, 0.15 #14564, 0.03 #1628) >> Best rule #4850 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 04cy8rb; 03h26tm; 021yc7p; 06rnl9; 0bbxx9b; 0b6mgp_; 02q9kqf; 0g9zcgx; >> query: (?x5653, ?x638) <- award_winner(?x5653, ?x1983), nominated_for(?x5653, ?x972), crewmember(?x638, ?x5653) >> conf = 0.28 => this is the best rule for 9 predicted values *> Best rule #64655 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1242 *> proper extension: 079vf; *> query: (?x5653, ?x573) <- profession(?x5653, ?x5654), award_winner(?x5653, ?x1983), nominated_for(?x1983, ?x573) *> conf = 0.09 ranks of expected_values: 20, 22, 113 EVAL 05bm4sm nominated_for 0dgq_kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 49.000 0.285 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 05bm4sm nominated_for 027s39y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 97.000 49.000 0.285 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 05bm4sm nominated_for 02c638 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 97.000 49.000 0.285 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 05bm4sm nominated_for 017gm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 97.000 49.000 0.285 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17504-043n1r5 PRED entity: 043n1r5 PRED relation: film! PRED expected values: 03dpqd => 85 concepts (49 used for prediction) PRED predicted values (max 10 best out of 934): 01wd9vs (0.49 #95810, 0.44 #89560, 0.43 #77063), 02zft0 (0.44 #89560, 0.43 #77063, 0.42 #74980), 03v0vd (0.20 #1630, 0.05 #87476, 0.05 #95811), 02tv80 (0.20 #1132, 0.03 #5296, 0.01 #13628), 0170qf (0.20 #365, 0.01 #12861, 0.01 #92008), 014x77 (0.20 #91, 0.01 #58408), 0k9j_ (0.20 #1552), 03dpqd (0.20 #828), 03h2d4 (0.20 #746), 05th8t (0.20 #445) >> Best rule #95810 for best value: >> intensional similarity = 3 >> extensional distance = 1179 >> proper extension: 03d17dg; >> query: (?x10077, ?x1802) <- nominated_for(?x1802, ?x10077), location(?x1802, ?x739), award_nominee(?x1802, ?x192) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #828 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0c0yh4; *> query: (?x10077, 03dpqd) <- titles(?x7131, ?x10077), film(?x538, ?x10077), ?x7131 = 03_gx *> conf = 0.20 ranks of expected_values: 8 EVAL 043n1r5 film! 03dpqd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 85.000 49.000 0.485 http://example.org/film/actor/film./film/performance/film #17503-07gp9 PRED entity: 07gp9 PRED relation: film_crew_role PRED expected values: 02rh1dz => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 29): 0dxtw (0.55 #110, 0.40 #282, 0.36 #898), 01vx2h (0.44 #283, 0.36 #111, 0.34 #146), 01pvkk (0.29 #1825, 0.28 #250, 0.28 #524), 094hwz (0.27 #115, 0.09 #287, 0.08 #218), 02rh1dz (0.22 #281, 0.20 #212, 0.19 #178), 02ynfr (0.20 #185, 0.18 #904, 0.17 #1350), 0215hd (0.18 #119, 0.18 #85, 0.14 #1353), 01xy5l_ (0.18 #114, 0.15 #183, 0.15 #286), 089g0h (0.18 #120, 0.14 #292, 0.12 #1354), 033smt (0.18 #128, 0.07 #471, 0.06 #231) >> Best rule #110 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0dnqr; >> query: (?x324, 0dxtw) <- nominated_for(?x4393, ?x324), nominated_for(?x2871, ?x324), profession(?x2871, ?x137), nominated_for(?x298, ?x324), ?x4393 = 0b6mgp_ >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #281 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 115 *> proper extension: 01q2nx; *> query: (?x324, 02rh1dz) <- executive_produced_by(?x324, ?x519), crewmember(?x324, ?x1933), film(?x1561, ?x324) *> conf = 0.22 ranks of expected_values: 5 EVAL 07gp9 film_crew_role 02rh1dz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 96.000 96.000 0.545 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17502-0ctw_b PRED entity: 0ctw_b PRED relation: nationality! PRED expected values: 02bfxb 089kpp => 229 concepts (108 used for prediction) PRED predicted values (max 10 best out of 4100): 0d1_f (0.68 #44534, 0.14 #17168, 0.06 #41459), 01xcfy (0.37 #178131, 0.29 #17009, 0.12 #41300), 0184jc (0.37 #178131, 0.25 #8103, 0.14 #16200), 015t7v (0.37 #178131, 0.25 #9631, 0.14 #17728), 0f0kz (0.37 #178131, 0.25 #8951, 0.14 #17048), 01b0k1 (0.37 #178131, 0.25 #11920, 0.14 #20017), 01fdc0 (0.37 #178131, 0.25 #9100, 0.12 #41488), 016zp5 (0.37 #178131, 0.25 #9792, 0.07 #34084), 04pf4r (0.37 #178131, 0.25 #9269, 0.07 #33561), 015wnl (0.37 #178131, 0.25 #9185, 0.07 #33477) >> Best rule #44534 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 06frc; >> query: (?x1023, ?x3444) <- jurisdiction_of_office(?x3444, ?x1023), capital(?x1023, ?x11743), contains(?x10150, ?x1023) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #178131 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 07zrf; 07ww5; *> query: (?x1023, ?x628) <- country(?x972, ?x1023), award(?x972, ?x143), nominated_for(?x628, ?x972) *> conf = 0.37 ranks of expected_values: 31 EVAL 0ctw_b nationality! 089kpp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 229.000 108.000 0.681 http://example.org/people/person/nationality EVAL 0ctw_b nationality! 02bfxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 229.000 108.000 0.681 http://example.org/people/person/nationality #17501-01ck6v PRED entity: 01ck6v PRED relation: award! PRED expected values: 03f0vvr 01bczm => 46 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2343): 0gcs9 (0.80 #33617, 0.79 #26894, 0.77 #60516), 0pk41 (0.80 #33617, 0.77 #60516, 0.76 #26893), 01vrz41 (0.57 #10376, 0.33 #289, 0.20 #27183), 0dw4g (0.57 #11714, 0.33 #1627, 0.18 #25158), 01wwvc5 (0.57 #10821, 0.33 #734, 0.18 #24265), 09889g (0.57 #11527, 0.20 #24971, 0.20 #28334), 012x4t (0.57 #10502, 0.17 #13863, 0.16 #17224), 01xzb6 (0.57 #11622, 0.15 #25066, 0.14 #28429), 0b_j2 (0.57 #11997, 0.12 #25441, 0.10 #28804), 0dl567 (0.43 #11228, 0.33 #7864, 0.33 #1141) >> Best rule #33617 for best value: >> intensional similarity = 6 >> extensional distance = 119 >> proper extension: 02x4x18; >> query: (?x7005, ?x2963) <- award(?x5391, ?x7005), award(?x1654, ?x7005), artists(?x114, ?x1654), award_winner(?x7005, ?x2963), role(?x5391, ?x212), film(?x5391, ?x1481) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #11715 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 02wh75; 01bgqh; 0c4z8; 01c92g; 03qbh5; *> query: (?x7005, 01bczm) <- award(?x6380, ?x7005), award(?x1654, ?x7005), instrumentalists(?x227, ?x1654), ?x6380 = 02s2wq, profession(?x1654, ?x220), artists(?x114, ?x1654) *> conf = 0.43 ranks of expected_values: 11, 341 EVAL 01ck6v award! 01bczm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 46.000 19.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01ck6v award! 03f0vvr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 19.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17500-01gq0b PRED entity: 01gq0b PRED relation: location_of_ceremony PRED expected values: 0f8l9c => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 32): 0cv3w (0.04 #634, 0.04 #993, 0.04 #393), 0k049 (0.03 #603, 0.03 #482, 0.02 #2163), 07fr_ (0.03 #311, 0.03 #431, 0.03 #551), 0r0m6 (0.02 #649, 0.02 #1368, 0.02 #288), 03gh4 (0.02 #358, 0.02 #838, 0.02 #1381), 04jpl (0.02 #358, 0.02 #838, 0.02 #1198), 03rjj (0.02 #358, 0.02 #838, 0.02 #1198), 02_286 (0.02 #2533, 0.02 #251, 0.01 #2172), 030qb3t (0.02 #1337, 0.02 #1577, 0.02 #1697), 0qxhc (0.02 #236) >> Best rule #634 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 014x77; 07jrjb; >> query: (?x1890, 0cv3w) <- award_winner(?x638, ?x1890), nominated_for(?x1890, ?x414), celebrity(?x10224, ?x1890) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01gq0b location_of_ceremony 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 125.000 0.045 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #17499-04lh6 PRED entity: 04lh6 PRED relation: citytown! PRED expected values: 086h6p => 158 concepts (63 used for prediction) PRED predicted values (max 10 best out of 581): 0gsg7 (0.25 #61, 0.06 #4918, 0.05 #8964), 05cl8y (0.25 #415, 0.06 #12557, 0.05 #13368), 02975m (0.25 #725, 0.05 #9628, 0.04 #10438), 01l50r (0.25 #684, 0.05 #9587, 0.04 #10397), 032j_n (0.25 #549, 0.05 #9452, 0.04 #10262), 07l1c (0.25 #326, 0.05 #9229, 0.04 #10039), 0p4wb (0.25 #22, 0.04 #9735, 0.04 #10545), 01dtcb (0.25 #385, 0.04 #12527, 0.04 #26300), 0146mv (0.25 #586, 0.04 #12728, 0.04 #13539), 06182p (0.25 #395, 0.04 #12537, 0.04 #13348) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02_286; 02z0j; >> query: (?x9026, 0gsg7) <- location(?x8741, ?x9026), origin(?x1136, ?x9026), contains(?x512, ?x9026), ?x8741 = 01p85y >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04lh6 citytown! 086h6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 63.000 0.250 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #17498-01pw9v PRED entity: 01pw9v PRED relation: people! PRED expected values: 041rx => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 46): 041rx (0.60 #697, 0.24 #1390, 0.23 #1544), 0222qb (0.33 #44, 0.04 #121, 0.03 #2971), 0x67 (0.24 #1088, 0.14 #857, 0.14 #1473), 033tf_ (0.12 #4634, 0.11 #2702, 0.11 #2934), 07hwkr (0.11 #243, 0.09 #628, 0.09 #1244), 048z7l (0.09 #733, 0.08 #117, 0.07 #425), 0xnvg (0.09 #783, 0.06 #4640, 0.06 #2940), 07bch9 (0.09 #1871, 0.05 #1794, 0.05 #4184), 013xrm (0.08 #1406, 0.06 #1560, 0.05 #3102), 0g5y6 (0.07 #730, 0.02 #1577, 0.02 #4353) >> Best rule #697 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 03bw6; >> query: (?x9519, 041rx) <- religion(?x9519, ?x7131), award_winner(?x3676, ?x9519), ?x7131 = 03_gx >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pw9v people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.600 http://example.org/people/ethnicity/people #17497-016yr0 PRED entity: 016yr0 PRED relation: award_nominee PRED expected values: 069ld1 => 108 concepts (36 used for prediction) PRED predicted values (max 10 best out of 906): 02lf70 (0.15 #30423, 0.12 #35106, 0.11 #32765), 04wvhz (0.11 #213, 0.07 #9573, 0.04 #18935), 0151w_ (0.11 #207, 0.04 #4887, 0.03 #9567), 03mcwq3 (0.11 #553, 0.04 #26295, 0.02 #5233), 06cgy (0.11 #328, 0.03 #9688, 0.03 #2668), 03pmty (0.11 #200, 0.03 #9560, 0.02 #23602), 015vq_ (0.11 #957, 0.03 #10317, 0.02 #61809), 017s11 (0.11 #107, 0.03 #2447, 0.02 #4787), 01mh8zn (0.11 #1785, 0.03 #4125, 0.02 #6465), 05pzdk (0.11 #1260, 0.03 #3600, 0.02 #5940) >> Best rule #30423 for best value: >> intensional similarity = 3 >> extensional distance = 231 >> proper extension: 01vw20_; 013ybx; >> query: (?x4327, ?x1991) <- profession(?x4327, ?x319), spouse(?x4327, ?x1991), award_nominee(?x5645, ?x4327) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #25923 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 204 *> proper extension: 06gh0t; *> query: (?x4327, 069ld1) <- profession(?x4327, ?x319), award(?x4327, ?x1670), ?x1670 = 0ck27z *> conf = 0.02 ranks of expected_values: 419 EVAL 016yr0 award_nominee 069ld1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 108.000 36.000 0.147 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17496-016cyt PRED entity: 016cyt PRED relation: parent_genre PRED expected values: 06cqb => 58 concepts (54 used for prediction) PRED predicted values (max 10 best out of 150): 06by7 (0.57 #3128, 0.43 #1324, 0.40 #1162), 05r6t (0.39 #3167, 0.24 #1038, 0.19 #1526), 06cqb (0.33 #1, 0.15 #327, 0.13 #492), 016cyt (0.33 #149, 0.12 #4254, 0.11 #2454), 0827d (0.25 #165, 0.14 #5558, 0.11 #657), 016clz (0.24 #987, 0.14 #3116, 0.08 #1966), 06j6l (0.22 #1017, 0.22 #689, 0.13 #525), 0gywn (0.22 #696, 0.20 #532, 0.11 #1187), 01243b (0.21 #1012, 0.17 #3141, 0.15 #1500), 03lty (0.20 #1981, 0.18 #2802, 0.18 #2639) >> Best rule #3128 for best value: >> intensional similarity = 5 >> extensional distance = 102 >> proper extension: 028cl7; 088vmr; 017ht; >> query: (?x13960, 06by7) <- parent_genre(?x13960, ?x6101), artists(?x6101, ?x4713), artists(?x6101, ?x2723), artist(?x3240, ?x4713), ?x2723 = 016fmf >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 0190y4; *> query: (?x13960, 06cqb) <- parent_genre(?x13960, ?x14580), parent_genre(?x11724, ?x13960), ?x14580 = 019z2l, artists(?x11724, ?x11953), artists(?x13960, ?x14291), ?x11953 = 01mskc3 *> conf = 0.33 ranks of expected_values: 3 EVAL 016cyt parent_genre 06cqb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 58.000 54.000 0.567 http://example.org/music/genre/parent_genre #17495-02ply6j PRED entity: 02ply6j PRED relation: religion PRED expected values: 03j6c => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 14): 0flw86 (0.33 #47, 0.06 #859, 0.04 #137), 03j6c (0.20 #21, 0.06 #859, 0.04 #517), 0c8wxp (0.19 #186, 0.17 #51, 0.15 #547), 0kpl (0.17 #55, 0.06 #190, 0.05 #145), 025t7ly (0.14 #102, 0.06 #859), 03_gx (0.08 #464, 0.08 #646, 0.07 #555), 092bf5 (0.04 #151, 0.03 #286, 0.03 #331), 0kq2 (0.03 #198, 0.02 #153, 0.02 #605), 06nzl (0.02 #195, 0.01 #420, 0.01 #285), 05sfs (0.02 #183) >> Best rule #47 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0qf43; >> query: (?x7123, 0flw86) <- nominated_for(?x7123, ?x4007), ?x4007 = 03hmt9b, award(?x7123, ?x102) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0jrqq; 030_3z; 0146mv; *> query: (?x7123, 03j6c) <- nominated_for(?x7123, ?x6053), award_nominee(?x7123, ?x2372), ?x6053 = 05qbbfb *> conf = 0.20 ranks of expected_values: 2 EVAL 02ply6j religion 03j6c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.333 http://example.org/people/person/religion #17494-095zvfg PRED entity: 095zvfg PRED relation: award_winner! PRED expected values: 0h_cssd => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 136): 0hr3c8y (0.60 #710, 0.56 #150, 0.50 #290), 0g55tzk (0.33 #276, 0.30 #416, 0.28 #836), 0hndn2q (0.29 #1439, 0.12 #39, 0.11 #179), 09gkdln (0.25 #121, 0.11 #261, 0.10 #401), 0hhtgcw (0.21 #1485, 0.02 #1345, 0.01 #1625), 073h1t (0.19 #867, 0.13 #1007, 0.13 #1147), 02jp5r (0.17 #628, 0.16 #908, 0.13 #1048), 0275n3y (0.17 #634, 0.15 #494, 0.12 #74), 073h9x (0.16 #889, 0.12 #609, 0.11 #1029), 0bvhz9 (0.15 #549, 0.12 #129, 0.11 #269) >> Best rule #710 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 03w1v2; 0f830f; 027dtv3; 08w7vj; 02lfns; 0fx0mw; 0c7t58; 05m9f9; 03cbtlj; 0bx0lc; ... >> query: (?x9151, 0hr3c8y) <- nominated_for(?x9151, ?x5648), nominated_for(?x9151, ?x493), ?x493 = 080dwhx, titles(?x53, ?x5648) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #28 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01wbg84; 03jvmp; 0gy6z9; 07s8hms; 0cjsxp; 04sry; *> query: (?x9151, 0h_cssd) <- nominated_for(?x9151, ?x5648), nominated_for(?x9151, ?x493), ?x493 = 080dwhx, executive_produced_by(?x5648, ?x3101) *> conf = 0.12 ranks of expected_values: 15 EVAL 095zvfg award_winner! 0h_cssd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 105.000 105.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17493-0163r3 PRED entity: 0163r3 PRED relation: instrumentalists! PRED expected values: 03gvt => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 125): 0342h (0.67 #2158, 0.64 #1728, 0.64 #2244), 018vs (0.34 #2598, 0.32 #2772, 0.31 #2166), 01vdm0 (0.31 #2847, 0.26 #2672, 0.26 #3626), 01s0ps (0.31 #2847, 0.26 #2672, 0.26 #3626), 06ch55 (0.30 #167, 0.13 #425, 0.12 #339), 013y1f (0.26 #2672, 0.26 #3626, 0.26 #3625), 0dwt5 (0.26 #2672, 0.26 #3626, 0.26 #3625), 02hnl (0.20 #120, 0.18 #2619, 0.18 #1239), 07gql (0.20 #128, 0.06 #645, 0.05 #731), 0l14j_ (0.20 #139, 0.05 #1776, 0.05 #2206) >> Best rule #2158 for best value: >> intensional similarity = 3 >> extensional distance = 304 >> proper extension: 094xh; 04mx7s; >> query: (?x6716, 0342h) <- instrumentalists(?x316, ?x6716), type_of_union(?x6716, ?x566), category(?x6716, ?x134) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #581 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 07yg2; 0394y; 08w4pm; *> query: (?x6716, 03gvt) <- artists(?x1127, ?x6716), artist(?x2039, ?x6716), inductee(?x1091, ?x6716) *> conf = 0.11 ranks of expected_values: 15 EVAL 0163r3 instrumentalists! 03gvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 113.000 113.000 0.670 http://example.org/music/instrument/instrumentalists #17492-03hj3b3 PRED entity: 03hj3b3 PRED relation: honored_for! PRED expected values: 0bzm81 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 110): 09p30_ (0.08 #194, 0.02 #682, 0.02 #804), 02hn5v (0.08 #155, 0.02 #643, 0.02 #765), 09k5jh7 (0.07 #315, 0.02 #1535, 0.02 #437), 09gkdln (0.07 #350, 0.02 #594, 0.02 #3522), 05qb8vx (0.07 #292, 0.02 #1512, 0.02 #414), 073hmq (0.05 #15, 0.04 #137, 0.01 #1479), 0bzjgq (0.05 #104, 0.04 #226, 0.01 #5003), 03tn9w (0.05 #80, 0.04 #202, 0.01 #5003), 05hmp6 (0.05 #74, 0.02 #440, 0.01 #562), 0d__c3 (0.05 #109, 0.02 #475, 0.01 #719) >> Best rule #194 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 02q6gfp; 01jw67; 02chhq; 01gvsn; >> query: (?x1944, 09p30_) <- costume_design_by(?x1944, ?x2109), country(?x1944, ?x94), nominated_for(?x749, ?x1944), ?x749 = 094qd5 >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #504 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 136 *> proper extension: 02d413; 0b2v79; 011yrp; 095zlp; 0ds11z; 04jwjq; 0fg04; 01r97z; 0p_sc; 017gl1; ... *> query: (?x1944, 0bzm81) <- costume_design_by(?x1944, ?x2109), film(?x788, ?x1944), award_winner(?x1105, ?x788) *> conf = 0.01 ranks of expected_values: 70 EVAL 03hj3b3 honored_for! 0bzm81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 72.000 72.000 0.080 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #17491-021996 PRED entity: 021996 PRED relation: major_field_of_study PRED expected values: 0g26h 0db86 02_7t 041y2 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 96): 0g26h (0.35 #2569, 0.33 #1764, 0.32 #1994), 062z7 (0.27 #2556, 0.27 #601, 0.26 #1981), 03g3w (0.26 #4743, 0.26 #6701, 0.25 #2787), 04rjg (0.26 #4045, 0.25 #18, 0.25 #2780), 0fdys (0.25 #36, 0.15 #4754, 0.15 #2798), 02_7t (0.24 #2590, 0.24 #1785, 0.21 #2015), 01540 (0.23 #1782, 0.22 #57, 0.19 #2587), 04x_3 (0.23 #1749, 0.20 #1979, 0.20 #2554), 01tbp (0.22 #1781, 0.19 #56, 0.19 #2586), 05qjt (0.20 #4035, 0.20 #4495, 0.19 #8) >> Best rule #2569 for best value: >> intensional similarity = 3 >> extensional distance = 191 >> proper extension: 0fht9f; >> query: (?x8427, 0g26h) <- school(?x2820, ?x8427), school(?x2820, ?x10104), major_field_of_study(?x10104, ?x1527) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 12, 18 EVAL 021996 major_field_of_study 041y2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 112.000 112.000 0.347 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 021996 major_field_of_study 02_7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 112.000 112.000 0.347 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 021996 major_field_of_study 0db86 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 112.000 112.000 0.347 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 021996 major_field_of_study 0g26h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.347 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17490-01fl3 PRED entity: 01fl3 PRED relation: award PRED expected values: 01c9jp => 81 concepts (57 used for prediction) PRED predicted values (max 10 best out of 219): 01bgqh (0.64 #9766, 0.52 #10172, 0.49 #10982), 01by1l (0.60 #10242, 0.50 #1733, 0.43 #9836), 026mfs (0.44 #1750, 0.30 #2965, 0.19 #11069), 03qbh5 (0.40 #9929, 0.31 #11145, 0.30 #11956), 01ckcd (0.35 #6007, 0.33 #5197, 0.32 #7222), 01c9jp (0.34 #3430, 0.30 #5860, 0.28 #5050), 03qbnj (0.33 #639, 0.23 #10363, 0.21 #9957), 02f72n (0.33 #9059, 0.21 #5817, 0.19 #6222), 01ckrr (0.33 #637, 0.17 #1042, 0.14 #2662), 02f6xy (0.32 #9113, 0.16 #10330, 0.14 #9924) >> Best rule #9766 for best value: >> intensional similarity = 7 >> extensional distance = 228 >> proper extension: 0ggl02; 0288fyj; 01x15dc; 016732; 01bmlb; 05mxw33; >> query: (?x1749, 01bgqh) <- award(?x1749, ?x3045), award(?x7162, ?x3045), award(?x4957, ?x3045), award(?x1270, ?x3045), ?x1270 = 0137n0, group(?x227, ?x4957), ?x7162 = 0ffgh >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #3430 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 42 *> proper extension: 01pfr3; 07c0j; 03t9sp; 03fbc; 016fmf; 0dm5l; 0249kn; 018ndc; 01rm8b; 03xhj6; ... *> query: (?x1749, 01c9jp) <- artists(?x1748, ?x1749), artists(?x1748, ?x2824), artists(?x1748, ?x1398), artists(?x1748, ?x654), group(?x227, ?x1749), ?x1398 = 01j4ls, ?x654 = 0kzy0, ?x2824 = 02w4fkq *> conf = 0.34 ranks of expected_values: 6 EVAL 01fl3 award 01c9jp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 57.000 0.643 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17489-01g63y PRED entity: 01g63y PRED relation: type_of_union! PRED expected values: 07s3vqk 0l8v5 06cv1 0blbxk 015rkw 01wxyx1 0443y3 015pxr 0qf3p 019pm_ 0lrh 01j5ws 01w7nww 01846t 03pvt 09r9dp 04n_g 01g23m 016dsy 07rd7 0kvnn 018z_c 03dpqd 0f7hc 015q43 01jb26 016yvw 016zp5 0347db 02lmk 0dx_q 01mh8zn 017c87 013zs9 0hsn_ 02s58t 01vh08 029ghl 01hgwkr 02p5hf 030hbp 057xn_m 02d45s 01vsn38 01pny5 01jz6d 0h6sv => 4 concepts (2 used for prediction) PRED predicted values (max 10 best out of 3748): 0l6px (0.64 #2703, 0.63 #2699, 0.62 #2704), 016gr2 (0.64 #2703, 0.63 #2699, 0.62 #2704), 05vsxz (0.64 #2703, 0.63 #2699, 0.62 #2704), 01ksr1 (0.64 #2703, 0.62 #2704, 0.62 #5402), 02l4rh (0.64 #2703, 0.62 #2704, 0.62 #5402), 01kb2j (0.64 #2703, 0.62 #2704, 0.62 #5402), 051wwp (0.64 #2703, 0.62 #2704, 0.62 #5402), 013knm (0.64 #2703, 0.62 #2704, 0.62 #5402), 03v3xp (0.64 #2703, 0.62 #2704, 0.62 #5402), 0227tr (0.64 #2703, 0.62 #2704, 0.62 #5402) >> Best rule #2703 for best value: >> intensional similarity = 60 >> extensional distance = 1 >> proper extension: 04ztj; >> query: (?x1873, ?x3293) <- type_of_union(?x11460, ?x1873), type_of_union(?x10423, ?x1873), type_of_union(?x10239, ?x1873), type_of_union(?x9892, ?x1873), type_of_union(?x9754, ?x1873), type_of_union(?x8966, ?x1873), type_of_union(?x8741, ?x1873), type_of_union(?x8640, ?x1873), type_of_union(?x7068, ?x1873), type_of_union(?x6957, ?x1873), type_of_union(?x5330, ?x1873), type_of_union(?x4819, ?x1873), type_of_union(?x4685, ?x1873), type_of_union(?x3065, ?x1873), type_of_union(?x2858, ?x1873), type_of_union(?x2782, ?x1873), type_of_union(?x2457, ?x1873), type_of_union(?x2237, ?x1873), type_of_union(?x2046, ?x1873), type_of_union(?x1047, ?x1873), type_of_union(?x919, ?x1873), type_of_union(?x702, ?x1873), type_of_union(?x426, ?x1873), type_of_union(?x192, ?x1873), ?x8741 = 01p85y, ?x702 = 01vvycq, artists(?x10853, ?x11460), ?x2237 = 01vs_v8, ?x8640 = 020hh3, ?x10423 = 01gc7h, ?x6957 = 03s9b, ?x8966 = 01qqtr, ?x4819 = 0c7xjb, award_winner(?x496, ?x2858), award_nominee(?x192, ?x3307), award_nominee(?x192, ?x3293), religion(?x9754, ?x1985), location_of_ceremony(?x1873, ?x362), award_winner(?x8533, ?x9892), ?x4685 = 0b478, award_winner(?x1389, ?x3065), ?x3307 = 01ksr1, film(?x2858, ?x2928), place_of_birth(?x919, ?x3014), ?x7068 = 01ts_3, people(?x3591, ?x426), ?x5330 = 02f2p7, ?x10853 = 0l8gh, award_nominee(?x488, ?x2457), award(?x426, ?x1254), award_winner(?x1135, ?x3293), ?x488 = 0159h6, award_nominee(?x2282, ?x919), ?x2782 = 014q2g, ?x2046 = 02mhfy, written_by(?x5134, ?x1047), award_nominee(?x3293, ?x406), award(?x919, ?x704), award(?x192, ?x112), instrumentalists(?x614, ?x10239) >> conf = 0.64 => this is the best rule for 49 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 17, 46, 196, 226, 233, 301, 316, 336, 387, 432, 455, 479, 552, 560, 607, 795, 798, 951, 986, 1245, 1272, 1386, 1543, 1580, 1642, 1684, 1814, 1832, 2008, 2033, 2091, 2315, 2433, 2440, 2459, 2556, 2568, 2670, 2783, 3286, 3381, 3677, 3720 EVAL 01g63y type_of_union! 0h6sv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01jz6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01pny5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01vsn38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 02d45s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 057xn_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 030hbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 02p5hf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01hgwkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 029ghl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01vh08 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 02s58t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 0hsn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 013zs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 017c87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01mh8zn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 0dx_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 02lmk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 0347db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 016zp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 016yvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01jb26 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 015q43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 0f7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 03dpqd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 018z_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 0kvnn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 07rd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 016dsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01g23m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 04n_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 09r9dp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 03pvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01846t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01w7nww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01j5ws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 0lrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 019pm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 0qf3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 015pxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 0443y3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 01wxyx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 015rkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 0blbxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 06cv1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 0l8v5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01g63y type_of_union! 07s3vqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 4.000 2.000 0.636 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17488-0cq8nx PRED entity: 0cq8nx PRED relation: list PRED expected values: 05glt => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.30 #30, 0.23 #107, 0.22 #100) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 03wbqc4; >> query: (?x9611, 05glt) <- genre(?x9611, ?x53), film(?x4652, ?x9611), nominated_for(?x4652, ?x499), place_of_death(?x4652, ?x1523) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cq8nx list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.305 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #17487-01gvpz PRED entity: 01gvpz PRED relation: genre PRED expected values: 03bxz7 => 88 concepts (84 used for prediction) PRED predicted values (max 10 best out of 125): 02l7c8 (0.58 #615, 0.57 #735, 0.55 #855), 03bxz7 (0.52 #535, 0.27 #415, 0.18 #2528), 05p553 (0.41 #4, 0.39 #124, 0.35 #2169), 04xvlr (0.36 #481, 0.35 #361, 0.34 #601), 01jfsb (0.35 #2540, 0.33 #1937, 0.33 #2660), 02kdv5l (0.33 #7817, 0.28 #1927, 0.27 #2650), 060__y (0.31 #256, 0.25 #856, 0.24 #736), 0hn10 (0.27 #9, 0.18 #2528, 0.09 #129), 03g3w (0.26 #384, 0.18 #2528, 0.17 #504), 03k9fj (0.23 #2779, 0.23 #3259, 0.21 #4099) >> Best rule #615 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 064n1pz; >> query: (?x8959, 02l7c8) <- nominated_for(?x749, ?x8959), ?x749 = 094qd5, film(?x902, ?x8959), language(?x8959, ?x254) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #535 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 82 *> proper extension: 06krf3; 0c9k8; 0qm9n; 0bs5k8r; 06nr2h; 0f4k49; 06cm5; 047myg9; 017180; 01qbg5; ... *> query: (?x8959, 03bxz7) <- nominated_for(?x4139, ?x8959), titles(?x1316, ?x8959), language(?x8959, ?x254), ?x1316 = 017fp, genre(?x8959, ?x53) *> conf = 0.52 ranks of expected_values: 2 EVAL 01gvpz genre 03bxz7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 84.000 0.576 http://example.org/film/film/genre #17486-02x17s4 PRED entity: 02x17s4 PRED relation: nominated_for PRED expected values: 017gl1 071nw5 02cbhg => 54 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1369): 017gl1 (0.73 #10777, 0.67 #6209, 0.54 #7732), 0bdjd (0.67 #7147, 0.54 #8670, 0.50 #4105), 0gmcwlb (0.63 #10822, 0.62 #7777, 0.44 #9300), 07024 (0.63 #11057, 0.40 #4968, 0.38 #8012), 095zlp (0.62 #7654, 0.60 #10699, 0.58 #6131), 01mgw (0.62 #8698, 0.60 #11743, 0.58 #7175), 02rcdc2 (0.62 #8008, 0.50 #3443, 0.42 #6485), 011ycb (0.62 #8334, 0.50 #3769, 0.42 #6811), 09gq0x5 (0.60 #10891, 0.50 #6323, 0.40 #9369), 0dr_4 (0.60 #10860, 0.50 #6292, 0.40 #4771) >> Best rule #10777 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 02rdxsh; >> query: (?x2341, 017gl1) <- nominated_for(?x2341, ?x8574), nominated_for(?x2341, ?x1496), ?x1496 = 011yqc, cinematography(?x8574, ?x7327) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 43, 172 EVAL 02x17s4 nominated_for 02cbhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 54.000 21.000 0.733 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x17s4 nominated_for 071nw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 54.000 21.000 0.733 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x17s4 nominated_for 017gl1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 21.000 0.733 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17485-01jb26 PRED entity: 01jb26 PRED relation: vacationer! PRED expected values: 07tp2 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 62): 0r0m6 (0.21 #69, 0.07 #569, 0.05 #1320), 05qtj (0.14 #72, 0.11 #572, 0.06 #2823), 03gh4 (0.14 #81, 0.09 #581, 0.09 #2082), 02j9z (0.07 #12, 0.06 #137, 0.06 #262), 0cv3w (0.07 #57, 0.05 #2058, 0.05 #2808), 0rnmy (0.07 #55, 0.05 #555, 0.02 #1056), 080h2 (0.07 #25, 0.02 #525, 0.02 #1151), 05jbn (0.07 #75, 0.02 #575, 0.01 #700), 02vzc (0.07 #40, 0.02 #540, 0.01 #665), 02fzs (0.07 #124, 0.01 #874, 0.01 #749) >> Best rule #69 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 012_53; 0993r; 01jbx1; 05r5w; 029_3; 02v60l; 01wc7p; 02b9g4; 02ts3h; 03f3yfj; ... >> query: (?x5268, 0r0m6) <- profession(?x5268, ?x1041), gender(?x5268, ?x514), ?x1041 = 03gjzk, participant(?x5268, ?x3673), person(?x9277, ?x5268) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jb26 vacationer! 07tp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 97.000 0.214 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #17484-01lvcs1 PRED entity: 01lvcs1 PRED relation: artists! PRED expected values: 02x8m => 110 concepts (86 used for prediction) PRED predicted values (max 10 best out of 224): 064t9 (0.47 #13, 0.44 #5612, 0.41 #8101), 016clz (0.41 #8403, 0.30 #2807, 0.28 #1872), 0gt_0v (0.33 #395, 0.11 #1328, 0.11 #1016), 0xhtw (0.30 #8725, 0.30 #2819, 0.27 #5616), 05bt6j (0.30 #5643, 0.29 #8442, 0.27 #8752), 08jyyk (0.28 #689, 0.17 #2870, 0.14 #1935), 016jny (0.27 #105, 0.21 #1037, 0.18 #1660), 02k_kn (0.27 #66, 0.19 #7778, 0.15 #8464), 01lyv (0.21 #8742, 0.20 #4392, 0.20 #4081), 0glt670 (0.21 #8129, 0.19 #7778, 0.18 #5019) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0197tq; 0pz91; 015_30; 0161sp; 04gycf; 01v40wd; 0kvnn; 013423; 01vtmw6; 01s1zk; ... >> query: (?x3492, 064t9) <- instrumentalists(?x214, ?x3492), location(?x3492, ?x2850), ?x2850 = 0cr3d >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #640 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 02pt7h_; *> query: (?x3492, 02x8m) <- role(?x3492, ?x716), ?x716 = 018vs, award_winner(?x3835, ?x3492) *> conf = 0.17 ranks of expected_values: 25 EVAL 01lvcs1 artists! 02x8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 110.000 86.000 0.467 http://example.org/music/genre/artists #17483-06nnj PRED entity: 06nnj PRED relation: adjoins! PRED expected values: 015fr => 124 concepts (80 used for prediction) PRED predicted values (max 10 best out of 394): 015fr (0.24 #57294, 0.23 #21975, 0.23 #33740), 07ylj (0.24 #57294, 0.23 #21975, 0.23 #33740), 06nnj (0.23 #21975, 0.23 #33740, 0.22 #61228), 0j3b (0.23 #21975, 0.22 #61228, 0.22 #61229), 059qw (0.20 #1254, 0.20 #471, 0.16 #61227), 0f8l9c (0.20 #823, 0.20 #40, 0.10 #14949), 0345h (0.20 #849, 0.20 #66, 0.09 #7134), 059j2 (0.20 #847, 0.20 #64, 0.04 #25113), 0dqyc (0.20 #1527, 0.20 #744, 0.02 #33699), 0d8h4 (0.20 #1506, 0.20 #723, 0.01 #7791) >> Best rule #57294 for best value: >> intensional similarity = 4 >> extensional distance = 263 >> proper extension: 03khn; 0df4y; >> query: (?x9051, ?x1203) <- adjoins(?x9459, ?x9051), adjoins(?x9459, ?x1203), film_release_region(?x186, ?x1203), contains(?x12315, ?x9459) >> conf = 0.24 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 06nnj adjoins! 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 80.000 0.237 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #17482-07ftc0 PRED entity: 07ftc0 PRED relation: profession PRED expected values: 01d_h8 => 86 concepts (45 used for prediction) PRED predicted values (max 10 best out of 55): 0dxtg (0.33 #4131, 0.29 #3689, 0.29 #3542), 01d_h8 (0.31 #4124, 0.31 #1329, 0.30 #1035), 09jwl (0.25 #606, 0.18 #2959, 0.18 #4283), 03gjzk (0.22 #1043, 0.22 #3690, 0.22 #14), 0cbd2 (0.17 #154, 0.16 #3536, 0.15 #5598), 018gz8 (0.15 #2074, 0.14 #2810, 0.14 #2663), 0np9r (0.15 #5463, 0.15 #5906, 0.14 #4726), 0nbcg (0.14 #619, 0.13 #472, 0.13 #766), 016z4k (0.14 #739, 0.13 #445, 0.12 #298), 0kyk (0.12 #617, 0.12 #3558, 0.12 #4588) >> Best rule #4131 for best value: >> intensional similarity = 4 >> extensional distance = 1107 >> proper extension: 01wdqrx; 05drq5; 0gcdzz; 017r2; 09ftwr; 09hnb; 053yx; 0p8jf; 04cw0j; 03pvt; ... >> query: (?x8180, 0dxtg) <- gender(?x8180, ?x231), student(?x4980, ?x8180), award(?x8180, ?x5923), ?x231 = 05zppz >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4124 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1107 *> proper extension: 01wdqrx; 05drq5; 0gcdzz; 017r2; 09ftwr; 09hnb; 053yx; 0p8jf; 04cw0j; 03pvt; ... *> query: (?x8180, 01d_h8) <- gender(?x8180, ?x231), student(?x4980, ?x8180), award(?x8180, ?x5923), ?x231 = 05zppz *> conf = 0.31 ranks of expected_values: 2 EVAL 07ftc0 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 45.000 0.329 http://example.org/people/person/profession #17481-03j0dp PRED entity: 03j0dp PRED relation: genre! PRED expected values: 0g4vmj8 => 55 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1889): 011wtv (0.67 #6395, 0.60 #2661, 0.56 #10129), 0jdgr (0.67 #4142, 0.60 #2276, 0.50 #6010), 03h_yy (0.62 #7544, 0.42 #13142, 0.36 #11277), 0fvr1 (0.60 #2229, 0.50 #5963, 0.50 #4095), 05ch98 (0.60 #3276, 0.50 #7010, 0.50 #5142), 0fh2v5 (0.60 #3529, 0.50 #7263, 0.50 #5395), 0645k5 (0.60 #2354, 0.50 #6088, 0.50 #4220), 0gfsq9 (0.60 #2329, 0.50 #6063, 0.50 #4195), 0435vm (0.60 #2535, 0.50 #6269, 0.50 #4401), 02qkwl (0.60 #3302, 0.50 #7036, 0.50 #5168) >> Best rule #6395 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 02xh1; >> query: (?x5722, 011wtv) <- genre(?x11685, ?x5722), genre(?x3251, ?x5722), ?x11685 = 017n9, film(?x1104, ?x3251), award(?x3251, ?x1063), nominated_for(?x1443, ?x3251), ?x1104 = 016tw3, nominated_for(?x1443, ?x2340), nominated_for(?x1443, ?x1199), ?x1199 = 0pv3x, ?x2340 = 0fpv_3_ >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3172 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 06n90; *> query: (?x5722, 0g4vmj8) <- genre(?x11685, ?x5722), genre(?x3251, ?x5722), ?x11685 = 017n9, film(?x1104, ?x3251), award(?x3251, ?x1063), nominated_for(?x1443, ?x3251), ?x1104 = 016tw3, ?x1443 = 054krc, film(?x8544, ?x3251), spouse(?x8544, ?x4536) *> conf = 0.40 ranks of expected_values: 151 EVAL 03j0dp genre! 0g4vmj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 19.000 0.667 http://example.org/film/film/genre #17480-03_87 PRED entity: 03_87 PRED relation: influenced_by! PRED expected values: 04cbtrw => 162 concepts (48 used for prediction) PRED predicted values (max 10 best out of 372): 0yxl (0.33 #1792, 0.33 #334, 0.12 #2765), 0pqzh (0.33 #428, 0.22 #1886, 0.06 #2859), 045bg (0.29 #1008, 0.24 #3440, 0.12 #2466), 03vrp (0.29 #1155, 0.22 #1640, 0.17 #182), 02wh0 (0.29 #1394, 0.19 #3826, 0.14 #4313), 07h1q (0.29 #1354, 0.17 #381, 0.14 #6695), 040rjq (0.29 #1428, 0.17 #455, 0.11 #1913), 07dnx (0.29 #3741, 0.14 #1309, 0.12 #1458), 06myp (0.29 #1385, 0.14 #3817, 0.12 #1458), 04cbtrw (0.29 #1073, 0.12 #2531, 0.12 #1458) >> Best rule #1792 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 08433; 040db; 06y8v; >> query: (?x6457, 0yxl) <- influenced_by(?x6457, ?x12345), profession(?x6457, ?x353), ?x12345 = 03_dj, influenced_by(?x118, ?x6457) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1073 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 081k8; *> query: (?x6457, 04cbtrw) <- influenced_by(?x6319, ?x6457), nationality(?x6457, ?x429), ?x6319 = 040_t, gender(?x6457, ?x231) *> conf = 0.29 ranks of expected_values: 10 EVAL 03_87 influenced_by! 04cbtrw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 162.000 48.000 0.333 http://example.org/influence/influence_node/influenced_by #17479-03cfjg PRED entity: 03cfjg PRED relation: artist! PRED expected values: 02p11jq => 95 concepts (54 used for prediction) PRED predicted values (max 10 best out of 82): 02p11jq (0.25 #13, 0.10 #1273, 0.08 #3227), 03rhqg (0.15 #296, 0.15 #1276, 0.15 #2820), 0181dw (0.12 #42, 0.12 #322, 0.11 #2145), 017l96 (0.12 #19, 0.10 #2823, 0.09 #4091), 043ljr (0.12 #17, 0.08 #3227, 0.03 #297), 06x2ww (0.12 #49, 0.08 #3227, 0.02 #2853), 02p4jf0 (0.12 #76, 0.08 #3227, 0.02 #216), 01clyr (0.12 #33, 0.07 #313, 0.07 #4105), 01cl0d (0.12 #55, 0.06 #335, 0.05 #2158), 0181hw (0.12 #51, 0.02 #1311, 0.01 #331) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01p9hgt; 01kv4mb; 02fn5r; 0ggjt; 0bhvtc; 0p_47; >> query: (?x3419, 02p11jq) <- award_nominee(?x3419, ?x367), nominated_for(?x3419, ?x2638), award_winner(?x1480, ?x3419) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cfjg artist! 02p11jq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 54.000 0.250 http://example.org/music/record_label/artist #17478-0flw86 PRED entity: 0flw86 PRED relation: religion! PRED expected values: 087z12 0d3f83 04s9n 03x31g => 38 concepts (27 used for prediction) PRED predicted values (max 10 best out of 4291): 049m19 (0.40 #5016, 0.33 #927, 0.25 #8085), 03xnq9_ (0.40 #4548, 0.33 #459, 0.25 #7617), 04v7k2 (0.40 #5092, 0.33 #1003, 0.25 #8161), 07_m9_ (0.40 #4474, 0.33 #385, 0.25 #7543), 0dj5q (0.40 #4635, 0.33 #546, 0.25 #7704), 01w58n3 (0.40 #4848, 0.25 #2802, 0.12 #20189), 03f1r6t (0.40 #4506, 0.25 #2460, 0.12 #19847), 02n1gr (0.33 #5844, 0.29 #6867, 0.27 #9935), 044pqn (0.33 #6073, 0.29 #7096, 0.20 #4028), 03vrnh (0.33 #5715, 0.29 #6738, 0.20 #3670) >> Best rule #5016 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 02vxy_; >> query: (?x492, 049m19) <- religion(?x11617, ?x492), religion(?x9070, ?x492), religion(?x6124, ?x492), religion(?x1020, ?x492), religion(?x111, ?x492), profession(?x11617, ?x5805), instrumentalists(?x716, ?x6124), team(?x9070, ?x12141), artists(?x283, ?x6124), type_of_union(?x111, ?x566), award(?x111, ?x112), student(?x1391, ?x111), award_nominee(?x294, ?x1020), draft(?x12141, ?x2569) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #9203 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 9 *> proper extension: 0631_; 01y0s9; 019cr; 021_0p; 05w5d; *> query: (?x492, ?x230) <- religion(?x111, ?x492), religion(?x3634, ?x492), religion(?x2146, ?x492), film_release_region(?x4610, ?x2146), film_release_region(?x141, ?x2146), administrative_parent(?x3411, ?x2146), film_distribution_medium(?x141, ?x81), contains(?x2146, ?x1391), genre(?x4610, ?x811), award(?x4610, ?x198), film(?x230, ?x4610), ?x3634 = 07b_l *> conf = 0.03 ranks of expected_values: 1210, 2751 EVAL 0flw86 religion! 03x31g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 38.000 27.000 0.400 http://example.org/people/person/religion EVAL 0flw86 religion! 04s9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 38.000 27.000 0.400 http://example.org/people/person/religion EVAL 0flw86 religion! 0d3f83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 38.000 27.000 0.400 http://example.org/people/person/religion EVAL 0flw86 religion! 087z12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 38.000 27.000 0.400 http://example.org/people/person/religion #17477-019f4v PRED entity: 019f4v PRED relation: ceremony PRED expected values: 09p3h7 => 68 concepts (60 used for prediction) PRED predicted values (max 10 best out of 127): 0bzm81 (0.71 #1406, 0.67 #1784, 0.60 #775), 0n8_m93 (0.71 #1490, 0.67 #1868, 0.60 #859), 0bvfqq (0.71 #1417, 0.67 #1795, 0.60 #786), 02hn5v (0.71 #1424, 0.67 #1802, 0.60 #793), 0bc773 (0.71 #1435, 0.67 #1813, 0.60 #804), 04110lv (0.71 #1483, 0.67 #1861, 0.60 #852), 02yvhx (0.71 #1457, 0.67 #1835, 0.60 #826), 02yxh9 (0.71 #1477, 0.67 #1855, 0.60 #846), 02yw5r (0.71 #1397, 0.67 #1775, 0.60 #766), 050yyb (0.71 #1421, 0.67 #1799, 0.60 #790) >> Best rule #1406 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0gs9p; 0k611; >> query: (?x1107, 0bzm81) <- nominated_for(?x1107, ?x11994), nominated_for(?x1107, ?x2112), ?x11994 = 0c5qvw, award_winner(?x1107, ?x276), award(?x286, ?x1107), ?x2112 = 0bm2g >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1387 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 02x4wr9; *> query: (?x1107, ?x8150) <- nominated_for(?x1107, ?x7750), award_winner(?x1107, ?x777), award(?x286, ?x1107), ?x777 = 05kfs, honored_for(?x8150, ?x7750) *> conf = 0.29 ranks of expected_values: 83 EVAL 019f4v ceremony 09p3h7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 68.000 60.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony #17476-060ny2 PRED entity: 060ny2 PRED relation: legislative_sessions! PRED expected values: 07t58 => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 6): 07t58 (0.91 #195, 0.90 #187, 0.90 #210), 0x2sv (0.08 #244, 0.06 #231), 0h6dy (0.07 #245, 0.04 #232), 0l_j_ (0.05 #246, 0.04 #233), 0162kb (0.02 #234, 0.02 #247), 030p4s (0.02 #248) >> Best rule #195 for best value: >> intensional similarity = 40 >> extensional distance = 36 >> proper extension: 01gst_; >> query: (?x6139, ?x2860) <- legislative_sessions(?x1829, ?x6139), legislative_sessions(?x1137, ?x6139), legislative_sessions(?x4665, ?x1829), legislative_sessions(?x2860, ?x1829), district_represented(?x1137, ?x7405), district_represented(?x1137, ?x5575), district_represented(?x1137, ?x4622), district_represented(?x1137, ?x1906), district_represented(?x1137, ?x1767), district_represented(?x1137, ?x1138), district_represented(?x1137, ?x961), district_represented(?x1137, ?x448), ?x7405 = 07_f2, ?x4622 = 04tgp, location(?x1568, ?x1138), contains(?x1138, ?x3026), jurisdiction_of_office(?x3959, ?x1138), legislative_sessions(?x2357, ?x1137), ?x448 = 03v1s, ?x1906 = 04rrx, religion(?x1138, ?x1985), religion(?x1138, ?x962), district_represented(?x1829, ?x6521), ?x1767 = 04rrd, time_zones(?x1138, ?x2088), country(?x6521, ?x94), adjoins(?x2768, ?x1138), state_province_region(?x3367, ?x1138), ?x3959 = 0f6c3, ?x1985 = 0c8wxp, contains(?x6521, ?x859), ?x4665 = 07t58, location(?x1376, ?x6521), partially_contains(?x1138, ?x14060), location(?x338, ?x5575), taxonomy(?x6521, ?x939), place_of_birth(?x2638, ?x6521), contains(?x8260, ?x5575), ?x961 = 03s0w, ?x962 = 05sfs >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 060ny2 legislative_sessions! 07t58 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.905 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #17475-01xvjb PRED entity: 01xvjb PRED relation: nominated_for! PRED expected values: 03hl6lc => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 197): 0gq9h (0.51 #1730, 0.51 #1968, 0.51 #6252), 0gs9p (0.50 #2446, 0.50 #1256, 0.49 #1970), 03hl6lc (0.48 #2035, 0.44 #1797, 0.42 #1559), 019f4v (0.44 #1959, 0.43 #1721, 0.40 #2435), 04dn09n (0.44 #1940, 0.43 #1702, 0.40 #2416), 02qyntr (0.43 #180, 0.35 #2084, 0.35 #1370), 040njc (0.41 #1197, 0.39 #1911, 0.39 #1435), 02qyp19 (0.41 #1191, 0.39 #1905, 0.39 #1429), 0gq_v (0.40 #6208, 0.29 #3828, 0.28 #6684), 099c8n (0.39 #1486, 0.38 #1248, 0.29 #1962) >> Best rule #1730 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0dyb1; 0p_qr; 0pd4f; 0j90s; 0qmfz; >> query: (?x8965, 0gq9h) <- nominated_for(?x1862, ?x8965), ?x1862 = 0gr51, country(?x8965, ?x94), ?x94 = 09c7w0 >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #2035 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 108 *> proper extension: 016z43; *> query: (?x8965, 03hl6lc) <- nominated_for(?x1862, ?x8965), ?x1862 = 0gr51, currency(?x8965, ?x170), film(?x496, ?x8965) *> conf = 0.48 ranks of expected_values: 3 EVAL 01xvjb nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 79.000 0.509 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17474-027l0b PRED entity: 027l0b PRED relation: award PRED expected values: 0gr4k => 111 concepts (108 used for prediction) PRED predicted values (max 10 best out of 293): 02x73k6 (0.33 #60, 0.09 #38790, 0.07 #17433), 09sb52 (0.33 #12160, 0.31 #17413, 0.28 #14585), 0gr4k (0.32 #4477, 0.22 #1245, 0.20 #1649), 0gr51 (0.31 #4544, 0.10 #10200, 0.10 #3736), 04dn09n (0.30 #4487, 0.14 #851, 0.11 #1255), 0fbtbt (0.29 #1040, 0.22 #1444, 0.20 #1848), 0gs9p (0.25 #4523, 0.08 #6947, 0.08 #18260), 0cqhk0 (0.25 #2461, 0.17 #37, 0.15 #13737), 0ck27z (0.24 #13020, 0.23 #15041, 0.23 #13424), 01by1l (0.23 #7384, 0.22 #3344, 0.20 #7788) >> Best rule #60 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 03vpf_; >> query: (?x2794, 02x73k6) <- film(?x2794, ?x9484), ?x9484 = 0291ck, profession(?x2794, ?x353) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4477 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 242 *> proper extension: 0qf43; 0b_c7; 04b19t; 04k25; 01q4qv; 01ycck; 01f7v_; 0171lb; 0gv5c; 015njf; ... *> query: (?x2794, 0gr4k) <- profession(?x2794, ?x353), award_winner(?x693, ?x2794), written_by(?x9484, ?x2794) *> conf = 0.32 ranks of expected_values: 3 EVAL 027l0b award 0gr4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 108.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17473-01jgpsh PRED entity: 01jgpsh PRED relation: film PRED expected values: 050xxm => 109 concepts (77 used for prediction) PRED predicted values (max 10 best out of 457): 0vjr (0.59 #93099, 0.51 #7161, 0.47 #87726), 0ndwt2w (0.09 #1001, 0.02 #13532, 0.02 #6371), 04k9y6 (0.09 #1042, 0.02 #15363), 035s95 (0.09 #341, 0.02 #7502, 0.02 #5711), 06lpmt (0.09 #686, 0.02 #15007, 0.02 #6056), 03wy8t (0.09 #1587, 0.02 #3377, 0.01 #14118), 0879bpq (0.09 #450, 0.02 #2240, 0.01 #14771), 03m4mj (0.09 #202, 0.02 #1992, 0.01 #5572), 0blpg (0.09 #657, 0.01 #7818, 0.01 #13188), 06znpjr (0.09 #1371, 0.01 #15692) >> Best rule #93099 for best value: >> intensional similarity = 3 >> extensional distance = 1315 >> proper extension: 04yywz; 02g8h; 0d_84; 0h1_w; 02nb2s; 04bs3j; 014x77; 0151ns; 0lzb8; 0kr5_; ... >> query: (?x6363, ?x5386) <- award(?x6363, ?x537), film(?x6363, ?x5020), nominated_for(?x6363, ?x5386) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jgpsh film 050xxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 77.000 0.589 http://example.org/film/actor/film./film/performance/film #17472-017j69 PRED entity: 017j69 PRED relation: school! PRED expected values: 0jmj7 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 92): 0jmj7 (0.75 #213, 0.67 #3527, 0.60 #1870), 051vz (0.27 #115, 0.16 #1403, 0.13 #1864), 0512p (0.27 #107, 0.12 #475, 0.12 #383), 04mjl (0.22 #63, 0.18 #155, 0.08 #247), 05m_8 (0.20 #371, 0.20 #1844, 0.18 #1383), 01yhm (0.18 #112, 0.12 #388, 0.12 #1400), 01ync (0.18 #132, 0.12 #408, 0.11 #40), 0jmk7 (0.18 #181, 0.09 #1469, 0.08 #457), 049n7 (0.18 #104, 0.09 #1853, 0.08 #380), 0jm64 (0.18 #147, 0.08 #423, 0.07 #607) >> Best rule #213 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 016sd3; >> query: (?x4410, 0jmj7) <- colors(?x4410, ?x663), school(?x465, ?x4410), currency(?x4410, ?x170) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017j69 school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.750 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #17471-030qb3t PRED entity: 030qb3t PRED relation: vacationer PRED expected values: 01vs_v8 => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 209): 016fnb (0.20 #276, 0.13 #449, 0.11 #4442), 03lt8g (0.20 #194, 0.13 #367, 0.08 #2449), 0bksh (0.20 #280, 0.11 #3229, 0.10 #4099), 05r5w (0.20 #246, 0.11 #4412, 0.09 #3195), 0bbf1f (0.20 #234, 0.11 #2314, 0.10 #1273), 04fzk (0.20 #263, 0.10 #1302, 0.09 #1650), 034x61 (0.20 #186, 0.10 #1225, 0.08 #2441), 019pm_ (0.20 #232, 0.09 #1619, 0.07 #1271), 026c1 (0.20 #210, 0.09 #3159, 0.07 #4376), 09yrh (0.20 #273, 0.09 #3222, 0.07 #1312) >> Best rule #276 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 07ytt; >> query: (?x1523, 016fnb) <- films(?x1523, ?x6103), country(?x1523, ?x94) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #558 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 0r5wt; *> query: (?x1523, 01vs_v8) <- place_of_birth(?x338, ?x1523), citytown(?x735, ?x1523), award_winner(?x3486, ?x735) *> conf = 0.10 ranks of expected_values: 32 EVAL 030qb3t vacationer 01vs_v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 154.000 154.000 0.200 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #17470-02p4jf0 PRED entity: 02p4jf0 PRED relation: artist PRED expected values: 01vv6_6 01wj5hp => 66 concepts (24 used for prediction) PRED predicted values (max 10 best out of 968): 0x3b7 (0.71 #3603, 0.50 #5260, 0.25 #1119), 016szr (0.57 #3654, 0.40 #5311, 0.25 #1170), 01kph_c (0.50 #1164, 0.30 #5305, 0.29 #3648), 01wg25j (0.43 #3926, 0.30 #5583, 0.29 #4756), 02mslq (0.43 #3337, 0.30 #4994, 0.25 #853), 0kzy0 (0.43 #3343, 0.30 #5000, 0.25 #859), 01817f (0.43 #3618, 0.30 #5275, 0.20 #1962), 01hgwkr (0.43 #3996, 0.30 #5653, 0.12 #828), 011lvx (0.43 #3840, 0.30 #5497, 0.06 #9636), 0g824 (0.43 #4592, 0.23 #6249, 0.20 #2106) >> Best rule #3603 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 041bnw; >> query: (?x10882, 0x3b7) <- artist(?x10882, ?x11455), artist(?x10882, ?x3202), artist(?x10882, ?x2518), award_nominee(?x4635, ?x2518), location(?x2518, ?x1523), origin(?x11455, ?x94), award_winner(?x3202, ?x1413), ?x4635 = 01l03w2, group(?x2592, ?x11455) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1065 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 01cl2y; 05cl8y; *> query: (?x10882, 01vv6_6) <- artist(?x10882, ?x11455), artist(?x10882, ?x5456), artist(?x10882, ?x2518), ?x2518 = 016sp_, award_nominee(?x11455, ?x2614), profession(?x5456, ?x131), origin(?x11455, ?x94), gender(?x5456, ?x231), award(?x5456, ?x1801) *> conf = 0.25 ranks of expected_values: 135, 789 EVAL 02p4jf0 artist 01wj5hp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 24.000 0.714 http://example.org/music/record_label/artist EVAL 02p4jf0 artist 01vv6_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 66.000 24.000 0.714 http://example.org/music/record_label/artist #17469-01wqpnm PRED entity: 01wqpnm PRED relation: instrumentalists! PRED expected values: 0342h => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 98): 0342h (0.66 #533, 0.65 #1413, 0.64 #269), 05r5c (0.49 #801, 0.45 #2037, 0.45 #1771), 05148p4 (0.47 #109, 0.46 #285, 0.45 #461), 0l14md (0.29 #96, 0.25 #272, 0.23 #448), 02hnl (0.28 #563, 0.17 #915, 0.16 #827), 03qjg (0.25 #580, 0.18 #316, 0.16 #1460), 0l14qv (0.24 #94, 0.23 #446, 0.18 #270), 03f5mt (0.18 #172, 0.11 #348, 0.06 #524), 0l14j_ (0.13 #495, 0.11 #319, 0.06 #847), 018j2 (0.12 #567, 0.09 #2067, 0.08 #1447) >> Best rule #533 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 018y81; 04bgy; 01k_0fp; >> query: (?x10198, 0342h) <- artists(?x3061, ?x10198), ?x3061 = 05bt6j, nationality(?x10198, ?x1310), ?x1310 = 02jx1 >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wqpnm instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.656 http://example.org/music/instrument/instrumentalists #17468-047lj PRED entity: 047lj PRED relation: film_release_region! PRED expected values: 01vksx 04n52p6 01jrbb 047p7fr 0btpm6 047p798 072hx4 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 1252): 0407yfx (0.88 #5157, 0.80 #11312, 0.76 #3926), 0dscrwf (0.88 #4974, 0.76 #3743, 0.69 #11129), 0gkz15s (0.85 #5006, 0.82 #11161, 0.65 #3775), 05c26ss (0.85 #5356, 0.79 #4125, 0.76 #11511), 06wbm8q (0.85 #5203, 0.76 #3972, 0.76 #11358), 02vxq9m (0.85 #4939, 0.74 #3708, 0.73 #11094), 0661m4p (0.85 #5176, 0.74 #3945, 0.73 #11331), 087wc7n (0.85 #5009, 0.73 #11164, 0.71 #3778), 0bh8tgs (0.85 #5529, 0.71 #11684, 0.71 #4298), 0dzlbx (0.82 #5511, 0.78 #11666, 0.76 #4280) >> Best rule #5157 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 03rjj; 0d060g; 04gzd; 0chghy; 01ls2; 03rt9; ... >> query: (?x404, 0407yfx) <- film_release_region(?x3217, ?x404), film_release_region(?x1916, ?x404), ?x3217 = 0gffmn8, ?x1916 = 0ch26b_ >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #5020 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 03rjj; 0d060g; 04gzd; 0chghy; 01ls2; 03rt9; ... *> query: (?x404, 01vksx) <- film_release_region(?x3217, ?x404), film_release_region(?x1916, ?x404), ?x3217 = 0gffmn8, ?x1916 = 0ch26b_ *> conf = 0.79 ranks of expected_values: 15, 20, 30, 42, 69, 109, 169 EVAL 047lj film_release_region! 072hx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 78.000 78.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047lj film_release_region! 047p798 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 78.000 78.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047lj film_release_region! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 78.000 78.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047lj film_release_region! 047p7fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 78.000 78.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047lj film_release_region! 01jrbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 78.000 78.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047lj film_release_region! 04n52p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 78.000 78.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047lj film_release_region! 01vksx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 78.000 78.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17467-0grwj PRED entity: 0grwj PRED relation: award_winner! PRED expected values: 04fgkf_ => 128 concepts (126 used for prediction) PRED predicted values (max 10 best out of 316): 0gqyl (0.33 #5166, 0.33 #4304, 0.33 #7754), 04fgkf_ (0.33 #5166, 0.33 #4304, 0.33 #7754), 01l29r (0.33 #5166, 0.33 #4304, 0.33 #7754), 02ppm4q (0.20 #2307, 0.18 #5753, 0.03 #22116), 0cjyzs (0.19 #12167, 0.18 #11737, 0.15 #1397), 02z1nbg (0.16 #2345, 0.16 #5791, 0.05 #14407), 0gqwc (0.16 #5672, 0.15 #2226, 0.07 #14288), 01by1l (0.14 #6573, 0.11 #8297, 0.11 #3986), 03x3wf (0.14 #6525, 0.11 #8249, 0.10 #3938), 0ck27z (0.14 #16889, 0.09 #28933, 0.09 #29363) >> Best rule #5166 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 01wd02c; >> query: (?x105, ?x1972) <- type_of_union(?x105, ?x1873), award(?x105, ?x1972), friend(?x1660, ?x105) >> conf = 0.33 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0grwj award_winner! 04fgkf_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 126.000 0.335 http://example.org/award/award_category/winners./award/award_honor/award_winner #17466-0ntxg PRED entity: 0ntxg PRED relation: currency PRED expected values: 09nqf => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.83 #19, 0.83 #18, 0.83 #38) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 0mwl2; 0nvrd; 0gyh; 0d0x8; 0p0cw; 0mpbj; 0l2lk; 0nf3h; 0m2by; 0ms1n; ... >> query: (?x10877, ?x170) <- contains(?x3818, ?x10877), administrative_division(?x10876, ?x10877), adjoins(?x10877, ?x11877), currency(?x11877, ?x170) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ntxg currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.835 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #17465-01dnws PRED entity: 01dnws PRED relation: role! PRED expected values: 01w4dy => 65 concepts (46 used for prediction) PRED predicted values (max 10 best out of 95): 0l14qv (0.83 #1926, 0.80 #3628, 0.80 #3545), 0l14j_ (0.82 #3541, 0.82 #1535, 0.82 #955), 02w3w (0.82 #3541, 0.82 #1535, 0.82 #955), 01xqw (0.82 #3541, 0.82 #1535, 0.82 #955), 01w4c9 (0.82 #3541, 0.82 #1535, 0.82 #955), 01w4dy (0.82 #3541, 0.82 #1535, 0.82 #955), 028tv0 (0.79 #3067, 0.71 #964, 0.67 #1445), 01vj9c (0.77 #2121, 0.69 #2209, 0.69 #3829), 05r5c (0.76 #379, 0.75 #3147, 0.72 #3908), 0bxl5 (0.75 #2726, 0.71 #1106, 0.67 #1490) >> Best rule #1926 for best value: >> intensional similarity = 22 >> extensional distance = 10 >> proper extension: 05ljv7; >> query: (?x2158, 0l14qv) <- role(?x5921, ?x2158), role(?x3296, ?x2158), role(?x3214, ?x2158), role(?x2798, ?x2158), role(?x2048, ?x2158), role(?x1212, ?x2158), role(?x960, ?x2158), role(?x227, ?x2158), role(?x5921, ?x316), ?x316 = 05r5c, ?x1212 = 07xzm, role(?x75, ?x2158), ?x227 = 0342h, role(?x922, ?x3296), role(?x960, ?x6449), ?x3214 = 02snj9, group(?x2798, ?x11551), ?x6449 = 014zz1, ?x11551 = 0cfgd, ?x2048 = 018j2, instrumentalists(?x2798, ?x6947), ?x6947 = 01vrnsk >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #3541 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 21 *> proper extension: 05148p4; *> query: (?x2158, ?x432) <- group(?x2158, ?x4791), role(?x2460, ?x2158), role(?x2309, ?x2158), role(?x716, ?x2158), ?x716 = 018vs, role(?x2460, ?x645), role(?x2460, ?x74), role(?x2158, ?x1969), role(?x2158, ?x432), role(?x4583, ?x2460), role(?x569, ?x2460), role(?x211, ?x2158), ?x74 = 03q5t, ?x569 = 07c6l, instrumentalists(?x2158, ?x226), ?x2309 = 06ncr, group(?x1969, ?x1929), ?x4583 = 0bmnm, ?x645 = 028tv0, role(?x5543, ?x1969), ?x5543 = 01kd57 *> conf = 0.82 ranks of expected_values: 6 EVAL 01dnws role! 01w4dy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 65.000 46.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/role #17464-02l4pj PRED entity: 02l4pj PRED relation: film PRED expected values: 035s95 => 115 concepts (55 used for prediction) PRED predicted values (max 10 best out of 320): 011ywj (0.57 #30320, 0.49 #3567, 0.49 #28536), 02chhq (0.57 #30320, 0.49 #3567, 0.49 #28536), 03bx2lk (0.03 #46375, 0.03 #8917, 0.03 #41022), 05sy_5 (0.03 #46375, 0.03 #8917, 0.03 #41022), 01jrbv (0.03 #46375, 0.03 #8917, 0.03 #41022), 04tqtl (0.03 #46375, 0.03 #8917, 0.03 #41022), 031hcx (0.03 #46375, 0.03 #8917, 0.03 #41022), 03177r (0.03 #46375, 0.03 #8917, 0.03 #41022), 05z43v (0.03 #46375, 0.03 #8917, 0.03 #41022), 02_kd (0.03 #46375, 0.03 #8917, 0.03 #41022) >> Best rule #30320 for best value: >> intensional similarity = 3 >> extensional distance = 852 >> proper extension: 04gcd1; 01gbbz; 0dx_q; 03bdm4; 06j8q_; >> query: (?x3461, ?x7974) <- film(?x3461, ?x1797), gender(?x3461, ?x514), award_winner(?x7974, ?x3461) >> conf = 0.57 => this is the best rule for 2 predicted values *> Best rule #18172 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 790 *> proper extension: 041h0; 019z7q; 0b82vw; 04zwjd; 04k25; 02dh86; 0p51w; 01l9v7n; 02wb6yq; 085pr; ... *> query: (?x3461, 035s95) <- location(?x3461, ?x13032), award_winner(?x7974, ?x3461), nationality(?x3461, ?x512) *> conf = 0.02 ranks of expected_values: 234 EVAL 02l4pj film 035s95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 115.000 55.000 0.575 http://example.org/film/actor/film./film/performance/film #17463-01whg97 PRED entity: 01whg97 PRED relation: artists! PRED expected values: 02qm5j => 132 concepts (79 used for prediction) PRED predicted values (max 10 best out of 259): 06by7 (0.69 #14022, 0.68 #935, 0.51 #7325), 064t9 (0.62 #1231, 0.53 #2448, 0.53 #5795), 0dl5d (0.41 #933, 0.26 #3670, 0.22 #7323), 05bt6j (0.41 #956, 0.25 #651, 0.25 #5824), 016clz (0.40 #5, 0.27 #3655, 0.25 #7308), 0xv2x (0.40 #151, 0.14 #1064, 0.08 #3801), 05jg58 (0.40 #119, 0.08 #3769, 0.07 #3955), 0glt670 (0.36 #344, 0.26 #9169, 0.25 #12519), 025sc50 (0.33 #658, 0.30 #5831, 0.27 #354), 0ggx5q (0.33 #686, 0.19 #9207, 0.18 #2512) >> Best rule #14022 for best value: >> intensional similarity = 4 >> extensional distance = 396 >> proper extension: 053y0s; 02rgz4; 01nqfh_; 0274ck; 01pr_j6; 01p45_v; 01qkqwg; 01tp5bj; 01m65sp; 0lzkm; ... >> query: (?x8149, 06by7) <- profession(?x8149, ?x131), artists(?x2249, ?x8149), artists(?x2249, ?x10145), ?x10145 = 0p76z >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #760 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 03g5jw; 015f7; *> query: (?x8149, 02qm5j) <- artist(?x7089, ?x8149), ?x7089 = 0181dw, artists(?x1000, ?x8149), influenced_by(?x8149, ?x1029) *> conf = 0.17 ranks of expected_values: 36 EVAL 01whg97 artists! 02qm5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 132.000 79.000 0.693 http://example.org/music/genre/artists #17462-05fnl9 PRED entity: 05fnl9 PRED relation: nationality PRED expected values: 09c7w0 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 51): 09c7w0 (0.80 #101, 0.76 #501, 0.76 #401), 02jx1 (0.11 #2634, 0.11 #3734, 0.11 #3034), 07ssc (0.11 #815, 0.09 #3716, 0.08 #4217), 03rjj (0.08 #5, 0.04 #3802, 0.02 #805), 0k6nt (0.08 #25), 0d060g (0.07 #107, 0.06 #307, 0.05 #2007), 03shp (0.07 #156), 03rk0 (0.05 #9752, 0.05 #9652, 0.05 #9952), 03_3d (0.04 #206, 0.04 #3802, 0.03 #306), 0345h (0.04 #3802, 0.03 #831, 0.02 #931) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 02pbp9; >> query: (?x1676, 09c7w0) <- award_winner(?x1265, ?x1676), actor(?x2009, ?x1676), ?x1265 = 05c1t6z >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fnl9 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.800 http://example.org/people/person/nationality #17461-0dt8xq PRED entity: 0dt8xq PRED relation: nominated_for! PRED expected values: 0gr4k => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 223): 03hj5vf (0.71 #12807, 0.70 #12806, 0.69 #13045), 027986c (0.71 #12807, 0.70 #12806, 0.69 #13045), 0gr4k (0.58 #1211, 0.37 #3819, 0.25 #3582), 099c8n (0.54 #1242, 0.36 #2901, 0.34 #3850), 02n9nmz (0.54 #1243, 0.34 #3851, 0.17 #16661), 0gq9h (0.50 #1248, 0.42 #10734, 0.41 #21173), 04kxsb (0.50 #1281, 0.32 #3889, 0.26 #10767), 02x17s4 (0.50 #1280, 0.29 #3888, 0.19 #2939), 0gs9p (0.42 #1250, 0.42 #10736, 0.38 #21175), 019f4v (0.42 #1239, 0.39 #10725, 0.37 #21164) >> Best rule #12807 for best value: >> intensional similarity = 4 >> extensional distance = 205 >> proper extension: 06mmr; >> query: (?x5070, ?x3190) <- category(?x5070, ?x134), award(?x5070, ?x3190), nominated_for(?x3190, ?x86), award(?x364, ?x3190) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #1211 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0b73_1d; 0kvgxk; 02xtxw; *> query: (?x5070, 0gr4k) <- category(?x5070, ?x134), nominated_for(?x384, ?x5070), ?x384 = 03hkv_r, film(?x2790, ?x5070) *> conf = 0.58 ranks of expected_values: 3 EVAL 0dt8xq nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 147.000 147.000 0.713 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17460-01nms7 PRED entity: 01nms7 PRED relation: film PRED expected values: 07sc6nw => 86 concepts (75 used for prediction) PRED predicted values (max 10 best out of 529): 05pdh86 (0.23 #751, 0.05 #37509, 0.04 #100022), 075wx7_ (0.23 #264, 0.05 #37509, 0.04 #100022), 03c7twt (0.15 #1670, 0.05 #37509, 0.04 #100022), 0c1sgd3 (0.15 #807, 0.03 #78588), 03nm_fh (0.15 #797, 0.03 #78588), 065_cjc (0.15 #1194, 0.02 #2980, 0.02 #6552), 0bvn25 (0.15 #50, 0.01 #17911, 0.01 #35772), 05q7874 (0.08 #1061, 0.05 #37509, 0.04 #100022), 0h7t36 (0.08 #1681, 0.05 #37509, 0.04 #100022), 01z452 (0.08 #1541, 0.05 #37509, 0.04 #100022) >> Best rule #751 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 04bdxl; 016khd; 03k7bd; 05mkhs; 062dn7; 08vr94; 086nl7; 0315q3; 07ldhs; 036hf4; ... >> query: (?x8099, 05pdh86) <- award_nominee(?x4046, ?x8099), location(?x8099, ?x335), ?x4046 = 07swvb >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nms7 film 07sc6nw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 75.000 0.231 http://example.org/film/actor/film./film/performance/film #17459-025cn2 PRED entity: 025cn2 PRED relation: profession PRED expected values: 0nbcg 025352 => 111 concepts (108 used for prediction) PRED predicted values (max 10 best out of 88): 02hrh1q (0.68 #7220, 0.68 #6470, 0.66 #6920), 09jwl (0.56 #770, 0.43 #1670, 0.40 #4070), 0nbcg (0.50 #783, 0.40 #1233, 0.36 #1083), 01c72t (0.45 #1225, 0.40 #1075, 0.35 #775), 016z4k (0.37 #1654, 0.29 #3904, 0.29 #4054), 0cbd2 (0.36 #157, 0.32 #307, 0.22 #2257), 0dz3r (0.33 #1652, 0.30 #602, 0.29 #4353), 0kyk (0.32 #331, 0.27 #181, 0.16 #2281), 01d_h8 (0.31 #8711, 0.31 #9311, 0.31 #10061), 0dxtg (0.30 #7219, 0.29 #6469, 0.29 #6919) >> Best rule #7220 for best value: >> intensional similarity = 2 >> extensional distance = 1228 >> proper extension: 0q1lp; 01f9mq; 03yf4d; >> query: (?x6164, 02hrh1q) <- nominated_for(?x6164, ?x4648), student(?x741, ?x6164) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #783 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 03c7ln; 0p3sf; 04k15; 0132k4; 082db; 01w9ph_; *> query: (?x6164, 0nbcg) <- place_of_death(?x6164, ?x191), gender(?x6164, ?x231), origin(?x6164, ?x739) *> conf = 0.50 ranks of expected_values: 3, 14 EVAL 025cn2 profession 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 111.000 108.000 0.683 http://example.org/people/person/profession EVAL 025cn2 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 108.000 0.683 http://example.org/people/person/profession #17458-02lf0c PRED entity: 02lf0c PRED relation: people! PRED expected values: 041rx => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 39): 041rx (0.63 #928, 0.20 #312, 0.19 #2006), 0x67 (0.13 #2090, 0.12 #1858, 0.12 #1088), 02w7gg (0.12 #2, 0.09 #541, 0.07 #2932), 07hwkr (0.12 #12, 0.04 #3173, 0.04 #2014), 033tf_ (0.10 #2009, 0.10 #1239, 0.09 #1085), 0g8_vp (0.09 #792, 0.02 #1870, 0.01 #2875), 0xnvg (0.09 #321, 0.08 #1245, 0.08 #1091), 0g5y6 (0.08 #961, 0.01 #3352, 0.01 #2735), 048z7l (0.08 #964, 0.06 #656, 0.05 #348), 013xrm (0.07 #944, 0.04 #636, 0.03 #3799) >> Best rule #928 for best value: >> intensional similarity = 2 >> extensional distance = 164 >> proper extension: 01w3v; 0mcf4; >> query: (?x595, 041rx) <- religion(?x595, ?x7131), ?x7131 = 03_gx >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lf0c people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.627 http://example.org/people/ethnicity/people #17457-0dcz8_ PRED entity: 0dcz8_ PRED relation: genre PRED expected values: 02kdv5l 04pbhw => 66 concepts (49 used for prediction) PRED predicted values (max 10 best out of 111): 07s9rl0 (0.78 #5161, 0.75 #5513, 0.69 #2698), 02kdv5l (0.67 #2582, 0.48 #237, 0.41 #120), 02l7c8 (0.33 #2008, 0.31 #1774, 0.29 #2711), 0hcr (0.29 #138, 0.25 #372, 0.25 #255), 04xvlr (0.27 #2699, 0.25 #3169, 0.18 #1762), 0lsxr (0.23 #2824, 0.21 #3176, 0.18 #1183), 06cvj (0.22 #1998, 0.10 #1764, 0.09 #2466), 060__y (0.19 #2712, 0.14 #4590, 0.14 #3182), 02n4kr (0.19 #2823, 0.17 #3175, 0.12 #4583), 03npn (0.17 #2822, 0.11 #3174, 0.09 #2586) >> Best rule #5161 for best value: >> intensional similarity = 5 >> extensional distance = 1295 >> proper extension: 06n90; 031t2d; 024l2y; 05zlld0; 034qbx; 09cxm4; 0d87hc; 0jqzt; 04sh80; >> query: (?x9715, 07s9rl0) <- genre(?x9715, ?x1510), genre(?x7393, ?x1510), genre(?x1366, ?x1510), ?x1366 = 07ng9k, ?x7393 = 02vz6dn >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2582 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 688 *> proper extension: 0bq8tmw; *> query: (?x9715, 02kdv5l) <- genre(?x9715, ?x1510), genre(?x9524, ?x1510), genre(?x1640, ?x1510), ?x9524 = 03whyr, ?x1640 = 0cd2vh9 *> conf = 0.67 ranks of expected_values: 2, 11 EVAL 0dcz8_ genre 04pbhw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 66.000 49.000 0.776 http://example.org/film/film/genre EVAL 0dcz8_ genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 49.000 0.776 http://example.org/film/film/genre #17456-0zcbl PRED entity: 0zcbl PRED relation: award PRED expected values: 02w9sd7 => 88 concepts (69 used for prediction) PRED predicted values (max 10 best out of 248): 09sb52 (0.73 #27359, 0.72 #16651, 0.71 #10700), 099tbz (0.73 #27359, 0.72 #16651, 0.71 #10700), 09cm54 (0.73 #27359, 0.72 #16651, 0.71 #10700), 05zr6wv (0.34 #809, 0.08 #5563, 0.08 #11113), 02x73k6 (0.25 #851, 0.15 #25769, 0.07 #1643), 0f4x7 (0.22 #822, 0.12 #3991, 0.12 #3595), 027dtxw (0.21 #796, 0.07 #5550, 0.07 #3569), 04kxsb (0.18 #915, 0.09 #3688, 0.09 #1707), 02x8n1n (0.17 #117, 0.12 #26565, 0.12 #26564), 02y_rq5 (0.17 #94, 0.12 #26565, 0.12 #26564) >> Best rule #27359 for best value: >> intensional similarity = 3 >> extensional distance = 2274 >> proper extension: 01ky2h; 01lcxbb; 01wz_ml; 0lzkm; 01vsy3q; 01t265; 08xz51; 0f6lx; 06lxn; >> query: (?x6980, ?x2183) <- award_winner(?x2183, ?x6980), award(?x2657, ?x2183), award_nominee(?x2657, ?x275) >> conf = 0.73 => this is the best rule for 3 predicted values *> Best rule #957 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 310 *> proper extension: 01vsps; *> query: (?x6980, 02w9sd7) <- award(?x6980, ?x4091), award(?x875, ?x4091), ?x875 = 032_jg *> conf = 0.14 ranks of expected_values: 20 EVAL 0zcbl award 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 88.000 69.000 0.728 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17455-06j6l PRED entity: 06j6l PRED relation: parent_genre! PRED expected values: 016ybr 05lwjc 018ysx 037n97 => 55 concepts (35 used for prediction) PRED predicted values (max 10 best out of 307): 0y3_8 (0.50 #796, 0.33 #1048, 0.33 #40), 059kh (0.50 #798, 0.33 #1050, 0.33 #42), 01h0kx (0.50 #878, 0.33 #1130, 0.33 #122), 0grjmv (0.50 #868, 0.33 #1120, 0.33 #112), 0dn16 (0.33 #1019, 0.33 #11, 0.25 #767), 01ym9b (0.33 #39, 0.25 #795, 0.25 #543), 03xnwz (0.33 #27, 0.25 #783, 0.25 #279), 01cbwl (0.33 #35, 0.25 #791, 0.25 #287), 016ybr (0.33 #99, 0.25 #855, 0.25 #351), 0m40d (0.33 #114, 0.25 #870, 0.25 #366) >> Best rule #796 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 06by7; >> query: (?x3319, 0y3_8) <- artists(?x3319, ?x5589), artists(?x3319, ?x4819), artists(?x3319, ?x4576), participant(?x4819, ?x932), nationality(?x4819, ?x94), ?x5589 = 044mfr, ?x4576 = 012z8_ >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #99 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 064t9; *> query: (?x3319, 016ybr) <- artists(?x3319, ?x8018), artists(?x3319, ?x6942), artists(?x3319, ?x5442), artists(?x3319, ?x4842), artists(?x3319, ?x4640), ?x4842 = 0hvbj, ?x6942 = 04b7xr, ?x5442 = 02jq1, ?x4640 = 018n6m, vacationer(?x583, ?x8018) *> conf = 0.33 ranks of expected_values: 9, 11, 144, 192 EVAL 06j6l parent_genre! 037n97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 35.000 0.500 http://example.org/music/genre/parent_genre EVAL 06j6l parent_genre! 018ysx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 55.000 35.000 0.500 http://example.org/music/genre/parent_genre EVAL 06j6l parent_genre! 05lwjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 55.000 35.000 0.500 http://example.org/music/genre/parent_genre EVAL 06j6l parent_genre! 016ybr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 55.000 35.000 0.500 http://example.org/music/genre/parent_genre #17454-0338g8 PRED entity: 0338g8 PRED relation: award_winner! PRED expected values: 09qrn4 => 74 concepts (54 used for prediction) PRED predicted values (max 10 best out of 165): 09qrn4 (0.32 #18173, 0.31 #14281, 0.30 #18172), 05zr6wv (0.31 #14281, 0.30 #18172, 0.30 #20337), 0gqwc (0.17 #75, 0.11 #508, 0.09 #16442), 02y_rq5 (0.17 #96, 0.11 #529, 0.09 #16442), 07h0cl (0.17 #170, 0.11 #603, 0.09 #16442), 02z1nbg (0.17 #195, 0.11 #628, 0.09 #16442), 09cn0c (0.17 #319, 0.11 #752, 0.09 #16442), 027b9k6 (0.17 #210, 0.11 #643, 0.09 #16442), 02y_j8g (0.17 #283, 0.11 #716, 0.09 #16442), 02z0dfh (0.17 #76, 0.11 #509, 0.03 #4332) >> Best rule #18173 for best value: >> intensional similarity = 4 >> extensional distance = 2270 >> proper extension: 01czx; 0134s5; 03_0p; 094xh; 015srx; 013w2r; 01q99h; 0ycp3; 01jcxwp; 03c3yf; ... >> query: (?x8036, ?x5235) <- award(?x8036, ?x5235), award(?x3853, ?x5235), category_of(?x5235, ?x2758), profession(?x3853, ?x296) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0338g8 award_winner! 09qrn4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 54.000 0.322 http://example.org/award/award_category/winners./award/award_honor/award_winner #17453-0m75g PRED entity: 0m75g PRED relation: category PRED expected values: 08mbj5d => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.73 #66, 0.72 #101, 0.72 #57) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 173 >> proper extension: 0_wm_; >> query: (?x7213, 08mbj5d) <- citytown(?x5846, ?x7213), school_type(?x5846, ?x3092), colors(?x5846, ?x332), organization(?x5510, ?x5846) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m75g category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.726 http://example.org/common/topic/webpage./common/webpage/category #17452-06qd3 PRED entity: 06qd3 PRED relation: film_release_region! PRED expected values: 02vxq9m 01gc7 0g5qs2k 0401sg 0872p_c 053rxgm 0_7w6 02yvct 0661ql3 03qnc6q 040b5k 03cw411 03q0r1 03nm_fh 0bt3j9 02h22 09v9mks 0g9zljd 02bg55 => 178 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1623): 03nm_fh (0.89 #26755, 0.88 #21049, 0.84 #29037), 0872p_c (0.89 #26364, 0.88 #29787, 0.83 #8103), 05p1tzf (0.88 #20595, 0.81 #22877, 0.81 #28583), 0407yj_ (0.86 #26551, 0.86 #10572, 0.81 #29974), 03qnc6q (0.86 #26513, 0.85 #23089, 0.84 #29936), 0bpm4yw (0.86 #26706, 0.84 #30129, 0.84 #21000), 0661ql3 (0.86 #26492, 0.84 #20786, 0.83 #8231), 0ds3t5x (0.86 #10307, 0.83 #8025, 0.82 #26286), 09g7vfw (0.86 #10618, 0.83 #8336, 0.80 #11759), 0j8f09z (0.86 #11284, 0.83 #9002, 0.80 #12425) >> Best rule #26755 for best value: >> intensional similarity = 6 >> extensional distance = 26 >> proper extension: 047lj; 07f1x; >> query: (?x1453, 03nm_fh) <- film_release_region(?x3053, ?x1453), film_release_region(?x2512, ?x1453), film_release_region(?x1498, ?x1453), nominated_for(?x1052, ?x3053), ?x1498 = 04jkpgv, ?x2512 = 07x4qr >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 7, 27, 28, 31, 34, 36, 40, 43, 44, 50, 51, 70, 71, 88, 123, 151 EVAL 06qd3 film_release_region! 02bg55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 0g9zljd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 09v9mks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 02h22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 0bt3j9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 03nm_fh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 03q0r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 03cw411 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 040b5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 03qnc6q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 0_7w6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 053rxgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 0872p_c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 0401sg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 0g5qs2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 01gc7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06qd3 film_release_region! 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 178.000 53.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17451-04rjg PRED entity: 04rjg PRED relation: films PRED expected values: 011ypx => 98 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1135): 02yvct (0.36 #9118, 0.20 #13891, 0.07 #22386), 095z4q (0.25 #4045, 0.10 #14120, 0.06 #11997), 03pc89 (0.25 #4143, 0.09 #9445, 0.05 #14218), 02j69w (0.25 #3944, 0.05 #17204, 0.05 #14019), 0bdjd (0.25 #4085, 0.05 #14160, 0.03 #16812), 064ndc (0.25 #4182, 0.05 #14257, 0.03 #16909), 01gglm (0.25 #4128, 0.05 #14203, 0.03 #16855), 0g0x9c (0.25 #4115, 0.05 #14190, 0.03 #16842), 023gxx (0.25 #3860, 0.05 #13935, 0.03 #16587), 0p_rk (0.25 #4113, 0.05 #14188, 0.03 #16840) >> Best rule #9118 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 07_m9_; 02z3r; 0mz2; 0kcc7; >> query: (?x2014, 02yvct) <- films(?x2014, ?x253), film(?x7837, ?x253), film_crew_role(?x253, ?x281), nominated_for(?x262, ?x253), nominated_for(?x198, ?x253), ?x281 = 02_n3z, country(?x253, ?x94) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #20973 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 55 *> proper extension: 0bxg3; *> query: (?x2014, 011ypx) <- films(?x2014, ?x253), film(?x7837, ?x253), production_companies(?x253, ?x1104), film(?x262, ?x253), titles(?x53, ?x253), award_winner(?x1039, ?x1104) *> conf = 0.02 ranks of expected_values: 638 EVAL 04rjg films 011ypx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 98.000 49.000 0.364 http://example.org/film/film_subject/films #17450-03spz PRED entity: 03spz PRED relation: country! PRED expected values: 01hp22 => 217 concepts (217 used for prediction) PRED predicted values (max 10 best out of 43): 06f41 (0.81 #442, 0.76 #657, 0.73 #1517), 03hr1p (0.81 #448, 0.72 #362, 0.69 #1523), 01cgz (0.80 #355, 0.79 #656, 0.72 #1387), 07jbh (0.77 #454, 0.68 #368, 0.65 #2045), 019tzd (0.69 #460, 0.68 #374, 0.55 #1191), 02y8z (0.69 #445, 0.60 #359, 0.58 #660), 01z27 (0.69 #443, 0.60 #357, 0.55 #658), 07gyv (0.68 #351, 0.67 #652, 0.64 #1383), 01sgl (0.67 #163, 0.64 #378, 0.58 #679), 09w1n (0.65 #449, 0.64 #363, 0.52 #1180) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 03rt9; ... >> query: (?x4743, 06f41) <- film_release_region(?x1228, ?x4743), ?x1228 = 05z_kps, jurisdiction_of_office(?x182, ?x4743) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #137 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0ck1d; *> query: (?x4743, 01hp22) <- contains(?x4743, ?x13064), vacationer(?x13064, ?x4119), entity_involved(?x7419, ?x4743) *> conf = 0.58 ranks of expected_values: 17 EVAL 03spz country! 01hp22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 217.000 217.000 0.808 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #17449-02r3zy PRED entity: 02r3zy PRED relation: artists! PRED expected values: 0xhtw => 83 concepts (33 used for prediction) PRED predicted values (max 10 best out of 267): 03lty (0.63 #5278, 0.32 #3114, 0.30 #3705), 064t9 (0.60 #1864, 0.55 #4647, 0.52 #321), 0xhtw (0.54 #5267, 0.44 #1250, 0.44 #3103), 059kh (0.43 #1592, 0.38 #975, 0.28 #2517), 06j6l (0.38 #1900, 0.30 #4683, 0.30 #6228), 05bt6j (0.36 #2203, 0.35 #3439, 0.35 #4678), 01243b (0.33 #43, 0.30 #3705, 0.27 #3748), 011j5x (0.33 #32, 0.30 #3705, 0.26 #2499), 0ggx5q (0.33 #386, 0.28 #1929, 0.20 #2237), 08jyyk (0.33 #67, 0.15 #2534, 0.13 #5317) >> Best rule #5278 for best value: >> intensional similarity = 5 >> extensional distance = 175 >> proper extension: 0274ck; 0zjpz; 01vv6_6; 01w8n89; 0bkg4; 01s7qqw; 027dpx; 018y81; 01vng3b; 01386_; ... >> query: (?x1060, 03lty) <- artists(?x5934, ?x1060), artists(?x5934, ?x8873), artists(?x5934, ?x8012), ?x8873 = 0232lm, ?x8012 = 01wt4wc >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #5267 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 175 *> proper extension: 0274ck; 0zjpz; 01vv6_6; 01w8n89; 0bkg4; 01s7qqw; 027dpx; 018y81; 01vng3b; 01386_; ... *> query: (?x1060, 0xhtw) <- artists(?x5934, ?x1060), artists(?x5934, ?x8873), artists(?x5934, ?x8012), ?x8873 = 0232lm, ?x8012 = 01wt4wc *> conf = 0.54 ranks of expected_values: 3 EVAL 02r3zy artists! 0xhtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 33.000 0.633 http://example.org/music/genre/artists #17448-03y9p40 PRED entity: 03y9p40 PRED relation: team! PRED expected values: 0cc8q3 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 8): 0cc8q3 (0.78 #91, 0.78 #194, 0.77 #93), 0b_6xf (0.78 #91, 0.77 #93, 0.75 #190), 0b_756 (0.78 #91, 0.77 #93, 0.75 #169), 0b_6x2 (0.78 #91, 0.77 #93, 0.75 #169), 0b_6s7 (0.78 #91, 0.77 #93, 0.75 #169), 0f9rw9 (0.78 #91, 0.77 #93, 0.75 #169), 0b_6h7 (0.78 #91, 0.77 #93, 0.75 #169), 0br1xn (0.78 #91, 0.77 #93, 0.75 #169) >> Best rule #91 for best value: >> intensional similarity = 28 >> extensional distance = 2 >> proper extension: 091tgz; >> query: (?x9833, ?x6002) <- team(?x12798, ?x9833), team(?x6583, ?x9833), team(?x2302, ?x9833), position(?x9833, ?x6848), position(?x9833, ?x4747), team(?x6848, ?x12141), team(?x6848, ?x12124), team(?x6848, ?x11805), team(?x6848, ?x9937), team(?x6848, ?x8228), team(?x6848, ?x6847), team(?x6848, ?x6089), colors(?x9833, ?x7203), team(?x10736, ?x6847), team(?x6002, ?x6847), ?x6089 = 0jmbv, ?x8228 = 0jmcv, ?x10736 = 0f9rw9, ?x6583 = 0b_75k, ?x9937 = 0jmjr, ?x11805 = 0jm5b, ?x12798 = 0b_770, sport(?x9833, ?x12913), ?x4747 = 02sf_r, ?x12124 = 0jmgb, school(?x12141, ?x581), ?x2302 = 0b_77q, draft(?x12141, ?x2569) >> conf = 0.78 => this is the best rule for 8 predicted values ranks of expected_values: 1 EVAL 03y9p40 team! 0cc8q3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.780 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #17447-0bbxx9b PRED entity: 0bbxx9b PRED relation: nominated_for PRED expected values: 0bdjd => 85 concepts (38 used for prediction) PRED predicted values (max 10 best out of 635): 04f52jw (0.80 #43540, 0.78 #45154, 0.78 #38701), 03176f (0.38 #643, 0.15 #22572, 0.03 #3223), 031786 (0.28 #11286, 0.25 #4835, 0.25 #1128), 0292qb (0.28 #11286, 0.25 #4835, 0.25 #6448), 09lxv9 (0.28 #11286, 0.25 #4835, 0.25 #6448), 050f0s (0.28 #11286, 0.25 #4835, 0.25 #6448), 06fcqw (0.28 #11286, 0.25 #4835, 0.25 #6448), 0d4htf (0.28 #11286, 0.25 #4835, 0.25 #6448), 02lk60 (0.28 #11286, 0.25 #4835, 0.25 #6448), 07nxnw (0.28 #11286, 0.25 #4835, 0.25 #6448) >> Best rule #43540 for best value: >> intensional similarity = 3 >> extensional distance = 1188 >> proper extension: 04wqr; 07s6tbm; 049k07; 09d5h; 04smkr; 0275_pj; 06mmb; 038g2x; 08wr3kg; 02xbw2; ... >> query: (?x3879, ?x1046) <- award_nominee(?x3879, ?x1622), award_winner(?x1046, ?x3879), nominated_for(?x3879, ?x2006) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #30636 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1051 *> proper extension: 01vvydl; 09fqtq; 04rcr; 02r3zy; 01wbgdv; 01k5t_3; 03g5jw; 015882; 0dvqq; 01trhmt; ... *> query: (?x3879, ?x810) <- award_nominee(?x3879, ?x1622), award_winner(?x2294, ?x3879), nominated_for(?x1622, ?x810) *> conf = 0.08 ranks of expected_values: 99 EVAL 0bbxx9b nominated_for 0bdjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 85.000 38.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17446-07h07 PRED entity: 07h07 PRED relation: profession PRED expected values: 0kyk => 95 concepts (91 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.83 #3420, 0.70 #754, 0.69 #8602), 01d_h8 (0.80 #154, 0.76 #450, 0.66 #1338), 02jknp (0.76 #452, 0.65 #156, 0.55 #2228), 0cbd2 (0.75 #7, 0.68 #303, 0.51 #1635), 0kyk (0.50 #30, 0.41 #326, 0.35 #1658), 03gjzk (0.44 #1347, 0.42 #607, 0.40 #163), 018gz8 (0.36 #757, 0.31 #1201, 0.29 #1053), 09jwl (0.28 #1055, 0.21 #759, 0.21 #3128), 05z96 (0.25 #43, 0.14 #339, 0.14 #2115), 02krf9 (0.20 #175, 0.19 #1359, 0.16 #1803) >> Best rule #3420 for best value: >> intensional similarity = 3 >> extensional distance = 427 >> proper extension: 031zkw; 01mqz0; 0241jw; 0k8y7; 0863x_; 01gw4f; 01fwf1; 0bdt8; 039x1k; 019l3m; ... >> query: (?x4008, 02hrh1q) <- award(?x4008, ?x899), nominated_for(?x899, ?x7664), ?x7664 = 046f3p >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0g72r; *> query: (?x4008, 0kyk) <- influenced_by(?x4008, ?x6457), influenced_by(?x4008, ?x4028), ?x4028 = 0lcx, influenced_by(?x6457, ?x712) *> conf = 0.50 ranks of expected_values: 5 EVAL 07h07 profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 95.000 91.000 0.830 http://example.org/people/person/profession #17445-02ptzz0 PRED entity: 02ptzz0 PRED relation: team! PRED expected values: 0355dz => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 51): 01pv51 (0.80 #1607, 0.79 #1505, 0.77 #1349), 02sf_r (0.80 #1626, 0.79 #1524, 0.71 #1420), 0355dz (0.79 #1954, 0.79 #1545, 0.79 #1389), 03558l (0.72 #1470, 0.69 #1134, 0.68 #1523), 02_j1w (0.63 #2982, 0.60 #3290, 0.54 #3188), 0619m3 (0.63 #1492, 0.54 #1956, 0.54 #2465), 02sdk9v (0.63 #3286, 0.58 #3184, 0.56 #3236), 02nzb8 (0.60 #3285, 0.54 #3183, 0.52 #3235), 0dgrmp (0.53 #2980, 0.53 #3186, 0.51 #3238), 02wszf (0.46 #2343, 0.40 #2395, 0.38 #2038) >> Best rule #1607 for best value: >> intensional similarity = 17 >> extensional distance = 18 >> proper extension: 0jm4v; >> query: (?x3798, 01pv51) <- position(?x3798, ?x5755), position(?x3798, ?x1579), ?x5755 = 0355dz, position(?x12141, ?x1579), position(?x8079, ?x1579), position(?x5756, ?x1579), position(?x1578, ?x1579), position(?x799, ?x1579), ?x1578 = 0jm2v, ?x5756 = 0jm4b, colors(?x12141, ?x332), draft(?x12141, ?x2569), school(?x12141, ?x581), ?x8079 = 04cxw5b, ?x799 = 0jm3v, team(?x9266, ?x3798), teams(?x1523, ?x12141) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1954 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 42 *> proper extension: 0jmm4; *> query: (?x3798, ?x6848) <- position(?x3798, ?x6848), position(?x3798, ?x5755), position(?x3798, ?x1579), team(?x5755, ?x10837), team(?x5755, ?x9937), team(?x5755, ?x5154), team(?x5755, ?x4571), team(?x5755, ?x2820), team(?x5755, ?x1578), team(?x5755, ?x660), ?x1578 = 0jm2v, ?x660 = 0jmdb, team(?x1579, ?x4369), team(?x1579, ?x2568), ?x4571 = 0jm6n, ?x9937 = 0jmjr, ?x2568 = 0jmcb, ?x10837 = 0jm7n, ?x2820 = 0jmj7, colors(?x4369, ?x332), position(?x5154, ?x1348) *> conf = 0.79 ranks of expected_values: 3 EVAL 02ptzz0 team! 0355dz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 66.000 0.800 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #17444-04g5k PRED entity: 04g5k PRED relation: film_release_region! PRED expected values: 0gtsx8c 0c3ybss 0gkz15s 0dtfn 04w7rn 01fmys 0dt8xq 06fcqw 0ndsl1x => 91 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1298): 08hmch (0.87 #9204, 0.87 #7906, 0.63 #13098), 017jd9 (0.87 #9667, 0.84 #8369, 0.59 #13561), 047vnkj (0.84 #9772, 0.84 #8474, 0.59 #13666), 0fpgp26 (0.84 #10205, 0.82 #8907, 0.61 #14099), 017gm7 (0.82 #9246, 0.80 #7948, 0.59 #13140), 0fpv_3_ (0.82 #9362, 0.80 #8064, 0.58 #22342), 0661ql3 (0.80 #9375, 0.80 #8077, 0.54 #13269), 0dzlbx (0.80 #9724, 0.78 #8426, 0.57 #11022), 017gl1 (0.80 #9194, 0.78 #7896, 0.54 #5300), 0dtfn (0.80 #9245, 0.78 #7947, 0.54 #5351) >> Best rule #9204 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 05v8c; >> query: (?x5482, 08hmch) <- film_release_region(?x1707, ?x5482), ?x1707 = 04n52p6, member_states(?x2106, ?x5482) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #9245 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 05v8c; *> query: (?x5482, 0dtfn) <- film_release_region(?x1707, ?x5482), ?x1707 = 04n52p6, member_states(?x2106, ?x5482) *> conf = 0.80 ranks of expected_values: 10, 20, 23, 25, 41, 46, 49, 84, 162 EVAL 04g5k film_release_region! 0ndsl1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 91.000 44.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04g5k film_release_region! 06fcqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 91.000 44.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04g5k film_release_region! 0dt8xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 91.000 44.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04g5k film_release_region! 01fmys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 91.000 44.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04g5k film_release_region! 04w7rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 91.000 44.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04g5k film_release_region! 0dtfn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 91.000 44.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04g5k film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 91.000 44.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04g5k film_release_region! 0c3ybss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 91.000 44.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04g5k film_release_region! 0gtsx8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 91.000 44.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17443-011lvx PRED entity: 011lvx PRED relation: student! PRED expected values: 01w5m => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 164): 078bz (0.25 #76, 0.04 #2701, 0.04 #15226), 02g839 (0.16 #10524, 0.13 #8949, 0.11 #12624), 0k__z (0.14 #832, 0.03 #4507, 0.02 #6607), 07tgn (0.11 #3166, 0.03 #17342, 0.02 #55143), 0bwfn (0.10 #14449, 0.10 #24950, 0.09 #16550), 017z88 (0.09 #10581, 0.06 #12156, 0.06 #20557), 0yjf0 (0.07 #3197, 0.02 #5297, 0.01 #8447), 0yls9 (0.07 #3374), 09f2j (0.07 #1208, 0.07 #1733, 0.06 #11708), 03ksy (0.07 #1155, 0.07 #1680, 0.05 #49982) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01vsykc; >> query: (?x7410, 078bz) <- artist(?x2931, ?x7410), spouse(?x7410, ?x4712), ?x2931 = 03rhqg, location_of_ceremony(?x7410, ?x3987) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #24780 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 301 *> proper extension: 07nznf; 02qjj7; 0z4s; 058kqy; 05zbm4; 016gr2; 016hvl; 05drq5; 048lv; 0162c8; ... *> query: (?x7410, 01w5m) <- gender(?x7410, ?x514), profession(?x7410, ?x524), student(?x817, ?x7410), ?x524 = 02jknp *> conf = 0.05 ranks of expected_values: 17 EVAL 011lvx student! 01w5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 149.000 149.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #17442-014zn0 PRED entity: 014zn0 PRED relation: people! PRED expected values: 0dq9p => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 47): 0gk4g (0.31 #1418, 0.31 #1483, 0.26 #2956), 0dq9p (0.19 #1489, 0.17 #1424, 0.15 #1168), 0qcr0 (0.14 #1474, 0.12 #2947, 0.11 #2499), 04p3w (0.12 #139, 0.12 #1419, 0.10 #1035), 019dmc (0.12 #176, 0.07 #304, 0.04 #368), 01mtqf (0.12 #132, 0.03 #260, 0.02 #324), 0h9dj (0.12 #136, 0.02 #328), 02y0js (0.12 #1475, 0.09 #2948, 0.09 #1410), 02knxx (0.07 #287, 0.07 #2465, 0.06 #2273), 04psf (0.07 #263, 0.03 #455, 0.03 #519) >> Best rule #1418 for best value: >> intensional similarity = 4 >> extensional distance = 215 >> proper extension: 02m30v; >> query: (?x11961, 0gk4g) <- profession(?x11961, ?x1032), people(?x10199, ?x11961), ?x1032 = 02hrh1q, symptom_of(?x4905, ?x10199) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #1489 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 236 *> proper extension: 0443c; *> query: (?x11961, 0dq9p) <- location(?x11961, ?x4622), people(?x10199, ?x11961), risk_factors(?x13099, ?x10199) *> conf = 0.19 ranks of expected_values: 2 EVAL 014zn0 people! 0dq9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.313 http://example.org/people/cause_of_death/people #17441-08cg36 PRED entity: 08cg36 PRED relation: artists PRED expected values: 0143q0 => 66 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1139): 01shhf (0.57 #3042, 0.50 #874, 0.38 #4125), 01y_rz (0.57 #3123, 0.50 #955, 0.38 #4206), 01vsxdm (0.50 #3354, 0.50 #103, 0.43 #2271), 0bk1p (0.50 #4084, 0.50 #833, 0.43 #3001), 01wg982 (0.50 #3437, 0.50 #186, 0.43 #2354), 0fpj4lx (0.50 #327, 0.43 #2495, 0.38 #3578), 012zng (0.50 #5556, 0.43 #2302, 0.38 #3385), 048tgl (0.50 #914, 0.43 #3082, 0.38 #4165), 01wqpnm (0.50 #923, 0.43 #3091, 0.38 #4174), 023p29 (0.50 #925, 0.43 #3093, 0.38 #4176) >> Best rule #3042 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 08jyyk; >> query: (?x14354, 01shhf) <- artists(?x14354, ?x8149), artists(?x14354, ?x8012), parent_genre(?x14354, ?x1572), ?x8149 = 01whg97, role(?x8012, ?x716), artists(?x9013, ?x8012), artists(?x6350, ?x8012), ?x6350 = 0296y, ?x9013 = 09nwwf, ?x716 = 018vs >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4924 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 8 *> proper extension: 088vmr; *> query: (?x14354, 0143q0) <- parent_genre(?x14354, ?x10306), parent_genre(?x14354, ?x5934), parent_genre(?x14354, ?x1572), ?x10306 = 09jw2, ?x5934 = 05r6t, artists(?x1572, ?x11182), artists(?x1572, ?x2461), award_winner(?x2461, ?x538), award_winner(?x884, ?x11182) *> conf = 0.20 ranks of expected_values: 164 EVAL 08cg36 artists 0143q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 66.000 19.000 0.571 http://example.org/music/genre/artists #17440-02gnmp PRED entity: 02gnmp PRED relation: institution! PRED expected values: 016t_3 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 18): 019v9k (0.69 #317, 0.65 #277, 0.64 #577), 02_xgp2 (0.58 #178, 0.57 #581, 0.57 #281), 016t_3 (0.53 #210, 0.52 #573, 0.50 #232), 04zx3q1 (0.35 #272, 0.30 #572, 0.29 #231), 022h5x (0.28 #1424, 0.21 #328, 0.18 #225), 0bjrnt (0.28 #1424, 0.18 #275, 0.13 #575), 02m4yg (0.28 #1424, 0.08 #243, 0.06 #584), 01ysy9 (0.28 #1424, 0.08 #227, 0.06 #249), 071tyz (0.28 #1424, 0.07 #238, 0.06 #579), 01gkg3 (0.28 #1424, 0.05 #94, 0.05 #74) >> Best rule #317 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 0frm7n; >> query: (?x11244, 019v9k) <- category(?x11244, ?x134), ?x134 = 08mbj5d, school(?x7357, ?x11244), season(?x7357, ?x701) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #210 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 02jztz; *> query: (?x11244, 016t_3) <- category(?x11244, ?x134), major_field_of_study(?x11244, ?x4321), organization(?x346, ?x11244), ?x4321 = 0g26h *> conf = 0.53 ranks of expected_values: 3 EVAL 02gnmp institution! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 144.000 144.000 0.686 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #17439-07kb5 PRED entity: 07kb5 PRED relation: nationality PRED expected values: 03rjj => 87 concepts (61 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.65 #4410, 0.62 #2703, 0.60 #5912), 035qy (0.50 #134, 0.33 #34, 0.25 #434), 0345h (0.44 #931, 0.40 #331, 0.32 #1231), 03rjj (0.40 #205, 0.25 #4610, 0.13 #805), 03rt9 (0.33 #613, 0.25 #4610, 0.20 #813), 07ssc (0.25 #415, 0.25 #4610, 0.20 #715), 02jx1 (0.25 #4610, 0.21 #1133, 0.15 #2535), 0f8l9c (0.21 #1022, 0.20 #1222, 0.20 #322), 0h7x (0.20 #735, 0.16 #1235, 0.16 #1436), 06q1r (0.12 #477, 0.12 #3705, 0.10 #777) >> Best rule #4410 for best value: >> intensional similarity = 2 >> extensional distance = 360 >> proper extension: 030pr; 01l3j; >> query: (?x712, 09c7w0) <- religion(?x712, ?x1985), ?x1985 = 0c8wxp >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #205 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 028p0; 034ks; *> query: (?x712, 03rjj) <- influenced_by(?x712, ?x7341), gender(?x712, ?x231), influenced_by(?x1279, ?x712), ?x231 = 05zppz, ?x7341 = 0m93 *> conf = 0.40 ranks of expected_values: 4 EVAL 07kb5 nationality 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 61.000 0.655 http://example.org/people/person/nationality #17438-01jq0j PRED entity: 01jq0j PRED relation: major_field_of_study PRED expected values: 06n6p 026bk => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 120): 01mkq (0.70 #1001, 0.67 #262, 0.64 #385), 02j62 (0.58 #278, 0.50 #401, 0.50 #155), 04rjg (0.58 #267, 0.50 #390, 0.49 #1006), 02lp1 (0.57 #997, 0.46 #627, 0.43 #381), 0g26h (0.45 #1029, 0.44 #1275, 0.43 #1522), 03g3w (0.43 #1013, 0.42 #274, 0.41 #1136), 062z7 (0.43 #1014, 0.42 #152, 0.37 #1137), 041y2 (0.42 #327, 0.36 #696, 0.36 #450), 0_jm (0.42 #183, 0.36 #429, 0.30 #2399), 01lj9 (0.40 #1026, 0.39 #533, 0.39 #1272) >> Best rule #1001 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 017zq0; >> query: (?x6953, 01mkq) <- student(?x6953, ?x5821), fraternities_and_sororities(?x6953, ?x3697), award_winner(?x154, ?x5821), participant(?x5821, ?x4397) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #276 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0bx8pn; *> query: (?x6953, 06n6p) <- school(?x2174, ?x6953), ?x2174 = 051vz, state_province_region(?x6953, ?x2623), fraternities_and_sororities(?x6953, ?x3697) *> conf = 0.17 ranks of expected_values: 43, 98 EVAL 01jq0j major_field_of_study 026bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 165.000 165.000 0.702 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01jq0j major_field_of_study 06n6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 165.000 165.000 0.702 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17437-06449 PRED entity: 06449 PRED relation: artist! PRED expected values: 043g7l => 128 concepts (122 used for prediction) PRED predicted values (max 10 best out of 117): 03rhqg (0.54 #433, 0.23 #850, 0.22 #294), 0g768 (0.50 #177, 0.22 #1567, 0.13 #6573), 0181dw (0.50 #182, 0.16 #877, 0.12 #1989), 0mzkr (0.50 #165, 0.14 #1555, 0.09 #1416), 01cl2y (0.38 #448, 0.11 #1004, 0.10 #2116), 011k1h (0.25 #1539, 0.23 #844, 0.12 #2373), 015_1q (0.22 #298, 0.21 #1966, 0.21 #6138), 01cl0d (0.17 #195, 0.17 #56, 0.09 #1585), 01cszh (0.17 #150, 0.11 #289, 0.09 #2374), 02p11jq (0.17 #152, 0.10 #6131, 0.10 #847) >> Best rule #433 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 03c7ln; 0kzy0; 01l4zqz; 045zr; 01vsy95; 011lvx; 0lsw9; 01hgwkr; >> query: (?x2940, 03rhqg) <- artist(?x9671, ?x2940), gender(?x2940, ?x231), ?x9671 = 041bnw >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #310 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 02qfhb; *> query: (?x2940, 043g7l) <- music(?x2211, ?x2940), performance_role(?x2940, ?x1495), award_winner(?x414, ?x2940) *> conf = 0.11 ranks of expected_values: 22 EVAL 06449 artist! 043g7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 128.000 122.000 0.538 http://example.org/music/record_label/artist #17436-06thjt PRED entity: 06thjt PRED relation: organization! PRED expected values: 060c4 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 13): 060c4 (0.84 #444, 0.82 #288, 0.82 #301), 0dq_5 (0.35 #243, 0.34 #477, 0.34 #347), 05k17c (0.25 #7, 0.17 #111, 0.16 #423), 07xl34 (0.22 #232, 0.20 #1116, 0.20 #661), 0hm4q (0.09 #554, 0.05 #1178, 0.05 #1139), 05c0jwl (0.05 #668, 0.04 #551, 0.04 #785), 01yc02 (0.03 #134, 0.02 #160, 0.02 #199), 0krdk (0.03 #133, 0.02 #159, 0.02 #198), 0dq3c (0.02 #144, 0.01 #235, 0.01 #339), 08jcfy (0.02 #688, 0.02 #792, 0.02 #896) >> Best rule #444 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 03p7gb; >> query: (?x10478, 060c4) <- institution(?x1368, ?x10478), country(?x10478, ?x94), category(?x10478, ?x134) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06thjt organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.841 http://example.org/organization/role/leaders./organization/leadership/organization #17435-07rfp PRED entity: 07rfp PRED relation: child! PRED expected values: 01dycg => 183 concepts (155 used for prediction) PRED predicted values (max 10 best out of 71): 06p8m (0.17 #67, 0.14 #150, 0.11 #234), 049ql1 (0.15 #1421, 0.15 #1337, 0.14 #1506), 09b3v (0.11 #1549, 0.11 #1634, 0.10 #1890), 01dycg (0.11 #220, 0.08 #555, 0.07 #642), 03d6fyn (0.10 #1382, 0.10 #1298, 0.09 #1467), 011k1h (0.10 #351, 0.08 #434, 0.07 #9836), 06q07 (0.10 #297, 0.07 #9836, 0.07 #634), 01_4lx (0.08 #559, 0.07 #731, 0.07 #646), 07733f (0.08 #580, 0.06 #1091, 0.04 #1854), 03jl0_ (0.08 #465, 0.02 #3515) >> Best rule #67 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 01qckn; 06zl7g; 0260p2; 06nfl; >> query: (?x13915, 06p8m) <- industry(?x13915, ?x10022), industry(?x13915, ?x245), place_founded(?x13915, ?x9559), ?x10022 = 020mfr, ?x9559 = 07dfk, ?x245 = 01mw1, citytown(?x13915, ?x9559) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #220 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 059wk; *> query: (?x13915, 01dycg) <- industry(?x13915, ?x10022), place_founded(?x13915, ?x9559), ?x10022 = 020mfr, citytown(?x10312, ?x9559), place_of_birth(?x256, ?x9559), state_province_region(?x3636, ?x9559), organization(?x4682, ?x10312) *> conf = 0.11 ranks of expected_values: 4 EVAL 07rfp child! 01dycg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 183.000 155.000 0.167 http://example.org/organization/organization/child./organization/organization_relationship/child #17434-01wbgdv PRED entity: 01wbgdv PRED relation: influenced_by! PRED expected values: 086qd => 124 concepts (57 used for prediction) PRED predicted values (max 10 best out of 72): 017yfz (0.05 #160, 0.03 #676, 0.02 #1708), 01vsy95 (0.05 #123, 0.03 #639, 0.02 #1671), 0167xy (0.03 #951, 0.03 #435, 0.02 #1983), 01hb6v (0.03 #6289, 0.03 #10419, 0.02 #15588), 02yl42 (0.03 #135, 0.02 #6330, 0.02 #13564), 03g5jw (0.03 #44, 0.02 #6755, 0.02 #560), 01vvyfh (0.03 #144, 0.02 #1176, 0.02 #660), 0gcs9 (0.03 #111, 0.02 #627, 0.01 #3208), 0821j (0.03 #358, 0.02 #874), 01t07j (0.03 #60, 0.02 #576) >> Best rule #160 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 01ccr8; >> query: (?x1128, 017yfz) <- profession(?x1128, ?x955), performance_role(?x1128, ?x1466), people(?x2510, ?x1128) >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #1106 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 0qmny; *> query: (?x1128, 086qd) <- artist(?x2039, ?x1128), artists(?x3928, ?x1128), ?x3928 = 0gywn *> conf = 0.02 ranks of expected_values: 26 EVAL 01wbgdv influenced_by! 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 124.000 57.000 0.051 http://example.org/influence/influence_node/influenced_by #17433-0chghy PRED entity: 0chghy PRED relation: jurisdiction_of_office! PRED expected values: 060bp 04syw 01_fjr => 214 concepts (214 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.77 #2466, 0.74 #603, 0.73 #2646), 060bp (0.75 #1241, 0.73 #681, 0.68 #741), 09n5b9 (0.61 #1712, 0.38 #2254, 0.37 #451), 0fkvn (0.56 #1705, 0.48 #304, 0.46 #1906), 0pqc5 (0.47 #2930, 0.41 #2950, 0.36 #3934), 0dq3c (0.36 #3627, 0.29 #42, 0.25 #62), 04syw (0.36 #3627, 0.26 #927, 0.23 #427), 01_fjr (0.36 #3627, 0.21 #196, 0.19 #156), 02079p (0.36 #3627, 0.14 #290, 0.14 #50), 0789n (0.25 #69, 0.19 #149, 0.17 #329) >> Best rule #2466 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 07z5n; 01p1b; 05tr7; 03676; 06s9y; 05br2; >> query: (?x390, 060c4) <- country(?x2978, ?x390), ?x2978 = 03_8r, olympics(?x390, ?x418) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1241 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 05v8c; 03gj2; 015qh; 01pj7; 087vz; 03shp; 03__y; 077qn; 0d05q4; 06vbd; ... *> query: (?x390, 060bp) <- contains(?x390, ?x901), film_release_region(?x66, ?x390), combatants(?x326, ?x390) *> conf = 0.75 ranks of expected_values: 2, 7, 8 EVAL 0chghy jurisdiction_of_office! 01_fjr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 214.000 214.000 0.771 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0chghy jurisdiction_of_office! 04syw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 214.000 214.000 0.771 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0chghy jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 214.000 214.000 0.771 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #17432-0d0x8 PRED entity: 0d0x8 PRED relation: contains PRED expected values: 0lhn5 => 196 concepts (131 used for prediction) PRED predicted values (max 10 best out of 2800): 0lhn5 (0.81 #87726, 0.81 #108201, 0.81 #157921), 02j416 (0.74 #108200, 0.72 #198868, 0.72 #195943), 0bqxw (0.74 #108200, 0.72 #198868, 0.71 #149146), 0gsgr (0.56 #201793, 0.48 #67252, 0.48 #108199), 03phgz (0.56 #201793, 0.48 #67252, 0.48 #108199), 0d0x8 (0.54 #157920, 0.51 #193017, 0.49 #330474), 09c7w0 (0.54 #157920, 0.51 #193017, 0.49 #330474), 0d0kn (0.54 #157920, 0.51 #193017, 0.49 #330474), 0kcd5 (0.48 #67252, 0.48 #108199, 0.48 #76025), 0gmf0nj (0.48 #67252, 0.48 #108199, 0.48 #76025) >> Best rule #87726 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 07cfx; 0jt5zcn; 01w0v; 0h924; 03lrc; 09ctj; 0dj0x; >> query: (?x3038, ?x2277) <- state_province_region(?x3416, ?x3038), state(?x2277, ?x3038), student(?x3416, ?x2639) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d0x8 contains 0lhn5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 131.000 0.811 http://example.org/location/location/contains #17431-01ksr1 PRED entity: 01ksr1 PRED relation: location PRED expected values: 030qb3t => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 91): 030qb3t (0.23 #2493, 0.23 #4902, 0.22 #8917), 04jpl (0.20 #17, 0.07 #4836, 0.07 #2427), 02_286 (0.16 #5659, 0.16 #8871, 0.16 #7265), 0r0m6 (0.09 #1020, 0.03 #3430, 0.02 #9051), 0cr3d (0.07 #25842, 0.06 #8175, 0.05 #70812), 0gkgp (0.06 #2410), 059rby (0.05 #4835, 0.04 #819, 0.04 #2426), 0cc56 (0.05 #6482, 0.05 #8088, 0.04 #11301), 01_d4 (0.05 #2512, 0.04 #102, 0.03 #4921), 01cx_ (0.04 #965, 0.04 #162, 0.02 #10603) >> Best rule #2493 for best value: >> intensional similarity = 2 >> extensional distance = 248 >> proper extension: 0glmv; >> query: (?x3307, 030qb3t) <- award_winner(?x8367, ?x3307), languages(?x3307, ?x254) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ksr1 location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.232 http://example.org/people/person/places_lived./people/place_lived/location #17430-01n8gr PRED entity: 01n8gr PRED relation: award_nominee! PRED expected values: 02f1c => 118 concepts (32 used for prediction) PRED predicted values (max 10 best out of 528): 02f1c (0.82 #23325, 0.80 #46649, 0.80 #69982), 03h_fk5 (0.50 #2961, 0.05 #21620, 0.05 #7626), 03cfjg (0.29 #3105, 0.06 #5437, 0.05 #7770), 01k_r5b (0.29 #3576, 0.06 #5908, 0.05 #8241), 0137n0 (0.29 #2589, 0.06 #4921, 0.05 #7254), 01lmj3q (0.29 #2388, 0.06 #4720, 0.03 #25712), 01vrx3g (0.29 #2387, 0.06 #4719, 0.03 #11716), 0x3b7 (0.29 #3314, 0.05 #7979, 0.05 #10311), 05sq20 (0.21 #3834, 0.15 #8499, 0.14 #10831), 016srn (0.21 #3042, 0.10 #7707, 0.09 #10039) >> Best rule #23325 for best value: >> intensional similarity = 4 >> extensional distance = 152 >> proper extension: 01vs14j; 033wx9; 016h9b; 0565cz; 0137hn; 0473q; 01mbwlb; >> query: (?x3358, ?x3235) <- profession(?x3358, ?x131), artists(?x1572, ?x3358), award_nominee(?x3358, ?x3235), ?x1572 = 06by7 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n8gr award_nominee! 02f1c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 32.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17429-05q5t0b PRED entity: 05q5t0b PRED relation: nominated_for PRED expected values: 03cvwkr 0372j5 => 47 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1406): 059lwy (0.78 #1580, 0.70 #6324, 0.69 #12655), 0n83s (0.78 #1580, 0.70 #6324, 0.69 #12655), 0d68qy (0.44 #364, 0.22 #18985, 0.06 #1945), 01bv8b (0.44 #386, 0.07 #6712, 0.07 #8294), 0vjr (0.44 #836, 0.06 #7162, 0.06 #8744), 05p9_ql (0.44 #1111, 0.06 #2692, 0.05 #7437), 0cs134 (0.44 #1472, 0.06 #3053, 0.04 #6216), 0431v3 (0.44 #856, 0.06 #2437, 0.04 #7182), 02czd5 (0.33 #1260, 0.22 #18985, 0.05 #7586), 05f4vxd (0.33 #779, 0.06 #7105, 0.06 #8687) >> Best rule #1580 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0cqhk0; 09qj50; 02z0dfh; 09qs08; 03qgjwc; 0hnf5vm; 0cqhmg; >> query: (?x3064, ?x2826) <- award(?x2826, ?x3064), nominated_for(?x3064, ?x599), award_winner(?x3064, ?x1213), award(?x3694, ?x3064), ?x3694 = 016tb7 >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #18985 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 179 *> proper extension: 02v1m7; 054ks3; 0g_w; 0dgshf6; 099vwn; 01c99j; 04zx08r; 0dgr5xp; 09v1lrz; *> query: (?x3064, ?x303) <- nominated_for(?x3064, ?x599), award(?x12710, ?x3064), award(?x6426, ?x3064), profession(?x12710, ?x319), nominated_for(?x6426, ?x303), film_release_distribution_medium(?x599, ?x81) *> conf = 0.22 ranks of expected_values: 89, 590 EVAL 05q5t0b nominated_for 0372j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 47.000 13.000 0.783 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05q5t0b nominated_for 03cvwkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 13.000 0.783 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17428-02r1c18 PRED entity: 02r1c18 PRED relation: production_companies PRED expected values: 03sb38 => 88 concepts (52 used for prediction) PRED predicted values (max 10 best out of 65): 05qd_ (0.18 #173, 0.10 #3524, 0.09 #2051), 020h2v (0.18 #221, 0.07 #712, 0.04 #2181), 03sb38 (0.16 #951, 0.03 #3567, 0.02 #2828), 02slt7 (0.12 #927, 0.04 #275, 0.04 #358), 086k8 (0.11 #3516, 0.11 #2043, 0.10 #1799), 030_1_ (0.10 #670, 0.07 #2139, 0.06 #1485), 016tw3 (0.09 #3526, 0.09 #910, 0.09 #175), 0g1rw (0.09 #171, 0.05 #987, 0.05 #662), 046b0s (0.09 #186, 0.04 #2391, 0.04 #677), 0jz9f (0.09 #164, 0.04 #247, 0.01 #411) >> Best rule #173 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0dnvn3; >> query: (?x1535, 05qd_) <- nominated_for(?x826, ?x1535), nominated_for(?x826, ?x945), ?x945 = 0b6tzs, film_format(?x1535, ?x6392) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #951 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 140 *> proper extension: 02vl9ln; *> query: (?x1535, 03sb38) <- country(?x1535, ?x789), country(?x1535, ?x94), ?x789 = 0f8l9c, nationality(?x51, ?x94) *> conf = 0.16 ranks of expected_values: 3 EVAL 02r1c18 production_companies 03sb38 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 88.000 52.000 0.182 http://example.org/film/film/production_companies #17427-01vvycq PRED entity: 01vvycq PRED relation: nationality PRED expected values: 09c7w0 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 83): 09c7w0 (0.83 #301, 0.73 #4703, 0.73 #9607), 02jx1 (0.45 #833, 0.41 #933, 0.28 #1633), 07ssc (0.36 #11913, 0.20 #1615, 0.18 #815), 0d060g (0.11 #407, 0.10 #507, 0.10 #1207), 06q1r (0.09 #877, 0.05 #577, 0.05 #977), 0f8l9c (0.09 #2424, 0.08 #2124, 0.07 #1922), 0h7x (0.08 #2036, 0.07 #1735, 0.07 #1935), 03rt9 (0.07 #1013, 0.02 #3315, 0.02 #1413), 0chghy (0.07 #1110, 0.04 #1810, 0.03 #2712), 03rk0 (0.06 #9754, 0.06 #10154, 0.06 #11155) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01wmxfs; >> query: (?x702, 09c7w0) <- award(?x702, ?x2563), artist(?x2190, ?x702), ?x2563 = 01cw51, people(?x2510, ?x702) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vvycq nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.833 http://example.org/people/person/nationality #17426-07g1sm PRED entity: 07g1sm PRED relation: genre PRED expected values: 07s9rl0 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.66 #2666, 0.66 #848, 0.65 #2302), 02kdv5l (0.53 #608, 0.34 #124, 0.32 #1334), 04xvlr (0.47 #7393, 0.47 #7392, 0.46 #4362), 024qqx (0.47 #7393, 0.47 #7392, 0.46 #4362), 01jfsb (0.45 #618, 0.34 #1344, 0.31 #1949), 03k9fj (0.42 #617, 0.37 #12, 0.36 #133), 05p553 (0.36 #1336, 0.35 #368, 0.34 #2549), 02l7c8 (0.33 #864, 0.31 #1227, 0.29 #4015), 01hmnh (0.30 #623, 0.29 #381, 0.25 #139), 06n90 (0.24 #619, 0.20 #135, 0.16 #1345) >> Best rule #2666 for best value: >> intensional similarity = 1 >> extensional distance = 530 >> proper extension: 01cgz; >> query: (?x7016, 07s9rl0) <- films(?x5673, ?x7016) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07g1sm genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.658 http://example.org/film/film/genre #17425-01l_pn PRED entity: 01l_pn PRED relation: film_format PRED expected values: 07fb8_ => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.22 #1, 0.20 #38, 0.18 #43), 0cj16 (0.14 #95, 0.13 #90, 0.12 #75), 017fx5 (0.06 #4, 0.06 #41, 0.05 #46) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 05dy7p; >> query: (?x5608, 07fb8_) <- crewmember(?x5608, ?x2887), film_crew_role(?x5608, ?x137), film_production_design_by(?x5608, ?x9062) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l_pn film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.219 http://example.org/film/film/film_format #17424-06znpjr PRED entity: 06znpjr PRED relation: currency PRED expected values: 09nqf => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.83 #22, 0.80 #29, 0.78 #71), 01nv4h (0.05 #65, 0.02 #156, 0.02 #51), 088n7 (0.02 #42), 02l6h (0.01 #46, 0.01 #81, 0.01 #186) >> Best rule #22 for best value: >> intensional similarity = 6 >> extensional distance = 62 >> proper extension: 01gwk3; >> query: (?x7878, 09nqf) <- film(?x7752, ?x7878), film_crew_role(?x7878, ?x1284), film_crew_role(?x7878, ?x1078), award_nominee(?x7752, ?x274), ?x1078 = 089fss, ?x1284 = 0ch6mp2 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06znpjr currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.828 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #17423-020x5r PRED entity: 020x5r PRED relation: film PRED expected values: 0g_zyp => 106 concepts (50 used for prediction) PRED predicted values (max 10 best out of 532): 07w8fz (0.10 #515, 0.03 #4097, 0.03 #5888), 02rrfzf (0.05 #547, 0.03 #4129, 0.03 #5920), 0p_rk (0.05 #1356, 0.03 #6729, 0.02 #17478), 0418wg (0.05 #402, 0.02 #20106, 0.02 #2193), 016z9n (0.05 #370, 0.02 #7534, 0.02 #2161), 035_2h (0.05 #920, 0.02 #8084, 0.01 #6293), 0_816 (0.05 #534, 0.02 #7698, 0.01 #5907), 09xbpt (0.05 #47, 0.02 #1838, 0.02 #17960), 06z8s_ (0.05 #130, 0.02 #1921, 0.02 #9085), 02704ff (0.05 #984, 0.02 #2775, 0.02 #9939) >> Best rule #515 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 02bfxb; 03xp8d5; >> query: (?x8161, 07w8fz) <- award(?x8161, ?x3435), award(?x8161, ?x601), student(?x6434, ?x8161), ?x601 = 0gr4k, ?x3435 = 03hl6lc >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #1593 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 02bfxb; 03xp8d5; *> query: (?x8161, 0g_zyp) <- award(?x8161, ?x3435), award(?x8161, ?x601), student(?x6434, ?x8161), ?x601 = 0gr4k, ?x3435 = 03hl6lc *> conf = 0.05 ranks of expected_values: 33 EVAL 020x5r film 0g_zyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 106.000 50.000 0.095 http://example.org/film/actor/film./film/performance/film #17422-057xn_m PRED entity: 057xn_m PRED relation: type_of_union PRED expected values: 01g63y => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.74 #65, 0.70 #69, 0.68 #33), 01g63y (0.20 #377, 0.18 #78, 0.17 #82), 0jgjn (0.05 #36, 0.04 #40, 0.02 #56) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 0pgjm; 0dpqk; 03dq9; 0dn44; >> query: (?x10864, 04ztj) <- gender(?x10864, ?x514), group(?x10864, ?x6699), award(?x10864, ?x2238), award_nominee(?x10864, ?x5536) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #377 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3674 *> proper extension: 019y64; 0j3v; 0fv6dr; 0dzkq; 099bk; 01ry0f; 0bk4s; 0j5b8; 09r1j5; 03sbs; ... *> query: (?x10864, ?x566) <- gender(?x10864, ?x514), nationality(?x10864, ?x94), gender(?x12683, ?x514), type_of_union(?x12683, ?x566) *> conf = 0.20 ranks of expected_values: 2 EVAL 057xn_m type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.739 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17421-06pj8 PRED entity: 06pj8 PRED relation: nominated_for PRED expected values: 0hfzr 08nvyr => 183 concepts (152 used for prediction) PRED predicted values (max 10 best out of 1025): 015w8_ (0.80 #126388, 0.80 #239988, 0.80 #124788), 0hx4y (0.80 #126388, 0.80 #239988, 0.80 #124788), 01flv_ (0.80 #126388, 0.80 #239988, 0.80 #124788), 0hfzr (0.80 #126388, 0.80 #239988, 0.80 #124788), 0bt4g (0.80 #126388, 0.80 #239988, 0.80 #124788), 0l76z (0.66 #44793, 0.63 #110390, 0.61 #97588), 02rb84n (0.60 #22397, 0.59 #28794, 0.53 #155188), 0322yj (0.60 #22397, 0.59 #28794, 0.53 #155188), 0k_9j (0.60 #22397, 0.59 #28794, 0.53 #155188), 0gmgwnv (0.60 #22397, 0.59 #28794, 0.53 #155188) >> Best rule #126388 for best value: >> intensional similarity = 3 >> extensional distance = 187 >> proper extension: 01bh6y; 02vkvcz; >> query: (?x2135, ?x1452) <- spouse(?x2135, ?x1802), award_winner(?x1452, ?x2135), nominated_for(?x2135, ?x531) >> conf = 0.80 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 130 EVAL 06pj8 nominated_for 08nvyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 183.000 152.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 06pj8 nominated_for 0hfzr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 183.000 152.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17420-01kwlwp PRED entity: 01kwlwp PRED relation: award_winner! PRED expected values: 01mh_q => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 101): 0jzphpx (0.24 #448, 0.09 #1818, 0.08 #311), 013b2h (0.15 #1858, 0.11 #1584, 0.10 #2406), 02rjjll (0.14 #416, 0.12 #1786, 0.11 #279), 0hndn2q (0.13 #449, 0.02 #5656, 0.02 #6341), 02cg41 (0.13 #1903, 0.11 #533, 0.09 #1629), 05pd94v (0.13 #1783, 0.10 #1509, 0.09 #1235), 09n4nb (0.13 #1827, 0.11 #320, 0.10 #457), 01s695 (0.12 #1784, 0.10 #1510, 0.09 #1099), 0g5b0q5 (0.12 #430, 0.02 #5637, 0.02 #6048), 01bx35 (0.11 #1788, 0.09 #1514, 0.08 #281) >> Best rule #448 for best value: >> intensional similarity = 3 >> extensional distance = 165 >> proper extension: 02lbrd; 0838y; 02ndj5; 0134pk; 0mjn2; 09jm8; >> query: (?x954, 0jzphpx) <- award_winner(?x7534, ?x954), award_winner(?x8500, ?x954), locations(?x8500, ?x1523) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #1867 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 437 *> proper extension: 0jdhp; 0770cd; 0d06m5; 0253b6; *> query: (?x954, 01mh_q) <- award_winner(?x1480, ?x954), ceremony(?x2212, ?x1480), ?x2212 = 02nbqh *> conf = 0.10 ranks of expected_values: 14 EVAL 01kwlwp award_winner! 01mh_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 103.000 103.000 0.240 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17419-0b_6h7 PRED entity: 0b_6h7 PRED relation: team PRED expected values: 0263cyj 026wlnm => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 16): 02py8_w (0.78 #92, 0.75 #114, 0.67 #169), 03y9p40 (0.75 #152, 0.75 #119, 0.72 #174), 02qk2d5 (0.75 #118, 0.72 #173, 0.71 #162), 026wlnm (0.75 #142, 0.69 #153, 0.69 #131), 091tgz (0.75 #138, 0.67 #171, 0.67 #105), 027yf83 (0.67 #101, 0.62 #123, 0.60 #46), 02ptzz0 (0.57 #78, 0.56 #133, 0.56 #100), 04088s0 (0.56 #102, 0.50 #135, 0.44 #146), 0263cyj (0.50 #29, 0.44 #150, 0.43 #62), 02r2qt7 (0.38 #148, 0.33 #104, 0.31 #126) >> Best rule #92 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 0b_77q; >> query: (?x5258, 02py8_w) <- instance_of_recurring_event(?x5258, ?x10863), team(?x5258, ?x12370), team(?x5258, ?x10171), team(?x5258, ?x5551), team(?x5258, ?x2303), ?x10171 = 026w398, team(?x6002, ?x5551), team(?x1348, ?x5551), ?x2303 = 02plv57, ?x6002 = 0cc8q3, ?x12370 = 026dqjm >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #142 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 14 *> proper extension: 0b_770; 0b_734; *> query: (?x5258, 026wlnm) <- instance_of_recurring_event(?x5258, ?x10863), team(?x5258, ?x10171), team(?x5258, ?x5551), team(?x5258, ?x4369), ?x4369 = 02pqcfz, team(?x10736, ?x5551), team(?x8824, ?x5551), ?x10736 = 0f9rw9, ?x10863 = 02jp2w, ?x8824 = 05g_nr, colors(?x10171, ?x4557) *> conf = 0.75 ranks of expected_values: 4, 9 EVAL 0b_6h7 team 026wlnm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 64.000 64.000 0.778 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_6h7 team 0263cyj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 64.000 64.000 0.778 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #17418-02fgdx PRED entity: 02fgdx PRED relation: student PRED expected values: 033p3_ => 149 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1566): 08k1lz (0.20 #1731, 0.07 #5899, 0.05 #16319), 016kb7 (0.12 #7609, 0.10 #9693, 0.02 #15945), 0d3k14 (0.10 #1847, 0.08 #3931, 0.07 #6015), 015qq1 (0.10 #1885, 0.08 #3969, 0.07 #6053), 0h96g (0.10 #828, 0.08 #2912, 0.07 #4996), 0m76b (0.10 #1751, 0.08 #3835, 0.07 #5919), 01ft2l (0.10 #577, 0.08 #2661, 0.07 #4745), 01zfmm (0.10 #440, 0.08 #2524, 0.07 #4608), 02pv_d (0.10 #1389, 0.08 #3473, 0.07 #5557), 03l3ln (0.10 #1151, 0.08 #3235, 0.07 #5319) >> Best rule #1731 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0gsgr; >> query: (?x3387, 08k1lz) <- organization(?x346, ?x3387), ?x346 = 060c4, contact_category(?x3387, ?x897), state_province_region(?x3387, ?x3634) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02fgdx student 033p3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 149.000 123.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #17417-02hzz PRED entity: 02hzz PRED relation: group! PRED expected values: 05148p4 => 90 concepts (71 used for prediction) PRED predicted values (max 10 best out of 121): 05148p4 (0.80 #1763, 0.77 #1588, 0.77 #1151), 0l14md (0.71 #1750, 0.69 #2014, 0.67 #2363), 03bx0bm (0.69 #1682, 0.65 #2033, 0.64 #2382), 05r5c (0.50 #355, 0.38 #1751, 0.33 #1664), 013y1f (0.50 #376, 0.33 #463, 0.27 #1772), 028tv0 (0.44 #2020, 0.40 #1756, 0.39 #2369), 07y_7 (0.43 #612, 0.24 #1746, 0.21 #1221), 06ncr (0.38 #1170, 0.27 #1782, 0.17 #1432), 03qjg (0.38 #1879, 0.36 #1791, 0.34 #2491), 01vj9c (0.31 #2807, 0.29 #2457, 0.29 #623) >> Best rule #1763 for best value: >> intensional similarity = 7 >> extensional distance = 43 >> proper extension: 06br6t; >> query: (?x8131, 05148p4) <- group(?x2297, ?x8131), group(?x228, ?x8131), artists(?x1572, ?x8131), role(?x1437, ?x2297), ?x1437 = 01vdm0, ?x228 = 0l14qv, role(?x317, ?x2297) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hzz group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 71.000 0.800 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17416-027t8fw PRED entity: 027t8fw PRED relation: cinematography! PRED expected values: 065zlr => 67 concepts (32 used for prediction) PRED predicted values (max 10 best out of 334): 03wy8t (0.04 #631, 0.04 #965, 0.04 #1633), 03cw411 (0.04 #453, 0.04 #787, 0.04 #1455), 0kbhf (0.04 #528, 0.04 #862, 0.04 #1530), 083skw (0.04 #416, 0.04 #750, 0.04 #1418), 0jvt9 (0.04 #440, 0.04 #774, 0.03 #1776), 0jymd (0.04 #796, 0.04 #1464, 0.04 #1130), 084qpk (0.04 #692, 0.04 #1360, 0.03 #1694), 037cr1 (0.02 #309, 0.02 #643, 0.02 #977), 02d003 (0.02 #236, 0.02 #570, 0.02 #904), 09zf_q (0.02 #168, 0.02 #502, 0.02 #836) >> Best rule #631 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 04qvl7; 06cv1; 0f3zf_; 079hvk; 05dppk; 04g865; 0dqzkv; 02rgz97; 07mb57; 06nz46; ... >> query: (?x7249, 03wy8t) <- cinematography(?x6798, ?x7249), profession(?x7249, ?x524), award(?x7249, ?x1243), film_release_distribution_medium(?x6798, ?x81) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027t8fw cinematography! 065zlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 32.000 0.041 http://example.org/film/film/cinematography #17415-028k2x PRED entity: 028k2x PRED relation: genre PRED expected values: 025s89p => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 68): 07s9rl0 (0.86 #1142, 0.62 #77, 0.60 #153), 05p553 (0.50 #538, 0.49 #690, 0.49 #614), 01hmnh (0.38 #318, 0.35 #166, 0.33 #14), 01jfsb (0.38 #87, 0.20 #163, 0.19 #315), 0lsxr (0.38 #85, 0.13 #466, 0.13 #1608), 01z4y (0.35 #700, 0.34 #624, 0.34 #548), 0c4xc (0.25 #645, 0.24 #569, 0.24 #949), 0jxy (0.23 #179, 0.19 #331, 0.08 #2087), 02kdv5l (0.20 #155, 0.14 #307, 0.06 #1144), 01t_vv (0.19 #105, 0.18 #410, 0.18 #562) >> Best rule #1142 for best value: >> intensional similarity = 3 >> extensional distance = 151 >> proper extension: 07qht4; >> query: (?x7657, 07s9rl0) <- genre(?x7657, ?x1013), genre(?x4040, ?x1013), ?x4040 = 02mt51 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #349 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 088tp3; *> query: (?x7657, 025s89p) <- genre(?x7657, ?x1013), genre(?x7305, ?x1013), ?x7305 = 031786 *> conf = 0.08 ranks of expected_values: 23 EVAL 028k2x genre 025s89p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 78.000 78.000 0.856 http://example.org/tv/tv_program/genre #17414-01vs4f3 PRED entity: 01vs4f3 PRED relation: influenced_by PRED expected values: 081k8 => 170 concepts (93 used for prediction) PRED predicted values (max 10 best out of 382): 032l1 (0.50 #1385, 0.17 #10038, 0.15 #13069), 0jt90f5 (0.50 #493, 0.05 #21637, 0.05 #36354), 03j24kf (0.43 #1730, 0.29 #13414, 0.27 #10383), 01vsyg9 (0.43 #1730, 0.29 #13414, 0.27 #10383), 0144l1 (0.43 #1730, 0.29 #13414, 0.27 #10383), 081k8 (0.33 #1452, 0.28 #17897, 0.16 #24822), 03f0324 (0.33 #1448, 0.10 #17893, 0.10 #31744), 01v9724 (0.25 #609, 0.18 #3637, 0.17 #1474), 02wh0 (0.25 #1245, 0.17 #1678, 0.15 #13362), 0379s (0.25 #941, 0.17 #1374, 0.15 #28563) >> Best rule #1385 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 073v6; >> query: (?x8579, 032l1) <- location(?x8579, ?x3301), peers(?x8579, ?x2170), influenced_by(?x8579, ?x6457), ?x6457 = 03_87 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1452 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 073v6; *> query: (?x8579, 081k8) <- location(?x8579, ?x3301), peers(?x8579, ?x2170), influenced_by(?x8579, ?x6457), ?x6457 = 03_87 *> conf = 0.33 ranks of expected_values: 6 EVAL 01vs4f3 influenced_by 081k8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 170.000 93.000 0.500 http://example.org/influence/influence_node/influenced_by #17413-05233hy PRED entity: 05233hy PRED relation: place_of_birth PRED expected values: 0r6c4 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 59): 02_286 (0.09 #2835, 0.08 #2131, 0.07 #6357), 01_d4 (0.09 #1474, 0.08 #770, 0.05 #3587), 030qb3t (0.08 #758, 0.04 #1462, 0.04 #49364), 0d6lp (0.04 #818, 0.03 #1522, 0.03 #2930), 0cr3d (0.04 #798, 0.03 #49404, 0.03 #5023), 01cx_ (0.04 #813, 0.03 #1517, 0.01 #22651), 0fpzwf (0.04 #910, 0.02 #11973), 017w_ (0.04 #1384), 0t_3w (0.04 #1101), 07ypt (0.04 #1029) >> Best rule #2835 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 01c59k; 052h3; 06c44; 0223g8; 0561xh; 01hkg9; 026c0p; 02m30v; >> query: (?x12512, 02_286) <- people(?x4322, ?x12512), ?x4322 = 0gk4g, nationality(?x12512, ?x94) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05233hy place_of_birth 0r6c4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 86.000 0.091 http://example.org/people/person/place_of_birth #17412-02tktw PRED entity: 02tktw PRED relation: currency PRED expected values: 09nqf => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.87 #57, 0.86 #120, 0.85 #148), 02l6h (0.03 #95, 0.03 #102, 0.02 #116), 01nv4h (0.02 #478, 0.02 #177, 0.02 #520), 088n7 (0.01 #413) >> Best rule #57 for best value: >> intensional similarity = 6 >> extensional distance = 36 >> proper extension: 01gc7; 011yxg; 011yph; 0cwy47; 020fcn; 0pb33; 0dr_4; 01pgp6; 0ds2n; 0jymd; ... >> query: (?x6293, 09nqf) <- production_companies(?x6293, ?x574), genre(?x6293, ?x53), film_crew_role(?x6293, ?x137), titles(?x812, ?x6293), film(?x4153, ?x6293), ?x574 = 016tt2 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tktw currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.868 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #17411-03hr1p PRED entity: 03hr1p PRED relation: sports! PRED expected values: 0l6mp => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 26): 06sks6 (0.89 #785, 0.84 #1033, 0.80 #759), 0lgxj (0.85 #1046, 0.84 #1045, 0.83 #521), 09x3r (0.85 #1046, 0.84 #1045, 0.83 #521), 0blg2 (0.84 #1045, 0.83 #204, 0.82 #798), 0l6ny (0.84 #1045, 0.83 #204, 0.82 #798), 0kbws (0.79 #435, 0.75 #854, 0.70 #744), 0l6mp (0.62 #503, 0.60 #273, 0.60 #246), 018qb4 (0.62 #511, 0.52 #203, 0.51 #288), 018ctl (0.57 #58, 0.51 #288, 0.51 #519), 09n48 (0.52 #203, 0.51 #288, 0.51 #519) >> Best rule #785 for best value: >> intensional similarity = 43 >> extensional distance = 17 >> proper extension: 03krj; >> query: (?x3127, 06sks6) <- country(?x3127, ?x2346), country(?x3127, ?x304), country(?x3127, ?x252), ?x304 = 0d0vqn, film_release_region(?x9941, ?x252), film_release_region(?x9349, ?x252), film_release_region(?x9345, ?x252), film_release_region(?x7693, ?x252), film_release_region(?x7692, ?x252), film_release_region(?x7524, ?x252), film_release_region(?x6620, ?x252), film_release_region(?x4518, ?x252), film_release_region(?x3000, ?x252), film_release_region(?x1364, ?x252), film_release_region(?x1173, ?x252), film_release_region(?x428, ?x252), film_release_region(?x409, ?x252), combatants(?x1140, ?x2346), ?x428 = 0h1cdwq, country(?x1948, ?x252), ?x1364 = 047msdk, ?x1948 = 0fy34l, adjoins(?x2346, ?x2146), ?x9941 = 024lt6, ?x3000 = 045j3w, sports(?x4255, ?x3127), sports(?x3971, ?x3127), contains(?x2346, ?x1885), ?x6620 = 0mbql, ?x7524 = 01cm8w, ?x1173 = 0872p_c, ?x409 = 0gtv7pk, ?x3971 = 0jhn7, ?x7692 = 0bt4g, ?x9349 = 0jdr0, teams(?x2346, ?x10896), ?x4518 = 0hgnl3t, ?x7693 = 0m63c, olympics(?x789, ?x4255), nationality(?x256, ?x252), ?x9345 = 014knw, location_of_ceremony(?x566, ?x252), country(?x536, ?x252) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #503 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 6 *> proper extension: 01cgz; *> query: (?x3127, 0l6mp) <- country(?x3127, ?x5274), country(?x3127, ?x5147), country(?x3127, ?x2513), country(?x3127, ?x1471), country(?x3127, ?x583), country(?x3127, ?x304), country(?x3127, ?x279), country(?x3127, ?x252), ?x304 = 0d0vqn, ?x252 = 03_3d, ?x5147 = 0d04z6, ?x2513 = 05b4w, sports(?x4255, ?x3127), sports(?x584, ?x3127), ?x584 = 0l98s, form_of_government(?x5274, ?x1926), ?x1471 = 07t21, olympics(?x5274, ?x2496), adjoins(?x5274, ?x789), film_release_region(?x9501, ?x5274), film_release_region(?x1071, ?x5274), time_zones(?x5274, ?x2864), ?x1926 = 018wl5, ?x583 = 015fr, participating_countries(?x4255, ?x7479), ?x9501 = 0g5qmbz, organization(?x279, ?x127), film_release_region(?x4041, ?x279), film_release_region(?x1707, ?x279), film_release_region(?x1108, ?x279), country(?x695, ?x5274), ?x1071 = 02d44q, medal(?x5274, ?x1242), olympics(?x279, ?x358), film_release_region(?x1064, ?x279), ?x4041 = 0gy2y8r, ?x1108 = 0jjy0, sports(?x4255, ?x171), contains(?x279, ?x481), ?x1707 = 04n52p6, ?x7479 = 0165b *> conf = 0.62 ranks of expected_values: 7 EVAL 03hr1p sports! 0l6mp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 44.000 44.000 0.895 http://example.org/olympics/olympic_games/sports #17410-02bp37 PRED entity: 02bp37 PRED relation: legislative_sessions! PRED expected values: 06bss 03txms => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 35): 06bss (0.80 #584, 0.75 #43, 0.73 #520), 0194xc (0.75 #43, 0.71 #678, 0.71 #502), 0d06m5 (0.75 #43, 0.71 #108, 0.67 #87), 01lct6 (0.75 #43, 0.71 #108, 0.67 #87), 03txms (0.71 #108, 0.67 #87, 0.65 #278), 02mjmr (0.71 #108, 0.67 #87, 0.65 #278), 06hx2 (0.71 #108, 0.65 #278, 0.59 #150), 0dq2k (0.33 #536, 0.25 #429, 0.23 #451), 042fk (0.29 #487, 0.27 #552, 0.23 #467), 0424m (0.25 #263, 0.09 #694, 0.04 #897) >> Best rule #584 for best value: >> intensional similarity = 31 >> extensional distance = 13 >> proper extension: 03z5xd; 02gkzs; >> query: (?x1829, 06bss) <- legislative_sessions(?x8607, ?x1829), legislative_sessions(?x5266, ?x1829), district_represented(?x1829, ?x6521), district_represented(?x1829, ?x3908), district_represented(?x1829, ?x3086), district_represented(?x1829, ?x2256), district_represented(?x1829, ?x335), legislative_sessions(?x1829, ?x6139), legislative_sessions(?x1829, ?x2861), legislative_sessions(?x1829, ?x1028), profession(?x5266, ?x5805), ?x6139 = 060ny2, basic_title(?x5266, ?x2358), ?x2256 = 07srw, gender(?x5266, ?x231), ?x8607 = 0226cw, religion(?x3086, ?x109), contains(?x94, ?x3086), location(?x5507, ?x3086), state(?x1248, ?x3908), student(?x1368, ?x5266), ?x1028 = 032ft5, ?x2861 = 03tcbx, ?x335 = 059rby, location(?x1376, ?x6521), location(?x1299, ?x3908), contains(?x6521, ?x859), people(?x4195, ?x5266), time_zones(?x6521, ?x1638), category(?x1248, ?x134), state_province_region(?x466, ?x3908) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 02bp37 legislative_sessions! 03txms CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 43.000 43.000 0.800 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 02bp37 legislative_sessions! 06bss CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.800 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #17409-02n4kr PRED entity: 02n4kr PRED relation: genre! PRED expected values: 026p_bs 01vfqh 045j3w 0gvs1kt 0900j5 093dqjy 02v5_g 05mrf_p 05v38p 0hv4t 01svry 0c9t0y 0cbn7c 09wnnb => 66 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1657): 02xs6_ (0.77 #20328, 0.71 #19427, 0.57 #21122), 06g77c (0.77 #20328, 0.57 #19009, 0.50 #5455), 01d259 (0.77 #20328, 0.57 #19560, 0.50 #6006), 05znxx (0.77 #20328, 0.50 #5902, 0.43 #21151), 0hv4t (0.77 #20328, 0.50 #6189, 0.40 #12965), 09p4w8 (0.77 #20328, 0.50 #5857, 0.40 #12633), 0bpbhm (0.77 #20328, 0.50 #5713, 0.40 #12489), 03ntbmw (0.77 #20328, 0.50 #6755, 0.33 #3369), 035zr0 (0.77 #20328, 0.40 #13085, 0.33 #1230), 0f40w (0.77 #20328, 0.33 #15577, 0.33 #3720) >> Best rule #20328 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 03npn; >> query: (?x600, ?x2288) <- genre(?x12720, ?x600), genre(?x9069, ?x600), genre(?x3826, ?x600), genre(?x2078, ?x600), film(?x7946, ?x12720), music(?x9069, ?x8374), titles(?x600, ?x2288), ?x3826 = 0yx7h >> conf = 0.77 => this is the best rule for 15 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 19, 63, 106, 143, 255, 260, 261, 560, 590, 628, 913, 969, 1094 EVAL 02n4kr genre! 09wnnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 0cbn7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 0c9t0y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 01svry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 0hv4t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 05v38p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 05mrf_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 02v5_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 093dqjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 0900j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 0gvs1kt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 045j3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 01vfqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 66.000 16.000 0.770 http://example.org/film/film/genre EVAL 02n4kr genre! 026p_bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 66.000 16.000 0.770 http://example.org/film/film/genre #17408-0lbd9 PRED entity: 0lbd9 PRED relation: sports PRED expected values: 071t0 0d1t3 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 58): 03fyrh (0.82 #235, 0.79 #1759, 0.79 #1758), 018w8 (0.82 #235, 0.79 #1759, 0.79 #1758), 02bkg (0.80 #807, 0.79 #1759, 0.79 #1758), 096f8 (0.79 #1759, 0.79 #1758, 0.79 #1153), 071t0 (0.79 #1759, 0.79 #1758, 0.79 #1153), 0d1t3 (0.79 #1759, 0.79 #1758, 0.79 #1153), 064vjs (0.79 #1759, 0.79 #1758, 0.79 #1153), 07_53 (0.71 #492, 0.64 #950, 0.53 #1481), 01sgl (0.69 #1757, 0.69 #542, 0.68 #843), 0w0d (0.59 #1463, 0.57 #474, 0.50 #435) >> Best rule #235 for best value: >> intensional similarity = 17 >> extensional distance = 2 >> proper extension: 0jkvj; >> query: (?x6464, ?x3641) <- olympics(?x3730, ?x6464), olympics(?x3728, ?x6464), olympics(?x1355, ?x6464), olympics(?x1023, ?x6464), olympics(?x456, ?x6464), olympics(?x6733, ?x6464), olympics(?x5182, ?x6464), ?x456 = 05qhw, sports(?x6464, ?x3641), ?x3728 = 087vz, ?x6733 = 01sgl, sports(?x391, ?x5182), ?x1355 = 0h7x, ?x1023 = 0ctw_b, country(?x3641, ?x1241), ?x3730 = 03shp, ?x1241 = 05cgv >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #1759 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 18 *> proper extension: 09n48; 0kbvv; *> query: (?x6464, ?x471) <- olympics(?x985, ?x6464), olympics(?x456, ?x6464), olympics(?x766, ?x6464), ?x456 = 05qhw, sports(?x6464, ?x471), sports(?x6464, ?x1967), country(?x471, ?x4737), sports(?x358, ?x766), contains(?x985, ?x8174), organization(?x985, ?x127), film_release_region(?x86, ?x4737), film_release_region(?x66, ?x985) *> conf = 0.79 ranks of expected_values: 5, 6 EVAL 0lbd9 sports 0d1t3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 72.000 72.000 0.818 http://example.org/user/jg/default_domain/olympic_games/sports EVAL 0lbd9 sports 071t0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 72.000 72.000 0.818 http://example.org/user/jg/default_domain/olympic_games/sports #17407-04b7xr PRED entity: 04b7xr PRED relation: artists! PRED expected values: 016clz 0y3_8 059kh => 101 concepts (82 used for prediction) PRED predicted values (max 10 best out of 238): 016clz (0.80 #3385, 0.29 #2155, 0.28 #9841), 02k_kn (0.35 #369, 0.24 #62, 0.23 #2212), 01lyv (0.31 #1875, 0.24 #32, 0.23 #3105), 05r6t (0.31 #3459, 0.11 #9915, 0.10 #2229), 025sc50 (0.31 #46, 0.24 #3119, 0.22 #1582), 0gywn (0.27 #361, 0.22 #54, 0.22 #3127), 02vjzr (0.24 #132, 0.21 #439, 0.20 #1668), 0xhtw (0.23 #3396, 0.21 #2166, 0.19 #10773), 03_d0 (0.23 #318, 0.20 #932, 0.20 #2468), 0glt670 (0.22 #6801, 0.22 #7416, 0.20 #3112) >> Best rule #3385 for best value: >> intensional similarity = 3 >> extensional distance = 259 >> proper extension: 0qmpd; >> query: (?x6942, 016clz) <- artists(?x2995, ?x6942), artists(?x2995, ?x8215), ?x8215 = 04_jsg >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14, 24 EVAL 04b7xr artists! 059kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 101.000 82.000 0.801 http://example.org/music/genre/artists EVAL 04b7xr artists! 0y3_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 101.000 82.000 0.801 http://example.org/music/genre/artists EVAL 04b7xr artists! 016clz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 82.000 0.801 http://example.org/music/genre/artists #17406-02w64f PRED entity: 02w64f PRED relation: current_club PRED expected values: 0hvgt => 146 concepts (84 used for prediction) PRED predicted values (max 10 best out of 356): 03x6m (0.60 #671, 0.50 #373, 0.43 #1121), 0138mv (0.50 #378, 0.40 #676, 0.29 #1126), 049f05 (0.43 #1309, 0.38 #1607, 0.22 #3852), 045xx (0.38 #1563, 0.25 #515, 0.22 #3808), 0266bd5 (0.38 #1613, 0.25 #565, 0.20 #1015), 03x726 (0.29 #1179, 0.25 #431, 0.20 #729), 02k9k9 (0.29 #1167, 0.25 #419, 0.20 #717), 023fb (0.29 #1099, 0.25 #351, 0.20 #649), 050fh (0.29 #1089, 0.25 #341, 0.20 #639), 02p8q1 (0.29 #1280, 0.25 #530, 0.20 #980) >> Best rule #671 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 03z8bw; >> query: (?x13154, 03x6m) <- colors(?x13154, ?x3189), colors(?x13154, ?x1101), current_club(?x13154, ?x3158), ?x3189 = 01g5v, colors(?x11771, ?x1101), colors(?x8694, ?x1101), colors(?x3387, ?x1101), ?x3387 = 02fgdx, position(?x11771, ?x60), ?x8694 = 011xy1 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #466 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 02s2lg; 03dj48; *> query: (?x13154, 0hvgt) <- sport(?x13154, ?x471), position(?x13154, ?x203), position(?x13154, ?x63), ?x63 = 02sdk9v, ?x203 = 0dgrmp, ?x471 = 02vx4, current_club(?x13154, ?x3158), ?x3158 = 0xbm, team(?x6430, ?x13154) *> conf = 0.25 ranks of expected_values: 38 EVAL 02w64f current_club 0hvgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 146.000 84.000 0.600 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #17405-0286gm1 PRED entity: 0286gm1 PRED relation: genre PRED expected values: 060__y => 115 concepts (103 used for prediction) PRED predicted values (max 10 best out of 115): 02l7c8 (0.56 #16, 0.39 #1831, 0.39 #1589), 03k9fj (0.37 #2189, 0.37 #2310, 0.34 #1100), 04xvlr (0.36 #1574, 0.35 #1816, 0.32 #2663), 02kdv5l (0.35 #2180, 0.35 #2301, 0.32 #607), 04xvh5 (0.35 #156, 0.22 #1850, 0.21 #1608), 05p553 (0.34 #8960, 0.34 #11627, 0.33 #4), 01jfsb (0.33 #617, 0.31 #3643, 0.31 #2311), 01hmnh (0.28 #2317, 0.27 #2196, 0.21 #3164), 060__y (0.26 #380, 0.23 #1832, 0.23 #1590), 0lsxr (0.25 #735, 0.23 #614, 0.23 #977) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0k7tq; >> query: (?x6269, 02l7c8) <- genre(?x6269, ?x53), film_sets_designed(?x12378, ?x6269), ?x12378 = 0c0tzp, nominated_for(?x2524, ?x6269) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #380 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 03lrqw; 0jdgr; 04t6fk; 059rc; 04tqtl; 07tw_b; 0dlngsd; 026wlxw; 0g_zyp; 09qycb; ... *> query: (?x6269, 060__y) <- genre(?x6269, ?x53), nominated_for(?x198, ?x6269), cinematography(?x6269, ?x2466), story_by(?x6269, ?x4264) *> conf = 0.26 ranks of expected_values: 9 EVAL 0286gm1 genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 115.000 103.000 0.556 http://example.org/film/film/genre #17404-0h1sz PRED entity: 0h1sz PRED relation: nutrient! PRED expected values: 09728 01nkt 0frq6 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 8): 01nkt (0.94 #598, 0.94 #570, 0.94 #531), 0frq6 (0.89 #390, 0.89 #387, 0.89 #69), 09728 (0.89 #69, 0.88 #220, 0.88 #113), 06x4c (0.89 #69, 0.88 #220, 0.88 #113), 0dcfv (0.89 #69, 0.88 #220, 0.88 #113), 01sh2 (0.03 #306, 0.02 #579, 0.02 #553), 04k8n (0.02 #579, 0.02 #565, 0.02 #553), 05wvs (0.02 #579, 0.02 #565, 0.02 #553) >> Best rule #598 for best value: >> intensional similarity = 120 >> extensional distance = 34 >> proper extension: 0f4k5; >> query: (?x10709, 01nkt) <- nutrient(?x9732, ?x10709), nutrient(?x9005, ?x10709), nutrient(?x6191, ?x10709), nutrient(?x5373, ?x10709), nutrient(?x5009, ?x10709), nutrient(?x2701, ?x10709), nutrient(?x6191, ?x13944), nutrient(?x6191, ?x13498), nutrient(?x6191, ?x13126), nutrient(?x6191, ?x12868), nutrient(?x6191, ?x12454), nutrient(?x6191, ?x12083), nutrient(?x6191, ?x11758), nutrient(?x6191, ?x11592), nutrient(?x6191, ?x11409), nutrient(?x6191, ?x11270), nutrient(?x6191, ?x10891), nutrient(?x6191, ?x10098), nutrient(?x6191, ?x9949), nutrient(?x6191, ?x9840), nutrient(?x6191, ?x9795), nutrient(?x6191, ?x9733), nutrient(?x6191, ?x9619), nutrient(?x6191, ?x9490), nutrient(?x6191, ?x9436), nutrient(?x6191, ?x9426), nutrient(?x6191, ?x9365), nutrient(?x6191, ?x8487), nutrient(?x6191, ?x8442), nutrient(?x6191, ?x8413), nutrient(?x6191, ?x8243), nutrient(?x6191, ?x7720), nutrient(?x6191, ?x7652), nutrient(?x6191, ?x7364), nutrient(?x6191, ?x7362), nutrient(?x6191, ?x7219), nutrient(?x6191, ?x7135), nutrient(?x6191, ?x6586), nutrient(?x6191, ?x6286), nutrient(?x6191, ?x6192), nutrient(?x6191, ?x6160), nutrient(?x6191, ?x6033), nutrient(?x6191, ?x5549), nutrient(?x6191, ?x5526), nutrient(?x6191, ?x5451), nutrient(?x6191, ?x5374), nutrient(?x6191, ?x5010), nutrient(?x6191, ?x4069), nutrient(?x6191, ?x3469), nutrient(?x6191, ?x3264), nutrient(?x6191, ?x3203), nutrient(?x6191, ?x2018), nutrient(?x6191, ?x1960), nutrient(?x6191, ?x1258), ?x12083 = 01n78x, ?x1258 = 0h1wg, ?x8487 = 014yzm, ?x7362 = 02kc5rj, ?x13126 = 02kc_w5, ?x7652 = 025s0s0, ?x3469 = 0h1zw, ?x11270 = 02kc008, ?x9733 = 0h1tz, ?x13944 = 0f4kp, ?x9732 = 05z55, ?x6192 = 06jry, ?x9949 = 02kd0rh, ?x11758 = 0q01m, ?x7720 = 025s7x6, nutrient(?x5009, ?x7894), nutrient(?x5009, ?x7431), nutrient(?x5009, ?x6026), nutrient(?x5009, ?x1304), ?x6286 = 02y_3rf, ?x5549 = 025s7j4, ?x9426 = 0h1yy, ?x9436 = 025sqz8, ?x3264 = 0dcfv, ?x1960 = 07hnp, ?x9840 = 02p0tjr, nutrient(?x2701, ?x9915), nutrient(?x2701, ?x3901), ?x3901 = 0466p20, ?x9365 = 04k8n, ?x8413 = 02kc4sf, ?x7219 = 0h1vg, ?x9915 = 025tkqy, ?x7135 = 025rsfk, ?x5373 = 0971v, ?x4069 = 0hqw8p_, ?x10891 = 0g5gq, ?x7894 = 0f4hc, ?x7431 = 09gwd, ?x8243 = 014d7f, ?x8442 = 02kcv4x, ?x12454 = 025rw19, nutrient(?x10612, ?x9795), taxonomy(?x2018, ?x939), ?x9490 = 0h1sg, ?x5010 = 0h1vz, nutrient(?x1257, ?x12868), ?x1257 = 09728, ?x1304 = 08lb68, ?x9005 = 04zpv, ?x6160 = 041r51, ?x6033 = 04zjxcz, ?x939 = 04n6k, ?x9619 = 0h1tg, ?x3203 = 04kl74p, ?x5451 = 05wvs, ?x13498 = 07q0m, ?x11409 = 0h1yf, ?x6586 = 05gh50, ?x7364 = 09gvd, ?x6026 = 025sf8g, ?x5526 = 09pbb, ?x11592 = 025sf0_, ?x10098 = 0h1_c, ?x5374 = 025s0zp, ?x10612 = 0frq6 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0h1sz nutrient! 0frq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.944 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0h1sz nutrient! 01nkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.944 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0h1sz nutrient! 09728 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.944 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #17403-0gwgn1k PRED entity: 0gwgn1k PRED relation: film_crew_role PRED expected values: 0dxtw => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 27): 02r96rf (0.72 #3, 0.70 #471, 0.68 #435), 0dxtw (0.40 #1096, 0.37 #1060, 0.36 #478), 01vx2h (0.36 #479, 0.33 #443, 0.31 #1061), 01pvkk (0.27 #2110, 0.27 #1098, 0.26 #1062), 02ynfr (0.18 #16, 0.17 #1102, 0.17 #484), 02rh1dz (0.14 #9, 0.13 #2643, 0.12 #81), 0215hd (0.14 #199, 0.13 #1105, 0.13 #487), 01xy5l_ (0.13 #2643, 0.12 #482, 0.11 #158), 089g0h (0.13 #2643, 0.11 #560, 0.10 #1070), 02_n3z (0.13 #2643, 0.10 #469, 0.10 #433) >> Best rule #3 for best value: >> intensional similarity = 6 >> extensional distance = 76 >> proper extension: 087wc7n; 08hmch; 0gj8t_b; 03bx2lk; 04zyhx; 0661m4p; 047svrl; 05q4y12; 0gffmn8; 0gjc4d3; ... >> query: (?x9322, 02r96rf) <- film_release_region(?x9322, ?x1536), film_release_region(?x9322, ?x429), film_release_region(?x9322, ?x205), ?x205 = 03rjj, ?x429 = 03rt9, ?x1536 = 06c1y >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1096 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 654 *> proper extension: 035xwd; 0963mq; 0c00zd0; 014zwb; 03mh_tp; 02tktw; 047rkcm; 01jnc_; *> query: (?x9322, 0dxtw) <- film(?x1335, ?x9322), film_crew_role(?x9322, ?x137), genre(?x9322, ?x258), produced_by(?x9322, ?x7090) *> conf = 0.40 ranks of expected_values: 2 EVAL 0gwgn1k film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 81.000 0.718 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17402-06n6p PRED entity: 06n6p PRED relation: major_field_of_study! PRED expected values: 07wrz 02zd460 01jq0j => 57 concepts (31 used for prediction) PRED predicted values (max 10 best out of 647): 01w5m (0.75 #1881, 0.64 #3060, 0.60 #6584), 03ksy (0.69 #9534, 0.62 #1882, 0.60 #706), 07szy (0.67 #2395, 0.59 #2985, 0.57 #1219), 02zd460 (0.64 #3135, 0.63 #3723, 0.57 #4309), 09f2j (0.64 #3120, 0.60 #4294, 0.59 #4883), 01w3v (0.64 #2957, 0.57 #4131, 0.56 #3545), 08815 (0.62 #1765, 0.60 #589, 0.50 #2944), 052nd (0.62 #1773, 0.40 #597, 0.29 #1186), 07wjk (0.60 #654, 0.57 #1243, 0.50 #1830), 07tgn (0.60 #604, 0.50 #1780, 0.43 #1193) >> Best rule #1881 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 01jzxy; 03g3w; 05qfh; >> query: (?x2921, 01w5m) <- major_field_of_study(?x13316, ?x2921), major_field_of_study(?x5035, ?x2921), major_field_of_study(?x581, ?x2921), ?x581 = 06pwq, major_field_of_study(?x2921, ?x2981), major_field_of_study(?x11690, ?x2921), school_type(?x5035, ?x3092), ?x13316 = 01stzp, institution(?x11690, ?x892), citytown(?x5035, ?x11731) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3135 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 20 *> proper extension: 01bt59; *> query: (?x2921, 02zd460) <- major_field_of_study(?x10861, ?x2921), major_field_of_study(?x5035, ?x2921), major_field_of_study(?x581, ?x2921), ?x581 = 06pwq, taxonomy(?x2921, ?x939), major_field_of_study(?x5035, ?x7979), organization(?x346, ?x5035), ?x7979 = 036nz, institution(?x620, ?x5035), major_field_of_study(?x865, ?x2921), citytown(?x10861, ?x659) *> conf = 0.64 ranks of expected_values: 4, 45, 363 EVAL 06n6p major_field_of_study! 01jq0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 57.000 31.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 06n6p major_field_of_study! 02zd460 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 57.000 31.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 06n6p major_field_of_study! 07wrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 57.000 31.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17401-05cj_j PRED entity: 05cj_j PRED relation: genre PRED expected values: 09blyk => 59 concepts (57 used for prediction) PRED predicted values (max 10 best out of 118): 07s9rl0 (0.74 #4766, 0.72 #1861, 0.65 #3373), 02kdv5l (0.59 #1281, 0.47 #2212, 0.40 #351), 03k9fj (0.44 #3266, 0.30 #1288, 0.28 #1055), 0lsxr (0.41 #472, 0.40 #356, 0.34 #1286), 02l7c8 (0.41 #594, 0.39 #2339, 0.37 #1874), 09q17 (0.33 #291, 0.20 #407, 0.14 #523), 06ppq (0.33 #209, 0.05 #2908, 0.04 #6043), 06cvj (0.30 #700, 0.29 #584, 0.26 #816), 0hn10 (0.26 #1278, 0.20 #357, 0.14 #473), 09blyk (0.26 #1278, 0.12 #2239, 0.09 #494) >> Best rule #4766 for best value: >> intensional similarity = 6 >> extensional distance = 1364 >> proper extension: 0170z3; 02d413; 0b76d_m; 014_x2; 0g22z; 018js4; 0sxg4; 083shs; 01br2w; 0140g4; ... >> query: (?x1708, 07s9rl0) <- genre(?x1708, ?x258), genre(?x2350, ?x258), genre(?x2052, ?x258), ?x2052 = 09tqkv2, genre(?x419, ?x258), film_release_region(?x2350, ?x87) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1278 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 150 *> proper extension: 0g60z; 0180mw; *> query: (?x1708, ?x714) <- nominated_for(?x5134, ?x1708), nominated_for(?x2094, ?x1708), titles(?x600, ?x1708), award_winner(?x2094, ?x11311), titles(?x714, ?x5134) *> conf = 0.26 ranks of expected_values: 10 EVAL 05cj_j genre 09blyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 59.000 57.000 0.736 http://example.org/film/film/genre #17400-026mg3 PRED entity: 026mg3 PRED relation: award! PRED expected values: 01m15br => 35 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2420): 0p_47 (0.78 #13472, 0.78 #30309, 0.78 #37044), 01kv4mb (0.78 #13472, 0.78 #37044, 0.78 #20206), 01lmj3q (0.78 #13472, 0.78 #37044, 0.78 #20206), 0pmw9 (0.78 #13472, 0.78 #37044, 0.78 #20206), 018ndc (0.78 #20206, 0.77 #23575, 0.77 #30308), 0m_v0 (0.33 #942, 0.16 #53882, 0.12 #43778), 01x15dc (0.33 #808, 0.16 #53882, 0.12 #43778), 0gcs9 (0.33 #817, 0.12 #10921, 0.11 #34493), 01vrncs (0.33 #260, 0.12 #10364, 0.09 #20467), 01vrx3g (0.33 #62, 0.12 #43778, 0.05 #70719) >> Best rule #13472 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 0bm70b; 0cjcbg; >> query: (?x341, ?x4566) <- award_winner(?x341, ?x4566), ceremony(?x341, ?x139), role(?x4566, ?x316) >> conf = 0.78 => this is the best rule for 4 predicted values *> Best rule #4498 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 019bnn; 026mml; *> query: (?x341, 01m15br) <- award_winner(?x341, ?x3917), ceremony(?x341, ?x139), ?x3917 = 0p_47 *> conf = 0.25 ranks of expected_values: 19 EVAL 026mg3 award! 01m15br CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 35.000 22.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17399-079yb PRED entity: 079yb PRED relation: country PRED expected values: 03rjj => 135 concepts (78 used for prediction) PRED predicted values (max 10 best out of 62): 09c7w0 (0.55 #700, 0.44 #786, 0.43 #1315), 03rjj (0.53 #4476, 0.52 #1310, 0.50 #523), 07kg3 (0.49 #3861, 0.48 #4126, 0.45 #522), 06mzp (0.33 #196, 0.06 #5811, 0.03 #2388), 059j2 (0.14 #1518, 0.13 #3099, 0.12 #2131), 07ssc (0.11 #1854, 0.11 #1066, 0.10 #1153), 03rk0 (0.10 #1884, 0.05 #4174, 0.04 #4436), 0345h (0.10 #642, 0.07 #6884, 0.06 #556), 02jx1 (0.08 #1170, 0.06 #1871, 0.05 #1083), 0f8l9c (0.07 #6884, 0.06 #546, 0.06 #5811) >> Best rule #700 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0f2tj; >> query: (?x11650, 09c7w0) <- place_of_death(?x4914, ?x11650), teams(?x11650, ?x8361), colors(?x8361, ?x663), type_of_union(?x4914, ?x566), award(?x4914, ?x9285) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #4476 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 143 *> proper extension: 0c8tk; 09c6w; 04vmp; 0hkpn; 015y2q; 09c17; 0fk98; *> query: (?x11650, ?x205) <- place_of_death(?x4914, ?x11650), contains(?x6408, ?x11650), contains(?x205, ?x11650), adjoins(?x6408, ?x9274), country(?x150, ?x205), film_release_region(?x66, ?x205) *> conf = 0.53 ranks of expected_values: 2 EVAL 079yb country 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 78.000 0.545 http://example.org/base/biblioness/bibs_location/country #17398-01271h PRED entity: 01271h PRED relation: instrumentalists! PRED expected values: 018vs => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 105): 0342h (0.77 #323, 0.68 #640, 0.64 #165), 018vs (0.50 #11, 0.44 #92, 0.44 #646), 03bx0bm (0.41 #1033, 0.40 #952, 0.04 #2306), 03qjg (0.33 #285, 0.20 #681, 0.19 #364), 0gkd1 (0.30 #1113, 0.28 #1430, 0.27 #2305), 02sgy (0.30 #1113, 0.28 #1430, 0.27 #2305), 01v1d8 (0.30 #1113, 0.28 #1430, 0.27 #2305), 0bxl5 (0.30 #1113, 0.28 #1430, 0.27 #2305), 01kcd (0.30 #1113, 0.28 #1430, 0.27 #2305), 026t6 (0.22 #84, 0.21 #163, 0.21 #638) >> Best rule #323 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 01vv126; 02wb6yq; 0gs6vr; 0kj34; >> query: (?x2945, 0342h) <- artists(?x302, ?x2945), instrumentalists(?x228, ?x2945), participant(?x6208, ?x2945) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0gr69; *> query: (?x2945, 018vs) <- role(?x2945, ?x7033), artists(?x302, ?x2945), ?x7033 = 0gkd1 *> conf = 0.50 ranks of expected_values: 2 EVAL 01271h instrumentalists! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.770 http://example.org/music/instrument/instrumentalists #17397-0h_cssd PRED entity: 0h_cssd PRED relation: award_winner PRED expected values: 0h0wc 095zvfg => 35 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1972): 02tr7d (0.50 #3319, 0.40 #4865, 0.21 #9500), 018ygt (0.50 #4053, 0.40 #5599, 0.15 #8687), 025mb_ (0.50 #4382, 0.40 #5928, 0.15 #9016), 031296 (0.50 #3648, 0.40 #5194, 0.15 #8282), 01wbg84 (0.50 #3123, 0.40 #4669, 0.15 #7757), 03yj_0n (0.50 #3628, 0.40 #5174, 0.15 #8262), 02sb1w (0.50 #4056, 0.40 #5602, 0.15 #8690), 08w7vj (0.50 #3203, 0.40 #4749, 0.15 #7837), 096lf_ (0.50 #4467, 0.40 #6013, 0.15 #9101), 0dyztm (0.50 #3996, 0.40 #5542, 0.15 #8630) >> Best rule #3319 for best value: >> intensional similarity = 21 >> extensional distance = 2 >> proper extension: 0hr3c8y; 0g55tzk; >> query: (?x2032, 02tr7d) <- honored_for(?x2032, ?x1803), ceremony(?x2523, ?x2032), ceremony(?x2375, ?x2032), film_release_region(?x1803, ?x1917), film_release_region(?x1803, ?x1499), nominated_for(?x2375, ?x6448), nominated_for(?x2375, ?x6149), nominated_for(?x2375, ?x4939), nominated_for(?x2375, ?x1163), nominated_for(?x2375, ?x1064), award(?x2938, ?x2375), film(?x72, ?x6448), ?x2938 = 01nwwl, ?x1917 = 01p1v, ?x1064 = 092vkg, film_crew_role(?x6448, ?x1284), ?x1499 = 01znc_, award_winner(?x2523, ?x698), film(?x4483, ?x6149), award_winner(?x1163, ?x1616), ?x4939 = 05hjnw >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #26244 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 54 *> proper extension: 0bzknt; *> query: (?x2032, ?x2551) <- honored_for(?x2032, ?x1803), ceremony(?x68, ?x2032), award_winner(?x1803, ?x2551), award(?x6886, ?x68), award(?x306, ?x68), nominated_for(?x68, ?x9456), nominated_for(?x68, ?x3500), nominated_for(?x68, ?x696), award(?x6748, ?x68), award(?x2590, ?x68), ?x9456 = 0yx_w, ?x6886 = 0gwjw0c, ?x6748 = 043hg, ?x3500 = 0221zw, nominated_for(?x618, ?x1803), ?x696 = 0209xj, ?x2590 = 0bsb4j, film(?x305, ?x306) *> conf = 0.41 ranks of expected_values: 13, 35 EVAL 0h_cssd award_winner 095zvfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 35.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0h_cssd award_winner 0h0wc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 35.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17396-018jcq PRED entity: 018jcq PRED relation: location! PRED expected values: 061y4q => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1199): 03rl84 (0.12 #15470, 0.03 #17988, 0.03 #25542), 03nb5v (0.11 #31540, 0.11 #26504, 0.11 #23986), 03nyts (0.10 #2323, 0.08 #4841, 0.07 #7359), 01r_t_ (0.10 #1250, 0.08 #3768, 0.07 #6286), 02rgz4 (0.10 #79, 0.08 #2597, 0.07 #5115), 06dn58 (0.10 #11627, 0.08 #14145, 0.07 #19181), 030hcs (0.10 #10391, 0.08 #12909, 0.07 #17945), 01yzhn (0.10 #12205, 0.08 #29831, 0.07 #19759), 01938t (0.10 #11433, 0.05 #29059, 0.04 #13951), 04t2l2 (0.10 #10097, 0.05 #27723, 0.04 #12615) >> Best rule #15470 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 02j71; >> query: (?x8889, 03rl84) <- administrative_parent(?x10980, ?x8889), contains(?x252, ?x10980), jurisdiction_of_office(?x1195, ?x10980) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018jcq location! 061y4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 103.000 0.125 http://example.org/people/person/places_lived./people/place_lived/location #17395-040db PRED entity: 040db PRED relation: gender PRED expected values: 05zppz => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #74, 0.91 #58, 0.89 #126), 02zsn (0.49 #332, 0.42 #67, 0.40 #63) >> Best rule #74 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 0177s6; 0dzkq; 04k15; 01dvtx; 0hgqq; 0kc6; 0k_mt; 0pqzh; 047g6; 0g72r; >> query: (?x2161, 05zppz) <- influenced_by(?x476, ?x2161), influenced_by(?x2161, ?x118), place_of_death(?x2161, ?x10610) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 040db gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.922 http://example.org/people/person/gender #17394-01_xtx PRED entity: 01_xtx PRED relation: award_nominee! PRED expected values: 06cgy => 124 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1113): 03yj_0n (0.81 #48930, 0.81 #37281, 0.81 #172390), 0bksh (0.23 #18638, 0.16 #37280, 0.14 #44269), 015t56 (0.22 #609, 0.11 #2939, 0.05 #19247), 0jfx1 (0.22 #522, 0.11 #2852, 0.04 #7510), 03lq43 (0.16 #13978, 0.10 #27960, 0.02 #7922), 0lx2l (0.13 #48929, 0.12 #34950, 0.11 #51260), 024n3z (0.11 #2929, 0.11 #599, 0.04 #7587), 086sj (0.11 #3284, 0.11 #954, 0.03 #5613), 0f276 (0.11 #2060, 0.08 #32619, 0.06 #4390), 0154qm (0.11 #740, 0.07 #10057, 0.07 #7728) >> Best rule #48930 for best value: >> intensional similarity = 3 >> extensional distance = 356 >> proper extension: 01sl1q; 0184jc; 04bdxl; 01vvydl; 0337vz; 01xdf5; 06dv3; 0m2wm; 09fb5; 032xhg; ... >> query: (?x3865, ?x1445) <- film(?x3865, ?x755), participant(?x2534, ?x3865), award_nominee(?x3865, ?x1445) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #32619 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 220 *> proper extension: 05m63c; 03_vx9; 0f2df; 01mmslz; 012_53; 018fmr; 0gmtm; 0jlv5; 012dtf; 012gbb; ... *> query: (?x3865, ?x92) <- film(?x3865, ?x755), participant(?x3865, ?x4782), film(?x92, ?x755) *> conf = 0.08 ranks of expected_values: 75 EVAL 01_xtx award_nominee! 06cgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 124.000 79.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17393-02wwmhc PRED entity: 02wwmhc PRED relation: music PRED expected values: 0fpjyd => 99 concepts (82 used for prediction) PRED predicted values (max 10 best out of 98): 02jxmr (0.22 #74, 0.05 #3863, 0.05 #4073), 0146pg (0.11 #10, 0.09 #2325, 0.09 #2535), 02bh9 (0.11 #51, 0.08 #261, 0.07 #1944), 0kvrb (0.11 #37, 0.03 #1720, 0.02 #2773), 016szr (0.10 #502, 0.05 #1133, 0.04 #1554), 0csdzz (0.10 #608, 0.04 #818, 0.03 #6080), 01tc9r (0.08 #275, 0.05 #4694, 0.05 #4064), 0150t6 (0.08 #256, 0.05 #467, 0.04 #2150), 03c_8t (0.08 #420, 0.05 #631, 0.03 #1262), 01x6v6 (0.08 #333, 0.05 #1385, 0.04 #6016) >> Best rule #74 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0bscw; 0bbw2z6; 01s3vk; 03bxp5; 0drnwh; 04jplwp; 03z9585; >> query: (?x10778, 02jxmr) <- genre(?x10778, ?x1403), costume_design_by(?x10778, ?x6327), ?x1403 = 02l7c8, ?x6327 = 03mfqm >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #2018 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 96 *> proper extension: 01gglm; *> query: (?x10778, 0fpjyd) <- story_by(?x10778, ?x117), film_crew_role(?x10778, ?x2095), film(?x2444, ?x10778), ?x2095 = 0dxtw *> conf = 0.01 ranks of expected_values: 91 EVAL 02wwmhc music 0fpjyd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 99.000 82.000 0.222 http://example.org/film/film/music #17392-0jz71 PRED entity: 0jz71 PRED relation: currency PRED expected values: 09nqf => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.80 #155, 0.79 #57, 0.78 #113), 01nv4h (0.05 #16, 0.04 #23, 0.03 #121), 02l6h (0.03 #137, 0.03 #130, 0.03 #144), 02gsvk (0.03 #104, 0.02 #230, 0.01 #34) >> Best rule #155 for best value: >> intensional similarity = 4 >> extensional distance = 262 >> proper extension: 03m5y9p; 03hp2y1; >> query: (?x10829, 09nqf) <- film_crew_role(?x10829, ?x137), country(?x10829, ?x94), language(?x10829, ?x254), crewmember(?x10829, ?x12096) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jz71 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.799 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #17391-0sxkh PRED entity: 0sxkh PRED relation: honored_for! PRED expected values: 09306z => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 115): 05c1t6z (0.13 #987, 0.02 #3549, 0.02 #5623), 02q690_ (0.12 #1030, 0.02 #6032, 0.02 #6520), 0gvstc3 (0.11 #1003, 0.02 #8079, 0.02 #9300), 03nnm4t (0.09 #1039, 0.02 #6041, 0.02 #3601), 09pj68 (0.09 #90, 0.04 #1066, 0.02 #334), 0bzmt8 (0.08 #206, 0.03 #572, 0.02 #694), 0gx_st (0.07 #1006, 0.01 #3568, 0.01 #5642), 0hr6lkl (0.07 #622, 0.06 #500, 0.05 #1598), 0lp_cd3 (0.06 #993, 0.01 #3555, 0.01 #6483), 0gmdkyy (0.05 #512, 0.05 #634, 0.03 #1610) >> Best rule #987 for best value: >> intensional similarity = 3 >> extensional distance = 161 >> proper extension: 01qn7n; 07hpv3; 09kn9; 0n2bh; 0gfzgl; 01cjhz; 03y3bp7; 01f3p_; 05sy2k_; 08cx5g; ... >> query: (?x4315, 05c1t6z) <- titles(?x714, ?x4315), titles(?x714, ?x2293), ?x2293 = 01q_y0 >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #8175 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1239 *> proper extension: 01p9hgt; 02xhpl; 01kv4mb; 0ggjt; 0bhvtc; 03cfjg; 01d8yn; 0p_47; 0pmw9; 016zfm; ... *> query: (?x4315, ?x78) <- nominated_for(?x601, ?x4315), award(?x164, ?x601), ceremony(?x601, ?x78) *> conf = 0.01 ranks of expected_values: 105 EVAL 0sxkh honored_for! 09306z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 89.000 89.000 0.135 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #17390-0bqdvt PRED entity: 0bqdvt PRED relation: award_nominee PRED expected values: 02qgqt 01kb2j => 79 concepts (28 used for prediction) PRED predicted values (max 10 best out of 838): 02qgqt (0.82 #4674, 0.81 #63089, 0.81 #63088), 01yb09 (0.82 #4674, 0.81 #63089, 0.81 #63088), 0gy6z9 (0.82 #4674, 0.81 #63089, 0.81 #63088), 01kb2j (0.69 #3543, 0.57 #1206, 0.40 #8217), 0bqdvt (0.54 #3399, 0.43 #1062, 0.17 #8073), 07r1h (0.54 #3771, 0.29 #1434, 0.16 #51403), 01tcf7 (0.46 #2592, 0.29 #255, 0.16 #51403), 0h0wc (0.33 #7562, 0.23 #2888, 0.16 #51403), 0hvb2 (0.29 #398, 0.17 #7409, 0.16 #51403), 019pm_ (0.29 #608, 0.16 #51403, 0.15 #2945) >> Best rule #4674 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 02qgqt; 02p65p; 0187y5; 01tcf7; 04t7ts; 02wgln; 02qgyv; 0408np; 01kb2j; 023kzp; ... >> query: (?x4509, ?x157) <- type_of_union(?x4509, ?x566), profession(?x4509, ?x1032), award_nominee(?x1251, ?x4509), award_nominee(?x157, ?x4509), ?x1251 = 01yb09 >> conf = 0.82 => this is the best rule for 3 predicted values ranks of expected_values: 1, 4 EVAL 0bqdvt award_nominee 01kb2j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 28.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 0bqdvt award_nominee 02qgqt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 28.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17389-0gkz3nz PRED entity: 0gkz3nz PRED relation: film_release_region PRED expected values: 03_3d 0ctw_b 03h64 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 139): 03h64 (0.84 #1079, 0.84 #495, 0.83 #787), 03_3d (0.83 #296, 0.80 #442, 0.80 #734), 05v8c (0.69 #742, 0.68 #450, 0.66 #1034), 0ctw_b (0.61 #458, 0.59 #750, 0.58 #1042), 06qd3 (0.58 #324, 0.51 #762, 0.49 #1054), 0h7x (0.58 #321, 0.45 #905, 0.43 #613), 047yc (0.57 #753, 0.56 #461, 0.55 #1045), 015qh (0.57 #474, 0.56 #766, 0.56 #1058), 016wzw (0.52 #788, 0.51 #1080, 0.50 #496), 06f32 (0.52 #786, 0.51 #494, 0.50 #1078) >> Best rule #1079 for best value: >> intensional similarity = 4 >> extensional distance = 190 >> proper extension: 0h1cdwq; 0bc1yhb; 02qyv3h; 0gfh84d; 02vz6dn; 04z_3pm; 0bs8hvm; 0cp0t91; 0gwlfnb; >> query: (?x4690, 03h64) <- film_release_region(?x4690, ?x1603), film_release_region(?x4690, ?x456), ?x1603 = 06bnz, ?x456 = 05qhw >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 0gkz3nz film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.839 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz3nz film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.839 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz3nz film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.839 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17388-0604m PRED entity: 0604m PRED relation: nationality! PRED expected values: 01dhpj => 99 concepts (61 used for prediction) PRED predicted values (max 10 best out of 4054): 08849 (0.71 #24409, 0.59 #40682, 0.01 #240033), 059xvg (0.33 #21392, 0.12 #37665, 0.09 #66143), 070px (0.25 #3907, 0.17 #24247, 0.05 #56793), 0p__8 (0.25 #22189, 0.12 #38462, 0.07 #62871), 053ksp (0.25 #3266, 0.08 #23606, 0.04 #39879), 03t0k1 (0.25 #741, 0.08 #21081, 0.04 #37354), 01l9p (0.25 #450, 0.08 #20790, 0.04 #37063), 018y2s (0.25 #284, 0.08 #20624, 0.04 #36897), 01x53m (0.25 #2902, 0.08 #23242, 0.04 #39515), 0fhxv (0.25 #1415, 0.08 #21755, 0.02 #54301) >> Best rule #24409 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 05v8c; >> query: (?x10569, ?x11617) <- contains(?x6956, ?x10569), country(?x14140, ?x10569), jurisdiction_of_office(?x11617, ?x10569), country(?x4045, ?x10569) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #14747 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 03shp; *> query: (?x10569, 01dhpj) <- contains(?x9122, ?x10569), olympics(?x10569, ?x2966), countries_spoken_in(?x254, ?x10569), official_language(?x10569, ?x5359), ?x9122 = 04wsz *> conf = 0.11 ranks of expected_values: 286 EVAL 0604m nationality! 01dhpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 99.000 61.000 0.708 http://example.org/people/person/nationality #17387-027lfrs PRED entity: 027lfrs PRED relation: student! PRED expected values: 088gzp => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 84): 08815 (0.13 #1056, 0.08 #2, 0.03 #4747), 015nl4 (0.13 #1121, 0.03 #3230, 0.02 #35936), 017j69 (0.08 #145, 0.07 #1199, 0.02 #2254), 07tg4 (0.08 #86, 0.07 #1140, 0.02 #11160), 0fr9jp (0.08 #345, 0.07 #1399, 0.02 #16690), 086xm (0.08 #92, 0.07 #1146, 0.01 #3255), 07tlg (0.08 #478, 0.07 #1532), 04rkkv (0.08 #307, 0.07 #1361), 01_qgp (0.08 #276, 0.07 #1330), 0bwfn (0.08 #8184, 0.07 #16092, 0.06 #17148) >> Best rule #1056 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 08jfkw; >> query: (?x14055, 08815) <- type_of_union(?x14055, ?x566), profession(?x14055, ?x11804), ?x566 = 04ztj, ?x11804 = 0q04f, place_of_birth(?x14055, ?x11134) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027lfrs student! 088gzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 108.000 0.133 http://example.org/education/educational_institution/students_graduates./education/education/student #17386-01n7q PRED entity: 01n7q PRED relation: adjoins PRED expected values: 0vmt => 158 concepts (149 used for prediction) PRED predicted values (max 10 best out of 568): 0b90_r (0.82 #110924, 0.81 #67788, 0.81 #52374), 0d060g (0.25 #2319, 0.25 #1549, 0.25 #779), 0j3b (0.25 #2367, 0.25 #1597, 0.25 #827), 07z5n (0.25 #8590, 0.25 #7820, 0.13 #20134), 0dc95 (0.25 #896, 0.17 #5519, 0.02 #45569), 0l2q3 (0.25 #3573, 0.01 #77032, 0.01 #44671), 0kv36 (0.25 #3738), 05tbn (0.23 #10184, 0.20 #11722, 0.17 #14031), 0498y (0.23 #10202, 0.13 #14049, 0.12 #15587), 03s5t (0.21 #112464, 0.14 #16298, 0.10 #31701) >> Best rule #110924 for best value: >> intensional similarity = 2 >> extensional distance = 733 >> proper extension: 0f4y_; 0mlyw; 0ldff; 0mm0p; 0n5_g; 059qw; 02m4d; 0nm8n; 0drr3; 03khn; ... >> query: (?x1227, ?x151) <- adjoins(?x1227, ?x726), adjoins(?x151, ?x1227) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #112464 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 761 *> proper extension: 01rxw2; *> query: (?x1227, ?x938) <- adjoins(?x1227, ?x1138), adjoins(?x1138, ?x938) *> conf = 0.21 ranks of expected_values: 12 EVAL 01n7q adjoins 0vmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 158.000 149.000 0.817 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #17385-0jfx1 PRED entity: 0jfx1 PRED relation: profession PRED expected values: 02jknp => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 75): 016z4k (0.65 #149, 0.53 #4, 0.48 #584), 0dz3r (0.58 #582, 0.51 #727, 0.47 #2), 0nbcg (0.56 #1914, 0.56 #609, 0.50 #174), 02jknp (0.45 #7548, 0.29 #1457, 0.24 #7403), 0cbd2 (0.45 #5951, 0.43 #6531, 0.42 #6822), 018gz8 (0.43 #1465, 0.30 #6671, 0.20 #2625), 03gjzk (0.41 #1463, 0.33 #2623, 0.31 #7554), 0n1h (0.33 #11, 0.30 #156, 0.28 #736), 01c72t (0.33 #3501, 0.31 #4371, 0.28 #6982), 0kyk (0.30 #5972, 0.29 #6552, 0.28 #6843) >> Best rule #149 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 058s57; 01w7nww; >> query: (?x2444, 016z4k) <- award_nominee(?x2444, ?x398), celebrity(?x5665, ?x2444), instrumentalists(?x227, ?x2444) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #7548 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 974 *> proper extension: 079vf; 01l1b90; 0qf43; 02g8h; 05g8ky; 02qjj7; 04rs03; 02nb2s; 025p38; 09byk; ... *> query: (?x2444, 02jknp) <- profession(?x2444, ?x319), ?x319 = 01d_h8 *> conf = 0.45 ranks of expected_values: 4 EVAL 0jfx1 profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 108.000 0.650 http://example.org/people/person/profession #17384-04mhbh PRED entity: 04mhbh PRED relation: category PRED expected values: 08mbj5d => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.72 #35, 0.71 #34, 0.60 #3) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 800 >> proper extension: 089tm; 01t_xp_; 01pfr3; 04lgymt; 04rcr; 0150jk; 02r3zy; 07c0j; 01v0sx2; 067mj; ... >> query: (?x9288, 08mbj5d) <- award(?x9288, ?x594), ceremony(?x594, ?x486), ?x486 = 02rjjll, award_winner(?x594, ?x593) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mhbh category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.721 http://example.org/common/topic/webpage./common/webpage/category #17383-0127s7 PRED entity: 0127s7 PRED relation: profession PRED expected values: 016z4k => 136 concepts (135 used for prediction) PRED predicted values (max 10 best out of 86): 09jwl (0.62 #1794, 0.61 #166, 0.59 #1646), 01d_h8 (0.55 #1485, 0.47 #2077, 0.46 #1189), 0nbcg (0.54 #327, 0.54 #919, 0.50 #771), 016z4k (0.50 #1779, 0.50 #1631, 0.47 #743), 0dxtg (0.43 #1493, 0.34 #1049, 0.30 #4603), 01c72t (0.36 #6687, 0.28 #2391, 0.27 #911), 0n1h (0.34 #1787, 0.30 #1639, 0.30 #751), 03gjzk (0.34 #4604, 0.30 #1050, 0.30 #4900), 0np9r (0.29 #4017, 0.21 #9797, 0.20 #9945), 0d1pc (0.28 #2566, 0.28 #1234, 0.27 #2665) >> Best rule #1794 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 04bpm6; 02_fj; 01vw20h; 06p03s; >> query: (?x5906, 09jwl) <- artists(?x474, ?x5906), award_winner(?x342, ?x5906), participant(?x970, ?x5906) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1779 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 04bpm6; 02_fj; 01vw20h; 06p03s; *> query: (?x5906, 016z4k) <- artists(?x474, ?x5906), award_winner(?x342, ?x5906), participant(?x970, ?x5906) *> conf = 0.50 ranks of expected_values: 4 EVAL 0127s7 profession 016z4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 136.000 135.000 0.621 http://example.org/people/person/profession #17382-050023 PRED entity: 050023 PRED relation: award_nominee PRED expected values: 05qsxy => 90 concepts (26 used for prediction) PRED predicted values (max 10 best out of 707): 03clrng (0.82 #2326, 0.81 #11629, 0.81 #39553), 02760sl (0.82 #2326, 0.81 #11629, 0.81 #39553), 026b7bz (0.82 #2326, 0.81 #11629, 0.81 #39553), 025vwmy (0.82 #2326, 0.81 #11629, 0.81 #39553), 026n998 (0.82 #2326, 0.81 #11629, 0.81 #39553), 05qsxy (0.82 #2326, 0.81 #11629, 0.81 #39553), 070m12 (0.77 #48859, 0.77 #37226, 0.76 #34898), 063lqs (0.77 #48859, 0.77 #37226, 0.76 #34898), 03ckxdg (0.77 #48859, 0.77 #37226, 0.76 #20936), 026n9h3 (0.76 #34898, 0.76 #60492, 0.76 #23263) >> Best rule #2326 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 02_2v2; >> query: (?x438, ?x439) <- award_nominee(?x2810, ?x438), award_nominee(?x439, ?x438), ?x2810 = 026n998, award_nominee(?x438, ?x2828) >> conf = 0.82 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 050023 award_nominee 05qsxy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 26.000 0.825 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17381-0gj8t_b PRED entity: 0gj8t_b PRED relation: film! PRED expected values: 018ygt => 86 concepts (62 used for prediction) PRED predicted values (max 10 best out of 857): 070yzk (0.22 #8306, 0.20 #56069, 0.20 #70606), 015pkc (0.12 #35303, 0.12 #41535, 0.11 #91376), 016ypb (0.09 #498, 0.04 #2574, 0.04 #4650), 0bdxs5 (0.09 #1501, 0.03 #3577, 0.03 #5653), 01wmxfs (0.08 #16611, 0.04 #107986, 0.04 #22842), 0lbj1 (0.08 #16611, 0.04 #22842, 0.03 #56070), 01wbgdv (0.08 #16611, 0.04 #22842, 0.03 #56070), 03j149k (0.08 #16611, 0.04 #22842, 0.03 #56070), 01s1zk (0.08 #16611, 0.04 #22842, 0.03 #120444), 0dw4g (0.08 #16611, 0.04 #22842, 0.03 #120444) >> Best rule #8306 for best value: >> intensional similarity = 5 >> extensional distance = 111 >> proper extension: 05fm6m; 0888c3; >> query: (?x1202, ?x8544) <- written_by(?x1202, ?x8544), category(?x1202, ?x134), currency(?x1202, ?x170), genre(?x1202, ?x53), film(?x815, ?x1202) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #15648 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 215 *> proper extension: 043h78; *> query: (?x1202, 018ygt) <- film_release_distribution_medium(?x1202, ?x81), ?x81 = 029j_, film(?x4474, ?x1202), influenced_by(?x5225, ?x4474), award_nominee(?x248, ?x4474) *> conf = 0.02 ranks of expected_values: 376 EVAL 0gj8t_b film! 018ygt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 86.000 62.000 0.218 http://example.org/film/actor/film./film/performance/film #17380-026gyn_ PRED entity: 026gyn_ PRED relation: genre PRED expected values: 060__y => 73 concepts (72 used for prediction) PRED predicted values (max 10 best out of 81): 07ssc (0.60 #5818, 0.59 #4772, 0.54 #2791), 02kdv5l (0.54 #581, 0.30 #1862, 0.30 #930), 01jfsb (0.46 #591, 0.38 #6413, 0.30 #940), 082gq (0.45 #376, 0.20 #7099, 0.13 #3051), 02l7c8 (0.44 #246, 0.42 #1643, 0.39 #5949), 03k9fj (0.44 #590, 0.27 #2801, 0.24 #1871), 05p553 (0.43 #6521, 0.40 #467, 0.31 #6869), 01hmnh (0.35 #596, 0.21 #6185, 0.18 #2807), 0lsxr (0.28 #937, 0.26 #124, 0.26 #472), 06n90 (0.25 #592, 0.20 #7099, 0.16 #941) >> Best rule #5818 for best value: >> intensional similarity = 4 >> extensional distance = 1049 >> proper extension: 01gglm; >> query: (?x1903, ?x512) <- film(?x294, ?x1903), titles(?x4757, ?x1903), titles(?x512, ?x1903), genre(?x573, ?x4757) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #247 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 02wk7b; *> query: (?x1903, 060__y) <- genre(?x1903, ?x53), ?x53 = 07s9rl0, award(?x1903, ?x749), ?x749 = 094qd5 *> conf = 0.24 ranks of expected_values: 11 EVAL 026gyn_ genre 060__y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 73.000 72.000 0.599 http://example.org/film/film/genre #17379-027y151 PRED entity: 027y151 PRED relation: crewmember! PRED expected values: 07jxpf 04hk0w => 115 concepts (112 used for prediction) PRED predicted values (max 10 best out of 306): 077q8x (0.37 #914, 0.36 #1524, 0.31 #1219), 07jnt (0.37 #914, 0.36 #1524, 0.04 #3049), 02vnmc9 (0.25 #252, 0.03 #556, 0.02 #861), 05dmmc (0.25 #147, 0.03 #451, 0.02 #756), 09fqgj (0.25 #294, 0.03 #598, 0.02 #903), 0dtfn (0.15 #657, 0.14 #962, 0.14 #1267), 07nxnw (0.10 #535, 0.10 #840, 0.10 #1145), 031t2d (0.10 #367, 0.10 #672, 0.10 #977), 01kff7 (0.10 #351, 0.10 #656, 0.10 #961), 024mpp (0.10 #739, 0.10 #1044, 0.09 #1349) >> Best rule #914 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 06cv1; 019fnv; >> query: (?x9769, ?x6169) <- crewmember(?x3919, ?x9769), nominated_for(?x9769, ?x6169), currency(?x3919, ?x170) >> conf = 0.37 => this is the best rule for 2 predicted values *> Best rule #608 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 0284n42; 076lxv; 027rwmr; 03h26tm; 021yc7p; 09rp4r_; 09pjnd; 0c94fn; 04ktcgn; 05x2t7; ... *> query: (?x9769, 04hk0w) <- award_winner(?x641, ?x9769), crewmember(?x385, ?x9769), award(?x9769, ?x500) *> conf = 0.03 ranks of expected_values: 88 EVAL 027y151 crewmember! 04hk0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 115.000 112.000 0.371 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember EVAL 027y151 crewmember! 07jxpf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 112.000 0.371 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #17378-0y62n PRED entity: 0y62n PRED relation: origin! PRED expected values: 03j149k => 114 concepts (61 used for prediction) PRED predicted values (max 10 best out of 267): 0127s7 (0.25 #10834, 0.08 #14444, 0.07 #8766), 01wf86y (0.25 #330, 0.05 #2390, 0.04 #1360), 01vvyc_ (0.25 #246, 0.05 #2306, 0.04 #1276), 01mvjl0 (0.25 #259, 0.04 #1289, 0.03 #1804), 04n2vgk (0.08 #927, 0.05 #2472, 0.04 #1442), 05crg7 (0.08 #565, 0.05 #2110, 0.04 #2627), 01z7s_ (0.07 #10833, 0.06 #30447, 0.06 #17541), 02sh8y (0.07 #10833, 0.06 #30447, 0.06 #30448), 03lq43 (0.06 #19608, 0.05 #25287, 0.05 #20125), 01pw2f1 (0.05 #20125, 0.05 #24770, 0.04 #25804) >> Best rule #10834 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 0cb4j; 0f2wj; 0f94t; 0wp9b; 030qb3t; 013yq; 0ftvz; 0r1jr; 0f__1; 0y2dl; ... >> query: (?x9233, ?x5906) <- contains(?x94, ?x9233), ?x94 = 09c7w0, place_of_birth(?x5906, ?x9233), artists(?x474, ?x5906) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #867 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0l2l_; *> query: (?x9233, 03j149k) <- contains(?x94, ?x9233), ?x94 = 09c7w0, adjoins(?x9233, ?x2850), featured_film_locations(?x7016, ?x9233) *> conf = 0.04 ranks of expected_values: 60 EVAL 0y62n origin! 03j149k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 114.000 61.000 0.252 http://example.org/music/artist/origin #17377-02s_qz PRED entity: 02s_qz PRED relation: award_winner! PRED expected values: 0hhtgcw => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 68): 09qvms (0.45 #436, 0.38 #295, 0.18 #6629), 09qftb (0.20 #113, 0.17 #254, 0.06 #395), 05c1t6z (0.20 #15, 0.17 #156, 0.04 #1143), 092_25 (0.20 #72, 0.17 #213, 0.03 #1341), 05pd94v (0.20 #2, 0.17 #143, 0.03 #566), 0bx6zs (0.20 #127, 0.17 #268, 0.02 #1396), 09q_6t (0.20 #8, 0.17 #149, 0.02 #1277), 09pnw5 (0.20 #103, 0.17 #244, 0.02 #2500), 03nnm4t (0.18 #6629, 0.10 #4372, 0.06 #356), 07y9ts (0.18 #6629, 0.10 #4372, 0.06 #350) >> Best rule #436 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 058ncz; 03zqc1; 035gjq; 06b0d2; 03lt8g; 05lb87; 030znt; 0443y3; 0pyg6; 038g2x; ... >> query: (?x8256, 09qvms) <- nominated_for(?x8256, ?x2078), award_nominee(?x444, ?x8256), ?x2078 = 03ln8b >> conf = 0.45 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02s_qz award_winner! 0hhtgcw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 99.000 0.448 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17376-050z2 PRED entity: 050z2 PRED relation: gender PRED expected values: 05zppz => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #53, 0.88 #39, 0.88 #23), 02zsn (0.46 #283, 0.36 #34, 0.36 #104) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 0c_drn; >> query: (?x4052, 05zppz) <- award(?x4052, ?x1443), music(?x1744, ?x4052), nationality(?x4052, ?x1310), type_of_union(?x4052, ?x566) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050z2 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.909 http://example.org/people/person/gender #17375-03x2qp PRED entity: 03x2qp PRED relation: artists PRED expected values: 01693z => 34 concepts (11 used for prediction) PRED predicted values (max 10 best out of 1047): 011_vz (0.61 #3021, 0.35 #4106, 0.29 #5192), 01vw20_ (0.56 #2419, 0.56 #247, 0.43 #1332), 07bzp (0.56 #568, 0.43 #1653, 0.28 #2740), 0134pk (0.56 #905, 0.36 #1990, 0.28 #3077), 020_4z (0.50 #2028, 0.44 #943, 0.33 #3115), 07mvp (0.50 #1675, 0.44 #590, 0.23 #3847), 01p95y0 (0.50 #2015, 0.22 #930, 0.18 #5273), 01vsy3q (0.44 #441, 0.43 #1526, 0.28 #2613), 01tw31 (0.44 #977, 0.43 #2062, 0.26 #4234), 01pny5 (0.44 #1062, 0.43 #2147, 0.23 #4319) >> Best rule #3021 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 016clz; 011j5x; 01_bkd; 06cp5; 01738f; 05jt_; 09nwwf; 03339m; 03w94xt; >> query: (?x14347, 011_vz) <- artists(?x14347, ?x379), artist(?x13837, ?x379), ?x13837 = 01w56k, award(?x379, ?x2634), award_winner(?x8994, ?x379), ?x2634 = 02f72n >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #784 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 7 *> proper extension: 07sbbz2; 015pdg; 0xhtw; 06by7; 03lty; 02yv6b; 05c6073; *> query: (?x14347, 01693z) <- artists(?x14347, ?x379), ?x379 = 089tm *> conf = 0.33 ranks of expected_values: 66 EVAL 03x2qp artists 01693z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 34.000 11.000 0.611 http://example.org/music/genre/artists #17374-07s467s PRED entity: 07s467s PRED relation: profession! PRED expected values: 0gs1_ 018p4y => 38 concepts (4 used for prediction) PRED predicted values (max 10 best out of 3568): 06cv1 (0.75 #12841, 0.71 #8602, 0.60 #123), 06jz0 (0.71 #11909, 0.62 #16148, 0.60 #3430), 036dyy (0.71 #11199, 0.62 #15438, 0.60 #2720), 02mz_6 (0.71 #10831, 0.62 #15070, 0.60 #2352), 02b29 (0.71 #10718, 0.62 #14957, 0.60 #2239), 052hl (0.71 #10677, 0.62 #14916, 0.60 #2198), 05mcjs (0.71 #10646, 0.62 #14885, 0.60 #2167), 06m6z6 (0.71 #9696, 0.62 #13935, 0.60 #1217), 0693l (0.71 #9400, 0.62 #13639, 0.60 #921), 01vsl3_ (0.71 #9303, 0.62 #13542, 0.60 #824) >> Best rule #12841 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0dgd_; >> query: (?x801, 06cv1) <- profession(?x6236, ?x801), profession(?x2683, ?x801), ?x6236 = 01xv77, award(?x2683, ?x537) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3830 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 01d_h8; 0cbd2; 02hrh1q; *> query: (?x801, 018p4y) <- profession(?x6236, ?x801), profession(?x2683, ?x801), profession(?x731, ?x801), ?x6236 = 01xv77, ?x2683 = 01dw9z, ?x731 = 09byk *> conf = 0.40 ranks of expected_values: 639, 961 EVAL 07s467s profession! 018p4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 38.000 4.000 0.750 http://example.org/people/person/profession EVAL 07s467s profession! 0gs1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 38.000 4.000 0.750 http://example.org/people/person/profession #17373-02ryx0 PRED entity: 02ryx0 PRED relation: nationality PRED expected values: 09c7w0 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.73 #4403, 0.73 #3703, 0.72 #3803), 02jx1 (0.24 #334, 0.20 #33, 0.19 #1335), 07ssc (0.12 #115, 0.12 #1016, 0.11 #1717), 0d060g (0.06 #1809, 0.06 #7, 0.05 #708), 03rk0 (0.05 #10149, 0.05 #9949, 0.05 #9749), 0f8l9c (0.04 #1023, 0.03 #222, 0.03 #1223), 0345h (0.04 #1533, 0.03 #2133, 0.03 #1232), 06q1r (0.04 #177, 0.03 #578, 0.03 #678), 0jgd (0.03 #301, 0.03 #1302, 0.03 #102), 03_3d (0.03 #301, 0.03 #1302, 0.02 #307) >> Best rule #4403 for best value: >> intensional similarity = 3 >> extensional distance = 1123 >> proper extension: 03h40_7; 06mm1x; >> query: (?x5949, 09c7w0) <- award_nominee(?x5949, ?x6011), student(?x2767, ?x5949), profession(?x6011, ?x987) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ryx0 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.734 http://example.org/people/person/nationality #17372-0m32_ PRED entity: 0m32_ PRED relation: type_of_union PRED expected values: 04ztj => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.79 #57, 0.79 #17, 0.78 #29), 01g63y (0.20 #173, 0.19 #14, 0.18 #10), 0jgjn (0.20 #173), 01bl8s (0.20 #173) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 354 >> proper extension: 012d40; 01j5ts; 02rchht; 083chw; 014zcr; 042l3v; 01wbg84; 09fb5; 02qjj7; 0z4s; ... >> query: (?x2774, 04ztj) <- profession(?x2774, ?x524), ?x524 = 02jknp, location(?x2774, ?x739) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m32_ type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.795 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17371-03m6j PRED entity: 03m6j PRED relation: combatants! PRED expected values: 03gqgt3 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 69): 0784z (0.83 #2126, 0.83 #934, 0.78 #867), 01tffp (0.83 #2126, 0.83 #934, 0.78 #867), 018vbf (0.83 #2126, 0.83 #934, 0.78 #867), 018w0j (0.82 #1067, 0.78 #837, 0.70 #904), 0ql86 (0.82 #1067, 0.67 #385, 0.62 #2592), 0gjw_ (0.82 #1035, 0.41 #1307, 0.33 #834), 0cbvg (0.71 #1097, 0.60 #963, 0.50 #228), 06k75 (0.70 #1550, 0.62 #1683, 0.48 #2075), 03jv8d (0.67 #250, 0.40 #985, 0.21 #1119), 07_nf (0.65 #1422, 0.36 #2541, 0.26 #3210) >> Best rule #2126 for best value: >> intensional similarity = 13 >> extensional distance = 31 >> proper extension: 01m3dv; 03x1x; 05pq3_; 0432mrk; 04bbb8; 043870; >> query: (?x14265, ?x12976) <- combatants(?x11814, ?x14265), entity_involved(?x11814, ?x11617), entity_involved(?x11814, ?x8437), locations(?x11814, ?x4743), type_of_union(?x8437, ?x566), basic_title(?x8437, ?x182), film_release_region(?x7493, ?x4743), geographic_distribution(?x11490, ?x4743), entity_involved(?x12976, ?x8437), adjoins(?x608, ?x4743), contains(?x4743, ?x5167), ?x7493 = 0btpm6, profession(?x11617, ?x5805) >> conf = 0.83 => this is the best rule for 3 predicted values *> Best rule #3781 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 57 *> proper extension: 03gj2; 0345h; 015qh; 06c1y; 01pj7; 059dn; 07jqh; *> query: (?x14265, 03gqgt3) <- combatants(?x11814, ?x14265), entity_involved(?x11814, ?x11617), entity_involved(?x11814, ?x8437), locations(?x11814, ?x14140), locations(?x11814, ?x4743), type_of_union(?x8437, ?x566), contains(?x9122, ?x14140), profession(?x11617, ?x5805), ?x9122 = 04wsz, gender(?x8437, ?x231), medal(?x4743, ?x422), country(?x1121, ?x4743), film_release_region(?x66, ?x4743) *> conf = 0.36 ranks of expected_values: 28 EVAL 03m6j combatants! 03gqgt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 77.000 77.000 0.833 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #17370-0dwtp PRED entity: 0dwtp PRED relation: role PRED expected values: 011_6p => 89 concepts (53 used for prediction) PRED predicted values (max 10 best out of 94): 05148p4 (0.82 #3254, 0.81 #577, 0.81 #95), 018j2 (0.82 #3254, 0.81 #577, 0.81 #95), 01vj9c (0.82 #3254, 0.81 #577, 0.81 #95), 0l14j_ (0.82 #3254, 0.81 #577, 0.81 #95), 01s0ps (0.82 #3254, 0.81 #577, 0.81 #95), 02sgy (0.82 #3254, 0.81 #577, 0.81 #95), 05ljv7 (0.82 #3254, 0.81 #577, 0.81 #95), 011_6p (0.82 #3254, 0.81 #577, 0.81 #95), 07m2y (0.82 #3254, 0.81 #577, 0.81 #95), 07y_7 (0.82 #2399, 0.79 #3255, 0.77 #2969) >> Best rule #3254 for best value: >> intensional similarity = 17 >> extensional distance = 11 >> proper extension: 016622; >> query: (?x885, ?x2157) <- role(?x885, ?x4769), role(?x885, ?x2297), role(?x3703, ?x885), role(?x2923, ?x885), role(?x894, ?x885), role(?x212, ?x885), role(?x6949, ?x885), role(?x885, ?x868), ?x3703 = 02dlh2, ?x4769 = 0dwt5, role(?x9413, ?x894), role(?x2157, ?x885), ?x212 = 026t6, ?x9413 = 07m2y, artist(?x2149, ?x6949), role(?x2964, ?x2923), role(?x2297, ?x1148) >> conf = 0.82 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 8 EVAL 0dwtp role 011_6p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 89.000 53.000 0.822 http://example.org/music/performance_role/regular_performances./music/group_membership/role #17369-01gbbz PRED entity: 01gbbz PRED relation: film PRED expected values: 02_1sj => 111 concepts (58 used for prediction) PRED predicted values (max 10 best out of 839): 0124k9 (0.63 #42990, 0.58 #93144, 0.56 #35825), 035s95 (0.09 #341, 0.04 #5715, 0.04 #87770), 016dj8 (0.09 #1115, 0.02 #29775, 0.02 #15445), 0661m4p (0.09 #376, 0.02 #7541, 0.02 #36201), 011yth (0.09 #300, 0.02 #3882, 0.01 #5674), 03m4mj (0.09 #202, 0.02 #12740, 0.01 #5576), 01shy7 (0.09 #2215, 0.05 #5798, 0.05 #32666), 0cqr0q (0.07 #3290, 0.04 #12246, 0.03 #17620), 03l6q0 (0.06 #4125, 0.02 #2334, 0.02 #66817), 034qzw (0.06 #5708, 0.04 #12872, 0.02 #63026) >> Best rule #42990 for best value: >> intensional similarity = 3 >> extensional distance = 309 >> proper extension: 01r42_g; 08m4c8; 03ds83; 0bkmf; >> query: (?x2894, ?x1542) <- participant(?x513, ?x2894), award_winner(?x4260, ?x2894), nominated_for(?x2894, ?x1542) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #21574 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 174 *> proper extension: 0l8v5; 0152cw; 012cph; 034np8; 01t2h2; 018swb; 0d7hg4; 03h_fk5; 0klh7; 02j9lm; ... *> query: (?x2894, 02_1sj) <- award(?x2894, ?x11179), location_of_ceremony(?x2894, ?x191), nominated_for(?x11179, ?x631) *> conf = 0.02 ranks of expected_values: 216 EVAL 01gbbz film 02_1sj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 111.000 58.000 0.629 http://example.org/film/actor/film./film/performance/film #17368-0jt90f5 PRED entity: 0jt90f5 PRED relation: influenced_by PRED expected values: 019gz => 183 concepts (103 used for prediction) PRED predicted values (max 10 best out of 419): 0p_47 (0.36 #2232, 0.21 #2657, 0.15 #1807), 081k8 (0.30 #579, 0.25 #1004, 0.23 #1429), 03hnd (0.30 #523, 0.25 #948, 0.23 #1373), 081lh (0.29 #2569, 0.23 #1719, 0.09 #5545), 01v9724 (0.25 #1025, 0.23 #1450, 0.20 #600), 03f0324 (0.25 #1000, 0.23 #1425, 0.20 #575), 040db (0.25 #904, 0.23 #1329, 0.20 #479), 01svq8 (0.21 #2541, 0.15 #2116, 0.14 #2966), 03_87 (0.20 #624, 0.17 #1049, 0.16 #3600), 037jz (0.20 #632, 0.17 #1057, 0.15 #1482) >> Best rule #2232 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 07ymr5; 02_j7t; >> query: (?x2343, 0p_47) <- location(?x2343, ?x7058), influenced_by(?x2343, ?x9597), story_by(?x4021, ?x9597), actor(?x9541, ?x2343) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #5059 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 03qcq; 03ft8; 041mt; 06lbp; 01y8d4; 04093; 0gs5q; 04mby; 0ky1; 018zvb; ... *> query: (?x2343, 019gz) <- location(?x2343, ?x7058), influenced_by(?x2343, ?x9597), story_by(?x4021, ?x9597), story_by(?x430, ?x2343) *> conf = 0.02 ranks of expected_values: 277 EVAL 0jt90f5 influenced_by 019gz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 103.000 0.357 http://example.org/influence/influence_node/influenced_by #17367-09c7w0 PRED entity: 09c7w0 PRED relation: country! PRED expected values: 034qrh 0jzw 01vksx 0cwy47 02d44q 02v63m 031t2d 0jym0 01hqhm 023p33 06ybb1 0cfhfz 014zwb 03f7xg 05zlld0 0gcrg 0pd6l 0n04r 0fb7sd 02r_pp 0b44shh 048rn 01q2nx 02pg45 01d259 0992d9 051ys82 02nczh 03p2xc 04tng0 02vjp3 02x3y41 05ch98 04gcyg 011xg5 01_1hw 02bqvs 0m_h6 02bj22 03bzyn4 04ynx7 07tlfx 03bdkd 09qljs 01qdmh 0170xl 07bxqz 03cffvv 04sh80 042g97 016z43 => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 676): 01f8f7 (0.33 #551, 0.33 #213, 0.21 #5284), 023g6w (0.33 #600, 0.29 #5333, 0.23 #10406), 04jkpgv (0.33 #373, 0.29 #5106, 0.20 #4429), 02gd6x (0.33 #535, 0.29 #5268, 0.15 #10341), 03cwwl (0.33 #633, 0.25 #3337, 0.20 #4689), 02vz6dn (0.33 #568, 0.25 #3272, 0.15 #10036), 0c0yh4 (0.33 #341, 0.25 #3045, 0.12 #10486), 0cmc26r (0.33 #450, 0.22 #8564, 0.20 #4506), 078mm1 (0.33 #593, 0.21 #5326, 0.20 #4649), 0fpkhkz (0.33 #371, 0.21 #5104, 0.13 #8485) >> Best rule #551 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0345h; >> query: (?x94, 01f8f7) <- nationality(?x51, ?x94), country(?x6343, ?x94), ?x6343 = 05n6sq >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #654 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 0345h; *> query: (?x94, 01qdmh) <- nationality(?x51, ?x94), country(?x6343, ?x94), ?x6343 = 05n6sq *> conf = 0.33 ranks of expected_values: 18, 48, 55, 57, 58, 65, 85, 99, 104, 110, 346, 351, 455, 463, 582, 585, 606, 654, 667, 672, 673, 674, 675, 676 EVAL 09c7w0 country! 016z43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 042g97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 04sh80 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 03cffvv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 07bxqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0170xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 01qdmh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 09qljs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 03bdkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 07tlfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 04ynx7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 03bzyn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 02bj22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0m_h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 02bqvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 01_1hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 011xg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 04gcyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 05ch98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 02x3y41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 02vjp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 04tng0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 03p2xc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 02nczh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 051ys82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0992d9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 01d259 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 02pg45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 01q2nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 048rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0b44shh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 02r_pp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0fb7sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0n04r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0pd6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0gcrg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 05zlld0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 03f7xg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 014zwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0cfhfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 06ybb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 023p33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 01hqhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0jym0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 031t2d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 02v63m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 02d44q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0cwy47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 01vksx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 0jzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.333 http://example.org/film/film/country EVAL 09c7w0 country! 034qrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 173.000 173.000 0.333 http://example.org/film/film/country #17366-017lb_ PRED entity: 017lb_ PRED relation: group! PRED expected values: 018vs => 85 concepts (52 used for prediction) PRED predicted values (max 10 best out of 110): 018vs (0.62 #1448, 0.61 #1785, 0.50 #431), 028tv0 (0.37 #1784, 0.36 #1447, 0.18 #1953), 02snj9 (0.33 #53, 0.25 #221, 0.17 #389), 0239kh (0.33 #21, 0.25 #189, 0.17 #357), 01vj9c (0.26 #1786, 0.23 #1449, 0.09 #1618), 06ncr (0.25 #455, 0.25 #203, 0.20 #287), 02fsn (0.25 #465, 0.20 #297, 0.17 #381), 03qjg (0.23 #1818, 0.17 #1481, 0.11 #1650), 026t6 (0.20 #255, 0.12 #423, 0.08 #2534), 04rzd (0.12 #1802, 0.08 #1859, 0.08 #1465) >> Best rule #1448 for best value: >> intensional similarity = 7 >> extensional distance = 88 >> proper extension: 0dm5l; 01rm8b; 016890; 048xh; 06gcn; 012vm6; >> query: (?x8226, 018vs) <- artists(?x2491, ?x8226), parent_genre(?x2491, ?x283), artist(?x2149, ?x8226), parent_genre(?x302, ?x2491), artists(?x2491, ?x8058), ?x8058 = 014pg1, group(?x227, ?x8226) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017lb_ group! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 52.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17365-03npn PRED entity: 03npn PRED relation: disciplines_or_subjects! PRED expected values: 058bzgm => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 39): 02r6nbc (0.33 #150, 0.25 #261, 0.17 #372), 04jhhng (0.33 #97, 0.14 #652, 0.14 #541), 045xh (0.20 #968, 0.14 #635, 0.07 #1080), 01b8bn (0.20 #954, 0.14 #621, 0.07 #1066), 0262x6 (0.20 #939, 0.14 #606, 0.07 #1051), 0262yt (0.20 #928, 0.14 #595, 0.07 #1040), 0265wl (0.20 #921, 0.14 #588, 0.07 #1033), 02664f (0.20 #915, 0.14 #582, 0.07 #1027), 02662b (0.20 #899, 0.14 #566, 0.07 #1011), 01bb1c (0.20 #990, 0.14 #657, 0.07 #1102) >> Best rule #150 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 01jfsb; >> query: (?x571, 02r6nbc) <- genre(?x7225, ?x571), genre(?x4680, ?x571), genre(?x4530, ?x571), genre(?x1252, ?x571), titles(?x571, ?x708), ?x4530 = 07j94, film_release_region(?x1252, ?x87), ?x7225 = 02mmwk, ?x4680 = 01f8hf >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #966 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 01htzx; 06q7n; *> query: (?x571, 058bzgm) <- genre(?x11336, ?x571), genre(?x9633, ?x571), genre(?x3413, ?x571), ?x3413 = 01f3p_, award_winner(?x11336, ?x3381), actor(?x11336, ?x560), nominated_for(?x686, ?x9633) *> conf = 0.10 ranks of expected_values: 26 EVAL 03npn disciplines_or_subjects! 058bzgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 51.000 51.000 0.333 http://example.org/award/award_category/disciplines_or_subjects #17364-03lrqw PRED entity: 03lrqw PRED relation: person PRED expected values: 01vsps => 85 concepts (72 used for prediction) PRED predicted values (max 10 best out of 146): 09b6zr (0.19 #455, 0.15 #1583, 0.01 #3088), 06c97 (0.19 #479, 0.09 #1607, 0.02 #3112), 0jw67 (0.19 #448, 0.06 #1576, 0.01 #3081), 01n4f8 (0.12 #407, 0.07 #1535), 0157m (0.09 #1534, 0.06 #406), 0c5vh (0.08 #365, 0.04 #1680, 0.03 #2055), 01v9724 (0.07 #1125, 0.06 #1503, 0.05 #750), 079vf (0.06 #377, 0.06 #939, 0.05 #1317), 06c0j (0.06 #556, 0.06 #1684), 034ls (0.06 #512, 0.04 #1640) >> Best rule #455 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 05_61y; >> query: (?x2463, 09b6zr) <- films(?x5011, ?x2463), currency(?x2463, ?x170), person(?x2463, ?x9574), genre(?x2463, ?x258) >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03lrqw person 01vsps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 72.000 0.188 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #17363-01j7mr PRED entity: 01j7mr PRED relation: honored_for! PRED expected values: 07z31v 07y9ts => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 78): 03gyp30 (0.21 #2509, 0.20 #95, 0.03 #1919), 07y9ts (0.21 #2509, 0.16 #5475, 0.11 #3992), 07z31v (0.21 #2509, 0.16 #5475, 0.11 #3992), 056878 (0.21 #2509, 0.11 #3992, 0.10 #5590), 03gwpw2 (0.20 #5, 0.10 #233, 0.10 #1829), 09pj68 (0.20 #83, 0.06 #1109, 0.06 #1907), 027hjff (0.20 #43, 0.06 #1183, 0.05 #1297), 0hndn2q (0.20 #142, 0.05 #1054, 0.04 #1168), 027n06w (0.20 #57, 0.03 #1197, 0.03 #1311), 05pd94v (0.16 #5475, 0.11 #3992, 0.10 #5590) >> Best rule #2509 for best value: >> intensional similarity = 2 >> extensional distance = 164 >> proper extension: 070g7; >> query: (?x3626, ?x1265) <- actor(?x3626, ?x2127), award_winner(?x1265, ?x2127) >> conf = 0.21 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3 EVAL 01j7mr honored_for! 07y9ts CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.212 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 01j7mr honored_for! 07z31v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.212 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #17362-01v_pj6 PRED entity: 01v_pj6 PRED relation: role PRED expected values: 013y1f => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 87): 05r5c (0.51 #627, 0.39 #1353, 0.39 #4048), 0342h (0.49 #1765, 0.41 #624, 0.40 #108), 018vs (0.33 #1865, 0.33 #1864, 0.32 #1553), 05148p4 (0.33 #1865, 0.33 #1864, 0.32 #1553), 02hnl (0.33 #1865, 0.33 #1864, 0.32 #1553), 013y1f (0.27 #655, 0.16 #1796, 0.14 #3765), 02sgy (0.25 #1766, 0.25 #3010, 0.24 #2390), 042v_gx (0.24 #2393, 0.23 #3013, 0.23 #1354), 05842k (0.23 #1838, 0.18 #3807, 0.17 #1526), 026t6 (0.22 #312, 0.21 #1763, 0.19 #622) >> Best rule #627 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 02fybl; >> query: (?x1674, 05r5c) <- role(?x1674, ?x1166), role(?x1674, ?x228), ?x1166 = 05148p4 >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #655 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 02fybl; *> query: (?x1674, 013y1f) <- role(?x1674, ?x1166), role(?x1674, ?x228), ?x1166 = 05148p4 *> conf = 0.27 ranks of expected_values: 6 EVAL 01v_pj6 role 013y1f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 124.000 124.000 0.508 http://example.org/music/artist/track_contributions./music/track_contribution/role #17361-02vxq9m PRED entity: 02vxq9m PRED relation: featured_film_locations PRED expected values: 04jpl => 89 concepts (67 used for prediction) PRED predicted values (max 10 best out of 72): 02_286 (0.28 #5051, 0.27 #4573, 0.27 #8163), 030qb3t (0.14 #5070, 0.14 #758, 0.14 #279), 01_d4 (0.14 #287, 0.04 #527, 0.03 #1007), 04jpl (0.13 #4562, 0.12 #8152, 0.12 #5040), 0rh6k (0.12 #1, 0.09 #241, 0.06 #5032), 080h2 (0.07 #3380, 0.04 #3860, 0.04 #8167), 06y57 (0.07 #582, 0.04 #3458, 0.03 #4655), 052p7 (0.05 #298, 0.03 #8201, 0.02 #5089), 02dtg (0.05 #252, 0.02 #972, 0.02 #2649), 05qtj (0.05 #335, 0.02 #575, 0.02 #814) >> Best rule #5051 for best value: >> intensional similarity = 4 >> extensional distance = 416 >> proper extension: 02v63m; 014zwb; >> query: (?x186, 02_286) <- film_crew_role(?x186, ?x137), genre(?x186, ?x225), featured_film_locations(?x186, ?x4271), nominated_for(?x185, ?x186) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #4562 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 398 *> proper extension: 03bzyn4; *> query: (?x186, 04jpl) <- film_crew_role(?x186, ?x137), genre(?x186, ?x225), featured_film_locations(?x186, ?x4271), nominated_for(?x112, ?x186) *> conf = 0.13 ranks of expected_values: 4 EVAL 02vxq9m featured_film_locations 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 89.000 67.000 0.278 http://example.org/film/film/featured_film_locations #17360-01gvxh PRED entity: 01gvxh PRED relation: legislative_sessions! PRED expected values: 0h6dy => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 5): 0h6dy (0.87 #62, 0.87 #61, 0.86 #81), 0162kb (0.87 #62, 0.87 #61, 0.86 #81), 030p4s (0.87 #62, 0.87 #61, 0.86 #81), 07t58 (0.86 #57, 0.84 #64, 0.83 #76), 0b3wk (0.85 #75, 0.84 #56, 0.82 #70) >> Best rule #62 for best value: >> intensional similarity = 77 >> extensional distance = 41 >> proper extension: 01grpq; 01grpc; 01grqd; 02glc4; 01gsvp; 01grp0; 01gtdd; 01grmk; 01grq1; >> query: (?x8777, ?x8776) <- district_represented(?x8777, ?x14129), district_represented(?x8777, ?x11542), district_represented(?x8777, ?x10063), district_represented(?x8777, ?x9311), district_represented(?x8777, ?x1905), legislative_sessions(?x11189, ?x8777), legislative_sessions(?x3473, ?x8777), legislative_sessions(?x8776, ?x11189), adjoins(?x11542, ?x6842), state(?x13347, ?x1905), taxonomy(?x14129, ?x939), contains(?x1905, ?x13458), contains(?x1905, ?x4199), contains(?x279, ?x11542), location(?x10607, ?x9311), adjoins(?x11993, ?x1905), adjoins(?x1274, ?x1905), adjoins(?x335, ?x1905), contains(?x9311, ?x13523), adjoins(?x1905, ?x1906), administrative_division(?x10718, ?x10063), currency(?x13458, ?x2244), partially_contains(?x11542, ?x10954), ?x10954 = 0lm0n, religion(?x1905, ?x7131), state_province_region(?x2327, ?x1905), time_zones(?x13523, ?x1638), religion(?x1274, ?x8613), religion(?x1274, ?x2672), major_field_of_study(?x2327, ?x742), ?x335 = 059rby, contains(?x1274, ?x3204), legislative_sessions(?x3099, ?x3473), institution(?x1305, ?x13458), district_represented(?x605, ?x1274), origin(?x10607, ?x94), location(?x1461, ?x1274), ?x2672 = 01y0s9, ?x1305 = 02mjs7, student(?x2327, ?x1422), institution(?x4981, ?x2327), institution(?x1368, ?x2327), artists(?x302, ?x10607), ?x4981 = 03bwzr4, organization(?x346, ?x2327), ?x1368 = 014mlp, location_of_ceremony(?x566, ?x1905), place_of_birth(?x2650, ?x1274), contains(?x11993, ?x8968), time_zones(?x11993, ?x2674), state(?x7328, ?x1274), major_field_of_study(?x13458, ?x2014), contains(?x10063, ?x1411), category(?x13347, ?x134), olympics(?x279, ?x5395), jurisdiction_of_office(?x182, ?x279), nationality(?x11596, ?x279), nationality(?x10587, ?x279), nationality(?x3664, ?x279), nationality(?x3325, ?x279), film_release_region(?x8292, ?x279), film_release_region(?x4518, ?x279), film_release_region(?x3276, ?x279), ?x8292 = 0cmf0m0, entity_involved(?x3278, ?x279), colors(?x4199, ?x663), ?x7131 = 03_gx, ?x5395 = 018qb4, ?x11596 = 0d_w7, second_level_divisions(?x279, ?x11578), ?x3276 = 0gjc4d3, ?x3325 = 073v6, ?x10587 = 021r6w, ?x3664 = 059xvg, ?x8613 = 04pk9, country(?x136, ?x279), ?x4518 = 0hgnl3t >> conf = 0.87 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 01gvxh legislative_sessions! 0h6dy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.870 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #17359-03n93 PRED entity: 03n93 PRED relation: award PRED expected values: 0gq9h => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 331): 0gq9h (0.67 #2108, 0.60 #1702, 0.55 #2920), 07bdd_ (0.53 #21182, 0.51 #8998, 0.50 #20776), 0c_dx (0.50 #1090, 0.33 #278), 05p1dby (0.42 #6604, 0.42 #9040, 0.39 #12694), 05pcn59 (0.39 #6172, 0.32 #11450, 0.28 #10232), 09sb52 (0.38 #11409, 0.34 #6131, 0.32 #10191), 0gr4k (0.33 #33, 0.25 #845, 0.11 #19524), 04dn09n (0.33 #44, 0.25 #856, 0.11 #10194), 03hkv_r (0.33 #16, 0.25 #828, 0.06 #36968), 02n9nmz (0.33 #70, 0.25 #882, 0.05 #30932) >> Best rule #2108 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 016ghw; >> query: (?x4058, 0gq9h) <- award_winner(?x4058, ?x1850), profession(?x4058, ?x319), ?x1850 = 017jv5, nationality(?x4058, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03n93 award 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17358-04bd8y PRED entity: 04bd8y PRED relation: nationality PRED expected values: 09c7w0 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 49): 09c7w0 (0.85 #201, 0.73 #602, 0.72 #1604), 04rrd (0.33 #7221), 0345h (0.31 #5114, 0.08 #501, 0.03 #4712), 0f8l9c (0.31 #5114, 0.08 #501, 0.03 #4712), 02jx1 (0.18 #6416, 0.12 #133, 0.12 #433), 07ssc (0.18 #6416, 0.08 #315, 0.08 #415), 0chghy (0.18 #6416, 0.08 #501, 0.03 #4712), 0b90_r (0.18 #6416, 0.08 #501, 0.03 #4712), 0d060g (0.18 #6416, 0.05 #708, 0.04 #1610), 03rt9 (0.18 #6416, 0.03 #4712, 0.03 #4711) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 040t74; >> query: (?x820, 09c7w0) <- award_nominee(?x820, ?x4586), award_nominee(?x336, ?x820), ?x4586 = 04bcb1 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bd8y nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.846 http://example.org/people/person/nationality #17357-020ffd PRED entity: 020ffd PRED relation: award_winner! PRED expected values: 0jt3qpk => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 90): 0gkxgfq (0.65 #107, 0.62 #248, 0.18 #530), 0jt3qpk (0.59 #43, 0.52 #184, 0.21 #466), 09g90vz (0.38 #406, 0.07 #2098, 0.07 #1957), 05c1t6z (0.13 #438, 0.12 #297, 0.10 #861), 03nnm4t (0.12 #356, 0.08 #497, 0.07 #779), 02q690_ (0.12 #347, 0.07 #1757, 0.05 #488), 0gvstc3 (0.11 #457, 0.08 #316, 0.08 #880), 09n4nb (0.10 #9872, 0.07 #753, 0.06 #894), 073hd1 (0.10 #9872, 0.06 #100, 0.05 #241), 09qvms (0.09 #1846, 0.08 #1987, 0.05 #2833) >> Best rule #107 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 086k8; >> query: (?x6171, 0gkxgfq) <- award_winner(?x6171, ?x690), award_nominee(?x3974, ?x6171), ?x3974 = 047q2wc >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 086k8; *> query: (?x6171, 0jt3qpk) <- award_winner(?x6171, ?x690), award_nominee(?x3974, ?x6171), ?x3974 = 047q2wc *> conf = 0.59 ranks of expected_values: 2 EVAL 020ffd award_winner! 0jt3qpk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 95.000 0.647 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17356-0rsjf PRED entity: 0rsjf PRED relation: place PRED expected values: 0rsjf => 118 concepts (66 used for prediction) PRED predicted values (max 10 best out of 134): 05jbn (0.24 #7224, 0.24 #7740, 0.15 #11349), 0rsjf (0.15 #11349, 0.14 #11865, 0.10 #10318), 0fwc0 (0.15 #11349, 0.14 #11865, 0.10 #10318), 0f2v0 (0.08 #74, 0.06 #14959, 0.03 #589), 0ply0 (0.08 #73, 0.06 #14959, 0.03 #588), 0rrwt (0.08 #258, 0.06 #14959, 0.03 #773), 0rhp6 (0.08 #208, 0.06 #14959, 0.03 #723), 0rn8q (0.08 #152, 0.06 #14959, 0.03 #667), 0rh7t (0.08 #145, 0.06 #14959, 0.03 #660), 0rk71 (0.08 #282, 0.06 #14959, 0.03 #797) >> Best rule #7224 for best value: >> intensional similarity = 4 >> extensional distance = 106 >> proper extension: 087vz; 010h9y; >> query: (?x6495, ?x4978) <- location(?x2269, ?x6495), place_of_birth(?x2269, ?x4978), role(?x2269, ?x227), gender(?x2269, ?x231) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #11349 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 164 *> proper extension: 0dg3n1; *> query: (?x6495, ?x4978) <- location(?x2269, ?x6495), role(?x2269, ?x227), location(?x2269, ?x4978), artists(?x482, ?x2269) *> conf = 0.15 ranks of expected_values: 2 EVAL 0rsjf place 0rsjf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 66.000 0.245 http://example.org/location/hud_county_place/place #17355-01ln5z PRED entity: 01ln5z PRED relation: genre PRED expected values: 02kdv5l => 86 concepts (59 used for prediction) PRED predicted values (max 10 best out of 97): 01jfsb (0.67 #13, 0.59 #497, 0.53 #618), 02kdv5l (0.66 #487, 0.66 #124, 0.65 #608), 07s9rl0 (0.64 #2909, 0.59 #4488, 0.58 #1), 0h9qh (0.53 #4609, 0.51 #3151, 0.49 #5705), 05p553 (0.39 #4371, 0.38 #1337, 0.36 #1458), 06n90 (0.34 #862, 0.33 #1104, 0.32 #377), 02l7c8 (0.29 #4504, 0.28 #3046, 0.27 #5478), 082gq (0.29 #31, 0.15 #2939, 0.14 #273), 0lsxr (0.26 #494, 0.25 #131, 0.24 #615), 0bkbm (0.25 #40, 0.09 #645, 0.09 #1251) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0413cff; >> query: (?x549, 01jfsb) <- genre(?x549, ?x811), featured_film_locations(?x549, ?x1273), language(?x549, ?x5359), ?x5359 = 0jzc >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #487 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 07ghq; *> query: (?x549, 02kdv5l) <- genre(?x549, ?x811), featured_film_locations(?x549, ?x1273), prequel(?x1074, ?x549), titles(?x811, ?x148) *> conf = 0.66 ranks of expected_values: 2 EVAL 01ln5z genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 59.000 0.667 http://example.org/film/film/genre #17354-0crlz PRED entity: 0crlz PRED relation: country PRED expected values: 0d060g 035qy => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 391): 03h64 (0.89 #2820, 0.75 #2689, 0.75 #1402), 05qhw (0.88 #5048, 0.81 #6691, 0.80 #1406), 0163v (0.88 #5089, 0.80 #3283, 0.80 #3083), 0f8l9c (0.87 #5876, 0.87 #4855, 0.87 #4658), 03_3d (0.87 #4643, 0.84 #6064, 0.82 #5254), 0345h (0.84 #5481, 0.84 #6090, 0.84 #6504), 015fr (0.84 #7890, 0.83 #6282, 0.82 #5265), 0d060g (0.84 #8316, 0.82 #8110, 0.82 #4236), 0jgd (0.84 #7890, 0.82 #4434, 0.82 #4231), 0d0vqn (0.82 #4238, 0.82 #3834, 0.82 #7891) >> Best rule #2820 for best value: >> intensional similarity = 50 >> extensional distance = 6 >> proper extension: 03hr1p; >> query: (?x5182, ?x2645) <- sports(?x6464, ?x5182), sports(?x4255, ?x5182), sports(?x2496, ?x5182), sports(?x2432, ?x5182), sports(?x775, ?x5182), country(?x5182, ?x2346), country(?x5182, ?x2152), ?x775 = 0l998, ?x2432 = 0nbjq, ?x2152 = 06mkj, olympics(?x2146, ?x4255), olympics(?x1453, ?x4255), olympics(?x1023, ?x4255), olympics(?x608, ?x4255), olympics(?x252, ?x4255), olympics(?x142, ?x4255), ?x2346 = 0d05w3, ?x2496 = 0sxrz, sports(?x4255, ?x2885), sports(?x4255, ?x766), ?x2885 = 07jjt, participating_countries(?x4255, ?x2645), ?x766 = 01hp22, ?x6464 = 0lbd9, ?x252 = 03_3d, ?x1453 = 06qd3, ?x1023 = 0ctw_b, ?x142 = 0jgd, olympics(?x1229, ?x4255), ?x2146 = 03rk0, film_release_region(?x11809, ?x2645), film_release_region(?x11395, ?x2645), film_release_region(?x7502, ?x2645), film_release_region(?x6761, ?x2645), film_release_region(?x4441, ?x2645), film_release_region(?x3812, ?x2645), film_release_region(?x3000, ?x2645), film_release_region(?x2168, ?x2645), film_release_region(?x1744, ?x2645), nationality(?x147, ?x2645), ?x1744 = 035yn8, ?x4441 = 0125xq, ?x6761 = 05ft32, ?x11395 = 05ypj5, ?x3000 = 045j3w, olympics(?x608, ?x1081), ?x3812 = 0c3xw46, ?x2168 = 0bx0l, ?x7502 = 0233bn, ?x11809 = 0b85mm >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #8316 for first EXPECTED value: *> intensional similarity = 40 *> extensional distance = 53 *> proper extension: 01dys; 0152n0; 09f6b; 01yfj; *> query: (?x5182, 0d060g) <- sports(?x2966, ?x5182), sports(?x775, ?x5182), country(?x5182, ?x2979), sports(?x775, ?x3641), olympics(?x2629, ?x775), country(?x3641, ?x1558), sports(?x1081, ?x3641), contains(?x2629, ?x10324), film_release_region(?x6528, ?x2629), film_release_region(?x5713, ?x2629), film_release_region(?x5162, ?x2629), film_release_region(?x3850, ?x2629), film_release_region(?x3745, ?x2629), film_release_region(?x3498, ?x2629), film_release_region(?x3292, ?x2629), film_release_region(?x3035, ?x2629), film_release_region(?x1451, ?x2629), film_release_region(?x972, ?x2629), combatants(?x3728, ?x2629), ?x6528 = 0dc_ms, ?x5713 = 0cc97st, location_of_ceremony(?x566, ?x2979), ?x3498 = 02fqrf, contains(?x6304, ?x2629), ?x3728 = 087vz, ?x3292 = 0gvs1kt, ?x3035 = 0j43swk, medal(?x2629, ?x422), nationality(?x690, ?x2629), ?x1451 = 04zyhx, olympics(?x6733, ?x775), ?x5162 = 0j3d9tn, combatants(?x326, ?x2629), ?x3850 = 047fjjr, administrative_area_type(?x2979, ?x2792), ?x1558 = 01mjq, ?x3745 = 03cw411, olympics(?x47, ?x2966), olympics(?x126, ?x2966), ?x972 = 017gl1 *> conf = 0.84 ranks of expected_values: 8, 57 EVAL 0crlz country 035qy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 43.000 43.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0crlz country 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 43.000 43.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #17353-018qql PRED entity: 018qql PRED relation: nationality PRED expected values: 09c7w0 => 133 concepts (96 used for prediction) PRED predicted values (max 10 best out of 90): 09c7w0 (0.74 #1205, 0.74 #3014, 0.73 #1306), 03rt9 (0.32 #213, 0.04 #816, 0.02 #2222), 02jx1 (0.21 #133, 0.15 #2846, 0.13 #836), 07ssc (0.14 #115, 0.12 #918, 0.11 #2828), 0d060g (0.09 #307, 0.08 #1010, 0.08 #609), 05bcl (0.09 #260, 0.02 #2812), 03rk0 (0.08 #46, 0.07 #8069, 0.07 #6767), 06m_5 (0.08 #83, 0.02 #785, 0.02 #1186), 0f8l9c (0.07 #122, 0.04 #2332, 0.04 #925), 0345h (0.07 #2341, 0.04 #5648, 0.04 #5948) >> Best rule #1205 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 06hzsx; 03jxw; 01p95y0; >> query: (?x13648, 09c7w0) <- gender(?x13648, ?x231), type_of_union(?x13648, ?x566), place_of_burial(?x13648, ?x3691), place_of_birth(?x13648, ?x1860) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018qql nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 96.000 0.742 http://example.org/people/person/nationality #17352-064_8sq PRED entity: 064_8sq PRED relation: language! PRED expected values: 0c0yh4 01hp5 02z3r8t 04dsnp 0340hj 04w7rn 0cd2vh9 04n52p6 035yn8 047n8xt 0661ql3 065dc4 0kv9d3 0bbw2z6 046488 01pvxl 040_lv 0p_tz 0gh6j94 04wddl 01k5y0 => 93 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1554): 01qb5d (0.71 #32263, 0.50 #4716, 0.33 #17007), 031hcx (0.71 #32263, 0.50 #21508, 0.46 #36872), 031778 (0.71 #32263, 0.50 #21508, 0.46 #36872), 0g9lm2 (0.71 #32263, 0.50 #21508, 0.46 #36872), 02sg5v (0.71 #32263, 0.50 #21508, 0.46 #36872), 0m313 (0.71 #32263, 0.50 #21508, 0.46 #36872), 03176f (0.71 #32263, 0.50 #21508, 0.46 #36872), 03hxsv (0.71 #32263, 0.50 #21508, 0.46 #36872), 05dptj (0.71 #32263, 0.50 #21508, 0.46 #36872), 03177r (0.71 #32263, 0.45 #46091, 0.43 #49166) >> Best rule #32263 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 071fb; >> query: (?x5607, ?x1586) <- language(?x7314, ?x5607), languages(?x10224, ?x5607), official_language(?x2804, ?x5607), film(?x10224, ?x1586), adjoins(?x2804, ?x1577), film_festivals(?x7314, ?x9080) >> conf = 0.71 => this is the best rule for 167 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 20, 36, 68, 169, 250, 251, 273, 281, 310, 340, 444, 475, 494, 538, 1065, 1308, 1353, 1363, 1421, 1456 EVAL 064_8sq language! 01k5y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 04wddl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 0gh6j94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 0p_tz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 040_lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 01pvxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 046488 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 0bbw2z6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 0kv9d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 065dc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 047n8xt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 035yn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 04n52p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 0cd2vh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 04w7rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 0340hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 04dsnp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 02z3r8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 01hp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 93.000 58.000 0.714 http://example.org/film/film/language EVAL 064_8sq language! 0c0yh4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 93.000 58.000 0.714 http://example.org/film/film/language #17351-01pv91 PRED entity: 01pv91 PRED relation: films! PRED expected values: 09b5t => 131 concepts (54 used for prediction) PRED predicted values (max 10 best out of 94): 07s2s (0.15 #1490, 0.07 #717, 0.06 #1645), 081pw (0.14 #158, 0.12 #312, 0.09 #2796), 0fx2s (0.12 #381, 0.10 #227, 0.06 #3019), 0d1w9 (0.10 #191, 0.08 #345, 0.05 #1583), 07jq_ (0.10 #236, 0.08 #390, 0.04 #5050), 018h2 (0.10 #177, 0.08 #331, 0.04 #2504), 01vq3 (0.08 #1744, 0.08 #505, 0.06 #4541), 0g1x2_ (0.08 #336, 0.05 #182, 0.04 #1574), 06d4h (0.08 #3922, 0.07 #4232, 0.07 #6413), 01d5g (0.07 #1656, 0.04 #728, 0.04 #1812) >> Best rule #1490 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 0dq626; 03twd6; 02n72k; >> query: (?x2539, 07s2s) <- genre(?x2539, ?x811), films(?x5954, ?x2539), ?x811 = 03k9fj, film(?x988, ?x2539) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pv91 films! 09b5t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 54.000 0.147 http://example.org/film/film_subject/films #17350-050t68 PRED entity: 050t68 PRED relation: award_nominee! PRED expected values: 05gml8 02p7_k => 79 concepts (31 used for prediction) PRED predicted values (max 10 best out of 882): 02ct_k (0.81 #50937, 0.81 #67147, 0.81 #62514), 021vwt (0.81 #50937, 0.81 #67147, 0.81 #62514), 02p65p (0.81 #50937, 0.81 #67147, 0.81 #62514), 05gml8 (0.81 #50937, 0.81 #67147, 0.81 #62514), 050t68 (0.52 #896, 0.21 #27782, 0.04 #48621), 02p7_k (0.48 #820, 0.02 #10080, 0.02 #3135), 0f6_x (0.28 #23151, 0.26 #69463, 0.25 #43989), 01d0b1 (0.28 #23151, 0.26 #69463, 0.25 #43989), 015qq1 (0.28 #23151, 0.26 #69463, 0.25 #43989), 01my4f (0.28 #23151, 0.26 #69463, 0.25 #43989) >> Best rule #50937 for best value: >> intensional similarity = 4 >> extensional distance = 1375 >> proper extension: 0knjh; >> query: (?x3932, ?x192) <- award_nominee(?x3932, ?x9655), award_nominee(?x3932, ?x192), award(?x9655, ?x678), category(?x9655, ?x134) >> conf = 0.81 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 6 EVAL 050t68 award_nominee! 02p7_k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 31.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 050t68 award_nominee! 05gml8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 79.000 31.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17349-06cm5 PRED entity: 06cm5 PRED relation: featured_film_locations PRED expected values: 02_286 => 51 concepts (43 used for prediction) PRED predicted values (max 10 best out of 43): 0dclg (0.15 #53, 0.01 #2456, 0.01 #2696), 02_286 (0.15 #1220, 0.14 #500, 0.14 #1941), 04jpl (0.09 #729, 0.08 #969, 0.06 #3373), 030qb3t (0.08 #279, 0.07 #3403, 0.06 #3162), 0h7h6 (0.08 #43, 0.02 #763, 0.01 #3166), 03pzf (0.08 #176), 0rh6k (0.04 #1201, 0.04 #1922, 0.04 #2404), 02nd_ (0.04 #596, 0.02 #1316, 0.02 #1796), 07b_l (0.03 #317, 0.02 #3441, 0.01 #1277), 027kp3 (0.02 #1300) >> Best rule #53 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 01cgz; >> query: (?x6137, 0dclg) <- films(?x1967, ?x6137), ?x1967 = 01cgz >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #1220 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 213 *> proper extension: 06gjk9; *> query: (?x6137, 02_286) <- nominated_for(?x1107, ?x6137), award_winner(?x6137, ?x1554), ?x1107 = 019f4v *> conf = 0.15 ranks of expected_values: 2 EVAL 06cm5 featured_film_locations 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 51.000 43.000 0.154 http://example.org/film/film/featured_film_locations #17348-04hwbq PRED entity: 04hwbq PRED relation: film_release_distribution_medium PRED expected values: 02nxhr => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 3): 07c52 (0.09 #39, 0.08 #47, 0.08 #63), 02nxhr (0.07 #1, 0.06 #14, 0.05 #10), 07z4p (0.06 #41, 0.06 #49, 0.06 #61) >> Best rule #39 for best value: >> intensional similarity = 5 >> extensional distance = 188 >> proper extension: 014lc_; 0ds35l9; 0g56t9t; 02vxq9m; 028_yv; 02vp1f_; 01gc7; 07gp9; 0ddfwj1; 0ds3t5x; ... >> query: (?x1259, 07c52) <- film_release_region(?x1259, ?x985), film_release_region(?x1259, ?x94), ?x985 = 0k6nt, ?x94 = 09c7w0, nominated_for(?x601, ?x1259) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 25 *> proper extension: 011yxg; 020fcn; 026p4q7; 019vhk; 049xgc; 0y_pg; *> query: (?x1259, 02nxhr) <- nominated_for(?x2706, ?x1259), nominated_for(?x1307, ?x1259), nominated_for(?x640, ?x1259), ?x640 = 02hsq3m, ?x1307 = 0gq9h, award(?x224, ?x2706) *> conf = 0.07 ranks of expected_values: 2 EVAL 04hwbq film_release_distribution_medium 02nxhr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 69.000 0.089 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17347-03mr85 PRED entity: 03mr85 PRED relation: music PRED expected values: 02wb6d => 72 concepts (56 used for prediction) PRED predicted values (max 10 best out of 91): 02sj1x (0.17 #477, 0.12 #1109, 0.10 #898), 02wb6d (0.11 #1180, 0.09 #1390, 0.09 #127), 02rf51g (0.09 #209, 0.08 #420, 0.05 #1262), 01pr6q7 (0.09 #62, 0.08 #273, 0.04 #483), 01l3mk3 (0.09 #145, 0.08 #356, 0.04 #987), 02bn75 (0.09 #144, 0.08 #355, 0.02 #1617), 05z_p6 (0.09 #3583, 0.09 #2736, 0.07 #3160), 02w670 (0.09 #1143, 0.09 #511, 0.07 #1353), 014kg4 (0.09 #2736, 0.08 #6116, 0.08 #6961), 018fmr (0.09 #2736, 0.08 #6961, 0.07 #3159) >> Best rule #477 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 03hjv97; 0c5dd; 04mzf8; 02q52q; 083skw; 0k4f3; 0f4yh; 0jymd; 048rn; 0bm2x; ... >> query: (?x12766, 02sj1x) <- film(?x574, ?x12766), film(?x8366, ?x12766), film_art_direction_by(?x12766, ?x4251), produced_by(?x12766, ?x11751) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1180 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 0k4kk; 01wb95; 0kb07; 0cq8qq; 0hvvf; *> query: (?x12766, 02wb6d) <- genre(?x12766, ?x1509), film_art_direction_by(?x12766, ?x4251), genre(?x5278, ?x1509), ?x5278 = 0bm2x *> conf = 0.11 ranks of expected_values: 2 EVAL 03mr85 music 02wb6d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 56.000 0.174 http://example.org/film/film/music #17346-01nkxvx PRED entity: 01nkxvx PRED relation: origin PRED expected values: 0pswc => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 88): 0kpys (0.33 #1654, 0.33 #1181, 0.20 #6613), 056_y (0.25 #87, 0.11 #2922, 0.10 #1741), 0f2rq (0.20 #575, 0.08 #1993, 0.05 #3410), 0f2v0 (0.19 #2670, 0.17 #2906, 0.04 #3142), 01b8w_ (0.17 #1095, 0.12 #1332, 0.11 #1568), 030qb3t (0.17 #978, 0.12 #3577, 0.11 #1451), 0fw4v (0.12 #2737, 0.11 #2973, 0.10 #1792), 0cr3d (0.12 #1237, 0.08 #1946, 0.03 #3363), 02dtg (0.11 #1427, 0.08 #1900, 0.05 #3317), 09c7w0 (0.10 #2363, 0.07 #2127, 0.06 #2600) >> Best rule #1654 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01m65sp; 043c4j; 04mky3; >> query: (?x8599, ?x2949) <- artists(?x2542, ?x8599), ?x2542 = 03xnwz, place_of_birth(?x8599, ?x2949) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nkxvx origin 0pswc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 113.000 0.333 http://example.org/music/artist/origin #17345-09r9m7 PRED entity: 09r9m7 PRED relation: profession PRED expected values: 0nbcg => 115 concepts (76 used for prediction) PRED predicted values (max 10 best out of 60): 02hrh1q (0.77 #7321, 0.76 #8490, 0.76 #7613), 0dxtg (0.60 #4544, 0.46 #2934, 0.46 #2496), 0nbcg (0.51 #3243, 0.49 #6169, 0.45 #4853), 02jknp (0.47 #2491, 0.44 #2929, 0.40 #4539), 016z4k (0.45 #1904, 0.44 #3072, 0.43 #3218), 0dz3r (0.41 #3216, 0.38 #1902, 0.37 #4241), 03gjzk (0.40 #4546, 0.36 #2498, 0.36 #2936), 039v1 (0.32 #3248, 0.26 #4858, 0.23 #5005), 0n1h (0.20 #1910, 0.17 #2056, 0.17 #3078), 0cbd2 (0.17 #5269, 0.14 #4538, 0.14 #4099) >> Best rule #7321 for best value: >> intensional similarity = 4 >> extensional distance = 1056 >> proper extension: 01vrx3g; 0436f4; 066m4g; 01wdqrx; 0n6f8; 0gcdzz; 012x4t; 02tr7d; 05fnl9; 015882; ... >> query: (?x5772, 02hrh1q) <- profession(?x5772, ?x319), award_winner(?x1821, ?x5772), profession(?x1384, ?x319), ?x1384 = 048lv >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #3243 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 416 *> proper extension: 032t2z; 01w923; 023l9y; 04cr6qv; 02r3cn; 018y81; 01ydzx; 0130sy; 04_jsg; *> query: (?x5772, 0nbcg) <- profession(?x5772, ?x1183), ?x1183 = 09jwl, instrumentalists(?x316, ?x5772) *> conf = 0.51 ranks of expected_values: 3 EVAL 09r9m7 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 76.000 0.769 http://example.org/people/person/profession #17344-027gs1_ PRED entity: 027gs1_ PRED relation: award! PRED expected values: 07s6tbm 098n5 06w58f => 46 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1966): 070j61 (0.82 #26949, 0.77 #13473, 0.49 #23579), 03c6vl (0.82 #26949, 0.77 #13473, 0.49 #23579), 012c6x (0.82 #26949, 0.77 #13473, 0.48 #23578), 0pyww (0.56 #24978, 0.40 #8133, 0.38 #21608), 018ygt (0.50 #5212, 0.36 #28794, 0.33 #32162), 025mb_ (0.44 #26191, 0.40 #9346, 0.38 #19452), 016tb7 (0.44 #24596, 0.40 #7751, 0.27 #27965), 0m66w (0.44 #25307, 0.36 #28676, 0.33 #32044), 04cl1 (0.40 #11459, 0.38 #21565, 0.36 #28304), 09yrh (0.40 #8032, 0.38 #18138, 0.33 #24877) >> Best rule #26949 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0cqhk0; 0cqhmg; >> query: (?x7510, ?x201) <- nominated_for(?x7510, ?x8536), nominated_for(?x7510, ?x7317), ?x7317 = 05p9_ql, nominated_for(?x1145, ?x8536), award_winner(?x7510, ?x201), ceremony(?x7510, ?x1265) >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #6307 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 09qvc0; *> query: (?x7510, 06w58f) <- nominated_for(?x7510, ?x8536), nominated_for(?x7510, ?x7317), nominated_for(?x7510, ?x6070), ?x7317 = 05p9_ql, nominated_for(?x1145, ?x8536), category_of(?x7510, ?x2758), ?x6070 = 03nt59 *> conf = 0.25 ranks of expected_values: 87, 120 EVAL 027gs1_ award! 06w58f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 16.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 027gs1_ award! 098n5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 46.000 16.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 027gs1_ award! 07s6tbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 16.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17343-03bnd9 PRED entity: 03bnd9 PRED relation: category PRED expected values: 08mbj5d => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #21, 0.91 #54, 0.90 #69) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 0288zy; 02g839; 06jk5_; 0bthb; 02hft3; 037s9x; 031n8c; 01cyd5; 017z88; 01swxv; ... >> query: (?x11474, 08mbj5d) <- school_type(?x11474, ?x1962), school_type(?x6501, ?x1962), currency(?x11474, ?x170), ?x6501 = 01ljpm >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bnd9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.909 http://example.org/common/topic/webpage./common/webpage/category #17342-0th3k PRED entity: 0th3k PRED relation: source PRED expected values: 0jbk9 => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #76, 0.80 #40, 0.78 #46) >> Best rule #76 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x13370, 0jbk9) <- category(?x13370, ?x134), ?x134 = 08mbj5d, place(?x13370, ?x13370) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0th3k source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #17341-04gnbv1 PRED entity: 04gnbv1 PRED relation: profession PRED expected values: 0dxtg 03gjzk => 107 concepts (103 used for prediction) PRED predicted values (max 10 best out of 61): 03gjzk (0.89 #313, 0.86 #1803, 0.86 #2101), 0dxtg (0.87 #311, 0.83 #907, 0.69 #1801), 02hrh1q (0.84 #3143, 0.83 #2547, 0.81 #3441), 02jknp (0.45 #4627, 0.45 #4478, 0.27 #603), 02krf9 (0.40 #27, 0.32 #2113, 0.32 #2262), 0cbd2 (0.27 #11328, 0.26 #12819, 0.25 #9688), 09jwl (0.20 #6279, 0.19 #5086, 0.19 #6130), 018gz8 (0.15 #613, 0.15 #2699, 0.15 #911), 016z4k (0.14 #451, 0.13 #1047, 0.12 #6264), 0np9r (0.14 #617, 0.13 #2107, 0.13 #2256) >> Best rule #313 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 04n7njg; 03ft8; 0jt90f5; 01jbx1; 023qfd; 02q6cv4; >> query: (?x4618, 03gjzk) <- profession(?x4618, ?x319), producer_type(?x4618, ?x632), tv_program(?x4618, ?x3310) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04gnbv1 profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 103.000 0.886 http://example.org/people/person/profession EVAL 04gnbv1 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 103.000 0.886 http://example.org/people/person/profession #17340-071ywj PRED entity: 071ywj PRED relation: film PRED expected values: 03m5y9p => 94 concepts (69 used for prediction) PRED predicted values (max 10 best out of 414): 01rwpj (0.29 #862, 0.01 #9762), 0dl6fv (0.14 #3258, 0.14 #1478, 0.01 #10378), 03177r (0.14 #2239, 0.13 #14241, 0.13 #17802), 03176f (0.14 #2479, 0.13 #14241, 0.13 #17802), 03hxsv (0.14 #2887, 0.13 #14241, 0.13 #17802), 031hcx (0.14 #3044, 0.13 #14241, 0.03 #10164), 03nfnx (0.14 #1393, 0.03 #4953, 0.02 #8513), 05pdh86 (0.14 #744, 0.03 #76545), 01jwxx (0.14 #841, 0.02 #9741), 016z9n (0.14 #365, 0.02 #30628, 0.02 #35968) >> Best rule #862 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0c_gcr; >> query: (?x3011, 01rwpj) <- profession(?x3011, ?x1032), film(?x3011, ?x504), ?x504 = 0g5qs2k >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 071ywj film 03m5y9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 69.000 0.286 http://example.org/film/actor/film./film/performance/film #17339-011yhm PRED entity: 011yhm PRED relation: genre PRED expected values: 0vgkd => 150 concepts (85 used for prediction) PRED predicted values (max 10 best out of 98): 02l7c8 (0.56 #2237, 0.45 #365, 0.43 #1067), 03k9fj (0.46 #244, 0.41 #1297, 0.41 #127), 01hmnh (0.42 #250, 0.29 #835, 0.28 #1303), 04xvlr (0.40 #1054, 0.30 #2224, 0.29 #352), 02kdv5l (0.38 #704, 0.37 #1406, 0.36 #119), 060__y (0.29 #1068, 0.26 #366, 0.25 #2238), 01t_vv (0.27 #636, 0.15 #8360, 0.12 #2274), 01g6gs (0.25 #1657, 0.23 #2125, 0.19 #1774), 06n90 (0.23 #245, 0.23 #128, 0.21 #1298), 06cvj (0.21 #8312, 0.15 #705, 0.13 #1641) >> Best rule #2237 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 03cfkrw; 01fwzk; >> query: (?x6553, 02l7c8) <- award(?x6553, ?x289), nominated_for(?x749, ?x6553), ?x749 = 094qd5 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #126 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 0b73_1d; 02pxmgz; 04t6fk; 0dnqr; 0hfzr; 07bwr; 02704ff; 011xg5; 01s7w3; 025s1wg; ... *> query: (?x6553, 0vgkd) <- executive_produced_by(?x6553, ?x3568), music(?x6553, ?x4644), edited_by(?x6553, ?x826), written_by(?x6553, ?x3572) *> conf = 0.09 ranks of expected_values: 28 EVAL 011yhm genre 0vgkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 150.000 85.000 0.559 http://example.org/film/film/genre #17338-0flw6 PRED entity: 0flw6 PRED relation: award_winner! PRED expected values: 05zksls => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 134): 013b2h (0.10 #2011, 0.07 #631, 0.06 #2563), 05pd94v (0.09 #1934, 0.06 #2486, 0.05 #2900), 02rjjll (0.09 #1937, 0.06 #2489, 0.05 #2903), 01s695 (0.09 #1935, 0.06 #2487, 0.05 #555), 0466p0j (0.09 #2007, 0.05 #2559, 0.04 #3525), 01c6qp (0.09 #1951, 0.06 #2503, 0.05 #571), 019bk0 (0.08 #1948, 0.05 #2500, 0.04 #3466), 02cg41 (0.08 #2055, 0.05 #2607, 0.05 #5091), 01bx35 (0.08 #1939, 0.05 #2491, 0.04 #3457), 0jzphpx (0.08 #591, 0.07 #1971, 0.04 #315) >> Best rule #2011 for best value: >> intensional similarity = 2 >> extensional distance = 489 >> proper extension: 09xwz; >> query: (?x4324, 013b2h) <- award_winner(?x4224, ?x4324), category(?x4324, ?x134) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #2381 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 591 *> proper extension: 02wrhj; *> query: (?x4324, 05zksls) <- nominated_for(?x4324, ?x825), film(?x4324, ?x2755), award_winner(?x4224, ?x4324) *> conf = 0.02 ranks of expected_values: 122 EVAL 0flw6 award_winner! 05zksls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 110.000 110.000 0.098 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17337-031hcx PRED entity: 031hcx PRED relation: film! PRED expected values: 015rkw 01f6zc 05kwx2 => 64 concepts (25 used for prediction) PRED predicted values (max 10 best out of 960): 0d5wn3 (0.45 #18617), 07ldhs (0.30 #877, 0.05 #14479, 0.04 #4138), 0171cm (0.25 #2486, 0.05 #14479, 0.04 #4138), 07swvb (0.20 #687, 0.05 #14479, 0.04 #4138), 0fthdk (0.20 #1581, 0.04 #4138, 0.04 #2069), 07ncs0 (0.20 #1077, 0.02 #5215, 0.02 #11419), 0n6f8 (0.20 #205), 0dgskx (0.17 #3215, 0.05 #14479, 0.04 #4138), 05kwx2 (0.17 #3153, 0.05 #14479, 0.04 #33095), 01nwwl (0.17 #2564, 0.05 #6701, 0.04 #4633) >> Best rule #18617 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 03n785; 012gk9; >> query: (?x7304, ?x4449) <- prequel(?x7304, ?x7305), film(?x488, ?x7304), nominated_for(?x4449, ?x7304) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #3153 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 04qw17; 01pv91; 04grkmd; 0bh8drv; 0dl6fv; *> query: (?x7304, 05kwx2) <- film(?x3860, ?x7304), film(?x988, ?x7304), ?x988 = 01tspc6, award_nominee(?x3860, ?x4046) *> conf = 0.17 ranks of expected_values: 9, 14, 15 EVAL 031hcx film! 05kwx2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 64.000 25.000 0.454 http://example.org/film/actor/film./film/performance/film EVAL 031hcx film! 01f6zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 64.000 25.000 0.454 http://example.org/film/actor/film./film/performance/film EVAL 031hcx film! 015rkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 64.000 25.000 0.454 http://example.org/film/actor/film./film/performance/film #17336-04b5l3 PRED entity: 04b5l3 PRED relation: position PRED expected values: 02lyr4 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 50): 01z9v6 (0.87 #237, 0.86 #312, 0.85 #355), 02lyr4 (0.86 #201, 0.85 #351, 0.84 #363), 02sddg (0.72 #319, 0.34 #274, 0.32 #101), 02dwpf (0.46 #116, 0.34 #274, 0.32 #507), 02dwn9 (0.46 #116, 0.34 #274, 0.32 #507), 017drs (0.46 #116, 0.34 #274, 0.32 #507), 02rsl1 (0.46 #116, 0.34 #274, 0.32 #507), 049k4w (0.46 #116, 0.34 #274, 0.32 #507), 01yvvn (0.46 #116, 0.34 #274, 0.32 #507), 02sg4b (0.46 #116, 0.34 #274, 0.32 #507) >> Best rule #237 for best value: >> intensional similarity = 25 >> extensional distance = 21 >> proper extension: 02h8p8; >> query: (?x11919, 01z9v6) <- colors(?x11919, ?x8271), colors(?x11919, ?x332), team(?x5727, ?x11919), ?x5727 = 02wszf, colors(?x6348, ?x8271), colors(?x2574, ?x8271), colors(?x1438, ?x8271), colors(?x662, ?x8271), ?x662 = 03lpp_, ?x2574 = 01y3v, colors(?x11559, ?x8271), colors(?x10038, ?x8271), colors(?x11587, ?x332), colors(?x10908, ?x332), colors(?x9172, ?x332), colors(?x8008, ?x332), ?x11559 = 02bpy_, ?x10038 = 06rkfs, ?x11587 = 02b190, ?x6348 = 021f30, ?x10908 = 03915c, currency(?x8008, ?x170), institution(?x1368, ?x8008), ?x9172 = 06rpd, ?x1438 = 0512p >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #201 for first EXPECTED value: *> intensional similarity = 33 *> extensional distance = 19 *> proper extension: 01d5z; 06wpc; *> query: (?x11919, 02lyr4) <- colors(?x11919, ?x332), team(?x8520, ?x11919), team(?x5727, ?x11919), team(?x4244, ?x11919), team(?x5727, ?x13260), team(?x5727, ?x10939), team(?x5727, ?x10279), team(?x5727, ?x8111), team(?x5727, ?x7060), team(?x5727, ?x6823), team(?x5727, ?x6074), team(?x5727, ?x4243), team(?x5727, ?x4208), team(?x5727, ?x2405), team(?x5727, ?x2011), team(?x5727, ?x1632), team(?x5727, ?x580), ?x4243 = 0713r, ?x6074 = 02__x, ?x2011 = 04913k, ?x10279 = 04wmvz, ?x8111 = 07147, ?x10939 = 0x0d, ?x4244 = 028c_8, ?x6823 = 07l8f, ?x1632 = 0cqt41, ?x8520 = 01z9v6, ?x7060 = 01slc, ?x580 = 05m_8, ?x13260 = 03qrh9, ?x2405 = 0x2p, sport(?x11919, ?x5063), ?x4208 = 061xq *> conf = 0.86 ranks of expected_values: 2 EVAL 04b5l3 position 02lyr4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 57.000 57.000 0.870 http://example.org/sports/sports_team/roster./baseball/baseball_roster_position/position #17335-03qsdpk PRED entity: 03qsdpk PRED relation: student PRED expected values: 01gy7r 032q8q 03x16f 04pp9s => 82 concepts (38 used for prediction) PRED predicted values (max 10 best out of 804): 083q7 (0.40 #1110, 0.33 #18, 0.29 #1764), 09b6zr (0.40 #960, 0.29 #2050, 0.25 #2706), 0kn4c (0.40 #899, 0.29 #1989, 0.25 #2426), 0d06m5 (0.33 #62, 0.20 #1154, 0.17 #1372), 02mjmr (0.33 #46, 0.20 #1138, 0.17 #1356), 0d0vj4 (0.33 #15, 0.20 #1107, 0.17 #1325), 014vk4 (0.33 #213, 0.20 #1305, 0.17 #1523), 02jr26 (0.33 #136, 0.20 #1228, 0.17 #1446), 012v1t (0.33 #122, 0.20 #1214, 0.17 #1432), 0c_md_ (0.33 #176, 0.20 #1268, 0.17 #1486) >> Best rule #1110 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 06ms6; >> query: (?x5614, 083q7) <- major_field_of_study(?x8046, ?x5614), major_field_of_study(?x1043, ?x5614), major_field_of_study(?x865, ?x5614), ?x1043 = 0kz2w, student(?x5614, ?x3395), major_field_of_study(?x8046, ?x1154), ?x1154 = 02lp1, person(?x6245, ?x3395) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3679 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 17 *> proper extension: 06ntj; *> query: (?x5614, 04pp9s) <- major_field_of_study(?x5614, ?x10046), major_field_of_study(?x5614, ?x8681), major_field_of_study(?x2008, ?x5614), major_field_of_study(?x3204, ?x8681), major_field_of_study(?x4980, ?x10046), ?x3204 = 01dq5z, ?x4980 = 01n6r0 *> conf = 0.05 ranks of expected_values: 109, 250, 581 EVAL 03qsdpk student 04pp9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 82.000 38.000 0.400 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 03qsdpk student 03x16f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 38.000 0.400 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 03qsdpk student 032q8q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 82.000 38.000 0.400 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 03qsdpk student 01gy7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 82.000 38.000 0.400 http://example.org/education/field_of_study/students_majoring./education/education/student #17334-0g_g2 PRED entity: 0g_g2 PRED relation: group! PRED expected values: 018vs => 93 concepts (76 used for prediction) PRED predicted values (max 10 best out of 113): 018vs (0.68 #1045, 0.67 #184, 0.61 #2163), 03qjg (0.42 #1079, 0.33 #218, 0.22 #2197), 05r5c (0.34 #1040, 0.23 #2158, 0.20 #437), 01vj9c (0.27 #2164, 0.26 #1046, 0.19 #1218), 0l14qv (0.26 #1038, 0.23 #2156, 0.17 #177), 013y1f (0.18 #1059, 0.13 #2177, 0.07 #1403), 042v_gx (0.17 #180, 0.13 #1041, 0.12 #1213), 04rzd (0.17 #202, 0.13 #1063, 0.12 #2181), 07y_7 (0.17 #174, 0.13 #1035, 0.11 #2153), 07brj (0.17 #191, 0.13 #1052, 0.10 #277) >> Best rule #1045 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 01qqwp9; >> query: (?x4957, 018vs) <- artists(?x1000, ?x4957), group(?x645, ?x4957), ?x645 = 028tv0, group(?x300, ?x4957) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g_g2 group! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 76.000 0.684 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17333-0478__m PRED entity: 0478__m PRED relation: influenced_by PRED expected values: 01vs_v8 09889g 0bk1p => 123 concepts (76 used for prediction) PRED predicted values (max 10 best out of 252): 04wqr (0.25 #12, 0.14 #447, 0.08 #1753), 01hmk9 (0.19 #2397, 0.08 #12411, 0.06 #14589), 0p_47 (0.19 #2284, 0.06 #12298, 0.06 #6635), 081lh (0.19 #2197, 0.06 #6548, 0.06 #12211), 014z8v (0.12 #2298, 0.10 #12312, 0.06 #14490), 01wp_jm (0.12 #2518, 0.07 #12532, 0.03 #14710), 014zfs (0.12 #2202, 0.05 #3072, 0.05 #14394), 01svq8 (0.12 #2602, 0.04 #6953, 0.03 #8260), 07c0j (0.11 #21342, 0.06 #2636, 0.03 #12215), 08433 (0.11 #21342, 0.04 #12212, 0.04 #14390) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 016fnb; >> query: (?x4593, 04wqr) <- award_nominee(?x4594, ?x4593), ?x4594 = 05vzw3, religion(?x4593, ?x1985) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #21342 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 464 *> proper extension: 055yr; *> query: (?x4593, ?x1029) <- influenced_by(?x4593, ?x4620), influenced_by(?x4620, ?x1029) *> conf = 0.11 ranks of expected_values: 15, 22, 181 EVAL 0478__m influenced_by 0bk1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 123.000 76.000 0.250 http://example.org/influence/influence_node/influenced_by EVAL 0478__m influenced_by 09889g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 123.000 76.000 0.250 http://example.org/influence/influence_node/influenced_by EVAL 0478__m influenced_by 01vs_v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 123.000 76.000 0.250 http://example.org/influence/influence_node/influenced_by #17332-03y3bp7 PRED entity: 03y3bp7 PRED relation: genre PRED expected values: 0c4xc => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 76): 07s9rl0 (0.66 #1771, 0.57 #1287, 0.55 #1369), 025s89p (0.50 #209, 0.50 #49, 0.35 #2333), 01hmnh (0.50 #15, 0.35 #2333, 0.33 #175), 0pr6f (0.50 #207, 0.35 #2333, 0.29 #369), 06n90 (0.50 #172, 0.35 #2333, 0.26 #1462), 01htzx (0.50 #176, 0.35 #2333, 0.25 #16), 0c4xc (0.44 #601, 0.43 #842, 0.42 #1004), 01t_vv (0.39 #271, 0.28 #833, 0.27 #592), 03k9fj (0.35 #2333, 0.25 #1460, 0.21 #332), 095bb (0.35 #2333, 0.25 #34, 0.21 #356) >> Best rule #1771 for best value: >> intensional similarity = 5 >> extensional distance = 198 >> proper extension: 05z43v; 06w7mlh; 045r_9; 06qxh; 070ltt; 07qht4; 03cf9ly; 04x4gj; 0d_rw; 05397h; >> query: (?x3102, 07s9rl0) <- genre(?x3102, ?x258), genre(?x10425, ?x258), genre(?x6529, ?x258), ?x6529 = 06823p, ?x10425 = 02x0fs9 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #601 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 020qr4; 02xhpl; 01cjhz; 0jq2r; 045qmr; 047m_w; 06f0k; *> query: (?x3102, 0c4xc) <- genre(?x3102, ?x258), ?x258 = 05p553, languages(?x3102, ?x254), ?x254 = 02h40lc *> conf = 0.44 ranks of expected_values: 7 EVAL 03y3bp7 genre 0c4xc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 56.000 56.000 0.655 http://example.org/tv/tv_program/genre #17331-023znp PRED entity: 023znp PRED relation: student PRED expected values: 026670 => 144 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1538): 0f4vbz (0.50 #2423, 0.03 #29506, 0.02 #56590), 083chw (0.25 #2109, 0.10 #6276, 0.08 #8359), 0ff3y (0.25 #4143, 0.06 #24975, 0.03 #31226), 051wwp (0.25 #2933, 0.05 #4167, 0.02 #52083), 03xp8d5 (0.25 #2816, 0.05 #4167, 0.02 #52083), 021bk (0.25 #2435, 0.05 #4167, 0.01 #23267), 016732 (0.25 #3251, 0.05 #13668, 0.05 #11584), 01g257 (0.25 #2322, 0.05 #10655, 0.04 #14822), 059_gf (0.25 #3057, 0.05 #13474, 0.04 #15557), 015wc0 (0.25 #3771, 0.03 #30854, 0.03 #24603) >> Best rule #2423 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0fr9jp; >> query: (?x3922, 0f4vbz) <- state_province_region(?x3922, ?x2020), student(?x3922, ?x4107), student(?x3922, ?x3581), award_nominee(?x2328, ?x4107), ?x3581 = 01l1hr >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3748 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0fr9jp; *> query: (?x3922, 026670) <- state_province_region(?x3922, ?x2020), student(?x3922, ?x4107), student(?x3922, ?x3581), award_nominee(?x2328, ?x4107), ?x3581 = 01l1hr *> conf = 0.25 ranks of expected_values: 82 EVAL 023znp student 026670 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 144.000 67.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/student #17330-01xq8v PRED entity: 01xq8v PRED relation: genre PRED expected values: 060__y 01hmnh => 123 concepts (79 used for prediction) PRED predicted values (max 10 best out of 96): 02kdv5l (0.60 #118, 0.47 #3170, 0.41 #1292), 03k9fj (0.60 #127, 0.34 #1888, 0.32 #2122), 03npn (0.51 #2464, 0.51 #5871, 0.51 #5753), 07yjb (0.51 #2464, 0.51 #5871, 0.51 #5753), 05p553 (0.41 #706, 0.41 #471, 0.37 #2349), 06n90 (0.40 #128, 0.28 #1655, 0.26 #1772), 01t_vv (0.33 #51, 0.15 #1106, 0.10 #4395), 0lsxr (0.33 #3176, 0.22 #241, 0.20 #124), 01hmnh (0.27 #1893, 0.25 #1659, 0.24 #1776), 0jxy (0.26 #980, 0.26 #1333, 0.20 #159) >> Best rule #118 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0pb33; 018nnz; 01dyvs; >> query: (?x7741, 02kdv5l) <- executive_produced_by(?x7741, ?x11965), genre(?x7741, ?x53), film(?x6917, ?x7741), ?x6917 = 0479b >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1893 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 167 *> proper extension: 0267wwv; *> query: (?x7741, 01hmnh) <- language(?x7741, ?x90), nominated_for(?x929, ?x7741), story_by(?x7741, ?x11410), production_companies(?x7741, ?x541) *> conf = 0.27 ranks of expected_values: 9, 12 EVAL 01xq8v genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 123.000 79.000 0.600 http://example.org/film/film/genre EVAL 01xq8v genre 060__y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 123.000 79.000 0.600 http://example.org/film/film/genre #17329-0d2fd7 PRED entity: 0d2fd7 PRED relation: industry PRED expected values: 01mw1 => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 46): 01mw1 (0.84 #471, 0.80 #1788, 0.77 #1082), 02q3wl (0.33 #31, 0.11 #172, 0.09 #266), 01mw2x (0.33 #34, 0.11 #175, 0.09 #269), 02vxn (0.23 #3861, 0.20 #707, 0.19 #2308), 01mf0 (0.22 #1270, 0.19 #2118, 0.18 #2918), 019z7b (0.22 #1270, 0.19 #2118, 0.18 #2918), 02jjt (0.20 #525, 0.18 #572, 0.16 #713), 03qh03g (0.15 #992, 0.15 #522, 0.15 #898), 02h400t (0.15 #542, 0.14 #589, 0.12 #730), 0hz28 (0.15 #546, 0.14 #593, 0.12 #734) >> Best rule #471 for best value: >> intensional similarity = 7 >> extensional distance = 17 >> proper extension: 02mdty; >> query: (?x9265, 01mw1) <- state_province_region(?x9265, ?x335), industry(?x9265, ?x10022), citytown(?x9265, ?x739), industry(?x13222, ?x10022), industry(?x5961, ?x10022), ?x5961 = 0123j6, ?x13222 = 021gk7 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d2fd7 industry 01mw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.842 http://example.org/business/business_operation/industry #17328-0bxqq PRED entity: 0bxqq PRED relation: adjoins! PRED expected values: 0l34j => 186 concepts (53 used for prediction) PRED predicted values (max 10 best out of 528): 0kpzy (0.25 #21118, 0.23 #34416, 0.16 #3429), 0bxqq (0.25 #21118, 0.23 #34416, 0.08 #4182), 0l2hf (0.25 #21118, 0.23 #34416, 0.05 #3312), 0kq0q (0.25 #21118, 0.23 #34416, 0.05 #3825), 0l34j (0.25 #21118, 0.23 #34416, 0.04 #4135), 0l2mg (0.25 #21118, 0.23 #34416, 0.02 #6126), 0n6mc (0.25 #21118, 0.23 #34416), 0l2xl (0.21 #3497, 0.17 #4280, 0.06 #7410), 0d6lp (0.16 #3290, 0.12 #4073, 0.05 #7203), 0l2vz (0.16 #3357, 0.08 #4140, 0.06 #2576) >> Best rule #21118 for best value: >> intensional similarity = 4 >> extensional distance = 126 >> proper extension: 0mwk9; >> query: (?x5892, ?x10702) <- adjoins(?x13522, ?x5892), county_seat(?x5892, ?x5893), contains(?x94, ?x5893), adjoins(?x13522, ?x10702) >> conf = 0.25 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5 EVAL 0bxqq adjoins! 0l34j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 186.000 53.000 0.252 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #17327-0136g9 PRED entity: 0136g9 PRED relation: award_nominee! PRED expected values: 02q42j_ => 98 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1153): 02yygk (0.81 #100205, 0.81 #86222, 0.81 #72238), 05szp (0.81 #100205, 0.81 #86222, 0.81 #72238), 01pfkw (0.81 #100205, 0.81 #86222, 0.81 #72238), 02q42j_ (0.81 #100205, 0.81 #86222, 0.81 #72238), 0136g9 (0.71 #281, 0.05 #102536, 0.02 #4941), 04lgymt (0.14 #102, 0.02 #23402, 0.01 #35053), 016732 (0.14 #1548, 0.01 #24848), 03f1r6t (0.14 #1233), 0l6px (0.12 #7494, 0.10 #2834, 0.05 #102536), 01ksr1 (0.12 #7738, 0.10 #3078, 0.05 #24048) >> Best rule #100205 for best value: >> intensional similarity = 3 >> extensional distance = 1291 >> proper extension: 01c1px; 02v49c; 0knjh; >> query: (?x1367, ?x3568) <- award_winner(?x1367, ?x12156), award_nominee(?x1367, ?x3568), gender(?x1367, ?x231) >> conf = 0.81 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 0136g9 award_nominee! 02q42j_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 47.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17326-028qyn PRED entity: 028qyn PRED relation: student! PRED expected values: 065y4w7 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 156): 065y4w7 (0.14 #3703, 0.06 #6867, 0.06 #5285), 01w5m (0.11 #2740, 0.11 #632, 0.11 #105), 0bwfn (0.11 #802, 0.11 #275, 0.10 #3964), 05zl0 (0.11 #729, 0.11 #202, 0.06 #2837), 03ksy (0.09 #1160, 0.06 #2741, 0.05 #7487), 01t8sr (0.09 #1088, 0.06 #2669, 0.01 #7415), 014zws (0.09 #1385, 0.03 #4020, 0.01 #5602), 02zd460 (0.09 #1224, 0.01 #24943, 0.01 #7551), 01jtp7 (0.09 #1110, 0.01 #5855, 0.01 #6382), 017z88 (0.07 #10626, 0.06 #17477, 0.06 #2717) >> Best rule #3703 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0c1pj; 02q_cc; 04wvhz; 0pz91; 0343h; 09gffmz; 0gz5hs; 06pj8; 081nh; 072twv; ... >> query: (?x10539, 065y4w7) <- award_winner(?x724, ?x10539), profession(?x10539, ?x106), award_nominee(?x163, ?x10539), organizations_founded(?x10539, ?x8489) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028qyn student! 065y4w7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.138 http://example.org/education/educational_institution/students_graduates./education/education/student #17325-05qsxy PRED entity: 05qsxy PRED relation: gender PRED expected values: 05zppz => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #1, 0.72 #60, 0.72 #158), 02zsn (0.51 #53, 0.29 #6, 0.28 #4) >> Best rule #1 for best value: >> intensional similarity = 2 >> extensional distance = 103 >> proper extension: 023qfd; 01r4zfk; 046_v; 0p_r5; >> query: (?x2543, 05zppz) <- type_of_union(?x2543, ?x566), tv_program(?x2543, ?x4011) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qsxy gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.790 http://example.org/people/person/gender #17324-0ks67 PRED entity: 0ks67 PRED relation: school_type PRED expected values: 05jxkf => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 17): 05jxkf (0.84 #993, 0.55 #377, 0.55 #421), 01rs41 (0.68 #554, 0.27 #4, 0.25 #1396), 05pcjw (0.40 #133, 0.38 #177, 0.38 #67), 01_srz (0.08 #486, 0.06 #530, 0.06 #222), 04399 (0.05 #496, 0.04 #474, 0.04 #540), 01y64 (0.05 #32, 0.03 #956, 0.03 #560), 02p0qmm (0.04 #118, 0.03 #580, 0.03 #448), 0bpgx (0.04 #371, 0.03 #459, 0.02 #591), 02dk5q (0.04 #358, 0.03 #446, 0.02 #578), 04qbv (0.04 #564, 0.03 #58, 0.02 #80) >> Best rule #993 for best value: >> intensional similarity = 3 >> extensional distance = 329 >> proper extension: 021l5s; 01y9st; 0269kx; 01zn4y; 057wlm; 02jx_v; 019tfm; >> query: (?x5807, 05jxkf) <- school_type(?x5807, ?x4994), school_type(?x8220, ?x4994), ?x8220 = 0c5x_ >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ks67 school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.840 http://example.org/education/educational_institution/school_type #17323-0b_5d PRED entity: 0b_5d PRED relation: film_crew_role PRED expected values: 09zzb8 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 26): 09zzb8 (0.75 #891, 0.70 #1526, 0.70 #2304), 0ch6mp2 (0.74 #899, 0.71 #268, 0.70 #1534), 02r96rf (0.64 #894, 0.60 #2307, 0.59 #152), 09vw2b7 (0.64 #898, 0.58 #2311, 0.56 #1533), 01vx2h (0.32 #903, 0.28 #2316, 0.28 #161), 01pvkk (0.28 #384, 0.28 #904, 0.28 #2317), 02ynfr (0.25 #815, 0.18 #908, 0.15 #277), 04pyp5 (0.25 #815, 0.12 #19, 0.06 #2322), 089g0h (0.25 #815, 0.10 #912, 0.09 #1733), 0d2b38 (0.25 #815, 0.10 #918, 0.10 #287) >> Best rule #891 for best value: >> intensional similarity = 3 >> extensional distance = 536 >> proper extension: 01q2nx; >> query: (?x2958, 09zzb8) <- genre(?x2958, ?x53), film_crew_role(?x2958, ?x2095), featured_film_locations(?x2958, ?x739) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_5d film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.751 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17322-0n08r PRED entity: 0n08r PRED relation: film_regional_debut_venue PRED expected values: 07zmj => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 16): 018cvf (0.12 #179, 0.10 #373, 0.10 #48), 0j63cyr (0.05 #371, 0.05 #242, 0.04 #46), 0kfhjq0 (0.05 #372, 0.04 #178, 0.03 #243), 07zmj (0.04 #193, 0.03 #387, 0.03 #95), 0gg7gsl (0.04 #171, 0.03 #73, 0.03 #40), 07751 (0.04 #173, 0.02 #367, 0.02 #42), 02_286 (0.03 #35, 0.02 #360, 0.01 #459), 04jpl (0.03 #33), 0g57ws5 (0.02 #183, 0.02 #248, 0.01 #377), 04_m9gk (0.01 #381, 0.01 #187, 0.01 #56) >> Best rule #179 for best value: >> intensional similarity = 5 >> extensional distance = 168 >> proper extension: 045j3w; 0bmc4cm; 0gtsxr4; 0fpgp26; >> query: (?x11065, 018cvf) <- nominated_for(?x350, ?x11065), film_release_region(?x11065, ?x1264), film_release_region(?x11065, ?x390), ?x390 = 0chghy, ?x1264 = 0345h >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #193 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 168 *> proper extension: 045j3w; 0bmc4cm; 0gtsxr4; 0fpgp26; *> query: (?x11065, 07zmj) <- nominated_for(?x350, ?x11065), film_release_region(?x11065, ?x1264), film_release_region(?x11065, ?x390), ?x390 = 0chghy, ?x1264 = 0345h *> conf = 0.04 ranks of expected_values: 4 EVAL 0n08r film_regional_debut_venue 07zmj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 76.000 0.118 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #17321-0j_c PRED entity: 0j_c PRED relation: profession PRED expected values: 03gjzk => 119 concepts (104 used for prediction) PRED predicted values (max 10 best out of 78): 03gjzk (0.61 #4074, 0.49 #592, 0.47 #1752), 0cbd2 (0.47 #4794, 0.47 #2618, 0.44 #6969), 018gz8 (0.41 #739, 0.38 #1029, 0.34 #1319), 0kyk (0.33 #2638, 0.31 #4814, 0.31 #6989), 09jwl (0.31 #1466, 0.23 #2047, 0.23 #1611), 0np9r (0.30 #1903, 0.30 #2194, 0.29 #308), 016z4k (0.25 #4, 0.22 #1454, 0.15 #2035), 01c72t (0.25 #21, 0.12 #2488, 0.11 #2923), 0dz3r (0.18 #1452, 0.17 #1597, 0.14 #6095), 0nbcg (0.18 #1478, 0.15 #2495, 0.14 #1043) >> Best rule #4074 for best value: >> intensional similarity = 2 >> extensional distance = 262 >> proper extension: 06v8s0; 04n7njg; 06w33f8; 01c58j; 0c8hct; 01f2f8; 01rc4p; 03rqww; 06zmg7m; 0bbxd3; ... >> query: (?x2465, 03gjzk) <- profession(?x2465, ?x1943), ?x1943 = 02krf9 >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j_c profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 104.000 0.614 http://example.org/people/person/profession #17320-0435vm PRED entity: 0435vm PRED relation: written_by PRED expected values: 09pl3s => 121 concepts (88 used for prediction) PRED predicted values (max 10 best out of 114): 02qzjj (0.35 #11101, 0.34 #15479, 0.34 #12448), 05mvd62 (0.32 #7395, 0.32 #5377, 0.31 #7731), 05prs8 (0.32 #7395, 0.32 #5377, 0.31 #7731), 09pl3s (0.14 #73, 0.04 #745, 0.02 #1082), 06pj8 (0.13 #13124, 0.03 #16155, 0.02 #19862), 07rd7 (0.13 #13124), 02bfxb (0.11 #96, 0.02 #4127, 0.02 #4465), 05183k (0.07 #45, 0.04 #4369, 0.03 #16155), 06dkzt (0.07 #266, 0.04 #938, 0.03 #602), 01xndd (0.07 #125, 0.02 #797, 0.01 #3819) >> Best rule #11101 for best value: >> intensional similarity = 5 >> extensional distance = 445 >> proper extension: 03m8y5; >> query: (?x3925, ?x11876) <- genre(?x3925, ?x812), film(?x382, ?x3925), film_crew_role(?x3925, ?x137), film(?x11876, ?x3925), titles(?x812, ?x80) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #73 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 26 *> proper extension: 053rxgm; 0ch26b_; 0cc5mcj; 017jd9; 026qnh6; 035w2k; 06fqlk; 07nxnw; 03whyr; *> query: (?x3925, 09pl3s) <- produced_by(?x3925, ?x1533), film(?x382, ?x3925), written_by(?x3925, ?x6001), film_crew_role(?x3925, ?x2091), ?x2091 = 02rh1dz *> conf = 0.14 ranks of expected_values: 4 EVAL 0435vm written_by 09pl3s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 121.000 88.000 0.350 http://example.org/film/film/written_by #17319-012s1d PRED entity: 012s1d PRED relation: film_crew_role PRED expected values: 09vw2b7 0ch6mp2 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 25): 0ch6mp2 (0.80 #673, 0.79 #408, 0.77 #2279), 09vw2b7 (0.69 #2278, 0.68 #407, 0.66 #672), 05smlt (0.40 #51, 0.25 #18, 0.25 #2102), 014kbl (0.40 #63, 0.25 #30, 0.25 #2102), 04pyp5 (0.25 #14, 0.10 #680, 0.08 #547), 02vs3x5 (0.25 #21, 0.08 #187, 0.06 #687), 02ynfr (0.25 #2102, 0.22 #679, 0.21 #414), 015h31 (0.22 #309, 0.16 #275, 0.11 #343), 02rh1dz (0.18 #310, 0.17 #109, 0.16 #1240), 0d2b38 (0.16 #323, 0.15 #424, 0.14 #622) >> Best rule #673 for best value: >> intensional similarity = 4 >> extensional distance = 243 >> proper extension: 0dq626; 047qxs; 0gbtbm; 0mbql; >> query: (?x5305, 0ch6mp2) <- films(?x13816, ?x5305), film_crew_role(?x5305, ?x137), film(?x123, ?x5305), ?x137 = 09zzb8 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 012s1d film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 012s1d film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17318-037mh8 PRED entity: 037mh8 PRED relation: major_field_of_study! PRED expected values: 03bwzr4 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 15): 03bwzr4 (0.67 #118, 0.63 #354, 0.63 #344), 01gkg3 (0.50 #63, 0.40 #105, 0.33 #35), 022h5x (0.40 #110, 0.36 #355, 0.35 #385), 07s6fsf (0.40 #99, 0.36 #355, 0.35 #385), 01ysy9 (0.36 #355, 0.35 #385, 0.34 #415), 02m4yg (0.36 #355, 0.35 #385, 0.34 #415), 01rr_d (0.36 #355, 0.35 #385, 0.34 #415), 013zdg (0.36 #355, 0.35 #385, 0.34 #415), 027f2w (0.36 #355, 0.35 #385, 0.34 #415), 028dcg (0.36 #355, 0.35 #385, 0.34 #415) >> Best rule #118 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 01540; >> query: (?x8221, 03bwzr4) <- major_field_of_study(?x7092, ?x8221), major_field_of_study(?x3437, ?x8221), ?x7092 = 01g7_r, institution(?x3437, ?x14069), institution(?x3437, ?x12276), ?x14069 = 03fcbb, ?x12276 = 018sg9 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 037mh8 major_field_of_study! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.667 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #17317-03jvmp PRED entity: 03jvmp PRED relation: production_companies! PRED expected values: 05h43ls => 118 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1157): 050gkf (0.50 #2504, 0.12 #17397, 0.12 #16251), 09m6kg (0.50 #2313, 0.08 #17206, 0.08 #16060), 0cn_b8 (0.50 #2704, 0.08 #17597, 0.08 #16451), 02qr69m (0.50 #2558, 0.07 #38078, 0.07 #41514), 0fh2v5 (0.50 #3321, 0.07 #38841, 0.06 #43423), 017d93 (0.50 #3003, 0.05 #38523, 0.05 #45397), 065zlr (0.50 #2560, 0.05 #38080, 0.05 #44954), 02_1sj (0.50 #2347, 0.05 #37867, 0.05 #44741), 023vcd (0.50 #3344, 0.05 #38864, 0.04 #43446), 05z43v (0.48 #17184, 0.48 #26353, 0.48 #28648) >> Best rule #2504 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04rtpt; >> query: (?x2246, 050gkf) <- production_companies(?x9496, ?x2246), production_companies(?x1315, ?x2246), nominated_for(?x2489, ?x9496), ?x1315 = 053tj7 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #10588 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 13 *> proper extension: 011k1h; 02bh8z; 01w5gp; 0b275x; 0plw; 07733f; 01scmq; *> query: (?x2246, 05h43ls) <- child(?x1908, ?x2246), ?x1908 = 0l8sx *> conf = 0.13 ranks of expected_values: 281 EVAL 03jvmp production_companies! 05h43ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 118.000 99.000 0.500 http://example.org/film/film/production_companies #17316-0pj8m PRED entity: 0pj8m PRED relation: artists! PRED expected values: 017_qw => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 203): 03_d0 (0.69 #5038, 0.19 #9122, 0.18 #6608), 017_qw (0.62 #1323, 0.36 #66, 0.26 #694), 06by7 (0.41 #6934, 0.40 #6619, 0.40 #9133), 064t9 (0.37 #16664, 0.36 #11640, 0.35 #18234), 0xhtw (0.25 #1903, 0.20 #2531, 0.19 #10997), 0155w (0.24 #1996, 0.20 #2624, 0.19 #10997), 06j6l (0.23 #5077, 0.22 #1936, 0.21 #6647), 016clz (0.22 #9115, 0.21 #16655, 0.20 #6916), 05bt6j (0.20 #16696, 0.19 #10997, 0.19 #9740), 01lyv (0.19 #664, 0.19 #10997, 0.19 #9740) >> Best rule #5038 for best value: >> intensional similarity = 2 >> extensional distance = 198 >> proper extension: 05563d; 0h08p; >> query: (?x7995, 03_d0) <- artists(?x7994, ?x7995), major_field_of_study(?x1390, ?x7994) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1323 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 63 *> proper extension: 0b6yp2; *> query: (?x7995, 017_qw) <- award(?x7995, ?x1079), ?x1079 = 0l8z1 *> conf = 0.62 ranks of expected_values: 2 EVAL 0pj8m artists! 017_qw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.690 http://example.org/music/genre/artists #17315-0r6cx PRED entity: 0r6cx PRED relation: place_founded! PRED expected values: 019rl6 => 167 concepts (61 used for prediction) PRED predicted values (max 10 best out of 192): 06nfl (0.33 #111, 0.04 #1456, 0.03 #1683), 07rfp (0.33 #104, 0.04 #1449, 0.03 #1676), 0260p2 (0.33 #100, 0.04 #1445, 0.03 #1672), 06zl7g (0.33 #99, 0.04 #1444, 0.03 #1671), 05b0f7 (0.33 #87, 0.04 #1432, 0.03 #1659), 01bvx1 (0.33 #83, 0.04 #1428, 0.03 #1655), 01qckn (0.33 #61, 0.04 #1406, 0.03 #1633), 01dycg (0.33 #54, 0.04 #1399, 0.03 #1626), 03pmfw (0.33 #18, 0.04 #1363, 0.03 #1590), 01zpmq (0.25 #148, 0.04 #1157, 0.04 #1495) >> Best rule #111 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 07dfk; >> query: (?x10916, 06nfl) <- contains(?x10916, ?x3021), citytown(?x10436, ?x10916), ?x10436 = 02pfymy, location(?x8863, ?x10916) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r6cx place_founded! 019rl6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 61.000 0.333 http://example.org/organization/organization/place_founded #17314-01h8sf PRED entity: 01h8sf PRED relation: organization! PRED expected values: 07xl34 => 261 concepts (261 used for prediction) PRED predicted values (max 10 best out of 13): 060c4 (0.73 #1185, 0.73 #1198, 0.73 #756), 05c0jwl (0.64 #122, 0.60 #187, 0.56 #70), 07xl34 (0.50 #63, 0.47 #505, 0.42 #427), 0dq_5 (0.25 #1023, 0.23 #841, 0.22 #1661), 08jcfy (0.16 #1848, 0.16 #1392, 0.15 #2187), 05k17c (0.16 #1848, 0.16 #1392, 0.15 #2187), 04n1q6 (0.16 #1848, 0.16 #1392, 0.15 #2187), 0hm4q (0.16 #1848, 0.16 #1392, 0.15 #2187), 02wlwtm (0.05 #195, 0.05 #234, 0.04 #299), 09d6p2 (0.02 #556, 0.01 #842) >> Best rule #1185 for best value: >> intensional similarity = 6 >> extensional distance = 203 >> proper extension: 01j_9c; 01wdl3; 01j_06; 01bvw5; 07xpm; 0f102; 078bz; 027xx3; 01c333; 01y17m; ... >> query: (?x13705, 060c4) <- institution(?x1368, ?x13705), ?x1368 = 014mlp, contains(?x13201, ?x13705), school_type(?x13705, ?x3092), major_field_of_study(?x13705, ?x2981), colors(?x13705, ?x332) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #63 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 07tgn; 07tg4; 01xrlm; 01dthg; *> query: (?x13705, 07xl34) <- institution(?x1368, ?x13705), institution(?x620, ?x13705), ?x1368 = 014mlp, contains(?x13201, ?x13705), currency(?x13705, ?x1099), ?x1099 = 01nv4h, ?x620 = 07s6fsf, major_field_of_study(?x13705, ?x2981) *> conf = 0.50 ranks of expected_values: 3 EVAL 01h8sf organization! 07xl34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 261.000 261.000 0.732 http://example.org/organization/role/leaders./organization/leadership/organization #17313-09g7vfw PRED entity: 09g7vfw PRED relation: film_release_region PRED expected values: 05r4w 04gzd 0ctw_b 035qy 06t8v 06wjf => 77 concepts (76 used for prediction) PRED predicted values (max 10 best out of 120): 035qy (0.92 #679, 0.92 #417, 0.92 #286), 05r4w (0.89 #920, 0.88 #657, 0.87 #1051), 04gzd (0.78 #661, 0.78 #137, 0.77 #924), 047lj (0.69 #139, 0.67 #663, 0.67 #401), 015qh (0.69 #948, 0.67 #685, 0.65 #1079), 0ctw_b (0.67 #280, 0.66 #673, 0.65 #936), 01mjq (0.61 #425, 0.59 #163, 0.59 #950), 06f32 (0.61 #309, 0.59 #178, 0.59 #440), 06qd3 (0.59 #683, 0.59 #421, 0.59 #290), 06t8v (0.58 #976, 0.57 #320, 0.56 #713) >> Best rule #679 for best value: >> intensional similarity = 7 >> extensional distance = 62 >> proper extension: 0c0nhgv; 04hwbq; 04n52p6; 0gd0c7x; 0crc2cp; 0c3xw46; 07s846j; 0h03fhx; 0dlngsd; 0fphf3v; >> query: (?x3423, 035qy) <- film_release_region(?x3423, ?x2146), film_release_region(?x3423, ?x1475), film_release_region(?x3423, ?x1229), ?x2146 = 03rk0, ?x1475 = 05qx1, olympics(?x1229, ?x418), country(?x3407, ?x1229) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 6, 10, 84 EVAL 09g7vfw film_release_region 06wjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 77.000 76.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09g7vfw film_release_region 06t8v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 76.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09g7vfw film_release_region 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 76.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09g7vfw film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 76.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09g7vfw film_release_region 04gzd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 76.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09g7vfw film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 76.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17312-07jmnh PRED entity: 07jmnh PRED relation: gender PRED expected values: 05zppz => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #3, 0.89 #11, 0.87 #13), 02zsn (0.46 #229, 0.46 #182, 0.46 #108) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0b5x23; >> query: (?x12209, 05zppz) <- award(?x12209, ?x4687), people(?x5025, ?x12209), ?x4687 = 03rbj2, nationality(?x12209, ?x2146) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07jmnh gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.909 http://example.org/people/person/gender #17311-01_srz PRED entity: 01_srz PRED relation: school_type! PRED expected values: 01mpwj 03x33n 02kbtf => 23 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1044): 02km0m (0.60 #3566, 0.43 #4678, 0.40 #3010), 01cyd5 (0.57 #5613, 0.50 #1717, 0.40 #2829), 04rwx (0.50 #6151, 0.44 #6709, 0.40 #7267), 049dk (0.50 #2261, 0.38 #6158, 0.33 #6716), 01j_cy (0.50 #2256, 0.38 #6153, 0.33 #6711), 07w0v (0.50 #2233, 0.38 #6130, 0.33 #6688), 07vyf (0.50 #2360, 0.38 #6257, 0.33 #6815), 0c5x_ (0.50 #2521, 0.38 #6418, 0.33 #6976), 02ldkf (0.50 #2656, 0.38 #6553, 0.33 #7111), 01rc6f (0.50 #2517, 0.38 #6414, 0.33 #6972) >> Best rule #3566 for best value: >> intensional similarity = 20 >> extensional distance = 3 >> proper extension: 07tf8; >> query: (?x1962, 02km0m) <- school_type(?x7596, ?x1962), school_type(?x7576, ?x1962), institution(?x620, ?x7576), organization(?x346, ?x7576), service_location(?x7596, ?x551), service_language(?x7596, ?x254), major_field_of_study(?x7596, ?x8925), major_field_of_study(?x7596, ?x4321), school_type(?x7576, ?x1507), ?x254 = 02h40lc, ?x4321 = 0g26h, ?x1507 = 01_9fk, currency(?x7576, ?x170), contains(?x3908, ?x7576), institution(?x4981, ?x7596), ?x4981 = 03bwzr4, school(?x685, ?x7596), ?x8925 = 01zc2w, ?x620 = 07s6fsf, school(?x1239, ?x7596) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2349 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 2 *> proper extension: 01_9fk; *> query: (?x1962, 03x33n) <- school_type(?x14319, ?x1962), school_type(?x8287, ?x1962), school_type(?x7596, ?x1962), school_type(?x7576, ?x1962), school_type(?x6501, ?x1962), school_type(?x466, ?x1962), ?x7576 = 0gy3w, ?x466 = 01pl14, state_province_region(?x6501, ?x335), institution(?x1368, ?x6501), major_field_of_study(?x7596, ?x7134), major_field_of_study(?x7596, ?x4321), major_field_of_study(?x7596, ?x3489), major_field_of_study(?x7596, ?x1154), ?x1154 = 02lp1, category(?x6501, ?x134), ?x3489 = 0193x, citytown(?x14319, ?x5015), student(?x7596, ?x5222), colors(?x7596, ?x332), major_field_of_study(?x5750, ?x7134), major_field_of_study(?x5055, ?x7134), ?x8287 = 02x9g_, ?x5750 = 01nnsv, ?x4321 = 0g26h, ?x5055 = 029d_ *> conf = 0.50 ranks of expected_values: 29, 73, 219 EVAL 01_srz school_type! 02kbtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 23.000 18.000 0.600 http://example.org/education/educational_institution/school_type EVAL 01_srz school_type! 03x33n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 23.000 18.000 0.600 http://example.org/education/educational_institution/school_type EVAL 01_srz school_type! 01mpwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 23.000 18.000 0.600 http://example.org/education/educational_institution/school_type #17310-0k525 PRED entity: 0k525 PRED relation: award PRED expected values: 02w9sd7 => 118 concepts (79 used for prediction) PRED predicted values (max 10 best out of 232): 05zvq6g (0.38 #1268, 0.10 #1671, 0.02 #2880), 09sb52 (0.31 #2458, 0.31 #2861, 0.31 #1249), 027dtxw (0.29 #2019, 0.09 #2422, 0.08 #810), 09sdmz (0.28 #2220, 0.10 #608, 0.08 #1011), 02x73k6 (0.28 #2075, 0.08 #866, 0.06 #2881), 0bdwqv (0.27 #2186, 0.17 #977, 0.10 #574), 040njc (0.25 #8, 0.18 #3636, 0.10 #2426), 0gq9h (0.25 #77, 0.13 #3705, 0.13 #27410), 0gr4k (0.25 #32, 0.13 #27410, 0.13 #24587), 04dn09n (0.25 #43, 0.13 #27410, 0.13 #24587) >> Best rule #1268 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 07lt7b; 0psss; 01qq_lp; 0gmtm; 02l4rh; 01syr4; 0p9gg; >> query: (?x11155, 05zvq6g) <- nationality(?x11155, ?x789), film(?x11155, ?x1744), type_of_union(?x11155, ?x566), ?x789 = 0f8l9c >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2184 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 163 *> proper extension: 0hwd8; *> query: (?x11155, 02w9sd7) <- nationality(?x11155, ?x304), award(?x11155, ?x3066), ?x3066 = 0gqy2 *> conf = 0.18 ranks of expected_values: 29 EVAL 0k525 award 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 118.000 79.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17309-03swmf PRED entity: 03swmf PRED relation: profession PRED expected values: 01d_h8 => 151 concepts (74 used for prediction) PRED predicted values (max 10 best out of 100): 01d_h8 (0.79 #2952, 0.78 #1624, 0.77 #1477), 0dxtg (0.67 #7837, 0.66 #6216, 0.66 #5032), 03gjzk (0.51 #3998, 0.48 #2960, 0.46 #603), 0kyk (0.43 #176, 0.26 #3866, 0.26 #4900), 0cbd2 (0.42 #1919, 0.34 #2215, 0.29 #154), 0d8qb (0.31 #520, 0.25 #961, 0.23 #667), 02krf9 (0.30 #1644, 0.28 #2972, 0.26 #1791), 0fj9f (0.29 #2113, 0.22 #4925, 0.21 #3891), 015cjr (0.23 #491, 0.19 #932, 0.15 #638), 01c72t (0.22 #2526, 0.20 #318, 0.16 #6962) >> Best rule #2952 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 012d40; 032v0v; 026c1; 0bs1yy; 0693l; 01rzqj; 01f7v_; 015c4g; 026dx; 0534v; ... >> query: (?x9156, 01d_h8) <- profession(?x9156, ?x524), award_winner(?x458, ?x9156), ?x524 = 02jknp, executive_produced_by(?x9199, ?x9156) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03swmf profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 74.000 0.793 http://example.org/people/person/profession #17308-03yj_0n PRED entity: 03yj_0n PRED relation: student! PRED expected values: 0234_c => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 25): 09f2j (0.08 #159, 0.03 #686, 0.03 #3321), 017z88 (0.08 #82, 0.03 #609, 0.03 #4825), 026gvfj (0.08 #111, 0.01 #1692), 0gjv_ (0.08 #206), 02mj7c (0.08 #165), 0bwfn (0.05 #5545, 0.05 #6072, 0.05 #8707), 065y4w7 (0.03 #13718, 0.03 #3703, 0.03 #12664), 015nl4 (0.03 #26423, 0.03 #11663, 0.03 #1648), 03ksy (0.02 #37003, 0.02 #6430, 0.02 #4849), 01w5m (0.02 #37002, 0.02 #21718, 0.02 #30677) >> Best rule #159 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01wbg84; 0f830f; 08w7vj; 02tr7d; 0fx0mw; 07s8hms; 0cjsxp; 0bx0lc; 0dyztm; 02l6dy; ... >> query: (?x3594, 09f2j) <- award_nominee(?x3594, ?x6360), profession(?x3594, ?x1032), ?x6360 = 02sb1w >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03yj_0n student! 0234_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 83.000 0.077 http://example.org/education/educational_institution/students_graduates./education/education/student #17307-0h7x PRED entity: 0h7x PRED relation: film_release_region! PRED expected values: 02vxq9m 0ddfwj1 0gkz15s 0jjy0 04jkpgv 01fmys 06w839_ 017jd9 02xbyr 0gt1k 03rg2b 02bg55 043tvp3 027m67 0btpm6 04z_3pm 0j8f09z 024lt6 0267wwv 0ccck7 => 218 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1553): 017jd9 (0.90 #32180, 0.89 #15756, 0.86 #33353), 01fmys (0.89 #15458, 0.88 #13112, 0.85 #31882), 0bwfwpj (0.89 #15351, 0.85 #13005, 0.80 #31775), 087wc7n (0.89 #15330, 0.82 #31754, 0.76 #32927), 0407yfx (0.89 #15472, 0.80 #31896, 0.76 #33069), 0661m4p (0.89 #15490, 0.78 #31914, 0.74 #33087), 0ch26b_ (0.89 #15446, 0.78 #31870, 0.73 #13100), 043tvp3 (0.86 #16053, 0.85 #14880, 0.82 #32477), 0jjy0 (0.86 #15361, 0.85 #13015, 0.79 #32958), 0bpm4yw (0.86 #15711, 0.82 #32135, 0.81 #13365) >> Best rule #32180 for best value: >> intensional similarity = 2 >> extensional distance = 38 >> proper extension: 0d060g; 0chghy; 06mzp; 0345h; 01mjq; 06bnz; 03rk0; 06f32; 06t8v; 04hqz; ... >> query: (?x1355, 017jd9) <- film_release_region(?x1724, ?x1355), ?x1724 = 02r8hh_ >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 8, 9, 11, 20, 37, 61, 76, 90, 95, 96, 114, 121, 128, 161, 220, 241, 253, 259 EVAL 0h7x film_release_region! 0ccck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 0267wwv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 024lt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 0j8f09z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 04z_3pm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 027m67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 043tvp3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 02bg55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 03rg2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 0gt1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 02xbyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 017jd9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 06w839_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 01fmys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 04jkpgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 0jjy0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 0ddfwj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h7x film_release_region! 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 218.000 54.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17306-01w524f PRED entity: 01w524f PRED relation: influenced_by PRED expected values: 041mt => 99 concepts (43 used for prediction) PRED predicted values (max 10 best out of 319): 08433 (0.15 #1766, 0.14 #3951, 0.09 #894), 032l1 (0.14 #11875, 0.11 #10565, 0.10 #16686), 01v9724 (0.14 #11964, 0.08 #3672, 0.07 #10654), 012vd6 (0.14 #4099, 0.02 #17641, 0.01 #14577), 081k8 (0.13 #10912, 0.11 #14565, 0.10 #16753), 0gz_ (0.13 #10912, 0.10 #12223, 0.09 #10579), 01vh096 (0.13 #10912, 0.10 #12223, 0.05 #13393), 0465_ (0.13 #10912, 0.10 #12223, 0.03 #10676), 03_87 (0.12 #11989, 0.09 #14612, 0.09 #13302), 03sbs (0.12 #10698, 0.09 #14631, 0.08 #3716) >> Best rule #1766 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0ggl02; >> query: (?x4237, 08433) <- gender(?x4237, ?x231), ?x231 = 05zppz, award(?x4237, ?x1565), ?x1565 = 01c4_6 >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #3989 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 01q_ph; *> query: (?x4237, 041mt) <- nationality(?x4237, ?x512), artist(?x4868, ?x4237), influenced_by(?x4237, ?x6975) *> conf = 0.06 ranks of expected_values: 53 EVAL 01w524f influenced_by 041mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 99.000 43.000 0.154 http://example.org/influence/influence_node/influenced_by #17305-016ntp PRED entity: 016ntp PRED relation: award PRED expected values: 01c4_6 => 137 concepts (109 used for prediction) PRED predicted values (max 10 best out of 331): 01bgqh (0.38 #3267, 0.32 #4879, 0.26 #10521), 01by1l (0.38 #1724, 0.35 #4545, 0.34 #6963), 01ck6h (0.38 #1734, 0.20 #928, 0.18 #2137), 01c99j (0.29 #3450, 0.25 #5062, 0.19 #6674), 01c427 (0.29 #3309, 0.20 #4921, 0.15 #11772), 026mfs (0.26 #13428, 0.19 #3353, 0.18 #4965), 02wh75 (0.25 #1621, 0.25 #9, 0.18 #2024), 0gqz2 (0.25 #1693, 0.22 #8141, 0.21 #10156), 02v1m7 (0.25 #1725, 0.20 #516, 0.17 #1322), 01ck6v (0.25 #1884, 0.20 #1078, 0.17 #2690) >> Best rule #3267 for best value: >> intensional similarity = 7 >> extensional distance = 19 >> proper extension: 02w4fkq; 018ndc; 0bqsy; >> query: (?x3168, 01bgqh) <- artists(?x3642, ?x3168), artists(?x3108, ?x3168), ?x3108 = 02w4v, artists(?x3642, ?x4162), artists(?x3642, ?x3118), ?x4162 = 01wy61y, ?x3118 = 01w02sy >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #895 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 016wvy; *> query: (?x3168, 01c4_6) <- role(?x3168, ?x1495), ?x1495 = 013y1f, artists(?x7329, ?x3168), artists(?x5379, ?x3168), ?x5379 = 08jyyk, ?x7329 = 016jny *> conf = 0.20 ranks of expected_values: 24 EVAL 016ntp award 01c4_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 137.000 109.000 0.381 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17304-0106dv PRED entity: 0106dv PRED relation: place_of_birth! PRED expected values: 053y0s => 210 concepts (157 used for prediction) PRED predicted values (max 10 best out of 2111): 053y0s (0.38 #39157, 0.38 #33935, 0.38 #135755), 0p_47 (0.38 #39157, 0.38 #33935, 0.38 #135755), 06mmb (0.38 #39157, 0.38 #33935, 0.38 #135755), 05w1vf (0.06 #2326, 0.06 #4936, 0.03 #7547), 017f4y (0.06 #2218, 0.06 #4828, 0.03 #7439), 01vvybv (0.06 #2160, 0.06 #4770, 0.03 #7381), 01693z (0.06 #1757, 0.06 #4367, 0.03 #6978), 04f7c55 (0.06 #1175, 0.06 #3785, 0.03 #6396), 01k70_ (0.06 #895, 0.06 #3505, 0.03 #6116), 02g5h5 (0.06 #743, 0.06 #3353, 0.03 #5964) >> Best rule #39157 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 0l39b; >> query: (?x10364, ?x130) <- county_seat(?x10365, ?x10364), location(?x130, ?x10364), citytown(?x6177, ?x10364), institution(?x1368, ?x6177) >> conf = 0.38 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0106dv place_of_birth! 053y0s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 157.000 0.384 http://example.org/people/person/place_of_birth #17303-02pv_d PRED entity: 02pv_d PRED relation: profession PRED expected values: 02jknp => 111 concepts (106 used for prediction) PRED predicted values (max 10 best out of 67): 02jknp (0.88 #1331, 0.88 #1625, 0.59 #1184), 02hrh1q (0.86 #1778, 0.82 #14866, 0.81 #15013), 0cbd2 (0.47 #447, 0.45 #594, 0.45 #2359), 0kyk (0.33 #469, 0.32 #616, 0.29 #764), 018gz8 (0.29 #15, 0.20 #3693, 0.19 #4134), 02krf9 (0.26 #3703, 0.26 #4144, 0.25 #2820), 09jwl (0.22 #2665, 0.20 #5900, 0.20 #7076), 05z96 (0.14 #482, 0.14 #2394, 0.14 #629), 0dz3r (0.14 #5885, 0.13 #7061, 0.12 #7796), 0nbcg (0.14 #13971, 0.14 #5913, 0.13 #7089) >> Best rule #1331 for best value: >> intensional similarity = 3 >> extensional distance = 246 >> proper extension: 04b19t; 0454s1; >> query: (?x8070, 02jknp) <- profession(?x8070, ?x319), film(?x8070, ?x1298), nominated_for(?x384, ?x1298) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pv_d profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 106.000 0.883 http://example.org/people/person/profession #17302-0340hj PRED entity: 0340hj PRED relation: featured_film_locations PRED expected values: 030qb3t => 79 concepts (69 used for prediction) PRED predicted values (max 10 best out of 85): 030qb3t (0.20 #38, 0.15 #1228, 0.13 #8146), 04jpl (0.12 #8117, 0.11 #8595, 0.11 #6687), 080h2 (0.07 #1927, 0.06 #737, 0.06 #261), 0rh6k (0.06 #8109, 0.05 #8587, 0.05 #4053), 01_d4 (0.06 #284, 0.05 #6724, 0.04 #8632), 0chgzm (0.06 #385, 0.02 #861, 0.02 #1099), 0cwx_ (0.06 #362, 0.02 #838, 0.02 #1076), 0qb62 (0.06 #449, 0.02 #925), 052p7 (0.05 #1009, 0.04 #771, 0.04 #2200), 06y57 (0.05 #1529, 0.04 #2005, 0.02 #2721) >> Best rule #38 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 06ys2; >> query: (?x1511, 030qb3t) <- nominated_for(?x4106, ?x1511), nominated_for(?x1733, ?x1511), ?x1733 = 015pkc, ?x4106 = 04fzk >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0340hj featured_film_locations 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 69.000 0.200 http://example.org/film/film/featured_film_locations #17301-02hp6p PRED entity: 02hp6p PRED relation: registering_agency PRED expected values: 03z19 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.86 #4, 0.85 #6, 0.85 #5) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 01rtm4; 01pq4w; 0kw4j; 03zw80; 035wtd; 033x5p; 017j69; 01j_5k; 01bk1y; 02d9nr; ... >> query: (?x11654, 03z19) <- currency(?x11654, ?x170), state_province_region(?x11654, ?x1755), colors(?x11654, ?x663) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hp6p registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.855 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #17300-038czx PRED entity: 038czx PRED relation: organization! PRED expected values: 060c4 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 16): 060c4 (0.75 #67, 0.71 #54, 0.71 #249), 07xl34 (0.29 #50, 0.22 #180, 0.21 #245), 0dq_5 (0.24 #152, 0.23 #165, 0.17 #492), 05k17c (0.10 #137, 0.10 #33, 0.08 #85), 01t7n9 (0.07 #365, 0.07 #431), 09n5b9 (0.07 #365, 0.07 #431), 02079p (0.07 #365, 0.07 #431), 0789n (0.07 #365, 0.07 #431), 0f6c3 (0.07 #365, 0.07 #431), 01gkgk (0.07 #365, 0.07 #431) >> Best rule #67 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 02d9nr; >> query: (?x6955, 060c4) <- contains(?x5259, ?x6955), location(?x286, ?x5259), colors(?x6955, ?x663), source(?x5259, ?x958) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 038czx organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.746 http://example.org/organization/role/leaders./organization/leadership/organization #17299-0jpdn PRED entity: 0jpdn PRED relation: profession PRED expected values: 0dxtg => 146 concepts (108 used for prediction) PRED predicted values (max 10 best out of 102): 02hrh1q (0.90 #11201, 0.90 #12671, 0.89 #15170), 02jknp (0.84 #5313, 0.55 #1922, 0.53 #1481), 0dxtg (0.84 #1927, 0.79 #1486, 0.77 #749), 01d_h8 (0.79 #5606, 0.77 #5312, 0.76 #4720), 0nbcg (0.77 #7688, 0.49 #2092, 0.45 #4155), 09jwl (0.54 #7676, 0.47 #2080, 0.47 #4143), 03gjzk (0.47 #4286, 0.46 #5614, 0.45 #751), 016z4k (0.42 #2066, 0.39 #4129, 0.34 #7662), 0dz3r (0.39 #4127, 0.38 #7660, 0.36 #2064), 018gz8 (0.34 #3553, 0.29 #1931, 0.26 #1490) >> Best rule #11201 for best value: >> intensional similarity = 5 >> extensional distance = 948 >> proper extension: 01sl1q; 0q9kd; 04bdxl; 0grwj; 01vvydl; 02qgqt; 04yywz; 07s3vqk; 02bfmn; 01xdf5; ... >> query: (?x8862, 02hrh1q) <- location(?x8862, ?x739), profession(?x8862, ?x353), nationality(?x8862, ?x94), film(?x8862, ?x3093), ?x94 = 09c7w0 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1927 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 03kpvp; 0n8bn; 0gd9k; 030g9z; *> query: (?x8862, 0dxtg) <- story_by(?x3093, ?x8862), film(?x395, ?x3093), film(?x8862, ?x8787) *> conf = 0.84 ranks of expected_values: 3 EVAL 0jpdn profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 146.000 108.000 0.904 http://example.org/people/person/profession #17298-0d6_s PRED entity: 0d6_s PRED relation: film_crew_role PRED expected values: 09zzb8 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 33): 09zzb8 (0.82 #923, 0.80 #709, 0.79 #566), 09vw2b7 (0.68 #572, 0.66 #1606, 0.64 #148), 0dxtw (0.42 #719, 0.39 #469, 0.39 #933), 02ynfr (0.22 #156, 0.20 #15, 0.18 #367), 015h31 (0.20 #9, 0.19 #502, 0.19 #610), 0d2b38 (0.19 #60, 0.14 #626, 0.13 #768), 02rh1dz (0.19 #115, 0.17 #575, 0.14 #1109), 0215hd (0.16 #88, 0.14 #1617, 0.12 #761), 094hwz (0.13 #119, 0.10 #543, 0.09 #295), 089g0h (0.13 #477, 0.12 #89, 0.12 #371) >> Best rule #923 for best value: >> intensional similarity = 5 >> extensional distance = 310 >> proper extension: 047svrl; >> query: (?x10405, 09zzb8) <- film_crew_role(?x10405, ?x2178), film_crew_role(?x10405, ?x1284), ?x2178 = 01pvkk, film_crew_role(?x4745, ?x1284), ?x4745 = 03b_fm5 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d6_s film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.824 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17297-01pgzn_ PRED entity: 01pgzn_ PRED relation: award_nominee PRED expected values: 01gbn6 => 134 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1091): 016vg8 (0.83 #60272, 0.82 #48678, 0.82 #60271), 01d1st (0.83 #60272, 0.82 #48678, 0.82 #60271), 011_3s (0.83 #60272, 0.82 #48678, 0.82 #60271), 0170s4 (0.83 #60272, 0.82 #48678, 0.82 #60271), 07h565 (0.83 #60272, 0.82 #48678, 0.82 #60271), 01pgzn_ (0.70 #7452, 0.67 #14406, 0.67 #12088), 0kszw (0.44 #5179, 0.14 #178495, 0.13 #99686), 01q_ph (0.33 #68, 0.15 #97366, 0.15 #99687), 03h_9lg (0.33 #4802, 0.14 #178495, 0.13 #99686), 02kxwk (0.33 #1009, 0.14 #178495, 0.13 #99686) >> Best rule #60272 for best value: >> intensional similarity = 3 >> extensional distance = 264 >> proper extension: 016jll; 020jqv; >> query: (?x2352, ?x7525) <- award_nominee(?x7525, ?x2352), artist(?x1124, ?x2352), nominated_for(?x7525, ?x1045) >> conf = 0.83 => this is the best rule for 5 predicted values *> Best rule #178495 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1236 *> proper extension: 01d1yr; 02_340; 01t9qj_; *> query: (?x2352, ?x496) <- award_nominee(?x495, ?x2352), film(?x2352, ?x1045), award_nominee(?x495, ?x496) *> conf = 0.14 ranks of expected_values: 183 EVAL 01pgzn_ award_nominee 01gbn6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 134.000 77.000 0.832 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17296-024tcq PRED entity: 024tcq PRED relation: district_represented PRED expected values: 05kj_ 0vmt 05fhy 081yw => 34 concepts (32 used for prediction) PRED predicted values (max 10 best out of 456): 05fkf (0.90 #140, 0.88 #309, 0.88 #308), 0vmt (0.90 #140, 0.88 #309, 0.88 #308), 07z1m (0.90 #140, 0.88 #309, 0.88 #308), 05kj_ (0.90 #140, 0.88 #309, 0.88 #308), 081yw (0.90 #140, 0.88 #309, 0.88 #308), 05fhy (0.90 #140, 0.88 #309, 0.88 #308), 01n4w (0.90 #140, 0.88 #309, 0.88 #308), 04ykg (0.90 #140, 0.88 #309, 0.88 #308), 0czr9_ (0.90 #140, 0.88 #309, 0.87 #71), 0694j (0.58 #557, 0.56 #634, 0.52 #504) >> Best rule #140 for best value: >> intensional similarity = 45 >> extensional distance = 2 >> proper extension: 077g7n; >> query: (?x3540, ?x726) <- district_represented(?x3540, ?x6895), district_represented(?x3540, ?x6226), district_represented(?x3540, ?x4758), district_represented(?x3540, ?x4105), district_represented(?x3540, ?x3818), district_represented(?x3540, ?x3670), district_represented(?x3540, ?x3634), district_represented(?x3540, ?x2831), district_represented(?x3540, ?x1782), district_represented(?x3540, ?x1767), legislative_sessions(?x3540, ?x4821), legislative_sessions(?x3540, ?x4730), legislative_sessions(?x3540, ?x355), ?x3818 = 03v0t, legislative_sessions(?x4821, ?x356), legislative_sessions(?x11605, ?x3540), legislative_sessions(?x9334, ?x3540), legislative_sessions(?x3445, ?x3540), ?x4730 = 02cg7g, ?x6226 = 03gh4, ?x3634 = 07b_l, ?x3670 = 05tbn, ?x4105 = 0824r, ?x355 = 0495ys, district_represented(?x4821, ?x726), ?x11605 = 024_vw, ?x1767 = 04rrd, ?x2831 = 0gyh, ?x6895 = 05fjf, location(?x3445, ?x108), student(?x1771, ?x3445), gender(?x3445, ?x514), ?x4758 = 0vbk, institution(?x1771, ?x12737), institution(?x1771, ?x9307), institution(?x1771, ?x6856), institution(?x1771, ?x3485), ?x9334 = 02hy5d, ?x9307 = 02kbtf, ?x12737 = 07wm6, major_field_of_study(?x1771, ?x3995), ?x3485 = 01mpwj, ?x3995 = 0fdys, ?x1782 = 0488g, ?x6856 = 0jkhr >> conf = 0.90 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 4, 5, 6 EVAL 024tcq district_represented 081yw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 34.000 32.000 0.898 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 024tcq district_represented 05fhy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 34.000 32.000 0.898 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 024tcq district_represented 0vmt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 34.000 32.000 0.898 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 024tcq district_represented 05kj_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 34.000 32.000 0.898 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #17295-0l35f PRED entity: 0l35f PRED relation: featured_film_locations! PRED expected values: 0c57yj => 112 concepts (82 used for prediction) PRED predicted values (max 10 best out of 626): 04j14qc (0.25 #601, 0.13 #2812, 0.13 #2075), 03hkch7 (0.25 #226, 0.13 #1700, 0.11 #3174), 0473rc (0.25 #454, 0.09 #7824, 0.07 #2665), 02fqxm (0.25 #734, 0.07 #2208, 0.06 #3682), 02sg5v (0.25 #54, 0.07 #1528, 0.06 #3002), 07bx6 (0.25 #547, 0.07 #2021, 0.06 #3495), 06rhz7 (0.25 #470, 0.07 #1944, 0.06 #3418), 0291hr (0.25 #599, 0.07 #2073, 0.06 #3547), 01s9vc (0.25 #684, 0.07 #2158, 0.06 #3632), 02p76f9 (0.25 #600, 0.07 #2074, 0.06 #3548) >> Best rule #601 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0d6lp; 0kq1l; >> query: (?x7369, 04j14qc) <- adjoins(?x7369, ?x13582), adjoins(?x7369, ?x3677), currency(?x13582, ?x170), ?x3677 = 0l2hf, adjoins(?x13582, ?x7460) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #21381 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 104 *> proper extension: 01bh3l; 0c7hq; 01d8l; 03v9w; 012wyq; *> query: (?x7369, ?x1734) <- contains(?x7369, ?x10937), category(?x10937, ?x134), contains(?x1227, ?x7369), featured_film_locations(?x1734, ?x10937) *> conf = 0.03 ranks of expected_values: 475 EVAL 0l35f featured_film_locations! 0c57yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 82.000 0.250 http://example.org/film/film/featured_film_locations #17294-01rrwf6 PRED entity: 01rrwf6 PRED relation: film PRED expected values: 03h3x5 => 67 concepts (47 used for prediction) PRED predicted values (max 10 best out of 704): 0bwhdbl (0.20 #1411, 0.03 #3202, 0.02 #4994), 0cqr0q (0.20 #1500, 0.03 #3291, 0.02 #5083), 0bpm4yw (0.20 #725, 0.02 #4308, 0.02 #15057), 0bhwhj (0.14 #21498, 0.13 #7167, 0.12 #10750), 0sxmx (0.10 #810, 0.06 #2601, 0.04 #4393), 08phg9 (0.10 #886, 0.06 #2677, 0.02 #18800), 04180vy (0.10 #1753, 0.06 #3544, 0.02 #5336), 0ds2l81 (0.10 #1440, 0.06 #3231, 0.02 #5023), 07l450 (0.10 #1577, 0.06 #3368, 0.02 #5160), 02wgk1 (0.10 #759, 0.04 #4342, 0.04 #7926) >> Best rule #1411 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02vntj; >> query: (?x478, 0bwhdbl) <- language(?x478, ?x254), profession(?x478, ?x1032), diet(?x478, ?x11141) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #4006 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 022s1m; 04hxyv; *> query: (?x478, 03h3x5) <- nationality(?x478, ?x94), ?x94 = 09c7w0, actor(?x2555, ?x478), language(?x478, ?x254) *> conf = 0.04 ranks of expected_values: 99 EVAL 01rrwf6 film 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 67.000 47.000 0.200 http://example.org/film/actor/film./film/performance/film #17293-0g3zpp PRED entity: 0g3zpp PRED relation: school PRED expected values: 0fht9f 01wdj_ 01jq0j 05x_5 => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 761): 06pwq (0.80 #1688, 0.75 #1361, 0.67 #1142), 065y4w7 (0.71 #1255, 0.62 #1582, 0.62 #1472), 0trv (0.50 #1680, 0.50 #619, 0.35 #1138), 03tw2s (0.50 #1533, 0.43 #1316, 0.39 #100), 01wdj_ (0.50 #619, 0.39 #100, 0.35 #1138), 0hd7j (0.50 #619, 0.39 #100, 0.35 #1138), 05x_5 (0.50 #619, 0.39 #100, 0.35 #1138), 01rc6f (0.50 #619, 0.39 #100, 0.35 #1138), 07wlf (0.50 #619, 0.39 #100, 0.35 #1138), 01jq34 (0.50 #619, 0.39 #100, 0.35 #1138) >> Best rule #1688 for best value: >> intensional similarity = 44 >> extensional distance = 8 >> proper extension: 025tn92; >> query: (?x685, 06pwq) <- draft(?x5773, ?x685), draft(?x5229, ?x685), draft(?x4170, ?x685), draft(?x1239, ?x685), school(?x685, ?x7596), school(?x685, ?x5621), team(?x1114, ?x5773), service_location(?x7596, ?x551), colors(?x7596, ?x332), major_field_of_study(?x7596, ?x6870), major_field_of_study(?x7596, ?x3490), major_field_of_study(?x7596, ?x3489), major_field_of_study(?x7596, ?x1695), position(?x1114, ?x180), administrative_parent(?x47, ?x551), ?x3489 = 0193x, teams(?x4090, ?x1239), school(?x260, ?x5621), service_location(?x11199, ?x551), service_location(?x8125, ?x551), service_location(?x5956, ?x551), ?x1695 = 06ms6, institution(?x11690, ?x5621), institution(?x865, ?x5621), student(?x5621, ?x525), ?x6870 = 01540, team(?x5412, ?x5773), team(?x1240, ?x1239), contains(?x94, ?x5621), student(?x7596, ?x5222), adjustment_currency(?x551, ?x170), sport(?x5773, ?x1083), school(?x4170, ?x546), ?x5956 = 01yfp7, taxonomy(?x551, ?x939), colors(?x5229, ?x663), award_nominee(?x525, ?x434), ?x3490 = 05qfh, ?x865 = 02h4rq6, ?x8125 = 06q07, ?x11199 = 069vt, colors(?x5621, ?x7203), major_field_of_study(?x11690, ?x314), award_winner(?x525, ?x7530) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #619 for first EXPECTED value: *> intensional similarity = 56 *> extensional distance = 1 *> proper extension: 03nt7j; *> query: (?x685, ?x581) <- draft(?x5773, ?x685), draft(?x4546, ?x685), draft(?x4469, ?x685), draft(?x4222, ?x685), draft(?x3658, ?x685), draft(?x1639, ?x685), draft(?x1576, ?x685), draft(?x1239, ?x685), ?x1639 = 07l24, team(?x10287, ?x5773), team(?x5412, ?x5773), school(?x685, ?x2497), school(?x685, ?x466), ?x466 = 01pl14, position_s(?x5773, ?x3346), position_s(?x5773, ?x2312), position_s(?x5773, ?x2147), position_s(?x5773, ?x1517), school(?x5773, ?x2171), school(?x5773, ?x581), ?x4469 = 043vc, ?x5412 = 03n69x, sport(?x5773, ?x1083), ?x4222 = 051q5, school(?x4779, ?x2171), ?x3346 = 02g_7z, ?x3658 = 03b3j, major_field_of_study(?x2171, ?x1154), ?x1576 = 05tfm, currency(?x2171, ?x170), ?x1154 = 02lp1, ?x1517 = 02g_6j, ?x170 = 09nqf, ?x4546 = 05gg4, student(?x2171, ?x3338), school(?x7158, ?x2497), colors(?x2171, ?x332), gender(?x10287, ?x231), team(?x2312, ?x6466), team(?x2312, ?x5491), ?x1239 = 01xvb, position(?x7892, ?x2312), sport(?x7158, ?x4833), contains(?x94, ?x2171), organization(?x346, ?x2497), major_field_of_study(?x2497, ?x4100), institution(?x734, ?x2171), colors(?x5773, ?x12676), ?x5491 = 086hg9, ?x2147 = 04nfpk, teams(?x2254, ?x7158), ?x7892 = 07kbp5, draft(?x7158, ?x8133), ?x6466 = 057xlyq, fraternities_and_sororities(?x2497, ?x3697), team(?x1348, ?x7158) *> conf = 0.50 ranks of expected_values: 5, 7, 31, 50 EVAL 0g3zpp school 05x_5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 19.000 19.000 0.800 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 0g3zpp school 01jq0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 19.000 19.000 0.800 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 0g3zpp school 01wdj_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 19.000 19.000 0.800 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 0g3zpp school 0fht9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 19.000 19.000 0.800 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #17292-0crfwmx PRED entity: 0crfwmx PRED relation: produced_by PRED expected values: 04jspq => 60 concepts (27 used for prediction) PRED predicted values (max 10 best out of 168): 026c1 (0.20 #460, 0.04 #1623, 0.03 #2793), 05zh9c (0.20 #566, 0.04 #1729, 0.03 #2899), 02q42j_ (0.11 #4874, 0.05 #2934, 0.02 #6426), 0b13g7 (0.11 #4782, 0.06 #2842, 0.02 #10219), 02tn0_ (0.10 #719, 0.04 #1882, 0.02 #3052), 03h304l (0.10 #577, 0.02 #1353, 0.02 #1740), 03h40_7 (0.10 #739, 0.02 #1902, 0.02 #3072), 027z0pl (0.10 #734, 0.02 #1897, 0.02 #3067), 054_mz (0.10 #407, 0.02 #1570, 0.02 #2740), 0fvf9q (0.08 #3893, 0.05 #4282, 0.04 #2340) >> Best rule #460 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 011ykb; >> query: (?x1022, 026c1) <- film(?x8702, ?x1022), film(?x2307, ?x1022), film(?x2156, ?x1022), ?x2307 = 011zd3, country(?x1022, ?x94), award(?x8702, ?x618) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0crfwmx produced_by 04jspq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 27.000 0.200 http://example.org/film/film/produced_by #17291-0ttxp PRED entity: 0ttxp PRED relation: place_of_birth! PRED expected values: 03cvfg => 106 concepts (69 used for prediction) PRED predicted values (max 10 best out of 257): 07663r (0.05 #7750, 0.02 #10364), 03fqv5 (0.05 #7749, 0.02 #10363), 06nd8c (0.05 #7732, 0.02 #10346), 01kkx2 (0.05 #7622, 0.02 #10236), 01f5q5 (0.05 #7595, 0.02 #10209), 0d0l91 (0.05 #7557, 0.02 #10171), 01r7t9 (0.05 #7539, 0.02 #10153), 0d500h (0.05 #7507, 0.02 #10121), 0hwqg (0.05 #7455, 0.02 #10069), 024jwt (0.05 #7438, 0.02 #10052) >> Best rule #7750 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 021996; 02l424; >> query: (?x9850, 07663r) <- contains(?x1705, ?x9850), time_zones(?x9850, ?x2674), place_of_birth(?x1092, ?x1705), dog_breed(?x1705, ?x1706) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ttxp place_of_birth! 03cvfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 69.000 0.050 http://example.org/people/person/place_of_birth #17290-0pkyh PRED entity: 0pkyh PRED relation: award_winner! PRED expected values: 01by1l => 137 concepts (129 used for prediction) PRED predicted values (max 10 best out of 313): 026mfs (0.41 #16740, 0.40 #19316, 0.40 #12874), 03qbh5 (0.41 #16740, 0.40 #19316, 0.40 #12874), 01ck6h (0.41 #16740, 0.40 #19316, 0.40 #12874), 01ck6v (0.41 #16740, 0.40 #19316, 0.40 #12874), 02f73p (0.41 #16740, 0.40 #19316, 0.40 #12874), 01by1l (0.26 #541, 0.20 #16422, 0.19 #14274), 02v1m7 (0.18 #542, 0.13 #1400, 0.11 #971), 02f6xy (0.17 #1056, 0.17 #1485, 0.12 #627), 054ks3 (0.17 #33478, 0.15 #42495, 0.15 #45935), 0gqz2 (0.17 #33478, 0.15 #42495, 0.15 #45935) >> Best rule #16740 for best value: >> intensional similarity = 3 >> extensional distance = 312 >> proper extension: 0436f4; 03f2_rc; 04rcr; 01vvycq; 07c0j; 05pdbs; 01wdqrx; 01p9hgt; 0244r8; 0ggl02; ... >> query: (?x2930, ?x724) <- award_winner(?x5623, ?x2930), award(?x2930, ?x724), role(?x5623, ?x227) >> conf = 0.41 => this is the best rule for 5 predicted values *> Best rule #541 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 0969fd; *> query: (?x2930, 01by1l) <- award_winner(?x5623, ?x2930), influenced_by(?x2930, ?x477), artists(?x505, ?x5623) *> conf = 0.26 ranks of expected_values: 6 EVAL 0pkyh award_winner! 01by1l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 137.000 129.000 0.409 http://example.org/award/award_category/winners./award/award_honor/award_winner #17289-02_4fn PRED entity: 02_4fn PRED relation: nationality PRED expected values: 06qd3 => 103 concepts (99 used for prediction) PRED predicted values (max 10 best out of 72): 09c7w0 (0.75 #401, 0.72 #3005, 0.71 #504), 0d05w3 (0.52 #502, 0.51 #503, 0.08 #250), 03_3d (0.52 #502, 0.51 #503, 0.02 #4812), 05b7q (0.52 #502, 0.51 #503, 0.02 #3105), 06f32 (0.52 #502, 0.51 #503, 0.02 #9715), 06qd3 (0.51 #503, 0.02 #3105, 0.01 #5408), 03rjj (0.20 #105, 0.04 #305, 0.03 #1609), 0f8l9c (0.17 #222, 0.10 #122, 0.03 #422), 02jx1 (0.11 #2537, 0.11 #2337, 0.11 #2837), 03rk0 (0.11 #3050, 0.10 #146, 0.08 #246) >> Best rule #401 for best value: >> intensional similarity = 5 >> extensional distance = 143 >> proper extension: 02qjj7; 08f3b1; 05zbm4; 017r2; 0lrh; 015v3r; 01vw26l; 0blt6; 016ksk; 02t_v1; ... >> query: (?x3470, 09c7w0) <- profession(?x3470, ?x987), people(?x8088, ?x3470), ?x987 = 0dxtg, geographic_distribution(?x8088, ?x7287), adjoins(?x7287, ?x1603) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #503 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 143 *> proper extension: 02qjj7; 08f3b1; 05zbm4; 017r2; 0lrh; 015v3r; 01vw26l; 0blt6; 016ksk; 02t_v1; ... *> query: (?x3470, ?x94) <- profession(?x3470, ?x987), people(?x8088, ?x3470), ?x987 = 0dxtg, geographic_distribution(?x8088, ?x7287), geographic_distribution(?x8088, ?x94), adjoins(?x7287, ?x1603) *> conf = 0.51 ranks of expected_values: 6 EVAL 02_4fn nationality 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 103.000 99.000 0.752 http://example.org/people/person/nationality #17288-07p__7 PRED entity: 07p__7 PRED relation: district_represented PRED expected values: 03v0t 0824r 0g0syc 03gh4 => 39 concepts (34 used for prediction) PRED predicted values (max 10 best out of 103): 07z1m (0.85 #454, 0.84 #407, 0.84 #408), 03v0t (0.85 #454, 0.84 #407, 0.84 #408), 0824r (0.85 #454, 0.84 #407, 0.84 #408), 01n4w (0.85 #454, 0.84 #407, 0.84 #408), 04ykg (0.85 #454, 0.84 #407, 0.84 #408), 03gh4 (0.85 #454, 0.84 #408, 0.84 #129), 0g0syc (0.85 #454, 0.84 #408, 0.84 #129), 0czr9_ (0.85 #454, 0.84 #408, 0.84 #129), 05kr_ (0.63 #243, 0.62 #151, 0.57 #379), 0694j (0.63 #243, 0.62 #151, 0.57 #379) >> Best rule #454 for best value: >> intensional similarity = 32 >> extensional distance = 9 >> proper extension: 03rl1g; >> query: (?x845, ?x1274) <- district_represented(?x845, ?x7405), district_represented(?x845, ?x4600), district_represented(?x845, ?x3908), district_represented(?x845, ?x3778), district_represented(?x845, ?x3038), district_represented(?x845, ?x1755), district_represented(?x845, ?x1025), district_represented(?x845, ?x335), ?x3908 = 04ly1, legislative_sessions(?x2860, ?x845), legislative_sessions(?x653, ?x845), ?x1755 = 01x73, legislative_sessions(?x652, ?x845), ?x7405 = 07_f2, ?x3038 = 0d0x8, district_represented(?x653, ?x1274), contains(?x335, ?x739), location(?x101, ?x335), ?x1025 = 04ych, state_province_region(?x741, ?x335), student(?x741, ?x881), location_of_ceremony(?x1652, ?x335), district_represented(?x12714, ?x335), contains(?x4600, ?x1087), country(?x4600, ?x94), institution(?x620, ?x741), ?x12714 = 05rrw9, citytown(?x312, ?x739), location(?x163, ?x739), religion(?x335, ?x492), featured_film_locations(?x89, ?x739), ?x3778 = 07h34 >> conf = 0.85 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3, 6, 7 EVAL 07p__7 district_represented 03gh4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 39.000 34.000 0.851 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07p__7 district_represented 0g0syc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 39.000 34.000 0.851 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07p__7 district_represented 0824r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 39.000 34.000 0.851 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07p__7 district_represented 03v0t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 39.000 34.000 0.851 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #17287-04306rv PRED entity: 04306rv PRED relation: language! PRED expected values: 02z9hqn 0d6b7 02r8hh_ 035yn8 08052t3 04t6fk 04z257 064lsn 02gd6x 0hwpz 04h4c9 0291ck 0fh2v5 => 88 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1727): 02h22 (0.80 #11255, 0.50 #5718, 0.33 #896), 0dmn0x (0.80 #11255, 0.17 #33759, 0.12 #33760), 06x43v (0.63 #41800, 0.50 #5985, 0.36 #15632), 078sj4 (0.63 #41800, 0.50 #5219, 0.33 #11652), 0ggbhy7 (0.63 #41800, 0.50 #5257, 0.33 #11690), 0bmfnjs (0.63 #41800, 0.50 #6134, 0.33 #1312), 05q96q6 (0.63 #41800, 0.50 #4952, 0.33 #130), 03rg2b (0.63 #41800, 0.50 #5790, 0.33 #968), 01mszz (0.63 #41800, 0.46 #35368, 0.33 #961), 0q9sg (0.63 #41800, 0.46 #35368, 0.11 #11929) >> Best rule #11255 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 0653m; >> query: (?x732, ?x5849) <- language(?x9349, ?x732), language(?x308, ?x732), award(?x308, ?x484), film_release_region(?x9349, ?x87), ?x484 = 0gq_v, languages(?x147, ?x732), nominated_for(?x574, ?x308), titles(?x732, ?x5849) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #5347 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 064_8sq; *> query: (?x732, 04z257) <- service_language(?x6717, ?x732), language(?x5429, ?x732), languages(?x147, ?x732), ?x5429 = 02psgq, major_field_of_study(?x735, ?x732), major_field_of_study(?x2014, ?x732), ?x6717 = 064f29 *> conf = 0.50 ranks of expected_values: 105, 113, 161, 297, 338, 400, 429, 432, 1094, 1578, 1592 EVAL 04306rv language! 0fh2v5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 0291ck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 04h4c9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 0hwpz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 02gd6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 04z257 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 04t6fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 08052t3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 035yn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 02r8hh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 0d6b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 27.000 0.796 http://example.org/film/film/language EVAL 04306rv language! 02z9hqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 27.000 0.796 http://example.org/film/film/language #17286-06w839_ PRED entity: 06w839_ PRED relation: nominated_for! PRED expected values: 09tqxt => 65 concepts (53 used for prediction) PRED predicted values (max 10 best out of 187): 09tqxt (0.50 #76, 0.43 #1749, 0.18 #793), 0drtkx (0.29 #1869, 0.25 #196, 0.18 #913), 02hsq3m (0.28 #1464, 0.23 #2899, 0.22 #3377), 099c8n (0.27 #774, 0.25 #57, 0.17 #1013), 018wdw (0.27 #897, 0.25 #180, 0.17 #1136), 02g3v6 (0.26 #1456, 0.23 #2891, 0.22 #2412), 05ztjjw (0.25 #10, 0.21 #2879, 0.19 #3357), 02qyxs5 (0.25 #111, 0.20 #1784, 0.18 #828), 0l8z1 (0.25 #52, 0.18 #769, 0.11 #7463), 040njc (0.25 #7, 0.17 #963, 0.14 #7418) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 06w99h3; 01jrbb; >> query: (?x3088, 09tqxt) <- film(?x9388, ?x3088), ?x9388 = 0309lm, film_release_region(?x3088, ?x142), genre(?x3088, ?x811), ?x811 = 03k9fj >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06w839_ nominated_for! 09tqxt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 53.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17285-0cjf0 PRED entity: 0cjf0 PRED relation: symptom_of PRED expected values: 072hv 0cycc 024c2 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 79): 01n3bm (0.67 #861, 0.57 #656, 0.55 #967), 0lcdk (0.60 #522, 0.33 #257, 0.33 #186), 02psvcf (0.57 #649, 0.50 #611, 0.38 #635), 0hgxh (0.56 #268, 0.45 #965, 0.44 #859), 072hv (0.56 #268, 0.40 #523, 0.33 #258), 02bft (0.56 #268, 0.33 #244, 0.32 #1019), 01rt5h (0.56 #268, 0.28 #1141, 0.22 #1021), 01pf6 (0.56 #268, 0.28 #1141, 0.12 #535), 01_qc_ (0.50 #284, 0.44 #856, 0.43 #651), 0dcp_ (0.50 #334, 0.34 #1144, 0.33 #592) >> Best rule #861 for best value: >> intensional similarity = 22 >> extensional distance = 7 >> proper extension: 02tfl8; >> query: (?x10717, 01n3bm) <- symptom_of(?x10717, ?x14024), symptom_of(?x10717, ?x11739), symptom_of(?x10717, ?x4291), people(?x4291, ?x10605), people(?x4291, ?x6370), people(?x4291, ?x4915), influenced_by(?x1236, ?x4915), risk_factors(?x14024, ?x11563), symptom_of(?x9118, ?x11739), symptom_of(?x4905, ?x11739), profession(?x10605, ?x2225), people(?x11563, ?x269), location(?x6370, ?x205), gender(?x6370, ?x231), ?x4905 = 01j6t0, risk_factors(?x4291, ?x13122), risk_factors(?x11739, ?x6781), location(?x4915, ?x1458), peers(?x10654, ?x10605), influenced_by(?x4915, ?x6810), ?x9118 = 0brgy, ?x6810 = 037jz >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #268 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 1 *> proper extension: 01j6t0; *> query: (?x10717, ?x9510) <- symptom_of(?x10717, ?x14562), symptom_of(?x10717, ?x14228), symptom_of(?x10717, ?x13485), symptom_of(?x10717, ?x10480), symptom_of(?x10717, ?x10199), symptom_of(?x10717, ?x9119), symptom_of(?x10717, ?x8675), symptom_of(?x10717, ?x7586), symptom_of(?x10717, ?x6656), symptom_of(?x10717, ?x6260), symptom_of(?x10717, ?x3799), award(?x14562, ?x12628), ?x7586 = 074m2, risk_factors(?x9119, ?x7007), ?x13485 = 07s4l, ?x3799 = 04psf, ?x8675 = 01gkcc, ?x6656 = 03p41, symptom_of(?x9438, ?x9119), notable_people_with_this_condition(?x14228, ?x5609), risk_factors(?x9510, ?x14228), ?x10199 = 02k6hp, symptom_of(?x6780, ?x14562), ?x10480 = 0h1n9, ?x6260 = 0dq9p, ?x9438 = 012qjw, ?x6780 = 0j5fv *> conf = 0.56 ranks of expected_values: 5, 29, 35 EVAL 0cjf0 symptom_of 024c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 59.000 59.000 0.667 http://example.org/medicine/symptom/symptom_of EVAL 0cjf0 symptom_of 0cycc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 59.000 59.000 0.667 http://example.org/medicine/symptom/symptom_of EVAL 0cjf0 symptom_of 072hv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 59.000 59.000 0.667 http://example.org/medicine/symptom/symptom_of #17284-01jz6d PRED entity: 01jz6d PRED relation: type_of_union PRED expected values: 01g63y => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.73 #13, 0.72 #25, 0.70 #101), 01g63y (0.14 #42, 0.14 #106, 0.14 #102) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 01vsy9_; >> query: (?x12922, 04ztj) <- profession(?x12922, ?x1581), people(?x2510, ?x12922), award_winner(?x10746, ?x12922), athlete(?x4833, ?x12922) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 02y0dd; *> query: (?x12922, 01g63y) <- team(?x12922, ?x1578), currency(?x12922, ?x170), teams(?x3501, ?x1578), team(?x1348, ?x1578) *> conf = 0.14 ranks of expected_values: 2 EVAL 01jz6d type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.733 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17283-0j1yf PRED entity: 0j1yf PRED relation: list PRED expected values: 026cl_m => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 1): 026cl_m (0.07 #3, 0.05 #115, 0.05 #87) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 012_53; >> query: (?x1896, 026cl_m) <- actor(?x13288, ?x1896), friend(?x1896, ?x3503), participant(?x5906, ?x1896) >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j1yf list 026cl_m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.071 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #17282-025mb_ PRED entity: 025mb_ PRED relation: award PRED expected values: 0cqgl9 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 289): 02py7pj (0.76 #44787, 0.71 #32100, 0.70 #7922), 09sb52 (0.42 #1227, 0.36 #13903, 0.35 #15091), 019bnn (0.33 #262, 0.05 #3431, 0.05 #38046), 05pcn59 (0.30 #475, 0.26 #2455, 0.25 #1267), 05q5t0b (0.30 #952, 0.06 #5704, 0.04 #4120), 05ztrmj (0.25 #1368, 0.21 #2556, 0.15 #1764), 0gkvb7 (0.23 #1612, 0.20 #2008, 0.20 #424), 0ck27z (0.21 #22274, 0.20 #22670, 0.20 #17519), 057xs89 (0.20 #949, 0.11 #5305, 0.10 #553), 02w9sd7 (0.20 #958, 0.11 #5314, 0.08 #15218) >> Best rule #44787 for best value: >> intensional similarity = 2 >> extensional distance = 1907 >> proper extension: 01t265; >> query: (?x9140, ?x2603) <- award_winner(?x2603, ?x9140), ceremony(?x2603, ?x1265) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #2564 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 011_3s; 04fzk; 01wy5m; 01x_d8; 08wjf4; 01bh6y; *> query: (?x9140, 0cqgl9) <- award(?x9140, ?x594), actor(?x5936, ?x9140), award_winner(?x758, ?x9140) *> conf = 0.11 ranks of expected_values: 56 EVAL 025mb_ award 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 135.000 135.000 0.764 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17281-0dcz8_ PRED entity: 0dcz8_ PRED relation: film_crew_role PRED expected values: 02r96rf => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 32): 02r96rf (0.72 #238, 0.70 #971, 0.65 #2040), 02ynfr (0.20 #981, 0.19 #1047, 0.18 #1081), 02rh1dz (0.17 #244, 0.17 #210, 0.13 #1102), 0215hd (0.14 #584, 0.14 #1050, 0.14 #1084), 015h31 (0.14 #243, 0.13 #1102, 0.12 #175), 0d2b38 (0.13 #1102, 0.12 #258, 0.10 #2778), 01xy5l_ (0.13 #1102, 0.12 #979, 0.10 #2778), 089g0h (0.13 #1102, 0.11 #985, 0.10 #2778), 02_n3z (0.13 #1102, 0.10 #2778, 0.10 #168), 089fss (0.13 #1102, 0.10 #2778, 0.08 #408) >> Best rule #238 for best value: >> intensional similarity = 4 >> extensional distance = 217 >> proper extension: 0cpllql; 0cc97st; 0cbn7c; 01xvjb; >> query: (?x9715, 02r96rf) <- film(?x1104, ?x9715), genre(?x9715, ?x258), story_by(?x9715, ?x12856), film_crew_role(?x9715, ?x137) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dcz8_ film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.717 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17280-01c65z PRED entity: 01c65z PRED relation: gender PRED expected values: 05zppz => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #83, 0.81 #85, 0.81 #63), 02zsn (0.47 #42, 0.46 #215, 0.46 #218) >> Best rule #83 for best value: >> intensional similarity = 4 >> extensional distance = 720 >> proper extension: 04rs03; 02pp_q_; 02kxbwx; 067jsf; 04wvhz; 01g4zr; 05m883; 03kwtb; 01b9ck; 05cv94; ... >> query: (?x12448, 05zppz) <- profession(?x12448, ?x319), ?x319 = 01d_h8, type_of_union(?x12448, ?x566), ?x566 = 04ztj >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c65z gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.813 http://example.org/people/person/gender #17279-027gy0k PRED entity: 027gy0k PRED relation: produced_by PRED expected values: 03ktjq => 117 concepts (93 used for prediction) PRED predicted values (max 10 best out of 179): 076_74 (0.20 #132), 0h1p (0.20 #66), 0sz28 (0.20 #42), 03ktjq (0.19 #589, 0.12 #1366, 0.03 #4083), 06chvn (0.12 #607, 0.08 #1384, 0.03 #8152), 086k8 (0.12 #27547, 0.11 #6209, 0.10 #34917), 03v1w7 (0.09 #612, 0.06 #1389, 0.03 #3717), 04zwtdy (0.08 #1130, 0.05 #1907, 0.03 #8152), 04pqqb (0.08 #954, 0.05 #1731, 0.03 #4060), 06dkzt (0.06 #688, 0.04 #1465, 0.02 #10481) >> Best rule #132 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 04grkmd; >> query: (?x6510, 076_74) <- film_crew_role(?x6510, ?x1284), film(?x3816, ?x6510), ?x3816 = 05mkhs, ?x1284 = 0ch6mp2 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #589 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 04tc1g; 018nnz; *> query: (?x6510, 03ktjq) <- production_companies(?x6510, ?x2548), ?x2548 = 046b0s, film(?x3210, ?x6510), film_release_distribution_medium(?x6510, ?x81) *> conf = 0.19 ranks of expected_values: 4 EVAL 027gy0k produced_by 03ktjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 93.000 0.200 http://example.org/film/film/produced_by #17278-0418wg PRED entity: 0418wg PRED relation: film_crew_role PRED expected values: 09vw2b7 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 25): 09vw2b7 (0.63 #837, 0.62 #605, 0.61 #505), 01vx2h (0.39 #276, 0.36 #343, 0.32 #543), 0dxtw (0.36 #609, 0.35 #841, 0.35 #1338), 0215hd (0.27 #49, 0.15 #515, 0.13 #115), 089g0h (0.18 #50, 0.16 #116, 0.12 #516), 02_n3z (0.18 #34, 0.10 #1, 0.09 #67), 02rh1dz (0.14 #274, 0.14 #341, 0.12 #541), 0d2b38 (0.13 #288, 0.12 #522, 0.11 #122), 015h31 (0.10 #173, 0.10 #273, 0.10 #540), 0263ycg (0.09 #81, 0.09 #48, 0.04 #180) >> Best rule #837 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 07l50vn; >> query: (?x2500, 09vw2b7) <- currency(?x2500, ?x170), country(?x2500, ?x94), film_crew_role(?x2500, ?x137) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0418wg film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.633 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17277-0ctw_b PRED entity: 0ctw_b PRED relation: film_release_region! PRED expected values: 0g5qs2k 01vksx 0dgst_d 04zyhx 03qnvdl 0gj9qxr 09146g 0hx4y 045j3w 09g7vfw 0h03fhx 0bh8x1y 0gkz3nz 0h95zbp 0cc97st 0ggbfwf 0btpm6 02vzpb 09tcg4 => 215 concepts (135 used for prediction) PRED predicted values (max 10 best out of 1679): 0dzlbx (0.87 #12130, 0.86 #19078, 0.84 #31826), 02vr3gz (0.87 #11972, 0.85 #24715, 0.82 #28190), 0bwfwpj (0.87 #11681, 0.79 #18629, 0.76 #24424), 0661ql3 (0.86 #29200, 0.85 #24564, 0.82 #31517), 0dgst_d (0.85 #24447, 0.83 #11704, 0.79 #31400), 0ds3t5x (0.85 #24358, 0.80 #28994, 0.79 #27833), 01vksx (0.85 #24411, 0.80 #29047, 0.79 #31364), 0btpm6 (0.84 #32116, 0.83 #29799, 0.76 #25163), 06fcqw (0.83 #29673, 0.83 #12294, 0.82 #25037), 02r8hh_ (0.83 #11747, 0.82 #24490, 0.74 #29126) >> Best rule #12130 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 09c7w0; 0b90_r; 0154j; 03rjj; 0d060g; 0chghy; 05qhw; 07ssc; 015fr; 0f8l9c; ... >> query: (?x1023, 0dzlbx) <- combatants(?x94, ?x1023), film_release_region(?x7009, ?x1023), contains(?x1023, ?x2396), ?x7009 = 0bs8s1p >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #24447 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 05r4w; 0jgd; 0d0vqn; 06mzp; 04v3q; 0h7x; 01znc_; 01p1v; 03rk0; 06mkj; ... *> query: (?x1023, 0dgst_d) <- country(?x972, ?x1023), film_release_region(?x5092, ?x1023), ?x5092 = 0gg5qcw *> conf = 0.85 ranks of expected_values: 5, 7, 8, 16, 22, 23, 24, 36, 44, 52, 53, 61, 104, 105, 107, 138, 197, 233, 293 EVAL 0ctw_b film_release_region! 09tcg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 02vzpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0ggbfwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0h95zbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0gkz3nz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0bh8x1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0h03fhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 09g7vfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 045j3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0hx4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 09146g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0gj9qxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 03qnvdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 04zyhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0dgst_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 01vksx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ctw_b film_release_region! 0g5qs2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 215.000 135.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17276-032t2z PRED entity: 032t2z PRED relation: instrumentalists! PRED expected values: 03gvt 01xqw => 138 concepts (75 used for prediction) PRED predicted values (max 10 best out of 117): 03gvt (0.72 #361, 0.37 #152, 0.36 #5409), 01vdm0 (0.52 #4641, 0.37 #152, 0.32 #1671), 0l14qv (0.44 #3109, 0.42 #3336, 0.42 #3335), 03bx0bm (0.44 #3109, 0.42 #3336, 0.42 #3335), 02snj9 (0.44 #3109, 0.42 #3336, 0.42 #3335), 03qjg (0.42 #3455, 0.38 #652, 0.29 #731), 018j2 (0.38 #182, 0.27 #3443, 0.14 #3521), 04rzd (0.38 #181, 0.25 #105, 0.22 #3442), 02hnl (0.37 #1776, 0.35 #867, 0.29 #636), 0l14j_ (0.25 #121, 0.10 #3874, 0.08 #655) >> Best rule #361 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 01309x; >> query: (?x642, 03gvt) <- instrumentalists(?x74, ?x642), role(?x642, ?x228), artist(?x2149, ?x642), profession(?x642, ?x5917), ?x5917 = 01b30l >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1, 48 EVAL 032t2z instrumentalists! 01xqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 138.000 75.000 0.722 http://example.org/music/instrument/instrumentalists EVAL 032t2z instrumentalists! 03gvt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 75.000 0.722 http://example.org/music/instrument/instrumentalists #17275-0gm34 PRED entity: 0gm34 PRED relation: award PRED expected values: 0gqy2 => 138 concepts (106 used for prediction) PRED predicted values (max 10 best out of 300): 0f4x7 (0.80 #10495, 0.76 #24624, 0.76 #15744), 0gqy2 (0.62 #569, 0.56 #973, 0.46 #9045), 04kxsb (0.62 #2550, 0.58 #3760, 0.43 #10216), 09sb52 (0.40 #3674, 0.38 #2464, 0.38 #40), 0gqwc (0.38 #74, 0.27 #2094, 0.18 #5324), 0gqyl (0.38 #105, 0.15 #15041, 0.13 #1316), 094qd5 (0.38 #44, 0.10 #21438, 0.10 #14980), 09qwmm (0.38 #33, 0.08 #21427, 0.08 #21832), 0bfvd4 (0.36 #8995, 0.24 #2539, 0.21 #3346), 0789_m (0.36 #8899, 0.23 #3653, 0.21 #2443) >> Best rule #10495 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 0kr5_; 0gs1_; 01wk3c; 0gpmp; >> query: (?x7458, ?x3209) <- award_winner(?x3209, ?x7458), film(?x7458, ?x675), nominated_for(?x3209, ?x4315), ?x4315 = 0sxkh >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #569 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 03fvqg; 03bpn6; 0m0hw; 03xx3m; *> query: (?x7458, 0gqy2) <- award_winner(?x591, ?x7458), film(?x7458, ?x8217), people(?x6260, ?x7458), ?x8217 = 04v89z *> conf = 0.62 ranks of expected_values: 2 EVAL 0gm34 award 0gqy2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 106.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17274-03shp PRED entity: 03shp PRED relation: country! PRED expected values: 03hr1p 035d1m 0486tv 0194d => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 42): 03hr1p (0.70 #436, 0.61 #772, 0.59 #856), 01lb14 (0.69 #222, 0.68 #432, 0.66 #768), 01hp22 (0.63 #49, 0.54 #427, 0.48 #91), 07bs0 (0.62 #431, 0.47 #221, 0.46 #977), 019tzd (0.61 #239, 0.50 #449, 0.48 #785), 0194d (0.60 #456, 0.58 #246, 0.57 #792), 01sgl (0.56 #243, 0.50 #453, 0.48 #789), 01z27 (0.56 #223, 0.50 #433, 0.48 #769), 0486tv (0.52 #448, 0.47 #238, 0.45 #826), 09w1n (0.50 #437, 0.44 #857, 0.44 #227) >> Best rule #436 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 01z215; >> query: (?x3730, 03hr1p) <- olympics(?x3730, ?x2966), combatants(?x9203, ?x3730), medal(?x3730, ?x422) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 9, 14 EVAL 03shp country! 0194d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 124.000 0.700 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03shp country! 0486tv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 124.000 124.000 0.700 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03shp country! 035d1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 124.000 124.000 0.700 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03shp country! 03hr1p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.700 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #17273-012c6x PRED entity: 012c6x PRED relation: place_of_death PRED expected values: 06_kh => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 20): 030qb3t (0.20 #413, 0.16 #22, 0.06 #782), 0k049 (0.13 #394, 0.11 #3, 0.02 #786), 0f2wj (0.11 #403, 0.04 #12, 0.01 #11505), 06_kh (0.07 #396, 0.05 #5, 0.02 #592), 04jpl (0.06 #398, 0.05 #7, 0.02 #2154), 02_286 (0.05 #404, 0.04 #13, 0.03 #4694), 0r0m6 (0.04 #60, 0.02 #451), 0r3w7 (0.02 #568), 01j2_7 (0.02 #186, 0.01 #577), 0fn7r (0.02 #159, 0.01 #550) >> Best rule #413 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 0l5yl; 0c921; 01kgg9; 09xvf7; >> query: (?x773, 030qb3t) <- award(?x773, ?x591), nationality(?x773, ?x94), place_of_burial(?x773, ?x7496) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #396 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 0l5yl; 0c921; 01kgg9; 09xvf7; *> query: (?x773, 06_kh) <- award(?x773, ?x591), nationality(?x773, ?x94), place_of_burial(?x773, ?x7496) *> conf = 0.07 ranks of expected_values: 4 EVAL 012c6x place_of_death 06_kh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 83.000 0.200 http://example.org/people/deceased_person/place_of_death #17272-0dl9_4 PRED entity: 0dl9_4 PRED relation: featured_film_locations PRED expected values: 04jpl => 113 concepts (84 used for prediction) PRED predicted values (max 10 best out of 89): 02_286 (0.23 #1221, 0.19 #2182, 0.17 #1461), 04jpl (0.15 #1210, 0.12 #1690, 0.11 #1450), 03rjj (0.14 #6, 0.11 #487, 0.03 #2648), 06m_5 (0.14 #146), 0d6lp (0.12 #313, 0.10 #793, 0.05 #1993), 0k424 (0.12 #443, 0.10 #923, 0.02 #2364), 0rh6k (0.11 #2403, 0.08 #3128, 0.08 #1202), 0h7h6 (0.10 #1004, 0.08 #1244, 0.06 #1484), 0jgd (0.10 #724), 030qb3t (0.10 #4130, 0.08 #8724, 0.08 #1240) >> Best rule #1221 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 04dsnp; 02phtzk; 02x0fs9; >> query: (?x5185, 02_286) <- nominated_for(?x1414, ?x5185), film_crew_role(?x5185, ?x137), honored_for(?x762, ?x5185), ?x762 = 03gwpw2, category(?x5185, ?x134) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #1210 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 04dsnp; 02phtzk; 02x0fs9; *> query: (?x5185, 04jpl) <- nominated_for(?x1414, ?x5185), film_crew_role(?x5185, ?x137), honored_for(?x762, ?x5185), ?x762 = 03gwpw2, category(?x5185, ?x134) *> conf = 0.15 ranks of expected_values: 2 EVAL 0dl9_4 featured_film_locations 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 84.000 0.231 http://example.org/film/film/featured_film_locations #17271-06z4wj PRED entity: 06z4wj PRED relation: award_winner! PRED expected values: 0d085 => 51 concepts (44 used for prediction) PRED predicted values (max 10 best out of 248): 02h3d1 (0.19 #177, 0.17 #606, 0.15 #1035), 0gs9p (0.16 #3512, 0.07 #2654, 0.07 #3083), 0d085 (0.15 #6865, 0.14 #248, 0.12 #677), 040njc (0.13 #3440, 0.07 #1724, 0.06 #2582), 019f4v (0.13 #3499, 0.06 #2641, 0.06 #3070), 0ck27z (0.12 #1809, 0.08 #5670, 0.08 #5241), 0gr4k (0.12 #2607, 0.11 #3036, 0.07 #3465), 09sb52 (0.11 #5189, 0.10 #5618, 0.08 #6476), 0gq9h (0.11 #3510, 0.10 #507, 0.09 #936), 09d28z (0.11 #3734, 0.07 #2876, 0.07 #3305) >> Best rule #177 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 0fvf9q; 0147dk; 04wvhz; 0fb1q; 01vw20h; 03flwk; 029h45; 01vrlr4; 06q8hf; 02kv5k; ... >> query: (?x6943, 02h3d1) <- award(?x6943, ?x3105), ?x3105 = 01l29r, profession(?x6943, ?x353) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #6865 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1519 *> proper extension: 01j53q; *> query: (?x6943, ?x3467) <- award_winner(?x6943, ?x12947), award_winner(?x3467, ?x12947) *> conf = 0.15 ranks of expected_values: 3 EVAL 06z4wj award_winner! 0d085 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 44.000 0.194 http://example.org/award/award_category/winners./award/award_honor/award_winner #17270-0mm0p PRED entity: 0mm0p PRED relation: contains! PRED expected values: 081yw => 109 concepts (53 used for prediction) PRED predicted values (max 10 best out of 96): 081yw (0.68 #18896, 0.66 #44116, 0.66 #30599), 01n7q (0.61 #5468, 0.60 #1876, 0.59 #9971), 09c7w0 (0.47 #18901, 0.47 #9896, 0.31 #4493), 06pvr (0.22 #5556, 0.19 #4656, 0.18 #6456), 05kj_ (0.21 #2739, 0.20 #3635, 0.18 #5431), 04_1l0v (0.17 #30150, 0.17 #27444, 0.16 #31051), 059rby (0.14 #12609, 0.14 #21615, 0.14 #22512), 05tbn (0.11 #11013, 0.11 #8316, 0.10 #15519), 041_3z (0.09 #3540, 0.08 #4436, 0.07 #6232), 05fjf (0.09 #7564, 0.08 #28270, 0.08 #20168) >> Best rule #18896 for best value: >> intensional similarity = 5 >> extensional distance = 188 >> proper extension: 04kbn; >> query: (?x8547, ?x4600) <- adjoins(?x8546, ?x8547), adjoins(?x8547, ?x11366), county_seat(?x11366, ?x11367), contains(?x4600, ?x11366), second_level_divisions(?x94, ?x11366) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mm0p contains! 081yw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 53.000 0.679 http://example.org/location/location/contains #17269-0697s PRED entity: 0697s PRED relation: olympics PRED expected values: 0jdk_ => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 42): 0jhn7 (0.69 #154, 0.68 #112, 0.63 #196), 06sks6 (0.67 #151, 0.65 #67, 0.65 #25), 0kbvb (0.62 #49, 0.61 #133, 0.59 #7), 0kbws (0.61 #182, 0.61 #140, 0.57 #98), 0jdk_ (0.59 #153, 0.57 #111, 0.54 #195), 0lgxj (0.43 #155, 0.42 #113, 0.40 #71), 0l6mp (0.42 #60, 0.41 #144, 0.40 #354), 0l6ny (0.39 #135, 0.38 #93, 0.35 #51), 0l998 (0.38 #90, 0.35 #132, 0.34 #342), 0l98s (0.38 #47, 0.31 #341, 0.30 #5) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 01n7q; 03gh4; >> query: (?x3016, 0jhn7) <- film_release_region(?x1012, ?x3016), jurisdiction_of_office(?x182, ?x3016), geographic_distribution(?x1571, ?x3016) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 01n7q; 03gh4; *> query: (?x3016, 0jdk_) <- film_release_region(?x1012, ?x3016), jurisdiction_of_office(?x182, ?x3016), geographic_distribution(?x1571, ?x3016) *> conf = 0.59 ranks of expected_values: 5 EVAL 0697s olympics 0jdk_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 64.000 0.694 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #17268-01g5v PRED entity: 01g5v PRED relation: colors! PRED expected values: 0223bl 070xg 024d8w 023fb 04mp75 0dwz3t 03x6m 02rjz5 06x76 02w59b 04l58n 04l59s 0bszz => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 884): 0jnmj (0.50 #2117, 0.33 #2636, 0.33 #1598), 0cgwt8 (0.50 #1905, 0.33 #1387, 0.33 #349), 01rlzn (0.50 #2212, 0.33 #1693, 0.22 #2731), 0mmd6 (0.50 #2319, 0.33 #1800, 0.22 #2838), 0lhp1 (0.50 #2081, 0.33 #1562, 0.22 #2600), 03lpp_ (0.44 #2602, 0.43 #2342, 0.40 #3122), 032498 (0.44 #2769, 0.43 #2509, 0.33 #3030), 0jnm_ (0.43 #2454, 0.40 #3234, 0.33 #2975), 01ct6 (0.43 #2343, 0.33 #2864, 0.33 #2603), 02hqt6 (0.43 #2493, 0.33 #2753, 0.33 #1715) >> Best rule #2117 for best value: >> intensional similarity = 26 >> extensional distance = 2 >> proper extension: 06kqt3; >> query: (?x3189, 0jnmj) <- colors(?x12302, ?x3189), colors(?x10178, ?x3189), colors(?x8223, ?x3189), colors(?x13166, ?x3189), colors(?x11312, ?x3189), colors(?x3333, ?x3189), colors(?x1085, ?x3189), category(?x3333, ?x134), student(?x12302, ?x2813), contains(?x362, ?x8223), currency(?x12302, ?x170), institution(?x620, ?x10178), season(?x3333, ?x701), ?x13166 = 0j6tr, ?x620 = 07s6fsf, state_province_region(?x10178, ?x1227), teams(?x1860, ?x3333), student(?x8223, ?x11159), award(?x11159, ?x458), people(?x5042, ?x11159), position(?x3333, ?x2010), school(?x3333, ?x735), award_nominee(?x3808, ?x2813), position(?x11312, ?x60), major_field_of_study(?x8223, ?x2601), team(?x5420, ?x1085) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2500 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 5 *> proper extension: 067z2v; *> query: (?x3189, 02rjz5) <- colors(?x12302, ?x3189), colors(?x10178, ?x3189), colors(?x9560, ?x3189), colors(?x8223, ?x3189), colors(?x8220, ?x3189), colors(?x6505, ?x3189), colors(?x5750, ?x3189), colors(?x11789, ?x3189), colors(?x7485, ?x3189), colors(?x3333, ?x3189), category(?x3333, ?x134), student(?x12302, ?x1594), contains(?x1310, ?x8223), currency(?x12302, ?x170), institution(?x865, ?x10178), institution(?x1526, ?x6505), major_field_of_study(?x5750, ?x1154), company(?x3428, ?x8220), contains(?x335, ?x9560), team(?x60, ?x7485), ?x1310 = 02jx1, organization(?x5510, ?x8220), school_type(?x6505, ?x4994), team(?x2302, ?x11789), student(?x5750, ?x652), school(?x3333, ?x735), ?x865 = 02h4rq6, ?x2302 = 0b_77q, registering_agency(?x9560, ?x1982), student(?x8223, ?x1515) *> conf = 0.43 ranks of expected_values: 18, 63, 83, 94, 121, 156, 191, 209, 218, 226, 233, 237, 742 EVAL 01g5v colors! 0bszz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 04l59s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 04l58n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 02w59b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 06x76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 02rjz5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 03x6m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 0dwz3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 04mp75 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 023fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 024d8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 070xg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.500 http://example.org/sports/sports_team/colors EVAL 01g5v colors! 0223bl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 20.000 20.000 0.500 http://example.org/sports/sports_team/colors #17267-03cxqp5 PRED entity: 03cxqp5 PRED relation: gender PRED expected values: 05zppz => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.71 #33, 0.71 #31, 0.71 #29), 02zsn (0.21 #2, 0.21 #6, 0.20 #8) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 3798 >> proper extension: 02ck1; 05wh0sh; 03f0324; 09n70c; 03_lf; 03yf5g; 079dy; >> query: (?x14527, 05zppz) <- nationality(?x14527, ?x94), film_release_region(?x54, ?x94), contains(?x94, ?x95) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cxqp5 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.712 http://example.org/people/person/gender #17266-01jc6q PRED entity: 01jc6q PRED relation: genre PRED expected values: 060__y => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 115): 02l7c8 (0.44 #15, 0.33 #5907, 0.31 #4107), 05p553 (0.35 #244, 0.33 #485, 0.32 #5296), 01jfsb (0.33 #2060, 0.30 #2900, 0.29 #5063), 02kdv5l (0.28 #2051, 0.28 #5054, 0.27 #5414), 082gq (0.28 #150, 0.20 #751, 0.19 #1838), 04xvlr (0.28 #1, 0.24 #1085, 0.21 #241), 0219x_ (0.28 #26, 0.15 #266, 0.10 #1110), 03k9fj (0.26 #130, 0.23 #3981, 0.23 #3260), 060__y (0.22 #377, 0.18 #1100, 0.17 #1221), 0lsxr (0.20 #1092, 0.20 #1455, 0.20 #971) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0g5q34q; 0d8w2n; >> query: (?x197, 02l7c8) <- films(?x2286, ?x197), ?x2286 = 018h2, language(?x197, ?x254) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #377 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 188 *> proper extension: 04tng0; *> query: (?x197, 060__y) <- genre(?x197, ?x53), nominated_for(?x3066, ?x197), ?x3066 = 0gqy2 *> conf = 0.22 ranks of expected_values: 9 EVAL 01jc6q genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 65.000 65.000 0.444 http://example.org/film/film/genre #17265-09b5t PRED entity: 09b5t PRED relation: films PRED expected values: 01pv91 => 2 concepts (2 used for prediction) PRED predicted values (max 10 best out of 19): 01q7h2 (0.01 #468), 0296vv (0.01 #416), 04lhc4 (0.01 #353), 03hxsv (0.01 #327), 09p4w8 (0.01 #244), 04x4vj (0.01 #232), 016y_f (0.01 #223), 03lv4x (0.01 #217), 0bs5k8r (0.01 #213), 015g28 (0.01 #196) >> Best rule #468 for best value: >> intensional similarity = 1 >> extensional distance = 96 >> proper extension: 09c7w0; 0342h; 02h40lc; 02211by; 02k54; 0dwsp; 07s6fsf; 0gbbt; 065y4w7; 02w7gg; ... >> query: (?x14701, 01q7h2) <- split_to(?x14701, ?x1959) >> conf = 0.01 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09b5t films 01pv91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 2.000 2.000 0.010 http://example.org/film/film_subject/films #17264-01516r PRED entity: 01516r PRED relation: group! PRED expected values: 0l14md => 90 concepts (70 used for prediction) PRED predicted values (max 10 best out of 123): 05148p4 (0.88 #906, 0.86 #1258, 0.80 #2317), 0l14md (0.63 #2128, 0.62 #3189, 0.62 #2392), 028tv0 (0.62 #545, 0.56 #634, 0.50 #810), 0l14qv (0.33 #1950, 0.30 #2302, 0.30 #802), 02snj9 (0.33 #57, 0.14 #413, 0.12 #589), 01vj9c (0.29 #2399, 0.28 #3905, 0.28 #3994), 05r5c (0.29 #364, 0.24 #3988, 0.24 #3899), 03qjg (0.27 #3495, 0.26 #3141, 0.23 #3939), 02fsn (0.25 #225, 0.20 #846, 0.17 #316), 0l14j_ (0.21 #2437, 0.19 #938, 0.17 #319) >> Best rule #906 for best value: >> intensional similarity = 9 >> extensional distance = 14 >> proper extension: 01t_xp_; 04r1t; 0dtd6; 07yg2; 03xhj6; 047cx; 07mvp; 0b_xm; 04k05; 027kwc; >> query: (?x8165, 05148p4) <- group(?x1750, ?x8165), artists(?x302, ?x8165), artist(?x6474, ?x8165), ?x6474 = 0g768, artists(?x302, ?x10561), artists(?x302, ?x1165), ?x1165 = 018y2s, ?x1750 = 02hnl, award_winner(?x725, ?x10561) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #2128 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 36 *> proper extension: 01czx; 0d193h; 018gm9; 01fmz6; 01k_yf; 015srx; 02vgh; 046p9; 017lb_; 02cw1m; ... *> query: (?x8165, 0l14md) <- group(?x1466, ?x8165), artists(?x7808, ?x8165), artist(?x6474, ?x8165), ?x1466 = 03bx0bm, artist(?x6474, ?x7909), ?x7909 = 03f3yfj, parent_genre(?x3642, ?x7808) *> conf = 0.63 ranks of expected_values: 2 EVAL 01516r group! 0l14md CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 70.000 0.875 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17263-02x8fs PRED entity: 02x8fs PRED relation: music PRED expected values: 05_pkf => 106 concepts (17 used for prediction) PRED predicted values (max 10 best out of 108): 01wwvc5 (0.42 #3174, 0.18 #3175, 0.15 #633), 01wwvd2 (0.18 #3175, 0.15 #633, 0.14 #844), 0146pg (0.17 #1277, 0.14 #854, 0.13 #1065), 0150t6 (0.12 #46, 0.10 #257, 0.09 #3009), 02jxkw (0.12 #142, 0.09 #1620, 0.09 #564), 02bh9 (0.12 #51, 0.09 #473, 0.08 #1318), 02jxmr (0.12 #74, 0.09 #496, 0.08 #707), 04ls53 (0.12 #79, 0.09 #501, 0.08 #712), 0bs1yy (0.12 #45, 0.09 #467, 0.08 #678), 0csdzz (0.12 #187, 0.09 #609, 0.08 #820) >> Best rule #3174 for best value: >> intensional similarity = 5 >> extensional distance = 83 >> proper extension: 0gxsh4; >> query: (?x5045, ?x2731) <- award_winner(?x5045, ?x4467), award_winner(?x5045, ?x2731), place_of_birth(?x4467, ?x6555), gender(?x2731, ?x231), role(?x2731, ?x227) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #3236 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 89 *> proper extension: 0dln8jk; *> query: (?x5045, 05_pkf) <- film(?x3768, ?x5045), currency(?x5045, ?x170), film_release_region(?x5045, ?x94), prequel(?x5045, ?x11174) *> conf = 0.01 ranks of expected_values: 107 EVAL 02x8fs music 05_pkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 106.000 17.000 0.417 http://example.org/film/film/music #17262-09v4bym PRED entity: 09v4bym PRED relation: nominated_for PRED expected values: 0233bn 02qd04y 08j7lh => 51 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1484): 01mgw (0.84 #1587, 0.82 #3174, 0.67 #2734), 01f8f7 (0.84 #1587, 0.82 #3174, 0.58 #7933), 02qd04y (0.84 #1587, 0.82 #3174, 0.58 #7933), 08j7lh (0.50 #2942, 0.43 #4529, 0.40 #1355), 0233bn (0.50 #2726, 0.40 #1139, 0.29 #4313), 0dckvs (0.50 #1645, 0.40 #58, 0.29 #3232), 03qnc6q (0.36 #5142, 0.09 #11489, 0.09 #16251), 0gmgwnv (0.34 #12067, 0.33 #16829, 0.32 #18418), 0m313 (0.34 #11119, 0.32 #15881, 0.30 #17470), 09gq0x5 (0.33 #16125, 0.33 #11363, 0.32 #17714) >> Best rule #1587 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 09v92_x; 09v51c2; 07kfzsg; >> query: (?x9377, ?x6788) <- nominated_for(?x9377, ?x6376), nominated_for(?x9377, ?x1625), award_winner(?x9377, ?x7740), ?x6376 = 01f85k, ?x1625 = 01f8gz, award(?x6788, ?x9377) >> conf = 0.84 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 4, 5 EVAL 09v4bym nominated_for 08j7lh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 20.000 0.842 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09v4bym nominated_for 02qd04y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 20.000 0.842 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09v4bym nominated_for 0233bn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 20.000 0.842 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17261-099md PRED entity: 099md PRED relation: profession! PRED expected values: 012c6x 0dj5q 082xp => 59 concepts (20 used for prediction) PRED predicted values (max 10 best out of 3148): 042kg (0.50 #50149, 0.50 #7893, 0.34 #16902), 019fz (0.50 #12357, 0.34 #16902, 0.33 #3906), 03d9v8 (0.50 #11467, 0.34 #16902, 0.33 #3016), 014vk4 (0.50 #12404, 0.34 #16902, 0.33 #3953), 0948xk (0.50 #11640, 0.34 #16902, 0.33 #3189), 03_nq (0.50 #11431, 0.34 #16902, 0.33 #2980), 01wj5hp (0.50 #15582, 0.34 #16902, 0.27 #70508), 05bnp0 (0.50 #12696, 0.34 #16902, 0.27 #67622), 03hzkq (0.50 #16241, 0.34 #16902, 0.25 #45819), 03f1zhf (0.50 #15894, 0.34 #16902, 0.25 #49697) >> Best rule #50149 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 0kyk; 0g0vx; >> query: (?x8290, 042kg) <- profession(?x13592, ?x8290), profession(?x7558, ?x8290), profession(?x7181, ?x8290), company(?x7558, ?x94), profession(?x7558, ?x10014), ?x10014 = 012qdp, student(?x7127, ?x7558), nationality(?x7181, ?x1264), people(?x6821, ?x7181), type_of_union(?x13592, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #71330 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 9 *> proper extension: 03jgz; 016fly; 0d8qb; *> query: (?x8290, 082xp) <- profession(?x10855, ?x8290), profession(?x7558, ?x8290), profession(?x7181, ?x8290), profession(?x5609, ?x8290), company(?x7558, ?x94), entity_involved(?x11436, ?x7181), film(?x10855, ?x1130), people(?x6821, ?x7181), type_of_union(?x5609, ?x566) *> conf = 0.45 ranks of expected_values: 22, 2467 EVAL 099md profession! 082xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 59.000 20.000 0.500 http://example.org/people/person/profession EVAL 099md profession! 0dj5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 20.000 0.500 http://example.org/people/person/profession EVAL 099md profession! 012c6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 20.000 0.500 http://example.org/people/person/profession #17260-01vrz41 PRED entity: 01vrz41 PRED relation: profession PRED expected values: 016z4k 09jwl => 98 concepts (95 used for prediction) PRED predicted values (max 10 best out of 57): 09jwl (0.81 #442, 0.75 #2289, 0.73 #4707), 016z4k (0.56 #429, 0.48 #2987, 0.47 #2276), 0nbcg (0.54 #2301, 0.52 #4719, 0.45 #5147), 025352 (0.41 #339, 0.18 #1902, 0.16 #765), 0dxtg (0.33 #295, 0.29 #8545, 0.28 #9397), 039v1 (0.31 #459, 0.30 #4724, 0.29 #2306), 03gjzk (0.27 #1291, 0.27 #4276, 0.26 #1149), 01c8w0 (0.25 #7, 0.18 #2706, 0.17 #3417), 0np9r (0.25 #18, 0.17 #4995, 0.15 #302), 02jknp (0.22 #8256, 0.21 #10386, 0.21 #9818) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 06y9c2; 02wb6yq; 04d_mtq; >> query: (?x1231, 09jwl) <- profession(?x1231, ?x131), friend(?x1231, ?x2524), instrumentalists(?x316, ?x1231) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01vrz41 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 95.000 0.812 http://example.org/people/person/profession EVAL 01vrz41 profession 016z4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 95.000 0.812 http://example.org/people/person/profession #17259-087vz PRED entity: 087vz PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 21): 060bp (0.68 #442, 0.66 #332, 0.64 #1873), 0pqc5 (0.67 #1083, 0.65 #841, 0.55 #1281), 0f6c3 (0.47 #580, 0.40 #250, 0.39 #1108), 0fkvn (0.41 #576, 0.40 #246, 0.39 #1170), 09n5b9 (0.41 #584, 0.40 #254, 0.35 #1178), 0p5vf (0.33 #101, 0.33 #34, 0.30 #167), 04syw (0.33 #28, 0.20 #183, 0.17 #293), 01zq91 (0.22 #36, 0.21 #213, 0.19 #103), 0377k9 (0.22 #37, 0.19 #104, 0.17 #170), 0dq3c (0.21 #46, 0.19 #311, 0.19 #91) >> Best rule #442 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 014tss; 024pcx; 0285m87; >> query: (?x3728, 060bp) <- combatants(?x3728, ?x789), jurisdiction_of_office(?x346, ?x3728), nationality(?x317, ?x789), combatants(?x1140, ?x789) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087vz jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.681 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #17258-0d07s PRED entity: 0d07s PRED relation: major_field_of_study PRED expected values: 0fdys => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 121): 01mkq (0.68 #6836, 0.50 #6092, 0.49 #5596), 02j62 (0.50 #6107, 0.45 #4619, 0.45 #5611), 03g3w (0.49 #2507, 0.45 #5607, 0.45 #6103), 062z7 (0.46 #2508, 0.35 #5608, 0.33 #5484), 037mh8 (0.43 #2550, 0.30 #5526, 0.28 #5650), 02lp1 (0.38 #5592, 0.37 #2492, 0.36 #6088), 0g26h (0.35 #540, 0.30 #5624, 0.29 #788), 0_jm (0.35 #556, 0.29 #804, 0.22 #5516), 05qjt (0.32 #6084, 0.31 #4472, 0.30 #4968), 05qfh (0.31 #4501, 0.30 #4997, 0.29 #2517) >> Best rule #6836 for best value: >> intensional similarity = 7 >> extensional distance = 199 >> proper extension: 03bwzr4; >> query: (?x7306, 01mkq) <- major_field_of_study(?x7306, ?x2014), major_field_of_study(?x10303, ?x2014), major_field_of_study(?x6894, ?x2014), major_field_of_study(?x5280, ?x2014), ?x6894 = 0cwx_, ?x5280 = 07vhb, ?x10303 = 03hpkp >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #6116 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 99 *> proper extension: 01nn7r; *> query: (?x7306, 0fdys) <- major_field_of_study(?x7306, ?x2014), ?x2014 = 04rjg, student(?x7306, ?x5345), gender(?x5345, ?x231) *> conf = 0.29 ranks of expected_values: 12 EVAL 0d07s major_field_of_study 0fdys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 152.000 152.000 0.682 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17257-0b7gr2 PRED entity: 0b7gr2 PRED relation: nominated_for PRED expected values: 07c72 => 81 concepts (27 used for prediction) PRED predicted values (max 10 best out of 108): 050f0s (0.50 #3531, 0.50 #287, 0.20 #5153), 07c72 (0.50 #3722, 0.25 #478, 0.21 #16230), 0q9jk (0.21 #16230, 0.20 #2881, 0.17 #4503), 03_8kz (0.21 #16230), 01h72l (0.21 #16230), 01bv8b (0.20 #2021, 0.07 #5265, 0.01 #15004), 0557yqh (0.20 #2173, 0.07 #5417), 011ykb (0.17 #4282, 0.13 #5904, 0.01 #9150), 011yn5 (0.17 #4092, 0.09 #12982, 0.01 #7336), 0yx1m (0.17 #4523, 0.09 #12982) >> Best rule #3531 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 03xpfzg; >> query: (?x11764, 050f0s) <- award_nominee(?x11764, ?x2952), award(?x11764, ?x11272), ?x2952 = 07_s4b >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3722 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 03xpfzg; *> query: (?x11764, 07c72) <- award_nominee(?x11764, ?x2952), award(?x11764, ?x11272), ?x2952 = 07_s4b *> conf = 0.50 ranks of expected_values: 2 EVAL 0b7gr2 nominated_for 07c72 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 27.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17256-0xkyn PRED entity: 0xkyn PRED relation: place PRED expected values: 0xkyn => 84 concepts (49 used for prediction) PRED predicted values (max 10 best out of 21): 0d35y (0.20 #3094, 0.07 #19095), 010cw1 (0.17 #5682, 0.16 #4130, 0.11 #15998), 0xkyn (0.17 #5682, 0.16 #4130, 0.07 #19095), 0xkq4 (0.17 #5682, 0.16 #4130), 0fvxz (0.11 #15998, 0.11 #16516, 0.09 #22), 0h6l4 (0.09 #376, 0.02 #891), 0xn7b (0.09 #372, 0.02 #887), 0xn7q (0.09 #346, 0.02 #861), 0xt3t (0.09 #345, 0.02 #860), 0hptm (0.09 #157, 0.02 #672) >> Best rule #3094 for best value: >> intensional similarity = 4 >> extensional distance = 203 >> proper extension: 0f8l9c; 01xd9; 0f2tj; 05fly; >> query: (?x11863, ?x4419) <- location(?x2226, ?x11863), award_nominee(?x2227, ?x2226), category(?x2226, ?x134), place_of_birth(?x2226, ?x4419) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #5682 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 235 *> proper extension: 0f2wj; 0288zy; 015zxh; 01c40n; 018mm4; 01q2sk; 0xrzh; 020923; 02zd460; 0k_p5; ... *> query: (?x11863, ?x1189) <- contains(?x321, ?x11863), adjoins(?x321, ?x322), county(?x1189, ?x321), source(?x321, ?x958) *> conf = 0.17 ranks of expected_values: 3 EVAL 0xkyn place 0xkyn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 49.000 0.196 http://example.org/location/hud_county_place/place #17255-03bx2lk PRED entity: 03bx2lk PRED relation: film_release_region PRED expected values: 015fr 0f8l9c 0k6nt 03gj2 059j2 01znc_ 03rj0 => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 107): 0f8l9c (0.90 #1043, 0.89 #914, 0.83 #268), 059j2 (0.84 #924, 0.76 #1053, 0.58 #278), 015fr (0.84 #912, 0.75 #266, 0.67 #137), 0k6nt (0.79 #1047, 0.76 #918, 0.58 #272), 03rk0 (0.78 #167, 0.75 #296, 0.68 #942), 01znc_ (0.77 #930, 0.67 #284, 0.67 #155), 03gj2 (0.73 #919, 0.67 #273, 0.67 #144), 047yc (0.70 #921, 0.67 #275, 0.56 #146), 0hzlz (0.67 #140, 0.50 #269, 0.29 #915), 03rj0 (0.59 #946, 0.58 #300, 0.48 #1075) >> Best rule #1043 for best value: >> intensional similarity = 4 >> extensional distance = 155 >> proper extension: 04969y; 053tj7; 040rmy; 0crh5_f; 0bhwhj; 064lsn; 0g9zljd; 07s3m4g; 0gh6j94; 05zvzf3; ... >> query: (?x1219, 0f8l9c) <- film_release_region(?x1219, ?x3749), film_release_region(?x1219, ?x1355), ?x1355 = 0h7x, jurisdiction_of_office(?x265, ?x3749) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 7, 10 EVAL 03bx2lk film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 52.000 52.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03bx2lk film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 52.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03bx2lk film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03bx2lk film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 52.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03bx2lk film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03bx2lk film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03bx2lk film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17254-01qvz8 PRED entity: 01qvz8 PRED relation: award PRED expected values: 07bdd_ => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 201): 0p9sw (0.39 #1399, 0.12 #249, 0.08 #2301), 07bdd_ (0.27 #9674, 0.27 #7601, 0.27 #10365), 04ljl_l (0.27 #9674, 0.27 #7601, 0.27 #10365), 05b4l5x (0.27 #9674, 0.27 #7601, 0.27 #10365), 0m7yy (0.26 #1047, 0.10 #1737, 0.07 #7958), 0gq9h (0.17 #8982, 0.14 #60, 0.13 #1440), 02pqp12 (0.17 #8982, 0.14 #56, 0.09 #976), 0gr51 (0.17 #8982, 0.14 #75, 0.09 #305), 040njc (0.17 #8982, 0.14 #7, 0.07 #1847), 019f4v (0.17 #8982, 0.10 #1431, 0.08 #1891) >> Best rule #1399 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 06mmr; >> query: (?x4709, 0p9sw) <- award(?x4709, ?x350), nominated_for(?x350, ?x7849), ?x7849 = 02z9rr >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #9674 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 989 *> proper extension: 097h2; 02_1ky; 019g8j; 0147w8; 02rq7nd; *> query: (?x4709, ?x102) <- award(?x4709, ?x1312), nominated_for(?x102, ?x4709), nominated_for(?x1312, ?x188) *> conf = 0.27 ranks of expected_values: 2 EVAL 01qvz8 award 07bdd_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.389 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #17253-0d060g PRED entity: 0d060g PRED relation: nationality! PRED expected values: 0l8v5 05slvm 01l4g5 0c00lh 012j8z 04mlmx 0821j 01p85y 036hf4 0fs9jn 0652ty 07gkgp 0894_x 01x2_q => 190 concepts (110 used for prediction) PRED predicted values (max 10 best out of 3957): 021bk (0.71 #43115, 0.46 #101906, 0.33 #4522), 0306bt (0.71 #43115, 0.46 #101906, 0.33 #415479), 01g42 (0.71 #43115, 0.46 #101906, 0.33 #415479), 058s44 (0.71 #43115, 0.46 #101906, 0.33 #415479), 0f4dx2 (0.71 #43115, 0.46 #101906, 0.33 #415479), 02qgqt (0.71 #43115, 0.46 #101906, 0.33 #415479), 01g257 (0.71 #43115, 0.46 #101906, 0.33 #415479), 02lgj6 (0.71 #43115, 0.46 #101906, 0.33 #415479), 0htlr (0.71 #43115, 0.46 #101906, 0.33 #415479), 058ncz (0.71 #43115, 0.46 #101906, 0.33 #415479) >> Best rule #43115 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0j5g9; >> query: (?x279, ?x157) <- contains(?x279, ?x7545), student(?x7545, ?x157), countries_spoken_in(?x393, ?x279) >> conf = 0.71 => this is the best rule for 128 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 35, 124, 130, 131, 140, 377, 1491, 2044, 3319, 3361, 3741 EVAL 0d060g nationality! 01x2_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 0894_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 07gkgp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 0652ty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 0fs9jn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 036hf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 01p85y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 0821j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 04mlmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 012j8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 0c00lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 01l4g5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 05slvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 190.000 110.000 0.714 http://example.org/people/person/nationality EVAL 0d060g nationality! 0l8v5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 190.000 110.000 0.714 http://example.org/people/person/nationality #17252-07z5n PRED entity: 07z5n PRED relation: organization PRED expected values: 07t65 => 85 concepts (75 used for prediction) PRED predicted values (max 10 best out of 16): 07t65 (0.90 #301, 0.90 #544, 0.89 #241), 04k4l (0.58 #543, 0.56 #645, 0.56 #644), 018cqq (0.58 #543, 0.56 #645, 0.56 #644), 0gkjy (0.38 #146, 0.31 #186, 0.27 #286), 0b6css (0.37 #189, 0.33 #249, 0.33 #229), 01rz1 (0.29 #262, 0.23 #382, 0.23 #545), 034h1h (0.21 #753, 0.18 #934, 0.03 #1443), 085h1 (0.13 #401, 0.04 #151, 0.03 #211), 02jxk (0.12 #223, 0.12 #383, 0.12 #263), 02_l9 (0.07 #939) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 05br2; 04hvw; >> query: (?x2291, 07t65) <- official_language(?x2291, ?x254), administrative_area_type(?x2291, ?x2792), organization(?x2291, ?x127) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07z5n organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 75.000 0.902 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #17251-016dsy PRED entity: 016dsy PRED relation: split_to PRED expected values: 016dsy => 148 concepts (65 used for prediction) No prediction ranks of expected_values: EVAL 016dsy split_to 016dsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 65.000 0.000 http://example.org/dataworld/gardening_hint/split_to #17250-0cgzj PRED entity: 0cgzj PRED relation: award PRED expected values: 0bdwqv => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 263): 0f4x7 (0.72 #11666, 0.71 #11665, 0.71 #8446), 02py7pj (0.71 #11665, 0.71 #8446, 0.70 #18912), 02x73k6 (0.50 #58, 0.15 #460, 0.07 #3275), 0bfvd4 (0.50 #112, 0.09 #917, 0.09 #3329), 0cqhk0 (0.40 #1644, 0.10 #6470, 0.10 #8481), 09sb52 (0.36 #6474, 0.34 #8485, 0.32 #7278), 0gq9h (0.35 #477, 0.13 #24543, 0.10 #2488), 04kxsb (0.32 #525, 0.25 #123, 0.10 #3340), 0gqy2 (0.25 #162, 0.21 #564, 0.12 #3379), 02w9sd7 (0.25 #168, 0.21 #570, 0.07 #21325) >> Best rule #11666 for best value: >> intensional similarity = 3 >> extensional distance = 1256 >> proper extension: 04cy8rb; 086k8; 03zqc1; 04lgymt; 017s11; 016tt2; 04rcr; 0g1rw; 0785v8; 0kx4m; ... >> query: (?x9866, ?x2060) <- award_winner(?x9866, ?x3931), award_winner(?x2060, ?x9866), award(?x269, ?x2060) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #170 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 016k6x; *> query: (?x9866, 0bdwqv) <- award(?x9866, ?x880), award(?x9866, ?x594), ?x594 = 02grdc, ?x880 = 0cqh46 *> conf = 0.25 ranks of expected_values: 14 EVAL 0cgzj award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 91.000 91.000 0.723 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17249-0fmqp6 PRED entity: 0fmqp6 PRED relation: award_winner! PRED expected values: 0c53zb => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 131): 0fz2y7 (0.33 #199, 0.11 #12097, 0.11 #12237), 0fz0c2 (0.25 #104, 0.18 #799, 0.17 #938), 0fy6bh (0.25 #47, 0.17 #186, 0.14 #881), 0c53zb (0.25 #61, 0.17 #200, 0.14 #895), 0fy59t (0.25 #114, 0.11 #12097, 0.11 #12237), 0d__c3 (0.17 #262, 0.10 #957, 0.10 #11679), 0c53vt (0.17 #249, 0.10 #11679, 0.07 #944), 0fk0xk (0.14 #912, 0.11 #1051, 0.10 #1190), 0dthsy (0.13 #623, 0.13 #484, 0.12 #762), 0fz20l (0.13 #609, 0.12 #748, 0.10 #887) >> Best rule #199 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 076lxv; 04gmp_z; >> query: (?x6921, 0fz2y7) <- award_nominee(?x2449, ?x6921), ?x2449 = 072twv, nominated_for(?x6921, ?x499) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #61 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 072twv; 0584j4n; *> query: (?x6921, 0c53zb) <- award_nominee(?x9825, ?x6921), place_of_death(?x6921, ?x1523), ?x9825 = 058vfp4 *> conf = 0.25 ranks of expected_values: 4 EVAL 0fmqp6 award_winner! 0c53zb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 132.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17248-041jlr PRED entity: 041jlr PRED relation: influenced_by PRED expected values: 03s9b => 143 concepts (23 used for prediction) PRED predicted values (max 10 best out of 275): 06whf (0.40 #982, 0.21 #2698, 0.12 #5704), 058vp (0.40 #1041, 0.12 #5763, 0.11 #2757), 032l1 (0.36 #5668, 0.36 #2662, 0.20 #946), 03_87 (0.36 #2775, 0.31 #5781, 0.20 #1059), 03sbs (0.28 #6232, 0.27 #1507, 0.25 #1936), 0gz_ (0.27 #6114, 0.20 #1818, 0.13 #1389), 0j3v (0.27 #1346, 0.25 #1775, 0.17 #6443), 05qmj (0.25 #6203, 0.15 #1907, 0.11 #2765), 084w8 (0.25 #2577, 0.20 #861, 0.11 #5583), 042q3 (0.25 #2075, 0.17 #6443, 0.13 #1646) >> Best rule #982 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 07h1q; >> query: (?x8767, 06whf) <- influenced_by(?x8767, ?x8768), influenced_by(?x8767, ?x4915), ?x4915 = 03f0324, ?x8768 = 07dnx, people(?x9332, ?x8767) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3215 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 0c1pj; 02kxbwx; 05drq5; 01f7j9; 04y8r; 0b_7k; 02l5rm; 0bzyh; 015njf; 0js9s; ... *> query: (?x8767, 03s9b) <- written_by(?x2403, ?x8767), award(?x8767, ?x1313), award(?x8767, ?x1198), ?x1198 = 02pqp12, ?x1313 = 0gs9p *> conf = 0.03 ranks of expected_values: 200 EVAL 041jlr influenced_by 03s9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 143.000 23.000 0.400 http://example.org/influence/influence_node/influenced_by #17247-0ds11z PRED entity: 0ds11z PRED relation: nominated_for! PRED expected values: 0kszw 03v1w7 => 99 concepts (38 used for prediction) PRED predicted values (max 10 best out of 791): 0bytkq (0.80 #30352, 0.78 #30351, 0.06 #9992), 03v1w7 (0.57 #72383, 0.47 #84059, 0.07 #25682), 077rj (0.49 #18677), 0c9c0 (0.38 #37356, 0.32 #72382, 0.03 #586), 0kszw (0.38 #37356, 0.32 #72382, 0.02 #2854), 05kwx2 (0.38 #37356, 0.32 #72382, 0.01 #20035), 014y6 (0.38 #37356, 0.32 #72382), 01gb54 (0.19 #9338, 0.19 #8016, 0.15 #63042), 0146pg (0.15 #121, 0.13 #7123, 0.06 #9459), 05qd_ (0.15 #63042, 0.14 #23347, 0.14 #42025) >> Best rule #30352 for best value: >> intensional similarity = 3 >> extensional distance = 252 >> proper extension: 01j95; >> query: (?x485, ?x3080) <- category(?x485, ?x134), award_winner(?x485, ?x3080), nominated_for(?x3080, ?x1842) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #72383 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 438 *> proper extension: 021gzd; *> query: (?x485, ?x6369) <- nominated_for(?x1532, ?x485), award(?x485, ?x484), film(?x2531, ?x485), produced_by(?x485, ?x6369) *> conf = 0.57 ranks of expected_values: 2, 5 EVAL 0ds11z nominated_for! 03v1w7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 38.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0ds11z nominated_for! 0kszw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 38.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17246-06__m6 PRED entity: 06__m6 PRED relation: language PRED expected values: 02h40lc => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.90 #179, 0.89 #1187, 0.89 #2793), 064_8sq (0.29 #22, 0.17 #259, 0.16 #140), 02bjrlw (0.29 #1, 0.07 #711, 0.06 #533), 04h9h (0.29 #43, 0.03 #753, 0.03 #813), 04306rv (0.15 #123, 0.14 #242, 0.09 #301), 06nm1 (0.14 #11, 0.14 #248, 0.12 #484), 07zrf (0.14 #3, 0.04 #121, 0.02 #358), 01lqm (0.14 #58, 0.02 #176, 0.01 #295), 03hkp (0.07 #133, 0.05 #252, 0.03 #370), 05qqm (0.05 #159, 0.03 #278, 0.01 #514) >> Best rule #179 for best value: >> intensional similarity = 5 >> extensional distance = 59 >> proper extension: 0g5q34q; >> query: (?x5991, 02h40lc) <- genre(?x5991, ?x2700), genre(?x5991, ?x809), country(?x5991, ?x94), ?x809 = 0vgkd, genre(?x808, ?x2700) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06__m6 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.902 http://example.org/film/film/language #17245-04pf4r PRED entity: 04pf4r PRED relation: gender PRED expected values: 05zppz => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #31, 0.89 #11, 0.89 #9), 02zsn (0.46 #190, 0.31 #86, 0.29 #114) >> Best rule #31 for best value: >> intensional similarity = 2 >> extensional distance = 185 >> proper extension: 07qy0b; 02vyw; 01mkn_d; 02bn75; 01mz9lt; 07zhd7; 089z0z; 07z4fy; >> query: (?x4019, 05zppz) <- profession(?x4019, ?x131), music(?x363, ?x4019) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04pf4r gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.893 http://example.org/people/person/gender #17244-0dgd_ PRED entity: 0dgd_ PRED relation: profession! PRED expected values: 02r5w9 0693l 044k8 0854hr 08mhyd 06t8b 03rqww 06p0s1 04cw0n4 => 41 concepts (18 used for prediction) PRED predicted values (max 10 best out of 4114): 05wm88 (0.78 #20374, 0.58 #24532, 0.50 #32847), 02b29 (0.78 #18835, 0.58 #22993, 0.50 #31308), 015pxr (0.78 #17227, 0.58 #21385, 0.50 #29700), 0dpqk (0.67 #22378, 0.67 #18220, 0.60 #14062), 021yw7 (0.67 #17723, 0.60 #13565, 0.58 #21881), 01_x6v (0.67 #17302, 0.60 #13144, 0.58 #21460), 01xndd (0.67 #17863, 0.60 #13705, 0.58 #22021), 09px1w (0.67 #19249, 0.58 #23407, 0.50 #31722), 015njf (0.67 #18158, 0.58 #22316, 0.50 #30631), 0bxtg (0.67 #16745, 0.50 #20903, 0.43 #29218) >> Best rule #20374 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 01d_h8; 0cbd2; 02hrh1q; 03gjzk; >> query: (?x2265, 05wm88) <- profession(?x10583, ?x2265), profession(?x6062, ?x2265), award(?x10583, ?x1243), award_nominee(?x6062, ?x815), cinematography(?x153, ?x6062), award_winner(?x2294, ?x6062), award_winner(?x1916, ?x6062) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #17541 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 01d_h8; 0cbd2; 02hrh1q; 03gjzk; *> query: (?x2265, 0693l) <- profession(?x10583, ?x2265), profession(?x6062, ?x2265), award(?x10583, ?x1243), award_nominee(?x6062, ?x815), cinematography(?x153, ?x6062), award_winner(?x2294, ?x6062), award_winner(?x1916, ?x6062) *> conf = 0.56 ranks of expected_values: 135, 208, 316, 1088, 4010 EVAL 0dgd_ profession! 04cw0n4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 18.000 0.778 http://example.org/people/person/profession EVAL 0dgd_ profession! 06p0s1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 18.000 0.778 http://example.org/people/person/profession EVAL 0dgd_ profession! 03rqww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 18.000 0.778 http://example.org/people/person/profession EVAL 0dgd_ profession! 06t8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 41.000 18.000 0.778 http://example.org/people/person/profession EVAL 0dgd_ profession! 08mhyd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 18.000 0.778 http://example.org/people/person/profession EVAL 0dgd_ profession! 0854hr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 18.000 0.778 http://example.org/people/person/profession EVAL 0dgd_ profession! 044k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 18.000 0.778 http://example.org/people/person/profession EVAL 0dgd_ profession! 0693l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 41.000 18.000 0.778 http://example.org/people/person/profession EVAL 0dgd_ profession! 02r5w9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 41.000 18.000 0.778 http://example.org/people/person/profession #17243-01b_d4 PRED entity: 01b_d4 PRED relation: student PRED expected values: 01846t => 170 concepts (71 used for prediction) PRED predicted values (max 10 best out of 2010): 03_nq (0.33 #1564, 0.25 #3657, 0.09 #20405), 02sdx (0.33 #1851, 0.25 #3944, 0.09 #20692), 0p50v (0.25 #3527, 0.10 #16088, 0.10 #13993), 0g8st4 (0.25 #3255, 0.10 #15816, 0.10 #13721), 01p0w_ (0.25 #4100, 0.10 #16661, 0.10 #14566), 0308kx (0.25 #2782, 0.10 #15343, 0.10 #13248), 0kh6b (0.17 #8988, 0.14 #29922, 0.14 #11081), 09jrf (0.17 #10446, 0.10 #18821, 0.05 #29287), 0dx97 (0.17 #9279, 0.04 #32306, 0.03 #34399), 0b_dh (0.17 #10241, 0.04 #33268, 0.02 #54203) >> Best rule #1564 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0lvng; >> query: (?x5539, 03_nq) <- school_type(?x5539, ?x3092), colors(?x5539, ?x3189), institution(?x1368, ?x5539), contains(?x455, ?x5539), ?x455 = 02j9z, ?x3189 = 01g5v >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01b_d4 student 01846t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 170.000 71.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #17242-0372j5 PRED entity: 0372j5 PRED relation: nominated_for! PRED expected values: 05zvj3m 05q5t0b => 100 concepts (99 used for prediction) PRED predicted values (max 10 best out of 210): 0gq9h (0.36 #8432, 0.36 #8671, 0.32 #2214), 019f4v (0.34 #2205, 0.33 #8423, 0.32 #8662), 02hsq3m (0.33 #746, 0.28 #5737, 0.25 #6695), 05zvj3m (0.33 #550, 0.28 #5737, 0.25 #6695), 0gs9p (0.33 #8434, 0.32 #8673, 0.26 #10346), 0gqy2 (0.28 #2274, 0.22 #8731, 0.21 #8492), 02g3v6 (0.28 #5737, 0.28 #738, 0.25 #6695), 0gr42 (0.28 #5737, 0.25 #6695, 0.25 #6696), 0hnf5vm (0.28 #5737, 0.25 #6695, 0.25 #6696), 05q5t0b (0.28 #5737, 0.25 #6695, 0.25 #6696) >> Best rule #8432 for best value: >> intensional similarity = 4 >> extensional distance = 364 >> proper extension: 05jf85; 02py4c8; 02ppg1r; 0cq8qq; 01s3vk; 02psgq; 043mk4y; 0p_rk; 0b4lkx; 01fwzk; ... >> query: (?x6751, 0gq9h) <- film(?x709, ?x6751), award(?x6751, ?x1336), film(?x6485, ?x6751), titles(?x7323, ?x6751) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #550 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 01hr1; *> query: (?x6751, 05zvj3m) <- film(?x382, ?x6751), film(?x2534, ?x6751), ?x2534 = 0lx2l, titles(?x7323, ?x6751) *> conf = 0.33 ranks of expected_values: 4, 10 EVAL 0372j5 nominated_for! 05q5t0b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 100.000 99.000 0.361 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0372j5 nominated_for! 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 100.000 99.000 0.361 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17241-0g8_vp PRED entity: 0g8_vp PRED relation: people PRED expected values: 03d_zl4 0d608 0hz_1 => 39 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2451): 0311wg (0.60 #5428, 0.40 #7142, 0.33 #15708), 018ygt (0.60 #7736, 0.40 #6022, 0.33 #2598), 044mvs (0.60 #8266, 0.40 #6552, 0.33 #3128), 0484q (0.60 #7865, 0.40 #6151, 0.33 #2727), 04nw9 (0.60 #5331, 0.40 #7045, 0.33 #1907), 086qd (0.60 #1714, 0.33 #273, 0.24 #1713), 01vsy7t (0.60 #1714, 0.24 #1713, 0.18 #25708), 01vs73g (0.55 #1712, 0.33 #1087, 0.24 #1713), 0415mzy (0.55 #1712, 0.24 #1713, 0.14 #18843), 0dl567 (0.55 #1712, 0.24 #1713, 0.14 #18843) >> Best rule #5428 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 07bch9; >> query: (?x5606, 0311wg) <- people(?x5606, ?x4593), people(?x5606, ?x2580), influenced_by(?x4593, ?x2138), award(?x4593, ?x724), ?x2580 = 0227tr, award_winner(?x528, ?x4593), award_nominee(?x4593, ?x1231), profession(?x4593, ?x131) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #34272 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 24 *> proper extension: 071x0k; 078vc; 078ds; 04czx7; *> query: (?x5606, ?x380) <- languages_spoken(?x5606, ?x5607), languages_spoken(?x5606, ?x254), ?x254 = 02h40lc, languages(?x8600, ?x5607), languages(?x380, ?x5607), language(?x4047, ?x5607), ?x4047 = 07s846j, ?x8600 = 0g7k2g, countries_spoken_in(?x5607, ?x172) *> conf = 0.01 ranks of expected_values: 2390 EVAL 0g8_vp people 0hz_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 27.000 0.600 http://example.org/people/ethnicity/people EVAL 0g8_vp people 0d608 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 27.000 0.600 http://example.org/people/ethnicity/people EVAL 0g8_vp people 03d_zl4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 27.000 0.600 http://example.org/people/ethnicity/people #17240-04dm2n PRED entity: 04dm2n PRED relation: inductee PRED expected values: 028rk => 9 concepts (9 used for prediction) PRED predicted values (max 10 best out of 3513): 01zlh5 (0.50 #418, 0.40 #734, 0.33 #1051), 0grwj (0.50 #474, 0.33 #159, 0.29 #1102), 0127xk (0.40 #765, 0.33 #1082, 0.33 #926), 03h_fk5 (0.40 #660, 0.33 #977, 0.33 #821), 028qyn (0.40 #759, 0.33 #1076, 0.33 #920), 014zfs (0.33 #167, 0.25 #482, 0.25 #323), 0ph2w (0.33 #205, 0.25 #520, 0.25 #361), 02tkzn (0.33 #232, 0.25 #547, 0.25 #388), 016kkx (0.33 #244, 0.25 #559, 0.25 #400), 01t9qj_ (0.33 #258, 0.25 #573, 0.25 #414) >> Best rule #418 for best value: >> intensional similarity = 32 >> extensional distance = 2 >> proper extension: 0g2c8; >> query: (?x14591, 01zlh5) <- inductee(?x14591, ?x9716), inductee(?x14591, ?x8924), person(?x6767, ?x9716), award_winner(?x1545, ?x8924), award_winner(?x9716, ?x5289), place_of_death(?x8924, ?x191), category(?x8924, ?x134), participant(?x8924, ?x703), profession(?x9716, ?x1041), award_winner(?x8459, ?x9716), influenced_by(?x986, ?x9716), profession(?x11630, ?x1041), profession(?x8113, ?x1041), profession(?x7796, ?x1041), profession(?x6944, ?x1041), profession(?x6382, ?x1041), profession(?x5462, ?x1041), profession(?x4631, ?x1041), profession(?x3975, ?x1041), profession(?x2182, ?x1041), profession(?x1630, ?x1041), ?x7796 = 030tj5, ?x11630 = 01rzxl, ?x8113 = 059fjj, ?x5462 = 0f5xn, ?x2182 = 01f7j9, ?x1630 = 027cxsm, ?x6944 = 02z2xdf, ?x3975 = 02v0ff, ceremony(?x8459, ?x873), ?x4631 = 0315q3, ?x6382 = 01wd9lv >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04dm2n inductee 028rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 9.000 9.000 0.500 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #17239-01w9mnm PRED entity: 01w9mnm PRED relation: gender PRED expected values: 05zppz => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.94 #27, 0.88 #41, 0.88 #37), 02zsn (0.46 #252, 0.45 #239, 0.26 #60) >> Best rule #27 for best value: >> intensional similarity = 5 >> extensional distance = 46 >> proper extension: 02qfhb; >> query: (?x8539, 05zppz) <- performance_role(?x8539, ?x228), role(?x8539, ?x227), role(?x228, ?x894), role(?x642, ?x228), ?x894 = 03m5k >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w9mnm gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.938 http://example.org/people/person/gender #17238-02k21g PRED entity: 02k21g PRED relation: student! PRED expected values: 014mlp => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 6): 014mlp (0.50 #6, 0.29 #26, 0.25 #86), 019v9k (0.12 #70, 0.11 #110, 0.02 #470), 028dcg (0.02 #178, 0.02 #258, 0.02 #338), 02h4rq6 (0.02 #203, 0.02 #463, 0.01 #263), 03mkk4 (0.01 #313), 02_xgp2 (0.01 #634) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 060j8b; 023mdt; >> query: (?x4490, 014mlp) <- film(?x4490, ?x2788), ?x2788 = 05q4y12, spouse(?x9754, ?x4490) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02k21g student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #17237-05gh50 PRED entity: 05gh50 PRED relation: nutrient! PRED expected values: 0971v => 58 concepts (55 used for prediction) PRED predicted values (max 10 best out of 11): 0971v (0.91 #99, 0.90 #101, 0.89 #16), 06x4c (0.91 #99, 0.90 #101, 0.89 #16), 0dcfv (0.91 #99, 0.90 #101, 0.89 #16), 01sh2 (0.03 #397, 0.02 #18), 04k8n (0.03 #397), 05wvs (0.03 #397), 025rw19 (0.02 #18), 025tkqy (0.02 #18), 014d7f (0.02 #18), 06jry (0.02 #18) >> Best rule #99 for best value: >> intensional similarity = 121 >> extensional distance = 9 >> proper extension: 025sf0_; 025rw19; >> query: (?x6586, ?x3264) <- nutrient(?x10612, ?x6586), nutrient(?x9732, ?x6586), nutrient(?x9489, ?x6586), nutrient(?x9005, ?x6586), nutrient(?x8298, ?x6586), nutrient(?x7719, ?x6586), nutrient(?x7057, ?x6586), nutrient(?x6285, ?x6586), nutrient(?x6191, ?x6586), nutrient(?x6159, ?x6586), nutrient(?x6032, ?x6586), nutrient(?x5009, ?x6586), nutrient(?x4068, ?x6586), nutrient(?x3900, ?x6586), nutrient(?x3468, ?x6586), nutrient(?x2701, ?x6586), nutrient(?x1959, ?x6586), nutrient(?x1303, ?x6586), nutrient(?x1257, ?x6586), ?x6191 = 014j1m, ?x10612 = 0frq6, ?x4068 = 0fbw6, ?x7719 = 0dj75, nutrient(?x3468, ?x14210), nutrient(?x3468, ?x13944), nutrient(?x3468, ?x13545), nutrient(?x3468, ?x13126), nutrient(?x3468, ?x12902), nutrient(?x3468, ?x12336), nutrient(?x3468, ?x12083), nutrient(?x3468, ?x11758), nutrient(?x3468, ?x11270), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10098), nutrient(?x3468, ?x9949), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x9840), nutrient(?x3468, ?x9733), nutrient(?x3468, ?x9619), nutrient(?x3468, ?x9490), nutrient(?x3468, ?x9436), nutrient(?x3468, ?x9365), nutrient(?x3468, ?x8442), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x7720), nutrient(?x3468, ?x7652), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7364), nutrient(?x3468, ?x7362), nutrient(?x3468, ?x7219), nutrient(?x3468, ?x7135), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x6160), nutrient(?x3468, ?x6026), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5526), nutrient(?x3468, ?x5451), nutrient(?x3468, ?x5010), nutrient(?x3468, ?x3203), nutrient(?x3468, ?x2702), nutrient(?x3468, ?x2018), nutrient(?x3468, ?x1960), nutrient(?x3468, ?x1304), nutrient(?x3468, ?x1258), ?x6159 = 033cnk, ?x1258 = 0h1wg, ?x6026 = 025sf8g, ?x9840 = 02p0tjr, ?x13126 = 02kc_w5, ?x9733 = 0h1tz, ?x9005 = 04zpv, ?x9489 = 07j87, ?x3203 = 04kl74p, ?x1257 = 09728, ?x14210 = 0f4k5, ?x7652 = 025s0s0, ?x12336 = 0f4l5, ?x9915 = 025tkqy, ?x8442 = 02kcv4x, ?x7219 = 0h1vg, ?x11758 = 0q01m, ?x7364 = 09gvd, ?x12083 = 01n78x, ?x9949 = 02kd0rh, ?x1304 = 08lb68, ?x13545 = 01w_3, ?x6192 = 06jry, ?x7720 = 025s7x6, ?x8298 = 037ls6, ?x13944 = 0f4kp, ?x7057 = 0fbdb, ?x8413 = 02kc4sf, ?x9490 = 0h1sg, ?x5009 = 0fjfh, ?x7431 = 09gwd, ?x6285 = 01645p, ?x1959 = 0f25w9, ?x10891 = 0g5gq, ?x1960 = 07hnp, ?x6032 = 01nkt, taxonomy(?x2018, ?x939), ?x5549 = 025s7j4, ?x6160 = 041r51, ?x11270 = 02kc008, ?x939 = 04n6k, ?x7135 = 025rsfk, ?x10098 = 0h1_c, ?x2702 = 0838f, ?x2701 = 0hkxq, ?x9436 = 025sqz8, nutrient(?x3264, ?x2018), ?x3900 = 061_f, ?x9732 = 05z55, ?x5010 = 0h1vz, ?x12902 = 0fzjh, ?x9365 = 04k8n, ?x1303 = 0fj52s, ?x9619 = 0h1tg, ?x5451 = 05wvs, ?x5526 = 09pbb, ?x7362 = 02kc5rj >> conf = 0.91 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 05gh50 nutrient! 0971v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 55.000 0.908 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #17236-035xwd PRED entity: 035xwd PRED relation: nominated_for! PRED expected values: 0l8z1 => 95 concepts (84 used for prediction) PRED predicted values (max 10 best out of 220): 0gq9h (0.44 #787, 0.40 #64, 0.38 #546), 0f4x7 (0.40 #26, 0.38 #508, 0.33 #749), 0l8z1 (0.40 #53, 0.38 #535, 0.25 #1981), 0gr4k (0.40 #27, 0.33 #750, 0.25 #509), 0gs96 (0.40 #92, 0.31 #2020, 0.28 #2502), 054krc (0.40 #71, 0.25 #553, 0.22 #794), 02qvyrt (0.40 #99, 0.25 #581, 0.22 #822), 03hkv_r (0.40 #15, 0.25 #497, 0.22 #738), 0gq_v (0.38 #1948, 0.30 #2430, 0.28 #3153), 04dn09n (0.33 #759, 0.25 #1000, 0.25 #518) >> Best rule #787 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 028_yv; 0h6r5; 0992d9; 0by17xn; >> query: (?x796, 0gq9h) <- featured_film_locations(?x796, ?x739), film(?x7530, ?x796), film(?x1554, ?x796), ?x1554 = 06cgy, ?x739 = 02_286, genre(?x796, ?x53), award_winner(?x2215, ?x7530) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #53 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0b2v79; 0jsf6; 0y_hb; *> query: (?x796, 0l8z1) <- featured_film_locations(?x796, ?x739), film(?x1554, ?x796), ?x1554 = 06cgy, music(?x796, ?x669), written_by(?x796, ?x8950), film(?x382, ?x796) *> conf = 0.40 ranks of expected_values: 3 EVAL 035xwd nominated_for! 0l8z1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 84.000 0.444 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17235-01qmy04 PRED entity: 01qmy04 PRED relation: category PRED expected values: 08mbj5d => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #13, 0.84 #9, 0.84 #23) >> Best rule #13 for best value: >> intensional similarity = 5 >> extensional distance = 122 >> proper extension: 02fybl; >> query: (?x12121, 08mbj5d) <- profession(?x12121, ?x1183), profession(?x12121, ?x131), ?x1183 = 09jwl, ?x131 = 0dz3r, location(?x12121, ?x1523) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qmy04 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.863 http://example.org/common/topic/webpage./common/webpage/category #17234-0kxf1 PRED entity: 0kxf1 PRED relation: genre PRED expected values: 07s9rl0 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 147): 07s9rl0 (0.77 #727, 0.68 #1817, 0.66 #2181), 01jfsb (0.49 #1951, 0.33 #1586, 0.33 #2072), 02l7c8 (0.38 #259, 0.37 #138, 0.36 #17), 03k9fj (0.38 #1950, 0.30 #375, 0.30 #617), 05p553 (0.35 #851, 0.35 #3395, 0.34 #4849), 01g6gs (0.31 #264, 0.27 #22, 0.16 #143), 04xvlr (0.30 #728, 0.21 #1818, 0.19 #2182), 06n90 (0.28 #1952, 0.18 #377, 0.18 #1587), 01hmnh (0.22 #1592, 0.21 #1108, 0.21 #1229), 0lsxr (0.21 #1703, 0.20 #1947, 0.19 #1825) >> Best rule #727 for best value: >> intensional similarity = 4 >> extensional distance = 162 >> proper extension: 027ct7c; 0cq8nx; >> query: (?x3599, 07s9rl0) <- language(?x3599, ?x254), nominated_for(?x1172, ?x3599), nominated_for(?x1243, ?x3599), ?x1243 = 0gr0m >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kxf1 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.768 http://example.org/film/film/genre #17233-0cf8qb PRED entity: 0cf8qb PRED relation: currency PRED expected values: 09nqf => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.85 #22, 0.81 #43, 0.80 #29), 01nv4h (0.03 #16, 0.03 #37, 0.03 #303), 02l6h (0.01 #32, 0.01 #333, 0.01 #480), 02gsvk (0.01 #62, 0.01 #20, 0.01 #83) >> Best rule #22 for best value: >> intensional similarity = 5 >> extensional distance = 124 >> proper extension: 02q52q; 0cp08zg; 01dc0c; 09yxcz; >> query: (?x7726, 09nqf) <- film_release_region(?x7726, ?x94), titles(?x812, ?x7726), production_companies(?x7726, ?x902), nominated_for(?x3186, ?x7726), category(?x7726, ?x134) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cf8qb currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.849 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #17232-0431v3 PRED entity: 0431v3 PRED relation: nominated_for! PRED expected values: 031296 => 55 concepts (29 used for prediction) PRED predicted values (max 10 best out of 918): 03rl84 (0.55 #2333, 0.46 #39655, 0.45 #48987), 03mdt (0.20 #3042, 0.14 #7708, 0.09 #709), 05gnf (0.16 #3784, 0.08 #10782, 0.08 #22443), 0gsg7 (0.13 #2683, 0.09 #350, 0.07 #9681), 03jvmp (0.09 #7451, 0.08 #14447, 0.08 #16779), 031296 (0.09 #55988, 0.04 #3113, 0.03 #10111), 0hvb2 (0.09 #55988, 0.03 #9702, 0.02 #21363), 011_3s (0.09 #55988, 0.02 #3020, 0.02 #12350), 0335fp (0.09 #55988, 0.02 #4032, 0.01 #13362), 04bcb1 (0.09 #55988, 0.02 #3351, 0.01 #12681) >> Best rule #2333 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 01_2n; >> query: (?x5561, ?x1065) <- genre(?x5561, ?x6674), actor(?x5561, ?x1065), ?x6674 = 01t_vv >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #55988 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 197 *> proper extension: 02zv4b; 070g7; 026bfsh; 01f39b; *> query: (?x5561, ?x2216) <- actor(?x5561, ?x1065), award_nominee(?x2216, ?x1065), film(?x1065, ?x1066) *> conf = 0.09 ranks of expected_values: 6 EVAL 0431v3 nominated_for! 031296 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 55.000 29.000 0.548 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17231-01qd_r PRED entity: 01qd_r PRED relation: colors PRED expected values: 01l849 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.44 #59, 0.38 #230, 0.38 #724), 01l849 (0.40 #1, 0.28 #58, 0.25 #362), 01g5v (0.27 #669, 0.25 #156, 0.25 #175), 03wkwg (0.20 #14, 0.17 #90, 0.12 #71), 036k5h (0.17 #82, 0.12 #177, 0.10 #139), 06fvc (0.15 #725, 0.14 #668, 0.13 #611), 09ggk (0.12 #72, 0.07 #680, 0.06 #737), 02rnmb (0.10 #31, 0.09 #88, 0.08 #69), 0jc_p (0.10 #214, 0.10 #385, 0.09 #404), 038hg (0.10 #239, 0.09 #733, 0.09 #220) >> Best rule #59 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 065y4w7; 07w0v; 027xx3; 0gl5_; >> query: (?x7660, 083jv) <- major_field_of_study(?x7660, ?x3213), ?x3213 = 0g4gr, school(?x2820, ?x7660) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 023znp; *> query: (?x7660, 01l849) <- major_field_of_study(?x7660, ?x3213), major_field_of_study(?x7660, ?x373), ?x3213 = 0g4gr, ?x373 = 02vxn *> conf = 0.40 ranks of expected_values: 2 EVAL 01qd_r colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.440 http://example.org/education/educational_institution/colors #17230-0jt90f5 PRED entity: 0jt90f5 PRED relation: story_by! PRED expected values: 03s9kp => 153 concepts (144 used for prediction) PRED predicted values (max 10 best out of 286): 04954r (0.33 #126, 0.02 #8238, 0.02 #10267), 034qmv (0.33 #5, 0.02 #8117, 0.02 #10146), 0ds5_72 (0.11 #950, 0.09 #1626, 0.06 #3654), 0184tc (0.11 #806, 0.09 #1482, 0.06 #3510), 01hvjx (0.11 #749, 0.05 #4467, 0.05 #4129), 0gfzfj (0.11 #999, 0.04 #5731, 0.03 #7421), 0gj9tn5 (0.11 #733, 0.04 #5465, 0.03 #7155), 0jdr0 (0.11 #967, 0.03 #6713, 0.02 #10432), 0crs0b8 (0.11 #963, 0.03 #6709, 0.02 #10428), 03cfkrw (0.11 #829, 0.03 #6575, 0.02 #10294) >> Best rule #126 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 04093; >> query: (?x2343, 04954r) <- influenced_by(?x2343, ?x6420), influenced_by(?x7180, ?x2343), ?x6420 = 014nvr, story_by(?x430, ?x2343) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jt90f5 story_by! 03s9kp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 144.000 0.333 http://example.org/film/film/story_by #17229-04rfq PRED entity: 04rfq PRED relation: award PRED expected values: 0gqwc => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 288): 01by1l (0.40 #113, 0.28 #3767, 0.23 #925), 01bgqh (0.40 #43, 0.24 #3697, 0.17 #5323), 0gq9h (0.28 #3326, 0.20 #78, 0.14 #10640), 0gr4k (0.26 #4062, 0.24 #7314, 0.24 #7313), 0gr51 (0.26 #4062, 0.24 #7314, 0.24 #7313), 0c4z8 (0.24 #3726, 0.20 #72, 0.15 #884), 09sb52 (0.23 #16287, 0.19 #21565, 0.19 #15475), 0czp_ (0.22 #5688, 0.04 #7619, 0.03 #4367), 0gqwc (0.21 #1699, 0.14 #1293, 0.13 #12261), 03hkv_r (0.21 #1234, 0.12 #9766, 0.06 #14232) >> Best rule #113 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01vs_v8; >> query: (?x13144, 01by1l) <- spouse(?x11626, ?x13144), profession(?x13144, ?x319), peers(?x13144, ?x9073), religion(?x13144, ?x1624) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1699 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 04wqr; 01v3bn; *> query: (?x13144, 0gqwc) <- nationality(?x13144, ?x94), place_of_burial(?x13144, ?x3691), ?x94 = 09c7w0, spouse(?x13144, ?x11626) *> conf = 0.21 ranks of expected_values: 9 EVAL 04rfq award 0gqwc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 110.000 110.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17228-03np63f PRED entity: 03np63f PRED relation: genre PRED expected values: 07s9rl0 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 121): 04xvlr (0.72 #4927, 0.60 #123, 0.60 #4926), 07s9rl0 (0.71 #1718, 0.71 #1842, 0.70 #124), 07ssc (0.60 #123, 0.60 #4926, 0.55 #1840), 05p553 (0.43 #2215, 0.39 #3328, 0.38 #2709), 01jfsb (0.43 #14, 0.32 #1978, 0.30 #3954), 02kdv5l (0.29 #3326, 0.28 #1967, 0.26 #4558), 03k9fj (0.26 #750, 0.25 #872, 0.23 #1239), 060__y (0.24 #1735, 0.23 #509, 0.23 #632), 04xvh5 (0.20 #404, 0.20 #527, 0.19 #650), 0lsxr (0.20 #1114, 0.19 #1358, 0.18 #1974) >> Best rule #4927 for best value: >> intensional similarity = 3 >> extensional distance = 1049 >> proper extension: 0c5dd; >> query: (?x7897, ?x162) <- titles(?x162, ?x7897), film(?x940, ?x7897), genre(?x144, ?x162) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1718 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 388 *> proper extension: 02qjv1p; *> query: (?x7897, 07s9rl0) <- titles(?x512, ?x7897), titles(?x512, ?x1710), ?x1710 = 05p3738, genre(?x7897, ?x1403) *> conf = 0.71 ranks of expected_values: 2 EVAL 03np63f genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 69.000 0.717 http://example.org/film/film/genre #17227-027gs1_ PRED entity: 027gs1_ PRED relation: ceremony PRED expected values: 07y_p6 => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 130): 0gpjbt (0.48 #1068, 0.34 #1848, 0.34 #2108), 09n4nb (0.47 #1084, 0.34 #1864, 0.34 #2124), 0466p0j (0.46 #1109, 0.33 #2149, 0.33 #1889), 05pd94v (0.46 #1042, 0.33 #2082, 0.33 #1822), 056878 (0.46 #1070, 0.33 #1850, 0.33 #2110), 02rjjll (0.46 #1045, 0.33 #1825, 0.33 #2085), 02cg41 (0.46 #1157, 0.33 #2197, 0.33 #1937), 01c6qp (0.45 #1058, 0.33 #1838, 0.33 #2098), 019bk0 (0.43 #1055, 0.31 #2095, 0.31 #1835), 01bx35 (0.43 #1047, 0.31 #2087, 0.31 #1827) >> Best rule #1068 for best value: >> intensional similarity = 1 >> extensional distance = 160 >> proper extension: 07n52; 02xzd9; >> query: (?x7510, 0gpjbt) <- category_of(?x7510, ?x2758) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #220 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0cjyzs; *> query: (?x7510, 07y_p6) <- award_winner(?x7510, ?x201), award(?x1266, ?x7510), ?x201 = 06j0md, ceremony(?x7510, ?x1265) *> conf = 0.33 ranks of expected_values: 17 EVAL 027gs1_ ceremony 07y_p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 45.000 45.000 0.481 http://example.org/award/award_category/winners./award/award_honor/ceremony #17226-01vv7sc PRED entity: 01vv7sc PRED relation: artists! PRED expected values: 07d2d => 121 concepts (113 used for prediction) PRED predicted values (max 10 best out of 243): 064t9 (0.59 #16146, 0.53 #3359, 0.50 #3967), 06by7 (0.52 #1846, 0.49 #933, 0.47 #7938), 029h7y (0.50 #39, 0.21 #1826, 0.10 #1560), 07d2d (0.50 #89, 0.19 #1610, 0.16 #697), 07s72n (0.50 #190, 0.05 #1711, 0.02 #1103), 01n4bh (0.50 #230, 0.03 #1751, 0.02 #1143), 0xhtw (0.37 #1841, 0.21 #1826, 0.21 #5494), 0glt670 (0.33 #344, 0.26 #12828, 0.25 #11003), 0y3_8 (0.29 #1566, 0.25 #45, 0.21 #1826), 06j6l (0.28 #3394, 0.28 #11009, 0.28 #12834) >> Best rule #16146 for best value: >> intensional similarity = 3 >> extensional distance = 595 >> proper extension: 05563d; 05xq9; 01j59b0; 06nv27; 02mq_y; 0123r4; 01kcms4; 013rfk; 08w4pm; 01516r; ... >> query: (?x1004, 064t9) <- artists(?x474, ?x1004), artists(?x474, ?x1989), ?x1989 = 04mn81 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #89 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0m19t; *> query: (?x1004, 07d2d) <- artists(?x3232, ?x1004), artists(?x474, ?x1004), ?x474 = 0m0jc, ?x3232 = 01ym9b *> conf = 0.50 ranks of expected_values: 4 EVAL 01vv7sc artists! 07d2d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 121.000 113.000 0.590 http://example.org/music/genre/artists #17225-02tk74 PRED entity: 02tk74 PRED relation: award PRED expected values: 0bb57s => 128 concepts (109 used for prediction) PRED predicted values (max 10 best out of 290): 09sb52 (0.56 #2869, 0.56 #3677, 0.50 #41), 094qd5 (0.48 #2873, 0.44 #3681, 0.17 #45), 02ppm4q (0.48 #2985, 0.44 #3793, 0.13 #15105), 0gqyl (0.44 #3741, 0.44 #2933, 0.24 #2125), 0gqwc (0.41 #3710, 0.40 #2902, 0.19 #1690), 099cng (0.37 #3722, 0.36 #2914, 0.06 #17458), 05p09zm (0.35 #4972, 0.33 #124, 0.31 #8204), 04dn09n (0.33 #44, 0.29 #448, 0.16 #4892), 03hkv_r (0.33 #16, 0.29 #420, 0.14 #2036), 05zr6wv (0.33 #17, 0.26 #4865, 0.24 #6481) >> Best rule #2869 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 01l2fn; 071ynp; 0ksrf8; 02cff1; 013zs9; >> query: (?x9807, 09sb52) <- nationality(?x9807, ?x94), award(?x9807, ?x941), ?x941 = 0fq9zdn >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #3073 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 01l2fn; 071ynp; 0ksrf8; 02cff1; 013zs9; *> query: (?x9807, 0bb57s) <- nationality(?x9807, ?x94), award(?x9807, ?x941), ?x941 = 0fq9zdn *> conf = 0.12 ranks of expected_values: 81 EVAL 02tk74 award 0bb57s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 128.000 109.000 0.560 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17224-02vkdwz PRED entity: 02vkdwz PRED relation: position! PRED expected values: 02px_23 => 33 concepts (26 used for prediction) PRED predicted values (max 10 best out of 392): 03b3j (0.87 #367, 0.83 #71, 0.82 #723), 05tfm (0.87 #367, 0.83 #71, 0.82 #723), 07kbp5 (0.83 #71, 0.82 #1103, 0.82 #1102), 0289q (0.83 #71, 0.82 #723, 0.80 #138), 06x76 (0.83 #71, 0.82 #723, 0.80 #138), 0fsb_6 (0.83 #71, 0.80 #138, 0.80 #1018), 0fht9f (0.83 #71, 0.80 #138, 0.80 #726), 0g0z58 (0.83 #71, 0.80 #138, 0.80 #726), 0fjzsy (0.83 #71, 0.80 #138, 0.80 #726), 02663p2 (0.83 #71, 0.80 #726, 0.77 #368) >> Best rule #367 for best value: >> intensional similarity = 26 >> extensional distance = 1 >> proper extension: 02g_6j; >> query: (?x706, ?x1576) <- position_s(?x706, ?x2573), position_s(?x706, ?x2312), team(?x706, ?x11061), team(?x706, ?x9748), team(?x706, ?x7892), team(?x706, ?x6294), team(?x706, ?x4546), team(?x706, ?x4189), team(?x706, ?x2526), team(?x706, ?x2148), team(?x706, ?x1576), ?x4189 = 026lg0s, position(?x706, ?x180), ?x2148 = 0fht9f, ?x2573 = 05b3ts, draft(?x1576, ?x4171), ?x2312 = 02qpbqj, sport(?x1576, ?x1083), ?x6294 = 02663p2, team(?x11282, ?x1576), ?x4171 = 092j54, ?x2526 = 03hfx6c, ?x4546 = 05gg4, ?x9748 = 0fsb_6, ?x11061 = 06x76, teams(?x3037, ?x7892) >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #1103 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 10 *> proper extension: 01snvb; *> query: (?x706, ?x7539) <- position(?x7539, ?x706), team(?x706, ?x9748), team(?x706, ?x9115), team(?x706, ?x4856), team(?x706, ?x3658), team(?x7079, ?x9115), ?x7079 = 08ns5s, ?x9748 = 0fsb_6, ?x3658 = 03b3j, position(?x4856, ?x1240), position_s(?x705, ?x706), team(?x11323, ?x4856), ?x1240 = 023wyl, school(?x4856, ?x3779), draft(?x4856, ?x685), state_province_region(?x3779, ?x3778), service_location(?x3779, ?x94), institution(?x620, ?x3779), citytown(?x3779, ?x4978) *> conf = 0.82 ranks of expected_values: 11 EVAL 02vkdwz position! 02px_23 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 33.000 26.000 0.867 http://example.org/sports/sports_team/roster./american_football/football_roster_position/position #17223-02dw1_ PRED entity: 02dw1_ PRED relation: group! PRED expected values: 0l14md 01hww_ 0l14j_ => 91 concepts (71 used for prediction) PRED predicted values (max 10 best out of 99): 0l14md (0.68 #1297, 0.62 #1672, 0.60 #2480), 03bx0bm (0.63 #1499, 0.58 #2679, 0.58 #2557), 0gkd1 (0.50 #124, 0.50 #119, 0.18 #1355), 0l14qv (0.50 #64, 0.46 #1296, 0.41 #1671), 028tv0 (0.41 #1489, 0.38 #2484, 0.38 #1676), 0l14j_ (0.36 #1328, 0.19 #1703, 0.18 #1516), 01vj9c (0.32 #1302, 0.28 #2423, 0.28 #2609), 0gghm (0.25 #88, 0.18 #1355, 0.17 #1357), 0xzly (0.25 #78, 0.18 #1355, 0.14 #126), 02w3w (0.25 #113, 0.18 #1355, 0.14 #126) >> Best rule #1297 for best value: >> intensional similarity = 10 >> extensional distance = 26 >> proper extension: 05crg7; 01qqwp9; 0123r4; 0qmpd; >> query: (?x5838, 0l14md) <- group(?x227, ?x5838), group(?x74, ?x5838), ?x227 = 0342h, role(?x7706, ?x74), role(?x4425, ?x74), role(?x3991, ?x74), role(?x228, ?x74), ?x4425 = 0979zs, ?x7706 = 0lsw9, ?x3991 = 05842k >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 22 EVAL 02dw1_ group! 0l14j_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 71.000 0.679 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02dw1_ group! 01hww_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 91.000 71.000 0.679 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02dw1_ group! 0l14md CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 71.000 0.679 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17222-02w5q6 PRED entity: 02w5q6 PRED relation: type_of_union PRED expected values: 04ztj => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.79 #85, 0.76 #141, 0.76 #369), 01g63y (0.33 #30, 0.27 #222, 0.26 #94), 0jgjn (0.02 #80, 0.01 #100, 0.01 #108) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 018dyl; 01vrnsk; 0pj8m; 01w0yrc; >> query: (?x6817, 04ztj) <- person(?x5201, ?x6817), nationality(?x6817, ?x94), film_crew_role(?x5201, ?x137), place_of_birth(?x6817, ?x6960) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02w5q6 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.787 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17221-0l3h PRED entity: 0l3h PRED relation: country! PRED expected values: 01cgz => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 56): 01cgz (0.78 #294, 0.73 #1526, 0.70 #1862), 071t0 (0.70 #1536, 0.70 #1872, 0.70 #1312), 03_8r (0.70 #1871, 0.70 #1255, 0.68 #975), 01lb14 (0.59 #1304, 0.57 #1864, 0.56 #1808), 07gyv (0.57 #1295, 0.56 #287, 0.55 #1239), 07jbh (0.57 #1323, 0.54 #1883, 0.53 #1827), 06f41 (0.55 #1303, 0.55 #1863, 0.55 #1527), 03hr1p (0.55 #1313, 0.55 #1817, 0.54 #1873), 06wrt (0.55 #1305, 0.52 #1809, 0.52 #1473), 0w0d (0.54 #1524, 0.54 #1300, 0.52 #516) >> Best rule #294 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0261m; >> query: (?x5622, 01cgz) <- contains(?x7273, ?x5622), ?x7273 = 07c5l, teams(?x5622, ?x6389) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l3h country! 01cgz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.778 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #17220-0gx1673 PRED entity: 0gx1673 PRED relation: award_winner PRED expected values: 07ss8_ 03j24kf => 63 concepts (49 used for prediction) PRED predicted values (max 10 best out of 944): 0gcs9 (0.78 #4979, 0.71 #3463, 0.70 #9526), 01lmj3q (0.64 #10641, 0.60 #9125, 0.57 #3062), 01m3b1t (0.57 #4086, 0.44 #5602, 0.40 #10149), 058s57 (0.57 #3269, 0.44 #4785, 0.40 #9332), 02qwg (0.56 #6562, 0.55 #12627, 0.33 #501), 0x3b7 (0.56 #6695, 0.55 #12760, 0.27 #11243), 02fn5r (0.56 #4916, 0.50 #14014, 0.50 #9463), 0hl3d (0.55 #12156, 0.44 #6091, 0.32 #16711), 016srn (0.50 #9553, 0.44 #5006, 0.43 #3490), 01w60_p (0.45 #12424, 0.44 #6359, 0.36 #10907) >> Best rule #4979 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 09n4nb; 02cg41; >> query: (?x8500, 0gcs9) <- ceremony(?x8929, ?x8500), ceremony(?x4837, ?x8500), ceremony(?x1389, ?x8500), ceremony(?x594, ?x8500), ceremony(?x528, ?x8500), ?x528 = 02g3gj, ?x1389 = 01c427, award_winner(?x8500, ?x163), ?x594 = 02grdc, ?x4837 = 03t5kl, ?x8929 = 02hdky >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #13643 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 9 *> proper extension: 0jzphpx; *> query: (?x8500, ?x527) <- ceremony(?x12833, ?x8500), ceremony(?x10102, ?x8500), ceremony(?x8331, ?x8500), ceremony(?x528, ?x8500), ?x12833 = 0257pw, ?x10102 = 031b91, award(?x1004, ?x8331), award_winner(?x528, ?x527) *> conf = 0.08 ranks of expected_values: 542, 795 EVAL 0gx1673 award_winner 03j24kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 63.000 49.000 0.778 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gx1673 award_winner 07ss8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 63.000 49.000 0.778 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17219-03mgx6z PRED entity: 03mgx6z PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.89 #96, 0.88 #91, 0.88 #51), 02nxhr (0.40 #400, 0.30 #320, 0.12 #12), 07z4p (0.30 #320, 0.07 #256, 0.06 #55), 07c52 (0.23 #353, 0.09 #254, 0.07 #239) >> Best rule #96 for best value: >> intensional similarity = 14 >> extensional distance = 34 >> proper extension: 0g5qs2k; 0hhggmy; >> query: (?x5791, 029j_) <- film_release_region(?x5791, ?x792), film_release_region(?x5791, ?x583), film_release_region(?x5791, ?x344), ?x344 = 04gzd, ?x792 = 0hzlz, film_release_region(?x3958, ?x583), film_release_region(?x3000, ?x583), film_release_region(?x2695, ?x583), film_release_region(?x2471, ?x583), ?x3000 = 045j3w, olympics(?x583, ?x391), ?x2695 = 047svrl, ?x2471 = 08052t3, ?x3958 = 0gyh2wm >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mgx6z film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17218-04ynx7 PRED entity: 04ynx7 PRED relation: film! PRED expected values: 08pth9 => 98 concepts (72 used for prediction) PRED predicted values (max 10 best out of 786): 0g2lq (0.48 #29129, 0.47 #24969, 0.47 #20808), 02cyfz (0.48 #29129, 0.47 #24969, 0.47 #20808), 06dv3 (0.29 #33, 0.17 #4193, 0.04 #45767), 0pz91 (0.25 #2291, 0.06 #14776, 0.06 #6451), 04wvhz (0.20 #16646, 0.12 #95689, 0.12 #95688), 04wp3s (0.17 #5135, 0.14 #975, 0.02 #15541), 04bdzg (0.17 #5259, 0.14 #1099, 0.01 #15665), 04954 (0.14 #1307, 0.08 #5467, 0.04 #45767), 017r13 (0.14 #1110, 0.08 #5270, 0.03 #9431), 0170s4 (0.14 #397, 0.08 #4557, 0.02 #124815) >> Best rule #29129 for best value: >> intensional similarity = 4 >> extensional distance = 220 >> proper extension: 0bm2g; >> query: (?x9872, ?x7837) <- film_format(?x9872, ?x909), nominated_for(?x7837, ?x9872), nominated_for(?x6463, ?x9872), award_winner(?x496, ?x7837) >> conf = 0.48 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 04ynx7 film! 08pth9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 72.000 0.481 http://example.org/film/actor/film./film/performance/film #17217-08gf93 PRED entity: 08gf93 PRED relation: executive_produced_by! PRED expected values: 05dss7 => 101 concepts (49 used for prediction) PRED predicted values (max 10 best out of 362): 02scbv (0.11 #386, 0.07 #913, 0.06 #1440), 043tvp3 (0.11 #384, 0.07 #911, 0.06 #1438), 0bt4g (0.05 #420, 0.04 #947, 0.03 #3055), 0mbql (0.05 #376, 0.04 #903, 0.03 #3011), 01f7kl (0.05 #134, 0.04 #661, 0.03 #2769), 0k_9j (0.05 #443, 0.04 #970, 0.03 #3078), 09gdh6k (0.05 #408, 0.04 #935, 0.03 #3570), 07b1gq (0.05 #201, 0.04 #728, 0.03 #1255), 04k9y6 (0.05 #341, 0.04 #868, 0.03 #1395), 0pb33 (0.05 #71, 0.04 #598, 0.03 #1125) >> Best rule #386 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 047q2wc; >> query: (?x11374, 02scbv) <- award_winner(?x1105, ?x11374), ?x1105 = 07bdd_, profession(?x11374, ?x319), ?x319 = 01d_h8 >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08gf93 executive_produced_by! 05dss7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 49.000 0.105 http://example.org/film/film/executive_produced_by #17216-01lvcs1 PRED entity: 01lvcs1 PRED relation: instrumentalists! PRED expected values: 05148p4 013y1f => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 118): 05148p4 (0.34 #635, 0.33 #248, 0.32 #2427), 02sgy (0.31 #696, 0.27 #1629, 0.26 #309), 0cfdd (0.31 #696, 0.27 #1629, 0.26 #309), 01vj9c (0.31 #696, 0.27 #1629, 0.26 #309), 06ch55 (0.20 #149, 0.10 #303, 0.07 #459), 013y1f (0.20 #104, 0.06 #336, 0.06 #1422), 02hnl (0.18 #571, 0.17 #1504, 0.17 #1425), 03t22m (0.17 #31, 0.07 #108, 0.03 #262), 0jtg0 (0.17 #43, 0.03 #274, 0.03 #2488), 03qjg (0.14 #1051, 0.14 #662, 0.14 #1362) >> Best rule #635 for best value: >> intensional similarity = 3 >> extensional distance = 199 >> proper extension: 01vd7hn; 03_0p; >> query: (?x3492, 05148p4) <- role(?x3492, ?x214), award_nominee(?x3492, ?x3493), instrumentalists(?x316, ?x3492) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 01lvcs1 instrumentalists! 013y1f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 109.000 109.000 0.343 http://example.org/music/instrument/instrumentalists EVAL 01lvcs1 instrumentalists! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.343 http://example.org/music/instrument/instrumentalists #17215-03lq43 PRED entity: 03lq43 PRED relation: profession PRED expected values: 02hrh1q => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 51): 02hrh1q (0.89 #4038, 0.88 #4783, 0.88 #3144), 01d_h8 (0.40 #304, 0.38 #5221, 0.38 #155), 0dz3r (0.33 #2, 0.28 #10431, 0.25 #151), 0dxtg (0.31 #3888, 0.29 #1057, 0.28 #4931), 02jknp (0.28 #10431, 0.27 #5223, 0.26 #1200), 03gjzk (0.28 #10431, 0.26 #1208, 0.25 #3890), 09jwl (0.28 #10431, 0.18 #2404, 0.18 #5086), 016z4k (0.28 #10431, 0.17 #4, 0.12 #153), 01c72t (0.28 #10431, 0.17 #25, 0.12 #174), 018gz8 (0.28 #10431, 0.13 #12386, 0.12 #12834) >> Best rule #4038 for best value: >> intensional similarity = 2 >> extensional distance = 540 >> proper extension: 01l1b90; 01wxyx1; 01wk7b7; 08b8vd; 0btyl; 04cr6qv; 02hhtj; 0mdyn; 04mlmx; 017m2y; ... >> query: (?x4042, 02hrh1q) <- participant(?x891, ?x4042), film(?x4042, ?x1941) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03lq43 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.895 http://example.org/people/person/profession #17214-0htww PRED entity: 0htww PRED relation: film! PRED expected values: 0ly5n => 100 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1177): 01n9d9 (0.44 #110355, 0.42 #72874, 0.42 #20819), 01pr6q7 (0.44 #110355, 0.42 #72874, 0.42 #20819), 021r6w (0.14 #95779, 0.13 #89532, 0.13 #24984), 06cgy (0.12 #250, 0.05 #29396, 0.04 #12740), 016z2j (0.12 #17043, 0.02 #25373, 0.02 #27454), 02ck7w (0.11 #5102, 0.07 #9265, 0.07 #11347), 0241jw (0.11 #4458, 0.07 #8621, 0.07 #10703), 0svqs (0.11 #5037, 0.07 #9200, 0.07 #11282), 03ym1 (0.11 #5174, 0.07 #9337, 0.07 #11419), 02gvwz (0.11 #4350, 0.07 #8513, 0.07 #10595) >> Best rule #110355 for best value: >> intensional similarity = 4 >> extensional distance = 689 >> proper extension: 047rkcm; >> query: (?x3137, ?x3811) <- nominated_for(?x3811, ?x3137), film_release_distribution_medium(?x3137, ?x2008), film_crew_role(?x3137, ?x137), film(?x541, ?x3137) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #13143 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 48 *> proper extension: 0k5px; *> query: (?x3137, 0ly5n) <- nominated_for(?x3811, ?x3137), nominated_for(?x3066, ?x3137), nominated_for(?x591, ?x3137), ?x591 = 0f4x7, ?x3066 = 0gqy2, film_release_distribution_medium(?x3137, ?x2008) *> conf = 0.02 ranks of expected_values: 577 EVAL 0htww film! 0ly5n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 55.000 0.444 http://example.org/film/actor/film./film/performance/film #17213-0qr8z PRED entity: 0qr8z PRED relation: location_of_ceremony! PRED expected values: 01csrl 01g42 => 108 concepts (55 used for prediction) PRED predicted values (max 10 best out of 956): 0h7pj (0.25 #957, 0.17 #1965, 0.17 #1713), 09889g (0.17 #1884, 0.15 #2136, 0.11 #2893), 01vsl3_ (0.15 #2083, 0.11 #2840, 0.08 #1579), 01kgg9 (0.14 #720, 0.12 #1224, 0.08 #1980), 05_2h8 (0.14 #662, 0.12 #1166, 0.08 #1922), 0c9c0 (0.14 #570, 0.11 #1326, 0.04 #3597), 06x58 (0.14 #545, 0.11 #1301, 0.04 #3572), 03lt8g (0.14 #527, 0.11 #1283, 0.04 #3554), 0gdqy (0.14 #726, 0.11 #1482, 0.04 #3753), 06wvj (0.14 #563, 0.11 #1319, 0.04 #3590) >> Best rule #957 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0f25y; >> query: (?x8654, 0h7pj) <- location_of_ceremony(?x9552, ?x8654), type_of_union(?x9552, ?x566), source(?x8654, ?x958), place_of_birth(?x9552, ?x739), ?x739 = 02_286 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qr8z location_of_ceremony! 01g42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 55.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony EVAL 0qr8z location_of_ceremony! 01csrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 55.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #17212-056rgc PRED entity: 056rgc PRED relation: profession PRED expected values: 01d_h8 => 99 concepts (63 used for prediction) PRED predicted values (max 10 best out of 45): 01d_h8 (0.67 #2946, 0.64 #1035, 0.37 #2652), 0dxtg (0.58 #2953, 0.56 #1042, 0.27 #7657), 03gjzk (0.28 #2660, 0.26 #1043, 0.25 #3689), 02krf9 (0.21 #1054, 0.21 #2965, 0.09 #2671), 09jwl (0.21 #3693, 0.18 #606, 0.18 #459), 0d1pc (0.18 #784, 0.17 #931, 0.17 #49), 0cbd2 (0.14 #2947, 0.14 #1036, 0.12 #8681), 016z4k (0.14 #3679, 0.12 #445, 0.12 #592), 0nbcg (0.13 #3705, 0.13 #4881, 0.11 #5763), 018gz8 (0.13 #2662, 0.13 #5455, 0.13 #3691) >> Best rule #2946 for best value: >> intensional similarity = 4 >> extensional distance = 424 >> proper extension: 0177s6; 025tdwc; 018ty9; 02qy3py; 05gc0h; 0k57l; 0gry51; >> query: (?x3195, 01d_h8) <- profession(?x3195, ?x1032), profession(?x3195, ?x524), ?x1032 = 02hrh1q, ?x524 = 02jknp >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 056rgc profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 63.000 0.669 http://example.org/people/person/profession #17211-09d3b7 PRED entity: 09d3b7 PRED relation: genre PRED expected values: 07s9rl0 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 89): 07s9rl0 (0.72 #6258, 0.68 #601, 0.68 #241), 05rwpb (0.57 #721), 082gq (0.50 #630, 0.12 #510, 0.11 #1232), 05p553 (0.42 #6262, 0.38 #7223, 0.34 #365), 01jfsb (0.36 #133, 0.30 #2297, 0.30 #1695), 04rlf (0.33 #65, 0.05 #305, 0.04 #425), 0bj8m2 (0.33 #49, 0.02 #2814, 0.02 #770), 02kdv5l (0.29 #2287, 0.28 #2768, 0.28 #603), 03k9fj (0.27 #854, 0.27 #1334, 0.26 #1815), 01hmnh (0.27 #138, 0.18 #1700, 0.18 #1340) >> Best rule #6258 for best value: >> intensional similarity = 3 >> extensional distance = 1394 >> proper extension: 0vgkd; >> query: (?x8677, 07s9rl0) <- genre(?x8677, ?x2753), genre(?x7150, ?x2753), ?x7150 = 01h18v >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09d3b7 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.721 http://example.org/film/film/genre #17210-012x8m PRED entity: 012x8m PRED relation: state_province_region PRED expected values: 07b_l => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 5): 059rby (0.25 #127, 0.17 #745, 0.17 #1734), 081yw (0.14 #431, 0.09 #555, 0.07 #678), 01n7q (0.07 #1378, 0.07 #1994, 0.06 #1501), 05k7sb (0.03 #1638, 0.02 #2749, 0.02 #2872), 07h34 (0.02 #2151, 0.02 #2277, 0.02 #2400) >> Best rule #127 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 0dd2f; >> query: (?x14545, 059rby) <- artist(?x14545, ?x1838), artist(?x14545, ?x1001), type_of_union(?x1001, ?x566), artists(?x302, ?x1001), instrumentalists(?x2048, ?x1001), ?x1838 = 012zng, role(?x1001, ?x1466), role(?x2048, ?x1574), role(?x74, ?x2048), group(?x2048, ?x997), ?x1574 = 0l15bq >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 012x8m state_province_region 07b_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 51.000 0.250 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #17209-0bvfqq PRED entity: 0bvfqq PRED relation: award_winner PRED expected values: 01q415 05ldnp => 30 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1405): 02mxbd (0.33 #3948, 0.12 #7021, 0.12 #17782), 05_k56 (0.33 #136, 0.12 #6282, 0.11 #7819), 01tc9r (0.33 #589, 0.09 #14421, 0.06 #6735), 03wd5tk (0.33 #2444, 0.08 #16278, 0.08 #17815), 026rm_y (0.33 #4317, 0.08 #18151, 0.06 #7390), 0127m7 (0.33 #1879, 0.06 #6489, 0.06 #8026), 047q2wc (0.33 #2138, 0.06 #6748, 0.06 #8285), 01pj5q (0.33 #2662, 0.06 #7272, 0.06 #8809), 027t8fw (0.33 #2609, 0.06 #7219, 0.06 #8756), 02kxwk (0.33 #2203, 0.06 #6813, 0.06 #8350) >> Best rule #3948 for best value: >> intensional similarity = 19 >> extensional distance = 4 >> proper extension: 05qb8vx; >> query: (?x2210, 02mxbd) <- ceremony(?x6860, ?x2210), ceremony(?x4573, ?x2210), ceremony(?x3458, ?x2210), ceremony(?x1079, ?x2210), ceremony(?x1053, ?x2210), ?x4573 = 0gq_d, ?x6860 = 018wdw, instance_of_recurring_event(?x2210, ?x3459), award_winner(?x2210, ?x157), nominated_for(?x3458, ?x7917), nominated_for(?x3458, ?x7336), nominated_for(?x3458, ?x5767), ?x5767 = 0ndwt2w, award(?x2871, ?x3458), titles(?x811, ?x7917), honored_for(?x2210, ?x861), ?x1079 = 0l8z1, ?x7336 = 0bdjd, ?x1053 = 0gqzz >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #30752 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 69 *> proper extension: 0h_9252; 0ds460j; *> query: (?x2210, ?x2426) <- ceremony(?x4573, ?x2210), ceremony(?x1245, ?x2210), ceremony(?x601, ?x2210), award(?x4505, ?x601), award(?x2793, ?x601), award_winner(?x4573, ?x2426), award_nominee(?x7188, ?x2793), award_winner(?x2210, ?x157), film(?x2793, ?x2057), award(?x3456, ?x4573), award(?x2524, ?x1245), ?x4505 = 012wg, ?x2524 = 0bmh4, award_winner(?x3455, ?x3456) *> conf = 0.02 ranks of expected_values: 615, 616 EVAL 0bvfqq award_winner 05ldnp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 20.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bvfqq award_winner 01q415 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 20.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17208-0dscrwf PRED entity: 0dscrwf PRED relation: film! PRED expected values: 017s11 => 76 concepts (63 used for prediction) PRED predicted values (max 10 best out of 72): 05rrtf (0.45 #2378, 0.45 #1039, 0.44 #2826), 016tw3 (0.20 #975, 0.19 #529, 0.18 #826), 086k8 (0.19 #966, 0.18 #2, 0.18 #743), 017s11 (0.15 #967, 0.15 #151, 0.15 #744), 03xq0f (0.15 #5, 0.14 #375, 0.14 #301), 016tt2 (0.15 #4, 0.13 #745, 0.13 #152), 05qd_ (0.15 #824, 0.14 #973, 0.14 #1196), 01795t (0.10 #388, 0.10 #166, 0.08 #462), 01gb54 (0.09 #993, 0.08 #770, 0.08 #844), 0g1rw (0.07 #601, 0.07 #2311, 0.06 #4553) >> Best rule #2378 for best value: >> intensional similarity = 4 >> extensional distance = 708 >> proper extension: 0jym0; 0170xl; 016z43; >> query: (?x511, ?x7690) <- film(?x3560, ?x511), production_companies(?x511, ?x7690), nominated_for(?x1336, ?x511), film_release_distribution_medium(?x511, ?x81) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #967 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 428 *> proper extension: 03h_yy; 035xwd; 09p35z; 0k4d7; 05_5rjx; 038bh3; 01q2nx; 02tktw; 04xx9s; *> query: (?x511, 017s11) <- produced_by(?x511, ?x4946), film_crew_role(?x511, ?x468), film(?x6969, ?x511), production_companies(?x511, ?x7690) *> conf = 0.15 ranks of expected_values: 4 EVAL 0dscrwf film! 017s11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 63.000 0.454 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #17207-02847m9 PRED entity: 02847m9 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.78 #1277, 0.78 #1168, 0.78 #839), 09vw2b7 (0.72 #1167, 0.69 #838, 0.68 #2752), 0dxtw (0.43 #2015, 0.40 #843, 0.38 #551), 0215hd (0.29 #163, 0.25 #379, 0.25 #271), 0d2b38 (0.29 #170, 0.25 #278, 0.22 #566), 089g0h (0.29 #164, 0.25 #272, 0.19 #380), 01pvkk (0.28 #2016, 0.28 #3316, 0.27 #2758), 02ynfr (0.25 #556, 0.21 #484, 0.20 #848), 01xy5l_ (0.20 #50, 0.19 #374, 0.16 #554), 0263ycg (0.20 #54, 0.09 #3834, 0.06 #270) >> Best rule #1277 for best value: >> intensional similarity = 5 >> extensional distance = 244 >> proper extension: 0d_2fb; >> query: (?x1619, 0ch6mp2) <- category(?x1619, ?x134), ?x134 = 08mbj5d, titles(?x5138, ?x1619), country(?x1619, ?x94), film_crew_role(?x1619, ?x137) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02847m9 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.785 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17206-09n5b9 PRED entity: 09n5b9 PRED relation: jurisdiction_of_office PRED expected values: 05kkh 05kj_ => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1539): 09c7w0 (0.78 #2980, 0.67 #3829, 0.67 #3406), 05kkh (0.62 #2558, 0.62 #2130, 0.60 #1279), 07srw (0.60 #1362, 0.50 #513, 0.43 #1788), 0h8d (0.60 #1417, 0.50 #568, 0.43 #1843), 05kj_ (0.50 #452, 0.41 #2543, 0.40 #1301), 03gh4 (0.50 #630, 0.40 #1479, 0.40 #1053), 0hjy (0.50 #461, 0.40 #1310, 0.40 #884), 0f8l9c (0.42 #5575, 0.39 #8142, 0.38 #6005), 0rh6k (0.41 #2543, 0.36 #7253, 0.33 #4), 0694j (0.41 #2543, 0.36 #7253, 0.33 #8532) >> Best rule #2980 for best value: >> intensional similarity = 20 >> extensional distance = 7 >> proper extension: 0dq3c; >> query: (?x10093, 09c7w0) <- jurisdiction_of_office(?x10093, ?x3778), jurisdiction_of_office(?x10093, ?x3038), jurisdiction_of_office(?x10093, ?x2977), jurisdiction_of_office(?x10093, ?x760), jurisdiction_of_office(?x10093, ?x448), contains(?x3038, ?x13736), contains(?x3038, ?x13267), contains(?x3038, ?x2497), location(?x120, ?x760), ?x13736 = 03wv2g, time_zones(?x3778, ?x1638), contains(?x448, ?x10763), religion(?x760, ?x109), location(?x396, ?x3778), ?x13267 = 0rwq6, ?x10763 = 0sqgt, contains(?x760, ?x552), ?x2497 = 0f1nl, partially_contains(?x2977, ?x10710), contains(?x3778, ?x1506) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2558 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 6 *> proper extension: 0789n; *> query: (?x10093, 05kkh) <- jurisdiction_of_office(?x10093, ?x3778), jurisdiction_of_office(?x10093, ?x3038), jurisdiction_of_office(?x10093, ?x2982), jurisdiction_of_office(?x10093, ?x938), jurisdiction_of_office(?x10093, ?x760), contains(?x3038, ?x2277), district_represented(?x176, ?x3778), contains(?x8260, ?x760), religion(?x760, ?x109), location(?x4473, ?x760), contains(?x3778, ?x1506), ?x8260 = 04_1l0v, state_province_region(?x2276, ?x3038), state(?x553, ?x760), contains(?x2982, ?x659), vacationer(?x760, ?x10915), adjoins(?x2256, ?x2982), gender(?x4473, ?x231), administrative_parent(?x5449, ?x938), artist(?x10727, ?x4473) *> conf = 0.62 ranks of expected_values: 2, 5 EVAL 09n5b9 jurisdiction_of_office 05kj_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 22.000 0.778 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 09n5b9 jurisdiction_of_office 05kkh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 22.000 22.000 0.778 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #17205-030x48 PRED entity: 030x48 PRED relation: gender PRED expected values: 02zsn => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.76 #3, 0.72 #129, 0.71 #21), 02zsn (0.60 #2, 0.46 #152, 0.45 #133) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 01_x6d; 02k4b2; 0q1lp; 02s529; 0dszr0; >> query: (?x3179, 05zppz) <- actor(?x2555, ?x3179), profession(?x3179, ?x1383), ?x1383 = 0np9r, religion(?x3179, ?x1985) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 01tpl1p; *> query: (?x3179, 02zsn) <- actor(?x7657, ?x3179), profession(?x3179, ?x1383), ?x1383 = 0np9r, ?x7657 = 028k2x *> conf = 0.60 ranks of expected_values: 2 EVAL 030x48 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.759 http://example.org/people/person/gender #17204-0gc_c_ PRED entity: 0gc_c_ PRED relation: film! PRED expected values: 0f13b => 97 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1060): 016tw3 (0.47 #52101, 0.47 #10420, 0.45 #52100), 018grr (0.47 #52101, 0.47 #10420, 0.45 #52100), 091yn0 (0.47 #52101, 0.47 #10420, 0.45 #52100), 0dt645q (0.22 #1765, 0.03 #16352), 04fcx7 (0.17 #6251, 0.14 #22924, 0.14 #22923), 0gg9_5q (0.17 #6251, 0.14 #22924, 0.14 #22923), 016ypb (0.17 #4667, 0.07 #13003, 0.07 #10920), 02gf_l (0.17 #5436, 0.07 #9604, 0.06 #3352), 01pk3z (0.16 #7239, 0.01 #78100, 0.01 #65595), 01lbp (0.16 #6401, 0.01 #71011, 0.01 #43912) >> Best rule #52101 for best value: >> intensional similarity = 4 >> extensional distance = 314 >> proper extension: 0g5pv3; 03l6q0; 03ct7jd; >> query: (?x3600, ?x1104) <- executive_produced_by(?x3600, ?x3744), film_release_distribution_medium(?x3600, ?x81), nominated_for(?x1104, ?x3600), award_nominee(?x846, ?x1104) >> conf = 0.47 => this is the best rule for 3 predicted values *> Best rule #1480 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0436yk; 08fbnx; *> query: (?x3600, 0f13b) <- titles(?x811, ?x3600), genre(?x3600, ?x1013), ?x1013 = 06n90, film(?x4832, ?x3600) *> conf = 0.11 ranks of expected_values: 56 EVAL 0gc_c_ film! 0f13b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 97.000 59.000 0.474 http://example.org/film/actor/film./film/performance/film #17203-0fq27fp PRED entity: 0fq27fp PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 6): 029j_ (0.83 #86, 0.83 #166, 0.82 #46), 07z4p (0.18 #403, 0.18 #397, 0.17 #366), 02nxhr (0.18 #403, 0.18 #397, 0.17 #366), 0735l (0.18 #403, 0.18 #397, 0.17 #366), 07c52 (0.18 #403, 0.18 #397, 0.17 #419), 0dq6p (0.17 #366) >> Best rule #86 for best value: >> intensional similarity = 9 >> extensional distance = 40 >> proper extension: 0ds33; 048scx; 03t97y; 020fcn; 01kff7; 0fdv3; 0260bz; 01hqk; 047gpsd; 04ynx7; ... >> query: (?x622, 029j_) <- film_crew_role(?x622, ?x2178), film_crew_role(?x622, ?x1171), film_crew_role(?x622, ?x468), genre(?x622, ?x53), ?x2178 = 01pvkk, currency(?x622, ?x1099), ?x468 = 02r96rf, crewmember(?x622, ?x3782), ?x1171 = 09vw2b7 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fq27fp film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17202-0_565 PRED entity: 0_565 PRED relation: place! PRED expected values: 0_565 => 75 concepts (35 used for prediction) PRED predicted values (max 10 best out of 17): 0mwk9 (0.06 #3101, 0.06 #3618, 0.04 #6198), 0_24q (0.05 #257, 0.04 #773, 0.04 #1290), 0l4vc (0.05 #231, 0.04 #747, 0.04 #1264), 068p2 (0.05 #105, 0.04 #621, 0.04 #1138), 0fvzz (0.04 #1431, 0.01 #1948), 0_jq4 (0.04 #1350, 0.01 #1867), 0_g_6 (0.04 #1311, 0.01 #1828), 0zrlp (0.04 #1301, 0.01 #1818), 0zlgm (0.04 #1150, 0.01 #1667), 0zygc (0.04 #1110, 0.01 #1627) >> Best rule #3101 for best value: >> intensional similarity = 3 >> extensional distance = 139 >> proper extension: 0mn0v; 0qlrh; >> query: (?x12295, ?x12296) <- time_zones(?x12295, ?x2674), county(?x12295, ?x12296), ?x2674 = 02hcv8 >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0_565 place! 0_565 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 35.000 0.064 http://example.org/location/hud_county_place/place #17201-0rh6k PRED entity: 0rh6k PRED relation: location! PRED expected values: 07r4c 012ycy => 229 concepts (185 used for prediction) PRED predicted values (max 10 best out of 3482): 049fgvm (0.70 #422038, 0.52 #158883, 0.50 #275566), 0p8jf (0.70 #422038, 0.52 #158883, 0.49 #419554), 01n1gc (0.70 #422038, 0.50 #275566, 0.49 #419554), 02t_8z (0.70 #422038, 0.49 #419554, 0.48 #352519), 0bdlj (0.70 #422038, 0.49 #419554, 0.48 #352519), 02vqpx8 (0.70 #422038, 0.49 #419554, 0.48 #352519), 01xwv7 (0.52 #158883, 0.50 #275566, 0.49 #419554), 0cwtm (0.50 #275566, 0.49 #419554, 0.48 #352519), 02779r4 (0.50 #275566, 0.49 #419554, 0.48 #352519), 07lmxq (0.50 #275566, 0.49 #419554, 0.48 #352519) >> Best rule #422038 for best value: >> intensional similarity = 3 >> extensional distance = 314 >> proper extension: 019xz9; >> query: (?x108, ?x4936) <- place_of_birth(?x4936, ?x108), time_zones(?x108, ?x2674), location(?x4936, ?x1227) >> conf = 0.70 => this is the best rule for 6 predicted values *> Best rule #3721 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 04wgh; *> query: (?x108, 07r4c) <- featured_film_locations(?x8605, ?x108), featured_film_locations(?x763, ?x108), ?x763 = 061681, language(?x8605, ?x254) *> conf = 0.14 ranks of expected_values: 183, 2130 EVAL 0rh6k location! 012ycy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 229.000 185.000 0.704 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0rh6k location! 07r4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 229.000 185.000 0.704 http://example.org/people/person/places_lived./people/place_lived/location #17200-043tvp3 PRED entity: 043tvp3 PRED relation: music PRED expected values: 02bh9 => 78 concepts (63 used for prediction) PRED predicted values (max 10 best out of 131): 01ycfv (0.33 #167, 0.03 #1637, 0.02 #5641), 02bh9 (0.17 #261, 0.08 #1943, 0.08 #1101), 02g1jh (0.17 #338, 0.07 #1598, 0.03 #758), 06cv1 (0.11 #214, 0.03 #1474, 0.01 #1054), 0150t6 (0.11 #466, 0.06 #676, 0.06 #256), 0146pg (0.08 #1902, 0.06 #640, 0.06 #4430), 01tc9r (0.07 #485, 0.03 #2801, 0.03 #4062), 04pf4r (0.07 #1538, 0.06 #278, 0.04 #2382), 02jxkw (0.06 #1192, 0.04 #1402, 0.03 #4562), 0csdzz (0.06 #1447, 0.05 #1868, 0.04 #3134) >> Best rule #167 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01_1hw; >> query: (?x6882, 01ycfv) <- film(?x382, ?x6882), executive_produced_by(?x6882, ?x8208), ?x8208 = 04fyhv, prequel(?x6882, ?x6429) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #261 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 025s1wg; *> query: (?x6882, 02bh9) <- film(?x541, ?x6882), country(?x6882, ?x205), ?x541 = 017s11, genre(?x6882, ?x53), prequel(?x6882, ?x6429) *> conf = 0.17 ranks of expected_values: 2 EVAL 043tvp3 music 02bh9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 63.000 0.333 http://example.org/film/film/music #17199-0jbyg PRED entity: 0jbyg PRED relation: category PRED expected values: 08mbj5d => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.87 #16, 0.86 #6, 0.83 #17) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 268 >> proper extension: 02mslq; 0frsw; 089pg7; 014g91; >> query: (?x6990, 08mbj5d) <- artist(?x3265, ?x6990), instrumentalists(?x227, ?x6990), award_winner(?x724, ?x6990) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jbyg category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.867 http://example.org/common/topic/webpage./common/webpage/category #17198-04gv3db PRED entity: 04gv3db PRED relation: film! PRED expected values: 015pxr => 121 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1140): 07y8l9 (0.50 #3032, 0.17 #5098, 0.09 #9230), 07m77x (0.50 #3595, 0.17 #5661, 0.05 #35126), 03n52j (0.50 #3015, 0.17 #5081, 0.03 #42273), 05txrz (0.50 #4894, 0.12 #6960, 0.08 #11092), 07cjqy (0.50 #4732, 0.09 #8864, 0.06 #12996), 0pgjm (0.50 #2280, 0.05 #35126, 0.04 #84716), 051wwp (0.50 #2934, 0.04 #84716, 0.03 #109512), 032w8h (0.33 #4411, 0.12 #6477, 0.08 #10609), 08vr94 (0.33 #4805, 0.12 #6871, 0.04 #84716), 0zcbl (0.33 #1210, 0.12 #7408, 0.03 #17738) >> Best rule #3032 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02stbw; >> query: (?x4479, 07y8l9) <- category(?x4479, ?x134), nominated_for(?x794, ?x4479), film(?x4478, ?x4479), ?x4478 = 028k57 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2413 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 02stbw; *> query: (?x4479, 015pxr) <- category(?x4479, ?x134), nominated_for(?x794, ?x4479), film(?x4478, ?x4479), ?x4478 = 028k57 *> conf = 0.25 ranks of expected_values: 19 EVAL 04gv3db film! 015pxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 121.000 73.000 0.500 http://example.org/film/actor/film./film/performance/film #17197-025vry PRED entity: 025vry PRED relation: type_of_union PRED expected values: 04ztj => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.77 #125, 0.77 #169, 0.77 #197), 01g63y (0.14 #30, 0.13 #162, 0.13 #361), 01bl8s (0.02 #59) >> Best rule #125 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 041h0; 0chsq; 021sv1; 012t1; 02whj; 02lkcc; 04nw9; 073bb; 0c6g29; 0b_fw; ... >> query: (?x681, 04ztj) <- people(?x1050, ?x681), place_of_death(?x681, ?x682), gender(?x681, ?x231), award_winner(?x1079, ?x681) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025vry type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.774 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17196-01914 PRED entity: 01914 PRED relation: featured_film_locations! PRED expected values: 0192hw => 297 concepts (232 used for prediction) PRED predicted values (max 10 best out of 719): 061681 (0.33 #782, 0.29 #4457, 0.29 #3722), 03r0g9 (0.29 #3937, 0.17 #997, 0.14 #4672), 06fqlk (0.25 #483, 0.17 #2688, 0.17 #1218), 02vxq9m (0.25 #11, 0.17 #2216, 0.07 #9566), 024l2y (0.20 #6729, 0.17 #849, 0.15 #8934), 04gv3db (0.20 #6935, 0.17 #1055, 0.14 #4730), 0dnkmq (0.19 #16123, 0.18 #12448, 0.14 #33034), 03ydlnj (0.18 #7937, 0.17 #1322, 0.14 #4997), 04pmnt (0.18 #7807, 0.17 #1192, 0.14 #4867), 033srr (0.18 #12038, 0.17 #1013, 0.15 #9098) >> Best rule #782 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 06c62; >> query: (?x206, 061681) <- location(?x6709, ?x206), state_province_region(?x9409, ?x206), month(?x206, ?x1650), locations(?x1931, ?x206) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6846 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01lxw6; *> query: (?x206, 0192hw) <- adjoins(?x14204, ?x206), locations(?x1931, ?x206), category(?x206, ?x134) *> conf = 0.10 ranks of expected_values: 162 EVAL 01914 featured_film_locations! 0192hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 297.000 232.000 0.333 http://example.org/film/film/featured_film_locations #17195-0872p_c PRED entity: 0872p_c PRED relation: film_release_region PRED expected values: 0d060g 015fr 06qd3 05v10 03ryn => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 135): 015fr (0.88 #777, 0.88 #1545, 0.86 #1673), 0d060g (0.86 #1796, 0.77 #2437, 0.77 #2052), 0ctw_b (0.75 #1809, 0.74 #1681, 0.74 #1553), 06mzp (0.69 #782, 0.58 #2062, 0.58 #1550), 06c1y (0.63 #1822, 0.45 #2463, 0.44 #2078), 06qd3 (0.58 #2075, 0.53 #2460, 0.53 #1563), 07ylj (0.52 #788, 0.48 #1812, 0.39 #1556), 0h7x (0.50 #792, 0.48 #1688, 0.47 #1560), 02k54 (0.48 #778, 0.44 #1546, 0.43 #1674), 01pj7 (0.46 #1827, 0.37 #2083, 0.37 #1571) >> Best rule #777 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0g5qs2k; 0dtfn; 0g9zljd; 0btpm6; >> query: (?x1173, 015fr) <- film(?x609, ?x1173), film_release_region(?x1173, ?x792), ?x792 = 0hzlz, nominated_for(?x102, ?x1173) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6, 11, 48 EVAL 0872p_c film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 97.000 97.000 0.881 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0872p_c film_release_region 05v10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 97.000 97.000 0.881 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0872p_c film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 97.000 0.881 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0872p_c film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.881 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0872p_c film_release_region 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.881 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17194-0g5879y PRED entity: 0g5879y PRED relation: country PRED expected values: 03rt9 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 124): 0345h (0.26 #1626, 0.23 #674, 0.14 #1450), 059j2 (0.23 #2538, 0.21 #531, 0.15 #4180), 03h64 (0.23 #2538, 0.21 #531, 0.12 #160), 0b90_r (0.23 #2538, 0.15 #4180, 0.12 #4119), 06mkj (0.23 #2538, 0.15 #4180, 0.12 #2656), 0jgd (0.23 #2538, 0.15 #4180, 0.12 #2656), 05r4w (0.23 #2538, 0.15 #4180, 0.12 #2656), 0k6nt (0.23 #2538, 0.12 #4119, 0.12 #591), 03rjj (0.21 #531, 0.15 #655, 0.15 #4180), 0154j (0.21 #531, 0.15 #4180, 0.12 #4119) >> Best rule #1626 for best value: >> intensional similarity = 4 >> extensional distance = 589 >> proper extension: 0cks1m; 02v5xg; 04svwx; >> query: (?x2685, 0345h) <- country(?x2685, ?x512), genre(?x2685, ?x53), region(?x54, ?x512), titles(?x512, ?x144) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #896 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 166 *> proper extension: 0gtvrv3; 047svrl; *> query: (?x2685, 03rt9) <- film_release_region(?x2685, ?x583), ?x583 = 015fr, nominated_for(?x2887, ?x2685), film_crew_role(?x2685, ?x137) *> conf = 0.03 ranks of expected_values: 33 EVAL 0g5879y country 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 92.000 92.000 0.264 http://example.org/film/film/country #17193-078mgh PRED entity: 078mgh PRED relation: gender PRED expected values: 05zppz => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #1, 0.71 #129, 0.71 #125), 02zsn (0.47 #16, 0.47 #12, 0.47 #10) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 024bbl; >> query: (?x8135, 05zppz) <- award_winner(?x2858, ?x8135), award_nominee(?x8135, ?x496), ?x496 = 0bxtg >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 078mgh gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.792 http://example.org/people/person/gender #17192-018w8 PRED entity: 018w8 PRED relation: sports! PRED expected values: 0blg2 => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 30): 0l6m5 (0.79 #198, 0.76 #428, 0.72 #745), 0lbbj (0.79 #198, 0.76 #428, 0.72 #745), 0blg2 (0.79 #198, 0.76 #428, 0.72 #745), 0lbd9 (0.79 #198, 0.76 #428, 0.72 #745), 06sks6 (0.79 #198, 0.72 #745, 0.72 #744), 0nbjq (0.71 #295, 0.50 #96, 0.43 #211), 0kbws (0.67 #199, 0.59 #314, 0.56 #429), 018qb4 (0.64 #303, 0.50 #104, 0.43 #219), 0lv1x (0.64 #291, 0.50 #92, 0.40 #148), 0lk8j (0.64 #292, 0.31 #407, 0.30 #525) >> Best rule #198 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 03tmr; 02vx4; 018jz; 09xp_; >> query: (?x4833, ?x391) <- athlete(?x4833, ?x1213), sport(?x660, ?x4833), sports(?x391, ?x4833), country(?x4833, ?x94), type_of_union(?x1213, ?x566), olympics(?x4833, ?x584) >> conf = 0.79 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 018w8 sports! 0blg2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 55.000 55.000 0.786 http://example.org/user/jg/default_domain/olympic_games/sports #17191-0gnbw PRED entity: 0gnbw PRED relation: gender PRED expected values: 05zppz => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #5, 0.87 #31, 0.87 #9), 02zsn (0.33 #4, 0.32 #12, 0.31 #22) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 03gm48; 01v3vp; 02tkzn; 01hbq0; >> query: (?x7269, 05zppz) <- award(?x7269, ?x2192), ?x2192 = 0bfvd4, film(?x7269, ?x167) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gnbw gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.911 http://example.org/people/person/gender #17190-0hgxh PRED entity: 0hgxh PRED relation: symptom_of! PRED expected values: 012qjw => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 66): 01cdt5 (0.88 #1184, 0.50 #279, 0.48 #671), 0cjf0 (0.76 #1037, 0.73 #983, 0.67 #644), 012qjw (0.67 #652, 0.67 #644, 0.60 #591), 0brgy (0.67 #651, 0.60 #591, 0.60 #475), 0f3kl (0.67 #913, 0.54 #948, 0.46 #1193), 0j5fv (0.54 #948, 0.50 #649, 0.48 #491), 097ns (0.54 #948, 0.46 #1193, 0.31 #868), 01pf6 (0.48 #671, 0.48 #491, 0.45 #1129), 0hg45 (0.48 #671, 0.45 #859, 0.40 #925), 0k95h (0.48 #491, 0.45 #1129, 0.36 #951) >> Best rule #1184 for best value: >> intensional similarity = 26 >> extensional distance = 15 >> proper extension: 01ddth; >> query: (?x9510, 01cdt5) <- symptom_of(?x13605, ?x9510), symptom_of(?x9509, ?x9510), symptom_of(?x3679, ?x9510), symptom_of(?x13605, ?x12870), symptom_of(?x13605, ?x7007), ?x12870 = 0dcqh, symptom_of(?x3679, ?x10480), symptom_of(?x3679, ?x7260), symptom_of(?x3679, ?x4959), risk_factors(?x7260, ?x2510), people(?x7260, ?x1737), risk_factors(?x10480, ?x4195), symptom_of(?x6780, ?x10480), ?x2510 = 0x67, ?x6780 = 0j5fv, ?x4959 = 01dcqj, symptom_of(?x9509, ?x14562), symptom_of(?x9509, ?x13744), symptom_of(?x9509, ?x11739), symptom_of(?x9509, ?x3799), risk_factors(?x7006, ?x11739), people(?x3799, ?x487), risk_factors(?x13744, ?x11678), people(?x7007, ?x2208), award(?x14562, ?x12628), ?x11678 = 0fltx >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #652 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 4 *> proper extension: 0d19y2; *> query: (?x9510, 012qjw) <- symptom_of(?x13605, ?x9510), symptom_of(?x9509, ?x9510), symptom_of(?x4905, ?x9510), symptom_of(?x3679, ?x9510), symptom_of(?x13605, ?x12870), symptom_of(?x13605, ?x7007), ?x3679 = 02tfl8, risk_factors(?x12870, ?x514), people(?x12870, ?x7183), notable_people_with_this_condition(?x12870, ?x2046), ?x7007 = 097ns, symptom_of(?x9509, ?x13744), symptom_of(?x9509, ?x11126), symptom_of(?x9509, ?x11064), symptom_of(?x9509, ?x3799), ?x13744 = 01qqwn, risk_factors(?x11126, ?x8523), ?x4905 = 01j6t0, risk_factors(?x11064, ?x231), people(?x11064, ?x7958), ?x8523 = 0c58k, ?x7958 = 04__f, ?x3799 = 04psf *> conf = 0.67 ranks of expected_values: 3 EVAL 0hgxh symptom_of! 012qjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 58.000 58.000 0.882 http://example.org/medicine/symptom/symptom_of #17189-01s9vc PRED entity: 01s9vc PRED relation: film_release_distribution_medium PRED expected values: 07z4p => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 3): 07z4p (0.74 #156, 0.50 #8, 0.16 #60), 07c52 (0.25 #6, 0.12 #46, 0.11 #86), 02nxhr (0.11 #73, 0.10 #77, 0.09 #21) >> Best rule #156 for best value: >> intensional similarity = 5 >> extensional distance = 135 >> proper extension: 016kv6; >> query: (?x10404, ?x81) <- film(?x2465, ?x10404), titles(?x4205, ?x10404), nominated_for(?x10404, ?x2094), film_release_region(?x2094, ?x87), film_release_distribution_medium(?x2094, ?x81) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01s9vc film_release_distribution_medium 07z4p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.742 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17188-07f_t4 PRED entity: 07f_t4 PRED relation: nominated_for! PRED expected values: 05ztjjw => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 192): 02g3ft (0.69 #13761, 0.67 #13058, 0.67 #4897), 0gq9h (0.36 #3092, 0.36 #1694, 0.35 #3559), 0k611 (0.32 #1705, 0.28 #3103, 0.27 #3570), 0gs9p (0.32 #3561, 0.32 #3094, 0.31 #1696), 019f4v (0.32 #3083, 0.31 #1685, 0.31 #3550), 099c8n (0.31 #1688, 0.23 #3319, 0.23 #290), 040njc (0.29 #1638, 0.26 #3036, 0.25 #3503), 0gq_v (0.26 #1651, 0.26 #5849, 0.25 #3049), 04dn09n (0.25 #1666, 0.24 #3064, 0.22 #3531), 02qyntr (0.25 #1807, 0.21 #3205, 0.20 #3672) >> Best rule #13761 for best value: >> intensional similarity = 3 >> extensional distance = 957 >> proper extension: 07bz5; >> query: (?x7672, ?x3508) <- award(?x7672, ?x3508), award(?x5626, ?x3508), location(?x5626, ?x1523) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1641 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 273 *> proper extension: 02wwmhc; *> query: (?x7672, 05ztjjw) <- film(?x1550, ?x7672), film_crew_role(?x7672, ?x468), honored_for(?x762, ?x7672), language(?x7672, ?x254) *> conf = 0.13 ranks of expected_values: 52 EVAL 07f_t4 nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 92.000 92.000 0.685 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17187-026zt PRED entity: 026zt PRED relation: partially_contains! PRED expected values: 01pj7 => 27 concepts (23 used for prediction) PRED predicted values (max 10 best out of 427): 0f8l9c (0.60 #102, 0.41 #1000, 0.38 #1555), 0d060g (0.50 #5, 0.40 #182, 0.25 #271), 09c7w0 (0.50 #1, 0.40 #178, 0.25 #267), 035qy (0.41 #1000, 0.38 #1555, 0.37 #1366), 03rjj (0.41 #1000, 0.38 #1555, 0.37 #1366), 06mzp (0.41 #1000, 0.38 #1555, 0.37 #1366), 01znc_ (0.41 #1000, 0.37 #1366, 0.37 #537), 06t8v (0.41 #1000, 0.37 #1366, 0.37 #537), 0bjv6 (0.41 #1000, 0.37 #1366, 0.37 #537), 04j53 (0.41 #1000, 0.37 #1366, 0.37 #537) >> Best rule #102 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 05g56; 065ky; >> query: (?x10517, 0f8l9c) <- partially_contains(?x1497, ?x10517), film_release_region(?x6527, ?x1497), film_release_region(?x6520, ?x1497), film_release_region(?x1283, ?x1497), combatants(?x3728, ?x1497), jurisdiction_of_office(?x182, ?x1497), ?x6520 = 02bg55, film_release_region(?x6527, ?x2152), film_release_region(?x6527, ?x344), ?x1283 = 0cnztc4, ?x344 = 04gzd, ?x2152 = 06mkj, olympics(?x1497, ?x584), ?x3728 = 087vz >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1000 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 11 *> proper extension: 02k1b; *> query: (?x10517, ?x1499) <- partially_contains(?x1536, ?x10517), partially_contains(?x1497, ?x10517), adjoins(?x1497, ?x1499), adjoins(?x1497, ?x1353), taxonomy(?x1497, ?x939), contains(?x455, ?x1497), film_release_region(?x66, ?x1353), contains(?x1536, ?x4962), service_location(?x127, ?x455), locations(?x9939, ?x1353), contains(?x1353, ?x7575) *> conf = 0.41 ranks of expected_values: 15 EVAL 026zt partially_contains! 01pj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 27.000 23.000 0.600 http://example.org/location/location/partially_contains #17186-032xhg PRED entity: 032xhg PRED relation: award PRED expected values: 0cqhk0 => 113 concepts (107 used for prediction) PRED predicted values (max 10 best out of 281): 09sb52 (0.50 #851, 0.36 #17053, 0.35 #8546), 05pcn59 (0.36 #892, 0.33 #2512, 0.30 #2917), 057xs89 (0.36 #972, 0.13 #2592, 0.11 #2997), 0ck27z (0.33 #4143, 0.33 #9003, 0.32 #5763), 0f4x7 (0.29 #841, 0.14 #9346, 0.13 #4486), 04kxsb (0.29 #937, 0.11 #9442, 0.10 #3772), 05zr6wv (0.27 #422, 0.21 #827, 0.18 #2447), 05p09zm (0.25 #2555, 0.23 #2960, 0.21 #935), 0gqwc (0.22 #1290, 0.19 #1695, 0.11 #3720), 09qv_s (0.21 #963, 0.09 #3798, 0.09 #2583) >> Best rule #851 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 03qmj9; >> query: (?x436, 09sb52) <- film(?x436, ?x1219), award_winner(?x6631, ?x436), ?x1219 = 03bx2lk >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4087 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 297 *> proper extension: 07sgfsl; 044gyq; 018n6m; 026v437; 01q9b9; 01wk7ql; *> query: (?x436, 0cqhk0) <- award_nominee(?x436, ?x665), award_winner(?x6631, ?x436), actor(?x2436, ?x436) *> conf = 0.21 ranks of expected_values: 12 EVAL 032xhg award 0cqhk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 113.000 107.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17185-01d4cb PRED entity: 01d4cb PRED relation: type_of_union PRED expected values: 04ztj => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #213, 0.70 #317, 0.70 #269), 01g63y (0.13 #66, 0.13 #18, 0.11 #402) >> Best rule #213 for best value: >> intensional similarity = 3 >> extensional distance = 579 >> proper extension: 03cvfg; 03l295; 01xyt7; 0cv72h; 01gct2; 054c1; 01g0jn; 049sb; >> query: (?x9128, 04ztj) <- student(?x5777, ?x9128), award_winner(?x9945, ?x9128), category_of(?x9945, ?x2421) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d4cb type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.723 http://example.org/people/person/spouse_s./people/marriage/type_of_union #17184-04b2qn PRED entity: 04b2qn PRED relation: film_crew_role PRED expected values: 02r96rf => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 26): 09zzb8 (0.74 #471, 0.73 #326, 0.73 #218), 02r96rf (0.71 #221, 0.70 #329, 0.65 #1202), 0dxtw (0.40 #11, 0.37 #481, 0.36 #1209), 01vx2h (0.33 #482, 0.32 #1210, 0.30 #337), 0215hd (0.29 #55, 0.15 #91, 0.13 #19), 089g0h (0.20 #20, 0.18 #56, 0.11 #382), 02ynfr (0.18 #52, 0.16 #341, 0.16 #1214), 01xy5l_ (0.18 #50, 0.13 #14, 0.11 #86), 089fss (0.18 #43, 0.08 #79, 0.07 #332), 02_n3z (0.13 #2, 0.12 #38, 0.08 #327) >> Best rule #471 for best value: >> intensional similarity = 3 >> extensional distance = 346 >> proper extension: 0fq27fp; 0963mq; 03m8y5; 02w86hz; 02j69w; 0bmch_x; 01jwxx; 0353xq; 0dll_t2; 02qyv3h; ... >> query: (?x7858, 09zzb8) <- film_crew_role(?x7858, ?x2178), genre(?x7858, ?x53), ?x2178 = 01pvkk >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #221 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 200 *> proper extension: 01gglm; *> query: (?x7858, 02r96rf) <- nominated_for(?x6980, ?x7858), film_format(?x7858, ?x6392), film_crew_role(?x7858, ?x1171), people(?x1446, ?x6980) *> conf = 0.71 ranks of expected_values: 2 EVAL 04b2qn film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.736 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17183-0b78hw PRED entity: 0b78hw PRED relation: student! PRED expected values: 04g7x => 131 concepts (129 used for prediction) PRED predicted values (max 10 best out of 96): 037mh8 (0.29 #296, 0.22 #420, 0.11 #1043), 04rjg (0.14 #264, 0.11 #574, 0.11 #388), 0h5k (0.12 #329, 0.04 #1014, 0.02 #1266), 05qjt (0.11 #565, 0.11 #379, 0.07 #1254), 02j62 (0.11 #398, 0.06 #584, 0.02 #1273), 0dc_v (0.11 #407, 0.06 #593, 0.02 #1282), 041y2 (0.11 #487, 0.05 #735, 0.05 #673), 0fdys (0.11 #1026, 0.06 #2644, 0.06 #2023), 02822 (0.10 #2646, 0.09 #1901, 0.09 #1963), 088tb (0.09 #504, 0.05 #628, 0.04 #752) >> Best rule #296 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0n00; 099bk; 01dvtx; 024jwt; 0cbgl; >> query: (?x4308, 037mh8) <- student(?x3437, ?x4308), ?x3437 = 02_xgp2, influenced_by(?x2608, ?x4308), student(?x4672, ?x4308) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2367 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 165 *> proper extension: 0f5zj6; *> query: (?x4308, ?x254) <- student(?x3437, ?x4308), institution(?x3437, ?x122), major_field_of_study(?x3437, ?x254), location(?x4308, ?x94) *> conf = 0.01 ranks of expected_values: 65 EVAL 0b78hw student! 04g7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 131.000 129.000 0.286 http://example.org/education/field_of_study/students_majoring./education/education/student #17182-0ljc_ PRED entity: 0ljc_ PRED relation: titles PRED expected values: 03r0rq => 130 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1550): 028k2x (0.55 #9358, 0.33 #1133, 0.14 #18719), 05f7w84 (0.55 #9358, 0.29 #12478, 0.14 #18719), 05nlzq (0.55 #9358, 0.14 #18719, 0.13 #12477), 02py4c8 (0.44 #9449, 0.42 #14130, 0.36 #12570), 06r1k (0.33 #3024, 0.33 #1465, 0.22 #10823), 08cx5g (0.33 #2127, 0.27 #13047, 0.17 #6806), 06f0k (0.33 #3089, 0.27 #14009, 0.17 #7768), 0jq2r (0.33 #2751, 0.27 #13671, 0.17 #7430), 01cjhz (0.33 #1944, 0.27 #12864, 0.17 #6623), 03j63k (0.33 #2631, 0.27 #13551, 0.17 #7310) >> Best rule #9358 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0g5lhl7; 03mdt; 01zcrv; 0146mv; 0kctd; >> query: (?x11954, ?x5938) <- titles(?x11954, ?x808), actor(?x808, ?x1125), genre(?x808, ?x809), program(?x11954, ?x5938), award_winner(?x3486, ?x11954) >> conf = 0.55 => this is the best rule for 3 predicted values *> Best rule #2972 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 07c52; *> query: (?x11954, 03r0rq) <- titles(?x11954, ?x8628), titles(?x11954, ?x1876), titles(?x11954, ?x808), ?x808 = 07hpv3, ?x1876 = 0584r4, genre(?x8628, ?x1844), ?x1844 = 01htzx *> conf = 0.33 ranks of expected_values: 49 EVAL 0ljc_ titles 03r0rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 130.000 23.000 0.551 http://example.org/media_common/netflix_genre/titles #17181-02d02 PRED entity: 02d02 PRED relation: team! PRED expected values: 040j2_ => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 106): 03n69x (0.40 #815, 0.33 #249, 0.33 #135), 0hcs3 (0.33 #1796, 0.29 #2930, 0.29 #1343), 04g9sq (0.33 #452, 0.20 #2152, 0.12 #3967), 054c1 (0.33 #94, 0.07 #3970, 0.07 #2135), 040j2_ (0.29 #1522, 0.26 #2430, 0.24 #2995), 0cg39k (0.25 #523, 0.07 #3625, 0.05 #4427), 03vrv9 (0.25 #539, 0.06 #3596, 0.06 #4512), 0444x (0.25 #541, 0.06 #3598, 0.06 #4514), 054fvj (0.25 #529, 0.02 #5427, 0.02 #5542), 01f492 (0.16 #2432, 0.14 #1863, 0.14 #1524) >> Best rule #815 for best value: >> intensional similarity = 19 >> extensional distance = 3 >> proper extension: 0512p; >> query: (?x8894, 03n69x) <- sport(?x8894, ?x5063), team(?x5727, ?x8894), team(?x4244, ?x8894), season(?x8894, ?x2406), school(?x8894, ?x466), colors(?x8894, ?x663), ?x5063 = 018jz, draft(?x8894, ?x1161), ?x466 = 01pl14, season(?x12042, ?x2406), season(?x7357, ?x2406), season(?x2405, ?x2406), season(?x580, ?x2406), ?x4244 = 028c_8, ?x580 = 05m_8, ?x12042 = 05xvj, ?x5727 = 02wszf, ?x7357 = 04mjl, ?x2405 = 0x2p >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1522 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 5 *> proper extension: 049n7; 0cqt41; 051vz; 01yjl; *> query: (?x8894, 040j2_) <- sport(?x8894, ?x5063), team(?x2010, ?x8894), season(?x8894, ?x701), school(?x8894, ?x735), school(?x8894, ?x466), colors(?x8894, ?x663), ?x5063 = 018jz, draft(?x8894, ?x8786), draft(?x8894, ?x1161), citytown(?x466, ?x1248), ?x1161 = 02x2khw, school(?x8901, ?x466), ?x8901 = 07l4z, ?x735 = 065y4w7, major_field_of_study(?x466, ?x947), ?x8786 = 02pq_x5, currency(?x466, ?x170) *> conf = 0.29 ranks of expected_values: 5 EVAL 02d02 team! 040j2_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 75.000 75.000 0.400 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #17180-05dppk PRED entity: 05dppk PRED relation: award_winner! PRED expected values: 0kvgnq => 106 concepts (65 used for prediction) PRED predicted values (max 10 best out of 159): 02gd6x (0.50 #57910, 0.47 #48823, 0.46 #35199), 03wy8t (0.17 #6815, 0.17 #3406, 0.16 #7952), 0kvgnq (0.17 #6815, 0.17 #3406, 0.16 #7952), 04smdd (0.17 #6815, 0.17 #3406, 0.16 #7952), 03m8y5 (0.17 #6815, 0.17 #3406, 0.16 #7952), 016z5x (0.17 #6815, 0.17 #3406, 0.16 #7952), 04jpk2 (0.11 #393), 048qrd (0.10 #1363), 01gvsn (0.05 #28387), 043n1r5 (0.05 #28387) >> Best rule #57910 for best value: >> intensional similarity = 3 >> extensional distance = 1486 >> proper extension: 0g5lhl7; 01zcrv; >> query: (?x2530, ?x7307) <- nominated_for(?x2530, ?x7307), award_winner(?x7307, ?x6957), award_winner(?x1243, ?x2530) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6815 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 53 *> proper extension: 09cdxn; 09myny; 071jrc; *> query: (?x2530, ?x518) <- award(?x2530, ?x77), cinematography(?x518, ?x2530) *> conf = 0.17 ranks of expected_values: 3 EVAL 05dppk award_winner! 0kvgnq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 65.000 0.497 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #17179-016vqk PRED entity: 016vqk PRED relation: artists! PRED expected values: 050sw4 => 110 concepts (42 used for prediction) PRED predicted values (max 10 best out of 218): 06by7 (0.65 #1858, 0.58 #2164, 0.57 #2470), 0glt670 (0.35 #8309, 0.30 #2795, 0.28 #5553), 0xhtw (0.30 #3077, 0.18 #8591, 0.17 #10433), 02lnbg (0.27 #974, 0.26 #1892, 0.25 #2810), 02x8m (0.27 #2773, 0.21 #8287, 0.20 #937), 02qdgx (0.25 #957, 0.21 #1875, 0.16 #2181), 03_d0 (0.25 #2766, 0.21 #3684, 0.20 #930), 016clz (0.25 #1841, 0.23 #3065, 0.23 #2453), 0ggx5q (0.24 #6507, 0.23 #993, 0.21 #2829), 017_qw (0.19 #10109, 0.19 #5878, 0.10 #9555) >> Best rule #1858 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 01pbxb; 0197tq; 01w61th; 03f5spx; 01vv7sc; 0lk90; 01vrt_c; 01vrz41; 09qr6; 04dqdk; ... >> query: (?x9008, 06by7) <- award_nominee(?x4960, ?x9008), artists(?x3061, ?x9008), nationality(?x9008, ?x94), ?x3061 = 05bt6j >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016vqk artists! 050sw4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 42.000 0.649 http://example.org/music/genre/artists #17178-0bwgc_ PRED entity: 0bwgc_ PRED relation: location PRED expected values: 0345h => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 99): 09bkv (0.50 #5636, 0.49 #12883, 0.48 #4024), 04jpl (0.50 #17, 0.33 #822, 0.25 #1626), 02_286 (0.33 #37, 0.14 #20966, 0.14 #29013), 030qb3t (0.22 #2496, 0.17 #3302, 0.15 #5720), 059rby (0.17 #821, 0.04 #1625, 0.04 #5653), 09tlh (0.17 #151, 0.04 #1760), 06y57 (0.17 #256, 0.01 #22795), 0dmy0 (0.07 #8053, 0.07 #4831, 0.06 #7248), 0cr3d (0.06 #3364, 0.05 #4976, 0.05 #43607), 01n7q (0.06 #3282, 0.05 #4088, 0.05 #4894) >> Best rule #5636 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 017b2p; 01npcy7; >> query: (?x11983, ?x10042) <- place_of_birth(?x11983, ?x10042), location_of_ceremony(?x11983, ?x10272), profession(?x11983, ?x1032) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bwgc_ location 0345h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 99.000 0.504 http://example.org/people/person/places_lived./people/place_lived/location #17177-0gqz2 PRED entity: 0gqz2 PRED relation: award! PRED expected values: 02q52q => 45 concepts (31 used for prediction) PRED predicted values (max 10 best out of 983): 0gmcwlb (0.57 #1147, 0.27 #5233, 0.25 #2169), 04v8x9 (0.50 #2082, 0.12 #7188, 0.07 #22482), 0ccd3x (0.50 #2501, 0.12 #7607, 0.05 #27598), 0bmhn (0.50 #2982, 0.11 #8088, 0.06 #10133), 07xtqq (0.50 #2076, 0.09 #12292, 0.09 #8204), 0c5dd (0.50 #2150, 0.08 #7256, 0.05 #27598), 04q827 (0.50 #3011, 0.07 #22482, 0.07 #26576), 0jqn5 (0.43 #1160, 0.27 #5246, 0.22 #3203), 0jyb4 (0.43 #1663, 0.22 #3706, 0.18 #5749), 0pv3x (0.38 #2153, 0.29 #1131, 0.18 #5217) >> Best rule #1147 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 09ly2r6; >> query: (?x1323, 0gmcwlb) <- award_winner(?x1323, ?x538), nominated_for(?x1323, ?x2097), award(?x9396, ?x1323), ?x9396 = 03975z >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1193 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 09ly2r6; *> query: (?x1323, 02q52q) <- award_winner(?x1323, ?x538), nominated_for(?x1323, ?x2097), award(?x9396, ?x1323), ?x9396 = 03975z *> conf = 0.14 ranks of expected_values: 144 EVAL 0gqz2 award! 02q52q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 45.000 31.000 0.571 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #17176-03ryks PRED entity: 03ryks PRED relation: role PRED expected values: 0l14qv 0395lw 013y1f 07_l6 => 173 concepts (101 used for prediction) PRED predicted values (max 10 best out of 107): 05148p4 (0.57 #620, 0.36 #533, 0.22 #1825), 0342h (0.57 #2158, 0.57 #2416, 0.56 #4495), 042v_gx (0.50 #179, 0.45 #2160, 0.43 #2505), 0l14qv (0.50 #91, 0.43 #609, 0.36 #522), 01vj9c (0.39 #2164, 0.38 #2422, 0.38 #2509), 026t6 (0.36 #519, 0.33 #2156, 0.33 #1811), 02qjv (0.33 #188, 0.17 #101, 0.14 #619), 0gkd1 (0.33 #165, 0.14 #683, 0.08 #604), 013y1f (0.30 #890, 0.25 #2527, 0.22 #2440), 0cfdd (0.29 #679, 0.20 #75, 0.18 #592) >> Best rule #620 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 082brv; 095x_; >> query: (?x6949, 05148p4) <- location(?x6949, ?x2454), role(?x6949, ?x3991), role(?x6949, ?x1437), ?x3991 = 05842k, ?x1437 = 01vdm0, type_of_union(?x6949, ?x566) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #91 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 04zwjd; *> query: (?x6949, 0l14qv) <- location(?x6949, ?x2454), role(?x6949, ?x922), role(?x6949, ?x74), award(?x6949, ?x2379), role(?x75, ?x922), ?x74 = 03q5t *> conf = 0.50 ranks of expected_values: 4, 9, 17, 73 EVAL 03ryks role 07_l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 173.000 101.000 0.571 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 03ryks role 013y1f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 173.000 101.000 0.571 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 03ryks role 0395lw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 173.000 101.000 0.571 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 03ryks role 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 173.000 101.000 0.571 http://example.org/music/artist/track_contributions./music/track_contribution/role #17175-04xfb PRED entity: 04xfb PRED relation: location PRED expected values: 0hzlz => 163 concepts (141 used for prediction) PRED predicted values (max 10 best out of 300): 02_286 (0.33 #36, 0.31 #102882, 0.27 #103686), 0rh6k (0.33 #4823, 0.21 #11249, 0.20 #8035), 0r0m6 (0.33 #217, 0.10 #8248, 0.09 #9052), 0h1k6 (0.33 #561, 0.10 #8592, 0.09 #9396), 030qb3t (0.25 #102928, 0.22 #103732, 0.18 #102125), 02jx1 (0.20 #2479, 0.17 #4086, 0.14 #5692), 0b_yz (0.20 #3761, 0.14 #6171, 0.10 #7777), 0cr3d (0.20 #1750, 0.09 #8979, 0.08 #18618), 0ljsz (0.20 #2150, 0.09 #9379, 0.07 #13396), 02h6_6p (0.20 #1736, 0.09 #8965, 0.06 #15391) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01vrncs; >> query: (?x8383, 02_286) <- student(?x2142, ?x8383), films(?x8383, ?x697), diet(?x8383, ?x3130), religion(?x8383, ?x8967) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #35382 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 09d5d5; *> query: (?x8383, 0hzlz) <- type_of_union(?x8383, ?x566), gender(?x8383, ?x231), location(?x8383, ?x362), ?x362 = 04jpl *> conf = 0.01 ranks of expected_values: 287 EVAL 04xfb location 0hzlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 163.000 141.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #17174-02s6sh PRED entity: 02s6sh PRED relation: role PRED expected values: 025cbm => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 114): 013y1f (0.55 #311, 0.49 #376, 0.45 #470), 0395lw (0.49 #376, 0.45 #470, 0.42 #188), 0l15bq (0.49 #376, 0.45 #470, 0.42 #188), 03bx0bm (0.49 #376, 0.45 #470, 0.42 #188), 01qbl (0.49 #376, 0.45 #470, 0.42 #188), 018vs (0.42 #1038, 0.33 #197, 0.32 #1787), 0bxl5 (0.27 #813, 0.14 #1092, 0.11 #1841), 0cfdd (0.25 #364, 0.22 #271, 0.14 #1672), 03gvt (0.23 #820, 0.22 #258, 0.15 #351), 07brj (0.22 #208, 0.14 #395, 0.13 #770) >> Best rule #311 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 01vsy7t; 01w3lzq; 01vsyjy; 02mx98; 04s5_s; >> query: (?x10989, 013y1f) <- nationality(?x10989, ?x94), role(?x10989, ?x228), ?x228 = 0l14qv, performance_role(?x10989, ?x1225) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #193 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0137g1; *> query: (?x10989, 025cbm) <- nationality(?x10989, ?x94), role(?x10989, ?x228), role(?x10989, ?x212), ?x228 = 0l14qv, ?x212 = 026t6 *> conf = 0.11 ranks of expected_values: 33 EVAL 02s6sh role 025cbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 109.000 109.000 0.550 http://example.org/music/artist/track_contributions./music/track_contribution/role #17173-03262k PRED entity: 03262k PRED relation: teams! PRED expected values: 06bnz => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 64): 02fvv (0.03 #792, 0.03 #1872, 0.03 #2142), 0m75g (0.02 #3939, 0.01 #429, 0.01 #159), 01jp4s (0.01 #531, 0.01 #261, 0.01 #801), 025r_t (0.01 #488, 0.01 #218, 0.01 #758), 0k33p (0.01 #469, 0.01 #199, 0.01 #739), 0ck6r (0.01 #468, 0.01 #198, 0.01 #738), 0135k2 (0.01 #459, 0.01 #189, 0.01 #729), 0g133 (0.01 #457, 0.01 #187, 0.01 #727), 07f5x (0.01 #451, 0.01 #181, 0.01 #721), 04sqj (0.01 #440, 0.01 #170, 0.01 #710) >> Best rule #792 for best value: >> intensional similarity = 12 >> extensional distance = 68 >> proper extension: 01xn7x1; 05hywl; >> query: (?x13054, 02fvv) <- sport(?x13054, ?x471), position(?x13054, ?x530), position(?x13054, ?x203), position(?x13054, ?x63), position(?x13054, ?x60), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x203 = 0dgrmp, ?x471 = 02vx4, ?x60 = 02nzb8, team(?x203, ?x13054), team(?x530, ?x13054) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03262k teams! 06bnz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 54.000 0.029 http://example.org/sports/sports_team_location/teams #17172-06kknt PRED entity: 06kknt PRED relation: major_field_of_study PRED expected values: 02822 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 119): 062z7 (0.57 #155, 0.35 #1289, 0.35 #2045), 03g3w (0.43 #1288, 0.42 #1792, 0.39 #1540), 02j62 (0.42 #2300, 0.38 #2048, 0.38 #3561), 01mkq (0.38 #1906, 0.38 #2032, 0.35 #2536), 04rjg (0.36 #1785, 0.36 #777, 0.35 #2793), 02lp1 (0.35 #1902, 0.34 #894, 0.31 #2532), 037mh8 (0.31 #1583, 0.29 #827, 0.26 #1205), 0fdys (0.29 #797, 0.28 #1805, 0.28 #923), 01lj9 (0.29 #168, 0.28 #1806, 0.27 #1554), 05qfh (0.29 #164, 0.25 #794, 0.24 #1298) >> Best rule #155 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01vg0s; >> query: (?x12063, 062z7) <- student(?x12063, ?x1365), award_winner(?x1365, ?x538), state_province_region(?x12063, ?x1227), politician(?x1912, ?x1365) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #799 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 09f2j; 01d34b; *> query: (?x12063, 02822) <- student(?x12063, ?x1365), award_winner(?x1365, ?x538), music(?x2116, ?x1365), award_winner(?x1118, ?x1365) *> conf = 0.21 ranks of expected_values: 22 EVAL 06kknt major_field_of_study 02822 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 139.000 139.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17171-02079p PRED entity: 02079p PRED relation: basic_title! PRED expected values: 019fz => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 192): 042fk (0.96 #459, 0.43 #455, 0.33 #72), 0424m (0.96 #459, 0.43 #417, 0.33 #34), 0dq2k (0.96 #459, 0.43 #412, 0.30 #565), 0rlz (0.96 #459, 0.25 #183, 0.20 #259), 0835q (0.96 #459, 0.25 #215, 0.20 #291), 02hy5d (0.96 #459, 0.14 #431, 0.10 #584), 016lh0 (0.96 #459, 0.14 #411, 0.10 #564), 0bymv (0.96 #459, 0.14 #397, 0.10 #550), 021sv1 (0.96 #459, 0.14 #384, 0.10 #537), 024_vw (0.96 #459, 0.14 #444, 0.10 #597) >> Best rule #459 for best value: >> intensional similarity = 23 >> extensional distance = 5 >> proper extension: 01dz7z; >> query: (?x6872, ?x652) <- basic_title(?x9765, ?x6872), basic_title(?x5932, ?x6872), nationality(?x5932, ?x94), politician(?x8714, ?x5932), ?x8714 = 0d075m, location(?x5932, ?x1705), ?x94 = 09c7w0, citytown(?x1768, ?x1705), time_zones(?x1705, ?x2674), featured_film_locations(?x9429, ?x1705), ?x9429 = 032sl_, location_of_ceremony(?x566, ?x1705), ?x566 = 04ztj, profession(?x9765, ?x3342), contains(?x1705, ?x9212), dog_breed(?x1705, ?x1706), place_of_birth(?x8229, ?x1705), ?x1706 = 0km5c, basic_title(?x5932, ?x2358), locations(?x7378, ?x1705), people(?x5741, ?x9765), basic_title(?x652, ?x2358), ?x2674 = 02hcv8 >> conf = 0.96 => this is the best rule for 11 predicted values No rule for expected values ranks of expected_values: EVAL 02079p basic_title! 019fz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 18.000 18.000 0.958 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #17170-0205dx PRED entity: 0205dx PRED relation: gender PRED expected values: 05zppz => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #13, 0.79 #63, 0.78 #71), 02zsn (0.50 #4, 0.44 #30, 0.43 #22) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 01c58j; 05rx__; 010p3; 02_wxh; 0168ql; >> query: (?x4767, 05zppz) <- profession(?x4767, ?x1383), profession(?x4767, ?x1041), ?x1041 = 03gjzk, ?x1383 = 0np9r >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0205dx gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.847 http://example.org/people/person/gender #17169-01797x PRED entity: 01797x PRED relation: award PRED expected values: 01by1l => 176 concepts (129 used for prediction) PRED predicted values (max 10 best out of 315): 01ck6h (0.38 #931, 0.17 #123, 0.15 #2547), 09sb52 (0.37 #23473, 0.34 #29537, 0.33 #28729), 01by1l (0.34 #12637, 0.34 #6173, 0.33 #11425), 01bgqh (0.32 #12567, 0.29 #11355, 0.27 #30348), 054ks3 (0.29 #143, 0.23 #7415, 0.22 #7011), 03qbnj (0.29 #234, 0.18 #12758, 0.17 #6294), 02f6xy (0.26 #1010, 0.17 #2626, 0.16 #7070), 0c4z8 (0.25 #2496, 0.24 #15020, 0.24 #880), 025m8l (0.25 #120, 0.19 #5372, 0.19 #2544), 02x17c2 (0.25 #221, 0.16 #5473, 0.15 #2645) >> Best rule #931 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 01svw8n; 023p29; >> query: (?x10396, 01ck6h) <- artists(?x1000, ?x10396), award_winner(?x9945, ?x10396), profession(?x10396, ?x220), ?x1000 = 0xhtw >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #12637 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 201 *> proper extension: 089tm; 01pfr3; 02r3zy; 07c0j; 01vsxdm; 03g5jw; 0frsw; 03fbc; 016fmf; 01vrwfv; ... *> query: (?x10396, 01by1l) <- artists(?x1572, ?x10396), award_winner(?x9945, ?x10396), artist(?x6474, ?x10396), ?x1572 = 06by7 *> conf = 0.34 ranks of expected_values: 3 EVAL 01797x award 01by1l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 176.000 129.000 0.382 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17168-07nx9j PRED entity: 07nx9j PRED relation: film PRED expected values: 0340hj 046f3p => 66 concepts (36 used for prediction) PRED predicted values (max 10 best out of 238): 063ykwt (0.53 #21422, 0.39 #14281, 0.38 #62489), 02z3r8t (0.25 #108, 0.01 #9033, 0.01 #16174), 043tvp3 (0.25 #1209), 0y_hb (0.25 #1110), 0992d9 (0.25 #988), 09v71cj (0.25 #725), 07x4qr (0.25 #402), 0gmcwlb (0.25 #206), 016z5x (0.25 #70), 017180 (0.20 #2972, 0.06 #57130) >> Best rule #21422 for best value: >> intensional similarity = 3 >> extensional distance = 1458 >> proper extension: 08_83x; >> query: (?x7585, ?x3787) <- profession(?x7585, ?x1032), nominated_for(?x7585, ?x3787), ?x1032 = 02hrh1q >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #57130 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1902 *> proper extension: 049tjg; *> query: (?x7585, ?x385) <- film(?x7585, ?x1753), nominated_for(?x4564, ?x1753), nominated_for(?x4564, ?x385) *> conf = 0.06 ranks of expected_values: 172 EVAL 07nx9j film 046f3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 36.000 0.526 http://example.org/film/actor/film./film/performance/film EVAL 07nx9j film 0340hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 66.000 36.000 0.526 http://example.org/film/actor/film./film/performance/film #17167-01vvyvk PRED entity: 01vvyvk PRED relation: location PRED expected values: 0n6dc => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 203): 02_286 (0.25 #2446, 0.18 #40195, 0.17 #15296), 07h34 (0.25 #195, 0.07 #4210, 0.02 #7422), 06y9v (0.25 #156, 0.02 #8990, 0.01 #7383), 030qb3t (0.24 #13736, 0.23 #14539, 0.22 #15342), 06yxd (0.20 #1049, 0.08 #2655, 0.04 #5064), 01ktz1 (0.20 #925, 0.04 #4137, 0.01 #4940), 0cr3d (0.17 #2554, 0.09 #3357, 0.08 #4963), 013yq (0.09 #1725, 0.06 #10560, 0.05 #5740), 01_d4 (0.09 #1708, 0.04 #4117, 0.03 #9740), 0k049 (0.09 #1614, 0.03 #15267, 0.03 #12055) >> Best rule #2446 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01nfys; >> query: (?x4474, 02_286) <- award_nominee(?x4474, ?x248), participant(?x3893, ?x4474), influenced_by(?x5225, ?x4474) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3813 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 43 *> proper extension: 046p9; *> query: (?x4474, 0n6dc) <- award(?x4474, ?x3488), ?x3488 = 02f71y *> conf = 0.02 ranks of expected_values: 62 EVAL 01vvyvk location 0n6dc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 105.000 105.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #17166-01pllx PRED entity: 01pllx PRED relation: film PRED expected values: 07h9gp => 120 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1093): 01vnbh (0.69 #25034, 0.63 #64375, 0.38 #137689), 01z452 (0.69 #25034, 0.63 #64375, 0.38 #137689), 0ptdz (0.17 #3544, 0.12 #1756, 0.02 #46460), 09lxv9 (0.17 #3292, 0.03 #47997, 0.02 #10444), 01f69m (0.17 #3523, 0.01 #37499), 0sxfd (0.17 #1999), 07xtqq (0.17 #1844), 03s6l2 (0.15 #3658, 0.14 #5446, 0.08 #1870), 0fvr1 (0.15 #3925, 0.14 #5713, 0.03 #16442), 01dc0c (0.12 #1451, 0.08 #3239, 0.05 #10391) >> Best rule #25034 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 01w7nww; >> query: (?x8927, ?x5236) <- award_nominee(?x8927, ?x5133), nominated_for(?x8927, ?x5236), celebrity(?x5197, ?x8927) >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #7417 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0p_pd; 02zyy4; 0137hn; 018phr; 01mskc3; *> query: (?x8927, 07h9gp) <- location(?x8927, ?x335), sibling(?x8927, ?x5834), award_nominee(?x91, ?x8927) *> conf = 0.03 ranks of expected_values: 378 EVAL 01pllx film 07h9gp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 120.000 111.000 0.692 http://example.org/film/actor/film./film/performance/film #17165-07c52 PRED entity: 07c52 PRED relation: film_release_distribution_medium! PRED expected values: 03g90h 0168ls 0ckrgs 03nqnnk 03lfd_ 0jqzt => 71 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1436): 0dgrwqr (0.50 #9656, 0.50 #6786, 0.43 #13964), 02wtp6 (0.50 #7125, 0.33 #9995, 0.33 #1381), 023vcd (0.50 #7055, 0.33 #9925, 0.33 #1311), 024lt6 (0.50 #7033, 0.33 #9903, 0.33 #1289), 0fpgp26 (0.50 #6970, 0.33 #9840, 0.33 #1226), 03lfd_ (0.50 #6943, 0.33 #9813, 0.33 #1199), 0bs8ndx (0.50 #6875, 0.33 #9745, 0.33 #1131), 03np63f (0.50 #6848, 0.33 #9718, 0.33 #1104), 0gvvf4j (0.50 #6806, 0.33 #9676, 0.33 #1062), 0233bn (0.50 #6788, 0.33 #9658, 0.33 #1044) >> Best rule #9656 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02nxhr; 0735l; >> query: (?x2008, 0dgrwqr) <- film_release_distribution_medium(?x8657, ?x2008), film_release_distribution_medium(?x785, ?x2008), nominated_for(?x2382, ?x8657), film_release_region(?x785, ?x87) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6943 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 07z4p; *> query: (?x2008, 03lfd_) <- film_release_distribution_medium(?x10346, ?x2008), film_release_distribution_medium(?x8657, ?x2008), film_release_distribution_medium(?x1108, ?x2008), ?x8657 = 030z4z, ?x10346 = 0dw4b0, produced_by(?x1108, ?x3873) *> conf = 0.50 ranks of expected_values: 6, 133, 655, 1238, 1400 EVAL 07c52 film_release_distribution_medium! 0jqzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 71.000 62.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 07c52 film_release_distribution_medium! 03lfd_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 71.000 62.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 07c52 film_release_distribution_medium! 03nqnnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 62.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 07c52 film_release_distribution_medium! 0ckrgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 62.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 07c52 film_release_distribution_medium! 0168ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 71.000 62.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 07c52 film_release_distribution_medium! 03g90h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 71.000 62.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17164-0b2v79 PRED entity: 0b2v79 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #85, 0.84 #54, 0.84 #6), 07z4p (0.10 #10, 0.03 #84, 0.03 #105), 07c52 (0.06 #3, 0.04 #287, 0.04 #251), 02nxhr (0.05 #44, 0.05 #91, 0.04 #65) >> Best rule #85 for best value: >> intensional similarity = 3 >> extensional distance = 411 >> proper extension: 058kh7; >> query: (?x195, 029j_) <- featured_film_locations(?x195, ?x1860), music(?x195, ?x9163), genre(?x195, ?x53) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b2v79 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.860 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17163-051q5 PRED entity: 051q5 PRED relation: colors PRED expected values: 083jv => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.62 #871, 0.61 #965, 0.61 #983), 019sc (0.40 #99, 0.39 #315, 0.37 #171), 04mkbj (0.40 #37, 0.33 #38, 0.33 #28), 06fvc (0.35 #966, 0.35 #274, 0.33 #984), 01g5v (0.35 #275, 0.29 #985, 0.29 #967), 03vtbc (0.33 #100, 0.26 #172, 0.21 #154), 0jc_p (0.33 #4, 0.15 #833, 0.14 #889), 02rnmb (0.20 #718, 0.19 #699, 0.17 #827), 03wkwg (0.17 #70, 0.15 #88, 0.15 #833), 036k5h (0.17 #23, 0.15 #833, 0.14 #889) >> Best rule #871 for best value: >> intensional similarity = 5 >> extensional distance = 139 >> proper extension: 04088s0; 026xxv_; >> query: (?x4222, 083jv) <- sport(?x4222, ?x1083), colors(?x4222, ?x332), team(?x7064, ?x4222), sport(?x6696, ?x1083), team(?x180, ?x6696) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051q5 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.624 http://example.org/sports/sports_team/colors #17162-06g4l PRED entity: 06g4l PRED relation: award PRED expected values: 0gr51 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 233): 0ck27z (0.25 #1301, 0.25 #1705, 0.23 #2511), 09sb52 (0.23 #8102, 0.23 #11733, 0.22 #10927), 0cqhk0 (0.16 #1246, 0.16 #1650, 0.12 #2456), 0cjyzs (0.16 #8868, 0.15 #1613, 0.14 #10886), 09qv3c (0.16 #8868, 0.15 #1613, 0.14 #10886), 09qs08 (0.16 #8868, 0.15 #1613, 0.14 #10886), 09qrn4 (0.16 #8868, 0.15 #1613, 0.14 #10886), 027gs1_ (0.16 #8868, 0.15 #1613, 0.14 #10886), 0gqwc (0.15 #3702, 0.12 #4105, 0.07 #5314), 03ccq3s (0.15 #1613, 0.14 #10886, 0.12 #18549) >> Best rule #1301 for best value: >> intensional similarity = 3 >> extensional distance = 498 >> proper extension: 06jntd; >> query: (?x10466, 0ck27z) <- award_winner(?x8132, ?x10466), actor(?x8132, ?x71), nominated_for(?x757, ?x8132) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3325 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 679 *> proper extension: 0c8hct; *> query: (?x10466, 0gr51) <- profession(?x10466, ?x987), type_of_union(?x10466, ?x566), ?x987 = 0dxtg *> conf = 0.10 ranks of expected_values: 20 EVAL 06g4l award 0gr51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 69.000 69.000 0.254 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17161-0fms83 PRED entity: 0fms83 PRED relation: nominated_for PRED expected values: 091z_p 07l50vn => 53 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1762): 0g5838s (0.86 #1595, 0.80 #4786, 0.78 #7976), 04nnpw (0.86 #1595, 0.80 #4786, 0.78 #7976), 01mgw (0.57 #9132, 0.50 #7536, 0.33 #12324), 02vr3gz (0.50 #3753, 0.50 #562, 0.25 #2158), 091z_p (0.50 #3439, 0.40 #5035, 0.26 #46283), 0gmcwlb (0.33 #6564, 0.29 #8160, 0.27 #40083), 0f4_l (0.33 #6700, 0.29 #8296, 0.20 #40219), 07w8fz (0.33 #6841, 0.29 #8437, 0.20 #40360), 04b2qn (0.33 #7584, 0.29 #9180, 0.20 #41103), 0209xj (0.33 #6474, 0.29 #8070, 0.19 #39993) >> Best rule #1595 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 05zvq6g; >> query: (?x11083, ?x534) <- award(?x8496, ?x11083), award(?x4696, ?x11083), award(?x534, ?x11083), ?x8496 = 0cvkv5, nominated_for(?x11083, ?x2501), executive_produced_by(?x4696, ?x6187), nominated_for(?x2275, ?x4696), nominated_for(?x68, ?x4696) >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #3439 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0fm3b5; *> query: (?x11083, 091z_p) <- award(?x4696, ?x11083), disciplines_or_subjects(?x11083, ?x524), disciplines_or_subjects(?x11083, ?x373), ?x4696 = 04nnpw, disciplines_or_subjects(?x7215, ?x524), ?x7215 = 09v92_x, ?x373 = 02vxn *> conf = 0.50 ranks of expected_values: 5, 27 EVAL 0fms83 nominated_for 07l50vn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 53.000 30.000 0.857 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fms83 nominated_for 091z_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 30.000 0.857 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17160-0g33q PRED entity: 0g33q PRED relation: role! PRED expected values: 0dwt5 => 60 concepts (49 used for prediction) PRED predicted values (max 10 best out of 106): 0l14md (0.88 #1632, 0.85 #2182, 0.85 #2498), 05r5c (0.88 #3925, 0.86 #1310, 0.85 #3271), 05148p4 (0.87 #2315, 0.84 #3299, 0.82 #2970), 0l14j_ (0.86 #1310, 0.85 #2065, 0.85 #2498), 02k84w (0.85 #2498, 0.84 #1953, 0.84 #1417), 03q5t (0.85 #2498, 0.84 #1953, 0.84 #1417), 0l14qv (0.80 #3393, 0.80 #2512, 0.78 #1323), 02sgy (0.80 #2192, 0.78 #1324, 0.77 #2081), 02hnl (0.78 #1249, 0.76 #3317, 0.75 #4472), 02k856 (0.78 #1160, 0.73 #210, 0.70 #208) >> Best rule #1632 for best value: >> intensional similarity = 30 >> extensional distance = 8 >> proper extension: 0bxl5; >> query: (?x4429, ?x1466) <- role(?x4429, ?x3296), role(?x4429, ?x2944), role(?x4429, ?x2785), role(?x4429, ?x2048), role(?x4429, ?x1466), role(?x4429, ?x316), ?x2785 = 0jtg0, role(?x3703, ?x2944), role(?x2206, ?x2944), ?x3703 = 02dlh2, role(?x1524, ?x2944), instrumentalists(?x2944, ?x7972), instrumentalists(?x2944, ?x1997), performance_role(?x1267, ?x2944), ?x2048 = 018j2, ?x1997 = 01wsl7c, group(?x3296, ?x3109), group(?x2944, ?x3516), role(?x885, ?x3296), ?x2206 = 07gql, role(?x2944, ?x212), ?x316 = 05r5c, group(?x1466, ?x9589), group(?x1466, ?x442), ?x9589 = 02cw1m, performance_role(?x248, ?x1466), ?x442 = 01t_xp_, role(?x115, ?x1466), ?x3516 = 05563d, ?x7972 = 0326tc >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #210 for first EXPECTED value: *> intensional similarity = 36 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x4429, ?x645) <- role(?x4429, ?x4913), role(?x4429, ?x4311), role(?x4429, ?x2956), role(?x4429, ?x2944), role(?x4429, ?x2798), role(?x4429, ?x2785), role(?x4429, ?x2048), role(?x4429, ?x1969), role(?x4429, ?x1495), role(?x4429, ?x316), role(?x4429, ?x315), role(?x4429, ?x75), ?x2785 = 0jtg0, ?x2944 = 0l14j_, ?x2048 = 018j2, ?x4311 = 01xqw, role(?x6039, ?x4429), role(?x5480, ?x4913), role(?x1166, ?x4913), role(?x212, ?x4913), ?x1495 = 013y1f, ?x1969 = 04rzd, ?x316 = 05r5c, group(?x4429, ?x5838), ?x1166 = 05148p4, role(?x2460, ?x4913), role(?x645, ?x4913), ?x75 = 07y_7, ?x2798 = 03qjg, ?x5480 = 01w4c9, ?x315 = 0l14md, ?x5838 = 02dw1_, role(?x4913, ?x1655), ?x212 = 026t6, ?x2460 = 01wy6, ?x2956 = 0myk8 *> conf = 0.73 ranks of expected_values: 20 EVAL 0g33q role! 0dwt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 60.000 49.000 0.885 http://example.org/music/performance_role/regular_performances./music/group_membership/role #17159-023p33 PRED entity: 023p33 PRED relation: language PRED expected values: 02h40lc => 96 concepts (94 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.90 #2374, 0.90 #1424, 0.89 #2911), 0653m (0.25 #12, 0.20 #130, 0.06 #1018), 03_9r (0.18 #482, 0.14 #5312, 0.12 #187), 064_8sq (0.16 #435, 0.15 #1444, 0.14 #258), 04306rv (0.14 #418, 0.10 #952, 0.10 #1249), 03x42 (0.14 #5312, 0.01 #463, 0.01 #938), 06nm1 (0.12 #365, 0.11 #1255, 0.11 #1670), 071fb (0.12 #195, 0.06 #372, 0.03 #313), 06b_j (0.08 #911, 0.06 #377, 0.06 #793), 02bjrlw (0.07 #1423, 0.06 #2373, 0.06 #2074) >> Best rule #2374 for best value: >> intensional similarity = 4 >> extensional distance = 551 >> proper extension: 0436yk; >> query: (?x2097, 02h40lc) <- film(?x12005, ?x2097), production_companies(?x2097, ?x3920), child(?x3920, ?x166), organization(?x4682, ?x3920) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023p33 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 94.000 0.899 http://example.org/film/film/language #17158-01b8w_ PRED entity: 01b8w_ PRED relation: place_of_birth! PRED expected values: 0c9c0 05c4fys => 116 concepts (55 used for prediction) PRED predicted values (max 10 best out of 2316): 09y20 (0.29 #88637, 0.28 #59959, 0.28 #10427), 01qn8k (0.29 #88637, 0.28 #59959, 0.28 #10427), 04rsd2 (0.25 #464, 0.05 #5677, 0.04 #2607), 01qrbf (0.25 #1487, 0.05 #6700, 0.04 #2607), 0ksrf8 (0.25 #1142, 0.05 #6355, 0.04 #114711), 0kbg6 (0.25 #2579, 0.05 #7792, 0.04 #114711), 026sb55 (0.25 #2556, 0.05 #7769, 0.04 #114711), 06lhbl (0.25 #2529, 0.05 #7742, 0.04 #114711), 02vkvcz (0.25 #2445, 0.05 #7658, 0.04 #114711), 07zhd7 (0.25 #2417, 0.05 #7630, 0.04 #114711) >> Best rule #88637 for best value: >> intensional similarity = 4 >> extensional distance = 114 >> proper extension: 0h3lt; 0r2gj; 0rxyk; 09f07; >> query: (?x9042, ?x1549) <- featured_film_locations(?x1015, ?x9042), place_of_birth(?x10243, ?x9042), profession(?x10243, ?x220), location(?x1549, ?x9042) >> conf = 0.29 => this is the best rule for 2 predicted values *> Best rule #122532 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 174 *> proper extension: 01cgxp; 03qhnx; *> query: (?x9042, ?x2790) <- featured_film_locations(?x1015, ?x9042), featured_film_locations(?x1015, ?x9699), film_release_distribution_medium(?x1015, ?x81), location(?x2790, ?x9699) *> conf = 0.02 ranks of expected_values: 1875, 2005 EVAL 01b8w_ place_of_birth! 05c4fys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 55.000 0.290 http://example.org/people/person/place_of_birth EVAL 01b8w_ place_of_birth! 0c9c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 55.000 0.290 http://example.org/people/person/place_of_birth #17157-0gyfp9c PRED entity: 0gyfp9c PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 32): 0ch6mp2 (0.79 #531, 0.77 #643, 0.76 #82), 02r96rf (0.71 #638, 0.71 #526, 0.69 #600), 09vw2b7 (0.66 #1204, 0.65 #530, 0.65 #642), 0dxtw (0.40 #1209, 0.37 #647, 0.37 #1135), 01vx2h (0.34 #648, 0.31 #536, 0.31 #610), 0215hd (0.25 #523, 0.20 #244, 0.19 #20), 02ynfr (0.25 #523, 0.18 #1214, 0.17 #652), 01xy5l_ (0.25 #523, 0.17 #15, 0.16 #89), 089g0h (0.25 #523, 0.17 #21, 0.12 #245), 02_n3z (0.25 #523, 0.14 #75, 0.11 #225) >> Best rule #531 for best value: >> intensional similarity = 4 >> extensional distance = 244 >> proper extension: 01gglm; >> query: (?x3226, 0ch6mp2) <- film(?x818, ?x3226), film_crew_role(?x3226, ?x137), film_format(?x3226, ?x6392), nominated_for(?x495, ?x3226) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gyfp9c film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.793 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17156-04bgy PRED entity: 04bgy PRED relation: profession PRED expected values: 02hrh1q => 130 concepts (94 used for prediction) PRED predicted values (max 10 best out of 84): 02hrh1q (0.82 #4487, 0.79 #2324, 0.74 #3190), 01d_h8 (0.78 #5202, 0.76 #5781, 0.75 #4046), 016z4k (0.56 #2457, 0.51 #3035, 0.49 #2890), 0dxtg (0.49 #4054, 0.47 #5210, 0.45 #5789), 03gjzk (0.48 #4056, 0.46 #5212, 0.44 #5791), 039v1 (0.47 #1909, 0.46 #898, 0.44 #3498), 02jknp (0.37 #5204, 0.36 #5783, 0.36 #4048), 0n1h (0.30 #2031, 0.28 #2321, 0.24 #1453), 0fnpj (0.29 #921, 0.21 #2799, 0.21 #4818), 04f2zj (0.25 #813, 0.20 #92, 0.16 #1968) >> Best rule #4487 for best value: >> intensional similarity = 5 >> extensional distance = 172 >> proper extension: 0jf1b; 016khd; 0gyx4; 06j8wx; 0m66w; 04r7p; 048hf; 01m42d0; 023nlj; 0cbkc; ... >> query: (?x6469, 02hrh1q) <- type_of_union(?x6469, ?x1873), type_of_union(?x6469, ?x566), ?x566 = 04ztj, ?x1873 = 01g63y, profession(?x6469, ?x131) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bgy profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 94.000 0.822 http://example.org/people/person/profession #17155-09kr66 PRED entity: 09kr66 PRED relation: people PRED expected values: 0j582 016z2j 027l0b 0kjgl => 31 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1780): 016z2j (0.33 #299, 0.23 #3724, 0.17 #2011), 01vrt_c (0.33 #150, 0.23 #3575, 0.17 #1862), 01twdk (0.33 #667, 0.17 #2379, 0.16 #16084), 01gbn6 (0.33 #1333, 0.17 #3045, 0.15 #4758), 0c0k1 (0.33 #1222, 0.17 #2934, 0.15 #4647), 06b_0 (0.33 #1060, 0.17 #2772, 0.11 #9623), 014x77 (0.33 #69, 0.17 #1781, 0.11 #6919), 0sz28 (0.33 #151, 0.17 #1863, 0.10 #1712), 04zn7g (0.33 #1685, 0.17 #3397, 0.08 #5110), 04gc65 (0.33 #1635, 0.17 #3347, 0.08 #5060) >> Best rule #299 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 041rx; >> query: (?x9943, 016z2j) <- people(?x9943, ?x9738), people(?x9943, ?x4956), people(?x9943, ?x123), ?x4956 = 023v4_, ?x9738 = 03rx9, award_winner(?x2183, ?x123), film(?x123, ?x1219), award_nominee(?x123, ?x1208) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 46, 91 EVAL 09kr66 people 0kjgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 31.000 25.000 0.333 http://example.org/people/ethnicity/people EVAL 09kr66 people 027l0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 31.000 25.000 0.333 http://example.org/people/ethnicity/people EVAL 09kr66 people 016z2j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 25.000 0.333 http://example.org/people/ethnicity/people EVAL 09kr66 people 0j582 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 25.000 0.333 http://example.org/people/ethnicity/people #17154-091n7z PRED entity: 091n7z PRED relation: location PRED expected values: 04tgp => 154 concepts (96 used for prediction) PRED predicted values (max 10 best out of 200): 0wq36 (0.55 #50692, 0.53 #41837, 0.51 #43446), 07b_l (0.50 #1796, 0.17 #5819, 0.14 #7427), 02j3w (0.25 #1034, 0.14 #2642, 0.07 #7469), 05tbn (0.25 #993, 0.12 #10646, 0.10 #3405), 04jpl (0.20 #3234, 0.12 #8866, 0.08 #5649), 059rby (0.20 #3233, 0.12 #8865, 0.08 #22545), 030qb3t (0.18 #10541, 0.17 #11345, 0.15 #13759), 02_286 (0.17 #41069, 0.15 #29805, 0.13 #40264), 04tgp (0.14 #2653, 0.04 #35402, 0.04 #13676), 01_d4 (0.14 #2515, 0.04 #24239, 0.04 #25043) >> Best rule #50692 for best value: >> intensional similarity = 5 >> extensional distance = 525 >> proper extension: 0399p; 07h1q; 02784z; 047g6; 015n8; 01h2_6; >> query: (?x12811, ?x13556) <- religion(?x12811, ?x109), place_of_birth(?x12811, ?x13556), location(?x8749, ?x13556), religion(?x3175, ?x109), artists(?x2937, ?x3175) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2653 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 02k4b2; *> query: (?x12811, 04tgp) <- religion(?x12811, ?x109), profession(?x12811, ?x1383), ?x109 = 01lp8, ?x1383 = 0np9r, gender(?x12811, ?x514) *> conf = 0.14 ranks of expected_values: 9 EVAL 091n7z location 04tgp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 154.000 96.000 0.546 http://example.org/people/person/places_lived./people/place_lived/location #17153-01k1k4 PRED entity: 01k1k4 PRED relation: film! PRED expected values: 01k53x => 57 concepts (46 used for prediction) PRED predicted values (max 10 best out of 790): 01vs_v8 (0.60 #66395, 0.58 #68471, 0.52 #16601), 0p__8 (0.60 #66395, 0.58 #68471, 0.52 #16601), 01r4hry (0.41 #18677, 0.41 #62245, 0.40 #20753), 03j1p2n (0.41 #18677, 0.41 #62245, 0.40 #20753), 0pz04 (0.25 #1419, 0.03 #72623, 0.03 #45649), 02ch1w (0.25 #1032, 0.02 #5180, 0.01 #9330), 02l3_5 (0.25 #1406, 0.01 #34608, 0.01 #5554), 0p_r5 (0.25 #2015, 0.01 #10313), 052hl (0.25 #1185, 0.01 #5333), 06nd8c (0.25 #2005) >> Best rule #66395 for best value: >> intensional similarity = 3 >> extensional distance = 1240 >> proper extension: 03cf9ly; >> query: (?x408, ?x2237) <- nominated_for(?x2237, ?x408), film(?x2237, ?x2160), award_winner(?x154, ?x2237) >> conf = 0.60 => this is the best rule for 2 predicted values *> Best rule #7854 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 134 *> proper extension: 0522wp; *> query: (?x408, 01k53x) <- region(?x408, ?x512), ?x512 = 07ssc *> conf = 0.03 ranks of expected_values: 177 EVAL 01k1k4 film! 01k53x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 57.000 46.000 0.596 http://example.org/film/actor/film./film/performance/film #17152-01x6v6 PRED entity: 01x6v6 PRED relation: role PRED expected values: 0680x0 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 73): 05r5c (0.33 #115, 0.30 #221, 0.29 #327), 01vdm0 (0.25 #140, 0.13 #352, 0.13 #246), 0342h (0.21 #323, 0.21 #1596, 0.20 #429), 02sgy (0.17 #431, 0.16 #325, 0.12 #1598), 01vj9c (0.17 #123, 0.08 #335, 0.07 #441), 05148p4 (0.17 #131, 0.05 #979, 0.05 #2042), 0214km (0.17 #208, 0.05 #420, 0.05 #526), 0319l (0.17 #142, 0.03 #354, 0.02 #460), 0dwvl (0.17 #19, 0.02 #1398, 0.02 #973), 018vs (0.15 #439, 0.13 #333, 0.07 #969) >> Best rule #115 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 032nwy; 0fpjd_g; 02j3d4; 0fhxv; 02qmncd; >> query: (?x6783, 05r5c) <- award_winner(?x6783, ?x4646), award(?x6783, ?x5224), ?x5224 = 025mbn >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #289 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0b82vw; 03n0q5; 04pf4r; 012wg; 02w670; 02sjp; 01m5m5b; *> query: (?x6783, 0680x0) <- music(?x385, ?x6783), origin(?x6783, ?x1523), nominated_for(?x6783, ?x6199) *> conf = 0.04 ranks of expected_values: 41 EVAL 01x6v6 role 0680x0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 113.000 113.000 0.333 http://example.org/music/artist/track_contributions./music/track_contribution/role #17151-05zvj3m PRED entity: 05zvj3m PRED relation: nominated_for PRED expected values: 034qrh 02v63m 034qzw 065zlr 0fz3b1 07pd_j 0372j5 026wlxw => 42 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1805): 06fpsx (0.72 #13931, 0.68 #21679, 0.65 #18581), 01mgw (0.60 #5761, 0.33 #1120, 0.26 #8858), 0btpm6 (0.60 #5752, 0.33 #1111, 0.17 #10398), 09q5w2 (0.50 #6333, 0.40 #4786, 0.33 #145), 0h95927 (0.50 #7317, 0.40 #5770, 0.25 #4223), 07s846j (0.50 #6779, 0.40 #5232, 0.21 #8329), 0gmgwnv (0.50 #7126, 0.26 #8676, 0.24 #10225), 05hjnw (0.50 #6935, 0.25 #3841, 0.22 #8485), 019vhk (0.50 #6592, 0.25 #3498, 0.18 #8142), 02c638 (0.50 #6486, 0.20 #4939, 0.19 #8036) >> Best rule #13931 for best value: >> intensional similarity = 4 >> extensional distance = 155 >> proper extension: 06196; >> query: (?x1691, ?x86) <- award(?x86, ?x1691), award(?x3927, ?x1691), award_winner(?x1691, ?x237), languages(?x3927, ?x254) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #4120 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 02p_04b; *> query: (?x1691, 0372j5) <- nominated_for(?x1691, ?x86), award(?x1460, ?x1691), ?x1460 = 02g87m *> conf = 0.25 ranks of expected_values: 148, 298, 300, 392, 455, 457, 672, 788 EVAL 05zvj3m nominated_for 026wlxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 16.000 0.716 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zvj3m nominated_for 0372j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 42.000 16.000 0.716 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zvj3m nominated_for 07pd_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 16.000 0.716 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zvj3m nominated_for 0fz3b1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 16.000 0.716 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zvj3m nominated_for 065zlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 16.000 0.716 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zvj3m nominated_for 034qzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 16.000 0.716 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zvj3m nominated_for 02v63m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 16.000 0.716 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zvj3m nominated_for 034qrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 16.000 0.716 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17150-02cq61 PRED entity: 02cq61 PRED relation: institution PRED expected values: 08815 07tg4 02mj7c 013719 04_j5s 04gxp2 => 23 concepts (22 used for prediction) PRED predicted values (max 10 best out of 929): 01w5m (0.86 #6935, 0.84 #9417, 0.80 #11279), 09f2j (0.86 #6998, 0.81 #8861, 0.80 #7618), 065y4w7 (0.80 #7450, 0.79 #6830, 0.75 #8693), 07wjk (0.79 #6884, 0.75 #8747, 0.73 #7504), 08815 (0.75 #8056, 0.74 #9298, 0.73 #7436), 01w3v (0.75 #8071, 0.73 #7451, 0.67 #5592), 02zd460 (0.74 #10116, 0.73 #7632, 0.71 #6392), 017j69 (0.73 #7600, 0.71 #6980, 0.71 #6360), 0gl5_ (0.73 #7714, 0.71 #7094, 0.71 #6474), 01jt2w (0.73 #7752, 0.71 #6512, 0.69 #8372) >> Best rule #6935 for best value: >> intensional similarity = 27 >> extensional distance = 12 >> proper extension: 07s6fsf; 028dcg; >> query: (?x7817, 01w5m) <- institution(?x7817, ?x13913), institution(?x7817, ?x3913), institution(?x7817, ?x3439), institution(?x7817, ?x2313), institution(?x7817, ?x1681), ?x1681 = 07szy, colors(?x3913, ?x7179), ?x3439 = 03ksy, major_field_of_study(?x2313, ?x8925), major_field_of_study(?x2313, ?x2606), ?x2606 = 062z7, list(?x2313, ?x2197), citytown(?x13913, ?x1860), ?x8925 = 01zc2w, student(?x2313, ?x8508), student(?x2313, ?x5559), company(?x4309, ?x2313), contains(?x94, ?x13913), service_language(?x2313, ?x254), religion(?x8508, ?x2694), ?x254 = 02h40lc, influenced_by(?x8508, ?x3542), award_nominee(?x8508, ?x2182), student(?x11614, ?x4309), colors(?x2313, ?x663), category(?x3913, ?x134), place_of_birth(?x5559, ?x2017) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #8056 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 14 *> proper extension: 02m4yg; *> query: (?x7817, 08815) <- institution(?x7817, ?x13913), institution(?x7817, ?x12293), institution(?x7817, ?x7545), institution(?x7817, ?x3913), institution(?x7817, ?x1681), contains(?x13174, ?x12293), school(?x465, ?x1681), currency(?x12293, ?x7888), school_type(?x13913, ?x3205), organization(?x346, ?x1681), major_field_of_study(?x1681, ?x10391), student(?x1681, ?x1580), contains(?x94, ?x13913), school(?x260, ?x1681), ?x94 = 09c7w0, major_field_of_study(?x4410, ?x10391), major_field_of_study(?x581, ?x10391), child(?x3913, ?x13029), location(?x2727, ?x13174), ?x581 = 06pwq, student(?x7545, ?x4744), student(?x7545, ?x881), category(?x13913, ?x134), ?x881 = 02lk1s, ?x4410 = 017j69, film(?x4744, ?x4745) *> conf = 0.75 ranks of expected_values: 5, 14, 155, 341, 350, 402 EVAL 02cq61 institution 04gxp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 23.000 22.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02cq61 institution 04_j5s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 23.000 22.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02cq61 institution 013719 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 23.000 22.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02cq61 institution 02mj7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 23.000 22.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02cq61 institution 07tg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 23.000 22.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02cq61 institution 08815 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 23.000 22.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #17149-0cy41 PRED entity: 0cy41 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.75 #17, 0.70 #45, 0.70 #41), 01bl8s (0.12 #19, 0.03 #107, 0.02 #147), 0jgjn (0.03 #148, 0.02 #156, 0.02 #164), 01g63y (0.01 #203, 0.01 #134) >> Best rule #17 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0hsqf; >> query: (?x11430, 04ztj) <- citytown(?x9078, ?x11430), administrative_parent(?x11430, ?x8264), category(?x11430, ?x134), contains(?x8264, ?x8977), combatants(?x8264, ?x6371) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cy41 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.750 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #17148-0h1x5f PRED entity: 0h1x5f PRED relation: featured_film_locations PRED expected values: 0qpjt 0fsv2 => 63 concepts (47 used for prediction) PRED predicted values (max 10 best out of 33): 02_286 (0.24 #740, 0.17 #500, 0.16 #981), 030qb3t (0.12 #39, 0.11 #519, 0.07 #1000), 0d6lp (0.09 #792, 0.02 #1994, 0.01 #4412), 04jpl (0.06 #249, 0.06 #9, 0.06 #729), 01cx_ (0.06 #311, 0.06 #71, 0.06 #551), 0cr3d (0.06 #786, 0.03 #6264), 01zv_ (0.06 #710), 0h7h6 (0.04 #1004, 0.03 #763, 0.03 #2690), 0dclg (0.04 #1014), 052p7 (0.03 #778, 0.02 #2705, 0.02 #5360) >> Best rule #740 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0g5q34q; 0d8w2n; >> query: (?x9701, 02_286) <- genre(?x9701, ?x714), films(?x14679, ?x9701), ?x714 = 0hn10 >> conf = 0.24 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0h1x5f featured_film_locations 0fsv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 47.000 0.242 http://example.org/film/film/featured_film_locations EVAL 0h1x5f featured_film_locations 0qpjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 47.000 0.242 http://example.org/film/film/featured_film_locations #17147-0l6mp PRED entity: 0l6mp PRED relation: locations PRED expected values: 0hsqf => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 35): 02h6_6p (0.33 #60, 0.25 #1582, 0.20 #1963), 0k3p (0.33 #324, 0.25 #1272, 0.06 #2288), 03khn (0.33 #737, 0.20 #1877, 0.17 #2649), 07dfk (0.33 #909, 0.20 #1862, 0.17 #2634), 01f62 (0.25 #1367, 0.25 #986, 0.20 #1940), 030qb3t (0.25 #1556, 0.20 #1937, 0.11 #3852), 06y57 (0.25 #1044, 0.17 #2768, 0.12 #2958), 013yq (0.25 #1383, 0.17 #2726, 0.12 #2916), 05qtj (0.25 #2953, 0.08 #4294, 0.06 #2288), 06mxs (0.25 #1242, 0.05 #3917, 0.04 #4204) >> Best rule #60 for best value: >> intensional similarity = 62 >> extensional distance = 1 >> proper extension: 0l98s; >> query: (?x2233, 02h6_6p) <- olympics(?x11872, ?x2233), olympics(?x5114, ?x2233), olympics(?x2236, ?x2233), olympics(?x1355, ?x2233), olympics(?x410, ?x2233), olympics(?x205, ?x2233), sports(?x2233, ?x5182), sports(?x2233, ?x471), ?x5182 = 0crlz, ?x410 = 01ls2, film_release_region(?x2350, ?x2236), film_release_region(?x633, ?x2236), film_release_region(?x504, ?x2236), ?x471 = 02vx4, country(?x2364, ?x2236), ?x2350 = 0661m4p, organization(?x2236, ?x127), ?x11872 = 03f2w, ?x633 = 0c40vxk, ?x5114 = 05vz3zq, adjoins(?x2236, ?x2146), film_release_region(?x9501, ?x205), film_release_region(?x9322, ?x205), film_release_region(?x8646, ?x205), film_release_region(?x5425, ?x205), film_release_region(?x5162, ?x205), film_release_region(?x3745, ?x205), film_release_region(?x3482, ?x205), film_release_region(?x3088, ?x205), film_release_region(?x2685, ?x205), film_release_region(?x2501, ?x205), film_release_region(?x2094, ?x205), film_release_region(?x1202, ?x205), ?x5425 = 02prwdh, ?x3088 = 06w839_, ?x9501 = 0g5qmbz, ?x3745 = 03cw411, member_states(?x7416, ?x205), olympics(?x2236, ?x784), contains(?x2236, ?x4344), country(?x5481, ?x205), ?x3482 = 017z49, ?x2094 = 05z7c, ?x504 = 0g5qs2k, nationality(?x101, ?x205), contains(?x205, ?x1356), ?x2685 = 0g5879y, location_of_ceremony(?x2182, ?x205), film_release_region(?x1425, ?x205), ?x1202 = 0gj8t_b, combatants(?x205, ?x94), country(?x1037, ?x205), ?x5162 = 0j3d9tn, ?x1355 = 0h7x, ?x9322 = 0gwgn1k, ?x8646 = 05zvzf3, location(?x4587, ?x205), country(?x2882, ?x205), countries_spoken_in(?x90, ?x205), ?x2501 = 040rmy, ?x2882 = 03rz2b, ?x1037 = 09_bl >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0l6mp locations 0hsqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 30.000 0.333 http://example.org/time/event/locations #17146-01ym8l PRED entity: 01ym8l PRED relation: company! PRED expected values: 05_wyz => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 39): 0dq_5 (0.83 #836, 0.81 #519, 0.81 #926), 0dq3c (0.66 #822, 0.52 #505, 0.48 #912), 01yc02 (0.54 #735, 0.50 #144, 0.50 #52), 05_wyz (0.46 #2117, 0.45 #927, 0.45 #1889), 09d6p2 (0.44 #3284, 0.42 #701, 0.40 #18), 02211by (0.44 #3284, 0.38 #140, 0.25 #232), 04192r (0.44 #3284, 0.33 #269, 0.33 #223), 0142rn (0.44 #3284, 0.33 #116, 0.25 #162), 01rk91 (0.44 #3284, 0.16 #6329, 0.13 #5876), 01kr6k (0.29 #936, 0.29 #529, 0.28 #1989) >> Best rule #836 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 01xdn1; 0168nq; 0jvs0; 035nm; 04fv0k; 01yx7f; 01_lh1; 0841v; >> query: (?x7151, 0dq_5) <- company(?x346, ?x7151), industry(?x7151, ?x6575), currency(?x7151, ?x170), service_language(?x7151, ?x254), jurisdiction_of_office(?x346, ?x47) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #2117 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 0c_j5d; 05qd_; 04qhdf; 03mnk; 02bh8z; 09b3v; 01jx9; 01yfp7; 056ws9; 019rl6; ... *> query: (?x7151, 05_wyz) <- company(?x346, ?x7151), industry(?x7151, ?x6575), currency(?x7151, ?x170), citytown(?x7151, ?x739) *> conf = 0.46 ranks of expected_values: 4 EVAL 01ym8l company! 05_wyz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 190.000 190.000 0.828 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #17145-053yx PRED entity: 053yx PRED relation: inductee! PRED expected values: 0g2c8 => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 5): 0g2c8 (0.41 #55, 0.34 #127, 0.32 #64), 06szd3 (0.10 #191, 0.07 #272, 0.06 #110), 04045y (0.04 #87, 0.01 #249), 0qjfl (0.03 #129, 0.03 #219, 0.03 #246), 04dm2n (0.02 #305, 0.02 #197, 0.01 #278) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 01ky2h; 044k8; 01mxt_; 04k05; 01nz1q6; 016m5c; >> query: (?x2835, 0g2c8) <- artists(?x119, ?x2835), peers(?x120, ?x2835), award_winner(?x2431, ?x2835) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 053yx inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.409 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #17144-04cbbz PRED entity: 04cbbz PRED relation: prequel! PRED expected values: 03clwtw => 123 concepts (91 used for prediction) PRED predicted values (max 10 best out of 65): 04cbbz (0.20 #96, 0.02 #1180, 0.02 #3809), 0140g4 (0.17 #365, 0.11 #546, 0.05 #728), 08984j (0.11 #661, 0.05 #843, 0.02 #1385), 0btyf5z (0.05 #765, 0.02 #1126, 0.01 #2940), 0fdv3 (0.05 #761, 0.01 #2391, 0.01 #2936), 0dfw0 (0.05 #810, 0.01 #2985), 02scbv (0.04 #6714, 0.04 #5989, 0.04 #3079), 06c0ns (0.04 #6714, 0.04 #5989, 0.04 #3079), 06ybb1 (0.04 #6714, 0.04 #5989, 0.04 #3079), 059lwy (0.04 #6714, 0.04 #5989, 0.04 #3079) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02qr3k8; >> query: (?x5441, 04cbbz) <- film(?x5440, ?x5441), ?x5440 = 016z51, genre(?x5441, ?x225) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04cbbz prequel! 03clwtw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 91.000 0.200 http://example.org/film/film/prequel #17143-05w5d PRED entity: 05w5d PRED relation: religion! PRED expected values: 06mz5 04ly1 05fjf => 42 concepts (39 used for prediction) PRED predicted values (max 10 best out of 505): 07srw (0.80 #657, 0.75 #939, 0.73 #728), 06mz5 (0.75 #507, 0.67 #225, 0.64 #719), 05fjf (0.73 #822, 0.73 #751, 0.71 #469), 0846v (0.73 #732, 0.71 #380, 0.70 #661), 04ly1 (0.71 #455, 0.64 #737, 0.61 #704), 05fky (0.64 #811, 0.61 #704, 0.60 #669), 07_f2 (0.61 #704, 0.57 #986, 0.56 #1915), 05kr_ (0.61 #704, 0.57 #986, 0.56 #1915), 0694j (0.61 #704, 0.57 #986, 0.56 #1915), 04ych (0.61 #704, 0.57 #986, 0.56 #1915) >> Best rule #657 for best value: >> intensional similarity = 13 >> extensional distance = 8 >> proper extension: 021_0p; >> query: (?x10107, 07srw) <- religion(?x6521, ?x10107), religion(?x2982, ?x10107), religion(?x2020, ?x10107), religion(?x448, ?x10107), ?x2982 = 01n4w, currency(?x2020, ?x170), ?x448 = 03v1s, location(?x237, ?x2020), contains(?x2020, ?x1151), adjoins(?x2020, ?x7405), state_province_region(?x1520, ?x2020), ?x6521 = 05mph, district_represented(?x176, ?x2020) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #507 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 6 *> proper extension: 01s5nb; *> query: (?x10107, 06mz5) <- religion(?x5622, ?x10107), religion(?x2982, ?x10107), religion(?x2831, ?x10107), religion(?x2768, ?x10107), religion(?x2713, ?x10107), religion(?x335, ?x10107), ?x2713 = 06btq, ?x2768 = 03s5t, jurisdiction_of_office(?x3959, ?x2982), place_of_birth(?x7552, ?x2982), time_zones(?x5622, ?x11506), district_represented(?x355, ?x335), contains(?x335, ?x322), location(?x101, ?x335), ?x2831 = 0gyh *> conf = 0.75 ranks of expected_values: 2, 3, 5 EVAL 05w5d religion! 05fjf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 42.000 39.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05w5d religion! 04ly1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 42.000 39.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05w5d religion! 06mz5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 42.000 39.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #17142-01k5y0 PRED entity: 01k5y0 PRED relation: nominated_for! PRED expected values: 0gs96 => 83 concepts (56 used for prediction) PRED predicted values (max 10 best out of 195): 0gq9h (0.41 #3104, 0.41 #2870, 0.41 #3573), 0gs9p (0.37 #3106, 0.37 #3575, 0.37 #2872), 019f4v (0.36 #2862, 0.36 #3096, 0.35 #3565), 0k611 (0.31 #2880, 0.31 #3114, 0.30 #3583), 040njc (0.30 #2816, 0.30 #3050, 0.29 #3519), 04dn09n (0.28 #5620, 0.25 #2843, 0.25 #3278), 05f4m9q (0.28 #5620, 0.25 #3278, 0.25 #8202), 05b1610 (0.28 #5620, 0.25 #3278, 0.25 #8202), 0gqz2 (0.28 #5620, 0.25 #3278, 0.25 #8202), 054ks3 (0.28 #5620, 0.25 #3278, 0.25 #8202) >> Best rule #3104 for best value: >> intensional similarity = 4 >> extensional distance = 465 >> proper extension: 075cph; >> query: (?x10752, 0gq9h) <- nominated_for(?x6911, ?x10752), award(?x6911, ?x198), honored_for(?x7100, ?x10752), genre(?x10752, ?x239) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #2896 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 461 *> proper extension: 02rjv2w; 0299hs; 02q_4ph; 0gbtbm; 019kyn; 072hx4; *> query: (?x10752, 0gs96) <- genre(?x10752, ?x239), honored_for(?x7100, ?x10752), film(?x3181, ?x10752) *> conf = 0.22 ranks of expected_values: 33 EVAL 01k5y0 nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 83.000 56.000 0.411 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17141-0kzy0 PRED entity: 0kzy0 PRED relation: instrumentalists! PRED expected values: 048j4l => 129 concepts (122 used for prediction) PRED predicted values (max 10 best out of 126): 0342h (0.77 #3138, 0.70 #1137, 0.65 #5316), 05148p4 (0.67 #537, 0.56 #975, 0.56 #1323), 01vj9c (0.41 #1391, 0.40 #3308, 0.38 #2614), 01vdm0 (0.35 #4352, 0.32 #3396, 0.28 #2702), 013y1f (0.35 #4352, 0.28 #2702, 0.28 #2701), 0395lw (0.32 #3396, 0.28 #2702, 0.28 #2701), 02hnl (0.32 #989, 0.29 #1337, 0.26 #1077), 016622 (0.28 #2702, 0.28 #2701, 0.27 #2441), 026t6 (0.24 #1307, 0.24 #1394, 0.22 #959), 03qjg (0.23 #3183, 0.22 #1354, 0.22 #1441) >> Best rule #3138 for best value: >> intensional similarity = 4 >> extensional distance = 223 >> proper extension: 015mrk; 011hdn; 015196; >> query: (?x654, 0342h) <- instrumentalists(?x228, ?x654), profession(?x654, ?x220), ?x220 = 016z4k, role(?x228, ?x75) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1384 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 0pgjm; *> query: (?x654, 048j4l) <- profession(?x654, ?x1614), group(?x654, ?x1749), role(?x654, ?x745), ?x1614 = 01c72t *> conf = 0.07 ranks of expected_values: 33 EVAL 0kzy0 instrumentalists! 048j4l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 129.000 122.000 0.773 http://example.org/music/instrument/instrumentalists #17140-0102t4 PRED entity: 0102t4 PRED relation: time_zones PRED expected values: 02fqwt => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 7): 02fqwt (0.79 #53, 0.72 #1, 0.68 #27), 02hczc (0.54 #447, 0.43 #583, 0.31 #170), 02hcv8 (0.51 #173, 0.43 #238, 0.43 #277), 02lcqs (0.23 #161, 0.21 #188, 0.21 #201), 02llzg (0.06 #252, 0.06 #265, 0.05 #1174), 03bdv (0.05 #215, 0.04 #254, 0.04 #306), 03plfd (0.01 #484, 0.01 #1272) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0mrs1; 0d1xh; 0mq17; 0mqs0; 0fxwx; 0mrhq; 0mpzm; 0mskq; 0ms1n; 0mr_8; >> query: (?x13421, 02fqwt) <- contains(?x3634, ?x13421), ?x3634 = 07b_l, source(?x13421, ?x958), ?x958 = 0jbk9 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0102t4 time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.788 http://example.org/location/location/time_zones #17139-01chc7 PRED entity: 01chc7 PRED relation: award_nominee PRED expected values: 01ycbq => 61 concepts (40 used for prediction) PRED predicted values (max 10 best out of 555): 01ycbq (0.81 #34926, 0.80 #51231, 0.25 #431), 02c6pq (0.76 #55890, 0.76 #51230, 0.76 #39584), 01chc7 (0.50 #732, 0.30 #69867, 0.18 #55891), 0hvb2 (0.18 #55891, 0.06 #2724, 0.04 #19023), 015t56 (0.18 #55891, 0.06 #2936, 0.03 #19235), 01pgzn_ (0.18 #55891, 0.05 #2824, 0.03 #12136), 06151l (0.18 #55891, 0.05 #2363, 0.03 #18662), 04w391 (0.18 #55891, 0.05 #3237, 0.02 #12549), 01mqc_ (0.18 #55891, 0.05 #4004, 0.02 #13316), 023kzp (0.18 #55891, 0.04 #3716, 0.03 #13028) >> Best rule #34926 for best value: >> intensional similarity = 2 >> extensional distance = 1236 >> proper extension: 0134w7; 0157m; 02_hj4; 06k02; 01dw9z; 029_3; 0k8y7; 02lymt; 03sww; 01wbsdz; ... >> query: (?x3274, ?x262) <- film(?x3274, ?x1224), award_nominee(?x262, ?x3274) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01chc7 award_nominee 01ycbq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 40.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17138-0j5ym PRED entity: 0j5ym PRED relation: entity_involved PRED expected values: 01_4z 01k31p => 76 concepts (39 used for prediction) PRED predicted values (max 10 best out of 227): 0chghy (0.40 #634, 0.33 #163, 0.20 #792), 06f32 (0.39 #786, 0.29 #2533, 0.23 #5873), 05vz3zq (0.39 #786, 0.29 #2533, 0.23 #5873), 09c7w0 (0.39 #786, 0.29 #2533, 0.23 #5873), 01k31p (0.33 #293, 0.20 #764, 0.20 #608), 0c_jc (0.33 #216, 0.20 #687, 0.17 #1163), 088q1s (0.33 #260, 0.20 #731, 0.15 #1682), 0c4b8 (0.33 #213, 0.20 #684, 0.15 #1635), 0d060g (0.33 #162, 0.20 #633, 0.12 #2380), 059j2 (0.33 #171, 0.20 #642, 0.08 #1433) >> Best rule #634 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 05nqz; 07_nf; >> query: (?x13682, 0chghy) <- combatants(?x13682, ?x5114), entity_involved(?x13682, ?x10154), people(?x6393, ?x10154), profession(?x10154, ?x5805), nationality(?x10154, ?x2346), ?x5114 = 05vz3zq >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #293 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 048n7; *> query: (?x13682, 01k31p) <- combatants(?x13682, ?x6465), combatants(?x13682, ?x94), entity_involved(?x13682, ?x10154), ?x10154 = 04xzm, ?x94 = 09c7w0, capital(?x6465, ?x9559), combatants(?x6465, ?x756) *> conf = 0.33 ranks of expected_values: 5, 30 EVAL 0j5ym entity_involved 01k31p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 76.000 39.000 0.400 http://example.org/base/culturalevent/event/entity_involved EVAL 0j5ym entity_involved 01_4z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 76.000 39.000 0.400 http://example.org/base/culturalevent/event/entity_involved #17137-0k9j_ PRED entity: 0k9j_ PRED relation: film PRED expected values: 016y_f => 100 concepts (65 used for prediction) PRED predicted values (max 10 best out of 765): 0ccck7 (0.60 #32152, 0.58 #110767, 0.48 #28579), 011ywj (0.16 #3220, 0.06 #12150, 0.04 #19294), 01bjbk (0.14 #1639, 0.03 #83970, 0.03 #100048), 01lsl (0.14 #1531, 0.03 #6889, 0.03 #10461), 075cph (0.14 #378, 0.03 #58959, 0.03 #46450), 05ypj5 (0.14 #1729, 0.03 #58959, 0.03 #46450), 0k4bc (0.14 #1261, 0.03 #58959, 0.03 #46450), 03nqnnk (0.14 #1022, 0.02 #2808, 0.01 #11738), 03hj3b3 (0.14 #306, 0.02 #2092, 0.01 #3878), 0147sh (0.14 #130, 0.02 #1916, 0.01 #3702) >> Best rule #32152 for best value: >> intensional similarity = 3 >> extensional distance = 643 >> proper extension: 02xb2bt; 02j8nx; 012g92; >> query: (?x9000, ?x11218) <- award_winner(?x9000, ?x1357), film(?x9000, ?x327), nominated_for(?x9000, ?x11218) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k9j_ film 016y_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 65.000 0.597 http://example.org/film/actor/film./film/performance/film #17136-0p_jc PRED entity: 0p_jc PRED relation: student! PRED expected values: 014mlp => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 13): 014mlp (0.35 #286, 0.33 #226, 0.17 #26), 02mjs7 (0.20 #5, 0.02 #185, 0.01 #245), 019v9k (0.08 #290, 0.06 #50, 0.04 #230), 028dcg (0.07 #298, 0.06 #238, 0.04 #78), 03mkk4 (0.07 #233, 0.04 #293, 0.02 #621), 016t_3 (0.06 #224, 0.04 #284, 0.02 #484), 0bkj86 (0.04 #229, 0.03 #289, 0.02 #609), 02h4rq6 (0.04 #283, 0.03 #223, 0.02 #621), 02_xgp2 (0.03 #294, 0.03 #594, 0.03 #614), 04zx3q1 (0.01 #602, 0.01 #643, 0.01 #743) >> Best rule #286 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 02kxbwx; 049dyj; 08m4c8; 072bb1; 0c9c0; 0klh7; 02rmfm; 01pkhw; 03_l8m; 05km8z; ... >> query: (?x11357, 014mlp) <- nominated_for(?x11357, ?x6884), student(?x8925, ?x11357), profession(?x11357, ?x987) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p_jc student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.346 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #17135-0bx0l PRED entity: 0bx0l PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.81 #96, 0.81 #171, 0.80 #6), 07c52 (0.09 #48, 0.08 #3, 0.06 #18), 07z4p (0.08 #5, 0.07 #50, 0.06 #20), 02nxhr (0.08 #2, 0.05 #47, 0.03 #162) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 587 >> proper extension: 01gglm; >> query: (?x2168, 029j_) <- film(?x294, ?x2168), award_winner(?x2168, ?x4561), production_companies(?x2168, ?x541) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bx0l film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17134-0m7yy PRED entity: 0m7yy PRED relation: award_winner PRED expected values: 087c7 01nzs7 01l87db 031rq5 0hm0k 02630g 01j53q 014hdb 017dpj => 57 concepts (38 used for prediction) PRED predicted values (max 10 best out of 2453): 017s11 (0.50 #7299, 0.50 #4896, 0.17 #79277), 07w21 (0.41 #14485, 0.35 #16886, 0.34 #21690), 016tt2 (0.38 #7306, 0.38 #4903, 0.17 #79277), 086k8 (0.38 #7261, 0.38 #4858, 0.14 #12066), 047q2wc (0.38 #8056, 0.38 #5653, 0.14 #12861), 0g1rw (0.38 #7338, 0.25 #4935, 0.07 #12143), 0415svh (0.33 #134, 0.17 #79277, 0.13 #55251), 05m9f9 (0.33 #1148, 0.17 #79277, 0.13 #55251), 04m_zp (0.33 #861, 0.17 #79277, 0.13 #55251), 04y8r (0.33 #466, 0.05 #24485, 0.04 #26889) >> Best rule #7299 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0p9sw; 07bdd_; 0gq9h; 05p1dby; 0gr42; 02x1z2s; >> query: (?x3486, 017s11) <- award_winner(?x3486, ?x7326), award(?x802, ?x3486), award_winner(?x802, ?x4064), nominated_for(?x803, ?x802), child(?x7326, ?x12664) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7207 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 03rbj2; *> query: (?x3486, ?x4064) <- award_winner(?x3486, ?x7326), award(?x802, ?x3486), award_winner(?x802, ?x4064), nominated_for(?x803, ?x802), company(?x1491, ?x7326) *> conf = 0.18 ranks of expected_values: 56, 190, 294, 295, 325, 1295 EVAL 0m7yy award_winner 017dpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 57.000 38.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0m7yy award_winner 014hdb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 38.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0m7yy award_winner 01j53q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 57.000 38.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0m7yy award_winner 02630g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 38.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0m7yy award_winner 0hm0k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 57.000 38.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0m7yy award_winner 031rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 57.000 38.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0m7yy award_winner 01l87db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 38.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0m7yy award_winner 01nzs7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 57.000 38.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0m7yy award_winner 087c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 38.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #17133-0hgnl3t PRED entity: 0hgnl3t PRED relation: genre PRED expected values: 07s9rl0 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 98): 07s9rl0 (0.83 #370, 0.81 #247, 0.80 #1600), 05p553 (0.42 #497, 0.40 #5, 0.36 #989), 03k9fj (0.42 #505, 0.40 #13, 0.33 #751), 01jfsb (0.34 #1121, 0.33 #1859, 0.33 #2845), 02kdv5l (0.33 #2834, 0.33 #1971, 0.30 #1725), 02l7c8 (0.31 #141, 0.29 #264, 0.29 #1617), 0hcr (0.30 #26, 0.17 #641, 0.17 #764), 02n4kr (0.30 #9, 0.14 #1116, 0.12 #2346), 02xlf (0.30 #56, 0.05 #2461, 0.05 #671), 0219x_ (0.27 #275, 0.27 #398, 0.22 #1382) >> Best rule #370 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 0cfhfz; >> query: (?x4518, 07s9rl0) <- nominated_for(?x899, ?x4518), film(?x2596, ?x4518), ?x899 = 02x1dht >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hgnl3t genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.828 http://example.org/film/film/genre #17132-039bp PRED entity: 039bp PRED relation: student! PRED expected values: 041y2 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 24): 02822 (0.17 #31, 0.14 #156, 0.09 #342), 03qsdpk (0.17 #99, 0.14 #161, 0.07 #347), 01x3g (0.14 #182), 01jzxy (0.09 #203), 0fdys (0.06 #340, 0.02 #466, 0.01 #590), 03g3w (0.05 #332, 0.02 #395, 0.01 #520), 062z7 (0.03 #333, 0.03 #396), 0w7c (0.03 #353, 0.01 #789), 01zc2w (0.03 #359, 0.01 #547), 05qjt (0.02 #316, 0.02 #379) >> Best rule #31 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03vpf_; >> query: (?x1119, 02822) <- film(?x1119, ?x9484), film(?x1119, ?x7711), ?x9484 = 0291ck, genre(?x7711, ?x53) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #362 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 225 *> proper extension: 05vzql; *> query: (?x1119, 041y2) <- profession(?x1119, ?x1032), student(?x1368, ?x1119) *> conf = 0.02 ranks of expected_values: 13 EVAL 039bp student! 041y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 100.000 100.000 0.167 http://example.org/education/field_of_study/students_majoring./education/education/student #17131-0g5qmbz PRED entity: 0g5qmbz PRED relation: language PRED expected values: 02h40lc => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 47): 02h40lc (0.99 #5049, 0.99 #5676, 0.98 #4842), 03_9r (0.20 #60, 0.08 #4175, 0.08 #843), 0653m (0.20 #268, 0.08 #897, 0.07 #3901), 0jzc (0.17 #431, 0.08 #905, 0.07 #1850), 07c9s (0.17 #120, 0.07 #275, 0.05 #483), 03x42 (0.14 #198, 0.07 #301, 0.06 #352), 012w70 (0.13 #269, 0.06 #424, 0.05 #2468), 0t_2 (0.10 #218, 0.08 #740, 0.07 #1004), 0880p (0.10 #245, 0.06 #400, 0.04 #767), 02hxcvy (0.08 #914, 0.06 #1859, 0.05 #493) >> Best rule #5049 for best value: >> intensional similarity = 9 >> extensional distance = 1110 >> proper extension: 0sxg4; 083shs; 06wzvr; 011yxg; 0dnvn3; 0ds11z; 01ln5z; 03h_yy; 02_1sj; 0170_p; ... >> query: (?x9501, 02h40lc) <- film_crew_role(?x9501, ?x137), language(?x9501, ?x5671), language(?x4699, ?x5671), language(?x3672, ?x5671), language(?x1595, ?x5671), ?x4699 = 08ct6, languages_spoken(?x3584, ?x5671), ?x3672 = 024mxd, ?x1595 = 05pbl56 >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g5qmbz language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.992 http://example.org/film/film/language #17130-09y20 PRED entity: 09y20 PRED relation: film PRED expected values: 0ds11z 03hxsv => 98 concepts (81 used for prediction) PRED predicted values (max 10 best out of 567): 0879bpq (0.11 #445, 0.10 #2220, 0.08 #3995), 08k40m (0.11 #479, 0.10 #2254, 0.08 #4029), 03y0pn (0.11 #1249, 0.10 #3024, 0.08 #4799), 05nlx4 (0.11 #1247, 0.10 #3022, 0.08 #4797), 0ddt_ (0.11 #469, 0.10 #2244, 0.08 #4019), 02t_h3 (0.11 #1746, 0.10 #3521, 0.08 #5296), 05vxdh (0.11 #769, 0.10 #2544, 0.08 #4319), 06w99h3 (0.10 #1801, 0.08 #3576, 0.06 #26), 02_kd (0.08 #4131, 0.06 #581, 0.05 #2356), 046f3p (0.08 #4868, 0.06 #1318, 0.05 #3093) >> Best rule #445 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 05vsxz; 0993r; 01qrbf; >> query: (?x1549, 0879bpq) <- award_nominee(?x1549, ?x8566), award_nominee(?x1549, ?x488), ?x488 = 0159h6, ?x8566 = 0djywgn >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #18860 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 297 *> proper extension: 05cljf; 026ps1; 0168cl; 01w61th; 01vv7sc; 016kjs; 015_30; 0136p1; 09k2t1; 01wgxtl; ... *> query: (?x1549, 03hxsv) <- location(?x1549, ?x9042), award_nominee(?x1549, ?x100), languages(?x1549, ?x254) *> conf = 0.01 ranks of expected_values: 343 EVAL 09y20 film 03hxsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 98.000 81.000 0.111 http://example.org/film/actor/film./film/performance/film EVAL 09y20 film 0ds11z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 81.000 0.111 http://example.org/film/actor/film./film/performance/film #17129-012x4t PRED entity: 012x4t PRED relation: award_winner! PRED expected values: 01htxr => 119 concepts (42 used for prediction) PRED predicted values (max 10 best out of 563): 09hnb (0.83 #52999, 0.83 #41758, 0.82 #59423), 01htxr (0.83 #52999, 0.82 #59423, 0.82 #64240), 03j24kf (0.52 #24093, 0.52 #38546, 0.48 #3213), 01vvlyt (0.52 #38546, 0.48 #3213, 0.48 #3212), 01w272y (0.52 #38546, 0.48 #3213, 0.48 #3212), 0197tq (0.29 #1628, 0.14 #64241, 0.11 #22), 012x4t (0.22 #248, 0.14 #64241, 0.05 #17669), 02qtywd (0.21 #3164, 0.14 #64241, 0.05 #17669), 01vwyqp (0.21 #2141, 0.14 #64241, 0.05 #17669), 0qdyf (0.21 #2125, 0.05 #17669, 0.02 #27825) >> Best rule #52999 for best value: >> intensional similarity = 3 >> extensional distance = 348 >> proper extension: 06lxn; >> query: (?x1660, ?x2698) <- artists(?x505, ?x1660), award_winner(?x521, ?x1660), award_winner(?x1660, ?x2698) >> conf = 0.83 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 012x4t award_winner! 01htxr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 42.000 0.827 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #17128-02x4wr9 PRED entity: 02x4wr9 PRED relation: nominated_for PRED expected values: 02s4l6 0djlxb 09gb_4p => 63 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1452): 0hfzr (0.78 #5286, 0.50 #11517, 0.44 #13076), 0gmgwnv (0.78 #5605, 0.50 #11836, 0.41 #13395), 017gl1 (0.77 #11031, 0.67 #4800, 0.62 #12590), 05hjnw (0.67 #5414, 0.66 #52984, 0.65 #49867), 026p4q7 (0.67 #5017, 0.63 #11248, 0.50 #12807), 03hmt9b (0.67 #5244, 0.60 #11475, 0.50 #13034), 011yqc (0.67 #4873, 0.60 #11104, 0.50 #12663), 095zlp (0.67 #4722, 0.50 #10953, 0.41 #12512), 011yg9 (0.67 #5561, 0.50 #11792, 0.41 #13351), 08zrbl (0.67 #5846, 0.43 #12077, 0.35 #13636) >> Best rule #5286 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 027dtxw; 040njc; 03hkv_r; 0gr4k; 02n9nmz; 04kxsb; 02qvyrt; >> query: (?x2532, 0hfzr) <- nominated_for(?x2532, ?x7081), nominated_for(?x2532, ?x2973), award(?x276, ?x2532), film(?x539, ?x2973), ?x7081 = 03q8xj >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #5355 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 027dtxw; 040njc; 03hkv_r; 0gr4k; 02n9nmz; 04kxsb; 02qvyrt; *> query: (?x2532, 09gb_4p) <- nominated_for(?x2532, ?x7081), nominated_for(?x2532, ?x2973), award(?x276, ?x2532), film(?x539, ?x2973), ?x7081 = 03q8xj *> conf = 0.56 ranks of expected_values: 27, 240, 1227 EVAL 02x4wr9 nominated_for 09gb_4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 63.000 37.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x4wr9 nominated_for 0djlxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 63.000 37.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x4wr9 nominated_for 02s4l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 63.000 37.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17127-0blfl PRED entity: 0blfl PRED relation: participating_countries PRED expected values: 06qd3 => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 355): 0chghy (0.86 #503, 0.85 #498, 0.83 #1982), 0h7x (0.86 #503, 0.85 #498, 0.77 #499), 03_3d (0.86 #503, 0.85 #498, 0.77 #499), 04g5k (0.86 #503, 0.85 #498, 0.77 #499), 0d05w3 (0.86 #503, 0.85 #498, 0.77 #499), 06bnz (0.86 #503, 0.85 #498, 0.77 #499), 0163v (0.86 #503, 0.85 #498, 0.77 #499), 0ctw_b (0.86 #503, 0.85 #498, 0.69 #497), 06qd3 (0.86 #503, 0.85 #498, 0.69 #497), 0345h (0.86 #503, 0.77 #499, 0.73 #1679) >> Best rule #503 for best value: >> intensional similarity = 58 >> extensional distance = 1 >> proper extension: 018ctl; >> query: (?x4424, ?x252) <- sports(?x4424, ?x8190), sports(?x4424, ?x5177), sports(?x4424, ?x3309), participating_countries(?x4424, ?x7430), participating_countries(?x4424, ?x7413), participating_countries(?x4424, ?x2513), participating_countries(?x4424, ?x2267), participating_countries(?x4424, ?x1917), participating_countries(?x4424, ?x1892), participating_countries(?x4424, ?x1497), participating_countries(?x4424, ?x1353), participating_countries(?x4424, ?x1003), participating_countries(?x4424, ?x985), participating_countries(?x4424, ?x789), participating_countries(?x4424, ?x456), participating_countries(?x4424, ?x172), ?x2513 = 05b4w, ?x3309 = 09w1n, ?x1892 = 02vzc, ?x8190 = 09_9n, ?x172 = 0154j, sports(?x8584, ?x5177), sports(?x8189, ?x5177), sports(?x2630, ?x5177), sports(?x1617, ?x5177), sports(?x418, ?x5177), medal(?x4424, ?x2132), olympics(?x94, ?x4424), ?x1353 = 035qy, ?x1497 = 015qh, country(?x5177, ?x1603), country(?x5177, ?x1355), country(?x5177, ?x404), country(?x5177, ?x252), ?x456 = 05qhw, ?x7413 = 04hqz, ?x2630 = 0swff, ?x1617 = 01f1jy, ?x1917 = 01p1v, ?x8584 = 01f1jf, ?x418 = 09n48, ?x985 = 0k6nt, ?x404 = 047lj, ?x2132 = 02lpp7, olympics(?x5177, ?x3729), ?x1355 = 0h7x, olympics(?x87, ?x3729), sports(?x3729, ?x150), ?x1603 = 06bnz, sports(?x3729, ?x2044), ?x2267 = 03rj0, olympics(?x1471, ?x3729), ?x8189 = 015l4k, ?x789 = 0f8l9c, locations(?x3729, ?x5036), ?x94 = 09c7w0, ?x1003 = 03gj2, nationality(?x4915, ?x7430) >> conf = 0.86 => this is the best rule for 11 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 9 EVAL 0blfl participating_countries 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 21.000 21.000 0.857 http://example.org/olympics/olympic_games/participating_countries #17126-0dj75 PRED entity: 0dj75 PRED relation: nutrient PRED expected values: 05wvs 025s7j4 04zjxcz 041r51 02kd0rh 0h1_c 0hkwr 0g5gq 0h1yf 01n78x => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 40): 025s7j4 (0.71 #443, 0.71 #424, 0.70 #441), 05wvs (0.70 #441, 0.62 #409, 0.57 #449), 0h1_c (0.70 #441, 0.62 #409, 0.57 #444), 0g5gq (0.70 #441, 0.62 #409, 0.57 #442), 04zjxcz (0.70 #441, 0.62 #409, 0.57 #448), 0h1yf (0.70 #441, 0.62 #409, 0.57 #446), 0hkwr (0.70 #441, 0.62 #409, 0.57 #451), 01n78x (0.70 #441, 0.62 #409, 0.50 #419), 041r51 (0.70 #441, 0.62 #409, 0.50 #374), 02kd0rh (0.70 #441, 0.62 #409, 0.50 #376) >> Best rule #443 for best value: >> intensional similarity = 76 >> extensional distance = 5 >> proper extension: 0dcfv; >> query: (?x7719, ?x5549) <- nutrient(?x7719, ?x12454), nutrient(?x7719, ?x9915), nutrient(?x7719, ?x9365), nutrient(?x7719, ?x8413), nutrient(?x7719, ?x8243), nutrient(?x7719, ?x6192), nutrient(?x7719, ?x5337), nutrient(?x7719, ?x3264), nutrient(?x7719, ?x2018), nutrient(?x10612, ?x8413), nutrient(?x9732, ?x8413), nutrient(?x9489, ?x8413), nutrient(?x9005, ?x8413), nutrient(?x8298, ?x8413), nutrient(?x7057, ?x8413), nutrient(?x6285, ?x8413), nutrient(?x6191, ?x8413), nutrient(?x6159, ?x8413), nutrient(?x6032, ?x8413), nutrient(?x5373, ?x8413), nutrient(?x5009, ?x8413), nutrient(?x4068, ?x8413), nutrient(?x3900, ?x8413), nutrient(?x3468, ?x8413), nutrient(?x2701, ?x8413), nutrient(?x1959, ?x8413), nutrient(?x1303, ?x8413), nutrient(?x1257, ?x8413), ?x10612 = 0frq6, ?x1303 = 0fj52s, ?x5337 = 06x4c, ?x2701 = 0hkxq, ?x9915 = 025tkqy, ?x5009 = 0fjfh, ?x8243 = 014d7f, ?x7057 = 0fbdb, ?x12454 = 025rw19, ?x9489 = 07j87, ?x6191 = 014j1m, ?x3900 = 061_f, ?x8298 = 037ls6, taxonomy(?x9365, ?x939), ?x9005 = 04zpv, ?x4068 = 0fbw6, ?x6285 = 01645p, ?x9732 = 05z55, ?x2018 = 01sh2, ?x1959 = 0f25w9, ?x939 = 04n6k, ?x6159 = 033cnk, ?x6192 = 06jry, ?x5373 = 0971v, ?x1257 = 09728, ?x6032 = 01nkt, nutrient(?x3468, ?x12083), nutrient(?x3468, ?x11409), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10453), nutrient(?x3468, ?x10195), nutrient(?x3468, ?x10098), nutrient(?x3468, ?x6160), nutrient(?x3468, ?x6033), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5451), ?x10195 = 0hkwr, ?x10453 = 075pwf, ?x5451 = 05wvs, ?x6033 = 04zjxcz, ?x12083 = 01n78x, ?x11409 = 0h1yf, ?x6160 = 041r51, ?x10098 = 0h1_c, ?x5549 = 025s7j4, ?x10891 = 0g5gq, nutrient(?x3264, ?x5549), nutrient(?x1303, ?x3264) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 EVAL 0dj75 nutrient 01n78x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dj75 nutrient 0h1yf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dj75 nutrient 0g5gq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dj75 nutrient 0hkwr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dj75 nutrient 0h1_c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dj75 nutrient 02kd0rh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dj75 nutrient 041r51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dj75 nutrient 04zjxcz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dj75 nutrient 025s7j4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dj75 nutrient 05wvs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #17125-0d1t3 PRED entity: 0d1t3 PRED relation: sports! PRED expected values: 0lbd9 => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 35): 0jkvj (0.82 #995, 0.79 #1296, 0.79 #1207), 0l6mp (0.82 #995, 0.79 #1296, 0.79 #1207), 0lv1x (0.79 #1296, 0.79 #1207, 0.79 #1151), 0nbjq (0.79 #1296, 0.79 #1207, 0.79 #1151), 0ldqf (0.79 #1296, 0.79 #1207, 0.79 #1151), 0lbd9 (0.79 #1296, 0.79 #1207, 0.79 #1295), 0l6vl (0.79 #1296, 0.79 #1207, 0.79 #1295), 06sks6 (0.77 #146, 0.77 #237, 0.77 #502), 018qb4 (0.67 #810, 0.67 #290, 0.64 #609), 016r9z (0.64 #662, 0.58 #420, 0.57 #402) >> Best rule #995 for best value: >> intensional similarity = 41 >> extensional distance = 24 >> proper extension: 07rlg; >> query: (?x4876, ?x2233) <- sports(?x2233, ?x4876), sports(?x778, ?x4876), country(?x4876, ?x1892), country(?x4876, ?x1273), country(?x4876, ?x456), ?x456 = 05qhw, sports(?x778, ?x6150), sports(?x778, ?x2315), sports(?x778, ?x2044), ?x6150 = 07_53, countries_spoken_in(?x5359, ?x1273), ?x2315 = 06wrt, sports(?x2134, ?x4876), ?x2044 = 06f41, olympics(?x3728, ?x2233), olympics(?x142, ?x778), film_release_region(?x10535, ?x1892), film_release_region(?x7493, ?x1892), film_release_region(?x6932, ?x1892), film_release_region(?x6235, ?x1892), film_release_region(?x5578, ?x1892), film_release_region(?x3135, ?x1892), film_release_region(?x1456, ?x1892), film_release_region(?x86, ?x1892), ?x6932 = 027pfg, countries_within(?x455, ?x1892), contains(?x6304, ?x1892), ?x3135 = 0bmc4cm, teams(?x1892, ?x12091), ?x10535 = 09v42sf, ?x86 = 0ds35l9, ?x5578 = 0ddj0x, ?x7493 = 0btpm6, organization(?x1892, ?x127), ?x1456 = 0cz8mkh, capital(?x1892, ?x11237), ?x6235 = 05b6rdt, ?x3728 = 087vz, olympics(?x1310, ?x2134), ?x142 = 0jgd, contains(?x1892, ?x7061) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #1296 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 49 *> proper extension: 09xp_; *> query: (?x4876, ?x2131) <- sports(?x2131, ?x4876), sports(?x2043, ?x4876), country(?x4876, ?x456), adjoins(?x344, ?x456), olympics(?x456, ?x418), film_release_region(?x7502, ?x456), film_release_region(?x7009, ?x456), film_release_region(?x6446, ?x456), film_release_region(?x5644, ?x456), film_release_region(?x5578, ?x456), film_release_region(?x4040, ?x456), film_release_region(?x3830, ?x456), film_release_region(?x3619, ?x456), film_release_region(?x2893, ?x456), film_release_region(?x2656, ?x456), film_release_region(?x2512, ?x456), film_release_region(?x2340, ?x456), member_states(?x2106, ?x456), sports(?x2131, ?x171), olympics(?x304, ?x2043), sports(?x2043, ?x471), ?x5644 = 0dll_t2, country(?x10585, ?x456), combatants(?x151, ?x456), ?x2512 = 07x4qr, ?x4040 = 02mt51, contains(?x456, ?x6265), ?x7009 = 0bs8s1p, ?x2340 = 0fpv_3_, ?x5578 = 0ddj0x, ?x3830 = 0gjcrrw, medal(?x456, ?x422), ?x10585 = 01gqfm, ?x418 = 09n48, ?x6446 = 089j8p, organization(?x456, ?x127), ?x2893 = 01jrbb, cinematography(?x7502, ?x7739), combatants(?x456, ?x3057), ?x3619 = 0fphgb, ?x2656 = 03qnc6q *> conf = 0.79 ranks of expected_values: 6 EVAL 0d1t3 sports! 0lbd9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 45.000 45.000 0.819 http://example.org/user/jg/default_domain/olympic_games/sports #17124-03c6vl PRED entity: 03c6vl PRED relation: place_of_birth PRED expected values: 03l2n => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 47): 0cr3d (0.12 #94, 0.06 #798, 0.05 #3614), 01531 (0.12 #105, 0.02 #9961, 0.02 #10665), 0c9cw (0.12 #671), 01n7q (0.12 #39), 01_d4 (0.10 #770, 0.04 #5698, 0.04 #3586), 02_286 (0.07 #11283, 0.07 #50712, 0.07 #7763), 030qb3t (0.04 #7094, 0.04 #9206, 0.04 #5686), 09c7w0 (0.04 #2817, 0.04 #4225, 0.04 #4929), 0rh6k (0.04 #2818, 0.03 #4930, 0.03 #1410), 0dclg (0.03 #782, 0.02 #3598, 0.02 #5710) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0d4fqn; 02ndbd; 0721cy; 03xp8d5; 01jgpsh; 030tj5; >> query: (?x9214, 0cr3d) <- award_nominee(?x635, ?x9214), ?x635 = 02pp_q_, profession(?x9214, ?x524) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #10025 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 365 *> proper extension: 0h1p; 0c01c; 038rzr; 027l0b; 02t_v1; 0kvqv; 02t__3; 015gy7; 07mvp; 02ts3h; ... *> query: (?x9214, 03l2n) <- award_winner(?x7189, ?x9214), award(?x7189, ?x4921), executive_produced_by(?x2833, ?x7189) *> conf = 0.01 ranks of expected_values: 43 EVAL 03c6vl place_of_birth 03l2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 83.000 83.000 0.125 http://example.org/people/person/place_of_birth #17123-09k23 PRED entity: 09k23 PRED relation: colors PRED expected values: 01l849 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 19): 019sc (0.45 #64, 0.37 #45, 0.31 #216), 083jv (0.41 #610, 0.41 #211, 0.41 #914), 01l849 (0.27 #115, 0.26 #495, 0.26 #1464), 06fvc (0.24 #231, 0.24 #212, 0.20 #98), 088fh (0.16 #82, 0.13 #158, 0.13 #234), 09ggk (0.16 #54, 0.10 #73, 0.08 #92), 036k5h (0.12 #366, 0.10 #385, 0.09 #233), 038hg (0.12 #221, 0.11 #240, 0.11 #620), 04mkbj (0.09 #922, 0.09 #1245, 0.09 #675), 0jc_p (0.08 #669, 0.08 #327, 0.08 #1087) >> Best rule #64 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0173s9; >> query: (?x12746, 019sc) <- contains(?x512, ?x12746), colors(?x12746, ?x3189), ?x512 = 07ssc, school_type(?x12746, ?x3092) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #115 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 42 *> proper extension: 01hhvg; 07vht; 0269kx; 01314k; 06b19; 07vfz; 01c57n; 02mg5r; *> query: (?x12746, 01l849) <- currency(?x12746, ?x1099), citytown(?x12746, ?x8771), organization(?x5510, ?x12746), ?x5510 = 07xl34, category(?x12746, ?x134) *> conf = 0.27 ranks of expected_values: 3 EVAL 09k23 colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 143.000 143.000 0.450 http://example.org/education/educational_institution/colors #17122-09t4hh PRED entity: 09t4hh PRED relation: profession PRED expected values: 0dxtg => 114 concepts (44 used for prediction) PRED predicted values (max 10 best out of 75): 0dxtg (0.65 #2805, 0.63 #600, 0.63 #747), 0cbd2 (0.61 #447, 0.57 #153, 0.48 #1770), 0kyk (0.38 #175, 0.29 #469, 0.27 #1792), 03gjzk (0.37 #601, 0.34 #748, 0.32 #307), 02krf9 (0.23 #4141, 0.21 #1054, 0.17 #1495), 02hv44_ (0.16 #1820, 0.15 #203, 0.14 #497), 018gz8 (0.14 #15, 0.13 #5896, 0.09 #4131), 0np9r (0.14 #19, 0.08 #5900, 0.07 #4135), 09jwl (0.13 #5898, 0.12 #5457, 0.12 #2369), 01c72t (0.13 #3697, 0.12 #5315, 0.12 #2962) >> Best rule #2805 for best value: >> intensional similarity = 4 >> extensional distance = 247 >> proper extension: 01c59k; 0k57l; >> query: (?x13516, 0dxtg) <- place_of_death(?x13516, ?x1659), profession(?x13516, ?x319), profession(?x10236, ?x319), ?x10236 = 0bq4j6 >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09t4hh profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 44.000 0.651 http://example.org/people/person/profession #17121-07y9ts PRED entity: 07y9ts PRED relation: ceremony! PRED expected values: 09qv3c 0gkr9q => 26 concepts (26 used for prediction) PRED predicted values (max 10 best out of 320): 09qv3c (0.83 #1276, 0.80 #1028, 0.40 #531), 0gqy2 (0.70 #3085, 0.63 #3580, 0.52 #1603), 0gq_d (0.67 #3120, 0.62 #3615, 0.50 #4855), 0gqwc (0.67 #3025, 0.61 #3520, 0.52 #1543), 0k611 (0.67 #3038, 0.61 #3533, 0.49 #4773), 0gkr9q (0.67 #1455, 0.60 #1207, 0.40 #710), 0gvx_ (0.66 #3099, 0.61 #3594, 0.48 #1617), 018wng (0.65 #2999, 0.60 #3494, 0.48 #1517), 0gqyl (0.65 #3046, 0.60 #3541, 0.48 #4781), 0p9sw (0.65 #2985, 0.58 #3480, 0.48 #1503) >> Best rule #1276 for best value: >> intensional similarity = 18 >> extensional distance = 10 >> proper extension: 05c1t6z; 0gvstc3; >> query: (?x5296, 09qv3c) <- award_winner(?x5296, ?x10340), award_winner(?x5296, ?x8439), award_winner(?x5296, ?x376), award_winner(?x8439, ?x3446), ceremony(?x4921, ?x5296), honored_for(?x5296, ?x2078), award(?x7604, ?x4921), award(?x6673, ?x4921), ?x7604 = 079kdz, award_winner(?x678, ?x8439), nationality(?x6673, ?x608), profession(?x8439, ?x1032), film(?x376, ?x377), program(?x1762, ?x2078), nominated_for(?x4921, ?x337), film(?x6673, ?x1493), actor(?x4339, ?x8439), nationality(?x10340, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 07y9ts ceremony! 0gkr9q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 26.000 26.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 07y9ts ceremony! 09qv3c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 26.000 26.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony #17120-0571m PRED entity: 0571m PRED relation: language PRED expected values: 06nm1 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 59): 064_8sq (0.27 #2928, 0.25 #547, 0.23 #254), 04306rv (0.27 #2928, 0.20 #178, 0.20 #62), 06nm1 (0.27 #2928, 0.20 #68, 0.17 #126), 02bjrlw (0.27 #2928, 0.10 #117, 0.10 #175), 02bv9 (0.27 #2928, 0.03 #4638), 06b_j (0.20 #22, 0.08 #374, 0.07 #1600), 02ztjwg (0.20 #89, 0.03 #147, 0.03 #205), 0jzc (0.10 #193, 0.07 #135, 0.06 #545), 07zrf (0.07 #118, 0.07 #176, 0.04 #235), 03_9r (0.06 #2054, 0.05 #1587, 0.05 #1762) >> Best rule #2928 for best value: >> intensional similarity = 3 >> extensional distance = 1114 >> proper extension: 07hpv3; 09kn9; 01cjhz; 05sy2k_; 02648p; 01p4wv; 099pks; 0jq2r; 06r4f; 025ljp; ... >> query: (?x3251, ?x90) <- titles(?x53, ?x3251), titles(?x53, ?x4037), languages(?x4037, ?x90) >> conf = 0.27 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0571m language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 80.000 80.000 0.267 http://example.org/film/film/language #17119-03qmj9 PRED entity: 03qmj9 PRED relation: languages PRED expected values: 02h40lc => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 16): 02h40lc (0.33 #2, 0.28 #119, 0.26 #1250), 06nm1 (0.04 #162, 0.01 #1410, 0.01 #1488), 05zjd (0.04 #174), 03_9r (0.03 #239, 0.01 #434, 0.01 #863), 064_8sq (0.02 #2589, 0.02 #2472, 0.02 #3837), 02bjrlw (0.02 #586, 0.01 #2458, 0.01 #2575), 06b_j (0.02 #991), 03k50 (0.02 #5349, 0.01 #5427, 0.01 #4099), 07c9s (0.01 #2626, 0.01 #2548, 0.01 #2743), 02hwhyv (0.01 #529) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0bdxs5; >> query: (?x1556, 02h40lc) <- artist(?x2190, ?x1556), film(?x1556, ?x6932), category(?x1556, ?x134), ?x6932 = 027pfg >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qmj9 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.333 http://example.org/people/person/languages #17118-04mqgr PRED entity: 04mqgr PRED relation: award! PRED expected values: 0h0yt => 48 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2902): 02645b (0.88 #23538, 0.86 #13449, 0.82 #23537), 01_x6d (0.88 #23538, 0.86 #13449, 0.82 #23537), 098n5 (0.86 #13449, 0.82 #23537, 0.82 #20174), 05pq9 (0.86 #13449, 0.82 #23537, 0.82 #20174), 06z4wj (0.86 #13449, 0.82 #23537, 0.82 #20174), 02fgpf (0.75 #20660, 0.71 #17297, 0.33 #10572), 01vrz41 (0.71 #17104, 0.62 #20467, 0.40 #7016), 012wg (0.71 #18103, 0.62 #21466, 0.33 #11378), 077rj (0.71 #18543, 0.62 #21906, 0.33 #11818), 0drc1 (0.57 #19206, 0.50 #22569, 0.33 #12481) >> Best rule #23538 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 024fxq; >> query: (?x2874, ?x2875) <- award_winner(?x2874, ?x5223), award_winner(?x2874, ?x2875), award(?x587, ?x2874), ?x5223 = 0178rl, profession(?x2875, ?x1032), ?x1032 = 02hrh1q >> conf = 0.88 => this is the best rule for 2 predicted values *> Best rule #35856 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 68 *> proper extension: 03m73lj; *> query: (?x2874, 0h0yt) <- ceremony(?x2874, ?x4141), ceremony(?x6878, ?x4141), award(?x190, ?x6878), ?x190 = 01k7d9, award_winner(?x4141, ?x163) *> conf = 0.01 ranks of expected_values: 2479 EVAL 04mqgr award! 0h0yt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 15.000 0.875 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17117-049w1q PRED entity: 049w1q PRED relation: film_crew_role PRED expected values: 09zzb8 02r96rf => 69 concepts (63 used for prediction) PRED predicted values (max 10 best out of 36): 09zzb8 (0.83 #630, 0.80 #371, 0.73 #297), 0ch6mp2 (0.80 #638, 0.79 #379, 0.71 #1237), 09vw2b7 (0.74 #378, 0.72 #230, 0.70 #637), 02r96rf (0.73 #337, 0.73 #374, 0.72 #263), 01vx2h (0.38 #198, 0.36 #642, 0.36 #124), 0d2b38 (0.25 #2137, 0.23 #1530, 0.22 #1869), 02_n3z (0.25 #2137, 0.23 #1530, 0.22 #1869), 089g0h (0.25 #2137, 0.23 #1530, 0.22 #1869), 02ynfr (0.25 #2137, 0.23 #387, 0.23 #17), 02vs3x5 (0.25 #2137, 0.18 #2175, 0.18 #1831) >> Best rule #630 for best value: >> intensional similarity = 6 >> extensional distance = 264 >> proper extension: 01gglm; >> query: (?x10860, 09zzb8) <- country(?x10860, ?x94), titles(?x53, ?x10860), film(?x6916, ?x10860), film_crew_role(?x10860, ?x2095), ?x2095 = 0dxtw, award_nominee(?x6916, ?x57) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 049w1q film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 63.000 0.831 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 049w1q film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 63.000 0.831 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17116-0221zw PRED entity: 0221zw PRED relation: currency PRED expected values: 09nqf => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.84 #183, 0.84 #106, 0.80 #127), 02l6h (0.20 #4, 0.09 #18, 0.08 #25), 0kz1h (0.20 #12, 0.01 #180, 0.01 #75), 01nv4h (0.04 #142, 0.04 #121, 0.04 #170), 0ptk_ (0.02 #45), 02gsvk (0.01 #244) >> Best rule #183 for best value: >> intensional similarity = 4 >> extensional distance = 152 >> proper extension: 0bh8yn3; 0ndsl1x; >> query: (?x3500, 09nqf) <- nominated_for(?x2657, ?x3500), film(?x166, ?x3500), film(?x10743, ?x3500), film_distribution_medium(?x3500, ?x81) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0221zw currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.838 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #17115-01sn3 PRED entity: 01sn3 PRED relation: teams PRED expected values: 01yhm => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 248): 0cqt41 (0.10 #29, 0.05 #7189, 0.05 #1103), 0jnlm (0.05 #350, 0.05 #1424, 0.05 #1066), 0jm74 (0.05 #146, 0.05 #1220, 0.05 #862), 01slc (0.05 #142, 0.05 #1216, 0.05 #858), 01yjl (0.05 #56, 0.05 #1130, 0.05 #772), 01y3v (0.05 #47, 0.05 #1121, 0.05 #763), 02d02 (0.05 #186, 0.05 #1260, 0.05 #902), 02fp3 (0.05 #185, 0.05 #1259, 0.05 #901), 02c_4 (0.05 #160, 0.05 #1234, 0.05 #876), 0hmtk (0.05 #315, 0.05 #1389, 0.05 #1031) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0n5d1; 01zqy6t; >> query: (?x4090, 0cqt41) <- contains(?x4090, ?x8354), origin(?x4140, ?x4090), adjoins(?x13626, ?x4090), source(?x4090, ?x958) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01sn3 teams 01yhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 193.000 193.000 0.100 http://example.org/sports/sports_team_location/teams #17114-0969fd PRED entity: 0969fd PRED relation: influenced_by! PRED expected values: 01hb6v => 149 concepts (60 used for prediction) PRED predicted values (max 10 best out of 409): 0dzkq (0.50 #2201, 0.14 #7367, 0.14 #6850), 0j0pf (0.33 #726, 0.17 #2800, 0.05 #22973), 0821j (0.33 #877, 0.17 #2951, 0.04 #6566), 01dzz7 (0.33 #572, 0.17 #2646, 0.02 #22819), 018fq (0.33 #727, 0.17 #2801, 0.01 #20388), 02yl42 (0.33 #135, 0.10 #7376, 0.08 #20315), 0p8jf (0.33 #112, 0.05 #14081, 0.05 #17189), 0q9t7 (0.33 #339, 0.01 #12754, 0.01 #23105), 07hgm (0.33 #1427, 0.01 #12803, 0.01 #13839), 0683n (0.25 #2415, 0.17 #2933, 0.11 #14309) >> Best rule #2201 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01h2_6; >> query: (?x10677, 0dzkq) <- influenced_by(?x10677, ?x10110), ?x10110 = 07h1q, place_of_death(?x10677, ?x739) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #16134 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 113 *> proper extension: 03j43; 07c37; 03sbs; 02ln1; 07h1q; 0jrg; 015n8; *> query: (?x10677, 01hb6v) <- influenced_by(?x10677, ?x10110), influenced_by(?x10110, ?x862), interests(?x10110, ?x713) *> conf = 0.10 ranks of expected_values: 21 EVAL 0969fd influenced_by! 01hb6v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 149.000 60.000 0.500 http://example.org/influence/influence_node/influenced_by #17113-013cr PRED entity: 013cr PRED relation: award PRED expected values: 099jhq 02x8n1n => 95 concepts (70 used for prediction) PRED predicted values (max 10 best out of 274): 09sb52 (0.37 #9714, 0.35 #9310, 0.33 #8907), 0fbtbt (0.21 #1037, 0.08 #634, 0.05 #27010), 02ppm4q (0.20 #157, 0.16 #11689, 0.14 #20155), 0ck27z (0.20 #92, 0.13 #7346, 0.13 #11377), 0gqwc (0.20 #74, 0.13 #9747, 0.12 #2492), 094qd5 (0.20 #45, 0.10 #2463, 0.10 #1657), 09qwmm (0.20 #34, 0.07 #9707, 0.07 #1646), 0bdw6t (0.20 #110, 0.05 #27010, 0.05 #12202), 099cng (0.20 #86, 0.05 #27010, 0.05 #5728), 099t8j (0.20 #141, 0.05 #27010, 0.05 #9814) >> Best rule #9714 for best value: >> intensional similarity = 3 >> extensional distance = 718 >> proper extension: 0gm34; 012g92; >> query: (?x1401, 09sb52) <- award_winner(?x7275, ?x1401), film(?x1401, ?x1402), film(?x4508, ?x7275) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #11689 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 919 *> proper extension: 02lg9w; 080knyg; 050t68; *> query: (?x1401, ?x451) <- award_winner(?x7275, ?x1401), film(?x1401, ?x1402), nominated_for(?x451, ?x7275) *> conf = 0.16 ranks of expected_values: 17, 24 EVAL 013cr award 02x8n1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 95.000 70.000 0.371 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 013cr award 099jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 95.000 70.000 0.371 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17112-03j0d PRED entity: 03j0d PRED relation: influenced_by PRED expected values: 03hnd => 142 concepts (55 used for prediction) PRED predicted values (max 10 best out of 394): 01v9724 (0.50 #1919, 0.40 #1047, 0.33 #3229), 0j3v (0.39 #3990, 0.38 #4422, 0.29 #5291), 081k8 (0.33 #3208, 0.33 #1898, 0.31 #3645), 03hnd (0.30 #4894, 0.17 #3152, 0.15 #3589), 032l1 (0.29 #4451, 0.23 #5320, 0.22 #4019), 03jxw (0.28 #4268, 0.25 #774, 0.21 #4700), 06myp (0.28 #4303, 0.23 #5604, 0.21 #4735), 048cl (0.26 #5463, 0.17 #12839, 0.17 #4594), 03sbs (0.26 #5884, 0.25 #12827, 0.23 #5451), 05qmj (0.26 #12798, 0.22 #4121, 0.16 #5422) >> Best rule #1919 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0c5tl; >> query: (?x10000, 01v9724) <- influenced_by(?x10974, ?x10000), influenced_by(?x2343, ?x10000), ?x2343 = 0jt90f5, gender(?x10000, ?x231), company(?x10974, ?x6056) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4894 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 01dzz7; *> query: (?x10000, 03hnd) <- influenced_by(?x3858, ?x10000), influenced_by(?x3858, ?x7828), ?x7828 = 014ps4, peers(?x6723, ?x3858) *> conf = 0.30 ranks of expected_values: 4 EVAL 03j0d influenced_by 03hnd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 142.000 55.000 0.500 http://example.org/influence/influence_node/influenced_by #17111-0dsvzh PRED entity: 0dsvzh PRED relation: region PRED expected values: 07ssc => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 07ssc (0.87 #190, 0.19 #75, 0.15 #144), 09c7w0 (0.02 #139, 0.02 #162, 0.01 #1093) >> Best rule #190 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 0522wp; >> query: (?x813, 07ssc) <- film_distribution_medium(?x813, ?x2099), ?x2099 = 0735l, film(?x609, ?x813) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dsvzh region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.868 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #17110-0fy59t PRED entity: 0fy59t PRED relation: award_winner PRED expected values: 019fnv => 36 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1272): 01vsgrn (0.40 #2395, 0.11 #3934, 0.06 #13163), 06fmdb (0.40 #2342), 0478__m (0.40 #2255), 02j3d4 (0.40 #2249), 0dl567 (0.40 #2158), 0p_47 (0.40 #2128), 01309x (0.40 #2083), 02l840 (0.40 #1637), 081nh (0.33 #341, 0.24 #21877, 0.24 #23415), 076lxv (0.33 #92, 0.15 #9227, 0.14 #15379) >> Best rule #2395 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 0hhtgcw; >> query: (?x8259, 01vsgrn) <- award_winner(?x8259, ?x9235), award_winner(?x8259, ?x6591), award_winner(?x8259, ?x4240), award_winner(?x8259, ?x1934), category(?x9235, ?x134), profession(?x9235, ?x987), produced_by(?x9234, ?x9235), gender(?x1934, ?x231), influenced_by(?x364, ?x9235), nominated_for(?x1934, ?x2425), ?x987 = 0dxtg, award(?x4240, ?x591), participant(?x1149, ?x4240), nationality(?x4240, ?x94), type_of_union(?x6591, ?x566), genre(?x9234, ?x53) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #21381 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 29 *> proper extension: 02yw5r; 02pgky2; *> query: (?x8259, 019fnv) <- award_winner(?x8259, ?x4926), award_winner(?x8259, ?x3519), award_winner(?x8259, ?x1934), ceremony(?x4573, ?x8259), ceremony(?x2209, ?x8259), ceremony(?x1243, ?x8259), ceremony(?x500, ?x8259), ?x500 = 0p9sw, ?x4573 = 0gq_d, instance_of_recurring_event(?x8259, ?x3459), profession(?x1934, ?x563), ?x2209 = 0gr42, nominated_for(?x1934, ?x3009), award_winner(?x1745, ?x3519), type_of_union(?x4926, ?x566), ?x1243 = 0gr0m, award_winner(?x5720, ?x3519), film_production_design_by(?x3009, ?x4830), genre(?x3009, ?x53) *> conf = 0.03 ranks of expected_values: 581 EVAL 0fy59t award_winner 019fnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 24.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17109-02s2ft PRED entity: 02s2ft PRED relation: award_winner PRED expected values: 06_bq1 => 97 concepts (41 used for prediction) PRED predicted values (max 10 best out of 668): 0301yj (0.82 #19318, 0.82 #66011, 0.82 #37024), 0151w_ (0.82 #66011, 0.82 #37024, 0.81 #66008), 0f6_dy (0.82 #66011, 0.82 #37024, 0.81 #66008), 04bdxl (0.48 #57954, 0.48 #56345, 0.38 #45076), 02qgqt (0.48 #57954, 0.48 #56345, 0.38 #45076), 0dlglj (0.48 #57954, 0.48 #56345, 0.38 #45076), 01yfm8 (0.48 #57954, 0.48 #56345, 0.38 #45076), 02xs5v (0.48 #57954, 0.48 #56345, 0.38 #45076), 0flw6 (0.48 #57954, 0.48 #56345, 0.38 #45076), 0h0wc (0.48 #57954, 0.48 #56345, 0.38 #45076) >> Best rule #19318 for best value: >> intensional similarity = 4 >> extensional distance = 845 >> proper extension: 0lzkm; >> query: (?x92, ?x10743) <- award_winner(?x10743, ?x92), award_winner(?x6066, ?x92), award(?x6066, ?x704), category(?x10743, ?x134) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #37025 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1019 *> proper extension: 0dbpyd; 0fvf9q; 0197tq; 06gp3f; 02rchht; 04cy8rb; 01lmj3q; 01r42_g; 086k8; 058ncz; ... *> query: (?x92, ?x1871) <- award_winner(?x5000, ?x92), award_winner(?x2122, ?x92), film(?x2122, ?x394), award_winner(?x1871, ?x5000) *> conf = 0.28 ranks of expected_values: 77 EVAL 02s2ft award_winner 06_bq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 97.000 41.000 0.818 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #17108-0418wg PRED entity: 0418wg PRED relation: nominated_for! PRED expected values: 032_jg => 86 concepts (42 used for prediction) PRED predicted values (max 10 best out of 572): 06t8b (0.52 #74637, 0.50 #41979, 0.46 #11661), 06chvn (0.48 #69972, 0.35 #55974), 032_jg (0.31 #13994, 0.30 #18658, 0.27 #95630), 014zcr (0.30 #18658, 0.27 #95630, 0.26 #37312), 0169dl (0.30 #18658, 0.27 #95630, 0.26 #37312), 0b_dy (0.30 #18658, 0.27 #95630, 0.26 #37312), 028r4y (0.30 #18658, 0.27 #95630, 0.26 #37312), 0h7pj (0.30 #18658, 0.27 #95630, 0.26 #37312), 044lyq (0.30 #18658, 0.27 #95630, 0.26 #37312), 06ltr (0.30 #18658, 0.27 #95630, 0.26 #37312) >> Best rule #74637 for best value: >> intensional similarity = 4 >> extensional distance = 783 >> proper extension: 0m3gy; 058kh7; >> query: (?x2500, ?x7903) <- genre(?x2500, ?x812), film(?x7903, ?x2500), film(?x286, ?x2500), genre(?x2009, ?x812) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #13994 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 206 *> proper extension: 02gqm3; *> query: (?x2500, ?x875) <- genre(?x2500, ?x225), film(?x875, ?x2500), sibling(?x875, ?x989), award_winner(?x1033, ?x875) *> conf = 0.31 ranks of expected_values: 3 EVAL 0418wg nominated_for! 032_jg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 42.000 0.515 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17107-0fh694 PRED entity: 0fh694 PRED relation: genre PRED expected values: 0lsxr => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 102): 05p553 (0.60 #3490, 0.42 #484, 0.42 #2170), 0lsxr (0.47 #969, 0.46 #1934, 0.38 #729), 02kdv5l (0.45 #6370, 0.32 #2288, 0.30 #4569), 04xvlr (0.40 #241, 0.33 #361, 0.27 #601), 06n90 (0.34 #6380, 0.19 #2298, 0.19 #2178), 0vgkd (0.33 #10, 0.29 #130, 0.13 #250), 03k9fj (0.32 #2297, 0.31 #2177, 0.28 #2417), 02l7c8 (0.32 #1460, 0.31 #2901, 0.31 #3021), 09blyk (0.30 #1957, 0.27 #992, 0.23 #2077), 060__y (0.27 #977, 0.22 #2422, 0.20 #5904) >> Best rule #3490 for best value: >> intensional similarity = 4 >> extensional distance = 282 >> proper extension: 05q4y12; >> query: (?x964, 05p553) <- titles(?x812, ?x964), production_companies(?x964, ?x382), titles(?x812, ?x8859), ?x8859 = 063_j5 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #969 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 0cqr0q; *> query: (?x964, 0lsxr) <- titles(?x812, ?x964), ?x812 = 01jfsb, films(?x5179, ?x964), nominated_for(?x286, ?x964) *> conf = 0.47 ranks of expected_values: 2 EVAL 0fh694 genre 0lsxr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 106.000 0.595 http://example.org/film/film/genre #17106-016kv6 PRED entity: 016kv6 PRED relation: cinematography PRED expected values: 06r_by => 67 concepts (41 used for prediction) PRED predicted values (max 10 best out of 39): 06r_by (0.08 #86, 0.07 #149, 0.07 #23), 0bqytm (0.05 #80, 0.05 #143, 0.04 #17), 0854hr (0.05 #271, 0.03 #82, 0.03 #145), 04qvl7 (0.05 #127, 0.04 #1, 0.03 #317), 06nz46 (0.04 #202, 0.04 #329), 02vx4c2 (0.04 #34, 0.03 #97, 0.03 #286), 07mb57 (0.04 #12, 0.03 #75, 0.03 #138), 0jsw9l (0.04 #51, 0.03 #114, 0.03 #177), 016ks_ (0.03 #1153, 0.03 #1802, 0.03 #2128), 05kfs (0.03 #1153, 0.03 #1802, 0.03 #2128) >> Best rule #86 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 02v8kmz; 0yyg4; 08r4x3; 048scx; 09p0ct; 072x7s; 026gyn_; 07yk1xz; 0j_t1; 019vhk; ... >> query: (?x3523, 06r_by) <- currency(?x3523, ?x170), genre(?x3523, ?x3506), ?x3506 = 03mqtr, nominated_for(?x591, ?x3523) >> conf = 0.08 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016kv6 cinematography 06r_by CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 41.000 0.081 http://example.org/film/film/cinematography #17105-05qzv PRED entity: 05qzv PRED relation: award PRED expected values: 0265vt => 151 concepts (141 used for prediction) PRED predicted values (max 10 best out of 307): 0262x6 (0.82 #1119, 0.67 #1521, 0.16 #13275), 02662b (0.75 #1282, 0.73 #880, 0.18 #478), 01bb1c (0.75 #30573, 0.75 #30572, 0.72 #49890), 0265vt (0.73 #1127, 0.58 #1529, 0.18 #725), 02664f (0.67 #1423, 0.64 #1021, 0.16 #13275), 0265wl (0.67 #1442, 0.45 #1040, 0.16 #13275), 0262zm (0.58 #1289, 0.55 #887, 0.16 #13275), 0ddd9 (0.40 #55, 0.13 #7694, 0.13 #9304), 045xh (0.33 #1581, 0.27 #1179, 0.16 #13275), 01tgwv (0.33 #1568, 0.27 #1166, 0.16 #13275) >> Best rule #1119 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 05x8n; >> query: (?x9982, 0262x6) <- award(?x9982, ?x8909), award(?x9982, ?x6687), award(?x9982, ?x575), ?x8909 = 040_9s0, ?x575 = 040vk98, ?x6687 = 0262yt >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1127 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 05x8n; *> query: (?x9982, 0265vt) <- award(?x9982, ?x8909), award(?x9982, ?x6687), award(?x9982, ?x575), ?x8909 = 040_9s0, ?x575 = 040vk98, ?x6687 = 0262yt *> conf = 0.73 ranks of expected_values: 4 EVAL 05qzv award 0265vt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 151.000 141.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17104-01k47c PRED entity: 01k47c PRED relation: role PRED expected values: 01vdm0 => 151 concepts (113 used for prediction) PRED predicted values (max 10 best out of 120): 05r5c (0.56 #539, 0.54 #327, 0.45 #752), 0342h (0.52 #2129, 0.48 #3087, 0.48 #641), 013y1f (0.46 #357, 0.39 #569, 0.28 #2231), 01vdm0 (0.46 #352, 0.33 #564, 0.21 #3864), 0l14qv (0.38 #324, 0.33 #536, 0.15 #3088), 018vs (0.35 #3190, 0.35 #3097, 0.31 #439), 02sgy (0.33 #643, 0.28 #1494, 0.26 #2131), 03gvt (0.32 #7135, 0.28 #3189, 0.24 #9816), 05842k (0.31 #505, 0.19 #717, 0.19 #4337), 05148p4 (0.30 #8098, 0.28 #3189, 0.24 #9816) >> Best rule #539 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 012x4t; 01309x; 04m2zj; >> query: (?x9074, 05r5c) <- artists(?x505, ?x9074), category(?x9074, ?x134), profession(?x9074, ?x5917), ?x5917 = 01b30l >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #352 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 01vrz41; 0407f; 01qgry; 0163r3; 01mr2g6; *> query: (?x9074, 01vdm0) <- artists(?x505, ?x9074), category(?x9074, ?x134), profession(?x9074, ?x5917), ?x5917 = 01b30l, nationality(?x9074, ?x512) *> conf = 0.46 ranks of expected_values: 4 EVAL 01k47c role 01vdm0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 151.000 113.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role #17103-05b1610 PRED entity: 05b1610 PRED relation: category PRED expected values: 08mbj5d => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.60 #6, 0.50 #4, 0.50 #3) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 07cbcy; >> query: (?x688, 08mbj5d) <- nominated_for(?x688, ?x1246), ?x1246 = 02pxmgz, award_winner(?x688, ?x800), award(?x702, ?x688) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05b1610 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.600 http://example.org/common/topic/webpage./common/webpage/category #17102-02cj_f PRED entity: 02cj_f PRED relation: location PRED expected values: 06pr6 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 236): 06pr6 (0.48 #59552, 0.47 #90928, 0.46 #13691), 02_286 (0.20 #37, 0.18 #20164, 0.17 #6478), 05tbn (0.20 #188, 0.09 #992, 0.08 #1797), 030qb3t (0.18 #20210, 0.17 #1692, 0.16 #24236), 01_d4 (0.09 #3323, 0.09 #906, 0.04 #4128), 0f2wj (0.09 #838, 0.08 #1643, 0.05 #3255), 04f_d (0.09 #912, 0.07 #4939, 0.07 #2523), 0cr3d (0.09 #3366, 0.07 #4976, 0.06 #15446), 0r4qq (0.09 #1134), 059_c (0.09 #862) >> Best rule #59552 for best value: >> intensional similarity = 4 >> extensional distance = 966 >> proper extension: 01gvr1; 01n5309; 0blbxk; 03j0br4; 01jbx1; 01v3vp; 06wm0z; 026g801; 01515w; 02p59ry; ... >> query: (?x9477, ?x7184) <- gender(?x9477, ?x231), award(?x9477, ?x3066), place_of_birth(?x9477, ?x7184), film(?x9477, ?x1308) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cj_f location 06pr6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.485 http://example.org/people/person/places_lived./people/place_lived/location #17101-0chghy PRED entity: 0chghy PRED relation: medal PRED expected values: 02lq5w => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 1): 02lq5w (0.86 #36, 0.83 #21, 0.82 #30) >> Best rule #36 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0193qj; 01d8l; >> query: (?x390, 02lq5w) <- combatants(?x326, ?x390), combatants(?x94, ?x390), olympics(?x390, ?x391) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0chghy medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 201.000 201.000 0.857 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #17100-01rhl PRED entity: 01rhl PRED relation: role PRED expected values: 05842k => 56 concepts (41 used for prediction) PRED predicted values (max 10 best out of 100): 0l14md (0.86 #1942, 0.85 #2451, 0.83 #1833), 05842k (0.85 #3844, 0.83 #1833, 0.83 #3744), 0mkg (0.85 #1726, 0.85 #1631, 0.83 #1426), 0342h (0.83 #1833, 0.83 #1421, 0.82 #197), 05148p4 (0.83 #1833, 0.82 #197, 0.82 #397), 05r5c (0.83 #1833, 0.82 #197, 0.82 #397), 0dwt5 (0.83 #1833, 0.82 #197, 0.82 #397), 018j2 (0.83 #1833, 0.82 #197, 0.82 #397), 0gkd1 (0.83 #1833, 0.82 #197, 0.82 #397), 0dwsp (0.83 #1833, 0.82 #197, 0.82 #397) >> Best rule #1942 for best value: >> intensional similarity = 29 >> extensional distance = 12 >> proper extension: 0bxl5; 0dwt5; >> query: (?x4616, 0l14md) <- role(?x4616, ?x3215), role(?x4616, ?x1574), role(?x4616, ?x868), role(?x4616, ?x780), role(?x4616, ?x314), ?x1574 = 0l15bq, ?x314 = 02sgy, role(?x1495, ?x3215), role(?x433, ?x3215), role(?x6774, ?x3215), role(?x4207, ?x3215), role(?x2765, ?x3215), role(?x2306, ?x3215), ?x2765 = 01w724, role(?x3215, ?x1166), role(?x219, ?x780), ?x868 = 0dwvl, role(?x3215, ?x645), ?x433 = 025cbm, role(?x7121, ?x4616), role(?x780, ?x1886), ?x4207 = 03k0yw, ?x1886 = 02k84w, role(?x4616, ?x4583), instrumentalists(?x780, ?x5623), ?x1495 = 013y1f, category(?x6774, ?x134), profession(?x2306, ?x131), award(?x7121, ?x247) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #3844 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 45 *> proper extension: 0859_; 06rvn; *> query: (?x4616, 05842k) <- role(?x4616, ?x1574), role(?x4616, ?x314), ?x1574 = 0l15bq, role(?x6449, ?x314), role(?x5990, ?x314), role(?x1750, ?x314), role(?x1495, ?x314), ?x5990 = 0192l, role(?x8921, ?x314), role(?x4207, ?x314), role(?x2862, ?x314), role(?x2799, ?x314), role(?x2796, ?x314), ?x1495 = 013y1f, artists(?x302, ?x8921), role(?x314, ?x214), award(?x2796, ?x247), ?x6449 = 014zz1, performance_role(?x1089, ?x314), artist(?x2241, ?x2796), award_nominee(?x1381, ?x4207), role(?x211, ?x1750), group(?x1750, ?x442), nationality(?x2862, ?x94), award(?x2799, ?x1801), profession(?x2799, ?x319) *> conf = 0.85 ranks of expected_values: 2 EVAL 01rhl role 05842k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 41.000 0.857 http://example.org/music/performance_role/track_performances./music/track_contribution/role #17099-04sj3 PRED entity: 04sj3 PRED relation: exported_to! PRED expected values: 03shp => 117 concepts (97 used for prediction) PRED predicted values (max 10 best out of 62): 09c7w0 (0.41 #171, 0.41 #115, 0.41 #624), 0345h (0.41 #624, 0.35 #170, 0.35 #965), 07ssc (0.41 #624, 0.35 #170, 0.35 #965), 03rjj (0.41 #624, 0.35 #170, 0.35 #965), 03rk0 (0.21 #27, 0.19 #84, 0.17 #255), 05r4w (0.19 #1025, 0.18 #114, 0.16 #568), 06q1r (0.16 #1067, 0.14 #838, 0.14 #271), 01z215 (0.14 #26, 0.12 #83, 0.10 #254), 0j1z8 (0.14 #8, 0.12 #65, 0.07 #405), 0ctw_b (0.14 #243, 0.14 #128, 0.13 #582) >> Best rule #171 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0853g; >> query: (?x8781, ?x94) <- exported_to(?x8781, ?x94), contains(?x2467, ?x8781), ?x94 = 09c7w0 >> conf = 0.41 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04sj3 exported_to! 03shp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 97.000 0.409 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #17098-0522wp PRED entity: 0522wp PRED relation: nationality PRED expected values: 0345h => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.77 #301, 0.74 #2312, 0.70 #1204), 02jx1 (0.13 #836, 0.12 #4844, 0.12 #936), 03rk0 (0.11 #246, 0.08 #346, 0.06 #3857), 0d060g (0.10 #910, 0.10 #1010, 0.09 #810), 07ssc (0.09 #5726, 0.09 #6227, 0.09 #6427), 03rt9 (0.06 #816, 0.05 #916, 0.04 #1016), 0d05w3 (0.03 #2261, 0.03 #3061, 0.02 #2861), 03rjj (0.03 #1208, 0.02 #2816, 0.02 #2216), 0chghy (0.03 #2221, 0.02 #4421, 0.02 #1113), 0f8l9c (0.02 #2833, 0.02 #3733, 0.02 #3033) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 014zfs; 026c1; 0jrqq; 0f502; 0f7hc; 04h6mm; 0gn30; 01fyzy; 029ghl; 034hck; ... >> query: (?x7619, 09c7w0) <- profession(?x7619, ?x319), award(?x7619, ?x1105), category(?x7619, ?x134), ?x1105 = 07bdd_ >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1134 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 03mv0b; 02g3w; 0drdv; *> query: (?x7619, 0345h) <- profession(?x7619, ?x987), award_winner(?x350, ?x7619), ?x987 = 0dxtg, category(?x7619, ?x134) *> conf = 0.02 ranks of expected_values: 11 EVAL 0522wp nationality 0345h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 101.000 101.000 0.769 http://example.org/people/person/nationality #17097-02r22gf PRED entity: 02r22gf PRED relation: award! PRED expected values: 07gp9 => 51 concepts (19 used for prediction) PRED predicted values (max 10 best out of 860): 0hfzr (0.62 #3404, 0.52 #2403, 0.33 #401), 017jd9 (0.60 #1448, 0.48 #3450, 0.38 #2449), 07cz2 (0.50 #1260, 0.25 #16013, 0.25 #7007), 0ywrc (0.48 #4003, 0.48 #3299, 0.43 #2298), 0209hj (0.48 #3002, 0.48 #2061, 0.38 #3062), 0pv3x (0.43 #2105, 0.38 #3106, 0.33 #103), 0dr_4 (0.40 #1146, 0.29 #3148, 0.25 #16013), 07gp9 (0.40 #1024, 0.25 #16013, 0.25 #7007), 0c0zq (0.38 #3884, 0.38 #2883, 0.25 #16013), 0m313 (0.38 #2008, 0.33 #3009, 0.33 #6) >> Best rule #3404 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 027dtxw; 02r0csl; 040njc; 04dn09n; 02rdxsh; 0l8z1; 019f4v; 02pqp12; 0gr0m; 0gq9h; ... >> query: (?x637, 0hfzr) <- nominated_for(?x637, ?x7214), nominated_for(?x637, ?x3157), story_by(?x7214, ?x12439), crewmember(?x7214, ?x4691), ?x3157 = 0ywrc >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1024 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 05ztjjw; 0p9sw; 02hsq3m; 099c8n; 054krc; 05ztrmj; 018wdw; *> query: (?x637, 07gp9) <- nominated_for(?x637, ?x11417), nominated_for(?x637, ?x7214), ?x7214 = 02dr9j, award(?x299, ?x637), film(?x719, ?x11417) *> conf = 0.40 ranks of expected_values: 8 EVAL 02r22gf award! 07gp9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 51.000 19.000 0.619 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #17096-07zl6m PRED entity: 07zl6m PRED relation: child! PRED expected values: 01_4lx => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 66): 049ql1 (0.09 #487, 0.08 #1673, 0.07 #2206), 09b3v (0.06 #3390, 0.05 #4260, 0.05 #4530), 04sv4 (0.06 #1395, 0.05 #1773, 0.05 #1737), 03d6fyn (0.05 #1634, 0.05 #2167, 0.05 #448), 0l8sx (0.05 #4245, 0.05 #3023, 0.04 #4317), 025txrl (0.05 #489, 0.04 #4588, 0.03 #2208), 0kx4m (0.05 #426, 0.03 #3450, 0.03 #4591), 06p8m (0.05 #485, 0.03 #3450, 0.03 #4322), 01dycg (0.05 #471, 0.03 #3450, 0.03 #4322), 07733f (0.05 #496, 0.03 #3450, 0.03 #4322) >> Best rule #487 for best value: >> intensional similarity = 6 >> extensional distance = 20 >> proper extension: 04vgq5; 01jx9; 0xwj; 0123j6; 08z84_; 02pfymy; 027lf1; 0225z1; 01skcy; 06zl7g; ... >> query: (?x13954, 049ql1) <- industry(?x13954, ?x10022), industry(?x13954, ?x245), category(?x13954, ?x134), ?x245 = 01mw1, ?x10022 = 020mfr, ?x134 = 08mbj5d >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #3450 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 98 *> proper extension: 0jz9f; 017s11; 0kx4m; 04f525m; 016tw3; 024rbz; 054lpb6; 01795t; 046b0s; 061dn_; ... *> query: (?x13954, ?x11468) <- industry(?x13954, ?x245), category(?x13954, ?x134), industry(?x13349, ?x245), industry(?x11070, ?x245), industry(?x4878, ?x245), industry(?x244, ?x245), citytown(?x244, ?x13566), service_location(?x11070, ?x94), service_language(?x13349, ?x254), ?x254 = 02h40lc, contact_category(?x11070, ?x897), child(?x11468, ?x4878), service_location(?x13349, ?x390) *> conf = 0.03 ranks of expected_values: 18 EVAL 07zl6m child! 01_4lx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 61.000 61.000 0.091 http://example.org/organization/organization/child./organization/organization_relationship/child #17095-0306ds PRED entity: 0306ds PRED relation: award_nominee PRED expected values: 0306bt => 91 concepts (51 used for prediction) PRED predicted values (max 10 best out of 614): 0h1nt (0.81 #16279, 0.81 #44183, 0.81 #109302), 06t74h (0.81 #16279, 0.81 #44183, 0.81 #109302), 0306bt (0.81 #16279, 0.81 #44183, 0.81 #109302), 03061d (0.81 #16279, 0.81 #44183, 0.81 #109302), 053y4h (0.81 #16279, 0.81 #44183, 0.81 #58134), 0306ds (0.71 #568, 0.42 #2894, 0.17 #109303), 0h0wc (0.29 #2876, 0.14 #60463, 0.02 #21479), 01gq0b (0.29 #2727, 0.02 #14354, 0.01 #21330), 0fx0mw (0.29 #719, 0.18 #118606, 0.17 #109303), 01wbg84 (0.29 #57, 0.18 #118606, 0.17 #109303) >> Best rule #16279 for best value: >> intensional similarity = 3 >> extensional distance = 512 >> proper extension: 01sl1q; 044mz_; 0q9kd; 02s2ft; 06qgvf; 0grwj; 01vvydl; 01k7d9; 02p65p; 01xdf5; ... >> query: (?x2615, ?x539) <- award_nominee(?x539, ?x2615), film(?x2615, ?x2655), actor(?x782, ?x2615) >> conf = 0.81 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0306ds award_nominee 0306bt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 51.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17094-0340hj PRED entity: 0340hj PRED relation: produced_by PRED expected values: 02xnjd => 65 concepts (46 used for prediction) PRED predicted values (max 10 best out of 136): 072vj (0.33 #9325, 0.24 #8548), 02xnjd (0.17 #273, 0.08 #661, 0.05 #1438), 05mvd62 (0.17 #244, 0.02 #632, 0.01 #4516), 02vyw (0.17 #123, 0.01 #8281, 0.01 #5173), 015pkc (0.10 #15154, 0.10 #3883, 0.10 #12043), 028r4y (0.10 #15154, 0.10 #3883, 0.10 #12043), 04fzk (0.10 #15154, 0.10 #3883, 0.10 #12043), 07rd7 (0.04 #1314, 0.03 #1703, 0.03 #2091), 0fvf9q (0.03 #2726, 0.03 #3501, 0.03 #9331), 04wvhz (0.03 #424, 0.03 #5476, 0.02 #5863) >> Best rule #9325 for best value: >> intensional similarity = 3 >> extensional distance = 721 >> proper extension: 016ztl; 0564x; >> query: (?x1511, ?x12894) <- film(?x541, ?x1511), film(?x12894, ?x1511), genre(?x1511, ?x53) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #273 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0fpkhkz; 09ps01; *> query: (?x1511, 02xnjd) <- nominated_for(?x4106, ?x1511), genre(?x1511, ?x53), ?x4106 = 04fzk *> conf = 0.17 ranks of expected_values: 2 EVAL 0340hj produced_by 02xnjd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 46.000 0.329 http://example.org/film/film/produced_by #17093-03hr1p PRED entity: 03hr1p PRED relation: country PRED expected values: 0h3y 0f8l9c 03gj2 03shp 0jhd => 45 concepts (44 used for prediction) PRED predicted values (max 10 best out of 278): 0f8l9c (0.93 #5841, 0.88 #4641, 0.86 #4193), 016wzw (0.85 #1786, 0.82 #148, 0.81 #4032), 03gj2 (0.82 #148, 0.81 #4032, 0.81 #298), 03spz (0.82 #148, 0.81 #4032, 0.81 #298), 02k54 (0.82 #148, 0.81 #298, 0.78 #2828), 01mk6 (0.82 #148, 0.81 #298, 0.78 #2828), 082fr (0.82 #148, 0.81 #298, 0.78 #2828), 05vz3zq (0.82 #148, 0.81 #298, 0.78 #2828), 087vz (0.82 #148, 0.81 #298, 0.78 #2828), 019pcs (0.82 #148, 0.81 #298, 0.78 #2828) >> Best rule #5841 for best value: >> intensional similarity = 38 >> extensional distance = 39 >> proper extension: 03rbzn; >> query: (?x3127, 0f8l9c) <- country(?x3127, ?x6305), country(?x3127, ?x792), country(?x3127, ?x429), olympics(?x3127, ?x778), second_level_divisions(?x429, ?x1788), country(?x4045, ?x6305), country(?x1967, ?x6305), ?x4045 = 06z6r, film_release_region(?x7897, ?x429), film_release_region(?x7170, ?x429), film_release_region(?x6446, ?x429), film_release_region(?x5270, ?x429), film_release_region(?x5109, ?x429), film_release_region(?x3938, ?x429), film_release_region(?x3252, ?x429), film_release_region(?x2655, ?x429), film_release_region(?x1744, ?x429), contains(?x792, ?x841), exported_to(?x792, ?x5360), nationality(?x294, ?x429), organization(?x429, ?x7416), ?x1744 = 035yn8, ?x7170 = 02pxst, country(?x148, ?x429), ?x3938 = 024mpp, ?x2655 = 0fpmrm3, ?x1967 = 01cgz, ?x3252 = 0gh8zks, ?x5109 = 0b44shh, jurisdiction_of_office(?x12279, ?x792), ?x7416 = 018cqq, ?x6446 = 089j8p, ?x7897 = 03np63f, film_release_region(?x66, ?x792), nationality(?x13005, ?x792), ?x5270 = 0bc1yhb, jurisdiction_of_office(?x182, ?x792), ?x13005 = 070px >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 15, 16, 22 EVAL 03hr1p country 0jhd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 45.000 44.000 0.927 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03hr1p country 03shp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 45.000 44.000 0.927 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03hr1p country 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 45.000 44.000 0.927 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03hr1p country 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 44.000 0.927 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03hr1p country 0h3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 45.000 44.000 0.927 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #17092-01jz6x PRED entity: 01jz6x PRED relation: profession PRED expected values: 02hrh1q => 93 concepts (91 used for prediction) PRED predicted values (max 10 best out of 52): 02hrh1q (0.89 #9049, 0.88 #2828, 0.88 #5346), 0dxtg (0.53 #310, 0.50 #458, 0.31 #1050), 01d_h8 (0.50 #450, 0.47 #302, 0.33 #598), 0np9r (0.29 #316, 0.28 #464, 0.19 #168), 09jwl (0.27 #5183, 0.21 #4460, 0.17 #2535), 0dz3r (0.27 #5183, 0.15 #4444, 0.12 #2), 016z4k (0.27 #5183, 0.14 #4446, 0.12 #4), 0d1pc (0.27 #5183, 0.12 #198, 0.12 #50), 01xr66 (0.27 #5183, 0.12 #64), 02krf9 (0.24 #322, 0.22 #470, 0.12 #618) >> Best rule #9049 for best value: >> intensional similarity = 3 >> extensional distance = 1817 >> proper extension: 070px; >> query: (?x10488, 02hrh1q) <- film(?x10488, ?x2102), gender(?x10488, ?x231), profession(?x10488, ?x1041) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jz6x profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 91.000 0.889 http://example.org/people/person/profession #17091-01hrqc PRED entity: 01hrqc PRED relation: role PRED expected values: 03qjg => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 48): 03bx0bm (0.47 #148, 0.36 #1795, 0.27 #211), 05r5c (0.27 #133, 0.20 #7, 0.18 #1780), 028tv0 (0.27 #138, 0.15 #201, 0.14 #75), 05148p4 (0.25 #1790, 0.14 #904, 0.14 #458), 03_vpw (0.20 #43, 0.13 #169, 0.08 #232), 018vs (0.16 #1786, 0.16 #391, 0.07 #76), 02hnl (0.15 #1801, 0.11 #915, 0.09 #406), 03qjg (0.13 #167, 0.09 #1814, 0.08 #230), 0l14md (0.12 #1779, 0.07 #384, 0.06 #893), 01vj9c (0.07 #1787, 0.07 #140, 0.04 #203) >> Best rule #148 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 02fybl; >> query: (?x7571, 03bx0bm) <- profession(?x7571, ?x319), profession(?x7571, ?x220), ?x220 = 016z4k, ?x319 = 01d_h8, role(?x7571, ?x1473) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #167 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 02fybl; *> query: (?x7571, 03qjg) <- profession(?x7571, ?x319), profession(?x7571, ?x220), ?x220 = 016z4k, ?x319 = 01d_h8, role(?x7571, ?x1473) *> conf = 0.13 ranks of expected_values: 8 EVAL 01hrqc role 03qjg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 91.000 91.000 0.467 http://example.org/music/group_member/membership./music/group_membership/role #17090-020jqv PRED entity: 020jqv PRED relation: artists! PRED expected values: 064t9 02w4v 02k_kn => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 205): 06by7 (0.45 #5066, 0.44 #653, 0.43 #23), 064t9 (0.44 #1906, 0.40 #8533, 0.37 #644), 017_qw (0.41 #1643, 0.40 #1328, 0.38 #2274), 06j6l (0.25 #1943, 0.23 #681, 0.21 #8570), 0ggx5q (0.23 #1974, 0.15 #712, 0.11 #8601), 05bt6j (0.22 #8565, 0.22 #676, 0.22 #5089), 016clz (0.22 #9469, 0.22 #8524, 0.21 #5048), 025sc50 (0.21 #1945, 0.15 #8572, 0.14 #683), 02lnbg (0.19 #1954, 0.13 #692, 0.10 #8581), 01lyv (0.19 #1928, 0.18 #5079, 0.18 #666) >> Best rule #5066 for best value: >> intensional similarity = 3 >> extensional distance = 382 >> proper extension: 07_3qd; 04mx7s; >> query: (?x10527, 06by7) <- instrumentalists(?x227, ?x10527), artist(?x8489, ?x10527), category(?x10527, ?x134) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1906 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 02wb6yq; *> query: (?x10527, 064t9) <- profession(?x10527, ?x353), nominated_for(?x10527, ?x7723), artist(?x8489, ?x10527) *> conf = 0.44 ranks of expected_values: 2, 21, 23 EVAL 020jqv artists! 02k_kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 93.000 93.000 0.445 http://example.org/music/genre/artists EVAL 020jqv artists! 02w4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 93.000 93.000 0.445 http://example.org/music/genre/artists EVAL 020jqv artists! 064t9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 93.000 0.445 http://example.org/music/genre/artists #17089-086k8 PRED entity: 086k8 PRED relation: film PRED expected values: 01r97z 04n52p6 0g5838s 0g54xkt 0dlngsd 04nnpw 0k4p0 01gwk3 02gpkt 017kz7 04jn6y7 => 141 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1386): 0ft18 (0.70 #42140, 0.68 #8157, 0.64 #42141), 01kff7 (0.70 #42140, 0.68 #8157, 0.64 #42141), 014zwb (0.70 #42140, 0.68 #8157, 0.64 #42141), 043h78 (0.70 #42140, 0.68 #8157, 0.64 #42141), 0dln8jk (0.70 #42140, 0.47 #19032, 0.33 #637), 0dqcs3 (0.70 #42140, 0.47 #19032, 0.17 #33982), 0fphf3v (0.70 #42140, 0.47 #19032, 0.17 #33982), 074rg9 (0.70 #42140, 0.47 #19032, 0.17 #33982), 0k5px (0.70 #42140, 0.47 #19032, 0.17 #33982), 0btpm6 (0.68 #8157, 0.64 #42141, 0.62 #55738) >> Best rule #42140 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 056ws9; 04rcl7; 02x2097; >> query: (?x382, ?x522) <- award(?x382, ?x500), nominated_for(?x382, ?x522), production_companies(?x339, ?x382) >> conf = 0.70 => this is the best rule for 9 predicted values *> Best rule #615 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 03xq0f; *> query: (?x382, 04nnpw) <- film(?x382, ?x5648), film(?x382, ?x3498), ?x3498 = 02fqrf, nominated_for(?x68, ?x5648) *> conf = 0.33 ranks of expected_values: 133, 395, 396, 488, 510, 515, 866, 874, 1063 EVAL 086k8 film 04jn6y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 086k8 film 017kz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 086k8 film 02gpkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 086k8 film 01gwk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 086k8 film 0k4p0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 086k8 film 04nnpw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 086k8 film 0dlngsd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 086k8 film 0g54xkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 086k8 film 0g5838s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 086k8 film 04n52p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 086k8 film 01r97z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 141.000 119.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #17088-0ptdz PRED entity: 0ptdz PRED relation: music PRED expected values: 02bh9 => 118 concepts (57 used for prediction) PRED predicted values (max 10 best out of 72): 02jxkw (0.20 #352, 0.08 #984, 0.04 #7101), 01r4hry (0.20 #353, 0.08 #985, 0.01 #3305), 04pf4r (0.17 #700, 0.05 #1753, 0.04 #2385), 03h610 (0.17 #709, 0.04 #1130, 0.04 #2816), 05y7hc (0.17 #546, 0.04 #1600, 0.02 #1179), 01hw6wq (0.17 #670, 0.02 #1091, 0.02 #6997), 02ryx0 (0.17 #530, 0.02 #4114, 0.02 #4325), 077rj (0.17 #527), 02bh9 (0.08 #893, 0.06 #1525, 0.05 #2368), 0b6yp2 (0.08 #894, 0.04 #1316, 0.03 #1947) >> Best rule #352 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0m5s5; >> query: (?x11909, 02jxkw) <- film(?x1104, ?x11909), genre(?x11909, ?x11523), genre(?x11909, ?x258), currency(?x11909, ?x170), ?x11523 = 07s2s, film_release_distribution_medium(?x11909, ?x81), ?x258 = 05p553 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #893 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 0fvr1; 07ghq; *> query: (?x11909, 02bh9) <- film(?x1104, ?x11909), genre(?x11909, ?x11523), currency(?x11909, ?x170), ?x11523 = 07s2s, language(?x11909, ?x90), film(?x2726, ?x11909) *> conf = 0.08 ranks of expected_values: 9 EVAL 0ptdz music 02bh9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 118.000 57.000 0.200 http://example.org/film/film/music #17087-01fpfn PRED entity: 01fpfn PRED relation: form_of_government! PRED expected values: 05r4w 07ssc 0ctw_b 06qd3 06mkj 06t2t 06f32 03__y 07fj_ 07twz 05c74 02k1b 04vg8 01p8s 0165v 04hvw => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 326): 0154j (0.50 #377, 0.40 #383, 0.33 #519), 0345h (0.50 #377, 0.40 #383, 0.33 #127), 09pmkv (0.50 #377, 0.40 #383, 0.33 #407), 015fr (0.50 #377, 0.40 #383, 0.33 #272), 015qh (0.50 #377, 0.40 #383, 0.33 #151), 0d05q4 (0.50 #377, 0.40 #383, 0.33 #561), 06bnz (0.50 #377, 0.40 #383, 0.33 #544), 05sb1 (0.50 #377, 0.40 #383, 0.33 #552), 02k1b (0.50 #377, 0.40 #383, 0.33 #338), 03gj2 (0.50 #377, 0.40 #383, 0.28 #388) >> Best rule #377 for best value: >> intensional similarity = 168 >> extensional distance = 1 >> proper extension: 01d9r3; >> query: (?x4763, ?x3951) <- form_of_government(?x8588, ?x4763), form_of_government(?x6428, ?x4763), form_of_government(?x4714, ?x4763), form_of_government(?x4569, ?x4763), form_of_government(?x4302, ?x4763), form_of_government(?x3918, ?x4763), form_of_government(?x3550, ?x4763), form_of_government(?x3120, ?x4763), form_of_government(?x2051, ?x4763), form_of_government(?x1471, ?x4763), form_of_government(?x1353, ?x4763), film_release_region(?x9529, ?x1471), film_release_region(?x8495, ?x1471), film_release_region(?x5271, ?x1471), film_release_region(?x3217, ?x1471), film_release_region(?x2746, ?x1471), film_release_region(?x1904, ?x1471), film_release_region(?x1642, ?x1471), film_release_region(?x1490, ?x1471), film_release_region(?x1470, ?x1471), film_release_region(?x1035, ?x1471), film_release_region(?x1022, ?x1471), film_release_region(?x504, ?x1471), ?x9529 = 0gwf191, ?x1470 = 03twd6, country(?x1121, ?x3120), nationality(?x4956, ?x1471), country(?x14109, ?x1471), ?x504 = 0g5qs2k, ?x8495 = 0ds5_72, country(?x6354, ?x1471), country(?x5989, ?x1471), country(?x4310, ?x1471), country(?x3885, ?x1471), country(?x3641, ?x1471), country(?x3554, ?x1471), country(?x2315, ?x1471), country(?x2266, ?x1471), ?x5989 = 019tzd, adjoins(?x456, ?x1471), ?x1904 = 09146g, ?x2746 = 04f52jw, ?x1022 = 0crfwmx, ?x6428 = 0j4b, olympics(?x2051, ?x1081), olympics(?x1471, ?x1277), combatants(?x3278, ?x3918), country(?x471, ?x2051), medal(?x1471, ?x422), ?x1035 = 08hmch, ?x1081 = 0l6m5, administrative_area_type(?x3120, ?x2792), currency(?x3550, ?x170), film_release_region(?x5271, ?x2645), film_release_region(?x5271, ?x1558), film_release_region(?x5271, ?x142), film_release_region(?x11218, ?x1353), film_release_region(?x9839, ?x1353), film_release_region(?x9174, ?x1353), film_release_region(?x7554, ?x1353), film_release_region(?x7538, ?x1353), film_release_region(?x7502, ?x1353), film_release_region(?x6931, ?x1353), film_release_region(?x5825, ?x1353), film_release_region(?x5162, ?x1353), film_release_region(?x4464, ?x1353), film_release_region(?x3566, ?x1353), film_release_region(?x3151, ?x1353), film_release_region(?x3081, ?x1353), film_release_region(?x2896, ?x1353), film_release_region(?x2893, ?x1353), film_release_region(?x2788, ?x1353), film_release_region(?x2655, ?x1353), film_release_region(?x2394, ?x1353), film_release_region(?x2340, ?x1353), film_release_region(?x2037, ?x1353), film_release_region(?x1916, ?x1353), film_release_region(?x1868, ?x1353), film_release_region(?x1625, ?x1353), film_release_region(?x1535, ?x1353), film_release_region(?x1293, ?x1353), film_release_region(?x1170, ?x1353), film_release_region(?x1069, ?x1353), ?x1277 = 0swbd, country(?x6494, ?x1471), ?x5825 = 067ghz, ?x7554 = 01mgw, ?x6354 = 09_b4, ?x4310 = 064vjs, ?x2340 = 0fpv_3_, vacationer(?x1353, ?x2237), ?x1868 = 0cc7hmk, nominated_for(?x4767, ?x5271), ?x2893 = 01jrbb, ?x2896 = 0645k5, ?x2394 = 0661ql3, exported_to(?x7833, ?x1353), ?x3641 = 03fyrh, ?x6931 = 09v3jyg, film_crew_role(?x5271, ?x8411), ?x4956 = 023v4_, ?x2655 = 0fpmrm3, ?x2037 = 0gvrws1, award_winner(?x7502, ?x1864), ?x8411 = 033smt, ?x4464 = 05pdh86, combatants(?x326, ?x1353), ?x2645 = 03h64, jurisdiction_of_office(?x182, ?x1353), film(?x8104, ?x7502), participating_countries(?x418, ?x1471), organization(?x3120, ?x127), organization(?x3550, ?x312), ?x5162 = 0j3d9tn, ?x2788 = 05q4y12, ?x1642 = 0bq8tmw, country(?x11110, ?x1353), film(?x7156, ?x3081), nominated_for(?x112, ?x1069), country(?x171, ?x456), currency(?x7538, ?x1099), cinematography(?x3081, ?x4997), combatants(?x94, ?x1353), ?x1170 = 09gdm7q, produced_by(?x1069, ?x1070), award(?x7502, ?x5923), contains(?x456, ?x6265), ?x3566 = 04jpk2, nominated_for(?x3177, ?x11110), ?x3554 = 035d1m, olympics(?x456, ?x391), combatants(?x3918, ?x6465), film_release_region(?x5052, ?x456), ?x2315 = 06wrt, combatants(?x7419, ?x4302), language(?x5271, ?x90), service_location(?x896, ?x456), ?x142 = 0jgd, ?x391 = 0l6vl, contains(?x455, ?x1471), ?x1916 = 0ch26b_, ?x1099 = 01nv4h, ?x1293 = 07g_0c, ?x9174 = 087pfc, taxonomy(?x4714, ?x939), adjoins(?x2051, ?x6431), ?x9839 = 0gy7bj4, participating_countries(?x778, ?x1353), country(?x12331, ?x2051), contains(?x4714, ?x11656), ?x3217 = 0gffmn8, ?x2266 = 01lb14, ?x3885 = 019w9j, film_production_design_by(?x1625, ?x9086), ?x1558 = 01mjq, nominated_for(?x3580, ?x7538), nominated_for(?x2596, ?x3081), capital(?x4569, ?x11662), contains(?x6956, ?x4302), ?x1535 = 02r1c18, ?x11218 = 0ccck7, ?x3151 = 0gtsxr4, ?x5052 = 04yg13l, nationality(?x1068, ?x1353), ?x1490 = 0fpkhkz, nominated_for(?x7739, ?x7502), ?x8588 = 0jhd, adjoins(?x3951, ?x4302) >> conf = 0.50 => this is the best rule for 33 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 9, 20, 34, 35, 36, 47, 52, 53, 55, 64, 74, 96, 105, 112, 142 EVAL 01fpfn form_of_government! 04hvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 0165v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 01p8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 04vg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 02k1b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 05c74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 07fj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 03__y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 06f32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 06t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 06mkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 06qd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 07ssc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 01fpfn form_of_government! 05r4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 6.000 6.000 0.500 http://example.org/location/country/form_of_government #17086-0bdw6t PRED entity: 0bdw6t PRED relation: ceremony PRED expected values: 0bxs_d => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 130): 03gyp30 (0.50 #368, 0.33 #238, 0.20 #1692), 09qvms (0.50 #273, 0.33 #143, 0.20 #1692), 027hjff (0.50 #313, 0.33 #183, 0.20 #1692), 092t4b (0.50 #308, 0.33 #178, 0.20 #1692), 0hr3c8y (0.50 #270, 0.33 #140, 0.10 #920), 092_25 (0.50 #326, 0.33 #196, 0.09 #976), 0bxs_d (0.50 #496, 0.23 #626, 0.22 #756), 058m5m4 (0.50 #311, 0.08 #831, 0.08 #961), 0gpjbt (0.48 #1068, 0.37 #1458, 0.36 #2370), 09n4nb (0.47 #1084, 0.36 #1474, 0.35 #2386) >> Best rule #368 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 09sb52; 0ck27z; >> query: (?x2071, 03gyp30) <- award(?x4482, ?x2071), nominated_for(?x2071, ?x337), ?x4482 = 03q95r >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #496 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0bp_b2; 0fbvqf; 09qv3c; 0bfvd4; *> query: (?x2071, 0bxs_d) <- award(?x5559, ?x2071), nominated_for(?x2071, ?x337), ?x5559 = 02tkzn, ceremony(?x2071, ?x1265) *> conf = 0.50 ranks of expected_values: 7 EVAL 0bdw6t ceremony 0bxs_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 46.000 46.000 0.500 http://example.org/award/award_category/winners./award/award_honor/ceremony #17085-01w3v PRED entity: 01w3v PRED relation: major_field_of_study PRED expected values: 02j62 => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 123): 02j62 (0.86 #10144, 0.64 #2647, 0.58 #1435), 062z7 (0.70 #725, 0.58 #826, 0.55 #1129), 04sh3 (0.42 #864, 0.40 #763, 0.36 #1167), 0db86 (0.42 #844, 0.40 #743, 0.32 #1147), 02jfc (0.42 #872, 0.30 #1276, 0.30 #771), 01zc2w (0.42 #861, 0.30 #760, 0.25 #2073), 04gb7 (0.40 #736, 0.33 #1746, 0.33 #837), 0pf2 (0.40 #731, 0.27 #2347, 0.25 #832), 02cm61 (0.40 #789, 0.25 #890, 0.25 #587), 02822 (0.40 #734, 0.25 #4372, 0.23 #4474) >> Best rule #10144 for best value: >> intensional similarity = 5 >> extensional distance = 181 >> proper extension: 020vx9; >> query: (?x741, 02j62) <- major_field_of_study(?x741, ?x947), major_field_of_study(?x10889, ?x947), major_field_of_study(?x6127, ?x947), ?x6127 = 0gjv_, ?x10889 = 01hl_w >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w3v major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.863 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17084-01jzxy PRED entity: 01jzxy PRED relation: major_field_of_study! PRED expected values: 02bqy 01ky7c => 57 concepts (27 used for prediction) PRED predicted values (max 10 best out of 750): 01w3v (0.69 #4045, 0.68 #5200, 0.67 #7513), 08815 (0.67 #1729, 0.57 #2305, 0.56 #4608), 07szy (0.67 #3497, 0.52 #7541, 0.52 #8117), 09f2j (0.63 #7670, 0.62 #4202, 0.62 #8246), 01j_cy (0.60 #2920, 0.50 #1193, 0.38 #4072), 0bwfn (0.58 #3744, 0.57 #2593, 0.52 #7788), 07tds (0.57 #2466, 0.56 #4769, 0.45 #8237), 01mpwj (0.57 #2420, 0.53 #5302, 0.52 #7615), 07wrz (0.57 #2368, 0.50 #4095, 0.45 #8139), 0j_sncb (0.57 #2391, 0.50 #4118, 0.42 #5273) >> Best rule #4045 for best value: >> intensional similarity = 12 >> extensional distance = 14 >> proper extension: 05qjt; 036hv; 02ky346; 04rjg; 062z7; 0fdys; 0g26h; 0dc_v; 02jfc; >> query: (?x2172, 01w3v) <- major_field_of_study(?x5288, ?x2172), major_field_of_study(?x4794, ?x2172), major_field_of_study(?x3439, ?x2172), major_field_of_study(?x2172, ?x2981), featured_film_locations(?x253, ?x4794), ?x3439 = 03ksy, student(?x4794, ?x11884), ?x5288 = 02zd460, student(?x2172, ?x3553), category(?x4794, ?x134), major_field_of_study(?x1200, ?x2172), location(?x11884, ?x13745) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #3652 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 10 *> proper extension: 02vxn; 02lp1; 02j62; 01540; *> query: (?x2172, 02bqy) <- major_field_of_study(?x5288, ?x2172), major_field_of_study(?x4794, ?x2172), major_field_of_study(?x4099, ?x2172), major_field_of_study(?x3439, ?x2172), major_field_of_study(?x2172, ?x2981), featured_film_locations(?x253, ?x4794), ?x3439 = 03ksy, student(?x4794, ?x1485), institution(?x620, ?x5288), fraternities_and_sororities(?x5288, ?x3697), ?x4099 = 01f1r4, company(?x3131, ?x5288) *> conf = 0.50 ranks of expected_values: 17, 29 EVAL 01jzxy major_field_of_study! 01ky7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 57.000 27.000 0.688 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01jzxy major_field_of_study! 02bqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 57.000 27.000 0.688 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17083-0xms9 PRED entity: 0xms9 PRED relation: time_zones PRED expected values: 02hcv8 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.85 #55, 0.82 #29, 0.81 #68), 02lcqs (0.44 #161, 0.23 #83, 0.21 #96), 02fqwt (0.17 #300, 0.16 #105, 0.16 #326), 02llzg (0.10 #199, 0.09 #108, 0.08 #225), 03bdv (0.07 #45, 0.05 #318, 0.05 #370), 02hczc (0.07 #262, 0.06 #80, 0.05 #210), 042g7t (0.01 #310, 0.01 #479, 0.01 #492), 03plfd (0.01 #361, 0.01 #582, 0.01 #595), 02lcrv (0.01 #111, 0.01 #124), 052vwh (0.01 #116) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0xrzh; 0xq63; 0n5bk; 0xpq9; 0xqf3; 0xszy; 010cw1; 0xmqf; >> query: (?x12021, 02hcv8) <- place_of_birth(?x494, ?x12021), contains(?x6895, ?x12021), ?x6895 = 05fjf >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xms9 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.850 http://example.org/location/location/time_zones #17082-0gd92 PRED entity: 0gd92 PRED relation: titles! PRED expected values: 01z4y => 99 concepts (41 used for prediction) PRED predicted values (max 10 best out of 71): 07c52 (0.51 #233, 0.45 #739, 0.45 #1048), 01z4y (0.50 #1769, 0.38 #2482, 0.28 #3706), 07s9rl0 (0.43 #307, 0.42 #103, 0.40 #2755), 01hmnh (0.40 #1045, 0.40 #736, 0.38 #230), 04xvlr (0.26 #1126, 0.25 #2758, 0.22 #1229), 0hn10 (0.21 #203, 0.20 #204, 0.20 #118), 05p553 (0.21 #203, 0.19 #914, 0.18 #3060), 06cvj (0.21 #203, 0.19 #914, 0.18 #3060), 0gsy3b (0.21 #203, 0.19 #914, 0.18 #3060), 0219x_ (0.21 #203, 0.19 #914, 0.18 #3060) >> Best rule #233 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 07hpv3; 0gfzgl; 03y3bp7; 05sy2k_; 02648p; 02sqkh; 0cskb; 03r0rq; 02xhwm; 02vjhf; ... >> query: (?x7501, 07c52) <- titles(?x2286, ?x7501), category(?x7501, ?x134), films(?x2286, ?x197) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #1769 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 334 *> proper extension: 0ds35l9; 0m313; 02y_lrp; 034qmv; 02v8kmz; 047q2k1; 06wzvr; 011yrp; 011yxg; 0bvn25; ... *> query: (?x7501, 01z4y) <- genre(?x7501, ?x258), nominated_for(?x1336, ?x7501), titles(?x2286, ?x7501), ?x258 = 05p553 *> conf = 0.50 ranks of expected_values: 2 EVAL 0gd92 titles! 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 41.000 0.510 http://example.org/media_common/netflix_genre/titles #17081-015qh PRED entity: 015qh PRED relation: combatants! PRED expected values: 02psqkz 05vz3zq => 146 concepts (85 used for prediction) PRED predicted values (max 10 best out of 265): 07ssc (0.83 #1220, 0.82 #812, 0.82 #1151), 0chghy (0.83 #1220, 0.82 #812, 0.82 #1151), 09c7w0 (0.83 #1220, 0.82 #812, 0.82 #1151), 059j2 (0.83 #1220, 0.82 #812, 0.82 #1151), 05vz3zq (0.83 #1220, 0.82 #812, 0.82 #1151), 035qy (0.83 #1220, 0.82 #812, 0.82 #1151), 015fr (0.83 #1220, 0.82 #812, 0.82 #1151), 02vzc (0.83 #1220, 0.82 #812, 0.82 #1151), 02psqkz (0.82 #812, 0.82 #1151, 0.82 #406), 05qhw (0.45 #342, 0.40 #748, 0.39 #1087) >> Best rule #1220 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0193qj; >> query: (?x1497, ?x1353) <- olympics(?x1497, ?x418), combatants(?x1497, ?x1353), country(?x359, ?x1353) >> conf = 0.83 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 9 EVAL 015qh combatants! 05vz3zq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 146.000 85.000 0.829 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants EVAL 015qh combatants! 02psqkz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 146.000 85.000 0.829 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #17080-0p07l PRED entity: 0p07l PRED relation: time_zones PRED expected values: 02hczc => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 11): 02hczc (0.91 #131, 0.87 #419, 0.87 #210), 02hcv8 (0.62 #606, 0.60 #892, 0.58 #931), 02lcqs (0.32 #70, 0.26 #83, 0.24 #803), 02fqwt (0.28 #105, 0.24 #276, 0.21 #525), 02lcrv (0.17 #20, 0.11 #46, 0.03 #72), 042g7t (0.17 #24, 0.11 #50, 0.03 #221), 02llzg (0.11 #43, 0.07 #568, 0.06 #1037), 03bdv (0.07 #1235, 0.03 #1104, 0.03 #570), 03plfd (0.03 #1017, 0.03 #1043, 0.03 #1056), 0gsrz4 (0.02 #1119, 0.02 #1145, 0.02 #1158) >> Best rule #131 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 0l2lk; >> query: (?x14300, ?x2088) <- county_seat(?x14300, ?x7770), adjoins(?x14300, ?x14360), time_zones(?x7770, ?x2088), location(?x230, ?x7770) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p07l time_zones 02hczc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.909 http://example.org/location/location/time_zones #17079-0hsph PRED entity: 0hsph PRED relation: inductee! PRED expected values: 027jbr => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 0hsph inductee! 027jbr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #17078-0456zg PRED entity: 0456zg PRED relation: music PRED expected values: 03kwtb => 55 concepts (43 used for prediction) PRED predicted values (max 10 best out of 88): 0146pg (0.48 #10, 0.21 #426, 0.18 #842), 01m5m5b (0.10 #394, 0.08 #1227, 0.07 #810), 0150t6 (0.10 #1294, 0.08 #1086, 0.07 #1919), 02bh9 (0.10 #466, 0.08 #1508, 0.07 #258), 01tc9r (0.08 #2979, 0.06 #3187, 0.06 #896), 03h610 (0.07 #283, 0.06 #1116, 0.04 #1741), 016szr (0.07 #495, 0.06 #2994, 0.06 #911), 01x6v6 (0.06 #953, 0.05 #329, 0.05 #1579), 07v4dm (0.05 #399, 0.04 #1232, 0.03 #1857), 02jxkw (0.05 #556, 0.04 #972, 0.04 #1389) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0vhm; 0180mw; 025x1t; >> query: (?x8358, 0146pg) <- nominated_for(?x1291, ?x8358), executive_produced_by(?x1619, ?x1291), peers(?x1291, ?x4960), location(?x1291, ?x1523) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1041 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 01vrwfv; *> query: (?x8358, ?x3069) <- nominated_for(?x4020, ?x8358), category(?x8358, ?x134), music(?x463, ?x4020), award_winner(?x3069, ?x4020) *> conf = 0.03 ranks of expected_values: 21 EVAL 0456zg music 03kwtb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 55.000 43.000 0.483 http://example.org/film/film/music #17077-0164qt PRED entity: 0164qt PRED relation: film! PRED expected values: 041c4 => 112 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1204): 018p4y (0.56 #93691, 0.48 #18735, 0.47 #52041), 02g1jh (0.48 #18735, 0.47 #52041, 0.47 #24981), 06mr6 (0.43 #3121, 0.40 #5202, 0.27 #7283), 03kpvp (0.30 #4794, 0.14 #2713, 0.13 #47878), 017lqp (0.27 #7854, 0.13 #47878, 0.12 #64531), 0143wl (0.14 #3150, 0.13 #47878, 0.12 #64531), 01846t (0.14 #2621, 0.13 #47878, 0.12 #64531), 0jw67 (0.14 #2695, 0.13 #47878, 0.12 #64531), 0131kb (0.14 #4100, 0.13 #47878, 0.12 #64531), 01bmlb (0.14 #3842, 0.13 #47878, 0.12 #64531) >> Best rule #93691 for best value: >> intensional similarity = 3 >> extensional distance = 677 >> proper extension: 06cs95; 019nnl; 0ddd0gc; 0kfv9; 01xr2s; 02hct1; 027tbrc; 01bv8b; 01j7mr; 063ykwt; ... >> query: (?x835, ?x3034) <- nominated_for(?x3034, ?x835), nominated_for(?x154, ?x835), languages(?x3034, ?x3592) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #47878 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 150 *> proper extension: 0dnvn3; 0blpg; 08984j; 023g6w; 02wwmhc; 09v8clw; *> query: (?x835, ?x971) <- nominated_for(?x835, ?x2160), film_crew_role(?x835, ?x137), genre(?x2160, ?x225), film(?x971, ?x2160) *> conf = 0.13 ranks of expected_values: 39 EVAL 0164qt film! 041c4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 112.000 73.000 0.558 http://example.org/film/actor/film./film/performance/film #17076-03sxd2 PRED entity: 03sxd2 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 33): 09vw2b7 (0.76 #259, 0.74 #693, 0.74 #621), 0dxtw (0.49 #263, 0.43 #82, 0.43 #697), 01vx2h (0.42 #264, 0.41 #919, 0.39 #698), 01pvkk (0.31 #1140, 0.30 #519, 0.28 #1877), 02ynfr (0.22 #703, 0.22 #523, 0.22 #269), 0215hd (0.20 #19, 0.19 #526, 0.17 #634), 0d2b38 (0.20 #26, 0.15 #98, 0.13 #533), 02_n3z (0.20 #1, 0.14 #109, 0.11 #508), 089g0h (0.20 #20, 0.14 #635, 0.13 #527), 033smt (0.20 #28, 0.11 #1054, 0.09 #2601) >> Best rule #259 for best value: >> intensional similarity = 5 >> extensional distance = 109 >> proper extension: 07k2mq; 0372j5; >> query: (?x1941, 09vw2b7) <- film_crew_role(?x1941, ?x1284), ?x1284 = 0ch6mp2, featured_film_locations(?x1941, ?x739), crewmember(?x1941, ?x9151), film(?x447, ?x1941) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03sxd2 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.757 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17075-09bymc PRED entity: 09bymc PRED relation: award_winner PRED expected values: 02p65p 02cllz 0427y => 33 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2603): 0hz_1 (0.62 #14948, 0.25 #8844, 0.17 #13422), 0g5lhl7 (0.50 #6496, 0.50 #4971, 0.40 #15654), 018ygt (0.50 #8582, 0.33 #13160, 0.33 #2478), 0jz9f (0.50 #4589, 0.33 #10, 0.25 #6114), 0lpjn (0.50 #6510, 0.33 #406, 0.25 #4985), 01pcq3 (0.50 #6208, 0.33 #104, 0.25 #4683), 04y79_n (0.50 #6291, 0.33 #187, 0.25 #4766), 0bwh6 (0.40 #10862, 0.33 #1704, 0.25 #3050), 05np4c (0.40 #11179, 0.33 #2021, 0.25 #8125), 01_njt (0.38 #4576, 0.33 #2698, 0.31 #22895) >> Best rule #14948 for best value: >> intensional similarity = 17 >> extensional distance = 6 >> proper extension: 0bx6zs; >> query: (?x8762, 0hz_1) <- award_winner(?x8762, ?x1762), award_winner(?x8762, ?x617), nominated_for(?x1762, ?x3169), honored_for(?x8762, ?x5810), honored_for(?x8762, ?x4848), award(?x617, ?x3105), award_winner(?x1762, ?x1394), ceremony(?x899, ?x8762), award_winner(?x1071, ?x617), nominated_for(?x617, ?x4596), award_winner(?x3105, ?x1047), ?x3169 = 030k94, nominated_for(?x68, ?x4848), award(?x4951, ?x3105), ?x4951 = 02lfp4, award_winner(?x4848, ?x2332), actor(?x5810, ?x56) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #4576 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 059x66; 0bc773; *> query: (?x8762, ?x665) <- award_winner(?x8762, ?x5504), award_winner(?x8762, ?x1762), award_winner(?x8762, ?x617), nominated_for(?x1762, ?x782), honored_for(?x8762, ?x4848), honored_for(?x8762, ?x2436), award(?x617, ?x3105), award_winner(?x1762, ?x1394), ceremony(?x899, ?x8762), award_winner(?x1071, ?x617), production_companies(?x4848, ?x902), ?x5504 = 02x7vq, nominated_for(?x3066, ?x4848), nominated_for(?x617, ?x4596), award_winner(?x3066, ?x92), award_winner(?x2436, ?x665) *> conf = 0.38 ranks of expected_values: 22, 84, 106 EVAL 09bymc award_winner 0427y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 33.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09bymc award_winner 02cllz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 33.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09bymc award_winner 02p65p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 33.000 23.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17074-04mhbh PRED entity: 04mhbh PRED relation: people! PRED expected values: 03lmx1 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 39): 041rx (0.33 #764, 0.28 #156, 0.25 #2968), 0x67 (0.21 #2974, 0.18 #3962, 0.18 #3430), 033tf_ (0.18 #83, 0.17 #1603, 0.14 #7), 09vc4s (0.14 #9, 0.06 #1605, 0.05 #1909), 02g7sp (0.14 #18, 0.02 #398, 0.02 #246), 048z7l (0.11 #191, 0.05 #799, 0.05 #1635), 0xnvg (0.11 #241, 0.09 #1609, 0.09 #2977), 02w7gg (0.10 #3118, 0.10 #3346, 0.10 #3194), 033qxt (0.09 #130), 07hwkr (0.08 #2976, 0.07 #3432, 0.07 #1608) >> Best rule #764 for best value: >> intensional similarity = 4 >> extensional distance = 289 >> proper extension: 0h5f5n; 04rs03; 08f3b1; 0jf1b; 02l840; 019z7q; 012t1; 0136g9; 0p_2r; 017r2; ... >> query: (?x9288, 041rx) <- award(?x9288, ?x537), profession(?x9288, ?x987), ?x987 = 0dxtg, people(?x5741, ?x9288) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #546 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 229 *> proper extension: 079vf; 03kpvp; 02_p5w; 01lly5; 02v406; 01twdk; 07y8l9; 015lhm; 06s6hs; 0143wl; ... *> query: (?x9288, 03lmx1) <- film(?x9288, ?x2746), film_release_region(?x2746, ?x2346), film_release_region(?x2746, ?x94), ?x2346 = 0d05w3, ?x94 = 09c7w0 *> conf = 0.03 ranks of expected_values: 22 EVAL 04mhbh people! 03lmx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 109.000 109.000 0.326 http://example.org/people/ethnicity/people #17073-0grwj PRED entity: 0grwj PRED relation: profession PRED expected values: 03gjzk => 115 concepts (107 used for prediction) PRED predicted values (max 10 best out of 79): 01d_h8 (0.86 #5080, 0.84 #4645, 0.84 #4790), 03gjzk (0.85 #3782, 0.83 #3492, 0.82 #3927), 0dxtg (0.67 #3491, 0.66 #3781, 0.64 #3926), 02jknp (0.50 #6, 0.49 #4646, 0.48 #5081), 09jwl (0.37 #1756, 0.37 #8716, 0.36 #7266), 018gz8 (0.35 #304, 0.27 #1174, 0.25 #449), 02krf9 (0.33 #2489, 0.32 #2924, 0.32 #3504), 0nbcg (0.27 #1769, 0.26 #6554, 0.26 #7279), 0np9r (0.25 #2628, 0.22 #453, 0.20 #1178), 016z4k (0.24 #6528, 0.23 #7398, 0.23 #7253) >> Best rule #5080 for best value: >> intensional similarity = 3 >> extensional distance = 376 >> proper extension: 02xnjd; 0glyyw; 03p01x; 0g_rs_; >> query: (?x105, 01d_h8) <- produced_by(?x7647, ?x105), profession(?x105, ?x106), genre(?x7647, ?x53) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #3782 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 214 *> proper extension: 06vqdf; *> query: (?x105, 03gjzk) <- nominated_for(?x105, ?x8837), program(?x105, ?x7647), profession(?x105, ?x106) *> conf = 0.85 ranks of expected_values: 2 EVAL 0grwj profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 107.000 0.857 http://example.org/people/person/profession #17072-06k75 PRED entity: 06k75 PRED relation: entity_involved PRED expected values: 03_lf => 58 concepts (33 used for prediction) PRED predicted values (max 10 best out of 225): 0cdbq (0.50 #155, 0.50 #39, 0.49 #2196), 0193qj (0.49 #2196, 0.44 #2195, 0.40 #528), 05kyr (0.49 #2196, 0.44 #2195, 0.40 #2199), 03b79 (0.49 #2196, 0.44 #2195, 0.33 #1264), 07ssc (0.49 #2196, 0.44 #2195, 0.29 #152), 02psqkz (0.49 #2196, 0.44 #2195, 0.29 #152), 0bq0p9 (0.49 #2196, 0.44 #2195, 0.29 #152), 02vzc (0.49 #2196, 0.44 #2195, 0.29 #152), 01rdm0 (0.49 #2196, 0.44 #2195, 0.29 #152), 0d060g (0.49 #2196, 0.44 #2195, 0.29 #152) >> Best rule #155 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 0k4y6; >> query: (?x7241, ?x4492) <- combatants(?x7241, ?x4492), combatants(?x7241, ?x1892), ?x4492 = 0cdbq, film_release_region(?x5827, ?x1892), film_release_region(?x3287, ?x1892), film_release_region(?x1451, ?x1892), ?x1451 = 04zyhx, ?x5827 = 0ggbfwf, country(?x453, ?x1892), combatants(?x1892, ?x1003), ?x3287 = 026njb5, entity_involved(?x7241, ?x3341) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3554 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 18 *> proper extension: 053_7s; *> query: (?x7241, 03_lf) <- combatants(?x7241, ?x4492), combatants(?x7241, ?x3142), combatants(?x7241, ?x1892), combatants(?x7241, ?x94), nationality(?x1221, ?x4492), entity_involved(?x7241, ?x9006), combatants(?x390, ?x3142), ?x94 = 09c7w0, film_release_region(?x2512, ?x1892), ?x2512 = 07x4qr *> conf = 0.05 ranks of expected_values: 171 EVAL 06k75 entity_involved 03_lf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 58.000 33.000 0.500 http://example.org/base/culturalevent/event/entity_involved #17071-04k4l PRED entity: 04k4l PRED relation: organizations_founded! PRED expected values: 083q7 => 110 concepts (47 used for prediction) PRED predicted values (max 10 best out of 76): 09bg4l (0.33 #37, 0.25 #266, 0.25 #151), 02yy8 (0.33 #106, 0.25 #335, 0.25 #220), 09gnn (0.25 #204, 0.20 #662, 0.20 #546), 0k6nt (0.20 #688, 0.20 #585, 0.14 #701), 059j2 (0.20 #589, 0.14 #705, 0.08 #1049), 0d0vqn (0.20 #576, 0.14 #692, 0.08 #1036), 04g61 (0.20 #623, 0.14 #739, 0.08 #1083), 03rt9 (0.20 #577, 0.14 #693, 0.08 #1037), 07ssc (0.20 #578, 0.14 #694, 0.08 #1038), 0154j (0.20 #575, 0.14 #691, 0.08 #1035) >> Best rule #37 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 07t65; >> query: (?x4230, 09bg4l) <- organization(?x7833, ?x4230), organization(?x2513, ?x4230), organization(?x1592, ?x4230), organization(?x985, ?x4230), organization(?x583, ?x4230), ?x7833 = 0jdx, ?x985 = 0k6nt, citytown(?x4230, ?x10610), ?x2513 = 05b4w, ?x1592 = 05v10, ?x583 = 015fr >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04k4l organizations_founded! 083q7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 47.000 0.333 http://example.org/organization/organization_founder/organizations_founded #17070-0ytc PRED entity: 0ytc PRED relation: teams! PRED expected values: 06c62 => 111 concepts (101 used for prediction) PRED predicted values (max 10 best out of 176): 04wgh (0.33 #40, 0.17 #850, 0.12 #1660), 031y2 (0.25 #735, 0.25 #465, 0.12 #2085), 079yb (0.25 #764, 0.17 #1034, 0.10 #17839), 01n43d (0.17 #1291, 0.08 #4533, 0.08 #4263), 01jp4s (0.17 #1341, 0.08 #4043, 0.04 #5936), 0947l (0.12 #2342, 0.11 #2612, 0.10 #17839), 07mgr (0.12 #2098, 0.09 #3719, 0.08 #3990), 06s_2 (0.12 #1826, 0.02 #7501, 0.02 #9121), 0hknf (0.12 #2413, 0.02 #7818, 0.01 #12409), 0fhsz (0.11 #2657, 0.01 #11843, 0.01 #12113) >> Best rule #40 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 03_9hm; >> query: (?x3823, 04wgh) <- team(?x5471, ?x3823), team(?x8360, ?x3823), position(?x3823, ?x530), position(?x3823, ?x203), position(?x3823, ?x60), ?x8360 = 0c2rr7, ?x60 = 02nzb8, ?x203 = 0dgrmp, ?x530 = 02_j1w >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ytc teams! 06c62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 101.000 0.333 http://example.org/sports/sports_team_location/teams #17069-0gt3p PRED entity: 0gt3p PRED relation: celebrities_impersonated! PRED expected values: 03m6t5 => 156 concepts (36 used for prediction) PRED predicted values (max 10 best out of 4): 03m6t5 (0.22 #68, 0.22 #132, 0.17 #35), 03d_zl4 (0.03 #62, 0.02 #38, 0.02 #71), 018grr (0.02 #2, 0.02 #18), 0pz04 (0.02 #40, 0.01 #48, 0.01 #64) >> Best rule #68 for best value: >> intensional similarity = 4 >> extensional distance = 170 >> proper extension: 01k7d9; 0byfz; 0h1_w; 028lc8; 015gw6; 01fdc0; 09p06; 0btyl; 02dth1; 0bqdvt; ... >> query: (?x7759, 03m6t5) <- award(?x7759, ?x102), people(?x268, ?x7759), nationality(?x7759, ?x279), film(?x7759, ?x5856) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gt3p celebrities_impersonated! 03m6t5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 36.000 0.221 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #17068-01c979 PRED entity: 01c979 PRED relation: profession! PRED expected values: 05fg2 01vzxld 0dq9wx => 67 concepts (28 used for prediction) PRED predicted values (max 10 best out of 4248): 017yfz (0.44 #22455, 0.40 #18222, 0.33 #9756), 03h502k (0.44 #22821, 0.40 #18588, 0.33 #10122), 0d0mbj (0.44 #23424, 0.33 #10725, 0.33 #6489), 016hvl (0.44 #21499, 0.33 #4564, 0.30 #25734), 0ky1 (0.44 #24636, 0.33 #7701, 0.20 #28871), 03gyh_z (0.43 #63498, 0.37 #63499, 0.37 #63500), 0dx97 (0.43 #63498, 0.37 #63499, 0.37 #63500), 05km8z (0.43 #63498, 0.37 #63499, 0.37 #63500), 04z_x4v (0.43 #63498, 0.37 #63500, 0.36 #67734), 05fg2 (0.43 #63498, 0.37 #63500, 0.36 #67734) >> Best rule #22455 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 0fj9f; >> query: (?x9674, 017yfz) <- profession(?x10777, ?x9674), profession(?x6512, ?x9674), influenced_by(?x11554, ?x6512), participant(?x10777, ?x2697), gender(?x6512, ?x231), ?x11554 = 03cdg, participant(?x56, ?x10777), participant(?x10777, ?x9374) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #63498 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 15 *> proper extension: 0db79; *> query: (?x9674, ?x1309) <- specialization_of(?x8368, ?x9674), specialization_of(?x2450, ?x9674), profession(?x11596, ?x8368), profession(?x1309, ?x8368), student(?x3878, ?x1309), profession(?x2803, ?x2450), gender(?x11596, ?x231), award_nominee(?x2803, ?x1039) *> conf = 0.43 ranks of expected_values: 10, 51, 2652 EVAL 01c979 profession! 0dq9wx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 67.000 28.000 0.444 http://example.org/people/person/profession EVAL 01c979 profession! 01vzxld CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 28.000 0.444 http://example.org/people/person/profession EVAL 01c979 profession! 05fg2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 67.000 28.000 0.444 http://example.org/people/person/profession #17067-07y9w5 PRED entity: 07y9w5 PRED relation: nominated_for! PRED expected values: 0gqzz => 65 concepts (58 used for prediction) PRED predicted values (max 10 best out of 192): 019f4v (0.53 #55, 0.45 #294, 0.28 #4359), 0l8z1 (0.53 #53, 0.45 #292, 0.18 #1965), 02x1z2s (0.42 #623, 0.34 #862, 0.13 #1101), 0gq9h (0.42 #64, 0.36 #303, 0.33 #4368), 099c8n (0.42 #58, 0.36 #297, 0.19 #1970), 0gs9p (0.37 #66, 0.32 #305, 0.27 #4370), 0k611 (0.37 #75, 0.32 #314, 0.24 #4379), 04dn09n (0.37 #36, 0.32 #275, 0.23 #13153), 040njc (0.37 #7, 0.32 #246, 0.23 #13153), 02qyntr (0.37 #182, 0.32 #421, 0.20 #2094) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 02qjv1p; >> query: (?x1477, 019f4v) <- nominated_for(?x2135, ?x1477), nominated_for(?x1723, ?x1477), ?x2135 = 06pj8, genre(?x1477, ?x600) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #529 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 016ztl; *> query: (?x1477, 0gqzz) <- genre(?x1477, ?x2540), ?x2540 = 0hcr, production_companies(?x1477, ?x541) *> conf = 0.29 ranks of expected_values: 16 EVAL 07y9w5 nominated_for! 0gqzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 65.000 58.000 0.526 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17066-083p7 PRED entity: 083p7 PRED relation: profession PRED expected values: 0fj9f => 122 concepts (100 used for prediction) PRED predicted values (max 10 best out of 96): 0fj9f (0.88 #2439, 0.88 #1992, 0.87 #2737), 02hrh1q (0.76 #611, 0.69 #12533, 0.69 #6273), 01d_h8 (0.31 #7009, 0.29 #4625, 0.29 #5370), 0dxtg (0.29 #6421, 0.28 #12383, 0.28 #4633), 0cbd2 (0.29 #3434, 0.27 #3285, 0.26 #3136), 0kyk (0.27 #1670, 0.27 #180, 0.25 #478), 012t_z (0.25 #13, 0.17 #758, 0.13 #162), 0c5lg (0.25 #81, 0.13 #230, 0.12 #379), 0dl08 (0.25 #107, 0.12 #554, 0.07 #256), 0d8qb (0.25 #80, 0.10 #974, 0.07 #1570) >> Best rule #2439 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 030dr; >> query: (?x1157, 0fj9f) <- nationality(?x1157, ?x94), basic_title(?x1157, ?x346), profession(?x1157, ?x3342) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 083p7 profession 0fj9f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 100.000 0.883 http://example.org/people/person/profession #17065-0gvbw PRED entity: 0gvbw PRED relation: place_founded PRED expected values: 0cr3d => 188 concepts (137 used for prediction) PRED predicted values (max 10 best out of 52): 02_286 (0.22 #920, 0.19 #1643, 0.19 #2369), 0d6lp (0.17 #88, 0.07 #614, 0.06 #810), 0rh6k (0.17 #68, 0.05 #266, 0.03 #856), 01_d4 (0.09 #1990, 0.07 #1331, 0.06 #2453), 06pwq (0.08 #139, 0.07 #599, 0.06 #206), 0qcrj (0.08 #196, 0.03 #656, 0.03 #1050), 0c75w (0.08 #185, 0.03 #841, 0.03 #907), 07dfk (0.08 #6207, 0.07 #3616, 0.05 #7806), 0y1rf (0.07 #643, 0.05 #1103, 0.05 #1169), 01smm (0.06 #240, 0.05 #370, 0.05 #305) >> Best rule #920 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0xwj; 0d2fd7; >> query: (?x2975, ?x739) <- currency(?x2975, ?x170), industry(?x2975, ?x13047), category(?x2975, ?x134), citytown(?x2975, ?x739) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gvbw place_founded 0cr3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 188.000 137.000 0.220 http://example.org/organization/organization/place_founded #17064-027ydt PRED entity: 027ydt PRED relation: student PRED expected values: 037s5h => 162 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1116): 015qq1 (0.11 #6082, 0.10 #8176, 0.04 #12364), 01mqh5 (0.11 #6074, 0.10 #8168, 0.03 #10262), 02779r4 (0.11 #5352, 0.10 #7446, 0.02 #11634), 02pv_d (0.11 #5584, 0.10 #7678, 0.02 #11866), 03l3ln (0.11 #5344, 0.10 #7438, 0.02 #11626), 0d3k14 (0.11 #6044, 0.10 #8138, 0.02 #12326), 084w8 (0.11 #4198, 0.10 #6292, 0.02 #10480), 02mslq (0.11 #4259, 0.05 #6353, 0.02 #10541), 024y6w (0.10 #7736, 0.06 #5642, 0.03 #9830), 022411 (0.10 #7971, 0.06 #5877, 0.03 #10065) >> Best rule #6082 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 0j_sncb; >> query: (?x6584, 015qq1) <- institution(?x865, ?x6584), colors(?x6584, ?x663), major_field_of_study(?x6584, ?x10391), currency(?x6584, ?x170), ?x10391 = 02jfc >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #10022 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 69 *> proper extension: 08qnnv; *> query: (?x6584, 037s5h) <- institution(?x1771, ?x6584), institution(?x1200, ?x6584), institution(?x865, ?x6584), ?x1771 = 019v9k, ?x1200 = 016t_3, ?x865 = 02h4rq6, currency(?x6584, ?x170) *> conf = 0.01 ranks of expected_values: 500 EVAL 027ydt student 037s5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 162.000 73.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #17063-03hj3b3 PRED entity: 03hj3b3 PRED relation: nominated_for! PRED expected values: 019f4v 02w9sd7 => 69 concepts (61 used for prediction) PRED predicted values (max 10 best out of 265): 04dn09n (0.66 #8594, 0.66 #3392, 0.66 #9729), 027c95y (0.66 #8594, 0.66 #3392, 0.66 #9729), 019f4v (0.59 #276, 0.53 #1858, 0.52 #50), 02qyntr (0.59 #395, 0.48 #169, 0.29 #1977), 09qv_s (0.44 #104, 0.37 #330, 0.17 #1686), 02w9sd7 (0.44 #114, 0.34 #340, 0.16 #1922), 02n9nmz (0.44 #280, 0.40 #54, 0.21 #1636), 03hkv_r (0.41 #240, 0.40 #14, 0.19 #1596), 0l8z1 (0.40 #48, 0.37 #274, 0.24 #4118), 09td7p (0.40 #83, 0.34 #309, 0.28 #1665) >> Best rule #8594 for best value: >> intensional similarity = 4 >> extensional distance = 864 >> proper extension: 0cwrr; 04glx0; 05h95s; 05fgr_; 05sy0cv; 06w7mlh; 07bz5; 06mmr; >> query: (?x1944, ?x591) <- award_winner(?x1944, ?x3002), award(?x1944, ?x2915), award(?x1944, ?x591), award_winner(?x2915, ?x157) >> conf = 0.66 => this is the best rule for 2 predicted values *> Best rule #276 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 39 *> proper extension: 0m313; 0_b3d; 0jyx6; 09p0ct; 011yqc; 0p_th; 026p4q7; 0ctb4g; 0yx7h; 0h6r5; ... *> query: (?x1944, 019f4v) <- nominated_for(?x2375, ?x1944), nominated_for(?x1972, ?x1944), nominated_for(?x591, ?x1944), ?x1972 = 0gqyl, ?x2375 = 04kxsb, award(?x123, ?x591) *> conf = 0.59 ranks of expected_values: 3, 6 EVAL 03hj3b3 nominated_for! 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 69.000 61.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hj3b3 nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 61.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17062-01nkcn PRED entity: 01nkcn PRED relation: organization! PRED expected values: 060c4 => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 16): 060c4 (0.81 #171, 0.81 #184, 0.79 #327), 07xl34 (0.32 #609, 0.28 #297, 0.27 #89), 0dq_5 (0.21 #477, 0.19 #529, 0.18 #1218), 05k17c (0.15 #1536, 0.14 #280, 0.11 #631), 0hm4q (0.15 #1536, 0.07 #606, 0.06 #658), 04n1q6 (0.15 #1536, 0.03 #58, 0.02 #71), 08jcfy (0.15 #1536, 0.02 #883, 0.02 #909), 05c0jwl (0.04 #239, 0.04 #1136, 0.04 #668), 01t7n9 (0.03 #1444, 0.02 #1901, 0.02 #2058), 0fkzq (0.03 #1444, 0.02 #1901, 0.02 #2058) >> Best rule #171 for best value: >> intensional similarity = 5 >> extensional distance = 115 >> proper extension: 02jztz; 03wv2g; >> query: (?x1699, 060c4) <- school(?x2820, ?x1699), currency(?x1699, ?x170), ?x170 = 09nqf, school_type(?x1699, ?x1507), contains(?x94, ?x1699) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nkcn organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.812 http://example.org/organization/role/leaders./organization/leadership/organization #17061-052hl PRED entity: 052hl PRED relation: influenced_by! PRED expected values: 0q9zc => 124 concepts (78 used for prediction) PRED predicted values (max 10 best out of 412): 05rx__ (0.10 #4391, 0.05 #815, 0.04 #14607), 01xwv7 (0.10 #931, 0.09 #4507, 0.07 #3486), 05ty4m (0.09 #4094, 0.08 #518, 0.06 #7158), 016_mj (0.08 #565, 0.07 #4141, 0.06 #3120), 040db (0.08 #17959, 0.07 #9268, 0.07 #20511), 05jm7 (0.08 #13416, 0.07 #12396, 0.07 #14441), 0ph2w (0.07 #22991, 0.05 #7305, 0.05 #24527), 01hmk9 (0.07 #22991, 0.05 #24527, 0.04 #2556), 052hl (0.07 #22991, 0.05 #24527, 0.01 #7416), 012gq6 (0.07 #22991, 0.05 #24527, 0.01 #2169) >> Best rule #4391 for best value: >> intensional similarity = 2 >> extensional distance = 103 >> proper extension: 01nrq5; 013qvn; 022q4j; 0p_jc; 0c5vh; 0btj0; >> query: (?x6771, 05rx__) <- film(?x6771, ?x463), influenced_by(?x1145, ?x6771) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #330 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 01y8d4; *> query: (?x6771, 0q9zc) <- award_winner(?x2794, ?x6771), written_by(?x1210, ?x6771), people(?x1050, ?x6771) *> conf = 0.02 ranks of expected_values: 222 EVAL 052hl influenced_by! 0q9zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 124.000 78.000 0.105 http://example.org/influence/influence_node/influenced_by #17060-01h788 PRED entity: 01h788 PRED relation: major_field_of_study! PRED expected values: 016t_3 => 48 concepts (34 used for prediction) PRED predicted values (max 10 best out of 22): 02_xgp2 (0.91 #298, 0.90 #163, 0.90 #274), 019v9k (0.87 #408, 0.81 #360, 0.77 #203), 016t_3 (0.87 #177, 0.86 #173, 0.86 #154), 02h4rq6 (0.85 #309, 0.74 #176, 0.73 #132), 03bwzr4 (0.69 #208, 0.62 #164, 0.59 #275), 0bkj86 (0.67 #137, 0.66 #293, 0.66 #269), 02mjs7 (0.58 #241, 0.53 #656, 0.41 #657), 0bjrnt (0.38 #156, 0.37 #267, 0.35 #179), 071tyz (0.33 #10, 0.31 #589, 0.31 #399), 02m4yg (0.33 #37, 0.28 #22, 0.26 #767) >> Best rule #298 for best value: >> intensional similarity = 23 >> extensional distance = 42 >> proper extension: 03nfmq; >> query: (?x14397, 02_xgp2) <- major_field_of_study(?x1368, ?x14397), major_field_of_study(?x734, ?x14397), ?x1368 = 014mlp, institution(?x734, ?x12374), institution(?x734, ?x9200), institution(?x734, ?x7950), institution(?x734, ?x2637), institution(?x734, ?x2497), institution(?x734, ?x2313), institution(?x734, ?x1667), institution(?x734, ?x216), institution(?x734, ?x122), ?x12374 = 0yl_3, ?x2637 = 0dy04, major_field_of_study(?x734, ?x947), ?x2497 = 0f1nl, ?x7950 = 01dbns, ?x947 = 036hv, ?x122 = 08815, ?x1667 = 03v6t, ?x2313 = 07wrz, colors(?x216, ?x663), ?x9200 = 0dzst >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #177 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 21 *> proper extension: 0dc_v; *> query: (?x14397, 016t_3) <- major_field_of_study(?x1368, ?x14397), major_field_of_study(?x734, ?x14397), ?x1368 = 014mlp, ?x734 = 04zx3q1, major_field_of_study(?x7097, ?x14397), student(?x7097, ?x6708), contains(?x455, ?x7097), currency(?x7097, ?x1099), award_nominee(?x2551, ?x6708), gender(?x6708, ?x231), nationality(?x6708, ?x1310), ?x2551 = 0h0wc, organization(?x5510, ?x7097), people(?x743, ?x6708) *> conf = 0.87 ranks of expected_values: 3 EVAL 01h788 major_field_of_study! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 48.000 34.000 0.909 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #17059-02tx6q PRED entity: 02tx6q PRED relation: profession! PRED expected values: 0b6mgp_ 02q9kqf 095zvfg => 50 concepts (19 used for prediction) PRED predicted values (max 10 best out of 4212): 02fybl (0.71 #19250, 0.67 #15019, 0.60 #10788), 03f1zhf (0.71 #20135, 0.67 #15904, 0.60 #11673), 01nhkxp (0.67 #15841, 0.60 #11610, 0.57 #20072), 0l12d (0.67 #13138, 0.60 #8907, 0.57 #17369), 01vsy7t (0.67 #14159, 0.60 #9928, 0.57 #18390), 0ddkf (0.67 #14913, 0.60 #10682, 0.57 #19144), 02cx90 (0.67 #14051, 0.60 #9820, 0.57 #18282), 014q2g (0.67 #13503, 0.60 #9272, 0.57 #17734), 0473q (0.67 #15044, 0.60 #10813, 0.57 #19275), 0161c2 (0.67 #13611, 0.60 #9380, 0.57 #17842) >> Best rule #19250 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0gbbt; >> query: (?x5654, 02fybl) <- profession(?x4594, ?x5654), profession(?x3607, ?x5654), profession(?x2242, ?x5654), ?x2242 = 09prnq, award_nominee(?x4594, ?x1989), award_nominee(?x527, ?x3607), award_winner(?x7810, ?x4594) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #8458 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0nbcg; *> query: (?x5654, ?x1989) <- profession(?x4594, ?x5654), profession(?x3607, ?x5654), profession(?x2242, ?x5654), ?x2242 = 09prnq, award_nominee(?x4594, ?x1989), ?x3607 = 0412f5y, award(?x4594, ?x528) *> conf = 0.30 ranks of expected_values: 966, 975, 978 EVAL 02tx6q profession! 095zvfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 19.000 0.714 http://example.org/people/person/profession EVAL 02tx6q profession! 02q9kqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 19.000 0.714 http://example.org/people/person/profession EVAL 02tx6q profession! 0b6mgp_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 19.000 0.714 http://example.org/people/person/profession #17058-04w1j9 PRED entity: 04w1j9 PRED relation: gender PRED expected values: 05zppz => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #15, 0.87 #13, 0.87 #3), 02zsn (0.46 #238, 0.31 #30, 0.29 #64) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 189 >> proper extension: 01mwsnc; 04bgy; 013rds; >> query: (?x4931, 05zppz) <- profession(?x4931, ?x319), type_of_union(?x4931, ?x566), executive_produced_by(?x3640, ?x4931) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04w1j9 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.874 http://example.org/people/person/gender #17057-0697s PRED entity: 0697s PRED relation: film_release_region! PRED expected values: 02vxq9m => 77 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1809): 0fpgp26 (0.66 #13030, 0.60 #14351, 0.59 #22278), 0bpm4yw (0.66 #12436, 0.60 #13757, 0.59 #21684), 02vxq9m (0.65 #11906, 0.61 #13227, 0.59 #9264), 0fpv_3_ (0.65 #12169, 0.59 #17454, 0.58 #13490), 04f52jw (0.64 #4294, 0.60 #12220, 0.60 #13541), 08hmch (0.63 #12007, 0.62 #13328, 0.62 #9365), 017jd9 (0.63 #12482, 0.59 #9840, 0.59 #4556), 017gm7 (0.63 #12049, 0.59 #9407, 0.58 #13370), 062zm5h (0.63 #12545, 0.54 #9903, 0.54 #21793), 047vnkj (0.62 #4663, 0.61 #13910, 0.60 #12589) >> Best rule #13030 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 05r4w; >> query: (?x3016, 0fpgp26) <- film_release_region(?x1012, ?x3016), member_states(?x7695, ?x3016), ?x7695 = 085h1 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #11906 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 05r4w; *> query: (?x3016, 02vxq9m) <- film_release_region(?x1012, ?x3016), member_states(?x7695, ?x3016), ?x7695 = 085h1 *> conf = 0.65 ranks of expected_values: 3 EVAL 0697s film_release_region! 02vxq9m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 42.000 0.659 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17056-0bfvw2 PRED entity: 0bfvw2 PRED relation: nominated_for PRED expected values: 04sskp => 45 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1445): 02py4c8 (0.79 #15821, 0.68 #17403, 0.67 #17404), 0h3mh3q (0.79 #15821, 0.68 #17403, 0.67 #17404), 0b76kw1 (0.79 #15821, 0.68 #17403, 0.67 #34815), 05c46y6 (0.60 #3557, 0.47 #14633, 0.38 #8300), 0ds3t5x (0.60 #3209, 0.40 #14285, 0.38 #7952), 011yg9 (0.60 #4079, 0.38 #8822, 0.33 #15155), 05v38p (0.60 #4173, 0.38 #8916, 0.33 #12082), 0bnzd (0.60 #4248, 0.38 #8991, 0.29 #5829), 0c0zq (0.60 #4536, 0.38 #9279, 0.29 #6117), 01g03q (0.47 #14022, 0.29 #7690, 0.29 #6109) >> Best rule #15821 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 02z0dfh; 09td7p; 099t8j; 02ppm4q; >> query: (?x375, ?x715) <- award(?x4367, ?x375), award(?x624, ?x375), ?x4367 = 02kxwk, award(?x715, ?x375), people(?x2510, ?x624) >> conf = 0.79 => this is the best rule for 3 predicted values *> Best rule #13880 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 09v82c0; *> query: (?x375, 04sskp) <- award(?x374, ?x375), nominated_for(?x375, ?x1434), ?x1434 = 0ddd0gc *> conf = 0.07 ranks of expected_values: 859 EVAL 0bfvw2 nominated_for 04sskp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 45.000 26.000 0.794 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17055-03bmmc PRED entity: 03bmmc PRED relation: major_field_of_study PRED expected values: 0jjw => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 117): 02j62 (0.47 #529, 0.41 #1521, 0.40 #3506), 04rjg (0.47 #518, 0.36 #3495, 0.35 #1014), 03g3w (0.47 #525, 0.35 #3502, 0.33 #1021), 04x_3 (0.42 #524, 0.22 #1020, 0.19 #1516), 01mkq (0.41 #1009, 0.38 #388, 0.33 #3490), 02lp1 (0.38 #384, 0.37 #509, 0.33 #1005), 03qsdpk (0.38 #422, 0.29 #298, 0.26 #1043), 062z7 (0.37 #1022, 0.36 #1518, 0.31 #401), 05qjt (0.37 #505, 0.25 #1497, 0.24 #3606), 02ky346 (0.37 #514, 0.22 #1010, 0.16 #1506) >> Best rule #529 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 08815; 052nd; 07vk2; 02301; 02ccqg; 07tds; 05zl0; 0gjv_; 01bk1y; 01qqv5; ... >> query: (?x5981, 02j62) <- institution(?x6117, ?x5981), contains(?x94, ?x5981), student(?x5981, ?x2408), ?x6117 = 02m4yg >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #497 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 0473m9; *> query: (?x5981, ?x742) <- institution(?x8398, ?x5981), institution(?x6117, ?x5981), institution(?x3386, ?x5981), ?x3386 = 03mkk4, major_field_of_study(?x6117, ?x742), ?x8398 = 028dcg *> conf = 0.13 ranks of expected_values: 72 EVAL 03bmmc major_field_of_study 0jjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 138.000 138.000 0.474 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17054-08zrbl PRED entity: 08zrbl PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.84 #159, 0.84 #169, 0.83 #138), 0735l (0.26 #84, 0.22 #16, 0.20 #153), 07c52 (0.10 #19, 0.07 #25, 0.07 #30), 02nxhr (0.05 #128, 0.04 #362, 0.04 #213), 07z4p (0.04 #245, 0.04 #211, 0.04 #286) >> Best rule #159 for best value: >> intensional similarity = 5 >> extensional distance = 121 >> proper extension: 02vxq9m; 0b2v79; 028_yv; 0yyg4; 01gc7; 011yxg; 095zlp; 0bth54; 0209hj; 05jzt3; ... >> query: (?x7911, 029j_) <- nominated_for(?x2379, ?x7911), nominated_for(?x2375, ?x7911), ?x2379 = 02qvyrt, nominated_for(?x1722, ?x7911), award(?x157, ?x2375) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08zrbl film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17053-015p3p PRED entity: 015p3p PRED relation: award_nominee! PRED expected values: 0bxtg => 89 concepts (37 used for prediction) PRED predicted values (max 10 best out of 846): 06bzwt (0.81 #83789, 0.81 #83790, 0.28 #60513), 0184jc (0.28 #60513, 0.18 #83791, 0.16 #58185), 0bxtg (0.28 #60513, 0.17 #86122, 0.16 #58185), 06151l (0.28 #60513, 0.16 #58185, 0.04 #34941), 0382m4 (0.28 #60513, 0.16 #58185, 0.02 #26933), 031k24 (0.28 #60513, 0.16 #58185, 0.02 #39020), 03mg35 (0.28 #60513, 0.16 #58185, 0.02 #37640), 02j9lm (0.28 #60513, 0.16 #58185, 0.02 #58835), 06gp3f (0.28 #60513, 0.16 #58185, 0.01 #25633), 05p5nc (0.28 #60513, 0.16 #58185, 0.01 #27155) >> Best rule #83789 for best value: >> intensional similarity = 4 >> extensional distance = 1578 >> proper extension: 09d5h; >> query: (?x6221, ?x1522) <- award_nominee(?x6221, ?x9449), award_nominee(?x6221, ?x1522), nominated_for(?x6221, ?x7756), award_winner(?x9449, ?x72) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #60513 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1139 *> proper extension: 018ndc; 06mj4; *> query: (?x6221, ?x496) <- award_nominee(?x5492, ?x6221), currency(?x5492, ?x170), award_nominee(?x496, ?x5492) *> conf = 0.28 ranks of expected_values: 3 EVAL 015p3p award_nominee! 0bxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 37.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #17052-0163r3 PRED entity: 0163r3 PRED relation: role PRED expected values: 0239kh => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 111): 0342h (0.40 #1421, 0.37 #1623, 0.36 #1929), 02sgy (0.25 #1423, 0.23 #1019, 0.23 #1625), 05148p4 (0.24 #2229, 0.23 #1822, 0.13 #1036), 06ncr (0.24 #2229, 0.23 #1822, 0.04 #2026), 042v_gx (0.23 #1425, 0.22 #9, 0.21 #1627), 0l14qv (0.22 #6, 0.16 #1422, 0.16 #1624), 01vj9c (0.22 #16, 0.16 #1432, 0.15 #1940), 0l15bq (0.22 #37, 0.08 #138, 0.06 #1453), 05842k (0.18 #1493, 0.17 #1695, 0.17 #2001), 018vs (0.17 #1430, 0.17 #1632, 0.16 #1938) >> Best rule #1421 for best value: >> intensional similarity = 2 >> extensional distance = 340 >> proper extension: 01vsxdm; 0dm5l; 016lj_; >> query: (?x6716, 0342h) <- artist(?x2039, ?x6716), role(?x6716, ?x316) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2026 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 470 *> proper extension: 06br6t; *> query: (?x6716, ?x74) <- role(?x6716, ?x4769), role(?x74, ?x4769) *> conf = 0.04 ranks of expected_values: 48 EVAL 0163r3 role 0239kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 83.000 83.000 0.398 http://example.org/music/artist/track_contributions./music/track_contribution/role #17051-03k545 PRED entity: 03k545 PRED relation: location PRED expected values: 04jpl => 111 concepts (84 used for prediction) PRED predicted values (max 10 best out of 159): 04jpl (0.32 #6441, 0.17 #11267, 0.13 #13678), 02_286 (0.28 #39416, 0.24 #47447, 0.24 #44234), 07b_l (0.17 #989, 0.04 #7416, 0.04 #9828), 059rby (0.09 #4834, 0.08 #819, 0.05 #39395), 06y57 (0.08 #1058, 0.07 #3467, 0.02 #8289), 04vmp (0.08 #1156, 0.06 #13211, 0.04 #5974), 01cx_ (0.08 #965, 0.05 #4980, 0.04 #5783), 0d0x8 (0.08 #963, 0.05 #4978, 0.04 #5781), 02j3w (0.08 #1031, 0.04 #7458, 0.04 #5849), 02cft (0.08 #1109, 0.04 #5927, 0.02 #7536) >> Best rule #6441 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 07m69t; >> query: (?x11470, 04jpl) <- nationality(?x11470, ?x1310), nationality(?x11470, ?x94), ?x94 = 09c7w0, ?x1310 = 02jx1 >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03k545 location 04jpl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 84.000 0.321 http://example.org/people/person/places_lived./people/place_lived/location #17050-06k75 PRED entity: 06k75 PRED relation: combatants PRED expected values: 06f32 => 48 concepts (38 used for prediction) PRED predicted values (max 10 best out of 367): 05vz3zq (0.56 #1995, 0.44 #232, 0.33 #170), 05qhw (0.50 #350, 0.50 #244, 0.44 #232), 012m_ (0.50 #542, 0.44 #232, 0.44 #818), 0chghy (0.50 #477, 0.44 #232, 0.42 #2598), 059z0 (0.50 #303, 0.44 #232, 0.33 #186), 07t21 (0.50 #350, 0.40 #349, 0.33 #116), 0jhd (0.50 #350, 0.40 #349, 0.33 #116), 0d0kn (0.50 #350, 0.40 #349, 0.33 #116), 03shp (0.50 #350, 0.40 #349, 0.33 #116), 04w8f (0.50 #350, 0.40 #349, 0.33 #116) >> Best rule #1995 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 0c6cwg; >> query: (?x7241, ?x5114) <- combatants(?x7241, ?x94), entity_involved(?x7241, ?x3341), student(?x12475, ?x3341), profession(?x3341, ?x3342), nationality(?x3341, ?x5114), combatants(?x151, ?x5114) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #232 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 081pw; *> query: (?x7241, ?x789) <- combatants(?x7241, ?x7430), combatants(?x7241, ?x6465), combatants(?x7241, ?x3918), combatants(?x7241, ?x279), locations(?x7241, ?x3357), ?x7430 = 01mk6, adjoins(?x3357, ?x1603), adjoins(?x2346, ?x3357), ?x6465 = 0193qj, combatants(?x3918, ?x789), ?x279 = 0d060g *> conf = 0.44 ranks of expected_values: 26 EVAL 06k75 combatants 06f32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 48.000 38.000 0.556 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #17049-0473q PRED entity: 0473q PRED relation: artists! PRED expected values: 0dl5d => 114 concepts (72 used for prediction) PRED predicted values (max 10 best out of 249): 016clz (0.46 #933, 0.45 #1243, 0.44 #1862), 08jyyk (0.40 #685, 0.38 #1924, 0.38 #1305), 02lnbg (0.35 #368, 0.19 #4698, 0.18 #4390), 0glt670 (0.33 #4372, 0.32 #5913, 0.26 #11786), 05w3f (0.31 #1275, 0.25 #655, 0.24 #2204), 06j6l (0.30 #5612, 0.30 #5920, 0.28 #11793), 0xhtw (0.30 #17330, 0.28 #1254, 0.27 #6814), 025sc50 (0.29 #5922, 0.27 #4381, 0.24 #11795), 05r6t (0.28 #1319, 0.25 #699, 0.22 #2248), 01lyv (0.27 #13634, 0.25 #8068, 0.22 #6214) >> Best rule #933 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 04bpm6; 06x4l_; 0lzkm; >> query: (?x7237, 016clz) <- role(?x7237, ?x716), ?x716 = 018vs, role(?x7237, ?x227), ?x227 = 0342h >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1567 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 04mx7s; *> query: (?x7237, 0dl5d) <- artist(?x2299, ?x7237), role(?x7237, ?x1750), ?x1750 = 02hnl *> conf = 0.25 ranks of expected_values: 11 EVAL 0473q artists! 0dl5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 114.000 72.000 0.462 http://example.org/music/genre/artists #17048-0djlxb PRED entity: 0djlxb PRED relation: film! PRED expected values: 01kb2j 03mp9s => 101 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1242): 01wy5m (0.72 #99802, 0.59 #149692, 0.47 #99801), 022wxh (0.47 #99801, 0.44 #43656, 0.44 #29109), 0d500h (0.47 #99801, 0.44 #29109, 0.44 #114355), 02ck7w (0.25 #936, 0.06 #60294, 0.06 #3015), 01v9l67 (0.25 #464, 0.06 #60294, 0.05 #72773), 09wj5 (0.25 #101, 0.06 #60294, 0.05 #72773), 02gvwz (0.25 #188, 0.06 #60294, 0.05 #72773), 0241jw (0.25 #296, 0.06 #60294, 0.05 #72773), 0svqs (0.25 #871, 0.06 #60294, 0.05 #72773), 01ps2h8 (0.25 #937, 0.06 #60294, 0.05 #72773) >> Best rule #99802 for best value: >> intensional similarity = 4 >> extensional distance = 699 >> proper extension: 03rtz1; 0407yj_; 04sskp; 04cf_l; 02q_x_l; >> query: (?x3275, ?x3078) <- language(?x3275, ?x254), genre(?x3275, ?x307), nominated_for(?x3078, ?x3275), participant(?x3078, ?x3210) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #906 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 017gl1; 05q96q6; 04mcw4; 017jd9; 027m5wv; *> query: (?x3275, 01kb2j) <- language(?x3275, ?x254), executive_produced_by(?x3275, ?x7189), film(?x3281, ?x3275), ?x3281 = 0154qm *> conf = 0.12 ranks of expected_values: 40, 183 EVAL 0djlxb film! 03mp9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 101.000 72.000 0.724 http://example.org/film/actor/film./film/performance/film EVAL 0djlxb film! 01kb2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 101.000 72.000 0.724 http://example.org/film/actor/film./film/performance/film #17047-053yx PRED entity: 053yx PRED relation: influenced_by! PRED expected values: 017_pb => 176 concepts (140 used for prediction) PRED predicted values (max 10 best out of 397): 0f0y8 (0.26 #14901, 0.24 #21579, 0.23 #14387), 024zq (0.26 #14901, 0.24 #21579, 0.23 #14387), 0lgm5 (0.26 #14901, 0.24 #21579, 0.23 #14387), 01ky2h (0.26 #14901, 0.24 #21579, 0.23 #14387), 01_k1z (0.25 #749, 0.03 #12055, 0.03 #13082), 02vyw (0.25 #645, 0.03 #11951, 0.03 #12978), 02n9k (0.25 #826, 0.03 #13159), 01gvr1 (0.25 #1027, 0.02 #13360), 01vsy3q (0.22 #6680, 0.15 #7194, 0.10 #16957), 0f6lx (0.22 #6680, 0.15 #7194, 0.10 #16957) >> Best rule #14901 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 07h1q; >> query: (?x2835, ?x120) <- peers(?x2835, ?x120), place_of_birth(?x2835, ?x13979), influenced_by(?x1872, ?x2835) >> conf = 0.26 => this is the best rule for 4 predicted values *> Best rule #8521 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0dzkq; 01bpn; 0gct_; 060_7; 03f3_p3; 0ct9_; 01vs4f3; 06y7d; *> query: (?x2835, 017_pb) <- peers(?x2835, ?x120), people(?x6260, ?x2835), risk_factors(?x10613, ?x6260) *> conf = 0.04 ranks of expected_values: 188 EVAL 053yx influenced_by! 017_pb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 176.000 140.000 0.259 http://example.org/influence/influence_node/influenced_by #17046-0bc773 PRED entity: 0bc773 PRED relation: instance_of_recurring_event PRED expected values: 0g_w => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 3): 0g_w (0.89 #92, 0.89 #179, 0.89 #162), 0c4ys (0.17 #202, 0.12 #220, 0.12 #228), 0gcf2r (0.09 #221, 0.09 #186, 0.09 #229) >> Best rule #92 for best value: >> intensional similarity = 19 >> extensional distance = 26 >> proper extension: 0fzrtf; >> query: (?x3579, 0g_w) <- award_winner(?x3579, ?x7088), award_winner(?x3579, ?x4414), ceremony(?x4573, ?x3579), ceremony(?x1323, ?x3579), ceremony(?x484, ?x3579), ?x484 = 0gq_v, ?x4573 = 0gq_d, honored_for(?x3579, ?x5028), ?x1323 = 0gqz2, award_nominee(?x190, ?x4414), award_winner(?x7088, ?x6104), award_winner(?x7088, ?x5298), award_winner(?x528, ?x7088), film(?x489, ?x5028), award_nominee(?x1238, ?x5298), role(?x6104, ?x1472), nominated_for(?x4414, ?x631), award(?x7088, ?x2379), award_winner(?x724, ?x5298) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bc773 instance_of_recurring_event 0g_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.893 http://example.org/time/event/instance_of_recurring_event #17045-02j62 PRED entity: 02j62 PRED relation: major_field_of_study! PRED expected values: 01jssp 06pwq 01w3v 0kz2w 04rwx 07w5rq 07xpm 0f1nl 0pspl 05t7c1 033x5p 09f2j 02zd460 02kzfw 01ljpm 07vjm 017y6l 031vy_ 012mzw 02pptm => 72 concepts (48 used for prediction) PRED predicted values (max 10 best out of 560): 09f2j (0.71 #6246, 0.67 #5372, 0.60 #4935), 07vhb (0.64 #8446, 0.50 #3198, 0.50 #2761), 06pwq (0.62 #8755, 0.62 #7880, 0.59 #9194), 04rwx (0.60 #4404, 0.58 #14025, 0.54 #7903), 012mzw (0.60 #4586, 0.50 #3275, 0.50 #1963), 01w3v (0.57 #6131, 0.54 #7881, 0.50 #10070), 0kz2w (0.57 #6136, 0.50 #2202, 0.40 #4825), 01cf5 (0.57 #6508, 0.40 #5197, 0.40 #4321), 0j_sncb (0.54 #7935, 0.50 #2251, 0.44 #8810), 02zd460 (0.53 #9323, 0.50 #15881, 0.50 #10198) >> Best rule #6246 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 0fdys; 037mh8; >> query: (?x2981, 09f2j) <- major_field_of_study(?x12051, ?x2981), major_field_of_study(?x10175, ?x2981), major_field_of_study(?x6732, ?x2981), major_field_of_study(?x3948, ?x2981), major_field_of_study(?x2327, ?x2981), institution(?x620, ?x3948), major_field_of_study(?x2981, ?x1527), school_type(?x12051, ?x3092), ?x2327 = 07wjk, major_field_of_study(?x3948, ?x10046), ?x10046 = 041y2, student(?x6732, ?x3961), ?x10175 = 03fgm >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 5, 6, 7, 10, 11, 18, 46, 67, 74, 141, 212, 264, 280, 300, 381, 448, 532 EVAL 02j62 major_field_of_study! 02pptm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 012mzw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 031vy_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 017y6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 07vjm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 01ljpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 02kzfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 02zd460 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 09f2j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 033x5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 05t7c1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 0pspl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 0f1nl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 07xpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 07w5rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 04rwx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 0kz2w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 01w3v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 06pwq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 01jssp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 72.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17044-02cpp PRED entity: 02cpp PRED relation: award PRED expected values: 02g3gj => 90 concepts (80 used for prediction) PRED predicted values (max 10 best out of 268): 01by1l (0.62 #2122, 0.28 #4936, 0.26 #28664), 054krc (0.52 #5314, 0.51 #10541, 0.46 #13759), 01ckcd (0.50 #2344, 0.44 #3148, 0.34 #13603), 0l8z1 (0.48 #5290, 0.39 #10517, 0.36 #14941), 02f72_ (0.44 #2238, 0.33 #2640, 0.25 #3846), 02f5qb (0.44 #2165, 0.23 #5783, 0.22 #2567), 02qvyrt (0.39 #5352, 0.35 #13797, 0.35 #15003), 01bgqh (0.38 #2053, 0.31 #4867, 0.28 #8486), 02f716 (0.38 #2185, 0.28 #2989, 0.25 #4999), 02f73p (0.38 #2196, 0.25 #990, 0.22 #2598) >> Best rule #2122 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 0ggl02; >> query: (?x5916, 01by1l) <- award(?x5916, ?x2180), award(?x5916, ?x1565), ?x1565 = 01c4_6, award(?x8060, ?x2180), award(?x1089, ?x2180), ?x8060 = 06mj4, ?x1089 = 01vrncs >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3241 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 16 *> proper extension: 03xhj6; 02vgh; *> query: (?x5916, 02g3gj) <- artists(?x3243, ?x5916), artists(?x474, ?x5916), artist(?x12666, ?x5916), ?x474 = 0m0jc, ?x3243 = 0y3_8, organization(?x4682, ?x12666) *> conf = 0.22 ranks of expected_values: 28 EVAL 02cpp award 02g3gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 90.000 80.000 0.625 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17043-0g69lg PRED entity: 0g69lg PRED relation: nominated_for PRED expected values: 015ppk => 91 concepts (54 used for prediction) PRED predicted values (max 10 best out of 294): 015ppk (0.69 #17833, 0.68 #16210, 0.66 #21076), 02qkq0 (0.69 #17833, 0.68 #16210, 0.66 #21076), 01cvtf (0.16 #53510, 0.16 #48645, 0.15 #43776), 017f3m (0.09 #72970, 0.09 #47022, 0.02 #18604), 08jgk1 (0.08 #231, 0.08 #16441, 0.07 #1852), 0330r (0.07 #11136, 0.06 #17623, 0.04 #14379), 04vr_f (0.06 #159, 0.06 #1780, 0.04 #3400), 072kp (0.06 #86, 0.05 #14674, 0.05 #26025), 0gj50 (0.06 #18435, 0.02 #37893, 0.01 #41134), 0828jw (0.06 #17122, 0.06 #9014, 0.05 #13878) >> Best rule #17833 for best value: >> intensional similarity = 3 >> extensional distance = 157 >> proper extension: 0g5lhl7; 03mdt; >> query: (?x6765, ?x3303) <- award_winner(?x3018, ?x6765), award_nominee(?x6765, ?x7043), program(?x6765, ?x3303) >> conf = 0.69 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0g69lg nominated_for 015ppk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 54.000 0.693 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #17042-0jmnl PRED entity: 0jmnl PRED relation: teams! PRED expected values: 0d9jr => 63 concepts (54 used for prediction) PRED predicted values (max 10 best out of 125): 01cx_ (0.33 #94, 0.25 #634, 0.20 #1176), 0f2v0 (0.25 #643, 0.20 #1185, 0.15 #9748), 01sn3 (0.25 #926, 0.11 #1467, 0.05 #4174), 02_286 (0.13 #1645, 0.10 #2457, 0.06 #3810), 030qb3t (0.11 #1402, 0.11 #1944, 0.07 #4650), 0ply0 (0.11 #1453, 0.05 #1995, 0.05 #2265), 0fvvz (0.11 #1391, 0.05 #2203, 0.03 #3015), 0fpzwf (0.07 #3114, 0.07 #3385, 0.06 #3926), 013yq (0.07 #3049, 0.07 #3320, 0.05 #4402), 0d6lp (0.07 #3071, 0.07 #3342, 0.05 #4424) >> Best rule #94 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 0bwjj; >> query: (?x13777, 01cx_) <- school(?x13777, ?x9200), school(?x13777, ?x1011), team(?x4834, ?x13777), draft(?x13777, ?x2569), ?x4834 = 03lh3v, ?x9200 = 0dzst, major_field_of_study(?x1011, ?x254), school(?x465, ?x1011) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3651 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 29 *> proper extension: 0fw9vx; 02yjk8; *> query: (?x13777, 0d9jr) <- sport(?x13777, ?x4833), ?x4833 = 018w8 *> conf = 0.03 ranks of expected_values: 40 EVAL 0jmnl teams! 0d9jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 63.000 54.000 0.333 http://example.org/sports/sports_team_location/teams #17041-07lqg0 PRED entity: 07lqg0 PRED relation: profession! PRED expected values: 06y7d => 77 concepts (50 used for prediction) PRED predicted values (max 10 best out of 4272): 042q3 (0.56 #33246, 0.45 #45984, 0.36 #67220), 01m3x5p (0.56 #35280, 0.17 #154213, 0.14 #124479), 0bdlj (0.56 #36356, 0.14 #155289, 0.14 #66082), 0m93 (0.50 #15138, 0.44 #40624, 0.43 #19387), 03s9v (0.50 #15109, 0.40 #10861, 0.38 #23607), 07hyk (0.50 #29061, 0.18 #50293, 0.15 #118257), 01tdnyh (0.45 #48376, 0.43 #18646, 0.40 #10149), 0b78hw (0.45 #48072, 0.26 #191135, 0.25 #26840), 0dpqk (0.44 #35592, 0.35 #137537, 0.35 #73819), 0144l1 (0.44 #34588, 0.29 #136533, 0.25 #162015) >> Best rule #33246 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 0cbd2; 04gc2; >> query: (?x13357, 042q3) <- profession(?x12146, ?x13357), organization(?x12146, ?x5250), influenced_by(?x7509, ?x12146), people(?x4195, ?x12146), influenced_by(?x12146, ?x1857), ?x7509 = 048cl, religion(?x12146, ?x4641) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #59453 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 10 *> proper extension: 012qdp; *> query: (?x13357, ?x2397) <- profession(?x12146, ?x13357), organization(?x12146, ?x5250), profession(?x12146, ?x3802), nationality(?x12146, ?x512), religion(?x12146, ?x4641), ?x4641 = 0n2g, profession(?x2397, ?x3802) *> conf = 0.28 ranks of expected_values: 230 EVAL 07lqg0 profession! 06y7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 50.000 0.556 http://example.org/people/person/profession #17040-0ggbhy7 PRED entity: 0ggbhy7 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #134, 0.82 #16, 0.81 #208), 02nxhr (0.04 #135, 0.03 #184, 0.03 #164), 07c52 (0.04 #28, 0.03 #255, 0.03 #165), 07z4p (0.03 #15, 0.02 #297, 0.02 #317) >> Best rule #134 for best value: >> intensional similarity = 2 >> extensional distance = 482 >> proper extension: 03kg2v; >> query: (?x3012, 029j_) <- genre(?x3012, ?x812), ?x812 = 01jfsb >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ggbhy7 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #17039-0h953 PRED entity: 0h953 PRED relation: inductee! PRED expected values: 06szd3 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 5): 0g2c8 (0.10 #19, 0.10 #28, 0.10 #172), 06szd3 (0.09 #2, 0.08 #11, 0.06 #74), 0qjfl (0.06 #3, 0.03 #12, 0.02 #264), 04dm2n (0.03 #8, 0.01 #89, 0.01 #44), 04045y (0.02 #33) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 04xjp; 081k8; 0m93; 0448r; 0dw6b; 06g4_; >> query: (?x8450, 0g2c8) <- influenced_by(?x6771, ?x8450), gender(?x8450, ?x231), languages(?x8450, ?x254) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 06c0j; *> query: (?x8450, 06szd3) <- people(?x6260, ?x8450), award_winner(?x537, ?x8450), ?x6260 = 0dq9p *> conf = 0.09 ranks of expected_values: 2 EVAL 0h953 inductee! 06szd3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.104 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #17038-03mp8k PRED entity: 03mp8k PRED relation: artist PRED expected values: 01l_vgt 015f7 02k5sc 06p03s => 173 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1671): 01pfkw (0.56 #7900, 0.47 #24491, 0.45 #11848), 01fh0q (0.50 #3781, 0.33 #4570, 0.33 #622), 023p29 (0.50 #1473, 0.33 #4631, 0.33 #3842), 01v40wd (0.50 #1039, 0.33 #4197, 0.29 #7359), 03xhj6 (0.43 #5816, 0.36 #15300, 0.33 #4236), 02vr7 (0.36 #15587, 0.29 #6103, 0.25 #1365), 07zft (0.33 #4561, 0.33 #2982, 0.33 #2192), 0c9d9 (0.33 #3172, 0.33 #2382, 0.33 #1592), 0fcsd (0.33 #9763, 0.33 #2652, 0.33 #1862), 01vw917 (0.33 #4381, 0.33 #3592, 0.33 #433) >> Best rule #7900 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 05s34b; >> query: (?x9492, ?x4420) <- state_province_region(?x9492, ?x335), company(?x4420, ?x9492), ?x335 = 059rby, artist(?x2149, ?x4420), profession(?x4420, ?x131) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #4164 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 0181dw; *> query: (?x9492, 015f7) <- artist(?x9492, ?x3894), artist(?x9492, ?x1953), state_province_region(?x9492, ?x335), location(?x3894, ?x1131), ?x1131 = 0cc56, artists(?x505, ?x1953) *> conf = 0.33 ranks of expected_values: 26, 95, 291, 292 EVAL 03mp8k artist 06p03s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 173.000 80.000 0.556 http://example.org/music/record_label/artist EVAL 03mp8k artist 02k5sc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 173.000 80.000 0.556 http://example.org/music/record_label/artist EVAL 03mp8k artist 015f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 173.000 80.000 0.556 http://example.org/music/record_label/artist EVAL 03mp8k artist 01l_vgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 173.000 80.000 0.556 http://example.org/music/record_label/artist #17037-0190yn PRED entity: 0190yn PRED relation: parent_genre! PRED expected values: 0283d => 65 concepts (41 used for prediction) PRED predicted values (max 10 best out of 278): 0283d (0.57 #348, 0.48 #2450, 0.33 #85), 06cp5 (0.48 #2440, 0.22 #3766, 0.17 #6598), 01ym9b (0.43 #566, 0.33 #1356, 0.30 #1092), 059kh (0.30 #1095, 0.17 #2408, 0.17 #1359), 011j5x (0.29 #290, 0.17 #2392, 0.13 #1607), 0190yn (0.29 #454, 0.12 #980, 0.12 #6597), 0y3_8 (0.25 #830, 0.20 #1621, 0.20 #1093), 03xnwz (0.25 #817, 0.17 #1344, 0.14 #554), 07s72n (0.25 #951, 0.14 #425, 0.13 #1742), 01h0kx (0.22 #2493, 0.14 #654, 0.14 #3819) >> Best rule #348 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 06cqb; 0glt670; 02pl5bx; >> query: (?x12818, 0283d) <- artists(?x12818, ?x11953), parent_genre(?x12818, ?x283), parent_genre(?x5630, ?x12818), artists(?x283, ?x1270), ?x11953 = 01mskc3, artist(?x4483, ?x1270), award_nominee(?x1270, ?x217) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0190yn parent_genre! 0283d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 41.000 0.571 http://example.org/music/genre/parent_genre #17036-0gydcp7 PRED entity: 0gydcp7 PRED relation: film_release_region PRED expected values: 0154j => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 184): 09c7w0 (0.93 #5858, 0.93 #6020, 0.81 #1625), 0d0vqn (0.91 #1145, 0.91 #1307, 0.90 #658), 0f8l9c (0.89 #1322, 0.89 #1160, 0.89 #673), 03h64 (0.85 #720, 0.85 #1207, 0.84 #1369), 035qy (0.84 #685, 0.83 #1172, 0.83 #1334), 03spz (0.84 #751, 0.81 #1400, 0.81 #1238), 0jgd (0.84 #653, 0.81 #1140, 0.80 #1302), 0154j (0.84 #1142, 0.83 #1304, 0.83 #655), 01znc_ (0.83 #694, 0.79 #1181, 0.77 #1343), 05b4w (0.83 #717, 0.77 #1204, 0.76 #1366) >> Best rule #5858 for best value: >> intensional similarity = 5 >> extensional distance = 1314 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; 018js4; ... >> query: (?x2093, 09c7w0) <- film_release_region(?x2093, ?x456), film_release_region(?x6175, ?x456), film_release_region(?x6121, ?x456), ?x6121 = 064lsn, ?x6175 = 0gg5kmg >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1142 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 178 *> proper extension: 014lc_; 0b76d_m; 0ds35l9; 0g56t9t; 0gtsx8c; 02vxq9m; 0c3ybss; 011yrp; 0gx1bnj; 0ddfwj1; ... *> query: (?x2093, 0154j) <- film_release_region(?x2093, ?x1603), film_release_region(?x2093, ?x456), ?x456 = 05qhw, ?x1603 = 06bnz, film(?x1289, ?x2093) *> conf = 0.84 ranks of expected_values: 8 EVAL 0gydcp7 film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 58.000 58.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #17035-01rzxl PRED entity: 01rzxl PRED relation: location_of_ceremony PRED expected values: 0rqyx => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 30): 0b90_r (0.11 #242, 0.03 #1438, 0.03 #1557), 0cv3w (0.09 #1230, 0.03 #3143, 0.03 #3860), 030qb3t (0.06 #497, 0.03 #1333, 0.02 #2050), 0dclg (0.06 #505, 0.03 #1341, 0.02 #2177), 02hrh0_ (0.05 #776, 0.01 #3167), 0f2v0 (0.05 #876, 0.03 #1713, 0.01 #2429), 031y2 (0.05 #1159, 0.03 #1519, 0.03 #1638), 06y57 (0.04 #1252, 0.03 #1371, 0.03 #1850), 027rn (0.04 #1196, 0.03 #1315, 0.03 #1794), 0d9jr (0.04 #1256, 0.03 #1375, 0.02 #2211) >> Best rule #242 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 05r5w; 01pctb; >> query: (?x11630, 0b90_r) <- actor(?x11042, ?x11630), profession(?x11630, ?x319), currency(?x11630, ?x170), category(?x11630, ?x134), program(?x11630, ?x10551) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01rzxl location_of_ceremony 0rqyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 154.000 0.111 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #17034-09nqf PRED entity: 09nqf PRED relation: currency! PRED expected values: 0tyql 0mtdx 0l2l_ 01m1zk 0fxyd 0n5fz 0n5df 0g_wn2 0l2lk 0kq39 0l35f 0jrtv 0mwxz 0m25p 0235l 0n2m7 0d1xh 0ntwb 0l2wt 0mws3 0l2sr 0kvt9 0dcdp 0fwc0 0n6mc 0frf6 0ntxg 0mnk7 0mpzm 0l3kx 0f4zv 0mwk9 0mnlq 0tz1j 0p07_ 0n6nl 0tnkg 0mwq7 0l_n1 0nrnz 0l2nd 0drrw 01m23s 014b6c 0k1jg 0mk59 => 8 concepts (8 used for prediction) No prediction ranks of expected_values: EVAL 09nqf currency! 0mk59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0k1jg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 014b6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01m23s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0drrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0l2nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0nrnz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0l_n1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0mwq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0tnkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0n6nl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0p07_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0tz1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0mnlq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0mwk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0f4zv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0l3kx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0mpzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0mnk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0ntxg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0frf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0n6mc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0fwc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0dcdp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0kvt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0l2sr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0mws3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0l2wt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0ntwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0d1xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0n2m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0235l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0m25p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0mwxz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0jrtv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0l35f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0kq39 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0l2lk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0g_wn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0n5df CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0n5fz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0fxyd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01m1zk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0l2l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0mtdx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0tyql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.000 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #17033-01vv6_6 PRED entity: 01vv6_6 PRED relation: artist! PRED expected values: 02p4jf0 => 137 concepts (112 used for prediction) PRED predicted values (max 10 best out of 114): 015_1q (0.57 #2757, 0.25 #2072, 0.24 #17), 017l96 (0.28 #2756, 0.25 #427, 0.12 #564), 03rhqg (0.22 #561, 0.20 #3849, 0.17 #1931), 01clyr (0.21 #31, 0.13 #579, 0.13 #305), 0n85g (0.19 #470, 0.11 #333, 0.10 #59), 0g768 (0.17 #35, 0.15 #720, 0.15 #1953), 02p3cr5 (0.16 #436, 0.07 #2765, 0.06 #710), 033hn8 (0.15 #1929, 0.14 #696, 0.12 #422), 01w40h (0.14 #711, 0.10 #26, 0.10 #1944), 01t04r (0.13 #746, 0.11 #1979, 0.08 #3897) >> Best rule #2757 for best value: >> intensional similarity = 4 >> extensional distance = 198 >> proper extension: 0m0hw; 016jll; >> query: (?x3472, 015_1q) <- profession(?x3472, ?x131), artist(?x2149, ?x3472), artist(?x2149, ?x642), ?x642 = 032t2z >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1031 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 99 *> proper extension: 03dq9; *> query: (?x3472, 02p4jf0) <- profession(?x3472, ?x131), gender(?x3472, ?x231), place_of_birth(?x3472, ?x6764), group(?x3472, ?x7013) *> conf = 0.02 ranks of expected_values: 75 EVAL 01vv6_6 artist! 02p4jf0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 137.000 112.000 0.575 http://example.org/music/record_label/artist #17032-08nvyr PRED entity: 08nvyr PRED relation: honored_for! PRED expected values: 0drtv8 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 118): 09k5jh7 (0.07 #4321, 0.07 #4682, 0.06 #7444), 050yyb (0.07 #4321, 0.07 #4682, 0.06 #7444), 09g90vz (0.07 #4321, 0.07 #4682, 0.06 #7444), 02wzl1d (0.07 #4321, 0.07 #4682, 0.06 #7444), 092c5f (0.07 #4321, 0.07 #4682, 0.06 #7444), 0bvfqq (0.07 #4321, 0.07 #4682, 0.06 #7444), 09q_6t (0.07 #4321, 0.07 #4682, 0.06 #7444), 092868 (0.07 #4321, 0.07 #4682, 0.06 #7444), 08pc1x (0.07 #4321, 0.07 #4682, 0.06 #7444), 02cg41 (0.07 #4321, 0.07 #4682, 0.06 #7444) >> Best rule #4321 for best value: >> intensional similarity = 4 >> extensional distance = 736 >> proper extension: 057__d; >> query: (?x4541, ?x2210) <- nominated_for(?x4940, ?x4541), film_crew_role(?x4541, ?x137), nominated_for(?x68, ?x4541), award_winner(?x2210, ?x4940) >> conf = 0.07 => this is the best rule for 11 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 02vxq9m; 07cyl; 03hxsv; 01k0vq; *> query: (?x4541, 0drtv8) <- nominated_for(?x2499, ?x4541), film_crew_role(?x4541, ?x137), prequel(?x4541, ?x4067), award(?x4541, ?x1079) *> conf = 0.02 ranks of expected_values: 46 EVAL 08nvyr honored_for! 0drtv8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 106.000 106.000 0.071 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #17031-04d_mtq PRED entity: 04d_mtq PRED relation: people! PRED expected values: 033tf_ => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 54): 0x67 (0.40 #2975, 0.25 #3203, 0.25 #3431), 033tf_ (0.33 #7, 0.22 #463, 0.21 #159), 041rx (0.25 #4185, 0.21 #5173, 0.21 #5706), 065b6q (0.21 #155, 0.07 #1599, 0.06 #1751), 07bch9 (0.14 #174, 0.09 #1618, 0.09 #1770), 09vc4s (0.11 #845, 0.08 #1529, 0.07 #1149), 07hwkr (0.10 #392, 0.09 #1608, 0.08 #620), 0dbxy (0.10 #426, 0.08 #122, 0.08 #578), 01qhm_ (0.09 #842, 0.08 #82, 0.08 #1374), 02w7gg (0.09 #5171, 0.09 #5095, 0.09 #5704) >> Best rule #2975 for best value: >> intensional similarity = 4 >> extensional distance = 327 >> proper extension: 016qtt; 03f2_rc; 01vvycq; 01wdqrx; 04mn81; 01vs_v8; 01wj9y9; 0cg9y; 01pgzn_; 03j0br4; ... >> query: (?x10353, 0x67) <- gender(?x10353, ?x231), profession(?x10353, ?x1032), artists(?x3061, ?x10353), people(?x3591, ?x10353) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 04cr6qv; *> query: (?x10353, 033tf_) <- vacationer(?x2673, ?x10353), people(?x3591, ?x10353), ?x3591 = 0xnvg, sibling(?x10353, ?x5514) *> conf = 0.33 ranks of expected_values: 2 EVAL 04d_mtq people! 033tf_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.398 http://example.org/people/ethnicity/people #17030-03v_5 PRED entity: 03v_5 PRED relation: time_zones PRED expected values: 02hcv8 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.72 #133, 0.67 #159, 0.54 #146), 02lcqs (0.50 #5, 0.31 #330, 0.27 #291), 02fqwt (0.32 #274, 0.31 #209, 0.29 #300), 02llzg (0.12 #342, 0.11 #199, 0.11 #1071), 02hczc (0.10 #184, 0.09 #28, 0.08 #939), 042g7t (0.09 #37, 0.05 #89, 0.03 #284), 02lcrv (0.09 #33, 0.05 #85, 0.01 #280), 03bdv (0.09 #357, 0.07 #514, 0.07 #618), 03plfd (0.02 #752, 0.02 #973, 0.02 #791), 0d2t4g (0.02 #204, 0.01 #360) >> Best rule #133 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 0f4zv; >> query: (?x1730, 02hcv8) <- source(?x1730, ?x958), contains(?x1730, ?x13963), contains(?x335, ?x13963), ?x335 = 059rby >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03v_5 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.720 http://example.org/location/location/time_zones #17029-0f0y8 PRED entity: 0f0y8 PRED relation: profession PRED expected values: 02hrh1q 01c72t 029bkp => 191 concepts (132 used for prediction) PRED predicted values (max 10 best out of 99): 02hrh1q (0.92 #14006, 0.71 #17545, 0.67 #12240), 01c72t (0.67 #318, 0.58 #760, 0.58 #3702), 01d_h8 (0.56 #1183, 0.45 #594, 0.35 #2360), 0dxtg (0.51 #12975, 0.45 #602, 0.40 #161), 0cbd2 (0.50 #1479, 0.49 #13409, 0.48 #10904), 039v1 (0.50 #1213, 0.36 #12113, 0.32 #2831), 0dz3r (0.45 #13110, 0.44 #12079, 0.44 #1179), 0n1h (0.45 #600, 0.44 #1189, 0.35 #2366), 02jknp (0.45 #596, 0.31 #1185, 0.26 #1921), 016z4k (0.45 #12670, 0.44 #15470, 0.43 #16059) >> Best rule #14006 for best value: >> intensional similarity = 4 >> extensional distance = 345 >> proper extension: 08b8vd; 02m30v; >> query: (?x120, 02hrh1q) <- people(?x11064, ?x120), profession(?x120, ?x1183), profession(?x2300, ?x1183), ?x2300 = 01ww2fs >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 11 EVAL 0f0y8 profession 029bkp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 191.000 132.000 0.922 http://example.org/people/person/profession EVAL 0f0y8 profession 01c72t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 132.000 0.922 http://example.org/people/person/profession EVAL 0f0y8 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 132.000 0.922 http://example.org/people/person/profession #17028-05ljv7 PRED entity: 05ljv7 PRED relation: role! PRED expected values: 05842k => 76 concepts (55 used for prediction) PRED predicted values (max 10 best out of 111): 03qjg (0.87 #544, 0.86 #2107, 0.86 #2050), 0342h (0.87 #544, 0.84 #103, 0.83 #104), 0dwt5 (0.87 #544, 0.84 #103, 0.83 #876), 0l14md (0.84 #2338, 0.78 #5101, 0.78 #213), 0bxl5 (0.83 #1390, 0.82 #1279, 0.80 #1059), 07c6l (0.80 #1116, 0.77 #1782, 0.76 #2116), 0cfdd (0.80 #1194, 0.77 #1860, 0.72 #327), 02fsn (0.79 #1940, 0.77 #1609, 0.70 #1163), 05148p4 (0.78 #213, 0.73 #4656, 0.72 #327), 05842k (0.78 #213, 0.71 #619, 0.71 #3943) >> Best rule #544 for best value: >> intensional similarity = 22 >> extensional distance = 4 >> proper extension: 026t6; 01vj9c; >> query: (?x1647, ?x212) <- role(?x4162, ?x1647), role(?x1647, ?x4769), role(?x1647, ?x2944), role(?x1647, ?x2798), role(?x1647, ?x885), role(?x1647, ?x316), role(?x1647, ?x212), role(?x1750, ?x1647), ?x4769 = 0dwt5, ?x2798 = 03qjg, ?x316 = 05r5c, ?x2944 = 0l14j_, role(?x1969, ?x1647), ?x1969 = 04rzd, group(?x1750, ?x10263), group(?x1750, ?x4942), role(?x1750, ?x2297), instrumentalists(?x1750, ?x300), ?x2297 = 051hrr, ?x885 = 0dwtp, ?x4942 = 05xq9, ?x10263 = 0mjn2 >> conf = 0.87 => this is the best rule for 3 predicted values *> Best rule #213 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x1647, ?x3215) <- role(?x12623, ?x1647), role(?x1647, ?x4769), role(?x1647, ?x2944), role(?x1647, ?x2798), role(?x1647, ?x432), role(?x1647, ?x316), role(?x1750, ?x1647), role(?x1166, ?x1647), ?x4769 = 0dwt5, ?x2798 = 03qjg, ?x316 = 05r5c, ?x2944 = 0l14j_, role(?x1969, ?x1647), ?x1969 = 04rzd, ?x1750 = 02hnl, ?x432 = 042v_gx, profession(?x12623, ?x220), family(?x1647, ?x1148), ?x1166 = 05148p4, role(?x1294, ?x1647), role(?x12623, ?x3215) *> conf = 0.78 ranks of expected_values: 10 EVAL 05ljv7 role! 05842k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 76.000 55.000 0.866 http://example.org/music/performance_role/track_performances./music/track_contribution/role #17027-01r3y2 PRED entity: 01r3y2 PRED relation: major_field_of_study PRED expected values: 088tb => 135 concepts (129 used for prediction) PRED predicted values (max 10 best out of 112): 03g3w (0.60 #260, 0.40 #1547, 0.35 #1196), 02lp1 (0.56 #244, 0.48 #712, 0.45 #595), 04rjg (0.56 #253, 0.43 #721, 0.38 #1540), 0g26h (0.48 #274, 0.46 #976, 0.45 #1327), 01mkq (0.48 #248, 0.44 #2357, 0.42 #1652), 02_7t (0.48 #296, 0.32 #1700, 0.31 #1349), 05qfh (0.36 #268, 0.36 #619, 0.32 #1555), 01tbp (0.35 #993, 0.33 #1695, 0.32 #291), 0fdys (0.32 #1207, 0.32 #271, 0.25 #1558), 04x_3 (0.32 #259, 0.31 #727, 0.30 #1195) >> Best rule #260 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 027ydt; 01yqqv; 01gwck; >> query: (?x3090, 03g3w) <- institution(?x865, ?x3090), colors(?x3090, ?x663), major_field_of_study(?x3090, ?x10391), ?x10391 = 02jfc >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1528 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 020vx9; *> query: (?x3090, 088tb) <- major_field_of_study(?x3090, ?x742), ?x742 = 05qjt, student(?x3090, ?x3547) *> conf = 0.07 ranks of expected_values: 65 EVAL 01r3y2 major_field_of_study 088tb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 135.000 129.000 0.600 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17026-04rfq PRED entity: 04rfq PRED relation: organizations_founded PRED expected values: 09xwz => 130 concepts (119 used for prediction) PRED predicted values (max 10 best out of 104): 09xwz (0.85 #912, 0.62 #1320, 0.12 #1295), 034h1h (0.30 #1252, 0.17 #2679, 0.02 #1459), 02_l9 (0.18 #1321), 01rz1 (0.17 #1229), 07t65 (0.14 #3, 0.05 #1221, 0.03 #2648), 030_1_ (0.14 #520, 0.10 #1029, 0.06 #1948), 061dn_ (0.11 #229, 0.09 #533, 0.06 #1042), 04gmlt (0.09 #557, 0.08 #860, 0.06 #1066), 056ws9 (0.09 #545, 0.08 #848, 0.06 #1054), 06dr9 (0.09 #1510, 0.05 #2933, 0.05 #3034) >> Best rule #912 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 05qd_; >> query: (?x13144, ?x11706) <- organizations_founded(?x13144, ?x1850), organizations_founded(?x11626, ?x1850), organizations_founded(?x11626, ?x11706), child(?x788, ?x1850) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04rfq organizations_founded 09xwz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 119.000 0.846 http://example.org/organization/organization_founder/organizations_founded #17025-019x62 PRED entity: 019x62 PRED relation: music! PRED expected values: 018js4 => 105 concepts (93 used for prediction) PRED predicted values (max 10 best out of 499): 042g97 (0.74 #5056, 0.52 #6068, 0.12 #19213), 05qm9f (0.08 #676, 0.02 #2698, 0.02 #3709), 01lbcqx (0.08 #821, 0.01 #2843), 0p9tm (0.08 #777, 0.01 #2799), 01s7w3 (0.04 #2891, 0.04 #6937, 0.04 #3902), 07bzz7 (0.04 #2548, 0.03 #3559, 0.02 #6594), 02rrfzf (0.04 #4368, 0.04 #3357, 0.03 #6392), 02ht1k (0.04 #3399, 0.03 #4410, 0.03 #2388), 09d3b7 (0.04 #3873, 0.03 #2862, 0.03 #4884), 08l0x2 (0.03 #1761, 0.02 #2772, 0.02 #6818) >> Best rule #5056 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 0gv07g; 01m7f5r; 07z4fy; >> query: (?x7088, ?x776) <- profession(?x7088, ?x131), nominated_for(?x7088, ?x776), music(?x2434, ?x7088) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #2032 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 03qd_; 01r6jt2; 0c_drn; 02fgp0; 01pbwwl; *> query: (?x7088, 018js4) <- award_winner(?x1974, ?x7088), music(?x776, ?x7088), award_nominee(?x4620, ?x7088) *> conf = 0.01 ranks of expected_values: 495 EVAL 019x62 music! 018js4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 105.000 93.000 0.737 http://example.org/film/film/music #17024-01lk0l PRED entity: 01lk0l PRED relation: award! PRED expected values: 02rchht => 42 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2140): 05qd_ (0.77 #30610, 0.73 #33990, 0.71 #6957), 04wvhz (0.62 #17138, 0.57 #7001, 0.50 #13759), 0g1rw (0.62 #30572, 0.62 #27192, 0.57 #6919), 086k8 (0.62 #30475, 0.62 #27095, 0.53 #33855), 016tt2 (0.54 #27154, 0.50 #10260, 0.47 #33914), 06cgy (0.52 #44311, 0.26 #47689, 0.16 #54062), 01795t (0.50 #10697, 0.44 #20832, 0.43 #7318), 02kv5k (0.50 #5622, 0.43 #9000, 0.38 #19137), 0gyx4 (0.50 #14767, 0.43 #8009, 0.38 #18146), 02q_cc (0.50 #17081, 0.43 #6944, 0.38 #13702) >> Best rule #30610 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 0p9sw; 018wng; 0gr42; 0gr07; 01lj_c; >> query: (?x7285, 05qd_) <- award(?x541, ?x7285), film(?x541, ?x10623), film(?x541, ?x2339), place_founded(?x541, ?x1523), film_release_distribution_medium(?x2339, ?x81), film_crew_role(?x10623, ?x137) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #10134 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 07bdd_; 05p1dby; 0gq_d; *> query: (?x7285, ?x164) <- award(?x3170, ?x7285), award(?x541, ?x7285), award(?x163, ?x7285), ?x541 = 017s11, produced_by(?x414, ?x163), award_nominee(?x3170, ?x164) *> conf = 0.22 ranks of expected_values: 288 EVAL 01lk0l award! 02rchht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 22.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #17023-023fb PRED entity: 023fb PRED relation: current_club! PRED expected values: 02rqxc => 91 concepts (64 used for prediction) PRED predicted values (max 10 best out of 27): 02ltg3 (0.44 #195, 0.25 #5, 0.21 #277), 02bh_v (0.33 #208, 0.25 #18, 0.19 #262), 02s2lg (0.25 #4, 0.22 #194, 0.20 #86), 02w64f (0.25 #27, 0.20 #109, 0.17 #136), 03_qrp (0.25 #40, 0.17 #122, 0.12 #176), 03d8m4 (0.25 #35, 0.17 #117, 0.11 #335), 03_qj1 (0.25 #36, 0.17 #118, 0.11 #696), 035qgm (0.25 #17, 0.12 #317, 0.11 #207), 033nzk (0.25 #2, 0.12 #165, 0.11 #192), 03ylxn (0.22 #213, 0.17 #240, 0.12 #323) >> Best rule #195 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 03fnmd; 0y9j; 0kq9l; 0cttx; >> query: (?x6670, 02ltg3) <- position(?x6670, ?x203), current_club(?x2427, ?x6670), ?x203 = 0dgrmp, category(?x6670, ?x134), ?x134 = 08mbj5d, team(?x60, ?x2427) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #307 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 30 *> proper extension: 01k2yr; 0371rb; 02b2np; 0bl8l; 085v7; 011v3; 02_lt; 06l22; 01k2xy; 01cwm1; ... *> query: (?x6670, 02rqxc) <- position(?x6670, ?x203), current_club(?x2427, ?x6670), team(?x6523, ?x6670), team(?x63, ?x6670), ?x63 = 02sdk9v, sport(?x6670, ?x471), team(?x7026, ?x2427) *> conf = 0.12 ranks of expected_values: 17 EVAL 023fb current_club! 02rqxc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 91.000 64.000 0.444 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #17022-06kl78 PRED entity: 06kl78 PRED relation: film_production_design_by PRED expected values: 03mdw3c => 100 concepts (77 used for prediction) PRED predicted values (max 10 best out of 19): 03mdw3c (0.09 #54, 0.02 #148, 0.01 #272), 02vxyl5 (0.05 #246, 0.04 #309, 0.03 #184), 05b5_tj (0.04 #93, 0.02 #155), 05b2gsm (0.04 #80, 0.02 #1162, 0.01 #1515), 09d5d5 (0.04 #63, 0.02 #376, 0.02 #1210), 026fd (0.04 #63, 0.02 #376, 0.02 #1210), 0fqyzz (0.04 #63, 0.02 #376, 0.02 #1210), 0d5wn3 (0.03 #354, 0.03 #104, 0.02 #772), 03cp7b3 (0.03 #120, 0.01 #596, 0.01 #275), 04kj2v (0.03 #97, 0.01 #252, 0.01 #475) >> Best rule #54 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 027r7k; >> query: (?x4772, 03mdw3c) <- nominated_for(?x3828, ?x4772), film(?x2549, ?x4772), nominated_for(?x5824, ?x4772), ?x5824 = 02qysm0 >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06kl78 film_production_design_by 03mdw3c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 77.000 0.091 http://example.org/film/film/film_production_design_by #17021-0jm9w PRED entity: 0jm9w PRED relation: draft PRED expected values: 06439y => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 17): 0f4vx0 (0.82 #233, 0.80 #165, 0.79 #996), 06439y (0.79 #996, 0.78 #824, 0.76 #771), 038c0q (0.79 #996, 0.78 #824, 0.76 #771), 0g3zpp (0.58 #258, 0.52 #773, 0.45 #980), 092j54 (0.58 #265, 0.50 #780, 0.45 #987), 09l0x9 (0.58 #268, 0.50 #783, 0.45 #990), 05vsb7 (0.50 #772, 0.50 #257, 0.42 #979), 02pq_rp (0.48 #572, 0.43 #969, 0.40 #25), 02r6gw6 (0.43 #577, 0.40 #30, 0.39 #974), 04f4z1k (0.43 #579, 0.40 #32, 0.39 #976) >> Best rule #233 for best value: >> intensional similarity = 10 >> extensional distance = 9 >> proper extension: 0bwjj; 0jm3b; 0jmh7; 0jmk7; >> query: (?x9995, 0f4vx0) <- team(?x5755, ?x9995), colors(?x9995, ?x332), sport(?x9995, ?x4833), draft(?x9995, ?x8133), team(?x13926, ?x9995), school(?x9995, ?x4296), ?x4833 = 018w8, team(?x5755, ?x7158), ?x7158 = 0jm4v, position(?x2568, ?x5755) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #996 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 60 *> proper extension: 043vc; *> query: (?x9995, ?x2569) <- team(?x5755, ?x9995), team(?x5755, ?x2820), team(?x5755, ?x1347), draft(?x9995, ?x8133), school(?x2820, ?x6973), school(?x2820, ?x466), ?x6973 = 05x_5, draft(?x1347, ?x2569), ?x466 = 01pl14, school(?x9995, ?x11713) *> conf = 0.79 ranks of expected_values: 2 EVAL 0jm9w draft 06439y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.818 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #17020-0ddd0gc PRED entity: 0ddd0gc PRED relation: nominated_for! PRED expected values: 0gkr9q => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 169): 0ck27z (0.69 #3391, 0.68 #10630, 0.67 #8591), 0bfvd4 (0.62 #83, 0.12 #2795, 0.12 #4152), 0gq9h (0.39 #8423, 0.35 #8650, 0.33 #8876), 0bdwqv (0.38 #124, 0.09 #2836, 0.08 #4193), 0gs9p (0.35 #8425, 0.31 #8652, 0.29 #8878), 019f4v (0.34 #8416, 0.30 #8643, 0.28 #8869), 0k611 (0.29 #8434, 0.26 #8661, 0.24 #8887), 040njc (0.28 #8371, 0.25 #8598, 0.24 #8824), 0gkr9q (0.28 #423, 0.21 #2005, 0.21 #649), 0gq_v (0.27 #8383, 0.26 #8610, 0.24 #8836) >> Best rule #3391 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 015g28; 03ffcz; 021gzd; 05fgr_; 05sy0cv; >> query: (?x1434, ?x1670) <- actor(?x1434, ?x3718), award_nominee(?x3718, ?x6258), award(?x1434, ?x1670) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #423 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 070ltt; 04x4gj; 0d_rw; *> query: (?x1434, 0gkr9q) <- genre(?x1434, ?x53), tv_program(?x6673, ?x1434), ?x53 = 07s9rl0 *> conf = 0.28 ranks of expected_values: 9 EVAL 0ddd0gc nominated_for! 0gkr9q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 74.000 74.000 0.685 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #17019-01s7w3 PRED entity: 01s7w3 PRED relation: category PRED expected values: 08mbj5d => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.30 #8, 0.28 #9, 0.28 #78) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 315 >> proper extension: 0243cq; 02rmd_2; 05q7874; 04h4c9; 07ykkx5; >> query: (?x9154, 08mbj5d) <- film(?x157, ?x9154), country(?x9154, ?x94), film_crew_role(?x9154, ?x2154), ?x2154 = 01vx2h >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01s7w3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.303 http://example.org/common/topic/webpage./common/webpage/category #17018-01nkxvx PRED entity: 01nkxvx PRED relation: gender PRED expected values: 02zsn => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #57, 0.81 #73, 0.81 #75), 02zsn (0.46 #265, 0.38 #52, 0.34 #20) >> Best rule #57 for best value: >> intensional similarity = 5 >> extensional distance = 193 >> proper extension: 01zmpg; >> query: (?x8599, 05zppz) <- nationality(?x8599, ?x94), profession(?x8599, ?x1183), profession(?x8599, ?x131), ?x1183 = 09jwl, ?x131 = 0dz3r >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #265 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4064 *> proper extension: 0jrg; *> query: (?x8599, ?x231) <- nationality(?x8599, ?x94), nationality(?x12159, ?x94), gender(?x12159, ?x231) *> conf = 0.46 ranks of expected_values: 2 EVAL 01nkxvx gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 136.000 0.841 http://example.org/people/person/gender #17017-0b_fw PRED entity: 0b_fw PRED relation: profession PRED expected values: 01d_h8 => 111 concepts (68 used for prediction) PRED predicted values (max 10 best out of 61): 01d_h8 (0.67 #3240, 0.67 #5299, 0.66 #1917), 0dxtg (0.62 #1923, 0.61 #5305, 0.58 #3246), 03gjzk (0.25 #5306, 0.23 #3247, 0.23 #9133), 0np9r (0.22 #4429, 0.21 #2077, 0.18 #460), 02krf9 (0.21 #3259, 0.21 #5318, 0.17 #1936), 09jwl (0.21 #4574, 0.20 #4133, 0.18 #8107), 018gz8 (0.15 #2220, 0.15 #2073, 0.14 #7663), 0d1pc (0.15 #3136, 0.14 #3871, 0.14 #4018), 0nbcg (0.14 #4587, 0.13 #4146, 0.13 #765), 01c72t (0.13 #757, 0.12 #3550, 0.10 #5168) >> Best rule #3240 for best value: >> intensional similarity = 4 >> extensional distance = 424 >> proper extension: 01g5kv; >> query: (?x2167, 01d_h8) <- profession(?x2167, ?x1032), profession(?x2167, ?x524), ?x524 = 02jknp, ?x1032 = 02hrh1q >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_fw profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 68.000 0.669 http://example.org/people/person/profession #17016-04d817 PRED entity: 04d817 PRED relation: position PRED expected values: 02_j1w => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 4): 02_j1w (0.87 #27, 0.86 #36, 0.86 #35), 0dgrmp (0.87 #27, 0.86 #36, 0.86 #35), 03f0fp (0.37 #499, 0.03 #109, 0.03 #102), 02md_2 (0.37 #499) >> Best rule #27 for best value: >> intensional similarity = 18 >> extensional distance = 7 >> proper extension: 049912; 02k9k9; >> query: (?x9389, ?x60) <- team(?x530, ?x9389), team(?x203, ?x9389), team(?x63, ?x9389), team(?x60, ?x9389), current_club(?x7294, ?x9389), ?x63 = 02sdk9v, ?x203 = 0dgrmp, teams(?x252, ?x7294), current_club(?x7294, ?x11421), current_club(?x7294, ?x9048), current_club(?x7294, ?x1599), team(?x12447, ?x7294), ?x11421 = 049f05, team(?x5471, ?x9048), sport(?x9048, ?x471), ?x530 = 02_j1w, team(?x8324, ?x1599), colors(?x1599, ?x663) >> conf = 0.87 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 04d817 position 02_j1w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.867 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #17015-0284n42 PRED entity: 0284n42 PRED relation: crewmember! PRED expected values: 027pfg => 82 concepts (49 used for prediction) PRED predicted values (max 10 best out of 306): 076zy_g (0.32 #1776, 0.03 #296, 0.02 #3259), 0dtfn (0.17 #341, 0.15 #1524, 0.14 #934), 0jqn5 (0.14 #49, 0.11 #939, 0.11 #1234), 0642xf3 (0.14 #158, 0.08 #455, 0.06 #1048), 0dfw0 (0.14 #149, 0.08 #446, 0.06 #1039), 0dnvn3 (0.14 #9, 0.08 #306, 0.05 #1489), 03nsm5x (0.14 #248, 0.06 #1138, 0.05 #1433), 060__7 (0.14 #260, 0.04 #557, 0.04 #853), 042zrm (0.14 #252, 0.04 #549, 0.04 #845), 07xvf (0.14 #238, 0.04 #535, 0.04 #831) >> Best rule #1776 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 094wz7q; 026dx; 03y_46; 09dvgb8; 03m49ly; 02vxyl5; >> query: (?x666, ?x5155) <- crewmember(?x3925, ?x666), genre(?x3925, ?x53), award_winner(?x5155, ?x666), film_crew_role(?x3925, ?x137) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #524 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 05f260; *> query: (?x666, 027pfg) <- award(?x666, ?x637), award(?x666, ?x500), ?x500 = 0p9sw, nominated_for(?x637, ?x144) *> conf = 0.04 ranks of expected_values: 176 EVAL 0284n42 crewmember! 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 82.000 49.000 0.322 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #17014-03rk0 PRED entity: 03rk0 PRED relation: medal PRED expected values: 02lq67 => 231 concepts (231 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.85 #42, 0.84 #34, 0.83 #52) >> Best rule #42 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 034cm; >> query: (?x2146, 02lq67) <- country(?x1352, ?x2146), contains(?x2146, ?x1391), service_location(?x1492, ?x2146) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rk0 medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 231.000 231.000 0.854 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #17013-014kyy PRED entity: 014kyy PRED relation: group! PRED expected values: 0342h => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 79): 0342h (0.89 #1488, 0.87 #1051, 0.83 #1575), 018vs (0.62 #1497, 0.61 #363, 0.60 #1060), 028tv0 (0.49 #449, 0.46 #362, 0.41 #1059), 03qjg (0.40 #309, 0.32 #483, 0.31 #1093), 05r5c (0.29 #444, 0.28 #1578, 0.27 #1404), 0l14qv (0.28 #268, 0.25 #6, 0.23 #1576), 0l14j_ (0.25 #51, 0.20 #313, 0.10 #1447), 07y_7 (0.25 #2, 0.16 #264, 0.12 #1398), 07gql (0.25 #36, 0.12 #298, 0.08 #1082), 07c6l (0.25 #10, 0.12 #272, 0.06 #1406) >> Best rule #1488 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 089tm; 01t_xp_; 01pfr3; 0150jk; 03t9sp; 01vrwfv; 03xhj6; 018gm9; 03d9d6; 01q99h; ... >> query: (?x12427, 0342h) <- artists(?x1572, ?x12427), ?x1572 = 06by7, group(?x315, ?x12427) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014kyy group! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.888 http://example.org/music/performance_role/regular_performances./music/group_membership/group #17012-028kj0 PRED entity: 028kj0 PRED relation: film_crew_role PRED expected values: 09vw2b7 01pvkk => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 25): 09vw2b7 (0.76 #285, 0.68 #1045, 0.67 #1205), 0d2b38 (0.56 #301, 0.55 #239, 0.12 #332), 01xy5l_ (0.51 #292, 0.41 #230, 0.22 #136), 01pvkk (0.43 #103, 0.33 #10, 0.30 #165), 0dxtw (0.39 #1049, 0.39 #1209, 0.37 #289), 02vs3x5 (0.29 #112, 0.20 #174, 0.06 #393), 015h31 (0.24 #225, 0.14 #318, 0.13 #287), 089fss (0.24 #222, 0.11 #284, 0.11 #128), 02ynfr (0.22 #138, 0.20 #201, 0.19 #388), 033smt (0.21 #241, 0.13 #303, 0.05 #272) >> Best rule #285 for best value: >> intensional similarity = 6 >> extensional distance = 68 >> proper extension: 03q8xj; 05zvzf3; >> query: (?x10596, 09vw2b7) <- film_crew_role(?x10596, ?x5136), film_crew_role(?x10596, ?x4305), ?x5136 = 089g0h, titles(?x2480, ?x10596), language(?x10596, ?x254), ?x4305 = 0215hd >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 028kj0 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 95.000 0.757 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 028kj0 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.757 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17011-028q7m PRED entity: 028q7m PRED relation: category PRED expected values: 08mbj5d => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.67 #8, 0.64 #12, 0.63 #9) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 1206 >> proper extension: 0n5yh; >> query: (?x11609, 08mbj5d) <- contains(?x3730, ?x11609), place_of_birth(?x12159, ?x3730), adjoins(?x3730, ?x1499), produced_by(?x2211, ?x12159) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028q7m category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.672 http://example.org/common/topic/webpage./common/webpage/category #17010-07r1_ PRED entity: 07r1_ PRED relation: award_winner! PRED expected values: 01mh_q => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 103): 01s695 (0.29 #144, 0.23 #990, 0.13 #4515), 02rjjll (0.29 #146, 0.19 #2261, 0.18 #1979), 01c6qp (0.29 #160, 0.17 #6082, 0.14 #865), 0bz6sb (0.27 #487, 0.10 #1756, 0.09 #1897), 0466p0j (0.25 #640, 0.18 #2191, 0.17 #2896), 013b2h (0.19 #3323, 0.14 #221, 0.14 #4310), 02cg41 (0.18 #1113, 0.14 #3369, 0.13 #4638), 01xqqp (0.18 #519, 0.14 #237, 0.14 #1083), 01bx35 (0.18 #994, 0.14 #148, 0.13 #2404), 09n4nb (0.16 #1176, 0.11 #2868, 0.11 #2304) >> Best rule #144 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0140t7; >> query: (?x7086, 01s695) <- artist(?x5634, ?x7086), artists(?x302, ?x7086), award(?x7086, ?x4488), artist(?x8738, ?x7086), ?x4488 = 02gdjb >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #230 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0140t7; *> query: (?x7086, 01mh_q) <- artist(?x5634, ?x7086), artists(?x302, ?x7086), award(?x7086, ?x4488), artist(?x8738, ?x7086), ?x4488 = 02gdjb *> conf = 0.14 ranks of expected_values: 12 EVAL 07r1_ award_winner! 01mh_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 112.000 112.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #17009-02fgmn PRED entity: 02fgmn PRED relation: genre! PRED expected values: 0fhzwl => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 398): 099pks (0.55 #666, 0.43 #1237, 0.36 #952), 07g9f (0.53 #1636, 0.33 #211, 0.31 #2786), 07gbf (0.47 #1625, 0.33 #484, 0.33 #200), 014gjp (0.45 #709, 0.39 #1854, 0.36 #1280), 02r1ysd (0.45 #693, 0.36 #1264, 0.33 #1838), 03nt59 (0.45 #676, 0.36 #1247, 0.33 #108), 0vjr (0.45 #663, 0.36 #1234, 0.33 #95), 0557yqh (0.45 #625, 0.36 #1196, 0.29 #911), 039cq4 (0.43 #1267, 0.31 #1713, 0.29 #2127), 0123qq (0.36 #801, 0.36 #1087, 0.33 #233) >> Best rule #666 for best value: >> intensional similarity = 11 >> extensional distance = 9 >> proper extension: 05p553; 01z4y; 06nbt; 03mqtr; 01t_vv; 0gs6m; 0c4xc; >> query: (?x12176, 099pks) <- genre(?x12105, ?x12176), genre(?x4637, ?x12176), nominated_for(?x6678, ?x12105), nominated_for(?x4563, ?x12105), ?x6678 = 05gnf, award_winner(?x1119, ?x4563), titles(?x2008, ?x12105), actor(?x4637, ?x1205), award(?x4563, ?x102), program(?x11249, ?x4637), category(?x4637, ?x134) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #180 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 07s9rl0; *> query: (?x12176, 0fhzwl) <- genre(?x14197, ?x12176), genre(?x11250, ?x12176), genre(?x4898, ?x12176), ?x14197 = 02rq7nd, actor(?x4898, ?x2200), ?x11250 = 01cvtf, program(?x6678, ?x4898), award_winner(?x4898, ?x8831), nominated_for(?x2041, ?x4898), ?x2041 = 0bdx29, participant(?x2200, ?x3308), country_of_origin(?x4898, ?x94), award_winner(?x192, ?x2200) *> conf = 0.33 ranks of expected_values: 69 EVAL 02fgmn genre! 0fhzwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 39.000 39.000 0.545 http://example.org/tv/tv_program/genre #17008-0h1x5f PRED entity: 0h1x5f PRED relation: genre PRED expected values: 07s9rl0 0219x_ => 97 concepts (65 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.82 #828, 0.81 #1301, 0.76 #591), 01z4y (0.63 #946, 0.51 #6975, 0.46 #3899), 02kdv5l (0.48 #6032, 0.37 #2129, 0.36 #4139), 02l7c8 (0.35 #251, 0.33 #4270, 0.33 #3677), 01jfsb (0.33 #2137, 0.31 #1547, 0.31 #5802), 04xvlr (0.32 #829, 0.28 #1302, 0.20 #592), 0lsxr (0.28 #599, 0.22 #1073, 0.20 #717), 0219x_ (0.24 #144, 0.23 #26, 0.17 #262), 060__y (0.23 #843, 0.21 #1316, 0.19 #724), 06cvj (0.21 #4259, 0.20 #950, 0.15 #4) >> Best rule #828 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 02phtzk; 0gy0l_; >> query: (?x9701, 07s9rl0) <- genre(?x9701, ?x258), nominated_for(?x1162, ?x9701), ?x1162 = 099c8n, titles(?x2480, ?x9701) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 0h1x5f genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 65.000 0.815 http://example.org/film/film/genre EVAL 0h1x5f genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 65.000 0.815 http://example.org/film/film/genre #17007-0tfc PRED entity: 0tfc PRED relation: student! PRED expected values: 0yldt => 149 concepts (121 used for prediction) PRED predicted values (max 10 best out of 220): 0dy04 (0.40 #1118, 0.17 #8980, 0.17 #8456), 03ksy (0.31 #4821, 0.19 #42050, 0.19 #42574), 0yls9 (0.25 #223, 0.14 #8085, 0.11 #10705), 07tg4 (0.25 #85, 0.14 #7423, 0.10 #2181), 0ymf1 (0.25 #522, 0.11 #11004, 0.10 #8384), 0138t4 (0.25 #401, 0.08 #5117, 0.06 #6166), 0d5fb (0.25 #509, 0.05 #8371, 0.04 #10991), 08815 (0.23 #4718, 0.19 #5767, 0.13 #41947), 0bwfn (0.20 #1321, 0.08 #3417, 0.08 #55323), 01stzp (0.17 #9418, 0.17 #4700, 0.17 #3652) >> Best rule #1118 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0399p; >> query: (?x12441, 0dy04) <- interests(?x12441, ?x1858), place_of_birth(?x12441, ?x13389), influenced_by(?x12441, ?x1857), ?x1857 = 026lj >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #14137 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 41 *> proper extension: 09jd9; *> query: (?x12441, 0yldt) <- student(?x12726, ?x12441), student(?x892, ?x12441), ?x892 = 07tgn, contains(?x512, ?x12726), category(?x12726, ?x134) *> conf = 0.07 ranks of expected_values: 49 EVAL 0tfc student! 0yldt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 149.000 121.000 0.400 http://example.org/education/educational_institution/students_graduates./education/education/student #17006-0571m PRED entity: 0571m PRED relation: currency PRED expected values: 09nqf => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.80 #57, 0.80 #50, 0.77 #29), 01nv4h (0.06 #23, 0.03 #79, 0.03 #107), 02l6h (0.06 #25, 0.01 #109, 0.01 #221) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 305 >> proper extension: 0gtsx8c; >> query: (?x3251, 09nqf) <- film(?x8663, ?x3251), crewmember(?x3251, ?x4703), profession(?x8663, ?x1032), film_crew_role(?x3251, ?x137) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0571m currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.798 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #17005-08s6mr PRED entity: 08s6mr PRED relation: film_crew_role PRED expected values: 01xy5l_ => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 27): 09vw2b7 (0.82 #39, 0.76 #138, 0.72 #105), 09zzb8 (0.81 #1490, 0.79 #828, 0.78 #1424), 0215hd (0.67 #283, 0.67 #117, 0.65 #150), 033smt (0.46 #157, 0.43 #58, 0.23 #290), 0dxtw (0.45 #639, 0.43 #705, 0.38 #1433), 01xy5l_ (0.39 #46, 0.38 #145, 0.36 #112), 015h31 (0.39 #41, 0.35 #140, 0.30 #74), 01pvkk (0.32 #44, 0.29 #640, 0.28 #673), 02ynfr (0.32 #710, 0.20 #644, 0.19 #114), 0ckd1 (0.25 #36, 0.24 #135, 0.13 #268) >> Best rule #39 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 09sh8k; 0bth54; 014kq6; 02725hs; 05p1qyh; 03r0g9; 05f4_n0; 0243cq; 057lbk; 033f8n; ... >> query: (?x7590, 09vw2b7) <- film_crew_role(?x7590, ?x2154), film_crew_role(?x7590, ?x281), ?x281 = 02_n3z, nominated_for(?x1053, ?x7590), ?x2154 = 01vx2h >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 26 *> proper extension: 09sh8k; 0bth54; 014kq6; 02725hs; 05p1qyh; 03r0g9; 05f4_n0; 0243cq; 057lbk; 033f8n; ... *> query: (?x7590, 01xy5l_) <- film_crew_role(?x7590, ?x2154), film_crew_role(?x7590, ?x281), ?x281 = 02_n3z, nominated_for(?x1053, ?x7590), ?x2154 = 01vx2h *> conf = 0.39 ranks of expected_values: 6 EVAL 08s6mr film_crew_role 01xy5l_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 84.000 84.000 0.821 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #17004-027xx3 PRED entity: 027xx3 PRED relation: major_field_of_study PRED expected values: 05qjt => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 134): 01tbp (0.50 #55, 0.43 #287, 0.42 #403), 036hv (0.50 #11, 0.30 #243, 0.29 #359), 04rjg (0.45 #250, 0.44 #366, 0.38 #1877), 02j62 (0.44 #2932, 0.38 #144, 0.37 #3629), 04x_3 (0.38 #140, 0.38 #256, 0.38 #372), 05qjt (0.36 #240, 0.35 #356, 0.31 #1867), 01bt59 (0.33 #73, 0.28 #305, 0.27 #421), 041y2 (0.33 #72, 0.19 #304, 0.19 #653), 03qsdpk (0.33 #43, 0.15 #159, 0.11 #1784), 03g3w (0.33 #2929, 0.32 #257, 0.31 #373) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 065y4w7; 01wdj_; 01ky7c; 0k__z; >> query: (?x3021, 01tbp) <- major_field_of_study(?x3021, ?x3213), major_field_of_study(?x3021, ?x1682), ?x1682 = 02ky346, ?x3213 = 0g4gr >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #240 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 45 *> proper extension: 01jssp; 06pwq; 01w3v; 04rwx; 03v6t; 07szy; 0bthb; 07vk2; 01s0_f; 01jswq; ... *> query: (?x3021, 05qjt) <- major_field_of_study(?x3021, ?x3213), major_field_of_study(?x3021, ?x1682), ?x1682 = 02ky346, major_field_of_study(?x1011, ?x3213), ?x1011 = 07w0v *> conf = 0.36 ranks of expected_values: 6 EVAL 027xx3 major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 85.000 85.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #17003-04gmp_z PRED entity: 04gmp_z PRED relation: place_of_death PRED expected values: 030qb3t => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 18): 030qb3t (0.29 #410, 0.29 #798, 0.25 #992), 0f2wj (0.08 #400, 0.08 #206, 0.08 #594), 0k_p5 (0.08 #476, 0.08 #282, 0.05 #864), 06_kh (0.05 #781, 0.04 #393, 0.04 #199), 04jpl (0.04 #1171), 0r540 (0.04 #419, 0.04 #225, 0.04 #613), 0cb4j (0.04 #399, 0.04 #205, 0.04 #593), 0r2gj (0.04 #298, 0.04 #686, 0.03 #880), 015zxh (0.04 #413, 0.03 #801, 0.02 #995), 071vr (0.04 #296, 0.03 #878, 0.02 #1072) >> Best rule #410 for best value: >> intensional similarity = 2 >> extensional distance = 22 >> proper extension: 035_2h; >> query: (?x2801, 030qb3t) <- award_winner(?x2801, ?x10609), film_sets_designed(?x10609, ?x499) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gmp_z place_of_death 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.292 http://example.org/people/deceased_person/place_of_death #17002-01jfr3y PRED entity: 01jfr3y PRED relation: artists! PRED expected values: 02xjb 026z9 => 129 concepts (62 used for prediction) PRED predicted values (max 10 best out of 264): 06by7 (0.65 #17420, 0.64 #16809, 0.59 #18640), 0y3_8 (0.43 #47, 0.27 #4623, 0.26 #4013), 0glt670 (0.42 #3702, 0.40 #2176, 0.40 #4007), 0gywn (0.37 #2191, 0.34 #4632, 0.33 #4022), 016clz (0.35 #17404, 0.25 #310, 0.23 #11298), 0m0jc (0.25 #314, 0.17 #3975, 0.14 #4585), 02x8m (0.25 #323, 0.16 #4594, 0.16 #15887), 016_nr (0.21 #1291, 0.12 #986, 0.11 #3427), 01lyv (0.21 #8579, 0.16 #5527, 0.15 #11021), 0xhtw (0.21 #17415, 0.20 #16804, 0.18 #17109) >> Best rule #17420 for best value: >> intensional similarity = 5 >> extensional distance = 594 >> proper extension: 01nqfh_; 01cv3n; 01vs14j; 01qvgl; 04zwjd; 01m65sp; 02bh9; 037hgm; 01vswwx; 02mq_y; ... >> query: (?x5878, 06by7) <- artists(?x3996, ?x5878), artists(?x3996, ?x6577), artists(?x3996, ?x6151), ?x6577 = 0gs6vr, participant(?x6151, ?x1017) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #4651 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 83 *> proper extension: 0dzc16; *> query: (?x5878, 026z9) <- artists(?x3996, ?x5878), ?x3996 = 02lnbg *> conf = 0.13 ranks of expected_values: 25, 105 EVAL 01jfr3y artists! 026z9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 129.000 62.000 0.653 http://example.org/music/genre/artists EVAL 01jfr3y artists! 02xjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 129.000 62.000 0.653 http://example.org/music/genre/artists #17001-08jcfy PRED entity: 08jcfy PRED relation: organization PRED expected values: 0m4yg => 26 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1012): 014xf6 (0.53 #12364, 0.28 #11593, 0.22 #6952), 07tgn (0.45 #771, 0.33 #25, 0.23 #7723), 0yls9 (0.45 #771, 0.22 #6952, 0.20 #1090), 0ym4t (0.45 #771, 0.22 #6952, 0.19 #11592), 0yl_w (0.45 #771, 0.22 #6951, 0.21 #6950), 0ymbl (0.45 #771, 0.22 #6951, 0.19 #11592), 0ymgk (0.45 #771, 0.22 #6951, 0.19 #11592), 0ym17 (0.45 #771, 0.22 #6951, 0.19 #11592), 0ym20 (0.45 #771, 0.22 #6951, 0.19 #11592), 0ym69 (0.45 #771, 0.21 #6950, 0.19 #11592) >> Best rule #12364 for best value: >> intensional similarity = 12 >> extensional distance = 10 >> proper extension: 0789n; 01dz7z; >> query: (?x11157, ?x8223) <- company(?x11157, ?x8223), contains(?x1310, ?x8223), contains(?x362, ?x8223), company(?x3484, ?x8223), organization(?x3484, ?x216), company(?x3484, ?x1665), company(?x3484, ?x122), taxonomy(?x1310, ?x939), ?x122 = 08815, service_location(?x2607, ?x362), ?x939 = 04n6k, ?x1665 = 04rwx >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #6951 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 6 *> proper extension: 09d6p2; *> query: (?x11157, ?x893) <- organization(?x11157, ?x13770), organization(?x11157, ?x6837), organization(?x11157, ?x2234), state_province_region(?x13770, ?x6885), currency(?x2234, ?x1099), school_type(?x13770, ?x3092), colors(?x6837, ?x3189), student(?x6837, ?x4649), category(?x2234, ?x134), profession(?x4649, ?x1032), currency(?x248, ?x1099), currency(?x622, ?x1099), currency(?x893, ?x1099) *> conf = 0.22 ranks of expected_values: 361 EVAL 08jcfy organization 0m4yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 26.000 16.000 0.526 http://example.org/organization/role/leaders./organization/leadership/organization #17000-05b6rdt PRED entity: 05b6rdt PRED relation: film_release_region PRED expected values: 03rt9 06t2t 03ryn => 123 concepts (115 used for prediction) PRED predicted values (max 10 best out of 220): 09c7w0 (0.94 #6637, 0.94 #8078, 0.93 #7213), 06t2t (0.82 #197, 0.81 #2937, 0.80 #53), 03_3d (0.79 #1737, 0.78 #5343, 0.78 #3754), 015qh (0.73 #180, 0.70 #36, 0.60 #2920), 01mjq (0.73 #183, 0.70 #39, 0.58 #5377), 03rt9 (0.72 #2895, 0.69 #5493, 0.69 #3760), 016wzw (0.68 #2941, 0.55 #201, 0.52 #921), 047yc (0.64 #2907, 0.56 #3772, 0.54 #5361), 06t8v (0.64 #212, 0.60 #68, 0.56 #2952), 06qd3 (0.63 #752, 0.61 #1764, 0.56 #1185) >> Best rule #6637 for best value: >> intensional similarity = 6 >> extensional distance = 257 >> proper extension: 0pd57; >> query: (?x6235, 09c7w0) <- genre(?x6235, ?x571), featured_film_locations(?x6235, ?x279), film_release_region(?x6235, ?x87), genre(?x723, ?x571), genre(?x3413, ?x571), ?x723 = 04fzfj >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #197 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 0fpv_3_; *> query: (?x6235, 06t2t) <- genre(?x6235, ?x571), film_crew_role(?x6235, ?x137), film_release_region(?x6235, ?x6691), film(?x4771, ?x6235), titles(?x571, ?x249), ?x6691 = 02k8k *> conf = 0.82 ranks of expected_values: 2, 6, 16 EVAL 05b6rdt film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 123.000 115.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b6rdt film_release_region 06t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 123.000 115.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b6rdt film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 123.000 115.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16999-0hcs3 PRED entity: 0hcs3 PRED relation: type_of_union PRED expected values: 04ztj => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.83 #69, 0.81 #57, 0.80 #89), 01g63y (0.14 #14, 0.14 #42, 0.12 #154), 0jgjn (0.02 #48, 0.01 #80, 0.01 #92) >> Best rule #69 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 03xp8d5; 0cj2w; >> query: (?x12323, 04ztj) <- place_of_birth(?x12323, ?x1860), inductee(?x12879, ?x12323), place_of_birth(?x2176, ?x1860), award_nominee(?x415, ?x2176) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hcs3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.831 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16998-01_1pv PRED entity: 01_1pv PRED relation: production_companies PRED expected values: 04rcl7 => 92 concepts (73 used for prediction) PRED predicted values (max 10 best out of 75): 054g1r (0.50 #2403, 0.44 #4893, 0.44 #4728), 03xq0f (0.50 #2403, 0.44 #4893, 0.44 #4728), 09b3v (0.40 #83, 0.40 #32, 0.38 #166), 04rcl7 (0.40 #71, 0.38 #154, 0.25 #986), 0kk9v (0.20 #34, 0.19 #117, 0.15 #949), 016tt2 (0.16 #502, 0.15 #1002, 0.15 #1333), 086k8 (0.15 #250, 0.14 #1910, 0.13 #4067), 016tw3 (0.15 #260, 0.14 #678, 0.14 #427), 056ws9 (0.14 #960, 0.05 #1953, 0.04 #1374), 05qd_ (0.14 #2330, 0.13 #2164, 0.13 #2413) >> Best rule #2403 for best value: >> intensional similarity = 4 >> extensional distance = 247 >> proper extension: 044g_k; 02rjv2w; 0299hs; 024mxd; 03q5db; 019kyn; 06fqlk; 072hx4; >> query: (?x2223, ?x609) <- production_companies(?x2223, ?x2156), film(?x609, ?x2223), film(?x2378, ?x2223), honored_for(?x5924, ?x2223) >> conf = 0.50 => this is the best rule for 2 predicted values *> Best rule #71 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 02x3lt7; 03bx2lk; 065z3_x; 0243cq; *> query: (?x2223, 04rcl7) <- genre(?x2223, ?x53), film(?x2378, ?x2223), titles(?x3920, ?x2223), ?x3920 = 09b3v *> conf = 0.40 ranks of expected_values: 4 EVAL 01_1pv production_companies 04rcl7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 92.000 73.000 0.495 http://example.org/film/film/production_companies #16997-0h3y PRED entity: 0h3y PRED relation: exported_to PRED expected values: 059j2 => 170 concepts (164 used for prediction) PRED predicted values (max 10 best out of 190): 0345h (0.32 #2040, 0.32 #2154, 0.32 #1810), 0d05w3 (0.32 #2040, 0.32 #2154, 0.32 #1810), 01znc_ (0.32 #2040, 0.32 #2154, 0.32 #1810), 05r4w (0.32 #2040, 0.32 #2154, 0.32 #1810), 0h3y (0.25 #399, 0.25 #117, 0.17 #850), 0j4b (0.21 #438, 0.19 #156, 0.18 #1230), 06s_2 (0.19 #167, 0.11 #788, 0.10 #956), 06tw8 (0.18 #436, 0.17 #1342, 0.16 #1228), 0jdx (0.14 #441, 0.12 #159, 0.12 #103), 07ssc (0.13 #1195, 0.13 #1309, 0.12 #233) >> Best rule #2040 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 01f08r; >> query: (?x291, ?x2346) <- exported_to(?x2346, ?x291), contains(?x2467, ?x291), country(?x150, ?x2346) >> conf = 0.32 => this is the best rule for 4 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0bk25; *> query: (?x291, 059j2) <- nationality(?x13901, ?x291), influenced_by(?x13901, ?x3712), ?x3712 = 0gz_ *> conf = 0.12 ranks of expected_values: 12 EVAL 0h3y exported_to 059j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 170.000 164.000 0.321 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #16996-027l0b PRED entity: 027l0b PRED relation: people! PRED expected values: 09kr66 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 48): 0x67 (0.27 #1359, 0.19 #3093, 0.18 #4068), 02w7gg (0.17 #152, 0.12 #3161, 0.11 #1502), 033tf_ (0.17 #81, 0.14 #1581, 0.13 #2338), 07bch9 (0.17 #97, 0.06 #1522, 0.06 #2354), 09vc4s (0.17 #83, 0.04 #1583, 0.03 #3917), 01g7zj (0.17 #125, 0.01 #1400, 0.01 #500), 0xnvg (0.10 #1512, 0.09 #1739, 0.08 #2494), 013xrm (0.09 #169, 0.05 #1444, 0.05 #1594), 013b6_ (0.09 #201, 0.04 #1476, 0.04 #1176), 07hwkr (0.07 #1361, 0.07 #2343, 0.07 #3320) >> Best rule #1359 for best value: >> intensional similarity = 3 >> extensional distance = 310 >> proper extension: 021sv1; 01w61th; 01kwlwp; 02whj; 01sbf2; 01ky2h; 019g40; 01wz3cx; 02mjmr; 0lgm5; ... >> query: (?x2794, 0x67) <- place_of_birth(?x2794, ?x6088), people(?x1050, ?x2794), category(?x2794, ?x134) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1166 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 246 *> proper extension: 0162c8; 030dx5; 04258w; 03f4k; 046_v; 059y0; 024t0y; 0f3nn; 02nygk; *> query: (?x2794, 09kr66) <- place_of_birth(?x2794, ?x6088), people(?x1050, ?x2794), ?x1050 = 041rx *> conf = 0.02 ranks of expected_values: 25 EVAL 027l0b people! 09kr66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 111.000 111.000 0.272 http://example.org/people/ethnicity/people #16995-014nvr PRED entity: 014nvr PRED relation: influenced_by! PRED expected values: 06hmd => 125 concepts (46 used for prediction) PRED predicted values (max 10 best out of 394): 040db (0.30 #76, 0.18 #590, 0.17 #1104), 01vdrw (0.30 #444, 0.18 #958, 0.08 #11241), 05jm7 (0.30 #140, 0.10 #8880, 0.10 #3224), 03772 (0.30 #202, 0.09 #716, 0.07 #10999), 0683n (0.27 #853, 0.25 #1367, 0.20 #339), 034bs (0.27 #668, 0.17 #1182, 0.12 #2724), 06jcc (0.27 #827, 0.14 #9053, 0.13 #8539), 0gd5z (0.27 #600, 0.12 #2656, 0.09 #8826), 03_87 (0.27 #775, 0.12 #2831, 0.08 #1289), 045bg (0.25 #1064, 0.07 #13405, 0.06 #12890) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 02y49; >> query: (?x6420, 040db) <- influenced_by(?x2343, ?x6420), profession(?x6420, ?x353), ?x2343 = 0jt90f5, type_of_union(?x6420, ?x566), gender(?x6420, ?x231) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #20063 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 282 *> proper extension: 07yg2; 0qmny; 016vn3; 06lxn; *> query: (?x6420, ?x2161) <- influenced_by(?x8699, ?x6420), influenced_by(?x3542, ?x8699), award(?x3542, ?x921), influenced_by(?x2161, ?x3542) *> conf = 0.07 ranks of expected_values: 161 EVAL 014nvr influenced_by! 06hmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 125.000 46.000 0.300 http://example.org/influence/influence_node/influenced_by #16994-03j24kf PRED entity: 03j24kf PRED relation: group PRED expected values: 07c0j => 172 concepts (109 used for prediction) PRED predicted values (max 10 best out of 99): 01v0sx2 (0.17 #4639, 0.10 #540, 0.09 #219), 01qqwp9 (0.13 #665, 0.12 #772, 0.12 #449), 07mvp (0.09 #259, 0.06 #1551, 0.04 #796), 015srx (0.09 #255, 0.05 #576, 0.04 #685), 0cfgd (0.09 #309, 0.04 #739, 0.04 #846), 01v0sxx (0.09 #298, 0.04 #835, 0.04 #943), 04k05 (0.09 #303, 0.04 #840, 0.04 #948), 07c0j (0.09 #648, 0.06 #432, 0.06 #1510), 0dw4g (0.09 #1545, 0.03 #4673, 0.03 #1437), 02r1tx7 (0.06 #1414, 0.05 #4650, 0.04 #1950) >> Best rule #4639 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 0jn5l; 01w5gg6; >> query: (?x4701, 01v0sx2) <- award_nominee(?x1660, ?x4701), artists(?x378, ?x4701), group(?x4701, ?x6202) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #648 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 07_3qd; *> query: (?x4701, 07c0j) <- role(?x4701, ?x2764), role(?x4701, ?x1574), ?x1574 = 0l15bq, role(?x2764, ?x228) *> conf = 0.09 ranks of expected_values: 8 EVAL 03j24kf group 07c0j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 172.000 109.000 0.167 http://example.org/music/group_member/membership./music/group_membership/group #16993-06rzwx PRED entity: 06rzwx PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.84 #189, 0.83 #179, 0.83 #325), 07z4p (0.21 #494, 0.21 #627, 0.14 #79), 07c52 (0.21 #494, 0.21 #627, 0.13 #77), 02nxhr (0.21 #494, 0.11 #37, 0.10 #107), 0735l (0.01 #78, 0.01 #83) >> Best rule #189 for best value: >> intensional similarity = 6 >> extensional distance = 259 >> proper extension: 0kv9d3; 08984j; 05nyqk; 06y611; >> query: (?x7114, 029j_) <- titles(?x7160, ?x7114), film(?x5604, ?x7114), category(?x5604, ?x134), film_crew_role(?x7114, ?x137), participant(?x5604, ?x3183), genre(?x7114, ?x53) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06rzwx film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.835 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #16992-0n1h PRED entity: 0n1h PRED relation: profession! PRED expected values: 01vvydl 012x4t 01wsl7c 0l56b 01cwhp 01wv9p 016fnb 026dx 0g824 0ffgh 02vwckw 01bbwp 0kc6 0blgl => 52 concepts (22 used for prediction) PRED predicted values (max 10 best out of 4106): 02fybl (0.75 #38884, 0.60 #30739, 0.60 #26666), 0473q (0.75 #38909, 0.60 #30764, 0.50 #14475), 014q2g (0.75 #37432, 0.60 #29287, 0.50 #12998), 01ydzx (0.75 #38754, 0.60 #30609, 0.50 #14320), 016jfw (0.62 #38559, 0.60 #30414, 0.50 #14125), 01l1sq (0.62 #37081, 0.60 #28936, 0.50 #12647), 01vsnff (0.62 #37244, 0.60 #29099, 0.50 #12810), 01l47f5 (0.62 #38661, 0.60 #30516, 0.41 #48876), 03f7m4h (0.62 #39283, 0.60 #31138, 0.41 #48876), 0phx4 (0.62 #37699, 0.60 #29554, 0.41 #48876) >> Best rule #38884 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0dz3r; 02hrh1q; 01c72t; >> query: (?x955, 02fybl) <- profession(?x6456, ?x955), profession(?x4620, ?x955), people(?x7790, ?x6456), ?x7790 = 06mvq, ?x4620 = 01vsy7t >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #30492 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 016z4k; *> query: (?x955, 0g824) <- profession(?x6456, ?x955), profession(?x4640, ?x955), profession(?x3126, ?x955), people(?x7322, ?x6456), artist(?x2190, ?x6456), ?x3126 = 0161c2, ?x4640 = 018n6m *> conf = 0.60 ranks of expected_values: 34, 37, 46, 62, 112, 132, 160, 288, 296, 532, 592, 594, 1189, 1235 EVAL 0n1h profession! 0blgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 0kc6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 01bbwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 02vwckw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 0ffgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 0g824 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 026dx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 016fnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 01wv9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 01cwhp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 0l56b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 01wsl7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 52.000 22.000 0.750 http://example.org/people/person/profession EVAL 0n1h profession! 01vvydl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 52.000 22.000 0.750 http://example.org/people/person/profession #16991-05m_jsg PRED entity: 05m_jsg PRED relation: genre PRED expected values: 02l7c8 => 60 concepts (59 used for prediction) PRED predicted values (max 10 best out of 81): 07s9rl0 (0.60 #3339, 0.60 #2027, 0.59 #2743), 03k9fj (0.37 #3824, 0.25 #10, 0.25 #1557), 02l7c8 (0.34 #966, 0.33 #1442, 0.30 #728), 0lsxr (0.29 #3821, 0.22 #245, 0.21 #126), 01hmnh (0.18 #730, 0.18 #3830, 0.17 #1563), 06n90 (0.18 #3825, 0.15 #249, 0.15 #487), 01t_vv (0.16 #1005, 0.15 #1481, 0.15 #767), 02n4kr (0.16 #3820, 0.12 #482, 0.12 #2628), 04xvlr (0.15 #3340, 0.14 #2744, 0.14 #2028), 060__y (0.14 #2041, 0.14 #2757, 0.13 #3353) >> Best rule #3339 for best value: >> intensional similarity = 3 >> extensional distance = 952 >> proper extension: 01jc6q; 03bdkd; >> query: (?x3921, 07s9rl0) <- award_winner(?x3921, ?x7830), film(?x10814, ?x3921), gender(?x10814, ?x514) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #966 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 404 *> proper extension: 027qgy; 047q2k1; 01sxly; 0pv2t; 0c5dd; 018f8; 03m4mj; 0sxfd; 07h9gp; 06rmdr; ... *> query: (?x3921, 02l7c8) <- country(?x3921, ?x94), genre(?x3921, ?x258), ?x258 = 05p553, nominated_for(?x5593, ?x3921) *> conf = 0.34 ranks of expected_values: 3 EVAL 05m_jsg genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 59.000 0.605 http://example.org/film/film/genre #16990-0k9p4 PRED entity: 0k9p4 PRED relation: teams PRED expected values: 04wmvz => 215 concepts (215 used for prediction) PRED predicted values (max 10 best out of 282): 06rny (0.17 #464, 0.06 #1182, 0.06 #1900), 0713r (0.17 #437, 0.06 #1155, 0.06 #1873), 0bwjj (0.17 #575, 0.06 #1293, 0.05 #2729), 0j2zj (0.17 #569, 0.06 #1287, 0.05 #2723), 02wvfxl (0.17 #460, 0.06 #1178, 0.05 #2614), 01d5z (0.17 #377, 0.06 #1095, 0.05 #2531), 06rpd (0.08 #918, 0.06 #1636, 0.06 #1995), 0jmk7 (0.06 #1738, 0.06 #1379, 0.06 #2097), 0jnq8 (0.06 #1664, 0.06 #1305, 0.06 #2023), 0jmjr (0.06 #1657, 0.06 #1298, 0.06 #2016) >> Best rule #464 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0mbf4; 0jpkg; >> query: (?x9417, 06rny) <- jurisdiction_of_office(?x10525, ?x9417), jurisdiction_of_office(?x1195, ?x9417), ?x1195 = 0pqc5, mode_of_transportation(?x9417, ?x4272), ?x10525 = 01q24l >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k9p4 teams 04wmvz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 215.000 0.167 http://example.org/sports/sports_team_location/teams #16989-0jdm8 PRED entity: 0jdm8 PRED relation: genre! PRED expected values: 01pvxl 04pk1f 0456zg => 35 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1976): 01cssf (0.74 #12985, 0.74 #7417, 0.33 #1946), 04x4vj (0.74 #12985, 0.74 #7417, 0.33 #2644), 021pqy (0.74 #7417, 0.60 #15629, 0.57 #19339), 0m313 (0.74 #7417, 0.60 #14852, 0.57 #18562), 03hjv97 (0.74 #7417, 0.60 #14956, 0.57 #18666), 03h_yy (0.74 #7417, 0.60 #14916, 0.57 #18626), 03s6l2 (0.74 #7417, 0.60 #14926, 0.50 #9360), 01hw5kk (0.74 #7417, 0.60 #13682, 0.50 #9971), 02nx2k (0.74 #7417, 0.60 #14230, 0.50 #8664), 06yykb (0.74 #7417, 0.60 #14408, 0.50 #8842) >> Best rule #12985 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 01q03; >> query: (?x10848, ?x4591) <- genre(?x7214, ?x10848), genre(?x6681, ?x10848), genre(?x5458, ?x10848), genre(?x4194, ?x10848), genre(?x3588, ?x10848), genre(?x2755, ?x10848), ?x3588 = 0900j5, film(?x1289, ?x5458), ?x6681 = 04y9mm8, film_crew_role(?x5458, ?x137), film_release_distribution_medium(?x2755, ?x81), costume_design_by(?x2755, ?x12521), award_nominee(?x1290, ?x1289), prequel(?x4194, ?x4591), nominated_for(?x298, ?x7214) >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #7417 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 2 *> proper extension: 02kdv5l; *> query: (?x10848, ?x54) <- genre(?x10752, ?x10848), genre(?x5458, ?x10848), genre(?x2755, ?x10848), ?x5458 = 05szq8z, country(?x10752, ?x94), nominated_for(?x484, ?x10752), film(?x5833, ?x2755), film(?x815, ?x2755), genre(?x2755, ?x53), ?x815 = 058kqy, participant(?x6187, ?x5833), genre(?x4664, ?x53), genre(?x54, ?x53), ?x4664 = 0fqt1ns, genre(?x273, ?x53) *> conf = 0.74 ranks of expected_values: 179, 509, 1528 EVAL 0jdm8 genre! 0456zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 35.000 16.000 0.738 http://example.org/film/film/genre EVAL 0jdm8 genre! 04pk1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 16.000 0.738 http://example.org/film/film/genre EVAL 0jdm8 genre! 01pvxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 35.000 16.000 0.738 http://example.org/film/film/genre #16988-0h0wd9 PRED entity: 0h0wd9 PRED relation: film! PRED expected values: 036jb => 98 concepts (46 used for prediction) PRED predicted values (max 10 best out of 173): 03thw4 (0.45 #6891, 0.42 #2752, 0.41 #9653), 07c0j (0.28 #5237, 0.13 #4133, 0.13 #1925), 03f2_rc (0.20 #552, 0.20 #287, 0.20 #276), 029m83 (0.20 #466, 0.20 #190, 0.02 #2390), 0gv40 (0.20 #116, 0.03 #668, 0.02 #2041), 02d6cy (0.20 #401, 0.01 #1227, 0.01 #2602), 076psv (0.14 #9651, 0.13 #4133, 0.13 #1925), 0fqjks (0.14 #9651, 0.13 #1925, 0.12 #275), 0c0tzp (0.14 #9651, 0.12 #9375, 0.12 #6890), 036jb (0.13 #4133, 0.13 #1925, 0.12 #275) >> Best rule #6891 for best value: >> intensional similarity = 4 >> extensional distance = 209 >> proper extension: 0g5qmbz; >> query: (?x10362, ?x4405) <- nominated_for(?x484, ?x10362), award_winner(?x10362, ?x4423), produced_by(?x10362, ?x4405), written_by(?x650, ?x4405) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #4133 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 150 *> proper extension: 0c3ybss; 09gdm7q; *> query: (?x10362, ?x1136) <- nominated_for(?x1136, ?x10362), film_festivals(?x10362, ?x3831), award_winner(?x2139, ?x1136) *> conf = 0.13 ranks of expected_values: 10 EVAL 0h0wd9 film! 036jb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 98.000 46.000 0.451 http://example.org/film/director/film #16987-01mr2g6 PRED entity: 01mr2g6 PRED relation: instrumentalists! PRED expected values: 0l14qv 02hnl 03qjg => 183 concepts (130 used for prediction) PRED predicted values (max 10 best out of 136): 05148p4 (0.50 #278, 0.41 #5190, 0.39 #2604), 018vs (0.40 #615, 0.39 #2596, 0.38 #2252), 03qjg (0.38 #482, 0.28 #1084, 0.25 #3152), 02hnl (0.33 #2618, 0.33 #1154, 0.33 #379), 018j2 (0.33 #383, 0.20 #641, 0.17 #1071), 026t6 (0.33 #348, 0.17 #2243, 0.17 #1123), 03m5k (0.30 #620, 0.25 #792, 0.17 #1050), 03bx0bm (0.26 #2067, 0.21 #3101, 0.04 #5430), 013y1f (0.25 #117, 0.17 #289, 0.12 #462), 0g2dz (0.25 #115, 0.17 #1149, 0.10 #632) >> Best rule #278 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01kv4mb; 02jg92; 01309x; >> query: (?x8272, 05148p4) <- instrumentalists(?x3716, ?x8272), artists(?x2664, ?x8272), type_of_union(?x8272, ?x566), ?x2664 = 01lyv, ?x3716 = 03gvt >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #482 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 01x66d; 016h4r; 06rgq; *> query: (?x8272, 03qjg) <- instrumentalists(?x316, ?x8272), artists(?x302, ?x8272), ?x316 = 05r5c, student(?x734, ?x8272), profession(?x8272, ?x131) *> conf = 0.38 ranks of expected_values: 3, 4, 12 EVAL 01mr2g6 instrumentalists! 03qjg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 183.000 130.000 0.500 http://example.org/music/instrument/instrumentalists EVAL 01mr2g6 instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 183.000 130.000 0.500 http://example.org/music/instrument/instrumentalists EVAL 01mr2g6 instrumentalists! 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 183.000 130.000 0.500 http://example.org/music/instrument/instrumentalists #16986-0fjyzt PRED entity: 0fjyzt PRED relation: story_by PRED expected values: 0hcvy => 92 concepts (54 used for prediction) PRED predicted values (max 10 best out of 42): 079vf (0.14 #218, 0.02 #2387, 0.02 #4555), 079ws (0.14 #347, 0.01 #2300, 0.01 #2516), 04r7jc (0.13 #3251), 0l99s (0.06 #559, 0.02 #1208), 0yxl (0.05 #154), 042v2 (0.05 #367, 0.02 #584), 0p8jf (0.05 #263, 0.01 #697), 025b3k (0.05 #380), 02hfp_ (0.04 #4771), 082mw (0.04 #560, 0.01 #1209) >> Best rule #218 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 06ys2; >> query: (?x5465, 079vf) <- nominated_for(?x7269, ?x5465), film(?x7269, ?x167), award_nominee(?x1733, ?x7269), ?x167 = 083shs >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fjyzt story_by 0hcvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 54.000 0.136 http://example.org/film/film/story_by #16985-0d060g PRED entity: 0d060g PRED relation: teams PRED expected values: 035tjy => 204 concepts (204 used for prediction) PRED predicted values (max 10 best out of 206): 0bszz (0.33 #716, 0.05 #4678, 0.04 #7199), 020wyp (0.20 #2133, 0.11 #2494, 0.08 #3214), 0cnk2q (0.20 #1801, 0.11 #2162, 0.08 #2882), 086x3 (0.11 #2521, 0.08 #3241, 0.06 #3961), 01l3vx (0.08 #2925, 0.06 #3645, 0.05 #4006), 03zrc_ (0.08 #2694, 0.05 #5575, 0.04 #5935), 03lygq (0.08 #3139, 0.04 #6020, 0.04 #7461), 01352_ (0.08 #2821, 0.04 #6062, 0.04 #7503), 04h5tx (0.08 #2767, 0.02 #21500), 04b8pv (0.08 #2663, 0.01 #26800, 0.01 #26440) >> Best rule #716 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0694j; >> query: (?x279, 0bszz) <- contains(?x279, ?x12737), contains(?x279, ?x9722), ?x9722 = 01fd26, colors(?x12737, ?x332) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d060g teams 035tjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 204.000 0.333 http://example.org/sports/sports_team_location/teams #16984-0dcdp PRED entity: 0dcdp PRED relation: currency PRED expected values: 09nqf => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.86 #27, 0.86 #26, 0.85 #29) >> Best rule #27 for best value: >> intensional similarity = 5 >> extensional distance = 101 >> proper extension: 0kwmc; >> query: (?x10149, ?x170) <- adjoins(?x4823, ?x10149), county_seat(?x10149, ?x4890), second_level_divisions(?x94, ?x10149), contains(?x335, ?x4823), currency(?x4823, ?x170) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dcdp currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.864 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #16983-01qn7n PRED entity: 01qn7n PRED relation: program! PRED expected values: 0cjdk => 70 concepts (67 used for prediction) PRED predicted values (max 10 best out of 47): 0b275x (0.40 #247, 0.40 #190, 0.25 #133), 0187wh (0.40 #254, 0.40 #197, 0.20 #311), 05gnf (0.25 #1557, 0.25 #1214, 0.25 #128), 0gsg7 (0.25 #59, 0.24 #1603, 0.22 #517), 0cjdk (0.23 #405, 0.15 #805, 0.15 #1148), 0g5lhl7 (0.20 #234, 0.10 #291, 0.07 #2825), 01f2w0 (0.20 #251, 0.10 #308, 0.04 #3418), 03mdt (0.17 #750, 0.16 #978, 0.15 #636), 07qht4 (0.12 #1029, 0.12 #1947, 0.11 #2641), 07c52 (0.12 #1029, 0.12 #1947, 0.11 #2641) >> Best rule #247 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 03y317; >> query: (?x273, 0b275x) <- titles(?x11671, ?x273), ?x11671 = 07qht4, genre(?x273, ?x53), languages(?x273, ?x254), ?x254 = 02h40lc >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #405 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 24 *> proper extension: 0phrl; *> query: (?x273, 0cjdk) <- actor(?x273, ?x969), participant(?x969, ?x286), film(?x969, ?x3133), celebrity(?x969, ?x4536), award(?x969, ?x2252) *> conf = 0.23 ranks of expected_values: 5 EVAL 01qn7n program! 0cjdk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 70.000 67.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #16982-09p5mwg PRED entity: 09p5mwg PRED relation: film_crew_role PRED expected values: 01xy5l_ => 97 concepts (92 used for prediction) PRED predicted values (max 10 best out of 35): 09zzb8 (0.84 #326, 0.80 #1391, 0.80 #1501), 01vx2h (0.48 #155, 0.43 #812, 0.43 #702), 0dxtw (0.47 #1068, 0.44 #1363, 0.43 #1510), 01pvkk (0.30 #557, 0.30 #2221, 0.29 #2296), 02ynfr (0.29 #88, 0.23 #341, 0.22 #1074), 0215hd (0.29 #91, 0.23 #344, 0.20 #1853), 089g0h (0.29 #92, 0.17 #1854, 0.16 #345), 0d2b38 (0.29 #98, 0.16 #351, 0.15 #571), 02rh1dz (0.20 #45, 0.19 #153, 0.18 #700), 01xy5l_ (0.19 #339, 0.15 #447, 0.15 #559) >> Best rule #326 for best value: >> intensional similarity = 10 >> extensional distance = 29 >> proper extension: 0czyxs; 03h_yy; 061681; 09p0ct; 03twd6; 05pbl56; 04n52p6; 07nt8p; 02rb607; 0f4m2z; ... >> query: (?x9752, 09zzb8) <- genre(?x9752, ?x3613), genre(?x9752, ?x604), genre(?x9752, ?x600), currency(?x9752, ?x170), film_crew_role(?x9752, ?x1171), ?x1171 = 09vw2b7, ?x604 = 0lsxr, ?x600 = 02n4kr, titles(?x3613, ?x3283), ?x3283 = 06gjk9 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #339 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 29 *> proper extension: 0czyxs; 03h_yy; 061681; 09p0ct; 03twd6; 05pbl56; 04n52p6; 07nt8p; 02rb607; 0f4m2z; ... *> query: (?x9752, 01xy5l_) <- genre(?x9752, ?x3613), genre(?x9752, ?x604), genre(?x9752, ?x600), currency(?x9752, ?x170), film_crew_role(?x9752, ?x1171), ?x1171 = 09vw2b7, ?x604 = 0lsxr, ?x600 = 02n4kr, titles(?x3613, ?x3283), ?x3283 = 06gjk9 *> conf = 0.19 ranks of expected_values: 10 EVAL 09p5mwg film_crew_role 01xy5l_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 97.000 92.000 0.839 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16981-042d1 PRED entity: 042d1 PRED relation: profession PRED expected values: 0fj9f 0g0vx => 165 concepts (127 used for prediction) PRED predicted values (max 10 best out of 113): 0fj9f (0.86 #5123, 0.86 #7805, 0.85 #4675), 01d_h8 (0.86 #11634, 0.31 #2390, 0.30 #18204), 02hrh1q (0.72 #18661, 0.71 #18811, 0.66 #10301), 0g0vx (0.50 #109, 0.36 #1748, 0.33 #407), 0dxtg (0.40 #11642, 0.31 #12686, 0.30 #18511), 0cbd2 (0.40 #1050, 0.28 #8949, 0.27 #1795), 02jknp (0.39 #11636, 0.22 #14621, 0.22 #15816), 099md (0.33 #371, 0.27 #1712, 0.25 #73), 012t_z (0.30 #1205, 0.25 #162, 0.22 #3738), 0kyk (0.29 #3607, 0.27 #1819, 0.27 #1670) >> Best rule #5123 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0226cw; 01mvpv; >> query: (?x10511, 0fj9f) <- legislative_sessions(?x10511, ?x7715), student(?x6919, ?x10511), basic_title(?x10511, ?x346), district_represented(?x7715, ?x335), legislative_sessions(?x1754, ?x7715) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 042d1 profession 0g0vx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 165.000 127.000 0.864 http://example.org/people/person/profession EVAL 042d1 profession 0fj9f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 127.000 0.864 http://example.org/people/person/profession #16980-0ch26b_ PRED entity: 0ch26b_ PRED relation: nominated_for! PRED expected values: 02r0csl 02pqp12 02qvyrt => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 204): 0gq_v (0.77 #9925, 0.75 #1103, 0.73 #2426), 02r22gf (0.77 #9925, 0.75 #1103, 0.73 #2426), 027c924 (0.75 #1103, 0.73 #2426, 0.72 #2427), 04dn09n (0.60 #31, 0.51 #2236, 0.45 #1135), 040njc (0.50 #1111, 0.49 #2212, 0.43 #2434), 0gqy2 (0.44 #3859, 0.41 #2313, 0.35 #990), 02pqp12 (0.40 #52, 0.38 #2257, 0.37 #1596), 05ztjjw (0.40 #9, 0.38 #231, 0.26 #1773), 02qvyrt (0.40 #83, 0.37 #1627, 0.25 #2730), 02r0csl (0.40 #5, 0.33 #1549, 0.32 #1109) >> Best rule #9925 for best value: >> intensional similarity = 3 >> extensional distance = 682 >> proper extension: 015g28; 06w7mlh; 07bz5; >> query: (?x1916, ?x500) <- award(?x1916, ?x500), award(?x382, ?x500), ceremony(?x500, ?x78) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #52 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 01gc7; 07024; 09sr0; *> query: (?x1916, 02pqp12) <- award(?x1916, ?x1107), award(?x1916, ?x637), film_release_region(?x1916, ?x87), ?x1107 = 019f4v, ?x637 = 02r22gf *> conf = 0.40 ranks of expected_values: 7, 9, 10 EVAL 0ch26b_ nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 95.000 95.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0ch26b_ nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 95.000 95.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0ch26b_ nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 95.000 95.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16979-06p8m PRED entity: 06p8m PRED relation: citytown PRED expected values: 0d6lp 06y57 07dfk => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 224): 07dfk (0.50 #10130, 0.50 #1681, 0.43 #3518), 0r6cx (0.50 #1719, 0.29 #3189, 0.14 #3923), 02_286 (0.49 #29435, 0.45 #34218, 0.45 #34953), 024bqj (0.38 #4238, 0.29 #3504, 0.18 #4605), 01n7q (0.33 #735, 0.08 #5142, 0.05 #34204), 01llj3 (0.26 #61041, 0.23 #43026, 0.22 #59196), 0cxgc (0.26 #61041, 0.23 #43026, 0.22 #59196), 049kw (0.26 #61041, 0.23 #43026, 0.22 #59196), 02jx1 (0.26 #61041, 0.23 #43026, 0.22 #59196), 07ssc (0.26 #61041, 0.23 #43026, 0.22 #59196) >> Best rule #10130 for best value: >> intensional similarity = 6 >> extensional distance = 22 >> proper extension: 01qf54; 07rfp; >> query: (?x11427, 07dfk) <- industry(?x11427, ?x10022), ?x10022 = 020mfr, citytown(?x11427, ?x362), place_of_birth(?x361, ?x362), location(?x7825, ?x362), film(?x7825, ?x3803) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 32, 36 EVAL 06p8m citytown 07dfk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.500 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 06p8m citytown 06y57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 183.000 183.000 0.500 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 06p8m citytown 0d6lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 183.000 183.000 0.500 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #16978-043q6n_ PRED entity: 043q6n_ PRED relation: profession PRED expected values: 01d_h8 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 59): 01d_h8 (0.86 #1206, 0.86 #2708, 0.85 #3309), 03gjzk (0.84 #2117, 0.83 #1517, 0.82 #1967), 02hrh1q (0.71 #6172, 0.70 #5720, 0.68 #4219), 0dxtg (0.71 #1515, 0.66 #1965, 0.66 #2115), 02jknp (0.52 #1809, 0.50 #3762, 0.48 #3462), 02krf9 (0.29 #1979, 0.29 #2129, 0.28 #2430), 0cbd2 (0.27 #1508, 0.20 #1958, 0.19 #2409), 064xm0 (0.25 #7958, 0.25 #64, 0.02 #514), 012t_z (0.25 #7958, 0.14 #313, 0.12 #1664), 09jwl (0.18 #920, 0.15 #10379, 0.15 #10529) >> Best rule #1206 for best value: >> intensional similarity = 3 >> extensional distance = 122 >> proper extension: 012rng; >> query: (?x1417, 01d_h8) <- produced_by(?x2037, ?x1417), film_crew_role(?x2037, ?x137), story_by(?x2037, ?x9982) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043q6n_ profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.863 http://example.org/people/person/profession #16977-05gpy PRED entity: 05gpy PRED relation: type_of_union PRED expected values: 04ztj => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.77 #113, 0.76 #117, 0.73 #65), 01g63y (0.20 #525, 0.13 #86, 0.13 #70), 01bl8s (0.20 #525, 0.08 #27, 0.07 #31), 0jgjn (0.20 #525) >> Best rule #113 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 0yfp; 04l3_z; 032v0v; 0c9c0; 02645b; 05ldnp; 0b478; 01rlxt; 0p__8; 01fyzy; ... >> query: (?x6320, 04ztj) <- location(?x6320, ?x728), gender(?x6320, ?x231), ?x231 = 05zppz, story_by(?x351, ?x6320) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05gpy type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.770 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16976-02bkdn PRED entity: 02bkdn PRED relation: award_nominee PRED expected values: 071ynp 047c9l => 100 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1037): 047c9l (0.81 #23002, 0.81 #48301, 0.81 #48300), 01gvr1 (0.81 #48301, 0.81 #48300, 0.81 #87405), 0f502 (0.81 #48301, 0.81 #48300, 0.81 #87405), 0hvb2 (0.81 #48301, 0.81 #48300, 0.81 #87405), 01x_d8 (0.81 #48301, 0.81 #48300, 0.81 #87405), 071ynp (0.81 #48301, 0.81 #48300, 0.81 #87405), 02f2p7 (0.81 #48301, 0.81 #48300, 0.81 #87405), 0372kf (0.81 #48301, 0.81 #48300, 0.81 #87405), 0525b (0.81 #48301, 0.81 #48300, 0.81 #87405), 0gx_p (0.81 #48301, 0.81 #48300, 0.81 #87405) >> Best rule #23002 for best value: >> intensional similarity = 3 >> extensional distance = 691 >> proper extension: 016sqs; 02vwckw; >> query: (?x1871, ?x3329) <- award_nominee(?x3329, ?x1871), award_nominee(?x1871, ?x92), student(?x4268, ?x3329) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 02bkdn award_nominee 047c9l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 38.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 02bkdn award_nominee 071ynp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 100.000 38.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16975-0gdh5 PRED entity: 0gdh5 PRED relation: artists! PRED expected values: 08jyyk => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 253): 0xhtw (0.49 #4009, 0.22 #2473, 0.22 #10156), 0ggx5q (0.45 #690, 0.36 #1304, 0.33 #1611), 06j6l (0.39 #660, 0.38 #1274, 0.31 #1581), 0cx7f (0.34 #4129, 0.11 #8738, 0.09 #3207), 017_qw (0.32 #8972, 0.26 #4360, 0.25 #7740), 0glt670 (0.31 #1267, 0.30 #9872, 0.30 #653), 025sc50 (0.31 #1276, 0.30 #662, 0.28 #969), 05w3f (0.30 #4029, 0.19 #1844, 0.13 #10176), 03lty (0.28 #4019, 0.19 #1844, 0.13 #10166), 01fh36 (0.27 #4078, 0.16 #3156, 0.12 #14529) >> Best rule #4009 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0167_s; 05563d; 02vgh; 01kcms4; 06gcn; 03k3b; 012vm6; 01l_w0; 0jn38; 0qmny; ... >> query: (?x2796, 0xhtw) <- artist(?x2241, ?x2796), artists(?x1380, ?x2796), ?x1380 = 0dl5d >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #4058 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 0167_s; 05563d; 02vgh; 01kcms4; 06gcn; 03k3b; 012vm6; 01l_w0; 0jn38; 0qmny; ... *> query: (?x2796, 08jyyk) <- artist(?x2241, ?x2796), artists(?x1380, ?x2796), ?x1380 = 0dl5d *> conf = 0.23 ranks of expected_values: 13 EVAL 0gdh5 artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 133.000 133.000 0.489 http://example.org/music/genre/artists #16974-01gsvb PRED entity: 01gsvb PRED relation: district_represented PRED expected values: 04rrd 04ly1 07_f2 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 957): 04rrd (0.89 #1239, 0.87 #1143, 0.85 #185), 07_f2 (0.88 #1217, 0.85 #185, 0.85 #184), 04ly1 (0.85 #185, 0.85 #184, 0.84 #328), 0vbk (0.85 #185, 0.85 #184, 0.84 #328), 04rrx (0.85 #185, 0.85 #184, 0.84 #328), 02xry (0.85 #185, 0.85 #184, 0.84 #328), 07b_l (0.85 #185, 0.85 #184, 0.84 #328), 0824r (0.85 #185, 0.85 #184, 0.84 #328), 03s0w (0.85 #185, 0.85 #184, 0.84 #328), 081mh (0.73 #898, 0.68 #1181, 0.64 #803) >> Best rule #1239 for best value: >> intensional similarity = 30 >> extensional distance = 26 >> proper extension: 02bp37; 01grmk; >> query: (?x7973, 04rrd) <- legislative_sessions(?x7714, ?x7973), district_represented(?x7973, ?x7058), district_represented(?x7973, ?x3778), district_represented(?x7973, ?x3670), district_represented(?x7973, ?x2713), district_represented(?x7973, ?x1426), district_represented(?x10291, ?x1426), contains(?x1426, ?x347), state_province_region(?x4077, ?x1426), religion(?x1426, ?x109), location(?x2227, ?x1426), location(?x1654, ?x1426), state_province_region(?x3178, ?x7058), ?x3670 = 05tbn, religion(?x7058, ?x2769), ?x2227 = 07ss8_, ?x2713 = 06btq, taxonomy(?x1426, ?x939), district_represented(?x7714, ?x1767), adjoins(?x108, ?x1426), ?x10291 = 01gtdd, contains(?x3778, ?x3779), vacationer(?x3778, ?x1896), school(?x1438, ?x3779), school(?x1883, ?x3779), gender(?x1654, ?x231), ?x1883 = 02qw1zx, category(?x3779, ?x134), ?x2769 = 019cr, legislative_sessions(?x2860, ?x7714) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 01gsvb district_represented 07_f2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.893 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gsvb district_represented 04ly1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.893 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gsvb district_represented 04rrd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.893 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #16973-0vkl2 PRED entity: 0vkl2 PRED relation: major_field_of_study PRED expected values: 05qt0 => 178 concepts (176 used for prediction) PRED predicted values (max 10 best out of 121): 01mkq (0.64 #2107, 0.60 #2968, 0.56 #3338), 02lp1 (0.54 #2964, 0.52 #3334, 0.51 #2103), 05qjt (0.44 #2099, 0.35 #2960, 0.32 #3330), 062z7 (0.43 #2981, 0.42 #2120, 0.40 #3351), 03g3w (0.40 #2980, 0.39 #3350, 0.37 #2857), 04rjg (0.40 #2112, 0.37 #2973, 0.36 #3343), 0g26h (0.39 #2996, 0.35 #3366, 0.33 #3736), 01lj9 (0.38 #2132, 0.33 #2993, 0.27 #3363), 05qfh (0.38 #2128, 0.30 #2989, 0.28 #3729), 04x_3 (0.38 #2118, 0.25 #3719, 0.25 #3349) >> Best rule #2107 for best value: >> intensional similarity = 5 >> extensional distance = 43 >> proper extension: 01w5m; 027mdh; 01jt2w; >> query: (?x10240, 01mkq) <- institution(?x4981, ?x10240), major_field_of_study(?x10240, ?x2981), ?x4981 = 03bwzr4, currency(?x10240, ?x1099), ?x2981 = 02j62 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #302 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 01y9st; *> query: (?x10240, 05qt0) <- currency(?x10240, ?x1099), colors(?x10240, ?x332), citytown(?x10240, ?x362), featured_film_locations(?x136, ?x362) *> conf = 0.14 ranks of expected_values: 35 EVAL 0vkl2 major_field_of_study 05qt0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 178.000 176.000 0.644 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16972-09v3hq_ PRED entity: 09v3hq_ PRED relation: country PRED expected values: 09c7w0 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 33): 09c7w0 (0.33 #1, 0.25 #4, 0.20 #121), 0d060g (0.08 #7, 0.04 #335, 0.04 #124), 07ssc (0.08 #7, 0.04 #335, 0.02 #131), 0chghy (0.08 #7, 0.03 #159, 0.02 #303), 03rjj (0.04 #335, 0.04 #124, 0.02 #131), 0345h (0.04 #335, 0.03 #159, 0.02 #131), 0f8l9c (0.04 #335, 0.02 #131, 0.02 #303), 03rt9 (0.04 #335, 0.02 #131, 0.02 #303), 02jx1 (0.04 #335, 0.02 #303, 0.01 #212), 03_3d (0.04 #335, 0.02 #303, 0.01 #212) >> Best rule #1 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0fvppk; >> query: (?x11867, 09c7w0) <- film(?x11867, ?x11037), film(?x11867, ?x3537), ?x3537 = 09lcsj, industry(?x11867, ?x373), ?x373 = 02vxn, titles(?x53, ?x11037) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09v3hq_ country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/country #16971-07plts PRED entity: 07plts PRED relation: colors! PRED expected values: 09kvv => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1065): 016sd3 (0.60 #3284, 0.57 #4259, 0.50 #2309), 01tntf (0.60 #3262, 0.43 #4237, 0.33 #5842), 0gjv_ (0.60 #3093, 0.43 #4068, 0.33 #5538), 0pz6q (0.60 #3254, 0.43 #4229, 0.33 #5699), 01b1mj (0.60 #2925, 0.43 #3900, 0.33 #5370), 021996 (0.50 #3677, 0.40 #3191, 0.40 #2703), 02607j (0.50 #3480, 0.40 #2994, 0.40 #2506), 03np_7 (0.50 #2381, 0.40 #3356, 0.38 #5313), 01s7pm (0.50 #2351, 0.40 #3326, 0.38 #5283), 02rv1w (0.50 #2293, 0.40 #3268, 0.38 #5225) >> Best rule #3284 for best value: >> intensional similarity = 39 >> extensional distance = 3 >> proper extension: 01g5v; >> query: (?x12170, 016sd3) <- colors(?x11009, ?x12170), colors(?x4341, ?x12170), colors(?x2388, ?x12170), child(?x10513, ?x2388), currency(?x2388, ?x170), ?x10513 = 03f2fw, contains(?x177, ?x2388), major_field_of_study(?x2388, ?x7134), school(?x2820, ?x2388), contains(?x335, ?x11009), major_field_of_study(?x7134, ?x3213), major_field_of_study(?x7134, ?x2981), major_field_of_study(?x865, ?x7134), major_field_of_study(?x7920, ?x7134), major_field_of_study(?x5486, ?x7134), major_field_of_study(?x3360, ?x7134), ?x2981 = 02j62, student(?x11009, ?x6263), location(?x932, ?x177), jurisdiction_of_office(?x900, ?x177), ?x5486 = 0g8rj, ?x3360 = 0fnmz, ?x3213 = 0g4gr, religion(?x177, ?x2769), religion(?x177, ?x2591), religion(?x177, ?x1624), district_represented(?x5252, ?x177), state_province_region(?x1091, ?x177), ?x865 = 02h4rq6, ?x2820 = 0jmj7, ?x5252 = 01gtcq, ?x335 = 059rby, ?x7920 = 01p79b, ?x2769 = 019cr, organization(?x346, ?x4341), ?x2591 = 0631_, adjoins(?x448, ?x177), ?x1624 = 051kv, category(?x177, ?x134) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3877 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 4 *> proper extension: 04mkbj; *> query: (?x12170, ?x481) <- colors(?x11009, ?x12170), colors(?x2388, ?x12170), child(?x10513, ?x2388), citytown(?x2388, ?x2504), school_type(?x2388, ?x1962), citytown(?x11009, ?x739), currency(?x11009, ?x170), school_type(?x7576, ?x1962), school_type(?x4603, ?x1962), school_type(?x3172, ?x1962), ?x7576 = 0gy3w, major_field_of_study(?x2388, ?x4321), major_field_of_study(?x2388, ?x1154), ?x3172 = 02zccd, major_field_of_study(?x8706, ?x4321), major_field_of_study(?x2895, ?x4321), major_field_of_study(?x2522, ?x4321), major_field_of_study(?x2166, ?x4321), student(?x4321, ?x744), institution(?x620, ?x2388), ?x739 = 02_286, state_province_region(?x2388, ?x177), ?x2166 = 01jtp7, ?x2522 = 022lly, contains(?x335, ?x11009), major_field_of_study(?x1368, ?x4321), major_field_of_study(?x4321, ?x2981), major_field_of_study(?x1668, ?x1154), major_field_of_study(?x5167, ?x1154), major_field_of_study(?x1768, ?x1154), major_field_of_study(?x481, ?x1154), organization(?x346, ?x2388), colors(?x6823, ?x12170), ?x8706 = 0trv, ?x335 = 059rby, ?x1368 = 014mlp, category(?x2388, ?x134), ?x4603 = 0hd7j, ?x5167 = 015cz0, ?x2895 = 0l2tk, ?x1768 = 09kvv *> conf = 0.20 ranks of expected_values: 492 EVAL 07plts colors! 09kvv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 19.000 19.000 0.600 http://example.org/education/educational_institution/colors #16970-0mlyw PRED entity: 0mlyw PRED relation: time_zones PRED expected values: 02lcqs => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 11): 02lcqs (0.88 #150, 0.87 #405, 0.86 #136), 02hcv8 (0.55 #821, 0.55 #860, 0.53 #167), 02fqwt (0.17 #1180, 0.16 #191, 0.14 #258), 02hczc (0.15 #192, 0.12 #43, 0.11 #84), 02llzg (0.07 #795, 0.07 #874, 0.07 #955), 03bdv (0.03 #944, 0.03 #797, 0.02 #1119), 03plfd (0.03 #801, 0.02 #880, 0.02 #961), 042g7t (0.02 #52, 0.01 #1124), 02lcrv (0.02 #48), 0gsrz4 (0.02 #905, 0.02 #959, 0.02 #1107) >> Best rule #150 for best value: >> intensional similarity = 7 >> extensional distance = 167 >> proper extension: 0d_kd; >> query: (?x3840, ?x2950) <- adjoins(?x12383, ?x3840), adjoins(?x10733, ?x3840), source(?x3840, ?x958), contains(?x4600, ?x10733), time_zones(?x12383, ?x2950), currency(?x12383, ?x170), county(?x10213, ?x12383) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mlyw time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.882 http://example.org/location/location/time_zones #16969-0sz28 PRED entity: 0sz28 PRED relation: award PRED expected values: 07cbcy => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 268): 0f4x7 (0.73 #1626, 0.72 #43121, 0.71 #30745), 02x4w6g (0.72 #43121, 0.71 #30745, 0.71 #31544), 02z13jg (0.72 #43121, 0.71 #30745, 0.71 #31544), 09cm54 (0.72 #43121, 0.71 #30745, 0.71 #31544), 027c95y (0.72 #43121, 0.71 #30745, 0.71 #31544), 027b9j5 (0.72 #43121, 0.71 #30745, 0.71 #31544), 027986c (0.72 #43121, 0.71 #30745, 0.71 #31544), 0gqy2 (0.46 #1757, 0.25 #959, 0.18 #28747), 0gq9h (0.33 #6863, 0.19 #1273, 0.12 #475), 040njc (0.25 #8, 0.24 #6795, 0.23 #1205) >> Best rule #1626 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 0gr36; 04n_g; 015d3h; 0l786; 0jvtp; 01vsy9_; 0p9qb; >> query: (?x1208, 0f4x7) <- location(?x1208, ?x242), award_winner(?x2915, ?x1208), ?x2915 = 027c95y >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #476 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 6 *> proper extension: 026_dq6; *> query: (?x1208, 07cbcy) <- participant(?x872, ?x1208), sibling(?x13442, ?x1208) *> conf = 0.25 ranks of expected_values: 11 EVAL 0sz28 award 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 121.000 121.000 0.730 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16968-036px PRED entity: 036px PRED relation: award PRED expected values: 0gkvb7 => 126 concepts (104 used for prediction) PRED predicted values (max 10 best out of 290): 01bgqh (0.53 #1243, 0.38 #2043, 0.35 #1643), 01by1l (0.52 #1312, 0.47 #1712, 0.38 #2112), 0gqz2 (0.50 #2081, 0.40 #1281, 0.33 #10482), 02f5qb (0.50 #1751, 0.22 #1351, 0.16 #13352), 0c4z8 (0.44 #2072, 0.38 #1272, 0.38 #10473), 02f716 (0.42 #1772, 0.17 #1372, 0.14 #13373), 02f72n (0.42 #1741, 0.15 #1341, 0.11 #13342), 02f73b (0.40 #1883, 0.23 #1483, 0.14 #2283), 02f72_ (0.38 #1825, 0.18 #1425, 0.13 #13426), 03qbh5 (0.35 #1401, 0.27 #1801, 0.25 #2201) >> Best rule #1243 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 0d193h; >> query: (?x4191, 01bgqh) <- award_winner(?x2420, ?x4191), award(?x4191, ?x2585), ?x2585 = 054ks3, artist(?x3240, ?x4191) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #5628 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 188 *> proper extension: 03gm48; 01f8ld; 011_3s; 05r5w; 026l37; 0dpqk; 07g7h2; 084m3; 01pjr7; 07d3x; ... *> query: (?x4191, 0gkvb7) <- award_winner(?x2430, ?x4191), type_of_union(?x4191, ?x566), currency(?x4191, ?x170), award(?x133, ?x2430) *> conf = 0.09 ranks of expected_values: 84 EVAL 036px award 0gkvb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 126.000 104.000 0.533 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16967-01gn36 PRED entity: 01gn36 PRED relation: profession PRED expected values: 018gz8 => 91 concepts (86 used for prediction) PRED predicted values (max 10 best out of 141): 01d_h8 (0.62 #8184, 0.55 #4825, 0.55 #590), 03gjzk (0.60 #159, 0.59 #451, 0.52 #1181), 018gz8 (0.56 #453, 0.55 #1183, 0.53 #161), 02jknp (0.48 #4827, 0.46 #4097, 0.42 #5265), 0cbd2 (0.47 #3220, 0.42 #3512, 0.41 #3366), 09jwl (0.42 #2353, 0.23 #7027, 0.22 #2646), 0dz3r (0.35 #2338, 0.16 #5113, 0.13 #7012), 0nbcg (0.34 #2365, 0.32 #7039, 0.15 #5140), 016z4k (0.33 #2340, 0.15 #5115, 0.11 #7014), 0kyk (0.30 #2217, 0.29 #3094, 0.29 #3240) >> Best rule #8184 for best value: >> intensional similarity = 5 >> extensional distance = 1573 >> proper extension: 012d40; 0fvf9q; 02p65p; 0337vz; 06151l; 01j5ts; 01l1b90; 06dv3; 0byfz; 0qf43; ... >> query: (?x4554, 01d_h8) <- profession(?x4554, ?x987), profession(?x8692, ?x987), profession(?x4353, ?x987), ?x8692 = 06dkzt, ?x4353 = 06mn7 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #453 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 07ymr5; 027xbpw; 021yw7; 01_x6d; 04h07s; 066l3y; 02dlfh; 044_7j; 0q1lp; 05vtbl; ... *> query: (?x4554, 018gz8) <- profession(?x4554, ?x1383), profession(?x4554, ?x987), ?x987 = 0dxtg, actor(?x5684, ?x4554), ?x1383 = 0np9r *> conf = 0.56 ranks of expected_values: 3 EVAL 01gn36 profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 86.000 0.619 http://example.org/people/person/profession #16966-0bdjd PRED entity: 0bdjd PRED relation: language PRED expected values: 02h40lc => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.90 #2097, 0.90 #2638, 0.90 #1379), 064_8sq (0.19 #1520, 0.18 #438, 0.18 #680), 04306rv (0.17 #663, 0.16 #362, 0.14 #601), 02bjrlw (0.15 #298, 0.13 #358, 0.13 #535), 06nm1 (0.12 #1449, 0.11 #308, 0.11 #368), 0jzc (0.10 #678, 0.06 #257, 0.06 #554), 06b_j (0.09 #681, 0.09 #320, 0.07 #1223), 0t_2 (0.07 #14, 0.02 #430, 0.02 #851), 03x42 (0.07 #50, 0.01 #347, 0.01 #407), 03_9r (0.06 #668, 0.05 #367, 0.05 #727) >> Best rule #2097 for best value: >> intensional similarity = 4 >> extensional distance = 454 >> proper extension: 03l6q0; 03cyslc; 03hp2y1; 01qdmh; >> query: (?x7336, 02h40lc) <- nominated_for(?x496, ?x7336), featured_film_locations(?x7336, ?x108), titles(?x53, ?x7336), film(?x540, ?x7336) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bdjd language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.904 http://example.org/film/film/language #16965-02lnhv PRED entity: 02lnhv PRED relation: student! PRED expected values: 0bwfn => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 152): 0bwfn (0.25 #1326, 0.08 #39725, 0.07 #10794), 0fr9jp (0.25 #1396, 0.06 #3500, 0.06 #7708), 06182p (0.25 #823, 0.03 #23441, 0.03 #15025), 01f1r4 (0.25 #1177, 0.02 #3807, 0.01 #5385), 065y4w7 (0.07 #7378, 0.07 #23158, 0.05 #39465), 07w0v (0.07 #1598, 0.04 #2124, 0.03 #10540), 0gl5_ (0.07 #1821, 0.04 #2347, 0.02 #39694), 09f2j (0.06 #3840, 0.05 #10678, 0.05 #14886), 015nl4 (0.05 #40570, 0.05 #24789, 0.05 #41622), 07szy (0.05 #5826, 0.03 #7404, 0.03 #2670) >> Best rule #1326 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0f4vbz; 0bq2g; >> query: (?x1207, 0bwfn) <- film(?x1207, ?x7480), participant(?x1725, ?x1207), profession(?x1207, ?x319), ?x7480 = 02vjp3 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lnhv student! 0bwfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #16964-059kh PRED entity: 059kh PRED relation: artists PRED expected values: 04dqdk 0p3r8 01vvyfh 03d9d6 04b7xr 01dq9q 01wqpnm 016vj5 => 69 concepts (5 used for prediction) PRED predicted values (max 10 best out of 974): 02p68d (0.71 #4772, 0.60 #3752, 0.60 #2731), 095x_ (0.71 #4765, 0.60 #3745, 0.60 #2724), 0178_w (0.71 #4652, 0.60 #3632, 0.60 #2611), 0b_j2 (0.71 #4636, 0.60 #3616, 0.60 #2595), 01vsy7t (0.71 #4458, 0.60 #3438, 0.60 #2417), 01797x (0.60 #3938, 0.60 #2917, 0.57 #4958), 02cw1m (0.60 #3881, 0.60 #2860, 0.57 #4901), 033s6 (0.60 #3866, 0.60 #2845, 0.57 #4886), 08w4pm (0.60 #3743, 0.60 #2722, 0.57 #4763), 04bgy (0.60 #3606, 0.60 #2585, 0.57 #4626) >> Best rule #4772 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 02qdgx; >> query: (?x3370, 02p68d) <- artists(?x3370, ?x6368), artists(?x3370, ?x4484), artists(?x3370, ?x3767), ?x3767 = 01wbz9, artist(?x382, ?x4484), award_winner(?x7535, ?x6368) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2901 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0xhtw; *> query: (?x3370, 01wqpnm) <- artists(?x3370, ?x6368), artists(?x3370, ?x4484), artists(?x3370, ?x3767), ?x3767 = 01wbz9, artist(?x382, ?x4484), ?x6368 = 0178kd *> conf = 0.60 ranks of expected_values: 21, 31, 36, 55, 82, 110, 115, 124 EVAL 059kh artists 016vj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 69.000 5.000 0.714 http://example.org/music/genre/artists EVAL 059kh artists 01wqpnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 69.000 5.000 0.714 http://example.org/music/genre/artists EVAL 059kh artists 01dq9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 69.000 5.000 0.714 http://example.org/music/genre/artists EVAL 059kh artists 04b7xr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 69.000 5.000 0.714 http://example.org/music/genre/artists EVAL 059kh artists 03d9d6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 69.000 5.000 0.714 http://example.org/music/genre/artists EVAL 059kh artists 01vvyfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 69.000 5.000 0.714 http://example.org/music/genre/artists EVAL 059kh artists 0p3r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 69.000 5.000 0.714 http://example.org/music/genre/artists EVAL 059kh artists 04dqdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 69.000 5.000 0.714 http://example.org/music/genre/artists #16963-07g0_ PRED entity: 07g0_ PRED relation: contains! PRED expected values: 049nq => 123 concepts (72 used for prediction) PRED predicted values (max 10 best out of 449): 09c7w0 (0.89 #62770, 0.63 #47528, 0.57 #56505), 049nq (0.81 #21528, 0.76 #12235, 0.75 #2684), 0dv0z (0.62 #15247, 0.61 #37669, 0.59 #17038), 07ssc (0.48 #20665, 0.32 #46663, 0.29 #5411), 04_1l0v (0.46 #23772, 0.45 #30053, 0.35 #35428), 02jx1 (0.40 #20719, 0.20 #46717, 0.17 #33272), 02qkt (0.31 #48770, 0.21 #44286, 0.16 #43392), 0f8l9c (0.27 #17986, 0.15 #45783, 0.14 #42148), 03rjj (0.25 #17949, 0.21 #22438, 0.19 #43056), 059rby (0.23 #27831, 0.15 #37689, 0.15 #38583) >> Best rule #62770 for best value: >> intensional similarity = 8 >> extensional distance = 640 >> proper extension: 013_gg; 07gdw; 054y8; 0dgfx; >> query: (?x10345, 09c7w0) <- contains(?x9186, ?x10345), contains(?x1229, ?x10345), adjoins(?x9186, ?x13631), adjoins(?x9186, ?x13356), state(?x9187, ?x9186), administrative_parent(?x13988, ?x13356), contains(?x10382, ?x13631), combatants(?x151, ?x1229) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #21528 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 46 *> proper extension: 0hc8h; *> query: (?x10345, ?x10382) <- country(?x10345, ?x1229), country(?x3407, ?x1229), contains(?x1229, ?x2351), olympics(?x1229, ?x418), first_level_division_of(?x1229, ?x10382) *> conf = 0.81 ranks of expected_values: 2 EVAL 07g0_ contains! 049nq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 123.000 72.000 0.888 http://example.org/location/location/contains #16962-04ktcgn PRED entity: 04ktcgn PRED relation: award_winner! PRED expected values: 018wdw => 83 concepts (70 used for prediction) PRED predicted values (max 10 best out of 207): 09sb52 (0.45 #471, 0.18 #4353, 0.14 #5215), 099tbz (0.45 #488, 0.11 #26738, 0.06 #5232), 02r22gf (0.41 #1295, 0.41 #863, 0.39 #6469), 0gr42 (0.19 #3136, 0.19 #3567, 0.18 #3998), 02r0csl (0.16 #5, 0.11 #26738, 0.10 #2593), 0gqxm (0.16 #177, 0.10 #2765, 0.10 #3197), 02hsq3m (0.15 #2623, 0.14 #3055, 0.14 #3486), 018wdw (0.12 #1131, 0.11 #26738, 0.11 #268), 07bdd_ (0.12 #928, 0.05 #29328, 0.05 #29327), 0gq_v (0.11 #1318, 0.11 #26738, 0.11 #1749) >> Best rule #471 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0f0kz; >> query: (?x1983, 09sb52) <- nominated_for(?x1983, ?x1392), ?x1392 = 017gm7, award(?x1983, ?x500) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1131 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 22 *> proper extension: 05f260; *> query: (?x1983, 018wdw) <- award(?x1983, ?x500), ?x500 = 0p9sw *> conf = 0.12 ranks of expected_values: 8 EVAL 04ktcgn award_winner! 018wdw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 83.000 70.000 0.455 http://example.org/award/award_category/winners./award/award_honor/award_winner #16961-0rqf1 PRED entity: 0rqf1 PRED relation: contains! PRED expected values: 09c7w0 => 110 concepts (73 used for prediction) PRED predicted values (max 10 best out of 402): 09c7w0 (0.80 #18783, 0.73 #16995, 0.72 #10732), 059rby (0.39 #44742, 0.27 #51010, 0.14 #63535), 01n7q (0.35 #24222, 0.30 #18858, 0.25 #19752), 04_1l0v (0.29 #14308, 0.16 #18336, 0.11 #37119), 05tbn (0.22 #44945, 0.15 #51213, 0.08 #47630), 0jgk3 (0.20 #1342, 0.08 #13861, 0.07 #6707), 0j_1v (0.20 #1690, 0.02 #11525, 0.02 #13313), 07c5l (0.19 #5759, 0.15 #12017, 0.14 #21856), 02jx1 (0.17 #24231, 0.14 #40336, 0.14 #51077), 05fjf (0.17 #4844, 0.10 #31673, 0.09 #47780) >> Best rule #18783 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 0s3y5; 0t_gg; 0136jw; >> query: (?x11345, 09c7w0) <- source(?x11345, ?x958), contains(?x2623, ?x11345), place_of_death(?x9552, ?x11345), jurisdiction_of_office(?x5742, ?x2623) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rqf1 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 73.000 0.803 http://example.org/location/location/contains #16960-01hr1 PRED entity: 01hr1 PRED relation: music PRED expected values: 07j8kh => 61 concepts (42 used for prediction) PRED predicted values (max 10 best out of 72): 05dbf (0.08 #5706, 0.07 #5918, 0.07 #211), 01kwsg (0.08 #5706, 0.07 #5918, 0.07 #211), 0lx2l (0.08 #5706, 0.07 #5918, 0.07 #211), 0dw4g (0.08 #5706, 0.07 #5918, 0.07 #211), 0146pg (0.07 #10, 0.06 #221, 0.05 #1066), 0150t6 (0.07 #46, 0.03 #257, 0.03 #2791), 0d_84 (0.07 #211, 0.07 #8035, 0.06 #5705), 01vswwx (0.07 #211, 0.07 #8035, 0.06 #5705), 02g1jh (0.05 #128, 0.02 #550, 0.02 #1819), 03h610 (0.05 #499, 0.05 #709, 0.03 #1133) >> Best rule #5706 for best value: >> intensional similarity = 4 >> extensional distance = 1166 >> proper extension: 09gdm7q; 0bh8yn3; 0k4d7; 065zlr; 0d1qmz; 0fsw_7; 01kf5lf; 03p2xc; 02r2j8; 02q0v8n; ... >> query: (?x339, ?x4702) <- genre(?x339, ?x604), nominated_for(?x4702, ?x339), award_winner(?x1998, ?x4702), award(?x4702, ?x112) >> conf = 0.08 => this is the best rule for 4 predicted values *> Best rule #523 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 183 *> proper extension: 064n1pz; 07l50vn; 05zvzf3; *> query: (?x339, 07j8kh) <- genre(?x339, ?x604), nominated_for(?x500, ?x339), ?x604 = 0lsxr, film(?x382, ?x339) *> conf = 0.03 ranks of expected_values: 20 EVAL 01hr1 music 07j8kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 61.000 42.000 0.076 http://example.org/film/film/music #16959-0464pz PRED entity: 0464pz PRED relation: genre PRED expected values: 0lsxr => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 81): 05p553 (0.55 #917, 0.52 #1748, 0.50 #1914), 01z4y (0.45 #931, 0.39 #1762, 0.37 #1928), 0c4xc (0.36 #292, 0.35 #956, 0.29 #1787), 0vgkd (0.32 #591, 0.21 #259, 0.20 #923), 03k9fj (0.23 #1008, 0.23 #592, 0.18 #2586), 01t_vv (0.23 #615, 0.22 #1529, 0.21 #283), 06nbt (0.23 #934, 0.19 #1350, 0.14 #602), 01z77k (0.22 #444, 0.19 #776, 0.17 #361), 06n90 (0.22 #2588, 0.16 #4500, 0.16 #4251), 01htzx (0.20 #1014, 0.19 #2592, 0.18 #2676) >> Best rule #917 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 0557yqh; 0524b41; 06qwh; 03nymk; 053x8hr; 06qw_; >> query: (?x1653, 05p553) <- nominated_for(?x8660, ?x1653), titles(?x3381, ?x1653), actor(?x1653, ?x1654), tv_program(?x6072, ?x1653) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2584 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 137 *> proper extension: 07qht4; *> query: (?x1653, 0lsxr) <- genre(?x1653, ?x12176), genre(?x4637, ?x12176), ?x4637 = 02sqkh *> conf = 0.16 ranks of expected_values: 13 EVAL 0464pz genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 90.000 90.000 0.550 http://example.org/tv/tv_program/genre #16958-0jmm4 PRED entity: 0jmm4 PRED relation: draft PRED expected values: 0f4vx0 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 15): 0f4vx0 (0.86 #147, 0.82 #87, 0.78 #178), 0g3zpp (0.42 #77, 0.38 #442, 0.34 #652), 02qw1zx (0.42 #77, 0.38 #445, 0.32 #168), 09l0x9 (0.42 #77, 0.38 #451, 0.32 #168), 05vsb7 (0.42 #77, 0.32 #168, 0.29 #441), 04f4z1k (0.42 #77, 0.32 #168, 0.27 #801), 03nt7j (0.42 #77, 0.32 #168, 0.26 #761), 02x2khw (0.42 #77, 0.32 #168, 0.26 #803), 092j54 (0.33 #448, 0.31 #763, 0.30 #658), 02z6872 (0.32 #168, 0.26 #764, 0.26 #803) >> Best rule #147 for best value: >> intensional similarity = 12 >> extensional distance = 20 >> proper extension: 0jm8l; >> query: (?x9049, 0f4vx0) <- school(?x9049, ?x3779), draft(?x9049, ?x8586), draft(?x9049, ?x2569), draft(?x5756, ?x8586), draft(?x4571, ?x8586), school(?x8586, ?x581), ?x2569 = 038c0q, major_field_of_study(?x3779, ?x1668), ?x5756 = 0jm4b, ?x4571 = 0jm6n, currency(?x3779, ?x170), citytown(?x3779, ?x4978) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jmm4 draft 0f4vx0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.864 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #16957-0265vt PRED entity: 0265vt PRED relation: award! PRED expected values: 03772 05qzv => 58 concepts (28 used for prediction) PRED predicted values (max 10 best out of 3887): 0210f1 (0.81 #26814, 0.80 #63683, 0.79 #87145), 0klw (0.81 #26814, 0.80 #63683, 0.79 #87145), 02xyl (0.81 #26814, 0.80 #63683, 0.79 #87145), 0821j (0.81 #26814, 0.80 #63683, 0.79 #87145), 02y49 (0.67 #36073, 0.64 #42777, 0.56 #32722), 01zkxv (0.56 #33645, 0.55 #40349, 0.50 #36996), 03hpr (0.50 #26331, 0.44 #33034, 0.44 #6704), 014dq7 (0.50 #16760, 0.44 #6704, 0.40 #3352), 01n4f8 (0.50 #16760, 0.44 #6704, 0.40 #3352), 0jt90f5 (0.50 #14011, 0.33 #34122, 0.33 #30771) >> Best rule #26814 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 02662b; 0262yt; 0262x6; >> query: (?x9285, ?x476) <- award_winner(?x9285, ?x7055), award_winner(?x9285, ?x476), award(?x12009, ?x9285), award(?x5333, ?x9285), award(?x2993, ?x9285), ?x12009 = 01g6bk, ?x5333 = 0b0pf, ?x7055 = 0210f1, place_of_birth(?x2993, ?x108) >> conf = 0.81 => this is the best rule for 4 predicted values *> Best rule #34982 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 02664f; *> query: (?x9285, 03772) <- award_winner(?x9285, ?x476), award(?x12009, ?x9285), award(?x9284, ?x9285), award(?x1287, ?x9285), award(?x12009, ?x14213), ?x9284 = 0gd_s, ?x1287 = 09dt7, ?x14213 = 01bb1c *> conf = 0.44 ranks of expected_values: 13, 14 EVAL 0265vt award! 05qzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 58.000 28.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0265vt award! 03772 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 58.000 28.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16956-01x2_q PRED entity: 01x2_q PRED relation: nationality PRED expected values: 0d060g => 60 concepts (43 used for prediction) PRED predicted values (max 10 best out of 94): 09c7w0 (0.91 #2410, 0.88 #2509, 0.87 #2609), 02jx1 (0.50 #2341, 0.27 #631, 0.19 #1235), 07ssc (0.42 #2122, 0.32 #2223, 0.27 #115), 0d060g (0.38 #2015, 0.33 #7, 0.19 #2114), 03rk0 (0.21 #2253, 0.06 #3555, 0.06 #3655), 03rjj (0.18 #2013, 0.09 #2112, 0.08 #2314), 0f8l9c (0.18 #2030, 0.15 #122, 0.09 #2129), 05bcl (0.15 #159, 0.05 #658, 0.05 #759), 06mkj (0.12 #146, 0.06 #247, 0.06 #446), 0chghy (0.09 #410, 0.09 #710, 0.09 #510) >> Best rule #2410 for best value: >> intensional similarity = 7 >> extensional distance = 2097 >> proper extension: 0h0jz; 018dnt; 02pkpfs; 01vs14j; 01wxyx1; 072twv; 059t6d; 0lzkm; 06whf; 01vswwx; ... >> query: (?x13210, 09c7w0) <- gender(?x13210, ?x231), nationality(?x13210, ?x1264), ?x231 = 05zppz, film_release_region(?x409, ?x1264), ?x409 = 0gtv7pk, religion(?x1264, ?x492), country(?x136, ?x1264) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #2015 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 275 *> proper extension: 01qscs; 04xjp; 02rxbmt; 060_7; 03c602; 01njxvw; *> query: (?x13210, 0d060g) <- gender(?x13210, ?x231), nationality(?x13210, ?x1264), ?x231 = 05zppz, film_release_region(?x5109, ?x1264), film_release_region(?x2896, ?x1264), first_level_division_of(?x1646, ?x1264), ?x5109 = 0b44shh, ?x2896 = 0645k5 *> conf = 0.38 ranks of expected_values: 4 EVAL 01x2_q nationality 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 60.000 43.000 0.912 http://example.org/people/person/nationality #16955-01m3x5p PRED entity: 01m3x5p PRED relation: place_of_birth PRED expected values: 0f2tj => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 74): 02_286 (0.13 #2132, 0.12 #723, 0.11 #4950), 030qb3t (0.07 #2167, 0.06 #2872, 0.05 #1462), 0cr3d (0.06 #2207, 0.05 #2912, 0.05 #798), 04jpl (0.05 #1416, 0.05 #712, 0.02 #34515), 01_d4 (0.05 #770, 0.04 #4293, 0.04 #3589), 03b12 (0.05 #1111, 0.04 #2520, 0.03 #3225), 01nl79 (0.05 #1245, 0.02 #2654, 0.02 #3359), 06_kh (0.05 #709, 0.01 #2118), 03l2n (0.04 #1577, 0.02 #2282, 0.02 #2987), 0hj6h (0.04 #1894, 0.02 #3304) >> Best rule #2132 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 02x8kk; 02x8mt; >> query: (?x4184, 02_286) <- sibling(?x1583, ?x4184), nationality(?x4184, ?x94), ?x94 = 09c7w0 >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #248 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 025tdwc; *> query: (?x4184, 0f2tj) <- profession(?x4184, ?x6476), profession(?x4184, ?x1183), ?x1183 = 09jwl, ?x6476 = 025352 *> conf = 0.03 ranks of expected_values: 28 EVAL 01m3x5p place_of_birth 0f2tj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 100.000 100.000 0.134 http://example.org/people/person/place_of_birth #16954-03z20c PRED entity: 03z20c PRED relation: production_companies PRED expected values: 06rq1k => 97 concepts (80 used for prediction) PRED predicted values (max 10 best out of 75): 06rq1k (0.50 #18, 0.06 #351, 0.04 #268), 054lpb6 (0.25 #15, 0.12 #513, 0.12 #348), 01gb54 (0.14 #370, 0.09 #947, 0.08 #2112), 017s11 (0.12 #418, 0.12 #583, 0.12 #501), 086k8 (0.12 #1743, 0.12 #1993, 0.11 #1410), 05qd_ (0.12 #920, 0.12 #343, 0.10 #1666), 0pz91 (0.12 #1824, 0.10 #2823, 0.10 #2074), 023tp8 (0.12 #1824, 0.10 #2823, 0.10 #2074), 02r251z (0.12 #1824, 0.10 #2823, 0.10 #2074), 01fyzy (0.12 #1824, 0.10 #2823, 0.10 #2074) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0ch3qr1; 0f2sx4; >> query: (?x2907, 06rq1k) <- nominated_for(?x1335, ?x2907), film_crew_role(?x2907, ?x137), nominated_for(?x4054, ?x2907), ?x1335 = 0pz91 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03z20c production_companies 06rq1k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 80.000 0.500 http://example.org/film/film/production_companies #16953-01wz01 PRED entity: 01wz01 PRED relation: nationality PRED expected values: 09c7w0 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.85 #201, 0.77 #2010, 0.76 #5419), 02jx1 (0.50 #33, 0.11 #133, 0.10 #1340), 0n2q0 (0.33 #7733, 0.27 #7531), 05kkh (0.33 #7733, 0.27 #7531), 04_1l0v (0.27 #7531), 029jpy (0.27 #7531), 02_286 (0.27 #7531), 059rby (0.27 #7531), 03rt9 (0.25 #13, 0.03 #1920, 0.01 #917), 07ssc (0.09 #2525, 0.08 #616, 0.08 #1322) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 01jz6d; >> query: (?x4173, 09c7w0) <- profession(?x4173, ?x1032), location(?x4173, ?x4090), ?x4090 = 01sn3 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wz01 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.850 http://example.org/people/person/nationality #16952-0gcpc PRED entity: 0gcpc PRED relation: film_festivals PRED expected values: 05ys0ws => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 13): 0bx_f_t (0.04 #100, 0.03 #226, 0.02 #163), 059_y8d (0.03 #107, 0.02 #128, 0.02 #149), 09rwjly (0.03 #113, 0.02 #134, 0.02 #155), 0kfhjq0 (0.03 #110, 0.02 #131, 0.02 #1328), 04_m9gk (0.02 #958, 0.01 #1210, 0.01 #1525), 04grdgy (0.02 #555, 0.02 #954, 0.02 #660), 0bmj62v (0.01 #1524, 0.01 #957, 0.01 #1587), 05ys0ws (0.01 #335, 0.01 #524, 0.01 #314), 0g57ws5 (0.01 #322, 0.01 #1099, 0.01 #1162), 0gg7gsl (0.01 #1240, 0.01 #1198, 0.01 #1576) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0cbl95; >> query: (?x4241, 0bx_f_t) <- films(?x5011, ?x4241), list(?x4241, ?x3004), award(?x4241, ?x1107), music(?x4241, ?x3483) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #335 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 204 *> proper extension: 0bs8hvm; *> query: (?x4241, 05ys0ws) <- film(?x1134, ?x4241), language(?x4241, ?x254), cinematography(?x4241, ?x5862) *> conf = 0.01 ranks of expected_values: 8 EVAL 0gcpc film_festivals 05ys0ws CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 104.000 104.000 0.037 http://example.org/film/film/film_festivals #16951-0ngg PRED entity: 0ngg PRED relation: profession PRED expected values: 0fj9f => 129 concepts (102 used for prediction) PRED predicted values (max 10 best out of 82): 02hrh1q (0.78 #1665, 0.74 #6166, 0.71 #465), 09jwl (0.43 #470, 0.36 #1370, 0.33 #1520), 02jknp (0.39 #1058, 0.38 #908, 0.33 #308), 01d_h8 (0.39 #1056, 0.37 #606, 0.33 #906), 03gjzk (0.36 #466, 0.23 #3616, 0.22 #2566), 0dxtg (0.35 #1064, 0.29 #914, 0.27 #6015), 019x4f (0.33 #266, 0.33 #116, 0.05 #866), 0cbd2 (0.33 #307, 0.21 #607, 0.19 #4357), 066dv (0.33 #250), 067nv (0.33 #205) >> Best rule #1665 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 015q43; >> query: (?x13877, 02hrh1q) <- type_of_union(?x13877, ?x566), location_of_ceremony(?x13877, ?x6959), gender(?x13877, ?x231), taxonomy(?x6959, ?x939), contains(?x6959, ?x5994) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #356 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 03_fk9; *> query: (?x13877, 0fj9f) <- gender(?x13877, ?x231), nationality(?x13877, ?x6329), ?x231 = 05zppz, place_of_birth(?x13877, ?x6959), ?x6959 = 06c62 *> conf = 0.17 ranks of expected_values: 16 EVAL 0ngg profession 0fj9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 129.000 102.000 0.780 http://example.org/people/person/profession #16950-0t_48 PRED entity: 0t_48 PRED relation: source PRED expected values: 0jbk9 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.95 #17, 0.95 #16, 0.92 #13) >> Best rule #17 for best value: >> intensional similarity = 5 >> extensional distance = 218 >> proper extension: 0_lr1; >> query: (?x13393, ?x958) <- county(?x13393, ?x9065), adjoins(?x9065, ?x4990), county(?x8171, ?x9065), time_zones(?x9065, ?x2674), source(?x8171, ?x958) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0t_48 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.955 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #16949-05mv4 PRED entity: 05mv4 PRED relation: major_field_of_study PRED expected values: 03g3w 0fdys => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 111): 03g3w (0.60 #361, 0.56 #248, 0.55 #474), 0fdys (0.60 #31, 0.38 #144, 0.33 #258), 02h40lc (0.56 #231, 0.50 #344, 0.50 #117), 0h5k (0.40 #357, 0.40 #17, 0.36 #470), 02lp1 (0.40 #9, 0.38 #122, 0.33 #236), 0g26h (0.40 #35, 0.38 #148, 0.33 #262), 0193x (0.40 #28, 0.38 #141, 0.33 #255), 0pf2 (0.40 #26, 0.38 #139, 0.33 #253), 03nfmq (0.40 #30, 0.30 #370, 0.27 #483), 04sh3 (0.40 #66, 0.27 #519, 0.27 #227) >> Best rule #361 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 07vk2; >> query: (?x4187, 03g3w) <- major_field_of_study(?x4187, ?x7979), major_field_of_study(?x4187, ?x2606), ?x7979 = 036nz, major_field_of_study(?x7816, ?x2606), ?x7816 = 015y3j >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05mv4 major_field_of_study 0fdys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.600 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05mv4 major_field_of_study 03g3w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.600 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16948-0lbp_ PRED entity: 0lbp_ PRED relation: place_of_burial! PRED expected values: 04n_g => 121 concepts (42 used for prediction) PRED predicted values (max 10 best out of 745): 022p06 (0.33 #179, 0.27 #864, 0.25 #453), 01t94_1 (0.33 #216, 0.25 #490, 0.18 #901), 0cf2h (0.33 #190, 0.25 #464, 0.18 #875), 0bkmf (0.33 #222, 0.25 #496, 0.18 #907), 03bw6 (0.33 #199, 0.25 #473, 0.18 #884), 0hnp7 (0.33 #188, 0.25 #462, 0.18 #873), 081nh (0.33 #153, 0.25 #427, 0.18 #838), 01200d (0.33 #256, 0.25 #530, 0.09 #941), 0p9qb (0.33 #246, 0.25 #520, 0.09 #931), 03n6r (0.33 #183, 0.25 #457, 0.09 #868) >> Best rule #179 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 018mmj; >> query: (?x7496, 022p06) <- place_of_burial(?x13194, ?x7496), place_of_burial(?x9569, ?x7496), award(?x13194, ?x435), ?x435 = 0bp_b2, people(?x1446, ?x9569), film(?x13194, ?x5294) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #960 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 0k_q_; *> query: (?x7496, ?x192) <- place_of_burial(?x13194, ?x7496), award(?x13194, ?x435), award(?x192, ?x435), nominated_for(?x435, ?x337), award_winner(?x8347, ?x13194) *> conf = 0.01 ranks of expected_values: 449 EVAL 0lbp_ place_of_burial! 04n_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 121.000 42.000 0.333 http://example.org/people/deceased_person/place_of_burial #16947-02qdrjx PRED entity: 02qdrjx PRED relation: film! PRED expected values: 01fyzy 01r9md => 93 concepts (58 used for prediction) PRED predicted values (max 10 best out of 980): 016z2j (0.26 #2461, 0.02 #6616, 0.02 #33610), 07r1h (0.25 #1086, 0.04 #38463, 0.02 #15623), 0fby2t (0.25 #750, 0.04 #19442, 0.03 #21518), 032xhg (0.25 #63, 0.03 #27059, 0.03 #2139), 013knm (0.25 #633, 0.03 #2709, 0.01 #50468), 0zcbl (0.25 #1218, 0.02 #15755, 0.02 #17832), 032w8h (0.25 #276, 0.02 #14813, 0.02 #25196), 023v4_ (0.25 #880, 0.02 #15417, 0.02 #17494), 08vr94 (0.25 #672, 0.02 #17286, 0.02 #54659), 05fnl9 (0.25 #265, 0.02 #6496, 0.01 #27261) >> Best rule #2461 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 030p35; 0vjr; >> query: (?x9441, 016z2j) <- nominated_for(?x72, ?x9441), film(?x72, ?x5270), award_nominee(?x71, ?x72), ?x5270 = 0bc1yhb >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #114205 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1105 *> proper extension: 06krf3; 016kv6; 02g5q1; *> query: (?x9441, ?x444) <- film(?x3272, ?x9441), titles(?x1510, ?x9441), place_of_birth(?x3272, ?x1860), award_nominee(?x444, ?x3272) *> conf = 0.04 ranks of expected_values: 169 EVAL 02qdrjx film! 01r9md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 58.000 0.258 http://example.org/film/actor/film./film/performance/film EVAL 02qdrjx film! 01fyzy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 93.000 58.000 0.258 http://example.org/film/actor/film./film/performance/film #16946-09v8clw PRED entity: 09v8clw PRED relation: genre PRED expected values: 01hmnh => 89 concepts (68 used for prediction) PRED predicted values (max 10 best out of 127): 07s9rl0 (0.77 #6068, 0.75 #8133, 0.72 #5341), 05p553 (0.51 #6434, 0.49 #4130, 0.48 #5707), 01jfsb (0.51 #2315, 0.49 #2194, 0.45 #980), 01hmnh (0.41 #1228, 0.36 #1956, 0.23 #2078), 02l7c8 (0.33 #16, 0.32 #379, 0.29 #5477), 06qm3 (0.33 #37, 0.18 #1817, 0.03 #8131), 06cvj (0.33 #3, 0.12 #487, 0.11 #6433), 06n90 (0.29 #1223, 0.27 #2195, 0.27 #2316), 0lsxr (0.24 #735, 0.24 #1098, 0.24 #5712), 04xvlr (0.23 #365, 0.20 #4371, 0.18 #5463) >> Best rule #6068 for best value: >> intensional similarity = 4 >> extensional distance = 1058 >> proper extension: 01h72l; >> query: (?x12423, 07s9rl0) <- genre(?x12423, ?x225), nominated_for(?x2444, ?x12423), genre(?x9802, ?x225), ?x9802 = 015qy1 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1228 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 126 *> proper extension: 03_wm6; *> query: (?x12423, 01hmnh) <- genre(?x12423, ?x811), film_crew_role(?x12423, ?x2154), ?x2154 = 01vx2h, ?x811 = 03k9fj *> conf = 0.41 ranks of expected_values: 4 EVAL 09v8clw genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 89.000 68.000 0.773 http://example.org/film/film/genre #16945-01k98nm PRED entity: 01k98nm PRED relation: nationality PRED expected values: 09c7w0 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 48): 09c7w0 (0.83 #4705, 0.82 #5107, 0.80 #401), 0mskq (0.33 #8415, 0.27 #8112), 07b_l (0.33 #8415, 0.27 #8112), 02jx1 (0.23 #733, 0.17 #1833, 0.16 #3136), 07ssc (0.10 #715, 0.09 #3018, 0.09 #3318), 0d060g (0.07 #307, 0.06 #907, 0.06 #1507), 03rk0 (0.06 #8461, 0.05 #9161, 0.05 #9061), 0345h (0.04 #831, 0.03 #2331, 0.03 #1731), 0d0vqn (0.03 #309, 0.02 #9818, 0.01 #709), 03rjj (0.03 #905, 0.02 #205, 0.02 #9818) >> Best rule #4705 for best value: >> intensional similarity = 2 >> extensional distance = 989 >> proper extension: 069d71; >> query: (?x3234, 09c7w0) <- location(?x3234, ?x1719), dog_breed(?x1719, ?x1706) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k98nm nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.828 http://example.org/people/person/nationality #16944-016s_5 PRED entity: 016s_5 PRED relation: instrumentalists! PRED expected values: 0gghm => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 121): 05r5c (0.70 #335, 0.54 #582, 0.51 #832), 026t6 (0.39 #413, 0.14 #495, 0.13 #1160), 03bx0bm (0.35 #575, 0.03 #2645, 0.03 #2646), 028tv0 (0.35 #575, 0.03 #2645, 0.03 #2646), 02sgy (0.30 #1240, 0.28 #825, 0.04 #498), 0214km (0.30 #1240, 0.28 #825, 0.03 #1899), 0l14qv (0.27 #333, 0.19 #415, 0.11 #745), 03gvt (0.27 #388, 0.08 #1217, 0.07 #800), 04rzd (0.15 #116, 0.15 #362, 0.14 #280), 07y_7 (0.15 #330, 0.08 #412, 0.07 #1159) >> Best rule #335 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 012x4t; 04cr6qv; 01vn0t_; >> query: (?x5452, 05r5c) <- instrumentalists(?x227, ?x5452), profession(?x5452, ?x6565), profession(?x5452, ?x220), ?x220 = 016z4k, ?x6565 = 0fnpj >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2645 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 579 *> proper extension: 0frsw; 028qdb; 01yzl2; 03d9d6; 09lwrt; 089pg7; 02ht0ln; *> query: (?x5452, ?x212) <- instrumentalists(?x716, ?x5452), role(?x74, ?x716), role(?x716, ?x212), artists(?x114, ?x5452) *> conf = 0.03 ranks of expected_values: 55 EVAL 016s_5 instrumentalists! 0gghm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 100.000 100.000 0.697 http://example.org/music/instrument/instrumentalists #16943-025b3k PRED entity: 025b3k PRED relation: religion PRED expected values: 0c8wxp => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 20): 0kpl (0.19 #1720, 0.19 #1540, 0.19 #235), 03_gx (0.18 #104, 0.17 #194, 0.17 #419), 0c8wxp (0.15 #726, 0.14 #951, 0.14 #1176), 092bf5 (0.12 #16, 0.07 #286, 0.06 #61), 0kq2 (0.09 #243, 0.09 #63, 0.09 #333), 0n2g (0.07 #553, 0.05 #1723, 0.05 #283), 051kv (0.06 #5, 0.03 #50, 0.03 #95), 03j6c (0.04 #561, 0.03 #3261, 0.03 #3171), 01lp8 (0.04 #541, 0.03 #586, 0.03 #46), 04pk9 (0.04 #560, 0.03 #110, 0.03 #1505) >> Best rule #1720 for best value: >> intensional similarity = 3 >> extensional distance = 254 >> proper extension: 05xq9; 0fpzzp; >> query: (?x9597, 0kpl) <- influenced_by(?x9597, ?x5336), influenced_by(?x2343, ?x9597), influenced_by(?x7180, ?x2343) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #726 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 106 *> proper extension: 04t2l2; 02g8h; 05g8ky; 01c58j; 01t07j; 07ymr5; 0bj9k; 018grr; 0jfx1; 0lx2l; ... *> query: (?x9597, 0c8wxp) <- profession(?x9597, ?x319), influenced_by(?x9597, ?x5336), gender(?x9597, ?x231), ?x319 = 01d_h8 *> conf = 0.15 ranks of expected_values: 3 EVAL 025b3k religion 0c8wxp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 123.000 0.191 http://example.org/people/person/religion #16942-01mxqyk PRED entity: 01mxqyk PRED relation: location PRED expected values: 02dtg => 113 concepts (111 used for prediction) PRED predicted values (max 10 best out of 111): 02dtg (0.36 #7237, 0.02 #3240, 0.01 #11282), 02_286 (0.17 #2449, 0.14 #1645, 0.13 #7274), 030qb3t (0.13 #22597, 0.12 #31441, 0.11 #47527), 01b8jj (0.11 #593, 0.03 #2201, 0.03 #3005), 03b12 (0.07 #519, 0.03 #2127, 0.02 #2931), 04jpl (0.07 #1625, 0.05 #2429, 0.05 #5645), 04rrd (0.07 #902, 0.04 #98), 013kcv (0.07 #846, 0.01 #9692), 0cr3d (0.05 #13815, 0.05 #31503, 0.05 #4165), 059rby (0.05 #2428, 0.04 #1624, 0.04 #22530) >> Best rule #7237 for best value: >> intensional similarity = 3 >> extensional distance = 247 >> proper extension: 0h7pj; >> query: (?x11621, ?x479) <- award_nominee(?x11621, ?x2614), award(?x11621, ?x567), origin(?x11621, ?x479) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mxqyk location 02dtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 111.000 0.358 http://example.org/people/person/places_lived./people/place_lived/location #16941-01m23s PRED entity: 01m23s PRED relation: time_zones PRED expected values: 02hcv8 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.67 #3, 0.49 #107, 0.47 #198), 02lcqs (0.25 #330, 0.22 #109, 0.21 #200), 02fqwt (0.20 #666, 0.19 #482, 0.19 #600), 02hczc (0.20 #666, 0.19 #482, 0.19 #600), 02lcrv (0.20 #666, 0.19 #482, 0.19 #600), 042g7t (0.20 #666, 0.19 #482, 0.19 #600), 02llzg (0.08 #212, 0.07 #121, 0.05 #420), 03bdv (0.06 #188, 0.05 #318, 0.05 #422), 03plfd (0.01 #361, 0.01 #544, 0.01 #1047) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 01k2wn; 02j04_; >> query: (?x13745, 02hcv8) <- contains(?x13940, ?x13745), contains(?x1755, ?x13745), source(?x13940, ?x958), ?x958 = 0jbk9, ?x1755 = 01x73 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m23s time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.667 http://example.org/location/location/time_zones #16940-0222qb PRED entity: 0222qb PRED relation: people PRED expected values: 0bytkq 0dxmyh 03_fk9 => 27 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1811): 0g824 (0.60 #5988, 0.25 #4289, 0.12 #9387), 0311wg (0.50 #3685, 0.40 #5384, 0.25 #1985), 01ttg5 (0.50 #3937, 0.20 #5636, 0.12 #9035), 0227tr (0.50 #3729, 0.20 #5428, 0.12 #8827), 04nw9 (0.50 #3590, 0.20 #5289, 0.09 #8688), 018ygt (0.50 #4282, 0.20 #5981, 0.08 #12780), 044mvs (0.50 #4804, 0.20 #6503, 0.08 #13302), 0484q (0.50 #4407, 0.20 #6106, 0.08 #12905), 06lvlf (0.50 #4233, 0.20 #5932, 0.07 #7633), 05vk_d (0.50 #4600, 0.20 #6299, 0.07 #8000) >> Best rule #5988 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 033tf_; 0x67; 0xnvg; >> query: (?x10035, 0g824) <- people(?x10035, ?x13161), people(?x10035, ?x9393), people(?x10035, ?x5500), people(?x10035, ?x1742), influenced_by(?x9392, ?x9393), award_winner(?x1243, ?x13161), nationality(?x1742, ?x205), location(?x9393, ?x8956), award_winner(?x5500, ?x2927), film(?x5500, ?x573), ?x2927 = 03jqw5 >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0222qb people 03_fk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 27.000 25.000 0.600 http://example.org/people/ethnicity/people EVAL 0222qb people 0dxmyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 27.000 25.000 0.600 http://example.org/people/ethnicity/people EVAL 0222qb people 0bytkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 27.000 25.000 0.600 http://example.org/people/ethnicity/people #16939-0378zn PRED entity: 0378zn PRED relation: film PRED expected values: 0fy34l 02825nf => 97 concepts (75 used for prediction) PRED predicted values (max 10 best out of 632): 0b3n61 (0.32 #12086, 0.18 #6722, 0.15 #8510), 0g9z_32 (0.26 #12004, 0.25 #1276, 0.20 #3064), 05c26ss (0.26 #11359, 0.18 #5995, 0.15 #7783), 034qzw (0.25 #333, 0.20 #2121, 0.18 #5697), 02825cv (0.25 #1142, 0.20 #2930, 0.18 #6506), 049xgc (0.25 #972, 0.20 #2760, 0.11 #4548), 02b6n9 (0.25 #1572, 0.20 #3360, 0.09 #6936), 095zlp (0.25 #60, 0.20 #1848, 0.09 #5424), 06q8qh (0.25 #606, 0.20 #2394, 0.09 #5970), 01cmp9 (0.25 #1048, 0.20 #2836, 0.09 #6412) >> Best rule #12086 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 01svw8n; 08hsww; 01w9wwg; >> query: (?x13847, 0b3n61) <- film(?x13847, ?x4304), genre(?x4304, ?x225), production_companies(?x4304, ?x1478), film(?x2790, ?x4304), ?x2790 = 0c9c0 >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #12049 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 45 *> proper extension: 01svw8n; 08hsww; 01w9wwg; *> query: (?x13847, 02825nf) <- film(?x13847, ?x4304), genre(?x4304, ?x225), production_companies(?x4304, ?x1478), film(?x2790, ?x4304), ?x2790 = 0c9c0 *> conf = 0.02 ranks of expected_values: 265 EVAL 0378zn film 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 97.000 75.000 0.319 http://example.org/film/actor/film./film/performance/film EVAL 0378zn film 0fy34l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 75.000 0.319 http://example.org/film/actor/film./film/performance/film #16938-02r1ysd PRED entity: 02r1ysd PRED relation: category PRED expected values: 08mbj5d => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.54 #6, 0.46 #17, 0.45 #4) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0cnjm0; >> query: (?x6726, 08mbj5d) <- honored_for(?x1265, ?x6726), honored_for(?x762, ?x6726), ?x762 = 03gwpw2, award_winner(?x1265, ?x669) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r1ysd category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.538 http://example.org/common/topic/webpage./common/webpage/category #16937-0drtkx PRED entity: 0drtkx PRED relation: ceremony PRED expected values: 0g5b0q5 0418154 => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 136): 05qb8vx (0.56 #464, 0.43 #328, 0.30 #1361), 0n8_m93 (0.56 #521, 0.43 #385, 0.20 #249), 02yvhx (0.56 #481, 0.43 #345, 0.20 #209), 0bvfqq (0.56 #440, 0.43 #304, 0.20 #168), 02hn5v (0.56 #447, 0.43 #311, 0.20 #175), 0gmdkyy (0.56 #437, 0.43 #301, 0.20 #165), 050yyb (0.56 #444, 0.43 #308, 0.20 #172), 02pgky2 (0.44 #493, 0.43 #357, 0.30 #1361), 02glmx (0.44 #485, 0.43 #349, 0.20 #213), 0bzm81 (0.44 #429, 0.29 #293, 0.18 #1245) >> Best rule #464 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0p9sw; 0l8z1; 02x1z2s; 018wdw; >> query: (?x8059, 05qb8vx) <- nominated_for(?x8059, ?x4707), nominated_for(?x8059, ?x3457), award(?x163, ?x8059), film_release_region(?x4707, ?x94), ?x3457 = 03x7hd, genre(?x4707, ?x258) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1361 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 117 *> proper extension: 06196; *> query: (?x8059, ?x762) <- award(?x3457, ?x8059), award(?x2705, ?x8059), ceremony(?x8059, ?x944), honored_for(?x762, ?x3457), nominated_for(?x2705, ?x4651) *> conf = 0.30 ranks of expected_values: 67, 93 EVAL 0drtkx ceremony 0418154 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 52.000 52.000 0.556 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0drtkx ceremony 0g5b0q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 52.000 52.000 0.556 http://example.org/award/award_category/winners./award/award_honor/ceremony #16936-01w3v PRED entity: 01w3v PRED relation: institution! PRED expected values: 019v9k => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 13): 019v9k (0.67 #868, 0.67 #568, 0.66 #552), 0bjrnt (0.60 #93, 0.57 #119, 0.53 #145), 01rr_d (0.40 #98, 0.36 #124, 0.33 #150), 02mjs7 (0.36 #391, 0.29 #118, 0.27 #144), 071tyz (0.30 #1575, 0.20 #95, 0.14 #121), 022h5x (0.30 #1575, 0.18 #875, 0.17 #546), 01ysy9 (0.30 #1575, 0.07 #994, 0.06 #561), 01kxxq (0.30 #1575, 0.03 #1048, 0.03 #1088), 01gkg3 (0.30 #1575, 0.02 #701, 0.02 #410), 028dcg (0.21 #126, 0.20 #413, 0.20 #152) >> Best rule #868 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 01b1mj; >> query: (?x741, 019v9k) <- institution(?x620, ?x741), school(?x2820, ?x741), currency(?x741, ?x170), student(?x741, ?x881) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w3v institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.675 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #16935-0pf2 PRED entity: 0pf2 PRED relation: major_field_of_study! PRED expected values: 02482c => 62 concepts (40 used for prediction) PRED predicted values (max 10 best out of 643): 01w5m (0.77 #7065, 0.75 #4750, 0.73 #7645), 07tgn (0.75 #6385, 0.71 #4071, 0.67 #7545), 07wrz (0.75 #4698, 0.71 #4119, 0.67 #5276), 07szy (0.75 #4675, 0.67 #6410, 0.67 #5253), 01w3v (0.75 #6383, 0.67 #5226, 0.62 #4648), 06pwq (0.70 #5802, 0.67 #6380, 0.62 #4645), 05mv4 (0.67 #2459, 0.60 #1880, 0.57 #4197), 07tg4 (0.67 #2987, 0.58 #6459, 0.57 #4145), 07tk7 (0.67 #3956, 0.57 #4535, 0.56 #5692), 09kvv (0.67 #3519, 0.57 #4098, 0.50 #1202) >> Best rule #7065 for best value: >> intensional similarity = 12 >> extensional distance = 11 >> proper extension: 02lp1; >> query: (?x3400, 01w5m) <- major_field_of_study(?x8715, ?x3400), major_field_of_study(?x5638, ?x3400), major_field_of_study(?x3439, ?x3400), ?x5638 = 02bqy, major_field_of_study(?x3437, ?x3400), major_field_of_study(?x8715, ?x2014), ?x2014 = 04rjg, ?x3437 = 02_xgp2, ?x3439 = 03ksy, colors(?x8715, ?x663), institution(?x620, ?x8715), contains(?x550, ?x8715) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #356 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 01mkq; *> query: (?x3400, 02482c) <- major_field_of_study(?x11640, ?x3400), major_field_of_study(?x8715, ?x3400), major_field_of_study(?x5638, ?x3400), major_field_of_study(?x3485, ?x3400), major_field_of_study(?x3439, ?x3400), major_field_of_study(?x122, ?x3400), ?x5638 = 02bqy, major_field_of_study(?x734, ?x3400), ?x3485 = 01mpwj, organization(?x346, ?x8715), ?x122 = 08815, ?x3439 = 03ksy, ?x11640 = 013719, institution(?x620, ?x8715) *> conf = 0.33 ranks of expected_values: 201 EVAL 0pf2 major_field_of_study! 02482c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 40.000 0.769 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16934-017gm7 PRED entity: 017gm7 PRED relation: story_by PRED expected values: 041h0 => 78 concepts (33 used for prediction) PRED predicted values (max 10 best out of 66): 041h0 (0.17 #221, 0.17 #5, 0.04 #440), 0c9xjl (0.17 #316), 03hnd (0.17 #273), 02bfxb (0.10 #4547, 0.06 #1084, 0.03 #1517), 0js9s (0.10 #4547, 0.06 #1084, 0.03 #1517), 042xh (0.09 #866, 0.03 #1515, 0.02 #1732), 0fx02 (0.07 #1144, 0.06 #2010, 0.06 #1577), 079vf (0.07 #653, 0.06 #869, 0.05 #1086), 046_v (0.04 #824, 0.03 #1257, 0.02 #1040), 0343h (0.04 #453, 0.02 #3268, 0.02 #1102) >> Best rule #221 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02tktw; >> query: (?x1392, 041h0) <- film(?x5282, ?x1392), film(?x4153, ?x1392), ?x4153 = 0294fd, award_nominee(?x5282, ?x629) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017gm7 story_by 041h0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 33.000 0.167 http://example.org/film/film/story_by #16933-01718w PRED entity: 01718w PRED relation: language PRED expected values: 0653m => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 29): 03_9r (0.20 #9, 0.07 #121, 0.05 #3453), 02hwhyv (0.20 #27, 0.01 #83, 0.01 #534), 064_8sq (0.16 #76, 0.15 #812, 0.15 #869), 04306rv (0.13 #60, 0.12 #738, 0.11 #910), 02bjrlw (0.09 #735, 0.09 #57, 0.08 #963), 0jzc (0.06 #74, 0.04 #581, 0.04 #924), 0653m (0.06 #66, 0.04 #1366, 0.04 #1141), 04h9h (0.04 #209, 0.03 #774, 0.03 #603), 02hxc3j (0.03 #62), 03hkp (0.03 #125, 0.02 #294, 0.02 #919) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0ds35l9; 02ntb8; 087pfc; >> query: (?x8063, 03_9r) <- award_winner(?x8063, ?x2596), film(?x11624, ?x8063), film_crew_role(?x8063, ?x137), ?x11624 = 063g7l >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #66 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 047gn4y; 07g_0c; *> query: (?x8063, 0653m) <- film(?x140, ?x8063), film_crew_role(?x8063, ?x3305), ?x3305 = 04pyp5 *> conf = 0.06 ranks of expected_values: 7 EVAL 01718w language 0653m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 78.000 78.000 0.200 http://example.org/film/film/language #16932-08nvyr PRED entity: 08nvyr PRED relation: nominated_for! PRED expected values: 0gq9h 03hl6lc => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 191): 0gq9h (0.68 #1373, 0.66 #2253, 0.56 #933), 0l8z1 (0.66 #10347, 0.66 #10788, 0.66 #14091), 02wypbh (0.66 #10347, 0.66 #10788, 0.66 #14091), 04kxsb (0.46 #961, 0.40 #1401, 0.37 #2281), 0gr4k (0.40 #1344, 0.38 #2224, 0.29 #904), 0f4x7 (0.39 #1343, 0.38 #2223, 0.31 #903), 0gqy2 (0.37 #1426, 0.37 #2306, 0.32 #986), 0gr0m (0.37 #930, 0.35 #1370, 0.35 #2250), 027dtxw (0.34 #883, 0.27 #3081, 0.25 #4182), 0p9sw (0.33 #898, 0.30 #1338, 0.29 #2218) >> Best rule #1373 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 0sxfd; 0j_t1; 0170th; 0kxf1; 0hv4t; 0hvvf; 0llcx; 0b4lkx; 02p86pb; 0bs5vty; >> query: (?x4541, 0gq9h) <- award_winner(?x4541, ?x3281), nominated_for(?x746, ?x4541), language(?x4541, ?x254), ?x746 = 04dn09n >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1, 15 EVAL 08nvyr nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 104.000 104.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 08nvyr nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16931-01hnb PRED entity: 01hnb PRED relation: organization! PRED expected values: 07xl34 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 17): 0dq_5 (0.25 #922, 0.24 #1066, 0.22 #693), 07xl34 (0.24 #538, 0.23 #863, 0.22 #623), 05k17c (0.23 #54, 0.21 #66, 0.18 #282), 09d6p2 (0.15 #914), 0hm4q (0.08 #31, 0.08 #559, 0.07 #620), 01t7n9 (0.07 #577, 0.02 #1551), 09n5b9 (0.07 #577, 0.02 #1551), 02079p (0.07 #577, 0.02 #1551), 0789n (0.07 #577, 0.02 #1551), 0f6c3 (0.07 #577, 0.02 #1551) >> Best rule #922 for best value: >> intensional similarity = 3 >> extensional distance = 512 >> proper extension: 0xbm; 0196bp; 02qdyj; 01dtcb; 01b39j; 01dycg; 01qckn; 03_c8p; 07s363; 0c0sl; ... >> query: (?x6770, 0dq_5) <- organization(?x346, ?x6770), citytown(?x6770, ?x8853), company(?x346, ?x94) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #538 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 286 *> proper extension: 08815; 0ymbl; 02583l; 07tl0; 05f7s1; 049dk; 01k8q5; 07w5rq; 031n8c; 017z88; ... *> query: (?x6770, 07xl34) <- organization(?x346, ?x6770), citytown(?x6770, ?x8853), contains(?x94, ?x6770), school_type(?x6770, ?x1044) *> conf = 0.24 ranks of expected_values: 2 EVAL 01hnb organization! 07xl34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 142.000 142.000 0.247 http://example.org/organization/role/leaders./organization/leadership/organization #16930-0379s PRED entity: 0379s PRED relation: influenced_by! PRED expected values: 084w8 => 112 concepts (42 used for prediction) PRED predicted values (max 10 best out of 413): 073bb (0.50 #1060, 0.33 #2560, 0.33 #61), 01hb6v (0.50 #1091, 0.33 #92, 0.22 #2591), 0bk5r (0.43 #2203, 0.15 #2499, 0.13 #500), 04hcw (0.43 #2278, 0.12 #9779, 0.10 #5779), 07dnx (0.43 #2349, 0.12 #9850, 0.10 #5850), 0dzkq (0.40 #3123, 0.33 #2622, 0.33 #123), 014ps4 (0.38 #4801, 0.33 #300, 0.29 #4300), 040db (0.33 #574, 0.33 #74, 0.30 #3074), 0683n (0.33 #328, 0.29 #2327, 0.25 #4829), 0lrh (0.33 #103, 0.29 #2102, 0.25 #1102) >> Best rule #1060 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 082_p; >> query: (?x2994, 073bb) <- profession(?x2994, ?x353), influenced_by(?x9284, ?x2994), influenced_by(?x4055, ?x2994), ?x9284 = 0gd_s, influenced_by(?x4055, ?x9851), ?x9851 = 04jvt >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 03_87; *> query: (?x2994, 084w8) <- profession(?x2994, ?x353), influenced_by(?x9284, ?x2994), influenced_by(?x4055, ?x2994), ?x9284 = 0gd_s, ?x4055 = 034bs *> conf = 0.33 ranks of expected_values: 18 EVAL 0379s influenced_by! 084w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 112.000 42.000 0.500 http://example.org/influence/influence_node/influenced_by #16929-01w9wwg PRED entity: 01w9wwg PRED relation: artist! PRED expected values: 01trtc => 80 concepts (61 used for prediction) PRED predicted values (max 10 best out of 98): 03rhqg (0.16 #844, 0.16 #1811, 0.15 #1673), 033hn8 (0.15 #13, 0.11 #3469, 0.11 #4576), 011k1h (0.15 #9, 0.10 #561, 0.10 #3327), 0n85g (0.15 #60, 0.10 #612, 0.08 #3378), 01trtc (0.15 #70, 0.09 #484, 0.09 #622), 01w40h (0.11 #27, 0.09 #1132, 0.09 #1685), 03mp8k (0.11 #64, 0.08 #1860, 0.07 #1998), 0mzkr (0.11 #24, 0.07 #438, 0.07 #1820), 0k_kr (0.11 #42, 0.06 #180, 0.05 #594), 01txts (0.11 #81, 0.01 #495, 0.01 #1877) >> Best rule #844 for best value: >> intensional similarity = 3 >> extensional distance = 274 >> proper extension: 016lj_; >> query: (?x6162, 03rhqg) <- category(?x6162, ?x134), artist(?x1954, ?x6162), role(?x6162, ?x212) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #70 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 0dm5l; *> query: (?x6162, 01trtc) <- award(?x6162, ?x3835), award(?x6162, ?x528), ?x528 = 02g3gj, award_winner(?x3835, ?x215) *> conf = 0.15 ranks of expected_values: 5 EVAL 01w9wwg artist! 01trtc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 80.000 61.000 0.163 http://example.org/music/record_label/artist #16928-030qb3t PRED entity: 030qb3t PRED relation: place_of_birth! PRED expected values: 06v8s0 06j0md 043q6n_ 02bh9 01vrkdt 042z_g 03fbb6 08s_lw 02vcp0 => 199 concepts (170 used for prediction) PRED predicted values (max 10 best out of 2692): 0bdxs5 (0.49 #335425, 0.38 #342936, 0.38 #237805), 09889g (0.49 #335425, 0.38 #237805, 0.37 #350444), 0j1yf (0.49 #335425, 0.38 #237805, 0.37 #350444), 0lk90 (0.49 #335425, 0.38 #237805, 0.37 #350444), 03mszl (0.49 #335425, 0.38 #237805, 0.37 #350444), 0146pg (0.49 #335425, 0.38 #237805, 0.37 #350444), 01s21dg (0.49 #335425, 0.38 #237805, 0.37 #350444), 0147dk (0.49 #335425, 0.38 #237805, 0.37 #350444), 01vvydl (0.49 #335425, 0.38 #237805, 0.37 #350444), 01pgzn_ (0.49 #335425, 0.38 #237805, 0.37 #350444) >> Best rule #335425 for best value: >> intensional similarity = 3 >> extensional distance = 178 >> proper extension: 01m3b7; >> query: (?x1523, ?x5216) <- location(?x5216, ?x1523), origin(?x250, ?x1523), place_of_birth(?x5216, ?x11163) >> conf = 0.49 => this is the best rule for 151 predicted values *> Best rule #222788 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 108 *> proper extension: 0sc6p; *> query: (?x1523, ?x1342) <- place_of_birth(?x4507, ?x1523), county(?x1523, ?x2949), award_winner(?x4507, ?x1342) *> conf = 0.02 ranks of expected_values: 1986, 2043, 2080 EVAL 030qb3t place_of_birth! 02vcp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 170.000 0.487 http://example.org/people/person/place_of_birth EVAL 030qb3t place_of_birth! 08s_lw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 170.000 0.487 http://example.org/people/person/place_of_birth EVAL 030qb3t place_of_birth! 03fbb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 170.000 0.487 http://example.org/people/person/place_of_birth EVAL 030qb3t place_of_birth! 042z_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 170.000 0.487 http://example.org/people/person/place_of_birth EVAL 030qb3t place_of_birth! 01vrkdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 170.000 0.487 http://example.org/people/person/place_of_birth EVAL 030qb3t place_of_birth! 02bh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 170.000 0.487 http://example.org/people/person/place_of_birth EVAL 030qb3t place_of_birth! 043q6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 170.000 0.487 http://example.org/people/person/place_of_birth EVAL 030qb3t place_of_birth! 06j0md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 199.000 170.000 0.487 http://example.org/people/person/place_of_birth EVAL 030qb3t place_of_birth! 06v8s0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 170.000 0.487 http://example.org/people/person/place_of_birth #16927-04w391 PRED entity: 04w391 PRED relation: award PRED expected values: 09sb52 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 257): 09sb52 (0.40 #9739, 0.32 #15395, 0.32 #15799), 01by1l (0.20 #8598, 0.10 #516, 0.08 #20718), 05b4l5x (0.17 #3239, 0.16 #4047, 0.16 #2026), 0ck27z (0.15 #15446, 0.14 #15850, 0.14 #9386), 01bgqh (0.14 #8529, 0.10 #447, 0.07 #4850), 03c7tr1 (0.14 #463, 0.14 #4100, 0.13 #3292), 05zr6wv (0.13 #421, 0.13 #32323, 0.13 #6079), 05ztrmj (0.13 #588, 0.13 #32323, 0.10 #2204), 0gqwc (0.13 #32323, 0.12 #7349, 0.12 #6945), 07cbcy (0.13 #32323, 0.12 #483, 0.08 #3716) >> Best rule #9739 for best value: >> intensional similarity = 3 >> extensional distance = 739 >> proper extension: 03mz9r; 04rsd2; 01qrbf; 03k48_; >> query: (?x3999, 09sb52) <- nationality(?x3999, ?x94), award_nominee(?x3999, ?x2352), participant(?x1338, ?x2352) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04w391 award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.401 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16926-017j69 PRED entity: 017j69 PRED relation: company! PRED expected values: 021q1c => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 41): 0dq_5 (0.72 #301, 0.71 #631, 0.70 #349), 0krdk (0.69 #338, 0.64 #620, 0.62 #290), 060c4 (0.56 #334, 0.55 #616, 0.53 #522), 05_wyz (0.47 #538, 0.40 #773, 0.39 #632), 0dq3c (0.46 #333, 0.45 #521, 0.44 #615), 01yc02 (0.41 #340, 0.38 #292, 0.30 #763), 09d6p2 (0.32 #539, 0.29 #633, 0.27 #774), 01kr6k (0.26 #359, 0.26 #547, 0.24 #641), 02211by (0.23 #287, 0.18 #523, 0.14 #758), 0142rn (0.20 #310, 0.13 #358, 0.13 #546) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 087c7; 045c7b; 04htfd; 077w0b; 07_dn; 02l48d; 07gyp7; >> query: (?x4410, 0dq_5) <- list(?x4410, ?x2197), contact_category(?x4410, ?x897), currency(?x4410, ?x170) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #247 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 01j_9c; 06pwq; 07w0v; 017d77; 07szy; 07vk2; 0j_sncb; 01r3y2; 03ksy; 01f1r4; ... *> query: (?x4410, 021q1c) <- major_field_of_study(?x4410, ?x10391), student(?x4410, ?x510), ?x10391 = 02jfc *> conf = 0.19 ranks of expected_values: 11 EVAL 017j69 company! 021q1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 87.000 87.000 0.725 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #16925-04306rv PRED entity: 04306rv PRED relation: service_language! PRED expected values: 018mxj => 62 concepts (56 used for prediction) PRED predicted values (max 10 best out of 138): 018mxj (0.60 #1212, 0.50 #675, 0.43 #1481), 05w3y (0.60 #1265, 0.50 #728, 0.40 #1801), 0cv9b (0.50 #543, 0.41 #2418, 0.36 #1882), 049mr (0.50 #761, 0.40 #1298, 0.40 #1164), 06q07 (0.50 #744, 0.40 #1281, 0.40 #1147), 02vk52z (0.50 #668, 0.40 #1205, 0.40 #1071), 06rfy5 (0.50 #799, 0.40 #1336, 0.40 #1202), 0lwkh (0.50 #782, 0.40 #1319, 0.40 #1185), 0z07 (0.40 #1305, 0.33 #234, 0.29 #1707), 045c7b (0.40 #1249, 0.33 #178, 0.27 #1918) >> Best rule #1212 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 06nm1; >> query: (?x732, 018mxj) <- language(?x10800, ?x732), language(?x308, ?x732), ?x308 = 011yxg, countries_spoken_in(?x732, ?x172), major_field_of_study(?x735, ?x732), languages(?x147, ?x732), nominated_for(?x91, ?x10800) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04306rv service_language! 018mxj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 56.000 0.600 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #16924-01qrb2 PRED entity: 01qrb2 PRED relation: major_field_of_study PRED expected values: 088tb => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 118): 01mkq (0.71 #261, 0.70 #505, 0.57 #1604), 02lp1 (0.63 #257, 0.58 #501, 0.51 #1600), 02j62 (0.53 #277, 0.51 #521, 0.46 #1620), 04rjg (0.47 #510, 0.47 #266, 0.41 #1609), 062z7 (0.46 #518, 0.45 #274, 0.36 #1617), 05qjt (0.45 #253, 0.44 #497, 0.37 #1596), 05qfh (0.43 #283, 0.42 #527, 0.34 #1626), 04x_3 (0.43 #272, 0.40 #516, 0.34 #1615), 03g3w (0.42 #517, 0.41 #273, 0.37 #2592), 01tbp (0.41 #307, 0.39 #551, 0.34 #1650) >> Best rule #261 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0cv_2; 02z_b; >> query: (?x9525, 01mkq) <- organization(?x346, ?x9525), organization(?x9525, ?x5487), category(?x9525, ?x134) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #10266 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 640 *> proper extension: 023p18; 01nmgc; 019q50; 0301dp; *> query: (?x9525, ?x742) <- institution(?x734, ?x9525), major_field_of_study(?x734, ?x742) *> conf = 0.05 ranks of expected_values: 58 EVAL 01qrb2 major_field_of_study 088tb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 134.000 134.000 0.706 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16923-0pd64 PRED entity: 0pd64 PRED relation: film_release_region PRED expected values: 03_3d 082fr => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 223): 03_3d (0.85 #494, 0.77 #3089, 0.76 #1467), 0chghy (0.84 #2607, 0.83 #3095, 0.80 #1473), 059j2 (0.84 #3120, 0.84 #2632, 0.80 #2470), 05r4w (0.84 #2597, 0.83 #3085, 0.81 #1787), 0k6nt (0.83 #1814, 0.80 #3112, 0.80 #2624), 07ssc (0.81 #1480, 0.79 #3102, 0.78 #2452), 035qy (0.79 #1501, 0.79 #2635, 0.75 #3123), 015fr (0.79 #2616, 0.76 #1482, 0.72 #3104), 016wzw (0.77 #561, 0.57 #73, 0.48 #1534), 0154j (0.75 #2599, 0.72 #1465, 0.69 #3087) >> Best rule #494 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0ds3t5x; 04hwbq; 0dtfn; 04jkpgv; 0661ql3; 03cw411; >> query: (?x7711, 03_3d) <- award(?x7711, ?x591), film_release_region(?x7711, ?x1003), nominated_for(?x8456, ?x7711), ?x1003 = 03gj2 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 46 EVAL 0pd64 film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 86.000 86.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0pd64 film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16922-03q45x PRED entity: 03q45x PRED relation: gender PRED expected values: 02zsn => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.90 #18, 0.50 #2, 0.50 #183), 05zppz (0.83 #37, 0.82 #87, 0.82 #35) >> Best rule #18 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 029b9k; 02c7lt; >> query: (?x7795, 02zsn) <- place_of_birth(?x7795, ?x8171), award(?x7795, ?x757), ?x757 = 09qj50 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q45x gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.897 http://example.org/people/person/gender #16921-06fpsx PRED entity: 06fpsx PRED relation: film_crew_role PRED expected values: 089g0h => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 22): 02r96rf (0.62 #962, 0.58 #825, 0.52 #381), 0dxtw (0.35 #969, 0.32 #832, 0.29 #388), 01vx2h (0.29 #970, 0.26 #833, 0.25 #11), 01pvkk (0.27 #971, 0.26 #834, 0.25 #114), 02ynfr (0.25 #15, 0.20 #49, 0.14 #974), 015h31 (0.25 #8, 0.20 #42, 0.07 #967), 089g0h (0.20 #53, 0.10 #121, 0.09 #978), 02rh1dz (0.20 #43, 0.09 #968, 0.08 #77), 02vs3x5 (0.20 #57, 0.07 #125, 0.05 #845), 0215hd (0.14 #120, 0.12 #977, 0.11 #396) >> Best rule #962 for best value: >> intensional similarity = 3 >> extensional distance = 1128 >> proper extension: 0k4d7; 03rz2b; 08gg47; 02w86hz; 0432_5; 02qyv3h; 012kyx; 0gtx63s; 09v42sf; 0jqzt; >> query: (?x7702, 02r96rf) <- film(?x91, ?x7702), language(?x7702, ?x254), film_crew_role(?x7702, ?x137) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #53 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 09rvwmy; *> query: (?x7702, 089g0h) <- film(?x11813, ?x7702), film(?x2602, ?x7702), ?x2602 = 072bb1, award(?x11813, ?x704) *> conf = 0.20 ranks of expected_values: 7 EVAL 06fpsx film_crew_role 089g0h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 68.000 68.000 0.616 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16920-03qlv7 PRED entity: 03qlv7 PRED relation: role PRED expected values: 0l14qv => 71 concepts (39 used for prediction) PRED predicted values (max 10 best out of 95): 01vj9c (0.91 #2659, 0.85 #3034, 0.85 #3313), 07m2y (0.89 #3021, 0.84 #359, 0.82 #1997), 042v_gx (0.84 #359, 0.82 #1997, 0.82 #2464), 0cfdd (0.84 #359, 0.82 #1997, 0.82 #2464), 0jtg0 (0.84 #359, 0.82 #1997, 0.82 #2464), 01vnt4 (0.84 #359, 0.82 #1997, 0.82 #1998), 02bxd (0.84 #359, 0.82 #1997, 0.82 #1998), 0l14qv (0.81 #2001, 0.78 #810, 0.71 #1814), 0mkg (0.81 #2562, 0.81 #2840, 0.71 #2189), 03bx0bm (0.80 #895, 0.67 #264, 0.61 #2828) >> Best rule #2659 for best value: >> intensional similarity = 19 >> extensional distance = 21 >> proper extension: 03q5t; 07brj; 0g2dz; 01dnws; 0l14j_; 02w3w; 05kms; >> query: (?x1332, 01vj9c) <- role(?x1332, ?x1147), role(?x1332, ?x227), role(?x9413, ?x1332), role(?x6039, ?x1332), role(?x716, ?x1332), role(?x75, ?x1332), role(?x5815, ?x1332), role(?x487, ?x1332), award_winner(?x487, ?x2698), award(?x487, ?x2212), ?x227 = 0342h, ?x716 = 018vs, ?x75 = 07y_7, group(?x1147, ?x6234), group(?x6039, ?x5838), profession(?x5815, ?x563), role(?x2205, ?x1147), role(?x74, ?x6039), instrumentalists(?x9413, ?x2945) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #2001 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 14 *> proper extension: 01vdm0; 0l15bq; *> query: (?x1332, 0l14qv) <- role(?x1332, ?x315), role(?x1332, ?x227), role(?x716, ?x1332), role(?x487, ?x1332), award_winner(?x487, ?x2698), award(?x487, ?x2212), ?x227 = 0342h, ?x716 = 018vs, people(?x3799, ?x487), role(?x487, ?x1472), ?x315 = 0l14md, role(?x228, ?x1472), category(?x487, ?x134), role(?x1472, ?x614), role(?x314, ?x1472), group(?x1472, ?x997) *> conf = 0.81 ranks of expected_values: 8 EVAL 03qlv7 role 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 71.000 39.000 0.913 http://example.org/music/performance_role/track_performances./music/track_contribution/role #16919-0641kkh PRED entity: 0641kkh PRED relation: award! PRED expected values: 05dbf 019pm_ 01d0fp => 52 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2459): 0794g (0.69 #57434, 0.68 #57433, 0.50 #912), 09l3p (0.67 #7970, 0.62 #11349, 0.25 #1216), 028knk (0.58 #7279, 0.54 #10658, 0.25 #525), 0478__m (0.52 #14840, 0.31 #18217, 0.13 #28355), 0kjrx (0.50 #2359, 0.46 #12492, 0.42 #9113), 05dbf (0.50 #7343, 0.46 #10722, 0.25 #589), 0f4vbz (0.50 #7340, 0.46 #10719, 0.25 #586), 0154qm (0.50 #7658, 0.46 #11037, 0.12 #21170), 01kb2j (0.50 #8246, 0.46 #11625, 0.12 #21758), 03mp9s (0.50 #8779, 0.46 #12158, 0.10 #22291) >> Best rule #57434 for best value: >> intensional similarity = 4 >> extensional distance = 190 >> proper extension: 05ztjjw; 02qt02v; >> query: (?x9770, ?x8445) <- nominated_for(?x9770, ?x339), award(?x324, ?x9770), award_winner(?x9770, ?x8445), award_nominee(?x221, ?x8445) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #7343 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 09qwmm; 094qd5; 099tbz; 0gqwc; 099cng; 0gqyl; 02x4x18; 027cyf7; *> query: (?x9770, 05dbf) <- award(?x5246, ?x9770), nominated_for(?x9770, ?x3820), ?x5246 = 046zh, film(?x8813, ?x3820) *> conf = 0.50 ranks of expected_values: 6, 19, 123 EVAL 0641kkh award! 01d0fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 52.000 18.000 0.689 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0641kkh award! 019pm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 52.000 18.000 0.689 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0641kkh award! 05dbf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 52.000 18.000 0.689 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16918-03s5t PRED entity: 03s5t PRED relation: adjoins! PRED expected values: 07srw => 191 concepts (96 used for prediction) PRED predicted values (max 10 best out of 526): 07srw (0.82 #35084, 0.81 #59260, 0.80 #21039), 07z1m (0.29 #1635, 0.25 #856, 0.12 #77), 07h34 (0.25 #965, 0.21 #1744, 0.18 #5637), 0vbk (0.19 #4125, 0.17 #1011, 0.16 #8802), 04ych (0.17 #7064, 0.16 #8623, 0.16 #3946), 0f8x_r (0.17 #1449, 0.14 #2228, 0.06 #4563), 0f8l9c (0.17 #2375, 0.10 #15623, 0.10 #3153), 05fjf (0.17 #2638, 0.10 #3416, 0.07 #1860), 05mph (0.16 #8858, 0.16 #4181, 0.15 #6520), 01n4w (0.15 #5598, 0.15 #4819, 0.13 #9497) >> Best rule #35084 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 0zqq; 01gpzx; >> query: (?x2768, ?x726) <- contains(?x8260, ?x2768), adjoins(?x2768, ?x726), first_level_division_of(?x2768, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s5t adjoins! 07srw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 96.000 0.819 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #16917-02frhbc PRED entity: 02frhbc PRED relation: month PRED expected values: 02xx5 => 214 concepts (214 used for prediction) PRED predicted values (max 10 best out of 1): 02xx5 (0.93 #3, 0.91 #14, 0.90 #10) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 02cl1; 01f62; 02h6_6p; 03h64; 03hrz; 0947l; >> query: (?x9605, 02xx5) <- location(?x476, ?x9605), month(?x9605, ?x1459), teams(?x9605, ?x9049) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02frhbc month 02xx5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 214.000 214.000 0.933 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #16916-034m8 PRED entity: 034m8 PRED relation: contains! PRED expected values: 07c5l => 102 concepts (99 used for prediction) PRED predicted values (max 10 best out of 118): 07c5l (0.75 #2189, 0.75 #395, 0.59 #25989), 02j71 (0.60 #79778, 0.59 #80677, 0.57 #67216), 04pnx (0.59 #25989, 0.50 #425, 0.31 #2219), 02qkt (0.58 #5722, 0.58 #18271, 0.57 #20959), 09c7w0 (0.41 #77988, 0.36 #61838, 0.30 #77092), 0dg3n1 (0.39 #4635, 0.37 #6428, 0.34 #11802), 02j9z (0.29 #22432, 0.29 #20640, 0.29 #17952), 04_1l0v (0.29 #38984, 0.28 #42568, 0.27 #35402), 0j0k (0.28 #23678, 0.27 #7547, 0.27 #20990), 05nrg (0.24 #3256, 0.21 #79779, 0.13 #24763) >> Best rule #2189 for best value: >> intensional similarity = 2 >> extensional distance = 14 >> proper extension: 01dtq1; >> query: (?x9459, 07c5l) <- contains(?x12315, ?x9459), ?x12315 = 06n3y >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034m8 contains! 07c5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 99.000 0.750 http://example.org/location/location/contains #16915-047byns PRED entity: 047byns PRED relation: category_of PRED expected values: 0gcf2r => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 3): 0gcf2r (0.90 #402, 0.88 #44, 0.86 #128), 0c4ys (0.56 #381, 0.38 #638, 0.38 #744), 0g_w (0.16 #298, 0.14 #362, 0.14 #383) >> Best rule #402 for best value: >> intensional similarity = 4 >> extensional distance = 154 >> proper extension: 03x3wf; 02gx2k; 01ck6v; 024dzn; 031b91; 02tj96; >> query: (?x882, ?x2758) <- ceremony(?x882, ?x4760), award_winner(?x882, ?x236), award_winner(?x4760, ?x829), instance_of_recurring_event(?x4760, ?x2758) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047byns category_of 0gcf2r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.897 http://example.org/award/award_category/category_of #16914-02ps55 PRED entity: 02ps55 PRED relation: category PRED expected values: 08mbj5d => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #34, 0.89 #73, 0.89 #75) >> Best rule #34 for best value: >> intensional similarity = 6 >> extensional distance = 134 >> proper extension: 0xxc; >> query: (?x11831, 08mbj5d) <- school_type(?x11831, ?x3092), currency(?x11831, ?x1099), institution(?x1368, ?x11831), institution(?x865, ?x11831), ?x865 = 02h4rq6, ?x1368 = 014mlp >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ps55 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 178.000 0.897 http://example.org/common/topic/webpage./common/webpage/category #16913-02pxmgz PRED entity: 02pxmgz PRED relation: genre PRED expected values: 06n90 => 114 concepts (102 used for prediction) PRED predicted values (max 10 best out of 156): 07s9rl0 (0.66 #3339, 0.65 #3816, 0.65 #4054), 05p553 (0.61 #719, 0.58 #124, 0.57 #838), 02kdv5l (0.50 #3, 0.49 #3580, 0.48 #6564), 02l7c8 (0.44 #373, 0.38 #9928, 0.38 #2282), 0lsxr (0.40 #8249, 0.33 #6570, 0.30 #2037), 03k9fj (0.38 #607, 0.37 #9326, 0.33 #12), 01hmnh (0.33 #18, 0.25 #3595, 0.25 #3952), 06n90 (0.32 #8133, 0.28 #608, 0.25 #3947), 02n4kr (0.30 #246, 0.27 #8128, 0.24 #2036), 02b5_l (0.25 #285, 0.10 #1119, 0.04 #5773) >> Best rule #3339 for best value: >> intensional similarity = 5 >> extensional distance = 195 >> proper extension: 025x1t; >> query: (?x1246, 07s9rl0) <- nominated_for(?x3873, ?x1246), film(?x3873, ?x1108), award(?x3873, ?x1198), award_nominee(?x382, ?x3873), ?x1198 = 02pqp12 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #8133 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 663 *> proper extension: 06n90; *> query: (?x1246, 06n90) <- genre(?x1246, ?x812), genre(?x11313, ?x812), genre(?x1108, ?x812), ?x11313 = 0by17xn, ?x1108 = 0jjy0 *> conf = 0.32 ranks of expected_values: 8 EVAL 02pxmgz genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 114.000 102.000 0.660 http://example.org/film/film/genre #16912-0ph24 PRED entity: 0ph24 PRED relation: nominated_for! PRED expected values: 0gkvb7 => 100 concepts (98 used for prediction) PRED predicted values (max 10 best out of 197): 0gq9h (0.39 #13688, 0.35 #14406, 0.33 #15362), 027gs1_ (0.36 #1144, 0.29 #1622, 0.27 #1861), 09qs08 (0.36 #1066, 0.29 #1544, 0.27 #1783), 0gs9p (0.35 #13690, 0.31 #14408, 0.30 #15364), 019f4v (0.34 #13679, 0.30 #14397, 0.28 #15353), 0fbtbt (0.33 #4943, 0.33 #6377, 0.32 #2552), 0k611 (0.29 #13699, 0.26 #14417, 0.24 #15373), 040njc (0.28 #13632, 0.25 #14350, 0.24 #15306), 0gkr9q (0.28 #4035, 0.27 #4992, 0.24 #3557), 0ck27z (0.27 #4854, 0.27 #3419, 0.22 #5571) >> Best rule #13688 for best value: >> intensional similarity = 3 >> extensional distance = 461 >> proper extension: 019kyn; 06mmr; >> query: (?x11726, 0gq9h) <- award_winner(?x11726, ?x4065), award(?x11726, ?x4386), honored_for(?x9450, ?x11726) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #980 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 072kp; 0124k9; 0584r4; 02hct1; 01j7mr; 0304nh; 05_z42; 039cq4; 04xbq3; *> query: (?x11726, 0gkvb7) <- program(?x4566, ?x11726), program(?x4065, ?x11726), nominated_for(?x882, ?x11726), program(?x2062, ?x11726) *> conf = 0.27 ranks of expected_values: 12 EVAL 0ph24 nominated_for! 0gkvb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 100.000 98.000 0.393 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16911-02qwg PRED entity: 02qwg PRED relation: artists! PRED expected values: 06cqb => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 260): 064t9 (0.64 #1246, 0.63 #1863, 0.59 #9880), 017_qw (0.55 #4688, 0.51 #10236, 0.39 #7155), 03lty (0.50 #10510, 0.20 #644, 0.16 #336), 0glt670 (0.47 #1272, 0.46 #1889, 0.44 #2814), 03_d0 (0.41 #12, 0.34 #320, 0.31 #628), 025sc50 (0.40 #1281, 0.39 #1898, 0.37 #2823), 02lnbg (0.40 #1290, 0.33 #1907, 0.30 #2832), 02x8m (0.36 #19, 0.16 #20968, 0.14 #4336), 01fh36 (0.34 #394, 0.23 #86, 0.16 #20968), 06j6l (0.33 #1896, 0.33 #9913, 0.32 #47) >> Best rule #1246 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 01q7cb_; 0285c; 01vv126; 03xl77; 02r3cn; 02jyhv; >> query: (?x3403, 064t9) <- participant(?x3403, ?x4608), artist(?x2931, ?x3403), participant(?x3403, ?x3321) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 02mslq; 0134tg; 0178_w; 07r1_; 0mjn2; *> query: (?x3403, 06cqb) <- award(?x3403, ?x4488), origin(?x3403, ?x362), ?x4488 = 02gdjb *> conf = 0.05 ranks of expected_values: 104 EVAL 02qwg artists! 06cqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 112.000 112.000 0.644 http://example.org/music/genre/artists #16910-03bdm4 PRED entity: 03bdm4 PRED relation: place_of_death PRED expected values: 0l1pj => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 51): 0f2wj (0.26 #594, 0.17 #206, 0.13 #1177), 030qb3t (0.22 #993, 0.22 #1967, 0.21 #1577), 0k049 (0.16 #585, 0.13 #1363, 0.13 #1558), 02_286 (0.13 #984, 0.12 #790, 0.09 #2929), 0k_p5 (0.11 #88, 0.11 #670, 0.06 #865), 05qtj (0.11 #64, 0.06 #841, 0.02 #2204), 0r3tq (0.11 #149, 0.03 #926, 0.02 #1120), 06_kh (0.11 #393, 0.05 #1950, 0.05 #587), 04jpl (0.08 #1367, 0.06 #5248, 0.04 #1172), 0r3tb (0.08 #310, 0.01 #1867) >> Best rule #594 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 09xvf7; >> query: (?x9710, 0f2wj) <- profession(?x9710, ?x1032), profession(?x9710, ?x524), ?x524 = 02jknp, ?x1032 = 02hrh1q, place_of_burial(?x9710, ?x3691) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #1863 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 71 *> proper extension: 016hvl; 01gzm2; 015rhv; 02bxjp; *> query: (?x9710, 0l1pj) <- profession(?x9710, ?x524), people(?x1158, ?x9710), ?x524 = 02jknp, award_winner(?x3066, ?x9710) *> conf = 0.01 ranks of expected_values: 39 EVAL 03bdm4 place_of_death 0l1pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 108.000 108.000 0.263 http://example.org/people/deceased_person/place_of_death #16909-0cpvcd PRED entity: 0cpvcd PRED relation: influenced_by PRED expected values: 04xm_ => 159 concepts (82 used for prediction) PRED predicted values (max 10 best out of 365): 05qmj (0.50 #4095, 0.47 #2360, 0.47 #4529), 043s3 (0.40 #1415, 0.32 #3584, 0.28 #4452), 032l1 (0.38 #523, 0.31 #956, 0.29 #3124), 06myp (0.38 #807, 0.23 #1240, 0.21 #3408), 0j3v (0.33 #3095, 0.31 #494, 0.30 #61), 015n8 (0.33 #4311, 0.28 #3877, 0.28 #4745), 081k8 (0.32 #5360, 0.25 #4927, 0.23 #6230), 048cl (0.31 #1100, 0.27 #1533, 0.22 #4570), 026lj (0.30 #45, 0.23 #3947, 0.22 #4381), 039n1 (0.27 #4227, 0.23 #1191, 0.22 #4661) >> Best rule #4095 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 04411; 045bg; 03j43; 0j3v; 0dzkq; 052h3; 0gz_; 099bk; 0372p; 01dvtx; ... >> query: (?x11500, 05qmj) <- influenced_by(?x11500, ?x3712), influenced_by(?x3711, ?x11500), interests(?x11500, ?x713), nationality(?x11500, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1641 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 07h1q; *> query: (?x11500, 04xm_) <- influenced_by(?x11500, ?x3712), influenced_by(?x3711, ?x11500), interests(?x11500, ?x713), place_of_birth(?x11500, ?x12817) *> conf = 0.13 ranks of expected_values: 48 EVAL 0cpvcd influenced_by 04xm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 159.000 82.000 0.500 http://example.org/influence/influence_node/influenced_by #16908-02lx0 PRED entity: 02lx0 PRED relation: contains! PRED expected values: 073q1 => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 83): 02j71 (0.60 #33979, 0.59 #34876, 0.56 #33082), 0dg3n1 (0.32 #1941, 0.28 #6410, 0.27 #5516), 09c7w0 (0.30 #39353, 0.30 #31297, 0.26 #14298), 04wsz (0.22 #497, 0.21 #33980, 0.09 #5858), 07c5l (0.22 #10222, 0.21 #33980, 0.20 #9329), 02j9z (0.21 #33980, 0.21 #16111, 0.20 #1814), 04pnx (0.21 #33980, 0.11 #3103, 0.10 #3997), 06n3y (0.21 #33980, 0.09 #3403, 0.08 #10552), 073q1 (0.21 #33980, 0.08 #408, 0.06 #1301), 059g4 (0.21 #33980, 0.06 #10290, 0.06 #4035) >> Best rule #33979 for best value: >> intensional similarity = 3 >> extensional distance = 586 >> proper extension: 0crjn65; 016v46; 0l9rg; 0h095; 03pbf; 0gqkd; 05314s; 01d8wq; 06hdk; 0174qm; ... >> query: (?x3656, ?x551) <- administrative_parent(?x3656, ?x551), administrative_parent(?x9816, ?x551), contains(?x7273, ?x9816) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #33980 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 586 *> proper extension: 0crjn65; 016v46; 0l9rg; 0h095; 03pbf; 0gqkd; 05314s; 01d8wq; 06hdk; 0174qm; ... *> query: (?x3656, ?x7273) <- administrative_parent(?x3656, ?x551), administrative_parent(?x9816, ?x551), contains(?x7273, ?x9816) *> conf = 0.21 ranks of expected_values: 9 EVAL 02lx0 contains! 073q1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 46.000 46.000 0.602 http://example.org/location/location/contains #16907-0286hyp PRED entity: 0286hyp PRED relation: film! PRED expected values: 015vql => 63 concepts (36 used for prediction) PRED predicted values (max 10 best out of 648): 03ctv8m (0.45 #66673, 0.43 #41668, 0.43 #50002), 07hhnl (0.45 #66673, 0.43 #50002, 0.42 #68757), 0j_c (0.12 #2494, 0.04 #10826, 0.04 #411), 0gn30 (0.09 #5116, 0.09 #9282, 0.08 #950), 02lkcc (0.06 #243, 0.04 #2326, 0.03 #4409), 016ggh (0.06 #33334, 0.06 #8121, 0.02 #31036), 0bmh4 (0.06 #33334, 0.05 #33333), 031y07 (0.06 #33334, 0.01 #7268), 0h0jz (0.06 #33334, 0.01 #8371), 0579tg2 (0.06 #33334) >> Best rule #66673 for best value: >> intensional similarity = 4 >> extensional distance = 1192 >> proper extension: 01fs__; 0d7vtk; >> query: (?x14075, ?x5692) <- nominated_for(?x484, ?x14075), nominated_for(?x5692, ?x14075), language(?x14075, ?x254), profession(?x5692, ?x2265) >> conf = 0.45 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0286hyp film! 015vql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 36.000 0.451 http://example.org/film/actor/film./film/performance/film #16906-05mv4 PRED entity: 05mv4 PRED relation: student PRED expected values: 085pr 04zkj5 => 123 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1584): 03fykz (0.12 #2848, 0.03 #13308, 0.02 #30044), 06l6nj (0.12 #3933, 0.02 #31129, 0.02 #8117), 02jr26 (0.12 #3308, 0.02 #43056, 0.02 #7492), 01h2_6 (0.12 #4118, 0.02 #8302, 0.01 #31314), 0d3k14 (0.06 #3946, 0.06 #8130, 0.04 #14406), 06hx2 (0.06 #3162, 0.04 #13622, 0.04 #7346), 0194xc (0.06 #3734, 0.04 #14194, 0.04 #7918), 09v6tz (0.06 #3434, 0.04 #5526, 0.03 #13894), 0k9j_ (0.06 #3648, 0.04 #5740, 0.02 #30844), 02cqbx (0.06 #3075, 0.04 #5167, 0.02 #30271) >> Best rule #2848 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 02482c; >> query: (?x4187, 03fykz) <- major_field_of_study(?x4187, ?x2172), ?x2172 = 01jzxy, contains(?x94, ?x4187) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #4742 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 06pwq; 01w3v; 01w5m; 09f2j; 0b1xl; 01nnsv; 0ks67; 0c5x_; *> query: (?x4187, 085pr) <- major_field_of_study(?x4187, ?x1695), student(?x4187, ?x201), institution(?x865, ?x4187), ?x1695 = 06ms6 *> conf = 0.02 ranks of expected_values: 529 EVAL 05mv4 student 04zkj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 65.000 0.118 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 05mv4 student 085pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 123.000 65.000 0.118 http://example.org/education/educational_institution/students_graduates./education/education/student #16905-0hwqz PRED entity: 0hwqz PRED relation: award PRED expected values: 09td7p => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 288): 09sb52 (0.36 #35418, 0.34 #38232, 0.33 #26976), 03c7tr1 (0.23 #460, 0.19 #2068, 0.17 #1666), 05p09zm (0.19 #1730, 0.17 #2936, 0.16 #4946), 05zr6wv (0.18 #1625, 0.18 #8459, 0.17 #8057), 05b4l5x (0.18 #408, 0.16 #2016, 0.15 #1614), 0gqyl (0.17 #1711, 0.16 #4123, 0.15 #7339), 0f4x7 (0.17 #31, 0.15 #8473, 0.14 #4051), 0hnf5vm (0.17 #186, 0.15 #40605, 0.14 #47040), 07cbcy (0.17 #77, 0.10 #4901, 0.09 #8519), 04ljl_l (0.17 #3, 0.10 #8445, 0.10 #8043) >> Best rule #35418 for best value: >> intensional similarity = 3 >> extensional distance = 1049 >> proper extension: 02zq43; 07lmxq; 0f830f; 08w7vj; 01v3s2_; 0bz5v2; 049k07; 02k6rq; 07hbxm; 06mmb; ... >> query: (?x5884, 09sb52) <- film(?x5884, ?x7415), award_nominee(?x5884, ?x2387), nominated_for(?x5884, ?x582) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #40605 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1273 *> proper extension: 0f721s; 0283xx2; 099ks0; 03lpbx; 02rf51g; *> query: (?x5884, ?x640) <- award_winner(?x2880, ?x5884), award_winner(?x7444, ?x5884), nominated_for(?x640, ?x7444) *> conf = 0.15 ranks of expected_values: 18 EVAL 0hwqz award 09td7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 160.000 160.000 0.360 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16904-02wmy PRED entity: 02wmy PRED relation: country PRED expected values: 02wmy => 92 concepts (64 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.43 #1573, 0.34 #1753, 0.22 #2617), 06n3y (0.15 #3549, 0.14 #2861, 0.14 #2859), 0j3b (0.15 #3549, 0.14 #2861, 0.14 #2859), 07c5l (0.14 #2861, 0.14 #2859, 0.14 #2858), 07ssc (0.13 #3436, 0.13 #3501, 0.11 #3566), 0d05w3 (0.10 #154, 0.05 #938, 0.05 #3087), 01nqj (0.10 #176, 0.01 #1629), 0chghy (0.07 #1581, 0.05 #1761, 0.02 #3000), 0k6nt (0.07 #257, 0.04 #438, 0.03 #739), 049nq (0.07 #293, 0.03 #834, 0.02 #1261) >> Best rule #1573 for best value: >> intensional similarity = 2 >> extensional distance = 80 >> proper extension: 05fly; 01gh6z; 018jmn; >> query: (?x9625, 09c7w0) <- jurisdiction_of_office(?x900, ?x9625), ?x900 = 0fkvn >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02wmy country 02wmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 64.000 0.427 http://example.org/location/administrative_division/country #16903-024dw0 PRED entity: 024dw0 PRED relation: profession PRED expected values: 09jwl => 134 concepts (98 used for prediction) PRED predicted values (max 10 best out of 75): 09jwl (0.85 #9504, 0.84 #2837, 0.83 #7577), 02hrh1q (0.82 #5648, 0.81 #4165, 0.80 #5796), 0nbcg (0.61 #2700, 0.58 #2848, 0.58 #3885), 0dz3r (0.58 #2819, 0.55 #7559, 0.55 #9486), 016z4k (0.50 #9044, 0.50 #2375, 0.50 #2227), 039v1 (0.50 #3445, 0.45 #9520, 0.44 #2705), 0d1pc (0.38 #1532, 0.34 #5337, 0.33 #1976), 01d_h8 (0.36 #5639, 0.35 #5343, 0.34 #5337), 0fnpj (0.34 #5100, 0.33 #208, 0.29 #6137), 012t_z (0.33 #1050, 0.33 #161, 0.22 #1939) >> Best rule #9504 for best value: >> intensional similarity = 5 >> extensional distance = 130 >> proper extension: 06cc_1; 0qf3p; >> query: (?x7506, 09jwl) <- role(?x7506, ?x2675), role(?x7506, ?x1166), family(?x2675, ?x2620), role(?x74, ?x1166), group(?x7506, ?x9196) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024dw0 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 98.000 0.848 http://example.org/people/person/profession #16902-02pby8 PRED entity: 02pby8 PRED relation: award PRED expected values: 04ljl_l => 101 concepts (83 used for prediction) PRED predicted values (max 10 best out of 235): 09sb52 (0.39 #15026, 0.37 #17456, 0.36 #446), 0ck27z (0.32 #498, 0.18 #19129, 0.15 #18318), 05zr6wv (0.29 #422, 0.15 #4472, 0.14 #4877), 07cbcy (0.25 #79, 0.10 #1294, 0.08 #1699), 05pcn59 (0.23 #4537, 0.23 #5347, 0.22 #4942), 02x73k6 (0.18 #466, 0.08 #18631, 0.07 #27544), 0cjyzs (0.16 #9017, 0.05 #19143, 0.05 #23600), 0fbtbt (0.16 #9143, 0.05 #23726, 0.04 #26156), 05b4l5x (0.15 #816, 0.14 #1221, 0.13 #28761), 0gqwc (0.15 #1290, 0.14 #885, 0.12 #6150) >> Best rule #15026 for best value: >> intensional similarity = 3 >> extensional distance = 849 >> proper extension: 0ggl02; 04bpm6; 01hw6wq; 017j6; 01vrkdt; 02bwc7; 0qf11; 02l0sf; 03j3pg9; >> query: (?x8038, 09sb52) <- award_nominee(?x8038, ?x286), award_nominee(?x286, ?x426), participant(?x286, ?x1735) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #28761 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1985 *> proper extension: 01nqfh_; 02qggqc; 0dky9n; 0f3zf_; 03y1mlp; 0p5mw; 0b82vw; 04zwjd; 04g865; 0b79gfg; ... *> query: (?x8038, ?x102) <- nationality(?x8038, ?x94), nominated_for(?x8038, ?x4331), nominated_for(?x102, ?x4331) *> conf = 0.13 ranks of expected_values: 36 EVAL 02pby8 award 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 101.000 83.000 0.394 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16901-0gqyl PRED entity: 0gqyl PRED relation: award_winner PRED expected values: 039x1k => 58 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1956): 09l3p (0.36 #2446, 0.35 #58697, 0.30 #31792), 0h0wc (0.36 #2446, 0.35 #58697, 0.30 #31792), 01x4sb (0.36 #2446, 0.35 #58697, 0.30 #31792), 01g23m (0.36 #2446, 0.35 #58697, 0.30 #31792), 0h1mt (0.36 #2446, 0.35 #58697, 0.30 #31792), 01vtj38 (0.36 #2446, 0.35 #58697, 0.30 #31792), 0kjrx (0.36 #2446, 0.35 #58697, 0.30 #31792), 02x7vq (0.36 #2446, 0.35 #58697, 0.30 #31792), 0dvld (0.36 #2446, 0.35 #58697, 0.30 #31792), 01hkhq (0.36 #2446, 0.35 #58697, 0.30 #31792) >> Best rule #2446 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0p9sw; 0l8z1; 0gr0m; 0gq9h; 0k611; >> query: (?x1972, ?x91) <- award(?x144, ?x1972), nominated_for(?x1972, ?x4179), award(?x91, ?x1972), ?x4179 = 0pd57 >> conf = 0.36 => this is the best rule for 92 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 37 EVAL 0gqyl award_winner 039x1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 58.000 26.000 0.360 http://example.org/award/award_category/winners./award/award_honor/award_winner #16900-02cgp8 PRED entity: 02cgp8 PRED relation: partially_contains! PRED expected values: 07h34 => 63 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1069): 0498y (0.31 #453, 0.27 #820, 0.17 #505), 07h34 (0.31 #453, 0.27 #820, 0.17 #228), 05fjf (0.31 #453, 0.27 #820, 0.08 #242), 059rby (0.31 #453, 0.27 #820, 0.08 #187), 02_286 (0.31 #453, 0.27 #820, 0.08 #192), 05kkh (0.31 #453, 0.27 #820, 0.06 #365), 0jhd (0.31 #453, 0.27 #820, 0.01 #727), 026mj (0.31 #453, 0.27 #820, 0.01 #727), 0jgx (0.31 #453, 0.27 #820, 0.01 #727), 0rh6k (0.31 #453, 0.27 #820, 0.01 #727) >> Best rule #453 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01smm; 0fcgd; >> query: (?x10710, ?x1499) <- partially_contains(?x2000, ?x10710), adjoins(?x1499, ?x2000), contains(?x455, ?x2000), contains(?x94, ?x10710) >> conf = 0.31 => this is the best rule for 14 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02cgp8 partially_contains! 07h34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 24.000 0.310 http://example.org/location/location/partially_contains #16899-045j3w PRED entity: 045j3w PRED relation: produced_by PRED expected values: 072vj => 93 concepts (51 used for prediction) PRED predicted values (max 10 best out of 152): 09zw90 (0.17 #360, 0.11 #748, 0.07 #4630), 02lymt (0.17 #174, 0.11 #562, 0.03 #9483), 0j_c (0.17 #80, 0.11 #468, 0.03 #4350), 04hw4b (0.17 #245, 0.11 #633, 0.02 #9554), 02bfxb (0.15 #1669, 0.12 #2445, 0.05 #7871), 0js9s (0.15 #1783, 0.12 #2559, 0.05 #7985), 0272kv (0.12 #2258, 0.07 #5361, 0.05 #9625), 0693l (0.12 #2049, 0.06 #5927, 0.05 #8251), 06chf (0.12 #2041, 0.03 #9408, 0.03 #5144), 01t6b4 (0.12 #2374, 0.08 #1598, 0.05 #6638) >> Best rule #360 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 04y9mm8; >> query: (?x3000, 09zw90) <- genre(?x3000, ?x812), genre(?x3000, ?x571), ?x571 = 03npn, country(?x3000, ?x94), film(?x2681, ?x3000), film(?x541, ?x3000), film_release_region(?x3000, ?x87), ?x812 = 01jfsb >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #5039 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 28 *> proper extension: 0ds11z; 06g77c; 03_wm6; 012kyx; 02nx2k; 06_sc3; 0gy30w; *> query: (?x3000, 072vj) <- genre(?x3000, ?x571), ?x571 = 03npn, category(?x3000, ?x134), country(?x3000, ?x252), film_release_region(?x66, ?x252), organization(?x252, ?x127) *> conf = 0.03 ranks of expected_values: 76 EVAL 045j3w produced_by 072vj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 93.000 51.000 0.167 http://example.org/film/film/produced_by #16898-016k6x PRED entity: 016k6x PRED relation: film PRED expected values: 0g9z_32 => 85 concepts (15 used for prediction) PRED predicted values (max 10 best out of 983): 0209hj (0.60 #23140, 0.42 #26701, 0.39 #26700), 02y_lrp (0.60 #23140, 0.32 #24920, 0.04 #14), 07w8fz (0.11 #511, 0.10 #2290, 0.08 #4069), 09xbpt (0.10 #1826, 0.08 #3605, 0.07 #47), 06z8s_ (0.10 #1908, 0.08 #3687, 0.07 #129), 07kdkfj (0.08 #4892, 0.07 #1334, 0.07 #3113), 0gg5qcw (0.08 #4428, 0.07 #870, 0.03 #2649), 0cz_ym (0.08 #3851, 0.07 #2072, 0.04 #293), 02704ff (0.08 #4534, 0.07 #2755, 0.04 #976), 0cd2vh9 (0.08 #3808, 0.04 #250, 0.03 #2029) >> Best rule #23140 for best value: >> intensional similarity = 3 >> extensional distance = 931 >> proper extension: 04smkr; 038g2x; 0dh73w; 03h3vtz; >> query: (?x4969, ?x146) <- nominated_for(?x4969, ?x146), film(?x4969, ?x363), award_winner(?x594, ?x4969) >> conf = 0.60 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 016k6x film 0g9z_32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 15.000 0.595 http://example.org/film/actor/film./film/performance/film #16897-01nczg PRED entity: 01nczg PRED relation: actor! PRED expected values: 0fpxp => 121 concepts (59 used for prediction) PRED predicted values (max 10 best out of 138): 039cq4 (0.11 #657, 0.05 #2506, 0.05 #3299), 01j7mr (0.08 #583, 0.03 #2432, 0.02 #1904), 0d68qy (0.06 #565, 0.03 #1886, 0.03 #3207), 03y3bp7 (0.06 #572, 0.03 #1893, 0.02 #2421), 026bfsh (0.04 #4588, 0.04 #11460, 0.03 #1681), 0180mw (0.04 #1969, 0.03 #5403, 0.03 #2497), 0jwl2 (0.04 #601, 0.03 #3243, 0.02 #11436), 08jgk1 (0.04 #550, 0.03 #1871, 0.03 #21), 02hct1 (0.04 #563, 0.03 #1884, 0.03 #34), 0fpxp (0.04 #677, 0.03 #1998, 0.02 #2526) >> Best rule #657 for best value: >> intensional similarity = 5 >> extensional distance = 51 >> proper extension: 02j8nx; >> query: (?x1594, 039cq4) <- profession(?x1594, ?x1146), profession(?x1594, ?x987), actor(?x416, ?x1594), ?x987 = 0dxtg, ?x1146 = 018gz8 >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #677 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 51 *> proper extension: 02j8nx; *> query: (?x1594, 0fpxp) <- profession(?x1594, ?x1146), profession(?x1594, ?x987), actor(?x416, ?x1594), ?x987 = 0dxtg, ?x1146 = 018gz8 *> conf = 0.04 ranks of expected_values: 10 EVAL 01nczg actor! 0fpxp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 121.000 59.000 0.113 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #16896-04gcyg PRED entity: 04gcyg PRED relation: film_release_region PRED expected values: 09c7w0 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 133): 09c7w0 (0.73 #1079, 0.70 #2871, 0.69 #1795), 0f8l9c (0.27 #32, 0.24 #1824, 0.22 #9003), 02vzc (0.27 #70, 0.24 #1862, 0.20 #9041), 03_3d (0.27 #10, 0.23 #1802, 0.20 #1265), 03h64 (0.27 #88, 0.23 #1880, 0.18 #9059), 03gj2 (0.27 #37, 0.20 #1471, 0.19 #1829), 07ssc (0.25 #1816, 0.21 #562, 0.21 #1458), 0d0vqn (0.23 #1804, 0.22 #2880, 0.22 #8983), 03rjj (0.23 #1800, 0.20 #2876, 0.19 #8979), 0chghy (0.23 #1809, 0.19 #2885, 0.19 #555) >> Best rule #1079 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 04lqvlr; >> query: (?x7947, 09c7w0) <- nominated_for(?x3458, ?x7947), genre(?x7947, ?x53), currency(?x7947, ?x170), ?x3458 = 0gqxm >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gcyg film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.727 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16895-01gf5h PRED entity: 01gf5h PRED relation: nominated_for PRED expected values: 03r0g9 => 128 concepts (56 used for prediction) PRED predicted values (max 10 best out of 304): 05lfwd (0.21 #17136, 0.20 #20381, 0.08 #7403), 04vr_f (0.17 #6649, 0.14 #16382, 0.12 #19627), 0ywrc (0.12 #476, 0.05 #15077, 0.03 #26434), 02cbhg (0.12 #1254, 0.05 #15855, 0.03 #12611), 02rx2m5 (0.12 #271, 0.05 #8383, 0.03 #11628), 02pxst (0.12 #1115, 0.02 #15716, 0.02 #27073), 0170th (0.10 #3655, 0.05 #8523, 0.03 #13390), 08gsvw (0.10 #3354, 0.02 #24445, 0.02 #32558), 0b1y_2 (0.09 #16664, 0.08 #6931, 0.08 #19909), 08fn5b (0.08 #7126, 0.07 #16859, 0.06 #20104) >> Best rule #17136 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0g5lhl7; >> query: (?x1001, 05lfwd) <- award_winner(?x5592, ?x1001), ?x5592 = 0275n3y, award_nominee(?x7027, ?x1001) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01gf5h nominated_for 03r0g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 56.000 0.209 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16894-0mws3 PRED entity: 0mws3 PRED relation: currency PRED expected values: 09nqf => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.87 #27, 0.87 #26, 0.86 #30) >> Best rule #27 for best value: >> intensional similarity = 5 >> extensional distance = 123 >> proper extension: 0d6lp; 0jgk3; 0fc_9; 0l2q3; 0mmpz; 0l3kx; 0m24v; 0mx5p; 0nzw2; 0ms6_; ... >> query: (?x9539, ?x170) <- contains(?x9539, ?x3650), adjoins(?x9539, ?x6478), adjoins(?x6478, ?x6490), second_level_divisions(?x94, ?x9539), currency(?x6478, ?x170) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mws3 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.872 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #16893-08q1tg PRED entity: 08q1tg PRED relation: people PRED expected values: 0lcx 024y6w => 22 concepts (8 used for prediction) PRED predicted values (max 10 best out of 754): 07z4p5 (0.60 #2058), 099p5 (0.50 #1808, 0.25 #3180, 0.25 #2494), 0h1_w (0.50 #1382, 0.25 #2754, 0.25 #2068), 01m42d0 (0.33 #1039, 0.25 #3096, 0.25 #2410), 024qwq (0.33 #439, 0.25 #3183, 0.25 #2497), 0hgqq (0.33 #183, 0.17 #687, 0.12 #2927), 053yx (0.33 #98, 0.12 #2842, 0.11 #4213), 040z9 (0.33 #1009, 0.12 #3066, 0.07 #4437), 045g4l (0.33 #519, 0.12 #3263, 0.07 #4634), 09qh1 (0.33 #123, 0.12 #2867, 0.07 #4238) >> Best rule #2058 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 0qcr0; 0m32h; >> query: (?x14040, ?x10040) <- people(?x14040, ?x6809), cinematography(?x2612, ?x6809), nationality(?x6809, ?x94), genre(?x2612, ?x53), written_by(?x2612, ?x4477), honored_for(?x8964, ?x2612), award(?x2612, ?x484), cinematography(?x2612, ?x10040) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08q1tg people 024y6w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 22.000 8.000 0.600 http://example.org/people/cause_of_death/people EVAL 08q1tg people 0lcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 22.000 8.000 0.600 http://example.org/people/cause_of_death/people #16892-0lbbj PRED entity: 0lbbj PRED relation: olympics! PRED expected values: 0345h => 64 concepts (60 used for prediction) PRED predicted values (max 10 best out of 274): 087vz (0.67 #1470, 0.60 #1075, 0.57 #2259), 0f8l9c (0.64 #7710, 0.64 #3972, 0.63 #6129), 03rjj (0.64 #3757, 0.62 #5526, 0.60 #3556), 059j2 (0.62 #5551, 0.60 #3581, 0.60 #1017), 0d0vqn (0.60 #3559, 0.60 #995, 0.60 #982), 0chghy (0.60 #985, 0.60 #798, 0.56 #6304), 05qhw (0.60 #3567, 0.51 #3750, 0.50 #5537), 0h7x (0.60 #824, 0.50 #3588, 0.40 #1024), 0b90_r (0.56 #6304, 0.51 #3750, 0.50 #3554), 02vzc (0.56 #6304, 0.51 #3750, 0.44 #1578) >> Best rule #1470 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 0blg2; >> query: (?x2369, 087vz) <- sports(?x2369, ?x1967), sports(?x2369, ?x359), olympics(?x3730, ?x2369), olympics(?x1497, ?x2369), olympics(?x1241, ?x2369), olympics(?x390, ?x2369), sports(?x2369, ?x766), ?x390 = 0chghy, olympics(?x172, ?x2369), locations(?x2369, ?x9559), ?x3730 = 03shp, medal(?x2369, ?x422), ?x1967 = 01cgz, ?x359 = 02bkg, olympics(?x1497, ?x7688), country(?x668, ?x1241), ?x7688 = 0jkvj >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #9890 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 40 *> proper extension: 018ctl; *> query: (?x2369, 0345h) <- sports(?x2369, ?x1121), country(?x1121, ?x10450), country(?x1121, ?x8958), country(?x1121, ?x8948), country(?x1121, ?x1174), country(?x1121, ?x789), ?x8958 = 01ppq, ?x789 = 0f8l9c, administrative_area_type(?x8948, ?x2792), nationality(?x7109, ?x10450), adjoins(?x1144, ?x10450), contains(?x6304, ?x1174), film_release_region(?x5092, ?x1174), olympics(?x252, ?x2369), ?x5092 = 0gg5qcw, contains(?x10450, ?x14378) *> conf = 0.50 ranks of expected_values: 21 EVAL 0lbbj olympics! 0345h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 64.000 60.000 0.667 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #16891-0bxtyq PRED entity: 0bxtyq PRED relation: category PRED expected values: 08mbj5d => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #20, 0.78 #22, 0.77 #21) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 492 >> proper extension: 01pbxb; 01vvydl; 0lbj1; 01vrx3g; 04rcr; 0146pg; 02r3zy; 01wbgdv; 07c0j; 01k5t_3; ... >> query: (?x10079, 08mbj5d) <- award_nominee(?x10079, ?x3145), artists(?x4910, ?x10079), gender(?x3145, ?x231) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bxtyq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.775 http://example.org/common/topic/webpage./common/webpage/category #16890-016yvw PRED entity: 016yvw PRED relation: award PRED expected values: 02x73k6 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 271): 02w9sd7 (0.72 #23503, 0.71 #21908, 0.71 #15534), 09qv_s (0.72 #23503, 0.71 #21908, 0.71 #15534), 099ck7 (0.72 #23503, 0.71 #21908, 0.71 #15534), 027b9j5 (0.72 #23503, 0.71 #21908, 0.71 #15534), 0gqy2 (0.45 #159, 0.15 #32266, 0.12 #26292), 027dtxw (0.45 #4, 0.13 #33461, 0.12 #29877), 09sdmz (0.27 #201, 0.15 #32266, 0.13 #33461), 02x73k6 (0.27 #58, 0.13 #33461, 0.08 #11152), 0bdwqv (0.23 #167, 0.15 #32266, 0.13 #33461), 099jhq (0.23 #19, 0.13 #33461, 0.12 #26292) >> Best rule #23503 for best value: >> intensional similarity = 3 >> extensional distance = 1561 >> proper extension: 028q6; 0l6qt; 07s3vqk; 05cljf; 02rchht; 0hl3d; 01vrx3g; 0prfz; 025h4z; 0m2l9; ... >> query: (?x5363, ?x2375) <- award_nominee(?x5363, ?x748), award_winner(?x2375, ?x5363), award(?x157, ?x2375) >> conf = 0.72 => this is the best rule for 4 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 02qgqt; 06dv3; 0p_pd; 0bl2g; 09fb5; 0z4s; 01wmxfs; 016khd; 039bp; 03f1zdw; ... *> query: (?x5363, 02x73k6) <- award_nominee(?x5363, ?x748), award_winner(?x2375, ?x5363), ?x2375 = 04kxsb *> conf = 0.27 ranks of expected_values: 8 EVAL 016yvw award 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 100.000 100.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16889-0g33q PRED entity: 0g33q PRED relation: role PRED expected values: 0dwt5 05kms => 67 concepts (42 used for prediction) PRED predicted values (max 10 best out of 102): 05kms (0.82 #3969, 0.80 #1387, 0.80 #1570), 05148p4 (0.82 #3861, 0.76 #2867, 0.75 #2846), 02hnl (0.82 #3861, 0.76 #2885, 0.75 #2783), 06ncr (0.76 #2159, 0.75 #1333, 0.71 #1035), 0dwtp (0.75 #1303, 0.71 #1005, 0.67 #906), 07xzm (0.75 #1309, 0.71 #1011, 0.67 #912), 0g33q (0.73 #1663, 0.71 #1259, 0.70 #1558), 06w7v (0.71 #1067, 0.67 #968, 0.65 #590), 07kc_ (0.71 #1008, 0.67 #909, 0.65 #590), 0bxl5 (0.71 #1051, 0.67 #952, 0.65 #590) >> Best rule #3969 for best value: >> intensional similarity = 24 >> extensional distance = 39 >> proper extension: 07c6l; >> query: (?x4429, ?x6039) <- role(?x4429, ?x2944), role(?x4429, ?x1969), ?x2944 = 0l14j_, role(?x1969, ?x3214), role(?x1969, ?x2888), role(?x1969, ?x885), instrumentalists(?x1969, ?x5635), instrumentalists(?x1969, ?x4140), instrumentalists(?x1969, ?x1970), ?x2888 = 02fsn, ?x1970 = 0zjpz, ?x5635 = 0kxbc, role(?x1225, ?x1969), role(?x4550, ?x1969), role(?x4044, ?x1969), performance_role(?x212, ?x1225), group(?x1969, ?x1929), role(?x6039, ?x4429), artists(?x302, ?x4550), ?x4140 = 01sb5r, role(?x642, ?x3214), gender(?x4550, ?x231), ?x885 = 0dwtp, ?x4044 = 01m15br >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 51 EVAL 0g33q role 05kms CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 42.000 0.825 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 0g33q role 0dwt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 67.000 42.000 0.825 http://example.org/music/performance_role/regular_performances./music/group_membership/role #16888-04b_jc PRED entity: 04b_jc PRED relation: film! PRED expected values: 0jz9f => 73 concepts (40 used for prediction) PRED predicted values (max 10 best out of 60): 017s11 (0.17 #378, 0.11 #832, 0.11 #303), 086k8 (0.15 #1436, 0.15 #1208, 0.15 #2055), 05qd_ (0.14 #309, 0.13 #1291, 0.12 #913), 016tw3 (0.14 #461, 0.12 #11, 0.12 #689), 03xq0f (0.14 #380, 0.13 #683, 0.12 #834), 016tt2 (0.12 #2369, 0.11 #2747, 0.11 #1438), 025jfl (0.11 #81, 0.11 #231, 0.10 #156), 0jz9f (0.10 #451, 0.10 #1, 0.09 #76), 020h2v (0.10 #45, 0.05 #195, 0.04 #1479), 01gb54 (0.09 #554, 0.08 #630, 0.08 #479) >> Best rule #378 for best value: >> intensional similarity = 4 >> extensional distance = 130 >> proper extension: 0dtw1x; 0g5q34q; >> query: (?x10732, 017s11) <- language(?x10732, ?x254), category(?x10732, ?x134), genre(?x10732, ?x258), ?x258 = 05p553 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #451 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 157 *> proper extension: 03twd6; 02vqhv0; 014nq4; 04grkmd; 0c57yj; 033srr; 034r25; 01q2nx; 0bq6ntw; 02qk3fk; ... *> query: (?x10732, 0jz9f) <- currency(?x10732, ?x170), genre(?x10732, ?x53), film_format(?x10732, ?x6392), ?x53 = 07s9rl0 *> conf = 0.10 ranks of expected_values: 8 EVAL 04b_jc film! 0jz9f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 73.000 40.000 0.167 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16887-095z4q PRED entity: 095z4q PRED relation: film_crew_role PRED expected values: 02r96rf => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 26): 02r96rf (0.73 #1468, 0.68 #1002, 0.66 #337), 0d2b38 (0.36 #57, 0.22 #224, 0.17 #2902), 01xy5l_ (0.28 #45, 0.20 #212, 0.17 #111), 0215hd (0.25 #50, 0.18 #217, 0.17 #116), 089g0h (0.22 #51, 0.17 #2902, 0.16 #84), 02rh1dz (0.19 #310, 0.19 #343, 0.18 #210), 02ynfr (0.18 #1478, 0.18 #13, 0.17 #2902), 02_n3z (0.17 #2902, 0.14 #35, 0.14 #1), 089fss (0.17 #2902, 0.13 #106, 0.12 #2367), 015h31 (0.17 #2902, 0.13 #141, 0.12 #175) >> Best rule #1468 for best value: >> intensional similarity = 3 >> extensional distance = 669 >> proper extension: 0gh6j94; >> query: (?x6507, 02r96rf) <- film_crew_role(?x6507, ?x1171), language(?x6507, ?x254), ?x1171 = 09vw2b7 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 095z4q film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.732 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16886-0jnkr PRED entity: 0jnkr PRED relation: colors PRED expected values: 03vtbc => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 19): 06fvc (0.66 #692, 0.64 #362, 0.57 #254), 01g5v (0.39 #928, 0.31 #946, 0.25 #752), 06kqt3 (0.33 #339, 0.33 #123, 0.20 #51), 01l849 (0.29 #289, 0.21 #470, 0.20 #73), 03vtbc (0.24 #421, 0.24 #385, 0.21 #367), 02rnmb (0.20 #499, 0.20 #84, 0.18 #408), 0jc_p (0.20 #58, 0.20 #40, 0.17 #633), 088fh (0.20 #96, 0.17 #168, 0.14 #258), 0680m7 (0.20 #90, 0.16 #578, 0.14 #252), 038hg (0.14 #451, 0.13 #862, 0.13 #810) >> Best rule #692 for best value: >> intensional similarity = 24 >> extensional distance = 74 >> proper extension: 0263cyj; >> query: (?x12977, 06fvc) <- colors(?x12977, ?x4557), colors(?x12977, ?x663), ?x663 = 083jv, teams(?x6084, ?x12977), colors(?x11632, ?x4557), colors(?x10100, ?x4557), colors(?x13795, ?x4557), colors(?x13480, ?x4557), colors(?x11195, ?x4557), colors(?x11153, ?x4557), colors(?x8826, ?x4557), colors(?x5175, ?x4557), colors(?x4907, ?x4557), colors(?x1639, ?x4557), ?x11195 = 0kwv2, ?x4907 = 01vqc7, ?x13795 = 044p4_, ?x13480 = 07sqbl, ?x8826 = 03x6w8, contains(?x279, ?x10100), team(?x60, ?x11153), ?x11632 = 0mbwf, ?x5175 = 051n13, ?x1639 = 07l24 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 15 *> proper extension: 01lpx8; *> query: (?x12977, 03vtbc) <- colors(?x12977, ?x4557), colors(?x12977, ?x663), ?x663 = 083jv, teams(?x6084, ?x12977), ?x4557 = 019sc, location(?x5910, ?x6084), film(?x5910, ?x103), award_nominee(?x5910, ?x3494), languages(?x5910, ?x254), profession(?x5910, ?x319), time_zones(?x6084, ?x2674), award(?x5910, ?x1336), place_of_birth(?x547, ?x6084) *> conf = 0.24 ranks of expected_values: 5 EVAL 0jnkr colors 03vtbc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 64.000 0.658 http://example.org/sports/sports_team/colors #16885-02bj6k PRED entity: 02bj6k PRED relation: award_winner! PRED expected values: 0266s9 => 99 concepts (65 used for prediction) PRED predicted values (max 10 best out of 191): 0266s9 (0.43 #21585, 0.43 #31806, 0.41 #29533), 0b6tzs (0.20 #97, 0.01 #8048, 0.01 #2369), 01s81 (0.20 #489, 0.01 #20937), 047vnkj (0.20 #597), 04jm_hq (0.20 #586), 0gkz15s (0.19 #4545, 0.18 #73846, 0.16 #13630), 0sxlb (0.19 #4545, 0.18 #73846, 0.16 #13630), 0cbv4g (0.19 #4545, 0.18 #73846, 0.16 #13630), 03vyw8 (0.19 #4545, 0.18 #73846, 0.16 #13630), 0322yj (0.19 #4545, 0.18 #73846, 0.16 #13630) >> Best rule #21585 for best value: >> intensional similarity = 2 >> extensional distance = 916 >> proper extension: 0kcdl; >> query: (?x7981, ?x11806) <- nominated_for(?x7981, ?x11806), genre(?x11806, ?x53) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bj6k award_winner! 0266s9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 65.000 0.430 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #16884-0pmhf PRED entity: 0pmhf PRED relation: award_nominee PRED expected values: 0785v8 => 114 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1224): 014v6f (0.81 #107393, 0.81 #137739, 0.81 #154082), 04qsdh (0.81 #107393, 0.81 #137739, 0.81 #154082), 027bs_2 (0.81 #107393, 0.81 #137739, 0.81 #154082), 0161sp (0.15 #84040, 0.15 #72365, 0.13 #63026), 06pjs (0.15 #84040, 0.15 #72365, 0.13 #63026), 046zh (0.15 #72365, 0.12 #95718, 0.11 #95717), 0h0wc (0.11 #551, 0.09 #2886, 0.04 #7554), 02qgyv (0.09 #23841, 0.05 #498, 0.05 #70527), 086k8 (0.08 #35073, 0.07 #51412, 0.07 #46744), 01q_ph (0.08 #7069, 0.04 #14072, 0.03 #18740) >> Best rule #107393 for best value: >> intensional similarity = 3 >> extensional distance = 700 >> proper extension: 029_3; >> query: (?x2596, ?x100) <- award_nominee(?x100, ?x2596), film(?x2596, ?x675), student(?x4794, ?x2596) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #150 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 02t__l; *> query: (?x2596, 0785v8) <- award_winner(?x3066, ?x2596), nominated_for(?x2596, ?x69), ?x3066 = 0gqy2 *> conf = 0.03 ranks of expected_values: 432 EVAL 0pmhf award_nominee 0785v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 114.000 73.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16883-06_kh PRED entity: 06_kh PRED relation: time_zones PRED expected values: 02lcqs => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 9): 02lcqs (0.61 #44, 0.47 #31, 0.35 #200), 02hcv8 (0.49 #393, 0.47 #458, 0.45 #575), 02fqwt (0.22 #144, 0.18 #742, 0.17 #1028), 02hczc (0.18 #93, 0.09 #301, 0.09 #158), 02llzg (0.12 #134, 0.11 #264, 0.11 #836), 03bdv (0.10 #6, 0.05 #591, 0.05 #526), 03plfd (0.02 #75, 0.02 #686, 0.02 #842), 052vwh (0.02 #77, 0.01 #233), 042g7t (0.02 #440) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0r3tb; >> query: (?x242, 02lcqs) <- place_of_death(?x7528, ?x242), award_winner(?x3332, ?x7528), county(?x242, ?x2949) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06_kh time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.609 http://example.org/location/location/time_zones #16882-0gwgn1k PRED entity: 0gwgn1k PRED relation: film! PRED expected values: 02wd48 => 91 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1069): 01vxqyl (0.43 #70684, 0.42 #95627, 0.41 #20787), 086nl7 (0.23 #785, 0.04 #2079, 0.02 #29886), 02k21g (0.23 #793, 0.04 #2079, 0.01 #48601), 0fby2t (0.19 #753, 0.05 #11146, 0.04 #4911), 05txrz (0.19 #765, 0.03 #46495, 0.02 #15315), 08vr94 (0.16 #675, 0.04 #2079, 0.03 #11068), 03q45x (0.16 #1351, 0.04 #2079, 0.02 #5509), 0f7hc (0.13 #2908, 0.07 #7065, 0.05 #19536), 07cjqy (0.13 #601, 0.02 #13072, 0.02 #15151), 0cmt6q (0.13 #1142, 0.02 #15692, 0.01 #36477) >> Best rule #70684 for best value: >> intensional similarity = 4 >> extensional distance = 587 >> proper extension: 03g9xj; >> query: (?x9322, ?x6797) <- nominated_for(?x6797, ?x9322), profession(?x6797, ?x1032), nationality(?x6797, ?x94), category(?x6797, ?x134) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gwgn1k film! 02wd48 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 49.000 0.434 http://example.org/film/actor/film./film/performance/film #16881-0bbgvp PRED entity: 0bbgvp PRED relation: film_festivals PRED expected values: 059_y8d => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 14): 04grdgy (0.04 #114, 0.03 #282, 0.03 #303), 04_m9gk (0.03 #118, 0.03 #286, 0.03 #307), 0j63cyr (0.02 #66, 0.01 #255, 0.01 #549), 0bmj62v (0.02 #12, 0.02 #117, 0.01 #75), 03nn7l2 (0.02 #17, 0.01 #269, 0.01 #227), 059_y8d (0.02 #233, 0.02 #254, 0.02 #275), 0hrcs29 (0.02 #99, 0.01 #78, 0.01 #183), 0gg7gsl (0.02 #547, 0.01 #757, 0.01 #820), 09rwjly (0.02 #323, 0.01 #344, 0.01 #197), 0g57ws5 (0.01 #70, 0.01 #175, 0.01 #490) >> Best rule #114 for best value: >> intensional similarity = 3 >> extensional distance = 193 >> proper extension: 0g5q34q; 0d8w2n; >> query: (?x11998, 04grdgy) <- genre(?x11998, ?x1403), featured_film_locations(?x11998, ?x1879), ?x1403 = 02l7c8 >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 332 *> proper extension: 03rz2b; *> query: (?x11998, 059_y8d) <- genre(?x11998, ?x1403), nominated_for(?x484, ?x11998), country(?x11998, ?x94), ?x1403 = 02l7c8 *> conf = 0.02 ranks of expected_values: 6 EVAL 0bbgvp film_festivals 059_y8d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 64.000 64.000 0.041 http://example.org/film/film/film_festivals #16880-046m59 PRED entity: 046m59 PRED relation: people! PRED expected values: 0x67 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 40): 041rx (0.21 #158, 0.17 #545, 0.15 #1085), 02w7gg (0.13 #156, 0.07 #2549, 0.06 #1006), 033tf_ (0.12 #470, 0.10 #315, 0.09 #1088), 0x67 (0.12 #396, 0.11 #241, 0.10 #87), 048z7l (0.11 #271, 0.03 #2510, 0.03 #503), 07hwkr (0.07 #243, 0.06 #320, 0.04 #1480), 0xnvg (0.06 #476, 0.06 #321, 0.05 #863), 01qhm_ (0.06 #4858, 0.04 #314, 0.04 #469), 07bch9 (0.06 #4858, 0.04 #873, 0.03 #2493), 09vc4s (0.06 #4858, 0.03 #317, 0.03 #472) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 01yznp; 02p21g; 0126rp; 027l0b; 0309jm; 014z8v; 03lgg; 03h502k; 02cbs0; 0bw87; ... >> query: (?x5460, 041rx) <- film(?x5460, ?x324), religion(?x5460, ?x2694), ?x2694 = 0kpl >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #396 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 448 *> proper extension: 0bx_q; 06sn8m; 0127xk; 047jhq; *> query: (?x5460, 0x67) <- award(?x5460, ?x1132), actor(?x4898, ?x5460), place_of_birth(?x5460, ?x12873) *> conf = 0.12 ranks of expected_values: 4 EVAL 046m59 people! 0x67 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 99.000 0.209 http://example.org/people/ethnicity/people #16879-0g7pm1 PRED entity: 0g7pm1 PRED relation: film_crew_role PRED expected values: 09zzb8 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 25): 09zzb8 (0.80 #398, 0.79 #143, 0.76 #57), 02r96rf (0.71 #4, 0.70 #117, 0.70 #514), 01pvkk (0.32 #95, 0.31 #123, 0.29 #152), 02_n3z (0.27 #115, 0.26 #87, 0.18 #2075), 015h31 (0.18 #121, 0.18 #2075, 0.13 #93), 02rh1dz (0.18 #2075, 0.17 #122, 0.15 #406), 020xn5 (0.18 #2075, 0.15 #92, 0.14 #120), 033smt (0.18 #2075, 0.15 #133, 0.12 #1621), 0ckd1 (0.18 #2075, 0.12 #1621, 0.10 #5), 05smlt (0.18 #2075, 0.12 #1621, 0.09 #2191) >> Best rule #398 for best value: >> intensional similarity = 4 >> extensional distance = 277 >> proper extension: 0m2kd; 08720; 0fr63l; 0jqp3; 020fcn; 07qg8v; 011yqc; 0ch26b_; 0bx0l; 0f4_l; ... >> query: (?x6798, 09zzb8) <- film_crew_role(?x6798, ?x2095), nominated_for(?x3190, ?x6798), film(?x541, ?x6798), ?x2095 = 0dxtw >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g7pm1 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.796 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16878-0gv40 PRED entity: 0gv40 PRED relation: award_winner! PRED expected values: 02wkmx => 132 concepts (124 used for prediction) PRED predicted values (max 10 best out of 283): 0gq9h (0.25 #76, 0.24 #505, 0.16 #35609), 05p1dby (0.20 #4824, 0.05 #13833, 0.05 #1821), 0m7yy (0.20 #17338, 0.07 #4896, 0.03 #2751), 054ky1 (0.19 #536, 0.13 #2681, 0.12 #107), 027c924 (0.19 #10, 0.17 #1726, 0.16 #1297), 07bdd_ (0.17 #4784, 0.05 #13793, 0.03 #21516), 0f4x7 (0.16 #35609, 0.14 #16732, 0.12 #888), 0gq_v (0.16 #35609, 0.14 #16732, 0.08 #5170), 0p9sw (0.16 #35609, 0.14 #16732, 0.07 #40330), 0gr42 (0.16 #35609, 0.14 #16732, 0.07 #40330) >> Best rule #76 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0jf1b; 01gzm2; 0p51w; 043gj; 01pp3p; 045cq; 03bw6; 01p87y; 029m83; 02hfp_; ... >> query: (?x4652, 0gq9h) <- place_of_death(?x4652, ?x1523), people(?x1050, ?x4652), film(?x4652, ?x4653) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1301 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 0bxtg; 06cv1; 0151w_; 01b9ck; 0162c8; 026c1; 034bgm; 0693l; 021yw7; 0gyx4; ... *> query: (?x4652, 02wkmx) <- participant(?x4652, ?x1149), nationality(?x4652, ?x94), film(?x4652, ?x4653) *> conf = 0.10 ranks of expected_values: 30 EVAL 0gv40 award_winner! 02wkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 132.000 124.000 0.250 http://example.org/award/award_category/winners./award/award_honor/award_winner #16877-04nnpw PRED entity: 04nnpw PRED relation: film! PRED expected values: 086k8 => 77 concepts (60 used for prediction) PRED predicted values (max 10 best out of 49): 0jz9f (0.60 #1, 0.09 #733, 0.08 #953), 03sb38 (0.55 #440, 0.46 #2806, 0.46 #1840), 086k8 (0.32 #75, 0.25 #148, 0.22 #295), 024rdh (0.26 #109, 0.25 #182, 0.05 #549), 016tw3 (0.21 #376, 0.17 #450, 0.15 #303), 017s11 (0.20 #3, 0.13 #3552, 0.12 #1400), 061dn_ (0.20 #23, 0.11 #96, 0.10 #169), 054g1r (0.20 #34, 0.09 #766, 0.08 #912), 016tt2 (0.17 #590, 0.16 #77, 0.15 #150), 05qd_ (0.16 #667, 0.16 #740, 0.14 #1331) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 07h9gp; 029k4p; 025s1wg; >> query: (?x4696, 0jz9f) <- film(?x10884, ?x4696), country(?x4696, ?x94), award(?x4696, ?x533), ?x10884 = 032j_n >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 040rmy; 02dpl9; 01_0f7; 065_cjc; 0h14ln; *> query: (?x4696, 086k8) <- film(?x609, ?x4696), country(?x4696, ?x2152), ?x2152 = 06mkj, titles(?x162, ?x4696) *> conf = 0.32 ranks of expected_values: 3 EVAL 04nnpw film! 086k8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 60.000 0.600 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16876-03y82t6 PRED entity: 03y82t6 PRED relation: award_winner! PRED expected values: 0hhtgcw => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 122): 01s695 (0.16 #3, 0.11 #1131, 0.11 #1272), 02rjjll (0.13 #1133, 0.11 #1274, 0.09 #3248), 013b2h (0.12 #1208, 0.12 #1349, 0.11 #80), 01c6qp (0.11 #1147, 0.11 #1288, 0.08 #19), 09g90vz (0.11 #406, 0.05 #6047, 0.05 #2098), 01bx35 (0.11 #7, 0.06 #1135, 0.06 #1276), 09n4nb (0.10 #1176, 0.09 #1317, 0.08 #3291), 05pd94v (0.10 #1130, 0.09 #1271, 0.08 #3245), 02cg41 (0.10 #1254, 0.09 #1395, 0.08 #126), 019bk0 (0.09 #1144, 0.08 #580, 0.08 #1285) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 02jyhv; >> query: (?x4740, 01s695) <- artists(?x3061, ?x4740), participant(?x4740, ?x6835), ?x3061 = 05bt6j >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #650 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 02d9k; 023n39; 01fxck; *> query: (?x4740, 0hhtgcw) <- profession(?x4740, ?x220), currency(?x4740, ?x170), friend(?x4740, ?x10864) *> conf = 0.08 ranks of expected_values: 12 EVAL 03y82t6 award_winner! 0hhtgcw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 112.000 112.000 0.158 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #16875-02vx4 PRED entity: 02vx4 PRED relation: sport! PRED expected values: 01453 03qx63 07r78j 044crp 01xbpn 0b256b 0690dn 01zhs3 01tqfs 03_lsr 0dwz3t 03x6w8 0175tv 019ltg 02_t6d 049dzz 032c7m 01l3wr 01rly6 0175rc 051qvn 0329qp 03tc8d 04czcb 03yvln 0byq0v 0bg4f9 04ck0_ 0lmm3 0mmd6 03mck3c 0h3c3g => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 347): 0mmd6 (0.58 #1827, 0.05 #2492), 0lmm3 (0.58 #1827, 0.05 #2492), 0175rc (0.58 #1827, 0.05 #2492), 01rly6 (0.58 #1827, 0.05 #2492), 049dzz (0.58 #1827, 0.05 #2492), 0dwz3t (0.58 #1827, 0.05 #2492), 01zhs3 (0.58 #1827, 0.05 #2492), 02gys2 (0.58 #1827, 0.05 #2492), 01453 (0.58 #1827, 0.05 #2492), 037css (0.58 #1827) >> Best rule #1827 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 0jm_; >> query: (?x471, ?x6161) <- athlete(?x471, ?x6134), athlete(?x471, ?x1935), sport(?x11153, ?x471), sport(?x10636, ?x471), sport(?x10493, ?x471), sport(?x2428, ?x471), team(?x60, ?x11153), teams(?x11289, ?x10493), category(?x2428, ?x134), colors(?x10636, ?x663), gender(?x6134, ?x231), team(?x1935, ?x6161) >> conf = 0.58 => this is the best rule for 129 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 9, 12, 88, 122, 297, 301, 303, 304, 307, 309, 310, 320, 324, 325, 329, 330, 337, 341 EVAL 02vx4 sport! 0h3c3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 03mck3c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 0mmd6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 0lmm3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 04ck0_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 0bg4f9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 0byq0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 03yvln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 04czcb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 03tc8d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 0329qp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 051qvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 0175rc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 01rly6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 01l3wr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 032c7m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 049dzz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 02_t6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 019ltg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 0175tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 03x6w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 0dwz3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 03_lsr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 01tqfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 01zhs3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 0690dn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 0b256b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 01xbpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 044crp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 07r78j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 03qx63 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 67.000 0.581 http://example.org/sports/sports_team/sport EVAL 02vx4 sport! 01453 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.581 http://example.org/sports/sports_team/sport #16874-09728 PRED entity: 09728 PRED relation: nutrient PRED expected values: 0h1zw 0h1vz 041r51 02kd0rh 0h1sz 0fzjh => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 42): 0h1vz (0.87 #24, 0.82 #56, 0.67 #516), 0h1zw (0.87 #24, 0.82 #56, 0.67 #516), 041r51 (0.87 #24, 0.82 #56, 0.67 #516), 0fzjh (0.87 #24, 0.82 #56, 0.67 #516), 0h1sz (0.87 #24, 0.82 #56, 0.67 #516), 02p0tjr (0.87 #24, 0.82 #56, 0.67 #516), 02y_3rf (0.87 #24, 0.82 #56, 0.67 #516), 07zqy (0.87 #24, 0.82 #56, 0.67 #516), 02kd0rh (0.87 #24, 0.82 #56, 0.67 #516), 08lb68 (0.87 #24, 0.82 #56, 0.67 #516) >> Best rule #24 for best value: >> intensional similarity = 138 >> extensional distance = 1 >> proper extension: 04zpv; >> query: (?x1257, ?x6160) <- nutrient(?x1257, ?x13944), nutrient(?x1257, ?x13498), nutrient(?x1257, ?x12868), nutrient(?x1257, ?x12454), nutrient(?x1257, ?x11758), nutrient(?x1257, ?x11592), nutrient(?x1257, ?x11409), nutrient(?x1257, ?x11270), nutrient(?x1257, ?x10891), nutrient(?x1257, ?x10098), nutrient(?x1257, ?x9915), nutrient(?x1257, ?x9733), nutrient(?x1257, ?x9619), nutrient(?x1257, ?x9490), nutrient(?x1257, ?x9436), nutrient(?x1257, ?x9426), nutrient(?x1257, ?x9365), nutrient(?x1257, ?x8487), nutrient(?x1257, ?x8442), nutrient(?x1257, ?x8413), nutrient(?x1257, ?x7894), nutrient(?x1257, ?x7720), nutrient(?x1257, ?x7652), nutrient(?x1257, ?x7431), nutrient(?x1257, ?x7364), nutrient(?x1257, ?x7362), nutrient(?x1257, ?x7219), nutrient(?x1257, ?x7135), nutrient(?x1257, ?x6586), nutrient(?x1257, ?x6192), nutrient(?x1257, ?x6033), nutrient(?x1257, ?x6026), nutrient(?x1257, ?x5549), nutrient(?x1257, ?x5526), nutrient(?x1257, ?x5451), nutrient(?x1257, ?x5374), nutrient(?x1257, ?x5337), nutrient(?x1257, ?x4069), nutrient(?x1257, ?x3203), nutrient(?x1257, ?x2702), nutrient(?x1257, ?x2018), nutrient(?x1257, ?x1960), nutrient(?x1257, ?x1258), ?x5526 = 09pbb, ?x11409 = 0h1yf, ?x9490 = 0h1sg, ?x7431 = 09gwd, ?x6192 = 06jry, ?x9436 = 025sqz8, ?x7219 = 0h1vg, ?x8413 = 02kc4sf, ?x6586 = 05gh50, ?x7135 = 025rsfk, ?x13498 = 07q0m, nutrient(?x7719, ?x12868), nutrient(?x7057, ?x12868), nutrient(?x6285, ?x12868), nutrient(?x6191, ?x12868), nutrient(?x6159, ?x12868), nutrient(?x5009, ?x12868), nutrient(?x4068, ?x12868), nutrient(?x2701, ?x12868), nutrient(?x1303, ?x12868), ?x12454 = 025rw19, ?x13944 = 0f4kp, ?x2018 = 01sh2, ?x1303 = 0fj52s, ?x7719 = 0dj75, ?x9365 = 04k8n, ?x7720 = 025s7x6, ?x6285 = 01645p, ?x5009 = 0fjfh, ?x8487 = 014yzm, ?x9915 = 025tkqy, ?x11592 = 025sf0_, ?x10098 = 0h1_c, nutrient(?x10612, ?x5549), nutrient(?x9732, ?x5549), nutrient(?x9489, ?x5549), nutrient(?x8298, ?x5549), nutrient(?x6032, ?x5549), nutrient(?x5373, ?x5549), nutrient(?x3900, ?x5549), nutrient(?x3468, ?x5549), nutrient(?x3264, ?x5549), nutrient(?x1959, ?x5549), ?x9426 = 0h1yy, ?x1959 = 0f25w9, ?x4069 = 0hqw8p_, ?x10891 = 0g5gq, ?x7894 = 0f4hc, ?x8442 = 02kcv4x, ?x2701 = 0hkxq, ?x11270 = 02kc008, ?x1960 = 07hnp, ?x6159 = 033cnk, ?x9489 = 07j87, ?x7057 = 0fbdb, ?x6032 = 01nkt, ?x11758 = 0q01m, ?x3264 = 0dcfv, ?x5451 = 05wvs, ?x9733 = 0h1tz, ?x5374 = 025s0zp, ?x1258 = 0h1wg, ?x4068 = 0fbw6, ?x6026 = 025sf8g, ?x10612 = 0frq6, ?x6033 = 04zjxcz, ?x7362 = 02kc5rj, ?x7364 = 09gvd, ?x6191 = 014j1m, nutrient(?x3468, ?x13545), nutrient(?x3468, ?x13126), nutrient(?x3468, ?x11784), nutrient(?x3468, ?x10709), nutrient(?x3468, ?x9949), nutrient(?x3468, ?x6160), nutrient(?x3468, ?x5010), nutrient(?x3468, ?x3469), nutrient(?x3468, ?x1304), ?x9949 = 02kd0rh, ?x2702 = 0838f, ?x13126 = 02kc_w5, ?x3900 = 061_f, ?x11784 = 07zqy, ?x7652 = 025s0s0, ?x5337 = 06x4c, ?x5010 = 0h1vz, ?x13545 = 01w_3, ?x8298 = 037ls6, ?x9619 = 0h1tg, ?x9732 = 05z55, ?x1304 = 08lb68, ?x3203 = 04kl74p, ?x10709 = 0h1sz, ?x3469 = 0h1zw, ?x5373 = 0971v >> conf = 0.87 => this is the best rule for 17 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 9 EVAL 09728 nutrient 0fzjh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.865 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 09728 nutrient 0h1sz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.865 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 09728 nutrient 02kd0rh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 22.000 0.865 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 09728 nutrient 041r51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.865 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 09728 nutrient 0h1vz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.865 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 09728 nutrient 0h1zw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.865 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #16873-013yq PRED entity: 013yq PRED relation: locations! PRED expected values: 0b_6s7 => 198 concepts (198 used for prediction) PRED predicted values (max 10 best out of 106): 0b_6x2 (0.22 #2983, 0.18 #1094, 0.16 #3810), 0b_6_l (0.20 #3047, 0.14 #4110, 0.14 #3874), 0b_6s7 (0.18 #1124, 0.15 #3013, 0.14 #1242), 0b_6xf (0.18 #1159, 0.14 #569, 0.12 #3520), 0b_6jz (0.15 #7825, 0.15 #6172, 0.14 #1095), 0bzrxn (0.14 #1114, 0.14 #7017, 0.12 #6191), 03jqfx (0.11 #7510, 0.10 #10111, 0.07 #14600), 0b_6h7 (0.09 #7003, 0.09 #6177, 0.08 #7830), 06k75 (0.09 #10089, 0.09 #7488, 0.07 #12095), 05t2fh4 (0.09 #583, 0.04 #8257, 0.03 #9320) >> Best rule #2983 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 02k54; >> query: (?x2277, 0b_6x2) <- citytown(?x2276, ?x2277), teams(?x2277, ?x2405), locations(?x3797, ?x2277) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1124 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 0dq16; *> query: (?x2277, 0b_6s7) <- citytown(?x7008, ?x2277), locations(?x3797, ?x2277), currency(?x7008, ?x170) *> conf = 0.18 ranks of expected_values: 3 EVAL 013yq locations! 0b_6s7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 198.000 198.000 0.217 http://example.org/time/event/locations #16872-0cc1v PRED entity: 0cc1v PRED relation: contains! PRED expected values: 04rrd => 77 concepts (34 used for prediction) PRED predicted values (max 10 best out of 81): 04rrd (0.86 #20685, 0.60 #21586, 0.52 #25181), 027rqbx (0.60 #21586, 0.02 #9365, 0.02 #10264), 09c7w0 (0.52 #25181, 0.51 #26081, 0.51 #25185), 07z1m (0.50 #92, 0.08 #10883, 0.07 #11781), 0cc1v (0.42 #20686, 0.42 #11689, 0.40 #25182), 0bx9y (0.42 #20686, 0.40 #25182, 0.39 #19786), 04_1l0v (0.30 #8544, 0.28 #5843, 0.24 #6744), 07c5l (0.20 #1293, 0.05 #5787, 0.05 #6688), 01n7q (0.19 #3671, 0.15 #4571, 0.15 #2772), 059rby (0.12 #18008, 0.12 #22505, 0.12 #21606) >> Best rule #20685 for best value: >> intensional similarity = 4 >> extensional distance = 284 >> proper extension: 0r2l7; 03v_5; 029cr; 0ply0; 071cn; 0sf9_; 02hrh0_; 0fpzwf; 07bcn; 0f67f; ... >> query: (?x10601, ?x1767) <- contains(?x10601, ?x8980), contains(?x1767, ?x8980), source(?x10601, ?x958), district_represented(?x176, ?x1767) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cc1v contains! 04rrd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 34.000 0.860 http://example.org/location/location/contains #16871-01kf5lf PRED entity: 01kf5lf PRED relation: prequel! PRED expected values: 0fsw_7 => 57 concepts (35 used for prediction) PRED predicted values (max 10 best out of 18): 01f85k (0.02 #290, 0.01 #471), 02xs6_ (0.01 #629), 0d1qmz (0.01 #361, 0.01 #245), 01c9d (0.01 #361), 0g5ptf (0.01 #361), 01633c (0.01 #361), 01kf5lf (0.01 #361), 02dwj (0.01 #361), 01kf4tt (0.01 #361), 03kxj2 (0.01 #361) >> Best rule #290 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 01s81; >> query: (?x5870, 01f85k) <- nominated_for(?x2507, ?x5870), award(?x5870, ?x6860), film_production_design_by(?x1851, ?x2507) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kf5lf prequel! 0fsw_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 35.000 0.022 http://example.org/film/film/prequel #16870-02664f PRED entity: 02664f PRED relation: award_winner PRED expected values: 03hpr => 59 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1336): 03rx9 (0.62 #11966, 0.60 #7013, 0.60 #4535), 02y49 (0.56 #17333, 0.56 #16771, 0.56 #14294), 04mhl (0.56 #14860, 0.56 #13367, 0.50 #12383), 03772 (0.56 #16003, 0.44 #13526, 0.40 #3618), 0fpzt5 (0.50 #11808, 0.50 #9904, 0.50 #9330), 04r68 (0.50 #11053, 0.50 #1147, 0.44 #13530), 0jt86 (0.50 #2271, 0.40 #7224, 0.38 #12177), 048_p (0.50 #1246, 0.40 #6199, 0.37 #9901), 05jm7 (0.46 #18169, 0.44 #15694, 0.25 #10740), 014ps4 (0.40 #42074, 0.38 #19038, 0.37 #9901) >> Best rule #11966 for best value: >> intensional similarity = 9 >> extensional distance = 6 >> proper extension: 01yz0x; 0262x6; >> query: (?x4418, 03rx9) <- award(?x8718, ?x4418), award(?x4417, ?x4418), award(?x3663, ?x4418), award(?x1752, ?x4418), ?x4417 = 04mhl, influenced_by(?x3663, ?x1089), disciplines_or_subjects(?x4418, ?x1013), location(?x8718, ?x1036), ?x1752 = 01dzz7 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #17005 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 0265wl; 040_9s0; *> query: (?x4418, 03hpr) <- award(?x12614, ?x4418), award(?x8908, ?x4418), award(?x8863, ?x4418), religion(?x8863, ?x1363), ?x8908 = 02y49, type_of_union(?x8863, ?x566), ?x12614 = 01k56k *> conf = 0.22 ranks of expected_values: 28 EVAL 02664f award_winner 03hpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 59.000 27.000 0.625 http://example.org/award/award_category/winners./award/award_honor/award_winner #16869-0g9wdmc PRED entity: 0g9wdmc PRED relation: nominated_for! PRED expected values: 0fq9zdn => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 219): 09cn0c (0.68 #10456, 0.68 #11158, 0.67 #10690), 0gr51 (0.61 #304, 0.60 #1000, 0.31 #536), 03hl6lc (0.56 #357, 0.54 #1053, 0.14 #1749), 0gq9h (0.50 #986, 0.50 #290, 0.48 #1682), 040njc (0.49 #932, 0.39 #236, 0.35 #1164), 02qyntr (0.49 #1102, 0.33 #406, 0.28 #1334), 0gs9p (0.46 #988, 0.44 #292, 0.40 #1684), 099c8n (0.46 #981, 0.33 #285, 0.27 #1213), 019f4v (0.41 #514, 0.38 #4924, 0.37 #978), 04dn09n (0.41 #960, 0.39 #264, 0.34 #496) >> Best rule #10456 for best value: >> intensional similarity = 4 >> extensional distance = 970 >> proper extension: 02rq7nd; >> query: (?x1803, ?x749) <- award(?x1803, ?x749), nominated_for(?x749, ?x6048), award(?x396, ?x749), honored_for(?x2707, ?x6048) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2129 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 141 *> proper extension: 0sxg4; 0yyg4; 0b73_1d; 04mzf8; 0sxfd; 0bs5k8r; 05c5z8j; 02r_pp; 0f42nz; 07cw4; ... *> query: (?x1803, 0fq9zdn) <- nominated_for(?x2551, ?x1803), nominated_for(?x68, ?x1803), film_festivals(?x1803, ?x11852) *> conf = 0.07 ranks of expected_values: 159 EVAL 0g9wdmc nominated_for! 0fq9zdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 85.000 85.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16868-04zqmj PRED entity: 04zqmj PRED relation: film PRED expected values: 0bth54 => 81 concepts (53 used for prediction) PRED predicted values (max 10 best out of 282): 04yc76 (0.05 #68025, 0.01 #2232, 0.01 #4022), 060v34 (0.05 #68025), 08r4x3 (0.04 #9103, 0.04 #1943, 0.03 #3733), 01shy7 (0.04 #423, 0.04 #2213, 0.03 #4003), 06_wqk4 (0.03 #1916, 0.02 #3706, 0.02 #10866), 017jd9 (0.03 #9730, 0.02 #18680, 0.02 #24050), 017gl1 (0.03 #9092, 0.02 #18042, 0.01 #23412), 020bv3 (0.03 #2108, 0.03 #3898, 0.03 #318), 02z3r8t (0.03 #1897, 0.02 #3687, 0.02 #107), 0fphf3v (0.03 #3152, 0.02 #1362, 0.02 #12102) >> Best rule #68025 for best value: >> intensional similarity = 2 >> extensional distance = 1904 >> proper extension: 0338lq; 030_1_; 024rdh; 04glx0; 032dg7; >> query: (?x11381, ?x570) <- award_nominee(?x8793, ?x11381), award_winner(?x570, ?x8793) >> conf = 0.05 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 04zqmj film 0bth54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 53.000 0.047 http://example.org/film/actor/film./film/performance/film #16867-03xnq9_ PRED entity: 03xnq9_ PRED relation: artists! PRED expected values: 05bt6j => 123 concepts (79 used for prediction) PRED predicted values (max 10 best out of 255): 06by7 (0.64 #329, 0.60 #1256, 0.57 #21972), 05bt6j (0.61 #1278, 0.42 #351, 0.33 #42), 025sc50 (0.53 #357, 0.37 #2520, 0.35 #2830), 06j6l (0.47 #355, 0.31 #1591, 0.31 #2828), 0glt670 (0.39 #349, 0.34 #2512, 0.33 #1585), 0ggx5q (0.36 #385, 0.29 #2782, 0.29 #1621), 03lty (0.36 #13013, 0.14 #8065, 0.13 #7137), 0xhtw (0.35 #13001, 0.33 #15, 0.20 #8053), 0cx7f (0.33 #137, 0.21 #14051, 0.13 #7247), 059kh (0.33 #47, 0.17 #1283, 0.14 #7157) >> Best rule #329 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 01nhkxp; 04vrxh; >> query: (?x5657, 06by7) <- artists(?x3996, ?x5657), artists(?x302, ?x5657), ?x302 = 016clz, artists(?x3996, ?x6666), ?x6666 = 05szp >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #1278 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 85 *> proper extension: 06y9c2; 01vsnff; 0qf3p; 01w524f; 01kph_c; 01hrqc; *> query: (?x5657, 05bt6j) <- artists(?x3996, ?x5657), artists(?x302, ?x5657), ?x302 = 016clz, artists(?x3996, ?x9395), ?x9395 = 09nhvw *> conf = 0.61 ranks of expected_values: 2 EVAL 03xnq9_ artists! 05bt6j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 123.000 79.000 0.639 http://example.org/music/genre/artists #16866-01y0s9 PRED entity: 01y0s9 PRED relation: religion! PRED expected values: 03v1s 04rrd 04rrx 02xry 03v0t 0vbk 050ks => 37 concepts (31 used for prediction) PRED predicted values (max 10 best out of 162): 04rrx (0.83 #914, 0.82 #752, 0.78 #428), 03v1s (0.83 #897, 0.82 #735, 0.78 #411), 050ks (0.83 #945, 0.82 #783, 0.78 #459), 02xry (0.82 #759, 0.78 #435, 0.75 #921), 04tgp (0.82 #770, 0.78 #446, 0.75 #932), 04rrd (0.82 #831, 0.75 #912, 0.75 #184), 05k7sb (0.75 #916, 0.75 #188, 0.73 #835), 059f4 (0.75 #900, 0.75 #172, 0.73 #819), 0rh6k (0.75 #163, 0.73 #810, 0.73 #729), 07srw (0.75 #271, 0.73 #756, 0.69 #1081) >> Best rule #914 for best value: >> intensional similarity = 13 >> extensional distance = 10 >> proper extension: 01s5nb; >> query: (?x2672, 04rrx) <- religion(?x2982, ?x2672), religion(?x961, ?x2672), religion(?x335, ?x2672), ?x961 = 03s0w, state_province_region(?x166, ?x335), location(?x2580, ?x335), district_represented(?x2019, ?x335), district_represented(?x759, ?x335), ?x759 = 043djx, contains(?x335, ?x322), ?x2019 = 01gtbb, award_nominee(?x2580, ?x192), administrative_parent(?x2982, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 11, 15 EVAL 01y0s9 religion! 050ks CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 31.000 0.833 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01y0s9 religion! 0vbk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 37.000 31.000 0.833 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01y0s9 religion! 03v0t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 37.000 31.000 0.833 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01y0s9 religion! 02xry CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 31.000 0.833 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01y0s9 religion! 04rrx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 31.000 0.833 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01y0s9 religion! 04rrd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 37.000 31.000 0.833 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01y0s9 religion! 03v1s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 31.000 0.833 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #16865-0ddd0gc PRED entity: 0ddd0gc PRED relation: actor PRED expected values: 01w1kyf => 61 concepts (49 used for prediction) PRED predicted values (max 10 best out of 636): 05mcjs (0.37 #2751, 0.36 #18345, 0.35 #13756), 05bnx3j (0.37 #2751, 0.36 #18345, 0.35 #13756), 06ns98 (0.37 #2751, 0.36 #18345, 0.35 #13756), 05xbx (0.37 #2751, 0.35 #13756, 0.35 #1834), 03q5dr (0.08 #2564, 0.07 #4398, 0.05 #8066), 06j8wx (0.07 #10088, 0.07 #13757, 0.05 #1345), 01tspc6 (0.07 #10088, 0.07 #13757, 0.03 #997), 016xk5 (0.07 #10088, 0.07 #13757, 0.03 #1464), 01hkhq (0.07 #10088, 0.07 #13757, 0.03 #1110), 015rkw (0.07 #10088, 0.07 #13757, 0.03 #1049) >> Best rule #2751 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 01h1bf; 06mr2s; 0h95b81; 01b7h8; >> query: (?x1434, ?x931) <- nominated_for(?x931, ?x1434), honored_for(?x1764, ?x1434), producer_type(?x1434, ?x632) >> conf = 0.37 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 0ddd0gc actor 01w1kyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 49.000 0.374 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #16864-027pdrh PRED entity: 027pdrh PRED relation: nationality PRED expected values: 07ssc => 104 concepts (100 used for prediction) PRED predicted values (max 10 best out of 116): 09c7w0 (0.80 #6943, 0.77 #4266, 0.76 #8133), 07ssc (0.55 #2395, 0.40 #199, 0.33 #15), 03rjj (0.40 #199, 0.08 #600, 0.08 #501), 0f8l9c (0.40 #199, 0.07 #1113, 0.04 #1212), 0345h (0.40 #199, 0.04 #1122, 0.03 #825), 03_3d (0.08 #403, 0.08 #502, 0.07 #700), 0d060g (0.08 #602, 0.07 #701, 0.04 #1892), 0h3y (0.08 #306, 0.04 #405, 0.04 #603), 03rk0 (0.05 #8870, 0.05 #9766, 0.05 #9069), 0d05w3 (0.04 #644, 0.03 #743, 0.02 #1140) >> Best rule #6943 for best value: >> intensional similarity = 3 >> extensional distance = 1911 >> proper extension: 07lmxq; 01nqfh_; 07s6tbm; 0162c8; 07ymr5; 02lq10; 0c01c; 08wr3kg; 06jvj7; 02wb6yq; ... >> query: (?x2572, 09c7w0) <- nominated_for(?x2572, ?x2168), nationality(?x2572, ?x1310), second_level_divisions(?x1310, ?x1156) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2395 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 426 *> proper extension: 0m77m; 01bpn; 082xp; 09jd9; *> query: (?x2572, 07ssc) <- award(?x2572, ?x1703), nationality(?x2572, ?x1310), nationality(?x9763, ?x1310), ?x9763 = 02p7xc *> conf = 0.55 ranks of expected_values: 2 EVAL 027pdrh nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 100.000 0.798 http://example.org/people/person/nationality #16863-0hcs3 PRED entity: 0hcs3 PRED relation: people! PRED expected values: 0x67 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 42): 0x67 (0.60 #550, 0.58 #627, 0.50 #87), 033tf_ (0.17 #1009, 0.16 #1317, 0.15 #1086), 041rx (0.13 #3165, 0.13 #1083, 0.13 #3319), 07bch9 (0.11 #254, 0.03 #4031, 0.03 #3492), 07mqps (0.11 #250, 0.03 #1407, 0.02 #1869), 02g7sp (0.09 #789, 0.09 #943, 0.07 #866), 02ctzb (0.09 #1248, 0.05 #2558, 0.05 #709), 07hwkr (0.07 #1322, 0.06 #1400, 0.06 #2170), 06v41q (0.06 #492, 0.04 #569, 0.03 #1339), 0fqz6 (0.06 #505, 0.04 #582, 0.03 #659) >> Best rule #550 for best value: >> intensional similarity = 6 >> extensional distance = 23 >> proper extension: 02qjj7; 03n69x; 03l295; 0cv72h; 01f492; 01sg7_; 012xdf; 0f2zc; 0cg39k; 054fvj; ... >> query: (?x12323, 0x67) <- team(?x12323, ?x8901), team(?x12323, ?x2067), school(?x8901, ?x466), profession(?x12323, ?x14261), draft(?x2067, ?x1161), team(?x261, ?x2067) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hcs3 people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.600 http://example.org/people/ethnicity/people #16862-0gvs1kt PRED entity: 0gvs1kt PRED relation: genre PRED expected values: 02n4kr => 92 concepts (91 used for prediction) PRED predicted values (max 10 best out of 111): 05p553 (0.40 #2550, 0.38 #853, 0.38 #1700), 02l7c8 (0.40 #138, 0.38 #381, 0.37 #623), 01jfsb (0.38 #6078, 0.34 #1830, 0.33 #4011), 02kdv5l (0.33 #4001, 0.33 #1820, 0.33 #3274), 060__y (0.26 #139, 0.23 #17, 0.21 #260), 04xvlr (0.25 #123, 0.23 #1, 0.22 #1576), 0lsxr (0.23 #6075, 0.20 #737, 0.19 #2555), 06n90 (0.22 #499, 0.20 #862, 0.19 #1104), 01hmnh (0.18 #867, 0.18 #4017, 0.18 #3290), 04t36 (0.15 #371, 0.15 #613, 0.11 #6) >> Best rule #2550 for best value: >> intensional similarity = 4 >> extensional distance = 499 >> proper extension: 0bz3jx; >> query: (?x3292, 05p553) <- genre(?x3292, ?x53), production_companies(?x3292, ?x382), film(?x496, ?x3292), produced_by(?x797, ?x496) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #6074 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1053 *> proper extension: 02vw1w2; 0d1qmz; *> query: (?x3292, 02n4kr) <- genre(?x3292, ?x53), film_release_distribution_medium(?x3292, ?x81), genre(?x5496, ?x53), ?x5496 = 07l50vn *> conf = 0.14 ranks of expected_values: 12 EVAL 0gvs1kt genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 92.000 91.000 0.403 http://example.org/film/film/genre #16861-064_8sq PRED entity: 064_8sq PRED relation: languages! PRED expected values: 039crh 0cbkc 02hh8j 0bqch 015010 => 77 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1537): 012d40 (0.50 #1199, 0.40 #2395, 0.40 #1797), 0prjs (0.50 #658, 0.40 #1854, 0.33 #3648), 0btpx (0.50 #1020, 0.40 #2216, 0.33 #4010), 015d3h (0.50 #837, 0.40 #2033, 0.33 #3827), 01_rh4 (0.50 #1373, 0.33 #177, 0.30 #5559), 02wk4d (0.50 #1507, 0.33 #311, 0.25 #909), 03f1zhf (0.50 #1684, 0.33 #488, 0.25 #1086), 01_x6v (0.50 #1309, 0.33 #113, 0.25 #711), 0hqly (0.50 #1735, 0.33 #539, 0.25 #1137), 01k3qj (0.50 #1575, 0.33 #379, 0.25 #977) >> Best rule #1199 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 03_9r; >> query: (?x5607, 012d40) <- language(?x7741, ?x5607), language(?x4375, ?x5607), language(?x1919, ?x5607), language(?x1470, ?x5607), currency(?x1919, ?x170), service_language(?x127, ?x5607), film_release_region(?x1470, ?x87), ?x7741 = 01xq8v, ?x4375 = 01rxyb >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3436 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 01z4y; *> query: (?x5607, 0cbkc) <- titles(?x5607, ?x9279), titles(?x5607, ?x2903), titles(?x5607, ?x2380), major_field_of_study(?x865, ?x5607), film_crew_role(?x2903, ?x2095), ?x2095 = 0dxtw, nominated_for(?x77, ?x9279), honored_for(?x762, ?x2380) *> conf = 0.40 ranks of expected_values: 12, 129, 420 EVAL 064_8sq languages! 015010 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 77.000 55.000 0.500 http://example.org/people/person/languages EVAL 064_8sq languages! 0bqch CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 55.000 0.500 http://example.org/people/person/languages EVAL 064_8sq languages! 02hh8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 55.000 0.500 http://example.org/people/person/languages EVAL 064_8sq languages! 0cbkc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 77.000 55.000 0.500 http://example.org/people/person/languages EVAL 064_8sq languages! 039crh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 77.000 55.000 0.500 http://example.org/people/person/languages #16860-02633g PRED entity: 02633g PRED relation: award PRED expected values: 05zr6wv => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 285): 01by1l (0.33 #111, 0.32 #14254, 0.24 #9406), 01bgqh (0.33 #43, 0.23 #14186, 0.17 #9338), 0c4z8 (0.33 #71, 0.17 #14214, 0.15 #9366), 01cky2 (0.33 #194, 0.08 #14337, 0.07 #9489), 09sb52 (0.32 #17012, 0.27 #15800, 0.24 #19033), 0gr51 (0.22 #907, 0.14 #1311, 0.14 #1715), 019f4v (0.22 #874, 0.08 #1278, 0.08 #1682), 040njc (0.21 #816, 0.17 #8, 0.10 #1220), 0gs9p (0.21 #887, 0.09 #3716, 0.07 #1291), 054ks3 (0.20 #14284, 0.14 #9436, 0.10 #1353) >> Best rule #111 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 012x4t; 027kmrb; >> query: (?x8065, 01by1l) <- profession(?x8065, ?x319), award_nominee(?x521, ?x8065), ?x521 = 0147dk >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 012x4t; 027kmrb; *> query: (?x8065, 05zr6wv) <- profession(?x8065, ?x319), award_nominee(?x521, ?x8065), ?x521 = 0147dk *> conf = 0.17 ranks of expected_values: 16 EVAL 02633g award 05zr6wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 97.000 97.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16859-025r_t PRED entity: 025r_t PRED relation: contains! PRED expected values: 07ssc => 145 concepts (92 used for prediction) PRED predicted values (max 10 best out of 284): 07ssc (0.98 #63596, 0.97 #56401, 0.91 #38528), 09c7w0 (0.69 #48345, 0.68 #22386, 0.65 #23281), 059rby (0.62 #26878, 0.25 #3597, 0.20 #7179), 02qkt (0.50 #32575, 0.42 #37944, 0.36 #47792), 03rk0 (0.34 #50267, 0.34 #49372, 0.31 #52059), 036wy (0.33 #2553, 0.25 #7028, 0.22 #13294), 0d060g (0.33 #908, 0.25 #3590, 0.10 #42089), 05kr_ (0.33 #1020, 0.25 #3702, 0.07 #35933), 07z1m (0.31 #34110, 0.29 #36795, 0.25 #4563), 04_1l0v (0.30 #51476, 0.26 #60432, 0.25 #55955) >> Best rule #63596 for best value: >> intensional similarity = 5 >> extensional distance = 143 >> proper extension: 0ymbl; 022_6; 0crjn65; 01k8q5; 0dplh; 0c_zj; 0fgj2; 013bqg; 01ykl0; 02f46y; ... >> query: (?x11117, 07ssc) <- contains(?x1976, ?x11117), contains(?x1310, ?x11117), jurisdiction_of_office(?x14694, ?x1976), place_of_birth(?x1975, ?x1976), ?x1310 = 02jx1 >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025r_t contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 92.000 0.979 http://example.org/location/location/contains #16858-0qlrh PRED entity: 0qlrh PRED relation: contains! PRED expected values: 05fjf => 102 concepts (68 used for prediction) PRED predicted values (max 10 best out of 533): 0n5kc (0.80 #45747, 0.80 #43949, 0.76 #59210), 05fjf (0.78 #49339, 0.77 #60112, 0.50 #1273), 09c7w0 (0.74 #18838, 0.73 #30500, 0.72 #34983), 01n7q (0.53 #8150, 0.50 #14426, 0.43 #10840), 01x73 (0.33 #2808, 0.31 #5497, 0.30 #3704), 0kpys (0.31 #18119, 0.24 #16324, 0.24 #10943), 0n5j7 (0.25 #1256, 0.25 #357, 0.14 #59211), 0m2fr (0.25 #5850, 0.22 #3161, 0.20 #4057), 04_1l0v (0.25 #2248, 0.20 #4040, 0.12 #5833), 0n5dt (0.25 #1615, 0.14 #59211, 0.10 #45748) >> Best rule #45747 for best value: >> intensional similarity = 6 >> extensional distance = 93 >> proper extension: 0mb2b; 0vp5f; >> query: (?x13665, ?x8766) <- category(?x13665, ?x134), county(?x13665, ?x8766), ?x134 = 08mbj5d, time_zones(?x13665, ?x2674), ?x2674 = 02hcv8, adjoins(?x6478, ?x8766) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #49339 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 96 *> proper extension: 0mmzt; 013hxv; 0mndw; 0gkgp; 0dzt9; 0fwc0; 0mm_4; 0mp08; 0mp36; 0yz30; *> query: (?x13665, ?x6895) <- category(?x13665, ?x134), county(?x13665, ?x8766), ?x134 = 08mbj5d, time_zones(?x13665, ?x2674), ?x2674 = 02hcv8, source(?x8766, ?x958), contains(?x6895, ?x8766) *> conf = 0.78 ranks of expected_values: 2 EVAL 0qlrh contains! 05fjf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 68.000 0.800 http://example.org/location/location/contains #16857-039crh PRED entity: 039crh PRED relation: program PRED expected values: 06hwzy => 146 concepts (127 used for prediction) PRED predicted values (max 10 best out of 27): 06hwzy (0.59 #106, 0.38 #333, 0.37 #257), 01f3p_ (0.56 #176, 0.24 #302, 0.09 #708), 0n2bh (0.56 #176, 0.09 #708, 0.08 #707), 01b7h8 (0.33 #43, 0.13 #93, 0.13 #294), 01j7mr (0.17 #158, 0.16 #108, 0.13 #83), 01h1bf (0.17 #283, 0.13 #435, 0.06 #384), 02zv4b (0.17 #28, 0.07 #78, 0.03 #103), 03gvm3t (0.14 #164, 0.05 #442, 0.03 #89), 0cpz4k (0.13 #84, 0.11 #159, 0.09 #336), 039cq4 (0.11 #389, 0.09 #288, 0.09 #162) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 016qtt; 01yznp; 0993r; 05r5w; 03f1d47; 02ktrs; >> query: (?x4407, 06hwzy) <- program(?x4407, ?x4891), profession(?x4407, ?x319), film(?x4407, ?x5024), person(?x3480, ?x4407) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039crh program 06hwzy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 127.000 0.594 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #16856-01l9p PRED entity: 01l9p PRED relation: actor! PRED expected values: 02hct1 => 123 concepts (117 used for prediction) PRED predicted values (max 10 best out of 79): 0294mx (0.11 #13002, 0.10 #14331, 0.10 #14597), 0g5qs2k (0.11 #13002, 0.10 #14331, 0.10 #14597), 06nr2h (0.09 #16989, 0.08 #23629, 0.07 #29202), 083shs (0.09 #16989, 0.08 #23629, 0.07 #29202), 0557yqh (0.08 #56, 0.01 #4831, 0.01 #5097), 030p35 (0.08 #81, 0.01 #876), 026bfsh (0.05 #892, 0.03 #7260, 0.03 #3546), 05lfwd (0.03 #7267, 0.03 #1429, 0.03 #1694), 034fl9 (0.03 #443, 0.02 #973, 0.02 #2300), 0dl6fv (0.03 #436, 0.02 #966, 0.01 #4946) >> Best rule #13002 for best value: >> intensional similarity = 3 >> extensional distance = 681 >> proper extension: 01sl1q; 01j5ts; 0p_pd; 0z4s; 054_mz; 03w1v2; 01gvr1; 01csvq; 058kqy; 0785v8; ... >> query: (?x1735, ?x504) <- location(?x1735, ?x1523), award_winner(?x504, ?x1735), award_nominee(?x1641, ?x1735) >> conf = 0.11 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01l9p actor! 02hct1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 117.000 0.115 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #16855-02p76f9 PRED entity: 02p76f9 PRED relation: nominated_for! PRED expected values: 05ztjjw 0l8z1 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 192): 05b4l5x (0.67 #238, 0.11 #2801, 0.10 #3034), 05b1610 (0.67 #262, 0.08 #20770, 0.08 #21236), 0f4x7 (0.52 #13304, 0.31 #1420, 0.29 #3517), 04ljl_l (0.50 #236, 0.22 #28669, 0.20 #23306), 07bdd_ (0.50 #283, 0.08 #5642, 0.08 #2846), 05f4m9q (0.50 #244, 0.08 #5603, 0.07 #6302), 07cbcy (0.50 #294, 0.07 #24705, 0.07 #1925), 019f4v (0.48 #1449, 0.42 #1216, 0.42 #3546), 0gq9h (0.46 #13342, 0.45 #1458, 0.43 #8914), 04kxsb (0.43 #558, 0.37 #13374, 0.24 #20275) >> Best rule #238 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01kff7; 02krdz; 01qvz8; 0ch3qr1; >> query: (?x8284, 05b4l5x) <- cinematography(?x8284, ?x4997), award(?x8284, ?x2325), nominated_for(?x2871, ?x8284), ?x2325 = 05p09zm >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1447 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 07xtqq; 016z5x; 0jzw; 0cwy47; 0pv3x; 0168ls; 02q52q; 0hmm7; 0bx0l; 083skw; ... *> query: (?x8284, 0l8z1) <- cinematography(?x8284, ?x4997), award(?x8284, ?x507), nominated_for(?x2871, ?x8284), film_production_design_by(?x8284, ?x3080) *> conf = 0.29 ranks of expected_values: 29, 34 EVAL 02p76f9 nominated_for! 0l8z1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 130.000 130.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02p76f9 nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 130.000 130.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16854-022_q8 PRED entity: 022_q8 PRED relation: profession PRED expected values: 0q04f => 133 concepts (107 used for prediction) PRED predicted values (max 10 best out of 81): 01d_h8 (0.85 #1320, 0.84 #1466, 0.82 #2050), 03gjzk (0.59 #2641, 0.42 #1473, 0.42 #3079), 0cbd2 (0.30 #3073, 0.28 #3657, 0.28 #3803), 09jwl (0.25 #17, 0.22 #4397, 0.21 #4543), 0nbcg (0.25 #29, 0.15 #4409, 0.14 #4555), 016z4k (0.25 #4, 0.12 #4384, 0.11 #4530), 039v1 (0.25 #34, 0.09 #4414, 0.08 #4560), 018gz8 (0.21 #3957, 0.17 #307, 0.15 #2643), 0kyk (0.17 #3969, 0.14 #3093, 0.13 #5867), 0np9r (0.17 #895, 0.16 #749, 0.15 #2647) >> Best rule #1320 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 013zyw; >> query: (?x5591, 01d_h8) <- student(?x2486, ?x5591), film(?x5591, ?x7516), written_by(?x6272, ?x5591), nominated_for(?x941, ?x7516) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #827 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 07hbxm; *> query: (?x5591, 0q04f) <- student(?x2486, ?x5591), profession(?x5591, ?x524), nominated_for(?x5591, ?x1863), ?x2486 = 015nl4 *> conf = 0.09 ranks of expected_values: 15 EVAL 022_q8 profession 0q04f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 133.000 107.000 0.848 http://example.org/people/person/profession #16853-03xj05 PRED entity: 03xj05 PRED relation: film_release_region PRED expected values: 09c7w0 => 115 concepts (108 used for prediction) PRED predicted values (max 10 best out of 273): 09c7w0 (0.80 #722, 0.78 #5034, 0.77 #362), 0h7x (0.61 #18900, 0.50 #11703, 0.47 #3594), 03rjj (0.50 #11703, 0.47 #3594, 0.46 #18899), 0d0vqn (0.29 #551, 0.28 #9187, 0.28 #13879), 03_3d (0.29 #549, 0.27 #729, 0.24 #9185), 059j2 (0.29 #584, 0.25 #13912, 0.25 #9220), 07ssc (0.29 #563, 0.24 #17664, 0.24 #16049), 0jgd (0.29 #544, 0.24 #9180, 0.23 #3419), 0chghy (0.29 #556, 0.23 #13884, 0.23 #1276), 0345h (0.29 #586, 0.22 #17687, 0.22 #13914) >> Best rule #722 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 056k77g; >> query: (?x10619, 09c7w0) <- genre(?x10619, ?x2605), genre(?x10619, ?x1626), ?x1626 = 03q4nz, film_crew_role(?x10619, ?x137), major_field_of_study(?x5638, ?x2605), student(?x5638, ?x2239) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xj05 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 108.000 0.800 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16852-07x4qr PRED entity: 07x4qr PRED relation: genre PRED expected values: 01hmnh => 63 concepts (61 used for prediction) PRED predicted values (max 10 best out of 88): 07s9rl0 (0.76 #4894, 0.59 #1073, 0.57 #3940), 01hmnh (0.48 #2282, 0.34 #1447, 0.33 #137), 02kdv5l (0.48 #1432, 0.48 #241, 0.41 #2267), 01jfsb (0.43 #250, 0.33 #131, 0.33 #2752), 02l7c8 (0.37 #2160, 0.27 #850, 0.27 #2518), 06n90 (0.33 #132, 0.29 #251, 0.25 #1442), 04xvlr (0.20 #1074, 0.16 #478, 0.14 #3941), 0lsxr (0.19 #247, 0.19 #2511, 0.18 #2749), 060__y (0.17 #1089, 0.17 #136, 0.15 #1685), 04pbhw (0.17 #294, 0.17 #175, 0.09 #771) >> Best rule #4894 for best value: >> intensional similarity = 3 >> extensional distance = 1330 >> proper extension: 05jyb2; 0cq8nx; 0c5qvw; >> query: (?x2512, 07s9rl0) <- genre(?x2512, ?x811), genre(?x10661, ?x811), ?x10661 = 06qv_ >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #2282 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 524 *> proper extension: 01h72l; 016ztl; *> query: (?x2512, 01hmnh) <- genre(?x2512, ?x811), genre(?x50, ?x811), genre(?x2628, ?x811), ?x2628 = 06wbm8q *> conf = 0.48 ranks of expected_values: 2 EVAL 07x4qr genre 01hmnh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 61.000 0.755 http://example.org/film/film/genre #16851-02vk52z PRED entity: 02vk52z PRED relation: contact_category PRED expected values: 03w5xm => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 2): 03w5xm (0.89 #65, 0.87 #94, 0.86 #102), 02zdwq (0.36 #66, 0.35 #76, 0.31 #101) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 01s73z; >> query: (?x127, 03w5xm) <- company(?x346, ?x127), service_language(?x127, ?x254), ?x346 = 060c4, contact_category(?x127, ?x3231) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vk52z contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.889 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #16850-0l8z1 PRED entity: 0l8z1 PRED relation: category_of PRED expected values: 0g_w => 66 concepts (63 used for prediction) PRED predicted values (max 10 best out of 3): 0g_w (0.81 #276, 0.62 #234, 0.60 #45), 0c4ys (0.64 #337, 0.48 #635, 0.42 #443), 0gcf2r (0.27 #401, 0.25 #530, 0.25 #422) >> Best rule #276 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 018wng; 0gr07; >> query: (?x1079, 0g_w) <- ceremony(?x1079, ?x8478), award(?x669, ?x1079), ?x8478 = 0bzjgq, award_winner(?x1079, ?x84) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l8z1 category_of 0g_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 63.000 0.810 http://example.org/award/award_category/category_of #16849-08swgx PRED entity: 08swgx PRED relation: participant! PRED expected values: 01trhmt => 75 concepts (31 used for prediction) PRED predicted values (max 10 best out of 55): 030vnj (0.08 #2924, 0.08 #2658, 0.08 #2392), 01q7cb_ (0.06 #25, 0.05 #290, 0.03 #555), 0dvmd (0.04 #1421, 0.02 #91, 0.02 #356), 01vs_v8 (0.03 #588, 0.03 #854, 0.02 #323), 0127s7 (0.03 #700, 0.02 #170, 0.02 #435), 01wxyx1 (0.03 #1385, 0.02 #55, 0.02 #320), 0151w_ (0.02 #1357, 0.02 #27, 0.02 #557), 0c6qh (0.02 #1398), 01p4vl (0.02 #3456, 0.02 #199, 0.02 #464), 019pm_ (0.02 #3456, 0.02 #341, 0.02 #606) >> Best rule #2924 for best value: >> intensional similarity = 3 >> extensional distance = 526 >> proper extension: 0152cw; 01wk7b7; 01z0rcq; 08b8vd; 0btyl; 0c2ry; 0gmtm; 03cn92; 0hwbd; 01wc7p; ... >> query: (?x2844, ?x8291) <- film(?x2844, ?x1045), profession(?x2844, ?x1032), participant(?x8291, ?x2844) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08swgx participant! 01trhmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 31.000 0.082 http://example.org/base/popstra/celebrity/canoodled./base/popstra/canoodled/participant #16848-01k6y1 PRED entity: 01k6y1 PRED relation: nationality! PRED expected values: 048cl => 146 concepts (83 used for prediction) PRED predicted values (max 10 best out of 4081): 0277c3 (0.67 #48807, 0.50 #101687, 0.48 #191170), 018dyl (0.67 #48807, 0.50 #101687, 0.48 #191170), 0l9k1 (0.67 #48807, 0.50 #101687, 0.48 #191170), 01h2_6 (0.67 #48807, 0.50 #101687, 0.48 #191170), 04kj2v (0.67 #48807, 0.50 #101687, 0.48 #191170), 0hskw (0.67 #48807, 0.50 #101687, 0.48 #191170), 0bqytm (0.67 #48807, 0.50 #101687, 0.48 #191170), 01kwld (0.67 #48807, 0.50 #101687, 0.48 #191170), 01dhpj (0.39 #191169, 0.34 #134227, 0.33 #2542), 08c7cz (0.39 #191169, 0.34 #134227, 0.22 #47091) >> Best rule #48807 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0h44w; >> query: (?x4493, ?x628) <- capital(?x4493, ?x1646), location(?x1221, ?x4493), vacationer(?x1646, ?x3466), place_of_birth(?x628, ?x1646), contains(?x1646, ?x196) >> conf = 0.67 => this is the best rule for 8 predicted values *> Best rule #47064 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0h44w; *> query: (?x4493, 048cl) <- capital(?x4493, ?x1646), location(?x1221, ?x4493), vacationer(?x1646, ?x3466), place_of_birth(?x628, ?x1646), contains(?x1646, ?x196) *> conf = 0.22 ranks of expected_values: 64 EVAL 01k6y1 nationality! 048cl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 146.000 83.000 0.667 http://example.org/people/person/nationality #16847-07tl0 PRED entity: 07tl0 PRED relation: student PRED expected values: 0c9c0 => 157 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1586): 0d3k14 (0.20 #8133, 0.07 #20691, 0.06 #31152), 01tnbn (0.20 #7338, 0.07 #19896, 0.01 #65931), 0n00 (0.15 #8371, 0.14 #4733, 0.13 #6825), 01tdnyh (0.15 #8371, 0.13 #7168, 0.13 #9261), 0tfc (0.15 #8371, 0.07 #20845, 0.07 #8287), 03s9v (0.15 #8371, 0.07 #5445, 0.04 #9630), 0jcx (0.15 #8371, 0.05 #19365, 0.04 #25641), 0l6qt (0.14 #4202, 0.13 #6294, 0.09 #8387), 041xl (0.14 #5454, 0.13 #9639, 0.11 #11734), 04cbtrw (0.14 #4656, 0.09 #470, 0.09 #8841) >> Best rule #8133 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 02l9wl; >> query: (?x1369, 0d3k14) <- student(?x1369, ?x12146), student(?x1369, ?x8085), organization(?x12146, ?x5250), religion(?x12146, ?x4641), languages(?x8085, ?x90) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07tl0 student 0c9c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 157.000 36.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #16846-01f7j9 PRED entity: 01f7j9 PRED relation: award_nominee PRED expected values: 0cv9fc => 113 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1403): 0cv9fc (0.81 #9354, 0.80 #51449, 0.80 #44433), 03ktjq (0.81 #9354, 0.80 #51449, 0.80 #44433), 03xp8d5 (0.19 #3363, 0.03 #43119, 0.02 #26749), 03xpf_7 (0.19 #3002, 0.02 #42758, 0.02 #54452), 03xpfzg (0.19 #4604, 0.02 #44360, 0.01 #56054), 07_s4b (0.19 #3003, 0.02 #42759, 0.01 #54453), 0721cy (0.19 #2824, 0.02 #42580, 0.01 #54274), 030_3z (0.17 #1075, 0.16 #30403, 0.07 #79511), 0415svh (0.17 #147, 0.16 #30403, 0.06 #2485), 01f7j9 (0.17 #464, 0.16 #30403, 0.06 #2802) >> Best rule #9354 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 02zyy4; >> query: (?x2182, ?x382) <- award_nominee(?x382, ?x2182), place_of_birth(?x2182, ?x1860), ?x1860 = 01_d4 >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01f7j9 award_nominee 0cv9fc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 40.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16845-01m3b1t PRED entity: 01m3b1t PRED relation: artist! PRED expected values: 02jjdr => 171 concepts (101 used for prediction) PRED predicted values (max 10 best out of 111): 03rhqg (0.22 #1276, 0.17 #3238, 0.16 #2536), 0g768 (0.21 #37, 0.15 #1997, 0.15 #1297), 01cf93 (0.17 #338, 0.13 #758, 0.13 #1038), 011k1h (0.14 #290, 0.13 #710, 0.12 #3652), 01dtcb (0.14 #327, 0.13 #747, 0.11 #467), 01w40h (0.14 #28, 0.11 #308, 0.11 #168), 033hn8 (0.13 #8838, 0.13 #3236, 0.13 #10382), 073tm9 (0.13 #1996, 0.08 #2136, 0.07 #5358), 043g7l (0.12 #3253, 0.11 #2131, 0.11 #3533), 01cl2y (0.12 #2550, 0.11 #2410, 0.09 #1290) >> Best rule #1276 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 01lcxbb; >> query: (?x7240, 03rhqg) <- origin(?x7240, ?x6769), artists(?x505, ?x7240), artist(?x3265, ?x7240), ?x505 = 03_d0 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1132 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 04bpm6; 0k7pf; 01kd57; 0bvzp; 01l3mk3; 06rgq; 01d4cb; 01c7p_; 02g40r; *> query: (?x7240, 02jjdr) <- award(?x7240, ?x1443), award_nominee(?x7240, ?x12304), music(?x805, ?x7240), role(?x7240, ?x2206) *> conf = 0.02 ranks of expected_values: 80 EVAL 01m3b1t artist! 02jjdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 171.000 101.000 0.222 http://example.org/music/record_label/artist #16844-0466p20 PRED entity: 0466p20 PRED relation: nutrient! PRED expected values: 0fjfh 033cnk => 25 concepts (16 used for prediction) PRED predicted values (max 10 best out of 24): 01nkt (0.91 #496, 0.91 #486, 0.90 #473), 0fjfh (0.90 #419, 0.90 #401, 0.90 #386), 0fj52s (0.90 #405, 0.90 #381, 0.90 #369), 0cxn2 (0.89 #481, 0.87 #238, 0.87 #223), 033cnk (0.88 #469, 0.88 #455, 0.88 #442), 07j87 (0.88 #66, 0.88 #64, 0.88 #46), 014j1m (0.88 #399, 0.88 #391, 0.88 #377), 0fbw6 (0.86 #435, 0.86 #418, 0.85 #402), 0f25w9 (0.85 #506, 0.85 #479, 0.85 #306), 0971v (0.83 #464, 0.83 #453, 0.83 #137) >> Best rule #496 for best value: >> intensional similarity = 138 >> extensional distance = 44 >> proper extension: 01w_3; 0f4k5; >> query: (?x3901, ?x6032) <- nutrient(?x10612, ?x3901), nutrient(?x9005, ?x3901), nutrient(?x7057, ?x3901), nutrient(?x2701, ?x3901), nutrient(?x7057, ?x13944), nutrient(?x7057, ?x12902), nutrient(?x7057, ?x12083), nutrient(?x7057, ?x11784), nutrient(?x7057, ?x11758), nutrient(?x7057, ?x11409), nutrient(?x7057, ?x11270), nutrient(?x7057, ?x10891), nutrient(?x7057, ?x10709), nutrient(?x7057, ?x10195), nutrient(?x7057, ?x10098), nutrient(?x7057, ?x9949), nutrient(?x7057, ?x9915), nutrient(?x7057, ?x9840), nutrient(?x7057, ?x9733), nutrient(?x7057, ?x9619), nutrient(?x7057, ?x9490), nutrient(?x7057, ?x9436), nutrient(?x7057, ?x9426), nutrient(?x7057, ?x9365), nutrient(?x7057, ?x8413), nutrient(?x7057, ?x7894), nutrient(?x7057, ?x7720), nutrient(?x7057, ?x7652), nutrient(?x7057, ?x7431), nutrient(?x7057, ?x7364), nutrient(?x7057, ?x7362), nutrient(?x7057, ?x7219), nutrient(?x7057, ?x7135), nutrient(?x7057, ?x6586), nutrient(?x7057, ?x6286), nutrient(?x7057, ?x6192), nutrient(?x7057, ?x6160), nutrient(?x7057, ?x6033), nutrient(?x7057, ?x6026), nutrient(?x7057, ?x5549), nutrient(?x7057, ?x5526), nutrient(?x7057, ?x5451), nutrient(?x7057, ?x5374), nutrient(?x7057, ?x5337), nutrient(?x7057, ?x5010), nutrient(?x7057, ?x4069), nutrient(?x7057, ?x3469), nutrient(?x7057, ?x3203), nutrient(?x7057, ?x2702), nutrient(?x7057, ?x2018), nutrient(?x7057, ?x1960), nutrient(?x7057, ?x1304), nutrient(?x7057, ?x1258), ?x2702 = 0838f, ?x9490 = 0h1sg, ?x10709 = 0h1sz, ?x9005 = 04zpv, nutrient(?x2701, ?x13498), nutrient(?x2701, ?x13126), nutrient(?x2701, ?x8487), nutrient(?x2701, ?x8442), ?x8413 = 02kc4sf, ?x5549 = 025s7j4, ?x10891 = 0g5gq, ?x7652 = 025s0s0, ?x7431 = 09gwd, ?x1304 = 08lb68, ?x9619 = 0h1tg, ?x11784 = 07zqy, ?x7720 = 025s7x6, ?x10612 = 0frq6, ?x9436 = 025sqz8, ?x6160 = 041r51, ?x7362 = 02kc5rj, nutrient(?x9732, ?x5374), nutrient(?x9489, ?x5374), nutrient(?x8298, ?x5374), nutrient(?x7719, ?x5374), nutrient(?x6191, ?x5374), nutrient(?x6159, ?x5374), nutrient(?x6032, ?x5374), nutrient(?x5373, ?x5374), nutrient(?x5009, ?x5374), nutrient(?x4068, ?x5374), nutrient(?x1959, ?x5374), nutrient(?x1303, ?x5374), nutrient(?x1257, ?x5374), ?x9915 = 025tkqy, ?x9733 = 0h1tz, ?x5010 = 0h1vz, ?x10098 = 0h1_c, ?x13944 = 0f4kp, ?x3469 = 0h1zw, ?x5526 = 09pbb, ?x10195 = 0hkwr, ?x1959 = 0f25w9, ?x7219 = 0h1vg, ?x1258 = 0h1wg, ?x12083 = 01n78x, ?x11270 = 02kc008, ?x8298 = 037ls6, ?x9732 = 05z55, ?x4068 = 0fbw6, ?x6026 = 025sf8g, ?x2018 = 01sh2, ?x4069 = 0hqw8p_, ?x6286 = 02y_3rf, ?x9840 = 02p0tjr, ?x7135 = 025rsfk, ?x8442 = 02kcv4x, ?x5451 = 05wvs, ?x6586 = 05gh50, ?x7719 = 0dj75, ?x6191 = 014j1m, ?x3203 = 04kl74p, ?x12902 = 0fzjh, ?x11409 = 0h1yf, ?x9365 = 04k8n, ?x7894 = 0f4hc, ?x5009 = 0fjfh, ?x1303 = 0fj52s, ?x5373 = 0971v, ?x9949 = 02kd0rh, ?x13498 = 07q0m, ?x6033 = 04zjxcz, ?x5337 = 06x4c, ?x6159 = 033cnk, ?x11758 = 0q01m, ?x7364 = 09gvd, ?x1960 = 07hnp, ?x13126 = 02kc_w5, ?x9426 = 0h1yy, ?x6032 = 01nkt, ?x1257 = 09728, ?x6192 = 06jry, ?x8487 = 014yzm, ?x9489 = 07j87, nutrient(?x2701, ?x9426) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #419 for first EXPECTED value: *> intensional similarity = 148 *> extensional distance = 40 *> proper extension: 01sh2; 06x4c; *> query: (?x3901, 0fjfh) <- nutrient(?x10612, ?x3901), nutrient(?x9005, ?x3901), nutrient(?x7057, ?x3901), nutrient(?x6285, ?x3901), nutrient(?x3900, ?x3901), nutrient(?x2701, ?x3901), ?x2701 = 0hkxq, ?x9005 = 04zpv, ?x3900 = 061_f, nutrient(?x6285, ?x13944), nutrient(?x6285, ?x13498), nutrient(?x6285, ?x12902), nutrient(?x6285, ?x12868), nutrient(?x6285, ?x12454), nutrient(?x6285, ?x11784), nutrient(?x6285, ?x11758), nutrient(?x6285, ?x11592), nutrient(?x6285, ?x11409), nutrient(?x6285, ?x11270), nutrient(?x6285, ?x10891), nutrient(?x6285, ?x10709), nutrient(?x6285, ?x10195), nutrient(?x6285, ?x10098), nutrient(?x6285, ?x9949), nutrient(?x6285, ?x9915), nutrient(?x6285, ?x9855), nutrient(?x6285, ?x9795), nutrient(?x6285, ?x9733), nutrient(?x6285, ?x9619), nutrient(?x6285, ?x9490), nutrient(?x6285, ?x9426), nutrient(?x6285, ?x9365), nutrient(?x6285, ?x8487), nutrient(?x6285, ?x8442), nutrient(?x6285, ?x8413), nutrient(?x6285, ?x8243), nutrient(?x6285, ?x7894), nutrient(?x6285, ?x7720), nutrient(?x6285, ?x7652), nutrient(?x6285, ?x7364), nutrient(?x6285, ?x7362), nutrient(?x6285, ?x7219), nutrient(?x6285, ?x6586), nutrient(?x6285, ?x6286), nutrient(?x6285, ?x6160), nutrient(?x6285, ?x6033), nutrient(?x6285, ?x6026), nutrient(?x6285, ?x5549), nutrient(?x6285, ?x5526), nutrient(?x6285, ?x5451), nutrient(?x6285, ?x5374), nutrient(?x6285, ?x5010), nutrient(?x6285, ?x4069), nutrient(?x6285, ?x3469), nutrient(?x6285, ?x3203), nutrient(?x6285, ?x2702), nutrient(?x6285, ?x1960), nutrient(?x6285, ?x1304), nutrient(?x6285, ?x1258), ?x7364 = 09gvd, ?x6160 = 041r51, ?x10098 = 0h1_c, ?x9855 = 0d9t0, ?x12868 = 03d49, ?x13944 = 0f4kp, ?x9733 = 0h1tz, ?x3469 = 0h1zw, ?x5010 = 0h1vz, ?x5549 = 025s7j4, ?x8442 = 02kcv4x, ?x11270 = 02kc008, ?x8487 = 014yzm, ?x12902 = 0fzjh, ?x7652 = 025s0s0, ?x12454 = 025rw19, ?x7362 = 02kc5rj, ?x11758 = 0q01m, ?x11409 = 0h1yf, ?x1258 = 0h1wg, ?x7894 = 0f4hc, ?x9949 = 02kd0rh, ?x10195 = 0hkwr, ?x9795 = 05v_8y, ?x4069 = 0hqw8p_, ?x5451 = 05wvs, ?x1304 = 08lb68, nutrient(?x7719, ?x8243), nutrient(?x6191, ?x8243), nutrient(?x6032, ?x8243), nutrient(?x5337, ?x8243), nutrient(?x4068, ?x8243), nutrient(?x3264, ?x8243), nutrient(?x1257, ?x8243), ?x7057 = 0fbdb, ?x5337 = 06x4c, ?x9426 = 0h1yy, ?x1960 = 07hnp, ?x8413 = 02kc4sf, nutrient(?x9732, ?x9915), nutrient(?x9489, ?x9915), nutrient(?x8298, ?x9915), nutrient(?x6159, ?x9915), nutrient(?x3468, ?x9915), nutrient(?x1303, ?x9915), ?x3203 = 04kl74p, ?x9619 = 0h1tg, ?x9489 = 07j87, ?x6159 = 033cnk, ?x9365 = 04k8n, ?x10891 = 0g5gq, ?x5374 = 025s0zp, ?x1303 = 0fj52s, ?x6191 = 014j1m, ?x2702 = 0838f, ?x6033 = 04zjxcz, ?x8298 = 037ls6, ?x4068 = 0fbw6, ?x10709 = 0h1sz, ?x3468 = 0cxn2, ?x13498 = 07q0m, ?x9732 = 05z55, ?x6286 = 02y_3rf, ?x11592 = 025sf0_, ?x6032 = 01nkt, ?x5526 = 09pbb, ?x7720 = 025s7x6, ?x6026 = 025sf8g, ?x9490 = 0h1sg, ?x3264 = 0dcfv, nutrient(?x10612, ?x14210), nutrient(?x10612, ?x13545), nutrient(?x10612, ?x12083), nutrient(?x10612, ?x9436), nutrient(?x10612, ?x7431), nutrient(?x10612, ?x6517), nutrient(?x10612, ?x6192), ?x6192 = 06jry, ?x7219 = 0h1vg, ?x14210 = 0f4k5, ?x7719 = 0dj75, ?x6586 = 05gh50, ?x6517 = 02kd8zw, ?x13545 = 01w_3, ?x11784 = 07zqy, ?x12083 = 01n78x, ?x9436 = 025sqz8, ?x1257 = 09728, ?x7431 = 09gwd *> conf = 0.90 ranks of expected_values: 2, 5 EVAL 0466p20 nutrient! 033cnk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 25.000 16.000 0.913 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0466p20 nutrient! 0fjfh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 16.000 0.913 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #16843-01n4w PRED entity: 01n4w PRED relation: religion PRED expected values: 072w0 => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 21): 0flw86 (0.55 #596, 0.48 #155, 0.44 #199), 01s5nb (0.55 #596, 0.40 #451, 0.39 #253), 03j6c (0.33 #8, 0.25 #52, 0.25 #30), 0kpl (0.33 #3, 0.25 #47, 0.25 #25), 07w8f (0.33 #16, 0.25 #60, 0.25 #38), 072w0 (0.23 #320, 0.23 #100, 0.22 #430), 04t_mf (0.07 #211, 0.05 #167, 0.04 #1540), 0n2g (0.05 #158, 0.04 #202, 0.03 #1420), 06yyp (0.05 #163, 0.04 #207, 0.03 #229), 042s9 (0.05 #176, 0.04 #220, 0.03 #242) >> Best rule #596 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 082pc; >> query: (?x2982, ?x109) <- adjoins(?x2982, ?x1024), country(?x2982, ?x94), religion(?x1024, ?x109) >> conf = 0.55 => this is the best rule for 2 predicted values *> Best rule #320 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 0g0syc; *> query: (?x2982, 072w0) <- district_represented(?x6728, ?x2982), district_represented(?x653, ?x2982), ?x653 = 070m6c, ?x6728 = 070mff *> conf = 0.23 ranks of expected_values: 6 EVAL 01n4w religion 072w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 220.000 220.000 0.548 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #16842-01vrt_c PRED entity: 01vrt_c PRED relation: profession PRED expected values: 016z4k 02hrh1q => 139 concepts (124 used for prediction) PRED predicted values (max 10 best out of 85): 02hrh1q (0.92 #1496, 0.91 #3716, 0.89 #7722), 016z4k (0.58 #9492, 0.46 #8749, 0.46 #1633), 01d_h8 (0.51 #450, 0.46 #153, 0.42 #2523), 0nbcg (0.50 #1661, 0.49 #328, 0.48 #9966), 0dxtg (0.35 #13, 0.31 #1051, 0.31 #5051), 0fj9f (0.29 #54, 0.11 #2128, 0.11 #1092), 0n1h (0.28 #1641, 0.27 #15276, 0.24 #11), 01c72t (0.28 #7582, 0.28 #9808, 0.27 #7285), 039v1 (0.27 #15276, 0.24 #9525, 0.23 #778), 03gjzk (0.27 #7871, 0.27 #8167, 0.27 #163) >> Best rule #1496 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 02w5q6; >> query: (?x1206, 02hrh1q) <- participant(?x1206, ?x5906), participant(?x1207, ?x1206), people(?x1050, ?x1206) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01vrt_c profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 124.000 0.917 http://example.org/people/person/profession EVAL 01vrt_c profession 016z4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 124.000 0.917 http://example.org/people/person/profession #16841-018p5f PRED entity: 018p5f PRED relation: list PRED expected values: 01ptsx => 197 concepts (197 used for prediction) PRED predicted values (max 10 best out of 5): 01ptsx (0.83 #376, 0.65 #467, 0.62 #383), 04k4rt (0.53 #403, 0.52 #466, 0.48 #543), 01pd60 (0.45 #545, 0.42 #503, 0.42 #405), 09g7thr (0.15 #764, 0.15 #785, 0.15 #890), 026cl_m (0.04 #451, 0.03 #920, 0.01 #990) >> Best rule #376 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 0gy1_; >> query: (?x7390, 01ptsx) <- company(?x4682, ?x7390), company(?x346, ?x7390), citytown(?x7390, ?x1719), ?x346 = 060c4, ?x4682 = 0dq_5 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018p5f list 01ptsx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 197.000 197.000 0.829 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #16840-03ksy PRED entity: 03ksy PRED relation: student PRED expected values: 011zf2 012wg 0bvzp 03q45x 0969fd 03xds => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 1602): 01tdnyh (0.25 #4875, 0.14 #2863, 0.12 #10909), 0l6qt (0.25 #4039, 0.14 #2027, 0.12 #10073), 0ff3y (0.25 #6012, 0.12 #12046, 0.11 #14058), 06y7d (0.19 #56321, 0.18 #60345, 0.11 #76443), 01zwy (0.19 #56321, 0.18 #60345, 0.11 #76443), 0x3r3 (0.19 #56321, 0.18 #60345, 0.11 #76443), 07n39 (0.19 #56321, 0.18 #60345, 0.11 #76443), 05fyss (0.19 #56321, 0.18 #60345, 0.11 #76443), 02cqbx (0.17 #8991, 0.09 #23072, 0.08 #29108), 0gt3p (0.17 #9333, 0.09 #23414, 0.08 #29450) >> Best rule #4875 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 039cpd; >> query: (?x3439, 01tdnyh) <- child(?x3439, ?x4278), contains(?x2020, ?x3439), category(?x3439, ?x134) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #8247 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 10 *> proper extension: 06y3r; 023p29; 0n839; *> query: (?x3439, 011zf2) <- list(?x3439, ?x2197), organizations_founded(?x3439, ?x5487) *> conf = 0.08 ranks of expected_values: 275, 453, 509, 1136, 1237 EVAL 03ksy student 03xds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 116.000 116.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 03ksy student 0969fd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 116.000 116.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 03ksy student 03q45x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 116.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 03ksy student 0bvzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 03ksy student 012wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 116.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 03ksy student 011zf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 116.000 116.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #16839-042kg PRED entity: 042kg PRED relation: student! PRED expected values: 05qjt => 111 concepts (95 used for prediction) PRED predicted values (max 10 best out of 41): 062z7 (0.31 #146, 0.18 #395, 0.15 #582), 0g26h (0.15 #281, 0.08 #94, 0.06 #156), 03g3w (0.12 #769, 0.11 #1019, 0.10 #707), 03nfmq (0.08 #90, 0.04 #464, 0.02 #1278), 04gb7 (0.07 #969, 0.06 #1408, 0.06 #158), 02j62 (0.05 #210, 0.05 #335, 0.04 #460), 06ms6 (0.05 #198, 0.05 #323, 0.04 #510), 036hv (0.05 #194, 0.05 #381, 0.04 #444), 041y2 (0.05 #300, 0.03 #737, 0.02 #799), 01lhf (0.05 #305, 0.02 #1054, 0.02 #1243) >> Best rule #146 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 034ls; >> query: (?x11290, 062z7) <- profession(?x11290, ?x2225), student(?x2497, ?x11290), basic_title(?x11290, ?x346), person(?x3124, ?x11290) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #441 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 011zf2; 0443c; *> query: (?x11290, 05qjt) <- award_winner(?x3846, ?x11290), nationality(?x11290, ?x94), student(?x2497, ?x11290), ?x3846 = 05qck *> conf = 0.04 ranks of expected_values: 12 EVAL 042kg student! 05qjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 111.000 95.000 0.312 http://example.org/education/field_of_study/students_majoring./education/education/student #16838-0275n3y PRED entity: 0275n3y PRED relation: ceremony! PRED expected values: 02x258x 02x1z2s => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 337): 02x1z2s (0.71 #397, 0.45 #653, 0.42 #1422), 02x258x (0.71 #350, 0.45 #606, 0.42 #1375), 0gqy2 (0.49 #8334, 0.49 #5763, 0.48 #7561), 018wng (0.48 #7474, 0.48 #6961, 0.48 #5676), 0k611 (0.48 #7512, 0.48 #6999, 0.48 #8285), 0gq_d (0.48 #8371, 0.47 #7598, 0.47 #7085), 0gqwc (0.48 #8272, 0.47 #6986, 0.46 #7499), 0gvx_ (0.48 #8349, 0.47 #7063, 0.46 #7576), 0gkts9 (0.47 #2173, 0.44 #1917, 0.27 #2687), 0gqyl (0.47 #5722, 0.46 #7520, 0.46 #7007) >> Best rule #397 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 03gwpw2; 0fqpc7d; 09bymc; >> query: (?x5592, 02x1z2s) <- honored_for(?x5592, ?x1135), award_winner(?x5592, ?x6324), award_winner(?x5592, ?x382), film(?x6324, ?x667), award_nominee(?x6324, ?x830), award_nominee(?x829, ?x6324), location(?x6324, ?x335), taxonomy(?x335, ?x939), state_province_region(?x166, ?x335), production_companies(?x339, ?x382) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0275n3y ceremony! 02x1z2s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0275n3y ceremony! 02x258x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony #16837-02h0f3 PRED entity: 02h0f3 PRED relation: film PRED expected values: 05ypj5 => 151 concepts (68 used for prediction) PRED predicted values (max 10 best out of 522): 014gjp (0.60 #119978, 0.60 #118187, 0.49 #112813), 05jyb2 (0.20 #14325, 0.15 #12534, 0.11 #3582), 026y3cf (0.20 #14325, 0.15 #12534, 0.11 #3582), 032016 (0.15 #2294, 0.05 #11247, 0.04 #5876), 0k5fg (0.15 #2880, 0.04 #18995, 0.04 #8253), 027pfg (0.15 #3013, 0.01 #35238, 0.01 #56724), 016dj8 (0.15 #2904, 0.01 #54824, 0.01 #110346), 02qr3k8 (0.10 #4871, 0.08 #1289, 0.07 #8452), 0gl3hr (0.10 #4680, 0.02 #19003, 0.02 #22583), 0k7tq (0.10 #4764, 0.01 #22667) >> Best rule #119978 for best value: >> intensional similarity = 3 >> extensional distance = 931 >> proper extension: 03f1zdw; 01438g; 01wb8bs; 01bcq; 02756j; 05lb30; 031k24; 026rm_y; 01bh6y; 01wk3c; ... >> query: (?x7550, ?x7551) <- award_winner(?x870, ?x7550), film(?x7550, ?x3294), nominated_for(?x7550, ?x7551) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #32168 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 225 *> proper extension: 08b8vd; 01d1yr; 011xjd; 01d5vk; 013qvn; 02cvp8; 03k1vm; 07tvwy; 0202p_; 026c0p; ... *> query: (?x7550, 05ypj5) <- people(?x6260, ?x7550), film(?x7550, ?x3294) *> conf = 0.01 ranks of expected_values: 377 EVAL 02h0f3 film 05ypj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 151.000 68.000 0.596 http://example.org/film/actor/film./film/performance/film #16836-04gxp2 PRED entity: 04gxp2 PRED relation: organization! PRED expected values: 05k17c => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 23): 060c4 (0.75 #15, 0.73 #146, 0.72 #159), 05k17c (0.29 #98, 0.15 #85, 0.12 #111), 07xl34 (0.21 #376, 0.20 #272, 0.20 #311), 0dq3c (0.09 #118, 0.03 #497, 0.02 #707), 0hm4q (0.07 #418, 0.07 #126, 0.05 #86), 08jcfy (0.07 #418, 0.02 #234, 0.02 #90), 016fly (0.07 #418), 07t3gd (0.07 #418), 02md_2 (0.07 #418), 01___w (0.07 #418) >> Best rule #15 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 02w2bc; 01j_cy; 015fs3; 037q2p; 02grjf; >> query: (?x13215, 060c4) <- contains(?x1906, ?x13215), ?x1906 = 04rrx, student(?x13215, ?x9684), category(?x13215, ?x134), school_type(?x13215, ?x3092) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #98 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 027mdh; *> query: (?x13215, 05k17c) <- institution(?x1519, ?x13215), school_type(?x13215, ?x3092), ?x1519 = 013zdg, category(?x13215, ?x134) *> conf = 0.29 ranks of expected_values: 2 EVAL 04gxp2 organization! 05k17c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.750 http://example.org/organization/role/leaders./organization/leadership/organization #16835-0h5j77 PRED entity: 0h5j77 PRED relation: award PRED expected values: 0gr42 => 98 concepts (70 used for prediction) PRED predicted values (max 10 best out of 241): 0fbtbt (0.60 #232, 0.44 #1038, 0.19 #1844), 09sb52 (0.41 #1249, 0.37 #12938, 0.28 #4070), 0gqy2 (0.27 #3388, 0.10 #8628, 0.09 #14271), 0ck27z (0.27 #2106, 0.24 #2912, 0.21 #6943), 0gq9h (0.24 #3300, 0.14 #12974, 0.11 #15392), 0279c15 (0.20 #136, 0.19 #19752, 0.18 #21366), 05zr6wv (0.20 #17, 0.15 #1226, 0.15 #11689), 04kxsb (0.20 #125, 0.15 #11689, 0.14 #17736), 05pcn59 (0.20 #80, 0.15 #11689, 0.14 #17736), 02w9sd7 (0.20 #170, 0.15 #11689, 0.14 #17736) >> Best rule #232 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 04h68j; >> query: (?x7507, 0fbtbt) <- award_nominee(?x10215, ?x7507), ?x10215 = 025y9fn, profession(?x7507, ?x1943), ?x1943 = 02krf9 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2533 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 458 *> proper extension: 0n6f8; 022_lg; 0gsg7; 0cjdk; 0kk9v; 05gnf; *> query: (?x7507, 0gr42) <- award_winner(?x8238, ?x7507), award_winner(?x4037, ?x7507), category(?x4037, ?x134) *> conf = 0.01 ranks of expected_values: 232 EVAL 0h5j77 award 0gr42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 98.000 70.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16834-0gtv7pk PRED entity: 0gtv7pk PRED relation: produced_by PRED expected values: 0ksf29 => 75 concepts (37 used for prediction) PRED predicted values (max 10 best out of 114): 025n3p (0.25 #100, 0.14 #488, 0.06 #1266), 06cv1 (0.14 #408, 0.11 #1186, 0.09 #1963), 03_gd (0.14 #417, 0.06 #1195, 0.05 #1583), 02xnjd (0.11 #1439, 0.11 #1827, 0.09 #2216), 01sl1q (0.10 #388, 0.05 #10103, 0.05 #12055), 0b478 (0.10 #948, 0.05 #1726, 0.04 #2895), 0fvf9q (0.10 #782, 0.04 #4285, 0.03 #10112), 0mdqp (0.10 #803, 0.04 #3140, 0.02 #4306), 0151ns (0.10 #388, 0.03 #776, 0.02 #1554), 03dbds (0.10 #1035) >> Best rule #100 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0401sg; 0bq6ntw; >> query: (?x409, 025n3p) <- film(?x56, ?x409), ?x56 = 01sl1q, film_release_region(?x409, ?x985), film_release_region(?x409, ?x87), ?x985 = 0k6nt, ?x87 = 05r4w >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gtv7pk produced_by 0ksf29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 37.000 0.250 http://example.org/film/film/produced_by #16833-0lcdk PRED entity: 0lcdk PRED relation: people PRED expected values: 0168dy => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 573): 02h48 (0.50 #4859, 0.42 #4858, 0.25 #4860), 012c6j (0.50 #4859, 0.42 #4858, 0.17 #8223), 0lrh (0.50 #4859, 0.42 #4858, 0.17 #7738), 07s3vqk (0.50 #4859, 0.42 #4858, 0.17 #7644), 024qwq (0.49 #4861, 0.49 #4857, 0.47 #4856), 053yx (0.49 #4861, 0.49 #4857, 0.47 #4856), 02vmzp (0.49 #4861, 0.49 #4857, 0.47 #4856), 01lc5 (0.49 #4861, 0.49 #4857, 0.47 #4856), 0h326 (0.49 #4861, 0.49 #4857, 0.47 #4856), 034qt_ (0.49 #4861, 0.49 #4857, 0.47 #4856) >> Best rule #4859 for best value: >> intensional similarity = 26 >> extensional distance = 2 >> proper extension: 0gk4g; >> query: (?x11392, ?x215) <- symptom_of(?x13487, ?x11392), symptom_of(?x6780, ?x11392), symptom_of(?x4905, ?x11392), ?x13487 = 01cdt5, symptom_of(?x6780, ?x14562), symptom_of(?x6780, ?x13131), symptom_of(?x6780, ?x10480), symptom_of(?x6780, ?x9898), symptom_of(?x6780, ?x6781), symptom_of(?x6780, ?x6656), symptom_of(?x6780, ?x1158), people(?x1158, ?x1159), risk_factors(?x1158, ?x8523), ?x14562 = 087z2, ?x8523 = 0c58k, risk_factors(?x11392, ?x268), symptom_of(?x10717, ?x6656), ?x10717 = 0cjf0, risk_factors(?x4291, ?x13131), ?x6781 = 035482, people(?x13131, ?x6358), people(?x9898, ?x12334), people(?x9898, ?x215), ?x4905 = 01j6t0, ?x10480 = 0h1n9, ?x12334 = 02h48 >> conf = 0.50 => this is the best rule for 4 predicted values *> Best rule #10240 for first EXPECTED value: *> intensional similarity = 31 *> extensional distance = 5 *> proper extension: 07jwr; 0dq9p; 02k6hp; 014w_8; 0h1wz; *> query: (?x11392, 0168dy) <- symptom_of(?x13487, ?x11392), symptom_of(?x6780, ?x11392), ?x13487 = 01cdt5, symptom_of(?x6780, ?x14562), symptom_of(?x6780, ?x13231), symptom_of(?x6780, ?x11307), symptom_of(?x6780, ?x10480), symptom_of(?x6780, ?x9898), symptom_of(?x6780, ?x6656), symptom_of(?x6780, ?x1158), people(?x1158, ?x1159), risk_factors(?x1158, ?x8524), risk_factors(?x1158, ?x8523), risk_factors(?x1158, ?x5802), risk_factors(?x1158, ?x231), ?x14562 = 087z2, ?x8523 = 0c58k, risk_factors(?x6655, ?x13231), risk_factors(?x11392, ?x268), ?x6656 = 03p41, risk_factors(?x5784, ?x1158), ?x6655 = 09d11, symptom_of(?x9438, ?x11307), ?x8524 = 01hbgs, ?x10480 = 0h1n9, ?x9438 = 012qjw, people(?x11307, ?x6745), ?x231 = 05zppz, ?x5802 = 0k95h, risk_factors(?x11307, ?x4195), ?x9898 = 09jg8 *> conf = 0.14 ranks of expected_values: 261 EVAL 0lcdk people 0168dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 47.000 47.000 0.497 http://example.org/people/cause_of_death/people #16832-06p03s PRED entity: 06p03s PRED relation: origin PRED expected values: 04jpl => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 91): 030qb3t (0.33 #34, 0.11 #1686, 0.07 #2158), 04jpl (0.20 #242, 0.13 #950, 0.08 #714), 02_286 (0.20 #1196, 0.09 #2140, 0.08 #724), 02ly_ (0.20 #336, 0.07 #1280, 0.07 #1044), 095l0 (0.20 #400, 0.02 #3468), 0fm2_ (0.20 #262, 0.02 #3330), 0b_yz (0.10 #654, 0.08 #890, 0.02 #2542), 0n6dc (0.10 #666, 0.06 #1846, 0.03 #2082), 04f_d (0.10 #515, 0.06 #1695, 0.02 #2403), 0f94t (0.10 #494, 0.06 #1674, 0.02 #2382) >> Best rule #34 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0mjn2; >> query: (?x11689, 030qb3t) <- award(?x11689, ?x4488), award(?x11689, ?x1389), ?x4488 = 02gdjb, category(?x11689, ?x134), ?x1389 = 01c427, artists(?x302, ?x11689) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #242 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0m19t; *> query: (?x11689, 04jpl) <- artists(?x6714, ?x11689), artists(?x3243, ?x11689), artists(?x497, ?x11689), ?x6714 = 07d2d, ?x497 = 0fd3y, parent_genre(?x996, ?x3243) *> conf = 0.20 ranks of expected_values: 2 EVAL 06p03s origin 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.333 http://example.org/music/artist/origin #16831-02p21g PRED entity: 02p21g PRED relation: special_performance_type PRED expected values: 01pb34 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.38 #8, 0.20 #3, 0.15 #29), 01kyvx (0.07 #268, 0.02 #17, 0.02 #494), 09_gdc (0.06 #7, 0.03 #28, 0.02 #179) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0285c; 01xllf; >> query: (?x1593, 01pb34) <- film(?x1593, ?x8495), ?x8495 = 0ds5_72, profession(?x1593, ?x987) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02p21g special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.375 http://example.org/film/actor/film./film/performance/special_performance_type #16830-02cm61 PRED entity: 02cm61 PRED relation: major_field_of_study! PRED expected values: 07vk2 02tz9z => 57 concepts (21 used for prediction) PRED predicted values (max 10 best out of 646): 01w3v (0.69 #1195, 0.69 #605, 0.69 #1785), 06pwq (0.64 #6508, 0.63 #4733, 0.62 #602), 012mzw (0.62 #1483, 0.62 #893, 0.56 #2073), 08815 (0.62 #591, 0.57 #3544, 0.56 #1771), 07szy (0.62 #633, 0.57 #2996, 0.54 #1223), 05zl0 (0.62 #1407, 0.57 #3180, 0.50 #1997), 09f2j (0.58 #2540, 0.54 #1356, 0.52 #10814), 01mpwj (0.52 #3661, 0.35 #4839, 0.31 #4250), 07wrz (0.50 #66, 0.48 #3608, 0.46 #1245), 05mv4 (0.50 #145, 0.33 #3687, 0.31 #1324) >> Best rule #1195 for best value: >> intensional similarity = 12 >> extensional distance = 11 >> proper extension: 05qjt; 04x_3; 0193x; 03nfmq; 01lj9; 0dc_v; 01tbp; >> query: (?x12363, 01w3v) <- major_field_of_study(?x5750, ?x12363), major_field_of_study(?x5288, ?x12363), major_field_of_study(?x4599, ?x12363), ?x5288 = 02zd460, ?x4599 = 07t90, major_field_of_study(?x1200, ?x12363), taxonomy(?x12363, ?x939), student(?x5750, ?x652), fraternities_and_sororities(?x5750, ?x3697), citytown(?x5750, ?x108), institution(?x865, ?x5750), ?x865 = 02h4rq6 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1235 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 11 *> proper extension: 05qjt; 04x_3; 0193x; 03nfmq; 01lj9; 0dc_v; 01tbp; *> query: (?x12363, 07vk2) <- major_field_of_study(?x5750, ?x12363), major_field_of_study(?x5288, ?x12363), major_field_of_study(?x4599, ?x12363), ?x5288 = 02zd460, ?x4599 = 07t90, major_field_of_study(?x1200, ?x12363), taxonomy(?x12363, ?x939), student(?x5750, ?x652), fraternities_and_sororities(?x5750, ?x3697), citytown(?x5750, ?x108), institution(?x865, ?x5750), ?x865 = 02h4rq6 *> conf = 0.31 ranks of expected_values: 74, 152 EVAL 02cm61 major_field_of_study! 02tz9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 57.000 21.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02cm61 major_field_of_study! 07vk2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 57.000 21.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16829-064_8sq PRED entity: 064_8sq PRED relation: major_field_of_study! PRED expected values: 019v9k => 89 concepts (83 used for prediction) PRED predicted values (max 10 best out of 20): 02_xgp2 (0.79 #373, 0.75 #352, 0.64 #1055), 04zx3q1 (0.79 #364, 0.62 #283, 0.58 #343), 019v9k (0.77 #1051, 0.68 #1333, 0.68 #1113), 0bkj86 (0.64 #368, 0.62 #287, 0.62 #1050), 03bwzr4 (0.64 #374, 0.62 #293, 0.53 #1056), 0bjrnt (0.50 #285, 0.42 #345, 0.33 #84), 01ysy9 (0.38 #301, 0.33 #361, 0.33 #100), 022h5x (0.33 #98, 0.30 #1065, 0.25 #179), 01rr_d (0.33 #95, 0.30 #1065, 0.25 #176), 013zdg (0.33 #85, 0.30 #1065, 0.25 #166) >> Best rule #373 for best value: >> intensional similarity = 7 >> extensional distance = 12 >> proper extension: 03ll3; >> query: (?x5607, 02_xgp2) <- major_field_of_study(?x5607, ?x1668), major_field_of_study(?x12086, ?x1668), major_field_of_study(?x581, ?x1668), major_field_of_study(?x122, ?x1668), ?x12086 = 07w6r, ?x581 = 06pwq, ?x122 = 08815 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1051 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 51 *> proper extension: 06ntj; *> query: (?x5607, 019v9k) <- major_field_of_study(?x5607, ?x1668), major_field_of_study(?x12086, ?x1668), major_field_of_study(?x4296, ?x1668), institution(?x1200, ?x12086), student(?x12086, ?x5086), ?x4296 = 07vyf *> conf = 0.77 ranks of expected_values: 3 EVAL 064_8sq major_field_of_study! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 83.000 0.786 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #16828-01w7nwm PRED entity: 01w7nwm PRED relation: award PRED expected values: 01c9dd => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 281): 01by1l (0.45 #1705, 0.39 #6083, 0.34 #5287), 01bgqh (0.39 #1635, 0.32 #4819, 0.31 #6013), 02f5qb (0.38 #953, 0.22 #1749, 0.15 #6127), 09sb52 (0.33 #22727, 0.33 #4021, 0.26 #13175), 03qbh5 (0.32 #1795, 0.27 #2193, 0.27 #4979), 01c427 (0.31 #483, 0.24 #881, 0.22 #1279), 03t5n3 (0.29 #1041, 0.18 #35424, 0.18 #1837), 02v1m7 (0.29 #910, 0.18 #1706, 0.15 #33035), 02f73b (0.25 #1477, 0.20 #1875, 0.19 #1079), 054ks3 (0.24 #2133, 0.22 #1735, 0.19 #6909) >> Best rule #1705 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 036px; >> query: (?x3175, 01by1l) <- artist(?x382, ?x3175), award_winner(?x827, ?x3175), currency(?x3175, ?x170) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #35424 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1531 *> proper extension: 0f721s; 01_8w2; 01p5yn; 0283xx2; 01j53q; 04rqd; 03lpbx; *> query: (?x3175, ?x3835) <- award_winner(?x3384, ?x3175), award_winner(?x3835, ?x3384) *> conf = 0.18 ranks of expected_values: 29 EVAL 01w7nwm award 01c9dd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 120.000 120.000 0.446 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16827-01s7ns PRED entity: 01s7ns PRED relation: nationality PRED expected values: 06mkj => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 48): 06mkj (0.44 #4977, 0.27 #12444, 0.01 #1538), 01s47p (0.44 #4977), 077qn (0.27 #12444, 0.25 #58), 02xry (0.27 #12444, 0.25 #11448), 03v9w (0.27 #12444), 087vz (0.27 #12444), 02j9z (0.27 #12444), 0d060g (0.25 #6, 0.06 #6580, 0.06 #602), 0f2v0 (0.25 #11448, 0.09 #994, 0.05 #4778), 0rn8q (0.25 #11448) >> Best rule #4977 for best value: >> intensional similarity = 3 >> extensional distance = 277 >> proper extension: 05fh2; >> query: (?x11026, ?x2152) <- place_of_birth(?x11026, ?x4698), location(?x2161, ?x4698), capital(?x2152, ?x4698) >> conf = 0.44 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01s7ns nationality 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.444 http://example.org/people/person/nationality #16826-07fj_ PRED entity: 07fj_ PRED relation: organization PRED expected values: 02vk52z 0b6css => 140 concepts (136 used for prediction) PRED predicted values (max 10 best out of 50): 02vk52z (0.89 #887, 0.87 #1685, 0.87 #1707), 01rz1 (0.57 #46, 0.42 #223, 0.42 #555), 0_2v (0.51 #114, 0.42 #380, 0.41 #357), 0b6css (0.48 #54, 0.39 #563, 0.38 #763), 041288 (0.45 #347, 0.45 #525, 0.34 #1634), 018cqq (0.39 #55, 0.35 #232, 0.33 #387), 04k4l (0.39 #226, 0.38 #780, 0.38 #824), 02jxk (0.32 #2513, 0.26 #47, 0.26 #224), 0j7v_ (0.32 #2513, 0.25 #1624, 0.25 #1381), 059dn (0.32 #2513, 0.10 #236, 0.09 #302) >> Best rule #887 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 059ss; >> query: (?x4521, 02vk52z) <- adjoins(?x4521, ?x291), contains(?x4521, ?x4522), organization(?x4521, ?x312) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 07fj_ organization 0b6css CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 136.000 0.890 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07fj_ organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 136.000 0.890 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #16825-09cdxn PRED entity: 09cdxn PRED relation: people! PRED expected values: 0dq9p => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 30): 0gk4g (0.14 #1331, 0.13 #1661, 0.12 #1859), 0qcr0 (0.08 #1322, 0.08 #1, 0.07 #1652), 0dq9p (0.08 #1470, 0.08 #1206, 0.07 #1074), 04p3w (0.06 #1464, 0.05 #1530, 0.05 #1926), 02y0js (0.05 #1455, 0.05 #1059, 0.05 #1191), 02k6hp (0.05 #1490, 0.04 #1094, 0.04 #1556), 08q1tg (0.04 #62, 0.04 #194, 0.04 #128), 0m32h (0.04 #23, 0.04 #155, 0.04 #89), 01bcp7 (0.04 #13, 0.04 #145, 0.04 #79), 0jdk0 (0.04 #5, 0.01 #732, 0.01 #798) >> Best rule #1331 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 0h1_w; 015wfg; 015qq1; 02vkvcz; >> query: (?x6115, 0gk4g) <- place_of_death(?x6115, ?x242), award(?x6115, ?x1243), nominated_for(?x6115, ?x2779) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1470 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 331 *> proper extension: 0cl_m; 01l3j; *> query: (?x6115, 0dq9p) <- place_of_death(?x6115, ?x242), place_of_birth(?x241, ?x242), county(?x242, ?x2949) *> conf = 0.08 ranks of expected_values: 3 EVAL 09cdxn people! 0dq9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 89.000 0.136 http://example.org/people/cause_of_death/people #16824-06pj8 PRED entity: 06pj8 PRED relation: participant! PRED expected values: 0157m => 166 concepts (134 used for prediction) PRED predicted values (max 10 best out of 410): 01vhrz (0.81 #48381, 0.80 #53474, 0.80 #56026), 02mjmr (0.44 #1273, 0.38 #16549, 0.06 #824), 0bq2g (0.44 #1273, 0.38 #16549, 0.02 #9167), 0343h (0.18 #3821, 0.09 #10184, 0.04 #21005), 029q_y (0.18 #1117, 0.08 #8119, 0.08 #16392), 046zh (0.12 #996, 0.08 #7361, 0.08 #7998), 01rr9f (0.12 #670, 0.07 #15945, 0.05 #7672), 01pqy_ (0.12 #993, 0.05 #7995, 0.04 #4178), 0cgfb (0.12 #1246, 0.05 #8248, 0.04 #4431), 02d9k (0.12 #759, 0.05 #7761, 0.04 #3944) >> Best rule #48381 for best value: >> intensional similarity = 2 >> extensional distance = 310 >> proper extension: 09x8ms; >> query: (?x2135, ?x6278) <- participant(?x2135, ?x6278), student(?x735, ?x2135) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #744 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0282x; *> query: (?x2135, 0157m) <- executive_produced_by(?x825, ?x2135), friend(?x2669, ?x2135), profession(?x2135, ?x319) *> conf = 0.06 ranks of expected_values: 27 EVAL 06pj8 participant! 0157m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 166.000 134.000 0.813 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #16823-01l849 PRED entity: 01l849 PRED relation: colors! PRED expected values: 065y4w7 06jk5_ 02jyr8 0bqxw 02897w 07vhb 02zkz7 03y5ky 0lbfv 01qd_r 04zwc 039d4 0jpkw 019_6d 03205_ 02cvcd 012gyf 09k23 => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 518): 01jszm (0.50 #4218, 0.40 #4589, 0.33 #3103), 01s7pm (0.50 #4417, 0.33 #2931, 0.33 #1817), 016ndm (0.50 #4186, 0.33 #2700, 0.33 #1214), 02bpy_ (0.50 #3666, 0.33 #2922, 0.33 #311), 06rkfs (0.50 #3623, 0.33 #2879, 0.33 #639), 01fsv9 (0.50 #3648, 0.33 #2904, 0.25 #4390), 0288zy (0.50 #3365, 0.33 #2621, 0.25 #4107), 02l424 (0.50 #3610, 0.33 #2866, 0.25 #4352), 0173s9 (0.50 #3585, 0.33 #2841, 0.25 #4327), 02ngbs (0.50 #3980, 0.33 #2123, 0.19 #5957) >> Best rule #4218 for best value: >> intensional similarity = 37 >> extensional distance = 2 >> proper extension: 03vtbc; >> query: (?x332, 01jszm) <- colors(?x11761, ?x332), colors(?x9768, ?x332), colors(?x9724, ?x332), colors(?x8287, ?x332), colors(?x8220, ?x332), colors(?x6988, ?x332), colors(?x2171, ?x332), colors(?x9995, ?x332), colors(?x8079, ?x332), colors(?x5233, ?x332), colors(?x705, ?x332), student(?x9724, ?x3051), citytown(?x2171, ?x8980), student(?x9768, ?x7749), school(?x3674, ?x9768), ?x9995 = 0jm9w, currency(?x9724, ?x170), position_s(?x705, ?x180), company(?x4228, ?x8287), major_field_of_study(?x9768, ?x1154), citytown(?x6988, ?x6987), student(?x6988, ?x2870), ?x5233 = 0j5m6, category(?x8220, ?x134), organization(?x346, ?x9768), institution(?x865, ?x8287), school_type(?x11761, ?x12633), contains(?x94, ?x2171), contains(?x2049, ?x8287), school(?x1632, ?x2171), teams(?x1523, ?x705), position(?x705, ?x1114), position(?x8079, ?x1348), school(?x4779, ?x2171), teams(?x2941, ?x8079), institution(?x734, ?x2171), ?x94 = 09c7w0 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4732 for first EXPECTED value: *> intensional similarity = 35 *> extensional distance = 3 *> proper extension: 03wkwg; *> query: (?x332, 0jpkw) <- colors(?x10240, ?x332), colors(?x9768, ?x332), colors(?x9724, ?x332), colors(?x8434, ?x332), colors(?x4824, ?x332), colors(?x2171, ?x332), colors(?x6847, ?x332), colors(?x1576, ?x332), student(?x9724, ?x3051), award_winner(?x3486, ?x2171), institution(?x865, ?x9768), school_type(?x9724, ?x4994), category(?x8434, ?x134), student(?x2171, ?x11371), student(?x2171, ?x10730), student(?x2171, ?x5562), student(?x4824, ?x1169), currency(?x8434, ?x170), state_province_region(?x2171, ?x1767), position(?x1576, ?x180), team(?x5897, ?x6847), profession(?x10730, ?x1943), film(?x10730, ?x1080), sport(?x1576, ?x1083), citytown(?x4824, ?x3014), award_nominee(?x11371, ?x827), contains(?x5174, ?x9768), ?x1943 = 02krf9, time_zones(?x10240, ?x5327), state_province_region(?x9724, ?x108), school(?x3674, ?x9768), draft(?x1576, ?x465), team(?x1579, ?x6847), award_winner(?x4242, ?x5562), major_field_of_study(?x9768, ?x1154) *> conf = 0.40 ranks of expected_values: 27, 121, 126, 174, 246, 297, 304, 305, 308, 321, 325, 329, 334, 347, 350, 355, 357, 502 EVAL 01l849 colors! 09k23 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 012gyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 02cvcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 03205_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 019_6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 0jpkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 039d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 04zwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 01qd_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 0lbfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 03y5ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 02zkz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 07vhb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 02897w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 0bqxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 02jyr8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 06jk5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01l849 colors! 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors #16822-0gl3hr PRED entity: 0gl3hr PRED relation: production_companies PRED expected values: 0g1rw => 81 concepts (73 used for prediction) PRED predicted values (max 10 best out of 57): 05qd_ (0.24 #174, 0.21 #92, 0.20 #586), 0g1rw (0.17 #254, 0.15 #584, 0.15 #90), 016tt2 (0.17 #250, 0.15 #86, 0.14 #580), 086k8 (0.14 #166, 0.13 #1487, 0.12 #1404), 016tw3 (0.13 #1497, 0.12 #1332, 0.11 #1414), 017s11 (0.11 #1488, 0.10 #1405, 0.09 #661), 054lpb6 (0.09 #1500, 0.08 #179, 0.08 #1335), 02jd_7 (0.07 #234, 0.05 #563, 0.04 #481), 01gb54 (0.06 #1523, 0.06 #778, 0.06 #3502), 0k9ctht (0.06 #125, 0.05 #371, 0.05 #619) >> Best rule #174 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 024mpp; 04cv9m; 06fqlk; >> query: (?x6243, 05qd_) <- written_by(?x6243, ?x4477), costume_design_by(?x6243, ?x2068), production_companies(?x6243, ?x13497) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #254 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 0jsqk; *> query: (?x6243, 0g1rw) <- film(?x3017, ?x6243), produced_by(?x6243, ?x4943), film_sets_designed(?x12725, ?x6243) *> conf = 0.17 ranks of expected_values: 2 EVAL 0gl3hr production_companies 0g1rw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 73.000 0.237 http://example.org/film/film/production_companies #16821-0ct_yc PRED entity: 0ct_yc PRED relation: nationality PRED expected values: 02jx1 => 85 concepts (49 used for prediction) PRED predicted values (max 10 best out of 119): 07ssc (0.96 #3096, 0.67 #3210, 0.48 #910), 09c7w0 (0.85 #4708, 0.83 #4304, 0.78 #4607), 02jx1 (0.82 #2387, 0.58 #828, 0.57 #2520), 03rk0 (0.78 #2434, 0.28 #4050, 0.27 #4149), 04jpl (0.56 #4807, 0.43 #2589, 0.35 #4808), 036wy (0.35 #4808, 0.35 #4505, 0.34 #2590), 06s_2 (0.33 #96, 0.25 #295, 0.09 #3698), 0ctw_b (0.25 #126, 0.24 #1194, 0.20 #325), 05cgv (0.25 #129, 0.24 #1194, 0.20 #328), 034m8 (0.25 #291, 0.24 #1194, 0.14 #489) >> Best rule #3096 for best value: >> intensional similarity = 6 >> extensional distance = 446 >> proper extension: 07z542; 04gtq43; >> query: (?x9779, ?x512) <- nationality(?x9779, ?x5622), adjustment_currency(?x5622, ?x170), organization(?x5622, ?x127), place_of_birth(?x9779, ?x14093), country(?x1121, ?x5622), country(?x14093, ?x512) >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #2387 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 221 *> proper extension: 0436f4; 026lj; 03jm6c; 01wz_ml; 019vgs; 02xwq9; 03nk3t; 09bx1k; 0kp2_; 05gnf9; ... *> query: (?x9779, ?x1310) <- gender(?x9779, ?x231), ?x231 = 05zppz, place_of_birth(?x9779, ?x14093), contains(?x362, ?x14093), second_level_divisions(?x1310, ?x14093) *> conf = 0.82 ranks of expected_values: 3 EVAL 0ct_yc nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 49.000 0.958 http://example.org/people/person/nationality #16820-043n0v_ PRED entity: 043n0v_ PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #69, 0.84 #64, 0.83 #109), 07z4p (0.25 #280, 0.21 #346, 0.07 #15), 07c52 (0.25 #280, 0.21 #346, 0.06 #18), 02nxhr (0.21 #346, 0.06 #83, 0.05 #65) >> Best rule #69 for best value: >> intensional similarity = 6 >> extensional distance = 165 >> proper extension: 035s95; 014nq4; 01q2nx; 08984j; 0bw20; 07p12s; 01qdmh; >> query: (?x5038, 029j_) <- genre(?x5038, ?x1626), genre(?x5038, ?x225), ?x225 = 02kdv5l, film_crew_role(?x5038, ?x468), genre(?x10147, ?x1626), ?x10147 = 04z4j2 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043n0v_ film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #16819-03rqww PRED entity: 03rqww PRED relation: profession PRED expected values: 0dgd_ => 112 concepts (62 used for prediction) PRED predicted values (max 10 best out of 52): 0dgd_ (0.92 #1511, 0.88 #918, 0.88 #1214), 02hrh1q (0.78 #4457, 0.76 #3421, 0.74 #2975), 01d_h8 (0.70 #2523, 0.67 #2671, 0.53 #5337), 0dxtg (0.67 #3568, 0.62 #2086, 0.61 #2530), 03gjzk (0.62 #2384, 0.58 #2236, 0.58 #2088), 01d30f (0.38 #366, 0.02 #1254, 0.02 #1403), 0cbd2 (0.37 #3562, 0.25 #303, 0.19 #4746), 018gz8 (0.25 #165, 0.22 #3572, 0.21 #2238), 0kyk (0.25 #325, 0.19 #3584, 0.13 #4768), 09jwl (0.15 #463, 0.15 #9055, 0.15 #6387) >> Best rule #1511 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 07xr3w; >> query: (?x8248, 0dgd_) <- cinematography(?x634, ?x8248), nationality(?x8248, ?x94), nominated_for(?x8248, ?x174), profession(?x8248, ?x524) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rqww profession 0dgd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 62.000 0.917 http://example.org/people/person/profession #16818-01rxw PRED entity: 01rxw PRED relation: adjoins! PRED expected values: 0j4b => 97 concepts (73 used for prediction) PRED predicted values (max 10 best out of 368): 01p1b (0.22 #57017, 0.22 #55453, 0.22 #50760), 01rxw (0.22 #57017, 0.22 #55453, 0.22 #50760), 02kcz (0.22 #57017, 0.22 #55453, 0.22 #50760), 06tw8 (0.22 #57017, 0.22 #55453, 0.22 #50760), 07dzf (0.22 #57017, 0.22 #55453, 0.22 #50760), 088vb (0.22 #57017, 0.22 #55453, 0.22 #50760), 0j4b (0.22 #57017, 0.22 #55453, 0.22 #50760), 0169t (0.22 #57017, 0.22 #55453, 0.22 #50760), 06dfg (0.22 #57017, 0.22 #55453, 0.22 #50760), 05rznz (0.22 #57017, 0.22 #55453, 0.22 #50760) >> Best rule #57017 for best value: >> intensional similarity = 4 >> extensional distance = 594 >> proper extension: 0mrf1; >> query: (?x6863, ?x1577) <- adjoins(?x6974, ?x6863), adjoins(?x2804, ?x6863), adjoins(?x2804, ?x1577), contains(?x6974, ?x14027) >> conf = 0.22 => this is the best rule for 11 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7 EVAL 01rxw adjoins! 0j4b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 73.000 0.221 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #16817-0crs0b8 PRED entity: 0crs0b8 PRED relation: genre PRED expected values: 01jfsb => 125 concepts (83 used for prediction) PRED predicted values (max 10 best out of 159): 01jfsb (0.76 #1651, 0.64 #5640, 0.64 #1183), 05p553 (0.72 #8687, 0.54 #7278, 0.42 #238), 02kdv5l (0.60 #2, 0.59 #1641, 0.53 #9154), 07ssc (0.60 #7037, 0.56 #9507, 0.51 #4451), 02n4kr (0.50 #593, 0.44 #945, 0.31 #1647), 03k9fj (0.41 #2938, 0.39 #3524, 0.39 #1767), 06n90 (0.40 #13, 0.35 #1535, 0.28 #2940), 01hmnh (0.33 #1538, 0.29 #2592, 0.29 #2943), 04xvlr (0.26 #6920, 0.25 #3279, 0.21 #2694), 04pbhw (0.25 #1577, 0.20 #55, 0.17 #2631) >> Best rule #1651 for best value: >> intensional similarity = 8 >> extensional distance = 49 >> proper extension: 05pbl56; 025n07; 02mpyh; 0ct2tf5; >> query: (?x9209, 01jfsb) <- film_crew_role(?x9209, ?x1171), film_crew_role(?x9209, ?x468), film_release_region(?x9209, ?x94), production_companies(?x9209, ?x7303), ?x468 = 02r96rf, genre(?x9209, ?x604), ?x1171 = 09vw2b7, ?x604 = 0lsxr >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0crs0b8 genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 83.000 0.765 http://example.org/film/film/genre #16816-01fx6y PRED entity: 01fx6y PRED relation: nominated_for! PRED expected values: 0p9sw 094qd5 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 208): 0gq9h (0.69 #991, 0.63 #2389, 0.60 #3088), 019f4v (0.67 #985, 0.61 #2383, 0.49 #3082), 0gs9p (0.60 #2391, 0.56 #993, 0.49 #3090), 04dn09n (0.50 #966, 0.40 #2364, 0.36 #3063), 0k611 (0.49 #3332, 0.48 #2400, 0.46 #1002), 04kxsb (0.46 #1025, 0.40 #2423, 0.21 #3355), 0gq_v (0.45 #3280, 0.40 #950, 0.36 #2348), 099c8n (0.41 #988, 0.31 #2386, 0.30 #4251), 0gr4k (0.39 #2355, 0.39 #3054, 0.31 #957), 0f4x7 (0.39 #2354, 0.34 #3053, 0.29 #956) >> Best rule #991 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 0hmr4; 01c9d; >> query: (?x6669, 0gq9h) <- nominated_for(?x1198, ?x6669), written_by(?x6669, ?x8268), ?x1198 = 02pqp12 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #3281 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 185 *> proper extension: 016kz1; 02q_4ph; 0fh2v5; *> query: (?x6669, 0p9sw) <- nominated_for(?x1243, ?x6669), nominated_for(?x1198, ?x6669), ?x1243 = 0gr0m, award(?x276, ?x1198) *> conf = 0.39 ranks of expected_values: 11, 13 EVAL 01fx6y nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 93.000 93.000 0.686 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01fx6y nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 93.000 93.000 0.686 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16815-01nfys PRED entity: 01nfys PRED relation: religion PRED expected values: 0c8wxp => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 13): 0c8wxp (0.25 #6, 0.24 #458, 0.16 #278), 03_gx (0.21 #104, 0.12 #240, 0.12 #512), 0kpl (0.15 #236, 0.14 #100, 0.14 #553), 03j6c (0.07 #789, 0.03 #1331, 0.02 #66), 0n2g (0.05 #239, 0.04 #375, 0.04 #511), 0kq2 (0.05 #63, 0.05 #244, 0.04 #199), 092bf5 (0.04 #61, 0.03 #106, 0.03 #152), 0flw86 (0.03 #770, 0.02 #2439, 0.02 #1312), 01lp8 (0.03 #91, 0.03 #182, 0.02 #46), 01spm (0.02 #444, 0.01 #354) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01z7_f; 050_qx; >> query: (?x9161, 0c8wxp) <- film(?x9161, ?x148), award_winner(?x9161, ?x8567), ?x8567 = 02cff1 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nfys religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.250 http://example.org/people/person/religion #16814-026spg PRED entity: 026spg PRED relation: instrumentalists! PRED expected values: 05r5c => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 58): 0342h (0.51 #94, 0.44 #986, 0.43 #717), 05r5c (0.48 #98, 0.42 #9, 0.34 #187), 05148p4 (0.33 #111, 0.23 #734, 0.23 #1003), 018vs (0.23 #103, 0.19 #2066, 0.17 #2868), 06ch55 (0.21 #84, 0.03 #173, 0.03 #796), 06ncr (0.17 #46, 0.08 #135, 0.05 #224), 02hnl (0.16 #125, 0.12 #36, 0.12 #214), 03qjg (0.16 #142, 0.12 #231, 0.12 #676), 0l14md (0.15 #186, 0.09 #631, 0.08 #275), 026t6 (0.12 #181, 0.08 #270, 0.08 #92) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 095x_; >> query: (?x4675, 0342h) <- artists(?x5300, ?x4675), ?x5300 = 02k_kn, profession(?x4675, ?x220) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #98 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 095x_; *> query: (?x4675, 05r5c) <- artists(?x5300, ?x4675), ?x5300 = 02k_kn, profession(?x4675, ?x220) *> conf = 0.48 ranks of expected_values: 2 EVAL 026spg instrumentalists! 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.508 http://example.org/music/instrument/instrumentalists #16813-01p4wv PRED entity: 01p4wv PRED relation: actor PRED expected values: 02hhtj 01twmp => 66 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1008): 0fx0mw (0.33 #2117, 0.02 #4904, 0.02 #8630), 01tfck (0.29 #5578, 0.29 #5579, 0.20 #171), 01zg98 (0.29 #5578, 0.29 #5579, 0.20 #340), 03vgp7 (0.29 #5578, 0.29 #5579, 0.18 #2791), 01tnxc (0.29 #5578, 0.29 #5579, 0.18 #2791), 030h95 (0.29 #5578, 0.29 #5579, 0.18 #2791), 01qscs (0.29 #5578, 0.29 #5579, 0.18 #2791), 04smkr (0.29 #5578, 0.29 #5579, 0.18 #2791), 04sx9_ (0.29 #5578, 0.29 #5579, 0.18 #2791), 0kjgl (0.29 #5578, 0.29 #5579, 0.18 #2791) >> Best rule #2117 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 080dwhx; 0464pz; >> query: (?x5307, 0fx0mw) <- actor(?x5307, ?x5467), award_winner(?x5467, ?x3139), award_winner(?x5467, ?x2281), ?x3139 = 0b_dy, award_winner(?x2282, ?x5467), location(?x2281, ?x1523) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6509 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 71 *> proper extension: 0n2bh; 01dvry; 097h2; 02xhwm; *> query: (?x5307, ?x2837) <- actor(?x5307, ?x5467), award(?x5467, ?x704), film(?x5467, ?x857), student(?x735, ?x5467), participant(?x2837, ?x5467) *> conf = 0.09 ranks of expected_values: 93 EVAL 01p4wv actor 01twmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 39.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 01p4wv actor 02hhtj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 66.000 39.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #16812-01wgcvn PRED entity: 01wgcvn PRED relation: film PRED expected values: 0bm2nq => 100 concepts (85 used for prediction) PRED predicted values (max 10 best out of 517): 09dv8h (0.17 #1166, 0.03 #100022, 0.02 #8310), 0gj8t_b (0.08 #180, 0.08 #1966, 0.01 #14468), 07gghl (0.08 #1173, 0.04 #4745, 0.02 #15461), 065zlr (0.08 #399, 0.04 #3971), 01l_pn (0.08 #965, 0.03 #126815, 0.03 #132174), 09cr8 (0.08 #284, 0.03 #132174, 0.02 #37790), 03t79f (0.08 #933, 0.03 #132174), 0fvr1 (0.08 #350, 0.03 #7494, 0.01 #14638), 0prrm (0.08 #860, 0.03 #11576, 0.03 #16934), 0295sy (0.08 #958, 0.02 #13460, 0.02 #8102) >> Best rule #1166 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 054c1; 01nr63; >> query: (?x3756, 09dv8h) <- profession(?x3756, ?x7361), film(?x3756, ?x857), ?x7361 = 01xr66 >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wgcvn film 0bm2nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 85.000 0.167 http://example.org/film/actor/film./film/performance/film #16811-0dn3n PRED entity: 0dn3n PRED relation: student! PRED expected values: 0bwfn => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 96): 03ksy (0.25 #105, 0.08 #30094, 0.04 #48517), 03np_7 (0.25 #488), 01d34b (0.20 #781, 0.03 #7095, 0.02 #29192), 03qdm (0.20 #934, 0.01 #4617, 0.01 #11456), 0bwfn (0.12 #1852, 0.12 #30263, 0.08 #32370), 027kp3 (0.08 #1204, 0.06 #1730, 0.01 #4361), 026gvfj (0.08 #1162, 0.03 #2740, 0.03 #2214), 01vmv_ (0.08 #1485), 01ky7c (0.08 #1275), 04s934 (0.08 #1267) >> Best rule #105 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 031zkw; >> query: (?x3070, 03ksy) <- participant(?x3070, ?x262), film(?x3070, ?x6778), ?x6778 = 01j5ql >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1852 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0cqt90; *> query: (?x3070, 0bwfn) <- location(?x3070, ?x335), participant(?x496, ?x3070), ?x335 = 059rby *> conf = 0.12 ranks of expected_values: 5 EVAL 0dn3n student! 0bwfn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 122.000 122.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #16810-01wz01 PRED entity: 01wz01 PRED relation: award_winner! PRED expected values: 06cgy => 68 concepts (30 used for prediction) PRED predicted values (max 10 best out of 487): 06cgy (0.82 #22382, 0.82 #9591, 0.82 #44768), 01wz01 (0.55 #2299, 0.32 #33577, 0.16 #41570), 0gy6z9 (0.32 #33577, 0.18 #2136, 0.16 #41570), 02yxwd (0.32 #33577, 0.18 #2316, 0.16 #41570), 042xrr (0.32 #33577, 0.18 #2385, 0.16 #41570), 07yp0f (0.32 #33577, 0.18 #2241, 0.16 #41570), 0hvb2 (0.32 #33577, 0.18 #1879, 0.16 #41570), 0151w_ (0.32 #33577, 0.16 #41570, 0.16 #19183), 02778qt (0.32 #33577, 0.16 #41570, 0.16 #19183), 01qr1_ (0.32 #33577, 0.16 #41570, 0.16 #19183) >> Best rule #22382 for best value: >> intensional similarity = 3 >> extensional distance = 1044 >> proper extension: 01y8d4; 0blgl; 011s9r; 08f3yq; >> query: (?x4173, ?x1554) <- award_winner(?x4173, ?x1554), award_winner(?x851, ?x4173), people(?x743, ?x1554) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wz01 award_winner! 06cgy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 30.000 0.825 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #16809-01j_5k PRED entity: 01j_5k PRED relation: school_type PRED expected values: 01rs41 => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.59 #196, 0.53 #148, 0.48 #1925), 01rs41 (0.55 #557, 0.52 #533, 0.52 #821), 05pcjw (0.47 #121, 0.46 #721, 0.46 #217), 01_srz (0.29 #27, 0.17 #291, 0.17 #219), 01_9fk (0.26 #746, 0.23 #1371, 0.22 #674), 07tf8 (0.23 #105, 0.21 #153, 0.21 #681), 04qbv (0.17 #16, 0.10 #304, 0.10 #3292), 04399 (0.15 #110, 0.10 #3292, 0.08 #230), 06cs1 (0.14 #30, 0.12 #54, 0.10 #3292), 047951 (0.10 #3292, 0.05 #200, 0.02 #392) >> Best rule #196 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 02pptm; 01tntf; 01bdhf; >> query: (?x6763, 05jxkf) <- major_field_of_study(?x6763, ?x1154), child(?x10513, ?x6763), colors(?x6763, ?x663) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #557 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 01rtm4; 04wlz2; 0288zy; 0kz2w; 01k2wn; 06jk5_; 033q4k; 04rwx; 02hft3; 037s9x; ... *> query: (?x6763, 01rs41) <- registering_agency(?x6763, ?x1982), colors(?x6763, ?x663), major_field_of_study(?x6763, ?x1154), currency(?x6763, ?x170) *> conf = 0.55 ranks of expected_values: 2 EVAL 01j_5k school_type 01rs41 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 180.000 180.000 0.591 http://example.org/education/educational_institution/school_type #16808-0plxn PRED entity: 0plxn PRED relation: place! PRED expected values: 0plxn => 80 concepts (21 used for prediction) PRED predicted values (max 10 best out of 7): 0q6lr (0.08 #876, 0.08 #361, 0.08 #1391), 0q8jl (0.08 #807, 0.08 #292, 0.08 #1322), 0q8s4 (0.08 #625, 0.08 #110, 0.08 #1140), 0qc7l (0.08 #993, 0.08 #478), 0lphb (0.08 #690, 0.08 #1205), 0fttg (0.08 #378, 0.08 #1408), 0q48z (0.08 #316, 0.08 #1346) >> Best rule #876 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 01wdl3; 02jyr8; 0325dj; >> query: (?x13051, 0q6lr) <- contains(?x2831, ?x13051), contains(?x94, ?x13051), ?x94 = 09c7w0, ?x2831 = 0gyh, category(?x13051, ?x134) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0plxn place! 0plxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 21.000 0.083 http://example.org/location/hud_county_place/place #16807-013sg6 PRED entity: 013sg6 PRED relation: gender PRED expected values: 05zppz => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #19, 0.88 #27, 0.86 #23), 02zsn (0.40 #6, 0.39 #12, 0.39 #10) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 0cg9y; >> query: (?x9587, 05zppz) <- profession(?x9587, ?x1032), ?x1032 = 02hrh1q, celebrities_impersonated(?x3649, ?x9587), ?x3649 = 03m6t5 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013sg6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.892 http://example.org/people/person/gender #16806-07yw6t PRED entity: 07yw6t PRED relation: type_of_union PRED expected values: 04ztj => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.79 #1, 0.77 #13, 0.77 #5), 01g63y (0.32 #225, 0.12 #34, 0.12 #90), 0jgjn (0.01 #12, 0.01 #20) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0265z9l; 081hvm; >> query: (?x4686, 04ztj) <- gender(?x4686, ?x231), award(?x4686, ?x4687), ?x4687 = 03rbj2, ?x231 = 05zppz >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07yw6t type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.787 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16805-05kh_ PRED entity: 05kh_ PRED relation: student! PRED expected values: 0bjqh => 107 concepts (88 used for prediction) PRED predicted values (max 10 best out of 122): 0bwfn (0.10 #802, 0.08 #7127, 0.08 #9235), 017z88 (0.06 #82, 0.04 #2718, 0.04 #3245), 025v3k (0.06 #120, 0.03 #1174, 0.03 #6445), 017hnw (0.06 #509, 0.03 #2090, 0.02 #1563), 07tgn (0.06 #7923, 0.03 #14774, 0.03 #16355), 03ksy (0.05 #8012, 0.04 #1687, 0.04 #14863), 065y4w7 (0.05 #8974, 0.05 #10028, 0.05 #6866), 01w5m (0.05 #3268, 0.04 #8011, 0.04 #9065), 06182p (0.05 #1352, 0.04 #1879, 0.03 #6623), 0fr9jp (0.04 #872, 0.02 #14048, 0.02 #11413) >> Best rule #802 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 0htlr; 0prjs; 022_lg; 01vhb0; 02v406; 01_f_5; 0l786; 011lvx; 01wk51; 03d0ns; ... >> query: (?x5601, 0bwfn) <- profession(?x5601, ?x524), ?x524 = 02jknp, spouse(?x1568, ?x5601) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05kh_ student! 0bjqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 88.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student #16804-04258w PRED entity: 04258w PRED relation: profession PRED expected values: 02jknp => 125 concepts (44 used for prediction) PRED predicted values (max 10 best out of 73): 02jknp (0.79 #883, 0.63 #4973, 0.56 #2635), 0kyk (0.57 #466, 0.40 #612, 0.33 #320), 03gjzk (0.40 #159, 0.37 #4979, 0.36 #4687), 018gz8 (0.40 #161, 0.33 #15, 0.26 #4689), 0np9r (0.40 #165, 0.33 #19, 0.13 #4693), 0cbd2 (0.33 #590, 0.33 #298, 0.25 #1028), 02krf9 (0.17 #4991, 0.16 #4699, 0.14 #901), 0747nrk (0.17 #349, 0.14 #495, 0.07 #641), 015btn (0.17 #392, 0.14 #538, 0.07 #684), 0fj9f (0.17 #345, 0.13 #637, 0.11 #1221) >> Best rule #883 for best value: >> intensional similarity = 6 >> extensional distance = 94 >> proper extension: 0j_c; 01pp3p; 012vct; 01d5vk; 03mv0b; 0hcvy; 05dxl_; 0py5b; 0gry51; >> query: (?x9566, 02jknp) <- people(?x9771, ?x9566), profession(?x9566, ?x9081), profession(?x9566, ?x319), profession(?x9296, ?x9081), ?x9296 = 04vt98, ?x319 = 01d_h8 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04258w profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 44.000 0.792 http://example.org/people/person/profession #16803-018fwv PRED entity: 018fwv PRED relation: film PRED expected values: 0cmdwwg => 115 concepts (49 used for prediction) PRED predicted values (max 10 best out of 681): 09fc83 (0.67 #3582, 0.60 #1791, 0.60 #883), 03ntbmw (0.20 #1771, 0.11 #3562, 0.04 #5353), 011yd2 (0.20 #355, 0.11 #2146, 0.01 #23632), 02bg8v (0.17 #2063), 09wnnb (0.11 #3418, 0.10 #1627, 0.02 #24904), 017gm7 (0.11 #2001, 0.03 #21695, 0.01 #62887), 017gl1 (0.11 #1933, 0.02 #21627, 0.01 #23419), 0bx0l (0.11 #2139, 0.02 #21833, 0.01 #37952), 0kt_4 (0.11 #3304), 03wh49y (0.11 #2743) >> Best rule #3582 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0h0jz; 0klh7; 0f0kz; 0svqs; 017khj; 031y07; 0309lm; 05xd_v; >> query: (?x13635, ?x5116) <- actor(?x5116, ?x13635), gender(?x13635, ?x231), ?x231 = 05zppz, film_release_region(?x5116, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #10081 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 0d608; 0mfj2; 025_ql1; *> query: (?x13635, 0cmdwwg) <- film(?x13635, ?x383), nationality(?x13635, ?x279), ?x279 = 0d060g, place_of_birth(?x13635, ?x108) *> conf = 0.02 ranks of expected_values: 374 EVAL 018fwv film 0cmdwwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 115.000 49.000 0.667 http://example.org/film/actor/film./film/performance/film #16802-0146pg PRED entity: 0146pg PRED relation: currency PRED expected values: 09nqf => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.33 #1, 0.27 #61, 0.24 #46), 01nv4h (0.03 #17, 0.03 #20, 0.03 #23), 02l6h (0.01 #15) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01f5q5; >> query: (?x669, 09nqf) <- nominated_for(?x669, ?x2050), ?x2050 = 01fmys, people(?x3584, ?x669) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0146pg currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #16801-01t2h2 PRED entity: 01t2h2 PRED relation: film PRED expected values: 02qd04y => 133 concepts (61 used for prediction) PRED predicted values (max 10 best out of 645): 05znbh7 (0.21 #1786, 0.17 #1092, 0.01 #8233), 043n0v_ (0.21 #1786, 0.01 #8005, 0.01 #96400), 08j7lh (0.21 #1786, 0.01 #96400), 02qd04y (0.21 #1786, 0.01 #96400), 0mb8c (0.21 #1786, 0.01 #96400), 05g8pg (0.21 #1786, 0.01 #96400), 01f8f7 (0.17 #1200, 0.05 #87472, 0.03 #87471), 01f85k (0.17 #1124, 0.05 #87472, 0.03 #87471), 0dx8gj (0.17 #640, 0.05 #87472, 0.03 #87471), 01f8gz (0.17 #250, 0.05 #87472, 0.03 #87471) >> Best rule #1786 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 042rnl; >> query: (?x1864, ?x3376) <- award_winner(?x9217, ?x1864), award_winner(?x5923, ?x1864), ?x5923 = 09v8db5, award(?x3376, ?x9217) >> conf = 0.21 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 01t2h2 film 02qd04y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 133.000 61.000 0.214 http://example.org/film/actor/film./film/performance/film #16800-05qdh PRED entity: 05qdh PRED relation: major_field_of_study! PRED expected values: 02csf => 76 concepts (67 used for prediction) PRED predicted values (max 10 best out of 118): 02csf (0.82 #2906, 0.82 #2902, 0.82 #2901), 02j62 (0.45 #1074, 0.44 #1688, 0.44 #899), 04rjg (0.45 #1065, 0.42 #1241, 0.26 #1679), 037mh8 (0.37 #1719, 0.33 #55, 0.25 #317), 01mkq (0.36 #1060, 0.33 #1236, 0.33 #10), 0fdys (0.33 #906, 0.33 #642, 0.33 #554), 062z7 (0.33 #109, 0.33 #22, 0.27 #1072), 05qfh (0.33 #1693, 0.33 #904, 0.27 #1079), 064_8sq (0.33 #912, 0.33 #560, 0.25 #299), 05qdh (0.33 #137, 0.30 #2372, 0.25 #224) >> Best rule #2906 for best value: >> intensional similarity = 6 >> extensional distance = 53 >> proper extension: 0j0k; >> query: (?x7017, ?x373) <- major_field_of_study(?x7017, ?x4268), major_field_of_study(?x7017, ?x373), taxonomy(?x7017, ?x939), major_field_of_study(?x373, ?x1695), major_field_of_study(?x7134, ?x4268), major_field_of_study(?x1527, ?x7134) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qdh major_field_of_study! 02csf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 67.000 0.822 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #16799-0gd5z PRED entity: 0gd5z PRED relation: profession PRED expected values: 0kyk => 163 concepts (104 used for prediction) PRED predicted values (max 10 best out of 105): 02hrh1q (0.84 #6087, 0.84 #5343, 0.80 #14238), 0kyk (0.63 #1658, 0.62 #1214, 0.62 #2694), 0dxtg (0.60 #1345, 0.52 #6975, 0.52 #2529), 01d_h8 (0.50 #13193, 0.40 #2522, 0.39 #4595), 09jwl (0.38 #9650, 0.38 #11280, 0.38 #907), 02hv44_ (0.35 #8443, 0.35 #8592, 0.34 #9335), 0d8qb (0.35 #8443, 0.35 #8592, 0.34 #9335), 016wtf (0.35 #8443, 0.35 #8592, 0.34 #9335), 025352 (0.35 #8592, 0.34 #9335, 0.33 #5775), 02jknp (0.33 #11564, 0.29 #12601, 0.29 #13342) >> Best rule #6087 for best value: >> intensional similarity = 4 >> extensional distance = 151 >> proper extension: 01pcrw; 01nfys; 014y6; >> query: (?x2485, 02hrh1q) <- student(?x2327, ?x2485), type_of_union(?x2485, ?x1873), profession(?x2485, ?x353), ?x1873 = 01g63y >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1658 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 33 *> proper extension: 01963w; 04r68; 048_p; 0g5ff; 05x8n; 0gppg; 0jt86; *> query: (?x2485, 0kyk) <- award(?x2485, ?x11388), award(?x2485, ?x575), ?x575 = 040vk98, location(?x2485, ?x1196), disciplines_or_subjects(?x11388, ?x5864) *> conf = 0.63 ranks of expected_values: 2 EVAL 0gd5z profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 163.000 104.000 0.843 http://example.org/people/person/profession #16798-0r00l PRED entity: 0r00l PRED relation: jurisdiction_of_office! PRED expected values: 01q24l => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.32 #1169, 0.10 #1719, 0.07 #1807), 01q24l (0.30 #189, 0.27 #13, 0.23 #57), 060bp (0.27 #1167, 0.08 #1717, 0.06 #551), 0fkvn (0.17 #1170, 0.17 #356, 0.11 #840), 0f6c3 (0.14 #359, 0.12 #843, 0.12 #1173), 09n5b9 (0.14 #363, 0.11 #847, 0.10 #1177), 04syw (0.07 #1172, 0.02 #1150, 0.02 #512), 0789n (0.06 #141, 0.06 #163, 0.03 #757), 0dq3c (0.06 #1168, 0.04 #134, 0.02 #156), 0p5vf (0.05 #1178, 0.02 #1156, 0.02 #144) >> Best rule #1169 for best value: >> intensional similarity = 1 >> extensional distance = 465 >> proper extension: 02jxk; 05vz3zq; 01mk6; 06srk; 05br2; 04hvw; 01gh6z; 018jmn; >> query: (?x11930, 060c4) <- jurisdiction_of_office(?x1195, ?x11930) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #189 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 0qy5v; 0r6rq; 01zlwg6; 0q_xk; 0qyzb; *> query: (?x11930, 01q24l) <- county(?x11930, ?x2949), contains(?x1227, ?x11930), ?x1227 = 01n7q *> conf = 0.30 ranks of expected_values: 2 EVAL 0r00l jurisdiction_of_office! 01q24l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.317 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #16797-0b6l1st PRED entity: 0b6l1st PRED relation: film_crew_role PRED expected values: 02r96rf 0ch6mp2 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.85 #326, 0.75 #1207, 0.75 #109), 0ch6mp2 (0.76 #333, 0.75 #1214, 0.75 #407), 02r96rf (0.71 #220, 0.67 #1210, 0.67 #953), 01vx2h (0.45 #192, 0.44 #156, 0.40 #228), 01pvkk (0.37 #412, 0.28 #448, 0.27 #193), 01xy5l_ (0.29 #87, 0.25 #123, 0.13 #231), 02ynfr (0.23 #416, 0.22 #161, 0.21 #379), 05smlt (0.20 #57, 0.18 #201, 0.14 #93), 02rh1dz (0.19 #227, 0.18 #373, 0.15 #556), 089g0h (0.14 #92, 0.12 #128, 0.11 #164) >> Best rule #326 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 0dq626; 04ddm4; 07qg8v; 03twd6; 07y9w5; 02r79_h; 05pbl56; 0f4m2z; 01ffx4; 04fv5b; ... >> query: (?x7208, 09zzb8) <- production_companies(?x7208, ?x3323), film_crew_role(?x7208, ?x1171), genre(?x7208, ?x600), ?x600 = 02n4kr >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #333 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 0dq626; 04ddm4; 07qg8v; 03twd6; 07y9w5; 02r79_h; 05pbl56; 0f4m2z; 01ffx4; 04fv5b; ... *> query: (?x7208, 0ch6mp2) <- production_companies(?x7208, ?x3323), film_crew_role(?x7208, ?x1171), genre(?x7208, ?x600), ?x600 = 02n4kr *> conf = 0.76 ranks of expected_values: 2, 3 EVAL 0b6l1st film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.854 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0b6l1st film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.854 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16796-01cwcr PRED entity: 01cwcr PRED relation: film PRED expected values: 02rrfzf 01npcx => 97 concepts (56 used for prediction) PRED predicted values (max 10 best out of 846): 063ykwt (0.59 #83828, 0.59 #65994, 0.58 #85612), 0ds5_72 (0.14 #3239, 0.02 #15722, 0.02 #13939), 0407yfx (0.12 #3910, 0.09 #5693, 0.01 #25313), 0qf2t (0.09 #24969, 0.08 #4398, 0.06 #6181), 09cr8 (0.08 #9200, 0.04 #23469, 0.03 #30605), 031778 (0.08 #3881, 0.06 #5664, 0.02 #23500), 085bd1 (0.08 #4016, 0.06 #5799, 0.02 #9366), 031786 (0.08 #4841, 0.06 #6624, 0.02 #26244), 01242_ (0.08 #4268, 0.06 #6051, 0.02 #11401), 0bh8x1y (0.08 #4359, 0.06 #6142) >> Best rule #83828 for best value: >> intensional similarity = 3 >> extensional distance = 1377 >> proper extension: 04yywz; 049tjg; 02g8h; 0d_84; 0h1_w; 02nb2s; 04bs3j; 014x77; 0151ns; 0lzb8; ... >> query: (?x7277, ?x3787) <- film(?x7277, ?x9016), nominated_for(?x7277, ?x3787), film(?x541, ?x9016) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #4530 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 01wsl7c; 03v36; *> query: (?x7277, 01npcx) <- award(?x7277, ?x102), nationality(?x7277, ?x6401), ?x6401 = 06q1r *> conf = 0.04 ranks of expected_values: 130, 741 EVAL 01cwcr film 01npcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 97.000 56.000 0.588 http://example.org/film/actor/film./film/performance/film EVAL 01cwcr film 02rrfzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 97.000 56.000 0.588 http://example.org/film/actor/film./film/performance/film #16795-0_b3d PRED entity: 0_b3d PRED relation: film_release_region PRED expected values: 0chghy 0k6nt => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 193): 0chghy (0.81 #1809, 0.79 #1972, 0.77 #1646), 0k6nt (0.79 #1824, 0.79 #1661, 0.76 #1987), 03_3d (0.79 #1641, 0.76 #1804, 0.74 #1967), 03h64 (0.77 #1870, 0.74 #1707, 0.73 #2033), 035qy (0.76 #1834, 0.75 #1671, 0.72 #1997), 01znc_ (0.74 #1842, 0.71 #1679, 0.70 #2005), 015fr (0.73 #1815, 0.72 #1652, 0.71 #1978), 05b4w (0.73 #1867, 0.69 #2030, 0.68 #1704), 0154j (0.72 #1802, 0.68 #1965, 0.67 #1147), 06bnz (0.68 #1847, 0.65 #2010, 0.64 #1684) >> Best rule #1809 for best value: >> intensional similarity = 5 >> extensional distance = 227 >> proper extension: 0gtsx8c; 087wc7n; 0crfwmx; 04zyhx; 0cz8mkh; 0gydcp7; 06v9_x; 0661m4p; 07x4qr; 05q4y12; ... >> query: (?x1002, 0chghy) <- film(?x2122, ?x1002), film_release_region(?x1002, ?x1003), film_release_region(?x1002, ?x87), ?x1003 = 03gj2, ?x87 = 05r4w >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0_b3d film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0_b3d film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16794-01gy7r PRED entity: 01gy7r PRED relation: student! PRED expected values: 03qsdpk => 139 concepts (136 used for prediction) PRED predicted values (max 10 best out of 46): 02822 (0.16 #590, 0.15 #341, 0.13 #527), 062z7 (0.15 #332, 0.05 #769, 0.05 #894), 06ms6 (0.11 #260, 0.03 #384, 0.02 #759), 01x3g (0.11 #305), 03qsdpk (0.08 #532, 0.08 #720, 0.08 #1095), 03g3w (0.08 #331, 0.06 #705, 0.06 #768), 0h5k (0.08 #327, 0.02 #1076, 0.02 #1139), 04gb7 (0.08 #344, 0.02 #781, 0.02 #906), 0g4gr (0.08 #335, 0.01 #521, 0.01 #584), 0fdys (0.07 #525, 0.06 #1151, 0.05 #1088) >> Best rule #590 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 05bnp0; 05gml8; 01yk13; 0pz7h; 0yfp; 049dyj; 03lt8g; 039bp; 0prjs; 0343h; ... >> query: (?x4228, 02822) <- people(?x1816, ?x4228), student(?x1368, ?x4228), film(?x4228, ?x1728) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #532 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 01gj8_; 05j12n; 0kst7v; *> query: (?x4228, 03qsdpk) <- profession(?x4228, ?x1032), student(?x1368, ?x4228), languages(?x4228, ?x254) *> conf = 0.08 ranks of expected_values: 5 EVAL 01gy7r student! 03qsdpk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 139.000 136.000 0.165 http://example.org/education/field_of_study/students_majoring./education/education/student #16793-0kxbc PRED entity: 0kxbc PRED relation: gender PRED expected values: 05zppz => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #39, 0.87 #31, 0.86 #43), 02zsn (0.46 #248, 0.46 #247, 0.28 #120) >> Best rule #39 for best value: >> intensional similarity = 4 >> extensional distance = 100 >> proper extension: 05qhnq; >> query: (?x5635, 05zppz) <- profession(?x5635, ?x2659), ?x2659 = 039v1, role(?x5635, ?x227), artists(?x302, ?x5635) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kxbc gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.873 http://example.org/people/person/gender #16792-09c7w0 PRED entity: 09c7w0 PRED relation: locations! PRED expected values: 0kbq => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 108): 0b_75k (0.25 #1300, 0.25 #925, 0.10 #11850), 0b_6lb (0.25 #1327, 0.25 #952, 0.09 #11877), 0b_6xf (0.25 #980, 0.09 #11905, 0.04 #14295), 0b_6zk (0.25 #906, 0.06 #2786, 0.06 #11831), 0b_6x2 (0.25 #1284, 0.06 #2789, 0.06 #11834), 0b_6qj (0.25 #943, 0.06 #11868, 0.05 #14511), 0b_6v_ (0.25 #940, 0.06 #11865, 0.04 #14508), 0b_6h7 (0.25 #915, 0.04 #11840, 0.04 #9705), 018wrk (0.25 #1252, 0.01 #10423, 0.01 #11426), 086m1 (0.20 #2064, 0.11 #5643, 0.08 #5330) >> Best rule #1300 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 04ych; 06wxw; >> query: (?x94, 0b_75k) <- contains(?x94, ?x12127), ?x12127 = 02tz9z >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4365 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 28 *> proper extension: 0hkb8; 016h5l; *> query: (?x94, 0kbq) <- place_of_burial(?x12602, ?x94), type_of_union(?x12602, ?x566) *> conf = 0.03 ranks of expected_values: 82 EVAL 09c7w0 locations! 0kbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 168.000 168.000 0.250 http://example.org/time/event/locations #16791-02b6n9 PRED entity: 02b6n9 PRED relation: genre PRED expected values: 0hn10 => 70 concepts (69 used for prediction) PRED predicted values (max 10 best out of 111): 07s9rl0 (0.76 #358, 0.76 #5969, 0.71 #477), 01jfsb (0.33 #1684, 0.31 #3592, 0.30 #3234), 02l7c8 (0.32 #2045, 0.31 #1210, 0.28 #1091), 02kdv5l (0.29 #1674, 0.29 #2150, 0.28 #3582), 03k9fj (0.28 #5979, 0.24 #2159, 0.22 #5620), 0lsxr (0.26 #1560, 0.25 #127, 0.25 #246), 060__y (0.22 #255, 0.16 #1569, 0.16 #850), 082gq (0.17 #984, 0.15 #863, 0.14 #30), 0hn10 (0.16 #128, 0.12 #604, 0.12 #485), 01hmnh (0.16 #3598, 0.16 #2166, 0.15 #5627) >> Best rule #358 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 0hgnl3t; 02x0fs9; >> query: (?x9533, 07s9rl0) <- nominated_for(?x157, ?x9533), film_crew_role(?x9533, ?x137), nominated_for(?x3435, ?x9533), ?x3435 = 03hl6lc >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 53 *> proper extension: 053tj7; 0d8w2n; *> query: (?x9533, 0hn10) <- genre(?x9533, ?x2753), production_companies(?x9533, ?x2549), ?x2753 = 0219x_ *> conf = 0.16 ranks of expected_values: 9 EVAL 02b6n9 genre 0hn10 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 70.000 69.000 0.764 http://example.org/film/film/genre #16790-0ycht PRED entity: 0ycht PRED relation: time_zones PRED expected values: 02hcv8 => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.80 #354, 0.62 #68, 0.59 #133), 02lcqs (0.37 #447, 0.36 #57, 0.36 #44), 02fqwt (0.23 #170, 0.21 #40, 0.20 #430), 02hczc (0.21 #41, 0.18 #262, 0.16 #236), 02llzg (0.15 #511, 0.14 #563, 0.14 #225), 03bdv (0.07 #58, 0.07 #1501, 0.07 #669), 042g7t (0.06 #89, 0.03 #258, 0.02 #544), 03plfd (0.02 #920, 0.02 #1375, 0.02 #1427), 052vwh (0.02 #961, 0.02 #1104), 02lcrv (0.01 #878, 0.01 #540, 0.01 #631) >> Best rule #354 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 0fm9_; 0drsm; 0dlhg; 0f6_4; 0f6_j; 0dc3_; 0f63n; 0fc2c; 0fc1_; 0fkhl; ... >> query: (?x12931, 02hcv8) <- source(?x12931, ?x958), ?x958 = 0jbk9, contains(?x335, ?x12931), ?x335 = 059rby >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ycht time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.800 http://example.org/location/location/time_zones #16789-05zl0 PRED entity: 05zl0 PRED relation: contains! PRED expected values: 0ljsz => 133 concepts (93 used for prediction) PRED predicted values (max 10 best out of 266): 05fjf (0.78 #74337, 0.75 #60899, 0.65 #21489), 07ssc (0.44 #75233, 0.25 #31, 0.14 #2718), 02qkt (0.34 #56763, 0.31 #58554, 0.31 #59451), 05k7sb (0.25 #132, 0.14 #2819, 0.12 #3714), 05l5n (0.25 #121, 0.14 #2808, 0.04 #12657), 01qh7 (0.25 #188, 0.07 #2875, 0.06 #10039), 0jt5zcn (0.25 #141, 0.07 #2828, 0.04 #6411), 0dg3n1 (0.21 #56571, 0.19 #58362, 0.19 #59259), 0j0k (0.18 #56794, 0.16 #59482, 0.16 #58585), 02jx1 (0.14 #2773, 0.12 #18889, 0.11 #74423) >> Best rule #74337 for best value: >> intensional similarity = 3 >> extensional distance = 340 >> proper extension: 0ymc8; 02_2kg; 09krm_; 06mvyf; 030nwm; >> query: (?x6056, ?x6895) <- state_province_region(?x6056, ?x6895), currency(?x6056, ?x170), contains(?x94, ?x6056) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1521 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0d2fd7; *> query: (?x6056, 0ljsz) <- state_province_region(?x6056, ?x6895), category(?x6056, ?x134), ?x6895 = 05fjf *> conf = 0.11 ranks of expected_values: 17 EVAL 05zl0 contains! 0ljsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 133.000 93.000 0.776 http://example.org/location/location/contains #16788-0146hc PRED entity: 0146hc PRED relation: campuses PRED expected values: 0146hc => 175 concepts (113 used for prediction) PRED predicted values (max 10 best out of 304): 01jq0j (0.29 #14197, 0.21 #33862, 0.20 #51353), 019dwp (0.29 #14197, 0.21 #33862, 0.20 #51353), 0146hc (0.29 #14197, 0.21 #33862, 0.20 #51353), 06182p (0.25 #286, 0.02 #1924, 0.02 #2470), 09s5q8 (0.04 #742, 0.02 #2380, 0.01 #2926), 016sd3 (0.04 #948, 0.01 #26214), 03l78j (0.04 #870, 0.01 #26214), 02fgdx (0.02 #1184, 0.02 #1730, 0.01 #2822), 07w0v (0.02 #1109, 0.02 #1655, 0.01 #2747), 02vnp2 (0.02 #1448, 0.02 #1994, 0.01 #4724) >> Best rule #14197 for best value: >> intensional similarity = 4 >> extensional distance = 154 >> proper extension: 026gvfj; >> query: (?x5844, ?x4916) <- major_field_of_study(?x5844, ?x5900), student(?x5844, ?x9232), student(?x4916, ?x9232), participant(?x9232, ?x4411) >> conf = 0.29 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0146hc campuses 0146hc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 175.000 113.000 0.291 http://example.org/education/educational_institution/campuses #16787-01l_w0 PRED entity: 01l_w0 PRED relation: group! PRED expected values: 06ncr => 71 concepts (61 used for prediction) PRED predicted values (max 10 best out of 119): 0l14md (0.73 #1027, 0.69 #772, 0.69 #517), 03qjg (0.50 #556, 0.44 #216, 0.31 #1491), 042v_gx (0.44 #179, 0.25 #519, 0.19 #774), 0l14qv (0.33 #175, 0.31 #515, 0.24 #2216), 05r5c (0.33 #178, 0.26 #1624, 0.25 #518), 04rzd (0.33 #200, 0.19 #540, 0.15 #2470), 01vj9c (0.31 #523, 0.29 #2139, 0.28 #2311), 013y1f (0.25 #536, 0.22 #196, 0.15 #2152), 02sgy (0.22 #176, 0.15 #2470, 0.12 #516), 06ncr (0.19 #547, 0.16 #2163, 0.15 #2248) >> Best rule #1027 for best value: >> intensional similarity = 9 >> extensional distance = 39 >> proper extension: 01t_xp_; 01wv9xn; 04r1t; 01czx; 05563d; 0d193h; 0g_g2; 0178kd; 013rfk; 0p76z; ... >> query: (?x8497, 0l14md) <- group(?x1750, ?x8497), group(?x1466, ?x8497), group(?x716, ?x8497), artists(?x1000, ?x8497), ?x1750 = 02hnl, ?x1466 = 03bx0bm, ?x1000 = 0xhtw, group(?x716, ?x8058), ?x8058 = 014pg1 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #547 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 14 *> proper extension: 01fl3; 047cx; 01kcms4; 01w5n51; *> query: (?x8497, 06ncr) <- group(?x1750, ?x8497), group(?x1466, ?x8497), artists(?x2809, ?x8497), artists(?x1000, ?x8497), ?x1750 = 02hnl, ?x1466 = 03bx0bm, artists(?x1000, ?x8004), ?x8004 = 01w9ph_, ?x2809 = 05w3f *> conf = 0.19 ranks of expected_values: 10 EVAL 01l_w0 group! 06ncr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 71.000 61.000 0.732 http://example.org/music/performance_role/regular_performances./music/group_membership/group #16786-0mww2 PRED entity: 0mww2 PRED relation: source PRED expected values: 0jbk9 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #28, 0.94 #27, 0.94 #31) >> Best rule #28 for best value: >> intensional similarity = 5 >> extensional distance = 140 >> proper extension: 0k3g3; >> query: (?x12846, ?x958) <- adjoins(?x12846, ?x12845), second_level_divisions(?x94, ?x12846), time_zones(?x12846, ?x2674), ?x2674 = 02hcv8, source(?x12845, ?x958) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mww2 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.944 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #16785-07vfj PRED entity: 07vfj PRED relation: major_field_of_study PRED expected values: 04rjg => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 114): 01mkq (0.69 #474, 0.68 #129, 0.67 #359), 04rjg (0.64 #134, 0.53 #709, 0.51 #249), 04x_3 (0.50 #140, 0.44 #370, 0.41 #255), 01lj9 (0.50 #151, 0.39 #266, 0.38 #496), 04sh3 (0.40 #873, 0.29 #413, 0.27 #528), 06ms6 (0.39 #131, 0.37 #246, 0.30 #476), 0g26h (0.39 #154, 0.33 #499, 0.32 #844), 0fdys (0.36 #150, 0.35 #725, 0.34 #265), 0dc_v (0.36 #155, 0.29 #270, 0.21 #385), 02ky346 (0.32 #130, 0.32 #245, 0.28 #475) >> Best rule #474 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 01prf3; >> query: (?x3813, 01mkq) <- organization(?x3813, ?x5487), organization(?x122, ?x5487), citytown(?x122, ?x9336) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #134 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 045c7b; *> query: (?x3813, 04rjg) <- organization(?x3813, ?x5487), company(?x3131, ?x3813), state_province_region(?x3813, ?x335) *> conf = 0.64 ranks of expected_values: 2 EVAL 07vfj major_field_of_study 04rjg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.688 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16784-0cfz_z PRED entity: 0cfz_z PRED relation: place_of_birth PRED expected values: 03rk0 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 65): 0hj6h (0.28 #11982, 0.20 #12689, 0.19 #12688), 04vmp (0.20 #12689, 0.17 #15509, 0.17 #7049), 02_286 (0.18 #9887, 0.17 #3544, 0.17 #2839), 0fpzwf (0.17 #911, 0.08 #4436, 0.07 #7255), 0p9z5 (0.10 #1780, 0.07 #7420, 0.07 #6715), 03b12 (0.08 #3932, 0.08 #3227, 0.08 #2521), 030qb3t (0.08 #13449, 0.08 #2168, 0.08 #14153), 04jpl (0.08 #3533, 0.08 #2828, 0.08 #11285), 013yq (0.08 #3604, 0.08 #2899, 0.02 #12062), 0rt80 (0.08 #4193, 0.08 #3488) >> Best rule #11982 for best value: >> intensional similarity = 5 >> extensional distance = 50 >> proper extension: 0p_pd; 0159h6; 01xsbh; 01w1kyf; >> query: (?x12098, ?x11914) <- profession(?x12098, ?x1032), sibling(?x11260, ?x12098), ?x1032 = 02hrh1q, nationality(?x12098, ?x2146), place_of_birth(?x11260, ?x11914) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #26076 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 205 *> proper extension: 0cfywh; *> query: (?x12098, ?x2146) <- nationality(?x12098, ?x2146), ?x2146 = 03rk0 *> conf = 0.01 ranks of expected_values: 51 EVAL 0cfz_z place_of_birth 03rk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 102.000 102.000 0.278 http://example.org/people/person/place_of_birth #16783-014dq7 PRED entity: 014dq7 PRED relation: award PRED expected values: 0g9wd99 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 274): 0g9wd99 (0.40 #1185, 0.33 #779, 0.23 #2403), 0gqy2 (0.38 #2602, 0.23 #5038, 0.14 #7069), 0ddd9 (0.33 #462, 0.25 #4522, 0.20 #1274), 0gr51 (0.33 #507, 0.21 #12689, 0.15 #24368), 0czp_ (0.33 #711, 0.06 #3959, 0.05 #4771), 0f4x7 (0.32 #8558, 0.29 #7340, 0.27 #4903), 0bdw6t (0.23 #2547, 0.14 #4983, 0.09 #11075), 0ck27z (0.21 #3341, 0.06 #24461, 0.06 #51664), 040vk98 (0.20 #1247, 0.19 #10587, 0.15 #2059), 01yz0x (0.20 #989, 0.16 #10735, 0.15 #2207) >> Best rule #1185 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 049gc; >> query: (?x1946, 0g9wd99) <- influenced_by(?x8720, ?x1946), influenced_by(?x1946, ?x118), ?x8720 = 027y_, location(?x1946, ?x739) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014dq7 award 0g9wd99 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16782-0bl3nn PRED entity: 0bl3nn PRED relation: film_crew_role PRED expected values: 01vx2h => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 32): 01vx2h (0.58 #167, 0.57 #71, 0.55 #266), 01pvkk (0.40 #104, 0.36 #136, 0.33 #8), 02ynfr (0.23 #271, 0.22 #630, 0.22 #663), 01xy5l_ (0.20 #106, 0.18 #138, 0.17 #170), 033smt (0.19 #184, 0.12 #1578, 0.11 #916), 094hwz (0.19 #171, 0.12 #1578, 0.11 #916), 0d2b38 (0.18 #479, 0.17 #182, 0.16 #640), 0215hd (0.18 #537, 0.18 #472, 0.15 #1064), 089g0h (0.16 #473, 0.15 #538, 0.13 #1065), 089fss (0.12 #1578, 0.11 #916, 0.10 #101) >> Best rule #167 for best value: >> intensional similarity = 8 >> extensional distance = 34 >> proper extension: 0d90m; 09sh8k; 01qb5d; 03t97y; 07sc6nw; 044g_k; 0340hj; 0fdv3; 028cg00; 0ddjy; ... >> query: (?x7239, 01vx2h) <- genre(?x7239, ?x13368), genre(?x7239, ?x1510), genre(?x7239, ?x225), genre(?x7566, ?x13368), ?x225 = 02kdv5l, featured_film_locations(?x7239, ?x362), ?x1510 = 01hmnh, film(?x1289, ?x7239) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bl3nn film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.583 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16781-04ly1 PRED entity: 04ly1 PRED relation: religion PRED expected values: 05w5d => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 27): 05sfs (0.80 #479, 0.78 #114, 0.74 #86), 05w5d (0.78 #493, 0.72 #128, 0.71 #241), 021_0p (0.64 #124, 0.62 #96, 0.61 #489), 03_gx (0.49 #485, 0.42 #120, 0.41 #92), 01s5nb (0.43 #495, 0.42 #158, 0.42 #130), 058x5 (0.42 #115, 0.41 #87, 0.37 #480), 0flw86 (0.40 #2221, 0.39 #1520, 0.39 #1324), 092bf5 (0.29 #683, 0.26 #655, 0.26 #992), 02t7t (0.25 #492, 0.25 #127, 0.24 #240), 03j6c (0.10 #182, 0.09 #1588, 0.09 #1532) >> Best rule #479 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 05kkh; 03v1s; 05kj_; 059f4; 05fhy; 059_c; 06mz5; 07z1m; 01x73; 04rrd; ... >> query: (?x3908, 05sfs) <- state_province_region(?x466, ?x3908), contains(?x3908, ?x1248), religion(?x3908, ?x109) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #493 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 05kkh; 03v1s; 05kj_; 059f4; 05fhy; 059_c; 06mz5; 07z1m; 01x73; 04rrd; ... *> query: (?x3908, 05w5d) <- state_province_region(?x466, ?x3908), contains(?x3908, ?x1248), religion(?x3908, ?x109) *> conf = 0.78 ranks of expected_values: 2 EVAL 04ly1 religion 05w5d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 166.000 166.000 0.804 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #16780-03ryn PRED entity: 03ryn PRED relation: country! PRED expected values: 02y8z => 205 concepts (205 used for prediction) PRED predicted values (max 10 best out of 48): 03hr1p (0.82 #1363, 0.82 #1891, 0.81 #1123), 06wrt (0.79 #1357, 0.79 #973, 0.77 #1117), 07jbh (0.76 #1373, 0.76 #893, 0.69 #701), 064vjs (0.75 #699, 0.68 #891, 0.68 #1371), 07jjt (0.75 #690, 0.62 #1362, 0.58 #1122), 035d1m (0.75 #694, 0.57 #550, 0.53 #1366), 02y8z (0.72 #880, 0.71 #1360, 0.68 #1120), 07gyv (0.72 #871, 0.68 #1351, 0.67 #2071), 03rbzn (0.69 #695, 0.66 #1895, 0.61 #1991), 07bs0 (0.69 #684, 0.59 #1884, 0.57 #540) >> Best rule #1363 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 05qx1; 0163v; 07f1x; >> query: (?x3749, 03hr1p) <- film_release_region(?x6095, ?x3749), film_release_region(?x249, ?x3749), ?x6095 = 0bq6ntw, ?x249 = 0c3ybss >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #880 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 0jgd; 0ctw_b; 06t8v; *> query: (?x3749, 02y8z) <- film_release_region(?x6536, ?x3749), film_release_region(?x5016, ?x3749), contains(?x3749, ?x11382), ?x5016 = 062zm5h, ?x6536 = 09gmmt6 *> conf = 0.72 ranks of expected_values: 7 EVAL 03ryn country! 02y8z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 205.000 205.000 0.824 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #16779-07cjqy PRED entity: 07cjqy PRED relation: participant PRED expected values: 0fby2t => 109 concepts (67 used for prediction) PRED predicted values (max 10 best out of 377): 0fby2t (0.82 #25626, 0.81 #27551, 0.81 #28833), 05ty4m (0.33 #21, 0.14 #662, 0.12 #1302), 09yrh (0.25 #7688, 0.05 #7362, 0.04 #4158), 0j1yf (0.14 #758, 0.12 #1398, 0.03 #2679), 08vr94 (0.14 #911, 0.12 #1551, 0.03 #5766), 0237fw (0.08 #7848, 0.07 #4643, 0.07 #8488), 0c6qh (0.06 #2087, 0.06 #11693, 0.06 #6571), 0cgfb (0.06 #2534, 0.06 #7018, 0.05 #5095), 0bksh (0.06 #2255, 0.05 #4816, 0.04 #5457), 02mjf2 (0.06 #2225, 0.05 #4786, 0.04 #6709) >> Best rule #25626 for best value: >> intensional similarity = 3 >> extensional distance = 406 >> proper extension: 03qcq; >> query: (?x3536, ?x4106) <- award_nominee(?x3536, ?x4631), participant(?x4106, ?x3536), participant(?x3536, ?x2221) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07cjqy participant 0fby2t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 67.000 0.823 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #16778-0h953 PRED entity: 0h953 PRED relation: people! PRED expected values: 041rx => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 42): 013xrm (0.61 #95, 0.52 #323, 0.47 #551), 041rx (0.40 #1600, 0.36 #2056, 0.35 #2360), 0x67 (0.23 #921, 0.18 #4874, 0.17 #5786), 033tf_ (0.15 #3883, 0.14 #4491, 0.13 #5251), 07bch9 (0.12 #630, 0.11 #1618, 0.08 #782), 0xnvg (0.11 #924, 0.10 #3889, 0.09 #4497), 02w7gg (0.11 #3879, 0.10 #5247, 0.10 #4943), 048z7l (0.09 #2889, 0.08 #951, 0.05 #1331), 09kr66 (0.09 #2889, 0.05 #650, 0.03 #878), 0dryh9k (0.08 #851, 0.06 #6020, 0.06 #6172) >> Best rule #95 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0h336; 0459z; 0g72r; >> query: (?x8450, 013xrm) <- nationality(?x8450, ?x1264), place_of_death(?x8450, ?x1523), ?x1264 = 0345h, influenced_by(?x6771, ?x8450) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1600 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 168 *> proper extension: 01dvtx; 0k57l; 030dx5; *> query: (?x8450, 041rx) <- nationality(?x8450, ?x94), place_of_death(?x8450, ?x1523), people(?x1423, ?x8450), ?x94 = 09c7w0 *> conf = 0.40 ranks of expected_values: 2 EVAL 0h953 people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 135.000 0.611 http://example.org/people/ethnicity/people #16777-063vn PRED entity: 063vn PRED relation: student! PRED expected values: 016t_3 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 18): 014mlp (0.41 #197, 0.40 #368, 0.35 #330), 04zx3q1 (0.31 #98, 0.10 #954, 0.07 #402), 019v9k (0.25 #163, 0.21 #125, 0.18 #410), 02_xgp2 (0.25 #243, 0.12 #167, 0.10 #954), 02h4rq6 (0.17 #327, 0.17 #3, 0.12 #365), 0bkj86 (0.14 #409, 0.12 #162, 0.10 #238), 013zdg (0.13 #332, 0.12 #370, 0.10 #954), 07s6fsf (0.12 #192, 0.09 #325, 0.08 #363), 016t_3 (0.10 #233, 0.10 #954, 0.09 #81), 02mjs7 (0.10 #63, 0.10 #954, 0.09 #329) >> Best rule #197 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 0dj5q; >> query: (?x1984, 014mlp) <- gender(?x1984, ?x231), basic_title(?x1984, ?x182), nationality(?x1984, ?x279), place_of_birth(?x1984, ?x2474), ?x231 = 05zppz >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 01dvtx; *> query: (?x1984, 016t_3) <- place_of_death(?x1984, ?x2474), profession(?x1984, ?x2225), religion(?x1984, ?x1985), student(?x7636, ?x1984) *> conf = 0.10 ranks of expected_values: 9 EVAL 063vn student! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 137.000 137.000 0.412 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #16776-015z4j PRED entity: 015z4j PRED relation: vacationer! PRED expected values: 01f08r => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 106): 03gh4 (0.33 #81, 0.33 #3828, 0.32 #4327), 06c62 (0.33 #87, 0.20 #211, 0.09 #832), 0f2v0 (0.33 #63, 0.13 #4434, 0.12 #5433), 0160w (0.33 #2, 0.11 #4373, 0.09 #5372), 07fr_ (0.33 #95, 0.08 #4466, 0.05 #3219), 05qtj (0.25 #5442, 0.20 #196, 0.18 #817), 0cv3w (0.20 #3804, 0.19 #3430, 0.19 #4428), 01p8s (0.20 #226, 0.14 #971, 0.10 #1470), 01_d4 (0.20 #163, 0.09 #660, 0.07 #908), 030qb3t (0.20 #160, 0.09 #781, 0.05 #1404) >> Best rule #81 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01pgzn_; >> query: (?x3020, 03gh4) <- participant(?x3020, ?x1416), participant(?x12130, ?x3020), ?x1416 = 0162c8, participant(?x4628, ?x12130) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2813 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 01_x6d; 03wbzp; *> query: (?x3020, 01f08r) <- participant(?x12130, ?x3020), nationality(?x3020, ?x94), gender(?x3020, ?x514), list(?x12130, ?x5160) *> conf = 0.02 ranks of expected_values: 57 EVAL 015z4j vacationer! 01f08r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 174.000 174.000 0.333 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #16775-04swx PRED entity: 04swx PRED relation: adjoins PRED expected values: 04w58 => 131 concepts (84 used for prediction) PRED predicted values (max 10 best out of 590): 04w58 (0.83 #55465, 0.82 #50838, 0.81 #47758), 0d05w3 (0.58 #11675, 0.38 #17071, 0.33 #18611), 04v3q (0.49 #63948, 0.09 #51608, 0.02 #43135), 0gtzp (0.49 #63948, 0.02 #50839), 01rhrd (0.49 #63948), 07ytt (0.49 #63948), 0d8h4 (0.42 #64720, 0.30 #50067, 0.14 #33894), 01c6yz (0.42 #64720, 0.30 #50067, 0.14 #33894), 0p0mx (0.42 #64720, 0.30 #50067, 0.09 #51608), 0cx2r (0.42 #64720, 0.30 #50067, 0.09 #51608) >> Best rule #55465 for best value: >> intensional similarity = 4 >> extensional distance = 168 >> proper extension: 0c75w; >> query: (?x12727, ?x3912) <- adjoins(?x12727, ?x455), adjoins(?x3912, ?x12727), adjoins(?x455, ?x1144), locations(?x1777, ?x455) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04swx adjoins 04w58 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 84.000 0.828 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #16774-05b5c PRED entity: 05b5c PRED relation: service_location PRED expected values: 0345h => 180 concepts (166 used for prediction) PRED predicted values (max 10 best out of 214): 0345h (0.54 #744, 0.46 #834, 0.45 #654), 03rjj (0.38 #728, 0.31 #818, 0.25 #186), 06mkj (0.38 #843, 0.31 #753, 0.25 #211), 02j71 (0.31 #735, 0.28 #10104, 0.27 #3731), 03rk0 (0.25 #210, 0.15 #842, 0.15 #752), 0h7x (0.25 #204, 0.15 #836, 0.15 #746), 07tp2 (0.25 #244, 0.09 #696, 0.08 #876), 03h64 (0.18 #580, 0.18 #9910, 0.17 #9638), 059j2 (0.18 #653, 0.17 #9638, 0.17 #2008), 06bnz (0.18 #658, 0.12 #2013, 0.11 #2925) >> Best rule #744 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 02qdyj; >> query: (?x13349, 0345h) <- service_location(?x13349, ?x550), service_location(?x13349, ?x512), ?x512 = 07ssc, industry(?x13349, ?x245), contact_category(?x13349, ?x6046), film_release_region(?x66, ?x550) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05b5c service_location 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 180.000 166.000 0.538 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #16773-040_9 PRED entity: 040_9 PRED relation: gender PRED expected values: 05zppz => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.96 #39, 0.95 #37, 0.92 #33), 02zsn (0.46 #178, 0.25 #36, 0.25 #159) >> Best rule #39 for best value: >> intensional similarity = 6 >> extensional distance = 47 >> proper extension: 079ws; 06hgj; 046_v; 02465; >> query: (?x3541, 05zppz) <- influenced_by(?x2161, ?x3541), influenced_by(?x1029, ?x3541), influenced_by(?x2161, ?x2625), place_of_birth(?x1029, ?x4356), influenced_by(?x476, ?x2161), ?x2625 = 03f70xs >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 040_9 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.959 http://example.org/people/person/gender #16772-03pvt PRED entity: 03pvt PRED relation: category PRED expected values: 08mbj5d => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.54 #12, 0.51 #17, 0.50 #1) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 06jzh; >> query: (?x3710, 08mbj5d) <- gender(?x3710, ?x231), person(?x1315, ?x3710), currency(?x3710, ?x170) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03pvt category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.540 http://example.org/common/topic/webpage./common/webpage/category #16771-0hg5 PRED entity: 0hg5 PRED relation: olympics PRED expected values: 018ctl => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 41): 0kbws (0.59 #53, 0.49 #172, 0.46 #211), 09n48 (0.53 #42, 0.41 #79, 0.41 #1135), 0kbvb (0.50 #46, 0.46 #7, 0.43 #126), 0jdk_ (0.50 #64, 0.33 #144, 0.31 #25), 0swbd (0.47 #50, 0.36 #130, 0.32 #208), 018ctl (0.47 #47, 0.32 #205, 0.31 #127), 0jhn7 (0.44 #65, 0.27 #1333, 0.24 #145), 0sx8l (0.41 #79, 0.41 #1135, 0.36 #1804), 0swff (0.38 #62, 0.31 #23, 0.27 #1333), 0l6m5 (0.34 #49, 0.27 #1333, 0.23 #10) >> Best rule #53 for best value: >> intensional similarity = 2 >> extensional distance = 30 >> proper extension: 087vz; 03f2w; >> query: (?x2756, 0kbws) <- participating_countries(?x1741, ?x2756), ?x1741 = 0sx8l >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 30 *> proper extension: 087vz; 03f2w; *> query: (?x2756, 018ctl) <- participating_countries(?x1741, ?x2756), ?x1741 = 0sx8l *> conf = 0.47 ranks of expected_values: 6 EVAL 0hg5 olympics 018ctl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 86.000 86.000 0.594 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #16770-013yq PRED entity: 013yq PRED relation: month PRED expected values: 040fb => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 1): 040fb (0.88 #18, 0.86 #20, 0.85 #17) >> Best rule #18 for best value: >> intensional similarity = 2 >> extensional distance = 47 >> proper extension: 0l0mk; >> query: (?x2277, 040fb) <- month(?x2277, ?x1459), location(?x624, ?x2277) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013yq month 040fb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.878 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #16769-02r1tx7 PRED entity: 02r1tx7 PRED relation: group! PRED expected values: 01797x => 84 concepts (52 used for prediction) PRED predicted values (max 10 best out of 206): 02whj (0.25 #16, 0.20 #790, 0.10 #3113), 01wg6y (0.25 #159, 0.20 #933, 0.10 #3256), 09g0h (0.25 #185, 0.20 #959, 0.10 #3282), 0jsg0m (0.25 #134, 0.20 #908, 0.10 #3231), 01p45_v (0.25 #22, 0.20 #796, 0.10 #3119), 048tgl (0.25 #556, 0.17 #1330, 0.12 #2294), 0jn5l (0.25 #485, 0.17 #1259, 0.12 #2223), 0qf11 (0.20 #658, 0.17 #1046, 0.14 #1818), 04d_mtq (0.20 #949, 0.10 #3272, 0.03 #3851), 04f7c55 (0.20 #878, 0.10 #3201, 0.03 #3780) >> Best rule #16 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 07qnf; 05563d; >> query: (?x2567, 02whj) <- group(?x2798, ?x2567), group(?x1750, ?x2567), group(?x1267, ?x2567), group(?x316, ?x2567), ?x1750 = 02hnl, group(?x6947, ?x2567), ?x1267 = 07brj, ?x316 = 05r5c, ?x2798 = 03qjg, artist(?x3888, ?x6947) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02r1tx7 group! 01797x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 52.000 0.250 http://example.org/music/group_member/membership./music/group_membership/group #16768-024cg8 PRED entity: 024cg8 PRED relation: major_field_of_study PRED expected values: 0fdys => 148 concepts (139 used for prediction) PRED predicted values (max 10 best out of 117): 03g3w (0.44 #1667, 0.27 #2423, 0.25 #2549), 062z7 (0.42 #1668, 0.25 #2802, 0.23 #4063), 02j62 (0.40 #1671, 0.37 #1545, 0.33 #1166), 037mh8 (0.40 #1710, 0.21 #449, 0.19 #827), 04rjg (0.36 #1660, 0.30 #147, 0.29 #777), 01mkq (0.33 #2915, 0.32 #1655, 0.31 #2537), 05qjt (0.30 #134, 0.26 #1647, 0.23 #260), 02lp1 (0.30 #1651, 0.25 #4046, 0.25 #2785), 01lj9 (0.30 #168, 0.22 #1681, 0.17 #2563), 0fdys (0.28 #1680, 0.14 #2940, 0.13 #2814) >> Best rule #1667 for best value: >> intensional similarity = 2 >> extensional distance = 48 >> proper extension: 0301dp; >> query: (?x13707, 03g3w) <- institution(?x1390, ?x13707), ?x1390 = 0bjrnt >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1680 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 48 *> proper extension: 0301dp; *> query: (?x13707, 0fdys) <- institution(?x1390, ?x13707), ?x1390 = 0bjrnt *> conf = 0.28 ranks of expected_values: 10 EVAL 024cg8 major_field_of_study 0fdys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 148.000 139.000 0.440 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16767-01fs_4 PRED entity: 01fs_4 PRED relation: nominated_for PRED expected values: 01rp13 => 183 concepts (104 used for prediction) PRED predicted values (max 10 best out of 549): 0199wf (0.29 #136204, 0.28 #149175, 0.26 #162145), 0yzbg (0.29 #1126), 01b9w3 (0.29 #667), 017f3m (0.14 #773, 0.03 #8882, 0.02 #104549), 02k_4g (0.14 #108, 0.02 #118477, 0.02 #103884), 0421v9q (0.14 #1050, 0.01 #38346), 05pbsry (0.14 #1617), 04zl8 (0.09 #4089, 0.08 #5710, 0.08 #7332), 016tvq (0.09 #4547, 0.08 #6168, 0.08 #7790), 04smdd (0.09 #3905, 0.08 #5526, 0.08 #7148) >> Best rule #136204 for best value: >> intensional similarity = 3 >> extensional distance = 716 >> proper extension: 02s2ft; 01k7d9; 06151l; 0byfz; 0bl2g; 0h1_w; 03zqc1; 0kr5_; 01mvth; 0f0p0; ... >> query: (?x3868, ?x10492) <- film(?x3868, ?x10492), type_of_union(?x3868, ?x566), award_winner(?x10618, ?x3868) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #12369 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 01xv77; *> query: (?x3868, 01rp13) <- film(?x3868, ?x10492), diet(?x3868, ?x3130), student(?x10478, ?x3868) *> conf = 0.02 ranks of expected_values: 210 EVAL 01fs_4 nominated_for 01rp13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 183.000 104.000 0.288 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16766-037gjc PRED entity: 037gjc PRED relation: film PRED expected values: 0f8j13 => 127 concepts (96 used for prediction) PRED predicted values (max 10 best out of 907): 01fx1l (0.50 #98534, 0.49 #114660, 0.49 #112868), 0g60z (0.50 #98534, 0.49 #114660, 0.49 #112868), 01f39b (0.20 #980, 0.08 #8146, 0.02 #29640), 0f40w (0.20 #363, 0.02 #18275, 0.01 #52311), 0258dh (0.14 #3075, 0.04 #6658), 07h9gp (0.14 #3849, 0.04 #7432, 0.04 #9223), 01633c (0.14 #3119, 0.04 #8494, 0.03 #19240), 0jvt9 (0.14 #2331, 0.04 #7706, 0.03 #29200), 03rtz1 (0.14 #1959, 0.04 #7334, 0.01 #107660), 02c638 (0.14 #2130, 0.04 #7505) >> Best rule #98534 for best value: >> intensional similarity = 3 >> extensional distance = 631 >> proper extension: 049tjg; 02g8h; 041h0; 02nb2s; 0151ns; 0456xp; 04shbh; 013cr; 01wjrn; 01xcqc; ... >> query: (?x4882, ?x337) <- location(?x4882, ?x11930), nominated_for(?x4882, ?x337), place_founded(?x2156, ?x11930) >> conf = 0.50 => this is the best rule for 2 predicted values *> Best rule #3357 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 01mmslz; *> query: (?x4882, 0f8j13) <- location(?x4882, ?x11930), nominated_for(?x4882, ?x337), ?x11930 = 0r00l *> conf = 0.14 ranks of expected_values: 18 EVAL 037gjc film 0f8j13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 127.000 96.000 0.502 http://example.org/film/actor/film./film/performance/film #16765-0cz_ym PRED entity: 0cz_ym PRED relation: film_crew_role PRED expected values: 02ynfr => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 27): 0dxtw (0.44 #212, 0.37 #689, 0.36 #587), 01vx2h (0.44 #213, 0.34 #860, 0.31 #996), 01pvkk (0.31 #214, 0.29 #1031, 0.28 #997), 01xy5l_ (0.27 #12, 0.13 #2668, 0.12 #80), 02ynfr (0.20 #82, 0.18 #48, 0.18 #218), 02rh1dz (0.16 #75, 0.13 #2668, 0.11 #994), 0215hd (0.15 #221, 0.15 #289, 0.15 #527), 0d2b38 (0.15 #228, 0.13 #24, 0.13 #2668), 089g0h (0.13 #2668, 0.11 #869, 0.11 #426), 04pyp5 (0.13 #2668, 0.11 #287, 0.10 #253) >> Best rule #212 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 09rfh9; >> query: (?x1877, 0dxtw) <- nominated_for(?x3019, ?x1877), featured_film_locations(?x1877, ?x739), ?x3019 = 057xs89, genre(?x1877, ?x53) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #82 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 0413cff; *> query: (?x1877, 02ynfr) <- person(?x1877, ?x5572), featured_film_locations(?x1877, ?x739), language(?x1877, ?x254) *> conf = 0.20 ranks of expected_values: 5 EVAL 0cz_ym film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 86.000 0.436 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16764-065y4w7 PRED entity: 065y4w7 PRED relation: organization! PRED expected values: 060c4 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 11): 060c4 (0.78 #439, 0.78 #28, 0.76 #82), 0dq_5 (0.52 #248, 0.49 #220, 0.48 #234), 05k17c (0.37 #94, 0.34 #385, 0.31 #358), 07t3gd (0.37 #94, 0.34 #385, 0.31 #358), 07xl34 (0.27 #50, 0.26 #170, 0.25 #24), 0hm4q (0.12 #8, 0.08 #180, 0.08 #141), 05c0jwl (0.05 #44, 0.04 #759, 0.04 #705), 08jcfy (0.05 #51, 0.02 #831, 0.02 #145), 0dq3c (0.03 #67, 0.01 #212, 0.01 #226), 04n1q6 (0.02 #377, 0.01 #391, 0.01 #561) >> Best rule #439 for best value: >> intensional similarity = 3 >> extensional distance = 153 >> proper extension: 02zcz3; 01fsv9; >> query: (?x735, 060c4) <- school(?x580, ?x735), institution(?x734, ?x735), school_type(?x735, ?x1044) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065y4w7 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.781 http://example.org/organization/role/leaders./organization/leadership/organization #16763-03_vx9 PRED entity: 03_vx9 PRED relation: award PRED expected values: 099flj => 133 concepts (113 used for prediction) PRED predicted values (max 10 best out of 262): 05p09zm (0.38 #1739, 0.35 #3355, 0.30 #2547), 09sb52 (0.38 #3271, 0.34 #4887, 0.34 #2463), 099ck7 (0.38 #1075, 0.20 #671, 0.09 #3499), 0279c15 (0.38 #944, 0.20 #540, 0.09 #28686), 0bfvd4 (0.38 #922, 0.07 #22334, 0.07 #25971), 05pcn59 (0.36 #3312, 0.30 #2504, 0.25 #10584), 03c7tr1 (0.26 #1673, 0.22 #2885, 0.21 #4097), 05b4l5x (0.26 #1622, 0.20 #410, 0.19 #10510), 07cbcy (0.25 #77, 0.20 #481, 0.19 #2501), 02ppm4q (0.25 #964, 0.20 #560, 0.14 #32323) >> Best rule #1739 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 06y9c2; 024dgj; 0261x8t; >> query: (?x950, 05p09zm) <- friend(?x949, ?x950), type_of_union(?x950, ?x566), participant(?x6157, ?x950) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #32323 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1464 *> proper extension: 030_1_; *> query: (?x950, ?x899) <- award_winner(?x4596, ?x950), award(?x950, ?x375), nominated_for(?x899, ?x4596) *> conf = 0.14 ranks of expected_values: 40 EVAL 03_vx9 award 099flj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 133.000 113.000 0.385 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16762-064t9 PRED entity: 064t9 PRED relation: parent_genre PRED expected values: 0ggx5q => 81 concepts (59 used for prediction) PRED predicted values (max 10 best out of 287): 0glt670 (0.65 #4690, 0.13 #7749, 0.11 #4850), 0gywn (0.36 #3412, 0.25 #3573, 0.25 #1962), 03_d0 (0.36 #5962, 0.24 #4189, 0.21 #8377), 017371 (0.33 #584, 0.25 #744, 0.22 #2350), 02w4v (0.33 #509, 0.25 #669, 0.20 #1312), 01243b (0.33 #2273, 0.20 #2595, 0.18 #2917), 05r9t (0.33 #61, 0.08 #3597, 0.06 #3758), 05r6t (0.30 #2620, 0.27 #2942, 0.25 #1816), 059kh (0.30 #2600, 0.27 #2922, 0.25 #1796), 09jw2 (0.30 #2668, 0.27 #2990, 0.25 #1864) >> Best rule #4690 for best value: >> intensional similarity = 7 >> extensional distance = 35 >> proper extension: 025tjk_; >> query: (?x671, 0glt670) <- parent_genre(?x671, ?x3319), artists(?x3319, ?x11371), artists(?x3319, ?x4640), artists(?x3319, ?x2334), ?x11371 = 01wlt3k, ?x2334 = 047sxrj, actor(?x5529, ?x4640) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #4343 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 05g7tj; *> query: (?x671, ?x2937) <- artists(?x671, ?x7972), artists(?x671, ?x1818), artists(?x2937, ?x1818), role(?x1818, ?x74), ?x7972 = 0326tc *> conf = 0.04 ranks of expected_values: 115 EVAL 064t9 parent_genre 0ggx5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 81.000 59.000 0.649 http://example.org/music/genre/parent_genre #16761-049qx PRED entity: 049qx PRED relation: award PRED expected values: 02f71y 02f777 => 128 concepts (101 used for prediction) PRED predicted values (max 10 best out of 326): 02f71y (0.78 #10101, 0.77 #22225, 0.77 #16971), 02f6ym (0.60 #1067, 0.27 #4703, 0.24 #3087), 01by1l (0.49 #9808, 0.40 #4152, 0.36 #4960), 05pcn59 (0.45 #5737, 0.31 #2101, 0.28 #8565), 01bgqh (0.40 #850, 0.38 #2870, 0.37 #9738), 02f5qb (0.40 #964, 0.31 #1368, 0.23 #1772), 01c99j (0.40 #1035, 0.29 #2651, 0.27 #4671), 054ks3 (0.40 #950, 0.28 #13475, 0.24 #16304), 02f73b (0.40 #1096, 0.27 #4732, 0.23 #1904), 03qbnj (0.40 #1042, 0.26 #9930, 0.21 #2658) >> Best rule #10101 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 028qdb; 01yzl2; 01dwrc; 011z3g; 02pt7h_; 0134wr; 014kyy; 012x03; >> query: (?x4394, ?x528) <- artists(?x3319, ?x4394), award_winner(?x9431, ?x4394), award_winner(?x528, ?x4394), ?x3319 = 06j6l >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 17 EVAL 049qx award 02f777 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 128.000 101.000 0.780 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 049qx award 02f71y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 101.000 0.780 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16760-05v10 PRED entity: 05v10 PRED relation: film_release_region! PRED expected values: 0872p_c 0bh8tgs 0cc97st => 92 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1334): 0fpgp26 (0.88 #7608, 0.83 #10204, 0.78 #1118), 0872p_c (0.88 #6619, 0.79 #9215, 0.74 #14407), 08hmch (0.86 #9200, 0.85 #6604, 0.74 #14392), 0gj8nq2 (0.85 #6890, 0.81 #9486, 0.77 #14678), 0cmf0m0 (0.85 #7528, 0.74 #10124, 0.67 #1038), 03nm_fh (0.83 #9669, 0.82 #7073, 0.77 #14861), 043tvp3 (0.83 #9983, 0.80 #7387, 0.77 #15175), 05pdh86 (0.83 #9634, 0.80 #7038, 0.72 #14826), 047vnkj (0.83 #9762, 0.80 #7166, 0.68 #14954), 01fmys (0.83 #9320, 0.78 #6724, 0.72 #14512) >> Best rule #7608 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 05r4w; 09c7w0; 0b90_r; 03rjj; 0d060g; 0d0vqn; 04gzd; 0chghy; 047lj; 01ls2; ... >> query: (?x1592, 0fpgp26) <- film_release_region(?x2441, ?x1592), adjoins(?x142, ?x1592), ?x2441 = 0cc5mcj >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #6619 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 05r4w; 09c7w0; 0b90_r; 03rjj; 0d060g; 0d0vqn; 04gzd; 0chghy; 047lj; 01ls2; ... *> query: (?x1592, 0872p_c) <- film_release_region(?x2441, ?x1592), adjoins(?x142, ?x1592), ?x2441 = 0cc5mcj *> conf = 0.88 ranks of expected_values: 2, 33, 74 EVAL 05v10 film_release_region! 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 92.000 53.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v10 film_release_region! 0bh8tgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 92.000 53.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05v10 film_release_region! 0872p_c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 53.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16759-0130sy PRED entity: 0130sy PRED relation: category PRED expected values: 08mbj5d => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #11, 0.83 #38, 0.83 #51) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 01l_vgt; 07yg2; 047cx; 012vm6; 0mjn2; 04k05; 0cfgd; 079kr; 0fsyx; >> query: (?x6838, 08mbj5d) <- artist(?x5744, ?x6838), artists(?x1380, ?x6838), ?x5744 = 01clyr >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0130sy category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.833 http://example.org/common/topic/webpage./common/webpage/category #16758-03g90h PRED entity: 03g90h PRED relation: film_release_distribution_medium PRED expected values: 07c52 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 4): 07c52 (0.10 #142, 0.10 #134, 0.09 #50), 07z4p (0.09 #144, 0.09 #136, 0.08 #108), 02nxhr (0.09 #113, 0.09 #117, 0.09 #129), 0735l (0.01 #75) >> Best rule #142 for best value: >> intensional similarity = 7 >> extensional distance = 221 >> proper extension: 053tj7; 0bmc4cm; 02qk3fk; >> query: (?x280, 07c52) <- film_release_region(?x280, ?x1229), film_release_region(?x280, ?x789), film_release_distribution_medium(?x280, ?x81), language(?x280, ?x254), ?x1229 = 059j2, film_release_region(?x4040, ?x789), ?x4040 = 02mt51 >> conf = 0.10 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03g90h film_release_distribution_medium 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.099 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #16757-0l34j PRED entity: 0l34j PRED relation: adjoins! PRED expected values: 0bxqq => 79 concepts (30 used for prediction) PRED predicted values (max 10 best out of 342): 0b90_r (0.40 #788, 0.02 #10987, 0.02 #14126), 0kpzy (0.33 #2652, 0.27 #1867, 0.17 #3437), 0l2xl (0.27 #1935, 0.25 #2720, 0.17 #3505), 0d6lp (0.27 #1728, 0.25 #2513, 0.17 #3298), 0l2vz (0.27 #1795, 0.25 #2580, 0.17 #3365), 0l2sr (0.25 #12553, 0.18 #2020, 0.17 #2805), 0bxqq (0.25 #12553, 0.09 #1837, 0.08 #2622), 0l34j (0.25 #12553, 0.09 #1790, 0.08 #2575), 0l2hf (0.25 #12553, 0.08 #2535, 0.01 #8027), 0f04v (0.22 #3434, 0.17 #5002, 0.09 #4218) >> Best rule #788 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 06pvr; >> query: (?x4412, 0b90_r) <- contains(?x4412, ?x4413), ?x4413 = 0gjcy >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #12553 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 169 *> proper extension: 0m2gk; 01279v; *> query: (?x4412, ?x3677) <- contains(?x4412, ?x4413), adjoins(?x4412, ?x7520), second_level_divisions(?x94, ?x7520), adjoins(?x7520, ?x3677) *> conf = 0.25 ranks of expected_values: 7 EVAL 0l34j adjoins! 0bxqq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 79.000 30.000 0.400 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #16756-07t58 PRED entity: 07t58 PRED relation: legislative_sessions PRED expected values: 0495ys 04gp1d 060ny2 01grrf 01grq1 => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 10): 060ny2 (0.84 #20, 0.84 #21, 0.83 #10), 04gp1d (0.84 #20, 0.84 #21, 0.83 #10), 0495ys (0.84 #20, 0.84 #21, 0.83 #10), 01grrf (0.84 #20, 0.84 #21, 0.83 #10), 01grq1 (0.84 #20, 0.84 #21, 0.80 #41), 04fhps (0.57 #39, 0.50 #18, 0.43 #29), 034_7s (0.50 #19, 0.43 #40, 0.43 #30), 03h_f4 (0.43 #27, 0.33 #16, 0.29 #37), 01gvxh (0.33 #15, 0.29 #36, 0.29 #26), 04lgybj (0.33 #12, 0.29 #33, 0.29 #23) >> Best rule #20 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 0x2sv; 0h6dy; 0l_j_; >> query: (?x4665, ?x3765) <- legislative_sessions(?x4665, ?x4730), legislative_sessions(?x4665, ?x1829), legislative_sessions(?x4665, ?x1028), district_represented(?x4730, ?x1906), legislative_sessions(?x1028, ?x355), state_province_region(?x266, ?x1906), district_represented(?x1829, ?x726), legislative_sessions(?x5266, ?x1028), legislative_sessions(?x1829, ?x3765), contains(?x1906, ?x169), time_zones(?x1906, ?x1638), currency(?x1906, ?x170), religion(?x1906, ?x109) >> conf = 0.84 => this is the best rule for 5 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 07t58 legislative_sessions 01grq1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.843 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions EVAL 07t58 legislative_sessions 01grrf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.843 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions EVAL 07t58 legislative_sessions 060ny2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.843 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions EVAL 07t58 legislative_sessions 04gp1d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.843 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions EVAL 07t58 legislative_sessions 0495ys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.843 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #16755-0309jm PRED entity: 0309jm PRED relation: special_performance_type PRED expected values: 01pb34 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.17 #41, 0.12 #18, 0.12 #8), 01kyvx (0.03 #171, 0.03 #191, 0.03 #161), 09_gdc (0.02 #17, 0.02 #85, 0.02 #7) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 106 >> proper extension: 01z7_f; 03ym1; 036hf4; >> query: (?x2825, 01pb34) <- film(?x2825, ?x2349), religion(?x2825, ?x2694), currency(?x2825, ?x170), ?x170 = 09nqf >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0309jm special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.167 http://example.org/film/actor/film./film/performance/special_performance_type #16754-03x42 PRED entity: 03x42 PRED relation: language! PRED expected values: 09gkx35 => 36 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1912): 0gtx63s (0.50 #3081, 0.33 #1351, 0.28 #17327), 0c0nhgv (0.50 #1892, 0.33 #162, 0.19 #5195), 0g4vmj8 (0.50 #2949, 0.33 #1219, 0.19 #5195), 0bs8ndx (0.50 #3084, 0.33 #1354, 0.19 #5195), 06zn2v2 (0.50 #2437, 0.33 #707, 0.19 #5195), 026hh0m (0.50 #3298, 0.33 #1568, 0.17 #5029), 0gldyz (0.50 #3326, 0.33 #1596, 0.17 #5057), 0kb57 (0.50 #2204, 0.33 #474, 0.17 #3935), 0pb33 (0.50 #1939, 0.33 #209, 0.17 #3670), 0cnztc4 (0.50 #1914, 0.33 #184, 0.17 #3645) >> Best rule #3081 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 0t_2; >> query: (?x12326, 0gtx63s) <- languages(?x2440, ?x12326), languages_spoken(?x7322, ?x12326), people(?x7322, ?x9339), people(?x7322, ?x8986), people(?x7322, ?x6456), people(?x7322, ?x5301), instrumentalists(?x316, ?x6456), ?x9339 = 03mstc, nationality(?x6456, ?x94), actor(?x3905, ?x8986), nationality(?x8986, ?x1310), group(?x5301, ?x5547) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #568 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x12326, 09gkx35) <- countries_spoken_in(?x12326, ?x512), countries_spoken_in(?x12326, ?x429), languages_spoken(?x5042, ?x12326), ?x429 = 03rt9, language(?x4927, ?x12326), language(?x1071, ?x12326), ?x4927 = 0j80w, ?x5042 = 0d7wh, languages(?x2440, ?x12326), ?x2440 = 01vvpjj, ?x1071 = 02d44q, ?x512 = 07ssc *> conf = 0.33 ranks of expected_values: 380 EVAL 03x42 language! 09gkx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 36.000 19.000 0.500 http://example.org/film/film/language #16753-05qbckf PRED entity: 05qbckf PRED relation: genre PRED expected values: 01jfsb => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 115): 07s9rl0 (0.61 #3576, 0.60 #4529, 0.59 #4769), 01jfsb (0.60 #4184, 0.49 #1919, 0.45 #6094), 03k9fj (0.54 #726, 0.44 #131, 0.43 #1799), 024qqx (0.51 #2146, 0.49 #7039, 0.48 #9906), 05p553 (0.48 #599, 0.43 #1195, 0.40 #480), 0lsxr (0.40 #9, 0.30 #485, 0.27 #6090), 01hmnh (0.31 #732, 0.30 #1805, 0.28 #1686), 02l7c8 (0.30 #2042, 0.28 #4068, 0.28 #8968), 0jtdp (0.25 #490, 0.24 #609, 0.19 #1205), 0219x_ (0.25 #503, 0.20 #5750, 0.16 #622) >> Best rule #3576 for best value: >> intensional similarity = 4 >> extensional distance = 299 >> proper extension: 027ct7c; 04nlb94; >> query: (?x1956, 07s9rl0) <- film_crew_role(?x1956, ?x2178), language(?x1956, ?x254), country(?x1956, ?x94), ?x2178 = 01pvkk >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #4184 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 376 *> proper extension: 0cks1m; 0dr1c2; 04svwx; *> query: (?x1956, 01jfsb) <- genre(?x1956, ?x6647), genre(?x1956, ?x225), ?x225 = 02kdv5l, disciplines_or_subjects(?x575, ?x6647) *> conf = 0.60 ranks of expected_values: 2 EVAL 05qbckf genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 106.000 0.608 http://example.org/film/film/genre #16752-0gjv_ PRED entity: 0gjv_ PRED relation: student PRED expected values: 041xl => 203 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1783): 09y20 (0.25 #2319, 0.14 #18993, 0.05 #64857), 06chf (0.25 #2527, 0.07 #19201, 0.05 #27539), 01qrbf (0.25 #3325, 0.07 #19999, 0.03 #65863), 01pbwwl (0.25 #3862, 0.07 #20536, 0.03 #66400), 06crng (0.25 #3380, 0.07 #20054, 0.03 #65918), 01jgpsh (0.25 #3188, 0.07 #19862, 0.03 #65726), 01pcrw (0.25 #2577, 0.07 #19251, 0.03 #65115), 06hx2 (0.14 #30245, 0.14 #13570, 0.08 #38582), 0194xc (0.14 #30817, 0.14 #14142, 0.08 #39154), 0d3k14 (0.14 #14353, 0.13 #22692, 0.12 #39365) >> Best rule #2319 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 08tyb_; >> query: (?x6127, 09y20) <- citytown(?x6127, ?x362), ?x362 = 04jpl, student(?x6127, ?x2143), producer_type(?x2143, ?x632), award_nominee(?x2143, ?x237) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #13767 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 03f2fw; *> query: (?x6127, 041xl) <- citytown(?x6127, ?x362), child(?x6127, ?x8223), major_field_of_study(?x8223, ?x742), student(?x8223, ?x1515), major_field_of_study(?x734, ?x742) *> conf = 0.14 ranks of expected_values: 18 EVAL 0gjv_ student 041xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 203.000 94.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #16751-02fwfb PRED entity: 02fwfb PRED relation: country PRED expected values: 0ctw_b => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 106): 09c7w0 (0.83 #2722, 0.81 #2782, 0.80 #5391), 07ssc (0.69 #139, 0.41 #559, 0.32 #439), 02jx1 (0.40 #5086, 0.07 #5329, 0.03 #4664), 05cgv (0.40 #5086), 03rk0 (0.22 #341, 0.14 #521, 0.09 #401), 0f8l9c (0.21 #502, 0.19 #322, 0.17 #562), 0chghy (0.17 #13, 0.14 #495, 0.12 #375), 0ctw_b (0.14 #386, 0.14 #506, 0.11 #326), 0d05w3 (0.14 #405, 0.14 #525, 0.08 #345), 06mkj (0.12 #402, 0.09 #522, 0.07 #5329) >> Best rule #2722 for best value: >> intensional similarity = 3 >> extensional distance = 769 >> proper extension: 04gknr; 08sfxj; 02gs6r; 047gpsd; 04ghz4m; 03nsm5x; 07ghq; 0564x; 0422v0; >> query: (?x7292, 09c7w0) <- film(?x752, ?x7292), award_winner(?x7292, ?x1019), country(?x7292, ?x1264) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #386 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 01f8gz; 02q3fdr; 031ldd; 047myg9; 0dkv90; 0581vn8; *> query: (?x7292, 0ctw_b) <- nominated_for(?x1019, ?x7292), genre(?x7292, ?x1626), ?x1626 = 03q4nz, film(?x752, ?x7292) *> conf = 0.14 ranks of expected_values: 8 EVAL 02fwfb country 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 92.000 92.000 0.831 http://example.org/film/film/country #16750-0j_1v PRED entity: 0j_1v PRED relation: source PRED expected values: 0jbk9 => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #44, 0.92 #47, 0.92 #32) >> Best rule #44 for best value: >> intensional similarity = 5 >> extensional distance = 274 >> proper extension: 0n5j_; 0fm9_; 0f4y_; 0jcgs; 0mx4_; 0mw89; 0mw93; 0cc56; 0m7fm; 0drsm; ... >> query: (?x13155, 0jbk9) <- second_level_divisions(?x94, ?x13155), adjoins(?x13155, ?x13582), adjoins(?x7460, ?x13155), ?x94 = 09c7w0, adjoins(?x13582, ?x7369) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j_1v source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.924 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #16749-015c1b PRED entity: 015c1b PRED relation: taxonomy PRED expected values: 04n6k => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.03 #46, 0.03 #30, 0.03 #31) >> Best rule #46 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14390, 04n6k) <- >> conf = 0.03 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015c1b taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.030 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #16748-01ccr8 PRED entity: 01ccr8 PRED relation: award PRED expected values: 02h3d1 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 274): 02lp0w (0.76 #14109, 0.74 #14916, 0.72 #6048), 01ck6h (0.37 #526, 0.12 #2138, 0.11 #1735), 0cqhk0 (0.34 #8503, 0.18 #6891, 0.18 #6488), 0gqyl (0.33 #105, 0.27 #912, 0.11 #17440), 094qd5 (0.33 #45, 0.15 #852, 0.09 #4479), 0gqwc (0.32 #881, 0.22 #74, 0.12 #10958), 01by1l (0.32 #516, 0.19 #13817, 0.18 #1725), 01bgqh (0.32 #447, 0.18 #1656, 0.18 #13748), 09sb52 (0.29 #2460, 0.28 #18989, 0.26 #10522), 0ck27z (0.28 #6946, 0.27 #4929, 0.26 #6543) >> Best rule #14109 for best value: >> intensional similarity = 3 >> extensional distance = 692 >> proper extension: 0grwj; 016qtt; 012d40; 028q6; 0byfz; 01vw87c; 089tm; 0prfz; 01pfr3; 06cv1; ... >> query: (?x8412, ?x5841) <- award(?x8412, ?x1058), award_winner(?x5841, ?x8412), category(?x8412, ?x134) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #5826 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 291 *> proper extension: 0gsg7; 09d5h; 0cjdk; 0kk9v; 05xbx; 05gnf; 01j7pt; 01zcrv; 0kctd; 0kcd5; *> query: (?x8412, 02h3d1) <- nominated_for(?x8412, ?x6694), category(?x8412, ?x134), award_winner(?x5841, ?x8412) *> conf = 0.02 ranks of expected_values: 218 EVAL 01ccr8 award 02h3d1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 100.000 100.000 0.762 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16747-09vc4s PRED entity: 09vc4s PRED relation: languages_spoken PRED expected values: 0t_2 => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 56): 0t_2 (0.67 #236, 0.64 #628, 0.60 #348), 064_8sq (0.50 #187, 0.28 #1402, 0.22 #1289), 02h40lc (0.42 #1066, 0.40 #1347, 0.40 #674), 0h407 (0.33 #50, 0.25 #498, 0.25 #162), 03x42 (0.33 #103, 0.20 #719, 0.19 #831), 0880p (0.31 #548, 0.29 #660, 0.27 #436), 06nm1 (0.27 #401, 0.25 #121, 0.23 #569), 0jzc (0.25 #297, 0.17 #185, 0.09 #1306), 03hkp (0.23 #517, 0.21 #629, 0.20 #349), 06b_j (0.23 #524, 0.21 #636, 0.18 #412) >> Best rule #236 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 065b6q; 01qhm_; >> query: (?x1816, 0t_2) <- people(?x1816, ?x5246), people(?x1816, ?x5111), people(?x1816, ?x3927), people(?x1816, ?x1992), ?x5246 = 046zh, award_winner(?x86, ?x3927), award_nominee(?x3927, ?x4046), category(?x1992, ?x134), film(?x3927, ?x365), award(?x5111, ?x1254) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09vc4s languages_spoken 0t_2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.667 http://example.org/people/ethnicity/languages_spoken #16746-01h8rk PRED entity: 01h8rk PRED relation: institution! PRED expected values: 0bkj86 022h5x => 246 concepts (112 used for prediction) PRED predicted values (max 10 best out of 19): 02_xgp2 (0.82 #342, 0.82 #302, 0.78 #524), 0bkj86 (0.71 #161, 0.67 #239, 0.67 #102), 07s6fsf (0.67 #98, 0.67 #78, 0.63 #235), 013zdg (0.58 #81, 0.53 #160, 0.44 #238), 03mkk4 (0.50 #105, 0.32 #1668, 0.31 #893), 027f2w (0.47 #299, 0.44 #279, 0.44 #339), 01ysy9 (0.44 #1769, 0.32 #1668, 0.31 #893), 02m4yg (0.44 #1769, 0.32 #1668, 0.31 #2053), 01gkg3 (0.44 #1769, 0.32 #1668, 0.31 #2053), 022h5x (0.42 #93, 0.42 #73, 0.35 #172) >> Best rule #342 for best value: >> intensional similarity = 8 >> extensional distance = 53 >> proper extension: 01gpkz; >> query: (?x5068, 02_xgp2) <- major_field_of_study(?x5068, ?x1668), institution(?x4981, ?x5068), institution(?x1200, ?x5068), institution(?x865, ?x5068), ?x1668 = 01mkq, ?x4981 = 03bwzr4, ?x1200 = 016t_3, student(?x865, ?x1117) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 01q0kg; 02zd460; 01nnsv; *> query: (?x5068, 0bkj86) <- major_field_of_study(?x5068, ?x1695), institution(?x734, ?x5068), school(?x1010, ?x5068), ?x1695 = 06ms6, season(?x1010, ?x701) *> conf = 0.71 ranks of expected_values: 2, 10 EVAL 01h8rk institution! 022h5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 246.000 112.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01h8rk institution! 0bkj86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 246.000 112.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #16745-084x96 PRED entity: 084x96 PRED relation: profession PRED expected values: 02dsz => 104 concepts (87 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.96 #12687, 0.91 #12836, 0.91 #12091), 0dxtg (0.78 #11196, 0.61 #11792, 0.41 #12537), 018gz8 (0.52 #8516, 0.28 #5087, 0.25 #6726), 01d_h8 (0.48 #12529, 0.40 #11784, 0.40 #11188), 03gjzk (0.40 #11794, 0.31 #12539, 0.31 #11198), 09jwl (0.37 #10903, 0.37 #10754, 0.36 #9264), 02jknp (0.35 #11190, 0.33 #12531, 0.30 #11786), 0nbcg (0.33 #330, 0.27 #10915, 0.26 #10766), 016z4k (0.33 #302, 0.24 #9248, 0.24 #4030), 0gbbt (0.33 #308, 0.19 #9244, 0.03 #5675) >> Best rule #12687 for best value: >> intensional similarity = 5 >> extensional distance = 2599 >> proper extension: 0184jc; 04bdxl; 05vsxz; 05d7rk; 07fq1y; 02qgqt; 04yywz; 02bfmn; 01j5ts; 06dv3; ... >> query: (?x13638, 02hrh1q) <- profession(?x13638, ?x1383), profession(?x8587, ?x1383), profession(?x3028, ?x1383), ?x8587 = 03hhd3, ?x3028 = 0f0kz >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #1399 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 01lly5; 01v3vp; 0219q; 01h1b; 06sn8m; 09wlpl; 01rcmg; 044_7j; 01x0sy; 0sw6y; ... *> query: (?x13638, 02dsz) <- language(?x13638, ?x254), actor(?x6840, ?x13638), actor(?x14241, ?x13638), genre(?x14241, ?x1510), film(?x296, ?x6840) *> conf = 0.04 ranks of expected_values: 30 EVAL 084x96 profession 02dsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 104.000 87.000 0.960 http://example.org/people/person/profession #16744-05qm9f PRED entity: 05qm9f PRED relation: award_winner PRED expected values: 06kbb6 => 84 concepts (20 used for prediction) PRED predicted values (max 10 best out of 164): 039wsf (0.49 #27933, 0.46 #24646, 0.46 #29577), 0cgzj (0.49 #27933, 0.46 #24646, 0.46 #29577), 06kbb6 (0.49 #27933, 0.46 #29577, 0.45 #31220), 01wd9lv (0.24 #31221, 0.15 #6572, 0.13 #9858), 07s3vqk (0.24 #31221, 0.15 #6572, 0.13 #9858), 0gm8_p (0.13 #16431, 0.10 #9859), 02sj1x (0.05 #21359, 0.02 #13710, 0.01 #2209), 07mkj0 (0.05 #21359, 0.01 #1470), 0bw87 (0.05 #21359, 0.01 #2718, 0.01 #14219), 01l1rw (0.05 #21359) >> Best rule #27933 for best value: >> intensional similarity = 3 >> extensional distance = 552 >> proper extension: 0h95b81; >> query: (?x6607, ?x9866) <- nominated_for(?x9866, ?x6607), honored_for(?x6606, ?x6607), award_winner(?x591, ?x9866) >> conf = 0.49 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 05qm9f award_winner 06kbb6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 20.000 0.489 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #16743-07kdkfj PRED entity: 07kdkfj PRED relation: nominated_for! PRED expected values: 06_wqk4 => 113 concepts (59 used for prediction) PRED predicted values (max 10 best out of 160): 06_wqk4 (0.84 #4833, 0.84 #5599, 0.82 #2798), 051ys82 (0.59 #5600, 0.53 #5602, 0.53 #5601), 09g8vhw (0.59 #5600, 0.53 #5602, 0.53 #5601), 05q7874 (0.59 #5600, 0.03 #931, 0.01 #1948), 05qbbfb (0.59 #5600, 0.03 #928, 0.01 #1945), 0bbw2z6 (0.59 #5600, 0.02 #1152, 0.01 #1661), 02q7yfq (0.59 #5600, 0.01 #2737), 0bs5f0b (0.59 #5600), 075wx7_ (0.53 #5602, 0.53 #5601, 0.50 #4835), 04jpg2p (0.53 #5602, 0.53 #5601, 0.50 #4835) >> Best rule #4833 for best value: >> intensional similarity = 4 >> extensional distance = 208 >> proper extension: 01p9hgt; 01kv4mb; 0ggjt; 0bhvtc; 03cfjg; 0p_47; 0pmw9; >> query: (?x7722, ?x857) <- nominated_for(?x2489, ?x7722), nominated_for(?x7722, ?x857), nominated_for(?x857, ?x8570), nominated_for(?x298, ?x8570) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07kdkfj nominated_for! 06_wqk4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 59.000 0.842 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #16742-0fqp6zk PRED entity: 0fqp6zk PRED relation: languages_spoken PRED expected values: 01c7y => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 54): 0t_2 (0.37 #124, 0.36 #236, 0.36 #292), 02h40lc (0.30 #114, 0.30 #226, 0.29 #58), 064_8sq (0.17 #75, 0.16 #131, 0.16 #187), 06nm1 (0.15 #9, 0.15 #289, 0.14 #65), 0880p (0.12 #44, 0.12 #100, 0.12 #156), 06b_j (0.12 #76, 0.12 #132, 0.11 #188), 03hkp (0.10 #13, 0.10 #69, 0.09 #125), 02bv9 (0.10 #81, 0.09 #137, 0.09 #249), 03x42 (0.09 #271, 0.09 #327, 0.07 #47), 06mp7 (0.07 #14, 0.07 #70, 0.07 #406) >> Best rule #124 for best value: >> intensional similarity = 6 >> extensional distance = 41 >> proper extension: 0ffjqy; >> query: (?x14515, 0t_2) <- people(?x14515, ?x10033), profession(?x10033, ?x4773), profession(?x10033, ?x1032), ?x4773 = 0d1pc, ?x1032 = 02hrh1q, award(?x10033, ?x10156) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #434 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 69 *> proper extension: 02rbdlq; 0g48m4; 03ttfc; 09v5bdn; 02g7sp; 0g6ff; 0g8_vp; 0bpjh3; 06gbnc; 06mvq; ... *> query: (?x14515, 01c7y) <- people(?x14515, ?x10033), profession(?x10033, ?x4773), film(?x10033, ?x5247), profession(?x10371, ?x4773), ?x10371 = 03_x5t *> conf = 0.04 ranks of expected_values: 26 EVAL 0fqp6zk languages_spoken 01c7y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 13.000 13.000 0.372 http://example.org/people/ethnicity/languages_spoken #16741-01ffx4 PRED entity: 01ffx4 PRED relation: film! PRED expected values: 01v42g 03crmd => 74 concepts (39 used for prediction) PRED predicted values (max 10 best out of 944): 030xr_ (0.41 #29156, 0.36 #20823, 0.32 #81219), 03xsby (0.41 #29156, 0.36 #20823, 0.32 #81219), 016zp5 (0.20 #979, 0.07 #3061, 0.04 #5143), 01ps2h8 (0.20 #943, 0.03 #3025, 0.02 #5107), 0p8r1 (0.15 #8915, 0.07 #2669, 0.06 #4751), 0f0kz (0.10 #2599, 0.06 #6763, 0.06 #4681), 03ym1 (0.10 #3096, 0.05 #7260, 0.04 #5178), 08qxx9 (0.10 #1520, 0.04 #5684, 0.04 #14013), 03hhd3 (0.10 #1492, 0.04 #5656, 0.03 #3574), 01l2fn (0.10 #263, 0.03 #2345, 0.02 #27336) >> Best rule #29156 for best value: >> intensional similarity = 4 >> extensional distance = 244 >> proper extension: 0kv2hv; 0jyx6; 03m4mj; 09z2b7; 021y7yw; 09p7fh; 0glnm; 0dx8gj; 02rq8k8; 01242_; ... >> query: (?x3201, ?x1914) <- genre(?x3201, ?x1403), award(?x3201, ?x1079), ?x1403 = 02l7c8, nominated_for(?x1914, ?x3201) >> conf = 0.41 => this is the best rule for 2 predicted values *> Best rule #2286 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 02x3lt7; 01vksx; 017gl1; 04hwbq; 0gmcwlb; 017gm7; 07qg8v; 0jqn5; 04w7rn; 0j6b5; ... *> query: (?x3201, 01v42g) <- film_release_region(?x3201, ?x1174), ?x1174 = 047yc, titles(?x162, ?x3201), award(?x3201, ?x1079) *> conf = 0.03 ranks of expected_values: 122 EVAL 01ffx4 film! 03crmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 39.000 0.414 http://example.org/film/actor/film./film/performance/film EVAL 01ffx4 film! 01v42g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 74.000 39.000 0.414 http://example.org/film/actor/film./film/performance/film #16740-020qjg PRED entity: 020qjg PRED relation: award_winner PRED expected values: 0jcx => 33 concepts (17 used for prediction) PRED predicted values (max 10 best out of 964): 081lh (0.67 #5132, 0.24 #7605, 0.04 #10075), 0jcx (0.50 #4941, 0.33 #723, 0.25 #3193), 04sry (0.50 #6563, 0.21 #9036, 0.05 #11506), 0h1p (0.50 #5372, 0.18 #7845, 0.05 #20202), 0jgwf (0.50 #6794, 0.15 #9267, 0.03 #16681), 06pj8 (0.50 #5379, 0.12 #7852, 0.05 #10322), 06mn7 (0.50 #5912, 0.12 #8385, 0.03 #10855), 01p87y (0.50 #6646, 0.12 #9119, 0.01 #11589), 07cbs (0.50 #4941), 02sdx (0.33 #2253, 0.25 #4723) >> Best rule #5132 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 040njc; 027c924; 03nqnk3; 027b9ly; >> query: (?x12587, 081lh) <- award_winner(?x12587, ?x5817), award_winner(?x12587, ?x2397), ?x5817 = 04ld94, student(?x2396, ?x2397), profession(?x2397, ?x3802), state_province_region(?x2396, ?x13715) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4941 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 05fmy; *> query: (?x12587, ?x5254) <- award_winner(?x12587, ?x12258), award_winner(?x12587, ?x2397), ?x2397 = 02m7r, peers(?x12258, ?x8991), profession(?x12258, ?x353), type_of_union(?x8991, ?x566), peers(?x5254, ?x8991) *> conf = 0.50 ranks of expected_values: 2 EVAL 020qjg award_winner 0jcx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 33.000 17.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #16739-0lfgr PRED entity: 0lfgr PRED relation: institution! PRED expected values: 02_xgp2 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 19): 02_xgp2 (0.62 #50, 0.61 #70, 0.60 #91), 016t_3 (0.62 #84, 0.50 #43, 0.48 #63), 07s6fsf (0.48 #82, 0.46 #123, 0.43 #183), 04zx3q1 (0.45 #42, 0.41 #22, 0.37 #62), 027f2w (0.38 #47, 0.37 #67, 0.34 #27), 013zdg (0.33 #46, 0.28 #26, 0.26 #128), 0bjrnt (0.26 #65, 0.25 #45, 0.19 #86), 01rr_d (0.24 #34, 0.23 #54, 0.18 #297), 03mkk4 (0.22 #131, 0.20 #191, 0.20 #69), 022h5x (0.20 #139, 0.19 #199, 0.14 #98) >> Best rule #50 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 02bh8z; >> query: (?x1809, 02_xgp2) <- list(?x1809, ?x2197), citytown(?x1809, ?x10995), company(?x8841, ?x1809) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lfgr institution! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.625 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #16738-033m23 PRED entity: 033m23 PRED relation: film PRED expected values: 02_qt => 104 concepts (81 used for prediction) PRED predicted values (max 10 best out of 562): 0180mw (0.39 #114648, 0.35 #118231, 0.33 #41201), 0by17xn (0.20 #1723, 0.01 #21428), 09dv8h (0.20 #1170), 02d49z (0.20 #777), 07sc6nw (0.20 #179), 03rtz1 (0.20 #168), 0422v0 (0.10 #3576, 0.04 #5367, 0.03 #94945), 03m5y9p (0.10 #3212, 0.04 #5003, 0.03 #94945), 0fvr1 (0.10 #2142, 0.04 #3933, 0.03 #94945), 01hqk (0.10 #2515, 0.04 #4306, 0.03 #7888) >> Best rule #114648 for best value: >> intensional similarity = 3 >> extensional distance = 1484 >> proper extension: 0627sn; >> query: (?x7835, ?x6482) <- type_of_union(?x7835, ?x566), ?x566 = 04ztj, nominated_for(?x7835, ?x6482) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #9589 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 0br1w; *> query: (?x7835, 02_qt) <- religion(?x7835, ?x7422), student(?x8398, ?x7835), nominated_for(?x7835, ?x6482) *> conf = 0.02 ranks of expected_values: 284 EVAL 033m23 film 02_qt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 104.000 81.000 0.386 http://example.org/film/actor/film./film/performance/film #16737-04jpg2p PRED entity: 04jpg2p PRED relation: film_crew_role PRED expected values: 089fss => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 20): 0dxtw (0.55 #60, 0.49 #86, 0.48 #452), 02rh1dz (0.27 #7, 0.20 #451, 0.19 #33), 02_n3z (0.23 #27, 0.20 #53, 0.18 #132), 094hwz (0.20 #11, 0.15 #37, 0.12 #63), 01xy5l_ (0.17 #62, 0.16 #219, 0.15 #141), 0263ycg (0.12 #64, 0.12 #38, 0.09 #143), 05smlt (0.12 #39, 0.10 #65, 0.08 #144), 089fss (0.08 #449, 0.07 #214, 0.07 #83), 0ckd1 (0.08 #29, 0.07 #55, 0.06 #134), 020xn5 (0.08 #32, 0.06 #137, 0.05 #215) >> Best rule #60 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 02vqhv0; 014zwb; 04grkmd; 033srr; 0640y35; 08c6k9; 026hh0m; 0dnkmq; >> query: (?x8570, 0dxtw) <- music(?x8570, ?x3410), film_crew_role(?x8570, ?x1966), film_crew_role(?x8570, ?x1284), ?x1966 = 015h31, ?x1284 = 0ch6mp2 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #449 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 366 *> proper extension: 0h95zbp; *> query: (?x8570, 089fss) <- film_crew_role(?x8570, ?x2154), ?x2154 = 01vx2h *> conf = 0.08 ranks of expected_values: 8 EVAL 04jpg2p film_crew_role 089fss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 83.000 83.000 0.550 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16736-01trhmt PRED entity: 01trhmt PRED relation: religion PRED expected values: 06nzl => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 22): 0c8wxp (0.29 #6, 0.24 #907, 0.23 #1267), 03_gx (0.19 #1546, 0.15 #1410, 0.13 #1817), 0kpl (0.16 #1406, 0.16 #1813, 0.16 #1542), 01lp8 (0.12 #1, 0.07 #361, 0.07 #586), 019cr (0.06 #11, 0.04 #146, 0.03 #236), 0v53x (0.06 #29, 0.02 #1335, 0.02 #164), 0kq2 (0.05 #1821, 0.05 #1414, 0.04 #2770), 092bf5 (0.05 #61, 0.04 #1277, 0.03 #196), 06nzl (0.04 #1141, 0.03 #330, 0.02 #105), 0flw86 (0.04 #407, 0.04 #858, 0.04 #137) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 01wrcxr; >> query: (?x2562, 0c8wxp) <- artists(?x5876, ?x2562), ?x5876 = 0ggx5q, friend(?x6383, ?x2562) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1141 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 149 *> proper extension: 012_53; *> query: (?x2562, 06nzl) <- profession(?x2562, ?x131), friend(?x6383, ?x2562) *> conf = 0.04 ranks of expected_values: 9 EVAL 01trhmt religion 06nzl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 136.000 136.000 0.294 http://example.org/people/person/religion #16735-06mmb PRED entity: 06mmb PRED relation: award_nominee PRED expected values: 05vsxz 0djywgn => 102 concepts (52 used for prediction) PRED predicted values (max 10 best out of 874): 0djywgn (0.81 #8850, 0.81 #86156, 0.81 #23283), 01yhvv (0.81 #7280, 0.81 #86156, 0.81 #23283), 06mmb (0.81 #7533, 0.80 #5205, 0.72 #12189), 03f1zdw (0.81 #86156, 0.81 #23283, 0.81 #104788), 032wdd (0.76 #18626, 0.76 #90815, 0.75 #88486), 0c01c (0.76 #18626, 0.76 #90815, 0.75 #88486), 03n08b (0.76 #18626, 0.76 #90815, 0.75 #88486), 05vsxz (0.75 #6993, 0.67 #4665, 0.64 #11649), 01x_d8 (0.74 #55883, 0.74 #58212, 0.01 #40986), 02pt6k_ (0.24 #17312, 0.11 #33611, 0.10 #14983) >> Best rule #8850 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 02zq43; 05tk7y; 0175wg; 02fz3w; >> query: (?x2559, 0djywgn) <- award_nominee(?x2559, ?x2487), award_nominee(?x2559, ?x488), ?x488 = 0159h6, ?x2487 = 04rsd2 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 06mmb award_nominee 0djywgn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 52.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 06mmb award_nominee 05vsxz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 102.000 52.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16734-019l68 PRED entity: 019l68 PRED relation: award_winner! PRED expected values: 0bdwft => 139 concepts (137 used for prediction) PRED predicted values (max 10 best out of 245): 02ppm4q (0.38 #40096, 0.37 #22849, 0.37 #27592), 0gqwc (0.38 #40096, 0.37 #22849, 0.37 #27592), 02z1nbg (0.21 #625, 0.09 #194, 0.05 #4937), 09sb52 (0.18 #472, 0.10 #28495, 0.10 #29357), 054ky1 (0.18 #109, 0.10 #7008, 0.07 #1835), 02kgb7 (0.18 #328), 09cn0c (0.16 #749, 0.05 #7648, 0.03 #2475), 0bdwft (0.16 #500, 0.04 #7399, 0.04 #13434), 094qd5 (0.14 #476, 0.06 #2202, 0.05 #4788), 027571b (0.14 #706, 0.04 #5018, 0.03 #2432) >> Best rule #40096 for best value: >> intensional similarity = 3 >> extensional distance = 1625 >> proper extension: 07k2d; >> query: (?x9055, ?x1245) <- award_winner(?x1972, ?x9055), award(?x144, ?x1972), award(?x9055, ?x1245) >> conf = 0.38 => this is the best rule for 2 predicted values *> Best rule #500 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 05slvm; *> query: (?x9055, 0bdwft) <- nominated_for(?x9055, ?x8456), award(?x9055, ?x2880), ?x2880 = 02ppm4q *> conf = 0.16 ranks of expected_values: 8 EVAL 019l68 award_winner! 0bdwft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 139.000 137.000 0.379 http://example.org/award/award_category/winners./award/award_honor/award_winner #16733-0j90s PRED entity: 0j90s PRED relation: nominated_for! PRED expected values: 0f4x7 0gqyl => 77 concepts (61 used for prediction) PRED predicted values (max 10 best out of 214): 04kxsb (0.67 #4163, 0.67 #4395, 0.66 #4858), 0k611 (0.55 #3075, 0.35 #3999, 0.33 #4231), 02qyp19 (0.52 #1388, 0.51 #1850, 0.33 #694), 040njc (0.45 #3014, 0.35 #2087, 0.33 #3938), 0gr4k (0.44 #3033, 0.38 #2106, 0.26 #3006), 0f4x7 (0.42 #3032, 0.32 #2105, 0.27 #4188), 0gr0m (0.38 #3064, 0.31 #2137, 0.27 #3988), 099c8n (0.36 #1903, 0.34 #1441, 0.33 #747), 02x1dht (0.35 #1891, 0.31 #1429, 0.20 #735), 09qwmm (0.35 #2107, 0.13 #1876, 0.12 #10878) >> Best rule #4163 for best value: >> intensional similarity = 4 >> extensional distance = 324 >> proper extension: 07l50vn; 05_61y; >> query: (?x7062, ?x749) <- genre(?x7062, ?x53), award(?x7062, ?x749), honored_for(?x6606, ?x7062), currency(?x7062, ?x170) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3032 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 253 *> proper extension: 0209xj; 0c0nhgv; 0sxfd; 02r1c18; 0k4kk; 0j_t1; 0gxfz; 07w8fz; 011ydl; 0kxf1; ... *> query: (?x7062, 0f4x7) <- genre(?x7062, ?x53), award_winner(?x7062, ?x4075), nominated_for(?x1307, ?x7062), ?x1307 = 0gq9h *> conf = 0.42 ranks of expected_values: 6, 13 EVAL 0j90s nominated_for! 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 77.000 61.000 0.669 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0j90s nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 61.000 0.669 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16732-02g0mx PRED entity: 02g0mx PRED relation: award PRED expected values: 0bfvw2 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 217): 02ppm4q (0.46 #1753, 0.08 #9756, 0.07 #4554), 09sb52 (0.45 #1639, 0.39 #1239, 0.36 #10042), 0gqwc (0.43 #1673, 0.14 #4074, 0.14 #2874), 03c7tr1 (0.42 #57, 0.33 #457, 0.24 #857), 09td7p (0.34 #1717, 0.06 #2517, 0.06 #2918), 094qd5 (0.31 #1643, 0.14 #2844, 0.13 #2443), 0bdwft (0.30 #1667, 0.17 #67, 0.12 #467), 0bfvw2 (0.27 #1614, 0.08 #414, 0.08 #9617), 099t8j (0.24 #1737, 0.05 #2938, 0.05 #3338), 05ztrmj (0.22 #1380, 0.17 #180, 0.12 #580) >> Best rule #1753 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 01dbgw; >> query: (?x3100, 02ppm4q) <- nominated_for(?x3100, ?x2815), award(?x3100, ?x1972), ?x1972 = 0gqyl >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1614 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 01dbgw; *> query: (?x3100, 0bfvw2) <- nominated_for(?x3100, ?x2815), award(?x3100, ?x1972), ?x1972 = 0gqyl *> conf = 0.27 ranks of expected_values: 8 EVAL 02g0mx award 0bfvw2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 81.000 81.000 0.458 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16731-0bj9k PRED entity: 0bj9k PRED relation: award PRED expected values: 07cbcy => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 298): 0f4x7 (0.76 #39412, 0.72 #44933, 0.70 #43748), 0cqh46 (0.76 #39412, 0.72 #44933, 0.70 #43748), 027986c (0.72 #44933, 0.70 #43748, 0.70 #39411), 027c95y (0.72 #44933, 0.70 #43748, 0.70 #39411), 09sb52 (0.32 #13041, 0.29 #17378, 0.29 #8706), 05pcn59 (0.24 #9140, 0.21 #13081, 0.21 #7170), 0ck27z (0.21 #22945, 0.20 #25703, 0.12 #37923), 01by1l (0.20 #20599, 0.19 #23751, 0.17 #1683), 05zr6wv (0.18 #9077, 0.15 #12624, 0.15 #18143), 01bgqh (0.18 #20532, 0.16 #23684, 0.16 #1616) >> Best rule #39412 for best value: >> intensional similarity = 2 >> extensional distance = 1907 >> proper extension: 02pp_q_; 03mv0b; 0c3dzk; 06lxn; >> query: (?x2035, ?x3247) <- award_winner(?x3247, ?x2035), ceremony(?x3247, ?x1265) >> conf = 0.76 => this is the best rule for 2 predicted values *> Best rule #43353 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2257 *> proper extension: 01nqfh_; 01nzs7; 09mfvx; 0n8bn; 04mlh8; 03cp7b3; 065d1h; 0kcdl; *> query: (?x2035, ?x198) <- nominated_for(?x2035, ?x6111), nominated_for(?x198, ?x6111) *> conf = 0.12 ranks of expected_values: 27 EVAL 0bj9k award 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 132.000 132.000 0.764 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16730-02cllz PRED entity: 02cllz PRED relation: award_nominee PRED expected values: 0h5g_ => 91 concepts (46 used for prediction) PRED predicted values (max 10 best out of 870): 0h5g_ (0.82 #6968, 0.82 #4645, 0.82 #48769), 07hbxm (0.82 #6968, 0.82 #4645, 0.82 #48769), 0lpjn (0.82 #6968, 0.82 #4645, 0.82 #48769), 0dgskx (0.68 #6147, 0.26 #8470, 0.08 #3824), 08_83x (0.68 #5865, 0.02 #26765, 0.01 #63922), 0bd2n4 (0.68 #5472, 0.02 #26372, 0.01 #63529), 0clvcx (0.68 #4945, 0.02 #25845, 0.01 #63002), 02w9895 (0.68 #4883, 0.02 #25783, 0.01 #62940), 05y5kf (0.68 #5784, 0.02 #26684, 0.01 #63841), 02cllz (0.67 #2845, 0.60 #523, 0.39 #7491) >> Best rule #6968 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0clvcx; >> query: (?x2457, ?x100) <- award(?x2457, ?x704), award_nominee(?x5144, ?x2457), award_nominee(?x100, ?x2457), ?x5144 = 017gxw >> conf = 0.82 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 02cllz award_nominee 0h5g_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 46.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16729-02c6pq PRED entity: 02c6pq PRED relation: type_of_union PRED expected values: 04ztj => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.75 #37, 0.75 #77, 0.74 #41), 01g63y (0.14 #22, 0.14 #18, 0.13 #146) >> Best rule #37 for best value: >> intensional similarity = 2 >> extensional distance = 477 >> proper extension: 01pnn3; >> query: (?x5299, 04ztj) <- religion(?x5299, ?x1985), nominated_for(?x5299, ?x1868) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02c6pq type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.752 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16728-05zr0xl PRED entity: 05zr0xl PRED relation: honored_for! PRED expected values: 0hn821n => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 84): 05c1t6z (0.25 #946, 0.24 #829, 0.20 #127), 02q690_ (0.24 #869, 0.23 #986, 0.19 #401), 03nnm4t (0.20 #878, 0.20 #995, 0.15 #59), 0hn821n (0.16 #5384, 0.09 #7140, 0.05 #696), 09v0p2c (0.16 #5384, 0.09 #7140, 0.01 #534), 0gx_st (0.13 #144, 0.13 #261, 0.12 #963), 0bxs_d (0.09 #915, 0.09 #1032, 0.07 #447), 0275n3y (0.09 #7140, 0.09 #60, 0.08 #177), 07y9ts (0.09 #7140, 0.07 #872, 0.07 #989), 07z31v (0.09 #7140, 0.07 #842, 0.06 #1076) >> Best rule #946 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 03_8kz; >> query: (?x8533, 05c1t6z) <- honored_for(?x873, ?x8533), nominated_for(?x1541, ?x8533), country_of_origin(?x8533, ?x94) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #5384 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 844 *> proper extension: 031786; *> query: (?x8533, ?x2213) <- award_winner(?x8533, ?x10011), award_winner(?x8533, ?x1541), award(?x10011, ?x2016), award_winner(?x2213, ?x1541) *> conf = 0.16 ranks of expected_values: 4 EVAL 05zr0xl honored_for! 0hn821n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 76.000 0.248 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #16727-04xrx PRED entity: 04xrx PRED relation: profession PRED expected values: 0dz3r 09jwl 01c72t => 162 concepts (161 used for prediction) PRED predicted values (max 10 best out of 80): 03gjzk (0.62 #584, 0.58 #441, 0.33 #2587), 09jwl (0.61 #9169, 0.58 #4164, 0.57 #7739), 0dz3r (0.58 #5580, 0.57 #1003, 0.51 #3006), 0cbd2 (0.44 #9731, 0.42 #13169, 0.42 #9874), 0dxtg (0.43 #9737, 0.42 #9880, 0.39 #7019), 018gz8 (0.33 #443, 0.24 #7022, 0.21 #9740), 01c72t (0.28 #14902, 0.23 #9603, 0.23 #11464), 02jknp (0.27 #1579, 0.24 #6299, 0.24 #9302), 0fnpj (0.27 #1056, 0.14 #9208, 0.13 #1771), 039v1 (0.23 #1319, 0.21 #9185, 0.20 #175) >> Best rule #584 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0p3r8; 0mdyn; >> query: (?x2614, 03gjzk) <- vacationer(?x2983, ?x2614), program(?x2614, ?x9788), participant(?x2614, ?x521) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #9169 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 240 *> proper extension: 051m56; *> query: (?x2614, 09jwl) <- award_nominee(?x2614, ?x527), artist(?x3265, ?x2614), type_of_union(?x2614, ?x566) *> conf = 0.61 ranks of expected_values: 2, 3, 7 EVAL 04xrx profession 01c72t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 162.000 161.000 0.625 http://example.org/people/person/profession EVAL 04xrx profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 162.000 161.000 0.625 http://example.org/people/person/profession EVAL 04xrx profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 162.000 161.000 0.625 http://example.org/people/person/profession #16726-01rbb PRED entity: 01rbb PRED relation: genre! PRED expected values: 065ym0c => 49 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2034): 07nt8p (0.40 #15399, 0.36 #17273, 0.33 #24775), 0645k5 (0.40 #15520, 0.36 #17394, 0.33 #24896), 016kv6 (0.40 #6234, 0.33 #2480, 0.17 #11874), 011ysn (0.40 #6225, 0.33 #2471, 0.17 #11865), 02v570 (0.40 #6976, 0.33 #3222, 0.17 #12616), 0sxmx (0.40 #6473, 0.17 #12113, 0.14 #35670), 05_61y (0.40 #6878, 0.17 #12518, 0.10 #31283), 064q5v (0.40 #6733, 0.17 #12373, 0.07 #31138), 0dlngsd (0.35 #15839, 0.33 #2691, 0.32 #19588), 03s6l2 (0.35 #15117, 0.33 #1969, 0.32 #16991) >> Best rule #15399 for best value: >> intensional similarity = 9 >> extensional distance = 18 >> proper extension: 02kdv5l; 05p553; 02n4kr; 0lsxr; 0hn10; 03k9fj; 01jfsb; 06n90; 02l7c8; 060__y; ... >> query: (?x13835, 07nt8p) <- genre(?x6782, ?x13835), award(?x6782, ?x3233), nominated_for(?x384, ?x6782), production_companies(?x6782, ?x5908), film_release_region(?x6782, ?x756), genre(?x6782, ?x53), ?x756 = 06npd, ?x53 = 07s9rl0, country(?x6782, ?x94) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #18779 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 20 *> proper extension: 04t36; 0hcr; *> query: (?x13835, ?x80) <- genre(?x6782, ?x13835), award(?x6782, ?x3233), nominated_for(?x384, ?x6782), production_companies(?x6782, ?x5908), film_release_region(?x6782, ?x756), film_release_region(?x6782, ?x142), genre(?x6782, ?x53), ?x756 = 06npd, ?x53 = 07s9rl0, film_release_region(?x80, ?x142) *> conf = 0.14 ranks of expected_values: 1606 EVAL 01rbb genre! 065ym0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 23.000 0.400 http://example.org/film/film/genre #16725-03rt9 PRED entity: 03rt9 PRED relation: film_release_region! PRED expected values: 0g56t9t 0gx1bnj 04969y 04jkpgv 03qnvdl 07x4qr 07f_7h 0kv238 0645k5 0g5838s 080nwsb 03q0r1 0bpm4yw 05pdh86 0j3d9tn 0cy__l 05b6rdt 09v9mks 0gfh84d 0dc_ms 0dgrwqr 0g5qmbz => 191 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1599): 05c26ss (0.96 #5497, 0.96 #5496, 0.82 #20152), 062zm5h (0.96 #5497, 0.96 #5496, 0.79 #20304), 0bh8yn3 (0.96 #5497, 0.96 #5496, 0.79 #19938), 0gmcwlb (0.96 #5497, 0.96 #5496, 0.76 #19908), 0dgst_d (0.96 #5497, 0.96 #5496, 0.76 #19900), 03qnc6q (0.96 #5497, 0.96 #5496, 0.76 #20024), 0gg5qcw (0.96 #5497, 0.96 #5496, 0.74 #13721), 0g4vmj8 (0.96 #5497, 0.96 #5496, 0.74 #20562), 0gg5kmg (0.96 #5497, 0.96 #5496, 0.74 #20449), 0cz8mkh (0.96 #5497, 0.96 #5496, 0.68 #13321) >> Best rule #5497 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 06rny; >> query: (?x429, ?x1263) <- split_to(?x3699, ?x429), film_release_region(?x1263, ?x3699) >> conf = 0.96 => this is the best rule for 32 predicted values *> Best rule #20208 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 05r4w; 0jgd; 03rjj; 03_3d; 0d0vqn; 04gzd; 0chghy; 01ls2; 05qhw; 015fr; ... *> query: (?x429, 0bpm4yw) <- film_release_region(?x3748, ?x429), film_release_region(?x1150, ?x429), ?x3748 = 05zlld0, ?x1150 = 0h3xztt *> conf = 0.88 ranks of expected_values: 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 49, 52, 58, 60, 74, 75, 77, 80, 93, 100, 160, 167 EVAL 03rt9 film_release_region! 0g5qmbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0dgrwqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0gfh84d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 09v9mks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 05b6rdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0cy__l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0j3d9tn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 05pdh86 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0bpm4yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 03q0r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 080nwsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0g5838s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0645k5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0kv238 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 07f_7h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 07x4qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 03qnvdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 04jkpgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 04969y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0gx1bnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rt9 film_release_region! 0g56t9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 191.000 75.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16724-0jqd3 PRED entity: 0jqd3 PRED relation: film_crew_role PRED expected values: 0dxtw => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.70 #851, 0.57 #161, 0.55 #889), 02r96rf (0.64 #846, 0.48 #656, 0.48 #884), 09vw2b7 (0.61 #850, 0.49 #660, 0.48 #888), 0dxtw (0.37 #855, 0.31 #665, 0.28 #165), 01vx2h (0.31 #856, 0.25 #894, 0.24 #666), 01pvkk (0.27 #857, 0.21 #895, 0.21 #1010), 02ynfr (0.14 #171, 0.14 #861, 0.13 #1014), 0215hd (0.11 #864, 0.10 #1017, 0.10 #1325), 02rh1dz (0.11 #854, 0.09 #664, 0.09 #11), 0d2b38 (0.10 #871, 0.08 #986, 0.08 #1332) >> Best rule #851 for best value: >> intensional similarity = 3 >> extensional distance = 623 >> proper extension: 0d6b7; 05dy7p; 040rmy; 03q8xj; >> query: (?x6309, 0ch6mp2) <- film(?x382, ?x6309), music(?x6309, ?x9946), film_crew_role(?x6309, ?x137) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #855 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 623 *> proper extension: 0d6b7; 05dy7p; 040rmy; 03q8xj; *> query: (?x6309, 0dxtw) <- film(?x382, ?x6309), music(?x6309, ?x9946), film_crew_role(?x6309, ?x137) *> conf = 0.37 ranks of expected_values: 4 EVAL 0jqd3 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 61.000 61.000 0.704 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16723-015ppk PRED entity: 015ppk PRED relation: award_winner PRED expected values: 0g69lg => 85 concepts (52 used for prediction) PRED predicted values (max 10 best out of 779): 02vqpx8 (0.59 #45904, 0.54 #45905, 0.53 #31146), 0347xl (0.52 #3279, 0.46 #14753, 0.45 #26226), 05cl2w (0.52 #3279, 0.46 #14753, 0.45 #26226), 0g69lg (0.40 #36065, 0.40 #42626, 0.39 #27867), 0f721s (0.39 #26227, 0.37 #36064, 0.37 #42625), 0347xz (0.32 #22949, 0.29 #13113, 0.21 #65571), 05gnf (0.17 #2718, 0.14 #45906, 0.14 #31147), 030znt (0.17 #1852, 0.12 #3491, 0.06 #19669), 0438pz (0.17 #2994, 0.12 #36066, 0.06 #4633), 06j8q_ (0.17 #3167, 0.06 #4806, 0.06 #19669) >> Best rule #45904 for best value: >> intensional similarity = 5 >> extensional distance = 154 >> proper extension: 04bp0l; >> query: (?x7116, ?x9335) <- genre(?x7116, ?x53), nominated_for(?x9335, ?x7116), nominated_for(?x3018, ?x7116), award_winner(?x6765, ?x3018), award_winner(?x6706, ?x9335) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #36065 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 139 *> proper extension: 063zky; *> query: (?x7116, ?x9571) <- program(?x9571, ?x7116), award_nominee(?x4034, ?x9571), nominated_for(?x9571, ?x3303) *> conf = 0.40 ranks of expected_values: 4 EVAL 015ppk award_winner 0g69lg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 52.000 0.589 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #16722-085ccd PRED entity: 085ccd PRED relation: genre PRED expected values: 07s9rl0 => 139 concepts (105 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.85 #12388, 0.84 #10685, 0.80 #12023), 05p553 (0.77 #8381, 0.77 #7650, 0.75 #9353), 01jfsb (0.71 #7294, 0.55 #8511, 0.48 #257), 0345h (0.62 #4367, 0.62 #8863, 0.61 #12021), 02kdv5l (0.56 #369, 0.52 #1338, 0.51 #8501), 06n90 (0.47 #380, 0.34 #258, 0.33 #1106), 04pbhw (0.44 #423, 0.34 #301, 0.19 #1513), 0hcr (0.36 #5850, 0.29 #146, 0.26 #2451), 02l7c8 (0.30 #8758, 0.29 #12282, 0.29 #8393), 0btmb (0.28 #455, 0.24 #333, 0.16 #576) >> Best rule #12388 for best value: >> intensional similarity = 8 >> extensional distance = 1068 >> proper extension: 02tqm5; 05jyb2; 05z43v; >> query: (?x2434, 07s9rl0) <- genre(?x2434, ?x1510), country(?x2434, ?x94), genre(?x9715, ?x1510), genre(?x6345, ?x1510), genre(?x4684, ?x1510), film(?x665, ?x9715), ?x6345 = 02gd6x, ?x4684 = 03nm_fh >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 085ccd genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 105.000 0.853 http://example.org/film/film/genre #16721-03g5_y PRED entity: 03g5_y PRED relation: influenced_by PRED expected values: 02g8h 01hmk9 => 82 concepts (24 used for prediction) PRED predicted values (max 10 best out of 298): 01hmk9 (0.12 #1085, 0.09 #651, 0.07 #1518), 081lh (0.11 #887, 0.06 #1753, 0.05 #453), 01k9lpl (0.10 #1175, 0.08 #741, 0.06 #1608), 081k8 (0.09 #2755, 0.08 #5354, 0.08 #3188), 03f0324 (0.08 #4050, 0.07 #1450, 0.07 #3184), 013tjc (0.08 #1241, 0.03 #4274, 0.03 #1674), 032l1 (0.08 #3989, 0.08 #2690, 0.07 #5289), 012vd6 (0.07 #1033, 0.06 #1899, 0.03 #599), 01wp_jm (0.07 #1206, 0.05 #1639, 0.05 #6501), 0ph2w (0.07 #985, 0.03 #3152, 0.03 #1851) >> Best rule #1085 for best value: >> intensional similarity = 2 >> extensional distance = 127 >> proper extension: 0d193h; 05xq9; 0lhn5; 014_lq; 07r1_; 01kcms4; 070b4; 07hgm; 0167xy; 0b1hw; >> query: (?x7872, 01hmk9) <- influenced_by(?x7872, ?x4112), award_winner(?x2431, ?x4112) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1, 55 EVAL 03g5_y influenced_by 01hmk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 24.000 0.116 http://example.org/influence/influence_node/influenced_by EVAL 03g5_y influenced_by 02g8h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 82.000 24.000 0.116 http://example.org/influence/influence_node/influenced_by #16720-059rc PRED entity: 059rc PRED relation: film! PRED expected values: 03xsby => 89 concepts (69 used for prediction) PRED predicted values (max 10 best out of 48): 0c41qv (0.55 #367, 0.54 #220, 0.50 #2578), 05qd_ (0.25 #8, 0.17 #154, 0.15 #1631), 016tw3 (0.17 #3329, 0.16 #2514, 0.16 #2072), 017s11 (0.15 #148, 0.13 #3321, 0.13 #2727), 0g1rw (0.13 #153, 0.07 #669, 0.07 #2511), 016tt2 (0.13 #812, 0.12 #516, 0.12 #1626), 03xq0f (0.11 #517, 0.11 #592, 0.11 #77), 054g1r (0.09 #547, 0.09 #622, 0.08 #1064), 01795t (0.09 #530, 0.09 #605, 0.06 #3336), 0jz9f (0.08 #1, 0.07 #1031, 0.07 #956) >> Best rule #367 for best value: >> intensional similarity = 5 >> extensional distance = 112 >> proper extension: 035xwd; 05p3738; 078sj4; 0x25q; 02ntb8; 09zf_q; 031786; 0hvvf; 05ch98; 01qz5; ... >> query: (?x2815, ?x7339) <- film(?x2373, ?x2815), production_companies(?x2815, ?x7339), production_companies(?x2815, ?x382), country(?x2815, ?x94), ?x382 = 086k8 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2151 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 758 *> proper extension: 0d90m; 0m313; 01jc6q; 02v8kmz; 0c3ybss; 02vp1f_; 047q2k1; 03g90h; 09xbpt; 08lr6s; ... *> query: (?x2815, 03xsby) <- film_release_region(?x2815, ?x94), titles(?x812, ?x2815), genre(?x2475, ?x812), film(?x4703, ?x2475) *> conf = 0.04 ranks of expected_values: 25 EVAL 059rc film! 03xsby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 89.000 69.000 0.548 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16719-029_3 PRED entity: 029_3 PRED relation: profession PRED expected values: 0dxtg => 147 concepts (112 used for prediction) PRED predicted values (max 10 best out of 96): 01d_h8 (0.93 #14369, 0.59 #2329, 0.53 #587), 0dxtg (0.87 #2772, 0.87 #2482, 0.72 #1031), 0cbd2 (0.51 #5375, 0.48 #5955, 0.46 #4360), 02jknp (0.43 #14371, 0.39 #2331, 0.38 #298), 09jwl (0.37 #14235, 0.34 #3500, 0.32 #13944), 0kyk (0.37 #4381, 0.36 #5976, 0.35 #5251), 0d8qb (0.33 #222, 0.31 #6529, 0.25 #9430), 01xr66 (0.33 #62, 0.06 #788, 0.05 #3980), 02krf9 (0.32 #3218, 0.31 #5683, 0.30 #2493), 0np9r (0.31 #1907, 0.31 #6529, 0.25 #9430) >> Best rule #14369 for best value: >> intensional similarity = 3 >> extensional distance = 1044 >> proper extension: 05drq5; 0b_dy; 04cw0j; 02_4fn; 03kpvp; 047q2wc; 01wxdn3; 05dxl_; 04dz_y7; 0gry51; ... >> query: (?x4065, 01d_h8) <- profession(?x4065, ?x4725), profession(?x11310, ?x4725), ?x11310 = 044kwr >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #2772 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 77 *> proper extension: 0dbpyd; 02773nt; 0pz7h; 057d89; 04n7njg; 0bg539; 03ft8; 0gz5hs; 0721cy; 01_x6v; ... *> query: (?x4065, 0dxtg) <- producer_type(?x4065, ?x632), tv_program(?x4065, ?x11726), profession(?x4065, ?x1032) *> conf = 0.87 ranks of expected_values: 2 EVAL 029_3 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 112.000 0.932 http://example.org/people/person/profession #16718-01nbq4 PRED entity: 01nbq4 PRED relation: profession PRED expected values: 015cjr => 82 concepts (67 used for prediction) PRED predicted values (max 10 best out of 107): 02hrh1q (0.90 #7080, 0.88 #5904, 0.88 #3103), 01d_h8 (0.59 #7513, 0.45 #2801, 0.39 #1772), 0dxtg (0.58 #7520, 0.40 #6638, 0.39 #5462), 09jwl (0.42 #2814, 0.25 #6350, 0.25 #1491), 0fj9f (0.38 #2555, 0.24 #6238, 0.12 #3290), 03gjzk (0.37 #1781, 0.30 #7522, 0.27 #6346), 02jknp (0.34 #7514, 0.25 #1479, 0.22 #2802), 04s2z (0.33 #651, 0.33 #357, 0.20 #210), 0nbcg (0.28 #2826, 0.22 #1503, 0.21 #1797), 015cjr (0.25 #490, 0.21 #2256, 0.19 #1226) >> Best rule #7080 for best value: >> intensional similarity = 4 >> extensional distance = 1400 >> proper extension: 016qtt; 01vrx3g; 01mvth; 03qd_; 03gm48; 0f0p0; 05fnl9; 0j_c; 0m31m; 01dw9z; ... >> query: (?x10227, 02hrh1q) <- profession(?x10227, ?x353), type_of_union(?x10227, ?x566), ?x566 = 04ztj, film(?x10227, ?x3854) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #490 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01hkhq; *> query: (?x10227, 015cjr) <- nationality(?x10227, ?x1310), type_of_union(?x10227, ?x566), ?x1310 = 02jx1, company(?x10227, ?x2776) *> conf = 0.25 ranks of expected_values: 10 EVAL 01nbq4 profession 015cjr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 82.000 67.000 0.897 http://example.org/people/person/profession #16717-01l4g5 PRED entity: 01l4g5 PRED relation: gender PRED expected values: 02zsn => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #19, 0.82 #73, 0.81 #77), 02zsn (0.46 #213, 0.46 #227, 0.46 #230) >> Best rule #19 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 02lz1s; >> query: (?x4855, 05zppz) <- profession(?x4855, ?x11127), profession(?x4855, ?x2348), profession(?x4855, ?x1183), ?x2348 = 0nbcg, ?x11127 = 05vyk, profession(?x4566, ?x1183), ?x4566 = 0pmw9 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #213 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4116 *> proper extension: 029rk; 01ty4; *> query: (?x4855, ?x231) <- profession(?x4855, ?x2348), profession(?x10539, ?x2348), profession(?x7924, ?x2348), gender(?x7924, ?x231), category(?x10539, ?x134) *> conf = 0.46 ranks of expected_values: 2 EVAL 01l4g5 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.875 http://example.org/people/person/gender #16716-01nzs7 PRED entity: 01nzs7 PRED relation: award_winner! PRED expected values: 0m7yy => 128 concepts (107 used for prediction) PRED predicted values (max 10 best out of 250): 0m7yy (0.80 #3637, 0.77 #3205, 0.67 #1476), 0ck27z (0.39 #14360, 0.32 #6143, 0.29 #11764), 02x4w6g (0.33 #547, 0.20 #1843, 0.06 #8759), 027986c (0.29 #913, 0.20 #1777, 0.17 #481), 0f4x7 (0.29 #895, 0.20 #1759, 0.17 #463), 09cm54 (0.29 #961, 0.20 #1825, 0.17 #529), 027c95y (0.29 #1022, 0.10 #2318, 0.09 #4047), 02w9sd7 (0.20 #1896, 0.17 #600, 0.14 #1032), 09cn0c (0.20 #2047, 0.17 #751, 0.10 #8963), 02y_rq5 (0.20 #1824, 0.17 #528, 0.10 #8740) >> Best rule #3637 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 0g5lhl7; 027_tg; 03lpbx; 04qb6g; >> query: (?x1648, 0m7yy) <- program(?x1648, ?x9082), award_winner(?x10284, ?x1648), program(?x12138, ?x9082), award_winner(?x4932, ?x12138), award_winner(?x1039, ?x12138), nationality(?x12138, ?x94) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nzs7 award_winner! 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 107.000 0.800 http://example.org/award/award_category/winners./award/award_honor/award_winner #16715-0c422z4 PRED entity: 0c422z4 PRED relation: award! PRED expected values: 0mdqp 0f4vbz 03kbb8 03q45x => 48 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2545): 03q45x (0.76 #23547, 0.72 #23546, 0.71 #50467), 0170pk (0.69 #10532, 0.08 #23990, 0.08 #27356), 0mdqp (0.60 #3529, 0.33 #166, 0.23 #26914), 0237fw (0.54 #10734, 0.33 #643, 0.20 #4006), 03ym1 (0.54 #11761, 0.08 #58865, 0.08 #48773), 02m501 (0.54 #12883, 0.07 #26341, 0.06 #29707), 0f7hc (0.50 #8072, 0.31 #11436, 0.20 #4708), 02t_99 (0.50 #8063, 0.07 #21519, 0.04 #18154), 030g9z (0.50 #9349, 0.05 #22805, 0.04 #26171), 0c6qh (0.46 #10751, 0.33 #660, 0.21 #14114) >> Best rule #23547 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 02py7pj; >> query: (?x2597, ?x7795) <- award_winner(?x2597, ?x7795), nominated_for(?x7795, ?x3630), program(?x7795, ?x6884), participant(?x7795, ?x906) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 106, 1072 EVAL 0c422z4 award! 03q45x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 21.000 0.759 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c422z4 award! 03kbb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 21.000 0.759 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c422z4 award! 0f4vbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 48.000 21.000 0.759 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c422z4 award! 0mdqp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 48.000 21.000 0.759 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16714-06_x996 PRED entity: 06_x996 PRED relation: award PRED expected values: 02n9nmz => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 174): 0gs9p (0.28 #1161, 0.28 #991, 0.26 #1159), 0gq9h (0.28 #989, 0.27 #291, 0.26 #1159), 09qv_s (0.27 #463, 0.27 #340, 0.26 #1159), 0f4x7 (0.26 #1159, 0.26 #1854, 0.26 #1853), 099c8n (0.26 #1159, 0.26 #1854, 0.26 #1853), 099ck7 (0.26 #1159, 0.26 #1854, 0.26 #1853), 04kxsb (0.26 #1159, 0.26 #1854, 0.26 #1853), 040njc (0.26 #1159, 0.26 #1854, 0.26 #1853), 019f4v (0.26 #1159, 0.26 #1854, 0.26 #1853), 02x17s4 (0.26 #1159, 0.26 #1854, 0.26 #1853) >> Best rule #1161 for best value: >> intensional similarity = 5 >> extensional distance = 67 >> proper extension: 064lsn; 02wk7b; >> query: (?x4086, ?x1313) <- film_crew_role(?x4086, ?x137), nominated_for(?x1313, ?x4086), nominated_for(?x198, ?x4086), ?x1313 = 0gs9p, ?x198 = 040njc >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #286 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 24 *> proper extension: 0gmgwnv; *> query: (?x4086, 02n9nmz) <- nominated_for(?x2853, ?x4086), nominated_for(?x1107, ?x4086), ?x2853 = 09qv_s, honored_for(?x2220, ?x4086), ?x1107 = 019f4v *> conf = 0.15 ranks of expected_values: 33 EVAL 06_x996 award 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 106.000 106.000 0.275 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #16713-0j1z8 PRED entity: 0j1z8 PRED relation: service_location! PRED expected values: 0p4wb => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 130): 01c6k4 (0.42 #555, 0.38 #6, 0.33 #829), 018mxj (0.33 #833, 0.33 #559, 0.29 #422), 0cv9b (0.33 #560, 0.29 #423, 0.27 #971), 0p4wb (0.29 #421, 0.29 #9, 0.26 #147), 07zl6m (0.26 #271, 0.24 #133, 0.21 #819), 069b85 (0.26 #267, 0.23 #1089, 0.21 #678), 05b5c (0.24 #128, 0.22 #951, 0.21 #814), 064f29 (0.24 #60, 0.22 #198, 0.21 #746), 04sv4 (0.24 #84, 0.22 #222, 0.21 #770), 0dmtp (0.24 #59, 0.21 #745, 0.17 #197) >> Best rule #555 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0jgd; 0d0vqn; 05qhw; 05v8c; 0k6nt; 05b4w; 03spz; >> query: (?x311, 01c6k4) <- film_release_region(?x8176, ?x311), ?x8176 = 0gvvm6l, geographic_distribution(?x1571, ?x311) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 0b90_r; 03rjj; 0chghy; 03rt9; 02k54; 0ctw_b; 06qd3; 01znc_; *> query: (?x311, 0p4wb) <- film_release_region(?x8193, ?x311), exported_to(?x311, ?x1780), ?x8193 = 03z9585 *> conf = 0.29 ranks of expected_values: 4 EVAL 0j1z8 service_location! 0p4wb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 96.000 0.417 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #16712-025twgt PRED entity: 025twgt PRED relation: film! PRED expected values: 017jv5 => 89 concepts (62 used for prediction) PRED predicted values (max 10 best out of 50): 0g1rw (0.50 #83, 0.40 #8, 0.38 #158), 017jv5 (0.40 #315, 0.36 #390, 0.33 #827), 086k8 (0.33 #603, 0.31 #527, 0.27 #452), 016tw3 (0.28 #838, 0.17 #1288, 0.15 #1363), 016tt2 (0.23 #529, 0.21 #379, 0.20 #454), 03xq0f (0.18 #1958, 0.16 #1582, 0.15 #530), 017s11 (0.17 #78, 0.15 #2933, 0.14 #1280), 03rwz3 (0.17 #119, 0.12 #194, 0.11 #269), 05qd_ (0.16 #1887, 0.15 #2639, 0.15 #836), 04mkft (0.12 #561, 0.09 #637, 0.08 #787) >> Best rule #83 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 014kq6; 01kf4tt; >> query: (?x11362, 0g1rw) <- nominated_for(?x11362, ?x11120), nominated_for(?x11362, ?x6077), nominated_for(?x11362, ?x3643), nominated_for(?x11362, ?x1262), film_release_distribution_medium(?x11362, ?x81), ?x6077 = 0g5pvv, ?x1262 = 0g5pv3, ?x3643 = 0d1qmz, currency(?x11120, ?x170) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 01kf3_9; *> query: (?x11362, 017jv5) <- nominated_for(?x11362, ?x6077), film_release_distribution_medium(?x11362, ?x81), ?x6077 = 0g5pvv, genre(?x11362, ?x812), genre(?x136, ?x812), ?x136 = 09sh8k *> conf = 0.40 ranks of expected_values: 2 EVAL 025twgt film! 017jv5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 62.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16711-0d6b7 PRED entity: 0d6b7 PRED relation: film_festivals PRED expected values: 05ys0xf => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 20): 05f5rsr (0.33 #30, 0.06 #550, 0.04 #50), 0kfhjq0 (0.27 #45, 0.17 #225, 0.11 #125), 0gg7gsl (0.19 #41, 0.13 #281, 0.11 #381), 04_m9gk (0.14 #392, 0.13 #292, 0.12 #452), 0j63cyr (0.12 #283, 0.12 #43, 0.10 #383), 03wf1p2 (0.12 #53, 0.10 #553, 0.05 #133), 0bmj62v (0.11 #391, 0.11 #451, 0.10 #291), 04grdgy (0.10 #388, 0.10 #448, 0.08 #548), 0g57ws5 (0.09 #387, 0.08 #447, 0.07 #287), 0hrcs29 (0.07 #134, 0.07 #294, 0.07 #234) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 026ck; >> query: (?x1546, 05f5rsr) <- influenced_by(?x2508, ?x1546), film_festivals(?x1546, ?x9080), film_festivals(?x3496, ?x9080), film(?x286, ?x3496) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #129 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 0djb3vw; 02pjc1h; 0jqn5; 02r79_h; 0fpkhkz; 04jkpgv; 02r1c18; 0gxtknx; 09k56b7; 01hqhm; ... *> query: (?x1546, 05ys0xf) <- film_release_region(?x1546, ?x87), film(?x8043, ?x1546), film_festivals(?x1546, ?x9080), nominated_for(?x4317, ?x1546) *> conf = 0.02 ranks of expected_values: 20 EVAL 0d6b7 film_festivals 05ys0xf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 79.000 79.000 0.333 http://example.org/film/film/film_festivals #16710-04rcr PRED entity: 04rcr PRED relation: influenced_by PRED expected values: 0167xy => 124 concepts (65 used for prediction) PRED predicted values (max 10 best out of 310): 014_lq (0.12 #7009, 0.07 #14026, 0.07 #18407), 01s7qqw (0.11 #6736, 0.07 #5420, 0.07 #10247), 03_87 (0.10 #15543, 0.09 #19930, 0.09 #22564), 014z8v (0.10 #10204, 0.10 #13710, 0.10 #5377), 081k8 (0.10 #22517, 0.09 #18126, 0.09 #24272), 032l1 (0.10 #19816, 0.10 #15429, 0.10 #22450), 05qmj (0.09 #22554, 0.07 #24309, 0.07 #27818), 03sbs (0.09 #22584, 0.07 #20827, 0.06 #24339), 02wh0 (0.09 #22745, 0.07 #24500, 0.07 #23624), 01k9lpl (0.09 #8200, 0.07 #5567, 0.07 #10394) >> Best rule #7009 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 0btj0; >> query: (?x646, ?x5329) <- influenced_by(?x5329, ?x646), category(?x646, ?x134), artists(?x302, ?x5329) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #5185 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 0jn38; 016lj_; 01518s; *> query: (?x646, 0167xy) <- artist(?x2931, ?x646), group(?x227, ?x646), artist(?x8738, ?x646) *> conf = 0.05 ranks of expected_values: 42 EVAL 04rcr influenced_by 0167xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 124.000 65.000 0.119 http://example.org/influence/influence_node/influenced_by #16709-0vrmb PRED entity: 0vrmb PRED relation: contains! PRED expected values: 09c7w0 => 159 concepts (90 used for prediction) PRED predicted values (max 10 best out of 275): 09c7w0 (0.99 #38476, 0.96 #68901, 0.78 #8049), 059rby (0.59 #52808, 0.13 #63546, 0.13 #78761), 01n7q (0.45 #2760, 0.37 #3655, 0.29 #5444), 06pvr (0.35 #2847, 0.33 #3742, 0.24 #5531), 04_1l0v (0.34 #28631, 0.33 #57264, 0.32 #80529), 05kr_ (0.33 #7281, 0.08 #74267, 0.06 #4598), 0nj7b (0.33 #487, 0.06 #7642, 0.01 #14801), 02qkt (0.32 #50449, 0.17 #44188, 0.16 #22709), 0d060g (0.31 #7168, 0.11 #8064, 0.10 #3590), 05kkh (0.26 #52797, 0.08 #78750, 0.08 #9849) >> Best rule #38476 for best value: >> intensional similarity = 6 >> extensional distance = 152 >> proper extension: 0tln7; 0fvwg; 0_lr1; 0pc6x; 0ttxp; 0t_4_; 0t6hk; 0qpsn; 01z1c; 0c5v2; ... >> query: (?x12794, 09c7w0) <- contains(?x11993, ?x12794), citytown(?x266, ?x12794), adjoins(?x11993, ?x1905), adjoins(?x11993, ?x279), ?x279 = 0d060g, adjoins(?x1905, ?x335) >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0vrmb contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 90.000 0.987 http://example.org/location/location/contains #16708-0418154 PRED entity: 0418154 PRED relation: honored_for PRED expected values: 027s39y => 29 concepts (19 used for prediction) PRED predicted values (max 10 best out of 869): 04p5cr (0.33 #3323, 0.25 #2146, 0.17 #6264), 05lfwd (0.33 #3276, 0.25 #2099, 0.16 #6804), 0kfv9 (0.33 #694, 0.20 #3524, 0.17 #2452), 0c0zq (0.33 #1099, 0.20 #1761, 0.10 #4699), 02rcwq0 (0.33 #1471, 0.17 #3234, 0.13 #6175), 03nt59 (0.33 #1531, 0.17 #3294, 0.13 #6235), 0b6tzs (0.33 #1226, 0.17 #2989, 0.11 #5341), 08zrbl (0.33 #1633, 0.17 #3396, 0.08 #6924), 0cw3yd (0.33 #1337, 0.17 #3100, 0.08 #6628), 0ctb4g (0.33 #1373, 0.17 #3136, 0.08 #3724) >> Best rule #3323 for best value: >> intensional similarity = 16 >> extensional distance = 4 >> proper extension: 09pj68; >> query: (?x7767, 04p5cr) <- ceremony(?x7965, ?x7767), ceremony(?x1107, ?x7767), ?x1107 = 019f4v, honored_for(?x7767, ?x1753), nominated_for(?x4922, ?x1753), award_winner(?x7767, ?x2963), award_winner(?x7767, ?x906), award_winner(?x7767, ?x798), film(?x4922, ?x186), ?x7965 = 054knh, award_nominee(?x237, ?x906), award(?x4922, ?x1198), profession(?x906, ?x353), program(?x906, ?x6884), program(?x798, ?x7756), award(?x2963, ?x462) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #227 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 1 *> proper extension: 04110lv; *> query: (?x7767, 027s39y) <- ceremony(?x1107, ?x7767), award(?x7815, ?x1107), award(?x2086, ?x1107), nominated_for(?x1107, ?x5278), nominated_for(?x1107, ?x4457), nominated_for(?x1107, ?x4231), nominated_for(?x1107, ?x4047), nominated_for(?x1107, ?x2840), nominated_for(?x1107, ?x810), ?x810 = 0jzw, award_winner(?x7767, ?x2443), ?x4047 = 07s846j, ?x7815 = 0184jw, ?x5278 = 0bm2x, ?x4457 = 0m_q0, country(?x2840, ?x94), ?x2443 = 0237fw, ?x4231 = 04j4tx, ?x2086 = 0h1p *> conf = 0.33 ranks of expected_values: 17 EVAL 0418154 honored_for 027s39y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 29.000 19.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #16707-01771z PRED entity: 01771z PRED relation: language PRED expected values: 02h40lc => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 40): 02h40lc (0.92 #1596, 0.90 #2245, 0.90 #415), 06b_j (0.38 #709, 0.23 #5327, 0.15 #613), 07zrf (0.38 #709, 0.23 #5327, 0.12 #62), 06nm1 (0.23 #5327, 0.14 #1605, 0.12 #70), 04306rv (0.23 #5327, 0.12 #64, 0.12 #654), 064_8sq (0.17 #1616, 0.16 #1439, 0.16 #671), 02bjrlw (0.15 #355, 0.15 #296, 0.12 #414), 04h9h (0.10 #397, 0.08 #456, 0.07 #1047), 03_9r (0.07 #2904, 0.07 #3556, 0.07 #3023), 0653m (0.05 #2906, 0.05 #3025, 0.04 #3558) >> Best rule #1596 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 018nnz; 01jr4j; >> query: (?x2749, 02h40lc) <- nominated_for(?x6746, ?x2749), production_companies(?x2749, ?x382), film(?x8763, ?x2749), genre(?x6746, ?x53) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01771z language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.920 http://example.org/film/film/language #16706-0206k5 PRED entity: 0206k5 PRED relation: state_province_region PRED expected values: 03v0t => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 90): 01n7q (0.43 #758, 0.34 #2368, 0.33 #634), 059rby (0.38 #8296, 0.38 #10889, 0.38 #10764), 03v0t (0.33 #53, 0.24 #14482, 0.18 #4334), 01w65s (0.24 #14482, 0.18 #4334), 07b_l (0.20 #543, 0.13 #420, 0.12 #5993), 05kkh (0.13 #372, 0.10 #618, 0.09 #991), 059_c (0.13 #387, 0.09 #1006, 0.06 #1749), 0d0x8 (0.13 #5987, 0.04 #3757, 0.04 #8461), 05fly (0.13 #11135, 0.12 #207, 0.05 #700), 01_d4 (0.11 #4085, 0.08 #1483, 0.08 #370) >> Best rule #758 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 027lf1; >> query: (?x10699, 01n7q) <- company(?x233, ?x10699), place_founded(?x10699, ?x1860), currency(?x10699, ?x170), adjoins(?x448, ?x1860) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #53 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0181hw; *> query: (?x10699, 03v0t) <- place_founded(?x10699, ?x1860), ?x1860 = 01_d4, industry(?x10699, ?x13321) *> conf = 0.33 ranks of expected_values: 3 EVAL 0206k5 state_province_region 03v0t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 133.000 0.429 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #16705-013q0p PRED entity: 013q0p PRED relation: genre PRED expected values: 0lsxr 01hwc6 07s2s => 75 concepts (74 used for prediction) PRED predicted values (max 10 best out of 94): 01jfsb (0.73 #1088, 0.44 #730, 0.43 #491), 07s9rl0 (0.64 #1556, 0.62 #599, 0.62 #1317), 01z4y (0.62 #5373, 0.61 #5493, 0.61 #4419), 03k9fj (0.45 #490, 0.44 #251, 0.41 #729), 02l7c8 (0.38 #1212, 0.29 #3482, 0.29 #4315), 06n90 (0.33 #253, 0.23 #731, 0.22 #492), 01hmnh (0.31 #497, 0.29 #736, 0.22 #258), 04xvlr (0.30 #1198, 0.18 #4301, 0.17 #1677), 0lsxr (0.29 #128, 0.27 #1084, 0.24 #368), 082gq (0.25 #1107, 0.12 #987, 0.11 #2184) >> Best rule #1088 for best value: >> intensional similarity = 4 >> extensional distance = 476 >> proper extension: 0cnztc4; 064n1pz; 0crh5_f; 0bmc4cm; 04lqvly; 0413cff; 07l50vn; 02h22; 03_wm6; 02qjv1p; ... >> query: (?x4717, 01jfsb) <- titles(?x2480, ?x4717), genre(?x4717, ?x5104), genre(?x11125, ?x5104), ?x11125 = 0gy4k >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 03nx8mj; 056xkh; *> query: (?x4717, 0lsxr) <- titles(?x2480, ?x4717), genre(?x4717, ?x225), film(?x1986, ?x4717), ?x1986 = 0gz5hs *> conf = 0.29 ranks of expected_values: 9, 16, 17 EVAL 013q0p genre 07s2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 75.000 74.000 0.728 http://example.org/film/film/genre EVAL 013q0p genre 01hwc6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 75.000 74.000 0.728 http://example.org/film/film/genre EVAL 013q0p genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 75.000 74.000 0.728 http://example.org/film/film/genre #16704-054ks3 PRED entity: 054ks3 PRED relation: award! PRED expected values: 012x4t 01fwj8 02lz1s 04xrx 01wmgrf 01l9v7n 025cn2 052hl 0133x7 03j1p2n 020fgy 01jgkj2 01q3_2 => 51 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2639): 019x62 (0.80 #22871, 0.79 #55556, 0.77 #68630), 03kwtb (0.80 #22871, 0.79 #55556, 0.77 #68630), 01k23t (0.80 #22871, 0.79 #55556, 0.77 #68630), 01k98nm (0.80 #22871, 0.79 #55556, 0.77 #68630), 01wd9vs (0.80 #22871, 0.79 #55556, 0.71 #68629), 0m_v0 (0.80 #22871, 0.79 #55556, 0.71 #68629), 0f8pz (0.80 #22871, 0.79 #55556, 0.71 #68629), 016jll (0.80 #22871, 0.79 #55556, 0.71 #68629), 0csdzz (0.62 #12662, 0.33 #2863, 0.12 #25734), 0478__m (0.57 #14341, 0.20 #43760, 0.15 #26138) >> Best rule #22871 for best value: >> intensional similarity = 6 >> extensional distance = 31 >> proper extension: 05ztjjw; >> query: (?x2585, ?x248) <- nominated_for(?x2585, ?x8084), nominated_for(?x2585, ?x1597), ?x1597 = 0dr_4, award_winner(?x2585, ?x248), music(?x8084, ?x84), currency(?x8084, ?x170) >> conf = 0.80 => this is the best rule for 8 predicted values *> Best rule #12333 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 0l8z1; 025m8y; 02qvyrt; 09ly2r6; *> query: (?x2585, 020fgy) <- award(?x9408, ?x2585), award(?x9163, ?x2585), award(?x5896, ?x2585), award(?x2275, ?x2585), ?x9163 = 02sjp, award_winner(?x308, ?x2275), location(?x2275, ?x108), award_winner(?x1232, ?x5896), profession(?x9408, ?x563) *> conf = 0.38 ranks of expected_values: 58, 91, 118, 313, 416, 443, 446, 447, 477, 492, 652, 861, 1701 EVAL 054ks3 award! 01q3_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 01jgkj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 020fgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 03j1p2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 0133x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 052hl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 025cn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 01l9v7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 01wmgrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 04xrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 02lz1s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 01fwj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ks3 award! 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 51.000 26.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16703-05r3qc PRED entity: 05r3qc PRED relation: language PRED expected values: 02h40lc => 91 concepts (82 used for prediction) PRED predicted values (max 10 best out of 47): 02h40lc (0.93 #535, 0.91 #357, 0.91 #298), 064_8sq (0.18 #200, 0.15 #377, 0.14 #1329), 02bjrlw (0.16 #119, 0.07 #711, 0.07 #830), 06nm1 (0.13 #70, 0.13 #366, 0.13 #307), 0653m (0.13 #71, 0.11 #130, 0.10 #604), 012w70 (0.13 #72, 0.06 #250, 0.05 #131), 04306rv (0.11 #715, 0.08 #1730, 0.08 #2334), 03_9r (0.08 #247, 0.08 #483, 0.07 #1136), 06b_j (0.08 #496, 0.06 #556, 0.06 #674), 0459q4 (0.07 #96, 0.06 #274, 0.04 #3646) >> Best rule #535 for best value: >> intensional similarity = 6 >> extensional distance = 121 >> proper extension: 072r5v; 0k2m6; >> query: (?x6167, 02h40lc) <- genre(?x6167, ?x225), film(?x1365, ?x6167), ?x225 = 02kdv5l, film_crew_role(?x6167, ?x2178), film_crew_role(?x6499, ?x2178), ?x6499 = 04xx9s >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05r3qc language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 82.000 0.927 http://example.org/film/film/language #16702-01hydr PRED entity: 01hydr PRED relation: parent_genre PRED expected values: 0fd3y 0kz10 => 65 concepts (50 used for prediction) PRED predicted values (max 10 best out of 207): 06by7 (0.83 #4259, 0.31 #5418, 0.31 #1642), 016clz (0.68 #2286, 0.47 #2614, 0.12 #1794), 08cyft (0.50 #524, 0.35 #2483, 0.33 #850), 01243b (0.41 #2310, 0.28 #2638, 0.14 #4272), 0283d (0.33 #230, 0.25 #392, 0.17 #717), 03mb9 (0.33 #66, 0.17 #552, 0.14 #1530), 03_d0 (0.33 #8, 0.14 #2290, 0.13 #2453), 01n4bh (0.33 #129, 0.07 #2608, 0.05 #2411), 0y3_8 (0.32 #2477, 0.31 #2642, 0.17 #518), 064t9 (0.32 #2455, 0.14 #4254, 0.12 #2620) >> Best rule #4259 for best value: >> intensional similarity = 11 >> extensional distance = 69 >> proper extension: 018ysx; 028cl7; 017ht; >> query: (?x14355, 06by7) <- parent_genre(?x14355, ?x474), artists(?x474, ?x6835), artists(?x474, ?x5916), artists(?x474, ?x4701), artists(?x474, ?x3894), artists(?x474, ?x498), ?x3894 = 01vxlbm, origin(?x498, ?x9929), ?x4701 = 03j24kf, award_nominee(?x6835, ?x140), artist(?x2149, ?x5916) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #493 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 07lnk; 03mb9; *> query: (?x14355, 0fd3y) <- artists(?x14355, ?x8806), ?x8806 = 01d_h, parent_genre(?x14355, ?x474), artists(?x474, ?x7683), artists(?x474, ?x1674), ?x1674 = 01v_pj6, artists(?x7329, ?x7683), ?x7329 = 016jny *> conf = 0.17 ranks of expected_values: 21, 47 EVAL 01hydr parent_genre 0kz10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 65.000 50.000 0.831 http://example.org/music/genre/parent_genre EVAL 01hydr parent_genre 0fd3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 65.000 50.000 0.831 http://example.org/music/genre/parent_genre #16701-05jbn PRED entity: 05jbn PRED relation: dog_breed PRED expected values: 01_gx_ => 204 concepts (204 used for prediction) PRED predicted values (max 10 best out of 1): 01_gx_ (0.83 #24, 0.78 #10, 0.74 #6) >> Best rule #24 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 0rh6k; 094jv; 01_d4; 04f_d; 0dclg; 0d6lp; 0ply0; 0f2v0; 019fh; 01sn3; ... >> query: (?x4978, 01_gx_) <- citytown(?x1506, ?x4978), location(?x105, ?x4978), dog_breed(?x4978, ?x1706) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05jbn dog_breed 01_gx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 204.000 204.000 0.833 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #16700-0177g PRED entity: 0177g PRED relation: people! PRED expected values: 012hw => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 52): 06z5s (0.46 #1432, 0.41 #846, 0.33 #25), 0gk4g (0.40 #75, 0.24 #660, 0.22 #3717), 051_y (0.33 #178, 0.11 #308, 0.11 #1089), 02y0js (0.22 #262, 0.17 #457, 0.17 #132), 02knxx (0.20 #97, 0.17 #487, 0.13 #617), 0dq9p (0.19 #1449, 0.16 #1058, 0.15 #1318), 012hw (0.18 #767, 0.17 #963, 0.17 #182), 0kh3 (0.17 #148, 0.11 #278, 0.06 #798), 0qcr0 (0.15 #2798, 0.15 #1888, 0.13 #2603), 02k6hp (0.14 #1599, 0.12 #1924, 0.12 #752) >> Best rule #1432 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 0cw10; >> query: (?x10986, ?x6821) <- people(?x13455, ?x10986), entity_involved(?x10764, ?x10986), type_of_union(?x10986, ?x566), entity_involved(?x10764, ?x9178), people(?x6821, ?x9178), gender(?x9178, ?x231) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #767 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 063vn; *> query: (?x10986, 012hw) <- people(?x13455, ?x10986), jurisdiction_of_office(?x10986, ?x205), gender(?x10986, ?x231), ?x231 = 05zppz, religion(?x10986, ?x1985), nationality(?x101, ?x205) *> conf = 0.18 ranks of expected_values: 7 EVAL 0177g people! 012hw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 142.000 142.000 0.463 http://example.org/people/cause_of_death/people #16699-0b_6mr PRED entity: 0b_6mr PRED relation: team PRED expected values: 026w398 02pyyld => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 27): 026w398 (0.83 #131, 0.80 #224, 0.79 #212), 02pzy52 (0.79 #213, 0.75 #225, 0.75 #132), 02plv57 (0.75 #79, 0.68 #204, 0.67 #159), 027yf83 (0.69 #175, 0.67 #186, 0.64 #116), 02pyyld (0.64 #111, 0.61 #134, 0.60 #55), 02pqcfz (0.63 #206, 0.61 #184, 0.61 #134), 04088s0 (0.61 #134, 0.56 #176, 0.56 #146), 02ptzz0 (0.61 #134, 0.56 #146, 0.50 #183), 03d5m8w (0.61 #134, 0.56 #146, 0.46 #249), 03d555l (0.61 #134, 0.56 #146, 0.46 #249) >> Best rule #131 for best value: >> intensional similarity = 9 >> extensional distance = 10 >> proper extension: 0b_6jz; >> query: (?x10673, 026w398) <- locations(?x10673, ?x6952), locations(?x10673, ?x659), team(?x10673, ?x5551), ?x5551 = 02pjzvh, place_of_birth(?x3853, ?x659), film(?x3853, ?x1259), administrative_division(?x6952, ?x8350), profession(?x3853, ?x296), award(?x3853, ?x401) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 0b_6mr team 02pyyld CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 73.000 73.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_6mr team 026w398 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #16698-015vq_ PRED entity: 015vq_ PRED relation: notable_people_with_this_condition! PRED expected values: 0g02vk => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 3): 029sk (0.06 #1, 0.02 #45, 0.02 #111), 0h99n (0.02 #32, 0.01 #362, 0.01 #296), 01g2q (0.01 #31) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02zyy4; >> query: (?x4128, 029sk) <- award_nominee(?x4128, ?x1677), ?x1677 = 021vwt, profession(?x4128, ?x524) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015vq_ notable_people_with_this_condition! 0g02vk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 98.000 0.062 http://example.org/medicine/disease/notable_people_with_this_condition #16697-036jb PRED entity: 036jb PRED relation: film PRED expected values: 0h0wd9 => 87 concepts (73 used for prediction) PRED predicted values (max 10 best out of 47): 087vnr5 (0.17 #699), 08952r (0.17 #352), 034qzw (0.17 #170), 026mfbr (0.17 #44), 05dmmc (0.10 #30678, 0.10 #31509, 0.09 #30677), 0h0wd9 (0.10 #30678, 0.10 #31509, 0.09 #30677), 02qjv1p (0.10 #30678, 0.09 #30677, 0.09 #31508), 043n1r5 (0.10 #1597), 08rr3p (0.10 #1058), 0296rz (0.02 #2433, 0.01 #3262) >> Best rule #699 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 02t_y3; >> query: (?x4512, 087vnr5) <- nationality(?x4512, ?x94), student(?x4750, ?x4512), ?x4750 = 04hgpt >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #30678 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1240 *> proper extension: 02x2097; 0kc9f; *> query: (?x4512, ?x10362) <- nominated_for(?x4512, ?x10362), award_winner(?x8459, ?x4512), genre(?x10362, ?x239) *> conf = 0.10 ranks of expected_values: 6 EVAL 036jb film 0h0wd9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 87.000 73.000 0.167 http://example.org/film/director/film #16696-0fb7sd PRED entity: 0fb7sd PRED relation: film! PRED expected values: 01wmxfs 069nzr 01jfrg => 102 concepts (51 used for prediction) PRED predicted values (max 10 best out of 959): 0b_dy (0.16 #2610, 0.14 #4687, 0.05 #10918), 064jjy (0.15 #35320, 0.14 #87262, 0.14 #16618), 014zcr (0.12 #36, 0.08 #6267, 0.04 #8344), 016yzz (0.12 #684, 0.04 #6915, 0.02 #8992), 0c6qh (0.12 #412, 0.04 #14951, 0.03 #31576), 0154qm (0.12 #10945, 0.09 #4714, 0.05 #2637), 0gg9_5q (0.12 #60252, 0.11 #64408, 0.11 #74796), 04y8r (0.11 #103880, 0.08 #22851, 0.08 #24929), 0169dl (0.11 #2476, 0.09 #4553, 0.08 #6630), 04shbh (0.11 #2241, 0.09 #4318, 0.07 #10549) >> Best rule #2610 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 0g3zrd; >> query: (?x4967, 0b_dy) <- nominated_for(?x2771, ?x4967), production_companies(?x4967, ?x1104), ?x2771 = 03m73lj, film_crew_role(?x4967, ?x468), film(?x1104, ?x86) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #6359 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 04lqvly; *> query: (?x4967, 01wmxfs) <- nominated_for(?x2771, ?x4967), genre(?x4967, ?x53), language(?x4967, ?x5359), ?x5359 = 0jzc, film_release_distribution_medium(?x4967, ?x81) *> conf = 0.04 ranks of expected_values: 226, 435, 770 EVAL 0fb7sd film! 01jfrg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 102.000 51.000 0.158 http://example.org/film/actor/film./film/performance/film EVAL 0fb7sd film! 069nzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 102.000 51.000 0.158 http://example.org/film/actor/film./film/performance/film EVAL 0fb7sd film! 01wmxfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 102.000 51.000 0.158 http://example.org/film/actor/film./film/performance/film #16695-0147jt PRED entity: 0147jt PRED relation: artists! PRED expected values: 016cjb => 133 concepts (43 used for prediction) PRED predicted values (max 10 best out of 230): 06by7 (0.71 #11206, 0.59 #1884, 0.50 #4059), 016clz (0.44 #5, 0.38 #11189, 0.25 #1867), 06j6l (0.39 #4086, 0.36 #7192, 0.25 #5950), 0xhtw (0.33 #17, 0.24 #7160, 0.23 #11201), 025sc50 (0.33 #4088, 0.23 #7194, 0.22 #1913), 017_qw (0.31 #2236, 0.22 #684, 0.22 #4410), 0gywn (0.29 #4096, 0.25 #7202, 0.24 #3165), 0155w (0.25 #1040, 0.21 #7251, 0.19 #419), 02k_kn (0.24 #377, 0.22 #1928, 0.19 #4103), 02w4v (0.24 #977, 0.19 #356, 0.16 #2840) >> Best rule #11206 for best value: >> intensional similarity = 3 >> extensional distance = 549 >> proper extension: 0m19t; 04r1t; 02r1tx7; 05563d; 03xhj6; 0394y; 018gm9; 05xq9; 06nv27; 02mq_y; ... >> query: (?x9103, 06by7) <- artists(?x12241, ?x9103), artists(?x12241, ?x6699), ?x6699 = 09lwrt >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6909 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 365 *> proper extension: 0f0y8; 01q7cb_; 01ky2h; 012zng; 0285c; 02jg92; 013v5j; 01vv126; 0lgm5; 03xl77; ... *> query: (?x9103, 016cjb) <- artists(?x671, ?x9103), location(?x9103, ?x3097), artist(?x9492, ?x9103), nationality(?x9103, ?x94) *> conf = 0.08 ranks of expected_values: 78 EVAL 0147jt artists! 016cjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 133.000 43.000 0.706 http://example.org/music/genre/artists #16694-01t_vv PRED entity: 01t_vv PRED relation: genre! PRED expected values: 06w99h3 02z3r8t 02pjc1h 02q56mk 02xtxw 0830vk 0c57yj 04smdd 0dnw1 01chpn 01l2b3 04b2qn => 55 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1811): 02jkkv (0.67 #15661, 0.62 #26251, 0.60 #10355), 05vxdh (0.67 #14893, 0.62 #25483, 0.60 #9587), 04h41v (0.67 #15143, 0.62 #25733, 0.50 #18673), 03m4mj (0.67 #14331, 0.62 #24921, 0.50 #17861), 0dnw1 (0.67 #15171, 0.62 #25761, 0.50 #4569), 0hmr4 (0.67 #14235, 0.62 #24825, 0.50 #3633), 05dptj (0.67 #15431, 0.60 #10125, 0.50 #26021), 07tj4c (0.67 #15805, 0.60 #10499, 0.50 #26395), 02rtqvb (0.67 #15879, 0.60 #10573, 0.50 #26469), 025rxjq (0.67 #15464, 0.60 #10158, 0.50 #26054) >> Best rule #15661 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 06cvj; 02l7c8; >> query: (?x6674, 02jkkv) <- genre(?x9533, ?x6674), genre(?x7432, ?x6674), genre(?x4927, ?x6674), genre(?x407, ?x6674), cinematography(?x9533, ?x7384), nominated_for(?x112, ?x407), language(?x9533, ?x254), film(?x2549, ?x9533), genre(?x273, ?x6674), nominated_for(?x157, ?x9533), award(?x7432, ?x68), ?x4927 = 0j80w >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #15171 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 06cvj; 02l7c8; *> query: (?x6674, 0dnw1) <- genre(?x9533, ?x6674), genre(?x7432, ?x6674), genre(?x4927, ?x6674), genre(?x407, ?x6674), cinematography(?x9533, ?x7384), nominated_for(?x112, ?x407), language(?x9533, ?x254), film(?x2549, ?x9533), genre(?x273, ?x6674), nominated_for(?x157, ?x9533), award(?x7432, ?x68), ?x4927 = 0j80w *> conf = 0.67 ranks of expected_values: 5, 24, 30, 52, 76, 83, 103, 132, 133, 225, 490, 717 EVAL 01t_vv genre! 04b2qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 01l2b3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 01chpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 0dnw1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 04smdd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 0c57yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 0830vk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 02xtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 02q56mk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 02pjc1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 02z3r8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 55.000 18.000 0.667 http://example.org/film/film/genre EVAL 01t_vv genre! 06w99h3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 18.000 0.667 http://example.org/film/film/genre #16693-05f5rsr PRED entity: 05f5rsr PRED relation: film_festivals! PRED expected values: 02z13jg 02wypbh => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1806): 0ddfwj1 (0.50 #1389, 0.33 #7, 0.25 #3693), 0cp08zg (0.50 #1561, 0.33 #179, 0.25 #3865), 09gq0x5 (0.50 #1419, 0.33 #37, 0.25 #3723), 0gyfp9c (0.40 #2371, 0.33 #3987, 0.33 #3066), 0g5838s (0.40 #2367, 0.33 #3062, 0.33 #2831), 0gvvm6l (0.40 #2487, 0.33 #3182, 0.33 #2951), 0h03fhx (0.40 #2406, 0.33 #3101, 0.33 #2870), 080lkt7 (0.40 #2177, 0.25 #1256, 0.11 #4259), 0b76t12 (0.40 #2109, 0.25 #1188, 0.11 #4191), 051ys82 (0.40 #2214, 0.25 #1293, 0.11 #4296) >> Best rule #1389 for best value: >> intensional similarity = 35 >> extensional distance = 2 >> proper extension: 0g57ws5; >> query: (?x9932, 0ddfwj1) <- film_festivals(?x5515, ?x9932), film_festivals(?x1452, ?x9932), music(?x5515, ?x8849), nominated_for(?x1245, ?x5515), nominated_for(?x591, ?x5515), production_companies(?x5515, ?x541), genre(?x5515, ?x53), film_release_region(?x1452, ?x4743), film_release_region(?x1452, ?x2513), film_release_region(?x1452, ?x1892), film_release_region(?x1452, ?x1603), film_release_region(?x1452, ?x1229), ?x2513 = 05b4w, film(?x1104, ?x1452), ?x1892 = 02vzc, award(?x5515, ?x850), production_companies(?x144, ?x1104), film_release_region(?x5515, ?x94), ?x591 = 0f4x7, ?x1229 = 059j2, nominated_for(?x2209, ?x1452), ?x4743 = 03spz, ?x1603 = 06bnz, award(?x197, ?x1245), award(?x10973, ?x1245), award(?x6314, ?x1245), award(?x4872, ?x1245), ?x10973 = 0g10g, ?x4872 = 02d42t, nominated_for(?x669, ?x1452), ?x6314 = 0c3p7, award(?x382, ?x2209), ceremony(?x2209, ?x78), ceremony(?x1245, ?x602), film_release_distribution_medium(?x1452, ?x81) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3211 for first EXPECTED value: *> intensional similarity = 40 *> extensional distance = 4 *> proper extension: 03wf1p2; *> query: (?x9932, 02wypbh) <- film_festivals(?x5515, ?x9932), film_festivals(?x1452, ?x9932), music(?x5515, ?x8849), nominated_for(?x591, ?x5515), language(?x5515, ?x254), nominated_for(?x1244, ?x5515), executive_produced_by(?x5515, ?x10522), genre(?x5515, ?x53), film_release_region(?x1452, ?x1453), film_release_region(?x1452, ?x390), nominated_for(?x1443, ?x1452), nominated_for(?x500, ?x1452), award_winner(?x1452, ?x2870), ?x1453 = 06qd3, nominated_for(?x500, ?x8769), nominated_for(?x500, ?x6030), nominated_for(?x500, ?x4970), nominated_for(?x500, ?x3433), nominated_for(?x500, ?x1597), nominated_for(?x500, ?x1077), nominated_for(?x500, ?x144), award(?x382, ?x500), award(?x324, ?x500), ?x8769 = 0bj25, award_winner(?x929, ?x2870), ?x1077 = 09q5w2, ?x6030 = 0sxgv, ?x3433 = 0299hs, ceremony(?x500, ?x1601), ceremony(?x500, ?x1449), ?x1597 = 0dr_4, award_winner(?x762, ?x2870), ?x144 = 0m313, nominated_for(?x2870, ?x1012), ?x1443 = 054krc, ?x1601 = 073hmq, ?x390 = 0chghy, award_winner(?x591, ?x157), ?x1449 = 059x66, ?x4970 = 0cqnss *> conf = 0.17 ranks of expected_values: 181, 192 EVAL 05f5rsr film_festivals! 02wypbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 22.000 22.000 0.500 http://example.org/film/film/film_festivals EVAL 05f5rsr film_festivals! 02z13jg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 22.000 0.500 http://example.org/film/film/film_festivals #16692-019rg5 PRED entity: 019rg5 PRED relation: country! PRED expected values: 03_8r => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 50): 071t0 (0.76 #2069, 0.74 #369, 0.71 #1119), 03_8r (0.74 #1468, 0.72 #2168, 0.71 #1118), 06wrt (0.70 #113, 0.58 #313, 0.55 #513), 03hr1p (0.60 #120, 0.60 #1020, 0.56 #370), 064vjs (0.60 #128, 0.58 #328, 0.58 #1028), 0194d (0.60 #143, 0.58 #1043, 0.53 #1493), 07bs0 (0.60 #112, 0.56 #312, 0.52 #1012), 019tzd (0.60 #136, 0.50 #1036, 0.50 #536), 0w0d (0.60 #1011, 0.55 #111, 0.52 #511), 07jjt (0.58 #317, 0.55 #117, 0.50 #517) >> Best rule #2069 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 027rn; 082fr; 087vz; 09lxtg; 05vz3zq; 0d04z6; 05r7t; 03f2w; >> query: (?x910, 071t0) <- film_release_region(?x186, ?x910), country(?x668, ?x910), olympics(?x910, ?x584) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1468 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 0166v; 06m_5; *> query: (?x910, 03_8r) <- currency(?x910, ?x170), olympics(?x910, ?x584), taxonomy(?x910, ?x939) *> conf = 0.74 ranks of expected_values: 2 EVAL 019rg5 country! 03_8r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 122.000 0.762 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #16691-06ch55 PRED entity: 06ch55 PRED relation: instrumentalists PRED expected values: 04zwjd 01p7b6b => 39 concepts (24 used for prediction) PRED predicted values (max 10 best out of 980): 01gg59 (0.75 #5013, 0.68 #4413, 0.62 #2017), 01vrncs (0.67 #2445, 0.50 #3044, 0.50 #46), 01vvycq (0.58 #2430, 0.50 #1831, 0.50 #31), 01vn35l (0.50 #2554, 0.50 #155, 0.43 #754), 018d6l (0.50 #2790, 0.50 #391, 0.43 #990), 018y81 (0.50 #2744, 0.50 #345, 0.38 #3343), 0c9d9 (0.50 #1811, 0.50 #11, 0.33 #2410), 01vsl3_ (0.50 #149, 0.43 #748, 0.42 #2548), 01p0vf (0.50 #384, 0.43 #983, 0.42 #2783), 09prnq (0.50 #114, 0.43 #713, 0.38 #1914) >> Best rule #5013 for best value: >> intensional similarity = 10 >> extensional distance = 22 >> proper extension: 07y_7; 0342h; 0l14md; 07c6l; 018vs; 03m5k; 01hww_; 02hnl; 07gql; 06ncr; ... >> query: (?x8168, 01gg59) <- instrumentalists(?x8168, ?x4139), award_winner(?x5123, ?x4139), award_winner(?x1079, ?x4139), ?x1079 = 0l8z1, award_winner(?x6011, ?x4139), gender(?x4139, ?x231), profession(?x4139, ?x1614), award(?x4139, ?x1232), ceremony(?x5123, ?x342), ?x231 = 05zppz >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #98 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 05r5c; 05148p4; *> query: (?x8168, 04zwjd) <- instrumentalists(?x8168, ?x4139), instrumentalists(?x8168, ?x217), instrumentalists(?x8168, ?x215), award_winner(?x5123, ?x4139), award_winner(?x1079, ?x4139), ?x1079 = 0l8z1, award_winner(?x6011, ?x4139), gender(?x4139, ?x231), profession(?x4139, ?x1614), ?x5123 = 025m98, ?x215 = 07s3vqk, ?x217 = 0197tq, ?x231 = 05zppz *> conf = 0.25 ranks of expected_values: 356, 693 EVAL 06ch55 instrumentalists 01p7b6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.750 http://example.org/music/instrument/instrumentalists EVAL 06ch55 instrumentalists 04zwjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 39.000 24.000 0.750 http://example.org/music/instrument/instrumentalists #16690-0w7s PRED entity: 0w7s PRED relation: specialization_of PRED expected values: 09j9h => 66 concepts (65 used for prediction) PRED predicted values (max 10 best out of 9): 0fj9f (0.25 #216, 0.09 #415, 0.07 #578), 012t_z (0.17 #234, 0.11 #299, 0.10 #368), 09j9h (0.11 #352, 0.05 #1167, 0.05 #1233), 0n1h (0.08 #465, 0.07 #498, 0.02 #1349), 03qh03g (0.05 #635, 0.04 #764, 0.03 #861), 02hrh1q (0.02 #986, 0.02 #1052, 0.02 #1085), 06q2q (0.02 #994, 0.02 #1027, 0.02 #1158), 01l5t6 (0.02 #1042, 0.02 #1374, 0.01 #1477), 0cbd2 (0.01 #1482) >> Best rule #216 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 062z7; >> query: (?x11820, 0fj9f) <- major_field_of_study(?x1771, ?x11820), major_field_of_study(?x7596, ?x11820), major_field_of_study(?x5306, ?x11820), major_field_of_study(?x2497, ?x11820), ?x1771 = 019v9k, ?x7596 = 012mzw, colors(?x2497, ?x332), ?x5306 = 0217m9, currency(?x2497, ?x170), school(?x685, ?x2497), school(?x7643, ?x2497), institution(?x620, ?x2497), ?x7643 = 02c_4 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #352 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 7 *> proper extension: 01lj9; 0db86; 01tbp; 01400v; *> query: (?x11820, 09j9h) <- major_field_of_study(?x1771, ?x11820), major_field_of_study(?x9768, ?x11820), major_field_of_study(?x6333, ?x11820), major_field_of_study(?x2948, ?x11820), major_field_of_study(?x2497, ?x11820), ?x1771 = 019v9k, ?x2497 = 0f1nl, institution(?x1368, ?x6333), institution(?x1526, ?x9768), school(?x700, ?x6333), student(?x2948, ?x129), currency(?x9768, ?x170) *> conf = 0.11 ranks of expected_values: 3 EVAL 0w7s specialization_of 09j9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 65.000 0.250 http://example.org/people/profession/specialization_of #16689-039cq4 PRED entity: 039cq4 PRED relation: nominated_for! PRED expected values: 0fc9js => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 195): 0fc9js (0.71 #6826, 0.70 #6354, 0.69 #6590), 09qvc0 (0.71 #6826, 0.70 #6354, 0.69 #6590), 0m7yy (0.71 #6826, 0.70 #6354, 0.69 #6590), 0gq9h (0.42 #4768, 0.35 #11122, 0.34 #10650), 07cbcy (0.39 #2179, 0.38 #3059, 0.33 #3766), 02w9sd7 (0.38 #3059, 0.33 #124, 0.33 #3766), 05p09zm (0.38 #3059, 0.33 #3766, 0.32 #4472), 04kxsb (0.38 #3059, 0.33 #3766, 0.32 #4472), 0cjyzs (0.38 #3059, 0.33 #3766, 0.32 #4472), 03ccq3s (0.38 #3059, 0.33 #3766, 0.32 #4472) >> Best rule #6826 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 0300ml; 02rq7nd; >> query: (?x6884, ?x537) <- award(?x6884, ?x537), genre(?x6884, ?x258), nominated_for(?x870, ?x6884) >> conf = 0.71 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 039cq4 nominated_for! 0fc9js CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.708 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16688-02vjp3 PRED entity: 02vjp3 PRED relation: music PRED expected values: 01m7f5r => 65 concepts (46 used for prediction) PRED predicted values (max 10 best out of 75): 0146pg (0.08 #10, 0.07 #2332, 0.06 #1700), 02bh9 (0.08 #51, 0.05 #262, 0.05 #1108), 08c9b0 (0.08 #83, 0.05 #294, 0.03 #505), 01d4cb (0.08 #162, 0.05 #373, 0.03 #584), 01ycfv (0.08 #167, 0.05 #378, 0.02 #1435), 0b6yp2 (0.08 #52, 0.05 #263, 0.02 #686), 07mvp (0.08 #117, 0.05 #328, 0.02 #751), 01r4hry (0.08 #143, 0.05 #354, 0.02 #777), 01r93l (0.06 #6538, 0.06 #7385, 0.06 #6114), 01v0sxx (0.05 #392, 0.03 #603, 0.02 #815) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0g56t9t; 02y_lrp; 02847m9; 0d_wms; 01w8g3; 02z2mr7; 02q7fl9; 064q5v; 05t54s; 0hz6mv2; >> query: (?x7480, 0146pg) <- country(?x7480, ?x512), person(?x7480, ?x269), film_release_distribution_medium(?x7480, ?x81), ?x512 = 07ssc >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02vjp3 music 01m7f5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 46.000 0.083 http://example.org/film/film/music #16687-02z6l5f PRED entity: 02z6l5f PRED relation: executive_produced_by! PRED expected values: 0gyy53 0660b9b 089j8p => 134 concepts (74 used for prediction) PRED predicted values (max 10 best out of 393): 0dgst_d (0.33 #64, 0.04 #3670, 0.03 #4186), 0bs8s1p (0.33 #382, 0.02 #13259, 0.02 #14290), 0bmfnjs (0.33 #455), 0bh8x1y (0.33 #262), 0gydcp7 (0.33 #110), 01pj_5 (0.13 #3853, 0.11 #2823, 0.10 #3338), 0bt4g (0.13 #4019, 0.10 #3504, 0.09 #4535), 0mbql (0.13 #3975, 0.10 #3460, 0.09 #4491), 01f7kl (0.13 #3738, 0.10 #3223, 0.09 #4254), 05z43v (0.12 #4122, 0.11 #15971, 0.11 #15455) >> Best rule #64 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02z2xdf; >> query: (?x4857, 0dgst_d) <- executive_produced_by(?x5819, ?x4857), executive_produced_by(?x4329, ?x4857), ?x4329 = 05c5z8j, ?x5819 = 02w9k1c, profession(?x4857, ?x319) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3252 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 0fvf9q; 0z4s; 02q_cc; 02lk1s; 0pz91; 0343h; 06pj8; 02vyw; 0b1f49; 05hj_k; ... *> query: (?x4857, 0gyy53) <- location(?x4857, ?x362), award_nominee(?x2803, ?x4857), executive_produced_by(?x1228, ?x4857), company(?x4857, ?x2776) *> conf = 0.05 ranks of expected_values: 116 EVAL 02z6l5f executive_produced_by! 089j8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 74.000 0.333 http://example.org/film/film/executive_produced_by EVAL 02z6l5f executive_produced_by! 0660b9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 74.000 0.333 http://example.org/film/film/executive_produced_by EVAL 02z6l5f executive_produced_by! 0gyy53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 134.000 74.000 0.333 http://example.org/film/film/executive_produced_by #16686-02fhtq PRED entity: 02fhtq PRED relation: parent_genre PRED expected values: 02w4v => 71 concepts (51 used for prediction) PRED predicted values (max 10 best out of 193): 06by7 (0.42 #510, 0.36 #839, 0.35 #1665), 0155w (0.33 #73, 0.25 #403, 0.23 #4307), 01lyv (0.33 #24, 0.25 #354, 0.23 #4307), 02fhtq (0.33 #145, 0.25 #475, 0.20 #309), 03_d0 (0.33 #9, 0.23 #4307, 0.18 #667), 01m1y (0.23 #4307, 0.20 #284, 0.12 #450), 02w4v (0.20 #195, 0.12 #361, 0.08 #525), 05r9t (0.20 #229, 0.01 #723, 0.01 #888), 05r6t (0.16 #8351, 0.16 #4194, 0.15 #7852), 03lty (0.13 #4158, 0.13 #8315, 0.13 #7816) >> Best rule #510 for best value: >> intensional similarity = 6 >> extensional distance = 24 >> proper extension: 025sc50; 02k_kn; 05jg58; >> query: (?x13737, 06by7) <- artists(?x13737, ?x7753), award_winner(?x5766, ?x7753), award_winner(?x7753, ?x1051), origin(?x7753, ?x4978), instrumentalists(?x2048, ?x7753), ?x2048 = 018j2 >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #195 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 03_d0; 01lyv; 0155w; *> query: (?x13737, 02w4v) <- artists(?x13737, ?x4635), parent_genre(?x8798, ?x13737), location(?x4635, ?x1426), award_winner(?x4635, ?x1051), award_nominee(?x4635, ?x2518), ?x8798 = 0gg8l *> conf = 0.20 ranks of expected_values: 7 EVAL 02fhtq parent_genre 02w4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 71.000 51.000 0.423 http://example.org/music/genre/parent_genre #16685-0gg8l PRED entity: 0gg8l PRED relation: parent_genre! PRED expected values: 016jhr => 61 concepts (52 used for prediction) PRED predicted values (max 10 best out of 265): 0mhfr (0.33 #552, 0.33 #286, 0.25 #1084), 016jny (0.33 #618, 0.33 #87, 0.25 #1150), 018ysx (0.33 #739, 0.33 #473, 0.25 #1271), 017371 (0.33 #677, 0.33 #411, 0.25 #1209), 064t9 (0.33 #807, 0.33 #541, 0.12 #1073), 0gg8l (0.33 #374, 0.25 #1172, 0.17 #906), 0155w (0.33 #89, 0.25 #797, 0.12 #1152), 0g_bh (0.33 #639, 0.23 #1974, 0.17 #905), 06hzq3 (0.33 #645, 0.17 #1442, 0.12 #1177), 05w3f (0.33 #563, 0.17 #829, 0.12 #1095) >> Best rule #552 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 06by7; >> query: (?x8798, 0mhfr) <- artists(?x8798, ?x7753), artists(?x8798, ?x6456), artists(?x8798, ?x4568), artists(?x8798, ?x4102), ?x7753 = 03mszl, award_winner(?x4568, ?x506), ?x6456 = 0k1bs, parent_genre(?x8798, ?x505), award_winner(?x5766, ?x4102) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #542 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 06by7; *> query: (?x8798, 016jhr) <- artists(?x8798, ?x7753), artists(?x8798, ?x6456), artists(?x8798, ?x4568), artists(?x8798, ?x4102), ?x7753 = 03mszl, award_winner(?x4568, ?x506), ?x6456 = 0k1bs, parent_genre(?x8798, ?x505), award_winner(?x5766, ?x4102) *> conf = 0.33 ranks of expected_values: 17 EVAL 0gg8l parent_genre! 016jhr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 61.000 52.000 0.333 http://example.org/music/genre/parent_genre #16684-01_c4 PRED entity: 01_c4 PRED relation: contains! PRED expected values: 04jpl => 139 concepts (61 used for prediction) PRED predicted values (max 10 best out of 205): 02jx1 (0.99 #41367, 0.98 #42265, 0.80 #48549), 07ssc (0.78 #6303, 0.71 #5408, 0.67 #49359), 09c7w0 (0.69 #21533, 0.65 #14346, 0.61 #12553), 04jpl (0.66 #15240, 0.66 #15241, 0.65 #54754), 048kw (0.66 #15241, 0.65 #54754, 0.65 #54753), 04_1l0v (0.43 #13001, 0.36 #14794, 0.35 #13898), 02qkt (0.31 #36240, 0.21 #54202, 0.19 #44326), 01_c4 (0.28 #51159, 0.26 #48462, 0.18 #44876), 059rby (0.25 #8082, 0.13 #20652, 0.12 #22445), 0345h (0.17 #29688, 0.17 #31483, 0.13 #10837) >> Best rule #41367 for best value: >> intensional similarity = 5 >> extensional distance = 221 >> proper extension: 022_6; 0crjn65; 0dplh; 0121c1; 0fgj2; 013bqg; 01t21q; 017_4z; 02ly_; 01t38b; ... >> query: (?x9491, 02jx1) <- contains(?x12774, ?x9491), administrative_parent(?x12774, ?x1310), contains(?x12774, ?x4049), ?x4049 = 0nccd, state_province_region(?x13052, ?x12774) >> conf = 0.99 => this is the best rule for 1 predicted values *> Best rule #15240 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 83 *> proper extension: 013d_f; 0s3pw; *> query: (?x9491, ?x362) <- adjoins(?x9491, ?x11049), category(?x9491, ?x134), ?x134 = 08mbj5d, contains(?x362, ?x11049), place_of_birth(?x361, ?x362) *> conf = 0.66 ranks of expected_values: 4 EVAL 01_c4 contains! 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 139.000 61.000 0.987 http://example.org/location/location/contains #16683-0h0jz PRED entity: 0h0jz PRED relation: award_winner! PRED expected values: 027c95y => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 208): 04ljl_l (0.34 #22369, 0.31 #12474, 0.31 #12905), 07cbcy (0.34 #22369, 0.31 #12474, 0.31 #12905), 0f4x7 (0.34 #22369, 0.31 #12474, 0.31 #12905), 0bdwqv (0.34 #22369, 0.31 #12474, 0.31 #12905), 04kxsb (0.34 #22369, 0.31 #12474, 0.31 #12905), 027dtxw (0.34 #22369, 0.31 #12474, 0.31 #12905), 09qv_s (0.34 #22369, 0.31 #12474, 0.31 #12905), 099ck7 (0.34 #22369, 0.31 #12474, 0.31 #12905), 0ck27z (0.16 #5256, 0.15 #5686, 0.13 #2246), 09sb52 (0.13 #4344, 0.12 #9934, 0.12 #11654) >> Best rule #22369 for best value: >> intensional similarity = 2 >> extensional distance = 1507 >> proper extension: 0qf43; 0d_84; 0h1_w; 041h0; 014x77; 0kr5_; 02w0dc0; 012c6x; 0htlr; 019z7q; ... >> query: (?x294, ?x102) <- award(?x294, ?x102), award_winner(?x1903, ?x294) >> conf = 0.34 => this is the best rule for 8 predicted values *> Best rule #157 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 88 *> proper extension: 03pvt; 0bx_q; 01vxqyl; *> query: (?x294, 027c95y) <- award(?x294, ?x102), ?x102 = 04ljl_l *> conf = 0.09 ranks of expected_values: 28 EVAL 0h0jz award_winner! 027c95y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 91.000 91.000 0.336 http://example.org/award/award_category/winners./award/award_honor/award_winner #16682-03j9ml PRED entity: 03j9ml PRED relation: nationality PRED expected values: 09c7w0 => 73 concepts (60 used for prediction) PRED predicted values (max 10 best out of 19): 09c7w0 (0.90 #201, 0.88 #1, 0.78 #1711), 0gx1l (0.32 #2316, 0.30 #5133), 0kpys (0.32 #2316, 0.30 #5133), 02jx1 (0.10 #133, 0.09 #2751, 0.09 #3254), 03_3d (0.10 #106, 0.06 #1515, 0.05 #1213), 07ssc (0.08 #2733, 0.07 #3236, 0.07 #3336), 0d060g (0.08 #913, 0.08 #107, 0.07 #1516), 03rk0 (0.07 #5583, 0.05 #2764, 0.05 #5280), 0f8l9c (0.03 #2941, 0.03 #3042, 0.02 #3743), 03rjj (0.02 #709, 0.02 #1614, 0.02 #1715) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 021yc7p; 053j4w4; 05683cn; >> query: (?x12280, 09c7w0) <- place_of_birth(?x12280, ?x1523), gender(?x12280, ?x514), ?x1523 = 030qb3t >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03j9ml nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 60.000 0.905 http://example.org/people/person/nationality #16681-01rzqj PRED entity: 01rzqj PRED relation: award PRED expected values: 0bdwqv => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 264): 03qbh5 (0.39 #603, 0.23 #1003, 0.14 #3003), 01bgqh (0.39 #443, 0.21 #843, 0.20 #2843), 09sb52 (0.35 #13641, 0.32 #21642, 0.32 #21242), 0cjyzs (0.34 #3705, 0.32 #4505, 0.32 #5305), 01by1l (0.33 #510, 0.20 #2910, 0.19 #910), 03qbnj (0.33 #630, 0.13 #1030, 0.09 #3030), 05pcn59 (0.32 #1281, 0.20 #6881, 0.20 #6481), 05zr6wv (0.32 #1217, 0.16 #5617, 0.16 #6417), 054ks3 (0.28 #540, 0.19 #940, 0.11 #2940), 01ck6h (0.28 #520, 0.17 #920, 0.13 #2120) >> Best rule #603 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 016kkx; >> query: (?x3366, 03qbh5) <- participant(?x4277, ?x3366), award_winner(?x3366, ?x1039), inductee(?x11145, ?x3366) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1770 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 63 *> proper extension: 01h4rj; *> query: (?x3366, 0bdwqv) <- award(?x3366, ?x2071), ?x2071 = 0bdw6t *> conf = 0.25 ranks of expected_values: 14 EVAL 01rzqj award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 106.000 106.000 0.389 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16680-0155w PRED entity: 0155w PRED relation: artists PRED expected values: 03h_fk5 01vv6_6 016s_5 0g824 01f9zw => 55 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1019): 0x3n (0.71 #6434, 0.70 #9396, 0.67 #5447), 019f9z (0.71 #6458, 0.70 #9420, 0.64 #10408), 016376 (0.71 #6790, 0.60 #9752, 0.55 #10740), 0gbwp (0.71 #6237, 0.60 #9199, 0.55 #10187), 024qwq (0.71 #6705, 0.60 #9667, 0.55 #10655), 012z8_ (0.71 #6280, 0.50 #9242, 0.50 #3319), 0g824 (0.67 #5454, 0.50 #9403, 0.45 #10391), 01vxlbm (0.67 #5241, 0.50 #9190, 0.45 #10178), 01wcp_g (0.67 #5013, 0.50 #8962, 0.45 #9950), 0ffgh (0.67 #5511, 0.50 #9460, 0.45 #10448) >> Best rule #6434 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 064t9; 02x8m; 0gywn; >> query: (?x7440, 0x3n) <- artists(?x7440, ?x9848), artists(?x7440, ?x6715), artists(?x7440, ?x5623), artist(?x1954, ?x5623), ?x9848 = 01wk7ql, instrumentalists(?x2048, ?x5623), ?x6715 = 011z3g, parent_genre(?x482, ?x7440), ?x2048 = 018j2 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5454 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 025sc50; 01fm07; *> query: (?x7440, 0g824) <- artists(?x7440, ?x9848), artists(?x7440, ?x6715), artists(?x7440, ?x5623), artists(?x7440, ?x5405), artists(?x7440, ?x366), artist(?x1954, ?x5623), ?x9848 = 01wk7ql, instrumentalists(?x227, ?x5623), ?x6715 = 011z3g, award_winner(?x139, ?x366), ?x5405 = 01vvlyt *> conf = 0.67 ranks of expected_values: 7, 30, 172, 177, 397 EVAL 0155w artists 01f9zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 24.000 0.714 http://example.org/music/genre/artists EVAL 0155w artists 0g824 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 55.000 24.000 0.714 http://example.org/music/genre/artists EVAL 0155w artists 016s_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 24.000 0.714 http://example.org/music/genre/artists EVAL 0155w artists 01vv6_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 55.000 24.000 0.714 http://example.org/music/genre/artists EVAL 0155w artists 03h_fk5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 24.000 0.714 http://example.org/music/genre/artists #16679-038hg PRED entity: 038hg PRED relation: colors! PRED expected values: 02kth6 01nmgc 0d5fb => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 578): 07lx1s (0.50 #2690, 0.40 #3132, 0.36 #4459), 01hjy5 (0.50 #2927, 0.40 #3369, 0.33 #2039), 01jq34 (0.50 #2261, 0.36 #4476, 0.33 #1819), 02vnp2 (0.50 #2527, 0.36 #4742, 0.33 #2085), 0gl6x (0.50 #2986, 0.33 #2098, 0.33 #1209), 0k__z (0.50 #2482, 0.33 #2040, 0.33 #1331), 021996 (0.50 #2929, 0.33 #2041, 0.33 #708), 02607j (0.50 #2744, 0.33 #1856, 0.33 #523), 02nq10 (0.50 #2514, 0.33 #2072, 0.33 #1183), 04cnp4 (0.50 #2485, 0.33 #2043, 0.33 #1154) >> Best rule #2690 for best value: >> intensional similarity = 32 >> extensional distance = 2 >> proper extension: 01g5v; >> query: (?x8047, 07lx1s) <- colors(?x12541, ?x8047), colors(?x10847, ?x8047), colors(?x12157, ?x8047), colors(?x11963, ?x8047), colors(?x9768, ?x8047), colors(?x9108, ?x8047), colors(?x1675, ?x8047), position(?x10847, ?x60), currency(?x12157, ?x170), major_field_of_study(?x9768, ?x1154), institution(?x865, ?x9768), citytown(?x9768, ?x5174), student(?x1675, ?x1875), category(?x9768, ?x134), major_field_of_study(?x11963, ?x947), major_field_of_study(?x12157, ?x4268), major_field_of_study(?x1675, ?x254), student(?x11963, ?x361), school(?x4171, ?x1675), team(?x2918, ?x12541), institution(?x8398, ?x1675), institution(?x734, ?x1675), school_type(?x11963, ?x3092), contains(?x94, ?x12157), ?x8398 = 028dcg, student(?x4268, ?x906), ?x9108 = 01v3k2, ?x734 = 04zx3q1, major_field_of_study(?x373, ?x4268), ?x254 = 02h40lc, team(?x13270, ?x12541), ?x373 = 02vxn >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #914 for first EXPECTED value: *> intensional similarity = 37 *> extensional distance = 1 *> proper extension: 019sc; *> query: (?x8047, 02kth6) <- colors(?x10847, ?x8047), colors(?x3702, ?x8047), colors(?x13029, ?x8047), colors(?x12157, ?x8047), colors(?x11963, ?x8047), colors(?x9912, ?x8047), colors(?x9768, ?x8047), colors(?x9570, ?x8047), colors(?x1675, ?x8047), position(?x10847, ?x60), currency(?x12157, ?x170), major_field_of_study(?x9768, ?x11820), institution(?x865, ?x9768), citytown(?x9768, ?x5174), student(?x1675, ?x1875), category(?x9768, ?x134), ?x11963 = 01bzs9, ?x11820 = 0w7s, organization(?x346, ?x1675), major_field_of_study(?x1675, ?x8221), major_field_of_study(?x1675, ?x6756), institution(?x1390, ?x12157), ?x8221 = 037mh8, child(?x3913, ?x13029), team(?x208, ?x3702), sport(?x10847, ?x471), institution(?x1200, ?x1675), contains(?x94, ?x9768), ?x9912 = 01p896, school(?x2820, ?x1675), major_field_of_study(?x8363, ?x6756), major_field_of_study(?x1667, ?x6756), ?x1667 = 03v6t, ?x346 = 060c4, ?x1200 = 016t_3, ?x8363 = 0k__z, currency(?x9570, ?x2244) *> conf = 0.33 ranks of expected_values: 89, 232 EVAL 038hg colors! 0d5fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 18.000 18.000 0.500 http://example.org/education/educational_institution/colors EVAL 038hg colors! 01nmgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 18.000 18.000 0.500 http://example.org/education/educational_institution/colors EVAL 038hg colors! 02kth6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 18.000 18.000 0.500 http://example.org/education/educational_institution/colors #16678-0d6lp PRED entity: 0d6lp PRED relation: citytown! PRED expected values: 0kc6x 027mdh 01xk7r 06p8m 06nfl => 241 concepts (215 used for prediction) PRED predicted values (max 10 best out of 704): 03qbm (0.38 #33574, 0.38 #48759, 0.33 #30376), 0dmtp (0.38 #33574, 0.38 #48759, 0.33 #30376), 01nds (0.16 #24549, 0.16 #10161, 0.16 #28545), 05cl8y (0.14 #5209, 0.12 #1210, 0.11 #21992), 049ql1 (0.12 #2178, 0.12 #1379, 0.11 #10172), 03d6fyn (0.12 #1793, 0.12 #994, 0.07 #5792), 0lk0l (0.12 #2290, 0.12 #1491, 0.07 #6289), 041pnt (0.12 #2243, 0.12 #1444, 0.07 #6242), 03sb38 (0.12 #1951, 0.12 #1152, 0.07 #5950), 09glbnt (0.12 #1079, 0.11 #9872, 0.09 #12269) >> Best rule #33574 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0gp5l6; >> query: (?x3125, ?x6404) <- country(?x3125, ?x94), citytown(?x1168, ?x3125), place_founded(?x6404, ?x3125) >> conf = 0.38 => this is the best rule for 2 predicted values *> Best rule #8001 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 04qdj; 0fw3f; *> query: (?x3125, 0kc6x) <- location_of_ceremony(?x6400, ?x3125), influenced_by(?x2161, ?x6400), religion(?x6400, ?x2694) *> conf = 0.06 ranks of expected_values: 235, 250, 421 EVAL 0d6lp citytown! 06nfl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 241.000 215.000 0.379 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 0d6lp citytown! 06p8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 241.000 215.000 0.379 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 0d6lp citytown! 01xk7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 241.000 215.000 0.379 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 0d6lp citytown! 027mdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 241.000 215.000 0.379 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 0d6lp citytown! 0kc6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 241.000 215.000 0.379 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #16677-04kj2v PRED entity: 04kj2v PRED relation: film_production_design_by! PRED expected values: 025twgf => 86 concepts (81 used for prediction) PRED predicted values (max 10 best out of 167): 0fsw_7 (0.58 #323, 0.52 #809, 0.47 #486), 04954r (0.58 #323, 0.52 #809, 0.47 #486), 02n72k (0.17 #324, 0.07 #273, 0.05 #436), 014kq6 (0.17 #324, 0.07 #196, 0.05 #359), 0g5pv3 (0.17 #324, 0.07 #180, 0.05 #343), 02qrv7 (0.17 #324, 0.07 #179, 0.05 #342), 02sg5v (0.17 #324, 0.07 #171, 0.05 #334), 025twgt (0.17 #324), 0fztbq (0.17 #324), 0g5pvv (0.17 #324) >> Best rule #323 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0fqjks; >> query: (?x2507, ?x2111) <- nominated_for(?x2507, ?x2111), film_production_design_by(?x2506, ?x2507), nominated_for(?x2506, ?x835) >> conf = 0.58 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 04kj2v film_production_design_by! 025twgf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 81.000 0.581 http://example.org/film/film/film_production_design_by #16676-03975z PRED entity: 03975z PRED relation: gender PRED expected values: 05zppz => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #5, 0.91 #17, 0.90 #3), 02zsn (0.31 #70, 0.30 #112, 0.29 #76) >> Best rule #5 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 02lfp4; 02zft0; 0163r3; >> query: (?x9396, 05zppz) <- award(?x9396, ?x1443), award(?x9396, ?x1323), ?x1323 = 0gqz2, nominated_for(?x9396, ?x697), ?x1443 = 054krc >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03975z gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.912 http://example.org/people/person/gender #16675-01hhvg PRED entity: 01hhvg PRED relation: school! PRED expected values: 02r6gw6 => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 20): 02qw1zx (0.25 #125, 0.21 #265, 0.16 #105), 0f4vx0 (0.24 #271, 0.23 #111, 0.19 #91), 025tn92 (0.19 #93, 0.19 #133, 0.16 #273), 03nt7j (0.19 #27, 0.16 #107, 0.15 #47), 09th87 (0.19 #135, 0.14 #661, 0.12 #35), 09l0x9 (0.16 #272, 0.12 #132, 0.10 #112), 05vsb7 (0.16 #101, 0.15 #261, 0.12 #121), 092j54 (0.16 #129, 0.13 #269, 0.12 #449), 038981 (0.14 #661, 0.12 #76, 0.06 #136), 038c0q (0.14 #661, 0.10 #266, 0.09 #126) >> Best rule #125 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 02hp70; >> query: (?x946, 02qw1zx) <- school_type(?x946, ?x1507), institution(?x1771, ?x946), ?x1507 = 01_9fk, ?x1771 = 019v9k >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #661 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 173 *> proper extension: 0frm7n; *> query: (?x946, ?x2569) <- school(?x2820, ?x946), category(?x946, ?x134), school(?x2820, ?x10945), draft(?x2820, ?x2569), school_type(?x10945, ?x1044) *> conf = 0.14 ranks of expected_values: 14 EVAL 01hhvg school! 02r6gw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 172.000 172.000 0.250 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #16674-031ldd PRED entity: 031ldd PRED relation: film_release_region PRED expected values: 0d0vqn 059j2 => 118 concepts (115 used for prediction) PRED predicted values (max 10 best out of 164): 09c7w0 (0.94 #2201, 0.94 #1863, 0.93 #10156), 0d0vqn (0.90 #4575, 0.90 #3730, 0.89 #1532), 0chghy (0.89 #1706, 0.85 #2044, 0.81 #3735), 05r4w (0.88 #1524, 0.85 #3722, 0.84 #4567), 059j2 (0.86 #1732, 0.86 #1563, 0.85 #2070), 0345h (0.86 #1734, 0.82 #2410, 0.79 #6299), 03rjj (0.86 #3727, 0.85 #2036, 0.84 #4572), 06mkj (0.84 #6326, 0.84 #2099, 0.84 #4635), 07ssc (0.82 #1544, 0.82 #2051, 0.78 #1713), 0k6nt (0.82 #3246, 0.81 #3753, 0.80 #2062) >> Best rule #2201 for best value: >> intensional similarity = 5 >> extensional distance = 88 >> proper extension: 0gj9qxr; 04q00lw; 02dpl9; 0ddcbd5; 043sct5; 0db94w; 01jwxx; 04nm0n0; 02qsqmq; 065_cjc; ... >> query: (?x6014, 09c7w0) <- titles(?x2645, ?x6014), film_release_distribution_medium(?x6014, ?x81), film_crew_role(?x6014, ?x137), film_release_region(?x6014, ?x142), film_release_region(?x80, ?x2645) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #4575 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 202 *> proper extension: 0bq8tmw; *> query: (?x6014, 0d0vqn) <- film_release_region(?x6014, ?x2645), film_release_region(?x6014, ?x252), nominated_for(?x9217, ?x6014), ?x2645 = 03h64, film_release_region(?x3000, ?x252) *> conf = 0.90 ranks of expected_values: 2, 5 EVAL 031ldd film_release_region 059j2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 118.000 115.000 0.944 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 031ldd film_release_region 0d0vqn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 115.000 0.944 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16673-064xp PRED entity: 064xp PRED relation: place_of_birth! PRED expected values: 02b25y => 184 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1850): 01m4yn (0.33 #1417, 0.17 #11866, 0.14 #17090), 0b_c7 (0.33 #300, 0.17 #10749, 0.14 #15973), 028p0 (0.33 #211, 0.17 #10660, 0.14 #15884), 05fh2 (0.33 #2426, 0.17 #12875, 0.14 #18099), 01t_z (0.25 #4595, 0.05 #30718, 0.02 #59452), 01ty4 (0.25 #5019, 0.02 #54651, 0.02 #59876), 06b4wb (0.25 #5004, 0.02 #54636, 0.02 #59861), 0gppg (0.25 #4680, 0.02 #54312, 0.02 #59537), 016kft (0.25 #4560, 0.02 #54192, 0.02 #59417), 0q9t7 (0.25 #4368, 0.02 #54000, 0.02 #59225) >> Best rule #1417 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 031y2; >> query: (?x13374, 01m4yn) <- place_of_birth(?x9903, ?x13374), time_zones(?x13374, ?x2864), contains(?x6408, ?x13374), ?x6408 = 07kg3, category(?x13374, ?x134) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #67920 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 72 *> proper extension: 0jcg8; 0cxgc; 012wyq; 01llj3; 09ctj; 0dj0x; *> query: (?x13374, ?x101) <- place_of_birth(?x9903, ?x13374), contains(?x13374, ?x6784), nationality(?x9903, ?x205), featured_film_locations(?x303, ?x205), nationality(?x101, ?x205) *> conf = 0.01 ranks of expected_values: 1677 EVAL 064xp place_of_birth! 02b25y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 184.000 30.000 0.333 http://example.org/people/person/place_of_birth #16672-0124ld PRED entity: 0124ld PRED relation: olympics! PRED expected values: 0h7x => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 457): 07ssc (0.70 #3489, 0.65 #2883, 0.64 #3692), 0345h (0.69 #2451, 0.69 #2286, 0.67 #1624), 0f8l9c (0.62 #4705, 0.62 #3290, 0.61 #4906), 0d060g (0.62 #4705, 0.61 #4906, 0.55 #604), 02vzc (0.55 #604, 0.50 #1687, 0.50 #606), 059j2 (0.55 #604, 0.48 #3300, 0.46 #2284), 06bnz (0.53 #1627, 0.42 #1683, 0.36 #2509), 04g5k (0.50 #1754, 0.43 #2580, 0.40 #1339), 04j53 (0.50 #1712, 0.43 #2538, 0.40 #1297), 06mkj (0.44 #2723, 0.43 #2520, 0.41 #3128) >> Best rule #3489 for best value: >> intensional similarity = 32 >> extensional distance = 21 >> proper extension: 0l6mp; 016r9z; 0jdk_; 018ljb; >> query: (?x7429, 07ssc) <- olympics(?x1264, ?x7429), olympics(?x279, ?x7429), sports(?x7429, ?x453), olympics(?x453, ?x418), sport(?x12734, ?x453), sport(?x8892, ?x453), country(?x453, ?x2188), country(?x453, ?x774), ?x774 = 06mzp, ?x279 = 0d060g, team(?x2918, ?x8892), colors(?x12734, ?x332), ?x2188 = 0163v, film_release_region(?x9194, ?x1264), film_release_region(?x7887, ?x1264), film_release_region(?x7629, ?x1264), film_release_region(?x2094, ?x1264), film_release_region(?x1803, ?x1264), featured_film_locations(?x2423, ?x1264), ?x9194 = 0fpgp26, titles(?x1264, ?x2434), country(?x1646, ?x1264), contains(?x1264, ?x196), country(?x150, ?x1264), nationality(?x380, ?x1264), country(?x136, ?x1264), ?x2094 = 05z7c, ?x7629 = 02825nf, genre(?x7887, ?x53), location(?x1221, ?x1264), ?x1803 = 0g9wdmc, film_release_region(?x1133, ?x1264) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4343 for first EXPECTED value: *> intensional similarity = 34 *> extensional distance = 25 *> proper extension: 0lbbj; *> query: (?x7429, 0h7x) <- olympics(?x94, ?x7429), sports(?x7429, ?x453), olympics(?x453, ?x418), sport(?x8541, ?x453), country(?x453, ?x774), film_release_region(?x7680, ?x774), film_release_region(?x7493, ?x774), film_release_region(?x6178, ?x774), film_release_region(?x3392, ?x774), film_release_region(?x3076, ?x774), film_release_region(?x2163, ?x774), film_release_region(?x1259, ?x774), film_release_region(?x1219, ?x774), film_release_region(?x124, ?x774), ?x7680 = 0gh6j94, contains(?x774, ?x1220), athlete(?x453, ?x11825), teams(?x9417, ?x8541), ?x94 = 09c7w0, ?x124 = 0g56t9t, official_language(?x774, ?x90), ?x1259 = 04hwbq, ?x2163 = 0j6b5, region(?x1315, ?x774), adjoins(?x9632, ?x774), ?x3392 = 0jwmp, olympics(?x774, ?x1608), teams(?x774, ?x11564), participating_countries(?x7429, ?x512), ?x1219 = 03bx2lk, ?x1608 = 09x3r, ?x7493 = 0btpm6, ?x3076 = 0g5838s, ?x6178 = 02v_r7d *> conf = 0.33 ranks of expected_values: 15 EVAL 0124ld olympics! 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 25.000 25.000 0.696 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #16671-0344gc PRED entity: 0344gc PRED relation: film! PRED expected values: 013w7j => 85 concepts (48 used for prediction) PRED predicted values (max 10 best out of 722): 01fh9 (0.65 #60370, 0.65 #70782, 0.64 #66617), 02yplc (0.33 #739, 0.03 #11144, 0.01 #13225), 02lf1j (0.33 #429, 0.01 #25402), 019vgs (0.33 #660, 0.01 #8984, 0.01 #11065), 05cx7x (0.33 #1300), 012q4n (0.25 #3217, 0.01 #15703, 0.01 #19866), 0169dl (0.12 #2482, 0.08 #10806, 0.03 #12887), 0jfx1 (0.12 #2486, 0.05 #14972, 0.04 #64534), 01wy5m (0.12 #2938, 0.04 #64534, 0.04 #11262), 01r93l (0.12 #2828, 0.04 #64534, 0.03 #15314) >> Best rule #60370 for best value: >> intensional similarity = 3 >> extensional distance = 749 >> proper extension: 025x1t; 0gxsh4; 06ys2; >> query: (?x898, ?x971) <- nominated_for(?x971, ?x898), participant(?x970, ?x971), award_winner(?x618, ?x971) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0344gc film! 013w7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 48.000 0.654 http://example.org/film/actor/film./film/performance/film #16670-03yxwq PRED entity: 03yxwq PRED relation: production_companies! PRED expected values: 03h3x5 => 143 concepts (128 used for prediction) PRED predicted values (max 10 best out of 1227): 06_wqk4 (0.33 #7010, 0.33 #1245, 0.27 #10466), 02q0k7v (0.33 #2011, 0.25 #3163, 0.22 #7776), 08gsvw (0.25 #2388, 0.21 #16218, 0.12 #32347), 02ylg6 (0.25 #2914, 0.20 #8679, 0.17 #1762), 02tgz4 (0.25 #3287, 0.20 #9052, 0.17 #2135), 03mh_tp (0.25 #2653, 0.16 #38374, 0.15 #52200), 03459x (0.25 #2694, 0.11 #26893, 0.10 #9611), 03rtz1 (0.22 #7038, 0.18 #10494, 0.17 #1273), 04tc1g (0.22 #7014, 0.18 #10470, 0.17 #1249), 01cssf (0.21 #16199, 0.19 #19655, 0.18 #23111) >> Best rule #7010 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 07733f; >> query: (?x6948, 06_wqk4) <- child(?x382, ?x6948), ?x382 = 086k8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #9511 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 018p5f; *> query: (?x6948, 03h3x5) <- category(?x6948, ?x134), award_winner(?x3486, ?x6948), ?x134 = 08mbj5d, place_founded(?x6948, ?x682) *> conf = 0.10 ranks of expected_values: 556 EVAL 03yxwq production_companies! 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 143.000 128.000 0.333 http://example.org/film/film/production_companies #16669-0fkx3 PRED entity: 0fkx3 PRED relation: jurisdiction_of_office PRED expected values: 0d05w3 0165b 0847q => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 746): 0hzlz (0.98 #1852, 0.88 #465, 0.77 #4648), 0f8l9c (0.98 #1852, 0.88 #465, 0.50 #6985), 03rj0 (0.98 #1852, 0.88 #465, 0.50 #6985), 035qy (0.98 #1852, 0.88 #465, 0.50 #6985), 03shp (0.98 #1852, 0.88 #465, 0.50 #6985), 01nqj (0.98 #1852, 0.88 #465, 0.50 #6985), 019rg5 (0.98 #1852, 0.88 #465, 0.50 #6985), 035dk (0.98 #1852, 0.88 #465, 0.50 #6985), 0345h (0.98 #1852, 0.88 #465, 0.50 #6985), 03rt9 (0.98 #1852, 0.88 #465, 0.50 #6985) >> Best rule #1852 for best value: >> intensional similarity = 29 >> extensional distance = 1 >> proper extension: 0fkvn; >> query: (?x14293, ?x47) <- basic_title(?x9548, ?x14293), religion(?x9548, ?x1985), jurisdiction_of_office(?x14293, ?x9494), jurisdiction_of_office(?x14293, ?x6842), jurisdiction_of_office(?x14293, ?x901), ?x1985 = 0c8wxp, state(?x2474, ?x6842), ?x901 = 07cfx, time_zones(?x6842, ?x2674), adjoins(?x728, ?x6842), adjoins(?x335, ?x6842), ?x728 = 059f4, basic_title(?x9548, ?x346), company(?x346, ?x94), jurisdiction_of_office(?x346, ?x47), state_province_region(?x481, ?x6842), basic_title(?x11290, ?x346), basic_title(?x1159, ?x346), ?x9494 = 0chgr2, colors(?x481, ?x663), major_field_of_study(?x481, ?x254), ?x11290 = 042kg, ?x1159 = 083q7, student(?x481, ?x2319), contains(?x279, ?x481), ?x335 = 059rby, participant(?x406, ?x9548), gender(?x9548, ?x231), category(?x481, ?x134) >> conf = 0.98 => this is the best rule for 147 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 24, 168, 221 EVAL 0fkx3 jurisdiction_of_office 0847q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 23.000 23.000 0.976 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkx3 jurisdiction_of_office 0165b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 23.000 23.000 0.976 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0fkx3 jurisdiction_of_office 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 23.000 23.000 0.976 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #16668-0bpm4yw PRED entity: 0bpm4yw PRED relation: costume_design_by PRED expected values: 03y1mlp => 58 concepts (42 used for prediction) PRED predicted values (max 10 best out of 14): 03y1mlp (0.17 #58, 0.06 #86, 0.05 #142), 03mfqm (0.06 #102, 0.05 #158, 0.05 #130), 02h1rt (0.05 #182, 0.04 #210, 0.03 #239), 0bytfv (0.03 #236, 0.03 #265, 0.02 #521), 05xf75 (0.02 #225, 0.02 #254, 0.01 #826), 01wy5m (0.02 #225, 0.02 #254, 0.01 #826), 02vntj (0.02 #225, 0.02 #254, 0.01 #826), 07lt7b (0.02 #225, 0.02 #254, 0.01 #826), 02cqbx (0.02 #526, 0.01 #497, 0.01 #871), 02mxbd (0.02 #556, 0.02 #498, 0.01 #527) >> Best rule #58 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 02qk3fk; >> query: (?x4336, 03y1mlp) <- film_release_region(?x4336, ?x5482), film_release_region(?x4336, ?x1790), ?x1790 = 01pj7, film_distribution_medium(?x4336, ?x81), ?x5482 = 04g5k >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bpm4yw costume_design_by 03y1mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 42.000 0.167 http://example.org/film/film/costume_design_by #16667-0bh8drv PRED entity: 0bh8drv PRED relation: country PRED expected values: 07ssc => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 57): 07ssc (0.87 #17, 0.32 #1737, 0.26 #568), 09c7w0 (0.80 #1415, 0.80 #1292, 0.79 #3009), 0f8l9c (0.26 #20, 0.15 #142, 0.12 #2107), 0345h (0.17 #272, 0.15 #824, 0.15 #579), 07s9rl0 (0.12 #1782, 0.06 #2580, 0.06 #5029), 03rjj (0.09 #7, 0.04 #558, 0.04 #1727), 0d060g (0.08 #253, 0.07 #131, 0.06 #2281), 0chghy (0.05 #932, 0.05 #319, 0.05 #748), 03h64 (0.05 #108, 0.04 #598, 0.04 #660), 059j2 (0.04 #27, 0.02 #3803, 0.01 #149) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 02w9k1c; >> query: (?x7516, 07ssc) <- genre(?x7516, ?x53), nominated_for(?x941, ?x7516), ?x941 = 0fq9zdn, nominated_for(?x5591, ?x7516) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bh8drv country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.870 http://example.org/film/film/country #16666-0dznvw PRED entity: 0dznvw PRED relation: award_winner PRED expected values: 06g60w 05683cn 0dg3jz => 33 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1359): 076lxv (0.50 #92, 0.30 #1626, 0.21 #6232), 09r9m7 (0.50 #900, 0.30 #2434, 0.17 #7040), 05qd_ (0.50 #118, 0.20 #1652, 0.18 #3069), 0gl88b (0.40 #1822, 0.25 #288, 0.17 #4893), 076psv (0.30 #2216, 0.25 #6822, 0.25 #682), 0579tg2 (0.30 #3043, 0.25 #1509, 0.12 #7649), 01vvdm (0.30 #2105, 0.16 #3641, 0.13 #5176), 02sj1x (0.25 #524, 0.21 #6664, 0.20 #2058), 012vct (0.25 #1070, 0.20 #2604, 0.18 #3069), 072twv (0.25 #343, 0.20 #1877, 0.17 #6483) >> Best rule #92 for best value: >> intensional similarity = 14 >> extensional distance = 2 >> proper extension: 0c53zb; >> query: (?x11428, 076lxv) <- ceremony(?x1323, ?x11428), ceremony(?x1313, ?x11428), ceremony(?x1307, ?x11428), ceremony(?x591, ?x11428), award_winner(?x11428, ?x5611), award_winner(?x11428, ?x2110), ?x591 = 0f4x7, ?x5611 = 02cqbx, costume_design_by(?x5183, ?x2110), ?x1323 = 0gqz2, ?x1313 = 0gs9p, honored_for(?x11428, ?x4841), place_of_death(?x2110, ?x1523), ?x1307 = 0gq9h >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #26119 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 66 *> proper extension: 0hr3c8y; 09qvms; 0h_cssd; 092t4b; 058m5m4; 027hjff; 092_25; 03gyp30; 09g90vz; 0g55tzk; *> query: (?x11428, ?x199) <- ceremony(?x591, ?x11428), award_winner(?x11428, ?x8401), nominated_for(?x591, ?x54), award(?x6426, ?x591), award(?x1922, ?x591), ?x6426 = 01tt43d, ?x1922 = 03mg35, award_winner(?x591, ?x157), award_nominee(?x199, ?x8401) *> conf = 0.10 ranks of expected_values: 55, 66, 290 EVAL 0dznvw award_winner 0dg3jz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 33.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0dznvw award_winner 05683cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 33.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0dznvw award_winner 06g60w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 33.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #16665-05bnp0 PRED entity: 05bnp0 PRED relation: people! PRED expected values: 048z7l => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 46): 033tf_ (0.25 #81, 0.20 #306, 0.18 #381), 07bch9 (0.25 #22, 0.10 #322, 0.06 #247), 041rx (0.24 #2178, 0.24 #2103, 0.21 #3078), 0x67 (0.18 #3084, 0.16 #4210, 0.14 #2184), 02w7gg (0.15 #302, 0.12 #227, 0.12 #77), 07hwkr (0.13 #536, 0.11 #686, 0.09 #611), 022dp5 (0.12 #123, 0.04 #573, 0.03 #648), 063k3h (0.11 #180, 0.03 #705, 0.03 #555), 01336l (0.11 #190, 0.03 #565, 0.02 #640), 03295l (0.11 #173) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02vg0; >> query: (?x123, 033tf_) <- award(?x123, ?x102), film(?x123, ?x3601), ?x3601 = 0830vk >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1239 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 310 *> proper extension: 017f4y; *> query: (?x123, 048z7l) <- participant(?x123, ?x1017), student(?x122, ?x123) *> conf = 0.04 ranks of expected_values: 23 EVAL 05bnp0 people! 048z7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 102.000 102.000 0.250 http://example.org/people/ethnicity/people #16664-0bkj86 PRED entity: 0bkj86 PRED relation: student PRED expected values: 01w8sf 0969fd => 25 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1297): 014vk4 (0.50 #3012, 0.33 #3442, 0.33 #1285), 02v406 (0.40 #2027, 0.33 #732, 0.33 #300), 083q7 (0.40 #2396, 0.33 #455, 0.25 #1534), 01mr2g6 (0.40 #2525, 0.33 #584, 0.25 #1663), 06y7d (0.33 #3010, 0.33 #1283, 0.33 #637), 01tdnyh (0.33 #2908, 0.33 #1181, 0.33 #535), 0969fd (0.33 #2997, 0.33 #1270, 0.33 #624), 01hbq0 (0.33 #3017, 0.33 #860, 0.33 #644), 02r34n (0.33 #2829, 0.33 #672, 0.33 #456), 04pp9s (0.33 #2984, 0.33 #827, 0.33 #611) >> Best rule #3012 for best value: >> intensional similarity = 24 >> extensional distance = 4 >> proper extension: 013zdg; >> query: (?x1526, 014vk4) <- institution(?x1526, ?x12489), institution(?x1526, ?x11244), institution(?x1526, ?x8706), institution(?x1526, ?x8354), institution(?x1526, ?x6177), institution(?x1526, ?x4338), institution(?x1526, ?x581), major_field_of_study(?x1526, ?x12035), major_field_of_study(?x1526, ?x3878), colors(?x8354, ?x663), major_field_of_study(?x12035, ?x1682), currency(?x8354, ?x170), ?x581 = 06pwq, ?x8706 = 0trv, student(?x1526, ?x476), company(?x3970, ?x12489), school_type(?x11244, ?x3092), school(?x1161, ?x6177), award_winner(?x575, ?x476), ?x4338 = 0bqxw, major_field_of_study(?x196, ?x3878), major_field_of_study(?x1667, ?x1682), category(?x11244, ?x134), contains(?x94, ?x11244) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2997 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 4 *> proper extension: 013zdg; *> query: (?x1526, 0969fd) <- institution(?x1526, ?x12489), institution(?x1526, ?x11244), institution(?x1526, ?x8706), institution(?x1526, ?x8354), institution(?x1526, ?x6177), institution(?x1526, ?x4338), institution(?x1526, ?x581), major_field_of_study(?x1526, ?x12035), major_field_of_study(?x1526, ?x3878), colors(?x8354, ?x663), major_field_of_study(?x12035, ?x1682), currency(?x8354, ?x170), ?x581 = 06pwq, ?x8706 = 0trv, student(?x1526, ?x476), company(?x3970, ?x12489), school_type(?x11244, ?x3092), school(?x1161, ?x6177), award_winner(?x575, ?x476), ?x4338 = 0bqxw, major_field_of_study(?x196, ?x3878), major_field_of_study(?x1667, ?x1682), category(?x11244, ?x134), contains(?x94, ?x11244) *> conf = 0.33 ranks of expected_values: 7, 95 EVAL 0bkj86 student 0969fd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 25.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 0bkj86 student 01w8sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 25.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #16663-0czyxs PRED entity: 0czyxs PRED relation: genre PRED expected values: 07s9rl0 => 105 concepts (39 used for prediction) PRED predicted values (max 10 best out of 110): 07s9rl0 (0.93 #4160, 0.90 #4276, 0.86 #2888), 01hmnh (0.75 #244, 0.67 #1169, 0.40 #129), 05p553 (0.61 #3007, 0.43 #580, 0.37 #4047), 03k9fj (0.47 #2435, 0.46 #2667, 0.46 #2782), 02l7c8 (0.38 #2553, 0.33 #2900, 0.32 #2323), 04xvlr (0.30 #2889, 0.29 #2542, 0.25 #4161), 082gq (0.25 #27, 0.22 #373, 0.22 #4302), 06l3bl (0.25 #35, 0.22 #381, 0.10 #956), 04xvh5 (0.25 #31, 0.12 #2918, 0.11 #377), 01j1n2 (0.25 #56, 0.11 #402, 0.07 #632) >> Best rule #4160 for best value: >> intensional similarity = 7 >> extensional distance = 196 >> proper extension: 0g22z; 0140g4; 01jc6q; 0c0yh4; 0yyg4; 0n0bp; 0jzw; 0m_mm; 0gjk1d; 026390q; ... >> query: (?x383, 07s9rl0) <- genre(?x383, ?x1509), produced_by(?x383, ?x7848), films(?x7455, ?x383), genre(?x8791, ?x1509), genre(?x6704, ?x1509), ?x6704 = 02wyzmv, ?x8791 = 0cqr0q >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0czyxs genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 39.000 0.929 http://example.org/film/film/genre #16662-0jhjl PRED entity: 0jhjl PRED relation: major_field_of_study PRED expected values: 01mkq => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 110): 02j62 (0.34 #1024, 0.28 #4500, 0.28 #4128), 01mkq (0.31 #760, 0.30 #512, 0.29 #1008), 02lp1 (0.28 #1004, 0.26 #508, 0.26 #756), 062z7 (0.24 #1021, 0.21 #4497, 0.21 #4746), 04rjg (0.24 #1013, 0.22 #517, 0.22 #765), 0g26h (0.22 #1036, 0.18 #4512, 0.18 #4761), 03g3w (0.22 #1020, 0.21 #4496, 0.21 #1764), 05qjt (0.22 #1000, 0.19 #752, 0.18 #1744), 0_jm (0.18 #1052, 0.14 #2292, 0.13 #3534), 0fdys (0.17 #785, 0.16 #537, 0.14 #289) >> Best rule #1024 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 01nmgc; >> query: (?x9409, 02j62) <- institution(?x865, ?x9409), contains(?x206, ?x9409), ?x865 = 02h4rq6 >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #760 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 158 *> proper extension: 0p4wb; 0300cp; 03d6fyn; 02d6ph; 05w3y; 019rl6; 04f0xq; 06q07; 01qygl; 0k9ts; ... *> query: (?x9409, 01mkq) <- list(?x9409, ?x2197), list(?x6505, ?x2197), organization(?x5510, ?x6505) *> conf = 0.31 ranks of expected_values: 2 EVAL 0jhjl major_field_of_study 01mkq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 56.000 0.336 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16661-0h7x PRED entity: 0h7x PRED relation: religion PRED expected values: 0c8wxp => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 36): 0c8wxp (0.46 #1664, 0.41 #1916, 0.39 #1952), 01lp8 (0.42 #1659, 0.35 #1911, 0.35 #1947), 051kv (0.41 #1663, 0.34 #1915, 0.34 #1951), 019cr (0.41 #1669, 0.34 #1921, 0.34 #1957), 0631_ (0.39 #1666, 0.33 #1918, 0.33 #1954), 05sfs (0.38 #1661, 0.32 #1913, 0.31 #1949), 04pk9 (0.38 #1677, 0.32 #1929, 0.31 #1965), 05w5d (0.36 #1681, 0.31 #1933, 0.30 #1969), 01y0s9 (0.30 #1667, 0.25 #1919, 0.25 #1955), 0flw86 (0.29 #218, 0.29 #362, 0.25 #326) >> Best rule #1664 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 05j49; 09b69; >> query: (?x1355, 0c8wxp) <- contains(?x455, ?x1355), contains(?x1355, ?x863), partially_contains(?x1355, ?x8154) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h7x religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.459 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #16660-02f8zw PRED entity: 02f8zw PRED relation: school_type PRED expected values: 05jxkf => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 17): 05jxkf (0.44 #76, 0.42 #196, 0.42 #750), 05pcjw (0.27 #73, 0.20 #361, 0.19 #627), 01rs41 (0.22 #631, 0.21 #365, 0.20 #655), 07tf8 (0.17 #81, 0.15 #201, 0.14 #105), 01_9fk (0.12 #194, 0.10 #74, 0.09 #362), 02p0qmm (0.07 #58, 0.05 #106, 0.04 #34), 01_srz (0.05 #363, 0.04 #677, 0.04 #459), 01y64 (0.03 #324, 0.02 #492, 0.02 #156), 01jlsn (0.03 #329, 0.03 #497, 0.02 #571), 0m4mb (0.02 #491, 0.02 #565, 0.01 #323) >> Best rule #76 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 08815; 01jssp; 052nd; 06pwq; 01w3v; 0277jc; 01j_cy; 07szy; 09kvv; 0bx8pn; ... >> query: (?x7661, 05jxkf) <- institution(?x2636, ?x7661), organization(?x4095, ?x7661), ?x2636 = 027f2w >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02f8zw school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.444 http://example.org/education/educational_institution/school_type #16659-02ndj5 PRED entity: 02ndj5 PRED relation: artists! PRED expected values: 01gbcf 01243b 01pfpt => 102 concepts (35 used for prediction) PRED predicted values (max 10 best out of 260): 08vlns (0.70 #1372, 0.04 #7279, 0.03 #7573), 05bt6j (0.64 #9773, 0.40 #1216, 0.33 #1510), 03_d0 (0.55 #9154, 0.43 #894, 0.25 #306), 016clz (0.51 #2362, 0.48 #1477, 0.46 #1772), 02lnbg (0.50 #1232, 0.16 #2707, 0.14 #9493), 02yv6b (0.41 #5107, 0.39 #2152, 0.25 #1858), 0233qs (0.40 #1390, 0.02 #7297, 0.01 #9651), 03ckfl9 (0.33 #151, 0.29 #1034, 0.09 #4872), 0grjmv (0.33 #134, 0.25 #4721, 0.23 #5902), 0m0jc (0.33 #9, 0.25 #4721, 0.23 #5902) >> Best rule #1372 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 03f0qd7; >> query: (?x9841, 08vlns) <- artists(?x9225, ?x9841), category(?x9841, ?x134), artist(?x2931, ?x9841), ?x9225 = 0g293 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 06br6t; *> query: (?x9841, 01243b) <- artists(?x9063, ?x9841), artists(?x2809, ?x9841), artists(?x497, ?x9841), group(?x227, ?x9841), ?x9063 = 0cx7f, ?x497 = 0fd3y, ?x2809 = 05w3f *> conf = 0.33 ranks of expected_values: 11, 32, 50 EVAL 02ndj5 artists! 01pfpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 102.000 35.000 0.700 http://example.org/music/genre/artists EVAL 02ndj5 artists! 01243b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 102.000 35.000 0.700 http://example.org/music/genre/artists EVAL 02ndj5 artists! 01gbcf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 102.000 35.000 0.700 http://example.org/music/genre/artists #16658-01zfzb PRED entity: 01zfzb PRED relation: film! PRED expected values: 02js6_ => 100 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1003): 05dtsb (0.17 #1177, 0.06 #3259, 0.01 #9502), 0dpqk (0.17 #895, 0.05 #5058, 0.04 #7139), 07r1h (0.17 #1090, 0.04 #7334, 0.02 #13577), 016fjj (0.17 #635, 0.03 #2717, 0.03 #8960), 01mmslz (0.17 #399, 0.03 #2481, 0.02 #10805), 02lf1j (0.17 #430, 0.03 #8755, 0.01 #48298), 014zcr (0.17 #37, 0.03 #33337, 0.02 #25011), 01pj3h (0.17 #1886, 0.02 #6049, 0.02 #8130), 09fb5 (0.17 #58, 0.02 #52090, 0.02 #56253), 046lt (0.17 #505, 0.02 #6749, 0.01 #35886) >> Best rule #1177 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01hvjx; 051zy_b; 0bt3j9; 01633c; >> query: (?x5320, 05dtsb) <- music(?x5320, ?x8013), award_winner(?x5320, ?x5319), ?x8013 = 01mh8zn, genre(?x5320, ?x53) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #6692 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 05p3738; *> query: (?x5320, 02js6_) <- titles(?x811, ?x5320), ?x811 = 03k9fj, currency(?x5320, ?x170), genre(?x5320, ?x53) *> conf = 0.02 ranks of expected_values: 527 EVAL 01zfzb film! 02js6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 62.000 0.167 http://example.org/film/actor/film./film/performance/film #16657-0lccn PRED entity: 0lccn PRED relation: role PRED expected values: 042v_gx => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 84): 05148p4 (0.33 #23, 0.12 #2087, 0.12 #1364), 02sgy (0.28 #418, 0.28 #6, 0.25 #625), 042v_gx (0.28 #420, 0.24 #1349, 0.22 #1453), 026t6 (0.28 #3, 0.16 #3208, 0.13 #1137), 0l14qv (0.22 #5, 0.16 #3210, 0.15 #1346), 013y1f (0.21 #448, 0.14 #1170, 0.14 #1377), 05842k (0.18 #490, 0.17 #3283, 0.16 #1419), 018vs (0.16 #3218, 0.14 #1354, 0.14 #2077), 01vj9c (0.16 #3220, 0.15 #1460, 0.15 #1356), 03qjg (0.13 #475, 0.08 #682, 0.07 #2127) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 04mx7s; >> query: (?x2319, 05148p4) <- artists(?x3243, ?x2319), role(?x2319, ?x227), ?x3243 = 0y3_8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #420 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 01vsyjy; 043c4j; 0kj34; 01t8399; 01wx756; 04mky3; *> query: (?x2319, 042v_gx) <- artists(?x7329, ?x2319), profession(?x2319, ?x220), ?x7329 = 016jny *> conf = 0.28 ranks of expected_values: 3 EVAL 0lccn role 042v_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 125.000 0.333 http://example.org/music/artist/track_contributions./music/track_contribution/role #16656-0gk4g PRED entity: 0gk4g PRED relation: risk_factors PRED expected values: 0jpmt => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 99): 0jpmt (0.67 #228, 0.56 #429, 0.55 #744), 012jc (0.50 #78, 0.40 #595, 0.33 #1007), 0k95h (0.50 #162, 0.33 #522, 0.33 #416), 02zsn (0.38 #1406, 0.33 #826, 0.25 #1088), 02y0js (0.22 #256, 0.21 #1982, 0.18 #1030), 0d19y2 (0.22 #256, 0.20 #706, 0.18 #1030), 0qcr0 (0.22 #256, 0.18 #1030, 0.18 #565), 0g02vk (0.22 #256, 0.18 #1030, 0.18 #565), 0gk4g (0.22 #256, 0.18 #1030, 0.18 #565), 0hg11 (0.22 #256, 0.18 #1030, 0.18 #565) >> Best rule #228 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0k95h; >> query: (?x4322, 0jpmt) <- risk_factors(?x6483, ?x4322), risk_factors(?x4322, ?x8523), risk_factors(?x4322, ?x231), ?x8523 = 0c58k, risk_factors(?x14098, ?x231), risk_factors(?x11064, ?x231), ?x11064 = 01n3bm, ?x14098 = 01k9gb >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gk4g risk_factors 0jpmt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.667 http://example.org/medicine/disease/risk_factors #16655-05xq9 PRED entity: 05xq9 PRED relation: influenced_by! PRED expected values: 0b1zz => 96 concepts (47 used for prediction) PRED predicted values (max 10 best out of 359): 05xq9 (0.25 #4830, 0.20 #1227, 0.17 #2773), 01w5n51 (0.20 #817, 0.17 #3394, 0.17 #2878), 01w3lzq (0.20 #717, 0.17 #3294, 0.17 #2778), 07r1_ (0.20 #795, 0.17 #3372, 0.17 #2341), 0b1zz (0.20 #759, 0.17 #3336, 0.17 #2305), 06crng (0.20 #1329, 0.17 #2875, 0.12 #4932), 01xwv7 (0.19 #6597, 0.17 #13291, 0.12 #20505), 016_mj (0.17 #12921, 0.11 #20135, 0.11 #18589), 0167xy (0.17 #15360, 0.16 #8663, 0.15 #14846), 01whg97 (0.16 #20596, 0.15 #15957, 0.14 #13382) >> Best rule #4830 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02vr7; >> query: (?x4942, 05xq9) <- influenced_by(?x1573, ?x4942), artists(?x2491, ?x4942), ?x2491 = 011j5x, artist(?x2023, ?x4942) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #759 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0dw4g; 070b4; 07hgm; *> query: (?x4942, 0b1zz) <- influenced_by(?x4942, ?x8149), artists(?x2491, ?x4942), artists(?x1000, ?x8149), ?x2491 = 011j5x, origin(?x4942, ?x3052) *> conf = 0.20 ranks of expected_values: 5 EVAL 05xq9 influenced_by! 0b1zz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 96.000 47.000 0.250 http://example.org/influence/influence_node/influenced_by #16654-05v10 PRED entity: 05v10 PRED relation: form_of_government PRED expected values: 06cx9 01d9r3 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 5): 01d9r3 (0.64 #4, 0.56 #9, 0.34 #99), 018wl5 (0.40 #22, 0.39 #37, 0.36 #12), 06cx9 (0.39 #96, 0.39 #216, 0.37 #141), 01q20 (0.34 #38, 0.32 #13, 0.32 #218), 026wp (0.11 #15, 0.09 #25, 0.07 #40) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02wmy; >> query: (?x1592, 01d9r3) <- contains(?x12315, ?x1592), ?x12315 = 06n3y, official_language(?x1592, ?x2502) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 05v10 form_of_government 01d9r3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.643 http://example.org/location/country/form_of_government EVAL 05v10 form_of_government 06cx9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.643 http://example.org/location/country/form_of_government #16653-01nln PRED entity: 01nln PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.89 #13, 0.88 #24, 0.88 #55) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 059z0; >> query: (?x6974, 0hzc9wc) <- adjoins(?x1241, ?x6974), taxonomy(?x6974, ?x939), official_language(?x6974, ?x254) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nln administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.885 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #16652-017dtf PRED entity: 017dtf PRED relation: genre PRED expected values: 0jxy => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 198): 01z4y (0.65 #1565, 0.60 #260, 0.50 #1156), 01t_vv (0.61 #1171, 0.60 #275, 0.32 #1580), 0jxy (0.60 #354, 0.46 #2122, 0.39 #2698), 06n90 (0.47 #1643, 0.46 #2122, 0.40 #337), 02kdv5l (0.46 #2122, 0.40 #327, 0.39 #2698), 0c4xc (0.42 #1589, 0.25 #527, 0.24 #2490), 03k9fj (0.41 #1641, 0.39 #2698, 0.33 #173), 01htzx (0.39 #2698, 0.36 #1646, 0.33 #178), 02l7c8 (0.39 #2698, 0.33 #14, 0.30 #339), 01jfsb (0.39 #2698, 0.16 #1468, 0.13 #2133) >> Best rule #1565 for best value: >> intensional similarity = 7 >> extensional distance = 79 >> proper extension: 01_2n; >> query: (?x10018, 01z4y) <- genre(?x10018, ?x2540), genre(?x10018, ?x258), actor(?x10018, ?x13457), type_of_union(?x13457, ?x566), film(?x13457, ?x1334), ?x258 = 05p553, genre(?x124, ?x2540) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #354 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 031kyy; 08cl7s; 02v5xg; 06xkst; 05631; *> query: (?x10018, 0jxy) <- genre(?x10018, ?x53), actor(?x10018, ?x13457), actor(?x10018, ?x11175), artists(?x671, ?x13457), profession(?x11175, ?x220), category(?x11175, ?x134), gender(?x11175, ?x514), special_performance_type(?x13457, ?x296) *> conf = 0.60 ranks of expected_values: 3 EVAL 017dtf genre 0jxy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 53.000 53.000 0.654 http://example.org/tv/tv_program/genre #16651-0c57yj PRED entity: 0c57yj PRED relation: featured_film_locations PRED expected values: 0l35f => 106 concepts (79 used for prediction) PRED predicted values (max 10 best out of 84): 04jpl (0.44 #4024, 0.15 #2606, 0.15 #4735), 05tbn (0.33 #76, 0.01 #4091), 0fvzz (0.33 #205), 0l4vc (0.33 #152), 02_286 (0.30 #10189, 0.30 #11372, 0.30 #10661), 0rh6k (0.25 #4016, 0.17 #709, 0.10 #1181), 030qb3t (0.21 #1454, 0.15 #2635, 0.15 #2871), 094jv (0.17 #751, 0.10 #1223, 0.01 #9028), 0cwx_ (0.17 #832, 0.10 #1304), 080h2 (0.11 #1440, 0.05 #9009, 0.05 #9247) >> Best rule #4024 for best value: >> intensional similarity = 5 >> extensional distance = 148 >> proper extension: 05_61y; >> query: (?x3859, 04jpl) <- language(?x3859, ?x254), featured_film_locations(?x3859, ?x1227), film_release_distribution_medium(?x3859, ?x81), contains(?x1227, ?x191), state_province_region(?x99, ?x1227) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c57yj featured_film_locations 0l35f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 79.000 0.440 http://example.org/film/film/featured_film_locations #16650-01zkxv PRED entity: 01zkxv PRED relation: influenced_by PRED expected values: 03j0d 07zl1 => 131 concepts (46 used for prediction) PRED predicted values (max 10 best out of 373): 0g5ff (0.40 #194, 0.38 #1489, 0.29 #1058), 05qzv (0.40 #333, 0.29 #1197, 0.25 #2491), 080r3 (0.40 #166, 0.29 #1030, 0.08 #2324), 0gd_s (0.33 #2469, 0.20 #7346, 0.20 #311), 040db (0.33 #486, 0.20 #55, 0.18 #4807), 06hmd (0.29 #2758, 0.12 #1463, 0.07 #3623), 0j0pf (0.27 #7345, 0.23 #8642, 0.19 #6913), 01v9724 (0.25 #1472, 0.14 #2767, 0.14 #4929), 03f0324 (0.23 #7930, 0.17 #583, 0.12 #5767), 03hnd (0.22 #3122, 0.17 #530, 0.15 #5714) >> Best rule #194 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01dzz7; 0gd_s; >> query: (?x576, 0g5ff) <- award(?x576, ?x12418), award(?x576, ?x8880), ?x8880 = 0262x6, ?x12418 = 045xh, influenced_by(?x576, ?x1287) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #765 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 05jm7; *> query: (?x576, 03j0d) <- award(?x576, ?x8880), ?x8880 = 0262x6, award_nominee(?x576, ?x12009) *> conf = 0.17 ranks of expected_values: 33, 142 EVAL 01zkxv influenced_by 07zl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 131.000 46.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 01zkxv influenced_by 03j0d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 131.000 46.000 0.400 http://example.org/influence/influence_node/influenced_by #16649-01sb5r PRED entity: 01sb5r PRED relation: location PRED expected values: 02_286 0xmp9 => 147 concepts (143 used for prediction) PRED predicted values (max 10 best out of 274): 01sn3 (0.42 #38520, 0.41 #41729, 0.36 #26489), 030qb3t (0.25 #19347, 0.23 #17741, 0.22 #21755), 02_286 (0.23 #77876, 0.18 #82696, 0.18 #73862), 0ftyc (0.20 #258), 07b_l (0.12 #4198, 0.07 #17658, 0.04 #8210), 0r0m6 (0.11 #1019, 0.07 #19481, 0.06 #17072), 0h7h6 (0.11 #892, 0.04 #4102, 0.02 #77929), 0h1k6 (0.11 #1362, 0.04 #4572, 0.02 #6979), 018d5b (0.11 #1540), 0cr3d (0.11 #1750, 0.09 #77984, 0.08 #7366) >> Best rule #38520 for best value: >> intensional similarity = 3 >> extensional distance = 291 >> proper extension: 016jll; >> query: (?x4140, ?x4090) <- artist(?x2039, ?x4140), origin(?x4140, ?x4090), profession(?x4140, ?x131) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #77876 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1515 *> proper extension: 02wrhj; 01h2_6; 0443c; *> query: (?x4140, 02_286) <- location(?x4140, ?x6453), place_of_birth(?x3956, ?x6453), teams(?x6453, ?x1759) *> conf = 0.23 ranks of expected_values: 3 EVAL 01sb5r location 0xmp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 143.000 0.417 http://example.org/people/person/places_lived./people/place_lived/location EVAL 01sb5r location 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 147.000 143.000 0.417 http://example.org/people/person/places_lived./people/place_lived/location #16648-05dy7p PRED entity: 05dy7p PRED relation: genre PRED expected values: 03bxz7 => 111 concepts (110 used for prediction) PRED predicted values (max 10 best out of 99): 02kdv5l (0.59 #238, 0.56 #711, 0.45 #2841), 03k9fj (0.49 #721, 0.36 #2969, 0.35 #1902), 01jfsb (0.43 #2852, 0.42 #3208, 0.42 #2021), 05p553 (0.38 #359, 0.36 #4625, 0.36 #6993), 0lsxr (0.36 #245, 0.36 #127, 0.33 #9), 06n90 (0.35 #723, 0.25 #2853, 0.25 #3209), 02l7c8 (0.32 #1434, 0.31 #5706, 0.30 #7123), 01hmnh (0.31 #727, 0.27 #1908, 0.25 #4045), 04xvlr (0.31 #1537, 0.26 #1419, 0.26 #1183), 060__y (0.25 #1435, 0.20 #1553, 0.19 #2025) >> Best rule #238 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0dckvs; 033g4d; 01f8gz; 0418wg; 02ryz24; 0ywrc; 05g8pg; 02fqrf; 0432_5; 047vnkj; ... >> query: (?x2402, 02kdv5l) <- film_crew_role(?x2402, ?x468), film(?x7310, ?x2402), language(?x2402, ?x2890), ?x2890 = 0653m >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 04xbq3; *> query: (?x2402, 03bxz7) <- nominated_for(?x7310, ?x2402), nominated_for(?x484, ?x2402), ?x7310 = 04sry *> conf = 0.18 ranks of expected_values: 12 EVAL 05dy7p genre 03bxz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 111.000 110.000 0.591 http://example.org/film/film/genre #16647-01g42 PRED entity: 01g42 PRED relation: type_of_union PRED expected values: 04ztj => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.91 #33, 0.87 #29, 0.87 #65), 01g63y (0.24 #46, 0.19 #22, 0.14 #42), 01bl8s (0.02 #59, 0.01 #39, 0.01 #43) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 01l3j; >> query: (?x8634, 04ztj) <- people(?x4322, ?x8634), religion(?x8634, ?x962), film(?x8634, ?x592) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01g42 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.910 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16646-084qpk PRED entity: 084qpk PRED relation: written_by PRED expected values: 01wg982 => 78 concepts (57 used for prediction) PRED predicted values (max 10 best out of 113): 02kxbwx (0.22 #689, 0.03 #2023, 0.03 #1022), 02kxbx3 (0.18 #767, 0.02 #2101, 0.02 #10112), 0534v (0.12 #832, 0.03 #1165, 0.02 #2166), 04y8r (0.11 #64, 0.08 #398, 0.02 #2066), 06q8hf (0.11 #1335), 05hj_k (0.11 #1335), 01m4yn (0.10 #2002, 0.09 #3672, 0.08 #5008), 018grr (0.08 #387, 0.02 #2055, 0.01 #1054), 01wg982 (0.08 #403), 0343h (0.08 #710, 0.03 #1710, 0.03 #1043) >> Best rule #689 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 016ztl; 0564x; >> query: (?x814, 02kxbwx) <- genre(?x814, ?x225), film(?x523, ?x814), written_by(?x814, ?x815), edited_by(?x3344, ?x523) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #403 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0ds5_72; *> query: (?x814, 01wg982) <- film(?x10126, ?x814), genre(?x814, ?x812), ?x10126 = 01xllf, titles(?x812, ?x80) *> conf = 0.08 ranks of expected_values: 9 EVAL 084qpk written_by 01wg982 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 78.000 57.000 0.216 http://example.org/film/film/written_by #16645-03k8th PRED entity: 03k8th PRED relation: films! PRED expected values: 0d6qjf => 113 concepts (51 used for prediction) PRED predicted values (max 10 best out of 64): 07jq_ (0.40 #236, 0.08 #2091, 0.07 #1779), 07s2s (0.31 #715, 0.25 #870, 0.22 #1179), 0bq3x (0.25 #338, 0.12 #1419, 0.09 #1883), 0d6qjf (0.25 #102, 0.08 #410, 0.08 #718), 07_nf (0.25 #67, 0.06 #1920, 0.06 #1301), 0fzyg (0.19 #825, 0.17 #1134, 0.09 #1907), 0ddct (0.15 #704, 0.11 #1168, 0.08 #1477), 01w1sx (0.11 #1325, 0.10 #2409, 0.08 #2100), 07yjb (0.11 #1299, 0.08 #1454, 0.08 #1608), 06d4h (0.10 #2361, 0.06 #7648, 0.06 #7492) >> Best rule #236 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 072x7s; >> query: (?x11296, 07jq_) <- genre(?x11296, ?x5104), film(?x1104, ?x11296), ?x5104 = 0bkbm, film_format(?x11296, ?x909) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #102 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 02p86pb; *> query: (?x11296, 0d6qjf) <- prequel(?x11296, ?x1470), films(?x13046, ?x11296), currency(?x11296, ?x170), ?x13046 = 0l8bg *> conf = 0.25 ranks of expected_values: 4 EVAL 03k8th films! 0d6qjf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 113.000 51.000 0.400 http://example.org/film/film_subject/films #16644-025j1t PRED entity: 025j1t PRED relation: nationality PRED expected values: 09c7w0 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.88 #5002, 0.83 #1101, 0.82 #6402), 03rt9 (0.12 #513, 0.02 #1013, 0.02 #5414), 02jx1 (0.11 #4834, 0.10 #5334, 0.10 #7934), 0d060g (0.10 #607, 0.08 #707, 0.07 #807), 07ssc (0.09 #5316, 0.09 #6616, 0.09 #4816), 03_3d (0.08 #706, 0.01 #11111, 0.01 #10910), 03rk0 (0.07 #6747, 0.06 #7747, 0.06 #9847), 0345h (0.04 #2631, 0.03 #2031, 0.03 #2831), 03spz (0.03 #967, 0.01 #1267), 0chghy (0.03 #5311, 0.03 #5711, 0.02 #5911) >> Best rule #5002 for best value: >> intensional similarity = 3 >> extensional distance = 580 >> proper extension: 079hvk; 08ff1k; 0564mx; 08qmfm; 0bq4j6; >> query: (?x6068, 09c7w0) <- student(?x1675, ?x6068), award_nominee(?x6068, ?x496), school(?x580, ?x1675) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025j1t nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.885 http://example.org/people/person/nationality #16643-01d8l PRED entity: 01d8l PRED relation: combatants! PRED expected values: 0flry => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 64): 03jqfx (0.69 #578, 0.63 #1868, 0.63 #1867), 081pw (0.63 #1418, 0.61 #1936, 0.58 #1353), 0845v (0.50 #774, 0.46 #970, 0.40 #517), 03gqgt3 (0.47 #1472, 0.45 #1990, 0.44 #761), 048n7 (0.42 #1375, 0.41 #1632, 0.38 #1825), 01gqg3 (0.40 #542, 0.38 #928, 0.29 #671), 01hwkn (0.40 #562, 0.32 #2496, 0.28 #1869), 0flry (0.40 #552, 0.28 #1869, 0.27 #1197), 01h6pn (0.33 #719, 0.29 #1043, 0.27 #1237), 0k4y6 (0.32 #2471, 0.29 #666, 0.28 #1869) >> Best rule #578 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03gk2; >> query: (?x10801, ?x9939) <- combatants(?x6982, ?x10801), ?x6982 = 0ql7q, contains(?x1558, ?x10801), entity_involved(?x9939, ?x10801) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #552 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 03gk2; *> query: (?x10801, 0flry) <- combatants(?x6982, ?x10801), ?x6982 = 0ql7q, contains(?x1558, ?x10801), entity_involved(?x9939, ?x10801) *> conf = 0.40 ranks of expected_values: 8 EVAL 01d8l combatants! 0flry CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 177.000 177.000 0.692 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #16642-02x201b PRED entity: 02x201b PRED relation: award_winner PRED expected values: 01vvycq => 55 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1872): 016szr (0.60 #8526, 0.59 #4947, 0.52 #14843), 02fgpf (0.60 #7813, 0.50 #10286, 0.50 #5339), 02cyfz (0.60 #7878, 0.50 #10351, 0.33 #456), 01l1rw (0.59 #4947, 0.52 #14843, 0.47 #4946), 02ryx0 (0.59 #4947, 0.52 #14843, 0.47 #4946), 02lfp4 (0.59 #4947, 0.52 #14843, 0.47 #4946), 06h7l7 (0.59 #4947, 0.52 #14843, 0.47 #4946), 09r9m7 (0.59 #4947, 0.52 #14843, 0.47 #4946), 023361 (0.59 #4947, 0.47 #4946, 0.44 #14842), 04zwjd (0.59 #4947, 0.47 #4946, 0.44 #14842) >> Best rule #8526 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 025m8y; 025m8l; >> query: (?x7099, 016szr) <- award(?x7167, ?x7099), nominated_for(?x7099, ?x1944), ?x7167 = 01wd9vs, ceremony(?x7099, ?x1747) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #12490 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 025m98; *> query: (?x7099, 01vvycq) <- award(?x7167, ?x7099), award(?x5949, ?x7099), nominated_for(?x7099, ?x3268), film_crew_role(?x3268, ?x137), ?x5949 = 02ryx0, award_winner(?x6011, ?x7167) *> conf = 0.29 ranks of expected_values: 113 EVAL 02x201b award_winner 01vvycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 55.000 28.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #16641-02r4qs PRED entity: 02r4qs PRED relation: profession PRED expected values: 0dz3r => 140 concepts (126 used for prediction) PRED predicted values (max 10 best out of 63): 09jwl (0.78 #765, 0.76 #1511, 0.75 #1212), 02hrh1q (0.74 #9566, 0.73 #8821, 0.71 #7330), 0dz3r (0.52 #1493, 0.51 #1194, 0.51 #747), 016z4k (0.48 #1196, 0.47 #1495, 0.46 #2390), 01c72t (0.37 #621, 0.36 #3903, 0.34 #770), 039v1 (0.32 #782, 0.31 #1229, 0.31 #3766), 0fnpj (0.30 #806, 0.29 #1253, 0.28 #1552), 0dxtg (0.30 #8075, 0.28 #7180, 0.28 #11206), 01d_h8 (0.29 #13136, 0.29 #8067, 0.29 #15371), 0n1h (0.27 #12, 0.23 #1055, 0.21 #459) >> Best rule #765 for best value: >> intensional similarity = 3 >> extensional distance = 136 >> proper extension: 0kvjrw; >> query: (?x1504, 09jwl) <- instrumentalists(?x1166, ?x1504), type_of_union(?x1504, ?x566), ?x1166 = 05148p4 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1493 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 185 *> proper extension: 01nqfh_; 032t2z; 016ntp; 023l9y; 04cr6qv; 04f7c55; 017g21; 01w9mnm; 02pt27; 048tgl; ... *> query: (?x1504, 0dz3r) <- instrumentalists(?x1166, ?x1504), ?x1166 = 05148p4, profession(?x1504, ?x2348) *> conf = 0.52 ranks of expected_values: 3 EVAL 02r4qs profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 126.000 0.775 http://example.org/people/person/profession #16640-06cp5 PRED entity: 06cp5 PRED relation: artists PRED expected values: 04n65n 02k5sc => 79 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1156): 01386_ (0.64 #9114, 0.43 #4845, 0.40 #1641), 01vw37m (0.60 #3764, 0.60 #2697, 0.43 #5901), 0bqvs2 (0.57 #6008, 0.40 #3871, 0.40 #2804), 01gx5f (0.56 #7763, 0.55 #8831, 0.48 #32031), 01shhf (0.55 #9400, 0.44 #8332, 0.43 #12602), 01w8n89 (0.55 #8853, 0.40 #1380, 0.36 #12055), 01vvycq (0.54 #10721, 0.47 #14993, 0.44 #13926), 01gf5h (0.50 #13941, 0.50 #11804, 0.47 #15008), 07h76 (0.50 #6948, 0.22 #8014, 0.20 #3745), 0mgcr (0.50 #6738, 0.13 #8540, 0.12 #13879) >> Best rule #9114 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 03339m; >> query: (?x6513, 01386_) <- artists(?x6513, ?x9155), artists(?x6513, ?x5536), artists(?x6513, ?x970), ?x9155 = 011_vz, profession(?x970, ?x524), parent_genre(?x6513, ?x1000), award_winner(?x1323, ?x5536), type_of_union(?x970, ?x566) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #32031 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 90 *> proper extension: 0ccxx6; 08s6r6; *> query: (?x6513, ?x3118) <- artists(?x6513, ?x9155), artists(?x6513, ?x7125), parent_genre(?x6513, ?x1000), group(?x227, ?x9155), group(?x3118, ?x9155), artist(?x8738, ?x7125) *> conf = 0.48 ranks of expected_values: 12, 219 EVAL 06cp5 artists 02k5sc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 79.000 40.000 0.636 http://example.org/music/genre/artists EVAL 06cp5 artists 04n65n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 79.000 40.000 0.636 http://example.org/music/genre/artists #16639-0jrqq PRED entity: 0jrqq PRED relation: award PRED expected values: 02qyp19 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 299): 02qt02v (0.76 #11856, 0.70 #33205, 0.70 #33204), 019f4v (0.43 #6383, 0.41 #4012, 0.38 #1642), 040njc (0.38 #6329, 0.37 #3958, 0.35 #7515), 02qyp19 (0.37 #2767, 0.17 #3952, 0.15 #6323), 0gq9h (0.36 #3627, 0.33 #1257, 0.33 #10346), 0gr4k (0.32 #2797, 0.25 #3982, 0.23 #6353), 03hkv_r (0.29 #804, 0.18 #2780, 0.15 #3965), 09sb52 (0.27 #7942, 0.27 #13869, 0.26 #17820), 01by1l (0.24 #11563, 0.20 #14724, 0.19 #15514), 02rdyk7 (0.23 #2850, 0.22 #6406, 0.21 #1665) >> Best rule #11856 for best value: >> intensional similarity = 3 >> extensional distance = 487 >> proper extension: 0191h5; 051m56; 026v1z; 0drdv; >> query: (?x3873, ?x350) <- award_nominee(?x382, ?x3873), category(?x3873, ?x134), award_winner(?x350, ?x3873) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #2767 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 05jjl; 06s1qy; 09xx0m; *> query: (?x3873, 02qyp19) <- gender(?x3873, ?x231), award(?x3873, ?x1862), ?x1862 = 0gr51 *> conf = 0.37 ranks of expected_values: 4 EVAL 0jrqq award 02qyp19 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 127.000 127.000 0.758 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16638-03wjb7 PRED entity: 03wjb7 PRED relation: performance_role PRED expected values: 0l15bq => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 26): 026t6 (0.25 #87, 0.20 #129, 0.07 #343), 0l14qv (0.25 #89, 0.06 #345, 0.06 #131), 0l14md (0.14 #133, 0.12 #91, 0.06 #347), 05r5c (0.12 #92, 0.10 #170, 0.09 #134), 042v_gx (0.12 #93, 0.03 #135, 0.02 #220), 03gvt (0.12 #121, 0.03 #163, 0.02 #420), 0gkd1 (0.12 #122, 0.03 #164, 0.01 #249), 018vs (0.12 #95, 0.03 #137, 0.01 #222), 03qjg (0.12 #114), 028tv0 (0.12 #94) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01w524f; 037hgm; 01hrqc; 02mx98; 01r0t_j; 04s5_s; >> query: (?x8403, 026t6) <- profession(?x8403, ?x220), artists(?x8385, ?x8403), ?x8385 = 01skxk >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #145 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 02qfhb; 0cj2w; *> query: (?x8403, 0l15bq) <- profession(?x8403, ?x1032), ?x1032 = 02hrh1q, instrumentalists(?x227, ?x8403), performance_role(?x8403, ?x1466) *> conf = 0.06 ranks of expected_values: 12 EVAL 03wjb7 performance_role 0l15bq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 67.000 67.000 0.250 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #16637-05v8c PRED entity: 05v8c PRED relation: country! PRED expected values: 03_8r 06z6r => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 54): 06z6r (0.88 #1434, 0.86 #786, 0.84 #3540), 06wrt (0.85 #231, 0.83 #609, 0.80 #285), 01lb14 (0.85 #230, 0.80 #284, 0.79 #608), 03_8r (0.81 #993, 0.79 #831, 0.77 #1749), 06f41 (0.80 #877, 0.77 #1741, 0.76 #823), 03fyrh (0.78 #189, 0.77 #243, 0.73 #297), 064vjs (0.77 #247, 0.73 #301, 0.72 #841), 03hr1p (0.75 #616, 0.71 #1426, 0.68 #1750), 07jbh (0.72 #843, 0.69 #249, 0.68 #1761), 01hp22 (0.71 #602, 0.57 #548, 0.56 #170) >> Best rule #1434 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 049nq; >> query: (?x550, 06z6r) <- contains(?x550, ?x4845), country(?x13030, ?x550), nationality(?x1408, ?x550) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 05v8c country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.878 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 05v8c country! 03_8r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 150.000 150.000 0.878 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #16636-0416y94 PRED entity: 0416y94 PRED relation: nominated_for! PRED expected values: 017s11 => 129 concepts (39 used for prediction) PRED predicted values (max 10 best out of 977): 0csdzz (0.44 #51381, 0.23 #11678, 0.05 #16128), 0b25vg (0.42 #23354, 0.36 #88767, 0.34 #65400), 07m77x (0.42 #23354, 0.36 #88767, 0.34 #65400), 0f7h2v (0.42 #23354, 0.36 #88767, 0.34 #65400), 0fvf9q (0.17 #56054, 0.16 #7006, 0.15 #72409), 0jz9f (0.15 #20, 0.09 #14033, 0.07 #2355), 014zcr (0.14 #2377, 0.08 #42, 0.05 #14055), 02mxbd (0.11 #29273, 0.11 #26938, 0.09 #33945), 0146pg (0.10 #56176, 0.09 #51502, 0.06 #79539), 03xp8d5 (0.09 #7962, 0.08 #77082, 0.05 #35987) >> Best rule #51381 for best value: >> intensional similarity = 5 >> extensional distance = 111 >> proper extension: 0c5qvw; >> query: (?x1318, ?x10634) <- genre(?x1318, ?x53), language(?x1318, ?x5607), costume_design_by(?x1318, ?x3685), music(?x1318, ?x10634), nominated_for(?x1774, ?x1318) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #77082 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 230 *> proper extension: 05f67hw; *> query: (?x1318, ?x541) <- films(?x13555, ?x1318), country(?x1318, ?x94), produced_by(?x1318, ?x9743), award_nominee(?x9743, ?x541) *> conf = 0.08 ranks of expected_values: 19 EVAL 0416y94 nominated_for! 017s11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 129.000 39.000 0.437 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16635-04qr6d PRED entity: 04qr6d PRED relation: award PRED expected values: 0b6k___ => 87 concepts (78 used for prediction) PRED predicted values (max 10 best out of 256): 054krc (0.48 #9832, 0.47 #10644, 0.46 #9426), 03rbj2 (0.41 #8751, 0.27 #12405, 0.22 #13623), 0l8z1 (0.38 #10620, 0.37 #9808, 0.36 #9402), 0gqz2 (0.33 #4953, 0.29 #4547, 0.25 #10231), 02qvyrt (0.30 #9872, 0.30 #10684, 0.29 #9466), 0fc9js (0.30 #6713, 0.03 #7931, 0.02 #15647), 03r8tl (0.28 #8631, 0.15 #12285, 0.14 #2541), 025m8y (0.27 #8220, 0.27 #9844, 0.25 #9438), 01by1l (0.24 #7827, 0.22 #14731, 0.20 #15950), 01bgqh (0.24 #7757, 0.18 #14661, 0.16 #15880) >> Best rule #9832 for best value: >> intensional similarity = 7 >> extensional distance = 84 >> proper extension: 012ljv; 02fgpf; 04zwjd; 01vyp_; 02cyfz; 0kvrb; 03h4mp; 037lyl; 04pf4r; 012ky3; ... >> query: (?x8756, 054krc) <- profession(?x8756, ?x987), artists(?x4910, ?x8756), ?x4910 = 017_qw, profession(?x10407, ?x987), profession(?x4987, ?x987), ?x4987 = 0dpqk, currency(?x10407, ?x170) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2656 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 0dfjb8; 05jjl; 01x2tm8; 071xj; 05wm88; *> query: (?x8756, 0b6k___) <- profession(?x8756, ?x6476), profession(?x8756, ?x987), profession(?x8756, ?x319), religion(?x8756, ?x8967), ?x6476 = 025352, ?x987 = 0dxtg, ?x319 = 01d_h8 *> conf = 0.14 ranks of expected_values: 21 EVAL 04qr6d award 0b6k___ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 87.000 78.000 0.477 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16634-02zcnq PRED entity: 02zcnq PRED relation: contains! PRED expected values: 09c7w0 => 154 concepts (101 used for prediction) PRED predicted values (max 10 best out of 275): 09c7w0 (0.98 #59028, 0.97 #40248, 0.93 #20574), 030qb3t (0.79 #1888, 0.28 #5464, 0.18 #11727), 02jx1 (0.39 #26024, 0.16 #64477, 0.14 #76105), 0bxqq (0.36 #52766, 0.26 #14311, 0.12 #8943), 04_1l0v (0.36 #52766), 0kpys (0.34 #5544, 0.21 #11807, 0.21 #1968), 059rby (0.24 #25958, 0.13 #14331, 0.13 #8963), 06pvr (0.20 #165, 0.14 #11792, 0.13 #27892), 0kpzy (0.20 #367, 0.09 #5731, 0.06 #11994), 0r2dp (0.20 #588, 0.06 #1482, 0.03 #4164) >> Best rule #59028 for best value: >> intensional similarity = 4 >> extensional distance = 335 >> proper extension: 063576; 0b5hj5; 04ld32; 02bf58; >> query: (?x4555, 09c7w0) <- contains(?x1227, ?x4555), institution(?x865, ?x4555), contains(?x1227, ?x13692), ?x13692 = 0b_cr >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zcnq contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 101.000 0.979 http://example.org/location/location/contains #16633-0ds2l81 PRED entity: 0ds2l81 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.84 #76, 0.82 #86, 0.81 #206), 07c52 (0.16 #23, 0.09 #73, 0.09 #153), 02nxhr (0.13 #47, 0.12 #52, 0.12 #67), 07z4p (0.07 #140, 0.07 #155, 0.07 #20), 0735l (0.01 #64) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 117 >> proper extension: 070fnm; 0c_j9x; 03lrqw; 03h3x5; 032016; 03l6q0; 02rq8k8; 014kkm; 01zfzb; 0295sy; ... >> query: (?x8377, 029j_) <- film(?x9388, ?x8377), cinematography(?x8377, ?x4974), nationality(?x9388, ?x94), profession(?x9388, ?x1032), special_performance_type(?x9388, ?x3558) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ds2l81 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.840 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #16632-0835q PRED entity: 0835q PRED relation: basic_title PRED expected values: 060c4 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 16): 060c4 (0.73 #259, 0.68 #483, 0.67 #387), 0789n (0.35 #680, 0.35 #664, 0.33 #216), 0dq3c (0.29 #338, 0.27 #258, 0.27 #370), 060bp (0.25 #529, 0.20 #561, 0.20 #241), 0p5vf (0.20 #250, 0.17 #106, 0.15 #314), 02079p (0.17 #89, 0.14 #153, 0.11 #217), 0pqc5 (0.14 #116, 0.11 #212, 0.07 #692), 0f6c3 (0.14 #118, 0.11 #214, 0.06 #454), 01t7n9 (0.14 #125, 0.11 #221, 0.06 #461), 01q24l (0.14 #123, 0.07 #876, 0.07 #892) >> Best rule #259 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 042fk; >> query: (?x11956, 060c4) <- taxonomy(?x11956, ?x939), profession(?x11956, ?x5805), people(?x6260, ?x11956), student(?x4672, ?x11956), basic_title(?x11956, ?x900) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0835q basic_title 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.727 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #16631-04z_3pm PRED entity: 04z_3pm PRED relation: film! PRED expected values: 05d6q1 => 86 concepts (67 used for prediction) PRED predicted values (max 10 best out of 56): 03xq0f (0.82 #967, 0.16 #523, 0.15 #1337), 01795t (0.48 #610, 0.07 #3432, 0.07 #832), 054g1r (0.30 #627, 0.07 #3598, 0.07 #997), 016tw3 (0.29 #85, 0.15 #1787, 0.15 #1861), 0jz9f (0.25 #223, 0.14 #149, 0.07 #963), 05qd_ (0.21 #971, 0.14 #749, 0.14 #157), 03xsby (0.20 #312, 0.15 #460, 0.10 #904), 024rdh (0.20 #333, 0.13 #1147, 0.12 #1221), 086k8 (0.18 #520, 0.18 #668, 0.17 #2744), 025jfl (0.18 #376, 0.15 #450, 0.14 #154) >> Best rule #967 for best value: >> intensional similarity = 6 >> extensional distance = 59 >> proper extension: 0522wp; >> query: (?x7887, 03xq0f) <- film(?x10629, ?x7887), category(?x7887, ?x134), film(?x10629, ?x4668), film(?x10629, ?x249), ?x249 = 0c3ybss, film(?x2280, ?x4668) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #416 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 15 *> proper extension: 0c0nhgv; 05z_kps; 047msdk; 0gmcwlb; 09k56b7; 0dgpwnk; 02j69w; 04nm0n0; 0dt8xq; 0g5q34q; ... *> query: (?x7887, 05d6q1) <- film_release_region(?x7887, ?x142), film_release_region(?x7887, ?x94), ?x142 = 0jgd, genre(?x7887, ?x53), film_festivals(?x7887, ?x3831), featured_film_locations(?x7887, ?x739), ?x94 = 09c7w0 *> conf = 0.18 ranks of expected_values: 11 EVAL 04z_3pm film! 05d6q1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 86.000 67.000 0.820 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16630-02yw26 PRED entity: 02yw26 PRED relation: artists PRED expected values: 016lj_ => 63 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1224): 01w8n89 (0.62 #2483, 0.60 #318, 0.57 #1400), 01gx5f (0.57 #1376, 0.50 #2459, 0.47 #6797), 01wg982 (0.54 #5606, 0.39 #7772, 0.35 #8855), 015196 (0.46 #6388, 0.33 #8554, 0.30 #9637), 01kd57 (0.44 #11343, 0.25 #2671, 0.22 #3755), 01386_ (0.41 #7086, 0.33 #10336, 0.31 #6004), 01j59b0 (0.41 #6976, 0.33 #10226, 0.31 #5894), 01vsxdm (0.41 #6605, 0.33 #9855, 0.28 #7689), 0191h5 (0.40 #652, 0.38 #2817, 0.33 #3901), 02ndj5 (0.40 #901, 0.38 #3066, 0.33 #4150) >> Best rule #2483 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 0fd3y; >> query: (?x13087, 01w8n89) <- artists(?x13087, ?x9463), artists(?x13087, ?x764), ?x764 = 0274ck, artists(?x3753, ?x9463), artists(?x302, ?x9463), ?x3753 = 01_bkd, group(?x227, ?x9463), artists(?x302, ?x1674), ?x1674 = 01v_pj6, origin(?x9463, ?x10980) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #6338 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 11 *> proper extension: 0xhtw; 03lty; 020ngt; 0hdf8; 05r6t; 0jmwg; 06bpt_; 05jt_; 09nwwf; *> query: (?x13087, 016lj_) <- artists(?x13087, ?x8012), artists(?x13087, ?x764), profession(?x764, ?x1183), performance_role(?x764, ?x1466), performance_role(?x764, ?x228), place_of_birth(?x764, ?x10858), ?x1183 = 09jwl, ?x1466 = 03bx0bm, ?x8012 = 01wt4wc, role(?x75, ?x228) *> conf = 0.38 ranks of expected_values: 31 EVAL 02yw26 artists 016lj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 63.000 27.000 0.625 http://example.org/music/genre/artists #16629-0b79gfg PRED entity: 0b79gfg PRED relation: nominated_for PRED expected values: 01kt_j => 90 concepts (42 used for prediction) PRED predicted values (max 10 best out of 488): 0dtfn (0.33 #1812, 0.15 #3433, 0.15 #55055), 0hx4y (0.33 #426, 0.15 #55055, 0.09 #13389), 07024 (0.33 #442, 0.15 #55055, 0.06 #13405), 0f4yh (0.33 #2152, 0.15 #55055, 0.04 #3773), 07gp9 (0.33 #39, 0.14 #13002, 0.14 #11381), 0ddjy (0.33 #349, 0.09 #13312, 0.09 #11691), 04gcyg (0.33 #2858, 0.08 #4479, 0.06 #9338), 0dr_4 (0.33 #228, 0.06 #13191, 0.06 #11570), 0dnqr (0.33 #446, 0.06 #13409, 0.06 #11788), 02c638 (0.33 #314, 0.06 #13277, 0.06 #11656) >> Best rule #1812 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 09pjnd; >> query: (?x2887, 0dtfn) <- crewmember(?x6099, ?x2887), crewmember(?x4848, ?x2887), award_winner(?x5585, ?x2887), ?x6099 = 0473rc, award_winner(?x4848, ?x1585) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6359 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 04bs3j; 0277470; 01t6b4; 03rwng; *> query: (?x2887, 01kt_j) <- nominated_for(?x2887, ?x7741), award_winner(?x5585, ?x2887), award(?x7741, ?x507), ?x5585 = 03nnm4t *> conf = 0.03 ranks of expected_values: 222 EVAL 0b79gfg nominated_for 01kt_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 90.000 42.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16628-015rmq PRED entity: 015rmq PRED relation: category PRED expected values: 08mbj5d => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.77 #47, 0.76 #11, 0.76 #21) >> Best rule #47 for best value: >> intensional similarity = 2 >> extensional distance = 980 >> proper extension: 0m19t; 04r1t; 02_5x9; 0167_s; 02r1tx7; 01qqwp9; 07yg2; 03xhj6; 0394y; 018gm9; ... >> query: (?x1373, 08mbj5d) <- artists(?x597, ?x1373), parent_genre(?x671, ?x597) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015rmq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.772 http://example.org/common/topic/webpage./common/webpage/category #16627-0ggbfwf PRED entity: 0ggbfwf PRED relation: film_release_region PRED expected values: 0154j 0chghy 0ctw_b => 71 concepts (67 used for prediction) PRED predicted values (max 10 best out of 138): 07ssc (0.88 #161, 0.87 #307, 0.81 #1184), 05v8c (0.86 #162, 0.69 #747, 0.69 #308), 01znc_ (0.85 #771, 0.81 #186, 0.74 #332), 0chghy (0.84 #301, 0.84 #740, 0.82 #1178), 0154j (0.84 #734, 0.81 #1172, 0.80 #295), 03rk0 (0.84 #198, 0.62 #783, 0.59 #344), 03rt9 (0.81 #159, 0.77 #744, 0.76 #305), 03_3d (0.79 #151, 0.76 #1320, 0.75 #736), 01p1v (0.77 #194, 0.59 #779, 0.57 #340), 047yc (0.70 #173, 0.63 #758, 0.51 #319) >> Best rule #161 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 01fmys; 0407yj_; 0gffmn8; 07s3m4g; 0fpgp26; >> query: (?x5827, 07ssc) <- language(?x5827, ?x3592), film_release_region(?x5827, ?x2843), film_release_region(?x5827, ?x1790), ?x1790 = 01pj7, ?x2843 = 016wzw >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #301 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 88 *> proper extension: 087wc7n; 053tj7; 0fq7dv_; 03mgx6z; 02qk3fk; 02825cv; 0gwjw0c; *> query: (?x5827, 0chghy) <- language(?x5827, ?x3592), film_release_region(?x5827, ?x2843), film_release_region(?x5827, ?x1790), ?x1790 = 01pj7, currency(?x2843, ?x170) *> conf = 0.84 ranks of expected_values: 4, 5, 11 EVAL 0ggbfwf film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 71.000 67.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ggbfwf film_release_region 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 71.000 67.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ggbfwf film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 71.000 67.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16626-06_wqk4 PRED entity: 06_wqk4 PRED relation: film_distribution_medium PRED expected values: 029j_ 02nxhr => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.32 #104, 0.12 #18, 0.11 #56), 029j_ (0.09 #1, 0.08 #100, 0.07 #58), 0dq6p (0.09 #3, 0.05 #54, 0.05 #16), 02nxhr (0.07 #101, 0.05 #35, 0.05 #15) >> Best rule #104 for best value: >> intensional similarity = 3 >> extensional distance = 363 >> proper extension: 0522wp; >> query: (?x857, 0735l) <- film(?x5854, ?x857), film(?x5854, ?x2006), ?x2006 = 031778 >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 017f3m; *> query: (?x857, 029j_) <- nominated_for(?x5246, ?x857), titles(?x2480, ?x857), ?x5246 = 046zh *> conf = 0.09 ranks of expected_values: 2, 4 EVAL 06_wqk4 film_distribution_medium 02nxhr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 69.000 0.318 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 06_wqk4 film_distribution_medium 029j_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 69.000 0.318 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #16625-06449 PRED entity: 06449 PRED relation: student! PRED expected values: 017z88 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 165): 02g839 (0.11 #25, 0.09 #3181, 0.08 #18962), 01w5m (0.10 #13780, 0.07 #20619, 0.06 #3786), 017z88 (0.09 #4815, 0.09 #7445, 0.09 #5867), 02_gzx (0.08 #2487, 0.02 #19320, 0.01 #4591), 0bwfn (0.07 #13950, 0.07 #7112, 0.06 #12372), 09f2j (0.07 #1736, 0.07 #4366, 0.06 #158), 065y4w7 (0.07 #1592, 0.05 #6852, 0.05 #4222), 03ksy (0.06 #13781, 0.04 #12729, 0.04 #10625), 01d34b (0.06 #7093, 0.04 #13931, 0.03 #10775), 078bz (0.06 #1128, 0.03 #602, 0.03 #2706) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01w923; 05n19y; >> query: (?x2940, 02g839) <- artists(?x4910, ?x2940), artist(?x8721, ?x2940), ?x4910 = 017_qw >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #4815 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 94 *> proper extension: 03n0q5; 012wg; 0dpqk; 02w670; 05cgy8; 095p3z; 01gz9n; 0csdzz; *> query: (?x2940, 017z88) <- award_winner(?x1443, ?x2940), music(?x2211, ?x2940), nominated_for(?x2940, ?x414) *> conf = 0.09 ranks of expected_values: 3 EVAL 06449 student! 017z88 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 133.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #16624-0gbtbm PRED entity: 0gbtbm PRED relation: featured_film_locations PRED expected values: 03pzf => 144 concepts (114 used for prediction) PRED predicted values (max 10 best out of 111): 02_286 (0.42 #25909, 0.33 #1694, 0.32 #15590), 030qb3t (0.30 #20886, 0.20 #9378, 0.17 #25928), 01jr6 (0.29 #2475, 0.02 #18288, 0.01 #25249), 03pzf (0.28 #4961, 0.17 #4004, 0.03 #21022), 04jpl (0.28 #20856, 0.25 #726, 0.20 #1204), 0345h (0.20 #1468, 0.11 #2904, 0.07 #4102), 0cv3w (0.20 #1504, 0.07 #4138, 0.07 #4377), 010h9y (0.20 #1626, 0.07 #4260, 0.07 #4499), 068p2 (0.20 #1528, 0.07 #4162, 0.07 #4401), 0ncj8 (0.20 #1519, 0.07 #4153, 0.07 #4392) >> Best rule #25909 for best value: >> intensional similarity = 5 >> extensional distance = 513 >> proper extension: 058kh7; >> query: (?x4529, 02_286) <- featured_film_locations(?x4529, ?x1658), genre(?x4529, ?x53), locations(?x2686, ?x1658), citytown(?x1306, ?x1658), place_of_birth(?x877, ?x1658) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #4961 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 02pcq92; *> query: (?x4529, 03pzf) <- featured_film_locations(?x4529, ?x1658), language(?x4529, ?x254), genre(?x4529, ?x53), ?x1658 = 0h7h6 *> conf = 0.28 ranks of expected_values: 4 EVAL 0gbtbm featured_film_locations 03pzf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 144.000 114.000 0.419 http://example.org/film/film/featured_film_locations #16623-02y6fz PRED entity: 02y6fz PRED relation: company PRED expected values: 06wpc 0jmnl => 41 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1013): 0537b (0.75 #3774, 0.60 #2786, 0.60 #2456), 060ppp (0.71 #3544, 0.67 #4534, 0.67 #3215), 087c7 (0.71 #3310, 0.67 #2981, 0.60 #2651), 01qygl (0.71 #3497, 0.62 #3826, 0.60 #2838), 01s73z (0.71 #3410, 0.60 #2751, 0.55 #1320), 019rl6 (0.67 #4451, 0.67 #3132, 0.62 #3790), 02r5dz (0.67 #3044, 0.60 #2714, 0.58 #4363), 0300cp (0.67 #3024, 0.60 #2694, 0.58 #4343), 07xyn1 (0.67 #3156, 0.60 #2826, 0.58 #4475), 0vlf (0.67 #3258, 0.60 #2928, 0.57 #3587) >> Best rule #3774 for best value: >> intensional similarity = 13 >> extensional distance = 6 >> proper extension: 01rk91; 01yc02; >> query: (?x6010, 0537b) <- company(?x6010, ?x9469), company(?x6010, ?x6404), company(?x6010, ?x3253), company(?x6010, ?x555), ?x9469 = 04sv4, state_province_region(?x6404, ?x1227), company(?x346, ?x3253), place_founded(?x6404, ?x3125), ?x346 = 060c4, currency(?x3253, ?x170), service_location(?x555, ?x1453), film_release_region(?x66, ?x1453), country(?x150, ?x1453) >> conf = 0.75 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02y6fz company 0jmnl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 35.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 02y6fz company 06wpc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 35.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #16622-03t79f PRED entity: 03t79f PRED relation: film! PRED expected values: 01rnxn 050zr4 => 99 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1257): 016dmx (0.44 #52012, 0.44 #49929, 0.37 #56173), 0f5xn (0.17 #3047, 0.06 #7208, 0.06 #17608), 04flrx (0.16 #22881, 0.15 #95699, 0.15 #87378), 06pj8 (0.16 #22881, 0.13 #47848, 0.13 #45766), 0h7pj (0.14 #3621, 0.04 #1542, 0.03 #68117), 03y2kr (0.13 #47847, 0.12 #45765, 0.12 #58255), 0f0kz (0.11 #4674, 0.09 #17155, 0.09 #12994), 0c6qh (0.11 #2492, 0.08 #56175, 0.08 #72814), 02p65p (0.11 #2100, 0.04 #81138, 0.02 #49952), 014zcr (0.11 #2116, 0.04 #37, 0.04 #4196) >> Best rule #52012 for best value: >> intensional similarity = 4 >> extensional distance = 334 >> proper extension: 014lc_; 01sxly; 0b60sq; 0209xj; 0kv2hv; 04969y; 0gjk1d; 0g5pv3; 07h9gp; 01dyvs; ... >> query: (?x5372, ?x3708) <- film_release_distribution_medium(?x5372, ?x81), executive_produced_by(?x5372, ?x2135), nominated_for(?x3708, ?x5372), genre(?x5372, ?x571) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2586 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0gyv0b4; *> query: (?x5372, 01rnxn) <- genre(?x5372, ?x571), film(?x400, ?x5372), film(?x6203, ?x5372), cinematography(?x2498, ?x6203) *> conf = 0.03 ranks of expected_values: 425, 1035 EVAL 03t79f film! 050zr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 99.000 47.000 0.445 http://example.org/film/actor/film./film/performance/film EVAL 03t79f film! 01rnxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 99.000 47.000 0.445 http://example.org/film/actor/film./film/performance/film #16621-014zn0 PRED entity: 014zn0 PRED relation: nationality PRED expected values: 09c7w0 => 112 concepts (89 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.87 #4014, 0.85 #4314, 0.83 #2406), 04_1l0v (0.32 #6329), 02jx1 (0.14 #2004, 0.13 #2638, 0.13 #2538), 07ssc (0.14 #2004, 0.12 #2620, 0.12 #2720), 0h7x (0.14 #2004, 0.08 #235, 0.04 #535), 0d060g (0.14 #2004, 0.05 #6234, 0.05 #5624), 06c1y (0.14 #2004, 0.03 #1241, 0.02 #1341), 0j5g9 (0.14 #2004, 0.02 #562, 0.02 #763), 012m_ (0.14 #2004, 0.01 #892, 0.01 #1593), 03rk0 (0.07 #7486, 0.07 #8491, 0.06 #8893) >> Best rule #4014 for best value: >> intensional similarity = 4 >> extensional distance = 384 >> proper extension: 027y_; 01tpl1p; 02xyl; >> query: (?x11961, 09c7w0) <- location(?x11961, ?x4622), gender(?x11961, ?x231), contains(?x4622, ?x1505), district_represented(?x176, ?x4622) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014zn0 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 89.000 0.865 http://example.org/people/person/nationality #16620-0flpy PRED entity: 0flpy PRED relation: languages PRED expected values: 02h40lc => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 7): 02h40lc (0.25 #2147, 0.24 #1874, 0.24 #3785), 064_8sq (0.05 #327, 0.04 #210, 0.02 #3798), 06b_j (0.02 #328, 0.02 #289, 0.02 #367), 06nm1 (0.02 #318, 0.02 #1878, 0.01 #396), 03k50 (0.02 #2656, 0.02 #1798, 0.02 #2617), 02bjrlw (0.01 #1093, 0.01 #1717), 03_9r (0.01 #512) >> Best rule #2147 for best value: >> intensional similarity = 4 >> extensional distance = 359 >> proper extension: 01sl1q; 06jzh; 01gvr1; 01csvq; 018db8; 0785v8; 066m4g; 07s6prs; 06mfvc; 026zvx7; ... >> query: (?x6290, 02h40lc) <- award(?x6290, ?x567), award_nominee(?x6290, ?x2731), location(?x6290, ?x11968), county_seat(?x10490, ?x11968) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0flpy languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.249 http://example.org/people/person/languages #16619-01xbgx PRED entity: 01xbgx PRED relation: contains! PRED expected values: 073q1 => 188 concepts (125 used for prediction) PRED predicted values (max 10 best out of 159): 02j71 (0.60 #72506, 0.60 #57289, 0.51 #35802), 073q1 (0.52 #69818, 0.51 #71611, 0.10 #14724), 09c7w0 (0.46 #67135, 0.45 #25957, 0.44 #103841), 02j9z (0.45 #102970, 0.35 #19714, 0.34 #20610), 04wsz (0.33 #3180, 0.29 #10336, 0.22 #30925), 0dg3n1 (0.32 #61023, 0.30 #60129, 0.29 #102202), 07c5l (0.31 #27242, 0.27 #51416, 0.26 #68420), 04_1l0v (0.29 #92654, 0.28 #46998, 0.23 #11183), 07ssc (0.20 #85972, 0.17 #1820, 0.15 #71643), 04pnx (0.19 #27272, 0.19 #41598, 0.17 #50552) >> Best rule #72506 for best value: >> intensional similarity = 2 >> extensional distance = 113 >> proper extension: 05x30m; 05l5n; 09tlh; 0nccd; 0gqkd; 04p3c; 09b9m; 02m77; 0m75g; 0rng; ... >> query: (?x7748, ?x551) <- location_of_ceremony(?x566, ?x7748), administrative_parent(?x7748, ?x551) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #69818 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 110 *> proper extension: 04pry; 0pf5y; 0f485; 0cvyp; 0n96z; *> query: (?x7748, ?x6304) <- location_of_ceremony(?x566, ?x7748), adjoins(?x8593, ?x7748), contains(?x6304, ?x8593) *> conf = 0.52 ranks of expected_values: 2 EVAL 01xbgx contains! 073q1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 188.000 125.000 0.600 http://example.org/location/location/contains #16618-07j8r PRED entity: 07j8r PRED relation: film_release_region PRED expected values: 0jgd 03rjj 05qhw 02vzc => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 171): 03rjj (0.83 #834, 0.74 #2322, 0.74 #2653), 02vzc (0.79 #885, 0.79 #719, 0.77 #2042), 0jgd (0.78 #831, 0.71 #1988, 0.71 #2319), 035qy (0.78 #866, 0.63 #2685, 0.63 #2354), 07ssc (0.78 #847, 0.72 #2004, 0.72 #2666), 03_3d (0.77 #836, 0.69 #1993, 0.69 #2324), 03h64 (0.77 #902, 0.68 #2390, 0.67 #2721), 015fr (0.77 #848, 0.62 #2667, 0.61 #2005), 05qhw (0.75 #845, 0.61 #2002, 0.61 #2664), 05b4w (0.73 #899, 0.62 #2056, 0.61 #2387) >> Best rule #834 for best value: >> intensional similarity = 4 >> extensional distance = 114 >> proper extension: 0ds3t5x; 0g5qs2k; 0407yfx; 047svrl; 01jrbb; 0cc97st; 0bdjd; 01mgw; 0ndsl1x; >> query: (?x2550, 03rjj) <- nominated_for(?x7068, ?x2550), film(?x166, ?x2550), film_release_region(?x2550, ?x1558), ?x1558 = 01mjq >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 9 EVAL 07j8r film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.828 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07j8r film_release_region 05qhw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 88.000 0.828 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07j8r film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.828 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07j8r film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.828 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16617-025mb_ PRED entity: 025mb_ PRED relation: award_winner! PRED expected values: 09qj50 => 129 concepts (122 used for prediction) PRED predicted values (max 10 best out of 311): 0m7yy (0.57 #7424, 0.11 #41775, 0.04 #12536), 0cqhk0 (0.39 #22589, 0.39 #31111, 0.38 #15768), 0gkts9 (0.39 #22589, 0.39 #31111, 0.38 #15768), 09qj50 (0.39 #22589, 0.39 #31111, 0.38 #15768), 0bdw1g (0.39 #22589, 0.39 #31111, 0.38 #15768), 0c422z4 (0.39 #22589, 0.39 #31111, 0.38 #15768), 0ck27z (0.27 #8616, 0.16 #12450, 0.14 #12876), 0gqyl (0.22 #105, 0.10 #5645, 0.08 #1383), 02z0dfh (0.22 #75, 0.10 #5615, 0.07 #2631), 02ppm4q (0.22 #154, 0.08 #1432, 0.07 #2284) >> Best rule #7424 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 0kc6x; 065y4w7; 0f721s; 01y67v; 01jq34; 0l2tk; 01w92; 01_8w2; 01p5yn; 01gl9g; ... >> query: (?x9140, 0m7yy) <- award_winner(?x4225, ?x9140), award(?x6884, ?x4225), ?x6884 = 039cq4 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #22589 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 949 *> proper extension: 01sl1q; 044mz_; 0184jc; 012ljv; 02s2ft; 01vvydl; 02qgqt; 0fvf9q; 02p65p; 0337vz; ... *> query: (?x9140, ?x594) <- award_winner(?x873, ?x9140), award_winner(?x3308, ?x9140), award(?x9140, ?x594) *> conf = 0.39 ranks of expected_values: 4 EVAL 025mb_ award_winner! 09qj50 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 129.000 122.000 0.567 http://example.org/award/award_category/winners./award/award_honor/award_winner #16616-0n2kw PRED entity: 0n2kw PRED relation: time_zones PRED expected values: 02hcv8 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 8): 02hcv8 (0.91 #3, 0.85 #147, 0.85 #79), 02lcqs (0.21 #138, 0.21 #31, 0.21 #70), 02fqwt (0.17 #478, 0.16 #413, 0.16 #465), 02hczc (0.10 #267, 0.10 #41, 0.09 #54), 02llzg (0.06 #390, 0.06 #376, 0.06 #348), 03bdv (0.03 #692, 0.03 #955, 0.03 #614), 03plfd (0.02 #396, 0.02 #409, 0.02 #435), 0gsrz4 (0.02 #380, 0.01 #394, 0.01 #433) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0z1cr; 029cr; 0yshw; 01snm; 0z20d; 0z2gq; 0yt73; 0z1vw; 0yzyn; 0z18v; ... >> query: (?x12306, 02hcv8) <- source(?x12306, ?x958), contains(?x177, ?x12306), ?x958 = 0jbk9, ?x177 = 05kkh >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n2kw time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.914 http://example.org/location/location/time_zones #16615-07kb5 PRED entity: 07kb5 PRED relation: profession PRED expected values: 05t4q => 84 concepts (42 used for prediction) PRED predicted values (max 10 best out of 79): 01c72t (0.99 #4176, 0.11 #3435, 0.10 #4324), 02hrh1q (0.70 #5500, 0.63 #3277, 0.43 #4907), 0dxtg (0.56 #4461, 0.47 #4906, 0.45 #1644), 06q2q (0.50 #192, 0.40 #2670, 0.33 #44), 04s2z (0.50 #211, 0.40 #2670, 0.33 #63), 05snw (0.50 #240, 0.40 #2670, 0.33 #92), 0kyk (0.48 #2997, 0.40 #5071, 0.39 #1513), 09jwl (0.47 #4171, 0.15 #3875, 0.13 #5357), 05t4q (0.40 #2670, 0.32 #6081, 0.25 #6232), 0h9c (0.40 #2670, 0.32 #6081, 0.25 #191) >> Best rule #4176 for best value: >> intensional similarity = 6 >> extensional distance = 302 >> proper extension: 0lbj1; 01nqfh_; 01w923; 01vyp_; 01zmpg; 01qdjm; 04pf4r; 050z2; 01l4g5; 0kxbc; ... >> query: (?x712, 01c72t) <- profession(?x712, ?x2413), profession(?x12345, ?x2413), profession(?x11460, ?x2413), ?x11460 = 0hqgp, influenced_by(?x1029, ?x12345), religion(?x12345, ?x4641) >> conf = 0.99 => this is the best rule for 1 predicted values *> Best rule #2670 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 168 *> proper extension: 04bs3j; 01n5309; 01c58j; 073bb; 0453t; 0gd5z; 01trhmt; 06449; 015f7; 02qwg; ... *> query: (?x712, ?x3801) <- profession(?x712, ?x353), influenced_by(?x712, ?x7341), languages(?x7341, ?x5359), profession(?x7341, ?x3801) *> conf = 0.40 ranks of expected_values: 9 EVAL 07kb5 profession 05t4q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 84.000 42.000 0.990 http://example.org/people/person/profession #16614-05g76 PRED entity: 05g76 PRED relation: position PRED expected values: 01z9v6 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 7): 01z9v6 (0.89 #107, 0.83 #205, 0.83 #156), 017drs (0.50 #52, 0.33 #3, 0.20 #31), 02dwpf (0.40 #27, 0.38 #55, 0.28 #118), 049k4w (0.40 #26, 0.38 #54, 0.25 #61), 02sg4b (0.33 #4, 0.25 #60, 0.25 #53), 01yvvn (0.33 #1, 0.25 #57, 0.20 #64), 02sddg (0.33 #7, 0.25 #56, 0.20 #35) >> Best rule #107 for best value: >> intensional similarity = 7 >> extensional distance = 16 >> proper extension: 04913k; 021f30; 03qrh9; >> query: (?x2067, 01z9v6) <- position(?x2067, ?x5727), position(?x2067, ?x2010), ?x5727 = 02wszf, colors(?x2067, ?x3189), ?x2010 = 02lyr4, colors(?x6908, ?x3189), ?x6908 = 01dthg >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05g76 position 01z9v6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.889 http://example.org/sports/sports_team/roster./baseball/baseball_roster_position/position #16613-016ywb PRED entity: 016ywb PRED relation: films! PRED expected values: 0flry => 75 concepts (24 used for prediction) PRED predicted values (max 10 best out of 48): 0bq3x (0.09 #30, 0.06 #187, 0.03 #975), 0fx2s (0.09 #73, 0.04 #388, 0.03 #545), 0cm2xh (0.06 #204, 0.02 #519, 0.01 #362), 0nbjq (0.06 #182), 0bynt (0.06 #167), 081pw (0.06 #475, 0.04 #318, 0.04 #2689), 07c52 (0.06 #335, 0.02 #1440, 0.02 #2072), 05489 (0.04 #367, 0.03 #1472, 0.02 #997), 07_nf (0.04 #382, 0.02 #539, 0.02 #1962), 06d4h (0.03 #515, 0.03 #2729, 0.03 #2254) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 09jcj6; 0gbfn9; 0660b9b; >> query: (?x7073, 0bq3x) <- country(?x7073, ?x512), film(?x1371, ?x7073), production_companies(?x7073, ?x9518), ?x9518 = 0283xx2 >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016ywb films! 0flry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 24.000 0.091 http://example.org/film/film_subject/films #16612-0r2dp PRED entity: 0r2dp PRED relation: location! PRED expected values: 0hqly => 153 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2097): 02_p5w (0.33 #723, 0.06 #3241, 0.03 #10796), 01w02sy (0.12 #28296, 0.08 #45924, 0.07 #48443), 0227tr (0.12 #2998, 0.05 #33218, 0.05 #48327), 016s0m (0.12 #4332, 0.03 #11887, 0.03 #37070), 099d4 (0.12 #4883, 0.02 #128277, 0.02 #100576), 0127m7 (0.10 #7555, 0.06 #15560, 0.05 #20597), 018grr (0.10 #7555, 0.05 #20524, 0.05 #18005), 032nl2 (0.10 #7555, 0.03 #16730, 0.03 #21767), 0gx_p (0.10 #7555, 0.03 #16383, 0.03 #21420), 0783m_ (0.10 #7555, 0.03 #15537, 0.03 #20574) >> Best rule #723 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0k9p4; >> query: (?x10526, 02_p5w) <- location(?x4628, ?x10526), contains(?x10526, ?x10297), ?x4628 = 016fnb, source(?x10526, ?x958) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r2dp location! 0hqly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 89.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #16611-05qjt PRED entity: 05qjt PRED relation: major_field_of_study! PRED expected values: 03g3w => 73 concepts (62 used for prediction) PRED predicted values (max 10 best out of 122): 0pf2 (0.84 #4315, 0.83 #3777, 0.82 #4934), 0_jm (0.60 #1012, 0.43 #1101, 0.33 #658), 05qfh (0.58 #1876, 0.50 #908, 0.50 #731), 064_8sq (0.50 #915, 0.50 #738, 0.43 #1092), 03g3w (0.50 #1868, 0.50 #900, 0.40 #988), 02j62 (0.50 #1961, 0.47 #2225, 0.43 #1080), 02822 (0.50 #1879, 0.44 #2410, 0.33 #471), 0fdys (0.50 #910, 0.40 #998, 0.33 #1878), 05qt0 (0.50 #922, 0.33 #656, 0.33 #482), 01400v (0.40 #1041, 0.33 #687, 0.33 #513) >> Best rule #4315 for best value: >> intensional similarity = 8 >> extensional distance = 72 >> proper extension: 06ntj; >> query: (?x742, ?x3400) <- major_field_of_study(?x4100, ?x742), major_field_of_study(?x742, ?x3400), major_field_of_study(?x1526, ?x3400), ?x1526 = 0bkj86, major_field_of_study(?x2497, ?x4100), major_field_of_study(?x947, ?x4100), major_field_of_study(?x865, ?x4100), school(?x660, ?x2497) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1868 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 10 *> proper extension: 04rjg; 0h5k; 0fdys; 01zc2w; *> query: (?x742, 03g3w) <- major_field_of_study(?x6333, ?x742), major_field_of_study(?x2313, ?x742), major_field_of_study(?x2166, ?x742), major_field_of_study(?x1681, ?x742), major_field_of_study(?x1391, ?x742), contains(?x2146, ?x1391), institution(?x620, ?x2166), student(?x742, ?x3335), organization(?x3484, ?x2166), service_language(?x6333, ?x254), ?x1681 = 07szy, ?x2313 = 07wrz *> conf = 0.50 ranks of expected_values: 5 EVAL 05qjt major_field_of_study! 03g3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 62.000 0.838 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #16610-02g3v6 PRED entity: 02g3v6 PRED relation: award! PRED expected values: 017gm7 => 42 concepts (13 used for prediction) PRED predicted values (max 10 best out of 758): 026p4q7 (0.67 #5327, 0.60 #3291, 0.40 #2272), 017gl1 (0.60 #3140, 0.50 #5176, 0.33 #89), 035_2h (0.50 #5623, 0.40 #3587, 0.40 #2568), 091z_p (0.50 #5253, 0.40 #3217, 0.40 #2198), 07gp9 (0.40 #3076, 0.33 #5112, 0.33 #25), 04jpg2p (0.40 #2870, 0.33 #5925, 0.20 #3889), 049xgc (0.40 #2598, 0.33 #5653, 0.20 #3617), 042y1c (0.40 #3282, 0.33 #5318, 0.20 #2263), 016z7s (0.40 #2237, 0.33 #5292, 0.20 #3256), 011yr9 (0.40 #3461, 0.33 #5497, 0.20 #2442) >> Best rule #5327 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0gq_v; >> query: (?x507, 026p4q7) <- nominated_for(?x507, ?x7801), nominated_for(?x507, ?x7741), nominated_for(?x507, ?x3845), ?x7741 = 01xq8v, nominated_for(?x2156, ?x3845), award_winner(?x7801, ?x3692) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #126 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 02hsq3m; *> query: (?x507, 017gm7) <- nominated_for(?x507, ?x7741), nominated_for(?x507, ?x3845), nominated_for(?x507, ?x708), ?x7741 = 01xq8v, ?x3845 = 0639bg, ?x708 = 0fg04 *> conf = 0.33 ranks of expected_values: 20 EVAL 02g3v6 award! 017gm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 42.000 13.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #16609-020_4z PRED entity: 020_4z PRED relation: nationality PRED expected values: 07ssc => 99 concepts (77 used for prediction) PRED predicted values (max 10 best out of 41): 09c7w0 (0.82 #892, 0.80 #397, 0.77 #1090), 07ssc (0.56 #4278, 0.35 #1698, 0.34 #1897), 0127c4 (0.28 #7049), 03rk0 (0.18 #6095, 0.12 #3019, 0.10 #2721), 06q1r (0.10 #6449, 0.05 #6250, 0.04 #5950), 0hzlz (0.10 #6449, 0.05 #6250, 0.04 #5950), 04xn_ (0.10 #6449), 0ctw_b (0.10 #6449), 0d060g (0.07 #4568, 0.05 #6250, 0.05 #304), 0345h (0.06 #6081, 0.05 #6250, 0.04 #5950) >> Best rule #892 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 01wdqrx; 0770cd; >> query: (?x10437, 09c7w0) <- profession(?x10437, ?x220), artists(?x3928, ?x10437), ?x3928 = 0gywn, people(?x5042, ?x10437) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #4278 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 597 *> proper extension: 02xb2bt; 09jd9; *> query: (?x10437, 07ssc) <- nationality(?x10437, ?x1310), contains(?x1310, ?x7918), contains(?x1310, ?x6419), ?x6419 = 0ny75, ?x7918 = 0gl6f *> conf = 0.56 ranks of expected_values: 2 EVAL 020_4z nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 77.000 0.819 http://example.org/people/person/nationality #16608-047vp1n PRED entity: 047vp1n PRED relation: film! PRED expected values: 061dn_ 032j_n => 67 concepts (52 used for prediction) PRED predicted values (max 10 best out of 73): 03xsby (0.44 #376, 0.10 #1372, 0.10 #618), 016tt2 (0.27 #4, 0.15 #304, 0.14 #1511), 017s11 (0.26 #153, 0.18 #3, 0.14 #1964), 086k8 (0.18 #2, 0.16 #302, 0.15 #378), 025jfl (0.18 #6, 0.09 #608, 0.08 #533), 016tw3 (0.17 #838, 0.16 #1139, 0.16 #763), 03xq0f (0.14 #607, 0.14 #1361, 0.12 #381), 05qd_ (0.12 #84, 0.11 #1894, 0.11 #1516), 024rdh (0.10 #1393, 0.10 #639, 0.08 #864), 01gb54 (0.09 #329, 0.08 #405, 0.08 #1914) >> Best rule #376 for best value: >> intensional similarity = 6 >> extensional distance = 95 >> proper extension: 024l2y; 01hqhm; 01ffx4; 0pd64; >> query: (?x7314, ?x1914) <- language(?x7314, ?x5607), ?x5607 = 064_8sq, genre(?x7314, ?x53), film_crew_role(?x7314, ?x468), film(?x368, ?x7314), production_companies(?x7314, ?x1914) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #174 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 25 *> proper extension: 05fm6m; 02825nf; 03h0byn; *> query: (?x7314, 061dn_) <- film(?x5944, ?x7314), titles(?x2480, ?x7314), film(?x5944, ?x5945), nominated_for(?x5944, ?x2436), award_nominee(?x436, ?x5944), ?x5945 = 05t0_2v *> conf = 0.07 ranks of expected_values: 15, 28 EVAL 047vp1n film! 032j_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 67.000 52.000 0.436 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 047vp1n film! 061dn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 67.000 52.000 0.436 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16607-0jfx1 PRED entity: 0jfx1 PRED relation: film PRED expected values: 0n_hp => 119 concepts (94 used for prediction) PRED predicted values (max 10 best out of 876): 06w99h3 (0.63 #44207, 0.63 #56585, 0.58 #118480), 04pk1f (0.63 #44207, 0.63 #56585, 0.58 #118480), 0ch26b_ (0.63 #44207, 0.63 #56585, 0.58 #118480), 09146g (0.17 #293, 0.06 #47744, 0.01 #42731), 049mql (0.17 #676, 0.04 #2444, 0.02 #13054), 0287477 (0.17 #1055, 0.04 #2823), 02vjp3 (0.17 #1285, 0.03 #17199, 0.02 #4821), 062zm5h (0.17 #848, 0.02 #7920, 0.02 #4384), 01xbxn (0.17 #1378, 0.02 #4914, 0.02 #6682), 0dzlbx (0.17 #842, 0.02 #4378, 0.02 #6146) >> Best rule #44207 for best value: >> intensional similarity = 3 >> extensional distance = 339 >> proper extension: 025hzx; >> query: (?x2444, ?x224) <- award_nominee(?x398, ?x2444), nominated_for(?x2444, ?x224), participant(?x2444, ?x117) >> conf = 0.63 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0jfx1 film 0n_hp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 94.000 0.633 http://example.org/film/actor/film./film/performance/film #16606-01ggc9 PRED entity: 01ggc9 PRED relation: profession PRED expected values: 02hrh1q 03gjzk => 129 concepts (128 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.89 #3292, 0.89 #15809, 0.89 #759), 0dxtg (0.48 #9549, 0.28 #12232, 0.28 #12679), 02jknp (0.45 #9543, 0.24 #9394, 0.22 #10586), 03gjzk (0.31 #9551, 0.31 #909, 0.29 #3144), 018gz8 (0.26 #16839, 0.17 #762, 0.17 #911), 0cbd2 (0.26 #16839, 0.16 #2986, 0.16 #9840), 09jwl (0.23 #1807, 0.22 #168, 0.22 #6426), 0np9r (0.22 #170, 0.21 #8514, 0.15 #1064), 0d1pc (0.17 #1690, 0.16 #2286, 0.14 #2733), 016z4k (0.15 #1792, 0.14 #6411, 0.13 #5368) >> Best rule #3292 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 044mz_; 02s2ft; 02p65p; 06gp3f; 083chw; 01wbg84; 05cj4r; 01tvz5j; 03rs8y; 025h4z; ... >> query: (?x10161, 02hrh1q) <- award_winner(?x10161, ?x820), actor(?x337, ?x10161), award_winner(?x102, ?x10161) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 01ggc9 profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 128.000 0.894 http://example.org/people/person/profession EVAL 01ggc9 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 128.000 0.894 http://example.org/people/person/profession #16605-017r2 PRED entity: 017r2 PRED relation: award PRED expected values: 01l78d => 168 concepts (161 used for prediction) PRED predicted values (max 10 best out of 315): 054ks3 (0.58 #952, 0.43 #26874, 0.32 #33762), 0c4z8 (0.50 #882, 0.43 #26804, 0.32 #33692), 01by1l (0.50 #922, 0.27 #26844, 0.22 #33732), 01bgqh (0.50 #853, 0.25 #26775, 0.22 #33663), 03qbh5 (0.42 #1016, 0.16 #26938, 0.13 #33826), 02x17c2 (0.42 #1030, 0.15 #26952, 0.12 #2650), 02f73p (0.42 #998, 0.09 #7883, 0.09 #6668), 02f73b (0.42 #1098, 0.09 #7983, 0.09 #5958), 0gqz2 (0.41 #2106, 0.38 #26813, 0.28 #33701), 0gs9p (0.36 #1295, 0.31 #3725, 0.12 #28027) >> Best rule #952 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0dw4g; >> query: (?x1645, 054ks3) <- award(?x1645, ?x1869), peers(?x12592, ?x1645), award(?x8661, ?x1869), ?x8661 = 02fgp0 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #8795 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 53 *> proper extension: 016yzz; 01q9b9; 040rjq; 02665kn; *> query: (?x1645, 01l78d) <- award(?x1645, ?x1869), profession(?x1645, ?x6421), profession(?x1645, ?x987), ?x987 = 0dxtg, ?x6421 = 02hv44_ *> conf = 0.33 ranks of expected_values: 24 EVAL 017r2 award 01l78d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 168.000 161.000 0.583 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16604-0bscw PRED entity: 0bscw PRED relation: film_crew_role PRED expected values: 01vx2h => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 28): 0dxtw (0.45 #434, 0.45 #398, 0.43 #363), 01vx2h (0.41 #719, 0.40 #789, 0.39 #399), 01pvkk (0.31 #116, 0.29 #400, 0.28 #436), 02ynfr (0.25 #50, 0.20 #724, 0.20 #794), 02rh1dz (0.20 #397, 0.18 #433, 0.17 #362), 01xy5l_ (0.17 #48, 0.14 #722, 0.13 #792), 0215hd (0.16 #727, 0.16 #797, 0.15 #657), 089g0h (0.14 #408, 0.13 #728, 0.13 #798), 0d2b38 (0.13 #414, 0.13 #734, 0.12 #804), 015h31 (0.12 #396, 0.12 #361, 0.11 #432) >> Best rule #434 for best value: >> intensional similarity = 4 >> extensional distance = 262 >> proper extension: 0gtsx8c; >> query: (?x1444, 0dxtw) <- language(?x1444, ?x254), film_crew_role(?x1444, ?x137), crewmember(?x1444, ?x6546), country(?x1444, ?x94) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #719 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 572 *> proper extension: 0cpllql; 0c40vxk; 0ds2n; 0gs973; 06__m6; 047bynf; 01mgw; 0gzlb9; 02mpyh; 01xvjb; ... *> query: (?x1444, 01vx2h) <- currency(?x1444, ?x170), film_crew_role(?x1444, ?x468), film(?x3138, ?x1444), ?x468 = 02r96rf *> conf = 0.41 ranks of expected_values: 2 EVAL 0bscw film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.451 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16603-013d7t PRED entity: 013d7t PRED relation: place_of_birth! PRED expected values: 0ckm4x => 141 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1384): 0bjkpt (0.41 #23514, 0.40 #60091, 0.40 #65317), 02fybl (0.41 #23514, 0.40 #60091, 0.40 #65317), 02yygk (0.25 #2084, 0.20 #4696, 0.07 #7311), 01k5t_3 (0.25 #205, 0.20 #2817, 0.07 #5432), 05w1vf (0.05 #12777, 0.03 #15389, 0.01 #18002), 017f4y (0.05 #12669, 0.03 #15281, 0.01 #17894), 01vvybv (0.05 #12611, 0.03 #15223, 0.01 #17836), 01693z (0.05 #12209, 0.03 #14821, 0.01 #17434), 04f7c55 (0.05 #11627, 0.03 #14239, 0.01 #16852), 01k70_ (0.05 #11347, 0.03 #13959, 0.01 #16572) >> Best rule #23514 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 0xkq4; 0fvxz; 0fvvz; 0s69k; 0mp3l; 0d234; 0cv3w; 0n6bs; 0v9qg; 02j3w; ... >> query: (?x5143, ?x5196) <- location(?x9298, ?x5143), location(?x5196, ?x5143), category(?x5143, ?x134), state(?x5143, ?x3908), artists(?x482, ?x9298) >> conf = 0.41 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 013d7t place_of_birth! 0ckm4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 141.000 67.000 0.415 http://example.org/people/person/place_of_birth #16602-05k7sb PRED entity: 05k7sb PRED relation: religion PRED expected values: 021_0p => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 25): 01y0s9 (0.62 #429, 0.59 #754, 0.57 #882), 021_0p (0.59 #435, 0.57 #888, 0.57 #862), 0flw86 (0.53 #1693, 0.50 #326, 0.42 #351), 072w0 (0.53 #1693, 0.40 #190, 0.25 #440), 02t7t (0.53 #1693, 0.28 #438, 0.25 #513), 058x5 (0.35 #752, 0.35 #880, 0.35 #854), 03j6c (0.25 #86, 0.25 #61, 0.25 #36), 0kpl (0.25 #80, 0.25 #55, 0.25 #30), 07w8f (0.25 #94, 0.25 #69, 0.25 #44), 0kq2 (0.22 #1182, 0.08 #3451) >> Best rule #429 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 011hq1; >> query: (?x2020, 01y0s9) <- location(?x1149, ?x2020), category(?x2020, ?x134), religion(?x2020, ?x109), award(?x1149, ?x375) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #435 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 011hq1; *> query: (?x2020, 021_0p) <- location(?x1149, ?x2020), category(?x2020, ?x134), religion(?x2020, ?x109), award(?x1149, ?x375) *> conf = 0.59 ranks of expected_values: 2 EVAL 05k7sb religion 021_0p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 164.000 164.000 0.625 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #16601-0jvt9 PRED entity: 0jvt9 PRED relation: music PRED expected values: 02sj1x => 68 concepts (57 used for prediction) PRED predicted values (max 10 best out of 129): 015wc0 (0.25 #176, 0.20 #387, 0.06 #1229), 012wg (0.25 #76, 0.20 #287, 0.01 #918), 0146pg (0.17 #2332, 0.16 #642, 0.09 #432), 02sj1x (0.09 #1109, 0.09 #1320, 0.03 #2589), 058vfp4 (0.08 #211, 0.07 #5714, 0.07 #2744), 04vzv4 (0.08 #211, 0.07 #5714, 0.07 #2744), 087v17 (0.08 #211, 0.07 #5714, 0.07 #2744), 06hzsx (0.08 #211, 0.07 #5714, 0.07 #2744), 09cdxn (0.08 #211, 0.07 #2744, 0.06 #5288), 01pr6q7 (0.05 #904, 0.04 #1115, 0.03 #1326) >> Best rule #176 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0jqd3; 05sbv3; >> query: (?x3294, 015wc0) <- film(?x4926, ?x3294), nominated_for(?x9825, ?x3294), ?x9825 = 058vfp4, list(?x3294, ?x3004) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1109 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 0fsw_7; 0fztbq; *> query: (?x3294, 02sj1x) <- film(?x2416, ?x3294), film_sets_designed(?x9825, ?x3294), film(?x788, ?x3294) *> conf = 0.09 ranks of expected_values: 4 EVAL 0jvt9 music 02sj1x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 57.000 0.250 http://example.org/film/film/music #16600-0c0k1 PRED entity: 0c0k1 PRED relation: award PRED expected values: 05ztrmj => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 256): 09sb52 (0.26 #3255, 0.23 #22149, 0.23 #4461), 0gq9h (0.21 #1282, 0.10 #3694, 0.08 #17764), 0ck27z (0.21 #6121, 0.12 #23005, 0.11 #23809), 05pcn59 (0.17 #3296, 0.17 #4502, 0.15 #2492), 040njc (0.17 #1214, 0.09 #3626, 0.08 #5636), 07bdd_ (0.16 #1270, 0.08 #868, 0.05 #7702), 0gqwc (0.14 #3289, 0.12 #4495, 0.11 #73), 01by1l (0.13 #2120, 0.11 #4130, 0.09 #16994), 05p1dby (0.12 #1312, 0.07 #26131, 0.04 #7744), 0gqyl (0.12 #104, 0.10 #1712, 0.09 #4526) >> Best rule #3255 for best value: >> intensional similarity = 3 >> extensional distance = 376 >> proper extension: 01g0jn; 06c0j; >> query: (?x8704, 09sb52) <- profession(?x8704, ?x319), award_winner(?x2060, ?x8704), participant(?x8704, ?x1397) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #986 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 191 *> proper extension: 01_k1z; *> query: (?x8704, 05ztrmj) <- profession(?x8704, ?x319), currency(?x8704, ?x170), ?x319 = 01d_h8 *> conf = 0.09 ranks of expected_values: 18 EVAL 0c0k1 award 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 99.000 99.000 0.262 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16599-02648p PRED entity: 02648p PRED relation: country_of_origin PRED expected values: 09c7w0 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 18): 09c7w0 (0.93 #179, 0.91 #420, 0.91 #623), 07ssc (0.70 #217, 0.52 #133, 0.33 #18), 03rjj (0.56 #136, 0.18 #656, 0.04 #127), 03_3d (0.26 #253, 0.21 #337, 0.20 #43), 03rt9 (0.18 #656, 0.17 #17, 0.04 #153), 02jx1 (0.09 #135, 0.08 #219, 0.02 #461), 07c52 (0.07 #61, 0.06 #92, 0.06 #103), 04jpl (0.04 #130, 0.03 #214), 05v8c (0.02 #238, 0.02 #353, 0.01 #644), 02gt5s (0.01 #633) >> Best rule #179 for best value: >> intensional similarity = 6 >> extensional distance = 26 >> proper extension: 026bfsh; >> query: (?x4108, 09c7w0) <- actor(?x4108, ?x13236), actor(?x4108, ?x4109), country_of_origin(?x4108, ?x279), actor(?x5955, ?x4109), award_nominee(?x13236, ?x3583), profession(?x4109, ?x563) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02648p country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.929 http://example.org/tv/tv_program/country_of_origin #16598-09mq4m PRED entity: 09mq4m PRED relation: award_winner! PRED expected values: 01bgqh => 103 concepts (77 used for prediction) PRED predicted values (max 10 best out of 278): 01bgqh (0.50 #895, 0.23 #3025, 0.19 #6392), 09sb52 (0.48 #8991, 0.11 #19646, 0.09 #25188), 01c4_6 (0.33 #514, 0.16 #1366, 0.04 #2644), 02x17c2 (0.32 #17048, 0.31 #14916, 0.31 #20886), 01c92g (0.26 #2226, 0.19 #948, 0.14 #3504), 01c427 (0.23 #20032, 0.20 #83, 0.19 #6392), 03qbnj (0.23 #20032, 0.19 #6392, 0.15 #27279), 02v1m7 (0.23 #20032, 0.19 #6392, 0.15 #27279), 01c99j (0.23 #20032, 0.19 #6392, 0.15 #27279), 02f72_ (0.23 #20032, 0.19 #6392, 0.15 #27279) >> Best rule #895 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 03cd1q; >> query: (?x1826, 01bgqh) <- award_winner(?x1827, ?x1826), gender(?x1826, ?x231), ?x1827 = 02nhxf, award_nominee(?x1826, ?x3062) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09mq4m award_winner! 01bgqh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 77.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #16597-01wk7ql PRED entity: 01wk7ql PRED relation: film PRED expected values: 05c9zr => 132 concepts (98 used for prediction) PRED predicted values (max 10 best out of 792): 02b61v (0.10 #1020, 0.09 #2812, 0.07 #4604), 0gj8t_b (0.10 #181, 0.07 #3765, 0.05 #10933), 034b6k (0.10 #1680, 0.07 #5264, 0.05 #12432), 01vksx (0.09 #1927, 0.07 #3719, 0.05 #135), 03y0pn (0.09 #3052, 0.07 #4844, 0.05 #1260), 05nlx4 (0.09 #3050, 0.07 #4842, 0.05 #1258), 017jd9 (0.09 #2573, 0.05 #11533, 0.05 #781), 017gl1 (0.09 #1935, 0.05 #10895, 0.05 #143), 014lc_ (0.09 #1794, 0.05 #2, 0.04 #3586), 03z20c (0.09 #2269, 0.05 #13021, 0.04 #4061) >> Best rule #1020 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 03j90; >> query: (?x9848, 02b61v) <- languages(?x9848, ?x254), award_winner(?x567, ?x9848), notable_people_with_this_condition(?x1502, ?x9848) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wk7ql film 05c9zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 98.000 0.095 http://example.org/film/actor/film./film/performance/film #16596-0j8f09z PRED entity: 0j8f09z PRED relation: film_crew_role PRED expected values: 02rh1dz => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 25): 02r96rf (0.76 #327, 0.66 #544, 0.62 #1158), 09vw2b7 (0.59 #331, 0.59 #1162, 0.58 #1018), 01vx2h (0.53 #336, 0.37 #589, 0.29 #1167), 01pvkk (0.39 #229, 0.38 #85, 0.33 #13), 0dxtw (0.37 #335, 0.35 #1166, 0.34 #1240), 04pyp5 (0.24 #234, 0.08 #90, 0.07 #342), 015h31 (0.23 #333, 0.16 #586, 0.09 #406), 089fss (0.23 #78, 0.10 #42, 0.07 #367), 0263ycg (0.20 #55, 0.13 #163, 0.13 #127), 02ynfr (0.19 #341, 0.15 #89, 0.15 #1028) >> Best rule #327 for best value: >> intensional similarity = 4 >> extensional distance = 158 >> proper extension: 03t97y; 0d_2fb; 0k4d7; 0gs973; 08sk8l; 05ch98; 02qdrjx; 03whyr; 016017; 0h63q6t; ... >> query: (?x9902, 02r96rf) <- country(?x9902, ?x94), film_crew_role(?x9902, ?x137), genre(?x9902, ?x1510), ?x1510 = 01hmnh >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #334 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 158 *> proper extension: 03t97y; 0d_2fb; 0k4d7; 0gs973; 08sk8l; 05ch98; 02qdrjx; 03whyr; 016017; 0h63q6t; ... *> query: (?x9902, 02rh1dz) <- country(?x9902, ?x94), film_crew_role(?x9902, ?x137), genre(?x9902, ?x1510), ?x1510 = 01hmnh *> conf = 0.17 ranks of expected_values: 11 EVAL 0j8f09z film_crew_role 02rh1dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 54.000 54.000 0.762 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16595-01w9wwg PRED entity: 01w9wwg PRED relation: artists! PRED expected values: 07gxw => 116 concepts (114 used for prediction) PRED predicted values (max 10 best out of 203): 0xhtw (0.40 #1241, 0.27 #935, 0.19 #17177), 05bt6j (0.34 #1266, 0.25 #1879, 0.24 #2800), 017_qw (0.31 #3431, 0.30 #3737, 0.30 #4043), 016clz (0.29 #2456, 0.27 #923, 0.25 #5), 03lty (0.28 #1252, 0.12 #17188, 0.12 #8606), 06j6l (0.26 #8933, 0.24 #4337, 0.23 #10770), 05r6t (0.25 #79, 0.09 #17239, 0.09 #15403), 09096d (0.25 #278), 0ck9l7 (0.25 #205), 0dl5d (0.22 #1244, 0.12 #8598, 0.11 #11355) >> Best rule #1241 for best value: >> intensional similarity = 2 >> extensional distance = 48 >> proper extension: 0285c; 01nn6c; 0phx4; 0bkg4; 027dpx; 044mfr; 018y81; 0130sy; 01vsyjy; 0jsg0m; ... >> query: (?x6162, 0xhtw) <- currency(?x6162, ?x170), group(?x6162, ?x5760) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #10777 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 466 *> proper extension: 06lxn; *> query: (?x6162, 07gxw) <- award_winner(?x12835, ?x6162), category(?x6162, ?x134), artists(?x671, ?x6162) *> conf = 0.02 ranks of expected_values: 158 EVAL 01w9wwg artists! 07gxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 116.000 114.000 0.400 http://example.org/music/genre/artists #16594-01352_ PRED entity: 01352_ PRED relation: current_club PRED expected values: 0hvgt => 84 concepts (62 used for prediction) PRED predicted values (max 10 best out of 724): 04ltf (0.50 #952, 0.36 #1245, 0.33 #1391), 0xbm (0.33 #901, 0.33 #462, 0.33 #315), 080_y (0.33 #986, 0.33 #400, 0.27 #1279), 011v3 (0.33 #485, 0.33 #43, 0.27 #1217), 0y9j (0.33 #932, 0.33 #493, 0.25 #1518), 0175rc (0.33 #991, 0.33 #552, 0.18 #1284), 0y54 (0.33 #450, 0.19 #1769, 0.18 #1182), 0138mv (0.33 #77, 0.19 #1838, 0.18 #1251), 0hvgt (0.33 #165, 0.19 #1485, 0.17 #899), 045xx (0.33 #66, 0.18 #1240, 0.17 #1680) >> Best rule #952 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 02rqxc; >> query: (?x12089, 04ltf) <- position(?x12089, ?x60), current_club(?x12089, ?x9089), current_club(?x12089, ?x7377), ?x7377 = 06l22, team(?x530, ?x12089), sport(?x9089, ?x471), team(?x3586, ?x9089) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 01l3vx; *> query: (?x12089, 0hvgt) <- team(?x8688, ?x12089), position(?x12089, ?x60), current_club(?x12089, ?x8750), current_club(?x12089, ?x7608), team(?x2201, ?x8750), ?x7608 = 01k9cc, team(?x8360, ?x8750), team(?x8688, ?x5914) *> conf = 0.33 ranks of expected_values: 9 EVAL 01352_ current_club 0hvgt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 84.000 62.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #16593-02nhxf PRED entity: 02nhxf PRED relation: ceremony PRED expected values: 01xqqp 0gx1673 => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 125): 01xqqp (0.76 #706, 0.59 #581, 0.57 #331), 0gx1673 (0.60 #480, 0.49 #730, 0.43 #355), 0n8_m93 (0.21 #3251, 0.16 #978, 0.10 #1978), 02yw5r (0.21 #3251, 0.15 #883, 0.10 #383), 09306z (0.21 #3251, 0.13 #969, 0.10 #469), 0bzn6_ (0.21 #3251, 0.13 #920, 0.10 #420), 07y_p6 (0.21 #3251, 0.07 #1458, 0.06 #1833), 092868 (0.21 #3251, 0.04 #874, 0.03 #1124), 08pc1x (0.21 #3251, 0.04 #873, 0.03 #1123), 0drtv8 (0.21 #3251, 0.04 #1430, 0.03 #1805) >> Best rule #706 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 01c9d1; 0257pw; >> query: (?x1827, 01xqqp) <- award(?x5512, ?x1827), award_winner(?x724, ?x5512), ceremony(?x1827, ?x342), ?x342 = 01s695 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02nhxf ceremony 0gx1673 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.760 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02nhxf ceremony 01xqqp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.760 http://example.org/award/award_category/winners./award/award_honor/ceremony #16592-02yxh9 PRED entity: 02yxh9 PRED relation: honored_for PRED expected values: 037q31 => 54 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1041): 0b6tzs (0.60 #1239, 0.33 #2969, 0.32 #4158), 0ctb4g (0.40 #1388, 0.18 #7325, 0.14 #2575), 02q6gfp (0.40 #1332, 0.18 #7269, 0.14 #2519), 03x7hd (0.40 #1392, 0.18 #7329, 0.14 #2579), 02rcdc2 (0.40 #1362, 0.12 #3143, 0.12 #7299), 02r1c18 (0.33 #2969, 0.32 #4158, 0.23 #8911), 01vfqh (0.33 #2969, 0.32 #4158, 0.23 #8911), 01jzyf (0.33 #2969, 0.32 #4158, 0.23 #8911), 0jzw (0.25 #640, 0.25 #45, 0.14 #1779), 0jqj5 (0.25 #906, 0.25 #311, 0.14 #1779) >> Best rule #1239 for best value: >> intensional similarity = 13 >> extensional distance = 3 >> proper extension: 03gwpw2; 02wzl1d; 03gt46z; >> query: (?x7144, 0b6tzs) <- ceremony(?x77, ?x7144), award_winner(?x7144, ?x7327), award_winner(?x7144, ?x3572), award_winner(?x7144, ?x826), ?x3572 = 02kxbx3, award(?x5096, ?x77), ceremony(?x77, ?x2707), nominated_for(?x7327, ?x1199), story_by(?x5096, ?x5434), ?x826 = 02kxbwx, genre(?x5096, ?x162), honored_for(?x2707, ?x414), award_winner(?x77, ?x2086) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #10099 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 21 *> proper extension: 0clfdj; 0hr6lkl; 0gvstc3; 0gx_st; 04n2r9h; 0275n3y; *> query: (?x7144, ?x144) <- ceremony(?x500, ?x7144), award_winner(?x7144, ?x7327), award_winner(?x7144, ?x3572), award(?x3572, ?x3435), award(?x3572, ?x68), award_nominee(?x3572, ?x163), ?x3435 = 03hl6lc, gender(?x3572, ?x231), award_winner(?x945, ?x3572), student(?x7545, ?x3572), ?x68 = 02qyp19, written_by(?x392, ?x3572), nominated_for(?x500, ?x2943), nominated_for(?x500, ?x144), genre(?x2943, ?x53), nominated_for(?x7327, ?x7883) *> conf = 0.01 ranks of expected_values: 921 EVAL 02yxh9 honored_for 037q31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #16591-02v49c PRED entity: 02v49c PRED relation: nationality PRED expected values: 09c7w0 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.83 #100, 0.83 #4181, 0.81 #596), 02jx1 (0.11 #1728, 0.10 #5507, 0.10 #5903), 07ssc (0.09 #6281, 0.09 #1710, 0.08 #5489), 03rk0 (0.06 #5718, 0.06 #5817, 0.06 #6015), 0f8l9c (0.03 #1316, 0.02 #6288, 0.02 #1016), 0d05w3 (0.02 #544, 0.02 #745, 0.01 #1344), 0chghy (0.02 #1204, 0.02 #1705, 0.02 #1004), 0345h (0.02 #625, 0.02 #30, 0.02 #6297), 0ctw_b (0.02 #26), 06q1r (0.02 #2071, 0.02 #1671, 0.02 #2270) >> Best rule #100 for best value: >> intensional similarity = 6 >> extensional distance = 170 >> proper extension: 01pr_j6; 03m_k0; 04pg29; 0gd9k; 08xz51; 023jq1; >> query: (?x8621, 09c7w0) <- profession(?x8621, ?x1041), profession(?x8621, ?x987), profession(?x8621, ?x319), ?x1041 = 03gjzk, ?x319 = 01d_h8, ?x987 = 0dxtg >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v49c nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.831 http://example.org/people/person/nationality #16590-0210hf PRED entity: 0210hf PRED relation: gender PRED expected values: 05zppz => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #35, 0.71 #151, 0.71 #117), 02zsn (0.43 #22, 0.42 #32, 0.40 #10) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 785 >> proper extension: 0dbpyd; 012d40; 0fvf9q; 02p65p; 06151l; 01j5ts; 02rchht; 0byfz; 083chw; 0qf43; ... >> query: (?x4746, 05zppz) <- award(?x4746, ?x1670), profession(?x4746, ?x319), ?x319 = 01d_h8 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0210hf gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.806 http://example.org/people/person/gender #16589-0dr1c2 PRED entity: 0dr1c2 PRED relation: actor PRED expected values: 04f62k => 89 concepts (43 used for prediction) PRED predicted values (max 10 best out of 829): 06_6j3 (0.54 #13975, 0.51 #13974), 0f8grf (0.54 #13975, 0.02 #31678, 0.02 #26049), 03d29b (0.33 #922, 0.25 #2786, 0.20 #7445), 01nsyf (0.33 #819, 0.25 #2683, 0.20 #7342), 01qvtwm (0.33 #884, 0.25 #2748, 0.20 #6476), 0f14q (0.29 #8204, 0.21 #12861, 0.20 #7273), 084m3 (0.29 #8036, 0.20 #7105, 0.14 #12693), 0f13b (0.29 #8110, 0.20 #7179, 0.14 #12767), 01_rh4 (0.29 #7726, 0.20 #6795, 0.14 #12383), 02gf_l (0.25 #3365, 0.20 #6161, 0.07 #22929) >> Best rule #13975 for best value: >> intensional similarity = 9 >> extensional distance = 15 >> proper extension: 04xbq3; >> query: (?x6839, ?x12753) <- film(?x12753, ?x6839), film(?x4632, ?x6839), program(?x13907, ?x6839), genre(?x6839, ?x53), gender(?x12753, ?x231), country_of_origin(?x6839, ?x252), student(?x11559, ?x4632), profession(?x4632, ?x1032), genre(?x54, ?x53) >> conf = 0.54 => this is the best rule for 2 predicted values *> Best rule #7405 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 0gxr1c; *> query: (?x6839, 04f62k) <- genre(?x6839, ?x2540), genre(?x6839, ?x811), genre(?x6839, ?x53), ?x811 = 03k9fj, actor(?x6839, ?x6118), ?x53 = 07s9rl0, category(?x6118, ?x134), ?x2540 = 0hcr, ?x134 = 08mbj5d, gender(?x6118, ?x514) *> conf = 0.20 ranks of expected_values: 24 EVAL 0dr1c2 actor 04f62k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 89.000 43.000 0.540 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #16588-0bdw6t PRED entity: 0bdw6t PRED relation: award_winner PRED expected values: 014zfs => 42 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1601): 040z9 (0.40 #4086, 0.31 #6544, 0.04 #9003), 01wbg84 (0.39 #58997, 0.39 #34414, 0.38 #54082), 01z7_f (0.39 #58997, 0.39 #34414, 0.38 #54082), 0hvb2 (0.39 #58997, 0.39 #34414, 0.38 #54082), 02k4gv (0.39 #58997, 0.39 #34414, 0.38 #54082), 0sw6g (0.39 #58997, 0.39 #34414, 0.38 #54082), 09r9dp (0.39 #58997, 0.39 #34414, 0.38 #54082), 01ggc9 (0.39 #58997, 0.39 #34414, 0.38 #54082), 0382m4 (0.39 #58997, 0.39 #34414, 0.38 #54082), 05fnl9 (0.39 #58997, 0.39 #34414, 0.38 #54082) >> Best rule #4086 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 027dtxw; 040njc; 0f4x7; 0gs9p; 0bfvd4; 04kxsb; 0gqy2; 0bdwqv; >> query: (?x2071, 040z9) <- award(?x269, ?x2071), nominated_for(?x2071, ?x337), ?x269 = 0byfz, ceremony(?x2071, ?x1265) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #5134 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 04ljl_l; 07cbcy; 02w9sd7; *> query: (?x2071, 014zfs) <- award(?x10161, ?x2071), award(?x269, ?x2071), nominated_for(?x2071, ?x337), ?x269 = 0byfz, film(?x10161, ?x408) *> conf = 0.08 ranks of expected_values: 534 EVAL 0bdw6t award_winner 014zfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 25.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #16587-04bdqk PRED entity: 04bdqk PRED relation: student! PRED expected values: 01d34b => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 91): 01r47h (0.33 #959), 01w5m (0.10 #8506, 0.05 #15858, 0.05 #26362), 0bwfn (0.09 #1325, 0.08 #24431, 0.08 #30210), 015nl4 (0.09 #1117, 0.06 #6893, 0.05 #4267), 03ksy (0.06 #8507, 0.05 #43698, 0.04 #45800), 08815 (0.05 #8403, 0.03 #6828, 0.03 #43594), 065y4w7 (0.05 #32052, 0.05 #1064, 0.04 #29949), 026gvfj (0.05 #2736, 0.04 #3786, 0.04 #5887), 09f2j (0.05 #1209, 0.04 #10661, 0.04 #4359), 07tgn (0.05 #1067, 0.03 #4217, 0.03 #2117) >> Best rule #959 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0h7pj; >> query: (?x10521, 01r47h) <- nominated_for(?x10521, ?x12393), profession(?x10521, ?x1032), nationality(?x10521, ?x94), ?x12393 = 07nnp_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8657 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 233 *> proper extension: 07_grx; *> query: (?x10521, 01d34b) <- location(?x10521, ?x739), student(?x9479, ?x10521), ?x739 = 02_286 *> conf = 0.04 ranks of expected_values: 12 EVAL 04bdqk student! 01d34b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 121.000 121.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #16586-0dbb3 PRED entity: 0dbb3 PRED relation: nationality PRED expected values: 09c7w0 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 46): 09c7w0 (0.81 #2609, 0.79 #2208, 0.78 #3112), 02jx1 (0.24 #2039, 0.23 #1035, 0.20 #33), 07ssc (0.21 #615, 0.20 #15, 0.18 #2423), 0chghy (0.20 #10, 0.03 #5028, 0.03 #2818), 03rk0 (0.10 #7873, 0.09 #1650, 0.08 #8173), 0345h (0.10 #1333, 0.06 #5150, 0.06 #4245), 0f8l9c (0.07 #722, 0.06 #1927, 0.06 #1324), 03rjj (0.07 #705, 0.03 #2513, 0.03 #2212), 0d060g (0.07 #908, 0.06 #6531, 0.06 #6129), 0h7x (0.06 #2443, 0.04 #2944, 0.04 #4249) >> Best rule #2609 for best value: >> intensional similarity = 3 >> extensional distance = 181 >> proper extension: 0584j4n; 07fzq3; >> query: (?x10559, 09c7w0) <- award_nominee(?x10559, ?x6207), place_of_death(?x10559, ?x739), citytown(?x166, ?x739) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dbb3 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.809 http://example.org/people/person/nationality #16585-0k3l5 PRED entity: 0k3l5 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 195 concepts (122 used for prediction) PRED predicted values (max 10 best out of 53): 09c7w0 (0.91 #520, 0.90 #346, 0.90 #642), 05k7sb (0.35 #1527, 0.21 #1498, 0.19 #72), 0k3k1 (0.19 #72), 0d060g (0.15 #1426, 0.01 #781), 05bcl (0.15 #1426), 03h64 (0.15 #1426), 0d05w3 (0.15 #1426), 03rk0 (0.15 #1426), 0345h (0.15 #1426), 07ssc (0.15 #1426) >> Best rule #520 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0mw89; 0drsm; 0d22f; 0m7d0; 0nryt; 0n5yh; 0n5fz; 0nvt9; 0n2q0; 0nm6k; ... >> query: (?x7309, 09c7w0) <- adjoins(?x7309, ?x4990), county_seat(?x7309, ?x3052), contains(?x7309, ?x4849), source(?x4990, ?x958) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k3l5 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 122.000 0.906 http://example.org/location/country/second_level_divisions #16584-03mg35 PRED entity: 03mg35 PRED relation: film PRED expected values: 01738w => 109 concepts (74 used for prediction) PRED predicted values (max 10 best out of 915): 02vqsll (0.70 #14316, 0.57 #59046, 0.41 #114514), 011yd2 (0.40 #355, 0.04 #132409), 0418wg (0.21 #3979, 0.05 #30819, 0.04 #2190), 03s9kp (0.20 #1762, 0.03 #5340, 0.03 #82307), 02c638 (0.20 #338, 0.03 #82307, 0.03 #93043), 03ntbmw (0.20 #1770, 0.02 #8927, 0.01 #10716), 01hv3t (0.20 #1292, 0.01 #12028, 0.01 #13818), 0bdjd (0.20 #1280), 09xbpt (0.17 #3625, 0.04 #132409, 0.04 #1836), 06z8s_ (0.14 #3708, 0.04 #30548, 0.04 #1919) >> Best rule #14316 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 02wb6yq; >> query: (?x1922, ?x2989) <- celebrity(?x3183, ?x1922), people(?x3591, ?x1922), nominated_for(?x1922, ?x2989) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #13654 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 02wb6yq; *> query: (?x1922, 01738w) <- celebrity(?x3183, ?x1922), people(?x3591, ?x1922), nominated_for(?x1922, ?x2989) *> conf = 0.02 ranks of expected_values: 451 EVAL 03mg35 film 01738w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 109.000 74.000 0.699 http://example.org/film/actor/film./film/performance/film #16583-07ylj PRED entity: 07ylj PRED relation: jurisdiction_of_office! PRED expected values: 02079p => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 19): 060bp (0.68 #463, 0.63 #862, 0.62 #505), 0f6c3 (0.53 #300, 0.45 #195, 0.41 #678), 09n5b9 (0.47 #304, 0.42 #199, 0.37 #430), 0fkvn (0.46 #296, 0.44 #191, 0.40 #674), 0pqc5 (0.41 #696, 0.41 #549, 0.38 #990), 01gkgk (0.36 #1765, 0.36 #1808, 0.08 #4), 0p5vf (0.26 #32, 0.20 #53, 0.20 #158), 04syw (0.20 #740, 0.20 #761, 0.18 #1034), 01zq91 (0.19 #13, 0.17 #55, 0.17 #97), 0fj45 (0.19 #39, 0.15 #18, 0.14 #123) >> Best rule #463 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 059ss; >> query: (?x1203, 060bp) <- adjoins(?x410, ?x1203), organization(?x1203, ?x127), contains(?x1203, ?x13229) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #135 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 0jgd; 0154j; 0d0vqn; 04gzd; 01ls2; 03rt9; 05qhw; 06npd; 03gj2; 09pmkv; ... *> query: (?x1203, 02079p) <- country(?x359, ?x1203), film_release_region(?x2163, ?x1203), ?x2163 = 0j6b5 *> conf = 0.09 ranks of expected_values: 16 EVAL 07ylj jurisdiction_of_office! 02079p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 114.000 114.000 0.677 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #16582-047csmy PRED entity: 047csmy PRED relation: film! PRED expected values: 083chw 01swck => 92 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1097): 05vk_d (0.69 #49798, 0.45 #74700, 0.45 #101674), 05mvd62 (0.45 #74700, 0.45 #101674, 0.42 #97525), 09pl3f (0.45 #74700, 0.45 #101674, 0.42 #97525), 02qzjj (0.45 #74700, 0.45 #101674, 0.42 #97525), 09pl3s (0.45 #74700, 0.45 #101674, 0.42 #97525), 01gb54 (0.45 #74700, 0.45 #101674, 0.42 #97525), 0hpt3 (0.45 #74700, 0.45 #101674, 0.42 #97525), 05qd_ (0.45 #74700, 0.45 #101674, 0.42 #97525), 0b6mgp_ (0.45 #74700, 0.45 #101674, 0.42 #97525), 04w391 (0.42 #4150, 0.41 #78850, 0.41 #51876) >> Best rule #49798 for best value: >> intensional similarity = 2 >> extensional distance = 478 >> proper extension: 01f3p_; >> query: (?x5277, ?x8638) <- nominated_for(?x8638, ?x5277), participant(?x3999, ?x8638) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #11173 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 98 *> proper extension: 0bmc4cm; *> query: (?x5277, 01swck) <- film(?x609, ?x5277), ?x609 = 03xq0f, nominated_for(?x154, ?x5277) *> conf = 0.03 ranks of expected_values: 231 EVAL 047csmy film! 01swck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 92.000 54.000 0.687 http://example.org/film/actor/film./film/performance/film EVAL 047csmy film! 083chw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 54.000 0.687 http://example.org/film/actor/film./film/performance/film #16581-017f3m PRED entity: 017f3m PRED relation: genre PRED expected values: 07s9rl0 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 81): 07s9rl0 (0.64 #2379, 0.56 #165, 0.54 #657), 05p553 (0.51 #743, 0.51 #907, 0.49 #989), 01z4y (0.36 #182, 0.34 #756, 0.34 #920), 0c4xc (0.26 #780, 0.25 #206, 0.25 #944), 0hcr (0.22 #3055, 0.20 #429, 0.19 #3139), 01t_vv (0.22 #198, 0.21 #444, 0.21 #362), 06n90 (0.19 #3049, 0.16 #2721, 0.16 #2391), 01htzx (0.17 #427, 0.17 #1903, 0.17 #345), 03k9fj (0.17 #3047, 0.15 #2389, 0.15 #3131), 01hmnh (0.16 #426, 0.16 #262, 0.15 #3052) >> Best rule #2379 for best value: >> intensional similarity = 3 >> extensional distance = 203 >> proper extension: 020qr4; 01cjhz; 0jq2r; 045qmr; 0dk0dj; 047m_w; 070ltt; 07qht4; 06f0k; 04x4gj; ... >> query: (?x4898, 07s9rl0) <- genre(?x4898, ?x604), genre(?x6288, ?x604), ?x6288 = 01chpn >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017f3m genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.639 http://example.org/tv/tv_program/genre #16580-0cw3yd PRED entity: 0cw3yd PRED relation: film! PRED expected values: 03xsby => 114 concepts (81 used for prediction) PRED predicted values (max 10 best out of 54): 0jz9f (0.26 #1, 0.25 #76, 0.15 #301), 025jfl (0.18 #306, 0.18 #81, 0.11 #381), 086k8 (0.17 #1580, 0.16 #2262, 0.16 #2339), 03xsby (0.15 #316, 0.10 #767, 0.09 #617), 016tw3 (0.15 #161, 0.13 #4165, 0.13 #1064), 03xq0f (0.14 #80, 0.13 #5, 0.13 #1433), 017s11 (0.14 #529, 0.13 #1960, 0.13 #829), 05qd_ (0.14 #2269, 0.13 #2799, 0.13 #2496), 016tt2 (0.12 #3548, 0.12 #3702, 0.12 #1735), 0fvppk (0.10 #357, 0.09 #57, 0.07 #432) >> Best rule #1 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 095zlp; 0pv3x; 09z2b7; 04qw17; 09k56b7; 02qr69m; 048htn; 05c46y6; 0f4vx; 02mt51; ... >> query: (?x2812, 0jz9f) <- featured_film_locations(?x2812, ?x10683), nominated_for(?x1245, ?x2812), nominated_for(?x618, ?x2812), ?x618 = 09qwmm, ?x1245 = 0gqwc >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #316 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 0963mq; *> query: (?x2812, 03xsby) <- featured_film_locations(?x2812, ?x10683), genre(?x2812, ?x2753), ?x2753 = 0219x_, film_crew_role(?x2812, ?x137) *> conf = 0.15 ranks of expected_values: 4 EVAL 0cw3yd film! 03xsby CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 114.000 81.000 0.261 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16579-013hvr PRED entity: 013hvr PRED relation: source PRED expected values: 0jbk9 => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #12, 0.65 #10, 0.64 #9) >> Best rule #12 for best value: >> intensional similarity = 1 >> extensional distance = 514 >> proper extension: 0mn0v; 0f04v; 0f2tj; 0_rwf; 0x335; 0_wm_; 010bnr; 0104lr; >> query: (?x12646, 0jbk9) <- place(?x12646, ?x12646) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013hvr source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #16578-0170qf PRED entity: 0170qf PRED relation: award PRED expected values: 0f4x7 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 279): 027dtxw (0.71 #16910, 0.71 #22811, 0.70 #25172), 0gq9h (0.33 #5577, 0.30 #4398, 0.14 #24385), 02w9sd7 (0.30 #161, 0.15 #20059, 0.14 #24385), 0gqwc (0.30 #71, 0.15 #4001, 0.13 #31072), 040njc (0.26 #5511, 0.22 #4332, 0.14 #24385), 05pcn59 (0.23 #3221, 0.22 #6366, 0.21 #3614), 05zr6wv (0.20 #17, 0.16 #3161, 0.16 #4734), 0bfvd4 (0.20 #111, 0.16 #12975, 0.15 #20059), 0gqyl (0.20 #101, 0.14 #24385, 0.13 #13762), 094qd5 (0.20 #43, 0.14 #24385, 0.13 #31072) >> Best rule #16910 for best value: >> intensional similarity = 3 >> extensional distance = 1208 >> proper extension: 04cy8rb; 01r42_g; 02pp_q_; 08wq0g; 01qkqwg; 08m4c8; 03jvmp; 0275_pj; 0g5lhl7; 06rnl9; ... >> query: (?x2280, ?x112) <- award_nominee(?x57, ?x2280), award_winner(?x112, ?x2280), award_winner(?x2141, ?x2280) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #20059 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1349 *> proper extension: 0lzkm; *> query: (?x2280, ?x704) <- award_winner(?x2141, ?x2280), nationality(?x2280, ?x512), award(?x2141, ?x704) *> conf = 0.15 ranks of expected_values: 22 EVAL 0170qf award 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 107.000 107.000 0.709 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16577-09hd16 PRED entity: 09hd16 PRED relation: award PRED expected values: 02xcb6n => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 244): 0cjyzs (0.35 #1727, 0.31 #2942, 0.28 #2132), 09sb52 (0.23 #9761, 0.22 #8951, 0.21 #9356), 0ck27z (0.23 #5763, 0.21 #8193, 0.12 #22685), 0gkr9q (0.20 #334, 0.15 #15391, 0.15 #22279), 0f_nbyh (0.20 #10, 0.15 #15391, 0.13 #23496), 02q1tc5 (0.18 #960, 0.08 #3795, 0.08 #3390), 0gr4k (0.17 #4083, 0.15 #6108, 0.12 #5298), 0cc8l6d (0.15 #15391, 0.06 #2605, 0.05 #1390), 03ccq3s (0.15 #1415, 0.15 #1820, 0.14 #605), 0gr51 (0.15 #4151, 0.12 #6176, 0.11 #5366) >> Best rule #1727 for best value: >> intensional similarity = 2 >> extensional distance = 167 >> proper extension: 0f721s; >> query: (?x4023, 0cjyzs) <- award_winner(?x2650, ?x4023), program(?x4023, ?x5810) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #22685 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2257 *> proper extension: 0fvppk; *> query: (?x4023, ?x435) <- nominated_for(?x4023, ?x5810), nominated_for(?x435, ?x5810) *> conf = 0.12 ranks of expected_values: 19 EVAL 09hd16 award 02xcb6n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 76.000 76.000 0.349 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16576-018z_c PRED entity: 018z_c PRED relation: religion PRED expected values: 03_gx => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 21): 0c8wxp (0.25 #186, 0.25 #231, 0.24 #51), 03_gx (0.09 #239, 0.08 #284, 0.08 #2357), 092bf5 (0.07 #466, 0.04 #511, 0.04 #601), 04pk9 (0.06 #200, 0.05 #245, 0.05 #290), 0v53x (0.06 #74, 0.04 #164, 0.02 #254), 0kpl (0.05 #505, 0.05 #1228, 0.04 #460), 03j6c (0.04 #1284, 0.03 #1824, 0.03 #876), 0n2g (0.03 #463, 0.02 #598, 0.02 #913), 051kv (0.03 #185, 0.02 #230, 0.02 #275), 0g5llry (0.03 #208, 0.01 #343) >> Best rule #186 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 03ds3; 0fb7c; 02_wxh; 0sx5w; 01svq8; >> query: (?x4414, 0c8wxp) <- type_of_union(?x4414, ?x566), location(?x4414, ?x1523), award(?x4414, ?x1245), person(?x3480, ?x4414) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #239 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 025p38; 012_53; 039crh; 0hfml; 010p3; 01rzxl; 035wq7; 01s7z0; 02pbp9; *> query: (?x4414, 03_gx) <- type_of_union(?x4414, ?x566), location(?x4414, ?x1523), program(?x4414, ?x2583), profession(?x4414, ?x319) *> conf = 0.09 ranks of expected_values: 2 EVAL 018z_c religion 03_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.250 http://example.org/people/person/religion #16575-03_x5t PRED entity: 03_x5t PRED relation: religion PRED expected values: 0c8wxp => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 22): 0c8wxp (0.26 #186, 0.22 #231, 0.21 #501), 0kpl (0.09 #100, 0.09 #55, 0.05 #1451), 03_gx (0.08 #1455, 0.07 #1771, 0.07 #509), 01lp8 (0.06 #271, 0.05 #226, 0.05 #136), 06nzl (0.05 #240, 0.04 #285, 0.02 #195), 03j6c (0.05 #1327, 0.04 #787, 0.02 #1778), 092bf5 (0.04 #196, 0.03 #151, 0.03 #466), 019cr (0.03 #191, 0.02 #236, 0.02 #416), 0flw86 (0.02 #1308, 0.02 #768, 0.02 #1759), 0kq2 (0.02 #198, 0.02 #2046, 0.02 #3171) >> Best rule #186 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 04d_mtq; >> query: (?x10371, 0c8wxp) <- nationality(?x10371, ?x94), vacationer(?x6959, ?x10371), place_of_birth(?x10371, ?x3501) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_x5t religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.261 http://example.org/people/person/religion #16574-042v_gx PRED entity: 042v_gx PRED relation: performance_role! PRED expected values: 018gkb => 88 concepts (64 used for prediction) PRED predicted values (max 10 best out of 886): 02rn_bj (0.50 #2875, 0.50 #848, 0.50 #599), 0m_v0 (0.50 #547, 0.43 #3663, 0.40 #1300), 0167v4 (0.50 #856, 0.43 #3663, 0.33 #1741), 01vn35l (0.43 #3663, 0.36 #3573, 0.25 #411), 07r4c (0.43 #3663, 0.29 #2342, 0.29 #2216), 02s6sh (0.43 #3663, 0.27 #3653, 0.25 #491), 0274ck (0.43 #3663, 0.25 #260, 0.18 #3547), 02qwg (0.43 #3663, 0.25 #795, 0.18 #3582), 0l12d (0.43 #3663, 0.25 #394, 0.18 #3556), 01vsyg9 (0.43 #3663, 0.25 #821, 0.17 #1706) >> Best rule #2875 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 05148p4; 07brj; 03qjg; >> query: (?x432, 02rn_bj) <- role(?x432, ?x716), role(?x9735, ?x432), role(?x1750, ?x432), role(?x74, ?x432), instrumentalists(?x432, ?x133), performance_role(?x9987, ?x432), ?x716 = 018vs, role(?x1291, ?x432), group(?x432, ?x442), ?x1750 = 02hnl, ?x9735 = 01wxdn3 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4297 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 14 *> proper extension: 0319l; *> query: (?x432, ?x1997) <- role(?x432, ?x212), role(?x1997, ?x432), role(?x75, ?x432), role(?x4425, ?x432), instrumentalists(?x432, ?x8328), role(?x1291, ?x432), origin(?x8328, ?x739), artists(?x302, ?x1997), ?x4425 = 0979zs *> conf = 0.07 ranks of expected_values: 182 EVAL 042v_gx performance_role! 018gkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 88.000 64.000 0.500 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #16573-03f1r6t PRED entity: 03f1r6t PRED relation: location PRED expected values: 02_286 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 204): 030qb3t (0.21 #33813, 0.19 #9720, 0.19 #29798), 02_286 (0.18 #55456, 0.17 #33767, 0.15 #3249), 0cr3d (0.09 #9782, 0.07 #12191, 0.07 #8979), 07h34 (0.07 #998, 0.06 #1801, 0.06 #195), 04lh6 (0.07 #1238, 0.06 #2041, 0.05 #3647), 0c_m3 (0.07 #1073, 0.06 #1876, 0.05 #3482), 02dtg (0.07 #827, 0.04 #6448, 0.04 #1630), 01n7q (0.06 #63, 0.05 #33793, 0.04 #32187), 059rby (0.06 #16, 0.04 #5637, 0.04 #1622), 013yq (0.06 #9756, 0.05 #8953, 0.04 #12165) >> Best rule #33813 for best value: >> intensional similarity = 3 >> extensional distance = 490 >> proper extension: 01kwld; 034x61; 01j5x6; 031zkw; 01mqz0; 0157m; 01l2fn; 015pxr; 0170s4; 07cjqy; ... >> query: (?x5222, 030qb3t) <- participant(?x4065, ?x5222), location(?x5222, ?x3014), profession(?x4065, ?x1032) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #55456 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1176 *> proper extension: 09ftwr; 0m32_; 01v3vp; 01mt1fy; 02dbn2; 01_k1z; 04jwp; 037d35; 05gpy; 0c8br; ... *> query: (?x5222, 02_286) <- gender(?x5222, ?x231), student(?x7596, ?x5222), location(?x5222, ?x3014) *> conf = 0.18 ranks of expected_values: 2 EVAL 03f1r6t location 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 124.000 0.209 http://example.org/people/person/places_lived./people/place_lived/location #16572-02cttt PRED entity: 02cttt PRED relation: organization! PRED expected values: 060c4 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 17): 060c4 (0.69 #288, 0.68 #28, 0.66 #145), 07xl34 (0.28 #128, 0.27 #24, 0.21 #63), 0dq_5 (0.24 #87, 0.23 #347, 0.23 #360), 05k17c (0.12 #46, 0.09 #423, 0.08 #85), 0hm4q (0.08 #125, 0.05 #619, 0.05 #684), 05c0jwl (0.06 #70, 0.04 #213, 0.04 #330), 04n1q6 (0.02 #71, 0.01 #318, 0.01 #331), 01t7n9 (0.02 #859), 0fkzq (0.02 #859), 09n5b9 (0.02 #859) >> Best rule #288 for best value: >> intensional similarity = 3 >> extensional distance = 305 >> proper extension: 015zyd; 01jssp; 0ym8f; 02s62q; 0l2tk; 0j_sncb; 01pq4w; 0kw4j; 023znp; 01ymvk; ... >> query: (?x918, 060c4) <- institution(?x1771, ?x918), major_field_of_study(?x918, ?x2502), currency(?x918, ?x170) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cttt organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.691 http://example.org/organization/role/leaders./organization/leadership/organization #16571-0btpm6 PRED entity: 0btpm6 PRED relation: honored_for! PRED expected values: 04110lv => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 118): 09gkdln (0.33 #103, 0.17 #341, 0.12 #222), 05qb8vx (0.17 #284, 0.08 #522, 0.07 #1355), 09g90vz (0.17 #343, 0.05 #581, 0.05 #1414), 0hr6lkl (0.13 #964, 0.09 #1678, 0.07 #607), 02wzl1d (0.12 #126, 0.03 #364, 0.03 #5839), 09pj68 (0.12 #207, 0.03 #5325, 0.02 #6039), 0bvfqq (0.12 #145, 0.02 #4668, 0.02 #5858), 0bq_mx (0.12 #232, 0.01 #10118, 0.01 #12618), 0gmdkyy (0.09 #976, 0.05 #2642, 0.05 #619), 09k5jh7 (0.08 #307, 0.05 #545, 0.05 #1378) >> Best rule #103 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02r1c18; >> query: (?x7493, 09gkdln) <- film_release_region(?x7493, ?x8483), nominated_for(?x1500, ?x7493), nominated_for(?x112, ?x7493), ?x8483 = 059g4 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #449 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 0d8w2n; *> query: (?x7493, 04110lv) <- region(?x7493, ?x512), films(?x9677, ?x7493), titles(?x8581, ?x7493) *> conf = 0.03 ranks of expected_values: 44 EVAL 0btpm6 honored_for! 04110lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 127.000 127.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #16570-0355dz PRED entity: 0355dz PRED relation: team PRED expected values: 02ptzz0 0jmhr => 27 concepts (10 used for prediction) PRED predicted values (max 10 best out of 959): 0bwjj (0.83 #8564, 0.82 #1903, 0.81 #8554), 0jmhr (0.83 #8564, 0.82 #1903, 0.81 #8554), 0jmbv (0.83 #8564, 0.82 #1903, 0.81 #8554), 01k8vh (0.83 #8564, 0.82 #1903, 0.81 #8554), 0jmmn (0.83 #8564, 0.82 #1903, 0.81 #8554), 0jmcb (0.83 #8564, 0.82 #1903, 0.81 #8554), 02ptzz0 (0.81 #8554, 0.81 #3800, 0.80 #1898), 02pqcfz (0.81 #8554, 0.81 #3800, 0.80 #1898), 03d5m8w (0.81 #8554, 0.81 #3800, 0.80 #1898), 0jmh7 (0.75 #9517, 0.75 #9516, 0.75 #9512) >> Best rule #8564 for best value: >> intensional similarity = 28 >> extensional distance = 5 >> proper extension: 0619m3; >> query: (?x5755, ?x11168) <- position(?x11168, ?x5755), position(?x10846, ?x5755), position(?x9995, ?x5755), position(?x4369, ?x5755), school(?x11168, ?x1011), team(?x12451, ?x10846), team(?x4368, ?x10846), team(?x2302, ?x10846), company(?x6010, ?x9995), ?x4368 = 0b_6x2, team(?x5755, ?x799), draft(?x9995, ?x8586), team(?x12451, ?x9576), team(?x12451, ?x4938), ?x8586 = 038981, teams(?x1523, ?x4369), ?x9576 = 02qk2d5, colors(?x4369, ?x332), ?x4938 = 027yf83, locations(?x12451, ?x2941), team(?x6002, ?x4369), location(?x71, ?x1523), place_of_birth(?x338, ?x1523), origin(?x250, ?x1523), ?x2302 = 0b_77q, contains(?x1523, ?x682), citytown(?x234, ?x1523), locations(?x867, ?x1523) >> conf = 0.83 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 7 EVAL 0355dz team 0jmhr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 27.000 10.000 0.831 http://example.org/sports/sports_position/players./sports/sports_team_roster/team EVAL 0355dz team 02ptzz0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 27.000 10.000 0.831 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #16569-01l87db PRED entity: 01l87db PRED relation: award_winner! PRED expected values: 0m7yy => 114 concepts (106 used for prediction) PRED predicted values (max 10 best out of 227): 023vrq (0.22 #323, 0.18 #755, 0.13 #2051), 02v1m7 (0.22 #114, 0.09 #546, 0.09 #1842), 054ks3 (0.18 #574, 0.11 #142, 0.10 #3598), 01bgqh (0.18 #475, 0.11 #43, 0.09 #1771), 0gq9h (0.18 #2238, 0.10 #6126, 0.08 #7854), 02g3ft (0.14 #2246, 0.05 #6134, 0.04 #7862), 02f6ym (0.14 #1554, 0.11 #258, 0.09 #690), 02f6xy (0.14 #1495, 0.07 #3655, 0.06 #4951), 01by1l (0.12 #9185, 0.11 #113, 0.10 #9617), 02sp_v (0.11 #161, 0.09 #1457, 0.09 #593) >> Best rule #323 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01w3v; >> query: (?x5745, 023vrq) <- category(?x5745, ?x134), ?x134 = 08mbj5d, organizations_founded(?x5745, ?x6202), religion(?x5745, ?x2694) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #2340 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 09gffmz; *> query: (?x5745, 0m7yy) <- profession(?x5745, ?x2348), organizations_founded(?x5745, ?x6202), profession(?x5650, ?x2348), ?x5650 = 029h45 *> conf = 0.04 ranks of expected_values: 114 EVAL 01l87db award_winner! 0m7yy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 114.000 106.000 0.222 http://example.org/award/award_category/winners./award/award_honor/award_winner #16568-014lc_ PRED entity: 014lc_ PRED relation: film_release_region PRED expected values: 01znc_ 03rk0 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 111): 06t2t (0.91 #200, 0.75 #1670, 0.72 #1817), 03h64 (0.88 #204, 0.83 #1674, 0.79 #1821), 05r4w (0.86 #1619, 0.83 #884, 0.82 #2354), 03rk0 (0.85 #196, 0.51 #1666, 0.47 #1813), 0jgd (0.82 #1621, 0.82 #886, 0.80 #1768), 01znc_ (0.79 #181, 0.76 #1651, 0.71 #1798), 04gzd (0.79 #155, 0.58 #1625, 0.53 #1772), 03rt9 (0.73 #1630, 0.71 #160, 0.70 #1777), 01p1v (0.71 #192, 0.55 #1662, 0.49 #1809), 016wzw (0.71 #205, 0.51 #1675, 0.49 #1822) >> Best rule #200 for best value: >> intensional similarity = 6 >> extensional distance = 32 >> proper extension: 02vxq9m; 0c40vxk; 053rxgm; 03qnvdl; 0ch26b_; 0_7w6; 0by1wkq; 0gd0c7x; 0407yfx; 0fpv_3_; ... >> query: (?x66, 06t2t) <- film_release_region(?x66, ?x4743), film_release_region(?x66, ?x2236), film_release_region(?x66, ?x583), ?x4743 = 03spz, ?x583 = 015fr, ?x2236 = 05sb1 >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #196 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 32 *> proper extension: 02vxq9m; 0c40vxk; 053rxgm; 03qnvdl; 0ch26b_; 0_7w6; 0by1wkq; 0gd0c7x; 0407yfx; 0fpv_3_; ... *> query: (?x66, 03rk0) <- film_release_region(?x66, ?x4743), film_release_region(?x66, ?x2236), film_release_region(?x66, ?x583), ?x4743 = 03spz, ?x583 = 015fr, ?x2236 = 05sb1 *> conf = 0.85 ranks of expected_values: 4, 6 EVAL 014lc_ film_release_region 03rk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 83.000 0.912 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 014lc_ film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 83.000 83.000 0.912 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16567-0_b3d PRED entity: 0_b3d PRED relation: film_crew_role PRED expected values: 0ch6mp2 06qc5 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.83 #969, 0.76 #865, 0.71 #1559), 02r96rf (0.69 #965, 0.65 #861, 0.61 #1797), 0dxtw (0.38 #973, 0.37 #869, 0.35 #731), 01xy5l_ (0.16 #81, 0.13 #975, 0.12 #47), 0215hd (0.15 #979, 0.14 #875, 0.12 #1049), 089g0h (0.13 #980, 0.10 #1570, 0.10 #738), 0d2b38 (0.12 #986, 0.10 #1056, 0.10 #1611), 0ckd1 (0.12 #38, 0.11 #72, 0.02 #862), 02rh1dz (0.11 #868, 0.11 #730, 0.10 #972), 02vs3x5 (0.09 #328, 0.06 #950, 0.06 #742) >> Best rule #969 for best value: >> intensional similarity = 4 >> extensional distance = 561 >> proper extension: 02y_lrp; 083shs; 0dnvn3; 0ds33; 01ln5z; 03h_yy; 02_1sj; 0170_p; 04fzfj; 035xwd; ... >> query: (?x1002, 0ch6mp2) <- genre(?x1002, ?x53), film_crew_role(?x1002, ?x1171), titles(?x512, ?x1002), ?x1171 = 09vw2b7 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 17 EVAL 0_b3d film_crew_role 06qc5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 86.000 86.000 0.826 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0_b3d film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.826 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16566-085jw PRED entity: 085jw PRED relation: group PRED expected values: 01qqwp9 => 88 concepts (35 used for prediction) PRED predicted values (max 10 best out of 826): 02vnpv (0.88 #4186, 0.77 #3818, 0.77 #3633), 047cx (0.83 #4811, 0.78 #2378, 0.76 #2012), 014pg1 (0.78 #2378, 0.76 #2012, 0.75 #1746), 0163m1 (0.78 #2378, 0.76 #2012, 0.75 #1680), 027kwc (0.78 #2378, 0.76 #2012, 0.68 #912), 0qmpd (0.78 #2378, 0.76 #2012, 0.68 #912), 048xh (0.78 #2378, 0.76 #2012, 0.68 #912), 01q99h (0.78 #2378, 0.76 #2012, 0.68 #912), 05crg7 (0.78 #2378, 0.76 #2012, 0.68 #912), 01lf293 (0.78 #2378, 0.76 #2012, 0.68 #912) >> Best rule #4186 for best value: >> intensional similarity = 21 >> extensional distance = 14 >> proper extension: 0bxl5; >> query: (?x3156, 02vnpv) <- group(?x3156, ?x9841), role(?x2460, ?x3156), role(?x75, ?x3156), performance_role(?x3156, ?x212), artists(?x9063, ?x9841), artists(?x1572, ?x9841), artists(?x1380, ?x9841), artists(?x1000, ?x9841), ?x1572 = 06by7, category(?x9841, ?x134), ?x2460 = 01wy6, artists(?x1000, ?x13136), artists(?x1000, ?x10565), artists(?x1000, ?x7966), artists(?x1000, ?x7570), ?x10565 = 0c9l1, ?x13136 = 03qkcn9, ?x7570 = 01dw_f, ?x7966 = 013rfk, ?x9063 = 0cx7f, ?x1380 = 0dl5d >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #2012 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 6 *> proper extension: 0l14j_; *> query: (?x3156, ?x1751) <- group(?x3156, ?x2901), role(?x2460, ?x3156), family(?x2944, ?x3156), family(?x3156, ?x4975), artists(?x1000, ?x2901), role(?x2460, ?x316), role(?x1524, ?x2460), ?x316 = 05r5c, group(?x2944, ?x1751), category(?x2901, ?x134), award(?x2901, ?x724), role(?x2460, ?x315), instrumentalists(?x2460, ?x680), role(?x2944, ?x432), role(?x2944, ?x615) *> conf = 0.76 ranks of expected_values: 28 EVAL 085jw group 01qqwp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 88.000 35.000 0.875 http://example.org/music/performance_role/regular_performances./music/group_membership/group #16565-016h9b PRED entity: 016h9b PRED relation: profession PRED expected values: 0dz3r => 112 concepts (101 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.72 #2383, 0.71 #2235, 0.68 #10542), 0nbcg (0.57 #5069, 0.56 #3439, 0.55 #4327), 0dz3r (0.50 #2, 0.46 #3410, 0.46 #5040), 01c72t (0.50 #23, 0.40 #467, 0.35 #1652), 039v1 (0.49 #3444, 0.37 #5074, 0.37 #4332), 01d_h8 (0.48 #1337, 0.40 #2078, 0.33 #2374), 02jknp (0.39 #895, 0.26 #1932, 0.25 #2080), 0fnpj (0.30 #60, 0.17 #1244, 0.16 #3021), 0dxtg (0.29 #2086, 0.28 #9491, 0.28 #901), 03gjzk (0.25 #2088, 0.24 #1347, 0.23 #10098) >> Best rule #2383 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 0sz28; 0m31m; 0mj0c; >> query: (?x2865, 02hrh1q) <- sibling(?x2865, ?x6129), profession(?x2865, ?x220), award(?x6129, ?x1232), nationality(?x6129, ?x512) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01l9v7n; 0163r3; 0473q; 017l4; *> query: (?x2865, 0dz3r) <- artists(?x5792, ?x2865), ?x5792 = 026z9, role(?x2865, ?x227) *> conf = 0.50 ranks of expected_values: 3 EVAL 016h9b profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 101.000 0.716 http://example.org/people/person/profession #16564-027pwzc PRED entity: 027pwzc PRED relation: season! PRED expected values: 06wpc 05xvj 051wf => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 303): 05xvj (0.88 #25, 0.83 #62, 0.80 #123), 01ync (0.88 #25, 0.83 #62, 0.79 #101), 06wpc (0.88 #25, 0.83 #62, 0.79 #101), 02__x (0.88 #25, 0.83 #62, 0.79 #101), 0x0d (0.88 #25, 0.83 #62, 0.79 #101), 07l8f (0.88 #25, 0.83 #62, 0.79 #101), 04wmvz (0.88 #25, 0.83 #62, 0.79 #101), 03lpp_ (0.88 #25, 0.83 #62, 0.79 #101), 051wf (0.88 #25, 0.83 #62, 0.79 #101), 02gtm4 (0.71 #75, 0.68 #73, 0.67 #22) >> Best rule #25 for best value: >> intensional similarity = 101 >> extensional distance = 1 >> proper extension: 0dx84s; >> query: (?x9498, ?x6074) <- season(?x11361, ?x9498), season(?x8901, ?x9498), season(?x8894, ?x9498), season(?x7725, ?x9498), season(?x7060, ?x9498), season(?x4243, ?x9498), season(?x4208, ?x9498), season(?x3333, ?x9498), season(?x2405, ?x9498), season(?x2174, ?x9498), season(?x2067, ?x9498), season(?x1823, ?x9498), season(?x1632, ?x9498), season(?x1438, ?x9498), season(?x1160, ?x9498), season(?x1010, ?x9498), season(?x700, ?x9498), season(?x580, ?x9498), season(?x260, ?x9498), ?x2067 = 05g76, ?x8894 = 02d02, ?x11361 = 03m1n, ?x2405 = 0x2p, ?x1823 = 01yhm, ?x260 = 01ypc, ?x1160 = 049n7, ?x7725 = 07l8x, ?x700 = 06x68, ?x580 = 05m_8, ?x4243 = 0713r, ?x4208 = 061xq, ?x1632 = 0cqt41, ?x3333 = 01yjl, ?x1438 = 0512p, ?x8901 = 07l4z, season(?x2174, ?x11501), season(?x2174, ?x10017), season(?x2174, ?x8529), season(?x2174, ?x8517), season(?x2174, ?x3431), school(?x2174, ?x9676), school(?x2174, ?x8706), school(?x2174, ?x6953), school(?x2174, ?x3948), school(?x2174, ?x3777), school(?x2174, ?x2948), school(?x2174, ?x735), school(?x2174, ?x581), position(?x2174, ?x8520), position(?x2174, ?x5727), position(?x2174, ?x4244), position(?x2174, ?x2010), ?x6953 = 01jq0j, ?x3948 = 025v3k, draft(?x2174, ?x8786), draft(?x2174, ?x8499), draft(?x2174, ?x4779), ?x7060 = 01slc, ?x2010 = 02lyr4, ?x735 = 065y4w7, ?x4779 = 02z6872, category(?x2174, ?x134), ?x8706 = 0trv, ?x1010 = 01d5z, major_field_of_study(?x2948, ?x5614), major_field_of_study(?x2948, ?x1154), teams(?x6088, ?x2174), school(?x1883, ?x2948), season(?x12042, ?x8529), season(?x6074, ?x8529), season(?x4487, ?x8529), ?x4244 = 028c_8, contains(?x94, ?x2948), student(?x2948, ?x129), school(?x2114, ?x2948), colors(?x2948, ?x3189), currency(?x9676, ?x170), institution(?x1368, ?x2948), ?x1154 = 02lp1, colors(?x2174, ?x332), ?x3431 = 025ygqm, ?x1368 = 014mlp, ?x5614 = 03qsdpk, ?x8786 = 02pq_x5, ?x8520 = 01z9v6, fraternities_and_sororities(?x2948, ?x3697), ?x4487 = 01ync, ?x8517 = 0285r5d, ?x10017 = 026fmqm, organization(?x346, ?x3777), place_of_birth(?x2794, ?x6088), ?x2114 = 01y49, ?x1883 = 02qw1zx, ?x581 = 06pwq, ?x12042 = 05xvj, ?x11501 = 027mvrc, team(?x5727, ?x662), contains(?x4105, ?x6088), ?x8499 = 02r6gw6, student(?x9676, ?x2259), institution(?x865, ?x3777) >> conf = 0.88 => this is the best rule for 9 predicted values ranks of expected_values: 1, 3, 9 EVAL 027pwzc season! 051wf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 14.000 14.000 0.875 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 027pwzc season! 05xvj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.875 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 027pwzc season! 06wpc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 14.000 14.000 0.875 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #16563-02wkmx PRED entity: 02wkmx PRED relation: award! PRED expected values: 0221zw 0qmhk 02v570 => 53 concepts (30 used for prediction) PRED predicted values (max 10 best out of 948): 03hmt9b (0.62 #6457, 0.56 #7468, 0.50 #1400), 0h1x5f (0.57 #4952, 0.50 #5963, 0.10 #12036), 0hfzr (0.56 #7490, 0.50 #6479, 0.50 #1422), 0404j37 (0.50 #6729, 0.44 #7740, 0.33 #661), 0_92w (0.50 #6170, 0.44 #7181, 0.33 #102), 017jd9 (0.50 #6526, 0.44 #7537, 0.33 #458), 0h6r5 (0.50 #6472, 0.44 #7483, 0.25 #1415), 0gmcwlb (0.50 #1132, 0.38 #6189, 0.33 #7200), 07xtqq (0.50 #1043, 0.38 #6100, 0.33 #7111), 0bl1_ (0.50 #1480, 0.38 #6537, 0.33 #7548) >> Best rule #6457 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 040njc; 019f4v; 02rdyk7; 02w_6xj; >> query: (?x372, 03hmt9b) <- award_winner(?x372, ?x7670), award(?x1365, ?x372), ?x7670 = 06b_0, award(?x810, ?x372) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #24301 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 194 *> proper extension: 0fq9zdv; 02qrwjt; *> query: (?x372, ?x5074) <- award_winner(?x372, ?x8019), award_winner(?x372, ?x7670), award(?x1365, ?x372), award_winner(?x5074, ?x7670), profession(?x8019, ?x319), award(?x810, ?x372) *> conf = 0.25 ranks of expected_values: 138, 140, 362 EVAL 02wkmx award! 02v570 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 30.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02wkmx award! 0qmhk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 30.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02wkmx award! 0221zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 30.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #16562-06thjt PRED entity: 06thjt PRED relation: student PRED expected values: 02n9k 04mky3 => 199 concepts (158 used for prediction) PRED predicted values (max 10 best out of 1746): 04411 (0.25 #2079, 0.25 #124, 0.20 #4283), 073v6 (0.25 #525, 0.20 #4684, 0.07 #10919), 049gc (0.25 #922, 0.20 #5081, 0.07 #11316), 013pp3 (0.25 #919, 0.20 #5078, 0.07 #11313), 06449 (0.25 #471, 0.20 #4630, 0.07 #10865), 01d494 (0.25 #264, 0.20 #4423, 0.07 #10658), 01zwy (0.25 #1469, 0.20 #5628, 0.07 #11863), 02rk45 (0.25 #1548, 0.20 #5707, 0.07 #11942), 02y49 (0.25 #1532, 0.20 #5691, 0.07 #11926), 050_qx (0.25 #1477, 0.20 #5636, 0.07 #11871) >> Best rule #2079 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 06dr9; >> query: (?x10478, ?x920) <- organizations_founded(?x920, ?x10478), influenced_by(?x920, ?x9600), ?x9600 = 039n1 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06thjt student 04mky3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 158.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06thjt student 02n9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 158.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #16561-0bkq7 PRED entity: 0bkq7 PRED relation: nominated_for! PRED expected values: 0gq9h => 79 concepts (78 used for prediction) PRED predicted values (max 10 best out of 218): 0gq9h (0.67 #60, 0.62 #3585, 0.62 #3350), 0gq_v (0.62 #725, 0.58 #960, 0.51 #2605), 0p9sw (0.50 #491, 0.33 #21, 0.31 #1196), 094qd5 (0.45 #3090, 0.18 #3795, 0.14 #5641), 040njc (0.42 #3297, 0.42 #3532, 0.31 #3062), 0k611 (0.42 #540, 0.39 #3595, 0.38 #3360), 0gs96 (0.42 #556, 0.38 #1026, 0.31 #791), 04kxsb (0.40 #3382, 0.39 #3617, 0.17 #5027), 04dn09n (0.39 #3324, 0.38 #3559, 0.31 #3089), 0gqy2 (0.36 #3644, 0.35 #3409, 0.33 #119) >> Best rule #60 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0ktpx; >> query: (?x8617, 0gq9h) <- film(?x9000, ?x8617), nominated_for(?x591, ?x8617), ?x9000 = 0k9j_, genre(?x8617, ?x53) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bkq7 nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 78.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16560-05p3738 PRED entity: 05p3738 PRED relation: genre PRED expected values: 02kdv5l => 83 concepts (43 used for prediction) PRED predicted values (max 10 best out of 114): 02kdv5l (0.82 #4894, 0.63 #4778, 0.57 #4545), 02l7c8 (0.81 #593, 0.55 #361, 0.41 #1759), 060__y (0.73 #362, 0.63 #3742, 0.47 #1526), 07ssc (0.60 #1163, 0.60 #813, 0.58 #3493), 05p553 (0.40 #235, 0.38 #4547, 0.30 #1631), 01t_vv (0.40 #282, 0.25 #166, 0.11 #4127), 01hmnh (0.40 #4209, 0.38 #4325, 0.34 #3975), 03g3w (0.37 #835, 0.33 #1068, 0.33 #22), 03bxz7 (0.37 #864, 0.33 #1097, 0.32 #747), 06l3bl (0.33 #34, 0.32 #847, 0.32 #730) >> Best rule #4894 for best value: >> intensional similarity = 12 >> extensional distance = 506 >> proper extension: 0d90m; 03qcfvw; 09sh8k; 02vxq9m; 06w99h3; 01k1k4; 0gtv7pk; 0ds33; 0b60sq; 04ddm4; ... >> query: (?x1710, 02kdv5l) <- country(?x1710, ?x512), genre(?x1710, ?x3515), genre(?x10531, ?x3515), genre(?x2928, ?x3515), genre(?x2699, ?x3515), genre(?x2026, ?x3515), genre(?x1077, ?x3515), ?x2928 = 07024, ?x1077 = 09q5w2, ?x2026 = 04kzqz, ?x2699 = 04t6fk, nominated_for(?x1020, ?x10531) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05p3738 genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 43.000 0.821 http://example.org/film/film/genre #16559-013pp3 PRED entity: 013pp3 PRED relation: influenced_by! PRED expected values: 0p8jf => 150 concepts (52 used for prediction) PRED predicted values (max 10 best out of 430): 026fd (0.40 #759, 0.25 #242, 0.20 #1791), 013pp3 (0.25 #221, 0.22 #1254, 0.20 #1770), 0683n (0.25 #338, 0.22 #1371, 0.09 #517), 0p8jf (0.25 #111, 0.20 #1660, 0.20 #628), 040_t (0.25 #255, 0.20 #1804, 0.20 #772), 01hb6v (0.25 #94, 0.20 #611, 0.11 #1127), 0d4jl (0.25 #116, 0.20 #633, 0.10 #1665), 040rjq (0.25 #484, 0.20 #1001, 0.10 #2033), 04cbtrw (0.25 #107, 0.20 #624, 0.10 #1656), 014ps4 (0.25 #310, 0.20 #827, 0.10 #1859) >> Best rule #759 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 045bg; 03f47xl; >> query: (?x5335, 026fd) <- influenced_by(?x5335, ?x3336), influenced_by(?x5335, ?x2994), award(?x5335, ?x575), ?x2994 = 0379s, ?x3336 = 032l1 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #111 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 01tz6vs; *> query: (?x5335, 0p8jf) <- influenced_by(?x5335, ?x11075), award(?x5335, ?x575), ?x11075 = 0113sg *> conf = 0.25 ranks of expected_values: 4 EVAL 013pp3 influenced_by! 0p8jf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 150.000 52.000 0.400 http://example.org/influence/influence_node/influenced_by #16558-099jhq PRED entity: 099jhq PRED relation: nominated_for PRED expected values: 04vr_f 02rx2m5 09tqkv2 07yk1xz 0gmgwnv 04q827 => 46 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1323): 02yvct (0.81 #4647, 0.77 #4646, 0.73 #1547), 01chpn (0.81 #4647, 0.77 #4646, 0.73 #1547), 016fyc (0.81 #4647, 0.77 #4646, 0.73 #1547), 050gkf (0.77 #4646, 0.73 #1547, 0.69 #15491), 04vr_f (0.60 #153, 0.56 #1701, 0.44 #4800), 0f4_l (0.60 #307, 0.50 #3405, 0.44 #4954), 04q827 (0.60 #1449, 0.44 #2997, 0.42 #4547), 07j94 (0.60 #670, 0.39 #5317, 0.33 #2218), 011ycb (0.60 #752, 0.33 #2300, 0.25 #3850), 071nw5 (0.60 #937, 0.33 #2485, 0.25 #4035) >> Best rule #4647 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0f4x7; 05pcn59; 05p09zm; 04kxsb; 057xs89; 02w9sd7; >> query: (?x451, ?x2189) <- award(?x1554, ?x451), award(?x2189, ?x451), film_release_region(?x2189, ?x87), ?x1554 = 06cgy >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 02x73k6; 0gqy2; 09sdmz; *> query: (?x451, 04vr_f) <- award(?x875, ?x451), award(?x2189, ?x451), ?x2189 = 02yvct, ?x875 = 032_jg *> conf = 0.60 ranks of expected_values: 5, 7, 15, 30, 46, 194 EVAL 099jhq nominated_for 04q827 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 46.000 18.000 0.807 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099jhq nominated_for 0gmgwnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 46.000 18.000 0.807 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099jhq nominated_for 07yk1xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 18.000 0.807 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099jhq nominated_for 09tqkv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 46.000 18.000 0.807 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099jhq nominated_for 02rx2m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 46.000 18.000 0.807 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099jhq nominated_for 04vr_f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 46.000 18.000 0.807 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16557-03295l PRED entity: 03295l PRED relation: languages_spoken PRED expected values: 07qv_ => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 149): 02h40lc (0.60 #267, 0.50 #161, 0.48 #1912), 03x42 (0.33 #98, 0.25 #204, 0.20 #310), 07qv_ (0.33 #30, 0.25 #136, 0.11 #932), 0jzc (0.33 #15, 0.06 #1925, 0.06 #1341), 064_8sq (0.25 #176, 0.23 #1927, 0.21 #866), 03_9r (0.25 #114, 0.11 #910, 0.07 #1175), 07zrf (0.25 #109, 0.05 #905, 0.04 #1011), 01r2l (0.20 #285, 0.06 #1930, 0.06 #1400), 0459q4 (0.20 #298, 0.04 #1943, 0.04 #1200), 012w70 (0.20 #275, 0.04 #1920, 0.04 #1177) >> Best rule #267 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 0d2by; >> query: (?x6711, 02h40lc) <- people(?x6711, ?x8166), people(?x6711, ?x8018), artist(?x6474, ?x8166), award(?x8166, ?x3488), ?x8018 = 09h4b5, artists(?x3319, ?x8166), ?x3319 = 06j6l, award_winner(?x3488, ?x702) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 071x0k; *> query: (?x6711, 07qv_) <- geographic_distribution(?x6711, ?x94), ?x94 = 09c7w0, languages_spoken(?x6711, ?x12965), languages_spoken(?x6711, ?x3592), ?x12965 = 01jb8r, languages_spoken(?x5741, ?x3592), languages_spoken(?x3584, ?x3592), ?x3584 = 07hwkr, people(?x5741, ?x133) *> conf = 0.33 ranks of expected_values: 3 EVAL 03295l languages_spoken 07qv_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 44.000 44.000 0.600 http://example.org/people/ethnicity/languages_spoken #16556-01m4kpp PRED entity: 01m4kpp PRED relation: award_winner! PRED expected values: 05qck => 147 concepts (113 used for prediction) PRED predicted values (max 10 best out of 338): 08_vwq (0.43 #1296, 0.43 #1132, 0.37 #48303), 0bs0bh (0.37 #48303, 0.37 #48302, 0.37 #48736), 0bfvd4 (0.37 #48303, 0.37 #48302, 0.37 #48736), 09qrn4 (0.29 #1101, 0.20 #2829, 0.12 #5418), 01c92g (0.29 #1825, 0.09 #7867, 0.08 #9161), 0ck27z (0.25 #22094, 0.23 #24683, 0.16 #17353), 09qv3c (0.25 #482, 0.10 #24159, 0.08 #4368), 0m7yy (0.24 #3634, 0.14 #1907, 0.10 #10538), 05f3q (0.23 #3332, 0.02 #8941, 0.02 #9804), 03x3wf (0.18 #7834, 0.14 #1792, 0.14 #9128) >> Best rule #1296 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 01vlj1g; 01yk13; 01y665; 0jmj; 01tsbmv; >> query: (?x12480, ?x6878) <- award(?x12480, ?x6878), award(?x12480, ?x1921), ?x6878 = 08_vwq, profession(?x12480, ?x987), ?x1921 = 0bs0bh >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1489 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02yygk; *> query: (?x12480, 05qck) <- actor(?x9098, ?x12480), artists(?x13359, ?x12480), profession(?x12480, ?x1041), ?x1041 = 03gjzk *> conf = 0.14 ranks of expected_values: 20 EVAL 01m4kpp award_winner! 05qck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 147.000 113.000 0.429 http://example.org/award/award_category/winners./award/award_honor/award_winner #16555-03cffvv PRED entity: 03cffvv PRED relation: country PRED expected values: 09c7w0 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.80 #1291, 0.80 #1539, 0.80 #614), 07ssc (0.40 #17, 0.30 #139, 0.26 #200), 0f8l9c (0.10 #1126, 0.10 #694, 0.10 #1248), 0345h (0.09 #2306, 0.09 #1317, 0.09 #2367), 015w9s (0.07 #736, 0.06 #1724, 0.06 #1723), 03mdt (0.07 #736, 0.06 #1724, 0.06 #1723), 03rjj (0.06 #2832, 0.05 #129, 0.04 #190), 0chghy (0.06 #2832, 0.05 #135, 0.04 #196), 0d060g (0.06 #253, 0.04 #2532, 0.04 #1546), 03_3d (0.04 #2347, 0.04 #3392, 0.04 #2531) >> Best rule #1291 for best value: >> intensional similarity = 4 >> extensional distance = 1076 >> proper extension: 0gtvrv3; >> query: (?x11610, 09c7w0) <- language(?x11610, ?x5607), film(?x3917, ?x11610), participant(?x3917, ?x496), location(?x3917, ?x739) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cffvv country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.800 http://example.org/film/film/country #16554-01hjy5 PRED entity: 01hjy5 PRED relation: fraternities_and_sororities PRED expected values: 035tlh => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 3): 035tlh (0.49 #11, 0.46 #14, 0.43 #23), 0325pb (0.36 #4, 0.34 #7, 0.31 #10), 04m8fy (0.05 #33, 0.04 #12, 0.04 #24) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0ks67; >> query: (?x8354, 035tlh) <- organization(?x8354, ?x5487), student(?x8354, ?x1287), organization(?x346, ?x8354) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hjy5 fraternities_and_sororities 035tlh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.490 http://example.org/education/university/fraternities_and_sororities #16553-03kpvp PRED entity: 03kpvp PRED relation: nationality PRED expected values: 09c7w0 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 71): 09c7w0 (0.81 #701, 0.76 #1001, 0.74 #1601), 07ssc (0.38 #215, 0.30 #415, 0.27 #7204), 02jx1 (0.29 #133, 0.20 #433, 0.12 #233), 01mjq (0.27 #7204, 0.10 #540, 0.06 #640), 0345h (0.27 #7204, 0.06 #631, 0.04 #8707), 0f8l9c (0.27 #7204, 0.04 #8707, 0.03 #5224), 0160w (0.27 #7204), 03rt9 (0.25 #213, 0.20 #413, 0.14 #113), 01zqy6t (0.25 #10609), 01n7q (0.25 #10609) >> Best rule #701 for best value: >> intensional similarity = 2 >> extensional distance = 56 >> proper extension: 02qggqc; >> query: (?x3692, 09c7w0) <- executive_produced_by(?x1262, ?x3692), nominated_for(?x1262, ?x836) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03kpvp nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.810 http://example.org/people/person/nationality #16552-017dpj PRED entity: 017dpj PRED relation: profession PRED expected values: 02hrh1q => 105 concepts (94 used for prediction) PRED predicted values (max 10 best out of 69): 02hrh1q (0.87 #12722, 0.82 #13014, 0.81 #13160), 02jknp (0.51 #2197, 0.43 #2927, 0.43 #3365), 018gz8 (0.50 #453, 0.50 #307, 0.43 #15), 09jwl (0.35 #1477, 0.31 #6735, 0.31 #1331), 0cbd2 (0.33 #444, 0.29 #6, 0.26 #590), 0nbcg (0.33 #6747, 0.24 #1489, 0.23 #1343), 0kyk (0.29 #27, 0.20 #319, 0.17 #465), 0dz3r (0.25 #6720, 0.17 #1462, 0.17 #1316), 016z4k (0.21 #1464, 0.19 #1610, 0.17 #1318), 0np9r (0.20 #603, 0.20 #311, 0.17 #457) >> Best rule #12722 for best value: >> intensional similarity = 3 >> extensional distance = 2853 >> proper extension: 063_t; 09ld6g; >> query: (?x10506, 02hrh1q) <- profession(?x10506, ?x1041), profession(?x9815, ?x1041), ?x9815 = 033jj1 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017dpj profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 94.000 0.875 http://example.org/people/person/profession #16551-07xtqq PRED entity: 07xtqq PRED relation: films! PRED expected values: 05vtw => 61 concepts (29 used for prediction) PRED predicted values (max 10 best out of 32): 081pw (0.08 #1246, 0.08 #624, 0.07 #1402), 06d4h (0.07 #663, 0.06 #1285, 0.06 #1441), 0fzyg (0.05 #1452, 0.05 #1296, 0.05 #674), 05489 (0.05 #672, 0.04 #1450, 0.03 #206), 07s2s (0.04 #1340, 0.04 #1496, 0.03 #718), 0bq3x (0.04 #1273, 0.04 #1429, 0.03 #651), 07c52 (0.04 #641, 0.03 #1419, 0.03 #1263), 01vq3 (0.04 #1439, 0.04 #1283, 0.03 #40), 018h2 (0.04 #643, 0.03 #1421, 0.03 #1265), 02_h0 (0.04 #719, 0.03 #1341, 0.03 #1497) >> Best rule #1246 for best value: >> intensional similarity = 2 >> extensional distance = 481 >> proper extension: 05f67hw; >> query: (?x407, 081pw) <- country(?x407, ?x94), films(?x4450, ?x407) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #691 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 328 *> proper extension: 01gglm; *> query: (?x407, 05vtw) <- award_winner(?x407, ?x230), films(?x4450, ?x407), nominated_for(?x406, ?x407) *> conf = 0.01 ranks of expected_values: 31 EVAL 07xtqq films! 05vtw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 61.000 29.000 0.077 http://example.org/film/film_subject/films #16550-0hx5f PRED entity: 0hx5f PRED relation: contains PRED expected values: 07wtc => 133 concepts (54 used for prediction) PRED predicted values (max 10 best out of 622): 07wtc (0.78 #97243, 0.77 #132619, 0.75 #67773), 01cwdk (0.17 #797, 0.07 #6691, 0.07 #9638), 07w4j (0.17 #266, 0.07 #6160, 0.07 #9107), 02lwv5 (0.17 #4692, 0.05 #25319, 0.05 #42998), 01t38b (0.17 #754, 0.05 #12542, 0.04 #15488), 02ps55 (0.17 #1975, 0.05 #13763, 0.04 #16709), 03_fmr (0.17 #4713, 0.05 #34179, 0.05 #37126), 021q2j (0.17 #4209, 0.05 #33675, 0.05 #36622), 03bmmc (0.17 #3725, 0.05 #33191, 0.05 #36138), 01stzp (0.17 #5399, 0.04 #17186, 0.02 #26026) >> Best rule #97243 for best value: >> intensional similarity = 5 >> extensional distance = 167 >> proper extension: 099ty; 0k3p; 015y2q; 0bdg5; 01s3v; 064xp; >> query: (?x12491, ?x11740) <- citytown(?x11740, ?x12491), place_of_birth(?x3856, ?x12491), contains(?x1310, ?x12491), contains(?x455, ?x11740), category(?x11740, ?x134) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hx5f contains 07wtc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 54.000 0.780 http://example.org/location/location/contains #16549-0163v PRED entity: 0163v PRED relation: country! PRED expected values: 0d1tm 09w1n => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 29): 07gyv (0.67 #61, 0.50 #148, 0.50 #90), 09w1n (0.67 #38, 0.50 #67, 0.43 #1452), 019tzd (0.58 #75, 0.43 #1452, 0.38 #1306), 07rlg (0.58 #59, 0.43 #1452, 0.38 #1306), 01gqfm (0.56 #83, 0.43 #1452, 0.38 #1306), 01sgl (0.56 #78, 0.43 #1452, 0.38 #1306), 035d1m (0.50 #69, 0.43 #1452, 0.38 #1306), 0d1t3 (0.50 #72, 0.43 #1452, 0.38 #1306), 096f8 (0.47 #62, 0.43 #1452, 0.38 #1306), 0d1tm (0.47 #60, 0.43 #1452, 0.38 #1306) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 05r4w; 09c7w0; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 04gzd; 0chghy; ... >> query: (?x2188, 07gyv) <- olympics(?x2188, ?x418), film_release_region(?x249, ?x2188), ?x249 = 0c3ybss >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 07ytt; *> query: (?x2188, 09w1n) <- administrative_parent(?x2188, ?x551), contains(?x455, ?x2188), ?x455 = 02j9z *> conf = 0.67 ranks of expected_values: 2, 10 EVAL 0163v country! 09w1n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.667 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0163v country! 0d1tm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 98.000 98.000 0.667 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #16548-07h1h5 PRED entity: 07h1h5 PRED relation: team PRED expected values: 029q3k => 121 concepts (37 used for prediction) PRED predicted values (max 10 best out of 269): 02b13y (0.84 #3176, 0.84 #3174, 0.82 #7142), 02q1hz (0.84 #3176, 0.84 #3174, 0.82 #7142), 0272vm (0.50 #1210, 0.33 #679, 0.07 #7028), 0fvly (0.33 #723, 0.20 #1518, 0.07 #7072), 01wx_y (0.33 #660, 0.20 #1455, 0.03 #4100), 02029f (0.33 #656, 0.07 #2243, 0.05 #2773), 017znw (0.33 #574, 0.06 #4014, 0.05 #6923), 0230rx (0.33 #762, 0.02 #7111), 02b1yn (0.33 #760, 0.02 #7109), 085v7 (0.27 #2173, 0.20 #1381, 0.12 #5613) >> Best rule #3176 for best value: >> intensional similarity = 6 >> extensional distance = 23 >> proper extension: 0g3b2z; >> query: (?x3586, ?x8673) <- team(?x3586, ?x8673), team(?x3586, ?x3587), position(?x8673, ?x60), team(?x3586, ?x8678), teams(?x985, ?x3587), current_club(?x3587, ?x202) >> conf = 0.84 => this is the best rule for 2 predicted values *> Best rule #4115 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 33 *> proper extension: 0784v1; 09ntbc; 0c11mj; 071pf2; 07nv3_; 09lhln; 0135nb; 0f1pyf; 05s_c38; 0bw7ly; ... *> query: (?x3586, 029q3k) <- athlete(?x471, ?x3586), team(?x3586, ?x4511), place_of_birth(?x3586, ?x8174), team(?x208, ?x4511), team(?x63, ?x4511) *> conf = 0.09 ranks of expected_values: 67 EVAL 07h1h5 team 029q3k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 121.000 37.000 0.838 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #16547-04mjl PRED entity: 04mjl PRED relation: season PRED expected values: 027mvrc => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 6): 027mvrc (0.81 #141, 0.79 #159, 0.79 #75), 02h7s73 (0.36 #76, 0.35 #136, 0.34 #160), 03c6s24 (0.36 #77, 0.33 #11, 0.33 #5), 04110b0 (0.35 #116, 0.34 #158, 0.33 #140), 03c74_8 (0.33 #1, 0.29 #73, 0.27 #133), 04n36qk (0.20 #36, 0.12 #90, 0.08 #126) >> Best rule #141 for best value: >> intensional similarity = 8 >> extensional distance = 25 >> proper extension: 07l8f; >> query: (?x7357, 027mvrc) <- position(?x7357, ?x2010), season(?x7357, ?x10017), school(?x7357, ?x9131), school(?x7357, ?x2711), draft(?x7357, ?x1633), ?x10017 = 026fmqm, organization(?x346, ?x2711), institution(?x620, ?x9131) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mjl season 027mvrc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.815 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #16546-072hv PRED entity: 072hv PRED relation: risk_factors PRED expected values: 0c58k => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 95): 01hbgs (0.83 #2465, 0.82 #4151, 0.80 #1269), 0jpmt (0.80 #1269, 0.77 #3787, 0.70 #768), 0c58k (0.80 #1269, 0.75 #3586, 0.75 #3543), 0fltx (0.80 #1269, 0.70 #768, 0.67 #1242), 05zppz (0.80 #1269, 0.70 #768, 0.50 #1670), 0k95h (0.80 #1269, 0.70 #768, 0.50 #831), 03p41 (0.64 #1850, 0.55 #292, 0.50 #654), 02zsn (0.57 #1679, 0.41 #2491, 0.33 #168), 012jc (0.55 #292, 0.50 #1670, 0.50 #929), 0qcr0 (0.55 #292, 0.33 #1457, 0.33 #487) >> Best rule #2465 for best value: >> intensional similarity = 24 >> extensional distance = 10 >> proper extension: 01k9gb; >> query: (?x11659, 01hbgs) <- risk_factors(?x11659, ?x13131), risk_factors(?x11659, ?x12536), risk_factors(?x8675, ?x13131), risk_factors(?x6655, ?x13131), risk_factors(?x6483, ?x13131), risk_factors(?x4291, ?x13131), ?x6655 = 09d11, notable_people_with_this_condition(?x4291, ?x4292), people(?x4291, ?x4055), symptom_of(?x9118, ?x4291), risk_factors(?x6483, ?x268), ?x9118 = 0brgy, ?x268 = 0qcr0, risk_factors(?x14024, ?x12536), risk_factors(?x7007, ?x12536), symptom_of(?x9438, ?x8675), ?x9438 = 012qjw, symptom_of(?x13713, ?x6483), risk_factors(?x7006, ?x7007), risk_factors(?x7007, ?x11160), symptom_of(?x9510, ?x7007), people(?x7007, ?x2208), ?x14024 = 0h1wz, ?x11160 = 012jc >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1269 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 4 *> proper extension: 01qqwn; *> query: (?x11659, ?x8524) <- risk_factors(?x11659, ?x13738), risk_factors(?x11659, ?x12536), symptom_of(?x6780, ?x11659), symptom_of(?x6780, ?x14562), symptom_of(?x6780, ?x13231), symptom_of(?x6780, ?x10480), symptom_of(?x6780, ?x9898), symptom_of(?x6780, ?x6781), ?x6781 = 035482, people(?x9898, ?x12334), ?x14562 = 087z2, ?x10480 = 0h1n9, ?x12334 = 02h48, symptom_of(?x9438, ?x9898), risk_factors(?x3799, ?x13738), symptom_of(?x5855, ?x12536), people(?x13231, ?x4309), risk_factors(?x6655, ?x13231), people(?x3799, ?x3800), symptom_of(?x11393, ?x3799), ?x3800 = 0ly5n, ?x9438 = 012qjw, risk_factors(?x13231, ?x8524) *> conf = 0.80 ranks of expected_values: 3 EVAL 072hv risk_factors 0c58k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.833 http://example.org/medicine/disease/risk_factors #16545-016fjj PRED entity: 016fjj PRED relation: type_of_union PRED expected values: 04ztj => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.77 #17, 0.75 #9, 0.73 #141), 01g63y (0.45 #329, 0.29 #6, 0.25 #2), 0jgjn (0.01 #20) >> Best rule #17 for best value: >> intensional similarity = 2 >> extensional distance = 88 >> proper extension: 01ztgm; 0522wp; 01vxqyl; >> query: (?x3701, 04ztj) <- award(?x3701, ?x102), ?x102 = 04ljl_l >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016fjj type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.767 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16544-019dwp PRED entity: 019dwp PRED relation: organization! PRED expected values: 060c4 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 16): 060c4 (0.82 #328, 0.82 #276, 0.82 #237), 07xl34 (0.38 #102, 0.33 #675, 0.32 #115), 0dq_5 (0.23 #907, 0.23 #946, 0.21 #543), 05k17c (0.12 #710, 0.11 #840, 0.10 #866), 01t7n9 (0.08 #222, 0.03 #1525, 0.03 #1789), 02079p (0.08 #222, 0.03 #1525, 0.03 #1789), 0789n (0.08 #222, 0.03 #1525, 0.03 #1789), 0f6c3 (0.08 #222, 0.03 #1525, 0.03 #1789), 01gkgk (0.08 #222, 0.03 #1525, 0.03 #1789), 0fkvn (0.08 #222, 0.03 #1525, 0.03 #1789) >> Best rule #328 for best value: >> intensional similarity = 5 >> extensional distance = 110 >> proper extension: 015fsv; >> query: (?x4916, 060c4) <- school_type(?x4916, ?x1507), contains(?x94, ?x4916), currency(?x4916, ?x170), school(?x580, ?x4916), ?x170 = 09nqf >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019dwp organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.821 http://example.org/organization/role/leaders./organization/leadership/organization #16543-0pc62 PRED entity: 0pc62 PRED relation: film! PRED expected values: 01jfrg => 70 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1103): 0150t6 (0.47 #74709, 0.47 #70555, 0.44 #68479), 04ktcgn (0.44 #68479, 0.42 #58105, 0.42 #37348), 02qzjj (0.44 #68479, 0.42 #58105, 0.42 #37348), 0284n42 (0.44 #68479, 0.42 #58105, 0.42 #37348), 020h2v (0.44 #68479, 0.42 #58105, 0.42 #37348), 0f6_x (0.33 #627, 0.12 #2702, 0.02 #25521), 01fyzy (0.33 #1059, 0.12 #3134, 0.02 #15580), 01skmp (0.33 #1173, 0.12 #3248, 0.02 #17768), 0k525 (0.33 #1838, 0.12 #3913, 0.02 #26732), 01ypsj (0.33 #1672, 0.12 #3747) >> Best rule #74709 for best value: >> intensional similarity = 4 >> extensional distance = 1134 >> proper extension: 0275kr; >> query: (?x667, ?x989) <- nominated_for(?x6730, ?x667), nominated_for(?x989, ?x667), location(?x989, ?x3007), student(?x11036, ?x6730) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #49804 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 763 *> proper extension: 01f3p_; 07wqr6; 0123qq; 0clpml; *> query: (?x667, ?x2857) <- nominated_for(?x6730, ?x667), participant(?x6730, ?x2857), location(?x6730, ?x3125) *> conf = 0.03 ranks of expected_values: 354 EVAL 0pc62 film! 01jfrg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 70.000 37.000 0.469 http://example.org/film/actor/film./film/performance/film #16542-063g7l PRED entity: 063g7l PRED relation: student! PRED expected values: 015fs3 => 115 concepts (70 used for prediction) PRED predicted values (max 10 best out of 124): 078bz (0.18 #1658, 0.18 #2712, 0.07 #4293), 09f2j (0.18 #1740, 0.07 #4375, 0.06 #2794), 04b_46 (0.17 #754, 0.17 #227, 0.12 #1281), 0h6rm (0.17 #671, 0.17 #144, 0.12 #1198), 01rtm4 (0.17 #531, 0.17 #4, 0.12 #1058), 03x33n (0.17 #129, 0.02 #6980, 0.02 #14359), 02qvvv (0.17 #99, 0.02 #6950, 0.01 #8531), 065y4w7 (0.12 #1068, 0.05 #3703, 0.04 #24785), 0lyjf (0.09 #1738, 0.06 #2792, 0.06 #2265), 017j69 (0.09 #1726, 0.06 #2780, 0.06 #2253) >> Best rule #1658 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 054c1; >> query: (?x11624, 078bz) <- film(?x11624, ?x86), profession(?x11624, ?x1032), team(?x11624, ?x4519) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 063g7l student! 015fs3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 70.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student #16541-0bxbr PRED entity: 0bxbr PRED relation: location! PRED expected values: 04bd8y => 89 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1466): 04bd8y (0.47 #78075, 0.47 #75556, 0.47 #95708), 021sv1 (0.12 #15115, 0.11 #10075, 0.10 #15114), 0444x (0.12 #15115, 0.11 #10075, 0.10 #15114), 07r4c (0.06 #1260, 0.03 #8816, 0.03 #3778), 09fb5 (0.06 #2569, 0.05 #5088, 0.04 #7607), 01vsy3q (0.05 #8547, 0.04 #11066, 0.04 #13586), 073749 (0.05 #13398, 0.04 #8359, 0.04 #10878), 01s21dg (0.05 #3483, 0.04 #11040, 0.04 #6002), 02lt8 (0.05 #3315, 0.04 #5834, 0.04 #15912), 023kzp (0.05 #3734, 0.04 #6253, 0.03 #8772) >> Best rule #78075 for best value: >> intensional similarity = 3 >> extensional distance = 436 >> proper extension: 0_3cs; 01sn04; 016v46; 0jp26; 01ykl0; 0d04z6; 02_n7; 01t8gz; 01rmjw; 062qg; ... >> query: (?x5962, ?x820) <- contains(?x94, ?x5962), place_of_birth(?x820, ?x5962), film(?x820, ?x1450) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bxbr location! 04bd8y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 71.000 0.474 http://example.org/people/person/places_lived./people/place_lived/location #16540-0fz0c2 PRED entity: 0fz0c2 PRED relation: ceremony! PRED expected values: 0p9sw 0gr4k => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 299): 0gs96 (0.93 #4007, 0.93 #3762, 0.92 #3270), 018wng (0.92 #5188, 0.92 #4451, 0.91 #4943), 0gr07 (0.88 #3351, 0.88 #3106, 0.87 #5071), 0gqng (0.88 #2705, 0.88 #2460, 0.87 #4916), 0p9sw (0.86 #5667, 0.85 #5174, 0.83 #4437), 0gr4k (0.83 #3952, 0.83 #3707, 0.81 #2233), 0gq_v (0.77 #5173, 0.77 #3945, 0.76 #3700), 018wdw (0.76 #3366, 0.76 #3858, 0.76 #8601), 0gqxm (0.76 #8601, 0.75 #7371, 0.73 #7618), 0gqzz (0.76 #8601, 0.75 #7371, 0.73 #7618) >> Best rule #4007 for best value: >> intensional similarity = 13 >> extensional distance = 28 >> proper extension: 0fzrhn; >> query: (?x7589, 0gs96) <- ceremony(?x1307, ?x7589), ceremony(?x1243, ?x7589), award_winner(?x7589, ?x11612), award_winner(?x7589, ?x5601), award_winner(?x3002, ?x5601), spouse(?x1568, ?x5601), nominated_for(?x1243, ?x4216), profession(?x11612, ?x1032), film(?x5601, ?x5378), ?x1307 = 0gq9h, award(?x185, ?x1243), award_winner(?x1243, ?x5528), ?x4216 = 0hfzr >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #5667 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 64 *> proper extension: 0fz20l; 0fz2y7; 0fzrtf; 0d__c3; 0dznvw; *> query: (?x7589, 0p9sw) <- ceremony(?x1079, ?x7589), award_winner(?x7589, ?x5601), award_winner(?x3002, ?x5601), honored_for(?x7589, ?x5183), award(?x669, ?x1079), nominated_for(?x1079, ?x6215), nominated_for(?x1079, ?x2402), nominated_for(?x1079, ?x2215), nominated_for(?x1079, ?x1496), nominated_for(?x1079, ?x186), ?x186 = 02vxq9m, ?x6215 = 0jyb4, ?x1496 = 011yqc, ?x2215 = 011yd2, award_winner(?x2402, ?x185) *> conf = 0.86 ranks of expected_values: 5, 6 EVAL 0fz0c2 ceremony! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 36.000 36.000 0.933 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0fz0c2 ceremony! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 36.000 36.000 0.933 http://example.org/award/award_category/winners./award/award_honor/ceremony #16539-01gn36 PRED entity: 01gn36 PRED relation: film PRED expected values: 01bn3l => 125 concepts (72 used for prediction) PRED predicted values (max 10 best out of 976): 03kx49 (0.29 #1341, 0.25 #3130, 0.12 #4919), 016dj8 (0.15 #10058, 0.03 #11847, 0.02 #29739), 027pfg (0.15 #10167, 0.02 #79943, 0.02 #19114), 027fwmt (0.14 #1592, 0.12 #5170, 0.12 #3381), 01d259 (0.14 #988, 0.12 #4566, 0.12 #2777), 042g97 (0.14 #1766, 0.12 #5344, 0.12 #3555), 01gvpz (0.14 #1507, 0.12 #5085, 0.12 #3296), 02_1sj (0.14 #80, 0.12 #1869, 0.08 #9025), 03mh94 (0.14 #64, 0.12 #1853, 0.06 #16167), 03q0r1 (0.14 #637, 0.12 #2426, 0.05 #18529) >> Best rule #1341 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01wp_jm; >> query: (?x4554, 03kx49) <- location(?x4554, ?x2504), influenced_by(?x6692, ?x4554), ?x6692 = 04l19_, profession(?x4554, ?x955) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1357 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 01wp_jm; *> query: (?x4554, 01bn3l) <- location(?x4554, ?x2504), influenced_by(?x6692, ?x4554), ?x6692 = 04l19_, profession(?x4554, ?x955) *> conf = 0.14 ranks of expected_values: 22 EVAL 01gn36 film 01bn3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 125.000 72.000 0.286 http://example.org/film/actor/film./film/performance/film #16538-01kff7 PRED entity: 01kff7 PRED relation: film! PRED expected values: 03cglm => 94 concepts (43 used for prediction) PRED predicted values (max 10 best out of 958): 01gbn6 (0.52 #2082, 0.48 #22896, 0.46 #72861), 012x4t (0.52 #2082, 0.48 #22896, 0.46 #72861), 0147dk (0.52 #2082, 0.48 #22896, 0.46 #72861), 01qg7c (0.52 #2082, 0.48 #22896, 0.45 #54122), 06s26c (0.52 #2082, 0.48 #22896, 0.45 #54122), 086k8 (0.52 #2082, 0.48 #22896, 0.45 #54122), 012d40 (0.12 #2098, 0.04 #16, 0.03 #31238), 05txrz (0.09 #2847, 0.04 #765, 0.03 #46559), 01wbg84 (0.09 #2129, 0.04 #47, 0.03 #35431), 086nl7 (0.09 #2867, 0.04 #785, 0.03 #46579) >> Best rule #2082 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 01hr1; 011yph; 0k2sk; 033g4d; 069q4f; 02pjc1h; 0yyts; 01dvbd; 02_sr1; 01hqk; ... >> query: (?x1372, ?x382) <- genre(?x1372, ?x5231), award_winner(?x1372, ?x382), nominated_for(?x102, ?x1372), ?x5231 = 0556j8 >> conf = 0.52 => this is the best rule for 6 predicted values *> Best rule #36430 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 233 *> proper extension: 0hgnl3t; *> query: (?x1372, 03cglm) <- film_crew_role(?x1372, ?x137), nominated_for(?x102, ?x1372), crewmember(?x1372, ?x666) *> conf = 0.02 ranks of expected_values: 381 EVAL 01kff7 film! 03cglm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 94.000 43.000 0.518 http://example.org/film/actor/film./film/performance/film #16537-01pl14 PRED entity: 01pl14 PRED relation: school! PRED expected values: 07l24 0jmnl => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 80): 05m_8 (0.42 #317, 0.20 #80, 0.20 #1), 07l8x (0.42 #374, 0.20 #58, 0.18 #690), 05tfm (0.40 #92, 0.40 #13, 0.25 #329), 01y49 (0.40 #98, 0.40 #19, 0.25 #335), 0cqt41 (0.40 #15, 0.33 #331, 0.20 #94), 05tg3 (0.40 #108, 0.25 #345, 0.20 #29), 051wf (0.40 #77, 0.20 #156, 0.17 #393), 051vz (0.33 #336, 0.20 #99, 0.20 #20), 04mjl (0.25 #213, 0.20 #134, 0.20 #55), 01yjl (0.25 #343, 0.20 #106, 0.20 #27) >> Best rule #317 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 07w0v; 03x33n; 03tw2s; 01jq0j; 0trv; 021w0_; >> query: (?x466, 05m_8) <- colors(?x466, ?x332), school(?x10279, ?x466), ?x10279 = 04wmvz, major_field_of_study(?x466, ?x947) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 07y0n; *> query: (?x466, 07l24) <- category(?x466, ?x134), company(?x3520, ?x466), ?x134 = 08mbj5d, ?x3520 = 03gkn5 *> conf = 0.22 ranks of expected_values: 17, 34 EVAL 01pl14 school! 0jmnl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 190.000 190.000 0.417 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01pl14 school! 07l24 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 190.000 190.000 0.417 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #16536-0sxmx PRED entity: 0sxmx PRED relation: nominated_for! PRED expected values: 02pqp12 03hl6lc => 107 concepts (100 used for prediction) PRED predicted values (max 10 best out of 213): 0gq9h (0.78 #11598, 0.71 #2104, 0.70 #2044), 02pqp12 (0.78 #11598, 0.70 #2044, 0.70 #2045), 040njc (0.64 #3642, 0.53 #2052, 0.42 #1823), 0gq_v (0.49 #1836, 0.34 #4336, 0.34 #2065), 04kxsb (0.46 #3723, 0.43 #2133, 0.27 #8499), 0l8z1 (0.42 #2095, 0.33 #3685, 0.32 #1866), 0gr4k (0.41 #2071, 0.36 #1842, 0.30 #10255), 02qvyrt (0.41 #3724, 0.33 #2134, 0.27 #2361), 054krc (0.39 #2110, 0.36 #1881, 0.28 #3700), 099c8n (0.39 #2099, 0.34 #3689, 0.28 #4370) >> Best rule #11598 for best value: >> intensional similarity = 3 >> extensional distance = 597 >> proper extension: 07bz5; >> query: (?x4734, ?x500) <- award(?x4734, ?x500), award_winner(?x4734, ?x777), ceremony(?x500, ?x78) >> conf = 0.78 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 19 EVAL 0sxmx nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 107.000 100.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0sxmx nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 100.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16535-047hpm PRED entity: 047hpm PRED relation: nationality PRED expected values: 03rjj => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 33): 01xbgx (0.40 #13909, 0.03 #477, 0.03 #774), 02jx1 (0.23 #627, 0.17 #528, 0.16 #230), 07ssc (0.09 #7953, 0.09 #14715, 0.09 #5466), 03rjj (0.08 #103, 0.06 #3868, 0.04 #1689), 0345h (0.08 #129, 0.03 #625, 0.03 #3003), 03rk0 (0.07 #12957, 0.07 #10969, 0.06 #13156), 0d060g (0.05 #11229, 0.05 #5161, 0.05 #10133), 0f8l9c (0.05 #219, 0.05 #5473, 0.05 #7960), 02k1b (0.03 #579, 0.03 #975, 0.02 #1273), 0h7x (0.03 #7575, 0.01 #3403) >> Best rule #13909 for best value: >> intensional similarity = 3 >> extensional distance = 2574 >> proper extension: 0784v1; >> query: (?x2837, ?x94) <- place_of_birth(?x2837, ?x1523), place_of_birth(?x3100, ?x1523), nationality(?x3100, ?x94) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #103 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 01tvz5j; 02nrdp; *> query: (?x2837, 03rjj) <- place_of_birth(?x2837, ?x1523), friend(?x9323, ?x2837), people(?x3591, ?x2837), ?x3591 = 0xnvg *> conf = 0.08 ranks of expected_values: 4 EVAL 047hpm nationality 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 157.000 157.000 0.401 http://example.org/people/person/nationality #16534-06rq1k PRED entity: 06rq1k PRED relation: company! PRED expected values: 01fyzy 0pz04 => 75 concepts (54 used for prediction) PRED predicted values (max 10 best out of 269): 0343h (0.33 #1680, 0.30 #1918, 0.25 #1206), 06pj8 (0.29 #745, 0.11 #3358, 0.10 #3595), 041c4 (0.22 #1759, 0.13 #2712, 0.06 #6987), 0pqzh (0.22 #1873, 0.07 #2587, 0.07 #2826), 01w_10 (0.17 #627, 0.07 #2527, 0.07 #8941), 0frmb1 (0.17 #623, 0.07 #2523, 0.07 #2762), 01xdf5 (0.17 #479, 0.07 #2379, 0.07 #2618), 09889g (0.17 #572, 0.07 #2472, 0.07 #2711), 01j7rd (0.17 #506, 0.07 #2406, 0.07 #2645), 02kz_ (0.17 #578, 0.03 #8180, 0.02 #9843) >> Best rule #1680 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 03zj9; >> query: (?x1836, 0343h) <- company(?x1335, ?x1836), influenced_by(?x364, ?x1335), written_by(?x821, ?x1335) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2137 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 015zyd; 04gvyp; 01sqd7; *> query: (?x1836, ?x541) <- organizations_founded(?x1335, ?x1836), award_nominee(?x541, ?x1335), nominated_for(?x1335, ?x821), influenced_by(?x364, ?x1335) *> conf = 0.06 ranks of expected_values: 89 EVAL 06rq1k company! 0pz04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 54.000 0.333 http://example.org/people/person/employment_history./business/employment_tenure/company EVAL 06rq1k company! 01fyzy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 75.000 54.000 0.333 http://example.org/people/person/employment_history./business/employment_tenure/company #16533-0j1z8 PRED entity: 0j1z8 PRED relation: country! PRED expected values: 03_8r => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 51): 06wrt (0.85 #321, 0.74 #627, 0.71 #423), 03hr1p (0.81 #328, 0.76 #430, 0.73 #838), 06f41 (0.81 #319, 0.74 #625, 0.74 #370), 0194d (0.81 #350, 0.66 #452, 0.65 #809), 03_8r (0.78 #837, 0.74 #480, 0.74 #327), 01lb14 (0.75 #677, 0.75 #779, 0.74 #320), 0w0d (0.74 #316, 0.71 #418, 0.68 #367), 01cgz (0.74 #369, 0.70 #828, 0.68 #420), 064vjs (0.70 #336, 0.64 #642, 0.61 #438), 02y8z (0.67 #324, 0.65 #375, 0.60 #630) >> Best rule #321 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 03rt9; >> query: (?x311, 06wrt) <- film_release_region(?x1392, ?x311), ?x1392 = 017gm7, exported_to(?x87, ?x311) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #837 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 027rn; 047lj; 01mjq; 05v10; 01pj7; 0bjv6; 07twz; 05r7t; 04hqz; *> query: (?x311, 03_8r) <- film_release_region(?x6528, ?x311), film_release_region(?x1392, ?x311), ?x1392 = 017gm7, film(?x665, ?x6528) *> conf = 0.78 ranks of expected_values: 5 EVAL 0j1z8 country! 03_8r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 101.000 0.852 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #16532-03975z PRED entity: 03975z PRED relation: profession PRED expected values: 01c72t => 112 concepts (109 used for prediction) PRED predicted values (max 10 best out of 67): 01c72t (0.67 #322, 0.60 #1663, 0.59 #1961), 0nbcg (0.48 #32, 0.44 #1820, 0.42 #777), 09jwl (0.47 #1807, 0.40 #168, 0.37 #764), 01c8w0 (0.39 #307, 0.28 #1648, 0.27 #1052), 016z4k (0.37 #1792, 0.21 #749, 0.18 #600), 0dz3r (0.35 #1790, 0.30 #896, 0.24 #747), 01d_h8 (0.35 #6564, 0.34 #3583, 0.34 #4925), 0dxtg (0.31 #7019, 0.30 #14622, 0.30 #3143), 02jknp (0.29 #3137, 0.24 #6566, 0.23 #5821), 025352 (0.27 #656, 0.22 #805, 0.13 #209) >> Best rule #322 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 03cfjg; 02cx72; 02lfp4; 02zft0; 01wqflx; >> query: (?x9396, 01c72t) <- award(?x9396, ?x1443), award_winner(?x4760, ?x9396), nominated_for(?x9396, ?x697), ?x1443 = 054krc >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03975z profession 01c72t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 109.000 0.667 http://example.org/people/person/profession #16531-01j_06 PRED entity: 01j_06 PRED relation: category PRED expected values: 08mbj5d => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #17, 0.91 #11, 0.91 #21) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 01bzw5; 01t8sr; 02183k; 017cy9; 01nnsv; 04p_hy; 020ddc; 029qzx; >> query: (?x1428, 08mbj5d) <- school(?x4243, ?x1428), position(?x4243, ?x261), team(?x10434, ?x4243) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j_06 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.912 http://example.org/common/topic/webpage./common/webpage/category #16530-01x73 PRED entity: 01x73 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.93 #137, 0.92 #105, 0.91 #45), 01g63y (0.23 #26, 0.09 #70, 0.08 #118), 0jgjn (0.15 #28, 0.05 #84, 0.05 #92), 01bl8s (0.02 #191, 0.01 #247, 0.01 #243) >> Best rule #137 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 01mb87; >> query: (?x1755, 04ztj) <- location(?x1897, ?x1755), location_of_ceremony(?x1545, ?x1755), time_zones(?x1755, ?x2674) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x73 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.930 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #16529-0tln7 PRED entity: 0tln7 PRED relation: source PRED expected values: 0jbk9 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.82 #3, 0.82 #2, 0.80 #7) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 151 >> proper extension: 0f2wj; 02_286; 030qb3t; 0dclg; 013yq; 0ftvz; 0f__1; 0cr3d; 0y2dl; 01jr6; ... >> query: (?x5015, 0jbk9) <- contains(?x3908, ?x5015), contains(?x94, ?x5015), ?x94 = 09c7w0, citytown(?x14319, ?x5015), location(?x1299, ?x3908) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tln7 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.824 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #16528-0dzc16 PRED entity: 0dzc16 PRED relation: type_of_union PRED expected values: 04ztj => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #53, 0.87 #21, 0.86 #33), 01g63y (0.36 #38, 0.35 #22, 0.34 #26), 0jgjn (0.02 #12, 0.02 #20) >> Best rule #53 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 08h79x; 0gm34; >> query: (?x4258, 04ztj) <- award_winner(?x725, ?x4258), spouse(?x4258, ?x9418), award(?x4258, ?x1827) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dzc16 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.872 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16527-0291ck PRED entity: 0291ck PRED relation: language PRED expected values: 04306rv => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 31): 064_8sq (0.25 #21, 0.17 #79, 0.15 #665), 06nm1 (0.25 #10, 0.10 #1181, 0.10 #1767), 02bjrlw (0.17 #59, 0.09 #352, 0.09 #586), 06b_j (0.17 #80, 0.07 #1193, 0.06 #724), 04306rv (0.13 #355, 0.12 #589, 0.11 #297), 01gp_d (0.08 #93, 0.02 #210, 0.02 #269), 0f8l9c (0.08 #63), 03_9r (0.08 #184, 0.07 #243, 0.07 #653), 0653m (0.06 #127, 0.05 #304, 0.04 #362), 04h9h (0.05 #452, 0.04 #335, 0.04 #686) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0p9tm; >> query: (?x9484, 064_8sq) <- nominated_for(?x601, ?x9484), film(?x11212, ?x9484), film_release_region(?x9484, ?x94), ?x11212 = 03vpf_ >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #355 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 111 *> proper extension: 06krf3; 015gm8; *> query: (?x9484, 04306rv) <- nominated_for(?x601, ?x9484), film(?x1119, ?x9484), film_release_region(?x9484, ?x94), costume_design_by(?x9484, ?x2109) *> conf = 0.13 ranks of expected_values: 5 EVAL 0291ck language 04306rv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 97.000 0.250 http://example.org/film/film/language #16526-01rm8b PRED entity: 01rm8b PRED relation: artist! PRED expected values: 01cf93 => 74 concepts (49 used for prediction) PRED predicted values (max 10 best out of 101): 03rhqg (0.43 #862, 0.38 #1003, 0.33 #1144), 0n85g (0.40 #346, 0.33 #205, 0.20 #628), 033hn8 (0.33 #1142, 0.33 #719, 0.33 #14), 015_1q (0.33 #725, 0.33 #20, 0.29 #866), 04fc6c (0.33 #783, 0.29 #924, 0.22 #1206), 03mp8k (0.33 #68, 0.28 #1901, 0.27 #1478), 0g768 (0.33 #179, 0.25 #1025, 0.24 #1589), 01clyr (0.33 #175, 0.25 #1021, 0.20 #598), 01cl2y (0.33 #172, 0.25 #1018, 0.20 #595), 02p3cr5 (0.33 #169, 0.25 #1015, 0.10 #2002) >> Best rule #862 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 0qmny; >> query: (?x3773, 03rhqg) <- artists(?x3928, ?x3773), ?x3928 = 0gywn, group(?x1166, ?x3773), group(?x227, ?x3773), ?x227 = 0342h, role(?x1166, ?x74), group(?x1166, ?x442), ?x442 = 01t_xp_ >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3584 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 111 *> proper extension: 01wgcvn; 015x1f; 01k_mc; 0flpy; *> query: (?x3773, 01cf93) <- artists(?x3928, ?x3773), artists(?x3061, ?x3773), ?x3928 = 0gywn, artists(?x3061, ?x8020), artists(?x3061, ?x6835), artist(?x12708, ?x3773), ?x6835 = 06mt91, participant(?x8020, ?x3397) *> conf = 0.06 ranks of expected_values: 43 EVAL 01rm8b artist! 01cf93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 74.000 49.000 0.429 http://example.org/music/record_label/artist #16525-017khj PRED entity: 017khj PRED relation: profession PRED expected values: 02hrh1q => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 55): 02hrh1q (0.89 #1515, 0.89 #315, 0.88 #165), 01d_h8 (0.36 #6, 0.32 #2708, 0.31 #4209), 03gjzk (0.36 #766, 0.25 #1066, 0.24 #1967), 0dxtg (0.34 #764, 0.31 #464, 0.29 #1815), 0np9r (0.27 #2102, 0.27 #172, 0.26 #322), 02jknp (0.27 #2102, 0.21 #3311, 0.21 #4961), 02krf9 (0.27 #2102, 0.16 #778, 0.09 #1078), 0n1h (0.27 #2102, 0.09 #12, 0.06 #1362), 09jwl (0.21 #620, 0.17 #3923, 0.17 #1370), 0cbd2 (0.16 #457, 0.14 #7510, 0.13 #4810) >> Best rule #1515 for best value: >> intensional similarity = 3 >> extensional distance = 1036 >> proper extension: 079vf; 05d7rk; 04yywz; 06688p; 01l1b90; 049tjg; 0d_84; 0h1_w; 01rrwf6; 02nb2s; ... >> query: (?x5545, 02hrh1q) <- film(?x5545, ?x1597), award_winner(?x1597, ?x574), films(?x8435, ?x1597) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017khj profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.889 http://example.org/people/person/profession #16524-0d7hg4 PRED entity: 0d7hg4 PRED relation: award_winner PRED expected values: 0h53p1 => 116 concepts (56 used for prediction) PRED predicted values (max 10 best out of 803): 0brkwj (0.81 #85312, 0.81 #70823, 0.81 #69212), 06jrhz (0.81 #85312, 0.81 #70823, 0.81 #69212), 08q3s0 (0.52 #85310, 0.52 #85309, 0.51 #90145), 0h5jg5 (0.52 #85310, 0.52 #85309, 0.51 #90145), 047cqr (0.52 #85310, 0.52 #85309, 0.40 #3219), 0d7hg4 (0.41 #38626, 0.40 #428, 0.33 #2037), 0h53p1 (0.41 #38626, 0.28 #88534, 0.26 #72433), 04wvhz (0.41 #38626, 0.26 #72433, 0.15 #65992), 059j4x (0.28 #88534, 0.26 #53115, 0.20 #1580), 01rzqj (0.28 #88534, 0.26 #53115, 0.15 #65992) >> Best rule #85312 for best value: >> intensional similarity = 3 >> extensional distance = 1333 >> proper extension: 030_1_; 04glx0; >> query: (?x2650, ?x5832) <- award_nominee(?x4022, ?x2650), award_winner(?x5832, ?x2650), nominated_for(?x4022, ?x5810) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #38626 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 209 *> proper extension: 02f9wb; 05gnf; 039cq4; *> query: (?x2650, ?x2802) <- award_winner(?x2650, ?x10340), tv_program(?x10340, ?x3413), award_winner(?x2802, ?x10340) *> conf = 0.41 ranks of expected_values: 7 EVAL 0d7hg4 award_winner 0h53p1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 116.000 56.000 0.814 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #16523-0f4_l PRED entity: 0f4_l PRED relation: nominated_for! PRED expected values: 0gs9p => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 202): 04dn09n (0.69 #1279, 0.67 #4476, 0.66 #7466), 02x4sn8 (0.69 #1279, 0.67 #4476, 0.66 #7466), 02qyp19 (0.69 #1279, 0.67 #4476, 0.66 #7466), 02wkmx (0.69 #1279, 0.67 #4476, 0.66 #7466), 027b9j5 (0.69 #1279, 0.67 #4476, 0.66 #7466), 02w_6xj (0.69 #1279, 0.67 #4476, 0.66 #7466), 027986c (0.69 #1279, 0.67 #4476, 0.66 #7466), 027c924 (0.69 #1279, 0.67 #4476, 0.66 #7466), 0gs9p (0.59 #1545, 0.58 #905, 0.55 #1758), 0p9sw (0.46 #1082, 0.29 #869, 0.28 #1509) >> Best rule #1279 for best value: >> intensional similarity = 6 >> extensional distance = 106 >> proper extension: 06mmr; >> query: (?x2177, ?x68) <- award(?x2177, ?x298), award(?x2177, ?x68), nominated_for(?x298, ?x1046), nominated_for(?x298, ?x144), ?x144 = 0m313, ?x1046 = 02qm_f >> conf = 0.69 => this is the best rule for 8 predicted values *> Best rule #1545 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 137 *> proper extension: 0jzw; 03hj3b3; 07cdz; 0bl1_; 0gwjw0c; 01gvts; *> query: (?x2177, 0gs9p) <- film(?x368, ?x2177), nominated_for(?x1587, ?x2177), nominated_for(?x1198, ?x2177), award(?x1118, ?x1587), ?x1198 = 02pqp12 *> conf = 0.59 ranks of expected_values: 9 EVAL 0f4_l nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 83.000 83.000 0.691 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16522-09qwmm PRED entity: 09qwmm PRED relation: ceremony PRED expected values: 0g55tzk => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 131): 0g55tzk (0.75 #258, 0.08 #651, 0.08 #782), 0n8_m93 (0.40 #502, 0.29 #371, 0.17 #633), 0bzm81 (0.40 #412, 0.29 #281, 0.17 #543), 02yvhx (0.40 #463, 0.29 #332, 0.17 #594), 0bvfqq (0.40 #423, 0.29 #292, 0.17 #554), 02yxh9 (0.40 #486, 0.29 #355, 0.17 #617), 0bc773 (0.40 #443, 0.29 #312, 0.17 #574), 02yw5r (0.40 #404, 0.29 #273, 0.17 #535), 02hn5v (0.40 #432, 0.29 #301, 0.17 #563), 0gmdkyy (0.40 #420, 0.29 #289, 0.17 #551) >> Best rule #258 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 02py7pj; >> query: (?x618, 0g55tzk) <- ceremony(?x618, ?x8347), ceremony(?x618, ?x3460), award_winner(?x618, ?x396), ?x8347 = 03gyp30, ?x3460 = 092t4b >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09qwmm ceremony 0g55tzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony #16521-02f777 PRED entity: 02f777 PRED relation: award! PRED expected values: 049qx 03h_0_z 01vtj38 => 49 concepts (8 used for prediction) PRED predicted values (max 10 best out of 4008): 018n6m (0.79 #20140, 0.79 #16784, 0.78 #3356), 01vwbts (0.40 #1351, 0.17 #6713, 0.13 #3357), 01vn0t_ (0.40 #2488, 0.02 #5846, 0.02 #9202), 01vrz41 (0.21 #3649, 0.19 #13426, 0.17 #7005), 01vz0g4 (0.20 #2359, 0.20 #6714, 0.06 #3358), 0ffgh (0.20 #2070, 0.19 #13426, 0.17 #6713), 01vvyc_ (0.20 #1691, 0.19 #13426, 0.17 #6713), 01vw37m (0.20 #1817, 0.19 #13426, 0.17 #6713), 06mt91 (0.20 #1965, 0.19 #13426, 0.16 #5323), 0dw4g (0.20 #1623, 0.19 #13426, 0.15 #8337) >> Best rule #20140 for best value: >> intensional similarity = 6 >> extensional distance = 133 >> proper extension: 01c9d1; >> query: (?x8458, ?x883) <- award_winner(?x8458, ?x5901), award_winner(?x8458, ?x883), origin(?x5901, ?x2552), award(?x521, ?x8458), artists(?x671, ?x5901), artist(?x3265, ?x883) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #13426 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 127 *> proper extension: 0bqsk5; *> query: (?x8458, ?x9418) <- award_winner(?x8458, ?x5901), award_winner(?x8458, ?x4842), origin(?x5901, ?x2552), profession(?x5901, ?x1032), award_nominee(?x4842, ?x9418), artist(?x2149, ?x5901) *> conf = 0.19 ranks of expected_values: 117, 274, 1436 EVAL 02f777 award! 01vtj38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 8.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f777 award! 03h_0_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 49.000 8.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f777 award! 049qx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 8.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16520-0jt86 PRED entity: 0jt86 PRED relation: people! PRED expected values: 041rx => 136 concepts (110 used for prediction) PRED predicted values (max 10 best out of 39): 041rx (0.63 #2391, 0.63 #2700, 0.62 #1082), 03w9bjf (0.25 #54, 0.02 #1055, 0.02 #1286), 048z7l (0.16 #733, 0.14 #348, 0.14 #271), 013b6_ (0.14 #361, 0.14 #284, 0.13 #1131), 09kr66 (0.14 #351, 0.14 #274, 0.09 #582), 07hwkr (0.12 #1167, 0.10 #1783, 0.10 #2014), 033tf_ (0.11 #3866, 0.11 #3011, 0.10 #4022), 013xrm (0.09 #1098, 0.07 #2716, 0.06 #2407), 0g5y6 (0.09 #2424, 0.08 #2733, 0.03 #730), 0x67 (0.09 #5732, 0.09 #5963, 0.09 #6348) >> Best rule #2391 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 015c1b; >> query: (?x11287, 041rx) <- religion(?x11287, ?x7131), gender(?x11287, ?x231), ?x7131 = 03_gx >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jt86 people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 110.000 0.628 http://example.org/people/ethnicity/people #16519-06mnbn PRED entity: 06mnbn PRED relation: people! PRED expected values: 0d7wh => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 34): 07bch9 (0.17 #23, 0.06 #254, 0.03 #1178), 06gbnc (0.17 #27, 0.04 #335), 09vc4s (0.17 #9, 0.03 #779, 0.02 #1164), 065b6q (0.17 #3, 0.02 #1158, 0.01 #850), 0bbz66j (0.17 #48), 02w7gg (0.16 #310, 0.14 #79, 0.09 #156), 0d7wh (0.14 #94, 0.09 #171, 0.06 #325), 03bkbh (0.14 #109, 0.09 #186, 0.02 #1187), 013xrm (0.14 #97, 0.04 #174, 0.02 #251), 059_w (0.14 #107, 0.04 #184, 0.02 #261) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01tsbmv; >> query: (?x4015, 07bch9) <- profession(?x4015, ?x1032), film(?x4015, ?x3612), ?x3612 = 04z257 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #94 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 06gh0t; *> query: (?x4015, 0d7wh) <- award_nominee(?x4015, ?x3717), award_nominee(?x4015, ?x2708), ?x3717 = 07fpm3, ?x2708 = 059t6d *> conf = 0.14 ranks of expected_values: 7 EVAL 06mnbn people! 0d7wh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 87.000 0.167 http://example.org/people/ethnicity/people #16518-036b_ PRED entity: 036b_ PRED relation: contains! PRED expected values: 0j3b => 102 concepts (91 used for prediction) PRED predicted values (max 10 best out of 175): 02j71 (0.64 #69007, 0.60 #70802, 0.59 #71701), 07ssc (0.55 #9880, 0.14 #78915, 0.13 #79812), 09c7w0 (0.52 #77989, 0.50 #51069, 0.50 #78886), 02qkt (0.51 #6614, 0.50 #11984, 0.49 #7509), 02jx1 (0.40 #9935, 0.12 #79867, 0.08 #78970), 02j9z (0.39 #6296, 0.35 #7191, 0.34 #8086), 0j0k (0.25 #13807, 0.25 #22767, 0.25 #12912), 04_1l0v (0.24 #39864, 0.23 #45245, 0.22 #42554), 07c5l (0.22 #34433, 0.22 #25473, 0.21 #38018), 05nrg (0.21 #70803, 0.18 #4148, 0.11 #34605) >> Best rule #69007 for best value: >> intensional similarity = 2 >> extensional distance = 552 >> proper extension: 01bh3l; 05ywg; 05x30m; 0crjn65; 05l5n; 016v46; 0l9rg; 02h6_6p; 09tlh; 0h095; ... >> query: (?x5700, ?x551) <- contains(?x2467, ?x5700), administrative_parent(?x5700, ?x551) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #25151 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 141 *> proper extension: 02psqkz; 01k6y1; 06jnv; 049nq; 088q1s; *> query: (?x5700, 0j3b) <- form_of_government(?x5700, ?x48), official_language(?x5700, ?x6753) *> conf = 0.02 ranks of expected_values: 72 EVAL 036b_ contains! 0j3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 102.000 91.000 0.639 http://example.org/location/location/contains #16517-09v3hq_ PRED entity: 09v3hq_ PRED relation: film PRED expected values: 08sfxj 05q_dw => 115 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1708): 05b6rdt (0.50 #979, 0.38 #2571, 0.33 #5755), 0k0rf (0.50 #794, 0.25 #2386, 0.23 #8754), 03hj3b3 (0.50 #269, 0.25 #1861, 0.22 #5045), 02_kd (0.50 #521, 0.25 #2113, 0.22 #5297), 016fyc (0.50 #46, 0.25 #1638, 0.22 #4822), 011yhm (0.50 #1036, 0.25 #2628, 0.22 #5812), 02xs6_ (0.50 #761, 0.25 #2353, 0.22 #5537), 02rb84n (0.38 #8213, 0.12 #1845, 0.11 #5029), 0dgq_kn (0.38 #2519, 0.33 #5703, 0.31 #8887), 014kq6 (0.38 #1898, 0.33 #5082, 0.31 #8266) >> Best rule #979 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0g1rw; 016tw3; >> query: (?x11867, 05b6rdt) <- film(?x11867, ?x10173), film(?x11867, ?x3084), film(?x11867, ?x1170), ?x3084 = 03mh_tp, ?x10173 = 01kqq7, country(?x1170, ?x94), titles(?x812, ?x1170) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2390 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 017s11; 016tt2; 01795t; 032dg7; *> query: (?x11867, 08sfxj) <- film(?x11867, ?x10173), film(?x11867, ?x3084), ?x3084 = 03mh_tp, nominated_for(?x637, ?x10173), film(?x914, ?x10173), country(?x10173, ?x94) *> conf = 0.12 ranks of expected_values: 684, 1671 EVAL 09v3hq_ film 05q_dw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 115.000 27.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 09v3hq_ film 08sfxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 115.000 27.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16516-018ygt PRED entity: 018ygt PRED relation: profession PRED expected values: 02hrh1q => 112 concepts (111 used for prediction) PRED predicted values (max 10 best out of 76): 02hrh1q (0.89 #5194, 0.89 #6970, 0.88 #3862), 0dxtg (0.67 #2085, 0.50 #1049, 0.29 #8153), 018gz8 (0.44 #312, 0.33 #460, 0.28 #11102), 09jwl (0.38 #4162, 0.37 #4754, 0.35 #2238), 02jknp (0.32 #599, 0.28 #11102, 0.28 #9917), 02krf9 (0.29 #2098, 0.28 #11102, 0.28 #9917), 0nbcg (0.28 #4175, 0.27 #1215, 0.27 #919), 0np9r (0.28 #11102, 0.28 #9917, 0.22 #1056), 0cbd2 (0.28 #11102, 0.28 #9917, 0.22 #1042), 0d1pc (0.28 #11102, 0.28 #9917, 0.17 #3898) >> Best rule #5194 for best value: >> intensional similarity = 2 >> extensional distance = 537 >> proper extension: 05m63c; 0d_84; 01ty7ll; 033hqf; 04bs3j; 0htlr; 03_vx9; 01q7cb_; 0456xp; 04shbh; ... >> query: (?x6324, 02hrh1q) <- participant(?x6324, ?x4775), film(?x6324, ?x667) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018ygt profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 111.000 0.892 http://example.org/people/person/profession #16515-01wz01 PRED entity: 01wz01 PRED relation: gender PRED expected values: 05zppz => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #150, 0.73 #152, 0.72 #179), 02zsn (0.53 #119, 0.38 #8, 0.37 #10) >> Best rule #150 for best value: >> intensional similarity = 2 >> extensional distance = 1867 >> proper extension: 099bk; 03bw6; 03fw60; 09x8ms; >> query: (?x4173, 05zppz) <- student(?x5807, ?x4173), institution(?x620, ?x5807) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wz01 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.727 http://example.org/people/person/gender #16514-0d060g PRED entity: 0d060g PRED relation: country! PRED expected values: 01hp22 01cgz 03_8r 018w8 0crlz 07_53 => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 10): 03_8r (0.82 #327, 0.79 #687, 0.77 #417), 01cgz (0.79 #65, 0.77 #218, 0.73 #74), 01hp22 (0.71 #172, 0.67 #82, 0.60 #208), 07_53 (0.48 #150, 0.47 #87, 0.46 #222), 018w8 (0.47 #85, 0.40 #40, 0.38 #175), 0crlz (0.40 #86, 0.40 #41, 0.31 #113), 06br8 (0.27 #739, 0.20 #43, 0.12 #115), 09xp_ (0.27 #739, 0.07 #89, 0.06 #116), 04lgq (0.27 #739), 037hz (0.20 #45, 0.07 #99, 0.07 #90) >> Best rule #327 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 05b7q; >> query: (?x279, 03_8r) <- combatants(?x279, ?x94), country(?x150, ?x279), administrative_area_type(?x279, ?x2792) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6 EVAL 0d060g country! 07_53 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.816 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d060g country! 0crlz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.816 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d060g country! 018w8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.816 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d060g country! 03_8r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.816 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d060g country! 01cgz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.816 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d060g country! 01hp22 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.816 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #16513-0581vn8 PRED entity: 0581vn8 PRED relation: category PRED expected values: 08mbj5d => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.32 #4, 0.32 #3, 0.31 #23) >> Best rule #4 for best value: >> intensional similarity = 5 >> extensional distance = 63 >> proper extension: 07sc6nw; 02vqhv0; 0k4d7; 08052t3; 05m_jsg; 0435vm; 033srr; 047vnkj; 0640y35; 027j9wd; ... >> query: (?x9250, 08mbj5d) <- film_crew_role(?x9250, ?x2154), film_crew_role(?x9250, ?x1966), film(?x1561, ?x9250), ?x1966 = 015h31, ?x2154 = 01vx2h >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0581vn8 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.323 http://example.org/common/topic/webpage./common/webpage/category #16512-036c_0 PRED entity: 036c_0 PRED relation: profession PRED expected values: 01d_h8 => 69 concepts (65 used for prediction) PRED predicted values (max 10 best out of 116): 01d_h8 (0.69 #298, 0.67 #444, 0.34 #8041), 0dxtg (0.59 #305, 0.58 #451, 0.33 #8194), 09jwl (0.22 #7467, 0.21 #8344, 0.20 #8637), 018gz8 (0.20 #307, 0.13 #2060, 0.13 #746), 0cbd2 (0.18 #299, 0.14 #445, 0.12 #8773), 0d1pc (0.17 #48, 0.06 #7205, 0.06 #6329), 01c8w0 (0.17 #8, 0.03 #8474, 0.03 #7743), 0nbcg (0.16 #7040, 0.15 #7625, 0.13 #8356), 0kyk (0.13 #319, 0.12 #7184, 0.09 #173), 0np9r (0.13 #7615, 0.13 #750, 0.13 #2064) >> Best rule #298 for best value: >> intensional similarity = 6 >> extensional distance = 88 >> proper extension: 083chw; 014zcr; 01wbg84; 0bxtg; 03f2_rc; 0187y5; 0mdqp; 0htlr; 015grj; 0f0p0; ... >> query: (?x2103, 01d_h8) <- profession(?x2103, ?x1943), profession(?x2103, ?x1032), profession(?x2103, ?x524), ?x1032 = 02hrh1q, ?x524 = 02jknp, ?x1943 = 02krf9 >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 036c_0 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 65.000 0.689 http://example.org/people/person/profession #16511-032clf PRED entity: 032clf PRED relation: film! PRED expected values: 02sjf5 => 98 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1078): 0fgg4 (0.25 #885, 0.20 #5051, 0.05 #11300), 016ks_ (0.25 #787, 0.20 #4953, 0.03 #17451), 044qx (0.25 #734, 0.20 #4900, 0.03 #71558), 0j_c (0.25 #411, 0.20 #4577, 0.02 #23324), 06cgy (0.25 #251, 0.20 #4417, 0.02 #43995), 04hpck (0.25 #171, 0.20 #4337, 0.02 #27250), 01r7t9 (0.25 #1882, 0.20 #6048, 0.02 #37293), 01x4sb (0.25 #1106, 0.20 #5272, 0.01 #44850), 04yywz (0.25 #19, 0.20 #4185, 0.01 #87508), 04gvt5 (0.25 #1700, 0.20 #5866) >> Best rule #885 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 028_yv; 0k5g9; >> query: (?x7379, 0fgg4) <- film_release_region(?x7379, ?x11872), film_release_region(?x7379, ?x985), film_release_region(?x7379, ?x390), film_release_region(?x7379, ?x94), ?x94 = 09c7w0, ?x985 = 0k6nt, ?x11872 = 03f2w, ?x390 = 0chghy >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 032clf film! 02sjf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 59.000 0.250 http://example.org/film/actor/film./film/performance/film #16510-0163m1 PRED entity: 0163m1 PRED relation: group! PRED expected values: 01s0ps => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 111): 018vs (0.66 #1088, 0.62 #1783, 0.62 #1706), 03bx0bm (0.61 #1098, 0.61 #1716, 0.58 #1793), 03qjg (0.40 #116, 0.40 #39, 0.32 #270), 04rzd (0.27 #103, 0.23 #257, 0.20 #26), 05r5c (0.24 #1779, 0.22 #1702, 0.21 #1084), 013y1f (0.20 #23, 0.14 #1796, 0.13 #1101), 0319l (0.20 #21, 0.13 #98, 0.09 #252), 042v_gx (0.20 #7, 0.10 #1780, 0.10 #1085), 0cfdd (0.20 #67, 0.09 #298, 0.07 #1852), 07brj (0.20 #16, 0.07 #1852, 0.06 #1712) >> Best rule #1088 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 0m19t; 04r1t; 02r1tx7; 05563d; 07yg2; 03xhj6; 0394y; 01j59b0; 06nv27; 02dw1_; ... >> query: (?x4010, 018vs) <- artists(?x505, ?x4010), group(?x1166, ?x4010), artist(?x382, ?x4010), ?x1166 = 05148p4 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #1852 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 181 *> proper extension: 01qqwp9; 02t3ln; 02mq_y; 0123r4; 0qmpd; 06br6t; *> query: (?x4010, ?x75) <- artists(?x505, ?x4010), group(?x3703, ?x4010), role(?x1472, ?x3703), role(?x75, ?x3703), ?x1472 = 0319l *> conf = 0.07 ranks of expected_values: 47 EVAL 0163m1 group! 01s0ps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 83.000 83.000 0.664 http://example.org/music/performance_role/regular_performances./music/group_membership/group #16509-0m19t PRED entity: 0m19t PRED relation: artist! PRED expected values: 01w56k => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 131): 0n85g (0.59 #3429, 0.50 #762, 0.40 #1183), 011k1h (0.36 #1271, 0.32 #2253, 0.31 #1691), 03rhqg (0.35 #1978, 0.33 #156, 0.25 #296), 02bh8z (0.33 #22, 0.25 #722, 0.25 #442), 0g768 (0.33 #36, 0.25 #736, 0.25 #456), 01dtcb (0.33 #186, 0.25 #606, 0.20 #886), 02p11jq (0.32 #3239, 0.07 #6891, 0.07 #5209), 015_1q (0.29 #5636, 0.22 #6057, 0.22 #6198), 0k_kr (0.25 #743, 0.20 #1164, 0.20 #1024), 04fc6c (0.25 #636, 0.20 #916, 0.09 #3508) >> Best rule #3429 for best value: >> intensional similarity = 10 >> extensional distance = 107 >> proper extension: 01q_ph; >> query: (?x498, 0n85g) <- artist(?x4483, ?x498), artist(?x4483, ?x5170), artist(?x4483, ?x4783), artist(?x4483, ?x3168), artist(?x4483, ?x2614), ?x4783 = 047cx, ?x2614 = 04xrx, child(?x7793, ?x4483), award_nominee(?x1321, ?x5170), role(?x3168, ?x227) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #394 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 0394y; 01p95y0; *> query: (?x498, 01w56k) <- artists(?x6173, ?x498), artists(?x497, ?x498), ?x6173 = 025tm81, parent_genre(?x2439, ?x497), artists(?x497, ?x5543), artists(?x2439, ?x1674), artist(?x4483, ?x498), ?x5543 = 01kd57 *> conf = 0.25 ranks of expected_values: 14 EVAL 0m19t artist! 01w56k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 71.000 71.000 0.587 http://example.org/music/record_label/artist #16508-0gtv7pk PRED entity: 0gtv7pk PRED relation: film! PRED expected values: 081bls => 110 concepts (88 used for prediction) PRED predicted values (max 10 best out of 84): 027jw0c (0.71 #1635, 0.47 #521, 0.46 #4684), 03xq0f (0.59 #2309, 0.58 #1268, 0.25 #5), 081bls (0.50 #40, 0.11 #115, 0.05 #486), 016tw3 (0.24 #457, 0.22 #86, 0.21 #680), 086k8 (0.24 #448, 0.21 #3198, 0.21 #1562), 05qd_ (0.23 #1123, 0.19 #1718, 0.18 #1272), 017s11 (0.20 #301, 0.19 #1341, 0.18 #746), 016tt2 (0.19 #450, 0.18 #525, 0.17 #2530), 0g1rw (0.14 #1047, 0.11 #1866, 0.08 #2016), 01795t (0.14 #1801, 0.13 #2841, 0.11 #761) >> Best rule #1635 for best value: >> intensional similarity = 6 >> extensional distance = 108 >> proper extension: 02qm_f; 020fcn; 011xg5; 0d6_s; >> query: (?x409, ?x9997) <- genre(?x409, ?x811), production_companies(?x409, ?x9997), film_crew_role(?x409, ?x468), ?x811 = 03k9fj, nominated_for(?x1007, ?x409), film(?x9997, ?x1009) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 06_sc3; *> query: (?x409, 081bls) <- genre(?x409, ?x3312), production_companies(?x409, ?x9997), prequel(?x409, ?x10590), ?x9997 = 027jw0c *> conf = 0.50 ranks of expected_values: 3 EVAL 0gtv7pk film! 081bls CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 88.000 0.710 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16507-067mj PRED entity: 067mj PRED relation: origin PRED expected values: 0hpyv => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 74): 0pmq2 (0.17 #31, 0.09 #267, 0.07 #503), 01hvzr (0.17 #233, 0.09 #469, 0.07 #705), 0fg6k (0.17 #160, 0.09 #396, 0.07 #632), 030qb3t (0.09 #2394, 0.09 #270, 0.09 #1922), 0d6lp (0.09 #301, 0.07 #1717, 0.07 #537), 01_d4 (0.09 #276, 0.07 #512, 0.06 #748), 0nbwf (0.09 #377, 0.07 #613, 0.06 #849), 04jpl (0.06 #4018, 0.06 #1658, 0.06 #4491), 02_286 (0.06 #2376, 0.06 #4264, 0.06 #1904), 0n95v (0.04 #1135, 0.04 #1371, 0.03 #1607) >> Best rule #31 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 01l_w0; 01k47c; 0p76z; 01pny5; >> query: (?x1412, 0pmq2) <- artists(?x7083, ?x1412), artists(?x6210, ?x1412), artists(?x1380, ?x1412), artists(?x1000, ?x1412), ?x6210 = 01fh36, ?x1380 = 0dl5d, ?x1000 = 0xhtw, ?x7083 = 02yv6b >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1075 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 21 *> proper extension: 025xt8y; 0ftps; 0191h5; 0140t7; 023322; 01mxnvc; *> query: (?x1412, 0hpyv) <- artists(?x6210, ?x1412), artists(?x1380, ?x1412), artists(?x1000, ?x1412), ?x6210 = 01fh36, ?x1380 = 0dl5d, artists(?x1000, ?x12880), artists(?x1000, ?x9463), ?x12880 = 011xhx, ?x9463 = 01shhf *> conf = 0.04 ranks of expected_values: 11 EVAL 067mj origin 0hpyv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 73.000 73.000 0.167 http://example.org/music/artist/origin #16506-01386_ PRED entity: 01386_ PRED relation: artists! PRED expected values: 06by7 08jyyk 0hdf8 => 129 concepts (51 used for prediction) PRED predicted values (max 10 best out of 290): 06by7 (0.67 #19, 0.61 #14566, 0.60 #2748), 064t9 (0.59 #4858, 0.55 #5160, 0.54 #9096), 02t8gf (0.46 #743, 0.07 #303, 0.06 #1214), 08jyyk (0.46 #974, 0.22 #367, 0.17 #64), 0glt670 (0.43 #5185, 0.42 #5487, 0.38 #6092), 02w4v (0.36 #1559, 0.22 #2125, 0.22 #344), 025sc50 (0.34 #5195, 0.33 #5497, 0.33 #2474), 02yv6b (0.33 #96, 0.28 #1614, 0.22 #2125), 01lyv (0.33 #31, 0.26 #1549, 0.23 #7905), 05hs4r (0.33 #1, 0.09 #1215, 0.06 #1519) >> Best rule #19 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0gr69; >> query: (?x6406, 06by7) <- artist(?x11715, ?x6406), role(?x6406, ?x316), ?x11715 = 015mlw, artists(?x2249, ?x6406), parent_genre(?x2072, ?x2249) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 31 EVAL 01386_ artists! 0hdf8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 129.000 51.000 0.667 http://example.org/music/genre/artists EVAL 01386_ artists! 08jyyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 51.000 0.667 http://example.org/music/genre/artists EVAL 01386_ artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 51.000 0.667 http://example.org/music/genre/artists #16505-02fv3t PRED entity: 02fv3t PRED relation: ceremony PRED expected values: 01c6qp => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 122): 01c6qp (0.92 #386, 0.91 #262, 0.91 #138), 0bz6sb (0.41 #869, 0.35 #2482, 0.27 #3847), 0clfdj (0.41 #869, 0.35 #2482, 0.27 #3847), 02pgky2 (0.22 #4220, 0.21 #4221, 0.13 #1565), 0bq_mx (0.22 #4220, 0.21 #4221), 03gt46z (0.22 #4220, 0.21 #4221), 05c1t6z (0.18 #1748, 0.18 #1996, 0.17 #1872), 02q690_ (0.17 #1792, 0.16 #1916, 0.16 #2040), 0gvstc3 (0.16 #1764, 0.16 #2012, 0.16 #1888), 03nnm4t (0.16 #1801, 0.15 #1925, 0.14 #2049) >> Best rule #386 for best value: >> intensional similarity = 7 >> extensional distance = 64 >> proper extension: 025m8l; 01dpdh; 01dk00; 0257w4; 026mff; 03t5b6; 01ckrr; 024_dt; >> query: (?x8369, 01c6qp) <- ceremony(?x8369, ?x6487), ceremony(?x8369, ?x5656), ceremony(?x8369, ?x139), ?x5656 = 0466p0j, ?x6487 = 01mh_q, award_winner(?x139, ?x4594), ?x4594 = 05vzw3 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fv3t ceremony 01c6qp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.924 http://example.org/award/award_category/winners./award/award_honor/ceremony #16504-02my3z PRED entity: 02my3z PRED relation: nationality PRED expected values: 0345h => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 26): 0345h (0.53 #129, 0.27 #7958, 0.03 #327), 0chghy (0.29 #6365, 0.25 #9, 0.05 #207), 0d060g (0.29 #6365, 0.05 #502, 0.05 #3984), 0ctw_b (0.29 #6365, 0.02 #224, 0.01 #1122), 02jx1 (0.14 #230, 0.12 #429, 0.12 #629), 07ssc (0.12 #212, 0.10 #411, 0.10 #710), 03rk0 (0.06 #8499, 0.06 #5119, 0.06 #5913), 03rt9 (0.03 #309, 0.02 #807, 0.02 #2499), 017fp (0.02 #1196, 0.02 #4378, 0.02 #2886), 07s9rl0 (0.02 #1196, 0.02 #4378, 0.02 #2886) >> Best rule #129 for best value: >> intensional similarity = 2 >> extensional distance = 55 >> proper extension: 07h1q; >> query: (?x11448, 0345h) <- people(?x5540, ?x11448), ?x5540 = 013xrm >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02my3z nationality 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.526 http://example.org/people/person/nationality #16503-02p2zq PRED entity: 02p2zq PRED relation: artist! PRED expected values: 056252 => 112 concepts (80 used for prediction) PRED predicted values (max 10 best out of 115): 015_1q (0.22 #1673, 0.21 #569, 0.21 #707), 0fb0v (0.18 #421, 0.10 #145, 0.09 #1111), 01w40h (0.16 #164, 0.12 #716, 0.09 #2096), 011k1h (0.16 #561, 0.11 #1803, 0.11 #699), 0n85g (0.15 #750, 0.13 #198, 0.09 #1026), 01clyr (0.15 #582, 0.13 #168, 0.10 #1134), 041bnw (0.14 #342, 0.06 #204, 0.05 #618), 03mp8k (0.13 #202, 0.11 #616, 0.07 #1030), 0k_kr (0.13 #179, 0.10 #731, 0.06 #1145), 01trtc (0.13 #208, 0.08 #5737, 0.07 #484) >> Best rule #1673 for best value: >> intensional similarity = 3 >> extensional distance = 225 >> proper extension: 0h08p; >> query: (?x7549, 015_1q) <- artists(?x6210, ?x7549), artists(?x6210, ?x6996), ?x6996 = 0132k4 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #318 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 06gcn; 0p76z; 0153nq; *> query: (?x7549, 056252) <- category(?x7549, ?x134), artist(?x5634, ?x7549), ?x5634 = 01cl2y *> conf = 0.05 ranks of expected_values: 40 EVAL 02p2zq artist! 056252 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 112.000 80.000 0.225 http://example.org/music/record_label/artist #16502-0rj0z PRED entity: 0rj0z PRED relation: origin! PRED expected values: 01vsxdm => 102 concepts (39 used for prediction) PRED predicted values (max 10 best out of 335): 0892sx (0.10 #1130, 0.10 #613, 0.09 #1647), 0g824 (0.10 #1310, 0.10 #793, 0.09 #1827), 05crg7 (0.10 #1083, 0.10 #566, 0.09 #1600), 094xh (0.10 #1256, 0.10 #739, 0.09 #1773), 04n2vgk (0.10 #1446, 0.10 #929, 0.09 #1963), 01wf86y (0.10 #1364, 0.10 #847, 0.09 #1881), 01vvyc_ (0.10 #1280, 0.10 #763, 0.09 #1797), 06lxn (0.10 #1548, 0.10 #1031, 0.09 #2065), 011xhx (0.10 #1541, 0.10 #1024, 0.09 #2058), 027kwc (0.10 #1539, 0.10 #1022, 0.09 #2056) >> Best rule #1130 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 02_286; 01_d4; 0dclg; 0d35y; 0b2ds; 0qpqn; 0l38x; 0d1y7; >> query: (?x3892, 0892sx) <- source(?x3892, ?x958), location_of_ceremony(?x12334, ?x3892), adjoins(?x3892, ?x6194), ?x958 = 0jbk9 >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0rj0z origin! 01vsxdm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 39.000 0.100 http://example.org/music/artist/origin #16501-018_lb PRED entity: 018_lb PRED relation: location PRED expected values: 0d04z6 => 125 concepts (103 used for prediction) PRED predicted values (max 10 best out of 204): 013gwb (0.76 #36870, 0.71 #20036, 0.70 #60926), 02_286 (0.26 #48132, 0.25 #36104, 0.23 #22475), 030qb3t (0.23 #22519, 0.20 #2483, 0.19 #10496), 0d6lp (0.20 #966, 0.17 #165, 0.07 #2568), 09c7w0 (0.15 #5611, 0.14 #4009, 0.02 #9617), 01n7q (0.13 #2464, 0.09 #3265, 0.08 #1663), 0r0m6 (0.13 #2618, 0.04 #4221, 0.03 #5823), 02frhbc (0.10 #1267, 0.02 #4472, 0.02 #6074), 0mzvm (0.10 #993), 0cr3d (0.10 #48238, 0.09 #36210, 0.08 #1744) >> Best rule #36870 for best value: >> intensional similarity = 3 >> extensional distance = 994 >> proper extension: 028p0; 014dq7; 03nbbv; 0d9v9q; 09wlpl; 03gvpk; 0399p; 02qnbs; 02x8kk; 03f4k; ... >> query: (?x11264, ?x9375) <- location(?x11264, ?x1310), place_of_birth(?x11264, ?x9375), teams(?x1310, ?x11309) >> conf = 0.76 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018_lb location 0d04z6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 103.000 0.757 http://example.org/people/person/places_lived./people/place_lived/location #16500-0hqly PRED entity: 0hqly PRED relation: people! PRED expected values: 048z7l => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 51): 041rx (0.29 #232, 0.27 #1452, 0.25 #1529), 0x67 (0.27 #2527, 0.26 #2374, 0.20 #1993), 02w7gg (0.12 #2, 0.11 #154, 0.09 #4280), 02ctzb (0.12 #242, 0.07 #394, 0.05 #166), 01qhm_ (0.12 #234, 0.05 #386, 0.05 #843), 063k3h (0.12 #258, 0.02 #4384, 0.02 #790), 0xnvg (0.11 #849, 0.09 #3679, 0.08 #1537), 013b6_ (0.08 #52, 0.05 #204, 0.03 #1577), 065b6q (0.08 #3, 0.05 #155, 0.03 #3670), 0dryh9k (0.08 #775, 0.06 #1463, 0.06 #2991) >> Best rule #232 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 0gs6vr; 03y2kr; 07bty; 024t0y; >> query: (?x11019, 041rx) <- people(?x1446, ?x11019), profession(?x11019, ?x967), ?x967 = 012t_z >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3706 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 720 *> proper extension: 049tjg; 02k6rq; *> query: (?x11019, 048z7l) <- people(?x1446, ?x11019), location(?x11019, ?x13801), film(?x11019, ?x6806) *> conf = 0.04 ranks of expected_values: 18 EVAL 0hqly people! 048z7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 113.000 113.000 0.293 http://example.org/people/ethnicity/people #16499-01vb6z PRED entity: 01vb6z PRED relation: profession PRED expected values: 0dxtg => 119 concepts (118 used for prediction) PRED predicted values (max 10 best out of 87): 02hrh1q (0.90 #9717, 0.88 #9276, 0.87 #10011), 0dxtg (0.90 #1922, 0.81 #6186, 0.81 #3834), 03gjzk (0.45 #1777, 0.45 #4571, 0.44 #5306), 018gz8 (0.45 #2514, 0.42 #1044, 0.40 #2661), 09jwl (0.37 #8399, 0.36 #8105, 0.35 #8546), 0kyk (0.34 #2086, 0.33 #4439, 0.31 #7380), 0nbcg (0.28 #8412, 0.26 #8118, 0.25 #8559), 016z4k (0.26 #8386, 0.24 #8533, 0.24 #8092), 02krf9 (0.25 #7352, 0.21 #172, 0.19 #3848), 0dz3r (0.25 #8384, 0.24 #8531, 0.24 #8090) >> Best rule #9717 for best value: >> intensional similarity = 3 >> extensional distance = 1190 >> proper extension: 04rsd2; 01v3bn; 01wz01; 062hgx; 06l9n8; 03k48_; 02__ww; >> query: (?x6698, 02hrh1q) <- award_nominee(?x4385, ?x6698), film(?x6698, ?x4565), profession(?x6698, ?x319) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1922 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 84 *> proper extension: 020x5r; 09xx0m; *> query: (?x6698, 0dxtg) <- award(?x6698, ?x746), ?x746 = 04dn09n *> conf = 0.90 ranks of expected_values: 2 EVAL 01vb6z profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 118.000 0.900 http://example.org/people/person/profession #16498-0gtxj2q PRED entity: 0gtxj2q PRED relation: film! PRED expected values: 016tw3 => 132 concepts (121 used for prediction) PRED predicted values (max 10 best out of 76): 04f525m (0.79 #6172, 0.76 #4769, 0.75 #5803), 016tw3 (0.65 #1843, 0.53 #2356, 0.45 #3164), 05qd_ (0.60 #3456, 0.33 #379, 0.22 #4409), 054g1r (0.36 #843, 0.31 #3481, 0.22 #330), 03xq0f (0.33 #375, 0.15 #1399, 0.14 #153), 086k8 (0.29 #4255, 0.25 #76, 0.23 #7791), 016tt2 (0.29 #152, 0.25 #226, 0.25 #78), 019v67 (0.25 #141, 0.22 #363, 0.14 #215), 017s11 (0.25 #77, 0.20 #4256, 0.18 #4846), 024rgt (0.25 #93, 0.14 #167, 0.12 #241) >> Best rule #6172 for best value: >> intensional similarity = 9 >> extensional distance = 586 >> proper extension: 0gzy02; 01kf3_9; 04t9c0; >> query: (?x4290, ?x963) <- language(?x4290, ?x254), film_release_distribution_medium(?x4290, ?x81), production_companies(?x4290, ?x963), film(?x963, ?x9478), film(?x963, ?x4269), film_crew_role(?x4269, ?x137), written_by(?x9478, ?x7352), genre(?x4290, ?x53), film(?x1914, ?x4290) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1843 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 92 *> proper extension: 02_1sj; 0170_p; 05cj_j; 050gkf; 0830vk; 0cn_b8; 0mcl0; 0hfzr; 05vxdh; 033fqh; ... *> query: (?x4290, 016tw3) <- language(?x4290, ?x254), film_release_distribution_medium(?x4290, ?x81), production_companies(?x4290, ?x963), film(?x963, ?x9478), film(?x963, ?x9292), ?x9478 = 0f8j13, citytown(?x963, ?x242), ?x9292 = 0h14ln *> conf = 0.65 ranks of expected_values: 2 EVAL 0gtxj2q film! 016tw3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 121.000 0.791 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16497-01lj9 PRED entity: 01lj9 PRED relation: student PRED expected values: 049gc => 62 concepts (41 used for prediction) PRED predicted values (max 10 best out of 326): 09b6zr (0.33 #569, 0.25 #1284, 0.20 #1522), 01zwy (0.33 #170, 0.20 #1599, 0.18 #2790), 01tdnyh (0.33 #114, 0.20 #1543, 0.14 #1781), 0jcx (0.33 #65, 0.20 #1494, 0.14 #1732), 059y0 (0.33 #213, 0.20 #1642, 0.14 #1880), 0bkg4 (0.33 #84, 0.20 #1513, 0.14 #1751), 08f3b1 (0.33 #487, 0.20 #1440, 0.08 #4539), 0tc7 (0.33 #515, 0.20 #1468, 0.06 #3852), 083q7 (0.25 #1210, 0.22 #2401, 0.14 #1686), 06c0j (0.25 #1424, 0.17 #3569, 0.14 #1900) >> Best rule #569 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 0g26h; >> query: (?x4100, 09b6zr) <- major_field_of_study(?x12127, ?x4100), major_field_of_study(?x11244, ?x4100), major_field_of_study(?x9847, ?x4100), major_field_of_study(?x7545, ?x4100), major_field_of_study(?x5280, ?x4100), major_field_of_study(?x3424, ?x4100), major_field_of_study(?x2313, ?x4100), major_field_of_study(?x741, ?x4100), ?x3424 = 01w5m, ?x11244 = 02gnmp, ?x9847 = 0187nd, ?x12127 = 02tz9z, ?x741 = 01w3v, ?x2313 = 07wrz, ?x7545 = 0bwfn, contains(?x94, ?x5280) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3457 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 10 *> proper extension: 06ntj; *> query: (?x4100, 049gc) <- major_field_of_study(?x4100, ?x1858), taxonomy(?x4100, ?x939), major_field_of_study(?x1327, ?x4100), interests(?x712, ?x1858), major_field_of_study(?x1771, ?x1858), major_field_of_study(?x3948, ?x1858), ?x1771 = 019v9k, ?x3948 = 025v3k *> conf = 0.08 ranks of expected_values: 109 EVAL 01lj9 student 049gc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 62.000 41.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #16496-070yzk PRED entity: 070yzk PRED relation: film PRED expected values: 06g77c 01shy7 => 114 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1098): 01g03q (0.48 #100104, 0.35 #80444, 0.35 #164441), 02ryz24 (0.43 #10725, 0.41 #5362, 0.28 #28604), 0gj8t_b (0.43 #10725, 0.41 #5362, 0.23 #50058), 05qbckf (0.43 #10725, 0.41 #5362, 0.23 #50058), 013q07 (0.20 #2144, 0.07 #7506, 0.04 #21810), 01dc0c (0.20 #3239, 0.02 #21118, 0.02 #12177), 034qmv (0.13 #7164, 0.09 #8952, 0.04 #21468), 0ch26b_ (0.12 #3876, 0.04 #12814, 0.03 #7451), 04ltlj (0.10 #3504, 0.07 #8866, 0.06 #5291), 01xbxn (0.10 #3180, 0.07 #8542, 0.06 #4967) >> Best rule #100104 for best value: >> intensional similarity = 3 >> extensional distance = 969 >> proper extension: 019fnv; 03f68r6; >> query: (?x8544, ?x9350) <- location(?x8544, ?x108), type_of_union(?x8544, ?x1873), nominated_for(?x8544, ?x9350) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #11149 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 0162c8; 0f4vbz; 0gyx4; 02hhtj; *> query: (?x8544, 01shy7) <- profession(?x8544, ?x319), ?x319 = 01d_h8, celebrity(?x4536, ?x8544) *> conf = 0.07 ranks of expected_values: 89, 777 EVAL 070yzk film 01shy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 114.000 103.000 0.484 http://example.org/film/actor/film./film/performance/film EVAL 070yzk film 06g77c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 114.000 103.000 0.484 http://example.org/film/actor/film./film/performance/film #16495-0cnl1c PRED entity: 0cnl1c PRED relation: currency PRED expected values: 09nqf => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.21 #32, 0.20 #35, 0.17 #157), 01nv4h (0.02 #11) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 608 >> proper extension: 079ws; >> query: (?x4332, 09nqf) <- award_winner(?x237, ?x4332), award_winner(?x4332, ?x275), currency(?x237, ?x170) >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cnl1c currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.210 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #16494-0vfs8 PRED entity: 0vfs8 PRED relation: place! PRED expected values: 0vfs8 => 101 concepts (54 used for prediction) PRED predicted values (max 10 best out of 38): 08809 (0.14 #3094, 0.13 #11350, 0.13 #11867), 02dtg (0.14 #20639, 0.13 #18059, 0.07 #9), 0vm39 (0.07 #238, 0.06 #753, 0.05 #1268), 0xckc (0.07 #188, 0.06 #703, 0.05 #1218), 0v9qg (0.07 #91, 0.06 #606, 0.05 #1121), 0vm5t (0.07 #493, 0.06 #1008, 0.04 #2038), 04pry (0.07 #385, 0.06 #900, 0.04 #1930), 0vm4s (0.07 #187, 0.06 #702, 0.04 #1732), 0f67f (0.07 #182, 0.05 #1212, 0.04 #1727), 0vrmb (0.07 #402, 0.03 #2462, 0.01 #12384) >> Best rule #3094 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 01zrs_; 01t3h6; >> query: (?x8115, ?x11359) <- contains(?x94, ?x8115), place_of_birth(?x8149, ?x8115), location(?x8149, ?x11359), influenced_by(?x8149, ?x1029) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #12384 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 348 *> proper extension: 0k049; 06_kh; 0s3y5; 02cl1; 02_286; 0f94t; 05ksh; 0284jb; 013jz2; 015zxh; ... *> query: (?x8115, ?x169) <- contains(?x1906, ?x8115), location(?x7550, ?x8115), district_represented(?x176, ?x1906), contains(?x1906, ?x169) *> conf = 0.01 ranks of expected_values: 32 EVAL 0vfs8 place! 0vfs8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 101.000 54.000 0.143 http://example.org/location/hud_county_place/place #16493-04j_gs PRED entity: 04j_gs PRED relation: film PRED expected values: 0gx1bnj => 101 concepts (76 used for prediction) PRED predicted values (max 10 best out of 668): 05fm6m (0.09 #1322, 0.07 #3111, 0.05 #4900), 07p62k (0.09 #354, 0.07 #2143, 0.05 #3932), 07x4qr (0.09 #405, 0.07 #2194, 0.03 #7562), 07h9gp (0.09 #266, 0.07 #2055, 0.03 #7423), 03l6q0 (0.09 #544, 0.07 #2333, 0.03 #7701), 02x3lt7 (0.09 #84, 0.03 #1873, 0.03 #7241), 01j7mr (0.08 #51891, 0.08 #50101, 0.07 #57259), 0prrm (0.08 #4440, 0.04 #862, 0.04 #9808), 03bx2lk (0.08 #3763, 0.03 #23443, 0.03 #32389), 02hxhz (0.06 #5490, 0.03 #3700, 0.02 #16224) >> Best rule #1322 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 05r5w; 01wc7p; >> query: (?x10512, 05fm6m) <- actor(?x3626, ?x10512), person(?x3775, ?x10512), film(?x10512, ?x7967) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 05r5w; 01wc7p; *> query: (?x10512, 0gx1bnj) <- actor(?x3626, ?x10512), person(?x3775, ?x10512), film(?x10512, ?x7967) *> conf = 0.04 ranks of expected_values: 53 EVAL 04j_gs film 0gx1bnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 101.000 76.000 0.087 http://example.org/film/actor/film./film/performance/film #16492-020_4z PRED entity: 020_4z PRED relation: artist! PRED expected values: 011k11 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 106): 0181dw (0.47 #1915, 0.20 #173, 0.19 #1111), 03rhqg (0.43 #15, 0.25 #551, 0.24 #685), 01cf93 (0.43 #54, 0.20 #188, 0.12 #724), 043g7l (0.32 #1905, 0.10 #1369, 0.10 #1235), 02p3cr5 (0.23 #293, 0.09 #427, 0.05 #829), 01cszh (0.22 #412, 0.22 #1886, 0.08 #2288), 033hn8 (0.22 #415, 0.20 #147, 0.18 #1889), 0g768 (0.20 #168, 0.18 #838, 0.18 #704), 01w40h (0.20 #160, 0.16 #428, 0.14 #26), 02p11jq (0.20 #146, 0.09 #548, 0.08 #950) >> Best rule #1915 for best value: >> intensional similarity = 5 >> extensional distance = 127 >> proper extension: 01pfkw; >> query: (?x10437, 0181dw) <- gender(?x10437, ?x231), profession(?x10437, ?x220), artist(?x9492, ?x10437), artist(?x9492, ?x4693), ?x4693 = 01vwbts >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #32 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 07bzp; 03c3yf; *> query: (?x10437, 011k11) <- artists(?x6210, ?x10437), artists(?x378, ?x10437), category(?x10437, ?x134), ?x378 = 07sbbz2, ?x6210 = 01fh36 *> conf = 0.14 ranks of expected_values: 19 EVAL 020_4z artist! 011k11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 119.000 119.000 0.473 http://example.org/music/record_label/artist #16491-01f7jt PRED entity: 01f7jt PRED relation: executive_produced_by PRED expected values: 030_3z => 97 concepts (87 used for prediction) PRED predicted values (max 10 best out of 103): 01f7j9 (0.24 #1005, 0.17 #251, 0.16 #3018), 0343h (0.16 #543, 0.10 #41, 0.08 #795), 04jspq (0.10 #1408, 0.10 #903, 0.10 #1659), 079vf (0.10 #2769, 0.08 #1764, 0.06 #5282), 06q8hf (0.10 #919, 0.10 #165, 0.10 #9718), 05hj_k (0.10 #850, 0.10 #96, 0.09 #9649), 0glyyw (0.10 #941, 0.07 #1446, 0.07 #2200), 030_3z (0.10 #9302, 0.10 #10554, 0.08 #357), 027rwmr (0.07 #1006, 0.05 #2265, 0.03 #3772), 03h26tm (0.07 #1006, 0.05 #2265, 0.03 #3772) >> Best rule #1005 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 0kv2hv; 0bshwmp; 04hwbq; 03s5lz; 04n52p6; 07h9gp; 0bcndz; 013q07; 01hvjx; 02vqsll; ... >> query: (?x10943, ?x2182) <- production_companies(?x10943, ?x1686), story_by(?x10943, ?x2182), award_winner(?x10943, ?x929), executive_produced_by(?x10943, ?x846) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #9302 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 326 *> proper extension: 075cph; *> query: (?x10943, ?x846) <- production_companies(?x10943, ?x1686), award(?x10943, ?x640), organizations_founded(?x846, ?x1686) *> conf = 0.10 ranks of expected_values: 8 EVAL 01f7jt executive_produced_by 030_3z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 97.000 87.000 0.241 http://example.org/film/film/executive_produced_by #16490-086nl7 PRED entity: 086nl7 PRED relation: film PRED expected values: 02825kb => 122 concepts (103 used for prediction) PRED predicted values (max 10 best out of 847): 06y_n (0.63 #92627, 0.62 #49875, 0.61 #85501), 039cq4 (0.63 #92627, 0.61 #85501, 0.55 #81937), 03cyslc (0.25 #1199, 0.17 #4761, 0.17 #2980), 0gwgn1k (0.25 #1541, 0.17 #3322, 0.15 #6884), 0g7pm1 (0.25 #1196, 0.17 #2977, 0.14 #8320), 05zpghd (0.25 #950, 0.17 #2731, 0.08 #4512), 0c3xw46 (0.25 #624, 0.17 #2405, 0.08 #4186), 0gj96ln (0.25 #1069, 0.17 #2850, 0.08 #4631), 03ynwqj (0.25 #1465, 0.17 #3246, 0.08 #5027), 03nfnx (0.17 #4957, 0.17 #3176, 0.15 #6738) >> Best rule #92627 for best value: >> intensional similarity = 3 >> extensional distance = 414 >> proper extension: 0d_84; 0456xp; 0h1m9; 0j582; 01mqz0; 03rl84; 01vhb0; 01wk7b7; 05hdf; 01pnn3; ... >> query: (?x4465, ?x6884) <- participant(?x1896, ?x4465), profession(?x4465, ?x353), nominated_for(?x4465, ?x6884) >> conf = 0.63 => this is the best rule for 2 predicted values *> Best rule #3002 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 03q45x; *> query: (?x4465, 02825kb) <- participant(?x1896, ?x4465), award_nominee(?x2390, ?x4465), cast_members(?x4465, ?x1942) *> conf = 0.17 ranks of expected_values: 20 EVAL 086nl7 film 02825kb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 122.000 103.000 0.628 http://example.org/film/actor/film./film/performance/film #16489-03v_5 PRED entity: 03v_5 PRED relation: place_of_birth! PRED expected values: 0cbgl => 163 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1809): 01zwy (0.40 #109767, 0.33 #2614, 0.31 #101927), 02t901 (0.20 #10340, 0.20 #5113, 0.20 #2499), 026xt5c (0.20 #10140, 0.20 #4913, 0.20 #2299), 0jt86 (0.20 #10127, 0.20 #4900, 0.20 #2286), 01kgxf (0.20 #9406, 0.20 #4179, 0.20 #1565), 01mkn_d (0.20 #9216, 0.20 #3989, 0.20 #1375), 030_3z (0.20 #8770, 0.20 #3543, 0.20 #929), 06rnl9 (0.20 #8394, 0.20 #3167, 0.20 #553), 036c_0 (0.20 #8222, 0.20 #2995, 0.20 #381), 0mfc0 (0.20 #9805, 0.20 #7191, 0.20 #4578) >> Best rule #109767 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 0fw4v; >> query: (?x1730, ?x8508) <- administrative_division(?x1730, ?x13866), category(?x1730, ?x134), location(?x8508, ?x1730), source(?x1730, ?x958) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #159419 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 138 *> proper extension: 0_wm_; *> query: (?x1730, ?x881) <- category(?x1730, ?x134), citytown(?x741, ?x1730), student(?x741, ?x881) *> conf = 0.06 ranks of expected_values: 83 EVAL 03v_5 place_of_birth! 0cbgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 163.000 68.000 0.398 http://example.org/people/person/place_of_birth #16488-026v437 PRED entity: 026v437 PRED relation: nominated_for PRED expected values: 080dwhx => 92 concepts (38 used for prediction) PRED predicted values (max 10 best out of 166): 080dwhx (0.84 #6490, 0.83 #60, 0.78 #29206), 0284b56 (0.22 #4144, 0.09 #56813, 0.05 #2522), 0fpmrm3 (0.17 #3635, 0.11 #2013, 0.09 #56813), 0kfv9 (0.17 #267, 0.11 #1889, 0.09 #56813), 02r1c18 (0.16 #1842, 0.15 #53563, 0.09 #56813), 011ywj (0.15 #53563, 0.09 #56813, 0.05 #2905), 0b6tzs (0.15 #53563, 0.05 #1752, 0.02 #34077), 06zsk51 (0.14 #14606), 011yhm (0.11 #2673, 0.09 #56813, 0.08 #1051), 02704ff (0.11 #2519) >> Best rule #6490 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 0301bq; >> query: (?x6359, ?x493) <- award(?x6359, ?x1670), ?x1670 = 0ck27z, award_winner(?x493, ?x6359) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026v437 nominated_for 080dwhx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 38.000 0.840 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16487-0139q5 PRED entity: 0139q5 PRED relation: special_performance_type PRED expected values: 09_gdc => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 4): 01pb34 (0.12 #8, 0.10 #13, 0.09 #18), 02t8yb (0.03 #44, 0.02 #59, 0.01 #64), 01kyvx (0.02 #222, 0.01 #360, 0.01 #381), 09_gdc (0.01 #325, 0.01 #188, 0.01 #382) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0342vg; 03cp7b3; >> query: (?x9809, 01pb34) <- place_of_birth(?x9809, ?x2645), ?x2645 = 03h64, award_winner(?x6376, ?x9809), profession(?x9809, ?x1032) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #325 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1707 *> proper extension: 01v42g; 06lht1; 02nwxc; 0gm34; 05p606; 0131kb; *> query: (?x9809, 09_gdc) <- gender(?x9809, ?x514), nationality(?x9809, ?x1310), film(?x9809, ?x2189) *> conf = 0.01 ranks of expected_values: 4 EVAL 0139q5 special_performance_type 09_gdc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 113.000 113.000 0.125 http://example.org/film/actor/film./film/performance/special_performance_type #16486-0168t PRED entity: 0168t PRED relation: currency PRED expected values: 09nqf => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.78 #202, 0.78 #227, 0.78 #185), 0ptk_ (0.08 #15, 0.03 #50, 0.01 #113), 02l6h (0.04 #296, 0.02 #346, 0.02 #374), 01nv4h (0.01 #125) >> Best rule #202 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 0160w; >> query: (?x11553, 09nqf) <- country(?x1121, ?x11553), participating_countries(?x1931, ?x11553), ?x1931 = 0kbws, ?x1121 = 0bynt >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0168t currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.783 http://example.org/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency #16485-042fgh PRED entity: 042fgh PRED relation: genre PRED expected values: 03k9fj 0btmb => 114 concepts (74 used for prediction) PRED predicted values (max 10 best out of 104): 0btmb (0.84 #7219, 0.50 #208, 0.40 #88), 07s9rl0 (0.76 #5049, 0.67 #721, 0.63 #3603), 082gq (0.73 #7129, 0.29 #391, 0.20 #751), 01jfsb (0.71 #8676, 0.62 #4215, 0.62 #3494), 03k9fj (0.67 #1212, 0.65 #6508, 0.62 #1332), 05p553 (0.55 #4327, 0.38 #1684, 0.36 #5776), 01hmnh (0.47 #1938, 0.40 #18, 0.36 #978), 02l7c8 (0.40 #736, 0.38 #856, 0.35 #3257), 0lsxr (0.38 #489, 0.32 #5419, 0.32 #2290), 04xvh5 (0.29 #395, 0.11 #3637, 0.11 #7615) >> Best rule #7219 for best value: >> intensional similarity = 6 >> extensional distance = 211 >> proper extension: 0c0wvx; 02qjv1p; >> query: (?x7425, ?x11401) <- genre(?x7425, ?x6888), genre(?x7672, ?x6888), genre(?x7239, ?x6888), ?x7672 = 07f_t4, split_to(?x11401, ?x6888), film(?x1289, ?x7239) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 042fgh genre 0btmb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 74.000 0.842 http://example.org/film/film/genre EVAL 042fgh genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 114.000 74.000 0.842 http://example.org/film/film/genre #16484-0gdqy PRED entity: 0gdqy PRED relation: type_of_union PRED expected values: 04ztj => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.93 #251, 0.93 #233, 0.93 #284), 01bl8s (0.10 #56, 0.09 #35, 0.08 #38), 0jgjn (0.01 #148) >> Best rule #251 for best value: >> intensional similarity = 3 >> extensional distance = 957 >> proper extension: 04sx9_; 030znt; 01d494; 02k6rq; 0hwd8; 05qsxy; 0p51w; 04gmp_z; 06rnl9; 01438g; ... >> query: (?x10354, 04ztj) <- award_winner(?x11779, ?x10354), type_of_union(?x10354, ?x1873), place_of_birth(?x10354, ?x4627) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gdqy type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.934 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16483-07tlfx PRED entity: 07tlfx PRED relation: film_release_region PRED expected values: 09c7w0 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 100): 09c7w0 (0.74 #4130, 0.73 #5387, 0.71 #1438), 0d060g (0.44 #3049, 0.43 #16338, 0.43 #13825), 0chghy (0.33 #17, 0.23 #10964, 0.23 #10066), 0jgd (0.33 #5, 0.22 #10952, 0.22 #4492), 0f8l9c (0.28 #10979, 0.27 #12778, 0.27 #10081), 0d0vqn (0.27 #10959, 0.27 #12758, 0.26 #10061), 06mkj (0.25 #11023, 0.25 #10125, 0.25 #4563), 02vzc (0.25 #4557, 0.24 #11017, 0.24 #12816), 07ssc (0.24 #10971, 0.24 #10073, 0.24 #12770), 0k6nt (0.24 #4523, 0.23 #10983, 0.23 #12782) >> Best rule #4130 for best value: >> intensional similarity = 3 >> extensional distance = 447 >> proper extension: 047gn4y; 02_1sj; 026mfbr; 08hmch; 02v63m; 0c00zd0; 03sxd2; 02vqhv0; 01j8wk; 0gydcp7; ... >> query: (?x9978, 09c7w0) <- film_crew_role(?x9978, ?x468), produced_by(?x9978, ?x2499), ?x468 = 02r96rf >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tlfx film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.739 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16482-02gvwz PRED entity: 02gvwz PRED relation: gender PRED expected values: 05zppz => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #150, 0.71 #128, 0.71 #120), 02zsn (0.51 #99, 0.33 #8, 0.31 #20) >> Best rule #150 for best value: >> intensional similarity = 2 >> extensional distance = 2862 >> proper extension: 0f0y8; 0c9d9; 05g8ky; 01vvy; 0c7ct; 06y9c2; 0274ck; 0hnlx; 0pcc0; 0bn9sc; ... >> query: (?x1194, 05zppz) <- type_of_union(?x1194, ?x566), ?x566 = 04ztj >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02gvwz gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.716 http://example.org/people/person/gender #16481-01pbs9w PRED entity: 01pbs9w PRED relation: profession PRED expected values: 03lgtv => 132 concepts (83 used for prediction) PRED predicted values (max 10 best out of 69): 09jwl (0.82 #5660, 0.72 #7896, 0.71 #8194), 01c72t (0.76 #1210, 0.67 #1952, 0.65 #2546), 02hrh1q (0.69 #11904, 0.64 #11607, 0.62 #10867), 016z4k (0.47 #5942, 0.47 #7880, 0.47 #5200), 01c8w0 (0.41 #1194, 0.39 #1936, 0.34 #898), 039v1 (0.36 #5677, 0.33 #36, 0.32 #5083), 01d_h8 (0.33 #1785, 0.33 #10858, 0.32 #11895), 0dxtg (0.28 #4018, 0.27 #6550, 0.27 #10866), 02jknp (0.27 #5500, 0.24 #6544, 0.23 #11897), 05vyk (0.27 #836, 0.18 #1132, 0.16 #1280) >> Best rule #5660 for best value: >> intensional similarity = 3 >> extensional distance = 179 >> proper extension: 01r4zfk; >> query: (?x5757, 09jwl) <- type_of_union(?x5757, ?x566), role(?x5757, ?x2764), nationality(?x5757, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1596 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 02mslq; 01vrncs; 01ky2h; 0k7pf; 01kd57; 09r9m7; 05_swj; 05y7hc; 019x62; 01l3mk3; ... *> query: (?x5757, 03lgtv) <- artists(?x505, ?x5757), instrumentalists(?x316, ?x5757), music(?x407, ?x5757), award_winner(?x7100, ?x5757) *> conf = 0.04 ranks of expected_values: 37 EVAL 01pbs9w profession 03lgtv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 132.000 83.000 0.818 http://example.org/people/person/profession #16480-01gg59 PRED entity: 01gg59 PRED relation: award_winner! PRED expected values: 02qvyrt => 109 concepts (108 used for prediction) PRED predicted values (max 10 best out of 279): 099vwn (0.42 #1712, 0.41 #2140, 0.38 #1284), 026mmy (0.42 #1712, 0.41 #2140, 0.38 #1284), 05zkcn5 (0.42 #1712, 0.41 #2140, 0.38 #1284), 02x17c2 (0.42 #1712, 0.41 #2140, 0.38 #1284), 025m8y (0.28 #952, 0.24 #3519, 0.21 #1381), 054ks3 (0.26 #1850, 0.23 #565, 0.13 #3560), 0c4z8 (0.19 #1783, 0.12 #498, 0.09 #3493), 01by1l (0.18 #110, 0.17 #537, 0.15 #6948), 05q8pss (0.15 #29492, 0.05 #3201, 0.04 #1919), 0b6k___ (0.15 #29492, 0.05 #33340, 0.01 #1924) >> Best rule #1712 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 01vsxdm; 0134s5; 04ls53; 02jqjm; 07mvp; 015cxv; 0bk1p; 01njxvw; 0p8h0; >> query: (?x3890, ?x462) <- award(?x3890, ?x462), category(?x3890, ?x134), music(?x3742, ?x3890) >> conf = 0.42 => this is the best rule for 4 predicted values *> Best rule #979 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 03h4mp; 08c9b0; *> query: (?x3890, 02qvyrt) <- award(?x3890, ?x4416), award(?x3890, ?x1443), award_winner(?x4416, ?x248), ?x1443 = 054krc *> conf = 0.13 ranks of expected_values: 11 EVAL 01gg59 award_winner! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 109.000 108.000 0.416 http://example.org/award/award_category/winners./award/award_honor/award_winner #16479-0f6lx PRED entity: 0f6lx PRED relation: films PRED expected values: 077q8x => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 8): 0djlxb (0.05 #1754, 0.05 #2285, 0.03 #4940), 080lkt7 (0.04 #3950, 0.02 #7667, 0.02 #7136), 042y1c (0.02 #7019, 0.02 #8081, 0.02 #9143), 0209hj (0.02 #9590, 0.01 #10652, 0.01 #11183), 0m313 (0.02 #9561, 0.01 #10623), 0cmc26r (0.01 #11882, 0.01 #14537, 0.01 #14006), 0b4lkx (0.01 #13688), 02vqsll (0.01 #13423) >> Best rule #1754 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 031x_3; >> query: (?x9021, 0djlxb) <- artists(?x505, ?x9021), award_winner(?x1088, ?x9021), ?x1088 = 03x3wf, origin(?x9021, ?x2017) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f6lx films 077q8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 165.000 0.050 http://example.org/film/film_subject/films #16478-02sdk9v PRED entity: 02sdk9v PRED relation: position! PRED expected values: 03fnmd => 29 concepts (12 used for prediction) PRED predicted values (max 10 best out of 319): 0346qt (0.84 #483, 0.82 #1133, 0.82 #1134), 05jx17 (0.84 #483, 0.82 #1133, 0.82 #1134), 01fwqn (0.84 #483, 0.82 #1133, 0.82 #1134), 083my7 (0.84 #483, 0.82 #1133, 0.82 #1134), 0fvly (0.84 #483, 0.82 #1133, 0.82 #1134), 0122wc (0.84 #483, 0.82 #1133, 0.82 #1134), 02hzx8 (0.84 #483, 0.82 #1133, 0.82 #1134), 05hc96 (0.84 #483, 0.82 #1133, 0.82 #1134), 016gp5 (0.84 #483, 0.82 #1133, 0.82 #1134), 03w7kx (0.84 #483, 0.82 #1133, 0.82 #1134) >> Best rule #483 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 0dgrmp; >> query: (?x63, ?x676) <- position(?x13538, ?x63), position(?x12269, ?x63), position(?x10878, ?x63), position(?x5207, ?x63), position(?x3587, ?x63), position(?x3032, ?x63), position(?x2074, ?x63), ?x3032 = 01j95f, team(?x63, ?x6153), team(?x63, ?x676), ?x10878 = 04mrfv, ?x3587 = 02s2lg, ?x2074 = 0j2pg, ?x13538 = 049m_l, ?x12269 = 0gfnqh, ?x6153 = 016gp5, ?x5207 = 0bl8l >> conf = 0.84 => this is the best rule for 114 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 84 EVAL 02sdk9v position! 03fnmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 29.000 12.000 0.836 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #16477-02p7_k PRED entity: 02p7_k PRED relation: award_nominee! PRED expected values: 050t68 => 91 concepts (27 used for prediction) PRED predicted values (max 10 best out of 620): 0785v8 (0.87 #2314, 0.81 #62473, 0.81 #50901), 0z4s (0.87 #2314, 0.81 #62473, 0.81 #50901), 017149 (0.87 #2314, 0.81 #62473, 0.81 #50901), 02t_vx (0.87 #2314, 0.81 #62473, 0.81 #50900), 02ch1w (0.87 #2314, 0.81 #62473, 0.81 #50900), 02p7_k (0.40 #818, 0.17 #37019, 0.15 #57844), 04myfb7 (0.27 #408, 0.26 #50902, 0.15 #57844), 04mz10g (0.27 #290, 0.26 #50902, 0.15 #57844), 065ydwb (0.27 #1312, 0.26 #50902, 0.15 #57844), 06b0d2 (0.27 #220, 0.26 #50902, 0.03 #11788) >> Best rule #2314 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0785v8; 05ml_s; 01pcq3; 04y79_n; 0f6_dy; 03mcwq3; 05th8t; 0b9dmk; 02rf1y; 08s_lw; ... >> query: (?x3660, ?x450) <- award_nominee(?x7992, ?x3660), award_nominee(?x3660, ?x450), film(?x3660, ?x2336), ?x7992 = 05wqr1 >> conf = 0.87 => this is the best rule for 5 predicted values *> Best rule #57844 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1150 *> proper extension: 0h1p; 09pl3s; 079hvk; 038rzr; 06449; 0kvqv; 037hgm; 0hwqz; 0flpy; 07g7h2; ... *> query: (?x3660, ?x3932) <- award_nominee(?x3660, ?x9655), student(?x3387, ?x3660), award_nominee(?x3932, ?x9655) *> conf = 0.15 ranks of expected_values: 64 EVAL 02p7_k award_nominee! 050t68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 91.000 27.000 0.865 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16476-01bv8b PRED entity: 01bv8b PRED relation: program! PRED expected values: 09d5h => 97 concepts (83 used for prediction) PRED predicted values (max 10 best out of 58): 05gnf (0.39 #129, 0.38 #14, 0.32 #187), 0gsg7 (0.21 #804, 0.21 #982, 0.21 #1505), 0cjdk (0.17 #235, 0.16 #349, 0.16 #406), 03mdt (0.14 #579, 0.14 #122, 0.13 #522), 09d5h (0.13 #1792, 0.13 #805, 0.13 #1678), 07c52 (0.12 #802, 0.11 #980, 0.10 #1503), 0g5lhl7 (0.08 #179, 0.06 #1451, 0.06 #867), 0187wh (0.06 #656, 0.05 #256, 0.05 #313), 01fsyp (0.06 #108, 0.06 #165, 0.04 #50), 0kctd (0.06 #86, 0.04 #543, 0.04 #600) >> Best rule #129 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 072kp; 019nnl; 0124k9; 08jgk1; 0584r4; 02xhpl; 01q_y0; 02hct1; 0d68qy; 0557yqh; ... >> query: (?x2710, 05gnf) <- genre(?x2710, ?x8534), nominated_for(?x678, ?x2710), nominated_for(?x3402, ?x2710), ?x8534 = 0c4xc >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1792 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 169 *> proper extension: 02rq7nd; *> query: (?x2710, 09d5h) <- genre(?x2710, ?x258), nominated_for(?x2016, ?x2710), nominated_for(?x2016, ?x10731), award(?x10731, ?x3486) *> conf = 0.13 ranks of expected_values: 5 EVAL 01bv8b program! 09d5h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 83.000 0.389 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #16475-02xbyr PRED entity: 02xbyr PRED relation: film_release_region PRED expected values: 0d0vqn 0chghy 0h7x 05qx1 01znc_ 02vzc 03rk0 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 167): 0d0vqn (0.93 #1326, 0.93 #2251, 0.92 #2912), 05r4w (0.91 #662, 0.87 #2909, 0.87 #2776), 01znc_ (0.88 #954, 0.85 #822, 0.83 #558), 0chghy (0.88 #801, 0.88 #537, 0.87 #2519), 02vzc (0.85 #963, 0.82 #2549, 0.82 #2681), 03rk0 (0.78 #966, 0.69 #2288, 0.68 #438), 01mjq (0.70 #824, 0.68 #428, 0.67 #1485), 01ls2 (0.64 #274, 0.63 #2256, 0.61 #934), 05qx1 (0.63 #953, 0.61 #1482, 0.59 #425), 0h7x (0.50 #1873, 0.49 #684, 0.48 #1477) >> Best rule #1326 for best value: >> intensional similarity = 9 >> extensional distance = 43 >> proper extension: 02vxq9m; 0dscrwf; 02x3lt7; 0c40vxk; 0ch26b_; 0gvrws1; 0661ql3; 04f52jw; 0407yj_; 0gj8nq2; ... >> query: (?x4707, 0d0vqn) <- film_release_region(?x4707, ?x1536), film_release_region(?x4707, ?x1174), film_release_region(?x4707, ?x205), film_release_region(?x4707, ?x172), ?x172 = 0154j, ?x1174 = 047yc, ?x205 = 03rjj, film(?x2156, ?x4707), ?x1536 = 06c1y >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 5, 6, 9, 10 EVAL 02xbyr film_release_region 03rk0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02xbyr film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02xbyr film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02xbyr film_release_region 05qx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 94.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02xbyr film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 94.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02xbyr film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02xbyr film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16474-09kvv PRED entity: 09kvv PRED relation: institution! PRED expected values: 016t_3 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 14): 016t_3 (0.71 #182, 0.69 #242, 0.66 #152), 07s6fsf (0.49 #181, 0.48 #241, 0.43 #286), 013zdg (0.41 #64, 0.38 #34, 0.33 #244), 01rr_d (0.38 #39, 0.29 #69, 0.28 #1410), 02m4yg (0.28 #1410, 0.25 #38, 0.24 #68), 022h5x (0.28 #1410, 0.25 #42, 0.17 #252), 071tyz (0.28 #1410, 0.14 #903, 0.14 #20), 01ysy9 (0.28 #1410, 0.07 #164, 0.06 #194), 01gkg3 (0.28 #1410, 0.01 #593), 02mjs7 (0.25 #33, 0.24 #63, 0.20 #183) >> Best rule #182 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0d06m5; 0d05fv; >> query: (?x1768, 016t_3) <- category(?x1768, ?x134), organization(?x1768, ?x5487), list(?x1768, ?x2197) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09kvv institution! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #16473-01ynzf PRED entity: 01ynzf PRED relation: producer_type PRED expected values: 0ckd1 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.36 #28, 0.32 #9, 0.25 #36) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 413 >> proper extension: 07nznf; 0q9kd; 0dbpyd; 016qtt; 012d40; 0fvf9q; 02p65p; 01xdf5; 04t2l2; 02rchht; ... >> query: (?x8974, 0ckd1) <- award(?x8974, ?x1862), award_nominee(?x5650, ?x8974), profession(?x8974, ?x1041), ?x1041 = 03gjzk >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ynzf producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.361 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #16472-0198b6 PRED entity: 0198b6 PRED relation: film_release_region PRED expected values: 07ssc 06t2t => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 190): 07ssc (0.81 #2007, 0.80 #3080, 0.80 #2927), 03spz (0.75 #2545, 0.72 #2085, 0.69 #399), 0154j (0.73 #3069, 0.73 #2916, 0.73 #1996), 0b90_r (0.73 #2455, 0.70 #2915, 0.67 #3068), 06bnz (0.71 #2495, 0.69 #2955, 0.68 #2035), 03rt9 (0.68 #2465, 0.66 #2925, 0.63 #2005), 06t2t (0.65 #2511, 0.64 #2971, 0.62 #365), 01mjq (0.62 #2493, 0.62 #347, 0.58 #2033), 0h7x (0.57 #2484, 0.57 #2024, 0.55 #2944), 0ctw_b (0.54 #2475, 0.53 #2935, 0.51 #3088) >> Best rule #2007 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0djb3vw; 04969y; 040rmy; 0g9zljd; 0cp08zg; >> query: (?x3886, 07ssc) <- nominated_for(?x4169, ?x3886), film_release_region(?x3886, ?x774), ?x774 = 06mzp, language(?x3886, ?x2502) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 0198b6 film_release_region 06t2t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 79.000 0.811 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0198b6 film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.811 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16471-0dn44 PRED entity: 0dn44 PRED relation: award_nominee! PRED expected values: 04yt7 => 49 concepts (27 used for prediction) PRED predicted values (max 10 best out of 792): 04yt7 (0.81 #13995, 0.81 #32656, 0.81 #34990), 03dq9 (0.81 #13995, 0.81 #32656, 0.81 #34990), 0hvb2 (0.07 #12055, 0.04 #7389, 0.03 #9723), 02p65p (0.07 #11688, 0.03 #9356, 0.03 #30348), 02qgyv (0.07 #12159, 0.04 #7493, 0.03 #30819), 03f1zdw (0.07 #11908, 0.04 #2578, 0.03 #246), 023kzp (0.07 #13052, 0.06 #8386, 0.04 #10720), 019pm_ (0.07 #12273, 0.05 #7607, 0.04 #9941), 015t56 (0.06 #12272, 0.04 #7606, 0.04 #9940), 0154qm (0.06 #12403, 0.03 #3073, 0.03 #31063) >> Best rule #13995 for best value: >> intensional similarity = 4 >> extensional distance = 501 >> proper extension: 0m2wm; 02zq43; 02k6rq; 04smkr; 07hbxm; 04rsd2; 06mmb; 02xbw2; 01wz01; 02qfhb; ... >> query: (?x11797, ?x4297) <- award_nominee(?x11797, ?x4988), award_nominee(?x11797, ?x4297), award(?x4988, ?x2375), ?x2375 = 04kxsb >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0dn44 award_nominee! 04yt7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 27.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16470-0wh3 PRED entity: 0wh3 PRED relation: category PRED expected values: 08mbj5d => 249 concepts (249 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #28, 0.82 #50, 0.81 #59) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 0r2bv; >> query: (?x1106, 08mbj5d) <- time_zones(?x1106, ?x2674), jurisdiction_of_office(?x1195, ?x1106), state(?x1106, ?x1906), source(?x1106, ?x958) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0wh3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 249.000 249.000 0.843 http://example.org/common/topic/webpage./common/webpage/category #16469-03qbh5 PRED entity: 03qbh5 PRED relation: award! PRED expected values: 01w61th 09k2t1 0gcs9 0qf11 01bczm 0g824 01jgkj2 01w5gg6 => 48 concepts (28 used for prediction) PRED predicted values (max 10 best out of 2405): 0127s7 (0.77 #39520, 0.71 #46108, 0.71 #39519), 01wyz92 (0.71 #46108, 0.71 #39519, 0.70 #69169), 02l840 (0.57 #10054, 0.50 #13348, 0.43 #19935), 01vw20h (0.57 #11123, 0.50 #1244, 0.38 #14417), 017959 (0.57 #12550, 0.50 #15844, 0.36 #22431), 0dvqq (0.57 #10487, 0.50 #13781, 0.36 #20368), 0kr_t (0.57 #11445, 0.50 #14739, 0.33 #18032), 0134pk (0.57 #12604, 0.50 #15898, 0.33 #19191), 07r1_ (0.57 #11876, 0.50 #15170, 0.33 #18463), 0d193h (0.57 #11042, 0.50 #14336, 0.33 #17629) >> Best rule #39520 for best value: >> intensional similarity = 3 >> extensional distance = 138 >> proper extension: 02qkk9_; 05f3q; 02kgb7; 02q3s; >> query: (?x4018, ?x5906) <- award_winner(?x4018, ?x5906), award(?x5906, ?x884), vacationer(?x362, ?x5906) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #28152 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 09sb52; 01bgqh; 03c7tr1; 0c4z8; 01by1l; 05p09zm; 01cky2; 03qbnj; *> query: (?x4018, 0g824) <- award(?x3065, ?x4018), award(?x2614, ?x4018), people(?x2510, ?x3065), award_winner(?x1389, ?x3065), ?x2614 = 04xrx *> conf = 0.40 ranks of expected_values: 56, 62, 73, 138, 235, 291, 310, 435 EVAL 03qbh5 award! 01w5gg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 28.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qbh5 award! 01jgkj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 48.000 28.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qbh5 award! 0g824 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 48.000 28.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qbh5 award! 01bczm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 48.000 28.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qbh5 award! 0qf11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 28.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qbh5 award! 0gcs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 48.000 28.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qbh5 award! 09k2t1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 48.000 28.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qbh5 award! 01w61th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 28.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16468-01m7pwq PRED entity: 01m7pwq PRED relation: artists! PRED expected values: 0xhtw 05bt6j => 120 concepts (53 used for prediction) PRED predicted values (max 10 best out of 249): 0xhtw (0.75 #1892, 0.27 #3143, 0.27 #10338), 064t9 (0.64 #3451, 0.62 #5327, 0.57 #639), 05bt6j (0.43 #669, 0.30 #10364, 0.28 #13499), 016clz (0.34 #3130, 0.32 #1879, 0.28 #10325), 0557q (0.33 #170, 0.25 #482, 0.03 #7048), 025sc50 (0.33 #3488, 0.29 #676, 0.28 #5364), 06j6l (0.32 #3486, 0.32 #5362, 0.29 #2235), 0glt670 (0.31 #3478, 0.27 #5354, 0.20 #2227), 01lyv (0.29 #4095, 0.23 #2845, 0.21 #1595), 02yv6b (0.27 #1975, 0.20 #5726, 0.17 #10421) >> Best rule #1892 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 01j59b0; 01516r; 01shhf; 02cw1m; 0p76z; 0167xy; 01518s; >> query: (?x9830, 0xhtw) <- artists(?x2249, ?x9830), artists(?x1572, ?x9830), ?x1572 = 06by7, ?x2249 = 03lty >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01m7pwq artists! 05bt6j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 53.000 0.750 http://example.org/music/genre/artists EVAL 01m7pwq artists! 0xhtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 53.000 0.750 http://example.org/music/genre/artists #16467-03qlv7 PRED entity: 03qlv7 PRED relation: instrumentalists PRED expected values: 01ky2h => 68 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1268): 01ky2h (0.75 #1847, 0.71 #1846, 0.53 #5553), 01vsy7t (0.71 #1846, 0.53 #5553, 0.52 #4316), 032nwy (0.71 #1846, 0.53 #5553, 0.52 #4316), 01kvqc (0.71 #1846, 0.53 #5553, 0.52 #4316), 01sb5r (0.67 #13198, 0.50 #4556, 0.50 #3318), 053yx (0.63 #1848, 0.48 #1844, 0.25 #4472), 02vr7 (0.62 #10954, 0.56 #13426, 0.56 #12807), 09hnb (0.62 #11249, 0.48 #1844, 0.28 #8638), 01w8n89 (0.56 #13576, 0.56 #13168, 0.50 #4526), 09prnq (0.56 #13075, 0.50 #4433, 0.50 #1963) >> Best rule #1847 for best value: >> intensional similarity = 19 >> extensional distance = 1 >> proper extension: 0342h; >> query: (?x1332, ?x1832) <- role(?x1332, ?x4425), role(?x1332, ?x3418), role(?x1332, ?x3328), role(?x1332, ?x1831), role(?x1332, ?x716), role(?x1332, ?x212), role(?x432, ?x1332), ?x3328 = 016622, instrumentalists(?x1332, ?x1333), ?x212 = 026t6, role(?x1832, ?x1332), role(?x4425, ?x74), ?x716 = 018vs, role(?x1332, ?x885), award_winner(?x159, ?x1333), ?x3418 = 02w4b, peers(?x1832, ?x2835), award_winner(?x1333, ?x158), role(?x547, ?x1831) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qlv7 instrumentalists 01ky2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 43.000 0.750 http://example.org/music/instrument/instrumentalists #16466-016z2j PRED entity: 016z2j PRED relation: participant PRED expected values: 01j851 => 110 concepts (93 used for prediction) PRED predicted values (max 10 best out of 308): 01j851 (0.82 #8435, 0.81 #9732, 0.80 #15572), 01g257 (0.60 #4543, 0.58 #2594, 0.58 #6490), 023s8 (0.27 #2595, 0.02 #30491, 0.01 #3844), 02nwxc (0.12 #386, 0.02 #2331, 0.01 #4280), 0gyx4 (0.11 #1606, 0.07 #4853, 0.06 #6151), 0sz28 (0.10 #731, 0.03 #1379, 0.02 #5275), 02js6_ (0.07 #3894, 0.05 #14275, 0.05 #10381), 04bdxl (0.07 #3894, 0.05 #14275, 0.05 #10381), 03knl (0.07 #3894, 0.05 #14275, 0.05 #10381), 0mm1q (0.07 #3894, 0.05 #14275, 0.05 #10381) >> Best rule #8435 for best value: >> intensional similarity = 3 >> extensional distance = 291 >> proper extension: 01l_vgt; 01xyt7; 02cg2v; >> query: (?x2373, ?x5889) <- place_of_birth(?x2373, ?x1131), type_of_union(?x2373, ?x566), participant(?x5889, ?x2373) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016z2j participant 01j851 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 93.000 0.816 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #16465-09hnb PRED entity: 09hnb PRED relation: religion PRED expected values: 092bf5 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 17): 0c8wxp (0.13 #51, 0.12 #186, 0.12 #1086), 092bf5 (0.08 #1126, 0.06 #106, 0.04 #421), 0kpl (0.07 #190, 0.06 #370, 0.05 #2037), 03_gx (0.07 #194, 0.05 #2536, 0.05 #2942), 03j6c (0.07 #66, 0.03 #111, 0.03 #156), 01lp8 (0.04 #676, 0.03 #541, 0.03 #631), 04pk9 (0.03 #335, 0.03 #110, 0.02 #380), 0flw86 (0.03 #1173, 0.02 #542, 0.02 #182), 0kq2 (0.03 #108, 0.02 #198, 0.02 #423), 02vxy_ (0.03 #124, 0.02 #349, 0.01 #439) >> Best rule #51 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 01lqf49; >> query: (?x2698, 0c8wxp) <- award(?x2698, ?x1079), artists(?x2936, ?x2698), ?x2936 = 029h7y >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #1126 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 352 *> proper extension: 0b4rf3; *> query: (?x2698, ?x7422) <- award_nominee(?x2698, ?x2747), artist(?x3265, ?x2747), religion(?x2747, ?x7422) *> conf = 0.08 ranks of expected_values: 2 EVAL 09hnb religion 092bf5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 124.000 0.133 http://example.org/people/person/religion #16464-01mk6 PRED entity: 01mk6 PRED relation: medal PRED expected values: 02lq67 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.86 #22, 0.85 #19, 0.83 #4) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 01z215; 05b7q; >> query: (?x7430, 02lq67) <- combatants(?x172, ?x7430), olympics(?x7430, ?x391), country(?x453, ?x7430), country(?x150, ?x172) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mk6 medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.857 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #16463-080z7 PRED entity: 080z7 PRED relation: institution! PRED expected values: 014mlp => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 23): 014mlp (0.78 #166, 0.75 #1966, 0.69 #491), 03bwzr4 (0.66 #175, 0.47 #222, 0.47 #664), 02_xgp2 (0.63 #173, 0.49 #267, 0.48 #662), 016t_3 (0.51 #164, 0.44 #397, 0.42 #489), 0bkj86 (0.51 #31, 0.51 #402, 0.50 #169), 07s6fsf (0.47 #162, 0.36 #209, 0.33 #1046), 04zx3q1 (0.37 #163, 0.34 #25, 0.32 #210), 013zdg (0.30 #215, 0.29 #30, 0.29 #53), 027f2w (0.27 #32, 0.26 #170, 0.24 #217), 01rr_d (0.23 #295, 0.22 #272, 0.20 #319) >> Best rule #166 for best value: >> intensional similarity = 5 >> extensional distance = 66 >> proper extension: 02y9bj; 0g8fs; >> query: (?x5778, 014mlp) <- contains(?x11743, ?x5778), major_field_of_study(?x5778, ?x1668), student(?x5778, ?x1294), category(?x11743, ?x134), ?x1668 = 01mkq >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 080z7 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.779 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #16462-073tm9 PRED entity: 073tm9 PRED relation: child! PRED expected values: 04fc6c => 195 concepts (158 used for prediction) PRED predicted values (max 10 best out of 228): 086k8 (0.36 #1069, 0.24 #1809, 0.20 #166), 061dn_ (0.25 #107, 0.10 #435, 0.07 #1504), 0jz9f (0.25 #83, 0.10 #411, 0.07 #1480), 0l8sx (0.23 #916, 0.21 #1080, 0.20 #7027), 02bh8z (0.23 #4478, 0.14 #1093, 0.09 #682), 09b3v (0.22 #4892, 0.21 #3072, 0.18 #684), 0fnmz (0.22 #4142, 0.20 #4886, 0.16 #5214), 049ql1 (0.20 #2205, 0.20 #2122, 0.16 #3195), 03d6fyn (0.15 #2084, 0.12 #3157, 0.09 #604), 02_l39 (0.14 #3605, 0.12 #7076, 0.11 #4926) >> Best rule #1069 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 04mkft; >> query: (?x6230, 086k8) <- state_province_region(?x6230, ?x335), child(?x7793, ?x6230), artist(?x7793, ?x7536), award_nominee(?x7536, ?x5297) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #2137 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 01scmq; *> query: (?x6230, ?x4483) <- industry(?x6230, ?x245), child(?x7793, ?x6230), ?x245 = 01mw1, child(?x7793, ?x4483) *> conf = 0.07 ranks of expected_values: 40 EVAL 073tm9 child! 04fc6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 195.000 158.000 0.357 http://example.org/organization/organization/child./organization/organization_relationship/child #16461-091z_p PRED entity: 091z_p PRED relation: film_crew_role PRED expected values: 0dxtw => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 35): 0ch6mp2 (0.78 #2069, 0.75 #1924, 0.74 #907), 09zzb8 (0.76 #901, 0.75 #2063, 0.72 #937), 0dxtw (0.40 #47, 0.39 #119, 0.38 #2073), 01pvkk (0.40 #48, 0.37 #192, 0.30 #912), 02ynfr (0.19 #2078, 0.18 #1933, 0.17 #124), 02rh1dz (0.18 #226, 0.17 #262, 0.15 #1527), 0215hd (0.15 #1936, 0.15 #343, 0.14 #2081), 0d2b38 (0.15 #242, 0.14 #350, 0.13 #278), 015h31 (0.15 #225, 0.13 #261, 0.12 #1526), 01xy5l_ (0.12 #338, 0.12 #1931, 0.11 #122) >> Best rule #2069 for best value: >> intensional similarity = 5 >> extensional distance = 622 >> proper extension: 02vqhv0; 06v9_x; 0gfh84d; 034qbx; 056xkh; 047p798; 028kj0; >> query: (?x1786, 0ch6mp2) <- film_release_distribution_medium(?x1786, ?x81), genre(?x1786, ?x53), film_crew_role(?x1786, ?x468), ?x81 = 029j_, ?x468 = 02r96rf >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 07l50vn; 06823p; *> query: (?x1786, 0dxtw) <- film_release_distribution_medium(?x1786, ?x81), award(?x1786, ?x143), nominated_for(?x12686, ?x1786), ?x12686 = 0fm3h2 *> conf = 0.40 ranks of expected_values: 3 EVAL 091z_p film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 119.000 0.777 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16460-01pj_5 PRED entity: 01pj_5 PRED relation: film! PRED expected values: 032j_n => 95 concepts (63 used for prediction) PRED predicted values (max 10 best out of 58): 03xq0f (0.88 #597, 0.88 #449, 0.83 #227), 032j_n (0.29 #205, 0.25 #57, 0.14 #131), 05qd_ (0.29 #231, 0.18 #379, 0.18 #453), 01795t (0.29 #92, 0.08 #462, 0.07 #2691), 020h2v (0.29 #118, 0.06 #1006, 0.05 #1303), 086k8 (0.22 #446, 0.18 #594, 0.18 #742), 016tt2 (0.20 #226, 0.18 #374, 0.17 #4), 016tw3 (0.18 #307, 0.17 #1047, 0.17 #11), 017s11 (0.17 #1262, 0.16 #1188, 0.16 #1411), 0jz9f (0.17 #1, 0.10 #1037, 0.09 #1558) >> Best rule #597 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 0522wp; >> query: (?x4500, 03xq0f) <- region(?x4500, ?x512), film(?x5636, ?x4500), ?x512 = 07ssc >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #205 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 01g3gq; 06bc59; 09qljs; *> query: (?x4500, 032j_n) <- language(?x4500, ?x254), genre(?x4500, ?x6625), currency(?x4500, ?x170), ?x6625 = 01585b *> conf = 0.29 ranks of expected_values: 2 EVAL 01pj_5 film! 032j_n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 63.000 0.881 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16459-0738b8 PRED entity: 0738b8 PRED relation: award_nominee! PRED expected values: 03q3sy => 111 concepts (60 used for prediction) PRED predicted values (max 10 best out of 949): 03q43g (0.82 #9324, 0.81 #128199, 0.81 #128198), 03q3sy (0.82 #9324, 0.81 #128199, 0.81 #128198), 05ty4m (0.76 #139855, 0.76 #88578, 0.76 #137523), 0mdqp (0.76 #137523, 0.75 #25640, 0.74 #137522), 011zd3 (0.15 #128200, 0.05 #486, 0.02 #58767), 02g5h5 (0.15 #128200, 0.04 #864, 0.02 #73128), 016z2j (0.15 #128200, 0.04 #503, 0.02 #72767), 023s8 (0.15 #128200, 0.04 #2146), 0738b8 (0.15 #128200, 0.04 #518, 0.01 #5179), 05sdxx (0.15 #128200, 0.04 #2024) >> Best rule #9324 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 03g5jw; 01w5n51; >> query: (?x2437, ?x436) <- award(?x2437, ?x678), influenced_by(?x2437, ?x9024), award_nominee(?x2437, ?x436) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0738b8 award_nominee! 03q3sy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 60.000 0.819 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16458-01cssf PRED entity: 01cssf PRED relation: nominated_for! PRED expected values: 05zr6wv => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 209): 02qvyrt (0.68 #8292, 0.68 #6908, 0.67 #7830), 0gq9h (0.38 #58, 0.32 #4660, 0.30 #2818), 0gs9p (0.38 #60, 0.28 #4662, 0.26 #6736), 0gs96 (0.31 #85, 0.15 #4687, 0.15 #3306), 019f4v (0.26 #4653, 0.25 #3272, 0.25 #4884), 040njc (0.24 #6676, 0.23 #7, 0.21 #4609), 0f4x7 (0.24 #6676, 0.23 #23, 0.20 #4625), 04kxsb (0.24 #6676, 0.19 #15429, 0.15 #4693), 054krc (0.24 #6676, 0.19 #15429, 0.14 #3976), 057xs89 (0.24 #6676, 0.19 #15429, 0.10 #1034) >> Best rule #8292 for best value: >> intensional similarity = 3 >> extensional distance = 986 >> proper extension: 06w7mlh; >> query: (?x638, ?x1198) <- award(?x638, ?x1198), nominated_for(?x1198, ?x89), award(?x276, ?x1198) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #6676 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 860 *> proper extension: 01j95; *> query: (?x638, ?x401) <- award_winner(?x638, ?x1890), titles(?x53, ?x638), award(?x1890, ?x401) *> conf = 0.24 ranks of expected_values: 17 EVAL 01cssf nominated_for! 05zr6wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 75.000 75.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16457-02kxx1 PRED entity: 02kxx1 PRED relation: student PRED expected values: 01qx13 => 167 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1416): 071xj (0.33 #3810, 0.11 #5899, 0.09 #7989), 087z12 (0.33 #5479, 0.09 #7569, 0.01 #13838), 0kh6b (0.33 #616, 0.04 #11064, 0.03 #17332), 015pxr (0.33 #326, 0.03 #10774, 0.02 #12863), 04xfb (0.33 #1455, 0.01 #11903, 0.01 #13992), 037jz (0.33 #1182, 0.01 #11630, 0.01 #13719), 03_hd (0.33 #774, 0.01 #11222, 0.01 #13311), 024dgj (0.33 #554, 0.01 #11002, 0.01 #13091), 0184dt (0.33 #388, 0.01 #10836, 0.01 #12925), 04kj2v (0.33 #384, 0.01 #10832, 0.01 #12921) >> Best rule #3810 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 031vy_; >> query: (?x11870, 071xj) <- contains(?x2146, ?x11870), student(?x11870, ?x11871), ?x11871 = 0djc3s, category(?x11870, ?x134), ?x134 = 08mbj5d >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6268 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 03w1lf; *> query: (?x11870, ?x4055) <- contains(?x12420, ?x11870), student(?x11870, ?x12200), award(?x12200, ?x1937), location(?x4055, ?x12420), ?x1937 = 03r8tl *> conf = 0.04 ranks of expected_values: 57 EVAL 02kxx1 student 01qx13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 167.000 63.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #16456-018ctl PRED entity: 018ctl PRED relation: olympics! PRED expected values: 09_94 => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 44): 01sgl (0.71 #895, 0.70 #803, 0.68 #850), 0bynt (0.65 #136, 0.60 #595, 0.58 #181), 03krj (0.65 #136, 0.58 #181, 0.57 #400), 096f8 (0.65 #136, 0.58 #181, 0.57 #366), 018w8 (0.65 #136, 0.58 #181, 0.57 #389), 02y74 (0.65 #136, 0.58 #181, 0.57 #398), 02bkg (0.65 #136, 0.58 #181, 0.57 #362), 06f41 (0.65 #136, 0.58 #181, 0.57 #371), 03hr1p (0.65 #136, 0.58 #181, 0.54 #272), 01cgz (0.65 #136, 0.58 #181, 0.54 #272) >> Best rule #895 for best value: >> intensional similarity = 53 >> extensional distance = 29 >> proper extension: 0lbd9; >> query: (?x784, 01sgl) <- olympics(?x453, ?x784), medal(?x784, ?x422), olympics(?x3855, ?x784), olympics(?x1790, ?x784), olympics(?x1355, ?x784), olympics(?x1264, ?x784), contains(?x1264, ?x196), country(?x2323, ?x1264), film_release_region(?x9902, ?x1264), film_release_region(?x8955, ?x1264), film_release_region(?x7246, ?x1264), film_release_region(?x5400, ?x1264), film_release_region(?x4615, ?x1264), film_release_region(?x3565, ?x1264), film_release_region(?x2441, ?x1264), film_release_region(?x2318, ?x1264), film_release_region(?x1315, ?x1264), film_release_region(?x1150, ?x1264), film_release_region(?x633, ?x1264), ?x9902 = 0j8f09z, olympics(?x1264, ?x778), film_release_region(?x430, ?x1355), participating_countries(?x784, ?x126), ?x2441 = 0cc5mcj, ?x1150 = 0h3xztt, ?x3565 = 0cp0ph6, currency(?x3855, ?x170), contains(?x1355, ?x863), ?x4615 = 0dlngsd, organization(?x1790, ?x127), administrative_parent(?x1264, ?x551), time_zones(?x1790, ?x2864), country(?x150, ?x1355), nationality(?x380, ?x1264), ?x633 = 0c40vxk, nationality(?x6406, ?x3855), ?x8955 = 0g4pl7z, service_location(?x555, ?x1264), film_release_distribution_medium(?x2323, ?x81), olympics(?x1355, ?x358), produced_by(?x1315, ?x1039), nationality(?x11500, ?x1355), contains(?x455, ?x1790), ?x551 = 02j71, member_states(?x2106, ?x1264), ?x5400 = 0bhwhj, genre(?x2323, ?x53), ?x11500 = 0cpvcd, film_sets_designed(?x8844, ?x7246), ?x778 = 0kbvb, film_release_region(?x5873, ?x3855), film_crew_role(?x2323, ?x137), ?x2318 = 06v9_x >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #181 for first EXPECTED value: *> intensional similarity = 60 *> extensional distance = 1 *> proper extension: 0kbws; *> query: (?x784, ?x1121) <- olympics(?x453, ?x784), medal(?x784, ?x422), olympics(?x5482, ?x784), olympics(?x3855, ?x784), olympics(?x3227, ?x784), olympics(?x3040, ?x784), olympics(?x2236, ?x784), olympics(?x792, ?x784), ?x3855 = 0jgx, participating_countries(?x784, ?x6572), participating_countries(?x784, ?x1499), ?x5482 = 04g5k, ?x6572 = 03548, ?x422 = 02lq67, film_release_region(?x10535, ?x1499), film_release_region(?x9501, ?x1499), film_release_region(?x8955, ?x1499), film_release_region(?x7832, ?x1499), film_release_region(?x7651, ?x1499), film_release_region(?x7629, ?x1499), film_release_region(?x7126, ?x1499), film_release_region(?x6321, ?x1499), film_release_region(?x6095, ?x1499), film_release_region(?x2714, ?x1499), film_release_region(?x2441, ?x1499), film_release_region(?x2394, ?x1499), film_release_region(?x1927, ?x1499), film_release_region(?x1707, ?x1499), film_release_region(?x1701, ?x1499), film_release_region(?x303, ?x1499), ?x6321 = 0gg8z1f, ?x1707 = 04n52p6, countries_within(?x455, ?x3040), participating_countries(?x418, ?x3040), partially_contains(?x6956, ?x1499), ?x2394 = 0661ql3, ?x7126 = 0ds1glg, ?x8955 = 0g4pl7z, ?x7651 = 0h95927, ?x1701 = 0bh8yn3, film(?x3273, ?x1927), locations(?x11802, ?x792), ?x10535 = 09v42sf, ?x2441 = 0cc5mcj, olympics(?x792, ?x778), ?x6095 = 0bq6ntw, member_states(?x7695, ?x3227), adjoins(?x3432, ?x792), award(?x303, ?x77), olympics(?x3040, ?x5176), nominated_for(?x68, ?x303), ?x7832 = 0fphf3v, category(?x7629, ?x134), adjustment_currency(?x3227, ?x170), ?x2714 = 0kv238, ?x9501 = 0g5qmbz, country(?x4045, ?x2236), country(?x1121, ?x2236), ?x77 = 0gqng, ?x4045 = 06z6r *> conf = 0.58 ranks of expected_values: 42 EVAL 018ctl olympics! 09_94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 24.000 24.000 0.710 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #16455-01g_k3 PRED entity: 01g_k3 PRED relation: category PRED expected values: 08mbj5d => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.62 #47, 0.61 #21, 0.60 #37) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 1283 >> proper extension: 02j416; >> query: (?x13351, 08mbj5d) <- contains(?x455, ?x13351), contains(?x455, ?x5482), locations(?x1777, ?x455), organization(?x5482, ?x127) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01g_k3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.616 http://example.org/common/topic/webpage./common/webpage/category #16454-0325dj PRED entity: 0325dj PRED relation: school! PRED expected values: 05vsb7 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 20): 02qw1zx (0.21 #105, 0.17 #205, 0.16 #145), 0f4vx0 (0.21 #211, 0.18 #111, 0.14 #151), 03nt7j (0.19 #107, 0.16 #147, 0.14 #187), 025tn92 (0.17 #13, 0.15 #213, 0.12 #113), 038981 (0.17 #16, 0.07 #216, 0.04 #236), 05vsb7 (0.16 #201, 0.15 #101, 0.12 #181), 092j54 (0.15 #109, 0.15 #209, 0.14 #149), 02pq_x5 (0.14 #157, 0.12 #217, 0.12 #117), 09l0x9 (0.14 #212, 0.12 #112, 0.10 #152), 09th87 (0.12 #115, 0.11 #155, 0.11 #215) >> Best rule #105 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 02zc7f; 03wv2g; >> query: (?x11387, 02qw1zx) <- fraternities_and_sororities(?x11387, ?x3697), currency(?x11387, ?x170), contains(?x94, ?x11387), school(?x387, ?x11387) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #201 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 119 *> proper extension: 0fht9f; 0frm7n; *> query: (?x11387, 05vsb7) <- school(?x387, ?x11387), teams(?x13949, ?x387), team(?x5412, ?x387) *> conf = 0.16 ranks of expected_values: 6 EVAL 0325dj school! 05vsb7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 96.000 0.209 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #16453-02t_99 PRED entity: 02t_99 PRED relation: religion PRED expected values: 0c8wxp => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 22): 0c8wxp (0.41 #366, 0.40 #636, 0.33 #6), 03_gx (0.21 #1500, 0.13 #464, 0.12 #194), 03j6c (0.10 #426, 0.09 #471, 0.09 #201), 0kpl (0.09 #190, 0.09 #325, 0.08 #415), 06nzl (0.07 #105, 0.06 #375, 0.06 #240), 0kq2 (0.07 #108, 0.06 #243, 0.05 #288), 0flw86 (0.06 #182, 0.04 #317, 0.04 #407), 01lp8 (0.06 #226, 0.05 #271, 0.03 #946), 04pk9 (0.05 #65, 0.02 #335, 0.02 #920), 019cr (0.04 #371, 0.03 #191, 0.03 #641) >> Best rule #366 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 01438g; >> query: (?x4638, 0c8wxp) <- location(?x4638, ?x1523), award(?x4638, ?x2325), ?x2325 = 05p09zm, participant(?x4638, ?x2534) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02t_99 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.408 http://example.org/people/person/religion #16452-0pkyh PRED entity: 0pkyh PRED relation: award PRED expected values: 03tcnt => 119 concepts (106 used for prediction) PRED predicted values (max 10 best out of 299): 02ddq4 (0.78 #38334, 0.77 #8782, 0.77 #16763), 01by1l (0.45 #112, 0.34 #12485, 0.33 #9293), 054ks3 (0.36 #140, 0.29 #539, 0.21 #2535), 09sb52 (0.35 #7625, 0.31 #14808, 0.30 #17203), 02x17c2 (0.27 #216, 0.21 #615, 0.18 #6786), 03qbnj (0.27 #230, 0.20 #3423, 0.19 #2625), 02f6xy (0.23 #3391, 0.21 #597, 0.21 #2593), 0c4z8 (0.22 #8453, 0.22 #16434, 0.21 #1667), 02wh75 (0.21 #408, 0.18 #9, 0.12 #2404), 05pcn59 (0.20 #14848, 0.20 #15247, 0.20 #17243) >> Best rule #38334 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 0dky9n; 01nrq5; 034bs; 06n9lt; 0627sn; 01t265; 051cc; 0bkmf; 03bdm4; 0f1jhc; ... >> query: (?x2930, ?x10316) <- award_winner(?x10316, ?x2930), award(?x1089, ?x10316), ceremony(?x10316, ?x139) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #564 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 02zrv7; *> query: (?x2930, 03tcnt) <- performance_role(?x2930, ?x1466), participant(?x2930, ?x6208), nationality(?x2930, ?x512) *> conf = 0.14 ranks of expected_values: 48 EVAL 0pkyh award 03tcnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 119.000 106.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16451-03sbs PRED entity: 03sbs PRED relation: influenced_by! PRED expected values: 099bk 0ct9_ => 102 concepts (50 used for prediction) PRED predicted values (max 10 best out of 476): 03f47xl (0.42 #742, 0.33 #252, 0.14 #4404), 0683n (0.33 #816, 0.33 #326, 0.17 #7170), 034bs (0.33 #638, 0.33 #148, 0.17 #1128), 014ps4 (0.33 #299, 0.33 #7143, 0.25 #789), 0dzkq (0.33 #120, 0.29 #4035, 0.28 #5012), 01hb6v (0.33 #89, 0.25 #1069, 0.25 #579), 043tg (0.33 #316, 0.25 #806, 0.15 #4231), 07lp1 (0.33 #399, 0.25 #889, 0.14 #4404), 013pp3 (0.33 #213, 0.25 #703, 0.14 #4404), 0n6kf (0.33 #183, 0.25 #673, 0.14 #4404) >> Best rule #742 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 040db; 0379s; 032l1; 03hnd; 099bk; 06whf; 0bk5r; 058vp; 015n8; >> query: (?x7250, 03f47xl) <- influenced_by(?x9982, ?x7250), ?x9982 = 05qzv, influenced_by(?x7250, ?x712), gender(?x712, ?x231) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #819 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 040db; 0379s; 032l1; 03hnd; 099bk; 06whf; 0bk5r; 058vp; 015n8; *> query: (?x7250, 0ct9_) <- influenced_by(?x9982, ?x7250), ?x9982 = 05qzv, influenced_by(?x7250, ?x712), gender(?x712, ?x231) *> conf = 0.25 ranks of expected_values: 35, 39 EVAL 03sbs influenced_by! 0ct9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 102.000 50.000 0.417 http://example.org/influence/influence_node/influenced_by EVAL 03sbs influenced_by! 099bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 102.000 50.000 0.417 http://example.org/influence/influence_node/influenced_by #16450-02whj PRED entity: 02whj PRED relation: artists! PRED expected values: 06by7 08jyyk 0k345 => 165 concepts (108 used for prediction) PRED predicted values (max 10 best out of 243): 06by7 (0.75 #20868, 0.62 #30381, 0.60 #4002), 064t9 (0.70 #23315, 0.56 #26385, 0.48 #28841), 017_qw (0.56 #980, 0.54 #1593, 0.30 #3738), 08jyyk (0.47 #679, 0.46 #11647, 0.45 #12874), 0ggq0m (0.46 #11647, 0.45 #12874, 0.43 #14103), 02yv6b (0.46 #11647, 0.45 #12874, 0.43 #14103), 0xhtw (0.46 #11647, 0.45 #12874, 0.43 #14103), 016clz (0.46 #11647, 0.45 #12874, 0.43 #14103), 05w3f (0.46 #11647, 0.45 #12874, 0.43 #14103), 0k345 (0.46 #11647, 0.45 #12874, 0.43 #14103) >> Best rule #20868 for best value: >> intensional similarity = 4 >> extensional distance = 347 >> proper extension: 01pr_j6; 0lk90; 01wz3cx; 05qw5; 09k2t1; 01x1cn2; 033wx9; 01l_vgt; 0p3r8; 01vxlbm; ... >> query: (?x1092, 06by7) <- gender(?x1092, ?x231), artists(?x7440, ?x1092), artists(?x7440, ?x2269), ?x2269 = 02jg92 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 10 EVAL 02whj artists! 0k345 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 165.000 108.000 0.748 http://example.org/music/genre/artists EVAL 02whj artists! 08jyyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 165.000 108.000 0.748 http://example.org/music/genre/artists EVAL 02whj artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 108.000 0.748 http://example.org/music/genre/artists #16449-0mwh1 PRED entity: 0mwh1 PRED relation: time_zones PRED expected values: 02hcv8 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.92 #253, 0.86 #556, 0.82 #740), 02fqwt (0.33 #68, 0.30 #108, 0.26 #148), 02lcqs (0.22 #126, 0.21 #192, 0.21 #574), 02hczc (0.20 #2, 0.17 #109, 0.14 #149), 042g7t (0.20 #11, 0.03 #118, 0.02 #78), 02lcrv (0.20 #7, 0.02 #74, 0.02 #246), 02llzg (0.07 #956, 0.06 #943, 0.06 #969), 03bdv (0.04 #998, 0.04 #1050, 0.03 #431), 03plfd (0.03 #949, 0.03 #962, 0.03 #975), 0gsrz4 (0.02 #1091, 0.02 #1105, 0.02 #1132) >> Best rule #253 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 0fw4v; >> query: (?x2744, ?x2674) <- county_seat(?x2744, ?x1494), source(?x2744, ?x958), time_zones(?x1494, ?x2674), ?x958 = 0jbk9 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwh1 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.916 http://example.org/location/location/time_zones #16448-0hd7j PRED entity: 0hd7j PRED relation: institution! PRED expected values: 019v9k => 149 concepts (85 used for prediction) PRED predicted values (max 10 best out of 17): 019v9k (0.75 #301, 0.70 #229, 0.67 #172), 0bkj86 (0.73 #40, 0.51 #319, 0.50 #340), 07s6fsf (0.66 #204, 0.64 #37, 0.52 #113), 02_xgp2 (0.64 #45, 0.62 #158, 0.58 #305), 027f2w (0.45 #42, 0.33 #137, 0.32 #302), 03mkk4 (0.35 #449, 0.33 #120, 0.31 #94), 022h5x (0.35 #449, 0.31 #94, 0.27 #51), 0bjrnt (0.35 #449, 0.31 #94, 0.20 #339), 02m4yg (0.35 #449, 0.31 #94, 0.20 #1347), 071tyz (0.35 #449, 0.31 #94, 0.20 #1347) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 01f1r4; 02bqy; 01hr11; 0ym17; >> query: (?x4603, 019v9k) <- currency(?x4603, ?x170), institution(?x734, ?x4603), major_field_of_study(?x4603, ?x1154), ?x1154 = 02lp1 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hd7j institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 85.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #16447-05fyss PRED entity: 05fyss PRED relation: student! PRED expected values: 022fj_ => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 180): 01jq34 (0.50 #57, 0.02 #584, 0.01 #6386), 03ksy (0.13 #3164, 0.07 #4220, 0.07 #2742), 065y4w7 (0.09 #1596, 0.07 #541, 0.06 #2123), 0bwfn (0.08 #4495, 0.08 #5022, 0.06 #3439), 01w5m (0.07 #2741, 0.06 #1687, 0.05 #10652), 07tg4 (0.07 #2722, 0.03 #4833, 0.03 #5360), 09f2j (0.06 #3851, 0.04 #3323, 0.04 #2268), 08815 (0.06 #3694, 0.04 #5276, 0.03 #5804), 015zyd (0.05 #2110, 0.03 #1583, 0.02 #4221), 07tk7 (0.05 #3078, 0.02 #5716) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 05gp3x; 02vqpx8; >> query: (?x6071, 01jq34) <- award_nominee(?x7044, ?x6071), profession(?x6071, ?x353), ?x7044 = 0crqcc >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05fyss student! 022fj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 123.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/student #16446-0bmhvpr PRED entity: 0bmhvpr PRED relation: nominated_for! PRED expected values: 0gg5qcw => 83 concepts (33 used for prediction) PRED predicted values (max 10 best out of 44): 03qnc6q (0.03 #588, 0.02 #2114, 0.02 #1096), 03cp4cn (0.03 #687, 0.02 #1195), 0h1x5f (0.03 #747, 0.01 #4308), 08nvyr (0.03 #640, 0.01 #4201), 0g9lm2 (0.03 #636, 0.01 #4197), 06fpsx (0.03 #722), 078sj4 (0.03 #593), 05k2xy (0.03 #580), 04jkpgv (0.03 #552), 09wnnb (0.02 #1003, 0.02 #1512, 0.02 #1767) >> Best rule #588 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 09tqkv2; 03hmt9b; >> query: (?x3784, 03qnc6q) <- film(?x286, ?x3784), nominated_for(?x1307, ?x3784), nominated_for(?x277, ?x3784), ?x1307 = 0gq9h, ?x277 = 0f_nbyh >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bmhvpr nominated_for! 0gg5qcw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 33.000 0.028 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #16445-013yq PRED entity: 013yq PRED relation: teams PRED expected values: 0jm64 => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 242): 0jmk7 (0.20 #301, 0.07 #1369, 0.07 #1013), 0jnq8 (0.20 #227, 0.07 #1295, 0.07 #939), 0jmjr (0.20 #220, 0.07 #1288, 0.07 #932), 04mjl (0.20 #154, 0.07 #1222, 0.07 #866), 02pqcfz (0.20 #81, 0.07 #1149, 0.07 #793), 04112r (0.20 #50, 0.07 #1118, 0.07 #762), 07k53y (0.20 #12, 0.07 #1080, 0.07 #724), 027yf83 (0.11 #447, 0.07 #1159, 0.07 #803), 0jmm4 (0.11 #549, 0.07 #905, 0.06 #1973), 01y49 (0.11 #393, 0.02 #6801, 0.02 #8225) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0rvty; >> query: (?x2277, 0jmk7) <- contains(?x94, ?x2277), featured_film_locations(?x2362, ?x2277), ?x2362 = 05p1qyh >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 013yq teams 0jm64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 164.000 164.000 0.200 http://example.org/sports/sports_team_location/teams #16444-01wz01 PRED entity: 01wz01 PRED relation: film PRED expected values: 08ct6 => 82 concepts (58 used for prediction) PRED predicted values (max 10 best out of 559): 06cm5 (0.20 #1071, 0.06 #51915, 0.06 #73405), 01xbxn (0.20 #1394, 0.03 #50124, 0.03 #53706), 02vjp3 (0.20 #1301, 0.03 #50124, 0.03 #53706), 04vr_f (0.10 #171, 0.06 #51915, 0.06 #73405), 011ypx (0.10 #1023, 0.06 #51915, 0.06 #73405), 01b195 (0.10 #361, 0.06 #51915, 0.06 #73405), 0h6r5 (0.10 #680, 0.06 #51915, 0.06 #73405), 03k8th (0.10 #1721, 0.06 #51915, 0.06 #73405), 05sns6 (0.10 #710, 0.06 #51915, 0.06 #73405), 01qbg5 (0.10 #1280, 0.06 #51915, 0.06 #73405) >> Best rule #1071 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0f4vbz; >> query: (?x4173, 06cm5) <- award_winner(?x851, ?x4173), ?x851 = 016khd, film(?x4173, ?x4174) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wz01 film 08ct6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 58.000 0.200 http://example.org/film/actor/film./film/performance/film #16443-02rtlp5 PRED entity: 02rtlp5 PRED relation: contains PRED expected values: 0n4mk => 96 concepts (27 used for prediction) PRED predicted values (max 10 best out of 21): 013hxv (0.04 #805, 0.02 #5893), 0ydpd (0.04 #54, 0.02 #5893), 0jkhr (0.04 #917, 0.01 #58959), 01jq4b (0.04 #767, 0.01 #58959), 0bx8pn (0.04 #226, 0.01 #58959), 0kcw2 (0.04 #2475), 013hvr (0.04 #2225), 0fvyg (0.04 #1813), 0fsb8 (0.04 #1304), 02ldmw (0.04 #1077) >> Best rule #805 for best value: >> intensional similarity = 2 >> extensional distance = 23 >> proper extension: 0n4m5; 0bx8pn; 0269kx; 0ygbf; 01jq4b; 0n4mk; 0jkhr; 02ldmw; 0jfqp; 0fsb8; ... >> query: (?x10532, 013hxv) <- contains(?x760, ?x10532), ?x760 = 05fkf >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02rtlp5 contains 0n4mk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 27.000 0.040 http://example.org/location/location/contains #16442-01dw_f PRED entity: 01dw_f PRED relation: artists! PRED expected values: 06rqw => 111 concepts (51 used for prediction) PRED predicted values (max 10 best out of 225): 025sc50 (0.61 #1555, 0.31 #45, 0.27 #2462), 02lnbg (0.52 #1564, 0.24 #54, 0.18 #3076), 03lty (0.47 #932, 0.35 #328, 0.17 #3350), 0gywn (0.41 #53, 0.29 #1563, 0.23 #3075), 06j6l (0.41 #1554, 0.38 #44, 0.30 #2461), 02x8m (0.34 #17, 0.11 #1829, 0.10 #2736), 02yv6b (0.33 #998, 0.30 #394, 0.15 #3416), 016clz (0.30 #3328, 0.23 #1816, 0.23 #8168), 05w3f (0.29 #941, 0.22 #337, 0.16 #3359), 02vjzr (0.28 #127, 0.16 #1637, 0.11 #3149) >> Best rule #1555 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 01l_vgt; 01v27pl; >> query: (?x7570, 025sc50) <- artists(?x5876, ?x7570), category(?x7570, ?x134), ?x5876 = 0ggx5q >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #379 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 04rcr; 05crg7; 07bzp; 06mj4; 03vhvp; 0c9l1; *> query: (?x7570, 06rqw) <- award_nominee(?x7088, ?x7570), artists(?x1000, ?x7570), ?x1000 = 0xhtw *> conf = 0.03 ranks of expected_values: 120 EVAL 01dw_f artists! 06rqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 111.000 51.000 0.614 http://example.org/music/genre/artists #16441-014knw PRED entity: 014knw PRED relation: films! PRED expected values: 081pw => 100 concepts (33 used for prediction) PRED predicted values (max 10 best out of 64): 081pw (0.09 #1409, 0.08 #2349, 0.08 #2507), 0fx2s (0.08 #1478, 0.07 #2576, 0.07 #2418), 03hzt (0.07 #134, 0.02 #1540, 0.02 #2638), 05489 (0.07 #1769, 0.06 #1145, 0.05 #2397), 03r8gp (0.06 #1495, 0.04 #89, 0.03 #2435), 0fzyg (0.06 #1771, 0.04 #3497, 0.04 #2399), 06d4h (0.06 #3486, 0.06 #1760, 0.06 #2388), 07c52 (0.06 #1738, 0.04 #2366, 0.04 #2524), 07s2s (0.05 #3542, 0.05 #1816, 0.03 #1504), 0bq3x (0.04 #3474, 0.04 #30, 0.03 #4414) >> Best rule #1409 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 0416y94; 02z0f6l; >> query: (?x9345, 081pw) <- award(?x9345, ?x591), films(?x5011, ?x9345), written_by(?x9345, ?x9320), genre(?x9345, ?x53) >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014knw films! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 33.000 0.087 http://example.org/film/film_subject/films #16440-0ldd PRED entity: 0ldd PRED relation: type_of_union PRED expected values: 04ztj => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.81 #113, 0.79 #97, 0.78 #105), 01g63y (0.29 #14, 0.19 #190, 0.17 #182), 01bl8s (0.06 #47, 0.03 #119, 0.03 #127), 0jgjn (0.01 #276, 0.01 #244) >> Best rule #113 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 0jvtp; 01bh6y; >> query: (?x12888, 04ztj) <- languages(?x12888, ?x254), people(?x9771, ?x12888), nationality(?x12888, ?x512), gender(?x12888, ?x514) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ldd type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.806 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16439-0fn5bx PRED entity: 0fn5bx PRED relation: languages PRED expected values: 02h40lc => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 15): 02h40lc (0.48 #80, 0.45 #394, 0.44 #355), 06nm1 (0.06 #45, 0.05 #84, 0.04 #123), 01r2l (0.06 #56, 0.04 #134), 064_8sq (0.05 #93, 0.02 #724, 0.02 #2206), 03_9r (0.05 #83, 0.02 #200, 0.01 #595), 03k50 (0.03 #515, 0.03 #554, 0.02 #3170), 0999q (0.02 #534, 0.02 #573, 0.01 #455), 07c9s (0.02 #524, 0.02 #563), 02bjrlw (0.02 #236, 0.02 #196, 0.02 #393), 03qsdpk (0.02 #275, 0.02 #432, 0.01 #235) >> Best rule #80 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01x1cn2; 01_rh4; 0432cd; 05j0wc; 08p1gp; >> query: (?x5200, 02h40lc) <- profession(?x5200, ?x1032), student(?x5614, ?x5200), ?x5614 = 03qsdpk >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fn5bx languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.476 http://example.org/people/person/languages #16438-02rky4 PRED entity: 02rky4 PRED relation: institution! PRED expected values: 013zdg => 215 concepts (215 used for prediction) PRED predicted values (max 10 best out of 22): 014mlp (0.82 #281, 0.79 #3616, 0.76 #1780), 016t_3 (0.64 #279, 0.43 #1778, 0.42 #1594), 03bwzr4 (0.59 #290, 0.52 #1720, 0.51 #1605), 07s6fsf (0.59 #277, 0.52 #346, 0.50 #70), 02_xgp2 (0.55 #288, 0.49 #2459, 0.44 #1787), 0bkj86 (0.50 #284, 0.40 #1783, 0.38 #192), 04zx3q1 (0.45 #278, 0.22 #1708, 0.22 #2519), 027f2w (0.36 #285, 0.20 #1600, 0.19 #1623), 022h5x (0.27 #296, 0.27 #181, 0.19 #1795), 028dcg (0.27 #295, 0.27 #180, 0.19 #1218) >> Best rule #281 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 03ksy; 08qnnv; >> query: (?x10368, 014mlp) <- institution(?x865, ?x10368), student(?x10368, ?x5617), nationality(?x5617, ?x94), service_language(?x10368, ?x254) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #283 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 03ksy; 08qnnv; *> query: (?x10368, 013zdg) <- institution(?x865, ?x10368), student(?x10368, ?x5617), nationality(?x5617, ?x94), service_language(?x10368, ?x254) *> conf = 0.27 ranks of expected_values: 11 EVAL 02rky4 institution! 013zdg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 215.000 215.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #16437-09v0p2c PRED entity: 09v0p2c PRED relation: honored_for PRED expected values: 0d68qy 02rzdcp 0404j37 => 28 concepts (24 used for prediction) PRED predicted values (max 10 best out of 657): 0d68qy (0.67 #4342, 0.67 #1794, 0.60 #6136), 039cq4 (0.67 #1794, 0.60 #2391, 0.50 #5387), 02rzdcp (0.67 #4388, 0.60 #2391, 0.50 #6182), 0kfv9 (0.60 #2391, 0.42 #4788, 0.40 #8375), 02hct1 (0.60 #2391, 0.40 #8375, 0.33 #8977), 01h72l (0.60 #2391, 0.40 #8375, 0.27 #4185), 01j7mr (0.57 #5602, 0.50 #2606, 0.50 #2009), 07zhjj (0.50 #2890, 0.50 #1695, 0.43 #5886), 06mr2s (0.50 #2676, 0.50 #1481, 0.43 #5672), 01b7h8 (0.50 #2927, 0.50 #1732, 0.43 #5923) >> Best rule #4342 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 02wzl1d; >> query: (?x5957, 0d68qy) <- award_winner(?x5957, ?x9500), award_winner(?x5957, ?x8229), award_winner(?x5957, ?x906), award_winner(?x5957, ?x829), award_nominee(?x9500, ?x6324), award_nominee(?x9500, ?x3082), ?x8229 = 0cp9f9, ?x906 = 0pz7h, program(?x829, ?x2528), ?x3082 = 02778qt, gender(?x829, ?x231), award_nominee(?x881, ?x829), award_winner(?x406, ?x6324), award_winner(?x3624, ?x6324), ?x3624 = 027hjff, award(?x6324, ?x102), student(?x3439, ?x829) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 153 EVAL 09v0p2c honored_for 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 28.000 24.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 09v0p2c honored_for 02rzdcp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 28.000 24.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 09v0p2c honored_for 0d68qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 24.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #16436-011yl_ PRED entity: 011yl_ PRED relation: genre PRED expected values: 02l7c8 => 94 concepts (89 used for prediction) PRED predicted values (max 10 best out of 88): 01jfsb (0.73 #1416, 0.37 #2118, 0.36 #3524), 0lsxr (0.40 #1412, 0.27 #242, 0.25 #8), 02kdv5l (0.39 #1406, 0.31 #2108, 0.30 #3514), 05p553 (0.37 #589, 0.34 #6564, 0.34 #6915), 060__y (0.35 #250, 0.29 #367, 0.25 #16), 02l7c8 (0.34 #600, 0.33 #2356, 0.33 #483), 04xvlr (0.32 #118, 0.31 #3866, 0.21 #235), 082gq (0.31 #29, 0.16 #731, 0.16 #1082), 03g3w (0.26 #140, 0.10 #3888, 0.08 #257), 03k9fj (0.22 #6571, 0.21 #7274, 0.21 #6805) >> Best rule #1416 for best value: >> intensional similarity = 4 >> extensional distance = 322 >> proper extension: 02pcq92; >> query: (?x3573, 01jfsb) <- featured_film_locations(?x3573, ?x362), genre(?x3573, ?x1316), genre(?x89, ?x1316), ?x89 = 015qsq >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #600 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 116 *> proper extension: 05c26ss; *> query: (?x3573, 02l7c8) <- featured_film_locations(?x3573, ?x362), nominated_for(?x4129, ?x3573), genre(?x3573, ?x53), production_companies(?x4009, ?x4129) *> conf = 0.34 ranks of expected_values: 6 EVAL 011yl_ genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 94.000 89.000 0.731 http://example.org/film/film/genre #16435-03vyw8 PRED entity: 03vyw8 PRED relation: titles! PRED expected values: 017fp => 94 concepts (44 used for prediction) PRED predicted values (max 10 best out of 52): 07ssc (0.38 #1326, 0.17 #8, 0.14 #109), 03bxz7 (0.33 #304, 0.32 #608, 0.21 #3954), 01z4y (0.19 #3684, 0.17 #2463, 0.17 #2970), 017fp (0.18 #529, 0.16 #225, 0.14 #1340), 01jfsb (0.17 #18, 0.14 #119, 0.13 #828), 09blyk (0.17 #45, 0.14 #146, 0.06 #349), 02l7c8 (0.17 #23, 0.14 #124, 0.05 #1341), 024qqx (0.13 #383, 0.12 #484, 0.11 #889), 03mqtr (0.11 #551, 0.09 #247, 0.08 #1362), 07c52 (0.10 #2763, 0.10 #3575, 0.10 #3168) >> Best rule #1326 for best value: >> intensional similarity = 3 >> extensional distance = 349 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x6058, 07ssc) <- titles(?x162, ?x6058), titles(?x162, ?x10049), ?x10049 = 01fx4k >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #529 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 183 *> proper extension: 072r5v; *> query: (?x6058, 017fp) <- genre(?x6058, ?x162), nominated_for(?x3195, ?x6058), ?x162 = 04xvlr, nominated_for(?x1716, ?x6058) *> conf = 0.18 ranks of expected_values: 4 EVAL 03vyw8 titles! 017fp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 44.000 0.385 http://example.org/media_common/netflix_genre/titles #16434-03mqj_ PRED entity: 03mqj_ PRED relation: colors PRED expected values: 038hg => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 17): 06fvc (0.53 #363, 0.46 #382, 0.45 #1278), 01g5v (0.39 #478, 0.39 #1298, 0.38 #744), 019sc (0.39 #1245, 0.33 #387, 0.33 #178), 038hg (0.24 #316, 0.23 #259, 0.20 #335), 06kqt3 (0.17 #16, 0.17 #1353, 0.15 #1414), 088fh (0.17 #1353, 0.15 #1414, 0.15 #1413), 01l849 (0.17 #1353, 0.15 #1414, 0.15 #1413), 0jc_p (0.17 #1353, 0.15 #1414, 0.15 #1413), 036k5h (0.17 #1353, 0.15 #1414, 0.15 #1413), 04d18d (0.17 #1353, 0.15 #1414, 0.15 #1413) >> Best rule #363 for best value: >> intensional similarity = 8 >> extensional distance = 53 >> proper extension: 01x4wq; >> query: (?x993, 06fvc) <- team(?x60, ?x993), teams(?x992, ?x993), ?x60 = 02nzb8, colors(?x993, ?x663), sport(?x993, ?x471), colors(?x7608, ?x663), colors(?x216, ?x663), ?x7608 = 01k9cc >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #316 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 43 *> proper extension: 0b256b; *> query: (?x993, 038hg) <- team(?x530, ?x993), team(?x63, ?x993), team(?x60, ?x993), ?x530 = 02_j1w, ?x60 = 02nzb8, colors(?x993, ?x663), position(?x993, ?x203), ?x663 = 083jv, ?x63 = 02sdk9v, ?x203 = 0dgrmp *> conf = 0.24 ranks of expected_values: 4 EVAL 03mqj_ colors 038hg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 96.000 0.527 http://example.org/sports/sports_team/colors #16433-01qqwp9 PRED entity: 01qqwp9 PRED relation: artists! PRED expected values: 06by7 03ckfl9 => 95 concepts (81 used for prediction) PRED predicted values (max 10 best out of 245): 08jyyk (0.84 #8574, 0.50 #5665, 0.48 #1571), 06by7 (0.81 #23330, 0.79 #5689, 0.76 #6948), 016clz (0.79 #19862, 0.78 #16394, 0.72 #20489), 0dl5d (0.72 #16723, 0.54 #18299, 0.53 #17668), 064t9 (0.58 #4105, 0.57 #12911, 0.56 #2220), 02yv6b (0.57 #1359, 0.48 #1571, 0.47 #11016), 05bt6j (0.57 #12911, 0.56 #7240, 0.56 #6971), 0155w (0.57 #12911, 0.50 #5665, 0.48 #1571), 0xhtw (0.57 #12911, 0.49 #23012, 0.49 #9776), 05w3f (0.50 #5665, 0.48 #1571, 0.48 #17687) >> Best rule #8574 for best value: >> intensional similarity = 11 >> extensional distance = 30 >> proper extension: 032t2z; 016wvy; >> query: (?x3207, 08jyyk) <- artists(?x10307, ?x3207), artists(?x9063, ?x3207), ?x9063 = 0cx7f, artists(?x10307, ?x7221), artists(?x10307, ?x3735), artists(?x10307, ?x3657), artists(?x10307, ?x1684), ?x3735 = 0lzkm, ?x7221 = 0191h5, ?x1684 = 01wv9xn, ?x3657 = 01w8n89 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #23330 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 391 *> proper extension: 02_t2t; *> query: (?x3207, 06by7) <- artists(?x9063, ?x3207), category(?x3207, ?x134), ?x134 = 08mbj5d, artists(?x9063, ?x11425), artists(?x9063, ?x7053), award_winner(?x3889, ?x7053), ?x11425 = 02vnpv, award(?x7053, ?x247) *> conf = 0.81 ranks of expected_values: 2, 13 EVAL 01qqwp9 artists! 03ckfl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 95.000 81.000 0.844 http://example.org/music/genre/artists EVAL 01qqwp9 artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 81.000 0.844 http://example.org/music/genre/artists #16432-016yzz PRED entity: 016yzz PRED relation: award PRED expected values: 01l78d => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 308): 0d085 (0.79 #6434, 0.67 #21716, 0.66 #5629), 0ck27z (0.40 #493, 0.25 #91, 0.12 #14969), 01by1l (0.31 #6142, 0.25 #8957, 0.18 #11772), 09sb52 (0.29 #14919, 0.28 #5670, 0.27 #6475), 01l78d (0.27 #1091, 0.24 #1493, 0.18 #17292), 0bs0bh (0.25 #102, 0.20 #504, 0.18 #17292), 02x4sn8 (0.25 #157, 0.20 #559, 0.13 #961), 0gr4k (0.25 #33, 0.20 #435, 0.13 #8075), 0bfvd4 (0.25 #114, 0.20 #516, 0.08 #2124), 0bp_b2 (0.25 #18, 0.20 #420, 0.08 #2028) >> Best rule #6434 for best value: >> intensional similarity = 3 >> extensional distance = 259 >> proper extension: 01vsxdm; >> query: (?x3980, ?x5863) <- role(?x3980, ?x2048), award_winner(?x5863, ?x3980), award(?x3980, ?x880) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1091 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0399p; *> query: (?x3980, 01l78d) <- nationality(?x3980, ?x94), influenced_by(?x3980, ?x4265), ?x4265 = 06whf *> conf = 0.27 ranks of expected_values: 5 EVAL 016yzz award 01l78d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 88.000 88.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16431-01hww_ PRED entity: 01hww_ PRED relation: group PRED expected values: 02dw1_ 01q99h => 67 concepts (46 used for prediction) PRED predicted values (max 10 best out of 724): 02dw1_ (0.78 #4015, 0.70 #2688, 0.65 #6100), 014pg1 (0.75 #1975, 0.71 #1599, 0.69 #3675), 02vnpv (0.69 #3729, 0.68 #5622, 0.67 #3539), 05563d (0.67 #2283, 0.64 #5500, 0.62 #3607), 047cx (0.67 #1171, 0.62 #3619, 0.62 #1919), 02vgh (0.67 #1207, 0.62 #1955, 0.60 #456), 09jvl (0.67 #902, 0.62 #2027, 0.60 #528), 06nv27 (0.67 #2303, 0.60 #428, 0.56 #3627), 03qkcn9 (0.67 #2426, 0.60 #551, 0.50 #3750), 02r1tx7 (0.64 #2837, 0.50 #1897, 0.45 #1682) >> Best rule #4015 for best value: >> intensional similarity = 23 >> extensional distance = 16 >> proper extension: 03q5t; 01v1d8; >> query: (?x1655, 02dw1_) <- role(?x4913, ?x1655), role(?x316, ?x1655), role(?x2309, ?x1655), ?x4913 = 03ndd, role(?x1655, ?x314), role(?x316, ?x2157), instrumentalists(?x316, ?x8305), instrumentalists(?x316, ?x7794), instrumentalists(?x316, ?x3657), instrumentalists(?x316, ?x1989), instrumentalists(?x316, ?x669), group(?x316, ?x997), role(?x3399, ?x316), ?x1989 = 04mn81, ?x3399 = 01gx5f, ?x7794 = 01k23t, role(?x1433, ?x316), ?x2157 = 011_6p, ?x1433 = 0239kh, ?x2309 = 06ncr, ?x3657 = 01w8n89, ?x8305 = 01vtg4q, nominated_for(?x669, ?x670) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12 EVAL 01hww_ group 01q99h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 67.000 46.000 0.778 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01hww_ group 02dw1_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 46.000 0.778 http://example.org/music/performance_role/regular_performances./music/group_membership/group #16430-0167v PRED entity: 0167v PRED relation: contains! PRED expected values: 073q1 => 56 concepts (49 used for prediction) PRED predicted values (max 10 best out of 156): 02j71 (0.60 #35803, 0.59 #36701, 0.56 #34905), 02j9z (0.57 #7184, 0.36 #1817, 0.33 #3606), 09c7w0 (0.52 #38497, 0.47 #39393, 0.43 #40289), 07ssc (0.41 #36734, 0.37 #37630, 0.13 #39422), 0dg3n1 (0.31 #2839, 0.28 #9101, 0.28 #13573), 07c5l (0.25 #6655, 0.24 #4867, 0.21 #14705), 02jx1 (0.24 #36789, 0.21 #37685, 0.12 #39477), 05nrg (0.21 #35804, 0.15 #565, 0.10 #1460), 04wsz (0.21 #35804, 0.13 #2286, 0.12 #4075), 04pnx (0.21 #35804, 0.11 #6685, 0.10 #4897) >> Best rule #35803 for best value: >> intensional similarity = 3 >> extensional distance = 586 >> proper extension: 0nm8n; >> query: (?x5445, ?x551) <- administrative_parent(?x5445, ?x551), administrative_parent(?x550, ?x551), contains(?x6304, ?x550) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #35804 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 586 *> proper extension: 0nm8n; *> query: (?x5445, ?x6304) <- administrative_parent(?x5445, ?x551), administrative_parent(?x550, ?x551), contains(?x6304, ?x550) *> conf = 0.21 ranks of expected_values: 13 EVAL 0167v contains! 073q1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 56.000 49.000 0.602 http://example.org/location/location/contains #16429-03csqj4 PRED entity: 03csqj4 PRED relation: award_winner! PRED expected values: 0fk0xk => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 134): 0fzrtf (0.25 #61, 0.20 #201, 0.06 #3421), 0d__c3 (0.22 #824, 0.08 #1524, 0.08 #3484), 0bzkvd (0.20 #393, 0.20 #253, 0.12 #1513), 0bzjgq (0.20 #398, 0.20 #258, 0.06 #1098), 0dth6b (0.20 #164, 0.08 #1424, 0.06 #2124), 073hd1 (0.20 #239, 0.05 #1359, 0.05 #1219), 0c4hnm (0.14 #688, 0.12 #1528, 0.11 #828), 0dthsy (0.14 #1186, 0.11 #1046, 0.10 #1606), 0fy6bh (0.11 #746, 0.09 #2146, 0.07 #3406), 0fk0xk (0.11 #777, 0.09 #2177, 0.05 #3437) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05728w1; >> query: (?x12010, 0fzrtf) <- people(?x4322, ?x12010), profession(?x12010, ?x1078), place_of_birth(?x12010, ?x3908), film_production_design_by(?x4870, ?x12010) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #777 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 09r9m7; 015dqj; *> query: (?x12010, 0fk0xk) <- nominated_for(?x12010, ?x1822), award_winner(?x1821, ?x12010), ?x1821 = 0ftlkg *> conf = 0.11 ranks of expected_values: 10 EVAL 03csqj4 award_winner! 0fk0xk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 122.000 122.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #16428-01q4qv PRED entity: 01q4qv PRED relation: gender PRED expected values: 05zppz => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #23, 0.87 #37, 0.87 #21), 02zsn (0.49 #155, 0.25 #74, 0.25 #76) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 197 >> proper extension: 02lk1s; 0hcvy; 06pcz0; >> query: (?x3177, 05zppz) <- award(?x3177, ?x198), profession(?x3177, ?x319), ?x319 = 01d_h8, written_by(?x5515, ?x3177) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q4qv gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.879 http://example.org/people/person/gender #16427-0ckm4x PRED entity: 0ckm4x PRED relation: location PRED expected values: 013d7t => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 235): 030qb3t (0.29 #887, 0.28 #4904, 0.19 #10529), 01cx_ (0.29 #967, 0.08 #2574, 0.08 #1770), 02_286 (0.19 #13697, 0.18 #80398, 0.18 #39410), 04jpl (0.17 #2428, 0.17 #1624, 0.07 #8855), 059rby (0.14 #820, 0.14 #36978, 0.11 #4034), 0d0x8 (0.14 #965, 0.08 #2572, 0.06 #4982), 0d739 (0.14 #1427, 0.02 #11069), 01n7q (0.13 #42649, 0.12 #37025, 0.06 #23363), 01531 (0.11 #4176, 0.07 #8996, 0.06 #9800), 0cr3d (0.08 #2556, 0.08 #1752, 0.08 #20231) >> Best rule #887 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0kjrx; >> query: (?x12353, 030qb3t) <- gender(?x12353, ?x514), ?x514 = 02zsn, language(?x12353, ?x254), religion(?x12353, ?x8140), film(?x12353, ?x6840) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ckm4x location 013d7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 114.000 0.286 http://example.org/people/person/places_lived./people/place_lived/location #16426-01jr4j PRED entity: 01jr4j PRED relation: music PRED expected values: 020fgy => 141 concepts (85 used for prediction) PRED predicted values (max 10 best out of 152): 015wc0 (0.44 #596, 0.33 #386, 0.33 #176), 02jxmr (0.22 #705, 0.11 #1335, 0.10 #3020), 0150t6 (0.18 #887, 0.11 #1307, 0.07 #2992), 04pf4r (0.18 #909, 0.11 #1329, 0.03 #3014), 0146pg (0.17 #2745, 0.17 #220, 0.14 #8855), 02p7xc (0.17 #383, 0.05 #1855, 0.04 #2066), 0drc1 (0.15 #1620, 0.05 #1200, 0.04 #2463), 02g1jh (0.11 #759, 0.11 #1179, 0.07 #2442), 0bvzp (0.11 #747, 0.01 #6014, 0.01 #6224), 023361 (0.11 #1411, 0.10 #3096, 0.09 #991) >> Best rule #596 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 05cj_j; 05z7c; 0k5g9; 02jr6k; 02r_pp; 01s9vc; >> query: (?x7149, 015wc0) <- production_companies(?x7149, ?x1104), nominated_for(?x7149, ?x5856), language(?x7149, ?x5607), ?x5856 = 0jwvf, languages(?x380, ?x5607), major_field_of_study(?x122, ?x5607) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1425 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 01j8wk; 0yx7h; 01jmyj; *> query: (?x7149, 020fgy) <- production_companies(?x7149, ?x1104), genre(?x7149, ?x4205), ?x4205 = 0c3351, film(?x2465, ?x7149), produced_by(?x1708, ?x2465), profession(?x2465, ?x319) *> conf = 0.05 ranks of expected_values: 31 EVAL 01jr4j music 020fgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 141.000 85.000 0.444 http://example.org/film/film/music #16425-06lgq8 PRED entity: 06lgq8 PRED relation: gender PRED expected values: 05zppz => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #39, 0.73 #76, 0.73 #136), 02zsn (0.52 #67, 0.42 #6, 0.35 #8) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 932 >> proper extension: 0mj0c; 04jwp; 0hky; 034ls; 01sg7_; 042f1; 01g0jn; >> query: (?x2076, 05zppz) <- student(?x2013, ?x2076), major_field_of_study(?x2013, ?x2014), ?x2014 = 04rjg >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06lgq8 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.739 http://example.org/people/person/gender #16424-03nx8mj PRED entity: 03nx8mj PRED relation: film! PRED expected values: 02lj6p 044zvm => 76 concepts (46 used for prediction) PRED predicted values (max 10 best out of 816): 01795t (0.43 #35340, 0.41 #91467, 0.41 #56131), 0q9kd (0.33 #4, 0.04 #56132, 0.03 #18708), 01ggc9 (0.33 #1726, 0.03 #22513, 0.02 #16277), 02lq10 (0.33 #357, 0.02 #4515, 0.01 #31539), 02fb1n (0.33 #334, 0.02 #4492, 0.01 #21121), 018grr (0.17 #339, 0.04 #4497, 0.04 #14890), 073749 (0.17 #707, 0.04 #4865, 0.04 #15258), 059j1m (0.17 #1469, 0.04 #16020, 0.03 #22256), 039bp (0.17 #181, 0.04 #56132, 0.03 #18708), 0kjrx (0.17 #1418, 0.03 #18708, 0.02 #36758) >> Best rule #35340 for best value: >> intensional similarity = 4 >> extensional distance = 389 >> proper extension: 09sh8k; 0bth54; 08gsvw; 0bwfwpj; 08hmch; 0jqp3; 0jyx6; 053rxgm; 02pxmgz; 0bscw; ... >> query: (?x4176, ?x2156) <- music(?x4176, ?x6664), film_crew_role(?x4176, ?x137), nominated_for(?x2156, ?x4176), ?x137 = 09zzb8 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #6097 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 112 *> proper extension: 04cf_l; *> query: (?x4176, 044zvm) <- titles(?x2480, ?x4176), country(?x4176, ?x94), ?x2480 = 01z4y, music(?x4176, ?x6664) *> conf = 0.03 ranks of expected_values: 224 EVAL 03nx8mj film! 044zvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 46.000 0.426 http://example.org/film/actor/film./film/performance/film EVAL 03nx8mj film! 02lj6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 46.000 0.426 http://example.org/film/actor/film./film/performance/film #16423-05pbl56 PRED entity: 05pbl56 PRED relation: film! PRED expected values: 03gm48 => 95 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1039): 02jxmr (0.56 #66650, 0.44 #6247, 0.37 #41651), 06mr6 (0.21 #1040, 0.05 #7287, 0.04 #28111), 017lqp (0.16 #1611, 0.08 #3693, 0.05 #7858), 0gg9_5q (0.13 #43736, 0.12 #58317, 0.12 #70816), 0j_c (0.12 #6655, 0.11 #2490, 0.11 #408), 03kpvp (0.11 #631, 0.04 #6878, 0.04 #27702), 016ypb (0.08 #2579, 0.05 #4661, 0.04 #6744), 014gf8 (0.08 #3090, 0.05 #5172, 0.04 #7255), 01j7z7 (0.08 #3406, 0.05 #5488, 0.04 #7571), 0p8r1 (0.08 #8913, 0.03 #25571, 0.02 #63067) >> Best rule #66650 for best value: >> intensional similarity = 4 >> extensional distance = 447 >> proper extension: 0gfzgl; 02sqkh; 025x1t; >> query: (?x1595, ?x2258) <- titles(?x812, ?x1595), nominated_for(?x2258, ?x1595), spouse(?x891, ?x2258), award_winner(?x2488, ?x2258) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #8481 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 07sc6nw; 02vqhv0; 05fcbk7; 06w839_; 04grkmd; 0435vm; 033srr; 0642xf3; 07bzz7; 02x3y41; ... *> query: (?x1595, 03gm48) <- genre(?x1595, ?x225), film_crew_role(?x1595, ?x1966), film(?x100, ?x1595), ?x1966 = 015h31 *> conf = 0.01 ranks of expected_values: 991 EVAL 05pbl56 film! 03gm48 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 95.000 36.000 0.561 http://example.org/film/actor/film./film/performance/film #16422-01v2xl PRED entity: 01v2xl PRED relation: contains! PRED expected values: 07ssc => 135 concepts (63 used for prediction) PRED predicted values (max 10 best out of 256): 02jx1 (0.89 #24262, 0.68 #9933, 0.66 #33219), 09c7w0 (0.77 #18805, 0.75 #17015, 0.73 #37614), 07ssc (0.74 #43886, 0.63 #25071, 0.63 #24207), 0345h (0.24 #29629, 0.17 #49344, 0.08 #43072), 0978r (0.22 #10053, 0.20 #9158, 0.18 #1997), 03rjj (0.22 #29557, 0.18 #40310, 0.15 #49272), 04jpl (0.18 #9868, 0.18 #8078, 0.15 #2707), 01n7q (0.17 #7239, 0.11 #18880, 0.11 #17090), 0f8l9c (0.16 #29594, 0.03 #16162, 0.02 #43037), 0d060g (0.13 #12546, 0.11 #13442, 0.08 #14338) >> Best rule #24262 for best value: >> intensional similarity = 4 >> extensional distance = 246 >> proper extension: 025ndl; >> query: (?x11602, 02jx1) <- contains(?x4221, ?x11602), time_zones(?x4221, ?x5327), contains(?x512, ?x4221), ?x512 = 07ssc >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #43886 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 386 *> proper extension: 0j603; 0fwdr; *> query: (?x11602, ?x512) <- contains(?x4221, ?x11602), administrative_parent(?x4221, ?x512), titles(?x512, ?x144), country(?x124, ?x512) *> conf = 0.74 ranks of expected_values: 3 EVAL 01v2xl contains! 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 135.000 63.000 0.887 http://example.org/location/location/contains #16421-043d4 PRED entity: 043d4 PRED relation: instrumentalists! PRED expected values: 07y_7 => 109 concepts (105 used for prediction) PRED predicted values (max 10 best out of 94): 05r5c (0.73 #186, 0.67 #538, 0.67 #274), 0342h (0.63 #5201, 0.62 #5112, 0.62 #5910), 07y_7 (0.40 #90, 0.33 #177, 0.25 #267), 05148p4 (0.34 #5218, 0.33 #5129, 0.32 #6017), 018vs (0.29 #5210, 0.28 #5919, 0.28 #5121), 02hnl (0.20 #389, 0.17 #5232, 0.17 #5143), 013y1f (0.20 #121, 0.12 #650, 0.07 #1090), 0d8lm (0.18 #5994, 0.01 #1936), 03qjg (0.16 #5249, 0.15 #5160, 0.14 #2431), 0l14md (0.13 #361, 0.13 #2386, 0.11 #5204) >> Best rule #186 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0g7k2g; >> query: (?x7559, 05r5c) <- artists(?x10853, ?x7559), artists(?x888, ?x7559), ?x888 = 05lls, profession(?x7559, ?x1614), ?x10853 = 0l8gh >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #90 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0h336; *> query: (?x7559, 07y_7) <- influenced_by(?x11512, ?x7559), influenced_by(?x3774, ?x7559), type_of_union(?x7559, ?x566), nationality(?x7559, ?x1355), ?x3774 = 04k15, instrumentalists(?x316, ?x11512) *> conf = 0.40 ranks of expected_values: 3 EVAL 043d4 instrumentalists! 07y_7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 105.000 0.727 http://example.org/music/instrument/instrumentalists #16420-02mt4k PRED entity: 02mt4k PRED relation: award PRED expected values: 02x4sn8 => 114 concepts (113 used for prediction) PRED predicted values (max 10 best out of 265): 0gr51 (0.72 #26223, 0.71 #18272, 0.71 #17076), 09sb52 (0.67 #1229, 0.60 #832, 0.50 #2817), 0gq9h (0.35 #4836, 0.35 #6424, 0.33 #5630), 040njc (0.27 #4771, 0.27 #6359, 0.25 #5565), 0gqy2 (0.27 #953, 0.19 #1350, 0.17 #159), 027dtxw (0.27 #797, 0.17 #3, 0.14 #1194), 05pcn59 (0.27 #870, 0.17 #76, 0.13 #33377), 02x4x18 (0.27 #921, 0.13 #33377, 0.12 #22248), 0ck27z (0.24 #2866, 0.17 #87, 0.15 #12793), 0bfvd4 (0.24 #1301, 0.17 #110, 0.13 #904) >> Best rule #26223 for best value: >> intensional similarity = 3 >> extensional distance = 1563 >> proper extension: 018p5f; 04qzm; 09jm8; >> query: (?x4871, ?x1862) <- award_nominee(?x91, ?x4871), award_winner(?x1862, ?x4871), award(?x361, ?x1862) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #6750 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 348 *> proper extension: 024c1b; *> query: (?x4871, ?x2902) <- produced_by(?x10800, ?x4871), nominated_for(?x2902, ?x10800) *> conf = 0.15 ranks of expected_values: 46 EVAL 02mt4k award 02x4sn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 114.000 113.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16419-04jwly PRED entity: 04jwly PRED relation: film_crew_role PRED expected values: 0ch6mp2 02vs3x5 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.80 #404, 0.75 #440, 0.75 #875), 02r96rf (0.70 #436, 0.68 #871, 0.61 #3193), 0dxtw (0.35 #879, 0.34 #3201, 0.33 #2474), 01vx2h (0.34 #880, 0.30 #954, 0.29 #445), 02ynfr (0.17 #449, 0.15 #161, 0.15 #884), 089g0h (0.15 #165, 0.12 #453, 0.12 #888), 0215hd (0.14 #887, 0.14 #416, 0.13 #164), 01xy5l_ (0.13 #882, 0.12 #159, 0.11 #447), 0d2b38 (0.12 #171, 0.12 #894, 0.12 #459), 02_n3z (0.11 #398, 0.09 #869, 0.08 #2391) >> Best rule #404 for best value: >> intensional similarity = 4 >> extensional distance = 243 >> proper extension: 020y73; 0gyy53; 023gxx; 0415ggl; 03m5y9p; 042zrm; 07jqjx; 03hp2y1; 09rvwmy; 02wtp6; >> query: (?x2833, 0ch6mp2) <- titles(?x53, ?x2833), nominated_for(?x1414, ?x2833), ?x53 = 07s9rl0, film_crew_role(?x2833, ?x137) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 16 EVAL 04jwly film_crew_role 02vs3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 117.000 117.000 0.796 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 04jwly film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.796 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16418-0cw3yd PRED entity: 0cw3yd PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.83 #96, 0.83 #101, 0.83 #51), 07c52 (0.07 #28, 0.05 #78, 0.04 #268), 07z4p (0.05 #30, 0.04 #80, 0.03 #165), 02nxhr (0.05 #47, 0.04 #152, 0.04 #52), 0735l (0.02 #24) >> Best rule #96 for best value: >> intensional similarity = 4 >> extensional distance = 276 >> proper extension: 02qk3fk; 0353tm; 06bc59; >> query: (?x2812, 029j_) <- genre(?x2812, ?x53), film_format(?x2812, ?x6392), film(?x5043, ?x2812), currency(?x2812, ?x2244) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cw3yd film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.835 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #16417-01n4w PRED entity: 01n4w PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 21): 0fkvn (0.77 #130, 0.75 #109, 0.72 #88), 0pqc5 (0.76 #1286, 0.69 #1412, 0.57 #1244), 060c4 (0.53 #1641, 0.52 #1557, 0.50 #1494), 060bp (0.46 #1639, 0.46 #1555, 0.44 #1492), 0fkzq (0.36 #2185, 0.36 #2228, 0.25 #267), 01gkgk (0.36 #2185, 0.36 #2228, 0.14 #27), 01t7n9 (0.17 #101, 0.14 #38, 0.12 #59), 0789n (0.15 #324, 0.14 #366, 0.14 #30), 01q24l (0.13 #1293, 0.13 #1251, 0.13 #1419), 04syw (0.12 #1645, 0.11 #1729, 0.09 #1309) >> Best rule #130 for best value: >> intensional similarity = 2 >> extensional distance = 45 >> proper extension: 0g0syc; >> query: (?x2982, 0fkvn) <- district_represented(?x6728, ?x2982), ?x6728 = 070mff >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n4w jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.766 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #16416-02zl4d PRED entity: 02zl4d PRED relation: award PRED expected values: 09sb52 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 249): 09sb52 (0.36 #851, 0.35 #1258, 0.34 #7334), 0ck27z (0.18 #8196, 0.16 #1310, 0.15 #6576), 05pcn59 (0.16 #82, 0.13 #487, 0.13 #24308), 027dtxw (0.14 #1216, 0.14 #814, 0.13 #5673), 0gqwc (0.13 #24308, 0.12 #26334, 0.12 #22687), 099cng (0.13 #24308, 0.12 #26334, 0.12 #22687), 07h0cl (0.13 #24308, 0.12 #26334, 0.12 #22687), 05zr6wv (0.13 #24308, 0.12 #22687, 0.12 #16610), 094qd5 (0.13 #24308, 0.12 #22687, 0.12 #16610), 054ks3 (0.13 #24308, 0.12 #22687, 0.12 #16610) >> Best rule #851 for best value: >> intensional similarity = 3 >> extensional distance = 465 >> proper extension: 024rbz; >> query: (?x11399, 09sb52) <- nominated_for(?x11399, ?x308), nominated_for(?x112, ?x308), ?x112 = 027dtxw >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zl4d award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.362 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16415-017l4 PRED entity: 017l4 PRED relation: nationality PRED expected values: 0d0vqn => 144 concepts (140 used for prediction) PRED predicted values (max 10 best out of 55): 0d0vqn (0.84 #9342, 0.84 #9946, 0.65 #3810), 09c7w0 (0.82 #3710, 0.80 #8235, 0.76 #4915), 0345h (0.65 #3810, 0.53 #7831, 0.49 #6219), 0k6nt (0.65 #3810, 0.53 #7831, 0.49 #6219), 01fvhp (0.47 #3709, 0.44 #4814, 0.44 #4311), 07ssc (0.37 #10147, 0.19 #3019, 0.18 #4728), 02jx1 (0.30 #3037, 0.23 #1434, 0.23 #4243), 03rt9 (0.20 #113, 0.04 #4223, 0.03 #6722), 0jgx (0.20 #58, 0.03 #6722, 0.03 #758), 03rk0 (0.17 #1146, 0.17 #1947, 0.16 #2148) >> Best rule #9342 for best value: >> intensional similarity = 3 >> extensional distance = 1116 >> proper extension: 070m12; >> query: (?x7799, ?x304) <- place_of_birth(?x7799, ?x5168), award(?x7799, ?x2379), country(?x5168, ?x304) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017l4 nationality 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 140.000 0.838 http://example.org/people/person/nationality #16414-0gjw_ PRED entity: 0gjw_ PRED relation: combatants PRED expected values: 0d060g => 43 concepts (38 used for prediction) PRED predicted values (max 10 best out of 323): 09c7w0 (0.45 #2209, 0.44 #2333, 0.44 #1713), 02psqkz (0.40 #659, 0.38 #1269, 0.33 #1024), 05kyr (0.40 #788, 0.33 #122, 0.22 #1465), 012m_ (0.40 #809, 0.33 #122, 0.22 #1465), 011zwl (0.40 #1464, 0.38 #1099, 0.35 #1960), 05vz3zq (0.38 #1399, 0.33 #122, 0.25 #424), 0d060g (0.33 #246, 0.33 #132, 0.33 #122), 0ctw_b (0.33 #143, 0.33 #122, 0.25 #489), 059j2 (0.33 #122, 0.25 #489, 0.25 #1732), 05v8c (0.33 #122, 0.25 #489, 0.25 #1354) >> Best rule #2209 for best value: >> intensional similarity = 8 >> extensional distance = 42 >> proper extension: 081pw; 01gjd0; 0d06vc; 0gfq9; 03c3jzx; 0cmc2; 031x2; 0cm2xh; 01h6pn; 0py8j; ... >> query: (?x10413, 09c7w0) <- combatants(?x10413, ?x2146), nationality(?x111, ?x2146), country(?x3411, ?x2146), contains(?x2146, ?x1391), adjoins(?x2146, ?x2236), country(?x257, ?x2146), film_release_region(?x1785, ?x2146), ?x1785 = 0gj9tn5 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #246 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 0bqtx; *> query: (?x10413, ?x279) <- combatants(?x10413, ?x6407), combatants(?x10413, ?x5738), combatants(?x10413, ?x2146), ?x2146 = 03rk0, combatants(?x6407, ?x279), combatants(?x11122, ?x5738), combatants(?x3278, ?x5738), ?x11122 = 0c3mz, ?x3278 = 0dl4z, capital(?x5738, ?x8751), ?x279 = 0d060g *> conf = 0.33 ranks of expected_values: 7 EVAL 0gjw_ combatants 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 43.000 38.000 0.455 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #16413-011lpr PRED entity: 011lpr PRED relation: inductee! PRED expected values: 06szd3 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 5): 06szd3 (0.27 #65, 0.22 #29, 0.12 #38), 0qjfl (0.06 #21, 0.03 #129, 0.02 #156), 0g2c8 (0.04 #28, 0.04 #82, 0.04 #37), 04dm2n (0.02 #62, 0.01 #80), 04045y (0.02 #69) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 02pp_q_; 01rgcg; 01kws3; 06z4wj; 03mstc; 02tn0_; 08nz99; 0488g9; >> query: (?x13880, 06szd3) <- profession(?x13880, ?x1041), place_of_death(?x13880, ?x9405), ?x1041 = 03gjzk, contains(?x94, ?x9405) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011lpr inductee! 06szd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.271 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #16412-01wgxtl PRED entity: 01wgxtl PRED relation: award PRED expected values: 03t5b6 => 112 concepts (94 used for prediction) PRED predicted values (max 10 best out of 252): 03t5kl (0.50 #1404, 0.50 #222, 0.40 #1798), 01cky2 (0.43 #1372, 0.38 #584, 0.25 #4130), 01bgqh (0.43 #1225, 0.34 #6347, 0.28 #4377), 03t5b6 (0.40 #1773, 0.30 #985, 0.17 #4137), 0c4z8 (0.38 #466, 0.36 #1254, 0.26 #6376), 02f79n (0.30 #1120, 0.25 #726, 0.17 #1908), 02f5qb (0.29 #1730, 0.29 #1336, 0.25 #548), 03qbh5 (0.29 #1382, 0.25 #6504, 0.24 #4534), 02f73p (0.29 #1365, 0.25 #577, 0.13 #10641), 02f6ym (0.29 #1433, 0.16 #4585, 0.15 #6555) >> Best rule #1404 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 09qr6; 04xrx; 01svw8n; 03y82t6; 02x_h0; 01vsgrn; 0g824; 06mt91; >> query: (?x2732, 03t5kl) <- award_nominee(?x2732, ?x827), participant(?x2614, ?x2732), gender(?x2732, ?x231), ?x827 = 02l840 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1773 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 03sww; 011z3g; 01wj5hp; *> query: (?x2732, 03t5b6) <- artists(?x671, ?x2732), award(?x2732, ?x9295), ?x9295 = 023vrq *> conf = 0.40 ranks of expected_values: 4 EVAL 01wgxtl award 03t5b6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 112.000 94.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16411-0163v PRED entity: 0163v PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.87 #18, 0.87 #17, 0.87 #45) >> Best rule #18 for best value: >> intensional similarity = 3 >> extensional distance = 92 >> proper extension: 04thp; >> query: (?x2188, 0hzc9wc) <- administrative_parent(?x2188, ?x551), currency(?x2188, ?x170), official_language(?x2188, ?x5671) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0163v administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.872 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #16410-01j5x6 PRED entity: 01j5x6 PRED relation: award PRED expected values: 0ck27z => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 249): 0ck27z (0.56 #905, 0.27 #14304, 0.21 #19582), 09sb52 (0.43 #447, 0.36 #4101, 0.36 #19124), 0hnf5vm (0.29 #596, 0.03 #3032, 0.03 #7904), 05pcn59 (0.22 #2924, 0.20 #14699, 0.19 #12669), 0cqhk0 (0.19 #14248, 0.13 #19526, 0.13 #19932), 05p09zm (0.18 #2967, 0.15 #7839, 0.14 #11900), 0gqwc (0.16 #1699, 0.16 #1293, 0.14 #2105), 05b4l5x (0.16 #1630, 0.14 #7720, 0.12 #3660), 05zr6wv (0.15 #11792, 0.14 #9762, 0.13 #15852), 03c7tr1 (0.15 #1683, 0.15 #2901, 0.14 #4931) >> Best rule #905 for best value: >> intensional similarity = 2 >> extensional distance = 14 >> proper extension: 0kctd; >> query: (?x891, 0ck27z) <- nominated_for(?x891, ?x8870), ?x8870 = 0fhzwl >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j5x6 award 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.562 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16409-0hskw PRED entity: 0hskw PRED relation: award_nominee PRED expected values: 01pcmd => 88 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1181): 03_80b (0.81 #51496, 0.81 #102998, 0.81 #91293), 0fb1q (0.81 #51496, 0.81 #102998, 0.81 #91293), 05zh9c (0.81 #51496, 0.81 #102998, 0.81 #91293), 01pcmd (0.81 #51496, 0.81 #102998, 0.81 #91293), 0h0wc (0.30 #105341, 0.05 #42685, 0.04 #49708), 03thw4 (0.30 #105341, 0.05 #3376, 0.04 #5716), 0hskw (0.30 #105341, 0.04 #107683, 0.04 #2341), 01_6dw (0.30 #105341, 0.04 #1498, 0.02 #3839), 0gl88b (0.30 #105341, 0.01 #51495), 0cg9f (0.30 #105341) >> Best rule #51496 for best value: >> intensional similarity = 3 >> extensional distance = 362 >> proper extension: 0jz9f; 0cb77r; 086k8; 03f2_rc; 017s11; 016tt2; 0g1rw; 05qd_; 027rwmr; 07c0j; ... >> query: (?x2733, ?x488) <- nominated_for(?x2733, ?x3404), costume_design_by(?x3404, ?x3685), award_nominee(?x488, ?x2733) >> conf = 0.81 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 0hskw award_nominee 01pcmd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 47.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16408-064_8sq PRED entity: 064_8sq PRED relation: official_language! PRED expected values: 0164v 04vjh => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 324): 04hqz (0.63 #1270, 0.62 #318, 0.51 #3331), 0697s (0.63 #1270, 0.62 #318, 0.51 #3331), 04wgh (0.63 #1270, 0.62 #318, 0.51 #3331), 01xbgx (0.63 #1270, 0.62 #318, 0.51 #3331), 04g61 (0.63 #1270, 0.62 #318, 0.51 #3331), 04hhv (0.63 #1270, 0.62 #318, 0.51 #3331), 07fj_ (0.63 #1270, 0.62 #318, 0.51 #3331), 03676 (0.63 #1270, 0.62 #318, 0.51 #3331), 0169t (0.63 #1270, 0.62 #318, 0.51 #3331), 0162v (0.63 #1270, 0.62 #318, 0.51 #3331) >> Best rule #1270 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 06nm1; >> query: (?x5607, ?x172) <- language(?x5365, ?x5607), language(?x1688, ?x5607), countries_spoken_in(?x5607, ?x172), service_language(?x127, ?x5607), ?x5365 = 05tgks, ?x1688 = 024l2y, official_language(?x279, ?x5607) >> conf = 0.63 => this is the best rule for 16 predicted values *> Best rule #145 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 0jzc; *> query: (?x5607, 04vjh) <- language(?x6365, ?x5607), language(?x2814, ?x5607), countries_spoken_in(?x5607, ?x172), service_language(?x127, ?x5607), ?x2814 = 078sj4, ?x6365 = 03n3gl, languages_spoken(?x1176, ?x5607) *> conf = 0.33 ranks of expected_values: 33, 130 EVAL 064_8sq official_language! 04vjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 91.000 91.000 0.632 http://example.org/location/country/official_language EVAL 064_8sq official_language! 0164v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 91.000 91.000 0.632 http://example.org/location/country/official_language #16407-0xbm PRED entity: 0xbm PRED relation: team! PRED expected values: 054kmq => 140 concepts (125 used for prediction) PRED predicted values (max 10 best out of 75): 054kmq (0.83 #1643, 0.82 #2284, 0.81 #1141), 0135nb (0.50 #231, 0.33 #445, 0.33 #17), 05_6_y (0.33 #429, 0.33 #1, 0.25 #215), 02rnns (0.25 #249, 0.25 #177, 0.20 #320), 0gtgp6 (0.25 #256, 0.25 #184, 0.17 #470), 09m465 (0.25 #265, 0.25 #193, 0.17 #479), 04v68c (0.25 #212, 0.20 #355, 0.17 #498), 0784v1 (0.25 #148, 0.20 #291, 0.17 #434), 08gwzt (0.20 #332, 0.17 #546, 0.13 #1571), 09r1j5 (0.20 #314, 0.17 #528, 0.12 #1098) >> Best rule #1643 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 07r78j; 05ns4g; 013xh7; 0ly8z; 03x73c; >> query: (?x3158, ?x6812) <- position(?x3158, ?x530), team(?x6812, ?x3158), current_club(?x2427, ?x3158), ?x530 = 02_j1w >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xbm team! 054kmq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 125.000 0.827 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #16406-027ct7c PRED entity: 027ct7c PRED relation: nominated_for! PRED expected values: 0gq9h => 90 concepts (81 used for prediction) PRED predicted values (max 10 best out of 220): 0l8z1 (0.74 #982, 0.40 #3079, 0.37 #1448), 040njc (0.68 #3036, 0.49 #4434, 0.44 #3502), 0gq9h (0.68 #4486, 0.64 #3554, 0.63 #3088), 02pqp12 (0.66 #3085, 0.40 #4483, 0.30 #2386), 0k611 (0.60 #1467, 0.58 #3098, 0.55 #3797), 04dn09n (0.55 #3062, 0.51 #4460, 0.40 #3528), 09sdmz (0.53 #2470, 0.48 #3402, 0.45 #839), 04kxsb (0.46 #3121, 0.41 #3587, 0.28 #4519), 02qvyrt (0.46 #3122, 0.26 #4520, 0.26 #1025), 099c8n (0.45 #2384, 0.42 #3316, 0.41 #3083) >> Best rule #982 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 01fmys; 016z9n; 0dnqr; 0f4yh; 0ptxj; 025rvx0; 02k1pr; 0f3m1; 0gltv; 09sr0; >> query: (?x5533, 0l8z1) <- nominated_for(?x484, ?x5533), genre(?x5533, ?x53), nominated_for(?x669, ?x5533), film(?x1850, ?x5533), ?x669 = 0146pg >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #4486 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 203 *> proper extension: 0bm2g; 0bmpm; 02tqm5; 0p_qr; 0yx1m; 01f69m; 016yxn; *> query: (?x5533, 0gq9h) <- nominated_for(?x1107, ?x5533), genre(?x5533, ?x53), nominated_for(?x669, ?x5533), film(?x1850, ?x5533), ?x1107 = 019f4v *> conf = 0.68 ranks of expected_values: 3 EVAL 027ct7c nominated_for! 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 81.000 0.742 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16405-01_f90 PRED entity: 01_f90 PRED relation: institution! PRED expected values: 0bkj86 019v9k => 130 concepts (67 used for prediction) PRED predicted values (max 10 best out of 23): 014mlp (0.80 #132, 0.78 #107, 0.71 #1077), 019v9k (0.80 #1210, 0.77 #1519, 0.67 #499), 02h4rq6 (0.79 #644, 0.73 #944, 0.73 #1075), 0bkj86 (0.67 #110, 0.61 #330, 0.60 #135), 02_xgp2 (0.67 #63, 0.60 #140, 0.60 #38), 04zx3q1 (0.67 #52, 0.50 #941, 0.40 #1434), 03bwzr4 (0.59 #956, 0.54 #656, 0.53 #628), 01ysy9 (0.52 #517, 0.44 #77, 0.34 #840), 07s6fsf (0.50 #51, 0.50 #941, 0.44 #103), 027f2w (0.50 #941, 0.44 #87, 0.40 #137) >> Best rule #132 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 02dj3; >> query: (?x12034, 014mlp) <- major_field_of_study(?x12034, ?x10380), state_province_region(?x12034, ?x2020), institution(?x1200, ?x12034), ?x10380 = 02stgt, major_field_of_study(?x1200, ?x254) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1210 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 250 *> proper extension: 01ngz1; 01nkcn; 01bvw5; 01rgdw; 0172jm; 02l1fn; 02dgq2; 01rc6f; 01csqg; 035gt8; ... *> query: (?x12034, 019v9k) <- major_field_of_study(?x12034, ?x10380), state_province_region(?x12034, ?x2020), institution(?x1200, ?x12034), major_field_of_study(?x10380, ?x2606), institution(?x1200, ?x8069), ?x8069 = 04bbpm *> conf = 0.80 ranks of expected_values: 2, 4 EVAL 01_f90 institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 67.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01_f90 institution! 0bkj86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 67.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #16404-02tr7d PRED entity: 02tr7d PRED relation: award_nominee! PRED expected values: 03v3xp => 83 concepts (34 used for prediction) PRED predicted values (max 10 best out of 801): 05cj4r (0.81 #2359, 0.81 #73691, 0.81 #43750), 015rkw (0.81 #73691, 0.81 #43750, 0.81 #73690), 015gw6 (0.81 #73691, 0.81 #43750, 0.81 #73690), 02sb1w (0.81 #73691, 0.81 #43750, 0.81 #73690), 0170s4 (0.81 #73691, 0.81 #43750, 0.81 #73690), 040981l (0.81 #73691, 0.81 #43750, 0.81 #73690), 01ksr1 (0.76 #52964, 0.75 #78298, 0.73 #78299), 03v3xp (0.76 #52964, 0.75 #78298, 0.62 #3100), 02tr7d (0.71 #4942, 0.69 #2639, 0.31 #7245), 017gxw (0.38 #8100, 0.16 #73692, 0.16 #75995) >> Best rule #2359 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02l4pj; 0755wz; >> query: (?x1669, 05cj4r) <- award_winner(?x1669, ?x5422), award(?x1669, ?x375), ?x5422 = 06j8wx >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #52964 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 985 *> proper extension: 012d40; 0byfz; 0c1pj; 05m883; 01f7j9; 04y8r; 03n0q5; 021yw7; 01_xtx; 03y82t6; ... *> query: (?x1669, ?x374) <- award_winner(?x493, ?x1669), award_nominee(?x368, ?x1669), award_winner(?x374, ?x1669) *> conf = 0.76 ranks of expected_values: 8 EVAL 02tr7d award_nominee! 03v3xp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 83.000 34.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16403-0c6qh PRED entity: 0c6qh PRED relation: award_nominee! PRED expected values: 02lj6p => 134 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1067): 04q5zw (0.81 #108734, 0.81 #113361, 0.81 #157316), 0p__8 (0.81 #108734, 0.81 #113361, 0.81 #157316), 0336mc (0.81 #108734, 0.81 #113361, 0.81 #157316), 026rm_y (0.81 #108734, 0.81 #113361, 0.81 #157316), 0509bl (0.81 #108734, 0.81 #113361, 0.81 #157316), 02xwgr (0.81 #108734, 0.81 #113361, 0.81 #157316), 044lyq (0.81 #108734, 0.81 #113361, 0.81 #157316), 018009 (0.81 #108734, 0.81 #113361, 0.81 #157316), 0z4s (0.76 #157315, 0.76 #198942, 0.75 #152690), 04g3p5 (0.76 #198942, 0.75 #189691, 0.75 #198941) >> Best rule #108734 for best value: >> intensional similarity = 3 >> extensional distance = 356 >> proper extension: 07nznf; 0337vz; 0lbj1; 0bl2g; 0c1pj; 01vrz41; 02lkcc; 058s57; 09f0bj; 0j_c; ... >> query: (?x2499, ?x192) <- film(?x2499, ?x349), participant(?x286, ?x2499), award_nominee(?x2499, ?x192) >> conf = 0.81 => this is the best rule for 8 predicted values *> Best rule #8805 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 22 *> proper extension: 065y4w7; *> query: (?x2499, 02lj6p) <- list(?x2499, ?x5160), award_winner(?x704, ?x2499) *> conf = 0.04 ranks of expected_values: 182 EVAL 0c6qh award_nominee! 02lj6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 134.000 86.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16402-076psv PRED entity: 076psv PRED relation: gender PRED expected values: 05zppz => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #45, 0.84 #43, 0.84 #33), 02zsn (0.29 #50, 0.28 #40, 0.27 #70) >> Best rule #45 for best value: >> intensional similarity = 1 >> extensional distance = 755 >> proper extension: 075wq; >> query: (?x4423, 05zppz) <- place_of_death(?x4423, ?x1523) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 076psv gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.845 http://example.org/people/person/gender #16401-01c9jp PRED entity: 01c9jp PRED relation: ceremony PRED expected values: 01bx35 => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 122): 01bx35 (0.86 #506, 0.83 #129, 0.54 #756), 0gx1673 (0.50 #105, 0.49 #607, 0.45 #230), 059x66 (0.39 #376, 0.38 #502, 0.27 #3628), 0bzjgq (0.39 #376, 0.38 #502, 0.27 #3628), 0bzn6_ (0.39 #376, 0.38 #502, 0.27 #3628), 0bzkvd (0.39 #376, 0.38 #502, 0.27 #3628), 09qftb (0.39 #376, 0.38 #502, 0.27 #3628), 09pnw5 (0.39 #376, 0.38 #502, 0.27 #3628), 026kqs9 (0.39 #376, 0.38 #502, 0.27 #3628), 09p30_ (0.39 #376, 0.38 #502, 0.27 #3628) >> Best rule #506 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 02flqd; >> query: (?x3647, 01bx35) <- award_winner(?x3647, ?x538), award(?x568, ?x3647), ceremony(?x3647, ?x2704), ?x2704 = 01mhwk >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c9jp ceremony 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.865 http://example.org/award/award_category/winners./award/award_honor/ceremony #16400-07cbcy PRED entity: 07cbcy PRED relation: award! PRED expected values: 09fb5 0sz28 0bj9k 01wxyx1 038rzr 0gnbw 0kjgl 018yj6 => 49 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2417): 0gn30 (0.80 #33185, 0.74 #6632, 0.71 #3316), 01fyzy (0.80 #33185, 0.74 #6632, 0.71 #3316), 0pz91 (0.80 #33185, 0.74 #6632, 0.71 #3316), 01p4vl (0.80 #33185, 0.74 #6632, 0.71 #3316), 0cbm64 (0.80 #33185, 0.74 #6632, 0.71 #3316), 02fcs2 (0.50 #3921, 0.50 #605, 0.15 #56412), 0jrqq (0.50 #4370, 0.50 #1054, 0.09 #7686), 0343h (0.50 #3646, 0.50 #330, 0.06 #6962), 02t_99 (0.50 #4632, 0.50 #1316, 0.04 #7948), 032v0v (0.50 #3736, 0.50 #420, 0.03 #7052) >> Best rule #33185 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 02581q; 03x3wf; 02581c; 01dpdh; 026mff; 0gq_d; 024_fw; 09lvl1; 024_41; 02fm4d; ... >> query: (?x1312, ?x1335) <- award(?x1145, ?x1312), award_winner(?x1312, ?x1335), inductee(?x9953, ?x1145) >> conf = 0.80 => this is the best rule for 5 predicted values *> Best rule #3833 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 05p1dby; *> query: (?x1312, 0bj9k) <- award(?x146, ?x1312), nominated_for(?x1312, ?x4839), ?x4839 = 0dqcs3, award_winner(?x1312, ?x269) *> conf = 0.17 ranks of expected_values: 88, 124, 147, 298, 562, 635, 811, 895 EVAL 07cbcy award! 018yj6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 17.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07cbcy award! 0kjgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07cbcy award! 0gnbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07cbcy award! 038rzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 49.000 17.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07cbcy award! 01wxyx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 17.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07cbcy award! 0bj9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 49.000 17.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07cbcy award! 0sz28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 17.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07cbcy award! 09fb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 17.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16399-019rg5 PRED entity: 019rg5 PRED relation: film_release_region! PRED expected values: 01fmys 0dc_ms => 132 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1845): 0fpgp26 (0.69 #44906, 0.62 #27667, 0.60 #42253), 01fmys (0.68 #8202, 0.58 #41353, 0.58 #4224), 0dtfn (0.68 #8115, 0.54 #41266, 0.53 #49223), 0bpm4yw (0.67 #44308, 0.65 #4526, 0.63 #41655), 017gl1 (0.66 #8064, 0.52 #43868, 0.51 #41215), 0fpv_3_ (0.66 #44041, 0.59 #26802, 0.57 #41388), 08hmch (0.65 #43878, 0.63 #8074, 0.61 #41225), 017jd9 (0.64 #44355, 0.59 #41702, 0.58 #8551), 047vnkj (0.63 #8658, 0.60 #44462, 0.57 #13962), 02d44q (0.63 #8080, 0.50 #4102, 0.48 #13384) >> Best rule #44906 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 0fhzf; >> query: (?x910, 0fpgp26) <- film_release_region(?x1150, ?x910), film_release_region(?x1150, ?x47), ?x47 = 027rn >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #8202 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 03_r3; 06qd3; 082fr; 05vz3zq; 0d04z6; 03f2w; *> query: (?x910, 01fmys) <- olympics(?x910, ?x418), olympics(?x910, ?x775), ?x775 = 0l998 *> conf = 0.68 ranks of expected_values: 2, 21 EVAL 019rg5 film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 132.000 105.000 0.688 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 019rg5 film_release_region! 01fmys CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 105.000 0.688 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16398-04k05 PRED entity: 04k05 PRED relation: category PRED expected values: 08mbj5d => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #26, 0.88 #46, 0.88 #21) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 01gv_f; >> query: (?x10671, 08mbj5d) <- award(?x10671, ?x1389), award_winner(?x10671, ?x4608), ?x1389 = 01c427 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04k05 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.914 http://example.org/common/topic/webpage./common/webpage/category #16397-01w8sf PRED entity: 01w8sf PRED relation: place_of_birth PRED expected values: 01z56h => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 85): 04jpl (0.25 #8, 0.11 #23247, 0.11 #21837), 02_286 (0.11 #5651, 0.11 #4243, 0.10 #10580), 0f2tj (0.10 #952, 0.03 #3064, 0.02 #10105), 01cx_ (0.10 #813, 0.02 #6445, 0.02 #14895), 0978r (0.10 #823, 0.01 #23358), 0yzyn (0.10 #1179), 01jr6 (0.10 #847), 05ksh (0.10 #741), 0f94t (0.10 #732), 0hptm (0.08 #1633, 0.08 #2337, 0.02 #12194) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0b13g7; 06rrzn; >> query: (?x2609, 04jpl) <- profession(?x2609, ?x319), award_nominee(?x647, ?x2609), ?x647 = 04r7jc >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w8sf place_of_birth 01z56h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 113.000 0.250 http://example.org/people/person/place_of_birth #16396-09tqxt PRED entity: 09tqxt PRED relation: nominated_for PRED expected values: 06w839_ 05c26ss => 35 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1654): 017gl1 (0.64 #3251, 0.54 #4811, 0.48 #9494), 026p4q7 (0.57 #9712, 0.56 #11272, 0.51 #12832), 04hwbq (0.55 #3290, 0.50 #1730, 0.47 #6411), 01mgw (0.55 #4248, 0.38 #12051, 0.36 #10491), 0fpv_3_ (0.55 #3447, 0.35 #6568, 0.33 #9690), 0661ql3 (0.53 #6578, 0.31 #5017, 0.30 #9700), 09gq0x5 (0.52 #9613, 0.51 #11173, 0.47 #6491), 0m313 (0.52 #9375, 0.51 #10935, 0.46 #12495), 0gmgwnv (0.51 #10304, 0.49 #11864, 0.45 #4061), 049xgc (0.49 #10218, 0.48 #11778, 0.43 #13338) >> Best rule #3251 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 027dtxw; 0gr4k; 02hsq3m; 02n9nmz; >> query: (?x1723, 017gl1) <- ceremony(?x1723, ?x472), nominated_for(?x1723, ?x6881), nominated_for(?x1723, ?x2868), nominated_for(?x1723, ?x2783), crewmember(?x6881, ?x929), film_release_region(?x2783, ?x1917), ?x1917 = 01p1v, ?x2868 = 0dr3sl >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #438 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 02x1z2s; *> query: (?x1723, 06w839_) <- ceremony(?x1723, ?x472), nominated_for(?x1723, ?x6881), nominated_for(?x1723, ?x2783), nominated_for(?x1723, ?x1797), ?x6881 = 07nxnw, film_release_region(?x2783, ?x87), ?x1797 = 050xxm, film(?x988, ?x2783) *> conf = 0.33 ranks of expected_values: 79, 1397 EVAL 09tqxt nominated_for 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 10.000 0.636 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09tqxt nominated_for 06w839_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 35.000 10.000 0.636 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16395-07rzf PRED entity: 07rzf PRED relation: people! PRED expected values: 02w7gg => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 49): 02w7gg (0.33 #2, 0.29 #1465, 0.28 #1157), 041rx (0.25 #81, 0.14 #2314, 0.12 #1852), 0x67 (0.20 #1781, 0.17 #2474, 0.12 #1319), 0xnvg (0.17 #167, 0.07 #1784, 0.05 #2554), 033tf_ (0.11 #1855, 0.10 #2394, 0.10 #2086), 02ctzb (0.11 #400, 0.07 #246, 0.06 #323), 0d7wh (0.08 #1557, 0.07 #248, 0.07 #1172), 07hwkr (0.08 #2399, 0.07 #2091, 0.07 #1860), 02g7sp (0.07 #249, 0.06 #557, 0.06 #326), 06gbnc (0.06 #643, 0.03 #1028, 0.03 #1105) >> Best rule #2 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0b_dy; >> query: (?x11465, 02w7gg) <- film(?x11465, ?x4749), film(?x11465, ?x3268), ?x3268 = 02x6dqb, nominated_for(?x1564, ?x4749), honored_for(?x4749, ?x188) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07rzf people! 02w7gg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.333 http://example.org/people/ethnicity/people #16394-0168ls PRED entity: 0168ls PRED relation: film_release_distribution_medium PRED expected values: 07c52 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 4): 07c52 (0.11 #34, 0.08 #30, 0.07 #14), 07z4p (0.10 #36, 0.07 #32, 0.06 #16), 02nxhr (0.08 #45, 0.07 #13, 0.07 #25), 0735l (0.01 #3) >> Best rule #34 for best value: >> intensional similarity = 4 >> extensional distance = 201 >> proper extension: 07s3m4g; >> query: (?x1547, 07c52) <- titles(?x162, ?x1547), genre(?x1547, ?x53), film_release_region(?x1547, ?x390), ?x390 = 0chghy >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0168ls film_release_distribution_medium 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.108 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #16393-015gm8 PRED entity: 015gm8 PRED relation: film_art_direction_by PRED expected values: 07hhnl => 84 concepts (71 used for prediction) PRED predicted values (max 10 best out of 28): 07hhnl (0.50 #9, 0.12 #64, 0.09 #92), 05683cn (0.20 #23, 0.10 #78, 0.08 #138), 0c4qzm (0.20 #22, 0.08 #138, 0.04 #77), 072twv (0.12 #141, 0.10 #113, 0.10 #58), 071jv5 (0.10 #25, 0.08 #138, 0.06 #249), 0dh73w (0.10 #6, 0.08 #138, 0.03 #144), 05v1sb (0.10 #34, 0.09 #145, 0.09 #90), 057bc6m (0.08 #138), 0584j4n (0.08 #138), 0520r2x (0.06 #28, 0.05 #84, 0.04 #111) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 02r_pp; >> query: (?x11597, 07hhnl) <- film_sets_designed(?x2716, ?x11597), nominated_for(?x198, ?x11597), production_companies(?x11597, ?x902), ?x2716 = 07h1tr >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015gm8 film_art_direction_by 07hhnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 71.000 0.500 http://example.org/film/film/film_art_direction_by #16392-02vqsll PRED entity: 02vqsll PRED relation: nominated_for! PRED expected values: 03hkv_r 02pqp12 04kxsb => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 203): 0gq_v (0.31 #2916, 0.30 #3808, 0.26 #5146), 05p1dby (0.30 #516, 0.19 #13160, 0.19 #12490), 0l8z1 (0.29 #3837, 0.27 #2945, 0.20 #5621), 0gr0m (0.29 #2950, 0.25 #3842, 0.22 #5180), 0gqy2 (0.28 #3008, 0.24 #5238, 0.22 #5684), 0p9sw (0.27 #2917, 0.26 #3809, 0.22 #1356), 07bdd_ (0.25 #493, 0.19 #13160, 0.19 #14722), 05f4m9q (0.25 #455, 0.19 #13160, 0.19 #14722), 04ljl_l (0.25 #449, 0.11 #3348, 0.10 #1118), 0gs96 (0.24 #2977, 0.21 #747, 0.20 #3869) >> Best rule #2916 for best value: >> intensional similarity = 3 >> extensional distance = 280 >> proper extension: 075cph; 047vnkj; >> query: (?x2989, 0gq_v) <- nominated_for(?x361, ?x2989), honored_for(?x2988, ?x2989), production_companies(?x2989, ?x1478) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #719 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 66 *> proper extension: 05y0cr; *> query: (?x2989, 02pqp12) <- country(?x2989, ?x789), ?x789 = 0f8l9c, award(?x2989, ?x2341) *> conf = 0.21 ranks of expected_values: 14, 29, 33 EVAL 02vqsll nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 73.000 73.000 0.312 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02vqsll nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 73.000 73.000 0.312 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02vqsll nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 73.000 73.000 0.312 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16391-0jyx6 PRED entity: 0jyx6 PRED relation: titles! PRED expected values: 01z4y => 88 concepts (65 used for prediction) PRED predicted values (max 10 best out of 71): 01z4y (0.50 #36, 0.39 #3647, 0.34 #448), 07s9rl0 (0.48 #206, 0.47 #722, 0.46 #927), 04xvlr (0.36 #209, 0.33 #725, 0.32 #930), 04jjy (0.29 #410, 0.04 #328, 0.04 #3509), 02l7c8 (0.27 #6011, 0.26 #6220, 0.23 #5802), 01jfsb (0.25 #20, 0.17 #123, 0.13 #4047), 0219x_ (0.23 #5802, 0.21 #1441, 0.21 #1338), 05p553 (0.21 #1441, 0.21 #1338, 0.21 #1853), 01g6gs (0.21 #1441, 0.21 #1338, 0.21 #1853), 06cvj (0.21 #1441, 0.21 #1338, 0.21 #1853) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 09jcj6; >> query: (?x1130, 01z4y) <- executive_produced_by(?x1130, ?x4562), film(?x986, ?x1130), genre(?x1130, ?x239), ?x986 = 081lh >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jyx6 titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 65.000 0.500 http://example.org/media_common/netflix_genre/titles #16390-02c638 PRED entity: 02c638 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #16, 0.83 #80, 0.83 #107), 02nxhr (0.05 #12, 0.05 #27, 0.04 #108), 07c52 (0.04 #180, 0.04 #257, 0.04 #195), 07z4p (0.04 #182, 0.03 #84, 0.03 #244) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 01sxly; 035xwd; 04smdd; 09p4w8; 0hvvf; 0pk1p; >> query: (?x2116, 029j_) <- genre(?x2116, ?x604), written_by(?x2116, ?x7761), ?x604 = 0lsxr >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02c638 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.832 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #16389-02756j PRED entity: 02756j PRED relation: actor! PRED expected values: 050kh5 => 140 concepts (127 used for prediction) PRED predicted values (max 10 best out of 92): 050kh5 (0.14 #1300, 0.13 #505, 0.06 #3157), 047q2k1 (0.12 #11144, 0.11 #3714, 0.10 #21763), 01j7mr (0.07 #320, 0.02 #2972), 026bfsh (0.06 #3811, 0.06 #627, 0.06 #4607), 02py4c8 (0.06 #542, 0.05 #807, 0.04 #1337), 0524b41 (0.06 #661, 0.05 #926, 0.04 #1456), 0d68qy (0.06 #567, 0.05 #832, 0.04 #1362), 027tbrc (0.06 #566, 0.04 #1361, 0.02 #1892), 03cv_gy (0.05 #889, 0.04 #1419, 0.02 #1685), 01vnbh (0.05 #887, 0.04 #1417, 0.02 #1683) >> Best rule #1300 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 04rs03; 08vxk5; 08kp57; 01mz9lt; 04cmrt; 08s0m7; 02x20c9; >> query: (?x6312, 050kh5) <- place_of_birth(?x6312, ?x7412), type_of_union(?x6312, ?x566), ?x7412 = 04vmp >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02756j actor! 050kh5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 127.000 0.136 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #16388-0ptj2 PRED entity: 0ptj2 PRED relation: place_of_death! PRED expected values: 032r1 => 146 concepts (95 used for prediction) PRED predicted values (max 10 best out of 772): 039n1 (0.17 #1251, 0.11 #3518, 0.10 #4276), 07_m9_ (0.17 #960, 0.11 #3227, 0.10 #3985), 017r2 (0.17 #807, 0.11 #3074, 0.10 #3832), 04xm_ (0.17 #1288, 0.11 #3555, 0.10 #4313), 0k_mt (0.17 #1297, 0.11 #3564, 0.10 #4322), 08c7cz (0.17 #1115, 0.11 #3382, 0.10 #4140), 03_f0 (0.17 #1148, 0.11 #3415, 0.10 #4173), 0j3v (0.17 #841, 0.10 #3866, 0.05 #6133), 0372p (0.09 #4695, 0.05 #11339, 0.05 #9827), 026lj (0.05 #11339, 0.05 #9827, 0.05 #15874) >> Best rule #1251 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0156q; 02h6_6p; 04kf4; 02z0j; >> query: (?x5115, 039n1) <- citytown(?x9988, ?x5115), contains(?x1264, ?x5115), place_of_death(?x8418, ?x5115), place_of_birth(?x4543, ?x5115), ?x1264 = 0345h >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #24189 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 02ly_; 0d58_; 07g0_; 079yb; 0cp6w; *> query: (?x5115, ?x2608) <- contains(?x1264, ?x5115), place_of_death(?x8418, ?x5115), influenced_by(?x8418, ?x4547), influenced_by(?x2608, ?x4547) *> conf = 0.03 ranks of expected_values: 508 EVAL 0ptj2 place_of_death! 032r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 146.000 95.000 0.167 http://example.org/people/deceased_person/place_of_death #16387-0345h PRED entity: 0345h PRED relation: featured_film_locations! PRED expected values: 072x7s => 234 concepts (162 used for prediction) PRED predicted values (max 10 best out of 693): 0413cff (0.16 #9850, 0.15 #17874, 0.14 #11309), 06fqlk (0.16 #22615, 0.05 #9963, 0.04 #15798), 05mrf_p (0.16 #22615, 0.05 #11314, 0.04 #45603), 02rq8k8 (0.16 #22615, 0.05 #11214, 0.04 #45503), 02qhlwd (0.16 #22615, 0.02 #100662, 0.02 #102122), 027gy0k (0.16 #22615, 0.02 #102122, 0.02 #102123), 0dmn0x (0.16 #22615, 0.02 #102123, 0.02 #100663), 0bmch_x (0.16 #22615, 0.02 #102123, 0.02 #100663), 061681 (0.14 #10990, 0.14 #3694, 0.11 #9531), 03ydlnj (0.14 #4229, 0.10 #6416, 0.10 #5687) >> Best rule #9850 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 088q4; >> query: (?x1264, 0413cff) <- contains(?x1264, ?x196), olympics(?x1264, ?x452), featured_film_locations(?x1470, ?x1264) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #21997 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 05kj_; 05rgl; 07srw; 0d35y; 068p2; 015jr; 0ht8h; 02frhbc; 0j95; 0snty; ... *> query: (?x1264, 072x7s) <- contains(?x1264, ?x1646), featured_film_locations(?x2189, ?x1646), featured_film_locations(?x1470, ?x1264) *> conf = 0.08 ranks of expected_values: 21 EVAL 0345h featured_film_locations! 072x7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 234.000 162.000 0.158 http://example.org/film/film/featured_film_locations #16386-025n07 PRED entity: 025n07 PRED relation: nominated_for! PRED expected values: 017s11 => 78 concepts (40 used for prediction) PRED predicted values (max 10 best out of 471): 043q6n_ (0.48 #56139, 0.37 #79532, 0.36 #74852), 027t8fw (0.40 #25730, 0.39 #28069, 0.38 #18715), 01xllf (0.30 #53799, 0.26 #28070, 0.23 #84212), 07rd7 (0.29 #7949, 0.04 #29001, 0.03 #10290), 02qggqc (0.29 #30411, 0.22 #11698, 0.22 #14037), 0f5xn (0.27 #79531, 0.26 #28070, 0.23 #84212), 0k6yt1 (0.27 #79531, 0.26 #28070, 0.23 #84212), 028pzq (0.26 #28070, 0.23 #84212, 0.22 #91231), 02bh9 (0.21 #7734, 0.06 #10075, 0.05 #12414), 0284n42 (0.17 #7138, 0.14 #120, 0.11 #9479) >> Best rule #56139 for best value: >> intensional similarity = 4 >> extensional distance = 559 >> proper extension: 0d7vtk; >> query: (?x2968, ?x1417) <- titles(?x8581, ?x2968), language(?x2968, ?x254), produced_by(?x2968, ?x1417), nominated_for(?x2858, ?x2968) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #4778 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 0f7hw; *> query: (?x2968, 017s11) <- film(?x5462, ?x2968), genre(?x2968, ?x225), ?x5462 = 0f5xn, film_release_distribution_medium(?x2968, ?x81) *> conf = 0.05 ranks of expected_values: 68 EVAL 025n07 nominated_for! 017s11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 78.000 40.000 0.476 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16385-01d_h PRED entity: 01d_h PRED relation: gender PRED expected values: 05zppz => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #41, 0.88 #15, 0.87 #39), 02zsn (0.51 #121, 0.46 #161, 0.27 #38) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 02p7xc; >> query: (?x8806, 05zppz) <- music(?x9421, ?x8806), genre(?x9421, ?x225), profession(?x8806, ?x131) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d_h gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.892 http://example.org/people/person/gender #16384-04353 PRED entity: 04353 PRED relation: profession PRED expected values: 02jknp => 144 concepts (60 used for prediction) PRED predicted values (max 10 best out of 45): 02jknp (0.90 #1312, 0.88 #1602, 0.88 #3197), 09jwl (0.27 #7850, 0.17 #5963, 0.17 #6398), 018gz8 (0.25 #1464, 0.18 #5236, 0.14 #3495), 0cbd2 (0.21 #5228, 0.18 #1456, 0.17 #876), 0kyk (0.20 #171, 0.17 #316, 0.14 #461), 0dgd_ (0.20 #172, 0.16 #1767, 0.14 #462), 0lgw7 (0.20 #189, 0.14 #479, 0.08 #334), 016z4k (0.16 #7838, 0.11 #5951, 0.11 #6386), 0nbcg (0.14 #7862, 0.09 #5975, 0.09 #6846), 0np9r (0.14 #888, 0.13 #1468, 0.11 #5240) >> Best rule #1312 for best value: >> intensional similarity = 4 >> extensional distance = 154 >> proper extension: 03ys2f; 03ysmg; >> query: (?x9313, 02jknp) <- award_winner(?x3471, ?x9313), profession(?x9313, ?x319), type_of_union(?x9313, ?x566), film(?x9313, ?x69) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04353 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 60.000 0.904 http://example.org/people/person/profession #16383-0171cm PRED entity: 0171cm PRED relation: type_of_union PRED expected values: 04ztj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.70 #289, 0.69 #69, 0.68 #237), 01g63y (0.24 #6, 0.17 #2, 0.15 #46), 0jgjn (0.04 #4) >> Best rule #289 for best value: >> intensional similarity = 1 >> extensional distance = 2084 >> proper extension: 05d7rk; 04yywz; 06688p; 01l1b90; 05bp8g; 05m63c; 01vw87c; 049tjg; 02g8h; 0d_84; ... >> query: (?x2556, 04ztj) <- film(?x2556, ?x144) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0171cm type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.698 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16382-03_d0 PRED entity: 03_d0 PRED relation: artists PRED expected values: 0b68vs 053yx 01l_vgt 01m15br 01wy61y 026spg 03h_fqv 01vsksr 021r7r 01wk7ql 02h9_l 014g91 => 63 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1017): 02pbrn (0.60 #7550, 0.50 #3755, 0.39 #1898), 020_4z (0.50 #8407, 0.50 #5561, 0.33 #9355), 03c3yf (0.50 #8179, 0.50 #5333, 0.33 #9127), 025xt8y (0.50 #4781, 0.50 #3832, 0.33 #7627), 0ftps (0.50 #4822, 0.50 #3873, 0.33 #7668), 053yx (0.50 #3036, 0.40 #6831, 0.33 #7779), 01309x (0.50 #5954, 0.39 #1898, 0.33 #7852), 0qdyf (0.50 #4965, 0.39 #1898, 0.33 #7811), 0249kn (0.50 #5888, 0.39 #1898, 0.33 #6642), 02cx90 (0.50 #6009, 0.39 #1898, 0.33 #6642) >> Best rule #7550 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 0gt_0v; >> query: (?x505, 02pbrn) <- artists(?x505, ?x5718), artists(?x505, ?x5048), artists(?x505, ?x3492), artists(?x505, ?x2698), ?x2698 = 09hnb, role(?x3492, ?x214), ?x5718 = 024zq, profession(?x5048, ?x131) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3036 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 01fbr2; 0cx6f; *> query: (?x505, 053yx) <- artists(?x505, ?x9134), artists(?x505, ?x3375), artists(?x505, ?x2698), artists(?x505, ?x2169), ?x2698 = 09hnb, parent_genre(?x119, ?x505), artist(?x6474, ?x9134), ?x3375 = 02pzc4, profession(?x2169, ?x131) *> conf = 0.50 ranks of expected_values: 6, 30, 64, 74, 114, 133, 155, 220, 279, 356, 498, 740 EVAL 03_d0 artists 014g91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 02h9_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 01wk7ql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 021r7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 01vsksr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 03h_fqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 026spg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 01wy61y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 01m15br CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 01l_vgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 053yx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 63.000 23.000 0.600 http://example.org/music/genre/artists EVAL 03_d0 artists 0b68vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 63.000 23.000 0.600 http://example.org/music/genre/artists #16381-01jzxy PRED entity: 01jzxy PRED relation: major_field_of_study! PRED expected values: 019v9k => 36 concepts (27 used for prediction) PRED predicted values (max 10 best out of 18): 019v9k (0.78 #119, 0.75 #178, 0.75 #257), 02h4rq6 (0.78 #116, 0.73 #213, 0.71 #175), 04zx3q1 (0.75 #77, 0.70 #96, 0.60 #40), 03bwzr4 (0.68 #202, 0.61 #143, 0.57 #123), 02m4yg (0.50 #30, 0.28 #350, 0.16 #125), 01rr_d (0.46 #311, 0.43 #94, 0.40 #113), 013zdg (0.46 #311, 0.43 #94, 0.40 #113), 027f2w (0.46 #311, 0.43 #94, 0.40 #113), 071tyz (0.46 #311, 0.43 #94, 0.40 #113), 022h5x (0.46 #311, 0.39 #38, 0.35 #75) >> Best rule #119 for best value: >> intensional similarity = 10 >> extensional distance = 35 >> proper extension: 07c52; >> query: (?x2172, 019v9k) <- major_field_of_study(?x13316, ?x2172), major_field_of_study(?x6501, ?x2172), institution(?x1526, ?x13316), country(?x6501, ?x94), student(?x6501, ?x1126), student(?x13316, ?x1211), major_field_of_study(?x13316, ?x9111), ?x9111 = 04sh3, major_field_of_study(?x6501, ?x4268), ?x4268 = 02822 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jzxy major_field_of_study! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 27.000 0.784 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #16380-02b29 PRED entity: 02b29 PRED relation: religion PRED expected values: 03_gx => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 21): 0c8wxp (0.26 #501, 0.23 #51, 0.20 #727), 0kpl (0.22 #641, 0.20 #280, 0.19 #867), 03_gx (0.21 #14, 0.18 #284, 0.16 #645), 0kq2 (0.10 #153, 0.07 #875, 0.07 #649), 092bf5 (0.07 #331, 0.03 #511, 0.03 #692), 01lp8 (0.05 #1, 0.03 #677, 0.03 #993), 02rsw (0.05 #24, 0.03 #249, 0.02 #384), 05w5d (0.05 #25, 0.01 #520), 0n2g (0.05 #508, 0.04 #870, 0.04 #1275), 019cr (0.05 #56, 0.02 #281, 0.01 #597) >> Best rule #501 for best value: >> intensional similarity = 4 >> extensional distance = 85 >> proper extension: 05hdf; >> query: (?x6914, 0c8wxp) <- spouse(?x6914, ?x9483), location(?x6914, ?x7405), people(?x1050, ?x6914), award_winner(?x12105, ?x6914) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 02yy8; *> query: (?x6914, 03_gx) <- spouse(?x6914, ?x9483), profession(?x6914, ?x319), influenced_by(?x12392, ?x6914) *> conf = 0.21 ranks of expected_values: 3 EVAL 02b29 religion 03_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 101.000 0.264 http://example.org/people/person/religion #16379-0hjy PRED entity: 0hjy PRED relation: films PRED expected values: 02rx2m5 => 208 concepts (185 used for prediction) PRED predicted values (max 10 best out of 12): 03hxsv (0.09 #327, 0.05 #858, 0.05 #1389), 0jqp3 (0.09 #48, 0.03 #4296, 0.03 #9606), 02fwfb (0.05 #905, 0.05 #1436, 0.04 #3560), 01znj1 (0.05 #817, 0.05 #1348, 0.04 #3472), 0bs5k8r (0.05 #744, 0.04 #3399, 0.03 #4461), 02725hs (0.05 #1175, 0.04 #3830, 0.03 #8078), 05q7874 (0.03 #5088, 0.02 #13584, 0.02 #23673), 04j4tx (0.03 #7644, 0.03 #9237, 0.02 #20388), 02j69w (0.02 #12448, 0.02 #18820, 0.02 #17227), 09q5w2 (0.02 #13324, 0.02 #18634, 0.02 #18103) >> Best rule #327 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 02j71; 028n3; >> query: (?x953, 03hxsv) <- administrative_parent(?x6497, ?x953), category(?x6497, ?x134), jurisdiction_of_office(?x1195, ?x6497) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hjy films 02rx2m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 208.000 185.000 0.091 http://example.org/film/film_subject/films #16378-0223xd PRED entity: 0223xd PRED relation: category_of PRED expected values: 0223xd => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 17): 0c4ys (0.33 #1, 0.25 #127), 07n52 (0.33 #79), 05x2s (0.06 #247, 0.04 #289, 0.02 #331), 01b8bn (0.06 #241, 0.04 #283, 0.02 #325), 04jhhng (0.04 #292, 0.02 #334), 01ppdy (0.04 #282, 0.02 #324), 0g_w (0.02 #298), 02v1ws (0.02 #337), 02r0d0 (0.02 #336), 01cd7p (0.02 #335) >> Best rule #1 for best value: >> intensional similarity = 46 >> extensional distance = 1 >> proper extension: 03x3wf; >> query: (?x14751, 0c4ys) <- disciplines_or_subjects(?x14751, ?x2605), major_field_of_study(?x13856, ?x2605), major_field_of_study(?x13316, ?x2605), major_field_of_study(?x9947, ?x2605), major_field_of_study(?x7418, ?x2605), major_field_of_study(?x7178, ?x2605), major_field_of_study(?x1681, ?x2605), major_field_of_study(?x1011, ?x2605), genre(?x6030, ?x2605), genre(?x5331, ?x2605), genre(?x2107, ?x2605), genre(?x1813, ?x2605), genre(?x1746, ?x2605), film(?x100, ?x2107), major_field_of_study(?x2605, ?x254), student(?x7178, ?x4563), currency(?x7178, ?x170), major_field_of_study(?x734, ?x2605), major_field_of_study(?x2014, ?x2605), school_type(?x7418, ?x3205), film(?x8568, ?x5331), ?x3205 = 01rs41, institution(?x620, ?x1681), student(?x1681, ?x1580), ?x1011 = 07w0v, film(?x1104, ?x5331), major_field_of_study(?x1681, ?x10391), nominated_for(?x68, ?x5331), fraternities_and_sororities(?x1681, ?x3697), ?x10391 = 02jfc, nominated_for(?x451, ?x2107), school(?x260, ?x1681), film_crew_role(?x5331, ?x137), produced_by(?x5331, ?x3568), executive_produced_by(?x2107, ?x1533), film(?x366, ?x6030), category(?x9947, ?x134), award(?x1813, ?x112), nominated_for(?x72, ?x1813), student(?x2605, ?x445), colors(?x13856, ?x3189), films(?x9829, ?x1746), citytown(?x13316, ?x1646), nominated_for(?x1622, ?x6030), film(?x510, ?x1746), school(?x465, ?x1681) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0223xd category_of 0223xd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 19.000 19.000 0.333 http://example.org/award/award_category/category_of #16377-03s6l2 PRED entity: 03s6l2 PRED relation: films! PRED expected values: 07c52 => 98 concepts (32 used for prediction) PRED predicted values (max 10 best out of 57): 05489 (0.09 #1306, 0.08 #681, 0.05 #993), 01w1sx (0.09 #91, 0.04 #2444, 0.03 #3232), 07c52 (0.09 #20, 0.04 #961, 0.03 #177), 04gb7 (0.09 #45, 0.03 #1299, 0.02 #830), 01d5g (0.09 #110, 0.02 #1364), 018w0j (0.09 #93, 0.01 #1347), 025t3bg (0.09 #57, 0.01 #686), 0fx2s (0.06 #702, 0.05 #858, 0.04 #1484), 06d4h (0.06 #1297, 0.04 #2237, 0.03 #4758), 0bq3x (0.06 #1284, 0.02 #2067, 0.02 #3171) >> Best rule #1306 for best value: >> intensional similarity = 5 >> extensional distance = 141 >> proper extension: 035xwd; 035s95; 0c34mt; 05_5rjx; 01j5ql; 0h14ln; >> query: (?x603, 05489) <- genre(?x603, ?x604), genre(?x603, ?x53), ?x53 = 07s9rl0, film(?x166, ?x603), ?x604 = 0lsxr >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 06z8s_; *> query: (?x603, 07c52) <- genre(?x603, ?x53), nominated_for(?x286, ?x603), film_crew_role(?x603, ?x137), ?x286 = 014zcr *> conf = 0.09 ranks of expected_values: 3 EVAL 03s6l2 films! 07c52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 32.000 0.091 http://example.org/film/film_subject/films #16376-0fbtbt PRED entity: 0fbtbt PRED relation: award! PRED expected values: 01gp_x 0b05xm 027hnjh 05y5fw 08qmfm => 38 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2673): 02kxbx3 (0.69 #14187, 0.18 #17492, 0.13 #20799), 02kxbwx (0.69 #13393, 0.16 #16698, 0.12 #20005), 019pkm (0.68 #52883, 0.68 #23133, 0.68 #19828), 08qvhv (0.68 #52883, 0.68 #23133, 0.68 #19828), 09hd6f (0.68 #52883, 0.68 #19828, 0.66 #23132), 0jmj (0.67 #4511, 0.57 #11119, 0.14 #52882), 014zcr (0.56 #13267, 0.33 #3355, 0.29 #9963), 05kfs (0.56 #13378, 0.18 #16683, 0.13 #19990), 026670 (0.56 #15925, 0.16 #19230, 0.12 #22537), 0js9s (0.56 #15090, 0.15 #18395, 0.11 #21702) >> Best rule #14187 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 02qyp19; 0f_nbyh; 03hkv_r; 0gr4k; 04dn09n; 02x1dht; 02n9nmz; 02pqp12; 0gq9h; 0gs9p; ... >> query: (?x4921, 02kxbx3) <- award(?x12138, ?x4921), award(?x7043, ?x4921), award(?x3260, ?x4921), ?x3260 = 05ldnp, award_winner(?x7043, ?x6071), award(?x687, ?x4921), executive_produced_by(?x8770, ?x12138) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #6608 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 0cqh46; 0ck27z; 0bdw6t; 0cqhb3; *> query: (?x4921, ?x9326) <- award(?x4299, ?x4921), award(?x3366, ?x4921), award(?x3260, ?x4921), award_nominee(?x9326, ?x4299), ?x3366 = 01rzqj, profession(?x3260, ?x319), ceremony(?x4921, ?x2126) *> conf = 0.17 ranks of expected_values: 177, 178, 197, 289, 331 EVAL 0fbtbt award! 08qmfm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 38.000 17.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fbtbt award! 05y5fw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 38.000 17.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fbtbt award! 027hnjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 38.000 17.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fbtbt award! 0b05xm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 38.000 17.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fbtbt award! 01gp_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 38.000 17.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16375-02211by PRED entity: 02211by PRED relation: company PRED expected values: 0k8z 01c7j1 => 32 concepts (23 used for prediction) PRED predicted values (max 10 best out of 719): 060ppp (0.78 #3202, 0.75 #2873, 0.71 #2544), 0300cp (0.78 #3010, 0.75 #2681, 0.71 #2352), 0z90c (0.71 #2471, 0.67 #3129, 0.67 #1813), 087c7 (0.71 #2309, 0.67 #2967, 0.62 #2638), 0vlf (0.71 #2585, 0.67 #3243, 0.62 #2914), 05njw (0.71 #2557, 0.62 #2886, 0.60 #1238), 02630g (0.71 #2445, 0.62 #2774, 0.60 #1126), 02r5dz (0.67 #3031, 0.62 #2702, 0.60 #726), 07xyn1 (0.67 #3143, 0.62 #2814, 0.60 #838), 04fv0k (0.67 #3168, 0.62 #2839, 0.57 #2510) >> Best rule #3202 for best value: >> intensional similarity = 19 >> extensional distance = 7 >> proper extension: 01yc02; >> query: (?x554, 060ppp) <- company(?x554, ?x7218), company(?x554, ?x555), industry(?x555, ?x5078), ?x7218 = 019rl6, service_location(?x555, ?x1603), film_release_region(?x5877, ?x1603), film_release_region(?x4690, ?x1603), film_release_region(?x3606, ?x1603), film_release_region(?x3088, ?x1603), film_release_region(?x2441, ?x1603), film_release_region(?x1364, ?x1603), olympics(?x1603, ?x418), ?x3088 = 06w839_, ?x1364 = 047msdk, ?x4690 = 0gkz3nz, country(?x150, ?x1603), ?x5877 = 02qyv3h, ?x3606 = 0gh65c5, ?x2441 = 0cc5mcj >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1397 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 4 *> proper extension: 04192r; *> query: (?x554, 0k8z) <- company(?x554, ?x9873), company(?x554, ?x9198), company(?x554, ?x555), industry(?x555, ?x5078), service_language(?x555, ?x7599), service_language(?x555, ?x5974), service_language(?x555, ?x254), service_location(?x555, ?x789), service_location(?x555, ?x94), ?x9873 = 01dfb6, ?x254 = 02h40lc, currency(?x9198, ?x170), ?x789 = 0f8l9c, countries_spoken_in(?x5974, ?x279), ?x94 = 09c7w0, list(?x9198, ?x5997), contact_category(?x555, ?x897), languages_spoken(?x7790, ?x7599) *> conf = 0.50 ranks of expected_values: 53, 223 EVAL 02211by company 01c7j1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 32.000 23.000 0.778 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 02211by company 0k8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 32.000 23.000 0.778 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #16374-0dc7hc PRED entity: 0dc7hc PRED relation: language PRED expected values: 02h40lc => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 52): 02h40lc (0.90 #297, 0.90 #1243, 0.89 #1184), 06nm1 (0.30 #129, 0.26 #247, 0.16 #660), 03_9r (0.25 #10, 0.17 #69, 0.11 #246), 04306rv (0.21 #241, 0.14 #300, 0.12 #654), 06b_j (0.14 #318, 0.11 #259, 0.07 #672), 064_8sq (0.12 #1204, 0.12 #1263, 0.12 #553), 02bjrlw (0.10 #119, 0.10 #591, 0.09 #650), 012w70 (0.10 #131, 0.06 #1480, 0.06 #721), 02hwhyv (0.10 #148, 0.06 #1480, 0.05 #266), 03hkp (0.10 #133, 0.06 #1480, 0.03 #4998) >> Best rule #297 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 08gsvw; 02sg5v; 08ct6; 0ndwt2w; 03clwtw; 0ft18; 025scjj; >> query: (?x9774, 02h40lc) <- film(?x788, ?x9774), written_by(?x9774, ?x3917), film(?x1867, ?x9774), ?x788 = 0g1rw >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dc7hc language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.905 http://example.org/film/film/language #16373-0h1v19 PRED entity: 0h1v19 PRED relation: film_release_region PRED expected values: 05r4w => 93 concepts (82 used for prediction) PRED predicted values (max 10 best out of 140): 0f8l9c (0.93 #1057, 0.93 #886, 0.90 #199), 07ssc (0.86 #192, 0.80 #1050, 0.79 #707), 06mkj (0.84 #240, 0.84 #1098, 0.84 #755), 05r4w (0.83 #860, 0.82 #1031, 0.82 #1202), 0345h (0.80 #213, 0.72 #728, 0.70 #1071), 059j2 (0.79 #1755, 0.78 #1069, 0.76 #2269), 03_3d (0.78 #180, 0.78 #867, 0.78 #1038), 03rjj (0.78 #178, 0.78 #1722, 0.77 #1036), 0chghy (0.78 #185, 0.78 #1729, 0.77 #1043), 03gj2 (0.78 #203, 0.72 #1747, 0.69 #718) >> Best rule #1057 for best value: >> intensional similarity = 4 >> extensional distance = 130 >> proper extension: 0gh6j94; >> query: (?x2738, 0f8l9c) <- film_release_region(?x2738, ?x1355), genre(?x2738, ?x239), ?x1355 = 0h7x, film_crew_role(?x2738, ?x137) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #860 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 112 *> proper extension: 0g5qs2k; 053rxgm; 0gvrws1; 0b_5d; 0dgpwnk; 02fqrf; 0g9zljd; 0g57wgv; 0gy4k; *> query: (?x2738, 05r4w) <- film_release_region(?x2738, ?x1355), genre(?x2738, ?x239), ?x1355 = 0h7x, nominated_for(?x382, ?x2738) *> conf = 0.83 ranks of expected_values: 4 EVAL 0h1v19 film_release_region 05r4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 82.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16372-02xwq9 PRED entity: 02xwq9 PRED relation: people! PRED expected values: 0x67 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 27): 041rx (0.13 #697, 0.13 #620, 0.12 #543), 0x67 (0.12 #87, 0.10 #1011, 0.10 #626), 033tf_ (0.08 #7, 0.08 #854, 0.08 #238), 0xnvg (0.08 #13, 0.07 #244, 0.05 #860), 06v41q (0.08 #29, 0.02 #260, 0.01 #876), 0g8_vp (0.08 #22, 0.01 #638, 0.01 #176), 038723 (0.08 #69), 02w7gg (0.07 #849, 0.06 #1080, 0.06 #1157), 07hwkr (0.06 #89, 0.04 #551, 0.04 #1706), 07bch9 (0.03 #562, 0.03 #870, 0.03 #254) >> Best rule #697 for best value: >> intensional similarity = 3 >> extensional distance = 733 >> proper extension: 09jd9; >> query: (?x4432, 041rx) <- award_winner(?x870, ?x4432), student(?x10576, ?x4432), place_of_birth(?x4432, ?x4356) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 01ty7ll; 01wz_ml; *> query: (?x4432, 0x67) <- profession(?x4432, ?x1032), place_of_birth(?x4432, ?x4356), ?x4356 = 06wxw *> conf = 0.12 ranks of expected_values: 2 EVAL 02xwq9 people! 0x67 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.133 http://example.org/people/ethnicity/people #16371-07ykkx5 PRED entity: 07ykkx5 PRED relation: genre PRED expected values: 01j1n2 => 77 concepts (42 used for prediction) PRED predicted values (max 10 best out of 92): 07qht4 (0.80 #1442, 0.78 #1563, 0.65 #961), 05p553 (0.56 #966, 0.54 #1085, 0.52 #1205), 06cvj (0.41 #364, 0.25 #3, 0.20 #124), 01jfsb (0.35 #2905, 0.31 #2182, 0.31 #494), 02kdv5l (0.30 #2051, 0.30 #842, 0.29 #2171), 03k9fj (0.30 #852, 0.22 #2061, 0.22 #3506), 04xvlr (0.29 #1322, 0.28 #1443, 0.27 #1929), 01hmnh (0.28 #857, 0.18 #2909, 0.17 #2066), 01t_vv (0.25 #53, 0.20 #174, 0.15 #294), 0lsxr (0.20 #490, 0.18 #1330, 0.18 #1451) >> Best rule #1442 for best value: >> intensional similarity = 4 >> extensional distance = 400 >> proper extension: 05jyb2; 0413cff; 02qjv1p; >> query: (?x13178, ?x11671) <- titles(?x11671, ?x13178), genre(?x2191, ?x11671), genre(?x13178, ?x53), ?x2191 = 0gfzgl >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #180 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 01qn7n; 03y317; 03g9xj; *> query: (?x13178, 01j1n2) <- titles(?x11671, ?x13178), titles(?x1403, ?x13178), ?x11671 = 07qht4, titles(?x1403, ?x4460), genre(?x2078, ?x1403), country(?x4460, ?x94) *> conf = 0.20 ranks of expected_values: 12 EVAL 07ykkx5 genre 01j1n2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 77.000 42.000 0.795 http://example.org/film/film/genre #16370-0159h6 PRED entity: 0159h6 PRED relation: award_nominee PRED expected values: 0175wg => 121 concepts (59 used for prediction) PRED predicted values (max 10 best out of 946): 0159h6 (0.88 #2405, 0.50 #4726, 0.43 #84), 0175wg (0.83 #4642, 0.82 #6963, 0.81 #104434), 0bwgc_ (0.83 #4642, 0.82 #6963, 0.81 #104434), 0kszw (0.25 #74265, 0.24 #118359, 0.14 #78906), 0f502 (0.25 #74265, 0.24 #118359, 0.09 #7966), 016yvw (0.25 #74265, 0.24 #118359, 0.09 #8214), 0z4s (0.25 #74265, 0.24 #118359, 0.05 #11683), 018grr (0.25 #74265, 0.24 #118359, 0.02 #25967), 059_gf (0.25 #74265, 0.24 #118359), 0h32q (0.25 #74265, 0.24 #118359) >> Best rule #2405 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 0m2wm; 02zq43; 0h5g_; 09y20; 05tk7y; 06mmb; 0993r; 020_95; 0175wg; 016xh5; ... >> query: (?x488, 0159h6) <- nominated_for(?x488, ?x715), award_nominee(?x2284, ?x488), ?x2284 = 07hbxm >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #4642 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 0m2wm; 02zq43; 0h5g_; 09y20; 05tk7y; 06mmb; 0993r; 020_95; 0175wg; 016xh5; ... *> query: (?x488, ?x100) <- nominated_for(?x488, ?x715), award_nominee(?x2284, ?x488), award_nominee(?x100, ?x488), ?x2284 = 07hbxm *> conf = 0.83 ranks of expected_values: 2 EVAL 0159h6 award_nominee 0175wg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 59.000 0.882 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16369-06vsbt PRED entity: 06vsbt PRED relation: award_winner! PRED expected values: 09g90vz => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 100): 09qvms (0.32 #13, 0.06 #293, 0.06 #2113), 09g90vz (0.28 #123, 0.17 #7421, 0.10 #543), 05c1t6z (0.17 #7421, 0.07 #435, 0.04 #155), 03nnm4t (0.17 #7421, 0.05 #493, 0.04 #73), 0418154 (0.17 #7421, 0.04 #387, 0.03 #527), 02wzl1d (0.17 #7421, 0.04 #11, 0.03 #2111), 0gvstc3 (0.17 #7421, 0.04 #1154, 0.03 #2134), 05zksls (0.17 #7421, 0.02 #1155, 0.02 #2135), 092t4b (0.07 #472, 0.04 #2012, 0.04 #2152), 03gyp30 (0.06 #536, 0.04 #2916, 0.04 #1236) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 04vmqg; >> query: (?x5505, 09qvms) <- award_nominee(?x5505, ?x3051), ?x3051 = 0gd_b_, gender(?x5505, ?x231) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 04vmqg; *> query: (?x5505, 09g90vz) <- award_nominee(?x5505, ?x3051), ?x3051 = 0gd_b_, gender(?x5505, ?x231) *> conf = 0.28 ranks of expected_values: 2 EVAL 06vsbt award_winner! 09g90vz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.320 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #16368-06f0dc PRED entity: 06f0dc PRED relation: legislative_sessions! PRED expected values: 0194xc => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 123): 0194xc (0.79 #320, 0.77 #432, 0.75 #266), 01lct6 (0.59 #116, 0.50 #218, 0.50 #164), 06hx2 (0.59 #116, 0.47 #367, 0.45 #531), 01mvpv (0.33 #183, 0.25 #306, 0.17 #220), 0dq2k (0.33 #170, 0.22 #386, 0.20 #350), 0rlz (0.25 #120, 0.16 #590, 0.14 #406), 0835q (0.25 #130, 0.16 #590, 0.13 #362), 03_nq (0.20 #357, 0.18 #376, 0.17 #393), 042fk (0.17 #400, 0.16 #590, 0.14 #418), 0fd_1 (0.16 #590, 0.09 #447, 0.09 #409) >> Best rule #320 for best value: >> intensional similarity = 36 >> extensional distance = 12 >> proper extension: 05l2z4; 03z5xd; 06r713; 060ny2; 04h1rz; >> query: (?x952, 0194xc) <- district_represented(?x952, ?x13269), district_represented(?x952, ?x3818), district_represented(?x952, ?x3634), district_represented(?x952, ?x3086), district_represented(?x952, ?x2977), district_represented(?x952, ?x1024), legislative_sessions(?x952, ?x6728), legislative_sessions(?x952, ?x3463), legislative_sessions(?x952, ?x1027), legislative_sessions(?x652, ?x952), ?x6728 = 070mff, legislative_sessions(?x1830, ?x952), state(?x405, ?x3818), currency(?x3086, ?x170), district_represented(?x5006, ?x3818), district_represented(?x2712, ?x3818), jurisdiction_of_office(?x13698, ?x3818), jurisdiction_of_office(?x900, ?x3086), religion(?x3818, ?x109), ?x1027 = 02bn_p, religion(?x3086, ?x962), adjoins(?x2982, ?x1024), taxonomy(?x1024, ?x939), ?x3463 = 02bqmq, ?x2712 = 01gst_, location(?x5285, ?x3634), location(?x2559, ?x3634), role(?x5285, ?x228), ?x2977 = 081mh, contains(?x3818, ?x1440), partially_contains(?x1024, ?x13214), contains(?x3634, ?x216), award(?x5285, ?x724), ?x5006 = 01gtc0, adjoins(?x13269, ?x12828), ?x2559 = 06mmb >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06f0dc legislative_sessions! 0194xc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.786 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #16367-03m6pk PRED entity: 03m6pk PRED relation: film PRED expected values: 03_gz8 03ydlnj => 111 concepts (69 used for prediction) PRED predicted values (max 10 best out of 930): 01cmp9 (0.33 #1047, 0.08 #8183, 0.06 #13535), 0ddt_ (0.33 #474, 0.05 #16530, 0.02 #23666), 0879bpq (0.33 #449, 0.02 #36131, 0.01 #23641), 0bz6sq (0.33 #1511, 0.02 #37193, 0.01 #80009), 0ddcbd5 (0.33 #670, 0.01 #32784), 0fzm0g (0.33 #1776), 03whyr (0.33 #1565), 05p3738 (0.33 #262), 04cj79 (0.25 #2378, 0.05 #16650, 0.02 #25571), 06_wqk4 (0.25 #1910, 0.04 #23318, 0.03 #39376) >> Best rule #1047 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0686zv; >> query: (?x6663, 01cmp9) <- film(?x6663, ?x9786), film(?x6663, ?x4664), ?x9786 = 06bc59, nationality(?x6663, ?x4221), film_format(?x4664, ?x10390) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #36803 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 261 *> proper extension: 06y8v; 0tfc; *> query: (?x6663, 03_gz8) <- nationality(?x6663, ?x4221), administrative_parent(?x9969, ?x4221), people(?x6736, ?x6663), state_province_region(?x4220, ?x4221) *> conf = 0.02 ranks of expected_values: 466 EVAL 03m6pk film 03ydlnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 69.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 03m6pk film 03_gz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 111.000 69.000 0.333 http://example.org/film/actor/film./film/performance/film #16366-05slvm PRED entity: 05slvm PRED relation: award PRED expected values: 0bsjcw => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 205): 0cqgl9 (0.50 #190, 0.19 #591, 0.15 #17249), 0gqwc (0.50 #73, 0.15 #17249, 0.14 #6819), 02x4x18 (0.50 #131, 0.15 #17249, 0.14 #6819), 0bfvw2 (0.50 #15, 0.15 #17249, 0.14 #6819), 0gkts9 (0.50 #166, 0.15 #17249, 0.14 #6819), 0cqhk0 (0.48 #438, 0.17 #37, 0.15 #17249), 0bdwft (0.33 #67, 0.14 #6819, 0.14 #468), 0ck27z (0.18 #2898, 0.15 #4102, 0.14 #492), 094qd5 (0.17 #43, 0.15 #17249, 0.14 #6819), 02z0dfh (0.17 #74, 0.15 #17249, 0.14 #6819) >> Best rule #190 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 030znt; 01bh6y; >> query: (?x4125, 0cqgl9) <- award_nominee(?x7512, ?x4125), nominated_for(?x4125, ?x167), ?x7512 = 01q9b9 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #17249 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2183 *> proper extension: 01w92; 04glx0; *> query: (?x4125, ?x375) <- award_nominee(?x5404, ?x4125), award_winner(?x375, ?x5404) *> conf = 0.15 ranks of expected_values: 19 EVAL 05slvm award 0bsjcw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 59.000 59.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16365-05jbn PRED entity: 05jbn PRED relation: location_of_ceremony! PRED expected values: 04ztj => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #45, 0.86 #37, 0.84 #89), 01g63y (0.30 #739, 0.30 #702, 0.30 #645), 0jgjn (0.07 #20, 0.07 #40, 0.06 #48), 01bl8s (0.04 #35, 0.03 #51, 0.02 #107) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 02k54; >> query: (?x4978, 04ztj) <- vacationer(?x4978, ?x3421), contains(?x4978, ?x1506), citytown(?x8559, ?x4978) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05jbn location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.875 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #16364-08xwck PRED entity: 08xwck PRED relation: profession PRED expected values: 02krf9 => 93 concepts (67 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.78 #3542, 0.73 #5454, 0.72 #601), 02jknp (0.42 #4124, 0.42 #3830, 0.39 #2353), 02krf9 (0.32 #1495, 0.30 #466, 0.30 #1936), 018gz8 (0.25 #603, 0.20 #15, 0.19 #1632), 0cbd2 (0.22 #3829, 0.22 #4123, 0.20 #741), 0np9r (0.20 #19, 0.18 #1342, 0.17 #166), 09jwl (0.20 #3399, 0.18 #5017, 0.18 #4576), 0kyk (0.13 #616, 0.12 #2969, 0.12 #4145), 0nbcg (0.13 #3412, 0.13 #5030, 0.12 #4589), 0dz3r (0.13 #5002, 0.12 #4561, 0.12 #3384) >> Best rule #3542 for best value: >> intensional similarity = 3 >> extensional distance = 800 >> proper extension: 049tjg; >> query: (?x8522, 02hrh1q) <- gender(?x8522, ?x231), people(?x1050, ?x8522), nominated_for(?x8522, ?x6439) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1495 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 144 *> proper extension: 02q6cv4; *> query: (?x8522, 02krf9) <- producer_type(?x8522, ?x632), award_nominee(?x8522, ?x65), program(?x8522, ?x4898) *> conf = 0.32 ranks of expected_values: 3 EVAL 08xwck profession 02krf9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 67.000 0.784 http://example.org/people/person/profession #16363-0fpzwf PRED entity: 0fpzwf PRED relation: time_zones PRED expected values: 02fqwt => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 12): 02fqwt (0.74 #950, 0.29 #456, 0.28 #105), 02hcv8 (0.46 #1395, 0.45 #1057, 0.44 #250), 02lcqs (0.35 #252, 0.32 #551, 0.26 #1059), 02llzg (0.16 #290, 0.14 #524, 0.14 #615), 02hczc (0.14 #457, 0.12 #171, 0.11 #67), 03bdv (0.12 #279, 0.12 #383, 0.12 #318), 03plfd (0.07 #296, 0.07 #530, 0.07 #686), 042g7t (0.04 #24, 0.02 #609, 0.02 #37), 052vwh (0.02 #818, 0.02 #623, 0.02 #988), 0gsrz4 (0.02 #1673) >> Best rule #950 for best value: >> intensional similarity = 2 >> extensional distance = 173 >> proper extension: 0jq27; >> query: (?x5771, ?x1638) <- administrative_division(?x5771, ?x10567), time_zones(?x10567, ?x1638) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fpzwf time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.739 http://example.org/location/location/time_zones #16362-064r9cb PRED entity: 064r9cb PRED relation: artist PRED expected values: 01s7ns => 48 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1093): 0565cz (0.40 #1027, 0.33 #1864, 0.33 #191), 01wg25j (0.40 #1455, 0.33 #2292, 0.33 #619), 016376 (0.40 #1585, 0.33 #2422, 0.33 #749), 0134s5 (0.40 #1071, 0.33 #1908, 0.33 #235), 0178kd (0.40 #1286, 0.33 #2123, 0.33 #450), 01wg6y (0.40 #1496, 0.33 #2333, 0.33 #660), 01lmj3q (0.40 #851, 0.33 #1688, 0.33 #15), 03f3yfj (0.40 #1408, 0.33 #2245, 0.33 #572), 01kp_1t (0.40 #1529, 0.33 #2366, 0.33 #693), 026spg (0.33 #2002, 0.33 #329, 0.25 #2838) >> Best rule #1027 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 0g768; 05clg8; >> query: (?x13380, 0565cz) <- artist(?x13380, ?x6208), artist(?x13380, ?x5225), artist(?x13380, ?x1388), ?x6208 = 07r4c, award_winner(?x1232, ?x5225), artists(?x505, ?x5225), award(?x5225, ?x724), participant(?x3397, ?x5225), award_nominee(?x1388, ?x527) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #7451 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 39 *> proper extension: 01gfq4; 081g_l; 01clyr; 011k11; 01cl0d; 05b0f7; 07gqbk; *> query: (?x13380, 01s7ns) <- artist(?x13380, ?x6797), artist(?x13380, ?x6208), nationality(?x6208, ?x94), role(?x6208, ?x74), location(?x6208, ?x362), gender(?x6797, ?x514), performance_role(?x6208, ?x1466), nominated_for(?x6797, ?x9322), country(?x54, ?x94) *> conf = 0.05 ranks of expected_values: 585 EVAL 064r9cb artist 01s7ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 19.000 0.400 http://example.org/music/record_label/artist #16361-0jmjr PRED entity: 0jmjr PRED relation: team! PRED expected values: 02_nkp => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 480): 02d9k (0.35 #2142, 0.10 #4304, 0.06 #1579), 04g9sq (0.33 #222, 0.30 #334, 0.28 #1796), 012xdf (0.33 #958, 0.29 #846, 0.27 #510), 02cg2v (0.27 #1122, 0.25 #1234, 0.23 #784), 03lh3v (0.27 #915, 0.18 #467, 0.14 #803), 01sg7_ (0.27 #957, 0.14 #2309, 0.14 #845), 02lm0t (0.20 #441, 0.15 #777, 0.15 #665), 04bsx1 (0.16 #4373, 0.07 #5403, 0.06 #5751), 03n69x (0.15 #4318, 0.14 #4546, 0.13 #2830), 03l26m (0.15 #2223, 0.14 #873, 0.10 #4297) >> Best rule #2142 for best value: >> intensional similarity = 9 >> extensional distance = 18 >> proper extension: 026xxv_; >> query: (?x9937, 02d9k) <- team(?x10097, ?x9937), location(?x10097, ?x1523), origin(?x250, ?x1523), contains(?x94, ?x1523), place_of_birth(?x338, ?x1523), featured_film_locations(?x718, ?x1523), contains(?x1523, ?x682), teams(?x1523, ?x705), ?x718 = 0hmr4 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1112 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 13 *> proper extension: 0jmfv; 0jmcv; *> query: (?x9937, 02_nkp) <- position(?x9937, ?x1348), draft(?x9937, ?x8542), draft(?x9937, ?x4979), school(?x9937, ?x6953), team(?x10097, ?x9937), ?x8542 = 09th87, ?x4979 = 0f4vx0, ?x1348 = 01pv51, school(?x2174, ?x6953), major_field_of_study(?x6953, ?x3213), season(?x2174, ?x2406) *> conf = 0.13 ranks of expected_values: 13 EVAL 0jmjr team! 02_nkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 62.000 62.000 0.350 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #16360-0nk3g PRED entity: 0nk3g PRED relation: artists PRED expected values: 019f9z => 49 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1175): 019f9z (0.77 #6000, 0.71 #3840, 0.56 #4920), 01wg25j (0.71 #4019, 0.46 #6179, 0.44 #5099), 020_4z (0.71 #4182, 0.44 #5262, 0.38 #6342), 01kx_81 (0.71 #3328, 0.44 #4408, 0.38 #5488), 01wd9lv (0.71 #3818, 0.44 #4898, 0.38 #5978), 011z3g (0.67 #4925, 0.57 #3845, 0.54 #6005), 0bs1g5r (0.67 #5074, 0.57 #3994, 0.50 #1833), 07s3vqk (0.67 #4333, 0.57 #3253, 0.50 #1092), 012vd6 (0.67 #4801, 0.57 #3721, 0.50 #1560), 01vvycq (0.67 #4369, 0.50 #1128, 0.46 #5449) >> Best rule #6000 for best value: >> intensional similarity = 7 >> extensional distance = 11 >> proper extension: 016cjb; >> query: (?x11960, 019f9z) <- artists(?x11960, ?x12593), artists(?x11960, ?x10712), ?x10712 = 016376, artists(?x1127, ?x12593), artist(?x6474, ?x12593), ?x1127 = 02x8m, ?x6474 = 0g768 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nk3g artists 019f9z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 26.000 0.769 http://example.org/music/genre/artists #16359-031k24 PRED entity: 031k24 PRED relation: film PRED expected values: 09gdm7q => 89 concepts (45 used for prediction) PRED predicted values (max 10 best out of 469): 02qr3k8 (0.07 #1281, 0.03 #6618, 0.02 #67106), 0dr_4 (0.07 #7117, 0.02 #5581), 09xbpt (0.05 #35582, 0.03 #47, 0.03 #74725), 06z8s_ (0.05 #35582, 0.03 #129, 0.03 #74725), 0b6tzs (0.05 #35582, 0.03 #74725, 0.03 #74724), 03s6l2 (0.05 #35582, 0.03 #74725, 0.03 #74724), 09q5w2 (0.05 #35582, 0.03 #74725, 0.03 #74724), 0cz_ym (0.05 #35582, 0.03 #74725, 0.03 #74724), 0dl6fv (0.05 #35582, 0.03 #74725, 0.03 #74724), 05znxx (0.05 #35582, 0.03 #74725, 0.03 #74724) >> Best rule #1281 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 012dr7; 012c6j; 01200d; 034cj9; >> query: (?x8066, 02qr3k8) <- type_of_union(?x8066, ?x566), award(?x8066, ?x591), ?x591 = 0f4x7 >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 031k24 film 09gdm7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 45.000 0.075 http://example.org/film/actor/film./film/performance/film #16358-071ynp PRED entity: 071ynp PRED relation: award_nominee! PRED expected values: 02bkdn => 92 concepts (31 used for prediction) PRED predicted values (max 10 best out of 637): 02bkdn (0.82 #27967, 0.81 #27966, 0.81 #62925), 03yj_0n (0.28 #72250, 0.17 #814, 0.17 #23305), 0cjsxp (0.28 #72250, 0.17 #871, 0.17 #23305), 02l6dy (0.28 #72250, 0.17 #1403, 0.17 #23305), 0bx0lc (0.28 #72250, 0.17 #1367, 0.17 #23305), 0f830f (0.28 #72250, 0.17 #108, 0.17 #23305), 0dyztm (0.28 #72250, 0.17 #1368, 0.17 #23305), 03w1v2 (0.28 #72250, 0.17 #88, 0.17 #23305), 08w7vj (0.28 #72250, 0.17 #173, 0.14 #27968), 027dtv3 (0.28 #72250, 0.17 #109, 0.14 #27968) >> Best rule #27967 for best value: >> intensional similarity = 3 >> extensional distance = 645 >> proper extension: 0kk9v; 0fqy4p; 05xbx; 0c41qv; 026v1z; >> query: (?x3225, ?x3224) <- award_nominee(?x3225, ?x3224), award_nominee(?x3224, ?x494), category(?x3225, ?x134) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 071ynp award_nominee! 02bkdn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 31.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16357-01jdxj PRED entity: 01jdxj PRED relation: teams! PRED expected values: 0rng => 105 concepts (104 used for prediction) PRED predicted values (max 10 best out of 144): 01fbb3 (0.33 #214, 0.25 #484, 0.20 #754), 0ck6r (0.20 #738, 0.12 #1548, 0.01 #5603), 04jpl (0.12 #1359, 0.07 #1629, 0.06 #1900), 0l3h (0.12 #1486, 0.01 #7431, 0.01 #8243), 02jx1 (0.10 #2433, 0.07 #10540, 0.06 #1933), 0ctw_b (0.10 #2433, 0.07 #10540, 0.05 #18105), 0k33p (0.07 #1819, 0.05 #2632, 0.02 #3714), 0h3y (0.07 #1627, 0.04 #2982, 0.03 #3252), 06s_2 (0.07 #1826, 0.01 #7501), 0pfd9 (0.06 #2140, 0.04 #2954, 0.04 #3224) >> Best rule #214 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 01x4wq; >> query: (?x7674, 01fbb3) <- position(?x7674, ?x530), position(?x7674, ?x60), sport(?x7674, ?x471), team(?x10244, ?x7674), ?x60 = 02nzb8, ?x10244 = 07zr66, ?x530 = 02_j1w, colors(?x7674, ?x1101), ?x1101 = 06fvc >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jdxj teams! 0rng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 104.000 0.333 http://example.org/sports/sports_team_location/teams #16356-02_j1w PRED entity: 02_j1w PRED relation: position! PRED expected values: 058dm9 04d817 071rlr 03x6rj 05p8bf9 => 10 concepts (10 used for prediction) PRED predicted values (max 10 best out of 168): 011v3 (0.82 #338, 0.82 #335, 0.82 #504), 02pp1 (0.82 #338, 0.82 #335, 0.82 #504), 0j46b (0.82 #338, 0.82 #335, 0.82 #504), 03x6m (0.82 #338, 0.82 #335, 0.82 #504), 019m9h (0.82 #338, 0.82 #335, 0.82 #504), 0371rb (0.82 #338, 0.82 #335, 0.82 #504), 0k_l4 (0.82 #338, 0.82 #335, 0.82 #504), 01z1r (0.82 #338, 0.82 #335, 0.82 #504), 02029f (0.82 #338, 0.82 #335, 0.82 #504), 01vqc7 (0.82 #338, 0.82 #335, 0.82 #504) >> Best rule #338 for best value: >> intensional similarity = 19 >> extensional distance = 1 >> proper extension: 02sdk9v; >> query: (?x530, ?x59) <- position(?x12091, ?x530), position(?x10501, ?x530), position(?x10294, ?x530), position(?x7358, ?x530), position(?x6727, ?x530), position(?x5567, ?x530), position(?x5071, ?x530), ?x7358 = 07sqnh, team(?x530, ?x12050), team(?x530, ?x11550), team(?x530, ?x59), ?x12091 = 035s37, ?x10501 = 03dj48, ?x10294 = 06ppc4, ?x6727 = 06ls0l, ?x5567 = 08132w, ?x12050 = 02k9k9, ?x5071 = 01hwgt, ?x11550 = 0fm6m8 >> conf = 0.82 => this is the best rule for 113 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 25, 108, 111, 128, 138 EVAL 02_j1w position! 05p8bf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 10.000 10.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position EVAL 02_j1w position! 03x6rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 10.000 10.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position EVAL 02_j1w position! 071rlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 10.000 10.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position EVAL 02_j1w position! 04d817 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 10.000 10.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position EVAL 02_j1w position! 058dm9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 10.000 10.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #16355-013hvr PRED entity: 013hvr PRED relation: time_zones PRED expected values: 02hcv8 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 7): 02hcv8 (0.77 #124, 0.74 #138, 0.74 #96), 02lcqs (0.20 #87, 0.19 #115, 0.18 #156), 02fqwt (0.18 #218, 0.15 #244, 0.14 #179), 03bdv (0.06 #170, 0.05 #236, 0.05 #197), 02hczc (0.05 #219, 0.05 #537, 0.05 #577), 02llzg (0.05 #1020, 0.05 #647, 0.05 #927), 03plfd (0.01 #1026, 0.01 #1144) >> Best rule #124 for best value: >> intensional similarity = 6 >> extensional distance = 249 >> proper extension: 017cjb; 01h8sf; >> query: (?x12646, ?x2674) <- contains(?x12068, ?x12646), contains(?x760, ?x12646), contains(?x12068, ?x13387), second_level_divisions(?x94, ?x12068), time_zones(?x13387, ?x2674), state(?x553, ?x760) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013hvr time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.773 http://example.org/location/location/time_zones #16354-02kth6 PRED entity: 02kth6 PRED relation: registering_agency PRED expected values: 03z19 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.85 #5, 0.84 #6, 0.82 #15) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 031n8c; 06mvyf; 036921; 032d52; >> query: (?x1609, 03z19) <- category(?x1609, ?x134), currency(?x1609, ?x170), organization(?x346, ?x1609), school_type(?x1609, ?x1044) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02kth6 registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.849 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #16353-0dwsp PRED entity: 0dwsp PRED relation: role PRED expected values: 026t6 01vdm0 => 86 concepts (61 used for prediction) PRED predicted values (max 10 best out of 89): 0l14md (0.88 #3487, 0.85 #3667, 0.83 #3042), 0395lw (0.86 #4032, 0.84 #1384, 0.82 #1559), 026t6 (0.85 #3665, 0.81 #3485, 0.81 #343), 01vdm0 (0.84 #1384, 0.83 #4452, 0.83 #4385), 0l15bq (0.84 #1384, 0.82 #1559, 0.82 #862), 02dlh2 (0.84 #1384, 0.82 #1559, 0.82 #862), 01w4c9 (0.84 #1384, 0.82 #1559, 0.82 #862), 01s0ps (0.81 #3526, 0.79 #4322, 0.78 #430), 05148p4 (0.81 #343, 0.80 #2881, 0.78 #430), 0dwsp (0.81 #343, 0.78 #430, 0.77 #2779) >> Best rule #3487 for best value: >> intensional similarity = 17 >> extensional distance = 14 >> proper extension: 0bxl5; >> query: (?x615, 0l14md) <- role(?x615, ?x5676), role(?x615, ?x4616), role(?x2592, ?x615), role(?x10239, ?x615), role(?x5391, ?x615), role(?x1715, ?x615), ?x4616 = 01rhl, film(?x5391, ?x1481), place_of_birth(?x5391, ?x12250), artists(?x3319, ?x10239), award_winner(?x8705, ?x1715), performance_role(?x2592, ?x7772), role(?x5676, ?x1332), profession(?x10239, ?x131), ?x3319 = 06j6l, artist(?x3874, ?x5391), role(?x1437, ?x615) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3665 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 18 *> proper extension: 07xzm; 02fsn; *> query: (?x615, 026t6) <- role(?x615, ?x5676), role(?x615, ?x4616), role(?x2592, ?x615), role(?x10239, ?x615), role(?x5391, ?x615), role(?x4595, ?x615), role(?x1715, ?x615), ?x4616 = 01rhl, film(?x5391, ?x1481), place_of_birth(?x5391, ?x12250), artists(?x378, ?x10239), award_winner(?x8705, ?x1715), performance_role(?x2592, ?x7772), role(?x5676, ?x1332), profession(?x10239, ?x131), artist(?x2149, ?x4595) *> conf = 0.85 ranks of expected_values: 3, 4 EVAL 0dwsp role 01vdm0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 61.000 0.875 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 0dwsp role 026t6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 61.000 0.875 http://example.org/music/performance_role/track_performances./music/track_contribution/role #16352-02h40lc PRED entity: 02h40lc PRED relation: official_language! PRED expected values: 07ww5 05bcl 06sw9 0168t => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 696): 06mzp (0.50 #605, 0.40 #1078, 0.33 #2025), 03_3d (0.47 #2131, 0.46 #2843, 0.46 #2962), 01ppq (0.47 #2131, 0.46 #2843, 0.46 #2962), 0chghy (0.47 #2131, 0.46 #2843, 0.46 #2962), 09pmkv (0.47 #2131, 0.46 #2843, 0.46 #2962), 03__y (0.47 #2131, 0.46 #2843, 0.46 #2962), 0697s (0.47 #2131, 0.46 #2843, 0.46 #2962), 03ryn (0.47 #2131, 0.46 #2843, 0.46 #2962), 0162b (0.47 #2131, 0.46 #2843, 0.46 #2962), 04hzj (0.47 #2131, 0.46 #2843, 0.46 #2962) >> Best rule #605 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 064_8sq; >> query: (?x254, 06mzp) <- languages(?x118, ?x254), official_language(?x183, ?x254), service_language(?x127, ?x254), language(?x1385, ?x254), countries_spoken_in(?x254, ?x126), ?x1385 = 044g_k >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #660 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 064_8sq; *> query: (?x254, 06sw9) <- languages(?x118, ?x254), official_language(?x183, ?x254), service_language(?x127, ?x254), language(?x1385, ?x254), countries_spoken_in(?x254, ?x126), ?x1385 = 044g_k *> conf = 0.25 ranks of expected_values: 55, 69, 139 EVAL 02h40lc official_language! 0168t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/location/country/official_language EVAL 02h40lc official_language! 06sw9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 76.000 76.000 0.500 http://example.org/location/country/official_language EVAL 02h40lc official_language! 05bcl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 76.000 76.000 0.500 http://example.org/location/country/official_language EVAL 02h40lc official_language! 07ww5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 76.000 76.000 0.500 http://example.org/location/country/official_language #16351-0135p7 PRED entity: 0135p7 PRED relation: category PRED expected values: 08mbj5d => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #4, 0.79 #6, 0.78 #18) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 019pwv; 02tz9z; >> query: (?x12881, 08mbj5d) <- contains(?x1025, ?x12881), contains(?x94, ?x12881), ?x1025 = 04ych, ?x94 = 09c7w0 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0135p7 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.800 http://example.org/common/topic/webpage./common/webpage/category #16350-045cq PRED entity: 045cq PRED relation: award PRED expected values: 040njc 019f4v => 172 concepts (149 used for prediction) PRED predicted values (max 10 best out of 342): 019f4v (0.38 #3306, 0.34 #8166, 0.34 #9381), 040njc (0.36 #8108, 0.36 #3248, 0.30 #9323), 0gs9p (0.36 #12229, 0.36 #3319, 0.35 #8179), 02pqp12 (0.32 #3310, 0.23 #9385, 0.23 #8170), 01bgqh (0.31 #2472, 0.20 #852, 0.19 #5712), 0gq9h (0.29 #3317, 0.28 #8177, 0.26 #12227), 01by1l (0.28 #2542, 0.22 #1732, 0.19 #2137), 09sb52 (0.27 #21911, 0.27 #25556, 0.25 #26771), 0gkvb7 (0.26 #431, 0.12 #836, 0.08 #26), 0gr51 (0.26 #8200, 0.24 #3340, 0.22 #9415) >> Best rule #3306 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 03xp8d5; >> query: (?x5573, 019f4v) <- film(?x5573, ?x3614), student(?x3136, ?x5573), people(?x1050, ?x5573) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 045cq award 019f4v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 149.000 0.385 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 045cq award 040njc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 149.000 0.385 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16349-03pzf PRED entity: 03pzf PRED relation: featured_film_locations! PRED expected values: 0gbtbm => 104 concepts (38 used for prediction) PRED predicted values (max 10 best out of 721): 09sh8k (0.29 #733, 0.08 #4373, 0.07 #2917), 0353tm (0.29 #1357, 0.05 #4997, 0.05 #2085), 06gb1w (0.29 #1038, 0.05 #4678, 0.04 #6134), 057lbk (0.29 #1037, 0.05 #4677, 0.04 #6133), 01qb5d (0.29 #783, 0.05 #4423, 0.04 #5879), 0czyxs (0.29 #750, 0.05 #4390, 0.04 #5846), 02vz6dn (0.25 #532, 0.14 #1260, 0.05 #4900), 02x0fs9 (0.25 #677, 0.14 #1405, 0.05 #2133), 048yqf (0.25 #657, 0.14 #1385, 0.04 #6481), 02__34 (0.25 #150, 0.14 #878, 0.04 #5974) >> Best rule #733 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 06gmr; >> query: (?x10683, 09sh8k) <- featured_film_locations(?x10515, ?x10683), ?x10515 = 0dnkmq, place_of_birth(?x4233, ?x10683) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1050 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 06gmr; *> query: (?x10683, 0gbtbm) <- featured_film_locations(?x10515, ?x10683), ?x10515 = 0dnkmq, place_of_birth(?x4233, ?x10683) *> conf = 0.14 ranks of expected_values: 71 EVAL 03pzf featured_film_locations! 0gbtbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 104.000 38.000 0.286 http://example.org/film/film/featured_film_locations #16348-065ky PRED entity: 065ky PRED relation: partially_contains! PRED expected values: 06mkj => 100 concepts (52 used for prediction) PRED predicted values (max 10 best out of 151): 0345h (0.40 #3907, 0.33 #773, 0.32 #1556), 03rjj (0.40 #3907, 0.33 #773, 0.32 #1556), 06mzp (0.40 #3907, 0.33 #773, 0.30 #1750), 0h7x (0.40 #3907, 0.25 #607, 0.25 #122), 06t8v (0.40 #3907, 0.25 #142, 0.20 #334), 04j53 (0.40 #3907, 0.25 #140, 0.20 #332), 04g61 (0.33 #773, 0.32 #1556, 0.32 #4596), 0hg5 (0.33 #773, 0.32 #1556, 0.32 #4596), 06mkj (0.33 #773, 0.32 #1556, 0.32 #4596), 04w58 (0.33 #773, 0.32 #1556, 0.32 #4596) >> Best rule #3907 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0fv_t; >> query: (?x12909, ?x205) <- partially_contains(?x789, ?x12909), contains(?x12909, ?x2756), partially_contains(?x789, ?x8154), partially_contains(?x205, ?x8154) >> conf = 0.40 => this is the best rule for 6 predicted values *> Best rule #773 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 0fb18; *> query: (?x12909, ?x172) <- partially_contains(?x789, ?x12909), contains(?x12909, ?x2756), contains(?x789, ?x790), adjoins(?x789, ?x172), administrative_parent(?x789, ?x551) *> conf = 0.33 ranks of expected_values: 9 EVAL 065ky partially_contains! 06mkj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 100.000 52.000 0.402 http://example.org/location/location/partially_contains #16347-03h3x5 PRED entity: 03h3x5 PRED relation: film! PRED expected values: 01rrwf6 0227tr 01fwpt 0143wl 03hhd3 01d_4t 0d9kl => 90 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1345): 086k8 (0.44 #103868, 0.43 #116332, 0.43 #110101), 06pj8 (0.43 #16614, 0.32 #54001, 0.14 #66465), 02_p5w (0.33 #647, 0.13 #6877, 0.08 #23492), 0pmw9 (0.33 #812, 0.04 #56078, 0.03 #103869), 02clgg (0.33 #1478, 0.04 #24323, 0.03 #28475), 03pmzt (0.33 #498, 0.03 #17112, 0.03 #23343), 016ks_ (0.33 #786, 0.02 #4939, 0.02 #17400), 03k7bd (0.33 #298, 0.02 #4451, 0.01 #39755), 0q9kd (0.33 #4, 0.02 #64390, 0.02 #22849), 01wb8bs (0.33 #684, 0.01 #11067) >> Best rule #103868 for best value: >> intensional similarity = 4 >> extensional distance = 1074 >> proper extension: 01gglm; >> query: (?x2642, ?x382) <- nominated_for(?x382, ?x2642), film(?x3917, ?x2642), country(?x2642, ?x94), award_winner(?x3917, ?x2124) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #17209 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 100 *> proper extension: 076xkdz; *> query: (?x2642, 01fwpt) <- genre(?x2642, ?x2540), ?x2540 = 0hcr, film(?x382, ?x2642) *> conf = 0.02 ranks of expected_values: 649, 650, 1036, 1206 EVAL 03h3x5 film! 0d9kl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 60.000 0.439 http://example.org/film/actor/film./film/performance/film EVAL 03h3x5 film! 01d_4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 90.000 60.000 0.439 http://example.org/film/actor/film./film/performance/film EVAL 03h3x5 film! 03hhd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 90.000 60.000 0.439 http://example.org/film/actor/film./film/performance/film EVAL 03h3x5 film! 0143wl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 60.000 0.439 http://example.org/film/actor/film./film/performance/film EVAL 03h3x5 film! 01fwpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 60.000 0.439 http://example.org/film/actor/film./film/performance/film EVAL 03h3x5 film! 0227tr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 60.000 0.439 http://example.org/film/actor/film./film/performance/film EVAL 03h3x5 film! 01rrwf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 60.000 0.439 http://example.org/film/actor/film./film/performance/film #16346-02825kb PRED entity: 02825kb PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #17, 0.83 #57, 0.82 #77), 07c52 (0.08 #14, 0.03 #130, 0.03 #140), 07z4p (0.04 #21, 0.03 #26, 0.02 #51), 02nxhr (0.03 #124, 0.03 #170, 0.03 #33) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 034qmv; 011yrp; 0hmr4; 07sc6nw; 04zyhx; 01_1pv; 02psgq; 0gh6j94; 034xyf; 014knw; ... >> query: (?x6984, 029j_) <- genre(?x6984, ?x258), language(?x6984, ?x732), ?x732 = 04306rv, ?x258 = 05p553 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02825kb film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #16345-035yg PRED entity: 035yg PRED relation: organization PRED expected values: 02vk52z 041288 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 50): 02vk52z (0.92 #551, 0.85 #375, 0.85 #221), 041288 (0.71 #38, 0.64 #60, 0.45 #258), 0b6css (0.44 #32, 0.36 #362, 0.36 #54), 04k4l (0.38 #181, 0.37 #291, 0.33 #335), 0_2v (0.37 #290, 0.36 #334, 0.34 #158), 018cqq (0.35 #187, 0.32 #1301, 0.32 #297), 0gkjy (0.34 #29, 0.34 #381, 0.32 #1301), 01rz1 (0.34 #354, 0.32 #1301, 0.32 #288), 02jxk (0.32 #1301, 0.19 #289, 0.19 #179), 059dn (0.32 #1301, 0.09 #235, 0.07 #301) >> Best rule #551 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 07z5n; 088xp; 04hzj; >> query: (?x8884, 02vk52z) <- form_of_government(?x8884, ?x1926), currency(?x8884, ?x170), jurisdiction_of_office(?x182, ?x8884) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 035yg organization 041288 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.917 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 035yg organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.917 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #16344-09gkx35 PRED entity: 09gkx35 PRED relation: genre PRED expected values: 05p553 => 78 concepts (77 used for prediction) PRED predicted values (max 10 best out of 116): 07s9rl0 (0.74 #2059, 0.74 #2301, 0.73 #1938), 01z4y (0.62 #5818, 0.61 #6307, 0.61 #4848), 05p553 (0.43 #610, 0.42 #3638, 0.41 #368), 03k9fj (0.40 #1343, 0.36 #738, 0.35 #1464), 02kdv5l (0.35 #608, 0.33 #366, 0.33 #2787), 02l7c8 (0.34 #1953, 0.33 #2316, 0.33 #1226), 01hmnh (0.32 #1349, 0.31 #1470, 0.28 #744), 06n90 (0.27 #618, 0.26 #134, 0.25 #255), 04xvlr (0.18 #3635, 0.18 #4728, 0.17 #5698), 0hcr (0.17 #387, 0.14 #1355, 0.13 #2203) >> Best rule #2059 for best value: >> intensional similarity = 3 >> extensional distance = 153 >> proper extension: 04lqvly; >> query: (?x3603, 07s9rl0) <- film_festivals(?x3603, ?x6828), genre(?x3603, ?x604), nominated_for(?x68, ?x3603) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #610 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 0hr41p6; *> query: (?x3603, 05p553) <- executive_produced_by(?x3603, ?x12392), genre(?x3603, ?x604), influenced_by(?x12392, ?x3117), award(?x12392, ?x68) *> conf = 0.43 ranks of expected_values: 3 EVAL 09gkx35 genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 77.000 0.742 http://example.org/film/film/genre #16343-0418wg PRED entity: 0418wg PRED relation: language PRED expected values: 02bjrlw => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 34): 04306rv (0.20 #169, 0.13 #114, 0.09 #224), 06nm1 (0.17 #120, 0.17 #10, 0.14 #65), 02bjrlw (0.14 #166, 0.10 #111, 0.08 #221), 06b_j (0.13 #185, 0.11 #130, 0.08 #20), 03_9r (0.11 #119, 0.10 #174, 0.06 #229), 0jzc (0.08 #18, 0.07 #183, 0.07 #73), 06mp7 (0.07 #69, 0.04 #179), 02hwyss (0.07 #93, 0.01 #148, 0.01 #258), 01r2l (0.06 #132, 0.03 #242, 0.01 #464), 012w70 (0.06 #231, 0.05 #121, 0.03 #398) >> Best rule #169 for best value: >> intensional similarity = 5 >> extensional distance = 82 >> proper extension: 0d7vtk; >> query: (?x2500, 04306rv) <- language(?x2500, ?x5607), language(?x2500, ?x254), ?x254 = 02h40lc, produced_by(?x2500, ?x6274), ?x5607 = 064_8sq >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 82 *> proper extension: 0d7vtk; *> query: (?x2500, 02bjrlw) <- language(?x2500, ?x5607), language(?x2500, ?x254), ?x254 = 02h40lc, produced_by(?x2500, ?x6274), ?x5607 = 064_8sq *> conf = 0.14 ranks of expected_values: 3 EVAL 0418wg language 02bjrlw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 66.000 0.202 http://example.org/film/film/language #16342-0dzkq PRED entity: 0dzkq PRED relation: influenced_by! PRED expected values: 01d494 0gd_s => 198 concepts (103 used for prediction) PRED predicted values (max 10 best out of 434): 01d494 (0.50 #1081, 0.26 #3654, 0.22 #8799), 0ct9_ (0.40 #858, 0.31 #16995, 0.30 #1374), 0dzkq (0.40 #640, 0.30 #1156, 0.25 #5786), 07h1q (0.33 #1955, 0.21 #6069, 0.20 #923), 040db (0.33 #1622, 0.21 #5736, 0.13 #38706), 01vdrw (0.33 #1990, 0.21 #6104, 0.10 #9265), 013pp3 (0.33 #1769, 0.17 #5883, 0.10 #9265), 0n6kf (0.33 #1738, 0.12 #5852, 0.11 #7397), 073v6 (0.33 #1664, 0.12 #5778, 0.10 #9265), 0lrh (0.33 #1651, 0.10 #9265, 0.08 #5765) >> Best rule #1081 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0ct9_; >> query: (?x3428, 01d494) <- influenced_by(?x8389, ?x3428), interests(?x3428, ?x6978), influenced_by(?x3428, ?x9600), ?x9600 = 039n1 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 83 EVAL 0dzkq influenced_by! 0gd_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 198.000 103.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 0dzkq influenced_by! 01d494 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 103.000 0.500 http://example.org/influence/influence_node/influenced_by #16341-03xf_m PRED entity: 03xf_m PRED relation: film_crew_role PRED expected values: 01vx2h => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 23): 0dxtw (0.41 #9, 0.38 #425, 0.36 #329), 01vx2h (0.34 #10, 0.32 #330, 0.31 #618), 02ynfr (0.17 #13, 0.17 #429, 0.15 #333), 0d2b38 (0.14 #22, 0.10 #342, 0.10 #1143), 0215hd (0.13 #336, 0.12 #1137, 0.12 #752), 02rh1dz (0.12 #8, 0.11 #328, 0.11 #616), 015h31 (0.10 #327, 0.09 #615, 0.09 #7), 089fss (0.09 #5, 0.07 #421, 0.06 #1126), 02_n3z (0.09 #737, 0.08 #1026, 0.08 #962), 04pyp5 (0.07 #14, 0.06 #430, 0.06 #334) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 08984j; >> query: (?x6281, 0dxtw) <- music(?x6281, ?x6910), film_crew_role(?x6281, ?x137), nominated_for(?x6281, ?x4848) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 08984j; *> query: (?x6281, 01vx2h) <- music(?x6281, ?x6910), film_crew_role(?x6281, ?x137), nominated_for(?x6281, ?x4848) *> conf = 0.34 ranks of expected_values: 2 EVAL 03xf_m film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 81.000 0.413 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16340-04sv4 PRED entity: 04sv4 PRED relation: service_location PRED expected values: 07ssc => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 251): 07ssc (0.47 #1770, 0.33 #197, 0.33 #105), 06bnz (0.33 #210, 0.14 #395, 0.12 #579), 0156q (0.33 #211, 0.06 #1784, 0.02 #8248), 02j71 (0.30 #2142, 0.29 #1956, 0.29 #1864), 03h64 (0.27 #8434, 0.14 #409, 0.12 #593), 03rjj (0.18 #1762, 0.14 #374, 0.12 #558), 05v8c (0.17 #1307, 0.14 #383, 0.12 #567), 0b90_r (0.14 #464, 0.14 #372, 0.14 #279), 06t2t (0.14 #402, 0.13 #1604, 0.12 #586), 059g4 (0.14 #427, 0.13 #1629, 0.12 #611) >> Best rule #1770 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 02slt7; >> query: (?x9469, 07ssc) <- service_language(?x9469, ?x7658), service_language(?x9469, ?x5607), languages_spoken(?x3584, ?x7658), service_location(?x9469, ?x390), ?x5607 = 064_8sq, olympics(?x390, ?x391) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sv4 service_location 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.471 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #16339-0241y7 PRED entity: 0241y7 PRED relation: story_by PRED expected values: 01vh096 => 81 concepts (46 used for prediction) PRED predicted values (max 10 best out of 18): 01lc5 (0.20 #183), 05jcn8 (0.07 #487), 04jspq (0.04 #549), 05_k56 (0.03 #449), 02cx72 (0.02 #2389, 0.02 #3259, 0.02 #2608), 01795t (0.02 #4783, 0.02 #4565, 0.01 #6745), 03j2gxx (0.01 #615), 03j90 (0.01 #612), 06lbp (0.01 #546), 01tz6vs (0.01 #531) >> Best rule #183 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 016z5x; 01sxly; >> query: (?x6140, 01lc5) <- award_winner(?x6140, ?x3732), film(?x9526, ?x6140), production_companies(?x6140, ?x3920), ?x9526 = 01gbn6 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0241y7 story_by 01vh096 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 46.000 0.200 http://example.org/film/film/story_by #16338-01j5sv PRED entity: 01j5sv PRED relation: film PRED expected values: 0b85mm => 146 concepts (44 used for prediction) PRED predicted values (max 10 best out of 671): 0h6r5 (0.17 #680, 0.16 #2472, 0.15 #7848), 02q7yfq (0.17 #1206, 0.11 #4790, 0.11 #2998), 0fphf3v (0.11 #4948, 0.11 #3156, 0.08 #1364), 06cm5 (0.11 #4656, 0.11 #2864, 0.08 #1072), 0jsf6 (0.11 #4675, 0.08 #1091, 0.07 #8259), 04ltlj (0.08 #1722, 0.05 #5306, 0.05 #3514), 03cvwkr (0.08 #136, 0.05 #3720, 0.05 #1928), 08mg_b (0.08 #1124, 0.05 #4708, 0.05 #2916), 01kqq7 (0.08 #1633, 0.05 #5217, 0.05 #3425), 04jplwp (0.08 #1374, 0.05 #4958, 0.05 #3166) >> Best rule #680 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 01fxck; 0g7k2g; 03crmd; >> query: (?x10965, 0h6r5) <- profession(?x10965, ?x1032), nationality(?x10965, ?x205), languages(?x10965, ?x90), location(?x10965, ?x6959), ?x205 = 03rjj >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #7134 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 01h4rj; *> query: (?x10965, 0b85mm) <- languages(?x10965, ?x5359), languages(?x10965, ?x90), type_of_union(?x10965, ?x566), ?x90 = 02bjrlw, official_language(?x291, ?x5359) *> conf = 0.04 ranks of expected_values: 132 EVAL 01j5sv film 0b85mm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 146.000 44.000 0.167 http://example.org/film/actor/film./film/performance/film #16337-049g_xj PRED entity: 049g_xj PRED relation: place_of_birth PRED expected values: 036k0s => 104 concepts (73 used for prediction) PRED predicted values (max 10 best out of 77): 0h7h6 (0.36 #11281, 0.36 #19035, 0.35 #22558), 02_286 (0.36 #11281, 0.36 #19035, 0.35 #22558), 0cr3d (0.09 #18423, 0.09 #21946, 0.07 #28297), 030qb3t (0.06 #2172, 0.06 #8516, 0.05 #9221), 01t3h6 (0.05 #704, 0.05 #1410, 0.03 #2116), 0ckhc (0.05 #508, 0.05 #1214, 0.03 #1920), 0b1t1 (0.05 #366, 0.05 #1072, 0.03 #1778), 05jbn (0.05 #176, 0.05 #882, 0.03 #1588), 0s5cg (0.05 #181, 0.03 #1593), 05ksh (0.05 #37, 0.03 #1449) >> Best rule #11281 for best value: >> intensional similarity = 3 >> extensional distance = 606 >> proper extension: 0hnlx; 04k15; 04hcw; 082db; 032md; 047g6; 0h326; >> query: (?x1530, ?x1658) <- location(?x1530, ?x1658), adjoins(?x1658, ?x6757), month(?x1658, ?x1459) >> conf = 0.36 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 049g_xj place_of_birth 036k0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 73.000 0.362 http://example.org/people/person/place_of_birth #16336-02q6gfp PRED entity: 02q6gfp PRED relation: film_crew_role PRED expected values: 02r96rf 01pvkk => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 26): 02r96rf (0.71 #1408, 0.63 #2304, 0.61 #2580), 01pvkk (0.28 #557, 0.28 #1896, 0.28 #2345), 02ynfr (0.22 #253, 0.19 #1419, 0.15 #2315), 02_n3z (0.18 #69, 0.11 #548, 0.10 #240), 0215hd (0.14 #85, 0.14 #1422, 0.13 #564), 02rh1dz (0.13 #1414, 0.09 #626, 0.09 #2586), 01xy5l_ (0.11 #1417, 0.10 #251, 0.10 #388), 0d2b38 (0.11 #1429, 0.10 #1910, 0.10 #571), 089g0h (0.11 #1423, 0.10 #1904, 0.10 #1526), 04pyp5 (0.09 #562, 0.06 #1420, 0.06 #459) >> Best rule #1408 for best value: >> intensional similarity = 3 >> extensional distance = 672 >> proper extension: 0gtsx8c; >> query: (?x2380, 02r96rf) <- country(?x2380, ?x512), film_crew_role(?x2380, ?x1171), ?x1171 = 09vw2b7 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02q6gfp film_crew_role 01pvkk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02q6gfp film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16335-01s1zk PRED entity: 01s1zk PRED relation: instrumentalists! PRED expected values: 02hnl => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 64): 05148p4 (0.52 #369, 0.41 #107, 0.36 #1856), 018vs (0.36 #1848, 0.34 #1671, 0.31 #2199), 02sgy (0.30 #1925, 0.26 #1748, 0.26 #960), 01vdm0 (0.30 #1925, 0.26 #1748, 0.26 #960), 0l14md (0.29 #94, 0.13 #1666, 0.13 #1843), 02hnl (0.19 #383, 0.19 #1870, 0.18 #1693), 0l14qv (0.18 #92, 0.11 #1841, 0.09 #2104), 03gvt (0.18 #152, 0.08 #1724, 0.07 #1901), 04rzd (0.16 #386, 0.10 #473, 0.10 #909), 03qjg (0.15 #2150, 0.15 #1710, 0.15 #1887) >> Best rule #369 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0lbj1; 0146pg; 01vvycq; 01gf5h; 01vrz41; 02r4qs; 01vs_v8; 021bk; 01wwvc5; 0gcs9; ... >> query: (?x7614, 05148p4) <- award_winner(?x7614, ?x1751), award(?x7614, ?x2238), ?x2238 = 025m8l, instrumentalists(?x227, ?x7614) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #383 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 0lbj1; 0146pg; 01vvycq; 01gf5h; 01vrz41; 02r4qs; 01vs_v8; 021bk; 01wwvc5; 0gcs9; ... *> query: (?x7614, 02hnl) <- award_winner(?x7614, ?x1751), award(?x7614, ?x2238), ?x2238 = 025m8l, instrumentalists(?x227, ?x7614) *> conf = 0.19 ranks of expected_values: 6 EVAL 01s1zk instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 92.000 92.000 0.516 http://example.org/music/instrument/instrumentalists #16334-01gsvb PRED entity: 01gsvb PRED relation: legislative_sessions PRED expected values: 01gsrl => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 44): 01gsrl (0.88 #591, 0.87 #838, 0.86 #1131), 01gtc0 (0.88 #591, 0.87 #838, 0.86 #1131), 01gssz (0.83 #1083, 0.83 #1082, 0.83 #640), 01gsvb (0.71 #481, 0.67 #826, 0.64 #1182), 01grqd (0.64 #1182, 0.63 #1035, 0.63 #938), 03rl1g (0.64 #1182, 0.63 #1035, 0.63 #938), 01grq1 (0.64 #1182, 0.63 #1035, 0.63 #938), 01grpq (0.64 #1182, 0.63 #1035, 0.63 #938), 01grpc (0.64 #1182, 0.63 #1035, 0.63 #938), 01grp0 (0.64 #1182, 0.63 #1035, 0.63 #938) >> Best rule #591 for best value: >> intensional similarity = 35 >> extensional distance = 6 >> proper extension: 01gsrl; >> query: (?x7973, ?x759) <- district_represented(?x7973, ?x6895), district_represented(?x7973, ?x4061), district_represented(?x7973, ?x3818), district_represented(?x7973, ?x3778), district_represented(?x7973, ?x2713), district_represented(?x7973, ?x760), ?x2713 = 06btq, legislative_sessions(?x4437, ?x7973), legislative_sessions(?x3973, ?x7973), legislative_sessions(?x759, ?x7973), ?x4061 = 0498y, contains(?x3818, ?x7418), contains(?x3818, ?x5737), contains(?x3818, ?x1860), legislative_sessions(?x7973, ?x10291), adjoins(?x3818, ?x961), colors(?x5737, ?x332), ?x3778 = 07h34, ?x760 = 05fkf, state_province_region(?x2313, ?x3818), country(?x3818, ?x94), ?x3973 = 01gssm, legislative_sessions(?x2860, ?x4437), administrative_division(?x11811, ?x3818), ?x6895 = 05fjf, location(?x396, ?x1860), legislative_sessions(?x5401, ?x759), locations(?x6583, ?x1860), place_of_birth(?x193, ?x1860), district_represented(?x4437, ?x1767), featured_film_locations(?x195, ?x1860), location_of_ceremony(?x566, ?x1860), ?x1767 = 04rrd, currency(?x7418, ?x170), partially_contains(?x3818, ?x4540) >> conf = 0.88 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01gsvb legislative_sessions 01gsrl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.875 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #16333-0h1p PRED entity: 0h1p PRED relation: profession PRED expected values: 0dxtg => 133 concepts (71 used for prediction) PRED predicted values (max 10 best out of 46): 0dxtg (0.83 #308, 0.78 #604, 0.73 #2084), 02hrh1q (0.75 #4898, 0.66 #3121, 0.65 #9786), 03gjzk (0.53 #1938, 0.52 #1790, 0.48 #1050), 02krf9 (0.32 #1210, 0.30 #1506, 0.30 #1654), 0cbd2 (0.29 #10514, 0.18 #154, 0.17 #746), 0kyk (0.29 #10514, 0.17 #29, 0.16 #473), 0dgd_ (0.26 #770, 0.18 #178, 0.17 #326), 09jwl (0.20 #9939, 0.19 #9050, 0.18 #7569), 0np9r (0.13 #908, 0.11 #1056, 0.11 #1204), 0nbcg (0.13 #9063, 0.12 #9952, 0.12 #7582) >> Best rule #308 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 02kxbwx; 06pj8; 02l5rm; 01f8ld; 02kxbx3; 0bzyh; 06mn7; 03xp8d5; 0gyx4; 0js9s; ... >> query: (?x2086, 0dxtg) <- award_winner(?x1819, ?x2086), award_winner(?x8364, ?x2086), ?x8364 = 09d28z, produced_by(?x2085, ?x2086) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h1p profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 71.000 0.833 http://example.org/people/person/profession #16332-09cm54 PRED entity: 09cm54 PRED relation: award! PRED expected values: 05hjnw => 40 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1374): 092vkg (0.40 #2073, 0.33 #94, 0.11 #1084), 011yth (0.40 #2158, 0.33 #179, 0.11 #1169), 08zrbl (0.33 #770, 0.32 #990, 0.22 #3960), 07s846j (0.33 #386, 0.20 #2365, 0.11 #1376), 064lsn (0.33 #609, 0.20 #2588, 0.11 #1599), 03xf_m (0.33 #627, 0.20 #2606, 0.11 #1617), 07g1sm (0.33 #688, 0.20 #2667, 0.07 #2969), 016yxn (0.33 #969, 0.20 #2948, 0.02 #13860), 0bl1_ (0.33 #456, 0.11 #1446, 0.10 #2435), 08vd2q (0.33 #361, 0.11 #1351, 0.10 #2340) >> Best rule #2073 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 054ky1; >> query: (?x1770, 092vkg) <- award_winner(?x1770, ?x1554), award_winner(?x1770, ?x269), ?x1554 = 06cgy, film(?x269, ?x1308) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1472 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 03nqnk3; *> query: (?x1770, 05hjnw) <- award_winner(?x1770, ?x4969), award_winner(?x1770, ?x3056), award_winner(?x1770, ?x269), ?x269 = 0byfz, participant(?x2258, ?x3056), profession(?x4969, ?x1032) *> conf = 0.11 ranks of expected_values: 55 EVAL 09cm54 award! 05hjnw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 40.000 14.000 0.400 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #16331-01yznp PRED entity: 01yznp PRED relation: program PRED expected values: 0fpxp => 93 concepts (76 used for prediction) PRED predicted values (max 10 best out of 17): 0cpz4k (0.20 #58, 0.04 #383, 0.03 #208), 0304nh (0.12 #34, 0.10 #59, 0.04 #134), 01h1bf (0.12 #31, 0.10 #56, 0.04 #381), 03gvm3t (0.10 #64, 0.01 #414), 01j7mr (0.09 #132, 0.06 #207, 0.05 #382), 01b7h8 (0.05 #393, 0.03 #218, 0.02 #268), 0124k9 (0.04 #127, 0.03 #202, 0.01 #427), 039cq4 (0.04 #162, 0.03 #237, 0.02 #863), 0ph24 (0.03 #247), 026bfsh (0.03 #385, 0.02 #260, 0.01 #360) >> Best rule #58 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0163t3; >> query: (?x425, 0cpz4k) <- person(?x424, ?x425), person(?x3480, ?x425), genre(?x424, ?x258) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01yznp program 0fpxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 76.000 0.200 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #16330-02r858_ PRED entity: 02r858_ PRED relation: country PRED expected values: 0d060g => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.80 #188, 0.79 #745, 0.79 #622), 0f8l9c (0.42 #20, 0.10 #393, 0.09 #1318), 0d060g (0.37 #3697, 0.04 #1799, 0.04 #1614), 02jx1 (0.37 #3697, 0.01 #402, 0.01 #152), 0345h (0.33 #28, 0.10 #1448, 0.10 #1633), 07ssc (0.32 #390, 0.21 #452, 0.21 #1622), 03rjj (0.25 #7, 0.04 #380, 0.03 #1305), 07s9rl0 (0.12 #435, 0.06 #620, 0.06 #2959), 03h64 (0.08 #47, 0.02 #605, 0.02 #976), 0d05w3 (0.08 #44, 0.02 #602, 0.02 #1649) >> Best rule #188 for best value: >> intensional similarity = 3 >> extensional distance = 426 >> proper extension: 0gtsx8c; >> query: (?x8277, 09c7w0) <- language(?x8277, ?x254), executive_produced_by(?x8277, ?x3828), film(?x4129, ?x8277) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3697 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1768 *> proper extension: 0cks1m; *> query: (?x8277, ?x94) <- film(?x1657, ?x8277), nationality(?x1657, ?x94), genre(?x8277, ?x53) *> conf = 0.37 ranks of expected_values: 3 EVAL 02r858_ country 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 62.000 0.799 http://example.org/film/film/country #16329-0g22z PRED entity: 0g22z PRED relation: film! PRED expected values: 03k7bd => 112 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1046): 01xv77 (0.38 #54049, 0.38 #60284, 0.02 #5253), 05kfs (0.20 #76910, 0.19 #87304, 0.18 #31184), 0f5xn (0.18 #11362, 0.04 #3046, 0.04 #5125), 04w1j9 (0.11 #122637, 0.11 #116402, 0.10 #43656), 05nn4k (0.11 #122637, 0.11 #116402, 0.10 #43656), 030_3z (0.11 #122637, 0.11 #116402, 0.10 #43656), 02q_cc (0.11 #122637, 0.11 #116402, 0.10 #43656), 015c4g (0.10 #778, 0.03 #25723, 0.02 #4935), 0jfx1 (0.08 #2482, 0.03 #48217, 0.03 #25349), 0f0kz (0.08 #2591, 0.02 #50404, 0.02 #62875) >> Best rule #54049 for best value: >> intensional similarity = 4 >> extensional distance = 393 >> proper extension: 0c5qvw; >> query: (?x153, ?x6236) <- award(?x153, ?x154), titles(?x53, ?x153), production_companies(?x153, ?x574), nominated_for(?x6236, ?x153) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #20786 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 181 *> proper extension: 0y_9q; *> query: (?x153, ?x100) <- award(?x153, ?x154), film(?x1870, ?x153), cinematography(?x153, ?x6062), award_nominee(?x1870, ?x100) *> conf = 0.03 ranks of expected_values: 201 EVAL 0g22z film! 03k7bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 112.000 65.000 0.382 http://example.org/film/actor/film./film/performance/film #16328-014b6c PRED entity: 014b6c PRED relation: contains! PRED expected values: 02v3m7 => 133 concepts (54 used for prediction) PRED predicted values (max 10 best out of 223): 09c7w0 (0.96 #13448, 0.69 #1793, 0.60 #898), 02v3m7 (0.63 #10755, 0.63 #10754, 0.63 #27801), 059rby (0.44 #10775, 0.43 #11672, 0.31 #26027), 01n7q (0.35 #32371, 0.32 #35064, 0.30 #37754), 07b_l (0.31 #12769, 0.26 #3803, 0.21 #5596), 04_1l0v (0.28 #9409, 0.27 #10306, 0.14 #13895), 03v0t (0.19 #19054, 0.18 #5607, 0.15 #4711), 02qkt (0.17 #28148, 0.12 #41609, 0.10 #42508), 02j9z (0.16 #27830, 0.05 #28730, 0.05 #26932), 0824r (0.16 #8314, 0.11 #28700, 0.09 #6522) >> Best rule #13448 for best value: >> intensional similarity = 5 >> extensional distance = 98 >> proper extension: 01pl14; 04yf_; 03l2n; 03l6bs; 0b2lw; >> query: (?x13921, 09c7w0) <- time_zones(?x13921, ?x1638), ?x1638 = 02fqwt, contains(?x1025, ?x13921), contains(?x1025, ?x7166), ?x7166 = 013gxt >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #10755 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 01zlx; *> query: (?x13921, ?x1025) <- time_zones(?x13921, ?x1638), adjoins(?x11877, ?x13921), ?x1638 = 02fqwt, contains(?x1025, ?x11877) *> conf = 0.63 ranks of expected_values: 2 EVAL 014b6c contains! 02v3m7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 54.000 0.960 http://example.org/location/location/contains #16327-0hn821n PRED entity: 0hn821n PRED relation: honored_for PRED expected values: 05zr0xl => 48 concepts (34 used for prediction) PRED predicted values (max 10 best out of 598): 039cq4 (0.60 #3361, 0.50 #6909, 0.50 #4544), 07zhjj (0.60 #3446, 0.50 #4629, 0.45 #7584), 06mr2s (0.60 #3234, 0.50 #4417, 0.45 #7372), 01b7h8 (0.60 #3483, 0.50 #4666, 0.44 #6441), 04xbq3 (0.60 #3461, 0.50 #4644, 0.44 #6419), 05zr0xl (0.50 #4028, 0.50 #2846, 0.50 #2256), 0524b41 (0.50 #3963, 0.50 #2781, 0.38 #5147), 0d68qy (0.50 #1922, 0.48 #9014, 0.45 #10195), 03nt59 (0.50 #4496, 0.40 #3313, 0.30 #6861), 01b_lz (0.40 #6698, 0.38 #5517, 0.36 #7288) >> Best rule #3361 for best value: >> intensional similarity = 17 >> extensional distance = 3 >> proper extension: 02q690_; 03nnm4t; >> query: (?x10010, 039cq4) <- honored_for(?x10010, ?x10447), honored_for(?x10010, ?x493), award_winner(?x10010, ?x496), ?x10447 = 07s8z_l, producer_type(?x493, ?x632), nominated_for(?x7310, ?x493), nominated_for(?x1669, ?x493), ceremony(?x5235, ?x10010), ceremony(?x1132, ?x10010), ceremony(?x870, ?x10010), ?x5235 = 09qrn4, ?x870 = 09qv3c, award_nominee(?x1669, ?x374), ?x1132 = 0bdwft, ?x7310 = 04sry, award_winner(?x945, ?x1669), ?x632 = 0ckd1 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4028 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 4 *> proper extension: 02wzl1d; *> query: (?x10010, 05zr0xl) <- honored_for(?x10010, ?x10447), honored_for(?x10010, ?x1434), honored_for(?x10010, ?x493), award_winner(?x10010, ?x496), honored_for(?x5585, ?x10447), award_winner(?x10447, ?x1285), actor(?x1434, ?x931), ceremony(?x5235, ?x10010), ceremony(?x870, ?x10010), ?x5585 = 03nnm4t, award_winner(?x493, ?x1669), nominated_for(?x368, ?x493), nominated_for(?x4728, ?x1434), award(?x237, ?x5235), ?x1669 = 02tr7d, ?x4728 = 04ldyx1, nominated_for(?x870, ?x758) *> conf = 0.50 ranks of expected_values: 6 EVAL 0hn821n honored_for 05zr0xl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 48.000 34.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #16326-04czcb PRED entity: 04czcb PRED relation: sport PRED expected values: 02vx4 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.88 #372, 0.88 #354, 0.87 #246), 0z74 (0.49 #452, 0.27 #616, 0.12 #109), 03tmr (0.19 #19, 0.12 #109, 0.11 #73), 018jz (0.13 #77, 0.12 #109, 0.10 #411), 0jm_ (0.12 #109, 0.09 #409, 0.08 #400), 018w8 (0.12 #109, 0.08 #22, 0.06 #76), 039yzs (0.12 #109, 0.04 #459, 0.03 #431), 09xp_ (0.12 #109, 0.01 #458) >> Best rule #372 for best value: >> intensional similarity = 13 >> extensional distance = 192 >> proper extension: 0371rb; 0gxkm; 01kwhf; 01vqc7; 051n13; 011v3; 0690dn; 02_lt; 0k_l4; 02nt75; ... >> query: (?x11849, 02vx4) <- position(?x11849, ?x530), team(?x203, ?x11849), colors(?x11849, ?x663), position(?x11176, ?x530), position(?x11131, ?x530), position(?x7631, ?x530), position(?x7486, ?x530), position(?x470, ?x530), ?x11176 = 027024, ?x7486 = 04ngn, ?x11131 = 0f1k__, ?x470 = 0223bl, ?x7631 = 02gjt4 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04czcb sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.876 http://example.org/sports/sports_team/sport #16325-0jmnl PRED entity: 0jmnl PRED relation: company! PRED expected values: 02y6fz => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 13): 02y6fz (0.25 #119, 0.25 #72, 0.20 #213), 02md_2 (0.19 #1559, 0.13 #1846, 0.12 #487), 06b1q (0.09 #949, 0.08 #665, 0.06 #1185), 02g_6x (0.04 #951, 0.03 #1187, 0.03 #1235), 0dq_5 (0.03 #1673, 0.03 #1292, 0.02 #1912), 01yc02 (0.03 #1236, 0.03 #1283, 0.03 #1330), 060c4 (0.02 #1513, 0.01 #1800), 033smt (0.02 #1541), 02_ssl (0.01 #991), 02sf_r (0.01 #991) >> Best rule #119 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 0jmk7; >> query: (?x13777, 02y6fz) <- school(?x13777, ?x9200), school(?x13777, ?x4955), draft(?x13777, ?x2569), ?x2569 = 038c0q, team(?x4834, ?x13777), major_field_of_study(?x9200, ?x12035), ?x4955 = 09f2j, ?x12035 = 01400v, state_province_region(?x9200, ?x760), institution(?x1771, ?x9200), institution(?x734, ?x9200), ?x734 = 04zx3q1, ?x1771 = 019v9k >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jmnl company! 02y6fz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.250 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #16324-011ycb PRED entity: 011ycb PRED relation: genre PRED expected values: 02p0szs => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 96): 04xvlr (0.59 #3778, 0.58 #2193, 0.52 #5597), 02l7c8 (0.37 #505, 0.30 #3673, 0.29 #1235), 05p553 (0.35 #248, 0.32 #7659, 0.32 #5479), 01jfsb (0.34 #135, 0.30 #4033, 0.29 #1231), 02kdv5l (0.30 #4022, 0.28 #124, 0.27 #5720), 03k9fj (0.25 #987, 0.25 #4032, 0.23 #1473), 082gq (0.24 #3079, 0.22 #1128, 0.19 #1614), 0lsxr (0.23 #131, 0.23 #497, 0.22 #1835), 060__y (0.22 #750, 0.21 #3674, 0.20 #140), 0hn10 (0.21 #254, 0.09 #4869, 0.08 #2080) >> Best rule #3778 for best value: >> intensional similarity = 3 >> extensional distance = 606 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x5013, ?x162) <- titles(?x162, ?x5013), titles(?x162, ?x3116), ?x3116 = 0qmd5 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #4869 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 955 *> proper extension: 0cwrr; 04glx0; 05h95s; 05fgr_; 05sy0cv; 06mmr; *> query: (?x5013, ?x53) <- award(?x5013, ?x8364), award(?x2943, ?x8364), genre(?x2943, ?x53) *> conf = 0.09 ranks of expected_values: 33 EVAL 011ycb genre 02p0szs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 72.000 72.000 0.590 http://example.org/film/film/genre #16323-03qhnx PRED entity: 03qhnx PRED relation: contains! PRED expected values: 03rjj 0bzjf => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 153): 09c7w0 (0.61 #13453, 0.58 #14351, 0.55 #37692), 0f8l9c (0.50 #14347, 0.36 #30500, 0.35 #8966), 07ssc (0.43 #2720, 0.14 #1824, 0.12 #7204), 02jx1 (0.29 #2775, 0.06 #22511, 0.04 #52140), 03rjj (0.25 #10, 0.20 #4492, 0.14 #2698), 0bzjf (0.25 #586, 0.14 #3274, 0.14 #2378), 01n7q (0.22 #9044, 0.20 #29680, 0.20 #28782), 0kpys (0.20 #4663, 0.14 #1077, 0.12 #8250), 02qkt (0.14 #1243, 0.11 #3932, 0.10 #4829), 059rby (0.14 #916, 0.11 #3605, 0.09 #21548) >> Best rule #13453 for best value: >> intensional similarity = 8 >> extensional distance = 21 >> proper extension: 09c17; >> query: (?x14673, 09c7w0) <- category(?x14673, ?x134), ?x134 = 08mbj5d, featured_film_locations(?x4786, ?x14673), film(?x2258, ?x4786), country(?x4786, ?x94), genre(?x4786, ?x53), costume_design_by(?x4786, ?x6327), nominated_for(?x3528, ?x4786) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2 *> proper extension: 05qtj; 07_pf; *> query: (?x14673, 03rjj) <- featured_film_locations(?x4786, ?x14673), ?x4786 = 0bbw2z6 *> conf = 0.25 ranks of expected_values: 5, 6 EVAL 03qhnx contains! 0bzjf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 73.000 0.609 http://example.org/location/location/contains EVAL 03qhnx contains! 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 73.000 0.609 http://example.org/location/location/contains #16322-058s57 PRED entity: 058s57 PRED relation: program PRED expected values: 01b7h8 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 16): 06hwzy (0.37 #240, 0.11 #6, 0.07 #110), 0cpz4k (0.22 #9, 0.02 #243, 0.01 #581), 01b7h8 (0.11 #123, 0.06 #227, 0.06 #71), 0275kr (0.11 #21), 01j7mr (0.10 #242, 0.03 #554, 0.02 #736), 025ljp (0.06 #70, 0.04 #122, 0.03 #174), 026bfsh (0.06 #245, 0.02 #661, 0.02 #453), 0304nh (0.04 #244, 0.01 #738, 0.01 #426), 03gvm3t (0.04 #119, 0.02 #379, 0.02 #223), 01h1bf (0.02 #241, 0.01 #579, 0.01 #553) >> Best rule #240 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 06mmb; 012_53; 01jb26; 035wq7; >> query: (?x1794, 06hwzy) <- profession(?x1794, ?x220), film(?x1794, ?x2558), person(?x3480, ?x1794) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 06crng; *> query: (?x1794, 01b7h8) <- diet(?x1794, ?x3130), award(?x1794, ?x1389), currency(?x1794, ?x170) *> conf = 0.11 ranks of expected_values: 3 EVAL 058s57 program 01b7h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 130.000 0.365 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #16321-05xpms PRED entity: 05xpms PRED relation: award_nominee! PRED expected values: 0cmt6q => 138 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1055): 072bb1 (0.81 #9281, 0.80 #120666, 0.80 #27844), 05p92jn (0.81 #10784, 0.73 #8463, 0.73 #6143), 05xpms (0.75 #11260, 0.73 #8939, 0.67 #6619), 05l0j5 (0.71 #4027, 0.69 #10988, 0.60 #8667), 043js (0.69 #9860, 0.67 #7539, 0.60 #5219), 0cmt6q (0.62 #10761, 0.53 #6120, 0.50 #3800), 0cj2t3 (0.32 #34807, 0.25 #9932, 0.25 #78896), 015pxr (0.32 #34807, 0.25 #9731, 0.25 #78896), 0bczgm (0.32 #34807, 0.25 #9911, 0.25 #78896), 06jnvs (0.32 #34807, 0.25 #78896, 0.24 #122989) >> Best rule #9281 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 04t2l2; 0cnl80; 08wq0g; 072bb1; 0bt4r4; 0bt7ws; 0cnl1c; 08hsww; 060j8b; 0cms7f; ... >> query: (?x9272, ?x237) <- award_winner(?x4333, ?x9272), award_winner(?x237, ?x9272), award_nominee(?x2912, ?x9272), ?x4333 = 0cnl09 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #10761 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 083chw; 0h3mrc; 0cnl09; *> query: (?x9272, 0cmt6q) <- award_winner(?x4332, ?x9272), award_nominee(?x2912, ?x9272), ?x4332 = 0cnl1c *> conf = 0.62 ranks of expected_values: 6 EVAL 05xpms award_nominee! 0cmt6q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 138.000 68.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16320-01npcx PRED entity: 01npcx PRED relation: film! PRED expected values: 01cwcr => 70 concepts (38 used for prediction) PRED predicted values (max 10 best out of 938): 0k525 (0.25 #1842, 0.09 #3920, 0.03 #5998), 041c4 (0.25 #893, 0.08 #5049, 0.04 #2971), 03knl (0.25 #158, 0.07 #68582, 0.05 #4314), 0143wl (0.25 #1066, 0.04 #3144, 0.03 #7301), 02zyy4 (0.25 #271, 0.04 #2349, 0.03 #4427), 01vs_v8 (0.25 #360, 0.04 #2438, 0.03 #4516), 016dp0 (0.25 #2013, 0.04 #4091, 0.03 #6169), 03ym1 (0.17 #3087, 0.07 #68582, 0.04 #78977), 0241jw (0.17 #2372, 0.04 #10685, 0.03 #16919), 0154qm (0.13 #2638, 0.05 #10951, 0.03 #6795) >> Best rule #1842 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 014kq6; 0b9rdk; >> query: (?x5598, 0k525) <- genre(?x5598, ?x225), costume_design_by(?x5598, ?x1500), film_production_design_by(?x5598, ?x5532), ?x225 = 02kdv5l >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #78977 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1193 *> proper extension: 0cwrr; *> query: (?x5598, ?x71) <- nominated_for(?x11879, ?x5598), film(?x11879, ?x8906), award_nominee(?x11879, ?x3034), film(?x71, ?x8906) *> conf = 0.04 ranks of expected_values: 204 EVAL 01npcx film! 01cwcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 70.000 38.000 0.250 http://example.org/film/actor/film./film/performance/film #16319-09b83 PRED entity: 09b83 PRED relation: citytown! PRED expected values: 01rz1 => 144 concepts (69 used for prediction) PRED predicted values (max 10 best out of 719): 01nds (0.16 #8659, 0.15 #3809, 0.14 #11085), 03_c8p (0.15 #3808, 0.08 #10276, 0.08 #7041), 064f29 (0.10 #8396, 0.09 #9204, 0.08 #10822), 09glbnt (0.08 #6746, 0.08 #3513, 0.07 #7554), 0338lq (0.08 #6491, 0.08 #3258, 0.07 #7299), 05cl8y (0.08 #6880, 0.08 #3647, 0.07 #7688), 01w5gp (0.08 #6814, 0.08 #3581, 0.07 #7622), 01dtcb (0.08 #6850, 0.08 #3617, 0.07 #7658), 0146mv (0.08 #7051, 0.08 #3818, 0.07 #7859), 06182p (0.08 #6860, 0.08 #3627, 0.07 #7668) >> Best rule #8659 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 0fhp9; 05ywg; 056_y; 0k9p4; 03pzf; 0mpbx; >> query: (?x10981, 01nds) <- contains(?x789, ?x10981), contains(?x10981, ?x12944), mode_of_transportation(?x10981, ?x6665), film_release_region(?x504, ?x789), ?x504 = 0g5qs2k >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09b83 citytown! 01rz1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 69.000 0.161 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #16318-08sfxj PRED entity: 08sfxj PRED relation: titles! PRED expected values: 03mqtr => 81 concepts (63 used for prediction) PRED predicted values (max 10 best out of 64): 07c52 (0.35 #525, 0.33 #26, 0.21 #725), 02l7c8 (0.26 #2192, 0.25 #297, 0.22 #2892), 03g3w (0.25 #297, 0.22 #2892, 0.22 #3295), 01z4y (0.21 #2124, 0.20 #5835, 0.20 #4330), 03mqtr (0.17 #141, 0.14 #240, 0.11 #1637), 01jfsb (0.16 #5423, 0.14 #5820, 0.13 #1711), 09b3v (0.13 #846, 0.07 #343, 0.04 #145), 01hmnh (0.13 #823, 0.12 #5429, 0.11 #5826), 024qqx (0.10 #1276, 0.09 #1176, 0.09 #1771), 09c7w0 (0.09 #3294, 0.08 #4698, 0.07 #2391) >> Best rule #525 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 02_1rq; 0cwrr; 0ddd0gc; 02hct1; 0gj50; 01b65l; 02md2d; 05_z42; 05lfwd; 0fkwzs; ... >> query: (?x5152, 07c52) <- award_winner(?x5152, ?x11965), place_of_birth(?x11965, ?x14306), program(?x11965, ?x11818), profession(?x11965, ?x319) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #141 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 01pvxl; 02ljhg; *> query: (?x5152, 03mqtr) <- film(?x574, ?x5152), genre(?x5152, ?x2605), film_release_distribution_medium(?x5152, ?x81), ?x2605 = 03g3w *> conf = 0.17 ranks of expected_values: 5 EVAL 08sfxj titles! 03mqtr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 81.000 63.000 0.350 http://example.org/media_common/netflix_genre/titles #16317-02gnmp PRED entity: 02gnmp PRED relation: student PRED expected values: 016yr0 => 156 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1462): 015qq1 (0.17 #1894, 0.14 #10266, 0.10 #14452), 023v4_ (0.17 #862, 0.14 #9234, 0.10 #13420), 03ft8 (0.17 #257, 0.10 #12815, 0.09 #17001), 023361 (0.17 #1455, 0.10 #14013, 0.09 #18199), 0444x (0.17 #1914, 0.07 #10286, 0.05 #14472), 02mjmr (0.17 #421, 0.07 #8793, 0.05 #12979), 030vnj (0.17 #1442, 0.07 #9814, 0.05 #14000), 0d_w7 (0.17 #1924, 0.07 #10296, 0.05 #14482), 02pjvc (0.17 #1010, 0.07 #9382, 0.05 #13568), 015grj (0.17 #130, 0.07 #8502, 0.05 #12688) >> Best rule #1894 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 05cwl_; >> query: (?x11244, 015qq1) <- state_province_region(?x11244, ?x1227), contains(?x1523, ?x11244), ?x1523 = 030qb3t, currency(?x11244, ?x170) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #127692 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 249 *> proper extension: 08tyb_; *> query: (?x11244, ?x338) <- student(?x11244, ?x13084), citytown(?x11244, ?x1523), category(?x11244, ?x134), place_of_birth(?x338, ?x1523) *> conf = 0.03 ranks of expected_values: 335 EVAL 02gnmp student 016yr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 156.000 97.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #16316-04n52p6 PRED entity: 04n52p6 PRED relation: film! PRED expected values: 086k8 => 101 concepts (74 used for prediction) PRED predicted values (max 10 best out of 67): 086k8 (0.53 #2, 0.28 #152, 0.23 #378), 046b0s (0.44 #1960), 05h4t7 (0.44 #1960), 05qd_ (0.29 #84, 0.19 #385, 0.16 #159), 03xq0f (0.22 #155, 0.17 #531, 0.16 #456), 016tt2 (0.19 #79, 0.14 #1585, 0.13 #980), 017s11 (0.17 #529, 0.16 #829, 0.15 #679), 016tw3 (0.16 #1895, 0.15 #2655, 0.14 #612), 01795t (0.12 #844, 0.10 #694, 0.10 #93), 01gb54 (0.10 #104, 0.06 #2294, 0.05 #1913) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 02ny6g; 0pk1p; >> query: (?x1707, 086k8) <- country(?x1707, ?x94), produced_by(?x1707, ?x5781), film(?x2373, ?x1707), ?x5781 = 03ktjq >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04n52p6 film! 086k8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 74.000 0.529 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16315-0193qj PRED entity: 0193qj PRED relation: organization PRED expected values: 04k4l => 117 concepts (108 used for prediction) PRED predicted values (max 10 best out of 46): 07t65 (0.92 #880, 0.91 #1902, 0.91 #1631), 02vk52z (0.82 #1229, 0.82 #437, 0.81 #731), 018cqq (0.59 #449, 0.50 #596, 0.50 #109), 01rz1 (0.56 #342, 0.50 #99, 0.50 #75), 0b6css (0.55 #448, 0.54 #277, 0.50 #327), 04k4l (0.52 #760, 0.50 #589, 0.50 #442), 0_2v (0.50 #125, 0.50 #101, 0.46 #1233), 02jxk (0.50 #100, 0.44 #343, 0.38 #269), 059dn (0.33 #185, 0.33 #113, 0.25 #1004), 0j7v_ (0.33 #103, 0.25 #737, 0.23 #272) >> Best rule #880 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 047yc; >> query: (?x6465, 07t65) <- combatants(?x6465, ?x756), contains(?x7456, ?x6465), olympics(?x6465, ?x2369), combatants(?x7747, ?x6465) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #760 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 31 *> proper extension: 09lxtg; 07twz; *> query: (?x6465, 04k4l) <- olympics(?x6465, ?x2369), capital(?x6465, ?x9559), ?x2369 = 0lbbj, capital(?x252, ?x9559), jurisdiction_of_office(?x182, ?x252) *> conf = 0.52 ranks of expected_values: 6 EVAL 0193qj organization 04k4l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 117.000 108.000 0.921 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #16314-0k0rf PRED entity: 0k0rf PRED relation: nominated_for PRED expected values: 05z7c => 145 concepts (71 used for prediction) PRED predicted values (max 10 best out of 382): 0jwvf (0.86 #2456, 0.85 #982, 0.84 #16484), 01jr4j (0.86 #2456, 0.85 #982, 0.84 #16484), 0k0rf (0.57 #878, 0.56 #1124, 0.54 #16482), 05z7c (0.54 #16482, 0.53 #16483, 0.44 #1041), 025twgf (0.54 #16482, 0.53 #16483, 0.05 #3173), 026p_bs (0.54 #16482, 0.53 #16483, 0.03 #2961), 03176f (0.17 #2331, 0.03 #10932, 0.03 #15370), 01kf3_9 (0.17 #5210, 0.07 #10864, 0.07 #9634), 027rpym (0.17 #628, 0.06 #1611, 0.05 #3334), 05dptj (0.17 #697, 0.06 #1680, 0.04 #2171) >> Best rule #2456 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 0pd64; >> query: (?x5134, ?x1708) <- film(?x382, ?x5134), genre(?x5134, ?x600), film(?x2465, ?x5134), nominated_for(?x1708, ?x5134), ?x600 = 02n4kr >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #16482 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 225 *> proper extension: 01p9hgt; 01kv4mb; 02fn5r; 0ggjt; 0bhvtc; 03cfjg; 0p_47; 0pmw9; *> query: (?x5134, ?x650) <- nominated_for(?x5134, ?x10404), nominated_for(?x5134, ?x4136), nominated_for(?x5856, ?x5134), nominated_for(?x746, ?x4136), nominated_for(?x4136, ?x2094), nominated_for(?x650, ?x10404) *> conf = 0.54 ranks of expected_values: 4 EVAL 0k0rf nominated_for 05z7c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 71.000 0.863 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #16313-046m59 PRED entity: 046m59 PRED relation: student! PRED expected values: 01s0_f => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 58): 01w5m (0.17 #105, 0.02 #632, 0.02 #47018), 015zyd (0.17 #1, 0.02 #1055, 0.01 #3690), 01vs5c (0.17 #181), 0bwfn (0.07 #1329, 0.06 #3964, 0.05 #12923), 017z88 (0.06 #1136, 0.03 #5352, 0.03 #12203), 07tg4 (0.05 #613, 0.02 #2194, 0.02 #9572), 07tgn (0.04 #544, 0.02 #9503, 0.02 #13720), 04b_46 (0.03 #1281, 0.02 #9186, 0.02 #4970), 065y4w7 (0.03 #10027, 0.03 #19515, 0.03 #22151), 015nl4 (0.03 #24313, 0.03 #5337, 0.03 #30112) >> Best rule #105 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 06y0xx; >> query: (?x5460, 01w5m) <- award_nominee(?x5460, ?x2389), type_of_union(?x5460, ?x566), ?x2389 = 0bgrsl >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 046m59 student! 01s0_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 102.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #16312-023vcd PRED entity: 023vcd PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.74 #295, 0.74 #1088, 0.74 #2208), 09vw2b7 (0.70 #294, 0.65 #1087, 0.63 #2207), 01pvkk (0.36 #660, 0.34 #228, 0.32 #876), 01vx2h (0.32 #1309, 0.32 #299, 0.32 #2212), 0215hd (0.25 #55, 0.21 #127, 0.17 #91), 02ynfr (0.25 #16, 0.21 #592, 0.19 #304), 089g0h (0.25 #56, 0.17 #92, 0.16 #128), 02_n3z (0.25 #37, 0.11 #73, 0.11 #109), 089fss (0.25 #5, 0.11 #293, 0.09 #1086), 094hwz (0.25 #15, 0.04 #1457, 0.04 #988) >> Best rule #295 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 06wzvr; 0ds33; 0pc62; 02hxhz; 06_wqk4; 0bshwmp; 02pxmgz; 02c6d; 01kff7; 075wx7_; ... >> query: (?x10246, 0ch6mp2) <- nominated_for(?x1312, ?x10246), film(?x794, ?x10246), ?x1312 = 07cbcy, film_crew_role(?x10246, ?x137) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023vcd film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.745 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16311-0n5_g PRED entity: 0n5_g PRED relation: contains! PRED expected values: 0gj4fx => 141 concepts (79 used for prediction) PRED predicted values (max 10 best out of 172): 059f4 (0.86 #65715, 0.85 #58508, 0.84 #70220), 05k7sb (0.75 #24299, 0.67 #67520, 0.66 #39602), 09c7w0 (0.72 #58509, 0.72 #45904, 0.70 #32397), 059g4 (0.55 #45905, 0.41 #36898, 0.40 #37798), 029jpy (0.55 #45905, 0.41 #36898, 0.40 #37798), 04_1l0v (0.55 #45905, 0.41 #36898, 0.40 #37798), 059rby (0.50 #7220, 0.48 #8122, 0.41 #23419), 07ssc (0.41 #6332, 0.40 #5432, 0.29 #3633), 02jx1 (0.29 #6387, 0.29 #3688, 0.27 #5487), 02qkt (0.23 #70570, 0.03 #59758, 0.02 #66965) >> Best rule #65715 for best value: >> intensional similarity = 4 >> extensional distance = 250 >> proper extension: 0crjn65; >> query: (?x8725, ?x728) <- administrative_parent(?x8725, ?x728), location(?x5620, ?x728), contains(?x728, ?x11397), organization(?x346, ?x11397) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #4372 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0174qm; *> query: (?x8725, 0gj4fx) <- administrative_parent(?x8725, ?x728), featured_film_locations(?x1944, ?x728), state(?x6188, ?x728), contains(?x728, ?x5088) *> conf = 0.14 ranks of expected_values: 18 EVAL 0n5_g contains! 0gj4fx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 141.000 79.000 0.857 http://example.org/location/location/contains #16310-01cv3n PRED entity: 01cv3n PRED relation: place_of_birth PRED expected values: 02_286 => 138 concepts (137 used for prediction) PRED predicted values (max 10 best out of 186): 01x73 (0.40 #16907, 0.33 #13384, 0.24 #46489), 02_286 (0.17 #1428, 0.11 #12698, 0.10 #44393), 0n95v (0.17 #1175, 0.08 #3288, 0.01 #11740), 0f2v0 (0.17 #831, 0.08 #2944, 0.01 #13511), 05ksh (0.17 #1446, 0.04 #68326, 0.01 #9897), 0d9jr (0.11 #2307, 0.04 #68326, 0.02 #17806), 094jv (0.11 #2174, 0.04 #68326, 0.02 #6400), 013yq (0.11 #2192, 0.04 #68326, 0.01 #12758), 018djs (0.11 #2778, 0.04 #68326), 04gxf (0.11 #2398, 0.04 #68326) >> Best rule #16907 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 011_vz; 017mbb; >> query: (?x680, ?x1755) <- artists(?x302, ?x680), origin(?x680, ?x1755), role(?x680, ?x1166), group(?x1166, ?x442) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1428 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0d608; *> query: (?x680, 02_286) <- profession(?x680, ?x1032), student(?x1151, ?x680), role(?x680, ?x2798), ?x2798 = 03qjg *> conf = 0.17 ranks of expected_values: 2 EVAL 01cv3n place_of_birth 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 137.000 0.400 http://example.org/people/person/place_of_birth #16309-02v2jy PRED entity: 02v2jy PRED relation: religion PRED expected values: 0c8wxp => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 29): 0c8wxp (0.44 #51, 0.41 #862, 0.39 #817), 03_gx (0.16 #14, 0.14 #1005, 0.11 #779), 0kpl (0.16 #10, 0.10 #235, 0.08 #685), 0kq2 (0.11 #18, 0.06 #333, 0.06 #198), 019cr (0.07 #56, 0.03 #371, 0.03 #101), 01lp8 (0.05 #1, 0.05 #316, 0.04 #181), 05w5d (0.05 #25, 0.04 #70, 0.01 #565), 03j6c (0.05 #786, 0.04 #1012, 0.04 #246), 0n2g (0.04 #553, 0.04 #193, 0.03 #2355), 092bf5 (0.04 #61, 0.02 #1188, 0.02 #1953) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 02qjj7; >> query: (?x13097, 0c8wxp) <- profession(?x13097, ?x1041), people(?x1446, ?x13097), ?x1446 = 033tf_, ?x1041 = 03gjzk >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v2jy religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.444 http://example.org/people/person/religion #16308-02qw2xb PRED entity: 02qw2xb PRED relation: person! PRED expected values: 0h1fktn => 108 concepts (89 used for prediction) PRED predicted values (max 10 best out of 11): 0h1fktn (0.40 #31, 0.02 #597, 0.01 #1165), 0g9lm2 (0.04 #93, 0.03 #164, 0.03 #306), 0dtw1x (0.04 #73, 0.03 #215, 0.03 #357), 0ds5_72 (0.02 #125, 0.02 #267, 0.01 #409), 0bx_hnp (0.02 #134, 0.01 #205, 0.01 #276), 02v570 (0.02 #189, 0.02 #260, 0.02 #331), 05_5_22 (0.02 #239, 0.01 #381, 0.01 #878), 058kh7 (0.02 #345, 0.02 #699, 0.01 #557), 03nqnnk (0.02 #529, 0.01 #175, 0.01 #814), 02847m9 (0.01 #646, 0.01 #150, 0.01 #789) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 05ztm4r; 0806vbn; 07s6prs; 080knyg; 07sgfsl; 0fn5bx; 03cxsvl; 032q8q; 077yk0; 0fn8jc; ... >> query: (?x7797, 0h1fktn) <- award_nominee(?x822, ?x7797), award_nominee(?x7797, ?x1796), ?x822 = 05b__vr >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qw2xb person! 0h1fktn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 89.000 0.400 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #16307-026z9 PRED entity: 026z9 PRED relation: artists PRED expected values: 012z8_ 01jfr3y 01vtj38 => 60 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1020): 012vd6 (0.71 #7797, 0.67 #5702, 0.60 #3608), 07s3vqk (0.71 #7342, 0.60 #9437, 0.60 #3153), 01vvycq (0.67 #4235, 0.60 #3188, 0.60 #2141), 0gbwp (0.67 #5577, 0.60 #3483, 0.60 #1388), 01wk7ql (0.67 #6103, 0.60 #4009, 0.60 #1914), 0x3n (0.67 #5788, 0.60 #3694, 0.60 #1599), 012z8_ (0.67 #5623, 0.60 #3529, 0.60 #1434), 0bs1g5r (0.67 #5957, 0.60 #3863, 0.60 #1768), 0407f (0.67 #5511, 0.60 #3417, 0.60 #1322), 03t9sp (0.67 #4311, 0.60 #2217, 0.57 #6405) >> Best rule #7797 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 016cjb; >> query: (?x5792, 012vd6) <- artists(?x5792, ?x6651), artists(?x5792, ?x4010), artists(?x5792, ?x1974), ?x1974 = 0136p1, parent_genre(?x3370, ?x5792), award(?x4010, ?x567), ?x6651 = 019f9z >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5623 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 02x8m; *> query: (?x5792, 012z8_) <- artists(?x5792, ?x10712), artists(?x5792, ?x8156), artists(?x5792, ?x1974), parent_genre(?x5792, ?x3928), ?x8156 = 046p9, award(?x1974, ?x528), instrumentalists(?x316, ?x1974), ?x10712 = 016376 *> conf = 0.67 ranks of expected_values: 7, 32, 144 EVAL 026z9 artists 01vtj38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 60.000 24.000 0.714 http://example.org/music/genre/artists EVAL 026z9 artists 01jfr3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 60.000 24.000 0.714 http://example.org/music/genre/artists EVAL 026z9 artists 012z8_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 60.000 24.000 0.714 http://example.org/music/genre/artists #16306-04pk1f PRED entity: 04pk1f PRED relation: nominated_for! PRED expected values: 03v1w7 => 106 concepts (51 used for prediction) PRED predicted values (max 10 best out of 899): 03v1w7 (0.45 #116775, 0.44 #112104, 0.37 #109768), 0b1f49 (0.45 #116775, 0.44 #112104, 0.37 #109768), 092ys_y (0.39 #14012, 0.33 #25684, 0.30 #79401), 0f0kz (0.32 #114439, 0.29 #93416, 0.29 #4672), 0kszw (0.32 #114439, 0.29 #93416, 0.29 #4672), 03hzl42 (0.32 #114439, 0.29 #93416, 0.29 #4672), 01xsc9 (0.32 #114439, 0.29 #93416, 0.29 #4672), 0ff2k (0.30 #7007, 0.12 #2337), 06pj8 (0.18 #35459, 0.06 #58810, 0.06 #65820), 0146pg (0.15 #35147, 0.14 #4793, 0.12 #11798) >> Best rule #116775 for best value: >> intensional similarity = 4 >> extensional distance = 639 >> proper extension: 016z9n; >> query: (?x6078, ?x3880) <- film(?x2531, ?x6078), nominated_for(?x143, ?x6078), produced_by(?x6078, ?x3880), film(?x382, ?x6078) >> conf = 0.45 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 04pk1f nominated_for! 03v1w7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 51.000 0.452 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16305-0gtv7pk PRED entity: 0gtv7pk PRED relation: film! PRED expected values: 0175wg => 132 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1422): 03359d (0.48 #22918, 0.29 #5067, 0.12 #15486), 0175wg (0.48 #22918, 0.14 #5187, 0.06 #15606), 02xs5v (0.23 #13909, 0.12 #15992, 0.10 #11826), 053xw6 (0.20 #11673, 0.06 #15839, 0.04 #103333), 0jbp0 (0.18 #16345, 0.15 #14262, 0.12 #22594), 03h_9lg (0.18 #14716, 0.15 #12633, 0.11 #8467), 016z2j (0.18 #17056, 0.15 #19139, 0.08 #31638), 079vf (0.16 #20841, 0.15 #12509, 0.14 #4173), 03ym1 (0.15 #13515, 0.12 #15598, 0.10 #19764), 02pk6x (0.15 #13503, 0.12 #15586, 0.08 #21835) >> Best rule #22918 for best value: >> intensional similarity = 7 >> extensional distance = 23 >> proper extension: 02vw1w2; >> query: (?x409, ?x558) <- genre(?x409, ?x1013), genre(?x409, ?x812), prequel(?x409, ?x10590), ?x812 = 01jfsb, ?x1013 = 06n90, language(?x10590, ?x254), film(?x558, ?x10590) >> conf = 0.48 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0gtv7pk film! 0175wg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 79.000 0.479 http://example.org/film/actor/film./film/performance/film #16304-0404j37 PRED entity: 0404j37 PRED relation: honored_for! PRED expected values: 09v0p2c => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 105): 0g5b0q5 (0.14 #14, 0.03 #966, 0.03 #1204), 09pj68 (0.07 #88, 0.06 #207, 0.03 #1040), 0bvfqq (0.07 #26, 0.06 #145, 0.02 #264), 09bymc (0.07 #103, 0.03 #1293, 0.03 #1412), 09p30_ (0.07 #70, 0.03 #1022, 0.03 #1260), 0n8_m93 (0.07 #101, 0.03 #220, 0.02 #577), 09qvms (0.07 #9, 0.03 #128, 0.02 #961), 0fqpc7d (0.07 #29, 0.03 #1219, 0.03 #981), 09p2r9 (0.07 #77, 0.03 #1029, 0.02 #1267), 092c5f (0.07 #10, 0.03 #962, 0.02 #1200) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0c38gj; >> query: (?x6448, 0g5b0q5) <- nominated_for(?x6729, ?x6448), nominated_for(?x500, ?x6448), ?x500 = 0p9sw, ?x6729 = 099ck7 >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0404j37 honored_for! 09v0p2c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 73.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #16303-0ch26b_ PRED entity: 0ch26b_ PRED relation: film_release_region PRED expected values: 0d0vqn 0345h 0jgx => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 101): 0d0vqn (0.90 #674, 0.87 #1210, 0.86 #1076), 0345h (0.83 #691, 0.74 #1227, 0.73 #1093), 03rj0 (0.56 #712, 0.49 #1114, 0.48 #1248), 06qd3 (0.47 #696, 0.44 #1098, 0.43 #1232), 06mzp (0.43 #683, 0.38 #1219, 0.37 #1085), 06t8v (0.43 #727, 0.34 #1129, 0.33 #1263), 015qh (0.42 #699, 0.35 #1101, 0.33 #1235), 0h7x (0.36 #1095, 0.36 #1229, 0.35 #693), 01pj7 (0.30 #704, 0.24 #1106, 0.24 #1240), 07f1x (0.30 #767, 0.25 #1169, 0.24 #1303) >> Best rule #674 for best value: >> intensional similarity = 5 >> extensional distance = 222 >> proper extension: 0djb3vw; 0fq27fp; 04969y; 0d6b7; 0bmc4cm; 0192hw; 026njb5; 07l50vn; 0h95zbp; 0g5q34q; ... >> query: (?x1916, 0d0vqn) <- genre(?x1916, ?x53), film_release_region(?x1916, ?x2152), film_release_region(?x1916, ?x279), ?x2152 = 06mkj, ?x279 = 0d060g >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 23 EVAL 0ch26b_ film_release_region 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 80.000 80.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ch26b_ film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ch26b_ film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16302-02gkxp PRED entity: 02gkxp PRED relation: school_type PRED expected values: 05pcjw => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 18): 05jxkf (0.43 #1588, 0.41 #1756, 0.41 #1492), 05pcjw (0.36 #193, 0.36 #145, 0.36 #97), 01rs41 (0.30 #1085, 0.29 #701, 0.29 #5), 07tf8 (0.21 #249, 0.19 #273, 0.19 #129), 01_9fk (0.16 #98, 0.15 #74, 0.14 #338), 01_srz (0.10 #2185, 0.09 #99, 0.08 #147), 06cs1 (0.10 #2185, 0.04 #126, 0.04 #174), 04399 (0.10 #2185, 0.03 #326, 0.02 #878), 01jlsn (0.10 #2185, 0.03 #1409, 0.03 #1481), 02p0qmm (0.05 #274, 0.05 #298, 0.04 #1018) >> Best rule #1588 for best value: >> intensional similarity = 4 >> extensional distance = 370 >> proper extension: 07b2yw; >> query: (?x10333, 05jxkf) <- institution(?x1771, ?x10333), contains(?x94, ?x10333), institution(?x1771, ?x6602), ?x6602 = 04bfg >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #193 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 059j2; *> query: (?x10333, 05pcjw) <- company(?x346, ?x10333), contains(?x94, ?x10333), basic_title(?x1157, ?x346) *> conf = 0.36 ranks of expected_values: 2 EVAL 02gkxp school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 147.000 0.430 http://example.org/education/educational_institution/school_type #16301-03qhnx PRED entity: 03qhnx PRED relation: time_zones PRED expected values: 02llzg => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 11): 02llzg (0.50 #17, 0.25 #56, 0.25 #43), 02hcv8 (0.40 #81, 0.33 #120, 0.33 #3), 02lcqs (0.33 #447, 0.32 #343, 0.31 #304), 02hczc (0.28 #197, 0.18 #392, 0.15 #236), 02fqwt (0.22 #66, 0.21 #170, 0.20 #79), 042g7t (0.12 #50, 0.11 #76, 0.08 #141), 052vwh (0.10 #324, 0.10 #103, 0.08 #142), 03bdv (0.05 #435, 0.05 #643, 0.05 #812), 03plfd (0.03 #439, 0.03 #803, 0.02 #920), 02lcrv (0.03 #319, 0.02 #423, 0.01 #527) >> Best rule #17 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 05qtj; 07_pf; >> query: (?x14673, 02llzg) <- featured_film_locations(?x4786, ?x14673), ?x4786 = 0bbw2z6 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qhnx time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.500 http://example.org/location/location/time_zones #16300-056vv PRED entity: 056vv PRED relation: countries_spoken_in! PRED expected values: 0k0sv => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 53): 02h40lc (0.36 #1838, 0.36 #1352, 0.34 #1082), 064_8sq (0.22 #288, 0.22 #936, 0.19 #450), 02ztjwg (0.19 #29, 0.13 #137, 0.13 #191), 06nm1 (0.19 #170, 0.19 #1088, 0.18 #1358), 04306rv (0.17 #5, 0.12 #383, 0.11 #545), 0jzc (0.15 #124, 0.15 #178, 0.15 #340), 02bjrlw (0.14 #1, 0.12 #109, 0.11 #163), 06b_j (0.13 #127, 0.13 #181, 0.09 #343), 05zjd (0.11 #454, 0.08 #1102, 0.08 #832), 0cjk9 (0.10 #112, 0.09 #166, 0.09 #328) >> Best rule #1838 for best value: >> intensional similarity = 3 >> extensional distance = 180 >> proper extension: 0h44w; >> query: (?x2979, 02h40lc) <- countries_spoken_in(?x13473, ?x2979), countries_spoken_in(?x13473, ?x1558), film_release_region(?x124, ?x1558) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0366c; *> query: (?x2979, 0k0sv) <- contains(?x6304, ?x2979), contains(?x455, ?x2979), ?x455 = 02j9z, ?x6304 = 02qkt *> conf = 0.03 ranks of expected_values: 41 EVAL 056vv countries_spoken_in! 0k0sv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 92.000 92.000 0.363 http://example.org/language/human_language/countries_spoken_in #16299-0q9nj PRED entity: 0q9nj PRED relation: category PRED expected values: 08mbj5d => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.43 #7, 0.40 #18, 0.40 #12) >> Best rule #7 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 03gvm3t; >> query: (?x9135, 08mbj5d) <- program(?x12476, ?x9135), program_creator(?x9135, ?x4720), genre(?x9135, ?x5728), ?x5728 = 09lmb, program(?x12476, ?x11082), nationality(?x4720, ?x94), country_of_origin(?x11082, ?x279), genre(?x11082, ?x53), profession(?x4720, ?x987), award(?x4720, ?x1105) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q9nj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.429 http://example.org/common/topic/webpage./common/webpage/category #16298-0cqhb3 PRED entity: 0cqhb3 PRED relation: award! PRED expected values: 01nvmd_ 069nzr => 49 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2683): 01wbg84 (0.84 #3359, 0.82 #13438, 0.81 #16798), 013pk3 (0.84 #3359, 0.82 #13438, 0.81 #16798), 016kft (0.84 #3359, 0.82 #13438, 0.81 #16798), 01vvb4m (0.67 #4196, 0.17 #10916, 0.06 #27717), 0bxtg (0.50 #3462, 0.33 #103, 0.17 #10182), 014gf8 (0.50 #5016, 0.17 #11736, 0.12 #47046), 0205dx (0.50 #4735, 0.17 #11455, 0.08 #14815), 0237fw (0.50 #4002, 0.17 #10722, 0.07 #27523), 07r1h (0.50 #5154, 0.17 #11874, 0.07 #28675), 0pmhf (0.50 #4051, 0.17 #10771, 0.07 #34298) >> Best rule #3359 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0fbtbt; >> query: (?x8250, ?x368) <- award_winner(?x8250, ?x368), nominated_for(?x8250, ?x10661), ?x10661 = 06qv_, award(?x286, ?x8250) >> conf = 0.84 => this is the best rule for 3 predicted values *> Best rule #10348 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 08_vwq; *> query: (?x8250, 01nvmd_) <- award_winner(?x8250, ?x368), award(?x3789, ?x8250), award_nominee(?x3789, ?x5505), ?x5505 = 06vsbt *> conf = 0.25 ranks of expected_values: 204, 310 EVAL 0cqhb3 award! 069nzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 17.000 0.844 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhb3 award! 01nvmd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 17.000 0.844 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16297-043sct5 PRED entity: 043sct5 PRED relation: film! PRED expected values: 05d6q1 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 102): 03xq0f (0.85 #2160, 0.17 #1194, 0.14 #302), 016tt2 (0.57 #152, 0.14 #2233, 0.13 #2902), 017s11 (0.40 #77, 0.25 #3, 0.15 #598), 03rwz3 (0.40 #118, 0.25 #44, 0.09 #1085), 086k8 (0.30 #224, 0.29 #150, 0.23 #745), 025jfl (0.25 #6, 0.20 #80, 0.13 #377), 017jv5 (0.20 #237, 0.07 #2318, 0.04 #4780), 024rdh (0.17 #854, 0.14 #185, 0.14 #780), 016tw3 (0.17 #1126, 0.15 #6648, 0.14 #3134), 05qd_ (0.16 #2164, 0.13 #2238, 0.12 #1198) >> Best rule #2160 for best value: >> intensional similarity = 9 >> extensional distance = 158 >> proper extension: 0522wp; >> query: (?x4430, 03xq0f) <- film(?x10629, ?x4430), film(?x10629, ?x8471), film(?x10629, ?x6005), film(?x10629, ?x2656), genre(?x2656, ?x53), film_release_region(?x8471, ?x2513), ?x6005 = 051ys82, film(?x368, ?x8471), ?x2513 = 05b4w >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #2201 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 158 *> proper extension: 0522wp; *> query: (?x4430, 05d6q1) <- film(?x10629, ?x4430), film(?x10629, ?x8471), film(?x10629, ?x6005), film(?x10629, ?x2656), genre(?x2656, ?x53), film_release_region(?x8471, ?x2513), ?x6005 = 051ys82, film(?x368, ?x8471), ?x2513 = 05b4w *> conf = 0.07 ranks of expected_values: 28 EVAL 043sct5 film! 05d6q1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 122.000 122.000 0.850 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16296-014488 PRED entity: 014488 PRED relation: award_nominee! PRED expected values: 03_6y 01p4vl => 78 concepts (36 used for prediction) PRED predicted values (max 10 best out of 968): 01p4vl (0.81 #32461, 0.81 #41736, 0.81 #41735), 08swgx (0.81 #32461, 0.81 #41736, 0.81 #41735), 03_6y (0.81 #32461, 0.81 #41736, 0.81 #41735), 05cx7x (0.81 #32461, 0.81 #41736, 0.81 #41735), 07h565 (0.81 #32461, 0.81 #41736, 0.81 #41735), 014488 (0.57 #3064, 0.25 #74207, 0.14 #37098), 02qgyv (0.32 #2810, 0.25 #74207, 0.14 #37098), 01kb2j (0.32 #3516, 0.25 #74207, 0.14 #37098), 02p65p (0.29 #2344, 0.25 #74207, 0.14 #37098), 0gy6z9 (0.29 #3057, 0.25 #74207, 0.14 #37098) >> Best rule #32461 for best value: >> intensional similarity = 3 >> extensional distance = 446 >> proper extension: 04nw9; 01vb403; 0l56b; 03wpmd; 038rzr; 0hwqz; 01tnbn; 07g7h2; 01kgxf; 01933d; ... >> query: (?x3324, ?x221) <- profession(?x3324, ?x319), religion(?x3324, ?x1985), award_nominee(?x3324, ?x221) >> conf = 0.81 => this is the best rule for 5 predicted values ranks of expected_values: 1, 3 EVAL 014488 award_nominee! 01p4vl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 36.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 014488 award_nominee! 03_6y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 36.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16295-0c31_ PRED entity: 0c31_ PRED relation: profession PRED expected values: 0dxtg => 96 concepts (33 used for prediction) PRED predicted values (max 10 best out of 58): 0dxtg (0.77 #3121, 0.69 #604, 0.67 #2084), 02hrh1q (0.76 #753, 0.75 #1049, 0.75 #1789), 0cbd2 (0.33 #6, 0.20 #154, 0.18 #3115), 03gjzk (0.32 #3123, 0.32 #2974, 0.31 #3271), 09jwl (0.23 #2830, 0.17 #4460, 0.17 #4016), 02krf9 (0.22 #2394, 0.21 #1358, 0.20 #1950), 0nbcg (0.21 #2843, 0.10 #4473, 0.10 #4029), 0dgd_ (0.20 #178, 0.13 #474, 0.12 #622), 018gz8 (0.14 #3125, 0.14 #1496, 0.13 #1644), 0kyk (0.14 #325, 0.13 #1509, 0.11 #2841) >> Best rule #3121 for best value: >> intensional similarity = 4 >> extensional distance = 885 >> proper extension: 0b05xm; 0bt23; 03k1vm; 02hblj; >> query: (?x8155, 0dxtg) <- type_of_union(?x8155, ?x566), profession(?x8155, ?x524), profession(?x12534, ?x524), ?x12534 = 05tjm3 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c31_ profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 33.000 0.767 http://example.org/people/person/profession #16294-01w7nww PRED entity: 01w7nww PRED relation: role PRED expected values: 01vdm0 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 75): 01vdm0 (0.36 #34, 0.19 #1308, 0.19 #246), 05r5c (0.29 #433, 0.28 #1709, 0.27 #964), 0l14qv (0.27 #6, 0.10 #3618, 0.09 #2662), 0342h (0.24 #3617, 0.24 #2763, 0.23 #3719), 05148p4 (0.24 #2763, 0.23 #3719, 0.22 #1807), 02sgy (0.19 #219, 0.16 #2663, 0.15 #3619), 01vj9c (0.19 #229, 0.12 #972, 0.09 #3629), 026t6 (0.19 #215, 0.10 #3615, 0.09 #3), 042v_gx (0.18 #10, 0.14 #2666, 0.13 #3622), 05842k (0.18 #81, 0.13 #1781, 0.12 #293) >> Best rule #34 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01wqpnm; >> query: (?x3176, 01vdm0) <- student(?x12732, ?x3176), award(?x3176, ?x7535), ?x7535 = 02f73b >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w7nww role 01vdm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.364 http://example.org/music/artist/track_contributions./music/track_contribution/role #16293-0ytph PRED entity: 0ytph PRED relation: place_of_birth! PRED expected values: 09fqd3 => 91 concepts (60 used for prediction) PRED predicted values (max 10 best out of 951): 01wk7ql (0.02 #5224, 0.02 #70506, 0.01 #65281), 01364q (0.02 #5224, 0.02 #70506, 0.01 #65281), 0k2mxq (0.02 #5224, 0.02 #70506, 0.01 #65281), 09f0bj (0.02 #5224, 0.02 #70506, 0.01 #65281), 08yx9q (0.02 #5224, 0.02 #70506, 0.01 #65281), 01_njt (0.02 #5224, 0.02 #70506, 0.01 #65281), 025b5y (0.02 #5224, 0.02 #70506, 0.01 #65281), 05np4c (0.02 #5224, 0.02 #70506, 0.01 #65281), 066m4g (0.02 #5224, 0.02 #70506, 0.01 #65281), 034x61 (0.02 #5224, 0.02 #70506, 0.01 #65281) >> Best rule #5224 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 062qg; 01gbzb; 0t6sb; 01t3h6; >> query: (?x12558, ?x848) <- place_of_birth(?x1918, ?x12558), state(?x12558, ?x177), award_winner(?x1918, ?x848) >> conf = 0.02 => this is the best rule for 14 predicted values No rule for expected values ranks of expected_values: EVAL 0ytph place_of_birth! 09fqd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 60.000 0.020 http://example.org/people/person/place_of_birth #16292-0282x PRED entity: 0282x PRED relation: people! PRED expected values: 02ctzb => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 53): 041rx (0.50 #466, 0.33 #158, 0.29 #2083), 0x67 (0.38 #1935, 0.29 #2628, 0.22 #549), 09vc4s (0.33 #163, 0.07 #3397, 0.06 #1087), 02rbdlq (0.33 #1, 0.04 #1464, 0.03 #1618), 02w7gg (0.26 #6240, 0.23 #7474, 0.18 #3852), 048z7l (0.17 #502, 0.16 #1195, 0.08 #1503), 013xrm (0.17 #2792, 0.14 #3023, 0.12 #2869), 02g7sp (0.17 #403, 0.11 #634, 0.08 #788), 033tf_ (0.12 #3703, 0.10 #2009, 0.10 #4165), 0xnvg (0.11 #629, 0.11 #3709, 0.11 #1168) >> Best rule #466 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 013pp3; 03ftmg; 0h0yt; >> query: (?x5345, 041rx) <- influenced_by(?x5345, ?x1136), student(?x3995, ?x5345), ?x3995 = 0fdys, type_of_union(?x5345, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1555 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 016gkf; 0h0p_; 082xp; *> query: (?x5345, 02ctzb) <- profession(?x5345, ?x2225), profession(?x5345, ?x987), ?x987 = 0dxtg, people(?x4322, ?x5345), ?x2225 = 0kyk *> conf = 0.08 ranks of expected_values: 16 EVAL 0282x people! 02ctzb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 167.000 167.000 0.500 http://example.org/people/ethnicity/people #16291-0k0rf PRED entity: 0k0rf PRED relation: nominated_for! PRED expected values: 05z7c => 141 concepts (71 used for prediction) PRED predicted values (max 10 best out of 587): 01s9vc (0.87 #1478, 0.84 #11365, 0.83 #5923), 05z7c (0.71 #1537, 0.50 #8148, 0.48 #14585), 0k0rf (0.57 #1619, 0.50 #8148, 0.48 #14585), 0d1qmz (0.50 #3307, 0.48 #5032, 0.33 #2072), 02qrv7 (0.50 #3240, 0.43 #4965, 0.33 #2005), 02n72k (0.50 #3386, 0.43 #5111, 0.33 #2151), 025twgt (0.50 #3447, 0.43 #5172, 0.33 #2212), 026p_bs (0.50 #8148, 0.08 #3219, 0.05 #4944), 025twgf (0.50 #8148, 0.03 #5179, 0.02 #11588), 0fztbq (0.43 #5171, 0.42 #3446, 0.22 #2211) >> Best rule #1478 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0h0wd9; >> query: (?x5134, ?x1708) <- currency(?x5134, ?x170), film(?x382, ?x5134), film_festivals(?x5134, ?x5415), nominated_for(?x5134, ?x10404), nominated_for(?x5134, ?x1708), produced_by(?x5134, ?x2465), film_release_region(?x10404, ?x94) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1537 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 02jr6k; *> query: (?x5134, 05z7c) <- currency(?x5134, ?x170), film(?x382, ?x5134), titles(?x600, ?x5134), film(?x2465, ?x5134), nominated_for(?x2717, ?x5134), ?x2717 = 0k5g9 *> conf = 0.71 ranks of expected_values: 2 EVAL 0k0rf nominated_for! 05z7c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 71.000 0.870 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #16290-0gt3p PRED entity: 0gt3p PRED relation: people! PRED expected values: 07bch9 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 43): 041rx (0.29 #384, 0.27 #992, 0.24 #4260), 0x67 (0.18 #3429, 0.18 #4265, 0.18 #4113), 07bch9 (0.12 #1010, 0.11 #402, 0.07 #174), 02w7gg (0.11 #4030, 0.09 #3422, 0.09 #4790), 02ctzb (0.10 #394, 0.07 #1002, 0.04 #1610), 0g8_vp (0.10 #857, 0.07 #249, 0.03 #2149), 0xnvg (0.09 #620, 0.08 #696, 0.08 #4040), 013xrm (0.08 #399, 0.07 #2147, 0.06 #1007), 063k3h (0.07 #1018, 0.06 #410, 0.02 #790), 03bkbh (0.07 #183, 0.07 #107, 0.04 #639) >> Best rule #384 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 01h2_6; >> query: (?x7759, 041rx) <- student(?x581, ?x7759), people(?x268, ?x7759), location(?x7759, ?x3125), people(?x1446, ?x7759) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1010 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 172 *> proper extension: 02wh0; *> query: (?x7759, 07bch9) <- people(?x268, ?x7759), people(?x1446, ?x7759), location(?x7759, ?x3125) *> conf = 0.12 ranks of expected_values: 3 EVAL 0gt3p people! 07bch9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 139.000 139.000 0.292 http://example.org/people/ethnicity/people #16289-02hsq3m PRED entity: 02hsq3m PRED relation: nominated_for PRED expected values: 01ln5z 0pb33 02wgk1 012s1d 0ndwt2w 02c7k4 0404j37 011xg5 01d2v1 => 51 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1283): 0hx4y (0.77 #4480, 0.66 #17928, 0.64 #17927), 011yd2 (0.77 #4480, 0.66 #17928, 0.64 #17927), 01f8hf (0.77 #4480, 0.66 #17928, 0.64 #17927), 07cz2 (0.77 #4480, 0.66 #17928, 0.64 #17927), 09gq0x5 (0.71 #4713, 0.67 #7699, 0.59 #12184), 0y_9q (0.71 #5244, 0.67 #8230, 0.56 #11219), 0209hj (0.71 #4569, 0.60 #3075, 0.56 #7555), 0m313 (0.71 #4492, 0.56 #7478, 0.52 #11963), 01cmp9 (0.71 #5346, 0.56 #8332, 0.52 #12817), 07vf5c (0.71 #5068, 0.56 #8054, 0.33 #12539) >> Best rule #4480 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02r0csl; 02w9sd7; 0gqxm; >> query: (?x640, ?x2215) <- nominated_for(?x640, ?x12108), ?x12108 = 02bqxb, award(?x2215, ?x640) >> conf = 0.77 => this is the best rule for 4 predicted values *> Best rule #11950 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 027h4yd; *> query: (?x640, ?x1012) <- award_winner(?x640, ?x2870), crewmember(?x1386, ?x2870), nominated_for(?x2870, ?x1012), award_winner(?x762, ?x2870) *> conf = 0.49 ranks of expected_values: 32, 33, 37, 81, 253, 265, 425, 634, 800 EVAL 02hsq3m nominated_for 01d2v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 51.000 17.000 0.765 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02hsq3m nominated_for 011xg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 51.000 17.000 0.765 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02hsq3m nominated_for 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 51.000 17.000 0.765 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02hsq3m nominated_for 02c7k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 17.000 0.765 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02hsq3m nominated_for 0ndwt2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 51.000 17.000 0.765 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02hsq3m nominated_for 012s1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 51.000 17.000 0.765 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02hsq3m nominated_for 02wgk1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 51.000 17.000 0.765 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02hsq3m nominated_for 0pb33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 17.000 0.765 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02hsq3m nominated_for 01ln5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 17.000 0.765 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16288-05gnf PRED entity: 05gnf PRED relation: nominated_for PRED expected values: 01b_lz => 139 concepts (106 used for prediction) PRED predicted values (max 10 best out of 641): 08jgk1 (0.83 #136961, 0.82 #40291, 0.82 #38908), 016tvq (0.83 #136961, 0.82 #136960, 0.80 #162735), 02md2d (0.83 #136961, 0.82 #136960, 0.80 #162735), 01cvtf (0.83 #136961, 0.82 #136960, 0.80 #162735), 01h1bf (0.83 #136961, 0.82 #136960, 0.80 #162735), 045r_9 (0.83 #136961, 0.82 #136960, 0.80 #162735), 04glx0 (0.83 #136961, 0.82 #136960, 0.80 #162735), 047csmy (0.40 #8891, 0.25 #18558, 0.18 #33068), 011yn5 (0.33 #844, 0.25 #4065, 0.14 #16955), 04vr_f (0.33 #157, 0.25 #3378, 0.14 #16268) >> Best rule #136961 for best value: >> intensional similarity = 3 >> extensional distance = 519 >> proper extension: 0q9kd; 0grwj; 01k7d9; 032xhg; 0c4f4; 02r_d4; 012c6x; 01yk13; 03gm48; 01sxq9; ... >> query: (?x6678, ?x1631) <- award_winner(?x1631, ?x6678), nominated_for(?x6678, ?x337), genre(?x1631, ?x239) >> conf = 0.83 => this is the best rule for 7 predicted values *> Best rule #35457 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 01nzs7; 01w5gp; 017vb_; 01j7pt; 0187wh; 0kctd; 025snf; 0kcd5; 02hmvw; 02fp82; ... *> query: (?x6678, ?x293) <- program(?x6678, ?x293), category(?x6678, ?x134), nominated_for(?x375, ?x293) *> conf = 0.26 ranks of expected_values: 26 EVAL 05gnf nominated_for 01b_lz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 139.000 106.000 0.832 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16287-05dl1s PRED entity: 05dl1s PRED relation: nominated_for! PRED expected values: 0fdtd7 => 61 concepts (34 used for prediction) PRED predicted values (max 10 best out of 215): 0fdtd7 (0.68 #4761, 0.68 #3093, 0.67 #5001), 0gq9h (0.38 #538, 0.32 #1727, 0.30 #1965), 02qwdhq (0.38 #110, 0.12 #5242, 0.10 #5002), 0gs9p (0.35 #540, 0.28 #1729, 0.27 #1967), 019f4v (0.31 #529, 0.26 #1718, 0.26 #1189), 0k611 (0.27 #549, 0.24 #1738, 0.22 #2452), 040njc (0.26 #1189, 0.25 #481, 0.23 #3809), 0gr4k (0.26 #1189, 0.25 #501, 0.23 #3809), 02pqp12 (0.26 #1189, 0.23 #3809, 0.20 #534), 0j298t8 (0.26 #1189, 0.23 #3809, 0.03 #6428) >> Best rule #4761 for best value: >> intensional similarity = 3 >> extensional distance = 986 >> proper extension: 02rq7nd; >> query: (?x11037, ?x8843) <- award(?x11037, ?x8843), nominated_for(?x8843, ?x278), award(?x8844, ?x8843) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05dl1s nominated_for! 0fdtd7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 34.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16286-01trf3 PRED entity: 01trf3 PRED relation: languages PRED expected values: 02h40lc => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 16): 02h40lc (0.36 #41, 0.35 #275, 0.35 #509), 064_8sq (0.08 #1015, 0.03 #1381, 0.03 #1771), 03_9r (0.04 #44, 0.03 #317, 0.03 #161), 06nm1 (0.04 #45, 0.02 #279, 0.01 #318), 03k50 (0.03 #433, 0.03 #511, 0.03 #745), 07c9s (0.03 #520, 0.02 #754, 0.01 #1262), 04306rv (0.03 #159, 0.02 #276, 0.01 #861), 04h9h (0.03 #186, 0.02 #303), 03hkp (0.03 #166, 0.02 #283), 0999q (0.02 #764, 0.02 #530, 0.02 #452) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 0c_md_; >> query: (?x4233, 02h40lc) <- person(?x124, ?x4233), profession(?x4233, ?x1032), student(?x1368, ?x4233) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01trf3 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.360 http://example.org/people/person/languages #16285-01bpc9 PRED entity: 01bpc9 PRED relation: profession PRED expected values: 02hrh1q => 109 concepts (68 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.87 #6597, 0.86 #8507, 0.86 #7477), 0cbd2 (0.70 #8207, 0.49 #2931, 0.47 #2344), 0nbcg (0.62 #760, 0.54 #1052, 0.52 #6759), 0dxtg (0.62 #2351, 0.40 #8214, 0.34 #4108), 0dz3r (0.57 #2048, 0.54 #2194, 0.52 #732), 01d_h8 (0.57 #1319, 0.48 #2489, 0.41 #4685), 039v1 (0.52 #1203, 0.30 #6764, 0.29 #3984), 03gjzk (0.32 #1329, 0.32 #2499, 0.29 #5573), 01c72t (0.32 #4556, 0.32 #2653, 0.31 #4410), 0n1h (0.27 #2788, 0.27 #1471, 0.22 #3082) >> Best rule #6597 for best value: >> intensional similarity = 4 >> extensional distance = 456 >> proper extension: 01ry0f; 018fwv; >> query: (?x1654, 02hrh1q) <- nationality(?x1654, ?x94), ?x94 = 09c7w0, location(?x1654, ?x739), actor(?x1653, ?x1654) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bpc9 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 68.000 0.873 http://example.org/people/person/profession #16284-05ty4m PRED entity: 05ty4m PRED relation: produced_by! PRED expected values: 03lrht => 100 concepts (91 used for prediction) PRED predicted values (max 10 best out of 303): 07_k0c0 (0.33 #532), 0bq8tmw (0.33 #139), 0124k9 (0.30 #18730, 0.15 #17793, 0.11 #8425), 0hr41p6 (0.30 #18730, 0.15 #17793, 0.11 #8425), 02ph9tm (0.25 #23414, 0.01 #5275), 02825cv (0.25 #23414), 07kb7vh (0.14 #9362, 0.02 #47755, 0.02 #35585), 0ddf2bm (0.09 #1818, 0.01 #5562, 0.01 #6498), 0h03fhx (0.07 #4162, 0.04 #5098, 0.02 #8843), 084qpk (0.04 #3812, 0.04 #2876, 0.03 #4748) >> Best rule #532 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01qbjg; >> query: (?x364, 07_k0c0) <- award(?x364, ?x3906), produced_by(?x86, ?x364), ?x3906 = 03ccq3s >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05ty4m produced_by! 03lrht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 91.000 0.333 http://example.org/film/film/produced_by #16283-059rby PRED entity: 059rby PRED relation: district_represented! PRED expected values: 03rl1g 01h7xx => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 7): 03rl1g (0.62 #57, 0.56 #204, 0.56 #141), 01h7xx (0.50 #206, 0.50 #143, 0.50 #59), 01gvxh (0.19 #200, 0.12 #284, 0.11 #263), 04lgybj (0.19 #198, 0.12 #282, 0.11 #261), 04fhps (0.17 #202, 0.11 #265, 0.10 #286), 03h_f4 (0.12 #201, 0.12 #285, 0.11 #264), 034_7s (0.10 #287, 0.09 #252, 0.09 #266) >> Best rule #57 for best value: >> intensional similarity = 2 >> extensional distance = 14 >> proper extension: 0g0syc; >> query: (?x335, 03rl1g) <- district_represented(?x5339, ?x335), ?x5339 = 02glc4 >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 059rby district_represented! 01h7xx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.625 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 059rby district_represented! 03rl1g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.625 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #16282-01n5sn PRED entity: 01n5sn PRED relation: parent_genre PRED expected values: 03_d0 => 51 concepts (36 used for prediction) PRED predicted values (max 10 best out of 242): 06by7 (0.87 #2273, 0.77 #2433, 0.72 #2595), 03lty (0.71 #2115, 0.27 #3413, 0.27 #3573), 03_d0 (0.38 #812, 0.33 #9, 0.27 #1132), 0xhtw (0.34 #1786, 0.14 #655, 0.12 #816), 02fhtq (0.33 #301, 0.25 #462, 0.18 #1264), 01m1y (0.33 #276, 0.25 #437, 0.14 #758), 0cx6f (0.33 #108, 0.25 #590, 0.12 #911), 016jny (0.31 #1841, 0.25 #550, 0.14 #710), 0gywn (0.30 #1974, 0.25 #522, 0.18 #1326), 017371 (0.29 #745, 0.25 #585, 0.12 #1716) >> Best rule #2273 for best value: >> intensional similarity = 11 >> extensional distance = 66 >> proper extension: 05hs4r; 01gbcf; 016clz; 0m0jc; 016jhr; 0xhtw; 061fhg; 01756d; 0mhfr; 03lty; ... >> query: (?x13055, 06by7) <- parent_genre(?x13055, ?x7440), artists(?x7440, ?x8305), artists(?x7440, ?x3740), artists(?x7440, ?x2347), artists(?x7440, ?x2319), artists(?x7440, ?x1720), ?x8305 = 01vtg4q, ?x3740 = 0fpj4lx, ?x1720 = 01qkqwg, ?x2319 = 0lccn, artist(?x3265, ?x2347) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #812 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 015pdg; 0dl5d; 0gg8l; *> query: (?x13055, 03_d0) <- parent_genre(?x13055, ?x7440), parent_genre(?x13055, ?x3319), ?x7440 = 0155w, artists(?x13055, ?x5904), award_nominee(?x5904, ?x2614), artists(?x3319, ?x2731), artists(?x3319, ?x1974), ?x1974 = 0136p1, award_nominee(?x2732, ?x2731) *> conf = 0.38 ranks of expected_values: 3 EVAL 01n5sn parent_genre 03_d0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 36.000 0.868 http://example.org/music/genre/parent_genre #16281-0l2lk PRED entity: 0l2lk PRED relation: currency PRED expected values: 09nqf => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.82 #37, 0.79 #5, 0.79 #30) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 0ml25; 0mxsm; 0mrhq; 0mnrb; >> query: (?x6775, 09nqf) <- time_zones(?x6775, ?x2950), source(?x6775, ?x958), administrative_division(?x9260, ?x6775), ?x958 = 0jbk9 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l2lk currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.822 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #16280-013d7t PRED entity: 013d7t PRED relation: location! PRED expected values: 0ckm4x => 168 concepts (49 used for prediction) PRED predicted values (max 10 best out of 2138): 01bpc9 (0.33 #288, 0.25 #5320, 0.25 #2804), 02lt8 (0.33 #797, 0.25 #5829, 0.25 #3313), 01ggc9 (0.33 #2057, 0.25 #7089, 0.25 #4573), 074tb5 (0.33 #1201, 0.25 #6233, 0.25 #3717), 025b5y (0.33 #1148, 0.25 #6180, 0.25 #3664), 0gs5q (0.33 #1775, 0.25 #6807, 0.25 #4291), 0154d7 (0.33 #1761, 0.25 #6793, 0.25 #4277), 07ss8_ (0.33 #402, 0.25 #5434, 0.25 #2918), 01h5f8 (0.33 #2272, 0.25 #7304, 0.25 #4788), 03q91d (0.33 #1549, 0.25 #6581, 0.25 #4065) >> Best rule #288 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 07z1m; >> query: (?x5143, 01bpc9) <- location(?x7164, ?x5143), location_of_ceremony(?x566, ?x5143), ?x7164 = 02fybl, contains(?x94, ?x5143) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #60257 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 03_3d; 059rby; 04jpl; 0f2wj; 05fkf; 0f8l9c; 0ctw_b; 01n7q; 02jx1; 035qy; ... *> query: (?x5143, 0ckm4x) <- location(?x7164, ?x5143), location_of_ceremony(?x566, ?x5143), celebrity(?x7164, ?x3705) *> conf = 0.02 ranks of expected_values: 1829 EVAL 013d7t location! 0ckm4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 168.000 49.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #16279-04fgkf_ PRED entity: 04fgkf_ PRED relation: award_winner PRED expected values: 0grwj => 58 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2398): 01jgpsh (0.40 #3886, 0.38 #6359, 0.33 #8833), 01l1ls (0.40 #4502, 0.25 #6975, 0.22 #9449), 02l3_5 (0.40 #4231, 0.25 #6704, 0.22 #9178), 01mbwlb (0.34 #51939, 0.32 #51941, 0.32 #51938), 01yg9y (0.34 #51939, 0.32 #51941, 0.32 #51938), 0grwj (0.33 #9, 0.32 #51941, 0.32 #51938), 01jbx1 (0.32 #51941, 0.32 #51938, 0.31 #9893), 01nczg (0.32 #51941, 0.32 #51938, 0.31 #9893), 01trf3 (0.32 #51941, 0.32 #51938, 0.28 #39568), 015pvh (0.32 #51941, 0.32 #51938, 0.28 #39568) >> Best rule #3886 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0gkvb7; 09qvf4; >> query: (?x7644, 01jgpsh) <- award(?x10754, ?x7644), award(?x3291, ?x7644), award(?x2894, ?x7644), ?x2894 = 01gbbz, ?x10754 = 0c1j_, people(?x2510, ?x3291), program(?x3291, ?x9668) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 0h53c_5; *> query: (?x7644, 0grwj) <- award(?x3291, ?x7644), award(?x2894, ?x7644), ?x2894 = 01gbbz, ?x3291 = 01jbx1, nominated_for(?x7644, ?x4891), ?x4891 = 0304nh *> conf = 0.33 ranks of expected_values: 6 EVAL 04fgkf_ award_winner 0grwj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 58.000 21.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #16278-02q3fdr PRED entity: 02q3fdr PRED relation: genre PRED expected values: 0bj8m2 => 94 concepts (71 used for prediction) PRED predicted values (max 10 best out of 89): 0bj8m2 (0.67 #46, 0.40 #164, 0.23 #632), 05p553 (0.66 #4581, 0.47 #1409, 0.46 #1526), 03_3d (0.59 #5282, 0.56 #4108, 0.54 #7989), 02kdv5l (0.58 #705, 0.54 #4814, 0.53 #588), 01jfsb (0.57 #4824, 0.33 #3533, 0.32 #832), 02l7c8 (0.50 #6003, 0.33 #1891, 0.30 #134), 06n90 (0.42 #716, 0.37 #599, 0.30 #1184), 01zhp (0.29 #1479, 0.28 #1362, 0.26 #543), 06qln (0.26 #331, 0.11 #565, 0.08 #1501), 0lsxr (0.23 #4821, 0.19 #3999, 0.18 #4234) >> Best rule #46 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 016ztl; >> query: (?x5936, 0bj8m2) <- film(?x2156, ?x5936), actor(?x5936, ?x489), award_nominee(?x100, ?x489), titles(?x252, ?x5936) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q3fdr genre 0bj8m2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 71.000 0.667 http://example.org/film/film/genre #16277-01kj0p PRED entity: 01kj0p PRED relation: type_of_union PRED expected values: 04ztj => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.71 #209, 0.71 #161, 0.71 #286), 01g63y (0.47 #265, 0.22 #18, 0.22 #22) >> Best rule #209 for best value: >> intensional similarity = 3 >> extensional distance = 1558 >> proper extension: 01h4rj; >> query: (?x2818, 04ztj) <- award(?x2818, ?x704), film(?x2818, ?x3482), film_release_region(?x3482, ?x87) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kj0p type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.709 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16276-0jwmp PRED entity: 0jwmp PRED relation: film_release_region PRED expected values: 05r4w 0b90_r 0chghy 0k6nt 03gj2 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 131): 059j2 (0.86 #1177, 0.84 #1666, 0.79 #2482), 0k6nt (0.84 #1657, 0.84 #1168, 0.80 #1331), 05r4w (0.84 #1632, 0.83 #1143, 0.80 #980), 0chghy (0.83 #1153, 0.83 #1642, 0.78 #1316), 0345h (0.81 #1179, 0.78 #1668, 0.71 #2484), 03gj2 (0.79 #1169, 0.76 #1658, 0.72 #1332), 05qhw (0.78 #1158, 0.74 #1647, 0.64 #1321), 035qy (0.77 #1181, 0.75 #1670, 0.65 #2486), 0154j (0.74 #1146, 0.72 #1635, 0.65 #1309), 01znc_ (0.73 #1190, 0.70 #1679, 0.69 #1027) >> Best rule #1177 for best value: >> intensional similarity = 6 >> extensional distance = 173 >> proper extension: 011yrp; 0ddfwj1; 05p1tzf; 02x3lt7; 087wc7n; 0jjy0; 03bx2lk; 0gmcwlb; 0dtfn; 017gm7; ... >> query: (?x3392, 059j2) <- film_release_region(?x3392, ?x2513), film_release_region(?x3392, ?x1892), film_release_region(?x3392, ?x512), ?x1892 = 02vzc, ?x2513 = 05b4w, ?x512 = 07ssc >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1657 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 218 *> proper extension: 03g90h; 0gj8t_b; 02c6d; 0gxtknx; 0ct5zc; 045j3w; 0gtsxr4; 0198b6; 02dpl9; 02prwdh; ... *> query: (?x3392, 0k6nt) <- film_release_region(?x3392, ?x2513), film_release_region(?x3392, ?x1892), film_release_region(?x3392, ?x512), ?x1892 = 02vzc, ?x2513 = 05b4w, combatants(?x512, ?x151) *> conf = 0.84 ranks of expected_values: 2, 3, 4, 6, 12 EVAL 0jwmp film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 74.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jwmp film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jwmp film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jwmp film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 74.000 74.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jwmp film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16275-0206k5 PRED entity: 0206k5 PRED relation: company! PRED expected values: 0dq3c => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 36): 0krdk (0.82 #2297, 0.78 #1320, 0.74 #1109), 0dq_5 (0.82 #2436, 0.81 #738, 0.79 #1120), 0dq3c (0.61 #342, 0.60 #639, 0.59 #723), 01yc02 (0.60 #135, 0.50 #221, 0.50 #178), 02211by (0.50 #87, 0.40 #130, 0.33 #216), 01kr6k (0.41 #323, 0.33 #449, 0.31 #1086), 02y6fz (0.25 #106, 0.20 #149, 0.18 #320), 04192r (0.20 #166, 0.17 #252, 0.17 #209), 021q0l (0.20 #136, 0.17 #222, 0.17 #179), 0142rn (0.18 #1085, 0.17 #364, 0.16 #3569) >> Best rule #2297 for best value: >> intensional similarity = 6 >> extensional distance = 71 >> proper extension: 061v5m; 0sxdg; 01dfb6; 07_dn; >> query: (?x10699, 0krdk) <- company(?x4792, ?x10699), currency(?x10699, ?x170), company(?x4792, ?x7326), company(?x4792, ?x5072), ?x7326 = 018_q8, ?x5072 = 045c7b >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #342 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 0168nq; 04f0xq; 01frpd; *> query: (?x10699, 0dq3c) <- company(?x4792, ?x10699), currency(?x10699, ?x170), ?x4792 = 05_wyz, list(?x10699, ?x8915), ?x8915 = 01pd60 *> conf = 0.61 ranks of expected_values: 3 EVAL 0206k5 company! 0dq3c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 151.000 151.000 0.822 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #16274-05k79 PRED entity: 05k79 PRED relation: artists! PRED expected values: 01h0kx => 87 concepts (36 used for prediction) PRED predicted values (max 10 best out of 275): 064t9 (0.91 #9186, 0.89 #9796, 0.88 #10405), 06by7 (0.62 #325, 0.60 #8282, 0.57 #7054), 05bt6j (0.55 #4319, 0.49 #8304, 0.47 #4014), 0ggx5q (0.52 #2821, 0.50 #684, 0.40 #4046), 06j6l (0.45 #10746, 0.33 #2793, 0.33 #9221), 02lnbg (0.44 #2801, 0.43 #664, 0.42 #4026), 0xhtw (0.44 #2151, 0.40 #6431, 0.40 #3680), 0dl5d (0.41 #3069, 0.38 #3683, 0.36 #2154), 025sc50 (0.41 #2794, 0.36 #4019, 0.33 #4629), 0glt670 (0.33 #2786, 0.23 #4926, 0.23 #6151) >> Best rule #9186 for best value: >> intensional similarity = 8 >> extensional distance = 336 >> proper extension: 03c7ln; 07s3vqk; 0197tq; 01l1b90; 0fp_v1x; 0147dk; 02mslq; 06cc_1; 0kzy0; 0168cl; ... >> query: (?x2005, 064t9) <- artists(?x474, ?x2005), artist(?x2931, ?x2005), artists(?x474, ?x2635), artists(?x474, ?x2237), artists(?x474, ?x1989), ?x1989 = 04mn81, ?x2237 = 01vs_v8, ?x2635 = 03fbc >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #4581 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 45 *> proper extension: 06y9c2; 03f5spx; 0lk90; 0lccn; 0840vq; 01wj18h; 02wb6yq; 01vvyfh; 049qx; 0czkbt; ... *> query: (?x2005, ?x2542) <- artists(?x3243, ?x2005), artists(?x2491, ?x2005), ?x3243 = 0y3_8, artist(?x2931, ?x2005), artists(?x2491, ?x8226), ?x8226 = 017lb_, parent_genre(?x2542, ?x2491) *> conf = 0.12 ranks of expected_values: 63 EVAL 05k79 artists! 01h0kx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 87.000 36.000 0.911 http://example.org/music/genre/artists #16273-0cj8x PRED entity: 0cj8x PRED relation: film PRED expected values: 04v89z => 116 concepts (81 used for prediction) PRED predicted values (max 10 best out of 893): 04954r (0.11 #615, 0.08 #7763, 0.06 #13124), 0bm2g (0.11 #337, 0.03 #7485, 0.03 #12846), 0bj25 (0.11 #1489, 0.02 #6850, 0.01 #21147), 013q07 (0.08 #11078, 0.08 #9291, 0.04 #21801), 0f42nz (0.08 #11629, 0.07 #22352, 0.06 #907), 026y3cf (0.08 #50043, 0.07 #53618, 0.07 #32169), 01lbcqx (0.08 #5021, 0.05 #13956, 0.05 #15743), 0ds5_72 (0.07 #12176, 0.06 #10389, 0.04 #22899), 06ztvyx (0.07 #9366, 0.03 #20089, 0.03 #21876), 031t2d (0.06 #9190, 0.06 #255, 0.03 #19913) >> Best rule #615 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 03mv0b; >> query: (?x3002, 04954r) <- gender(?x3002, ?x231), people(?x10199, ?x3002), special_performance_type(?x3002, ?x4832) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #1416 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 03mv0b; *> query: (?x3002, 04v89z) <- gender(?x3002, ?x231), people(?x10199, ?x3002), special_performance_type(?x3002, ?x4832) *> conf = 0.06 ranks of expected_values: 13 EVAL 0cj8x film 04v89z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 116.000 81.000 0.111 http://example.org/film/actor/film./film/performance/film #16272-01n1gc PRED entity: 01n1gc PRED relation: film PRED expected values: 05567m => 83 concepts (44 used for prediction) PRED predicted values (max 10 best out of 252): 0k4f3 (0.05 #2235, 0.02 #4023), 0b3n61 (0.03 #6722, 0.02 #4934, 0.02 #8510), 017jd9 (0.03 #13295, 0.02 #27599, 0.02 #6143), 01shy7 (0.03 #5786, 0.02 #7574, 0.02 #14726), 016dj8 (0.03 #6477, 0.02 #4689, 0.02 #8265), 017gl1 (0.03 #5507, 0.02 #12659, 0.02 #7295), 0dcz8_ (0.03 #44701), 04cppj (0.03 #44701), 0blpg (0.03 #2443, 0.02 #4231, 0.01 #6019), 03wy8t (0.03 #3373, 0.02 #6949, 0.02 #8737) >> Best rule #2235 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 01w3v; 0mcf4; >> query: (?x3768, 0k4f3) <- religion(?x3768, ?x7131), ?x7131 = 03_gx, category(?x3768, ?x134) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01n1gc film 05567m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 44.000 0.053 http://example.org/film/actor/film./film/performance/film #16271-02hxcvy PRED entity: 02hxcvy PRED relation: language! PRED expected values: 02fqrf => 46 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1782): 02w86hz (0.87 #12025, 0.82 #1719, 0.72 #10306), 0dc7hc (0.87 #12025, 0.55 #24047, 0.06 #54983), 09gdm7q (0.82 #1719, 0.72 #10306, 0.48 #8588), 0ddbjy4 (0.82 #1719, 0.72 #10306, 0.48 #8588), 02bg55 (0.82 #1719, 0.72 #10306, 0.48 #8588), 047vnkj (0.82 #1719, 0.48 #8588, 0.46 #10308), 0h3xztt (0.82 #1719, 0.48 #8588, 0.46 #10308), 02r8hh_ (0.82 #1719, 0.48 #8588, 0.46 #10308), 0h03fhx (0.82 #1719, 0.48 #8588, 0.46 #10308), 02yvct (0.82 #1719, 0.48 #8588, 0.46 #10308) >> Best rule #12025 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 0999q; >> query: (?x9113, ?x3742) <- languages_spoken(?x7838, ?x9113), countries_spoken_in(?x9113, ?x3016), countries_spoken_in(?x9113, ?x2146), languages(?x12189, ?x9113), languages(?x6189, ?x9113), ?x2146 = 03rk0, location(?x12189, ?x9315), religion(?x6189, ?x492), gender(?x6189, ?x231), ?x3016 = 0697s, film(?x12189, ?x3742), people(?x7838, ?x111) >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #1719 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x9113, ?x66) <- language(?x8657, ?x9113), language(?x8381, ?x9113), language(?x5001, ?x9113), language(?x4529, ?x9113), languages(?x6189, ?x9113), film(?x2382, ?x8657), genre(?x8657, ?x258), film(?x1445, ?x8657), countries_spoken_in(?x9113, ?x2236), film_release_region(?x1178, ?x2236), film_release_region(?x1173, ?x2236), film_release_region(?x66, ?x2236), ?x1173 = 0872p_c, country(?x1121, ?x2236), film_release_region(?x8657, ?x94), ?x1178 = 053rxgm, ?x4529 = 0gbtbm, ?x5001 = 09q23x, ?x8381 = 0h2zvzr *> conf = 0.82 ranks of expected_values: 32 EVAL 02hxcvy language! 02fqrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 46.000 32.000 0.872 http://example.org/film/film/language #16270-07vyf PRED entity: 07vyf PRED relation: fraternities_and_sororities PRED expected values: 0325pb 035tlh => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 3): 0325pb (0.47 #16, 0.41 #32, 0.38 #48), 035tlh (0.37 #30, 0.36 #26, 0.34 #33), 04m8fy (0.05 #15, 0.05 #21, 0.03 #56) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 06mkj; 0d05w3; >> query: (?x4296, 0325pb) <- school(?x8133, ?x4296), school(?x8133, ?x4955), ?x4955 = 09f2j >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07vyf fraternities_and_sororities 035tlh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.468 http://example.org/education/university/fraternities_and_sororities EVAL 07vyf fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.468 http://example.org/education/university/fraternities_and_sororities #16269-0rrhp PRED entity: 0rrhp PRED relation: contains! PRED expected values: 09c7w0 => 116 concepts (48 used for prediction) PRED predicted values (max 10 best out of 186): 09c7w0 (0.97 #34911, 0.97 #8952, 0.81 #42080), 04_1l0v (0.37 #25050, 0.16 #9399, 0.16 #4926), 0jrxx (0.27 #505, 0.14 #6265, 0.07 #1400), 01n7q (0.27 #3660, 0.21 #14396, 0.20 #11711), 02jx1 (0.25 #34100, 0.12 #24242, 0.07 #22454), 059rby (0.24 #24175, 0.14 #32243, 0.14 #34033), 04ly1 (0.23 #5606, 0.02 #32459, 0.02 #34249), 07ssc (0.17 #34045, 0.09 #22399, 0.08 #24187), 0jrtv (0.14 #6265, 0.09 #3580, 0.09 #410), 0jgj7 (0.14 #6265, 0.09 #3580, 0.09 #644) >> Best rule #34911 for best value: >> intensional similarity = 5 >> extensional distance = 915 >> proper extension: 015zyd; 08815; 05kkh; 05zjtn4; 01rtm4; 01jssp; 04wlz2; 05krk; 01j_9c; 01fpvz; ... >> query: (?x14166, 09c7w0) <- contains(?x2623, ?x14166), location(?x91, ?x2623), contains(?x2623, ?x5124), ?x5124 = 0rkkv, religion(?x2623, ?x109) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rrhp contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 48.000 0.973 http://example.org/location/location/contains #16268-0192l PRED entity: 0192l PRED relation: role PRED expected values: 01bns_ => 63 concepts (44 used for prediction) PRED predicted values (max 10 best out of 114): 01vj9c (0.88 #1907, 0.88 #1815, 0.88 #3605), 013y1f (0.84 #2170, 0.82 #1943, 0.78 #2056), 0g2dz (0.83 #1569, 0.82 #443, 0.82 #1121), 0l14j_ (0.83 #1569, 0.82 #443, 0.82 #1121), 01bns_ (0.83 #1569, 0.82 #443, 0.82 #1121), 018j2 (0.83 #4771, 0.75 #785, 0.72 #337), 05r5c (0.82 #3830, 0.81 #2823, 0.80 #1470), 02hnl (0.80 #1573, 0.80 #1502, 0.77 #1615), 0l14md (0.80 #1469, 0.79 #2143, 0.77 #2822), 06w7v (0.80 #1547, 0.75 #1319, 0.75 #1094) >> Best rule #1907 for best value: >> intensional similarity = 25 >> extensional distance = 15 >> proper extension: 02k84w; 02fsn; 0dwt5; >> query: (?x5990, ?x745) <- role(?x5990, ?x5417), role(?x5990, ?x1969), role(?x5990, ?x1437), role(?x5990, ?x1432), role(?x5990, ?x1166), role(?x5417, ?x2310), role(?x5417, ?x736), ?x2310 = 0gghm, ?x736 = 06w87, ?x1969 = 04rzd, role(?x2964, ?x5417), role(?x1166, ?x745), instrumentalists(?x5417, ?x642), group(?x1166, ?x6699), ?x1432 = 0395lw, role(?x6225, ?x1166), instrumentalists(?x1166, ?x11633), instrumentalists(?x1166, ?x7233), ?x7233 = 01lz4tf, ?x6225 = 01vng3b, ?x745 = 01vj9c, ?x2964 = 0565cz, ?x1437 = 01vdm0, ?x11633 = 01ww_vs, ?x6699 = 09lwrt >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1569 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 8 *> proper extension: 0395lw; 018j2; *> query: (?x5990, ?x2377) <- role(?x5990, ?x5417), role(?x5990, ?x1267), role(?x5990, ?x614), role(?x5990, ?x432), ?x5417 = 02w3w, role(?x2377, ?x5990), role(?x1473, ?x5990), ?x432 = 042v_gx, ?x614 = 0mkg, role(?x2764, ?x1473), role(?x1750, ?x1473), role(?x716, ?x1473), role(?x433, ?x1473), ?x1750 = 02hnl, role(?x211, ?x1473), instrumentalists(?x1473, ?x3667), instrumentalists(?x1473, ?x1660), role(?x1473, ?x1495), ?x1267 = 07brj, ?x433 = 025cbm, group(?x5990, ?x4791), ?x1660 = 012x4t, group(?x1473, ?x2635), ?x3667 = 0phx4, ?x716 = 018vs, ?x2764 = 01s0ps, ?x1495 = 013y1f *> conf = 0.83 ranks of expected_values: 5 EVAL 0192l role 01bns_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 63.000 44.000 0.882 http://example.org/music/performance_role/track_performances./music/track_contribution/role #16267-0404wqb PRED entity: 0404wqb PRED relation: award_winner! PRED expected values: 0cqhk0 => 101 concepts (75 used for prediction) PRED predicted values (max 10 best out of 194): 0cqhk0 (0.37 #22039, 0.37 #18149, 0.30 #9939), 0ck27z (0.26 #957, 0.25 #2253, 0.17 #525), 09sb52 (0.19 #3929, 0.15 #5225, 0.14 #8683), 09qs08 (0.15 #26794, 0.15 #26361, 0.10 #17284), 099tbz (0.11 #3946, 0.08 #5242, 0.07 #8700), 0cqhmg (0.10 #17284, 0.04 #20742, 0.03 #27227), 09qv3c (0.10 #17284, 0.02 #483, 0.02 #4803), 0gqwc (0.07 #75, 0.07 #9581, 0.06 #2667), 0f4x7 (0.06 #9105, 0.06 #3919, 0.05 #9970), 01by1l (0.06 #16532, 0.06 #16964, 0.06 #14372) >> Best rule #22039 for best value: >> intensional similarity = 3 >> extensional distance = 1269 >> proper extension: 01vd7hn; 03_0p; 09bx1k; 0c_drn; 05683cn; >> query: (?x10814, ?x678) <- gender(?x10814, ?x514), award_winner(?x10814, ?x1784), award(?x10814, ?x678) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0404wqb award_winner! 0cqhk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 75.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner #16266-0n96z PRED entity: 0n96z PRED relation: contains! PRED expected values: 02jx1 => 139 concepts (76 used for prediction) PRED predicted values (max 10 best out of 347): 02jx1 (0.96 #34858, 0.90 #35753, 0.81 #34048), 09c7w0 (0.84 #59900, 0.67 #21449, 0.64 #22343), 0dg3n1 (0.54 #8193, 0.13 #50210, 0.10 #54682), 04_1l0v (0.44 #16531, 0.39 #17425, 0.32 #21002), 01n7q (0.41 #43872, 0.40 #30463, 0.26 #25098), 048kw (0.25 #2472, 0.02 #45375, 0.01 #48059), 0cxgc (0.20 #3332, 0.17 #5119, 0.17 #4225), 02qkt (0.20 #50402, 0.18 #54874, 0.17 #16427), 05fjf (0.19 #30759, 0.15 #44168, 0.10 #37911), 0d060g (0.16 #43809, 0.16 #30400, 0.09 #56333) >> Best rule #34858 for best value: >> intensional similarity = 5 >> extensional distance = 173 >> proper extension: 0crjn65; 01k8q5; 0dplh; 0121c1; 0c_zj; 0fgj2; 013bqg; 02f46y; 01b_d4; 01cwdk; ... >> query: (?x14093, ?x1310) <- contains(?x12774, ?x14093), contains(?x512, ?x14093), ?x512 = 07ssc, administrative_parent(?x12774, ?x1310), administrative_parent(?x4049, ?x12774) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n96z contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 76.000 0.963 http://example.org/location/location/contains #16265-02kk_c PRED entity: 02kk_c PRED relation: nominated_for! PRED expected values: 02lf1j => 78 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1154): 0bxtg (0.84 #49096, 0.84 #35066, 0.83 #65460), 0g2lq (0.84 #49096, 0.84 #35066, 0.83 #65460), 03mdt (0.84 #49096, 0.84 #35066, 0.81 #28050), 04rtpt (0.72 #4676, 0.69 #14024, 0.69 #18701), 037gjc (0.58 #28052, 0.56 #56110, 0.55 #32729), 03cglm (0.58 #28052, 0.56 #56110, 0.55 #32729), 0gd_b_ (0.58 #28052, 0.56 #56110, 0.55 #32729), 05fnl9 (0.58 #28052, 0.56 #56110, 0.55 #32729), 01j4ls (0.58 #28052, 0.55 #32729, 0.55 #37407), 0219q (0.58 #28052, 0.55 #32729, 0.55 #37407) >> Best rule #49096 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 01xr2s; >> query: (?x4881, ?x4036) <- award_winner(?x4881, ?x4036), languages(?x4881, ?x254), actor(?x4881, ?x1398), nominated_for(?x4036, ?x4037) >> conf = 0.84 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 02kk_c nominated_for! 02lf1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 50.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16264-02f2p7 PRED entity: 02f2p7 PRED relation: award PRED expected values: 07h0cl => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 254): 09sb52 (0.34 #10520, 0.33 #8907, 0.33 #12132), 01by1l (0.16 #514, 0.10 #15023, 0.10 #4141), 0gqwc (0.16 #880, 0.14 #6925, 0.13 #25392), 0ck27z (0.15 #10571, 0.15 #10168, 0.15 #12183), 05b4l5x (0.15 #812, 0.13 #25392, 0.12 #22973), 0gqyl (0.14 #6955, 0.13 #25392, 0.12 #22973), 05pcn59 (0.14 #81, 0.14 #8947, 0.13 #2902), 02g2yr (0.14 #261, 0.13 #25392, 0.13 #10076), 09cn0c (0.14 #321, 0.13 #10076, 0.02 #1127), 027571b (0.14 #276, 0.13 #10076, 0.02 #1082) >> Best rule #10520 for best value: >> intensional similarity = 3 >> extensional distance = 1141 >> proper extension: 03zqc1; 06lgq8; 02xb2bt; 0308kx; 012g92; >> query: (?x5330, 09sb52) <- film(?x5330, ?x1490), award_nominee(?x5330, ?x3329), award_winner(?x3329, ?x2100) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #25392 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2131 *> proper extension: 0dbpyd; 01vvycq; 01gf5h; 01ztgm; 01t6b4; 030_1m; 0h1p; 05b4rcb; 01r216; 01hw6wq; ... *> query: (?x5330, ?x375) <- award_nominee(?x5330, ?x8674), award(?x5330, ?x1008), award(?x8674, ?x375) *> conf = 0.13 ranks of expected_values: 37 EVAL 02f2p7 award 07h0cl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 92.000 92.000 0.343 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16263-01s7qqw PRED entity: 01s7qqw PRED relation: organizations_founded PRED expected values: 01w5gp => 98 concepts (64 used for prediction) PRED predicted values (max 10 best out of 5): 01sqd7 (0.03 #363, 0.02 #669, 0.01 #873), 06dr9 (0.01 #3461, 0.01 #3972, 0.01 #4176), 01cl2y (0.01 #852), 015mlw (0.01 #998), 073tm9 (0.01 #962) >> Best rule #363 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 03g5jw; 05xq9; 0167xy; >> query: (?x5208, 01sqd7) <- influenced_by(?x5208, ?x3917), participant(?x1817, ?x3917), award_winner(?x3917, ?x2124) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01s7qqw organizations_founded 01w5gp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 64.000 0.027 http://example.org/organization/organization_founder/organizations_founded #16262-02fz3w PRED entity: 02fz3w PRED relation: award_nominee! PRED expected values: 02zq43 => 92 concepts (39 used for prediction) PRED predicted values (max 10 best out of 865): 020_95 (0.84 #2320, 0.84 #4640, 0.82 #39437), 0993r (0.84 #2320, 0.84 #4640, 0.82 #39437), 0175wg (0.84 #2320, 0.84 #4640, 0.82 #39437), 02fz3w (0.62 #4290, 0.41 #6610, 0.14 #90478), 02zq43 (0.62 #2379, 0.32 #4699, 0.14 #90478), 0dvmd (0.33 #685, 0.03 #44761, 0.03 #47082), 0gy6z9 (0.33 #733, 0.02 #21613, 0.02 #74965), 018ygt (0.33 #1447, 0.02 #8407, 0.02 #22327), 042xrr (0.33 #1080, 0.02 #8040, 0.02 #19640), 09fb5 (0.33 #67, 0.02 #7027, 0.02 #18627) >> Best rule #2320 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 06jzh; 02yxwd; >> query: (?x9236, ?x100) <- award_nominee(?x9236, ?x100), film(?x9236, ?x4158), ?x4158 = 0g83dv >> conf = 0.84 => this is the best rule for 3 predicted values *> Best rule #2379 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0159h6; 01yhvv; 04rsd2; 0k269; 016xh5; 01qrbf; *> query: (?x9236, 02zq43) <- award_nominee(?x9236, ?x489), place_of_birth(?x9236, ?x13696), ?x489 = 0h5g_ *> conf = 0.62 ranks of expected_values: 5 EVAL 02fz3w award_nominee! 02zq43 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 39.000 0.837 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16261-0gnbw PRED entity: 0gnbw PRED relation: celebrities_impersonated! PRED expected values: 03m6t5 => 101 concepts (73 used for prediction) PRED predicted values (max 10 best out of 7): 03m6t5 (0.84 #143, 0.75 #150, 0.64 #3), 0pz04 (0.21 #7, 0.16 #147, 0.14 #154), 04s430 (0.14 #5, 0.05 #152, 0.03 #12), 01n5309 (0.07 #1, 0.06 #148, 0.03 #8), 018grr (0.07 #2, 0.03 #9, 0.02 #16), 0d608 (0.02 #146, 0.02 #153), 0f7hc (0.02 #151) >> Best rule #143 for best value: >> intensional similarity = 2 >> extensional distance = 111 >> proper extension: 0d9kl; 057ph; 0dng4; >> query: (?x7269, 03m6t5) <- celebrities_impersonated(?x6707, ?x7269), type_of_union(?x6707, ?x566) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gnbw celebrities_impersonated! 03m6t5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 73.000 0.841 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #16260-08y2fn PRED entity: 08y2fn PRED relation: story_by PRED expected values: 01v9724 => 99 concepts (77 used for prediction) PRED predicted values (max 10 best out of 26): 01tz6vs (0.25 #314, 0.17 #964, 0.08 #1614), 01v9724 (0.20 #531, 0.03 #3564), 027hnjh (0.20 #513), 01gp_x (0.20 #472), 01q415 (0.11 #1328, 0.08 #1977, 0.06 #2194), 0c4y8 (0.06 #2330), 03_dj (0.04 #2797, 0.04 #3447, 0.03 #3665), 03j2gxx (0.04 #2779, 0.03 #3647), 0jt90f5 (0.04 #3279, 0.03 #3497, 0.01 #5883), 07d3x (0.04 #3415) >> Best rule #314 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 09rfpk; >> query: (?x7424, 01tz6vs) <- program(?x2776, ?x7424), titles(?x512, ?x7424), ?x512 = 07ssc, nominated_for(?x940, ?x7424) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #531 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0gydcp7; *> query: (?x7424, 01v9724) <- nominated_for(?x940, ?x7424), ?x940 = 03d_w3h, film(?x11302, ?x7424), place_of_birth(?x11302, ?x682) *> conf = 0.20 ranks of expected_values: 2 EVAL 08y2fn story_by 01v9724 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 77.000 0.250 http://example.org/film/film/story_by #16259-02183k PRED entity: 02183k PRED relation: major_field_of_study PRED expected values: 04rjg => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 119): 04rjg (0.67 #729, 0.54 #965, 0.45 #1321), 02_7t (0.67 #772, 0.42 #1008, 0.40 #1364), 02j62 (0.61 #739, 0.58 #975, 0.57 #5942), 01mkq (0.56 #724, 0.54 #960, 0.50 #1316), 02lp1 (0.56 #720, 0.54 #956, 0.45 #1312), 0fdys (0.50 #747, 0.33 #1102, 0.30 #1339), 03g3w (0.45 #1091, 0.43 #5939, 0.41 #1566), 0g4gr (0.42 #976, 0.28 #740, 0.28 #1332), 01tbp (0.39 #767, 0.38 #1003, 0.30 #1359), 01lj9 (0.39 #748, 0.35 #1340, 0.33 #984) >> Best rule #729 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 01w5m; >> query: (?x3416, 04rjg) <- major_field_of_study(?x3416, ?x10046), major_field_of_study(?x3416, ?x4321), ?x10046 = 041y2, institution(?x865, ?x3416), ?x4321 = 0g26h >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02183k major_field_of_study 04rjg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16258-036qs_ PRED entity: 036qs_ PRED relation: student! PRED expected values: 02j62 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 5): 02822 (0.03 #528, 0.03 #403, 0.03 #964), 02vxn (0.02 #4, 0.01 #66, 0.01 #128), 03qsdpk (0.01 #969, 0.01 #533, 0.01 #1155), 0w7c (0.01 #539, 0.01 #975, 0.01 #414), 03g3w (0.01 #642) >> Best rule #528 for best value: >> intensional similarity = 3 >> extensional distance = 760 >> proper extension: 02k6rq; >> query: (?x7102, 02822) <- student(?x3424, ?x7102), location(?x7102, ?x8181), film(?x7102, ?x1488) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 036qs_ student! 02j62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 80.000 0.030 http://example.org/education/field_of_study/students_majoring./education/education/student #16257-036jv PRED entity: 036jv PRED relation: artists PRED expected values: 016kjs 01wgxtl 01vw37m => 53 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1149): 09889g (0.69 #9005, 0.50 #10075, 0.24 #13287), 0gbwp (0.56 #8908, 0.45 #9978, 0.25 #3556), 0127s7 (0.56 #9092, 0.41 #10162, 0.33 #13374), 01wyz92 (0.50 #1372, 0.47 #5348, 0.34 #20344), 011z3g (0.50 #9154, 0.45 #10224, 0.31 #13436), 01vwyqp (0.50 #8835, 0.45 #9905, 0.27 #13117), 01vtj38 (0.50 #9213, 0.41 #10283, 0.27 #13495), 09qr6 (0.50 #8650, 0.41 #9720, 0.25 #3298), 01dwrc (0.50 #9077, 0.36 #10147, 0.27 #16574), 01wgxtl (0.50 #1285, 0.34 #4278, 0.28 #7487) >> Best rule #9005 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 064t9; 02x8m; 021_z5; 05bt6j; 06j6l; 025sc50; 0gywn; 02lnbg; 026z9; 0ggx5q; ... >> query: (?x11545, 09889g) <- artists(?x11545, ?x8169), friend(?x3481, ?x8169), place_of_death(?x8169, ?x1523), profession(?x8169, ?x131), ?x1523 = 030qb3t, people(?x9888, ?x8169) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1285 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 0glt670; 016_nr; *> query: (?x11545, 01wgxtl) <- artists(?x11545, ?x8169), artists(?x11545, ?x4476), artists(?x11545, ?x3494), ?x8169 = 01vz0g4, parent_genre(?x11545, ?x2937), ?x4476 = 01vw20h, award_nominee(?x3494, ?x286), nominated_for(?x3494, ?x1642) *> conf = 0.50 ranks of expected_values: 10, 21, 67 EVAL 036jv artists 01vw37m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 53.000 34.000 0.688 http://example.org/music/genre/artists EVAL 036jv artists 01wgxtl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 53.000 34.000 0.688 http://example.org/music/genre/artists EVAL 036jv artists 016kjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 53.000 34.000 0.688 http://example.org/music/genre/artists #16256-04pmnt PRED entity: 04pmnt PRED relation: currency PRED expected values: 09nqf => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.83 #183, 0.81 #253, 0.80 #211), 01nv4h (0.11 #540, 0.04 #247, 0.04 #100), 02l6h (0.11 #540, 0.02 #207, 0.02 #172), 02gsvk (0.11 #540, 0.02 #146, 0.02 #153), 088n7 (0.11 #540), 0kz1h (0.11 #540), 0ptk_ (0.11 #540) >> Best rule #183 for best value: >> intensional similarity = 8 >> extensional distance = 216 >> proper extension: 02d44q; >> query: (?x6148, 09nqf) <- nominated_for(?x1243, ?x6148), country(?x6148, ?x94), film_crew_role(?x6148, ?x1171), film_crew_role(?x6148, ?x137), ?x1171 = 09vw2b7, featured_film_locations(?x6148, ?x362), film_crew_role(?x11685, ?x137), ?x11685 = 017n9 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04pmnt currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.835 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #16255-03j43 PRED entity: 03j43 PRED relation: profession PRED expected values: 0dxtg => 179 concepts (178 used for prediction) PRED predicted values (max 10 best out of 98): 02hrh1q (0.73 #15021, 0.71 #18174, 0.71 #16823), 0cbd2 (0.70 #3160, 0.69 #6910, 0.65 #4810), 0dxtg (0.53 #1667, 0.50 #314, 0.46 #9319), 03gjzk (0.50 #316, 0.41 #1669, 0.22 #13222), 01d_h8 (0.50 #306, 0.40 #1959, 0.39 #1809), 018gz8 (0.50 #318, 0.24 #1671, 0.21 #13224), 0kyk (0.47 #3184, 0.45 #1082, 0.43 #4834), 0nbcg (0.40 #1986, 0.36 #2436, 0.27 #2586), 09jwl (0.40 #1973, 0.32 #2423, 0.31 #2573), 02jknp (0.39 #1811, 0.29 #17716, 0.27 #7361) >> Best rule #15021 for best value: >> intensional similarity = 4 >> extensional distance = 411 >> proper extension: 07q1v4; 03lt8g; 0157m; 0127m7; 0bt4r4; 01w02sy; 01ksr1; 03bnv; 04gycf; 01309x; ... >> query: (?x2080, 02hrh1q) <- award(?x2080, ?x921), religion(?x2080, ?x7131), type_of_union(?x2080, ?x566), location(?x2080, ?x4627) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1667 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 0j1yf; 015f7; *> query: (?x2080, 0dxtg) <- nationality(?x2080, ?x789), influenced_by(?x2080, ?x3941), type_of_union(?x2080, ?x566), sibling(?x3941, ?x7334), award(?x2080, ?x921) *> conf = 0.53 ranks of expected_values: 3 EVAL 03j43 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 179.000 178.000 0.726 http://example.org/people/person/profession #16254-05vzql PRED entity: 05vzql PRED relation: gender PRED expected values: 02zsn => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.75 #148, 0.74 #78, 0.74 #192), 02zsn (0.68 #50, 0.53 #255, 0.47 #194) >> Best rule #148 for best value: >> intensional similarity = 3 >> extensional distance = 758 >> proper extension: 09lhln; 0djvzd; 08h79x; 0ct_yc; >> query: (?x10579, 05zppz) <- nationality(?x10579, ?x2146), administrative_parent(?x2146, ?x551), organization(?x2146, ?x127) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 0c7ct; 026_dq6; 0517bc; 0cgfb; 0b_4z; *> query: (?x10579, 02zsn) <- category(?x10579, ?x134), profession(?x10579, ?x4773), ?x134 = 08mbj5d, ?x4773 = 0d1pc *> conf = 0.68 ranks of expected_values: 2 EVAL 05vzql gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 147.000 0.746 http://example.org/people/person/gender #16253-032dg7 PRED entity: 032dg7 PRED relation: film PRED expected values: 01dvbd 0gtvpkw => 150 concepts (109 used for prediction) PRED predicted values (max 10 best out of 1582): 072hx4 (0.50 #1552, 0.40 #3132, 0.17 #11032), 047gpsd (0.44 #7375, 0.25 #1055, 0.22 #15275), 0372j5 (0.40 #2640, 0.33 #7380, 0.25 #1060), 0404j37 (0.40 #2587, 0.25 #1007, 0.22 #7327), 0dq626 (0.40 #1619, 0.25 #39, 0.17 #9519), 01_1pv (0.40 #1894, 0.25 #314, 0.17 #9794), 0mbql (0.40 #2622, 0.25 #1042, 0.17 #10522), 05650n (0.40 #2472, 0.25 #892, 0.17 #10372), 03176f (0.40 #2210, 0.25 #630, 0.17 #10110), 031778 (0.40 #1854, 0.25 #274, 0.17 #9754) >> Best rule #1552 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 054g1r; >> query: (?x8796, 072hx4) <- award(?x8796, ?x1105), film(?x8796, ?x7208), ?x7208 = 0b6l1st >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #439 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 054g1r; *> query: (?x8796, 01dvbd) <- award(?x8796, ?x1105), film(?x8796, ?x7208), ?x7208 = 0b6l1st *> conf = 0.25 ranks of expected_values: 38, 473 EVAL 032dg7 film 0gtvpkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 150.000 109.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 032dg7 film 01dvbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 150.000 109.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16252-04wsz PRED entity: 04wsz PRED relation: locations! PRED expected values: 03gqgt3 => 109 concepts (68 used for prediction) PRED predicted values (max 10 best out of 117): 0k4y6 (0.43 #567, 0.27 #942, 0.27 #817), 01gqg3 (0.33 #330, 0.33 #82, 0.29 #454), 06k75 (0.31 #1547, 0.14 #8205, 0.11 #6309), 01w1sx (0.29 #1706, 0.29 #460, 0.27 #959), 0845v (0.29 #507, 0.27 #757, 0.19 #2007), 01hwkn (0.27 #853, 0.17 #2604, 0.17 #1353), 03jqfx (0.22 #4073, 0.16 #5201, 0.15 #3324), 086m1 (0.18 #807, 0.18 #683, 0.17 #2558), 0jnh (0.18 #837, 0.18 #2337, 0.14 #6223), 0bqtx (0.18 #720, 0.06 #5348, 0.06 #6230) >> Best rule #567 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 034cm; >> query: (?x9122, 0k4y6) <- contains(?x9122, ?x4743), contains(?x9122, ?x4092), capital(?x4092, ?x13482), locations(?x2391, ?x4092), service_location(?x555, ?x9122), combatants(?x7419, ?x4743) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1359 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0jdd; *> query: (?x9122, 03gqgt3) <- locations(?x326, ?x9122), combatants(?x326, ?x1003), ?x1003 = 03gj2 *> conf = 0.17 ranks of expected_values: 22 EVAL 04wsz locations! 03gqgt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 109.000 68.000 0.429 http://example.org/time/event/locations #16251-0qf3p PRED entity: 0qf3p PRED relation: artist! PRED expected values: 0mzkr => 181 concepts (121 used for prediction) PRED predicted values (max 10 best out of 116): 015_1q (0.49 #10933, 0.40 #417, 0.30 #1215), 017l96 (0.43 #6270, 0.36 #2810, 0.20 #1214), 02p11jq (0.33 #7597, 0.20 #145, 0.11 #8529), 0mzkr (0.30 #689, 0.27 #8541, 0.20 #822), 01dtcb (0.27 #6295, 0.20 #707, 0.13 #9758), 01w40h (0.25 #26, 0.14 #8543, 0.12 #1622), 043g7l (0.21 #10543, 0.20 #294, 0.14 #13204), 033hn8 (0.20 #2806, 0.20 #279, 0.17 #2407), 02bh8z (0.20 #153, 0.20 #5606, 0.10 #10535), 026s90 (0.20 #834, 0.13 #967, 0.12 #1100) >> Best rule #10933 for best value: >> intensional similarity = 5 >> extensional distance = 300 >> proper extension: 05563d; >> query: (?x2600, 015_1q) <- artist(?x2931, ?x2600), artist(?x2931, ?x9210), artist(?x2931, ?x6876), ?x9210 = 03d2k, category(?x6876, ?x134) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #689 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01wt4wc; 020_4z; 01mxnvc; *> query: (?x2600, 0mzkr) <- artist(?x2149, ?x2600), gender(?x2600, ?x231), artists(?x10306, ?x2600), ?x10306 = 09jw2 *> conf = 0.30 ranks of expected_values: 4 EVAL 0qf3p artist! 0mzkr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 181.000 121.000 0.493 http://example.org/music/record_label/artist #16250-07t_x PRED entity: 07t_x PRED relation: form_of_government PRED expected values: 01d9r3 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 5): 01d9r3 (0.48 #119, 0.38 #259, 0.36 #269), 01fpfn (0.44 #92, 0.43 #277, 0.42 #257), 01q20 (0.33 #168, 0.28 #23, 0.28 #93), 018wl5 (0.33 #276, 0.32 #1, 0.31 #91), 026wp (0.09 #95, 0.09 #155, 0.09 #160) >> Best rule #119 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 02wm6l; >> query: (?x6305, 01d9r3) <- form_of_government(?x6305, ?x48), ?x48 = 06cx9 >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07t_x form_of_government 01d9r3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.479 http://example.org/location/country/form_of_government #16249-0bdjd PRED entity: 0bdjd PRED relation: films! PRED expected values: 07_nf => 92 concepts (30 used for prediction) PRED predicted values (max 10 best out of 68): 01cgz (0.40 #18, 0.06 #486, 0.03 #3312), 06d4h (0.13 #510, 0.10 #42, 0.07 #3336), 0fx2s (0.11 #540, 0.06 #3366, 0.05 #3840), 018jz (0.10 #41), 081pw (0.09 #471, 0.08 #3771, 0.07 #3297), 05489 (0.07 #519, 0.04 #3819, 0.04 #3345), 07c52 (0.07 #487, 0.04 #3155, 0.03 #3787), 018w8 (0.07 #38), 07_nf (0.06 #534, 0.03 #2099, 0.02 #3360), 03r8gp (0.05 #401, 0.04 #557, 0.03 #3225) >> Best rule #18 for best value: >> intensional similarity = 2 >> extensional distance = 28 >> proper extension: 01cgz; >> query: (?x7336, 01cgz) <- films(?x1083, ?x7336), athlete(?x1083, ?x445) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #534 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 99 *> proper extension: 02rjv2w; 01c9d; *> query: (?x7336, 07_nf) <- country(?x7336, ?x94), nominated_for(?x1107, ?x7336), films(?x1083, ?x7336), ?x1107 = 019f4v *> conf = 0.06 ranks of expected_values: 9 EVAL 0bdjd films! 07_nf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 92.000 30.000 0.400 http://example.org/film/film_subject/films #16248-039xcr PRED entity: 039xcr PRED relation: nationality PRED expected values: 09c7w0 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 105): 09c7w0 (0.85 #3920, 0.84 #3314, 0.81 #3011), 0n58p (0.33 #6137, 0.32 #4524, 0.28 #5532), 05fjf (0.33 #6137, 0.32 #4524, 0.28 #5532), 07ssc (0.25 #15, 0.13 #417, 0.12 #2322), 02jx1 (0.13 #1239, 0.11 #435, 0.11 #1340), 0b90_r (0.11 #103, 0.02 #8948, 0.02 #8847), 03rt9 (0.08 #314, 0.02 #2723, 0.02 #2823), 0f8l9c (0.07 #323, 0.06 #222, 0.03 #2932), 03rk0 (0.06 #246, 0.06 #7690, 0.06 #7790), 0345h (0.06 #231, 0.05 #2540, 0.03 #3446) >> Best rule #3920 for best value: >> intensional similarity = 4 >> extensional distance = 1099 >> proper extension: 07m69t; >> query: (?x10058, 09c7w0) <- location(?x10058, ?x10059), source(?x10059, ?x958), contains(?x6895, ?x10059), time_zones(?x10059, ?x2674) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039xcr nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.854 http://example.org/people/person/nationality #16247-06b_j PRED entity: 06b_j PRED relation: languages! PRED expected values: 032l1 0k8y7 => 69 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1101): 0448r (0.55 #3026, 0.50 #1730, 0.42 #4972), 01j5sv (0.50 #1878, 0.45 #3174, 0.33 #582), 040nwr (0.50 #1926, 0.40 #2574, 0.33 #630), 0bdt8 (0.50 #1652, 0.36 #2948, 0.33 #356), 01ps2h8 (0.50 #1593, 0.36 #2889, 0.33 #297), 01q8fxx (0.50 #1894, 0.36 #3190, 0.33 #598), 03crmd (0.50 #1857, 0.36 #3153, 0.33 #561), 02lf70 (0.50 #1392, 0.36 #2688, 0.33 #96), 02pk6x (0.50 #1611, 0.33 #315, 0.27 #2907), 0dqcm (0.50 #1788, 0.33 #492, 0.27 #3084) >> Best rule #3026 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 02bjrlw; 04306rv; 03_9r; 06nm1; 0jzc; 01r2l; 04h9h; >> query: (?x5671, 0448r) <- official_language(?x404, ?x5671), languages_spoken(?x3584, ?x5671), language(?x5441, ?x5671), production_companies(?x5441, ?x1561), featured_film_locations(?x5441, ?x8654), nominated_for(?x5338, ?x5441), film_release_distribution_medium(?x5441, ?x81), major_field_of_study(?x1681, ?x5671) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #240 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x5671, 0k8y7) <- official_language(?x404, ?x5671), languages_spoken(?x3584, ?x5671), language(?x9349, ?x5671), language(?x7947, ?x5671), language(?x5441, ?x5671), language(?x4786, ?x5671), language(?x1015, ?x5671), ?x5441 = 04cbbz, ?x1015 = 04dsnp, ?x7947 = 04gcyg, ?x4786 = 0bbw2z6, ?x9349 = 0jdr0 *> conf = 0.33 ranks of expected_values: 185, 929 EVAL 06b_j languages! 0k8y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 69.000 20.000 0.545 http://example.org/people/person/languages EVAL 06b_j languages! 032l1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 69.000 20.000 0.545 http://example.org/people/person/languages #16246-01jnc_ PRED entity: 01jnc_ PRED relation: film! PRED expected values: 01nm3s 01k_r5b 05mc99 => 89 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1290): 0pz91 (0.33 #4361, 0.29 #6435, 0.07 #33406), 01fyzy (0.33 #5206, 0.29 #7280, 0.03 #36325), 03xb2w (0.33 #5025, 0.29 #7099, 0.02 #29921), 014z8v (0.33 #2781, 0.03 #17302, 0.01 #27677), 030vmc (0.30 #18670, 0.22 #24896, 0.19 #56015), 01kh2m1 (0.25 #716, 0.05 #20746, 0.03 #85063), 01yk13 (0.25 #141, 0.04 #8438, 0.01 #22962), 0ywqc (0.25 #1782, 0.03 #20453, 0.02 #12154), 05hj0n (0.25 #84, 0.02 #22905), 04954 (0.25 #1302, 0.01 #24123, 0.01 #15823) >> Best rule #4361 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0c8tkt; 02f6g5; 0f2sx4; 01d2v1; >> query: (?x9507, 0pz91) <- featured_film_locations(?x9507, ?x1860), film(?x7522, ?x9507), film_crew_role(?x9507, ?x468), ?x7522 = 0d608, production_companies(?x9507, ?x1104) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4835 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0c8tkt; 02f6g5; 0f2sx4; 01d2v1; *> query: (?x9507, 01nm3s) <- featured_film_locations(?x9507, ?x1860), film(?x7522, ?x9507), film_crew_role(?x9507, ?x468), ?x7522 = 0d608, production_companies(?x9507, ?x1104) *> conf = 0.17 ranks of expected_values: 18, 742 EVAL 01jnc_ film! 05mc99 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 63.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 01jnc_ film! 01k_r5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 63.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 01jnc_ film! 01nm3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 89.000 63.000 0.333 http://example.org/film/actor/film./film/performance/film #16245-027z0pl PRED entity: 027z0pl PRED relation: executive_produced_by! PRED expected values: 03h4fq7 => 102 concepts (59 used for prediction) PRED predicted values (max 10 best out of 282): 03h4fq7 (0.33 #288, 0.07 #811, 0.05 #24098), 0bt4g (0.14 #1985, 0.13 #2509, 0.12 #3033), 0mbql (0.14 #1943, 0.13 #2467, 0.12 #2991), 01f7kl (0.14 #1700, 0.13 #2224, 0.12 #2748), 049xgc (0.11 #1366, 0.09 #1889, 0.09 #2413), 01pj_5 (0.11 #1294, 0.09 #1817, 0.09 #2341), 0gwjw0c (0.11 #1428, 0.09 #1951, 0.09 #2475), 03s6l2 (0.11 #1067, 0.09 #1590, 0.09 #2114), 0fsd9t (0.11 #1507, 0.09 #2030, 0.09 #2554), 0234j5 (0.11 #1489, 0.09 #2012, 0.09 #2536) >> Best rule #288 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 03h304l; >> query: (?x10430, 03h4fq7) <- executive_produced_by(?x146, ?x10430), award_nominee(?x10430, ?x902), ?x146 = 02y_lrp >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027z0pl executive_produced_by! 03h4fq7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 59.000 0.333 http://example.org/film/film/executive_produced_by #16244-03205_ PRED entity: 03205_ PRED relation: colors PRED expected values: 01l849 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.44 #102, 0.43 #42, 0.41 #62), 01g5v (0.32 #124, 0.29 #44, 0.28 #104), 01l849 (0.25 #481, 0.25 #501, 0.24 #441), 06fvc (0.19 #43, 0.18 #103, 0.18 #83), 019sc (0.18 #508, 0.17 #488, 0.16 #448), 04mkbj (0.12 #51, 0.12 #71, 0.12 #31), 036k5h (0.12 #106, 0.12 #126, 0.11 #46), 038hg (0.09 #513, 0.09 #493, 0.07 #453), 0jc_p (0.09 #45, 0.09 #105, 0.09 #85), 09ggk (0.06 #496, 0.06 #396, 0.06 #216) >> Best rule #102 for best value: >> intensional similarity = 2 >> extensional distance = 92 >> proper extension: 0kz2w; 01xk7r; >> query: (?x11714, 083jv) <- colors(?x11714, ?x8632), currency(?x11714, ?x170) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #481 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 399 *> proper extension: 06pwq; 01w3v; 024y8p; 01w5m; 01b1pf; 031q3w; 02gr81; 071_8; 0204jh; 09f2j; ... *> query: (?x11714, 01l849) <- colors(?x11714, ?x8632), school_type(?x11714, ?x3205) *> conf = 0.25 ranks of expected_values: 3 EVAL 03205_ colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 65.000 0.436 http://example.org/education/educational_institution/colors #16243-03gjzk PRED entity: 03gjzk PRED relation: profession! PRED expected values: 0grwj 06j0md 02773nt 01t6b4 0p_2r 01r216 071dcs 0j_c 03qmx_f 0q5hw 07_s4b 05ldnp 027xbpw 0k269 016tb7 016ksk 03y9ccy 0kh6b 01vtqml 04snp2 01xcr4 07b3r9 039crh 03f4xvm 04gnbv1 05nn4k 02bvt 03sww 01h8f 09zmys 0bvg70 0988cp 015pvh 03q43g 02r251z 03wh8kl 02__7n 01vz80y 070j61 03xx9l 03q45x 08d9z7 02661h 0cp9f9 02lj6p 03w9sgh 01kt17 03wh8pq 01hcj2 01ggc9 09hd6f 06j8q_ 0b_dh 02zl4d 01pj3h 0b7gr2 021npv 0b1s_q 090gpr 034cj9 01b3bp => 31 concepts (19 used for prediction) PRED predicted values (max 10 best out of 3668): 028k57 (0.77 #14462, 0.70 #3616, 0.50 #26488), 030g9z (0.77 #14462, 0.70 #3616, 0.50 #17003), 06rq2l (0.77 #14462, 0.70 #3616, 0.50 #13389), 08vr94 (0.77 #14462, 0.70 #3616, 0.50 #11840), 0q5hw (0.77 #14462, 0.70 #3616, 0.50 #15152), 02tf1y (0.77 #14462, 0.70 #3616, 0.50 #13302), 02_l96 (0.77 #14462, 0.70 #3616, 0.50 #12197), 0fby2t (0.77 #14462, 0.70 #3616, 0.50 #11971), 0jfx1 (0.77 #14462, 0.70 #3616, 0.50 #25881), 09889g (0.77 #14462, 0.70 #3616, 0.50 #26629) >> Best rule #14462 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 018gz8; >> query: (?x1041, ?x1017) <- profession(?x6151, ?x1041), profession(?x4233, ?x1041), profession(?x3924, ?x1041), student(?x5638, ?x3924), participant(?x6151, ?x1017), ?x4233 = 01trf3 >> conf = 0.77 => this is the best rule for 178 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 12, 15, 43, 84, 99, 145, 180, 188, 211, 212, 258, 271, 284, 286, 341, 366, 374, 378, 399, 400, 401, 473, 475, 478, 479, 482, 483, 484, 488, 490, 496, 500, 501, 626, 627, 634, 639, 672, 686, 764, 765, 822, 888, 936, 996, 1034, 1136, 1138, 1264, 1273, 1562, 1629, 1642, 1832, 1917, 1922, 2587, 2606, 2637, 2908 EVAL 03gjzk profession! 01b3bp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 034cj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 090gpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0b1s_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 021npv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0b7gr2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 01pj3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 02zl4d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0b_dh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 06j8q_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 09hd6f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 01ggc9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 01hcj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 03wh8pq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 01kt17 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 03w9sgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 02lj6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0cp9f9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 02661h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 08d9z7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 03q45x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 03xx9l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 070j61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 01vz80y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 02__7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 03wh8kl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 02r251z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 03q43g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 015pvh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0988cp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0bvg70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 09zmys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 01h8f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 03sww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 02bvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 05nn4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 04gnbv1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 03f4xvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 039crh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 07b3r9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 01xcr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 04snp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 01vtqml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0kh6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 03y9ccy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 016ksk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 016tb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0k269 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 027xbpw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 05ldnp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 07_s4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0q5hw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 03qmx_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0j_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 071dcs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 01r216 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0p_2r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 01t6b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 02773nt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 06j0md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 19.000 0.773 http://example.org/people/person/profession EVAL 03gjzk profession! 0grwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 31.000 19.000 0.773 http://example.org/people/person/profession #16242-06hwzy PRED entity: 06hwzy PRED relation: program! PRED expected values: 0164nb 039crh 02v60l 01wc7p 03xx9l 01yzhn 0ck91 01mbwlb => 86 concepts (39 used for prediction) PRED predicted values (max 10 best out of 415): 025ldg (0.33 #884, 0.33 #26, 0.29 #1156), 02b9g4 (0.33 #44, 0.29 #1174, 0.25 #507), 01pfkw (0.33 #29, 0.25 #492, 0.25 #425), 02ktrs (0.33 #62, 0.25 #525, 0.25 #458), 01wmjkb (0.33 #51, 0.25 #514, 0.25 #447), 05szp (0.33 #42, 0.25 #505, 0.25 #438), 043zg (0.33 #38, 0.25 #501, 0.25 #434), 01gbbz (0.33 #17, 0.25 #480, 0.25 #413), 04xrx (0.33 #15, 0.25 #478, 0.25 #411), 047sxrj (0.33 #14, 0.25 #477, 0.25 #410) >> Best rule #884 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 025ljp; >> query: (?x2583, 025ldg) <- program(?x4983, ?x2583), program(?x3421, ?x2583), honored_for(?x10010, ?x2583), award_nominee(?x3175, ?x4983), award_winner(?x3064, ?x3421), ceremony(?x686, ?x10010), people(?x2510, ?x4983), vacationer(?x1036, ?x3421), award(?x4983, ?x724), award_winner(?x10010, ?x496), profession(?x3421, ?x1032) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #419 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 0cpz4k; *> query: (?x2583, 0164nb) <- program(?x11208, ?x2583), program(?x7474, ?x2583), program(?x4983, ?x2583), program(?x3417, ?x2583), nominated_for(?x11208, ?x8608), people(?x3584, ?x11208), profession(?x11208, ?x319), award_nominee(?x11208, ?x8609), student(?x1665, ?x7474), film(?x3417, ?x6256), artist(?x1954, ?x4983), crewmember(?x6256, ?x1933) *> conf = 0.25 ranks of expected_values: 30, 33 EVAL 06hwzy program! 01mbwlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 39.000 0.333 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program EVAL 06hwzy program! 0ck91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 39.000 0.333 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program EVAL 06hwzy program! 01yzhn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 39.000 0.333 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program EVAL 06hwzy program! 03xx9l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 39.000 0.333 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program EVAL 06hwzy program! 01wc7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 39.000 0.333 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program EVAL 06hwzy program! 02v60l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 39.000 0.333 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program EVAL 06hwzy program! 039crh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 86.000 39.000 0.333 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program EVAL 06hwzy program! 0164nb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 86.000 39.000 0.333 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #16241-0l3kx PRED entity: 0l3kx PRED relation: source PRED expected values: 0jbk9 => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #83, 0.93 #82, 0.92 #64) >> Best rule #83 for best value: >> intensional similarity = 5 >> extensional distance = 233 >> proper extension: 0f4y_; 0n5_g; 09dfcj; 0l2mg; 0n4z2; >> query: (?x11658, ?x958) <- adjoins(?x10134, ?x11658), time_zones(?x11658, ?x1638), source(?x10134, ?x958), second_level_divisions(?x94, ?x11658), adjoins(?x11150, ?x10134) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l3kx source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.928 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #16240-02jxsq PRED entity: 02jxsq PRED relation: gender PRED expected values: 05zppz => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 8): 05zppz (0.88 #29, 0.88 #33, 0.87 #35), 02zsn (0.49 #119, 0.49 #124, 0.46 #141), 0fltx (0.12 #93, 0.12 #108), 012jc (0.12 #93, 0.12 #108), 01hbgs (0.12 #93, 0.12 #108), 0c58k (0.12 #93, 0.12 #108), 0jpmt (0.12 #93, 0.12 #108), 0k95h (0.12 #93, 0.12 #108) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 01x2tm8; 03d6wsd; 044pqn; 044prt; 044ptm; >> query: (?x10200, 05zppz) <- award(?x10200, ?x4687), religion(?x10200, ?x8967), ?x4687 = 03rbj2, ?x8967 = 03j6c >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jxsq gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.885 http://example.org/people/person/gender #16239-01cwhp PRED entity: 01cwhp PRED relation: award PRED expected values: 05zkcn5 02f79n => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 322): 02q3s (0.76 #19161, 0.76 #7984, 0.74 #23555), 02f5qb (0.41 #553, 0.18 #2948, 0.17 #1750), 02f73p (0.41 #585, 0.17 #1782, 0.15 #2581), 0gqz2 (0.41 #4871, 0.23 #4791, 0.23 #20359), 09sb52 (0.36 #10020, 0.36 #9222, 0.34 #6029), 05pcn59 (0.36 #6069, 0.34 #7665, 0.26 #3274), 02f6ym (0.35 #652, 0.23 #7039, 0.14 #3047), 02f72_ (0.35 #624, 0.14 #3019, 0.14 #1821), 02f73b (0.29 #681, 0.22 #7068, 0.17 #1479), 02f777 (0.29 #704, 0.22 #7091, 0.12 #1502) >> Best rule #19161 for best value: >> intensional similarity = 3 >> extensional distance = 338 >> proper extension: 01qkqwg; 0c_mvb; 01wz_ml; 0lzkm; 0191h5; 01jllg1; 024y6w; 051m56; 06lxn; >> query: (?x2461, ?x2139) <- award_winner(?x2461, ?x538), artists(?x671, ?x2461), award_winner(?x2139, ?x2461) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #5128 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 67 *> proper extension: 01vswwx; *> query: (?x2461, 02f79n) <- location(?x2461, ?x2474), award(?x2461, ?x2585), ?x2585 = 054ks3 *> conf = 0.16 ranks of expected_values: 61, 102 EVAL 01cwhp award 02f79n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 143.000 143.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01cwhp award 05zkcn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 143.000 143.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16238-0xrzh PRED entity: 0xrzh PRED relation: source PRED expected values: 0jbk9 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #98, 0.91 #27, 0.90 #37) >> Best rule #98 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 0_rwf; 0_wm_; 010bnr; >> query: (?x3807, 0jbk9) <- category(?x3807, ?x134), ?x134 = 08mbj5d, place(?x3807, ?x3807) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xrzh source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #16237-01q9b9 PRED entity: 01q9b9 PRED relation: profession PRED expected values: 02jknp => 103 concepts (70 used for prediction) PRED predicted values (max 10 best out of 85): 0cbd2 (0.70 #288, 0.56 #3386, 0.56 #3250), 02jknp (0.45 #7062, 0.40 #4806, 0.40 #2828), 09jwl (0.45 #439, 0.44 #721, 0.43 #862), 016z4k (0.40 #427, 0.29 #709, 0.28 #3532), 0dz3r (0.38 #425, 0.26 #3530, 0.25 #5083), 0xzm (0.33 #3387, 0.30 #4376, 0.30 #4375), 0nbcg (0.31 #733, 0.30 #451, 0.30 #874), 025352 (0.30 #4376, 0.30 #4375, 0.07 #195), 018gz8 (0.29 #1707, 0.29 #578, 0.28 #1001), 02krf9 (0.28 #2845, 0.14 #4823, 0.12 #7079) >> Best rule #288 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 0j3v; 02wh0; >> query: (?x7512, 0cbd2) <- influenced_by(?x7512, ?x5004), gender(?x7512, ?x514), ?x5004 = 081k8 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #7062 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 974 *> proper extension: 042l3v; 04rs03; 02nb2s; 025p38; 09byk; 042rnl; 019z7q; 067jsf; 01g4zr; 02lnhv; ... *> query: (?x7512, 02jknp) <- profession(?x7512, ?x319), ?x319 = 01d_h8 *> conf = 0.45 ranks of expected_values: 2 EVAL 01q9b9 profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 70.000 0.703 http://example.org/people/person/profession #16236-02vw1w2 PRED entity: 02vw1w2 PRED relation: actor PRED expected values: 0814k3 => 101 concepts (76 used for prediction) PRED predicted values (max 10 best out of 70): 05v954 (0.35 #683, 0.27 #615, 0.21 #816), 0ckm4x (0.33 #257, 0.33 #125, 0.29 #722), 0814k3 (0.33 #189, 0.33 #58, 0.21 #785), 066l3y (0.33 #216, 0.33 #84, 0.21 #746), 084x96 (0.33 #262, 0.33 #130, 0.11 #792), 091n7z (0.33 #127, 0.21 #789, 0.17 #857), 09wlpl (0.33 #159, 0.21 #755, 0.17 #823), 044_7j (0.33 #36, 0.17 #831, 0.17 #233), 05j0wc (0.33 #47, 0.17 #244, 0.12 #709), 05z775 (0.33 #52, 0.17 #249, 0.08 #847) >> Best rule #683 for best value: >> intensional similarity = 9 >> extensional distance = 15 >> proper extension: 0b60sq; 076xkdz; 015qy1; >> query: (?x1419, 05v954) <- genre(?x1419, ?x812), genre(?x1419, ?x225), ?x225 = 02kdv5l, actor(?x1419, ?x51), film(?x296, ?x1419), genre(?x6994, ?x812), genre(?x3218, ?x812), ?x6994 = 01v1ln, ?x3218 = 0ds2n >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #189 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 0dd6bf; *> query: (?x1419, 0814k3) <- genre(?x1419, ?x571), film(?x12484, ?x1419), ?x12484 = 04f62k, genre(?x1889, ?x571), genre(?x599, ?x571), ?x1889 = 028cg00, titles(?x571, ?x708), film_release_region(?x599, ?x87) *> conf = 0.33 ranks of expected_values: 3 EVAL 02vw1w2 actor 0814k3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 76.000 0.353 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #16235-013w2r PRED entity: 013w2r PRED relation: group! PRED expected values: 02hnl => 83 concepts (60 used for prediction) PRED predicted values (max 10 best out of 99): 02hnl (0.89 #440, 0.84 #606, 0.82 #1353), 03qjg (0.50 #209, 0.29 #292, 0.28 #624), 01vj9c (0.28 #2505, 0.27 #1422, 0.26 #2253), 0l14qv (0.26 #420, 0.25 #171, 0.24 #1084), 05r5c (0.26 #1999, 0.24 #1833, 0.24 #1750), 06ncr (0.25 #200, 0.21 #449, 0.14 #283), 07y_7 (0.25 #168, 0.16 #417, 0.14 #251), 042v_gx (0.25 #174, 0.14 #257, 0.14 #1336), 018j2 (0.25 #194, 0.14 #277, 0.11 #443), 0l14j_ (0.21 #462, 0.14 #545, 0.12 #213) >> Best rule #440 for best value: >> intensional similarity = 8 >> extensional distance = 17 >> proper extension: 05crg7; 018gm9; 0g_g2; 02jqjm; 0178kd; 015cqh; 017mbb; 01v0sxx; >> query: (?x5858, 02hnl) <- artists(?x3061, ?x5858), artists(?x1000, ?x5858), artists(?x671, ?x5858), ?x1000 = 0xhtw, ?x3061 = 05bt6j, group(?x227, ?x5858), artists(?x671, ?x8035), ?x8035 = 095x_ >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013w2r group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 60.000 0.895 http://example.org/music/performance_role/regular_performances./music/group_membership/group #16234-017r2 PRED entity: 017r2 PRED relation: nationality PRED expected values: 0345h => 136 concepts (123 used for prediction) PRED predicted values (max 10 best out of 47): 0345h (0.85 #10637, 0.78 #10839, 0.77 #11244), 09c7w0 (0.80 #603, 0.79 #4913, 0.79 #9532), 017v_ (0.37 #12054, 0.33 #11750, 0.33 #12360), 02jx1 (0.33 #735, 0.25 #133, 0.19 #233), 0h7x (0.29 #35, 0.16 #535, 0.10 #1237), 07ssc (0.25 #115, 0.24 #717, 0.21 #415), 0f8l9c (0.25 #322, 0.14 #22, 0.11 #8826), 05vz3zq (0.15 #370, 0.02 #2473, 0.01 #2975), 01mk6 (0.14 #80, 0.03 #882, 0.02 #1782), 03rk0 (0.12 #246, 0.11 #8826, 0.09 #9274) >> Best rule #10637 for best value: >> intensional similarity = 4 >> extensional distance = 1075 >> proper extension: 07_3qd; 0457w0; 02rnns; 04mx7s; 09hd6f; 0bhtzw; >> query: (?x1645, ?x1264) <- gender(?x1645, ?x231), place_of_birth(?x1645, ?x12866), ?x231 = 05zppz, country(?x12866, ?x1264) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017r2 nationality 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 123.000 0.846 http://example.org/people/person/nationality #16233-01vvy PRED entity: 01vvy PRED relation: influenced_by PRED expected values: 06c44 => 155 concepts (42 used for prediction) PRED predicted values (max 10 best out of 141): 081k8 (0.17 #2779, 0.14 #1468, 0.07 #13730), 082db (0.12 #667, 0.03 #5915, 0.03 #3727), 048cl (0.12 #2421, 0.10 #5047, 0.06 #4610), 0420y (0.12 #2591, 0.07 #1717, 0.06 #3028), 015n8 (0.12 #2598, 0.07 #8731, 0.06 #5224), 099bk (0.12 #2297, 0.06 #4923, 0.04 #4486), 06myp (0.12 #2562, 0.06 #5188, 0.04 #4751), 03_87 (0.12 #2389, 0.05 #8522, 0.05 #15529), 0379s (0.11 #2701, 0.07 #1390, 0.06 #4890), 03sbs (0.09 #8542, 0.06 #5035, 0.06 #2409) >> Best rule #2779 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 06b_0; >> query: (?x598, 081k8) <- profession(?x598, ?x1614), nationality(?x598, ?x789), people(?x6734, ?x598), ?x789 = 0f8l9c, ?x6734 = 03ts0c >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1072 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 0hl3d; 025vry; 0hnlx; 0kvrb; 02ck1; 09h_q; 02vr7; 012201; 05ccxr; 01k47c; ... *> query: (?x598, 06c44) <- profession(?x598, ?x1614), nationality(?x598, ?x789), gender(?x598, ?x231), artists(?x5640, ?x598), ?x5640 = 01wqlc *> conf = 0.07 ranks of expected_values: 20 EVAL 01vvy influenced_by 06c44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 155.000 42.000 0.167 http://example.org/influence/influence_node/influenced_by #16232-01nr63 PRED entity: 01nr63 PRED relation: location PRED expected values: 05k7sb => 107 concepts (67 used for prediction) PRED predicted values (max 10 best out of 87): 02dtg (0.54 #12071, 0.51 #14486, 0.51 #20925), 02_286 (0.15 #11302, 0.15 #6473, 0.15 #37), 030qb3t (0.14 #19399, 0.13 #7326, 0.13 #11348), 0cr3d (0.08 #949, 0.06 #11410, 0.06 #18656), 0rh6k (0.06 #808, 0.06 #40255, 0.05 #30587), 0chrx (0.06 #1209, 0.03 #2818, 0.03 #3622), 01cx_ (0.06 #40255, 0.05 #30587, 0.04 #967), 05k7sb (0.06 #40255, 0.05 #30587, 0.03 #4131), 09c7w0 (0.06 #40255, 0.05 #30587, 0.01 #6439), 0r0m6 (0.05 #1827, 0.03 #2631, 0.03 #3435) >> Best rule #12071 for best value: >> intensional similarity = 4 >> extensional distance = 691 >> proper extension: 01sl1q; 044mz_; 07nznf; 04bdxl; 079vf; 05bnp0; 01vvydl; 04yywz; 02p65p; 0337vz; ... >> query: (?x12412, ?x479) <- people(?x3584, ?x12412), nationality(?x12412, ?x94), ?x94 = 09c7w0, place_of_birth(?x12412, ?x479) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #40255 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1461 *> proper extension: 099bk; 0cl_m; *> query: (?x12412, ?x94) <- gender(?x12412, ?x231), student(?x10945, ?x12412), contains(?x94, ?x10945), institution(?x620, ?x10945) *> conf = 0.06 ranks of expected_values: 8 EVAL 01nr63 location 05k7sb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 107.000 67.000 0.537 http://example.org/people/person/places_lived./people/place_lived/location #16231-04cbtrw PRED entity: 04cbtrw PRED relation: category PRED expected values: 08mbj5d => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.41 #38, 0.40 #130, 0.39 #7) >> Best rule #38 for best value: >> intensional similarity = 3 >> extensional distance = 248 >> proper extension: 03g5jw; 016ppr; >> query: (?x2934, 08mbj5d) <- influenced_by(?x2934, ?x5988), award(?x2934, ?x8842), place_of_birth(?x5988, ?x11794) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cbtrw category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.408 http://example.org/common/topic/webpage./common/webpage/category #16230-0glnm PRED entity: 0glnm PRED relation: genre PRED expected values: 01g6gs => 82 concepts (61 used for prediction) PRED predicted values (max 10 best out of 94): 07s9rl0 (0.73 #3048, 0.71 #5858, 0.69 #4101), 02kdv5l (0.35 #1409, 0.33 #1877, 0.31 #2111), 01g6gs (0.34 #487, 0.31 #136, 0.29 #955), 01jfsb (0.32 #1417, 0.31 #1768, 0.31 #1885), 03k9fj (0.30 #1181, 0.29 #1416, 0.27 #1533), 04xvlr (0.26 #1525, 0.24 #821, 0.20 #3049), 0lsxr (0.24 #1647, 0.21 #1061, 0.20 #1413), 060__y (0.24 #1538, 0.20 #2358, 0.19 #834), 06n90 (0.19 #5517, 0.15 #1418, 0.15 #1886), 01hmnh (0.19 #718, 0.19 #3531, 0.17 #2711) >> Best rule #3048 for best value: >> intensional similarity = 4 >> extensional distance = 620 >> proper extension: 04z_x4v; >> query: (?x3438, 07s9rl0) <- nominated_for(?x1079, ?x3438), nominated_for(?x3811, ?x3438), nominated_for(?x1079, ?x11218), ?x11218 = 0ccck7 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #487 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 048rn; 0jqb8; *> query: (?x3438, 01g6gs) <- genre(?x3438, ?x239), film_art_direction_by(?x3438, ?x8402), production_companies(?x3438, ?x5537) *> conf = 0.34 ranks of expected_values: 3 EVAL 0glnm genre 01g6gs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 61.000 0.730 http://example.org/film/film/genre #16229-043js PRED entity: 043js PRED relation: award PRED expected values: 0cqhk0 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 232): 02z13jg (0.72 #35706, 0.70 #34099, 0.69 #35705), 0cqhk0 (0.67 #839, 0.43 #1641, 0.31 #1240), 0cjyzs (0.43 #1709, 0.38 #1308, 0.13 #34501), 03ccq3s (0.29 #1800, 0.25 #1399, 0.13 #34501), 0bp_b2 (0.29 #18, 0.13 #34501, 0.13 #4412), 0cqhmg (0.29 #359, 0.13 #34501, 0.13 #4412), 0gqy2 (0.22 #563, 0.13 #34501, 0.12 #33296), 0f4x7 (0.22 #432, 0.13 #34501, 0.12 #33296), 04kxsb (0.22 #524, 0.13 #34501, 0.12 #33296), 0gqwc (0.22 #475, 0.13 #34501, 0.12 #2079) >> Best rule #35706 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x2657, ?x2071) <- award_winner(?x2071, ?x2657), award(?x269, ?x2071) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #839 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 08wq0g; 0bt4r4; 08hsww; *> query: (?x2657, 0cqhk0) <- award_nominee(?x2657, ?x6622), award_nominee(?x2657, ?x4333), ?x4333 = 0cnl09, ?x6622 = 05p92jn *> conf = 0.67 ranks of expected_values: 2 EVAL 043js award 0cqhk0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16228-047byns PRED entity: 047byns PRED relation: award! PRED expected values: 0bz5v2 => 55 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2919): 01l1ls (0.33 #2729, 0.19 #9467, 0.08 #16210), 03xp8d5 (0.33 #1243, 0.15 #11352, 0.11 #18094), 046lt (0.33 #809, 0.13 #26962, 0.12 #33705), 0ph2w (0.33 #1141, 0.13 #26962, 0.12 #33705), 01lct6 (0.33 #3104, 0.12 #10109, 0.04 #9842), 01n5309 (0.33 #146, 0.12 #74153, 0.12 #13481), 0b1f49 (0.33 #1077, 0.12 #74153, 0.12 #7815), 0fz27v (0.33 #2895, 0.12 #74153, 0.08 #13004), 01jgpsh (0.33 #1853, 0.12 #8591, 0.10 #11962), 01vb403 (0.33 #512, 0.08 #10621, 0.08 #7250) >> Best rule #2729 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0fc9js; >> query: (?x882, 01l1ls) <- award(?x4065, ?x882), award(?x2790, ?x882), ceremony(?x882, ?x4760), ?x4065 = 029_3, ?x2790 = 0c9c0 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #33704 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 157 *> proper extension: 0fm3h2; *> query: (?x882, ?x1726) <- award(?x8139, ?x882), nominated_for(?x882, ?x3626), participant(?x8139, ?x1942), award_nominee(?x1726, ?x8139) *> conf = 0.13 ranks of expected_values: 82 EVAL 047byns award! 0bz5v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 55.000 23.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16227-01dwrc PRED entity: 01dwrc PRED relation: artists! PRED expected values: 0ggx5q => 69 concepts (23 used for prediction) PRED predicted values (max 10 best out of 255): 0ggx5q (0.67 #68, 0.59 #2147, 0.27 #662), 06by7 (0.53 #3586, 0.49 #2991, 0.48 #3883), 016clz (0.42 #302, 0.38 #1193, 0.37 #1490), 03_d0 (0.31 #5361, 0.18 #2684, 0.16 #4767), 02ny8t (0.21 #2201, 0.06 #3093, 0.06 #3390), 01lyv (0.20 #4490, 0.19 #4788, 0.18 #3598), 0xhtw (0.20 #907, 0.18 #5665, 0.18 #5069), 02k_kn (0.18 #3026, 0.17 #3323, 0.16 #2728), 02vjzr (0.17 #2202, 0.17 #2796, 0.13 #3094), 07d2d (0.17 #83, 0.15 #380, 0.06 #1271) >> Best rule #68 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0dm5l; >> query: (?x5760, 0ggx5q) <- award(?x5760, ?x2180), award(?x5760, ?x528), ?x2180 = 02v1m7, ?x528 = 02g3gj >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dwrc artists! 0ggx5q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 23.000 0.667 http://example.org/music/genre/artists #16226-0fq9zcx PRED entity: 0fq9zcx PRED relation: award! PRED expected values: 0159h6 0l6px => 50 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1649): 02l4pj (0.43 #7707, 0.33 #4329, 0.25 #11087), 0dvld (0.43 #8506, 0.33 #5128, 0.12 #11886), 03bxsw (0.33 #4294, 0.29 #7672, 0.25 #11052), 0lpjn (0.33 #4143, 0.29 #7521, 0.13 #64214), 07yp0f (0.33 #4467, 0.29 #7845, 0.12 #11225), 03d_w3h (0.33 #3597, 0.29 #6975, 0.12 #10355), 01tspc6 (0.33 #3612, 0.29 #6990, 0.09 #10136), 02f2p7 (0.33 #4936, 0.29 #8314, 0.09 #10136), 07lt7b (0.33 #3535, 0.29 #6913, 0.08 #30564), 04fzk (0.33 #4531, 0.29 #7909, 0.07 #31560) >> Best rule #7707 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0fq9zdn; 05zvq6g; 09ly2r6; >> query: (?x13107, 02l4pj) <- nominated_for(?x13107, ?x7516), nominated_for(?x13107, ?x1813), ?x7516 = 0bh8drv, award(?x1017, ?x13107), award(?x1813, ?x68), film(?x72, ?x1813) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3477 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 02y_rq5; *> query: (?x13107, 0159h6) <- nominated_for(?x13107, ?x7516), ?x7516 = 0bh8drv, award(?x11983, ?x13107), award(?x4398, ?x13107), ?x4398 = 0h32q, nominated_for(?x11983, ?x7275) *> conf = 0.33 ranks of expected_values: 17, 18 EVAL 0fq9zcx award! 0l6px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 50.000 21.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fq9zcx award! 0159h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 50.000 21.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16225-0fqpc7d PRED entity: 0fqpc7d PRED relation: honored_for PRED expected values: 03d34x8 07s846j => 28 concepts (15 used for prediction) PRED predicted values (max 10 best out of 856): 09k56b7 (0.33 #1287, 0.33 #113, 0.25 #701), 05f4vxd (0.33 #298, 0.25 #886, 0.18 #2646), 080dwhx (0.33 #23, 0.25 #611, 0.17 #1784), 02r5qtm (0.33 #242, 0.25 #830, 0.17 #2003), 07s846j (0.33 #234, 0.25 #822, 0.17 #1408), 09tqkv2 (0.33 #116, 0.25 #704, 0.17 #1290), 04f6hhm (0.33 #478, 0.25 #1066, 0.17 #1652), 0cp0790 (0.33 #408, 0.25 #996, 0.17 #1582), 02pqs8l (0.18 #2565, 0.17 #1391, 0.12 #3153), 0fhzwl (0.18 #2841, 0.09 #4016, 0.08 #3429) >> Best rule #1287 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 09p2r9; 02yxh9; 0g55tzk; >> query: (?x2245, 09k56b7) <- award_winner(?x2245, ?x1222), nationality(?x1222, ?x1310), award_winner(?x5699, ?x1222), award_winner(?x2531, ?x1222), ?x5699 = 03y_46, award_nominee(?x1222, ?x1739), film(?x1739, ?x708), award_winner(?x3461, ?x1739), ?x2531 = 0kszw, student(?x2486, ?x1739), nominated_for(?x1222, ?x144), ceremony(?x899, ?x2245), ?x3461 = 02l4pj, honored_for(?x2245, ?x1259) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #234 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 0g5b0q5; *> query: (?x2245, 07s846j) <- award_winner(?x2245, ?x4835), award_winner(?x2245, ?x1222), ?x1222 = 03f1zdw, honored_for(?x2245, ?x2098), honored_for(?x2245, ?x1861), ceremony(?x899, ?x2245), nominated_for(?x91, ?x1861), titles(?x2480, ?x1861), ?x4835 = 01wy5m, titles(?x812, ?x2098), award(?x286, ?x899), nominated_for(?x899, ?x10425), award_nominee(?x91, ?x92), genre(?x2098, ?x600), award_winner(?x10425, ?x7156) *> conf = 0.33 ranks of expected_values: 5, 13 EVAL 0fqpc7d honored_for 07s846j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 28.000 15.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0fqpc7d honored_for 03d34x8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 28.000 15.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #16224-019fh PRED entity: 019fh PRED relation: dog_breed PRED expected values: 0km3f => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 1): 0km3f (0.90 #11, 0.88 #13, 0.88 #5) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 013yq; 01cx_; 0d6lp; 03l2n; 05jbn; 01smm; >> query: (?x3689, 0km3f) <- dog_breed(?x3689, ?x1706), location(?x3688, ?x3689), award_winner(?x3688, ?x369), contains(?x94, ?x3689) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019fh dog_breed 0km3f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.902 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #16223-05ywg PRED entity: 05ywg PRED relation: mode_of_transportation PRED expected values: 025t3bg 01bjv => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 4): 01bjv (0.86 #38, 0.82 #18, 0.82 #54), 025t3bg (0.75 #89, 0.74 #81, 0.74 #73), 0k4j (0.06 #43, 0.04 #35, 0.03 #51), 06d_3 (0.02 #80, 0.02 #92, 0.01 #116) >> Best rule #38 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 080h2; 01_d4; 052p7; 01cx_; >> query: (?x1458, 01bjv) <- contains(?x1558, ?x1458), contains(?x455, ?x1558), month(?x1458, ?x1459), adjoins(?x1558, ?x456), featured_film_locations(?x3693, ?x1458) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05ywg mode_of_transportation 01bjv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.862 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 05ywg mode_of_transportation 025t3bg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.862 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #16222-0bynt PRED entity: 0bynt PRED relation: country PRED expected values: 0154j 03_3d 0chghy 015fr 04v3q 07ww5 01z215 03rk0 07z5n 0hg5 0fv4v 04hqz 03676 05c74 04hhv 06m_5 04wlh 06nnj 04vs9 01nqj 03188 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 96): 015fr (0.89 #1050, 0.77 #825, 0.75 #488), 03_3d (0.83 #1274, 0.80 #1462, 0.80 #897), 0chghy (0.83 #750, 0.80 #600, 0.79 #1049), 0154j (0.75 #522, 0.68 #1047, 0.60 #598), 04g61 (0.60 #390, 0.50 #616, 0.50 #315), 0345_ (0.60 #389, 0.40 #615, 0.38 #1500), 05b7q (0.53 #916, 0.50 #206, 0.49 #371), 03rk0 (0.50 #606, 0.50 #305, 0.50 #268), 04hqz (0.50 #620, 0.50 #319, 0.50 #282), 04w8f (0.50 #346, 0.50 #272, 0.50 #235) >> Best rule #1050 for best value: >> intensional similarity = 13 >> extensional distance = 17 >> proper extension: 07rlg; 06z68; 019tzd; 0194d; 01gqfm; >> query: (?x1121, 015fr) <- country(?x1121, ?x7871), country(?x1121, ?x6827), country(?x1121, ?x1355), sports(?x3729, ?x1121), sports(?x2496, ?x1121), organization(?x6827, ?x127), taxonomy(?x6827, ?x939), olympics(?x359, ?x2496), sports(?x2496, ?x171), ?x1355 = 0h7x, currency(?x6827, ?x170), ?x3729 = 0jdk_, olympics(?x7871, ?x2966) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 8, 9, 11, 12, 13, 14, 15, 16, 18, 40, 43, 44, 45, 46, 47, 63, 65 EVAL 0bynt country 03188 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 01nqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 04vs9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 06nnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 04wlh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 06m_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 04hhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 05c74 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 03676 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 04hqz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 0fv4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 0hg5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 07z5n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 03rk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 01z215 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 07ww5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 04v3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0bynt country 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #16221-02stgt PRED entity: 02stgt PRED relation: major_field_of_study! PRED expected values: 015fsv => 58 concepts (33 used for prediction) PRED predicted values (max 10 best out of 677): 01mpwj (0.77 #3047, 0.58 #1876, 0.44 #3634), 02bqy (0.69 #3132, 0.67 #1961, 0.44 #3719), 01w5m (0.67 #1875, 0.65 #4219, 0.62 #3633), 06pwq (0.67 #1771, 0.65 #4115, 0.62 #6463), 09f2j (0.67 #1935, 0.65 #4279, 0.56 #9567), 08815 (0.67 #1760, 0.62 #3518, 0.62 #2931), 07w0v (0.67 #1193, 0.50 #609, 0.40 #9410), 02zd460 (0.65 #5470, 0.64 #6642, 0.62 #4883), 07tds (0.62 #3096, 0.58 #1925, 0.56 #3683), 07szy (0.62 #2386, 0.58 #1800, 0.55 #4144) >> Best rule #3047 for best value: >> intensional similarity = 13 >> extensional distance = 11 >> proper extension: 01lhy; 04rjg; 03g3w; >> query: (?x10380, 01mpwj) <- major_field_of_study(?x11415, ?x10380), major_field_of_study(?x3439, ?x10380), major_field_of_study(?x892, ?x10380), major_field_of_study(?x11690, ?x10380), major_field_of_study(?x1526, ?x10380), ?x3439 = 03ksy, ?x892 = 07tgn, major_field_of_study(?x10380, ?x2606), ?x1526 = 0bkj86, institution(?x11690, ?x7660), category(?x11415, ?x134), currency(?x11415, ?x170), ?x7660 = 01qd_r >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #11744 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 49 *> proper extension: 02sgy; *> query: (?x10380, ?x6083) <- major_field_of_study(?x4981, ?x10380), institution(?x4981, ?x8903), institution(?x4981, ?x7154), institution(?x4981, ?x6083), institution(?x4981, ?x5280), ?x8903 = 01wqg8, list(?x7154, ?x2197), fraternities_and_sororities(?x6083, ?x3697), state_province_region(?x6083, ?x2623), student(?x5280, ?x1942), state_province_region(?x5280, ?x1227) *> conf = 0.19 ranks of expected_values: 281 EVAL 02stgt major_field_of_study! 015fsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 58.000 33.000 0.769 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16220-0fpmrm3 PRED entity: 0fpmrm3 PRED relation: language PRED expected values: 02h40lc => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 30): 02h40lc (0.89 #357, 0.89 #1013, 0.89 #594), 064_8sq (0.17 #22, 0.15 #318, 0.15 #1152), 06nm1 (0.11 #1022, 0.11 #366, 0.11 #70), 04306rv (0.09 #1016, 0.09 #835, 0.09 #301), 06b_j (0.08 #1034, 0.06 #615, 0.05 #853), 02bjrlw (0.07 #1012, 0.06 #771, 0.06 #891), 03_9r (0.05 #1021, 0.05 #900, 0.05 #2750), 0653m (0.05 #842, 0.04 #902, 0.04 #1203), 0jzc (0.04 #1031, 0.04 #850, 0.03 #20), 012w70 (0.04 #843, 0.03 #903, 0.03 #1024) >> Best rule #357 for best value: >> intensional similarity = 3 >> extensional distance = 210 >> proper extension: 04fzfj; 02rqwhl; 02q56mk; 07w8fz; 0286gm1; 012kyx; 0dp7wt; >> query: (?x2655, 02h40lc) <- nominated_for(?x1641, ?x2655), genre(?x2655, ?x53), nominated_for(?x385, ?x2655) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fpmrm3 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.892 http://example.org/film/film/language #16219-030qb3t PRED entity: 030qb3t PRED relation: place_of_death! PRED expected values: 02lxj_ 01wj9y9 04gmp_z 01vvdm 02d6cy 02x0bdb 0523v5y 04rvy8 0qdwr 0hsmh 02cvp8 02c7lt 011s9r 01nr63 05mc7y => 150 concepts (139 used for prediction) PRED predicted values (max 10 best out of 1091): 0gl88b (0.10 #1299, 0.04 #45895, 0.04 #1919), 0hgqq (0.10 #1414, 0.04 #2034, 0.04 #16737), 01fxfk (0.10 #1816, 0.04 #2436, 0.04 #16737), 01dbk6 (0.10 #1440, 0.04 #2060, 0.03 #3299), 034cj9 (0.10 #1841, 0.04 #2461, 0.03 #3700), 01dbhb (0.10 #1835, 0.04 #2455, 0.03 #3694), 0f3nn (0.10 #1819, 0.04 #2439, 0.03 #3678), 0164y7 (0.10 #1818, 0.04 #2438, 0.03 #3677), 06y7d (0.10 #1786, 0.04 #2406, 0.03 #3645), 0cpvcd (0.10 #1754, 0.04 #2374, 0.03 #3613) >> Best rule #1299 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 07751; >> query: (?x1523, 0gl88b) <- vacationer(?x1523, ?x793), film_regional_debut_venue(?x2954, ?x1523) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 030qb3t place_of_death! 05mc7y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 01nr63 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 011s9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 02c7lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 02cvp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 0hsmh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 0qdwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 04rvy8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 0523v5y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 02x0bdb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 02d6cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 01vvdm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 04gmp_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 01wj9y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death EVAL 030qb3t place_of_death! 02lxj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 139.000 0.100 http://example.org/people/deceased_person/place_of_death #16218-0gx_p PRED entity: 0gx_p PRED relation: film PRED expected values: 02tktw => 134 concepts (94 used for prediction) PRED predicted values (max 10 best out of 852): 0bt3j9 (0.66 #24964, 0.50 #888, 0.44 #144435), 06gb1w (0.25 #732, 0.03 #2515, 0.03 #4299), 03mh94 (0.25 #64, 0.03 #1847, 0.03 #167616), 01qb5d (0.25 #137, 0.03 #3704, 0.03 #167616), 0d90m (0.25 #8, 0.03 #3575, 0.03 #167616), 0bvn25 (0.15 #3617, 0.03 #49976, 0.03 #30363), 06_wqk4 (0.12 #126, 0.09 #1909, 0.04 #130168), 03cd0x (0.12 #934, 0.06 #73106, 0.05 #83805), 026qnh6 (0.12 #820, 0.06 #73106, 0.05 #83805), 02v8kmz (0.12 #28, 0.03 #1811, 0.02 #30341) >> Best rule #24964 for best value: >> intensional similarity = 3 >> extensional distance = 277 >> proper extension: 01507p; >> query: (?x6278, ?x5142) <- participant(?x338, ?x6278), place_of_birth(?x6278, ?x13451), nominated_for(?x6278, ?x5142) >> conf = 0.66 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gx_p film 02tktw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 94.000 0.661 http://example.org/film/actor/film./film/performance/film #16217-01q9mk PRED entity: 01q9mk PRED relation: category PRED expected values: 08mbj5d => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #39, 0.08 #15, 0.08 #16) >> Best rule #39 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x7435, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q9mk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #16216-0ds11z PRED entity: 0ds11z PRED relation: honored_for! PRED expected values: 02pgky2 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 116): 0275n3y (0.10 #2120, 0.04 #5508, 0.04 #3935), 04n2r9h (0.10 #277, 0.08 #2092, 0.07 #35), 03gwpw2 (0.10 #2062, 0.05 #1699, 0.04 #4119), 03nnm4t (0.10 #2119, 0.04 #5507, 0.04 #3813), 05c1t6z (0.09 #2067, 0.06 #3761, 0.05 #5455), 02q690_ (0.07 #2110, 0.05 #5498, 0.05 #3804), 09gkdln (0.07 #105, 0.05 #952, 0.05 #589), 05qb8vx (0.07 #47, 0.05 #289, 0.04 #410), 09k5jh7 (0.07 #70, 0.05 #312, 0.04 #554), 092c5f (0.07 #9, 0.05 #251, 0.03 #2066) >> Best rule #2120 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 06hwzy; 0cnjm0; >> query: (?x485, 0275n3y) <- honored_for(?x944, ?x485), award_winner(?x944, ?x8556), role(?x8556, ?x614) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #1769 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 175 *> proper extension: 080dwhx; 06cs95; 0jzw; 019nnl; 0124k9; 08jgk1; 0bq8tmw; 09146g; 03d34x8; 01hqhm; ... *> query: (?x485, 02pgky2) <- nominated_for(?x4314, ?x485), award(?x4314, ?x1053), produced_by(?x124, ?x4314), category(?x485, ?x134) *> conf = 0.03 ranks of expected_values: 42 EVAL 0ds11z honored_for! 02pgky2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 108.000 108.000 0.101 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #16215-01lrrt PRED entity: 01lrrt PRED relation: genre! PRED expected values: 021y7yw 09y6pb => 42 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1868): 03s6l2 (0.67 #14985, 0.60 #11260, 0.60 #7533), 01rnly (0.67 #14657, 0.60 #12796, 0.50 #16521), 03bxp5 (0.67 #16018, 0.60 #12293, 0.40 #10429), 0mcl0 (0.67 #15560, 0.60 #11835, 0.40 #9971), 083shs (0.67 #14917, 0.60 #11192, 0.40 #9328), 0gyy53 (0.67 #15394, 0.60 #11669, 0.40 #9805), 09z2b7 (0.67 #15142, 0.60 #11417, 0.33 #13278), 01gvsn (0.67 #16659, 0.60 #12934, 0.33 #14795), 0bs5k8r (0.67 #15633, 0.60 #11908, 0.33 #13769), 083skw (0.67 #15331, 0.60 #11606, 0.33 #2294) >> Best rule #14985 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 060__y; >> query: (?x6530, 03s6l2) <- genre(?x6679, ?x6530), genre(?x2833, ?x6530), genre(?x898, ?x6530), ?x6679 = 0drnwh, award(?x898, ?x618), nominated_for(?x10583, ?x2833), film_crew_role(?x2833, ?x137), film(?x1253, ?x2833), film(?x971, ?x898), ?x10583 = 0jsw9l, film_release_region(?x898, ?x94), films(?x5069, ?x898) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #15304 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 060__y; *> query: (?x6530, 021y7yw) <- genre(?x6679, ?x6530), genre(?x2833, ?x6530), genre(?x898, ?x6530), ?x6679 = 0drnwh, award(?x898, ?x618), nominated_for(?x10583, ?x2833), film_crew_role(?x2833, ?x137), film(?x1253, ?x2833), film(?x971, ?x898), ?x10583 = 0jsw9l, film_release_region(?x898, ?x94), films(?x5069, ?x898) *> conf = 0.50 ranks of expected_values: 236, 456 EVAL 01lrrt genre! 09y6pb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 16.000 0.667 http://example.org/film/film/genre EVAL 01lrrt genre! 021y7yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 16.000 0.667 http://example.org/film/film/genre #16214-016k6x PRED entity: 016k6x PRED relation: people! PRED expected values: 03w9bjf => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 39): 07bch9 (0.25 #396, 0.06 #846, 0.06 #921), 041rx (0.23 #1204, 0.23 #3079, 0.23 #1729), 0x67 (0.20 #759, 0.19 #1735, 0.19 #3085), 013xrm (0.18 #393, 0.04 #3094, 0.03 #693), 03bkbh (0.14 #405, 0.06 #30, 0.05 #180), 033tf_ (0.14 #6, 0.13 #681, 0.13 #606), 0xnvg (0.10 #612, 0.09 #687, 0.09 #762), 0d7wh (0.06 #465, 0.03 #615, 0.03 #690), 07hwkr (0.06 #3087, 0.06 #1287, 0.06 #836), 01qhm_ (0.05 #5, 0.05 #680, 0.04 #605) >> Best rule #396 for best value: >> intensional similarity = 3 >> extensional distance = 304 >> proper extension: 083p7; 0k4gf; 083pr; 0j3v; 09bg4l; 04k15; 0372p; 01dvtx; 02lt8; 06c97; ... >> query: (?x4969, 07bch9) <- people(?x743, ?x4969), people(?x743, ?x875), ?x875 = 032_jg >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #52 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 91 *> proper extension: 02qgqt; 06dv3; 0byfz; 0h0jz; 0p_pd; 0bl2g; 0z4s; 0h5g_; 0bxtg; 018db8; ... *> query: (?x4969, 03w9bjf) <- award(?x4969, ?x2375), profession(?x4969, ?x1032), ?x2375 = 04kxsb *> conf = 0.01 ranks of expected_values: 36 EVAL 016k6x people! 03w9bjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 90.000 90.000 0.255 http://example.org/people/ethnicity/people #16213-06gn7r PRED entity: 06gn7r PRED relation: gender PRED expected values: 05zppz => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #19, 0.89 #63, 0.89 #85), 02zsn (0.46 #217, 0.46 #334, 0.46 #278) >> Best rule #19 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 081hvm; >> query: (?x8296, 05zppz) <- type_of_union(?x8296, ?x566), location(?x8296, ?x8297), award(?x8296, ?x4687), ?x566 = 04ztj, ?x4687 = 03rbj2 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06gn7r gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.909 http://example.org/people/person/gender #16212-0560w PRED entity: 0560w PRED relation: group! PRED expected values: 01vj9c => 81 concepts (55 used for prediction) PRED predicted values (max 10 best out of 100): 05148p4 (0.69 #1869, 0.68 #1781, 0.67 #724), 018vs (0.66 #981, 0.65 #893, 0.65 #805), 028tv0 (0.54 #804, 0.50 #892, 0.50 #628), 05r5c (0.43 #535, 0.25 #1768, 0.23 #1856), 03qjg (0.29 #576, 0.25 #752, 0.23 #1809), 01vj9c (0.27 #1863, 0.25 #1775, 0.25 #718), 0l14qv (0.25 #1766, 0.23 #1854, 0.21 #709), 042v_gx (0.23 #800, 0.21 #536, 0.19 #888), 013y1f (0.14 #556, 0.13 #1877, 0.12 #732), 02k856 (0.14 #579, 0.10 #491, 0.08 #755) >> Best rule #1869 for best value: >> intensional similarity = 5 >> extensional distance = 183 >> proper extension: 02dw1_; >> query: (?x11704, 05148p4) <- artists(?x2249, ?x11704), parent_genre(?x2249, ?x1572), artists(?x2249, ?x6469), group(?x227, ?x11704), place_of_death(?x6469, ?x362) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1863 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 183 *> proper extension: 02dw1_; *> query: (?x11704, 01vj9c) <- artists(?x2249, ?x11704), parent_genre(?x2249, ?x1572), artists(?x2249, ?x6469), group(?x227, ?x11704), place_of_death(?x6469, ?x362) *> conf = 0.27 ranks of expected_values: 6 EVAL 0560w group! 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 55.000 0.686 http://example.org/music/performance_role/regular_performances./music/group_membership/group #16211-02qwdhq PRED entity: 02qwdhq PRED relation: nominated_for PRED expected values: 02hfk5 => 42 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1825): 017jd9 (0.61 #3884, 0.47 #7061, 0.45 #5472), 026p4q7 (0.57 #3533, 0.47 #5121, 0.47 #6710), 0dr_4 (0.57 #3398, 0.47 #4986, 0.47 #6575), 017gl1 (0.54 #3306, 0.44 #6483, 0.42 #4894), 03hmt9b (0.50 #3771, 0.40 #5359, 0.37 #6948), 019vhk (0.50 #3591, 0.35 #5179, 0.35 #6768), 011yqc (0.46 #3383, 0.40 #4971, 0.37 #6560), 07024 (0.46 #3609, 0.40 #5197, 0.37 #6786), 0m313 (0.46 #3187, 0.38 #4775, 0.37 #6364), 049xgc (0.46 #4049, 0.38 #5637, 0.37 #7226) >> Best rule #3884 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 018wng; 0gq_d; 027h4yd; >> query: (?x2599, 017jd9) <- award(?x1622, ?x2599), crewmember(?x407, ?x1622), award_nominee(?x1622, ?x3879), award_winner(?x3618, ?x1622) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #750 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 02qysm0; *> query: (?x2599, 02hfk5) <- award(?x8277, ?x2599), award(?x4111, ?x2599), ?x4111 = 0cmc26r, nominated_for(?x2599, ?x9996), ?x8277 = 02r858_, ?x9996 = 03cwwl *> conf = 0.33 ranks of expected_values: 49 EVAL 02qwdhq nominated_for 02hfk5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 42.000 12.000 0.607 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16210-0kc9f PRED entity: 0kc9f PRED relation: citytown PRED expected values: 0r00l => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 145): 02_286 (0.56 #5539, 0.56 #5170, 0.53 #4802), 0r00l (0.38 #6174, 0.24 #9486, 0.19 #10590), 030qb3t (0.33 #28, 0.29 #4079, 0.25 #1133), 0cc56 (0.33 #387, 0.10 #1492, 0.09 #2228), 0r04p (0.16 #9678, 0.14 #13730, 0.12 #14098), 0rj4g (0.12 #1332, 0.09 #2436, 0.09 #2068), 04jpl (0.10 #14000, 0.10 #1480, 0.09 #15104), 013yq (0.09 #2252, 0.07 #10721, 0.07 #11828), 0f04v (0.09 #8253, 0.07 #14513, 0.07 #11568), 0r6cx (0.09 #8352, 0.07 #11667, 0.05 #14612) >> Best rule #5539 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01l50r; >> query: (?x13952, 02_286) <- program(?x13952, ?x13288), actor(?x13288, ?x5906), child(?x3920, ?x13952), location(?x5906, ?x739) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #6174 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 02swsm; *> query: (?x13952, 0r00l) <- child(?x3920, ?x13952), ?x3920 = 09b3v *> conf = 0.38 ranks of expected_values: 2 EVAL 0kc9f citytown 0r00l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.562 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #16209-057__d PRED entity: 057__d PRED relation: film! PRED expected values: 0147dk 01l9p => 106 concepts (57 used for prediction) PRED predicted values (max 10 best out of 951): 01l79yc (0.46 #118661, 0.44 #68702, 0.43 #49967), 0bqytm (0.46 #118661, 0.44 #68702, 0.43 #49967), 0372kf (0.37 #9247, 0.09 #5084, 0.09 #3003), 0mj1l (0.22 #308, 0.18 #4470, 0.18 #2389), 04t7ts (0.22 #211, 0.18 #4373, 0.14 #6455), 030xr_ (0.22 #1591, 0.18 #5753, 0.14 #7835), 04gc65 (0.22 #1974, 0.14 #8218, 0.09 #6136), 01wmxfs (0.18 #4292, 0.18 #2211, 0.14 #6374), 01j7z7 (0.18 #5485, 0.18 #3404, 0.14 #7567), 03swmf (0.18 #5732, 0.18 #3651, 0.14 #7814) >> Best rule #118661 for best value: >> intensional similarity = 4 >> extensional distance = 903 >> proper extension: 0gtvrv3; >> query: (?x8633, ?x5014) <- nominated_for(?x5014, ?x8633), film(?x7391, ?x8633), film_crew_role(?x8633, ?x137), award_winner(?x591, ?x7391) >> conf = 0.46 => this is the best rule for 2 predicted values *> Best rule #4244 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01719t; 01k60v; 043mk4y; 0gm2_0; *> query: (?x8633, 0147dk) <- titles(?x53, ?x8633), film(?x6777, ?x8633), ?x6777 = 05nzw6, nominated_for(?x5014, ?x8633) *> conf = 0.09 ranks of expected_values: 62 EVAL 057__d film! 01l9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 57.000 0.460 http://example.org/film/actor/film./film/performance/film EVAL 057__d film! 0147dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 106.000 57.000 0.460 http://example.org/film/actor/film./film/performance/film #16208-017g21 PRED entity: 017g21 PRED relation: artists! PRED expected values: 0dl5d 0cx7f => 150 concepts (80 used for prediction) PRED predicted values (max 10 best out of 266): 06by7 (0.60 #6904, 0.56 #2840, 0.55 #23514), 016clz (0.56 #319, 0.37 #5635, 0.37 #2511), 064t9 (0.55 #23506, 0.49 #5020, 0.49 #4083), 03lty (0.49 #6911, 0.28 #343, 0.22 #8476), 0dl5d (0.38 #20, 0.27 #648, 0.26 #6902), 08jyyk (0.33 #1948, 0.31 #1635, 0.29 #3512), 06j6l (0.30 #23541, 0.25 #22291, 0.25 #19157), 025sc50 (0.28 #2557, 0.26 #5057, 0.25 #6306), 02yv6b (0.26 #6983, 0.24 #2293, 0.23 #101), 05bt6j (0.26 #23536, 0.26 #6926, 0.25 #5674) >> Best rule #6904 for best value: >> intensional similarity = 4 >> extensional distance = 135 >> proper extension: 02t3ln; >> query: (?x7252, 06by7) <- category(?x7252, ?x134), artists(?x1000, ?x7252), ?x134 = 08mbj5d, ?x1000 = 0xhtw >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 032t2z; 01kx_81; 0ftps; 0144l1; 016h9b; 0gcs9; 01tv3x2; 04kjrv; 04mx7s; 01wg6y; ... *> query: (?x7252, 0dl5d) <- role(?x7252, ?x228), instrumentalists(?x1166, ?x7252), artist(?x2149, ?x7252), ?x228 = 0l14qv *> conf = 0.38 ranks of expected_values: 5, 19 EVAL 017g21 artists! 0cx7f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 150.000 80.000 0.599 http://example.org/music/genre/artists EVAL 017g21 artists! 0dl5d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 150.000 80.000 0.599 http://example.org/music/genre/artists #16207-0k8y7 PRED entity: 0k8y7 PRED relation: award PRED expected values: 0gqwc => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 274): 09sb52 (0.34 #23132, 0.33 #12195, 0.33 #16650), 0gqy2 (0.33 #1380, 0.26 #2190, 0.22 #5835), 0f4x7 (0.27 #1246, 0.20 #2056, 0.20 #5701), 05pcn59 (0.27 #2512, 0.18 #4942, 0.17 #7373), 05p09zm (0.25 #2554, 0.17 #124, 0.11 #4579), 0gq9h (0.25 #483, 0.13 #37677, 0.12 #1698), 054ky1 (0.21 #514, 0.18 #28765, 0.17 #21471), 01by1l (0.20 #13886, 0.11 #21177, 0.10 #19556), 03c7tr1 (0.20 #2489, 0.12 #4514, 0.11 #4919), 05b4l5x (0.18 #2436, 0.12 #4461, 0.11 #6892) >> Best rule #23132 for best value: >> intensional similarity = 3 >> extensional distance = 1181 >> proper extension: 02lg9w; 06lgq8; 0f6_dy; 02xb2bt; 050t68; 0308kx; 06lht1; 050_qx; 012g92; >> query: (?x4285, 09sb52) <- film(?x4285, ?x4559), award_nominee(?x8371, ?x4285), type_of_union(?x8371, ?x566) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #4935 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 212 *> proper extension: 0gm34; *> query: (?x4285, 0gqwc) <- film(?x4285, ?x4559), participant(?x4285, ?x3701), award_winner(?x3001, ?x4285) *> conf = 0.18 ranks of expected_values: 19 EVAL 0k8y7 award 0gqwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 120.000 120.000 0.336 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16206-080lkt7 PRED entity: 080lkt7 PRED relation: film_crew_role PRED expected values: 01vx2h => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 34): 09zzb8 (0.80 #1369, 0.74 #5307, 0.73 #343), 02r96rf (0.79 #2251, 0.78 #2099, 0.78 #2405), 09vw2b7 (0.76 #464, 0.72 #2409, 0.70 #2255), 01vx2h (0.54 #1419, 0.44 #2108, 0.42 #2260), 0dxtw (0.38 #1955, 0.38 #5318, 0.38 #392), 015h31 (0.38 #1416, 0.29 #1532, 0.29 #1493), 01pvkk (0.33 #546, 0.30 #698, 0.29 #1459), 01xy5l_ (0.27 #320, 0.26 #700, 0.20 #16), 02vs3x5 (0.27 #330, 0.22 #710, 0.12 #5114), 04pyp5 (0.27 #323, 0.12 #5114, 0.11 #703) >> Best rule #1369 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 02d44q; >> query: (?x4643, 09zzb8) <- titles(?x162, ?x4643), featured_film_locations(?x4643, ?x739), film_release_region(?x4643, ?x87), ?x162 = 04xvlr, film_crew_role(?x4643, ?x1284) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1419 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 48 *> proper extension: 03mh94; 02qm_f; 01c22t; 0j6b5; 0879bpq; 01jrbb; 07nxnw; *> query: (?x4643, 01vx2h) <- music(?x4643, ?x4644), film(?x123, ?x4643), genre(?x4643, ?x2540), ?x2540 = 0hcr, film_crew_role(?x4643, ?x1284) *> conf = 0.54 ranks of expected_values: 4 EVAL 080lkt7 film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 167.000 167.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16205-02hxhz PRED entity: 02hxhz PRED relation: genre PRED expected values: 07s9rl0 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 101): 07s9rl0 (0.63 #4762, 0.62 #2077, 0.62 #4640), 09q17 (0.61 #6847, 0.54 #6846, 0.52 #1832), 01z4y (0.61 #6847, 0.54 #6846, 0.52 #1832), 01jfsb (0.44 #501, 0.35 #1600, 0.34 #2699), 02kdv5l (0.43 #369, 0.38 #247, 0.34 #735), 02l7c8 (0.36 #139, 0.33 #2947, 0.30 #4656), 03k9fj (0.35 #866, 0.34 #1110, 0.32 #988), 0lsxr (0.28 #497, 0.20 #1351, 0.19 #1718), 01hmnh (0.27 #873, 0.26 #1117, 0.25 #629), 06cvj (0.25 #248, 0.21 #126, 0.21 #2934) >> Best rule #4762 for best value: >> intensional similarity = 3 >> extensional distance = 849 >> proper extension: 0cvkv5; >> query: (?x821, 07s9rl0) <- nominated_for(?x541, ?x821), award(?x821, ?x1312), genre(?x821, ?x258) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hxhz genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.629 http://example.org/film/film/genre #16204-04v8x9 PRED entity: 04v8x9 PRED relation: award PRED expected values: 0gs96 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 201): 0l8z1 (0.43 #498, 0.16 #4999, 0.16 #4549), 0gqwc (0.29 #281, 0.16 #4557, 0.16 #2081), 0gr51 (0.29 #296, 0.11 #2546, 0.11 #1871), 02qyntr (0.29 #614, 0.11 #1739, 0.10 #3089), 054krc (0.29 #513, 0.09 #17574, 0.09 #2538), 0gr4k (0.28 #13737, 0.27 #14642, 0.27 #12610), 0gs96 (0.18 #4357, 0.18 #4582, 0.17 #1206), 09tqxt (0.18 #5019, 0.14 #9298, 0.13 #9748), 0gr0m (0.17 #955, 0.16 #4556, 0.15 #3655), 05pcn59 (0.17 #60, 0.05 #22756, 0.03 #5236) >> Best rule #498 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0h1v19; 0jyb4; >> query: (?x499, 0l8z1) <- film_release_region(?x499, ?x304), award(?x499, ?x1703), ?x1703 = 0k611, film(?x382, ?x499) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4357 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 0k4f3; 0fy66; 04vvh9; 024lff; 027ct7c; 0jqd3; 063hp4; *> query: (?x499, 0gs96) <- film_sets_designed(?x10609, ?x499), film(?x382, ?x499), genre(?x499, ?x53), award_winner(?x499, ?x877) *> conf = 0.18 ranks of expected_values: 7 EVAL 04v8x9 award 0gs96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 145.000 145.000 0.429 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #16203-069d71 PRED entity: 069d71 PRED relation: profession PRED expected values: 01445t => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 125): 02hrh1q (0.70 #2265, 0.69 #2115, 0.68 #1815), 0gl2ny2 (0.57 #969, 0.42 #1569, 0.42 #1419), 01445t (0.50 #474, 0.43 #324, 0.27 #624), 018gz8 (0.33 #168, 0.33 #18, 0.10 #2118), 01d_h8 (0.28 #3456, 0.28 #3006, 0.27 #2706), 0dxtg (0.27 #5864, 0.27 #3014, 0.26 #3464), 09jwl (0.22 #1670, 0.19 #4070, 0.18 #5120), 03gjzk (0.19 #5866, 0.18 #3466, 0.18 #1966), 02jknp (0.18 #8109, 0.18 #7209, 0.17 #8259), 0np9r (0.17 #172, 0.17 #22, 0.11 #3472) >> Best rule #2265 for best value: >> intensional similarity = 4 >> extensional distance = 958 >> proper extension: 06sn8m; 02hy9p; 01lct6; 033071; >> query: (?x13333, 02hrh1q) <- location(?x13333, ?x6084), place_of_birth(?x547, ?x6084), teams(?x6084, ?x1639), dog_breed(?x6084, ?x1706) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #474 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 019tzd; *> query: (?x13333, 01445t) <- athlete(?x1557, ?x13333), olympics(?x1557, ?x2748), ?x2748 = 0c_tl, country(?x1557, ?x608), film_release_region(?x141, ?x608) *> conf = 0.50 ranks of expected_values: 3 EVAL 069d71 profession 01445t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 68.000 0.697 http://example.org/people/person/profession #16202-01chpn PRED entity: 01chpn PRED relation: genre PRED expected values: 01t_vv => 90 concepts (60 used for prediction) PRED predicted values (max 10 best out of 94): 0gf28 (0.40 #529, 0.38 #412, 0.29 #178), 03k9fj (0.39 #594, 0.25 #1413, 0.25 #2584), 01t_vv (0.38 #168, 0.21 #402, 0.19 #519), 02l7c8 (0.37 #833, 0.32 #3645, 0.32 #4114), 01jfsb (0.33 #9, 0.33 #5871, 0.31 #1063), 03bxz7 (0.33 #52, 0.21 #872, 0.18 #286), 03mqtr (0.33 #26, 0.13 #729, 0.10 #143), 01f9r0 (0.33 #72, 0.06 #775, 0.05 #306), 02kdv5l (0.33 #5864, 0.29 #1993, 0.28 #1524), 060__y (0.29 #2473, 0.29 #2356, 0.26 #717) >> Best rule #529 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 01lbcqx; >> query: (?x6288, 0gf28) <- genre(?x6288, ?x2700), ?x2700 = 06nbt, film(?x91, ?x6288) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #168 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 08cfr1; 03p2xc; 0jqb8; 03wy8t; *> query: (?x6288, 01t_vv) <- genre(?x6288, ?x2700), genre(?x6288, ?x53), ?x2700 = 06nbt, film(?x91, ?x6288), ?x53 = 07s9rl0 *> conf = 0.38 ranks of expected_values: 3 EVAL 01chpn genre 01t_vv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 60.000 0.395 http://example.org/film/film/genre #16201-02g_7z PRED entity: 02g_7z PRED relation: team PRED expected values: 025v1sx => 33 concepts (24 used for prediction) PRED predicted values (max 10 best out of 988): 0289q (0.86 #5472, 0.84 #11863, 0.83 #7300), 051q5 (0.86 #5472, 0.84 #11863, 0.83 #7300), 05tg3 (0.86 #5472, 0.84 #11863, 0.83 #7300), 01ct6 (0.86 #5472, 0.84 #11863, 0.83 #7300), 025v26c (0.86 #5472, 0.83 #7300, 0.83 #3650), 0fgg8c (0.86 #5472, 0.83 #7300, 0.83 #3650), 02vklm3 (0.86 #5472, 0.83 #7300, 0.83 #3650), 086hg9 (0.86 #5472, 0.83 #7300, 0.82 #1823), 025v1sx (0.84 #11863, 0.83 #3650, 0.83 #10952), 02wvfxz (0.78 #18261, 0.69 #21915, 0.67 #21914) >> Best rule #5472 for best value: >> intensional similarity = 34 >> extensional distance = 1 >> proper extension: 047g8h; >> query: (?x3346, ?x684) <- team(?x3346, ?x9115), team(?x3346, ?x6696), team(?x3346, ?x6645), team(?x3346, ?x5773), team(?x3346, ?x5229), team(?x3346, ?x4723), team(?x3346, ?x4519), team(?x3346, ?x4494), team(?x3346, ?x4469), team(?x3346, ?x3658), team(?x3346, ?x3347), team(?x3346, ?x3114), team(?x3346, ?x2114), team(?x3346, ?x1516), ?x6696 = 0fjzsy, ?x3658 = 03b3j, position(?x3346, ?x1717), ?x1516 = 0ft5vs, position(?x4222, ?x3346), position(?x1718, ?x3346), position(?x684, ?x3346), ?x4222 = 051q5, ?x1718 = 0fgg8c, ?x3114 = 070xg, ?x4469 = 043vc, ?x4519 = 084l5, ?x2114 = 01y49, ?x9115 = 0g0z58, ?x5773 = 06rny, ?x4494 = 057xkj_, ?x3347 = 03gqb0k, ?x6645 = 0wsr, ?x4723 = 043tz8m, position(?x5229, ?x180) >> conf = 0.86 => this is the best rule for 8 predicted values *> Best rule #11863 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 5 *> proper extension: 0b13yt; *> query: (?x3346, ?x6570) <- team(?x3346, ?x11061), team(?x3346, ?x7892), team(?x3346, ?x7078), team(?x3346, ?x6696), team(?x3346, ?x6645), team(?x3346, ?x5822), team(?x3346, ?x5773), team(?x3346, ?x4519), team(?x3346, ?x2574), team(?x3346, ?x1576), ?x7078 = 0ws7, category(?x6696, ?x134), position(?x7892, ?x2147), ?x1576 = 05tfm, ?x2574 = 01y3v, position_s(?x6570, ?x3346), school(?x5773, ?x2171), ?x4519 = 084l5, ?x2147 = 04nfpk, team(?x5412, ?x5773), ?x2171 = 01jq34, draft(?x5773, ?x685), position_s(?x5773, ?x3113), ?x6645 = 0wsr, colors(?x6570, ?x663), ?x11061 = 06x76, ?x5822 = 03wnh *> conf = 0.84 ranks of expected_values: 9 EVAL 02g_7z team 025v1sx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 33.000 24.000 0.860 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #16200-03rjj PRED entity: 03rjj PRED relation: contains! PRED expected values: 02j9z => 225 concepts (170 used for prediction) PRED predicted values (max 10 best out of 271): 09c7w0 (0.50 #6275, 0.48 #141604, 0.47 #92307), 02j9z (0.42 #33184, 0.38 #14364, 0.34 #91437), 04_1l0v (0.40 #92754, 0.34 #105303, 0.34 #125917), 02j71 (0.37 #51978, 0.27 #75277, 0.04 #101270), 0j0k (0.37 #74758, 0.30 #66692, 0.28 #60416), 07ssc (0.34 #129051, 0.24 #106676, 0.23 #22431), 02jx1 (0.34 #129051, 0.16 #106731, 0.14 #123672), 03rjj (0.33 #4492, 0.33 #3597, 0.33 #2701), 04swx (0.33 #2556, 0.33 #762, 0.25 #3587), 0dg3n1 (0.29 #143549, 0.29 #91564, 0.28 #117554) >> Best rule #6275 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 0r2kh; >> query: (?x205, 09c7w0) <- location(?x7555, ?x205), ?x7555 = 01kmd4 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #33184 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0fq8f; *> query: (?x205, 02j9z) <- film_release_region(?x5827, ?x205), film_release_region(?x1463, ?x205), film_crew_role(?x5827, ?x137), ?x1463 = 0gtvrv3 *> conf = 0.42 ranks of expected_values: 2 EVAL 03rjj contains! 02j9z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 225.000 170.000 0.500 http://example.org/location/location/contains #16199-01kp_1t PRED entity: 01kp_1t PRED relation: award_nominee PRED expected values: 086qd => 121 concepts (34 used for prediction) PRED predicted values (max 10 best out of 805): 01wgxtl (0.25 #607, 0.08 #7637, 0.04 #14666), 01vw20h (0.12 #1060, 0.10 #8090, 0.08 #15119), 02l840 (0.12 #160, 0.10 #7190, 0.08 #21250), 0677ng (0.12 #1651, 0.08 #8681, 0.03 #15710), 026yqrr (0.12 #1461, 0.07 #8491, 0.04 #15520), 03j3pg9 (0.12 #2096, 0.07 #9126, 0.04 #16155), 03f19q4 (0.12 #1234, 0.07 #8264, 0.03 #15293), 01q32bd (0.12 #847, 0.07 #7877, 0.03 #17249), 0837ql (0.12 #1145, 0.07 #8175, 0.03 #33952), 016kjs (0.12 #230, 0.05 #7260, 0.04 #16632) >> Best rule #607 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01wwvc5; 0gbwp; 09889g; 05szp; 0ffgh; 02h9_l; >> query: (?x9528, 01wgxtl) <- artists(?x1952, ?x9528), type_of_union(?x9528, ?x566), artist(?x2299, ?x9528), ?x1952 = 021_z5 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #14516 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 120 *> proper extension: 04gycf; *> query: (?x9528, 086qd) <- artists(?x671, ?x9528), type_of_union(?x9528, ?x566), award_nominee(?x9528, ?x11621), ?x671 = 064t9 *> conf = 0.02 ranks of expected_values: 364 EVAL 01kp_1t award_nominee 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 121.000 34.000 0.250 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16198-07xvf PRED entity: 07xvf PRED relation: nominated_for! PRED expected values: 0p9sw => 101 concepts (85 used for prediction) PRED predicted values (max 10 best out of 178): 0p9sw (0.45 #1941, 0.43 #2181, 0.32 #261), 0gq9h (0.30 #1984, 0.29 #4625, 0.29 #3905), 0gs9p (0.30 #3907, 0.29 #4627, 0.26 #12787), 0gq_v (0.29 #1940, 0.29 #2180, 0.22 #2401), 0k611 (0.28 #1995, 0.28 #2235, 0.28 #315), 02r22gf (0.27 #2189, 0.27 #1949, 0.22 #2401), 019f4v (0.27 #3896, 0.26 #4616, 0.26 #2215), 0gr0m (0.26 #1981, 0.25 #2221, 0.22 #2401), 02hsq3m (0.25 #30, 0.24 #2190, 0.24 #1950), 0gr42 (0.25 #91, 0.22 #2011, 0.22 #2251) >> Best rule #1941 for best value: >> intensional similarity = 3 >> extensional distance = 185 >> proper extension: 080dwhx; 07gbf; >> query: (?x7373, 0p9sw) <- nominated_for(?x9391, ?x7373), award_nominee(?x3879, ?x9391), crewmember(?x392, ?x9391) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07xvf nominated_for! 0p9sw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 85.000 0.449 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16197-02ldmw PRED entity: 02ldmw PRED relation: student PRED expected values: 02__ww => 136 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1211): 01tnbn (0.33 #1056, 0.12 #3141, 0.04 #5227), 03p9hl (0.33 #2072, 0.02 #6243), 01vh3r (0.33 #1956), 018_lb (0.33 #1863), 03rgvr (0.33 #1779), 03xk1_ (0.33 #1745), 02tk74 (0.33 #1673), 02fz3w (0.33 #1583), 01k47c (0.33 #1563), 0mb5x (0.33 #1450) >> Best rule #1056 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02l9wl; >> query: (?x7744, 01tnbn) <- student(?x7744, ?x9924), student(?x7744, ?x5915), ?x9924 = 03yk8z, film(?x5915, ?x1642) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02ldmw student 02__ww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 73.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #16196-02y_2y PRED entity: 02y_2y PRED relation: film PRED expected values: 0gzlb9 => 114 concepts (64 used for prediction) PRED predicted values (max 10 best out of 676): 01xr2s (0.59 #105401, 0.59 #108975, 0.59 #108974), 0180mw (0.59 #105401, 0.59 #108975, 0.59 #108974), 0gzlb9 (0.59 #105401, 0.59 #108975, 0.59 #108974), 03mh94 (0.11 #64, 0.07 #1850, 0.06 #3636), 06lpmt (0.07 #684, 0.04 #2470, 0.03 #4256), 0f4k49 (0.07 #822, 0.04 #2608, 0.03 #4394), 0h03fhx (0.07 #778, 0.04 #2564, 0.03 #4350), 06nr2h (0.07 #734, 0.04 #2520, 0.03 #4306), 01f39b (0.07 #977, 0.03 #4549, 0.02 #2763), 0k4p0 (0.07 #985, 0.03 #4557, 0.02 #2771) >> Best rule #105401 for best value: >> intensional similarity = 3 >> extensional distance = 1315 >> proper extension: 016qtt; 05ty4m; 05cj4r; 0436f4; 01rr9f; 03f2_rc; 01gvr1; 01mvth; 03qd_; 05ml_s; ... >> query: (?x4470, ?x2042) <- nominated_for(?x4470, ?x2042), award(?x4470, ?x102), film(?x4470, ?x1644) >> conf = 0.59 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 02y_2y film 0gzlb9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 64.000 0.589 http://example.org/film/actor/film./film/performance/film #16195-03flwk PRED entity: 03flwk PRED relation: profession PRED expected values: 02jknp => 103 concepts (72 used for prediction) PRED predicted values (max 10 best out of 55): 02jknp (0.68 #1175, 0.62 #153, 0.48 #2928), 03gjzk (0.50 #451, 0.48 #7461, 0.47 #4540), 09jwl (0.42 #2062, 0.39 #2354, 0.18 #1039), 05sxg2 (0.40 #439, 0.38 #585, 0.37 #731), 0dz3r (0.37 #2339, 0.29 #2047, 0.17 #586), 016z4k (0.35 #2341, 0.27 #2049, 0.10 #442), 01c72t (0.34 #1044, 0.27 #2067, 0.15 #898), 0nbcg (0.32 #2367, 0.31 #2075, 0.23 #1052), 02hv44_ (0.29 #786, 0.29 #56, 0.24 #932), 0cbd2 (0.29 #6, 0.24 #1174, 0.21 #2781) >> Best rule #1175 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 0klw; 033rq; >> query: (?x5100, 02jknp) <- profession(?x5100, ?x319), award(?x5100, ?x1862), ?x1862 = 0gr51 >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03flwk profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 72.000 0.682 http://example.org/people/person/profession #16194-02qlp4 PRED entity: 02qlp4 PRED relation: film! PRED expected values: 044lyq => 67 concepts (38 used for prediction) PRED predicted values (max 10 best out of 491): 024rgt (0.42 #64446, 0.37 #60287, 0.36 #16630), 0bxtyq (0.36 #24946, 0.36 #31184, 0.34 #45736), 01pgzn_ (0.33 #383, 0.25 #2461, 0.04 #62366), 03q45x (0.33 #1351, 0.25 #3429), 044rvb (0.25 #2180, 0.02 #8415, 0.02 #16733), 04fzk (0.25 #2785, 0.01 #9020, 0.01 #6941), 01gkmx (0.25 #3661, 0.01 #61870, 0.01 #68107), 0161h5 (0.25 #3901), 01l_yg (0.25 #3733), 01ccr8 (0.25 #3541) >> Best rule #64446 for best value: >> intensional similarity = 3 >> extensional distance = 1124 >> proper extension: 01h72l; 0cp08zg; >> query: (?x10902, ?x2549) <- genre(?x10902, ?x811), titles(?x8581, ?x10902), nominated_for(?x2549, ?x10902) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02qlp4 film! 044lyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 38.000 0.421 http://example.org/film/actor/film./film/performance/film #16193-01v15f PRED entity: 01v15f PRED relation: contains! PRED expected values: 06bnz => 80 concepts (68 used for prediction) PRED predicted values (max 10 best out of 220): 09c7w0 (0.70 #21508, 0.67 #22406, 0.67 #27784), 02qkt (0.40 #42468, 0.39 #43366, 0.37 #6619), 07c5l (0.33 #395, 0.25 #1291, 0.19 #14732), 06n3y (0.33 #726, 0.25 #1622, 0.14 #2518), 07ssc (0.32 #13473, 0.30 #15265, 0.29 #16161), 03rjj (0.29 #1802, 0.16 #5386, 0.15 #11659), 03spz (0.25 #1182, 0.07 #52880, 0.07 #54678), 02jx1 (0.21 #23387, 0.19 #33247, 0.19 #13528), 01n7q (0.21 #46683, 0.19 #31445, 0.18 #56558), 02j9z (0.20 #43047, 0.19 #42149, 0.14 #12573) >> Best rule #21508 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 04ykg; 0c_m3; 0b2lw; 0ggh3; 04gxf; >> query: (?x13748, 09c7w0) <- teams(?x13748, ?x12438), category(?x13748, ?x134), ?x134 = 08mbj5d, team(?x60, ?x12438), jurisdiction_of_office(?x1195, ?x13748) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2793 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 9 *> proper extension: 03hrz; 0gyvgw; *> query: (?x13748, 06bnz) <- teams(?x13748, ?x12438), colors(?x12438, ?x3189), team(?x63, ?x12438), current_club(?x7464, ?x12438), ?x63 = 02sdk9v, position(?x12438, ?x530), ?x3189 = 01g5v *> conf = 0.18 ranks of expected_values: 12 EVAL 01v15f contains! 06bnz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 80.000 68.000 0.698 http://example.org/location/location/contains #16192-04lqvly PRED entity: 04lqvly PRED relation: genre PRED expected values: 0hcr => 90 concepts (66 used for prediction) PRED predicted values (max 10 best out of 156): 0hcr (0.85 #259, 0.75 #25, 0.47 #2021), 05p553 (0.75 #4, 0.70 #238, 0.35 #2117), 03k9fj (0.73 #246, 0.62 #12, 0.36 #2008), 02l7c8 (0.72 #2956, 0.38 #3309, 0.34 #1190), 03mqtr (0.62 #7295, 0.61 #7650, 0.61 #2820), 03spz (0.58 #2819, 0.57 #939, 0.56 #5764), 02kdv5l (0.48 #2232, 0.36 #1762, 0.32 #704), 01zhp (0.45 #310, 0.31 #76, 0.13 #2072), 01jfsb (0.42 #1773, 0.42 #2243, 0.33 #2361), 01hmnh (0.39 #253, 0.38 #19, 0.25 #2249) >> Best rule #259 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0b3n61; 0cmf0m0; >> query: (?x3965, 0hcr) <- nominated_for(?x1723, ?x3965), film(?x5959, ?x3965), film_crew_role(?x3965, ?x468), ?x1723 = 09tqxt >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04lqvly genre 0hcr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 66.000 0.848 http://example.org/film/film/genre #16191-012wg PRED entity: 012wg PRED relation: student! PRED expected values: 03ksy => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 211): 0bwfn (0.23 #30784, 0.11 #1852, 0.09 #13424), 01qd_r (0.22 #806, 0.11 #1858, 0.03 #16061), 01cyd5 (0.20 #53), 01w5m (0.16 #2208, 0.15 #3786, 0.11 #9046), 07tgn (0.15 #3699, 0.10 #4751, 0.08 #7907), 02g839 (0.11 #551, 0.05 #1603, 0.04 #30535), 017rbx (0.11 #867, 0.05 #1919, 0.04 #7179), 0yjf0 (0.11 #574, 0.05 #1626, 0.03 #2678), 01722w (0.11 #830, 0.05 #1882, 0.02 #5564), 01ymvk (0.11 #646, 0.05 #1698, 0.02 #5906) >> Best rule #30784 for best value: >> intensional similarity = 3 >> extensional distance = 519 >> proper extension: 06688p; 06y9c2; 012_53; 03gkn5; 01v3bn; 01s7qqw; 027dpx; 01_k1z; 0k1bs; 046chh; ... >> query: (?x4505, 0bwfn) <- profession(?x4505, ?x2348), student(?x2909, ?x4505), currency(?x2909, ?x170) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #2209 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0yfp; 08433; 03ft8; 098n5; 0fx02; 0klw; 06n9lt; 056wb; 096hm; 0hcvy; ... *> query: (?x4505, 03ksy) <- written_by(?x4504, ?x4505), story_by(?x4513, ?x4505), people(?x4659, ?x4505) *> conf = 0.11 ranks of expected_values: 12 EVAL 012wg student! 03ksy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 125.000 125.000 0.228 http://example.org/education/educational_institution/students_graduates./education/education/student #16190-0f102 PRED entity: 0f102 PRED relation: major_field_of_study PRED expected values: 062z7 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 119): 02h40lc (0.67 #956, 0.33 #361, 0.26 #837), 02lp1 (0.63 #368, 0.55 #3464, 0.44 #1916), 062z7 (0.63 #383, 0.45 #3717, 0.38 #1812), 02j62 (0.59 #386, 0.56 #981, 0.56 #4315), 05qfh (0.52 #392, 0.34 #1940, 0.32 #1106), 05qjt (0.52 #364, 0.32 #1912, 0.29 #1078), 03g3w (0.47 #1930, 0.37 #382, 0.34 #1334), 0g26h (0.47 #3494, 0.39 #2184, 0.39 #1112), 04rjg (0.44 #376, 0.44 #1924, 0.40 #138), 01540 (0.44 #416, 0.29 #1011, 0.27 #1130) >> Best rule #956 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 01k2wn; 0dplh; 01q460; 0pspl; 027kp3; 02hmw9; 01jvxb; 0677j; 0vkl2; 014d4v; ... >> query: (?x2682, 02h40lc) <- major_field_of_study(?x2682, ?x732), contains(?x94, ?x2682), organization(?x346, ?x2682), languages(?x147, ?x732) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #383 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 08815; 049dk; 02zd460; *> query: (?x2682, 062z7) <- fraternities_and_sororities(?x2682, ?x10424), major_field_of_study(?x2682, ?x2601), school_type(?x2682, ?x1507), ?x2601 = 04x_3 *> conf = 0.63 ranks of expected_values: 3 EVAL 0f102 major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 153.000 153.000 0.673 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16189-07fb6 PRED entity: 07fb6 PRED relation: country! PRED expected values: 06z6r => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 56): 06z6r (0.82 #489, 0.82 #546, 0.81 #375), 03_8r (0.75 #479, 0.71 #365, 0.71 #707), 01cgz (0.70 #71, 0.67 #698, 0.66 #812), 071t0 (0.65 #708, 0.65 #822, 0.64 #537), 01lb14 (0.53 #358, 0.53 #814, 0.52 #700), 03hr1p (0.51 #367, 0.50 #709, 0.50 #823), 0194d (0.50 #107, 0.38 #392, 0.37 #848), 07gyv (0.49 #520, 0.48 #349, 0.45 #691), 07jbh (0.49 #378, 0.46 #549, 0.44 #720), 06f41 (0.48 #813, 0.48 #699, 0.48 #357) >> Best rule #489 for best value: >> intensional similarity = 3 >> extensional distance = 86 >> proper extension: 04gzd; 019rg5; 03gj2; 05cgv; 01znc_; 02vzc; 03rj0; 06t2t; 03h64; 0697s; ... >> query: (?x8378, 06z6r) <- currency(?x8378, ?x170), administrative_parent(?x8378, ?x551), medal(?x8378, ?x1242) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07fb6 country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #16188-01_njt PRED entity: 01_njt PRED relation: people! PRED expected values: 01xhh5 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 33): 041rx (0.15 #389, 0.14 #466, 0.14 #312), 02w7gg (0.14 #156, 0.10 #79, 0.07 #1773), 0x67 (0.11 #241, 0.10 #934, 0.10 #549), 033tf_ (0.11 #238, 0.10 #84, 0.10 #7), 0xnvg (0.08 #244, 0.07 #552, 0.06 #1168), 048z7l (0.05 #656, 0.04 #117, 0.04 #425), 09vc4s (0.04 #86, 0.04 #240, 0.03 #548), 07bch9 (0.04 #100, 0.04 #1255, 0.04 #1486), 065b6q (0.04 #80, 0.03 #157, 0.03 #465), 07hwkr (0.04 #2091, 0.04 #320, 0.03 #628) >> Best rule #389 for best value: >> intensional similarity = 2 >> extensional distance = 251 >> proper extension: 017c87; >> query: (?x8167, 041rx) <- spouse(?x8167, ?x8070), award_winner(?x995, ?x8167) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01_njt people! 01xhh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 103.000 0.146 http://example.org/people/ethnicity/people #16187-0f2rq PRED entity: 0f2rq PRED relation: place_of_birth! PRED expected values: 0c7xjb => 210 concepts (193 used for prediction) PRED predicted values (max 10 best out of 2112): 030hcs (0.38 #59835, 0.33 #483893, 0.33 #442267), 0br1w (0.38 #59835, 0.33 #483893, 0.33 #442267), 030vnj (0.38 #59835, 0.33 #483893, 0.33 #442267), 01vxqyl (0.38 #59835, 0.33 #483893, 0.33 #442267), 06jzh (0.38 #59835, 0.33 #483893, 0.33 #442267), 033wx9 (0.33 #18211, 0.27 #166498, 0.26 #374624), 02wb6yq (0.33 #18211, 0.27 #166498, 0.26 #374624), 018ndc (0.33 #18211, 0.27 #166498, 0.26 #374624), 01vyv9 (0.12 #10405, 0.09 #202921, 0.06 #306981), 0d3k14 (0.12 #10405, 0.06 #306981, 0.06 #189912) >> Best rule #59835 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0dq16; >> query: (?x5719, ?x400) <- locations(?x3797, ?x5719), location(?x400, ?x5719), county_seat(?x11836, ?x5719) >> conf = 0.38 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 0f2rq place_of_birth! 0c7xjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 210.000 193.000 0.385 http://example.org/people/person/place_of_birth #16186-01bb9r PRED entity: 01bb9r PRED relation: production_companies PRED expected values: 046b0s => 72 concepts (56 used for prediction) PRED predicted values (max 10 best out of 95): 05qd_ (0.13 #173, 0.12 #1409, 0.09 #1739), 016tt2 (0.12 #167, 0.09 #1403, 0.08 #250), 01gb54 (0.11 #201, 0.07 #696, 0.07 #284), 016tw3 (0.11 #11, 0.10 #93, 0.09 #175), 054lpb6 (0.11 #14, 0.08 #96, 0.06 #3579), 017s11 (0.08 #166, 0.07 #1402, 0.06 #1238), 0g1rw (0.07 #89, 0.06 #7, 0.05 #1407), 0kx4m (0.06 #172, 0.04 #255, 0.04 #338), 030_1m (0.06 #179, 0.04 #262, 0.04 #345), 020h2v (0.06 #59, 0.04 #223, 0.03 #389) >> Best rule #173 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 0m313; 011yxg; 0ds3t5x; 0dnvn3; 0ds33; 0dqytn; 0jzw; 0pv2t; 06_wqk4; 0kv2hv; ... >> query: (?x2955, 05qd_) <- award_winner(?x2955, ?x286), honored_for(?x2955, ?x9258), production_companies(?x2955, ?x382) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #187 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 0m313; 011yxg; 0ds3t5x; 0dnvn3; 0ds33; 0dqytn; 0jzw; 0pv2t; 06_wqk4; 0kv2hv; ... *> query: (?x2955, 046b0s) <- award_winner(?x2955, ?x286), honored_for(?x2955, ?x9258), production_companies(?x2955, ?x382) *> conf = 0.04 ranks of expected_values: 19 EVAL 01bb9r production_companies 046b0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 72.000 56.000 0.135 http://example.org/film/film/production_companies #16185-08952r PRED entity: 08952r PRED relation: film! PRED expected values: 02lkcc 07m77x => 92 concepts (62 used for prediction) PRED predicted values (max 10 best out of 871): 0mdqp (0.33 #119, 0.04 #35351, 0.04 #8409), 02lhm2 (0.33 #962, 0.03 #68387, 0.03 #82896), 018ygt (0.33 #1114, 0.03 #68387, 0.03 #82896), 022s1m (0.33 #2009, 0.02 #8226, 0.02 #12371), 01j7z7 (0.33 #1320, 0.02 #7537, 0.02 #9610), 02lj6p (0.33 #1489, 0.02 #7706, 0.02 #13923), 016_mj (0.33 #295, 0.02 #25165, 0.02 #31382), 020ffd (0.33 #1083, 0.02 #36315, 0.01 #25953), 016tbr (0.33 #1737, 0.01 #12099), 0c33pl (0.33 #1381, 0.01 #36613) >> Best rule #119 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0b3n61; >> query: (?x4304, 0mdqp) <- language(?x4304, ?x254), film(?x8311, ?x4304), film(?x2790, ?x4304), artists(?x1000, ?x8311), ?x2790 = 0c9c0 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5681 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02825cv; *> query: (?x4304, 07m77x) <- language(?x4304, ?x254), category(?x4304, ?x134), film(?x2353, ?x4304), ?x2353 = 02qgyv *> conf = 0.12 ranks of expected_values: 64, 636 EVAL 08952r film! 07m77x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 92.000 62.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 08952r film! 02lkcc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 92.000 62.000 0.333 http://example.org/film/actor/film./film/performance/film #16184-06nnj PRED entity: 06nnj PRED relation: country! PRED expected values: 0bynt => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 56): 0bynt (0.86 #1523, 0.86 #1467, 0.85 #515), 03_8r (0.76 #696, 0.73 #976, 0.73 #640), 071t0 (0.72 #921, 0.71 #1145, 0.71 #809), 01cgz (0.67 #631, 0.67 #687, 0.66 #743), 01lb14 (0.57 #689, 0.54 #801, 0.54 #913), 06f41 (0.56 #688, 0.55 #912, 0.50 #1136), 03hr1p (0.56 #698, 0.54 #922, 0.51 #586), 07gyv (0.54 #679, 0.51 #791, 0.48 #567), 07jbh (0.51 #708, 0.49 #932, 0.48 #820), 06wrt (0.49 #690, 0.49 #914, 0.48 #578) >> Best rule #1523 for best value: >> intensional similarity = 4 >> extensional distance = 126 >> proper extension: 047yc; 0d0kn; 05sb1; 03h64; 04w8f; 07dzf; 04vs9; >> query: (?x9051, 0bynt) <- organization(?x9051, ?x127), country(?x4045, ?x9051), member_states(?x7695, ?x9051), ?x7695 = 085h1 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06nnj country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.859 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #16183-02yl42 PRED entity: 02yl42 PRED relation: profession PRED expected values: 0kyk => 125 concepts (116 used for prediction) PRED predicted values (max 10 best out of 90): 02hrh1q (0.79 #15515, 0.76 #16112, 0.70 #15963), 0dxtg (0.71 #10447, 0.70 #11192, 0.51 #2099), 0kyk (0.67 #1222, 0.64 #1669, 0.61 #1818), 01d_h8 (0.52 #12228, 0.41 #2092, 0.38 #10440), 09jwl (0.41 #5385, 0.39 #7173, 0.37 #10006), 018gz8 (0.36 #911, 0.33 #2103, 0.23 #3893), 03gjzk (0.36 #909, 0.32 #2101, 0.31 #3429), 02jknp (0.35 #12229, 0.32 #10441, 0.31 #11186), 016z4k (0.31 #3429, 0.30 #5370, 0.28 #7158), 0nbcg (0.31 #3429, 0.28 #7186, 0.27 #5398) >> Best rule #15515 for best value: >> intensional similarity = 4 >> extensional distance = 2837 >> proper extension: 06v8s0; 07lmxq; 018dnt; 01pr_j6; 01yh3y; 01c59k; 0g51l1; 04xjp; 07sgfsl; 01m65sp; ... >> query: (?x3663, 02hrh1q) <- nationality(?x3663, ?x94), profession(?x3663, ?x353), profession(?x1278, ?x353), ?x1278 = 016hvl >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1222 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 048_p; 05x8n; *> query: (?x3663, 0kyk) <- award(?x3663, ?x1288), ?x1288 = 02662b, location(?x3663, ?x335) *> conf = 0.67 ranks of expected_values: 3 EVAL 02yl42 profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 116.000 0.793 http://example.org/people/person/profession #16182-05pyrb PRED entity: 05pyrb PRED relation: film! PRED expected values: 01kwh5j => 123 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1246): 01vs8ng (0.33 #2055, 0.25 #6224, 0.20 #10394), 02v92l (0.33 #1668, 0.25 #5837, 0.20 #10007), 01nsyf (0.33 #1850, 0.25 #6019, 0.20 #10189), 0ywqc (0.33 #3877, 0.22 #16387, 0.13 #30979), 01wbg84 (0.33 #2132, 0.11 #14642, 0.08 #47999), 03pmzt (0.33 #2583, 0.11 #15093, 0.07 #29685), 016ks_ (0.33 #2872, 0.11 #15382, 0.07 #29974), 018ygt (0.33 #3205, 0.11 #15715, 0.07 #30307), 032zg9 (0.33 #2920, 0.11 #15430, 0.07 #30022), 022s1m (0.33 #4105, 0.11 #16615, 0.07 #31207) >> Best rule #2055 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 02gs6r; >> query: (?x5732, 01vs8ng) <- film_release_distribution_medium(?x5732, ?x81), genre(?x5732, ?x2540), currency(?x5732, ?x170), actor(?x5732, ?x3788), ?x2540 = 0hcr, film_release_region(?x5732, ?x252), ?x252 = 03_3d >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05pyrb film! 01kwh5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 73.000 0.333 http://example.org/film/actor/film./film/performance/film #16181-0373qg PRED entity: 0373qg PRED relation: category PRED expected values: 08mbj5d => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #17, 0.90 #31, 0.90 #30) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 228 >> proper extension: 03pmfw; >> query: (?x3199, 08mbj5d) <- citytown(?x3199, ?x3198), organization(?x346, ?x3199), ?x346 = 060c4 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0373qg category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.900 http://example.org/common/topic/webpage./common/webpage/category #16180-0161c2 PRED entity: 0161c2 PRED relation: vacationer! PRED expected values: 04jpl => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 98): 05qtj (0.28 #2180, 0.14 #815, 0.11 #71), 03gh4 (0.15 #700, 0.15 #2189, 0.11 #1321), 0cv3w (0.15 #2166, 0.07 #1546, 0.07 #2414), 04jpl (0.11 #9, 0.07 #2118, 0.07 #1374), 0162v (0.11 #42, 0.06 #290, 0.06 #414), 02fzs (0.11 #123, 0.06 #371, 0.06 #495), 0fhsz (0.11 #118, 0.06 #490, 0.03 #1235), 07751 (0.11 #60, 0.06 #432, 0.03 #1177), 06c62 (0.10 #830, 0.08 #2195, 0.06 #1327), 0b90_r (0.09 #2112, 0.05 #2484, 0.05 #623) >> Best rule #2180 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 01xyt7; >> query: (?x3126, 05qtj) <- vacationer(?x3501, ?x3126), participant(?x3126, ?x1206), type_of_union(?x3126, ?x566), award_winner(?x2855, ?x3126) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 018y2s; 01w02sy; 03bnv; 03y82t6; 01vswwx; 044mfr; 04kjrv; *> query: (?x3126, 04jpl) <- profession(?x3126, ?x2659), religion(?x3126, ?x1985), instrumentalists(?x227, ?x3126), ?x2659 = 039v1, participant(?x1206, ?x3126) *> conf = 0.11 ranks of expected_values: 4 EVAL 0161c2 vacationer! 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 127.000 127.000 0.284 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #16179-02mjmr PRED entity: 02mjmr PRED relation: location PRED expected values: 02hrh0_ => 124 concepts (122 used for prediction) PRED predicted values (max 10 best out of 341): 02hrh0_ (0.52 #56856, 0.48 #34443, 0.47 #82470), 013n2h (0.40 #1204, 0.25 #4407, 0.22 #5209), 030qb3t (0.33 #32122, 0.27 #36124, 0.24 #39325), 0tl6d (0.33 #378, 0.02 #86474), 0qt85 (0.20 #2216, 0.12 #4618, 0.12 #3017), 0ftvg (0.20 #2112, 0.12 #4514, 0.12 #2913), 0hyxv (0.20 #1008, 0.12 #3411, 0.11 #5013), 0rd6b (0.20 #1326, 0.12 #4529, 0.11 #5331), 050ks (0.20 #1136, 0.12 #4339, 0.11 #5141), 05jbn (0.12 #4253, 0.12 #2652, 0.11 #5055) >> Best rule #56856 for best value: >> intensional similarity = 3 >> extensional distance = 394 >> proper extension: 033jj1; >> query: (?x2669, ?x5193) <- profession(?x2669, ?x353), place_of_birth(?x2669, ?x5193), participant(?x286, ?x2669) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02mjmr location 02hrh0_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 122.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location #16178-0683n PRED entity: 0683n PRED relation: people! PRED expected values: 041rx => 152 concepts (131 used for prediction) PRED predicted values (max 10 best out of 50): 041rx (0.25 #81, 0.23 #1930, 0.22 #4), 07hwkr (0.19 #243, 0.13 #320, 0.10 #474), 013b6_ (0.17 #207, 0.11 #53, 0.08 #1079), 0x67 (0.17 #5017, 0.16 #1397, 0.16 #4709), 013xrm (0.11 #20, 0.10 #1021, 0.10 #1253), 019lrz (0.11 #38), 0g6ff (0.10 #252, 0.08 #1079, 0.06 #714), 063k3h (0.10 #262, 0.08 #1079, 0.05 #493), 048z7l (0.08 #117, 0.07 #348, 0.05 #1966), 0xnvg (0.08 #90, 0.06 #2555, 0.05 #1554) >> Best rule #81 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 03qcq; >> query: (?x8389, 041rx) <- influenced_by(?x8389, ?x118), type_of_union(?x8389, ?x566), gender(?x8389, ?x231), ?x118 = 084w8 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0683n people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 131.000 0.250 http://example.org/people/ethnicity/people #16177-042ly5 PRED entity: 042ly5 PRED relation: award_nominee PRED expected values: 0f502 => 89 concepts (30 used for prediction) PRED predicted values (max 10 best out of 871): 0gx_p (0.80 #70047, 0.80 #70046, 0.80 #14010), 03knl (0.80 #70047, 0.80 #70046, 0.80 #14010), 0kszw (0.24 #65376, 0.20 #545, 0.17 #49033), 09yhzs (0.24 #65376, 0.17 #49033, 0.15 #67711), 02qgyv (0.24 #65376, 0.17 #49033, 0.15 #67711), 06dv3 (0.24 #65376, 0.17 #49033, 0.15 #67711), 03zz8b (0.24 #65376, 0.17 #49033, 0.15 #67711), 011zd3 (0.24 #65376, 0.17 #49033, 0.15 #67711), 0686zv (0.24 #65376, 0.17 #49033, 0.15 #67711), 030xr_ (0.24 #65376, 0.17 #49033, 0.15 #67711) >> Best rule #70047 for best value: >> intensional similarity = 2 >> extensional distance = 1292 >> proper extension: 01q415; 02z6l5f; 01lvzbl; 01wj5hp; 024qwq; 06vqdf; >> query: (?x7255, ?x5058) <- location(?x7255, ?x1523), award_nominee(?x5058, ?x7255) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #65376 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1287 *> proper extension: 0bq4j6; *> query: (?x7255, ?x2307) <- award_nominee(?x7255, ?x3756), award_nominee(?x3756, ?x2307), participant(?x3756, ?x2857) *> conf = 0.24 ranks of expected_values: 13 EVAL 042ly5 award_nominee 0f502 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 89.000 30.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16176-0bmhvpr PRED entity: 0bmhvpr PRED relation: film_release_region PRED expected values: 05v8c 0k6nt 01pj7 06mkj 05b4w 01crd5 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 127): 09c7w0 (0.94 #5277, 0.94 #4870, 0.94 #6494), 06mkj (0.88 #1124, 0.88 #582, 0.86 #1530), 0k6nt (0.84 #1505, 0.83 #1099, 0.82 #557), 05b4w (0.81 #1130, 0.80 #588, 0.75 #1941), 05v8c (0.71 #549, 0.66 #1091, 0.55 #1902), 0ctw_b (0.64 #1100, 0.53 #558, 0.49 #1911), 06qd3 (0.61 #568, 0.54 #1110, 0.48 #1516), 0h7x (0.55 #565, 0.44 #1513, 0.42 #1783), 06mzp (0.53 #1095, 0.50 #1906, 0.47 #553), 05qx1 (0.51 #571, 0.40 #1113, 0.31 #1924) >> Best rule #5277 for best value: >> intensional similarity = 4 >> extensional distance = 683 >> proper extension: 09sh8k; 034qmv; 018js4; 01br2w; 06w99h3; 0c3ybss; 02vp1f_; 047gn4y; 0bvn25; 0m2kd; ... >> query: (?x3784, 09c7w0) <- film(?x617, ?x3784), film_release_region(?x3784, ?x87), currency(?x3784, ?x170), nominated_for(?x1179, ?x3784) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #1124 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 93 *> proper extension: 05z_kps; 04zyhx; 0by1wkq; 07x4qr; 0407yj_; 0gtsxr4; 09gkx35; 0gh65c5; 0gjcrrw; 0gy2y8r; ... *> query: (?x3784, 06mkj) <- film(?x617, ?x3784), film_release_region(?x3784, ?x3277), film_release_region(?x3784, ?x1892), ?x1892 = 02vzc, ?x3277 = 06t8v *> conf = 0.88 ranks of expected_values: 2, 3, 4, 5, 13, 21 EVAL 0bmhvpr film_release_region 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 84.000 84.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmhvpr film_release_region 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmhvpr film_release_region 06mkj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmhvpr film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 84.000 84.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmhvpr film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmhvpr film_release_region 05v8c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16175-03gh4 PRED entity: 03gh4 PRED relation: featured_film_locations! PRED expected values: 0pc62 => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 764): 0dpl44 (0.25 #1241, 0.14 #3419, 0.12 #4145), 0c57yj (0.25 #995, 0.14 #3173, 0.12 #3899), 0298n7 (0.25 #1291, 0.14 #3469, 0.12 #4195), 0315w4 (0.25 #1074, 0.14 #3252, 0.12 #3978), 04dsnp (0.15 #10230, 0.13 #11682, 0.11 #15313), 0473rc (0.15 #10613, 0.11 #15696, 0.10 #19328), 047csmy (0.13 #13460, 0.12 #10556, 0.11 #15639), 0872p_c (0.12 #7337, 0.08 #28396, 0.08 #10242), 024l2y (0.12 #7373, 0.06 #16087, 0.05 #45132), 04gv3db (0.12 #7577, 0.06 #16291, 0.04 #25006) >> Best rule #1241 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 07h34; >> query: (?x6226, 0dpl44) <- vacationer(?x6226, ?x3754), state(?x3704, ?x6226), artists(?x671, ?x3754) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03gh4 featured_film_locations! 0pc62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 187.000 187.000 0.250 http://example.org/film/film/featured_film_locations #16174-02lq10 PRED entity: 02lq10 PRED relation: profession PRED expected values: 02hrh1q => 81 concepts (69 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.91 #4187, 0.89 #7914, 0.89 #7616), 01d_h8 (0.66 #5967, 0.43 #602, 0.42 #304), 0cbd2 (0.49 #1199, 0.47 #1348, 0.38 #454), 0dxtg (0.43 #5975, 0.34 #1355, 0.34 #1206), 03gjzk (0.42 #314, 0.42 #165, 0.37 #2847), 02jknp (0.33 #8, 0.33 #5969, 0.25 #306), 018gz8 (0.23 #614, 0.15 #1955, 0.14 #4190), 09jwl (0.20 #3000, 0.20 #2106, 0.19 #2702), 02krf9 (0.17 #177, 0.15 #1518, 0.15 #2859), 0np9r (0.15 #7921, 0.15 #7623, 0.14 #9116) >> Best rule #4187 for best value: >> intensional similarity = 4 >> extensional distance = 888 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 04bdxl; 079vf; 06qgvf; 0grwj; 05d7rk; 05bnp0; ... >> query: (?x2217, 02hrh1q) <- film(?x2217, ?x197), nationality(?x2217, ?x94), profession(?x2217, ?x2225), people(?x743, ?x2217) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lq10 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 69.000 0.910 http://example.org/people/person/profession #16173-013m_x PRED entity: 013m_x PRED relation: location! PRED expected values: 06hmd => 137 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2039): 078jt5 (0.46 #196422, 0.46 #133468, 0.45 #115839), 01797x (0.25 #4612, 0.25 #2094, 0.22 #7130), 0b78hw (0.25 #3370, 0.25 #852, 0.22 #5888), 032r1 (0.25 #2315, 0.22 #7351, 0.12 #4833), 01wp8w7 (0.25 #2778, 0.22 #5296, 0.11 #15368), 01yzhn (0.25 #4650, 0.22 #7168, 0.10 #19758), 01vtmw6 (0.25 #3880, 0.22 #6398, 0.07 #16470), 02yl42 (0.25 #3224, 0.22 #5742, 0.07 #15814), 0p_pd (0.25 #2566, 0.22 #5084, 0.07 #15156), 06mmb (0.25 #474, 0.12 #2992, 0.11 #5510) >> Best rule #196422 for best value: >> intensional similarity = 3 >> extensional distance = 342 >> proper extension: 02p3my; >> query: (?x5658, ?x3018) <- location(?x4819, ?x5658), place_of_birth(?x3018, ?x5658), nominated_for(?x3018, ?x6706) >> conf = 0.46 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 013m_x location! 06hmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 80.000 0.459 http://example.org/people/person/places_lived./people/place_lived/location #16172-02rb607 PRED entity: 02rb607 PRED relation: nominated_for! PRED expected values: 0gqng => 106 concepts (100 used for prediction) PRED predicted values (max 10 best out of 213): 02wkmx (0.68 #11966, 0.67 #7897, 0.67 #15077), 0gs9p (0.45 #543, 0.37 #5804, 0.36 #7244), 0gr4k (0.45 #505, 0.24 #1222, 0.23 #744), 0gq9h (0.42 #780, 0.42 #6762, 0.41 #7242), 019f4v (0.41 #8670, 0.39 #6754, 0.39 #6994), 0gq_v (0.40 #498, 0.31 #7199, 0.31 #6719), 040njc (0.40 #724, 0.31 #6946, 0.31 #6706), 0k611 (0.37 #791, 0.34 #5813, 0.33 #6773), 09v92_x (0.36 #424, 0.12 #16276, 0.11 #185), 09v51c2 (0.36 #446, 0.11 #207, 0.08 #8855) >> Best rule #11966 for best value: >> intensional similarity = 3 >> extensional distance = 855 >> proper extension: 06mmr; >> query: (?x2403, ?x7965) <- award(?x2403, ?x7965), award_winner(?x2403, ?x8767), nominated_for(?x7965, ?x161) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 040b5k; 0dmn0x; *> query: (?x2403, 0gqng) <- nominated_for(?x5959, ?x2403), nominated_for(?x1243, ?x2403), film_format(?x2403, ?x6392), ?x5959 = 024rdh *> conf = 0.33 ranks of expected_values: 12 EVAL 02rb607 nominated_for! 0gqng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 106.000 100.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16171-02v2lh PRED entity: 02v2lh PRED relation: artists PRED expected values: 01nqfh_ => 59 concepts (21 used for prediction) PRED predicted values (max 10 best out of 3531): 01vtj38 (0.75 #3865, 0.68 #10291, 0.62 #2795), 03t9sp (0.62 #3331, 0.50 #5472, 0.50 #2261), 0415mzy (0.62 #3710, 0.50 #5851, 0.50 #2640), 0473q (0.56 #4928, 0.50 #2787, 0.45 #7069), 049qx (0.50 #5730, 0.50 #3589, 0.50 #2519), 01pfr3 (0.50 #3235, 0.50 #2165, 0.50 #1094), 011z3g (0.50 #2738, 0.44 #4879, 0.40 #5949), 03xhj6 (0.50 #2526, 0.44 #4667, 0.40 #5737), 0bqsy (0.50 #3563, 0.40 #5704, 0.38 #2493), 01309x (0.50 #2452, 0.40 #5663, 0.38 #3522) >> Best rule #3865 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0ggx5q; >> query: (?x12590, 01vtj38) <- artists(?x12590, ?x7018), artists(?x12590, ?x2306), ?x7018 = 01sxd1, award_winner(?x1854, ?x2306), gender(?x2306, ?x231), role(?x2306, ?x228), ?x1854 = 025m8y, award_winner(?x1656, ?x2306) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 06by7; *> query: (?x12590, 01nqfh_) <- artists(?x12590, ?x10039), artists(?x12590, ?x7018), artists(?x12590, ?x2306), ?x7018 = 01sxd1, award_winner(?x1079, ?x2306), artists(?x4910, ?x2306), gender(?x2306, ?x231), ?x10039 = 0ftqr, ?x4910 = 017_qw, type_of_union(?x2306, ?x566) *> conf = 0.33 ranks of expected_values: 247 EVAL 02v2lh artists 01nqfh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 59.000 21.000 0.750 http://example.org/music/genre/artists #16170-07vn_9 PRED entity: 07vn_9 PRED relation: category PRED expected values: 08mbj5d => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.32 #9, 0.32 #2, 0.30 #4) >> Best rule #9 for best value: >> intensional similarity = 6 >> extensional distance = 63 >> proper extension: 03s5lz; 03twd6; 03sxd2; 01b195; 03wbqc4; 0992d9; 0f4_2k; 0b7l4x; 0k7tq; 01j5ql; ... >> query: (?x10799, 08mbj5d) <- genre(?x10799, ?x604), ?x604 = 0lsxr, films(?x3530, ?x10799), film_crew_role(?x10799, ?x468), film(?x1324, ?x10799), award_nominee(?x1324, ?x157) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07vn_9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.323 http://example.org/common/topic/webpage./common/webpage/category #16169-099d4 PRED entity: 099d4 PRED relation: profession PRED expected values: 01d_h8 => 139 concepts (65 used for prediction) PRED predicted values (max 10 best out of 87): 01d_h8 (0.80 #153, 0.75 #2960, 0.73 #4283), 03gjzk (0.44 #3261, 0.36 #8995, 0.36 #6202), 018gz8 (0.34 #3263, 0.30 #162, 0.22 #3410), 0nbcg (0.29 #30, 0.12 #324, 0.10 #5778), 0n1h (0.29 #11, 0.12 #305, 0.06 #3553), 039v1 (0.29 #35, 0.12 #329, 0.03 #5783), 0cbd2 (0.25 #3402, 0.25 #1332, 0.23 #2961), 02krf9 (0.23 #2979, 0.21 #4302, 0.20 #4449), 0np9r (0.21 #3267, 0.19 #7972, 0.15 #1492), 0kyk (0.20 #1353, 0.19 #3423, 0.17 #1649) >> Best rule #153 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 03cxsvl; 01q9b9; >> query: (?x12180, 01d_h8) <- nationality(?x12180, ?x94), location(?x12180, ?x3125), profession(?x12180, ?x987), ?x3125 = 0d6lp, ?x987 = 0dxtg >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 099d4 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 65.000 0.800 http://example.org/people/person/profession #16168-0284b56 PRED entity: 0284b56 PRED relation: nominated_for! PRED expected values: 02w9sd7 => 91 concepts (90 used for prediction) PRED predicted values (max 10 best out of 188): 099c8n (0.50 #292, 0.35 #528, 0.23 #764), 09sb52 (0.50 #271, 0.25 #9686, 0.24 #11575), 02x4wr9 (0.43 #574, 0.20 #338, 0.08 #1518), 09td7p (0.40 #328, 0.39 #564, 0.25 #9686), 099t8j (0.40 #340, 0.33 #104, 0.30 #576), 02x17s4 (0.40 #330, 0.30 #566, 0.17 #3778), 03hkv_r (0.40 #251, 0.26 #487, 0.25 #9686), 09sdmz (0.40 #379, 0.25 #9686, 0.25 #11576), 0gqy2 (0.40 #358, 0.25 #9686, 0.24 #11575), 027dtxw (0.40 #240, 0.25 #9686, 0.24 #11575) >> Best rule #292 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 078sj4; >> query: (?x5706, 099c8n) <- award_winner(?x5706, ?x156), nominated_for(?x995, ?x5706), nominated_for(?x5706, ?x813), ?x995 = 099tbz >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #125 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 03hj5lq; *> query: (?x5706, 02w9sd7) <- award_winner(?x5706, ?x1564), nominated_for(?x899, ?x5706), genre(?x5706, ?x53), ?x1564 = 01g257 *> conf = 0.33 ranks of expected_values: 18 EVAL 0284b56 nominated_for! 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 91.000 90.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16167-0l3n4 PRED entity: 0l3n4 PRED relation: time_zones PRED expected values: 02hcv8 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.87 #524, 0.85 #406, 0.84 #579), 02fqwt (0.22 #485, 0.19 #170, 0.19 #118), 02lcqs (0.21 #278, 0.21 #793, 0.20 #570), 02hczc (0.14 #67, 0.10 #354, 0.10 #922), 02llzg (0.08 #370, 0.07 #845, 0.07 #779), 03bdv (0.04 #887, 0.03 #1229, 0.03 #953), 03plfd (0.03 #865, 0.03 #878, 0.02 #904), 042g7t (0.02 #616, 0.02 #377, 0.01 #219), 0gsrz4 (0.02 #982, 0.02 #996, 0.01 #1010) >> Best rule #524 for best value: >> intensional similarity = 4 >> extensional distance = 252 >> proper extension: 0mrf1; >> query: (?x8815, ?x2674) <- source(?x8815, ?x958), adjoins(?x4357, ?x8815), currency(?x4357, ?x170), time_zones(?x4357, ?x2674) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l3n4 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.869 http://example.org/location/location/time_zones #16166-02dr9j PRED entity: 02dr9j PRED relation: film_crew_role PRED expected values: 01pvkk 02ynfr => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.78 #684, 0.78 #556, 0.74 #2020), 01pvkk (0.30 #688, 0.30 #1111, 0.28 #2024), 02rh1dz (0.20 #461, 0.18 #299, 0.18 #73), 015h31 (0.20 #460, 0.15 #298, 0.13 #784), 0d2b38 (0.19 #475, 0.13 #313, 0.12 #508), 02ynfr (0.18 #563, 0.17 #691, 0.17 #45), 0215hd (0.14 #2030, 0.13 #468, 0.12 #694), 089g0h (0.12 #469, 0.11 #2031, 0.10 #1118), 01xy5l_ (0.11 #2026, 0.11 #562, 0.10 #464), 033smt (0.10 #477, 0.09 #315, 0.08 #420) >> Best rule #684 for best value: >> intensional similarity = 4 >> extensional distance = 389 >> proper extension: 0dq626; 09p35z; 0cnztc4; 04zyhx; 0d_2fb; 0gj8nq2; 09rsjpv; 04z257; 03z106; 05_5rjx; ... >> query: (?x7214, 0ch6mp2) <- country(?x7214, ?x94), genre(?x7214, ?x53), film_crew_role(?x7214, ?x2095), ?x2095 = 0dxtw >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #688 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 389 *> proper extension: 0dq626; 09p35z; 0cnztc4; 04zyhx; 0d_2fb; 0gj8nq2; 09rsjpv; 04z257; 03z106; 05_5rjx; ... *> query: (?x7214, 01pvkk) <- country(?x7214, ?x94), genre(?x7214, ?x53), film_crew_role(?x7214, ?x2095), ?x2095 = 0dxtw *> conf = 0.30 ranks of expected_values: 2, 6 EVAL 02dr9j film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 98.000 0.783 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02dr9j film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.783 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16165-03nk3t PRED entity: 03nk3t PRED relation: nationality PRED expected values: 02jx1 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 55): 09c7w0 (0.74 #4218, 0.73 #3617, 0.73 #2812), 07ssc (0.43 #215, 0.37 #3415, 0.34 #1908), 03rjj (0.37 #3415, 0.12 #105, 0.05 #505), 0f8l9c (0.37 #3415, 0.04 #902, 0.03 #2332), 02jx1 (0.34 #1908, 0.33 #7526, 0.33 #7929), 0glh3 (0.34 #1908, 0.33 #7526, 0.33 #7929), 0978r (0.25 #3213, 0.25 #1003), 02j9z (0.25 #3213), 03rk0 (0.09 #2356, 0.08 #2957, 0.08 #2556), 0d060g (0.09 #909, 0.06 #1010, 0.05 #1712) >> Best rule #4218 for best value: >> intensional similarity = 3 >> extensional distance = 1298 >> proper extension: 03qcq; 02knnd; 05cv94; 02zyy4; 01qkqwg; 03mz9r; 04rsd2; 062hgx; 0fwy0h; 0jn5l; ... >> query: (?x4472, 09c7w0) <- award_nominee(?x361, ?x4472), profession(?x4472, ?x319), place_of_birth(?x4472, ?x1156) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1908 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 589 *> proper extension: 0399p; 07h1q; 02784z; 015n8; *> query: (?x4472, ?x512) <- place_of_birth(?x4472, ?x1156), religion(?x4472, ?x4641), contains(?x512, ?x1156) *> conf = 0.34 ranks of expected_values: 5 EVAL 03nk3t nationality 02jx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 92.000 0.735 http://example.org/people/person/nationality #16164-0h6sv PRED entity: 0h6sv PRED relation: instrumentalists! PRED expected values: 05r5c => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 109): 05r5c (0.73 #538, 0.64 #970, 0.64 #890), 0342h (0.60 #3272, 0.58 #5574, 0.57 #2656), 05148p4 (0.43 #2496, 0.43 #2317, 0.38 #2228), 018vs (0.34 #2665, 0.31 #5492, 0.30 #5671), 06ch55 (0.32 #964, 0.22 #1317, 0.17 #1671), 07y_7 (0.27 #531, 0.20 #267, 0.14 #972), 01xqw (0.25 #245, 0.20 #334, 0.12 #510), 03qjg (0.19 #2704, 0.16 #3320, 0.15 #2527), 0l14md (0.17 #978, 0.12 #889, 0.11 #2214), 02hnl (0.17 #5605, 0.17 #5514, 0.16 #2242) >> Best rule #538 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 082db; >> query: (?x13167, 05r5c) <- gender(?x13167, ?x231), artists(?x3597, ?x13167), ?x3597 = 021dvj, instrumentalists(?x3296, ?x13167) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h6sv instrumentalists! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.727 http://example.org/music/instrument/instrumentalists #16163-0ch3qr1 PRED entity: 0ch3qr1 PRED relation: film! PRED expected values: 03f1r6t 06rq2l => 69 concepts (40 used for prediction) PRED predicted values (max 10 best out of 540): 02dlfh (0.68 #35332, 0.64 #60272, 0.63 #62353), 02r251z (0.45 #51958, 0.43 #66511, 0.42 #68589), 017s11 (0.45 #51958, 0.43 #66511, 0.42 #68589), 06cgy (0.22 #250, 0.03 #6483, 0.02 #43894), 0252fh (0.22 #1351, 0.02 #9663, 0.01 #5505), 015c4g (0.22 #776, 0.02 #23640, 0.02 #11166), 02lk1s (0.20 #20785, 0.20 #29097, 0.19 #33253), 02yxwd (0.14 #2817, 0.02 #4894, 0.01 #38151), 09yhzs (0.14 #2587, 0.01 #27019), 015p3p (0.11 #1091, 0.07 #3168, 0.01 #7324) >> Best rule #35332 for best value: >> intensional similarity = 3 >> extensional distance = 638 >> proper extension: 0n2bh; 0gfzgl; 01f3p_; 0cskb; 025x1t; 0gxsh4; 0clpml; 06ys2; >> query: (?x5672, ?x2857) <- nominated_for(?x2857, ?x5672), participant(?x521, ?x2857), participant(?x2857, ?x6187) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #3649 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 12 *> proper extension: 09v38qj; *> query: (?x5672, 06rq2l) <- honored_for(?x6297, ?x5672), ?x6297 = 0hhtgcw *> conf = 0.07 ranks of expected_values: 58 EVAL 0ch3qr1 film! 06rq2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 69.000 40.000 0.676 http://example.org/film/actor/film./film/performance/film EVAL 0ch3qr1 film! 03f1r6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 40.000 0.676 http://example.org/film/actor/film./film/performance/film #16162-0gs6m PRED entity: 0gs6m PRED relation: genre! PRED expected values: 030k94 01b_lz => 44 concepts (35 used for prediction) PRED predicted values (max 10 best out of 408): 030k94 (0.67 #933, 0.64 #5020, 0.33 #50), 07gbf (0.67 #1377, 0.60 #789, 0.57 #1673), 01kt_j (0.67 #1401, 0.60 #813, 0.57 #1697), 02r1ysd (0.67 #1007, 0.50 #418, 0.40 #712), 0431v3 (0.67 #980, 0.33 #97, 0.25 #391), 05p9_ql (0.67 #1016, 0.33 #133, 0.25 #427), 01b_lz (0.64 #5020, 0.50 #348, 0.40 #642), 02sqkh (0.64 #5020, 0.50 #376, 0.40 #670), 030p35 (0.64 #5020, 0.33 #966, 0.33 #83), 03d34x8 (0.60 #617, 0.50 #1205, 0.50 #323) >> Best rule #933 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 01z4y; >> query: (?x7120, 030k94) <- genre(?x782, ?x7120), titles(?x2008, ?x782), nominated_for(?x6913, ?x782), nominated_for(?x1762, ?x782), ?x6913 = 01my4f, nominated_for(?x783, ?x782), ?x1762 = 0gsg7, ?x783 = 0fbvqf >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 0gs6m genre! 01b_lz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 44.000 35.000 0.667 http://example.org/tv/tv_program/genre EVAL 0gs6m genre! 030k94 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 35.000 0.667 http://example.org/tv/tv_program/genre #16161-09jcj6 PRED entity: 09jcj6 PRED relation: production_companies PRED expected values: 05nn2c => 91 concepts (75 used for prediction) PRED predicted values (max 10 best out of 79): 024rbz (0.45 #4044, 0.45 #4126, 0.45 #3882), 016tw3 (0.25 #12, 0.12 #573, 0.12 #3162), 016tt2 (0.25 #4, 0.12 #325, 0.08 #565), 0c_j5d (0.25 #6, 0.06 #487, 0.02 #1618), 086k8 (0.17 #403, 0.15 #243, 0.12 #163), 01795t (0.17 #101, 0.06 #1714, 0.05 #2421), 01gb54 (0.15 #278, 0.07 #2458, 0.07 #3187), 02jd_7 (0.12 #228, 0.03 #870, 0.02 #1921), 017s11 (0.12 #564, 0.12 #324, 0.10 #644), 05qd_ (0.11 #1460, 0.11 #4948, 0.10 #4867) >> Best rule #4044 for best value: >> intensional similarity = 5 >> extensional distance = 721 >> proper extension: 0c5dd; 047msdk; 0jnwx; 01_1pv; 02rcdc2; 0kb57; 0c8qq; 0glnm; 0jymd; 0n04r; ... >> query: (?x4688, ?x1414) <- film(?x3705, ?x4688), titles(?x812, ?x4688), film(?x1414, ?x4688), production_companies(?x4688, ?x1478), award(?x3705, ?x749) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #589 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 71 *> proper extension: 047svrl; *> query: (?x4688, 05nn2c) <- film(?x844, ?x4688), titles(?x2480, ?x4688), executive_produced_by(?x4688, ?x4857), ?x2480 = 01z4y, production_companies(?x4688, ?x1478) *> conf = 0.01 ranks of expected_values: 48 EVAL 09jcj6 production_companies 05nn2c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 91.000 75.000 0.454 http://example.org/film/film/production_companies #16160-02hy9p PRED entity: 02hy9p PRED relation: nationality PRED expected values: 09c7w0 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.87 #2505, 0.82 #5015, 0.76 #1203), 02jx1 (0.40 #10846, 0.11 #1936, 0.10 #2236), 07ssc (0.40 #10846, 0.09 #2218, 0.09 #2118), 0d060g (0.40 #10846, 0.05 #5224, 0.05 #2913), 03_3d (0.40 #10846, 0.04 #1508, 0.01 #4616), 03rjj (0.40 #10846, 0.04 #105, 0.03 #807), 0f8l9c (0.40 #10846, 0.03 #724, 0.03 #1224), 03rt9 (0.40 #10846, 0.02 #1215, 0.02 #2919), 0345h (0.40 #10846, 0.02 #432, 0.02 #4641), 03rk0 (0.18 #246, 0.16 #1348, 0.15 #1548) >> Best rule #2505 for best value: >> intensional similarity = 3 >> extensional distance = 207 >> proper extension: 079vf; 01wjrn; 0203v; 01ky2h; 0g51l1; 049_zz; 01m65sp; 03kpvp; 0fpj4lx; 03hbzj; ... >> query: (?x8159, 09c7w0) <- place_of_birth(?x8159, ?x739), profession(?x8159, ?x987), ?x739 = 02_286 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hy9p nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.866 http://example.org/people/person/nationality #16159-01grpq PRED entity: 01grpq PRED relation: district_represented PRED expected values: 07h34 0498y 07_f2 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 461): 0498y (0.88 #1169, 0.88 #212, 0.87 #427), 07_f2 (0.88 #212, 0.87 #427, 0.87 #538), 07h34 (0.88 #212, 0.87 #427, 0.84 #1025), 05kkh (0.87 #427, 0.84 #1025, 0.83 #267), 04ly1 (0.87 #427, 0.84 #1025, 0.83 #267), 03v1s (0.79 #1026, 0.73 #1250, 0.73 #1249), 0gyh (0.79 #1026, 0.73 #1250, 0.73 #1249), 04ych (0.79 #1026, 0.73 #1250, 0.73 #1249), 04tgp (0.79 #1026, 0.73 #1250, 0.73 #1249), 050ks (0.79 #1026, 0.73 #1250, 0.73 #1249) >> Best rule #1169 for best value: >> intensional similarity = 35 >> extensional distance = 15 >> proper extension: 06f0dc; >> query: (?x4787, 0498y) <- legislative_sessions(?x4787, ?x11142), legislative_sessions(?x4787, ?x10638), district_represented(?x4787, ?x7518), district_represented(?x4787, ?x1767), district_represented(?x4787, ?x1426), district_represented(?x4787, ?x728), ?x1767 = 04rrd, legislative_sessions(?x5742, ?x4787), legislative_sessions(?x4812, ?x4787), legislative_sessions(?x2860, ?x10638), adjoins(?x279, ?x728), contains(?x728, ?x7564), state(?x7600, ?x728), contains(?x94, ?x728), district_represented(?x12714, ?x728), district_represented(?x7944, ?x728), district_represented(?x759, ?x728), district_represented(?x653, ?x728), religion(?x728, ?x10681), religion(?x728, ?x2769), ?x759 = 043djx, ?x12714 = 05rrw9, ?x7518 = 026mj, ?x653 = 070m6c, location(?x5620, ?x728), currency(?x7564, ?x170), ?x2769 = 019cr, time_zones(?x7564, ?x2674), ?x10681 = 01s5nb, district_represented(?x11142, ?x3778), ?x1426 = 07z1m, contains(?x7564, ?x5638), profession(?x5742, ?x5805), ?x7944 = 01h7xx, jurisdiction_of_office(?x900, ?x728) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 01grpq district_represented 07_f2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01grpq district_represented 0498y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01grpq district_represented 07h34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #16158-070zc PRED entity: 070zc PRED relation: adjoins PRED expected values: 07nf6 => 209 concepts (121 used for prediction) PRED predicted values (max 10 best out of 636): 06rf7 (0.50 #2752, 0.40 #4288, 0.16 #10436), 04p0c (0.46 #8624, 0.26 #10159, 0.25 #11696), 09krp (0.31 #8826, 0.25 #11898, 0.25 #2677), 09hzw (0.25 #2876, 0.25 #57677, 0.23 #72291), 07nf6 (0.25 #2824, 0.23 #72291, 0.23 #85374), 09ksp (0.25 #2659, 0.20 #4195, 0.16 #10343), 0d331 (0.25 #3343, 0.20 #4111, 0.14 #4880), 070zc (0.25 #57677, 0.23 #72291, 0.23 #85374), 0156q (0.23 #72291, 0.23 #85374, 0.23 #88454), 0345h (0.23 #72291, 0.23 #85374, 0.23 #88454) >> Best rule #2752 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04p0c; 09hzw; >> query: (?x10524, 06rf7) <- contains(?x1264, ?x10524), adjoins(?x10524, ?x7049), ?x7049 = 017wh, state(?x5560, ?x10524) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2824 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 04p0c; 09hzw; *> query: (?x10524, 07nf6) <- contains(?x1264, ?x10524), adjoins(?x10524, ?x7049), ?x7049 = 017wh, state(?x5560, ?x10524) *> conf = 0.25 ranks of expected_values: 5 EVAL 070zc adjoins 07nf6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 209.000 121.000 0.500 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #16157-01prf3 PRED entity: 01prf3 PRED relation: state_province_region PRED expected values: 05tbn => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 71): 05tbn (0.82 #6079, 0.79 #8058, 0.74 #9677), 059rby (0.50 #7938, 0.50 #1492, 0.50 #620), 01n7q (0.50 #1629, 0.45 #2991, 0.38 #3366), 03v0t (0.33 #299, 0.17 #1787, 0.14 #2160), 09c7w0 (0.25 #17620, 0.22 #13525, 0.17 #16007), 05k7sb (0.22 #2758, 0.22 #2634, 0.17 #1765), 0dclg (0.22 #13525, 0.17 #16007, 0.17 #1858), 0rh6k (0.17 #1735, 0.11 #2728, 0.11 #2604), 07h34 (0.17 #1786, 0.11 #2779, 0.11 #2655), 07z1m (0.15 #3493, 0.12 #3868, 0.05 #4736) >> Best rule #6079 for best value: >> intensional similarity = 5 >> extensional distance = 63 >> proper extension: 08wjc1; 01vg13; 02ngbs; 03hpkp; 060ppp; 02grjf; >> query: (?x10530, ?x3670) <- citytown(?x10530, ?x2254), country(?x10530, ?x94), ?x94 = 09c7w0, location(?x120, ?x2254), state(?x2254, ?x3670) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01prf3 state_province_region 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.821 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #16156-0dc_ms PRED entity: 0dc_ms PRED relation: film_release_region PRED expected values: 0jgd 03_3d 03rt9 015fr 019rg5 0345h 05qx1 01mjq 05b4w 06t8v 0161c => 78 concepts (74 used for prediction) PRED predicted values (max 10 best out of 238): 03h64 (0.93 #559, 0.90 #687, 0.89 #815), 0345h (0.90 #660, 0.87 #788, 0.87 #916), 015fr (0.86 #651, 0.85 #779, 0.84 #523), 0jgd (0.86 #515, 0.85 #771, 0.83 #643), 05b4w (0.85 #685, 0.84 #557, 0.80 #813), 03_3d (0.83 #645, 0.80 #773, 0.80 #517), 03rt9 (0.78 #649, 0.78 #777, 0.77 #521), 06t8v (0.61 #570, 0.55 #826, 0.52 #954), 05qx1 (0.59 #538, 0.52 #666, 0.51 #794), 01mjq (0.59 #796, 0.57 #1052, 0.56 #924) >> Best rule #559 for best value: >> intensional similarity = 7 >> extensional distance = 67 >> proper extension: 0gtsx8c; 02vxq9m; 0ds3t5x; 0g5qs2k; 0dscrwf; 0gkz15s; 01vksx; 017gl1; 08hmch; 0h3xztt; ... >> query: (?x6528, 03h64) <- film_release_region(?x6528, ?x2843), film_release_region(?x6528, ?x2146), film_release_region(?x6528, ?x1603), ?x2146 = 03rk0, film(?x665, ?x6528), ?x1603 = 06bnz, ?x2843 = 016wzw >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #660 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 84 *> proper extension: 0h95zbp; 07s3m4g; 0j8f09z; *> query: (?x6528, 0345h) <- film_release_region(?x6528, ?x2152), film_release_region(?x6528, ?x2146), film_release_region(?x6528, ?x985), ?x2146 = 03rk0, genre(?x6528, ?x225), ?x2152 = 06mkj, ?x985 = 0k6nt *> conf = 0.90 ranks of expected_values: 2, 3, 4, 5, 6, 7, 8, 9, 10, 23, 52 EVAL 0dc_ms film_release_region 0161c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dc_ms film_release_region 06t8v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dc_ms film_release_region 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dc_ms film_release_region 01mjq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dc_ms film_release_region 05qx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dc_ms film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dc_ms film_release_region 019rg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dc_ms film_release_region 015fr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dc_ms film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dc_ms film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dc_ms film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 74.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16155-01kstn9 PRED entity: 01kstn9 PRED relation: place_of_birth PRED expected values: 02_286 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 122): 06wxw (0.32 #18313, 0.27 #83099, 0.27 #11973), 02_286 (0.12 #2131, 0.11 #4244, 0.11 #4949), 01_d4 (0.08 #2178, 0.06 #7108, 0.05 #4996), 05jbn (0.06 #37327, 0.05 #40144, 0.05 #4930), 013kcv (0.04 #23, 0.02 #727, 0.02 #1431), 013d7t (0.04 #186, 0.02 #890, 0.02 #1594), 0zlgm (0.04 #177, 0.01 #3698), 043yj (0.04 #583), 0rv97 (0.04 #324), 0xq63 (0.04 #236) >> Best rule #18313 for best value: >> intensional similarity = 3 >> extensional distance = 332 >> proper extension: 049tjg; 0j3v; 0dzkq; 07c37; 0399p; 02wh0; 0b5x23; 0436zq; 01h2_6; 0qkj7; >> query: (?x3539, ?x4356) <- people(?x3538, ?x3539), location(?x3539, ?x4356), place_of_birth(?x543, ?x4356) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #2131 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 02whj; 01vyp_; 014z8v; 015wfg; 0674cw; 02lvtb; 015xp4; 02jq1; 01vtmw6; 0bdlj; ... *> query: (?x3539, 02_286) <- category(?x3539, ?x134), award(?x3539, ?x341), people(?x3538, ?x3539) *> conf = 0.12 ranks of expected_values: 2 EVAL 01kstn9 place_of_birth 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 134.000 134.000 0.315 http://example.org/people/person/place_of_birth #16154-03l6q0 PRED entity: 03l6q0 PRED relation: film! PRED expected values: 0362q0 => 81 concepts (50 used for prediction) PRED predicted values (max 10 best out of 79): 0162c8 (0.29 #861, 0.18 #1136), 04sry (0.20 #716, 0.11 #2092, 0.07 #2369), 0362q0 (0.20 #681, 0.08 #1506), 02qzjj (0.20 #813, 0.01 #5776), 064jjy (0.18 #1296, 0.14 #1021), 04jspq (0.14 #982, 0.09 #1257, 0.01 #5670), 01nr36 (0.14 #1028, 0.09 #1303), 01pfkw (0.14 #3578, 0.13 #3302, 0.12 #10197), 042xrr (0.09 #1649, 0.04 #1648, 0.02 #1098), 0993r (0.09 #1649, 0.04 #1648, 0.02 #1098) >> Best rule #861 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 05dss7; 03p2xc; 0ptdz; >> query: (?x3317, 0162c8) <- film(?x5410, ?x3317), genre(?x3317, ?x1729), genre(?x4847, ?x1729), ?x5410 = 05bpg3, ?x4847 = 0dln8jk >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #681 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 04vr_f; 05zlld0; *> query: (?x3317, 0362q0) <- film(?x4606, ?x3317), genre(?x3317, ?x571), award_winner(?x3317, ?x4420), ?x4606 = 042xrr *> conf = 0.20 ranks of expected_values: 3 EVAL 03l6q0 film! 0362q0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 81.000 50.000 0.286 http://example.org/film/director/film #16153-053rxgm PRED entity: 053rxgm PRED relation: executive_produced_by PRED expected values: 0g_rs_ => 93 concepts (64 used for prediction) PRED predicted values (max 10 best out of 112): 04h6mm (0.25 #120, 0.01 #3157), 0ksf29 (0.25 #51, 0.01 #3088), 03swmf (0.25 #202), 0tc7 (0.17 #758, 0.17 #565, 0.05 #757), 012d40 (0.17 #508, 0.01 #3547), 01twdk (0.14 #1123, 0.08 #871, 0.03 #1375), 079vf (0.09 #1518, 0.08 #760, 0.07 #1012), 06pj8 (0.08 #813, 0.07 #5368, 0.06 #6125), 02pq9yv (0.08 #843, 0.01 #2363, 0.01 #2616), 05hj_k (0.07 #2376, 0.07 #2629, 0.07 #1108) >> Best rule #120 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05nyqk; >> query: (?x1178, 04h6mm) <- film_crew_role(?x1178, ?x1171), nominated_for(?x5338, ?x1178), ?x5338 = 0gn30, ?x1171 = 09vw2b7 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3795 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 89 *> proper extension: 09g8vhw; 02725hs; 05fgt1; 04ydr95; 0642xf3; 05zpghd; 02qsqmq; 0660b9b; 0640y35; 0b7l4x; ... *> query: (?x1178, 0g_rs_) <- film_crew_role(?x1178, ?x2472), film_release_region(?x1178, ?x456), ?x2472 = 01xy5l_, form_of_government(?x456, ?x6441) *> conf = 0.01 ranks of expected_values: 105 EVAL 053rxgm executive_produced_by 0g_rs_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 93.000 64.000 0.250 http://example.org/film/film/executive_produced_by #16152-036dyy PRED entity: 036dyy PRED relation: award PRED expected values: 02xj3rw => 131 concepts (125 used for prediction) PRED predicted values (max 10 best out of 322): 026mmy (0.78 #40306, 0.70 #33857, 0.70 #41113), 0cjyzs (0.37 #10987, 0.32 #11793, 0.29 #509), 0gs9p (0.36 #3707, 0.33 #2901, 0.31 #886), 09sb52 (0.35 #18580, 0.33 #1653, 0.32 #25834), 05p09zm (0.35 #4960, 0.23 #6572, 0.22 #6975), 0ck27z (0.32 #20646, 0.29 #495, 0.28 #13391), 0fbtbt (0.32 #11113, 0.29 #635, 0.27 #11919), 02pqp12 (0.31 #877, 0.27 #3698, 0.25 #2892), 019f4v (0.29 #3694, 0.26 #2082, 0.23 #873), 0cqhk0 (0.29 #440, 0.22 #5679, 0.18 #13336) >> Best rule #40306 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 06lxn; >> query: (?x8274, ?x10881) <- award_winner(?x10881, ?x8274), award(?x1282, ?x10881), ceremony(?x10881, ?x139) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #50384 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3385 *> proper extension: 01jcxwp; 01fchy; 01dpts; 0560w; 07n3s; 07k2d; 014_xj; *> query: (?x8274, ?x2071) <- award(?x8274, ?x5235), award(?x12037, ?x5235), award(?x12037, ?x2071) *> conf = 0.03 ranks of expected_values: 249 EVAL 036dyy award 02xj3rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 131.000 125.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16151-05zr6wv PRED entity: 05zr6wv PRED relation: nominated_for PRED expected values: 034qrh 01cssf 0dr3sl 02xtxw 057lbk => 39 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1570): 07bx6 (0.68 #12419, 0.68 #24841, 0.67 #21737), 0ct2tf5 (0.68 #12419, 0.68 #24841, 0.67 #21737), 01y9jr (0.68 #12419, 0.68 #24841, 0.67 #21737), 02xtxw (0.68 #12419, 0.68 #24841, 0.67 #21737), 049xgc (0.67 #848, 0.20 #18632, 0.16 #14823), 02yvct (0.67 #304, 0.20 #18632, 0.15 #14279), 0m313 (0.67 #9, 0.19 #13984, 0.16 #21747), 011yl_ (0.67 #509, 0.14 #3610, 0.14 #14484), 095zlp (0.56 #49, 0.20 #18632, 0.15 #3150), 0b6tzs (0.56 #122, 0.20 #18632, 0.14 #14097) >> Best rule #12419 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 06196; >> query: (?x401, ?x408) <- award_winner(?x401, ?x147), award(?x408, ?x401), award(?x9780, ?x401), film(?x9780, ?x136) >> conf = 0.68 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 178, 179, 345, 526 EVAL 05zr6wv nominated_for 057lbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 16.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zr6wv nominated_for 02xtxw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 39.000 16.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zr6wv nominated_for 0dr3sl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 39.000 16.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zr6wv nominated_for 01cssf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 39.000 16.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zr6wv nominated_for 034qrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 39.000 16.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16150-0hcr PRED entity: 0hcr PRED relation: genre! PRED expected values: 0b60sq 0_7w6 050f0s 0gtsxr4 05c26ss 027s39y 04lqvly 05sw5b 0dh8v4 0d4htf 04g73n 099bhp 05t0zfv => 107 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1814): 02w86hz (0.67 #44399, 0.62 #56669, 0.60 #33877), 01hw5kk (0.67 #44465, 0.60 #33943, 0.57 #51474), 034qmv (0.67 #43829, 0.60 #33307, 0.40 #35059), 0crc2cp (0.67 #44306, 0.60 #33784, 0.40 #35536), 0gc_c_ (0.62 #56649, 0.60 #33857, 0.57 #51388), 05pdd86 (0.62 #57113, 0.60 #34321, 0.50 #44843), 01738w (0.62 #57176, 0.60 #34384, 0.50 #44906), 0d4htf (0.60 #28952, 0.57 #51738, 0.50 #41221), 06zn1c (0.60 #29677, 0.50 #45454, 0.50 #41946), 05nlx4 (0.60 #34506, 0.50 #57298, 0.50 #45028) >> Best rule #44399 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 02l7c8; >> query: (?x2540, 02w86hz) <- genre(?x8752, ?x2540), genre(?x6000, ?x2540), genre(?x5732, ?x2540), genre(?x5538, ?x2540), genre(?x419, ?x2540), ?x6000 = 027j9wd, program(?x2159, ?x419), written_by(?x5538, ?x6037), ?x8752 = 076xkdz, country(?x5732, ?x252) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #28952 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 04t36; *> query: (?x2540, 0d4htf) <- genre(?x6881, ?x2540), genre(?x2868, ?x2540), genre(?x802, ?x2540), ?x802 = 0cwrr, film_release_region(?x2868, ?x404), ?x404 = 047lj, film_crew_role(?x6881, ?x1171) *> conf = 0.60 ranks of expected_values: 8, 39, 43, 50, 59, 117, 155, 162, 382, 709, 1038, 1039, 1140 EVAL 0hcr genre! 05t0zfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 099bhp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 04g73n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 0d4htf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 0dh8v4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 05sw5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 04lqvly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 027s39y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 0gtsxr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 050f0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 0_7w6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 107.000 75.000 0.667 http://example.org/film/film/genre EVAL 0hcr genre! 0b60sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 107.000 75.000 0.667 http://example.org/film/film/genre #16149-04mnts PRED entity: 04mnts PRED relation: colors PRED expected values: 06fvc => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.45 #155, 0.41 #136, 0.41 #174), 06fvc (0.43 #154, 0.41 #230, 0.41 #59), 019sc (0.39 #711, 0.28 #634, 0.27 #653), 038hg (0.14 #31, 0.14 #879, 0.13 #658), 088fh (0.14 #879, 0.12 #666, 0.11 #386), 02rnmb (0.14 #879, 0.12 #666, 0.10 #944), 01l849 (0.14 #879, 0.12 #666, 0.10 #944), 06kqt3 (0.14 #879, 0.12 #666, 0.10 #944), 0jc_p (0.12 #666, 0.10 #944, 0.10 #943), 04mkbj (0.12 #666, 0.10 #944, 0.10 #943) >> Best rule #155 for best value: >> intensional similarity = 9 >> extensional distance = 105 >> proper extension: 03tc8d; >> query: (?x4116, 01g5v) <- team(?x203, ?x4116), team(?x63, ?x4116), ?x203 = 0dgrmp, position(?x4116, ?x60), colors(?x4116, ?x663), ?x60 = 02nzb8, ?x63 = 02sdk9v, colors(?x1087, ?x663), ?x1087 = 01b1mj >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #154 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 105 *> proper extension: 03tc8d; *> query: (?x4116, 06fvc) <- team(?x203, ?x4116), team(?x63, ?x4116), ?x203 = 0dgrmp, position(?x4116, ?x60), colors(?x4116, ?x663), ?x60 = 02nzb8, ?x63 = 02sdk9v, colors(?x1087, ?x663), ?x1087 = 01b1mj *> conf = 0.43 ranks of expected_values: 2 EVAL 04mnts colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 60.000 60.000 0.449 http://example.org/sports/sports_team/colors #16148-01yndb PRED entity: 01yndb PRED relation: award PRED expected values: 02qvyrt => 88 concepts (64 used for prediction) PRED predicted values (max 10 best out of 242): 02581q (0.47 #1213, 0.46 #811, 0.45 #1615), 01ck6h (0.40 #123, 0.33 #525, 0.10 #4545), 01by1l (0.32 #1319, 0.31 #8153, 0.31 #3731), 01ckrr (0.30 #232, 0.25 #634, 0.09 #6262), 01c4_6 (0.30 #90, 0.25 #492, 0.04 #6120), 09sb52 (0.27 #19739, 0.19 #22151, 0.19 #20945), 01bgqh (0.26 #8083, 0.25 #7279, 0.24 #3661), 03qbh5 (0.21 #8246, 0.21 #7442, 0.20 #3824), 01d38g (0.20 #3646, 0.12 #7264, 0.11 #8068), 02f73p (0.20 #188, 0.17 #590, 0.10 #8228) >> Best rule #1213 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 028q6; 0lbj1; 01lmj3q; 026ps1; 01l4zqz; 011zf2; 03xgm3; 07z542; 01ww2fs; 0ggjt; ... >> query: (?x9144, 02581q) <- award_nominee(?x9144, ?x4343), ?x4343 = 02cx90, role(?x9144, ?x432), award(?x9144, ?x2420) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #4148 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 185 *> proper extension: 0kp2_; *> query: (?x9144, 02qvyrt) <- award(?x9144, ?x2420), role(?x9144, ?x432), place_of_birth(?x9144, ?x1005) *> conf = 0.11 ranks of expected_values: 44 EVAL 01yndb award 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 88.000 64.000 0.474 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16147-03r8tl PRED entity: 03r8tl PRED relation: award! PRED expected values: 08d6bd 02n1gr => 58 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2733): 015npr (0.82 #6714, 0.82 #16788, 0.81 #6715), 0f5zj6 (0.82 #6714, 0.82 #16788, 0.81 #6715), 02jxsq (0.82 #6714, 0.82 #16788, 0.81 #6715), 03m3nzf (0.50 #5970, 0.42 #16790, 0.40 #16789), 02ply6j (0.42 #16790, 0.25 #5430, 0.20 #15504), 0969vz (0.40 #12205, 0.40 #8847, 0.33 #2132), 0cvbb9q (0.40 #11838, 0.40 #8480, 0.33 #1765), 02x0dzw (0.40 #15943, 0.25 #5869, 0.13 #80619), 07jmnh (0.33 #3208, 0.25 #6565, 0.20 #16639), 01k6nm (0.33 #3156, 0.25 #6513, 0.20 #16587) >> Best rule #6714 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 09sb52; >> query: (?x1937, ?x1445) <- award_winner(?x1937, ?x11447), award_winner(?x1937, ?x7031), award_winner(?x1937, ?x1445), award(?x1936, ?x1937), ?x11447 = 03j367r, nominated_for(?x1937, ?x257), location(?x7031, ?x7412) >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #2594 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 03rbj2; *> query: (?x1937, 02n1gr) <- award_winner(?x1937, ?x11447), award(?x1936, ?x1937), ?x11447 = 03j367r, nominated_for(?x1937, ?x657), ?x657 = 04jwjq *> conf = 0.33 ranks of expected_values: 25, 31 EVAL 03r8tl award! 02n1gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 58.000 24.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03r8tl award! 08d6bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 58.000 24.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16146-032_jg PRED entity: 032_jg PRED relation: student! PRED expected values: 01w5m 01nnsv => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 140): 02gr81 (0.33 #132, 0.04 #2240, 0.03 #3821), 03ksy (0.20 #633, 0.09 #1687, 0.06 #4322), 017z88 (0.20 #609, 0.03 #13258, 0.03 #17474), 03hdz8 (0.20 #788), 0bwfn (0.16 #1329, 0.06 #2383, 0.05 #3964), 01stzp (0.11 #3146, 0.11 #3673), 01mpwj (0.09 #1688, 0.05 #4323, 0.04 #4850), 026gvfj (0.08 #1165, 0.01 #15922, 0.01 #24355), 01y06y (0.08 #3130, 0.07 #3657, 0.01 #7346), 01lhdt (0.07 #3422, 0.06 #2895) >> Best rule #132 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01tnxc; >> query: (?x875, 02gr81) <- award_nominee(?x192, ?x875), sibling(?x989, ?x875), ?x192 = 02p65p >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1686 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 0c7ct; *> query: (?x875, 01w5m) <- nationality(?x875, ?x94), sibling(?x875, ?x989), participant(?x989, ?x287) *> conf = 0.06 ranks of expected_values: 11, 66 EVAL 032_jg student! 01nnsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 127.000 127.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 032_jg student! 01w5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 127.000 127.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #16145-07yjb PRED entity: 07yjb PRED relation: titles PRED expected values: 029k4p 04nlb94 => 59 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1823): 03nm_fh (0.36 #4661, 0.35 #3107, 0.12 #6898), 05pdh86 (0.36 #4661, 0.35 #3107, 0.06 #6857), 04fv5b (0.33 #795, 0.15 #10120, 0.15 #8567), 0c34mt (0.33 #489, 0.15 #9814, 0.15 #8261), 04sh80 (0.33 #1503, 0.11 #10828, 0.11 #9275), 04fjzv (0.33 #1473, 0.07 #10798, 0.07 #9245), 07f_7h (0.33 #359, 0.07 #9684, 0.07 #8131), 02pcq92 (0.33 #1494, 0.07 #10819, 0.07 #9266), 09rfh9 (0.33 #1410, 0.07 #10735, 0.07 #9182), 0fq7dv_ (0.33 #257, 0.07 #9582, 0.07 #8029) >> Best rule #4661 for best value: >> intensional similarity = 9 >> extensional distance = 12 >> proper extension: 01sgl; >> query: (?x7173, ?x4464) <- titles(?x7173, ?x11565), titles(?x7173, ?x8414), titles(?x7173, ?x7741), film(?x1104, ?x11565), nominated_for(?x929, ?x7741), genre(?x7741, ?x53), films(?x7173, ?x4464), language(?x8414, ?x254), film(?x1286, ?x8414) >> conf = 0.36 => this is the best rule for 2 predicted values *> Best rule #715 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 03npn; *> query: (?x7173, 029k4p) <- titles(?x7173, ?x11565), titles(?x7173, ?x8886), titles(?x7173, ?x8414), titles(?x7173, ?x7741), titles(?x7173, ?x7463), titles(?x7173, ?x6536), ?x8414 = 0gy30w, ?x8886 = 076xkps, ?x7741 = 01xq8v, ?x6536 = 09gmmt6, production_companies(?x7463, ?x738), film_crew_role(?x11565, ?x281) *> conf = 0.33 ranks of expected_values: 37, 1496 EVAL 07yjb titles 04nlb94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 59.000 22.000 0.356 http://example.org/media_common/netflix_genre/titles EVAL 07yjb titles 029k4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 59.000 22.000 0.356 http://example.org/media_common/netflix_genre/titles #16144-02j04_ PRED entity: 02j04_ PRED relation: organization! PRED expected values: 060c4 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 16): 060c4 (0.81 #80, 0.76 #379, 0.75 #288), 07xl34 (0.37 #37, 0.24 #206, 0.24 #154), 05k17c (0.29 #7, 0.23 #20, 0.16 #137), 0dq_5 (0.21 #867, 0.17 #1075, 0.17 #1101), 0hm4q (0.06 #684, 0.06 #164, 0.06 #190), 05c0jwl (0.04 #603, 0.04 #759, 0.04 #629), 01t7n9 (0.04 #1171, 0.04 #1198, 0.03 #1343), 02079p (0.04 #1171, 0.04 #1198, 0.03 #1343), 0789n (0.04 #1171, 0.04 #1198, 0.03 #1343), 0f6c3 (0.04 #1171, 0.04 #1198, 0.03 #1343) >> Best rule #80 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 02zkz7; >> query: (?x7271, 060c4) <- currency(?x7271, ?x170), colors(?x7271, ?x3189), ?x170 = 09nqf, ?x3189 = 01g5v >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02j04_ organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.814 http://example.org/organization/role/leaders./organization/leadership/organization #16143-02yy88 PRED entity: 02yy88 PRED relation: parent_genre PRED expected values: 03lty => 64 concepts (39 used for prediction) PRED predicted values (max 10 best out of 178): 06by7 (0.95 #2300, 0.64 #2625, 0.55 #3607), 03lty (0.61 #1977, 0.50 #342, 0.34 #3610), 0296y (0.33 #59, 0.14 #2448, 0.14 #2123), 052smk (0.33 #141, 0.14 #2448, 0.14 #2123), 05r6t (0.30 #2664, 0.28 #2013, 0.27 #2339), 0xhtw (0.28 #1971, 0.16 #2297, 0.15 #2622), 011j5x (0.25 #345, 0.25 #182, 0.20 #507), 0dl5d (0.25 #338, 0.19 #1973, 0.13 #1791), 01243b (0.25 #352, 0.17 #2638, 0.17 #1987), 016clz (0.25 #328, 0.14 #3596, 0.14 #2614) >> Best rule #2300 for best value: >> intensional similarity = 8 >> extensional distance = 42 >> proper extension: 018ysx; >> query: (?x11973, 06by7) <- parent_genre(?x13652, ?x11973), parent_genre(?x11973, ?x8011), parent_genre(?x11973, ?x482), artists(?x8011, ?x8165), ?x8165 = 01516r, artists(?x482, ?x2269), ?x2269 = 02jg92, parent_genre(?x8011, ?x2249) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #1977 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 34 *> proper extension: 01gbcf; 02srgf; 01h0kx; 0d4xmp; *> query: (?x11973, 03lty) <- parent_genre(?x13652, ?x11973), parent_genre(?x11973, ?x8011), artists(?x8011, ?x2073), ?x2073 = 01czx, parent_genre(?x8011, ?x2249), artists(?x2249, ?x3420), ?x3420 = 0134s5 *> conf = 0.61 ranks of expected_values: 2 EVAL 02yy88 parent_genre 03lty CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 39.000 0.955 http://example.org/music/genre/parent_genre #16142-0vfs8 PRED entity: 0vfs8 PRED relation: location! PRED expected values: 01whg97 => 98 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1392): 01whg97 (0.68 #12593, 0.53 #17633, 0.53 #22673), 03l26m (0.20 #2291, 0.17 #4810, 0.05 #12365), 05ry0p (0.20 #2161, 0.17 #4680, 0.05 #12235), 023mdt (0.20 #1865, 0.17 #4384, 0.05 #11939), 022yb4 (0.20 #1710, 0.17 #4229, 0.05 #11784), 017c87 (0.20 #1758, 0.17 #4277, 0.05 #11832), 03mp9s (0.20 #1404, 0.17 #3923, 0.03 #11478), 01vw20h (0.20 #903, 0.17 #3422, 0.03 #10977), 0443c (0.20 #2502, 0.17 #5021, 0.03 #12576), 01vh3r (0.20 #2340, 0.17 #4859, 0.03 #12414) >> Best rule #12593 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 07mgr; >> query: (?x8115, ?x8149) <- place_of_birth(?x8149, ?x8115), artists(?x1000, ?x8149), languages(?x8149, ?x254) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0vfs8 location! 01whg97 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 10.000 0.676 http://example.org/people/person/places_lived./people/place_lived/location #16141-0cwx_ PRED entity: 0cwx_ PRED relation: student PRED expected values: 024bbl 019pkm 03cvv4 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 1644): 034bgm (0.18 #4563, 0.02 #89688, 0.02 #58543), 0641g8 (0.17 #2923, 0.03 #11227, 0.03 #29912), 0cqt90 (0.17 #2704, 0.03 #11008, 0.02 #40074), 02rchht (0.17 #2100, 0.03 #10404, 0.02 #39470), 0c1jh (0.17 #3697, 0.03 #12001, 0.02 #57676), 0438pz (0.17 #3593, 0.03 #11897, 0.01 #30582), 0835q (0.17 #4020, 0.03 #12324, 0.01 #31009), 0716t2 (0.17 #4005, 0.03 #12309, 0.01 #30994), 03xpfzg (0.17 #3999, 0.03 #12303, 0.01 #30988), 0b455l (0.17 #3774, 0.03 #12078, 0.01 #30763) >> Best rule #4563 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 015zxh; >> query: (?x6894, 034bgm) <- contains(?x3670, ?x6894), adjoins(?x331, ?x6894), institution(?x620, ?x331) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cwx_ student 03cvv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0cwx_ student 019pkm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0cwx_ student 024bbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student #16140-0hskw PRED entity: 0hskw PRED relation: award_winner! PRED expected values: 0h_9252 0c4hgj => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 122): 09p2r9 (0.21 #1681, 0.08 #372, 0.03 #4573), 09q_6t (0.18 #148, 0.11 #428, 0.08 #288), 09pnw5 (0.15 #382, 0.08 #522, 0.07 #2623), 0hndn2q (0.12 #179, 0.12 #319, 0.08 #459), 09p3h7 (0.12 #210, 0.08 #490, 0.08 #350), 073h1t (0.12 #167, 0.06 #447, 0.04 #307), 059x66 (0.12 #158, 0.03 #438, 0.03 #998), 0bxs_d (0.11 #534, 0.06 #254, 0.05 #2915), 02q690_ (0.10 #1745, 0.08 #2025, 0.08 #2865), 05c1t6z (0.09 #1696, 0.08 #2816, 0.07 #3516) >> Best rule #1681 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 024c1b; >> query: (?x2733, ?x6631) <- produced_by(?x3605, ?x2733), currency(?x3605, ?x170), honored_for(?x6631, ?x3605) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2158 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 157 *> proper extension: 052gzr; 02dbp7; 03y3dk; *> query: (?x2733, 0h_9252) <- produced_by(?x2734, ?x2733), award_winner(?x198, ?x2733), award_winner(?x2126, ?x2733) *> conf = 0.03 ranks of expected_values: 96 EVAL 0hskw award_winner! 0c4hgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 117.000 0.208 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0hskw award_winner! 0h_9252 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 117.000 117.000 0.208 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #16139-0lccn PRED entity: 0lccn PRED relation: artists! PRED expected values: 064t9 => 135 concepts (133 used for prediction) PRED predicted values (max 10 best out of 221): 017_qw (0.51 #5593, 0.48 #4057, 0.43 #6826), 064t9 (0.49 #8316, 0.43 #9852, 0.43 #11388), 06j6l (0.30 #8349, 0.26 #9885, 0.25 #12035), 0glt670 (0.29 #8344, 0.22 #8651, 0.21 #14803), 025sc50 (0.27 #8351, 0.21 #14810, 0.21 #11423), 016clz (0.26 #5844, 0.25 #6153, 0.25 #7999), 03_d0 (0.26 #11, 0.20 #8006, 0.19 #14762), 01lyv (0.23 #10180, 0.22 #9873, 0.22 #9258), 0gywn (0.22 #670, 0.20 #8359, 0.20 #1285), 0ggq0m (0.20 #2777, 0.19 #14762, 0.15 #5852) >> Best rule #5593 for best value: >> intensional similarity = 2 >> extensional distance = 162 >> proper extension: 02rgz4; 08c9b0; 02z81h; 01l79yc; 03_f0; 063tn; 0dr5y; 0c73z; 0383f; >> query: (?x2319, 017_qw) <- music(?x10796, ?x2319), artists(?x284, ?x2319) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #8316 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 243 *> proper extension: 03fbc; 0163m1; 07bzp; 01dq9q; 01w5n51; 03vhvp; *> query: (?x2319, 064t9) <- award_nominee(?x2319, ?x1181), artists(?x284, ?x2319), origin(?x2319, ?x2474) *> conf = 0.49 ranks of expected_values: 2 EVAL 0lccn artists! 064t9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 133.000 0.506 http://example.org/music/genre/artists #16138-07cz2 PRED entity: 07cz2 PRED relation: award PRED expected values: 02g3ft => 93 concepts (78 used for prediction) PRED predicted values (max 10 best out of 186): 02g3ft (0.30 #980, 0.29 #2285, 0.28 #915), 0262s1 (0.29 #2285, 0.28 #915, 0.27 #3886), 02g2yr (0.29 #2285, 0.28 #915, 0.27 #3886), 02g3gw (0.29 #2285, 0.28 #915, 0.27 #3886), 02r22gf (0.29 #2285, 0.28 #915, 0.27 #3886), 02qyntr (0.29 #2285, 0.28 #915, 0.27 #3886), 05zr6wv (0.29 #2285, 0.28 #915, 0.27 #3886), 02g2wv (0.29 #2285, 0.28 #915, 0.27 #3886), 02g3v6 (0.29 #2285, 0.28 #915, 0.27 #3886), 0gs9p (0.22 #3719, 0.20 #4632, 0.20 #2803) >> Best rule #980 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0fsw_7; >> query: (?x2770, 02g3ft) <- award(?x2770, ?x2209), award(?x2770, ?x298), nominated_for(?x298, ?x69), ?x2209 = 0gr42 >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07cz2 award 02g3ft CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 78.000 0.303 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #16137-0h6r5 PRED entity: 0h6r5 PRED relation: award PRED expected values: 02rdxsh => 81 concepts (77 used for prediction) PRED predicted values (max 10 best out of 184): 0gqyl (0.27 #9144, 0.27 #9602, 0.27 #9601), 0gq9h (0.27 #9144, 0.27 #9602, 0.27 #9601), 0k611 (0.27 #9144, 0.27 #9602, 0.27 #9601), 0gs9p (0.27 #9144, 0.27 #9602, 0.27 #9601), 02qyntr (0.27 #9144, 0.27 #9602, 0.27 #9601), 0gr4k (0.27 #9144, 0.27 #9602, 0.27 #9601), 019f4v (0.27 #9144, 0.27 #9602, 0.27 #9601), 04dn09n (0.27 #9144, 0.27 #9602, 0.27 #9601), 04kxsb (0.27 #9144, 0.27 #9602, 0.27 #9601), 0gqy2 (0.27 #9144, 0.27 #9602, 0.27 #9601) >> Best rule #9144 for best value: >> intensional similarity = 3 >> extensional distance = 953 >> proper extension: 0g60z; 02_1q9; 080dwhx; 02_1rq; 03kq98; 072kp; 039fgy; 0kfpm; 02k_4g; 04969y; ... >> query: (?x4093, ?x384) <- nominated_for(?x1991, ?x4093), nominated_for(?x384, ?x4093), award(?x4093, ?x198) >> conf = 0.27 => this is the best rule for 12 predicted values *> Best rule #507 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 0hz6mv2; *> query: (?x4093, 02rdxsh) <- genre(?x4093, ?x6887), film_format(?x4093, ?x909), ?x6887 = 03bxz7 *> conf = 0.10 ranks of expected_values: 28 EVAL 0h6r5 award 02rdxsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 81.000 77.000 0.269 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #16136-015076 PRED entity: 015076 PRED relation: sibling PRED expected values: 018db8 => 172 concepts (118 used for prediction) PRED predicted values (max 10 best out of 106): 018db8 (0.81 #1518, 0.80 #4793, 0.79 #933), 024dw0 (0.33 #66, 0.03 #1700, 0.02 #2636), 013v5j (0.19 #832, 0.07 #598, 0.05 #949), 04d_mtq (0.14 #328, 0.12 #911, 0.09 #1378), 04cr6qv (0.14 #281, 0.12 #864, 0.04 #1331), 01z7s_ (0.14 #283, 0.04 #1333, 0.02 #2620), 026_dq6 (0.14 #317, 0.04 #1367, 0.01 #3355), 01pllx (0.14 #310, 0.03 #1711, 0.02 #2647), 0gbwp (0.12 #851, 0.07 #617, 0.04 #1318), 09889g (0.12 #860, 0.05 #977, 0.04 #3784) >> Best rule #1518 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 045g4l; 02g5bf; >> query: (?x11259, ?x793) <- sibling(?x793, ?x11259), profession(?x11259, ?x1032), people(?x5855, ?x11259) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015076 sibling 018db8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 118.000 0.812 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #16135-060bp PRED entity: 060bp PRED relation: basic_title! PRED expected values: 0dj5q 0lzcs => 16 concepts (16 used for prediction) PRED predicted values (max 10 best out of 143): 07cbs (0.50 #364, 0.33 #90, 0.29 #574), 042f1 (0.50 #251, 0.33 #114, 0.25 #388), 0424m (0.38 #372, 0.33 #98, 0.27 #443), 042fk (0.38 #408, 0.33 #134, 0.27 #479), 042d1 (0.38 #390, 0.33 #116, 0.25 #253), 0dq2k (0.38 #366, 0.27 #437, 0.21 #576), 083pr (0.33 #79, 0.25 #353, 0.25 #216), 0c_md_ (0.33 #113, 0.25 #387, 0.25 #250), 03_js (0.33 #109, 0.25 #383, 0.25 #246), 06c97 (0.33 #93, 0.25 #367, 0.25 #230) >> Best rule #364 for best value: >> intensional similarity = 17 >> extensional distance = 6 >> proper extension: 01dz7z; >> query: (?x182, 07cbs) <- basic_title(?x3864, ?x182), basic_title(?x1984, ?x182), basic_title(?x1590, ?x182), profession(?x3864, ?x3802), profession(?x3864, ?x3342), profession(?x12258, ?x3802), influenced_by(?x3864, ?x10500), place_of_birth(?x1590, ?x6357), politician(?x13622, ?x3864), profession(?x10654, ?x3342), ?x12258 = 019fz, ?x10654 = 042q3, people(?x5056, ?x1590), profession(?x1984, ?x2225), type_of_union(?x1590, ?x566), place_of_birth(?x1984, ?x2474), nationality(?x1984, ?x279) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #100 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 1 *> proper extension: 060c4; *> query: (?x182, 0dj5q) <- jurisdiction_of_office(?x182, ?x7413), jurisdiction_of_office(?x182, ?x3635), jurisdiction_of_office(?x182, ?x2000), jurisdiction_of_office(?x182, ?x985), ?x3635 = 019pcs, ?x2000 = 0d0kn, ?x7413 = 04hqz, film_release_region(?x10404, ?x985), film_release_region(?x6620, ?x985), film_release_region(?x5564, ?x985), film_release_region(?x3958, ?x985), film_release_region(?x2550, ?x985), film_release_region(?x1547, ?x985), film_release_region(?x1069, ?x985), film_release_region(?x303, ?x985), ?x6620 = 0mbql, ?x3958 = 0gyh2wm, ?x5564 = 03yvf2, olympics(?x985, ?x391), ?x10404 = 01s9vc, ?x2550 = 07j8r, country(?x150, ?x985), ?x303 = 011yrp, ?x1069 = 0jqp3, ?x1547 = 0168ls *> conf = 0.33 ranks of expected_values: 35, 74 EVAL 060bp basic_title! 0lzcs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 16.000 16.000 0.500 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title EVAL 060bp basic_title! 0dj5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 16.000 16.000 0.500 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #16134-01wwvd2 PRED entity: 01wwvd2 PRED relation: award_winner! PRED expected values: 01xqqp => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 120): 013b2h (0.29 #79, 0.21 #919, 0.15 #1759), 01mhwk (0.29 #40, 0.20 #180, 0.10 #880), 02rjjll (0.29 #5, 0.14 #845, 0.13 #1685), 056878 (0.29 #32, 0.13 #172, 0.13 #1012), 0466p0j (0.29 #75, 0.13 #215, 0.10 #1755), 01mh_q (0.22 #928, 0.14 #1768, 0.12 #1068), 02cg41 (0.19 #405, 0.14 #125, 0.13 #1105), 0gpjbt (0.16 #1009, 0.11 #589, 0.08 #2129), 05pd94v (0.14 #2, 0.13 #142, 0.09 #982), 09n4nb (0.14 #47, 0.13 #187, 0.09 #1727) >> Best rule #79 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01f9zw; >> query: (?x4467, 013b2h) <- award_winner(?x3835, ?x4467), company(?x4467, ?x6230), ?x3835 = 01cky2 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #935 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 05crg7; 01vd7hn; 028qdb; 01l03w2; 01fmz6; 016890; 02pt7h_; 0134wr; 014kyy; *> query: (?x4467, 01xqqp) <- award_winner(?x4467, ?x2138), award_winner(?x2139, ?x4467), ?x2139 = 01by1l *> conf = 0.10 ranks of expected_values: 18 EVAL 01wwvd2 award_winner! 01xqqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 126.000 126.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #16133-027z0pl PRED entity: 027z0pl PRED relation: produced_by! PRED expected values: 03z9585 => 95 concepts (66 used for prediction) PRED predicted values (max 10 best out of 656): 02y_lrp (0.30 #21571, 0.29 #938, 0.22 #36570), 04jpg2p (0.17 #773, 0.12 #15939, 0.03 #15774), 02qkwl (0.17 #740, 0.06 #2813, 0.06 #37508), 03z9585 (0.17 #750, 0.06 #2813, 0.06 #37508), 0ds11z (0.17 #40, 0.06 #2813, 0.06 #37508), 0b1y_2 (0.17 #261, 0.06 #2813, 0.06 #37508), 09g7vfw (0.17 #303, 0.06 #2813, 0.06 #37508), 0416y94 (0.17 #119, 0.06 #2813, 0.06 #37508), 0bv8h2 (0.17 #316, 0.06 #2813, 0.06 #37508), 01q2nx (0.17 #497, 0.05 #13125, 0.02 #31883) >> Best rule #21571 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 0q9kd; 0grwj; 012d40; 02qgqt; 0fvf9q; 02p65p; 083chw; 014zcr; 0h5f5n; 05ty4m; ... >> query: (?x10430, ?x146) <- award_nominee(?x902, ?x10430), nominated_for(?x10430, ?x146), executive_produced_by(?x103, ?x10430) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #750 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 05183k; *> query: (?x10430, 03z9585) <- award_nominee(?x1533, ?x10430), nominated_for(?x10430, ?x146), ?x1533 = 05prs8 *> conf = 0.17 ranks of expected_values: 4 EVAL 027z0pl produced_by! 03z9585 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 95.000 66.000 0.301 http://example.org/film/film/produced_by #16132-07cdz PRED entity: 07cdz PRED relation: film_sets_designed! PRED expected values: 053j4w4 => 72 concepts (50 used for prediction) PRED predicted values (max 10 best out of 21): 076lxv (0.06 #50, 0.03 #26, 0.03 #75), 076psv (0.06 #54, 0.02 #153, 0.02 #203), 02q4mt (0.06 #73, 0.02 #172, 0.02 #520), 0bl2g (0.06 #73, 0.02 #172, 0.02 #520), 03nqbvz (0.06 #73, 0.02 #172, 0.01 #296), 057bc6m (0.05 #59, 0.03 #35, 0.02 #158), 0cb77r (0.05 #49, 0.03 #74, 0.02 #297), 07h1tr (0.04 #77, 0.03 #52, 0.03 #101), 053vcrp (0.03 #63, 0.02 #88, 0.02 #39), 058vfp4 (0.03 #62) >> Best rule #50 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 07bz5; >> query: (?x3510, 076lxv) <- nominated_for(?x398, ?x3510), list(?x3510, ?x3004), honored_for(?x762, ?x3510) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #156 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 140 *> proper extension: 0g5pv3; 05cj_j; 0d1qmz; *> query: (?x3510, 053j4w4) <- nominated_for(?x398, ?x3510), genre(?x3510, ?x53), film(?x5404, ?x3510), film_production_design_by(?x3510, ?x5894) *> conf = 0.02 ranks of expected_values: 11 EVAL 07cdz film_sets_designed! 053j4w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 72.000 50.000 0.062 http://example.org/film/film_set_designer/film_sets_designed #16131-0ycfj PRED entity: 0ycfj PRED relation: origin PRED expected values: 0mzww => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 84): 030qb3t (0.16 #1922, 0.14 #2158, 0.12 #4519), 04jpl (0.16 #1186, 0.15 #1658, 0.15 #3783), 02_286 (0.13 #1904, 0.12 #724, 0.11 #2140), 0d9jr (0.12 #806, 0.11 #1514, 0.08 #2222), 01cx_ (0.11 #1480, 0.10 #64, 0.09 #300), 02cft (0.10 #110, 0.06 #1054, 0.05 #1526), 06y57 (0.10 #93, 0.06 #801, 0.03 #2217), 0d2lt (0.10 #227, 0.03 #2115, 0.03 #2351), 0m75g (0.09 #361, 0.06 #597, 0.06 #1069), 05fjf (0.09 #356, 0.06 #592, 0.05 #1536) >> Best rule #1922 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0150jk; 0dtd6; 01rm8b; 0560w; 0bsj9; 027kwc; >> query: (?x10813, 030qb3t) <- group(?x1750, ?x10813), award(?x10813, ?x2877), artist(?x8738, ?x10813), ?x1750 = 02hnl >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ycfj origin 0mzww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 94.000 0.161 http://example.org/music/artist/origin #16130-03_lsr PRED entity: 03_lsr PRED relation: sport PRED expected values: 02vx4 => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 7): 02vx4 (0.51 #281, 0.50 #263, 0.50 #182), 0z74 (0.27 #435, 0.23 #415), 03tmr (0.05 #416, 0.04 #426), 0jm_ (0.04 #418, 0.04 #428), 018jz (0.04 #420, 0.03 #430), 018w8 (0.03 #429), 039yzs (0.01 #432) >> Best rule #281 for best value: >> intensional similarity = 11 >> extensional distance = 157 >> proper extension: 06xl8z; >> query: (?x8406, 02vx4) <- position(?x8406, ?x530), position(?x8406, ?x203), position(?x8406, ?x63), position(?x8406, ?x60), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x203 = 0dgrmp, ?x60 = 02nzb8, team(?x203, ?x8406), position(?x8406, ?x63), team(?x63, ?x8406) >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_lsr sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.509 http://example.org/sports/sports_team/sport #16129-015fsv PRED entity: 015fsv PRED relation: category PRED expected values: 08mbj5d => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #12, 0.90 #16, 0.90 #23) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 0frm7n; >> query: (?x9249, 08mbj5d) <- school(?x2820, ?x9249), school(?x2820, ?x331), ?x331 = 01jssp >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015fsv category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.912 http://example.org/common/topic/webpage./common/webpage/category #16128-01stj9 PRED entity: 01stj9 PRED relation: major_field_of_study PRED expected values: 040p_q => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 96): 0g26h (0.39 #545, 0.39 #295, 0.39 #1295), 02lp1 (0.39 #262, 0.38 #387, 0.37 #1262), 02j62 (0.37 #532, 0.37 #407, 0.36 #282), 01mkq (0.34 #391, 0.34 #266, 0.34 #1266), 062z7 (0.31 #529, 0.30 #404, 0.30 #1279), 04rjg (0.30 #521, 0.27 #1271, 0.27 #396), 02_7t (0.29 #567, 0.26 #1317, 0.25 #1192), 01tbp (0.26 #312, 0.24 #437, 0.22 #687), 04x_3 (0.26 #402, 0.22 #527, 0.21 #277), 03g3w (0.23 #2403, 0.22 #7281, 0.22 #528) >> Best rule #545 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 0frm7n; >> query: (?x12736, 0g26h) <- category(?x12736, ?x134), ?x134 = 08mbj5d, school(?x12042, ?x12736), season(?x12042, ?x701) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #452 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 88 *> proper extension: 05kj_; 07szy; 01jsn5; 0j_sncb; 0pspl; 01q0kg; 01jzyx; 05x_5; 015fs3; 02jztz; *> query: (?x12736, 040p_q) <- category(?x12736, ?x134), ?x134 = 08mbj5d, contains(?x94, ?x12736), school(?x1633, ?x12736) *> conf = 0.09 ranks of expected_values: 37 EVAL 01stj9 major_field_of_study 040p_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 127.000 127.000 0.390 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16127-0hcr PRED entity: 0hcr PRED relation: genre! PRED expected values: 01hn_t 0jwl2 08cl7s 03nymk => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 363): 014gjp (0.62 #6534, 0.33 #375, 0.14 #5548), 03nymk (0.62 #6546, 0.33 #387, 0.14 #5560), 01rf57 (0.60 #2770, 0.50 #1780, 0.33 #306), 0jwl2 (0.60 #3766, 0.40 #3521, 0.20 #3026), 08cl7s (0.50 #2107, 0.33 #4819, 0.33 #137), 0l76z (0.50 #6478, 0.33 #319, 0.29 #5492), 0557yqh (0.50 #6456, 0.33 #297, 0.14 #5470), 0d68qy (0.50 #6439, 0.33 #280, 0.14 #5453), 0124k9 (0.50 #6424, 0.33 #265, 0.14 #5438), 0431v3 (0.40 #3049, 0.38 #6495, 0.33 #336) >> Best rule #6534 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 01z4y; 0c4xc; >> query: (?x2540, 014gjp) <- genre(?x8717, ?x2540), genre(?x5314, ?x2540), genre(?x1395, ?x2540), country_of_origin(?x5314, ?x252), actor(?x8717, ?x5779), ?x1395 = 019nnl >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #6546 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 01z4y; 0c4xc; *> query: (?x2540, 03nymk) <- genre(?x8717, ?x2540), genre(?x5314, ?x2540), genre(?x1395, ?x2540), country_of_origin(?x5314, ?x252), actor(?x8717, ?x5779), ?x1395 = 019nnl *> conf = 0.62 ranks of expected_values: 2, 4, 5, 181 EVAL 0hcr genre! 03nymk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.625 http://example.org/tv/tv_program/genre EVAL 0hcr genre! 08cl7s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 102.000 0.625 http://example.org/tv/tv_program/genre EVAL 0hcr genre! 0jwl2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 102.000 0.625 http://example.org/tv/tv_program/genre EVAL 0hcr genre! 01hn_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 102.000 102.000 0.625 http://example.org/tv/tv_program/genre #16126-0gyh2wm PRED entity: 0gyh2wm PRED relation: film! PRED expected values: 0jz9f 016tt2 => 75 concepts (58 used for prediction) PRED predicted values (max 10 best out of 51): 086k8 (0.17 #2025, 0.17 #1444, 0.16 #2825), 017s11 (0.17 #77, 0.14 #149, 0.14 #1085), 05qd_ (0.14 #1451, 0.13 #2032, 0.13 #1163), 016tt2 (0.13 #222, 0.13 #2027, 0.12 #1446), 025jfl (0.12 #6, 0.07 #224, 0.06 #152), 0fqy4p (0.12 #26, 0.03 #172, 0.02 #1615), 03xq0f (0.11 #367, 0.10 #655, 0.10 #295), 061dn_ (0.10 #96, 0.09 #168, 0.06 #528), 0g1rw (0.10 #226, 0.07 #2031, 0.07 #1959), 09tlc8 (0.09 #1515) >> Best rule #2025 for best value: >> intensional similarity = 4 >> extensional distance = 900 >> proper extension: 05dy7p; 03_wm6; 0d8w2n; >> query: (?x3958, 086k8) <- country(?x3958, ?x94), production_companies(?x3958, ?x1414), film(?x1104, ?x3958), genre(?x3958, ?x53) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #222 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 148 *> proper extension: 0cq8nx; *> query: (?x3958, 016tt2) <- country(?x3958, ?x512), film(?x1104, ?x3958), ?x512 = 07ssc, award(?x3958, ?x13107) *> conf = 0.13 ranks of expected_values: 4, 19 EVAL 0gyh2wm film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 58.000 0.167 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0gyh2wm film! 0jz9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 75.000 58.000 0.167 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16125-01s7w3 PRED entity: 01s7w3 PRED relation: titles! PRED expected values: 024qqx => 112 concepts (95 used for prediction) PRED predicted values (max 10 best out of 59): 07s9rl0 (0.46 #307, 0.34 #718, 0.30 #2877), 03npn (0.33 #113, 0.11 #419, 0.04 #1449), 01z4y (0.27 #1061, 0.23 #342, 0.20 #2497), 07c52 (0.22 #642, 0.20 #30, 0.14 #3216), 04xvlr (0.20 #2261, 0.19 #2880, 0.19 #2673), 024qqx (0.20 #591, 0.17 #285, 0.16 #1004), 01hmnh (0.17 #129, 0.14 #1465, 0.14 #3006), 01jfsb (0.17 #224, 0.12 #1764, 0.12 #1662), 0c3351 (0.17 #256, 0.07 #460, 0.07 #1796), 09blyk (0.17 #251, 0.05 #2406, 0.05 #2098) >> Best rule #307 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 04kkz8; 02ylg6; 0284b56; 0_9wr; >> query: (?x9154, 07s9rl0) <- film(?x157, ?x9154), music(?x9154, ?x2392), ?x157 = 02qgqt, nominated_for(?x495, ?x9154) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #591 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 0ddjy; 06wbm8q; *> query: (?x9154, 024qqx) <- executive_produced_by(?x9154, ?x1533), film_crew_role(?x9154, ?x2154), ?x2154 = 01vx2h, award(?x9154, ?x640) *> conf = 0.20 ranks of expected_values: 6 EVAL 01s7w3 titles! 024qqx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 112.000 95.000 0.462 http://example.org/media_common/netflix_genre/titles #16124-0dfw0 PRED entity: 0dfw0 PRED relation: film_crew_role PRED expected values: 09zzb8 01vx2h => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.80 #2052, 0.80 #309, 0.77 #1058), 0dxtw (0.48 #2060, 0.44 #2300, 0.43 #2163), 01vx2h (0.45 #964, 0.43 #454, 0.43 #1239), 01pvkk (0.35 #79, 0.33 #147, 0.33 #727), 02ynfr (0.27 #322, 0.25 #14, 0.22 #968), 015h31 (0.19 #110, 0.16 #452, 0.15 #246), 0d2b38 (0.17 #262, 0.15 #978, 0.14 #944), 01xy5l_ (0.16 #457, 0.15 #559, 0.13 #899), 0215hd (0.16 #221, 0.15 #2617, 0.15 #563), 089g0h (0.15 #86, 0.14 #2069, 0.13 #256) >> Best rule #2052 for best value: >> intensional similarity = 4 >> extensional distance = 226 >> proper extension: 03ckwzc; 04gknr; 0963mq; 03t97y; 0jjy0; 07g_0c; 04zyhx; 03sxd2; 02vqhv0; 04g9gd; ... >> query: (?x4902, 09zzb8) <- crewmember(?x4902, ?x9391), film_crew_role(?x4902, ?x1284), genre(?x4902, ?x225), ?x1284 = 0ch6mp2 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0dfw0 film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 126.000 0.803 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dfw0 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.803 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16123-03p2xc PRED entity: 03p2xc PRED relation: language PRED expected values: 02h40lc => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.89 #1369, 0.89 #474, 0.89 #533), 064_8sq (0.12 #435, 0.12 #732, 0.12 #2736), 06b_j (0.12 #2736, 0.11 #2555, 0.11 #318), 06nm1 (0.12 #2736, 0.11 #2555, 0.11 #306), 04306rv (0.12 #2736, 0.11 #2555, 0.10 #300), 0jzc (0.12 #2736, 0.11 #2555, 0.08 #315), 05zjd (0.12 #2736, 0.11 #2555, 0.08 #144), 02bjrlw (0.12 #2736, 0.11 #2555, 0.07 #237), 03k50 (0.12 #2736, 0.11 #2555, 0.07 #245), 03_9r (0.12 #2736, 0.11 #2555, 0.05 #659) >> Best rule #1369 for best value: >> intensional similarity = 4 >> extensional distance = 1466 >> proper extension: 0czyxs; 01h7bb; 04ddm4; 03hjv97; 02847m9; 0m491; 0j6b5; 0bby9p5; 0kcn7; 03h3x5; ... >> query: (?x7128, 02h40lc) <- film(?x4928, ?x7128), award_nominee(?x4928, ?x91), award_winner(?x2173, ?x4928), genre(?x7128, ?x53) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03p2xc language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.893 http://example.org/film/film/language #16122-01dzz7 PRED entity: 01dzz7 PRED relation: gender PRED expected values: 05zppz => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #65, 0.87 #23, 0.86 #51), 02zsn (0.73 #77, 0.24 #114, 0.24 #112) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 332 >> proper extension: 0k4gf; 02rzmzk; >> query: (?x1752, 05zppz) <- influenced_by(?x1752, ?x7828), influenced_by(?x1752, ?x1287), place_of_death(?x1287, ?x9341), type_of_union(?x7828, ?x566) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dzz7 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.868 http://example.org/people/person/gender #16121-09qxq7 PRED entity: 09qxq7 PRED relation: artists PRED expected values: 015882 01wz3cx 01ydzx => 52 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1157): 011z3g (0.67 #2729, 0.60 #3796, 0.60 #1661), 01vvycq (0.67 #2183, 0.60 #1115, 0.50 #3250), 01wk7ql (0.67 #3025, 0.55 #5160, 0.50 #4092), 016s0m (0.67 #2951, 0.50 #4018, 0.45 #5086), 012vd6 (0.67 #2609, 0.40 #3676, 0.40 #1541), 0bs1g5r (0.67 #2878, 0.40 #3945, 0.40 #1810), 0407f (0.67 #2413, 0.40 #3480, 0.40 #1345), 01pq5j7 (0.67 #2599, 0.40 #3666, 0.40 #1531), 07s3vqk (0.67 #2147, 0.40 #3214, 0.40 #1079), 0cg9y (0.67 #2314, 0.36 #4449, 0.30 #3381) >> Best rule #2729 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 06j6l; >> query: (?x12800, 011z3g) <- artists(?x12800, ?x4741), artists(?x12800, ?x1181), role(?x4741, ?x227), vacationer(?x126, ?x4741), category(?x4741, ?x134), ?x1181 = 0b68vs, profession(?x4741, ?x131) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4867 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 9 *> proper extension: 02qdgx; *> query: (?x12800, 01ydzx) <- artists(?x12800, ?x8488), artists(?x12800, ?x4741), artists(?x12800, ?x3740), ?x4741 = 01s21dg, influenced_by(?x8323, ?x8488), diet(?x3740, ?x3130), profession(?x3740, ?x220) *> conf = 0.45 ranks of expected_values: 85, 275, 316 EVAL 09qxq7 artists 01ydzx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 52.000 26.000 0.667 http://example.org/music/genre/artists EVAL 09qxq7 artists 01wz3cx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 26.000 0.667 http://example.org/music/genre/artists EVAL 09qxq7 artists 015882 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 52.000 26.000 0.667 http://example.org/music/genre/artists #16120-01pjr7 PRED entity: 01pjr7 PRED relation: student! PRED expected values: 0g2jl => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 198): 065y4w7 (0.12 #541, 0.06 #14, 0.05 #10027), 0bwfn (0.09 #1329, 0.09 #17139, 0.08 #6072), 03ksy (0.08 #12227, 0.06 #3795, 0.06 #1160), 01w5m (0.06 #12226, 0.05 #3267, 0.05 #23293), 04b_46 (0.06 #754, 0.06 #227, 0.03 #15510), 07tg4 (0.06 #86, 0.04 #3775, 0.03 #21693), 09f2j (0.06 #159, 0.04 #686, 0.03 #14915), 021w0_ (0.06 #324, 0.02 #851, 0.01 #1905), 01q7q2 (0.06 #293, 0.02 #820, 0.01 #1874), 052nd (0.06 #9, 0.02 #536, 0.01 #1590) >> Best rule #541 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 01c6l; >> query: (?x7624, 065y4w7) <- profession(?x7624, ?x319), film(?x7624, ?x6099), currency(?x7624, ?x170) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pjr7 student! 0g2jl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 135.000 0.122 http://example.org/education/educational_institution/students_graduates./education/education/student #16119-03jj93 PRED entity: 03jj93 PRED relation: languages PRED expected values: 02h40lc => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 15): 02h40lc (0.29 #353, 0.28 #80, 0.28 #392), 064_8sq (0.07 #1952, 0.07 #1210, 0.03 #756), 02bjrlw (0.07 #1952, 0.07 #1210, 0.02 #586), 06nm1 (0.07 #1952, 0.07 #1210, 0.01 #201), 04306rv (0.07 #1952, 0.07 #1210, 0.01 #276), 0t_2 (0.07 #1952, 0.07 #1210, 0.01 #360), 097kp (0.07 #1952, 0.07 #1210), 0k0sv (0.07 #1952, 0.07 #1210), 03hkp (0.07 #1952, 0.07 #1210), 03k50 (0.02 #2346, 0.02 #1409, 0.02 #2502) >> Best rule #353 for best value: >> intensional similarity = 4 >> extensional distance = 443 >> proper extension: 01r42_g; 02l840; 066m4g; 035gjq; 06w2sn5; 02d9k; 08m4c8; 01wgxtl; 01vw20_; 02wb6yq; ... >> query: (?x11651, 02h40lc) <- profession(?x11651, ?x1032), participant(?x398, ?x11651), ?x1032 = 02hrh1q, nominated_for(?x398, ?x406) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03jj93 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.285 http://example.org/people/person/languages #16118-02qjb7z PRED entity: 02qjb7z PRED relation: contains! PRED expected values: 0d060g => 85 concepts (51 used for prediction) PRED predicted values (max 10 best out of 167): 0d060g (0.83 #18810, 0.73 #15227, 0.64 #18811), 036k0s (0.73 #18809, 0.67 #4481, 0.60 #6272), 09c7w0 (0.65 #17020, 0.55 #5379, 0.53 #40335), 07ssc (0.29 #26007, 0.27 #26904, 0.22 #6304), 02jx1 (0.23 #26959, 0.21 #14417, 0.18 #29652), 01n7q (0.19 #26053, 0.17 #26950, 0.14 #4559), 0694j (0.12 #2162, 0.07 #28665, 0.03 #4852), 059g4 (0.12 #2254, 0.03 #28232, 0.02 #40795), 015jr (0.12 #2205, 0.03 #4895, 0.02 #5790), 0j95 (0.12 #2352, 0.02 #5937, 0.02 #7728) >> Best rule #18810 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 0pbhz; 0d1yn; 0jq27; 09hzc; 0fjsl; >> query: (?x11016, ?x279) <- administrative_division(?x11016, ?x2541), contains(?x279, ?x2541), film_release_region(?x66, ?x279), country(?x150, ?x279) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qjb7z contains! 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 51.000 0.827 http://example.org/location/location/contains #16117-06mkj PRED entity: 06mkj PRED relation: combatants! PRED expected values: 0845v => 263 concepts (263 used for prediction) PRED predicted values (max 10 best out of 60): 03jqfx (0.58 #1543, 0.58 #3971, 0.56 #4209), 0845v (0.56 #539, 0.29 #183, 0.12 #420), 081pw (0.50 #1484, 0.50 #1067, 0.48 #1307), 048n7 (0.44 #498, 0.42 #1505, 0.41 #1148), 01cpp0 (0.44 #529, 0.19 #1119, 0.18 #1891), 01hwkn (0.38 #462, 0.33 #581, 0.15 #4195), 01_3rn (0.38 #443, 0.22 #503, 0.18 #1153), 0dr7s (0.38 #461, 0.22 #580, 0.08 #9936), 02tvsn (0.38 #467, 0.04 #1653, 0.04 #4200), 0cm2xh (0.33 #841, 0.33 #487, 0.30 #1317) >> Best rule #1543 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 09c7w0; 0jgd; 0d060g; 0chghy; 07ssc; 06mzp; 0f8l9c; 03gj2; 059j2; 035qy; ... >> query: (?x2152, ?x9939) <- olympics(?x2152, ?x391), film_release_region(?x66, ?x2152), entity_involved(?x9939, ?x2152) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #539 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 7 *> proper extension: 0dv0z; 043870; *> query: (?x2152, 0845v) <- combatants(?x10176, ?x2152), ?x10176 = 01gqg3 *> conf = 0.56 ranks of expected_values: 2 EVAL 06mkj combatants! 0845v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 263.000 263.000 0.583 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #16116-01pnn3 PRED entity: 01pnn3 PRED relation: film PRED expected values: 02_1sj => 156 concepts (134 used for prediction) PRED predicted values (max 10 best out of 931): 013q07 (0.33 #7508, 0.33 #5720, 0.04 #45056), 0295sy (0.33 #959, 0.25 #2747, 0.17 #6323), 06_wqk4 (0.33 #126, 0.25 #1914, 0.06 #16218), 06z8s_ (0.33 #129, 0.25 #1917, 0.04 #16221), 0blpg (0.33 #656, 0.25 #2444, 0.04 #38204), 01cz7r (0.33 #1323, 0.25 #3111, 0.04 #96553), 0418wg (0.33 #401, 0.25 #2189, 0.04 #14705), 03s6l2 (0.33 #82, 0.25 #1870, 0.03 #153770), 0pv54 (0.33 #957, 0.25 #2745, 0.03 #153770), 014l6_ (0.33 #526, 0.25 #2314, 0.03 #153770) >> Best rule #7508 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 073749; >> query: (?x2647, 013q07) <- film(?x2647, ?x6099), film(?x2647, ?x408), ?x408 = 01k1k4, featured_film_locations(?x6099, ?x957) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #62659 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 198 *> proper extension: 0168dy; *> query: (?x2647, 02_1sj) <- participant(?x1231, ?x2647), participant(?x917, ?x2647), people(?x5606, ?x917), nominated_for(?x2647, ?x6099) *> conf = 0.01 ranks of expected_values: 621 EVAL 01pnn3 film 02_1sj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 156.000 134.000 0.333 http://example.org/film/actor/film./film/performance/film #16115-04jpl PRED entity: 04jpl PRED relation: time_zones PRED expected values: 03bdv => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 13): 03bdv (0.71 #534, 0.25 #344, 0.17 #461), 02llzg (0.36 #17, 0.25 #407, 0.24 #225), 02lcqs (0.32 #213, 0.28 #421, 0.27 #395), 02hcv8 (0.31 #497, 0.31 #1265, 0.30 #1291), 02fqwt (0.28 #183, 0.28 #326, 0.21 #652), 02hczc (0.13 #496, 0.13 #327, 0.11 #718), 03plfd (0.11 #544, 0.10 #687, 0.09 #296), 052vwh (0.07 #25, 0.06 #51, 0.05 #272), 0d2t4g (0.06 #48, 0.03 #126, 0.02 #451), 042g7t (0.06 #818, 0.05 #297, 0.04 #961) >> Best rule #534 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 029jpy; >> query: (?x362, ?x5327) <- place_of_birth(?x361, ?x362), contains(?x362, ?x12308), time_zones(?x12308, ?x5327) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04jpl time_zones 03bdv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.710 http://example.org/location/location/time_zones #16114-01g969 PRED entity: 01g969 PRED relation: location PRED expected values: 080h2 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 144): 0mzvm (0.53 #12063, 0.48 #29758, 0.47 #53894), 030qb3t (0.24 #8125, 0.23 #8929, 0.23 #1692), 02_286 (0.19 #842, 0.18 #12905, 0.18 #20141), 0d6lp (0.12 #168, 0.05 #973, 0.05 #2581), 0f2wj (0.12 #34, 0.04 #4859, 0.03 #6468), 0hptm (0.12 #303, 0.03 #1108, 0.02 #27645), 07b_l (0.12 #187, 0.01 #6621, 0.01 #8229), 0f2s6 (0.12 #474, 0.01 #2887), 0mmzt (0.12 #239), 0r0m6 (0.09 #1827, 0.06 #3435, 0.06 #5043) >> Best rule #12063 for best value: >> intensional similarity = 4 >> extensional distance = 334 >> proper extension: 02w5q6; >> query: (?x9783, ?x3764) <- profession(?x9783, ?x1032), participant(?x9783, ?x9782), ?x1032 = 02hrh1q, place_of_birth(?x9783, ?x3764) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #4879 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 124 *> proper extension: 07q0g5; *> query: (?x9783, 080h2) <- profession(?x9783, ?x1032), type_of_union(?x9783, ?x566), participant(?x9782, ?x9783), actor(?x4203, ?x9783) *> conf = 0.02 ranks of expected_values: 94 EVAL 01g969 location 080h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 91.000 91.000 0.527 http://example.org/people/person/places_lived./people/place_lived/location #16113-01kp66 PRED entity: 01kp66 PRED relation: award PRED expected values: 09td7p => 97 concepts (84 used for prediction) PRED predicted values (max 10 best out of 259): 0bdwft (0.79 #13445, 0.77 #11862, 0.76 #26503), 09cn0c (0.72 #21360, 0.71 #20170, 0.71 #21359), 027571b (0.72 #21360, 0.71 #20170, 0.71 #21359), 02z1nbg (0.72 #21360, 0.71 #20170, 0.71 #21359), 027b9k6 (0.72 #21360, 0.71 #20170, 0.71 #21359), 02y_j8g (0.72 #21360, 0.71 #20170, 0.71 #21359), 09sb52 (0.53 #1618, 0.38 #828, 0.33 #433), 0ck27z (0.44 #877, 0.25 #482, 0.14 #11157), 09qwmm (0.33 #32, 0.26 #1612, 0.17 #427), 09td7p (0.31 #1694, 0.14 #22151, 0.13 #20566) >> Best rule #13445 for best value: >> intensional similarity = 3 >> extensional distance = 1380 >> proper extension: 025vry; 0126rp; 057hz; 03dpqd; 01n44c; 087yty; 014l4w; 07h76; 07mvp; 011z3g; ... >> query: (?x4234, ?x1245) <- award_winner(?x1245, ?x4234), nominated_for(?x1245, ?x144), ceremony(?x1245, ?x78) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1694 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 66 *> proper extension: 01wk51; *> query: (?x4234, 09td7p) <- type_of_union(?x4234, ?x1873), award(?x4234, ?x2880), ?x2880 = 02ppm4q *> conf = 0.31 ranks of expected_values: 10 EVAL 01kp66 award 09td7p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 97.000 84.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16112-033qxt PRED entity: 033qxt PRED relation: languages_spoken PRED expected values: 0jzc => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 68): 02h40lc (0.81 #534, 0.74 #587, 0.69 #641), 0880p (0.67 #202, 0.50 #95, 0.40 #149), 0t_2 (0.50 #438, 0.50 #65, 0.47 #385), 06b_j (0.50 #72, 0.40 #126, 0.33 #179), 03k50 (0.44 #273, 0.32 #320, 0.30 #107), 07c9s (0.44 #281, 0.32 #320, 0.30 #107), 0swlx (0.40 #156, 0.25 #102, 0.17 #209), 02hxcvy (0.33 #297, 0.32 #320, 0.30 #107), 0688f (0.33 #301, 0.20 #142, 0.11 #1341), 064_8sq (0.32 #497, 0.32 #320, 0.30 #107) >> Best rule #534 for best value: >> intensional similarity = 18 >> extensional distance = 29 >> proper extension: 02w7gg; 033tf_; 071x0k; 0x67; 09v5bdn; 03lmx1; 0d7wh; 0g8_vp; 059_w; 03bkbh; ... >> query: (?x12078, 02h40lc) <- languages_spoken(?x12078, ?x13310), languages_spoken(?x12078, ?x3966), language(?x8456, ?x3966), language(?x7114, ?x3966), language(?x6798, ?x3966), language(?x4178, ?x3966), language(?x715, ?x3966), ?x715 = 02py4c8, languages(?x9762, ?x3966), languages(?x8085, ?x3966), ?x4178 = 0bmssv, ?x8456 = 02k1pr, ?x6798 = 0g7pm1, ?x7114 = 06rzwx, language(?x1724, ?x13310), countries_spoken_in(?x13310, ?x279), ?x8085 = 0448r, ?x9762 = 03f1zhf >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #176 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 041rx; 018s6c; *> query: (?x12078, 0jzc) <- languages_spoken(?x12078, ?x13310), languages_spoken(?x12078, ?x3966), ?x3966 = 03hkp, people(?x12078, ?x2817), languages_spoken(?x9428, ?x13310), language(?x6270, ?x13310), language(?x1724, ?x13310), nominated_for(?x5959, ?x6270), ?x9428 = 048z7l, film_release_region(?x6270, ?x2316), ?x2316 = 06t2t, film_festivals(?x6270, ?x2686), film_release_distribution_medium(?x1724, ?x81) *> conf = 0.17 ranks of expected_values: 34 EVAL 033qxt languages_spoken 0jzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 28.000 28.000 0.806 http://example.org/people/ethnicity/languages_spoken #16111-03wd5tk PRED entity: 03wd5tk PRED relation: profession PRED expected values: 02pjxr => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 76): 02pjxr (0.77 #1085, 0.76 #1685, 0.74 #2285), 02hrh1q (0.76 #9915, 0.74 #11715, 0.73 #12615), 01d_h8 (0.48 #2106, 0.48 #1956, 0.39 #3306), 02jknp (0.40 #1958, 0.33 #908, 0.32 #2408), 03gjzk (0.40 #1966, 0.31 #1366, 0.31 #2116), 0dxtg (0.36 #2864, 0.34 #2114, 0.32 #2414), 02krf9 (0.33 #28, 0.19 #2428, 0.19 #1378), 089fss (0.33 #17, 0.18 #1667, 0.15 #1067), 02ynfr (0.33 #41, 0.08 #1091, 0.06 #2291), 01c72t (0.31 #1375, 0.20 #325, 0.17 #2125) >> Best rule #1085 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0bytkq; 0d5wn3; 05b2gsm; 05km8z; >> query: (?x5894, 02pjxr) <- type_of_union(?x5894, ?x566), film_production_design_by(?x3510, ?x5894), award_winner(?x1793, ?x5894) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03wd5tk profession 02pjxr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.769 http://example.org/people/person/profession #16110-0hnjt PRED entity: 0hnjt PRED relation: profession PRED expected values: 02hv44_ => 142 concepts (97 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.72 #4898, 0.70 #10227, 0.70 #11559), 0kyk (0.54 #4174, 0.47 #2989, 0.46 #769), 01d_h8 (0.52 #9036, 0.50 #13625, 0.49 #8148), 05z96 (0.50 #42, 0.41 #190, 0.26 #338), 02hv44_ (0.48 #649, 0.26 #797, 0.25 #57), 02jknp (0.45 #9037, 0.42 #9777, 0.41 #8149), 03gjzk (0.41 #8156, 0.38 #9784, 0.37 #9044), 09jwl (0.32 #13489, 0.20 #462, 0.17 #8308), 018gz8 (0.27 #2384, 0.23 #4309, 0.22 #460), 0nbcg (0.22 #13502, 0.15 #475, 0.11 #5656) >> Best rule #4898 for best value: >> intensional similarity = 4 >> extensional distance = 298 >> proper extension: 03w1v2; 021_rm; 0h1mt; 048lv; 013cr; 049g_xj; 01bpc9; 01gzm2; 030h95; 05ztm4r; ... >> query: (?x4738, 02hrh1q) <- nationality(?x4738, ?x94), profession(?x4738, ?x353), location(?x4738, ?x739), ?x739 = 02_286 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #649 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 05qd_; 061dn_; 03m9c8; *> query: (?x4738, 02hv44_) <- award(?x4738, ?x7606), award(?x4738, ?x921), ?x7606 = 01l78d, award_winner(?x921, ?x118) *> conf = 0.48 ranks of expected_values: 5 EVAL 0hnjt profession 02hv44_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 142.000 97.000 0.717 http://example.org/people/person/profession #16109-07_m2 PRED entity: 07_m2 PRED relation: people! PRED expected values: 034qg => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 55): 0gk4g (0.24 #1181, 0.24 #2028, 0.24 #2093), 032s66 (0.19 #634, 0.04 #2586, 0.04 #1479), 07jwr (0.17 #465, 0.12 #725, 0.12 #790), 0dq9p (0.17 #2035, 0.17 #2100, 0.13 #1905), 02k6hp (0.14 #687, 0.12 #752, 0.12 #817), 02y0js (0.12 #2085, 0.11 #1694, 0.09 #4170), 034qg (0.11 #618, 0.11 #292, 0.04 #1463), 01tf_6 (0.11 #290, 0.10 #681, 0.09 #811), 051_y (0.11 #633, 0.04 #2065, 0.04 #2130), 0148xv (0.11 #325, 0.03 #1301, 0.02 #1041) >> Best rule #1181 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 07c37; >> query: (?x10923, 0gk4g) <- student(?x8052, ?x10923), influenced_by(?x4679, ?x10923), people(?x6821, ?x10923), people(?x6821, ?x4387), instrumentalists(?x227, ?x4387) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #618 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 0gct_; 07_m9_; *> query: (?x10923, 034qg) <- profession(?x10923, ?x955), people(?x6821, ?x10923), nationality(?x10923, ?x10382), ?x955 = 0n1h *> conf = 0.11 ranks of expected_values: 7 EVAL 07_m2 people! 034qg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 151.000 151.000 0.243 http://example.org/people/cause_of_death/people #16108-049xgc PRED entity: 049xgc PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #36, 0.83 #67, 0.83 #78), 07c52 (0.06 #23, 0.03 #190, 0.03 #180), 02nxhr (0.05 #128, 0.05 #133, 0.04 #101), 07z4p (0.04 #10, 0.04 #40, 0.03 #93) >> Best rule #36 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 0353xq; 01svry; 02mc5v; 04sh80; 03wjm2; >> query: (?x5648, 029j_) <- titles(?x53, ?x5648), executive_produced_by(?x5648, ?x3101), written_by(?x5648, ?x1532) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049xgc film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #16107-0257__ PRED entity: 0257__ PRED relation: ceremony PRED expected values: 02rjjll 0gpjbt 056878 0jzphpx 01xqqp => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 128): 0gpjbt (0.92 #408, 0.91 #792, 0.89 #24), 056878 (0.88 #923, 0.88 #411, 0.87 #27), 02rjjll (0.87 #771, 0.86 #387, 0.85 #259), 01xqqp (0.71 #341, 0.71 #853, 0.70 #469), 0jzphpx (0.68 #930, 0.68 #290, 0.67 #418), 02yxh9 (0.38 #3332, 0.18 #602, 0.17 #1626), 05c1t6z (0.22 #1676, 0.22 #1292, 0.21 #1164), 0gvstc3 (0.20 #1693, 0.19 #1309, 0.19 #1181), 0gx_st (0.20 #672, 0.18 #1696, 0.18 #1312), 02q690_ (0.20 #1338, 0.20 #1722, 0.20 #1210) >> Best rule #408 for best value: >> intensional similarity = 6 >> extensional distance = 64 >> proper extension: 025m8l; 025mb9; 0248jb; 02v703; 02fm4d; 024_dt; >> query: (?x12819, 0gpjbt) <- ceremony(?x12819, ?x9431), ceremony(?x12819, ?x1362), award_winner(?x12819, ?x8080), ?x1362 = 019bk0, profession(?x8080, ?x563), ?x9431 = 02cg41 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 0257__ ceremony 01xqqp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.924 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0257__ ceremony 0jzphpx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.924 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0257__ ceremony 056878 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.924 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0257__ ceremony 0gpjbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.924 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0257__ ceremony 02rjjll CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.924 http://example.org/award/award_category/winners./award/award_honor/ceremony #16106-02j62 PRED entity: 02j62 PRED relation: major_field_of_study! PRED expected values: 062z7 => 78 concepts (73 used for prediction) PRED predicted values (max 10 best out of 108): 06ms6 (0.87 #3509, 0.84 #3436, 0.84 #3432), 037mh8 (0.87 #3509, 0.84 #3436, 0.84 #3432), 062z7 (0.87 #3509, 0.84 #3436, 0.84 #3432), 04sh3 (0.84 #3436, 0.84 #3432, 0.84 #3430), 0g4gr (0.84 #3436, 0.84 #3432, 0.84 #3430), 06mq7 (0.84 #3436, 0.84 #3432, 0.84 #3430), 0mg1w (0.84 #3436, 0.84 #3432, 0.84 #3430), 02j62 (0.50 #715, 0.44 #1627, 0.41 #2323), 02822 (0.50 #789, 0.40 #999, 0.33 #25), 02h40lc (0.50 #835, 0.33 #1259, 0.33 #1189) >> Best rule #3509 for best value: >> intensional similarity = 10 >> extensional distance = 54 >> proper extension: 06ntj; >> query: (?x2981, ?x2172) <- major_field_of_study(?x2981, ?x9111), major_field_of_study(?x2981, ?x9079), major_field_of_study(?x2981, ?x2172), major_field_of_study(?x122, ?x9111), major_field_of_study(?x4187, ?x2172), student(?x9111, ?x10394), ?x4187 = 05mv4, major_field_of_study(?x3513, ?x9079), currency(?x3513, ?x170), school(?x799, ?x3513) >> conf = 0.87 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 02j62 major_field_of_study! 062z7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 73.000 0.867 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #16105-01xwqn PRED entity: 01xwqn PRED relation: nationality PRED expected values: 09c7w0 => 105 concepts (102 used for prediction) PRED predicted values (max 10 best out of 95): 09c7w0 (0.84 #8445, 0.84 #8344, 0.82 #2205), 03rjj (0.65 #306, 0.06 #2310, 0.05 #2410), 05fjf (0.51 #8752, 0.49 #9162, 0.36 #8753), 059rby (0.32 #8958, 0.30 #8446, 0.24 #5626), 02jx1 (0.17 #133, 0.11 #7067, 0.11 #4045), 07ssc (0.13 #4632, 0.12 #4027, 0.12 #3827), 03rk0 (0.12 #3958, 0.09 #2149, 0.09 #4562), 0345h (0.10 #1432, 0.08 #2837, 0.08 #1332), 0d060g (0.06 #7141, 0.05 #3014, 0.05 #3717), 0h7x (0.06 #1436, 0.05 #2037, 0.03 #2841) >> Best rule #8445 for best value: >> intensional similarity = 3 >> extensional distance = 1637 >> proper extension: 07m69t; >> query: (?x10963, ?x94) <- location(?x10963, ?x13853), contains(?x94, ?x13853), ?x94 = 09c7w0 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xwqn nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 102.000 0.839 http://example.org/people/person/nationality #16104-07_k0c0 PRED entity: 07_k0c0 PRED relation: film_crew_role PRED expected values: 09vw2b7 0ch6mp2 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 24): 09vw2b7 (0.89 #161, 0.87 #130, 0.63 #820), 0ch6mp2 (0.77 #131, 0.74 #162, 0.72 #821), 01pvkk (0.37 #133, 0.35 #164, 0.27 #1324), 02ynfr (0.27 #137, 0.25 #168, 0.25 #13), 01xy5l_ (0.18 #1908, 0.17 #166, 0.17 #135), 015h31 (0.18 #1908, 0.17 #163, 0.15 #132), 02vs3x5 (0.18 #1908, 0.13 #1750, 0.12 #20), 04pyp5 (0.18 #1908, 0.13 #1750, 0.12 #14), 089g0h (0.18 #1908, 0.13 #1750, 0.12 #171), 02_n3z (0.18 #1908, 0.13 #1750, 0.10 #63) >> Best rule #161 for best value: >> intensional similarity = 5 >> extensional distance = 108 >> proper extension: 02q56mk; 0kv238; 0125xq; 0dfw0; 08phg9; 01q2nx; 0640y35; 01738w; 01y9jr; 063fh9; ... >> query: (?x5687, 09vw2b7) <- film_crew_role(?x5687, ?x2091), film_crew_role(?x5687, ?x137), ?x2091 = 02rh1dz, film_crew_role(?x4479, ?x137), ?x4479 = 04gv3db >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07_k0c0 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.891 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 07_k0c0 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.891 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16103-06cm5 PRED entity: 06cm5 PRED relation: currency PRED expected values: 09nqf => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.81 #29, 0.78 #71, 0.77 #50), 01nv4h (0.08 #9, 0.02 #226, 0.02 #303), 088n7 (0.01 #56) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 206 >> proper extension: 0299hs; >> query: (?x6137, 09nqf) <- award(?x6137, ?x6909), nominated_for(?x6909, ?x2770), ?x2770 = 07cz2 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06cm5 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.808 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #16102-03mh94 PRED entity: 03mh94 PRED relation: film! PRED expected values: 0k269 02v0ff => 54 concepts (34 used for prediction) PRED predicted values (max 10 best out of 708): 0bjkpt (0.53 #8292, 0.48 #55970, 0.44 #58044), 0154d7 (0.31 #11859, 0.25 #3568, 0.02 #18079), 01tnbn (0.31 #11433, 0.25 #3142, 0.01 #50821), 0gr36 (0.25 #2571, 0.23 #10862), 027_sn (0.25 #3309, 0.15 #11600), 01vy_v8 (0.25 #2803, 0.15 #11094), 0c0k1 (0.23 #11864, 0.12 #3573, 0.04 #16012), 0335fp (0.22 #5523, 0.12 #13814, 0.02 #22105), 06m6p7 (0.18 #9654, 0.01 #34531, 0.01 #38675), 01fyzy (0.15 #11421, 0.12 #3130, 0.02 #23858) >> Best rule #8292 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 016z9n; 0571m; 03phtz; >> query: (?x463, ?x4563) <- nominated_for(?x4563, ?x463), film(?x8663, ?x463), ?x8663 = 05cl2w, genre(?x463, ?x258) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #15121 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0879bpq; 08k40m; 0gh65c5; 024mxd; 0gs973; *> query: (?x463, 0k269) <- film(?x3651, ?x463), film(?x2938, ?x463), ?x2938 = 01nwwl, award_winner(?x458, ?x3651) *> conf = 0.04 ranks of expected_values: 240 EVAL 03mh94 film! 02v0ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 34.000 0.528 http://example.org/film/actor/film./film/performance/film EVAL 03mh94 film! 0k269 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 54.000 34.000 0.528 http://example.org/film/actor/film./film/performance/film #16101-0d06m5 PRED entity: 0d06m5 PRED relation: person! PRED expected values: 0g9z_32 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 45): 05t54s (0.25 #43, 0.14 #457, 0.14 #250), 064q5v (0.25 #36, 0.14 #243, 0.11 #381), 06929s (0.25 #21, 0.14 #228, 0.11 #366), 0413cff (0.25 #24, 0.06 #300, 0.05 #507), 0g9z_32 (0.25 #45, 0.06 #321, 0.05 #528), 0bh8tgs (0.25 #25, 0.06 #301, 0.05 #508), 0g9lm2 (0.25 #22, 0.05 #436, 0.05 #3059), 02847m9 (0.25 #8, 0.05 #422, 0.04 #3045), 0bx_hnp (0.17 #753, 0.04 #2065, 0.04 #3100), 02v570 (0.14 #254, 0.11 #392, 0.10 #461) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0157m; >> query: (?x3445, 05t54s) <- jurisdiction_of_office(?x3445, ?x94), student(?x1519, ?x3445), ?x1519 = 013zdg >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #45 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0157m; *> query: (?x3445, 0g9z_32) <- jurisdiction_of_office(?x3445, ?x94), student(?x1519, ?x3445), ?x1519 = 013zdg *> conf = 0.25 ranks of expected_values: 5 EVAL 0d06m5 person! 0g9z_32 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 143.000 143.000 0.250 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #16100-018phr PRED entity: 018phr PRED relation: profession PRED expected values: 09jwl => 106 concepts (46 used for prediction) PRED predicted values (max 10 best out of 58): 09jwl (0.81 #2082, 0.80 #2231, 0.79 #2380), 02hrh1q (0.76 #5621, 0.70 #3558, 0.69 #4591), 016z4k (0.59 #885, 0.56 #444, 0.47 #591), 039v1 (0.48 #477, 0.38 #2099, 0.38 #2248), 01c72t (0.40 #318, 0.30 #1644, 0.30 #1940), 01d_h8 (0.35 #740, 0.29 #1182, 0.29 #1330), 0n1h (0.26 #893, 0.23 #3114, 0.23 #3261), 0dxtg (0.24 #748, 0.23 #1338, 0.23 #6061), 02jknp (0.24 #742, 0.21 #1184, 0.20 #1332), 03gjzk (0.20 #6063, 0.20 #4592, 0.19 #5769) >> Best rule #2082 for best value: >> intensional similarity = 3 >> extensional distance = 262 >> proper extension: 01pbxb; 0f0y8; 028q6; 03c7ln; 0lbj1; 01vw87c; 01vrx3g; 0fp_v1x; 0m2l9; 01wl38s; ... >> query: (?x7937, 09jwl) <- artists(?x1928, ?x7937), role(?x7937, ?x75), profession(?x7937, ?x131) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018phr profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 46.000 0.811 http://example.org/people/person/profession #16099-039cq4 PRED entity: 039cq4 PRED relation: tv_program! PRED expected values: 021bk 06jnvs 0bqs56 01svq8 => 86 concepts (71 used for prediction) PRED predicted values (max 10 best out of 244): 0p_2r (0.34 #4705, 0.27 #2926, 0.26 #6173), 0284gcb (0.33 #15, 0.09 #1316, 0.08 #1643), 02778tk (0.33 #130, 0.03 #5196, 0.02 #2894), 07ymr5 (0.27 #2926, 0.23 #3089, 0.22 #3088), 0pz04 (0.27 #2926, 0.23 #3089, 0.22 #3088), 0j1yf (0.27 #2926, 0.23 #3089, 0.22 #3088), 05gnf (0.27 #2926, 0.22 #3088, 0.16 #1463), 025mb_ (0.27 #2926, 0.15 #4867, 0.04 #8763), 02778pf (0.26 #6173, 0.23 #3089, 0.22 #3088), 086nl7 (0.23 #3089, 0.22 #3088, 0.17 #1790) >> Best rule #4705 for best value: >> intensional similarity = 2 >> extensional distance = 118 >> proper extension: 0dk0dj; 054gwt; >> query: (?x6884, ?x1422) <- genre(?x6884, ?x258), program_creator(?x6884, ?x1422) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #3145 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 77 *> proper extension: 06r4f; *> query: (?x6884, 06jnvs) <- actor(?x6884, ?x692), tv_program(?x236, ?x6884) *> conf = 0.05 ranks of expected_values: 46, 190 EVAL 039cq4 tv_program! 01svq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 71.000 0.340 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program EVAL 039cq4 tv_program! 0bqs56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 86.000 71.000 0.340 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program EVAL 039cq4 tv_program! 06jnvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 86.000 71.000 0.340 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program EVAL 039cq4 tv_program! 021bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 71.000 0.340 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #16098-04xzm PRED entity: 04xzm PRED relation: place_of_death PRED expected values: 01914 => 185 concepts (184 used for prediction) PRED predicted values (max 10 best out of 59): 05qtj (0.31 #4156, 0.29 #5325, 0.21 #4740), 03pbf (0.25 #246, 0.02 #12325, 0.01 #18567), 01j2_7 (0.25 #381), 0mp3l (0.14 #1592, 0.12 #1787, 0.11 #1982), 0f2rq (0.14 #1643, 0.12 #1838, 0.11 #2033), 04f_d (0.14 #1589, 0.12 #1784, 0.10 #2955), 030qb3t (0.13 #26911, 0.13 #25939, 0.13 #25551), 03l2n (0.12 #1818, 0.08 #4352, 0.08 #4158), 0rh6k (0.11 #10327, 0.11 #11690, 0.11 #7409), 06_kh (0.11 #7216, 0.07 #10719, 0.07 #10524) >> Best rule #4156 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 0399p; >> query: (?x10154, 05qtj) <- influenced_by(?x10154, ?x7509), gender(?x10154, ?x231), ?x231 = 05zppz, ?x7509 = 048cl, people(?x6393, ?x10154) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #2146 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 0d060g; 0c4b8; 088q1s; *> query: (?x10154, 01914) <- entity_involved(?x13682, ?x10154), entity_involved(?x9203, ?x10154), ?x9203 = 048n7, combatants(?x13682, ?x2629), film_release_region(?x66, ?x2629) *> conf = 0.10 ranks of expected_values: 15 EVAL 04xzm place_of_death 01914 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 185.000 184.000 0.308 http://example.org/people/deceased_person/place_of_death #16097-095nx PRED entity: 095nx PRED relation: student! PRED expected values: 01j_cy => 133 concepts (124 used for prediction) PRED predicted values (max 10 best out of 163): 017j69 (0.33 #145, 0.06 #10688, 0.04 #8579), 07szy (0.20 #1621, 0.14 #5312, 0.13 #5839), 078bz (0.20 #2185, 0.12 #3239, 0.09 #9565), 01pl14 (0.20 #2116, 0.12 #3170, 0.08 #4751), 09f2j (0.20 #2267, 0.12 #3321, 0.08 #4902), 01jpqb (0.20 #2468, 0.08 #5103, 0.05 #7213), 02fjzt (0.20 #2247, 0.08 #4882, 0.05 #6992), 037fqp (0.20 #1760, 0.05 #7032, 0.03 #11249), 02g839 (0.12 #10041, 0.04 #13733, 0.03 #15314), 08815 (0.10 #3691, 0.07 #8963, 0.07 #13182) >> Best rule #145 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03vrv9; >> query: (?x13842, 017j69) <- team(?x13842, ?x12141), notable_people_with_this_condition(?x6656, ?x13842), profession(?x13842, ?x319), ?x319 = 01d_h8 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 095nx student! 01j_cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 124.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #16096-02w0dc0 PRED entity: 02w0dc0 PRED relation: costume_design_by! PRED expected values: 014zwb => 115 concepts (38 used for prediction) PRED predicted values (max 10 best out of 303): 0_816 (0.27 #1535, 0.01 #4792, 0.01 #3832), 01y9r2 (0.27 #1535, 0.01 #4408, 0.01 #2109), 0hv1t (0.27 #1535, 0.01 #4407), 0f42nz (0.18 #1445, 0.17 #1637, 0.12 #870), 017jd9 (0.12 #854, 0.11 #1045, 0.09 #1429), 017gm7 (0.12 #791, 0.11 #982, 0.09 #1366), 017gl1 (0.12 #784, 0.11 #975, 0.09 #1359), 027fwmt (0.12 #944, 0.11 #1135, 0.09 #1519), 0ft18 (0.12 #921, 0.11 #1112, 0.09 #1496), 0cbn7c (0.12 #917, 0.11 #1108, 0.09 #1492) >> Best rule #1535 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 03cp7b3; 0cbxl0; >> query: (?x771, ?x1255) <- costume_design_by(?x770, ?x771), gender(?x771, ?x231), ?x231 = 05zppz, nominated_for(?x771, ?x1255) >> conf = 0.27 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 02w0dc0 costume_design_by! 014zwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 38.000 0.273 http://example.org/film/film/costume_design_by #16095-09gdm7q PRED entity: 09gdm7q PRED relation: film! PRED expected values: 031k24 => 81 concepts (47 used for prediction) PRED predicted values (max 10 best out of 921): 0f5xn (0.15 #970, 0.06 #11385, 0.06 #21800), 062dn7 (0.15 #662, 0.05 #2745, 0.03 #19409), 07swvb (0.15 #698, 0.05 #2781, 0.03 #9030), 07ldhs (0.15 #887, 0.05 #2970, 0.03 #9219), 0gnbw (0.08 #1271, 0.07 #5437, 0.05 #3354), 0f0kz (0.08 #516, 0.06 #6765, 0.05 #8848), 03h_9lg (0.08 #132, 0.06 #16796, 0.06 #20962), 01wy5m (0.08 #859, 0.05 #9191, 0.03 #7108), 0c9xjl (0.08 #972, 0.05 #3055, 0.05 #7221), 079vf (0.08 #8, 0.05 #12506, 0.05 #20838) >> Best rule #970 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0ch26b_; 02yvct; 0879bpq; 05pdh86; 03nm_fh; 02xbyr; 06fcqw; >> query: (?x1170, 0f5xn) <- film_release_region(?x1170, ?x3749), language(?x1170, ?x254), ?x3749 = 03ryn, films(?x14661, ?x1170) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #7658 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 61 *> proper extension: 02vxq9m; 0h1cdwq; 0dscrwf; 087wc7n; 03qnvdl; 0gd0c7x; 0gvrws1; 0fpv_3_; 0661m4p; 04f52jw; ... *> query: (?x1170, 031k24) <- film_release_region(?x1170, ?x3749), film_release_region(?x1170, ?x142), language(?x1170, ?x254), ?x3749 = 03ryn, ?x142 = 0jgd *> conf = 0.02 ranks of expected_values: 523 EVAL 09gdm7q film! 031k24 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 81.000 47.000 0.154 http://example.org/film/actor/film./film/performance/film #16094-06k5_ PRED entity: 06k5_ PRED relation: vacationer PRED expected values: 01vs_v8 => 187 concepts (89 used for prediction) PRED predicted values (max 10 best out of 127): 06_bq1 (0.17 #683, 0.04 #2833, 0.04 #3012), 01l2fn (0.11 #2356, 0.05 #5227, 0.03 #3431), 01cwhp (0.09 #2738, 0.08 #2917, 0.02 #7765), 07r1h (0.08 #3001, 0.05 #2463, 0.04 #2822), 01f492 (0.06 #3737, 0.05 #4635, 0.04 #4815), 016fnb (0.06 #2254, 0.05 #5125, 0.05 #2434), 04fzk (0.06 #2241, 0.05 #2421, 0.02 #5112), 01dw4q (0.06 #2151, 0.04 #2690, 0.04 #2869), 01vs_v8 (0.06 #2189, 0.04 #3265, 0.03 #3983), 0mm1q (0.06 #2268, 0.04 #3344, 0.03 #4062) >> Best rule #683 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03shp; >> query: (?x11134, 06_bq1) <- adjoins(?x11134, ?x2236), contains(?x11134, ?x12923), ?x2236 = 05sb1, contains(?x2146, ?x11134) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #2189 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0chgzm; 09f8q; 0nc7s; 021npd; 0f8j6; *> query: (?x11134, 01vs_v8) <- place_of_birth(?x14055, ?x11134), contains(?x2146, ?x11134), profession(?x14055, ?x11804), ?x11804 = 0q04f *> conf = 0.06 ranks of expected_values: 9 EVAL 06k5_ vacationer 01vs_v8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 187.000 89.000 0.167 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #16093-025tm81 PRED entity: 025tm81 PRED relation: parent_genre! PRED expected values: 012x7b => 57 concepts (27 used for prediction) PRED predicted values (max 10 best out of 277): 01h0kx (0.50 #1176, 0.50 #651, 0.43 #1439), 06cp5 (0.50 #599, 0.33 #1124, 0.33 #75), 0dl5d (0.50 #540, 0.33 #1065, 0.33 #16), 0781g (0.50 #677, 0.33 #1202, 0.33 #153), 0cx7f (0.37 #787, 0.33 #114, 0.27 #1049), 0fd3y (0.37 #787, 0.33 #8, 0.27 #1049), 016jny (0.37 #787, 0.29 #1397, 0.25 #609), 01ym9b (0.37 #787, 0.25 #826, 0.25 #300), 016clz (0.37 #787, 0.25 #792, 0.25 #529), 01243b (0.37 #787, 0.25 #823, 0.25 #560) >> Best rule #1176 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 064t9; 07v64s; >> query: (?x6173, 01h0kx) <- parent_genre(?x13401, ?x6173), parent_genre(?x9342, ?x6173), artists(?x6173, ?x498), ?x9342 = 0grjmv, artists(?x13401, ?x8032), artists(?x302, ?x498), people(?x2510, ?x8032), ?x302 = 016clz >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #191 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 05w3f; *> query: (?x6173, 012x7b) <- parent_genre(?x13401, ?x6173), parent_genre(?x6714, ?x6173), ?x13401 = 0509cr, artists(?x6173, ?x498), artists(?x6714, ?x7972), ?x7972 = 0326tc, artist(?x4483, ?x498), category(?x498, ?x134) *> conf = 0.33 ranks of expected_values: 31 EVAL 025tm81 parent_genre! 012x7b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 57.000 27.000 0.500 http://example.org/music/genre/parent_genre #16092-05_wyz PRED entity: 05_wyz PRED relation: company PRED expected values: 02zs4 0hpt3 01ym8l 035nm 07_dn => 33 concepts (19 used for prediction) PRED predicted values (max 10 best out of 711): 01qygl (0.75 #3163, 0.67 #2264, 0.60 #1966), 01s73z (0.75 #3085, 0.67 #2186, 0.57 #2787), 03sc8 (0.71 #2776, 0.62 #3074, 0.50 #2175), 0537b (0.67 #2218, 0.62 #3117, 0.60 #1920), 07gyp7 (0.67 #2368, 0.62 #3267, 0.57 #2969), 0178g (0.67 #2164, 0.62 #3063, 0.57 #2765), 01_lh1 (0.67 #2318, 0.62 #3217, 0.43 #2919), 0841v (0.67 #2355, 0.57 #2956, 0.50 #3254), 09b3v (0.62 #3066, 0.50 #2167, 0.43 #2768), 0hpt3 (0.62 #3025, 0.50 #2126, 0.43 #2727) >> Best rule #3163 for best value: >> intensional similarity = 17 >> extensional distance = 6 >> proper extension: 01kr6k; >> query: (?x4792, 01qygl) <- company(?x4792, ?x10133), company(?x4792, ?x5072), company(?x4792, ?x1492), company(?x4792, ?x555), company(?x4792, ?x502), service_language(?x555, ?x90), service_location(?x555, ?x8483), service_location(?x555, ?x985), currency(?x1492, ?x170), contact_category(?x555, ?x897), citytown(?x10133, ?x3125), list(?x1492, ?x5997), industry(?x5072, ?x5615), ?x502 = 087c7, ?x8483 = 059g4, film_release_region(?x1170, ?x985), ?x1170 = 09gdm7q >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3025 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 6 *> proper extension: 01kr6k; *> query: (?x4792, 0hpt3) <- company(?x4792, ?x10133), company(?x4792, ?x5072), company(?x4792, ?x1492), company(?x4792, ?x555), company(?x4792, ?x502), service_language(?x555, ?x90), service_location(?x555, ?x8483), service_location(?x555, ?x985), currency(?x1492, ?x170), contact_category(?x555, ?x897), citytown(?x10133, ?x3125), list(?x1492, ?x5997), industry(?x5072, ?x5615), ?x502 = 087c7, ?x8483 = 059g4, film_release_region(?x1170, ?x985), ?x1170 = 09gdm7q *> conf = 0.62 ranks of expected_values: 10, 16, 24, 28, 51 EVAL 05_wyz company 07_dn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 33.000 19.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 05_wyz company 035nm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 33.000 19.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 05_wyz company 01ym8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 33.000 19.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 05_wyz company 0hpt3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 33.000 19.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 05_wyz company 02zs4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 33.000 19.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #16091-03z19 PRED entity: 03z19 PRED relation: registering_agency! PRED expected values: 02kth6 01j_cy 0bthb 02bjhv 07wlf 02q636 01c333 03fmfs 01jzyx 03zj9 01ljpm 01y20v 038czx 04cnp4 05nrkb 01pcj4 01tntf 02hp6p 0558_1 02grjf 02tz9z 01cf5 => 109 concepts (109 used for prediction) No prediction ranks of expected_values: EVAL 03z19 registering_agency! 01cf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 02tz9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 02grjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 0558_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 02hp6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 01tntf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 01pcj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 05nrkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 04cnp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 038czx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 01y20v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 01ljpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 03zj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 01jzyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 03fmfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 01c333 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 02q636 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 07wlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 02bjhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 0bthb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 01j_cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency EVAL 03z19 registering_agency! 02kth6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.000 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #16090-015np0 PRED entity: 015np0 PRED relation: people! PRED expected values: 02y0js => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 32): 02y0js (0.25 #68, 0.06 #332, 0.05 #662), 02k6hp (0.25 #103, 0.05 #367, 0.04 #499), 0gk4g (0.19 #406, 0.18 #340, 0.17 #670), 0dq9p (0.11 #347, 0.10 #413, 0.09 #611), 04p3w (0.10 #341, 0.10 #407, 0.08 #671), 0qcr0 (0.09 #133, 0.08 #397, 0.07 #331), 01psyx (0.09 #177, 0.02 #1167, 0.02 #507), 01qqwn (0.09 #193, 0.02 #391, 0.02 #457), 0g02vk (0.09 #167), 0m32h (0.05 #551, 0.04 #419, 0.04 #485) >> Best rule #68 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0379s; 04107; >> query: (?x8772, 02y0js) <- place_of_birth(?x8772, ?x10338), place_of_death(?x8772, ?x10852), place_of_burial(?x10239, ?x10338) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015np0 people! 02y0js CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.250 http://example.org/people/cause_of_death/people #16089-016kft PRED entity: 016kft PRED relation: award_nominee! PRED expected values: 02p8v8 => 96 concepts (37 used for prediction) PRED predicted values (max 10 best out of 968): 016kft (0.30 #2001, 0.26 #86222, 0.20 #37285), 0bgrsl (0.30 #507), 02p8v8 (0.26 #86222, 0.20 #37285, 0.10 #2050), 07qcbw (0.26 #86222, 0.02 #11449), 046zh (0.26 #86222, 0.01 #12889, 0.01 #38522), 06r3p2 (0.26 #86222), 0438pz (0.26 #86222), 05gnf (0.26 #86222), 03q1vd (0.20 #37285, 0.10 #596, 0.04 #12248), 05b_7n (0.20 #37285, 0.03 #5820, 0.02 #25634) >> Best rule #2001 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 030znt; >> query: (?x9359, 016kft) <- award_nominee(?x9359, ?x5460), film(?x9359, ?x4231), ?x5460 = 046m59 >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #86222 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1624 *> proper extension: 0f721s; 06jntd; 0283xx2; 099ks0; 03lpbx; *> query: (?x9359, ?x1204) <- award_winner(?x4898, ?x9359), nominated_for(?x1204, ?x4898) *> conf = 0.26 ranks of expected_values: 3 EVAL 016kft award_nominee! 02p8v8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 37.000 0.300 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16088-01w92 PRED entity: 01w92 PRED relation: citytown PRED expected values: 09bkv => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 102): 02_286 (0.60 #6253, 0.50 #9189, 0.50 #5152), 030qb3t (0.43 #1495, 0.24 #9570, 0.23 #3697), 013yq (0.15 #6281, 0.14 #1510, 0.12 #9217), 0nbfm (0.14 #2450, 0.14 #2083, 0.05 #6854), 024bqj (0.14 #1299, 0.12 #9373, 0.12 #5336), 06_kh (0.14 #2206, 0.07 #4775, 0.05 #6977), 04vmp (0.14 #2367, 0.05 #12277, 0.05 #8239), 0r04p (0.14 #7077, 0.13 #8912, 0.12 #9647), 0r00l (0.14 #7253, 0.12 #9823, 0.11 #11291), 07dfk (0.12 #9387, 0.08 #4249, 0.08 #3515) >> Best rule #6253 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0kcdl; 01l50r; 02w_l9; 0bmj2y; 01fsyp; >> query: (?x3487, 02_286) <- citytown(?x3487, ?x362), program(?x3487, ?x6023), featured_film_locations(?x136, ?x362) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #27904 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 745 *> proper extension: 01rr31; 01h8sf; *> query: (?x3487, ?x1310) <- state_province_region(?x3487, ?x362), contains(?x362, ?x4692), contains(?x1310, ?x4692) *> conf = 0.03 ranks of expected_values: 50 EVAL 01w92 citytown 09bkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 123.000 123.000 0.600 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #16087-01t2h2 PRED entity: 01t2h2 PRED relation: nominated_for PRED expected values: 02qd04y => 83 concepts (47 used for prediction) PRED predicted values (max 10 best out of 206): 065ym0c (0.29 #24305, 0.29 #21063, 0.29 #25926), 027m67 (0.29 #24305, 0.29 #21063, 0.29 #25926), 031ldd (0.29 #24305, 0.29 #21063, 0.29 #25926), 0bc1yhb (0.29 #24305, 0.29 #21063, 0.29 #25926), 040b5k (0.29 #24305, 0.29 #21063, 0.29 #25926), 0dkv90 (0.09 #53481), 01f8f7 (0.09 #53481), 01f85k (0.09 #53481), 0dx8gj (0.09 #53481), 01f8gz (0.09 #53481) >> Best rule #24305 for best value: >> intensional similarity = 3 >> extensional distance = 931 >> proper extension: 0785v8; 02xb2bt; 01wb8bs; 05typm; 02qw2xb; >> query: (?x1864, ?x2889) <- nominated_for(?x1864, ?x5826), film(?x1864, ?x2889), award_winner(?x5923, ?x1864) >> conf = 0.29 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 01t2h2 nominated_for 02qd04y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 47.000 0.291 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16086-02lymt PRED entity: 02lymt PRED relation: currency PRED expected values: 09nqf => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.32 #19, 0.31 #7, 0.28 #13) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 242 >> proper extension: 01l1b90; 01wmxfs; 01fkv0; 01wxyx1; 0b_fw; 0136pk; 03n_7k; 01wk7b7; 0qf3p; 01w7nwm; ... >> query: (?x4777, 09nqf) <- gender(?x4777, ?x231), film(?x4777, ?x3639), ?x231 = 05zppz, participant(?x4777, ?x971) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lymt currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.320 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #16085-01sbv9 PRED entity: 01sbv9 PRED relation: film! PRED expected values: 0c9c0 => 108 concepts (66 used for prediction) PRED predicted values (max 10 best out of 973): 0mdqp (0.65 #47858, 0.59 #76997, 0.45 #74915), 0150t6 (0.45 #74915, 0.45 #124859, 0.45 #118617), 056ws9 (0.45 #74915, 0.45 #124859, 0.45 #33292), 02gf_l (0.15 #3349, 0.04 #7509, 0.03 #15834), 02_p5w (0.15 #2726, 0.03 #19373, 0.02 #63068), 05dbf (0.14 #365, 0.06 #6605, 0.04 #21172), 05bnp0 (0.14 #13, 0.05 #2093, 0.03 #4173), 09h4b5 (0.14 #1395, 0.05 #3475, 0.01 #9715), 051wwp (0.14 #877, 0.03 #5037, 0.02 #7117), 031k24 (0.14 #1408, 0.03 #5568, 0.02 #7648) >> Best rule #47858 for best value: >> intensional similarity = 4 >> extensional distance = 272 >> proper extension: 0431v3; 0gvsh7l; >> query: (?x10192, ?x794) <- nominated_for(?x794, ?x10192), award_winner(?x10192, ?x1835), nominated_for(?x1723, ?x10192), celebrity(?x710, ?x794) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #6714 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 0522wp; *> query: (?x10192, 0c9c0) <- film(?x902, ?x10192), category(?x10192, ?x134), film_distribution_medium(?x10192, ?x2099), ?x2099 = 0735l *> conf = 0.04 ranks of expected_values: 220 EVAL 01sbv9 film! 0c9c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 108.000 66.000 0.646 http://example.org/film/actor/film./film/performance/film #16084-02k54 PRED entity: 02k54 PRED relation: country! PRED expected values: 06z6r 07_53 => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 37): 06z6r (0.86 #536, 0.86 #2942, 0.84 #2905), 01lb14 (0.86 #526, 0.73 #1007, 0.71 #748), 03hr1p (0.82 #531, 0.75 #753, 0.72 #346), 07jjt (0.67 #1011, 0.67 #345, 0.63 #1048), 03rbzn (0.64 #535, 0.61 #350, 0.57 #757), 01z27 (0.64 #527, 0.57 #749, 0.56 #934), 01dys (0.64 #523, 0.54 #745, 0.50 #79), 019tzd (0.62 #949, 0.61 #1023, 0.59 #542), 02_5h (0.62 #81, 0.55 #525, 0.50 #747), 09w1n (0.61 #347, 0.59 #532, 0.54 #754) >> Best rule #536 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 07fj_; >> query: (?x608, 06z6r) <- time_zones(?x608, ?x10735), country(?x766, ?x608), ?x766 = 01hp22 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 21 EVAL 02k54 country! 07_53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 187.000 187.000 0.864 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02k54 country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 187.000 0.864 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #16083-0h326 PRED entity: 0h326 PRED relation: location PRED expected values: 0156q => 125 concepts (92 used for prediction) PRED predicted values (max 10 best out of 162): 02_286 (0.35 #47398, 0.28 #21705, 0.24 #51412), 030qb3t (0.33 #47443, 0.29 #14529, 0.24 #21750), 095l0 (0.14 #483, 0.08 #1285, 0.03 #3695), 0cr3d (0.14 #16999, 0.12 #18603, 0.09 #17801), 02h6_6p (0.13 #1734, 0.10 #2538, 0.03 #3342), 0h7x (0.11 #3211, 0.10 #3212, 0.10 #2481), 09c7w0 (0.11 #3211, 0.07 #2407, 0.05 #2411), 04jpl (0.10 #47378, 0.08 #819, 0.08 #51392), 0d6lp (0.08 #969, 0.04 #47528, 0.03 #3379), 02frhbc (0.08 #1269, 0.03 #3679, 0.03 #4481) >> Best rule #47398 for best value: >> intensional similarity = 4 >> extensional distance = 661 >> proper extension: 01l2fn; 0pyg6; 0738b8; 062dn7; 01900g; 021yzs; 03hh89; 028r4y; 03q3sy; 01520h; ... >> query: (?x14208, 02_286) <- location(?x14208, ?x863), profession(?x14208, ?x1032), film(?x14208, ?x3755), mode_of_transportation(?x863, ?x4272) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #3299 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 083q7; 0k4gf; 01pcmd; 01jqr_5; 03pm9; 053yx; 0lgm5; 045cq; 07c37; 0l99s; ... *> query: (?x14208, 0156q) <- gender(?x14208, ?x231), people(?x1158, ?x14208), ?x1158 = 02y0js, location(?x14208, ?x863) *> conf = 0.03 ranks of expected_values: 33 EVAL 0h326 location 0156q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 125.000 92.000 0.345 http://example.org/people/person/places_lived./people/place_lived/location #16082-04rfq PRED entity: 04rfq PRED relation: award_winner! PRED expected values: 0gqwc => 95 concepts (90 used for prediction) PRED predicted values (max 10 best out of 309): 0f4x7 (0.32 #7783, 0.20 #3057, 0.06 #8246), 0gr51 (0.32 #7783, 0.14 #2162, 0.12 #6486), 03nqnk3 (0.32 #7783, 0.12 #3593, 0.08 #1431), 040njc (0.32 #7783, 0.12 #3466, 0.08 #1304), 0l8z1 (0.32 #7783, 0.08 #3090, 0.04 #7414), 025m8y (0.29 #964, 0.17 #1828, 0.10 #5286), 01by1l (0.28 #1841, 0.14 #977, 0.14 #545), 054ks3 (0.22 #1870, 0.15 #1438, 0.14 #1006), 0gq9h (0.18 #7428, 0.12 #6996, 0.08 #11317), 0gr4k (0.18 #2161, 0.16 #5619, 0.15 #6485) >> Best rule #7783 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 05qd_; >> query: (?x13144, ?x198) <- organizations_founded(?x13144, ?x1850), organizations_founded(?x11265, ?x1850), award(?x11265, ?x198) >> conf = 0.32 => this is the best rule for 5 predicted values *> Best rule #15634 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 178 *> proper extension: 01hkhq; 02j9lm; 0d1_f; 094xh; 012v1t; 03k545; 0739z6; *> query: (?x13144, 0gqwc) <- type_of_union(?x13144, ?x566), gender(?x13144, ?x514), ?x514 = 02zsn, religion(?x13144, ?x1624) *> conf = 0.10 ranks of expected_values: 50 EVAL 04rfq award_winner! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 95.000 90.000 0.321 http://example.org/award/award_category/winners./award/award_honor/award_winner #16081-04b7xr PRED entity: 04b7xr PRED relation: artist! PRED expected values: 03rhqg => 96 concepts (62 used for prediction) PRED predicted values (max 10 best out of 99): 033hn8 (0.25 #13, 0.12 #291, 0.12 #1960), 02bh8z (0.25 #21, 0.05 #438, 0.04 #1968), 03q58q (0.25 #90, 0.02 #229, 0.02 #507), 015_1q (0.22 #436, 0.19 #3225, 0.19 #575), 03rhqg (0.19 #154, 0.17 #571, 0.17 #2101), 0g768 (0.14 #175, 0.13 #314, 0.12 #1983), 023rwm (0.14 #280, 0.03 #1949, 0.03 #2088), 01dtcb (0.12 #45, 0.12 #462, 0.10 #184), 0181dw (0.12 #41, 0.11 #1153, 0.11 #180), 017l96 (0.12 #18, 0.11 #157, 0.11 #1965) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02_jkc; >> query: (?x6942, 033hn8) <- award_winner(?x7088, ?x6942), ?x7088 = 019x62, award_nominee(?x8049, ?x6942) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #154 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 01x1cn2; 024dgj; 0163m1; 0bqsy; 0qf11; 049qx; 015xp4; 02jq1; 044mfr; 015srx; ... *> query: (?x6942, 03rhqg) <- artists(?x3319, ?x6942), artists(?x1572, ?x6942), ?x1572 = 06by7, ?x3319 = 06j6l *> conf = 0.19 ranks of expected_values: 5 EVAL 04b7xr artist! 03rhqg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 96.000 62.000 0.250 http://example.org/music/record_label/artist #16080-0fvwz PRED entity: 0fvwz PRED relation: time_zones PRED expected values: 02fqwt => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 11): 02fqwt (0.69 #40, 0.67 #53, 0.67 #14), 02hcv8 (0.44 #863, 0.43 #915, 0.43 #902), 02lcqs (0.22 #162, 0.19 #305, 0.19 #1437), 02llzg (0.20 #122, 0.19 #135, 0.07 #955), 02hczc (0.19 #769, 0.19 #1437, 0.17 #67), 02lcrv (0.19 #769, 0.17 #1410, 0.16 #1356), 042g7t (0.19 #769, 0.16 #1356, 0.04 #76), 03bdv (0.06 #566, 0.05 #397, 0.05 #579), 05jphn (0.03 #118), 03plfd (0.02 #388, 0.01 #427, 0.01 #1406) >> Best rule #40 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 0nf3h; 0ndh6; 014b6c; >> query: (?x11964, 02fqwt) <- contains(?x1025, ?x11964), contains(?x94, ?x11964), ?x1025 = 04ych, contains(?x94, ?x946), currency(?x946, ?x170) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fvwz time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.692 http://example.org/location/location/time_zones #16079-023l9y PRED entity: 023l9y PRED relation: nationality PRED expected values: 02jx1 => 130 concepts (80 used for prediction) PRED predicted values (max 10 best out of 34): 02jx1 (0.82 #6238, 0.81 #7052, 0.81 #7257), 07ssc (0.79 #6343, 0.79 #6748, 0.51 #1705), 09c7w0 (0.67 #4335, 0.66 #7959, 0.66 #6137), 0134bf (0.50 #302, 0.49 #3421, 0.47 #6749), 0j5g9 (0.40 #262, 0.04 #1664, 0.02 #2169), 06q1r (0.21 #1679, 0.11 #2184, 0.10 #2688), 0chghy (0.10 #312, 0.08 #412, 0.07 #812), 03_3d (0.08 #408, 0.05 #608, 0.04 #1008), 0d060g (0.08 #1714, 0.07 #2821, 0.07 #1509), 03rt9 (0.06 #515, 0.05 #715, 0.02 #6149) >> Best rule #6238 for best value: >> intensional similarity = 5 >> extensional distance = 468 >> proper extension: 07nznf; 079vf; 0fvf9q; 01xdf5; 07lmxq; 018dnt; 02r_d4; 03m8lq; 02w0dc0; 05kfs; ... >> query: (?x4595, ?x1310) <- place_of_birth(?x4595, ?x12223), contains(?x512, ?x12223), country(?x12223, ?x1310), location(?x2284, ?x12223), award_nominee(?x100, ?x2284) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023l9y nationality 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 80.000 0.817 http://example.org/people/person/nationality #16078-018ysx PRED entity: 018ysx PRED relation: parent_genre PRED expected values: 06j6l 0133_p => 40 concepts (30 used for prediction) PRED predicted values (max 10 best out of 279): 03_d0 (0.68 #1779, 0.33 #171, 0.33 #9), 0xhtw (0.48 #1301, 0.17 #175, 0.13 #657), 016jny (0.45 #1195, 0.13 #712, 0.12 #1677), 064t9 (0.40 #1459, 0.24 #1620, 0.11 #3727), 03lty (0.37 #3895, 0.35 #4055, 0.17 #1306), 02fhtq (0.33 #140, 0.17 #302, 0.15 #625), 06j6l (0.23 #517, 0.20 #1000, 0.20 #676), 02yv6b (0.20 #707, 0.17 #384, 0.17 #225), 01243b (0.18 #481, 0.16 #1475, 0.16 #831), 05w3f (0.17 #1312, 0.17 #186, 0.12 #1633) >> Best rule #1779 for best value: >> intensional similarity = 11 >> extensional distance = 32 >> proper extension: 01lxd4; 08lpkq; 0gt_0v; 054nbl; 05g7tj; 0p9xd; 0cx6f; 0f_j1; 0h08p; 015y_n; ... >> query: (?x13294, 03_d0) <- parent_genre(?x13294, ?x7440), artists(?x7440, ?x9246), artists(?x7440, ?x7477), artists(?x7440, ?x4628), artists(?x7440, ?x1720), nationality(?x9246, ?x94), ?x1720 = 01qkqwg, ?x7477 = 028hc2, award(?x9246, ?x724), role(?x4628, ?x1466), award_nominee(?x4628, ?x140) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #517 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 11 *> proper extension: 01lyv; 01fbr2; 017510; 0133_p; 017323; 01n5sn; 01750n; *> query: (?x13294, 06j6l) <- parent_genre(?x13294, ?x7440), parent_genre(?x13294, ?x2664), ?x7440 = 0155w, artists(?x2664, ?x8311), artists(?x2664, ?x6467), artists(?x2664, ?x2518), artists(?x2664, ?x1291), ?x1291 = 01kx_81, artist(?x382, ?x8311), award(?x8311, ?x1323), profession(?x6467, ?x131), participant(?x2518, ?x1735) *> conf = 0.23 ranks of expected_values: 7, 11 EVAL 018ysx parent_genre 0133_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 40.000 30.000 0.676 http://example.org/music/genre/parent_genre EVAL 018ysx parent_genre 06j6l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 40.000 30.000 0.676 http://example.org/music/genre/parent_genre #16077-03f4k PRED entity: 03f4k PRED relation: people! PRED expected values: 09969 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 41): 0gk4g (0.22 #340, 0.16 #736, 0.14 #2387), 02k6hp (0.22 #367, 0.16 #763, 0.09 #1160), 07s4l (0.20 #124, 0.06 #652, 0.03 #916), 074m2 (0.20 #95, 0.06 #623, 0.03 #887), 0gg4h (0.17 #234, 0.10 #432, 0.08 #564), 0x2fg (0.17 #236, 0.10 #434, 0.08 #566), 012hw (0.15 #580, 0.11 #382, 0.05 #976), 04p3w (0.15 #671, 0.11 #605, 0.08 #1266), 0dq9p (0.11 #1140, 0.11 #677, 0.10 #3384), 01l2m3 (0.11 #676, 0.11 #346, 0.05 #2195) >> Best rule #340 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0b5x23; >> query: (?x9593, 0gk4g) <- place_of_death(?x9593, ?x682), sibling(?x11034, ?x9593), people(?x1050, ?x9593) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03f4k people! 09969 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 140.000 0.222 http://example.org/people/cause_of_death/people #16076-0k57l PRED entity: 0k57l PRED relation: gender PRED expected values: 05zppz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #15, 0.90 #35, 0.90 #41), 02zsn (0.56 #109, 0.47 #134, 0.47 #88) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 07g2b; 045bg; 06dl_; 0ph2w; 06whf; 081k8; 049gc; 03_87; 02y49; 05qzv; ... >> query: (?x8472, 05zppz) <- place_of_death(?x8472, ?x739), profession(?x8472, ?x524), influenced_by(?x12689, ?x8472), film(?x12689, ?x3755) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k57l gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.917 http://example.org/people/person/gender #16075-0k3p PRED entity: 0k3p PRED relation: month PRED expected values: 040fv => 286 concepts (286 used for prediction) PRED predicted values (max 10 best out of 1): 040fv (0.87 #47, 0.86 #95, 0.83 #65) >> Best rule #47 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0l0mk; >> query: (?x8252, ?x2255) <- month(?x8252, ?x6303), state(?x8252, ?x3407), seasonal_months(?x6303, ?x2255) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k3p month 040fv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 286.000 286.000 0.874 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #16074-01c9d PRED entity: 01c9d PRED relation: genre PRED expected values: 02l7c8 => 112 concepts (90 used for prediction) PRED predicted values (max 10 best out of 121): 04xvlr (0.73 #5624, 0.72 #9806, 0.72 #10046), 07ssc (0.67 #1433, 0.67 #1432, 0.63 #5623), 05p553 (0.60 #7778, 0.46 #4548, 0.43 #8494), 01jfsb (0.57 #4437, 0.52 #5036, 0.38 #8503), 02l7c8 (0.53 #256, 0.52 #136, 0.47 #7791), 01hmnh (0.40 #2770, 0.19 #5041, 0.18 #1092), 02kdv5l (0.38 #1076, 0.33 #8492, 0.30 #5025), 0lsxr (0.37 #4433, 0.26 #368, 0.23 #844), 03k9fj (0.35 #5035, 0.28 #8502, 0.25 #2764), 03bxz7 (0.29 #532, 0.17 #2087, 0.17 #1487) >> Best rule #5624 for best value: >> intensional similarity = 4 >> extensional distance = 669 >> proper extension: 0140g4; 02v8kmz; 07g_0c; 0g3zrd; 05jyb2; 0c38gj; 06t6dz; 02z44tp; 03p2xc; 0cbn7c; ... >> query: (?x11385, ?x162) <- genre(?x11385, ?x53), titles(?x162, ?x11385), genre(?x4174, ?x162), ?x4174 = 07nxvj >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #256 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 047myg9; *> query: (?x11385, 02l7c8) <- genre(?x11385, ?x1509), titles(?x512, ?x11385), language(?x11385, ?x254), ?x1509 = 060__y, olympics(?x512, ?x358) *> conf = 0.53 ranks of expected_values: 5 EVAL 01c9d genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 112.000 90.000 0.726 http://example.org/film/film/genre #16073-0n2z PRED entity: 0n2z PRED relation: place_of_death! PRED expected values: 03flwk => 207 concepts (91 used for prediction) PRED predicted values (max 10 best out of 645): 05qmj (0.36 #4542, 0.13 #4541, 0.08 #11357), 08959 (0.20 #2248, 0.10 #6793, 0.10 #5279), 0835q (0.20 #2170, 0.10 #6715, 0.10 #5201), 03_nq (0.20 #1974, 0.10 #6519, 0.10 #5005), 0c_jc (0.20 #1777, 0.10 #6322, 0.10 #4808), 0dq2k (0.20 #1758, 0.10 #6303, 0.10 #4789), 083pr (0.20 #1575, 0.10 #6120, 0.10 #4606), 0jf1b (0.20 #1533, 0.10 #6078, 0.10 #4564), 0j3v (0.17 #3109, 0.12 #3869, 0.03 #15988), 040db (0.17 #3103, 0.02 #3783, 0.01 #45519) >> Best rule #4542 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02m77; 02z0j; >> query: (?x11096, ?x6015) <- place_of_birth(?x6015, ?x11096), location(?x4930, ?x11096), capital(?x11095, ?x11096), interests(?x6015, ?x713) >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0n2z place_of_death! 03flwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 207.000 91.000 0.364 http://example.org/people/deceased_person/place_of_death #16072-0k__z PRED entity: 0k__z PRED relation: student PRED expected values: 01ypsj => 166 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1878): 01gbn6 (0.25 #3718, 0.03 #22483, 0.02 #24568), 024t0y (0.25 #4073, 0.03 #22838, 0.02 #24923), 07g7h2 (0.25 #3216, 0.02 #28236, 0.02 #30321), 02zft0 (0.12 #9389, 0.06 #21899, 0.05 #23984), 015qq1 (0.12 #10226, 0.06 #22736, 0.04 #18566), 02t_w8 (0.12 #9259, 0.06 #21769, 0.04 #32194), 03l3ln (0.12 #9492, 0.06 #22002, 0.03 #19917), 02pv_d (0.12 #9730, 0.06 #22240, 0.03 #20155), 0f13b (0.12 #9809, 0.04 #13979, 0.03 #20234), 07s93v (0.11 #10673, 0.07 #16928, 0.05 #25268) >> Best rule #3718 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 016tw3; >> query: (?x8363, 01gbn6) <- organization(?x346, ?x8363), child(?x3360, ?x8363), category(?x8363, ?x134), featured_film_locations(?x5128, ?x8363) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k__z student 01ypsj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 166.000 95.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #16071-012x7b PRED entity: 012x7b PRED relation: parent_genre! PRED expected values: 02w6s3 => 41 concepts (26 used for prediction) PRED predicted values (max 10 best out of 270): 02t8gf (0.25 #119, 0.20 #662, 0.10 #1206), 0b_6yv (0.25 #487, 0.15 #1031, 0.15 #1303), 0bt7w (0.25 #361, 0.10 #1177, 0.10 #1448), 0175zz (0.25 #429, 0.08 #973, 0.05 #1245), 016y3j (0.23 #947, 0.15 #1219, 0.14 #2303), 0g_bh (0.18 #1739, 0.18 #2280, 0.15 #924), 0ccxx6 (0.15 #1035, 0.15 #1307, 0.14 #2121), 01_bkd (0.15 #1134, 0.11 #2490, 0.09 #2762), 06cp5 (0.15 #1163, 0.09 #4153, 0.09 #4424), 05jt_ (0.11 #3366, 0.11 #2546, 0.10 #1190) >> Best rule #119 for best value: >> intensional similarity = 17 >> extensional distance = 2 >> proper extension: 029fbr; >> query: (?x12831, 02t8gf) <- parent_genre(?x12831, ?x7220), parent_genre(?x12831, ?x2809), artists(?x2809, ?x12670), artists(?x2809, ?x8060), artists(?x2809, ?x8004), artists(?x2809, ?x7882), artists(?x2809, ?x7233), artists(?x2809, ?x5872), parent_genre(?x2809, ?x505), role(?x12670, ?x1166), ?x7233 = 01lz4tf, ?x7220 = 0mmp3, ?x5872 = 01bpnd, gender(?x8004, ?x231), award_winner(?x725, ?x7882), ?x8060 = 06mj4, award(?x7882, ?x4018) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2984 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 41 *> proper extension: 02rp117; 04qftx; 04qvq0; 0264jc6; 03rlps; *> query: (?x12831, ?x13782) <- parent_genre(?x12831, ?x2809), parent_genre(?x12831, ?x2439), artists(?x2809, ?x12670), artists(?x2809, ?x6406), artists(?x2809, ?x4550), artists(?x2809, ?x2908), parent_genre(?x2809, ?x505), role(?x12670, ?x1166), ?x6406 = 01386_, location(?x12670, ?x2624), ?x4550 = 0180w8, artist(?x441, ?x2908), parent_genre(?x13782, ?x2439) *> conf = 0.04 ranks of expected_values: 118 EVAL 012x7b parent_genre! 02w6s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 41.000 26.000 0.250 http://example.org/music/genre/parent_genre #16070-02n9k PRED entity: 02n9k PRED relation: student! PRED expected values: 06thjt => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 272): 03ksy (0.25 #1160, 0.17 #106, 0.15 #7484), 01mpwj (0.25 #1161, 0.15 #7485, 0.15 #9066), 08815 (0.25 #1056, 0.14 #6326, 0.11 #7907), 07tg4 (0.17 #86, 0.12 #3248, 0.12 #613), 017v3q (0.17 #245, 0.12 #772, 0.09 #1826), 01n951 (0.17 #286, 0.12 #813, 0.09 #1867), 07tk7 (0.17 #442, 0.04 #26266, 0.03 #22050), 017v71 (0.14 #2303, 0.12 #1249, 0.05 #6519), 07tgn (0.12 #1071, 0.12 #544, 0.09 #1598), 0yldt (0.12 #1568, 0.12 #1041, 0.09 #2095) >> Best rule #1160 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0d06m5; 09b6zr; 0d05fv; >> query: (?x7893, 03ksy) <- type_of_union(?x7893, ?x566), location(?x7893, ?x108), ?x108 = 0rh6k, politician(?x8714, ?x7893) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #5141 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 0gv5c; *> query: (?x7893, 06thjt) <- type_of_union(?x7893, ?x566), place_of_birth(?x7893, ?x739), ?x739 = 02_286, languages(?x7893, ?x254) *> conf = 0.10 ranks of expected_values: 22 EVAL 02n9k student! 06thjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 164.000 164.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #16069-0fjyzt PRED entity: 0fjyzt PRED relation: genre PRED expected values: 0lsxr => 108 concepts (88 used for prediction) PRED predicted values (max 10 best out of 94): 03mqtr (0.73 #2338, 0.60 #2337, 0.60 #5853), 05p553 (0.43 #2692, 0.42 #4328, 0.36 #3508), 02kdv5l (0.32 #2104, 0.29 #1987, 0.28 #4560), 03k9fj (0.27 #2114, 0.24 #4220, 0.24 #3516), 03bxz7 (0.26 #52, 0.24 #168, 0.18 #2272), 06cvj (0.25 #2691, 0.24 #4327, 0.10 #5383), 0lsxr (0.24 #124, 0.22 #9129, 0.22 #474), 01hmnh (0.22 #2118, 0.16 #2001, 0.16 #4224), 017fp (0.21 #14, 0.19 #130, 0.16 #2234), 02n4kr (0.18 #1057, 0.16 #940, 0.15 #9128) >> Best rule #2338 for best value: >> intensional similarity = 4 >> extensional distance = 293 >> proper extension: 02qjv1p; >> query: (?x5465, ?x3506) <- titles(?x3506, ?x5465), award_winner(?x5465, ?x5363), genre(?x12113, ?x3506), ?x12113 = 0gt14 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 016ks5; *> query: (?x5465, 0lsxr) <- award(?x5465, ?x4894), ?x4894 = 027b9j5, award_winner(?x5465, ?x5363), genre(?x5465, ?x53) *> conf = 0.24 ranks of expected_values: 7 EVAL 0fjyzt genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 108.000 88.000 0.732 http://example.org/film/film/genre #16068-0j43swk PRED entity: 0j43swk PRED relation: film! PRED expected values: 03_wtr 06cl2w => 78 concepts (41 used for prediction) PRED predicted values (max 10 best out of 969): 01f8ld (0.44 #56154, 0.43 #60315, 0.41 #51994), 043kzcr (0.44 #56154, 0.43 #60315, 0.41 #51994), 016khd (0.20 #134, 0.01 #37567, 0.01 #4293), 016dmx (0.11 #39513), 05fnl9 (0.10 #269, 0.05 #2349, 0.03 #56155), 015wnl (0.10 #650, 0.03 #2730, 0.03 #15205), 05xf75 (0.10 #1489, 0.03 #3569, 0.03 #7728), 0154qm (0.10 #562, 0.03 #2642, 0.02 #46317), 0dvmd (0.10 #528, 0.03 #2608, 0.02 #17162), 01ps2h8 (0.10 #939, 0.03 #3019, 0.02 #11336) >> Best rule #56154 for best value: >> intensional similarity = 4 >> extensional distance = 762 >> proper extension: 0dnvn3; 03h_yy; 04kzqz; 03l6q0; 05n6sq; 04xx9s; 047gpsd; 02bj22; 02x2jl_; >> query: (?x3035, ?x2516) <- film(?x541, ?x3035), award_winner(?x3035, ?x2516), film(?x6708, ?x3035), award_nominee(?x525, ?x6708) >> conf = 0.44 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0j43swk film! 06cl2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 41.000 0.441 http://example.org/film/actor/film./film/performance/film EVAL 0j43swk film! 03_wtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 41.000 0.441 http://example.org/film/actor/film./film/performance/film #16067-01bgqh PRED entity: 01bgqh PRED relation: award! PRED expected values: 04lgymt 0150jk 02l840 09mq4m 016z1t 01vw37m 01vtmw6 01vttb9 01w5jwb 01x0yrt 016ppr 015bwt => 40 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2176): 0dvqq (0.79 #42105, 0.78 #42104, 0.50 #7082), 0140t7 (0.79 #42105, 0.78 #42104, 0.43 #15499), 0m_v0 (0.79 #42105, 0.78 #42104, 0.23 #12954), 023p29 (0.79 #42105, 0.78 #42104, 0.23 #12954), 02_jkc (0.79 #42105, 0.78 #42104, 0.23 #12954), 018x3 (0.79 #42105, 0.78 #42104, 0.23 #12954), 05pdbs (0.79 #42105, 0.78 #42104, 0.16 #32386), 0gbwp (0.62 #10770, 0.33 #1055, 0.30 #14009), 01vvycq (0.60 #3380, 0.43 #13096, 0.25 #9857), 02f1c (0.60 #5653, 0.38 #12130, 0.26 #15369) >> Best rule #42105 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 02r0d0; >> query: (?x724, ?x5493) <- award_winner(?x724, ?x5493), award(?x5493, ?x247), category_of(?x724, ?x2421) >> conf = 0.79 => this is the best rule for 7 predicted values *> Best rule #5314 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 05q8pss; *> query: (?x724, 01vttb9) <- award(?x4646, ?x724), award(?x2083, ?x724), instrumentalists(?x227, ?x4646), ?x2083 = 02zmh5 *> conf = 0.60 ranks of expected_values: 11, 21, 39, 86, 103, 112, 125, 146, 194, 343, 347, 798 EVAL 01bgqh award! 015bwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 016ppr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 01x0yrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 01w5jwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 01vttb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 01vtmw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 01vw37m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 016z1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 09mq4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 02l840 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 0150jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01bgqh award! 04lgymt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 40.000 15.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16066-03fqv5 PRED entity: 03fqv5 PRED relation: award PRED expected values: 02rdyk7 => 116 concepts (103 used for prediction) PRED predicted values (max 10 best out of 279): 09d28z (0.69 #24424, 0.69 #19616, 0.68 #12409), 027c924 (0.69 #24424, 0.69 #19616, 0.68 #12409), 02rdyk7 (0.52 #486, 0.36 #1288, 0.19 #2088), 0gr51 (0.48 #495, 0.31 #6098, 0.30 #3698), 04dn09n (0.43 #442, 0.33 #1244, 0.31 #3645), 09sb52 (0.40 #39, 0.27 #18453, 0.27 #16853), 0789_m (0.40 #19, 0.08 #3222, 0.08 #4422), 0f_nbyh (0.33 #409, 0.21 #2411, 0.21 #2011), 03nqnk3 (0.33 #530, 0.18 #1332, 0.12 #2132), 0fbtbt (0.32 #6632, 0.19 #10235, 0.12 #1830) >> Best rule #24424 for best value: >> intensional similarity = 4 >> extensional distance = 1431 >> proper extension: 07c0j; 065jlv; 046b0s; 04qmr; 01svw8n; 0dw4g; 03d9d6; 09cdxn; 010xjr; 01rnpy; ... >> query: (?x12960, ?x289) <- nominated_for(?x12960, ?x8000), award_winner(?x289, ?x12960), award(?x12960, ?x198), award_winner(?x8000, ?x1250) >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #486 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 03xp8d5; *> query: (?x12960, 02rdyk7) <- nominated_for(?x12960, ?x7628), award_winner(?x8364, ?x12960), student(?x122, ?x12960), ?x8364 = 09d28z *> conf = 0.52 ranks of expected_values: 3 EVAL 03fqv5 award 02rdyk7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 103.000 0.689 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16065-07j94 PRED entity: 07j94 PRED relation: film_release_region PRED expected values: 09c7w0 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 108): 09c7w0 (0.73 #182, 0.70 #719, 0.70 #5927), 0chghy (0.31 #8981, 0.26 #196, 0.24 #375), 03rk0 (0.31 #8981, 0.22 #3768, 0.11 #433), 0d0vqn (0.28 #370, 0.27 #191, 0.26 #8096), 03_3d (0.28 #368, 0.27 #189, 0.22 #2518), 06mkj (0.28 #434, 0.26 #255, 0.25 #7620), 059j2 (0.28 #403, 0.25 #224, 0.23 #8129), 0f8l9c (0.27 #211, 0.27 #7576, 0.27 #8116), 0jgd (0.26 #363, 0.25 #184, 0.22 #1617), 03rjj (0.26 #366, 0.23 #7552, 0.22 #8092) >> Best rule #182 for best value: >> intensional similarity = 4 >> extensional distance = 100 >> proper extension: 02wk7b; >> query: (?x4530, 09c7w0) <- nominated_for(?x1198, ?x4530), genre(?x4530, ?x53), film_crew_role(?x4530, ?x1284), ?x1198 = 02pqp12 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07j94 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.725 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16064-0123r4 PRED entity: 0123r4 PRED relation: group! PRED expected values: 02hnl => 68 concepts (61 used for prediction) PRED predicted values (max 10 best out of 117): 02hnl (0.82 #2911, 0.82 #3079, 0.80 #2416), 01vj9c (0.72 #3053, 0.72 #3052, 0.66 #1651), 03_vpw (0.72 #3053, 0.72 #3052, 0.66 #1651), 0mkg (0.50 #172, 0.43 #501, 0.30 #914), 013y1f (0.50 #188, 0.35 #1342, 0.25 #601), 04rzd (0.50 #193, 0.29 #522, 0.25 #2885), 0g2dz (0.50 #187, 0.25 #600, 0.21 #247), 03qjg (0.46 #1362, 0.32 #2929, 0.32 #3182), 028tv0 (0.42 #3398, 0.42 #1907, 0.40 #3485), 026t6 (0.40 #414, 0.29 #496, 0.25 #2885) >> Best rule #2911 for best value: >> intensional similarity = 9 >> extensional distance = 94 >> proper extension: 033s6; >> query: (?x6202, 02hnl) <- group(?x248, ?x6202), group(?x2888, ?x6202), group(?x228, ?x6202), role(?x74, ?x2888), role(?x569, ?x228), instrumentalists(?x228, ?x8308), instrumentalists(?x228, ?x3667), ?x8308 = 04mx7s, ?x3667 = 0phx4 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0123r4 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 61.000 0.823 http://example.org/music/performance_role/regular_performances./music/group_membership/group #16063-01g0jn PRED entity: 01g0jn PRED relation: person! PRED expected values: 087vnr5 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 29): 05t54s (0.07 #674, 0.04 #1164, 0.03 #1304), 02q7fl9 (0.07 #665, 0.01 #1577, 0.01 #1787), 058kh7 (0.06 #762, 0.03 #902, 0.03 #972), 03nqnnk (0.04 #1786, 0.03 #1364, 0.02 #1716), 0g9lm2 (0.03 #1213, 0.03 #1493, 0.02 #1986), 05_61y (0.03 #1303, 0.02 #1163, 0.02 #2146), 02847m9 (0.03 #1199, 0.02 #2463, 0.02 #2603), 0dtw1x (0.03 #4138, 0.03 #4208, 0.03 #4908), 012jfb (0.03 #2910, 0.02 #2700, 0.02 #3820), 02v570 (0.02 #1028, 0.02 #1098, 0.02 #2712) >> Best rule #674 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02fcs2; 06pwf6; 0c_md_; 03h40_7; >> query: (?x12116, 05t54s) <- gender(?x12116, ?x231), student(?x1681, ?x12116), ?x1681 = 07szy >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #1244 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 01vsy7t; *> query: (?x12116, 087vnr5) <- gender(?x12116, ?x231), participant(?x2614, ?x12116), artist(?x3265, ?x2614), religion(?x12116, ?x1985) *> conf = 0.02 ranks of expected_values: 22 EVAL 01g0jn person! 087vnr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 150.000 150.000 0.071 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #16062-01gkmx PRED entity: 01gkmx PRED relation: film PRED expected values: 0421ng => 100 concepts (71 used for prediction) PRED predicted values (max 10 best out of 846): 01z452 (0.69 #8896, 0.66 #44479, 0.60 #83623), 08phg9 (0.25 #881, 0.07 #4439, 0.01 #32906), 014lc_ (0.25 #2, 0.01 #23131), 0mbql (0.25 #1167), 02rx2m5 (0.22 #3850, 0.04 #126331, 0.02 #12746), 03bx2lk (0.22 #1963, 0.03 #12638, 0.03 #16197), 084qpk (0.22 #1899, 0.02 #21470, 0.02 #12574), 0h7t36 (0.15 #5232, 0.04 #126331), 0b76t12 (0.15 #3848, 0.04 #126331), 092vkg (0.15 #3714, 0.04 #126331) >> Best rule #8896 for best value: >> intensional similarity = 2 >> extensional distance = 123 >> proper extension: 07jrjb; >> query: (?x9257, ?x7491) <- nominated_for(?x9257, ?x7491), celebrity(?x3581, ?x9257) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #4414 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 02mt4k; *> query: (?x9257, 0421ng) <- award_nominee(?x9257, ?x91), profession(?x9257, ?x319), ?x91 = 04bdxl *> conf = 0.04 ranks of expected_values: 206 EVAL 01gkmx film 0421ng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 100.000 71.000 0.686 http://example.org/film/actor/film./film/performance/film #16061-0dwt5 PRED entity: 0dwt5 PRED relation: role! PRED expected values: 0g33q => 83 concepts (55 used for prediction) PRED predicted values (max 10 best out of 101): 02sgy (0.85 #2684, 0.80 #2877, 0.79 #2874), 05r5c (0.84 #2870, 0.82 #2967, 0.82 #2970), 0l14md (0.84 #2870, 0.82 #2967, 0.82 #2968), 04rzd (0.84 #2870, 0.82 #2967, 0.82 #2968), 03qjg (0.84 #2870, 0.82 #2967, 0.82 #2968), 01wy6 (0.84 #2870, 0.82 #2967, 0.82 #2968), 0dwr4 (0.84 #2870, 0.82 #2967, 0.80 #2774), 0g2dz (0.79 #2802, 0.67 #2899, 0.64 #2304), 028tv0 (0.77 #2689, 0.75 #3768, 0.71 #1395), 018j2 (0.75 #2512, 0.71 #2812, 0.70 #3795) >> Best rule #2684 for best value: >> intensional similarity = 19 >> extensional distance = 11 >> proper extension: 02snj9; >> query: (?x4769, 02sgy) <- role(?x4769, ?x2764), role(?x4769, ?x2206), role(?x4769, ?x1166), role(?x4769, ?x614), role(?x4769, ?x315), role(?x4769, ?x227), ?x1166 = 05148p4, ?x614 = 0mkg, ?x2764 = 01s0ps, group(?x4769, ?x11425), role(?x2592, ?x4769), role(?x10237, ?x4769), ?x227 = 0342h, role(?x2392, ?x2206), instrumentalists(?x2206, ?x669), group(?x2206, ?x1751), artists(?x1380, ?x11425), role(?x2206, ?x3161), ?x315 = 0l14md >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #2346 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 9 *> proper extension: 0mkg; *> query: (?x4769, 0g33q) <- role(?x4769, ?x2059), role(?x4769, ?x1437), role(?x2206, ?x4769), role(?x1466, ?x4769), role(?x227, ?x4769), role(?x1437, ?x214), role(?x5494, ?x1437), role(?x4646, ?x1437), role(?x2865, ?x1437), role(?x2784, ?x1437), role(?x1181, ?x1437), role(?x959, ?x1437), ?x959 = 03f5spx, ?x4646 = 0fhxv, ?x2784 = 0137g1, ?x1466 = 03bx0bm, ?x2059 = 0dwr4, instrumentalists(?x1437, ?x226), sibling(?x2865, ?x6129), ?x2206 = 07gql, award(?x5494, ?x1565), ?x227 = 0342h, nationality(?x1181, ?x512) *> conf = 0.55 ranks of expected_values: 56 EVAL 0dwt5 role! 0g33q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 83.000 55.000 0.846 http://example.org/music/performance_role/regular_performances./music/group_membership/role #16060-0837ql PRED entity: 0837ql PRED relation: award_nominee! PRED expected values: 01wlt3k => 102 concepts (54 used for prediction) PRED predicted values (max 10 best out of 967): 016kjs (0.82 #97582, 0.82 #76670, 0.81 #62730), 0677ng (0.82 #97582, 0.82 #76670, 0.81 #62730), 01q32bd (0.82 #97582, 0.82 #76670, 0.81 #62730), 03f19q4 (0.82 #97582, 0.82 #76670, 0.81 #62730), 067nsm (0.82 #97582, 0.82 #76670, 0.81 #62730), 01wlt3k (0.45 #2221, 0.06 #4544, 0.03 #32423), 0837ql (0.36 #1138, 0.06 #31340, 0.03 #54574), 01vsgrn (0.27 #1299, 0.08 #31501, 0.06 #3622), 01yzl2 (0.27 #1280, 0.05 #31482, 0.03 #15218), 02x_h0 (0.27 #1281, 0.05 #31483, 0.02 #45423) >> Best rule #97582 for best value: >> intensional similarity = 3 >> extensional distance = 562 >> proper extension: 02zq43; >> query: (?x4836, ?x1125) <- award_nominee(?x4475, ?x4836), award_nominee(?x4836, ?x1125), origin(?x4475, ?x2740) >> conf = 0.82 => this is the best rule for 5 predicted values *> Best rule #2221 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01wbsdz; *> query: (?x4836, 01wlt3k) <- award_nominee(?x10012, ?x4836), award_nominee(?x9623, ?x4836), category(?x9623, ?x134), ?x10012 = 03j3pg9 *> conf = 0.45 ranks of expected_values: 6 EVAL 0837ql award_nominee! 01wlt3k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 102.000 54.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16059-040db PRED entity: 040db PRED relation: influenced_by! PRED expected values: 014ps4 040rjq => 166 concepts (69 used for prediction) PRED predicted values (max 10 best out of 452): 014ps4 (0.57 #3318, 0.21 #4823, 0.20 #1812), 045bg (0.50 #3553, 0.21 #4555, 0.14 #2012), 013pp3 (0.43 #4736, 0.20 #1725, 0.14 #2012), 06hmd (0.43 #3230, 0.07 #10027, 0.07 #16042), 0d4jl (0.40 #1622, 0.29 #3128, 0.23 #4130), 073bb (0.40 #1570, 0.23 #4078, 0.17 #2574), 084w8 (0.40 #1511, 0.20 #3520, 0.20 #1008), 0mb5x (0.40 #1836, 0.20 #1333, 0.15 #4344), 0lcx (0.40 #3665, 0.14 #2012, 0.08 #24059), 06mn7 (0.33 #166, 0.20 #1172, 0.08 #13037) >> Best rule #3318 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 06kb_; 03f47xl; 037jz; 0ky1; >> query: (?x2161, 014ps4) <- influenced_by(?x2161, ?x6400), influenced_by(?x2161, ?x1279), influenced_by(?x476, ?x2161), gender(?x1279, ?x231), ?x6400 = 06lbp >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 82 EVAL 040db influenced_by! 040rjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 166.000 69.000 0.571 http://example.org/influence/influence_node/influenced_by EVAL 040db influenced_by! 014ps4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 69.000 0.571 http://example.org/influence/influence_node/influenced_by #16058-0vmt PRED entity: 0vmt PRED relation: adjoins! PRED expected values: 01n7q => 143 concepts (108 used for prediction) PRED predicted values (max 10 best out of 1327): 03s5t (0.24 #48377, 0.23 #65541, 0.22 #74906), 05mph (0.24 #48377, 0.23 #65541, 0.22 #74906), 0846v (0.24 #48377, 0.23 #65541, 0.22 #74906), 05kj_ (0.24 #48377, 0.23 #65541, 0.22 #74906), 0vmt (0.24 #48377, 0.23 #65541, 0.22 #74906), 05fhy (0.24 #48377, 0.23 #65541, 0.22 #74906), 01n7q (0.24 #48377, 0.23 #65541, 0.22 #74906), 0488g (0.24 #48377, 0.21 #47597, 0.20 #81147), 07b_l (0.24 #48377, 0.21 #47597, 0.20 #81147), 0183z2 (0.24 #48377, 0.21 #47597, 0.20 #81147) >> Best rule #48377 for best value: >> intensional similarity = 3 >> extensional distance = 200 >> proper extension: 0mvxt; >> query: (?x938, ?x726) <- adjoins(?x1138, ?x938), administrative_division(?x4419, ?x938), adjoins(?x1138, ?x726) >> conf = 0.24 => this is the best rule for 10 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7 EVAL 0vmt adjoins! 01n7q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 143.000 108.000 0.239 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #16057-01frpd PRED entity: 01frpd PRED relation: organization! PRED expected values: 0dq_5 => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 37): 0dq_5 (0.85 #116, 0.82 #226, 0.81 #307), 060c4 (0.54 #1653, 0.53 #1588, 0.52 #1758), 0krdk (0.34 #1928, 0.32 #1408, 0.27 #1799), 0dq3c (0.27 #1799, 0.27 #1365, 0.26 #2075), 05_wyz (0.24 #1530, 0.21 #1815, 0.21 #1813), 01kr6k (0.21 #1815, 0.21 #1813, 0.21 #1861), 07xl34 (0.17 #1727, 0.16 #1320, 0.16 #1898), 05k17c (0.09 #333, 0.08 #1697, 0.08 #1948), 01yc02 (0.07 #124, 0.06 #180, 0.05 #289), 05c0jwl (0.05 #2037, 0.05 #2081, 0.05 #2133) >> Best rule #116 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 05w3y; >> query: (?x14236, 0dq_5) <- list(?x14236, ?x8915), ?x8915 = 01pd60, place_founded(?x14236, ?x659), citytown(?x14236, ?x1523), category(?x14236, ?x134) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01frpd organization! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.846 http://example.org/organization/role/leaders./organization/leadership/organization #16056-01q460 PRED entity: 01q460 PRED relation: major_field_of_study PRED expected values: 062z7 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 119): 01mkq (0.62 #371, 0.55 #3231, 0.50 #847), 03g3w (0.48 #382, 0.40 #620, 0.39 #858), 062z7 (0.48 #383, 0.38 #3838, 0.35 #859), 04rjg (0.43 #732, 0.43 #971, 0.40 #1209), 037mh8 (0.41 #423, 0.33 #661, 0.31 #899), 05qjt (0.41 #364, 0.29 #4058, 0.28 #4177), 05qfh (0.41 #390, 0.27 #628, 0.26 #866), 01540 (0.41 #416, 0.27 #654, 0.26 #892), 0g26h (0.39 #3257, 0.33 #3852, 0.31 #397), 04x_3 (0.34 #381, 0.25 #619, 0.24 #857) >> Best rule #371 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 08815; 07tgn; 0dplh; 0160nk; 04jhp; >> query: (?x3354, 01mkq) <- major_field_of_study(?x3354, ?x9111), institution(?x865, ?x3354), ?x9111 = 04sh3, organization(?x346, ?x3354), ?x865 = 02h4rq6 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #383 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 08815; 07tgn; 0dplh; 0160nk; 04jhp; *> query: (?x3354, 062z7) <- major_field_of_study(?x3354, ?x9111), institution(?x865, ?x3354), ?x9111 = 04sh3, organization(?x346, ?x3354), ?x865 = 02h4rq6 *> conf = 0.48 ranks of expected_values: 3 EVAL 01q460 major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 144.000 144.000 0.621 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16055-04vr_f PRED entity: 04vr_f PRED relation: crewmember PRED expected values: 095zvfg => 113 concepts (67 used for prediction) PRED predicted values (max 10 best out of 50): 095zvfg (0.38 #86, 0.04 #563, 0.03 #325), 09rp4r_ (0.17 #9, 0.12 #105, 0.02 #920), 051z6rz (0.12 #77, 0.03 #554, 0.03 #1327), 04wp63 (0.06 #235, 0.04 #187, 0.03 #376), 03m49ly (0.06 #560, 0.03 #322, 0.03 #897), 0b79gfg (0.04 #640, 0.04 #400, 0.03 #305), 05bm4sm (0.04 #171, 0.02 #648, 0.02 #1516), 0c94fn (0.04 #536, 0.03 #1068, 0.02 #970), 04sry (0.04 #96, 0.03 #1298, 0.03 #2364), 018ygt (0.04 #96, 0.03 #1298, 0.03 #2364) >> Best rule #86 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 05dy7p; >> query: (?x1135, 095zvfg) <- nominated_for(?x7310, ?x1135), country(?x1135, ?x94), ?x7310 = 04sry, nominated_for(?x112, ?x1135) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04vr_f crewmember 095zvfg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 67.000 0.375 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #16054-0f4_l PRED entity: 0f4_l PRED relation: film! PRED expected values: 0jz9f => 61 concepts (47 used for prediction) PRED predicted values (max 10 best out of 45): 05qd_ (0.33 #8, 0.25 #82, 0.21 #379), 054g1r (0.33 #34, 0.25 #108, 0.08 #183), 020h2v (0.33 #44, 0.25 #118, 0.08 #193), 016tt2 (0.25 #78, 0.15 #449, 0.13 #598), 0g1rw (0.23 #230, 0.20 #304, 0.17 #156), 086k8 (0.21 #373, 0.19 #596, 0.19 #447), 016tw3 (0.15 #233, 0.15 #976, 0.14 #1276), 017s11 (0.12 #1044, 0.12 #969, 0.12 #894), 01gb54 (0.10 #399, 0.08 #473, 0.07 #622), 01795t (0.09 #834, 0.08 #166, 0.08 #240) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0c3zjn7; >> query: (?x2177, 05qd_) <- film(?x14135, ?x2177), ?x14135 = 01nd6v, nominated_for(?x112, ?x2177) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #521 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 110 *> proper extension: 0fq27fp; *> query: (?x2177, 0jz9f) <- film_crew_role(?x2177, ?x1171), film_release_region(?x2177, ?x94) *> conf = 0.07 ranks of expected_values: 13 EVAL 0f4_l film! 0jz9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 61.000 47.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16053-0jsg0m PRED entity: 0jsg0m PRED relation: instrumentalists! PRED expected values: 0342h => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 119): 0342h (0.84 #1455, 0.83 #1540, 0.70 #2562), 0319l (0.43 #852, 0.42 #426, 0.41 #1108), 01vj9c (0.43 #852, 0.42 #426, 0.41 #1108), 03qjg (0.27 #134, 0.27 #389, 0.21 #1754), 02hnl (0.27 #118, 0.25 #33, 0.23 #373), 06ncr (0.19 #382, 0.12 #1235, 0.11 #1064), 026t6 (0.18 #1282, 0.17 #1025, 0.17 #684), 06w7v (0.18 #155, 0.15 #410, 0.11 #1605), 04rzd (0.18 #121, 0.15 #1486, 0.15 #1571), 0l14qv (0.18 #91, 0.15 #687, 0.12 #1028) >> Best rule #1455 for best value: >> intensional similarity = 4 >> extensional distance = 156 >> proper extension: 016qtt; 06y9c2; 01cv3n; 0152cw; 01gf5h; 01p9hgt; 01w923; 0zjpz; 0144l1; 01vsnff; ... >> query: (?x7459, 0342h) <- profession(?x7459, ?x2659), instrumentalists(?x316, ?x7459), ?x2659 = 039v1, artists(?x378, ?x7459) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jsg0m instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.835 http://example.org/music/instrument/instrumentalists #16052-045cq PRED entity: 045cq PRED relation: profession PRED expected values: 02jknp => 166 concepts (95 used for prediction) PRED predicted values (max 10 best out of 91): 02jknp (0.88 #7704, 0.88 #9629, 0.88 #5780), 02hrh1q (0.84 #8747, 0.82 #10376, 0.80 #2530), 0dxtg (0.75 #4750, 0.73 #9635, 0.73 #2973), 03gjzk (0.70 #1939, 0.68 #1051, 0.67 #2235), 09jwl (0.31 #4164, 0.30 #5348, 0.24 #2091), 0cbd2 (0.30 #2670, 0.24 #7407, 0.24 #4595), 0kyk (0.25 #1213, 0.19 #2693, 0.18 #7430), 018gz8 (0.22 #2533, 0.21 #4310, 0.21 #2237), 0nbcg (0.21 #4176, 0.21 #5360, 0.16 #919), 0np9r (0.19 #2241, 0.17 #7570, 0.17 #3130) >> Best rule #7704 for best value: >> intensional similarity = 3 >> extensional distance = 211 >> proper extension: 05drq5; 04b19t; 01ycck; 012vct; 0522wp; 013tcv; >> query: (?x5573, 02jknp) <- film(?x5573, ?x3614), profession(?x5573, ?x319), ?x319 = 01d_h8 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045cq profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 95.000 0.883 http://example.org/people/person/profession #16051-0nf3h PRED entity: 0nf3h PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 212 concepts (90 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.88 #264, 0.88 #253, 0.88 #241), 04ych (0.33 #188, 0.20 #393, 0.18 #432), 0nf3h (0.33 #188, 0.20 #393, 0.18 #432), 0488g (0.14 #225, 0.09 #773, 0.09 #851), 02jx1 (0.06 #1035, 0.05 #1147, 0.04 #100), 03rt9 (0.04 #216, 0.02 #510, 0.02 #562), 03rjj (0.03 #469, 0.03 #278, 0.02 #304), 0f8l9c (0.02 #754, 0.02 #793, 0.02 #333), 07ssc (0.01 #1032) >> Best rule #264 for best value: >> intensional similarity = 5 >> extensional distance = 112 >> proper extension: 0nm6z; >> query: (?x7165, ?x94) <- contains(?x1025, ?x7165), adjoins(?x7165, ?x14338), second_level_divisions(?x94, ?x14338), vacationer(?x1025, ?x1634), district_represented(?x176, ?x1025) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nf3h second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 90.000 0.877 http://example.org/location/country/second_level_divisions #16050-0cnl09 PRED entity: 0cnl09 PRED relation: place_of_birth PRED expected values: 01_d4 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 39): 013yq (0.08 #79, 0.05 #783, 0.02 #1487), 0qkcb (0.08 #292, 0.03 #996, 0.02 #1700), 03l2n (0.08 #169, 0.03 #873, 0.02 #16194), 01m1_d (0.08 #553, 0.03 #1257, 0.02 #16194), 0d04z6 (0.08 #187, 0.03 #891, 0.02 #16194), 0t_07 (0.08 #448, 0.03 #1152, 0.02 #16194), 0d9jr (0.08 #194, 0.03 #898, 0.01 #28868), 01qh7 (0.08 #104, 0.03 #808, 0.01 #28868), 0dclg (0.08 #78, 0.02 #5006, 0.02 #16194), 0cr3d (0.08 #798, 0.04 #9246, 0.04 #2910) >> Best rule #79 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0cnl80; >> query: (?x4333, 013yq) <- award_winner(?x6633, ?x4333), award_winner(?x3624, ?x4333), ?x6633 = 0cl0bk >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #54281 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2749 *> proper extension: 02qggqc; 07qnf; 07m69t; 01l3j; 01nvdc; 03cxqp5; *> query: (?x4333, 01_d4) <- nationality(?x4333, ?x94), ?x94 = 09c7w0 *> conf = 0.03 ranks of expected_values: 13 EVAL 0cnl09 place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 87.000 87.000 0.083 http://example.org/people/person/place_of_birth #16049-0chsq PRED entity: 0chsq PRED relation: location PRED expected values: 030qb3t => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 195): 02_286 (0.26 #49849, 0.24 #54669, 0.19 #73946), 030qb3t (0.22 #18564, 0.19 #54715, 0.18 #19367), 01n7q (0.18 #866, 0.06 #7291, 0.06 #1669), 0dclg (0.17 #117, 0.04 #5739, 0.04 #4133), 07ssc (0.17 #26, 0.03 #5648, 0.02 #4042), 0g284 (0.17 #115, 0.02 #4131, 0.01 #5737), 0k049 (0.15 #4016, 0.14 #12050, 0.13 #8835), 0cr3d (0.10 #49957, 0.08 #2554, 0.07 #74054), 0cc56 (0.09 #860, 0.08 #4073, 0.07 #5679), 0r0m6 (0.09 #1021, 0.06 #7446, 0.06 #1824) >> Best rule #49849 for best value: >> intensional similarity = 2 >> extensional distance = 1333 >> proper extension: 02r3cn; 032md; 0bqch; 0466k4; >> query: (?x510, 02_286) <- location(?x510, ?x5867), adjoins(?x5867, ?x1860) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #18564 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 413 *> proper extension: 013vdl; 01fxck; *> query: (?x510, 030qb3t) <- gender(?x510, ?x231), film(?x510, ?x499), languages(?x510, ?x254) *> conf = 0.22 ranks of expected_values: 2 EVAL 0chsq location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 126.000 0.262 http://example.org/people/person/places_lived./people/place_lived/location #16048-034b6k PRED entity: 034b6k PRED relation: nominated_for! PRED expected values: 02g3v6 018wdw => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 185): 0gq9h (0.40 #301, 0.36 #1253, 0.30 #1015), 019f4v (0.40 #292, 0.32 #1244, 0.27 #1006), 0gs9p (0.40 #303, 0.30 #1255, 0.26 #1017), 0k611 (0.40 #312, 0.28 #1264, 0.24 #1502), 099c8n (0.40 #295, 0.26 #1962, 0.23 #1009), 02pqp12 (0.40 #297, 0.24 #1011, 0.22 #1249), 04dn09n (0.40 #273, 0.22 #1225, 0.20 #5037), 0gr4k (0.40 #264, 0.18 #10269, 0.18 #5742), 02x17s4 (0.40 #334, 0.14 #2001, 0.11 #3669), 09qv_s (0.40 #354, 0.12 #2021, 0.11 #3689) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0dj0m5; 09cr8; 01cmp9; >> query: (?x10742, 0gq9h) <- award_winner(?x10742, ?x2214), film(?x3708, ?x10742), country(?x10742, ?x94), ?x3708 = 013knm >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #497 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 02qhqz4; 0cc846d; 0d_wms; 062zjtt; 0bs8ndx; 076xkps; 042g97; *> query: (?x10742, 02g3v6) <- country(?x10742, ?x94), genre(?x10742, ?x6888), ?x6888 = 04pbhw *> conf = 0.20 ranks of expected_values: 60, 64 EVAL 034b6k nominated_for! 018wdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 86.000 86.000 0.400 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 034b6k nominated_for! 02g3v6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 86.000 86.000 0.400 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #16047-03ckwzc PRED entity: 03ckwzc PRED relation: film_crew_role PRED expected values: 02_n3z 02ynfr => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 24): 01xy5l_ (0.61 #460, 0.53 #123, 0.48 #516), 01vx2h (0.60 #92, 0.54 #64, 0.50 #457), 02_n3z (0.59 #141, 0.56 #29, 0.47 #85), 02ynfr (0.55 #181, 0.54 #69, 0.53 #97), 01pvkk (0.34 #2021, 0.33 #1652, 0.29 #262), 015h31 (0.22 #455, 0.18 #511, 0.17 #539), 033smt (0.20 #469, 0.19 #553, 0.18 #525), 02rh1dz (0.19 #653, 0.19 #625, 0.17 #7), 06qc5 (0.17 #21, 0.04 #386, 0.03 #330), 0ckd1 (0.15 #59, 0.13 #87, 0.11 #143) >> Best rule #460 for best value: >> intensional similarity = 5 >> extensional distance = 106 >> proper extension: 02hfk5; >> query: (?x805, 01xy5l_) <- film_crew_role(?x805, ?x4305), film_crew_role(?x805, ?x1078), ?x4305 = 0215hd, profession(?x5571, ?x1078), location(?x5571, ?x3125) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #141 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 0b7l4x; 0421v9q; 07p12s; *> query: (?x805, 02_n3z) <- film_crew_role(?x805, ?x4305), ?x4305 = 0215hd, film(?x804, ?x805), category(?x805, ?x134) *> conf = 0.59 ranks of expected_values: 3, 4 EVAL 03ckwzc film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.611 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 03ckwzc film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.611 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16046-051vz PRED entity: 051vz PRED relation: draft PRED expected values: 02rl201 047dpm0 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 14): 047dpm0 (0.82 #253, 0.82 #239, 0.81 #72), 02rl201 (0.82 #243, 0.81 #72, 0.77 #1048), 092j54 (0.54 #939, 0.53 #682, 0.52 #452), 0g3zpp (0.54 #934, 0.52 #447, 0.50 #677), 09l0x9 (0.54 #941, 0.50 #684, 0.48 #698), 05vsb7 (0.52 #446, 0.52 #690, 0.50 #933), 03nt7j (0.48 #938, 0.48 #451, 0.46 #526), 02qw1zx (0.41 #936, 0.38 #449, 0.38 #679), 025tn92 (0.40 #1043, 0.36 #917, 0.35 #1019), 0f4vx0 (0.40 #1041, 0.36 #917, 0.35 #1019) >> Best rule #253 for best value: >> intensional similarity = 10 >> extensional distance = 9 >> proper extension: 04wmvz; >> query: (?x2174, 047dpm0) <- season(?x2174, ?x2406), team(?x2010, ?x2174), ?x2406 = 03c6sl9, school(?x2174, ?x2948), draft(?x2174, ?x1161), major_field_of_study(?x2948, ?x5615), ?x5615 = 011s0, ?x1161 = 02x2khw, currency(?x2948, ?x170), student(?x2948, ?x129) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 051vz draft 047dpm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.818 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 051vz draft 02rl201 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.818 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #16045-02k4gv PRED entity: 02k4gv PRED relation: participant PRED expected values: 0kxbc => 99 concepts (51 used for prediction) PRED predicted values (max 10 best out of 188): 0kxbc (0.81 #8372, 0.81 #10949, 0.81 #7081), 03_wj_ (0.07 #7082, 0.07 #141, 0.05 #5790), 044mjy (0.07 #7082, 0.07 #543, 0.05 #5790), 07f3xb (0.07 #7082, 0.05 #5790, 0.05 #7080), 01kwld (0.07 #7082, 0.05 #5790, 0.05 #7080), 01sl1q (0.07 #7082, 0.05 #5790, 0.05 #7080), 03_wtr (0.07 #7082, 0.05 #5790, 0.05 #7080), 03x22w (0.07 #7082, 0.05 #5790, 0.05 #7080), 031ydm (0.07 #7082, 0.05 #5790, 0.05 #7080), 044mm6 (0.07 #7082, 0.05 #5790, 0.05 #7080) >> Best rule #8372 for best value: >> intensional similarity = 3 >> extensional distance = 356 >> proper extension: 01vvydl; 01dw4q; 01n5309; 018y2s; 01vrt_c; 0137n0; 09qr6; 01yhvv; 04nw9; 058s57; ... >> query: (?x5507, ?x5635) <- award_nominee(?x5507, ?x56), participant(?x5635, ?x5507), film(?x5507, ?x6009) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02k4gv participant 0kxbc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 51.000 0.810 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #16044-06c97 PRED entity: 06c97 PRED relation: type_of_union PRED expected values: 04ztj => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #213, 0.88 #153, 0.87 #293), 01g63y (0.25 #14, 0.23 #122, 0.22 #86), 01bl8s (0.20 #641, 0.19 #686, 0.02 #311), 0jgjn (0.02 #316, 0.01 #348, 0.01 #360) >> Best rule #213 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 06f5j; >> query: (?x5572, 04ztj) <- entity_involved(?x7455, ?x5572), nationality(?x5572, ?x94), profession(?x5572, ?x2225), religion(?x5572, ?x3616) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06c97 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.885 http://example.org/people/person/spouse_s./people/marriage/type_of_union #16043-01trf3 PRED entity: 01trf3 PRED relation: profession PRED expected values: 0dxtg 0np9r => 125 concepts (115 used for prediction) PRED predicted values (max 10 best out of 76): 0dxtg (0.84 #3983, 0.81 #602, 0.81 #2366), 01d_h8 (0.57 #3387, 0.56 #3093, 0.55 #3534), 02jknp (0.36 #13388, 0.32 #1184, 0.28 #2066), 0np9r (0.33 #460, 0.31 #754, 0.26 #1048), 0cbd2 (0.31 #1918, 0.27 #1330, 0.25 #1477), 02krf9 (0.31 #3259, 0.31 #760, 0.26 #7081), 09jwl (0.27 #3692, 0.26 #605, 0.20 #6485), 0kyk (0.26 #1792, 0.20 #1204, 0.20 #1645), 015cjr (0.20 #195, 0.19 #783, 0.16 #1077), 0nbcg (0.15 #3705, 0.13 #6498, 0.13 #4881) >> Best rule #3983 for best value: >> intensional similarity = 3 >> extensional distance = 174 >> proper extension: 03mz9r; 0275_pj; 01gp_x; 03bx_5q; 04gtdnh; 03fykz; 04crrxr; 0grmhb; 0564mx; 055sjw; ... >> query: (?x4233, 0dxtg) <- tv_program(?x4233, ?x6884), profession(?x4233, ?x1032), actor(?x6884, ?x692) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 01trf3 profession 0np9r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 115.000 0.835 http://example.org/people/person/profession EVAL 01trf3 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 115.000 0.835 http://example.org/people/person/profession #16042-06j2v PRED entity: 06j2v PRED relation: people PRED expected values: 01lc5 => 22 concepts (6 used for prediction) PRED predicted values (max 10 best out of 3251): 0lkr7 (0.14 #4164, 0.13 #7617, 0.13 #9344), 06cgy (0.14 #3649, 0.13 #7102, 0.13 #8829), 01pk3z (0.14 #4240, 0.13 #7693, 0.13 #9420), 0315q3 (0.14 #4108, 0.13 #7561, 0.13 #9288), 0g824 (0.11 #4352, 0.11 #2626, 0.10 #6079), 04f7c55 (0.11 #4268, 0.11 #2542, 0.10 #5995), 0311wg (0.11 #3744, 0.11 #2018, 0.10 #5471), 06crk (0.11 #2622, 0.10 #6075, 0.10 #7801), 0132k4 (0.11 #4418, 0.10 #7871, 0.10 #9598), 0bx_q (0.11 #4261, 0.10 #7714, 0.10 #9441) >> Best rule #4164 for best value: >> intensional similarity = 6 >> extensional distance = 26 >> proper extension: 0g48m4; 01qhm_; 09vc4s; 0x67; 07hwkr; 0xnvg; 013xrm; 07bch9; 03ts0c; 06gbnc; ... >> query: (?x13372, 0lkr7) <- people(?x13372, ?x6363), nationality(?x6363, ?x94), award_winner(?x537, ?x6363), geographic_distribution(?x13372, ?x583), award_winner(?x635, ?x6363), ?x94 = 09c7w0 >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06j2v people 01lc5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 22.000 6.000 0.143 http://example.org/people/ethnicity/people #16041-06mn7 PRED entity: 06mn7 PRED relation: location PRED expected values: 01531 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 153): 0cc56 (0.70 #57038, 0.49 #39364, 0.41 #80338), 030qb3t (0.17 #16146, 0.15 #45872, 0.14 #36232), 0cr3d (0.10 #947, 0.09 #1750, 0.08 #144), 04jpl (0.06 #28934, 0.05 #75535, 0.05 #77945), 0vzm (0.05 #172, 0.03 #2581, 0.03 #4187), 01n7q (0.04 #865, 0.04 #18535, 0.04 #15323), 059rby (0.04 #16080, 0.04 #4031, 0.04 #45806), 0chrx (0.04 #2010, 0.02 #6828, 0.01 #2813), 01cx_ (0.03 #3374, 0.03 #162, 0.03 #965), 0rh6k (0.03 #3216, 0.02 #56238, 0.02 #16068) >> Best rule #57038 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 07h1h5; 04m2zj; 04hqbbz; 0kbn5; 0b9f7t; >> query: (?x4353, ?x1131) <- location(?x4353, ?x739), place_of_birth(?x4353, ?x1131) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2566 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 96 *> proper extension: 01wd3l; 0crqcc; *> query: (?x4353, 01531) <- nominated_for(?x4353, ?x1547), award(?x4353, ?x601), ?x601 = 0gr4k *> conf = 0.03 ranks of expected_values: 16 EVAL 06mn7 location 01531 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 112.000 112.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #16040-03xnq9_ PRED entity: 03xnq9_ PRED relation: instrumentalists! PRED expected values: 042v_gx => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 90): 0342h (0.71 #1607, 0.45 #6419, 0.44 #6240), 05r5c (0.45 #1611, 0.36 #4816, 0.35 #3748), 018vs (0.37 #1616, 0.24 #3753, 0.22 #4821), 05148p4 (0.34 #1624, 0.30 #467, 0.27 #289), 02hnl (0.18 #1638, 0.16 #303, 0.14 #3775), 03qjg (0.14 #1655, 0.13 #2367, 0.12 #498), 0l14qv (0.12 #362, 0.08 #2142, 0.08 #2231), 0l14md (0.12 #453, 0.11 #1165, 0.11 #987), 026t6 (0.12 #448, 0.11 #270, 0.11 #1605), 04rzd (0.09 #1641, 0.07 #484, 0.06 #4846) >> Best rule #1607 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 016qtt; 0197tq; 01vvycq; 03f5spx; 01gf5h; 01vv7sc; 01v_pj6; 012zng; 01wsl7c; 01wwvt2; ... >> query: (?x5657, 0342h) <- category(?x5657, ?x134), artists(?x302, ?x5657), profession(?x5657, ?x220), ?x302 = 016clz >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #277 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 01vrncs; 0j1yf; 04gycf; 01fs_4; 03h502k; *> query: (?x5657, 042v_gx) <- religion(?x5657, ?x109), participant(?x8018, ?x5657), artists(?x302, ?x5657), place_of_birth(?x5657, ?x1523) *> conf = 0.08 ranks of expected_values: 11 EVAL 03xnq9_ instrumentalists! 042v_gx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 133.000 133.000 0.709 http://example.org/music/instrument/instrumentalists #16039-02x8fs PRED entity: 02x8fs PRED relation: film! PRED expected values: 0p_pd => 74 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1100): 01wwvc5 (0.57 #54022, 0.56 #39475, 0.50 #10387), 01wwvd2 (0.50 #10387, 0.49 #29086, 0.43 #78966), 086nl7 (0.25 #786, 0.08 #4940, 0.07 #9095), 0mdqp (0.25 #119, 0.04 #27127, 0.04 #8428), 01nfys (0.25 #1570, 0.04 #9879, 0.03 #16111), 0dzf_ (0.25 #809, 0.04 #9118, 0.03 #25740), 0b25vg (0.25 #1770, 0.04 #10079, 0.02 #18389), 02mjf2 (0.25 #775, 0.04 #9084, 0.02 #13239), 01q_ph (0.25 #57, 0.04 #12521, 0.03 #27065), 0fby2t (0.25 #754, 0.04 #13218, 0.02 #40232) >> Best rule #54022 for best value: >> intensional similarity = 4 >> extensional distance = 491 >> proper extension: 0gj50; 02qkq0; >> query: (?x5045, ?x2731) <- award_winner(?x5045, ?x2731), award_nominee(?x4476, ?x2731), friend(?x2280, ?x4476), award(?x2731, ?x567) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2131 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 05b6rdt; 01d2v1; *> query: (?x5045, 0p_pd) <- genre(?x5045, ?x225), titles(?x2480, ?x5045), featured_film_locations(?x5045, ?x739), film(?x4771, ?x5045), ?x4771 = 0h96g *> conf = 0.17 ranks of expected_values: 24 EVAL 02x8fs film! 0p_pd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 74.000 42.000 0.575 http://example.org/film/actor/film./film/performance/film #16038-0ff3y PRED entity: 0ff3y PRED relation: written_by! PRED expected values: 01lbcqx => 175 concepts (159 used for prediction) PRED predicted values (max 10 best out of 263): 09qycb (0.31 #16534, 0.30 #23147, 0.29 #11905), 03cvwkr (0.08 #51, 0.02 #7988, 0.01 #9972), 09fc83 (0.08 #346, 0.01 #9605, 0.01 #10267), 02ph9tm (0.06 #1088, 0.03 #10348, 0.02 #6379), 0ds2n (0.06 #866, 0.02 #6157, 0.02 #16077), 04fzfj (0.06 #698, 0.02 #5989, 0.02 #15909), 0199wf (0.06 #1279, 0.02 #6570, 0.02 #7893), 0hv8w (0.06 #1030, 0.02 #6321, 0.02 #7644), 0dnqr (0.06 #852, 0.02 #6143, 0.02 #7466), 0ddt_ (0.06 #846, 0.02 #6137, 0.02 #7460) >> Best rule #16534 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 0884hk; >> query: (?x13298, ?x10349) <- profession(?x13298, ?x987), story_by(?x10349, ?x13298), gender(?x13298, ?x231), ?x987 = 0dxtg >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #28322 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 164 *> proper extension: 01w3v; 0mcf4; *> query: (?x13298, 01lbcqx) <- religion(?x13298, ?x7131), ?x7131 = 03_gx *> conf = 0.02 ranks of expected_values: 77 EVAL 0ff3y written_by! 01lbcqx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 175.000 159.000 0.315 http://example.org/film/film/written_by #16037-04v8x9 PRED entity: 04v8x9 PRED relation: film_festivals PRED expected values: 059_y8d => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 18): 05ys0ws (0.07 #62, 0.02 #335, 0.01 #167), 04_m9gk (0.06 #76, 0.06 #223, 0.05 #139), 03nn7l2 (0.05 #206, 0.04 #227, 0.04 #269), 059_y8d (0.04 #233, 0.04 #86, 0.04 #170), 05ys0wz (0.04 #46, 0.02 #88, 0.01 #151), 0gg7gsl (0.04 #127, 0.03 #64, 0.02 #211), 0bmj62v (0.03 #264, 0.03 #159, 0.03 #306), 0g57ws5 (0.03 #70, 0.03 #154, 0.02 #217), 05f5rsr (0.03 #74, 0.02 #95, 0.02 #137), 0hrcs29 (0.03 #78, 0.02 #141, 0.02 #456) >> Best rule #62 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 04tng0; >> query: (?x499, 05ys0ws) <- titles(?x4757, ?x499), honored_for(?x7226, ?x499), film_art_direction_by(?x499, ?x2801) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 07bz5; *> query: (?x499, 059_y8d) <- nominated_for(?x510, ?x499), list(?x499, ?x3004), award(?x499, ?x198) *> conf = 0.04 ranks of expected_values: 4 EVAL 04v8x9 film_festivals 059_y8d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 93.000 0.071 http://example.org/film/film/film_festivals #16036-0jpy_ PRED entity: 0jpy_ PRED relation: location_of_ceremony! PRED expected values: 04ztj => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.80 #17, 0.74 #25, 0.67 #45), 0jgjn (0.04 #20, 0.03 #28, 0.03 #48), 01g63y (0.01 #18, 0.01 #66, 0.01 #70), 01bl8s (0.01 #19, 0.01 #27) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 09f07; >> query: (?x12472, 04ztj) <- place_of_birth(?x305, ?x12472), citytown(?x13514, ?x12472), featured_film_locations(?x6543, ?x12472) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jpy_ location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.797 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #16035-01ypsj PRED entity: 01ypsj PRED relation: award PRED expected values: 03c7tr1 => 135 concepts (113 used for prediction) PRED predicted values (max 10 best out of 258): 09sb52 (0.55 #4463, 0.40 #9288, 0.39 #7277), 05p09zm (0.42 #5351, 0.15 #3341, 0.12 #1331), 0bfvd4 (0.38 #1322, 0.11 #4538, 0.10 #11373), 05ztrmj (0.35 #5410, 0.27 #1792, 0.15 #2194), 03c7tr1 (0.34 #5285, 0.19 #3275, 0.13 #6893), 07cbcy (0.33 #79, 0.19 #3295, 0.16 #5305), 0cqhk0 (0.33 #37, 0.17 #8078, 0.16 #8480), 0bdw6t (0.33 #111, 0.12 #1317, 0.07 #10966), 09v92_x (0.31 #2287, 0.25 #3091, 0.18 #1885), 09v8db5 (0.31 #2261, 0.21 #3065, 0.09 #1859) >> Best rule #4463 for best value: >> intensional similarity = 5 >> extensional distance = 200 >> proper extension: 06151l; 05slvm; 0205dx; 03q3sy; >> query: (?x9813, 09sb52) <- film(?x9813, ?x4276), film_crew_role(?x4276, ?x137), film_format(?x4276, ?x6392), genre(?x4276, ?x162), ?x162 = 04xvlr >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #5285 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 215 *> proper extension: 0cbm64; *> query: (?x9813, 03c7tr1) <- award(?x9813, ?x3064), award(?x3421, ?x3064), ?x3421 = 05r5w, nominated_for(?x3064, ?x599) *> conf = 0.34 ranks of expected_values: 5 EVAL 01ypsj award 03c7tr1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 135.000 113.000 0.554 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16034-0f2v0 PRED entity: 0f2v0 PRED relation: location! PRED expected values: 01w61th 02vy5j 01jz6d => 220 concepts (159 used for prediction) PRED predicted values (max 10 best out of 2258): 04bdxl (0.53 #213272, 0.52 #190686, 0.50 #203235), 0cgzj (0.53 #213272, 0.52 #190686, 0.50 #203235), 01dw_f (0.53 #213272, 0.50 #203235, 0.50 #363803), 02dztn (0.53 #213272, 0.50 #203235, 0.50 #298577), 03f0qd7 (0.52 #190686, 0.50 #363803, 0.50 #298577), 02fcs2 (0.50 #298577, 0.48 #346241, 0.48 #57716), 0261g5l (0.50 #298577, 0.48 #346241, 0.48 #57716), 02vwckw (0.40 #326173, 0.37 #238367, 0.33 #183160), 01fmz6 (0.33 #183160, 0.32 #265966, 0.31 #268475), 073749 (0.21 #5820, 0.15 #25893, 0.14 #10839) >> Best rule #213272 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 0_vn7; 0rh7t; 01t8gz; 04gxf; 0prxp; >> query: (?x3501, ?x7718) <- place_of_birth(?x7718, ?x3501), teams(?x3501, ?x1578), film(?x7718, ?x339) >> conf = 0.53 => this is the best rule for 4 predicted values *> Best rule #306102 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 173 *> proper extension: 0t015; 0fm2_; 0_7z2; 0_ytw; 0c5_3; 04kf4; 0fvwg; 0mnm2; 0_lr1; 015y2q; ... *> query: (?x3501, ?x1683) <- citytown(?x9022, ?x3501), contains(?x3501, ?x4904), student(?x4904, ?x1683) *> conf = 0.10 ranks of expected_values: 167, 635, 1263 EVAL 0f2v0 location! 01jz6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 220.000 159.000 0.530 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0f2v0 location! 02vy5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 220.000 159.000 0.530 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0f2v0 location! 01w61th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 220.000 159.000 0.530 http://example.org/people/person/places_lived./people/place_lived/location #16033-0gr51 PRED entity: 0gr51 PRED relation: award! PRED expected values: 014zcr 01q_ph 03s9b 027d5g5 026670 0b455l 06g4l => 54 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2657): 02mt4k (0.78 #49213, 0.65 #65621, 0.65 #52493), 052hl (0.78 #49213, 0.65 #65621, 0.65 #52493), 0169dl (0.78 #49213, 0.65 #65621, 0.65 #52493), 012wg (0.78 #49213, 0.65 #65621, 0.65 #52493), 06n9lt (0.78 #49213, 0.65 #65621, 0.65 #52493), 0gv5c (0.78 #49213, 0.65 #52493, 0.64 #65618), 03_gd (0.70 #3448, 0.50 #13289, 0.46 #6728), 014zcr (0.62 #13174, 0.60 #3333, 0.56 #16455), 0c00lh (0.56 #14643, 0.50 #17924, 0.40 #4802), 026670 (0.56 #19090, 0.50 #15809, 0.40 #5968) >> Best rule #49213 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 02581q; 02wh75; 02g3gj; 01d38g; 02grdc; 018wng; 01bgqh; 03x3wf; 0c4z8; 02g8mp; ... >> query: (?x1862, ?x2422) <- award(?x4732, ?x1862), award_winner(?x1862, ?x2422), award_nominee(?x8572, ?x4732), category_of(?x1862, ?x3459) >> conf = 0.78 => this is the best rule for 6 predicted values *> Best rule #13174 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 02qyp19; 0f_nbyh; 03hkv_r; 0gr4k; 02x17s4; 02x4wr9; *> query: (?x1862, 014zcr) <- award(?x826, ?x1862), nominated_for(?x1862, ?x2928), award(?x2928, ?x500), ?x826 = 02kxbwx *> conf = 0.62 ranks of expected_values: 8, 10, 32, 50, 275, 388, 803 EVAL 0gr51 award! 06g4l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 21.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr51 award! 0b455l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 54.000 21.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr51 award! 026670 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 54.000 21.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr51 award! 027d5g5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 21.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr51 award! 03s9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 54.000 21.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr51 award! 01q_ph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 54.000 21.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr51 award! 014zcr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 54.000 21.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16032-06wpc PRED entity: 06wpc PRED relation: company! PRED expected values: 02y6fz => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 13): 0dq_5 (0.17 #255, 0.11 #492, 0.08 #823), 01yc02 (0.17 #246, 0.11 #483, 0.08 #814), 02y6fz (0.12 #356, 0.11 #452, 0.06 #972), 02md_2 (0.11 #444, 0.08 #728, 0.06 #964), 06b1q (0.11 #1098, 0.07 #1578, 0.07 #1529), 02g_6x (0.03 #1580, 0.03 #1531, 0.03 #1434), 060c4 (0.02 #3754), 02dwpf (0.01 #1141, 0.01 #1140), 01z9v6 (0.01 #1141, 0.01 #1140), 028c_8 (0.01 #1141, 0.01 #1140) >> Best rule #255 for best value: >> intensional similarity = 21 >> extensional distance = 4 >> proper extension: 05g76; >> query: (?x7399, 0dq_5) <- sport(?x7399, ?x5063), team(?x8520, ?x7399), team(?x261, ?x7399), season(?x7399, ?x8517), season(?x7399, ?x2406), position(?x7399, ?x5727), ?x2406 = 03c6sl9, position(?x11673, ?x8520), position(?x8995, ?x8520), position(?x6348, ?x8520), position(?x4208, ?x8520), position(?x2011, ?x8520), ?x4208 = 061xq, draft(?x7399, ?x3334), ?x6348 = 021f30, school(?x7399, ?x735), ?x261 = 02dwn9, ?x8517 = 0285r5d, ?x2011 = 04913k, ?x11673 = 02gtm4, ?x8995 = 01d6g >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #356 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 6 *> proper extension: 098knd; *> query: (?x7399, 02y6fz) <- sport(?x7399, ?x5063), colors(?x7399, ?x5325), sports(?x778, ?x5063), sport(?x4243, ?x5063), athlete(?x5063, ?x13779), colors(?x8937, ?x5325), colors(?x8825, ?x5325), ?x8825 = 02x9cv, teams(?x3125, ?x4243), country(?x5063, ?x94), student(?x4955, ?x13779), school_type(?x8937, ?x3092) *> conf = 0.12 ranks of expected_values: 3 EVAL 06wpc company! 02y6fz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 79.000 0.167 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #16031-01yx7f PRED entity: 01yx7f PRED relation: place_founded PRED expected values: 0d6lp => 210 concepts (174 used for prediction) PRED predicted values (max 10 best out of 53): 0fsb8 (0.33 #66, 0.33 #48, 0.22 #1394), 02_286 (0.12 #811, 0.10 #1738, 0.08 #1338), 02kx3 (0.08 #197, 0.04 #529, 0.02 #3119), 01_d4 (0.08 #550, 0.06 #3202, 0.06 #1082), 06pwq (0.08 #205, 0.06 #875, 0.06 #1073), 0y1rf (0.08 #585, 0.05 #1512, 0.05 #1645), 0qcrj (0.08 #262, 0.05 #328, 0.04 #598), 07dfk (0.07 #5968, 0.06 #6433, 0.06 #3367), 0d6lp (0.06 #755, 0.06 #689, 0.05 #1284), 030qb3t (0.05 #1741, 0.05 #3331, 0.03 #613) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 024y8p; >> query: (?x10637, ?x8993) <- organization(?x4682, ?x10637), citytown(?x10637, ?x8993), state_province_region(?x10637, ?x760), ?x8993 = 0fsb8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #755 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 30 *> proper extension: 02lv2v; *> query: (?x10637, 0d6lp) <- state_province_region(?x10637, ?x760), currency(?x10637, ?x170), citytown(?x10637, ?x8993), service_location(?x10637, ?x335), contact_category(?x10637, ?x6046) *> conf = 0.06 ranks of expected_values: 9 EVAL 01yx7f place_founded 0d6lp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 210.000 174.000 0.333 http://example.org/organization/organization/place_founded #16030-01wdcxk PRED entity: 01wdcxk PRED relation: instrumentalists! PRED expected values: 0342h => 148 concepts (108 used for prediction) PRED predicted values (max 10 best out of 121): 0342h (0.84 #2251, 0.84 #2509, 0.84 #2595), 018vs (0.51 #1741, 0.49 #2087, 0.45 #2259), 05148p4 (0.43 #451, 0.41 #2697, 0.39 #709), 0l14j_ (0.33 #397, 0.33 #311, 0.25 #139), 03bx0bm (0.28 #1034, 0.25 #1988, 0.24 #1469), 02hnl (0.25 #121, 0.22 #1763, 0.22 #1591), 0mkg (0.25 #97, 0.06 #2171, 0.06 #872), 03ndd (0.25 #156, 0.05 #4927, 0.04 #4149), 07y_7 (0.21 #690, 0.17 #260, 0.14 #2162), 06w7v (0.20 #243, 0.17 #415, 0.17 #329) >> Best rule #2251 for best value: >> intensional similarity = 6 >> extensional distance = 118 >> proper extension: 01p9hgt; 01s21dg; >> query: (?x10094, 0342h) <- instrumentalists(?x316, ?x10094), profession(?x10094, ?x2659), profession(?x10094, ?x1183), ?x2659 = 039v1, ?x1183 = 09jwl, artist(?x2299, ?x10094) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wdcxk instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 108.000 0.842 http://example.org/music/instrument/instrumentalists #16029-0dvmd PRED entity: 0dvmd PRED relation: award_winner! PRED expected values: 0275n3y => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 117): 092c5f (0.11 #14, 0.07 #154, 0.05 #3234), 0275n3y (0.11 #75, 0.06 #355, 0.05 #1475), 09pnw5 (0.11 #102, 0.05 #242, 0.05 #1502), 013b2h (0.10 #5400, 0.09 #4980, 0.09 #5120), 02rjjll (0.10 #5325, 0.08 #4905, 0.08 #285), 05pd94v (0.10 #5322, 0.07 #4902, 0.07 #5042), 0466p0j (0.09 #5396, 0.07 #4976, 0.06 #7636), 0g55tzk (0.09 #556, 0.05 #136, 0.03 #6436), 01s695 (0.09 #5323, 0.08 #4903, 0.07 #5043), 02cg41 (0.09 #5445, 0.08 #5025, 0.07 #5165) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 01pj5q; 014g_s; >> query: (?x3101, 092c5f) <- award(?x3101, ?x3019), participant(?x1733, ?x3101), ?x3019 = 057xs89 >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 01pj5q; 014g_s; *> query: (?x3101, 0275n3y) <- award(?x3101, ?x3019), participant(?x1733, ?x3101), ?x3019 = 057xs89 *> conf = 0.11 ranks of expected_values: 2 EVAL 0dvmd award_winner! 0275n3y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.105 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #16028-01cw7s PRED entity: 01cw7s PRED relation: award_winner PRED expected values: 01w7nww 026spg 02h9_l => 37 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1584): 02z4b_8 (0.42 #4043, 0.33 #6513, 0.33 #1572), 0pyg6 (0.38 #12356, 0.33 #2470, 0.33 #17298), 03f1d47 (0.33 #2470, 0.33 #1127, 0.33 #17298), 0197tq (0.33 #2498, 0.33 #27, 0.27 #4968), 01pq5j7 (0.33 #3653, 0.33 #1182, 0.27 #6123), 01wqmm8 (0.33 #2470, 0.33 #17298, 0.32 #9884), 03f3yfj (0.33 #2470, 0.33 #17298, 0.32 #9884), 0gbwp (0.33 #2470, 0.33 #17298, 0.32 #9884), 01vt9p3 (0.33 #2470, 0.33 #17298, 0.32 #9884), 012vd6 (0.33 #2470, 0.33 #17298, 0.32 #9884) >> Best rule #4043 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01d38g; 09sb52; 01bgqh; 0c4z8; 01c427; 01by1l; 01cky2; 03qbh5; 03qbnj; >> query: (?x6652, 02z4b_8) <- award(?x2614, ?x6652), ?x2614 = 04xrx, award_winner(?x6652, ?x2138), ceremony(?x6652, ?x139) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #2470 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 01c99j; *> query: (?x6652, ?x1128) <- award(?x6042, ?x6652), award(?x2614, ?x6652), award(?x1128, ?x6652), ?x2614 = 04xrx, ?x6042 = 01wrcxr, ceremony(?x6652, ?x139) *> conf = 0.33 ranks of expected_values: 14, 38, 51 EVAL 01cw7s award_winner 02h9_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 37.000 15.000 0.417 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01cw7s award_winner 026spg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 37.000 15.000 0.417 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01cw7s award_winner 01w7nww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 37.000 15.000 0.417 http://example.org/award/award_category/winners./award/award_honor/award_winner #16027-016jny PRED entity: 016jny PRED relation: artists PRED expected values: 01wsl7c 01w02sy 01sb5r 028qdb 0khth 0ddkf 07m4c 02vnpv 014cw2 => 61 concepts (31 used for prediction) PRED predicted values (max 10 best out of 931): 0bdxs5 (0.78 #11833, 0.38 #9816, 0.33 #4778), 0lk90 (0.78 #11154, 0.33 #4099, 0.33 #1076), 03y82t6 (0.67 #11481, 0.33 #4426, 0.33 #1403), 01jfr3y (0.67 #11585, 0.33 #1507, 0.25 #9568), 01304j (0.64 #15033, 0.50 #6966, 0.43 #8982), 01cwhp (0.62 #9250, 0.44 #11267, 0.36 #13285), 01vw20_ (0.60 #7285, 0.57 #8294, 0.50 #6278), 0fq117k (0.60 #7667, 0.50 #6660, 0.33 #4646), 016s_5 (0.60 #7514, 0.50 #6507, 0.33 #4493), 0gr69 (0.60 #7657, 0.50 #6650, 0.33 #4636) >> Best rule #11833 for best value: >> intensional similarity = 9 >> extensional distance = 7 >> proper extension: 0dn16; 02lnbg; 0ggx5q; 02ny8t; >> query: (?x7329, 0bdxs5) <- artists(?x7329, ?x10671), artists(?x7329, ?x8913), artists(?x7329, ?x6418), artists(?x7329, ?x5566), award_winner(?x10671, ?x4608), award_nominee(?x3235, ?x6418), ?x5566 = 01_ztw, award(?x8913, ?x3647), ?x3647 = 01c9jp >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6388 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 02yv6b; *> query: (?x7329, 01sb5r) <- artists(?x7329, ?x10671), artists(?x7329, ?x7345), artists(?x7329, ?x4642), ?x10671 = 04k05, parent_genre(?x837, ?x7329), parent_genre(?x7329, ?x1572), gender(?x7345, ?x231), ?x4642 = 0394y *> conf = 0.50 ranks of expected_values: 68, 83, 91, 101, 350, 362, 512, 789, 800 EVAL 016jny artists 014cw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 31.000 0.778 http://example.org/music/genre/artists EVAL 016jny artists 02vnpv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 61.000 31.000 0.778 http://example.org/music/genre/artists EVAL 016jny artists 07m4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 61.000 31.000 0.778 http://example.org/music/genre/artists EVAL 016jny artists 0ddkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 31.000 0.778 http://example.org/music/genre/artists EVAL 016jny artists 0khth CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 31.000 0.778 http://example.org/music/genre/artists EVAL 016jny artists 028qdb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 31.000 0.778 http://example.org/music/genre/artists EVAL 016jny artists 01sb5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 61.000 31.000 0.778 http://example.org/music/genre/artists EVAL 016jny artists 01w02sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 31.000 0.778 http://example.org/music/genre/artists EVAL 016jny artists 01wsl7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 61.000 31.000 0.778 http://example.org/music/genre/artists #16026-09nyf PRED entity: 09nyf PRED relation: place_of_death! PRED expected values: 06qn87 => 155 concepts (43 used for prediction) PRED predicted values (max 10 best out of 395): 01y8d4 (0.20 #1907, 0.11 #2665, 0.07 #4178), 01h2_6 (0.20 #2205, 0.07 #4476, 0.06 #5992), 03ryks (0.03 #19688, 0.03 #31053, 0.02 #16656), 0h326 (0.02 #10596, 0.02 #11354, 0.02 #13625), 05f0r8 (0.02 #10590, 0.02 #11348, 0.02 #13619), 01l3j (0.02 #10585, 0.02 #11343, 0.02 #13614), 034cj9 (0.02 #10578, 0.02 #11336, 0.02 #13607), 067x44 (0.02 #10576, 0.02 #11334, 0.02 #13605), 058z1hb (0.02 #10572, 0.02 #11330, 0.02 #13601), 02rf51g (0.02 #10570, 0.02 #11328, 0.02 #13599) >> Best rule #1907 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 09c7w0; 06mkj; >> query: (?x2454, 01y8d4) <- location(?x6949, ?x2454), entity_involved(?x6982, ?x2454), contains(?x789, ?x2454), entity_involved(?x6982, ?x9602), ?x9602 = 0285m87 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09nyf place_of_death! 06qn87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 43.000 0.200 http://example.org/people/deceased_person/place_of_death #16025-03qmj9 PRED entity: 03qmj9 PRED relation: instrumentalists! PRED expected values: 0l14md 07xzm => 152 concepts (90 used for prediction) PRED predicted values (max 10 best out of 117): 05148p4 (0.60 #106, 0.41 #1656, 0.37 #3467), 03qjg (0.60 #136, 0.22 #1514, 0.19 #1686), 018vs (0.46 #1476, 0.40 #1648, 0.36 #3459), 03gvt (0.40 #150, 0.11 #1700, 0.10 #1096), 02hnl (0.23 #1670, 0.21 #1066, 0.20 #1411), 026t6 (0.20 #89, 0.18 #1553, 0.17 #1035), 06w7v (0.20 #157, 0.13 #845, 0.12 #1535), 013y1f (0.20 #117, 0.09 #1667, 0.08 #1495), 0mkg (0.20 #96, 0.05 #1646, 0.04 #1474), 0dwtp (0.20 #102, 0.04 #1134, 0.03 #1393) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01p0vf; >> query: (?x1556, 05148p4) <- film(?x1556, ?x365), artists(?x1555, ?x1556), instrumentalists(?x2048, ?x1556), ?x2048 = 018j2 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1125 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 101 *> proper extension: 01yznp; 01wk7b7; 02wk4d; *> query: (?x1556, 0l14md) <- film(?x1556, ?x365), instrumentalists(?x2048, ?x1556), category(?x1556, ?x134), role(?x74, ?x2048) *> conf = 0.16 ranks of expected_values: 14, 30 EVAL 03qmj9 instrumentalists! 07xzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 152.000 90.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 03qmj9 instrumentalists! 0l14md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 152.000 90.000 0.600 http://example.org/music/instrument/instrumentalists #16024-0lk8j PRED entity: 0lk8j PRED relation: sports PRED expected values: 01cgz => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 48): 02vx4 (0.86 #152, 0.86 #77, 0.85 #39), 01cgz (0.78 #73, 0.78 #112, 0.78 #111), 06f41 (0.78 #73, 0.78 #112, 0.78 #111), 07jjt (0.78 #73, 0.78 #112, 0.78 #111), 07_53 (0.62 #62, 0.57 #100, 0.38 #175), 0w0d (0.54 #44, 0.50 #82, 0.48 #157), 018w8 (0.50 #96, 0.48 #171, 0.46 #58), 03krj (0.46 #66, 0.43 #104, 0.38 #179), 03_8r (0.46 #52, 0.43 #90, 0.33 #165), 07bs0 (0.38 #45, 0.36 #83, 0.33 #305) >> Best rule #152 for best value: >> intensional similarity = 8 >> extensional distance = 19 >> proper extension: 018wrk; 0l6ny; 09x3r; 0blg2; 06sks6; 0lgxj; 018qb4; >> query: (?x2131, 02vx4) <- sports(?x2131, ?x3127), sports(?x2131, ?x1967), medal(?x2131, ?x422), olympics(?x94, ?x2131), ?x422 = 02lq67, ?x1967 = 01cgz, country(?x3127, ?x87), olympics(?x3127, ?x778) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #73 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 11 *> proper extension: 0l6vl; 0l98s; 0l998; 0kbvb; 0l6m5; 0lv1x; 0lbbj; 0nbjq; 0sxrz; 0jdk_; ... *> query: (?x2131, ?x171) <- sports(?x2131, ?x3127), sports(?x2131, ?x2315), sports(?x2131, ?x1967), sports(?x2131, ?x171), medal(?x2131, ?x422), olympics(?x94, ?x2131), ?x422 = 02lq67, ?x1967 = 01cgz, ?x3127 = 03hr1p, ?x2315 = 06wrt *> conf = 0.78 ranks of expected_values: 2 EVAL 0lk8j sports 01cgz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 41.000 0.857 http://example.org/user/jg/default_domain/olympic_games/sports #16023-029d_ PRED entity: 029d_ PRED relation: major_field_of_study PRED expected values: 04x_3 => 187 concepts (183 used for prediction) PRED predicted values (max 10 best out of 93): 062z7 (0.62 #638, 0.44 #1493, 0.40 #1127), 04rjg (0.45 #508, 0.43 #386, 0.41 #1485), 02j62 (0.45 #9676, 0.37 #1496, 0.33 #31), 01mkq (0.43 #381, 0.39 #9660, 0.38 #625), 01540 (0.41 #1526, 0.38 #671, 0.35 #1160), 03g3w (0.38 #637, 0.33 #1492, 0.33 #27), 0_jm (0.38 #669, 0.33 #303, 0.20 #1158), 05qjt (0.37 #1473, 0.35 #1107, 0.33 #8), 01lj9 (0.33 #285, 0.33 #41, 0.31 #773), 0fdys (0.33 #40, 0.31 #772, 0.26 #1505) >> Best rule #638 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0kw4j; 017j69; >> query: (?x5055, 062z7) <- currency(?x5055, ?x170), registering_agency(?x5055, ?x1982), major_field_of_study(?x5055, ?x7134), ?x7134 = 02_7t >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 07tds; *> query: (?x5055, 04x_3) <- contains(?x3670, ?x5055), major_field_of_study(?x5055, ?x1154), ?x3670 = 05tbn, service_location(?x5055, ?x94) *> conf = 0.33 ranks of expected_values: 15 EVAL 029d_ major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 187.000 183.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #16022-06cddt PRED entity: 06cddt PRED relation: actor! PRED expected values: 03y3bp7 => 94 concepts (80 used for prediction) PRED predicted values (max 10 best out of 62): 039cq4 (0.60 #924, 0.57 #1189, 0.50 #1719), 0557yqh (0.25 #321, 0.12 #1646, 0.05 #1911), 0d68qy (0.20 #832, 0.20 #567, 0.14 #1097), 01j7mr (0.14 #1910, 0.03 #4032, 0.02 #3236), 08jgk1 (0.05 #1877, 0.04 #3203, 0.03 #2673), 0124k9 (0.05 #1876, 0.03 #2407, 0.02 #2937), 0fpxp (0.05 #2004, 0.02 #2270, 0.02 #2535), 06qv_ (0.05 #2066, 0.01 #2332), 05_z42 (0.05 #1958, 0.01 #2224), 02md2d (0.05 #1926, 0.01 #2192) >> Best rule #924 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 08vr94; >> query: (?x9711, 039cq4) <- cast_members(?x3927, ?x9711), people(?x4195, ?x9711), type_of_union(?x9711, ?x566), ?x566 = 04ztj, award_nominee(?x4046, ?x3927) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7468 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 288 *> proper extension: 02m92h; *> query: (?x9711, 03y3bp7) <- profession(?x9711, ?x1146), profession(?x9711, ?x1032), ?x1146 = 018gz8, profession(?x12733, ?x1032), profession(?x7999, ?x1032), ?x12733 = 01ckhj, ?x7999 = 05gc0h *> conf = 0.01 ranks of expected_values: 61 EVAL 06cddt actor! 03y3bp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 94.000 80.000 0.600 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #16021-0m68w PRED entity: 0m68w PRED relation: location PRED expected values: 030qb3t => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 170): 02_286 (0.31 #24873, 0.31 #25675, 0.31 #27277), 030qb3t (0.29 #7293, 0.28 #2486, 0.25 #24919), 0njpq (0.25 #13619, 0.04 #25638), 09c7w0 (0.17 #804, 0.04 #31249, 0.03 #51284), 03s5t (0.17 #142), 03h64 (0.14 #938), 02jx1 (0.12 #872, 0.02 #2474, 0.02 #3276), 04jpl (0.11 #27257, 0.11 #25655, 0.11 #28860), 07ssc (0.11 #827), 0cc56 (0.10 #12874, 0.05 #2460, 0.04 #6466) >> Best rule #24873 for best value: >> intensional similarity = 3 >> extensional distance = 1084 >> proper extension: 04vrxh; >> query: (?x12255, 02_286) <- profession(?x12255, ?x1146), location(?x12255, ?x9331), county(?x9331, ?x7409) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #7293 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 468 *> proper extension: 059x0w; *> query: (?x12255, 030qb3t) <- location(?x12255, ?x4743), film_release_region(?x66, ?x4743), award_winner(?x4828, ?x12255) *> conf = 0.29 ranks of expected_values: 2 EVAL 0m68w location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.313 http://example.org/people/person/places_lived./people/place_lived/location #16020-04fjzv PRED entity: 04fjzv PRED relation: currency PRED expected values: 09nqf => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.80 #148, 0.79 #141, 0.79 #134), 01nv4h (0.04 #9, 0.04 #16, 0.03 #30), 02gsvk (0.04 #20, 0.02 #27, 0.02 #55), 02l6h (0.03 #67, 0.02 #130, 0.02 #46) >> Best rule #148 for best value: >> intensional similarity = 4 >> extensional distance = 434 >> proper extension: 0140g4; 0qm8b; 0g3zrd; 0bmpm; 07w8fz; 02ht1k; 0dgq_kn; 05r3qc; 05fm6m; 0888c3; >> query: (?x11209, 09nqf) <- production_companies(?x11209, ?x382), film(?x406, ?x11209), nominated_for(?x350, ?x11209), film(?x4353, ?x11209) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04fjzv currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.803 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #16019-0dsvzh PRED entity: 0dsvzh PRED relation: film! PRED expected values: 0jz9f => 113 concepts (105 used for prediction) PRED predicted values (max 10 best out of 96): 017jv5 (0.50 #14, 0.08 #607, 0.07 #311), 086k8 (0.40 #76, 0.27 #225, 0.26 #373), 016tw3 (0.30 #307, 0.25 #10, 0.15 #1123), 0g1rw (0.25 #7, 0.10 #2020, 0.09 #600), 05qd_ (0.23 #231, 0.21 #899, 0.21 #1121), 016tt2 (0.17 #671, 0.15 #1339, 0.15 #1794), 05s_k6 (0.17 #286, 0.11 #508, 0.06 #880), 01gb54 (0.14 #399, 0.13 #251, 0.13 #102), 04mkft (0.13 #258, 0.10 #852, 0.09 #554), 024rgt (0.13 #93, 0.07 #242, 0.07 #464) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0k0rf; >> query: (?x813, 017jv5) <- genre(?x813, ?x3613), nominated_for(?x5706, ?x813), written_by(?x813, ?x989), ?x3613 = 09blyk >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2014 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 190 *> proper extension: 05y0cr; *> query: (?x813, 0jz9f) <- genre(?x813, ?x53), honored_for(?x762, ?x813), featured_film_locations(?x813, ?x3052) *> conf = 0.09 ranks of expected_values: 15 EVAL 0dsvzh film! 0jz9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 113.000 105.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16018-07t90 PRED entity: 07t90 PRED relation: institution! PRED expected values: 014mlp 03mkk4 028dcg 022h5x => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 17): 014mlp (0.82 #167, 0.74 #148, 0.69 #672), 0bkj86 (0.68 #97, 0.67 #225, 0.64 #170), 07s6fsf (0.61 #164, 0.48 #145, 0.48 #219), 04zx3q1 (0.58 #92, 0.52 #220, 0.46 #165), 013zdg (0.43 #169, 0.40 #6, 0.37 #96), 0bjrnt (0.37 #95, 0.33 #149, 0.30 #113), 03mkk4 (0.37 #99, 0.33 #27, 0.30 #153), 01rr_d (0.30 #157, 0.26 #103, 0.20 #357), 022h5x (0.29 #179, 0.22 #216, 0.21 #106), 02m4yg (0.26 #102, 0.14 #84, 0.11 #156) >> Best rule #167 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 05krk; 01pl14; 06pwq; 01w3v; 07szy; 0bx8pn; 01q0kg; 017j69; 09f2j; 01h8rk; ... >> query: (?x4599, 014mlp) <- major_field_of_study(?x4599, ?x1695), school(?x1639, ?x4599), ?x1695 = 06ms6 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 9, 13 EVAL 07t90 institution! 022h5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 117.000 117.000 0.821 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07t90 institution! 028dcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 117.000 117.000 0.821 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07t90 institution! 03mkk4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 117.000 117.000 0.821 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07t90 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.821 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #16017-0qm8b PRED entity: 0qm8b PRED relation: production_companies PRED expected values: 025hwq => 73 concepts (59 used for prediction) PRED predicted values (max 10 best out of 65): 030_1_ (0.19 #262, 0.16 #510, 0.11 #1172), 05qd_ (0.16 #173, 0.16 #255, 0.15 #1165), 01gb54 (0.14 #782, 0.10 #531, 0.10 #201), 086k8 (0.14 #579, 0.14 #331, 0.13 #829), 01795t (0.12 #267, 0.10 #1094, 0.05 #1922), 0kk9v (0.12 #280, 0.07 #1107, 0.03 #1771), 016tt2 (0.12 #748, 0.10 #912, 0.10 #415), 016tw3 (0.11 #93, 0.10 #423, 0.09 #1250), 0kx4m (0.09 #502, 0.05 #1164, 0.02 #1581), 054lpb6 (0.08 #1005, 0.07 #759, 0.06 #3164) >> Best rule #262 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0bx_hnp; >> query: (?x1586, 030_1_) <- nominated_for(?x1585, ?x1586), genre(?x1586, ?x2605), crewmember(?x599, ?x1585), major_field_of_study(?x122, ?x2605) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1631 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 305 *> proper extension: 03g90h; 085ccd; *> query: (?x1586, 025hwq) <- crewmember(?x1586, ?x1585), film(?x629, ?x1586), film_crew_role(?x1586, ?x468) *> conf = 0.02 ranks of expected_values: 57 EVAL 0qm8b production_companies 025hwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 73.000 59.000 0.188 http://example.org/film/film/production_companies #16016-03nm_fh PRED entity: 03nm_fh PRED relation: film! PRED expected values: 07ncs0 => 78 concepts (43 used for prediction) PRED predicted values (max 10 best out of 813): 03h610 (0.46 #72889, 0.45 #22907, 0.42 #49978), 01f6zc (0.08 #943, 0.02 #7189, 0.02 #32179), 0f5xn (0.06 #969, 0.04 #5133, 0.03 #3051), 0c6qh (0.06 #415, 0.03 #12908, 0.03 #6661), 0f0kz (0.06 #517, 0.03 #29671, 0.02 #8846), 09l3p (0.06 #749, 0.02 #21573, 0.02 #13242), 01vsn38 (0.06 #1855, 0.02 #22679, 0.02 #6019), 02xs5v (0.06 #1406, 0.02 #5570, 0.02 #22230), 012d40 (0.06 #16, 0.02 #14591, 0.01 #25005), 02gvwz (0.06 #188, 0.01 #29342, 0.01 #8517) >> Best rule #72889 for best value: >> intensional similarity = 3 >> extensional distance = 817 >> proper extension: 01h1bf; 02kk_c; 03d17dg; 0275kr; 0gxsh4; >> query: (?x4684, ?x4964) <- award_winner(?x4684, ?x4964), location(?x4964, ?x1523), gender(?x4964, ?x231) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #3170 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 01cgz; *> query: (?x4684, 07ncs0) <- films(?x7173, ?x4684), titles(?x7173, ?x7463), genre(?x7463, ?x225) *> conf = 0.03 ranks of expected_values: 103 EVAL 03nm_fh film! 07ncs0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 78.000 43.000 0.458 http://example.org/film/actor/film./film/performance/film #16015-0gkjy PRED entity: 0gkjy PRED relation: organization! PRED expected values: 088q4 01nln 01nyl 04hvw => 39 concepts (16 used for prediction) PRED predicted values (max 10 best out of 278): 07ssc (0.93 #1097, 0.90 #1094, 0.90 #1375), 01nln (0.93 #1097, 0.90 #1094, 0.90 #1375), 04hvw (0.93 #1097, 0.90 #1094, 0.90 #1375), 088q4 (0.93 #1097, 0.90 #1094, 0.90 #1375), 02vzc (0.93 #1097, 0.90 #1094, 0.90 #1375), 059j2 (0.93 #1097, 0.90 #1094, 0.90 #1375), 0k6nt (0.93 #1097, 0.90 #1094, 0.90 #1375), 0d0vqn (0.93 #1097, 0.90 #1094, 0.90 #1375), 03rjj (0.93 #1097, 0.90 #1094, 0.90 #1375), 03rt9 (0.93 #1097, 0.90 #1094, 0.90 #1375) >> Best rule #1097 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02hcxm; >> query: (?x4753, ?x47) <- organization(?x13717, ?x4753), organization(?x7035, ?x4753), organization(?x7035, ?x312), organization(?x47, ?x312), organizations_founded(?x3563, ?x312), category(?x13717, ?x134) >> conf = 0.93 => this is the best rule for 146 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3, 4, 46 EVAL 0gkjy organization! 04hvw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 39.000 16.000 0.932 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0gkjy organization! 01nyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 39.000 16.000 0.932 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0gkjy organization! 01nln CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 39.000 16.000 0.932 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0gkjy organization! 088q4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 39.000 16.000 0.932 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #16014-01f9zw PRED entity: 01f9zw PRED relation: artists! PRED expected values: 0155w => 157 concepts (86 used for prediction) PRED predicted values (max 10 best out of 230): 025sc50 (0.51 #3173, 0.44 #51, 0.38 #9419), 06by7 (0.50 #648, 0.49 #3769, 0.49 #12824), 02x8m (0.48 #332, 0.34 #3141, 0.23 #9387), 0glt670 (0.34 #3164, 0.32 #5975, 0.31 #10658), 05bt6j (0.34 #12222, 0.33 #12847, 0.30 #671), 03_d0 (0.33 #325, 0.27 #3134, 0.21 #9380), 0ggx5q (0.32 #1329, 0.31 #1641, 0.31 #3514), 02lnbg (0.29 #3494, 0.27 #2869, 0.26 #1933), 016clz (0.25 #8436, 0.24 #9061, 0.24 #3752), 02b71x (0.24 #467, 0.10 #3276, 0.06 #154) >> Best rule #3173 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 0qmny; >> query: (?x8856, 025sc50) <- origin(?x8856, ?x1523), artists(?x3928, ?x8856), ?x3928 = 0gywn >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #8539 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 212 *> proper extension: 01q7cb_; 01nn6c; 0jn5l; 0gs6vr; *> query: (?x8856, 0155w) <- location(?x8856, ?x1523), instrumentalists(?x227, ?x8856), artists(?x671, ?x8856), ?x227 = 0342h *> conf = 0.21 ranks of expected_values: 12 EVAL 01f9zw artists! 0155w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 157.000 86.000 0.507 http://example.org/music/genre/artists #16013-0ps1q PRED entity: 0ps1q PRED relation: contains! PRED expected values: 0345h => 90 concepts (25 used for prediction) PRED predicted values (max 10 best out of 103): 0345h (0.97 #3672, 0.89 #3589, 0.89 #2775), 09c7w0 (0.73 #5390, 0.62 #10783, 0.58 #17074), 02jx1 (0.45 #4576, 0.25 #6373, 0.25 #7271), 09krp (0.40 #1347, 0.11 #3145, 0.10 #4042), 059rby (0.31 #4509, 0.26 #5407, 0.17 #6306), 017v_ (0.29 #1904, 0.14 #2803, 0.10 #3700), 07ssc (0.25 #4521, 0.14 #6318, 0.14 #8115), 07nf6 (0.24 #2405, 0.11 #3304, 0.05 #4201), 05k7sb (0.19 #5520, 0.13 #6419, 0.12 #7317), 01n7q (0.12 #17149, 0.04 #20744) >> Best rule #3672 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 0156q; 02h6_6p; 0qb1z; 03hrz; 04kf4; 0d331; 06fz_; 02z0j; 02hrb2; 0d58_; ... >> query: (?x14214, 0345h) <- contains(?x7934, ?x14214), contains(?x7934, ?x12661), adjoins(?x3623, ?x7934), ?x12661 = 0d3ff, adjoins(?x2985, ?x3623) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ps1q contains! 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 25.000 0.973 http://example.org/location/location/contains #16012-01h910 PRED entity: 01h910 PRED relation: profession PRED expected values: 02hrh1q => 117 concepts (101 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.93 #3080, 0.90 #6438, 0.89 #2934), 01d_h8 (0.55 #444, 0.51 #1320, 0.47 #736), 0cbd2 (0.49 #2635, 0.45 #2197, 0.44 #3511), 09jwl (0.37 #3667, 0.37 #7171, 0.37 #1623), 02jknp (0.33 #154, 0.32 #446, 0.29 #1322), 0kyk (0.33 #2655, 0.29 #3531, 0.28 #3969), 02krf9 (0.30 #24, 0.29 #1776, 0.28 #1922), 0d1pc (0.30 #9930, 0.29 #12415, 0.28 #10077), 016z4k (0.28 #1610, 0.27 #3654, 0.23 #7158), 0nbcg (0.28 #1635, 0.26 #3679, 0.26 #7183) >> Best rule #3080 for best value: >> intensional similarity = 3 >> extensional distance = 402 >> proper extension: 04n7njg; 0bkq_8; 02pbp9; >> query: (?x6190, 02hrh1q) <- award_winner(?x2710, ?x6190), profession(?x6190, ?x987), actor(?x3180, ?x6190) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01h910 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 101.000 0.926 http://example.org/people/person/profession #16011-04sv4 PRED entity: 04sv4 PRED relation: currency PRED expected values: 09nqf => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.84 #205, 0.84 #201, 0.84 #247), 0kz1h (0.38 #136, 0.32 #462, 0.02 #200), 02l6h (0.23 #558, 0.02 #208), 01nv4h (0.02 #194, 0.02 #232, 0.01 #269) >> Best rule #205 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 0217m9; 01bk1y; 02grjf; >> query: (?x9469, ?x170) <- state_province_region(?x9469, ?x4600), company(?x346, ?x9469), currency(?x9469, ?x170), ?x346 = 060c4 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sv4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.843 http://example.org/business/business_operation/revenue./measurement_unit/dated_money_value/currency #16010-0jt3tjf PRED entity: 0jt3tjf PRED relation: geographic_distribution! PRED expected values: 0g6ff => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 34): 0d29z (0.29 #21, 0.24 #141, 0.19 #662), 071x0k (0.19 #3, 0.19 #123, 0.14 #644), 04mvp8 (0.14 #154, 0.12 #34, 0.11 #675), 0g6ff (0.13 #201, 0.08 #130, 0.08 #90), 01xhh5 (0.13 #201, 0.08 #20, 0.07 #140), 06mvq (0.06 #18, 0.04 #138, 0.03 #219), 012f86 (0.06 #32, 0.04 #152, 0.03 #273), 013b6_ (0.06 #27, 0.04 #147, 0.03 #268), 0g48m4 (0.05 #1602, 0.05 #1002, 0.04 #2162), 01rv7x (0.05 #142, 0.05 #263, 0.05 #663) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 07ww5; >> query: (?x9455, 0d29z) <- country(?x2315, ?x9455), adjoins(?x9455, ?x404), ?x2315 = 06wrt >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #201 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 93 *> proper extension: 03pn9; 0f1_p; 06k5_; 0393g; *> query: (?x9455, ?x5590) <- adjoins(?x4073, ?x9455), official_language(?x4073, ?x5671), geographic_distribution(?x5590, ?x4073) *> conf = 0.13 ranks of expected_values: 4 EVAL 0jt3tjf geographic_distribution! 0g6ff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 82.000 0.288 http://example.org/people/ethnicity/geographic_distribution #16009-017g21 PRED entity: 017g21 PRED relation: role PRED expected values: 02snj9 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 120): 05148p4 (0.32 #1887, 0.29 #1404, 0.28 #1451), 028tv0 (0.28 #371, 0.24 #674, 0.22 #492), 048j4l (0.28 #1451, 0.27 #1995, 0.27 #1632), 03qjg (0.25 #38, 0.14 #338, 0.13 #1005), 02hnl (0.22 #1898, 0.19 #25, 0.18 #386), 05r5c (0.20 #1396, 0.20 #973, 0.19 #1879), 02fsn (0.18 #361, 0.18 #422, 0.15 #543), 01vj9c (0.11 #311, 0.08 #978, 0.08 #858), 02sgy (0.11 #305, 0.07 #972, 0.06 #366), 06w7v (0.07 #352, 0.04 #838, 0.03 #2057) >> Best rule #1887 for best value: >> intensional similarity = 4 >> extensional distance = 207 >> proper extension: 0lbj1; 0fp_v1x; 06cc_1; 032t2z; 06y9c2; 01cv3n; 01vvycq; 03qd_; 01wdqrx; 01kx_81; ... >> query: (?x7252, 05148p4) <- role(?x7252, ?x227), profession(?x7252, ?x655), group(?x227, ?x8864), ?x8864 = 070b4 >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #345 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 0191h5; 04m2zj; 06br6t; *> query: (?x7252, 02snj9) <- role(?x7252, ?x227), artists(?x2809, ?x7252), ?x2809 = 05w3f *> conf = 0.05 ranks of expected_values: 27 EVAL 017g21 role 02snj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 134.000 134.000 0.321 http://example.org/music/group_member/membership./music/group_membership/role #16008-019g8j PRED entity: 019g8j PRED relation: nominated_for! PRED expected values: 074qgb => 94 concepts (40 used for prediction) PRED predicted values (max 10 best out of 993): 08jtv5 (0.54 #60870, 0.54 #65559, 0.53 #65556), 0sw62 (0.54 #60870, 0.54 #65559, 0.53 #65556), 01tszq (0.54 #60870, 0.54 #65559, 0.53 #65556), 024my5 (0.54 #60870, 0.54 #65559, 0.53 #65556), 01n7qlf (0.54 #65559, 0.53 #65556, 0.52 #56186), 0sw6y (0.54 #65559, 0.53 #65556, 0.52 #56186), 0146mv (0.43 #25757, 0.29 #21075, 0.28 #60871), 09b0xs (0.33 #12377, 0.25 #14717, 0.25 #10035), 01x209s (0.33 #1426, 0.25 #6109, 0.03 #29527), 044f7 (0.33 #1238, 0.25 #5921, 0.03 #7025) >> Best rule #60870 for best value: >> intensional similarity = 5 >> extensional distance = 98 >> proper extension: 0304nh; 023ny6; >> query: (?x11599, ?x10109) <- actor(?x11599, ?x10109), program(?x11453, ?x11599), genre(?x11599, ?x2540), award(?x11599, ?x3263), location(?x10109, ?x1523) >> conf = 0.54 => this is the best rule for 4 predicted values *> Best rule #16390 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 03cwwl; *> query: (?x11599, ?x129) <- nominated_for(?x3263, ?x11599), award(?x9519, ?x3263), award(?x2691, ?x3263), award(?x129, ?x3263), ?x2691 = 067pl7, profession(?x9519, ?x319) *> conf = 0.07 ranks of expected_values: 75 EVAL 019g8j nominated_for! 074qgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 94.000 40.000 0.542 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #16007-0gtsxr4 PRED entity: 0gtsxr4 PRED relation: film_release_region PRED expected values: 0jgd 04gzd 07ssc => 53 concepts (36 used for prediction) PRED predicted values (max 10 best out of 162): 05qhw (0.92 #310, 0.91 #608, 0.90 #1055), 03gj2 (0.87 #1067, 0.86 #918, 0.85 #1812), 07ssc (0.87 #461, 0.84 #908, 0.81 #312), 0jgd (0.86 #1791, 0.85 #301, 0.85 #1046), 03spz (0.85 #387, 0.80 #685, 0.80 #1132), 03_3d (0.79 #2389, 0.77 #452, 0.75 #2688), 04gzd (0.74 #604, 0.73 #306, 0.72 #1051), 05v8c (0.74 #611, 0.70 #1058, 0.69 #462), 016wzw (0.73 #358, 0.70 #656, 0.67 #507), 015qh (0.73 #338, 0.63 #636, 0.60 #1828) >> Best rule #310 for best value: >> intensional similarity = 10 >> extensional distance = 24 >> proper extension: 08hmch; 0gj8t_b; 0gj8nq2; 043tvp3; 03z9585; >> query: (?x3151, 05qhw) <- film_release_region(?x3151, ?x3277), film_release_region(?x3151, ?x2267), film_release_region(?x3151, ?x1536), film_release_region(?x3151, ?x172), film_release_region(?x3151, ?x151), ?x172 = 0154j, ?x151 = 0b90_r, ?x1536 = 06c1y, ?x2267 = 03rj0, ?x3277 = 06t8v >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #461 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 37 *> proper extension: 0g56t9t; 02vxq9m; 02x3lt7; 0gkz15s; 017gl1; 0dgst_d; 017gm7; 02r8hh_; 0ch26b_; 09k56b7; ... *> query: (?x3151, 07ssc) <- film_release_region(?x3151, ?x1892), film_release_region(?x3151, ?x1536), film_release_region(?x3151, ?x172), film_release_region(?x3151, ?x151), ?x172 = 0154j, ?x151 = 0b90_r, ?x1536 = 06c1y, nominated_for(?x1053, ?x3151), ?x1892 = 02vzc *> conf = 0.87 ranks of expected_values: 3, 4, 7 EVAL 0gtsxr4 film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 53.000 36.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gtsxr4 film_release_region 04gzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 36.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gtsxr4 film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 53.000 36.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16006-02dr9j PRED entity: 02dr9j PRED relation: film! PRED expected values: 016tw3 => 117 concepts (95 used for prediction) PRED predicted values (max 10 best out of 58): 03xq0f (0.58 #598, 0.57 #970, 0.56 #376), 017s11 (0.25 #151, 0.13 #77, 0.13 #2380), 05qd_ (0.22 #454, 0.21 #898, 0.20 #9), 086k8 (0.22 #521, 0.22 #76, 0.21 #225), 016tw3 (0.20 #11, 0.16 #1866, 0.15 #456), 016tt2 (0.17 #671, 0.16 #1636, 0.16 #1266), 01795t (0.11 #1947, 0.09 #833, 0.09 #1132), 0g1rw (0.10 #305, 0.09 #1640, 0.09 #8), 01gb54 (0.09 #474, 0.08 #400, 0.07 #918), 04mkft (0.09 #1001, 0.09 #407, 0.08 #629) >> Best rule #598 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 04nlb94; >> query: (?x7214, 03xq0f) <- nominated_for(?x298, ?x7214), genre(?x7214, ?x53), film_distribution_medium(?x7214, ?x81) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 013q0p; 02754c9; *> query: (?x7214, 016tw3) <- film(?x6589, ?x7214), country(?x7214, ?x1264), ?x1264 = 0345h, nominated_for(?x298, ?x7214) *> conf = 0.20 ranks of expected_values: 5 EVAL 02dr9j film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 117.000 95.000 0.578 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #16005-02hczc PRED entity: 02hczc PRED relation: time_zones! PRED expected values: 02cl1 050l8 01n4w 05fjy 0k39j 0x44q 0fsv2 0xtz9 0p07l 0flbm => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 2018): 05fjy (0.90 #2363, 0.88 #3544, 0.87 #1180), 050l8 (0.90 #2363, 0.88 #3544, 0.87 #1180), 0j95 (0.90 #2363, 0.88 #3544, 0.87 #1180), 01n4w (0.77 #1178, 0.77 #3542, 0.73 #5914), 0mk7z (0.77 #1178, 0.77 #3542, 0.72 #4724), 0d1y7 (0.77 #1178, 0.77 #3542, 0.72 #4724), 04rrx (0.77 #1178, 0.77 #3542, 0.72 #4724), 02dtg (0.77 #1178, 0.77 #3542, 0.72 #4724), 059f4 (0.77 #1178, 0.77 #3542, 0.72 #4724), 02gt5s (0.77 #1178, 0.77 #3542, 0.72 #4724) >> Best rule #2363 for best value: >> intensional similarity = 32 >> extensional distance = 2 >> proper extension: 02fqwt; >> query: (?x2088, ?x2049) <- time_zones(?x12644, ?x2088), time_zones(?x12087, ?x2088), time_zones(?x8260, ?x2088), time_zones(?x8093, ?x2088), time_zones(?x5449, ?x2088), time_zones(?x3983, ?x2088), time_zones(?x3086, ?x2088), time_zones(?x279, ?x2088), ?x279 = 0d060g, source(?x12087, ?x958), administrative_division(?x12644, ?x2049), featured_film_locations(?x603, ?x3983), place_of_birth(?x8092, ?x8093), location(?x12382, ?x8093), location(?x1817, ?x3983), religion(?x3086, ?x2769), religion(?x3086, ?x2672), film(?x8092, ?x365), award_nominee(?x8092, ?x262), ?x2672 = 01y0s9, location(?x5507, ?x3086), dog_breed(?x3983, ?x1706), locations(?x4368, ?x3983), contains(?x3086, ?x3087), ?x958 = 0jbk9, adjoins(?x5449, ?x9887), contains(?x8260, ?x6895), ?x4368 = 0b_6x2, district_represented(?x605, ?x3086), ?x6895 = 05fjf, citytown(?x4296, ?x3983), ?x2769 = 019cr >> conf = 0.90 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2, 4, 23, 30, 92, 638, 989, 1293, 1305 EVAL 02hczc time_zones! 0flbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 13.000 13.000 0.897 http://example.org/location/location/time_zones EVAL 02hczc time_zones! 0p07l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 13.000 13.000 0.897 http://example.org/location/location/time_zones EVAL 02hczc time_zones! 0xtz9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 13.000 13.000 0.897 http://example.org/location/location/time_zones EVAL 02hczc time_zones! 0fsv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 13.000 13.000 0.897 http://example.org/location/location/time_zones EVAL 02hczc time_zones! 0x44q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 13.000 13.000 0.897 http://example.org/location/location/time_zones EVAL 02hczc time_zones! 0k39j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 13.000 13.000 0.897 http://example.org/location/location/time_zones EVAL 02hczc time_zones! 05fjy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.897 http://example.org/location/location/time_zones EVAL 02hczc time_zones! 01n4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 13.000 13.000 0.897 http://example.org/location/location/time_zones EVAL 02hczc time_zones! 050l8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.897 http://example.org/location/location/time_zones EVAL 02hczc time_zones! 02cl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 13.000 13.000 0.897 http://example.org/location/location/time_zones #16004-09sb52 PRED entity: 09sb52 PRED relation: award! PRED expected values: 04bdxl 02qgqt 06dv3 014zcr 01wmxfs 0292l3 02lxj_ 01l9p 03k7bd 016z2j 0169dl 019f2f 03xpsrx 0161sp 0391jz 04w391 015vq_ 0f502 016vg8 0bksh 053y4h 04wp3s 018ygt 03x400 016nff 0z05l 0c35b1 07rhpg 050zr4 0gpprt 049t4g 01jz6x 02d45s 02zl4d => 49 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2216): 0gjvqm (0.84 #11943, 0.80 #5971, 0.78 #14929), 01713c (0.84 #11943, 0.80 #5971, 0.78 #14929), 02p65p (0.84 #11943, 0.80 #5971, 0.78 #14929), 0zcbl (0.84 #11943, 0.80 #5971, 0.78 #14929), 03n_7k (0.84 #11943, 0.80 #5971, 0.78 #14929), 069nzr (0.84 #11943, 0.80 #5971, 0.78 #14929), 03kbb8 (0.84 #11943, 0.80 #5971, 0.78 #14929), 09yhzs (0.84 #11943, 0.80 #5971, 0.78 #14929), 0l6px (0.84 #11943, 0.80 #5971, 0.78 #14929), 027f7dj (0.84 #11943, 0.80 #5971, 0.78 #14929) >> Best rule #11943 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 05zr6wv; 05pcn59; 02w9sd7; 0fbtbt; 07z2lx; 07kjk7c; >> query: (?x704, ?x72) <- award_winner(?x704, ?x72), award(?x496, ?x704), award(?x144, ?x704), ?x496 = 0bxtg >> conf = 0.84 => this is the best rule for 22 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 20, 26, 28, 32, 35, 62, 78, 79, 82, 87, 89, 112, 120, 140, 141, 233, 292, 334, 335, 340, 352, 374, 584, 588, 590, 591, 592, 593, 594, 595, 596, 630, 767, 823 EVAL 09sb52 award! 02zl4d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 02d45s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 01jz6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 049t4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 0gpprt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 050zr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 07rhpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 0c35b1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 0z05l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 016nff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 03x400 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 018ygt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 04wp3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 053y4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 0bksh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 016vg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 0f502 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 015vq_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 04w391 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 0391jz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 0161sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 03xpsrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 019f2f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 0169dl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 016z2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 03k7bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 01l9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 02lxj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 0292l3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 01wmxfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 014zcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 06dv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 02qgqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sb52 award! 04bdxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 17.000 0.841 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #16003-02z7f3 PRED entity: 02z7f3 PRED relation: artists PRED expected values: 06y9c2 01wt4wc => 69 concepts (30 used for prediction) PRED predicted values (max 10 best out of 976): 0fpj4lx (0.64 #14421, 0.45 #8999, 0.43 #6831), 03sww (0.55 #9116, 0.50 #10201, 0.35 #16705), 01w8n89 (0.50 #10076, 0.48 #14413, 0.45 #8991), 011_vz (0.43 #7352, 0.36 #9520, 0.33 #10605), 01q7cb_ (0.43 #6563, 0.28 #14153, 0.22 #16320), 017j6 (0.42 #10050, 0.36 #8965, 0.35 #2168), 01gx5f (0.42 #10052, 0.36 #8967, 0.33 #1380), 0285c (0.40 #3395, 0.36 #8814, 0.35 #2168), 01vw20_ (0.40 #14342, 0.36 #8920, 0.33 #10005), 0lsw9 (0.40 #3956, 0.35 #2168, 0.33 #703) >> Best rule #14421 for best value: >> intensional similarity = 9 >> extensional distance = 23 >> proper extension: 02w4v; 09qxq7; 02278y; >> query: (?x10676, 0fpj4lx) <- artists(?x10676, ?x7125), artists(?x8031, ?x7125), artists(?x6513, ?x7125), artists(?x5934, ?x7125), artist(?x2299, ?x7125), ?x2299 = 033hn8, ?x6513 = 06cp5, ?x5934 = 05r6t, ?x8031 = 01738f >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #9403 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 9 *> proper extension: 05r6t; 06cp5; 0jmwg; 01738f; 09nwwf; 04f73rc; *> query: (?x10676, 01wt4wc) <- parent_genre(?x10676, ?x13938), artists(?x10676, ?x7125), parent_genre(?x7124, ?x13938), parent_genre(?x14400, ?x10676), artists(?x7124, ?x10639), ?x7125 = 01jcxwp, ?x10639 = 03q_w5 *> conf = 0.36 ranks of expected_values: 11, 35 EVAL 02z7f3 artists 01wt4wc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 69.000 30.000 0.640 http://example.org/music/genre/artists EVAL 02z7f3 artists 06y9c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 69.000 30.000 0.640 http://example.org/music/genre/artists #16002-01633c PRED entity: 01633c PRED relation: film_crew_role PRED expected values: 09zzb8 09vw2b7 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.77 #181, 0.76 #544, 0.75 #109), 02r96rf (0.68 #547, 0.63 #2547, 0.63 #1166), 09vw2b7 (0.68 #551, 0.62 #1170, 0.60 #188), 01vx2h (0.34 #555, 0.29 #2083, 0.29 #2555), 02ynfr (0.24 #196, 0.21 #124, 0.20 #559), 089g0h (0.16 #128, 0.13 #200, 0.12 #2908), 0215hd (0.15 #562, 0.14 #272, 0.13 #127), 01xy5l_ (0.13 #122, 0.12 #2908, 0.12 #194), 089fss (0.12 #43, 0.12 #2908, 0.08 #550), 02rh1dz (0.12 #554, 0.12 #2908, 0.11 #191) >> Best rule #181 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 0g5qs2k; 0164qt; 0jyx6; 04vr_f; 0bscw; 02q5g1z; 0htww; 0gvs1kt; 02fqrf; 024mpp; ... >> query: (?x7654, 09zzb8) <- film(?x2416, ?x7654), genre(?x7654, ?x239), film_crew_role(?x7654, ?x1284), costume_design_by(?x7654, ?x3685) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01633c film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.770 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01633c film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.770 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #16001-0159h6 PRED entity: 0159h6 PRED relation: award_nominee! PRED expected values: 07b2lv => 95 concepts (35 used for prediction) PRED predicted values (max 10 best out of 894): 0dvld (0.83 #2312, 0.81 #73974, 0.81 #73973), 07b2lv (0.83 #2312, 0.81 #73974, 0.81 #73973), 02qgyv (0.50 #2798, 0.17 #80911, 0.05 #18980), 023kzp (0.46 #3686, 0.17 #80911, 0.03 #19868), 01kb2j (0.46 #3504, 0.17 #80911, 0.02 #56668), 01yb09 (0.46 #2560, 0.17 #80911, 0.01 #18742), 04t7ts (0.42 #2577, 0.17 #80911, 0.02 #18759), 02qgqt (0.38 #2330, 0.17 #80911, 0.03 #39315), 0gy6z9 (0.38 #3042, 0.17 #80911, 0.03 #21536), 02p65p (0.33 #2337, 0.17 #80911, 0.16 #69349) >> Best rule #2312 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01tspc6; 09y20; 065jlv; 0170qf; 0l6px; 0kszw; 062dn7; 013_vh; 025t9b; 03y_46; >> query: (?x488, ?x100) <- award_nominee(?x488, ?x100), film(?x488, ?x7304), ?x7304 = 031hcx >> conf = 0.83 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0159h6 award_nominee! 07b2lv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 35.000 0.835 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #16000-0c57yj PRED entity: 0c57yj PRED relation: genre PRED expected values: 06qm3 01t_vv => 113 concepts (88 used for prediction) PRED predicted values (max 10 best out of 99): 03k9fj (0.50 #129, 0.48 #5496, 0.38 #10267), 02kdv5l (0.47 #597, 0.42 #835, 0.41 #716), 01jfsb (0.46 #487, 0.46 #606, 0.43 #1082), 01hmnh (0.33 #135, 0.27 #254, 0.26 #10273), 06n90 (0.33 #131, 0.27 #250, 0.26 #607), 070yc (0.33 #211, 0.02 #925, 0.02 #806), 04xvlr (0.32 #7514, 0.29 #4173, 0.19 #3935), 0hcr (0.27 #260, 0.14 #5508, 0.13 #3123), 03bxz7 (0.25 #54, 0.17 #173, 0.16 #4226), 01j1n2 (0.25 #59, 0.07 #3398, 0.05 #4231) >> Best rule #129 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0ddjy; 0f4yh; 08mg_b; 0f3m1; >> query: (?x3859, 03k9fj) <- written_by(?x3859, ?x2367), film(?x7980, ?x3859), ?x2367 = 02fcs2, film(?x7923, ?x3859), award_nominee(?x7923, ?x450) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1719 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 206 *> proper extension: 06zn1c; *> query: (?x3859, 01t_vv) <- genre(?x3859, ?x258), ?x258 = 05p553, film(?x2367, ?x3859), film(?x7980, ?x3859) *> conf = 0.18 ranks of expected_values: 14, 30 EVAL 0c57yj genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 113.000 88.000 0.500 http://example.org/film/film/genre EVAL 0c57yj genre 06qm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 113.000 88.000 0.500 http://example.org/film/film/genre #15999-05r4w PRED entity: 05r4w PRED relation: combatants! PRED expected values: 0k4y6 => 210 concepts (210 used for prediction) PRED predicted values (max 10 best out of 78): 081pw (0.52 #3172, 0.46 #1368, 0.43 #1990), 0gfq9 (0.42 #566, 0.40 #318, 0.30 #256), 02h2z_ (0.42 #609, 0.30 #361, 0.17 #1230), 048n7 (0.40 #271, 0.35 #3193, 0.33 #1389), 06k75 (0.40 #325, 0.33 #573, 0.20 #4737), 01fc7p (0.40 #313, 0.33 #561, 0.13 #1555), 08qz1l (0.40 #352, 0.33 #600, 0.12 #4764), 0d06vc (0.33 #67, 0.30 #254, 0.27 #378), 01gjd0 (0.33 #562, 0.30 #252, 0.25 #624), 07j9n (0.33 #89, 0.25 #151, 0.22 #2264) >> Best rule #3172 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0193qj; >> query: (?x87, 081pw) <- combatants(?x5503, ?x87), olympics(?x87, ?x778), films(?x5503, ?x2133) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #272 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0cdbq; *> query: (?x87, 0k4y6) <- combatants(?x5503, ?x87), partially_contains(?x1144, ?x87), contains(?x87, ?x7809) *> conf = 0.20 ranks of expected_values: 21 EVAL 05r4w combatants! 0k4y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 210.000 210.000 0.522 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #15998-01kcd PRED entity: 01kcd PRED relation: family! PRED expected values: 02bxd 07m2y => 71 concepts (61 used for prediction) PRED predicted values (max 10 best out of 122): 018vs (0.33 #620, 0.33 #551, 0.33 #79), 02k84w (0.33 #119, 0.25 #302, 0.17 #666), 042v_gx (0.33 #7, 0.17 #714, 0.17 #640), 02sgy (0.33 #5, 0.17 #638, 0.17 #630), 02k856 (0.33 #45, 0.17 #678, 0.17 #586), 01v8y9 (0.33 #48, 0.17 #681, 0.17 #589), 0dwtp (0.25 #357, 0.25 #282, 0.20 #464), 0239kh (0.25 #292, 0.20 #474, 0.17 #656), 0l14qv (0.18 #1417, 0.17 #718, 0.17 #1683), 0mbct (0.18 #1462, 0.17 #1643, 0.17 #682) >> Best rule #620 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 01vj9c; >> query: (?x2620, ?x716) <- role(?x2620, ?x3409), role(?x2620, ?x716), performance_role(?x2620, ?x228), role(?x2620, ?x4471), ?x716 = 018vs, family(?x569, ?x2620), ?x3409 = 0680x0, role(?x4471, ?x885), role(?x4471, ?x314), ?x314 = 02sgy, ?x885 = 0dwtp, family(?x228, ?x1148), role(?x1147, ?x228), role(?x2725, ?x228), role(?x130, ?x228), ?x2725 = 0l1589, ?x1147 = 07kc_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #616 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 4 *> proper extension: 01vj9c; *> query: (?x2620, 07m2y) <- role(?x2620, ?x3409), role(?x2620, ?x716), performance_role(?x2620, ?x228), role(?x2620, ?x4471), ?x716 = 018vs, family(?x569, ?x2620), ?x3409 = 0680x0, role(?x4471, ?x885), role(?x4471, ?x314), ?x314 = 02sgy, ?x885 = 0dwtp, family(?x228, ?x1148), role(?x1147, ?x228), role(?x2725, ?x228), role(?x130, ?x228), ?x2725 = 0l1589, ?x1147 = 07kc_ *> conf = 0.17 ranks of expected_values: 15, 73 EVAL 01kcd family! 07m2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 71.000 61.000 0.333 http://example.org/music/instrument/family EVAL 01kcd family! 02bxd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 71.000 61.000 0.333 http://example.org/music/instrument/family #15997-04vmqg PRED entity: 04vmqg PRED relation: award_nominee! PRED expected values: 03lt8g => 94 concepts (49 used for prediction) PRED predicted values (max 10 best out of 814): 03lt8g (0.82 #4637, 0.82 #4636, 0.81 #37084), 030znt (0.82 #4637, 0.82 #4636, 0.81 #37084), 02s_qz (0.82 #4637, 0.82 #4636, 0.81 #37084), 04vmqg (0.61 #4393, 0.58 #6713, 0.56 #2075), 05lb87 (0.48 #2590, 0.42 #4910, 0.34 #7228), 0308kx (0.46 #5592, 0.43 #3272, 0.39 #954), 03w4sh (0.43 #3801, 0.42 #6121, 0.34 #8439), 058ncz (0.38 #4732, 0.33 #94, 0.33 #4638), 01wb8bs (0.33 #4638, 0.28 #897, 0.26 #3215), 09r9dp (0.33 #4638, 0.14 #92707, 0.11 #851) >> Best rule #4637 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 058ncz; >> query: (?x10004, ?x3602) <- award_nominee(?x10004, ?x4976), award_nominee(?x10004, ?x3602), ?x4976 = 05683p >> conf = 0.82 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 04vmqg award_nominee! 03lt8g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 49.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15996-04w7rn PRED entity: 04w7rn PRED relation: films! PRED expected values: 06bvp => 83 concepts (31 used for prediction) PRED predicted values (max 10 best out of 64): 018h2 (0.24 #331, 0.03 #3155, 0.03 #3466), 07c52 (0.23 #329, 0.04 #1582, 0.03 #3308), 04jjy (0.19 #317, 0.03 #3452, 0.03 #3296), 01cgz (0.16 #328, 0.02 #3307, 0.02 #3152), 081pw (0.15 #158, 0.08 #3448, 0.07 #3137), 0fx2s (0.12 #72, 0.10 #227, 0.05 #3361), 0fzyg (0.12 #208, 0.05 #1616, 0.05 #3498), 05489 (0.07 #206, 0.05 #1614, 0.04 #3185), 0kbq (0.07 #259, 0.02 #1041, 0.02 #727), 06d4h (0.07 #1605, 0.07 #3331, 0.06 #3176) >> Best rule #331 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 01cgz; >> query: (?x1518, 018h2) <- films(?x1510, ?x1518), titles(?x1510, ?x6610), genre(?x6610, ?x53) >> conf = 0.24 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04w7rn films! 06bvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 31.000 0.240 http://example.org/film/film_subject/films #15995-01y888 PRED entity: 01y888 PRED relation: institution! PRED expected values: 01ysy9 => 191 concepts (106 used for prediction) PRED predicted values (max 10 best out of 20): 014mlp (0.90 #1305, 0.89 #1350, 0.86 #1967), 03bwzr4 (0.89 #56, 0.64 #206, 0.62 #163), 02h4rq6 (0.84 #46, 0.82 #153, 0.72 #1784), 019v9k (0.77 #1592, 0.75 #333, 0.71 #1486), 016t_3 (0.74 #47, 0.61 #68, 0.57 #197), 04zx3q1 (0.68 #45, 0.48 #195, 0.44 #173), 07s6fsf (0.63 #44, 0.41 #473, 0.41 #517), 013zdg (0.42 #51, 0.29 #2135, 0.29 #1848), 0bjrnt (0.32 #50, 0.29 #2135, 0.29 #1848), 03mkk4 (0.32 #54, 0.21 #204, 0.20 #182) >> Best rule #1305 for best value: >> intensional similarity = 5 >> extensional distance = 322 >> proper extension: 015zyd; 0l2tk; 02fy0z; 0373qg; 033x5p; 017cy9; 01jzyx; 09s5q8; 01j_5k; 02hmw9; ... >> query: (?x4031, 014mlp) <- organization(?x4095, ?x4031), institution(?x1526, ?x4031), major_field_of_study(?x4031, ?x2606), institution(?x1526, ?x10497), ?x10497 = 02m0b0 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2135 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 444 *> proper extension: 0288zy; 02cttt; 01nkcn; 027xx3; 01hb1t; 0kqj1; 02cbvn; 03x83_; 0hd7j; 01rr31; ... *> query: (?x4031, ?x734) <- school_type(?x4031, ?x3092), institution(?x1526, ?x4031), major_field_of_study(?x4031, ?x2981), major_field_of_study(?x2981, ?x1527), major_field_of_study(?x734, ?x2981) *> conf = 0.29 ranks of expected_values: 11 EVAL 01y888 institution! 01ysy9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 191.000 106.000 0.901 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15994-029_3 PRED entity: 029_3 PRED relation: languages PRED expected values: 02h40lc => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 18): 02h40lc (0.43 #197, 0.38 #392, 0.33 #704), 03k50 (0.09 #1603, 0.06 #2149, 0.03 #1174), 07c9s (0.06 #1612, 0.04 #2158, 0.02 #1183), 03_9r (0.06 #512, 0.04 #668, 0.03 #785), 064_8sq (0.05 #2825, 0.04 #3566, 0.04 #3176), 03hkp (0.04 #673, 0.03 #829, 0.03 #946), 04h9h (0.04 #693, 0.03 #849, 0.03 #966), 04306rv (0.04 #666, 0.02 #1602, 0.02 #1173), 06nm1 (0.03 #825, 0.02 #981, 0.02 #1137), 0t_2 (0.03 #828, 0.02 #1062, 0.02 #1491) >> Best rule #197 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01jbx1; >> query: (?x4065, 02h40lc) <- producer_type(?x4065, ?x632), program(?x4065, ?x2710), special_performance_type(?x4065, ?x4832) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 029_3 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.429 http://example.org/people/person/languages #15993-01wxyx1 PRED entity: 01wxyx1 PRED relation: location PRED expected values: 01xd9 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 172): 01xd9 (0.36 #1693, 0.06 #889, 0.01 #3302), 030qb3t (0.28 #2495, 0.26 #8126, 0.25 #10540), 02cft (0.21 #1915, 0.12 #1111, 0.03 #3524), 02_286 (0.19 #4059, 0.18 #28187, 0.18 #24165), 0cc56 (0.10 #57, 0.07 #4079, 0.06 #861), 0cr3d (0.10 #145, 0.06 #21057, 0.06 #37140), 04rrd (0.10 #98, 0.03 #3315, 0.03 #5728), 0qpqn (0.10 #453, 0.02 #5279, 0.02 #6083), 01ppq (0.10 #430), 017cjb (0.10 #72) >> Best rule #1693 for best value: >> intensional similarity = 2 >> extensional distance = 40 >> proper extension: 0f1pyf; 02y0dd; >> query: (?x2108, 01xd9) <- nationality(?x2108, ?x429), ?x429 = 03rt9 >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wxyx1 location 01xd9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.357 http://example.org/people/person/places_lived./people/place_lived/location #15992-0y62n PRED entity: 0y62n PRED relation: county_seat! PRED expected values: 0y62n => 166 concepts (152 used for prediction) PRED predicted values (max 10 best out of 99): 0n58p (0.21 #2015, 0.03 #5319, 0.03 #7881), 0n5fz (0.21 #2015, 0.03 #5319, 0.03 #7881), 0n5df (0.21 #2015, 0.03 #5319, 0.03 #7881), 02_286 (0.14 #548, 0.10 #8431, 0.10 #13007), 0cc56 (0.12 #8, 0.08 #191, 0.06 #556), 09c7w0 (0.10 #8431, 0.10 #13007, 0.10 #7696), 0mnrb (0.08 #360, 0.08 #542, 0.02 #2375), 02cl1 (0.08 #187, 0.08 #369, 0.02 #2384), 0mpbx (0.08 #305, 0.06 #670, 0.06 #854), 0fw4v (0.08 #257, 0.01 #4476, 0.01 #5393) >> Best rule #2015 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0r679; 0rqyx; 0d8jf; 0rjg8; 0n6mc; 0ntxg; 0mhdz; 0r6c4; 01zqy6t; 0qcrj; >> query: (?x9233, ?x10131) <- source(?x9233, ?x958), adjoins(?x10059, ?x9233), county(?x10059, ?x10131) >> conf = 0.21 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0y62n county_seat! 0y62n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 166.000 152.000 0.214 http://example.org/location/us_county/county_seat #15991-02y_lrp PRED entity: 02y_lrp PRED relation: language PRED expected values: 02h40lc => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.93 #600, 0.90 #1743, 0.90 #1080), 04306rv (0.49 #361, 0.10 #244, 0.09 #544), 064_8sq (0.15 #261, 0.15 #442, 0.14 #979), 06nm1 (0.12 #250, 0.11 #609, 0.11 #908), 02bjrlw (0.08 #599, 0.07 #1742, 0.07 #240), 03_9r (0.07 #309, 0.06 #249, 0.06 #729), 0653m (0.07 #12, 0.04 #1332, 0.04 #1933), 03x42 (0.07 #50, 0.01 #170, 0.01 #349), 06b_j (0.06 #202, 0.06 #322, 0.06 #262), 04h9h (0.05 #103, 0.04 #463, 0.03 #282) >> Best rule #600 for best value: >> intensional similarity = 4 >> extensional distance = 300 >> proper extension: 0jymd; 04fjzv; >> query: (?x146, 02h40lc) <- country(?x146, ?x94), award_winner(?x146, ?x902), titles(?x2480, ?x146), featured_film_locations(?x146, ?x1658) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y_lrp language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.927 http://example.org/film/film/language #15990-0cv3w PRED entity: 0cv3w PRED relation: location! PRED expected values: 01yznp => 242 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2468): 04myfb7 (0.56 #228430, 0.53 #353927, 0.47 #286160), 03d9d6 (0.39 #200816, 0.30 #356437, 0.30 #358947), 023kzp (0.33 #16276, 0.21 #26317, 0.20 #13765), 05d1y (0.33 #1675, 0.10 #14226, 0.08 #16737), 098sv2 (0.33 #2233, 0.07 #19805, 0.05 #29847), 0gl88b (0.29 #7902, 0.25 #15432, 0.20 #12921), 02t__3 (0.29 #8753, 0.20 #6244, 0.17 #16283), 0bdxs5 (0.29 #9290, 0.20 #14309, 0.12 #21841), 01q_ph (0.29 #7582, 0.18 #22644, 0.17 #15112), 073749 (0.29 #8332, 0.17 #15862, 0.16 #28414) >> Best rule #228430 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 05sb1; 0nc7s; >> query: (?x3026, ?x11750) <- jurisdiction_of_office(?x1195, ?x3026), place_of_birth(?x11750, ?x3026), people(?x5269, ?x11750) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #396590 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 220 *> proper extension: 059f4; 04v3q; 0l2hf; 0qr4n; 04ly1; 03__y; 01m1zk; 0h3lt; 0d331; 0ht8h; ... *> query: (?x3026, ?x436) <- featured_film_locations(?x437, ?x3026), film(?x436, ?x437) *> conf = 0.05 ranks of expected_values: 1342 EVAL 0cv3w location! 01yznp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 242.000 162.000 0.561 http://example.org/people/person/places_lived./people/place_lived/location #15989-02dr9j PRED entity: 02dr9j PRED relation: film! PRED expected values: 0241jw => 110 concepts (53 used for prediction) PRED predicted values (max 10 best out of 882): 01xcfy (0.55 #87443, 0.54 #89529, 0.40 #58295), 02jxmr (0.55 #87443, 0.54 #89529, 0.40 #58295), 05bm4sm (0.40 #58295, 0.39 #4165, 0.39 #33307), 04ktcgn (0.40 #58295, 0.39 #4165, 0.39 #33307), 02h1rt (0.40 #58295, 0.39 #4165, 0.39 #33307), 01y_px (0.25 #365, 0.05 #2447, 0.02 #17023), 02zhkz (0.25 #1276, 0.05 #3358, 0.02 #5441), 01fwk3 (0.25 #462, 0.01 #4627, 0.01 #21282), 026fd (0.25 #1049), 0154qm (0.15 #2645, 0.06 #4728, 0.05 #8892) >> Best rule #87443 for best value: >> intensional similarity = 4 >> extensional distance = 635 >> proper extension: 0275kr; >> query: (?x7214, ?x2891) <- nominated_for(?x4691, ?x7214), nominated_for(?x2891, ?x7214), award_winner(?x143, ?x4691), spouse(?x399, ?x2891) >> conf = 0.55 => this is the best rule for 2 predicted values *> Best rule #2378 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 07gp9; 0dqytn; 017gl1; 017gm7; 024l2y; 011yd2; 0fpv_3_; 0661ql3; 026p4q7; 07cz2; ... *> query: (?x7214, 0241jw) <- award(?x7214, ?x640), produced_by(?x7214, ?x3434), ?x640 = 02hsq3m, award_winner(?x7214, ?x1983) *> conf = 0.15 ranks of expected_values: 13 EVAL 02dr9j film! 0241jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 110.000 53.000 0.555 http://example.org/film/actor/film./film/performance/film #15988-01336l PRED entity: 01336l PRED relation: languages_spoken PRED expected values: 02hwhyv => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 52): 02h40lc (0.54 #1722, 0.41 #1566, 0.33 #157), 06nm1 (0.50 #426, 0.43 #374, 0.38 #894), 064_8sq (0.41 #955, 0.25 #903, 0.24 #1841), 06mp7 (0.33 #274, 0.10 #1836, 0.10 #1512), 01r2l (0.33 #71, 0.07 #1739, 0.06 #1478), 0459q4 (0.33 #83, 0.06 #1490, 0.04 #1751), 012w70 (0.33 #61, 0.06 #1468, 0.04 #1729), 0653m (0.33 #60, 0.04 #1728, 0.03 #1467), 06b_j (0.21 #1424, 0.17 #280, 0.16 #1164), 05zjd (0.17 #335, 0.17 #283, 0.14 #387) >> Best rule #1722 for best value: >> intensional similarity = 6 >> extensional distance = 44 >> proper extension: 078ds; 04czx7; >> query: (?x9648, 02h40lc) <- languages_spoken(?x9648, ?x3592), languages_spoken(?x5269, ?x3592), languages_spoken(?x2510, ?x3592), ?x2510 = 0x67, people(?x5269, ?x2963), ?x2963 = 0gcs9 >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1171 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 17 *> proper extension: 0d29z; *> query: (?x9648, 02hwhyv) <- geographic_distribution(?x9648, ?x94), ?x94 = 09c7w0 *> conf = 0.05 ranks of expected_values: 42 EVAL 01336l languages_spoken 02hwhyv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 48.000 48.000 0.543 http://example.org/people/ethnicity/languages_spoken #15987-03spz PRED entity: 03spz PRED relation: nationality! PRED expected values: 0f1vrl 016k62 0dhrqx => 213 concepts (138 used for prediction) PRED predicted values (max 10 best out of 4078): 016k62 (0.40 #507195, 0.32 #405750, 0.18 #499080), 0969fd (0.40 #507195, 0.18 #499080, 0.05 #27695), 059xvg (0.14 #65966, 0.14 #25394, 0.14 #21337), 0jcx (0.14 #69919, 0.12 #90207, 0.12 #53691), 0p__8 (0.14 #26188, 0.14 #22131, 0.13 #34302), 054k_8 (0.13 #13873, 0.12 #38216, 0.09 #21988), 07y_r (0.13 #15749, 0.12 #19807, 0.08 #40092), 03y3dk (0.13 #14833, 0.12 #18891, 0.08 #39176), 01qq_lp (0.13 #13319, 0.12 #17377, 0.08 #37662), 082mw (0.13 #14468, 0.09 #22583, 0.09 #34754) >> Best rule #507195 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 06y9v; 0fgj2; 02ly_; 0125q1; 0k_s5; 0dzz_; >> query: (?x4743, ?x4862) <- contains(?x4743, ?x10519), location(?x12255, ?x4743), place_of_birth(?x4862, ?x10519) >> conf = 0.40 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 03spz nationality! 0dhrqx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 213.000 138.000 0.397 http://example.org/people/person/nationality EVAL 03spz nationality! 016k62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 213.000 138.000 0.397 http://example.org/people/person/nationality EVAL 03spz nationality! 0f1vrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 213.000 138.000 0.397 http://example.org/people/person/nationality #15986-01kwsg PRED entity: 01kwsg PRED relation: profession PRED expected values: 02hrh1q => 105 concepts (104 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.92 #3713, 0.92 #4453, 0.90 #3861), 0dxtg (0.61 #4304, 0.42 #456, 0.30 #7562), 09jwl (0.33 #18, 0.22 #1646, 0.21 #1202), 0d1pc (0.33 #346, 0.12 #1678, 0.12 #2714), 0cbd2 (0.31 #450, 0.17 #302, 0.15 #8000), 03gjzk (0.30 #458, 0.28 #902, 0.27 #1642), 018gz8 (0.25 #312, 0.18 #608, 0.17 #904), 0np9r (0.21 #4608, 0.20 #5941, 0.20 #5053), 02krf9 (0.21 #4318, 0.10 #2986, 0.10 #618), 0dz3r (0.20 #150, 0.16 #1630, 0.12 #8292) >> Best rule #3713 for best value: >> intensional similarity = 3 >> extensional distance = 618 >> proper extension: 04n7njg; 01vq3nl; >> query: (?x4702, 02hrh1q) <- actor(?x9222, ?x4702), nominated_for(?x4702, ?x339), profession(?x4702, ?x319) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kwsg profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 104.000 0.924 http://example.org/people/person/profession #15985-08xvpn PRED entity: 08xvpn PRED relation: genre PRED expected values: 060__y => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 90): 02l7c8 (0.51 #135, 0.39 #3746, 0.34 #255), 05p553 (0.38 #5778, 0.35 #3373, 0.35 #2173), 01jfsb (0.33 #2661, 0.31 #5786, 0.30 #2301), 02kdv5l (0.28 #1568, 0.27 #3371, 0.25 #2651), 060__y (0.25 #136, 0.20 #376, 0.20 #256), 03k9fj (0.25 #1577, 0.22 #1095, 0.22 #4463), 082gq (0.20 #390, 0.20 #270, 0.19 #1717), 0lsxr (0.20 #972, 0.19 #851, 0.19 #730), 01hmnh (0.19 #17, 0.18 #1810, 0.18 #1584), 04xvh5 (0.19 #34, 0.18 #1810, 0.13 #394) >> Best rule #135 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 0m313; 01jc6q; 011yxg; 07xtqq; 095zlp; 0hmr4; 0pv2t; 05jzt3; 0344gc; 02d44q; ... >> query: (?x9801, 02l7c8) <- language(?x9801, ?x254), nominated_for(?x1245, ?x9801), film(?x105, ?x9801), ?x1245 = 0gqwc >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #136 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 138 *> proper extension: 0m313; 01jc6q; 011yxg; 07xtqq; 095zlp; 0hmr4; 0pv2t; 05jzt3; 0344gc; 02d44q; ... *> query: (?x9801, 060__y) <- language(?x9801, ?x254), nominated_for(?x1245, ?x9801), film(?x105, ?x9801), ?x1245 = 0gqwc *> conf = 0.25 ranks of expected_values: 5 EVAL 08xvpn genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 62.000 0.507 http://example.org/film/film/genre #15984-05y7hc PRED entity: 05y7hc PRED relation: role PRED expected values: 01vdm0 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 85): 0342h (0.41 #2610, 0.41 #2819, 0.40 #3027), 01vdm0 (0.32 #137, 0.27 #3682, 0.27 #2847), 07y_7 (0.32 #2710, 0.23 #2292, 0.23 #833), 02sgy (0.29 #319, 0.26 #2612, 0.26 #2821), 042v_gx (0.25 #1051, 0.24 #1988, 0.23 #2823), 013y1f (0.23 #142, 0.18 #558, 0.17 #350), 0l14qv (0.23 #110, 0.17 #318, 0.17 #2298), 05842k (0.19 #391, 0.19 #599, 0.18 #2371), 018vs (0.18 #2619, 0.17 #2828, 0.17 #326), 01vj9c (0.17 #2830, 0.17 #3038, 0.16 #2621) >> Best rule #2610 for best value: >> intensional similarity = 3 >> extensional distance = 293 >> proper extension: 03c7ln; 0c9d9; 07_3qd; 01w923; 01tp5bj; 0565cz; 0gkg6; 0bkg4; 01wy61y; 023l9y; ... >> query: (?x6910, 0342h) <- role(?x6910, ?x316), category(?x6910, ?x134), instrumentalists(?x75, ?x6910) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #137 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 03ryks; 017l4; *> query: (?x6910, 01vdm0) <- award(?x6910, ?x2379), ?x2379 = 02qvyrt, role(?x6910, ?x316) *> conf = 0.32 ranks of expected_values: 2 EVAL 05y7hc role 01vdm0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 106.000 0.410 http://example.org/music/artist/track_contributions./music/track_contribution/role #15983-0l14md PRED entity: 0l14md PRED relation: group PRED expected values: 03g5jw 05k79 0167_s 0fcsd 0394y 02dw1_ 0l8g0 048xh 0b_xm 0187x8 01516r 01fchy 016lj_ 01s560x 079kr => 87 concepts (62 used for prediction) PRED predicted values (max 10 best out of 602): 02dw1_ (0.64 #1969, 0.64 #1320, 0.62 #755), 07m4c (0.62 #767, 0.50 #366, 0.50 #202), 02t3ln (0.62 #748, 0.50 #266, 0.50 #183), 0gr69 (0.62 #763, 0.50 #198, 0.42 #2791), 01cblr (0.62 #750, 0.50 #185, 0.36 #1315), 0fcsd (0.62 #744, 0.50 #179, 0.36 #1309), 0b_xm (0.55 #1659, 0.53 #2473, 0.50 #768), 03k3b (0.55 #1336, 0.50 #771, 0.50 #206), 048xh (0.53 #2471, 0.50 #201, 0.46 #1817), 01rm8b (0.50 #742, 0.50 #341, 0.50 #177) >> Best rule #1969 for best value: >> intensional similarity = 12 >> extensional distance = 12 >> proper extension: 03m5k; >> query: (?x315, 02dw1_) <- role(?x460, ?x315), role(?x315, ?x2309), role(?x315, ?x614), role(?x315, ?x316), role(?x212, ?x315), ?x316 = 05r5c, ?x614 = 0mkg, role(?x315, ?x4311), instrumentalists(?x315, ?x226), group(?x315, ?x379), ?x2309 = 06ncr, instrumentalists(?x4311, ?x764) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 7, 9, 12, 14, 16, 17, 18, 35, 36, 52, 58, 62, 69 EVAL 0l14md group 079kr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 01s560x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 016lj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 01fchy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 01516r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 0187x8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 0b_xm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 048xh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 0l8g0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 02dw1_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 0394y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 0fcsd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 0167_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 05k79 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l14md group 03g5jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 87.000 62.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group #15982-027rpym PRED entity: 027rpym PRED relation: film_format PRED expected values: 01dc60 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 4): 07fb8_ (0.17 #26, 0.16 #99, 0.16 #131), 0cj16 (0.11 #116, 0.11 #106, 0.11 #293), 017fx5 (0.08 #60, 0.05 #129, 0.05 #209), 01dc60 (0.06 #15, 0.03 #20, 0.02 #30) >> Best rule #26 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 01kff7; 07w8fz; 0ch3qr1; 0y_yw; 01jw67; 0p9tm; 02z9rr; >> query: (?x4865, 07fb8_) <- cinematography(?x4865, ?x4863), film(?x1666, ?x4865), nominated_for(?x1745, ?x4865), country(?x4865, ?x94) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0gxfz; 0gl3hr; *> query: (?x4865, 01dc60) <- cinematography(?x4865, ?x4863), film(?x1666, ?x4865), costume_design_by(?x4865, ?x2069), film_art_direction_by(?x4865, ?x2304) *> conf = 0.06 ranks of expected_values: 4 EVAL 027rpym film_format 01dc60 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 125.000 125.000 0.170 http://example.org/film/film/film_format #15981-024tv_ PRED entity: 024tv_ PRED relation: category PRED expected values: 08mbj5d => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14797, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024tv_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #15980-02qggqc PRED entity: 02qggqc PRED relation: gender PRED expected values: 05zppz => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #23, 0.87 #19, 0.87 #37), 02zsn (0.49 #93, 0.46 #130, 0.25 #76) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 166 >> proper extension: 0147dk; 02lk1s; 04wvhz; 01t2h2; 02f2dn; 01rzqj; 07lwsz; 01f7v_; 0bjkpt; 06chvn; ... >> query: (?x707, 05zppz) <- executive_produced_by(?x8570, ?x707), genre(?x8570, ?x1510), film_crew_role(?x8570, ?x137), nominated_for(?x298, ?x8570) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qggqc gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.887 http://example.org/people/person/gender #15979-01wbsdz PRED entity: 01wbsdz PRED relation: profession PRED expected values: 0dz3r => 142 concepts (101 used for prediction) PRED predicted values (max 10 best out of 78): 09jwl (0.83 #2834, 0.78 #6981, 0.77 #5796), 0nbcg (0.64 #2847, 0.58 #2403, 0.56 #7290), 016z4k (0.64 #2376, 0.56 #2820, 0.55 #7115), 0dz3r (0.56 #3560, 0.56 #6965, 0.54 #2670), 01d_h8 (0.49 #7561, 0.48 #1934, 0.46 #7709), 039v1 (0.41 #1221, 0.39 #2408, 0.35 #1073), 03gjzk (0.38 #4904, 0.36 #7569, 0.33 #162), 0dxtg (0.33 #161, 0.33 #7568, 0.28 #7716), 064xm0 (0.33 #62, 0.32 #3114, 0.32 #3113), 02jknp (0.33 #156, 0.25 #304, 0.22 #1936) >> Best rule #2834 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 0285c; >> query: (?x5882, 09jwl) <- participant(?x1953, ?x5882), currency(?x5882, ?x170), instrumentalists(?x1166, ?x5882), artist(?x1124, ?x5882) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #3560 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 0lbj1; 0147dk; 01wmxfs; 016kjs; 01kx_81; 09qr6; 06w2sn5; 058s57; 01wgxtl; 014q2g; ... *> query: (?x5882, 0dz3r) <- participant(?x1953, ?x5882), currency(?x5882, ?x170), award_nominee(?x5479, ?x5882), artist(?x1124, ?x5882) *> conf = 0.56 ranks of expected_values: 4 EVAL 01wbsdz profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 142.000 101.000 0.833 http://example.org/people/person/profession #15978-02gpkt PRED entity: 02gpkt PRED relation: film! PRED expected values: 0162c8 => 104 concepts (66 used for prediction) PRED predicted values (max 10 best out of 83): 03dbds (0.25 #176, 0.03 #998), 03h304l (0.23 #4118, 0.22 #9347, 0.21 #8245), 012d40 (0.19 #3842, 0.14 #4947, 0.12 #3843), 01900g (0.12 #3843, 0.11 #13746, 0.11 #10446), 06pj8 (0.09 #1693, 0.07 #1144, 0.04 #3891), 0js9s (0.09 #703, 0.02 #2075, 0.02 #2350), 026670 (0.09 #781, 0.01 #4076), 0343h (0.07 #311, 0.05 #1133, 0.04 #1407), 026fd (0.06 #691, 0.01 #3986), 0162c8 (0.05 #1134, 0.04 #312, 0.03 #1408) >> Best rule #176 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 035s95; 0992d9; >> query: (?x7541, 03dbds) <- film(?x2549, ?x7541), film(?x1561, ?x7541), ?x1561 = 030_1m, language(?x7541, ?x254), ?x2549 = 024rgt >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1134 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 0d90m; 01k1k4; 01vksx; 01qb5d; 0p9lw; 053rxgm; 031778; 0dr3sl; 03177r; 0hx4y; ... *> query: (?x7541, 0162c8) <- film(?x541, ?x7541), prequel(?x4038, ?x7541), language(?x7541, ?x254), film_distribution_medium(?x7541, ?x2099) *> conf = 0.05 ranks of expected_values: 10 EVAL 02gpkt film! 0162c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 104.000 66.000 0.250 http://example.org/film/director/film #15977-015q1n PRED entity: 015q1n PRED relation: colors PRED expected values: 01jnf1 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.38 #182, 0.38 #942, 0.37 #822), 01l849 (0.29 #201, 0.29 #381, 0.28 #741), 019sc (0.24 #207, 0.23 #107, 0.23 #307), 06fvc (0.23 #303, 0.19 #203, 0.16 #403), 0jc_p (0.22 #44, 0.12 #284, 0.10 #584), 036k5h (0.19 #305, 0.17 #205, 0.12 #145), 088fh (0.12 #26, 0.09 #66, 0.07 #2241), 03wkwg (0.12 #175, 0.11 #135, 0.10 #155), 067z2v (0.11 #49, 0.11 #89, 0.10 #109), 04d18d (0.11 #59, 0.09 #219, 0.06 #159) >> Best rule #182 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 05xb7q; >> query: (?x6271, 083jv) <- school_type(?x6271, ?x1507), state_province_region(?x6271, ?x1782), institution(?x734, ?x6271), ?x734 = 04zx3q1 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #511 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 115 *> proper extension: 01hhvg; 01b1mj; 03v6t; 07szy; 01jsn5; 03ksy; 01q0kg; 07tds; 02zd460; 035ktt; ... *> query: (?x6271, 01jnf1) <- school_type(?x6271, ?x1507), school(?x1578, ?x6271), contains(?x94, ?x6271), currency(?x6271, ?x170) *> conf = 0.08 ranks of expected_values: 14 EVAL 015q1n colors 01jnf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 130.000 130.000 0.382 http://example.org/education/educational_institution/colors #15976-043sct5 PRED entity: 043sct5 PRED relation: film_regional_debut_venue PRED expected values: 02_286 => 127 concepts (123 used for prediction) PRED predicted values (max 10 best out of 24): 04grdgy (0.25 #750, 0.19 #1418, 0.19 #280), 015hr (0.20 #107, 0.16 #890, 0.16 #419), 0gg7gsl (0.12 #318, 0.11 #412, 0.04 #914), 0kfhjq0 (0.11 #232, 0.10 #420, 0.09 #891), 0j63cyr (0.11 #324, 0.08 #889, 0.08 #418), 07751 (0.10 #885, 0.10 #414, 0.07 #320), 02_286 (0.07 #879, 0.04 #314, 0.02 #721), 07zmj (0.06 #434, 0.06 #905, 0.04 #340), 059_y8d (0.04 #258, 0.02 #384), 09rwjly (0.04 #269, 0.02 #739, 0.01 #960) >> Best rule #750 for best value: >> intensional similarity = 9 >> extensional distance = 130 >> proper extension: 03mh_tp; 0jqj5; >> query: (?x4430, ?x9189) <- genre(?x4430, ?x162), film(?x10629, ?x4430), film_crew_role(?x4430, ?x2095), film_festivals(?x4430, ?x9189), production_companies(?x299, ?x10629), film_crew_role(?x10201, ?x2095), film_crew_role(?x9524, ?x2095), ?x10201 = 0bm2nq, ?x9524 = 03whyr >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #879 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 151 *> proper extension: 01kqq7; *> query: (?x4430, 02_286) <- film_regional_debut_venue(?x4430, ?x1658), genre(?x4430, ?x162), country(?x4430, ?x1453), film_release_region(?x6587, ?x1453), olympics(?x1453, ?x418), nationality(?x4389, ?x1453), ?x6587 = 07s3m4g, film_release_region(?x2029, ?x1453) *> conf = 0.07 ranks of expected_values: 7 EVAL 043sct5 film_regional_debut_venue 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 127.000 123.000 0.252 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #15975-06w2sn5 PRED entity: 06w2sn5 PRED relation: artists! PRED expected values: 064t9 02ny8t => 163 concepts (129 used for prediction) PRED predicted values (max 10 best out of 258): 064t9 (0.85 #9101, 0.79 #29146, 0.64 #2209), 06by7 (0.66 #33228, 0.53 #649, 0.50 #1904), 0glt670 (0.57 #356, 0.50 #2864, 0.42 #10696), 02lnbg (0.53 #9148, 0.43 #374, 0.42 #2569), 05bt6j (0.48 #1300, 0.40 #2554, 0.32 #4120), 016clz (0.44 #1259, 0.38 #6898, 0.35 #33210), 02x8m (0.43 #333, 0.17 #7229, 0.16 #29152), 06j6l (0.42 #9138, 0.40 #29183, 0.37 #677), 0gywn (0.30 #9147, 0.30 #2881, 0.29 #373), 01lyv (0.27 #5989, 0.26 #662, 0.26 #18829) >> Best rule #9101 for best value: >> intensional similarity = 2 >> extensional distance = 91 >> proper extension: 01v27pl; >> query: (?x1462, 064t9) <- artists(?x5876, ?x1462), ?x5876 = 0ggx5q >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11 EVAL 06w2sn5 artists! 02ny8t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 163.000 129.000 0.849 http://example.org/music/genre/artists EVAL 06w2sn5 artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 129.000 0.849 http://example.org/music/genre/artists #15974-048lv PRED entity: 048lv PRED relation: participant! PRED expected values: 01rr9f => 98 concepts (45 used for prediction) PRED predicted values (max 10 best out of 293): 01rr9f (0.83 #15311, 0.83 #2554, 0.83 #18504), 0d_84 (0.83 #15311, 0.83 #2554, 0.83 #18504), 03lt8g (0.27 #1347, 0.09 #18505, 0.05 #1917), 09yrh (0.18 #1597, 0.09 #18505, 0.08 #2237), 0cgfb (0.18 #1887, 0.09 #18505, 0.04 #1250), 02d9k (0.18 #1399, 0.09 #18505, 0.04 #762), 029q_y (0.15 #1758, 0.09 #18505, 0.08 #2398), 014zcr (0.10 #1935, 0.09 #18505, 0.05 #4483), 01pqy_ (0.09 #1632, 0.09 #18505, 0.06 #355), 0c6qh (0.09 #1448, 0.09 #18505, 0.05 #1917) >> Best rule #15311 for best value: >> intensional similarity = 3 >> extensional distance = 485 >> proper extension: 0285c; 02_j7t; 01_rh4; 04cr6qv; 03xnq9_; 04f7c55; 06tp4h; 09nhvw; 012x2b; 022q4j; ... >> query: (?x1384, ?x513) <- participant(?x1384, ?x513), participant(?x513, ?x489), film(?x1384, ?x394) >> conf = 0.83 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 048lv participant! 01rr9f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 45.000 0.832 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #15973-04b2qn PRED entity: 04b2qn PRED relation: award PRED expected values: 09sb52 => 82 concepts (78 used for prediction) PRED predicted values (max 10 best out of 204): 02rdxsh (0.41 #220, 0.27 #11363, 0.26 #440), 0gqyl (0.27 #11364, 0.27 #11363, 0.26 #440), 0gr4k (0.27 #11364, 0.27 #11363, 0.26 #440), 0gs9p (0.27 #11364, 0.27 #11363, 0.26 #440), 0gq9h (0.27 #11364, 0.27 #11363, 0.26 #440), 0gqy2 (0.27 #11364, 0.27 #11363, 0.26 #440), 019f4v (0.27 #11364, 0.27 #11363, 0.26 #440), 054krc (0.27 #11364, 0.27 #11363, 0.26 #440), 02x17s4 (0.27 #11364, 0.27 #11363, 0.26 #440), 09qv_s (0.27 #11364, 0.27 #11363, 0.26 #440) >> Best rule #220 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 02h22; >> query: (?x7858, ?x1063) <- nominated_for(?x1063, ?x7858), nominated_for(?x601, ?x7858), ?x601 = 0gr4k, nominated_for(?x1063, ?x3965), ?x3965 = 04lqvly >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #13985 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1236 *> proper extension: 01vrwfv; 05gnf; 03d17dg; 0gxsh4; 06ys2; 04bp0l; *> query: (?x7858, ?x995) <- nominated_for(?x8167, ?x7858), award_winner(?x8167, ?x848), award_winner(?x995, ?x8167) *> conf = 0.11 ranks of expected_values: 31 EVAL 04b2qn award 09sb52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 82.000 78.000 0.410 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #15972-025rcc PRED entity: 025rcc PRED relation: institution! PRED expected values: 02h4rq6 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.84 #26, 0.66 #463, 0.63 #786), 019v9k (0.76 #31, 0.61 #284, 0.59 #791), 014mlp (0.75 #28, 0.68 #1272, 0.65 #834), 03bwzr4 (0.69 #37, 0.49 #474, 0.46 #290), 02_xgp2 (0.59 #35, 0.51 #472, 0.48 #288), 07s6fsf (0.39 #24, 0.36 #70, 0.36 #277), 04zx3q1 (0.31 #25, 0.28 #2357, 0.27 #278), 027f2w (0.31 #32, 0.25 #285, 0.22 #469), 013zdg (0.30 #122, 0.25 #30, 0.24 #352), 0bjrnt (0.16 #29, 0.13 #328, 0.12 #282) >> Best rule #26 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 08qnnv; >> query: (?x5887, 02h4rq6) <- category(?x5887, ?x134), currency(?x5887, ?x170), major_field_of_study(?x5887, ?x6870), ?x6870 = 01540 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025rcc institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.843 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15971-01pbs9w PRED entity: 01pbs9w PRED relation: award PRED expected values: 02qvyrt => 125 concepts (99 used for prediction) PRED predicted values (max 10 best out of 279): 01by1l (0.49 #16190, 0.45 #17397, 0.36 #12572), 0c4z8 (0.49 #3287, 0.48 #1277, 0.43 #14141), 02qvyrt (0.39 #5351, 0.39 #2135, 0.39 #1331), 025m8y (0.39 #1303, 0.35 #2107, 0.30 #5323), 01c92g (0.37 #10949, 0.17 #899, 0.16 #14969), 025m8l (0.35 #1323, 0.32 #3333, 0.30 #2529), 04njml (0.33 #2511, 0.22 #1305, 0.18 #3315), 01bgqh (0.33 #10897, 0.31 #12505, 0.30 #16123), 02gdjb (0.30 #1424, 0.25 #2228, 0.25 #3836), 02x17c2 (0.28 #2629, 0.23 #14287, 0.22 #7051) >> Best rule #16190 for best value: >> intensional similarity = 5 >> extensional distance = 382 >> proper extension: 02mslq; 025xt8y; 03f5spx; 0ftps; 01kvqc; 05crg7; 0dtd6; 01w60_p; 0892sx; 016fmf; ... >> query: (?x5757, 01by1l) <- award(?x5757, ?x2585), award(?x9321, ?x2585), award(?x6382, ?x2585), ?x9321 = 0140t7, ?x6382 = 01wd9lv >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #5351 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 97 *> proper extension: 025vry; *> query: (?x5757, 02qvyrt) <- profession(?x5757, ?x131), award_winner(?x1323, ?x5757), music(?x407, ?x5757), artists(?x505, ?x5757) *> conf = 0.39 ranks of expected_values: 3 EVAL 01pbs9w award 02qvyrt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 99.000 0.492 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15970-01bpn PRED entity: 01bpn PRED relation: type_of_union PRED expected values: 04ztj => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.84 #214, 0.83 #291, 0.82 #323), 01g63y (0.25 #57, 0.17 #99, 0.14 #95), 01bl8s (0.06 #84, 0.05 #92, 0.04 #120), 0jgjn (0.01 #258) >> Best rule #214 for best value: >> intensional similarity = 4 >> extensional distance = 163 >> proper extension: 0443c; >> query: (?x4309, 04ztj) <- people(?x13231, ?x4309), award_winner(?x921, ?x4309), location(?x4309, ?x13227), nationality(?x4309, ?x512) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bpn type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.836 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15969-0bxtg PRED entity: 0bxtg PRED relation: award_nominee PRED expected values: 015p3p => 107 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1081): 04wvhz (0.81 #115919, 0.81 #81127, 0.80 #41722), 017149 (0.81 #115919, 0.81 #81127, 0.80 #41722), 07ym6ss (0.81 #115919, 0.81 #81127, 0.80 #41722), 01s7zw (0.81 #115919, 0.81 #81127, 0.80 #41722), 025n3p (0.81 #115919, 0.81 #81127, 0.80 #41722), 04wp2p (0.81 #115919, 0.81 #81127, 0.80 #41722), 025y9fn (0.81 #115919, 0.81 #81127, 0.80 #41722), 078mgh (0.81 #115919, 0.81 #81127, 0.80 #41722), 044zvm (0.81 #115919, 0.81 #81127, 0.80 #41722), 036jb (0.81 #115919, 0.81 #81127, 0.80 #41722) >> Best rule #115919 for best value: >> intensional similarity = 3 >> extensional distance = 1110 >> proper extension: 044mz_; 0184jc; 012ljv; 02s2ft; 05vsxz; 0dbpyd; 07fq1y; 02qgqt; 0fvf9q; 0l6qt; ... >> query: (?x496, ?x495) <- award_winner(?x496, ?x525), nominated_for(?x496, ?x69), award_nominee(?x495, ?x496) >> conf = 0.81 => this is the best rule for 10 predicted values *> Best rule #102000 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1001 *> proper extension: 0283xx2; *> query: (?x496, ?x1104) <- award_winner(?x496, ?x1039), award_winner(?x69, ?x496), award_nominee(?x1039, ?x1104) *> conf = 0.18 ranks of expected_values: 55 EVAL 0bxtg award_nominee 015p3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 107.000 53.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15968-0c5x_ PRED entity: 0c5x_ PRED relation: institution! PRED expected values: 019v9k => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 18): 016t_3 (0.79 #40, 0.72 #78, 0.64 #97), 0bkj86 (0.79 #44, 0.71 #101, 0.70 #120), 04zx3q1 (0.71 #39, 0.53 #96, 0.52 #115), 019v9k (0.64 #102, 0.64 #45, 0.64 #255), 027f2w (0.59 #84, 0.57 #46, 0.51 #103), 013zdg (0.50 #43, 0.40 #100, 0.37 #119), 03mkk4 (0.50 #47, 0.29 #104, 0.28 #85), 0bjrnt (0.43 #42, 0.31 #80, 0.22 #156), 01rr_d (0.43 #51, 0.24 #261, 0.23 #89), 028dcg (0.36 #53, 0.18 #91, 0.17 #1406) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 09c7w0; >> query: (?x8220, 016t_3) <- company(?x3428, ?x8220), organization(?x8220, ?x5487), influenced_by(?x3428, ?x2080) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #102 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 045c7b; 0cv_2; 02z_b; *> query: (?x8220, 019v9k) <- citytown(?x8220, ?x5783), state_province_region(?x8220, ?x1227), organization(?x8220, ?x5487) *> conf = 0.64 ranks of expected_values: 4 EVAL 0c5x_ institution! 019v9k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 95.000 95.000 0.786 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15967-0gjcy PRED entity: 0gjcy PRED relation: location! PRED expected values: 0gt3p => 110 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1570): 0c6g1l (0.15 #453, 0.03 #8007, 0.03 #2971), 02t__3 (0.08 #1224, 0.05 #8778, 0.03 #3742), 012v1t (0.08 #1218, 0.05 #8772, 0.02 #11290), 01k53x (0.08 #1947, 0.04 #6983, 0.03 #4465), 086sj (0.08 #808, 0.03 #8362, 0.02 #10880), 0k8y7 (0.08 #844, 0.03 #8398, 0.02 #10916), 01w02sy (0.08 #596, 0.03 #8150, 0.02 #10668), 0j5q3 (0.08 #1424, 0.03 #8978, 0.02 #11496), 02pjvc (0.08 #1181, 0.03 #8735, 0.02 #11253), 0prfz (0.08 #49, 0.03 #7603, 0.02 #10121) >> Best rule #453 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0l2hf; >> query: (?x4413, 0c6g1l) <- place_of_birth(?x9567, ?x4413), contains(?x2632, ?x4413), time_zones(?x4413, ?x2950), ?x2632 = 06pvr >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #1554 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0l2hf; *> query: (?x4413, 0gt3p) <- place_of_birth(?x9567, ?x4413), contains(?x2632, ?x4413), time_zones(?x4413, ?x2950), ?x2632 = 06pvr *> conf = 0.08 ranks of expected_values: 43 EVAL 0gjcy location! 0gt3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 110.000 47.000 0.154 http://example.org/people/person/places_lived./people/place_lived/location #15966-015f7 PRED entity: 015f7 PRED relation: profession PRED expected values: 0nbcg => 124 concepts (108 used for prediction) PRED predicted values (max 10 best out of 89): 09jwl (0.61 #6218, 0.57 #2468, 0.56 #9971), 0nbcg (0.55 #318, 0.51 #607, 0.49 #2480), 016z4k (0.54 #726, 0.49 #2455, 0.48 #1878), 0dxtg (0.52 #1023, 0.43 #5347, 0.43 #4771), 01d_h8 (0.51 #1016, 0.41 #872, 0.41 #3899), 0cbd2 (0.46 #5197, 0.45 #7074, 0.44 #5341), 01c72t (0.43 #2040, 0.37 #579, 0.34 #6491), 0d1pc (0.37 #579, 0.34 #6491, 0.34 #8512), 012t_z (0.37 #579, 0.34 #6491, 0.34 #8512), 039v1 (0.37 #579, 0.34 #6491, 0.34 #8512) >> Best rule #6218 for best value: >> intensional similarity = 3 >> extensional distance = 405 >> proper extension: 01sfmyk; >> query: (?x3397, 09jwl) <- artist(?x5666, ?x3397), profession(?x3397, ?x131), location(?x3397, ?x4622) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #318 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 01vvycq; 01r9fv; 01v_pj6; 09hnb; 0137g1; 0gdh5; 03h_fk5; 01vn35l; 0gcs9; 01w7nww; ... *> query: (?x3397, 0nbcg) <- award(?x3397, ?x7535), profession(?x3397, ?x131), ?x7535 = 02f73b *> conf = 0.55 ranks of expected_values: 2 EVAL 015f7 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 108.000 0.612 http://example.org/people/person/profession #15965-0c01c PRED entity: 0c01c PRED relation: film PRED expected values: 01k0vq => 141 concepts (106 used for prediction) PRED predicted values (max 10 best out of 815): 07zhjj (0.66 #25047, 0.63 #57253, 0.61 #32204), 0cskb (0.66 #25047, 0.63 #57253, 0.61 #32204), 03bx2lk (0.10 #184, 0.04 #5551, 0.03 #18074), 09g8vhw (0.10 #325, 0.03 #3903, 0.03 #12848), 02ryz24 (0.10 #468, 0.03 #11202, 0.02 #32672), 02ntb8 (0.10 #839, 0.03 #7995, 0.02 #6206), 011yn5 (0.10 #927, 0.03 #11661, 0.02 #8083), 02yvct (0.10 #351, 0.02 #7507, 0.01 #96972), 0qm98 (0.10 #222, 0.02 #3800, 0.01 #7378), 026390q (0.10 #187, 0.02 #5554, 0.01 #7343) >> Best rule #25047 for best value: >> intensional similarity = 3 >> extensional distance = 209 >> proper extension: 0bdt8; 01qqtr; >> query: (?x2560, ?x8775) <- participant(?x1986, ?x2560), award_winner(?x2560, ?x1460), nominated_for(?x2560, ?x8775) >> conf = 0.66 => this is the best rule for 2 predicted values *> Best rule #177140 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1284 *> proper extension: 0cjdk; 014l4w; 04qb6g; *> query: (?x2560, ?x6684) <- award_winner(?x6066, ?x2560), award_winner(?x6684, ?x6066) *> conf = 0.06 ranks of expected_values: 15 EVAL 0c01c film 01k0vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 141.000 106.000 0.656 http://example.org/film/actor/film./film/performance/film #15964-02s2wq PRED entity: 02s2wq PRED relation: gender PRED expected values: 05zppz => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #11, 0.81 #9, 0.77 #13), 02zsn (0.38 #8, 0.31 #90, 0.31 #6) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 028q6; 0lbj1; 01vrx3g; 01wl38s; 03qd_; 01gf5h; 03kwtb; 0bg539; 03cs_z7; 0pgjm; ... >> query: (?x6380, 05zppz) <- award_nominee(?x6380, ?x1751), role(?x6380, ?x227), profession(?x6380, ?x220) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02s2wq gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.832 http://example.org/people/person/gender #15963-04v68c PRED entity: 04v68c PRED relation: type_of_union PRED expected values: 04ztj => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.69 #125, 0.68 #153, 0.68 #69), 01g63y (0.20 #157, 0.20 #227, 0.19 #260), 0jgjn (0.01 #100) >> Best rule #125 for best value: >> intensional similarity = 7 >> extensional distance = 1652 >> proper extension: 016qtt; 05ty4m; 02zq43; 0436f4; 0d4fqn; 01vvycq; 01mvth; 05ml_s; 04bd8y; 066m4g; ... >> query: (?x13846, 04ztj) <- gender(?x13846, ?x231), ?x231 = 05zppz, profession(?x13846, ?x7623), profession(?x9739, ?x7623), profession(?x7907, ?x7623), nationality(?x7907, ?x279), team(?x9739, ?x8515) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04v68c type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.693 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15962-01w92 PRED entity: 01w92 PRED relation: award_winner! PRED expected values: 0381pn => 93 concepts (60 used for prediction) PRED predicted values (max 10 best out of 601): 03jvmp (0.62 #28942, 0.53 #88438, 0.53 #94873), 026v1z (0.50 #3147, 0.12 #6361, 0.07 #12795), 016gr2 (0.50 #27509, 0.02 #82178, 0.02 #83786), 0h0yt (0.50 #28568, 0.02 #83237, 0.02 #84845), 015rkw (0.46 #27600, 0.03 #59761, 0.02 #83877), 015gw6 (0.46 #27673, 0.01 #82342, 0.01 #83950), 02k6rq (0.46 #27648, 0.01 #82317, 0.01 #83925), 02l4rh (0.46 #28489, 0.01 #83158, 0.01 #84766), 051wwp (0.46 #28188, 0.01 #82857, 0.01 #84465), 09fqtq (0.46 #27390, 0.01 #82059, 0.01 #83667) >> Best rule #28942 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0g2mbn; >> query: (?x3487, ?x2246) <- award_nominee(?x3487, ?x2246), nominated_for(?x2246, ?x6023), ?x6023 = 0bbm7r >> conf = 0.62 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w92 award_winner! 0381pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 60.000 0.615 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #15961-0pqz3 PRED entity: 0pqz3 PRED relation: place! PRED expected values: 0pqz3 => 101 concepts (78 used for prediction) PRED predicted values (max 10 best out of 182): 030qb3t (0.16 #19591, 0.10 #20624, 0.10 #22688), 0pqz3 (0.16 #19591, 0.10 #20624, 0.10 #22688), 02xry (0.16 #19591, 0.10 #20624, 0.10 #22688), 0th3k (0.12 #443, 0.02 #1475, 0.01 #3537), 0fvvg (0.12 #396), 0tgcy (0.12 #277), 0d9y6 (0.12 #129), 0tct_ (0.12 #112), 0f2rq (0.03 #655, 0.02 #1171, 0.01 #1686), 0c_m3 (0.03 #648, 0.02 #1164, 0.01 #1679) >> Best rule #19591 for best value: >> intensional similarity = 4 >> extensional distance = 306 >> proper extension: 03hrz; 04kf4; 0k3p; 019fv4; 064xp; 03kfl; 0hknf; 0fnc_; >> query: (?x14081, ?x1523) <- time_zones(?x14081, ?x1638), place_of_birth(?x2444, ?x14081), contains(?x94, ?x14081), location(?x2444, ?x1523) >> conf = 0.16 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0pqz3 place! 0pqz3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 78.000 0.155 http://example.org/location/hud_county_place/place #15960-063y9fp PRED entity: 063y9fp PRED relation: genre PRED expected values: 07s9rl0 => 45 concepts (43 used for prediction) PRED predicted values (max 10 best out of 82): 03k9fj (0.67 #369, 0.43 #488, 0.38 #608), 05p553 (0.64 #1910, 0.52 #361, 0.37 #1671), 07s9rl0 (0.54 #5003, 0.54 #4169, 0.53 #4646), 01jfsb (0.45 #489, 0.35 #251, 0.33 #132), 01zhp (0.36 #433, 0.04 #1029, 0.04 #1743), 0lsxr (0.35 #247, 0.17 #1795, 0.17 #2630), 03npn (0.33 #7, 0.14 #483, 0.12 #245), 01q03 (0.33 #5, 0.12 #243, 0.03 #1911), 0jdm8 (0.33 #81, 0.06 #319, 0.02 #1272), 02l7c8 (0.25 #1922, 0.25 #4780, 0.25 #4899) >> Best rule #369 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 076xkdz; 0564x; >> query: (?x9169, 03k9fj) <- film_release_region(?x9169, ?x94), production_companies(?x9169, ?x4585), genre(?x9169, ?x2540), ?x2540 = 0hcr >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5003 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1863 *> proper extension: 05jyb2; 0cvkv5; *> query: (?x9169, 07s9rl0) <- genre(?x9169, ?x1013), genre(?x419, ?x1013), genre(?x4446, ?x1013), film_release_region(?x4446, ?x87) *> conf = 0.54 ranks of expected_values: 3 EVAL 063y9fp genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 45.000 43.000 0.672 http://example.org/film/film/genre #15959-0c3351 PRED entity: 0c3351 PRED relation: genre! PRED expected values: 04ynx7 => 52 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1824): 08720 (0.79 #3652, 0.77 #3653, 0.77 #1827), 0f4_2k (0.79 #3652, 0.77 #3653, 0.77 #1827), 01s9vc (0.79 #3652, 0.77 #3653, 0.77 #1827), 07cyl (0.79 #3652, 0.77 #3653, 0.77 #1827), 0k4fz (0.79 #3652, 0.77 #3653, 0.77 #1827), 0jymd (0.79 #3652, 0.77 #3653, 0.77 #1827), 05k2xy (0.79 #3652, 0.77 #3653, 0.77 #1827), 09gb_4p (0.79 #3652, 0.77 #3653, 0.77 #1827), 033qdy (0.79 #3652, 0.77 #3653, 0.77 #1827), 0jjy0 (0.79 #3652, 0.77 #3653, 0.77 #1827) >> Best rule #3652 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 02n4kr; 01jfsb; >> query: (?x4205, ?x599) <- genre(?x2009, ?x4205), titles(?x4205, ?x3986), titles(?x4205, ?x2525), titles(?x4205, ?x1708), titles(?x4205, ?x599), ?x1708 = 05cj_j, ?x3986 = 0jymd, genre(?x1246, ?x4205), film_crew_role(?x2525, ?x137) >> conf = 0.79 => this is the best rule for 23 predicted values *> Best rule #12579 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 01htzx; *> query: (?x4205, 04ynx7) <- genre(?x11336, ?x4205), genre(?x8870, ?x4205), actor(?x11336, ?x1515), ?x8870 = 0fhzwl, ?x1515 = 07f3xb, nominated_for(?x3381, ?x11336) *> conf = 0.29 ranks of expected_values: 457 EVAL 0c3351 genre! 04ynx7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 20.000 0.786 http://example.org/film/film/genre #15958-06crng PRED entity: 06crng PRED relation: influenced_by PRED expected values: 01hmk9 01wp_jm => 149 concepts (63 used for prediction) PRED predicted values (max 10 best out of 346): 03f70xs (0.50 #68, 0.04 #10874, 0.03 #6550), 081k8 (0.25 #154, 0.13 #17879, 0.09 #23067), 032l1 (0.25 #88, 0.11 #17813, 0.10 #19542), 03_87 (0.25 #201, 0.10 #19655, 0.09 #23114), 03f0324 (0.25 #150, 0.09 #19604, 0.09 #17875), 08433 (0.25 #21, 0.08 #15562, 0.06 #5639), 01rgr (0.25 #321, 0.08 #15562, 0.03 #18046), 085gk (0.25 #412, 0.08 #15562, 0.02 #6894), 0lrh (0.25 #73, 0.05 #10879, 0.05 #16427), 0hky (0.25 #192, 0.03 #5810, 0.03 #10134) >> Best rule #68 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01w9ph_; >> query: (?x7527, 03f70xs) <- profession(?x7527, ?x1032), ?x1032 = 02hrh1q, influenced_by(?x7527, ?x2208), ?x2208 = 041mt >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4108 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 01xdf5; 04t2l2; 0p_pd; 04bs3j; 0mdqp; 0pz7h; 0343h; 034np8; 018grr; 01j7rd; ... *> query: (?x7527, 01hmk9) <- profession(?x7527, ?x1041), film(?x7527, ?x1202), influenced_by(?x7527, ?x2125), ?x1041 = 03gjzk *> conf = 0.19 ranks of expected_values: 16, 21 EVAL 06crng influenced_by 01wp_jm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 149.000 63.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 06crng influenced_by 01hmk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 149.000 63.000 0.500 http://example.org/influence/influence_node/influenced_by #15957-0mw89 PRED entity: 0mw89 PRED relation: time_zones PRED expected values: 02hcv8 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.88 #226, 0.86 #423, 0.86 #476), 02fqwt (0.24 #318, 0.21 #450, 0.19 #227), 02lcqs (0.21 #550, 0.21 #414, 0.21 #217), 02hczc (0.10 #319, 0.10 #519, 0.09 #411), 02llzg (0.07 #965, 0.07 #978, 0.06 #1031), 03bdv (0.04 #1046, 0.03 #1512, 0.03 #1197), 03plfd (0.03 #984, 0.03 #1011, 0.02 #997), 0gsrz4 (0.02 #1131, 0.02 #995, 0.01 #1075), 042g7t (0.02 #237, 0.01 #998, 0.01 #944), 02lcrv (0.01 #351, 0.01 #364) >> Best rule #226 for best value: >> intensional similarity = 5 >> extensional distance = 126 >> proper extension: 0nvrd; 0n5yh; 0mkdm; 0l30v; 0fc_9; 0l2q3; 0mrq3; 0mmpz; 0l3kx; 0m24v; ... >> query: (?x990, ?x2674) <- adjoins(?x990, ?x1302), time_zones(?x1302, ?x2674), second_level_divisions(?x94, ?x1302), contains(?x3670, ?x1302), county(?x10261, ?x990) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mw89 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.877 http://example.org/location/location/time_zones #15956-0j_t1 PRED entity: 0j_t1 PRED relation: crewmember PRED expected values: 09dvgb8 => 61 concepts (56 used for prediction) PRED predicted values (max 10 best out of 35): 02q9kqf (0.14 #30, 0.05 #77, 0.02 #124), 02lp3c (0.14 #31, 0.04 #219, 0.04 #125), 03m49ly (0.14 #35, 0.03 #751, 0.03 #946), 0b79gfg (0.14 #18, 0.02 #734, 0.02 #1897), 027y151 (0.14 #40, 0.02 #1533, 0.02 #1919), 0b6mgp_ (0.14 #22, 0.02 #163, 0.01 #258), 095zvfg (0.10 #85, 0.02 #754, 0.02 #1531), 04wp63 (0.03 #183, 0.02 #278, 0.02 #422), 06rnl9 (0.03 #157, 0.02 #252, 0.02 #301), 0284n42 (0.03 #720, 0.03 #1497, 0.02 #915) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 09ps01; 03m5y9p; >> query: (?x2719, 02q9kqf) <- film(?x3960, ?x2719), genre(?x2719, ?x307), film(?x12367, ?x2719), ?x12367 = 04gc65 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #175 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 07xtqq; 04v8x9; 0hmr4; 0dtfn; 0283_zv; 0bx0l; 0b_5d; 0hfzr; 097zcz; 0pd4f; ... *> query: (?x2719, 09dvgb8) <- list(?x2719, ?x3004), genre(?x2719, ?x307), honored_for(?x7940, ?x2719) *> conf = 0.02 ranks of expected_values: 30 EVAL 0j_t1 crewmember 09dvgb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 61.000 56.000 0.143 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #15955-02b0y3 PRED entity: 02b0y3 PRED relation: position PRED expected values: 0dgrmp => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 3): 0dgrmp (0.78 #34, 0.77 #63, 0.76 #23), 02md_2 (0.59 #38, 0.32 #99, 0.31 #102), 02qvgy (0.53 #43, 0.50 #96) >> Best rule #34 for best value: >> intensional similarity = 23 >> extensional distance = 163 >> proper extension: 02279c; 051n13; 050fh; 01rl_3; 015_z1; 0177gl; 04991x; 046f25; 07245g; 02b0yk; ... >> query: (?x5403, 0dgrmp) <- position(?x5403, ?x12598), position(?x5403, ?x530), position(?x5403, ?x63), position(?x5403, ?x60), team(?x7705, ?x5403), position(?x11748, ?x12598), position(?x4364, ?x12598), team(?x12598, ?x12057), team(?x12598, ?x10443), ?x11748 = 02b0_m, ?x63 = 02sdk9v, ?x4364 = 065zf3p, ?x12057 = 03mg5f, ?x10443 = 03j6_5, ?x60 = 02nzb8, position(?x12991, ?x530), position(?x10725, ?x530), position(?x9270, ?x530), position(?x6705, ?x530), ?x10725 = 03l7tr, ?x12991 = 09hyvp, ?x6705 = 03j70d, ?x9270 = 04h4zx >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02b0y3 position 0dgrmp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.782 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #15954-0gd0c7x PRED entity: 0gd0c7x PRED relation: film_release_region PRED expected values: 0b90_r 059j2 0d05w3 0161c 07twz => 93 concepts (90 used for prediction) PRED predicted values (max 10 best out of 160): 09c7w0 (0.92 #8270, 0.92 #8389, 0.92 #8509), 059j2 (0.90 #618, 0.89 #976, 0.88 #260), 0b90_r (0.86 #1078, 0.85 #1676, 0.83 #1317), 01mjq (0.60 #1341, 0.59 #267, 0.59 #148), 09pmkv (0.49 #1092, 0.47 #496, 0.45 #973), 07twz (0.48 #1019, 0.41 #303, 0.40 #1377), 0hzlz (0.48 #6469, 0.45 #7551, 0.35 #3591), 06npd (0.41 #252, 0.33 #1087, 0.31 #3349), 0161c (0.35 #172, 0.29 #291, 0.27 #649), 02jx1 (0.35 #3591) >> Best rule #8270 for best value: >> intensional similarity = 6 >> extensional distance = 1325 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; 018js4; ... >> query: (?x1999, 09c7w0) <- film_release_region(?x1999, ?x1264), combatants(?x13022, ?x1264), film_release_region(?x634, ?x1264), participating_countries(?x418, ?x1264), country(?x136, ?x1264), ?x634 = 0gx9rvq >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #618 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 39 *> proper extension: 0gtv7pk; 0401sg; 0bh8yn3; 0by1wkq; 0661m4p; 0645k5; 02dpl9; 0db94w; 0dzlbx; 0bc1yhb; ... *> query: (?x1999, 059j2) <- film_release_region(?x1999, ?x2513), film_release_region(?x1999, ?x1264), ?x1264 = 0345h, genre(?x1999, ?x1013), ?x1013 = 06n90, ?x2513 = 05b4w, film(?x72, ?x1999) *> conf = 0.90 ranks of expected_values: 2, 3, 6, 9, 11 EVAL 0gd0c7x film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 90.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gd0c7x film_release_region 0161c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 93.000 90.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gd0c7x film_release_region 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 93.000 90.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gd0c7x film_release_region 059j2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 90.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gd0c7x film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 90.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15953-01xr2s PRED entity: 01xr2s PRED relation: genre PRED expected values: 01htzx => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 80): 05p553 (0.49 #1235, 0.48 #2391, 0.46 #1648), 01z4y (0.37 #1495, 0.37 #1248, 0.33 #756), 0hcr (0.31 #2980, 0.19 #4131, 0.19 #3637), 01t_vv (0.29 #444, 0.24 #608, 0.21 #936), 01jfsb (0.27 #258, 0.20 #340, 0.19 #422), 02n4kr (0.27 #255, 0.17 #8, 0.15 #419), 0c4xc (0.26 #1273, 0.24 #2263, 0.23 #2015), 06n90 (0.23 #1738, 0.22 #2974, 0.18 #4042), 01htzx (0.23 #263, 0.19 #2978, 0.19 #1742), 03k9fj (0.20 #2972, 0.18 #257, 0.17 #1736) >> Best rule #1235 for best value: >> intensional similarity = 4 >> extensional distance = 114 >> proper extension: 05r1_t; 03y317; 0h95b81; 06qxh; 03cf9ly; 01j95; >> query: (?x2042, 05p553) <- country_of_origin(?x2042, ?x94), program(?x4155, ?x2042), ?x94 = 09c7w0, titles(?x2008, ?x2042) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #263 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 07wqr6; *> query: (?x2042, 01htzx) <- genre(?x2042, ?x604), nominated_for(?x4155, ?x2042), actor(?x2042, ?x4606), ?x604 = 0lsxr *> conf = 0.23 ranks of expected_values: 9 EVAL 01xr2s genre 01htzx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 92.000 92.000 0.491 http://example.org/tv/tv_program/genre #15952-01hvjx PRED entity: 01hvjx PRED relation: nominated_for! PRED expected values: 02x1z2s => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 207): 02x1z2s (0.64 #869, 0.52 #1351, 0.50 #1110), 09tqxt (0.64 #800, 0.47 #318, 0.47 #1282), 0gqzz (0.55 #774, 0.43 #1256, 0.41 #1015), 0drtkx (0.36 #921, 0.32 #439, 0.29 #1162), 0gq9h (0.32 #6331, 0.30 #2715, 0.28 #2956), 02qyxs5 (0.32 #353, 0.23 #835, 0.21 #1076), 0p9sw (0.28 #2672, 0.26 #2913, 0.24 #3154), 0gr0m (0.28 #2712, 0.25 #3194, 0.24 #2953), 0k611 (0.28 #2726, 0.25 #2967, 0.25 #75), 019f4v (0.27 #6322, 0.27 #2947, 0.27 #2706) >> Best rule #869 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0g56t9t; 06w99h3; 03mh94; 0cpllql; 02qm_f; 0k2sk; 01c22t; 04hwbq; 07y9w5; 050xxm; ... >> query: (?x2349, 02x1z2s) <- genre(?x2349, ?x2540), film_crew_role(?x2349, ?x468), ?x2540 = 0hcr, nominated_for(?x902, ?x2349) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hvjx nominated_for! 02x1z2s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.636 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15951-08809 PRED entity: 08809 PRED relation: contains! PRED expected values: 04rrx => 106 concepts (69 used for prediction) PRED predicted values (max 10 best out of 161): 04rrx (0.68 #28640, 0.65 #58178, 0.64 #56388), 04_1l0v (0.36 #5821, 0.33 #9401, 0.32 #6716), 02qkt (0.31 #20036, 0.25 #22722, 0.23 #34359), 01n7q (0.24 #77, 0.22 #2763, 0.22 #3658), 06pvr (0.24 #165, 0.18 #2851, 0.17 #3746), 059rby (0.15 #23290, 0.15 #24185, 0.12 #35823), 07ssc (0.15 #44780, 0.15 #48359, 0.13 #13457), 02j9z (0.15 #19717, 0.13 #22403, 0.10 #34040), 0dg3n1 (0.15 #34167, 0.12 #22530, 0.10 #19844), 05fjf (0.12 #373, 0.11 #24539, 0.10 #2164) >> Best rule #28640 for best value: >> intensional similarity = 2 >> extensional distance = 266 >> proper extension: 01xhb_; >> query: (?x11359, ?x1906) <- citytown(?x9249, ?x11359), state_province_region(?x9249, ?x1906) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08809 contains! 04rrx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 69.000 0.677 http://example.org/location/location/contains #15950-01jc6q PRED entity: 01jc6q PRED relation: music PRED expected values: 01c7p_ => 66 concepts (51 used for prediction) PRED predicted values (max 10 best out of 101): 0146pg (0.14 #10, 0.08 #219, 0.08 #429), 02t__l (0.11 #5663, 0.08 #1887, 0.08 #3564), 0g476 (0.11 #5663, 0.08 #1887, 0.08 #3564), 0jf1b (0.11 #5663, 0.08 #1887, 0.08 #3564), 016szr (0.10 #81, 0.03 #3016, 0.02 #1549), 02jxkw (0.10 #142, 0.03 #771, 0.02 #1190), 01pr6q7 (0.08 #271, 0.08 #481, 0.07 #900), 06qn87 (0.08 #1887, 0.08 #3564, 0.07 #2306), 01p7b6b (0.08 #1887, 0.08 #3564, 0.07 #2306), 015wc0 (0.08 #594, 0.06 #2901, 0.05 #384) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01br2w; 02py4c8; 0yyts; 0dgpwnk; 0f4yh; 04lqvly; 0bmssv; 03tps5; 033fqh; 09q23x; ... >> query: (?x197, 0146pg) <- language(?x197, ?x3966), ?x3966 = 03hkp, genre(?x197, ?x53) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jc6q music 01c7p_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 51.000 0.143 http://example.org/film/film/music #15949-01ync PRED entity: 01ync PRED relation: sport PRED expected values: 018jz => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 9): 018jz (0.89 #357, 0.86 #228, 0.83 #133), 02vx4 (0.55 #349, 0.54 #623, 0.51 #677), 0jm_ (0.37 #240, 0.36 #39, 0.36 #221), 018w8 (0.32 #277, 0.28 #296, 0.27 #287), 03tmr (0.20 #65, 0.16 #219, 0.15 #320), 039yzs (0.12 #280, 0.06 #326, 0.04 #411), 0z74 (0.07 #154, 0.06 #190, 0.02 #217), 09xp_ (0.06 #224, 0.04 #353, 0.04 #400), 06f3l (0.03 #191) >> Best rule #357 for best value: >> intensional similarity = 7 >> extensional distance = 118 >> proper extension: 023fxp; >> query: (?x4487, ?x5063) <- team(?x11844, ?x4487), team(?x11844, ?x10939), team(?x11844, ?x6074), colors(?x10939, ?x4557), teams(?x4419, ?x10939), sport(?x6074, ?x5063), teams(?x659, ?x4487) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ync sport 018jz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.892 http://example.org/sports/sports_team/sport #15948-01trhmt PRED entity: 01trhmt PRED relation: type_of_union PRED expected values: 04ztj => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.71 #361, 0.70 #73, 0.70 #345), 01g63y (0.21 #94, 0.21 #106, 0.20 #210), 0jgjn (0.01 #28, 0.01 #36) >> Best rule #361 for best value: >> intensional similarity = 2 >> extensional distance = 1155 >> proper extension: 0f0p0; 03xmy1; 01nrq5; 03bpn6; 01skmp; 01gvyp; 01h4rj; 01g4bk; 039xcr; 045931; ... >> query: (?x2562, 04ztj) <- award_winner(?x567, ?x2562), film(?x2562, ?x3921) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01trhmt type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.714 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15947-06q8hf PRED entity: 06q8hf PRED relation: produced_by! PRED expected values: 047fjjr 0gwjw0c => 112 concepts (98 used for prediction) PRED predicted values (max 10 best out of 441): 0m313 (0.38 #35943, 0.31 #11346, 0.31 #946), 01cmp9 (0.38 #35943, 0.31 #11346, 0.31 #946), 05fgr_ (0.38 #35943, 0.31 #946, 0.30 #32159), 0dgst_d (0.20 #116, 0.12 #2007, 0.09 #2952), 0gwjw0c (0.20 #654, 0.12 #2545, 0.09 #3490), 04jplwp (0.20 #737, 0.12 #2628, 0.09 #3573), 019vhk (0.20 #251, 0.12 #2142, 0.09 #3087), 0b6l1st (0.18 #3513, 0.07 #5405, 0.04 #4460), 0gd0c7x (0.18 #3005, 0.03 #5842, 0.03 #6788), 03tbg6 (0.18 #3712, 0.03 #6549, 0.03 #7495) >> Best rule #35943 for best value: >> intensional similarity = 3 >> extensional distance = 252 >> proper extension: 0bs8d; >> query: (?x7324, ?x144) <- award_winner(?x3105, ?x7324), produced_by(?x2287, ?x7324), nominated_for(?x7324, ?x144) >> conf = 0.38 => this is the best rule for 3 predicted values *> Best rule #654 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 01hmk9; *> query: (?x7324, 0gwjw0c) <- award_nominee(?x6771, ?x7324), ?x6771 = 052hl, nominated_for(?x7324, ?x144) *> conf = 0.20 ranks of expected_values: 5 EVAL 06q8hf produced_by! 0gwjw0c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 112.000 98.000 0.380 http://example.org/film/film/produced_by EVAL 06q8hf produced_by! 047fjjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 98.000 0.380 http://example.org/film/film/produced_by #15946-01ppdy PRED entity: 01ppdy PRED relation: award! PRED expected values: 01q415 => 60 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2783): 013pp3 (0.79 #30440, 0.77 #77786, 0.77 #84553), 07w21 (0.74 #10243, 0.67 #6861, 0.44 #23773), 01dzz7 (0.61 #7218, 0.53 #10600, 0.42 #30895), 048_p (0.56 #8395, 0.53 #11777, 0.39 #32072), 05jm7 (0.56 #7838, 0.47 #11220, 0.30 #34899), 01k56k (0.50 #10052, 0.47 #13434, 0.41 #26964), 09dt7 (0.50 #7077, 0.47 #10459, 0.33 #27370), 0gd_s (0.50 #9430, 0.47 #12812, 0.33 #29723), 014ps4 (0.50 #9040, 0.47 #12422, 0.30 #29333), 018fq (0.47 #11640, 0.44 #8258, 0.32 #31935) >> Best rule #30440 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 0gr4k; 03nqnk3; 0262yt; 02xj3rw; 01f7d; >> query: (?x10548, ?x4795) <- award_winner(?x10548, ?x4795), award(?x10974, ?x10548), type_of_union(?x10974, ?x566), influenced_by(?x10974, ?x6457), ?x6457 = 03_87 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #587 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0c_dx; 02tzwd; *> query: (?x10548, 01q415) <- award_winner(?x10548, ?x4795), award(?x10974, ?x10548), award(?x8389, ?x10548), ?x10974 = 01vdrw, gender(?x8389, ?x231), influenced_by(?x3663, ?x8389), influenced_by(?x8389, ?x118) *> conf = 0.25 ranks of expected_values: 44 EVAL 01ppdy award! 01q415 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 60.000 26.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15945-0hzc9wc PRED entity: 0hzc9wc PRED relation: administrative_area_type! PRED expected values: 03_r3 05v8c 047yc 0h7x 01p1v 0163v 05sb1 03rj0 0d05w3 04w8f 019pcs 06vbd 03spz 07dzf 04g5k 03548 01nln 0jhd 04vs9 05rznz => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 0hzc9wc administrative_area_type! 05rznz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 04vs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 0jhd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 01nln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 03548 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 04g5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 07dzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 03spz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 06vbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 019pcs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 04w8f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 03rj0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 05sb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 0163v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 01p1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 047yc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type EVAL 0hzc9wc administrative_area_type! 03_r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #15944-03v1w7 PRED entity: 03v1w7 PRED relation: executive_produced_by! PRED expected values: 04ydr95 => 117 concepts (105 used for prediction) PRED predicted values (max 10 best out of 210): 0yyts (0.10 #13824, 0.05 #662, 0.04 #16484), 04jpg2p (0.10 #13824, 0.04 #16484, 0.04 #19674), 0jyb4 (0.10 #13824, 0.04 #16484, 0.04 #19674), 0qmjd (0.10 #13824, 0.04 #16484, 0.04 #19674), 047wh1 (0.10 #13824, 0.04 #16484, 0.04 #19674), 027pfg (0.10 #13824, 0.04 #16484, 0.04 #19674), 01y9r2 (0.10 #13824, 0.04 #16484, 0.04 #19674), 05szq8z (0.10 #13824, 0.04 #16484, 0.04 #19674), 0h1v19 (0.10 #680, 0.05 #149, 0.04 #1743), 0194zl (0.10 #811, 0.03 #2405, 0.02 #11976) >> Best rule #13824 for best value: >> intensional similarity = 2 >> extensional distance = 211 >> proper extension: 02qggqc; >> query: (?x6369, ?x2370) <- executive_produced_by(?x4404, ?x6369), nominated_for(?x6369, ?x2370) >> conf = 0.10 => this is the best rule for 8 predicted values No rule for expected values ranks of expected_values: EVAL 03v1w7 executive_produced_by! 04ydr95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 105.000 0.102 http://example.org/film/film/executive_produced_by #15943-04hgpt PRED entity: 04hgpt PRED relation: school! PRED expected values: 06x76 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 91): 0jmj7 (0.65 #2121, 0.64 #2212, 0.60 #301), 05m_8 (0.18 #1277, 0.16 #1641, 0.14 #913), 07l8x (0.17 #337, 0.14 #1338, 0.10 #519), 051vz (0.16 #1297, 0.13 #296, 0.13 #478), 01yhm (0.13 #657, 0.13 #293, 0.13 #475), 07l4z (0.13 #341, 0.13 #1342, 0.13 #523), 07147 (0.13 #338, 0.12 #1339, 0.10 #1703), 0jmm4 (0.13 #708, 0.11 #890, 0.08 #1345), 0jmk7 (0.13 #361, 0.10 #543, 0.09 #1362), 03wnh (0.13 #324, 0.10 #506, 0.09 #688) >> Best rule #2121 for best value: >> intensional similarity = 3 >> extensional distance = 137 >> proper extension: 02jyr8; 02zcz3; 016sd3; >> query: (?x4750, 0jmj7) <- organization(?x346, ?x4750), school(?x2574, ?x4750), currency(?x4750, ?x170) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #1355 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 74 *> proper extension: 0fht9f; 0frm7n; *> query: (?x4750, 06x76) <- school(?x2574, ?x4750), position(?x2574, ?x180) *> conf = 0.04 ranks of expected_values: 75 EVAL 04hgpt school! 06x76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 94.000 94.000 0.647 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #15942-0c3xpwy PRED entity: 0c3xpwy PRED relation: country_of_origin PRED expected values: 09c7w0 => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.88 #67, 0.85 #134, 0.84 #180), 02jx1 (0.47 #258, 0.01 #168, 0.01 #269), 03_3d (0.23 #114, 0.11 #160, 0.11 #193), 07ssc (0.10 #313, 0.10 #291, 0.10 #267), 0d060g (0.10 #59, 0.07 #115, 0.07 #15), 030qb3t (0.01 #78), 05v8c (0.01 #167, 0.01 #121, 0.01 #303), 03rt9 (0.01 #108, 0.01 #119, 0.01 #130), 03rjj (0.01 #102, 0.01 #113, 0.01 #124) >> Best rule #67 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 063zky; >> query: (?x5663, 09c7w0) <- program(?x11526, ?x5663), executive_produced_by(?x324, ?x11526), location(?x11526, ?x1523), written_by(?x8370, ?x11526) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c3xpwy country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.875 http://example.org/tv/tv_program/country_of_origin #15941-0jrv_ PRED entity: 0jrv_ PRED relation: parent_genre PRED expected values: 01dqhq => 69 concepts (44 used for prediction) PRED predicted values (max 10 best out of 210): 05r6t (0.87 #2964, 0.50 #1509, 0.50 #539), 06by7 (0.45 #2276, 0.45 #3417, 0.45 #3580), 0dl5d (0.33 #15, 0.25 #1633, 0.20 #825), 01243b (0.33 #28, 0.23 #2938, 0.20 #838), 016clz (0.33 #4, 0.20 #814, 0.18 #4379), 0p9xd (0.33 #98, 0.20 #908, 0.13 #5354), 0xhtw (0.29 #1144, 0.27 #2273, 0.25 #1792), 0jrv_ (0.25 #591, 0.22 #2045, 0.20 #2205), 01_bkd (0.25 #522, 0.22 #1976, 0.20 #2136), 02yv6b (0.25 #389, 0.20 #875, 0.20 #712) >> Best rule #2964 for best value: >> intensional similarity = 6 >> extensional distance = 45 >> proper extension: 028cl7; 088vmr; >> query: (?x10930, 05r6t) <- parent_genre(?x10930, ?x7808), parent_genre(?x7808, ?x5934), artists(?x7808, ?x11627), artists(?x7808, ?x1004), ?x1004 = 01vv7sc, ?x11627 = 03j_hq >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #374 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 03lty; 07bbw; *> query: (?x10930, 01dqhq) <- artists(?x10930, ?x11704), artists(?x10930, ?x7125), artists(?x10930, ?x3024), artists(?x10930, ?x646), parent_genre(?x5580, ?x10930), ?x3024 = 0gkg6, ?x11704 = 0560w, ?x646 = 04rcr, ?x7125 = 01jcxwp, parent_genre(?x10930, ?x2249) *> conf = 0.25 ranks of expected_values: 13 EVAL 0jrv_ parent_genre 01dqhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 69.000 44.000 0.872 http://example.org/music/genre/parent_genre #15940-01g23m PRED entity: 01g23m PRED relation: award_nominee PRED expected values: 02qgqt => 128 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1155): 05dbf (0.82 #9342, 0.81 #42040, 0.81 #147124), 01qq_lp (0.82 #9342, 0.81 #42040, 0.81 #147124), 01cyjx (0.82 #9342, 0.81 #42040, 0.81 #147124), 0151w_ (0.26 #168142, 0.24 #79405, 0.05 #2539), 0h0wc (0.26 #168142, 0.24 #79405, 0.04 #551), 03f1zdw (0.26 #168142, 0.24 #79405, 0.04 #248), 01kb2j (0.26 #168142, 0.24 #79405, 0.04 #1204), 06mt91 (0.26 #168142, 0.24 #79405, 0.03 #17899), 0bq2g (0.26 #168142, 0.24 #79405, 0.03 #14807), 01g23m (0.26 #168142, 0.24 #79405, 0.03 #31271) >> Best rule #9342 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 027dtv3; 05k2s_; 0343h; 02g87m; 03jqw5; 07swvb; 0315q3; 03y82t6; 0hqcy; 026fd; ... >> query: (?x4005, ?x396) <- award_winner(?x748, ?x4005), award_nominee(?x396, ?x4005), participant(?x8206, ?x4005) >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #30379 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 215 *> proper extension: 01sl1q; 04bdxl; 06dv3; 014zcr; 01qscs; 01q_ph; 09fb5; 01dw4q; 02lfcm; 03w1v2; ... *> query: (?x4005, 02qgqt) <- award_winner(?x748, ?x4005), participant(?x6515, ?x4005), film(?x4005, ?x6005) *> conf = 0.04 ranks of expected_values: 105 EVAL 01g23m award_nominee 02qgqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 128.000 72.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15939-036hv PRED entity: 036hv PRED relation: major_field_of_study! PRED expected values: 02301 025v3k 01jsk6 => 55 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1306): 07szy (0.78 #2854, 0.77 #5672, 0.75 #4545), 07tds (0.67 #2977, 0.64 #4105, 0.60 #3541), 01w5m (0.67 #1803, 0.62 #2366, 0.62 #5747), 08815 (0.67 #2819, 0.60 #3383, 0.50 #4510), 07t90 (0.67 #1849, 0.55 #4103, 0.50 #4666), 02bqy (0.64 #4139, 0.50 #1885, 0.50 #1321), 03v6t (0.62 #5670, 0.58 #4543, 0.38 #2289), 04rwx (0.58 #4542, 0.55 #3979, 0.54 #5669), 025v3k (0.58 #4637, 0.54 #5764, 0.50 #1820), 01j_cy (0.58 #4544, 0.54 #5671, 0.50 #1163) >> Best rule #2854 for best value: >> intensional similarity = 10 >> extensional distance = 7 >> proper extension: 02ky346; 03g3w; 0fdys; 01r4k; >> query: (?x947, 07szy) <- major_field_of_study(?x11963, ?x947), major_field_of_study(?x5085, ?x947), major_field_of_study(?x947, ?x2606), ?x11963 = 01bzs9, major_field_of_study(?x734, ?x947), organization(?x346, ?x5085), ?x346 = 060c4, currency(?x5085, ?x2244), institution(?x2636, ?x5085), contains(?x279, ?x5085) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4637 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 10 *> proper extension: 01mkq; 062z7; *> query: (?x947, 025v3k) <- major_field_of_study(?x11963, ?x947), major_field_of_study(?x8363, ?x947), major_field_of_study(?x6973, ?x947), major_field_of_study(?x466, ?x947), major_field_of_study(?x947, ?x2606), organization(?x5510, ?x11963), student(?x11963, ?x361), colors(?x11963, ?x1101), state_province_region(?x8363, ?x1227), major_field_of_study(?x734, ?x947), ?x6973 = 05x_5, school(?x260, ?x466) *> conf = 0.58 ranks of expected_values: 9, 72, 106 EVAL 036hv major_field_of_study! 01jsk6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 55.000 21.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 036hv major_field_of_study! 025v3k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 55.000 21.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 036hv major_field_of_study! 02301 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 55.000 21.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #15938-0lbj1 PRED entity: 0lbj1 PRED relation: role PRED expected values: 01vj9c => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 114): 05r5c (0.48 #841, 0.47 #632, 0.47 #528), 042v_gx (0.40 #529, 0.31 #633, 0.29 #946), 01vdm0 (0.35 #552, 0.32 #656, 0.31 #865), 06ncr (0.30 #1459, 0.26 #1877, 0.24 #4176), 05148p4 (0.30 #1459, 0.21 #543, 0.15 #647), 02fsn (0.30 #1459, 0.06 #1002, 0.05 #689), 02sgy (0.29 #110, 0.28 #526, 0.26 #630), 03bx0bm (0.27 #833, 0.03 #4177), 04q7r (0.26 #1877, 0.24 #4176, 0.24 #2087), 048j4l (0.26 #1877, 0.24 #2087, 0.24 #2192) >> Best rule #841 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 09g0h; >> query: (?x248, 05r5c) <- religion(?x248, ?x7422), role(?x248, ?x227), nationality(?x248, ?x512) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #743 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 02qfhb; *> query: (?x248, 01vj9c) <- award_nominee(?x248, ?x3403), profession(?x248, ?x131), performance_role(?x248, ?x1466) *> conf = 0.22 ranks of expected_values: 12 EVAL 0lbj1 role 01vj9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 109.000 109.000 0.484 http://example.org/music/artist/track_contributions./music/track_contribution/role #15937-0jrtv PRED entity: 0jrtv PRED relation: currency PRED expected values: 09nqf => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.87 #59, 0.87 #58, 0.86 #66) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 245 >> proper extension: 0p07l; >> query: (?x7460, ?x170) <- adjoins(?x7460, ?x13582), currency(?x13582, ?x170), adjoins(?x13582, ?x7369), second_level_divisions(?x94, ?x7460) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jrtv currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.874 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #15936-03cn92 PRED entity: 03cn92 PRED relation: nationality PRED expected values: 09c7w0 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.86 #101, 0.81 #5006, 0.78 #401), 02jx1 (0.12 #1835, 0.10 #6040, 0.10 #733), 07ssc (0.09 #1817, 0.08 #6022, 0.08 #6824), 03rk0 (0.08 #4751, 0.08 #1948, 0.07 #3649), 0d060g (0.07 #107, 0.05 #707, 0.05 #307), 03rt9 (0.06 #213, 0.05 #313, 0.03 #5406), 06q1r (0.06 #277, 0.05 #377, 0.02 #6285), 05bcl (0.06 #260, 0.05 #360), 0345h (0.03 #1232, 0.02 #2033, 0.02 #1733), 0f8l9c (0.03 #5406, 0.03 #722, 0.02 #4025) >> Best rule #101 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01vw87c; >> query: (?x5408, 09c7w0) <- participant(?x9526, ?x5408), film(?x5408, ?x1311), film(?x9526, ?x518), ?x518 = 016z5x >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cn92 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.857 http://example.org/people/person/nationality #15935-0d060g PRED entity: 0d060g PRED relation: administrative_parent PRED expected values: 02j71 => 182 concepts (97 used for prediction) PRED predicted values (max 10 best out of 52): 02j71 (0.79 #13023, 0.78 #12337, 0.77 #9459), 09c7w0 (0.52 #7942, 0.46 #8764, 0.36 #8626), 0d060g (0.33 #6, 0.03 #12879, 0.02 #4380), 07ssc (0.10 #828, 0.09 #419, 0.06 #6173), 049nq (0.10 #368, 0.06 #777, 0.04 #1187), 0345h (0.08 #12212, 0.05 #4124, 0.04 #6188), 0f8l9c (0.06 #6180, 0.05 #4116, 0.02 #6590), 059g4 (0.05 #6709, 0.01 #12463, 0.01 #5885), 07c5l (0.05 #6709, 0.01 #12463, 0.01 #5885), 03rjj (0.05 #12190, 0.04 #5339, 0.04 #6166) >> Best rule #13023 for best value: >> intensional similarity = 2 >> extensional distance = 110 >> proper extension: 02khs; 07f5x; 0jt3tjf; >> query: (?x279, 02j71) <- adjoins(?x279, ?x94), olympics(?x279, ?x358) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d060g administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 97.000 0.795 http://example.org/base/aareas/schema/administrative_area/administrative_parent #15934-02hgm4 PRED entity: 02hgm4 PRED relation: ceremony PRED expected values: 01mhwk => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 120): 01mhwk (0.79 #776, 0.65 #156, 0.60 #1148), 0bc773 (0.36 #3102, 0.27 #5341, 0.26 #5342), 05c1t6z (0.19 #2119, 0.17 #2243, 0.12 #3486), 0gvstc3 (0.17 #2134, 0.16 #2258, 0.10 #3501), 02q690_ (0.17 #2287, 0.17 #2163, 0.11 #3406), 0n8_m93 (0.16 #1839, 0.14 #2335, 0.14 #2211), 0bzm81 (0.16 #1752, 0.14 #2248, 0.14 #2124), 03nnm4t (0.15 #2296, 0.15 #2172, 0.11 #3415), 0gx_st (0.15 #2137, 0.15 #2261, 0.10 #3131), 02yvhx (0.15 #1802, 0.13 #2298, 0.13 #2174) >> Best rule #776 for best value: >> intensional similarity = 5 >> extensional distance = 76 >> proper extension: 03q_g6; 02flq1; 02x4wb; 01c9d1; >> query: (?x2561, 01mhwk) <- award_winner(?x2561, ?x1832), artist(?x7089, ?x1832), ceremony(?x2561, ?x9431), award(?x1238, ?x2561), ?x9431 = 02cg41 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hgm4 ceremony 01mhwk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.795 http://example.org/award/award_category/winners./award/award_honor/ceremony #15933-04h1rz PRED entity: 04h1rz PRED relation: district_represented PRED expected values: 03gh4 => 38 concepts (34 used for prediction) PRED predicted values (max 10 best out of 93): 03gh4 (0.82 #1581, 0.81 #1650, 0.80 #131), 01x73 (0.80 #1388, 0.80 #1333, 0.80 #131), 03v1s (0.80 #1319, 0.80 #131, 0.79 #1963), 05tbn (0.80 #1349, 0.80 #131, 0.74 #2218), 05fjf (0.80 #1365, 0.80 #131, 0.72 #2305), 050l8 (0.80 #1338, 0.80 #131, 0.71 #1391), 04rrx (0.80 #1337, 0.80 #131, 0.71 #1391), 03s0w (0.80 #1325, 0.80 #131, 0.71 #1391), 07srw (0.80 #1339, 0.80 #131, 0.71 #1391), 05fjy (0.80 #1362, 0.80 #131, 0.71 #1391) >> Best rule #1581 for best value: >> intensional similarity = 37 >> extensional distance = 9 >> proper extension: 02bqn1; >> query: (?x6743, 03gh4) <- legislative_sessions(?x6743, ?x3766), legislative_sessions(?x6743, ?x1137), legislative_sessions(?x6743, ?x653), legislative_sessions(?x6743, ?x606), legislative_sessions(?x1028, ?x6743), legislative_sessions(?x9569, ?x6743), legislative_sessions(?x8607, ?x6743), legislative_sessions(?x6742, ?x6743), legislative_sessions(?x652, ?x6743), legislative_sessions(?x1137, ?x605), district_represented(?x1137, ?x5575), district_represented(?x1137, ?x2020), district_represented(?x1137, ?x1227), district_represented(?x1137, ?x1138), district_represented(?x1137, ?x1025), district_represented(?x1137, ?x448), ?x2020 = 05k7sb, ?x1227 = 01n7q, ?x653 = 070m6c, ?x5575 = 05fjy, legislative_sessions(?x2860, ?x1137), ?x1025 = 04ych, ?x8607 = 0226cw, student(?x13141, ?x6742), student(?x3821, ?x6742), jurisdiction_of_office(?x6742, ?x94), religion(?x6742, ?x2769), ?x1138 = 059_c, ?x605 = 077g7n, ?x448 = 03v1s, ?x606 = 03ww_x, currency(?x13141, ?x170), ?x3766 = 02gkzs, organization(?x346, ?x3821), ?x9569 = 0194xc, institution(?x620, ?x3821), ?x652 = 021sv1 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04h1rz district_represented 03gh4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 34.000 0.818 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #15932-0gtzp PRED entity: 0gtzp PRED relation: form_of_government PRED expected values: 06cx9 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 6): 01q20 (0.50 #34, 0.40 #28, 0.40 #22), 01fpfn (0.50 #51, 0.33 #45, 0.31 #231), 018wl5 (0.33 #140, 0.33 #32, 0.33 #8), 01d9r3 (0.26 #197, 0.23 #233, 0.20 #485), 06cx9 (0.23 #529, 0.21 #253, 0.21 #481), 026wp (0.14 #258, 0.14 #180, 0.14 #90) >> Best rule #34 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 02jx1; >> query: (?x14334, 01q20) <- capital(?x14334, ?x4627), citytown(?x4619, ?x4627), contains(?x4627, ?x2593), featured_film_locations(?x1685, ?x4627), location(?x11985, ?x4627), location(?x11354, ?x4627), month(?x4627, ?x1459), ?x11985 = 01vh3r, spouse(?x317, ?x11354) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #529 for first EXPECTED value: *> intensional similarity = 1 *> extensional distance = 134 *> proper extension: 09472; *> query: (?x14334, 06cx9) <- capital(?x14334, ?x4627) *> conf = 0.23 ranks of expected_values: 5 EVAL 0gtzp form_of_government 06cx9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.500 http://example.org/location/country/form_of_government #15931-0b_734 PRED entity: 0b_734 PRED relation: team PRED expected values: 02ptzz0 03d555l 027yf83 02qk2d5 02pzy52 => 27 concepts (27 used for prediction) PRED predicted values (max 10 best out of 335): 02qk2d5 (0.80 #188, 0.77 #237, 0.77 #230), 02q4ntp (0.77 #286, 0.77 #247, 0.77 #232), 027yf83 (0.77 #226, 0.73 #198, 0.71 #82), 026xxv_ (0.75 #270, 0.75 #216, 0.73 #283), 026w398 (0.73 #205, 0.71 #122, 0.71 #82), 026dqjm (0.71 #82, 0.70 #193, 0.67 #209), 02pzy52 (0.71 #82, 0.69 #234, 0.69 #275), 02ptzz0 (0.71 #82, 0.67 #209, 0.66 #236), 02pjzvh (0.71 #82, 0.67 #209, 0.66 #236), 02plv57 (0.71 #82, 0.67 #209, 0.66 #236) >> Best rule #188 for best value: >> intensional similarity = 34 >> extensional distance = 8 >> proper extension: 0b_6qj; >> query: (?x13209, 02qk2d5) <- team(?x13209, ?x9833), team(?x13209, ?x9147), team(?x13209, ?x8528), team(?x13209, ?x6003), team(?x13209, ?x5032), team(?x13045, ?x5032), team(?x12451, ?x5032), team(?x7378, ?x5032), team(?x7042, ?x5032), team(?x6802, ?x5032), team(?x6583, ?x5032), team(?x6002, ?x5032), team(?x2302, ?x5032), sport(?x5032, ?x12913), ?x6802 = 0br1x_, ?x6003 = 02py8_w, ?x6002 = 0cc8q3, ?x6583 = 0b_75k, ?x12451 = 0b_6xf, colors(?x5032, ?x663), ?x12913 = 039yzs, ?x9147 = 0263cyj, ?x13045 = 0bqthy, ?x7042 = 0b_72t, ?x2302 = 0b_77q, ?x9833 = 03y9p40, instance_of_recurring_event(?x13209, ?x10863), team(?x12798, ?x8528), team(?x10673, ?x8528), team(?x4368, ?x8528), locations(?x7378, ?x1705), ?x12798 = 0b_770, ?x10673 = 0b_6mr, ?x4368 = 0b_6x2 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 7, 8, 11 EVAL 0b_734 team 02pzy52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 27.000 27.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_734 team 02qk2d5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 27.000 27.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_734 team 027yf83 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 27.000 27.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_734 team 03d555l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 27.000 27.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_734 team 02ptzz0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 27.000 27.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #15930-01gwk3 PRED entity: 01gwk3 PRED relation: film_crew_role PRED expected values: 014kbl => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 24): 015h31 (0.28 #214, 0.24 #84, 0.21 #318), 0215hd (0.28 #89, 0.24 #323, 0.22 #37), 02_n3z (0.28 #79, 0.22 #27, 0.14 #235), 05smlt (0.25 #12, 0.16 #168, 0.10 #90), 020xn5 (0.24 #1850, 0.14 #57, 0.10 #83), 04pyp5 (0.24 #1850, 0.09 #139, 0.09 #953), 02vs3x5 (0.24 #1850, 0.07 #67, 0.06 #1172), 033smt (0.21 #96, 0.14 #70, 0.14 #226), 0ckd1 (0.14 #81, 0.13 #29, 0.12 #3), 094hwz (0.13 #320, 0.12 #8, 0.12 #216) >> Best rule #214 for best value: >> intensional similarity = 6 >> extensional distance = 41 >> proper extension: 031t2d; 0gfsq9; 02mmwk; >> query: (?x6429, 015h31) <- film_crew_role(?x6429, ?x2154), crewmember(?x6429, ?x666), ?x2154 = 01vx2h, produced_by(?x6429, ?x519), featured_film_locations(?x6429, ?x1523), genre(?x6429, ?x225) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #101 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 085bd1; 017kz7; 02gqm3; *> query: (?x6429, 014kbl) <- story_by(?x6429, ?x800), film(?x2387, ?x6429), country(?x6429, ?x1264), genre(?x6429, ?x225), ?x1264 = 0345h *> conf = 0.07 ranks of expected_values: 13 EVAL 01gwk3 film_crew_role 014kbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 138.000 138.000 0.279 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15929-04zx3q1 PRED entity: 04zx3q1 PRED relation: institution PRED expected values: 02cttt 04rwx 05d9y_ 01qd_r 0bsnm 013nky 01l8t8 => 24 concepts (21 used for prediction) PRED predicted values (max 10 best out of 596): 078bz (0.80 #6179, 0.73 #7291, 0.73 #6734), 0pspl (0.75 #7881, 0.75 #5654, 0.69 #8998), 01s0_f (0.75 #7834, 0.73 #6722, 0.67 #8393), 0g2jl (0.73 #7628, 0.73 #7071, 0.70 #6516), 01nnsv (0.73 #7404, 0.70 #6292, 0.67 #7959), 01jssp (0.73 #7228, 0.70 #6116, 0.67 #7783), 05krk (0.70 #6118, 0.67 #5004, 0.65 #2223), 01jsk6 (0.70 #6527, 0.67 #5413, 0.65 #2223), 04rwx (0.70 #6149, 0.67 #5035, 0.65 #2223), 015cz0 (0.70 #6274, 0.67 #5160, 0.65 #2223) >> Best rule #6179 for best value: >> intensional similarity = 25 >> extensional distance = 8 >> proper extension: 013zdg; >> query: (?x734, 078bz) <- institution(?x734, ?x9525), institution(?x734, ?x5068), institution(?x734, ?x4955), institution(?x734, ?x4889), institution(?x734, ?x4410), institution(?x734, ?x4341), institution(?x734, ?x1681), institution(?x734, ?x741), student(?x734, ?x920), ?x741 = 01w3v, school(?x12956, ?x5068), major_field_of_study(?x9525, ?x4100), major_field_of_study(?x734, ?x10417), ?x12956 = 051wf, currency(?x5068, ?x170), institution(?x620, ?x4889), ?x4955 = 09f2j, school_type(?x4341, ?x3092), student(?x4889, ?x7731), major_field_of_study(?x4341, ?x6756), ?x1681 = 07szy, ?x4410 = 017j69, ?x4100 = 01lj9, ?x620 = 07s6fsf, major_field_of_study(?x546, ?x10417) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #6149 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 8 *> proper extension: 013zdg; *> query: (?x734, 04rwx) <- institution(?x734, ?x9525), institution(?x734, ?x5068), institution(?x734, ?x4955), institution(?x734, ?x4889), institution(?x734, ?x4410), institution(?x734, ?x4341), institution(?x734, ?x1681), institution(?x734, ?x741), student(?x734, ?x920), ?x741 = 01w3v, school(?x12956, ?x5068), major_field_of_study(?x9525, ?x4100), major_field_of_study(?x734, ?x10417), ?x12956 = 051wf, currency(?x5068, ?x170), institution(?x620, ?x4889), ?x4955 = 09f2j, school_type(?x4341, ?x3092), student(?x4889, ?x7731), major_field_of_study(?x4341, ?x6756), ?x1681 = 07szy, ?x4410 = 017j69, ?x4100 = 01lj9, ?x620 = 07s6fsf, major_field_of_study(?x546, ?x10417) *> conf = 0.70 ranks of expected_values: 9, 56, 65, 177, 263, 289, 591 EVAL 04zx3q1 institution 01l8t8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 24.000 21.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 04zx3q1 institution 013nky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 24.000 21.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 04zx3q1 institution 0bsnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 24.000 21.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 04zx3q1 institution 01qd_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 24.000 21.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 04zx3q1 institution 05d9y_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 24.000 21.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 04zx3q1 institution 04rwx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 24.000 21.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 04zx3q1 institution 02cttt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 24.000 21.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15928-07cz2 PRED entity: 07cz2 PRED relation: honored_for! PRED expected values: 02yw5r => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 119): 04n2r9h (0.09 #280, 0.07 #402, 0.06 #890), 0gvstc3 (0.08 #881, 0.04 #2711, 0.02 #4177), 05c1t6z (0.08 #865, 0.04 #2695, 0.03 #4039), 03nnm4t (0.08 #917, 0.03 #2747, 0.02 #3480), 02pgky2 (0.07 #320, 0.05 #442, 0.04 #76), 02q690_ (0.06 #908, 0.04 #2738, 0.03 #3471), 0hr6lkl (0.05 #378, 0.05 #256, 0.04 #12), 03gwpw2 (0.05 #249, 0.04 #371, 0.04 #5), 03gyp30 (0.05 #346, 0.04 #468, 0.04 #102), 09k5jh7 (0.05 #315, 0.04 #437, 0.04 #193) >> Best rule #280 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 05r3qc; >> query: (?x2770, 04n2r9h) <- nominated_for(?x6860, ?x2770), ?x6860 = 018wdw, titles(?x8581, ?x2770) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #3540 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 686 *> proper extension: 02nf2c; 03j63k; 0m123; 02_1ky; 019g8j; 0147w8; 0300ml; *> query: (?x2770, ?x78) <- award(?x2770, ?x1703), nominated_for(?x1703, ?x5711), award_winner(?x5711, ?x457), ceremony(?x1703, ?x78) *> conf = 0.02 ranks of expected_values: 83 EVAL 07cz2 honored_for! 02yw5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 100.000 100.000 0.086 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15927-01bcq PRED entity: 01bcq PRED relation: film PRED expected values: 03tps5 => 97 concepts (62 used for prediction) PRED predicted values (max 10 best out of 435): 01q_y0 (0.65 #10725, 0.59 #69714, 0.55 #28602), 01633c (0.12 #3112, 0.08 #1325, 0.02 #17415), 03q0r1 (0.11 #5997, 0.10 #7785, 0.08 #9573), 0p9lw (0.11 #5507, 0.10 #7295, 0.08 #9083), 0bpm4yw (0.11 #6084, 0.10 #7872, 0.08 #9660), 099bhp (0.11 #15919, 0.07 #19495, 0.05 #21283), 05sw5b (0.10 #13328, 0.09 #15116, 0.07 #18692), 047csmy (0.10 #13426, 0.09 #15214, 0.07 #18790), 03x7hd (0.08 #9497, 0.08 #11286, 0.06 #5921), 0dc7hc (0.08 #10525, 0.08 #12314, 0.04 #15890) >> Best rule #10725 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0h5g_; 0169dl; 01j5ws; 04fzk; 07cn2c; 01wy5m; 08wjf4; 01nms7; 01bh6y; 015p37; ... >> query: (?x4919, ?x2293) <- nationality(?x4919, ?x94), nominated_for(?x4919, ?x2293), actor(?x596, ?x4919) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01bcq film 03tps5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 62.000 0.649 http://example.org/film/actor/film./film/performance/film #15926-06btq PRED entity: 06btq PRED relation: district_represented! PRED expected values: 02bn_p 01gtcc 070mff => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 25): 070mff (0.85 #420, 0.83 #195, 0.78 #45), 01gtcc (0.74 #37, 0.43 #976, 0.42 #187), 02bn_p (0.69 #179, 0.67 #404, 0.65 #29), 02bqm0 (0.57 #416, 0.54 #191, 0.54 #341), 02bqmq (0.52 #35, 0.52 #185, 0.52 #335), 02bqn1 (0.48 #31, 0.44 #406, 0.44 #331), 02cg7g (0.43 #40, 0.43 #976, 0.43 #415), 02gkzs (0.43 #39, 0.43 #976, 0.41 #414), 02glc4 (0.43 #976, 0.35 #42, 0.28 #417), 03rtmz (0.43 #976, 0.30 #34, 0.30 #409) >> Best rule #420 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 0g0syc; >> query: (?x2713, 070mff) <- district_represented(?x4437, ?x2713), district_represented(?x4437, ?x177), ?x177 = 05kkh >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 06btq district_represented! 070mff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 173.000 0.852 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 06btq district_represented! 01gtcc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 173.000 0.852 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 06btq district_represented! 02bn_p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 173.000 0.852 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #15925-0bzk8w PRED entity: 0bzk8w PRED relation: ceremony! PRED expected values: 0p9sw 0gq9h => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 350): 0p9sw (0.95 #3417, 0.94 #3174, 0.89 #5366), 0gq9h (0.89 #1994, 0.88 #1751, 0.88 #2967), 0gs96 (0.87 #4939, 0.87 #3721, 0.85 #3964), 0gr42 (0.83 #3233, 0.81 #3476, 0.78 #5425), 018wdw (0.75 #9506, 0.75 #7548, 0.68 #3816), 0gqxm (0.75 #9506, 0.75 #7548, 0.47 #1334), 02x201b (0.75 #9506, 0.75 #7548, 0.27 #7059), 0gqzz (0.75 #9506, 0.75 #7548, 0.27 #1011), 0czp_ (0.75 #9506, 0.75 #7548, 0.25 #194), 054ky1 (0.29 #4621, 0.27 #7059, 0.25 #67) >> Best rule #3417 for best value: >> intensional similarity = 18 >> extensional distance = 35 >> proper extension: 0bzknt; >> query: (?x602, 0p9sw) <- ceremony(?x5409, ?x602), ceremony(?x1972, ?x602), ceremony(?x1703, ?x602), ceremony(?x1245, ?x602), ceremony(?x77, ?x602), award_winner(?x602, ?x2530), ?x1703 = 0k611, ?x1245 = 0gqwc, ?x5409 = 0gr07, ceremony(?x77, ?x78), award(?x1872, ?x77), award(?x303, ?x77), nominated_for(?x77, ?x1786), ?x1972 = 0gqyl, ?x1786 = 091z_p, ?x78 = 073hkh, award_winner(?x77, ?x2086), honored_for(?x602, ?x1744) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0bzk8w ceremony! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.946 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzk8w ceremony! 0p9sw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.946 http://example.org/award/award_category/winners./award/award_honor/ceremony #15924-018gkb PRED entity: 018gkb PRED relation: place_of_birth PRED expected values: 0tgcy => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 111): 04lh6 (0.17 #1037, 0.11 #1741, 0.01 #13713), 0c8tk (0.17 #859, 0.11 #1563, 0.01 #13535), 013d7t (0.17 #890, 0.11 #1594), 030qb3t (0.11 #1462, 0.04 #40898, 0.04 #55687), 01llj3 (0.11 #2038), 02_286 (0.07 #53538, 0.07 #55652, 0.06 #64104), 0nbrp (0.05 #2644, 0.02 #3348, 0.02 #4053), 0rng (0.05 #2425, 0.02 #3834, 0.02 #4538), 0cr3d (0.04 #14178, 0.03 #53613, 0.03 #55727), 01_d4 (0.03 #55699, 0.03 #53585, 0.03 #38798) >> Best rule #1037 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 01gg59; 03j24kf; 01vrnsk; 016j2t; >> query: (?x11161, 04lh6) <- instrumentalists(?x2888, ?x11161), instrumentalists(?x1166, ?x11161), ?x1166 = 05148p4, ?x2888 = 02fsn, award(?x11161, ?x2634), gender(?x11161, ?x231) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018gkb place_of_birth 0tgcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 103.000 0.167 http://example.org/people/person/place_of_birth #15923-02ctc6 PRED entity: 02ctc6 PRED relation: film! PRED expected values: 016tw3 => 54 concepts (37 used for prediction) PRED predicted values (max 10 best out of 51): 05qd_ (0.55 #457, 0.15 #84, 0.12 #309), 056ws9 (0.42 #1050), 03xq0f (0.39 #453, 0.13 #305, 0.09 #601), 01795t (0.29 #318, 0.09 #18, 0.06 #168), 086k8 (0.23 #2, 0.16 #77, 0.16 #152), 054g1r (0.17 #334, 0.06 #1009, 0.06 #482), 016tw3 (0.16 #986, 0.13 #533, 0.12 #2343), 016tt2 (0.15 #79, 0.14 #4, 0.13 #229), 02cx72 (0.13 #822, 0.12 #900, 0.07 #821), 0150t6 (0.13 #822, 0.12 #900, 0.07 #821) >> Best rule #457 for best value: >> intensional similarity = 3 >> extensional distance = 348 >> proper extension: 0522wp; >> query: (?x3211, 05qd_) <- film(?x4564, ?x3211), film(?x4564, ?x1173), ?x1173 = 0872p_c >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #986 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 816 *> proper extension: 0gtsx8c; 07kb7vh; *> query: (?x3211, 016tw3) <- film_crew_role(?x3211, ?x468), film(?x338, ?x3211), production_companies(?x3211, ?x4564) *> conf = 0.16 ranks of expected_values: 7 EVAL 02ctc6 film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 54.000 37.000 0.551 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15922-05p8bf9 PRED entity: 05p8bf9 PRED relation: position PRED expected values: 02_j1w => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 5): 02nzb8 (0.91 #30, 0.87 #35, 0.86 #56), 02_j1w (0.83 #69, 0.81 #42, 0.78 #47), 03f0fp (0.50 #98, 0.49 #51, 0.43 #97), 02qvgy (0.50 #98, 0.49 #51, 0.43 #97), 02md_2 (0.49 #51, 0.43 #97, 0.33 #104) >> Best rule #30 for best value: >> intensional similarity = 27 >> extensional distance = 383 >> proper extension: 03qx63; 01k2yr; 0371rb; 0264v8r; 0gxkm; 03fhm5; 02mplj; 01_lhg; 0bl8l; 04mp9q; ... >> query: (?x13530, ?x60) <- position(?x13530, ?x203), position(?x13530, ?x63), ?x203 = 0dgrmp, team(?x60, ?x13530), ?x63 = 02sdk9v, team(?x60, ?x11582), team(?x60, ?x11044), team(?x60, ?x10633), team(?x60, ?x9860), team(?x60, ?x8606), team(?x60, ?x7820), team(?x60, ?x6381), team(?x60, ?x928), position(?x12496, ?x60), position(?x8885, ?x60), ?x8885 = 01rlzn, ?x6381 = 02b1b5, ?x10633 = 059nf5, position(?x11631, ?x60), ?x7820 = 021mkg, ?x11044 = 098r1q, ?x8606 = 02wwr5n, ?x928 = 02279c, ?x11631 = 0515zg, ?x12496 = 0425j7, ?x11582 = 03mgbf, ?x9860 = 02hzx8 >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #69 for first EXPECTED value: *> intensional similarity = 31 *> extensional distance = 688 *> proper extension: 03mqj_; 0266sb_; 08036w; 04hzfz; 01yxbw; 041n28; 05hywl; 0b256b; 02b0_6; 07q9q2; ... *> query: (?x13530, 02_j1w) <- position(?x13530, ?x203), team(?x203, ?x13580), team(?x203, ?x13503), team(?x203, ?x12663), team(?x203, ?x10987), team(?x203, ?x10557), team(?x203, ?x10189), team(?x203, ?x6340), team(?x203, ?x5918), team(?x203, ?x3582), team(?x203, ?x1833), team(?x203, ?x1759), team(?x203, ?x1026), position(?x14012, ?x203), position(?x11406, ?x203), position(?x10636, ?x203), ?x11406 = 02b1d0, ?x1759 = 01kkg5, ?x1026 = 056xx8, ?x6340 = 0j47s, ?x13580 = 01_1kk, ?x10987 = 077jpc, ?x13503 = 04gj8r, ?x12663 = 035l_9, ?x10557 = 01l0__, ?x3582 = 03fhm5, ?x14012 = 0ghd6l, ?x1833 = 07r78j, ?x5918 = 01xn5th, ?x10636 = 04h54p, ?x10189 = 02_t6d *> conf = 0.83 ranks of expected_values: 2 EVAL 05p8bf9 position 02_j1w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 23.000 23.000 0.910 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #15921-026xxv_ PRED entity: 026xxv_ PRED relation: teams! PRED expected values: 0jfqp => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 129): 01_d4 (0.33 #60, 0.17 #1415, 0.10 #5477), 0fr0t (0.20 #653, 0.17 #1196, 0.12 #1736), 0d9y6 (0.20 #402, 0.17 #945, 0.12 #1757), 0snty (0.20 #508, 0.17 #1051, 0.11 #2133), 0f__1 (0.20 #623, 0.17 #894, 0.11 #1976), 071cn (0.17 #1194, 0.12 #1734, 0.11 #2004), 0fvvz (0.17 #852, 0.10 #3020, 0.09 #3834), 01sn3 (0.17 #1470, 0.09 #3910, 0.05 #4991), 0rh6k (0.17 #1357, 0.06 #4067, 0.05 #4878), 0t6hk (0.12 #1847, 0.10 #2932, 0.10 #2389) >> Best rule #60 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0jm74; >> query: (?x8728, 01_d4) <- colors(?x8728, ?x663), team(?x11924, ?x8728), sport(?x8728, ?x12913), ?x11924 = 054c1, ?x663 = 083jv >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026xxv_ teams! 0jfqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.333 http://example.org/sports/sports_team_location/teams #15920-0bh8yn3 PRED entity: 0bh8yn3 PRED relation: honored_for! PRED expected values: 0hhtgcw => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 74): 09q_6t (0.17 #126, 0.03 #492, 0.02 #2200), 02ywhz (0.17 #189), 0hhtgcw (0.16 #3295, 0.03 #561, 0.03 #2513), 0hr6lkl (0.12 #256, 0.06 #378, 0.05 #1354), 0hndn2q (0.12 #276, 0.03 #1130, 0.03 #1374), 0fqpc7d (0.08 #151, 0.03 #517, 0.02 #639), 0g5b0q5 (0.08 #136, 0.03 #624, 0.02 #380), 02glmx (0.08 #190, 0.02 #434, 0.02 #678), 0g55tzk (0.08 #242, 0.02 #608, 0.02 #730), 09p2r9 (0.08 #201, 0.02 #2275, 0.01 #2031) >> Best rule #126 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 06nr2h; >> query: (?x1701, 09q_6t) <- film(?x1738, ?x1701), film(?x382, ?x1701), production_companies(?x1701, ?x4585), ?x1738 = 0170pk >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #3295 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 706 *> proper extension: 03czz87; *> query: (?x1701, ?x6297) <- award_winner(?x1701, ?x9084), type_of_union(?x9084, ?x566), award_winner(?x6297, ?x9084) *> conf = 0.16 ranks of expected_values: 3 EVAL 0bh8yn3 honored_for! 0hhtgcw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15919-0yx7h PRED entity: 0yx7h PRED relation: language PRED expected values: 02h40lc => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.92 #356, 0.91 #179, 0.89 #1308), 064_8sq (0.18 #258, 0.16 #376, 0.15 #555), 04306rv (0.18 #241, 0.14 #5, 0.11 #359), 06nm1 (0.18 #247, 0.14 #11, 0.10 #1377), 06b_j (0.10 #259, 0.09 #556, 0.07 #318), 0jzc (0.07 #138, 0.05 #315, 0.05 #2797), 02bjrlw (0.07 #594, 0.06 #534, 0.06 #475), 03_9r (0.05 #246, 0.05 #3226, 0.05 #2797), 06mp7 (0.05 #252, 0.05 #2797, 0.03 #549), 03hkp (0.05 #251, 0.02 #1261, 0.02 #667) >> Best rule #356 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 05css_; 02tktw; 01jr4j; 078mm1; >> query: (?x3826, 02h40lc) <- titles(?x4205, ?x3826), ?x4205 = 0c3351, film(?x3557, ?x3826), genre(?x3826, ?x53) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0yx7h language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.921 http://example.org/film/film/language #15918-02f1c PRED entity: 02f1c PRED relation: award_nominee PRED expected values: 01n8gr => 112 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1020): 02fn5r (0.82 #11697, 0.81 #42113, 0.81 #49132), 01n8gr (0.82 #11697, 0.81 #42113, 0.81 #49132), 02pzc4 (0.76 #126340, 0.75 #109963, 0.74 #51473), 0kftt (0.76 #126340, 0.75 #109963, 0.74 #51473), 06lxn (0.76 #126340, 0.75 #109963, 0.74 #51473), 02l840 (0.11 #14195, 0.07 #46950, 0.06 #9514), 01vw20h (0.09 #15093, 0.05 #47848, 0.05 #24451), 02cx90 (0.09 #22067, 0.08 #29087, 0.07 #40785), 02zft0 (0.08 #3746, 0.04 #6085, 0.03 #10763), 01wd9vs (0.07 #3965, 0.05 #6304, 0.03 #10982) >> Best rule #11697 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 03h610; >> query: (?x8799, ?x133) <- nominated_for(?x8799, ?x2165), artists(?x671, ?x8799), award_nominee(?x133, ?x8799) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02f1c award_nominee 01n8gr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 54.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15917-036px PRED entity: 036px PRED relation: artists! PRED expected values: 0mhfr => 138 concepts (64 used for prediction) PRED predicted values (max 10 best out of 223): 064t9 (0.64 #1578, 0.56 #4079, 0.56 #327), 025sc50 (0.42 #1615, 0.35 #2240, 0.24 #1302), 0glt670 (0.40 #2230, 0.39 #1605, 0.20 #6917), 0xhtw (0.34 #12830, 0.27 #6269, 0.26 #7518), 05bt6j (0.33 #6295, 0.31 #7544, 0.30 #4109), 06j6l (0.32 #1613, 0.30 #1300, 0.29 #2238), 0mhfr (0.31 #338, 0.29 #1901, 0.12 #25), 016clz (0.28 #18133, 0.27 #7505, 0.27 #6256), 0gywn (0.27 #1623, 0.26 #1310, 0.21 #6622), 0ggx5q (0.27 #1644, 0.22 #2269, 0.19 #80) >> Best rule #1578 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 01vvydl; 012d40; 0lbj1; 01vw87c; 0147dk; 0kzy0; 02l840; 081lh; 016kjs; 014zfs; ... >> query: (?x4191, 064t9) <- artists(?x1572, ?x4191), category(?x4191, ?x134), currency(?x4191, ?x170), award_winner(?x2420, ?x4191) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #338 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 02lvtb; *> query: (?x4191, 0mhfr) <- artists(?x2664, ?x4191), category(?x4191, ?x134), currency(?x4191, ?x170), ?x2664 = 01lyv *> conf = 0.31 ranks of expected_values: 7 EVAL 036px artists! 0mhfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 138.000 64.000 0.641 http://example.org/music/genre/artists #15916-01q_wyj PRED entity: 01q_wyj PRED relation: role PRED expected values: 02sgy => 121 concepts (54 used for prediction) PRED predicted values (max 10 best out of 120): 05r5c (0.51 #731, 0.42 #2690, 0.41 #3929), 05842k (0.50 #489, 0.41 #904, 0.33 #801), 026t6 (0.47 #930, 0.47 #829, 0.43 #3), 018vs (0.43 #736, 0.38 #826, 0.33 #4646), 018j2 (0.38 #826, 0.33 #4646, 0.33 #4232), 05148p4 (0.38 #826, 0.33 #4646, 0.33 #4232), 01vdm0 (0.35 #754, 0.30 #1168, 0.29 #2405), 02sgy (0.34 #2275, 0.33 #211, 0.31 #729), 0l14qv (0.27 #728, 0.20 #831, 0.18 #1142), 0l14md (0.26 #418, 0.22 #833, 0.14 #7) >> Best rule #731 for best value: >> intensional similarity = 5 >> extensional distance = 47 >> proper extension: 07_3qd; >> query: (?x8282, 05r5c) <- instrumentalists(?x212, ?x8282), artists(?x1000, ?x8282), role(?x8282, ?x745), ?x745 = 01vj9c, artist(?x1954, ?x8282) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #2275 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 215 *> proper extension: 01vd7hn; *> query: (?x8282, 02sgy) <- instrumentalists(?x716, ?x8282), instrumentalists(?x227, ?x8282), gender(?x8282, ?x231), ?x227 = 0342h, role(?x8282, ?x432), role(?x74, ?x716) *> conf = 0.34 ranks of expected_values: 8 EVAL 01q_wyj role 02sgy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 121.000 54.000 0.510 http://example.org/music/artist/track_contributions./music/track_contribution/role #15915-018mmj PRED entity: 018mmj PRED relation: place_of_burial! PRED expected values: 036jp8 05f0r8 => 82 concepts (51 used for prediction) PRED predicted values (max 10 best out of 138): 0l786 (0.25 #600, 0.20 #156, 0.18 #710), 0h1m9 (0.20 #118, 0.14 #451, 0.14 #340), 0c921 (0.20 #179, 0.14 #512, 0.14 #401), 04wqr (0.20 #113, 0.14 #446, 0.14 #335), 02v2jy (0.20 #214, 0.14 #547, 0.14 #436), 0btj0 (0.20 #212, 0.14 #545, 0.14 #434), 0gpmp (0.20 #209, 0.14 #542, 0.14 #431), 01kkx2 (0.20 #207, 0.14 #540, 0.14 #429), 0127xk (0.20 #204, 0.14 #537, 0.14 #426), 04f9r2 (0.20 #198, 0.14 #531, 0.14 #420) >> Best rule #600 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0lbp_; >> query: (?x3691, 0l786) <- place_of_burial(?x4057, ?x3691), place_of_burial(?x1357, ?x3691), participant(?x4057, ?x544), gender(?x4057, ?x514), profession(?x1357, ?x319) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018mmj place_of_burial! 05f0r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 51.000 0.250 http://example.org/people/deceased_person/place_of_burial EVAL 018mmj place_of_burial! 036jp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 51.000 0.250 http://example.org/people/deceased_person/place_of_burial #15914-0hcvy PRED entity: 0hcvy PRED relation: profession PRED expected values: 0d8qb => 128 concepts (92 used for prediction) PRED predicted values (max 10 best out of 120): 02hrh1q (0.88 #12200, 0.88 #9734, 0.87 #10314), 02jknp (0.64 #2472, 0.59 #7117, 0.58 #6246), 03gjzk (0.46 #5091, 0.44 #3930, 0.43 #5236), 05z96 (0.28 #3232, 0.27 #1200, 0.23 #1491), 015btn (0.23 #1451, 0.14 #679, 0.12 #969), 0d8qb (0.23 #1451, 0.11 #8272, 0.10 #2107), 099md (0.23 #1451, 0.04 #6019, 0.03 #5950), 018gz8 (0.23 #5383, 0.21 #8577, 0.20 #5673), 0np9r (0.22 #11482, 0.18 #9741, 0.16 #9016), 02krf9 (0.20 #5248, 0.20 #6265, 0.20 #4523) >> Best rule #12200 for best value: >> intensional similarity = 2 >> extensional distance = 721 >> proper extension: 03zqc1; 0clvcx; 06lgq8; 02xb2bt; 0308kx; >> query: (?x11271, 02hrh1q) <- award(?x11271, ?x11388), actor(?x531, ?x11271) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1451 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 073v6; 06whf; *> query: (?x11271, ?x9081) <- influenced_by(?x11271, ?x6810), influenced_by(?x11271, ?x5612), ?x5612 = 058vp, profession(?x11271, ?x353), ?x353 = 0cbd2, profession(?x6810, ?x9081) *> conf = 0.23 ranks of expected_values: 6 EVAL 0hcvy profession 0d8qb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 128.000 92.000 0.878 http://example.org/people/person/profession #15913-01y64_ PRED entity: 01y64_ PRED relation: award_nominee! PRED expected values: 04y9dk => 96 concepts (43 used for prediction) PRED predicted values (max 10 best out of 748): 01kj0p (0.84 #4667, 0.84 #4666, 0.84 #14003), 0lpjn (0.84 #4667, 0.84 #4666, 0.84 #14003), 0jfx1 (0.84 #4666, 0.84 #14003, 0.82 #2332), 01y64_ (0.57 #3379, 0.50 #1045, 0.24 #10380), 0171cm (0.44 #7553, 0.43 #12223, 0.40 #9888), 04y9dk (0.43 #2749, 0.33 #415, 0.20 #9750), 0dgskx (0.40 #8509, 0.36 #10844, 0.36 #13179), 0151w_ (0.40 #9538, 0.36 #7203, 0.32 #11873), 016xk5 (0.36 #10936, 0.32 #8601, 0.29 #13271), 03y_46 (0.36 #10680, 0.32 #8345, 0.29 #13015) >> Best rule #4667 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 02wgln; >> query: (?x4440, ?x1958) <- award_nominee(?x4440, ?x2818), award_nominee(?x4440, ?x2805), award_nominee(?x4440, ?x1958), ?x2805 = 0lpjn, ?x2818 = 01kj0p, nationality(?x1958, ?x94) >> conf = 0.84 => this is the best rule for 2 predicted values *> Best rule #2749 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 02wgln; *> query: (?x4440, 04y9dk) <- award_nominee(?x4440, ?x2818), award_nominee(?x4440, ?x2805), award_nominee(?x4440, ?x1958), ?x2805 = 0lpjn, ?x2818 = 01kj0p, nationality(?x1958, ?x94) *> conf = 0.43 ranks of expected_values: 6 EVAL 01y64_ award_nominee! 04y9dk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 43.000 0.845 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15912-02nvg1 PRED entity: 02nvg1 PRED relation: major_field_of_study PRED expected values: 02_7t => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 115): 02j62 (0.56 #154, 0.50 #523, 0.43 #4094), 03g3w (0.54 #3104, 0.30 #1134, 0.29 #1380), 062z7 (0.53 #3105, 0.31 #151, 0.30 #1135), 03nfmq (0.49 #1145, 0.47 #1022, 0.16 #407), 02lp1 (0.47 #504, 0.43 #996, 0.42 #1119), 04rjg (0.44 #143, 0.41 #512, 0.37 #1250), 02_7t (0.44 #188, 0.38 #434, 0.37 #311), 01lj9 (0.44 #163, 0.35 #532, 0.26 #1147), 01mkq (0.39 #1245, 0.39 #5678, 0.38 #507), 06ms6 (0.38 #140, 0.24 #509, 0.20 #1001) >> Best rule #154 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 0k__z; 026ssfj; >> query: (?x7900, 02j62) <- major_field_of_study(?x7900, ?x4321), major_field_of_study(?x7900, ?x3213), state_province_region(?x7900, ?x3818), ?x3213 = 0g4gr, ?x4321 = 0g26h >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #188 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 0k__z; 026ssfj; *> query: (?x7900, 02_7t) <- major_field_of_study(?x7900, ?x4321), major_field_of_study(?x7900, ?x3213), state_province_region(?x7900, ?x3818), ?x3213 = 0g4gr, ?x4321 = 0g26h *> conf = 0.44 ranks of expected_values: 7 EVAL 02nvg1 major_field_of_study 02_7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 137.000 137.000 0.562 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #15911-016vqk PRED entity: 016vqk PRED relation: student! PRED expected values: 01wdj_ => 96 concepts (93 used for prediction) PRED predicted values (max 10 best out of 79): 0bwfn (0.09 #12899, 0.09 #17107, 0.05 #30260), 0lyjf (0.07 #157, 0.02 #1209, 0.01 #11729), 017z88 (0.06 #12706, 0.05 #16914, 0.03 #5868), 03ksy (0.05 #12730, 0.04 #30091, 0.04 #16938), 065y4w7 (0.05 #16846, 0.04 #29999, 0.04 #12638), 02g839 (0.04 #1603, 0.03 #7915, 0.03 #11071), 01r3y2 (0.04 #89, 0.02 #2193, 0.02 #1141), 0234_c (0.04 #416), 07szy (0.04 #40), 09f2j (0.03 #12783, 0.03 #16991, 0.03 #30144) >> Best rule #12899 for best value: >> intensional similarity = 3 >> extensional distance = 487 >> proper extension: 02f9wb; >> query: (?x9008, 0bwfn) <- award_winner(?x4960, ?x9008), award_winner(?x7884, ?x9008), student(?x7939, ?x9008) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016vqk student! 01wdj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 93.000 0.094 http://example.org/education/educational_institution/students_graduates./education/education/student #15910-03wy8t PRED entity: 03wy8t PRED relation: film! PRED expected values: 04fzk 01cj6y 0pgm3 => 100 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1667): 0gn30 (0.40 #3018, 0.33 #5091, 0.04 #13383), 04yywz (0.40 #2093, 0.33 #4166, 0.02 #51862), 02vyw (0.33 #45620, 0.33 #4147, 0.30 #45619), 01_f_5 (0.33 #4147, 0.30 #45619, 0.28 #72583), 0crqcc (0.33 #4147, 0.30 #45619, 0.28 #72583), 0p_pd (0.33 #53, 0.09 #8347, 0.09 #16637), 02114t (0.33 #635, 0.05 #44181, 0.05 #8929), 01pk3z (0.33 #984, 0.03 #17568, 0.03 #15495), 03fbb6 (0.33 #975, 0.03 #17559, 0.02 #75634), 0sz28 (0.26 #14702, 0.21 #18848, 0.20 #20922) >> Best rule #3018 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0140g4; 0jsf6; 08984j; >> query: (?x9755, 0gn30) <- written_by(?x9755, ?x986), genre(?x9755, ?x53), film(?x4867, ?x9755), film(?x5636, ?x9755), ?x4867 = 01gw4f >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #13195 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 05dy7p; 0crh5_f; *> query: (?x9755, 01cj6y) <- country(?x9755, ?x94), production_companies(?x9755, ?x7980), titles(?x53, ?x9755), ?x7980 = 020h2v *> conf = 0.08 ranks of expected_values: 82, 409, 572 EVAL 03wy8t film! 0pgm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 39.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 03wy8t film! 01cj6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 100.000 39.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 03wy8t film! 04fzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 39.000 0.400 http://example.org/film/actor/film./film/performance/film #15909-01k0vq PRED entity: 01k0vq PRED relation: film! PRED expected values: 016tw3 => 98 concepts (76 used for prediction) PRED predicted values (max 10 best out of 68): 016tw3 (0.65 #2637, 0.46 #1653, 0.45 #3248), 061dn_ (0.33 #249, 0.33 #24, 0.25 #99), 0jz9f (0.33 #226, 0.25 #76, 0.12 #376), 02rr_z4 (0.33 #65, 0.17 #290, 0.02 #1116), 03xq0f (0.27 #305, 0.22 #981, 0.21 #906), 032j_n (0.25 #133, 0.06 #1787, 0.04 #508), 086k8 (0.18 #302, 0.17 #1579, 0.17 #1053), 017s11 (0.18 #378, 0.15 #1355, 0.15 #2412), 05qd_ (0.15 #384, 0.15 #985, 0.14 #910), 016tt2 (0.15 #905, 0.14 #1431, 0.13 #1506) >> Best rule #2637 for best value: >> intensional similarity = 4 >> extensional distance = 511 >> proper extension: 0djb3vw; 04dsnp; 091z_p; 072r5v; >> query: (?x7579, ?x1104) <- film_crew_role(?x7579, ?x1171), nominated_for(?x1691, ?x7579), production_companies(?x7579, ?x1104), film(?x1104, ?x86) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k0vq film! 016tw3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 76.000 0.650 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15908-01trhmt PRED entity: 01trhmt PRED relation: profession PRED expected values: 0nbcg 047rgpy => 89 concepts (70 used for prediction) PRED predicted values (max 10 best out of 72): 09jwl (0.66 #162, 0.53 #307, 0.53 #17), 0dxtg (0.57 #447, 0.42 #2332, 0.42 #1462), 0cbd2 (0.51 #1745, 0.45 #2325, 0.44 #2470), 0nbcg (0.46 #609, 0.42 #319, 0.39 #5106), 018gz8 (0.45 #450, 0.24 #1030, 0.22 #1465), 01d_h8 (0.42 #4, 0.40 #1889, 0.40 #439), 0n1h (0.42 #10, 0.26 #300, 0.25 #1170), 0kyk (0.37 #1767, 0.31 #2492, 0.30 #1477), 03gjzk (0.34 #448, 0.27 #3639, 0.26 #2623), 02dsz (0.28 #8127, 0.05 #53, 0.05 #2083) >> Best rule #162 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 0lbj1; 01vw87c; 0c9d9; 0fp_v1x; 06cv1; 01wl38s; 01cv3n; 015grj; 01pr_j6; 014zfs; ... >> query: (?x2562, 09jwl) <- profession(?x2562, ?x1614), profession(?x2562, ?x1032), ?x1032 = 02hrh1q, ?x1614 = 01c72t >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #609 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 123 *> proper extension: 0qmny; *> query: (?x2562, 0nbcg) <- artists(?x3928, ?x2562), ?x3928 = 0gywn *> conf = 0.46 ranks of expected_values: 4, 32 EVAL 01trhmt profession 047rgpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 89.000 70.000 0.660 http://example.org/people/person/profession EVAL 01trhmt profession 0nbcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 89.000 70.000 0.660 http://example.org/people/person/profession #15907-0315q3 PRED entity: 0315q3 PRED relation: film PRED expected values: 0315w4 0c9t0y 056xkh => 104 concepts (96 used for prediction) PRED predicted values (max 10 best out of 793): 04yc76 (0.63 #53554, 0.62 #55340, 0.58 #105335), 01chpn (0.18 #1106, 0.04 #4676, 0.02 #17171), 01qb559 (0.18 #1299, 0.04 #4869, 0.02 #17364), 047vnkj (0.18 #907, 0.02 #4477, 0.01 #24114), 0yx1m (0.18 #1428, 0.02 #4998), 078sj4 (0.18 #451, 0.02 #5806, 0.02 #16516), 0b76t12 (0.18 #290), 03nfnx (0.12 #3184, 0.05 #4969, 0.04 #8539), 0ndsl1x (0.12 #3295, 0.04 #71408, 0.03 #6865), 0bxxzb (0.12 #2958, 0.04 #71408, 0.03 #89263) >> Best rule #53554 for best value: >> intensional similarity = 2 >> extensional distance = 420 >> proper extension: 03pmty; 027cxsm; 01fh9; 01v9l67; 021yw7; 02bwc7; 012dr7; 01p0vf; 048wrb; 020l9r; ... >> query: (?x4631, ?x437) <- nominated_for(?x4631, ?x437), participant(?x4631, ?x400) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #26587 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 177 *> proper extension: 06y9c2; *> query: (?x4631, 056xkh) <- participant(?x2194, ?x4631), type_of_union(?x4631, ?x566), profession(?x4631, ?x319) *> conf = 0.02 ranks of expected_values: 242, 464 EVAL 0315q3 film 056xkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 104.000 96.000 0.629 http://example.org/film/actor/film./film/performance/film EVAL 0315q3 film 0c9t0y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 104.000 96.000 0.629 http://example.org/film/actor/film./film/performance/film EVAL 0315q3 film 0315w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 96.000 0.629 http://example.org/film/actor/film./film/performance/film #15906-0cxn2 PRED entity: 0cxn2 PRED relation: nutrient PRED expected values: 025s0zp 014d7f 014yzm 025sf0_ 025rw19 07q0m => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 36): 025rw19 (0.80 #376, 0.78 #359, 0.77 #19), 025sf0_ (0.77 #19, 0.70 #375, 0.67 #358), 025s0zp (0.77 #19, 0.70 #368, 0.67 #351), 0d9t0 (0.77 #19, 0.70 #374, 0.67 #357), 03d49 (0.77 #19, 0.67 #361, 0.60 #378), 014yzm (0.77 #19, 0.60 #371, 0.56 #354), 07q0m (0.77 #19, 0.60 #379, 0.56 #362), 05v_8y (0.77 #19, 0.60 #373, 0.56 #356), 0dcfv (0.77 #19, 0.56 #365, 0.56 #349), 014d7f (0.77 #19, 0.50 #370, 0.44 #353) >> Best rule #376 for best value: >> intensional similarity = 52 >> extensional distance = 8 >> proper extension: 0dcfv; >> query: (?x3468, 025rw19) <- nutrient(?x3468, ?x9915), nutrient(?x3468, ?x9365), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5337), nutrient(?x3468, ?x2018), nutrient(?x10612, ?x8413), nutrient(?x9732, ?x8413), nutrient(?x9489, ?x8413), nutrient(?x9005, ?x8413), nutrient(?x8298, ?x8413), nutrient(?x7719, ?x8413), nutrient(?x7057, ?x8413), nutrient(?x6285, ?x8413), nutrient(?x6191, ?x8413), nutrient(?x6159, ?x8413), nutrient(?x6032, ?x8413), nutrient(?x5373, ?x8413), nutrient(?x5009, ?x8413), nutrient(?x4068, ?x8413), nutrient(?x3900, ?x8413), nutrient(?x2701, ?x8413), nutrient(?x1959, ?x8413), nutrient(?x1303, ?x8413), nutrient(?x1257, ?x8413), ?x1959 = 0f25w9, ?x5373 = 0971v, ?x7057 = 0fbdb, ?x10612 = 0frq6, ?x6191 = 014j1m, ?x5337 = 06x4c, ?x1303 = 0fj52s, ?x1257 = 09728, ?x7719 = 0dj75, ?x9915 = 025tkqy, ?x3900 = 061_f, ?x2018 = 01sh2, taxonomy(?x9365, ?x939), ?x4068 = 0fbw6, ?x2701 = 0hkxq, ?x5549 = 025s7j4, ?x6285 = 01645p, ?x6192 = 06jry, ?x6159 = 033cnk, ?x6032 = 01nkt, ?x9489 = 07j87, ?x9732 = 05z55, ?x8298 = 037ls6, ?x939 = 04n6k, ?x9005 = 04zpv, ?x5009 = 0fjfh >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 6, 7, 10 EVAL 0cxn2 nutrient 07q0m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0cxn2 nutrient 025rw19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0cxn2 nutrient 025sf0_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0cxn2 nutrient 014yzm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0cxn2 nutrient 014d7f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0cxn2 nutrient 025s0zp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #15905-0bxxzb PRED entity: 0bxxzb PRED relation: genre PRED expected values: 01jfsb => 78 concepts (68 used for prediction) PRED predicted values (max 10 best out of 86): 07s9rl0 (0.61 #715, 0.59 #953, 0.59 #477), 01jfsb (0.55 #368, 0.52 #606, 0.49 #2987), 01hmnh (0.36 #3353, 0.26 #1191, 0.24 #136), 02l7c8 (0.32 #253, 0.27 #2872, 0.27 #5624), 06n90 (0.30 #3348, 0.28 #2988, 0.27 #131), 0lsxr (0.27 #1079, 0.26 #1191, 0.25 #1437), 06cvj (0.26 #1191, 0.19 #3, 0.11 #2860), 0gf28 (0.26 #1191, 0.12 #63, 0.05 #2801), 09q17 (0.26 #1191, 0.05 #1370, 0.03 #298), 04xvlr (0.21 #240, 0.20 #716, 0.20 #478) >> Best rule #715 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 0g5qmbz; >> query: (?x6628, 07s9rl0) <- language(?x6628, ?x5671), language(?x6628, ?x254), ?x5671 = 06b_j, nominated_for(?x1312, ?x6628), languages(?x50, ?x254) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #368 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 03z9585; *> query: (?x6628, 01jfsb) <- language(?x6628, ?x5671), ?x5671 = 06b_j, film_crew_role(?x6628, ?x137), film(?x400, ?x6628) *> conf = 0.55 ranks of expected_values: 2 EVAL 0bxxzb genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 68.000 0.607 http://example.org/film/film/genre #15904-0f8l9c PRED entity: 0f8l9c PRED relation: location! PRED expected values: 081nh => 315 concepts (188 used for prediction) PRED predicted values (max 10 best out of 2168): 032r1 (0.27 #45069, 0.25 #47584, 0.22 #37523), 0prfz (0.25 #30229, 0.22 #35260, 0.18 #42806), 0g7k2g (0.24 #457812, 0.24 #349647, 0.19 #201233), 01l_vgt (0.24 #457812, 0.24 #349647, 0.19 #201233), 023kzp (0.22 #74155, 0.10 #189868, 0.10 #199930), 03rl84 (0.17 #45632, 0.12 #30540, 0.12 #106001), 0hnp7 (0.17 #46510, 0.12 #31418, 0.11 #36449), 018_lb (0.17 #47501, 0.12 #32409, 0.11 #37440), 014g9y (0.17 #75084, 0.12 #107784, 0.10 #137968), 0227tr (0.15 #53297, 0.14 #28143, 0.14 #128757) >> Best rule #45069 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0h7x; >> query: (?x789, 032r1) <- country(?x790, ?x789), film_release_region(?x66, ?x789), second_level_divisions(?x789, ?x9632) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #73383 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0k049; 0fr0t; *> query: (?x789, 081nh) <- jurisdiction_of_office(?x182, ?x789), location_of_ceremony(?x3580, ?x789), locations(?x3278, ?x789) *> conf = 0.06 ranks of expected_values: 751 EVAL 0f8l9c location! 081nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 315.000 188.000 0.273 http://example.org/people/person/places_lived./people/place_lived/location #15903-01wrcxr PRED entity: 01wrcxr PRED relation: group PRED expected values: 01v0sx2 => 121 concepts (38 used for prediction) PRED predicted values (max 10 best out of 32): 01v0sx2 (0.09 #437, 0.07 #5, 0.06 #1086), 011_vz (0.03 #183, 0.03 #291, 0.02 #399), 0cbm64 (0.03 #185, 0.03 #293), 0hvbj (0.03 #139, 0.02 #355, 0.02 #571), 01qqwp9 (0.02 #345, 0.02 #1642, 0.02 #1102), 015srx (0.02 #365, 0.02 #1013, 0.01 #797), 016fmf (0.02 #342, 0.02 #558, 0.02 #450), 09jm8 (0.02 #412, 0.02 #628), 07c0j (0.02 #328, 0.01 #652), 0kr_t (0.02 #362, 0.01 #794) >> Best rule #437 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 02h9_l; >> query: (?x6042, 01v0sx2) <- award(?x6042, ?x4796), artists(?x505, ?x6042), ?x4796 = 01c99j >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wrcxr group 01v0sx2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 38.000 0.092 http://example.org/music/group_member/membership./music/group_membership/group #15902-04mkft PRED entity: 04mkft PRED relation: category PRED expected values: 08mbj5d => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #95, 0.82 #94, 0.81 #127) >> Best rule #95 for best value: >> intensional similarity = 4 >> extensional distance = 267 >> proper extension: 04wlz2; >> query: (?x5854, ?x134) <- citytown(?x5854, ?x11930), category(?x11930, ?x134), location(?x1208, ?x11930), time_zones(?x11930, ?x2950) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mkft category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.818 http://example.org/common/topic/webpage./common/webpage/category #15901-012mzw PRED entity: 012mzw PRED relation: major_field_of_study PRED expected values: 04x_3 02j62 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 107): 01mkq (0.64 #2217, 0.63 #1997, 0.52 #1886), 03g3w (0.52 #1236, 0.48 #906, 0.40 #2008), 02j62 (0.50 #2231, 0.48 #2011, 0.45 #1900), 04rjg (0.46 #2001, 0.46 #2221, 0.40 #899), 04x_3 (0.40 #905, 0.27 #2007, 0.26 #2227), 0fdys (0.36 #915, 0.36 #2237, 0.34 #2017), 037mh8 (0.36 #939, 0.33 #2261, 0.33 #2041), 02ky346 (0.32 #896, 0.21 #1998, 0.21 #1887), 04gb7 (0.30 #1249, 0.24 #919, 0.22 #2021), 02h40lc (0.28 #886, 0.21 #2208, 0.19 #1988) >> Best rule #2217 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 0jhjl; 017lvd; 04jhp; 0jksm; >> query: (?x7596, 01mkq) <- institution(?x620, ?x7596), major_field_of_study(?x7596, ?x742), list(?x7596, ?x2197) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #2231 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 0jhjl; 017lvd; 04jhp; 0jksm; *> query: (?x7596, 02j62) <- institution(?x620, ?x7596), major_field_of_study(?x7596, ?x742), list(?x7596, ?x2197) *> conf = 0.50 ranks of expected_values: 3, 5 EVAL 012mzw major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 114.000 0.645 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 012mzw major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 114.000 114.000 0.645 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #15900-043t8t PRED entity: 043t8t PRED relation: music PRED expected values: 0jn5l => 69 concepts (23 used for prediction) PRED predicted values (max 10 best out of 74): 01tc9r (0.25 #65, 0.18 #910, 0.03 #2593), 04ls53 (0.17 #289, 0.10 #500, 0.07 #712), 0jn5l (0.17 #306, 0.10 #517, 0.07 #729), 01pbs9w (0.17 #315, 0.10 #526, 0.07 #738), 01l1rw (0.17 #313, 0.10 #524, 0.07 #736), 03h610 (0.10 #498, 0.07 #710, 0.05 #1133), 02_33l (0.10 #617, 0.07 #829), 02bn75 (0.10 #565, 0.07 #777), 021bk (0.10 #457, 0.07 #669), 0pgjm (0.10 #442, 0.07 #654) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01q_y0; >> query: (?x4651, 01tc9r) <- nominated_for(?x2263, ?x4651), nominated_for(?x397, ?x4651), ?x2263 = 01y_px, film(?x397, ?x2463) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #306 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 03b1sb; *> query: (?x4651, 0jn5l) <- film(?x400, ?x4651), film(?x241, ?x4651), ?x241 = 01j5ts, produced_by(?x4651, ?x163), award_nominee(?x147, ?x400) *> conf = 0.17 ranks of expected_values: 3 EVAL 043t8t music 0jn5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 23.000 0.250 http://example.org/film/film/music #15899-059j2 PRED entity: 059j2 PRED relation: service_location! PRED expected values: 07zl6m => 244 concepts (231 used for prediction) PRED predicted values (max 10 best out of 142): 01c6k4 (0.48 #3414, 0.46 #1841, 0.38 #4729), 064f29 (0.46 #1893, 0.29 #975, 0.27 #5175), 077w0b (0.38 #1898, 0.29 #980, 0.26 #3864), 07zl6m (0.31 #1962, 0.29 #1044, 0.26 #3928), 0k9ts (0.31 #1923, 0.27 #2316, 0.26 #3889), 04fv0k (0.31 #1917, 0.26 #3883, 0.21 #4674), 06_9lg (0.30 #15329, 0.30 #12043, 0.27 #10992), 05b5c (0.29 #2481, 0.29 #1039, 0.27 #4189), 01xdn1 (0.29 #933, 0.22 #1326, 0.15 #1851), 0cv9b (0.26 #3417, 0.26 #6176, 0.24 #2368) >> Best rule #3414 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 015fr; 0345h; 01pj7; 05b4w; >> query: (?x1229, 01c6k4) <- combatants(?x151, ?x1229), film_release_region(?x1364, ?x1229), ?x1364 = 047msdk >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1962 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 02j71; *> query: (?x1229, 07zl6m) <- administrative_parent(?x7655, ?x1229), administrative_parent(?x3407, ?x1229), adjoins(?x10793, ?x7655), administrative_parent(?x3408, ?x3407) *> conf = 0.31 ranks of expected_values: 4 EVAL 059j2 service_location! 07zl6m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 244.000 231.000 0.478 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #15898-030_1_ PRED entity: 030_1_ PRED relation: production_companies! PRED expected values: 0b73_1d 02wgbb 01s7w3 => 110 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1156): 0vhm (0.49 #6667, 0.25 #34455, 0.24 #28897), 03tn80 (0.47 #32232, 0.45 #32231, 0.42 #14448), 01s7w3 (0.47 #32232, 0.45 #32231, 0.42 #14448), 026p4q7 (0.47 #32232, 0.45 #32231, 0.42 #14448), 0260bz (0.47 #32232, 0.45 #32231, 0.42 #14448), 07j94 (0.47 #32232, 0.45 #32231, 0.42 #14448), 0g22z (0.47 #32232, 0.45 #32231, 0.42 #14448), 0h21v2 (0.47 #32232, 0.45 #32231, 0.42 #14448), 060__7 (0.47 #32232, 0.45 #32231, 0.42 #14448), 02q3fdr (0.47 #32232, 0.45 #32231, 0.42 #14448) >> Best rule #6667 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0gsg7; >> query: (?x1686, ?x5219) <- award_winner(?x6678, ?x1686), organizations_founded(?x846, ?x1686), award_winner(?x5219, ?x1686) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #32232 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 03qx_f; *> query: (?x1686, ?x153) <- organizations_founded(?x846, ?x1686), produced_by(?x1847, ?x846), produced_by(?x153, ?x846), nominated_for(?x640, ?x1847) *> conf = 0.47 ranks of expected_values: 3, 33 EVAL 030_1_ production_companies! 01s7w3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 101.000 0.490 http://example.org/film/film/production_companies EVAL 030_1_ production_companies! 02wgbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 101.000 0.490 http://example.org/film/film/production_companies EVAL 030_1_ production_companies! 0b73_1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 110.000 101.000 0.490 http://example.org/film/film/production_companies #15897-026rm_y PRED entity: 026rm_y PRED relation: award_nominee PRED expected values: 06r_by => 108 concepts (61 used for prediction) PRED predicted values (max 10 best out of 952): 0c6qh (0.83 #11706, 0.81 #128758, 0.81 #56181), 0336mc (0.83 #11706, 0.81 #135786, 0.81 #107688), 0p__8 (0.83 #11706, 0.81 #107688, 0.81 #65548), 026rm_y (0.62 #1912, 0.25 #6593, 0.22 #107689), 06r_by (0.62 #1414, 0.20 #6095, 0.16 #8437), 01541z (0.30 #9808, 0.02 #42575, 0.02 #63650), 06b0d2 (0.28 #9591, 0.04 #42358, 0.04 #51723), 01pcq3 (0.28 #9532, 0.03 #51664, 0.03 #61031), 05wqr1 (0.28 #11146, 0.02 #64988, 0.02 #62645), 04myfb7 (0.28 #9782, 0.01 #42549, 0.01 #61281) >> Best rule #11706 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 04y79_n; 04myfb7; 06lgq8; 0b9dmk; 065ydwb; 08s_lw; >> query: (?x8740, ?x815) <- award_nominee(?x815, ?x8740), award_winner(?x8964, ?x8740), ?x8964 = 09gkdln >> conf = 0.83 => this is the best rule for 3 predicted values *> Best rule #1414 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 058kqy; 0c6qh; 01q6bg; 0p__8; 0336mc; 08qxx9; *> query: (?x8740, 06r_by) <- award_nominee(?x4543, ?x8740), award_winner(?x2220, ?x8740), ?x4543 = 07m9cm *> conf = 0.62 ranks of expected_values: 5 EVAL 026rm_y award_nominee 06r_by CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 61.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15896-0k8y7 PRED entity: 0k8y7 PRED relation: languages PRED expected values: 06b_j => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 25): 03k50 (0.22 #231, 0.19 #269, 0.17 #345), 07c9s (0.19 #278, 0.14 #240, 0.10 #354), 064_8sq (0.17 #128, 0.14 #812, 0.14 #1610), 02bjrlw (0.17 #115, 0.09 #685, 0.08 #799), 04306rv (0.17 #116, 0.06 #40, 0.06 #268), 055qm (0.08 #289, 0.08 #251, 0.07 #365), 0999q (0.08 #288, 0.03 #250, 0.03 #364), 09s02 (0.06 #301, 0.06 #263, 0.03 #377), 06mp7 (0.06 #124, 0.03 #276, 0.03 #618), 01c7y (0.06 #296, 0.03 #258, 0.03 #372) >> Best rule #231 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 03wpmd; >> query: (?x4285, 03k50) <- languages(?x4285, ?x254), special_performance_type(?x4285, ?x4832), place_of_birth(?x4285, ?x3125) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #4143 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1624 *> proper extension: 015c1b; *> query: (?x4285, ?x3592) <- people(?x9943, ?x4285), languages_spoken(?x9943, ?x3592) *> conf = 0.04 ranks of expected_values: 14 EVAL 0k8y7 languages 06b_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 137.000 137.000 0.222 http://example.org/people/person/languages #15895-094jv PRED entity: 094jv PRED relation: location_of_ceremony! PRED expected values: 0cj8x => 183 concepts (125 used for prediction) PRED predicted values (max 10 best out of 198): 01933d (0.05 #954, 0.03 #3251, 0.03 #5544), 0dvld (0.05 #915, 0.03 #659, 0.02 #8819), 06lbp (0.05 #413, 0.05 #156, 0.03 #923), 012cph (0.05 #278, 0.05 #21, 0.03 #788), 0gdqy (0.05 #481, 0.05 #224, 0.02 #1502), 0c9c0 (0.05 #323, 0.05 #66, 0.02 #1344), 06wvj (0.05 #316, 0.05 #59, 0.02 #1337), 06x58 (0.05 #298, 0.05 #41, 0.02 #1319), 03lt8g (0.05 #280, 0.05 #23, 0.02 #1301), 054k_8 (0.05 #393, 0.05 #136, 0.02 #1414) >> Best rule #954 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0mnm2; >> query: (?x1705, 01933d) <- place_of_death(?x4072, ?x1705), state(?x1705, ?x1767), contains(?x1705, ?x1768) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 094jv location_of_ceremony! 0cj8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 183.000 125.000 0.053 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #15894-01g7_r PRED entity: 01g7_r PRED relation: organization! PRED expected values: 060c4 => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.86 #242, 0.85 #203, 0.84 #417), 0dq_5 (0.67 #63, 0.57 #529, 0.56 #355), 02zdwq (0.33 #333, 0.29 #201, 0.27 #374), 07xl34 (0.27 #371, 0.23 #330, 0.23 #780), 05k17c (0.25 #35, 0.13 #541, 0.13 #619), 0hm4q (0.09 #341, 0.07 #168, 0.06 #1327), 0dq3c (0.07 #55, 0.05 #108, 0.03 #1347), 05c0jwl (0.04 #985, 0.04 #1025, 0.04 #1220), 0krdk (0.03 #1347, 0.01 #349, 0.01 #496), 01kr6k (0.03 #1347) >> Best rule #242 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 07wrz; 012mzw; 01n4w_; >> query: (?x7092, 060c4) <- institution(?x865, ?x7092), ?x865 = 02h4rq6, currency(?x7092, ?x170), colors(?x7092, ?x663) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01g7_r organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.860 http://example.org/organization/role/leaders./organization/leadership/organization #15893-03bggl PRED entity: 03bggl PRED relation: film PRED expected values: 01wb95 0pd57 => 116 concepts (81 used for prediction) PRED predicted values (max 10 best out of 443): 01lbcqx (0.13 #5033, 0.07 #8615, 0.06 #6824), 0jvt9 (0.13 #4122, 0.07 #7704, 0.06 #5913), 03rg2b (0.13 #4677, 0.07 #8259, 0.05 #11841), 09qycb (0.12 #1647, 0.10 #3438, 0.04 #5229), 01wb95 (0.09 #4205, 0.05 #7787, 0.04 #11369), 04v89z (0.09 #5002, 0.05 #8584, 0.04 #6793), 0jswp (0.09 #4130, 0.04 #5921, 0.04 #7712), 0bl06 (0.09 #4567, 0.04 #6358, 0.04 #8149), 0gxfz (0.09 #4018, 0.04 #5809, 0.04 #7600), 04wddl (0.09 #5116, 0.04 #8698, 0.02 #10489) >> Best rule #5033 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 09qh1; 01vsps; 01p4r3; >> query: (?x11277, 01lbcqx) <- place_of_death(?x11277, ?x682), nominated_for(?x11277, ?x7231), award(?x11277, ?x3247), celebrities_impersonated(?x3649, ?x11277) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #4205 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 09qh1; 01vsps; 01p4r3; *> query: (?x11277, 01wb95) <- place_of_death(?x11277, ?x682), nominated_for(?x11277, ?x7231), award(?x11277, ?x3247), celebrities_impersonated(?x3649, ?x11277) *> conf = 0.09 ranks of expected_values: 5, 113 EVAL 03bggl film 0pd57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 116.000 81.000 0.130 http://example.org/film/actor/film./film/performance/film EVAL 03bggl film 01wb95 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 116.000 81.000 0.130 http://example.org/film/actor/film./film/performance/film #15892-02x8n1n PRED entity: 02x8n1n PRED relation: award! PRED expected values: 01n30p => 41 concepts (11 used for prediction) PRED predicted values (max 10 best out of 1127): 0404j37 (0.42 #3034, 0.42 #2683, 0.25 #7079), 0gmcwlb (0.38 #2143, 0.31 #122, 0.15 #6188), 0hfzr (0.38 #2432, 0.17 #6477, 0.13 #7490), 0c0zq (0.38 #2916, 0.15 #895, 0.14 #6961), 0209hj (0.38 #2083, 0.13 #6128, 0.11 #7141), 03hmt9b (0.33 #2410, 0.23 #389, 0.17 #6455), 09gq0x5 (0.33 #2193, 0.23 #172, 0.15 #6238), 07s846j (0.33 #2417, 0.14 #6462, 0.10 #7475), 0f4_l (0.31 #216, 0.25 #2237, 0.22 #10111), 0209xj (0.31 #61, 0.08 #2082, 0.08 #6127) >> Best rule #3034 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 02rdxsh; >> query: (?x2252, ?x6448) <- nominated_for(?x2252, ?x6448), nominated_for(?x2252, ?x4098), ?x6448 = 0404j37, award(?x5109, ?x2252), film(?x1424, ?x4098) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #10111 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 192 *> proper extension: 02qyp19; 0gqng; 04ljl_l; 027dtxw; 02r0csl; 05b4l5x; 040njc; 0f_nbyh; 027c924; 05f4m9q; ... *> query: (?x2252, ?x394) <- nominated_for(?x2252, ?x394), award(?x5492, ?x2252), award(?x2317, ?x2252), type_of_union(?x2317, ?x566), film(?x5492, ?x603) *> conf = 0.22 ranks of expected_values: 39 EVAL 02x8n1n award! 01n30p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 41.000 11.000 0.417 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #15891-09qv3c PRED entity: 09qv3c PRED relation: award! PRED expected values: 01v3vp 086nl7 0f7hc 0gs1_ 0f13b 01w0yrc => 52 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2700): 01rcmg (0.82 #6692, 0.81 #10038, 0.79 #3346), 02xwq9 (0.82 #6692, 0.81 #10038, 0.79 #3346), 01nrq5 (0.82 #6692, 0.81 #10038, 0.79 #3346), 01gn36 (0.82 #6692, 0.81 #10038, 0.79 #3346), 02ct_k (0.82 #6692, 0.81 #10038, 0.79 #3346), 01h910 (0.60 #5144, 0.50 #8490, 0.25 #1798), 0q5hw (0.60 #4113, 0.50 #7459, 0.25 #767), 0q9zc (0.60 #5758, 0.50 #9104, 0.25 #2412), 018ygt (0.60 #5186, 0.38 #8532, 0.25 #1840), 04t2l2 (0.60 #3387, 0.38 #6733, 0.25 #41) >> Best rule #6692 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0cqhk0; 0cjyzs; 09qrn4; >> query: (?x870, ?x71) <- award_winner(?x870, ?x71), award(?x4676, ?x870), nominated_for(?x870, ?x758), ?x4676 = 04cl1 >> conf = 0.82 => this is the best rule for 5 predicted values *> Best rule #2834 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0bs0bh; *> query: (?x870, 01w0yrc) <- award_winner(?x870, ?x71), award(?x12282, ?x870), award(?x10415, ?x870), ?x12282 = 02s529, actor(?x12533, ?x10415) *> conf = 0.25 ranks of expected_values: 102, 318, 319, 327, 374, 856 EVAL 09qv3c award! 01w0yrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 52.000 20.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qv3c award! 0f13b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 20.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qv3c award! 0gs1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 20.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qv3c award! 0f7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 20.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qv3c award! 086nl7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 20.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qv3c award! 01v3vp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 20.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15890-02hwyss PRED entity: 02hwyss PRED relation: language! PRED expected values: 0bmch_x 0642xf3 => 35 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1843): 0dr_4 (0.71 #3692, 0.50 #8881, 0.50 #7149), 0ckrnn (0.69 #8642, 0.63 #12103, 0.59 #13833), 05nlx4 (0.69 #8642, 0.63 #12103, 0.59 #13833), 02qrv7 (0.62 #5183, 0.50 #8825, 0.50 #7093), 0164qt (0.62 #5183, 0.47 #19023, 0.47 #22481), 01kf4tt (0.62 #5183, 0.47 #19023, 0.47 #22481), 01kf3_9 (0.62 #5183, 0.47 #19023, 0.47 #22481), 015qsq (0.62 #5183, 0.47 #19023, 0.47 #22481), 02n72k (0.62 #5183, 0.47 #19023, 0.43 #22482), 0d1qmz (0.62 #5183, 0.47 #19023, 0.43 #22482) >> Best rule #3692 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 02bjrlw; 02h40lc; 06mp7; 064_8sq; >> query: (?x10580, 0dr_4) <- official_language(?x1499, ?x10580), language(?x11362, ?x10580), language(?x2345, ?x10580), countries_spoken_in(?x10580, ?x1355), nominated_for(?x11362, ?x835), ?x2345 = 0c_j9x, languages_spoken(?x3584, ?x10580), film_release_region(?x2394, ?x1355), currency(?x1355, ?x170), olympics(?x1355, ?x418), ?x2394 = 0661ql3 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2520 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 04306rv; *> query: (?x10580, 0bmch_x) <- official_language(?x1499, ?x10580), language(?x11362, ?x10580), language(?x2345, ?x10580), countries_spoken_in(?x10580, ?x3227), countries_spoken_in(?x10580, ?x1355), nominated_for(?x11362, ?x835), ?x2345 = 0c_j9x, languages_spoken(?x3584, ?x10580), ?x1355 = 0h7x, genre(?x11362, ?x5104), ?x5104 = 0bkbm, adjoins(?x1353, ?x3227) *> conf = 0.33 ranks of expected_values: 201, 1476 EVAL 02hwyss language! 0642xf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 20.000 0.714 http://example.org/film/film/language EVAL 02hwyss language! 0bmch_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 35.000 20.000 0.714 http://example.org/film/film/language #15889-01xqw PRED entity: 01xqw PRED relation: role! PRED expected values: 03bx0bm => 59 concepts (49 used for prediction) PRED predicted values (max 10 best out of 93): 03bx0bm (0.86 #1094, 0.83 #647, 0.82 #2611), 04rzd (0.83 #442, 0.83 #2412, 0.81 #4381), 0g2dz (0.83 #442, 0.83 #2412, 0.81 #4381), 0mkg (0.83 #442, 0.83 #2412, 0.81 #4381), 03m5k (0.83 #442, 0.83 #2412, 0.81 #4381), 05kms (0.83 #442, 0.83 #2412, 0.81 #4381), 01c3q (0.83 #442, 0.83 #2412, 0.81 #4381), 01xqw (0.70 #1937, 0.69 #953, 0.67 #684), 0l14qv (0.70 #1879, 0.69 #2768, 0.69 #3039), 02snj9 (0.67 #491, 0.60 #1160, 0.47 #2461) >> Best rule #1094 for best value: >> intensional similarity = 7 >> extensional distance = 12 >> proper extension: 014zz1; >> query: (?x4311, 03bx0bm) <- role(?x4311, ?x1662), role(?x2956, ?x4311), instrumentalists(?x4311, ?x562), role(?x1662, ?x1432), ?x2956 = 0myk8, group(?x4311, ?x1945), ?x1432 = 0395lw >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xqw role! 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 49.000 0.857 http://example.org/music/performance_role/regular_performances./music/group_membership/role #15888-04wmvz PRED entity: 04wmvz PRED relation: school PRED expected values: 01jswq 01qrb2 => 57 concepts (54 used for prediction) PRED predicted values (max 10 best out of 344): 06pwq (0.60 #2185, 0.50 #913, 0.43 #1096), 06fq2 (0.50 #1764, 0.44 #2127, 0.44 #3044), 02pptm (0.50 #1774, 0.43 #1229, 0.33 #2137), 01vs5c (0.50 #992, 0.40 #2264, 0.39 #3364), 01dzg0 (0.44 #2155, 0.43 #1428, 0.43 #1247), 025v3k (0.43 #1141, 0.33 #2049, 0.33 #232), 01q0kg (0.43 #1146, 0.33 #237, 0.30 #363), 0bx8pn (0.40 #2199, 0.33 #2018, 0.33 #927), 012vwb (0.38 #1866, 0.38 #1684, 0.33 #2047), 01rc6f (0.38 #1945, 0.13 #2861, 0.13 #2678) >> Best rule #2185 for best value: >> intensional similarity = 13 >> extensional distance = 8 >> proper extension: 0jmj7; >> query: (?x10279, 06pwq) <- colors(?x10279, ?x1101), school(?x10279, ?x8706), colors(?x12015, ?x1101), colors(?x11278, ?x1101), colors(?x8363, ?x1101), colors(?x7912, ?x1101), currency(?x11278, ?x170), ?x8706 = 0trv, team(?x60, ?x12015), ?x7912 = 06b19, draft(?x10279, ?x1161), major_field_of_study(?x8363, ?x7134), ?x7134 = 02_7t >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #363 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 05m_8; *> query: (?x10279, ?x581) <- season(?x10279, ?x11501), colors(?x10279, ?x663), school(?x10279, ?x6953), draft(?x10279, ?x10600), draft(?x10279, ?x3334), ?x10600 = 04f4z1k, ?x11501 = 027mvrc, position(?x10279, ?x12238), ?x12238 = 02dwpf, ?x6953 = 01jq0j, school(?x3334, ?x581) *> conf = 0.30 ranks of expected_values: 40, 153 EVAL 04wmvz school 01qrb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 57.000 54.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 04wmvz school 01jswq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 57.000 54.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #15887-0hv8w PRED entity: 0hv8w PRED relation: film! PRED expected values: 0g2lq => 83 concepts (63 used for prediction) PRED predicted values (max 10 best out of 943): 0343h (0.61 #122894, 0.59 #129145, 0.58 #131229), 02vyw (0.45 #74986, 0.43 #56239, 0.43 #72902), 01vy_v8 (0.17 #734, 0.04 #6984, 0.04 #131230), 0f5xn (0.13 #971, 0.05 #13470, 0.05 #15553), 02yxwd (0.13 #745, 0.05 #9078, 0.04 #13244), 09l3p (0.13 #750, 0.04 #131230, 0.04 #23664), 0169dl (0.09 #401, 0.05 #8734, 0.05 #12900), 0gr36 (0.09 #498, 0.04 #2581, 0.03 #6748), 0k269 (0.09 #611, 0.04 #131230, 0.03 #6861), 01tnbn (0.09 #1075, 0.04 #131230, 0.03 #7325) >> Best rule #122894 for best value: >> intensional similarity = 3 >> extensional distance = 1330 >> proper extension: 0n2bh; 0gfzgl; 0cskb; 03cf9ly; >> query: (?x5473, ?x6755) <- nominated_for(?x6755, ?x5473), film(?x6755, ?x430), music(?x430, ?x5757) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #3451 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 07bz5; *> query: (?x5473, 0g2lq) <- award_winner(?x5473, ?x1387), list(?x5473, ?x3004), student(?x735, ?x1387) *> conf = 0.01 ranks of expected_values: 641 EVAL 0hv8w film! 0g2lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 83.000 63.000 0.608 http://example.org/film/actor/film./film/performance/film #15886-0l38x PRED entity: 0l38x PRED relation: county! PRED expected values: 0r8c8 => 120 concepts (61 used for prediction) PRED predicted values (max 10 best out of 194): 0r8bh (0.66 #4295, 0.63 #6448, 0.61 #7985), 0r8c8 (0.63 #6448, 0.57 #8292, 0.53 #6447), 0r4h3 (0.12 #845, 0.03 #2071, 0.03 #2684), 01zlwg6 (0.12 #744, 0.03 #1970, 0.03 #2583), 0r4qq (0.12 #720, 0.03 #1946, 0.03 #2559), 0r3tq (0.12 #814, 0.03 #2653, 0.02 #3268), 0r3wm (0.12 #796, 0.03 #2635, 0.02 #3250), 0r3tb (0.12 #753, 0.03 #2592, 0.02 #3207), 0jbrr (0.08 #1196, 0.04 #1503, 0.04 #1809), 0r2dp (0.08 #1107, 0.04 #1414, 0.04 #1720) >> Best rule #4295 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 0m2gk; >> query: (?x11967, ?x11966) <- contains(?x11967, ?x11966), adjoins(?x2949, ?x11967), state(?x11966, ?x1227), source(?x11967, ?x958) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #6448 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 108 *> proper extension: 0mww2; *> query: (?x11967, ?x6367) <- contains(?x11967, ?x6367), adjoins(?x2949, ?x11967), source(?x6367, ?x958), currency(?x11967, ?x170) *> conf = 0.63 ranks of expected_values: 2 EVAL 0l38x county! 0r8c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 61.000 0.663 http://example.org/location/hud_county_place/county #15885-01m1zk PRED entity: 01m1zk PRED relation: currency PRED expected values: 09nqf => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.52 #100, 0.45 #120, 0.45 #117) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 378 >> proper extension: 0f4y_; 0nj1c; 0n5_g; 0k3ll; 0mws3; 0n5y4; 0cc1v; 043z0; 0drr3; 0nt4s; ... >> query: (?x4074, 09nqf) <- source(?x4074, ?x958), time_zones(?x4074, ?x2674), ?x2674 = 02hcv8, ?x958 = 0jbk9 >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m1zk currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.524 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #15884-09qvf4 PRED entity: 09qvf4 PRED relation: award! PRED expected values: 04bs3j 01dy7j 07m77x => 49 concepts (10 used for prediction) PRED predicted values (max 10 best out of 2645): 0pz7h (0.81 #3344, 0.71 #26764, 0.70 #30112), 07z1_q (0.81 #3344, 0.70 #30112, 0.69 #30111), 0bq2g (0.81 #3344, 0.70 #30112, 0.69 #30111), 031sg0 (0.81 #3344, 0.69 #30111, 0.68 #26763), 05typm (0.81 #3344, 0.69 #30111, 0.68 #26763), 02773nt (0.40 #182, 0.18 #26766, 0.18 #6691), 0284gcb (0.40 #364, 0.18 #26766, 0.18 #6691), 0d4fqn (0.40 #136, 0.18 #26766, 0.18 #6691), 02778yp (0.40 #1529, 0.18 #6691, 0.14 #10036), 02bvt (0.40 #1389, 0.18 #6691, 0.14 #10036) >> Best rule #3344 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0cjyzs; 03ccq3s; 0cqhmg; >> query: (?x4225, ?x488) <- award_winner(?x4225, ?x906), award_winner(?x4225, ?x488), ?x906 = 0pz7h, nominated_for(?x4225, ?x631), ceremony(?x4225, ?x1265) >> conf = 0.81 => this is the best rule for 5 predicted values *> Best rule #2544 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0cjyzs; 03ccq3s; 0cqhmg; *> query: (?x4225, 07m77x) <- award_winner(?x4225, ?x906), ?x906 = 0pz7h, nominated_for(?x4225, ?x631), ceremony(?x4225, ?x1265) *> conf = 0.20 ranks of expected_values: 91, 161, 224 EVAL 09qvf4 award! 07m77x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 49.000 10.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qvf4 award! 01dy7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 49.000 10.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qvf4 award! 04bs3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 10.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15883-0kzy0 PRED entity: 0kzy0 PRED relation: role PRED expected values: 05148p4 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 90): 0342h (0.44 #1964, 0.31 #330, 0.30 #921), 03bx0bm (0.31 #1982, 0.26 #873, 0.23 #218), 05148p4 (0.27 #1977, 0.22 #408, 0.22 #2173), 018vs (0.23 #983, 0.23 #931, 0.21 #275), 028tv0 (0.17 #144, 0.12 #1973, 0.12 #864), 05r5c (0.15 #1968, 0.15 #2025, 0.15 #5899), 013y1f (0.15 #2025, 0.13 #916, 0.12 #1114), 01vdm0 (0.15 #2025, 0.13 #916, 0.12 #1114), 0395lw (0.15 #2025, 0.13 #916, 0.12 #1114), 016622 (0.15 #2025, 0.13 #916, 0.12 #1114) >> Best rule #1964 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 094xh; >> query: (?x654, 0342h) <- award(?x654, ?x4912), group(?x654, ?x1749), artist(?x2149, ?x654), role(?x654, ?x316) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1977 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 82 *> proper extension: 094xh; *> query: (?x654, 05148p4) <- award(?x654, ?x4912), group(?x654, ?x1749), artist(?x2149, ?x654), role(?x654, ?x316) *> conf = 0.27 ranks of expected_values: 3 EVAL 0kzy0 role 05148p4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 146.000 146.000 0.440 http://example.org/music/group_member/membership./music/group_membership/role #15882-037hgm PRED entity: 037hgm PRED relation: student! PRED expected values: 0bqxw => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 151): 07vhb (0.18 #694, 0.03 #3324, 0.02 #3850), 0dbns (0.11 #482, 0.02 #5216, 0.01 #6268), 0bwfn (0.10 #12898, 0.09 #34992, 0.08 #36570), 02g839 (0.10 #11071, 0.08 #6337, 0.08 #17909), 065y4w7 (0.09 #540, 0.05 #12638, 0.05 #34732), 01w5m (0.09 #630, 0.05 #6416, 0.05 #17988), 01q7q2 (0.09 #818, 0.04 #2922, 0.02 #4500), 01k7xz (0.09 #592, 0.03 #3222, 0.02 #3748), 01n6r0 (0.09 #685), 017z88 (0.08 #6394, 0.05 #17966, 0.05 #12180) >> Best rule #694 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 07w21; 0151w_; 04ld94; 051z6rz; 091yn0; 09px1w; 01k53x; 022q32; >> query: (?x4759, 07vhb) <- place_of_birth(?x4759, ?x3976), ?x3976 = 01jr6, nationality(?x4759, ?x94), ?x94 = 09c7w0 >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 037hgm student! 0bqxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 143.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student #15881-047tsx3 PRED entity: 047tsx3 PRED relation: film_release_region PRED expected values: 05qhw 02k54 01p1v => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 108): 09c7w0 (0.92 #11490, 0.92 #11634, 0.92 #7904), 05qhw (0.85 #156, 0.66 #4324, 0.66 #3461), 0k6nt (0.82 #20, 0.81 #2034, 0.79 #164), 0d060g (0.78 #149, 0.65 #1732, 0.64 #4174), 06bnz (0.75 #181, 0.60 #3486, 0.59 #4349), 01znc_ (0.75 #177, 0.71 #464, 0.69 #3482), 0ctw_b (0.67 #165, 0.47 #3470, 0.46 #4333), 04gzd (0.61 #151, 0.41 #3456, 0.41 #1734), 01p1v (0.56 #188, 0.37 #3493, 0.36 #4356), 015qh (0.56 #176, 0.37 #3481, 0.35 #4344) >> Best rule #11490 for best value: >> intensional similarity = 5 >> extensional distance = 1327 >> proper extension: 02d413; 0g22z; 018js4; 0b2v79; 01jc6q; 027qgy; 047q2k1; 0ckr7s; 08lr6s; 016fyc; ... >> query: (?x3981, 09c7w0) <- film_release_region(?x3981, ?x390), film_release_region(?x3981, ?x311), nationality(?x72, ?x390), country(?x3838, ?x311), olympics(?x390, ?x418) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #156 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 108 *> proper extension: 0gtsx8c; 0jjy0; 0gj8t_b; 04zyhx; 02r8hh_; 0gd0c7x; 0661m4p; 07f_7h; 06w839_; 026njb5; ... *> query: (?x3981, 05qhw) <- film_crew_role(?x3981, ?x137), film_release_region(?x3981, ?x2629), film_release_region(?x3981, ?x2152), ?x2152 = 06mkj, ?x2629 = 06f32 *> conf = 0.85 ranks of expected_values: 2, 9, 19 EVAL 047tsx3 film_release_region 01p1v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 107.000 107.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047tsx3 film_release_region 02k54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 107.000 107.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047tsx3 film_release_region 05qhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15880-069nzr PRED entity: 069nzr PRED relation: award PRED expected values: 0cqhb3 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 235): 09sb52 (0.70 #9317, 0.70 #30384, 0.70 #11748), 0bdw6t (0.70 #9317, 0.70 #30384, 0.70 #11748), 0ck27z (0.31 #902, 0.31 #1712, 0.21 #2522), 0cqhk0 (0.20 #846, 0.18 #1656, 0.13 #4087), 040njc (0.16 #14991, 0.14 #14585, 0.12 #29978), 0f_nbyh (0.16 #14991, 0.14 #14585, 0.12 #29978), 04dn09n (0.16 #14991, 0.14 #14585, 0.12 #29978), 02pqp12 (0.16 #14991, 0.14 #14585, 0.12 #29978), 0cqhb3 (0.16 #14991, 0.14 #14585, 0.12 #29978), 0gq9h (0.16 #14991, 0.14 #14585, 0.12 #26332) >> Best rule #9317 for best value: >> intensional similarity = 3 >> extensional distance = 1131 >> proper extension: 025jfl; 02r3zy; 03g5jw; 0dvqq; 03fbc; 04qmr; 0hvbj; 01dwrc; 0gr69; 02k5sc; ... >> query: (?x5030, ?x704) <- award_nominee(?x1676, ?x5030), film(?x1676, ?x1202), award_winner(?x704, ?x5030) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #14991 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1426 *> proper extension: 030_1_; 03jvmp; 0g5lhl7; 01w92; 05xbx; 04glx0; 0187x8; *> query: (?x5030, ?x704) <- award_nominee(?x1676, ?x5030), award_winner(?x5030, ?x969), award_winner(?x704, ?x1676) *> conf = 0.16 ranks of expected_values: 9 EVAL 069nzr award 0cqhb3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 95.000 95.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15879-01_p6t PRED entity: 01_p6t PRED relation: location PRED expected values: 06y57 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 140): 030qb3t (0.28 #8115, 0.26 #11328, 0.24 #83), 02_286 (0.23 #8872, 0.21 #11282, 0.18 #13692), 0r0m6 (0.07 #3430, 0.05 #1020, 0.03 #11462), 04jpl (0.07 #7246, 0.06 #16885, 0.06 #17688), 0cr3d (0.06 #4161, 0.06 #145, 0.06 #948), 0k049 (0.06 #8, 0.06 #811, 0.05 #1615), 01n7q (0.06 #63, 0.05 #3276, 0.05 #4079), 0cc56 (0.06 #11302, 0.05 #8892, 0.05 #7286), 02cft (0.06 #14459, 0.01 #7535, 0.01 #8338), 0clz7 (0.06 #14459) >> Best rule #8115 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 05m63c; 04bs3j; 0htlr; 04shbh; 0n6f8; 0prjs; 031zkw; 0f2df; 03xmy1; 0285c; ... >> query: (?x5758, 030qb3t) <- film(?x5758, ?x414), participant(?x6424, ?x5758), languages(?x5758, ?x254) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #17926 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 529 *> proper extension: 02wr2r; 01d6jf; 05g7q; *> query: (?x5758, 06y57) <- film(?x5758, ?x414), award_winner(?x5758, ?x237), location(?x5758, ?x3699) *> conf = 0.01 ranks of expected_values: 104 EVAL 01_p6t location 06y57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 99.000 99.000 0.275 http://example.org/people/person/places_lived./people/place_lived/location #15878-0cms7f PRED entity: 0cms7f PRED relation: award_nominee PRED expected values: 05p92jn => 96 concepts (37 used for prediction) PRED predicted values (max 10 best out of 593): 0cnl09 (0.83 #2332, 0.81 #69960, 0.81 #86285), 08hsww (0.83 #2332, 0.81 #69960, 0.81 #86285), 060j8b (0.83 #2332, 0.81 #69960, 0.81 #86285), 043js (0.83 #2332, 0.81 #69960, 0.81 #86285), 05p92jn (0.83 #2332, 0.81 #69960, 0.81 #86285), 0cmt6q (0.83 #2332, 0.81 #69960, 0.81 #86285), 08wq0g (0.77 #74625, 0.77 #18655, 0.77 #46634), 0cms7f (0.73 #3783, 0.73 #1451, 0.17 #69961), 027cxsm (0.18 #341, 0.17 #69961, 0.17 #72293), 0cj2nl (0.18 #884, 0.17 #69961, 0.17 #72293) >> Best rule #2332 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 083chw; 072bb1; 0bt4r4; 0h3mrc; 0cnl1c; 060j8b; 0cl0bk; 04zkj5; 05xpms; >> query: (?x6263, ?x275) <- award_nominee(?x275, ?x6263), award_nominee(?x274, ?x6263), place_of_birth(?x6263, ?x5147), ?x274 = 0cnl80 >> conf = 0.83 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5 EVAL 0cms7f award_nominee 05p92jn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 96.000 37.000 0.828 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15877-0167km PRED entity: 0167km PRED relation: currency PRED expected values: 09nqf => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.28 #55, 0.26 #103, 0.26 #22), 01nv4h (0.08 #14, 0.07 #38, 0.07 #29), 02l6h (0.01 #21, 0.01 #30, 0.01 #36) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 130 >> proper extension: 024yxd; >> query: (?x5879, 09nqf) <- profession(?x5879, ?x2348), origin(?x5879, ?x5036), category(?x5879, ?x134), ?x2348 = 0nbcg >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0167km currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.280 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #15876-0ch26b_ PRED entity: 0ch26b_ PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.82 #234, 0.80 #96, 0.80 #239), 02nxhr (0.11 #72, 0.11 #77, 0.10 #92), 07c52 (0.09 #145, 0.07 #104, 0.07 #130), 07z4p (0.07 #147, 0.06 #132, 0.05 #40), 0735l (0.01 #59) >> Best rule #234 for best value: >> intensional similarity = 3 >> extensional distance = 926 >> proper extension: 058kh7; >> query: (?x1916, 029j_) <- film(?x3028, ?x1916), music(?x1916, ?x3910), award_nominee(?x230, ?x3028) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ch26b_ film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.819 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15875-0b1y_2 PRED entity: 0b1y_2 PRED relation: film_crew_role PRED expected values: 01vx2h => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 20): 01vx2h (0.49 #97, 0.31 #1066, 0.31 #273), 0dxtw (0.36 #1065, 0.35 #1393, 0.34 #1571), 01pvkk (0.29 #10, 0.28 #1097, 0.27 #1395), 015h31 (0.20 #152, 0.16 #94, 0.15 #329), 020xn5 (0.16 #93, 0.03 #269, 0.02 #1062), 02rh1dz (0.14 #95, 0.14 #7, 0.10 #1064), 02vs3x5 (0.14 #18, 0.07 #282, 0.07 #135), 089fss (0.12 #33, 0.09 #239, 0.07 #1061), 0263ycg (0.12 #102, 0.04 #249, 0.03 #131), 033smt (0.10 #167, 0.10 #109, 0.07 #344) >> Best rule #97 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 01gglm; >> query: (?x2920, 01vx2h) <- language(?x2920, ?x254), award_winner(?x2920, ?x902), film_crew_role(?x2920, ?x2472), ?x2472 = 01xy5l_ >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b1y_2 film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.490 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15874-0plw PRED entity: 0plw PRED relation: state_province_region PRED expected values: 07z1m => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 75): 01n7q (0.64 #2460, 0.60 #1483, 0.60 #1361), 09c7w0 (0.23 #5135, 0.22 #21792, 0.20 #8812), 07b_l (0.19 #3470, 0.18 #5258, 0.16 #19457), 07z1m (0.18 #5258, 0.16 #19457, 0.12 #755), 0488g (0.18 #5258, 0.16 #19457, 0.12 #761), 04ych (0.18 #5258, 0.16 #19457, 0.12 #749), 01vsb_ (0.18 #5258, 0.16 #19457, 0.12 #835), 03v0t (0.18 #5258, 0.16 #19457, 0.09 #5799), 04rrd (0.18 #5258, 0.12 #4793, 0.12 #3324), 081yw (0.12 #3358, 0.10 #7153, 0.09 #5929) >> Best rule #2460 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 032dg7; >> query: (?x12850, 01n7q) <- child(?x1908, ?x12850), state_province_region(?x12850, ?x335), industry(?x12850, ?x6575), major_field_of_study(?x6575, ?x2606) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #5258 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 0974y; *> query: (?x12850, ?x1767) <- child(?x1908, ?x12850), state_province_region(?x12850, ?x335), industry(?x12850, ?x6575), industry(?x3379, ?x6575), state_province_region(?x3379, ?x1767) *> conf = 0.18 ranks of expected_values: 4 EVAL 0plw state_province_region 07z1m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 196.000 196.000 0.643 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #15873-01qx13 PRED entity: 01qx13 PRED relation: student! PRED expected values: 02kxx1 => 158 concepts (93 used for prediction) PRED predicted values (max 10 best out of 166): 03w1lf (0.12 #2459, 0.07 #10365, 0.06 #15636), 031vy_ (0.12 #2385, 0.03 #12926, 0.03 #13453), 02kxx1 (0.12 #2566, 0.03 #13107, 0.03 #13634), 03gdf1 (0.12 #2561, 0.02 #9413, 0.02 #10467), 07tg4 (0.12 #18006, 0.05 #14844, 0.04 #34355), 0h6rm (0.11 #2779, 0.06 #3833, 0.05 #18064), 09f2j (0.11 #2794, 0.06 #3848, 0.05 #5429), 01q0kg (0.11 #2769, 0.06 #3823, 0.03 #7512), 07tgn (0.09 #17937, 0.04 #34286, 0.03 #37450), 02cw8s (0.08 #6921, 0.04 #19045, 0.03 #20099) >> Best rule #2459 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 06gn7r; >> query: (?x2992, 03w1lf) <- location(?x2992, ?x8297), ?x8297 = 0cvw9, type_of_union(?x2992, ?x566), ?x566 = 04ztj >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #2566 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 06gn7r; *> query: (?x2992, 02kxx1) <- location(?x2992, ?x8297), ?x8297 = 0cvw9, type_of_union(?x2992, ?x566), ?x566 = 04ztj *> conf = 0.12 ranks of expected_values: 3 EVAL 01qx13 student! 02kxx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 158.000 93.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #15872-0459z PRED entity: 0459z PRED relation: artists! PRED expected values: 0ggq0m => 141 concepts (123 used for prediction) PRED predicted values (max 10 best out of 211): 06by7 (0.46 #2860, 0.46 #10756, 0.43 #6647), 064t9 (0.43 #6007, 0.39 #15799, 0.38 #7588), 05lls (0.33 #16, 0.33 #3786, 0.27 #2223), 0ggq0m (0.33 #13, 0.33 #3786, 0.27 #2220), 06q6jz (0.33 #3786, 0.26 #7892, 0.24 #7891), 0l8gh (0.33 #3786, 0.26 #7892, 0.24 #7891), 021dvj (0.33 #3786, 0.26 #7892, 0.24 #7891), 0gywn (0.32 #6054, 0.25 #7635, 0.21 #2898), 0155w (0.30 #6735, 0.29 #2948, 0.27 #3897), 03_d0 (0.30 #8534, 0.30 #8851, 0.24 #4744) >> Best rule #2860 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 01pbxb; 07s3vqk; 01vrncs; 0k4gf; 0gt_k; 03h_fk5; 0gcs9; 04k15; 01w524f; 03j24kf; ... >> query: (?x11512, 06by7) <- influenced_by(?x862, ?x11512), instrumentalists(?x316, ?x11512), ?x316 = 05r5c, location(?x11512, ?x863) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 06y9c2; *> query: (?x11512, 0ggq0m) <- nationality(?x11512, ?x1264), ?x1264 = 0345h, instrumentalists(?x316, ?x11512), ?x316 = 05r5c *> conf = 0.33 ranks of expected_values: 4 EVAL 0459z artists! 0ggq0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 141.000 123.000 0.458 http://example.org/music/genre/artists #15871-0154d7 PRED entity: 0154d7 PRED relation: gender PRED expected values: 05zppz => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #7, 0.82 #21, 0.79 #11), 02zsn (0.55 #189, 0.46 #243, 0.45 #224) >> Best rule #7 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 04gcd1; 0c9c0; 01nxzv; >> query: (?x8685, 05zppz) <- profession(?x8685, ?x1383), profession(?x8685, ?x353), ?x1383 = 0np9r, film(?x8685, ?x1386), ?x353 = 0cbd2 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0154d7 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.857 http://example.org/people/person/gender #15870-06yj20 PRED entity: 06yj20 PRED relation: place_of_birth PRED expected values: 0xpp5 => 77 concepts (43 used for prediction) PRED predicted values (max 10 best out of 79): 0cr3d (0.33 #94, 0.22 #3617, 0.09 #5026), 01snm (0.20 #1648, 0.05 #4467, 0.05 #5171), 05fkf (0.20 #724, 0.03 #30297, 0.03 #15501), 0r7fy (0.20 #1458, 0.02 #9911, 0.01 #11320), 0bxc4 (0.17 #2758, 0.11 #4167, 0.03 #15501), 0lphb (0.14 #3075, 0.03 #15501), 06gmr (0.10 #4263, 0.05 #6375, 0.04 #8488), 0jfqp (0.09 #22547, 0.08 #16206, 0.03 #30297), 02_286 (0.09 #15520, 0.08 #17635, 0.08 #14815), 03b12 (0.05 #4635, 0.02 #6043, 0.02 #7452) >> Best rule #94 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 054c1; >> query: (?x12478, 0cr3d) <- student(?x1884, ?x12478), athlete(?x471, ?x12478), ?x1884 = 0bx8pn, profession(?x12478, ?x7623), sport(?x59, ?x471) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06yj20 place_of_birth 0xpp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 43.000 0.333 http://example.org/people/person/place_of_birth #15869-026rm_y PRED entity: 026rm_y PRED relation: nationality PRED expected values: 0h7x => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.80 #5570, 0.77 #8153, 0.76 #7856), 0h7x (0.45 #1293, 0.27 #9646, 0.02 #1227), 01nhhz (0.45 #1293), 07ssc (0.27 #9646, 0.17 #1208, 0.10 #213), 02jx1 (0.16 #1225, 0.12 #230, 0.11 #1325), 03rk0 (0.11 #2930, 0.10 #2532, 0.10 #2831), 0f8l9c (0.07 #1215, 0.02 #9867, 0.02 #10263), 0d060g (0.05 #5977, 0.05 #4281, 0.05 #900), 03rjj (0.04 #1198, 0.04 #698, 0.03 #1497), 06q1r (0.03 #1269, 0.02 #5148, 0.02 #3060) >> Best rule #5570 for best value: >> intensional similarity = 2 >> extensional distance = 1344 >> proper extension: 0466k4; >> query: (?x8740, 09c7w0) <- location(?x8740, ?x863), adjoins(?x7182, ?x863) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1293 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 302 *> proper extension: 05fh2; *> query: (?x8740, ?x1355) <- place_of_birth(?x8740, ?x863), capital(?x1355, ?x863) *> conf = 0.45 ranks of expected_values: 2 EVAL 026rm_y nationality 0h7x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.802 http://example.org/people/person/nationality #15868-01t110 PRED entity: 01t110 PRED relation: award_winner! PRED expected values: 019bk0 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 107): 02rjjll (0.25 #146, 0.11 #851, 0.10 #2684), 01c6qp (0.18 #583, 0.12 #160, 0.09 #865), 01s695 (0.17 #10577, 0.12 #144, 0.12 #849), 019bk0 (0.17 #10577, 0.12 #157, 0.11 #16), 013b2h (0.16 #362, 0.14 #503, 0.14 #644), 09n4nb (0.12 #189, 0.12 #612, 0.11 #48), 0466p0j (0.12 #217, 0.12 #499, 0.11 #76), 0gpjbt (0.12 #170, 0.11 #29, 0.11 #875), 056878 (0.12 #173, 0.11 #32, 0.09 #596), 01mh_q (0.12 #230, 0.09 #935, 0.09 #1358) >> Best rule #146 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0249kn; 018ndc; 0mjn2; >> query: (?x6461, 02rjjll) <- artists(?x10833, ?x6461), ?x10833 = 06924p, origin(?x6461, ?x13006) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #10577 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1354 *> proper extension: 02vyh; 035_2h; 039cq4; *> query: (?x6461, ?x342) <- award_winner(?x1751, ?x6461), award(?x1751, ?x724), award_winner(?x342, ?x1751) *> conf = 0.17 ranks of expected_values: 4 EVAL 01t110 award_winner! 019bk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 126.000 126.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15867-076psv PRED entity: 076psv PRED relation: film_sets_designed PRED expected values: 03mr85 => 116 concepts (90 used for prediction) PRED predicted values (max 10 best out of 93): 01k7b0 (0.70 #524, 0.07 #875, 0.07 #874), 048rn (0.15 #131, 0.14 #393, 0.14 #306), 0bkq7 (0.15 #158, 0.09 #420, 0.09 #333), 014knw (0.15 #165, 0.09 #427, 0.09 #340), 04wddl (0.15 #163, 0.09 #425, 0.09 #338), 029jt9 (0.15 #161, 0.09 #423, 0.09 #336), 0dnw1 (0.15 #143, 0.09 #405, 0.09 #318), 0jwvf (0.15 #140, 0.09 #402, 0.09 #315), 0bl06 (0.15 #139, 0.09 #401, 0.09 #314), 0k4kk (0.15 #95, 0.09 #357, 0.09 #270) >> Best rule #524 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0584j4n; >> query: (?x4423, ?x951) <- nominated_for(?x4423, ?x951), award_nominee(?x199, ?x4423), film_sets_designed(?x4423, ?x4300) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 076psv film_sets_designed 03mr85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 90.000 0.704 http://example.org/film/film_set_designer/film_sets_designed #15866-042z_g PRED entity: 042z_g PRED relation: actor! PRED expected values: 02kk_c => 65 concepts (54 used for prediction) PRED predicted values (max 10 best out of 42): 09fc83 (0.10 #355, 0.10 #90, 0.08 #620), 01fx1l (0.10 #365, 0.10 #100, 0.05 #1160), 039c26 (0.10 #314, 0.08 #579, 0.06 #844), 0pc_l (0.10 #506, 0.08 #771, 0.06 #1036), 0147w8 (0.10 #498, 0.08 #763, 0.06 #1028), 024rwx (0.10 #371, 0.08 #636, 0.06 #901), 0bsxd3 (0.10 #252, 0.08 #782, 0.06 #1047), 09v38qj (0.10 #228, 0.08 #758, 0.06 #1023), 09rfpk (0.10 #202, 0.08 #732, 0.06 #997), 07g9f (0.10 #201, 0.08 #731, 0.06 #996) >> Best rule #355 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 050t68; 015vq_; 057_yx; >> query: (?x5099, 09fc83) <- award_nominee(?x5099, ?x4137), award_nominee(?x5099, ?x1677), ?x1677 = 021vwt, ?x4137 = 0410cp >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 042z_g actor! 02kk_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 54.000 0.100 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #15865-013pk3 PRED entity: 013pk3 PRED relation: role PRED expected values: 05148p4 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 25): 0342h (0.25 #5, 0.19 #137, 0.16 #1260), 03bx0bm (0.14 #1279, 0.12 #1477, 0.11 #24), 05r5c (0.11 #9, 0.07 #1264, 0.06 #141), 05148p4 (0.10 #1274, 0.08 #1472, 0.07 #151), 02hnl (0.07 #31, 0.05 #1286, 0.04 #1484), 0l14qv (0.07 #6, 0.03 #138, 0.02 #1261), 018vs (0.04 #1270, 0.04 #2661, 0.04 #1468), 03qjg (0.04 #1299, 0.04 #44, 0.03 #1497), 028tv0 (0.04 #1467, 0.04 #1269, 0.04 #2660), 0l14md (0.04 #8, 0.03 #2654, 0.03 #140) >> Best rule #5 for best value: >> intensional similarity = 2 >> extensional distance = 26 >> proper extension: 03xhj6; 0jn38; >> query: (?x7638, 0342h) <- artist(?x382, ?x7638), ?x382 = 086k8 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1274 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 332 *> proper extension: 094xh; *> query: (?x7638, 05148p4) <- award(?x7638, ?x1670), type_of_union(?x7638, ?x566), artist(?x382, ?x7638) *> conf = 0.10 ranks of expected_values: 4 EVAL 013pk3 role 05148p4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 109.000 0.250 http://example.org/music/group_member/membership./music/group_membership/role #15864-03gwpw2 PRED entity: 03gwpw2 PRED relation: award_winner PRED expected values: 024rbz 0gsg7 => 49 concepts (32 used for prediction) PRED predicted values (max 10 best out of 2400): 01hkhq (0.50 #20155, 0.50 #3393, 0.50 #1869), 01713c (0.50 #4774, 0.41 #36581, 0.38 #32004), 0h0wc (0.50 #1879, 0.40 #9492, 0.33 #27789), 0h5f5n (0.50 #1552, 0.40 #9165, 0.33 #12214), 018ygt (0.50 #2472, 0.39 #32957, 0.38 #20758), 025b5y (0.50 #3904, 0.38 #20666, 0.29 #3044), 02y_2y (0.50 #2202, 0.38 #20488, 0.25 #3726), 0pz7h (0.50 #4682, 0.33 #13821, 0.29 #26022), 015q43 (0.50 #5348, 0.33 #14487, 0.29 #17536), 01_njt (0.50 #20979, 0.25 #4217, 0.25 #2693) >> Best rule #20155 for best value: >> intensional similarity = 16 >> extensional distance = 6 >> proper extension: 09qvms; 027hjff; 09pj68; >> query: (?x762, 01hkhq) <- honored_for(?x762, ?x8870), honored_for(?x762, ?x5808), honored_for(?x762, ?x1015), honored_for(?x762, ?x945), honored_for(?x762, ?x813), award_winner(?x762, ?x2776), nominated_for(?x198, ?x945), featured_film_locations(?x945, ?x151), nominated_for(?x2776, ?x4063), ?x5808 = 05lfwd, award_winner(?x8870, ?x879), award_winner(?x2246, ?x2776), genre(?x813, ?x53), award(?x8870, ?x435), film_release_distribution_medium(?x1015, ?x81), nominated_for(?x1254, ?x813) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #48775 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 50 *> proper extension: 0gx_st; 03nnm4t; *> query: (?x762, ?x163) <- honored_for(?x762, ?x945), award_winner(?x762, ?x2776), nominated_for(?x198, ?x945), genre(?x945, ?x604), award_winner(?x6597, ?x2776), award_nominee(?x2776, ?x5007), award_winner(?x5592, ?x2776), ?x5592 = 0275n3y, award_winner(?x945, ?x163), genre(?x493, ?x604) *> conf = 0.36 ranks of expected_values: 52, 72 EVAL 03gwpw2 award_winner 0gsg7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 49.000 32.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03gwpw2 award_winner 024rbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 49.000 32.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15863-01trxd PRED entity: 01trxd PRED relation: citytown PRED expected values: 0156q => 152 concepts (98 used for prediction) PRED predicted values (max 10 best out of 124): 0156q (0.70 #8867, 0.70 #3693, 0.69 #9237), 0345h (0.33 #369, 0.29 #3692, 0.29 #13664), 02h6_6p (0.15 #1159, 0.12 #790, 0.03 #2637), 02_286 (0.13 #3338, 0.12 #9620, 0.11 #10359), 04kf4 (0.12 #863, 0.08 #1232, 0.02 #1601), 04jpl (0.11 #3330, 0.07 #8504, 0.07 #8874), 030qb3t (0.09 #3351, 0.06 #8525, 0.06 #8895), 02z0j (0.08 #1310, 0.02 #1679, 0.02 #2048), 03hrz (0.08 #1168, 0.02 #1537, 0.02 #1906), 0cy41 (0.08 #1379, 0.01 #2857) >> Best rule #8867 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 01y888; 080z7; >> query: (?x13080, ?x1646) <- institution(?x865, ?x13080), contains(?x1646, ?x13080), mode_of_transportation(?x1646, ?x4272), place_of_birth(?x628, ?x1646) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01trxd citytown 0156q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 98.000 0.701 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #15862-0217g PRED entity: 0217g PRED relation: risk_factors! PRED expected values: 09d11 => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 85): 02bft (0.75 #248, 0.61 #1778, 0.60 #699), 09d11 (0.75 #248, 0.60 #699, 0.50 #272), 07jwr (0.75 #248, 0.60 #699, 0.48 #1323), 02k6hp (0.70 #1045, 0.67 #988, 0.67 #609), 02y0js (0.67 #576, 0.60 #1012, 0.56 #955), 0gk4g (0.67 #581, 0.44 #960, 0.40 #1017), 0dq9p (0.63 #1760, 0.60 #329, 0.53 #2084), 05mdx (0.58 #1341, 0.50 #268, 0.40 #328), 0h1wz (0.57 #816, 0.44 #893, 0.41 #895), 0hg45 (0.50 #1050, 0.50 #614, 0.45 #1236) >> Best rule #248 for best value: >> intensional similarity = 18 >> extensional distance = 2 >> proper extension: 0h1wz; >> query: (?x13738, ?x4291) <- risk_factors(?x11659, ?x13738), risk_factors(?x11392, ?x13738), risk_factors(?x8675, ?x13738), risk_factors(?x11659, ?x13131), risk_factors(?x11659, ?x12536), symptom_of(?x9509, ?x11659), symptom_of(?x4905, ?x11659), symptom_of(?x9438, ?x8675), ?x4905 = 01j6t0, people(?x13131, ?x6358), notable_people_with_this_condition(?x13131, ?x8708), ?x9509 = 0gxb2, ?x9438 = 012qjw, risk_factors(?x11392, ?x268), symptom_of(?x3679, ?x12536), people(?x268, ?x8579), risk_factors(?x4291, ?x13131), ?x8579 = 01vs4f3 >> conf = 0.75 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0217g risk_factors! 09d11 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 34.000 34.000 0.750 http://example.org/medicine/disease/risk_factors #15861-05qgd9 PRED entity: 05qgd9 PRED relation: category PRED expected values: 08mbj5d => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #104, 0.90 #89, 0.89 #141) >> Best rule #104 for best value: >> intensional similarity = 5 >> extensional distance = 168 >> proper extension: 0yl_j; >> query: (?x12026, 08mbj5d) <- currency(?x12026, ?x170), citytown(?x12026, ?x2298), location(?x1165, ?x2298), time_zones(?x2298, ?x2674), contains(?x1426, ?x2298) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qgd9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 220.000 220.000 0.918 http://example.org/common/topic/webpage./common/webpage/category #15860-0gwjw0c PRED entity: 0gwjw0c PRED relation: produced_by PRED expected values: 06q8hf => 94 concepts (72 used for prediction) PRED predicted values (max 10 best out of 203): 0693l (0.38 #8905, 0.26 #1935, 0.26 #12004), 026rm_y (0.18 #4260, 0.17 #3485, 0.17 #15493), 0dvmd (0.18 #4260, 0.17 #3485, 0.17 #15493), 0f5xn (0.17 #3485, 0.17 #15493, 0.16 #18204), 06r_by (0.17 #3485, 0.17 #15493, 0.16 #18204), 01wmxfs (0.17 #3485, 0.17 #15493, 0.16 #18204), 021yc7p (0.17 #3485, 0.17 #15493, 0.16 #18204), 02x2t07 (0.17 #3485, 0.17 #15493, 0.16 #18204), 02bfxb (0.11 #114, 0.03 #1662, 0.03 #2436), 06q8hf (0.08 #6584, 0.08 #3097, 0.08 #6583) >> Best rule #8905 for best value: >> intensional similarity = 3 >> extensional distance = 395 >> proper extension: 0pvms; >> query: (?x6886, ?x3117) <- produced_by(?x6886, ?x4060), written_by(?x6886, ?x3117), film(?x828, ?x6886) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #6584 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 237 *> proper extension: 09p35z; 02v63m; 01j8wk; 07bwr; 048rn; 01q2nx; 02tktw; 05ch98; 01_1hw; 03n0cd; ... *> query: (?x6886, ?x6690) <- produced_by(?x6886, ?x4060), executive_produced_by(?x6886, ?x6690), profession(?x6690, ?x319), produced_by(?x1444, ?x6690) *> conf = 0.08 ranks of expected_values: 10 EVAL 0gwjw0c produced_by 06q8hf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 94.000 72.000 0.378 http://example.org/film/film/produced_by #15859-04ltlj PRED entity: 04ltlj PRED relation: currency PRED expected values: 09nqf => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.81 #162, 0.78 #246, 0.78 #78), 088n7 (0.06 #14), 01nv4h (0.03 #177, 0.03 #184, 0.03 #261), 02gsvk (0.03 #69, 0.02 #55, 0.02 #132), 02l6h (0.02 #18, 0.02 #4, 0.02 #32) >> Best rule #162 for best value: >> intensional similarity = 4 >> extensional distance = 603 >> proper extension: 047svrl; 07k2mq; 01gglm; >> query: (?x11276, 09nqf) <- nominated_for(?x1554, ?x11276), titles(?x162, ?x11276), production_companies(?x11276, ?x1561), award(?x1561, ?x2022) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ltlj currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.810 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #15858-07x4c PRED entity: 07x4c PRED relation: category PRED expected values: 08mbj5d => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #52, 0.90 #46, 0.90 #47) >> Best rule #52 for best value: >> intensional similarity = 4 >> extensional distance = 308 >> proper extension: 0kz2w; 0ym8f; 01nnsv; 07tjf; 0ym1n; >> query: (?x7127, 08mbj5d) <- major_field_of_study(?x7127, ?x1682), student(?x7127, ?x2663), colors(?x7127, ?x332), major_field_of_study(?x734, ?x1682) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07x4c category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.900 http://example.org/common/topic/webpage./common/webpage/category #15857-02lcqs PRED entity: 02lcqs PRED relation: time_zones! PRED expected values: 09c7w0 06_kh 0r2l7 0mlyw 0r5lz 07bcn 0235l 0qzhw 010t4v 0n6mc 0r6ff 0mmpz 0mhdz 0mmpm 0r02m => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1852): 0mmpm (0.84 #1065, 0.68 #10704, 0.67 #8555), 0mmpz (0.84 #1065, 0.68 #10704, 0.67 #8555), 0n6mc (0.84 #1065, 0.68 #10704, 0.45 #5341), 09c7w0 (0.83 #10710, 0.78 #8560, 0.75 #11791), 015zxh (0.68 #10704, 0.67 #8555, 0.64 #9630), 05rgl (0.68 #10704, 0.64 #9630, 0.63 #11795), 0846v (0.68 #10704, 0.64 #9630, 0.63 #11795), 050l8 (0.68 #10704, 0.64 #9630, 0.63 #11795), 0r6ff (0.68 #10704, 0.64 #9630, 0.45 #5341), 04rrx (0.68 #10704, 0.63 #11795, 0.52 #6419) >> Best rule #1065 for best value: >> intensional similarity = 32 >> extensional distance = 1 >> proper extension: 02hcv8; >> query: (?x2950, ?x11525) <- time_zones(?x13566, ?x2950), time_zones(?x11653, ?x2950), time_zones(?x6637, ?x2950), time_zones(?x3026, ?x2950), time_zones(?x2552, ?x2950), time_zones(?x1227, ?x2950), time_zones(?x1036, ?x2950), location(?x496, ?x2552), second_level_divisions(?x94, ?x11653), teams(?x2552, ?x580), jurisdiction_of_office(?x1195, ?x2552), ?x1195 = 0pqc5, location(?x1773, ?x3026), contains(?x1227, ?x191), state_province_region(?x2931, ?x1227), state_province_region(?x902, ?x1227), month(?x3026, ?x1459), featured_film_locations(?x155, ?x2552), mode_of_transportation(?x1036, ?x4272), student(?x6637, ?x395), location_of_ceremony(?x397, ?x3026), artist(?x2931, ?x133), film(?x902, ?x103), district_represented(?x176, ?x1227), religion(?x1227, ?x109), film(?x397, ?x696), featured_film_locations(?x136, ?x1036), organization(?x4682, ?x2931), award_winner(?x1861, ?x1773), production_companies(?x66, ?x902), award(?x397, ?x591), county(?x13566, ?x11525) >> conf = 0.84 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2, 3, 4, 9, 15, 22, 26, 36, 37, 89, 96, 98, 649, 650 EVAL 02lcqs time_zones! 0r02m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 0mmpm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 0mhdz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 0mmpz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 0r6ff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 0n6mc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 010t4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 0qzhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 0235l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 07bcn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 0r5lz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 0mlyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 0r2l7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 06_kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 13.000 13.000 0.840 http://example.org/location/location/time_zones EVAL 02lcqs time_zones! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.840 http://example.org/location/location/time_zones #15856-0ftlx PRED entity: 0ftlx PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 21): 0pqc5 (0.51 #512, 0.47 #673, 0.47 #97), 060c4 (0.30 #1132, 0.26 #1017, 0.23 #1708), 060bp (0.27 #1130, 0.23 #1015, 0.19 #1706), 0f6c3 (0.22 #907, 0.22 #1298, 0.22 #1091), 0fkvn (0.22 #1087, 0.22 #903, 0.21 #1294), 09n5b9 (0.22 #911, 0.20 #1302, 0.19 #1095), 0p5vf (0.10 #797, 0.09 #3043, 0.09 #635), 01zq91 (0.09 #3043, 0.09 #2973, 0.09 #2926), 01q24l (0.08 #729, 0.08 #590, 0.07 #752), 0fkzq (0.07 #1100, 0.06 #1307, 0.06 #916) >> Best rule #512 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 01s3v; >> query: (?x5237, 0pqc5) <- place_of_birth(?x9009, ?x5237), citytown(?x12613, ?x5237), country(?x5237, ?x3730), teams(?x5237, ?x8914) >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ftlx jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.507 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #15855-0gqy2 PRED entity: 0gqy2 PRED relation: ceremony PRED expected values: 0gmdkyy 05qb8vx 0c53zb => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 80): 0gpjbt (0.61 #1283, 0.36 #2311, 0.34 #2470), 09n4nb (0.60 #1297, 0.35 #2325, 0.34 #2484), 0466p0j (0.59 #1313, 0.35 #2341, 0.33 #2500), 05pd94v (0.59 #1265, 0.33 #2293, 0.33 #2452), 02cg41 (0.58 #1334, 0.35 #2362, 0.33 #2521), 02rjjll (0.58 #1268, 0.34 #2296, 0.33 #2455), 056878 (0.58 #1286, 0.34 #2314, 0.34 #2473), 01c6qp (0.57 #1279, 0.33 #2307, 0.33 #2466), 01mh_q (0.54 #1318, 0.32 #2346, 0.31 #2505), 01bx35 (0.54 #1269, 0.32 #2297, 0.32 #2456) >> Best rule #1283 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 07n52; 02xzd9; >> query: (?x3066, 0gpjbt) <- category_of(?x3066, ?x3459), category_of(?x720, ?x3459), disciplines_or_subjects(?x720, ?x373) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #494 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0gqwc; 0gqyl; 02x201b; *> query: (?x3066, 0gmdkyy) <- award_winner(?x3066, ?x92), nominated_for(?x3066, ?x10752), ?x10752 = 01k5y0, ceremony(?x3066, ?x78) *> conf = 0.50 ranks of expected_values: 15, 16, 21 EVAL 0gqy2 ceremony 0c53zb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 52.000 52.000 0.614 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqy2 ceremony 05qb8vx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 52.000 52.000 0.614 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqy2 ceremony 0gmdkyy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 52.000 52.000 0.614 http://example.org/award/award_category/winners./award/award_honor/ceremony #15854-07qy0b PRED entity: 07qy0b PRED relation: student! PRED expected values: 065y4w7 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 146): 01pcj4 (0.25 #369, 0.04 #1423, 0.04 #896), 065y4w7 (0.12 #14, 0.09 #2649, 0.07 #4230), 08815 (0.12 #2, 0.03 #21609, 0.03 #18447), 02301 (0.12 #74, 0.02 #5344, 0.02 #6398), 09f2j (0.10 #2794, 0.08 #2267, 0.08 #4375), 017z88 (0.09 #2717, 0.08 #2190, 0.08 #8514), 0bwfn (0.07 #1329, 0.07 #802, 0.05 #6072), 01w5m (0.07 #1159, 0.07 #632, 0.05 #2213), 02g839 (0.05 #2133, 0.05 #5295, 0.04 #8984), 015nl4 (0.05 #4810, 0.04 #1121, 0.04 #594) >> Best rule #369 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 03n0q5; >> query: (?x3371, 01pcj4) <- music(?x6099, ?x3371), country(?x6099, ?x94), sibling(?x6783, ?x3371), ?x94 = 09c7w0 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 03n0q5; *> query: (?x3371, 065y4w7) <- music(?x6099, ?x3371), country(?x6099, ?x94), sibling(?x6783, ?x3371), ?x94 = 09c7w0 *> conf = 0.12 ranks of expected_values: 2 EVAL 07qy0b student! 065y4w7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #15853-02ylg6 PRED entity: 02ylg6 PRED relation: film_release_region PRED expected values: 03_3d 0chghy 035qy => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 95): 0chghy (0.86 #612, 0.85 #914, 0.83 #2123), 03gj2 (0.84 #625, 0.80 #927, 0.77 #2136), 03_3d (0.81 #910, 0.81 #608, 0.77 #1665), 035qy (0.78 #2145, 0.75 #634, 0.72 #936), 05qhw (0.76 #616, 0.75 #2127, 0.73 #918), 01znc_ (0.74 #641, 0.69 #943, 0.69 #2152), 06bnz (0.68 #2156, 0.61 #645, 0.60 #947), 06t2t (0.63 #2172, 0.59 #661, 0.57 #963), 0ctw_b (0.52 #2137, 0.43 #626, 0.42 #928), 015qh (0.45 #2151, 0.37 #640, 0.35 #942) >> Best rule #612 for best value: >> intensional similarity = 4 >> extensional distance = 146 >> proper extension: 03bx2lk; 045j3w; 0gffmn8; 0mb8c; 07s3m4g; 07ghq; 0fpgp26; 072hx4; >> query: (?x5347, 0chghy) <- film_release_region(?x5347, ?x1453), film_release_region(?x5347, ?x87), ?x1453 = 06qd3, ?x87 = 05r4w >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4 EVAL 02ylg6 film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.865 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02ylg6 film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.865 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02ylg6 film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.865 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15852-02lwv5 PRED entity: 02lwv5 PRED relation: organization! PRED expected values: 060c4 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 11): 060c4 (0.71 #327, 0.69 #483, 0.67 #665), 0dq_5 (0.35 #113, 0.34 #139, 0.25 #373), 07xl34 (0.24 #206, 0.21 #284, 0.20 #297), 05k17c (0.24 #46, 0.14 #150, 0.13 #20), 05c0jwl (0.09 #200, 0.07 #278, 0.06 #239), 0hm4q (0.06 #450, 0.05 #463, 0.05 #385), 08jcfy (0.03 #285, 0.02 #454, 0.02 #220), 04n1q6 (0.02 #279, 0.02 #201, 0.02 #214), 0dq3c (0.01 #105, 0.01 #131), 02wlwtm (0.01 #130) >> Best rule #327 for best value: >> intensional similarity = 4 >> extensional distance = 219 >> proper extension: 01xk7r; 01r3w7; 01p896; >> query: (?x11009, 060c4) <- student(?x11009, ?x6263), colors(?x11009, ?x12170), currency(?x11009, ?x170), colors(?x6823, ?x12170) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lwv5 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.715 http://example.org/organization/role/leaders./organization/leadership/organization #15851-03c7tr1 PRED entity: 03c7tr1 PRED relation: nominated_for PRED expected values: 09sh8k 02sg5v 0fphf3v 0419kt => 51 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1362): 06bd5j (0.76 #3098, 0.73 #12393, 0.71 #18593), 01kff7 (0.62 #7919, 0.50 #11018, 0.33 #174), 0kv2hv (0.62 #7860, 0.43 #10959, 0.33 #115), 0cn_b8 (0.62 #8288, 0.43 #11387, 0.33 #543), 0gc_c_ (0.62 #8263, 0.43 #11362, 0.33 #518), 0gwgn1k (0.62 #9074, 0.36 #12173, 0.33 #1329), 02nczh (0.50 #7173, 0.50 #2526, 0.45 #10272), 01hqk (0.50 #11478, 0.50 #8379, 0.25 #3732), 0c9k8 (0.50 #1966, 0.45 #9712, 0.33 #6613), 0_9l_ (0.50 #3040, 0.45 #10786, 0.33 #7687) >> Best rule #3098 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 094qd5; 02y_rq5; >> query: (?x1007, ?x504) <- award(?x7754, ?x1007), award(?x5064, ?x1007), student(?x5306, ?x5064), ?x7754 = 0421st, award(?x504, ?x1007) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #9232 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 04ljl_l; 05b1610; 05p09zm; *> query: (?x1007, 0419kt) <- award(?x5064, ?x1007), ?x5064 = 02_l96, nominated_for(?x1007, ?x146) *> conf = 0.50 ranks of expected_values: 38, 208, 581 EVAL 03c7tr1 nominated_for 0419kt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 51.000 26.000 0.760 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03c7tr1 nominated_for 0fphf3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 51.000 26.000 0.760 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03c7tr1 nominated_for 02sg5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 26.000 0.760 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03c7tr1 nominated_for 09sh8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 26.000 0.760 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15850-01cblr PRED entity: 01cblr PRED relation: award PRED expected values: 02f6yz => 62 concepts (48 used for prediction) PRED predicted values (max 10 best out of 238): 01by1l (0.50 #2912, 0.39 #3313, 0.38 #3713), 0c4z8 (0.50 #2872, 0.39 #3273, 0.33 #4073), 01bgqh (0.46 #2843, 0.35 #3244, 0.35 #1643), 02x17c2 (0.45 #4217, 0.44 #3817, 0.42 #3417), 0gqz2 (0.42 #3282, 0.42 #2881, 0.36 #4082), 026mfs (0.40 #6130, 0.27 #4529, 0.13 #3329), 0c422z4 (0.35 #5743, 0.09 #3201, 0.04 #18010), 02f73b (0.33 #284, 0.29 #3084, 0.25 #1884), 01c99j (0.33 #3023, 0.26 #3424, 0.25 #3824), 02f72n (0.30 #1344, 0.25 #544, 0.22 #6946) >> Best rule #2912 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 02zj61; >> query: (?x4909, 01by1l) <- award(?x4909, ?x4416), award(?x4909, ?x2855), ?x4416 = 099vwn, award(?x5225, ?x2855), ?x5225 = 01pq5j7 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7118 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 131 *> proper extension: 0hvbj; 015bwt; *> query: (?x4909, 02f6yz) <- group(?x1969, ?x4909), award(?x4909, ?x1389), role(?x1969, ?x2798), role(?x1969, ?x315), ?x315 = 0l14md, ?x2798 = 03qjg *> conf = 0.21 ranks of expected_values: 21 EVAL 01cblr award 02f6yz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 62.000 48.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15849-0fpj9pm PRED entity: 0fpj9pm PRED relation: profession PRED expected values: 039v1 => 198 concepts (134 used for prediction) PRED predicted values (max 10 best out of 145): 02hrh1q (0.90 #14005, 0.89 #13123, 0.89 #11796), 01d_h8 (0.62 #12673, 0.41 #11640, 0.37 #14144), 039v1 (0.59 #2388, 0.51 #8132, 0.50 #2684), 0dz3r (0.58 #6185, 0.56 #1473, 0.54 #4127), 01c72t (0.40 #317, 0.40 #23, 0.39 #4295), 0n1h (0.40 #11, 0.38 #1335, 0.34 #2808), 09lbv (0.40 #19, 0.31 #5026, 0.30 #313), 05z96 (0.40 #41, 0.20 #335, 0.15 #924), 0d1pc (0.32 #7360, 0.20 #1667, 0.19 #7852), 04f2zj (0.32 #2007, 0.20 #95, 0.17 #831) >> Best rule #14005 for best value: >> intensional similarity = 3 >> extensional distance = 348 >> proper extension: 01jb26; 0333wf; 078jnn; 03h8_g; >> query: (?x7236, 02hrh1q) <- participant(?x2012, ?x7236), profession(?x7236, ?x220), participant(?x6262, ?x7236) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2388 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 04cr6qv; *> query: (?x7236, 039v1) <- participant(?x7236, ?x6262), role(?x7236, ?x227), ?x227 = 0342h, instrumentalists(?x716, ?x7236) *> conf = 0.59 ranks of expected_values: 3 EVAL 0fpj9pm profession 039v1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 198.000 134.000 0.900 http://example.org/people/person/profession #15848-031y07 PRED entity: 031y07 PRED relation: film PRED expected values: 0gzy02 => 108 concepts (62 used for prediction) PRED predicted values (max 10 best out of 957): 01vw8k (0.25 #651, 0.08 #4225, 0.03 #34605), 026fs38 (0.25 #1293, 0.04 #4867, 0.02 #8441), 0dr_4 (0.25 #247, 0.04 #3821, 0.01 #71734), 026gyn_ (0.25 #297, 0.04 #3871), 01lbcqx (0.25 #1449, 0.03 #13958, 0.03 #6810), 09fqgj (0.25 #1659, 0.02 #8807, 0.02 #5233), 047wh1 (0.25 #886, 0.02 #11608, 0.02 #4460), 03x7hd (0.25 #559, 0.02 #4133, 0.01 #34513), 015bpl (0.25 #1389, 0.02 #4963, 0.01 #8537), 012gk9 (0.25 #1509, 0.02 #5083) >> Best rule #651 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0h0jz; 017khj; >> query: (?x5693, 01vw8k) <- award(?x5693, ?x1921), film(?x5693, ?x1763), student(?x2486, ?x5693), ?x1763 = 02bg8v >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1831 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0131kb; *> query: (?x5693, 0gzy02) <- award(?x5693, ?x2192), film(?x5693, ?x1763), people(?x11064, ?x5693), ?x2192 = 0bfvd4 *> conf = 0.11 ranks of expected_values: 16 EVAL 031y07 film 0gzy02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 108.000 62.000 0.250 http://example.org/film/actor/film./film/performance/film #15847-0m2rv PRED entity: 0m2rv PRED relation: place PRED expected values: 0m2rv => 140 concepts (89 used for prediction) PRED predicted values (max 10 best out of 215): 02_286 (0.15 #25796, 0.12 #36121, 0.03 #1560), 013f1h (0.15 #25796, 0.12 #36121), 01n7q (0.15 #25796, 0.12 #36121), 0qpqn (0.12 #36121, 0.08 #31474, 0.08 #43872), 03spz (0.12 #36121, 0.08 #31474, 0.08 #43872), 0m2rv (0.12 #36121, 0.08 #31474, 0.08 #43872), 02dtg (0.07 #9, 0.05 #524, 0.04 #1039), 0xckc (0.07 #188, 0.05 #703, 0.04 #1218), 01fq7 (0.07 #4, 0.05 #519, 0.04 #1034), 0vm39 (0.07 #238, 0.05 #753, 0.04 #1268) >> Best rule #25796 for best value: >> intensional similarity = 4 >> extensional distance = 335 >> proper extension: 01t8gz; 017w_; >> query: (?x3372, ?x739) <- contains(?x94, ?x3372), place_of_birth(?x3593, ?x3372), location(?x3593, ?x739), award_winner(?x372, ?x3593) >> conf = 0.15 => this is the best rule for 3 predicted values *> Best rule #36121 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 480 *> proper extension: 01c1nm; 01423b; *> query: (?x3372, ?x739) <- contains(?x94, ?x3372), place_of_birth(?x3593, ?x3372), location(?x3593, ?x739), gender(?x3593, ?x231) *> conf = 0.12 ranks of expected_values: 6 EVAL 0m2rv place 0m2rv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 140.000 89.000 0.146 http://example.org/location/hud_county_place/place #15846-06v9sf PRED entity: 06v9sf PRED relation: entity_involved! PRED expected values: 02h2z_ => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 84): 02kxjx (0.83 #608, 0.67 #234, 0.58 #879), 02h2z_ (0.58 #879, 0.51 #2522, 0.48 #2585), 0dl4z (0.50 #257, 0.45 #507, 0.43 #382), 03w6sj (0.48 #2585, 0.33 #230, 0.28 #2140), 048n7 (0.36 #522, 0.33 #272, 0.29 #397), 0cm2xh (0.35 #827, 0.24 #2907, 0.22 #3039), 0gfq9 (0.33 #6, 0.25 #69, 0.24 #2907), 075k5 (0.33 #25, 0.25 #88, 0.24 #2907), 07_nf (0.28 #705, 0.24 #1022, 0.23 #1150), 06k75 (0.26 #830, 0.24 #2907, 0.22 #3039) >> Best rule #608 for best value: >> intensional similarity = 8 >> extensional distance = 10 >> proper extension: 07_m9_; 0dj5q; 088q1s; >> query: (?x3057, 02kxjx) <- entity_involved(?x13967, ?x3057), combatants(?x13967, ?x1023), entity_involved(?x13967, ?x12361), entity_involved(?x13967, ?x5531), entity_involved(?x13967, ?x2663), ?x2663 = 028rk, ?x12361 = 018q7, ?x5531 = 0bxjv >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #879 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 21 *> proper extension: 083q7; 03pn9; 05wh0sh; 01hnp; 04jvt; 03l5m1; 05hks; 0lzcs; 01llxp; 011zwl; *> query: (?x3057, ?x12031) <- entity_involved(?x13967, ?x3057), entity_involved(?x10764, ?x3057), combatants(?x13967, ?x1023), entity_involved(?x13967, ?x2663), combatants(?x10764, ?x13069), entity_involved(?x12031, ?x2663), ?x13069 = 01rdm0, organizations_founded(?x2663, ?x11089) *> conf = 0.58 ranks of expected_values: 2 EVAL 06v9sf entity_involved! 02h2z_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 50.000 50.000 0.833 http://example.org/base/culturalevent/event/entity_involved #15845-03mh_tp PRED entity: 03mh_tp PRED relation: produced_by PRED expected values: 0gg9_5q => 67 concepts (45 used for prediction) PRED predicted values (max 10 best out of 153): 01v80y (0.33 #301, 0.05 #2231, 0.05 #2619), 0q9kd (0.20 #390, 0.17 #776, 0.02 #8116), 0d_skg (0.20 #616, 0.17 #1002, 0.02 #5642), 04fyhv (0.19 #2210, 0.18 #2598, 0.14 #2984), 02lf0c (0.14 #1182, 0.09 #3502, 0.08 #3888), 06pj8 (0.14 #1226, 0.03 #5866, 0.03 #9728), 01t6b4 (0.14 #2361, 0.10 #2747, 0.05 #1973), 092kgw (0.10 #1740, 0.01 #9471, 0.01 #12172), 05ty4m (0.06 #3102, 0.06 #3491, 0.06 #3877), 04q5zw (0.06 #3198, 0.06 #3587, 0.06 #3973) >> Best rule #301 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 035xwd; >> query: (?x3084, 01v80y) <- film(?x8796, ?x3084), film(?x1104, ?x3084), film(?x1461, ?x3084), ?x8796 = 032dg7, award(?x1461, ?x401), ?x1104 = 016tw3 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03mh_tp produced_by 0gg9_5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 45.000 0.333 http://example.org/film/film/produced_by #15844-042f1 PRED entity: 042f1 PRED relation: people! PRED expected values: 024c2 => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 45): 0dq9p (0.25 #413, 0.20 #1007, 0.20 #875), 07jwr (0.22 #207, 0.21 #537, 0.20 #273), 0gk4g (0.20 #868, 0.19 #670, 0.16 #1396), 0c58k (0.20 #96, 0.11 #228, 0.04 #1284), 02k6hp (0.17 #499, 0.13 #631, 0.12 #1423), 012hw (0.13 #1174, 0.11 #250, 0.10 #976), 02y0js (0.12 #662, 0.11 #794, 0.10 #860), 0qcr0 (0.12 #133, 0.09 #3104, 0.09 #331), 02knxx (0.12 #164, 0.09 #362, 0.05 #824), 01l2m3 (0.11 #742, 0.10 #280, 0.09 #1204) >> Best rule #413 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 042kg; 0835q; >> query: (?x9765, 0dq9p) <- basic_title(?x9765, ?x900), politician(?x8714, ?x9765), ?x900 = 0fkvn, taxonomy(?x9765, ?x939), jurisdiction_of_office(?x9765, ?x3778) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 042f1 people! 024c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 172.000 0.250 http://example.org/people/cause_of_death/people #15843-02q9kqf PRED entity: 02q9kqf PRED relation: award PRED expected values: 0p9sw => 95 concepts (79 used for prediction) PRED predicted values (max 10 best out of 299): 0p9sw (0.79 #21491, 0.76 #28797, 0.71 #16624), 0gqz2 (0.47 #2106, 0.25 #80, 0.14 #1701), 054ks3 (0.38 #2168, 0.12 #142, 0.09 #1763), 09sb52 (0.30 #13421, 0.29 #6123, 0.29 #4500), 0gqy2 (0.30 #570, 0.14 #24329, 0.13 #5678), 0c4z8 (0.25 #2097, 0.25 #71, 0.10 #476), 025m8l (0.25 #119, 0.18 #2145, 0.13 #12976), 025m8y (0.25 #99, 0.14 #2125, 0.10 #504), 040njc (0.21 #3656, 0.14 #24329, 0.13 #5678), 04njml (0.21 #2127, 0.12 #101, 0.06 #1722) >> Best rule #21491 for best value: >> intensional similarity = 3 >> extensional distance = 1380 >> proper extension: 01vw87c; 02r3zy; 015_30; 0126rp; 01fwk3; 057hz; 0ph2w; 0m_31; 03f1d47; 01pq5j7; ... >> query: (?x6232, ?x500) <- award_winner(?x500, ?x6232), nominated_for(?x500, ?x144), ceremony(?x500, ?x78) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q9kqf award 0p9sw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 79.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15842-047vnkj PRED entity: 047vnkj PRED relation: film! PRED expected values: 02cpb7 => 66 concepts (36 used for prediction) PRED predicted values (max 10 best out of 975): 094tsh6 (0.44 #60245, 0.44 #54011, 0.42 #62322), 032v0v (0.17 #31159, 0.14 #27004, 0.02 #64401), 02qjpv5 (0.15 #2077, 0.11 #37395), 03h304l (0.15 #2077, 0.11 #37395), 04wvhz (0.15 #2077, 0.11 #37395), 014v6f (0.12 #5120, 0.08 #70640, 0.03 #17582), 01w1kyf (0.12 #5060, 0.08 #70640, 0.02 #74800), 0svqs (0.12 #872, 0.08 #7105, 0.05 #9181), 0170pk (0.11 #2357, 0.05 #8589, 0.04 #16896), 01f7dd (0.09 #5361, 0.08 #70640, 0.05 #19902) >> Best rule #60245 for best value: >> intensional similarity = 3 >> extensional distance = 967 >> proper extension: 0gcrg; >> query: (?x5271, ?x4767) <- nominated_for(?x4767, ?x5271), film_crew_role(?x5271, ?x137), genre(?x5271, ?x53) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #17448 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 117 *> proper extension: 03d34x8; 0c3xpwy; 07gbf; *> query: (?x5271, 02cpb7) <- nominated_for(?x9391, ?x5271), award_winner(?x5271, ?x2415), crewmember(?x392, ?x9391), profession(?x9391, ?x5654) *> conf = 0.02 ranks of expected_values: 607 EVAL 047vnkj film! 02cpb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 66.000 36.000 0.441 http://example.org/film/actor/film./film/performance/film #15841-013719 PRED entity: 013719 PRED relation: institution! PRED expected values: 014mlp 02_xgp2 02cq61 => 137 concepts (83 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.94 #1461, 0.89 #1084, 0.78 #157), 016t_3 (0.82 #962, 0.74 #315, 0.71 #241), 02_xgp2 (0.81 #324, 0.72 #208, 0.71 #250), 019v9k (0.78 #159, 0.78 #141, 0.77 #269), 03bwzr4 (0.78 #326, 0.76 #396, 0.75 #252), 07s6fsf (0.76 #383, 0.71 #239, 0.67 #313), 0bkj86 (0.74 #319, 0.67 #245, 0.66 #413), 04zx3q1 (0.62 #240, 0.59 #408, 0.59 #314), 022h5x (0.58 #406, 0.45 #622, 0.43 #1006), 02mjs7 (0.57 #114, 0.36 #235, 0.32 #524) >> Best rule #1461 for best value: >> intensional similarity = 10 >> extensional distance = 289 >> proper extension: 017ztv; >> query: (?x11640, 014mlp) <- category(?x11640, ?x134), organization(?x5510, ?x11640), institution(?x7636, ?x11640), institution(?x7636, ?x7546), institution(?x7636, ?x6132), institution(?x7636, ?x5085), ?x5085 = 02dj3, ?x7546 = 01_qgp, major_field_of_study(?x6132, ?x2314), currency(?x6132, ?x1099) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 17 EVAL 013719 institution! 02cq61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 137.000 83.000 0.938 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 013719 institution! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 83.000 0.938 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 013719 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 83.000 0.938 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15840-04j5jl PRED entity: 04j5jl PRED relation: profession! PRED expected values: 065d1h => 54 concepts (21 used for prediction) PRED predicted values (max 10 best out of 4226): 02633g (0.75 #16970, 0.71 #15374, 0.67 #11132), 01j7rd (0.75 #16970, 0.67 #9083, 0.57 #13325), 0fb1q (0.75 #16970, 0.67 #9431, 0.57 #13673), 0126rp (0.75 #16970, 0.67 #9082, 0.57 #13324), 01xwqn (0.75 #16970, 0.67 #12083, 0.57 #16325), 01xwv7 (0.75 #16970, 0.67 #11959, 0.57 #16201), 049fgvm (0.75 #16970, 0.57 #14905, 0.50 #10663), 018009 (0.75 #16970, 0.50 #9844, 0.50 #5602), 014z8v (0.75 #16970, 0.50 #9765, 0.50 #5523), 02lj6p (0.75 #16970, 0.50 #11325, 0.50 #7083) >> Best rule #16970 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 02jknp; >> query: (?x14546, ?x318) <- profession(?x7183, ?x14546), gender(?x7183, ?x231), nominated_for(?x7183, ?x1210), influenced_by(?x4318, ?x7183), influenced_by(?x318, ?x7183), location(?x7183, ?x3964), ?x4318 = 018009 >> conf = 0.75 => this is the best rule for 19 predicted values *> Best rule #16205 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 02jknp; *> query: (?x14546, 065d1h) <- profession(?x7183, ?x14546), gender(?x7183, ?x231), nominated_for(?x7183, ?x1210), influenced_by(?x4318, ?x7183), location(?x7183, ?x3964), ?x4318 = 018009 *> conf = 0.43 ranks of expected_values: 608 EVAL 04j5jl profession! 065d1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 21.000 0.747 http://example.org/people/person/profession #15839-0cj2w PRED entity: 0cj2w PRED relation: profession PRED expected values: 09jwl => 158 concepts (86 used for prediction) PRED predicted values (max 10 best out of 91): 09jwl (0.73 #9800, 0.71 #893, 0.70 #12286), 01d_h8 (0.70 #6869, 0.64 #9059, 0.64 #6723), 02jknp (0.59 #6725, 0.58 #9061, 0.57 #6871), 0nbcg (0.52 #8936, 0.49 #6162, 0.48 #11567), 0cbd2 (0.50 #445, 0.47 #1175, 0.42 #2197), 0kyk (0.50 #319, 0.43 #173, 0.40 #465), 03gjzk (0.46 #6877, 0.43 #890, 0.43 #160), 016z4k (0.43 #9787, 0.42 #4092, 0.41 #11542), 02hv44_ (0.43 #201, 0.30 #347, 0.20 #493), 0dz3r (0.42 #4674, 0.42 #12271, 0.41 #11540) >> Best rule #9800 for best value: >> intensional similarity = 4 >> extensional distance = 286 >> proper extension: 0kvjrw; >> query: (?x11322, 09jwl) <- instrumentalists(?x8168, ?x11322), profession(?x11322, ?x1146), profession(?x10310, ?x1146), ?x10310 = 04gr35 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cj2w profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 86.000 0.729 http://example.org/people/person/profession #15838-0fs9jn PRED entity: 0fs9jn PRED relation: nationality PRED expected values: 0d060g => 103 concepts (60 used for prediction) PRED predicted values (max 10 best out of 73): 09c7w0 (0.85 #903, 0.72 #3208, 0.72 #2304), 03rjj (0.33 #5, 0.04 #1103, 0.03 #3609), 02_286 (0.25 #5915), 059rby (0.25 #5915), 0d060g (0.17 #107, 0.12 #407, 0.11 #307), 07ssc (0.14 #215, 0.09 #2018, 0.09 #1918), 0chghy (0.14 #210, 0.05 #610, 0.04 #1103), 0345h (0.14 #231, 0.04 #1103, 0.03 #3609), 03rt9 (0.14 #213, 0.04 #1103, 0.03 #3609), 06q1r (0.14 #277, 0.03 #677, 0.02 #577) >> Best rule #903 for best value: >> intensional similarity = 3 >> extensional distance = 520 >> proper extension: 02rn_bj; >> query: (?x10136, 09c7w0) <- profession(?x10136, ?x1032), student(?x8056, ?x10136), currency(?x8056, ?x170) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #107 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 03m8lq; 02xv8m; 0gpprt; 01gkmx; *> query: (?x10136, 0d060g) <- film(?x10136, ?x5271), student(?x8056, ?x10136), ?x5271 = 047vnkj, people(?x412, ?x10136) *> conf = 0.17 ranks of expected_values: 5 EVAL 0fs9jn nationality 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 103.000 60.000 0.851 http://example.org/people/person/nationality #15837-02ny6g PRED entity: 02ny6g PRED relation: nominated_for! PRED expected values: 02g3v6 => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 161): 05ztjjw (0.34 #728, 0.08 #1207, 0.08 #1447), 07cbcy (0.33 #64, 0.19 #1741, 0.19 #1502), 02hsq3m (0.30 #748, 0.14 #2901, 0.11 #3140), 0gq9h (0.30 #1740, 0.30 #1501, 0.29 #1979), 019f4v (0.26 #1731, 0.26 #1492, 0.26 #1970), 02r22gf (0.26 #747, 0.12 #1705, 0.12 #1944), 04dn09n (0.24 #1712, 0.24 #1473, 0.23 #1233), 0gs9p (0.23 #1503, 0.22 #1742, 0.22 #2459), 0gqy2 (0.22 #1800, 0.22 #1561, 0.22 #1321), 05ztrmj (0.22 #10525, 0.21 #854, 0.20 #9806) >> Best rule #728 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 013q07; 013q0p; 0m63c; >> query: (?x3639, 05ztjjw) <- film(?x318, ?x3639), genre(?x3639, ?x225), nominated_for(?x3019, ?x3639), ?x3019 = 057xs89 >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #740 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 013q07; 013q0p; 0m63c; *> query: (?x3639, 02g3v6) <- film(?x318, ?x3639), genre(?x3639, ?x225), nominated_for(?x3019, ?x3639), ?x3019 = 057xs89 *> conf = 0.21 ranks of expected_values: 22 EVAL 02ny6g nominated_for! 02g3v6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 54.000 54.000 0.343 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15836-04q5zw PRED entity: 04q5zw PRED relation: produced_by! PRED expected values: 076xkps => 112 concepts (75 used for prediction) PRED predicted values (max 10 best out of 875): 07b1gq (0.11 #6586), 03qnc6q (0.10 #1881, 0.02 #19760, 0.01 #1171), 02mpyh (0.10 #1881, 0.02 #19760, 0.01 #1717), 0cn_b8 (0.10 #1881, 0.01 #1270), 09wnnb (0.10 #1881), 0bpbhm (0.10 #1881), 0g22z (0.08 #7, 0.03 #2830, 0.03 #4711), 060__7 (0.08 #774, 0.03 #2656, 0.02 #3597), 01s7w3 (0.08 #810, 0.03 #1750, 0.02 #4574), 026p4q7 (0.08 #214, 0.02 #2096, 0.02 #19760) >> Best rule #6586 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 04093; >> query: (?x3223, ?x3640) <- profession(?x3223, ?x987), story_by(?x3640, ?x3223), ?x987 = 0dxtg >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04q5zw produced_by! 076xkps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 75.000 0.109 http://example.org/film/film/produced_by #15835-03qsdpk PRED entity: 03qsdpk PRED relation: major_field_of_study! PRED expected values: 086xm => 93 concepts (57 used for prediction) PRED predicted values (max 10 best out of 643): 012mzw (0.71 #4133, 0.60 #1382, 0.33 #2483), 06pwq (0.70 #6069, 0.70 #5514, 0.61 #11021), 03ksy (0.70 #5612, 0.67 #11119, 0.67 #8368), 07szy (0.70 #6096, 0.67 #2789, 0.67 #2239), 07w0v (0.70 #6628, 0.50 #11029, 0.50 #6077), 02zd460 (0.67 #8986, 0.67 #8436, 0.67 #7886), 07t90 (0.67 #2354, 0.60 #1253, 0.57 #4004), 07wrz (0.67 #5014, 0.60 #1161, 0.57 #3912), 07tds (0.67 #5107, 0.60 #6212, 0.50 #5657), 05zl0 (0.67 #2413, 0.50 #6270, 0.50 #2963) >> Best rule #4133 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 0_jm; >> query: (?x5614, 012mzw) <- major_field_of_study(?x10333, ?x5614), major_field_of_study(?x9575, ?x5614), major_field_of_study(?x8008, ?x5614), major_field_of_study(?x5145, ?x5614), major_field_of_study(?x735, ?x5614), ?x5145 = 0b1xl, organization(?x346, ?x8008), time_zones(?x9575, ?x2950), category(?x10333, ?x134), school(?x465, ?x735), institution(?x1368, ?x10333) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5046 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 7 *> proper extension: 01lj9; *> query: (?x5614, 086xm) <- major_field_of_study(?x11474, ?x5614), major_field_of_study(?x7545, ?x5614), major_field_of_study(?x4955, ?x5614), major_field_of_study(?x122, ?x5614), student(?x5614, ?x396), ?x7545 = 0bwfn, ?x4955 = 09f2j, major_field_of_study(?x254, ?x5614), school_type(?x11474, ?x1044), ?x122 = 08815 *> conf = 0.44 ranks of expected_values: 51 EVAL 03qsdpk major_field_of_study! 086xm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 93.000 57.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #15834-01520h PRED entity: 01520h PRED relation: gender PRED expected values: 05zppz => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #9, 0.75 #17, 0.74 #7), 02zsn (0.35 #6, 0.33 #22, 0.32 #56) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 0p_pd; 0h1_w; 012cj0; 012c6x; 03ds3; 0f0p0; 09qh1; 015vq_; 016yr0; 0f502; ... >> query: (?x6755, 05zppz) <- award(?x6755, ?x591), film(?x6755, ?x430), ?x591 = 0f4x7 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01520h gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.912 http://example.org/people/person/gender #15833-0bcndz PRED entity: 0bcndz PRED relation: nominated_for! PRED expected values: 027rpym => 97 concepts (38 used for prediction) PRED predicted values (max 10 best out of 137): 027rpym (0.85 #6324, 0.81 #505, 0.80 #4548), 02jr6k (0.19 #625, 0.07 #1382, 0.04 #6191), 05cj_j (0.19 #549, 0.07 #1306, 0.04 #6115), 075cph (0.19 #578, 0.05 #1335, 0.04 #6144), 01jr4j (0.19 #706, 0.05 #1463, 0.04 #6272), 05z7c (0.19 #564, 0.05 #1321, 0.04 #6130), 0k5g9 (0.12 #584, 0.05 #1341, 0.04 #6150), 01s9vc (0.12 #745, 0.05 #1502, 0.04 #6311), 05css_ (0.12 #661, 0.05 #1418, 0.04 #6227), 0k0rf (0.12 #648, 0.04 #1405, 0.03 #6214) >> Best rule #6324 for best value: >> intensional similarity = 3 >> extensional distance = 226 >> proper extension: 02fn5r; >> query: (?x1745, ?x4865) <- nominated_for(?x4404, ?x1745), nominated_for(?x484, ?x4404), nominated_for(?x1745, ?x4865) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bcndz nominated_for! 027rpym CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 38.000 0.847 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #15832-05xpv PRED entity: 05xpv PRED relation: student! PRED expected values: 078bz => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 239): 09f2j (0.17 #684, 0.15 #1209, 0.05 #47937), 017z88 (0.17 #607, 0.14 #2182, 0.05 #1132), 01vg13 (0.17 #744, 0.07 #2319, 0.05 #1269), 08815 (0.17 #527, 0.06 #4727, 0.05 #1052), 01d34b (0.17 #781, 0.05 #1306, 0.04 #17056), 015q1n (0.17 #737, 0.05 #1262, 0.04 #2312), 03qdm (0.17 #932, 0.05 #1457, 0.01 #22983), 02gr81 (0.17 #657, 0.05 #1182), 01ymvk (0.17 #646, 0.05 #1171), 02s62q (0.17 #577, 0.05 #1102) >> Best rule #684 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 05bnp0; 01nvmd_; 052hl; 02z1yj; >> query: (?x8774, 09f2j) <- student(?x9307, ?x8774), place_of_birth(?x8774, ?x739), people(?x9943, ?x8774), ?x9943 = 09kr66 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #32628 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 565 *> proper extension: 044mz_; 07nznf; 079vf; 02p65p; 06688p; 0197tq; 04t2l2; 0d_84; 0h5f5n; 01wbg84; ... *> query: (?x8774, 078bz) <- student(?x9307, ?x8774), place_of_birth(?x8774, ?x739), people(?x1050, ?x8774) *> conf = 0.02 ranks of expected_values: 123 EVAL 05xpv student! 078bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 161.000 161.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #15831-01y81r PRED entity: 01y81r PRED relation: service_location PRED expected values: 06y57 => 51 concepts (48 used for prediction) PRED predicted values (max 10 best out of 145): 09c7w0 (0.97 #4222, 0.90 #4319, 0.87 #4416), 06y57 (0.83 #4126, 0.59 #382, 0.33 #53), 0chghy (0.82 #2423, 0.44 #1538, 0.40 #1645), 0d060g (0.79 #3656, 0.53 #2419, 0.50 #1835), 07ssc (0.74 #3189, 0.64 #3570, 0.57 #1074), 0847q (0.59 #382, 0.30 #769, 0.19 #2119), 0dv9v (0.59 #382, 0.30 #769, 0.19 #1728), 03kjh (0.59 #382, 0.30 #769, 0.19 #1728), 012q8y (0.59 #382, 0.30 #769, 0.19 #1728), 07cfx (0.59 #382, 0.30 #769, 0.19 #1728) >> Best rule #4222 for best value: >> intensional similarity = 11 >> extensional distance = 110 >> proper extension: 07tds; 01yfp7; 04f0xq; 0vlf; >> query: (?x5919, 09c7w0) <- service_location(?x5919, ?x11731), service_location(?x5919, ?x8963), contains(?x390, ?x8963), service_location(?x555, ?x390), taxonomy(?x390, ?x939), location_of_ceremony(?x566, ?x8963), place_founded(?x9077, ?x8963), location(?x2865, ?x11731), instrumentalists(?x316, ?x2865), role(?x2865, ?x74), profession(?x2865, ?x220) >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #4126 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 94 *> proper extension: 03bnb; *> query: (?x5919, ?x5036) <- service_location(?x5919, ?x8963), service_location(?x5919, ?x8602), service_location(?x10016, ?x8963), organization(?x4682, ?x10016), category(?x5919, ?x134), service_location(?x10016, ?x5036), service_language(?x10016, ?x254), ?x254 = 02h40lc, program(?x10016, ?x14197), ?x4682 = 0dq_5, ?x134 = 08mbj5d, service_language(?x5919, ?x254) *> conf = 0.83 ranks of expected_values: 2 EVAL 01y81r service_location 06y57 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 51.000 48.000 0.973 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #15830-034ls PRED entity: 034ls PRED relation: award_winner! PRED expected values: 05qck => 153 concepts (147 used for prediction) PRED predicted values (max 10 best out of 287): 05f3q (0.30 #2901, 0.23 #6357, 0.19 #7221), 05p09zm (0.20 #125, 0.17 #557, 0.15 #6173), 05qck (0.20 #2785, 0.16 #11857, 0.16 #10993), 02grdc (0.20 #2624, 0.15 #6080, 0.14 #896), 024fz9 (0.20 #3232, 0.10 #2800, 0.08 #5824), 019f4v (0.20 #67, 0.09 #3523, 0.07 #12163), 0gs9p (0.20 #80, 0.09 #3536, 0.05 #22977), 02qt02v (0.20 #169, 0.09 #3625, 0.05 #9241), 02pqp12 (0.20 #71, 0.09 #3527, 0.05 #18647), 04dn09n (0.20 #44, 0.09 #3500, 0.04 #58757) >> Best rule #2901 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0b22w; 042kg; >> query: (?x7540, 05f3q) <- basic_title(?x7540, ?x265), taxonomy(?x7540, ?x939), student(?x122, ?x7540), company(?x7540, ?x94) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #2785 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0b22w; 042kg; *> query: (?x7540, 05qck) <- basic_title(?x7540, ?x265), taxonomy(?x7540, ?x939), student(?x122, ?x7540), company(?x7540, ?x94) *> conf = 0.20 ranks of expected_values: 3 EVAL 034ls award_winner! 05qck CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 153.000 147.000 0.300 http://example.org/award/award_category/winners./award/award_honor/award_winner #15829-06mmb PRED entity: 06mmb PRED relation: program PRED expected values: 0bsxd3 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 26): 07pd_j (0.14 #152, 0.13 #101), 01j7mr (0.13 #184, 0.10 #133, 0.10 #159), 01b7h8 (0.13 #245, 0.11 #195, 0.05 #220), 01h1bf (0.13 #233, 0.11 #81, 0.10 #132), 039cq4 (0.11 #189, 0.07 #239), 026bfsh (0.11 #111, 0.08 #187, 0.06 #162), 0304nh (0.11 #84, 0.07 #236, 0.07 #135), 0cpz4k (0.08 #210, 0.07 #235, 0.04 #185), 0275kr (0.07 #95, 0.05 #146, 0.02 #247), 03gvm3t (0.05 #241, 0.04 #191, 0.02 #216) >> Best rule #152 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 01wf86y; >> query: (?x2559, ?x6684) <- person(?x3480, ?x2559), nominated_for(?x2559, ?x6684), profession(?x2559, ?x1032) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06mmb program 0bsxd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 88.000 0.136 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #15828-01fyzy PRED entity: 01fyzy PRED relation: profession PRED expected values: 018gz8 => 103 concepts (86 used for prediction) PRED predicted values (max 10 best out of 66): 03gjzk (0.83 #158, 0.80 #12, 0.77 #450), 018gz8 (0.52 #14, 0.37 #160, 0.24 #452), 02krf9 (0.52 #316, 0.28 #24, 0.21 #462), 09jwl (0.39 #2936, 0.36 #3958, 0.36 #1184), 0cbd2 (0.33 #152, 0.23 #882, 0.23 #444), 0np9r (0.32 #18, 0.27 #164, 0.18 #456), 015cjr (0.28 #47, 0.17 #193, 0.06 #485), 0dz3r (0.28 #1170, 0.27 #2922, 0.21 #6135), 0nbcg (0.28 #2949, 0.27 #1197, 0.26 #6162), 016z4k (0.27 #2924, 0.25 #1172, 0.23 #3946) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 03mz9r; >> query: (?x5975, 03gjzk) <- tv_program(?x5975, ?x6884), profession(?x5975, ?x319), category(?x5975, ?x134) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 01jbx1; 021yw7; 01pfkw; *> query: (?x5975, 018gz8) <- tv_program(?x5975, ?x6884), profession(?x5975, ?x319), participant(?x5975, ?x1335) *> conf = 0.52 ranks of expected_values: 2 EVAL 01fyzy profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 86.000 0.833 http://example.org/people/person/profession #15827-032sl_ PRED entity: 032sl_ PRED relation: film! PRED expected values: 046qq => 60 concepts (45 used for prediction) PRED predicted values (max 10 best out of 978): 01t6b4 (0.11 #33206), 0c0k1 (0.10 #3579, 0.05 #18107, 0.02 #9804), 0f5xn (0.08 #3042, 0.04 #17570, 0.03 #19646), 016ypb (0.08 #2570, 0.01 #4645, 0.01 #50303), 081lh (0.07 #8461, 0.02 #29216, 0.02 #22990), 01j7z7 (0.07 #10376, 0.04 #83011, 0.03 #1320), 0tc7 (0.06 #2466, 0.01 #19070, 0.01 #23220), 03f2_rc (0.06 #8385, 0.01 #20839), 014v6f (0.06 #966, 0.04 #5116, 0.04 #7191), 02m501 (0.06 #1684, 0.04 #5834, 0.04 #7909) >> Best rule #33206 for best value: >> intensional similarity = 3 >> extensional distance = 728 >> proper extension: 0c0yh4; 05jf85; 0209xj; 0416y94; 01kff7; 014zwb; 07w8fz; 0g54xkt; 016kv6; 02qzmz6; ... >> query: (?x9429, ?x1285) <- titles(?x812, ?x9429), genre(?x9429, ?x53), produced_by(?x9429, ?x1285) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #4889 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 02c6d; 02bg8v; 085bd1; 05fcbk7; 0bs5k8r; 0dr_9t7; 09fc83; 03nqnnk; 02z0f6l; 025ts_z; ... *> query: (?x9429, 046qq) <- film_release_region(?x9429, ?x94), ?x94 = 09c7w0, language(?x9429, ?x254) *> conf = 0.04 ranks of expected_values: 49 EVAL 032sl_ film! 046qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 60.000 45.000 0.105 http://example.org/film/actor/film./film/performance/film #15826-0l76z PRED entity: 0l76z PRED relation: nominated_for! PRED expected values: 03xpsrx => 106 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1073): 0m66w (0.84 #88745, 0.80 #74731, 0.80 #49035), 03fg0r (0.84 #88745, 0.80 #74731, 0.80 #49035), 05m9f9 (0.84 #88745, 0.80 #74731, 0.80 #49035), 015c2f (0.84 #88745, 0.80 #74731, 0.80 #49035), 06pj8 (0.78 #30359, 0.73 #51373, 0.71 #30358), 04wvhz (0.78 #30359, 0.73 #51373, 0.71 #30358), 02q_cc (0.78 #30359, 0.73 #51373, 0.71 #30358), 06chf (0.78 #30359, 0.73 #51373, 0.71 #30358), 0g2lq (0.78 #30359, 0.73 #51373, 0.71 #30358), 030_3z (0.73 #51373, 0.71 #30358, 0.70 #46700) >> Best rule #88745 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 03ffcz; 05h95s; >> query: (?x4588, ?x2813) <- genre(?x4588, ?x239), award_winner(?x4588, ?x2813), actor(?x4588, ?x1205), nominated_for(?x2813, ?x2586) >> conf = 0.84 => this is the best rule for 4 predicted values *> Best rule #16954 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 70 *> proper extension: 026wlxw; *> query: (?x4588, 03xpsrx) <- nominated_for(?x6724, ?x4588), nominated_for(?x3906, ?x4588), nominated_for(?x6724, ?x5060), award(?x1631, ?x3906), ?x5060 = 05f4vxd *> conf = 0.01 ranks of expected_values: 946 EVAL 0l76z nominated_for! 03xpsrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 106.000 45.000 0.837 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15825-0cn_b8 PRED entity: 0cn_b8 PRED relation: film_distribution_medium PRED expected values: 0735l => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.85 #103, 0.22 #30, 0.21 #55), 029j_ (0.19 #20, 0.18 #8, 0.17 #32), 0dq6p (0.14 #22, 0.13 #34, 0.08 #77), 02nxhr (0.12 #100, 0.11 #46, 0.11 #52), 07z4p (0.04 #13, 0.02 #25, 0.02 #37) >> Best rule #103 for best value: >> intensional similarity = 2 >> extensional distance = 135 >> proper extension: 0522wp; >> query: (?x3752, 0735l) <- film(?x609, ?x3752), ?x609 = 03xq0f >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cn_b8 film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.847 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #15824-0kqj1 PRED entity: 0kqj1 PRED relation: contains! PRED expected values: 09c7w0 => 191 concepts (119 used for prediction) PRED predicted values (max 10 best out of 237): 09c7w0 (0.86 #6261, 0.83 #40252, 0.82 #3579), 0k3l5 (0.60 #42038, 0.18 #3973, 0.14 #6655), 01n7q (0.46 #5442, 0.38 #7231, 0.33 #2760), 02_286 (0.33 #2725, 0.29 #41186, 0.22 #38503), 01qh7 (0.33 #188, 0.17 #1976, 0.16 #16286), 02jx1 (0.31 #4557, 0.18 #37653, 0.17 #46597), 07ssc (0.23 #4502, 0.14 #19707, 0.13 #37598), 059rby (0.23 #41163, 0.19 #38480, 0.17 #40269), 03kxzm (0.18 #4451, 0.14 #7133, 0.02 #42018), 01nl79 (0.17 #3428, 0.08 #6110, 0.06 #7899) >> Best rule #6261 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01f07x; >> query: (?x4278, 09c7w0) <- contains(?x3052, ?x4278), contains(?x2020, ?x4278), ?x3052 = 01cx_, location(?x237, ?x2020) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kqj1 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 119.000 0.857 http://example.org/location/location/contains #15823-03jj93 PRED entity: 03jj93 PRED relation: film PRED expected values: 0cc5qkt => 134 concepts (72 used for prediction) PRED predicted values (max 10 best out of 812): 031786 (0.38 #42746, 0.03 #42232, 0.02 #65386), 031778 (0.38 #42746, 0.02 #41279, 0.02 #64433), 02fqxm (0.38 #42746), 0prrm (0.07 #6202, 0.04 #41823, 0.03 #9764), 0bvn25 (0.06 #5392, 0.04 #14297, 0.03 #30326), 04gv3db (0.06 #6094, 0.03 #41715, 0.03 #70212), 034qzw (0.06 #5675, 0.03 #14580, 0.02 #30609), 01shy7 (0.05 #5765, 0.04 #68102, 0.04 #14670), 0b3n61 (0.05 #6695, 0.03 #4914, 0.02 #15600), 05zpghd (0.05 #6295, 0.02 #2733, 0.02 #4514) >> Best rule #42746 for best value: >> intensional similarity = 4 >> extensional distance = 444 >> proper extension: 06jzh; 022769; 038rzr; 071ynp; 02jtjz; 04w391; 01fx2g; 0cmt6q; 0436kgz; 01yfm8; ... >> query: (?x11651, ?x12720) <- film(?x11651, ?x5627), award(?x11651, ?x102), titles(?x812, ?x5627), prequel(?x5627, ?x12720) >> conf = 0.38 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 03jj93 film 0cc5qkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 72.000 0.381 http://example.org/film/actor/film./film/performance/film #15822-0l8z1 PRED entity: 0l8z1 PRED relation: nominated_for PRED expected values: 035xwd 029zqn 0260bz 04jwly 011ysn 011yl_ 0404j37 07jnt 02p76f9 02cbg0 09tcg4 02jxrw => 48 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1514): 08nvyr (0.78 #14475, 0.78 #17370, 0.77 #11577), 01qz5 (0.78 #14475, 0.78 #17370, 0.77 #11577), 02zk08 (0.78 #14475, 0.78 #17370, 0.77 #11577), 02rjv2w (0.78 #14475, 0.78 #17370, 0.77 #11577), 026gyn_ (0.78 #14475, 0.78 #17370, 0.77 #11577), 0glbqt (0.78 #14475, 0.78 #17370, 0.77 #11577), 034xyf (0.78 #14475, 0.78 #17370, 0.77 #11577), 02jxrw (0.78 #14475, 0.78 #17370, 0.77 #11577), 0kcn7 (0.78 #14475, 0.78 #17370, 0.77 #11577), 0jnwx (0.78 #14475, 0.78 #17370, 0.77 #11577) >> Best rule #14475 for best value: >> intensional similarity = 4 >> extensional distance = 119 >> proper extension: 02qyp19; 0gqng; 027dtxw; 02r0csl; 040njc; 0bfvw2; 03hkv_r; 0bp_b2; 099jhq; 0gq_v; ... >> query: (?x1079, ?x776) <- award(?x669, ?x1079), award(?x776, ?x1079), ceremony(?x1079, ?x78), nominated_for(?x1079, ?x167) >> conf = 0.78 => this is the best rule for 15 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 8, 19, 38, 142, 205, 210, 216, 346, 536, 590, 616, 692 EVAL 0l8z1 nominated_for 02jxrw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 09tcg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 02cbg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 02p76f9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 07jnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 011yl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 011ysn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 04jwly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 0260bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 029zqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0l8z1 nominated_for 035xwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 23.000 0.776 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15821-02sf_r PRED entity: 02sf_r PRED relation: team PRED expected values: 02plv57 => 25 concepts (11 used for prediction) PRED predicted values (max 10 best out of 897): 0jmk7 (0.84 #5675, 0.83 #5674, 0.81 #4731), 0jm7n (0.84 #5675, 0.83 #5674, 0.81 #5673), 0jmcb (0.84 #5675, 0.83 #5674, 0.81 #5673), 04cxw5b (0.84 #5675, 0.81 #4731, 0.81 #5673), 0fw9vx (0.84 #5675, 0.81 #4731, 0.81 #5673), 02pzy52 (0.84 #5675, 0.81 #5673, 0.81 #1888), 0jmnl (0.78 #6627, 0.64 #7573, 0.38 #8516), 02pyyld (0.75 #9460, 0.61 #8517, 0.61 #8515), 02r2qt7 (0.75 #9460, 0.61 #8517, 0.61 #8515), 026wlnm (0.75 #9460, 0.61 #8517, 0.61 #8515) >> Best rule #5675 for best value: >> intensional similarity = 27 >> extensional distance = 5 >> proper extension: 0619m3; >> query: (?x4747, ?x4571) <- position(?x10837, ?x4747), position(?x9833, ?x4747), position(?x4571, ?x4747), position(?x1578, ?x4747), team(?x4747, ?x5756), ?x1578 = 0jm2v, position(?x10837, ?x6848), teams(?x4090, ?x10837), place_of_birth(?x971, ?x4090), school(?x10837, ?x1675), location(?x3402, ?x4090), team(?x9974, ?x9833), team(?x8527, ?x9833), ?x6848 = 02_ssl, locations(?x8527, ?x2277), ?x2277 = 013yq, contains(?x177, ?x4090), sport(?x4571, ?x4833), draft(?x10837, ?x2569), team(?x8527, ?x4938), team(?x8527, ?x2303), ?x4938 = 027yf83, dog_breed(?x4090, ?x1706), ?x9974 = 0b_6pv, source(?x4090, ?x958), time_zones(?x4090, ?x2674), ?x2303 = 02plv57 >> conf = 0.84 => this is the best rule for 6 predicted values *> Best rule #5679 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 5 *> proper extension: 0619m3; *> query: (?x4747, ?x3798) <- position(?x10837, ?x4747), position(?x9833, ?x4747), position(?x4571, ?x4747), position(?x1578, ?x4747), team(?x4747, ?x5756), ?x1578 = 0jm2v, position(?x10837, ?x6848), teams(?x4090, ?x10837), place_of_birth(?x971, ?x4090), school(?x10837, ?x1675), location(?x3402, ?x4090), team(?x9974, ?x9833), team(?x8527, ?x9833), ?x6848 = 02_ssl, locations(?x8527, ?x2277), ?x2277 = 013yq, contains(?x177, ?x4090), sport(?x4571, ?x4833), draft(?x10837, ?x2569), team(?x8527, ?x4938), team(?x8527, ?x3798), team(?x8527, ?x2303), ?x4938 = 027yf83, dog_breed(?x4090, ?x1706), ?x9974 = 0b_6pv, source(?x4090, ?x958), time_zones(?x4090, ?x2674), ?x2303 = 02plv57 *> conf = 0.25 ranks of expected_values: 65 EVAL 02sf_r team 02plv57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 25.000 11.000 0.835 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #15820-0342h PRED entity: 0342h PRED relation: instrumentalists PRED expected values: 0274ck 01vrt_c 01j4ls 019g40 05qw5 01wy61y 025ldg 013423 01l47f5 01vrnsk 0f_y9 0jsg0m 0dw3l 02vcp0 027hm_ 0bdxs5 0140t7 01hgwkr 0417z2 01wdcxk 01wqpnm 01y_rz => 85 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1120): 07zft (0.60 #2526, 0.43 #6680, 0.36 #5901), 01wvxw1 (0.56 #4925, 0.50 #3029, 0.49 #9339), 03bnv (0.56 #4925, 0.50 #1366, 0.49 #9339), 01tp5bj (0.56 #4925, 0.50 #1090, 0.49 #9339), 01271h (0.56 #4925, 0.49 #9339, 0.48 #9079), 016h9b (0.56 #4925, 0.49 #9339, 0.48 #9079), 01wwvc5 (0.56 #4925, 0.49 #9339, 0.48 #9079), 01nhkxp (0.56 #4925, 0.49 #9339, 0.48 #9079), 0bkf4 (0.56 #4925, 0.49 #9339, 0.48 #9079), 016jfw (0.56 #4925, 0.49 #9339, 0.48 #9079) >> Best rule #2526 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0l14qv; 0l14md; 05r5c; >> query: (?x227, 07zft) <- performance_role(?x1089, ?x227), role(?x219, ?x227), performance_role(?x75, ?x227), instrumentalists(?x227, ?x11443), role(?x74, ?x227), ?x11443 = 01vv6xv >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4925 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 0mkg; 018j2; *> query: (?x227, ?x1292) <- role(?x74, ?x227), instrumentalists(?x227, ?x2824), role(?x1292, ?x227), group(?x227, ?x4909), ?x4909 = 01cblr, nationality(?x2824, ?x94) *> conf = 0.56 ranks of expected_values: 22, 55, 59, 60, 94, 100, 107, 113, 120, 121, 141, 159, 162, 200, 208, 236, 240, 356, 384, 552, 684, 712 EVAL 0342h instrumentalists 01y_rz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 01wqpnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 01wdcxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 0417z2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 01hgwkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 0140t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 0bdxs5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 027hm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 02vcp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 0dw3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 0jsg0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 0f_y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 01vrnsk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 01l47f5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 013423 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 025ldg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 01wy61y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 05qw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 019g40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 01j4ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 01vrt_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists EVAL 0342h instrumentalists 0274ck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 85.000 73.000 0.600 http://example.org/music/instrument/instrumentalists #15819-06pj8 PRED entity: 06pj8 PRED relation: participant PRED expected values: 0157m => 165 concepts (149 used for prediction) PRED predicted values (max 10 best out of 374): 07r1h (0.80 #19889, 0.07 #11964, 0.04 #7472), 02mjmr (0.36 #12191, 0.25 #7699, 0.02 #11090), 0bq2g (0.36 #12191, 0.25 #7699, 0.01 #11797), 0237fw (0.14 #803, 0.03 #5936, 0.03 #7218), 0151w_ (0.09 #1348, 0.04 #2630, 0.03 #7762), 063b4k (0.09 #1919, 0.04 #3201), 0343h (0.09 #5133, 0.05 #8982, 0.04 #15398), 014zcr (0.08 #21190, 0.07 #19265, 0.04 #42353), 0bbf1f (0.06 #11747, 0.04 #7897, 0.03 #30349), 04mlmx (0.06 #41694, 0.06 #16682, 0.05 #38488) >> Best rule #19889 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 04bs3j; 0n6f8; 01qvgl; 0157m; 02mjmr; 01gbbz; 046lt; 024dgj; 044qx; 046qq; ... >> query: (?x2135, ?x6187) <- student(?x735, ?x2135), award_winner(?x747, ?x2135), participant(?x6187, ?x2135) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5882 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 58 *> proper extension: 017l4; *> query: (?x2135, 0157m) <- currency(?x2135, ?x170), executive_produced_by(?x825, ?x2135) *> conf = 0.02 ranks of expected_values: 242 EVAL 06pj8 participant 0157m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 165.000 149.000 0.801 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #15818-02gkzs PRED entity: 02gkzs PRED relation: legislative_sessions! PRED expected values: 03ww_x => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 36): 070mff (0.85 #1210, 0.83 #117, 0.83 #1371), 03ww_x (0.82 #807, 0.82 #690, 0.82 #1411), 02gkzs (0.80 #615, 0.80 #663, 0.80 #809), 01gst_ (0.43 #1218, 0.39 #810, 0.39 #578), 01gsvb (0.43 #913, 0.41 #1237, 0.39 #1030), 01gsvp (0.39 #810, 0.39 #578, 0.39 #616), 01gtcc (0.39 #810, 0.39 #578, 0.39 #616), 01grr2 (0.39 #810, 0.39 #578, 0.39 #616), 01gtc0 (0.39 #810, 0.39 #578, 0.39 #616), 01gtbb (0.39 #810, 0.39 #578, 0.39 #616) >> Best rule #1210 for best value: >> intensional similarity = 27 >> extensional distance = 34 >> proper extension: 01h7xx; >> query: (?x3766, ?x653) <- legislative_sessions(?x3766, ?x4730), legislative_sessions(?x3766, ?x653), district_represented(?x3766, ?x2020), district_represented(?x3766, ?x1755), legislative_sessions(?x652, ?x3766), ?x2020 = 05k7sb, legislative_sessions(?x2860, ?x4730), contains(?x1755, ?x503), contains(?x94, ?x1755), district_represented(?x653, ?x7518), district_represented(?x653, ?x4758), district_represented(?x653, ?x2623), location(?x4587, ?x1755), location(?x1897, ?x1755), religion(?x1755, ?x109), ?x7518 = 026mj, partially_contains(?x1755, ?x10954), location_of_ceremony(?x1545, ?x1755), state_province_region(?x122, ?x1755), profession(?x4587, ?x1032), ?x4758 = 0vbk, jurisdiction_of_office(?x3959, ?x1755), ?x3959 = 0f6c3, ?x2623 = 02xry, film(?x4587, ?x1444), award_winner(?x591, ?x4587), participant(?x1897, ?x3546) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #807 for first EXPECTED value: *> intensional similarity = 33 *> extensional distance = 16 *> proper extension: 04gp1d; *> query: (?x3766, ?x356) <- legislative_sessions(?x3766, ?x2861), legislative_sessions(?x3766, ?x653), legislative_sessions(?x3766, ?x606), legislative_sessions(?x3766, ?x356), district_represented(?x3766, ?x2977), district_represented(?x3766, ?x2256), district_represented(?x3766, ?x2020), district_represented(?x3766, ?x448), ?x2861 = 03tcbx, location(?x5312, ?x2256), adjoins(?x448, ?x177), contains(?x448, ?x11474), contains(?x448, ?x6973), religion(?x448, ?x2591), religion(?x448, ?x109), state_province_region(?x2895, ?x2256), legislative_sessions(?x11605, ?x606), ?x109 = 01lp8, currency(?x2256, ?x170), ?x11605 = 024_vw, ?x2977 = 081mh, state(?x12953, ?x448), ?x653 = 070m6c, state_province_region(?x4348, ?x448), major_field_of_study(?x2895, ?x10417), institution(?x1368, ?x11474), category(?x6973, ?x134), legislative_sessions(?x2860, ?x3766), ?x2591 = 0631_, ?x2020 = 05k7sb, adjoins(?x2768, ?x2256), major_field_of_study(?x11474, ?x5614), ?x10417 = 01r4k *> conf = 0.82 ranks of expected_values: 2 EVAL 02gkzs legislative_sessions! 03ww_x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 40.000 40.000 0.847 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #15817-025twgf PRED entity: 025twgf PRED relation: music PRED expected values: 01cbt3 => 116 concepts (105 used for prediction) PRED predicted values (max 10 best out of 103): 015wc0 (0.33 #807, 0.33 #387, 0.22 #597), 0146pg (0.29 #1482, 0.28 #2112, 0.26 #1692), 05yzt_ (0.20 #153, 0.05 #1625, 0.04 #1835), 02vyw (0.20 #57, 0.05 #1529, 0.04 #1739), 02ryx0 (0.20 #110, 0.01 #3685, 0.01 #3896), 02wb6d (0.17 #338, 0.11 #758, 0.11 #548), 01cbt3 (0.16 #1142, 0.10 #2193, 0.07 #2403), 01l9v7n (0.16 #1098, 0.04 #3833, 0.03 #4675), 012201 (0.11 #572, 0.10 #1623, 0.07 #1833), 02jxmr (0.11 #495, 0.06 #915, 0.05 #3649) >> Best rule #807 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0k5g9; 02jr6k; 0k0rf; >> query: (?x8737, 015wc0) <- country(?x8737, ?x512), currency(?x8737, ?x170), nominated_for(?x10404, ?x8737), nominated_for(?x1708, ?x10404), film(?x1104, ?x10404), ?x1708 = 05cj_j >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1142 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 0140g4; *> query: (?x8737, 01cbt3) <- country(?x8737, ?x512), prequel(?x11362, ?x8737), nominated_for(?x11362, ?x1851), film(?x3022, ?x1851), films(?x5954, ?x1851) *> conf = 0.16 ranks of expected_values: 7 EVAL 025twgf music 01cbt3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 116.000 105.000 0.333 http://example.org/film/film/music #15816-044mvs PRED entity: 044mvs PRED relation: gender PRED expected values: 05zppz => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.71 #200, 0.71 #196, 0.71 #202), 02zsn (0.40 #15, 0.40 #19, 0.36 #29) >> Best rule #200 for best value: >> intensional similarity = 1 >> extensional distance = 4067 >> proper extension: 05_6_y; 01x66d; 045bg; 07_3qd; 028p0; 0kn4c; 03fghg; 0784v1; 063vn; 09ntbc; ... >> query: (?x10188, 05zppz) <- nationality(?x10188, ?x94) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044mvs gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.711 http://example.org/people/person/gender #15815-01grnp PRED entity: 01grnp PRED relation: district_represented PRED expected values: 05tbn => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 175): 05tbn (0.94 #1528, 0.93 #639, 0.91 #1392), 0g0syc (0.93 #639, 0.89 #959, 0.88 #746), 07h34 (0.93 #639, 0.89 #959, 0.86 #53), 05kkh (0.93 #639, 0.89 #959, 0.86 #53), 0rh6k (0.77 #1393, 0.61 #1394, 0.58 #799), 03v1s (0.76 #1284, 0.75 #1504, 0.74 #1616), 04tgp (0.76 #1284, 0.72 #1391, 0.67 #1648), 0gyh (0.76 #1284, 0.72 #1391, 0.65 #1358), 04ych (0.76 #1284, 0.72 #1391, 0.63 #1293), 03v0t (0.76 #1284, 0.72 #1391, 0.62 #1014) >> Best rule #1528 for best value: >> intensional similarity = 27 >> extensional distance = 30 >> proper extension: 02bqn1; 03tcbx; 03rtmz; 02gkzs; 02cg7g; 02glc4; >> query: (?x1754, 05tbn) <- legislative_sessions(?x4787, ?x1754), district_represented(?x1754, ?x6895), district_represented(?x1754, ?x3038), district_represented(?x1754, ?x2020), district_represented(?x1754, ?x1426), district_represented(?x1754, ?x728), ?x6895 = 05fjf, legislative_sessions(?x5978, ?x1754), ?x2020 = 05k7sb, contains(?x1426, ?x347), location(?x5200, ?x1426), district_represented(?x3540, ?x728), adjoins(?x3778, ?x1426), religion(?x1426, ?x962), jurisdiction_of_office(?x5254, ?x1426), ?x3540 = 024tcq, adjoins(?x279, ?x728), ?x962 = 05sfs, state_province_region(?x4077, ?x1426), time_zones(?x3038, ?x2674), partially_contains(?x1426, ?x10710), award_nominee(?x5200, ?x2359), ?x2359 = 0783m_, contains(?x94, ?x3778), contains(?x3038, ?x2277), location(?x396, ?x3778), state_province_region(?x2064, ?x728) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01grnp district_represented 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.938 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #15814-0p9tm PRED entity: 0p9tm PRED relation: nominated_for! PRED expected values: 0164w8 => 73 concepts (40 used for prediction) PRED predicted values (max 10 best out of 798): 018gqj (0.79 #21032, 0.79 #46741, 0.78 #70118), 0164w8 (0.79 #21032, 0.79 #46741, 0.78 #70118), 024yxd (0.79 #21032, 0.79 #46741, 0.78 #70118), 03vpf_ (0.37 #11685, 0.31 #18695, 0.28 #49080), 02l3_5 (0.37 #11685, 0.31 #18695, 0.28 #49080), 06m6p7 (0.37 #11685, 0.31 #18695, 0.28 #49080), 02vyw (0.20 #768, 0.04 #5442, 0.03 #3105), 0bj9k (0.20 #412, 0.03 #16770, 0.03 #88815), 012201 (0.20 #1789, 0.03 #88815, 0.03 #93490), 05yzt_ (0.20 #1799, 0.03 #88815, 0.03 #93490) >> Best rule #21032 for best value: >> intensional similarity = 3 >> extensional distance = 164 >> proper extension: 0dnvn3; 03s6l2; 01b195; 0bbw2z6; 02wwmhc; >> query: (?x7846, ?x3527) <- nominated_for(?x1822, ?x7846), genre(?x7846, ?x225), award_winner(?x7846, ?x3527) >> conf = 0.79 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0p9tm nominated_for! 0164w8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 40.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15813-02z3r8t PRED entity: 02z3r8t PRED relation: country PRED expected values: 0f8l9c => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 58): 07ssc (0.50 #136, 0.33 #16, 0.32 #2855), 02jx1 (0.35 #3571, 0.12 #148, 0.04 #328), 0d060g (0.35 #3571, 0.08 #368, 0.06 #909), 03rk0 (0.35 #3571, 0.04 #459, 0.03 #4902), 03spz (0.35 #3571, 0.03 #4902), 0345h (0.30 #267, 0.22 #207, 0.21 #327), 0f8l9c (0.14 #319, 0.13 #680, 0.12 #3590), 03rjj (0.12 #126, 0.11 #186, 0.10 #246), 04v3q (0.12 #144, 0.03 #4902), 07s9rl0 (0.12 #2900, 0.07 #962, 0.06 #6352) >> Best rule #136 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 026zlh9; >> query: (?x755, 07ssc) <- genre(?x755, ?x53), film(?x2156, ?x755), film(?x5043, ?x755), ?x5043 = 015q43 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #319 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 0d6b7; *> query: (?x755, 0f8l9c) <- music(?x755, ?x10574), film_festivals(?x755, ?x9189), written_by(?x755, ?x4295), film_crew_role(?x755, ?x468) *> conf = 0.14 ranks of expected_values: 7 EVAL 02z3r8t country 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 115.000 115.000 0.500 http://example.org/film/film/country #15812-0cnl1c PRED entity: 0cnl1c PRED relation: award_nominee! PRED expected values: 05l0j5 => 84 concepts (20 used for prediction) PRED predicted values (max 10 best out of 750): 05p92jn (0.82 #3824, 0.75 #1503, 0.40 #6143), 04t2l2 (0.82 #2320, 0.79 #9281, 0.78 #6960), 05l0j5 (0.67 #1707, 0.59 #4028, 0.40 #6347), 0cnl1c (0.65 #3327, 0.58 #1006, 0.33 #16244), 0cmt6q (0.53 #3801, 0.42 #1480, 0.25 #6120), 027cxsm (0.33 #16244, 0.30 #7296, 0.29 #32483), 0cj2t3 (0.33 #16244, 0.29 #32483, 0.28 #34805), 06jnvs (0.33 #16244, 0.29 #32483, 0.28 #34805), 03qmfzx (0.33 #16244, 0.29 #32483, 0.28 #34805), 048wrb (0.33 #16244, 0.29 #32483, 0.28 #34805) >> Best rule #3824 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 043js; 0cmt6q; 05l0j5; >> query: (?x4332, 05p92jn) <- award_nominee(?x7663, ?x4332), award_nominee(?x6634, ?x4332), ?x6634 = 0cj36c, ?x7663 = 04zkj5 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1707 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0bt4r4; *> query: (?x4332, 05l0j5) <- award_winner(?x4332, ?x6263), award_winner(?x4332, ?x237), ?x237 = 04t2l2, ?x6263 = 0cms7f *> conf = 0.67 ranks of expected_values: 3 EVAL 0cnl1c award_nominee! 05l0j5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 20.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15811-0dt39 PRED entity: 0dt39 PRED relation: award_winner PRED expected values: 06crk => 27 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1404): 02m7r (0.33 #499, 0.04 #22270, 0.03 #2973), 06cgy (0.26 #2783, 0.05 #15155, 0.04 #17630), 09fb5 (0.20 #2537, 0.06 #14909, 0.05 #19858), 0d6d2 (0.17 #4252, 0.06 #16624, 0.05 #19099), 0z4s (0.17 #2545, 0.05 #14917, 0.04 #19866), 039bp (0.17 #2686, 0.05 #15058, 0.04 #17533), 07w21 (0.16 #5022, 0.16 #7497, 0.12 #9971), 06pj8 (0.14 #2910, 0.07 #15282, 0.06 #20231), 04sry (0.14 #4096, 0.07 #16468, 0.06 #18943), 0bj9k (0.14 #2893, 0.07 #15265, 0.05 #17740) >> Best rule #499 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 06zrp44; >> query: (?x11301, 02m7r) <- award_winner(?x11301, ?x11055), award_winner(?x11301, ?x10913), award_winner(?x11301, ?x3335), ?x11055 = 02sdx, ?x3335 = 0jcx, ?x10913 = 059y0 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dt39 award_winner 06crk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 27.000 17.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #15810-05nqz PRED entity: 05nqz PRED relation: entity_involved PRED expected values: 09py7 03_lf => 68 concepts (46 used for prediction) PRED predicted values (max 10 best out of 414): 0193qj (0.60 #58, 0.15 #1614, 0.12 #2700), 01h3dj (0.44 #696, 0.40 #2396, 0.40 #68), 01hnp (0.40 #71, 0.09 #1157, 0.08 #1470), 02psqkz (0.36 #1123, 0.23 #1593, 0.22 #665), 09c7w0 (0.36 #935, 0.12 #318, 0.10 #2799), 0285m87 (0.33 #3037, 0.25 #4284, 0.25 #556), 01m41_ (0.33 #1514, 0.25 #432, 0.21 #4317), 0j5b8 (0.33 #1460, 0.25 #378, 0.17 #4263), 0chghy (0.30 #785, 0.27 #938, 0.12 #2646), 07ssc (0.30 #786, 0.25 #479, 0.25 #322) >> Best rule #58 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 0cm2xh; >> query: (?x5352, 0193qj) <- locations(?x5352, ?x2517), locations(?x5352, ?x344), contains(?x344, ?x1249), entity_involved(?x5352, ?x1892), adjoins(?x7430, ?x2517), time_zones(?x1249, ?x10735), olympics(?x1892, ?x391), combatants(?x1003, ?x1892), taxonomy(?x344, ?x939), ?x1003 = 03gj2 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #871 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 8 *> proper extension: 048n7; 0727h; *> query: (?x5352, 03_lf) <- locations(?x5352, ?x2188), contains(?x455, ?x2188), taxonomy(?x2188, ?x939), entity_involved(?x5352, ?x1892), country(?x453, ?x1892), film_release_region(?x9345, ?x1892), film_release_region(?x3491, ?x1892), ?x3491 = 0gtvpkw, medal(?x1892, ?x422), film(?x902, ?x9345) *> conf = 0.10 ranks of expected_values: 101, 180 EVAL 05nqz entity_involved 03_lf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 68.000 46.000 0.600 http://example.org/base/culturalevent/event/entity_involved EVAL 05nqz entity_involved 09py7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 68.000 46.000 0.600 http://example.org/base/culturalevent/event/entity_involved #15809-040_lv PRED entity: 040_lv PRED relation: genre PRED expected values: 07s9rl0 => 88 concepts (58 used for prediction) PRED predicted values (max 10 best out of 98): 07s9rl0 (0.66 #473, 0.66 #2724, 0.65 #1064), 02kdv5l (0.54 #5099, 0.35 #830, 0.32 #3678), 01z4y (0.51 #6883, 0.51 #5813, 0.51 #5694), 02l7c8 (0.44 #251, 0.33 #1672, 0.28 #1197), 03k9fj (0.43 #5106, 0.25 #4040, 0.25 #4395), 01jfsb (0.42 #838, 0.34 #3686, 0.34 #2142), 060__y (0.33 #16, 0.24 #370, 0.16 #1079), 06cvj (0.33 #240, 0.20 #1661, 0.10 #595), 0lsxr (0.27 #599, 0.25 #480, 0.20 #1309), 06n90 (0.25 #5108, 0.15 #839, 0.14 #3687) >> Best rule #473 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 05_61y; >> query: (?x6036, 07s9rl0) <- genre(?x6036, ?x2753), film_release_distribution_medium(?x6036, ?x81), ?x2753 = 0219x_, films(?x9268, ?x6036) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 040_lv genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 58.000 0.659 http://example.org/film/film/genre #15808-015zxh PRED entity: 015zxh PRED relation: place_of_death! PRED expected values: 06w38l => 129 concepts (49 used for prediction) PRED predicted values (max 10 best out of 719): 014dq7 (0.33 #65, 0.25 #1571, 0.03 #13556), 0h326 (0.33 #751, 0.25 #2257, 0.02 #7532), 05f0r8 (0.33 #745, 0.25 #2251, 0.02 #7526), 01l3j (0.33 #740, 0.25 #2246, 0.02 #7521), 067x44 (0.33 #731, 0.25 #2237, 0.02 #7512), 058z1hb (0.33 #728, 0.25 #2234, 0.02 #7509), 02rf51g (0.33 #726, 0.25 #2232, 0.02 #7507), 02nygk (0.33 #725, 0.25 #2231, 0.02 #7506), 01c5d5 (0.33 #718, 0.25 #2224, 0.02 #7499), 01g04k (0.33 #717, 0.25 #2223, 0.02 #7498) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 030qb3t; >> query: (?x1659, 014dq7) <- location(?x1947, ?x1659), ?x1947 = 06dl_, country(?x1659, ?x94), place_of_birth(?x4149, ?x1659) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015zxh place_of_death! 06w38l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 49.000 0.333 http://example.org/people/deceased_person/place_of_death #15807-024mxd PRED entity: 024mxd PRED relation: genre PRED expected values: 06n90 => 79 concepts (77 used for prediction) PRED predicted values (max 10 best out of 126): 07s9rl0 (0.79 #6971, 0.78 #7089, 0.77 #1182), 05p553 (0.58 #6738, 0.33 #6856, 0.33 #6620), 06n90 (0.50 #131, 0.35 #5681, 0.33 #2492), 0bkbm (0.50 #394, 0.34 #630, 0.27 #748), 02n4kr (0.50 #8, 0.31 #5439, 0.27 #2251), 0lsxr (0.49 #836, 0.47 #5796, 0.35 #4023), 03k9fj (0.41 #1783, 0.40 #355, 0.38 #1075), 02l7c8 (0.32 #4975, 0.29 #1197, 0.27 #8759), 09blyk (0.25 #31, 0.14 #622, 0.12 #267), 0c3351 (0.25 #37, 0.14 #628, 0.12 #273) >> Best rule #6971 for best value: >> intensional similarity = 8 >> extensional distance = 1267 >> proper extension: 0170z3; 02d413; 014_x2; 034qmv; 0g22z; 018js4; 0sxg4; 083shs; 01br2w; 01jc6q; ... >> query: (?x3672, 07s9rl0) <- genre(?x3672, ?x812), titles(?x812, ?x80), genre(?x7738, ?x812), genre(?x1511, ?x812), genre(?x836, ?x812), ?x7738 = 01y9r2, written_by(?x836, ?x3692), ?x1511 = 0340hj >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #131 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 01_mdl; 0d_wms; 08mg_b; 042fgh; *> query: (?x3672, 06n90) <- nominated_for(?x3672, ?x1072), film(?x1119, ?x3672), genre(?x3672, ?x225), language(?x3672, ?x254), ?x1119 = 039bp, ?x254 = 02h40lc *> conf = 0.50 ranks of expected_values: 3 EVAL 024mxd genre 06n90 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 77.000 0.793 http://example.org/film/film/genre #15806-0dcfv PRED entity: 0dcfv PRED relation: nutrient! PRED expected values: 061_f 01645p => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 17): 04zpv (0.94 #556, 0.94 #538, 0.94 #532), 0fjfh (0.93 #670, 0.92 #756, 0.92 #632), 061_f (0.90 #877, 0.90 #842, 0.90 #827), 0cxn2 (0.86 #748, 0.86 #732, 0.86 #630), 0f25w9 (0.85 #579, 0.85 #565, 0.85 #417), 037ls6 (0.85 #580, 0.85 #573, 0.85 #417), 09728 (0.85 #583, 0.85 #564, 0.85 #417), 01645p (0.85 #417, 0.85 #415, 0.85 #441), 0frq6 (0.85 #417, 0.85 #415, 0.85 #441), 0971v (0.85 #417, 0.85 #415, 0.85 #441) >> Best rule #556 for best value: >> intensional similarity = 85 >> extensional distance = 30 >> proper extension: 025s7x6; 0h1sg; 0h1tg; 0h1tz; >> query: (?x3264, 04zpv) <- nutrient(?x7719, ?x3264), nutrient(?x7057, ?x3264), nutrient(?x6159, ?x3264), nutrient(?x6032, ?x3264), nutrient(?x4068, ?x3264), nutrient(?x1303, ?x3264), ?x7057 = 0fbdb, nutrient(?x4068, ?x13944), nutrient(?x4068, ?x13498), nutrient(?x4068, ?x12902), nutrient(?x4068, ?x12454), nutrient(?x4068, ?x11784), nutrient(?x4068, ?x11758), nutrient(?x4068, ?x11592), nutrient(?x4068, ?x11409), nutrient(?x4068, ?x11270), nutrient(?x4068, ?x10195), nutrient(?x4068, ?x10098), nutrient(?x4068, ?x9915), nutrient(?x4068, ?x9855), nutrient(?x4068, ?x9840), nutrient(?x4068, ?x9795), nutrient(?x4068, ?x9708), nutrient(?x4068, ?x9436), nutrient(?x4068, ?x9426), nutrient(?x4068, ?x9365), nutrient(?x4068, ?x8413), nutrient(?x4068, ?x7894), nutrient(?x4068, ?x7431), nutrient(?x4068, ?x7364), nutrient(?x4068, ?x7362), nutrient(?x4068, ?x6586), nutrient(?x4068, ?x6286), nutrient(?x4068, ?x6160), nutrient(?x4068, ?x6033), nutrient(?x4068, ?x6026), nutrient(?x4068, ?x5549), nutrient(?x4068, ?x5526), nutrient(?x4068, ?x5451), nutrient(?x4068, ?x5374), nutrient(?x4068, ?x5337), nutrient(?x4068, ?x5010), nutrient(?x4068, ?x1960), nutrient(?x4068, ?x1258), ?x9436 = 025sqz8, ?x6286 = 02y_3rf, ?x8413 = 02kc4sf, ?x5374 = 025s0zp, ?x13498 = 07q0m, ?x1258 = 0h1wg, ?x7719 = 0dj75, ?x1303 = 0fj52s, ?x7894 = 0f4hc, ?x11758 = 0q01m, ?x13944 = 0f4kp, ?x5451 = 05wvs, ?x12902 = 0fzjh, ?x7364 = 09gvd, ?x9795 = 05v_8y, ?x10195 = 0hkwr, ?x5337 = 06x4c, ?x6160 = 041r51, ?x9426 = 0h1yy, ?x6032 = 01nkt, ?x6586 = 05gh50, ?x11784 = 07zqy, ?x9855 = 0d9t0, ?x6026 = 025sf8g, ?x9365 = 04k8n, ?x9708 = 061xhr, ?x6159 = 033cnk, ?x10098 = 0h1_c, ?x5526 = 09pbb, ?x7431 = 09gwd, ?x9840 = 02p0tjr, ?x11270 = 02kc008, ?x7362 = 02kc5rj, ?x9915 = 025tkqy, ?x5010 = 0h1vz, ?x12454 = 025rw19, ?x1960 = 07hnp, ?x5549 = 025s7j4, ?x11409 = 0h1yf, ?x6033 = 04zjxcz, ?x11592 = 025sf0_ >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #877 for first EXPECTED value: *> intensional similarity = 85 *> extensional distance = 61 *> proper extension: 027g6p7; *> query: (?x3264, 061_f) <- nutrient(?x7057, ?x3264), nutrient(?x6159, ?x3264), nutrient(?x7057, ?x13944), nutrient(?x7057, ?x12902), nutrient(?x7057, ?x12454), nutrient(?x7057, ?x11784), nutrient(?x7057, ?x11758), nutrient(?x7057, ?x11592), nutrient(?x7057, ?x11409), nutrient(?x7057, ?x10891), nutrient(?x7057, ?x9949), nutrient(?x7057, ?x9915), nutrient(?x7057, ?x9855), nutrient(?x7057, ?x9795), nutrient(?x7057, ?x9733), nutrient(?x7057, ?x9426), nutrient(?x7057, ?x8413), nutrient(?x7057, ?x7894), nutrient(?x7057, ?x7720), nutrient(?x7057, ?x7652), nutrient(?x7057, ?x7431), nutrient(?x7057, ?x7219), nutrient(?x7057, ?x7135), nutrient(?x7057, ?x6160), nutrient(?x7057, ?x6033), nutrient(?x7057, ?x6026), nutrient(?x7057, ?x5549), nutrient(?x7057, ?x5451), nutrient(?x7057, ?x5337), nutrient(?x7057, ?x5010), nutrient(?x7057, ?x4069), nutrient(?x7057, ?x3469), nutrient(?x7057, ?x3203), nutrient(?x7057, ?x1304), nutrient(?x7057, ?x1258), ?x5549 = 025s7j4, ?x7431 = 09gwd, ?x1304 = 08lb68, ?x9949 = 02kd0rh, nutrient(?x9732, ?x7135), nutrient(?x9489, ?x7135), nutrient(?x8298, ?x7135), nutrient(?x5373, ?x7135), nutrient(?x5009, ?x7135), nutrient(?x3468, ?x7135), nutrient(?x1257, ?x7135), ?x8413 = 02kc4sf, ?x9855 = 0d9t0, ?x9733 = 0h1tz, ?x7219 = 0h1vg, ?x9732 = 05z55, ?x3469 = 0h1zw, ?x7894 = 0f4hc, ?x3468 = 0cxn2, ?x6160 = 041r51, ?x9489 = 07j87, ?x5451 = 05wvs, ?x12902 = 0fzjh, ?x9915 = 025tkqy, ?x10891 = 0g5gq, ?x11784 = 07zqy, ?x1258 = 0h1wg, ?x5337 = 06x4c, ?x3203 = 04kl74p, ?x13944 = 0f4kp, ?x6033 = 04zjxcz, ?x4069 = 0hqw8p_, ?x11592 = 025sf0_, ?x6026 = 025sf8g, ?x5373 = 0971v, ?x11409 = 0h1yf, ?x11758 = 0q01m, ?x9795 = 05v_8y, nutrient(?x6159, ?x13498), nutrient(?x6159, ?x8487), ?x7720 = 025s7x6, ?x9426 = 0h1yy, ?x7652 = 025s0s0, ?x13498 = 07q0m, ?x5009 = 0fjfh, ?x1257 = 09728, ?x8298 = 037ls6, ?x8487 = 014yzm, ?x5010 = 0h1vz, ?x12454 = 025rw19 *> conf = 0.90 ranks of expected_values: 3, 8 EVAL 0dcfv nutrient! 01645p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 41.000 41.000 0.938 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dcfv nutrient! 061_f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 41.000 0.938 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #15805-01w5jwb PRED entity: 01w5jwb PRED relation: role PRED expected values: 0l14qv => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 78): 0342h (0.43 #213, 0.40 #2191, 0.39 #2400), 05r5c (0.40 #2404, 0.40 #217, 0.38 #2195), 042v_gx (0.31 #218, 0.23 #2196, 0.21 #2405), 02sgy (0.27 #215, 0.25 #2193, 0.24 #2402), 013y1f (0.23 #245, 0.15 #2223, 0.14 #2432), 0l14qv (0.22 #214, 0.16 #2401, 0.16 #2192), 05842k (0.20 #287, 0.18 #2265, 0.18 #2474), 018vs (0.19 #223, 0.17 #2201, 0.17 #2410), 026t6 (0.17 #2398, 0.15 #2189, 0.14 #211), 05148p4 (0.15 #232, 0.12 #24, 0.12 #2419) >> Best rule #213 for best value: >> intensional similarity = 5 >> extensional distance = 86 >> proper extension: 01vd7hn; >> query: (?x8722, 0342h) <- award(?x8722, ?x12835), award(?x8722, ?x2139), ?x2139 = 01by1l, role(?x8722, ?x745), award_winner(?x12835, ?x3493) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #214 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 86 *> proper extension: 01vd7hn; *> query: (?x8722, 0l14qv) <- award(?x8722, ?x12835), award(?x8722, ?x2139), ?x2139 = 01by1l, role(?x8722, ?x745), award_winner(?x12835, ?x3493) *> conf = 0.22 ranks of expected_values: 6 EVAL 01w5jwb role 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 88.000 0.432 http://example.org/music/artist/track_contributions./music/track_contribution/role #15804-0c1d0 PRED entity: 0c1d0 PRED relation: teams PRED expected values: 04l590 => 213 concepts (213 used for prediction) PRED predicted values (max 10 best out of 206): 04l58n (0.14 #1034, 0.06 #25935, 0.05 #20887), 01d6g (0.14 #913, 0.03 #3073, 0.03 #3433), 0d3fdn (0.12 #1415, 0.09 #21248, 0.06 #25935), 0jnpv (0.12 #1357, 0.09 #21248, 0.06 #25935), 0jm4b (0.12 #1184, 0.06 #25935, 0.05 #20887), 0jmgb (0.12 #1382, 0.06 #25935, 0.05 #20887), 02r2qt7 (0.12 #1219, 0.06 #25935, 0.05 #20887), 051q5 (0.12 #1157, 0.06 #25935, 0.05 #20887), 0512p (0.12 #1106, 0.06 #25935, 0.05 #20887), 03by7wc (0.12 #1216, 0.06 #25935, 0.05 #20887) >> Best rule #1034 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01lxw6; >> query: (?x8263, 04l58n) <- locations(?x3797, ?x8263), currency(?x8263, ?x170), country(?x8263, ?x94), contains(?x2713, ?x8263) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c1d0 teams 04l590 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 213.000 213.000 0.143 http://example.org/sports/sports_team_location/teams #15803-0qr4n PRED entity: 0qr4n PRED relation: contains! PRED expected values: 0vmt => 88 concepts (42 used for prediction) PRED predicted values (max 10 best out of 197): 0vmt (0.80 #11628, 0.69 #26852, 0.69 #26851), 01n7q (0.41 #25136, 0.14 #13495, 0.14 #11705), 0m2by (0.33 #471, 0.25 #2259, 0.05 #3153), 0m24v (0.33 #1611, 0.01 #6082, 0.01 #6977), 07srw (0.20 #3721, 0.19 #4615, 0.15 #5509), 01n4w (0.18 #3760, 0.17 #4654, 0.13 #5548), 04_1l0v (0.15 #5815, 0.14 #6710, 0.09 #3132), 059rby (0.10 #9857, 0.09 #2701, 0.09 #18810), 07z1m (0.10 #9929, 0.09 #18882, 0.08 #17092), 05tbn (0.10 #10955, 0.06 #19909, 0.06 #17224) >> Best rule #11628 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 0f04v; >> query: (?x3832, ?x94) <- county_seat(?x11275, ?x3832), source(?x11275, ?x958), contains(?x94, ?x11275), contains(?x11275, ?x13006) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qr4n contains! 0vmt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 42.000 0.801 http://example.org/location/location/contains #15802-0b7gxq PRED entity: 0b7gxq PRED relation: profession PRED expected values: 0cbd2 => 98 concepts (73 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.75 #4603, 0.69 #8303, 0.69 #5047), 01d_h8 (0.62 #6223, 0.50 #7999, 0.44 #2818), 02krf9 (0.58 #174, 0.50 #26, 0.39 #470), 0cbd2 (0.50 #155, 0.38 #7, 0.36 #303), 02jknp (0.42 #6225, 0.38 #2820, 0.34 #8001), 018gz8 (0.33 #164, 0.28 #2828, 0.26 #10809), 0np9r (0.26 #10809, 0.26 #10808, 0.25 #4589), 015cjr (0.26 #10809, 0.26 #10808, 0.25 #4589), 08z956 (0.26 #10809, 0.26 #10808, 0.25 #4589), 0kyk (0.26 #10809, 0.26 #10808, 0.25 #4589) >> Best rule #4603 for best value: >> intensional similarity = 3 >> extensional distance = 1219 >> proper extension: 04n32; >> query: (?x7552, 02hrh1q) <- award_nominee(?x7552, ?x1630), award(?x7552, ?x2016), participant(?x1630, ?x7040) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #155 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 04t2l2; 027cxsm; 015pxr; 0bt4r4; 0cj2t3; 06jnvs; 0h3mrc; 08hsww; 048wrb; 03qmfzx; *> query: (?x7552, 0cbd2) <- award_nominee(?x7552, ?x3896), award_winner(?x5296, ?x7552), ?x3896 = 0cj2nl *> conf = 0.50 ranks of expected_values: 4 EVAL 0b7gxq profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 73.000 0.745 http://example.org/people/person/profession #15801-05b5c PRED entity: 05b5c PRED relation: place_founded PRED expected values: 0hzgf => 163 concepts (143 used for prediction) PRED predicted values (max 10 best out of 87): 0d6lp (0.20 #1063, 0.18 #1197, 0.15 #1330), 0r5wt (0.20 #230, 0.14 #358, 0.08 #943), 0d7k1z (0.20 #236, 0.14 #364, 0.08 #949), 080h2 (0.20 #273, 0.13 #1052, 0.09 #793), 0f2rq (0.20 #235, 0.07 #1078, 0.04 #1474), 013d7t (0.20 #233, 0.07 #1076, 0.04 #1472), 07dfk (0.19 #1614, 0.18 #1873, 0.12 #3164), 02_286 (0.17 #1383, 0.14 #336, 0.14 #2225), 030qb3t (0.15 #1772, 0.10 #3320, 0.08 #1450), 026mj (0.14 #372, 0.12 #1221, 0.10 #1354) >> Best rule #1063 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 043ljr; >> query: (?x13349, 0d6lp) <- place_founded(?x13349, ?x11236), adjoins(?x11236, ?x11237), country(?x11236, ?x1892), category(?x11236, ?x134) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05b5c place_founded 0hzgf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 163.000 143.000 0.200 http://example.org/organization/organization/place_founded #15800-0jcx PRED entity: 0jcx PRED relation: award_winner! PRED expected values: 020qjg => 205 concepts (188 used for prediction) PRED predicted values (max 10 best out of 329): 02g3ft (0.25 #3510, 0.20 #4795, 0.14 #6508), 02g3gw (0.25 #3716, 0.20 #5001, 0.14 #6714), 02rdyk7 (0.25 #1376, 0.20 #1804, 0.12 #3944), 02wkmx (0.25 #1299, 0.20 #1727, 0.04 #22702), 0gqng (0.25 #1286, 0.20 #1714, 0.04 #8564), 0gq9h (0.25 #3930, 0.17 #9496, 0.14 #16345), 02sp_v (0.25 #589, 0.15 #8295, 0.09 #5726), 03x3wf (0.25 #493, 0.15 #8199, 0.08 #6059), 01by1l (0.25 #541, 0.15 #8247, 0.08 #6107), 05f3q (0.25 #3305, 0.12 #2877, 0.12 #2449) >> Best rule #3510 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 09gnn; >> query: (?x3335, 02g3ft) <- nationality(?x3335, ?x94), profession(?x3335, ?x353), peers(?x9836, ?x3335), organizations_founded(?x3335, ?x11768) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4665 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 041mt; 02m7r; 034bs; 060_7; 06myp; 059y0; 032r1; *> query: (?x3335, 020qjg) <- location(?x3335, ?x1264), peers(?x9836, ?x3335), country(?x136, ?x1264), film_release_region(?x66, ?x1264) *> conf = 0.20 ranks of expected_values: 25 EVAL 0jcx award_winner! 020qjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 205.000 188.000 0.250 http://example.org/award/award_category/winners./award/award_honor/award_winner #15799-016dsy PRED entity: 016dsy PRED relation: location PRED expected values: 059rby => 146 concepts (125 used for prediction) PRED predicted values (max 10 best out of 290): 02_286 (0.98 #24111, 0.98 #39354, 0.98 #30529), 0n9r8 (0.77 #25679, 0.76 #70604, 0.75 #81036), 030qb3t (0.40 #62663, 0.29 #64267, 0.27 #5700), 0k049 (0.33 #8, 0.06 #52964, 0.05 #60182), 04jpl (0.27 #2425, 0.21 #17669, 0.21 #60191), 06c62 (0.20 #1137, 0.03 #53291, 0.02 #60509), 059rby (0.13 #52972, 0.10 #59388, 0.10 #60190), 0h924 (0.12 #2125, 0.07 #2928, 0.05 #70603), 0cv5l (0.12 #2371, 0.07 #3174, 0.01 #18418), 0bdg5 (0.12 #2069, 0.03 #10091) >> Best rule #24111 for best value: >> intensional similarity = 4 >> extensional distance = 235 >> proper extension: 049tjg; 033hqf; 0bz5v2; 01wjrn; 02v406; 0g2mbn; 02pk6x; 023n39; 02zhkz; 024my5; ... >> query: (?x4082, 02_286) <- location(?x4082, ?x11072), location_of_ceremony(?x5951, ?x11072), ?x5951 = 0dvld, film(?x4082, ?x3441) >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #52972 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 483 *> proper extension: 02qjj7; 02zyy4; 049k07; 06mmb; 03m6t5; 073749; 039crh; 06jw0s; 03bxh; 06hx2; ... *> query: (?x4082, 059rby) <- location(?x4082, ?x11072), location_of_ceremony(?x5951, ?x11072), diet(?x5951, ?x3130), nominated_for(?x5951, ?x1597) *> conf = 0.13 ranks of expected_values: 7 EVAL 016dsy location 059rby CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 146.000 125.000 0.983 http://example.org/people/person/places_lived./people/place_lived/location #15798-025hl8 PRED entity: 025hl8 PRED relation: people PRED expected values: 01934k => 56 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1849): 04wqr (0.50 #2071, 0.05 #30258, 0.04 #24075), 02cj_f (0.50 #2500, 0.05 #30687, 0.04 #24504), 0gyy0 (0.43 #9317, 0.40 #5194, 0.33 #1755), 0chsq (0.40 #5515, 0.33 #11008, 0.25 #15821), 0jrny (0.40 #4920, 0.29 #9043, 0.25 #3545), 05v45k (0.40 #5431, 0.29 #9554, 0.25 #4056), 016gkf (0.40 #5027, 0.29 #9150, 0.25 #3652), 0b22w (0.40 #5996, 0.29 #9429, 0.25 #3931), 0436zq (0.40 #6088, 0.22 #11581, 0.17 #16394), 0121rx (0.33 #7520, 0.33 #2018, 0.29 #8894) >> Best rule #2071 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 06z5s; >> query: (?x3680, 04wqr) <- people(?x3680, ?x6852), award_winner(?x749, ?x6852), film(?x6852, ?x2370), nominated_for(?x749, ?x4460), award(?x396, ?x749), ?x4460 = 0yxm1, spouse(?x9256, ?x6852) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #687 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 02knxx; *> query: (?x3680, ?x538) <- people(?x3680, ?x6852), award_winner(?x5180, ?x6852), award_winner(?x1245, ?x6852), award_winner(?x749, ?x6852), film(?x6852, ?x2370), ?x749 = 094qd5, ?x1245 = 0gqwc, award_winner(?x5180, ?x13106), award_winner(?x5180, ?x538), ceremony(?x5180, ?x4141), nationality(?x6852, ?x94), ?x13106 = 018417 *> conf = 0.04 ranks of expected_values: 749 EVAL 025hl8 people 01934k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 45.000 0.500 http://example.org/people/cause_of_death/people #15797-02j62 PRED entity: 02j62 PRED relation: student PRED expected values: 0154qm 036qs_ 0c_md_ => 78 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1162): 06whf (0.50 #1965, 0.40 #2898, 0.33 #98), 05l4yg (0.50 #2244, 0.20 #2710, 0.03 #10201), 0kn4c (0.33 #3528, 0.33 #726, 0.33 #260), 083q7 (0.33 #952, 0.33 #719, 0.25 #2353), 0d06m5 (0.33 #1000, 0.25 #2401, 0.20 #3335), 042xh (0.33 #233, 0.25 #2100, 0.20 #3033), 02mjmr (0.33 #983, 0.25 #2384, 0.17 #3785), 02jr26 (0.33 #1077, 0.25 #2478, 0.17 #3879), 0d0vj4 (0.33 #949, 0.25 #2350, 0.17 #3751), 0c_md_ (0.33 #1120, 0.25 #2521, 0.17 #3922) >> Best rule #1965 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 0fdys; >> query: (?x2981, 06whf) <- major_field_of_study(?x13088, ?x2981), major_field_of_study(?x11707, ?x2981), major_field_of_study(?x7092, ?x2981), major_field_of_study(?x3813, ?x2981), ?x13088 = 01s753, major_field_of_study(?x734, ?x2981), major_field_of_study(?x2981, ?x1527), organization(?x346, ?x11707), colors(?x3813, ?x3189), company(?x1159, ?x7092) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1120 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 062z7; *> query: (?x2981, 0c_md_) <- major_field_of_study(?x7918, ?x2981), major_field_of_study(?x6127, ?x2981), major_field_of_study(?x3354, ?x2981), major_field_of_study(?x2327, ?x2981), major_field_of_study(?x1675, ?x2981), ?x2327 = 07wjk, ?x1675 = 01j_cy, ?x7918 = 0gl6f, institution(?x1368, ?x6127), ?x1368 = 014mlp, contains(?x2316, ?x3354), major_field_of_study(?x1527, ?x2981) *> conf = 0.33 ranks of expected_values: 10, 189, 310 EVAL 02j62 student 0c_md_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 78.000 68.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 02j62 student 036qs_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 78.000 68.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 02j62 student 0154qm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 78.000 68.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/student #15796-0j5g9 PRED entity: 0j5g9 PRED relation: contains PRED expected values: 02049g => 189 concepts (137 used for prediction) PRED predicted values (max 10 best out of 2653): 01_5bb (0.85 #140836, 0.83 #167245, 0.83 #99758), 0j5g9 (0.52 #299283, 0.39 #287547, 0.25 #3477), 0yl27 (0.52 #299283, 0.25 #9313, 0.20 #15181), 05bcl (0.52 #299283, 0.25 #3447, 0.20 #18116), 07ssc (0.52 #299283, 0.06 #178982, 0.02 #217128), 027xq5 (0.40 #20226, 0.25 #8490, 0.25 #5557), 09vzz (0.40 #19847, 0.25 #8111, 0.25 #5178), 01b1nk (0.40 #20343, 0.25 #8607, 0.25 #5674), 024cg8 (0.40 #20192, 0.25 #8456, 0.25 #5523), 011pcj (0.40 #20392, 0.25 #8656, 0.25 #5723) >> Best rule #140836 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0lwkz; >> query: (?x4221, ?x9969) <- contains(?x512, ?x4221), administrative_parent(?x9969, ?x4221), time_zones(?x4221, ?x5327) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #5783 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0yl27; *> query: (?x4221, 02049g) <- state_province_region(?x4220, ?x4221), contains(?x4221, ?x14206), ?x14206 = 0205m3 *> conf = 0.25 ranks of expected_values: 246 EVAL 0j5g9 contains 02049g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 189.000 137.000 0.849 http://example.org/location/location/contains #15795-03cn92 PRED entity: 03cn92 PRED relation: award PRED expected values: 0bdwft => 110 concepts (77 used for prediction) PRED predicted values (max 10 best out of 266): 0gq9h (0.47 #2906, 0.38 #1290, 0.33 #1694), 0gqwc (0.36 #2095, 0.23 #4923, 0.20 #6943), 0gqy2 (0.35 #5014, 0.35 #4610, 0.29 #7034), 018wng (0.31 #1254, 0.24 #1658, 0.09 #850), 09sb52 (0.31 #4889, 0.30 #11760, 0.30 #5293), 0bdwft (0.31 #2089, 0.13 #6129, 0.11 #4917), 0f4x7 (0.29 #4879, 0.29 #4475, 0.25 #31), 040njc (0.27 #2836, 0.15 #2432, 0.12 #412), 0gqyl (0.25 #2126, 0.23 #4954, 0.23 #4550), 0gq_d (0.25 #1435, 0.19 #1839, 0.09 #1031) >> Best rule #2906 for best value: >> intensional similarity = 4 >> extensional distance = 189 >> proper extension: 032v0v; 05nn4k; 0bvg70; 0dqmt0; 04fyhv; 02qzjj; >> query: (?x5408, 0gq9h) <- award(?x5408, ?x5409), place_of_birth(?x5408, ?x4733), award(?x902, ?x5409), ?x902 = 05qd_ >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #2089 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 018z_c; *> query: (?x5408, 0bdwft) <- award(?x5408, ?x4225), type_of_union(?x5408, ?x566), gender(?x5408, ?x514), ?x4225 = 09qvf4 *> conf = 0.31 ranks of expected_values: 6 EVAL 03cn92 award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 110.000 77.000 0.471 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15794-01k9gb PRED entity: 01k9gb PRED relation: people PRED expected values: 03n6r 041b4j => 69 concepts (38 used for prediction) PRED predicted values (max 10 best out of 3565): 0gyy0 (0.50 #3837, 0.33 #16946, 0.25 #14189), 01w9ph_ (0.50 #1733, 0.08 #4829, 0.08 #6897), 015076 (0.50 #1932, 0.08 #24001, 0.07 #25386), 06hx2 (0.40 #4398, 0.29 #9918, 0.20 #18884), 05hks (0.40 #4628, 0.29 #10148, 0.20 #19114), 04xfb (0.40 #4514, 0.29 #10034, 0.20 #19000), 0b22w (0.33 #17059, 0.29 #10157, 0.20 #19123), 0chsq (0.33 #705, 0.25 #13129, 0.25 #2776), 0436zq (0.33 #1280, 0.25 #13704, 0.25 #3351), 0jrny (0.33 #16670, 0.25 #14601, 0.25 #3561) >> Best rule #3837 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 01dcqj; >> query: (?x14098, 0gyy0) <- people(?x14098, ?x11913), risk_factors(?x14098, ?x8023), ?x8023 = 0jpmt, film(?x11913, ?x8985), location(?x11913, ?x14602), titles(?x1316, ?x8985), titles(?x53, ?x8985), ?x1316 = 017fp, ?x53 = 07s9rl0 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2071 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 01tf_6; *> query: (?x14098, ?x65) <- people(?x14098, ?x2608), influenced_by(?x2608, ?x6037), influenced_by(?x2608, ?x3325), ?x6037 = 0hky, people(?x1050, ?x2608), type_of_union(?x2608, ?x566), profession(?x2608, ?x2225), people(?x1050, ?x65), location(?x3325, ?x2474), influenced_by(?x3325, ?x2162) *> conf = 0.03 ranks of expected_values: 838, 1137 EVAL 01k9gb people 041b4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 69.000 38.000 0.500 http://example.org/people/cause_of_death/people EVAL 01k9gb people 03n6r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 69.000 38.000 0.500 http://example.org/people/cause_of_death/people #15793-0gjk1d PRED entity: 0gjk1d PRED relation: films! PRED expected values: 04gb7 => 87 concepts (41 used for prediction) PRED predicted values (max 10 best out of 47): 0fx2s (0.40 #73, 0.08 #539, 0.07 #849), 081pw (0.20 #3, 0.12 #158, 0.11 #314), 0g1x2_ (0.20 #27, 0.03 #958, 0.03 #1580), 06d4h (0.12 #198, 0.11 #354, 0.08 #509), 0fzyg (0.12 #209, 0.11 #365, 0.06 #1607), 0bq3x (0.12 #185, 0.11 #341, 0.05 #806), 04jjy (0.12 #162, 0.11 #318, 0.03 #3442), 0htp (0.12 #275, 0.11 #431), 05f4p (0.12 #250, 0.11 #406), 02_h0 (0.09 #876, 0.04 #721, 0.04 #2278) >> Best rule #73 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 011ysn; >> query: (?x1209, 0fx2s) <- film(?x1208, ?x1209), films(?x12672, ?x1209), ?x1208 = 0sz28, nominated_for(?x591, ?x1209) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1598 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 270 *> proper extension: 0k4d7; *> query: (?x1209, 04gb7) <- film(?x1208, ?x1209), films(?x12672, ?x1209), type_of_union(?x1208, ?x566), produced_by(?x1209, ?x8491) *> conf = 0.04 ranks of expected_values: 19 EVAL 0gjk1d films! 04gb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 87.000 41.000 0.400 http://example.org/film/film_subject/films #15792-01z4y PRED entity: 01z4y PRED relation: titles PRED expected values: 026mfbr 0jyx6 05cj_j 0j_tw 065zlr 03m8y5 03mh_tp 04grkmd 0blpg 07kb7vh 02qzh2 07sgdw 0dt8xq 04t9c0 02pg45 02704ff 04h41v 02q7fl9 0421v9q 094g2z 0372j5 03p2xc 01jft4 0gd92 06x43v 02wgbb 0291hr 01jnc_ 058kh7 0df2zx => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1322): 03h_yy (0.50 #2686, 0.27 #13207, 0.26 #15835), 02s4l6 (0.50 #2889, 0.15 #18667, 0.14 #12095), 03hfmm (0.50 #3693, 0.15 #19471, 0.14 #12899), 0353xq (0.50 #3294, 0.14 #12500, 0.14 #13815), 011yxg (0.50 #2663, 0.14 #11869, 0.14 #13184), 05ypj5 (0.50 #3884, 0.11 #19662, 0.10 #13090), 01rnly (0.50 #3757, 0.10 #12963, 0.09 #14278), 01sxdy (0.50 #3057, 0.10 #12263, 0.09 #13578), 0296rz (0.25 #3817, 0.23 #14338, 0.22 #16966), 07z6xs (0.25 #3267, 0.23 #13788, 0.22 #16416) >> Best rule #2686 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 04xvlr; 04t36; >> query: (?x2480, 03h_yy) <- titles(?x2480, ?x7832), titles(?x2480, ?x6272), genre(?x631, ?x2480), ?x6272 = 041td_, film_release_region(?x7832, ?x87) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3097 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 04xvlr; 04t36; *> query: (?x2480, 0blpg) <- titles(?x2480, ?x7832), titles(?x2480, ?x6272), genre(?x631, ?x2480), ?x6272 = 041td_, film_release_region(?x7832, ?x87) *> conf = 0.25 ranks of expected_values: 290, 335, 353, 745, 895, 961, 978, 1023, 1051, 1054, 1057, 1070, 1225, 1239 EVAL 01z4y titles 0df2zx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 058kh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 01jnc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 0291hr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 02wgbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 06x43v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 0gd92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 01jft4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 03p2xc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 0372j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 094g2z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 0421v9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 02q7fl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 04h41v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 02704ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 02pg45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 04t9c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 0dt8xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 07sgdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 02qzh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 07kb7vh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 0blpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 04grkmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 03mh_tp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 03m8y5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 065zlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 0j_tw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 05cj_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 0jyx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 01z4y titles 026mfbr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 76.000 0.500 http://example.org/media_common/netflix_genre/titles #15791-01wdj_ PRED entity: 01wdj_ PRED relation: contains! PRED expected values: 09c7w0 => 158 concepts (79 used for prediction) PRED predicted values (max 10 best out of 318): 09c7w0 (0.82 #24146, 0.81 #48295, 0.79 #5367), 059rby (0.26 #23271, 0.17 #4490, 0.16 #49206), 02_286 (0.25 #23294, 0.17 #4513, 0.09 #7195), 02jx1 (0.22 #4557, 0.19 #17971, 0.14 #35856), 04tgp (0.17 #279, 0.12 #1173, 0.09 #2067), 02xry (0.17 #57405, 0.08 #8209, 0.08 #9998), 05mph (0.17 #358, 0.07 #3934, 0.04 #57600), 026mj (0.17 #416, 0.02 #8462, 0.02 #9357), 03v0t (0.16 #57474, 0.07 #5596, 0.05 #45840), 01n7q (0.14 #25118, 0.13 #16174, 0.13 #18857) >> Best rule #24146 for best value: >> intensional similarity = 5 >> extensional distance = 101 >> proper extension: 062qg; 012q8y; >> query: (?x2830, ?x94) <- contains(?x11901, ?x2830), contains(?x2831, ?x2830), teams(?x11901, ?x5491), country(?x2831, ?x94), district_represented(?x176, ?x2831) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wdj_ contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 79.000 0.816 http://example.org/location/location/contains #15790-05h4t7 PRED entity: 05h4t7 PRED relation: production_companies! PRED expected values: 07cz2 => 57 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1800): 07cz2 (0.49 #15941, 0.46 #15940, 0.46 #15942), 0b6l1st (0.49 #15941, 0.46 #15940, 0.46 #15942), 0bmch_x (0.49 #15941, 0.46 #15940, 0.46 #15942), 0127ps (0.49 #15941, 0.46 #15940, 0.46 #15942), 0gy0l_ (0.33 #2115, 0.19 #6670, 0.10 #43256), 011xg5 (0.33 #2052, 0.19 #6607, 0.10 #43256), 08fn5b (0.33 #1592, 0.19 #6147, 0.10 #43256), 02q0k7v (0.33 #1983, 0.19 #6538, 0.10 #43256), 01hqk (0.33 #1611, 0.19 #6166, 0.10 #43256), 05sns6 (0.33 #466, 0.17 #2742, 0.09 #17546) >> Best rule #15941 for best value: >> intensional similarity = 6 >> extensional distance = 31 >> proper extension: 015zyd; 0fb0v; 03rhqg; 054g1r; 04gvyp; 03qx_f; 09xwz; >> query: (?x1186, ?x3330) <- organizations_founded(?x5781, ?x1186), produced_by(?x3330, ?x5781), produced_by(?x1185, ?x5781), film_release_distribution_medium(?x1185, ?x81), genre(?x1185, ?x225), film_release_region(?x3330, ?x94) >> conf = 0.49 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 05h4t7 production_companies! 07cz2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 42.000 0.485 http://example.org/film/film/production_companies #15789-063_j5 PRED entity: 063_j5 PRED relation: film_crew_role PRED expected values: 0dxtw => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 34): 0dxtw (0.39 #185, 0.38 #927, 0.36 #821), 01vx2h (0.33 #822, 0.31 #928, 0.31 #10), 01xy5l_ (0.31 #13, 0.23 #48, 0.13 #118), 01pvkk (0.28 #187, 0.27 #1246, 0.27 #929), 015h31 (0.23 #42, 0.23 #7, 0.13 #2197), 02ynfr (0.18 #933, 0.15 #1250, 0.15 #581), 0215hd (0.15 #53, 0.15 #18, 0.13 #936), 02rh1dz (0.15 #43, 0.15 #8, 0.13 #2197), 0d2b38 (0.15 #60, 0.15 #25, 0.13 #2197), 05smlt (0.15 #55, 0.15 #20, 0.13 #2197) >> Best rule #185 for best value: >> intensional similarity = 4 >> extensional distance = 149 >> proper extension: 0dckvs; >> query: (?x8859, 0dxtw) <- nominated_for(?x338, ?x8859), genre(?x8859, ?x604), ?x604 = 0lsxr, film_crew_role(?x8859, ?x137) >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 063_j5 film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.391 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15788-0164nb PRED entity: 0164nb PRED relation: category PRED expected values: 08mbj5d => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.48 #14, 0.47 #11, 0.47 #5) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 0frmb1; >> query: (?x3817, 08mbj5d) <- gender(?x3817, ?x231), ?x231 = 05zppz, person(?x3480, ?x3817) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0164nb category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.480 http://example.org/common/topic/webpage./common/webpage/category #15787-0bt7ws PRED entity: 0bt7ws PRED relation: religion PRED expected values: 0631_ => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 18): 0c8wxp (0.21 #276, 0.21 #682, 0.21 #591), 0kpl (0.09 #55, 0.06 #1227, 0.06 #190), 03_gx (0.09 #1231, 0.08 #1997, 0.08 #2043), 03j6c (0.05 #923, 0.04 #968, 0.03 #832), 0kq2 (0.04 #243, 0.02 #1235, 0.02 #2543), 0631_ (0.04 #233, 0.01 #1225), 092bf5 (0.03 #286, 0.03 #511, 0.03 #601), 01lp8 (0.03 #271, 0.03 #361, 0.02 #586), 0flw86 (0.03 #272, 0.03 #813, 0.02 #904), 0n2g (0.02 #508, 0.02 #238, 0.02 #2538) >> Best rule #276 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 0n6f8; 031zkw; 0285c; 03rl84; 046lt; 0161c2; 02wb6yq; 01jbx1; 01_rh4; 049qx; ... >> query: (?x3852, 0c8wxp) <- location(?x3852, ?x7993), languages(?x3852, ?x12394), participant(?x3852, ?x2602) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 108 *> proper extension: 04bs3j; 044qx; *> query: (?x3852, 0631_) <- participant(?x3852, ?x2602), student(?x5614, ?x2602) *> conf = 0.04 ranks of expected_values: 6 EVAL 0bt7ws religion 0631_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 112.000 112.000 0.215 http://example.org/people/person/religion #15786-012fvq PRED entity: 012fvq PRED relation: educational_institution PRED expected values: 012fvq => 158 concepts (108 used for prediction) PRED predicted values (max 10 best out of 324): 012fvq (0.21 #57748, 0.01 #14563, 0.01 #39384), 01_k7f (0.21 #57748), 0qlnr (0.20 #312, 0.08 #851, 0.07 #1390), 01g7_r (0.20 #235, 0.08 #774, 0.04 #2391), 0cwx_ (0.08 #764, 0.07 #1303, 0.06 #1842), 0bthb (0.08 #577, 0.06 #1655, 0.03 #3272), 01j_5k (0.08 #753, 0.06 #1831, 0.03 #3448), 06rkfs (0.08 #908, 0.03 #3603, 0.02 #5761), 015y3j (0.08 #805, 0.03 #3500, 0.01 #9434), 037s9x (0.08 #583, 0.03 #3278, 0.01 #9212) >> Best rule #57748 for best value: >> intensional similarity = 4 >> extensional distance = 444 >> proper extension: 06xpp7; 053mhx; 05bjp6; >> query: (?x3576, ?x10045) <- contains(?x3670, ?x3576), student(?x3576, ?x3497), student(?x10045, ?x3497), gender(?x3497, ?x231) >> conf = 0.21 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 012fvq educational_institution 012fvq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 108.000 0.207 http://example.org/education/educational_institution_campus/educational_institution #15785-02rsz0 PRED entity: 02rsz0 PRED relation: location PRED expected values: 01d8wq => 75 concepts (40 used for prediction) PRED predicted values (max 10 best out of 87): 04jpl (0.24 #11292, 0.23 #8072, 0.18 #17736), 02_286 (0.12 #12118, 0.11 #4873, 0.11 #7288), 030qb3t (0.09 #3307, 0.08 #4919, 0.07 #30685), 01n7q (0.06 #4899, 0.05 #10533, 0.04 #9729), 0b_yz (0.05 #550, 0.04 #1356, 0.04 #2162), 05l5n (0.05 #101, 0.04 #907, 0.04 #1713), 07ssc (0.05 #26, 0.03 #805, 0.02 #21744), 0978r (0.05 #175, 0.02 #981, 0.02 #1787), 0144wg (0.05 #489, 0.02 #1295, 0.02 #2101), 0125q1 (0.05 #318, 0.02 #1124, 0.02 #1930) >> Best rule #11292 for best value: >> intensional similarity = 4 >> extensional distance = 154 >> proper extension: 0lbj1; 032t2z; 01l2fn; 06t61y; 04kj2v; 062dn7; 04pf4r; 01fwf1; 01qn8k; 03975z; ... >> query: (?x12474, 04jpl) <- nationality(?x12474, ?x1310), profession(?x12474, ?x353), ?x1310 = 02jx1, people(?x4195, ?x12474) >> conf = 0.24 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02rsz0 location 01d8wq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 40.000 0.244 http://example.org/people/person/places_lived./people/place_lived/location #15784-0bd2n4 PRED entity: 0bd2n4 PRED relation: student! PRED expected values: 07tg4 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 54): 0bwfn (0.08 #5008, 0.07 #15010, 0.06 #1852), 07tg4 (0.07 #85, 0.07 #611, 0.02 #10611), 015nl4 (0.07 #66, 0.06 #592, 0.05 #1644), 017z88 (0.07 #81, 0.05 #1659, 0.04 #4815), 0m4yg (0.07 #364, 0.01 #17894, 0.01 #1942), 031ns1 (0.07 #517, 0.01 #17894), 01f6ss (0.07 #516, 0.01 #17894), 01nn7r (0.07 #489, 0.01 #17894), 07tgn (0.06 #543, 0.02 #17911, 0.02 #10543), 09f2j (0.05 #1736, 0.03 #4892, 0.03 #2262) >> Best rule #5008 for best value: >> intensional similarity = 2 >> extensional distance = 1146 >> proper extension: 02xnjd; >> query: (?x3718, 0bwfn) <- award_nominee(?x931, ?x3718), student(?x1848, ?x3718) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 02qflgv; 02w9895; 0clvcx; 0l6px; 07fpm3; 0dsb_yy; 05y5kf; 08_83x; 01x4sb; 0dgskx; ... *> query: (?x3718, 07tg4) <- award_nominee(?x3718, ?x931), award_winner(?x2371, ?x3718), ?x2371 = 02xb2bt *> conf = 0.07 ranks of expected_values: 2 EVAL 0bd2n4 student! 07tg4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.080 http://example.org/education/educational_institution/students_graduates./education/education/student #15783-04jn6y7 PRED entity: 04jn6y7 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.84 #197, 0.84 #192, 0.83 #264), 07c52 (0.22 #434, 0.21 #477, 0.18 #400), 07z4p (0.22 #434, 0.21 #477, 0.18 #400), 02nxhr (0.21 #477, 0.18 #400, 0.18 #384), 0735l (0.18 #400, 0.18 #384) >> Best rule #197 for best value: >> intensional similarity = 5 >> extensional distance = 469 >> proper extension: 0d90m; 083shs; 01br2w; 02vp1f_; 03g90h; 060v34; 02x3lt7; 04fzfj; 03ckwzc; 0dsvzh; ... >> query: (?x12693, 029j_) <- country(?x12693, ?x94), film_crew_role(?x12693, ?x1171), ?x1171 = 09vw2b7, ?x94 = 09c7w0, currency(?x12693, ?x170) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04jn6y7 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15782-0djd3 PRED entity: 0djd3 PRED relation: place_of_birth! PRED expected values: 02wb6yq => 150 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1940): 02byfd (0.38 #88782, 0.37 #143623, 0.34 #107063), 0410cp (0.38 #88782, 0.37 #143623, 0.34 #107063), 01p8r8 (0.38 #88782, 0.37 #143623, 0.34 #107063), 06fc0b (0.17 #1624, 0.03 #12068, 0.02 #32957), 03mcwq3 (0.17 #474, 0.03 #10918, 0.02 #31807), 0168dy (0.08 #4833, 0.03 #7444, 0.03 #10055), 06pjs (0.08 #4514, 0.03 #7125, 0.03 #9736), 051cc (0.08 #4380, 0.03 #6991, 0.03 #9602), 06t8b (0.08 #4250, 0.03 #6861, 0.03 #9472), 02b9g4 (0.08 #4067, 0.03 #6678, 0.03 #9289) >> Best rule #88782 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 01j8yr; 0xpp5; 0pzmf; 0tr3p; 042tq; 09snz; 0rk71; 0rmby; 0_z91; 0r785; ... >> query: (?x6683, ?x2789) <- time_zones(?x6683, ?x2088), county_seat(?x10698, ?x6683), location(?x2789, ?x6683) >> conf = 0.38 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0djd3 place_of_birth! 02wb6yq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 105.000 0.379 http://example.org/people/person/place_of_birth #15781-03rhqg PRED entity: 03rhqg PRED relation: artist PRED expected values: 01czx 01wwvt2 01vvpjj 0249kn 01k98nm 01309x 03sww 01n44c 094xh 05sq20 06tp4h 0ddkf 04b7xr 048xh 07m4c 02hzz 01tl50z 01yndb 01jgkj2 016l09 023p29 => 112 concepts (106 used for prediction) PRED predicted values (max 10 best out of 778): 0137hn (0.60 #4025, 0.50 #4748, 0.50 #1854), 01k23t (0.50 #1936, 0.40 #4107, 0.40 #3384), 03f7jfh (0.50 #2013, 0.40 #4184, 0.40 #3461), 020_4z (0.50 #4981, 0.40 #4258, 0.25 #2087), 0mjn2 (0.40 #4250, 0.33 #4973, 0.25 #2802), 0gps0z (0.40 #4225, 0.33 #4948, 0.25 #2054), 02k5sc (0.40 #4109, 0.33 #4832, 0.25 #1938), 01bczm (0.40 #3960, 0.33 #4683, 0.25 #1789), 0889x (0.40 #4315, 0.33 #5038, 0.25 #2144), 0frsw (0.40 #3742, 0.33 #4465, 0.25 #1571) >> Best rule #4025 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 01clyr; >> query: (?x2931, 0137hn) <- artist(?x2931, ?x10744), artist(?x2931, ?x4712), artist(?x2931, ?x4537), ?x10744 = 01t8399, artists(?x1000, ?x4712), people(?x1050, ?x4537), award_winner(?x3045, ?x4712) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1631 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 01dtcb; 01q940; *> query: (?x2931, 01k98nm) <- artist(?x2931, ?x10744), artist(?x2931, ?x4712), ?x10744 = 01t8399, child(?x3887, ?x2931), languages(?x4712, ?x254), instrumentalists(?x227, ?x4712) *> conf = 0.25 ranks of expected_values: 33, 120, 150, 156, 167, 182, 194, 214, 226, 232, 238, 247, 300, 304, 449, 494, 580, 589, 610, 627, 685 EVAL 03rhqg artist 023p29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 016l09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 01jgkj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 01yndb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 01tl50z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 02hzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 07m4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 048xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 04b7xr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 0ddkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 06tp4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 05sq20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 094xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 01n44c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 03sww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 01309x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 01k98nm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 0249kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 01vvpjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 01wwvt2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 106.000 0.600 http://example.org/music/record_label/artist EVAL 03rhqg artist 01czx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 112.000 106.000 0.600 http://example.org/music/record_label/artist #15780-02f79n PRED entity: 02f79n PRED relation: award! PRED expected values: 01vs14j 01cwhp 0qf3p 01vsyg9 01k23t 03f3yfj => 41 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2669): 01vsgrn (0.93 #10035, 0.93 #10034, 0.86 #6690), 01w60_p (0.93 #10035, 0.93 #10034, 0.86 #6690), 0gbwp (0.50 #7789, 0.33 #4445, 0.23 #17826), 0gdh5 (0.50 #7438, 0.33 #4094, 0.21 #17475), 0478__m (0.50 #8001, 0.33 #4657, 0.20 #18038), 01wf86y (0.50 #8857, 0.33 #5513, 0.14 #18894), 01wj18h (0.50 #7559, 0.33 #4215, 0.12 #17596), 0f502 (0.38 #11259, 0.10 #27992, 0.10 #24643), 019pm_ (0.38 #10774, 0.08 #14120, 0.06 #60240), 01f6zc (0.38 #11572, 0.07 #14918, 0.07 #28305) >> Best rule #10035 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 01ckbq; 02f6ym; 02f777; >> query: (?x10169, ?x5906) <- award_winner(?x10169, ?x5906), award_winner(?x10169, ?x1206), ?x1206 = 01vrt_c, award(?x115, ?x10169), award(?x5906, ?x884), award_nominee(?x6207, ?x5906) >> conf = 0.93 => this is the best rule for 2 predicted values *> Best rule #8919 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 01ckbq; 02f6ym; 02f777; *> query: (?x10169, 01k23t) <- award_winner(?x10169, ?x5906), award_winner(?x10169, ?x1206), ?x1206 = 01vrt_c, award(?x115, ?x10169), award(?x5906, ?x884), award_nominee(?x6207, ?x5906) *> conf = 0.17 ranks of expected_values: 239, 280, 536, 1663, 1670 EVAL 02f79n award! 03f3yfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 41.000 18.000 0.930 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f79n award! 01k23t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 41.000 18.000 0.930 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f79n award! 01vsyg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 18.000 0.930 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f79n award! 0qf3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 18.000 0.930 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f79n award! 01cwhp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 18.000 0.930 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f79n award! 01vs14j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 18.000 0.930 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15779-0pgm3 PRED entity: 0pgm3 PRED relation: nominated_for PRED expected values: 02ywwy => 73 concepts (34 used for prediction) PRED predicted values (max 10 best out of 230): 02mc5v (0.26 #24323, 0.25 #9729, 0.25 #8107), 0g9z_32 (0.26 #24323, 0.25 #9729, 0.25 #8107), 065zlr (0.26 #24323, 0.25 #9729, 0.25 #8107), 07h9gp (0.26 #24323, 0.25 #9729, 0.25 #8107), 048qrd (0.07 #304, 0.01 #51890, 0.01 #1622), 03ln8b (0.06 #3545, 0.02 #34355, 0.02 #29490), 039cq4 (0.06 #2707, 0.03 #7570, 0.02 #4328), 05f4vxd (0.05 #4041, 0.02 #798), 0258dh (0.05 #1142, 0.01 #1622), 0d6b7 (0.05 #222, 0.01 #1622) >> Best rule #24323 for best value: >> intensional similarity = 3 >> extensional distance = 1074 >> proper extension: 049tjg; 02wrhj; 02k6rq; 050_qx; >> query: (?x12710, ?x1728) <- type_of_union(?x12710, ?x566), nominated_for(?x12710, ?x1811), film(?x12710, ?x1728) >> conf = 0.26 => this is the best rule for 4 predicted values *> Best rule #1622 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 02zmh5; 01vwbts; 0dw4g; 02zft0; 019x62; 02zj61; *> query: (?x12710, ?x599) <- award(?x12710, ?x4317), award(?x12710, ?x3064), nominated_for(?x3064, ?x599), ?x4317 = 05q8pss *> conf = 0.01 ranks of expected_values: 211 EVAL 0pgm3 nominated_for 02ywwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 73.000 34.000 0.260 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15778-02ky346 PRED entity: 02ky346 PRED relation: major_field_of_study! PRED expected values: 07wjk 0p5wz 01w5m 01f2xy => 113 concepts (77 used for prediction) PRED predicted values (max 10 best out of 594): 01w5m (0.75 #8319, 0.73 #13249, 0.67 #9415), 07w0v (0.67 #8780, 0.62 #8232, 0.60 #11518), 07t90 (0.67 #8913, 0.62 #8365, 0.56 #9461), 07vyf (0.64 #13285, 0.40 #11641, 0.40 #5070), 025v3k (0.60 #5051, 0.56 #8884, 0.50 #8336), 0bwfn (0.56 #21078, 0.55 #13415, 0.50 #11771), 0cwx_ (0.56 #9558, 0.50 #8462, 0.45 #13392), 01f1r4 (0.56 #8889, 0.50 #8341, 0.44 #9437), 01bk1y (0.56 #9040, 0.50 #8492, 0.44 #9588), 01j_cy (0.55 #13181, 0.50 #11537, 0.44 #8799) >> Best rule #8319 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 05qjt; 02lp1; 01mkq; 0db86; 01r4k; >> query: (?x1682, 01w5m) <- major_field_of_study(?x8363, ?x1682), major_field_of_study(?x581, ?x1682), major_field_of_study(?x6117, ?x1682), student(?x8363, ?x2046), ?x6117 = 02m4yg, company(?x346, ?x8363), ?x581 = 06pwq >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 27, 111, 571 EVAL 02ky346 major_field_of_study! 01f2xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 113.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02ky346 major_field_of_study! 01w5m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02ky346 major_field_of_study! 0p5wz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 113.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02ky346 major_field_of_study! 07wjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 113.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #15777-01jrvr6 PRED entity: 01jrvr6 PRED relation: student! PRED expected values: 08815 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 186): 07tgn (0.34 #15242, 0.07 #8417, 0.05 #6317), 01mpwj (0.33 #631, 0.20 #1156, 0.15 #11131), 086xm (0.33 #92, 0.20 #1142, 0.02 #7442), 0lvng (0.33 #794, 0.20 #1319, 0.01 #11294), 0bwfn (0.14 #48051, 0.13 #49101, 0.13 #49626), 065y4w7 (0.10 #3164, 0.09 #46216, 0.08 #4739), 017z88 (0.09 #15832, 0.09 #13207, 0.09 #16357), 09f2j (0.09 #1733, 0.08 #2258, 0.07 #48985), 01k7xz (0.09 #1641, 0.07 #6366, 0.07 #12141), 02g839 (0.09 #1600, 0.07 #2650, 0.05 #6850) >> Best rule #15242 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 0l6qt; 041h0; 0136g9; 02lq10; 01hb6v; 016h4r; 03f5vvx; 043s3; 04yt7; 01tdnyh; ... >> query: (?x4807, 07tgn) <- student(?x3439, ?x4807), student(?x3439, ?x587), major_field_of_study(?x3439, ?x254), ?x587 = 07g2b >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #46204 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 818 *> proper extension: 0byfz; 02c4s; 043js; 0lpjn; 0f0kz; 01fdc0; 025t9b; 034bs; 05slvm; 0h32q; ... *> query: (?x4807, 08815) <- student(?x3439, ?x4807), student(?x3439, ?x6138), profession(?x4807, ?x1614), place_of_burial(?x6138, ?x7496) *> conf = 0.07 ranks of expected_values: 22 EVAL 01jrvr6 student! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 160.000 160.000 0.339 http://example.org/education/educational_institution/students_graduates./education/education/student #15776-06x43v PRED entity: 06x43v PRED relation: country PRED expected values: 0345h => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 33): 07ssc (0.65 #1083, 0.43 #252, 0.29 #1363), 03h64 (0.29 #1363, 0.10 #45, 0.09 #163), 0345h (0.23 #262, 0.16 #203, 0.15 #559), 03rjj (0.15 #242, 0.08 #1073, 0.07 #2917), 03_3d (0.12 #1074, 0.07 #2917, 0.06 #243), 0d060g (0.09 #244, 0.07 #2917, 0.07 #185), 0chghy (0.07 #2917, 0.05 #486, 0.05 #842), 06mkj (0.07 #2917, 0.04 #275, 0.02 #513), 0d0vqn (0.07 #2917, 0.04 #246, 0.01 #1077), 01mjq (0.07 #2917, 0.03 #34, 0.03 #211) >> Best rule #1083 for best value: >> intensional similarity = 3 >> extensional distance = 553 >> proper extension: 05hd32; >> query: (?x7514, 07ssc) <- country(?x7514, ?x789), olympics(?x789, ?x452), location_of_ceremony(?x3580, ?x789) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #262 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 140 *> proper extension: 02vl9ln; *> query: (?x7514, 0345h) <- country(?x7514, ?x789), country(?x7514, ?x94), ?x789 = 0f8l9c, film_release_region(?x54, ?x94) *> conf = 0.23 ranks of expected_values: 3 EVAL 06x43v country 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 55.000 55.000 0.652 http://example.org/film/film/country #15775-0g69lg PRED entity: 0g69lg PRED relation: type_of_union PRED expected values: 04ztj => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.75 #13, 0.74 #85, 0.71 #133), 01g63y (0.12 #146, 0.12 #134, 0.12 #166) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 04l3_z; 02p21g; 03ft8; 0126rp; 0jt90f5; 01gbbz; 01jbx1; 029_3; 014z8v; 02y_2y; ... >> query: (?x6765, 04ztj) <- producer_type(?x6765, ?x632), location(?x6765, ?x4074), ?x632 = 0ckd1 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g69lg type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.748 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15774-03kg2v PRED entity: 03kg2v PRED relation: film_crew_role PRED expected values: 09vw2b7 => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 33): 09vw2b7 (0.78 #1072, 0.76 #40, 0.75 #489), 0dxtw (0.48 #974, 0.44 #1076, 0.44 #1457), 015h31 (0.25 #76, 0.24 #315, 0.17 #902), 02ynfr (0.23 #497, 0.22 #1322, 0.21 #737), 0d2b38 (0.22 #92, 0.16 #918, 0.15 #2095), 02rh1dz (0.20 #973, 0.19 #180, 0.19 #834), 0215hd (0.20 #500, 0.18 #1083, 0.17 #1325), 01xy5l_ (0.19 #495, 0.15 #1078, 0.14 #1320), 04pyp5 (0.18 #49, 0.12 #391, 0.12 #152), 089g0h (0.17 #501, 0.17 #1326, 0.16 #1084) >> Best rule #1072 for best value: >> intensional similarity = 6 >> extensional distance = 188 >> proper extension: 0hgnl3t; >> query: (?x2917, 09vw2b7) <- film_release_region(?x2917, ?x94), film_crew_role(?x2917, ?x468), film(?x7980, ?x2917), ?x468 = 02r96rf, produced_by(?x2917, ?x1285), music(?x2917, ?x9064) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03kg2v film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.784 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15773-0l8g0 PRED entity: 0l8g0 PRED relation: artists! PRED expected values: 011j5x => 96 concepts (37 used for prediction) PRED predicted values (max 10 best out of 259): 0xhtw (0.72 #3979, 0.60 #321, 0.49 #3673), 06by7 (0.68 #3372, 0.68 #2459, 0.63 #4288), 016jhr (0.60 #622, 0.22 #11279, 0.20 #318), 05bt6j (0.49 #8885, 0.33 #4308, 0.33 #4922), 0cx7f (0.47 #2267, 0.47 #1658, 0.40 #743), 064t9 (0.47 #7638, 0.47 #7029, 0.47 #4894), 011j5x (0.43 #1251, 0.26 #3079, 0.22 #11279), 0155w (0.42 #1930, 0.22 #11279, 0.20 #407), 03lty (0.40 #332, 0.36 #3990, 0.28 #3684), 01fh36 (0.40 #691, 0.33 #995, 0.27 #3739) >> Best rule #3979 for best value: >> intensional similarity = 6 >> extensional distance = 99 >> proper extension: 06br6t; >> query: (?x6234, 0xhtw) <- artists(?x2808, ?x6234), group(?x212, ?x6234), artists(?x2808, ?x9841), artists(?x2808, ?x7896), ?x9841 = 02ndj5, ?x7896 = 03k3b >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1251 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 12 *> proper extension: 01tp5bj; 01y_rz; *> query: (?x6234, 011j5x) <- artists(?x5934, ?x6234), artists(?x2809, ?x6234), ?x5934 = 05r6t, artists(?x2809, ?x7459), artists(?x2809, ?x1838), ?x1838 = 012zng, ?x7459 = 0jsg0m *> conf = 0.43 ranks of expected_values: 7 EVAL 0l8g0 artists! 011j5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 96.000 37.000 0.723 http://example.org/music/genre/artists #15772-0dr31 PRED entity: 0dr31 PRED relation: category PRED expected values: 08mbj5d => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.72 #8, 0.72 #7, 0.72 #6) >> Best rule #8 for best value: >> intensional similarity = 5 >> extensional distance = 70 >> proper extension: 01c0cc; 03pbf; 04p0c; 05t7c1; 0pmcz; 0ptj2; 017gry; 06fz_; 0727_; 06jtd; ... >> query: (?x11045, 08mbj5d) <- contains(?x8264, ?x11045), contains(?x1264, ?x11045), ?x1264 = 0345h, adjoins(?x8264, ?x7934), contains(?x7934, ?x9402) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dr31 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.722 http://example.org/common/topic/webpage./common/webpage/category #15771-04t2l2 PRED entity: 04t2l2 PRED relation: award_winner! PRED expected values: 027n06w => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 103): 0275n3y (0.10 #7040, 0.10 #6763, 0.05 #488), 09g90vz (0.10 #7040, 0.10 #6763, 0.04 #1501), 02q690_ (0.10 #7040, 0.10 #6763, 0.03 #478), 05c1t6z (0.10 #7040, 0.10 #6763, 0.03 #2637), 058m5m4 (0.10 #7040, 0.10 #6763, 0.03 #1435), 027n06w (0.10 #7040, 0.10 #6763, 0.03 #72), 03nnm4t (0.10 #7040, 0.10 #6763, 0.03 #2557), 02wzl1d (0.10 #7040, 0.10 #6763, 0.03 #287), 0gx_st (0.10 #7040, 0.10 #6763, 0.02 #2659), 0drtv8 (0.10 #7040, 0.10 #6763, 0.02 #341) >> Best rule #7040 for best value: >> intensional similarity = 3 >> extensional distance = 1975 >> proper extension: 0jz9f; 06jzh; 025jfl; 02r3zy; 043q6n_; 03g5jw; 01795t; 0dvqq; 04qmr; 09b3v; ... >> query: (?x237, ?x3624) <- award_nominee(?x237, ?x3924), award_nominee(?x905, ?x237), award_winner(?x3624, ?x3924) >> conf = 0.10 => this is the best rule for 16 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 04t2l2 award_winner! 027n06w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 78.000 78.000 0.102 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15770-01738w PRED entity: 01738w PRED relation: film! PRED expected values: 03mg35 01rnxn => 79 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1134): 01pcbg (0.60 #2647, 0.43 #6785, 0.38 #8854), 07m77x (0.50 #1532, 0.43 #7739, 0.40 #3601), 07y8l9 (0.50 #964, 0.43 #7171, 0.40 #3033), 028k57 (0.50 #784, 0.43 #6991, 0.40 #2853), 051wwp (0.50 #867, 0.43 #7074, 0.40 #2936), 02qx69 (0.50 #550, 0.29 #6757, 0.25 #8826), 06gb2q (0.50 #1269, 0.29 #7476, 0.25 #9545), 03n52j (0.43 #7154, 0.40 #3016, 0.38 #9223), 073749 (0.40 #2771, 0.29 #6909, 0.25 #8978), 028d4v (0.40 #2458, 0.29 #6596, 0.25 #8665) >> Best rule #2647 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0pdp8; 02stbw; 02ht1k; >> query: (?x6411, 01pcbg) <- film(?x1345, ?x6411), titles(?x1510, ?x6411), genre(?x6411, ?x225), ?x1345 = 0pgjm, film(?x1104, ?x6411) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #20998 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 95 *> proper extension: 0267wwv; *> query: (?x6411, 03mg35) <- film_crew_role(?x6411, ?x2178), ?x2178 = 01pvkk, produced_by(?x6411, ?x8345), language(?x6411, ?x254), music(?x6411, ?x8374) *> conf = 0.02 ranks of expected_values: 452 EVAL 01738w film! 01rnxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 58.000 0.600 http://example.org/film/actor/film./film/performance/film EVAL 01738w film! 03mg35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 79.000 58.000 0.600 http://example.org/film/actor/film./film/performance/film #15769-0fhp9 PRED entity: 0fhp9 PRED relation: place_of_death! PRED expected values: 043d4 => 218 concepts (97 used for prediction) PRED predicted values (max 10 best out of 781): 06myp (0.50 #2257, 0.19 #2256, 0.18 #6018), 043tg (0.25 #1899, 0.11 #2258, 0.08 #5660), 0dzkq (0.25 #1633, 0.11 #2258, 0.08 #5394), 045bg (0.25 #1543, 0.11 #2258, 0.08 #5304), 0ct9_ (0.25 #1918, 0.08 #5679, 0.05 #7184), 0399p (0.25 #1900, 0.08 #5661, 0.05 #7166), 01rgr (0.25 #1996, 0.08 #5757, 0.05 #7262), 07ym0 (0.25 #1913, 0.08 #5674, 0.05 #7179), 06whf (0.25 #1677, 0.08 #5438, 0.05 #6943), 03j43 (0.25 #1577, 0.08 #5338, 0.05 #6843) >> Best rule #2257 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05qtj; 02z0j; >> query: (?x863, ?x10895) <- location(?x10895, ?x863), influenced_by(?x8441, ?x10895), capital(?x1355, ?x863), ?x8441 = 0c1fs >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #18809 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 0fvwg; 0t6hk; *> query: (?x863, ?x7559) <- contains(?x1355, ?x863), place_of_death(?x7386, ?x863), contains(?x863, ?x2637), influenced_by(?x7386, ?x7559) *> conf = 0.05 ranks of expected_values: 105 EVAL 0fhp9 place_of_death! 043d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 218.000 97.000 0.500 http://example.org/people/deceased_person/place_of_death #15768-05q96q6 PRED entity: 05q96q6 PRED relation: titles! PRED expected values: 03k9fj => 86 concepts (39 used for prediction) PRED predicted values (max 10 best out of 46): 07s9rl0 (0.50 #102, 0.39 #1844, 0.34 #406), 07ssc (0.34 #509, 0.34 #414, 0.16 #508), 017fp (0.25 #124, 0.09 #1866, 0.07 #428), 0hn10 (0.25 #15, 0.02 #1858, 0.02 #3598), 0d63kt (0.25 #85), 018h2 (0.25 #33), 04jjy (0.25 #20), 01jfsb (0.22 #221, 0.17 #1862, 0.14 #836), 01z4y (0.20 #3106, 0.18 #339, 0.18 #749), 02kdv5l (0.17 #3276, 0.17 #2047, 0.17 #1842) >> Best rule #102 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 09m6kg; 09q5w2; 020fcn; 011yqc; 0cz_ym; 050gkf; >> query: (?x1038, 07s9rl0) <- film(?x2803, ?x1038), film(?x262, ?x1038), ?x262 = 06dv3, film_crew_role(?x1038, ?x137) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #119 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 09m6kg; 09q5w2; 020fcn; 011yqc; 0cz_ym; 050gkf; *> query: (?x1038, 03k9fj) <- film(?x2803, ?x1038), film(?x262, ?x1038), ?x262 = 06dv3, film_crew_role(?x1038, ?x137) *> conf = 0.12 ranks of expected_values: 15 EVAL 05q96q6 titles! 03k9fj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 86.000 39.000 0.500 http://example.org/media_common/netflix_genre/titles #15767-01cbt3 PRED entity: 01cbt3 PRED relation: music! PRED expected values: 0g9yrw 0fsw_7 025twgf => 123 concepts (91 used for prediction) PRED predicted values (max 10 best out of 811): 0dtfn (0.66 #2003, 0.07 #21025, 0.07 #21023), 0cq86w (0.66 #2003, 0.07 #21025, 0.07 #21023), 06mmr (0.66 #2003, 0.07 #21025, 0.07 #21023), 01_1pv (0.04 #213, 0.02 #5219, 0.01 #6220), 02rrfzf (0.04 #7329, 0.03 #2324, 0.03 #13335), 02ht1k (0.04 #1365, 0.02 #5370, 0.02 #6371), 01s7w3 (0.04 #5867, 0.04 #6868, 0.03 #13875), 09d3b7 (0.03 #5838, 0.03 #6839, 0.03 #1833), 08l0x2 (0.03 #3747, 0.02 #8752, 0.02 #4748), 07bzz7 (0.03 #6529, 0.03 #1523, 0.02 #8531) >> Best rule #2003 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 012ljv; 03f2_rc; 0146pg; 025vry; 03kwtb; 0pgjm; 0bwh6; 0244r8; 0l12d; 02lz1s; ... >> query: (?x5251, ?x1386) <- music(?x351, ?x5251), award_winner(?x1079, ?x5251), award_winner(?x1386, ?x5251) >> conf = 0.66 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 01cbt3 music! 025twgf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 91.000 0.661 http://example.org/film/film/music EVAL 01cbt3 music! 0fsw_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 91.000 0.661 http://example.org/film/film/music EVAL 01cbt3 music! 0g9yrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 91.000 0.661 http://example.org/film/film/music #15766-043zg PRED entity: 043zg PRED relation: award PRED expected values: 02f77y => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 274): 02681vq (0.56 #830, 0.38 #52, 0.13 #43182), 01bgqh (0.52 #1208, 0.38 #2764, 0.26 #6654), 02681xs (0.44 #959, 0.25 #181, 0.03 #16908), 09sb52 (0.41 #2373, 0.40 #23379, 0.34 #3540), 02681_5 (0.39 #1151, 0.25 #373, 0.15 #44350), 02w7fs (0.39 #1119, 0.25 #341, 0.15 #44350), 01by1l (0.37 #1276, 0.31 #2832, 0.29 #16836), 026rsl9 (0.33 #1102, 0.25 #324, 0.03 #4992), 03qbh5 (0.30 #1363, 0.27 #2919, 0.25 #196), 01c99j (0.30 #1384, 0.23 #2940, 0.18 #6830) >> Best rule #830 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01wbl_r; 01wj18h; 0bqsy; 01wv9p; 0127s7; 0cc5tgk; 020hyj; 03c602; 01s7ns; 03x82v; >> query: (?x5364, 02681vq) <- artists(?x12082, ?x5364), ?x12082 = 08vlns, award(?x5364, ?x154) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1028 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 01wbl_r; 01wj18h; 0bqsy; 01wv9p; 0127s7; 0cc5tgk; 020hyj; 03c602; 01s7ns; 03x82v; *> query: (?x5364, 02f77y) <- artists(?x12082, ?x5364), ?x12082 = 08vlns, award(?x5364, ?x154) *> conf = 0.22 ranks of expected_values: 18 EVAL 043zg award 02f77y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 123.000 123.000 0.556 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15765-02lf0c PRED entity: 02lf0c PRED relation: award_winner! PRED expected values: 0fdtd7 => 137 concepts (116 used for prediction) PRED predicted values (max 10 best out of 272): 0fdtd7 (0.35 #2594, 0.34 #8211, 0.34 #37609), 0f_nbyh (0.35 #2594, 0.34 #8211, 0.34 #37609), 01l29r (0.35 #2594, 0.34 #8211, 0.34 #37609), 0bm70b (0.35 #2594, 0.34 #8211, 0.34 #37609), 0cjyzs (0.26 #1835, 0.26 #2268, 0.23 #3565), 0gs9p (0.23 #8291, 0.17 #13909, 0.16 #6994), 019f4v (0.19 #8278, 0.14 #12600, 0.14 #13896), 02qwdhq (0.16 #37175, 0.16 #37174, 0.08 #2161), 02qysm0 (0.16 #37175, 0.16 #37174, 0.08 #2161), 0fbtbt (0.16 #1959, 0.15 #3689, 0.15 #2825) >> Best rule #2594 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 0dbpyd; 0d4fqn; 02778pf; 0284gcb; 0crx5w; 0261g5l; 02_2v2; 071dcs; 0h53p1; 026n6cs; ... >> query: (?x595, ?x277) <- award_winner(?x7927, ?x595), award(?x595, ?x277), program(?x595, ?x6375) >> conf = 0.35 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 02lf0c award_winner! 0fdtd7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 116.000 0.345 http://example.org/award/award_category/winners./award/award_honor/award_winner #15764-0dfcn PRED entity: 0dfcn PRED relation: location_of_ceremony! PRED expected values: 04ztj => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.55 #25, 0.54 #33, 0.50 #73), 01g63y (0.14 #290, 0.13 #221, 0.10 #359), 0jgjn (0.04 #28, 0.04 #32, 0.03 #44), 01bl8s (0.01 #27) >> Best rule #25 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 04jpl; 02jx1; 030qb3t; 01m1zk; 0j5g9; 07tds; 0d9y6; 02m77; 0167q3; 0qpqn; ... >> query: (?x14377, 04ztj) <- contains(?x6226, ?x14377), featured_film_locations(?x11998, ?x14377), location(?x2415, ?x6226), location_of_ceremony(?x940, ?x6226) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dfcn location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.553 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #15763-03mszl PRED entity: 03mszl PRED relation: student! PRED expected values: 02g839 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 137): 02g839 (0.12 #25, 0.07 #8441, 0.07 #551), 04b_46 (0.12 #226, 0.07 #752, 0.06 #8116), 07tgn (0.12 #17, 0.07 #543, 0.05 #2121), 01qdhx (0.12 #503, 0.07 #1029, 0.05 #2607), 019n9w (0.12 #312, 0.07 #838, 0.05 #2416), 01cwdk (0.12 #204, 0.07 #730, 0.05 #2308), 01qd_r (0.12 #280, 0.02 #8170), 0ym8f (0.12 #26), 0bwfn (0.09 #29730, 0.09 #30782, 0.08 #28152), 02bbyw (0.07 #757, 0.05 #2335, 0.02 #3913) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0djywgn; >> query: (?x7753, 02g839) <- award_nominee(?x7753, ?x1051), role(?x7753, ?x227), student(?x11691, ?x7753) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mszl student! 02g839 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #15762-03v3xp PRED entity: 03v3xp PRED relation: award_nominee! PRED expected values: 02tr7d => 105 concepts (46 used for prediction) PRED predicted values (max 10 best out of 957): 02_hj4 (0.82 #39428, 0.82 #13914, 0.81 #4640), 051wwp (0.82 #39428, 0.82 #13914, 0.81 #4640), 02k6rq (0.82 #39428, 0.82 #13914, 0.81 #4640), 09fqtq (0.82 #39428, 0.82 #13914, 0.81 #4640), 015rkw (0.77 #90460, 0.76 #30152, 0.76 #102059), 02tr7d (0.77 #90460, 0.76 #95099, 0.76 #81173), 09yrh (0.16 #27833, 0.16 #23194, 0.13 #41748), 032_jg (0.16 #27833, 0.16 #23194, 0.11 #55666), 02p65p (0.16 #97421, 0.14 #106698, 0.09 #27859), 0dgskx (0.16 #97421, 0.14 #106698, 0.08 #29332) >> Best rule #39428 for best value: >> intensional similarity = 3 >> extensional distance = 405 >> proper extension: 01j5ts; 023tp8; 0m2wm; 0prfz; 0l8v5; 0h5g_; 0c4f4; 06cv1; 06jzh; 01n5309; ... >> query: (?x3604, ?x374) <- award_nominee(?x3604, ?x374), participant(?x3604, ?x2258), award_nominee(?x1223, ?x3604) >> conf = 0.82 => this is the best rule for 4 predicted values *> Best rule #90460 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1084 *> proper extension: 024y6w; *> query: (?x3604, ?x1223) <- award_nominee(?x3604, ?x1672), film(?x1672, ?x814), award_winner(?x3604, ?x1223) *> conf = 0.77 ranks of expected_values: 6 EVAL 03v3xp award_nominee! 02tr7d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 105.000 46.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15761-0170th PRED entity: 0170th PRED relation: featured_film_locations PRED expected values: 0rh6k => 82 concepts (74 used for prediction) PRED predicted values (max 10 best out of 80): 030qb3t (0.19 #279, 0.12 #2682, 0.10 #1240), 02_286 (0.16 #8450, 0.15 #5549, 0.15 #6758), 0rh6k (0.11 #1, 0.06 #241, 0.04 #4808), 095w_ (0.11 #36, 0.03 #516, 0.03 #756), 04jpl (0.06 #1450, 0.06 #8439, 0.05 #9887), 0d6lp (0.06 #312, 0.05 #1032, 0.02 #3676), 07b_l (0.06 #317, 0.04 #1278, 0.03 #2238), 0b90_r (0.06 #244, 0.03 #1205, 0.03 #2165), 0rxyk (0.06 #428, 0.01 #1389, 0.01 #2109), 0rvty (0.06 #372, 0.01 #1333, 0.01 #2053) >> Best rule #279 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02_1sj; 0963mq; 035s95; 03mh_tp; >> query: (?x2757, 030qb3t) <- film(?x2415, ?x2757), ?x2415 = 0170s4, film_crew_role(?x2757, ?x137) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 059rc; 049mql; *> query: (?x2757, 0rh6k) <- nominated_for(?x777, ?x2757), ?x777 = 05kfs, nominated_for(?x591, ?x2757) *> conf = 0.11 ranks of expected_values: 3 EVAL 0170th featured_film_locations 0rh6k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 74.000 0.188 http://example.org/film/film/featured_film_locations #15760-0249fn PRED entity: 0249fn PRED relation: ceremony PRED expected values: 056878 => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 125): 056878 (0.90 #150, 0.87 #775, 0.87 #525), 0gx1673 (0.53 #230, 0.52 #355, 0.51 #855), 03nnm4t (0.47 #1001, 0.33 #2753, 0.21 #4131), 0bz6sb (0.33 #3004, 0.14 #3755, 0.12 #1555), 02yv_b (0.33 #3004, 0.14 #3755, 0.12 #1520), 09p3h7 (0.33 #3004, 0.14 #3755, 0.02 #936), 05c1t6z (0.23 #1387, 0.18 #2137, 0.18 #1637), 0gvstc3 (0.21 #1403, 0.17 #2153, 0.16 #1903), 02q690_ (0.20 #1431, 0.17 #1931, 0.17 #1806), 0gx_st (0.18 #1406, 0.15 #1656, 0.15 #1906) >> Best rule #150 for best value: >> intensional similarity = 7 >> extensional distance = 57 >> proper extension: 02wh75; 026mg3; 01d38g; 01bgqh; 02g8mp; 01c9f2; 01c4_6; 02gx2k; 02nhxf; 025m8y; ... >> query: (?x6378, 056878) <- ceremony(?x6378, ?x6487), ceremony(?x6378, ?x3121), ceremony(?x6378, ?x1362), ?x1362 = 019bk0, ?x6487 = 01mh_q, award(?x2169, ?x6378), ?x3121 = 09n4nb >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0249fn ceremony 056878 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.898 http://example.org/award/award_category/winners./award/award_honor/ceremony #15759-0gfmc_ PRED entity: 0gfmc_ PRED relation: film PRED expected values: 05dss7 => 80 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1714): 01z452 (0.71 #6364, 0.68 #22273, 0.64 #19089), 01jzyf (0.71 #6364, 0.68 #22273, 0.64 #19089), 0cp0t91 (0.23 #6059, 0.20 #1286, 0.18 #15602), 035s95 (0.20 #5075, 0.19 #8256, 0.17 #6666), 0gtvpkw (0.20 #504, 0.17 #5277, 0.15 #3685), 040b5k (0.20 #414, 0.13 #5187, 0.12 #8368), 08k40m (0.20 #428, 0.13 #6792, 0.12 #9972), 07f_7h (0.20 #370, 0.11 #3551, 0.10 #6734), 0jwvf (0.20 #902, 0.10 #7266, 0.10 #5675), 0ctb4g (0.20 #494, 0.10 #6858, 0.09 #10038) >> Best rule #6364 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 0jz9f; 086k8; 017s11; 016tt2; 025jfl; 0338lq; 0g1rw; 05qd_; 04f525m; 016tw3; ... >> query: (?x6862, ?x3706) <- film(?x6862, ?x1209), production_companies(?x3706, ?x6862), nominated_for(?x1245, ?x1209), ?x1245 = 0gqwc >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #1034 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 01dtcb; *> query: (?x6862, 05dss7) <- child(?x1104, ?x6862), child(?x752, ?x6862), ?x1104 = 016tw3, production_companies(?x339, ?x752) *> conf = 0.20 ranks of expected_values: 16 EVAL 0gfmc_ film 05dss7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 80.000 16.000 0.707 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15758-03xsby PRED entity: 03xsby PRED relation: child PRED expected values: 09mfvx => 149 concepts (132 used for prediction) PRED predicted values (max 10 best out of 188): 030_1m (0.33 #12, 0.14 #2055, 0.13 #2226), 031rq5 (0.31 #1411, 0.19 #3630, 0.15 #1581), 02j_j0 (0.20 #398, 0.15 #1418, 0.09 #908), 093h7p (0.20 #435, 0.14 #605, 0.11 #775), 0c_j5d (0.20 #346, 0.14 #516, 0.11 #686), 0kc9f (0.20 #493, 0.14 #663, 0.11 #833), 02w_l9 (0.20 #482, 0.14 #652, 0.11 #822), 02swsm (0.20 #468, 0.14 #638, 0.11 #808), 04rcl7 (0.20 #439, 0.14 #609, 0.11 #779), 032dg7 (0.20 #423, 0.14 #593, 0.11 #763) >> Best rule #12 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 017s11; >> query: (?x1914, 030_1m) <- film(?x1914, ?x8679), film(?x1914, ?x3921), production_companies(?x781, ?x1914), ?x8679 = 023g6w, genre(?x3921, ?x225) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03xsby child 09mfvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 149.000 132.000 0.333 http://example.org/organization/organization/child./organization/organization_relationship/child #15757-06w38l PRED entity: 06w38l PRED relation: award PRED expected values: 0gr4k => 113 concepts (96 used for prediction) PRED predicted values (max 10 best out of 280): 0gr4k (0.92 #2457, 0.50 #33, 0.35 #3669), 04dn09n (0.54 #2468, 0.39 #3680, 0.37 #4084), 0gq9h (0.50 #78, 0.49 #2502, 0.33 #3714), 02qyp19 (0.50 #405, 0.44 #3637, 0.39 #4041), 03hl6lc (0.50 #583, 0.43 #2603, 0.42 #4219), 0gs9p (0.50 #484, 0.39 #3716, 0.32 #2504), 019f4v (0.50 #471, 0.32 #3703, 0.30 #2491), 040njc (0.42 #3644, 0.38 #412, 0.33 #4856), 02pqp12 (0.38 #475, 0.28 #3707, 0.22 #4919), 03nqnk3 (0.33 #135, 0.14 #2559, 0.12 #4983) >> Best rule #2457 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 05drq5; 02bfxb; 0hw1j; 03xp8d5; >> query: (?x12891, 0gr4k) <- award(?x12891, ?x8153), award(?x12891, ?x1862), ?x1862 = 0gr51, award_winner(?x8153, ?x9073), ?x9073 = 064177 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06w38l award 0gr4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 96.000 0.919 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15756-01fx2g PRED entity: 01fx2g PRED relation: religion PRED expected values: 01lp8 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 21): 0c8wxp (0.33 #51, 0.25 #276, 0.23 #546), 04pk9 (0.20 #20, 0.02 #1010, 0.02 #1145), 051kv (0.11 #95, 0.10 #230, 0.05 #185), 06nzl (0.11 #60, 0.05 #330, 0.04 #690), 03_gx (0.09 #1951, 0.08 #2223, 0.08 #1905), 0kpl (0.06 #595, 0.06 #1901, 0.06 #1947), 01lp8 (0.06 #766, 0.05 #136, 0.05 #811), 05sfs (0.06 #93, 0.05 #228), 092bf5 (0.05 #466, 0.05 #421, 0.04 #736), 0g5llry (0.05 #163, 0.01 #793, 0.01 #838) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 015pkc; 015t56; >> query: (?x5240, 0c8wxp) <- film(?x5240, ?x5966), film(?x5240, ?x2085), ?x2085 = 0kvgxk, film_release_distribution_medium(?x5966, ?x81) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #766 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 133 *> proper extension: 04d_mtq; 057xn_m; *> query: (?x5240, 01lp8) <- nationality(?x5240, ?x94), friend(?x5240, ?x2444), profession(?x5240, ?x1032) *> conf = 0.06 ranks of expected_values: 7 EVAL 01fx2g religion 01lp8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 120.000 120.000 0.333 http://example.org/people/person/religion #15755-0gz6b6g PRED entity: 0gz6b6g PRED relation: film_release_region PRED expected values: 015fr 06f32 077qn => 76 concepts (60 used for prediction) PRED predicted values (max 10 best out of 103): 035qy (0.79 #2466, 0.76 #3071, 0.70 #3373), 015fr (0.77 #2450, 0.76 #3055, 0.69 #3357), 03_3d (0.75 #2440, 0.74 #3045, 0.72 #3347), 07ww5 (0.72 #2434, 0.51 #1826, 0.50 #2282), 0d060g (0.72 #2441, 0.69 #3046, 0.68 #3348), 0b90_r (0.72 #2438, 0.67 #3043, 0.60 #3345), 03spz (0.67 #2526, 0.60 #3131, 0.56 #3433), 06qd3 (0.55 #2470, 0.51 #3075, 0.46 #3377), 0ctw_b (0.54 #2458, 0.51 #3063, 0.44 #3365), 01mjq (0.52 #2477, 0.50 #3082, 0.46 #3384) >> Best rule #2466 for best value: >> intensional similarity = 6 >> extensional distance = 203 >> proper extension: 014lc_; 0ds35l9; 0gtsx8c; 028_yv; 03g90h; 01gc7; 011yrp; 0ds3t5x; 0gtv7pk; 0dscrwf; ... >> query: (?x2627, 035qy) <- film_release_region(?x2627, ?x1264), film_release_region(?x2627, ?x1003), film_release_region(?x2627, ?x390), ?x1264 = 0345h, ?x390 = 0chghy, ?x1003 = 03gj2 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #2450 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 203 *> proper extension: 014lc_; 0ds35l9; 0gtsx8c; 028_yv; 03g90h; 01gc7; 011yrp; 0ds3t5x; 0gtv7pk; 0dscrwf; ... *> query: (?x2627, 015fr) <- film_release_region(?x2627, ?x1264), film_release_region(?x2627, ?x1003), film_release_region(?x2627, ?x390), ?x1264 = 0345h, ?x390 = 0chghy, ?x1003 = 03gj2 *> conf = 0.77 ranks of expected_values: 2, 16, 31 EVAL 0gz6b6g film_release_region 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 76.000 60.000 0.785 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gz6b6g film_release_region 06f32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 76.000 60.000 0.785 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gz6b6g film_release_region 015fr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 60.000 0.785 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15754-05650n PRED entity: 05650n PRED relation: currency PRED expected values: 09nqf => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.82 #78, 0.81 #85, 0.81 #50), 01nv4h (0.06 #23, 0.04 #37, 0.03 #177), 088n7 (0.03 #49, 0.01 #70), 02gsvk (0.03 #132, 0.02 #62, 0.01 #146), 02l6h (0.02 #193, 0.01 #382, 0.01 #403) >> Best rule #78 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0ds35l9; 0czyxs; 01k1k4; 0ds11z; 0g5qs2k; 02hxhz; 01vksx; 03cvwkr; 0bwfwpj; 0k2sk; ... >> query: (?x5839, 09nqf) <- nominated_for(?x1053, ?x5839), film(?x382, ?x5839), produced_by(?x5839, ?x6629), film_distribution_medium(?x5839, ?x627) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05650n currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.817 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #15753-07ymr5 PRED entity: 07ymr5 PRED relation: award PRED expected values: 05p09zm => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 282): 09sb52 (0.48 #2883, 0.39 #13846, 0.33 #13034), 09qj50 (0.33 #858, 0.22 #2076, 0.17 #1264), 05zvj3m (0.33 #906, 0.22 #2124, 0.15 #6497), 0c422z4 (0.29 #1768, 0.22 #2174, 0.17 #956), 0cqhk0 (0.24 #3691, 0.17 #1255, 0.17 #849), 05pcn59 (0.22 #5360, 0.22 #4954, 0.17 #9827), 01by1l (0.22 #6610, 0.16 #22334, 0.14 #10670), 04ljl_l (0.20 #409, 0.16 #22334, 0.13 #21927), 04kxsb (0.19 #2969, 0.13 #4999, 0.13 #5405), 01bgqh (0.18 #6540, 0.16 #22334, 0.13 #21927) >> Best rule #2883 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 0g51l1; >> query: (?x1942, 09sb52) <- award_winner(?x6884, ?x1942), profession(?x1942, ?x319), nominated_for(?x829, ?x6884) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #20708 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1437 *> proper extension: 03jvmp; 0g5lhl7; 01w92; 04glx0; *> query: (?x1942, ?x749) <- award_winner(?x1896, ?x1942), award_nominee(?x3210, ?x1942), award_winner(?x749, ?x3210) *> conf = 0.16 ranks of expected_values: 37 EVAL 07ymr5 award 05p09zm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 88.000 88.000 0.481 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15752-0c0zq PRED entity: 0c0zq PRED relation: film_festivals PRED expected values: 0bmj62v => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 19): 0bmj62v (0.07 #96, 0.04 #54, 0.04 #33), 0g57ws5 (0.06 #112, 0.04 #28, 0.02 #343), 03nn7l2 (0.06 #17, 0.04 #59, 0.04 #38), 04_m9gk (0.04 #55, 0.04 #34, 0.04 #139), 0hrcs29 (0.04 #57, 0.04 #36, 0.02 #288), 059_y8d (0.04 #44, 0.04 #23, 0.02 #86), 09rwjly (0.04 #155, 0.04 #176, 0.03 #71), 0j63cyr (0.04 #150, 0.02 #108, 0.02 #234), 0bx_f_t (0.03 #142, 0.02 #121, 0.02 #226), 0gg7gsl (0.03 #148, 0.02 #85, 0.02 #106) >> Best rule #96 for best value: >> intensional similarity = 5 >> extensional distance = 44 >> proper extension: 0sxg4; 0fgpvf; 0gmcwlb; 011yqc; 0dr_4; 026p4q7; 040b5k; 0ctb4g; 01s3vk; 0y_9q; ... >> query: (?x9452, 0bmj62v) <- nominated_for(?x1245, ?x9452), nominated_for(?x143, ?x9452), ?x143 = 02r0csl, award(?x2372, ?x1245), ?x2372 = 0l6px >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c0zq film_festivals 0bmj62v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.065 http://example.org/film/film/film_festivals #15751-06mkj PRED entity: 06mkj PRED relation: partially_contains PRED expected values: 065ky => 274 concepts (226 used for prediction) PRED predicted values (max 10 best out of 38): 05r4w (0.33 #80, 0.09 #709, 0.04 #3180), 0lcd (0.27 #724, 0.25 #173, 0.21 #804), 065ky (0.25 #189, 0.20 #268, 0.14 #506), 05g56 (0.25 #187, 0.20 #266, 0.14 #504), 0k3nk (0.25 #210, 0.18 #644, 0.18 #605), 06c6l (0.25 #227, 0.18 #661, 0.18 #622), 09glw (0.20 #294, 0.10 #2683, 0.09 #2121), 06mkj (0.18 #717, 0.07 #3188, 0.03 #2070), 0lm0n (0.17 #6135, 0.17 #6214, 0.15 #5780), 026zt (0.16 #2448, 0.14 #498, 0.11 #3727) >> Best rule #80 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03v9w; >> query: (?x2152, 05r4w) <- contains(?x2152, ?x11167), contains(?x2152, ?x4698), ?x4698 = 056_y, teams(?x11167, ?x11195) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #189 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 03_3d; 0f8l9c; *> query: (?x2152, 065ky) <- film_release_region(?x6661, ?x2152), ?x6661 = 0k7tq, titles(?x2152, ?x534) *> conf = 0.25 ranks of expected_values: 3 EVAL 06mkj partially_contains 065ky CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 274.000 226.000 0.333 http://example.org/location/location/partially_contains #15750-0bq_mx PRED entity: 0bq_mx PRED relation: honored_for PRED expected values: 06929s 05lfwd => 43 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1071): 011ywj (0.57 #4158, 0.57 #4043, 0.40 #4639), 03ln8b (0.50 #3091, 0.50 #2497, 0.33 #1310), 05lfwd (0.50 #2719, 0.33 #3313, 0.33 #1532), 092vkg (0.50 #1836, 0.33 #1243, 0.25 #2430), 09p0ct (0.50 #1857, 0.33 #1264, 0.25 #2451), 05jzt3 (0.50 #1827, 0.33 #1234, 0.25 #2421), 0drnwh (0.50 #2186, 0.09 #6349, 0.05 #11698), 09m6kg (0.43 #3573, 0.30 #4169, 0.13 #6546), 011yxg (0.43 #3576, 0.30 #4172, 0.13 #6549), 017gl1 (0.43 #3615, 0.30 #4211, 0.12 #7183) >> Best rule #4158 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 0clfdj; 092t4b; 09p3h7; 02yvhx; >> query: (?x10809, ?x8367) <- honored_for(?x10809, ?x6079), award_winner(?x10809, ?x2248), film(?x3307, ?x6079), award_nominee(?x3307, ?x6998), award_winner(?x3307, ?x1669), ?x6998 = 0755wz, participant(?x3307, ?x2221), nominated_for(?x3435, ?x6079), nominated_for(?x1703, ?x6079), award_winner(?x8367, ?x3307), award(?x707, ?x1703), ?x3435 = 03hl6lc >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2719 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 0275n3y; *> query: (?x10809, 05lfwd) <- honored_for(?x10809, ?x10249), honored_for(?x10809, ?x6079), award_winner(?x10809, ?x2802), film(?x10092, ?x6079), film(?x3307, ?x6079), film(?x193, ?x6079), ?x3307 = 01ksr1, currency(?x10092, ?x170), titles(?x2008, ?x10249), award_winner(?x5810, ?x2802), producer_type(?x10249, ?x632), film_release_region(?x6079, ?x94), student(?x1043, ?x10092), award_winner(?x4921, ?x2802), award_winner(?x193, ?x192), student(?x11785, ?x2802) *> conf = 0.50 ranks of expected_values: 3, 73 EVAL 0bq_mx honored_for 05lfwd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 43.000 31.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0bq_mx honored_for 06929s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 43.000 31.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15749-01qnfc PRED entity: 01qnfc PRED relation: languages PRED expected values: 03_9r => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 11): 02h40lc (0.17 #1528, 0.17 #1567, 0.16 #1723), 02hwhyv (0.09 #196, 0.04 #665, 0.03 #3165), 03_9r (0.09 #979, 0.08 #900, 0.03 #201), 064_8sq (0.05 #289, 0.05 #93, 0.05 #132), 03k50 (0.05 #82, 0.04 #160, 0.03 #864), 07c9s (0.05 #91, 0.04 #169, 0.02 #873), 09bnf (0.05 #117, 0.04 #195), 0999q (0.05 #101, 0.04 #179), 02bjrlw (0.03 #275, 0.03 #744), 04306rv (0.02 #277, 0.01 #746) >> Best rule #1528 for best value: >> intensional similarity = 7 >> extensional distance = 2240 >> proper extension: 06v8s0; 01sl1q; 044mz_; 0184jc; 04bdxl; 02s2ft; 079vf; 05vsxz; 06qgvf; 0grwj; ... >> query: (?x12722, 02h40lc) <- nationality(?x12722, ?x252), gender(?x12722, ?x231), profession(?x12722, ?x524), profession(?x2167, ?x524), profession(?x1021, ?x524), ?x1021 = 0f0p0, ?x2167 = 0b_fw >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #979 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 866 *> proper extension: 0ngg; *> query: (?x12722, ?x2164) <- nationality(?x12722, ?x252), gender(?x12722, ?x231), ?x231 = 05zppz, nationality(?x2306, ?x252), official_language(?x252, ?x2164), location(?x2306, ?x9559) *> conf = 0.09 ranks of expected_values: 3 EVAL 01qnfc languages 03_9r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.169 http://example.org/people/person/languages #15748-027xq5 PRED entity: 027xq5 PRED relation: category PRED expected values: 08mbj5d => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #19, 0.90 #18, 0.90 #14) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 280 >> proper extension: 01y888; 0143hl; 01lnyf; 057bxr; 01xrlm; 0lwyk; 0jhjl; 058z2d; 04jhp; 0jksm; >> query: (?x13781, 08mbj5d) <- citytown(?x13781, ?x4030), place_of_birth(?x194, ?x4030), major_field_of_study(?x13781, ?x373), location(?x2745, ?x4030) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027xq5 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.904 http://example.org/common/topic/webpage./common/webpage/category #15747-0mmd6 PRED entity: 0mmd6 PRED relation: current_club! PRED expected values: 03ylxn => 94 concepts (78 used for prediction) PRED predicted values (max 10 best out of 47): 02s2lg (0.47 #338, 0.42 #253, 0.21 #387), 02bh_v (0.36 #322, 0.33 #377, 0.26 #350), 0cnk2q (0.33 #30, 0.30 #193, 0.29 #360), 03y_f8 (0.33 #86, 0.29 #307, 0.21 #1062), 02pp1 (0.30 #215, 0.25 #270, 0.21 #387), 01352_ (0.29 #329, 0.21 #387, 0.19 #332), 02w64f (0.29 #331, 0.21 #387, 0.19 #332), 01_lhg (0.25 #254, 0.25 #117, 0.21 #387), 03dj48 (0.25 #379, 0.25 #130, 0.21 #387), 02rqxc (0.21 #387, 0.20 #591, 0.19 #332) >> Best rule #338 for best value: >> intensional similarity = 11 >> extensional distance = 17 >> proper extension: 06ls0l; 05dkbr; 0466hh; >> query: (?x13542, 02s2lg) <- position(?x13542, ?x63), ?x63 = 02sdk9v, current_club(?x4406, ?x13542), current_club(?x4406, ?x12617), current_club(?x4406, ?x11507), current_club(?x4406, ?x8585), current_club(?x4406, ?x3791), ?x12617 = 0cttx, teams(?x2611, ?x11507), colors(?x3791, ?x663), ?x8585 = 04ltf >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1081 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 69 *> proper extension: 02jgm0; 03m10r; 06khkb; 04mp9q; 021mkg; 0kq9l; 06z9f8; *> query: (?x13542, 03ylxn) <- position(?x13542, ?x63), ?x63 = 02sdk9v, current_club(?x4406, ?x13542), current_club(?x4406, ?x6552), current_club(?x4406, ?x3791), sport(?x3791, ?x471), category(?x6552, ?x134), colors(?x3791, ?x663) *> conf = 0.15 ranks of expected_values: 22 EVAL 0mmd6 current_club! 03ylxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 94.000 78.000 0.474 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #15746-01gvpz PRED entity: 01gvpz PRED relation: nominated_for! PRED expected values: 0gqwc => 75 concepts (59 used for prediction) PRED predicted values (max 10 best out of 201): 0f4x7 (0.35 #25, 0.26 #261, 0.19 #1205), 019f4v (0.33 #53, 0.24 #525, 0.24 #1233), 0gq9h (0.32 #62, 0.26 #534, 0.25 #1242), 0gs9p (0.29 #64, 0.25 #536, 0.24 #1244), 04dn09n (0.27 #35, 0.21 #1215, 0.21 #507), 0k611 (0.26 #72, 0.24 #544, 0.23 #308), 0gqy2 (0.24 #120, 0.20 #592, 0.19 #356), 040njc (0.24 #7, 0.18 #951, 0.18 #1187), 02qvyrt (0.23 #94, 0.20 #8974, 0.20 #9683), 0l8z1 (0.23 #51, 0.20 #8974, 0.20 #9683) >> Best rule #25 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 03xj05; >> query: (?x8959, 0f4x7) <- titles(?x1316, ?x8959), currency(?x8959, ?x170), ?x1316 = 017fp, nominated_for(?x484, ?x8959) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #9683 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1125 *> proper extension: 0584r4; 01xr2s; 01rp13; 06qwh; 01dvry; 023ny6; 06qv_; 0pc_l; 015pnb; *> query: (?x8959, ?x1079) <- titles(?x307, ?x8959), nominated_for(?x4139, ?x8959), nominated_for(?x484, ?x8959), award(?x4139, ?x1079) *> conf = 0.20 ranks of expected_values: 22 EVAL 01gvpz nominated_for! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 75.000 59.000 0.348 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15745-02t_v1 PRED entity: 02t_v1 PRED relation: place_of_birth PRED expected values: 01531 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 27): 01531 (0.28 #40150, 0.27 #44378, 0.27 #45083), 0cr3d (0.12 #94, 0.04 #10657, 0.03 #12068), 025569 (0.12 #658), 01lfy (0.12 #293), 02_286 (0.07 #47215, 0.06 #4948, 0.06 #40169), 030qb3t (0.04 #47250, 0.04 #17665, 0.04 #15552), 01_d4 (0.03 #1474, 0.03 #47262, 0.03 #42329), 0dclg (0.02 #1486, 0.02 #782, 0.02 #2191), 04jpl (0.02 #35227, 0.02 #38045, 0.02 #3529), 01cx_ (0.02 #813, 0.02 #2222, 0.02 #2926) >> Best rule #40150 for best value: >> intensional similarity = 3 >> extensional distance = 1745 >> proper extension: 03qcq; 084w8; 0f0y8; 053y0s; 05cljf; 05g8ky; 042l3v; 0h5f5n; 02qjj7; 0fp_v1x; ... >> query: (?x4051, ?x3014) <- location(?x4051, ?x3014), gender(?x4051, ?x231), ?x231 = 05zppz >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02t_v1 place_of_birth 01531 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.282 http://example.org/people/person/place_of_birth #15744-01309x PRED entity: 01309x PRED relation: profession PRED expected values: 02hrh1q 025352 => 134 concepts (81 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.81 #5533, 0.80 #5097, 0.79 #4516), 0nbcg (0.57 #1192, 0.56 #1482, 0.54 #1047), 025352 (0.53 #346, 0.49 #56, 0.13 #637), 0dz3r (0.47 #1454, 0.47 #1889, 0.46 #1164), 03gjzk (0.34 #1322, 0.34 #1612, 0.23 #11496), 0dxtg (0.33 #303, 0.32 #13, 0.27 #8733), 0n1h (0.32 #1173, 0.29 #446, 0.28 #2043), 01d_h8 (0.32 #3492, 0.31 #4362, 0.31 #9308), 02hv44_ (0.27 #344, 0.27 #54, 0.05 #8774), 0fnpj (0.22 #2234, 0.20 #202, 0.19 #2818) >> Best rule #5533 for best value: >> intensional similarity = 2 >> extensional distance = 377 >> proper extension: 02v60l; 02yy8; 02m30v; >> query: (?x3632, 02hrh1q) <- profession(?x3632, ?x220), spouse(?x4537, ?x3632) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01309x profession 025352 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 134.000 81.000 0.807 http://example.org/people/person/profession EVAL 01309x profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 81.000 0.807 http://example.org/people/person/profession #15743-02hft3 PRED entity: 02hft3 PRED relation: colors PRED expected values: 01g5v => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.44 #222, 0.40 #1282, 0.39 #202), 01g5v (0.33 #224, 0.30 #204, 0.30 #64), 01l849 (0.25 #1481, 0.25 #1141, 0.25 #1381), 019sc (0.20 #127, 0.19 #1187, 0.19 #1347), 06fvc (0.19 #143, 0.18 #1343, 0.18 #1183), 04mkbj (0.17 #10, 0.12 #150, 0.11 #70), 03wkwg (0.17 #15, 0.07 #195, 0.07 #35), 09ggk (0.17 #16, 0.07 #236, 0.06 #1436), 038hg (0.14 #312, 0.12 #112, 0.12 #132), 036k5h (0.13 #25, 0.13 #65, 0.11 #1125) >> Best rule #222 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 02d9nr; >> query: (?x1977, 083jv) <- citytown(?x1977, ?x13817), registering_agency(?x1977, ?x1982), ?x1982 = 03z19, colors(?x1977, ?x3315) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #224 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 02d9nr; *> query: (?x1977, 01g5v) <- citytown(?x1977, ?x13817), registering_agency(?x1977, ?x1982), ?x1982 = 03z19, colors(?x1977, ?x3315) *> conf = 0.33 ranks of expected_values: 2 EVAL 02hft3 colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 147.000 0.439 http://example.org/education/educational_institution/colors #15742-0dt645q PRED entity: 0dt645q PRED relation: profession PRED expected values: 02hv44_ => 88 concepts (37 used for prediction) PRED predicted values (max 10 best out of 119): 09jwl (0.96 #5033, 0.85 #2968, 0.85 #1789), 016z4k (0.72 #2511, 0.59 #1921, 0.58 #1774), 0nbcg (0.60 #2538, 0.59 #3275, 0.59 #2980), 0dz3r (0.52 #2951, 0.45 #3246, 0.43 #1919), 01d_h8 (0.46 #1037, 0.45 #2070, 0.42 #2365), 0dxtg (0.38 #1045, 0.33 #2078, 0.26 #3555), 0cbd2 (0.35 #1038, 0.10 #2071, 0.08 #4136), 018gz8 (0.33 #165, 0.27 #3558, 0.26 #3852), 015h31 (0.33 #175, 0.05 #3392, 0.03 #2681), 02jknp (0.29 #2072, 0.27 #1039, 0.22 #2367) >> Best rule #5033 for best value: >> intensional similarity = 7 >> extensional distance = 649 >> proper extension: 0f0y8; 053y0s; 028q6; 03c7ln; 0411q; 0c9d9; 01lmj3q; 0fp_v1x; 0m2l9; 032nwy; ... >> query: (?x10418, 09jwl) <- profession(?x10418, ?x2659), profession(?x8391, ?x2659), profession(?x7233, ?x2659), profession(?x1413, ?x2659), ?x7233 = 01lz4tf, ?x8391 = 01693z, ?x1413 = 01p9hgt >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #3392 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 173 *> proper extension: 06y9c2; 025xt8y; 01w923; 0p5mw; 01vv126; 01vsy95; 01w8n89; 050z2; 01mxt_; 02r3cn; ... *> query: (?x10418, ?x524) <- profession(?x10418, ?x2659), profession(?x10418, ?x1383), ?x2659 = 039v1, profession(?x5562, ?x1383), profession(?x1690, ?x1383), award_nominee(?x989, ?x1690), profession(?x5562, ?x524) *> conf = 0.05 ranks of expected_values: 49 EVAL 0dt645q profession 02hv44_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 88.000 37.000 0.955 http://example.org/people/person/profession #15741-09nwwf PRED entity: 09nwwf PRED relation: artists PRED expected values: 0137g1 044mfr 0ycp3 04vrxh 016376 => 61 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1185): 03t9sp (0.75 #7451, 0.73 #11639, 0.70 #10593), 017_hq (0.75 #8308, 0.60 #11450, 0.60 #6214), 016t0h (0.70 #11455, 0.64 #12501, 0.62 #8313), 01304j (0.67 #10375, 0.62 #9328, 0.57 #7233), 01vvycq (0.62 #7376, 0.60 #10518, 0.60 #5282), 0qf3p (0.62 #7527, 0.60 #10669, 0.60 #5433), 01pny5 (0.62 #9399, 0.60 #5211, 0.57 #7304), 016vn3 (0.62 #8239, 0.60 #11381, 0.55 #12427), 016fmf (0.62 #7539, 0.60 #5445, 0.50 #10681), 02bgmr (0.62 #7840, 0.60 #5746, 0.50 #10982) >> Best rule #7451 for best value: >> intensional similarity = 9 >> extensional distance = 6 >> proper extension: 011j5x; 059kh; >> query: (?x9013, 03t9sp) <- artists(?x9013, ?x7682), artists(?x9013, ?x5478), artists(?x9013, ?x1751), ?x7682 = 01323p, instrumentalists(?x2309, ?x5478), ?x2309 = 06ncr, award_winner(?x724, ?x1751), group(?x75, ?x1751), award_winner(?x3065, ?x1751) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6107 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 06by7; *> query: (?x9013, 04vrxh) <- artists(?x9013, ?x8156), artists(?x9013, ?x7682), artists(?x9013, ?x5478), artists(?x9013, ?x1751), ?x7682 = 01323p, instrumentalists(?x2309, ?x5478), ?x2309 = 06ncr, award_winner(?x724, ?x1751), group(?x75, ?x1751), ?x8156 = 046p9 *> conf = 0.60 ranks of expected_values: 22, 219, 259, 264, 475 EVAL 09nwwf artists 016376 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 32.000 0.750 http://example.org/music/genre/artists EVAL 09nwwf artists 04vrxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 61.000 32.000 0.750 http://example.org/music/genre/artists EVAL 09nwwf artists 0ycp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 32.000 0.750 http://example.org/music/genre/artists EVAL 09nwwf artists 044mfr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 32.000 0.750 http://example.org/music/genre/artists EVAL 09nwwf artists 0137g1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 32.000 0.750 http://example.org/music/genre/artists #15740-01h8f PRED entity: 01h8f PRED relation: profession PRED expected values: 03gjzk => 107 concepts (59 used for prediction) PRED predicted values (max 10 best out of 64): 0dxtg (0.79 #589, 0.77 #445, 0.71 #7511), 03gjzk (0.58 #1025, 0.33 #1313, 0.33 #1745), 09jwl (0.37 #1461, 0.36 #2902, 0.36 #3046), 0d1pc (0.33 #46, 0.09 #190, 0.07 #335), 01c72t (0.31 #4348, 0.14 #3195, 0.14 #1466), 0nbcg (0.26 #3201, 0.26 #2913, 0.25 #3057), 018gz8 (0.24 #303, 0.18 #448, 0.17 #1315), 016z4k (0.24 #3034, 0.23 #1449, 0.23 #2890), 0dz3r (0.22 #3032, 0.21 #4473, 0.21 #1159), 0np9r (0.22 #1607, 0.21 #885, 0.19 #740) >> Best rule #589 for best value: >> intensional similarity = 5 >> extensional distance = 82 >> proper extension: 0brkwj; 03p01x; >> query: (?x5217, 0dxtg) <- profession(?x5217, ?x524), profession(?x5217, ?x353), ?x353 = 0cbd2, ?x524 = 02jknp, nationality(?x5217, ?x94) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1025 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 141 *> proper extension: 01f8ld; 015njf; 026w_gk; 03v40v; 0dbc1s; 04353; 03hzkq; 0drdv; *> query: (?x5217, 03gjzk) <- type_of_union(?x5217, ?x566), profession(?x5217, ?x1943), profession(?x5217, ?x1032), ?x1032 = 02hrh1q, ?x1943 = 02krf9 *> conf = 0.58 ranks of expected_values: 2 EVAL 01h8f profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 59.000 0.786 http://example.org/people/person/profession #15739-090gk3 PRED entity: 090gk3 PRED relation: type_of_union PRED expected values: 04ztj => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.74 #17, 0.73 #21, 0.70 #13), 01g63y (0.21 #189, 0.15 #30, 0.13 #42), 0jgjn (0.21 #189, 0.01 #8, 0.01 #12), 01bl8s (0.21 #189) >> Best rule #17 for best value: >> intensional similarity = 5 >> extensional distance = 144 >> proper extension: 05d7rk; 025p38; 067jsf; 01pr_j6; 02vmzp; 025tdwc; 0jrqq; 0288crq; 02wxvtv; 02xfrd; ... >> query: (?x12520, 04ztj) <- profession(?x12520, ?x1032), ?x1032 = 02hrh1q, nationality(?x12520, ?x2146), ?x2146 = 03rk0, gender(?x12520, ?x514) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 090gk3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.740 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15738-0kjrx PRED entity: 0kjrx PRED relation: film PRED expected values: 0gmd3k7 => 128 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1202): 0gyv0b4 (0.62 #55184, 0.50 #97907, 0.48 #106809), 0f42nz (0.10 #11581, 0.08 #15141, 0.07 #8021), 05h43ls (0.09 #3972, 0.02 #32453, 0.01 #68056), 02qzh2 (0.09 #689, 0.05 #6029, 0.04 #11369), 03bzjpm (0.09 #1307, 0.04 #4867, 0.04 #22667), 034qrh (0.09 #63, 0.04 #5403, 0.03 #10743), 06w839_ (0.09 #507, 0.04 #5847, 0.03 #11187), 02x8fs (0.09 #858, 0.03 #7978, 0.03 #11538), 02825cv (0.09 #1134, 0.03 #11814, 0.03 #6474), 0888c3 (0.09 #1407, 0.03 #12087, 0.03 #6747) >> Best rule #55184 for best value: >> intensional similarity = 3 >> extensional distance = 289 >> proper extension: 04cbtrw; 0hwbd; 0mbs8; >> query: (?x8134, ?x4375) <- participant(?x8134, ?x5979), nationality(?x5979, ?x94), award_winner(?x4375, ?x8134) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #35602 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 185 *> proper extension: 03fghg; 025tdwc; 02h8hr; 01kymm; 01wphh2; 01d6jf; 0jpdn; 01kwh5j; 01rmnp; 0392kz; ... *> query: (?x8134, ?x66) <- special_performance_type(?x8134, ?x3558), film(?x3558, ?x66) *> conf = 0.02 ranks of expected_values: 490 EVAL 0kjrx film 0gmd3k7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 128.000 97.000 0.618 http://example.org/film/actor/film./film/performance/film #15737-09rp4r_ PRED entity: 09rp4r_ PRED relation: award_winner! PRED expected values: 0bzm__ => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 134): 073h1t (0.21 #166, 0.17 #304, 0.17 #442), 02jp5r (0.17 #345, 0.17 #207, 0.17 #483), 073h9x (0.17 #189, 0.14 #465, 0.14 #603), 026kq4q (0.15 #46, 0.05 #5247, 0.02 #1427), 02glmx (0.11 #633, 0.11 #909, 0.10 #357), 0fy6bh (0.11 #600, 0.11 #876, 0.09 #1014), 0d__c3 (0.11 #675, 0.11 #951, 0.09 #537), 0bzm81 (0.10 #299, 0.10 #161, 0.09 #437), 059x66 (0.10 #295, 0.10 #157, 0.09 #433), 073hkh (0.10 #278, 0.10 #140, 0.09 #416) >> Best rule #166 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 094wz7q; >> query: (?x1622, 073h1t) <- crewmember(?x407, ?x1622), award_nominee(?x3879, ?x1622), nominated_for(?x1622, ?x810), crewmember(?x1046, ?x3879) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #5247 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1324 *> proper extension: 0gp9mp; 01ky2h; 02lq10; 02wb6yq; 07xr3w; 0lzkm; 02z6l5f; 0627sn; 02f9wb; 08xz51; ... *> query: (?x1622, ?x3001) <- award_winner(?x3618, ?x1622), gender(?x1622, ?x231), award_winner(?x3618, ?x8612), award_winner(?x3001, ?x8612) *> conf = 0.05 ranks of expected_values: 45 EVAL 09rp4r_ award_winner! 0bzm__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 105.000 105.000 0.207 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15736-0vgkd PRED entity: 0vgkd PRED relation: genre! PRED expected values: 0fhzwl => 62 concepts (29 used for prediction) PRED predicted values (max 10 best out of 346): 07gbf (0.67 #3553, 0.67 #2990, 0.60 #1593), 020qr4 (0.67 #3649, 0.56 #5614, 0.55 #6459), 01kt_j (0.60 #1616, 0.57 #4417, 0.50 #3013), 01h72l (0.60 #2280, 0.56 #5644, 0.50 #3679), 04f6hhm (0.60 #1552, 0.50 #2949, 0.43 #4353), 06dfz1 (0.60 #1561, 0.50 #2958, 0.43 #4362), 01fs__ (0.60 #2649, 0.50 #3207, 0.40 #1810), 01j67j (0.60 #2560, 0.50 #3118, 0.40 #1721), 099pks (0.60 #2337, 0.45 #6546, 0.44 #5701), 0557yqh (0.60 #2298, 0.33 #3134, 0.33 #611) >> Best rule #3553 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 03npn; >> query: (?x809, 07gbf) <- genre(?x11336, ?x809), genre(?x6439, ?x809), genre(?x9452, ?x809), award_winner(?x9452, ?x1384), nominated_for(?x68, ?x9452), film(?x92, ?x9452), program(?x65, ?x6439), language(?x9452, ?x254), ?x11336 = 0qmk5, ?x68 = 02qyp19 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2970 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 0c3351; *> query: (?x809, 0fhzwl) <- genre(?x2009, ?x809), genre(?x9452, ?x809), award_winner(?x9452, ?x1384), nominated_for(?x1587, ?x9452), nominated_for(?x112, ?x9452), ?x2009 = 03d34x8, award(?x4764, ?x112), ?x4764 = 021yzs, ?x1587 = 02rdyk7 *> conf = 0.50 ranks of expected_values: 25 EVAL 0vgkd genre! 0fhzwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 62.000 29.000 0.667 http://example.org/tv/tv_program/genre #15735-02lw8j PRED entity: 02lw8j PRED relation: parent_genre PRED expected values: 06by7 => 58 concepts (48 used for prediction) PRED predicted values (max 10 best out of 193): 03lty (0.95 #1661, 0.40 #2818, 0.33 #1170), 0xhtw (0.85 #1492, 0.33 #343, 0.29 #508), 06by7 (0.63 #2815, 0.54 #1495, 0.50 #1332), 02w4v (0.33 #31, 0.17 #196, 0.14 #526), 05r6t (0.31 #1534, 0.23 #2854, 0.21 #1697), 05w3f (0.31 #1505, 0.10 #851, 0.09 #1014), 016jny (0.18 #1059, 0.17 #1387, 0.17 #401), 017371 (0.18 #1095, 0.17 #1423, 0.17 #272), 05bt6j (0.17 #1181, 0.17 #360, 0.17 #195), 016clz (0.17 #1155, 0.17 #334, 0.15 #1483) >> Best rule #1661 for best value: >> intensional similarity = 10 >> extensional distance = 37 >> proper extension: 0jf1v; 02srgf; 028cl7; 0d4xmp; >> query: (?x14090, 03lty) <- parent_genre(?x14090, ?x11040), artists(?x11040, ?x9206), artists(?x11040, ?x7868), artists(?x11040, ?x7013), artists(?x11040, ?x5126), ?x7868 = 0knhk, ?x9206 = 017mbb, parent_genre(?x11040, ?x1000), ?x7013 = 081wh1, ?x5126 = 03h502k >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #2815 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 91 *> proper extension: 01gbcf; 018ysx; 017ht; *> query: (?x14090, 06by7) <- parent_genre(?x14090, ?x11040), artists(?x11040, ?x10813), artists(?x11040, ?x9206), artists(?x11040, ?x7868), artist(?x3050, ?x7868), ?x10813 = 0ycfj, group(?x227, ?x7868), ?x3050 = 0229rs, ?x9206 = 017mbb *> conf = 0.63 ranks of expected_values: 3 EVAL 02lw8j parent_genre 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 58.000 48.000 0.949 http://example.org/music/genre/parent_genre #15734-0pr6f PRED entity: 0pr6f PRED relation: genre! PRED expected values: 015w8_ 01hn_t => 38 concepts (18 used for prediction) PRED predicted values (max 10 best out of 562): 0gxr1c (0.75 #1912, 0.71 #1634, 0.70 #2741), 07ng9k (0.62 #1673, 0.60 #2502, 0.57 #1395), 03g9xj (0.62 #1848, 0.57 #1570, 0.56 #2401), 0dr1c2 (0.60 #1227, 0.57 #1502, 0.50 #2609), 03r0rq (0.60 #1310, 0.44 #2416, 0.43 #1585), 0q6g3 (0.57 #1627, 0.50 #2734, 0.50 #1905), 06qw_ (0.57 #1636, 0.50 #1914, 0.44 #2467), 06qv_ (0.57 #1594, 0.50 #1872, 0.44 #2425), 06qxh (0.57 #1580, 0.50 #1858, 0.44 #2411), 06r4f (0.57 #1559, 0.50 #1837, 0.44 #2390) >> Best rule #1912 for best value: >> intensional similarity = 16 >> extensional distance = 6 >> proper extension: 01hmnh; >> query: (?x10023, 0gxr1c) <- genre(?x11035, ?x10023), genre(?x8976, ?x10023), genre(?x6694, ?x10023), genre(?x5852, ?x10023), genre(?x419, ?x10023), ?x419 = 020qr4, nominated_for(?x2400, ?x11035), actor(?x5852, ?x9650), type_of_union(?x2400, ?x566), ?x9650 = 0q1lp, titles(?x2008, ?x6694), tv_program(?x5562, ?x6694), country_of_origin(?x6694, ?x94), nominated_for(?x7510, ?x6694), titles(?x7712, ?x11035), category(?x8976, ?x134) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #324 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 1 *> proper extension: 0hcr; *> query: (?x10023, 015w8_) <- genre(?x11035, ?x10023), genre(?x8837, ?x10023), genre(?x7566, ?x10023), genre(?x5938, ?x10023), genre(?x5852, ?x10023), genre(?x4339, ?x10023), ?x11035 = 06r1k, ?x7566 = 05h95s, languages(?x4339, ?x254), ?x5852 = 024rwx, ?x5938 = 05f7w84, actor(?x4339, ?x7835), actor(?x8837, ?x3808), location(?x7835, ?x2645), category(?x7835, ?x134), nationality(?x7835, ?x94), nominated_for(?x105, ?x8837) *> conf = 0.33 ranks of expected_values: 65, 258 EVAL 0pr6f genre! 01hn_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 38.000 18.000 0.750 http://example.org/tv/tv_program/genre EVAL 0pr6f genre! 015w8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 38.000 18.000 0.750 http://example.org/tv/tv_program/genre #15733-02jxsq PRED entity: 02jxsq PRED relation: nationality PRED expected values: 03rk0 => 108 concepts (107 used for prediction) PRED predicted values (max 10 best out of 23): 03rk0 (0.89 #1949, 0.83 #1547, 0.82 #1447), 09c7w0 (0.77 #801, 0.74 #4013, 0.73 #4113), 055vr (0.29 #6836, 0.27 #8951), 07ssc (0.11 #8849, 0.09 #5944, 0.09 #6245), 05sb1 (0.11 #8849, 0.08 #7539, 0.06 #2704), 02jx1 (0.10 #733, 0.09 #5153, 0.09 #2636), 0d060g (0.06 #6338, 0.05 #6640, 0.05 #6741), 0bq0p9 (0.06 #2704, 0.04 #1119, 0.02 #1821), 0f8l9c (0.05 #722, 0.05 #822, 0.04 #2625), 0345h (0.05 #1732, 0.04 #4747, 0.04 #2634) >> Best rule #1949 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 084z0w; 07yw6t; 0fr7nt; 0969vz; 087z12; 08kp57; 04ch23; 07jmnh; 026sv5l; >> query: (?x10200, 03rk0) <- profession(?x10200, ?x1032), award(?x10200, ?x4687), ?x4687 = 03rbj2, ?x1032 = 02hrh1q >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jxsq nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 107.000 0.894 http://example.org/people/person/nationality #15732-01kb2j PRED entity: 01kb2j PRED relation: student! PRED expected values: 019v9k => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 9): 014mlp (0.09 #246, 0.08 #106, 0.08 #66), 02h4rq6 (0.08 #3, 0.03 #83, 0.02 #243), 02_xgp2 (0.08 #94, 0.04 #134, 0.04 #154), 019v9k (0.03 #70, 0.03 #130, 0.03 #250), 04zx3q1 (0.03 #82, 0.02 #122, 0.02 #142), 0bkj86 (0.03 #89, 0.02 #249, 0.02 #109), 028dcg (0.02 #118, 0.02 #78, 0.02 #198), 016t_3 (0.02 #224, 0.01 #204, 0.01 #544), 03mkk4 (0.01 #93, 0.01 #253, 0.01 #573) >> Best rule #246 for best value: >> intensional similarity = 3 >> extensional distance = 329 >> proper extension: 01h2_6; >> query: (?x5097, 014mlp) <- religion(?x5097, ?x2694), student(?x6912, ?x5097), people(?x5741, ?x5097) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #70 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 0456xp; 0n6f8; 03rl84; 01skmp; 02jr26; 0chw_; 023mdt; 0btxr; *> query: (?x5097, 019v9k) <- nominated_for(?x5097, ?x414), award(?x5097, ?x2478), ?x2478 = 02x4x18 *> conf = 0.03 ranks of expected_values: 4 EVAL 01kb2j student! 019v9k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 96.000 0.088 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #15731-0d0kn PRED entity: 0d0kn PRED relation: contains! PRED expected values: 02qkt => 127 concepts (98 used for prediction) PRED predicted values (max 10 best out of 155): 02qkt (0.80 #1240, 0.64 #20013, 0.60 #23589), 02j71 (0.72 #47376, 0.69 #23243, 0.67 #50951), 09c7w0 (0.51 #65262, 0.45 #10728, 0.45 #12516), 04_1l0v (0.45 #11174, 0.41 #12962, 0.33 #22798), 0dg3n1 (0.45 #34123, 0.29 #44848, 0.27 #31442), 07c5l (0.28 #14696, 0.27 #19166, 0.23 #32575), 06n3y (0.18 #5192, 0.14 #9660, 0.13 #19496), 07ssc (0.17 #26850, 0.15 #67078, 0.13 #22380), 04pnx (0.15 #19196, 0.15 #14726, 0.13 #5785), 04wsz (0.14 #20164, 0.11 #497, 0.11 #17484) >> Best rule #1240 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 059z0; 024pcx; >> query: (?x2000, 02qkt) <- contains(?x455, ?x2000), adjoins(?x1499, ?x2000), ?x455 = 02j9z >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d0kn contains! 02qkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 98.000 0.800 http://example.org/location/location/contains #15730-0f2tj PRED entity: 0f2tj PRED relation: source PRED expected values: 0jbk9 => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #152, 0.83 #51, 0.80 #104) >> Best rule #152 for best value: >> intensional similarity = 1 >> extensional distance = 514 >> proper extension: 0zrlp; 013hvr; 0vqcq; >> query: (?x6769, 0jbk9) <- place(?x6769, ?x6769) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2tj source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #15729-0jgx PRED entity: 0jgx PRED relation: medal PRED expected values: 02lpp7 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.77 #13, 0.75 #15, 0.74 #31) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 04w4s; >> query: (?x3855, 02lpp7) <- jurisdiction_of_office(?x182, ?x3855), olympics(?x3855, ?x3110), ?x3110 = 0kbvv >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgx medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.771 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #15728-01kd57 PRED entity: 01kd57 PRED relation: profession PRED expected values: 0dz3r => 101 concepts (79 used for prediction) PRED predicted values (max 10 best out of 58): 02hrh1q (0.82 #7047, 0.79 #2939, 0.75 #7339), 0dz3r (0.64 #2, 0.43 #4250, 0.42 #2781), 01c72t (0.62 #2362, 0.57 #1776, 0.56 #2214), 039v1 (0.40 #1496, 0.33 #912, 0.30 #1934), 0n1h (0.36 #11, 0.28 #9519, 0.27 #1910), 01c8w0 (0.32 #739, 0.26 #1761, 0.25 #2347), 01d_h8 (0.29 #10108, 0.28 #6892, 0.28 #8208), 0dxtg (0.28 #9519, 0.27 #10116, 0.26 #11272), 0fnpj (0.28 #9519, 0.26 #11272, 0.23 #2838), 02jknp (0.28 #9519, 0.26 #11272, 0.21 #7) >> Best rule #7047 for best value: >> intensional similarity = 3 >> extensional distance = 827 >> proper extension: 07hbxm; >> query: (?x5543, 02hrh1q) <- location(?x5543, ?x10683), award_nominee(?x5494, ?x5543), participant(?x1656, ?x5494) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 02ryx0; *> query: (?x5543, 0dz3r) <- award_winner(?x725, ?x5543), music(?x586, ?x5543), performance_role(?x5543, ?x228), profession(?x5543, ?x220) *> conf = 0.64 ranks of expected_values: 2 EVAL 01kd57 profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 79.000 0.818 http://example.org/people/person/profession #15727-0175wg PRED entity: 0175wg PRED relation: award_nominee! PRED expected values: 02zq43 0159h6 => 61 concepts (31 used for prediction) PRED predicted values (max 10 best out of 568): 0159h6 (0.84 #85, 0.81 #13921, 0.81 #16243), 01l2fn (0.81 #13921, 0.81 #16243, 0.81 #71941), 0m2wm (0.81 #13921, 0.81 #16243, 0.81 #71941), 020_95 (0.81 #13921, 0.81 #71941, 0.81 #34808), 0175wg (0.79 #1342, 0.16 #71942, 0.16 #53373), 02zq43 (0.68 #60, 0.16 #71942, 0.16 #53373), 0dvld (0.16 #71942, 0.16 #53373, 0.15 #55694), 06dv3 (0.16 #71942, 0.16 #53373, 0.15 #55694), 0170qf (0.16 #71942, 0.16 #53373, 0.15 #55694), 0171cm (0.16 #71942, 0.16 #53373, 0.15 #55694) >> Best rule #85 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 05vsxz; 0m2wm; 02zq43; 0159h6; 0h5g_; 03f1zdw; 01yhvv; 09y20; 05tk7y; 02cllz; ... >> query: (?x5743, 0159h6) <- award_nominee(?x2487, ?x5743), gender(?x5743, ?x514), ?x2487 = 04rsd2 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 0175wg award_nominee! 0159h6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 31.000 0.842 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 0175wg award_nominee! 02zq43 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 61.000 31.000 0.842 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15726-03tps5 PRED entity: 03tps5 PRED relation: crewmember PRED expected values: 0b79gfg => 103 concepts (74 used for prediction) PRED predicted values (max 10 best out of 32): 0c94fn (0.27 #59, 0.25 #11, 0.03 #202), 092ys_y (0.07 #164, 0.04 #355, 0.04 #259), 016szr (0.06 #287, 0.03 #1975, 0.03 #2265), 0bl2g (0.06 #287, 0.03 #1975, 0.03 #2265), 04wp63 (0.06 #377, 0.06 #281, 0.04 #186), 02xc1w4 (0.06 #266, 0.06 #218, 0.05 #362), 04ktcgn (0.05 #300, 0.04 #347, 0.04 #251), 0bbxx9b (0.04 #356, 0.04 #165, 0.04 #403), 0b79gfg (0.04 #162, 0.03 #209, 0.02 #400), 0284n42 (0.04 #577, 0.03 #195, 0.03 #386) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0266s9; >> query: (?x4409, 0c94fn) <- nominated_for(?x1691, ?x4409), titles(?x2480, ?x4409), nominated_for(?x4850, ?x4409), ?x4850 = 016szr >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #162 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 0bmc4cm; *> query: (?x4409, 0b79gfg) <- nominated_for(?x1691, ?x4409), language(?x4409, ?x254), film_release_distribution_medium(?x4409, ?x81), region(?x4409, ?x512) *> conf = 0.04 ranks of expected_values: 9 EVAL 03tps5 crewmember 0b79gfg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 103.000 74.000 0.273 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #15725-02vyw PRED entity: 02vyw PRED relation: profession PRED expected values: 01d_h8 => 142 concepts (141 used for prediction) PRED predicted values (max 10 best out of 92): 02hrh1q (0.89 #16921, 0.88 #12804, 0.87 #15304), 01d_h8 (0.89 #2799, 0.87 #7062, 0.86 #9561), 01c72t (0.57 #3844, 0.10 #463, 0.09 #2668), 0cbd2 (0.49 #5887, 0.48 #8827, 0.46 #10298), 018gz8 (0.47 #4131, 0.36 #5160, 0.33 #2220), 09jwl (0.37 #3839, 0.20 #11191, 0.20 #14279), 02krf9 (0.34 #1054, 0.31 #172, 0.28 #3994), 0kyk (0.34 #8848, 0.33 #28, 0.33 #5908), 0nbcg (0.29 #3852, 0.26 #18232, 0.21 #1941), 0dgd_ (0.26 #18232, 0.25 #10144, 0.16 #176) >> Best rule #16921 for best value: >> intensional similarity = 2 >> extensional distance = 2012 >> proper extension: 08b8vd; 02hhtj; 0mdyn; 04_jsg; 069z_5; 04j5fx; 01vs8ng; >> query: (?x3662, 02hrh1q) <- film(?x3662, ?x810), profession(?x3662, ?x524) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #2799 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 108 *> proper extension: 02g8h; *> query: (?x3662, 01d_h8) <- produced_by(?x6213, ?x3662), nominated_for(?x6111, ?x6213), award(?x3662, ?x198) *> conf = 0.89 ranks of expected_values: 2 EVAL 02vyw profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 142.000 141.000 0.892 http://example.org/people/person/profession #15724-062dn7 PRED entity: 062dn7 PRED relation: currency PRED expected values: 09nqf => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.40 #13, 0.40 #7, 0.37 #4), 01nv4h (0.08 #2, 0.04 #41, 0.02 #62) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 165 >> proper extension: 01npcy7; >> query: (?x3860, 09nqf) <- participant(?x3860, ?x3861), participant(?x3860, ?x4046), profession(?x3860, ?x319) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 062dn7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.401 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #15723-0q9jk PRED entity: 0q9jk PRED relation: genre PRED expected values: 01z4y => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 80): 01z4y (0.63 #1015, 0.43 #516, 0.36 #1929), 07s9rl0 (0.54 #583, 0.53 #4323, 0.53 #499), 01t_vv (0.28 #1031, 0.24 #616, 0.21 #865), 0hcr (0.24 #1016, 0.22 #4341, 0.19 #4425), 01htzx (0.23 #349, 0.21 #682, 0.21 #931), 01z77k (0.21 #278, 0.13 #3687, 0.12 #1940), 06n90 (0.21 #345, 0.20 #678, 0.20 #927), 03k9fj (0.19 #343, 0.17 #4333, 0.16 #2006), 06nbt (0.18 #1018, 0.15 #686, 0.14 #935), 0vgkd (0.17 #425, 0.16 #508, 0.15 #592) >> Best rule #1015 for best value: >> intensional similarity = 2 >> extensional distance = 95 >> proper extension: 020qr4; 01cjhz; 0jq2r; 045qmr; 047m_w; 06f0k; >> query: (?x8132, 01z4y) <- genre(?x8132, ?x258), ?x258 = 05p553 >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q9jk genre 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.629 http://example.org/tv/tv_program/genre #15722-0160w PRED entity: 0160w PRED relation: contains! PRED expected values: 07c5l => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 262): 059g4 (0.75 #45717, 0.21 #143480, 0.18 #4945), 02j71 (0.67 #106681, 0.66 #48409, 0.65 #55580), 09c7w0 (0.67 #51100, 0.63 #52893, 0.59 #73509), 02qkt (0.59 #58617, 0.59 #46065, 0.57 #57720), 0dg3n1 (0.53 #46771, 0.32 #65597, 0.31 #38700), 04_1l0v (0.40 #68581, 0.38 #64100, 0.36 #11209), 02j9z (0.40 #44848, 0.31 #41262, 0.29 #58298), 06mkj (0.37 #78885, 0.04 #62891, 0.03 #69164), 07c5l (0.32 #19221, 0.32 #13843, 0.30 #17428), 0d060g (0.30 #120140, 0.12 #30493, 0.11 #22426) >> Best rule #45717 for best value: >> intensional similarity = 2 >> extensional distance = 51 >> proper extension: 04fh3; >> query: (?x126, ?x8483) <- countries_within(?x8483, ?x126), time_zones(?x126, ?x2674) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #19221 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 32 *> proper extension: 0n3g; *> query: (?x126, 07c5l) <- form_of_government(?x126, ?x1926), vacationer(?x126, ?x872) *> conf = 0.32 ranks of expected_values: 9 EVAL 0160w contains! 07c5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 168.000 168.000 0.755 http://example.org/location/location/contains #15721-0cy07 PRED entity: 0cy07 PRED relation: contains! PRED expected values: 02jx1 => 104 concepts (43 used for prediction) PRED predicted values (max 10 best out of 270): 02jx1 (0.88 #28619, 0.73 #27810, 0.68 #15203), 09c7w0 (0.67 #21466, 0.60 #20572, 0.56 #8051), 02qkt (0.50 #16098, 0.01 #11077), 01n7q (0.27 #8125, 0.18 #17963, 0.15 #9914), 0d060g (0.20 #8956, 0.08 #21476, 0.07 #5378), 0kpys (0.17 #8228, 0.10 #18066, 0.08 #12699), 04jpl (0.17 #4492, 0.12 #15225, 0.11 #27746), 05kr_ (0.10 #5490, 0.09 #7278, 0.08 #9068), 059rby (0.09 #18800, 0.08 #16118, 0.08 #17012), 0j5g9 (0.09 #3836, 0.03 #6519, 0.03 #27984) >> Best rule #28619 for best value: >> intensional similarity = 5 >> extensional distance = 192 >> proper extension: 0zc6f; 0crjn65; 01k8q5; 0dplh; 0c_zj; 0fgj2; 013bqg; 0hsb3; 0125q1; 0yls9; ... >> query: (?x13829, ?x1310) <- contains(?x11933, ?x13829), contains(?x512, ?x13829), ?x512 = 07ssc, contains(?x1310, ?x11933), time_zones(?x1310, ?x5327) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cy07 contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 43.000 0.885 http://example.org/location/location/contains #15720-0bdw1g PRED entity: 0bdw1g PRED relation: nominated_for PRED expected values: 03j63k 02zk08 => 49 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1297): 05lfwd (0.67 #2465, 0.25 #890, 0.08 #4040), 0hz55 (0.67 #2333, 0.08 #3908, 0.05 #5484), 01fx1l (0.65 #14202, 0.64 #9462, 0.63 #6306), 030p35 (0.65 #14202, 0.64 #9462, 0.63 #6306), 0gvsh7l (0.65 #14202, 0.64 #9462, 0.63 #6306), 01cvtf (0.65 #14202, 0.64 #9462, 0.63 #6306), 0d66j2 (0.65 #14202, 0.64 #9462, 0.63 #6306), 015ppk (0.58 #2655, 0.25 #1080, 0.06 #4230), 03d34x8 (0.58 #1859, 0.05 #3434, 0.04 #8165), 039c26 (0.58 #2059, 0.05 #3634, 0.04 #13107) >> Best rule #2465 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 09v7wsg; >> query: (?x686, 05lfwd) <- award_winner(?x686, ?x376), nominated_for(?x686, ?x337), ceremony(?x686, ?x1265), ?x337 = 0g60z >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1100 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0bfvw2; 0cqh6z; *> query: (?x686, 03j63k) <- award_winner(?x686, ?x376), award(?x11601, ?x686), ?x11601 = 0ck91, nominated_for(?x686, ?x337) *> conf = 0.25 ranks of expected_values: 19, 457 EVAL 0bdw1g nominated_for 02zk08 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 16.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bdw1g nominated_for 03j63k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 49.000 16.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15719-02l424 PRED entity: 02l424 PRED relation: organization! PRED expected values: 060c4 => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.86 #816, 0.85 #619, 0.85 #355), 0dq_5 (0.60 #9, 0.57 #836, 0.55 #521), 02md_2 (0.35 #486, 0.34 #895, 0.32 #657), 07xl34 (0.30 #997, 0.28 #233, 0.28 #207), 05k17c (0.15 #1243, 0.11 #1615, 0.11 #1019), 0dq3c (0.08 #1728, 0.01 #407, 0.01 #513), 01t7n9 (0.08 #1728), 01q24l (0.08 #1728), 02079p (0.08 #1728), 0789n (0.08 #1728) >> Best rule #816 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 01pl14; 06jk5_; 017d77; 033q4k; 04rwx; 01j_cy; 01s0_f; 07wlf; 0l2tk; 01c333; ... >> query: (?x9620, 060c4) <- country(?x9620, ?x94), contains(?x177, ?x9620), institution(?x620, ?x9620), major_field_of_study(?x9620, ?x1327) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02l424 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.862 http://example.org/organization/role/leaders./organization/leadership/organization #15718-02vqpx8 PRED entity: 02vqpx8 PRED relation: gender PRED expected values: 05zppz => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #55, 0.84 #63, 0.84 #53), 02zsn (0.36 #4, 0.29 #58, 0.29 #60) >> Best rule #55 for best value: >> intensional similarity = 2 >> extensional distance = 481 >> proper extension: 075wq; >> query: (?x7043, 05zppz) <- place_of_death(?x7043, ?x6769), contains(?x6769, ?x5596) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vqpx8 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.847 http://example.org/people/person/gender #15717-087yty PRED entity: 087yty PRED relation: cinematography! PRED expected values: 018f8 => 128 concepts (88 used for prediction) PRED predicted values (max 10 best out of 346): 0bj25 (0.25 #293, 0.06 #1677, 0.06 #1331), 0jqb8 (0.25 #303, 0.05 #2033, 0.03 #3071), 0k4fz (0.25 #163, 0.05 #1893, 0.03 #2931), 0kbhf (0.10 #1929, 0.09 #891, 0.06 #1237), 0jymd (0.10 #1862, 0.05 #2900, 0.04 #3246), 0jvt9 (0.10 #1839, 0.04 #3223, 0.04 #3569), 03wy8t (0.09 #1002, 0.06 #2386, 0.06 #1348), 03bdkd (0.09 #1021, 0.06 #1367, 0.05 #2059), 0kvb6p (0.09 #983, 0.06 #1329, 0.05 #2021), 02q_4ph (0.09 #834, 0.06 #1180, 0.05 #1872) >> Best rule #293 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 087v17; 071jrc; >> query: (?x5862, 0bj25) <- place_of_death(?x5862, ?x5895), cinematography(?x4241, ?x5862), award(?x5862, ?x1243), ?x5895 = 0k_p5 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 087yty cinematography! 018f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 88.000 0.250 http://example.org/film/film/cinematography #15716-01qdhx PRED entity: 01qdhx PRED relation: student PRED expected values: 01j6mff => 147 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1370): 010hn (0.33 #367, 0.25 #2459, 0.20 #4551), 01h5f8 (0.33 #1913, 0.25 #4005, 0.20 #6097), 02p8v8 (0.33 #1661, 0.25 #3753, 0.20 #5845), 0grwj (0.25 #2099, 0.20 #4191, 0.14 #6283), 06j8q_ (0.14 #8087), 01_xtx (0.07 #8996, 0.05 #11089, 0.03 #15275), 037lyl (0.05 #9028, 0.03 #11121, 0.03 #15307), 0bw87 (0.05 #9518, 0.03 #11611, 0.03 #15797), 02mjmr (0.05 #8787, 0.03 #10880, 0.03 #15066), 013pp3 (0.05 #9291, 0.03 #11384, 0.03 #15570) >> Best rule #367 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01pq4w; >> query: (?x13109, 010hn) <- contains(?x4978, ?x13109), ?x4978 = 05jbn, currency(?x13109, ?x170), student(?x13109, ?x1795) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01qdhx student 01j6mff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 98.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #15715-06823p PRED entity: 06823p PRED relation: nominated_for! PRED expected values: 0fm3b5 => 87 concepts (50 used for prediction) PRED predicted values (max 10 best out of 206): 02w_6xj (0.69 #925, 0.69 #1388, 0.68 #4399), 0gq9h (0.59 #1679, 0.41 #2373, 0.41 #3531), 019f4v (0.47 #1670, 0.38 #2364, 0.36 #3522), 0k611 (0.42 #1688, 0.32 #2382, 0.30 #3540), 040njc (0.42 #1625, 0.31 #2319, 0.30 #3477), 04dn09n (0.40 #1653, 0.28 #2347, 0.25 #3505), 02pqp12 (0.37 #1675, 0.22 #2369, 0.21 #3527), 02qyntr (0.36 #1795, 0.24 #2489, 0.23 #3647), 0gr4k (0.35 #1644, 0.24 #2338, 0.23 #3496), 0gr0m (0.34 #1676, 0.27 #2370, 0.24 #3528) >> Best rule #925 for best value: >> intensional similarity = 4 >> extensional distance = 144 >> proper extension: 026njb5; 04lqvlr; >> query: (?x6529, ?x68) <- award(?x6529, ?x68), film_release_distribution_medium(?x6529, ?x81), film_format(?x6529, ?x6392), nominated_for(?x533, ?x6529) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #11585 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x6529, ?x198) <- award(?x6529, ?x5398), award(?x4610, ?x5398), award(?x4610, ?x198) *> conf = 0.12 ranks of expected_values: 84 EVAL 06823p nominated_for! 0fm3b5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 87.000 50.000 0.690 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15714-01jq34 PRED entity: 01jq34 PRED relation: school! PRED expected values: 0jml5 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 89): 01yhm (0.16 #552, 0.14 #463, 0.13 #730), 0jmm4 (0.13 #604, 0.13 #782, 0.11 #515), 051vz (0.13 #555, 0.11 #1356, 0.11 #733), 01slc (0.12 #1568, 0.09 #1746, 0.09 #2013), 07147 (0.11 #509, 0.10 #1577, 0.09 #598), 05xvj (0.11 #618, 0.10 #1597, 0.09 #529), 01d5z (0.11 #1522, 0.09 #1344, 0.09 #543), 01yjl (0.11 #1541, 0.09 #473, 0.09 #1719), 06x68 (0.11 #1519, 0.09 #2142, 0.08 #1964), 0713r (0.11 #1547, 0.09 #2170, 0.08 #1992) >> Best rule #552 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 045c7b; >> query: (?x2171, 01yhm) <- organization(?x2171, ?x5487), citytown(?x2171, ?x8980), state_province_region(?x2171, ?x1767) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #1381 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 97 *> proper extension: 04344j; 03cz83; 0jpn8; 0ghvb; *> query: (?x2171, 0jml5) <- major_field_of_study(?x2171, ?x742), fraternities_and_sororities(?x2171, ?x3697), school_type(?x2171, ?x1507) *> conf = 0.03 ranks of expected_values: 77 EVAL 01jq34 school! 0jml5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 84.000 84.000 0.156 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #15713-04vjh PRED entity: 04vjh PRED relation: official_language PRED expected values: 064_8sq => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.43 #1507, 0.38 #1894, 0.37 #1980), 064_8sq (0.20 #58, 0.19 #1520, 0.18 #617), 06nm1 (0.18 #266, 0.17 #309, 0.14 #610), 05zjd (0.15 #2495, 0.06 #707, 0.05 #922), 04306rv (0.06 #1510, 0.05 #48, 0.05 #91), 071fb (0.05 #55, 0.05 #98, 0.05 #141), 02hwyss (0.04 #30, 0.04 #73, 0.03 #159), 032f6 (0.04 #39, 0.02 #555, 0.02 #512), 0x82 (0.04 #38, 0.01 #1242, 0.01 #1543), 03hkp (0.04 #10, 0.01 #268) >> Best rule #1507 for best value: >> intensional similarity = 2 >> extensional distance = 159 >> proper extension: 03gk2; 03b79; 02psqkz; 01k6y1; 06jnv; 012m_; >> query: (?x10451, 02h40lc) <- official_language(?x10451, ?x5359), service_language(?x610, ?x5359) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 54 *> proper extension: 0366c; *> query: (?x10451, 064_8sq) <- official_language(?x10451, ?x5359), time_zones(?x10451, ?x5327) *> conf = 0.20 ranks of expected_values: 2 EVAL 04vjh official_language 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.435 http://example.org/location/country/official_language #15712-011ypx PRED entity: 011ypx PRED relation: honored_for! PRED expected values: 09qftb => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 119): 09p2r9 (0.10 #4480, 0.05 #199, 0.03 #1410), 03gwpw2 (0.10 #4480, 0.04 #1337, 0.04 #1942), 0275n3y (0.10 #4480, 0.04 #2484, 0.04 #63), 04n2r9h (0.10 #4480, 0.04 #1368, 0.04 #1973), 02yvhx (0.10 #4480, 0.03 #185, 0.03 #548), 0n8_m93 (0.10 #4480, 0.03 #223, 0.01 #1434), 09qftb (0.10 #4480, 0.02 #581, 0.02 #1429), 073h9x (0.10 #4480, 0.02 #1009, 0.01 #403), 019bk0 (0.10 #4480), 02ywhz (0.08 #187, 0.04 #66, 0.02 #429) >> Best rule #4480 for best value: >> intensional similarity = 3 >> extensional distance = 945 >> proper extension: 08cx5g; 0c3xpwy; 03czz87; >> query: (?x5927, ?x1362) <- award_winner(?x5927, ?x989), nominated_for(?x4563, ?x5927), award_winner(?x1362, ?x4563) >> conf = 0.10 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7 EVAL 011ypx honored_for! 09qftb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 73.000 73.000 0.102 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15711-07m4c PRED entity: 07m4c PRED relation: inductee! PRED expected values: 0g2c8 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 4): 0g2c8 (0.56 #55, 0.47 #154, 0.46 #208), 0qjfl (0.12 #39, 0.09 #84, 0.07 #111), 027jbr (0.07 #117, 0.05 #842, 0.01 #333), 06szd3 (0.06 #443, 0.05 #497, 0.05 #506) >> Best rule #55 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 01vsxdm; 01v0sxx; >> query: (?x7544, 0g2c8) <- artists(?x1572, ?x7544), group(?x1473, ?x7544), group(?x716, ?x7544), music(?x1069, ?x7544), ?x716 = 018vs, role(?x74, ?x1473) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07m4c inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.556 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #15710-05r79 PRED entity: 05r79 PRED relation: interests! PRED expected values: 07c37 0420y => 61 concepts (44 used for prediction) PRED predicted values (max 10 best out of 68): 03sbs (0.60 #111, 0.57 #173, 0.45 #92), 01bpn (0.60 #108, 0.57 #170, 0.33 #14), 045bg (0.60 #96, 0.45 #92, 0.43 #158), 015n8 (0.60 #122, 0.45 #92, 0.43 #184), 04hcw (0.50 #78, 0.45 #92, 0.43 #174), 0m93 (0.45 #237, 0.33 #146, 0.31 #326), 03j43 (0.45 #92, 0.40 #98, 0.33 #4), 02ln1 (0.45 #92, 0.33 #23, 0.29 #91), 05qmj (0.45 #92, 0.33 #16, 0.29 #91), 01h2_6 (0.45 #92, 0.33 #29, 0.29 #185) >> Best rule #111 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 04s0m; >> query: (?x1858, 03sbs) <- interests(?x8430, ?x1858), interests(?x5796, ?x1858), interests(?x1857, ?x1858), interests(?x712, ?x1858), taxonomy(?x1858, ?x939), ?x712 = 07kb5, influenced_by(?x10677, ?x8430), religion(?x8430, ?x2694), influenced_by(?x5796, ?x12146), profession(?x10677, ?x353), ?x12146 = 01lwx, ?x1857 = 026lj >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #92 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 0gt_hv; *> query: (?x1858, ?x2080) <- interests(?x8430, ?x1858), interests(?x4033, ?x1858), interests(?x1857, ?x1858), peers(?x3428, ?x8430), influenced_by(?x8430, ?x1236), ?x4033 = 043s3, ?x1857 = 026lj, religion(?x8430, ?x2694), gender(?x8430, ?x231), company(?x8430, ?x5288), religion(?x3428, ?x7131), influenced_by(?x1737, ?x8430), influenced_by(?x3428, ?x2080) *> conf = 0.45 ranks of expected_values: 12, 31 EVAL 05r79 interests! 0420y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 61.000 44.000 0.600 http://example.org/user/alexander/philosophy/philosopher/interests EVAL 05r79 interests! 07c37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 61.000 44.000 0.600 http://example.org/user/alexander/philosophy/philosopher/interests #15709-02xs6_ PRED entity: 02xs6_ PRED relation: nominated_for! PRED expected values: 057xs89 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 213): 02hsq3m (0.43 #268, 0.20 #2180, 0.18 #746), 02z0dfh (0.36 #62, 0.19 #14589, 0.19 #16026), 054krc (0.29 #69, 0.19 #14589, 0.19 #16026), 09qwmm (0.29 #27, 0.19 #16026, 0.19 #14829), 057xs89 (0.29 #359, 0.19 #16026, 0.19 #14829), 09sdmz (0.29 #145, 0.19 #16026, 0.19 #14829), 02g3v6 (0.29 #260, 0.15 #1216, 0.15 #1694), 05ztrmj (0.29 #374, 0.15 #852, 0.12 #3481), 0gq9h (0.26 #4605, 0.23 #3170, 0.23 #10344), 0p9sw (0.24 #2171, 0.21 #3366, 0.18 #737) >> Best rule #268 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 02vqhv0; 03177r; 04ydr95; 0bpm4yw; 0125xq; 0gfh84d; 031786; 02yxbc; 0btpm6; 01xq8v; ... >> query: (?x4991, 02hsq3m) <- film(?x5316, ?x4991), genre(?x4991, ?x53), film_crew_role(?x4991, ?x137), ?x5316 = 01f6zc >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #359 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 02vqhv0; 03177r; 04ydr95; 0bpm4yw; 0125xq; 0gfh84d; 031786; 02yxbc; 0btpm6; 01xq8v; ... *> query: (?x4991, 057xs89) <- film(?x5316, ?x4991), genre(?x4991, ?x53), film_crew_role(?x4991, ?x137), ?x5316 = 01f6zc *> conf = 0.29 ranks of expected_values: 5 EVAL 02xs6_ nominated_for! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 78.000 0.429 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15708-01k98nm PRED entity: 01k98nm PRED relation: artists! PRED expected values: 064t9 => 88 concepts (53 used for prediction) PRED predicted values (max 10 best out of 229): 06by7 (0.77 #9427, 0.56 #11938, 0.48 #336), 064t9 (0.59 #953, 0.58 #327, 0.53 #14), 06j6l (0.34 #50, 0.33 #989, 0.30 #1617), 025sc50 (0.30 #991, 0.30 #52, 0.26 #1619), 0glt670 (0.29 #982, 0.27 #43, 0.25 #5371), 016clz (0.27 #9409, 0.26 #1257, 0.24 #6900), 0gywn (0.26 #373, 0.24 #60, 0.23 #999), 0xhtw (0.25 #9422, 0.20 #6913, 0.19 #9108), 01lyv (0.24 #3484, 0.21 #5364, 0.18 #8812), 03_d0 (0.20 #325, 0.18 #951, 0.18 #2833) >> Best rule #9427 for best value: >> intensional similarity = 3 >> extensional distance = 505 >> proper extension: 0qmpd; >> query: (?x3234, 06by7) <- artists(?x3061, ?x3234), artists(?x3061, ?x6838), ?x6838 = 0130sy >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #953 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 160 *> proper extension: 014hr0; *> query: (?x3234, 064t9) <- award(?x3234, ?x2139), ?x2139 = 01by1l, award_nominee(?x3235, ?x3234) *> conf = 0.59 ranks of expected_values: 2 EVAL 01k98nm artists! 064t9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 53.000 0.767 http://example.org/music/genre/artists #15707-0gyy0 PRED entity: 0gyy0 PRED relation: award PRED expected values: 0bdwqv => 129 concepts (122 used for prediction) PRED predicted values (max 10 best out of 269): 0bdwqv (0.57 #2575, 0.49 #4981, 0.46 #5382), 0bfvd4 (0.43 #2519, 0.33 #4925, 0.31 #5326), 027dtxw (0.43 #2410, 0.26 #4816, 0.25 #4), 09sb52 (0.40 #2445, 0.36 #4851, 0.33 #5252), 02x73k6 (0.40 #2465, 0.20 #59, 0.20 #4871), 0cqh46 (0.37 #2456, 0.25 #4862, 0.23 #5263), 0gq9h (0.26 #1279, 0.22 #477, 0.19 #7294), 02w9sd7 (0.26 #2573, 0.18 #4177, 0.17 #4979), 09sdmz (0.26 #2609, 0.14 #5015, 0.14 #5416), 054ky1 (0.22 #508, 0.15 #38901, 0.13 #1310) >> Best rule #2575 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 02qgqt; 0byfz; 017149; 01yk13; 048lv; 0170pk; 03k7bd; 01ycbq; 0bj9k; 0170qf; ... >> query: (?x8473, 0bdwqv) <- award(?x8473, ?x3066), award(?x8473, ?x458), ?x458 = 0789_m, ?x3066 = 0gqy2 >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gyy0 award 0bdwqv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 122.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15706-0cr3d PRED entity: 0cr3d PRED relation: place_founded! PRED expected values: 0gvbw => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 94): 01_4mn (0.33 #214, 0.20 #325, 0.03 #436), 043g7l (0.33 #142, 0.20 #253, 0.03 #364), 0dq23 (0.33 #203, 0.20 #314, 0.03 #425), 01ynvx (0.33 #201, 0.20 #312, 0.03 #423), 01hlwv (0.33 #190, 0.20 #301, 0.03 #412), 032j_n (0.33 #177, 0.20 #288, 0.03 #399), 01dfb6 (0.33 #169, 0.20 #280, 0.03 #391), 0xwj (0.33 #140, 0.20 #251, 0.03 #362), 01xdn1 (0.33 #121, 0.20 #232, 0.03 #343), 024rgt (0.33 #120, 0.20 #231, 0.03 #342) >> Best rule #214 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02_286; >> query: (?x2850, 01_4mn) <- location(?x7823, ?x2850), location(?x4712, ?x2850), ?x4712 = 03f0fnk, film(?x7823, ?x2529) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cr3d place_founded! 0gvbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 114.000 0.333 http://example.org/organization/organization/place_founded #15705-093l8p PRED entity: 093l8p PRED relation: currency PRED expected values: 09nqf => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.79 #36, 0.78 #50, 0.77 #134), 01nv4h (0.04 #16, 0.03 #142, 0.03 #51), 02l6h (0.02 #144, 0.02 #81, 0.01 #18), 02gsvk (0.01 #175) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 06fqlk; >> query: (?x7584, 09nqf) <- film_crew_role(?x7584, ?x137), honored_for(?x7573, ?x7584), featured_film_locations(?x7584, ?x3832), genre(?x7584, ?x53) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 093l8p currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.786 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #15704-0693l PRED entity: 0693l PRED relation: profession PRED expected values: 0dgd_ => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 84): 0dgd_ (0.88 #2202, 0.86 #1767, 0.27 #897), 03gjzk (0.56 #1896, 0.50 #1606, 0.50 #736), 0kyk (0.47 #3216, 0.44 #2491, 0.30 #12181), 018gz8 (0.39 #1173, 0.37 #2623, 0.31 #1028), 02krf9 (0.33 #1908, 0.25 #3793, 0.23 #6403), 09jwl (0.28 #1030, 0.26 #1175, 0.22 #5670), 0d1pc (0.21 #3962, 0.17 #5557, 0.17 #7297), 0np9r (0.20 #742, 0.19 #2627, 0.19 #3352), 02hv44_ (0.19 #634, 0.19 #2809, 0.18 #199), 0nbcg (0.19 #1043, 0.18 #1188, 0.14 #4233) >> Best rule #2202 for best value: >> intensional similarity = 2 >> extensional distance = 55 >> proper extension: 0280mv7; 04cw0n4; 026sb55; >> query: (?x3117, 0dgd_) <- cinematography(?x814, ?x3117), nationality(?x3117, ?x94) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0693l profession 0dgd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.877 http://example.org/people/person/profession #15703-059rby PRED entity: 059rby PRED relation: origin! PRED expected values: 01sbf2 0892sx => 172 concepts (167 used for prediction) PRED predicted values (max 10 best out of 484): 0g824 (0.25 #790, 0.20 #1305, 0.20 #62322), 05qw5 (0.25 #583, 0.20 #1098, 0.20 #62322), 06nv27 (0.25 #218, 0.20 #1247, 0.15 #2276), 05crg7 (0.25 #564, 0.20 #1079, 0.10 #3138), 0892sx (0.25 #611, 0.20 #1126, 0.09 #3701), 04n2vgk (0.25 #925, 0.20 #1440, 0.09 #4015), 0146pg (0.25 #531, 0.20 #1046, 0.06 #78302), 02rn_bj (0.25 #874, 0.20 #1389, 0.06 #59227), 01wv9p (0.25 #168, 0.20 #1197, 0.05 #9438), 0d193h (0.25 #172, 0.20 #1201, 0.05 #9442) >> Best rule #790 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 02_286; 0r0m6; >> query: (?x335, 0g824) <- location(?x8927, ?x335), contains(?x335, ?x322), ?x8927 = 01pllx >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #611 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 02_286; 0r0m6; *> query: (?x335, 0892sx) <- location(?x8927, ?x335), contains(?x335, ?x322), ?x8927 = 01pllx *> conf = 0.25 ranks of expected_values: 5 EVAL 059rby origin! 0892sx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 172.000 167.000 0.250 http://example.org/music/artist/origin EVAL 059rby origin! 01sbf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 167.000 0.250 http://example.org/music/artist/origin #15702-01rwcgb PRED entity: 01rwcgb PRED relation: profession PRED expected values: 0dz3r 09jwl => 135 concepts (66 used for prediction) PRED predicted values (max 10 best out of 61): 09jwl (0.97 #4547, 0.80 #3085, 0.78 #749), 016z4k (0.61 #1464, 0.60 #1318, 0.55 #2194), 01d_h8 (0.60 #298, 0.42 #1612, 0.40 #1758), 0dz3r (0.55 #1316, 0.55 #4384, 0.55 #1462), 01c72t (0.50 #24, 0.40 #170, 0.36 #3674), 039v1 (0.48 #766, 0.46 #912, 0.42 #1058), 0dxtg (0.40 #306, 0.23 #3958, 0.23 #1620), 01c8w0 (0.38 #593, 0.25 #447, 0.25 #9), 0n1h (0.31 #888, 0.30 #742, 0.26 #1034), 0fnpj (0.28 #4441, 0.25 #2103, 0.25 #2249) >> Best rule #4547 for best value: >> intensional similarity = 5 >> extensional distance = 168 >> proper extension: 01wg3q; 01w9k25; >> query: (?x10591, 09jwl) <- instrumentalists(?x227, ?x10591), profession(?x10591, ?x6476), origin(?x10591, ?x4335), profession(?x8661, ?x6476), ?x8661 = 02fgp0 >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 01rwcgb profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 66.000 0.971 http://example.org/people/person/profession EVAL 01rwcgb profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 135.000 66.000 0.971 http://example.org/people/person/profession #15701-01yznp PRED entity: 01yznp PRED relation: type_of_union PRED expected values: 04ztj => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.74 #257, 0.74 #125, 0.74 #249), 01g63y (0.33 #2, 0.18 #54, 0.16 #134), 01bl8s (0.01 #119) >> Best rule #257 for best value: >> intensional similarity = 3 >> extensional distance = 722 >> proper extension: 0lrh; 082_p; 06y7d; >> query: (?x425, 04ztj) <- profession(?x425, ?x353), award(?x425, ?x7041), religion(?x425, ?x2694) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01yznp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.744 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15700-0d234 PRED entity: 0d234 PRED relation: contains PRED expected values: 01hx2t => 139 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2267): 02sjgpq (0.09 #179751, 0.07 #94288, 0.06 #53037), 01hx2t (0.09 #179751, 0.07 #94288, 0.06 #53037), 015zyd (0.09 #179751, 0.07 #94288, 0.06 #53037), 08qnnv (0.09 #179751, 0.07 #94288, 0.04 #120810), 04x8mj (0.08 #13302, 0.07 #4464, 0.03 #69289), 02bhj4 (0.08 #12769, 0.07 #3931, 0.03 #68756), 02yr3z (0.08 #12712, 0.07 #3874, 0.03 #68699), 07tds (0.08 #12383, 0.07 #3545, 0.03 #68370), 017y26 (0.08 #12332, 0.07 #3494, 0.03 #68319), 02htv6 (0.08 #13819, 0.07 #4981, 0.02 #69806) >> Best rule #179751 for best value: >> intensional similarity = 4 >> extensional distance = 245 >> proper extension: 022_6; 020skc; 09d4_; 0125q1; 09f8q; 0fcrg; >> query: (?x2622, ?x6315) <- contains(?x726, ?x2622), country(?x2622, ?x94), location(?x1400, ?x2622), student(?x6315, ?x1400) >> conf = 0.09 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0d234 contains 01hx2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 139.000 87.000 0.092 http://example.org/location/location/contains #15699-01k5zk PRED entity: 01k5zk PRED relation: film PRED expected values: 0jqn5 => 99 concepts (50 used for prediction) PRED predicted values (max 10 best out of 604): 027r9t (0.09 #1246, 0.05 #8394, 0.05 #4820), 03bzjpm (0.06 #3101, 0.06 #1314, 0.06 #4888), 01l_pn (0.06 #2753, 0.05 #6327, 0.05 #8114), 06z8s_ (0.06 #1917, 0.05 #3704, 0.04 #9066), 06_wqk4 (0.06 #14424, 0.06 #3701, 0.05 #16211), 03bx2lk (0.06 #185, 0.05 #1972, 0.04 #9121), 03s6l2 (0.06 #83, 0.05 #1870, 0.04 #7231), 0f40w (0.06 #361, 0.05 #2148, 0.03 #5722), 02825cv (0.06 #1141, 0.05 #4715, 0.04 #6502), 040_lv (0.06 #1046, 0.03 #4620, 0.03 #6407) >> Best rule #1246 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 05m63c; 01q_ph; 01pcq3; 0lk90; 0sz28; 0j1yf; 03rl84; 07ss8_; 01vs_v8; 05dbf; ... >> query: (?x3585, 027r9t) <- participant(?x2035, ?x3585), profession(?x3585, ?x1032), languages(?x3585, ?x254) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #2008 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 014zcr; 01dw4q; 01n5309; 018db8; 01lbp; 0151w_; 0456xp; 01ztgm; 01pw2f1; 015pkc; ... *> query: (?x3585, 0jqn5) <- participant(?x2035, ?x3585), profession(?x3585, ?x1032), vacationer(?x291, ?x3585) *> conf = 0.02 ranks of expected_values: 429 EVAL 01k5zk film 0jqn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 99.000 50.000 0.091 http://example.org/film/actor/film./film/performance/film #15698-02m97n PRED entity: 02m97n PRED relation: citytown PRED expected values: 04jpl 09bkv => 103 concepts (47 used for prediction) PRED predicted values (max 10 best out of 497): 09bkv (0.94 #8517, 0.33 #231, 0.25 #967), 04jpl (0.93 #6670, 0.49 #10004, 0.32 #13337), 02_286 (0.82 #12233, 0.64 #13716, 0.62 #14086), 030qb3t (0.70 #7433, 0.29 #1104, 0.07 #2618), 0d9jr (0.50 #3449, 0.50 #3079, 0.25 #4558), 05l5n (0.49 #8183, 0.32 #10034, 0.21 #13367), 0r04p (0.47 #2695, 0.21 #4916, 0.18 #6027), 0f2rq (0.31 #1972, 0.12 #4566, 0.04 #11233), 052bw (0.30 #1669, 0.17 #3893, 0.06 #7968), 0nbrp (0.29 #1104, 0.04 #3699, 0.04 #3329) >> Best rule #8517 for best value: >> intensional similarity = 7 >> extensional distance = 49 >> proper extension: 018mxj; >> query: (?x14743, ?x10042) <- citytown(?x14743, ?x13447), country(?x13447, ?x512), citytown(?x8294, ?x13447), citytown(?x8294, ?x10042), ?x512 = 07ssc, contains(?x362, ?x13447), place_of_birth(?x361, ?x362) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02m97n citytown 09bkv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 47.000 0.944 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 02m97n citytown 04jpl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 47.000 0.944 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #15697-02pdhz PRED entity: 02pdhz PRED relation: school! PRED expected values: 0jmj7 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 94): 0jmj7 (0.45 #29, 0.39 #3792, 0.38 #5014), 05m_8 (0.19 #285, 0.09 #3, 0.07 #3672), 04wmvz (0.19 #362, 0.06 #1396, 0.04 #2430), 051vz (0.18 #23, 0.12 #117, 0.11 #305), 06rny (0.18 #51, 0.12 #145, 0.08 #239), 0jm64 (0.18 #55, 0.07 #337, 0.06 #149), 0jm4b (0.18 #50, 0.06 #144, 0.04 #238), 02d02 (0.12 #164, 0.11 #352, 0.09 #70), 0jm6n (0.12 #137, 0.09 #43, 0.08 #231), 0cqt41 (0.11 #300, 0.09 #18, 0.07 #488) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01xcgf; >> query: (?x12667, 0jmj7) <- school_type(?x12667, ?x3205), state_province_region(?x12667, ?x760), ?x760 = 05fkf >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pdhz school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.455 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #15696-04znsy PRED entity: 04znsy PRED relation: profession PRED expected values: 0kyk => 124 concepts (95 used for prediction) PRED predicted values (max 10 best out of 55): 0dxtg (0.54 #1337, 0.53 #1484, 0.53 #1043), 01d_h8 (0.44 #6, 0.34 #1917, 0.34 #5592), 09jwl (0.43 #2369, 0.38 #2957, 0.37 #3104), 016z4k (0.32 #2356, 0.28 #2944, 0.27 #3091), 0nbcg (0.30 #2382, 0.27 #2970, 0.26 #6645), 0np9r (0.30 #1048, 0.29 #1489, 0.28 #1342), 02krf9 (0.29 #172, 0.14 #1495, 0.14 #1054), 0dz3r (0.29 #2354, 0.26 #2942, 0.24 #3089), 0d1pc (0.25 #11909, 0.17 #2254, 0.14 #343), 0n1h (0.25 #11909, 0.16 #2364, 0.14 #2952) >> Best rule #1337 for best value: >> intensional similarity = 4 >> extensional distance = 267 >> proper extension: 0dszr0; >> query: (?x9211, 0dxtg) <- profession(?x9211, ?x1146), profession(?x9211, ?x1032), ?x1032 = 02hrh1q, ?x1146 = 018gz8 >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #11909 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1673 *> proper extension: 0584j4n; 06mm1x; *> query: (?x9211, ?x220) <- award_nominee(?x2614, ?x9211), nominated_for(?x9211, ?x2742), profession(?x2614, ?x220) *> conf = 0.25 ranks of expected_values: 11 EVAL 04znsy profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 124.000 95.000 0.535 http://example.org/people/person/profession #15695-0mpdw PRED entity: 0mpdw PRED relation: contains PRED expected values: 0mnm2 => 191 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1974): 0mp3l (0.78 #94320, 0.78 #106112, 0.76 #50112), 0g8rj (0.42 #44216, 0.36 #47164, 0.10 #716), 05qgd9 (0.42 #44216, 0.36 #47164, 0.10 #2033), 0qplq (0.11 #22802, 0.09 #31645, 0.09 #8061), 0qpsn (0.11 #22778, 0.09 #31621, 0.09 #8037), 0qpjt (0.11 #21943, 0.09 #30786, 0.09 #7202), 0dzt9 (0.10 #1478, 0.10 #120856, 0.08 #129702), 0mnzd (0.10 #158, 0.06 #14899, 0.06 #20795), 02zr0z (0.10 #2378, 0.06 #17119, 0.06 #23015), 01stj9 (0.10 #2256, 0.06 #16997, 0.06 #22893) >> Best rule #94320 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 0h7x; 0fqyc; 0f1_p; 01qtj9; 07kg3; 0d8rs; 03p7r; 068cn; 06nrt; 0g39h; ... >> query: (?x9460, ?x2298) <- administrative_parent(?x9460, ?x1426), adjoins(?x5998, ?x9460), administrative_division(?x2298, ?x9460), country(?x2298, ?x94) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1050 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01gpzx; *> query: (?x9460, 0mnm2) <- administrative_parent(?x9460, ?x1426), administrative_division(?x2298, ?x9460), adjoins(?x9460, ?x5998), second_level_divisions(?x94, ?x2298) *> conf = 0.10 ranks of expected_values: 18 EVAL 0mpdw contains 0mnm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 191.000 82.000 0.783 http://example.org/location/location/contains #15694-0pz91 PRED entity: 0pz91 PRED relation: people! PRED expected values: 041rx => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 46): 041rx (0.24 #1621, 0.22 #1005, 0.21 #928), 0xnvg (0.21 #90, 0.21 #167, 0.11 #475), 0x67 (0.18 #2243, 0.15 #780, 0.14 #87), 048z7l (0.14 #40, 0.08 #1041, 0.07 #579), 07hwkr (0.14 #12, 0.07 #89, 0.05 #166), 013xrm (0.14 #20, 0.06 #1868, 0.05 #2484), 03bkbh (0.14 #32, 0.03 #2958, 0.03 #3189), 06mvq (0.14 #34), 033tf_ (0.14 #1778, 0.13 #1932, 0.11 #3164), 07bch9 (0.11 #870, 0.10 #1409, 0.09 #639) >> Best rule #1621 for best value: >> intensional similarity = 2 >> extensional distance = 131 >> proper extension: 02x8kk; >> query: (?x1335, 041rx) <- location(?x1335, ?x2850), ?x2850 = 0cr3d >> conf = 0.24 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pz91 people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.241 http://example.org/people/ethnicity/people #15693-026g4l_ PRED entity: 026g4l_ PRED relation: award_nominee PRED expected values: 016yzz => 113 concepts (45 used for prediction) PRED predicted values (max 10 best out of 874): 03m9c8 (0.81 #18692, 0.80 #81783, 0.77 #9347), 016yzz (0.81 #18692, 0.80 #81783, 0.77 #9347), 026g4l_ (0.36 #3689, 0.18 #93471, 0.18 #95810), 03v1w7 (0.29 #1471, 0.02 #15490, 0.02 #22500), 05qd_ (0.27 #2519, 0.18 #93471, 0.18 #95810), 07h07 (0.18 #93471, 0.18 #3253, 0.18 #95810), 061dn_ (0.18 #93471, 0.18 #3116, 0.18 #95810), 0yfp (0.18 #93471, 0.18 #2538, 0.18 #95810), 01vhrz (0.18 #93471, 0.18 #4340, 0.18 #95810), 01j2xj (0.18 #93471, 0.18 #3501, 0.18 #95810) >> Best rule #18692 for best value: >> intensional similarity = 4 >> extensional distance = 149 >> proper extension: 01yhvv; 04rsd2; 0408np; 03_6y; 0bqdvt; 01z7s_; 01qrbf; >> query: (?x5714, ?x163) <- award_nominee(?x6534, ?x5714), award_nominee(?x163, ?x5714), award(?x6534, ?x7850), ?x7850 = 07kjk7c >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 026g4l_ award_nominee 016yzz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 45.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15692-06_bq1 PRED entity: 06_bq1 PRED relation: nationality PRED expected values: 09c7w0 => 136 concepts (134 used for prediction) PRED predicted values (max 10 best out of 79): 09c7w0 (0.81 #2116, 0.79 #6732, 0.77 #1916), 02jx1 (0.36 #10853, 0.15 #941, 0.12 #2749), 07ssc (0.36 #10853, 0.12 #15, 0.10 #7047), 01n7q (0.33 #13169, 0.32 #10049), 0chghy (0.07 #110, 0.04 #716, 0.04 #1018), 0d060g (0.05 #10458, 0.05 #6938, 0.05 #2122), 03rk0 (0.05 #13315, 0.05 #13215, 0.05 #13415), 03rjj (0.04 #1114, 0.03 #1415, 0.03 #410), 0ctw_b (0.04 #203, 0.03 #12159, 0.03 #11654), 06f32 (0.04 #203, 0.03 #12159, 0.03 #11654) >> Best rule #2116 for best value: >> intensional similarity = 3 >> extensional distance = 175 >> proper extension: 01wk7b7; 01pfkw; 03kxp7; >> query: (?x7046, 09c7w0) <- participant(?x7046, ?x10053), profession(?x7046, ?x4773), actor(?x2191, ?x7046) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06_bq1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 134.000 0.808 http://example.org/people/person/nationality #15691-07s846j PRED entity: 07s846j PRED relation: award PRED expected values: 03hkv_r => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 214): 063y_ky (0.34 #218, 0.25 #219, 0.25 #87), 03hkv_r (0.34 #218, 0.25 #13, 0.24 #873), 0gq9h (0.34 #218, 0.24 #873, 0.24 #1310), 09qv_s (0.34 #218, 0.24 #873, 0.24 #1310), 040njc (0.34 #218, 0.24 #873, 0.24 #1310), 0f4x7 (0.34 #218, 0.24 #873, 0.24 #1310), 099tbz (0.34 #218, 0.24 #873, 0.24 #1310), 04kxsb (0.34 #218, 0.24 #873, 0.24 #1310), 099ck7 (0.34 #218, 0.24 #873, 0.24 #1310), 0p9sw (0.34 #218, 0.24 #873, 0.24 #1310) >> Best rule #218 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 07gp9; 0fpv_3_; 05c46y6; 0bmhvpr; 0hfzr; 011ykb; >> query: (?x4047, ?x112) <- nominated_for(?x2456, ?x4047), nominated_for(?x1703, ?x4047), nominated_for(?x112, ?x4047), ?x2456 = 063y_ky, film_crew_role(?x4047, ?x468), ?x1703 = 0k611 >> conf = 0.34 => this is the best rule for 17 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07s846j award 03hkv_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 104.000 0.342 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #15690-0bymv PRED entity: 0bymv PRED relation: religion PRED expected values: 02rsw => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 33): 0c8wxp (0.56 #1265, 0.54 #1097, 0.40 #1559), 07y1z (0.29 #250, 0.25 #334, 0.25 #124), 03_gx (0.28 #3211, 0.21 #3506, 0.18 #3041), 02rsw (0.25 #316, 0.21 #484, 0.17 #1072), 051kv (0.25 #88, 0.20 #382, 0.15 #802), 0631_ (0.21 #469, 0.20 #1519, 0.18 #1393), 03j6c (0.19 #565, 0.08 #1868, 0.08 #3600), 0kpl (0.17 #3590, 0.16 #3293, 0.16 #1984), 07x21 (0.14 #455, 0.10 #1337, 0.10 #833), 0n2g (0.12 #305, 0.07 #1313, 0.06 #2028) >> Best rule #1265 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 036jp8; 019n7x; >> query: (?x2357, 0c8wxp) <- celebrities_impersonated(?x5915, ?x2357), nationality(?x2357, ?x94), ?x94 = 09c7w0, religion(?x2357, ?x962) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #316 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0d1_f; *> query: (?x2357, 02rsw) <- basic_title(?x2357, ?x2358), location_of_ceremony(?x2357, ?x2254), jurisdiction_of_office(?x2357, ?x94), religion(?x2357, ?x962) *> conf = 0.25 ranks of expected_values: 4 EVAL 0bymv religion 02rsw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 145.000 0.564 http://example.org/people/person/religion #15689-0h6r5 PRED entity: 0h6r5 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #94, 0.82 #21, 0.82 #222), 07c52 (0.04 #3, 0.03 #219, 0.03 #69), 07z4p (0.03 #10, 0.03 #71, 0.03 #35), 02nxhr (0.03 #95, 0.03 #22, 0.03 #125) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 683 >> proper extension: 0192hw; 0g5q34q; 0gh6j94; >> query: (?x4093, 029j_) <- genre(?x4093, ?x53), featured_film_locations(?x4093, ?x739), language(?x4093, ?x90) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h6r5 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.832 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15688-02d02 PRED entity: 02d02 PRED relation: season PRED expected values: 025ygws => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 2): 025ygws (0.85 #35, 0.83 #19, 0.81 #41), 04n36qk (0.25 #4, 0.14 #24, 0.14 #12) >> Best rule #35 for best value: >> intensional similarity = 11 >> extensional distance = 18 >> proper extension: 06x68; >> query: (?x8894, 025ygws) <- school(?x8894, ?x5486), team(?x2010, ?x8894), draft(?x8894, ?x1161), colors(?x5486, ?x3315), major_field_of_study(?x5486, ?x254), school(?x465, ?x5486), season(?x8894, ?x9498), student(?x5486, ?x118), ?x9498 = 027pwzc, organization(?x346, ?x5486), teams(?x479, ?x8894) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d02 season 025ygws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.850 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #15687-02wh0 PRED entity: 02wh0 PRED relation: student! PRED expected values: 0m7yh 01y06y => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 260): 01stzp (0.43 #5781, 0.24 #11051, 0.20 #16324), 07tgn (0.25 #5814, 0.20 #6341, 0.07 #25319), 0dy04 (0.20 #11138, 0.17 #1125, 0.14 #4814), 0277jc (0.19 #13176, 0.16 #20030, 0.16 #15813), 01y06y (0.17 #3657, 0.17 #2603, 0.17 #1549), 07tg4 (0.17 #2194, 0.17 #1667, 0.14 #4302), 03ksy (0.17 #2214, 0.17 #1160, 0.10 #30151), 01wqg8 (0.17 #2433, 0.17 #1379, 0.07 #8230), 07tk7 (0.17 #2023, 0.14 #4658, 0.09 #7820), 01tpvt (0.17 #2869, 0.14 #8666, 0.07 #9720) >> Best rule #5781 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0l9k1; >> query: (?x11097, 01stzp) <- people(?x5540, ?x11097), nationality(?x11097, ?x1264), religion(?x11097, ?x2694), ?x1264 = 0345h, people(?x6260, ?x11097) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3657 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 026c1; 07bsj; *> query: (?x11097, 01y06y) <- people(?x5540, ?x11097), nationality(?x11097, ?x1264), diet(?x11097, ?x3130), gender(?x11097, ?x231), ?x5540 = 013xrm *> conf = 0.17 ranks of expected_values: 5, 47 EVAL 02wh0 student! 01y06y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 201.000 201.000 0.429 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 02wh0 student! 0m7yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 201.000 201.000 0.429 http://example.org/education/educational_institution/students_graduates./education/education/student #15686-02fqrf PRED entity: 02fqrf PRED relation: production_companies PRED expected values: 02hvd => 88 concepts (60 used for prediction) PRED predicted values (max 10 best out of 63): 03xq0f (0.34 #2090, 0.33 #2761, 0.32 #3941), 086k8 (0.34 #2090, 0.33 #2761, 0.32 #3941), 046b0s (0.18 #107, 0.06 #1445, 0.06 #607), 09b3v (0.15 #1537, 0.06 #33, 0.05 #952), 01795t (0.14 #1526, 0.14 #105, 0.05 #271), 05qd_ (0.14 #93, 0.12 #509, 0.12 #10), 0kk9v (0.14 #118, 0.05 #1539, 0.03 #201), 04f525m (0.12 #11, 0.03 #427, 0.02 #763), 024rgt (0.11 #108, 0.06 #25, 0.05 #274), 016tw3 (0.11 #1601, 0.10 #2186, 0.08 #2522) >> Best rule #2090 for best value: >> intensional similarity = 4 >> extensional distance = 299 >> proper extension: 011yxg; 01ln5z; 0170_p; 09p35z; 03ckwzc; 0b73_1d; 02rqwhl; 07y9w5; 0340hj; 01pgp6; ... >> query: (?x3498, ?x382) <- film(?x382, ?x3498), film_crew_role(?x3498, ?x137), genre(?x3498, ?x53), category(?x3498, ?x134) >> conf = 0.34 => this is the best rule for 2 predicted values *> Best rule #371 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 0ds11z; 0dsvzh; 02hxhz; 05h43ls; 05c5z8j; 03bzyn4; *> query: (?x3498, 02hvd) <- film(?x382, ?x3498), film_crew_role(?x3498, ?x137), film_distribution_medium(?x3498, ?x81), written_by(?x3498, ?x7106) *> conf = 0.04 ranks of expected_values: 29 EVAL 02fqrf production_companies 02hvd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 88.000 60.000 0.337 http://example.org/film/film/production_companies #15685-085h1 PRED entity: 085h1 PRED relation: category PRED expected values: 08mbj5d => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #115, 0.83 #114, 0.83 #109) >> Best rule #115 for best value: >> intensional similarity = 6 >> extensional distance = 573 >> proper extension: 02185j; >> query: (?x7695, ?x134) <- citytown(?x7695, ?x10610), citytown(?x4031, ?x10610), major_field_of_study(?x4031, ?x2606), institution(?x1526, ?x4031), category(?x4031, ?x134), organization(?x4095, ?x4031) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 085h1 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.833 http://example.org/common/topic/webpage./common/webpage/category #15684-01nzz8 PRED entity: 01nzz8 PRED relation: profession PRED expected values: 018gz8 0np9r => 108 concepts (90 used for prediction) PRED predicted values (max 10 best out of 57): 03gjzk (0.32 #2250, 0.32 #2101, 0.24 #2847), 01d_h8 (0.32 #2689, 0.31 #5818, 0.31 #3732), 0dxtg (0.31 #3740, 0.30 #2100, 0.30 #2249), 02jknp (0.23 #157, 0.21 #2691, 0.21 #902), 0np9r (0.22 #1362, 0.21 #1809, 0.20 #1958), 018gz8 (0.19 #17, 0.14 #3296, 0.14 #762), 09jwl (0.18 #4639, 0.17 #7470, 0.17 #6427), 0cbd2 (0.17 #1646, 0.16 #305, 0.16 #901), 02krf9 (0.14 #2262, 0.14 #2113, 0.10 #3753), 0nbcg (0.12 #6440, 0.11 #7930, 0.11 #9867) >> Best rule #2250 for best value: >> intensional similarity = 3 >> extensional distance = 840 >> proper extension: 0kcdl; >> query: (?x5523, 03gjzk) <- nominated_for(?x5523, ?x5386), actor(?x5386, ?x300), award_winner(?x5386, ?x2307) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1362 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 549 *> proper extension: 059xvg; 02_wxh; 0dxmyh; 07gknc; *> query: (?x5523, 0np9r) <- gender(?x5523, ?x231), actor(?x8627, ?x5523), ?x231 = 05zppz *> conf = 0.22 ranks of expected_values: 5, 6 EVAL 01nzz8 profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 90.000 0.324 http://example.org/people/person/profession EVAL 01nzz8 profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 90.000 0.324 http://example.org/people/person/profession #15683-01gbn6 PRED entity: 01gbn6 PRED relation: award_nominee PRED expected values: 01pgzn_ => 110 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1297): 0170s4 (0.81 #56100, 0.81 #81815, 0.81 #49087), 050zr4 (0.81 #56100, 0.81 #81815, 0.81 #49087), 03n_7k (0.13 #520, 0.13 #12205, 0.05 #74803), 06dv3 (0.13 #43, 0.11 #11728, 0.06 #4717), 031k24 (0.13 #1796, 0.09 #13481, 0.03 #6470), 0gy6z9 (0.13 #742, 0.07 #12427, 0.04 #3079), 0151w_ (0.13 #206, 0.07 #11891, 0.03 #60981), 0184jc (0.13 #5, 0.07 #11690, 0.03 #4679), 034zc0 (0.13 #1364, 0.05 #13049, 0.02 #87854), 0154qm (0.09 #12422, 0.06 #5411, 0.05 #10085) >> Best rule #56100 for best value: >> intensional similarity = 3 >> extensional distance = 581 >> proper extension: 0f830f; 03_wpf; 02fgm7; 03h3vtz; >> query: (?x9526, ?x2353) <- award_winner(?x78, ?x9526), film(?x9526, ?x518), award_nominee(?x2353, ?x9526) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #500 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 0785v8; 048lv; 0170pk; 01fh9; 0170qf; 0170s4; 0blq0z; 0flw6; 015c4g; 03ym1; *> query: (?x9526, 01pgzn_) <- award(?x9526, ?x3066), award(?x9526, ?x2853), ?x2853 = 09qv_s, ?x3066 = 0gqy2 *> conf = 0.09 ranks of expected_values: 31 EVAL 01gbn6 award_nominee 01pgzn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 110.000 50.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15682-01vx2h PRED entity: 01vx2h PRED relation: film_crew_role! PRED expected values: 03mh94 0bscw 05sxzwc 0fpkhkz 01hvjx 0cc5mcj 05q54f5 0b1y_2 0x25q 04tqtl 04grkmd 02w86hz 08fn5b 0c38gj 080lkt7 0fqt1ns 0dfw0 09r94m 03cd0x 0d4htf 0241y7 0gj96ln 03xf_m 03hxsv 047bynf 063fh9 0bl3nn 031786 07xvf 02fj8n 0btpm6 02q0k7v 0dkv90 0ndsl1x 0dc7hc 0crd8q6 0466s8n => 25 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1210): 07xvf (0.71 #11603, 0.70 #13425, 0.60 #8870), 0ds33 (0.71 #10970, 0.70 #12792, 0.60 #8237), 0639bg (0.71 #10353, 0.60 #13086, 0.60 #8531), 0c3z0 (0.71 #12359, 0.60 #8715, 0.57 #10537), 06_x996 (0.71 #12200, 0.60 #8556, 0.57 #10378), 03ckwzc (0.71 #11911, 0.60 #8267, 0.57 #10089), 09txzv (0.71 #11985, 0.60 #8341, 0.57 #10163), 05zpghd (0.71 #12345, 0.57 #10523, 0.50 #13256), 0b1y_2 (0.71 #12097, 0.57 #10275, 0.50 #7542), 0466s8n (0.71 #12690, 0.57 #10868, 0.50 #13601) >> Best rule #11603 for best value: >> intensional similarity = 25 >> extensional distance = 5 >> proper extension: 02rh1dz; 01pvkk; >> query: (?x2154, 07xvf) <- film_crew_role(?x10397, ?x2154), film_crew_role(?x9133, ?x2154), film_crew_role(?x6615, ?x2154), film_crew_role(?x4047, ?x2154), film_crew_role(?x3606, ?x2154), film_crew_role(?x3455, ?x2154), film_crew_role(?x2896, ?x2154), film_crew_role(?x1430, ?x2154), film_crew_role(?x1372, ?x2154), film_crew_role(?x936, ?x2154), country(?x6615, ?x94), film(?x1522, ?x6615), ?x3606 = 0gh65c5, award(?x4047, ?x289), film_release_region(?x4047, ?x1499), film_release_region(?x4047, ?x410), genre(?x936, ?x225), story_by(?x2896, ?x8753), ?x1499 = 01znc_, language(?x1430, ?x254), nominated_for(?x1307, ?x9133), ?x1372 = 01kff7, ?x10397 = 085wqm, ?x410 = 01ls2, honored_for(?x2220, ?x3455) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9, 10, 25, 35, 39, 45, 65, 67, 71, 94, 145, 156, 182, 184, 191, 200, 202, 225, 262, 276, 282, 290, 291, 302, 488, 511, 634, 638, 668, 673, 680, 681, 698, 821, 842, 846 EVAL 01vx2h film_crew_role! 0466s8n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0crd8q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0dc7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0ndsl1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0dkv90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 02q0k7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 02fj8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 07xvf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 031786 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0bl3nn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 063fh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 047bynf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 03hxsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 03xf_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0gj96ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0241y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0d4htf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 03cd0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 09r94m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0dfw0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0fqt1ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 080lkt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0c38gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 08fn5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 02w86hz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 04grkmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 04tqtl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0x25q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0b1y_2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 05q54f5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0cc5mcj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 01hvjx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0fpkhkz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 05sxzwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 0bscw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01vx2h film_crew_role! 03mh94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 25.000 15.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15681-08nvyr PRED entity: 08nvyr PRED relation: film! PRED expected values: 04f525m => 81 concepts (49 used for prediction) PRED predicted values (max 10 best out of 49): 02z2xdf (0.36 #148, 0.16 #1335, 0.10 #2978), 03qmx_f (0.36 #148, 0.16 #1335, 0.10 #2978), 086k8 (0.22 #3422, 0.19 #300, 0.19 #226), 016tw3 (0.19 #3430, 0.18 #308, 0.15 #234), 016tt2 (0.18 #228, 0.18 #154, 0.17 #375), 01gb54 (0.17 #472, 0.17 #100, 0.07 #766), 03xq0f (0.17 #450, 0.13 #78, 0.10 #229), 017s11 (0.17 #3423, 0.14 #742, 0.13 #1340), 02pq9yv (0.16 #1335, 0.10 #2978, 0.09 #147), 0c6qh (0.16 #1335, 0.10 #2978, 0.09 #147) >> Best rule #148 for best value: >> intensional similarity = 5 >> extensional distance = 58 >> proper extension: 025x1t; >> query: (?x4541, ?x2689) <- nominated_for(?x2689, ?x4541), award_nominee(?x2689, ?x5527), award_nominee(?x2689, ?x4552), ?x4552 = 030_3z, nominated_for(?x5527, ?x1135) >> conf = 0.36 => this is the best rule for 2 predicted values *> Best rule #3429 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1094 *> proper extension: 0cks1m; *> query: (?x4541, 04f525m) <- film(?x4533, ?x4541), production_companies(?x4534, ?x4533), category(?x4533, ?x134) *> conf = 0.02 ranks of expected_values: 33 EVAL 08nvyr film! 04f525m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 81.000 49.000 0.358 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15680-03jht PRED entity: 03jht PRED relation: influenced_by PRED expected values: 05qmj => 122 concepts (41 used for prediction) PRED predicted values (max 10 best out of 318): 03f0324 (0.50 #151, 0.16 #2732, 0.14 #3593), 03sbs (0.48 #7101, 0.41 #4521, 0.36 #651), 06myp (0.36 #801, 0.28 #1662, 0.26 #2952), 02lt8 (0.36 #549, 0.28 #1840, 0.24 #3129), 081k8 (0.33 #1876, 0.29 #1015, 0.29 #3165), 05qmj (0.27 #621, 0.27 #7071, 0.23 #4491), 0379s (0.27 #508, 0.17 #1799, 0.17 #1369), 01tz6vs (0.27 #605, 0.17 #1466, 0.14 #4475), 03jxw (0.26 #2918, 0.12 #337, 0.11 #1628), 04xjp (0.25 #57, 0.24 #917, 0.22 #1778) >> Best rule #151 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 07h1q; >> query: (?x9308, 03f0324) <- influenced_by(?x9308, ?x12259), influenced_by(?x9308, ?x8768), ?x8768 = 07dnx, religion(?x12259, ?x2694), influenced_by(?x12259, ?x3712) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #621 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 02wh0; *> query: (?x9308, 05qmj) <- nationality(?x9308, ?x774), influenced_by(?x2161, ?x9308), influenced_by(?x9308, ?x3336), influenced_by(?x9308, ?x2240), ?x2240 = 0j3v, ?x3336 = 032l1 *> conf = 0.27 ranks of expected_values: 6 EVAL 03jht influenced_by 05qmj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 41.000 0.500 http://example.org/influence/influence_node/influenced_by #15679-01shhf PRED entity: 01shhf PRED relation: group! PRED expected values: 02hnl => 91 concepts (63 used for prediction) PRED predicted values (max 10 best out of 123): 02hnl (0.79 #2596, 0.79 #2330, 0.78 #2242), 05148p4 (0.76 #2496, 0.75 #2940, 0.75 #2585), 03bx0bm (0.74 #1618, 0.72 #1529, 0.70 #1440), 028tv0 (0.62 #895, 0.55 #1249, 0.53 #1339), 01vj9c (0.38 #896, 0.33 #1517, 0.28 #1606), 02k856 (0.33 #51, 0.25 #492, 0.09 #2656), 0l14qv (0.28 #2482, 0.28 #2926, 0.28 #2128), 03qjg (0.27 #2349, 0.26 #3324, 0.26 #3060), 05r5c (0.26 #2484, 0.26 #2130, 0.25 #2928), 01v1d8 (0.25 #939, 0.09 #2656, 0.09 #4428) >> Best rule #2596 for best value: >> intensional similarity = 8 >> extensional distance = 100 >> proper extension: 01qqwp9; 01fmz6; 01k_yf; 0143q0; 0838y; 01jkqfz; 070b4; 09jvl; 07n3s; 07n68; >> query: (?x9463, 02hnl) <- artists(?x8289, ?x9463), artists(?x2249, ?x9463), group(?x716, ?x9463), artists(?x2249, ?x6067), parent_genre(?x8289, ?x2937), ?x716 = 018vs, category(?x9463, ?x134), instrumentalists(?x212, ?x6067) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01shhf group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 63.000 0.794 http://example.org/music/performance_role/regular_performances./music/group_membership/group #15678-045hz5 PRED entity: 045hz5 PRED relation: nationality PRED expected values: 03rk0 => 116 concepts (89 used for prediction) PRED predicted values (max 10 best out of 29): 03rk0 (0.85 #246, 0.84 #6631, 0.82 #1148), 09c7w0 (0.71 #6429, 0.69 #7539, 0.69 #7741), 0yyh (0.32 #6834, 0.28 #7944), 02jx1 (0.10 #2844, 0.10 #5958, 0.10 #2743), 07ssc (0.09 #5037, 0.09 #6040, 0.09 #5238), 03shp (0.09 #457, 0.06 #858, 0.06 #558), 03_3d (0.08 #1409, 0.05 #1610, 0.04 #1208), 016zwt (0.08 #286, 0.06 #387), 0d060g (0.06 #609, 0.05 #709, 0.05 #7142), 0f8l9c (0.05 #423, 0.04 #824, 0.03 #4742) >> Best rule #246 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 02756j; >> query: (?x13153, 03rk0) <- award(?x13153, ?x10156), location(?x13153, ?x5384), ?x10156 = 03r8v_, gender(?x13153, ?x514) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045hz5 nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 89.000 0.846 http://example.org/people/person/nationality #15677-024pcx PRED entity: 024pcx PRED relation: combatants PRED expected values: 0f8l9c => 196 concepts (144 used for prediction) PRED predicted values (max 10 best out of 224): 01d8l (0.88 #770, 0.85 #927, 0.83 #6929), 0dv0z (0.88 #770, 0.85 #927, 0.83 #6929), 024pcx (0.57 #758, 0.56 #914, 0.45 #1226), 0f8l9c (0.57 #708, 0.51 #3813, 0.51 #5844), 09c7w0 (0.54 #3799, 0.47 #2945, 0.42 #4117), 0chghy (0.50 #2951, 0.49 #3805, 0.42 #4280), 0154j (0.44 #1549, 0.44 #2947, 0.42 #4276), 02psqkz (0.44 #968, 0.39 #1587, 0.33 #4706), 0d060g (0.44 #2949, 0.42 #2251, 0.41 #3803), 05vz3zq (0.44 #2992, 0.40 #4321, 0.39 #1594) >> Best rule #770 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0f8l9c; 0g78xc; 07l75; 01d8l; >> query: (?x9328, ?x8949) <- combatants(?x1777, ?x9328), combatants(?x9328, ?x9602), combatants(?x9328, ?x1611), combatants(?x8949, ?x9328), ?x1611 = 025ndl, combatants(?x6829, ?x9602) >> conf = 0.88 => this is the best rule for 2 predicted values *> Best rule #708 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 0f8l9c; 0g78xc; 07l75; 01d8l; *> query: (?x9328, 0f8l9c) <- combatants(?x1777, ?x9328), combatants(?x9328, ?x9602), combatants(?x9328, ?x1611), combatants(?x8949, ?x9328), ?x1611 = 025ndl, combatants(?x6829, ?x9602) *> conf = 0.57 ranks of expected_values: 4 EVAL 024pcx combatants 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 196.000 144.000 0.879 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #15676-0jswp PRED entity: 0jswp PRED relation: list PRED expected values: 05glt => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.33 #9, 0.15 #23, 0.11 #51) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 023p33; >> query: (?x3369, 05glt) <- film(?x5913, ?x3369), nominated_for(?x1323, ?x3369), ?x1323 = 0gqz2 >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jswp list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.333 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #15675-0136jw PRED entity: 0136jw PRED relation: time_zones PRED expected values: 02hcv8 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.88 #107, 0.82 #120, 0.67 #3), 02lcqs (0.40 #57, 0.35 #83, 0.31 #70), 02fqwt (0.22 #40, 0.20 #92, 0.18 #287), 02llzg (0.16 #147, 0.09 #173, 0.05 #862), 02hczc (0.10 #80, 0.08 #262, 0.07 #249), 03bdv (0.04 #539, 0.04 #474, 0.03 #630), 042g7t (0.04 #128, 0.01 #154), 02lcrv (0.01 #150), 03plfd (0.01 #1037) >> Best rule #107 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 0mn0v; >> query: (?x6567, 02hcv8) <- currency(?x6567, ?x170), ?x170 = 09nqf, county(?x6567, ?x8616) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0136jw time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.875 http://example.org/location/location/time_zones #15674-01w8n89 PRED entity: 01w8n89 PRED relation: artists! PRED expected values: 01_bkd => 116 concepts (37 used for prediction) PRED predicted values (max 10 best out of 236): 01_bkd (0.71 #906, 0.40 #619, 0.32 #2298), 05bt6j (0.61 #4347, 0.42 #1471, 0.35 #1758), 064t9 (0.46 #8918, 0.45 #9493, 0.44 #9205), 0xv2x (0.40 #707, 0.36 #994, 0.32 #2298), 05w3f (0.39 #1752, 0.26 #2329, 0.25 #316), 02yv6b (0.32 #6986, 0.32 #1234, 0.30 #1809), 05r6t (0.32 #2298, 0.27 #6971, 0.24 #9483), 0mmp3 (0.32 #2298, 0.24 #9483, 0.22 #1435), 0163zw (0.32 #2298, 0.24 #9483, 0.22 #1435), 01243b (0.32 #2298, 0.24 #9483, 0.22 #1435) >> Best rule #906 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0c9l1; >> query: (?x3657, 01_bkd) <- artists(?x9248, ?x3657), artists(?x6107, ?x3657), ?x9248 = 02t8gf, parent_genre(?x8747, ?x6107), ?x8747 = 0g_bh >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w8n89 artists! 01_bkd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 37.000 0.714 http://example.org/music/genre/artists #15673-049gc PRED entity: 049gc PRED relation: place_of_birth PRED expected values: 0ftxw => 162 concepts (158 used for prediction) PRED predicted values (max 10 best out of 194): 03v1s (0.29 #49338, 0.28 #87417, 0.28 #48632), 0ftxw (0.29 #49338, 0.28 #87417, 0.28 #48632), 05k7sb (0.29 #49338, 0.28 #87417, 0.28 #48632), 02_286 (0.15 #1429, 0.14 #66981, 0.11 #19045), 0cr3d (0.11 #799, 0.08 #6435, 0.06 #4321), 0vzm (0.09 #2231, 0.02 #7162, 0.02 #7866), 0cc56 (0.08 #33, 0.06 #4964, 0.05 #7782), 02cl1 (0.08 #16, 0.03 #7765, 0.02 #3538), 01snm (0.08 #239, 0.02 #3761, 0.02 #5170), 0ws0h (0.08 #283, 0.02 #5214, 0.02 #5919) >> Best rule #49338 for best value: >> intensional similarity = 4 >> extensional distance = 398 >> proper extension: 0l6qt; 02rchht; 083chw; 014zcr; 01vw87c; 0h5f5n; 01wbg84; 05cj4r; 09fb5; 0159h6; ... >> query: (?x5346, ?x448) <- type_of_union(?x5346, ?x566), location(?x5346, ?x448), profession(?x5346, ?x987), ?x987 = 0dxtg >> conf = 0.29 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 049gc place_of_birth 0ftxw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 162.000 158.000 0.289 http://example.org/people/person/place_of_birth #15672-05xb7q PRED entity: 05xb7q PRED relation: contains! PRED expected values: 05sb1 => 158 concepts (72 used for prediction) PRED predicted values (max 10 best out of 324): 05sb1 (0.84 #55604, 0.79 #53807, 0.79 #54704), 075mb (0.83 #25991, 0.82 #46628, 0.82 #24199), 030qb3t (0.82 #2790, 0.06 #18920, 0.05 #1894), 09c7w0 (0.77 #45735, 0.74 #47532, 0.69 #21513), 01n7q (0.68 #2767, 0.17 #31452, 0.14 #18897), 02jx1 (0.35 #12632, 0.17 #31461, 0.16 #26078), 0xnt5 (0.29 #422, 0.02 #12967), 065zr (0.29 #151, 0.02 #12696), 0kpys (0.23 #2870), 0d060g (0.22 #7181, 0.12 #910, 0.08 #27798) >> Best rule #55604 for best value: >> intensional similarity = 6 >> extensional distance = 321 >> proper extension: 0975t6; >> query: (?x5968, ?x2236) <- state_province_region(?x5968, ?x2365), category(?x5968, ?x134), contains(?x5967, ?x5968), country(?x2365, ?x2236), film_release_region(?x66, ?x2236), olympics(?x2236, ?x2233) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05xb7q contains! 05sb1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 72.000 0.836 http://example.org/location/location/contains #15671-027dtxw PRED entity: 027dtxw PRED relation: award_winner PRED expected values: 02cllz => 36 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1600): 039bp (0.62 #7364, 0.62 #5118, 0.50 #2455), 06cgy (0.50 #5214, 0.50 #306, 0.14 #2761), 02qgqt (0.50 #17, 0.38 #4925, 0.36 #27003), 016yvw (0.50 #1200, 0.38 #6108, 0.14 #3655), 0bj9k (0.50 #412, 0.36 #27003, 0.36 #31916), 04__f (0.50 #1710, 0.36 #27003, 0.36 #31916), 0d6d2 (0.50 #1756, 0.36 #27003, 0.36 #31916), 01wmxfs (0.50 #146, 0.36 #27003, 0.36 #31916), 01g42 (0.50 #1829, 0.36 #27003, 0.36 #31916), 01vvb4m (0.50 #652, 0.25 #5560, 0.14 #3107) >> Best rule #7364 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0279c15; 0gqy2; >> query: (?x112, ?x1119) <- award(?x1119, ?x112), award(?x395, ?x112), ?x1119 = 039bp, nominated_for(?x112, ?x144), award_nominee(?x395, ?x919) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #19632 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 195 *> proper extension: 0m7yy; 02wwsh8; 03ybrwc; 02vl9ln; 0468g4r; *> query: (?x112, ?x192) <- award_winner(?x112, ?x395), award_nominee(?x192, ?x395), award(?x144, ?x112) *> conf = 0.08 ranks of expected_values: 519 EVAL 027dtxw award_winner 02cllz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 14.000 0.625 http://example.org/award/award_category/winners./award/award_honor/award_winner #15670-02f2dn PRED entity: 02f2dn PRED relation: award PRED expected values: 0fq9zdn => 110 concepts (84 used for prediction) PRED predicted values (max 10 best out of 261): 02z1nbg (0.70 #31538, 0.69 #13795, 0.69 #9066), 02y_rq5 (0.50 #878, 0.13 #23652, 0.06 #11126), 0gqwc (0.44 #859, 0.13 #23652, 0.13 #33115), 0789_m (0.29 #20, 0.06 #5142, 0.06 #1596), 05pcn59 (0.25 #865, 0.18 #1259, 0.16 #4017), 02x4w6g (0.25 #502, 0.14 #108, 0.13 #23652), 019f4v (0.25 #457, 0.14 #26019, 0.13 #1245), 0gs9p (0.25 #469, 0.14 #26019, 0.12 #1257), 0fq9zdn (0.25 #842, 0.14 #26019, 0.12 #28385), 02x73k6 (0.25 #451, 0.13 #23652, 0.13 #33115) >> Best rule #31538 for best value: >> intensional similarity = 3 >> extensional distance = 1907 >> proper extension: 06lxn; >> query: (?x2646, ?x1008) <- award_winner(?x1972, ?x2646), award_winner(?x1008, ?x2646), ceremony(?x1972, ?x78) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #842 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 014g9y; *> query: (?x2646, 0fq9zdn) <- award(?x2646, ?x1008), award_nominee(?x2646, ?x396), ?x1008 = 05zvq6g *> conf = 0.25 ranks of expected_values: 9 EVAL 02f2dn award 0fq9zdn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 110.000 84.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15669-04411 PRED entity: 04411 PRED relation: student! PRED expected values: 01q7q2 => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 234): 0bjqh (0.33 #45, 0.17 #3729, 0.12 #5310), 07x4c (0.33 #1309, 0.17 #3413, 0.09 #6574), 065y4w7 (0.29 #4752, 0.22 #5806, 0.20 #2643), 01w5m (0.27 #6420, 0.18 #3684, 0.18 #3156), 01stzp (0.25 #2088, 0.17 #8403, 0.17 #7878), 01zzy3 (0.25 #2052, 0.08 #8367, 0.08 #7842), 0m7yh (0.25 #1851, 0.08 #8166, 0.08 #7641), 09f2j (0.22 #5949, 0.20 #2786, 0.12 #10155), 01tpvt (0.20 #2861, 0.17 #3388, 0.06 #9702), 01dyk8 (0.20 #2967, 0.17 #3494, 0.06 #9808) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 081nh; >> query: (?x920, 0bjqh) <- profession(?x920, ?x13369), organizations_founded(?x920, ?x10478), student(?x10478, ?x2208), country(?x10478, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04411 student! 01q7q2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 181.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #15668-09fqd3 PRED entity: 09fqd3 PRED relation: gender PRED expected values: 05zppz => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #5, 0.79 #7, 0.78 #3), 02zsn (0.27 #12, 0.25 #14, 0.24 #16) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 298 >> proper extension: 03f2_rc; 01vvycq; 02lk1s; 015grj; 017r2; 02_hj4; 03jm6c; 0bt4r4; 0p8jf; 017yfz; ... >> query: (?x10723, 05zppz) <- location(?x10723, ?x6084), profession(?x10723, ?x353), ?x353 = 0cbd2 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09fqd3 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.797 http://example.org/people/person/gender #15667-07w3r PRED entity: 07w3r PRED relation: citytown PRED expected values: 013kcv => 156 concepts (130 used for prediction) PRED predicted values (max 10 best out of 91): 09c7w0 (0.25 #28771, 0.24 #30983, 0.22 #7371), 013kcv (0.25 #28771, 0.24 #30983, 0.22 #7371), 02_286 (0.18 #10705, 0.17 #34688, 0.15 #7386), 0fvzg (0.15 #15115, 0.04 #20284, 0.03 #8849), 05jbn (0.11 #108, 0.10 #476, 0.04 #845), 094jv (0.11 #34, 0.01 #14779), 0ygbf (0.11 #130), 010y34 (0.11 #128), 0ply0 (0.11 #76), 01_d4 (0.10 #406, 0.04 #775, 0.03 #14783) >> Best rule #28771 for best value: >> intensional similarity = 4 >> extensional distance = 366 >> proper extension: 02cw8s; 027xq5; >> query: (?x2150, ?x859) <- major_field_of_study(?x2150, ?x7134), contains(?x859, ?x2150), student(?x2150, ?x5133), place_of_birth(?x803, ?x859) >> conf = 0.25 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07w3r citytown 013kcv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 156.000 130.000 0.250 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #15666-0c0tzp PRED entity: 0c0tzp PRED relation: award_winner! PRED expected values: 0cq7kw => 99 concepts (54 used for prediction) PRED predicted values (max 10 best out of 208): 09d38d (0.47 #26073, 0.45 #55557, 0.44 #29474), 0cqnss (0.47 #26073, 0.45 #55557, 0.44 #29474), 0ft18 (0.24 #3401, 0.24 #2267), 0k7tq (0.24 #3401, 0.24 #2267), 0cy__l (0.24 #3401, 0.24 #2267), 0cq7kw (0.24 #3401, 0.24 #2267), 05dmmc (0.20 #3889, 0.04 #7290, 0.04 #5022), 0cwy47 (0.18 #2365, 0.15 #1231, 0.15 #44216), 0bcndz (0.15 #1316, 0.15 #44216, 0.12 #2450), 0kvb6p (0.15 #2068, 0.15 #44216, 0.12 #3202) >> Best rule #26073 for best value: >> intensional similarity = 3 >> extensional distance = 976 >> proper extension: 06vqdf; >> query: (?x12378, ?x4970) <- award_winner(?x4280, ?x12378), award_winner(?x199, ?x12378), nominated_for(?x12378, ?x4970) >> conf = 0.47 => this is the best rule for 2 predicted values *> Best rule #3401 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0cb77r; 076lxv; 07h1tr; 057dxsg; 04_1nk; 053j4w4; 051x52f; 057bc6m; 05b49tt; 058vfp4; ... *> query: (?x12378, ?x4280) <- award_nominee(?x12378, ?x2068), award_winner(?x1793, ?x12378), film_sets_designed(?x12378, ?x4280) *> conf = 0.24 ranks of expected_values: 6 EVAL 0c0tzp award_winner! 0cq7kw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 99.000 54.000 0.466 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #15665-016xh5 PRED entity: 016xh5 PRED relation: award_nominee! PRED expected values: 0h5g_ 01yhvv => 95 concepts (35 used for prediction) PRED predicted values (max 10 best out of 866): 0h5g_ (0.84 #2318, 0.83 #4638, 0.83 #4637), 01yhvv (0.84 #2318, 0.83 #4638, 0.83 #4637), 0djywgn (0.84 #2318, 0.83 #4638, 0.83 #4637), 016xh5 (0.79 #3726, 0.50 #1407, 0.20 #4639), 02wgln (0.25 #406, 0.20 #4639, 0.16 #76506), 03x400 (0.25 #1498, 0.20 #4639, 0.16 #76506), 0hskw (0.25 #592, 0.20 #4639, 0.16 #76506), 0bwgc_ (0.25 #2264, 0.20 #4639, 0.16 #76506), 0171cm (0.20 #4639, 0.16 #76506, 0.14 #81144), 0hvb2 (0.20 #4639, 0.16 #76506, 0.14 #81144) >> Best rule #2318 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0159h6; 09y20; >> query: (?x6122, ?x100) <- award_nominee(?x6122, ?x8566), award_nominee(?x6122, ?x5951), award_nominee(?x6122, ?x100), ?x8566 = 0djywgn, ?x5951 = 0dvld >> conf = 0.84 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2 EVAL 016xh5 award_nominee! 01yhvv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 35.000 0.844 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 016xh5 award_nominee! 0h5g_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 35.000 0.844 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15664-016fnb PRED entity: 016fnb PRED relation: profession PRED expected values: 0n1h 0nbcg => 117 concepts (116 used for prediction) PRED predicted values (max 10 best out of 74): 09jwl (0.85 #1769, 0.83 #1623, 0.81 #601), 0nbcg (0.60 #30, 0.57 #1636, 0.57 #1782), 039v1 (0.45 #181, 0.40 #1641, 0.40 #1787), 01d_h8 (0.40 #3219, 0.36 #4536, 0.34 #1172), 0n1h (0.38 #594, 0.31 #302, 0.23 #2493), 01c72t (0.36 #2359, 0.31 #4846, 0.30 #4992), 0dxtg (0.27 #13025, 0.27 #7619, 0.27 #6886), 03gjzk (0.26 #1181, 0.26 #13599, 0.25 #13452), 02jknp (0.26 #13599, 0.25 #13452, 0.21 #444), 0kyk (0.26 #13599, 0.25 #13452, 0.20 #28) >> Best rule #1769 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 09g0h; >> query: (?x4628, 09jwl) <- instrumentalists(?x228, ?x4628), group(?x4628, ?x2723), profession(?x4628, ?x131) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 04mn81; 01vs_v8; 01nhkxp; *> query: (?x4628, 0nbcg) <- instrumentalists(?x228, ?x4628), award_nominee(?x4628, ?x5478), ?x5478 = 01yzl2 *> conf = 0.60 ranks of expected_values: 2, 5 EVAL 016fnb profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 116.000 0.846 http://example.org/people/person/profession EVAL 016fnb profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 116.000 0.846 http://example.org/people/person/profession #15663-0hgxh PRED entity: 0hgxh PRED relation: symptom_of PRED expected values: 09jg8 087z2 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 89): 0h1n9 (0.71 #1425, 0.60 #1149, 0.60 #1046), 0d19y2 (0.60 #1055, 0.57 #1434, 0.50 #2223), 0gk4g (0.60 #1129, 0.43 #1405, 0.33 #1289), 09jg8 (0.57 #1422, 0.50 #2211, 0.50 #778), 07jwr (0.50 #1288, 0.50 #760, 0.40 #1025), 02k6hp (0.50 #1308, 0.50 #888, 0.33 #120), 074m2 (0.50 #774, 0.40 #1039, 0.33 #1302), 0hg11 (0.50 #763, 0.40 #1028, 0.33 #1291), 035482 (0.50 #715, 0.40 #1140, 0.29 #1416), 0dq9p (0.50 #1295, 0.33 #59, 0.25 #767) >> Best rule #1425 for best value: >> intensional similarity = 20 >> extensional distance = 5 >> proper extension: 02tfl8; >> query: (?x9510, 0h1n9) <- symptom_of(?x9510, ?x11064), symptom_of(?x9510, ?x7007), symptom_of(?x9510, ?x7006), people(?x7007, ?x10907), people(?x7007, ?x2208), risk_factors(?x9119, ?x7007), location(?x2208, ?x151), religion(?x2208, ?x1985), influenced_by(?x2208, ?x1029), influenced_by(?x1089, ?x2208), artists(?x378, ?x10907), instrumentalists(?x716, ?x10907), gender(?x10907, ?x231), location(?x10907, ?x11585), people(?x7006, ?x1946), student(?x3424, ?x2208), risk_factors(?x7006, ?x11160), ?x716 = 018vs, profession(?x10907, ?x131), ?x11064 = 01n3bm >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1422 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 5 *> proper extension: 02tfl8; *> query: (?x9510, 09jg8) <- symptom_of(?x9510, ?x11064), symptom_of(?x9510, ?x7007), symptom_of(?x9510, ?x7006), people(?x7007, ?x10907), people(?x7007, ?x2208), risk_factors(?x9119, ?x7007), location(?x2208, ?x151), religion(?x2208, ?x1985), influenced_by(?x2208, ?x1029), influenced_by(?x1089, ?x2208), artists(?x378, ?x10907), instrumentalists(?x716, ?x10907), gender(?x10907, ?x231), location(?x10907, ?x11585), people(?x7006, ?x1946), student(?x3424, ?x2208), risk_factors(?x7006, ?x11160), ?x716 = 018vs, profession(?x10907, ?x131), ?x11064 = 01n3bm *> conf = 0.57 ranks of expected_values: 4, 39 EVAL 0hgxh symptom_of 087z2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 57.000 57.000 0.714 http://example.org/medicine/symptom/symptom_of EVAL 0hgxh symptom_of 09jg8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 57.000 57.000 0.714 http://example.org/medicine/symptom/symptom_of #15662-05g3b PRED entity: 05g3b PRED relation: sport PRED expected values: 0jm_ => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 7): 0jm_ (0.91 #530, 0.88 #439, 0.87 #521), 02vx4 (0.57 #1233, 0.53 #1335, 0.51 #1295), 018w8 (0.50 #350, 0.28 #1262, 0.28 #1289), 018jz (0.44 #596, 0.35 #975, 0.30 #866), 03tmr (0.20 #925, 0.11 #455, 0.09 #537), 039yzs (0.17 #353, 0.06 #1349, 0.04 #949), 09xp_ (0.12 #930) >> Best rule #530 for best value: >> intensional similarity = 11 >> extensional distance = 21 >> proper extension: 0ws7; 06rpd; >> query: (?x729, 0jm_) <- position(?x729, ?x7079), position(?x729, ?x1717), position(?x729, ?x1240), position(?x729, ?x180), ?x7079 = 08ns5s, draft(?x729, ?x465), ?x1240 = 023wyl, school(?x729, ?x1681), team(?x3113, ?x729), ?x1717 = 02g_6x, position_s(?x179, ?x180) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05g3b sport 0jm_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.913 http://example.org/sports/sports_team/sport #15661-0gfh84d PRED entity: 0gfh84d PRED relation: genre PRED expected values: 0hfjk => 62 concepts (47 used for prediction) PRED predicted values (max 10 best out of 116): 05p553 (0.53 #730, 0.34 #4866, 0.34 #1579), 02kdv5l (0.48 #1456, 0.44 #1940, 0.44 #1819), 01jfsb (0.42 #12, 0.42 #1466, 0.42 #1829), 03k9fj (0.37 #374, 0.35 #858, 0.33 #979), 02l7c8 (0.30 #3293, 0.27 #3415, 0.27 #742), 06n90 (0.22 #497, 0.22 #1467, 0.22 #618), 01hmnh (0.22 #260, 0.21 #381, 0.21 #865), 0hcr (0.21 #387, 0.19 #871, 0.17 #992), 04xvlr (0.21 #1, 0.18 #3278, 0.16 #3400), 060__y (0.18 #1592, 0.17 #3294, 0.15 #3416) >> Best rule #730 for best value: >> intensional similarity = 5 >> extensional distance = 87 >> proper extension: 04cf_l; >> query: (?x6527, 05p553) <- language(?x6527, ?x254), written_by(?x6527, ?x565), ?x254 = 02h40lc, category(?x565, ?x134), award_nominee(?x4239, ?x565) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #5108 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1276 *> proper extension: 06wzvr; 0gcrg; *> query: (?x6527, ?x5276) <- film_crew_role(?x6527, ?x468), film_crew_role(?x6520, ?x468), film_crew_role(?x1688, ?x468), film_crew_role(?x103, ?x468), genre(?x1688, ?x5276), film_release_region(?x6520, ?x87), production_companies(?x103, ?x7690) *> conf = 0.04 ranks of expected_values: 47 EVAL 0gfh84d genre 0hfjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 62.000 47.000 0.528 http://example.org/film/film/genre #15660-06nm1 PRED entity: 06nm1 PRED relation: major_field_of_study! PRED expected values: 014mlp => 80 concepts (79 used for prediction) PRED predicted values (max 10 best out of 23): 02_xgp2 (0.89 #407, 0.71 #314, 0.67 #937), 016t_3 (0.86 #304, 0.78 #1065, 0.78 #397), 02h4rq6 (0.83 #396, 0.79 #303, 0.74 #926), 014mlp (0.79 #929, 0.79 #423, 0.78 #1067), 019v9k (0.79 #310, 0.72 #403, 0.72 #933), 03bwzr4 (0.71 #315, 0.61 #408, 0.53 #938), 0bkj86 (0.67 #402, 0.63 #932, 0.59 #1070), 04zx3q1 (0.67 #395, 0.58 #419, 0.57 #302), 03mkk4 (0.49 #1086, 0.48 #1112, 0.48 #1111), 022h5x (0.49 #1086, 0.48 #1112, 0.48 #1111) >> Best rule #407 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 03nfmq; 01tbp; >> query: (?x2502, 02_xgp2) <- major_field_of_study(?x6056, ?x2502), major_field_of_study(?x3149, ?x2502), ?x6056 = 05zl0, student(?x2502, ?x9156), colors(?x3149, ?x663), institution(?x1368, ?x3149) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #929 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 41 *> proper extension: 06ntj; *> query: (?x2502, 014mlp) <- major_field_of_study(?x2502, ?x5607), major_field_of_study(?x5974, ?x2502), major_field_of_study(?x6417, ?x5607), major_field_of_study(?x2895, ?x5607), major_field_of_study(?x5607, ?x90), contains(?x2256, ?x2895), ?x6417 = 01t0dy *> conf = 0.79 ranks of expected_values: 4 EVAL 06nm1 major_field_of_study! 014mlp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 80.000 79.000 0.889 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #15659-06sn8m PRED entity: 06sn8m PRED relation: nationality PRED expected values: 09c7w0 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 68): 09c7w0 (0.81 #501, 0.81 #2204, 0.80 #3608), 02jx1 (0.40 #8137, 0.40 #8035, 0.33 #33), 03_3d (0.40 #8137, 0.40 #8035, 0.12 #2610), 07ssc (0.40 #8137, 0.40 #8035, 0.10 #1516), 0d060g (0.40 #8137, 0.40 #8035, 0.10 #2410), 03rt9 (0.40 #8137, 0.40 #8035, 0.10 #213), 0f8l9c (0.40 #8137, 0.40 #8035, 0.03 #3729), 0345h (0.40 #8137, 0.40 #8035, 0.03 #3738), 03rjj (0.40 #8137, 0.40 #8035, 0.02 #705), 059rby (0.33 #7833) >> Best rule #501 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0678gl; >> query: (?x6962, 09c7w0) <- profession(?x6962, ?x1383), ?x1383 = 0np9r, actor(?x10826, ?x6962), location(?x6962, ?x739) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06sn8m nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.815 http://example.org/people/person/nationality #15658-02x20c9 PRED entity: 02x20c9 PRED relation: executive_produced_by! PRED expected values: 08g_jw => 89 concepts (43 used for prediction) PRED predicted values (max 10 best out of 209): 01jft4 (0.33 #403, 0.07 #2528, 0.06 #3060), 0drnwh (0.33 #381, 0.07 #2506, 0.06 #3038), 0287477 (0.33 #350, 0.07 #2475, 0.06 #3007), 02y_lrp (0.33 #9, 0.07 #2134, 0.06 #2666), 03qcfvw (0.33 #5, 0.07 #2130, 0.06 #2662), 03h4fq7 (0.33 #292, 0.03 #2417, 0.03 #2949), 0bt4g (0.10 #2548, 0.05 #3611, 0.04 #4673), 0mbql (0.10 #2504, 0.05 #3567, 0.04 #4629), 01f7kl (0.10 #2259, 0.05 #3322, 0.04 #4384), 049xgc (0.07 #2449, 0.06 #2981, 0.05 #3512) >> Best rule #403 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03h304l; >> query: (?x13826, 01jft4) <- place_of_birth(?x13826, ?x7412), type_of_union(?x13826, ?x566), executive_produced_by(?x1246, ?x13826), ?x1246 = 02pxmgz >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02x20c9 executive_produced_by! 08g_jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 43.000 0.333 http://example.org/film/film/executive_produced_by #15657-0fw2d3 PRED entity: 0fw2d3 PRED relation: team PRED expected values: 05169r => 76 concepts (24 used for prediction) PRED predicted values (max 10 best out of 462): 0bl8l (0.81 #3948, 0.79 #2106, 0.79 #2105), 0dwz3t (0.50 #392, 0.40 #918, 0.33 #129), 027pwl (0.33 #30, 0.25 #293, 0.20 #819), 01tqfs (0.33 #95, 0.25 #358, 0.20 #884), 0j2pg (0.33 #18, 0.25 #281, 0.20 #807), 02s2ys (0.33 #166, 0.25 #429, 0.20 #955), 02b17f (0.33 #167, 0.25 #430, 0.20 #956), 0cj_v7 (0.33 #120, 0.25 #383, 0.20 #909), 02rh_0 (0.33 #170, 0.25 #433, 0.20 #959), 085v7 (0.25 #320, 0.20 #846, 0.12 #1372) >> Best rule #3948 for best value: >> intensional similarity = 7 >> extensional distance = 62 >> proper extension: 0c11mj; 071pf2; 0fv6dr; 09lhln; 0f1pyf; 0bw7ly; 0457w0; 09r1j5; 0djvzd; 02rnns; ... >> query: (?x7703, ?x1085) <- nationality(?x7703, ?x1499), gender(?x7703, ?x231), team(?x7703, ?x2096), team(?x7703, ?x1085), team(?x7703, ?x5708), position(?x2096, ?x530), ?x530 = 02_j1w >> conf = 0.81 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fw2d3 team 05169r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 24.000 0.808 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #15656-02gjp PRED entity: 02gjp PRED relation: category PRED expected values: 08mbj5d => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.75 #17, 0.75 #16, 0.74 #59) >> Best rule #17 for best value: >> intensional similarity = 6 >> extensional distance = 34 >> proper extension: 01fy2s; >> query: (?x14152, ?x134) <- contains(?x4302, ?x14152), time_zones(?x4302, ?x10735), contains(?x4302, ?x13593), ?x10735 = 03plfd, category(?x13593, ?x134), ?x134 = 08mbj5d >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02gjp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.750 http://example.org/common/topic/webpage./common/webpage/category #15655-0gd_s PRED entity: 0gd_s PRED relation: nationality PRED expected values: 09c7w0 => 125 concepts (121 used for prediction) PRED predicted values (max 10 best out of 41): 09c7w0 (0.83 #2401, 0.80 #7624, 0.79 #5011), 0f8l9c (0.33 #122, 0.26 #3502, 0.09 #722), 0h3y (0.33 #108, 0.02 #1008, 0.02 #1308), 01t12z (0.33 #9836, 0.33 #11350, 0.32 #9533), 02_286 (0.33 #9836, 0.33 #11350, 0.32 #9533), 059rby (0.33 #9836, 0.33 #11350, 0.32 #9533), 02jx1 (0.26 #3502, 0.22 #333, 0.15 #2133), 03rt9 (0.26 #3502, 0.05 #1013, 0.04 #1313), 014tss (0.26 #3502, 0.01 #1976), 035qy (0.26 #3502) >> Best rule #2401 for best value: >> intensional similarity = 3 >> extensional distance = 188 >> proper extension: 03lh3v; 01vw917; 040j2_; 04g9sq; >> query: (?x9284, 09c7w0) <- place_of_birth(?x9284, ?x1005), people(?x2510, ?x9284), ?x2510 = 0x67 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gd_s nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 121.000 0.832 http://example.org/people/person/nationality #15654-01gfhk PRED entity: 01gfhk PRED relation: adjoins! PRED expected values: 04sqj => 255 concepts (136 used for prediction) PRED predicted values (max 10 best out of 612): 04sqj (0.86 #44016, 0.85 #39298, 0.85 #42443), 0d060g (0.50 #2369, 0.25 #3154, 0.12 #19659), 0vmt (0.33 #831, 0.25 #3974, 0.17 #7118), 01gfhk (0.33 #762, 0.20 #5478, 0.14 #8620), 01zlx (0.33 #2291, 0.20 #7006, 0.10 #29864), 0rh6k (0.30 #11788, 0.20 #5504, 0.07 #14931), 0f8l9c (0.27 #36192, 0.15 #28332, 0.14 #63719), 05kr_ (0.25 #3248, 0.25 #2463, 0.14 #15033), 0vm5t (0.25 #3909, 0.25 #3124, 0.10 #13337), 0vrmb (0.25 #3837, 0.25 #3052, 0.10 #13265) >> Best rule #44016 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 01_c4; >> query: (?x13910, ?x8181) <- administrative_parent(?x13910, ?x151), adjoins(?x13910, ?x8181), category(?x13910, ?x134), contains(?x7273, ?x151) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gfhk adjoins! 04sqj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 255.000 136.000 0.860 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #15653-03q2t9 PRED entity: 03q2t9 PRED relation: profession PRED expected values: 02hrh1q => 111 concepts (82 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.94 #4691, 0.76 #8651, 0.76 #451), 09jwl (0.77 #3525, 0.76 #2499, 0.74 #2353), 01d_h8 (0.75 #9667, 0.53 #5858, 0.50 #4), 0dxtg (0.55 #7331, 0.54 #5866, 0.49 #9675), 02jknp (0.37 #9669, 0.29 #5860, 0.26 #7325), 0n1h (0.37 #302, 0.35 #1324, 0.29 #594), 039v1 (0.33 #3542, 0.33 #2516, 0.32 #2370), 01c72t (0.32 #2797, 0.32 #1628, 0.32 #4992), 0fnpj (0.30 #788, 0.28 #204, 0.20 #3566), 0d1pc (0.28 #486, 0.17 #6781, 0.17 #924) >> Best rule #4691 for best value: >> intensional similarity = 4 >> extensional distance = 361 >> proper extension: 01sfmyk; >> query: (?x5456, 02hrh1q) <- artist(?x2931, ?x5456), profession(?x5456, ?x1041), profession(?x4719, ?x1041), ?x4719 = 08hsww >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q2t9 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 82.000 0.939 http://example.org/people/person/profession #15652-07w21 PRED entity: 07w21 PRED relation: award PRED expected values: 02tzwd 0g9wd99 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 274): 040_9s0 (0.72 #37672, 0.70 #18442, 0.69 #37671), 06196 (0.72 #37672, 0.70 #18442, 0.69 #37671), 047xyn (0.69 #37671, 0.67 #36101, 0.67 #36100), 0g9wd99 (0.56 #752, 0.50 #1144, 0.14 #360), 01ppdy (0.40 #1121, 0.33 #729, 0.06 #4649), 0grw_ (0.33 #700, 0.30 #1092, 0.14 #308), 0262s1 (0.29 #341, 0.08 #3869, 0.06 #4261), 01tgwv (0.27 #5057, 0.26 #6234, 0.24 #5842), 058bzgm (0.24 #5066, 0.22 #4282, 0.19 #5851), 04hddx (0.22 #748, 0.20 #1140, 0.16 #7021) >> Best rule #37672 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 04cy8rb; 01r42_g; 0f830f; 02pp_q_; 025p38; 08wq0g; 025vry; 08w7vj; 0dky9n; 067jsf; ... >> query: (?x476, ?x12418) <- award_winner(?x12418, ?x476), award(?x576, ?x12418) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #752 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 01q415; 040_t; *> query: (?x476, 0g9wd99) <- award(?x476, ?x7111), ?x7111 = 0c_dx, location(?x476, ?x9605) *> conf = 0.56 ranks of expected_values: 4, 37 EVAL 07w21 award 0g9wd99 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 113.000 113.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07w21 award 02tzwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 113.000 113.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15651-043c4j PRED entity: 043c4j PRED relation: role PRED expected values: 01p970 => 150 concepts (77 used for prediction) PRED predicted values (max 10 best out of 118): 05r5c (0.58 #1282, 0.56 #399, 0.55 #5230), 0342h (0.53 #5227, 0.48 #5721, 0.47 #1083), 01vdm0 (0.42 #1896, 0.42 #2092, 0.40 #1304), 02sgy (0.40 #1183, 0.37 #2069, 0.37 #1281), 0bxl5 (0.40 #165, 0.25 #263, 0.17 #1244), 01vj9c (0.36 #603, 0.34 #1684, 0.32 #1289), 013y1f (0.33 #525, 0.33 #426, 0.28 #1309), 042v_gx (0.33 #1283, 0.32 #2071, 0.30 #1185), 0l14qv (0.33 #397, 0.28 #2068, 0.26 #1280), 018vs (0.29 #1682, 0.26 #2173, 0.25 #1977) >> Best rule #1282 for best value: >> intensional similarity = 5 >> extensional distance = 55 >> proper extension: 03c7ln; 01vv7sc; 0137g1; 06x4l_; 03xl77; 01wgjj5; 06rgq; 01nkxvx; 01dhjz; 02g40r; >> query: (?x7683, 05r5c) <- artists(?x1127, ?x7683), role(?x7683, ?x3991), ?x3991 = 05842k, artists(?x1127, ?x7597), ?x7597 = 03c3yf >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #2659 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 96 *> proper extension: 03193l; *> query: (?x7683, ?x228) <- performance_role(?x7683, ?x1750), profession(?x7683, ?x1183), nationality(?x7683, ?x94), performance_role(?x228, ?x1750), artists(?x302, ?x7683) *> conf = 0.10 ranks of expected_values: 45 EVAL 043c4j role 01p970 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 150.000 77.000 0.579 http://example.org/music/artist/track_contributions./music/track_contribution/role #15650-07xl34 PRED entity: 07xl34 PRED relation: organization PRED expected values: 01v3ht 02cbvn 0hd7j 01b_d4 02_cx_ 01hnb 0ymb6 01nmgc 01g4yw 01gpkz 01h8sf => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1775): 0bqxw (0.63 #5275, 0.51 #5272, 0.51 #5268), 01bm_ (0.63 #5275, 0.51 #5272, 0.51 #5268), 018sg9 (0.63 #5275, 0.51 #5272, 0.51 #5268), 0h6rm (0.63 #5275, 0.51 #5272, 0.51 #5268), 05hf_5 (0.63 #5275, 0.51 #5272, 0.51 #5268), 049dk (0.50 #2029, 0.40 #2687, 0.33 #1371), 01tx9m (0.50 #2223, 0.40 #2881, 0.33 #1565), 0gsgr (0.50 #2295, 0.40 #2953, 0.33 #1637), 054lpb6 (0.40 #2673, 0.33 #1357, 0.25 #2015), 02630g (0.40 #2902, 0.25 #2244, 0.15 #10164) >> Best rule #5275 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0789n; 01dz7z; >> query: (?x5510, ?x4390) <- company(?x5510, ?x4390), company(?x5510, ?x2621), contains(?x6885, ?x4390), contains(?x94, ?x2621), company(?x4095, ?x4390), organization(?x4095, ?x1220), adjoins(?x9758, ?x2621) >> conf = 0.63 => this is the best rule for 5 predicted values *> Best rule #1571 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 060c4; *> query: (?x5510, 02_cx_) <- organization(?x5510, ?x11963), organization(?x5510, ?x11632), organization(?x5510, ?x11559), organization(?x5510, ?x8220), organization(?x5510, ?x5280), organization(?x5510, ?x2327), organization(?x5510, ?x2013), student(?x11963, ?x361), ?x2327 = 07wjk, contains(?x94, ?x11559), major_field_of_study(?x5280, ?x742), institution(?x865, ?x2013), state_province_region(?x8220, ?x1227), ?x11632 = 0mbwf *> conf = 0.33 ranks of expected_values: 160, 329, 381, 672, 678, 701, 707, 715, 728, 1740, 1745 EVAL 07xl34 organization 01h8sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 07xl34 organization 01gpkz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 07xl34 organization 01g4yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 07xl34 organization 01nmgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 07xl34 organization 0ymb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 07xl34 organization 01hnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 07xl34 organization 02_cx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 07xl34 organization 01b_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 07xl34 organization 0hd7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 07xl34 organization 02cbvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 07xl34 organization 01v3ht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 28.000 0.633 http://example.org/organization/role/leaders./organization/leadership/organization #15649-02fsn PRED entity: 02fsn PRED relation: instrumentalists PRED expected values: 0dw3l => 90 concepts (64 used for prediction) PRED predicted values (max 10 best out of 937): 01sb5r (0.67 #17209, 0.67 #8711, 0.60 #12959), 018y81 (0.60 #7609, 0.60 #5793, 0.57 #9426), 01vw20_ (0.60 #5005, 0.57 #10458, 0.50 #4402), 0zjpz (0.60 #4945, 0.50 #12826, 0.50 #12220), 09prnq (0.60 #4958, 0.50 #17089, 0.50 #12839), 03bnv (0.60 #12304, 0.50 #8662, 0.50 #4426), 07zft (0.60 #7747, 0.50 #8959, 0.50 #4723), 0bg539 (0.60 #7323, 0.50 #4299, 0.43 #10355), 04m2zj (0.60 #13172, 0.43 #9529, 0.42 #17422), 01vsyg9 (0.60 #5167, 0.40 #13048, 0.38 #20332) >> Best rule #17209 for best value: >> intensional similarity = 20 >> extensional distance = 10 >> proper extension: 0395lw; >> query: (?x2888, 01sb5r) <- role(?x7869, ?x2888), role(?x1225, ?x2888), role(?x885, ?x2888), role(?x75, ?x2888), instrumentalists(?x2888, ?x7937), ?x885 = 0dwtp, role(?x315, ?x2888), role(?x569, ?x7869), instrumentalists(?x75, ?x3168), ?x3168 = 016ntp, role(?x75, ?x2785), group(?x75, ?x5303), group(?x75, ?x3516), role(?x2328, ?x75), ?x5303 = 02mq_y, ?x3516 = 05563d, group(?x2888, ?x2521), ?x2785 = 0jtg0, artists(?x1928, ?x7937), ?x1225 = 01qbl >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5283 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 3 *> proper extension: 018vs; 018j2; *> query: (?x2888, 0dw3l) <- role(?x7869, ?x2888), role(?x885, ?x2888), role(?x75, ?x2888), instrumentalists(?x2888, ?x9298), ?x885 = 0dwtp, role(?x315, ?x2888), role(?x3991, ?x7869), instrumentalists(?x75, ?x3168), ?x3168 = 016ntp, role(?x75, ?x1147), group(?x75, ?x11425), group(?x75, ?x9196), ?x9298 = 016j2t, ?x1147 = 07kc_, role(?x2888, ?x3215), role(?x894, ?x7869), ?x9196 = 0qmpd, ?x3991 = 05842k, ?x11425 = 02vnpv *> conf = 0.40 ranks of expected_values: 132 EVAL 02fsn instrumentalists 0dw3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 90.000 64.000 0.667 http://example.org/music/instrument/instrumentalists #15648-0130sy PRED entity: 0130sy PRED relation: gender PRED expected values: 05zppz => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #31, 0.88 #49, 0.87 #37), 02zsn (0.45 #159, 0.40 #6, 0.34 #169) >> Best rule #31 for best value: >> intensional similarity = 6 >> extensional distance = 48 >> proper extension: 026dx; >> query: (?x6838, 05zppz) <- profession(?x6838, ?x1614), profession(?x6838, ?x1183), ?x1614 = 01c72t, role(?x6838, ?x894), location(?x6838, ?x5174), ?x1183 = 09jwl >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0130sy gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.880 http://example.org/people/person/gender #15647-04rrx PRED entity: 04rrx PRED relation: religion PRED expected values: 01y0s9 => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 21): 01y0s9 (0.65 #213, 0.57 #236, 0.54 #167), 058x5 (0.40 #211, 0.35 #94, 0.33 #234), 0flw86 (0.39 #1017, 0.38 #831, 0.38 #785), 03j6c (0.33 #10, 0.25 #33, 0.09 #1119), 0kpl (0.33 #5, 0.25 #28, 0.06 #1663), 07w8f (0.33 #17, 0.25 #40, 0.06 #1663), 02t7t (0.27 #221, 0.24 #244, 0.24 #175), 04t_mf (0.04 #1123, 0.04 #1193, 0.02 #1053), 078tg (0.03 #1035, 0.03 #1058, 0.03 #1128), 0n2g (0.03 #1115, 0.03 #1185, 0.02 #1022) >> Best rule #213 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 0846v; >> query: (?x1906, 01y0s9) <- contains(?x1906, ?x169), adjoins(?x177, ?x1906), religion(?x1906, ?x109), district_represented(?x176, ?x1906) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04rrx religion 01y0s9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.646 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #15646-0778p PRED entity: 0778p PRED relation: student PRED expected values: 02y7sr => 165 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1143): 0f4dx2 (0.17 #4705, 0.09 #8891, 0.02 #13079), 03rwng (0.17 #5164, 0.09 #9350, 0.01 #113037), 02jm9c (0.17 #6278, 0.09 #10464, 0.01 #16746), 099d4 (0.17 #6173, 0.09 #10359, 0.01 #16641), 048hf (0.17 #5542, 0.09 #9728, 0.01 #16010), 02l0sf (0.17 #5367, 0.09 #9553, 0.01 #15835), 0flpy (0.17 #5282, 0.09 #9468, 0.01 #15750), 01pk3z (0.17 #5153, 0.09 #9339, 0.01 #15621), 026_w57 (0.17 #4775, 0.09 #8961, 0.01 #15243), 07n39 (0.09 #10054, 0.01 #116812, 0.01 #131464) >> Best rule #4705 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0d9jr; 010tkc; 0ckhc; >> query: (?x3543, 0f4dx2) <- category(?x3543, ?x134), contains(?x4600, ?x3543), contains(?x94, ?x3543), ?x4600 = 081yw, ?x94 = 09c7w0 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #113037 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 281 *> proper extension: 05d9y_; *> query: (?x3543, ?x275) <- citytown(?x3543, ?x5267), organization(?x346, ?x3543), contains(?x94, ?x3543), place_of_birth(?x275, ?x5267) *> conf = 0.01 ranks of expected_values: 612 EVAL 0778p student 02y7sr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 165.000 84.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #15645-07_hy PRED entity: 07_hy PRED relation: diet! PRED expected values: 049dyj 0170s4 01ttg5 024bbl 0137hn 01p0w_ => 2 concepts (2 used for prediction) PRED predicted values (max 10 best out of 3613): 05ry0p (0.33 #132, 0.33 #100, 0.14 #133), 0227vl (0.33 #132, 0.33 #87, 0.14 #133), 09889g (0.33 #146, 0.33 #52, 0.08 #144), 0fhxv (0.33 #145, 0.33 #46, 0.08 #144), 03bnv (0.33 #151, 0.33 #26, 0.05 #122), 026c1 (0.33 #17, 0.17 #160, 0.17 #159), 01f7dd (0.33 #70, 0.17 #153, 0.08 #156), 03xl77 (0.33 #22, 0.15 #131, 0.04 #139), 01pcq3 (0.33 #6, 0.14 #133, 0.05 #117), 0f5xn (0.33 #55, 0.12 #113, 0.05 #117) >> Best rule #132 for best value: >> intensional similarity = 292 >> extensional distance = 1 >> proper extension: 07_jd; >> query: (?x11141, ?x10792) <- diet(?x11259, ?x11141), diet(?x9442, ?x11141), diet(?x8876, ?x11141), diet(?x8839, ?x11141), diet(?x7402, ?x11141), diet(?x7236, ?x11141), diet(?x7164, ?x11141), diet(?x6236, ?x11141), diet(?x5853, ?x11141), diet(?x5550, ?x11141), diet(?x4929, ?x11141), diet(?x4701, ?x11141), diet(?x4295, ?x11141), diet(?x3176, ?x11141), diet(?x2796, ?x11141), diet(?x2793, ?x11141), diet(?x1794, ?x11141), diet(?x940, ?x11141), diet(?x875, ?x11141), ?x2793 = 0b_7k, award_nominee(?x8898, ?x4929), award_nominee(?x6157, ?x4929), award_nominee(?x397, ?x4929), award(?x3176, ?x9295), award(?x3176, ?x7535), award(?x3176, ?x3835), award(?x3176, ?x2634), award(?x3176, ?x1389), ?x9295 = 023vrq, nationality(?x3176, ?x94), people(?x1050, ?x4929), award_nominee(?x3175, ?x3176), award_nominee(?x1735, ?x3176), film(?x397, ?x696), artists(?x9630, ?x3176), artists(?x3928, ?x3176), artists(?x505, ?x3176), actor(?x1766, ?x397), participant(?x2012, ?x4929), award_nominee(?x3176, ?x1641), award_winner(?x834, ?x397), location(?x397, ?x335), artist(?x5021, ?x3176), participant(?x10792, ?x11259), ?x5550 = 01bczm, people(?x1446, ?x397), award_nominee(?x2518, ?x9442), award_winner(?x4382, ?x9442), award_winner(?x2420, ?x9442), artist(?x2931, ?x9442), location(?x11259, ?x5865), profession(?x3176, ?x131), award(?x397, ?x591), award_nominee(?x827, ?x3175), ?x2420 = 026mfs, artists(?x9630, ?x9631), artists(?x9630, ?x7951), artists(?x9630, ?x7570), artists(?x9630, ?x3667), artists(?x9630, ?x3390), artists(?x9630, ?x1206), artists(?x9630, ?x959), award_winner(?x486, ?x3175), artists(?x3319, ?x3175), ?x1794 = 058s57, ?x827 = 02l840, profession(?x11259, ?x1183), ceremony(?x1389, ?x9431), ceremony(?x1389, ?x8500), ceremony(?x1389, ?x5656), ceremony(?x1389, ?x3121), ceremony(?x1389, ?x139), profession(?x8876, ?x8709), profession(?x8876, ?x1383), profession(?x8876, ?x319), film(?x4929, ?x5945), film(?x4929, ?x3441), film(?x3176, ?x9507), gender(?x8876, ?x231), diet(?x10792, ?x3130), award(?x6383, ?x1389), award(?x5798, ?x1389), artist(?x382, ?x3175), ?x5798 = 01vvyc_, ?x3390 = 017j6, participant(?x11259, ?x3138), award(?x8060, ?x7535), award(?x5547, ?x7535), award(?x5493, ?x7535), award(?x4960, ?x7535), award(?x4646, ?x7535), award(?x3997, ?x7535), award(?x2807, ?x7535), type_of_union(?x8898, ?x566), people(?x2510, ?x3176), ?x1383 = 0np9r, ?x2807 = 03h_fk5, place_of_birth(?x8876, ?x479), participant(?x6157, ?x513), award_nominee(?x10792, ?x1343), film(?x10792, ?x6199), place_of_birth(?x3175, ?x1860), film(?x11259, ?x2947), award(?x6157, ?x112), film(?x9442, ?x5122), ?x566 = 04ztj, genre(?x5945, ?x225), award(?x9442, ?x4018), award(?x9442, ?x2576), ?x1183 = 09jwl, actor(?x9340, ?x8876), student(?x2228, ?x8876), participant(?x10792, ?x3308), ?x2796 = 0gdh5, language(?x9507, ?x254), executive_produced_by(?x3441, ?x3662), nominated_for(?x143, ?x3441), location(?x6157, ?x739), participant(?x950, ?x6157), ?x3997 = 0gbwp, titles(?x53, ?x3441), genre(?x9507, ?x258), film_crew_role(?x9507, ?x1284), film_crew_role(?x9507, ?x1171), nominated_for(?x6157, ?x54), ?x53 = 07s9rl0, currency(?x5945, ?x170), country(?x5945, ?x512), ?x6236 = 01xv77, instrumentalists(?x227, ?x3176), nominated_for(?x397, ?x9941), ?x7951 = 01vt5c_, participant(?x7613, ?x8898), parent_genre(?x119, ?x505), artists(?x505, ?x10025), artists(?x505, ?x6382), artists(?x505, ?x6124), artists(?x505, ?x3756), artists(?x505, ?x3399), ?x3756 = 01wgcvn, ?x3399 = 01gx5f, location(?x8898, ?x1523), ?x6124 = 0277c3, profession(?x3175, ?x2225), location_of_ceremony(?x8898, ?x191), ?x3121 = 09n4nb, actor(?x7424, ?x940), parent_genre(?x9630, ?x12988), gender(?x940, ?x514), award(?x3175, ?x3391), actor(?x5852, ?x5853), ?x4960 = 09889g, award_winner(?x508, ?x8898), ?x1171 = 09vw2b7, ?x3391 = 02f76h, category(?x9442, ?x134), ?x5493 = 0kr_t, award_nominee(?x794, ?x397), location(?x940, ?x362), artists(?x3928, ?x9639), artists(?x3928, ?x6819), award_winner(?x1389, ?x3234), written_by(?x2947, ?x1387), award(?x5853, ?x1670), participant(?x804, ?x10792), ?x8060 = 06mj4, award_winner(?x157, ?x3138), nominated_for(?x1104, ?x5945), parent_genre(?x2823, ?x3928), ?x5547 = 0dw4g, award(?x1270, ?x4018), ?x7570 = 01dw_f, ?x319 = 01d_h8, award_nominee(?x1538, ?x5853), ?x512 = 07ssc, ceremony(?x4018, ?x5766), ?x6382 = 01wd9lv, participant(?x8898, ?x1397), nominated_for(?x4393, ?x2947), film(?x8898, ?x814), honored_for(?x9921, ?x3441), ?x6383 = 0g824, film(?x5853, ?x5323), award_winner(?x693, ?x8898), location(?x7402, ?x6357), award_nominee(?x156, ?x3138), participant(?x1890, ?x4929), ?x9631 = 09z1lg, ?x1284 = 0ch6mp2, participant(?x6157, ?x2891), award_nominee(?x940, ?x13236), award_nominee(?x940, ?x3583), parent_genre(?x505, ?x6989), student(?x741, ?x940), category_of(?x1389, ?x2421), award_nominee(?x7402, ?x6677), award(?x7908, ?x3835), location(?x3138, ?x1131), friend(?x8898, ?x917), nominated_for(?x4295, ?x1988), profession(?x4929, ?x1359), award(?x8898, ?x401), ?x1523 = 030qb3t, ?x8500 = 0gx1673, ?x7613 = 029q_y, participant(?x4295, ?x1582), award(?x3583, ?x8250), nominated_for(?x500, ?x2947), actor(?x4108, ?x13236), ?x5766 = 013b2h, artist(?x2931, ?x9706), artist(?x2931, ?x6854), film(?x4295, ?x6855), film(?x4295, ?x2899), film(?x4295, ?x2644), student(?x122, ?x6157), nationality(?x7402, ?x429), award(?x4295, ?x1245), ?x6854 = 0178_w, program(?x1762, ?x9340), participant(?x4295, ?x1942), ?x9431 = 02cg41, nominated_for(?x2899, ?x2366), genre(?x1988, ?x571), ?x959 = 03f5spx, ?x1245 = 0gqwc, ?x5656 = 0466p0j, origin(?x8839, ?x13164), nominated_for(?x8898, ?x1120), written_by(?x3441, ?x6275), award(?x3138, ?x384), ?x4701 = 03j24kf, film_release_distribution_medium(?x5945, ?x81), nominated_for(?x3145, ?x9340), people(?x5741, ?x3583), ?x1670 = 0ck27z, film_crew_role(?x1988, ?x2154), film_release_region(?x1988, ?x87), nominated_for(?x4190, ?x3441), film(?x940, ?x9599), film(?x940, ?x7897), award(?x940, ?x686), student(?x3490, ?x4295), artists(?x5876, ?x8839), titles(?x812, ?x1988), ?x9639 = 0gps0z, nominated_for(?x68, ?x1988), award(?x3321, ?x2634), languages(?x875, ?x2502), ?x6819 = 02pt7h_, student(?x1368, ?x3583), award(?x7359, ?x2576), award(?x6467, ?x2576), profession(?x11208, ?x8709), profession(?x10963, ?x8709), ?x10025 = 02yygk, ?x11208 = 03h8_g, award(?x1800, ?x4382), ?x7908 = 01vs73g, production_companies(?x2899, ?x847), participant(?x3583, ?x1335), nominated_for(?x7402, ?x6439), ?x2154 = 01vx2h, ?x7164 = 02fybl, ?x1206 = 01vrt_c, ?x139 = 05pd94v, sibling(?x989, ?x875), location(?x10792, ?x2850), written_by(?x6855, ?x2332), place_of_birth(?x585, ?x6357), award(?x2947, ?x10747), participant(?x875, ?x3604), nominated_for(?x1063, ?x7897), ?x5876 = 0ggx5q, ?x10963 = 01xwqn, film_crew_role(?x2644, ?x1078), film_release_region(?x2644, ?x410), honored_for(?x3624, ?x9599), participant(?x1735, ?x521), ?x3667 = 0phx4, ?x6467 = 01l47f5, participant(?x1880, ?x6157), award_winner(?x762, ?x875), ?x3321 = 03bnv, role(?x7236, ?x212), award_winner(?x6439, ?x2554), ?x7359 = 01k_n63, ?x4646 = 0fhxv, ?x1270 = 0137n0, ?x9706 = 01fchy, religion(?x3175, ?x109), award_nominee(?x192, ?x875) >> conf = 0.33 => this is the best rule for 2 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 292 *> extensional distance = 1 *> proper extension: 07_jd; *> query: (?x11141, 01ttg5) <- diet(?x11259, ?x11141), diet(?x9442, ?x11141), diet(?x8876, ?x11141), diet(?x8839, ?x11141), diet(?x7402, ?x11141), diet(?x7236, ?x11141), diet(?x7164, ?x11141), diet(?x6236, ?x11141), diet(?x5853, ?x11141), diet(?x5550, ?x11141), diet(?x4929, ?x11141), diet(?x4701, ?x11141), diet(?x4295, ?x11141), diet(?x3176, ?x11141), diet(?x2796, ?x11141), diet(?x2793, ?x11141), diet(?x1794, ?x11141), diet(?x940, ?x11141), diet(?x875, ?x11141), ?x2793 = 0b_7k, award_nominee(?x8898, ?x4929), award_nominee(?x6157, ?x4929), award_nominee(?x397, ?x4929), award(?x3176, ?x9295), award(?x3176, ?x7535), award(?x3176, ?x3835), award(?x3176, ?x2634), award(?x3176, ?x1389), ?x9295 = 023vrq, nationality(?x3176, ?x94), people(?x1050, ?x4929), award_nominee(?x3175, ?x3176), award_nominee(?x1735, ?x3176), film(?x397, ?x696), artists(?x9630, ?x3176), artists(?x3928, ?x3176), artists(?x505, ?x3176), actor(?x1766, ?x397), participant(?x2012, ?x4929), award_nominee(?x3176, ?x1641), award_winner(?x834, ?x397), location(?x397, ?x335), artist(?x5021, ?x3176), participant(?x10792, ?x11259), ?x5550 = 01bczm, people(?x1446, ?x397), award_nominee(?x2518, ?x9442), award_winner(?x4382, ?x9442), award_winner(?x2420, ?x9442), artist(?x2931, ?x9442), location(?x11259, ?x5865), profession(?x3176, ?x131), award(?x397, ?x591), award_nominee(?x827, ?x3175), ?x2420 = 026mfs, artists(?x9630, ?x9631), artists(?x9630, ?x7951), artists(?x9630, ?x7570), artists(?x9630, ?x3667), artists(?x9630, ?x3390), artists(?x9630, ?x1206), artists(?x9630, ?x959), award_winner(?x486, ?x3175), artists(?x3319, ?x3175), ?x1794 = 058s57, ?x827 = 02l840, profession(?x11259, ?x1183), ceremony(?x1389, ?x9431), ceremony(?x1389, ?x8500), ceremony(?x1389, ?x5656), ceremony(?x1389, ?x3121), ceremony(?x1389, ?x139), profession(?x8876, ?x8709), profession(?x8876, ?x1383), profession(?x8876, ?x319), film(?x4929, ?x5945), film(?x4929, ?x3441), film(?x3176, ?x9507), gender(?x8876, ?x231), diet(?x10792, ?x3130), award(?x6383, ?x1389), award(?x5798, ?x1389), artist(?x382, ?x3175), ?x5798 = 01vvyc_, ?x3390 = 017j6, participant(?x11259, ?x3138), award(?x8060, ?x7535), award(?x5547, ?x7535), award(?x5493, ?x7535), award(?x4960, ?x7535), award(?x4646, ?x7535), award(?x3997, ?x7535), award(?x2807, ?x7535), type_of_union(?x8898, ?x566), people(?x2510, ?x3176), ?x1383 = 0np9r, ?x2807 = 03h_fk5, place_of_birth(?x8876, ?x479), participant(?x6157, ?x513), award_nominee(?x10792, ?x1343), film(?x10792, ?x6199), place_of_birth(?x3175, ?x1860), film(?x11259, ?x2947), award(?x6157, ?x112), film(?x9442, ?x5122), ?x566 = 04ztj, genre(?x5945, ?x225), award(?x9442, ?x4018), award(?x9442, ?x2576), ?x1183 = 09jwl, actor(?x9340, ?x8876), student(?x2228, ?x8876), participant(?x10792, ?x3308), ?x2796 = 0gdh5, language(?x9507, ?x254), executive_produced_by(?x3441, ?x3662), nominated_for(?x143, ?x3441), location(?x6157, ?x739), participant(?x950, ?x6157), ?x3997 = 0gbwp, titles(?x53, ?x3441), genre(?x9507, ?x258), film_crew_role(?x9507, ?x1284), film_crew_role(?x9507, ?x1171), nominated_for(?x6157, ?x54), ?x53 = 07s9rl0, currency(?x5945, ?x170), country(?x5945, ?x512), ?x6236 = 01xv77, instrumentalists(?x227, ?x3176), nominated_for(?x397, ?x9941), ?x7951 = 01vt5c_, participant(?x7613, ?x8898), parent_genre(?x119, ?x505), artists(?x505, ?x10025), artists(?x505, ?x6382), artists(?x505, ?x6124), artists(?x505, ?x3756), artists(?x505, ?x3399), ?x3756 = 01wgcvn, ?x3399 = 01gx5f, location(?x8898, ?x1523), ?x6124 = 0277c3, profession(?x3175, ?x2225), location_of_ceremony(?x8898, ?x191), ?x3121 = 09n4nb, actor(?x7424, ?x940), parent_genre(?x9630, ?x12988), gender(?x940, ?x514), award(?x3175, ?x3391), actor(?x5852, ?x5853), ?x4960 = 09889g, award_winner(?x508, ?x8898), ?x1171 = 09vw2b7, ?x3391 = 02f76h, category(?x9442, ?x134), ?x5493 = 0kr_t, award_nominee(?x794, ?x397), location(?x940, ?x362), artists(?x3928, ?x9639), artists(?x3928, ?x6819), award_winner(?x1389, ?x3234), written_by(?x2947, ?x1387), award(?x5853, ?x1670), participant(?x804, ?x10792), ?x8060 = 06mj4, award_winner(?x157, ?x3138), nominated_for(?x1104, ?x5945), parent_genre(?x2823, ?x3928), ?x5547 = 0dw4g, award(?x1270, ?x4018), ?x7570 = 01dw_f, ?x319 = 01d_h8, award_nominee(?x1538, ?x5853), ?x512 = 07ssc, ceremony(?x4018, ?x5766), ?x6382 = 01wd9lv, participant(?x8898, ?x1397), nominated_for(?x4393, ?x2947), film(?x8898, ?x814), honored_for(?x9921, ?x3441), ?x6383 = 0g824, film(?x5853, ?x5323), award_winner(?x693, ?x8898), location(?x7402, ?x6357), award_nominee(?x156, ?x3138), participant(?x1890, ?x4929), ?x9631 = 09z1lg, ?x1284 = 0ch6mp2, participant(?x6157, ?x2891), award_nominee(?x940, ?x13236), award_nominee(?x940, ?x3583), parent_genre(?x505, ?x6989), student(?x741, ?x940), category_of(?x1389, ?x2421), award_nominee(?x7402, ?x6677), award(?x7908, ?x3835), location(?x3138, ?x1131), friend(?x8898, ?x917), nominated_for(?x4295, ?x1988), profession(?x4929, ?x1359), award(?x8898, ?x401), ?x1523 = 030qb3t, ?x8500 = 0gx1673, ?x7613 = 029q_y, participant(?x4295, ?x1582), award(?x3583, ?x8250), nominated_for(?x500, ?x2947), actor(?x4108, ?x13236), ?x5766 = 013b2h, artist(?x2931, ?x9706), artist(?x2931, ?x6854), film(?x4295, ?x6855), film(?x4295, ?x2899), film(?x4295, ?x2644), student(?x122, ?x6157), nationality(?x7402, ?x429), award(?x4295, ?x1245), ?x6854 = 0178_w, program(?x1762, ?x9340), participant(?x4295, ?x1942), ?x9431 = 02cg41, nominated_for(?x2899, ?x2366), genre(?x1988, ?x571), ?x959 = 03f5spx, ?x1245 = 0gqwc, ?x5656 = 0466p0j, origin(?x8839, ?x13164), nominated_for(?x8898, ?x1120), written_by(?x3441, ?x6275), award(?x3138, ?x384), ?x4701 = 03j24kf, film_release_distribution_medium(?x5945, ?x81), nominated_for(?x3145, ?x9340), people(?x5741, ?x3583), ?x1670 = 0ck27z, film_crew_role(?x1988, ?x2154), film_release_region(?x1988, ?x87), nominated_for(?x4190, ?x3441), film(?x940, ?x9599), film(?x940, ?x7897), award(?x940, ?x686), student(?x3490, ?x4295), artists(?x5876, ?x8839), titles(?x812, ?x1988), ?x9639 = 0gps0z, nominated_for(?x68, ?x1988), award(?x3321, ?x2634), languages(?x875, ?x2502), ?x6819 = 02pt7h_, student(?x1368, ?x3583), award(?x7359, ?x2576), award(?x6467, ?x2576), profession(?x11208, ?x8709), profession(?x10963, ?x8709), ?x10025 = 02yygk, ?x11208 = 03h8_g, award(?x1800, ?x4382), ?x7908 = 01vs73g, production_companies(?x2899, ?x847), participant(?x3583, ?x1335), nominated_for(?x7402, ?x6439), ?x2154 = 01vx2h, ?x7164 = 02fybl, ?x1206 = 01vrt_c, ?x139 = 05pd94v, sibling(?x989, ?x875), location(?x10792, ?x2850), written_by(?x6855, ?x2332), place_of_birth(?x585, ?x6357), award(?x2947, ?x10747), participant(?x875, ?x3604), nominated_for(?x1063, ?x7897), ?x5876 = 0ggx5q, ?x10963 = 01xwqn, film_crew_role(?x2644, ?x1078), film_release_region(?x2644, ?x410), honored_for(?x3624, ?x9599), participant(?x1735, ?x521), ?x3667 = 0phx4, ?x6467 = 01l47f5, participant(?x1880, ?x6157), award_winner(?x762, ?x875), ?x3321 = 03bnv, role(?x7236, ?x212), award_winner(?x6439, ?x2554), ?x7359 = 01k_n63, ?x4646 = 0fhxv, ?x1270 = 0137n0, ?x9706 = 01fchy, religion(?x3175, ?x109), award_nominee(?x192, ?x875) *> conf = 0.33 ranks of expected_values: 40, 57, 637, 1664, 1705 EVAL 07_hy diet! 01p0w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 2.000 2.000 0.333 http://example.org/base/eating/practicer_of_diet/diet EVAL 07_hy diet! 0137hn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 2.000 2.000 0.333 http://example.org/base/eating/practicer_of_diet/diet EVAL 07_hy diet! 024bbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 2.000 2.000 0.333 http://example.org/base/eating/practicer_of_diet/diet EVAL 07_hy diet! 01ttg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 2.000 2.000 0.333 http://example.org/base/eating/practicer_of_diet/diet EVAL 07_hy diet! 0170s4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 2.000 2.000 0.333 http://example.org/base/eating/practicer_of_diet/diet EVAL 07_hy diet! 049dyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 2.000 2.000 0.333 http://example.org/base/eating/practicer_of_diet/diet #15644-02k21g PRED entity: 02k21g PRED relation: cast_members PRED expected values: 07ymr5 => 123 concepts (105 used for prediction) PRED predicted values (max 10 best out of 3): 03q45x (0.85 #4, 0.62 #3, 0.05 #56), 02k21g (0.69 #2, 0.03 #54), 07ymr5 (0.62 #1, 0.05 #56, 0.03 #53) >> Best rule #4 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 04h07s; 04s430; 030wkp; 06cddt; >> query: (?x4490, ?x905) <- cast_members(?x905, ?x4490), profession(?x4490, ?x1032) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 11 *> proper extension: 04h07s; 04s430; 030wkp; 06cddt; *> query: (?x4490, 07ymr5) <- cast_members(?x905, ?x4490), profession(?x4490, ?x1032) *> conf = 0.62 ranks of expected_values: 3 EVAL 02k21g cast_members 07ymr5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 105.000 0.851 http://example.org/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members #15643-0hfzr PRED entity: 0hfzr PRED relation: nominated_for! PRED expected values: 02r0csl 0p9sw 0gq9h => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 185): 0gq9h (0.77 #6949, 0.68 #2437, 0.68 #9345), 0gs9p (0.77 #6949, 0.68 #9345, 0.68 #11089), 04dn09n (0.77 #6949, 0.68 #9345, 0.68 #11089), 0gq_v (0.77 #6949, 0.68 #9345, 0.68 #11089), 027b9ly (0.68 #9345, 0.68 #11089, 0.68 #10868), 09d28z (0.68 #9345, 0.68 #11089, 0.68 #10868), 02qt02v (0.68 #9345, 0.67 #11088, 0.66 #6948), 0p9sw (0.43 #2404, 0.40 #451, 0.35 #885), 099c8n (0.34 #915, 0.32 #2868, 0.32 #1132), 094qd5 (0.27 #898, 0.27 #1115, 0.16 #4370) >> Best rule #6949 for best value: >> intensional similarity = 5 >> extensional distance = 676 >> proper extension: 06qv_; >> query: (?x4216, ?x1079) <- award(?x4216, ?x1079), award(?x4216, ?x1033), award_winner(?x1033, ?x192), nominated_for(?x1079, ?x167), ceremony(?x1079, ?x78) >> conf = 0.77 => this is the best rule for 4 predicted values ranks of expected_values: 1, 8, 36 EVAL 0hfzr nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0hfzr nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 73.000 73.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0hfzr nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 73.000 73.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15642-02wgbb PRED entity: 02wgbb PRED relation: titles! PRED expected values: 01z4y => 87 concepts (61 used for prediction) PRED predicted values (max 10 best out of 64): 01z4y (0.46 #446, 0.28 #4486, 0.27 #756), 01hmnh (0.43 #2586, 0.43 #2508, 0.42 #2481), 017fp (0.33 #229, 0.12 #331, 0.08 #4787), 02l7c8 (0.33 #230, 0.04 #4475, 0.03 #1575), 07s9rl0 (0.26 #4764, 0.26 #5499, 0.26 #5604), 01jfsb (0.25 #122, 0.09 #5518, 0.09 #4678), 0glj9q (0.25 #145), 024qqx (0.19 #388, 0.14 #2146, 0.14 #2250), 04xvlr (0.19 #3419, 0.17 #5398, 0.17 #3938), 09q17 (0.18 #5813, 0.18 #4868, 0.18 #5498) >> Best rule #446 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 04svwx; >> query: (?x7800, 01z4y) <- person(?x7800, ?x12065), genre(?x7800, ?x258), country(?x7800, ?x94), ?x258 = 05p553 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wgbb titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 61.000 0.464 http://example.org/media_common/netflix_genre/titles #15641-084nh PRED entity: 084nh PRED relation: people! PRED expected values: 03bkbh => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 47): 03bkbh (0.55 #648, 0.50 #879, 0.41 #1110), 041rx (0.24 #1775, 0.24 #1698, 0.22 #2470), 02w7gg (0.22 #926, 0.17 #156, 0.12 #464), 033tf_ (0.15 #2009, 0.12 #2164, 0.10 #4168), 0xnvg (0.15 #1476, 0.09 #1168, 0.09 #2015), 07mqps (0.14 #250, 0.09 #558, 0.08 #712), 0x67 (0.12 #2090, 0.12 #395, 0.12 #2245), 0d7wh (0.12 #479, 0.11 #941, 0.05 #1557), 07hwkr (0.12 #474, 0.09 #551, 0.08 #705), 013b6_ (0.12 #361, 0.07 #1054, 0.07 #1747) >> Best rule #648 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 059t6d; 01vzxmq; 04x1_w; >> query: (?x11412, 03bkbh) <- nationality(?x11412, ?x429), ?x429 = 03rt9, location(?x11412, ?x1591), languages(?x11412, ?x254) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 084nh people! 03bkbh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.545 http://example.org/people/ethnicity/people #15640-0164v PRED entity: 0164v PRED relation: countries_spoken_in! PRED expected values: 064_8sq => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 56): 064_8sq (0.35 #2577, 0.31 #186, 0.27 #74), 02h40lc (0.35 #2577, 0.30 #58, 0.29 #1290), 06nm1 (0.21 #1128, 0.21 #1408, 0.21 #1464), 071fb (0.20 #182, 0.19 #70, 0.17 #238), 0jzc (0.17 #352, 0.16 #520, 0.16 #632), 05zjd (0.12 #582, 0.11 #806, 0.11 #78), 04306rv (0.10 #901, 0.10 #509, 0.10 #621), 02ztjwg (0.10 #533, 0.10 #701, 0.10 #757), 0x82 (0.08 #104, 0.07 #272, 0.07 #328), 012v8 (0.08 #434, 0.07 #546, 0.07 #714) >> Best rule #2577 for best value: >> intensional similarity = 3 >> extensional distance = 161 >> proper extension: 0j3b; 05rgl; 059qw; 02j7k; 02613; 04swx; 0d8h4; >> query: (?x8857, ?x5607) <- adjoins(?x8857, ?x8948), administrative_parent(?x8948, ?x551), official_language(?x8948, ?x5607) >> conf = 0.35 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0164v countries_spoken_in! 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.353 http://example.org/language/human_language/countries_spoken_in #15639-0h3y PRED entity: 0h3y PRED relation: time_zones PRED expected values: 02llzg => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 12): 03plfd (0.64 #1893, 0.63 #1866, 0.58 #1839), 03bdv (0.64 #1893, 0.63 #1866, 0.58 #1839), 02llzg (0.63 #1866, 0.58 #1839, 0.58 #1799), 02hcv8 (0.35 #1882, 0.34 #1855, 0.33 #1828), 02fqwt (0.21 #575, 0.19 #653, 0.18 #40), 0gsrz4 (0.19 #386, 0.11 #138, 0.09 #321), 02lcqs (0.15 #1192, 0.15 #1857, 0.15 #1884), 02hczc (0.14 #15, 0.12 #576, 0.12 #54), 042g7t (0.14 #24, 0.11 #89, 0.09 #115), 05jphn (0.14 #26, 0.07 #39, 0.05 #52) >> Best rule #1893 for best value: >> intensional similarity = 2 >> extensional distance = 575 >> proper extension: 0nj1c; 0mmr1; 0ntwb; 0msck; >> query: (?x291, ?x10735) <- adjoins(?x291, ?x4120), time_zones(?x4120, ?x10735) >> conf = 0.64 => this is the best rule for 2 predicted values *> Best rule #1866 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 565 *> proper extension: 0nh57; 0l2mg; 0mvxt; 0mww2; *> query: (?x291, ?x5327) <- adjoins(?x10451, ?x291), adjoins(?x4521, ?x291), adjoins(?x2856, ?x4521), time_zones(?x10451, ?x5327) *> conf = 0.63 ranks of expected_values: 3 EVAL 0h3y time_zones 02llzg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 157.000 157.000 0.636 http://example.org/location/location/time_zones #15638-072twv PRED entity: 072twv PRED relation: award_winner! PRED expected values: 0d__c3 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 131): 0fz2y7 (0.40 #195, 0.12 #331, 0.12 #10477), 09gkdln (0.25 #5986, 0.20 #8979, 0.04 #5966), 0bzkvd (0.25 #5986, 0.20 #8979, 0.04 #2014), 0c6vcj (0.20 #235, 0.20 #99, 0.13 #779), 0fz20l (0.20 #596, 0.13 #732, 0.08 #868), 0fv89q (0.20 #118, 0.10 #662, 0.04 #4489), 0dthsy (0.13 #745, 0.08 #881, 0.04 #4489), 0d__c3 (0.12 #392, 0.12 #10477, 0.08 #2024), 0c53vt (0.12 #10477, 0.04 #4489, 0.04 #924), 09q_6t (0.11 #960, 0.11 #1096, 0.05 #1368) >> Best rule #195 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0584j4n; >> query: (?x2449, 0fz2y7) <- award_nominee(?x2449, ?x6921), nominated_for(?x2449, ?x785), ?x6921 = 0fmqp6 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #392 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 03gyh_z; *> query: (?x2449, 0d__c3) <- award_nominee(?x2449, ?x2801), nominated_for(?x2449, ?x785), ?x2801 = 04gmp_z *> conf = 0.12 ranks of expected_values: 8 EVAL 072twv award_winner! 0d__c3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 132.000 132.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15637-0pgm3 PRED entity: 0pgm3 PRED relation: nationality PRED expected values: 09c7w0 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.87 #805, 0.82 #1405, 0.82 #502), 0d060g (0.37 #4423, 0.30 #6736, 0.06 #308), 059rby (0.33 #8047), 0m2fr (0.29 #4825), 01x73 (0.29 #4825), 03rt9 (0.14 #113, 0.07 #214, 0.02 #514), 02jx1 (0.14 #1037, 0.11 #2141, 0.10 #4355), 07ssc (0.11 #1923, 0.11 #1319, 0.11 #416), 03rk0 (0.06 #8693, 0.05 #8593, 0.05 #8493), 03rjj (0.05 #206, 0.05 #105, 0.02 #1913) >> Best rule #805 for best value: >> intensional similarity = 3 >> extensional distance = 206 >> proper extension: 01d494; >> query: (?x12710, 09c7w0) <- place_of_birth(?x12710, ?x739), gender(?x12710, ?x231), ?x739 = 02_286 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pgm3 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.870 http://example.org/people/person/nationality #15636-06by7 PRED entity: 06by7 PRED relation: artists PRED expected values: 03c7ln 01vw87c 0fp_v1x 04rcr 0152cw 01vrncs 02whj 09qr6 01fl3 05crg7 03gr7w 02_5x9 0285c 04mn81 0161sp 0k7pf 01qqwp9 02wb6yq 03bnv 017vkx 0mgcr 07yg2 03f4xvm 03f0fnk 03f0vvr 01cblr 01mwsnc 09889g 02jqjm 013w2r 01bpnd 0127s7 01386_ 01vrnsk 02p2zq 01k3qj 043c4j 01lf293 06rgq 01nkxvx 01d_h 013w8y 017b2p 01fh0q 0167v4 07hgm 01q3_2 023p29 01p95y0 016vn3 01nz1q6 01ww_vs 01qmy04 0153nq => 76 concepts (58 used for prediction) PRED predicted values (max 10 best out of 837): 01dwrc (0.64 #24046, 0.50 #10088, 0.50 #7298), 06mt91 (0.64 #24100, 0.50 #10142, 0.47 #4884), 0197tq (0.62 #16755, 0.56 #22338, 0.50 #13964), 013w8y (0.60 #11682, 0.50 #17965, 0.50 #8892), 045zr (0.60 #11287, 0.50 #4313, 0.47 #4884), 01386_ (0.57 #15011, 0.50 #5940, 0.47 #4884), 016ntp (0.57 #14814, 0.47 #4884, 0.44 #20397), 02w4fkq (0.56 #22464, 0.50 #16881, 0.50 #8505), 09889g (0.56 #19116, 0.50 #23302, 0.50 #10042), 01vrncs (0.56 #19582, 0.50 #16790, 0.50 #6322) >> Best rule #24046 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 0ggx5q; 035wcs; >> query: (?x1572, 01dwrc) <- artists(?x1572, ?x10712), artists(?x1572, ?x9631), artists(?x1572, ?x5544), artists(?x1572, ?x1997), artist(?x2299, ?x10712), ?x5544 = 0415mzy, award_nominee(?x9631, ?x1566), role(?x1997, ?x227) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #11682 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 016jny; *> query: (?x1572, 013w8y) <- artists(?x1572, ?x6418), artists(?x1572, ?x2187), artists(?x1572, ?x1997), artists(?x1572, ?x487), parent_genre(?x114, ?x1572), ?x6418 = 013423, award_nominee(?x3426, ?x2187), award_winner(?x486, ?x487), award(?x1997, ?x4796) *> conf = 0.60 ranks of expected_values: 4, 6, 9, 10, 17, 19, 34, 58, 63, 72, 80, 86, 95, 103, 106, 118, 124, 130, 134, 142, 157, 172, 176, 179, 192, 207, 210, 211, 212, 223, 226, 230, 231, 237, 244, 245, 249, 253, 271, 288, 306, 311, 314, 321, 327, 338, 342, 360, 375, 381, 417, 480, 572, 648 EVAL 06by7 artists 0153nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01qmy04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01ww_vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01nz1q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 016vn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01p95y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 023p29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01q3_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 07hgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 0167v4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01fh0q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 017b2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 013w8y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01d_h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01nkxvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 06rgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01lf293 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 043c4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01k3qj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 02p2zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01vrnsk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01386_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 0127s7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01bpnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 013w2r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 02jqjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 09889g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01mwsnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01cblr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 03f0vvr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 03f0fnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 03f4xvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 07yg2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 0mgcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 017vkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 03bnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 02wb6yq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01qqwp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 0k7pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 0161sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 04mn81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 0285c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 02_5x9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 03gr7w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 05crg7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01fl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 09qr6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 02whj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01vrncs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 0152cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 04rcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 0fp_v1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 01vw87c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 76.000 58.000 0.636 http://example.org/music/genre/artists EVAL 06by7 artists 03c7ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 58.000 0.636 http://example.org/music/genre/artists #15635-080dwhx PRED entity: 080dwhx PRED relation: nominated_for! PRED expected values: 0bdw6t => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 191): 0ck27z (0.77 #10348, 0.69 #5407, 0.68 #4231), 0p9sw (0.44 #5898, 0.23 #8013, 0.23 #8248), 0gq9h (0.38 #8056, 0.38 #8763, 0.37 #8528), 0gs9p (0.35 #8293, 0.34 #8765, 0.33 #8530), 019f4v (0.34 #8047, 0.33 #8754, 0.33 #8519), 0gq_v (0.29 #5897, 0.27 #8247, 0.27 #8012), 0k611 (0.29 #8067, 0.29 #5952, 0.28 #8774), 0bdw6t (0.29 #85, 0.22 #4080, 0.22 #15764), 040njc (0.28 #7999, 0.27 #8234, 0.27 #11055), 02r22gf (0.27 #5906, 0.19 #15293, 0.19 #15292) >> Best rule #10348 for best value: >> intensional similarity = 2 >> extensional distance = 691 >> proper extension: 085bd1; 037q31; 09hy79; 0k2m6; 04qk12; 0j8f09z; >> query: (?x493, ?x9640) <- award(?x493, ?x9640), ceremony(?x9640, ?x1265) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 06hwzy; *> query: (?x493, 0bdw6t) <- honored_for(?x10010, ?x493), ?x10010 = 0hn821n, country_of_origin(?x493, ?x94) *> conf = 0.29 ranks of expected_values: 8 EVAL 080dwhx nominated_for! 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 72.000 72.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15634-047p7fr PRED entity: 047p7fr PRED relation: film_release_region PRED expected values: 0154j 047lj 02vzc 01p1v => 130 concepts (102 used for prediction) PRED predicted values (max 10 best out of 264): 09c7w0 (0.94 #3744, 0.93 #9290, 0.93 #8989), 0154j (0.83 #3895, 0.81 #5543, 0.79 #5394), 02vzc (0.81 #6185, 0.80 #6784, 0.80 #3937), 01znc_ (0.78 #3928, 0.73 #5427, 0.71 #5576), 0d060g (0.77 #5545, 0.75 #5396, 0.71 #602), 05b4w (0.76 #3949, 0.74 #5597, 0.74 #5448), 0ctw_b (0.75 #1065, 0.54 #3913, 0.52 #5561), 03rt9 (0.70 #3904, 0.67 #1056, 0.65 #5552), 047yc (0.67 #1068, 0.57 #621, 0.54 #3916), 015qh (0.58 #1079, 0.48 #5575, 0.48 #3927) >> Best rule #3744 for best value: >> intensional similarity = 5 >> extensional distance = 117 >> proper extension: 09sh8k; 06w99h3; 09m6kg; 047gn4y; 0bth54; 026mfbr; 06_wqk4; 092vkg; 053rxgm; 0pb33; ... >> query: (?x2961, 09c7w0) <- film_release_region(?x2961, ?x2645), film_crew_role(?x2961, ?x4305), ?x4305 = 0215hd, genre(?x2961, ?x53), country(?x766, ?x2645) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #3895 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 121 *> proper extension: 014lc_; 0401sg; 0h3xztt; 053tj7; 0407yfx; 0407yj_; 0j43swk; 03mgx6z; 0m63c; 0g4pl7z; *> query: (?x2961, 0154j) <- film_release_region(?x2961, ?x3699), film_release_region(?x2961, ?x1353), film_release_region(?x2961, ?x1003), genre(?x2961, ?x53), ?x1003 = 03gj2, contains(?x3699, ?x429), production_companies(?x2961, ?x13152), ?x1353 = 035qy *> conf = 0.83 ranks of expected_values: 2, 3, 15, 17 EVAL 047p7fr film_release_region 01p1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 130.000 102.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047p7fr film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 102.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047p7fr film_release_region 047lj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 130.000 102.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047p7fr film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 102.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15633-04bbv7 PRED entity: 04bbv7 PRED relation: artists! PRED expected values: 01243b => 134 concepts (80 used for prediction) PRED predicted values (max 10 best out of 271): 05bt6j (0.55 #16903, 0.46 #6284, 0.36 #19400), 06j6l (0.39 #12534, 0.35 #8475, 0.33 #15968), 0glt670 (0.38 #17212, 0.33 #976, 0.26 #7529), 0gywn (0.34 #12544, 0.33 #58, 0.32 #8485), 025sc50 (0.33 #986, 0.32 #12536, 0.31 #15970), 0ggx5q (0.33 #1015, 0.29 #7568, 0.24 #15999), 0m0jc (0.33 #8, 0.26 #13744, 0.25 #6249), 02lnbg (0.33 #995, 0.24 #7548, 0.23 #15979), 0y3_8 (0.33 #47, 0.23 #6288, 0.19 #16280), 059kh (0.33 #49, 0.21 #6290, 0.16 #9412) >> Best rule #16903 for best value: >> intensional similarity = 5 >> extensional distance = 344 >> proper extension: 08w4pm; 01lf293; 01v0sxx; >> query: (?x9269, 05bt6j) <- artists(?x302, ?x9269), artists(?x302, ?x6461), artists(?x302, ?x2697), ?x2697 = 033wx9, ?x6461 = 01t110 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #978 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0j1yf; 01m65sp; 0bqsy; 01tpl1p; *> query: (?x9269, 01243b) <- artists(?x302, ?x9269), profession(?x9269, ?x1032), actor(?x50, ?x9269), ?x302 = 016clz *> conf = 0.17 ranks of expected_values: 31 EVAL 04bbv7 artists! 01243b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 134.000 80.000 0.546 http://example.org/music/genre/artists #15632-0cwy47 PRED entity: 0cwy47 PRED relation: featured_film_locations PRED expected values: 06c62 => 95 concepts (47 used for prediction) PRED predicted values (max 10 best out of 102): 02_286 (0.30 #6679, 0.29 #730, 0.28 #5961), 030qb3t (0.25 #37, 0.14 #6218, 0.14 #5980), 052p7 (0.07 #293, 0.03 #768, 0.02 #6717), 06y57 (0.07 #337, 0.02 #5569, 0.02 #6043), 0gkgp (0.07 #395, 0.02 #1107, 0.02 #1344), 0ftvg (0.07 #408, 0.02 #1357), 0j5g9 (0.07 #324), 0d6lp (0.06 #782, 0.05 #1256, 0.03 #10721), 0rh6k (0.06 #6662, 0.05 #6182, 0.05 #7380), 06c62 (0.06 #601, 0.03 #839, 0.01 #1788) >> Best rule #6679 for best value: >> intensional similarity = 4 >> extensional distance = 549 >> proper extension: 03qcfvw; 09sh8k; 034qmv; 018js4; 047q2k1; 05p1tzf; 02x3lt7; 0fg04; 04dsnp; 02v63m; ... >> query: (?x951, 02_286) <- genre(?x951, ?x53), nominated_for(?x8401, ?x951), nationality(?x8401, ?x94), featured_film_locations(?x951, ?x362) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #601 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0gtx63s; 0hz6mv2; *> query: (?x951, 06c62) <- genre(?x951, ?x6887), ?x6887 = 03bxz7, film_release_region(?x951, ?x512), ?x512 = 07ssc *> conf = 0.06 ranks of expected_values: 10 EVAL 0cwy47 featured_film_locations 06c62 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 95.000 47.000 0.299 http://example.org/film/film/featured_film_locations #15631-0lsxr PRED entity: 0lsxr PRED relation: genre! PRED expected values: 0464pz 0431v3 0fhzwl 07g9f 01cvtf 0147w8 02rq7nd => 63 concepts (49 used for prediction) PRED predicted values (max 10 best out of 660): 0fhzwl (0.50 #4082, 0.50 #3802, 0.50 #3520), 0h3mh3q (0.50 #3534, 0.50 #1859, 0.43 #4657), 07g9f (0.50 #3550, 0.50 #1875, 0.43 #4673), 09v38qj (0.50 #3580, 0.50 #1905, 0.43 #4703), 01rf57 (0.50 #3695, 0.50 #1738, 0.40 #2856), 0gxr1c (0.50 #3887, 0.50 #1930, 0.33 #5568), 0gvsh7l (0.50 #3781, 0.50 #1824, 0.33 #4061), 0c3xpwy (0.50 #3726, 0.50 #1769, 0.33 #3444), 02pqs8l (0.50 #1732, 0.33 #3689, 0.33 #3407), 0d7vtk (0.50 #1865, 0.33 #3822, 0.33 #3540) >> Best rule #4082 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 09blyk; >> query: (?x604, 0fhzwl) <- genre(?x10088, ?x604), genre(?x6352, ?x604), genre(?x6079, ?x604), genre(?x3534, ?x604), award_winner(?x6079, ?x193), nominated_for(?x112, ?x6079), genre(?x5047, ?x604), produced_by(?x6352, ?x2367), country(?x10088, ?x94), ?x5047 = 02rcwq0, ?x112 = 027dtxw, award(?x3534, ?x298) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 27, 28, 29, 42 EVAL 0lsxr genre! 02rq7nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 63.000 49.000 0.500 http://example.org/tv/tv_program/genre EVAL 0lsxr genre! 0147w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 49.000 0.500 http://example.org/tv/tv_program/genre EVAL 0lsxr genre! 01cvtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 63.000 49.000 0.500 http://example.org/tv/tv_program/genre EVAL 0lsxr genre! 07g9f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 49.000 0.500 http://example.org/tv/tv_program/genre EVAL 0lsxr genre! 0fhzwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 49.000 0.500 http://example.org/tv/tv_program/genre EVAL 0lsxr genre! 0431v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 63.000 49.000 0.500 http://example.org/tv/tv_program/genre EVAL 0lsxr genre! 0464pz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 63.000 49.000 0.500 http://example.org/tv/tv_program/genre #15630-01w20rx PRED entity: 01w20rx PRED relation: student! PRED expected values: 02bpy_ => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 67): 09f2j (0.12 #686, 0.11 #1213, 0.02 #20712), 065y4w7 (0.12 #541, 0.06 #1068, 0.03 #34271), 02g839 (0.06 #3714, 0.05 #10565, 0.04 #11619), 0fr9jp (0.06 #1399, 0.04 #4034, 0.04 #1926), 0k__z (0.06 #1362, 0.04 #2416, 0.02 #3997), 019vv1 (0.06 #1502, 0.02 #4664, 0.01 #5718), 0bwfn (0.05 #34532, 0.05 #20828, 0.04 #24517), 029qzx (0.04 #1986, 0.03 #3040, 0.02 #3567), 0cwx_ (0.04 #1822, 0.03 #2876, 0.02 #4984), 0trv (0.04 #1900, 0.02 #4535, 0.01 #5589) >> Best rule #686 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 04mn81; 01wwvt2; 01w8n89; 0xsk8; 020_4z; 0ql36; >> query: (?x10628, 09f2j) <- artists(?x9013, ?x10628), artists(?x1127, ?x10628), location(?x10628, ?x4852), profession(?x10628, ?x131), ?x9013 = 09nwwf, ?x1127 = 02x8m >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w20rx student! 02bpy_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 112.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #15629-0265z9l PRED entity: 0265z9l PRED relation: profession PRED expected values: 02hrh1q => 114 concepts (98 used for prediction) PRED predicted values (max 10 best out of 78): 02hrh1q (0.91 #1665, 0.90 #2265, 0.90 #3315), 01d_h8 (0.46 #606, 0.42 #906, 0.38 #756), 02jknp (0.36 #608, 0.33 #908, 0.26 #758), 0dxtg (0.29 #10065, 0.28 #14270, 0.28 #7965), 0d1pc (0.25 #52, 0.12 #1702, 0.12 #3052), 03gjzk (0.20 #166, 0.20 #2266, 0.19 #1666), 09jwl (0.19 #8871, 0.19 #9621, 0.18 #8121), 0cbd2 (0.18 #3457, 0.17 #5257, 0.15 #7058), 018gz8 (0.16 #4818, 0.16 #5719, 0.13 #1818), 0np9r (0.15 #4072, 0.14 #12176, 0.14 #13377) >> Best rule #1665 for best value: >> intensional similarity = 4 >> extensional distance = 281 >> proper extension: 05m63c; 0m2wm; 02zq43; 04wqr; 03m8lq; 01j5x6; 0285c; 04smkr; 05wjnt; 01_rh4; ... >> query: (?x7082, 02hrh1q) <- languages(?x7082, ?x254), film(?x7082, ?x697), ?x254 = 02h40lc, people(?x5025, ?x7082) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0265z9l profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 98.000 0.908 http://example.org/people/person/profession #15628-073749 PRED entity: 073749 PRED relation: film PRED expected values: 026hxwx 0888c3 => 60 concepts (45 used for prediction) PRED predicted values (max 10 best out of 522): 0888c3 (0.49 #12456, 0.49 #17794, 0.45 #5338), 06lpmt (0.14 #680, 0.03 #44485, 0.02 #7797), 01633c (0.14 #1318, 0.02 #3097, 0.01 #8435), 01jrbv (0.14 #548, 0.02 #2327), 0gh65c5 (0.14 #592, 0.01 #4150), 01bn3l (0.14 #1349), 0421ng (0.14 #855), 017jd9 (0.07 #4333, 0.02 #18569, 0.02 #16789), 04gv3db (0.07 #748, 0.04 #7865, 0.03 #44485), 03q0r1 (0.07 #632, 0.03 #7749, 0.01 #4190) >> Best rule #12456 for best value: >> intensional similarity = 3 >> extensional distance = 527 >> proper extension: 0lgsq; 03y1mlp; 06w33f8; 027pdrh; 01f8ld; 0bytfv; 02mxbd; 01l79yc; 03mfqm; 05b2gsm; ... >> query: (?x4107, ?x8182) <- gender(?x4107, ?x514), nominated_for(?x4107, ?x8182), ?x514 = 02zsn >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1, 184 EVAL 073749 film 0888c3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 45.000 0.494 http://example.org/film/actor/film./film/performance/film EVAL 073749 film 026hxwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 60.000 45.000 0.494 http://example.org/film/actor/film./film/performance/film #15627-012v1t PRED entity: 012v1t PRED relation: legislative_sessions PRED expected values: 024tcq => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 37): 024tcq (0.75 #198, 0.67 #13, 0.62 #309), 03rtmz (0.58 #196, 0.51 #519, 0.50 #307), 032ft5 (0.51 #519, 0.50 #191, 0.50 #6), 03ww_x (0.51 #519, 0.50 #189, 0.50 #4), 02glc4 (0.51 #519, 0.50 #207, 0.38 #318), 03tcbx (0.51 #519, 0.33 #195, 0.33 #10), 04h1rz (0.51 #519, 0.33 #211, 0.33 #26), 05l2z4 (0.51 #519, 0.33 #188, 0.33 #3), 0495ys (0.51 #519, 0.33 #187, 0.33 #2), 04gp1d (0.51 #519, 0.33 #200, 0.33 #15) >> Best rule #198 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0bymv; >> query: (?x5932, 024tcq) <- legislative_sessions(?x5932, ?x6933), legislative_sessions(?x5932, ?x952), nationality(?x5932, ?x94), ?x6933 = 024tkd, ?x952 = 06f0dc >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012v1t legislative_sessions 024tcq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 173.000 0.750 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #15626-01wn718 PRED entity: 01wn718 PRED relation: artists! PRED expected values: 011j5x => 140 concepts (81 used for prediction) PRED predicted values (max 10 best out of 250): 064t9 (0.64 #322, 0.63 #1867, 0.61 #2794), 025sc50 (0.59 #360, 0.48 #1596, 0.35 #1905), 06by7 (0.51 #10531, 0.48 #1258, 0.44 #11149), 06j6l (0.43 #1594, 0.36 #358, 0.34 #1903), 0ggx5q (0.36 #389, 0.26 #1934, 0.25 #2861), 0xhtw (0.33 #635, 0.29 #10526, 0.25 #944), 03lty (0.33 #647, 0.22 #956, 0.18 #10538), 02lnbg (0.32 #369, 0.31 #1914, 0.29 #2841), 059kh (0.29 #977, 0.24 #668, 0.14 #10559), 0gywn (0.27 #368, 0.26 #1604, 0.24 #1913) >> Best rule #322 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 04qmr; >> query: (?x3977, 064t9) <- participant(?x4476, ?x3977), award(?x3977, ?x4837), award_nominee(?x521, ?x4476), ?x4837 = 03t5kl >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #960 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 0gkg6; 018gm9; 01tv3x2; 04_jsg; 07rnh; 012ycy; *> query: (?x3977, 011j5x) <- artists(?x5934, ?x3977), ?x5934 = 05r6t, artist(?x2190, ?x3977) *> conf = 0.25 ranks of expected_values: 14 EVAL 01wn718 artists! 011j5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 140.000 81.000 0.636 http://example.org/music/genre/artists #15625-03qjg PRED entity: 03qjg PRED relation: instrumentalists PRED expected values: 01vvycq 02b25y 036px 01vsyg9 01mr2g6 01tw31 => 84 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1494): 0gcs9 (0.71 #544, 0.68 #1631, 0.67 #2176), 01vsyg9 (0.71 #544, 0.68 #1631, 0.67 #6279), 01wl38s (0.71 #544, 0.68 #1631, 0.66 #2178), 015p3p (0.71 #544, 0.68 #1631, 0.66 #2178), 01vsy7t (0.71 #544, 0.68 #1631, 0.66 #2178), 0137g1 (0.71 #544, 0.68 #1631, 0.66 #2178), 01vtmw6 (0.71 #544, 0.68 #1631, 0.66 #2178), 0lgsq (0.71 #544, 0.68 #1631, 0.66 #2178), 023l9y (0.71 #544, 0.68 #1631, 0.66 #2178), 01wxdn3 (0.71 #544, 0.68 #1631, 0.66 #2178) >> Best rule #544 for best value: >> intensional similarity = 13 >> extensional distance = 1 >> proper extension: 0342h; >> query: (?x2798, ?x300) <- role(?x4429, ?x2798), role(?x2459, ?x2798), role(?x780, ?x2798), role(?x2865, ?x2798), ?x2865 = 016h9b, role(?x300, ?x2798), ?x4429 = 0g33q, role(?x74, ?x2798), instrumentalists(?x2798, ?x3200), ?x2459 = 021bmf, ?x780 = 01qzyz, ?x3200 = 01wj18h, group(?x2798, ?x997) >> conf = 0.71 => this is the best rule for 10 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 25, 93, 109, 139, 224 EVAL 03qjg instrumentalists 01tw31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 84.000 58.000 0.711 http://example.org/music/instrument/instrumentalists EVAL 03qjg instrumentalists 01mr2g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 84.000 58.000 0.711 http://example.org/music/instrument/instrumentalists EVAL 03qjg instrumentalists 01vsyg9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 58.000 0.711 http://example.org/music/instrument/instrumentalists EVAL 03qjg instrumentalists 036px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 84.000 58.000 0.711 http://example.org/music/instrument/instrumentalists EVAL 03qjg instrumentalists 02b25y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 84.000 58.000 0.711 http://example.org/music/instrument/instrumentalists EVAL 03qjg instrumentalists 01vvycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 84.000 58.000 0.711 http://example.org/music/instrument/instrumentalists #15624-01pgk0 PRED entity: 01pgk0 PRED relation: profession PRED expected values: 016z4k => 177 concepts (122 used for prediction) PRED predicted values (max 10 best out of 85): 03gjzk (0.84 #7461, 0.84 #744, 0.83 #8922), 01d_h8 (0.67 #13889, 0.66 #9352, 0.59 #3364), 0nbcg (0.67 #613, 0.55 #2219, 0.55 #1635), 0dxtg (0.66 #7460, 0.65 #8921, 0.65 #5561), 09jwl (0.63 #8633, 0.62 #1916, 0.62 #3522), 016z4k (0.52 #4092, 0.50 #1610, 0.48 #9935), 0dz3r (0.46 #4236, 0.45 #3506, 0.45 #5696), 018gz8 (0.42 #454, 0.41 #2060, 0.39 #2498), 0n1h (0.36 #4245, 0.36 #3515, 0.34 #5705), 0fnpj (0.33 #58, 0.15 #1956, 0.10 #11160) >> Best rule #7461 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 01r216; >> query: (?x11992, 03gjzk) <- producer_type(?x11992, ?x632), place_of_birth(?x11992, ?x10364), gender(?x11992, ?x514) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #4092 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 85 *> proper extension: 07c0j; 0cbm64; *> query: (?x11992, 016z4k) <- artists(?x671, ?x11992), ?x671 = 064t9, participant(?x7613, ?x11992) *> conf = 0.52 ranks of expected_values: 6 EVAL 01pgk0 profession 016z4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 177.000 122.000 0.845 http://example.org/people/person/profession #15623-04v3q PRED entity: 04v3q PRED relation: film_release_region! PRED expected values: 0c40vxk 0g9wdmc => 160 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1641): 017jd9 (0.93 #31498, 0.93 #28921, 0.88 #48249), 0jjy0 (0.93 #28468, 0.83 #31045, 0.79 #34910), 08hmch (0.90 #47786, 0.89 #28458, 0.88 #34900), 0bpm4yw (0.90 #48204, 0.85 #35318, 0.85 #28876), 09k56b7 (0.90 #29866, 0.82 #47905, 0.82 #35019), 0gj9tn5 (0.90 #29837, 0.82 #34990, 0.78 #28548), 047msdk (0.89 #28494, 0.83 #31071, 0.76 #29783), 024mpp (0.89 #28825, 0.80 #31402, 0.79 #30114), 04f52jw (0.88 #35110, 0.85 #47996, 0.83 #31245), 0661ql3 (0.88 #47959, 0.86 #29920, 0.82 #35073) >> Best rule #31498 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 06npd; 03ryn; 07f1x; >> query: (?x1061, 017jd9) <- film_release_region(?x6684, ?x1061), olympics(?x1061, ?x2966), ?x6684 = 07pd_j >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #29840 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d0vqn; 04gzd; 0chghy; ... *> query: (?x1061, 0g9wdmc) <- film_release_region(?x6321, ?x1061), film_release_region(?x428, ?x1061), ?x6321 = 0gg8z1f, ?x428 = 0h1cdwq, country(?x4045, ?x1061) *> conf = 0.86 ranks of expected_values: 21, 127 EVAL 04v3q film_release_region! 0g9wdmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 160.000 48.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04v3q film_release_region! 0c40vxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 160.000 48.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15622-01zh29 PRED entity: 01zh29 PRED relation: people! PRED expected values: 09743 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 63): 0dryh9k (0.56 #324, 0.50 #93, 0.33 #247), 041rx (0.25 #389, 0.23 #543, 0.21 #1390), 0bpjh3 (0.25 #102, 0.17 #256, 0.11 #333), 02sch9 (0.25 #112, 0.11 #343, 0.03 #651), 0x67 (0.25 #934, 0.10 #3553, 0.09 #3090), 033tf_ (0.20 #469, 0.15 #1085, 0.15 #854), 07hwkr (0.17 #705, 0.10 #474, 0.10 #551), 01rv7x (0.17 #270, 0.06 #809, 0.04 #963), 03kbr (0.17 #280), 0xnvg (0.12 #398, 0.11 #706, 0.09 #1322) >> Best rule #324 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05nw9m; 08624h; 044pqn; >> query: (?x8073, 0dryh9k) <- place_of_birth(?x8073, ?x9466), ?x9466 = 0dlv0, profession(?x8073, ?x319) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #999 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 09r1j5; *> query: (?x8073, 09743) <- gender(?x8073, ?x231), religion(?x8073, ?x492), ?x492 = 0flw86 *> conf = 0.02 ranks of expected_values: 52 EVAL 01zh29 people! 09743 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 124.000 124.000 0.556 http://example.org/people/ethnicity/people #15621-02ljhg PRED entity: 02ljhg PRED relation: genre PRED expected values: 0hfjk => 70 concepts (64 used for prediction) PRED predicted values (max 10 best out of 89): 0hfjk (0.72 #3052, 0.69 #705, 0.69 #647), 01jfsb (0.43 #246, 0.40 #363, 0.38 #480), 04xvlr (0.42 #706, 0.39 #823, 0.38 #940), 05p553 (0.39 #1060, 0.35 #1645, 0.35 #3759), 03k9fj (0.38 #127, 0.35 #597, 0.33 #10), 060__y (0.32 #837, 0.29 #954, 0.28 #720), 01hmnh (0.22 #16, 0.21 #721, 0.18 #955), 04pbhw (0.22 #53, 0.13 #405, 0.10 #522), 02n4kr (0.21 #242, 0.20 #359, 0.14 #476), 082gq (0.19 #850, 0.19 #967, 0.18 #733) >> Best rule #3052 for best value: >> intensional similarity = 3 >> extensional distance = 889 >> proper extension: 03kq98; 01q_y0; >> query: (?x7757, ?x8280) <- titles(?x8280, ?x7757), nominated_for(?x338, ?x7757), genre(?x148, ?x8280) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ljhg genre 0hfjk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 64.000 0.715 http://example.org/film/film/genre #15620-0dqyw PRED entity: 0dqyw PRED relation: place_founded! PRED expected values: 01hpf6 => 89 concepts (81 used for prediction) PRED predicted values (max 10 best out of 86): 075znj (0.12 #76, 0.06 #298, 0.05 #409), 01swdw (0.12 #80, 0.05 #413, 0.03 #635), 01hpf6 (0.07 #2224, 0.07 #889, 0.06 #1223), 01nds (0.07 #2224, 0.07 #889, 0.06 #1223), 06nfl (0.06 #333, 0.05 #444, 0.03 #666), 07rfp (0.06 #326, 0.05 #437, 0.03 #659), 0260p2 (0.06 #322, 0.05 #433, 0.03 #655), 06zl7g (0.06 #321, 0.05 #432, 0.03 #654), 05b0f7 (0.06 #309, 0.05 #420, 0.03 #642), 01bvx1 (0.06 #305, 0.05 #416, 0.03 #638) >> Best rule #76 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 049wm; >> query: (?x10980, 075znj) <- contains(?x252, ?x10980), ?x252 = 03_3d, administrative_division(?x10980, ?x8889) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #2224 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 98 *> proper extension: 03902; *> query: (?x10980, ?x11304) <- citytown(?x11304, ?x10980), jurisdiction_of_office(?x1195, ?x10980) *> conf = 0.07 ranks of expected_values: 3 EVAL 0dqyw place_founded! 01hpf6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 81.000 0.125 http://example.org/organization/organization/place_founded #15619-0f4yh PRED entity: 0f4yh PRED relation: list PRED expected values: 05glt => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 3): 05glt (0.26 #9, 0.25 #114, 0.25 #107), 09g7thr (0.02 #729), 01ptsx (0.01 #733) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 03cfkrw; 0b2qtl; 0kt_4; >> query: (?x3535, 05glt) <- award(?x3535, ?x500), nominated_for(?x198, ?x3535), ?x198 = 040njc, story_by(?x3535, ?x1387) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f4yh list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.265 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #15618-01wbg84 PRED entity: 01wbg84 PRED relation: nominated_for PRED expected values: 024hbv => 98 concepts (48 used for prediction) PRED predicted values (max 10 best out of 251): 06zsk51 (0.47 #19407, 0.46 #22645, 0.42 #12940), 07bwr (0.28 #27501, 0.28 #29122, 0.28 #24265), 0640m69 (0.28 #27501, 0.28 #29122, 0.28 #24265), 02tgz4 (0.28 #27501, 0.28 #29122, 0.28 #24265), 0f2sx4 (0.28 #27501, 0.28 #29122, 0.28 #24265), 027pfg (0.28 #27501, 0.28 #29122, 0.28 #24265), 0f4_l (0.28 #27501, 0.28 #29122, 0.28 #24265), 01vfqh (0.28 #27501, 0.28 #29122, 0.28 #24265), 01c22t (0.28 #27501, 0.28 #29122, 0.28 #24265), 0cp0t91 (0.28 #27501, 0.28 #29122, 0.28 #24265) >> Best rule #19407 for best value: >> intensional similarity = 3 >> extensional distance = 559 >> proper extension: 01zmpg; 0277990; 0bqsy; 018z_c; 08n__5; 030b93; 01q9b9; 07q0g5; 0f87jy; 01gw8b; ... >> query: (?x368, ?x493) <- award_nominee(?x368, ?x369), actor(?x493, ?x368), award(?x368, ?x401) >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wbg84 nominated_for 024hbv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 48.000 0.469 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15617-0g5pvv PRED entity: 0g5pvv PRED relation: language PRED expected values: 02h40lc => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 43): 02h40lc (0.95 #3094, 0.95 #3270, 0.95 #3973), 02bv9 (0.63 #2446, 0.61 #2564, 0.04 #900), 012w70 (0.33 #187, 0.07 #246, 0.07 #2459), 064_8sq (0.25 #313, 0.20 #894, 0.19 #953), 06nm1 (0.25 #127, 0.19 #651, 0.18 #709), 02bjrlw (0.25 #117, 0.19 #524, 0.17 #175), 0653m (0.25 #186, 0.08 #2223, 0.05 #2458), 04306rv (0.19 #296, 0.18 #703, 0.17 #761), 0jzc (0.17 #78, 0.12 #136, 0.08 #194), 0349s (0.17 #102, 0.06 #335, 0.05 #800) >> Best rule #3094 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 01f7gh; 0283_zv; 0946bb; 0gjcrrw; 02qzh2; 0ch3qr1; 0kvbl6; 02q7yfq; 0pd64; 025twgf; ... >> query: (?x6077, 02h40lc) <- nominated_for(?x836, ?x6077), genre(?x6077, ?x604), film_release_region(?x6077, ?x94), language(?x6077, ?x8650) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g5pvv language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.955 http://example.org/film/film/language #15616-0tln7 PRED entity: 0tln7 PRED relation: place! PRED expected values: 0tln7 => 117 concepts (57 used for prediction) PRED predicted values (max 10 best out of 95): 0fvvz (0.28 #7216, 0.25 #24, 0.20 #539), 013d7t (0.25 #123, 0.20 #638, 0.07 #1153), 0f2tj (0.20 #686, 0.06 #16510), 04ly1 (0.19 #9283, 0.17 #10831, 0.13 #8767), 09c7w0 (0.19 #9283, 0.17 #10831, 0.13 #8767), 013gz (0.07 #1532, 0.07 #2047, 0.06 #2562), 0tn9j (0.07 #1421, 0.07 #1936, 0.06 #2451), 0tk02 (0.07 #1358, 0.07 #1873, 0.06 #2388), 0tln7 (0.06 #16510, 0.02 #3094, 0.01 #10314), 019tfm (0.02 #3094, 0.01 #10314, 0.01 #17541) >> Best rule #7216 for best value: >> intensional similarity = 5 >> extensional distance = 112 >> proper extension: 0r04p; 0bxbr; 0bxbb; 0_lr1; 0ttxp; 0f0z_; 0mzy7; 0k_mf; 0xn7b; 0fttg; ... >> query: (?x5015, ?x1248) <- citytown(?x14319, ?x5015), contains(?x3908, ?x5015), contains(?x94, ?x5015), ?x94 = 09c7w0, administrative_division(?x1248, ?x3908) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #16510 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 201 *> proper extension: 0qm40; *> query: (?x5015, ?x1248) <- state(?x5015, ?x3908), category(?x5015, ?x134), state(?x1248, ?x3908), adjoins(?x3908, ?x3634) *> conf = 0.06 ranks of expected_values: 9 EVAL 0tln7 place! 0tln7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 117.000 57.000 0.284 http://example.org/location/hud_county_place/place #15615-0ly5n PRED entity: 0ly5n PRED relation: film PRED expected values: 0htww => 138 concepts (120 used for prediction) PRED predicted values (max 10 best out of 529): 04954r (0.11 #22108, 0.10 #9571, 0.09 #14944), 02qr3k8 (0.11 #3081, 0.05 #22782, 0.05 #46065), 0pd57 (0.11 #2491, 0.02 #13237, 0.02 #16819), 03rg2b (0.08 #10049, 0.07 #15422, 0.05 #29750), 0k5fg (0.07 #20792, 0.06 #19001, 0.05 #26165), 01lbcqx (0.07 #22943, 0.07 #30107, 0.07 #15779), 0jvt9 (0.07 #22031, 0.07 #14867, 0.05 #29195), 04v89z (0.07 #13957, 0.05 #22912, 0.05 #46195), 0k4kk (0.07 #16390, 0.07 #14599, 0.05 #21763), 01f39b (0.07 #29635, 0.05 #22471, 0.05 #36799) >> Best rule #22108 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 0gm34; 01l3j; >> query: (?x3800, 04954r) <- type_of_union(?x3800, ?x566), celebrities_impersonated(?x3649, ?x3800), nationality(?x3800, ?x94), people(?x3799, ?x3800) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #9470 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 012v9y; *> query: (?x3800, 0htww) <- celebrities_impersonated(?x3649, ?x3800), profession(?x3800, ?x1032), place_of_death(?x3800, ?x242), location(?x3800, ?x3818) *> conf = 0.03 ranks of expected_values: 161 EVAL 0ly5n film 0htww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 138.000 120.000 0.107 http://example.org/film/actor/film./film/performance/film #15614-06r_by PRED entity: 06r_by PRED relation: cinematography! PRED expected values: 016kv6 => 92 concepts (42 used for prediction) PRED predicted values (max 10 best out of 335): 0ch26b_ (0.75 #2327, 0.74 #1661, 0.03 #8323), 060__7 (0.75 #2327, 0.74 #1661, 0.03 #8323), 0gwjw0c (0.75 #2327, 0.74 #1661, 0.03 #9655), 084qpk (0.09 #23, 0.04 #1351, 0.04 #1684), 0422v0 (0.09 #331, 0.03 #663, 0.02 #1327), 0g_zyp (0.09 #299, 0.03 #631, 0.02 #1295), 02ptczs (0.09 #298, 0.03 #630, 0.02 #1294), 02b6n9 (0.09 #293, 0.03 #625, 0.02 #1289), 0c0zq (0.09 #291, 0.03 #623, 0.02 #1287), 03n0cd (0.09 #283, 0.03 #615, 0.02 #1279) >> Best rule #2327 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 0854hr; >> query: (?x6062, ?x1916) <- nominated_for(?x6062, ?x1916), cinematography(?x9056, ?x6062), film(?x1244, ?x9056) >> conf = 0.75 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 06r_by cinematography! 016kv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 42.000 0.754 http://example.org/film/film/cinematography #15613-089kpp PRED entity: 089kpp PRED relation: music! PRED expected values: 084qpk => 136 concepts (85 used for prediction) PRED predicted values (max 10 best out of 917): 02rrfzf (0.29 #1333, 0.09 #2340, 0.06 #9389), 0pc62 (0.16 #10071, 0.03 #2066, 0.03 #3073), 0ds33 (0.16 #10071, 0.01 #8095, 0.01 #9102), 02qzh2 (0.16 #10071), 02725hs (0.16 #10071), 0f40w (0.16 #10071), 084qpk (0.14 #1079, 0.06 #2086, 0.05 #3093), 03tbg6 (0.14 #1943, 0.03 #2950, 0.03 #20143), 04g73n (0.14 #1807, 0.03 #2814, 0.03 #20143), 01br2w (0.14 #1020, 0.03 #2027, 0.03 #20143) >> Best rule #1333 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02_hj4; >> query: (?x12768, 02rrfzf) <- nominated_for(?x12768, ?x3093), award_nominee(?x523, ?x12768), ?x3093 = 04tqtl, profession(?x523, ?x319) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1079 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02_hj4; *> query: (?x12768, 084qpk) <- nominated_for(?x12768, ?x3093), award_nominee(?x523, ?x12768), ?x3093 = 04tqtl, profession(?x523, ?x319) *> conf = 0.14 ranks of expected_values: 7 EVAL 089kpp music! 084qpk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 136.000 85.000 0.286 http://example.org/film/film/music #15612-0181dw PRED entity: 0181dw PRED relation: artist PRED expected values: 02r3zy 0137n0 0892sx 01n8gr 025ldg 0133x7 01dq9q 01wj5hp => 159 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1111): 01vxlbm (0.50 #4051, 0.44 #5571, 0.40 #6331), 011z3g (0.50 #4239, 0.44 #5759, 0.40 #6519), 0892sx (0.50 #3952, 0.44 #5472, 0.30 #6232), 01k3qj (0.50 #2778, 0.20 #6583, 0.20 #3542), 0bk1p (0.50 #2878, 0.20 #6683, 0.20 #3642), 01vrz41 (0.50 #2337, 0.20 #6142, 0.20 #3101), 017mbb (0.50 #2893, 0.20 #6698, 0.20 #3657), 0232lm (0.50 #2870, 0.20 #6675, 0.20 #3634), 01s21dg (0.40 #3349, 0.38 #4110, 0.33 #5630), 0m_v0 (0.40 #3265, 0.33 #979, 0.33 #218) >> Best rule #4051 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 015_1q; >> query: (?x7089, 01vxlbm) <- artist(?x7089, ?x7902), artist(?x7089, ?x7794), artist(?x7089, ?x6383), artist(?x7089, ?x3539), ?x6383 = 0g824, instrumentalists(?x227, ?x7794), people(?x268, ?x7902), nationality(?x3539, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3952 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 015_1q; *> query: (?x7089, 0892sx) <- artist(?x7089, ?x7902), artist(?x7089, ?x7794), artist(?x7089, ?x6383), artist(?x7089, ?x3539), ?x6383 = 0g824, instrumentalists(?x227, ?x7794), people(?x268, ?x7902), nationality(?x3539, ?x94) *> conf = 0.50 ranks of expected_values: 3, 57, 169, 225, 284, 457, 693, 1050 EVAL 0181dw artist 01wj5hp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 159.000 69.000 0.500 http://example.org/music/record_label/artist EVAL 0181dw artist 01dq9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 159.000 69.000 0.500 http://example.org/music/record_label/artist EVAL 0181dw artist 0133x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 159.000 69.000 0.500 http://example.org/music/record_label/artist EVAL 0181dw artist 025ldg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 159.000 69.000 0.500 http://example.org/music/record_label/artist EVAL 0181dw artist 01n8gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 159.000 69.000 0.500 http://example.org/music/record_label/artist EVAL 0181dw artist 0892sx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 159.000 69.000 0.500 http://example.org/music/record_label/artist EVAL 0181dw artist 0137n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 159.000 69.000 0.500 http://example.org/music/record_label/artist EVAL 0181dw artist 02r3zy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 159.000 69.000 0.500 http://example.org/music/record_label/artist #15611-0hpt3 PRED entity: 0hpt3 PRED relation: company! PRED expected values: 05_wyz => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 35): 0dq3c (0.51 #1899, 0.50 #2112, 0.45 #3165), 014l7h (0.50 #785, 0.50 #489, 0.43 #405), 05_wyz (0.45 #2125, 0.44 #902, 0.41 #2167), 02y6fz (0.40 #148, 0.22 #444, 0.18 #570), 09d6p2 (0.34 #2126, 0.33 #270, 0.33 #185), 02211by (0.24 #1730, 0.22 #426, 0.20 #130), 02k13d (0.21 #772, 0.20 #476, 0.18 #1024), 01rk91 (0.20 #466, 0.20 #128, 0.17 #677), 0142rn (0.20 #1750, 0.17 #1920, 0.15 #2133), 04192r (0.18 #629, 0.18 #586, 0.13 #1934) >> Best rule #1899 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 01s73z; 07l1c; 0z90c; 01dfb6; >> query: (?x2021, 0dq3c) <- company(?x346, ?x2021), contact_category(?x2021, ?x897), list(?x2021, ?x7472) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #2125 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 0537b; *> query: (?x2021, 05_wyz) <- company(?x4682, ?x2021), ?x4682 = 0dq_5, list(?x2021, ?x7472) *> conf = 0.45 ranks of expected_values: 3 EVAL 0hpt3 company! 05_wyz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 160.000 160.000 0.509 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #15610-024_dt PRED entity: 024_dt PRED relation: award_winner PRED expected values: 0h6sv => 49 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1576): 0h6sv (0.40 #56913, 0.37 #49488, 0.36 #51964), 0164r9 (0.33 #51963, 0.30 #42063, 0.30 #49487), 09889g (0.29 #8546, 0.23 #15969, 0.14 #33291), 0149xx (0.27 #34638, 0.26 #17316, 0.22 #44539), 0127gn (0.27 #34638, 0.26 #17316, 0.22 #44539), 03_0p (0.27 #34638, 0.26 #17316, 0.22 #44539), 01vrlr4 (0.27 #34638, 0.26 #17316, 0.19 #47013), 014hr0 (0.27 #34638, 0.26 #17316, 0.19 #47013), 01wd9lv (0.24 #1420, 0.21 #3893, 0.16 #6366), 0dw4g (0.20 #1258, 0.18 #3731, 0.12 #6204) >> Best rule #56913 for best value: >> intensional similarity = 5 >> extensional distance = 246 >> proper extension: 02f6yz; >> query: (?x12458, ?x13167) <- award_winner(?x12458, ?x352), award(?x13167, ?x12458), award_winner(?x2324, ?x13167), ceremony(?x2324, ?x3121), ?x3121 = 09n4nb >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024_dt award_winner 0h6sv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 23.000 0.396 http://example.org/award/award_category/winners./award/award_honor/award_winner #15609-02prwdh PRED entity: 02prwdh PRED relation: language PRED expected values: 02h40lc => 71 concepts (61 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.89 #2363, 0.89 #1694, 0.89 #1391), 03x42 (0.66 #2967, 0.61 #1753, 0.08 #110), 064_8sq (0.27 #22, 0.23 #142, 0.15 #624), 04306rv (0.25 #65, 0.10 #367, 0.10 #607), 06b_j (0.15 #143, 0.09 #23, 0.08 #385), 06nm1 (0.11 #253, 0.10 #1098, 0.10 #192), 06mp7 (0.09 #16, 0.08 #136, 0.02 #197), 02bjrlw (0.09 #603, 0.08 #363, 0.07 #243), 012w70 (0.08 #73, 0.03 #1950, 0.03 #796), 02hwyss (0.08 #102, 0.01 #345) >> Best rule #2363 for best value: >> intensional similarity = 4 >> extensional distance = 992 >> proper extension: 0jym0; 0c_j9x; 09p7fh; 0cfhfz; 02r_pp; 02pg45; 04gcyg; 01_1hw; 0170xl; 07bxqz; ... >> query: (?x5425, 02h40lc) <- film_release_distribution_medium(?x5425, ?x81), film(?x7147, ?x5425), place_of_birth(?x7147, ?x6357), award_winner(?x1265, ?x7147) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02prwdh language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 61.000 0.892 http://example.org/film/film/language #15608-043q6n_ PRED entity: 043q6n_ PRED relation: nationality PRED expected values: 09c7w0 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 54): 09c7w0 (0.86 #802, 0.84 #1402, 0.82 #4009), 0345h (0.34 #2904, 0.04 #3708, 0.02 #532), 02jx1 (0.11 #133, 0.10 #4841, 0.10 #5341), 07ssc (0.09 #4223, 0.09 #4823, 0.09 #5323), 0d060g (0.08 #307, 0.05 #407, 0.05 #1608), 0h7x (0.08 #335, 0.05 #435, 0.04 #3708), 03rjj (0.05 #405, 0.05 #205, 0.05 #506), 03gj2 (0.05 #226, 0.04 #3708, 0.03 #1527), 03rk0 (0.04 #3708, 0.04 #8256, 0.03 #8456), 03spz (0.04 #3708, 0.03 #968, 0.03 #1068) >> Best rule #802 for best value: >> intensional similarity = 2 >> extensional distance = 63 >> proper extension: 01_rh4; 04h07s; 0hgqq; 021r7r; 01mr2g6; 0f1jhc; 0ccqd7; 0sw62; 05xd_v; 049sb; ... >> query: (?x1417, 09c7w0) <- student(?x4955, ?x1417), ?x4955 = 09f2j >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043q6n_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.862 http://example.org/people/person/nationality #15607-044mfr PRED entity: 044mfr PRED relation: role PRED expected values: 042v_gx => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 90): 0342h (0.50 #5, 0.42 #2449, 0.40 #5102), 03bx0bm (0.33 #107, 0.27 #1702, 0.26 #4461), 028tv0 (0.33 #107, 0.27 #1702, 0.26 #4461), 05r5c (0.31 #2983, 0.30 #5106, 0.26 #2453), 02sgy (0.29 #2451, 0.25 #7, 0.24 #751), 05842k (0.25 #81, 0.21 #3055, 0.20 #188), 01vj9c (0.25 #17, 0.17 #5114, 0.15 #4371), 0l14qv (0.25 #6, 0.15 #5103, 0.15 #4360), 0dwt5 (0.25 #88, 0.07 #832, 0.06 #939), 01679d (0.25 #54, 0.03 #798, 0.03 #905) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02fybl; >> query: (?x5589, 0342h) <- participant(?x4782, ?x5589), ?x4782 = 0bksh, role(?x5589, ?x227), profession(?x5589, ?x1032) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2454 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 0p5mw; *> query: (?x5589, 042v_gx) <- place_of_birth(?x5589, ?x1523), profession(?x5589, ?x2659), ?x2659 = 039v1, nationality(?x5589, ?x94) *> conf = 0.22 ranks of expected_values: 14 EVAL 044mfr role 042v_gx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 159.000 159.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #15606-0j4b PRED entity: 0j4b PRED relation: country! PRED expected values: 01cgz => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 51): 01cgz (0.70 #65, 0.64 #1034, 0.63 #218), 071t0 (0.67 #278, 0.66 #431, 0.65 #380), 07gyv (0.57 #211, 0.56 #262, 0.54 #415), 01lb14 (0.54 #271, 0.54 #373, 0.53 #424), 06f41 (0.54 #270, 0.53 #423, 0.51 #219), 06wrt (0.52 #374, 0.51 #425, 0.50 #272), 0486tv (0.52 #89, 0.49 #242, 0.48 #293), 0194d (0.50 #403, 0.50 #301, 0.49 #454), 07jbh (0.49 #441, 0.48 #390, 0.48 #288), 03hr1p (0.48 #381, 0.48 #279, 0.47 #432) >> Best rule #65 for best value: >> intensional similarity = 2 >> extensional distance = 31 >> proper extension: 07ytt; >> query: (?x6428, 01cgz) <- countries_within(?x2467, ?x6428), religion(?x6428, ?x962) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j4b country! 01cgz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.697 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #15605-01n7q PRED entity: 01n7q PRED relation: location! PRED expected values: 03jldb 014z8v => 196 concepts (149 used for prediction) PRED predicted values (max 10 best out of 2375): 02vx4c2 (0.45 #194792, 0.45 #295876, 0.44 #320530), 0721cy (0.45 #194792, 0.45 #295876, 0.44 #320530), 0259r0 (0.28 #283548, 0.27 #318064, 0.24 #212051), 03q_w5 (0.28 #283548, 0.27 #318064, 0.24 #212051), 01zwy (0.25 #11543, 0.25 #9078, 0.25 #6613), 032r1 (0.25 #12125, 0.25 #9660, 0.25 #7195), 04z0g (0.25 #11009, 0.25 #8544, 0.25 #6079), 01797x (0.25 #11910, 0.25 #9445, 0.25 #6980), 099d4 (0.25 #12175, 0.25 #9710, 0.25 #7245), 06crk (0.25 #11116, 0.25 #8651, 0.25 #6186) >> Best rule #194792 for best value: >> intensional similarity = 2 >> extensional distance = 96 >> proper extension: 0bwtj; >> query: (?x1227, ?x2285) <- place_of_birth(?x2285, ?x1227), adjoins(?x1227, ?x726) >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #30377 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 11 *> proper extension: 0r1yc; *> query: (?x1227, 014z8v) <- jurisdiction_of_office(?x2387, ?x1227), featured_film_locations(?x3859, ?x1227) *> conf = 0.08 ranks of expected_values: 315, 1553 EVAL 01n7q location! 014z8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 196.000 149.000 0.453 http://example.org/people/person/places_lived./people/place_lived/location EVAL 01n7q location! 03jldb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 196.000 149.000 0.453 http://example.org/people/person/places_lived./people/place_lived/location #15604-09zmys PRED entity: 09zmys PRED relation: film PRED expected values: 01gvts => 120 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1055): 06g77c (0.72 #17866, 0.65 #51808, 0.64 #66097), 03nqnnk (0.22 #19653, 0.17 #12506, 0.15 #23227), 02qmsr (0.12 #406, 0.06 #3979, 0.06 #9339), 011ywj (0.12 #1434, 0.04 #10367, 0.03 #5007), 02cbhg (0.12 #1402, 0.04 #10335, 0.03 #17481), 06gb1w (0.12 #733, 0.04 #9666, 0.02 #50754), 01qb5d (0.12 #138, 0.04 #9071, 0.02 #32298), 0d90m (0.12 #8, 0.04 #8941, 0.02 #32168), 0h1fktn (0.10 #11687, 0.06 #18834, 0.04 #2754), 031hcx (0.09 #4846, 0.06 #1273, 0.04 #10206) >> Best rule #17866 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 0bkmf; >> query: (?x5521, ?x2547) <- award_winner(?x7451, ?x5521), nominated_for(?x5521, ?x2547), participant(?x5521, ?x8143) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #6613 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 01_j71; 01wf86y; *> query: (?x5521, 01gvts) <- award(?x5521, ?x4225), nominated_for(?x5521, ?x2547), ?x4225 = 09qvf4 *> conf = 0.02 ranks of expected_values: 518 EVAL 09zmys film 01gvts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 120.000 75.000 0.723 http://example.org/film/actor/film./film/performance/film #15603-03q5t PRED entity: 03q5t PRED relation: instrumentalists PRED expected values: 01vsy3q 01vsqvs => 94 concepts (55 used for prediction) PRED predicted values (max 10 best out of 3022): 01vw20_ (0.75 #13760, 0.62 #10040, 0.60 #18699), 01vsy3q (0.75 #2467, 0.71 #2465, 0.62 #9253), 0lsw9 (0.75 #2467, 0.71 #2465, 0.62 #9253), 028qdb (0.75 #2467, 0.71 #2465, 0.62 #9253), 0b_j2 (0.75 #2467, 0.71 #2465, 0.62 #9253), 04zwjd (0.75 #2467, 0.71 #2465, 0.62 #9253), 0bvzp (0.75 #2467, 0.71 #2465, 0.62 #9253), 03ryks (0.71 #2465, 0.62 #9253, 0.59 #1848), 03h502k (0.71 #2465, 0.62 #9253, 0.59 #1848), 04s5_s (0.71 #2465, 0.62 #9253, 0.59 #1848) >> Best rule #13760 for best value: >> intensional similarity = 19 >> extensional distance = 6 >> proper extension: 02hnl; >> query: (?x74, 01vw20_) <- role(?x74, ?x745), role(?x2785, ?x74), role(?x2158, ?x74), role(?x1166, ?x74), role(?x885, ?x74), role(?x645, ?x74), ?x885 = 0dwtp, role(?x1818, ?x74), ?x745 = 01vj9c, ?x1166 = 05148p4, role(?x2923, ?x2158), group(?x2158, ?x4791), role(?x433, ?x74), instrumentalists(?x74, ?x642), ?x2923 = 02k856, instrumentalists(?x2158, ?x226), ?x645 = 028tv0, ?x2785 = 0jtg0, group(?x74, ?x4715) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2467 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x74, ?x6399) <- role(?x74, ?x745), role(?x4429, ?x74), role(?x2158, ?x74), role(?x1166, ?x74), role(?x885, ?x74), ?x885 = 0dwtp, role(?x6399, ?x74), role(?x5126, ?x74), ?x745 = 01vj9c, ?x1166 = 05148p4, ?x2158 = 01dnws, role(?x74, ?x6039), role(?x2865, ?x74), role(?x433, ?x74), instrumentalists(?x74, ?x642), ?x2865 = 016h9b, ?x6039 = 05kms, ?x4429 = 0g33q, award_winner(?x594, ?x6399), ?x5126 = 03h502k *> conf = 0.75 ranks of expected_values: 2, 465 EVAL 03q5t instrumentalists 01vsqvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 94.000 55.000 0.750 http://example.org/music/instrument/instrumentalists EVAL 03q5t instrumentalists 01vsy3q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 55.000 0.750 http://example.org/music/instrument/instrumentalists #15602-0pqzh PRED entity: 0pqzh PRED relation: religion PRED expected values: 07w8f => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 28): 03_gx (0.50 #14, 0.21 #1634, 0.21 #1184), 0c8wxp (0.29 #456, 0.27 #1221, 0.26 #816), 092bf5 (0.29 #196, 0.17 #286, 0.07 #1096), 0kpl (0.25 #280, 0.25 #10, 0.23 #865), 01lp8 (0.14 #181, 0.11 #226, 0.08 #271), 051kv (0.14 #185, 0.08 #275, 0.04 #2435), 0kq2 (0.08 #1503, 0.08 #378, 0.08 #2808), 07w8f (0.08 #395, 0.07 #665, 0.04 #1295), 0631_ (0.08 #323, 0.05 #2348, 0.04 #2438), 02vxy_ (0.06 #439, 0.02 #1879, 0.02 #1069) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 019z7q; >> query: (?x11404, 03_gx) <- influenced_by(?x11404, ?x5336), profession(?x11404, ?x987), written_by(?x11681, ?x11404), ?x5336 = 02kz_ >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #395 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 014nvr; *> query: (?x11404, 07w8f) <- nationality(?x11404, ?x94), influenced_by(?x2343, ?x11404), ?x2343 = 0jt90f5 *> conf = 0.08 ranks of expected_values: 8 EVAL 0pqzh religion 07w8f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 163.000 163.000 0.500 http://example.org/people/person/religion #15601-09dvgb8 PRED entity: 09dvgb8 PRED relation: award PRED expected values: 02r22gf => 100 concepts (69 used for prediction) PRED predicted values (max 10 best out of 242): 02r22gf (0.50 #439, 0.35 #844, 0.34 #1249), 09sb52 (0.31 #3688, 0.29 #3283, 0.26 #4903), 018wng (0.21 #446, 0.07 #41, 0.03 #2879), 0gs9p (0.20 #79, 0.18 #2917, 0.14 #20260), 0gqwc (0.20 #74, 0.13 #21476, 0.13 #24315), 04kxsb (0.20 #126, 0.13 #21476, 0.13 #24315), 05p1dby (0.20 #107, 0.12 #512, 0.06 #3755), 018wdw (0.19 #13775, 0.18 #15397, 0.14 #27962), 040njc (0.19 #2846, 0.14 #20260, 0.14 #22288), 0gq9h (0.18 #2915, 0.17 #482, 0.14 #20260) >> Best rule #439 for best value: >> intensional similarity = 2 >> extensional distance = 22 >> proper extension: 05f260; >> query: (?x7648, 02r22gf) <- award(?x7648, ?x500), ?x500 = 0p9sw >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09dvgb8 award 02r22gf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 69.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15600-0c6vcj PRED entity: 0c6vcj PRED relation: award_winner PRED expected values: 05x2t7 05v1sb 053vcrp => 32 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1125): 081nh (0.18 #9576, 0.14 #18830, 0.13 #14204), 0cw67g (0.15 #6027, 0.14 #1408, 0.13 #2947), 02fgpf (0.14 #263, 0.13 #1802, 0.12 #3341), 0133sq (0.14 #1442, 0.12 #4520, 0.12 #6061), 0146pg (0.13 #9317, 0.12 #3159, 0.11 #13945), 02sj1x (0.13 #9762, 0.11 #8223, 0.11 #23634), 02cqbx (0.13 #10111, 0.10 #20905, 0.07 #22444), 076lxv (0.13 #9328, 0.09 #13956, 0.08 #6250), 072twv (0.13 #9581, 0.07 #8042, 0.07 #21914), 09r9m7 (0.13 #10136, 0.07 #22469, 0.07 #24008) >> Best rule #9576 for best value: >> intensional similarity = 17 >> extensional distance = 36 >> proper extension: 0gwdy4; >> query: (?x7226, 081nh) <- award_winner(?x7226, ?x4423), honored_for(?x7226, ?x10276), ceremony(?x2222, ?x7226), ceremony(?x1323, ?x7226), award(?x144, ?x2222), award_winner(?x2222, ?x771), award(?x1779, ?x2222), list(?x10276, ?x3004), award(?x10005, ?x1323), award(?x2963, ?x1323), award(?x483, ?x1323), place_of_death(?x10005, ?x739), influenced_by(?x1573, ?x483), award_winner(?x1323, ?x1934), location(?x483, ?x1275), religion(?x2963, ?x1985), award_winner(?x2924, ?x4423) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #6446 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 24 *> proper extension: 0fzrtf; 04110lv; 0d__c3; *> query: (?x7226, 05x2t7) <- award_winner(?x7226, ?x510), honored_for(?x7226, ?x499), ceremony(?x3617, ?x7226), ceremony(?x3066, ?x7226), ceremony(?x2222, ?x7226), ceremony(?x1862, ?x7226), ceremony(?x1323, ?x7226), ceremony(?x1245, ?x7226), ceremony(?x720, ?x7226), ceremony(?x591, ?x7226), ceremony(?x500, ?x7226), ?x2222 = 0gs96, ?x3617 = 0gvx_, ?x3066 = 0gqy2, ?x1862 = 0gr51, ?x1245 = 0gqwc, category_of(?x720, ?x3459), ceremony(?x720, ?x5369), ?x500 = 0p9sw, ?x1323 = 0gqz2, ?x591 = 0f4x7, ?x5369 = 0ftlxj, award_winner(?x720, ?x382) *> conf = 0.08 ranks of expected_values: 69, 484, 486 EVAL 0c6vcj award_winner 053vcrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 17.000 0.184 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0c6vcj award_winner 05v1sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 17.000 0.184 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0c6vcj award_winner 05x2t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 32.000 17.000 0.184 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15599-024lt6 PRED entity: 024lt6 PRED relation: film_release_region PRED expected values: 0d060g 0d0vqn 0345h 0h7x 03spz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 215): 0d0vqn (0.91 #468, 0.90 #6, 0.90 #1238), 03h64 (0.87 #63, 0.75 #525, 0.75 #1603), 0345h (0.84 #29, 0.82 #1569, 0.76 #337), 06bnz (0.83 #43, 0.70 #1583, 0.61 #505), 035qy (0.78 #1571, 0.78 #31, 0.75 #493), 0d060g (0.76 #5, 0.71 #1545, 0.65 #1083), 01mjq (0.76 #41, 0.51 #503, 0.50 #349), 03spz (0.75 #94, 0.63 #1634, 0.57 #556), 05v8c (0.75 #16, 0.53 #1556, 0.53 #478), 0b90_r (0.73 #3, 0.67 #1543, 0.65 #465) >> Best rule #468 for best value: >> intensional similarity = 5 >> extensional distance = 148 >> proper extension: 08hmch; 0gtvrv3; 0bh8yn3; 07x4qr; 023gxx; 0c3xw46; 02dpl9; 0gy2y8r; 06tpmy; 047vnkj; ... >> query: (?x9941, 0d0vqn) <- nominated_for(?x397, ?x9941), film_release_region(?x9941, ?x1892), film_release_region(?x9941, ?x1499), ?x1892 = 02vzc, ?x1499 = 01znc_ >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 6, 8, 16 EVAL 024lt6 film_release_region 03spz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 024lt6 film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 98.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 024lt6 film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 024lt6 film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 024lt6 film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15598-01jfsb PRED entity: 01jfsb PRED relation: genre! PRED expected values: 03cf9ly => 62 concepts (32 used for prediction) PRED predicted values (max 10 best out of 804): 0d_rw (0.62 #4233, 0.33 #4514, 0.33 #3379), 03g9xj (0.56 #4442, 0.50 #4161, 0.33 #3307), 01f3p_ (0.50 #4021, 0.43 #3451, 0.40 #2318), 0qmk5 (0.50 #3340, 0.43 #3624, 0.33 #1360), 0266s9 (0.50 #3352, 0.40 #2503, 0.33 #5336), 03_b1g (0.50 #3371, 0.40 #2522, 0.33 #1391), 02rcwq0 (0.50 #3200, 0.40 #2070, 0.33 #1220), 05sy2k_ (0.50 #4029, 0.33 #4310, 0.33 #2891), 06w7mlh (0.50 #4146, 0.33 #3008, 0.33 #463), 0170k0 (0.50 #3286, 0.33 #1306, 0.33 #1023) >> Best rule #4233 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 09n3wz; >> query: (?x812, 0d_rw) <- genre(?x9514, ?x812), genre(?x2009, ?x812), award(?x2009, ?x783), ?x9514 = 0h3mh3q, honored_for(?x1265, ?x2009) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #4209 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 09n3wz; *> query: (?x812, 03cf9ly) <- genre(?x9514, ?x812), genre(?x2009, ?x812), award(?x2009, ?x783), ?x9514 = 0h3mh3q, honored_for(?x1265, ?x2009) *> conf = 0.38 ranks of expected_values: 41 EVAL 01jfsb genre! 03cf9ly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 62.000 32.000 0.625 http://example.org/tv/tv_program/genre #15597-0162c8 PRED entity: 0162c8 PRED relation: film PRED expected values: 02gpkt => 125 concepts (106 used for prediction) PRED predicted values (max 10 best out of 485): 04xbq3 (0.58 #31324, 0.55 #31323, 0.35 #6594), 06dfz1 (0.55 #31323, 0.35 #6594, 0.28 #34621), 0crd8q6 (0.21 #37920, 0.21 #45341, 0.02 #74207), 04sh80 (0.17 #812, 0.06 #4109, 0.03 #3284), 0gj9tn5 (0.17 #139, 0.03 #2611, 0.03 #3436), 0322yj (0.17 #820, 0.03 #3292, 0.03 #4117), 0315rp (0.17 #689, 0.03 #3161, 0.03 #3986), 011xg5 (0.17 #686, 0.03 #3158, 0.03 #3983), 0k_9j (0.17 #670, 0.03 #3142, 0.03 #3967), 02mmwk (0.17 #612, 0.03 #3084, 0.03 #3909) >> Best rule #31324 for best value: >> intensional similarity = 3 >> extensional distance = 231 >> proper extension: 030pr; 0p51w; 03bw6; >> query: (?x1416, ?x9188) <- nominated_for(?x1416, ?x9188), film(?x1416, ?x755), film(?x4165, ?x9188) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0162c8 film 02gpkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 106.000 0.578 http://example.org/film/director/film #15596-04yqlk PRED entity: 04yqlk PRED relation: award_nominee PRED expected values: 0jmj => 78 concepts (33 used for prediction) PRED predicted values (max 10 best out of 600): 0jmj (0.81 #62985, 0.81 #62984, 0.81 #30321), 03x3qv (0.81 #62985, 0.81 #62984, 0.81 #30321), 04yqlk (0.67 #8029, 0.62 #1032, 0.58 #5697), 030hbp (0.60 #9145, 0.40 #4480, 0.38 #2148), 01ggc9 (0.60 #9101, 0.38 #2104, 0.33 #6769), 02mqc4 (0.53 #7960, 0.50 #5628, 0.39 #18660), 011_3s (0.40 #7729, 0.39 #18660, 0.30 #3064), 02qgyv (0.25 #5164, 0.25 #499, 0.20 #7496), 018ygt (0.20 #3792, 0.19 #39654, 0.17 #48986), 0dvmd (0.20 #3026, 0.19 #39654, 0.17 #48986) >> Best rule #62985 for best value: >> intensional similarity = 3 >> extensional distance = 1481 >> proper extension: 01pbxb; 03qcq; 016qtt; 028q6; 0l6qt; 04qvl7; 0cb77r; 05cljf; 0byfz; 0h5f5n; ... >> query: (?x4408, ?x820) <- type_of_union(?x4408, ?x566), award_nominee(?x820, ?x4408), award_nominee(?x1870, ?x820) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 04yqlk award_nominee 0jmj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 33.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15595-06m61 PRED entity: 06m61 PRED relation: profession PRED expected values: 039v1 => 93 concepts (88 used for prediction) PRED predicted values (max 10 best out of 98): 02hrh1q (0.70 #2091, 0.69 #1495, 0.65 #9804), 016z4k (0.69 #892, 0.47 #152, 0.45 #4), 0dz3r (0.49 #1630, 0.45 #298, 0.44 #3262), 01d_h8 (0.48 #450, 0.37 #2674, 0.36 #2822), 0cbd2 (0.41 #4306, 0.39 #3416, 0.39 #4008), 0dxtg (0.38 #2830, 0.38 #2682, 0.36 #2978), 01c72t (0.36 #24, 0.33 #616, 0.29 #1652), 039v1 (0.36 #3296, 0.35 #776, 0.25 #1664), 03gjzk (0.36 #2832, 0.35 #2684, 0.34 #1200), 0n1h (0.30 #604, 0.27 #900, 0.22 #1640) >> Best rule #2091 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 02dh86; 0hfml; 0pqzh; 054c1; 049sb; >> query: (?x4840, 02hrh1q) <- award_winner(?x594, ?x4840), inductee(?x1091, ?x4840), profession(?x4840, ?x1183) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #3296 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 255 *> proper extension: 02fybl; 01r4zfk; 09g0h; *> query: (?x4840, 039v1) <- role(?x4840, ?x227), profession(?x4840, ?x1183), nationality(?x4840, ?x94) *> conf = 0.36 ranks of expected_values: 8 EVAL 06m61 profession 039v1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 93.000 88.000 0.697 http://example.org/people/person/profession #15594-0n0bp PRED entity: 0n0bp PRED relation: nominated_for! PRED expected values: 0gr0m 0gq9h => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 192): 0gq9h (0.79 #753, 0.40 #1677, 0.38 #1215), 04kxsb (0.50 #783, 0.25 #5778, 0.24 #6703), 04dn09n (0.38 #726, 0.27 #957, 0.25 #263), 02w9sd7 (0.38 #812, 0.12 #1043, 0.12 #8090), 0gqwc (0.33 #751, 0.25 #288, 0.25 #57), 0p9sw (0.33 #713, 0.22 #1637, 0.21 #2561), 0gr51 (0.33 #767, 0.21 #998, 0.20 #1229), 09qv_s (0.29 #802, 0.12 #1264, 0.12 #8090), 0gr0m (0.25 #750, 0.25 #287, 0.24 #1674), 0l8z1 (0.25 #743, 0.25 #280, 0.21 #1667) >> Best rule #753 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 064lsn; >> query: (?x592, 0gq9h) <- award_winner(?x592, ?x8240), award(?x592, ?x591), ?x591 = 0f4x7, participant(?x5239, ?x8240) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 0n0bp nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.792 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0n0bp nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 67.000 67.000 0.792 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15593-03hhd3 PRED entity: 03hhd3 PRED relation: location PRED expected values: 030qb3t => 109 concepts (108 used for prediction) PRED predicted values (max 10 best out of 109): 030qb3t (0.26 #20940, 0.22 #37799, 0.19 #35391), 04jpl (0.11 #20876, 0.10 #37735, 0.09 #45758), 0cr3d (0.10 #143, 0.09 #48292, 0.08 #35453), 059rby (0.07 #818, 0.06 #20875, 0.05 #37734), 01_d4 (0.07 #902, 0.05 #1704, 0.05 #3308), 0r0m6 (0.06 #216, 0.03 #15458, 0.02 #16260), 01n7q (0.05 #20920, 0.04 #37779, 0.04 #35371), 0rh6k (0.05 #2410, 0.05 #806, 0.05 #4014), 02jx1 (0.05 #871, 0.03 #69, 0.03 #4079), 01531 (0.04 #15398, 0.04 #11386, 0.04 #48305) >> Best rule #20940 for best value: >> intensional similarity = 3 >> extensional distance = 873 >> proper extension: 017r2; >> query: (?x8587, 030qb3t) <- location(?x8587, ?x739), award(?x8587, ?x2071), film_release_region(?x204, ?x739) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hhd3 location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 108.000 0.264 http://example.org/people/person/places_lived./people/place_lived/location #15592-037s9x PRED entity: 037s9x PRED relation: organization! PRED expected values: 060c4 => 217 concepts (217 used for prediction) PRED predicted values (max 10 best out of 14): 060c4 (0.85 #457, 0.85 #340, 0.84 #418), 0dq_5 (0.34 #1023, 0.31 #867, 0.30 #724), 07xl34 (0.26 #102, 0.20 #1116, 0.19 #1870), 05k17c (0.14 #33, 0.14 #1125, 0.13 #618), 0hm4q (0.05 #2036, 0.05 #736, 0.05 #1945), 05c0jwl (0.05 #1240, 0.05 #1227, 0.05 #1279), 08jcfy (0.05 #90, 0.02 #1234, 0.02 #1416), 0fkzq (0.03 #2368, 0.02 #2577, 0.02 #2683), 09n5b9 (0.03 #2368, 0.02 #2577, 0.02 #2683), 0f6c3 (0.03 #2368, 0.02 #2577, 0.02 #2683) >> Best rule #457 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 04bbpm; 0jpn8; >> query: (?x1981, 060c4) <- colors(?x1981, ?x1101), currency(?x1981, ?x170), colors(?x260, ?x1101) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 037s9x organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 217.000 217.000 0.849 http://example.org/organization/role/leaders./organization/leadership/organization #15591-01jq4b PRED entity: 01jq4b PRED relation: school! PRED expected values: 06x68 049n7 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 88): 0jmj7 (0.66 #2934, 0.65 #3198, 0.65 #2758), 05m_8 (0.21 #1147, 0.18 #531, 0.18 #1851), 06rpd (0.16 #598, 0.11 #2465, 0.11 #1214), 01slc (0.15 #1906, 0.15 #586, 0.14 #2434), 051vz (0.15 #551, 0.14 #199, 0.13 #1167), 07l8x (0.15 #593, 0.11 #2465, 0.11 #2265), 06rny (0.14 #226, 0.11 #2465, 0.11 #578), 0jm4b (0.14 #225, 0.11 #2465, 0.08 #137), 0jm64 (0.14 #230, 0.11 #2465, 0.08 #142), 01yjl (0.13 #1703, 0.11 #2465, 0.11 #1879) >> Best rule #2934 for best value: >> intensional similarity = 3 >> extensional distance = 152 >> proper extension: 08815; 0cchk3; 015fsv; 02rky4; 02c9dj; >> query: (?x5907, 0jmj7) <- currency(?x5907, ?x170), school(?x4571, ?x5907), ?x170 = 09nqf >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #1679 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 82 *> proper extension: 01b1mj; 01j_06; 0f1nl; 04hgpt; 01tx9m; 017v3q; 02y9bj; 01qgr3; 0gy3w; 012mzw; *> query: (?x5907, 06x68) <- currency(?x5907, ?x170), school(?x1161, ?x5907), contains(?x94, ?x5907) *> conf = 0.12 ranks of expected_values: 18, 21 EVAL 01jq4b school! 049n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 141.000 141.000 0.662 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01jq4b school! 06x68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 141.000 141.000 0.662 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #15590-0ds3t5x PRED entity: 0ds3t5x PRED relation: nominated_for! PRED expected values: 0f_nbyh 0c422z4 => 93 concepts (86 used for prediction) PRED predicted values (max 10 best out of 190): 0gqyl (0.69 #9822, 0.68 #10270, 0.66 #10269), 0gs9p (0.49 #277, 0.40 #1169, 0.36 #4294), 019f4v (0.49 #271, 0.38 #4288, 0.37 #1163), 0k611 (0.43 #1177, 0.40 #285, 0.32 #4302), 04dn09n (0.43 #254, 0.40 #1146, 0.27 #3825), 0gqy2 (0.40 #330, 0.32 #1222, 0.28 #4347), 0gq_v (0.37 #240, 0.32 #4257, 0.31 #3811), 02pqp12 (0.35 #1166, 0.25 #274, 0.20 #3845), 02qyntr (0.34 #1280, 0.25 #388, 0.22 #3959), 0f4x7 (0.32 #246, 0.27 #4263, 0.25 #1138) >> Best rule #9822 for best value: >> intensional similarity = 3 >> extensional distance = 970 >> proper extension: 02nf2c; 02rjv2w; 011yfd; 02pg45; 059lwy; 05_61y; 08cfr1; 03j63k; 0m123; 097h2; ... >> query: (?x385, ?x618) <- award(?x385, ?x618), award(?x3461, ?x618), award_nominee(?x3461, ?x157) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1122 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 140 *> proper extension: 083shs; 011yxg; 0ds11z; 048scx; 020fcn; 01719t; 09z2b7; 029zqn; 09cr8; 0cz_ym; ... *> query: (?x385, 0f_nbyh) <- film_crew_role(?x385, ?x137), nominated_for(?x1162, ?x385), nominated_for(?x624, ?x385), ?x1162 = 099c8n *> conf = 0.27 ranks of expected_values: 21, 117 EVAL 0ds3t5x nominated_for! 0c422z4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 93.000 86.000 0.690 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0ds3t5x nominated_for! 0f_nbyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 93.000 86.000 0.690 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15589-012c6x PRED entity: 012c6x PRED relation: profession PRED expected values: 02krf9 099md => 86 concepts (68 used for prediction) PRED predicted values (max 10 best out of 60): 01d_h8 (0.70 #1182, 0.67 #1329, 0.33 #3387), 0dxtg (0.61 #1189, 0.58 #1336, 0.33 #160), 0kyk (0.40 #28, 0.29 #322, 0.25 #469), 02krf9 (0.33 #172, 0.23 #1201, 0.21 #1348), 0np9r (0.20 #2224, 0.17 #3694, 0.16 #8086), 09jwl (0.17 #2810, 0.17 #752, 0.16 #7073), 0cbd2 (0.17 #154, 0.14 #1330, 0.14 #1183), 0xzm (0.17 #253), 018gz8 (0.16 #8086, 0.13 #3690, 0.13 #6483), 04j5jl (0.16 #8086) >> Best rule #1182 for best value: >> intensional similarity = 3 >> extensional distance = 398 >> proper extension: 04rs03; 04l3_z; 01g4zr; 01p45_v; 01c59k; 01c58j; 01wg982; 021lby; 05h72z; 0674cw; ... >> query: (?x773, 01d_h8) <- place_of_birth(?x773, ?x1523), profession(?x773, ?x524), ?x524 = 02jknp >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #172 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 09d5h; *> query: (?x773, 02krf9) <- award_winner(?x7551, ?x773), award_winner(?x7510, ?x773), ?x7551 = 014gjp *> conf = 0.33 ranks of expected_values: 4, 46 EVAL 012c6x profession 099md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 86.000 68.000 0.698 http://example.org/people/person/profession EVAL 012c6x profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 68.000 0.698 http://example.org/people/person/profession #15588-028cg00 PRED entity: 028cg00 PRED relation: film! PRED expected values: 04jb97 => 94 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1126): 014x77 (0.33 #2172, 0.08 #118636, 0.07 #4253), 07f3xb (0.33 #2322, 0.08 #118636, 0.07 #4403), 014g_s (0.33 #3887, 0.08 #118636, 0.03 #5968), 08x5c_ (0.33 #1949, 0.03 #24843, 0.03 #31086), 01ypsj (0.33 #1678), 06k02 (0.33 #373), 0151ns (0.14 #4255, 0.06 #20906, 0.04 #33392), 03kpvp (0.11 #23526, 0.07 #25607, 0.07 #63070), 041c4 (0.11 #11301, 0.10 #13382, 0.07 #25870), 06ltr (0.11 #11353, 0.09 #28003, 0.07 #30084) >> Best rule #2172 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 03t97y; >> query: (?x1889, 014x77) <- film(?x2580, ?x1889), genre(?x1889, ?x225), prequel(?x1889, ?x1074), ?x2580 = 0227tr, film_crew_role(?x1889, ?x2095), ?x2095 = 0dxtw >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #53447 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 117 *> proper extension: 0432_5; *> query: (?x1889, 04jb97) <- film(?x2580, ?x1889), genre(?x1889, ?x225), prequel(?x1889, ?x1074), film_crew_role(?x1889, ?x137), award_winner(?x704, ?x2580) *> conf = 0.02 ranks of expected_values: 832 EVAL 028cg00 film! 04jb97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 94.000 57.000 0.333 http://example.org/film/actor/film./film/performance/film #15587-085pr PRED entity: 085pr PRED relation: student! PRED expected values: 05mv4 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 205): 08815 (0.33 #2, 0.05 #3158, 0.05 #1580), 0bwfn (0.11 #3430, 0.10 #4482, 0.10 #8164), 02zd460 (0.10 #695, 0.07 #1221, 0.01 #19632), 07tgn (0.10 #1069, 0.06 #8960, 0.05 #5277), 065y4w7 (0.10 #2118, 0.06 #10535, 0.05 #4748), 03ksy (0.08 #9048, 0.06 #5365, 0.06 #10100), 0ylvj (0.07 #1252, 0.05 #726, 0.02 #1778), 09f2j (0.06 #3314, 0.05 #8048, 0.05 #684), 07tg4 (0.06 #9029, 0.05 #5346, 0.04 #11133), 07w0v (0.06 #2124, 0.03 #7910, 0.03 #5806) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03fqv5; >> query: (?x3527, 08815) <- nominated_for(?x3527, ?x8000), written_by(?x518, ?x3527), ?x8000 = 0b4lkx, award_winner(?x601, ?x3527) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 085pr student! 05mv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 119.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #15586-0nt4s PRED entity: 0nt4s PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 94 concepts (84 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.89 #448, 0.88 #518, 0.88 #222), 0nt4s (0.25 #459, 0.13 #289, 0.13 #803), 04_1l0v (0.13 #354), 02jx1 (0.07 #298, 0.06 #751, 0.06 #860), 03rjj (0.05 #164, 0.04 #177, 0.03 #190), 0f8l9c (0.04 #349, 0.03 #477, 0.03 #384), 06mkj (0.02 #78, 0.01 #173, 0.01 #186), 07ssc (0.01 #942, 0.01 #994), 03rt9 (0.01 #532, 0.01 #602) >> Best rule #448 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 0l30v; >> query: (?x11235, 09c7w0) <- county(?x2879, ?x11235), currency(?x11235, ?x170), contains(?x94, ?x2879) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nt4s second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 84.000 0.887 http://example.org/location/country/second_level_divisions #15585-0lcdk PRED entity: 0lcdk PRED relation: risk_factors PRED expected values: 01hbgs => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 100): 0jpmt (0.89 #1817, 0.67 #812, 0.50 #781), 01hbgs (0.88 #1694, 0.67 #813, 0.67 #812), 0c58k (0.68 #2094, 0.68 #2061, 0.68 #1941), 0fltx (0.67 #854, 0.67 #812, 0.50 #792), 05zppz (0.67 #812, 0.53 #1671, 0.44 #1278), 0k95h (0.67 #812, 0.44 #1278, 0.38 #1343), 01psyx (0.57 #1970, 0.33 #155, 0.25 #446), 01mtqf (0.57 #1970, 0.21 #2286, 0.13 #988), 01rt5h (0.57 #1970, 0.21 #2286, 0.13 #988), 0d19y2 (0.56 #1197, 0.51 #1724, 0.50 #567) >> Best rule #1817 for best value: >> intensional similarity = 22 >> extensional distance = 17 >> proper extension: 0qcr0; 01mtqf; >> query: (?x11392, 0jpmt) <- risk_factors(?x11392, ?x13738), risk_factors(?x11392, ?x11393), risk_factors(?x11392, ?x268), risk_factors(?x6655, ?x268), risk_factors(?x6655, ?x11160), risk_factors(?x6655, ?x8524), ?x11160 = 012jc, symptom_of(?x10717, ?x6655), symptom_of(?x4905, ?x6655), risk_factors(?x5784, ?x6655), ?x8524 = 01hbgs, ?x4905 = 01j6t0, people(?x6655, ?x6975), risk_factors(?x11659, ?x13738), risk_factors(?x3799, ?x13738), people(?x3799, ?x4553), symptom_of(?x6780, ?x11659), ?x4553 = 01vyv9, ?x10717 = 0cjf0, risk_factors(?x5118, ?x11393), ?x6780 = 0j5fv, ?x5118 = 01bcp7 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1694 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 15 *> proper extension: 0h9dj; 017s1k; 0m32h; 01k9gb; *> query: (?x11392, 01hbgs) <- risk_factors(?x11392, ?x13738), risk_factors(?x11392, ?x11393), risk_factors(?x11392, ?x268), risk_factors(?x6655, ?x268), risk_factors(?x6483, ?x268), ?x6655 = 09d11, risk_factors(?x5118, ?x11393), risk_factors(?x6483, ?x1158), risk_factors(?x11659, ?x13738), risk_factors(?x8675, ?x13738), risk_factors(?x3799, ?x13738), ?x1158 = 02y0js, people(?x3799, ?x3800), people(?x3799, ?x1774), ?x5118 = 01bcp7, ?x3800 = 0ly5n, symptom_of(?x10717, ?x8675), symptom_of(?x4905, ?x8675), ?x1774 = 01gzm2, ?x10717 = 0cjf0, symptom_of(?x9509, ?x11659), ?x9509 = 0gxb2, ?x4905 = 01j6t0 *> conf = 0.88 ranks of expected_values: 2 EVAL 0lcdk risk_factors 01hbgs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 52.000 0.895 http://example.org/medicine/disease/risk_factors #15584-019f9z PRED entity: 019f9z PRED relation: award PRED expected values: 01c9jp 031b3h => 124 concepts (119 used for prediction) PRED predicted values (max 10 best out of 292): 01bgqh (0.44 #2862, 0.32 #4474, 0.31 #2459), 02hgm4 (0.40 #137, 0.27 #1346, 0.25 #943), 02f6ym (0.37 #3482, 0.16 #7512, 0.16 #5497), 01by1l (0.35 #5350, 0.34 #3335, 0.33 #2932), 02f73b (0.34 #3510, 0.21 #2704, 0.20 #4719), 02f71y (0.34 #3406, 0.15 #7436, 0.14 #4212), 02f777 (0.34 #3533, 0.14 #2727, 0.13 #5548), 03qbh5 (0.33 #3026, 0.29 #3429, 0.24 #4638), 02w7fs (0.33 #757, 0.05 #30633, 0.05 #37083), 02681vq (0.33 #455, 0.04 #4082, 0.04 #4485) >> Best rule #2862 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 01kwsg; 027kmrb; 03rwz3; 01gbn6; >> query: (?x6651, 01bgqh) <- award_nominee(?x6651, ?x12102), award_nominee(?x12102, ?x1660), ?x1660 = 012x4t >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #201 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 032nwy; *> query: (?x6651, 031b3h) <- artists(?x505, ?x6651), place_of_birth(?x6651, ?x2254), ?x2254 = 0dclg, ?x505 = 03_d0 *> conf = 0.20 ranks of expected_values: 26, 35 EVAL 019f9z award 031b3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 124.000 119.000 0.444 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f9z award 01c9jp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 124.000 119.000 0.444 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15583-01wb95 PRED entity: 01wb95 PRED relation: film! PRED expected values: 017jv5 => 61 concepts (41 used for prediction) PRED predicted values (max 10 best out of 57): 016tt2 (0.40 #4, 0.24 #79, 0.22 #229), 03xq0f (0.20 #5, 0.19 #230, 0.18 #305), 05qd_ (0.20 #9, 0.15 #159, 0.15 #309), 086k8 (0.17 #302, 0.17 #227, 0.16 #152), 017s11 (0.15 #528, 0.12 #1505, 0.11 #2111), 016tw3 (0.13 #2879, 0.12 #1664, 0.12 #86), 0g1rw (0.11 #608, 0.09 #760, 0.09 #233), 054g1r (0.10 #410, 0.10 #335, 0.08 #260), 0jz9f (0.09 #526, 0.07 #451, 0.06 #1053), 017jv5 (0.09 #240, 0.09 #315, 0.08 #90) >> Best rule #4 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0cwy47; 0dr_4; 026qnh6; >> query: (?x3783, 016tt2) <- genre(?x3783, ?x162), nominated_for(?x2209, ?x3783), ?x2209 = 0gr42, language(?x3783, ?x254), ?x162 = 04xvlr >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #240 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 76 *> proper extension: 07gp9; 0gzy02; 04v8x9; 0ds33; 0bth54; 0pc62; 0fr63l; 01vksx; 017gl1; 0k2sk; ... *> query: (?x3783, 017jv5) <- genre(?x3783, ?x162), nominated_for(?x2209, ?x3783), ?x2209 = 0gr42, language(?x3783, ?x254), titles(?x162, ?x144) *> conf = 0.09 ranks of expected_values: 10 EVAL 01wb95 film! 017jv5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 61.000 41.000 0.400 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15582-01wp8w7 PRED entity: 01wp8w7 PRED relation: award_winner! PRED expected values: 01mhwk => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 106): 02rjjll (0.20 #428, 0.19 #569, 0.18 #992), 01mhwk (0.17 #12269, 0.14 #605, 0.13 #1169), 01mh_q (0.15 #512, 0.14 #653, 0.13 #1217), 0gpjbt (0.14 #2144, 0.12 #170, 0.09 #4964), 0466p0j (0.14 #1768, 0.11 #358, 0.10 #5011), 013b2h (0.13 #3041, 0.13 #5156, 0.12 #5015), 01s695 (0.13 #2118, 0.11 #3951, 0.11 #849), 01bx35 (0.12 #430, 0.12 #571, 0.11 #853), 02cg41 (0.12 #549, 0.12 #690, 0.11 #1113), 05pd94v (0.12 #3950, 0.12 #143, 0.10 #4937) >> Best rule #428 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0kftt; 04n32; >> query: (?x1521, 02rjjll) <- inductee(?x1091, ?x1521), role(?x1521, ?x214), gender(?x1521, ?x231) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #12269 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1134 *> proper extension: 0f721s; 030_1_; 03jvmp; 0g5lhl7; 01w92; 01p5yn; 031rq5; 0g9zcgx; 0283xx2; *> query: (?x1521, ?x2704) <- award_winner(?x219, ?x1521), award_winner(?x4018, ?x1521), award_winner(?x2704, ?x219) *> conf = 0.17 ranks of expected_values: 2 EVAL 01wp8w7 award_winner! 01mhwk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 126.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15581-05lb65 PRED entity: 05lb65 PRED relation: award_nominee PRED expected values: 030znt 026zvx7 02s_qz 03x16f => 67 concepts (30 used for prediction) PRED predicted values (max 10 best out of 642): 05lb30 (0.83 #2325, 0.82 #4651, 0.82 #4653), 030znt (0.83 #2325, 0.82 #4651, 0.82 #4653), 02s_qz (0.83 #2325, 0.82 #4651, 0.81 #55818), 06hgym (0.83 #2325, 0.82 #4651, 0.81 #55818), 05lb65 (0.73 #6201, 0.73 #1548, 0.71 #3874), 026zvx7 (0.57 #2880, 0.55 #554, 0.47 #5207), 03x16f (0.57 #4226, 0.47 #6553, 0.45 #1900), 09r9dp (0.29 #41861, 0.27 #44188, 0.18 #58146), 0bbvr84 (0.29 #41861, 0.27 #44188, 0.18 #58146), 0d810y (0.29 #41861, 0.27 #44188, 0.18 #58146) >> Best rule #2325 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 05dxl5; >> query: (?x6851, ?x444) <- award_nominee(?x515, ?x6851), award_nominee(?x444, ?x6851), award_winner(?x6851, ?x2578), ?x515 = 058ncz >> conf = 0.83 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3, 6, 7 EVAL 05lb65 award_nominee 03x16f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 67.000 30.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 05lb65 award_nominee 02s_qz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 30.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 05lb65 award_nominee 026zvx7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 67.000 30.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 05lb65 award_nominee 030znt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 30.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15580-01yzl2 PRED entity: 01yzl2 PRED relation: award PRED expected values: 02g3gj 03t5kl => 85 concepts (69 used for prediction) PRED predicted values (max 10 best out of 257): 02nhxf (0.79 #9317, 0.78 #21880, 0.76 #15395), 02f6yz (0.46 #1130, 0.43 #1940, 0.23 #2750), 09sb52 (0.43 #8547, 0.26 #16652, 0.25 #19894), 01bgqh (0.42 #853, 0.36 #1663, 0.29 #3283), 03t5n3 (0.39 #655, 0.38 #1465, 0.37 #250), 03t5kl (0.39 #633, 0.35 #1443, 0.32 #228), 01c9jp (0.38 #1000, 0.36 #1810, 0.24 #2620), 02v1m7 (0.33 #923, 0.32 #1733, 0.30 #518), 02f72n (0.33 #956, 0.32 #1766, 0.22 #551), 03tcnt (0.33 #977, 0.32 #1787, 0.15 #2597) >> Best rule #9317 for best value: >> intensional similarity = 3 >> extensional distance = 461 >> proper extension: 0m0hw; >> query: (?x5478, ?x1827) <- award(?x5478, ?x2139), artist(?x10426, ?x5478), award_winner(?x1827, ?x5478) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #633 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0147dk; 02l840; 016kjs; 04mn81; 01wwvc5; 01wgxtl; 0126y2; 0412f5y; 04qmr; 01q32bd; ... *> query: (?x5478, 03t5kl) <- award_nominee(?x4476, ?x5478), ?x4476 = 01vw20h, artists(?x302, ?x5478) *> conf = 0.39 ranks of expected_values: 6, 51 EVAL 01yzl2 award 03t5kl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 85.000 69.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01yzl2 award 02g3gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 85.000 69.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15579-01cycq PRED entity: 01cycq PRED relation: film! PRED expected values: 05lb65 => 97 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1005): 030_1m (0.53 #2080, 0.45 #29120, 0.29 #68639), 020h2v (0.53 #2080, 0.45 #29120, 0.29 #68639), 023kzp (0.20 #1057, 0.02 #11455, 0.01 #7295), 02wr6r (0.20 #1667), 079vf (0.11 #4167, 0.08 #8326, 0.07 #6246), 02lkcc (0.10 #243, 0.06 #4402, 0.04 #2323), 04fzk (0.10 #708, 0.06 #4867, 0.03 #9026), 03knl (0.10 #158, 0.05 #4317, 0.04 #6396), 01vsn38 (0.10 #1853, 0.04 #8091, 0.03 #6012), 044rvb (0.10 #102, 0.03 #4261, 0.03 #6340) >> Best rule #2080 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 03p2xc; >> query: (?x7834, ?x1561) <- genre(?x7834, ?x13467), genre(?x7834, ?x3515), ?x13467 = 05mrx8, nominated_for(?x1561, ?x7834), genre(?x5169, ?x3515), ?x5169 = 04jm_hq >> conf = 0.53 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01cycq film! 05lb65 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 50.000 0.526 http://example.org/film/actor/film./film/performance/film #15578-02xh1 PRED entity: 02xh1 PRED relation: genre! PRED expected values: 063_j5 => 64 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1852): 0hv4t (0.80 #3710, 0.79 #1854, 0.76 #3711), 0ktpx (0.80 #3710, 0.79 #1854, 0.72 #11126), 0296rz (0.60 #7258, 0.54 #10966, 0.50 #5405), 03cp4cn (0.60 #6694, 0.50 #4841, 0.46 #10402), 0c34mt (0.60 #6159, 0.50 #4306, 0.38 #9867), 011ywj (0.60 #7034, 0.50 #5181, 0.33 #1470), 060__7 (0.60 #7061, 0.50 #3353, 0.33 #1497), 0y_yw (0.60 #6650, 0.50 #4797, 0.33 #1086), 0fvr1 (0.60 #5925, 0.46 #9633, 0.43 #7780), 02vjp3 (0.60 #6893, 0.33 #1329, 0.31 #12457) >> Best rule #3710 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 07s9rl0; 02l7c8; >> query: (?x11108, ?x5818) <- genre(?x9209, ?x11108), genre(?x6149, ?x11108), titles(?x11108, ?x6653), titles(?x11108, ?x5818), film(?x187, ?x6653), ?x9209 = 0crs0b8, award(?x6653, ?x746), nominated_for(?x112, ?x6653), ?x6149 = 016ks5 >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #7106 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 04xvlr; 02n4kr; *> query: (?x11108, 063_j5) <- genre(?x11685, ?x11108), genre(?x6149, ?x11108), titles(?x11108, ?x6653), ?x6653 = 0hv4t, award(?x6149, ?x834), film(?x731, ?x11685), music(?x6149, ?x9408), nominated_for(?x4128, ?x6149) *> conf = 0.40 ranks of expected_values: 535 EVAL 02xh1 genre! 063_j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 64.000 10.000 0.800 http://example.org/film/film/genre #15577-01psyx PRED entity: 01psyx PRED relation: people PRED expected values: 015d3h => 56 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1087): 04__f (0.60 #1673, 0.25 #5687, 0.22 #6355), 042d1 (0.40 #1148, 0.17 #3154, 0.12 #5831), 0407f (0.33 #3450, 0.33 #107, 0.25 #5458), 053yx (0.33 #93, 0.27 #8120, 0.25 #12130), 0chsq (0.33 #12, 0.27 #8039, 0.18 #8707), 0gyy0 (0.33 #3046, 0.25 #5723, 0.22 #6391), 014z8v (0.33 #2813, 0.25 #5490, 0.22 #6158), 0137hn (0.33 #269, 0.25 #5620, 0.17 #3612), 0jrny (0.33 #2777, 0.22 #6122, 0.20 #6792), 016gkf (0.33 #2881, 0.22 #6226, 0.20 #6896) >> Best rule #1673 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 01n3bm; >> query: (?x11563, 04__f) <- people(?x11563, ?x10693), people(?x11563, ?x9044), people(?x11563, ?x8240), people(?x11563, ?x1528), award_winner(?x5180, ?x8240), type_of_union(?x8240, ?x566), category(?x8240, ?x134), participant(?x7676, ?x8240), profession(?x1528, ?x1529), student(?x7338, ?x10693), ?x566 = 04ztj, nominated_for(?x9044, ?x6215), place_of_death(?x1528, ?x12372), location(?x9044, ?x739) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7359 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 8 *> proper extension: 0cycc; *> query: (?x11563, ?x538) <- people(?x11563, ?x11554), people(?x11563, ?x9849), people(?x11563, ?x8240), people(?x11563, ?x5803), people(?x11563, ?x1528), award_winner(?x5180, ?x8240), influenced_by(?x11554, ?x2240), award(?x11554, ?x921), nationality(?x9849, ?x94), type_of_union(?x5803, ?x566), religion(?x1528, ?x109), film(?x9849, ?x1210), ?x921 = 0ddd9, profession(?x5803, ?x10014), award_winner(?x5180, ?x538) *> conf = 0.04 ranks of expected_values: 868 EVAL 01psyx people 015d3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 29.000 0.600 http://example.org/people/cause_of_death/people #15576-0jmmn PRED entity: 0jmmn PRED relation: teams! PRED expected values: 07bcn => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 67): 01cx_ (0.33 #94, 0.25 #366, 0.17 #639), 0d9jr (0.33 #271, 0.20 #272, 0.14 #544), 01_d4 (0.25 #332, 0.17 #605, 0.14 #877), 081yw (0.20 #272, 0.08 #545, 0.06 #816), 09c7w0 (0.20 #272, 0.08 #545, 0.06 #816), 0f2tj (0.17 #697, 0.14 #969, 0.11 #1239), 0c_m3 (0.17 #679, 0.11 #1221, 0.09 #1762), 030qb3t (0.14 #2491, 0.14 #2221, 0.14 #867), 0cr3d (0.14 #900, 0.09 #1440, 0.07 #2524), 0fpzwf (0.11 #1225, 0.09 #1766, 0.09 #1495) >> Best rule #94 for best value: >> intensional similarity = 25 >> extensional distance = 1 >> proper extension: 0bwjj; >> query: (?x5419, 01cx_) <- position(?x5419, ?x6848), position(?x5419, ?x5755), position(?x5419, ?x4747), position(?x5419, ?x1348), draft(?x5419, ?x8542), draft(?x5419, ?x8133), draft(?x5419, ?x4979), ?x1348 = 01pv51, ?x8542 = 09th87, ?x8133 = 025tn92, ?x6848 = 02_ssl, ?x4979 = 0f4vx0, ?x5755 = 0355dz, ?x4747 = 02sf_r, school(?x5419, ?x4599), contains(?x5267, ?x4599), major_field_of_study(?x4599, ?x6870), major_field_of_study(?x4599, ?x2981), mode_of_transportation(?x5267, ?x4272), ?x6870 = 01540, student(?x4599, ?x3273), citytown(?x3543, ?x5267), ?x2981 = 02j62, teams(?x5267, ?x3114), month(?x5267, ?x1459) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jmmn teams! 07bcn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 48.000 0.333 http://example.org/sports/sports_team_location/teams #15575-024lt6 PRED entity: 024lt6 PRED relation: film_regional_debut_venue PRED expected values: 0kfhjq0 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 18): 018cvf (0.12 #329, 0.12 #224, 0.11 #189), 0prpt (0.10 #201, 0.10 #236, 0.09 #306), 015hr (0.08 #118, 0.08 #466, 0.07 #396), 0kfhjq0 (0.06 #16, 0.05 #258, 0.04 #223), 0gg7gsl (0.06 #8, 0.04 #111, 0.03 #250), 07751 (0.05 #182, 0.04 #217, 0.04 #426), 0j63cyr (0.04 #256, 0.04 #500, 0.04 #326), 07zmj (0.04 #239, 0.04 #204, 0.03 #448), 02_286 (0.04 #210, 0.03 #419, 0.03 #175), 0g57ws5 (0.02 #21, 0.02 #193, 0.01 #333) >> Best rule #329 for best value: >> intensional similarity = 5 >> extensional distance = 265 >> proper extension: 0gx1bnj; 03wh49y; >> query: (?x9941, 018cvf) <- film_release_region(?x9941, ?x2152), film_release_region(?x9941, ?x172), genre(?x9941, ?x53), ?x172 = 0154j, country(?x689, ?x2152) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 83 *> proper extension: 0gkz15s; 0c0nhgv; 0fpv_3_; 06wbm8q; 0m63c; *> query: (?x9941, 0kfhjq0) <- film_release_region(?x9941, ?x1499), film_release_region(?x9941, ?x1023), nominated_for(?x397, ?x9941), ?x1499 = 01znc_, ?x1023 = 0ctw_b *> conf = 0.06 ranks of expected_values: 4 EVAL 024lt6 film_regional_debut_venue 0kfhjq0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 97.000 0.124 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #15574-0jdr0 PRED entity: 0jdr0 PRED relation: nominated_for! PRED expected values: 040njc => 123 concepts (92 used for prediction) PRED predicted values (max 10 best out of 212): 0gq9h (0.71 #1737, 0.54 #4844, 0.50 #64), 0gr0m (0.68 #21533, 0.68 #21293, 0.68 #16742), 0gr4k (0.50 #27, 0.46 #505, 0.45 #1700), 019f4v (0.48 #4835, 0.42 #5792, 0.40 #10334), 0gq_v (0.45 #1693, 0.35 #10520, 0.35 #10299), 0gqy2 (0.39 #4903, 0.37 #1796, 0.33 #5860), 0f4x7 (0.38 #4806, 0.31 #5763, 0.31 #504), 04dn09n (0.35 #5773, 0.34 #4816, 0.30 #10315), 040njc (0.34 #4787, 0.33 #10286, 0.32 #14834), 0p9sw (0.33 #4801, 0.30 #10300, 0.28 #5758) >> Best rule #1737 for best value: >> intensional similarity = 6 >> extensional distance = 36 >> proper extension: 0jyx6; 0c5dd; 0kb57; 0cq8qq; 0gnkb; 0gndh; 0bkq7; 0cq8nx; 0symg; 02q_ncg; >> query: (?x9349, 0gq9h) <- genre(?x9349, ?x11108), genre(?x9349, ?x1805), film(?x10758, ?x9349), nominated_for(?x1313, ?x9349), ?x1805 = 01g6gs, genre(?x6597, ?x11108) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4787 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 132 *> proper extension: 0gzy02; 02rjv2w; 015whm; 0pd6l; 0hfzr; 06nr2h; 011ykb; 01qbg5; 04q827; 04vq33; *> query: (?x9349, 040njc) <- genre(?x9349, ?x53), films(?x10849, ?x9349), ?x53 = 07s9rl0, language(?x9349, ?x254), honored_for(?x11428, ?x9349) *> conf = 0.34 ranks of expected_values: 9 EVAL 0jdr0 nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 123.000 92.000 0.711 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15573-04cbtrw PRED entity: 04cbtrw PRED relation: influenced_by PRED expected values: 073v6 040_9 0zm1 03_87 07lp1 => 158 concepts (63 used for prediction) PRED predicted values (max 10 best out of 350): 032l1 (0.53 #9558, 0.38 #948, 0.36 #3531), 03_87 (0.44 #1059, 0.36 #3642, 0.26 #9669), 058vp (0.31 #1042, 0.18 #3625, 0.14 #9652), 084w8 (0.25 #864, 0.23 #9474, 0.23 #3447), 081k8 (0.25 #1013, 0.23 #3596, 0.20 #9623), 02kz_ (0.25 #1028, 0.23 #3611, 0.13 #9638), 0g5ff (0.25 #4064, 0.16 #5784, 0.16 #1481), 07dnx (0.25 #1154, 0.14 #3737, 0.09 #9764), 06whf (0.23 #9594, 0.19 #984, 0.18 #3567), 013tjc (0.23 #2952, 0.05 #6398, 0.05 #6829) >> Best rule #9558 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 014dq7; 0399p; 06hgj; 02m4t; >> query: (?x2934, 032l1) <- influenced_by(?x2934, ?x4915), influenced_by(?x4915, ?x2240), influenced_by(?x9173, ?x4915), ?x9173 = 01x53m >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1059 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 07h1q; *> query: (?x2934, 03_87) <- influenced_by(?x2934, ?x4915), influenced_by(?x2934, ?x2161), ?x4915 = 03f0324, people(?x11665, ?x2934), nationality(?x2161, ?x142) *> conf = 0.44 ranks of expected_values: 2, 13, 18, 40, 43 EVAL 04cbtrw influenced_by 07lp1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 158.000 63.000 0.529 http://example.org/influence/influence_node/influenced_by EVAL 04cbtrw influenced_by 03_87 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 158.000 63.000 0.529 http://example.org/influence/influence_node/influenced_by EVAL 04cbtrw influenced_by 0zm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 158.000 63.000 0.529 http://example.org/influence/influence_node/influenced_by EVAL 04cbtrw influenced_by 040_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 158.000 63.000 0.529 http://example.org/influence/influence_node/influenced_by EVAL 04cbtrw influenced_by 073v6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 158.000 63.000 0.529 http://example.org/influence/influence_node/influenced_by #15572-01grq1 PRED entity: 01grq1 PRED relation: district_represented PRED expected values: 07_f2 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1068): 07_f2 (0.91 #569, 0.86 #1432, 0.86 #566), 05kkh (0.91 #569, 0.86 #566, 0.86 #880), 04ly1 (0.91 #569, 0.86 #566, 0.86 #880), 03v1s (0.86 #566, 0.79 #723, 0.70 #1448), 04ych (0.86 #566, 0.79 #723, 0.65 #1402), 04tgp (0.86 #566, 0.79 #723, 0.64 #1478), 0gyh (0.86 #566, 0.79 #723, 0.62 #1393), 03v0t (0.86 #566, 0.79 #723, 0.60 #620), 050ks (0.86 #566, 0.79 #723, 0.60 #620), 0vbk (0.62 #1393, 0.57 #1136, 0.57 #1032) >> Best rule #569 for best value: >> intensional similarity = 39 >> extensional distance = 3 >> proper extension: 077g7n; 070m6c; >> query: (?x11142, ?x7405) <- district_represented(?x11142, ?x7518), district_represented(?x11142, ?x6895), district_represented(?x11142, ?x4754), district_represented(?x11142, ?x4061), district_represented(?x11142, ?x3670), district_represented(?x11142, ?x2713), district_represented(?x11142, ?x2020), district_represented(?x11142, ?x1767), district_represented(?x11142, ?x760), district_represented(?x11142, ?x728), legislative_sessions(?x10638, ?x11142), legislative_sessions(?x7914, ?x11142), legislative_sessions(?x3973, ?x11142), ?x1767 = 04rrd, ?x728 = 059f4, ?x2020 = 05k7sb, legislative_sessions(?x4812, ?x10638), legislative_sessions(?x2860, ?x10638), ?x760 = 05fkf, legislative_sessions(?x5978, ?x10638), ?x2713 = 06btq, ?x3670 = 05tbn, district_represented(?x7914, ?x7405), district_represented(?x7914, ?x177), legislative_sessions(?x6021, ?x3973), legislative_sessions(?x5005, ?x3973), legislative_sessions(?x7944, ?x6021), ?x7518 = 026mj, ?x2860 = 0b3wk, district_represented(?x5005, ?x4622), ?x6895 = 05fjf, ?x4061 = 0498y, entity_involved(?x8416, ?x5978), ?x4622 = 04tgp, basic_title(?x5978, ?x346), ?x177 = 05kkh, ?x4754 = 0g0syc, student(?x6056, ?x5978), type_of_union(?x5978, ?x566) >> conf = 0.91 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 01grq1 district_represented 07_f2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.911 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #15571-023gxx PRED entity: 023gxx PRED relation: production_companies PRED expected values: 09b3v => 87 concepts (70 used for prediction) PRED predicted values (max 10 best out of 58): 09b3v (0.39 #278, 0.21 #32, 0.06 #196), 054g1r (0.32 #4540, 0.32 #1566, 0.30 #4374), 0kk9v (0.26 #34, 0.13 #280, 0.06 #198), 04rcl7 (0.17 #317, 0.05 #71, 0.03 #564), 086k8 (0.17 #495, 0.15 #413, 0.13 #1814), 016tw3 (0.14 #505, 0.14 #1824, 0.13 #1578), 01gb54 (0.12 #530, 0.10 #1603, 0.09 #1685), 05qd_ (0.12 #1987, 0.11 #1576, 0.11 #1822), 054lpb6 (0.12 #1581, 0.12 #1827, 0.11 #1663), 017s11 (0.11 #414, 0.09 #496, 0.08 #1569) >> Best rule #278 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 016ztl; >> query: (?x3081, 09b3v) <- film(?x2156, ?x3081), genre(?x3081, ?x53), language(?x3081, ?x254), ?x2156 = 01795t >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023gxx production_companies 09b3v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 70.000 0.391 http://example.org/film/film/production_companies #15570-0b7xl8 PRED entity: 0b7xl8 PRED relation: profession PRED expected values: 05sxg2 => 92 concepts (88 used for prediction) PRED predicted values (max 10 best out of 47): 02hrh1q (0.69 #1057, 0.68 #9402, 0.67 #1206), 0dxtg (0.49 #758, 0.48 #2546, 0.48 #5527), 02jknp (0.48 #752, 0.46 #1497, 0.46 #901), 03gjzk (0.47 #5529, 0.32 #2399, 0.32 #909), 02hv44_ (0.33 #58, 0.03 #12278, 0.03 #7062), 0cbd2 (0.22 #6, 0.13 #5520, 0.12 #8202), 05sxg2 (0.22 #1, 0.05 #150, 0.05 #299), 09jwl (0.22 #1658, 0.20 #3148, 0.20 #2105), 0dz3r (0.16 #1641, 0.14 #2088, 0.13 #3429), 0nbcg (0.16 #1671, 0.13 #2118, 0.13 #3459) >> Best rule #1057 for best value: >> intensional similarity = 3 >> extensional distance = 658 >> proper extension: 01n4f8; 01mkn_d; >> query: (?x8684, 02hrh1q) <- award_nominee(?x8684, ?x7324), gender(?x8684, ?x231), executive_produced_by(?x144, ?x7324) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0yfp; *> query: (?x8684, 05sxg2) <- type_of_union(?x8684, ?x566), award_nominee(?x8684, ?x3170), ?x3170 = 04cw0j *> conf = 0.22 ranks of expected_values: 7 EVAL 0b7xl8 profession 05sxg2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 92.000 88.000 0.689 http://example.org/people/person/profession #15569-01n30p PRED entity: 01n30p PRED relation: genre PRED expected values: 05p553 01j1n2 => 87 concepts (66 used for prediction) PRED predicted values (max 10 best out of 89): 07s9rl0 (0.96 #6430, 0.75 #2143, 0.74 #7622), 05p553 (0.74 #243, 0.43 #7626, 0.38 #124), 01jfsb (0.44 #370, 0.36 #13, 0.32 #1084), 02kdv5l (0.41 #360, 0.30 #1074, 0.30 #955), 02l7c8 (0.38 #136, 0.30 #2159, 0.29 #6446), 03k9fj (0.28 #964, 0.26 #1083, 0.24 #1440), 0lsxr (0.27 #10, 0.26 #248, 0.21 #1319), 02n4kr (0.27 #9, 0.17 #366, 0.13 #1080), 04xvlr (0.26 #2144, 0.17 #6431, 0.17 #2977), 060__y (0.21 #2160, 0.18 #18, 0.18 #1208) >> Best rule #6430 for best value: >> intensional similarity = 3 >> extensional distance = 1050 >> proper extension: 0vgkd; >> query: (?x8158, 07s9rl0) <- genre(?x8158, ?x6674), genre(?x9350, ?x6674), ?x9350 = 01g03q >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #243 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 0g5q34q; *> query: (?x8158, 05p553) <- country(?x8158, ?x94), genre(?x8158, ?x809), language(?x8158, ?x254), ?x809 = 0vgkd *> conf = 0.74 ranks of expected_values: 2, 17 EVAL 01n30p genre 01j1n2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 87.000 66.000 0.956 http://example.org/film/film/genre EVAL 01n30p genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 66.000 0.956 http://example.org/film/film/genre #15568-01bb9r PRED entity: 01bb9r PRED relation: genre PRED expected values: 05p553 => 67 concepts (48 used for prediction) PRED predicted values (max 10 best out of 97): 05p553 (0.85 #477, 0.36 #2134, 0.36 #595), 02kdv5l (0.63 #2368, 0.49 #1301, 0.36 #711), 01jfsb (0.47 #2377, 0.33 #2496, 0.32 #838), 02l7c8 (0.42 #488, 0.28 #606, 0.27 #3800), 03bxz7 (0.33 #53, 0.25 #171, 0.10 #1590), 01hmnh (0.33 #1316, 0.18 #2502, 0.16 #2383), 04xvlr (0.30 #355, 0.20 #1182, 0.20 #1656), 06cvj (0.29 #476, 0.12 #594, 0.10 #948), 06n90 (0.29 #2378, 0.26 #1311, 0.15 #603), 0219x_ (0.25 #499, 0.12 #1563, 0.10 #1919) >> Best rule #477 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 08cfr1; >> query: (?x2955, 05p553) <- film(?x976, ?x2955), genre(?x2955, ?x6674), award_winner(?x976, ?x1674), ?x6674 = 01t_vv >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bb9r genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 48.000 0.848 http://example.org/film/film/genre #15567-05d8vw PRED entity: 05d8vw PRED relation: artist! PRED expected values: 015_1q => 120 concepts (79 used for prediction) PRED predicted values (max 10 best out of 114): 04kjvt (0.50 #218, 0.33 #78, 0.02 #7982), 033hn8 (0.25 #154, 0.21 #574, 0.15 #714), 015_1q (0.21 #3661, 0.21 #580, 0.20 #300), 03mp8k (0.21 #626, 0.15 #766, 0.14 #906), 043g7l (0.20 #312, 0.18 #592, 0.12 #732), 011k1h (0.20 #290, 0.12 #2951, 0.10 #5191), 017l96 (0.20 #299, 0.11 #3240, 0.09 #4360), 01trtc (0.20 #352, 0.09 #2033, 0.09 #1332), 03rhqg (0.17 #716, 0.17 #2957, 0.15 #856), 0181dw (0.17 #742, 0.15 #602, 0.14 #882) >> Best rule #218 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01kp_1t; >> query: (?x2055, 04kjvt) <- artists(?x13245, ?x2055), award_winner(?x342, ?x2055), ?x13245 = 02qcqkl, artist(?x6474, ?x2055) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3661 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 334 *> proper extension: 014g91; *> query: (?x2055, 015_1q) <- artists(?x671, ?x2055), award_winner(?x342, ?x2055), artist(?x6474, ?x2055) *> conf = 0.21 ranks of expected_values: 3 EVAL 05d8vw artist! 015_1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 79.000 0.500 http://example.org/music/record_label/artist #15566-02bb47 PRED entity: 02bb47 PRED relation: major_field_of_study PRED expected values: 0g26h => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 120): 01mkq (0.57 #2438, 0.45 #4499, 0.43 #2922), 02j62 (0.56 #151, 0.54 #272, 0.43 #2454), 04rjg (0.49 #2443, 0.44 #140, 0.38 #261), 037mh8 (0.44 #188, 0.38 #309, 0.28 #2491), 03g3w (0.40 #2450, 0.33 #147, 0.32 #4511), 05qjt (0.40 #2432, 0.28 #4008, 0.28 #2553), 0g26h (0.40 #2829, 0.39 #3436, 0.38 #1012), 062z7 (0.38 #2572, 0.38 #269, 0.35 #2451), 0_jm (0.38 #1028, 0.25 #2966, 0.24 #2845), 01lj9 (0.38 #2463, 0.33 #160, 0.31 #2584) >> Best rule #2438 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 08qnnv; >> query: (?x3212, 01mkq) <- institution(?x3437, ?x3212), company(?x3131, ?x3212), major_field_of_study(?x3212, ?x1154), ?x3437 = 02_xgp2 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2829 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 84 *> proper extension: 02hp6p; *> query: (?x3212, 0g26h) <- institution(?x620, ?x3212), contains(?x94, ?x3212), currency(?x3212, ?x170), ?x620 = 07s6fsf *> conf = 0.40 ranks of expected_values: 7 EVAL 02bb47 major_field_of_study 0g26h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 113.000 113.000 0.569 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #15565-0hwpz PRED entity: 0hwpz PRED relation: film! PRED expected values: 016tw3 => 87 concepts (74 used for prediction) PRED predicted values (max 10 best out of 47): 016tw3 (0.50 #2533, 0.49 #2834, 0.47 #2986), 03rwz3 (0.25 #43, 0.04 #1531, 0.04 #1087), 03xq0f (0.23 #303, 0.20 #227, 0.14 #1492), 086k8 (0.22 #301, 0.20 #527, 0.20 #750), 05qd_ (0.20 #458, 0.20 #82, 0.18 #231), 017s11 (0.20 #151, 0.14 #528, 0.14 #825), 0g1rw (0.20 #81, 0.08 #755, 0.08 #829), 01f_mw (0.20 #122, 0.02 #796, 0.02 #870), 01gb54 (0.12 #403, 0.08 #628, 0.08 #702), 054g1r (0.12 #257, 0.09 #333, 0.08 #2492) >> Best rule #2533 for best value: >> intensional similarity = 3 >> extensional distance = 778 >> proper extension: 0gj9qxr; 03_wm6; 02pcq92; >> query: (?x7444, ?x1104) <- film(?x574, ?x7444), currency(?x7444, ?x170), production_companies(?x7444, ?x1104) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hwpz film! 016tw3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 74.000 0.502 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15564-02bj6k PRED entity: 02bj6k PRED relation: film PRED expected values: 0416y94 => 96 concepts (48 used for prediction) PRED predicted values (max 10 best out of 585): 0266s9 (0.35 #78248, 0.30 #39120, 0.25 #28449), 062zm5h (0.08 #853, 0.03 #35563, 0.01 #2631), 02ryz24 (0.08 #465, 0.02 #7577, 0.01 #9355), 0pc62 (0.08 #93, 0.01 #8983, 0.01 #7205), 01shy7 (0.06 #421, 0.04 #7533, 0.04 #3977), 0m313 (0.06 #13, 0.02 #19571, 0.01 #21349), 0b6tzs (0.06 #138, 0.01 #5472), 04tqtl (0.06 #507, 0.01 #7619, 0.01 #2285), 011ysn (0.06 #563, 0.01 #7675, 0.01 #5897), 059rc (0.06 #452, 0.01 #5786) >> Best rule #78248 for best value: >> intensional similarity = 3 >> extensional distance = 1500 >> proper extension: 056ws9; 081bls; 032dg7; 02x2097; 03f68r6; >> query: (?x7981, ?x1868) <- award(?x7981, ?x112), award_winner(?x693, ?x7981), nominated_for(?x7981, ?x1868) >> conf = 0.35 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02bj6k film 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 48.000 0.351 http://example.org/film/actor/film./film/performance/film #15563-02mc79 PRED entity: 02mc79 PRED relation: profession PRED expected values: 02jknp => 115 concepts (82 used for prediction) PRED predicted values (max 10 best out of 58): 02jknp (0.88 #3632, 0.88 #4357, 0.87 #4212), 02krf9 (0.35 #603, 0.30 #2488, 0.28 #3068), 0np9r (0.30 #2627, 0.30 #3497, 0.14 #7703), 0cbd2 (0.20 #2906, 0.20 #876, 0.19 #2326), 09jwl (0.18 #9006, 0.17 #5670, 0.17 #10167), 0kyk (0.15 #2636, 0.14 #461, 0.13 #3506), 0nbcg (0.13 #9019, 0.12 #9309, 0.11 #11630), 012t_z (0.12 #12, 0.10 #2042, 0.10 #2187), 0dgd_ (0.12 #27, 0.08 #4377, 0.08 #4232), 0dz3r (0.12 #8993, 0.11 #9283, 0.10 #11604) >> Best rule #3632 for best value: >> intensional similarity = 3 >> extensional distance = 205 >> proper extension: 01twdk; 0k_mt; >> query: (?x8071, 02jknp) <- film(?x8071, ?x8072), profession(?x8071, ?x319), award_winner(?x4386, ?x8071) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02mc79 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 82.000 0.884 http://example.org/people/person/profession #15562-046p9 PRED entity: 046p9 PRED relation: artists! PRED expected values: 0m0jc => 72 concepts (37 used for prediction) PRED predicted values (max 10 best out of 279): 06j6l (0.64 #1858, 0.62 #3969, 0.62 #4271), 05bt6j (0.62 #1249, 0.61 #2759, 0.60 #41), 03_d0 (0.62 #917, 0.43 #1823, 0.38 #4236), 01243b (0.50 #1248, 0.21 #5469, 0.19 #4867), 02k_kn (0.45 #2780, 0.21 #1874, 0.20 #1571), 025sc50 (0.43 #1860, 0.36 #6692, 0.35 #4273), 0xhtw (0.40 #6355, 0.39 #5747, 0.38 #6051), 02vjzr (0.38 #1336, 0.32 #2846, 0.17 #6772), 03ckfl9 (0.33 #455, 0.25 #757, 0.13 #1663), 0glt670 (0.32 #1850, 0.31 #7585, 0.30 #3961) >> Best rule #1858 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 028qyn; >> query: (?x8156, 06j6l) <- award(?x8156, ?x2877), artists(?x1127, ?x8156), ?x1127 = 02x8m, award(?x7951, ?x2877), ?x7951 = 01vt5c_ >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #4835 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 107 *> proper extension: 012zng; 0285c; 03xl77; 01wy61y; 03f6fl0; 01wgjj5; 01vng3b; 01386_; 05qhnq; 01k3qj; ... *> query: (?x8156, 0m0jc) <- origin(?x8156, ?x362), artists(?x302, ?x8156), ?x302 = 016clz, artist(?x2299, ?x8156) *> conf = 0.16 ranks of expected_values: 44 EVAL 046p9 artists! 0m0jc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 72.000 37.000 0.643 http://example.org/music/genre/artists #15561-0p8h0 PRED entity: 0p8h0 PRED relation: artists! PRED expected values: 0mhfr => 82 concepts (68 used for prediction) PRED predicted values (max 10 best out of 296): 05bt6j (0.69 #2537, 0.49 #4402, 0.48 #15588), 02w4v (0.61 #5957, 0.45 #3159, 0.43 #3470), 017_qw (0.59 #6906, 0.56 #9079, 0.54 #10009), 026z9 (0.50 #6611, 0.14 #701, 0.13 #13754), 016clz (0.47 #4984, 0.41 #10881, 0.40 #1562), 02k_kn (0.44 #1313, 0.32 #4425, 0.30 #3181), 0xhtw (0.42 #13070, 0.40 #11516, 0.40 #10893), 06j6l (0.38 #17764, 0.34 #8442, 0.34 #4717), 01lyv (0.35 #3149, 0.33 #3460, 0.33 #5947), 016zgj (0.33 #1086, 0.33 #462, 0.30 #1708) >> Best rule #2537 for best value: >> intensional similarity = 8 >> extensional distance = 14 >> proper extension: 01vsqvs; >> query: (?x13145, 05bt6j) <- artists(?x8385, ?x13145), artists(?x7329, ?x13145), artists(?x671, ?x13145), ?x671 = 064t9, ?x7329 = 016jny, artists(?x8385, ?x4237), parent_genre(?x8385, ?x302), ?x4237 = 01w524f >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 06rgq; 01wx756; *> query: (?x13145, 0mhfr) <- artists(?x7329, ?x13145), artists(?x2809, ?x13145), artists(?x671, ?x13145), ?x671 = 064t9, ?x7329 = 016jny, music(?x1069, ?x13145), artist(?x3265, ?x13145), parent_genre(?x2809, ?x505) *> conf = 0.25 ranks of expected_values: 21 EVAL 0p8h0 artists! 0mhfr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 82.000 68.000 0.688 http://example.org/music/genre/artists #15560-016622 PRED entity: 016622 PRED relation: role! PRED expected values: 013y1f => 77 concepts (45 used for prediction) PRED predicted values (max 10 best out of 107): 06ncr (0.87 #3281, 0.87 #3212, 0.86 #4389), 013y1f (0.87 #3197, 0.83 #3643, 0.83 #4644), 0bxl5 (0.87 #3228, 0.78 #3674, 0.77 #2439), 0jtg0 (0.84 #560, 0.84 #3728, 0.84 #2042), 0395lw (0.84 #560, 0.84 #3728, 0.84 #2601), 042v_gx (0.84 #560, 0.84 #3728, 0.84 #2601), 05842k (0.84 #560, 0.84 #2601, 0.84 #2600), 04rzd (0.83 #3650, 0.81 #3317, 0.80 #4317), 0l14md (0.83 #2163, 0.78 #1593, 0.74 #2034), 02fsn (0.82 #3556, 0.82 #1986, 0.77 #2887) >> Best rule #3281 for best value: >> intensional similarity = 26 >> extensional distance = 13 >> proper extension: 0jtg0; >> query: (?x3328, ?x2309) <- role(?x3328, ?x1473), role(?x3703, ?x3328), role(?x2377, ?x3328), role(?x2048, ?x3328), role(?x1663, ?x3328), role(?x1437, ?x3328), role(?x1332, ?x3328), role(?x716, ?x3328), role(?x3328, ?x2309), ?x2048 = 018j2, role(?x1663, ?x1212), role(?x1663, ?x8172), role(?x3239, ?x1332), ?x2309 = 06ncr, ?x3239 = 03qmg1, instrumentalists(?x1332, ?x120), group(?x3328, ?x3516), group(?x3703, ?x1945), role(?x645, ?x3703), role(?x1433, ?x3703), role(?x1260, ?x2377), ?x716 = 018vs, ?x1437 = 01vdm0, ?x8172 = 06rvn, role(?x1473, ?x3215), instrumentalists(?x1473, ?x1660) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #3197 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 13 *> proper extension: 0jtg0; *> query: (?x3328, 013y1f) <- role(?x3328, ?x1473), role(?x3703, ?x3328), role(?x2377, ?x3328), role(?x2048, ?x3328), role(?x1663, ?x3328), role(?x1437, ?x3328), role(?x1332, ?x3328), role(?x716, ?x3328), role(?x3328, ?x2309), ?x2048 = 018j2, role(?x1663, ?x1212), role(?x1663, ?x8172), role(?x3239, ?x1332), ?x2309 = 06ncr, ?x3239 = 03qmg1, instrumentalists(?x1332, ?x120), group(?x3328, ?x3516), group(?x3703, ?x1945), role(?x645, ?x3703), role(?x1433, ?x3703), role(?x1260, ?x2377), ?x716 = 018vs, ?x1437 = 01vdm0, ?x8172 = 06rvn, role(?x1473, ?x3215), instrumentalists(?x1473, ?x1660) *> conf = 0.87 ranks of expected_values: 2 EVAL 016622 role! 013y1f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 45.000 0.867 http://example.org/music/performance_role/track_performances./music/track_contribution/role #15559-02079p PRED entity: 02079p PRED relation: jurisdiction_of_office PRED expected values: 0hjy 0ctw_b 07ylj 0vh3 => 22 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1425): 01n7q (0.78 #2740, 0.67 #4173, 0.66 #2742), 07_f2 (0.78 #2740, 0.66 #912, 0.66 #2742), 059rby (0.78 #2740, 0.66 #912, 0.66 #2742), 050ks (0.78 #2740, 0.66 #912, 0.66 #2742), 06btq (0.78 #2740, 0.66 #912, 0.66 #2742), 0694j (0.78 #2740, 0.66 #912, 0.56 #3653), 0chgr2 (0.78 #2740, 0.66 #912, 0.56 #3653), 05fly (0.78 #2740, 0.66 #912, 0.56 #3653), 0847q (0.78 #2740, 0.66 #912, 0.56 #3653), 0g39h (0.78 #2740, 0.66 #912, 0.50 #2640) >> Best rule #2740 for best value: >> intensional similarity = 18 >> extensional distance = 2 >> proper extension: 0fkx3; >> query: (?x6872, ?x108) <- jurisdiction_of_office(?x6872, ?x12854), jurisdiction_of_office(?x6872, ?x4776), jurisdiction_of_office(?x6872, ?x2020), district_represented(?x5256, ?x4776), district_represented(?x952, ?x4776), contains(?x4776, ?x2034), religion(?x4776, ?x8613), legislative_sessions(?x355, ?x952), jurisdiction_of_office(?x3959, ?x4776), ?x12854 = 06mtq, legislative_sessions(?x652, ?x952), legislative_sessions(?x2860, ?x5256), adjoins(?x12573, ?x4776), basic_title(?x6138, ?x3959), state_province_region(?x1513, ?x2020), contains(?x2020, ?x1151), jurisdiction_of_office(?x3959, ?x108), religion(?x2317, ?x8613) >> conf = 0.78 => this is the best rule for 90 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 44, 62, 131 EVAL 02079p jurisdiction_of_office 0vh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 22.000 21.000 0.778 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 02079p jurisdiction_of_office 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 22.000 21.000 0.778 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 02079p jurisdiction_of_office 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 22.000 21.000 0.778 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 02079p jurisdiction_of_office 0hjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 22.000 21.000 0.778 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #15558-07xvf PRED entity: 07xvf PRED relation: language PRED expected values: 02h40lc => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 57): 02h40lc (0.91 #727, 0.91 #426, 0.90 #1504), 064_8sq (0.35 #81, 0.27 #22, 0.25 #203), 06nm1 (0.13 #435, 0.12 #855, 0.11 #1452), 04306rv (0.11 #669, 0.11 #1745, 0.11 #186), 02bjrlw (0.10 #182, 0.10 #243, 0.10 #121), 06b_j (0.09 #23, 0.08 #867, 0.07 #687), 0jzc (0.09 #20, 0.07 #140, 0.06 #201), 05qqm (0.09 #41, 0.03 #3287, 0.02 #222), 02ztjwg (0.09 #32, 0.03 #3287, 0.02 #91), 07zrf (0.09 #3, 0.03 #3287, 0.02 #62) >> Best rule #727 for best value: >> intensional similarity = 4 >> extensional distance = 303 >> proper extension: 02d413; 0b2v79; 01sxly; 0hmr4; 0pv2t; 0hv1t; 0qm98; 0p_th; 07h9gp; 03hj3b3; ... >> query: (?x7373, 02h40lc) <- film(?x300, ?x7373), award(?x7373, ?x6860), film(?x1104, ?x7373), featured_film_locations(?x7373, ?x1061) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07xvf language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.908 http://example.org/film/film/language #15557-01dw4q PRED entity: 01dw4q PRED relation: film PRED expected values: 02rrfzf => 83 concepts (57 used for prediction) PRED predicted values (max 10 best out of 736): 03ln8b (0.34 #82354, 0.07 #14321, 0.07 #19692), 08fn5b (0.13 #4276, 0.11 #6066, 0.10 #696), 06r2h (0.11 #6887, 0.07 #5097, 0.03 #62657), 02_fz3 (0.10 #1383, 0.08 #3173, 0.07 #4963), 063y9fp (0.10 #1530, 0.08 #3320, 0.07 #5110), 04y9mm8 (0.10 #1188, 0.08 #2978, 0.07 #4768), 04fzfj (0.10 #105, 0.08 #1895, 0.07 #3685), 0879bpq (0.10 #450, 0.08 #2240, 0.03 #62657), 09cr8 (0.10 #285, 0.07 #3865, 0.06 #5655), 05qbckf (0.10 #309, 0.07 #3889, 0.06 #5679) >> Best rule #82354 for best value: >> intensional similarity = 2 >> extensional distance = 1429 >> proper extension: 0dky9n; >> query: (?x444, ?x2078) <- nationality(?x444, ?x94), award_winner(?x2078, ?x444) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #16658 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 144 *> proper extension: 0m2wm; 01n4f8; 01l1hr; 01v40wd; *> query: (?x444, 02rrfzf) <- award_nominee(?x444, ?x516), participant(?x445, ?x444), participant(?x444, ?x286) *> conf = 0.01 ranks of expected_values: 452 EVAL 01dw4q film 02rrfzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 83.000 57.000 0.343 http://example.org/film/actor/film./film/performance/film #15556-01_j71 PRED entity: 01_j71 PRED relation: type_of_union PRED expected values: 04ztj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.70 #37, 0.70 #187, 0.69 #105), 01g63y (0.19 #6, 0.16 #74, 0.15 #10) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 338 >> proper extension: 067xw; 032md; 030dx5; >> query: (?x3446, 04ztj) <- profession(?x3446, ?x1032), people(?x1050, ?x3446), ?x1050 = 041rx >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_j71 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.697 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15555-01grp0 PRED entity: 01grp0 PRED relation: legislative_sessions! PRED expected values: 0424m => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 27): 024_vw (0.54 #646, 0.42 #935, 0.41 #758), 0fd_1 (0.51 #849, 0.48 #734, 0.46 #880), 03_nq (0.51 #849, 0.48 #734, 0.46 #880), 0424m (0.51 #849, 0.48 #734, 0.46 #880), 0rlz (0.51 #849, 0.48 #734, 0.46 #880), 021sv1 (0.50 #623, 0.43 #969, 0.41 #998), 02hy5d (0.50 #642, 0.41 #988, 0.39 #1017), 0bymv (0.50 #624, 0.38 #736, 0.38 #822), 0d3qd0 (0.50 #628, 0.36 #712, 0.34 #826), 0226cw (0.46 #639, 0.43 #985, 0.41 #1014) >> Best rule #646 for best value: >> intensional similarity = 38 >> extensional distance = 22 >> proper extension: 024tkd; >> query: (?x7715, 024_vw) <- district_represented(?x7715, ?x7405), district_represented(?x7715, ?x4776), district_represented(?x7715, ?x4061), district_represented(?x7715, ?x2020), ?x7405 = 07_f2, legislative_sessions(?x11142, ?x7715), legislative_sessions(?x7715, ?x4812), legislative_sessions(?x2860, ?x7715), legislative_sessions(?x2860, ?x7973), legislative_sessions(?x2860, ?x6712), legislative_sessions(?x2860, ?x5977), legislative_sessions(?x2860, ?x4437), legislative_sessions(?x2860, ?x2976), legislative_sessions(?x2860, ?x2712), legislative_sessions(?x2860, ?x1137), legislative_sessions(?x2860, ?x653), ?x1137 = 02bqn1, ?x4437 = 01gsrl, ?x4061 = 0498y, ?x7973 = 01gsvb, ?x2712 = 01gst_, ?x2976 = 03rtmz, ?x6712 = 01gst9, ?x653 = 070m6c, district_represented(?x11142, ?x4754), state_province_region(?x9827, ?x2020), state_province_region(?x6912, ?x2020), state_province_region(?x3485, ?x2020), jurisdiction_of_office(?x10093, ?x2020), contains(?x2020, ?x1151), ?x10093 = 09n5b9, contains(?x94, ?x3485), ?x4754 = 0g0syc, ?x5977 = 06r713, institution(?x1200, ?x9827), colors(?x6912, ?x663), religion(?x2020, ?x109), category(?x4776, ?x134) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #849 for first EXPECTED value: *> intensional similarity = 35 *> extensional distance = 30 *> proper extension: 01gsvb; *> query: (?x7715, ?x5742) <- district_represented(?x7715, ?x7405), district_represented(?x7715, ?x4061), district_represented(?x7715, ?x2020), district_represented(?x7715, ?x1767), religion(?x7405, ?x2769), legislative_sessions(?x7715, ?x4787), contains(?x7405, ?x1476), ?x2020 = 05k7sb, country(?x7405, ?x94), district_represented(?x10803, ?x7405), district_represented(?x6933, ?x7405), district_represented(?x4821, ?x7405), district_represented(?x3766, ?x7405), district_represented(?x3540, ?x7405), ?x6933 = 024tkd, legislative_sessions(?x5742, ?x4787), ?x4821 = 02bqm0, religion(?x1767, ?x7422), location(?x820, ?x1767), contains(?x1767, ?x1396), district_represented(?x4787, ?x7518), ?x10803 = 01gt99, adjoins(?x108, ?x1767), jurisdiction_of_office(?x10093, ?x1767), jurisdiction_of_office(?x3959, ?x1767), award_nominee(?x820, ?x1870), ?x3959 = 0f6c3, ?x7422 = 092bf5, award_nominee(?x336, ?x820), ?x7518 = 026mj, ?x2769 = 019cr, ?x10093 = 09n5b9, ?x3540 = 024tcq, ?x3766 = 02gkzs, ?x4061 = 0498y *> conf = 0.51 ranks of expected_values: 4 EVAL 01grp0 legislative_sessions! 0424m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 39.000 39.000 0.542 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #15554-02s2wq PRED entity: 02s2wq PRED relation: instrumentalists! PRED expected values: 0l14md => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 94): 03bx0bm (0.42 #787, 0.39 #524), 05148p4 (0.40 #718, 0.37 #455, 0.35 #3169), 018vs (0.40 #710, 0.30 #3161, 0.29 #1235), 02hnl (0.22 #732, 0.18 #1257, 0.17 #3183), 03qjg (0.21 #749, 0.19 #662, 0.18 #925), 0l14md (0.13 #705, 0.13 #3156, 0.12 #2020), 026t6 (0.13 #701, 0.12 #2016, 0.12 #4555), 04rzd (0.12 #385, 0.10 #735, 0.10 #472), 06ncr (0.12 #742, 0.08 #479, 0.08 #1708), 0l14qv (0.11 #703, 0.10 #1492, 0.09 #2628) >> Best rule #787 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 01gx5f; >> query: (?x6380, ?x227) <- type_of_union(?x6380, ?x566), artists(?x302, ?x6380), role(?x6380, ?x227) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #705 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 186 *> proper extension: 01gx5f; *> query: (?x6380, 0l14md) <- type_of_union(?x6380, ?x566), artists(?x302, ?x6380), role(?x6380, ?x227) *> conf = 0.13 ranks of expected_values: 6 EVAL 02s2wq instrumentalists! 0l14md CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 126.000 126.000 0.416 http://example.org/music/instrument/instrumentalists #15553-06b_0 PRED entity: 06b_0 PRED relation: location_of_ceremony PRED expected values: 01t21q => 148 concepts (94 used for prediction) PRED predicted values (max 10 best out of 23): 0cv3w (0.07 #35, 0.03 #751, 0.03 #1347), 05qtj (0.05 #174, 0.04 #477, 0.04 #412), 01_d4 (0.05 #262, 0.02 #621, 0.02 #740), 0k049 (0.03 #959, 0.02 #1435, 0.02 #1554), 0n3g (0.02 #659, 0.02 #778, 0.01 #1136), 02_286 (0.02 #1563, 0.02 #4550, 0.01 #4789), 04jpl (0.02 #1559, 0.01 #4427, 0.01 #2038), 07fr_ (0.02 #789, 0.01 #909, 0.01 #1266), 03rjj (0.02 #721, 0.01 #960, 0.01 #1436), 059rby (0.01 #3113, 0.01 #3232) >> Best rule #35 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 014zcr; 02kxbwx; 0h1p; 0184dt; 0693l; 02kxbx3; 0bzyh; 0c00lh; 0c12h; 01_f_5; ... >> query: (?x7670, 0cv3w) <- nominated_for(?x7670, ?x6121), award(?x7670, ?x2532), award(?x7670, ?x198), ?x198 = 040njc, ?x2532 = 02x4wr9 >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06b_0 location_of_ceremony 01t21q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 94.000 0.071 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #15552-0xtz9 PRED entity: 0xtz9 PRED relation: time_zones PRED expected values: 02hczc => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.46 #133, 0.43 #224, 0.42 #159), 02hczc (0.33 #2, 0.16 #743, 0.16 #716), 02fqwt (0.22 #40, 0.20 #157, 0.20 #79), 02lcqs (0.19 #57, 0.18 #31, 0.18 #200), 02lcrv (0.16 #743, 0.16 #716), 042g7t (0.16 #716), 02llzg (0.06 #147, 0.06 #212, 0.05 #680), 03bdv (0.05 #149, 0.04 #110, 0.04 #123), 03plfd (0.02 #283, 0.01 #739, 0.01 #805) >> Best rule #133 for best value: >> intensional similarity = 3 >> extensional distance = 321 >> proper extension: 0m2gk; 0nh0f; 0nvd8; 0k3ll; 0mws3; 0n5y4; 0nh57; 0cc1v; 043z0; 0nt4s; ... >> query: (?x14086, 02hcv8) <- source(?x14086, ?x958), ?x958 = 0jbk9, contains(?x14086, ?x11713) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 02482c; 0f25y; 02jztz; *> query: (?x14086, 02hczc) <- contains(?x5575, ?x14086), contains(?x94, ?x14086), ?x94 = 09c7w0, ?x5575 = 05fjy, category(?x14086, ?x134) *> conf = 0.33 ranks of expected_values: 2 EVAL 0xtz9 time_zones 02hczc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.455 http://example.org/location/location/time_zones #15551-01wk7b7 PRED entity: 01wk7b7 PRED relation: people! PRED expected values: 038723 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 53): 0x67 (0.28 #857, 0.21 #1319, 0.19 #3245), 0xnvg (0.25 #90, 0.19 #1091, 0.16 #1168), 041rx (0.25 #81, 0.19 #2084, 0.18 #158), 02w7gg (0.24 #772, 0.13 #2005, 0.12 #2159), 033tf_ (0.16 #2626, 0.16 #2857, 0.15 #3473), 07hwkr (0.14 #474, 0.08 #936, 0.07 #2862), 09vc4s (0.14 #1164, 0.11 #1087, 0.07 #2859), 07bch9 (0.13 #562, 0.08 #947, 0.07 #485), 01qhm_ (0.08 #1084, 0.08 #1161, 0.08 #1777), 0cn68 (0.08 #289, 0.08 #366, 0.07 #443) >> Best rule #857 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 086qd; 0127xk; >> query: (?x2435, 0x67) <- actor(?x1280, ?x2435), profession(?x2435, ?x1183), profession(?x2435, ?x1041), ?x1183 = 09jwl, ?x1041 = 03gjzk >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #377 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 01m42d0; *> query: (?x2435, 038723) <- film(?x2435, ?x5684), ?x5684 = 01f39b, profession(?x2435, ?x1032), actor(?x1280, ?x2435) *> conf = 0.08 ranks of expected_values: 11 EVAL 01wk7b7 people! 038723 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 120.000 120.000 0.278 http://example.org/people/ethnicity/people #15550-015rmq PRED entity: 015rmq PRED relation: award_winner! PRED expected values: 01xqqp => 130 concepts (127 used for prediction) PRED predicted values (max 10 best out of 129): 013b2h (0.43 #221, 0.25 #80, 0.14 #3466), 02cg41 (0.33 #549, 0.17 #691, 0.13 #565), 01xqqp (0.33 #519, 0.13 #565, 0.10 #1225), 019bk0 (0.25 #16, 0.14 #157, 0.13 #565), 01c6qp (0.22 #442, 0.18 #10595, 0.17 #9466), 05pd94v (0.22 #425, 0.17 #567, 0.14 #143), 01bx35 (0.22 #430, 0.14 #148, 0.13 #565), 01mhwk (0.22 #464, 0.13 #565, 0.08 #1694), 08pc1x (0.22 #562, 0.02 #14693, 0.02 #3384), 09n4nb (0.18 #10595, 0.17 #9466, 0.17 #11443) >> Best rule #221 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01p9hgt; 01wg6y; >> query: (?x1373, 013b2h) <- award_winner(?x2324, ?x1373), role(?x1373, ?x316), profession(?x1373, ?x8353), ?x8353 = 028kk_ >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #519 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 06fmdb; 01vrlr4; *> query: (?x1373, 01xqqp) <- award_winner(?x5765, ?x1373), award_winner(?x2324, ?x1373), award_nominee(?x8583, ?x1373), ?x2324 = 02581c, ceremony(?x5765, ?x139) *> conf = 0.33 ranks of expected_values: 3 EVAL 015rmq award_winner! 01xqqp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 127.000 0.429 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15549-03z0l6 PRED entity: 03z0l6 PRED relation: student! PRED expected values: 01xrlm => 138 concepts (114 used for prediction) PRED predicted values (max 10 best out of 188): 07tg4 (0.22 #6398, 0.14 #612, 0.13 #4820), 0dplh (0.20 #54, 0.09 #580, 0.05 #1106), 01bzs9 (0.20 #459, 0.05 #1511, 0.05 #985), 01pcj4 (0.20 #368, 0.03 #1946, 0.03 #6154), 015nl4 (0.17 #1645, 0.15 #4801, 0.09 #2697), 07tgn (0.14 #6329, 0.10 #4751, 0.07 #1595), 0bwfn (0.13 #30793, 0.12 #30267, 0.11 #32371), 03ksy (0.09 #30625, 0.08 #30099, 0.07 #32203), 065y4w7 (0.08 #30533, 0.07 #30007, 0.07 #32111), 01w5m (0.07 #30624, 0.07 #30098, 0.07 #24834) >> Best rule #6398 for best value: >> intensional similarity = 5 >> extensional distance = 100 >> proper extension: 01v42g; >> query: (?x9991, 07tg4) <- nationality(?x9991, ?x1310), student(?x6675, ?x9991), citytown(?x6675, ?x7483), ?x1310 = 02jx1, location(?x3667, ?x7483) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03z0l6 student! 01xrlm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 114.000 0.216 http://example.org/education/educational_institution/students_graduates./education/education/student #15548-0cbn7c PRED entity: 0cbn7c PRED relation: film_release_region PRED expected values: 0345h 03f2w => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 150): 05qhw (0.92 #170, 0.68 #2387, 0.59 #1437), 0345h (0.88 #192, 0.76 #2409, 0.72 #1459), 015fr (0.87 #174, 0.69 #2391, 0.64 #1441), 03rt9 (0.87 #169, 0.57 #2386, 0.49 #1436), 07ssc (0.85 #172, 0.75 #2389, 0.73 #1439), 03gj2 (0.85 #183, 0.72 #2400, 0.68 #1450), 05b4w (0.83 #224, 0.65 #2441, 0.62 #1491), 06bnz (0.82 #204, 0.61 #2421, 0.54 #1471), 06t2t (0.78 #221, 0.55 #2438, 0.49 #1488), 05v8c (0.78 #173, 0.48 #2390, 0.40 #1440) >> Best rule #170 for best value: >> intensional similarity = 5 >> extensional distance = 58 >> proper extension: 0gtsx8c; >> query: (?x7864, 05qhw) <- film_release_region(?x7864, ?x2645), film_release_region(?x7864, ?x172), executive_produced_by(?x7864, ?x6488), ?x2645 = 03h64, ?x172 = 0154j >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #192 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 58 *> proper extension: 0gtsx8c; *> query: (?x7864, 0345h) <- film_release_region(?x7864, ?x2645), film_release_region(?x7864, ?x172), executive_produced_by(?x7864, ?x6488), ?x2645 = 03h64, ?x172 = 0154j *> conf = 0.88 ranks of expected_values: 2, 75 EVAL 0cbn7c film_release_region 03f2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 78.000 78.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cbn7c film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 78.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15547-02zkz7 PRED entity: 02zkz7 PRED relation: contains! PRED expected values: 09c7w0 => 198 concepts (103 used for prediction) PRED predicted values (max 10 best out of 376): 09c7w0 (0.83 #87949, 0.83 #65498, 0.83 #64603), 04ly1 (0.80 #54726, 0.79 #41259, 0.79 #37665), 0tk02 (0.73 #16144, 0.72 #15247, 0.72 #55623), 01n7q (0.40 #19806, 0.27 #30566, 0.26 #7255), 0f2tj (0.38 #3956, 0.33 #2162, 0.25 #1265), 0fvvz (0.25 #979, 0.17 #1876, 0.15 #89747), 04rrx (0.22 #4613, 0.10 #12684, 0.07 #18063), 02dtg (0.22 #4517, 0.03 #7208, 0.03 #26036), 02jx1 (0.18 #35060, 0.16 #39551, 0.15 #22505), 05tbn (0.18 #5609, 0.16 #7401, 0.14 #30712) >> Best rule #87949 for best value: >> intensional similarity = 6 >> extensional distance = 245 >> proper extension: 01lhdt; >> query: (?x6075, ?x94) <- state_province_region(?x6075, ?x3908), currency(?x6075, ?x170), contains(?x3908, ?x466), location(?x1299, ?x3908), adjoins(?x3908, ?x3634), administrative_parent(?x3908, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zkz7 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 103.000 0.830 http://example.org/location/location/contains #15546-02ppm4q PRED entity: 02ppm4q PRED relation: award! PRED expected values: 04bdxl 0159h6 013knm 01y64_ 01jgpsh 0jlv5 01vtj38 => 51 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2574): 0fb1q (0.71 #43013, 0.70 #49631, 0.70 #26462), 02l3_5 (0.68 #52945, 0.67 #49630, 0.67 #49629), 02vntj (0.68 #52945, 0.67 #49630, 0.67 #49629), 013knm (0.68 #52945, 0.67 #49630, 0.67 #49629), 04znsy (0.68 #52945, 0.67 #49630, 0.67 #49629), 039x1k (0.68 #52945, 0.67 #49630, 0.67 #49629), 04jlgp (0.68 #52945, 0.67 #49630, 0.67 #49629), 07lt7b (0.60 #10073, 0.29 #9921, 0.17 #6766), 0159h6 (0.50 #10016, 0.33 #13324, 0.29 #9921), 01fx5l (0.50 #8407, 0.29 #9921, 0.20 #11714) >> Best rule #43013 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 02qkk9_; 02r9qt; >> query: (?x2880, ?x4670) <- award_winner(?x2880, ?x8081), award_winner(?x2880, ?x4670), celebrity(?x4670, ?x2499), actor(?x758, ?x8081) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #52945 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 155 *> proper extension: 05ztjjw; 02qt02v; *> query: (?x2880, ?x2185) <- nominated_for(?x2880, ?x7275), nominated_for(?x2880, ?x5353), award_winner(?x2880, ?x2185), film(?x1401, ?x7275), written_by(?x5353, ?x986) *> conf = 0.68 ranks of expected_values: 4, 9, 165, 208, 482, 862, 1827 EVAL 02ppm4q award! 01vtj38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 19.000 0.708 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02ppm4q award! 0jlv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 19.000 0.708 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02ppm4q award! 01jgpsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 19.000 0.708 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02ppm4q award! 01y64_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 51.000 19.000 0.708 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02ppm4q award! 013knm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 51.000 19.000 0.708 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02ppm4q award! 0159h6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 51.000 19.000 0.708 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02ppm4q award! 04bdxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 19.000 0.708 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15545-01dz7z PRED entity: 01dz7z PRED relation: basic_title! PRED expected values: 0d06m5 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 160): 07cbs (0.60 #184, 0.57 #806, 0.57 #648), 042d1 (0.43 #833, 0.43 #675, 0.43 #597), 042fk (0.43 #694, 0.40 #230, 0.39 #937), 0dq2k (0.43 #650, 0.40 #186, 0.33 #1286), 0424m (0.43 #656, 0.40 #192, 0.33 #1292), 0c_md_ (0.40 #208, 0.39 #937, 0.36 #1017), 06c97 (0.40 #187, 0.39 #937, 0.36 #1017), 034ls (0.40 #196, 0.39 #937, 0.36 #1017), 0f7fy (0.40 #195, 0.33 #426, 0.33 #349), 03_js (0.40 #203, 0.33 #434, 0.33 #357) >> Best rule #184 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 060c4; 0789n; >> query: (?x14269, 07cbs) <- basic_title(?x7893, ?x14269), company(?x14269, ?x94), ?x94 = 09c7w0, place_of_birth(?x7893, ?x739), politician(?x8714, ?x7893), ?x8714 = 0d075m, profession(?x7893, ?x9682), location(?x7893, ?x13062), contains(?x322, ?x13062), place_of_birth(?x8124, ?x13062) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1017 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 07xl34; *> query: (?x14269, ?x12525) <- company(?x14269, ?x94), company(?x12525, ?x94), company(?x9684, ?x94), contains(?x7273, ?x94), type_of_union(?x9684, ?x566), ?x566 = 04ztj, student(?x1681, ?x9684), profession(?x9684, ?x1032), basic_title(?x12525, ?x346) *> conf = 0.36 ranks of expected_values: 19 EVAL 01dz7z basic_title! 0d06m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 42.000 42.000 0.600 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #15544-07g_0c PRED entity: 07g_0c PRED relation: film_release_region PRED expected values: 03_3d 06mzp 06mkj => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 152): 06mkj (0.87 #841, 0.87 #1469, 0.87 #1155), 05r4w (0.84 #1259, 0.83 #788, 0.82 #1102), 03h64 (0.84 #1166, 0.83 #852, 0.81 #1480), 05b4w (0.78 #1320, 0.76 #849, 0.75 #1163), 03_3d (0.78 #1105, 0.77 #1419, 0.76 #791), 06t2t (0.77 #1160, 0.76 #846, 0.73 #1474), 03spz (0.77 #1511, 0.76 #1197, 0.75 #883), 0d060g (0.76 #1263, 0.72 #1106, 0.72 #792), 06bnz (0.75 #829, 0.74 #1143, 0.73 #1457), 05v8c (0.61 #1429, 0.60 #1115, 0.59 #1272) >> Best rule #841 for best value: >> intensional similarity = 8 >> extensional distance = 108 >> proper extension: 0401sg; 053tj7; 0fq7dv_; 045j3w; 0j43swk; 01sby_; 03mgx6z; 02qk3fk; 072hx4; >> query: (?x1293, 06mkj) <- film_release_region(?x1293, ?x2267), film_release_region(?x1293, ?x1003), film_release_region(?x1293, ?x985), film_release_region(?x1293, ?x142), ?x1003 = 03gj2, ?x2267 = 03rj0, ?x142 = 0jgd, ?x985 = 0k6nt >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 14 EVAL 07g_0c film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07g_0c film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 68.000 68.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07g_0c film_release_region 03_3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 68.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15543-0ft7sr PRED entity: 0ft7sr PRED relation: award PRED expected values: 02g3v6 => 104 concepts (78 used for prediction) PRED predicted values (max 10 best out of 259): 0gq_v (0.93 #4447, 0.93 #4064, 0.82 #5256), 09sb52 (0.30 #4488, 0.29 #5297, 0.25 #5701), 02g3v6 (0.25 #832, 0.18 #428, 0.14 #1236), 0gqz2 (0.21 #4932, 0.11 #6065, 0.06 #13430), 0gq9h (0.19 #4929, 0.13 #12944, 0.13 #19828), 0gqwc (0.19 #4926, 0.16 #4522, 0.14 #5331), 02x2gy0 (0.18 #538, 0.17 #942, 0.14 #1346), 054ks3 (0.17 #4993, 0.11 #6065, 0.07 #13491), 05pcn59 (0.16 #4529, 0.14 #5338, 0.12 #5742), 094qd5 (0.14 #4896, 0.13 #4492, 0.12 #5301) >> Best rule #4447 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 04kj2v; 07h1tr; 057dxsg; 076psv; 04_1nk; 0fmqp6; 051z6mv; 05b49tt; 058vfp4; 05683cn; ... >> query: (?x1779, ?x484) <- award_winner(?x484, ?x1779), nominated_for(?x1779, ?x1255), award(?x1779, ?x2222), ?x484 = 0gq_v >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #832 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 03gt0c5; *> query: (?x1779, 02g3v6) <- type_of_union(?x1779, ?x566), award(?x1779, ?x2222), costume_design_by(?x2345, ?x1779), titles(?x162, ?x2345) *> conf = 0.25 ranks of expected_values: 3 EVAL 0ft7sr award 02g3v6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 78.000 0.926 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15542-04z4j2 PRED entity: 04z4j2 PRED relation: film! PRED expected values: 0170pk => 61 concepts (37 used for prediction) PRED predicted values (max 10 best out of 991): 02q42j_ (0.11 #33287, 0.11 #20802, 0.11 #27043), 0b13g7 (0.11 #33287, 0.11 #20802, 0.11 #27043), 015wnl (0.10 #647, 0.07 #2727, 0.03 #11047), 07jmnh (0.10 #4038, 0.05 #8198, 0.04 #10278), 0tj9 (0.10 #4099, 0.05 #8259, 0.04 #10339), 08y7b9 (0.10 #4020, 0.05 #8180, 0.04 #10260), 01zh29 (0.10 #3489, 0.05 #7649, 0.04 #9729), 0292l3 (0.10 #2311, 0.05 #6471, 0.04 #8551), 0170pk (0.08 #12761, 0.03 #10681, 0.03 #21083), 0dt645q (0.07 #10083, 0.07 #8003) >> Best rule #33287 for best value: >> intensional similarity = 4 >> extensional distance = 494 >> proper extension: 01gglm; >> query: (?x10147, ?x3568) <- film(?x2900, ?x10147), nominated_for(?x2900, ?x3610), award_nominee(?x2900, ?x221), executive_produced_by(?x10147, ?x3568) >> conf = 0.11 => this is the best rule for 2 predicted values *> Best rule #12761 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 91 *> proper extension: 0g3zrd; 0415ggl; 05q7874; *> query: (?x10147, 0170pk) <- film(?x629, ?x10147), film(?x1104, ?x10147), genre(?x10147, ?x6887), ?x6887 = 03bxz7 *> conf = 0.08 ranks of expected_values: 9 EVAL 04z4j2 film! 0170pk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 61.000 37.000 0.115 http://example.org/film/actor/film./film/performance/film #15541-0cj2w PRED entity: 0cj2w PRED relation: gender PRED expected values: 05zppz => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #13, 0.90 #75, 0.88 #45), 02zsn (0.49 #206, 0.45 #122, 0.44 #175) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 014g91; 04f9r2; >> query: (?x11322, 05zppz) <- role(?x11322, ?x316), award_winner(?x11698, ?x11322), award(?x11322, ?x591), people(?x6260, ?x11322) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cj2w gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.909 http://example.org/people/person/gender #15540-059y0 PRED entity: 059y0 PRED relation: peers! PRED expected values: 06crk => 215 concepts (115 used for prediction) PRED predicted values (max 10 best out of 124): 09889g (0.11 #3107, 0.10 #3225, 0.10 #3580), 053yx (0.10 #3564, 0.06 #4512, 0.05 #1793), 0f0y8 (0.10 #1654, 0.09 #591, 0.08 #2245), 07g2b (0.10 #1657, 0.09 #594, 0.07 #3547), 06g4_ (0.09 #687, 0.07 #805, 0.06 #1041), 03j24kf (0.09 #623, 0.05 #3931, 0.05 #3813), 01c58j (0.09 #603, 0.05 #3675, 0.05 #1666), 07n39 (0.09 #675, 0.05 #3865, 0.05 #1738), 044k8 (0.09 #1803, 0.05 #3574, 0.04 #4522), 024zq (0.09 #639, 0.05 #1702, 0.05 #1821) >> Best rule #3107 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 01zkxv; 01vvycq; 01kx_81; 0343h; 0l12d; 01ky2h; 06pj8; 01vs_v8; 02m7r; 0jcx; ... >> query: (?x10913, 09889g) <- award_winner(?x11301, ?x10913), gender(?x10913, ?x231), peers(?x10913, ?x2397), location(?x10913, ?x985) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 059y0 peers! 06crk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 115.000 0.114 http://example.org/influence/influence_node/peers./influence/peer_relationship/peers #15539-08720 PRED entity: 08720 PRED relation: featured_film_locations PRED expected values: 07t90 => 117 concepts (105 used for prediction) PRED predicted values (max 10 best out of 111): 02_286 (0.31 #15890, 0.30 #13728, 0.30 #14208), 030qb3t (0.20 #3162, 0.16 #518, 0.15 #12067), 0d6lp (0.17 #72, 0.03 #3195, 0.03 #2235), 04jpl (0.14 #11796, 0.14 #12278, 0.14 #10109), 080h2 (0.10 #1223, 0.08 #2428, 0.05 #6994), 0rh6k (0.08 #240, 0.07 #5288, 0.07 #2164), 0qr8z (0.08 #388, 0.03 #1348, 0.03 #2553), 0156q (0.08 #280, 0.03 #1480, 0.02 #759), 035v3 (0.08 #445, 0.02 #1405, 0.02 #1645), 03jn4 (0.08 #457, 0.02 #1657) >> Best rule #15890 for best value: >> intensional similarity = 3 >> extensional distance = 638 >> proper extension: 047qxs; 03m8y5; 042fgh; 01jnc_; 03whyr; 058kh7; >> query: (?x641, 02_286) <- featured_film_locations(?x641, ?x5267), film(?x6360, ?x641), award_winner(?x6360, ?x368) >> conf = 0.31 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08720 featured_film_locations 07t90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 105.000 0.306 http://example.org/film/film/featured_film_locations #15538-01ky2h PRED entity: 01ky2h PRED relation: instrumentalists! PRED expected values: 03qlv7 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 104): 0342h (0.72 #3404, 0.69 #5677, 0.67 #2096), 05r5c (0.55 #1053, 0.54 #618, 0.53 #531), 03qlv7 (0.50 #113, 0.40 #26, 0.29 #3661), 0l14j_ (0.40 #54, 0.38 #141, 0.08 #315), 05148p4 (0.39 #1066, 0.38 #631, 0.37 #1764), 018vs (0.32 #4634, 0.31 #5247, 0.31 #5686), 02hnl (0.30 #210, 0.19 #3609, 0.18 #1080), 01wy6 (0.25 #134, 0.20 #47, 0.08 #656), 06ch55 (0.25 #343, 0.16 #604, 0.12 #169), 0l14md (0.20 #182, 0.15 #2709, 0.14 #2883) >> Best rule #3404 for best value: >> intensional similarity = 4 >> extensional distance = 171 >> proper extension: 03qmj9; 0fq117k; >> query: (?x1832, 0342h) <- instrumentalists(?x1831, ?x1832), award_winner(?x3121, ?x1832), category(?x1832, ?x134), artist(?x7089, ?x1832) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #113 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 01dhjz; *> query: (?x1832, 03qlv7) <- instrumentalists(?x1831, ?x1832), ?x1831 = 03t22m, profession(?x1832, ?x1183) *> conf = 0.50 ranks of expected_values: 3 EVAL 01ky2h instrumentalists! 03qlv7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 146.000 146.000 0.717 http://example.org/music/instrument/instrumentalists #15537-01qncf PRED entity: 01qncf PRED relation: film! PRED expected values: 08k881 => 70 concepts (24 used for prediction) PRED predicted values (max 10 best out of 691): 08x5c_ (0.15 #1949, 0.01 #18604), 04l19_ (0.15 #1173), 01g1lp (0.13 #20819, 0.11 #4163), 01kb2j (0.11 #2991, 0.08 #910, 0.04 #7154), 01wbg84 (0.11 #2128, 0.04 #6291, 0.03 #4210), 0htlr (0.11 #2228, 0.03 #6391, 0.01 #4310), 023n39 (0.11 #3283, 0.03 #7446, 0.01 #5365), 01j5sd (0.11 #3527, 0.03 #7690, 0.01 #5609), 02pby8 (0.11 #3481, 0.03 #7644), 02t_v1 (0.11 #2780, 0.03 #6943) >> Best rule #1949 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 02tktw; 048tv9; 02g5q1; 0d99k_; >> query: (?x2251, 08x5c_) <- film(?x9384, ?x2251), film(?x7346, ?x2251), ?x9384 = 05mlqj, profession(?x7346, ?x1032), ?x1032 = 02hrh1q >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #1024 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 02tktw; 048tv9; 02g5q1; 0d99k_; *> query: (?x2251, 08k881) <- film(?x9384, ?x2251), film(?x7346, ?x2251), ?x9384 = 05mlqj, profession(?x7346, ?x1032), ?x1032 = 02hrh1q *> conf = 0.08 ranks of expected_values: 66 EVAL 01qncf film! 08k881 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 70.000 24.000 0.154 http://example.org/film/actor/film./film/performance/film #15536-0g5pvv PRED entity: 0g5pvv PRED relation: genre PRED expected values: 02kdv5l => 152 concepts (114 used for prediction) PRED predicted values (max 10 best out of 123): 07s9rl0 (0.85 #12409, 0.85 #13245, 0.75 #12766), 02kdv5l (0.78 #1315, 0.75 #3943, 0.73 #1794), 0bkbm (0.67 #1471, 0.67 #1351, 0.60 #1113), 07ssc (0.59 #12885, 0.56 #4538, 0.54 #6689), 01hmnh (0.56 #5272, 0.51 #12544, 0.46 #6228), 06n90 (0.47 #2984, 0.43 #1684, 0.33 #1803), 082gq (0.47 #2984, 0.31 #4418, 0.30 #478), 0d2rhq (0.47 #2984, 0.31 #4418, 0.30 #478), 04pbhw (0.45 #1608, 0.27 #1847, 0.24 #3040), 02n4kr (0.45 #9556, 0.30 #7055, 0.26 #7412) >> Best rule #12409 for best value: >> intensional similarity = 7 >> extensional distance = 431 >> proper extension: 08g_jw; >> query: (?x6077, 07s9rl0) <- genre(?x6077, ?x604), language(?x6077, ?x8650), featured_film_locations(?x6077, ?x739), genre(?x7087, ?x604), genre(?x2211, ?x604), ?x7087 = 0bnzd, ?x2211 = 07nt8p >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1315 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 025twgt; *> query: (?x6077, 02kdv5l) <- music(?x6077, ?x3030), nominated_for(?x6077, ?x11362), nominated_for(?x6077, ?x1261), ?x1261 = 02qrv7, genre(?x6077, ?x604), prequel(?x11362, ?x8737) *> conf = 0.78 ranks of expected_values: 2 EVAL 0g5pvv genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 152.000 114.000 0.850 http://example.org/film/film/genre #15535-080lkt7 PRED entity: 080lkt7 PRED relation: film! PRED expected values: 09r9dp => 95 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1081): 0170qf (0.25 #366, 0.17 #2445, 0.08 #8683), 02qgqt (0.25 #17, 0.17 #2096, 0.06 #62379), 02wr6r (0.25 #1666, 0.17 #3745, 0.04 #7904), 02l4pj (0.25 #590, 0.17 #2669, 0.04 #8907), 01nwwl (0.25 #502, 0.17 #2581, 0.03 #12978), 01jgpsh (0.25 #1125, 0.17 #3204, 0.03 #13601), 01515w (0.25 #1084, 0.17 #3163, 0.03 #85254), 0z4s (0.25 #67, 0.17 #2146, 0.03 #89479), 0klh7 (0.25 #488, 0.17 #2567, 0.02 #17123), 09nz_c (0.25 #1694, 0.17 #3773, 0.01 #66154) >> Best rule #366 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 033qdy; >> query: (?x4643, 0170qf) <- genre(?x4643, ?x53), language(?x4643, ?x254), film(?x10491, ?x4643), ?x10491 = 030hbp, film(?x609, ?x4643) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #38079 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 141 *> proper extension: 0bmpm; 07w8fz; 05z43v; *> query: (?x4643, 09r9dp) <- genre(?x4643, ?x2540), film(?x1031, ?x4643), titles(?x1316, ?x4643), gender(?x1031, ?x231), major_field_of_study(?x1200, ?x2540) *> conf = 0.01 ranks of expected_values: 772 EVAL 080lkt7 film! 09r9dp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 95.000 60.000 0.250 http://example.org/film/actor/film./film/performance/film #15534-055sjw PRED entity: 055sjw PRED relation: place_of_birth PRED expected values: 09c7w0 => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 53): 0s5cg (0.14 #181), 02_286 (0.07 #21140, 0.06 #4947, 0.06 #5651), 0cr3d (0.05 #1502, 0.04 #5022, 0.04 #4318), 01_d4 (0.05 #770, 0.04 #3586, 0.04 #1474), 0rh6k (0.04 #706, 0.02 #2818, 0.02 #3522), 030qb3t (0.04 #2870, 0.03 #1462, 0.03 #9206), 09c7w0 (0.03 #2817, 0.03 #3521, 0.03 #4225), 0dclg (0.02 #1486, 0.01 #7822, 0.01 #12751), 0yc7f (0.02 #1687), 01531 (0.02 #6441, 0.02 #7145, 0.01 #2217) >> Best rule #181 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 0f721s; 0cjdk; 0glmv; 02vntj; >> query: (?x9871, 0s5cg) <- award_winner(?x3180, ?x9871), ?x3180 = 07c72 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2817 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 215 *> proper extension: 04rtpt; *> query: (?x9871, 09c7w0) <- program(?x9871, ?x2555), award_winner(?x2555, ?x4459) *> conf = 0.03 ranks of expected_values: 7 EVAL 055sjw place_of_birth 09c7w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 41.000 41.000 0.143 http://example.org/people/person/place_of_birth #15533-02g5bf PRED entity: 02g5bf PRED relation: place_of_death PRED expected values: 04vmp => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 41): 04vmp (0.29 #302, 0.14 #1274, 0.13 #886), 030qb3t (0.19 #4102, 0.15 #6044, 0.15 #6820), 0f2rq (0.12 #474, 0.08 #669, 0.02 #2999), 0k049 (0.10 #4083, 0.09 #6025, 0.08 #6995), 02_286 (0.10 #7394, 0.10 #6811, 0.09 #7005), 0cvw9 (0.09 #1281, 0.07 #893, 0.04 #2640), 04jpl (0.06 #2726, 0.05 #3891, 0.05 #1367), 06_kh (0.05 #7192, 0.04 #7970, 0.03 #1753), 09c17 (0.04 #1719, 0.02 #2496, 0.02 #2690), 05tbn (0.04 #1606, 0.02 #2383, 0.02 #2577) >> Best rule #302 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02vmzp; 01n8_g; 03vrnh; 01k6nm; >> query: (?x12024, 04vmp) <- award_winner(?x4443, ?x12024), sibling(?x12024, ?x10074), award(?x2065, ?x4443), ?x2065 = 015npr >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02g5bf place_of_death 04vmp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.286 http://example.org/people/deceased_person/place_of_death #15532-0fq7dv_ PRED entity: 0fq7dv_ PRED relation: film_distribution_medium PRED expected values: 029j_ => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.17 #1, 0.14 #6, 0.08 #59), 02nxhr (0.17 #2, 0.08 #22, 0.07 #17), 0735l (0.15 #24, 0.14 #19, 0.13 #14), 07z4p (0.03 #222, 0.02 #348), 07c52 (0.03 #222, 0.02 #348) >> Best rule #1 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 06_sc3; >> query: (?x1915, 029j_) <- country(?x1915, ?x390), genre(?x1915, ?x600), genre(?x1915, ?x571), ?x571 = 03npn, ?x390 = 0chghy, language(?x1915, ?x254), titles(?x600, ?x394) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fq7dv_ film_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.167 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #15531-0vhm PRED entity: 0vhm PRED relation: genre PRED expected values: 095bb => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 75): 07s9rl0 (0.58 #660, 0.57 #1977, 0.56 #2553), 01htzx (0.50 #17, 0.26 #676, 0.22 #594), 01z4y (0.43 #347, 0.39 #1664, 0.35 #1087), 06n90 (0.38 #13, 0.29 #424, 0.27 #590), 01hmnh (0.38 #16, 0.24 #593, 0.24 #427), 0215n (0.38 #576, 0.12 #2635, 0.12 #1812), 025s89p (0.29 #462, 0.27 #628, 0.21 #792), 01z77k (0.28 #521, 0.16 #111, 0.15 #2580), 03k9fj (0.27 #588, 0.26 #422, 0.25 #11), 0c4xc (0.27 #1688, 0.25 #1111, 0.25 #1194) >> Best rule #660 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 063zky; >> query: (?x5219, 07s9rl0) <- program(?x2135, ?x5219), award(?x2135, ?x198), nominated_for(?x2135, ?x531), executive_produced_by(?x825, ?x2135) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #448 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 07ng9k; 0jwl2; 026bfsh; 01lk02; 04mx8h4; 03d3ht; 017dtf; 04svwx; 0gxr1c; 045nc5; ... *> query: (?x5219, 095bb) <- actor(?x5219, ?x12244), language(?x12244, ?x254), category(?x12244, ?x134), film(?x12244, ?x1022) *> conf = 0.26 ranks of expected_values: 11 EVAL 0vhm genre 095bb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 104.000 104.000 0.585 http://example.org/tv/tv_program/genre #15530-02vjp3 PRED entity: 02vjp3 PRED relation: crewmember PRED expected values: 0272kv => 84 concepts (63 used for prediction) PRED predicted values (max 10 best out of 33): 095zvfg (0.10 #83, 0.07 #410, 0.07 #873), 03m49ly (0.10 #80, 0.07 #870, 0.06 #266), 092ys_y (0.10 #65, 0.06 #855, 0.03 #158), 0b6mgp_ (0.10 #67, 0.05 #114, 0.03 #206), 021yc7p (0.10 #54, 0.04 #844, 0.03 #147), 02q9kqf (0.10 #75, 0.04 #865, 0.03 #168), 0js9s (0.10 #78, 0.03 #171, 0.03 #217), 01yznp (0.10 #48, 0.02 #281), 0284n42 (0.08 #840, 0.06 #236, 0.06 #330), 0b79gfg (0.07 #853, 0.03 #992, 0.03 #1039) >> Best rule #83 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0dtw1x; 0bhwhj; >> query: (?x7480, 095zvfg) <- film(?x382, ?x7480), person(?x7480, ?x269), crewmember(?x7480, ?x1983), film_release_distribution_medium(?x7480, ?x81) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02vjp3 crewmember 0272kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 63.000 0.100 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #15529-01qdjm PRED entity: 01qdjm PRED relation: artists! PRED expected values: 01fbr2 => 115 concepts (103 used for prediction) PRED predicted values (max 10 best out of 231): 03_d0 (0.67 #325, 0.50 #12, 0.42 #638), 064t9 (0.56 #14128, 0.46 #1581, 0.45 #8489), 06by7 (0.55 #2217, 0.53 #5671, 0.52 #1276), 06j6l (0.50 #51, 0.37 #677, 0.33 #364), 037n97 (0.50 #257, 0.33 #570, 0.13 #1196), 0xhtw (0.42 #5666, 0.33 #957, 0.27 #3784), 016clz (0.42 #9737, 0.29 #5653, 0.27 #2199), 0dl5d (0.37 #960, 0.25 #5669, 0.17 #3787), 03lty (0.35 #5678, 0.18 #3796, 0.17 #969), 0827d (0.33 #317, 0.06 #3142, 0.06 #1571) >> Best rule #325 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01vsy95; 02lbrd; >> query: (?x2747, 03_d0) <- award_winner(?x8409, ?x2747), ?x8409 = 03ncb2, award_winner(?x342, ?x2747) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #15055 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 697 *> proper extension: 07lmxq; 06jzh; 01yhvv; 01l2fn; 07ymr5; 0bgrsl; 08swgx; 06jvj7; 0347xl; 027xbpw; ... *> query: (?x2747, ?x505) <- nationality(?x2747, ?x94), award_nominee(?x2698, ?x2747), artists(?x505, ?x2698) *> conf = 0.16 ranks of expected_values: 28 EVAL 01qdjm artists! 01fbr2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 115.000 103.000 0.667 http://example.org/music/genre/artists #15528-02_p5w PRED entity: 02_p5w PRED relation: actor! PRED expected values: 0jwl2 => 116 concepts (79 used for prediction) PRED predicted values (max 10 best out of 161): 063zky (0.25 #108, 0.22 #634, 0.19 #1423), 043qqt5 (0.25 #224, 0.19 #1539, 0.17 #487), 015w8_ (0.25 #46, 0.19 #1361, 0.17 #309), 0jwl2 (0.25 #73, 0.18 #862, 0.15 #1125), 025x1t (0.25 #221, 0.17 #484, 0.11 #747), 01h72l (0.25 #38, 0.17 #301, 0.11 #564), 0fpxp (0.25 #148, 0.03 #1726, 0.03 #2254), 072kp (0.17 #273, 0.09 #799, 0.08 #1062), 0gxsh4 (0.17 #485, 0.09 #1011, 0.08 #1274), 0q9jk (0.17 #417, 0.09 #943, 0.08 #1206) >> Best rule #108 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02y0yt; >> query: (?x3758, 063zky) <- film(?x3758, ?x11149), film(?x3758, ?x1893), ?x1893 = 0jnwx, film(?x4463, ?x11149), award_winner(?x2183, ?x4463) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #73 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 02y0yt; *> query: (?x3758, 0jwl2) <- film(?x3758, ?x11149), film(?x3758, ?x1893), ?x1893 = 0jnwx, film(?x4463, ?x11149), award_winner(?x2183, ?x4463) *> conf = 0.25 ranks of expected_values: 4 EVAL 02_p5w actor! 0jwl2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 79.000 0.250 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #15527-06czyr PRED entity: 06czyr PRED relation: student! PRED expected values: 017z88 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 94): 0bwfn (0.06 #275, 0.06 #32952, 0.05 #29789), 065y4w7 (0.06 #14, 0.05 #4757, 0.05 #5811), 09f2j (0.05 #3848, 0.03 #686, 0.03 #5956), 04b_46 (0.04 #227, 0.04 #4970, 0.03 #10240), 017z88 (0.04 #19583, 0.03 #22218, 0.03 #28542), 015nl4 (0.03 #29581, 0.03 #31162, 0.03 #31689), 0fr9jp (0.03 #872, 0.03 #4034, 0.03 #1399), 07szy (0.03 #567, 0.03 #4256, 0.03 #2148), 01w5m (0.03 #11172, 0.02 #7483, 0.02 #32254), 08815 (0.03 #4745, 0.03 #5799, 0.03 #1583) >> Best rule #275 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 01fsyp; >> query: (?x5599, 0bwfn) <- award_winner(?x5592, ?x5599), award_winner(?x3624, ?x5599), ?x5592 = 0275n3y, ceremony(?x618, ?x3624) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #19583 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 325 *> proper extension: 03gm48; *> query: (?x5599, 017z88) <- award(?x5599, ?x1058), actor(?x5808, ?x5599), award_winner(?x3624, ?x5599) *> conf = 0.04 ranks of expected_values: 5 EVAL 06czyr student! 017z88 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 135.000 135.000 0.061 http://example.org/education/educational_institution/students_graduates./education/education/student #15526-061xq PRED entity: 061xq PRED relation: colors PRED expected values: 083jv => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.87 #830, 0.64 #415, 0.63 #523), 01g5v (0.50 #75, 0.40 #39, 0.32 #1653), 06fvc (0.40 #38, 0.39 #416, 0.36 #977), 02rnmb (0.40 #66, 0.38 #336, 0.32 #498), 03vtbc (0.31 #151, 0.29 #205, 0.29 #187), 036k5h (0.25 #23, 0.15 #1893, 0.14 #866), 0jc_p (0.20 #94, 0.20 #58, 0.17 #1688), 07plts (0.15 #1893, 0.14 #866, 0.12 #921), 088fh (0.15 #1893, 0.14 #866, 0.12 #921), 06kqt3 (0.15 #1893, 0.14 #866, 0.12 #921) >> Best rule #830 for best value: >> intensional similarity = 10 >> extensional distance = 69 >> proper extension: 026w398; >> query: (?x4208, 083jv) <- colors(?x4208, ?x4557), colors(?x4208, ?x332), ?x4557 = 019sc, colors(?x12737, ?x332), colors(?x5679, ?x332), colors(?x2497, ?x332), citytown(?x12737, ?x1275), currency(?x12737, ?x2244), ?x2497 = 0f1nl, school_type(?x5679, ?x3092) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 061xq colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.873 http://example.org/sports/sports_team/colors #15525-06jcc PRED entity: 06jcc PRED relation: influenced_by PRED expected values: 034bs 0hky 03j0d => 167 concepts (73 used for prediction) PRED predicted values (max 10 best out of 425): 032l1 (0.33 #3088, 0.29 #3516, 0.16 #10377), 048cl (0.33 #2372, 0.25 #1516, 0.10 #24443), 03j0d (0.33 #3330, 0.19 #2901, 0.14 #3758), 03_87 (0.28 #3197, 0.25 #2768, 0.22 #2340), 03f0324 (0.28 #3148, 0.16 #7862, 0.15 #9150), 084w8 (0.28 #3001, 0.14 #3429, 0.12 #7715), 06kb_ (0.25 #2724, 0.12 #1868, 0.10 #24443), 06hmd (0.25 #1879, 0.07 #6164, 0.06 #27450), 01tz6vs (0.22 #3172, 0.14 #3600, 0.07 #7886), 040db (0.22 #3054, 0.12 #1769, 0.12 #1341) >> Best rule #3088 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 084w8; 014dq7; 040db; 0jt90f5; 03vrp; 058vp; 03f47xl; 0683n; 0yxl; 07dnx; ... >> query: (?x7861, 032l1) <- influenced_by(?x7861, ?x4808), influenced_by(?x7861, ?x4072), location(?x4808, ?x1227), award(?x4808, ?x921), ?x4072 = 02lt8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3330 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 084w8; 014dq7; 040db; 0jt90f5; 03vrp; 058vp; 03f47xl; 0683n; 0yxl; 07dnx; ... *> query: (?x7861, 03j0d) <- influenced_by(?x7861, ?x4808), influenced_by(?x7861, ?x4072), location(?x4808, ?x1227), award(?x4808, ?x921), ?x4072 = 02lt8 *> conf = 0.33 ranks of expected_values: 3, 55, 98 EVAL 06jcc influenced_by 03j0d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 167.000 73.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 06jcc influenced_by 0hky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 167.000 73.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 06jcc influenced_by 034bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 167.000 73.000 0.333 http://example.org/influence/influence_node/influenced_by #15524-0f4hc PRED entity: 0f4hc PRED relation: nutrient! PRED expected values: 01nkt 033cnk 014j1m => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 12): 01nkt (0.93 #565, 0.93 #559, 0.92 #533), 014j1m (0.92 #500, 0.92 #672, 0.90 #420), 033cnk (0.89 #24, 0.89 #644, 0.89 #125), 06x4c (0.89 #24, 0.89 #125, 0.89 #160), 0dcfv (0.89 #24, 0.89 #125, 0.89 #160), 01sh2 (0.05 #479, 0.02 #400, 0.02 #597), 04k8n (0.05 #479, 0.02 #597, 0.02 #626), 05wvs (0.05 #479, 0.02 #597, 0.02 #626), 025rw19 (0.02 #400, 0.01 #360), 06jry (0.02 #400, 0.01 #360) >> Best rule #565 for best value: >> intensional similarity = 118 >> extensional distance = 28 >> proper extension: 0f4k5; >> query: (?x7894, ?x6032) <- nutrient(?x10612, ?x7894), nutrient(?x9732, ?x7894), nutrient(?x9489, ?x7894), nutrient(?x9005, ?x7894), nutrient(?x8298, ?x7894), nutrient(?x5373, ?x7894), nutrient(?x4068, ?x7894), nutrient(?x3468, ?x7894), nutrient(?x2701, ?x7894), nutrient(?x1959, ?x7894), ?x9732 = 05z55, ?x10612 = 0frq6, ?x5373 = 0971v, nutrient(?x9489, ?x13498), nutrient(?x9489, ?x12902), nutrient(?x9489, ?x12454), nutrient(?x9489, ?x12083), nutrient(?x9489, ?x11758), nutrient(?x9489, ?x11592), nutrient(?x9489, ?x11409), nutrient(?x9489, ?x10891), nutrient(?x9489, ?x10709), nutrient(?x9489, ?x10098), nutrient(?x9489, ?x9949), nutrient(?x9489, ?x9915), nutrient(?x9489, ?x9840), nutrient(?x9489, ?x9619), nutrient(?x9489, ?x9490), nutrient(?x9489, ?x9426), nutrient(?x9489, ?x7720), nutrient(?x9489, ?x7652), nutrient(?x9489, ?x7431), nutrient(?x9489, ?x7364), nutrient(?x9489, ?x7362), nutrient(?x9489, ?x7219), nutrient(?x9489, ?x7135), nutrient(?x9489, ?x6586), nutrient(?x9489, ?x6160), nutrient(?x9489, ?x6033), nutrient(?x9489, ?x6026), nutrient(?x9489, ?x5549), nutrient(?x9489, ?x5526), nutrient(?x9489, ?x5451), nutrient(?x9489, ?x5374), nutrient(?x9489, ?x5010), nutrient(?x9489, ?x3469), nutrient(?x9489, ?x2702), nutrient(?x9489, ?x2018), nutrient(?x9489, ?x1304), nutrient(?x2701, ?x13126), nutrient(?x2701, ?x12868), nutrient(?x2701, ?x9795), nutrient(?x2701, ?x9436), nutrient(?x2701, ?x9365), nutrient(?x2701, ?x8487), nutrient(?x2701, ?x8442), nutrient(?x2701, ?x6286), nutrient(?x2701, ?x6192), nutrient(?x2701, ?x5337), nutrient(?x2701, ?x4069), ?x2018 = 01sh2, ?x9490 = 0h1sg, nutrient(?x4068, ?x1960), ?x11592 = 025sf0_, ?x9949 = 02kd0rh, ?x1960 = 07hnp, ?x6160 = 041r51, ?x7652 = 025s0s0, ?x3468 = 0cxn2, ?x12868 = 03d49, ?x5010 = 0h1vz, ?x4069 = 0hqw8p_, ?x10891 = 0g5gq, ?x5374 = 025s0zp, ?x12902 = 0fzjh, nutrient(?x6032, ?x6286), ?x7135 = 025rsfk, ?x3469 = 0h1zw, ?x6033 = 04zjxcz, ?x13498 = 07q0m, ?x9619 = 0h1tg, ?x6586 = 05gh50, ?x12083 = 01n78x, ?x12454 = 025rw19, ?x5526 = 09pbb, ?x10709 = 0h1sz, ?x11409 = 0h1yf, nutrient(?x1959, ?x13545), nutrient(?x1959, ?x6517), ?x5549 = 025s7j4, ?x7431 = 09gwd, ?x9840 = 02p0tjr, ?x6517 = 02kd8zw, ?x9795 = 05v_8y, ?x9365 = 04k8n, ?x8442 = 02kcv4x, nutrient(?x8298, ?x10453), ?x1304 = 08lb68, ?x10453 = 075pwf, ?x5451 = 05wvs, ?x6032 = 01nkt, ?x6026 = 025sf8g, ?x7720 = 025s7x6, ?x10098 = 0h1_c, ?x13126 = 02kc_w5, ?x13545 = 01w_3, ?x7219 = 0h1vg, ?x2702 = 0838f, ?x9436 = 025sqz8, ?x6192 = 06jry, ?x9915 = 025tkqy, ?x7362 = 02kc5rj, ?x11758 = 0q01m, ?x8487 = 014yzm, ?x9005 = 04zpv, ?x7364 = 09gvd, ?x9426 = 0h1yy, ?x5337 = 06x4c >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0f4hc nutrient! 014j1m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.933 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0f4hc nutrient! 033cnk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.933 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0f4hc nutrient! 01nkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.933 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #15523-0cqt90 PRED entity: 0cqt90 PRED relation: location PRED expected values: 0cc56 => 124 concepts (81 used for prediction) PRED predicted values (max 10 best out of 233): 0ccvx (0.69 #52916, 0.67 #42496, 0.64 #28868), 030qb3t (0.29 #29750, 0.21 #62618, 0.19 #40974), 0dclg (0.20 #115, 0.12 #24053, 0.06 #916), 09c7w0 (0.20 #3, 0.06 #804, 0.02 #5610), 04jpl (0.19 #31288, 0.10 #62553, 0.06 #58545), 01n7q (0.13 #17698, 0.09 #31333, 0.07 #3266), 0cc56 (0.13 #1658, 0.10 #13683, 0.09 #12079), 01531 (0.12 #957, 0.04 #1759, 0.04 #13784), 0cr3d (0.10 #61878, 0.08 #4149, 0.07 #58672), 06yxd (0.06 #1046, 0.04 #17882, 0.02 #31517) >> Best rule #52916 for best value: >> intensional similarity = 4 >> extensional distance = 776 >> proper extension: 03j90; >> query: (?x3884, ?x4253) <- location(?x3884, ?x335), award_winner(?x102, ?x3884), place_of_birth(?x3884, ?x4253), award(?x123, ?x102) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1658 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 012v1t; *> query: (?x3884, 0cc56) <- location(?x3884, ?x739), ?x739 = 02_286, religion(?x3884, ?x1985), ?x1985 = 0c8wxp *> conf = 0.13 ranks of expected_values: 7 EVAL 0cqt90 location 0cc56 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 124.000 81.000 0.694 http://example.org/people/person/places_lived./people/place_lived/location #15522-0315w4 PRED entity: 0315w4 PRED relation: film! PRED expected values: 0315q3 => 124 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1126): 043zg (0.67 #131216, 0.64 #133301, 0.60 #162458), 01kwsg (0.12 #11253, 0.06 #2924, 0.02 #34158), 0jfx1 (0.12 #2490, 0.09 #6654, 0.07 #12902), 0j_c (0.12 #2494, 0.09 #6658, 0.06 #108305), 073w14 (0.12 #2843, 0.06 #108305, 0.05 #4925), 09nz_c (0.12 #3780, 0.06 #108305, 0.04 #7944), 0z4s (0.12 #2151, 0.06 #108305, 0.04 #6315), 016z2j (0.12 #2473, 0.06 #108305, 0.03 #21213), 01f6zc (0.11 #11358, 0.06 #108305, 0.06 #9275), 0127m7 (0.11 #10820, 0.02 #62886, 0.01 #12903) >> Best rule #131216 for best value: >> intensional similarity = 4 >> extensional distance = 653 >> proper extension: 0gfzgl; 01f3p_; 0cskb; >> query: (?x4799, ?x5364) <- titles(?x812, ?x4799), nominated_for(?x5364, ?x4799), film(?x5364, ?x3507), participant(?x5364, ?x4777) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #27895 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 131 *> proper extension: 0353tm; *> query: (?x4799, 0315q3) <- featured_film_locations(?x4799, ?x1227), film_format(?x4799, ?x909), country(?x4799, ?x94), film_release_distribution_medium(?x4799, ?x81) *> conf = 0.02 ranks of expected_values: 728 EVAL 0315w4 film! 0315q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 79.000 0.667 http://example.org/film/actor/film./film/performance/film #15521-044lyq PRED entity: 044lyq PRED relation: award_winner! PRED expected values: 0ck27z => 89 concepts (75 used for prediction) PRED predicted values (max 10 best out of 203): 0ck27z (0.67 #525, 0.64 #93, 0.62 #957), 09sb52 (0.37 #21178, 0.37 #21611, 0.35 #4323), 0fbvqf (0.37 #21178, 0.37 #21611, 0.35 #4323), 0bdwqv (0.14 #3890, 0.09 #171, 0.08 #603), 0789_m (0.14 #3890, 0.09 #21, 0.08 #453), 08_vwq (0.14 #3890, 0.09 #270, 0.08 #702), 099tbz (0.11 #1354, 0.08 #2219, 0.08 #6541), 0cqhk0 (0.09 #6088, 0.08 #901, 0.07 #6520), 0f4x7 (0.07 #2192, 0.07 #7378, 0.04 #3921), 054ky1 (0.06 #2271, 0.05 #4000, 0.04 #7025) >> Best rule #525 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0d810y; >> query: (?x7242, 0ck27z) <- award_nominee(?x7242, ?x4507), ?x4507 = 08pth9, award_winner(?x7242, ?x3051) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044lyq award_winner! 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 75.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #15520-014zcr PRED entity: 014zcr PRED relation: award PRED expected values: 05zr6wv 09sb52 0gs9p 0gr51 0bdw6t 04kxsb => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 267): 027c95y (0.71 #4183, 0.71 #34594, 0.70 #34975), 0gs9p (0.46 #2726, 0.35 #4249, 0.20 #7289), 09sb52 (0.41 #24360, 0.35 #12959, 0.33 #14479), 0gr51 (0.31 #2745, 0.26 #4268, 0.16 #7308), 0fbtbt (0.30 #6294, 0.29 #7815, 0.28 #3251), 05zr6wv (0.29 #13, 0.27 #1533, 0.26 #1153), 05ztrmj (0.29 #164, 0.26 #924, 0.21 #1684), 07cbcy (0.29 #65, 0.22 #825, 0.17 #1205), 057xs89 (0.29 #142, 0.22 #902, 0.16 #1662), 0cjyzs (0.28 #6174, 0.28 #7695, 0.25 #3131) >> Best rule #4183 for best value: >> intensional similarity = 2 >> extensional distance = 157 >> proper extension: 0yxl; >> query: (?x286, ?x198) <- participant(?x444, ?x286), award_winner(?x198, ?x286) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2726 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 123 *> proper extension: 045cq; 0m9c1; *> query: (?x286, 0gs9p) <- award_winner(?x1442, ?x286), film(?x286, ?x3133) *> conf = 0.46 ranks of expected_values: 2, 3, 4, 6, 11, 53 EVAL 014zcr award 04kxsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 133.000 133.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 014zcr award 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 133.000 133.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 014zcr award 0gr51 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 014zcr award 0gs9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 014zcr award 09sb52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 014zcr award 05zr6wv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 133.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15519-07ssc PRED entity: 07ssc PRED relation: countries_within! PRED expected values: 02j9z => 209 concepts (209 used for prediction) PRED predicted values (max 10 best out of 7): 0j0k (0.45 #128, 0.33 #72, 0.33 #3), 02j9z (0.45 #114, 0.44 #263, 0.41 #283), 059g4 (0.31 #162, 0.22 #113, 0.20 #37), 02qkt (0.28 #635, 0.27 #581, 0.24 #598), 0dg3n1 (0.21 #632, 0.20 #652, 0.20 #164), 017jq (0.04 #134), 07ssc (0.04 #134) >> Best rule #128 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 07bxhl; 04ty8; >> query: (?x512, 0j0k) <- country(?x150, ?x512), region(?x54, ?x512), olympics(?x512, ?x391) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #114 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 0jgd; 0d0vqn; 01ls2; 05v8c; 015fr; 0k6nt; 03gj2; 0345h; 06qd3; 01znc_; ... *> query: (?x512, 02j9z) <- nationality(?x111, ?x512), film_release_region(?x2878, ?x512), ?x2878 = 0hx4y *> conf = 0.45 ranks of expected_values: 2 EVAL 07ssc countries_within! 02j9z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 209.000 209.000 0.455 http://example.org/base/locations/continents/countries_within #15518-03gvt PRED entity: 03gvt PRED relation: role PRED expected values: 02qjv 04rzd => 80 concepts (58 used for prediction) PRED predicted values (max 10 best out of 89): 02hnl (0.87 #2369, 0.69 #2178, 0.67 #748), 0342h (0.86 #3188, 0.86 #2763, 0.86 #413), 0l14j_ (0.86 #2763, 0.86 #413, 0.84 #3103), 05148p4 (0.86 #2763, 0.86 #413, 0.84 #583), 01v1d8 (0.86 #413, 0.84 #583, 0.84 #830), 0dwtp (0.86 #413, 0.84 #583, 0.84 #830), 01399x (0.86 #413, 0.84 #583, 0.84 #830), 06w7v (0.86 #413, 0.84 #583, 0.84 #830), 0cfdd (0.86 #413, 0.84 #583, 0.84 #830), 0979zs (0.86 #413, 0.84 #583, 0.84 #830) >> Best rule #2369 for best value: >> intensional similarity = 16 >> extensional distance = 13 >> proper extension: 01w4dy; >> query: (?x3716, 02hnl) <- role(?x2460, ?x3716), role(?x2158, ?x3716), role(?x316, ?x3716), role(?x314, ?x3716), role(?x228, ?x3716), role(?x3716, ?x432), ?x432 = 042v_gx, ?x314 = 02sgy, ?x228 = 0l14qv, role(?x2158, ?x4311), ?x4311 = 01xqw, role(?x1147, ?x2158), ?x2460 = 01wy6, performance_role(?x2698, ?x316), role(?x115, ?x316), instrumentalists(?x316, ?x130) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #4396 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 35 *> proper extension: 02pprs; 07xzm; 01679d; 0l14j_; 011k_j; 02w3w; *> query: (?x3716, 04rzd) <- role(?x3161, ?x3716), role(?x780, ?x3716), role(?x3716, ?x614), instrumentalists(?x3716, ?x130), role(?x645, ?x3161), role(?x2731, ?x3716), profession(?x130, ?x131), ?x614 = 0mkg, instrumentalists(?x780, ?x5623), role(?x2459, ?x3716), profession(?x2731, ?x220), award_nominee(?x2731, ?x827), role(?x219, ?x780) *> conf = 0.78 ranks of expected_values: 13, 48 EVAL 03gvt role 04rzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 80.000 58.000 0.867 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 03gvt role 02qjv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 80.000 58.000 0.867 http://example.org/music/performance_role/track_performances./music/track_contribution/role #15517-0d_rw PRED entity: 0d_rw PRED relation: program! PRED expected values: 07y2b => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 54): 03mdt (0.36 #1520, 0.08 #3202, 0.08 #3032), 0187wh (0.33 #81, 0.29 #137, 0.20 #361), 0b275x (0.33 #74, 0.29 #130, 0.18 #410), 0gsg7 (0.30 #338, 0.24 #3028, 0.23 #3198), 05gnf (0.25 #13, 0.19 #3039, 0.19 #3209), 027_tg (0.25 #8, 0.17 #64, 0.14 #120), 0cjdk (0.20 #340, 0.13 #1237, 0.13 #2974), 0ljc_ (0.20 #364, 0.09 #420, 0.08 #1093), 02hmvw (0.14 #1163, 0.12 #210, 0.11 #826), 022tfp (0.12 #205, 0.11 #317, 0.09 #429) >> Best rule #1520 for best value: >> intensional similarity = 6 >> extensional distance = 42 >> proper extension: 0bx_hnp; >> query: (?x13221, 03mdt) <- program(?x2062, ?x13221), languages(?x13221, ?x254), ?x254 = 02h40lc, award_winner(?x6339, ?x2062), contact_category(?x2062, ?x897), film(?x2062, ?x4504) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #767 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 16 *> proper extension: 01rf57; 06qwh; 07gbf; 07g9f; 06qxh; 03cf9ly; 06qw_; *> query: (?x13221, 07y2b) <- genre(?x13221, ?x1510), genre(?x13221, ?x1013), ?x1013 = 06n90, program(?x2062, ?x13221), country_of_origin(?x13221, ?x94), ?x94 = 09c7w0, genre(?x5458, ?x1510), genre(?x5116, ?x1510), ?x5116 = 09fc83, ?x5458 = 05szq8z *> conf = 0.11 ranks of expected_values: 12 EVAL 0d_rw program! 07y2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 70.000 70.000 0.364 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #15516-01b9z4 PRED entity: 01b9z4 PRED relation: currency PRED expected values: 09nqf => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.43 #1, 0.32 #19, 0.31 #10), 01nv4h (0.03 #20, 0.02 #29, 0.01 #32) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02gf_l; >> query: (?x9647, 09nqf) <- film(?x9647, ?x5002), ?x5002 = 03tn80, actor(?x1849, ?x9647) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01b9z4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.429 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #15515-0dr3sl PRED entity: 0dr3sl PRED relation: film_release_region PRED expected values: 0b90_r 02k54 0345h => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 160): 05r4w (0.91 #1966, 0.89 #844, 0.87 #3090), 0b90_r (0.88 #284, 0.87 #846, 0.86 #1968), 0d060g (0.87 #848, 0.82 #1970, 0.77 #6), 0345h (0.86 #1990, 0.85 #3114, 0.84 #1569), 07ssc (0.83 #855, 0.82 #3101, 0.80 #1556), 05b4w (0.83 #894, 0.80 #2016, 0.77 #52), 04gzd (0.79 #851, 0.69 #9, 0.68 #1973), 03rk0 (0.71 #887, 0.59 #2009, 0.53 #325), 07f1x (0.62 #103, 0.59 #383, 0.42 #2067), 02k54 (0.62 #14, 0.53 #294, 0.32 #575) >> Best rule #1966 for best value: >> intensional similarity = 5 >> extensional distance = 109 >> proper extension: 0gtsx8c; 0c3ybss; 0gj8t_b; 0gtvpkw; 0gy2y8r; 06zn2v2; 0gtt5fb; 0dll_t2; 0bq6ntw; 0gg5kmg; ... >> query: (?x2868, 05r4w) <- film_release_region(?x2868, ?x1917), film_release_region(?x2868, ?x1229), language(?x2868, ?x254), ?x1229 = 059j2, ?x1917 = 01p1v >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #284 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 02r8hh_; 06w839_; 0gtsxr4; 0fpgp26; *> query: (?x2868, 0b90_r) <- film_release_region(?x2868, ?x2629), nominated_for(?x3911, ?x2868), ?x2629 = 06f32, ?x3911 = 02x1z2s *> conf = 0.88 ranks of expected_values: 2, 4, 10 EVAL 0dr3sl film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 85.000 0.910 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dr3sl film_release_region 02k54 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 85.000 85.000 0.910 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dr3sl film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.910 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15514-01chpn PRED entity: 01chpn PRED relation: honored_for! PRED expected values: 02hn5v => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 119): 0gvstc3 (0.10 #1117, 0.05 #2085, 0.04 #4747), 03gwpw2 (0.10 #1095, 0.06 #5, 0.05 #1821), 0hr6lkl (0.09 #375, 0.08 #496, 0.06 #738), 02wzl1d (0.09 #7, 0.05 #1097, 0.04 #854), 03nnm4t (0.08 #1153, 0.04 #5267, 0.04 #1879), 0clfdj (0.07 #365, 0.07 #486, 0.06 #2), 0g5b0q5 (0.07 #377, 0.07 #498, 0.05 #1104), 0hndn2q (0.07 #395, 0.07 #516, 0.05 #758), 0gmdkyy (0.07 #387, 0.07 #508, 0.05 #750), 02jp5r (0.07 #421, 0.07 #542, 0.05 #784) >> Best rule #1117 for best value: >> intensional similarity = 3 >> extensional distance = 141 >> proper extension: 07s8z_l; >> query: (?x6288, 0gvstc3) <- category(?x6288, ?x134), ?x134 = 08mbj5d, honored_for(?x6238, ?x6288) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #880 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 80 *> proper extension: 048scx; 0416y94; 0kvgxk; 0dx8gj; 07jxpf; 02chhq; *> query: (?x6288, 02hn5v) <- nominated_for(?x2341, ?x6288), nominated_for(?x601, ?x6288), award(?x167, ?x601), ?x2341 = 02x17s4, ceremony(?x601, ?x78) *> conf = 0.04 ranks of expected_values: 36 EVAL 01chpn honored_for! 02hn5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 91.000 91.000 0.105 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15513-07c9s PRED entity: 07c9s PRED relation: countries_spoken_in PRED expected values: 0hzlz 02wt0 04vs9 => 65 concepts (54 used for prediction) PRED predicted values (max 10 best out of 322): 0697s (0.67 #2034, 0.60 #1318, 0.57 #2211), 0162v (0.60 #1299, 0.50 #2015, 0.50 #943), 0hzlz (0.57 #2163, 0.50 #2340, 0.50 #1986), 06t2t (0.56 #4468, 0.55 #1600, 0.55 #3210), 07ytt (0.40 #1402, 0.33 #3367, 0.33 #3188), 01ppq (0.40 #1397, 0.33 #2113, 0.29 #2290), 01nln (0.40 #1373, 0.33 #2089, 0.29 #2266), 04hhv (0.40 #1387, 0.33 #2103, 0.29 #2280), 03_xj (0.40 #1356, 0.33 #2072, 0.29 #2249), 07z5n (0.40 #1305, 0.33 #2021, 0.29 #2198) >> Best rule #2034 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 02hxcvy; >> query: (?x5121, 0697s) <- languages(?x12675, ?x5121), languages(?x10828, ?x5121), languages(?x9253, ?x5121), languages(?x7295, ?x5121), language(?x2340, ?x5121), countries_spoken_in(?x5121, ?x6307), ?x12675 = 040nwr, film(?x7295, ?x657), type_of_union(?x10828, ?x566), profession(?x9253, ?x319), religion(?x7295, ?x492), gender(?x10828, ?x231), countries_within(?x6956, ?x6307), spouse(?x9039, ?x7295) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2163 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 5 *> proper extension: 0121sr; *> query: (?x5121, 0hzlz) <- languages(?x12675, ?x5121), languages(?x10828, ?x5121), languages(?x9253, ?x5121), languages(?x7295, ?x5121), language(?x2340, ?x5121), countries_spoken_in(?x5121, ?x6307), ?x12675 = 040nwr, film(?x7295, ?x657), type_of_union(?x10828, ?x566), profession(?x9253, ?x319), jurisdiction_of_office(?x346, ?x6307), languages_spoken(?x9347, ?x5121), country(?x668, ?x6307) *> conf = 0.57 ranks of expected_values: 3, 51, 90 EVAL 07c9s countries_spoken_in 04vs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 65.000 54.000 0.667 http://example.org/language/human_language/countries_spoken_in EVAL 07c9s countries_spoken_in 02wt0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 65.000 54.000 0.667 http://example.org/language/human_language/countries_spoken_in EVAL 07c9s countries_spoken_in 0hzlz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 54.000 0.667 http://example.org/language/human_language/countries_spoken_in #15512-045c66 PRED entity: 045c66 PRED relation: award_nominee! PRED expected values: 06jzh => 83 concepts (22 used for prediction) PRED predicted values (max 10 best out of 534): 07s8r0 (0.83 #6966, 0.82 #6965, 0.81 #23218), 05th8t (0.83 #6966, 0.82 #6965, 0.81 #23218), 06jzh (0.62 #104, 0.16 #51085, 0.15 #34829), 045c66 (0.50 #306, 0.16 #51085, 0.15 #34829), 043kzcr (0.19 #540, 0.16 #51085, 0.15 #34829), 02tk74 (0.19 #6967, 0.15 #9289, 0.10 #11611), 02bkdn (0.16 #51085, 0.15 #34829, 0.12 #392), 03yj_0n (0.16 #51085, 0.15 #34829, 0.12 #812), 02bfmn (0.16 #51085, 0.15 #34829, 0.12 #36), 0cjsxp (0.16 #51085, 0.15 #34829, 0.12 #869) >> Best rule #6966 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 066m4g; 012x4t; 01s21dg; 01p0w_; >> query: (?x1486, ?x3289) <- award_nominee(?x1486, ?x3289), film(?x3289, ?x2973), friend(?x9807, ?x1486) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #104 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0fthdk; *> query: (?x1486, 06jzh) <- award_nominee(?x1486, ?x3289), ?x3289 = 0347xl, film(?x1486, ?x1487) *> conf = 0.62 ranks of expected_values: 3 EVAL 045c66 award_nominee! 06jzh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 22.000 0.825 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15511-0h7x PRED entity: 0h7x PRED relation: nationality! PRED expected values: 0p51w 07_m9_ 026rm_y => 173 concepts (129 used for prediction) PRED predicted values (max 10 best out of 4065): 0p51w (0.34 #113302, 0.33 #36419, 0.32 #283253), 0459z (0.34 #113302, 0.33 #36419, 0.24 #80931), 01pr6q7 (0.34 #113302, 0.33 #36419, 0.11 #5148), 026rm_y (0.34 #113302, 0.33 #36419, 0.08 #10839), 04k15 (0.34 #113302, 0.33 #36419, 0.08 #9182), 019r_1 (0.34 #113302, 0.33 #36419, 0.07 #13579), 0280mv7 (0.32 #283253, 0.11 #5574, 0.08 #9620), 09gnn (0.24 #80931, 0.15 #11377, 0.07 #35657), 03s9v (0.24 #80931, 0.15 #10340, 0.07 #34620), 042q3 (0.24 #80931, 0.15 #11431, 0.04 #80223) >> Best rule #113302 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 02psqkz; 01m41_; >> query: (?x1355, ?x862) <- capital(?x1355, ?x863), location(?x862, ?x863), location_of_ceremony(?x566, ?x863) >> conf = 0.34 => this is the best rule for 6 predicted values ranks of expected_values: 1, 4, 2993 EVAL 0h7x nationality! 026rm_y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 173.000 129.000 0.335 http://example.org/people/person/nationality EVAL 0h7x nationality! 07_m9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 129.000 0.335 http://example.org/people/person/nationality EVAL 0h7x nationality! 0p51w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 129.000 0.335 http://example.org/people/person/nationality #15510-0p9qb PRED entity: 0p9qb PRED relation: award_winner! PRED expected values: 0c4hx0 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 133): 0c4hx0 (0.33 #269, 0.33 #128, 0.04 #9448), 0c53zb (0.17 #202, 0.06 #1471, 0.05 #1753), 0d__c3 (0.17 #266, 0.06 #548, 0.05 #1535), 0fz2y7 (0.17 #201, 0.04 #9448, 0.02 #1470), 0jzphpx (0.17 #180, 0.02 #9345, 0.02 #462), 073hkh (0.06 #283, 0.04 #9448, 0.01 #2257), 0h_9252 (0.06 #340, 0.04 #9448, 0.01 #2314), 0gmdkyy (0.06 #312, 0.04 #9448, 0.01 #594), 09g90vz (0.06 #406, 0.04 #3226, 0.04 #3085), 09gkdln (0.06 #404, 0.04 #3224, 0.03 #3083) >> Best rule #269 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 012wg; 0m593; 0dqcm; >> query: (?x11011, 0c4hx0) <- nominated_for(?x11011, ?x4504), ?x4504 = 0cq7kw, award_winner(?x458, ?x11011), gender(?x11011, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p9qb award_winner! 0c4hx0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15509-01b195 PRED entity: 01b195 PRED relation: genre PRED expected values: 01jfsb => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 88): 05p553 (0.37 #3507, 0.36 #3747, 0.36 #3867), 02kdv5l (0.34 #363, 0.33 #845, 0.32 #965), 01jfsb (0.33 #2791, 0.33 #855, 0.33 #3273), 02l7c8 (0.30 #5206, 0.29 #5927, 0.28 #136), 04xvlr (0.27 #241, 0.21 #1812, 0.20 #2418), 03k9fj (0.27 #612, 0.26 #372, 0.25 #854), 03bxz7 (0.25 #56, 0.10 #1381, 0.10 #777), 082gq (0.21 #752, 0.21 #1114, 0.20 #1356), 06n90 (0.19 #374, 0.17 #614, 0.16 #3153), 060__y (0.19 #1342, 0.18 #1100, 0.17 #2434) >> Best rule #3507 for best value: >> intensional similarity = 3 >> extensional distance = 691 >> proper extension: 02d413; 0140g4; 047gn4y; 0ds3t5x; 016z5x; 0gjk1d; 09gq0x5; 0g3zrd; 0fpmrm3; 047svrl; ... >> query: (?x2262, 05p553) <- nominated_for(?x71, ?x2262), production_companies(?x2262, ?x902), titles(?x53, ?x2262) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #2791 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 583 *> proper extension: 0h95zbp; *> query: (?x2262, 01jfsb) <- film_crew_role(?x2262, ?x137), ?x137 = 09zzb8, production_companies(?x2262, ?x902) *> conf = 0.33 ranks of expected_values: 3 EVAL 01b195 genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 84.000 0.368 http://example.org/film/film/genre #15508-02wh0 PRED entity: 02wh0 PRED relation: influenced_by! PRED expected values: 045bg 01dvtx 0x3r3 => 227 concepts (119 used for prediction) PRED predicted values (max 10 best out of 449): 045bg (0.50 #3935, 0.43 #1986, 0.40 #523), 06whf (0.40 #157, 0.26 #6492, 0.20 #645), 043tg (0.40 #316, 0.20 #32650, 0.20 #4216), 040_t (0.40 #246, 0.20 #734, 0.17 #4633), 0d4jl (0.40 #113, 0.20 #601, 0.15 #2441), 041xl (0.40 #278, 0.20 #766, 0.15 #2441), 0683n (0.33 #1301, 0.20 #4225, 0.20 #325), 05jm7 (0.33 #1111, 0.19 #37659, 0.15 #2441), 0jt90f5 (0.33 #1053, 0.15 #2441, 0.14 #4387), 0nk72 (0.30 #4227, 0.29 #2278, 0.19 #52160) >> Best rule #3935 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 04xjp; >> query: (?x11097, 045bg) <- influenced_by(?x11554, ?x11097), influenced_by(?x8430, ?x11097), influenced_by(?x1737, ?x11097), ?x8430 = 0ct9_, influenced_by(?x3335, ?x11554), gender(?x1737, ?x231) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 13, 126 EVAL 02wh0 influenced_by! 0x3r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 227.000 119.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 02wh0 influenced_by! 01dvtx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 227.000 119.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 02wh0 influenced_by! 045bg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 227.000 119.000 0.500 http://example.org/influence/influence_node/influenced_by #15507-01dvms PRED entity: 01dvms PRED relation: award_winner! PRED expected values: 0fk0xk => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 127): 0fk0xk (0.30 #7333, 0.10 #8603, 0.06 #219), 027hjff (0.12 #57, 0.06 #480, 0.05 #4569), 0ftlkg (0.10 #8603, 0.01 #2846, 0.01 #1718), 09bymc (0.06 #121, 0.06 #544, 0.05 #967), 092_25 (0.06 #72, 0.06 #495, 0.03 #4584), 03gyp30 (0.06 #117, 0.06 #4629, 0.05 #1386), 092t4b (0.06 #52, 0.05 #2308, 0.05 #898), 0275n3y (0.06 #75, 0.05 #921, 0.04 #2331), 0g55tzk (0.06 #137, 0.05 #983, 0.04 #4649), 0clfdj (0.06 #4, 0.05 #850, 0.03 #6490) >> Best rule #7333 for best value: >> intensional similarity = 3 >> extensional distance = 966 >> proper extension: 01zcrv; >> query: (?x4349, ?x5723) <- award_winner(?x3902, ?x4349), award_winner(?x11597, ?x4349), honored_for(?x5723, ?x11597) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dvms award_winner! 0fk0xk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.301 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15506-0c_j5d PRED entity: 0c_j5d PRED relation: production_companies! PRED expected values: 0d90m 0cd2vh9 048yqf => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 1217): 05sns6 (0.50 #472, 0.27 #74244, 0.12 #26742), 02fttd (0.50 #538, 0.27 #74244, 0.12 #26808), 0g22z (0.50 #13, 0.12 #26283, 0.08 #63973), 0fzm0g (0.50 #1138, 0.12 #27408, 0.08 #15985), 0ndsl1x (0.50 #972, 0.12 #27242, 0.08 #15819), 026f__m (0.50 #854, 0.12 #27124, 0.08 #15701), 08s6mr (0.50 #836, 0.12 #27106, 0.08 #15683), 026hxwx (0.50 #733, 0.12 #27003, 0.08 #15580), 0640y35 (0.50 #654, 0.12 #26924, 0.08 #15501), 03yvf2 (0.50 #620, 0.12 #26890, 0.08 #15467) >> Best rule #472 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 016tt2; 0c41qv; >> query: (?x738, 05sns6) <- production_companies(?x8787, ?x738), production_companies(?x4664, ?x738), ?x8787 = 048vhl, film(?x96, ?x4664), ?x96 = 079vf >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7995 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0gy1_; *> query: (?x738, ?x97) <- company(?x4682, ?x738), company(?x7976, ?x738), ?x4682 = 0dq_5, executive_produced_by(?x97, ?x7976) *> conf = 0.49 ranks of expected_values: 14, 20, 104 EVAL 0c_j5d production_companies! 048yqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 191.000 191.000 0.500 http://example.org/film/film/production_companies EVAL 0c_j5d production_companies! 0cd2vh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 191.000 191.000 0.500 http://example.org/film/film/production_companies EVAL 0c_j5d production_companies! 0d90m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 191.000 191.000 0.500 http://example.org/film/film/production_companies #15505-017v3q PRED entity: 017v3q PRED relation: institution! PRED expected values: 02h4rq6 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.77 #381, 0.76 #293, 0.75 #246), 02_xgp2 (0.62 #121, 0.59 #33, 0.53 #276), 03bwzr4 (0.62 #123, 0.54 #278, 0.53 #303), 016t_3 (0.53 #92, 0.51 #269, 0.50 #114), 04zx3q1 (0.45 #112, 0.41 #90, 0.38 #178), 027f2w (0.44 #96, 0.43 #8, 0.38 #118), 07s6fsf (0.43 #291, 0.39 #379, 0.38 #244), 013zdg (0.40 #161, 0.33 #117, 0.29 #29), 01rr_d (0.29 #38, 0.28 #1121, 0.26 #170), 03mkk4 (0.29 #10, 0.18 #32, 0.17 #1211) >> Best rule #381 for best value: >> intensional similarity = 1 >> extensional distance = 191 >> proper extension: 0fht9f; >> query: (?x6919, 02h4rq6) <- school(?x12956, ?x6919) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017v3q institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.767 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15504-01rnxn PRED entity: 01rnxn PRED relation: film PRED expected values: 03t79f 01738w => 92 concepts (47 used for prediction) PRED predicted values (max 10 best out of 291): 08bytj (0.60 #28602, 0.59 #35752, 0.59 #41115), 02qr3k8 (0.33 #1287, 0.02 #31676, 0.02 #47763), 03nqnnk (0.33 #1023, 0.01 #9958, 0.01 #13532), 0gy4k (0.33 #3495), 01s9vc (0.33 #3434), 01jr4j (0.33 #3035), 0k7tq (0.33 #2969), 0jqd3 (0.33 #2902), 05css_ (0.33 #2803), 0ktpx (0.33 #2792) >> Best rule #28602 for best value: >> intensional similarity = 3 >> extensional distance = 931 >> proper extension: 016qtt; 05cj4r; 0436f4; 03f2_rc; 01gvr1; 01mvth; 04bd8y; 03gm48; 015grj; 0f0p0; ... >> query: (?x2991, ?x7756) <- film(?x2991, ?x1708), award_winner(?x1033, ?x2991), nominated_for(?x2991, ?x7756) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #31518 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1080 *> proper extension: 04kj2v; *> query: (?x2991, 01738w) <- nationality(?x2991, ?x94), film(?x2991, ?x3505), nominated_for(?x591, ?x3505), films(?x7455, ?x3505) *> conf = 0.01 ranks of expected_values: 284 EVAL 01rnxn film 01738w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 92.000 47.000 0.596 http://example.org/film/actor/film./film/performance/film EVAL 01rnxn film 03t79f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 47.000 0.596 http://example.org/film/actor/film./film/performance/film #15503-019vhk PRED entity: 019vhk PRED relation: film_festivals PRED expected values: 09rwjly => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 13): 0g57ws5 (0.11 #7, 0.05 #91, 0.04 #28), 0j63cyr (0.03 #45, 0.02 #150, 0.02 #486), 0bx_f_t (0.02 #100), 04grdgy (0.02 #114, 0.02 #282, 0.02 #51), 04_m9gk (0.02 #286, 0.02 #223, 0.02 #601), 0kfhjq0 (0.02 #110, 0.02 #89, 0.02 #47), 09rwjly (0.02 #134, 0.02 #71, 0.02 #92), 0gg7gsl (0.02 #64, 0.02 #484, 0.02 #85), 0bmj62v (0.02 #810, 0.02 #54, 0.01 #600), 059_y8d (0.02 #86, 0.02 #44, 0.01 #338) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 03wy8t; >> query: (?x2852, 0g57ws5) <- genre(?x2852, ?x53), film(?x7310, ?x2852), language(?x2852, ?x254), ?x7310 = 04sry >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #134 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 341 *> proper extension: 0170z3; 0m313; 028_yv; 02_fm2; 09m6kg; 011yrp; 011yxg; 0bvn25; 0dq626; 0czyxs; ... *> query: (?x2852, 09rwjly) <- genre(?x2852, ?x53), film(?x489, ?x2852), films(?x11988, ?x2852), film_crew_role(?x2852, ?x137) *> conf = 0.02 ranks of expected_values: 7 EVAL 019vhk film_festivals 09rwjly CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 69.000 69.000 0.111 http://example.org/film/film/film_festivals #15502-04sh80 PRED entity: 04sh80 PRED relation: film! PRED expected values: 013cr 01xcqc 04twmk => 68 concepts (24 used for prediction) PRED predicted values (max 10 best out of 959): 0bwh6 (0.33 #216, 0.25 #2295, 0.07 #10613), 01n1gc (0.33 #647, 0.13 #11044, 0.12 #8964), 01fx2g (0.33 #933, 0.07 #11330, 0.05 #15488), 0c1pj (0.33 #92, 0.07 #10489, 0.03 #12568), 0dpqk (0.33 #894, 0.07 #11291, 0.03 #15449), 01gy7r (0.33 #728, 0.07 #11125, 0.03 #15283), 0347xl (0.33 #564, 0.07 #10961, 0.03 #15119), 015p3p (0.25 #3174, 0.20 #7333, 0.03 #13571), 0mfj2 (0.25 #5693, 0.13 #11932, 0.10 #16090), 018ygt (0.25 #9436, 0.13 #11516, 0.05 #15674) >> Best rule #216 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 033pf1; >> query: (?x11681, 0bwh6) <- film(?x7522, ?x11681), ?x7522 = 0d608, film(?x382, ?x11681), genre(?x11681, ?x571), ?x571 = 03npn >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #16858 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 47 *> proper extension: 07gp9; 0bth54; 0fg04; 0b73_1d; 05q96q6; 02pxmgz; 04w7rn; 02r1c18; 0fdv3; 0cz_ym; ... *> query: (?x11681, 013cr) <- film(?x12436, ?x11681), executive_produced_by(?x11681, ?x4552), edited_by(?x11681, ?x4215), film_release_distribution_medium(?x11681, ?x81), gender(?x12436, ?x231) *> conf = 0.02 ranks of expected_values: 691, 866 EVAL 04sh80 film! 04twmk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 24.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 04sh80 film! 01xcqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 24.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 04sh80 film! 013cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 24.000 0.333 http://example.org/film/actor/film./film/performance/film #15501-03q45x PRED entity: 03q45x PRED relation: people! PRED expected values: 07hwkr => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 37): 041rx (0.23 #312, 0.14 #1082, 0.14 #1236), 07bch9 (0.22 #177, 0.14 #331, 0.10 #254), 013xrm (0.14 #97, 0.11 #174, 0.10 #251), 019kn7 (0.14 #123, 0.11 #200, 0.10 #277), 02ctzb (0.14 #92, 0.10 #246, 0.03 #1093), 0x67 (0.14 #318, 0.11 #164, 0.10 #3629), 048z7l (0.14 #348, 0.04 #964, 0.03 #1041), 033tf_ (0.11 #1085, 0.11 #161, 0.10 #238), 09vc4s (0.11 #163, 0.10 #240, 0.04 #1087), 038723 (0.11 #223, 0.09 #377, 0.03 #454) >> Best rule #312 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0m0nq; >> query: (?x7795, 041rx) <- profession(?x7795, ?x1146), spouse(?x6569, ?x7795), ?x1146 = 018gz8 >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #936 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 337 *> proper extension: 0157m; 02fb1n; 010hn; 01x1cn2; 01yd8v; 05r5w; 07g2v; 0d06m5; 024dgj; 0btyl; ... *> query: (?x7795, 07hwkr) <- profession(?x7795, ?x987), spouse(?x6569, ?x7795), award(?x7795, ?x757) *> conf = 0.05 ranks of expected_values: 13 EVAL 03q45x people! 07hwkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 87.000 87.000 0.227 http://example.org/people/ethnicity/people #15500-02qkwl PRED entity: 02qkwl PRED relation: written_by PRED expected values: 06dkzt => 113 concepts (78 used for prediction) PRED predicted values (max 10 best out of 124): 072vj (0.25 #333, 0.02 #1679, 0.01 #3025), 0237jb (0.25 #234, 0.02 #3262, 0.02 #8646), 0d6484 (0.16 #5721, 0.16 #4039, 0.15 #14134), 05nn4k (0.16 #5721, 0.16 #4039, 0.15 #14134), 032v0v (0.10 #721, 0.07 #1395, 0.03 #3750), 05183k (0.10 #717, 0.05 #1391, 0.03 #2401), 08hp53 (0.10 #725, 0.05 #1399, 0.02 #3754), 02bfxb (0.10 #768, 0.05 #1442, 0.02 #7499), 0c3ns (0.10 #731, 0.02 #1405, 0.01 #2751), 09pl3f (0.08 #1193, 0.03 #5231, 0.02 #5567) >> Best rule #333 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 011yqc; >> query: (?x8001, 072vj) <- film_format(?x8001, ?x909), film_crew_role(?x8001, ?x137), nominated_for(?x2209, ?x8001), film(?x4681, ?x8001), ?x4681 = 024bbl >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3968 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 108 *> proper extension: 0dnvn3; 0m491; 02vqhv0; 04g9gd; 014nq4; 04grkmd; 05c9zr; 034r25; 06tpmy; 0660b9b; ... *> query: (?x8001, 06dkzt) <- film_format(?x8001, ?x909), film_crew_role(?x8001, ?x1284), film_crew_role(?x8001, ?x1171), film(?x521, ?x8001), ?x1171 = 09vw2b7, produced_by(?x8001, ?x4660), ?x1284 = 0ch6mp2 *> conf = 0.03 ranks of expected_values: 20 EVAL 02qkwl written_by 06dkzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 113.000 78.000 0.250 http://example.org/film/film/written_by #15499-03f0324 PRED entity: 03f0324 PRED relation: student! PRED expected values: 09hgk => 138 concepts (135 used for prediction) PRED predicted values (max 10 best out of 266): 0g8rj (0.25 #176, 0.20 #1230, 0.10 #5975), 07x4c (0.25 #259, 0.20 #1313, 0.05 #6058), 01p7x7 (0.25 #956, 0.03 #9390, 0.02 #8863), 01w5m (0.20 #1159, 0.15 #9066, 0.14 #8539), 01w3v (0.20 #1597, 0.06 #5287, 0.05 #5814), 015ln1 (0.20 #1779, 0.06 #5469, 0.05 #5996), 02zd460 (0.20 #1752, 0.06 #5442, 0.03 #37949), 01hjy5 (0.20 #1360), 01stzp (0.17 #3147, 0.09 #9999, 0.07 #4201), 01lhdt (0.17 #2896, 0.03 #37949, 0.03 #7113) >> Best rule #176 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 040db; 02lt8; >> query: (?x4915, 0g8rj) <- nationality(?x4915, ?x7430), influenced_by(?x10275, ?x4915), influenced_by(?x4915, ?x2994), ?x10275 = 03hpr, influenced_by(?x2994, ?x2162) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4745 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0n6f8; *> query: (?x4915, ?x122) <- diet(?x4915, ?x3130), student(?x5179, ?x4915), major_field_of_study(?x122, ?x5179), major_field_of_study(?x734, ?x5179) *> conf = 0.02 ranks of expected_values: 183 EVAL 03f0324 student! 09hgk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 138.000 135.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #15498-0p_2r PRED entity: 0p_2r PRED relation: inductee! PRED expected values: 06szd3 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 4): 06szd3 (0.11 #110, 0.11 #20, 0.10 #29), 04045y (0.05 #33, 0.04 #51, 0.03 #60), 0g2c8 (0.04 #442, 0.04 #181, 0.04 #451), 0qjfl (0.02 #210, 0.02 #75, 0.01 #93) >> Best rule #110 for best value: >> intensional similarity = 2 >> extensional distance = 99 >> proper extension: 05g8ky; 0f1vrl; 024swd; 0bbxd3; 01s7z0; >> query: (?x1422, 06szd3) <- profession(?x1422, ?x319), program_creator(?x6884, ?x1422) >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p_2r inductee! 06szd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.109 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #15497-016kv6 PRED entity: 016kv6 PRED relation: genre PRED expected values: 01jfsb => 74 concepts (39 used for prediction) PRED predicted values (max 10 best out of 88): 02qfv5d (0.64 #2229, 0.63 #2465, 0.58 #1407), 05p553 (0.45 #120, 0.40 #237, 0.38 #3), 02l7c8 (0.44 #15, 0.33 #249, 0.32 #1304), 01jfsb (0.37 #833, 0.32 #598, 0.30 #715), 04xvlr (0.29 #2112, 0.27 #2348, 0.24 #1290), 0lsxr (0.25 #359, 0.25 #8, 0.22 #829), 03k9fj (0.24 #1183, 0.23 #832, 0.22 #597), 060__y (0.22 #2127, 0.21 #2363, 0.19 #1305), 04228s (0.19 #73, 0.09 #190, 0.07 #307), 01t_vv (0.18 #402, 0.17 #285, 0.14 #168) >> Best rule #2229 for best value: >> intensional similarity = 4 >> extensional distance = 397 >> proper extension: 02qjv1p; >> query: (?x3523, ?x11405) <- titles(?x11405, ?x3523), titles(?x53, ?x3523), ?x53 = 07s9rl0, genre(?x3523, ?x225) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #833 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 117 *> proper extension: 025n07; *> query: (?x3523, 01jfsb) <- language(?x3523, ?x2502), ?x2502 = 06nm1, nominated_for(?x777, ?x3523) *> conf = 0.37 ranks of expected_values: 4 EVAL 016kv6 genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 39.000 0.636 http://example.org/film/film/genre #15496-01l3j PRED entity: 01l3j PRED relation: profession PRED expected values: 02hrh1q => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 109): 02hrh1q (0.90 #13515, 0.88 #7515, 0.88 #9915), 01d_h8 (0.40 #6, 0.35 #4656, 0.34 #7806), 0dxtg (0.40 #14, 0.29 #3314, 0.29 #314), 02jknp (0.40 #8, 0.27 #1208, 0.24 #6758), 0nbcg (0.39 #483, 0.35 #3483, 0.32 #3633), 09jwl (0.37 #14121, 0.36 #13220, 0.36 #12320), 03gjzk (0.29 #316, 0.23 #7516, 0.20 #4666), 018gz8 (0.29 #318, 0.23 #918, 0.19 #1668), 01c72t (0.28 #475, 0.27 #2875, 0.26 #3025), 01c8w0 (0.28 #459, 0.14 #309, 0.11 #2859) >> Best rule #13515 for best value: >> intensional similarity = 3 >> extensional distance = 789 >> proper extension: 0b_dy; 03kpvp; 073749; 01515w; 01520h; 023n39; 02jyhv; 02dlfh; 065mm1; 045931; ... >> query: (?x13735, 02hrh1q) <- film(?x13735, ?x5499), film_release_region(?x5499, ?x87), cinematography(?x5499, ?x7118) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l3j profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.895 http://example.org/people/person/profession #15495-080h2 PRED entity: 080h2 PRED relation: location! PRED expected values: 0f276 01g969 => 226 concepts (160 used for prediction) PRED predicted values (max 10 best out of 2277): 023kzp (0.33 #1209, 0.21 #31249, 0.20 #33753), 03hh89 (0.33 #1106, 0.20 #6114, 0.17 #8617), 05x2t7 (0.33 #371, 0.20 #5379, 0.17 #7882), 073749 (0.33 #798, 0.20 #5806, 0.17 #8309), 0c6qh (0.33 #459, 0.20 #5467, 0.17 #7970), 0gl88b (0.33 #370, 0.17 #17894, 0.16 #30410), 0151ns (0.33 #84, 0.16 #30124, 0.15 #32628), 014g9y (0.33 #2131, 0.16 #32171, 0.15 #34675), 01w02sy (0.33 #593, 0.15 #33137, 0.11 #30633), 0pyww (0.33 #976, 0.12 #28513, 0.12 #26010) >> Best rule #1209 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 030qb3t; >> query: (?x1036, 023kzp) <- teams(?x1036, ?x934), location(?x4371, ?x1036), ?x4371 = 05txrz, citytown(?x4267, ?x1036) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #29512 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0jpkg; *> query: (?x1036, 0f276) <- adjoins(?x1036, ?x10586), citytown(?x4267, ?x1036), mode_of_transportation(?x1036, ?x4272) *> conf = 0.06 ranks of expected_values: 1016 EVAL 080h2 location! 01g969 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 226.000 160.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location EVAL 080h2 location! 0f276 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 226.000 160.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #15494-0h10vt PRED entity: 0h10vt PRED relation: student! PRED expected values: 07tg4 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 56): 0ks67 (0.10 #189, 0.08 #1239, 0.02 #18909), 0bwfn (0.10 #800, 0.09 #4476, 0.09 #3426), 0cwx_ (0.10 #766, 0.01 #28077, 0.01 #9167), 065y4w7 (0.05 #1589, 0.05 #3165, 0.04 #4215), 015nl4 (0.05 #67, 0.05 #8993, 0.04 #1117), 08815 (0.05 #2, 0.04 #1052, 0.03 #8928), 04b_46 (0.05 #227, 0.04 #1277, 0.03 #9153), 01d34b (0.05 #256, 0.04 #1306, 0.02 #4982), 05nrkb (0.05 #349, 0.04 #1399, 0.02 #9275), 033gn8 (0.05 #378, 0.04 #1428, 0.02 #9304) >> Best rule #189 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 09fb5; 0pz7h; 06cgy; 015rkw; 030hcs; 0hvb2; 0gy6z9; 01qr1_; 07yp0f; 01wz01; ... >> query: (?x9561, 0ks67) <- award_winner(?x9561, ?x6324), ?x6324 = 018ygt, film(?x9561, ?x1263) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #4812 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 805 *> proper extension: 0pcc0; 03f70xs; 052h3; 0372p; 016lh0; 021r7r; 046rfv; 02ln1; 0738y5; 016wvy; ... *> query: (?x9561, 07tg4) <- gender(?x9561, ?x231), people(?x743, ?x9561), student(?x11614, ?x9561) *> conf = 0.02 ranks of expected_values: 31 EVAL 0h10vt student! 07tg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 109.000 109.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student #15493-05fjy PRED entity: 05fjy PRED relation: district_represented! PRED expected values: 077g7n => 230 concepts (230 used for prediction) PRED predicted values (max 10 best out of 44): 077g7n (0.93 #708, 0.89 #620, 0.89 #576), 03rl1g (0.57 #617, 0.54 #1013, 0.53 #573), 043djx (0.55 #666, 0.54 #622, 0.53 #578), 01h7xx (0.49 #648, 0.48 #1044, 0.47 #692), 01gt99 (0.47 #700, 0.44 #612, 0.43 #1052), 01gtdd (0.47 #609, 0.46 #653, 0.45 #697), 03tcbx (0.45 #2333, 0.43 #2422, 0.27 #716), 03z5xd (0.45 #2333, 0.43 #2422, 0.20 #713), 03ww_x (0.45 #2333, 0.43 #2422, 0.18 #93), 01gst_ (0.45 #671, 0.42 #583, 0.41 #1023) >> Best rule #708 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0g0syc; >> query: (?x5575, 077g7n) <- district_represented(?x6728, ?x5575), district_represented(?x952, ?x5575), ?x952 = 06f0dc, ?x6728 = 070mff >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fjy district_represented! 077g7n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 230.000 230.000 0.927 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #15492-057d89 PRED entity: 057d89 PRED relation: student! PRED expected values: 02mj7c => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 144): 01jq34 (0.20 #584, 0.20 #57, 0.10 #3219), 03ksy (0.11 #13808, 0.10 #15389, 0.10 #14335), 065y4w7 (0.11 #3703, 0.10 #541, 0.10 #14), 01w5m (0.10 #632, 0.10 #105, 0.05 #16442), 06kknt (0.10 #994, 0.10 #467, 0.04 #1521), 0lyjf (0.10 #684, 0.10 #157, 0.04 #1211), 04gd8j (0.10 #895, 0.10 #368), 0k__z (0.10 #835, 0.10 #308), 05zl0 (0.10 #202, 0.04 #5999, 0.03 #3364), 0bx8pn (0.10 #572, 0.03 #3734, 0.02 #4261) >> Best rule #584 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0pqzh; >> query: (?x1056, 01jq34) <- place_of_death(?x1056, ?x1523), tv_program(?x1056, ?x2829), program(?x2062, ?x2829) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 057d89 student! 02mj7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 119.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #15491-0gn30 PRED entity: 0gn30 PRED relation: celebrities_impersonated! PRED expected values: 03m6t5 => 127 concepts (75 used for prediction) PRED predicted values (max 10 best out of 7): 03m6t5 (0.29 #3, 0.11 #59, 0.10 #141), 03d_zl4 (0.14 #6, 0.02 #144, 0.02 #78), 04s430 (0.11 #13, 0.07 #29, 0.02 #143), 0pz04 (0.11 #16, 0.04 #80, 0.04 #146), 01n5309 (0.07 #17, 0.04 #105, 0.03 #81), 0f7hc (0.01 #76, 0.01 #84), 0d608 (0.01 #223) >> Best rule #3 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 0qkj7; >> query: (?x5338, 03m6t5) <- person(?x7415, ?x5338), ?x7415 = 02qr3k8 >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gn30 celebrities_impersonated! 03m6t5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 75.000 0.286 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #15490-0fzrhn PRED entity: 0fzrhn PRED relation: ceremony! PRED expected values: 0gr0m => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 333): 0gs9p (0.89 #4942, 0.89 #3666, 0.88 #3175), 0gr51 (0.89 #3666, 0.88 #3175, 0.88 #4467), 0gr0m (0.89 #3666, 0.88 #3175, 0.83 #4452), 0gqng (0.89 #3666, 0.88 #3175, 0.77 #3913), 0gr07 (0.89 #3666, 0.88 #3175, 0.77 #3913), 0l8z1 (0.89 #3666, 0.88 #3175, 0.77 #3913), 018wdw (0.89 #3666, 0.88 #3175, 0.77 #3913), 0gqxm (0.71 #1095, 0.57 #3049, 0.56 #3540), 02g3ft (0.61 #1710, 0.12 #7581, 0.12 #9292), 0262s1 (0.61 #1710, 0.12 #9292, 0.10 #4891) >> Best rule #4942 for best value: >> intensional similarity = 15 >> extensional distance = 35 >> proper extension: 073hmq; >> query: (?x11984, 0gs9p) <- award_winner(?x11984, ?x2109), ceremony(?x4573, ?x11984), ceremony(?x2222, ?x11984), ceremony(?x1972, ?x11984), award_nominee(?x5611, ?x2109), honored_for(?x11984, ?x8984), nominated_for(?x2109, ?x1746), ?x1972 = 0gqyl, type_of_union(?x2109, ?x566), award_winner(?x4445, ?x5611), religion(?x5611, ?x1985), category_of(?x4573, ?x3459), ?x2222 = 0gs96, award_winner(?x4653, ?x5611), nationality(?x2109, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #3666 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 14 *> proper extension: 073h9x; *> query: (?x11984, ?x77) <- award_winner(?x11984, ?x2109), ceremony(?x4573, ?x11984), ceremony(?x3066, ?x11984), ceremony(?x601, ?x11984), costume_design_by(?x9484, ?x2109), award_winner(?x12679, ?x2109), ?x601 = 0gr4k, award_winner(?x9667, ?x2109), ?x3066 = 0gqy2, ceremony(?x1243, ?x9667), ceremony(?x1079, ?x9667), ceremony(?x77, ?x9667), ?x4573 = 0gq_d, ?x1079 = 0l8z1, ?x1243 = 0gr0m, genre(?x9484, ?x258) *> conf = 0.89 ranks of expected_values: 3 EVAL 0fzrhn ceremony! 0gr0m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 42.000 42.000 0.892 http://example.org/award/award_category/winners./award/award_honor/ceremony #15489-09fb5 PRED entity: 09fb5 PRED relation: award_winner! PRED expected values: 09q_6t 0bzjvm => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 118): 01bx35 (0.22 #279, 0.17 #7, 0.10 #415), 02rjjll (0.22 #277, 0.05 #2453, 0.04 #2045), 01s695 (0.17 #3, 0.11 #275, 0.10 #411), 05c1t6z (0.17 #15, 0.10 #423, 0.07 #695), 09p2r9 (0.12 #225, 0.04 #1313, 0.03 #1177), 09bymc (0.12 #252, 0.02 #1340, 0.02 #5964), 0bzjvm (0.12 #243), 09306z (0.12 #241), 013b2h (0.11 #350, 0.05 #2526, 0.05 #2118), 03nnm4t (0.11 #345, 0.05 #753, 0.04 #2521) >> Best rule #279 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 04bgy; 01k_0fp; >> query: (?x406, 01bx35) <- film(?x406, ?x3566), ?x3566 = 04jpk2 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #243 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01rw116; *> query: (?x406, 0bzjvm) <- film(?x406, ?x3566), film(?x406, ?x407), ?x407 = 07xtqq, production_companies(?x3566, ?x541) *> conf = 0.12 ranks of expected_values: 7, 21 EVAL 09fb5 award_winner! 0bzjvm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 141.000 141.000 0.222 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09fb5 award_winner! 09q_6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 141.000 141.000 0.222 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15488-0cc5qkt PRED entity: 0cc5qkt PRED relation: genre PRED expected values: 01j1n2 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 116): 05p553 (0.39 #244, 0.37 #846, 0.36 #4340), 02kdv5l (0.38 #1, 0.36 #121, 0.32 #2891), 03k9fj (0.38 #11, 0.32 #131, 0.26 #372), 01jfsb (0.37 #2902, 0.34 #253, 0.32 #4229), 02l7c8 (0.33 #2665, 0.32 #2545, 0.30 #2185), 060__y (0.23 #2186, 0.21 #17, 0.19 #5074), 06n90 (0.21 #13, 0.16 #1097, 0.16 #254), 0lsxr (0.19 #729, 0.19 #2777, 0.19 #3380), 01hmnh (0.18 #619, 0.18 #259, 0.18 #1102), 017fp (0.17 #2184, 0.12 #15, 0.12 #5072) >> Best rule #244 for best value: >> intensional similarity = 4 >> extensional distance = 205 >> proper extension: 01hvjx; 025n07; 0bxsk; >> query: (?x3596, 05p553) <- nominated_for(?x669, ?x3596), executive_produced_by(?x3596, ?x4552), film_release_region(?x3596, ?x94), production_companies(?x3596, ?x1686) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1807 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 339 *> proper extension: 04bp0l; *> query: (?x3596, ?x53) <- nominated_for(?x7980, ?x3596), film(?x7980, ?x4027), film(?x7980, ?x3220), genre(?x4027, ?x53), language(?x3220, ?x90) *> conf = 0.06 ranks of expected_values: 35 EVAL 0cc5qkt genre 01j1n2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 105.000 105.000 0.386 http://example.org/film/film/genre #15487-05q5t0b PRED entity: 05q5t0b PRED relation: category PRED expected values: 08mbj5d => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.33 #1, 0.27 #49, 0.16 #7) >> Best rule #1 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 07cbcy; >> query: (?x3064, 08mbj5d) <- award(?x6426, ?x3064), award(?x1398, ?x3064), nominated_for(?x3064, ?x3507), performance_role(?x1398, ?x1466), ?x3507 = 03459x, ?x6426 = 01tt43d >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05q5t0b category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.333 http://example.org/common/topic/webpage./common/webpage/category #15486-04zx3q1 PRED entity: 04zx3q1 PRED relation: major_field_of_study PRED expected values: 04x_3 0l5mz => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 117): 02h40lc (0.82 #1143, 0.75 #436, 0.74 #523), 0l5mz (0.75 #436, 0.74 #523, 0.73 #1188), 0g4gr (0.75 #436, 0.74 #523, 0.73 #1053), 0_jm (0.75 #436, 0.74 #523, 0.73 #1053), 03qsdpk (0.75 #436, 0.74 #523, 0.73 #1053), 04_tv (0.75 #436, 0.74 #523, 0.73 #1053), 01jzxy (0.75 #436, 0.74 #523, 0.73 #1053), 041y2 (0.75 #436, 0.74 #523, 0.73 #1053), 02stgt (0.75 #436, 0.74 #523, 0.73 #1053), 04306rv (0.75 #436, 0.74 #523, 0.73 #1053) >> Best rule #1143 for best value: >> intensional similarity = 27 >> extensional distance = 9 >> proper extension: 01gkg3; >> query: (?x734, 02h40lc) <- institution(?x734, ?x10861), institution(?x734, ?x7707), institution(?x734, ?x6271), institution(?x734, ?x1768), major_field_of_study(?x734, ?x2981), major_field_of_study(?x734, ?x2606), major_field_of_study(?x10576, ?x2981), major_field_of_study(?x7546, ?x2981), major_field_of_study(?x6973, ?x2981), major_field_of_study(?x4031, ?x2981), major_field_of_study(?x2621, ?x2981), ?x7546 = 01_qgp, currency(?x10861, ?x170), major_field_of_study(?x2981, ?x9079), ?x170 = 09nqf, ?x2621 = 07vht, major_field_of_study(?x1768, ?x5900), ?x6973 = 05x_5, institution(?x1526, ?x4031), contains(?x1782, ?x6271), ?x10576 = 0g2jl, major_field_of_study(?x8095, ?x2606), ?x8095 = 02mp0g, school(?x799, ?x7707), taxonomy(?x9079, ?x939), student(?x6271, ?x1129), student(?x2606, ?x677) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #436 for first EXPECTED value: *> intensional similarity = 31 *> extensional distance = 2 *> proper extension: 014mlp; *> query: (?x734, ?x254) <- institution(?x734, ?x10861), institution(?x734, ?x7545), institution(?x734, ?x6814), institution(?x734, ?x1011), major_field_of_study(?x734, ?x10391), major_field_of_study(?x734, ?x5864), major_field_of_study(?x734, ?x2606), major_field_of_study(?x734, ?x1682), major_field_of_study(?x734, ?x1668), major_field_of_study(?x734, ?x866), ?x6814 = 03tw2s, ?x5864 = 04g51, ?x7545 = 0bwfn, ?x1668 = 01mkq, major_field_of_study(?x1011, ?x3213), major_field_of_study(?x1011, ?x254), ?x2606 = 062z7, school(?x465, ?x1011), major_field_of_study(?x1200, ?x1682), ?x866 = 088tb, category(?x1011, ?x134), ?x3213 = 0g4gr, profession(?x5609, ?x1682), school_type(?x10861, ?x3205), student(?x1011, ?x400), ?x10391 = 02jfc, currency(?x10861, ?x170), school(?x260, ?x1011), ?x465 = 05vsb7, ?x1200 = 016t_3, major_field_of_study(?x331, ?x1682) *> conf = 0.75 ranks of expected_values: 2, 12 EVAL 04zx3q1 major_field_of_study 0l5mz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 24.000 24.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 04zx3q1 major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 24.000 24.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #15485-026qnh6 PRED entity: 026qnh6 PRED relation: film_crew_role PRED expected values: 09vw2b7 0ch6mp2 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 25): 0ch6mp2 (0.88 #507, 0.86 #474, 0.79 #1139), 09vw2b7 (0.64 #1640, 0.62 #239, 0.62 #1138), 0dxtw (0.58 #10, 0.46 #243, 0.43 #76), 015h31 (0.24 #75, 0.20 #242, 0.13 #642), 01xy5l_ (0.22 #111, 0.22 #78, 0.18 #245), 02ynfr (0.19 #1647, 0.18 #246, 0.17 #1145), 0215hd (0.18 #148, 0.16 #82, 0.15 #483), 0d2b38 (0.17 #23, 0.16 #89, 0.16 #256), 05smlt (0.17 #18, 0.12 #251, 0.11 #84), 089g0h (0.16 #83, 0.14 #250, 0.12 #1950) >> Best rule #507 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0170z3; 0ds35l9; 0d90m; 03qcfvw; 03g90h; 01gc7; 011yxg; 01k1k4; 0ds11z; 02hxhz; ... >> query: (?x4810, 0ch6mp2) <- region(?x4810, ?x512), titles(?x162, ?x4810), film_crew_role(?x4810, ?x137), film(?x844, ?x4810) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 026qnh6 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.878 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 026qnh6 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.878 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15484-0qkj7 PRED entity: 0qkj7 PRED relation: nationality PRED expected values: 09c7w0 => 82 concepts (63 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.89 #3117, 0.86 #1002, 0.81 #2508), 0n5bk (0.33 #2911, 0.32 #3015, 0.30 #2912), 05fjf (0.33 #2911, 0.32 #3015, 0.30 #2912), 02jx1 (0.17 #333, 0.12 #734, 0.11 #834), 0345h (0.14 #432, 0.08 #732, 0.07 #832), 0d060g (0.14 #408, 0.05 #3925, 0.05 #508), 07ssc (0.11 #1117, 0.10 #1420, 0.09 #1520), 03rk0 (0.08 #1551, 0.07 #1148, 0.07 #1451), 03rjj (0.05 #606, 0.04 #806, 0.02 #4629), 0d05q4 (0.05 #662) >> Best rule #3117 for best value: >> intensional similarity = 4 >> extensional distance = 455 >> proper extension: 01pbxb; 05m63c; 01t6b4; 01mqz0; 012x4t; 0f1vrl; 01qdjm; 01zfmm; 016srn; 0blt6; ... >> query: (?x13834, 09c7w0) <- location(?x13834, ?x3807), type_of_union(?x13834, ?x566), ?x566 = 04ztj, county_seat(?x7492, ?x3807) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qkj7 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 63.000 0.893 http://example.org/people/person/nationality #15483-016ztl PRED entity: 016ztl PRED relation: actor PRED expected values: 04mlh8 => 162 concepts (97 used for prediction) PRED predicted values (max 10 best out of 162): 0814k3 (0.44 #757, 0.25 #580, 0.24 #1525), 0ckm4x (0.44 #1468, 0.28 #1764, 0.25 #2356), 07cn2c (0.43 #365, 0.25 #1074, 0.21 #1192), 05j0wc (0.38 #571, 0.29 #453, 0.29 #335), 0chrwb (0.38 #538, 0.25 #1424, 0.25 #1010), 091n7z (0.33 #762, 0.25 #1471, 0.22 #1767), 06sn8m (0.33 #82, 0.20 #200, 0.17 #259), 0fthdk (0.33 #95, 0.20 #213, 0.17 #272), 017vkx (0.33 #8), 066l3y (0.31 #1434, 0.28 #1730, 0.22 #725) >> Best rule #757 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 03gyvwg; >> query: (?x5955, 0814k3) <- film(?x296, ?x5955), actor(?x5955, ?x4214), genre(?x5955, ?x53), film(?x7030, ?x5955), student(?x263, ?x4214), ?x7030 = 07k2x >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #201 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 02gs6r; *> query: (?x5955, 04mlh8) <- actor(?x5955, ?x9238), actor(?x5955, ?x3785), titles(?x1510, ?x5955), film_release_distribution_medium(?x5955, ?x81), actor(?x7566, ?x9238), music(?x5955, ?x535), profession(?x3785, ?x1146) *> conf = 0.20 ranks of expected_values: 38 EVAL 016ztl actor 04mlh8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 162.000 97.000 0.444 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #15482-017s11 PRED entity: 017s11 PRED relation: award_nominee! PRED expected values: 0g1rw 06rq2l => 149 concepts (112 used for prediction) PRED predicted values (max 10 best out of 980): 026c1 (0.81 #212776, 0.81 #224341, 0.81 #238221), 0fvf9q (0.81 #212776, 0.81 #224341, 0.81 #238221), 04fyhv (0.81 #212776, 0.81 #224341, 0.81 #238221), 04cw0j (0.81 #212776, 0.81 #224341, 0.81 #238221), 024rdh (0.81 #212776, 0.81 #224341, 0.81 #238221), 01gb54 (0.27 #8013, 0.20 #17264, 0.19 #45019), 01b9ck (0.25 #9510, 0.21 #14136, 0.20 #16449), 02hy9p (0.25 #1794, 0.20 #17981, 0.18 #8730), 063472 (0.25 #855, 0.18 #7791, 0.14 #5479), 0hqcy (0.25 #1131, 0.13 #17318, 0.11 #19631) >> Best rule #212776 for best value: >> intensional similarity = 3 >> extensional distance = 875 >> proper extension: 05bnp0; 01j5ts; 0h5f5n; 023tp8; 01p7yb; 0bl2g; 032xhg; 0159h6; 0c4f4; 0187y5; ... >> query: (?x541, ?x163) <- award_winner(?x2245, ?x541), nominated_for(?x541, ?x770), award_nominee(?x541, ?x163) >> conf = 0.81 => this is the best rule for 5 predicted values *> Best rule #80949 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 05f260; *> query: (?x541, ?x1335) <- production_companies(?x821, ?x541), film(?x541, ?x186), produced_by(?x821, ?x1335) *> conf = 0.10 ranks of expected_values: 114, 162 EVAL 017s11 award_nominee! 06rq2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 149.000 112.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 017s11 award_nominee! 0g1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 149.000 112.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15481-01k5y0 PRED entity: 01k5y0 PRED relation: edited_by PRED expected values: 0dky9n => 67 concepts (56 used for prediction) PRED predicted values (max 10 best out of 21): 03q8ch (0.12 #159, 0.10 #275, 0.03 #335), 08h79x (0.07 #106, 0.05 #135, 0.01 #732), 0343h (0.05 #269, 0.04 #153, 0.01 #299), 02qggqc (0.05 #120, 0.04 #384, 0.02 #717), 04cy8rb (0.04 #147, 0.01 #323, 0.01 #805), 096hm (0.04 #714, 0.01 #352, 0.01 #925), 027pdrh (0.02 #302, 0.02 #272, 0.01 #724), 04wp63 (0.02 #288), 02lp3c (0.02 #163, 0.01 #731, 0.01 #279), 03cp7b3 (0.02 #257) >> Best rule #159 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 064n1pz; >> query: (?x10752, 03q8ch) <- genre(?x10752, ?x239), honored_for(?x7100, ?x10752), nominated_for(?x484, ?x10752), film_distribution_medium(?x10752, ?x81) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01k5y0 edited_by 0dky9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 56.000 0.122 http://example.org/film/film/edited_by #15480-0gkgp PRED entity: 0gkgp PRED relation: location! PRED expected values: 02zrv7 => 160 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1920): 01gz9n (0.49 #153572, 0.46 #125880, 0.44 #118328), 09fb5 (0.22 #50, 0.18 #7604, 0.07 #22712), 0lkr7 (0.22 #1016, 0.14 #8570, 0.07 #11088), 0q9kd (0.22 #2, 0.14 #7556, 0.04 #10074), 0c6qh (0.22 #460, 0.09 #8014, 0.09 #10532), 01nms7 (0.22 #1633, 0.09 #9187, 0.07 #11705), 09yrh (0.22 #913, 0.09 #8467, 0.07 #18539), 01ggc9 (0.22 #2059, 0.09 #9613, 0.05 #19685), 039crh (0.22 #884, 0.09 #8438, 0.05 #18510), 037s5h (0.22 #1944, 0.09 #9498, 0.04 #12016) >> Best rule #153572 for best value: >> intensional similarity = 4 >> extensional distance = 309 >> proper extension: 01vskn; >> query: (?x9394, ?x9964) <- location(?x11925, ?x9394), category(?x9394, ?x134), place_of_birth(?x9964, ?x9394), profession(?x11925, ?x1032) >> conf = 0.49 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gkgp location! 02zrv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 81.000 0.491 http://example.org/people/person/places_lived./people/place_lived/location #15479-016ky6 PRED entity: 016ky6 PRED relation: music PRED expected values: 01tc9r => 81 concepts (60 used for prediction) PRED predicted values (max 10 best out of 98): 01tc9r (0.67 #3167, 0.50 #212, 0.50 #65), 0b455l (0.25 #3168, 0.21 #3166, 0.15 #213), 02sh8y (0.25 #3168, 0.21 #3166, 0.15 #213), 0146pg (0.16 #2121, 0.14 #433, 0.10 #856), 02jxmr (0.09 #287, 0.05 #2607, 0.04 #920), 0bs1yy (0.09 #258, 0.04 #1102, 0.03 #2368), 0150t6 (0.08 #892, 0.06 #1103, 0.05 #259), 0bxtg (0.07 #6759, 0.06 #7394, 0.06 #8027), 04pf4r (0.06 #1125, 0.05 #281, 0.04 #2391), 02jxkw (0.06 #1199, 0.04 #988, 0.03 #1619) >> Best rule #3167 for best value: >> intensional similarity = 5 >> extensional distance = 152 >> proper extension: 02vxq9m; 01jc6q; 0fh694; 0_92w; 053rxgm; 02c638; 0bx0l; 082scv; 01jrbb; 0jswp; ... >> query: (?x5812, ?x3910) <- genre(?x5812, ?x53), award_winner(?x5812, ?x5813), award_winner(?x5812, ?x3910), student(?x9847, ?x5813), music(?x69, ?x3910) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016ky6 music 01tc9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 60.000 0.672 http://example.org/film/film/music #15478-09d6p2 PRED entity: 09d6p2 PRED relation: jurisdiction_of_office PRED expected values: 06tw8 => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 564): 09c7w0 (0.78 #8339, 0.58 #11125, 0.54 #12987), 02k54 (0.62 #467, 0.62 #466, 0.56 #4164), 03rk0 (0.62 #467, 0.62 #466, 0.56 #4164), 01crd5 (0.62 #467, 0.62 #466, 0.56 #4164), 05sb1 (0.62 #467, 0.62 #466, 0.56 #4164), 016zwt (0.62 #467, 0.62 #466, 0.56 #4164), 0jt3tjf (0.62 #467, 0.62 #466, 0.56 #4164), 01p1b (0.62 #467, 0.62 #466, 0.56 #4164), 04w8f (0.62 #467, 0.62 #466, 0.40 #11585), 05cc1 (0.62 #467, 0.62 #466, 0.40 #11585) >> Best rule #8339 for best value: >> intensional similarity = 9 >> extensional distance = 7 >> proper extension: 0f6c3; 02079p; 01t7n9; >> query: (?x5161, 09c7w0) <- basic_title(?x3341, ?x5161), politician(?x14092, ?x3341), jurisdiction_of_office(?x5161, ?x5114), partially_contains(?x455, ?x5114), country(?x359, ?x5114), combatants(?x326, ?x5114), olympics(?x5114, ?x7688), ?x7688 = 0jkvj, combatants(?x94, ?x5114) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #467 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 060c4; *> query: (?x5161, ?x291) <- company(?x5161, ?x11051), company(?x5161, ?x7545), jurisdiction_of_office(?x5161, ?x4120), ?x11051 = 07_dn, adjoins(?x4120, ?x6827), adjoins(?x4120, ?x291), locations(?x11216, ?x4120), adjustment_currency(?x6827, ?x170), member_states(?x7695, ?x6827), student(?x7545, ?x157), service_location(?x7545, ?x94) *> conf = 0.62 ranks of expected_values: 11 EVAL 09d6p2 jurisdiction_of_office 06tw8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 50.000 50.000 0.778 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #15477-01z56h PRED entity: 01z56h PRED relation: place_of_birth! PRED expected values: 01w8sf => 135 concepts (61 used for prediction) PRED predicted values (max 10 best out of 473): 0dsb_yy (0.33 #995, 0.25 #6219, 0.25 #3607), 01pnn3 (0.25 #3113, 0.15 #26125, 0.06 #91448), 08_438 (0.25 #5175, 0.15 #26125, 0.06 #91448), 01hb6v (0.15 #26125, 0.06 #91448, 0.04 #8329), 02d42t (0.04 #8837, 0.04 #11450, 0.03 #14062), 082_p (0.04 #9695, 0.04 #12308, 0.03 #14920), 059xnf (0.04 #9297, 0.04 #11910, 0.03 #14522), 01cbt3 (0.04 #8912, 0.04 #11525, 0.03 #14137), 01pcql (0.04 #8538, 0.04 #11151, 0.03 #13763), 03dq9 (0.04 #9986, 0.04 #12599, 0.03 #15211) >> Best rule #995 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0grd7; >> query: (?x13696, 0dsb_yy) <- country(?x13696, ?x512), contains(?x11868, ?x13696), ?x11868 = 03lrc, place_of_birth(?x9236, ?x13696), ?x512 = 07ssc >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01z56h place_of_birth! 01w8sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 61.000 0.333 http://example.org/people/person/place_of_birth #15476-025jfl PRED entity: 025jfl PRED relation: production_companies! PRED expected values: 0djkrp => 120 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1555): 09gb_4p (0.47 #5654, 0.43 #22614, 0.30 #20351), 08s6mr (0.47 #5654, 0.43 #22614, 0.30 #22615), 02d44q (0.47 #5654, 0.42 #12438, 0.41 #9046), 07vfy4 (0.47 #5654, 0.30 #20351, 0.30 #22615), 01rwpj (0.47 #5654, 0.30 #20351, 0.30 #22615), 03lfd_ (0.47 #5654, 0.30 #22615, 0.29 #20350), 017180 (0.47 #5654, 0.01 #24879), 09sh8k (0.43 #22614, 0.30 #20351, 0.30 #22615), 0fzm0g (0.43 #22614, 0.30 #20351, 0.30 #22615), 0kvgxk (0.43 #22614, 0.30 #20351, 0.30 #22615) >> Best rule #5654 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0g5lhl7; >> query: (?x617, ?x1071) <- award_nominee(?x617, ?x7324), nominated_for(?x617, ?x1071), award_winner(?x762, ?x617), production_companies(?x616, ?x617) >> conf = 0.47 => this is the best rule for 7 predicted values *> Best rule #22614 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 63 *> proper extension: 09d5h; 05xbx; 0mgkg; *> query: (?x617, ?x6427) <- film(?x617, ?x6427), film(?x617, ?x5724), film(?x617, ?x2458), genre(?x5724, ?x53), film(?x2457, ?x2458), production_companies(?x6427, ?x8394) *> conf = 0.43 ranks of expected_values: 11 EVAL 025jfl production_companies! 0djkrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 120.000 22.000 0.467 http://example.org/film/film/production_companies #15475-02tjl3 PRED entity: 02tjl3 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.84 #114, 0.83 #67, 0.83 #83), 07z4p (0.20 #181, 0.10 #10, 0.09 #20), 07c52 (0.20 #181, 0.07 #8, 0.07 #18), 02nxhr (0.20 #181, 0.05 #7, 0.04 #17), 0735l (0.20 #181) >> Best rule #114 for best value: >> intensional similarity = 4 >> extensional distance = 535 >> proper extension: 0413cff; 02pcq92; 0d8w2n; >> query: (?x5520, 029j_) <- titles(?x53, ?x5520), genre(?x5520, ?x2605), featured_film_locations(?x5520, ?x1767), language(?x5520, ?x254) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tjl3 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.836 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15474-054fvj PRED entity: 054fvj PRED relation: company PRED expected values: 0cjdk => 91 concepts (75 used for prediction) PRED predicted values (max 10 best out of 103): 09c7w0 (0.40 #579, 0.16 #2510, 0.10 #3476), 0gsg7 (0.33 #26, 0.20 #796, 0.09 #2342), 09d5h (0.33 #33, 0.07 #1961, 0.05 #2349), 01q0kg (0.33 #64, 0.07 #1992, 0.03 #3154), 05l71 (0.33 #62, 0.07 #1990, 0.03 #3152), 0g5lhl7 (0.23 #2358, 0.03 #2551, 0.02 #4871), 05g49 (0.20 #660, 0.11 #1236, 0.07 #2010), 03b3j (0.20 #631, 0.11 #1207, 0.07 #1981), 01w5m (0.20 #627, 0.10 #3139, 0.06 #4878), 0152x_ (0.20 #846, 0.08 #1618, 0.07 #1811) >> Best rule #579 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0c_md_; >> query: (?x9952, 09c7w0) <- location(?x9952, ?x2504), company(?x9952, ?x6678), athlete(?x1083, ?x9952), ?x1083 = 0jm_ >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 054fvj company 0cjdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 75.000 0.400 http://example.org/people/person/employment_history./business/employment_tenure/company #15473-03cwwl PRED entity: 03cwwl PRED relation: prequel PRED expected values: 0401sg => 114 concepts (55 used for prediction) PRED predicted values (max 10 best out of 35): 06_sc3 (0.33 #152, 0.02 #877, 0.01 #1420), 057lbk (0.03 #260, 0.02 #623, 0.01 #985), 080dfr7 (0.03 #352, 0.01 #1439), 09rfh9 (0.03 #350, 0.01 #1437), 048vhl (0.03 #339, 0.01 #1426), 01pj_5 (0.03 #263, 0.01 #1350), 0bmssv (0.03 #257, 0.01 #1344), 01qb5d (0.03 #197, 0.01 #1284), 0d90m (0.03 #183, 0.01 #1270), 02mc5v (0.03 #330) >> Best rule #152 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 080dfr7; >> query: (?x9996, 06_sc3) <- film_crew_role(?x9996, ?x281), film(?x5743, ?x9996), ?x5743 = 0175wg, genre(?x9996, ?x812), ?x812 = 01jfsb >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03cwwl prequel 0401sg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 55.000 0.333 http://example.org/film/film/prequel #15472-05gp3x PRED entity: 05gp3x PRED relation: producer_type PRED expected values: 0ckd1 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.74 #2, 0.73 #5, 0.70 #6) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 05hrq4; >> query: (?x6072, 0ckd1) <- award(?x6072, ?x6024), award_winner(?x12729, ?x6072), program_creator(?x1653, ?x6072) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05gp3x producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.737 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #15471-02r0d0 PRED entity: 02r0d0 PRED relation: award_winner PRED expected values: 098sx => 54 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1958): 07w21 (0.58 #27249, 0.50 #14858, 0.48 #32282), 014ps4 (0.58 #27249, 0.50 #14858, 0.33 #4183), 0dz46 (0.58 #27249, 0.33 #11834, 0.33 #4405), 06jcc (0.58 #27249, 0.33 #4190, 0.29 #14095), 04x56 (0.58 #27249, 0.33 #4618, 0.18 #21956), 0g5ff (0.58 #27249, 0.33 #3831, 0.18 #14859), 0jt90f5 (0.58 #27249, 0.33 #2958, 0.18 #14859), 03hpr (0.58 #27249, 0.33 #4626, 0.17 #12055), 06d6y (0.58 #27249, 0.33 #4399, 0.17 #11828), 03vrp (0.58 #27249, 0.33 #3593, 0.17 #11022) >> Best rule #27249 for best value: >> intensional similarity = 9 >> extensional distance = 19 >> proper extension: 05vc71; >> query: (?x14025, ?x476) <- award_winner(?x14025, ?x13624), award_winner(?x4879, ?x13624), nationality(?x13624, ?x512), place_of_birth(?x13624, ?x362), ?x512 = 07ssc, place_of_death(?x587, ?x362), award_winner(?x4879, ?x476), category(?x362, ?x134), student(?x892, ?x13624) >> conf = 0.58 => this is the best rule for 13 predicted values *> Best rule #14858 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 5 *> proper extension: 027dtxw; *> query: (?x14025, ?x9519) <- award_winner(?x14025, ?x13624), award(?x13624, ?x11084), award(?x13624, ?x8909), award_winner(?x11084, ?x9519), student(?x892, ?x13624), award_winner(?x8909, ?x476), ?x892 = 07tgn, disciplines_or_subjects(?x8909, ?x1510), ?x1510 = 01hmnh *> conf = 0.50 ranks of expected_values: 15 EVAL 02r0d0 award_winner 098sx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 54.000 32.000 0.581 http://example.org/award/award_category/winners./award/award_honor/award_winner #15470-01mqc_ PRED entity: 01mqc_ PRED relation: languages PRED expected values: 02h40lc => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 9): 02h40lc (0.31 #431, 0.30 #587, 0.30 #275), 064_8sq (0.05 #405, 0.05 #171, 0.05 #288), 02bjrlw (0.04 #157, 0.03 #235, 0.02 #742), 03k50 (0.02 #1876, 0.02 #2071, 0.02 #1213), 04306rv (0.02 #237, 0.01 #315, 0.01 #354), 04h9h (0.01 #186), 03hkp (0.01 #166), 06nm1 (0.01 #357), 07c9s (0.01 #1885, 0.01 #2080, 0.01 #1222) >> Best rule #431 for best value: >> intensional similarity = 3 >> extensional distance = 366 >> proper extension: 01sl1q; 04bdxl; 01j5ts; 06dv3; 014zcr; 023tp8; 0m2wm; 01qscs; 0prfz; 01q_ph; ... >> query: (?x7525, 02h40lc) <- participant(?x7525, ?x4126), award_nominee(?x221, ?x7525), film(?x7525, ?x1045) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mqc_ languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.310 http://example.org/people/person/languages #15469-01w5gg6 PRED entity: 01w5gg6 PRED relation: award PRED expected values: 03qbh5 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 292): 09sb52 (0.43 #4101, 0.33 #3695, 0.32 #24402), 01by1l (0.38 #5391, 0.31 #7827, 0.30 #9451), 01bgqh (0.32 #855, 0.28 #5321, 0.28 #1667), 054ks3 (0.30 #143, 0.24 #4609, 0.20 #3391), 01ck6h (0.26 #529, 0.20 #2153, 0.18 #1341), 0c4z8 (0.26 #884, 0.23 #2102, 0.23 #9410), 03qbh5 (0.23 #9545, 0.22 #4673, 0.22 #5485), 01c99j (0.23 #1040, 0.18 #1446, 0.17 #1852), 03qbnj (0.22 #235, 0.19 #1047, 0.17 #1859), 01ckrr (0.22 #233, 0.16 #2263, 0.11 #2669) >> Best rule #4101 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 02xb2bt; 03ym1; 01bh6y; 01wk3c; >> query: (?x9241, 09sb52) <- nationality(?x9241, ?x512), award_nominee(?x2807, ?x9241), film(?x9241, ?x4828), ?x512 = 07ssc >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #9545 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 237 *> proper extension: 03_0p; *> query: (?x9241, 03qbh5) <- instrumentalists(?x227, ?x9241), category(?x9241, ?x134), award_nominee(?x9241, ?x2807) *> conf = 0.23 ranks of expected_values: 7 EVAL 01w5gg6 award 03qbh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 99.000 99.000 0.426 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15468-01vh18t PRED entity: 01vh18t PRED relation: profession PRED expected values: 02jknp => 103 concepts (66 used for prediction) PRED predicted values (max 10 best out of 76): 0cbd2 (0.58 #587, 0.33 #442, 0.24 #1602), 01d_h8 (0.39 #296, 0.36 #876, 0.32 #1311), 0np9r (0.33 #20, 0.25 #745, 0.21 #5095), 03gjzk (0.30 #304, 0.26 #159, 0.23 #9151), 05sxg2 (0.30 #291, 0.04 #436, 0.03 #871), 018gz8 (0.24 #161, 0.14 #5961, 0.13 #4076), 02jknp (0.23 #3198, 0.23 #1313, 0.22 #3053), 09jwl (0.20 #5818, 0.19 #598, 0.19 #6398), 0nbcg (0.18 #610, 0.13 #5830, 0.13 #1190), 0d1pc (0.16 #1498, 0.14 #918, 0.10 #4108) >> Best rule #587 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 028p0; 04xjp; 07ym0; 058w5; 01rgr; 085gk; 0ldd; >> query: (?x9404, 0cbd2) <- nationality(?x9404, ?x94), profession(?x9404, ?x3746), ?x3746 = 05z96 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #3198 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 412 *> proper extension: 012cph; 03jm6c; 09bx1k; 09cdxn; 0bdlj; 06gn7r; 01zwy; 04v048; 02rybfn; 02jxsq; ... *> query: (?x9404, 02jknp) <- place_of_death(?x9404, ?x2866), award(?x9404, ?x783), nominated_for(?x783, ?x337) *> conf = 0.23 ranks of expected_values: 7 EVAL 01vh18t profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 103.000 66.000 0.578 http://example.org/people/person/profession #15467-01l29r PRED entity: 01l29r PRED relation: award_winner PRED expected values: 026g4l_ => 52 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1462): 0178rl (0.60 #10993, 0.33 #13448, 0.33 #3632), 01r6jt2 (0.60 #10628, 0.18 #36824, 0.17 #13083), 0f8pz (0.60 #10641, 0.17 #13096, 0.05 #27832), 017s11 (0.57 #17278, 0.50 #19734, 0.43 #4908), 03v1xb (0.57 #12271, 0.45 #36823, 0.43 #4908), 0147dk (0.57 #12271, 0.43 #4908, 0.43 #12269), 0fvf9q (0.57 #12271, 0.43 #4908, 0.43 #12269), 02q42j_ (0.57 #12271, 0.43 #4908, 0.43 #12269), 0grwj (0.57 #12271, 0.43 #4908, 0.43 #12269), 04wvhz (0.57 #12271, 0.43 #4908, 0.43 #12269) >> Best rule #10993 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0gqz2; 02h3d1; >> query: (?x3105, 0178rl) <- award(?x12947, ?x3105), award(?x9044, ?x3105), award(?x3381, ?x3105), ?x12947 = 0164y7, produced_by(?x3534, ?x9044), program_creator(?x4037, ?x3381), executive_produced_by(?x2370, ?x9044) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #28295 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 06bwtj; *> query: (?x3105, 026g4l_) <- ceremony(?x3105, ?x10337), ceremony(?x3105, ?x4141), ?x4141 = 0h_9252, ?x10337 = 0ds460j *> conf = 0.11 ranks of expected_values: 404 EVAL 01l29r award_winner 026g4l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #15466-01svw8n PRED entity: 01svw8n PRED relation: location_of_ceremony PRED expected values: 0r0m6 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 20): 0d1qn (0.10 #153, 0.10 #34), 0cv3w (0.08 #273, 0.04 #511, 0.03 #630), 0k049 (0.08 #242, 0.01 #2504, 0.01 #1790), 03gh4 (0.08 #301), 02_286 (0.02 #608, 0.02 #370, 0.01 #2870), 0lhn5 (0.02 #655, 0.01 #536), 0d9jr (0.02 #656), 0pswc (0.02 #459, 0.01 #578), 06q1r (0.02 #421, 0.01 #540), 03rk0 (0.02 #383, 0.01 #502) >> Best rule #153 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 07lt7b; 03k7bd; 043js; 01qq_lp; 031k24; >> query: (?x3930, 0d1qn) <- award(?x3930, ?x704), award_nominee(?x3930, ?x5363), ?x5363 = 016yvw >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #2074 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 482 *> proper extension: 01my_c; 0b4rf3; *> query: (?x3930, 0r0m6) <- award_nominee(?x3930, ?x2275), participant(?x2275, ?x6187) *> conf = 0.01 ranks of expected_values: 14 EVAL 01svw8n location_of_ceremony 0r0m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 103.000 103.000 0.100 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #15465-0h3c3g PRED entity: 0h3c3g PRED relation: sport PRED expected values: 02vx4 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.88 #138, 0.88 #101, 0.88 #517), 0z74 (0.49 #218, 0.47 #136, 0.26 #751), 0jm_ (0.14 #120, 0.13 #419, 0.11 #293), 03tmr (0.13 #118, 0.11 #209, 0.11 #291), 018jz (0.11 #394, 0.11 #131, 0.10 #421), 018w8 (0.07 #130, 0.06 #121, 0.06 #420), 039yzs (0.03 #639, 0.03 #603, 0.03 #630), 09xp_ (0.02 #123, 0.01 #638) >> Best rule #138 for best value: >> intensional similarity = 11 >> extensional distance = 75 >> proper extension: 0223bl; 0xbm; 0k_l4; 04ltf; 049n2l; 04mvk7; 06zpgb2; 014nzp; >> query: (?x14073, 02vx4) <- position(?x14073, ?x203), colors(?x14073, ?x4557), ?x203 = 0dgrmp, colors(?x6223, ?x4557), colors(?x1520, ?x4557), colors(?x13795, ?x4557), colors(?x10939, ?x4557), ?x13795 = 044p4_, organization(?x3484, ?x1520), ?x6223 = 05d9y_, team(?x2010, ?x10939) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h3c3g sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.883 http://example.org/sports/sports_team/sport #15464-0j43swk PRED entity: 0j43swk PRED relation: nominated_for! PRED expected values: 094qd5 0k611 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 178): 02z1nbg (0.66 #8857, 0.66 #8856, 0.66 #6638), 09d28z (0.66 #8857, 0.66 #8856, 0.66 #6638), 027c924 (0.66 #8857, 0.66 #8856, 0.66 #6638), 0gs9p (0.58 #1159, 0.44 #54, 0.33 #2485), 0k611 (0.52 #61, 0.50 #1166, 0.29 #2492), 0gr0m (0.52 #51, 0.33 #1156, 0.23 #2482), 094qd5 (0.52 #32, 0.22 #8634, 0.19 #1137), 0gr4k (0.48 #24, 0.44 #1129, 0.21 #2676), 0f4x7 (0.44 #23, 0.42 #1128, 0.23 #2898), 0gqy2 (0.41 #108, 0.37 #1213, 0.24 #2539) >> Best rule #8857 for best value: >> intensional similarity = 2 >> extensional distance = 1002 >> proper extension: 034fl9; 02_1ky; 02rq7nd; >> query: (?x3035, ?x3902) <- award(?x3035, ?x3902), nominated_for(?x68, ?x3035) >> conf = 0.66 => this is the best rule for 3 predicted values *> Best rule #61 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 09p7fh; 04jwly; 0mcl0; 0286gm1; 0c0zq; *> query: (?x3035, 0k611) <- music(?x3035, ?x10634), nominated_for(?x1245, ?x3035), nominated_for(?x746, ?x3035), ?x746 = 04dn09n, ?x1245 = 0gqwc *> conf = 0.52 ranks of expected_values: 5, 7 EVAL 0j43swk nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 80.000 80.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0j43swk nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 80.000 80.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15463-01cwkq PRED entity: 01cwkq PRED relation: gender PRED expected values: 02zsn => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #39, 0.72 #11, 0.71 #185), 02zsn (0.52 #166, 0.52 #161, 0.51 #10) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 319 >> proper extension: 04rs03; 01cv3n; 08f3b1; 0m77m; 02vmzp; 041mt; 0180w8; 0hgqq; 0kvjrw; 0cc63l; ... >> query: (?x11198, 05zppz) <- profession(?x11198, ?x1032), religion(?x11198, ?x7131), taxonomy(?x7131, ?x939) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2243 *> proper extension: 0411q; 01dzz7; 01wg982; 010hn; 01kph_c; 01lvzbl; 0280mv7; 013423; 01mv_n; 01vt5c_; ... *> query: (?x11198, ?x231) <- award_nominee(?x7255, ?x11198), gender(?x7255, ?x231) *> conf = 0.52 ranks of expected_values: 2 EVAL 01cwkq gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 108.000 0.723 http://example.org/people/person/gender #15462-023n39 PRED entity: 023n39 PRED relation: profession PRED expected values: 02jknp => 108 concepts (42 used for prediction) PRED predicted values (max 10 best out of 73): 01d_h8 (0.45 #1476, 0.45 #2946, 0.44 #2505), 0dxtg (0.40 #1042, 0.39 #2218, 0.37 #2512), 03gjzk (0.36 #1043, 0.34 #2513, 0.33 #14), 0nbcg (0.33 #30, 0.19 #3705, 0.15 #1500), 0dz3r (0.33 #2, 0.17 #3677, 0.15 #737), 0n1h (0.33 #11, 0.11 #746, 0.10 #3686), 0d1pc (0.26 #637, 0.19 #784, 0.17 #931), 09jwl (0.26 #753, 0.23 #1635, 0.23 #1782), 02jknp (0.24 #2506, 0.21 #1183, 0.21 #2065), 018gz8 (0.20 #457, 0.20 #2956, 0.19 #1045) >> Best rule #1476 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 01vw87c; 0pz7h; 01kx_81; 09qr6; 07ss8_; 0qf3p; 01wyz92; 021yw7; 02hhtj; 0g824; ... >> query: (?x6849, 01d_h8) <- profession(?x6849, ?x353), film(?x6849, ?x2757), participant(?x6850, ?x6849), currency(?x6849, ?x170) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2506 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 249 *> proper extension: 02vptk_; *> query: (?x6849, 02jknp) <- currency(?x6849, ?x170), student(?x9318, ?x6849), ?x170 = 09nqf *> conf = 0.24 ranks of expected_values: 9 EVAL 023n39 profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 108.000 42.000 0.452 http://example.org/people/person/profession #15461-01914 PRED entity: 01914 PRED relation: month PRED expected values: 04w_7 05lf_ => 277 concepts (277 used for prediction) PRED predicted values (max 10 best out of 2): 04w_7 (0.93 #127, 0.93 #121, 0.93 #113), 05lf_ (0.88 #62, 0.86 #122, 0.86 #114) >> Best rule #127 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 03czqs; >> query: (?x206, 04w_7) <- location_of_ceremony(?x566, ?x206), month(?x206, ?x7298), ?x7298 = 04wzr >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01914 month 05lf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 277.000 277.000 0.932 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 01914 month 04w_7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 277.000 277.000 0.932 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #15460-04g61 PRED entity: 04g61 PRED relation: film_release_region! PRED expected values: 040rmy => 175 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1382): 053rxgm (0.80 #6750, 0.72 #9396, 0.68 #21304), 08hmch (0.76 #22611, 0.75 #46427, 0.75 #21288), 017jd9 (0.76 #23087, 0.75 #21764, 0.73 #7210), 047vnkj (0.75 #21871, 0.73 #7317, 0.68 #23194), 0bpm4yw (0.73 #7164, 0.73 #23041, 0.72 #21718), 043tvp3 (0.73 #7539, 0.71 #23416, 0.70 #31355), 04f52jw (0.73 #6948, 0.71 #22825, 0.70 #21502), 05qbckf (0.73 #6854, 0.70 #21408, 0.67 #9500), 0661ql3 (0.73 #6910, 0.68 #22787, 0.68 #30726), 0dzlbx (0.73 #7268, 0.68 #23145, 0.68 #46961) >> Best rule #6750 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 017v_; >> query: (?x5274, 053rxgm) <- combatants(?x5274, ?x172), time_zones(?x5274, ?x2864), entity_involved(?x11183, ?x5274) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #46615 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 54 *> proper extension: 07zrf; *> query: (?x5274, 040rmy) <- country(?x695, ?x5274), film(?x1222, ?x695) *> conf = 0.55 ranks of expected_values: 156 EVAL 04g61 film_release_region! 040rmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 175.000 96.000 0.800 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15459-0bxtg PRED entity: 0bxtg PRED relation: participant PRED expected values: 02v3yy => 108 concepts (69 used for prediction) PRED predicted values (max 10 best out of 281): 02v3yy (0.80 #14694, 0.80 #19162, 0.80 #9580), 0gcs9 (0.80 #14694, 0.80 #19162, 0.80 #9580), 0bxtg (0.20 #33, 0.03 #4502, 0.02 #5141), 0gx_p (0.15 #1697, 0.13 #2336, 0.08 #2975), 0mm1q (0.15 #1649, 0.13 #2288, 0.08 #2927), 0f4vbz (0.15 #1422, 0.13 #2061, 0.08 #2700), 0gy6z9 (0.15 #1503, 0.13 #2142, 0.08 #2781), 014zcr (0.12 #3210, 0.09 #5126, 0.08 #2572), 02mjmr (0.12 #3374, 0.08 #2736, 0.08 #1458), 05hj0n (0.08 #1915, 0.07 #2554, 0.07 #9579) >> Best rule #14694 for best value: >> intensional similarity = 3 >> extensional distance = 339 >> proper extension: 01r42_g; 066m4g; 035gjq; 07c0j; 014zfs; 02lxj_; 0l12d; 08m4c8; 0443y3; 0hskw; ... >> query: (?x496, ?x2963) <- award_nominee(?x495, ?x496), nominated_for(?x496, ?x69), participant(?x2963, ?x496) >> conf = 0.80 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0bxtg participant 02v3yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 69.000 0.803 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #15458-02l5rm PRED entity: 02l5rm PRED relation: written_by! PRED expected values: 01jw67 => 93 concepts (43 used for prediction) PRED predicted values (max 10 best out of 197): 0jqj5 (0.44 #9912, 0.36 #3304, 0.35 #7929), 0bz3jx (0.03 #1103, 0.03 #443, 0.02 #2425), 03wy8t (0.03 #1257, 0.02 #3240, 0.02 #6545), 07vf5c (0.03 #282, 0.01 #3586, 0.01 #942), 04q827 (0.03 #627), 0gy0l_ (0.03 #566), 02p86pb (0.03 #565), 01gglm (0.03 #528), 072r5v (0.03 #519), 0gmd3k7 (0.03 #431) >> Best rule #9912 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 0b05xm; >> query: (?x2967, ?x5129) <- written_by(?x11065, ?x2967), nominated_for(?x2967, ?x5129), gender(?x2967, ?x231) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02l5rm written_by! 01jw67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 43.000 0.436 http://example.org/film/film/written_by #15457-09yrh PRED entity: 09yrh PRED relation: people! PRED expected values: 038723 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 52): 0x67 (0.30 #2260, 0.20 #2335, 0.17 #4060), 09kr66 (0.25 #41, 0.04 #641, 0.03 #791), 041rx (0.21 #5179, 0.21 #2779, 0.20 #6454), 033tf_ (0.18 #232, 0.18 #82, 0.16 #1207), 02ctzb (0.13 #239, 0.08 #689, 0.06 #1364), 09vc4s (0.10 #759, 0.10 #159, 0.10 #84), 02w7gg (0.10 #4952, 0.09 #6302, 0.08 #6452), 01qhm_ (0.09 #906, 0.09 #381, 0.08 #606), 065b6q (0.09 #153, 0.06 #753, 0.06 #1353), 07hwkr (0.08 #1812, 0.07 #4287, 0.07 #2787) >> Best rule #2260 for best value: >> intensional similarity = 2 >> extensional distance = 297 >> proper extension: 032t2z; 016lh0; 021r7r; 024t0y; >> query: (?x4536, 0x67) <- people(?x3591, ?x4536), currency(?x4536, ?x170) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #817 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 04shbh; *> query: (?x4536, 038723) <- participant(?x513, ?x4536), award(?x4536, ?x757), celebrity(?x4536, ?x969) *> conf = 0.02 ranks of expected_values: 30 EVAL 09yrh people! 038723 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 115.000 115.000 0.301 http://example.org/people/ethnicity/people #15456-016tw3 PRED entity: 016tw3 PRED relation: film PRED expected values: 0g5qs2k 087wc7n 03fts 02yvct 01f7kl 02ctc6 0ddcbd5 0gtxj2q 02j69w 05_5_22 012jfb 065_cjc 02dr9j 0hwpz 01k0vq 085wqm 076tw54 => 124 concepts (100 used for prediction) PRED predicted values (max 10 best out of 1420): 060__7 (0.72 #4187, 0.70 #29309, 0.69 #22331), 07p62k (0.72 #4187, 0.70 #29309, 0.69 #22331), 02z9rr (0.72 #4187, 0.70 #29309, 0.69 #22331), 05zlld0 (0.72 #4187, 0.68 #34893, 0.65 #20935), 0jwmp (0.72 #4187, 0.68 #34893, 0.65 #20935), 09m6kg (0.72 #4187, 0.68 #34893, 0.65 #20935), 01jft4 (0.72 #4187, 0.68 #34893, 0.65 #20935), 035bcl (0.72 #4187, 0.68 #34893, 0.65 #20935), 03tps5 (0.72 #4187, 0.68 #34893, 0.65 #20935), 02qr69m (0.72 #4187, 0.68 #34893, 0.65 #20935) >> Best rule #4187 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0g1rw; 0gfmc_; >> query: (?x1104, ?x253) <- film(?x1104, ?x6553), production_companies(?x253, ?x1104), ?x6553 = 011yhm >> conf = 0.72 => this is the best rule for 30 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 17, 19, 131, 676, 914, 919, 976, 981, 1025, 1213, 1275, 1276, 1277 EVAL 016tw3 film 076tw54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 085wqm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 01k0vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 0hwpz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 02dr9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 065_cjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 012jfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 05_5_22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 02j69w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 0gtxj2q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 0ddcbd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 02ctc6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 01f7kl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 03fts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 087wc7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 016tw3 film 0g5qs2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 100.000 0.723 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15455-022lly PRED entity: 022lly PRED relation: institution! PRED expected values: 016t_3 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 22): 014mlp (0.73 #191, 0.68 #1093, 0.68 #75), 02_xgp2 (0.58 #12, 0.50 #339, 0.47 #129), 07s6fsf (0.58 #1, 0.45 #24, 0.42 #141), 03bwzr4 (0.55 #223, 0.54 #131, 0.53 #108), 016t_3 (0.51 #143, 0.50 #73, 0.50 #50), 0bkj86 (0.42 #8, 0.40 #335, 0.39 #194), 027f2w (0.33 #9, 0.22 #56, 0.22 #79), 022h5x (0.33 #20, 0.22 #137, 0.20 #114), 04zx3q1 (0.28 #329, 0.25 #2, 0.25 #188), 013zdg (0.25 #147, 0.25 #7, 0.23 #193) >> Best rule #191 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 01jswq; 01f1r4; 0m9_5; 02zd460; 0ks67; 08qnnv; 0160nk; >> query: (?x2522, 014mlp) <- category(?x2522, ?x134), school(?x1823, ?x2522), school_type(?x2522, ?x3092), student(?x2522, ?x4065) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #143 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 100 *> proper extension: 01b1mj; *> query: (?x2522, 016t_3) <- school(?x1823, ?x2522), colors(?x2522, ?x663), school(?x1161, ?x2522) *> conf = 0.51 ranks of expected_values: 5 EVAL 022lly institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 138.000 0.735 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15454-02qzh2 PRED entity: 02qzh2 PRED relation: executive_produced_by PRED expected values: 05hj_k => 76 concepts (52 used for prediction) PRED predicted values (max 10 best out of 80): 0343h (0.22 #294, 0.20 #546, 0.16 #1050), 0gd9k (0.22 #504, 0.11 #756, 0.09 #1261), 05hj_k (0.12 #1359, 0.08 #5652, 0.05 #5147), 030_3z (0.11 #360, 0.08 #612, 0.06 #1116), 0gg9_5q (0.06 #846, 0.05 #1351, 0.03 #5644), 0glyyw (0.05 #1449, 0.04 #5742, 0.04 #2709), 04q5zw (0.05 #1342, 0.03 #5635, 0.02 #2097), 06pj8 (0.04 #5609, 0.04 #1316, 0.03 #6618), 079vf (0.04 #5556, 0.02 #6314, 0.02 #1515), 03c9pqt (0.04 #5800, 0.02 #6809, 0.02 #5295) >> Best rule #294 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0dnqr; 0f4yh; 0f3m1; >> query: (?x4160, 0343h) <- nominated_for(?x667, ?x4160), nominated_for(?x350, ?x667), edited_by(?x4160, ?x7984), written_by(?x5024, ?x7984) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1359 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 0m313; 02y_lrp; 03s6l2; 04fzfj; 092vkg; 0bshwmp; 09q5w2; 0c0nhgv; 0872p_c; 04hwbq; ... *> query: (?x4160, 05hj_k) <- film_crew_role(?x4160, ?x137), nominated_for(?x667, ?x4160), film(?x521, ?x4160), executive_produced_by(?x4160, ?x7324) *> conf = 0.12 ranks of expected_values: 3 EVAL 02qzh2 executive_produced_by 05hj_k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 76.000 52.000 0.222 http://example.org/film/film/executive_produced_by #15453-04mjl PRED entity: 04mjl PRED relation: team! PRED expected values: 0hcs3 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 112): 03n69x (0.40 #245, 0.30 #921, 0.24 #1601), 0hcs3 (0.33 #96, 0.29 #2699, 0.26 #2924), 06s27s (0.33 #214, 0.20 #1002, 0.12 #2593), 019g65 (0.24 #1659, 0.21 #1431, 0.20 #303), 0cg39k (0.23 #1306, 0.18 #1648, 0.12 #2216), 01f492 (0.20 #950, 0.12 #2541, 0.11 #2653), 04g9sq (0.20 #446, 0.10 #4072, 0.09 #2032), 0f2zc (0.20 #624, 0.08 #1189, 0.07 #7804), 02d9k (0.18 #1929, 0.07 #6685, 0.03 #6118), 03vrv9 (0.18 #1664, 0.15 #1322, 0.12 #2232) >> Best rule #245 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 02fbb5; >> query: (?x7357, 03n69x) <- teams(?x1523, ?x7357), team(?x11844, ?x7357), colors(?x7357, ?x663), team(?x11844, ?x4208), sport(?x4208, ?x5063), location(?x3118, ?x1523), location(?x2281, ?x1523), ?x3118 = 01w02sy, award_nominee(?x2281, ?x192) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #96 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 07147; *> query: (?x7357, 0hcs3) <- school(?x7357, ?x3779), season(?x7357, ?x2406), ?x3779 = 01pq4w, ?x2406 = 03c6sl9, position(?x7357, ?x2010), team(?x8110, ?x7357), draft(?x7357, ?x10600), draft(?x7357, ?x8499), draft(?x7357, ?x3334), ?x3334 = 02pq_rp, ?x10600 = 04f4z1k, ?x8499 = 02r6gw6 *> conf = 0.33 ranks of expected_values: 2 EVAL 04mjl team! 0hcs3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.400 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #15452-09jd9 PRED entity: 09jd9 PRED relation: profession PRED expected values: 0cbd2 0kyk => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 102): 0cbd2 (0.82 #457, 0.67 #7, 0.57 #157), 0dxtg (0.69 #2414, 0.67 #3164, 0.67 #2864), 0kyk (0.67 #31, 0.57 #181, 0.55 #481), 02hrh1q (0.63 #12319, 0.62 #14871, 0.62 #15772), 01d_h8 (0.50 #2406, 0.50 #6, 0.45 #2856), 02jknp (0.41 #2408, 0.38 #2858, 0.33 #8), 03gjzk (0.29 #3166, 0.29 #3316, 0.26 #3767), 0n1h (0.25 #312, 0.14 #17712, 0.14 #17411), 09jwl (0.20 #770, 0.20 #8121, 0.19 #10373), 05z96 (0.19 #1694, 0.18 #494, 0.14 #194) >> Best rule #457 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 03j2gxx; >> query: (?x13624, 0cbd2) <- story_by(?x596, ?x13624), student(?x892, ?x13624), ?x892 = 07tgn >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 09jd9 profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 128.000 0.818 http://example.org/people/person/profession EVAL 09jd9 profession 0cbd2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.818 http://example.org/people/person/profession #15451-01vsn38 PRED entity: 01vsn38 PRED relation: type_of_union PRED expected values: 01g63y => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2): 01g63y (0.47 #193, 0.45 #206, 0.21 #4), 0jgjn (0.02 #9) >> Best rule #193 for best value: >> intensional similarity = 3 >> extensional distance = 1854 >> proper extension: 0288fyj; 01vd7hn; 0521d_3; 0bm9xk; >> query: (?x11233, ?x566) <- nationality(?x11233, ?x94), award_nominee(?x11233, ?x4520), type_of_union(?x4520, ?x566) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vsn38 type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.465 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15450-02tq2r PRED entity: 02tq2r PRED relation: award PRED expected values: 03rbj2 => 126 concepts (119 used for prediction) PRED predicted values (max 10 best out of 250): 03rbj2 (0.64 #224, 0.56 #1844, 0.50 #1439), 03r8v_ (0.39 #1152, 0.35 #747, 0.22 #5202), 09sb52 (0.25 #19886, 0.24 #21101, 0.24 #17051), 0b6k___ (0.20 #9129, 0.12 #10749, 0.11 #2649), 05zr6wv (0.19 #4067, 0.17 #6497, 0.15 #7712), 05ztrmj (0.17 #4235, 0.11 #6665, 0.09 #7880), 04fgkf_ (0.16 #3126, 0.15 #3936, 0.15 #5556), 0f4x7 (0.16 #3271, 0.15 #4081, 0.12 #6106), 0gkvb7 (0.15 #3672, 0.14 #2862, 0.12 #4482), 0h53c_5 (0.13 #3858, 0.11 #3048, 0.11 #5478) >> Best rule #224 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 087z12; 03j367r; 044ptm; >> query: (?x6098, 03rbj2) <- nationality(?x6098, ?x2146), film(?x6098, ?x5247), award(?x6098, ?x1937), ?x1937 = 03r8tl >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tq2r award 03rbj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 119.000 0.643 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15449-01k0vq PRED entity: 01k0vq PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.89 #46, 0.89 #21, 0.88 #26), 07c52 (0.11 #3, 0.06 #68, 0.06 #13), 02nxhr (0.11 #7, 0.04 #128, 0.04 #138), 07z4p (0.06 #70, 0.05 #75, 0.05 #40) >> Best rule #46 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 02vw1w2; 01kf4tt; 027j9wd; 0fztbq; 025twgt; >> query: (?x7579, 029j_) <- prequel(?x7348, ?x7579), genre(?x7579, ?x1403), prequel(?x7579, ?x6684), film(?x1460, ?x7579) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k0vq film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15448-09pl3s PRED entity: 09pl3s PRED relation: written_by! PRED expected values: 0435vm => 148 concepts (91 used for prediction) PRED predicted values (max 10 best out of 368): 03ct7jd (0.43 #5911, 0.17 #18389, 0.16 #22982), 0421v9q (0.26 #4597, 0.09 #9196), 0435vm (0.14 #253, 0.02 #3535, 0.01 #4193), 0ndwt2w (0.12 #1039, 0.02 #9579, 0.01 #11548), 0dcz8_ (0.12 #1247, 0.01 #4531, 0.01 #5845), 0dc_ms (0.12 #1098, 0.01 #4382, 0.01 #5696), 06zn2v2 (0.12 #950, 0.01 #4234, 0.01 #5548), 091z_p (0.12 #765, 0.01 #4049, 0.01 #5363), 0g9z_32 (0.07 #1793, 0.05 #3763, 0.03 #2449), 02wgbb (0.07 #1819, 0.03 #4447, 0.02 #7733) >> Best rule #5911 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 0d05fv; 013zyw; 072vj; >> query: (?x2442, ?x2441) <- location(?x2442, ?x279), produced_by(?x2441, ?x2442), written_by(?x3748, ?x2442) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 05mvd62; 05vk_d; *> query: (?x2442, 0435vm) <- nominated_for(?x2442, ?x5277), gender(?x2442, ?x231), ?x5277 = 047csmy *> conf = 0.14 ranks of expected_values: 3 EVAL 09pl3s written_by! 0435vm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 148.000 91.000 0.431 http://example.org/film/film/written_by #15447-05c26ss PRED entity: 05c26ss PRED relation: genre PRED expected values: 03k9fj 0hcr => 86 concepts (66 used for prediction) PRED predicted values (max 10 best out of 110): 07s9rl0 (0.57 #7938, 0.57 #7449, 0.56 #7081), 02kdv5l (0.54 #2687, 0.54 #2931, 0.54 #2565), 01jfsb (0.50 #257, 0.46 #2697, 0.46 #2575), 03k9fj (0.44 #744, 0.42 #1842, 0.39 #2696), 02l7c8 (0.34 #1725, 0.34 #4167, 0.33 #505), 06cvj (0.33 #4, 0.23 #4154, 0.21 #4886), 0bbc17 (0.33 #102, 0.11 #590, 0.04 #8060), 011ys5 (0.33 #96, 0.11 #584, 0.04 #8060), 09q17 (0.33 #63, 0.06 #551, 0.04 #2137), 01hmnh (0.29 #1849, 0.28 #2581, 0.28 #2703) >> Best rule #7938 for best value: >> intensional similarity = 2 >> extensional distance = 1433 >> proper extension: 02qjv1p; >> query: (?x3839, 07s9rl0) <- genre(?x3839, ?x258), nominated_for(?x5970, ?x3839) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #744 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 03ln8b; 05zr0xl; *> query: (?x3839, 03k9fj) <- nominated_for(?x5970, ?x3839), category(?x3839, ?x134), ?x134 = 08mbj5d, organizations_founded(?x2135, ?x5970) *> conf = 0.44 ranks of expected_values: 4, 11 EVAL 05c26ss genre 0hcr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 86.000 66.000 0.571 http://example.org/film/film/genre EVAL 05c26ss genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 66.000 0.571 http://example.org/film/film/genre #15446-02q0k7v PRED entity: 02q0k7v PRED relation: genre PRED expected values: 060__y 0bkbm => 105 concepts (52 used for prediction) PRED predicted values (max 10 best out of 98): 05p553 (0.71 #5810, 0.38 #6048, 0.38 #2016), 02n4kr (0.53 #1546, 0.53 #716, 0.18 #4382), 0lsxr (0.48 #1547, 0.44 #717, 0.29 #1073), 03k9fj (0.43 #129, 0.40 #2850, 0.39 #247), 02l7c8 (0.38 #4872, 0.32 #251, 0.31 #5822), 01hmnh (0.32 #135, 0.29 #253, 0.27 #1318), 09blyk (0.29 #1569, 0.27 #739, 0.06 #266), 06n90 (0.27 #2851, 0.22 #12, 0.18 #958), 04xvlr (0.26 #1420, 0.24 #1658, 0.23 #1776), 03g3w (0.22 #379, 0.17 #615, 0.17 #1681) >> Best rule #5810 for best value: >> intensional similarity = 3 >> extensional distance = 815 >> proper extension: 0vgkd; 04svwx; >> query: (?x7694, 05p553) <- genre(?x7694, ?x3515), genre(?x3549, ?x3515), ?x3549 = 017kct >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1435 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 104 *> proper extension: 01c9d; *> query: (?x7694, 060__y) <- film(?x3101, ?x7694), films(?x2391, ?x7694), locations(?x2391, ?x4092) *> conf = 0.22 ranks of expected_values: 11, 17 EVAL 02q0k7v genre 0bkbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 105.000 52.000 0.714 http://example.org/film/film/genre EVAL 02q0k7v genre 060__y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 105.000 52.000 0.714 http://example.org/film/film/genre #15445-07ddz9 PRED entity: 07ddz9 PRED relation: award_nominee! PRED expected values: 016fjj => 71 concepts (39 used for prediction) PRED predicted values (max 10 best out of 649): 0gx_p (0.81 #30261, 0.81 #62851, 0.81 #20948), 07ddz9 (0.43 #2108, 0.10 #18620, 0.09 #83802), 016fjj (0.43 #835, 0.10 #18620, 0.09 #83802), 03061d (0.28 #60523, 0.26 #25605, 0.11 #4594), 04twmk (0.28 #60523, 0.26 #25605, 0.11 #4344), 084m3 (0.28 #60523, 0.26 #25605, 0.11 #3998), 043js (0.28 #60523, 0.26 #25605, 0.05 #2910), 0btpx (0.28 #60523, 0.26 #25605, 0.02 #15821), 030znt (0.28 #60523, 0.26 #25605, 0.02 #35193), 07k2p6 (0.28 #60523, 0.26 #25605) >> Best rule #30261 for best value: >> intensional similarity = 3 >> extensional distance = 1177 >> proper extension: 03cd1q; >> query: (?x10167, ?x3756) <- gender(?x10167, ?x231), award_nominee(?x10167, ?x3756), location(?x10167, ?x12583) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #835 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 03yrkt; *> query: (?x10167, 016fjj) <- award_nominee(?x1871, ?x10167), award_nominee(?x10167, ?x6278), ?x6278 = 0gx_p *> conf = 0.43 ranks of expected_values: 3 EVAL 07ddz9 award_nominee! 016fjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 39.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15444-0fvr1 PRED entity: 0fvr1 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #62, 0.84 #72, 0.83 #103), 02nxhr (0.11 #12, 0.10 #17, 0.06 #43), 07c52 (0.10 #23, 0.04 #89, 0.03 #218), 07z4p (0.03 #220, 0.02 #147, 0.02 #194) >> Best rule #62 for best value: >> intensional similarity = 7 >> extensional distance = 183 >> proper extension: 0n_hp; >> query: (?x2184, 029j_) <- titles(?x1510, ?x2184), film(?x6613, ?x2184), film(?x2626, ?x2184), nominated_for(?x2183, ?x2184), genre(?x2184, ?x53), artist(?x5634, ?x6613), award(?x2626, ?x112) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fvr1 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15443-0bzmt8 PRED entity: 0bzmt8 PRED relation: ceremony! PRED expected values: 0p9sw 0gr4k 0gqwc 0gqxm => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 331): 0gqwc (0.92 #5175, 0.91 #5419, 0.90 #4931), 0gr4k (0.87 #3669, 0.86 #3424, 0.83 #4900), 0p9sw (0.86 #5382, 0.85 #5138, 0.85 #4894), 04dn09n (0.75 #1454, 0.43 #3186, 0.29 #4170), 094qd5 (0.75 #1454, 0.33 #726, 0.31 #5367), 04kxsb (0.75 #1454, 0.31 #5367, 0.24 #1452), 019f4v (0.75 #1454, 0.29 #3200, 0.29 #2956), 02pqp12 (0.75 #1454, 0.20 #8289, 0.17 #8288), 040njc (0.75 #1454, 0.20 #8289, 0.17 #8288), 02qyntr (0.75 #1454, 0.14 #2672, 0.11 #8044) >> Best rule #5175 for best value: >> intensional similarity = 15 >> extensional distance = 51 >> proper extension: 0fz20l; >> query: (?x7100, 0gqwc) <- award_winner(?x7100, ?x8734), gender(?x8734, ?x514), ceremony(?x4573, ?x7100), ceremony(?x3066, ?x7100), instance_of_recurring_event(?x7100, ?x3459), ?x3066 = 0gqy2, award(?x8734, ?x3722), award(?x8734, ?x1254), award_winner(?x1132, ?x8734), nationality(?x8734, ?x94), ?x4573 = 0gq_d, ceremony(?x3722, ?x873), honored_for(?x7100, ?x251), award(?x715, ?x3722), award_winner(?x1254, ?x1641) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 15 EVAL 0bzmt8 ceremony! 0gqxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 35.000 35.000 0.925 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzmt8 ceremony! 0gqwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.925 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzmt8 ceremony! 0gr4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.925 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzmt8 ceremony! 0p9sw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.925 http://example.org/award/award_category/winners./award/award_honor/ceremony #15442-01k7xz PRED entity: 01k7xz PRED relation: student PRED expected values: 0gppg => 101 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1188): 06jkm (0.33 #1907, 0.11 #3995, 0.10 #8171), 03_nq (0.33 #1562, 0.11 #3650, 0.06 #7826), 06hx2 (0.33 #1068, 0.11 #3156, 0.04 #9420), 0194xc (0.33 #1638, 0.11 #3726, 0.04 #9990), 0d3k14 (0.33 #1851, 0.11 #3939, 0.04 #10203), 0cbgl (0.33 #2082, 0.11 #4170, 0.04 #12522), 01cv3n (0.33 #88, 0.11 #2176, 0.04 #10528), 01kwsg (0.33 #812, 0.11 #2900, 0.04 #11252), 0mj0c (0.33 #647, 0.11 #2735, 0.04 #11087), 01ty4 (0.33 #1972, 0.11 #4060, 0.04 #12412) >> Best rule #1907 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03ksy; >> query: (?x2484, 06jkm) <- student(?x2484, ?x4165), contains(?x94, ?x2484), ?x4165 = 02mqc4, major_field_of_study(?x2484, ?x2314) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #75179 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 398 *> proper extension: 0fvly; *> query: (?x2484, ?x875) <- citytown(?x2484, ?x3007), location(?x875, ?x3007), category(?x3007, ?x134) *> conf = 0.01 ranks of expected_values: 1184 EVAL 01k7xz student 0gppg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 101.000 75.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #15441-09_b4 PRED entity: 09_b4 PRED relation: country PRED expected values: 0chghy => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 455): 0345h (0.92 #4506, 0.89 #3546, 0.88 #4890), 06c1y (0.83 #1794, 0.82 #1397, 0.78 #1199), 0chghy (0.83 #5066, 0.81 #4487, 0.80 #4680), 07ssc (0.82 #5834, 0.81 #4300, 0.79 #6207), 0jgd (0.81 #2550, 0.75 #2152, 0.75 #968), 01pj7 (0.78 #1205, 0.75 #1800, 0.73 #1403), 015fr (0.75 #4108, 0.75 #2563, 0.75 #981), 0b90_r (0.75 #2153, 0.75 #969, 0.73 #3904), 01p1v (0.75 #2194, 0.73 #3945, 0.73 #1549), 06qd3 (0.75 #999, 0.73 #3934, 0.72 #2147) >> Best rule #4506 for best value: >> intensional similarity = 62 >> extensional distance = 24 >> proper extension: 02y74; >> query: (?x6354, 0345h) <- country(?x6354, ?x1023), country(?x6354, ?x456), sports(?x418, ?x6354), film_release_region(?x7887, ?x456), film_release_region(?x7275, ?x456), film_release_region(?x6520, ?x456), film_release_region(?x6446, ?x456), film_release_region(?x6321, ?x456), film_release_region(?x6270, ?x456), film_release_region(?x6247, ?x456), film_release_region(?x5713, ?x456), film_release_region(?x4643, ?x456), film_release_region(?x4610, ?x456), film_release_region(?x4607, ?x456), film_release_region(?x4446, ?x456), film_release_region(?x3423, ?x456), film_release_region(?x3135, ?x456), film_release_region(?x1490, ?x456), film_release_region(?x1392, ?x456), film_release_region(?x1219, ?x456), film_release_region(?x1150, ?x456), film_release_region(?x1002, ?x456), film_release_region(?x791, ?x456), film_release_region(?x299, ?x456), film_release_region(?x249, ?x456), film_release_region(?x80, ?x456), ?x249 = 0c3ybss, combatants(?x456, ?x8687), ?x4607 = 0h03fhx, country(?x3598, ?x456), ?x7275 = 0g4vmj8, ?x3423 = 09g7vfw, ?x6270 = 0g9zljd, olympics(?x456, ?x3971), ?x6247 = 09v9mks, administrative_parent(?x6265, ?x456), ?x7887 = 04z_3pm, contains(?x456, ?x8989), ?x1490 = 0fpkhkz, ?x80 = 0b76d_m, adjoins(?x456, ?x344), country(?x1498, ?x456), ?x1150 = 0h3xztt, ?x299 = 01gc7, ?x6446 = 089j8p, ?x1219 = 03bx2lk, ?x3598 = 03rbzn, ?x1023 = 0ctw_b, ?x3971 = 0jhn7, combatants(?x8687, ?x1003), ?x791 = 087wc7n, ?x5713 = 0cc97st, genre(?x4643, ?x53), ?x6520 = 02bg55, category(?x4643, ?x134), ?x4610 = 017jd9, ?x3135 = 0bmc4cm, ?x6321 = 0gg8z1f, ?x1392 = 017gm7, ?x1002 = 0_b3d, ?x4446 = 0db94w, ?x1498 = 04jkpgv >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #5066 for first EXPECTED value: *> intensional similarity = 58 *> extensional distance = 34 *> proper extension: 07_53; *> query: (?x6354, 0chghy) <- sports(?x418, ?x6354), country(?x6354, ?x404), country(?x6354, ?x304), country(?x6354, ?x252), locations(?x7241, ?x404), country(?x5396, ?x404), organization(?x404, ?x127), film_release_region(?x9194, ?x404), film_release_region(?x6931, ?x404), film_release_region(?x1956, ?x404), film_release_region(?x1915, ?x404), ?x5396 = 0486tv, ?x6931 = 09v3jyg, ?x1915 = 0fq7dv_, olympics(?x6354, ?x784), film_release_region(?x9961, ?x304), film_release_region(?x9839, ?x304), film_release_region(?x9565, ?x304), film_release_region(?x9501, ?x304), film_release_region(?x7016, ?x304), film_release_region(?x6932, ?x304), film_release_region(?x6621, ?x304), film_release_region(?x5713, ?x304), film_release_region(?x4841, ?x304), film_release_region(?x3886, ?x304), film_release_region(?x3524, ?x304), film_release_region(?x2104, ?x304), film_release_region(?x2037, ?x304), film_release_region(?x1625, ?x304), film_release_region(?x1293, ?x304), film_release_region(?x607, ?x304), film_release_region(?x124, ?x304), ?x252 = 03_3d, ?x5713 = 0cc97st, teams(?x304, ?x2619), ?x1293 = 07g_0c, ?x2104 = 0j_tw, ?x4841 = 0k4fz, ?x3886 = 0198b6, ?x6932 = 027pfg, ?x124 = 0g56t9t, olympics(?x304, ?x391), ?x9501 = 0g5qmbz, capital(?x304, ?x5168), ?x3524 = 06r2_, ?x6621 = 0h63gl9, ?x7016 = 07g1sm, ?x1956 = 05qbckf, film_crew_role(?x607, ?x137), ?x9839 = 0gy7bj4, genre(?x9961, ?x1014), ?x9194 = 0fpgp26, film_release_distribution_medium(?x9961, ?x81), ?x9565 = 0hz6mv2, film(?x2156, ?x607), ?x2037 = 0gvrws1, ?x1625 = 01f8gz, award(?x607, ?x102) *> conf = 0.83 ranks of expected_values: 3 EVAL 09_b4 country 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 34.000 34.000 0.923 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #15440-05148p4 PRED entity: 05148p4 PRED relation: role! PRED expected values: 0dq630k => 74 concepts (71 used for prediction) PRED predicted values (max 10 best out of 87): 03m5k (0.84 #77, 0.83 #2479, 0.83 #4898), 03gvt (0.84 #77, 0.83 #2479, 0.83 #4898), 01s0ps (0.84 #77, 0.83 #2479, 0.83 #4898), 0dwr4 (0.84 #77, 0.83 #2479, 0.83 #4898), 01rhl (0.84 #77, 0.83 #2479, 0.83 #4898), 042v_gx (0.84 #77, 0.83 #2479, 0.82 #1081), 01dnws (0.84 #77, 0.83 #2479, 0.82 #1081), 02w3w (0.84 #77, 0.83 #2479, 0.82 #1081), 02hnl (0.84 #77, 0.83 #2479, 0.82 #1081), 01xqw (0.84 #77, 0.83 #2479, 0.82 #1081) >> Best rule #77 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0l14md; >> query: (?x1166, ?x885) <- group(?x1166, ?x5618), ?x5618 = 03d9d6, role(?x569, ?x1166), role(?x1166, ?x885), instrumentalists(?x1166, ?x130), ?x569 = 07c6l, role(?x248, ?x1166) >> conf = 0.84 => this is the best rule for 13 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 13 EVAL 05148p4 role! 0dq630k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 74.000 71.000 0.844 http://example.org/music/performance_role/track_performances./music/track_contribution/role #15439-0nvvw PRED entity: 0nvvw PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 180 concepts (120 used for prediction) PRED predicted values (max 10 best out of 40): 09c7w0 (0.90 #450, 0.90 #439, 0.88 #714), 03v0t (0.26 #701, 0.26 #762, 0.25 #750), 0nvvw (0.13 #1245, 0.07 #1357, 0.05 #1438), 03rt9 (0.11 #66, 0.08 #16, 0.02 #880), 02jx1 (0.06 #1318, 0.06 #1353, 0.05 #1488), 03rjj (0.05 #211, 0.04 #313, 0.03 #198), 0f8l9c (0.03 #178, 0.02 #1034, 0.02 #318), 0d060g (0.02 #354, 0.01 #402, 0.01 #964), 07ssc (0.02 #1315, 0.01 #1472, 0.01 #1485), 0z4_0 (0.01 #964) >> Best rule #450 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 0m2gk; >> query: (?x13596, ?x94) <- source(?x13596, ?x958), county(?x405, ?x13596), ?x958 = 0jbk9, country(?x405, ?x94) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nvvw second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 180.000 120.000 0.897 http://example.org/location/country/second_level_divisions #15438-01v3vp PRED entity: 01v3vp PRED relation: nationality PRED expected values: 09c7w0 => 105 concepts (72 used for prediction) PRED predicted values (max 10 best out of 75): 09c7w0 (0.86 #5639, 0.86 #2307, 0.85 #5535), 03v0t (0.32 #6046, 0.31 #5536, 0.26 #6856), 07ssc (0.29 #116, 0.09 #2721, 0.07 #5857), 05kj_ (0.26 #6856, 0.26 #4119, 0.26 #4723), 0mx5p (0.26 #6856, 0.26 #4119, 0.01 #3311), 02mf7 (0.26 #4723, 0.25 #4925, 0.21 #603), 02_286 (0.26 #4723, 0.25 #4925, 0.21 #603), 059rby (0.26 #4723, 0.25 #4925, 0.21 #603), 0chghy (0.14 #111, 0.04 #914, 0.03 #512), 0j5g9 (0.14 #163, 0.03 #865, 0.02 #966) >> Best rule #5639 for best value: >> intensional similarity = 4 >> extensional distance = 988 >> proper extension: 02mslq; 019y64; 01d494; 05qsxy; 0b6yp2; 01xyt7; 0frmb1; 01gct2; 019g65; 02vptk_; ... >> query: (?x4109, 09c7w0) <- student(?x4980, ?x4109), institution(?x865, ?x4980), major_field_of_study(?x4980, ?x742), school(?x1115, ?x4980) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v3vp nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 72.000 0.859 http://example.org/people/person/nationality #15437-0mtdx PRED entity: 0mtdx PRED relation: time_zones PRED expected values: 02hcv8 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.90 #184, 0.83 #118, 0.81 #238), 02fqwt (0.67 #40, 0.56 #53, 0.50 #27), 02hczc (0.25 #28, 0.13 #200, 0.11 #266), 042g7t (0.25 #37, 0.11 #50, 0.02 #275), 02lcrv (0.25 #33, 0.11 #46), 02lcqs (0.20 #175, 0.19 #336, 0.18 #493), 02llzg (0.06 #772, 0.06 #838, 0.05 #613), 03bdv (0.03 #562, 0.03 #575, 0.03 #958), 03plfd (0.02 #739, 0.02 #752, 0.02 #778) >> Best rule #184 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 0n5y4; >> query: (?x2003, ?x2674) <- administrative_division(?x2004, ?x2003), second_level_divisions(?x94, ?x2003), ?x94 = 09c7w0, time_zones(?x2004, ?x2674) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mtdx time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.897 http://example.org/location/location/time_zones #15436-01ttg5 PRED entity: 01ttg5 PRED relation: profession PRED expected values: 025352 => 176 concepts (175 used for prediction) PRED predicted values (max 10 best out of 87): 09jwl (0.82 #606, 0.80 #1048, 0.78 #3696), 01c72t (0.61 #3995, 0.60 #6496, 0.60 #8554), 0dz3r (0.57 #2944, 0.55 #1032, 0.54 #1326), 039v1 (0.45 #623, 0.38 #476, 0.37 #12244), 01d_h8 (0.44 #4419, 0.42 #5742, 0.41 #8389), 0n1h (0.43 #305, 0.38 #747, 0.35 #1041), 02jknp (0.33 #7, 0.29 #301, 0.25 #1037), 0d1pc (0.32 #4168, 0.31 #4904, 0.28 #5639), 0dxtg (0.29 #18406, 0.29 #14873, 0.29 #307), 03gjzk (0.29 #308, 0.28 #4428, 0.27 #5163) >> Best rule #606 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0kxbc; 0fpj9pm; >> query: (?x3934, 09jwl) <- diet(?x3934, ?x3130), participant(?x3934, ?x7233), role(?x3934, ?x1466) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #794 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 01vv7sc; *> query: (?x3934, 025352) <- diet(?x3934, ?x3130), origin(?x3934, ?x682), award_nominee(?x3934, ?x2392) *> conf = 0.19 ranks of expected_values: 19 EVAL 01ttg5 profession 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 176.000 175.000 0.818 http://example.org/people/person/profession #15435-026p4q7 PRED entity: 026p4q7 PRED relation: film_crew_role PRED expected values: 0dxtw => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 23): 0215hd (0.58 #138, 0.15 #1107, 0.14 #920), 089g0h (0.53 #139, 0.12 #1108, 0.11 #514), 0d2b38 (0.45 #145, 0.13 #238, 0.12 #176), 0dxtw (0.44 #224, 0.41 #350, 0.38 #1100), 02_n3z (0.31 #125, 0.09 #63, 0.09 #94), 01pvkk (0.29 #101, 0.28 #1256, 0.28 #914), 02ynfr (0.22 #135, 0.20 #166, 0.20 #228), 02rh1dz (0.21 #223, 0.17 #349, 0.16 #161), 033smt (0.14 #147, 0.07 #178, 0.06 #366), 094hwz (0.12 #103, 0.07 #165, 0.07 #227) >> Best rule #138 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 01gglm; 09rvwmy; >> query: (?x2490, 0215hd) <- film(?x1289, ?x2490), nominated_for(?x846, ?x2490), film_crew_role(?x2490, ?x2472), ?x2472 = 01xy5l_ >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #224 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 166 *> proper extension: 026mfbr; 02vqhv0; 047qxs; 02qhqz4; 0cc846d; 03kg2v; 0crc2cp; 014nq4; 0gjc4d3; 09g7vfw; ... *> query: (?x2490, 0dxtw) <- film(?x1289, ?x2490), film_crew_role(?x2490, ?x468), story_by(?x2490, ?x6163), ?x468 = 02r96rf *> conf = 0.44 ranks of expected_values: 4 EVAL 026p4q7 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 87.000 0.577 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15434-047svrl PRED entity: 047svrl PRED relation: film_release_region PRED expected values: 07ssc 07ylj 06bnz 03rj0 05b4w => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 246): 03h64 (0.90 #772, 0.84 #1058, 0.83 #1773), 06bnz (0.85 #749, 0.71 #1035, 0.71 #2323), 07ssc (0.84 #1013, 0.79 #1871, 0.79 #727), 05b4w (0.83 #769, 0.75 #1913, 0.74 #1770), 02vzc (0.80 #1757, 0.80 #1042, 0.80 #2330), 03spz (0.79 #801, 0.70 #1802, 0.69 #1945), 04gzd (0.76 #722, 0.59 #1008, 0.56 #1723), 03rj0 (0.70 #765, 0.61 #1766, 0.59 #1051), 05v8c (0.70 #728, 0.59 #2302, 0.57 #1872), 047yc (0.68 #736, 0.50 #1022, 0.49 #1737) >> Best rule #772 for best value: >> intensional similarity = 6 >> extensional distance = 92 >> proper extension: 087wc7n; 0crfwmx; 0407yj_; 045j3w; 0fpgp26; 072hx4; >> query: (?x2695, 03h64) <- film_release_region(?x2695, ?x1497), film_release_region(?x2695, ?x151), film_release_region(?x2695, ?x142), ?x151 = 0b90_r, ?x142 = 0jgd, ?x1497 = 015qh >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #749 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 92 *> proper extension: 087wc7n; 0crfwmx; 0407yj_; 045j3w; 0fpgp26; 072hx4; *> query: (?x2695, 06bnz) <- film_release_region(?x2695, ?x1497), film_release_region(?x2695, ?x151), film_release_region(?x2695, ?x142), ?x151 = 0b90_r, ?x142 = 0jgd, ?x1497 = 015qh *> conf = 0.85 ranks of expected_values: 2, 3, 4, 8, 18 EVAL 047svrl film_release_region 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047svrl film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 100.000 100.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047svrl film_release_region 06bnz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047svrl film_release_region 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 100.000 100.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047svrl film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15433-051wf PRED entity: 051wf PRED relation: season PRED expected values: 027pwzc 026fmqm => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 12): 03c6sl9 (0.89 #198, 0.71 #74, 0.66 #222), 0dx84s (0.85 #202, 0.71 #78, 0.63 #226), 0285r5d (0.85 #199, 0.71 #75, 0.60 #223), 026fmqm (0.85 #204, 0.71 #80, 0.60 #228), 027mvrc (0.81 #205, 0.71 #81, 0.60 #57), 027pwzc (0.71 #79, 0.70 #203, 0.51 #227), 05kcgsf (0.57 #73, 0.52 #197, 0.50 #37), 04110b0 (0.57 #77, 0.49 #97, 0.40 #53), 02h7s73 (0.49 #97, 0.43 #82, 0.40 #58), 03c6s24 (0.49 #97, 0.43 #83, 0.40 #59) >> Best rule #198 for best value: >> intensional similarity = 16 >> extensional distance = 25 >> proper extension: 01d5z; 049n7; 0512p; 01yhm; 051vz; 01ync; 02__x; 0x0d; 03m1n; >> query: (?x12956, 03c6sl9) <- school(?x12956, ?x10666), school(?x12956, ?x4955), major_field_of_study(?x10666, ?x1668), school(?x8111, ?x4955), institution(?x4981, ?x4955), student(?x4955, ?x669), major_field_of_study(?x4955, ?x4268), major_field_of_study(?x4955, ?x3995), ?x4981 = 03bwzr4, ?x3995 = 0fdys, major_field_of_study(?x10170, ?x4268), draft(?x8111, ?x1161), season(?x12956, ?x3431), state_province_region(?x10666, ?x4622), award_winner(?x1386, ?x669), ?x10170 = 01_r9k >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #204 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 25 *> proper extension: 01d5z; 049n7; 0512p; 01yhm; 051vz; 01ync; 02__x; 0x0d; 03m1n; *> query: (?x12956, 026fmqm) <- school(?x12956, ?x10666), school(?x12956, ?x4955), major_field_of_study(?x10666, ?x1668), school(?x8111, ?x4955), institution(?x4981, ?x4955), student(?x4955, ?x669), major_field_of_study(?x4955, ?x4268), major_field_of_study(?x4955, ?x3995), ?x4981 = 03bwzr4, ?x3995 = 0fdys, major_field_of_study(?x10170, ?x4268), draft(?x8111, ?x1161), season(?x12956, ?x3431), state_province_region(?x10666, ?x4622), award_winner(?x1386, ?x669), ?x10170 = 01_r9k *> conf = 0.85 ranks of expected_values: 4, 6 EVAL 051wf season 026fmqm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 80.000 80.000 0.889 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 051wf season 027pwzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 80.000 80.000 0.889 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #15432-02psgq PRED entity: 02psgq PRED relation: honored_for! PRED expected values: 0ftlkg => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 102): 05c1t6z (0.16 #1221, 0.06 #2673, 0.06 #2915), 02q690_ (0.15 #1264, 0.05 #3926, 0.05 #2716), 0gvstc3 (0.13 #1237, 0.04 #3899, 0.04 #3536), 0275n3y (0.12 #306, 0.07 #1274, 0.04 #3936), 03gwpw2 (0.12 #247, 0.05 #368, 0.05 #2546), 02wzl1d (0.12 #249, 0.05 #1217, 0.03 #2548), 0bvhz9 (0.12 #355, 0.04 #839, 0.03 #2654), 0bvfqq (0.11 #26, 0.05 #389, 0.02 #1599), 03nnm4t (0.10 #1273, 0.04 #3935, 0.04 #2725), 02hn5v (0.10 #396, 0.08 #154, 0.04 #517) >> Best rule #1221 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 0n2bh; 0gfzgl; 03y3bp7; 01f3p_; 02sqkh; 06dfz1; 07wqr6; 03g9xj; 0h95b81; 0cskb; ... >> query: (?x5429, 05c1t6z) <- titles(?x90, ?x5429), nominated_for(?x8476, ?x5429), major_field_of_study(?x3995, ?x90) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #1593 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 155 *> proper extension: 05f4_n0; 02q_4ph; 0125xq; 0fsw_7; 08cfr1; 072zl1; 0k2m6; 0kt_4; 0jdr0; *> query: (?x5429, 0ftlkg) <- genre(?x5429, ?x53), award(?x5429, ?x372), language(?x5429, ?x254), story_by(?x5429, ?x8573) *> conf = 0.01 ranks of expected_values: 87 EVAL 02psgq honored_for! 0ftlkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 77.000 77.000 0.156 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15431-0lbj1 PRED entity: 0lbj1 PRED relation: artists! PRED expected values: 06cqb 064t9 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 249): 064t9 (0.79 #930, 0.57 #3067, 0.43 #13450), 06j6l (0.48 #963, 0.32 #3100, 0.28 #1878), 0glt670 (0.45 #3093, 0.22 #12560, 0.21 #13170), 0gywn (0.43 #972, 0.24 #361, 0.23 #3109), 02k_kn (0.43 #979, 0.16 #16189, 0.15 #2200), 025sc50 (0.40 #964, 0.38 #3101, 0.21 #7684), 0xhtw (0.38 #1239, 0.34 #323, 0.25 #5820), 01fh36 (0.29 #1305, 0.27 #83, 0.12 #1610), 0dl5d (0.29 #1242, 0.17 #326, 0.14 #1547), 02lnbg (0.29 #973, 0.24 #3110, 0.18 #3721) >> Best rule #930 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 01wbgdv; 0pyg6; 02b25y; 0259r0; 03bxwtd; 01vwyqp; 026spg; 0c7xjb; 01k_mc; 02s2wq; ... >> query: (?x248, 064t9) <- award_nominee(?x248, ?x3403), artists(?x9007, ?x248), ?x9007 = 02vjzr >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 51 EVAL 0lbj1 artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.786 http://example.org/music/genre/artists EVAL 0lbj1 artists! 06cqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 117.000 117.000 0.786 http://example.org/music/genre/artists #15430-0yxl PRED entity: 0yxl PRED relation: influenced_by! PRED expected values: 047cqr => 117 concepts (53 used for prediction) PRED predicted values (max 10 best out of 486): 014ps4 (0.23 #1337, 0.09 #5957, 0.08 #12127), 03772 (0.15 #1229, 0.07 #5849, 0.07 #23128), 02ghq (0.15 #1472, 0.07 #6092, 0.07 #5065), 01zkxv (0.15 #1044, 0.04 #4637, 0.04 #9266), 01vs4f3 (0.15 #1377, 0.04 #4970, 0.04 #5483), 0kbg6 (0.15 #1537, 0.02 #4617, 0.02 #5130), 06jcc (0.14 #5959, 0.09 #4932, 0.07 #23128), 040db (0.13 #11894, 0.09 #5724, 0.08 #7268), 02yl42 (0.13 #6296, 0.13 #4756, 0.12 #5783), 0yxl (0.12 #1900, 0.08 #3439, 0.07 #23128) >> Best rule #1337 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01t_xp_; >> query: (?x8753, 014ps4) <- award(?x8753, ?x10678), award(?x8753, ?x10270), ?x10270 = 06196, award_winner(?x10678, ?x476) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #6592 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 66 *> proper extension: 01dvtx; *> query: (?x8753, 047cqr) <- influenced_by(?x8753, ?x10000), influenced_by(?x8753, ?x8452), student(?x3490, ?x8452), profession(?x8753, ?x353), peers(?x10000, ?x5334) *> conf = 0.01 ranks of expected_values: 436 EVAL 0yxl influenced_by! 047cqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 117.000 53.000 0.231 http://example.org/influence/influence_node/influenced_by #15429-07t3gd PRED entity: 07t3gd PRED relation: company PRED expected values: 03v6t 03mnk => 33 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1898): 04rwx (0.43 #1994, 0.43 #1667, 0.43 #1340), 08815 (0.43 #1311, 0.38 #2623, 0.33 #3610), 03s7h (0.38 #2875, 0.33 #583, 0.30 #4521), 03ksy (0.38 #2693, 0.33 #401, 0.30 #4339), 09c7w0 (0.36 #4923, 0.33 #330, 0.25 #6904), 05zl0 (0.33 #451, 0.33 #122, 0.29 #2085), 02zd460 (0.33 #3387, 0.33 #108, 0.29 #1417), 01bm_ (0.33 #141, 0.30 #4076, 0.29 #2104), 0537b (0.33 #465, 0.30 #4732, 0.18 #5058), 04sv4 (0.33 #529, 0.30 #4796, 0.14 #2163) >> Best rule #1994 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 02md_2; >> query: (?x5652, 04rwx) <- company(?x5652, ?x7618), company(?x5652, ?x6836), company(?x5652, ?x741), company(?x5652, ?x581), contains(?x1156, ?x6836), school_type(?x6836, ?x3092), major_field_of_study(?x741, ?x10417), place_of_birth(?x4472, ?x1156), category(?x1156, ?x134), state_province_region(?x741, ?x335), ?x10417 = 01r4k, major_field_of_study(?x7618, ?x1527), institution(?x620, ?x7618), country(?x581, ?x94) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4660 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 8 *> proper extension: 01rk91; 0krdk; 05smlt; *> query: (?x5652, 03mnk) <- company(?x5652, ?x6836), company(?x5652, ?x6123), company(?x5652, ?x4278), colors(?x6123, ?x3189), student(?x6123, ?x2600), school_type(?x6836, ?x3092), major_field_of_study(?x4278, ?x4321), major_field_of_study(?x4278, ?x1527), currency(?x6836, ?x1099), ?x1527 = 04_tv, citytown(?x4278, ?x3052), ?x4321 = 0g26h, student(?x4278, ?x3291), category(?x6123, ?x134) *> conf = 0.10 ranks of expected_values: 240, 357 EVAL 07t3gd company 03mnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 22.000 0.429 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 07t3gd company 03v6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 33.000 22.000 0.429 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #15428-0kwmc PRED entity: 0kwmc PRED relation: source PRED expected values: 0jbk9 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #16, 0.91 #30, 0.91 #62) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 0f4y_; 0nj1c; 0n5_g; 0nm8n; 0drr3; 0n4z2; >> query: (?x12797, 0jbk9) <- administrative_parent(?x12797, ?x2831), district_represented(?x176, ?x2831), taxonomy(?x2831, ?x939), contains(?x94, ?x2831) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kwmc source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.930 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #15427-04m_kpx PRED entity: 04m_kpx PRED relation: nationality PRED expected values: 03rk0 => 96 concepts (94 used for prediction) PRED predicted values (max 10 best out of 97): 03rk0 (0.84 #1146, 0.83 #1246, 0.09 #1646), 09c7w0 (0.76 #1401, 0.73 #1501, 0.71 #701), 07ssc (0.13 #315, 0.12 #815, 0.12 #1915), 02jx1 (0.12 #4634, 0.11 #4034, 0.11 #4534), 0345h (0.07 #531, 0.06 #1331, 0.03 #1931), 06q1r (0.07 #177, 0.07 #377, 0.04 #1377), 014tss (0.07 #376, 0.04 #576, 0.01 #1376), 0d060g (0.06 #1307, 0.05 #2407, 0.05 #1607), 05bcl (0.05 #460, 0.03 #660, 0.03 #760), 0d0vqn (0.04 #1009, 0.03 #609, 0.03 #709) >> Best rule #1146 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 02756j; 0265z9l; 0b5x23; >> query: (?x10707, 03rk0) <- type_of_union(?x10707, ?x566), gender(?x10707, ?x231), ?x566 = 04ztj, people(?x5025, ?x10707), ?x5025 = 0dryh9k >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04m_kpx nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 94.000 0.839 http://example.org/people/person/nationality #15426-04wp2p PRED entity: 04wp2p PRED relation: nominated_for PRED expected values: 06ybb1 => 98 concepts (51 used for prediction) PRED predicted values (max 10 best out of 578): 02qjv1p (0.50 #1323, 0.09 #56732, 0.02 #7806), 03cv_gy (0.25 #848, 0.02 #23536, 0.01 #28402), 08bytj (0.25 #1205, 0.01 #23893, 0.01 #41726), 07g9f (0.12 #1464, 0.09 #56732, 0.02 #19289), 03nt59 (0.12 #958, 0.09 #56732), 080dwhx (0.12 #60, 0.05 #22748, 0.04 #17885), 02py4c8 (0.12 #99, 0.04 #1719, 0.01 #21167), 0180mw (0.12 #1039, 0.03 #23727, 0.03 #28593), 04vr_f (0.12 #159, 0.03 #24469, 0.03 #29335), 07024 (0.12 #443, 0.03 #24753, 0.02 #32859) >> Best rule #1323 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 03jvmp; >> query: (?x10589, 02qjv1p) <- nominated_for(?x10589, ?x4037), award_winner(?x496, ?x10589), ?x4037 = 015g28 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #82667 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1490 *> proper extension: 057hz; 01pcql; 02t_v1; 034f0d; 087yty; 03kxp7; 012gbb; 03wdsbz; *> query: (?x10589, ?x103) <- award(?x10589, ?x688), award_winner(?x4037, ?x10589), nominated_for(?x688, ?x103) *> conf = 0.01 ranks of expected_values: 525 EVAL 04wp2p nominated_for 06ybb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 98.000 51.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15425-044f7 PRED entity: 044f7 PRED relation: profession PRED expected values: 01d_h8 => 87 concepts (72 used for prediction) PRED predicted values (max 10 best out of 78): 01d_h8 (0.84 #4470, 0.84 #1014, 0.80 #1734), 02hrh1q (0.79 #1597, 0.73 #6638, 0.73 #4189), 0cbd2 (0.51 #3751, 0.47 #4039, 0.42 #5336), 016z4k (0.50 #4, 0.26 #5621, 0.24 #5765), 025352 (0.50 #56, 0.09 #488, 0.08 #1352), 09jwl (0.40 #449, 0.37 #5634, 0.36 #881), 018gz8 (0.38 #159, 0.25 #4897, 0.23 #4335), 0nbcg (0.33 #28, 0.33 #460, 0.29 #5645), 01c72t (0.33 #21, 0.19 #453, 0.16 #885), 0kyk (0.33 #3770, 0.31 #4058, 0.28 #5355) >> Best rule #4470 for best value: >> intensional similarity = 2 >> extensional distance = 377 >> proper extension: 024c1b; >> query: (?x5562, 01d_h8) <- produced_by(?x7723, ?x5562), film(?x803, ?x7723) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044f7 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 72.000 0.839 http://example.org/people/person/profession #15424-02gx2k PRED entity: 02gx2k PRED relation: category_of PRED expected values: 0c4ys => 45 concepts (40 used for prediction) PRED predicted values (max 10 best out of 4): 0c4ys (0.92 #85, 0.90 #43, 0.88 #22), 0gcf2r (0.20 #128, 0.12 #404, 0.11 #425), 0g_w (0.08 #405, 0.07 #426, 0.07 #490), 058vy5 (0.01 #74) >> Best rule #85 for best value: >> intensional similarity = 6 >> extensional distance = 93 >> proper extension: 02581q; 02g3gj; 03x3wf; 02581c; 03t5kl; 025m98; 03t5n3; 03q_g6; 01cw7s; 019bnn; ... >> query: (?x1584, 0c4ys) <- ceremony(?x1584, ?x6487), ceremony(?x3647, ?x6487), ceremony(?x1854, ?x6487), award_winner(?x6487, ?x352), ?x1854 = 025m8y, ?x3647 = 01c9jp >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02gx2k category_of 0c4ys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 40.000 0.916 http://example.org/award/award_category/category_of #15423-04jpl PRED entity: 04jpl PRED relation: place_of_birth! PRED expected values: 0psss 016nvh => 189 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2611): 0892sx (0.52 #5115, 0.48 #46041, 0.48 #35809), 0phx4 (0.52 #5115, 0.48 #46041, 0.48 #35809), 01vzz1c (0.52 #5115, 0.48 #46041, 0.48 #35809), 0150t6 (0.52 #5115, 0.45 #61386, 0.43 #127885), 01vt5c_ (0.52 #5115, 0.45 #61386, 0.43 #127885), 0140t7 (0.52 #5115, 0.45 #61386, 0.43 #127885), 01vw20h (0.52 #5115, 0.45 #61386, 0.43 #127885), 0167km (0.52 #5115, 0.45 #61386, 0.43 #127885), 0czkbt (0.52 #5115, 0.45 #61386, 0.43 #127885), 01bpnd (0.52 #5115, 0.45 #61386, 0.43 #127885) >> Best rule #5115 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0fvxz; >> query: (?x362, ?x361) <- location(?x4476, ?x362), location(?x361, ?x362), ?x4476 = 01vw20h, place_of_birth(?x1950, ?x362) >> conf = 0.52 => this is the best rule for 101 predicted values *> Best rule #140675 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 015g7; 067z4; 0fs1v; 0889d; 0d6nx; 01fqm; 0dlwj; 0fnm3; 0fs44; 07m_f; ... *> query: (?x362, ?x57) <- capital(?x1310, ?x362), nationality(?x57, ?x1310), contains(?x1310, ?x892) *> conf = 0.04 ranks of expected_values: 1575 EVAL 04jpl place_of_birth! 016nvh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 189.000 162.000 0.524 http://example.org/people/person/place_of_birth EVAL 04jpl place_of_birth! 0psss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 189.000 162.000 0.524 http://example.org/people/person/place_of_birth #15422-06z4wj PRED entity: 06z4wj PRED relation: profession PRED expected values: 0dxtg => 99 concepts (65 used for prediction) PRED predicted values (max 10 best out of 105): 02hrh1q (0.97 #3494, 0.82 #7410, 0.77 #1319), 0dxtg (0.91 #3203, 0.75 #593, 0.64 #158), 01d_h8 (0.74 #3341, 0.70 #3196, 0.62 #586), 02jknp (0.63 #3197, 0.50 #3342, 0.45 #2182), 09jwl (0.46 #2628, 0.29 #6253, 0.25 #6979), 02hv44_ (0.40 #55, 0.30 #925, 0.27 #200), 0np9r (0.36 #7271, 0.11 #3790, 0.11 #1760), 018gz8 (0.34 #1321, 0.31 #596, 0.25 #1901), 05sxg2 (0.33 #291, 0.27 #1596, 0.26 #871), 0dz3r (0.33 #2612, 0.17 #6237, 0.17 #292) >> Best rule #3494 for best value: >> intensional similarity = 5 >> extensional distance = 150 >> proper extension: 01p45_v; 02_j7t; 013v5j; 0gkg6; 01_rh4; 0p3r8; 039crh; 0hgqq; 01h8f; 02w5q6; ... >> query: (?x6943, 02hrh1q) <- profession(?x6943, ?x11804), profession(?x6943, ?x2225), ?x2225 = 0kyk, profession(?x3281, ?x11804), ?x3281 = 0154qm >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #3203 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 135 *> proper extension: 01r216; *> query: (?x6943, 0dxtg) <- award(?x6943, ?x9766), written_by(?x6243, ?x6943), award(?x2733, ?x9766), ?x2733 = 0hskw *> conf = 0.91 ranks of expected_values: 2 EVAL 06z4wj profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 65.000 0.967 http://example.org/people/person/profession #15421-0bvzp PRED entity: 0bvzp PRED relation: profession PRED expected values: 028kk_ => 131 concepts (87 used for prediction) PRED predicted values (max 10 best out of 92): 0nbcg (0.87 #8017, 0.47 #7290, 0.47 #7582), 02hrh1q (0.86 #4368, 0.83 #4658, 0.82 #7710), 09jwl (0.70 #7570, 0.69 #7278, 0.65 #4083), 0dz3r (0.56 #8426, 0.50 #147, 0.45 #3487), 03gjzk (0.45 #1175, 0.32 #1610, 0.31 #304), 016z4k (0.43 #2761, 0.41 #6681, 0.39 #8573), 01d_h8 (0.33 #1167, 0.32 #4651, 0.31 #1602), 0dxtg (0.33 #1173, 0.31 #302, 0.30 #2333), 02jknp (0.27 #733, 0.23 #1458, 0.22 #6102), 039v1 (0.27 #7587, 0.27 #7295, 0.22 #3520) >> Best rule #8017 for best value: >> intensional similarity = 3 >> extensional distance = 484 >> proper extension: 025tdwc; 01fxck; 0b57p6; >> query: (?x6399, 0nbcg) <- profession(?x6399, ?x563), profession(?x1934, ?x563), ?x1934 = 0b82vw >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #3122 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 137 *> proper extension: 0dhqyw; 03c_8t; *> query: (?x6399, 028kk_) <- artists(?x888, ?x6399), artists(?x888, ?x13167), ?x13167 = 0h6sv *> conf = 0.09 ranks of expected_values: 28 EVAL 0bvzp profession 028kk_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 131.000 87.000 0.874 http://example.org/people/person/profession #15420-076xkps PRED entity: 076xkps PRED relation: film_crew_role PRED expected values: 05smlt => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 24): 015h31 (0.38 #34, 0.26 #225, 0.26 #170), 033smt (0.34 #46, 0.22 #182, 0.21 #237), 02_n3z (0.34 #137, 0.33 #55, 0.32 #219), 02ynfr (0.30 #10, 0.25 #119, 0.25 #64), 020xn5 (0.17 #33, 0.16 #142, 0.15 #115), 0263ycg (0.17 #39, 0.15 #66, 0.14 #230), 089fss (0.15 #114, 0.15 #59, 0.14 #223), 04pyp5 (0.13 #384, 0.12 #1897, 0.10 #1785), 02vs3x5 (0.13 #384, 0.12 #1897, 0.10 #1785), 02zdwq (0.13 #384, 0.10 #42, 0.10 #1785) >> Best rule #34 for best value: >> intensional similarity = 8 >> extensional distance = 27 >> proper extension: 0415ggl; >> query: (?x8886, 015h31) <- film_crew_role(?x8886, ?x7591), film_crew_role(?x8886, ?x4305), film_crew_role(?x8886, ?x2472), film_crew_role(?x8886, ?x137), ?x7591 = 0d2b38, ?x2472 = 01xy5l_, ?x4305 = 0215hd, ?x137 = 09zzb8 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 27 *> proper extension: 0415ggl; *> query: (?x8886, 05smlt) <- film_crew_role(?x8886, ?x7591), film_crew_role(?x8886, ?x4305), film_crew_role(?x8886, ?x2472), film_crew_role(?x8886, ?x137), ?x7591 = 0d2b38, ?x2472 = 01xy5l_, ?x4305 = 0215hd, ?x137 = 09zzb8 *> conf = 0.10 ranks of expected_values: 12 EVAL 076xkps film_crew_role 05smlt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 83.000 83.000 0.379 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15419-02wypbh PRED entity: 02wypbh PRED relation: film_festivals PRED expected values: 05f5rsr => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 18): 05f5rsr (0.41 #91, 0.24 #111, 0.15 #71), 0hrcs29 (0.22 #114, 0.06 #655, 0.06 #675), 059_y8d (0.21 #102, 0.05 #643, 0.05 #663), 04_m9gk (0.10 #654, 0.10 #674, 0.02 #113), 0bmj62v (0.09 #653, 0.09 #673), 0gg7gsl (0.08 #642, 0.08 #662, 0.02 #101), 04grdgy (0.08 #650, 0.08 #670), 0kfhjq0 (0.08 #646, 0.08 #666), 09rwjly (0.07 #649, 0.07 #669), 0g57ws5 (0.07 #648, 0.07 #668) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0jqn5; 01_vfy; 05whq_9; 01q4qv; 01ycck; 0qmhk; 040z9; 081l_; 037w7r; 0gdqy; ... >> query: (?x10597, 05f5rsr) <- film_festivals(?x10597, ?x11231), film_festivals(?x4169, ?x11231), place_of_birth(?x4169, ?x4271), award_winner(?x1625, ?x4169) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wypbh film_festivals 05f5rsr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.405 http://example.org/film/film/film_festivals #15418-01ww2fs PRED entity: 01ww2fs PRED relation: profession PRED expected values: 016z4k => 106 concepts (96 used for prediction) PRED predicted values (max 10 best out of 60): 016z4k (0.80 #151, 0.64 #298, 0.46 #3242), 0nbcg (0.49 #3858, 0.48 #3563, 0.47 #2531), 0dz3r (0.42 #3240, 0.42 #737, 0.42 #1031), 01c72t (0.34 #758, 0.32 #2523, 0.31 #2817), 01d_h8 (0.28 #4421, 0.28 #7064, 0.28 #9126), 0n1h (0.28 #7064, 0.28 #9126, 0.28 #9274), 025352 (0.28 #7064, 0.28 #9126, 0.28 #9274), 029bkp (0.28 #7064, 0.28 #9126, 0.28 #9274), 04f2zj (0.28 #7064, 0.28 #9126, 0.28 #9274), 03lgtv (0.28 #7064, 0.28 #9126, 0.28 #9274) >> Best rule #151 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 051m56; >> query: (?x2300, 016z4k) <- award_winner(?x6562, ?x2300), award_winner(?x1480, ?x2300), ?x6562 = 05sq20 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ww2fs profession 016z4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 96.000 0.800 http://example.org/people/person/profession #15417-01xy5l_ PRED entity: 01xy5l_ PRED relation: profession! PRED expected values: 018dyl => 63 concepts (31 used for prediction) PRED predicted values (max 10 best out of 4122): 01vsl3_ (0.67 #51734, 0.60 #30523, 0.50 #17796), 02qwg (0.67 #51927, 0.60 #30716, 0.43 #73142), 02cx90 (0.67 #52278, 0.60 #31067, 0.43 #73493), 0161c2 (0.67 #51834, 0.60 #30623, 0.37 #68805), 01w02sy (0.67 #51832, 0.60 #30621, 0.37 #68803), 01wp8w7 (0.67 #51313, 0.60 #30102, 0.33 #81014), 03j24kf (0.60 #31209, 0.56 #52420, 0.48 #99094), 02fybl (0.60 #32035, 0.56 #53246, 0.48 #74461), 014q2g (0.60 #30515, 0.56 #51726, 0.38 #72941), 0473q (0.60 #32060, 0.56 #53271, 0.38 #74486) >> Best rule #51734 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0dz3r; 02hrh1q; 018gz8; >> query: (?x2472, 01vsl3_) <- profession(?x6512, ?x2472), profession(?x4741, ?x2472), ?x4741 = 01s21dg, influenced_by(?x477, ?x6512), student(?x892, ?x6512) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #52255 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0dz3r; 02hrh1q; 018gz8; *> query: (?x2472, 018dyl) <- profession(?x6512, ?x2472), profession(?x4741, ?x2472), ?x4741 = 01s21dg, influenced_by(?x477, ?x6512), student(?x892, ?x6512) *> conf = 0.33 ranks of expected_values: 433 EVAL 01xy5l_ profession! 018dyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 63.000 31.000 0.667 http://example.org/people/person/profession #15416-03cwwl PRED entity: 03cwwl PRED relation: film_format PRED expected values: 07fb8_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.31 #17, 0.20 #1, 0.19 #44), 0cj16 (0.20 #3, 0.16 #35, 0.15 #24), 017fx5 (0.08 #41, 0.07 #31, 0.04 #78) >> Best rule #17 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 09rfh9; >> query: (?x9996, 07fb8_) <- featured_film_locations(?x9996, ?x1658), currency(?x9996, ?x170), prequel(?x8234, ?x9996), genre(?x9996, ?x812), ?x812 = 01jfsb >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cwwl film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.308 http://example.org/film/film/film_format #15415-016ppr PRED entity: 016ppr PRED relation: group! PRED expected values: 03bx0bm => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 57): 0342h (0.81 #373, 0.29 #2489, 0.28 #3136), 02hnl (0.71 #401, 0.26 #2517, 0.23 #3164), 03bx0bm (0.68 #396, 0.21 #1960, 0.18 #3067), 05148p4 (0.65 #390, 0.24 #2506, 0.22 #3153), 018vs (0.61 #383, 0.21 #2499, 0.21 #2039), 0l14md (0.52 #376, 0.20 #2032, 0.19 #2492), 05r5c (0.35 #377, 0.09 #2493, 0.08 #3140), 028tv0 (0.32 #382, 0.12 #2498, 0.12 #1118), 03qjg (0.16 #420, 0.10 #2536, 0.09 #3183), 06ncr (0.16 #411, 0.06 #2527, 0.06 #2067) >> Best rule #373 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 02cpp; 0ycfj; >> query: (?x10740, 0342h) <- artist(?x3265, ?x10740), award(?x10740, ?x8994), ?x8994 = 02f6yz, artists(?x671, ?x10740) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #396 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 02cpp; 0ycfj; *> query: (?x10740, 03bx0bm) <- artist(?x3265, ?x10740), award(?x10740, ?x8994), ?x8994 = 02f6yz, artists(?x671, ?x10740) *> conf = 0.68 ranks of expected_values: 3 EVAL 016ppr group! 03bx0bm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.806 http://example.org/music/performance_role/regular_performances./music/group_membership/group #15414-0bfvw2 PRED entity: 0bfvw2 PRED relation: award_winner PRED expected values: 030hbp => 48 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1719): 0154qm (0.50 #17959, 0.50 #3172, 0.50 #707), 02x7vq (0.50 #19717, 0.50 #18490, 0.44 #16025), 0lpjn (0.50 #3066, 0.50 #601, 0.40 #17853), 013knm (0.50 #3266, 0.50 #801, 0.40 #18053), 0f4vbz (0.50 #2464, 0.50 #462, 0.36 #44357), 02vntj (0.50 #3394, 0.50 #929, 0.33 #8324), 02ktrs (0.50 #4760, 0.50 #2295, 0.33 #9690), 02l3_5 (0.50 #9149, 0.50 #4219, 0.30 #19006), 02f2dn (0.50 #3022, 0.33 #7952, 0.25 #557), 0fb1q (0.50 #3152, 0.33 #8082, 0.25 #687) >> Best rule #17959 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 09sb52; 02z0dfh; 0ck27z; 03qgjwc; >> query: (?x375, 0154qm) <- award(?x5504, ?x375), award(?x2258, ?x375), nominated_for(?x375, ?x293), ?x5504 = 02x7vq, award_winner(?x2488, ?x2258) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #41891 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 209 *> proper extension: 02pzxlw; 0fqpg6b; *> query: (?x375, ?x190) <- award(?x1641, ?x375), nominated_for(?x375, ?x293), award_nominee(?x190, ?x1641) *> conf = 0.05 ranks of expected_values: 583 EVAL 0bfvw2 award_winner 030hbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 21.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #15413-027km64 PRED entity: 027km64 PRED relation: award_nominee! PRED expected values: 066yfh => 86 concepts (26 used for prediction) PRED predicted values (max 10 best out of 770): 062cg6 (0.81 #32624, 0.81 #25633, 0.80 #53599), 066yfh (0.40 #2291, 0.38 #4621, 0.18 #41947), 07rd7 (0.38 #3332, 0.20 #1002, 0.18 #41947), 01vhrz (0.25 #4336, 0.20 #2006, 0.18 #41947), 0bjkpt (0.20 #1224, 0.18 #41947, 0.17 #48938), 01pw9v (0.20 #2029, 0.15 #60590, 0.12 #4359), 025hzx (0.20 #2156, 0.12 #4486), 0bxtg (0.18 #41947, 0.17 #48938, 0.15 #60590), 07lwsz (0.18 #41947, 0.17 #48938, 0.15 #60590), 07ym6ss (0.18 #41947, 0.17 #48938, 0.15 #60590) >> Best rule #32624 for best value: >> intensional similarity = 4 >> extensional distance = 1135 >> proper extension: 038rzr; 01k5zk; 01wn718; 01933d; 05jjl; 0227vl; >> query: (?x5202, ?x2691) <- profession(?x5202, ?x1032), location(?x5202, ?x7328), gender(?x5202, ?x514), award_nominee(?x5202, ?x2691) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #2291 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 07rd7; 01pw9v; 066yfh; *> query: (?x5202, 066yfh) <- profession(?x5202, ?x1032), award_nominee(?x2691, ?x5202), ?x2691 = 067pl7, type_of_union(?x5202, ?x566) *> conf = 0.40 ranks of expected_values: 2 EVAL 027km64 award_nominee! 066yfh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 26.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15412-017f3m PRED entity: 017f3m PRED relation: nominated_for! PRED expected values: 0bdw6t => 76 concepts (74 used for prediction) PRED predicted values (max 10 best out of 190): 0m7yy (0.70 #4415, 0.70 #3717, 0.70 #3252), 027gs1_ (0.38 #183, 0.33 #415, 0.26 #3667), 0gq9h (0.36 #11212, 0.27 #12375, 0.26 #11445), 0gs9p (0.32 #11214, 0.24 #11447, 0.23 #12377), 04g2jz2 (0.31 #3018, 0.23 #3019, 0.21 #6043), 02sp_v (0.31 #3018, 0.23 #3019, 0.21 #6043), 0cjyzs (0.31 #80, 0.28 #1936, 0.28 #1472), 0bdw6t (0.31 #82, 0.27 #314, 0.22 #3101), 09qs08 (0.31 #107, 0.27 #339, 0.21 #1499), 09qrn4 (0.31 #161, 0.27 #393, 0.19 #2017) >> Best rule #4415 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 03j63k; 0m123; 097h2; 02gl58; 02_1ky; 019g8j; >> query: (?x4898, ?x3184) <- actor(?x4898, ?x1204), award(?x4898, ?x3184), nominated_for(?x686, ?x4898), genre(?x4898, ?x604) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #82 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 045r_9; *> query: (?x4898, 0bdw6t) <- award_winner(?x4898, ?x6678), ?x6678 = 05gnf, nominated_for(?x686, ?x4898) *> conf = 0.31 ranks of expected_values: 8 EVAL 017f3m nominated_for! 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 76.000 74.000 0.702 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15411-0chghy PRED entity: 0chghy PRED relation: form_of_government PRED expected values: 018wl5 => 240 concepts (240 used for prediction) PRED predicted values (max 10 best out of 5): 01fpfn (0.41 #303, 0.41 #413, 0.39 #453), 06cx9 (0.39 #927, 0.35 #806, 0.34 #872), 018wl5 (0.37 #152, 0.37 #272, 0.36 #277), 01d9r3 (0.31 #799, 0.31 #930, 0.31 #809), 026wp (0.12 #145, 0.12 #60, 0.11 #75) >> Best rule #303 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 01z215; 03gyl; 04xn_; 016zwt; >> query: (?x390, 01fpfn) <- participating_countries(?x418, ?x390), nationality(?x72, ?x390), country(?x901, ?x390) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #152 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 0b90_r; 01ls2; 047yc; 01p1v; 06f32; *> query: (?x390, 018wl5) <- film_release_region(?x5713, ?x390), country(?x150, ?x390), ?x5713 = 0cc97st *> conf = 0.37 ranks of expected_values: 3 EVAL 0chghy form_of_government 018wl5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 240.000 240.000 0.411 http://example.org/location/country/form_of_government #15410-01wmgrf PRED entity: 01wmgrf PRED relation: award PRED expected values: 054ks3 => 120 concepts (118 used for prediction) PRED predicted values (max 10 best out of 279): 01bgqh (0.52 #845, 0.43 #444, 0.27 #1647), 02f6ym (0.46 #1058, 0.15 #1459, 0.14 #1860), 01by1l (0.45 #914, 0.38 #513, 0.35 #1716), 03qbnj (0.37 #1033, 0.16 #1835, 0.14 #1434), 03qbh5 (0.36 #605, 0.28 #1006, 0.25 #1808), 01c427 (0.35 #886, 0.22 #84, 0.19 #485), 026mg3 (0.33 #12, 0.14 #35292, 0.11 #2017), 09sb52 (0.32 #23301, 0.28 #2447, 0.26 #22900), 0c4z8 (0.25 #874, 0.22 #5285, 0.20 #3681), 054ks3 (0.25 #942, 0.19 #5353, 0.19 #4952) >> Best rule #845 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 01wz3cx; 01wf86y; >> query: (?x3122, 01bgqh) <- profession(?x3122, ?x955), award(?x3122, ?x4796), ?x4796 = 01c99j >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #942 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 01wz3cx; 01wf86y; *> query: (?x3122, 054ks3) <- profession(?x3122, ?x955), award(?x3122, ?x4796), ?x4796 = 01c99j *> conf = 0.25 ranks of expected_values: 10 EVAL 01wmgrf award 054ks3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 120.000 118.000 0.523 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15409-0f4yh PRED entity: 0f4yh PRED relation: nominated_for! PRED expected values: 016j68 => 166 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1228): 04wp63 (0.83 #123794, 0.82 #105103, 0.81 #81743), 03q8ch (0.83 #123794, 0.82 #105103, 0.81 #81743), 02fcs2 (0.73 #30366, 0.62 #4671, 0.61 #7007), 030_3z (0.48 #18686, 0.03 #36042, 0.02 #17356), 0c0k1 (0.42 #49044, 0.33 #28029, 0.32 #91085), 01846t (0.42 #49044, 0.33 #28029, 0.32 #91085), 02wgln (0.42 #49044, 0.32 #91085, 0.23 #28028), 016j68 (0.42 #49044, 0.32 #91085, 0.23 #28028), 0kx4m (0.29 #4672, 0.18 #91084, 0.16 #109776), 0343h (0.26 #67728, 0.26 #98095, 0.25 #60721) >> Best rule #123794 for best value: >> intensional similarity = 4 >> extensional distance = 239 >> proper extension: 0cwrr; 08cx5g; 05fgr_; 0275kr; 03czz87; >> query: (?x3535, ?x1643) <- award_winner(?x3535, ?x1643), nominated_for(?x669, ?x3535), category(?x3535, ?x134), nominated_for(?x1643, ?x1386) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #49044 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 92 *> proper extension: 047bynf; *> query: (?x3535, ?x1958) <- nominated_for(?x112, ?x3535), ?x112 = 027dtxw, film_release_distribution_medium(?x3535, ?x81), film(?x1958, ?x3535) *> conf = 0.42 ranks of expected_values: 8 EVAL 0f4yh nominated_for! 016j68 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 166.000 55.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15408-05qtj PRED entity: 05qtj PRED relation: film_regional_debut_venue! PRED expected values: 0gffmn8 => 232 concepts (230 used for prediction) PRED predicted values (max 10 best out of 39): 0crh5_f (0.15 #2105, 0.13 #4342, 0.11 #3783), 0gffmn8 (0.12 #804, 0.11 #3601, 0.11 #1922), 0btpm6 (0.12 #888, 0.11 #1260, 0.10 #2193), 01s9vc (0.12 #922, 0.11 #1294, 0.10 #2227), 0bpm4yw (0.12 #823, 0.11 #1195, 0.06 #1941), 0dr_4 (0.12 #774, 0.11 #1146, 0.06 #1892), 01sby_ (0.10 #2149, 0.10 #1402, 0.10 #2522), 0b44shh (0.10 #2146, 0.10 #1399, 0.10 #2519), 0blpg (0.10 #2123, 0.10 #1376, 0.10 #2496), 0hv81 (0.09 #4958, 0.07 #3654, 0.07 #4212) >> Best rule #2105 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0135p7; >> query: (?x4627, 0crh5_f) <- place_of_birth(?x10634, ?x4627), place_of_birth(?x981, ?x4627), friend(?x981, ?x3861), music(?x1318, ?x10634) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #804 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0c499; *> query: (?x4627, 0gffmn8) <- place_of_birth(?x981, ?x4627), friend(?x981, ?x3861), capital(?x789, ?x4627) *> conf = 0.12 ranks of expected_values: 2 EVAL 05qtj film_regional_debut_venue! 0gffmn8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 232.000 230.000 0.150 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #15407-02dbn2 PRED entity: 02dbn2 PRED relation: nationality PRED expected values: 0chghy => 100 concepts (65 used for prediction) PRED predicted values (max 10 best out of 57): 09c7w0 (0.74 #911, 0.74 #1113, 0.73 #1815), 0chghy (0.37 #2015, 0.36 #3320, 0.35 #5227), 0ctw_b (0.37 #2015, 0.36 #3320, 0.35 #5227), 02jx1 (0.33 #33, 0.21 #133, 0.11 #2349), 07ssc (0.29 #4824, 0.27 #5529, 0.25 #115), 0d060g (0.29 #4824, 0.27 #5529, 0.04 #2925), 0b90_r (0.29 #4824, 0.27 #5529, 0.03 #6532), 03rk0 (0.10 #146, 0.07 #4569, 0.07 #4870), 0d05w3 (0.08 #150, 0.01 #1964, 0.01 #857), 0d0vqn (0.06 #109) >> Best rule #911 for best value: >> intensional similarity = 4 >> extensional distance = 946 >> proper extension: 084w8; 0l6qt; 0197tq; 02rchht; 0h5f5n; 0fp_v1x; 07w21; 041h0; 026ps1; 06cv1; ... >> query: (?x4809, 09c7w0) <- profession(?x4809, ?x1032), award(?x4809, ?x14647), student(?x5035, ?x4809), location(?x4809, ?x5036) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #2015 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1144 *> proper extension: 0hm0k; 04qb6g; *> query: (?x4809, ?x390) <- award_winner(?x4351, ?x4809), genre(?x4351, ?x53), country(?x4351, ?x390) *> conf = 0.37 ranks of expected_values: 2 EVAL 02dbn2 nationality 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 65.000 0.737 http://example.org/people/person/nationality #15406-04swx PRED entity: 04swx PRED relation: partially_contains PRED expected values: 03rjj => 137 concepts (78 used for prediction) PRED predicted values (max 10 best out of 67): 0lm0n (0.42 #1411, 0.24 #2003, 0.19 #409), 0lcd (0.40 #164, 0.33 #52, 0.33 #14), 01znc_ (0.40 #194, 0.31 #861, 0.21 #2580), 05vz3zq (0.40 #199, 0.31 #861, 0.21 #2580), 0cdbq (0.40 #197, 0.31 #861, 0.21 #2580), 0jgx (0.40 #196, 0.31 #861, 0.21 #2580), 065ky (0.33 #30, 0.31 #861, 0.06 #335), 05g56 (0.33 #28, 0.31 #861, 0.06 #333), 047lj (0.31 #861, 0.21 #2580, 0.20 #191), 049nq (0.31 #861, 0.21 #2580, 0.20 #209) >> Best rule #1411 for best value: >> intensional similarity = 2 >> extensional distance = 51 >> proper extension: 0mvsg; >> query: (?x12727, 0lm0n) <- partially_contains(?x12727, ?x789), contains(?x789, ?x790) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #983 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 0978r; 096gm; 06pr6; 04swd; 09b83; 01llj3; *> query: (?x12727, ?x205) <- contains(?x12727, ?x8958), contains(?x12727, ?x2856), contains(?x455, ?x8958), ?x455 = 02j9z, contains(?x205, ?x2856) *> conf = 0.01 ranks of expected_values: 52 EVAL 04swx partially_contains 03rjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 137.000 78.000 0.415 http://example.org/location/location/partially_contains #15405-022qw7 PRED entity: 022qw7 PRED relation: people! PRED expected values: 03bkbh => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 43): 041rx (0.32 #312, 0.25 #158, 0.24 #543), 0xnvg (0.29 #167, 0.18 #321, 0.17 #244), 0x67 (0.18 #164, 0.17 #241, 0.16 #318), 033tf_ (0.18 #1240, 0.13 #1162, 0.12 #1781), 02w7gg (0.15 #2, 0.06 #3319, 0.06 #1776), 013b6_ (0.11 #207, 0.08 #361, 0.06 #284), 07bch9 (0.09 #870, 0.04 #1101, 0.03 #1024), 02ctzb (0.08 #862, 0.03 #554, 0.03 #2407), 048z7l (0.07 #194, 0.05 #348, 0.04 #1427), 013xrm (0.06 #559, 0.03 #1717, 0.03 #1872) >> Best rule #312 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 01r42_g; 0lsw9; 0d0l91; >> query: (?x9132, 041rx) <- profession(?x9132, ?x1032), location(?x9132, ?x4253), ?x4253 = 0ccvx, gender(?x9132, ?x231) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1265 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 329 *> proper extension: 099bk; 0j5b8; 0cl_m; 03d6q; 04pwg; *> query: (?x9132, 03bkbh) <- gender(?x9132, ?x231), religion(?x9132, ?x1985), ?x1985 = 0c8wxp *> conf = 0.04 ranks of expected_values: 17 EVAL 022qw7 people! 03bkbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 91.000 91.000 0.316 http://example.org/people/ethnicity/people #15404-018p4y PRED entity: 018p4y PRED relation: location PRED expected values: 03gh4 01qs54 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 86): 01xd9 (0.45 #2490, 0.36 #3293, 0.02 #22548), 02cft (0.27 #2711, 0.21 #3514, 0.07 #1909), 015q02 (0.25 #801), 030qb3t (0.24 #4897, 0.23 #10512, 0.22 #8908), 02_286 (0.17 #9665, 0.16 #26514, 0.15 #10467), 04jpl (0.14 #1621, 0.12 #2423, 0.10 #3226), 059rby (0.14 #818, 0.06 #4831, 0.05 #10446), 01cx_ (0.14 #964, 0.02 #9790, 0.02 #4175), 01x73 (0.14 #897, 0.01 #8921, 0.01 #10525), 02xry (0.14 #934, 0.01 #21793, 0.01 #9760) >> Best rule #2490 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0f1pyf; 02y0dd; >> query: (?x11879, 01xd9) <- nationality(?x11879, ?x429), ?x429 = 03rt9, location(?x11879, ?x1310) >> conf = 0.45 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018p4y location 01qs54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.455 http://example.org/people/person/places_lived./people/place_lived/location EVAL 018p4y location 03gh4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.455 http://example.org/people/person/places_lived./people/place_lived/location #15403-059j2 PRED entity: 059j2 PRED relation: country! PRED expected values: 07bs0 => 263 concepts (263 used for prediction) PRED predicted values (max 10 best out of 30): 064vjs (0.87 #1005, 0.81 #645, 0.77 #1215), 01z27 (0.82 #367, 0.70 #277, 0.65 #1087), 01cgz (0.78 #1086, 0.74 #996, 0.70 #2886), 03rbzn (0.74 #1001, 0.68 #731, 0.65 #791), 0486tv (0.74 #740, 0.70 #1010, 0.61 #1100), 07bs0 (0.73 #365, 0.70 #785, 0.70 #275), 03fyrh (0.70 #1002, 0.64 #372, 0.63 #732), 09w1n (0.69 #639, 0.59 #1269, 0.59 #1389), 0dwxr (0.64 #373, 0.60 #793, 0.58 #733), 06z68 (0.63 #736, 0.60 #796, 0.57 #1096) >> Best rule #1005 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 06npd; 015qh; 0d05w3; 05b4w; 07f1x; >> query: (?x1229, 064vjs) <- film_release_region(?x385, ?x1229), ?x385 = 0ds3t5x, combatants(?x151, ?x1229) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #365 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 02vzc; *> query: (?x1229, 07bs0) <- film_release_region(?x3425, ?x1229), film_release_region(?x2746, ?x1229), ?x3425 = 0qm9n, ?x2746 = 04f52jw *> conf = 0.73 ranks of expected_values: 6 EVAL 059j2 country! 07bs0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 263.000 263.000 0.870 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #15402-02f2dn PRED entity: 02f2dn PRED relation: film PRED expected values: 0f40w => 89 concepts (65 used for prediction) PRED predicted values (max 10 best out of 649): 026p4q7 (0.72 #35632, 0.71 #1782, 0.65 #35631), 03nqnnk (0.06 #1020, 0.03 #89086, 0.03 #83741), 027m5wv (0.06 #1053, 0.03 #89086), 02z3r8t (0.06 #108, 0.03 #83741, 0.03 #81959), 031hcx (0.06 #1267, 0.02 #4830, 0.02 #6612), 0cfhfz (0.06 #492, 0.02 #4055, 0.01 #34341), 09lxv9 (0.06 #1498, 0.01 #3280, 0.01 #35347), 0m313 (0.06 #13, 0.01 #5358, 0.01 #19611), 0bz3jx (0.06 #1135, 0.01 #4698, 0.01 #34984), 02x2jl_ (0.06 #1747) >> Best rule #35632 for best value: >> intensional similarity = 3 >> extensional distance = 1113 >> proper extension: 02wrhj; >> query: (?x2646, ?x7009) <- nominated_for(?x2646, ?x7009), genre(?x7009, ?x812), film(?x2646, ?x3845) >> conf = 0.72 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02f2dn film 0f40w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 65.000 0.722 http://example.org/film/actor/film./film/performance/film #15401-03sb38 PRED entity: 03sb38 PRED relation: production_companies! PRED expected values: 02r1c18 02725hs 02vqsll 0b7l4x 09sr0 03hp2y1 02fqxm => 141 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1580): 02x3y41 (0.50 #3100, 0.39 #1123, 0.34 #12345), 0glqh5_ (0.50 #2834, 0.33 #589, 0.25 #9566), 0b7l4x (0.50 #2903, 0.33 #658, 0.17 #9635), 0cz_ym (0.50 #2448, 0.33 #203, 0.17 #9180), 03mh_tp (0.50 #2584, 0.17 #9316, 0.05 #11560), 02vqsll (0.50 #2573, 0.08 #9305, 0.04 #16837), 065_cjc (0.50 #2990, 0.08 #9722, 0.01 #17962), 02r1c18 (0.50 #2409, 0.08 #9141, 0.01 #17962), 0cwfgz (0.39 #1123, 0.34 #12345, 0.34 #12344), 0bs8ndx (0.39 #1123, 0.34 #12345, 0.34 #12344) >> Best rule #3100 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 054lpb6; 02j_j0; >> query: (?x7303, 02x3y41) <- production_companies(?x4048, ?x7303), production_companies(?x3430, ?x7303), production_companies(?x2547, ?x7303), production_companies(?x324, ?x7303), nominated_for(?x323, ?x324), ?x4048 = 0ddcbd5, ?x3430 = 0ctb4g, titles(?x600, ?x2547) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2903 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 054lpb6; 02j_j0; *> query: (?x7303, 0b7l4x) <- production_companies(?x4048, ?x7303), production_companies(?x3430, ?x7303), production_companies(?x2547, ?x7303), production_companies(?x324, ?x7303), nominated_for(?x323, ?x324), ?x4048 = 0ddcbd5, ?x3430 = 0ctb4g, titles(?x600, ?x2547) *> conf = 0.50 ranks of expected_values: 3, 6, 8, 28, 118, 150, 254 EVAL 03sb38 production_companies! 02fqxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 141.000 16.000 0.500 http://example.org/film/film/production_companies EVAL 03sb38 production_companies! 03hp2y1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 141.000 16.000 0.500 http://example.org/film/film/production_companies EVAL 03sb38 production_companies! 09sr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 141.000 16.000 0.500 http://example.org/film/film/production_companies EVAL 03sb38 production_companies! 0b7l4x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 141.000 16.000 0.500 http://example.org/film/film/production_companies EVAL 03sb38 production_companies! 02vqsll CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 141.000 16.000 0.500 http://example.org/film/film/production_companies EVAL 03sb38 production_companies! 02725hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 141.000 16.000 0.500 http://example.org/film/film/production_companies EVAL 03sb38 production_companies! 02r1c18 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 141.000 16.000 0.500 http://example.org/film/film/production_companies #15400-06w87 PRED entity: 06w87 PRED relation: role PRED expected values: 03qjg => 46 concepts (45 used for prediction) PRED predicted values (max 10 best out of 119): 0342h (0.92 #1566, 0.91 #2558, 0.91 #2441), 0l14md (0.90 #4868, 0.90 #4507, 0.88 #3661), 018vs (0.88 #3031, 0.88 #2928, 0.87 #2447), 0mkg (0.87 #1579, 0.86 #2201, 0.78 #2445), 04rzd (0.86 #1317, 0.85 #467, 0.75 #1249), 03bx0bm (0.82 #1114, 0.81 #4289, 0.80 #1482), 01s0ps (0.82 #1143, 0.75 #1265, 0.71 #655), 07y_7 (0.78 #3519, 0.76 #348, 0.73 #2321), 03qjg (0.76 #3836, 0.73 #1750, 0.71 #2254), 018j2 (0.76 #3819, 0.73 #355, 0.68 #229) >> Best rule #1566 for best value: >> intensional similarity = 30 >> extensional distance = 13 >> proper extension: 01v1d8; >> query: (?x736, ?x227) <- role(?x736, ?x1166), role(?x736, ?x645), role(?x736, ?x228), ?x645 = 028tv0, performance_role(?x736, ?x1969), ?x228 = 0l14qv, ?x1166 = 05148p4, performance_role(?x75, ?x736), role(?x736, ?x227), instrumentalists(?x736, ?x2987), role(?x1955, ?x736), instrumentalists(?x227, ?x12422), instrumentalists(?x227, ?x11689), instrumentalists(?x227, ?x5879), instrumentalists(?x227, ?x3934), instrumentalists(?x227, ?x1165), instrumentalists(?x227, ?x226), ?x5879 = 0167km, role(?x227, ?x3296), group(?x227, ?x11749), ?x11689 = 06p03s, ?x11749 = 016t0h, performance_role(?x227, ?x1267), role(?x227, ?x868), origin(?x3934, ?x682), diet(?x3934, ?x3130), program(?x226, ?x9788), ?x3296 = 07_l6, ?x12422 = 01p0w_, gender(?x1165, ?x231) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #3836 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 32 *> proper extension: 03_vpw; *> query: (?x736, 03qjg) <- role(?x736, ?x2923), role(?x736, ?x645), ?x645 = 028tv0, role(?x2923, ?x1750), role(?x2923, ?x745), role(?x1969, ?x2923), role(?x227, ?x2923), role(?x3215, ?x2923), role(?x2923, ?x885), ?x227 = 0342h, ?x1750 = 02hnl, ?x745 = 01vj9c, group(?x2923, ?x5329), role(?x4918, ?x2923), role(?x3202, ?x2923), ?x1969 = 04rzd, instrumentalists(?x2923, ?x2242), ?x3215 = 0bxl5, profession(?x3202, ?x131), nominated_for(?x3202, ?x2638), gender(?x3202, ?x231), ?x885 = 0dwtp, ?x2638 = 02fn5r, role(?x5676, ?x736) *> conf = 0.76 ranks of expected_values: 9 EVAL 06w87 role 03qjg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 46.000 45.000 0.917 http://example.org/music/performance_role/regular_performances./music/group_membership/role #15399-04lgybj PRED entity: 04lgybj PRED relation: legislative_sessions PRED expected values: 04fhps => 9 concepts (9 used for prediction) PRED predicted values (max 10 best out of 56): 04fhps (0.78 #238, 0.78 #237, 0.50 #56), 04lgybj (0.50 #191, 0.33 #134, 0.33 #78), 07p__7 (0.44 #367, 0.43 #303, 0.42 #429), 02bqmq (0.44 #379, 0.43 #315, 0.42 #441), 06f0dc (0.44 #368, 0.43 #304, 0.42 #430), 03ww_x (0.43 #243, 0.42 #364, 0.41 #300), 02gkzs (0.43 #320, 0.42 #384, 0.40 #446), 060ny2 (0.42 #398, 0.41 #334, 0.41 #277), 03tcbx (0.42 #377, 0.41 #313, 0.40 #439), 03rtmz (0.41 #314, 0.40 #378, 0.39 #440) >> Best rule #238 for best value: >> intensional similarity = 103 >> extensional distance = 2 >> proper extension: 04fhps; >> query: (?x3473, ?x11189) <- district_represented(?x3473, ?x14386), district_represented(?x3473, ?x10063), district_represented(?x3473, ?x9370), district_represented(?x3473, ?x9311), district_represented(?x3473, ?x3824), district_represented(?x3473, ?x3474), district_represented(?x3473, ?x1905), ?x3474 = 05j49, adjoins(?x335, ?x1905), state_province_region(?x7066, ?x1905), state_province_region(?x6038, ?x1905), state_province_region(?x4199, ?x1905), state_province_region(?x3091, ?x1905), contains(?x1905, ?x13811), contains(?x1905, ?x12456), contains(?x1905, ?x10683), contains(?x1905, ?x9061), contains(?x1905, ?x1196), adjoins(?x1905, ?x1906), first_level_division_of(?x1905, ?x279), state_province_region(?x266, ?x1906), contains(?x1906, ?x169), district_represented(?x4730, ?x1906), district_represented(?x3540, ?x1906), district_represented(?x2976, ?x1906), district_represented(?x2861, ?x1906), district_represented(?x1829, ?x1906), district_represented(?x1137, ?x1906), district_represented(?x845, ?x1906), district_represented(?x606, ?x1906), district_represented(?x605, ?x1906), ?x4730 = 02cg7g, ?x605 = 077g7n, ?x10063 = 0j95, religion(?x1906, ?x10107), religion(?x1906, ?x8613), religion(?x1906, ?x2769), religion(?x1906, ?x1985), religion(?x1906, ?x962), category(?x6038, ?x134), ?x8613 = 04pk9, ?x845 = 07p__7, legislative_sessions(?x11189, ?x3473), legislative_sessions(?x10543, ?x3473), ?x3540 = 024tcq, location(?x5574, ?x1906), jurisdiction_of_office(?x3959, ?x1906), adjoins(?x1906, ?x448), ?x2976 = 03rtmz, ?x1137 = 02bqn1, ?x1829 = 02bp37, major_field_of_study(?x4199, ?x1154), ?x9370 = 059t8, administrative_parent(?x1906, ?x94), ?x2861 = 03tcbx, major_field_of_study(?x6038, ?x4100), ?x10543 = 03h_f4, organization(?x346, ?x6038), ?x962 = 05sfs, institution(?x1200, ?x6038), colors(?x6038, ?x332), currency(?x6038, ?x2244), state(?x12755, ?x1905), location(?x917, ?x1196), school_type(?x4199, ?x3092), contains(?x335, ?x322), state_province_region(?x166, ?x335), location(?x101, ?x335), district_represented(?x355, ?x335), ?x1985 = 0c8wxp, adjoins(?x9311, ?x2049), featured_film_locations(?x97, ?x10683), jurisdiction_of_office(?x1195, ?x13811), institution(?x1368, ?x3091), institution(?x865, ?x3091), citytown(?x11304, ?x12456), location_of_ceremony(?x566, ?x1906), place_of_birth(?x2025, ?x1196), student(?x4199, ?x2033), ?x606 = 03ww_x, institution(?x1390, ?x4199), taxonomy(?x9311, ?x939), ?x14386 = 0h5qxv, contains(?x9311, ?x9570), student(?x3091, ?x595), institution(?x2759, ?x7066), ?x1368 = 014mlp, ?x2769 = 019cr, ?x3824 = 04s7y, currency(?x335, ?x170), legislative_sessions(?x3099, ?x3473), ?x865 = 02h4rq6, major_field_of_study(?x7066, ?x1668), time_zones(?x9061, ?x2674), ?x566 = 04ztj, ?x10107 = 05w5d, colors(?x4199, ?x663), jurisdiction_of_office(?x12303, ?x335), legislative_sessions(?x8776, ?x11189), colors(?x3091, ?x3364), contains(?x8260, ?x1906), ?x3959 = 0f6c3, ?x1668 = 01mkq >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04lgybj legislative_sessions 04fhps CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 9.000 9.000 0.778 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #15398-0_wm_ PRED entity: 0_wm_ PRED relation: contains! PRED expected values: 07h34 => 127 concepts (67 used for prediction) PRED predicted values (max 10 best out of 389): 07h34 (0.81 #43057, 0.76 #57415, 0.76 #32292), 09c7w0 (0.76 #57415, 0.76 #32292, 0.76 #42163), 04rrx (0.37 #24215, 0.06 #15370, 0.06 #18959), 01n7q (0.31 #13524, 0.25 #78, 0.24 #20703), 07b_l (0.30 #9187, 0.30 #3807, 0.28 #7393), 059rby (0.22 #916, 0.12 #20, 0.11 #1812), 04ych (0.20 #3649, 0.10 #9029, 0.09 #10822), 0mr_8 (0.20 #3411, 0.10 #4308, 0.08 #13273), 05k7sb (0.17 #8201, 0.14 #5511, 0.12 #133), 02xry (0.17 #11817, 0.12 #18995, 0.10 #21686) >> Best rule #43057 for best value: >> intensional similarity = 7 >> extensional distance = 88 >> proper extension: 02hrh0_; >> query: (?x12181, ?x3778) <- citytown(?x6824, ?x12181), source(?x12181, ?x958), contains(?x3778, ?x6824), state_province_region(?x1506, ?x3778), jurisdiction_of_office(?x900, ?x3778), district_represented(?x6728, ?x3778), ?x6728 = 070mff >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_wm_ contains! 07h34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 67.000 0.813 http://example.org/location/location/contains #15397-0gtsxr4 PRED entity: 0gtsxr4 PRED relation: film! PRED expected values: 03jldb 015t7v => 74 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1084): 0p8r1 (0.14 #6832, 0.12 #8914, 0.10 #10996), 0p_pd (0.12 #54, 0.05 #2136, 0.05 #4218), 01pk3z (0.12 #988, 0.04 #17645, 0.04 #19727), 01gy7r (0.12 #728, 0.04 #25715, 0.04 #19467), 02l3_5 (0.12 #1411, 0.03 #13903, 0.03 #15985), 02v60l (0.12 #818, 0.03 #25805, 0.03 #19557), 01rr9f (0.12 #80, 0.03 #25067, 0.03 #18819), 0l8v5 (0.12 #59, 0.03 #25046, 0.03 #18798), 0gx_p (0.12 #1110, 0.03 #17767, 0.03 #19849), 0pgm3 (0.12 #2005, 0.03 #18662, 0.03 #20744) >> Best rule #6832 for best value: >> intensional similarity = 8 >> extensional distance = 42 >> proper extension: 03twd6; 0dlngsd; 0hv27; >> query: (?x3151, 0p8r1) <- film_release_region(?x3151, ?x1892), film_release_region(?x3151, ?x985), film_release_region(?x3151, ?x87), genre(?x3151, ?x811), ?x985 = 0k6nt, ?x1892 = 02vzc, ?x811 = 03k9fj, ?x87 = 05r4w >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2980 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 17 *> proper extension: 03t97y; 02nx2k; *> query: (?x3151, 015t7v) <- language(?x3151, ?x254), ?x254 = 02h40lc, genre(?x3151, ?x811), genre(?x3151, ?x571), ?x571 = 03npn, ?x811 = 03k9fj, country(?x3151, ?x94) *> conf = 0.05 ranks of expected_values: 115 EVAL 0gtsxr4 film! 015t7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 74.000 46.000 0.136 http://example.org/film/actor/film./film/performance/film EVAL 0gtsxr4 film! 03jldb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 46.000 0.136 http://example.org/film/actor/film./film/performance/film #15396-01l47f5 PRED entity: 01l47f5 PRED relation: profession PRED expected values: 0nbcg => 83 concepts (82 used for prediction) PRED predicted values (max 10 best out of 51): 0nbcg (0.48 #2603, 0.47 #600, 0.47 #2460), 01d_h8 (0.31 #1721, 0.31 #2866, 0.29 #6446), 0n1h (0.28 #582, 0.28 #6155, 0.27 #6299), 02jknp (0.28 #6155, 0.27 #6299, 0.25 #9018), 029bkp (0.28 #6155, 0.27 #6299, 0.25 #9018), 03lgtv (0.28 #6155, 0.27 #6299, 0.25 #9018), 0dxtg (0.26 #4307, 0.25 #8457, 0.25 #10746), 0cbd2 (0.25 #9018, 0.12 #10882, 0.11 #11598), 025352 (0.25 #9018, 0.07 #1343, 0.06 #914), 012t_z (0.25 #9018, 0.07 #583, 0.06 #440) >> Best rule #2603 for best value: >> intensional similarity = 2 >> extensional distance = 459 >> proper extension: 03c7ln; 0c9d9; 01nqfh_; 07_3qd; 01w923; 01tp5bj; 0gkg6; 0bkg4; 01wy61y; 023l9y; ... >> query: (?x6467, 0nbcg) <- category(?x6467, ?x134), instrumentalists(?x212, ?x6467) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l47f5 profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 82.000 0.484 http://example.org/people/person/profession #15395-0fht9f PRED entity: 0fht9f PRED relation: school! PRED expected values: 0g3zpp => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 20): 05vsb7 (0.48 #389, 0.47 #326, 0.47 #283), 0f4vx0 (0.42 #251, 0.40 #293, 0.35 #336), 02qw1zx (0.40 #724, 0.36 #348, 0.36 #496), 09l0x9 (0.37 #731, 0.36 #348, 0.36 #503), 03nt7j (0.36 #348, 0.35 #332, 0.35 #802), 092j54 (0.36 #348, 0.35 #334, 0.35 #802), 0g3zpp (0.36 #348, 0.35 #802, 0.35 #513), 047dpm0 (0.25 #259, 0.24 #344, 0.21 #510), 02r6gw6 (0.25 #505, 0.20 #733, 0.20 #296), 09th87 (0.20 #297, 0.18 #340, 0.17 #255) >> Best rule #389 for best value: >> intensional similarity = 15 >> extensional distance = 19 >> proper extension: 01r3y2; 0lyjf; 0g8rj; 08qnnv; 0gy3w; 027ybp; >> query: (?x2148, 05vsb7) <- school(?x4170, ?x2148), colors(?x4170, ?x4557), position_s(?x4170, ?x1717), position_s(?x4170, ?x1240), position_s(?x4170, ?x180), team(?x2573, ?x4170), ?x1717 = 02g_6x, ?x1240 = 023wyl, draft(?x4170, ?x1883), ?x4557 = 019sc, school(?x1883, ?x3779), ?x180 = 01r3hr, ?x3779 = 01pq4w, position(?x4170, ?x935), team(?x10361, ?x4170) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #348 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 15 *> proper extension: 02183k; 01vs5c; *> query: (?x2148, ?x465) <- school(?x4170, ?x2148), colors(?x4170, ?x5325), colors(?x4170, ?x4557), position_s(?x4170, ?x11424), position_s(?x4170, ?x1717), position_s(?x4170, ?x1240), team(?x2573, ?x4170), ?x1717 = 02g_6x, ?x1240 = 023wyl, draft(?x4170, ?x1883), draft(?x4170, ?x685), draft(?x4170, ?x465), ?x4557 = 019sc, ?x1883 = 02qw1zx, colors(?x8228, ?x5325), ?x8228 = 0jmcv, position(?x4170, ?x935), ?x685 = 0g3zpp, position(?x10339, ?x11424) *> conf = 0.36 ranks of expected_values: 7 EVAL 0fht9f school! 0g3zpp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 99.000 99.000 0.476 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #15394-076_74 PRED entity: 076_74 PRED relation: profession PRED expected values: 0dxtg => 110 concepts (97 used for prediction) PRED predicted values (max 10 best out of 117): 0dxtg (0.88 #1041, 0.87 #600, 0.87 #306), 02jknp (0.77 #300, 0.74 #1770, 0.69 #1329), 02hrh1q (0.72 #9276, 0.69 #7805, 0.69 #13689), 03gjzk (0.67 #5012, 0.50 #455, 0.47 #1631), 012t_z (0.33 #11, 0.17 #746, 0.14 #893), 02krf9 (0.24 #1202, 0.21 #2084, 0.19 #320), 018gz8 (0.17 #5014, 0.16 #2074, 0.15 #4720), 09jwl (0.17 #6487, 0.17 #7663, 0.16 #13989), 0np9r (0.17 #461, 0.10 #2078, 0.10 #12666), 01c72t (0.17 #170, 0.10 #317, 0.09 #13994) >> Best rule #1041 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 02r5w9; 01_vfy; 0gr36; 042v2; 016dp0; >> query: (?x3862, 0dxtg) <- award(?x3862, ?x601), profession(?x3862, ?x319), award_winner(?x2085, ?x3862), ?x601 = 0gr4k >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 076_74 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 97.000 0.881 http://example.org/people/person/profession #15393-0cks1m PRED entity: 0cks1m PRED relation: film! PRED expected values: 0392kz => 95 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1192): 0dt645q (0.36 #20525, 0.25 #12189, 0.16 #33029), 02_p5w (0.33 #15237, 0.25 #8985, 0.17 #13153), 0392kz (0.33 #3830, 0.25 #10083, 0.17 #14251), 0f4vbz (0.29 #17037, 0.14 #23289, 0.08 #56637), 03hzl42 (0.29 #17464, 0.14 #23716, 0.06 #32052), 02gf_l (0.25 #9609, 0.17 #15861, 0.17 #13777), 03pmzt (0.25 #10920, 0.17 #15088, 0.14 #17172), 01wy5m (0.25 #9199, 0.17 #13367, 0.08 #21703), 01v3vp (0.25 #9049, 0.17 #13217, 0.08 #21553), 0c1pj (0.25 #8431, 0.17 #12599, 0.08 #20935) >> Best rule #20525 for best value: >> intensional similarity = 11 >> extensional distance = 9 >> proper extension: 015qy1; >> query: (?x5633, 0dt645q) <- country(?x5633, ?x252), genre(?x5633, ?x2540), genre(?x5633, ?x1510), genre(?x5633, ?x225), ?x252 = 03_3d, ?x225 = 02kdv5l, ?x2540 = 0hcr, genre(?x4241, ?x1510), genre(?x1366, ?x1510), ?x1366 = 07ng9k, ?x4241 = 0gcpc >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #3830 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 0ckr7s; *> query: (?x5633, 0392kz) <- film(?x902, ?x5633), film(?x296, ?x5633), genre(?x5633, ?x2540), genre(?x5633, ?x1510), genre(?x5633, ?x1403), ?x1403 = 02l7c8, actor(?x5633, ?x4134), ?x1510 = 01hmnh, film(?x13175, ?x5633), genre(?x419, ?x2540) *> conf = 0.33 ranks of expected_values: 3 EVAL 0cks1m film! 0392kz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 54.000 0.364 http://example.org/film/actor/film./film/performance/film #15392-01v90t PRED entity: 01v90t PRED relation: gender PRED expected values: 05zppz => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #15, 0.87 #13, 0.87 #31), 02zsn (0.46 #179, 0.45 #182, 0.29 #140) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 02t__l; >> query: (?x7209, 05zppz) <- award(?x7209, ?x1033), type_of_union(?x7209, ?x566), ?x1033 = 02x73k6, profession(?x7209, ?x987) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v90t gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.918 http://example.org/people/person/gender #15391-01n4w_ PRED entity: 01n4w_ PRED relation: institution! PRED expected values: 01rr_d => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 19): 02_xgp2 (0.53 #389, 0.52 #558, 0.51 #431), 016t_3 (0.50 #3, 0.48 #382, 0.47 #424), 0bkj86 (0.46 #280, 0.42 #385, 0.42 #6), 03bwzr4 (0.46 #857, 0.46 #645, 0.45 #391), 07s6fsf (0.42 #1, 0.38 #43, 0.36 #802), 04zx3q1 (0.42 #2, 0.28 #423, 0.28 #276), 027f2w (0.42 #7, 0.23 #428, 0.23 #386), 03mkk4 (0.42 #9, 0.17 #388, 0.16 #283), 028dcg (0.33 #17, 0.17 #59, 0.17 #101), 0bjrnt (0.25 #5, 0.16 #279, 0.13 #806) >> Best rule #389 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 0ym8f; 01jt2w; 0ylzs; >> query: (?x11185, 02_xgp2) <- student(?x11185, ?x10871), institution(?x865, ?x11185), colors(?x11185, ?x663), influenced_by(?x10871, ?x117) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 08tyb_; *> query: (?x11185, 01rr_d) <- student(?x11185, ?x10871), influenced_by(?x10871, ?x2208), ?x2208 = 041mt *> conf = 0.25 ranks of expected_values: 11 EVAL 01n4w_ institution! 01rr_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 152.000 152.000 0.531 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15390-0dt8xq PRED entity: 0dt8xq PRED relation: honored_for! PRED expected values: 03gwpw2 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 98): 073h1t (0.33 #21, 0.02 #384, 0.02 #3530), 0bzmt8 (0.17 #83, 0.02 #1656, 0.02 #688), 02q690_ (0.09 #2353, 0.05 #6709, 0.05 #5499), 04n2r9h (0.09 #399, 0.04 #2456, 0.04 #157), 05c1t6z (0.09 #2310, 0.06 #5456, 0.05 #6666), 09gkdln (0.07 #710, 0.06 #589, 0.05 #1678), 0gvstc3 (0.07 #2326, 0.06 #2689, 0.04 #6682), 0418154 (0.06 #576, 0.06 #334, 0.04 #1665), 09k5jh7 (0.06 #554, 0.04 #1159, 0.03 #1643), 03nnm4t (0.06 #2361, 0.04 #2603, 0.04 #6717) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 06g77c; 0hfzr; 05znxx; 0bcp9b; >> query: (?x5070, 073h1t) <- nominated_for(?x298, ?x5070), language(?x5070, ?x10486), titles(?x9694, ?x5070), ?x10486 = 05qqm >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2546 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 201 *> proper extension: 0cwrr; *> query: (?x5070, 03gwpw2) <- nominated_for(?x2790, ?x5070), category(?x5070, ?x134), award(?x5070, ?x834) *> conf = 0.05 ranks of expected_values: 14 EVAL 0dt8xq honored_for! 03gwpw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 126.000 126.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15389-0lbd9 PRED entity: 0lbd9 PRED relation: medal PRED expected values: 02lq5w => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1): 02lq5w (0.87 #30, 0.85 #28, 0.84 #37) >> Best rule #30 for best value: >> intensional similarity = 10 >> extensional distance = 21 >> proper extension: 06sks6; >> query: (?x6464, 02lq5w) <- olympics(?x5114, ?x6464), olympics(?x142, ?x6464), sports(?x6464, ?x2885), ?x2885 = 07jjt, locations(?x5352, ?x5114), combatants(?x279, ?x5114), medal(?x6464, ?x422), administrative_area_type(?x142, ?x2792), film_release_region(?x80, ?x142), olympics(?x2315, ?x6464) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lbd9 medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.870 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #15388-01q_22 PRED entity: 01q_22 PRED relation: place_of_birth! PRED expected values: 0mbhr => 105 concepts (57 used for prediction) PRED predicted values (max 10 best out of 882): 0ddfph (0.11 #2566, 0.10 #5179, 0.08 #7792), 045hz5 (0.11 #2547, 0.10 #5160, 0.08 #12999), 04fkg4 (0.11 #2251, 0.10 #4864, 0.08 #12703), 07yw6t (0.11 #964, 0.08 #6190, 0.08 #11416), 04v7k2 (0.11 #2554, 0.08 #7780, 0.08 #13006), 01rwcgb (0.11 #2188, 0.08 #7414, 0.08 #12640), 05vzql (0.11 #2185, 0.08 #7411, 0.08 #12637), 03m3nzf (0.11 #1899, 0.08 #7125, 0.08 #12351), 01gg59 (0.11 #765, 0.08 #5991, 0.08 #11217), 0b66qd (0.11 #2610, 0.08 #7836, 0.08 #13062) >> Best rule #2566 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0cvw9; >> query: (?x14563, 0ddfph) <- contains(?x6106, ?x14563), contains(?x2146, ?x14563), service_location(?x10867, ?x14563), ?x2146 = 03rk0, ?x10867 = 06_9lg, administrative_division(?x14563, ?x6106) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01q_22 place_of_birth! 0mbhr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 57.000 0.111 http://example.org/people/person/place_of_birth #15387-01fwpt PRED entity: 01fwpt PRED relation: film PRED expected values: 03h3x5 025rxjq => 106 concepts (61 used for prediction) PRED predicted values (max 10 best out of 652): 01xdxy (0.60 #74903, 0.58 #51716, 0.58 #49931), 0639bg (0.40 #632, 0.10 #2415, 0.02 #5981), 0jwmp (0.29 #2332, 0.20 #549), 04zl8 (0.24 #2705, 0.01 #9837), 011yg9 (0.20 #1026, 0.10 #2809, 0.02 #4592), 08k40m (0.20 #482, 0.10 #2265, 0.01 #9397), 0bh8drv (0.20 #1306, 0.10 #3089), 03mh94 (0.20 #64, 0.05 #1847, 0.02 #53564), 05z43v (0.20 #1350, 0.05 #3133, 0.02 #4916), 02z0f6l (0.20 #1214, 0.05 #2997, 0.02 #4780) >> Best rule #74903 for best value: >> intensional similarity = 4 >> extensional distance = 890 >> proper extension: 049tjg; 0m2wm; 02zq43; 04wqr; 0f830f; 025p38; 08w7vj; 0bz5v2; 04cf09; 01wjrn; ... >> query: (?x3477, ?x9487) <- gender(?x3477, ?x514), location(?x3477, ?x739), film(?x3477, ?x1259), nominated_for(?x3477, ?x9487) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3987 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 02r3cn; 030dx5; 045g4l; *> query: (?x3477, 03h3x5) <- type_of_union(?x3477, ?x566), location(?x3477, ?x739), sibling(?x9257, ?x3477), ?x566 = 04ztj *> conf = 0.02 ranks of expected_values: 354, 432 EVAL 01fwpt film 025rxjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 106.000 61.000 0.601 http://example.org/film/actor/film./film/performance/film EVAL 01fwpt film 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 106.000 61.000 0.601 http://example.org/film/actor/film./film/performance/film #15386-03tbg6 PRED entity: 03tbg6 PRED relation: currency PRED expected values: 09nqf => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.82 #15, 0.82 #64, 0.82 #141), 01nv4h (0.03 #9, 0.03 #212, 0.03 #219), 02l6h (0.02 #186, 0.01 #368, 0.01 #312), 02gsvk (0.02 #160, 0.01 #174, 0.01 #55), 088n7 (0.01 #56) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 01fmys; 09w6br; >> query: (?x10455, 09nqf) <- film(?x478, ?x10455), region(?x10455, ?x512), films(?x12273, ?x10455) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03tbg6 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.821 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #15385-03y5ky PRED entity: 03y5ky PRED relation: colors PRED expected values: 01l849 09q2t => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.40 #2, 0.38 #44, 0.36 #128), 01g5v (0.33 #25, 0.27 #130, 0.25 #109), 01l849 (0.25 #190, 0.24 #169, 0.23 #295), 04mkbj (0.20 #11, 0.18 #74, 0.13 #158), 03wkwg (0.20 #16, 0.17 #37, 0.12 #58), 01jnf1 (0.18 #96, 0.18 #75, 0.10 #117), 019sc (0.17 #29, 0.15 #890, 0.15 #239), 067z2v (0.17 #31, 0.12 #52, 0.07 #1282), 06fvc (0.15 #108, 0.14 #129, 0.13 #339), 036k5h (0.13 #153, 0.12 #90, 0.12 #69) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 022lly; 012mzw; 02zy1z; >> query: (?x6201, 083jv) <- state_province_region(?x6201, ?x448), currency(?x6201, ?x170), student(?x6201, ?x2445), ?x448 = 03v1s >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #190 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 196 *> proper extension: 015zyd; 08815; 01jssp; 04wlz2; 05krk; 01pl14; 01j_9c; 06pwq; 02w2bc; 0288zy; ... *> query: (?x6201, 01l849) <- state_province_region(?x6201, ?x448), currency(?x6201, ?x170), student(?x6201, ?x2445), district_represented(?x176, ?x448) *> conf = 0.25 ranks of expected_values: 3, 20 EVAL 03y5ky colors 09q2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 121.000 121.000 0.400 http://example.org/education/educational_institution/colors EVAL 03y5ky colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 121.000 121.000 0.400 http://example.org/education/educational_institution/colors #15384-05h5nb8 PRED entity: 05h5nb8 PRED relation: award! PRED expected values: 0170th 011ysn => 45 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1074): 0hfzr (0.60 #4503, 0.50 #3481, 0.43 #5525), 05hjnw (0.57 #5609, 0.50 #4587, 0.50 #3565), 017jd9 (0.50 #3528, 0.40 #4550, 0.36 #5572), 0gmcwlb (0.50 #4212, 0.38 #3190, 0.36 #5234), 0209hj (0.50 #4151, 0.38 #3129, 0.29 #5173), 0cq806 (0.50 #4948, 0.38 #3926, 0.21 #5970), 0ywrc (0.50 #4395, 0.38 #3373, 0.21 #5417), 0qm98 (0.50 #3204, 0.30 #4226, 0.29 #5248), 07xtqq (0.50 #4120, 0.25 #3098, 0.21 #5142), 03hmt9b (0.40 #4481, 0.38 #3459, 0.36 #5503) >> Best rule #4503 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 04dn09n; 02pqp12; 0gq9h; 02x4wr9; 02x4sn8; >> query: (?x9171, 0hfzr) <- award_winner(?x9171, ?x10271), award_winner(?x9171, ?x8645), award(?x3960, ?x9171), ?x3960 = 0bzyh, profession(?x10271, ?x319), nationality(?x8645, ?x512) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4356 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 04dn09n; 02pqp12; 0gq9h; 02x4wr9; 02x4sn8; *> query: (?x9171, 0170th) <- award_winner(?x9171, ?x10271), award_winner(?x9171, ?x8645), award(?x3960, ?x9171), ?x3960 = 0bzyh, profession(?x10271, ?x319), nationality(?x8645, ?x512) *> conf = 0.20 ranks of expected_values: 119, 157 EVAL 05h5nb8 award! 011ysn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 45.000 17.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 05h5nb8 award! 0170th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 45.000 17.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #15383-05c0jwl PRED entity: 05c0jwl PRED relation: organization PRED expected values: 0yldt => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1099): 0gf14 (0.50 #2993, 0.40 #4523, 0.40 #3756), 0mbwf (0.50 #2903, 0.40 #4433, 0.40 #3666), 022jr5 (0.50 #2545, 0.40 #4075, 0.40 #3308), 017cy9 (0.50 #2505, 0.40 #4035, 0.40 #3268), 03x33n (0.50 #2476, 0.40 #4006, 0.40 #3239), 01y9pk (0.50 #2381, 0.40 #3911, 0.40 #3144), 011kn2 (0.50 #3020, 0.40 #4550, 0.40 #3783), 0hpv3 (0.50 #2777, 0.40 #4307, 0.40 #3540), 0885n (0.50 #2648, 0.40 #4178, 0.40 #3411), 071_8 (0.50 #2492, 0.40 #4022, 0.40 #3255) >> Best rule #2993 for best value: >> intensional similarity = 25 >> extensional distance = 2 >> proper extension: 060c4; >> query: (?x2361, 0gf14) <- organization(?x2361, ?x14209), organization(?x2361, ?x11987), organization(?x2361, ?x11350), organization(?x2361, ?x4365), organization(?x2361, ?x1369), company(?x900, ?x11350), student(?x14209, ?x6654), category(?x4365, ?x134), jurisdiction_of_office(?x900, ?x177), company(?x2998, ?x11987), currency(?x11987, ?x1099), basic_title(?x744, ?x900), nominated_for(?x6654, ?x4329), school_type(?x4365, ?x5931), institution(?x1200, ?x14209), contains(?x1310, ?x11350), profession(?x6654, ?x353), major_field_of_study(?x1369, ?x2605), citytown(?x1369, ?x3301), student(?x1369, ?x8085), institution(?x1526, ?x1369), student(?x4365, ?x4988), major_field_of_study(?x11987, ?x5179), student(?x11987, ?x4951), student(?x11350, ?x3028) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7645 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 6 *> proper extension: 01yc02; *> query: (?x2361, ?x1153) <- organization(?x2361, ?x14287), organization(?x2361, ?x11350), organization(?x2361, ?x7306), organization(?x2361, ?x4365), organization(?x2361, ?x893), state_province_region(?x4365, ?x3302), category(?x7306, ?x134), citytown(?x4365, ?x3301), state_province_region(?x14287, ?x2235), state_province_region(?x11350, ?x10603), location(?x5345, ?x3301), citytown(?x893, ?x1841), place_of_birth(?x698, ?x3301), contains(?x2235, ?x4887), time_zones(?x3301, ?x5327), location(?x981, ?x2235), contains(?x512, ?x3301), ?x134 = 08mbj5d, administrative_parent(?x3302, ?x1310), administrative_parent(?x11747, ?x3302), citytown(?x1153, ?x1841), contains(?x10603, ?x5084), place_of_burial(?x4055, ?x10603), place_of_birth(?x4649, ?x1841), location(?x6319, ?x1841) *> conf = 0.27 ranks of expected_values: 294 EVAL 05c0jwl organization 0yldt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 13.000 13.000 0.500 http://example.org/organization/role/leaders./organization/leadership/organization #15382-05d6kv PRED entity: 05d6kv PRED relation: film PRED expected values: 05p3738 => 71 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1747): 04t9c0 (0.69 #15948, 0.66 #27109, 0.64 #31895), 03mh_tp (0.33 #2045, 0.25 #14803, 0.23 #13208), 02v570 (0.33 #4347, 0.25 #1157, 0.09 #18699), 09v71cj (0.33 #2248, 0.22 #5438, 0.18 #7033), 035s95 (0.30 #14657, 0.17 #25818, 0.17 #9872), 02rb84n (0.25 #14608, 0.25 #255, 0.23 #13013), 0cp0t91 (0.25 #1292, 0.20 #26806, 0.20 #15645), 0gtvpkw (0.25 #505, 0.20 #14858, 0.17 #2100), 014kq6 (0.25 #309, 0.20 #14662, 0.17 #1904), 0dgq_kn (0.25 #929, 0.20 #15282, 0.17 #2524) >> Best rule #15948 for best value: >> intensional similarity = 7 >> extensional distance = 18 >> proper extension: 086k8; 025jfl; 030_1m; 01gb54; 024rdh; >> query: (?x2972, ?x5353) <- film(?x2972, ?x9565), film(?x2972, ?x4446), production_companies(?x5353, ?x2972), genre(?x4446, ?x571), film_festivals(?x9565, ?x11852), film_release_region(?x4446, ?x2346), ?x2346 = 0d05w3 >> conf = 0.69 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05d6kv film 05p3738 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 20.000 0.691 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15381-04f525m PRED entity: 04f525m PRED relation: organizations_founded! PRED expected values: 04fyhv => 171 concepts (31 used for prediction) PRED predicted values (max 10 best out of 58): 0bzyh (0.26 #3231), 0343h (0.25 #576, 0.20 #1358, 0.17 #1469), 04411 (0.25 #568, 0.04 #3020), 01vhrz (0.20 #1967, 0.12 #3193, 0.12 #519), 044kwr (0.20 #208, 0.07 #1879, 0.07 #1768), 0qdwr (0.20 #189, 0.07 #1860, 0.07 #1749), 03cdg (0.17 #321, 0.12 #432, 0.11 #877), 06q8hf (0.13 #1957, 0.06 #2068, 0.05 #2404), 081nh (0.12 #3147, 0.12 #473, 0.07 #1921), 06pj8 (0.12 #692, 0.10 #2365, 0.08 #3144) >> Best rule #3231 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 03z19; >> query: (?x963, ?x3960) <- child(?x1914, ?x963), organizations_founded(?x4854, ?x963), company(?x3960, ?x1914) >> conf = 0.26 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04f525m organizations_founded! 04fyhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 31.000 0.256 http://example.org/organization/organization_founder/organizations_founded #15380-030hbp PRED entity: 030hbp PRED relation: award_winner! PRED expected values: 0bfvw2 => 84 concepts (69 used for prediction) PRED predicted values (max 10 best out of 233): 0cqhk0 (0.37 #17294, 0.37 #17293, 0.37 #10806), 09qj50 (0.37 #17294, 0.37 #17293, 0.37 #10806), 0cqhmg (0.37 #17294, 0.37 #17293, 0.37 #10806), 0ck27z (0.37 #17294, 0.37 #17293, 0.37 #10806), 0bdwft (0.37 #17294, 0.37 #17293, 0.37 #10806), 0bdx29 (0.37 #17294, 0.37 #17293, 0.37 #10806), 0bb57s (0.37 #17294, 0.37 #17293, 0.37 #10806), 09sb52 (0.17 #907, 0.13 #5660, 0.13 #7821), 0cqgl9 (0.16 #17727, 0.10 #24214, 0.08 #190), 0bfvw2 (0.16 #17727, 0.10 #24214, 0.06 #26807) >> Best rule #17294 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 02mslq; 01czx; 016fmf; 0134s5; 024dgj; 02lbrd; 0h005; 0khth; 0134tg; 01mxt_; ... >> query: (?x10491, ?x757) <- award_winner(?x7573, ?x10491), award(?x10491, ?x757) >> conf = 0.37 => this is the best rule for 7 predicted values *> Best rule #17727 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1462 *> proper extension: 0hm0k; 04qb6g; *> query: (?x10491, ?x375) <- award_winner(?x715, ?x10491), award(?x715, ?x375) *> conf = 0.16 ranks of expected_values: 10 EVAL 030hbp award_winner! 0bfvw2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 84.000 69.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner #15379-0mnm2 PRED entity: 0mnm2 PRED relation: place PRED expected values: 0mnm2 => 203 concepts (118 used for prediction) PRED predicted values (max 10 best out of 232): 0dzt9 (0.25 #780, 0.20 #2326, 0.20 #1811), 09c7w0 (0.24 #31476, 0.22 #28895, 0.19 #31992), 07z1m (0.24 #31476, 0.22 #28895, 0.19 #31992), 0mnm2 (0.24 #31476, 0.22 #28895, 0.19 #31992), 013h9 (0.20 #2373, 0.05 #59869, 0.05 #8561), 094jv (0.17 #2613, 0.14 #3128, 0.11 #3644), 0mm_4 (0.11 #4429, 0.08 #4944, 0.05 #8555), 01cx_ (0.11 #3672, 0.05 #7798, 0.05 #7282), 0mnzd (0.11 #4151, 0.05 #8277, 0.04 #10854), 01tlmw (0.11 #3618, 0.04 #9807, 0.03 #11868) >> Best rule #780 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0mp3l; 0dzt9; >> query: (?x7548, 0dzt9) <- contains(?x7548, ?x11185), state(?x7548, ?x1426), ?x1426 = 07z1m, currency(?x7548, ?x170), institution(?x865, ?x11185) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #31476 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 127 *> proper extension: 06_kh; 0l4vc; 0k_mf; 0kdqw; *> query: (?x7548, ?x1426) <- citytown(?x3949, ?x7548), contains(?x1426, ?x3949), contains(?x94, ?x3949), major_field_of_study(?x3949, ?x6870), ?x94 = 09c7w0 *> conf = 0.24 ranks of expected_values: 4 EVAL 0mnm2 place 0mnm2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 203.000 118.000 0.250 http://example.org/location/hud_county_place/place #15378-01zlh5 PRED entity: 01zlh5 PRED relation: person! PRED expected values: 012jfb => 105 concepts (103 used for prediction) PRED predicted values (max 10 best out of 41): 0dtw1x (0.18 #486, 0.03 #1596, 0.02 #2081), 0bx_hnp (0.17 #270, 0.08 #546, 0.04 #477), 02847m9 (0.17 #216, 0.05 #492, 0.03 #561), 037q31 (0.17 #251, 0.03 #458, 0.03 #527), 0dtzkt (0.08 #273, 0.07 #549, 0.01 #618), 04dsnp (0.08 #212, 0.05 #488, 0.01 #419), 0g9lm2 (0.08 #229, 0.03 #505, 0.02 #712), 0hz6mv2 (0.08 #267, 0.03 #543, 0.01 #612), 05_5_22 (0.07 #509, 0.01 #716), 0bhwhj (0.05 #511) >> Best rule #486 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 0gr69; >> query: (?x8205, 0dtw1x) <- category(?x8205, ?x134), person(?x2742, ?x8205), gender(?x8205, ?x231) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #518 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 0gr69; *> query: (?x8205, 012jfb) <- category(?x8205, ?x134), person(?x2742, ?x8205), gender(?x8205, ?x231) *> conf = 0.04 ranks of expected_values: 12 EVAL 01zlh5 person! 012jfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 105.000 103.000 0.176 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #15377-0sxns PRED entity: 0sxns PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.81 #145, 0.81 #16, 0.81 #84), 02nxhr (0.03 #162, 0.03 #234, 0.03 #269), 07c52 (0.03 #275, 0.02 #333, 0.02 #322), 07z4p (0.02 #209, 0.02 #45, 0.02 #20) >> Best rule #145 for best value: >> intensional similarity = 3 >> extensional distance = 1037 >> proper extension: 0gtvrv3; 047svrl; 01gglm; >> query: (?x6174, 029j_) <- nominated_for(?x986, ?x6174), currency(?x6174, ?x170), film(?x4520, ?x6174) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sxns film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.811 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15376-050z2 PRED entity: 050z2 PRED relation: performance_role PRED expected values: 0d8lm => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 119): 03bx0bm (0.50 #305, 0.43 #599, 0.41 #1442), 05r5c (0.15 #590, 0.13 #1025, 0.12 #991), 0342h (0.13 #1025, 0.12 #256, 0.12 #1246), 02sgy (0.13 #1025, 0.12 #257, 0.12 #1246), 042v_gx (0.13 #1025, 0.12 #1246, 0.10 #297), 04rzd (0.13 #1025, 0.12 #1246, 0.10 #126), 05148p4 (0.13 #1025, 0.12 #1246, 0.05 #110), 018vs (0.13 #1025, 0.12 #1246, 0.05 #110), 011k_j (0.13 #1025, 0.12 #1246, 0.05 #110), 0cfdd (0.13 #1025, 0.12 #1246, 0.05 #110) >> Best rule #305 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 02b25y; >> query: (?x4052, 03bx0bm) <- artists(?x284, ?x4052), performance_role(?x4052, ?x1574), religion(?x4052, ?x1985), performance_role(?x1574, ?x1433) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #70 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 06gd4; *> query: (?x4052, 0d8lm) <- artists(?x497, ?x4052), performance_role(?x4052, ?x212), role(?x4052, ?x227), ?x497 = 0fd3y *> conf = 0.12 ranks of expected_values: 25 EVAL 050z2 performance_role 0d8lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 168.000 168.000 0.500 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #15375-01938t PRED entity: 01938t PRED relation: award_winner! PRED expected values: 0fz2y7 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 128): 05hmp6 (0.15 #87, 0.04 #510, 0.03 #1497), 0d__c3 (0.15 #125, 0.04 #1535, 0.03 #548), 0fz0c2 (0.15 #106, 0.03 #1516, 0.03 #529), 0c53zb (0.08 #61, 0.05 #484, 0.03 #1471), 0c53vt (0.08 #112, 0.03 #1522, 0.02 #18332), 0fy6bh (0.08 #47, 0.03 #752, 0.02 #1457), 0c4hx0 (0.08 #128, 0.03 #551, 0.02 #1538), 0fy59t (0.08 #116, 0.03 #539, 0.02 #18332), 0dthsy (0.08 #67, 0.03 #1477, 0.03 #772), 0ftlkg (0.08 #26, 0.02 #1436, 0.02 #18332) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 072twv; 01v5h; >> query: (?x6745, 05hmp6) <- place_of_death(?x6745, ?x682), people(?x913, ?x6745), ?x682 = 0f2wj >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #18332 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2935 *> proper extension: 067mj; 054lpb6; 05k79; 0dtd6; 01czx; 01rm8b; 0fcsd; 047cx; 01k_yf; 015srx; ... *> query: (?x6745, ?x78) <- award(?x6745, ?x1245), ceremony(?x1245, ?x78) *> conf = 0.02 ranks of expected_values: 85 EVAL 01938t award_winner! 0fz2y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 140.000 140.000 0.154 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15374-0klw PRED entity: 0klw PRED relation: influenced_by PRED expected values: 02mpb => 119 concepts (79 used for prediction) PRED predicted values (max 10 best out of 343): 09dt7 (0.57 #32, 0.22 #1340, 0.05 #6573), 0g5ff (0.39 #1503, 0.29 #195, 0.07 #6736), 040db (0.29 #56, 0.17 #1364, 0.07 #6597), 06bng (0.29 #282, 0.17 #1590, 0.05 #25287), 014ps4 (0.29 #246, 0.11 #1554, 0.07 #21795), 03rx9 (0.29 #329, 0.07 #22668, 0.07 #21795), 0821j (0.29 #295, 0.07 #22668, 0.07 #21795), 041h0 (0.22 #1318, 0.14 #10, 0.06 #6551), 03_87 (0.22 #1511, 0.13 #6744, 0.12 #6309), 05qzv (0.17 #1644, 0.14 #336, 0.07 #22668) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01zkxv; 01dzz7; 05jm7; 03hpr; 01g6bk; >> query: (?x4895, 09dt7) <- award_nominee(?x4353, ?x4895), award(?x4895, ?x8880), ?x8880 = 0262x6 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1576 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 07zl1; *> query: (?x4895, 02mpb) <- influenced_by(?x1683, ?x4895), award(?x4895, ?x3337), ?x3337 = 01yz0x *> conf = 0.17 ranks of expected_values: 12 EVAL 0klw influenced_by 02mpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 119.000 79.000 0.571 http://example.org/influence/influence_node/influenced_by #15373-0rp46 PRED entity: 0rp46 PRED relation: contains! PRED expected values: 02xry => 94 concepts (66 used for prediction) PRED predicted values (max 10 best out of 443): 02xry (0.82 #2684, 0.78 #3579, 0.78 #7157), 01n7q (0.20 #24225, 0.19 #32278, 0.18 #17964), 07ssc (0.15 #41176, 0.12 #42070, 0.11 #43859), 059rby (0.13 #34904, 0.11 #8070, 0.11 #913), 04_1l0v (0.12 #33546, 0.10 #30863, 0.07 #41595), 05fjf (0.11 #373, 0.10 #20048, 0.08 #18260), 05k7sb (0.11 #132, 0.08 #35017, 0.08 #24280), 02jx1 (0.10 #41231, 0.09 #49281, 0.09 #42125), 0kpys (0.10 #27906, 0.09 #19855, 0.09 #32381), 02_286 (0.09 #34927, 0.03 #19717, 0.03 #8093) >> Best rule #2684 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0qr4n; 0l_q9; 0q8sw; 034lk7; 0fw3f; >> query: (?x3259, ?x2623) <- source(?x3259, ?x958), administrative_division(?x3259, ?x11986), administrative_parent(?x11986, ?x2623), contains(?x94, ?x3259) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rp46 contains! 02xry CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 66.000 0.824 http://example.org/location/location/contains #15372-027dtxw PRED entity: 027dtxw PRED relation: award! PRED expected values: 03f1zdw 0prjs 02cllz 06mnps 0f7hc 0427y 0141kz => 54 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2590): 015c4g (0.70 #52777, 0.66 #49478, 0.22 #27611), 09fb5 (0.70 #52777, 0.66 #49478, 0.22 #6660), 0f5xn (0.70 #52777, 0.66 #49478, 0.22 #8155), 01ycbq (0.70 #52777, 0.66 #49478, 0.22 #7097), 0170qf (0.70 #52777, 0.66 #49478, 0.20 #10457), 015wnl (0.70 #52777, 0.66 #49478, 0.17 #4309), 02kxbwx (0.47 #13358, 0.44 #16490, 0.39 #16657), 0151w_ (0.47 #13411, 0.44 #16490, 0.37 #26604), 01fh9 (0.44 #7079, 0.33 #3781, 0.26 #26869), 081lh (0.44 #16708, 0.29 #13409, 0.20 #10110) >> Best rule #52777 for best value: >> intensional similarity = 4 >> extensional distance = 190 >> proper extension: 01c9dd; >> query: (?x112, ?x406) <- award(?x395, ?x112), nominated_for(?x112, ?x144), film(?x395, ?x394), award_winner(?x112, ?x406) >> conf = 0.70 => this is the best rule for 6 predicted values *> Best rule #6909 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 03hkv_r; 054krc; 0gr51; 0gqy2; 09sdmz; *> query: (?x112, 0prjs) <- award(?x12435, ?x112), nominated_for(?x112, ?x2814), ?x2814 = 078sj4, award_nominee(?x12435, ?x262) *> conf = 0.33 ranks of expected_values: 70, 84, 121, 737, 993, 1188, 1215 EVAL 027dtxw award! 0141kz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 17.000 0.697 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 027dtxw award! 0427y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 17.000 0.697 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 027dtxw award! 0f7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 54.000 17.000 0.697 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 027dtxw award! 06mnps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 17.000 0.697 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 027dtxw award! 02cllz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 17.000 0.697 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 027dtxw award! 0prjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 54.000 17.000 0.697 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 027dtxw award! 03f1zdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 54.000 17.000 0.697 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15371-016lj_ PRED entity: 016lj_ PRED relation: artists! PRED expected values: 0296y 02yw26 04f73rc => 120 concepts (48 used for prediction) PRED predicted values (max 10 best out of 264): 0xhtw (0.88 #10761, 0.81 #8298, 0.78 #9219), 06by7 (0.79 #12612, 0.74 #12921, 0.71 #9532), 016clz (0.78 #8898, 0.71 #3985, 0.67 #3372), 05r6t (0.69 #6827, 0.56 #5597, 0.50 #1918), 06cp5 (0.65 #7451, 0.50 #1621, 0.40 #10434), 08jyyk (0.62 #5276, 0.43 #4049, 0.33 #3743), 02yv6b (0.60 #2853, 0.50 #2240, 0.40 #10434), 0dl5d (0.57 #4001, 0.55 #7687, 0.50 #5228), 09nwwf (0.57 #4115, 0.50 #5342, 0.50 #1663), 064t9 (0.54 #6144, 0.44 #11679, 0.42 #11990) >> Best rule #10761 for best value: >> intensional similarity = 12 >> extensional distance = 72 >> proper extension: 01pbxb; 01wl38s; 0144l1; 09prnq; 0qdyf; 016ntp; 01nn6c; 01wbz9; 06gd4; 0180w8; ... >> query: (?x10106, 0xhtw) <- artist(?x441, ?x10106), artists(?x10676, ?x10106), artists(?x10471, ?x10106), artists(?x2249, ?x10106), artists(?x10471, ?x3657), role(?x10106, ?x3991), parent_genre(?x10676, ?x5934), ?x3657 = 01w8n89, artists(?x2249, ?x8708), artists(?x2249, ?x1955), ?x8708 = 01vn0t_, ?x1955 = 0285c >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #6743 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 13 *> proper extension: 04rcr; 0mgcr; *> query: (?x10106, ?x5934) <- artist(?x441, ?x10106), artists(?x10676, ?x10106), group(?x645, ?x10106), ?x645 = 028tv0, parent_genre(?x10676, ?x13938), parent_genre(?x10676, ?x5934), ?x13938 = 04f73rc *> conf = 0.35 ranks of expected_values: 25, 30, 34 EVAL 016lj_ artists! 04f73rc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 120.000 48.000 0.878 http://example.org/music/genre/artists EVAL 016lj_ artists! 02yw26 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 120.000 48.000 0.878 http://example.org/music/genre/artists EVAL 016lj_ artists! 0296y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 120.000 48.000 0.878 http://example.org/music/genre/artists #15370-0170z3 PRED entity: 0170z3 PRED relation: films! PRED expected values: 0d1w9 => 84 concepts (39 used for prediction) PRED predicted values (max 10 best out of 42): 01cgz (0.57 #19, 0.03 #485, 0.02 #2187), 018jz (0.14 #41), 05489 (0.08 #206, 0.05 #978, 0.05 #824), 081pw (0.08 #2171, 0.07 #777, 0.07 #2483), 0fx2s (0.06 #537, 0.05 #845, 0.05 #2239), 0fzyg (0.06 #980, 0.05 #2532, 0.05 #826), 07c52 (0.05 #486, 0.04 #794, 0.04 #948), 04jjy (0.05 #7, 0.03 #1244, 0.03 #473), 0nbjq (0.05 #25), 02_5h (0.05 #12) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01cgz; >> query: (?x54, 01cgz) <- films(?x4833, ?x54), sports(?x391, ?x4833), olympics(?x4833, ?x584) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1273 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 357 *> proper extension: 01br2w; 02_fm2; 0bvn25; 0dq626; 0czyxs; 02_1sj; 01hp5; 061681; 03ckwzc; 0963mq; ... *> query: (?x54, 0d1w9) <- film_crew_role(?x54, ?x1284), films(?x4833, ?x54), genre(?x54, ?x53) *> conf = 0.03 ranks of expected_values: 24 EVAL 0170z3 films! 0d1w9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 84.000 39.000 0.571 http://example.org/film/film_subject/films #15369-092vkg PRED entity: 092vkg PRED relation: award PRED expected values: 02x4w6g => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 192): 0gqyl (0.29 #2949, 0.28 #3855, 0.27 #14274), 09sb52 (0.29 #2949, 0.28 #3855, 0.27 #14274), 03hkv_r (0.29 #2949, 0.28 #3855, 0.27 #14274), 02n9nmz (0.29 #2949, 0.28 #3855, 0.27 #14274), 0gs9p (0.29 #2949, 0.28 #3855, 0.27 #14274), 0gq9h (0.29 #2949, 0.28 #3855, 0.27 #14274), 02z0dfh (0.29 #2949, 0.28 #3855, 0.27 #14274), 02ppm4q (0.29 #2949, 0.28 #3855, 0.27 #14274), 099t8j (0.29 #2949, 0.28 #3855, 0.27 #14274), 09td7p (0.29 #2949, 0.28 #3855, 0.27 #14274) >> Best rule #2949 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 0524b41; 04f6hhm; 04mx8h4; 02py9yf; >> query: (?x1064, ?x277) <- nominated_for(?x277, ?x1064), category(?x1064, ?x134), honored_for(?x1112, ?x1064) >> conf = 0.29 => this is the best rule for 17 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 15 EVAL 092vkg award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 121.000 121.000 0.289 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #15368-0d90m PRED entity: 0d90m PRED relation: executive_produced_by PRED expected values: 021lby => 84 concepts (59 used for prediction) PRED predicted values (max 10 best out of 87): 05hj_k (0.21 #349, 0.20 #600, 0.09 #97), 06q8hf (0.14 #418, 0.13 #669, 0.09 #1170), 07nznf (0.11 #2007, 0.03 #4011, 0.02 #3760), 0glyyw (0.09 #187, 0.04 #3194, 0.03 #4700), 03c9pqt (0.07 #999, 0.07 #497, 0.07 #748), 02465 (0.07 #477, 0.07 #728), 06pj8 (0.07 #3312, 0.07 #1810, 0.06 #2061), 0tc7 (0.07 #563), 0343h (0.04 #2298, 0.04 #2048, 0.04 #3299), 02q_cc (0.04 #3034, 0.04 #2284, 0.03 #3285) >> Best rule #349 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 017gl1; 017gm7; 04w7rn; 065dc4; 017jd9; 0170xl; >> query: (?x97, 05hj_k) <- genre(?x97, ?x225), film(?x5661, ?x97), ?x5661 = 03ym1, film(?x574, ?x97) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2070 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 77 *> proper extension: 0353tm; *> query: (?x97, 021lby) <- featured_film_locations(?x97, ?x739), film_release_region(?x97, ?x94), genre(?x97, ?x225), film_distribution_medium(?x97, ?x81) *> conf = 0.01 ranks of expected_values: 84 EVAL 0d90m executive_produced_by 021lby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 84.000 59.000 0.214 http://example.org/film/film/executive_produced_by #15367-07zl6m PRED entity: 07zl6m PRED relation: service_language PRED expected values: 064_8sq 02bv9 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 100): 064_8sq (0.67 #98, 0.67 #91, 0.66 #475), 02bv9 (0.67 #91, 0.53 #146, 0.33 #66), 02bjrlw (0.50 #128, 0.50 #73, 0.33 #37), 05zjd (0.50 #82, 0.33 #137, 0.33 #119), 01r2l (0.33 #27, 0.25 #282, 0.25 #173), 06b_j (0.33 #26, 0.25 #80, 0.17 #219), 0jzc (0.33 #24, 0.17 #219, 0.12 #170), 03_9r (0.25 #75, 0.24 #328, 0.17 #130), 02hwhyv (0.25 #85, 0.17 #219, 0.17 #140), 01gp_d (0.25 #86, 0.17 #141, 0.17 #123) >> Best rule #98 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 04fv0k; >> query: (?x13954, 064_8sq) <- industry(?x13954, ?x245), service_location(?x13954, ?x789), service_location(?x13954, ?x279), service_location(?x13954, ?x94), ?x789 = 0f8l9c, industry(?x14014, ?x245), industry(?x13564, ?x245), industry(?x4683, ?x245), ?x279 = 0d060g, organization(?x4682, ?x4683), citytown(?x14014, ?x5076), place_founded(?x13564, ?x3269), country(?x54, ?x94), film_release_region(?x303, ?x94), contains(?x94, ?x95), nationality(?x51, ?x94), country_of_origin(?x50, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07zl6m service_language 02bv9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.667 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 07zl6m service_language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.667 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #15366-01bv8b PRED entity: 01bv8b PRED relation: genre PRED expected values: 01z4y => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 78): 01z4y (0.85 #184, 0.81 #101, 0.80 #267), 07s9rl0 (0.62 #333, 0.55 #667, 0.55 #1663), 0hcr (0.39 #1266, 0.36 #1931, 0.22 #3097), 01t_vv (0.34 #533, 0.29 #34, 0.28 #366), 06n90 (0.31 #1925, 0.19 #3091, 0.18 #1260), 06nbt (0.23 #270, 0.20 #520, 0.15 #1268), 01hmnh (0.22 #1928, 0.15 #1263, 0.15 #3094), 0vgkd (0.21 #10, 0.19 #93, 0.17 #342), 03k9fj (0.20 #1923, 0.17 #1673, 0.17 #3089), 01htzx (0.20 #1679, 0.19 #1015, 0.19 #1929) >> Best rule #184 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 02nf2c; >> query: (?x2710, 01z4y) <- award(?x2710, ?x2603), award(?x1057, ?x2603), nominated_for(?x2603, ?x10618), ?x10618 = 01fszq >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bv8b genre 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.848 http://example.org/tv/tv_program/genre #15365-0gnjh PRED entity: 0gnjh PRED relation: film! PRED expected values: 0h7dd => 64 concepts (43 used for prediction) PRED predicted values (max 10 best out of 571): 0523v5y (0.44 #72940, 0.42 #89614, 0.42 #66684), 05233hy (0.44 #72940, 0.42 #89614, 0.42 #66684), 0k9ctht (0.44 #72940, 0.42 #89614, 0.42 #66684), 0blpnz (0.44 #72940, 0.42 #89614, 0.42 #66684), 0cyhq (0.42 #89614, 0.42 #66684, 0.42 #72939), 044qx (0.08 #733, 0.07 #2818, 0.03 #9069), 0j_c (0.08 #410, 0.06 #4579, 0.05 #2495), 04__f (0.06 #1384, 0.05 #3469, 0.03 #7637), 0chsq (0.06 #79, 0.05 #2164), 017lqp (0.06 #1612, 0.04 #3697, 0.03 #9948) >> Best rule #72940 for best value: >> intensional similarity = 3 >> extensional distance = 1302 >> proper extension: 01h72l; >> query: (?x6604, ?x5537) <- nominated_for(?x5537, ?x6604), genre(?x6604, ?x239), award_nominee(?x5537, ?x2465) >> conf = 0.44 => this is the best rule for 4 predicted values *> Best rule #1069 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 0fxmbn; *> query: (?x6604, 0h7dd) <- film_art_direction_by(?x6604, ?x8402), film_release_distribution_medium(?x6604, ?x81), music(?x6604, ?x3811) *> conf = 0.02 ranks of expected_values: 243 EVAL 0gnjh film! 0h7dd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 64.000 43.000 0.439 http://example.org/film/actor/film./film/performance/film #15364-02wt0 PRED entity: 02wt0 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #53, 0.87 #17, 0.86 #93), 01g63y (0.12 #6, 0.07 #18, 0.05 #94), 0jgjn (0.07 #96, 0.04 #100, 0.04 #168), 01bl8s (0.01 #199) >> Best rule #53 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 01c1nm; 01dg3s; >> query: (?x2290, 04ztj) <- contains(?x10150, ?x2290), administrative_parent(?x2290, ?x551), location_of_ceremony(?x11835, ?x2290) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wt0 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.885 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #15363-0ytc PRED entity: 0ytc PRED relation: colors PRED expected values: 06fvc => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 17): 01g5v (0.88 #938, 0.58 #803, 0.53 #251), 083jv (0.64 #477, 0.64 #668, 0.64 #1393), 06fvc (0.51 #802, 0.41 #1184, 0.39 #1241), 019sc (0.33 #7, 0.31 #444, 0.30 #825), 02rnmb (0.25 #32, 0.20 #70, 0.18 #533), 09ggk (0.20 #53, 0.18 #533, 0.15 #1700), 01l849 (0.15 #1700, 0.15 #1699, 0.15 #1698), 038hg (0.15 #1700, 0.15 #1699, 0.15 #1698), 036k5h (0.15 #1700, 0.15 #1699, 0.15 #1698), 0jc_p (0.15 #1700, 0.15 #1699, 0.15 #1698) >> Best rule #938 for best value: >> intensional similarity = 7 >> extensional distance = 119 >> proper extension: 03d555l; >> query: (?x3823, 01g5v) <- colors(?x3823, ?x3621), colors(?x12245, ?x3621), colors(?x7667, ?x3621), colors(?x3813, ?x3621), ?x3813 = 07vfj, ?x12245 = 03fn5s, ?x7667 = 023zd7 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #802 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 98 *> proper extension: 05jx2d; 03y_f8; 0g701n; 04k3r_; 01n_2f; 037mp6; 04zw9hs; 037mjv; 01xn5th; 01xn6mc; ... *> query: (?x3823, 06fvc) <- sport(?x3823, ?x471), position(?x3823, ?x530), ?x471 = 02vx4, ?x530 = 02_j1w, colors(?x3823, ?x3621), colors(?x3363, ?x3621), ?x3363 = 01fjz9 *> conf = 0.51 ranks of expected_values: 3 EVAL 0ytc colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 107.000 107.000 0.876 http://example.org/sports/sports_team/colors #15362-02pv_d PRED entity: 02pv_d PRED relation: award_winner! PRED expected values: 02w_6xj => 121 concepts (100 used for prediction) PRED predicted values (max 10 best out of 278): 0gs9p (0.38 #2629, 0.17 #927, 0.14 #3907), 019f4v (0.37 #15313, 0.36 #39555, 0.36 #39554), 0gq9h (0.37 #15313, 0.36 #39555, 0.36 #39554), 040njc (0.37 #15313, 0.36 #39555, 0.36 #39554), 02n9nmz (0.37 #15313, 0.36 #39555, 0.36 #39554), 02rdyk7 (0.37 #15313, 0.36 #39555, 0.36 #39554), 0f_nbyh (0.37 #15313, 0.36 #39555, 0.36 #39554), 05b1610 (0.37 #15313, 0.36 #39555, 0.36 #39554), 09cm54 (0.25 #94, 0.22 #519, 0.06 #1796), 02w9sd7 (0.25 #162, 0.22 #587, 0.06 #1864) >> Best rule #2629 for best value: >> intensional similarity = 2 >> extensional distance = 95 >> proper extension: 09p06; >> query: (?x8070, 0gs9p) <- award(?x8070, ?x1107), ?x1107 = 019f4v >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2786 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 95 *> proper extension: 09p06; *> query: (?x8070, 02w_6xj) <- award(?x8070, ?x1107), ?x1107 = 019f4v *> conf = 0.23 ranks of expected_values: 12 EVAL 02pv_d award_winner! 02w_6xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 121.000 100.000 0.381 http://example.org/award/award_category/winners./award/award_honor/award_winner #15361-032clf PRED entity: 032clf PRED relation: film_release_region PRED expected values: 035qy 02vzc 03h64 07twz => 129 concepts (116 used for prediction) PRED predicted values (max 10 best out of 150): 0345h (0.82 #6086, 0.78 #2581, 0.76 #3058), 02vzc (0.82 #3079, 0.81 #3397, 0.81 #7866), 03h64 (0.82 #3096, 0.81 #6124, 0.80 #3414), 035qy (0.81 #6088, 0.76 #3060, 0.71 #2583), 0154j (0.78 #6058, 0.73 #3030, 0.67 #7817), 05qhw (0.77 #6067, 0.71 #3039, 0.67 #2562), 06bnz (0.75 #6100, 0.69 #3072, 0.64 #2595), 0b90_r (0.73 #6057, 0.63 #3029, 0.62 #7816), 05b4w (0.72 #6121, 0.69 #2616, 0.69 #3093), 05v8c (0.67 #2564, 0.55 #3041, 0.54 #6069) >> Best rule #6086 for best value: >> intensional similarity = 8 >> extensional distance = 158 >> proper extension: 014lc_; 0b76d_m; 0ds3t5x; 0dckvs; 0djb3vw; 05p1tzf; 0c40vxk; 0gx9rvq; 04969y; 017gl1; ... >> query: (?x7379, 0345h) <- film_release_region(?x7379, ?x789), film_release_region(?x7379, ?x304), film_release_region(?x7379, ?x279), film_release_region(?x7379, ?x252), ?x789 = 0f8l9c, ?x304 = 0d0vqn, ?x252 = 03_3d, ?x279 = 0d060g >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #3079 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 49 *> proper extension: 040rmy; 07l50vn; 0gh6j94; 0hz6mv2; *> query: (?x7379, 02vzc) <- film_format(?x7379, ?x6392), film_release_region(?x7379, ?x789), film_release_region(?x7379, ?x390), film_release_region(?x7379, ?x252), ?x252 = 03_3d, ?x789 = 0f8l9c, ?x390 = 0chghy *> conf = 0.82 ranks of expected_values: 2, 3, 4, 38 EVAL 032clf film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 129.000 116.000 0.825 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 032clf film_release_region 03h64 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 116.000 0.825 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 032clf film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 116.000 0.825 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 032clf film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 116.000 0.825 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15360-0htlr PRED entity: 0htlr PRED relation: people! PRED expected values: 0xnvg => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 43): 0xnvg (0.42 #88, 0.09 #240, 0.09 #924), 041rx (0.24 #1603, 0.24 #1144, 0.24 #1831), 033tf_ (0.17 #234, 0.15 #158, 0.13 #1147), 0222qb (0.17 #119, 0.03 #1184, 0.03 #955), 0x67 (0.16 #3816, 0.15 #3588, 0.14 #2752), 02w7gg (0.13 #914, 0.12 #1830, 0.12 #1602), 07hwkr (0.10 #11, 0.10 #543, 0.08 #619), 048z7l (0.10 #39, 0.04 #267, 0.04 #647), 06v41q (0.08 #104, 0.03 #180, 0.03 #256), 07mqps (0.08 #94, 0.02 #170, 0.02 #2761) >> Best rule #88 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 04cw0n4; >> query: (?x914, 0xnvg) <- nationality(?x914, ?x205), nationality(?x914, ?x94), ?x94 = 09c7w0, ?x205 = 03rjj >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0htlr people! 0xnvg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.417 http://example.org/people/ethnicity/people #15359-0psxp PRED entity: 0psxp PRED relation: location! PRED expected values: 01x4sb 04__f => 174 concepts (121 used for prediction) PRED predicted values (max 10 best out of 2127): 05drr9 (0.59 #82926, 0.49 #37683, 0.48 #70358), 01x4sb (0.59 #82926, 0.49 #37683, 0.48 #70358), 01wqflx (0.49 #37683, 0.48 #70358, 0.48 #32657), 02v60l (0.32 #45221), 02wlk (0.20 #4922, 0.11 #108062, 0.08 #9946), 06pcz0 (0.20 #4765, 0.11 #108062, 0.08 #9789), 025mb_ (0.20 #4359, 0.11 #108062, 0.08 #9383), 02s529 (0.20 #4883, 0.11 #108062, 0.08 #9907), 012gq6 (0.20 #3184, 0.11 #108062, 0.08 #8208), 02p21g (0.20 #2786, 0.11 #108062, 0.08 #7810) >> Best rule #82926 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 0nbfm; 011wdm; >> query: (?x5867, ?x5620) <- contains(?x3818, ?x5867), place_of_birth(?x5620, ?x5867), citytown(?x4410, ?x5867), participant(?x8146, ?x5620) >> conf = 0.59 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 827 EVAL 0psxp location! 04__f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 174.000 121.000 0.587 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0psxp location! 01x4sb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 174.000 121.000 0.587 http://example.org/people/person/places_lived./people/place_lived/location #15358-0by1wkq PRED entity: 0by1wkq PRED relation: film_release_region PRED expected values: 0jgd 077qn => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 133): 09c7w0 (0.92 #3685, 0.82 #138, 0.82 #1638), 0jgd (0.85 #140, 0.83 #276, 0.82 #549), 016wzw (0.58 #321, 0.57 #185, 0.52 #594), 06qd3 (0.53 #437, 0.51 #301, 0.48 #574), 01ls2 (0.52 #282, 0.51 #146, 0.49 #555), 05qx1 (0.50 #304, 0.43 #168, 0.39 #440), 047lj (0.48 #281, 0.38 #145, 0.34 #554), 06c1y (0.48 #170, 0.47 #306, 0.46 #579), 09pmkv (0.45 #292, 0.43 #156, 0.37 #565), 0h7x (0.45 #298, 0.40 #1662, 0.40 #162) >> Best rule #3685 for best value: >> intensional similarity = 3 >> extensional distance = 1328 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 0g56t9t; 09sh8k; 0m313; 034qmv; 0g22z; ... >> query: (?x1927, 09c7w0) <- film_release_region(?x1927, ?x789), form_of_government(?x789, ?x4763), olympics(?x789, ?x391) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #140 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 115 *> proper extension: 0gkz15s; 0gj8t_b; 0jqn5; 0bq8tmw; 0bh8yn3; 06wbm8q; 0kv238; 0gffmn8; 05zlld0; 03q0r1; ... *> query: (?x1927, 0jgd) <- film_release_region(?x1927, ?x2316), film_release_region(?x1927, ?x1023), film_release_region(?x1927, ?x789), ?x789 = 0f8l9c, ?x1023 = 0ctw_b, ?x2316 = 06t2t *> conf = 0.85 ranks of expected_values: 2, 13 EVAL 0by1wkq film_release_region 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 51.000 51.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0by1wkq film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 51.000 51.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15357-04411 PRED entity: 04411 PRED relation: organization PRED expected values: 01prf3 => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 24): 07wrz (0.80 #437, 0.80 #436, 0.12 #1907), 06thjt (0.80 #436, 0.12 #1907), 034h1h (0.80 #422, 0.11 #1893), 02_l9 (0.33 #112, 0.22 #185, 0.16 #574), 02hcxm (0.25 #34, 0.22 #228, 0.17 #131), 01prf3 (0.17 #116, 0.11 #263, 0.11 #189), 07t65 (0.06 #1884, 0.01 #4915), 02vk52z (0.06 #1883, 0.01 #4914), 02jxk (0.06 #1886), 03lb_v (0.06 #483, 0.05 #1240, 0.04 #727) >> Best rule #437 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 08815; 06pwq; 01w3v; 07szy; 09kvv; 01w5m; 03ksy; 07tds; 02zd460; 01p5xy; ... >> query: (?x920, ?x2313) <- organizations_founded(?x920, ?x10478), organizations_founded(?x920, ?x2313), citytown(?x2313, ?x1860), country(?x10478, ?x94), currency(?x2313, ?x170) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #116 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 09bg4l; 0d3k14; 019fz; *> query: (?x920, 01prf3) <- influenced_by(?x920, ?x3941), place_of_death(?x920, ?x739), type_of_union(?x920, ?x566), organizations_founded(?x920, ?x2313) *> conf = 0.17 ranks of expected_values: 6 EVAL 04411 organization 01prf3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 203.000 203.000 0.800 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #15356-01l1ls PRED entity: 01l1ls PRED relation: nationality PRED expected values: 09c7w0 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 28): 09c7w0 (0.84 #2004, 0.83 #1103, 0.83 #1203), 0mskq (0.33 #8439), 07b_l (0.33 #8439), 07ssc (0.11 #2219, 0.10 #1318, 0.09 #6542), 02jx1 (0.10 #6560, 0.10 #6157, 0.09 #5554), 03rk0 (0.06 #8283, 0.06 #7579, 0.06 #8183), 0d060g (0.06 #3720, 0.06 #708, 0.05 #2311), 0345h (0.05 #531, 0.03 #1434, 0.02 #2235), 0f2w0 (0.04 #2104, 0.03 #1303, 0.01 #3814), 03gj2 (0.03 #1429, 0.02 #526, 0.01 #928) >> Best rule #2004 for best value: >> intensional similarity = 3 >> extensional distance = 156 >> proper extension: 04n7njg; 07s6tbm; 0162c8; 01my_c; 0564mx; 08qmfm; 07f7jp; >> query: (?x9583, 09c7w0) <- profession(?x9583, ?x353), producer_type(?x9583, ?x632), place_of_birth(?x9583, ?x1719) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l1ls nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.835 http://example.org/people/person/nationality #15355-0233bn PRED entity: 0233bn PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 25): 0ch6mp2 (0.80 #81, 0.76 #531, 0.74 #568), 09vw2b7 (0.64 #567, 0.62 #530, 0.62 #977), 0dxtw (0.44 #48, 0.39 #85, 0.37 #346), 01vx2h (0.33 #49, 0.31 #347, 0.30 #983), 02ynfr (0.33 #17, 0.20 #91, 0.18 #352), 01pvkk (0.26 #574, 0.26 #537, 0.26 #87), 02rh1dz (0.20 #84, 0.12 #158, 0.11 #345), 0d2b38 (0.13 #101, 0.10 #362, 0.09 #175), 0215hd (0.12 #581, 0.10 #991, 0.10 #355), 089g0h (0.10 #582, 0.10 #356, 0.09 #545) >> Best rule #81 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 03t97y; >> query: (?x7502, 0ch6mp2) <- production_companies(?x7502, ?x12671), titles(?x812, ?x7502), film_crew_role(?x7502, ?x137), prequel(?x4604, ?x7502) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0233bn film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.803 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15354-025s7j4 PRED entity: 025s7j4 PRED relation: nutrient! PRED expected values: 0dj75 04zpv => 58 concepts (53 used for prediction) PRED predicted values (max 10 best out of 70): 04zpv (0.94 #333, 0.93 #294, 0.93 #286), 0dj75 (0.89 #88, 0.88 #130, 0.88 #105), 0f4kp (0.03 #28, 0.03 #27, 0.03 #37), 0fzjh (0.03 #28, 0.03 #27, 0.03 #37), 025rw19 (0.03 #28, 0.03 #27, 0.03 #37), 0q01m (0.03 #28, 0.03 #27, 0.03 #37), 025sf0_ (0.03 #28, 0.03 #27, 0.03 #37), 02kc008 (0.03 #28, 0.03 #27, 0.03 #37), 025tkqy (0.03 #28, 0.03 #27, 0.03 #37), 0h1tz (0.03 #28, 0.03 #27, 0.03 #37) >> Best rule #333 for best value: >> intensional similarity = 106 >> extensional distance = 34 >> proper extension: 075pwf; >> query: (?x5549, 04zpv) <- nutrient(?x10612, ?x5549), nutrient(?x6191, ?x5549), nutrient(?x6159, ?x5549), nutrient(?x6032, ?x5549), nutrient(?x5373, ?x5549), nutrient(?x4068, ?x5549), nutrient(?x3900, ?x5549), nutrient(?x3468, ?x5549), nutrient(?x6191, ?x13944), nutrient(?x6191, ?x13498), nutrient(?x6191, ?x13126), nutrient(?x6191, ?x12902), nutrient(?x6191, ?x12454), nutrient(?x6191, ?x12083), nutrient(?x6191, ?x11758), nutrient(?x6191, ?x11592), nutrient(?x6191, ?x11409), nutrient(?x6191, ?x11270), nutrient(?x6191, ?x10891), nutrient(?x6191, ?x10709), nutrient(?x6191, ?x10098), nutrient(?x6191, ?x9949), nutrient(?x6191, ?x9840), nutrient(?x6191, ?x9733), nutrient(?x6191, ?x9619), nutrient(?x6191, ?x9490), nutrient(?x6191, ?x9436), nutrient(?x6191, ?x9426), nutrient(?x6191, ?x8487), nutrient(?x6191, ?x8442), nutrient(?x6191, ?x7720), nutrient(?x6191, ?x7652), nutrient(?x6191, ?x7364), nutrient(?x6191, ?x7362), nutrient(?x6191, ?x7219), nutrient(?x6191, ?x7135), nutrient(?x6191, ?x6586), nutrient(?x6191, ?x6286), nutrient(?x6191, ?x6192), nutrient(?x6191, ?x6160), nutrient(?x6191, ?x6033), nutrient(?x6191, ?x5526), nutrient(?x6191, ?x5451), nutrient(?x6191, ?x5374), nutrient(?x6191, ?x5010), nutrient(?x6191, ?x4069), nutrient(?x6191, ?x3469), nutrient(?x6191, ?x3203), nutrient(?x6191, ?x1258), ?x5526 = 09pbb, ?x7364 = 09gvd, ?x7652 = 025s0s0, ?x5374 = 025s0zp, ?x12902 = 0fzjh, ?x12454 = 025rw19, ?x9840 = 02p0tjr, ?x11270 = 02kc008, ?x6159 = 033cnk, ?x4069 = 0hqw8p_, ?x9426 = 0h1yy, ?x9436 = 025sqz8, nutrient(?x4068, ?x9915), nutrient(?x4068, ?x7894), nutrient(?x4068, ?x7431), nutrient(?x4068, ?x6026), ?x7431 = 09gwd, ?x8442 = 02kcv4x, ?x6586 = 05gh50, ?x6160 = 041r51, ?x7219 = 0h1vg, ?x7720 = 025s7x6, ?x6033 = 04zjxcz, ?x13498 = 07q0m, ?x7894 = 0f4hc, ?x10709 = 0h1sz, ?x9949 = 02kd0rh, ?x12083 = 01n78x, ?x3468 = 0cxn2, ?x9733 = 0h1tz, ?x10098 = 0h1_c, ?x6032 = 01nkt, ?x6286 = 02y_3rf, ?x6026 = 025sf8g, ?x3469 = 0h1zw, nutrient(?x3900, ?x3901), ?x9490 = 0h1sg, ?x3901 = 0466p20, ?x1258 = 0h1wg, ?x5010 = 0h1vz, ?x11758 = 0q01m, ?x9915 = 025tkqy, ?x7135 = 025rsfk, ?x5373 = 0971v, ?x11592 = 025sf0_, ?x7362 = 02kc5rj, ?x8487 = 014yzm, ?x13126 = 02kc_w5, ?x13944 = 0f4kp, nutrient(?x10612, ?x12336), ?x12336 = 0f4l5, ?x10891 = 0g5gq, ?x5451 = 05wvs, ?x6192 = 06jry, ?x3203 = 04kl74p, ?x11409 = 0h1yf, ?x9619 = 0h1tg >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 025s7j4 nutrient! 04zpv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 53.000 0.944 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 025s7j4 nutrient! 0dj75 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 53.000 0.944 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #15353-0h6dy PRED entity: 0h6dy PRED relation: legislative_sessions PRED expected values: 01gvxh => 7 concepts (7 used for prediction) PRED predicted values (max 10 best out of 53): 01gvxh (0.79 #269, 0.56 #107, 0.50 #161), 01gt99 (0.33 #267), 01grmk (0.33 #266), 01gtdd (0.33 #265), 01gssz (0.33 #264), 01gsvb (0.33 #261), 01h7xx (0.33 #260), 01grp0 (0.33 #259), 01grr2 (0.33 #258), 04h1rz (0.33 #256) >> Best rule #269 for best value: >> intensional similarity = 93 >> extensional distance = 4 >> proper extension: 0b3wk; 07t58; >> query: (?x12796, ?x8777) <- legislative_sessions(?x12796, ?x11190), legislative_sessions(?x12796, ?x11189), legislative_sessions(?x12796, ?x3473), district_represented(?x11190, ?x11542), district_represented(?x11190, ?x10063), district_represented(?x11190, ?x9311), district_represented(?x11190, ?x7468), district_represented(?x11190, ?x3824), district_represented(?x11190, ?x3474), district_represented(?x11190, ?x1905), first_level_division_of(?x3474, ?x279), adjoins(?x11542, ?x9370), adjoins(?x11542, ?x7058), adjoins(?x11542, ?x6842), state_province_region(?x12356, ?x11542), contains(?x11542, ?x12135), contains(?x3474, ?x5678), adjoins(?x13765, ?x11542), ?x7058 = 050ks, category(?x12796, ?x134), legislative_sessions(?x11189, ?x8777), contains(?x12971, ?x3474), district_represented(?x11189, ?x14386), district_represented(?x11189, ?x14129), district_represented(?x11189, ?x12125), district_represented(?x11189, ?x10544), state(?x6224, ?x3474), ?x6842 = 0694j, taxonomy(?x11542, ?x939), ?x939 = 04n6k, capital(?x7468, ?x8916), state_province_region(?x1914, ?x7468), contains(?x7468, ?x1036), adjoins(?x7468, ?x953), partially_contains(?x11542, ?x10954), ?x1914 = 03xsby, legislative_sessions(?x3099, ?x8777), location(?x8720, ?x7468), contains(?x10063, ?x10718), adjoins(?x7387, ?x1905), adjoins(?x1274, ?x1905), contains(?x1905, ?x2243), contains(?x1905, ?x1196), capital(?x3824, ?x1275), featured_film_locations(?x5313, ?x10063), contains(?x9311, ?x9570), ?x1274 = 04ykg, currency(?x1905, ?x2244), vacationer(?x7468, ?x4884), contains(?x3824, ?x3825), location(?x10607, ?x9311), jurisdiction_of_office(?x14293, ?x14386), state(?x12755, ?x1905), time_zones(?x1905, ?x2674), adjoins(?x4198, ?x3824), ?x134 = 08mbj5d, state_province_region(?x12737, ?x3824), religion(?x1905, ?x7422), religion(?x1905, ?x1985), religion(?x1905, ?x962), religion(?x1905, ?x492), religion(?x1905, ?x109), partially_contains(?x7468, ?x6195), ?x492 = 0flw86, ?x7422 = 092bf5, ?x6195 = 0k3nk, capital(?x9311, ?x1637), ?x962 = 05sfs, ?x10954 = 0lm0n, time_zones(?x7468, ?x2950), featured_film_locations(?x1721, ?x7468), ?x109 = 01lp8, state_province_region(?x11632, ?x10063), contains(?x12125, ?x8823), adjoins(?x12125, ?x12854), ?x4198 = 05fky, jurisdiction_of_office(?x900, ?x12125), ?x900 = 0fkvn, country(?x12125, ?x390), location(?x927, ?x12125), ?x1985 = 0c8wxp, ?x2950 = 02lcqs, first_level_division_of(?x14129, ?x279), adjoins(?x1905, ?x7387), legislative_sessions(?x8777, ?x11190), district_represented(?x3473, ?x3474), state_province_region(?x2243, ?x1905), district_represented(?x8777, ?x14386), district_represented(?x3473, ?x9370), adjoins(?x10063, ?x9311), district_represented(?x8777, ?x7468), administrative_division(?x10718, ?x10063), adjoins(?x10544, ?x10063) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h6dy legislative_sessions 01gvxh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 7.000 7.000 0.786 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #15352-048_p PRED entity: 048_p PRED relation: gender PRED expected values: 05zppz => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #11, 0.80 #7, 0.80 #65), 02zsn (0.47 #172, 0.47 #169, 0.46 #185) >> Best rule #11 for best value: >> intensional similarity = 5 >> extensional distance = 140 >> proper extension: 07nznf; 0415svh; 05fnl9; 04y8r; 0d7hg4; 0h53p1; 06chf; 078jt5; 03m_k0; 07lwsz; ... >> query: (?x5506, 05zppz) <- award(?x5506, ?x10678), award(?x5034, ?x10678), award(?x3338, ?x10678), award_winner(?x1375, ?x3338), ?x5034 = 03772 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 048_p gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.838 http://example.org/people/person/gender #15351-06mtq PRED entity: 06mtq PRED relation: adjoins! PRED expected values: 05ff6 => 203 concepts (103 used for prediction) PRED predicted values (max 10 best out of 547): 06mtq (0.33 #2257, 0.29 #3818, 0.24 #79777), 07_f2 (0.33 #1114, 0.29 #2677, 0.10 #8933), 05ff6 (0.29 #3822, 0.24 #79777, 0.24 #52398), 0d060g (0.29 #2355, 0.18 #4698, 0.17 #792), 0vh3 (0.24 #52398, 0.23 #43798, 0.22 #48487), 07z5n (0.20 #4028, 0.09 #4810, 0.06 #5592), 0b90_r (0.18 #4692, 0.14 #2349, 0.11 #5474), 0694j (0.17 #1085, 0.14 #2648, 0.11 #25323), 059rby (0.17 #800, 0.14 #2363, 0.11 #6270), 059f4 (0.17 #816, 0.14 #2379, 0.10 #8635) >> Best rule #2257 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 07cfx; >> query: (?x12854, 06mtq) <- country(?x12854, ?x390), jurisdiction_of_office(?x10118, ?x12854), capital(?x12854, ?x8963), ?x10118 = 0p5vf >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3822 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0chgzm; *> query: (?x12854, 05ff6) <- country(?x12854, ?x390), ?x390 = 0chghy, contains(?x390, ?x12854) *> conf = 0.29 ranks of expected_values: 3 EVAL 06mtq adjoins! 05ff6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 203.000 103.000 0.333 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #15350-0pd64 PRED entity: 0pd64 PRED relation: honored_for! PRED expected values: 0bzknt => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 102): 0bzjvm (0.09 #96, 0.04 #462, 0.03 #584), 03tn9w (0.06 #80, 0.04 #446, 0.03 #568), 0bzmt8 (0.06 #84, 0.02 #450, 0.02 #572), 0dthsy (0.06 #56, 0.02 #422, 0.02 #544), 0hr6lkl (0.05 #988, 0.05 #1720, 0.04 #1842), 0d__c3 (0.04 #597, 0.04 #475, 0.03 #109), 0gmdkyy (0.04 #1000, 0.03 #634, 0.03 #1732), 0n8_m93 (0.04 #713, 0.03 #1567, 0.03 #1079), 03gwpw2 (0.04 #249, 0.02 #3909, 0.02 #4031), 0275n3y (0.04 #1650, 0.03 #2260, 0.03 #1284) >> Best rule #96 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0y_9q; >> query: (?x7711, 0bzjvm) <- list(?x7711, ?x3004), genre(?x7711, ?x53), film_crew_role(?x7711, ?x137), award_winner(?x7711, ?x1431) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #435 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 81 *> proper extension: 07bz5; *> query: (?x7711, 0bzknt) <- list(?x7711, ?x3004), award(?x7711, ?x591), award_winner(?x7711, ?x1431) *> conf = 0.01 ranks of expected_values: 98 EVAL 0pd64 honored_for! 0bzknt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 92.000 92.000 0.086 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15349-016zfm PRED entity: 016zfm PRED relation: actor PRED expected values: 01p47r => 96 concepts (73 used for prediction) PRED predicted values (max 10 best out of 875): 02ndbd (0.28 #1859, 0.19 #13012, 0.19 #17664), 0gz5hs (0.22 #151, 0.02 #28044, 0.02 #30832), 02gf_l (0.12 #6146, 0.06 #10793, 0.04 #9863), 021yw7 (0.12 #3082, 0.03 #17028, 0.03 #18889), 02r_d4 (0.11 #1910, 0.11 #51, 0.06 #3768), 01pcz9 (0.11 #485, 0.06 #4202, 0.06 #5131), 05xpms (0.11 #2564, 0.06 #4422, 0.06 #5351), 0pyww (0.11 #1323, 0.06 #4111, 0.06 #5040), 02clgg (0.11 #2514, 0.06 #4372, 0.06 #7160), 0pz7h (0.11 #1003, 0.06 #4720, 0.06 #7509) >> Best rule #1859 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 072kp; 08jgk1; 03ln8b; 02hct1; 0d68qy; 01s81; 030cx; 05f4vxd; 0b005; 039cq4; ... >> query: (?x6248, ?x856) <- program_creator(?x6248, ?x856), nominated_for(?x7510, ?x6248), genre(?x6248, ?x258), ?x7510 = 027gs1_ >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016zfm actor 01p47r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 73.000 0.280 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #15348-02h9_l PRED entity: 02h9_l PRED relation: nationality PRED expected values: 09c7w0 => 119 concepts (115 used for prediction) PRED predicted values (max 10 best out of 92): 09c7w0 (0.84 #2604, 0.81 #901, 0.80 #2403), 02jx1 (0.41 #1033, 0.25 #2503, 0.15 #4439), 03spz (0.25 #2503, 0.10 #667, 0.01 #11521), 0d060g (0.25 #2503, 0.10 #1207, 0.07 #2510), 07ssc (0.24 #1015, 0.10 #3420, 0.09 #7928), 0ctw_b (0.12 #427, 0.03 #1528, 0.02 #1828), 03rk0 (0.10 #6857, 0.08 #6056, 0.08 #6156), 01ls2 (0.06 #811, 0.03 #1411, 0.02 #1612), 05r7t (0.06 #878, 0.03 #1478, 0.02 #1679), 06q1r (0.06 #1077, 0.01 #2881, 0.01 #3482) >> Best rule #2604 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 0443c; >> query: (?x10148, 09c7w0) <- award_winner(?x4796, ?x10148), people(?x2510, ?x10148), location(?x10148, ?x2277), ?x2510 = 0x67 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02h9_l nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 115.000 0.836 http://example.org/people/person/nationality #15347-06s27s PRED entity: 06s27s PRED relation: team PRED expected values: 04913k => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 503): 07147 (0.50 #175, 0.40 #529, 0.33 #883), 06wpc (0.40 #509, 0.33 #863, 0.29 #1217), 0x2p (0.33 #752, 0.29 #1106, 0.25 #2522), 01ypc (0.29 #1067, 0.17 #713, 0.12 #2483), 051vz (0.25 #40, 0.20 #394, 0.17 #748), 04mjl (0.25 #151, 0.20 #505, 0.17 #859), 01d5z (0.25 #19, 0.20 #373, 0.17 #727), 05g76 (0.25 #35, 0.20 #389, 0.17 #743), 02__x (0.25 #110, 0.20 #464, 0.17 #818), 07l4z (0.25 #203, 0.20 #557, 0.17 #911) >> Best rule #175 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 040j2_; 0hcs3; >> query: (?x12826, 07147) <- team(?x12826, ?x12956), team(?x12826, ?x10939), school(?x12956, ?x5621), school(?x12956, ?x4955), ?x5621 = 01vs5c, season(?x10939, ?x2406), ?x4955 = 09f2j, school(?x10939, ?x581), athlete(?x5063, ?x12826), draft(?x10939, ?x1161), team(?x2010, ?x10939), team(?x11844, ?x12956) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4965 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 32 *> proper extension: 037gjc; 014g_s; *> query: (?x12826, ?x662) <- team(?x12826, ?x10939), athlete(?x5063, ?x12826), nationality(?x12826, ?x1229), sport(?x6074, ?x5063), sport(?x1160, ?x5063), sport(?x662, ?x5063), colors(?x6074, ?x663), school(?x1160, ?x581), team(?x2010, ?x10939), team(?x8110, ?x6074), team(?x7533, ?x1160) *> conf = 0.07 ranks of expected_values: 135 EVAL 06s27s team 04913k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 79.000 79.000 0.500 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #15346-01hmk9 PRED entity: 01hmk9 PRED relation: award_winner! PRED expected values: 09n4nb => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 129): 013b2h (0.20 #221, 0.09 #6425, 0.08 #6566), 0jzphpx (0.20 #180, 0.08 #321, 0.06 #6102), 0466p0j (0.20 #217, 0.06 #6139, 0.06 #6562), 0hndn2q (0.20 #181, 0.04 #1591, 0.04 #604), 03tn9w (0.20 #235, 0.02 #658, 0.02 #799), 0bc773 (0.20 #195, 0.02 #618, 0.02 #1041), 0gmdkyy (0.20 #171, 0.02 #594, 0.02 #1017), 0bzkgg (0.17 #326, 0.03 #1454, 0.03 #3287), 05c1t6z (0.10 #1566, 0.08 #2271, 0.05 #2553), 02rjjll (0.09 #2261, 0.08 #6350, 0.07 #6491) >> Best rule #221 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01k9lpl; >> query: (?x7183, 013b2h) <- influenced_by(?x10560, ?x7183), influenced_by(?x10101, ?x7183), ?x10101 = 01wp_jm, ?x10560 = 01xwv7 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #6393 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 444 *> proper extension: 01jq34; 07bzp; *> query: (?x7183, 09n4nb) <- category(?x7183, ?x134), award_winner(?x7183, ?x2300) *> conf = 0.06 ranks of expected_values: 24 EVAL 01hmk9 award_winner! 09n4nb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 131.000 131.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15345-0bxbr PRED entity: 0bxbr PRED relation: county PRED expected values: 0bx9y => 98 concepts (74 used for prediction) PRED predicted values (max 10 best out of 77): 0bx9y (0.30 #685, 0.29 #292, 0.20 #489), 04rrd (0.22 #393, 0.14 #2355, 0.14 #2749), 09c7w0 (0.22 #393, 0.14 #2355, 0.14 #2749), 0dn8b (0.14 #346, 0.10 #739, 0.10 #543), 0cc1v (0.14 #307, 0.10 #700, 0.10 #504), 0kpys (0.14 #2171, 0.14 #2565, 0.14 #1583), 0l2xl (0.12 #1832, 0.12 #1439, 0.06 #3994), 0cc07 (0.10 #738, 0.10 #542, 0.06 #934), 0m2fr (0.05 #1643, 0.05 #3805, 0.05 #1839), 0m27n (0.05 #2607, 0.04 #3198, 0.04 #3787) >> Best rule #685 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0d8jf; 0dhml; 0txrs; >> query: (?x5962, 0bx9y) <- contains(?x1767, ?x5962), contains(?x94, ?x5962), ?x94 = 09c7w0, ?x1767 = 04rrd, time_zones(?x5962, ?x2674) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bxbr county 0bx9y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 74.000 0.300 http://example.org/location/hud_county_place/county #15344-0252fh PRED entity: 0252fh PRED relation: location PRED expected values: 02_286 => 134 concepts (132 used for prediction) PRED predicted values (max 10 best out of 83): 02_286 (0.33 #36, 0.32 #38511, 0.30 #837), 030qb3t (0.25 #38557, 0.24 #11299, 0.23 #2485), 0cr3d (0.17 #144, 0.09 #12162, 0.08 #1746), 013yq (0.17 #118, 0.02 #10534, 0.02 #2521), 0vzm (0.17 #171, 0.02 #38646, 0.01 #50670), 0xhj2 (0.17 #605), 04jpl (0.12 #38491, 0.09 #50515, 0.07 #11233), 0cc56 (0.10 #857, 0.05 #9670, 0.05 #50555), 05tbn (0.10 #987, 0.02 #15408, 0.01 #4992), 0ycht (0.10 #1490) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0pz91; 03h_0_z; 092ggq; >> query: (?x7780, 02_286) <- award(?x7780, ?x3066), film(?x7780, ?x9322), profession(?x7780, ?x1032), ?x9322 = 0gwgn1k >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0252fh location 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 132.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #15343-01n9d9 PRED entity: 01n9d9 PRED relation: profession PRED expected values: 01d_h8 => 120 concepts (116 used for prediction) PRED predicted values (max 10 best out of 144): 01d_h8 (0.87 #2540, 0.86 #1944, 0.83 #1198), 02hrh1q (0.81 #312, 0.80 #16408, 0.71 #5083), 0dxtg (0.69 #2398, 0.69 #1355, 0.69 #1802), 03gjzk (0.40 #2549, 0.39 #1207, 0.35 #1953), 09jwl (0.28 #13284, 0.17 #15221, 0.16 #8217), 02krf9 (0.23 #1816, 0.23 #1518, 0.23 #1369), 0cbd2 (0.23 #2989, 0.22 #7, 0.22 #2690), 0nbcg (0.19 #13297, 0.12 #926, 0.11 #777), 0kyk (0.16 #2713, 0.15 #3012, 0.15 #30), 018gz8 (0.15 #17, 0.10 #5086, 0.10 #5533) >> Best rule #2540 for best value: >> intensional similarity = 3 >> extensional distance = 297 >> proper extension: 0q9kd; 079vf; 0grwj; 016qtt; 0fvf9q; 04t2l2; 06dv3; 014zcr; 02g8h; 042l3v; ... >> query: (?x4075, 01d_h8) <- type_of_union(?x4075, ?x566), produced_by(?x592, ?x4075), profession(?x4075, ?x524) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n9d9 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 116.000 0.866 http://example.org/people/person/profession #15342-0jzphpx PRED entity: 0jzphpx PRED relation: ceremony! PRED expected values: 0c4z8 025m8l 02581c 026mml 01ckcd 02ddq4 02x4wb 024fxq 0257__ => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 266): 02x4wb (0.88 #1955, 0.83 #1758, 0.82 #1561), 03qbnj (0.88 #1902, 0.75 #1705, 0.73 #1508), 03qbh5 (0.88 #1890, 0.75 #1693, 0.73 #1496), 03q27t (0.88 #1960, 0.75 #1763, 0.73 #1566), 01c9dd (0.88 #1942, 0.75 #1745, 0.73 #1548), 026mml (0.83 #1738, 0.82 #1344, 0.81 #1935), 01ckcd (0.83 #1751, 0.81 #1948, 0.73 #1554), 025m8l (0.82 #1451, 0.80 #1057, 0.75 #1845), 02ddq4 (0.81 #1953, 0.75 #1756, 0.73 #1559), 03tcnt (0.81 #1870, 0.73 #1476, 0.70 #1082) >> Best rule #1955 for best value: >> intensional similarity = 10 >> extensional distance = 14 >> proper extension: 05pd94v; >> query: (?x2431, 02x4wb) <- ceremony(?x12458, ?x2431), ceremony(?x9594, ?x2431), ceremony(?x341, ?x2431), award_winner(?x2431, ?x5949), award_winner(?x2431, ?x1894), ?x341 = 026mg3, music(?x1944, ?x5949), ?x9594 = 02flqd, award_winner(?x1079, ?x1894), award(?x6399, ?x12458) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 7, 8, 9, 12, 15, 16, 21 EVAL 0jzphpx ceremony! 0257__ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0jzphpx ceremony! 024fxq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0jzphpx ceremony! 02x4wb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0jzphpx ceremony! 02ddq4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0jzphpx ceremony! 01ckcd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0jzphpx ceremony! 026mml CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0jzphpx ceremony! 02581c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0jzphpx ceremony! 025m8l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0jzphpx ceremony! 0c4z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 56.000 56.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony #15341-01wv9xn PRED entity: 01wv9xn PRED relation: artists! PRED expected values: 05w3f => 88 concepts (46 used for prediction) PRED predicted values (max 10 best out of 264): 06by7 (0.88 #4638, 0.78 #12939, 0.77 #11094), 016clz (0.85 #7391, 0.57 #2156, 0.50 #12307), 064t9 (0.58 #9859, 0.57 #9244, 0.44 #11087), 05w3f (0.52 #5269, 0.50 #1265, 0.46 #2804), 01243b (0.50 #349, 0.33 #42, 0.27 #1578), 03lty (0.43 #5260, 0.41 #9565, 0.36 #6491), 05bt6j (0.38 #12961, 0.33 #3117, 0.33 #4967), 05r6t (0.38 #1310, 0.33 #82, 0.27 #1618), 0b_6yv (0.33 #250, 0.25 #1478, 0.18 #1786), 034487 (0.33 #64, 0.17 #1907, 0.12 #1292) >> Best rule #4638 for best value: >> intensional similarity = 5 >> extensional distance = 47 >> proper extension: 01wp8w7; 0gt_k; 01w60_p; 01vs_v8; 0pj9t; 0407f; 01wz_ml; 01309x; 012z8_; 0p7h7; ... >> query: (?x1684, 06by7) <- artists(?x5379, ?x1684), award_winner(?x4912, ?x1684), artists(?x5379, ?x9463), ?x9463 = 01shhf, inductee(?x1091, ?x1684) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #5269 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 56 *> proper extension: 032t2z; 09prnq; 01hw6wq; 01nn6c; 01w8n89; 0bkg4; 06gd4; 0qf11; 027dpx; 018y81; ... *> query: (?x1684, 05w3f) <- artists(?x7436, ?x1684), artists(?x1380, ?x1684), ?x1380 = 0dl5d, artists(?x7436, ?x8004), artists(?x7436, ?x7896), ?x7896 = 03k3b, ?x8004 = 01w9ph_ *> conf = 0.52 ranks of expected_values: 4 EVAL 01wv9xn artists! 05w3f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 46.000 0.878 http://example.org/music/genre/artists #15340-01xwqn PRED entity: 01xwqn PRED relation: influenced_by PRED expected values: 063_t 0121rx => 155 concepts (80 used for prediction) PRED predicted values (max 10 best out of 383): 014zfs (0.29 #3021, 0.24 #3450, 0.21 #4309), 01k9lpl (0.25 #4592, 0.25 #2446, 0.25 #306), 01svq8 (0.25 #418, 0.24 #3845, 0.21 #3416), 0q9zc (0.25 #269, 0.18 #4555, 0.12 #3696), 049fgvm (0.25 #203, 0.18 #3630, 0.14 #4489), 0ph2w (0.25 #116, 0.17 #2256, 0.12 #1400), 02z3zp (0.25 #261, 0.08 #2401, 0.07 #3259), 049gc (0.25 #169, 0.08 #2309, 0.07 #3167), 0j6cj (0.25 #249, 0.06 #3676, 0.04 #4535), 01mxt_ (0.25 #179, 0.06 #3606, 0.04 #4465) >> Best rule #3021 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 03g5_y; 04gr35; >> query: (?x10963, 014zfs) <- influenced_by(?x10963, ?x4065), influenced_by(?x10963, ?x3917), ?x3917 = 0p_47, participant(?x4065, ?x1145), location(?x10963, ?x739) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #20149 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 145 *> proper extension: 014_lq; 03ftmg; 0bk1p; *> query: (?x10963, ?x4554) <- influenced_by(?x10963, ?x4112), influenced_by(?x10963, ?x3917), influenced_by(?x10963, ?x397), award_nominee(?x241, ?x397), influenced_by(?x4112, ?x4554), award(?x3917, ?x384) *> conf = 0.10 ranks of expected_values: 48 EVAL 01xwqn influenced_by 0121rx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 80.000 0.286 http://example.org/influence/influence_node/influenced_by EVAL 01xwqn influenced_by 063_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 155.000 80.000 0.286 http://example.org/influence/influence_node/influenced_by #15339-0304nh PRED entity: 0304nh PRED relation: category PRED expected values: 08mbj5d => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.44 #2, 0.43 #5, 0.42 #15) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 05fgr_; >> query: (?x4891, 08mbj5d) <- program(?x7983, ?x4891), award(?x4891, ?x7644), producer_type(?x4891, ?x632), award_nominee(?x2614, ?x7983) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0304nh category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.444 http://example.org/common/topic/webpage./common/webpage/category #15338-025rw19 PRED entity: 025rw19 PRED relation: nutrient! PRED expected values: 0cxn2 => 58 concepts (54 used for prediction) PRED predicted values (max 10 best out of 69): 0cxn2 (0.90 #236, 0.89 #74, 0.89 #40), 04k8n (0.20 #12, 0.04 #37, 0.04 #16), 05wvs (0.20 #12, 0.04 #37, 0.04 #16), 01sh2 (0.20 #12, 0.04 #37, 0.04 #16), 0f4kp (0.04 #37, 0.04 #16, 0.04 #13), 0fzjh (0.04 #37, 0.04 #16, 0.04 #13), 0q01m (0.04 #37, 0.04 #16, 0.04 #13), 0h1yf (0.04 #37, 0.04 #16, 0.04 #13), 02kc008 (0.04 #37, 0.04 #16, 0.04 #13), 0g5gq (0.04 #37, 0.04 #16, 0.04 #13) >> Best rule #236 for best value: >> intensional similarity = 107 >> extensional distance = 29 >> proper extension: 0f4l5; >> query: (?x12454, 0cxn2) <- nutrient(?x10612, ?x12454), nutrient(?x9732, ?x12454), nutrient(?x9005, ?x12454), nutrient(?x8298, ?x12454), nutrient(?x7057, ?x12454), nutrient(?x6191, ?x12454), nutrient(?x6159, ?x12454), nutrient(?x1959, ?x12454), nutrient(?x1257, ?x12454), nutrient(?x6191, ?x13944), nutrient(?x6191, ?x13126), nutrient(?x6191, ?x12902), nutrient(?x6191, ?x12083), nutrient(?x6191, ?x11784), nutrient(?x6191, ?x11758), nutrient(?x6191, ?x11409), nutrient(?x6191, ?x11270), nutrient(?x6191, ?x10709), nutrient(?x6191, ?x10195), nutrient(?x6191, ?x10098), nutrient(?x6191, ?x9949), nutrient(?x6191, ?x9840), nutrient(?x6191, ?x9490), nutrient(?x6191, ?x9426), nutrient(?x6191, ?x9365), nutrient(?x6191, ?x8442), nutrient(?x6191, ?x8413), nutrient(?x6191, ?x7720), nutrient(?x6191, ?x7652), nutrient(?x6191, ?x7364), nutrient(?x6191, ?x7362), nutrient(?x6191, ?x7219), nutrient(?x6191, ?x7135), nutrient(?x6191, ?x6286), nutrient(?x6191, ?x6192), nutrient(?x6191, ?x6160), nutrient(?x6191, ?x6033), nutrient(?x6191, ?x5549), nutrient(?x6191, ?x5526), nutrient(?x6191, ?x5451), nutrient(?x6191, ?x5010), nutrient(?x6191, ?x4069), nutrient(?x6191, ?x3469), nutrient(?x6191, ?x3203), nutrient(?x6191, ?x2702), nutrient(?x6191, ?x2018), nutrient(?x6191, ?x1960), nutrient(?x6191, ?x1258), ?x10709 = 0h1sz, ?x9490 = 0h1sg, ?x10195 = 0hkwr, ?x13944 = 0f4kp, ?x5010 = 0h1vz, ?x5549 = 025s7j4, nutrient(?x1257, ?x7894), nutrient(?x1257, ?x7431), nutrient(?x1257, ?x6026), ?x9840 = 02p0tjr, ?x7431 = 09gwd, ?x9365 = 04k8n, ?x7652 = 025s0s0, ?x2018 = 01sh2, ?x13126 = 02kc_w5, nutrient(?x7057, ?x3901), nutrient(?x7057, ?x1304), ?x6286 = 02y_3rf, ?x6033 = 04zjxcz, ?x6160 = 041r51, ?x11270 = 02kc008, ?x5451 = 05wvs, ?x7894 = 0f4hc, ?x7364 = 09gvd, ?x9732 = 05z55, ?x7135 = 025rsfk, ?x10098 = 0h1_c, ?x3901 = 0466p20, ?x1304 = 08lb68, ?x5526 = 09pbb, nutrient(?x8298, ?x10453), ?x10612 = 0frq6, ?x7362 = 02kc5rj, ?x3203 = 04kl74p, ?x10453 = 075pwf, ?x1959 = 0f25w9, ?x6192 = 06jry, ?x7219 = 0h1vg, nutrient(?x9005, ?x14210), nutrient(?x9005, ?x13545), ?x11758 = 0q01m, ?x11409 = 0h1yf, ?x8413 = 02kc4sf, ?x6159 = 033cnk, ?x13545 = 01w_3, ?x12083 = 01n78x, ?x1960 = 07hnp, ?x4069 = 0hqw8p_, ?x7720 = 025s7x6, ?x14210 = 0f4k5, ?x1258 = 0h1wg, ?x11784 = 07zqy, ?x9426 = 0h1yy, ?x3469 = 0h1zw, ?x6026 = 025sf8g, ?x12902 = 0fzjh, ?x8442 = 02kcv4x, ?x9949 = 02kd0rh, ?x2702 = 0838f >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025rw19 nutrient! 0cxn2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 54.000 0.903 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #15337-0419kt PRED entity: 0419kt PRED relation: story_by PRED expected values: 04m_zp => 91 concepts (54 used for prediction) PRED predicted values (max 10 best out of 45): 05ldnp (0.12 #50), 081k8 (0.06 #737, 0.01 #4630, 0.01 #7019), 02qjpv5 (0.05 #2806, 0.05 #1944), 0fx02 (0.04 #276, 0.04 #493, 0.03 #2434), 0343h (0.04 #668, 0.03 #234, 0.02 #451), 0794g (0.04 #216, 0.02 #5846, 0.02 #433), 016tt2 (0.04 #216, 0.02 #5846, 0.02 #433), 01f7dd (0.04 #216, 0.02 #650, 0.02 #649), 079vf (0.03 #218, 0.02 #435, 0.02 #2161), 02nygk (0.03 #426, 0.02 #643, 0.02 #2369) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02ny6g; >> query: (?x11372, 05ldnp) <- nominated_for(?x3308, ?x11372), ?x3308 = 0794g, genre(?x11372, ?x225), film_release_distribution_medium(?x11372, ?x81) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0419kt story_by 04m_zp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 54.000 0.125 http://example.org/film/film/story_by #15336-094hwz PRED entity: 094hwz PRED relation: film_crew_role! PRED expected values: 0fpv_3_ => 55 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1662): 024l2y (0.80 #7649, 0.67 #6406, 0.50 #2677), 06znpjr (0.80 #8439, 0.50 #7196, 0.50 #3467), 0dp7wt (0.70 #8429, 0.67 #7186, 0.50 #3457), 01gwk3 (0.70 #8265, 0.67 #7022, 0.48 #16971), 06fqlk (0.70 #8272, 0.67 #7029, 0.37 #16978), 02b61v (0.70 #8184, 0.67 #6941, 0.33 #16890), 0pc62 (0.70 #7527, 0.67 #6284, 0.33 #16233), 0ct2tf5 (0.70 #8559, 0.50 #7316, 0.50 #3587), 08c6k9 (0.70 #8533, 0.50 #7290, 0.50 #3561), 0435vm (0.70 #7925, 0.50 #6682, 0.50 #2953) >> Best rule #7649 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 02r96rf; 02rh1dz; 0dxtw; >> query: (?x2848, 024l2y) <- film_crew_role(?x3845, ?x2848), film_crew_role(?x2163, ?x2848), film_release_region(?x2163, ?x1499), film(?x2156, ?x2163), ?x3845 = 0639bg, ?x1499 = 01znc_ >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #7734 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 02r96rf; 02rh1dz; 0dxtw; *> query: (?x2848, 0fpv_3_) <- film_crew_role(?x3845, ?x2848), film_crew_role(?x2163, ?x2848), film_release_region(?x2163, ?x1499), film(?x2156, ?x2163), ?x3845 = 0639bg, ?x1499 = 01znc_ *> conf = 0.30 ranks of expected_values: 809 EVAL 094hwz film_crew_role! 0fpv_3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 30.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15335-02q52q PRED entity: 02q52q PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.81 #154, 0.81 #278, 0.81 #268), 0735l (0.24 #49, 0.21 #65, 0.20 #31), 02nxhr (0.10 #22, 0.05 #61, 0.04 #123), 07c52 (0.04 #99, 0.04 #73, 0.03 #366), 07z4p (0.04 #75, 0.03 #64, 0.03 #101) >> Best rule #154 for best value: >> intensional similarity = 4 >> extensional distance = 179 >> proper extension: 01f7kl; 02q_4ph; 07kdkfj; 0422v0; >> query: (?x1804, 029j_) <- language(?x1804, ?x254), nominated_for(?x484, ?x1804), cinematography(?x1804, ?x9552), film(?x10226, ?x1804) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q52q film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15334-01vwbts PRED entity: 01vwbts PRED relation: role PRED expected values: 026t6 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 119): 05r5c (0.39 #2408, 0.39 #1885, 0.36 #2095), 0l14qv (0.33 #109, 0.18 #839, 0.16 #2826), 02hnl (0.33 #1982, 0.26 #2296, 0.25 #3135), 02sgy (0.30 #1259, 0.26 #946, 0.25 #2827), 05842k (0.28 #183, 0.19 #2479, 0.18 #913), 03bx0bm (0.26 #940, 0.24 #1045, 0.24 #209), 042v_gx (0.25 #9, 0.23 #949, 0.22 #1886), 01vj9c (0.23 #956, 0.20 #850, 0.16 #2416), 018vs (0.19 #954, 0.18 #1267, 0.18 #2835), 013y1f (0.19 #977, 0.18 #871, 0.13 #2858) >> Best rule #2408 for best value: >> intensional similarity = 3 >> extensional distance = 247 >> proper extension: 09g0h; >> query: (?x4693, 05r5c) <- place_of_birth(?x4693, ?x362), role(?x4693, ?x227), profession(?x4693, ?x220) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #2824 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 317 *> proper extension: 02fybl; *> query: (?x4693, 026t6) <- profession(?x4693, ?x1183), ?x1183 = 09jwl, role(?x4693, ?x227) *> conf = 0.18 ranks of expected_values: 11 EVAL 01vwbts role 026t6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 114.000 114.000 0.394 http://example.org/music/artist/track_contributions./music/track_contribution/role #15333-07q0m PRED entity: 07q0m PRED relation: nutrient! PRED expected values: 0f25w9 0cxn2 0fbdb 037ls6 => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 14): 0fbdb (0.94 #638, 0.94 #630, 0.93 #574), 0f25w9 (0.89 #20, 0.89 #74, 0.89 #383), 037ls6 (0.89 #20, 0.89 #74, 0.89 #185), 0cxn2 (0.89 #20, 0.89 #74, 0.89 #185), 06x4c (0.89 #20, 0.89 #74, 0.89 #185), 0dcfv (0.89 #20, 0.89 #74, 0.89 #185), 01sh2 (0.04 #19, 0.03 #656, 0.03 #624), 04k8n (0.04 #19, 0.03 #656, 0.03 #624), 05wvs (0.04 #19, 0.03 #656, 0.03 #624), 025rw19 (0.01 #107, 0.01 #92, 0.01 #152) >> Best rule #638 for best value: >> intensional similarity = 121 >> extensional distance = 31 >> proper extension: 0466p20; >> query: (?x13498, ?x7057) <- nutrient(?x10612, ?x13498), nutrient(?x9732, ?x13498), nutrient(?x9489, ?x13498), nutrient(?x9005, ?x13498), nutrient(?x6285, ?x13498), nutrient(?x6191, ?x13498), nutrient(?x6032, ?x13498), nutrient(?x3900, ?x13498), nutrient(?x2701, ?x13498), ?x2701 = 0hkxq, ?x9005 = 04zpv, ?x10612 = 0frq6, nutrient(?x9489, ?x13944), nutrient(?x9489, ?x12902), nutrient(?x9489, ?x12454), nutrient(?x9489, ?x12083), nutrient(?x9489, ?x11784), nutrient(?x9489, ?x11758), nutrient(?x9489, ?x11592), nutrient(?x9489, ?x11409), nutrient(?x9489, ?x11270), nutrient(?x9489, ?x10891), nutrient(?x9489, ?x10709), nutrient(?x9489, ?x10195), nutrient(?x9489, ?x10098), nutrient(?x9489, ?x9915), nutrient(?x9489, ?x9840), nutrient(?x9489, ?x9733), nutrient(?x9489, ?x9708), nutrient(?x9489, ?x9619), nutrient(?x9489, ?x9490), nutrient(?x9489, ?x9426), nutrient(?x9489, ?x8413), nutrient(?x9489, ?x7364), nutrient(?x9489, ?x7362), nutrient(?x9489, ?x7219), nutrient(?x9489, ?x7135), nutrient(?x9489, ?x6586), nutrient(?x9489, ?x6160), nutrient(?x9489, ?x6033), nutrient(?x9489, ?x6026), nutrient(?x9489, ?x5549), nutrient(?x9489, ?x5451), nutrient(?x9489, ?x5374), nutrient(?x9489, ?x5010), nutrient(?x9489, ?x3469), nutrient(?x9489, ?x2018), nutrient(?x9489, ?x1304), nutrient(?x9489, ?x1258), ?x12902 = 0fzjh, ?x10709 = 0h1sz, ?x7362 = 02kc5rj, ?x6285 = 01645p, ?x13944 = 0f4kp, ?x10891 = 0g5gq, ?x11592 = 025sf0_, ?x2018 = 01sh2, nutrient(?x6191, ?x13126), nutrient(?x6191, ?x12868), nutrient(?x6191, ?x9855), nutrient(?x6191, ?x9795), nutrient(?x6191, ?x9436), nutrient(?x6191, ?x6286), nutrient(?x6191, ?x5337), nutrient(?x6191, ?x4069), nutrient(?x6191, ?x3264), nutrient(?x6191, ?x3203), ?x3469 = 0h1zw, ?x7364 = 09gvd, ?x6586 = 05gh50, ?x5374 = 025s0zp, ?x5549 = 025s7j4, ?x12868 = 03d49, ?x9619 = 0h1tg, ?x5337 = 06x4c, ?x9855 = 0d9t0, ?x6033 = 04zjxcz, ?x11758 = 0q01m, ?x9426 = 0h1yy, ?x13126 = 02kc_w5, ?x6026 = 025sf8g, ?x9840 = 02p0tjr, nutrient(?x6032, ?x14210), nutrient(?x6032, ?x13545), ?x1304 = 08lb68, ?x8413 = 02kc4sf, ?x11784 = 07zqy, nutrient(?x8298, ?x3203), nutrient(?x7057, ?x3203), nutrient(?x3468, ?x3203), nutrient(?x1959, ?x3203), ?x12454 = 025rw19, ?x5451 = 05wvs, ?x10098 = 0h1_c, ?x10195 = 0hkwr, ?x12083 = 01n78x, ?x9708 = 061xhr, ?x9915 = 025tkqy, ?x9436 = 025sqz8, ?x9490 = 0h1sg, ?x1258 = 0h1wg, nutrient(?x9732, ?x6517), ?x1959 = 0f25w9, ?x11270 = 02kc008, ?x11409 = 0h1yf, ?x4069 = 0hqw8p_, ?x13545 = 01w_3, ?x6160 = 041r51, ?x8298 = 037ls6, ?x7135 = 025rsfk, ?x6517 = 02kd8zw, ?x3264 = 0dcfv, ?x7057 = 0fbdb, ?x5010 = 0h1vz, ?x3900 = 061_f, ?x9733 = 0h1tz, ?x9795 = 05v_8y, ?x14210 = 0f4k5, ?x7219 = 0h1vg, ?x6286 = 02y_3rf, ?x3468 = 0cxn2 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 07q0m nutrient! 037ls6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.939 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 07q0m nutrient! 0fbdb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.939 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 07q0m nutrient! 0cxn2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.939 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 07q0m nutrient! 0f25w9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.939 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #15332-056252 PRED entity: 056252 PRED relation: artist PRED expected values: 02p2zq => 41 concepts (17 used for prediction) PRED predicted values (max 10 best out of 980): 01vw8mh (0.60 #2014, 0.38 #2849, 0.33 #1180), 02cpp (0.50 #2930, 0.40 #2095, 0.33 #1261), 0p76z (0.40 #2388, 0.38 #3223, 0.33 #1554), 0ycp3 (0.40 #2153, 0.38 #2988, 0.33 #1319), 01q99h (0.40 #2108, 0.33 #1274, 0.33 #439), 048xh (0.40 #2205, 0.33 #1371, 0.33 #536), 01323p (0.40 #2226, 0.33 #1392, 0.33 #557), 0bk1p (0.40 #2330, 0.33 #1496, 0.33 #661), 02vcp0 (0.40 #2255, 0.33 #1421, 0.33 #586), 07c0j (0.40 #1723, 0.33 #889, 0.33 #54) >> Best rule #2014 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 01n2m6; >> query: (?x7743, 01vw8mh) <- artist(?x7743, ?x5901), artist(?x7743, ?x3358), artist(?x7743, ?x654), ?x5901 = 01wgfp6, currency(?x654, ?x170), artist(?x2149, ?x654), award_nominee(?x3358, ?x3235), profession(?x3358, ?x131), ?x2149 = 011k1h, category(?x3358, ?x134), award(?x654, ?x4912) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3046 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 03rhqg; 01cl2y; 02y21l; *> query: (?x7743, 02p2zq) <- artist(?x7743, ?x5901), category(?x5901, ?x134), award_winner(?x3045, ?x5901), artists(?x7267, ?x5901), location(?x5901, ?x2552), award(?x5901, ?x2634), award_winner(?x5901, ?x9407), ?x7267 = 03mb9, ?x2634 = 02f72n *> conf = 0.25 ranks of expected_values: 152 EVAL 056252 artist 02p2zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 41.000 17.000 0.600 http://example.org/music/record_label/artist #15331-0797c7 PRED entity: 0797c7 PRED relation: citytown PRED expected values: 0cv3w => 104 concepts (91 used for prediction) PRED predicted values (max 10 best out of 86): 0cv3w (0.40 #432, 0.20 #64, 0.12 #1905), 0f04v (0.29 #1624, 0.18 #2729, 0.17 #888), 02_286 (0.26 #23628, 0.24 #24366, 0.24 #10702), 0bxbr (0.20 #134, 0.17 #1238, 0.17 #870), 0r5wt (0.20 #101, 0.12 #1942, 0.07 #4520), 0k_q_ (0.20 #48, 0.12 #1889, 0.07 #3363), 06kx2 (0.20 #679, 0.03 #11796, 0.02 #22135), 0r6ff (0.17 #1372, 0.17 #1004, 0.12 #2109), 01m1zk (0.17 #1195, 0.14 #1563, 0.10 #2300), 0y1rf (0.17 #1365, 0.14 #1733, 0.10 #2470) >> Best rule #432 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01jpyb; 01jpqb; >> query: (?x12242, 0cv3w) <- category(?x12242, ?x134), ?x134 = 08mbj5d, state_province_region(?x12242, ?x1138), ?x1138 = 059_c >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0797c7 citytown 0cv3w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 91.000 0.400 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #15330-04r1t PRED entity: 04r1t PRED relation: category PRED expected values: 08mbj5d => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #42, 0.85 #89, 0.84 #91) >> Best rule #42 for best value: >> intensional similarity = 7 >> extensional distance = 118 >> proper extension: 01lmj3q; 01ww2fs; 016sp_; 02fn5r; 03yf3z; 0137g1; 01wmgrf; 0ggjt; 016srn; 0bhvtc; ... >> query: (?x1929, 08mbj5d) <- artists(?x7083, ?x1929), artists(?x2664, ?x1929), ?x2664 = 01lyv, artists(?x7083, ?x10025), artists(?x7083, ?x3244), participant(?x1462, ?x3244), artist(?x3265, ?x10025) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04r1t category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.850 http://example.org/common/topic/webpage./common/webpage/category #15329-05gnf PRED entity: 05gnf PRED relation: award_winner PRED expected values: 07ymr5 => 184 concepts (79 used for prediction) PRED predicted values (max 10 best out of 386): 039cq4 (0.80 #89879, 0.60 #11233, 0.60 #10748), 014hdb (0.80 #89879), 04glx0 (0.60 #11233, 0.12 #21949, 0.11 #23554), 024hbv (0.60 #11233), 01cvtf (0.60 #11233), 045r_9 (0.60 #11233), 016tvq (0.60 #11233), 02czd5 (0.60 #11233), 01fs__ (0.60 #11233), 0dsx3f (0.60 #11233) >> Best rule #89879 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 01j53q; >> query: (?x6678, ?x1762) <- award_winner(?x2062, ?x6678), award_winner(?x1762, ?x6678), service_language(?x2062, ?x254), award_winner(?x6339, ?x2062) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #9927 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0j1yf; 07ymr5; 09px1w; *> query: (?x6678, 07ymr5) <- award_winner(?x631, ?x6678), award_winner(?x6678, ?x6447), ?x6447 = 091yn0 *> conf = 0.40 ranks of expected_values: 17 EVAL 05gnf award_winner 07ymr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 184.000 79.000 0.804 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #15328-05c6073 PRED entity: 05c6073 PRED relation: artists PRED expected values: 01w5n51 => 65 concepts (25 used for prediction) PRED predicted values (max 10 best out of 997): 03t9sp (0.73 #6518, 0.71 #4386, 0.67 #7584), 02k5sc (0.67 #2832, 0.50 #3898, 0.43 #4966), 0259r0 (0.67 #2348, 0.43 #4482, 0.31 #8747), 01s21dg (0.67 #2549, 0.31 #8948, 0.29 #11082), 01vxlbm (0.62 #8865, 0.57 #4600, 0.50 #10999), 03f5spx (0.57 #4321, 0.50 #2187, 0.46 #8586), 011z3g (0.57 #4860, 0.50 #2726, 0.40 #1660), 0gdh5 (0.57 #4483, 0.50 #2349, 0.40 #1283), 01323p (0.57 #4953, 0.50 #2819, 0.36 #7085), 01vvycq (0.57 #4310, 0.50 #2176, 0.32 #11775) >> Best rule #6518 for best value: >> intensional similarity = 9 >> extensional distance = 9 >> proper extension: 011j5x; 059kh; >> query: (?x12498, 03t9sp) <- artists(?x12498, ?x2635), artists(?x12498, ?x2005), artists(?x12498, ?x475), parent_genre(?x3243, ?x12498), award(?x475, ?x247), ?x2005 = 05k79, group(?x716, ?x2635), ?x716 = 018vs, artist(?x2149, ?x2635) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1747 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 016clz; 0m0jc; *> query: (?x12498, 01w5n51) <- artists(?x12498, ?x2005), artists(?x12498, ?x1004), artists(?x12498, ?x475), artists(?x12498, ?x317), ?x475 = 01pfr3, parent_genre(?x3243, ?x12498), ?x1004 = 01vv7sc, ?x2005 = 05k79, role(?x317, ?x227) *> conf = 0.40 ranks of expected_values: 100 EVAL 05c6073 artists 01w5n51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 65.000 25.000 0.727 http://example.org/music/genre/artists #15327-02_fz3 PRED entity: 02_fz3 PRED relation: film_crew_role PRED expected values: 02r96rf 0dxtw => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 32): 02r96rf (0.81 #175, 0.65 #688, 0.63 #141), 0dxtw (0.49 #182, 0.37 #148, 0.37 #695), 02rh1dz (0.22 #43, 0.21 #181, 0.20 #9), 02ynfr (0.22 #186, 0.20 #14, 0.19 #699), 0d2b38 (0.19 #92, 0.16 #196, 0.15 #58), 094hwz (0.19 #47, 0.09 #2674, 0.07 #81), 015h31 (0.18 #180, 0.09 #2674, 0.08 #110), 0215hd (0.18 #189, 0.14 #702, 0.12 #85), 01xy5l_ (0.15 #184, 0.10 #80, 0.10 #697), 089g0h (0.15 #190, 0.11 #52, 0.10 #326) >> Best rule #175 for best value: >> intensional similarity = 3 >> extensional distance = 337 >> proper extension: 0gx1bnj; 0h1cdwq; 03t97y; 07sc6nw; 0cz8mkh; 05p3738; 028cg00; 02qhqz4; 08052t3; 0cc846d; ... >> query: (?x7945, 02r96rf) <- language(?x7945, ?x254), film_crew_role(?x7945, ?x2154), ?x2154 = 01vx2h >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02_fz3 film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.814 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_fz3 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.814 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15326-0cc5qkt PRED entity: 0cc5qkt PRED relation: edited_by PRED expected values: 03q8ch => 115 concepts (89 used for prediction) PRED predicted values (max 10 best out of 24): 03q8ch (0.50 #13, 0.44 #43, 0.09 #190), 06pj8 (0.07 #9, 0.03 #39, 0.02 #1190), 02qggqc (0.06 #92, 0.04 #180, 0.03 #686), 0136g9 (0.05 #683, 0.05 #446, 0.04 #802), 02kxbx3 (0.04 #101, 0.03 #339, 0.02 #428), 0bn3jg (0.04 #146, 0.03 #58, 0.02 #87), 02lp3c (0.03 #165, 0.03 #135, 0.02 #194), 092ys_y (0.03 #209, 0.02 #208, 0.01 #1672), 0c94fn (0.03 #209, 0.02 #208, 0.01 #1672), 04cy8rb (0.03 #119, 0.03 #328, 0.02 #90) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0mbql; >> query: (?x3596, 03q8ch) <- production_companies(?x3596, ?x1686), music(?x3596, ?x669), ?x1686 = 030_1_, written_by(?x3596, ?x1367) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cc5qkt edited_by 03q8ch CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 89.000 0.500 http://example.org/film/film/edited_by #15325-0661m4p PRED entity: 0661m4p PRED relation: films! PRED expected values: 07s2s => 79 concepts (28 used for prediction) PRED predicted values (max 10 best out of 50): 07c52 (0.12 #177, 0.02 #3485, 0.02 #2533), 081pw (0.06 #946, 0.03 #2836, 0.03 #789), 03r8gp (0.06 #560, 0.03 #717, 0.03 #1033), 0bxg3 (0.06 #550, 0.03 #707, 0.01 #1336), 0fx2s (0.06 #1016, 0.03 #859, 0.02 #3856), 054yc0 (0.05 #1233, 0.01 #2017), 07s2s (0.05 #1512, 0.05 #1668, 0.04 #2454), 018jz (0.03 #669, 0.01 #2555), 0kbq (0.03 #891, 0.03 #1361, 0.03 #1048), 0jm_ (0.03 #794, 0.03 #1107, 0.02 #1891) >> Best rule #177 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 02q56mk; 07yvsn; >> query: (?x2350, 07c52) <- produced_by(?x2350, ?x1533), film(?x12359, ?x2350), film(?x4465, ?x2350), ?x12359 = 02js_6, award_nominee(?x2390, ?x4465) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #1512 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 77 *> proper extension: 014lc_; 09sh8k; 0140g4; 011yxg; 07gp9; 01hr1; 09xbpt; 01k1k4; 061681; 0164qt; ... *> query: (?x2350, 07s2s) <- produced_by(?x2350, ?x1533), film(?x488, ?x2350), prequel(?x2350, ?x1673), film_release_distribution_medium(?x2350, ?x81) *> conf = 0.05 ranks of expected_values: 7 EVAL 0661m4p films! 07s2s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 79.000 28.000 0.125 http://example.org/film/film_subject/films #15324-0kfhjq0 PRED entity: 0kfhjq0 PRED relation: film_festivals! PRED expected values: 024lt6 => 32 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1849): 023vcd (0.79 #4058, 0.79 #3155, 0.77 #2927), 02825nf (0.79 #4058, 0.79 #3155, 0.77 #2927), 04cppj (0.79 #4058, 0.79 #3155, 0.77 #2927), 0c8tkt (0.79 #4058, 0.79 #3155, 0.77 #2927), 0462hhb (0.40 #1899, 0.33 #2807, 0.25 #3714), 0fq27fp (0.33 #451, 0.29 #2936, 0.25 #899), 0b76d_m (0.33 #444, 0.25 #3385, 0.25 #892), 0fpmrm3 (0.33 #496, 0.25 #944, 0.22 #3887), 0g5qmbz (0.33 #638, 0.25 #1086, 0.22 #4029), 0g9zljd (0.33 #588, 0.25 #1036, 0.22 #3979) >> Best rule #4058 for best value: >> intensional similarity = 34 >> extensional distance = 7 >> proper extension: 0fpkxfd; 0g57ws5; 0hrcs29; >> query: (?x6557, ?x1743) <- film_festivals(?x6751, ?x6557), film_festivals(?x6684, ?x6557), film_festivals(?x6556, ?x6557), film_festivals(?x4694, ?x6557), film_regional_debut_venue(?x10246, ?x6557), film_regional_debut_venue(?x1743, ?x6557), production_companies(?x10246, ?x1104), film_release_region(?x4694, ?x1003), film_release_region(?x4694, ?x279), production_companies(?x6556, ?x752), nominated_for(?x1312, ?x10246), film(?x5410, ?x6556), ?x1003 = 03gj2, nominated_for(?x298, ?x6684), film_release_region(?x6684, ?x1892), film(?x794, ?x10246), profession(?x5410, ?x319), film(?x875, ?x6684), film_crew_role(?x6684, ?x137), genre(?x6556, ?x258), genre(?x9329, ?x258), genre(?x7541, ?x258), genre(?x3919, ?x258), genre(?x1184, ?x258), ?x279 = 0d060g, ?x7541 = 02gpkt, ?x319 = 01d_h8, genre(?x419, ?x258), ?x3919 = 05_5rjx, ?x1184 = 02v63m, ?x1892 = 02vzc, ?x9329 = 034hwx, film_release_distribution_medium(?x10246, ?x81), film(?x382, ?x6751) >> conf = 0.79 => this is the best rule for 4 predicted values *> Best rule #1115 for first EXPECTED value: *> intensional similarity = 37 *> extensional distance = 2 *> proper extension: 02_286; *> query: (?x6557, ?x66) <- film_regional_debut_venue(?x10475, ?x6557), film_regional_debut_venue(?x6556, ?x6557), film_regional_debut_venue(?x6516, ?x6557), film_release_region(?x6516, ?x4743), film_release_region(?x6516, ?x3749), film_release_region(?x6516, ?x2645), film_release_region(?x6516, ?x1603), film_release_region(?x6516, ?x1023), film_release_region(?x6516, ?x774), film_release_region(?x6516, ?x550), film_release_region(?x6516, ?x304), currency(?x6516, ?x170), ?x550 = 05v8c, film_crew_role(?x6556, ?x1171), ?x774 = 06mzp, film(?x4782, ?x6556), ?x1171 = 09vw2b7, titles(?x2480, ?x6556), ?x2645 = 03h64, film(?x1461, ?x6516), film_crew_role(?x10475, ?x4305), ?x304 = 0d0vqn, film_release_distribution_medium(?x6516, ?x81), ?x4743 = 03spz, film_release_region(?x5271, ?x1023), film_release_region(?x3191, ?x1023), film_release_region(?x66, ?x1023), ?x3749 = 03ryn, film_crew_role(?x7263, ?x4305), ?x7263 = 0292qb, country(?x150, ?x1023), capital(?x1023, ?x11743), ?x5271 = 047vnkj, ?x3191 = 0crc2cp, ?x1603 = 06bnz, written_by(?x6556, ?x8235), nominated_for(?x1691, ?x6516) *> conf = 0.02 ranks of expected_values: 765 EVAL 0kfhjq0 film_festivals! 024lt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 18.000 0.795 http://example.org/film/film/film_festivals #15323-02g9z1 PRED entity: 02g9z1 PRED relation: type_of_union PRED expected values: 04ztj => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.76 #37, 0.76 #33, 0.76 #25), 01g63y (0.19 #369, 0.15 #10, 0.14 #126), 0jgjn (0.19 #369), 01bl8s (0.19 #369) >> Best rule #37 for best value: >> intensional similarity = 2 >> extensional distance = 562 >> proper extension: 01xyt7; >> query: (?x12161, 04ztj) <- religion(?x12161, ?x492), award_winner(?x3499, ?x12161) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02g9z1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.764 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15322-02slt7 PRED entity: 02slt7 PRED relation: contact_category PRED expected values: 02zdwq => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 2): 02zdwq (0.46 #34, 0.43 #32, 0.40 #28), 014dgf (0.23 #79, 0.23 #71, 0.22 #73) >> Best rule #34 for best value: >> intensional similarity = 8 >> extensional distance = 26 >> proper extension: 06_9lg; >> query: (?x3331, 02zdwq) <- service_language(?x3331, ?x5607), service_location(?x3331, ?x789), contact_category(?x3331, ?x897), contains(?x789, ?x790), location_of_ceremony(?x3580, ?x789), origin(?x3382, ?x789), location(?x4536, ?x789), award_nominee(?x513, ?x4536) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02slt7 contact_category 02zdwq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.464 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #15321-08gsvw PRED entity: 08gsvw PRED relation: film_crew_role PRED expected values: 02r96rf 0dxtw => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 26): 02r96rf (0.81 #287, 0.73 #1065, 0.64 #1919), 0dxtw (0.59 #294, 0.41 #1072, 0.38 #1926), 01pvkk (0.35 #295, 0.30 #682, 0.29 #542), 02rh1dz (0.27 #293, 0.18 #80, 0.13 #1071), 02ynfr (0.24 #299, 0.19 #1077, 0.18 #51), 089g0h (0.20 #303, 0.12 #19, 0.12 #1543), 0215hd (0.19 #302, 0.17 #18, 0.14 #1542), 015h31 (0.19 #292, 0.09 #1532, 0.09 #1105), 01xy5l_ (0.18 #297, 0.17 #13, 0.12 #1075), 0d2b38 (0.18 #309, 0.12 #25, 0.11 #1549) >> Best rule #287 for best value: >> intensional similarity = 5 >> extensional distance = 252 >> proper extension: 0d_2fb; >> query: (?x787, 02r96rf) <- genre(?x787, ?x225), film_crew_role(?x787, ?x2154), film_crew_role(?x787, ?x1171), ?x1171 = 09vw2b7, ?x2154 = 01vx2h >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 08gsvw film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.815 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 08gsvw film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.815 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15320-02gw_w PRED entity: 02gw_w PRED relation: place_of_birth! PRED expected values: 0jgwf => 104 concepts (38 used for prediction) PRED predicted values (max 10 best out of 953): 0326tc (0.33 #4262, 0.20 #6872, 0.03 #19922), 01j5x6 (0.33 #2747, 0.20 #5357, 0.03 #18407), 037jz (0.33 #1412, 0.20 #9242, 0.03 #19682), 0g8st4 (0.33 #1385, 0.20 #9215, 0.03 #19655), 0f8pz (0.33 #751, 0.20 #8581, 0.03 #19021), 0350l7 (0.20 #6576, 0.01 #35288), 0h0p_ (0.20 #6449, 0.01 #35161), 022g44 (0.20 #6240, 0.01 #34952), 0509bl (0.20 #5580, 0.01 #34292), 04jlgp (0.06 #10839, 0.05 #70484, 0.04 #13449) >> Best rule #4262 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 049kw; >> query: (?x13588, 0326tc) <- contains(?x11432, ?x13588), contains(?x362, ?x13588), place_of_birth(?x3718, ?x13588), ?x11432 = 0cxgc, ?x362 = 04jpl >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02gw_w place_of_birth! 0jgwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 38.000 0.333 http://example.org/people/person/place_of_birth #15319-01vj9c PRED entity: 01vj9c PRED relation: role! PRED expected values: 0lbj1 01wsl7c 016ntp 01vrkdt 01vng3b 01mxnvc 02qtywd => 83 concepts (60 used for prediction) PRED predicted values (max 10 best out of 939): 03ryks (0.75 #13953, 0.70 #12745, 0.60 #6300), 03h502k (0.75 #10270, 0.50 #13491, 0.50 #3832), 04s5_s (0.62 #10457, 0.60 #5625, 0.50 #9252), 0140t7 (0.60 #12826, 0.60 #6381, 0.56 #11616), 09hnb (0.60 #6135, 0.56 #12179, 0.43 #14594), 0326tc (0.60 #5535, 0.50 #15603, 0.50 #10367), 01gx5f (0.60 #6173, 0.50 #13423, 0.50 #10202), 04kjrv (0.60 #5499, 0.40 #5901, 0.38 #10331), 06x4l_ (0.56 #10977, 0.50 #13395, 0.50 #8969), 01l4g5 (0.56 #11467, 0.50 #15901, 0.50 #10261) >> Best rule #13953 for best value: >> intensional similarity = 14 >> extensional distance = 10 >> proper extension: 0dwvl; 0dwt5; >> query: (?x745, 03ryks) <- role(?x6162, ?x745), role(?x5883, ?x745), role(?x2785, ?x745), role(?x2059, ?x745), role(?x1432, ?x745), role(?x1212, ?x745), group(?x2785, ?x1945), ?x2059 = 0dwr4, ?x1212 = 07xzm, role(?x75, ?x2785), role(?x645, ?x2785), participant(?x5883, ?x4662), award_nominee(?x399, ?x6162), ?x1432 = 0395lw >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6821 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 07y_7; *> query: (?x745, 02qtywd) <- role(?x6124, ?x745), role(?x3166, ?x745), role(?x7033, ?x745), role(?x2785, ?x745), ?x2785 = 0jtg0, group(?x745, ?x4182), role(?x75, ?x745), role(?x211, ?x745), instrumentalists(?x7033, ?x5508), artist(?x2931, ?x6124), ?x5508 = 0jn5l, award_nominee(?x2698, ?x3166), ?x4182 = 07yg2 *> conf = 0.50 ranks of expected_values: 13, 15, 19, 37, 67, 168, 195 EVAL 01vj9c role! 02qtywd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 83.000 60.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vj9c role! 01mxnvc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 83.000 60.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vj9c role! 01vng3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 83.000 60.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vj9c role! 01vrkdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 83.000 60.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vj9c role! 016ntp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 83.000 60.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vj9c role! 01wsl7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 83.000 60.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vj9c role! 0lbj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 83.000 60.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role #15318-024jwt PRED entity: 024jwt PRED relation: profession PRED expected values: 0kyk => 111 concepts (89 used for prediction) PRED predicted values (max 10 best out of 84): 0dxtg (0.68 #731, 0.65 #1883, 0.58 #1451), 0kyk (0.47 #5499, 0.37 #1321, 0.31 #3194), 09jwl (0.29 #447, 0.27 #591, 0.23 #15), 018gz8 (0.25 #2737, 0.22 #445, 0.22 #589), 0np9r (0.25 #2737, 0.19 #3475, 0.18 #4915), 0nbcg (0.23 #459, 0.22 #603, 0.19 #27), 05z96 (0.21 #38, 0.13 #2054, 0.12 #2630), 0fj9f (0.20 #338, 0.11 #1202, 0.09 #4372), 01c72t (0.20 #452, 0.19 #596, 0.15 #20), 0dz3r (0.17 #434, 0.16 #578, 0.11 #4180) >> Best rule #731 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 02zyy4; 02lf1j; 03lgg; 062cg6; 02mz_6; 01x2tm8; 0k_mt; 042kbj; 03hzkq; 0b57p6; ... >> query: (?x10694, 0dxtg) <- profession(?x10694, ?x353), profession(?x10694, ?x319), ?x353 = 0cbd2, ?x319 = 01d_h8 >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #5499 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 685 *> proper extension: 0hnlx; 06hmd; 06c97; 01_k0d; 0h336; 022q32; 0177g; 081t6; *> query: (?x10694, 0kyk) <- profession(?x10694, ?x353), profession(?x11766, ?x353), ?x11766 = 02ndf1 *> conf = 0.47 ranks of expected_values: 2 EVAL 024jwt profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 89.000 0.681 http://example.org/people/person/profession #15317-0bs5vty PRED entity: 0bs5vty PRED relation: nominated_for! PRED expected values: 03hj5vf => 82 concepts (67 used for prediction) PRED predicted values (max 10 best out of 202): 03hl6lc (0.68 #5041, 0.67 #5040, 0.66 #9398), 099tbz (0.60 #41, 0.49 #270, 0.20 #13754), 0gr0m (0.60 #53, 0.40 #282, 0.36 #740), 02qvyrt (0.60 #87, 0.31 #316, 0.20 #1919), 0k611 (0.51 #294, 0.50 #65, 0.35 #752), 040njc (0.51 #235, 0.50 #6, 0.32 #1838), 02qyntr (0.50 #171, 0.45 #400, 0.25 #2003), 02pqp12 (0.50 #52, 0.42 #281, 0.23 #1884), 0fhpv4 (0.50 #131, 0.35 #360, 0.17 #2520), 02r22gf (0.50 #25, 0.31 #254, 0.20 #712) >> Best rule #5041 for best value: >> intensional similarity = 3 >> extensional distance = 511 >> proper extension: 06mmr; >> query: (?x10241, ?x1862) <- honored_for(?x1442, ?x10241), award(?x10241, ?x1862), nominated_for(?x1862, ?x69) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #13754 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1262 *> proper extension: 0clpml; *> query: (?x10241, ?x154) <- nominated_for(?x4367, ?x10241), student(?x3387, ?x4367), award(?x4367, ?x154) *> conf = 0.20 ranks of expected_values: 51 EVAL 0bs5vty nominated_for! 03hj5vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 82.000 67.000 0.682 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15316-04b_46 PRED entity: 04b_46 PRED relation: student PRED expected values: 05bnp0 02qgqt 058ncz 0c9xjl => 119 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1529): 0306ds (0.33 #398, 0.20 #2457, 0.05 #4516), 02vntj (0.33 #688, 0.20 #2747, 0.05 #4806), 015wc0 (0.33 #1665, 0.20 #3724, 0.05 #5783), 01l1rw (0.33 #981, 0.20 #3040, 0.05 #5099), 03rs8y (0.33 #45, 0.20 #2104, 0.05 #4163), 02lgj6 (0.33 #220, 0.20 #2279, 0.04 #6397), 06rq2l (0.33 #1555, 0.20 #3614, 0.03 #22656), 0203v (0.33 #240, 0.20 #2299, 0.03 #8476), 081lh (0.33 #130, 0.20 #2189, 0.03 #8366), 012t1 (0.33 #142, 0.20 #2201, 0.03 #8378) >> Best rule #398 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0bwfn; >> query: (?x6611, 0306ds) <- student(?x6611, ?x11437), contains(?x94, ?x6611), ?x11437 = 06pcz0, major_field_of_study(?x6611, ?x373) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #63 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0bwfn; *> query: (?x6611, 058ncz) <- student(?x6611, ?x11437), contains(?x94, ?x6611), ?x11437 = 06pcz0, major_field_of_study(?x6611, ?x373) *> conf = 0.33 ranks of expected_values: 19, 36, 74, 128 EVAL 04b_46 student 0c9xjl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 119.000 63.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 04b_46 student 058ncz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 119.000 63.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 04b_46 student 02qgqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 119.000 63.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 04b_46 student 05bnp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 119.000 63.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #15315-0gl3hr PRED entity: 0gl3hr PRED relation: film! PRED expected values: 0g1rw => 100 concepts (66 used for prediction) PRED predicted values (max 10 best out of 53): 05f260 (0.47 #3805, 0.47 #3729, 0.47 #4183), 05qd_ (0.30 #84, 0.25 #9, 0.24 #159), 086k8 (0.25 #2, 0.18 #1063, 0.17 #1139), 016tt2 (0.25 #4, 0.18 #459, 0.18 #231), 017jv5 (0.20 #90, 0.11 #165, 0.10 #545), 0k9ctht (0.17 #34, 0.10 #109, 0.05 #336), 0g1rw (0.16 #235, 0.15 #538, 0.13 #310), 016tw3 (0.15 #1454, 0.15 #4653, 0.15 #3740), 017s11 (0.12 #2748, 0.12 #2134, 0.12 #3426), 0jz9f (0.11 #378, 0.08 #151, 0.08 #1214) >> Best rule #3805 for best value: >> intensional similarity = 3 >> extensional distance = 818 >> proper extension: 01xbxn; >> query: (?x6243, ?x13497) <- production_companies(?x6243, ?x13497), nominated_for(?x198, ?x6243), language(?x6243, ?x254) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #235 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 0bmpm; *> query: (?x6243, 0g1rw) <- country(?x6243, ?x94), ?x94 = 09c7w0, film_release_distribution_medium(?x6243, ?x81), film_art_direction_by(?x6243, ?x2304) *> conf = 0.16 ranks of expected_values: 7 EVAL 0gl3hr film! 0g1rw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 100.000 66.000 0.471 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15314-0418154 PRED entity: 0418154 PRED relation: ceremony! PRED expected values: 0drtkx => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 355): 01by1l (0.81 #511, 0.73 #6158, 0.73 #5981), 02wh75 (0.81 #511, 0.73 #5909, 0.71 #4109), 0257pw (0.81 #511, 0.73 #6149, 0.71 #4349), 02hdky (0.81 #511, 0.73 #6117, 0.71 #4317), 024_fw (0.81 #511, 0.73 #6071, 0.71 #4271), 025mb9 (0.81 #511, 0.73 #6045, 0.71 #4245), 02nbqh (0.81 #511, 0.73 #5985, 0.71 #4185), 02v1m7 (0.81 #511, 0.73 #5982, 0.71 #4182), 01c4_6 (0.81 #511, 0.73 #5965, 0.71 #4165), 02ddqh (0.81 #511, 0.73 #6015, 0.71 #4215) >> Best rule #511 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 01s695; >> query: (?x7767, ?x159) <- award_winner(?x7767, ?x8269), award_winner(?x7767, ?x5951), award_winner(?x7767, ?x2963), award(?x8269, ?x102), award(?x2963, ?x2962), ceremony(?x746, ?x7767), profession(?x2963, ?x220), role(?x2963, ?x227), award_winner(?x2054, ?x2963), ?x2962 = 02ddqh, type_of_union(?x2963, ?x566), ?x5951 = 0dvld, location(?x8269, ?x1591), award_winner(?x247, ?x2963), ceremony(?x159, ?x2054) >> conf = 0.81 => this is the best rule for 113 predicted values *> Best rule #4357 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 5 *> proper extension: 0466p0j; *> query: (?x7767, ?x401) <- award_winner(?x7767, ?x8269), award_winner(?x7767, ?x5951), award_winner(?x7767, ?x2963), award(?x8269, ?x102), ?x2963 = 0gcs9, profession(?x8269, ?x319), location(?x8269, ?x1591), award(?x7277, ?x102), nominated_for(?x102, ?x103), ceremony(?x746, ?x7767), ?x7277 = 01cwcr, award_nominee(?x398, ?x5951), award(?x1812, ?x102), award_winner(?x102, ?x7958), award(?x5951, ?x401) *> conf = 0.46 ranks of expected_values: 168 EVAL 0418154 ceremony! 0drtkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 38.000 38.000 0.813 http://example.org/award/award_category/winners./award/award_honor/ceremony #15313-0gr07 PRED entity: 0gr07 PRED relation: award! PRED expected values: 01y_px 0svqs 0gry51 => 58 concepts (25 used for prediction) PRED predicted values (max 10 best out of 3021): 02hh8j (0.85 #6735, 0.79 #74102, 0.79 #74101), 01t265 (0.85 #6735, 0.79 #43788, 0.78 #47157), 0byfz (0.60 #6783, 0.27 #13520, 0.12 #43838), 0gs1_ (0.60 #8636, 0.27 #15373, 0.11 #18741), 07r1h (0.60 #8537, 0.18 #15274, 0.15 #18642), 03ym1 (0.60 #8412, 0.18 #15149, 0.15 #28623), 0c6qh (0.60 #7394, 0.18 #14131, 0.13 #24237), 016k6x (0.60 #8189, 0.18 #14926, 0.13 #41875), 0171cm (0.60 #7412, 0.18 #14149, 0.11 #41098), 0237fw (0.60 #7377, 0.18 #14114, 0.11 #17482) >> Best rule #6735 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 02qyp19; >> query: (?x5409, ?x1853) <- award_winner(?x5409, ?x12392), award_winner(?x5409, ?x1853), ?x12392 = 040rjq, award(?x5408, ?x5409), ceremony(?x5409, ?x78), gender(?x5408, ?x514) >> conf = 0.85 => this is the best rule for 2 predicted values *> Best rule #7318 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 02w9sd7; *> query: (?x5409, 01y_px) <- award_winner(?x5409, ?x12392), award(?x8460, ?x5409), influenced_by(?x12392, ?x3117), ?x8460 = 063_t, award_winner(?x1448, ?x12392) *> conf = 0.20 ranks of expected_values: 304, 1792 EVAL 0gr07 award! 0gry51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 25.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr07 award! 0svqs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 25.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr07 award! 01y_px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 25.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15312-0140t7 PRED entity: 0140t7 PRED relation: group PRED expected values: 0dtd6 => 159 concepts (80 used for prediction) PRED predicted values (max 10 best out of 93): 02mq_y (0.25 #141, 0.09 #783, 0.02 #2820), 01v0sx2 (0.12 #433, 0.10 #1931, 0.10 #647), 01dq9q (0.12 #483, 0.02 #1553, 0.01 #4025), 01qqwp9 (0.10 #1626, 0.09 #770, 0.08 #1412), 02r1tx7 (0.10 #551, 0.09 #1193, 0.07 #979), 0dw4g (0.09 #360, 0.05 #574, 0.05 #681), 09jm8 (0.08 #943, 0.06 #1157, 0.02 #2013), 0g_g2 (0.06 #460, 0.05 #567, 0.04 #888), 07mvp (0.06 #473, 0.05 #687, 0.04 #2296), 0frsw (0.06 #443, 0.05 #657, 0.03 #978) >> Best rule #141 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 05qhnq; >> query: (?x9321, 02mq_y) <- role(?x9321, ?x3215), role(?x9321, ?x1267), ?x1267 = 07brj, artists(?x505, ?x9321), ?x3215 = 0bxl5 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #547 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 01vs14j; 03j0br4; 01whg97; 02bc74; *> query: (?x9321, 0dtd6) <- instrumentalists(?x1750, ?x9321), award(?x9321, ?x1232), ?x1750 = 02hnl, film(?x9321, ?x5570) *> conf = 0.05 ranks of expected_values: 13 EVAL 0140t7 group 0dtd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 159.000 80.000 0.250 http://example.org/music/group_member/membership./music/group_membership/group #15311-03rjj PRED entity: 03rjj PRED relation: member_states! PRED expected values: 02jxk => 246 concepts (246 used for prediction) PRED predicted values (max 10 best out of 7): 02jxk (0.50 #15, 0.43 #22, 0.38 #29), 01rz1 (0.14 #64, 0.13 #34, 0.11 #48), 07t65 (0.14 #64, 0.13 #34, 0.11 #48), 02vk52z (0.14 #64, 0.13 #34, 0.11 #48), 0b6css (0.14 #64, 0.13 #34, 0.11 #48), 04k4l (0.14 #64, 0.13 #34, 0.11 #48), 0_2v (0.14 #64, 0.13 #34, 0.11 #48) >> Best rule #15 for best value: >> intensional similarity = 2 >> extensional distance = 20 >> proper extension: 059dn; 07jqh; 03m7d; 0v74; >> query: (?x205, 02jxk) <- combatants(?x13022, ?x205), ?x13022 = 03gqgt3 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rjj member_states! 02jxk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 246.000 246.000 0.500 http://example.org/user/ktrueman/default_domain/international_organization/member_states #15310-05qfh PRED entity: 05qfh PRED relation: major_field_of_study! PRED expected values: 028dcg => 71 concepts (53 used for prediction) PRED predicted values (max 10 best out of 14): 01ysy9 (0.50 #14, 0.45 #525, 0.43 #115), 071tyz (0.45 #525, 0.41 #132, 0.33 #78), 022h5x (0.45 #525, 0.41 #132, 0.33 #510), 01rr_d (0.45 #525, 0.41 #132, 0.33 #510), 013zdg (0.45 #525, 0.41 #132, 0.33 #510), 027f2w (0.45 #525, 0.41 #132, 0.33 #510), 028dcg (0.45 #525, 0.41 #132, 0.33 #510), 03mkk4 (0.45 #525, 0.41 #132, 0.33 #510), 02m4yg (0.41 #132, 0.33 #510, 0.32 #479), 07s6fsf (0.41 #132, 0.33 #510, 0.32 #479) >> Best rule #14 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 01mkq; 02j62; >> query: (?x3490, 01ysy9) <- major_field_of_study(?x3437, ?x3490), major_field_of_study(?x8706, ?x3490), major_field_of_study(?x8016, ?x3490), major_field_of_study(?x4980, ?x3490), ?x4980 = 01n6r0, ?x3437 = 02_xgp2, ?x8706 = 0trv, major_field_of_study(?x1668, ?x3490), ?x8016 = 02yxjs >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #525 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 78 *> proper extension: 03ll3; *> query: (?x3490, ?x865) <- major_field_of_study(?x4268, ?x3490), major_field_of_study(?x9724, ?x4268), student(?x4268, ?x906), major_field_of_study(?x4268, ?x373), organizations_founded(?x9724, ?x5487), major_field_of_study(?x735, ?x373), major_field_of_study(?x865, ?x4268) *> conf = 0.45 ranks of expected_values: 7 EVAL 05qfh major_field_of_study! 028dcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 71.000 53.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #15309-06rgq PRED entity: 06rgq PRED relation: artists! PRED expected values: 06by7 01lyv => 113 concepts (111 used for prediction) PRED predicted values (max 10 best out of 220): 06by7 (0.64 #8288, 0.61 #4613, 0.50 #2777), 01lyv (0.60 #4625, 0.57 #2789, 0.23 #11973), 0glt670 (0.51 #3102, 0.45 #4326, 0.43 #3408), 025sc50 (0.47 #3110, 0.46 #3416, 0.38 #4334), 02lnbg (0.44 #3119, 0.35 #3425, 0.33 #4343), 06j6l (0.42 #3108, 0.35 #3414, 0.33 #4332), 0ggx5q (0.40 #3139, 0.35 #3445, 0.27 #4363), 05lls (0.30 #321, 0.25 #14, 0.08 #627), 0xhtw (0.28 #4608, 0.21 #8283, 0.18 #21454), 0gywn (0.26 #12303, 0.25 #4342, 0.24 #3424) >> Best rule #8288 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 0150jk; 01r9fv; 03t9sp; 01fl3; 0dtd6; 016fmf; 01rm8b; 0fcsd; 01cblr; 0g_g2; ... >> query: (?x8490, 06by7) <- award(?x8490, ?x724), artists(?x3061, ?x8490), ?x3061 = 05bt6j >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 06rgq artists! 01lyv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 111.000 0.638 http://example.org/music/genre/artists EVAL 06rgq artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 111.000 0.638 http://example.org/music/genre/artists #15308-046qq PRED entity: 046qq PRED relation: type_of_union PRED expected values: 04ztj => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.86 #21, 0.83 #29, 0.83 #37), 01g63y (0.20 #10, 0.17 #54, 0.17 #70) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 012v1t; >> query: (?x4277, 04ztj) <- location(?x4277, ?x11843), nationality(?x4277, ?x94), politician(?x1912, ?x4277) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 046qq type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.857 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15307-014pg1 PRED entity: 014pg1 PRED relation: group! PRED expected values: 01vj9c => 78 concepts (57 used for prediction) PRED predicted values (max 10 best out of 101): 06ncr (0.33 #388, 0.33 #100, 0.25 #749), 03qjg (0.33 #323, 0.24 #2126, 0.23 #1620), 02fsn (0.33 #108, 0.20 #180, 0.17 #324), 026t6 (0.33 #74, 0.20 #146, 0.17 #290), 013y1f (0.33 #19, 0.13 #2399, 0.13 #1028), 018j2 (0.33 #23, 0.11 #455, 0.09 #527), 051hrr (0.33 #99, 0.11 #748, 0.07 #1036), 07brj (0.33 #85, 0.09 #806, 0.08 #1310), 07_l6 (0.33 #117, 0.07 #2164, 0.06 #838), 0dwt5 (0.33 #128, 0.07 #2164, 0.05 #1641) >> Best rule #388 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 01tp5bj; 01ww_vs; >> query: (?x8058, 06ncr) <- artists(?x6101, ?x8058), artists(?x3370, ?x8058), artists(?x3167, ?x8058), ?x3370 = 059kh, artists(?x6101, ?x2521), ?x2521 = 0frsw, parent_genre(?x283, ?x6101), ?x3167 = 0xjl2 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2387 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 184 *> proper extension: 02_5x9; 01qqwp9; 07yg2; 02t3ln; 02dw1_; 0knhk; 03k3b; 012vm6; 0qmny; 0qmpd; ... *> query: (?x8058, 01vj9c) <- artists(?x302, ?x8058), group(?x1466, ?x8058), group(?x1466, ?x7865), ?x7865 = 02k5sc, role(?x115, ?x1466) *> conf = 0.27 ranks of expected_values: 14 EVAL 014pg1 group! 01vj9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 78.000 57.000 0.333 http://example.org/music/performance_role/regular_performances./music/group_membership/group #15306-0ldd PRED entity: 0ldd PRED relation: profession PRED expected values: 0kyk => 133 concepts (96 used for prediction) PRED predicted values (max 10 best out of 88): 02hrh1q (0.83 #8639, 0.82 #9663, 0.81 #9956), 0kyk (0.56 #905, 0.54 #467, 0.46 #4557), 01d_h8 (0.56 #4827, 0.52 #12587, 0.47 #6291), 02jknp (0.48 #4828, 0.45 #6292, 0.43 #12588), 03gjzk (0.40 #12595, 0.36 #4835, 0.30 #2790), 09jwl (0.37 #13183, 0.36 #13329, 0.33 #4985), 0nbcg (0.34 #4705, 0.34 #4998, 0.29 #3245), 05t4q (0.34 #11410, 0.34 #9065, 0.33 #10676), 0fj9f (0.34 #11410, 0.34 #9065, 0.33 #10676), 018gz8 (0.34 #3960, 0.25 #162, 0.22 #8495) >> Best rule #8639 for best value: >> intensional similarity = 5 >> extensional distance = 304 >> proper extension: 05bnp0; 0fvf9q; 01j5ts; 01wbg84; 01p7yb; 0l8v5; 0bxtg; 0151ns; 0146pg; 03m8lq; ... >> query: (?x12888, 02hrh1q) <- nationality(?x12888, ?x512), place_of_birth(?x12888, ?x14172), languages(?x12888, ?x254), ?x254 = 02h40lc, profession(?x12888, ?x353) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #905 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 032l1; 03f0324; 03f47xl; 03j0d; 0pqzh; *> query: (?x12888, 0kyk) <- nationality(?x12888, ?x512), influenced_by(?x12888, ?x4072), gender(?x12888, ?x514), place_of_birth(?x12888, ?x14172), ?x4072 = 02lt8 *> conf = 0.56 ranks of expected_values: 2 EVAL 0ldd profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 96.000 0.833 http://example.org/people/person/profession #15305-07s3vqk PRED entity: 07s3vqk PRED relation: type_of_union PRED expected values: 01g63y => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 3): 01g63y (0.16 #145, 0.16 #217, 0.15 #313), 0jgjn (0.06 #3), 01bl8s (0.03 #26, 0.02 #38, 0.02 #11) >> Best rule #145 for best value: >> intensional similarity = 2 >> extensional distance = 384 >> proper extension: 03j90; >> query: (?x215, 01g63y) <- place_of_birth(?x215, ?x13295), languages(?x215, ?x254) >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07s3vqk type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.161 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15304-0gq_d PRED entity: 0gq_d PRED relation: ceremony PRED expected values: 073hmq 0fz2y7 02yvhx 0fk0xk 0c4hx0 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 81): 02yvhx (0.90 #1021, 0.89 #940, 0.89 #859), 0fk0xk (0.87 #455, 0.85 #293, 0.83 #860), 073hmq (0.83 #746, 0.78 #827, 0.77 #260), 0c4hx0 (0.83 #883, 0.77 #316, 0.76 #721), 0fz2y7 (0.77 #281, 0.76 #686, 0.75 #605), 0gpjbt (0.61 #1397, 0.54 #1478, 0.52 #1559), 09n4nb (0.60 #1410, 0.53 #1491, 0.50 #1572), 0466p0j (0.59 #1425, 0.52 #1506, 0.51 #1587), 05pd94v (0.59 #1378, 0.52 #1459, 0.50 #1540), 02rjjll (0.58 #1381, 0.52 #1462, 0.50 #1543) >> Best rule #1021 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 0gqxm; >> query: (?x4573, 02yvhx) <- ceremony(?x4573, ?x7884), ceremony(?x4573, ?x2822), ceremony(?x1245, ?x2822), ?x7884 = 09306z, ?x1245 = 0gqwc, award(?x382, ?x4573) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 0gq_d ceremony 0c4hx0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq_d ceremony 0fk0xk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq_d ceremony 02yvhx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq_d ceremony 0fz2y7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq_d ceremony 073hmq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony #15303-01bcq PRED entity: 01bcq PRED relation: profession PRED expected values: 02hrh1q => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 135): 02hrh1q (0.92 #1515, 0.88 #9915, 0.88 #11267), 0np9r (0.71 #2122, 0.70 #2722, 0.63 #1822), 03gjzk (0.34 #4516, 0.33 #6616, 0.32 #6166), 01d_h8 (0.32 #4656, 0.32 #6906, 0.31 #5706), 0dxtg (0.32 #4514, 0.30 #5864, 0.30 #6164), 0cbd2 (0.30 #1957, 0.29 #1357, 0.29 #1207), 018gz8 (0.29 #468, 0.29 #18, 0.25 #168), 02jknp (0.29 #8, 0.25 #158, 0.21 #9608), 0kyk (0.27 #1981, 0.27 #1081, 0.26 #1681), 0fj9f (0.26 #1706, 0.20 #2006, 0.19 #1256) >> Best rule #1515 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 01dy7j; 01q9b9; 039x1k; 01kgg9; >> query: (?x4919, 02hrh1q) <- award(?x4919, ?x3989), nationality(?x4919, ?x94), ?x3989 = 0bsjcw >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bcq profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.917 http://example.org/people/person/profession #15302-042ly5 PRED entity: 042ly5 PRED relation: award PRED expected values: 05zr6wv => 98 concepts (67 used for prediction) PRED predicted values (max 10 best out of 241): 099vwn (0.43 #217, 0.13 #27145, 0.13 #26333), 04kxsb (0.38 #531, 0.14 #126, 0.13 #27145), 05pcn59 (0.29 #81, 0.23 #2916, 0.20 #3726), 0gqy2 (0.25 #570, 0.16 #7050, 0.13 #8670), 0f4x7 (0.25 #436, 0.15 #6916, 0.14 #31), 02x73k6 (0.25 #465, 0.14 #24307, 0.13 #20254), 09qv_s (0.25 #557, 0.14 #152, 0.13 #27145), 02w9sd7 (0.25 #576, 0.14 #171, 0.09 #7056), 02x4w6g (0.25 #519, 0.07 #2949, 0.07 #6999), 03c7tr1 (0.22 #1273, 0.14 #24307, 0.13 #27145) >> Best rule #217 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 03yrkt; >> query: (?x7255, 099vwn) <- award_nominee(?x3701, ?x7255), ?x3701 = 016fjj, gender(?x7255, ?x231) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #827 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0jbp0; 01mylz; 02g9z1; *> query: (?x7255, 05zr6wv) <- nominated_for(?x7255, ?x5142), film(?x7255, ?x4392), ?x4392 = 06gb1w *> conf = 0.19 ranks of expected_values: 11 EVAL 042ly5 award 05zr6wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 98.000 67.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15301-05f4m9q PRED entity: 05f4m9q PRED relation: award! PRED expected values: 05kfs 0d608 070j61 02mc79 017yxq 04vt98 0l15n 02r0st6 => 54 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2833): 01qg7c (0.81 #26697, 0.68 #36715, 0.65 #36714), 02l5rm (0.81 #26697, 0.65 #36714, 0.62 #33377), 017yxq (0.81 #26697, 0.65 #36714, 0.62 #33377), 019z7q (0.81 #26697, 0.65 #36714, 0.62 #33377), 04vt98 (0.81 #26697, 0.65 #36714, 0.62 #33377), 02kxbx3 (0.70 #21002, 0.20 #7655, 0.19 #27676), 04sry (0.70 #22128, 0.20 #8781, 0.16 #28802), 02_l96 (0.67 #14823, 0.60 #11487, 0.50 #4813), 01fyzy (0.60 #11753, 0.50 #15089, 0.50 #5079), 04wvhz (0.60 #6922, 0.25 #3586, 0.20 #20269) >> Best rule #26697 for best value: >> intensional similarity = 5 >> extensional distance = 44 >> proper extension: 058vy5; >> query: (?x350, ?x916) <- award_winner(?x350, ?x916), award_winner(?x350, ?x702), award(?x702, ?x567), role(?x702, ?x316), peers(?x702, ?x4960) >> conf = 0.81 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 5, 11, 22, 25, 73, 134, 267 EVAL 05f4m9q award! 02r0st6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 54.000 18.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05f4m9q award! 0l15n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 54.000 18.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05f4m9q award! 04vt98 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 54.000 18.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05f4m9q award! 017yxq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 54.000 18.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05f4m9q award! 02mc79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 54.000 18.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05f4m9q award! 070j61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 54.000 18.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05f4m9q award! 0d608 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 54.000 18.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05f4m9q award! 05kfs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 54.000 18.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15300-0m7yh PRED entity: 0m7yh PRED relation: currency PRED expected values: 02l6h => 197 concepts (197 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.74 #695, 0.71 #365, 0.69 #463), 01nv4h (0.22 #100, 0.19 #23, 0.18 #617), 0ptk_ (0.18 #617, 0.15 #1038, 0.08 #66), 0kz1h (0.18 #617, 0.15 #1038, 0.08 #68), 02l6h (0.18 #617, 0.15 #1038, 0.08 #102), 02gsvk (0.18 #617, 0.06 #34, 0.04 #41) >> Best rule #695 for best value: >> intensional similarity = 5 >> extensional distance = 359 >> proper extension: 02fs_d; >> query: (?x7508, 09nqf) <- state_province_region(?x7508, ?x7934), institution(?x3437, ?x7508), adjoins(?x3623, ?x7934), location(?x4884, ?x7934), contains(?x1264, ?x7934) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #617 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 328 *> proper extension: 037s9x; 01g0p5; 038czx; *> query: (?x7508, ?x170) <- state_province_region(?x7508, ?x7934), major_field_of_study(?x7508, ?x2605), school_type(?x7508, ?x3092), school_type(?x5596, ?x3092), currency(?x5596, ?x170) *> conf = 0.18 ranks of expected_values: 5 EVAL 0m7yh currency 02l6h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 197.000 197.000 0.742 http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency #15299-01g257 PRED entity: 01g257 PRED relation: award PRED expected values: 099t8j => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 256): 05pcn59 (0.81 #1599, 0.79 #1598, 0.76 #2000), 0gqyl (0.81 #1599, 0.79 #1598, 0.76 #2000), 0gr4k (0.29 #431, 0.09 #9589, 0.07 #13615), 04ljl_l (0.29 #3, 0.08 #5597, 0.06 #18781), 03hkv_r (0.29 #414, 0.05 #813, 0.04 #13598), 02x4sn8 (0.29 #552, 0.02 #14537, 0.02 #9742), 05p09zm (0.22 #3719, 0.20 #2121, 0.17 #3319), 03c7tr1 (0.20 #3255, 0.19 #855, 0.19 #2057), 0gkvb7 (0.20 #1224, 0.19 #824, 0.18 #1625), 04fgkf_ (0.19 #1083, 0.15 #1483, 0.14 #1884) >> Best rule #1599 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 05cljf; 025ldg; 05szp; 03f3yfj; 02p68d; 02h9_l; >> query: (?x1564, ?x2456) <- award_winner(?x2456, ?x1564), program(?x1564, ?x631), award(?x286, ?x2456) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #9589 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 702 *> proper extension: 079vf; 02hsgn; *> query: (?x1564, ?x462) <- film(?x1564, ?x6076), award_winner(?x156, ?x1564), nominated_for(?x462, ?x6076) *> conf = 0.09 ranks of expected_values: 88 EVAL 01g257 award 099t8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 113.000 113.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15298-09bjv PRED entity: 09bjv PRED relation: location! PRED expected values: 07f8wg => 165 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1871): 012ljv (0.55 #22640, 0.54 #25156, 0.53 #22639), 01vh3r (0.29 #9884, 0.25 #7367, 0.16 #35039), 0dx97 (0.29 #8612, 0.25 #6095, 0.13 #26221), 044mvs (0.29 #9609, 0.25 #7092, 0.13 #27218), 07ym0 (0.29 #9247, 0.25 #6730, 0.13 #26856), 03bxh (0.25 #6181, 0.22 #16244, 0.22 #13729), 01nz1q6 (0.25 #7213, 0.22 #17276, 0.22 #14761), 026rm_y (0.25 #6800, 0.14 #9317, 0.13 #26926), 08c7cz (0.25 #6547, 0.14 #9064, 0.13 #26673), 01lwx (0.25 #7388, 0.14 #9905, 0.12 #30029) >> Best rule #22640 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0ftkx; >> query: (?x461, ?x84) <- capital(?x7413, ?x461), state_province_region(?x8082, ?x461), place_of_birth(?x84, ?x461), nominated_for(?x84, ?x83) >> conf = 0.55 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09bjv location! 07f8wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 91.000 0.554 http://example.org/people/person/places_lived./people/place_lived/location #15297-02s_qz PRED entity: 02s_qz PRED relation: award_nominee! PRED expected values: 05lb65 => 94 concepts (56 used for prediction) PRED predicted values (max 10 best out of 600): 05lb65 (0.81 #129891, 0.81 #129888, 0.81 #90459), 038g2x (0.81 #129888, 0.81 #90459, 0.81 #90458), 06b0d2 (0.69 #4857, 0.60 #2538, 0.37 #9495), 01rs5p (0.62 #6799, 0.60 #4480, 0.37 #11437), 03w4sh (0.53 #3803, 0.50 #6122, 0.37 #10760), 02s_qz (0.50 #6456, 0.47 #4137, 0.42 #11094), 0308kx (0.50 #5593, 0.33 #3274, 0.26 #10231), 05lb87 (0.44 #4910, 0.42 #9548, 0.33 #2591), 030znt (0.44 #4911, 0.40 #2592, 0.26 #9549), 058ncz (0.44 #4732, 0.33 #2413, 0.25 #88138) >> Best rule #129891 for best value: >> intensional similarity = 4 >> extensional distance = 1776 >> proper extension: 03xsby; 09pl3s; 01wn718; 037hgm; 0b80__; 0237jb; 01dhpj; 0ckcvk; 03cd1q; 03csqj4; >> query: (?x8256, ?x516) <- award_nominee(?x8256, ?x4976), award_nominee(?x8256, ?x516), student(?x6611, ?x516), award_winner(?x4976, ?x444) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02s_qz award_nominee! 05lb65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 56.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15296-02cyfz PRED entity: 02cyfz PRED relation: award PRED expected values: 02qvyrt => 132 concepts (110 used for prediction) PRED predicted values (max 10 best out of 317): 02qvyrt (0.42 #5666, 0.42 #5270, 0.42 #1309), 09sb52 (0.30 #22224, 0.30 #26581, 0.30 #26977), 099vwn (0.25 #1001, 0.09 #5358, 0.09 #4565), 0257__ (0.25 #376, 0.05 #32483, 0.04 #39612), 01by1l (0.23 #9218, 0.23 #899, 0.22 #8426), 02gdjb (0.23 #1400, 0.17 #3380, 0.16 #2192), 01c9jp (0.21 #579, 0.17 #14659, 0.14 #1767), 04njml (0.18 #3264, 0.16 #4056, 0.16 #3660), 01ckcd (0.18 #724, 0.14 #1912, 0.10 #1120), 01c99j (0.17 #1010, 0.17 #14659, 0.10 #9329) >> Best rule #5666 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 012ljv; 06cv1; 03f2_rc; 01wl38s; 0146pg; 03qd_; 01vrncs; 07c0j; 01kx_81; 03kwtb; ... >> query: (?x2214, 02qvyrt) <- award(?x2214, ?x724), nominated_for(?x2214, ?x253), music(?x161, ?x2214) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cyfz award 02qvyrt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 110.000 0.422 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15295-02r99xw PRED entity: 02r99xw PRED relation: nationality PRED expected values: 03rk0 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 67): 03rk0 (0.84 #246, 0.62 #46, 0.56 #146), 09c7w0 (0.68 #1001, 0.67 #601, 0.67 #301), 02jx1 (0.10 #3401, 0.09 #3333, 0.09 #4334), 07ssc (0.10 #3401, 0.08 #2815, 0.08 #3315), 05sb1 (0.10 #3401, 0.02 #6107, 0.02 #5805), 0d060g (0.05 #2307, 0.04 #2707, 0.04 #3207), 03rjj (0.03 #505, 0.02 #2105, 0.02 #2505), 03rt9 (0.02 #913, 0.02 #1113, 0.02 #613), 0345h (0.02 #3331, 0.02 #2831, 0.02 #6107), 0chghy (0.02 #510, 0.02 #410, 0.02 #6107) >> Best rule #246 for best value: >> intensional similarity = 2 >> extensional distance = 83 >> proper extension: 0cfywh; >> query: (?x13898, 03rk0) <- people(?x5025, ?x13898), ?x5025 = 0dryh9k >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r99xw nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.835 http://example.org/people/person/nationality #15294-01j4ls PRED entity: 01j4ls PRED relation: profession PRED expected values: 09jwl => 160 concepts (125 used for prediction) PRED predicted values (max 10 best out of 88): 09jwl (0.80 #3126, 0.76 #6239, 0.75 #6091), 0nbcg (0.57 #2991, 0.54 #3139, 0.53 #4028), 0dz3r (0.53 #1926, 0.52 #1482, 0.50 #2074), 01c72t (0.44 #319, 0.39 #1207, 0.37 #6984), 01d_h8 (0.40 #8598, 0.40 #153, 0.36 #10384), 0d1pc (0.40 #198, 0.25 #50, 0.22 #4789), 039v1 (0.37 #1664, 0.37 #3144, 0.31 #10117), 03gjzk (0.33 #458, 0.32 #8607, 0.29 #5199), 0n1h (0.33 #1639, 0.30 #2971, 0.27 #3119), 0np9r (0.33 #464, 0.21 #3424, 0.21 #14553) >> Best rule #3126 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0m2l9; 01gf5h; 02whj; 01wp8w7; 0pkyh; 01wbz9; 0qf11; 03f0vvr; 016s_5; 0167km; ... >> query: (?x1398, 09jwl) <- award(?x1398, ?x10169), award(?x1398, ?x2322), award_winner(?x10169, ?x521), ?x2322 = 01ck6h >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j4ls profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 125.000 0.805 http://example.org/people/person/profession #15293-0f25w9 PRED entity: 0f25w9 PRED relation: nutrient PRED expected values: 025tkqy 0g5gq 0fzjh 07q0m 0f4kp 0f4k5 => 23 concepts (21 used for prediction) PRED predicted values (max 10 best out of 40): 025tkqy (0.77 #436, 0.75 #471, 0.75 #454), 0g5gq (0.77 #436, 0.75 #443, 0.75 #427), 0f4kp (0.77 #436, 0.71 #466, 0.69 #472), 01sh2 (0.77 #436, 0.71 #466, 0.69 #468), 0fzjh (0.77 #436, 0.71 #466, 0.69 #467), 06x4c (0.77 #436, 0.71 #466, 0.69 #469), 07q0m (0.77 #436, 0.71 #466, 0.69 #461), 0hkwr (0.77 #436, 0.71 #466, 0.64 #30), 02y_3rf (0.77 #436, 0.71 #466, 0.64 #30), 0466p20 (0.77 #436, 0.71 #466, 0.64 #30) >> Best rule #436 for best value: >> intensional similarity = 76 >> extensional distance = 10 >> proper extension: 06x4c; >> query: (?x1959, ?x10195) <- nutrient(?x1959, ?x13126), nutrient(?x1959, ?x12454), nutrient(?x1959, ?x9365), nutrient(?x1959, ?x6192), nutrient(?x1959, ?x6026), nutrient(?x1959, ?x5549), nutrient(?x1959, ?x4069), nutrient(?x1959, ?x2702), nutrient(?x1959, ?x1258), ?x12454 = 025rw19, nutrient(?x10612, ?x1258), nutrient(?x9732, ?x1258), nutrient(?x9489, ?x1258), nutrient(?x9005, ?x1258), nutrient(?x7719, ?x1258), nutrient(?x7057, ?x1258), nutrient(?x6285, ?x1258), nutrient(?x6191, ?x1258), nutrient(?x6032, ?x1258), nutrient(?x5373, ?x1258), nutrient(?x5009, ?x1258), nutrient(?x4068, ?x1258), nutrient(?x3900, ?x1258), nutrient(?x3468, ?x1258), nutrient(?x1303, ?x1258), nutrient(?x1257, ?x1258), nutrient(?x8298, ?x4069), nutrient(?x6159, ?x4069), nutrient(?x2701, ?x4069), ?x8298 = 037ls6, ?x6192 = 06jry, ?x9489 = 07j87, ?x6159 = 033cnk, ?x6285 = 01645p, ?x9005 = 04zpv, ?x7057 = 0fbdb, taxonomy(?x9365, ?x939), ?x6191 = 014j1m, ?x2702 = 0838f, ?x2701 = 0hkxq, ?x5009 = 0fjfh, ?x6026 = 025sf8g, ?x4068 = 0fbw6, ?x5549 = 025s7j4, ?x9732 = 05z55, ?x1257 = 09728, ?x1303 = 0fj52s, ?x3900 = 061_f, ?x10612 = 0frq6, ?x6032 = 01nkt, ?x7719 = 0dj75, ?x5373 = 0971v, ?x939 = 04n6k, nutrient(?x3468, ?x14210), nutrient(?x3468, ?x13944), nutrient(?x3468, ?x12902), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10453), nutrient(?x3468, ?x10195), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x6286), nutrient(?x3468, ?x5337), nutrient(?x3468, ?x2018), ?x10453 = 075pwf, ?x6286 = 02y_3rf, ?x10891 = 0g5gq, ?x13944 = 0f4kp, ?x2018 = 01sh2, ?x5337 = 06x4c, ?x14210 = 0f4k5, ?x12902 = 0fzjh, ?x9915 = 025tkqy, nutrient(?x8298, ?x9365), nutrient(?x9005, ?x13126), nutrient(?x3468, ?x9365), nutrient(?x2701, ?x13126) >> conf = 0.77 => this is the best rule for 12 predicted values ranks of expected_values: 1, 2, 3, 5, 7, 11 EVAL 0f25w9 nutrient 0f4k5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 23.000 21.000 0.766 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0f25w9 nutrient 0f4kp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 21.000 0.766 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0f25w9 nutrient 07q0m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 23.000 21.000 0.766 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0f25w9 nutrient 0fzjh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 23.000 21.000 0.766 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0f25w9 nutrient 0g5gq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 21.000 0.766 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0f25w9 nutrient 025tkqy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 21.000 0.766 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #15292-02ln0f PRED entity: 02ln0f PRED relation: fraternities_and_sororities PRED expected values: 035tlh => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 3): 0325pb (0.40 #61, 0.38 #58, 0.37 #67), 035tlh (0.31 #68, 0.31 #59, 0.29 #44), 04m8fy (0.08 #12, 0.05 #18, 0.05 #84) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 04hgpt; >> query: (?x5754, 0325pb) <- colors(?x5754, ?x1101), currency(?x5754, ?x170), school(?x12852, ?x5754), institution(?x865, ?x5754) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #68 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 05kj_; *> query: (?x5754, 035tlh) <- category(?x5754, ?x134), contains(?x94, ?x5754), school(?x12852, ?x5754) *> conf = 0.31 ranks of expected_values: 2 EVAL 02ln0f fraternities_and_sororities 035tlh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 187.000 187.000 0.400 http://example.org/education/university/fraternities_and_sororities #15291-01htzx PRED entity: 01htzx PRED relation: genre! PRED expected values: 01xr2s 05h95s 0h63q6t 04svwx => 44 concepts (27 used for prediction) PRED predicted values (max 10 best out of 763): 01hvv0 (0.73 #5504, 0.50 #1443, 0.40 #1968), 019g8j (0.73 #5504, 0.40 #2309, 0.40 #2046), 0ctzf1 (0.67 #3524, 0.60 #2216, 0.50 #3262), 01h72l (0.60 #1864, 0.50 #1339, 0.45 #3697), 019nnl (0.60 #2634, 0.50 #1322, 0.45 #3680), 0584r4 (0.60 #2641, 0.50 #1329, 0.36 #3687), 06y_n (0.60 #2802, 0.50 #1490, 0.36 #3848), 02r1ysd (0.57 #2992, 0.33 #638, 0.32 #4306), 07ng9k (0.56 #3417, 0.50 #3155, 0.50 #1585), 07g9f (0.53 #4116, 0.43 #3066, 0.40 #2544) >> Best rule #5504 for best value: >> intensional similarity = 14 >> extensional distance = 32 >> proper extension: 0jxy; >> query: (?x1844, ?x8017) <- genre(?x7488, ?x1844), genre(?x5938, ?x1844), country_of_origin(?x7488, ?x94), actor(?x5938, ?x2594), actor(?x5938, ?x478), genre(?x7488, ?x53), genre(?x5938, ?x809), program(?x6678, ?x7488), actor(?x8017, ?x2594), gender(?x2594, ?x514), type_of_union(?x478, ?x566), genre(?x670, ?x809), ?x53 = 07s9rl0, instrumentalists(?x316, ?x2594) >> conf = 0.73 => this is the best rule for 2 predicted values *> Best rule #1435 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 05p553; *> query: (?x1844, 05h95s) <- genre(?x10284, ?x1844), genre(?x10278, ?x1844), genre(?x9327, ?x1844), genre(?x8644, ?x1844), genre(?x6839, ?x1844), genre(?x5938, ?x1844), genre(?x4108, ?x1844), ?x5938 = 05f7w84, genre(?x6839, ?x1510), actor(?x4108, ?x4014), ?x10278 = 03r0rq, program_creator(?x9327, ?x1683), program(?x1394, ?x8644), actor(?x10284, ?x4327), ?x1510 = 01hmnh, titles(?x2008, ?x4108), country_of_origin(?x10284, ?x94) *> conf = 0.50 ranks of expected_values: 13, 24, 31, 116 EVAL 01htzx genre! 04svwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 44.000 27.000 0.727 http://example.org/tv/tv_program/genre EVAL 01htzx genre! 0h63q6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 44.000 27.000 0.727 http://example.org/tv/tv_program/genre EVAL 01htzx genre! 05h95s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 44.000 27.000 0.727 http://example.org/tv/tv_program/genre EVAL 01htzx genre! 01xr2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 44.000 27.000 0.727 http://example.org/tv/tv_program/genre #15290-0fgpvf PRED entity: 0fgpvf PRED relation: nominated_for! PRED expected values: 0gr0m 02qvyrt 02ppm4q => 88 concepts (78 used for prediction) PRED predicted values (max 10 best out of 205): 019f4v (0.64 #515, 0.39 #4924, 0.35 #6316), 0gq9h (0.59 #523, 0.47 #4932, 0.44 #6324), 0gr0m (0.55 #520, 0.36 #4929, 0.28 #6321), 0gr4k (0.50 #489, 0.31 #953, 0.30 #6290), 0l8z1 (0.50 #513, 0.30 #4922, 0.25 #6314), 040njc (0.50 #470, 0.27 #4879, 0.27 #6271), 02qyntr (0.50 #638, 0.26 #6439, 0.24 #1393), 02pqp12 (0.50 #519, 0.25 #6320, 0.24 #1393), 0gqwc (0.45 #289, 0.36 #521, 0.27 #6322), 02qvyrt (0.45 #556, 0.23 #2181, 0.22 #6357) >> Best rule #515 for best value: >> intensional similarity = 6 >> extensional distance = 20 >> proper extension: 0yyts; 02qr69m; 0c9k8; >> query: (?x695, 019f4v) <- genre(?x695, ?x53), nominated_for(?x749, ?x695), nominated_for(?x484, ?x695), film_release_region(?x695, ?x94), ?x749 = 094qd5, ?x484 = 0gq_v >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #520 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 20 *> proper extension: 0yyts; 02qr69m; 0c9k8; *> query: (?x695, 0gr0m) <- genre(?x695, ?x53), nominated_for(?x749, ?x695), nominated_for(?x484, ?x695), film_release_region(?x695, ?x94), ?x749 = 094qd5, ?x484 = 0gq_v *> conf = 0.55 ranks of expected_values: 3, 10, 20 EVAL 0fgpvf nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 88.000 78.000 0.636 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fgpvf nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 88.000 78.000 0.636 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fgpvf nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 88.000 78.000 0.636 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15289-0mn0v PRED entity: 0mn0v PRED relation: category PRED expected values: 08mbj5d => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #64, 0.78 #101, 0.78 #108) >> Best rule #64 for best value: >> intensional similarity = 3 >> extensional distance = 196 >> proper extension: 0tct_; 0fvvg; 0t_48; 0dzs0; >> query: (?x2673, 08mbj5d) <- time_zones(?x2673, ?x2674), ?x2674 = 02hcv8, place(?x2673, ?x2673) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mn0v category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.778 http://example.org/common/topic/webpage./common/webpage/category #15288-0fhzwl PRED entity: 0fhzwl PRED relation: category PRED expected values: 08mbj5d => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.45 #8, 0.44 #7, 0.43 #6) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 01h72l; 02kk_c; 0c3xpwy; 03czz87; >> query: (?x8870, 08mbj5d) <- actor(?x8870, ?x879), nominated_for(?x7382, ?x8870), honored_for(?x762, ?x8870) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fhzwl category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.450 http://example.org/common/topic/webpage./common/webpage/category #15287-0853g PRED entity: 0853g PRED relation: exported_to PRED expected values: 0chghy 0d05w3 => 187 concepts (179 used for prediction) PRED predicted values (max 10 best out of 76): 07fsv (0.27 #597, 0.25 #657, 0.09 #2299), 047t_ (0.27 #590, 0.25 #650, 0.07 #2292), 0345h (0.25 #202, 0.20 #324, 0.15 #1904), 03rjj (0.25 #185, 0.20 #307, 0.13 #1887), 059j2 (0.25 #201, 0.20 #323, 0.13 #1903), 0f8l9c (0.25 #196, 0.20 #318, 0.10 #1898), 06t2t (0.25 #215, 0.20 #337, 0.10 #1917), 06mkj (0.25 #212, 0.20 #334, 0.08 #1914), 06mzp (0.25 #195, 0.20 #317, 0.03 #1897), 03rt9 (0.25 #191, 0.20 #313, 0.03 #1893) >> Best rule #597 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0b90_r; 03rjj; 0chghy; 0ctw_b; 02wt0; >> query: (?x11743, 07fsv) <- featured_film_locations(?x573, ?x11743), exported_to(?x11743, ?x94), award(?x573, ?x1107) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1918 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 05r4w; 06s_2; *> query: (?x11743, 0d05w3) <- exported_to(?x11743, ?x94), contains(?x94, ?x95), geographic_distribution(?x1423, ?x94) *> conf = 0.13 ranks of expected_values: 20, 22 EVAL 0853g exported_to 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 187.000 179.000 0.273 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to EVAL 0853g exported_to 0chghy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 187.000 179.000 0.273 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #15286-01bt59 PRED entity: 01bt59 PRED relation: major_field_of_study! PRED expected values: 0bthb 078bz 02gr81 07vyf => 56 concepts (21 used for prediction) PRED predicted values (max 10 best out of 617): 01w3v (0.71 #2868, 0.69 #6302, 0.67 #4586), 04rwx (0.71 #2893, 0.67 #2322, 0.62 #3467), 09f2j (0.69 #5883, 0.67 #5311, 0.64 #7026), 01j_cy (0.67 #5184, 0.62 #5756, 0.57 #6899), 025v3k (0.67 #5270, 0.62 #5842, 0.57 #6985), 05zl0 (0.67 #5362, 0.62 #5934, 0.57 #7077), 07t90 (0.62 #3584, 0.58 #5300, 0.56 #4155), 01qqv5 (0.62 #3789, 0.57 #3215, 0.56 #4360), 065y4w7 (0.62 #6301, 0.61 #8018, 0.60 #8589), 07w0v (0.58 #5164, 0.57 #2874, 0.56 #4019) >> Best rule #2868 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 02lp1; >> query: (?x10264, 01w3v) <- major_field_of_study(?x11516, ?x10264), major_field_of_study(?x1681, ?x10264), major_field_of_study(?x1667, ?x10264), major_field_of_study(?x331, ?x10264), ?x1667 = 03v6t, major_field_of_study(?x1771, ?x10264), currency(?x11516, ?x170), ?x1681 = 07szy, ?x331 = 01jssp, institution(?x1771, ?x12863), institution(?x1771, ?x8715), ?x12863 = 02c9dj, ?x8715 = 01wv24 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1857 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 09s1f; *> query: (?x10264, 07vyf) <- major_field_of_study(?x8363, ?x10264), major_field_of_study(?x2830, ?x10264), major_field_of_study(?x1681, ?x10264), major_field_of_study(?x1667, ?x10264), major_field_of_study(?x331, ?x10264), institution(?x865, ?x1667), ?x331 = 01jssp, ?x865 = 02h4rq6, ?x8363 = 0k__z, currency(?x1667, ?x170), fraternities_and_sororities(?x1667, ?x3697), contains(?x2831, ?x2830), school(?x465, ?x1681) *> conf = 0.50 ranks of expected_values: 26, 28, 34, 158 EVAL 01bt59 major_field_of_study! 07vyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 56.000 21.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01bt59 major_field_of_study! 02gr81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 56.000 21.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01bt59 major_field_of_study! 078bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 56.000 21.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01bt59 major_field_of_study! 0bthb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 56.000 21.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #15285-09ggk PRED entity: 09ggk PRED relation: colors! PRED expected values: 01ync 04l5b4 => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 961): 04l5d0 (0.60 #2360, 0.52 #358, 0.43 #3446), 0j5m6 (0.60 #2249, 0.52 #358, 0.43 #3335), 0329gm (0.52 #358, 0.50 #2769, 0.33 #612), 07k53y (0.52 #358, 0.50 #1084, 0.33 #368), 051vz (0.52 #358, 0.40 #2189, 0.38 #3632), 03lpp_ (0.52 #358, 0.40 #2160, 0.38 #3603), 05tfm (0.52 #358, 0.40 #2178, 0.38 #3621), 03915c (0.52 #358, 0.40 #2398, 0.38 #3841), 0jm9w (0.52 #358, 0.40 #2373, 0.38 #3816), 04b5l3 (0.52 #358, 0.40 #2434, 0.38 #3877) >> Best rule #2360 for best value: >> intensional similarity = 36 >> extensional distance = 3 >> proper extension: 01g5v; >> query: (?x9778, 04l5d0) <- colors(?x12370, ?x9778), colors(?x12141, ?x9778), colors(?x9412, ?x9778), colors(?x3777, ?x9778), team(?x9908, ?x12370), team(?x8527, ?x12370), team(?x7042, ?x12370), team(?x5258, ?x12370), team(?x2302, ?x12370), ?x9908 = 0b_6lb, ?x5258 = 0b_6h7, school(?x685, ?x3777), position(?x12141, ?x4747), position(?x12141, ?x1579), major_field_of_study(?x3777, ?x1154), ?x4747 = 02sf_r, ?x7042 = 0b_72t, ?x1154 = 02lp1, currency(?x3777, ?x170), team(?x4570, ?x12370), team(?x13842, ?x12141), current_club(?x4972, ?x9412), contains(?x94, ?x3777), ?x2302 = 0b_77q, team(?x1579, ?x10409), profession(?x13842, ?x319), school(?x3333, ?x3777), organization(?x346, ?x3777), ?x4570 = 03558l, ?x8527 = 0b_6v_, sport(?x9412, ?x471), ?x10409 = 0jmh7, season(?x3333, ?x701), school(?x3333, ?x6177), sport(?x12141, ?x4833), ?x6177 = 01tx9m >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 43 *> extensional distance = 1 *> proper extension: 083jv; *> query: (?x9778, 01ync) <- colors(?x12370, ?x9778), colors(?x12141, ?x9778), colors(?x9412, ?x9778), colors(?x2148, ?x9778), colors(?x13610, ?x9778), colors(?x7900, ?x9778), colors(?x6675, ?x9778), colors(?x3136, ?x9778), team(?x9974, ?x12370), team(?x9908, ?x12370), team(?x5258, ?x12370), colors(?x12370, ?x332), ?x9908 = 0b_6lb, ?x5258 = 0b_6h7, ?x12141 = 0jmk7, ?x9412 = 01r5xw, ?x13610 = 02kj7g, team(?x2312, ?x2148), team(?x2147, ?x2148), team(?x1792, ?x2148), team(?x706, ?x2148), team(?x180, ?x2148), ?x180 = 01r3hr, ?x706 = 02vkdwz, registering_agency(?x3136, ?x1982), ?x2312 = 02qpbqj, ?x7900 = 02nvg1, student(?x3136, ?x5366), ?x9974 = 0b_6pv, position(?x12370, ?x4570), school_type(?x6675, ?x3092), currency(?x3136, ?x170), institution(?x865, ?x6675), ?x4570 = 03558l, contains(?x94, ?x3136), ?x1792 = 05zm34, colors(?x11789, ?x332), colors(?x5733, ?x332), colors(?x4780, ?x332), ?x11789 = 02pyyld, ?x2147 = 04nfpk, ?x5733 = 03zj9, ?x4780 = 017cy9 *> conf = 0.33 ranks of expected_values: 160, 257 EVAL 09ggk colors! 04l5b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.600 http://example.org/sports/sports_team/colors EVAL 09ggk colors! 01ync CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 20.000 20.000 0.600 http://example.org/sports/sports_team/colors #15284-03hj5lq PRED entity: 03hj5lq PRED relation: featured_film_locations PRED expected values: 0xn5b 0xszy => 80 concepts (38 used for prediction) PRED predicted values (max 10 best out of 66): 02_286 (0.21 #979, 0.17 #2416, 0.16 #1937), 0h7h6 (0.12 #43, 0.03 #521, 0.03 #1481), 0kygv (0.12 #141), 030qb3t (0.08 #278, 0.08 #1956, 0.08 #2195), 04jpl (0.08 #248, 0.08 #6006, 0.07 #1207), 0rh6k (0.04 #2397, 0.04 #1918, 0.03 #2157), 07b_l (0.04 #315, 0.02 #1035, 0.02 #554), 01_d4 (0.03 #2443, 0.02 #1485, 0.02 #1964), 0d6lp (0.03 #549, 0.02 #789, 0.02 #310), 080h2 (0.03 #2180, 0.03 #1941, 0.03 #6740) >> Best rule #979 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 02wk7b; >> query: (?x6076, 02_286) <- nominated_for(?x1972, ?x6076), ?x1972 = 0gqyl, titles(?x53, ?x6076), film_release_distribution_medium(?x6076, ?x81) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03hj5lq featured_film_locations 0xszy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 38.000 0.210 http://example.org/film/film/featured_film_locations EVAL 03hj5lq featured_film_locations 0xn5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 38.000 0.210 http://example.org/film/film/featured_film_locations #15283-063vn PRED entity: 063vn PRED relation: type_of_union PRED expected values: 04ztj => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.85 #206, 0.84 #182, 0.82 #186), 01g63y (0.61 #324, 0.57 #117, 0.54 #319), 01bl8s (0.22 #214, 0.02 #152, 0.02 #180) >> Best rule #206 for best value: >> intensional similarity = 5 >> extensional distance = 102 >> proper extension: 01k31p; >> query: (?x1984, 04ztj) <- profession(?x1984, ?x5805), profession(?x1984, ?x3342), ?x5805 = 0fj9f, profession(?x8462, ?x3342), location(?x8462, ?x961) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 063vn type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.846 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15282-04y9mm8 PRED entity: 04y9mm8 PRED relation: genre PRED expected values: 02kdv5l => 88 concepts (33 used for prediction) PRED predicted values (max 10 best out of 91): 02kdv5l (0.71 #1065, 0.62 #2010, 0.61 #1655), 03k9fj (0.67 #246, 0.50 #128, 0.48 #1899), 07s9rl0 (0.58 #709, 0.56 #591, 0.50 #945), 02xlf (0.50 #288, 0.50 #170, 0.33 #52), 02l7c8 (0.50 #722, 0.38 #958, 0.38 #486), 09kqc (0.50 #236, 0.33 #354, 0.02 #2007), 0lsxr (0.43 #1069, 0.34 #2486, 0.34 #3672), 01hmnh (0.34 #1905, 0.32 #2850, 0.32 #1668), 0hfjk (0.33 #62, 0.17 #298, 0.11 #652), 0gsy3b (0.29 #447, 0.08 #1273, 0.03 #1745) >> Best rule #1065 for best value: >> intensional similarity = 5 >> extensional distance = 47 >> proper extension: 09rfh9; >> query: (?x6681, 02kdv5l) <- production_companies(?x6681, ?x3462), country(?x6681, ?x94), genre(?x6681, ?x812), ?x812 = 01jfsb, prequel(?x7678, ?x6681) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04y9mm8 genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 33.000 0.714 http://example.org/film/film/genre #15281-03cvvlg PRED entity: 03cvvlg PRED relation: honored_for! PRED expected values: 058m5m4 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 106): 05zksls (0.07 #4515, 0.04 #28, 0.02 #1736), 09gkdln (0.07 #4515, 0.03 #350, 0.02 #1814), 09p30_ (0.07 #4515, 0.03 #1780, 0.02 #1170), 0hr6lkl (0.07 #4515, 0.03 #1720, 0.02 #1110), 0hndn2q (0.07 #4515, 0.03 #398, 0.03 #520), 0275n3y (0.07 #4515, 0.02 #1772, 0.02 #4090), 0drtv8 (0.07 #4515, 0.02 #1763, 0.02 #1885), 0bzmt8 (0.07 #4515, 0.02 #1182, 0.01 #1792), 058m5m4 (0.07 #4515, 0.02 #167, 0.02 #289), 073hmq (0.07 #4515, 0.02 #991, 0.02 #259) >> Best rule #4515 for best value: >> intensional similarity = 4 >> extensional distance = 832 >> proper extension: 0gtvrv3; >> query: (?x8438, ?x1601) <- language(?x8438, ?x254), nominated_for(?x4666, ?x8438), place_of_birth(?x4666, ?x3014), award_winner(?x1601, ?x4666) >> conf = 0.07 => this is the best rule for 14 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 9 EVAL 03cvvlg honored_for! 058m5m4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 82.000 82.000 0.071 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15280-069d71 PRED entity: 069d71 PRED relation: sibling PRED expected values: 069d68 => 124 concepts (64 used for prediction) PRED predicted values (max 10 best out of 15): 016cff (0.10 #535, 0.03 #581, 0.02 #1238), 015z4j (0.10 #490, 0.03 #581, 0.02 #1193), 051q39 (0.03 #581), 069d71 (0.03 #581), 01my95 (0.03 #581), 09n70c (0.03 #581), 03xyp_ (0.03 #581), 01qx13 (0.03 #581), 01gkmx (0.02 #898, 0.02 #1131), 02v60l (0.02 #857, 0.02 #1090) >> Best rule #535 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 019tzd; >> query: (?x13333, 016cff) <- athlete(?x1557, ?x13333), country(?x1557, ?x172), ?x172 = 0154j >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 069d71 sibling 069d68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 64.000 0.100 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #15279-0bkg4 PRED entity: 0bkg4 PRED relation: gender PRED expected values: 05zppz => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #85, 0.86 #53, 0.86 #25), 02zsn (0.46 #239, 0.38 #76, 0.34 #84) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 141 >> proper extension: 0770cd; >> query: (?x3867, 05zppz) <- profession(?x3867, ?x131), role(?x3867, ?x227), group(?x3867, ?x8999), artists(?x1000, ?x3867) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bkg4 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.867 http://example.org/people/person/gender #15278-047jhq PRED entity: 047jhq PRED relation: nationality PRED expected values: 03rk0 => 128 concepts (88 used for prediction) PRED predicted values (max 10 best out of 34): 03rk0 (0.81 #2751, 0.57 #46, 0.26 #5837), 09c7w0 (0.77 #4924, 0.76 #3712, 0.76 #3409), 075_t2 (0.42 #5330, 0.41 #3914, 0.33 #6648), 055vr (0.26 #5837, 0.26 #6142), 02jx1 (0.18 #2234, 0.15 #2435, 0.14 #1834), 07ssc (0.17 #916, 0.15 #2216, 0.13 #2316), 0d060g (0.10 #107, 0.08 #2812, 0.08 #4023), 0j5g9 (0.10 #162, 0.05 #1263, 0.05 #1163), 012m_ (0.10 #291, 0.05 #1592, 0.03 #1992), 0d05w3 (0.09 #350, 0.06 #1051, 0.05 #3058) >> Best rule #2751 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 099ks0; >> query: (?x12616, 03rk0) <- award_winner(?x10156, ?x12616), award(?x3129, ?x10156), nominated_for(?x10156, ?x4444), ?x4444 = 09fn1w >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047jhq nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 88.000 0.815 http://example.org/people/person/nationality #15277-014xf6 PRED entity: 014xf6 PRED relation: institution! PRED expected values: 02h4rq6 02_xgp2 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 22): 02_xgp2 (0.81 #300, 0.81 #344, 0.55 #608), 02h4rq6 (0.81 #291, 0.79 #335, 0.65 #1084), 03bwzr4 (0.77 #302, 0.76 #346, 0.47 #610), 014mlp (0.69 #293, 0.67 #1843, 0.66 #1086), 0bkj86 (0.62 #296, 0.60 #340, 0.50 #273), 04zx3q1 (0.54 #334, 0.52 #290, 0.33 #598), 07s6fsf (0.50 #289, 0.48 #333, 0.36 #45), 027f2w (0.44 #297, 0.43 #341, 0.30 #274), 02mjs7 (0.34 #247, 0.33 #269, 0.28 #225), 013zdg (0.31 #295, 0.30 #339, 0.30 #228) >> Best rule #300 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 08815; 01jssp; 05krk; 052nd; 06pwq; 065y4w7; 07tgn; 04rwx; 07szy; 09kvv; ... >> query: (?x8223, 02_xgp2) <- student(?x8223, ?x2354), list(?x8223, ?x2197), award(?x2354, ?x1443) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 014xf6 institution! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.812 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014xf6 institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.812 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15276-0969vz PRED entity: 0969vz PRED relation: profession PRED expected values: 02jknp => 75 concepts (42 used for prediction) PRED predicted values (max 10 best out of 44): 01d_h8 (0.69 #6, 0.38 #155, 0.38 #751), 0dxtg (0.69 #14, 0.31 #3740, 0.28 #4932), 02jknp (0.69 #8, 0.27 #753, 0.26 #157), 03gjzk (0.21 #3741, 0.18 #1058, 0.18 #1803), 0cbd2 (0.19 #7, 0.12 #4031, 0.12 #1497), 09jwl (0.16 #2553, 0.16 #4937, 0.16 #4043), 01d30f (0.12 #71, 0.01 #518, 0.01 #667), 018gz8 (0.12 #2551, 0.11 #2849, 0.11 #5829), 015cjr (0.12 #199, 0.11 #348, 0.05 #497), 0np9r (0.12 #2555, 0.11 #5833, 0.11 #2853) >> Best rule #6 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 04rs03; 015npr; 0cc63l; 03fw4y; 0kvsb; 05q9g1; 071xj; 03hzkq; 05xd8x; 0285xqh; ... >> query: (?x7335, 01d_h8) <- profession(?x7335, ?x1032), type_of_union(?x7335, ?x566), ?x566 = 04ztj, award(?x7335, ?x4443), ?x4443 = 0b6k___ >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 04rs03; 015npr; 0cc63l; 03fw4y; 0kvsb; 05q9g1; 071xj; 03hzkq; 05xd8x; 0285xqh; ... *> query: (?x7335, 02jknp) <- profession(?x7335, ?x1032), type_of_union(?x7335, ?x566), ?x566 = 04ztj, award(?x7335, ?x4443), ?x4443 = 0b6k___ *> conf = 0.69 ranks of expected_values: 3 EVAL 0969vz profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 75.000 42.000 0.688 http://example.org/people/person/profession #15275-0h95927 PRED entity: 0h95927 PRED relation: executive_produced_by PRED expected values: 06q8hf => 81 concepts (45 used for prediction) PRED predicted values (max 10 best out of 83): 06q8hf (0.14 #924, 0.09 #6464, 0.09 #4701), 04jspq (0.05 #1412, 0.05 #908, 0.04 #3427), 02q42j_ (0.05 #894, 0.02 #2407, 0.02 #4671), 0b13g7 (0.05 #844, 0.02 #6384, 0.02 #4621), 0m593 (0.05 #912, 0.01 #3180), 06pj8 (0.05 #4590, 0.05 #6353, 0.03 #2075), 0glyyw (0.04 #6486, 0.04 #4723, 0.02 #11036), 048lv (0.04 #293, 0.02 #546, 0.02 #799), 01b9ck (0.04 #288, 0.02 #541, 0.02 #794), 02465 (0.04 #478, 0.02 #731) >> Best rule #924 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 07w8fz; >> query: (?x7651, 06q8hf) <- executive_produced_by(?x7651, ?x4060), nominated_for(?x1307, ?x7651), ?x1307 = 0gq9h >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h95927 executive_produced_by 06q8hf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 45.000 0.143 http://example.org/film/film/executive_produced_by #15274-03f1d47 PRED entity: 03f1d47 PRED relation: place_of_birth PRED expected values: 0z1vw => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 183): 02_286 (0.15 #1427, 0.14 #2131, 0.13 #7059), 01_d4 (0.15 #1474, 0.14 #2178, 0.06 #4290), 0h7h6 (0.12 #58, 0.02 #5690, 0.01 #16954), 0d9jr (0.11 #3714, 0.02 #52808, 0.01 #16386), 030qb3t (0.10 #7094, 0.04 #57790, 0.04 #58495), 0dclg (0.09 #782, 0.06 #3598, 0.04 #14158), 01snm (0.09 #943, 0.06 #3759, 0.02 #52808), 0r0ss (0.09 #1222, 0.06 #4038), 068p2 (0.09 #866, 0.02 #10018), 0v1xg (0.08 #1727, 0.07 #2431, 0.07 #3135) >> Best rule #1427 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0lbj1; 01vrz41; 01vs_v8; 09hnb; 0161sp; 01w7nwm; 0478__m; 016fnb; 0fhxv; 01xzb6; ... >> query: (?x4983, 02_286) <- award(?x4983, ?x4892), student(?x735, ?x4983), ?x4892 = 02f72_, award_nominee(?x3175, ?x4983) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03f1d47 place_of_birth 0z1vw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 103.000 0.154 http://example.org/people/person/place_of_birth #15273-02q87z6 PRED entity: 02q87z6 PRED relation: genre PRED expected values: 01jfsb => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 90): 01jfsb (0.74 #4353, 0.74 #3021, 0.72 #374), 04xvlr (0.74 #4353, 0.74 #3021, 0.72 #7856), 09blyk (0.74 #4353, 0.74 #3021, 0.72 #7856), 0lsxr (0.46 #370, 0.24 #1094, 0.23 #612), 02l7c8 (0.40 #16, 0.38 #1463, 0.34 #1825), 017fp (0.40 #15, 0.26 #1824, 0.22 #1462), 05p553 (0.37 #6530, 0.37 #5441, 0.37 #6289), 0c3351 (0.34 #399, 0.20 #37, 0.07 #641), 02kdv5l (0.33 #244, 0.32 #1691, 0.31 #3023), 060__y (0.29 #1464, 0.26 #1826, 0.22 #3522) >> Best rule #4353 for best value: >> intensional similarity = 4 >> extensional distance = 589 >> proper extension: 03kx49; 033pf1; >> query: (?x5964, ?x162) <- titles(?x162, ?x5964), music(?x5964, ?x4428), film(?x494, ?x5964), genre(?x144, ?x162) >> conf = 0.74 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 02q87z6 genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.738 http://example.org/film/film/genre #15272-02vzpb PRED entity: 02vzpb PRED relation: film_release_region PRED expected values: 0d0vqn 03gj2 0ctw_b => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 162): 0d0vqn (0.88 #523, 0.44 #182, 0.43 #11), 059j2 (0.85 #553, 0.33 #212, 0.31 #2090), 03rjj (0.84 #519, 0.43 #7, 0.33 #178), 06mkj (0.84 #582, 0.33 #241, 0.32 #2119), 0chghy (0.83 #527, 0.33 #186, 0.29 #2064), 07ssc (0.82 #534, 0.56 #193, 0.43 #22), 05qhw (0.81 #532, 0.33 #191, 0.29 #20), 03gj2 (0.81 #545, 0.33 #204, 0.29 #33), 03h64 (0.80 #592, 0.33 #251, 0.29 #80), 0345h (0.80 #555, 0.33 #214, 0.29 #43) >> Best rule #523 for best value: >> intensional similarity = 3 >> extensional distance = 191 >> proper extension: 01sby_; >> query: (?x10029, 0d0vqn) <- film(?x1641, ?x10029), film_release_region(?x10029, ?x2267), ?x2267 = 03rj0 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8, 26 EVAL 02vzpb film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 53.000 53.000 0.876 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzpb film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 53.000 53.000 0.876 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzpb film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.876 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15271-01vw37m PRED entity: 01vw37m PRED relation: profession PRED expected values: 08z956 => 102 concepts (99 used for prediction) PRED predicted values (max 10 best out of 63): 0dxtg (0.60 #9014, 0.51 #9739, 0.48 #5530), 02jknp (0.45 #5524, 0.44 #2182, 0.41 #9008), 0nbcg (0.44 #463, 0.43 #173, 0.41 #5108), 03gjzk (0.39 #4222, 0.36 #303, 0.36 #2189), 016z4k (0.38 #1019, 0.38 #1599, 0.37 #3050), 0d1pc (0.28 #7842, 0.26 #11033, 0.22 #628), 0n1h (0.28 #7842, 0.26 #11033, 0.19 #3057), 0fnpj (0.28 #7842, 0.26 #11033, 0.10 #492), 02dsz (0.28 #7842, 0.14 #198, 0.04 #779), 0kyk (0.28 #7842, 0.12 #461, 0.11 #1767) >> Best rule #9014 for best value: >> intensional similarity = 3 >> extensional distance = 1600 >> proper extension: 0d0vj4; 06y3r; 081t6; >> query: (?x6264, 0dxtg) <- profession(?x6264, ?x319), profession(?x11626, ?x319), ?x11626 = 01hdht >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1816 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 255 *> proper extension: 01xyt7; 02vptk_; 02_nkp; *> query: (?x6264, 08z956) <- currency(?x6264, ?x170), student(?x11415, ?x6264) *> conf = 0.02 ranks of expected_values: 42 EVAL 01vw37m profession 08z956 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 102.000 99.000 0.597 http://example.org/people/person/profession #15270-0dsvzh PRED entity: 0dsvzh PRED relation: nominated_for! PRED expected values: 0gqyl => 118 concepts (115 used for prediction) PRED predicted values (max 10 best out of 214): 04jhhng (0.70 #5215, 0.69 #5453, 0.67 #12571), 0gq9h (0.44 #9549, 0.42 #10498, 0.40 #11210), 019f4v (0.40 #2187, 0.38 #9541, 0.37 #10490), 0gqy2 (0.40 #595, 0.33 #121, 0.32 #2254), 099c8n (0.40 #531, 0.33 #57, 0.28 #3612), 027dtxw (0.40 #478, 0.33 #4, 0.21 #2137), 09sdmz (0.40 #617, 0.20 #854, 0.20 #380), 02x17s4 (0.40 #568, 0.20 #331, 0.17 #15179), 03hkv_r (0.40 #489, 0.20 #252, 0.16 #6416), 02x73k6 (0.40 #523, 0.20 #286, 0.16 #2182) >> Best rule #5215 for best value: >> intensional similarity = 3 >> extensional distance = 209 >> proper extension: 0124k9; 01b66d; 017f3m; 05p9_ql; 019g8j; >> query: (?x813, ?x2577) <- category(?x813, ?x134), award(?x813, ?x2577), nominated_for(?x995, ?x813) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #10516 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 451 *> proper extension: 04nl83; 02py4c8; 0dgst_d; 0k4kk; 0b76kw1; 0fpv_3_; 02q6gfp; 0gxfz; 097zcz; 02ppg1r; ... *> query: (?x813, 0gqyl) <- honored_for(?x762, ?x813), film(?x525, ?x813), nominated_for(?x995, ?x813) *> conf = 0.24 ranks of expected_values: 32 EVAL 0dsvzh nominated_for! 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 118.000 115.000 0.695 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15269-07r78j PRED entity: 07r78j PRED relation: sport PRED expected values: 02vx4 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.88 #520, 0.87 #529, 0.85 #780), 0z74 (0.47 #674, 0.46 #712, 0.44 #637), 0jm_ (0.23 #475, 0.20 #566, 0.19 #612), 03tmr (0.21 #254, 0.19 #309, 0.17 #372), 018w8 (0.19 #312, 0.17 #375, 0.16 #257), 018jz (0.14 #376, 0.14 #605, 0.13 #660), 039yzs (0.07 #315, 0.06 #378, 0.05 #260), 09xp_ (0.05 #615, 0.03 #755, 0.02 #661) >> Best rule #520 for best value: >> intensional similarity = 9 >> extensional distance = 73 >> proper extension: 03y_f8; 03yl2t; 044l47; 02rytm; 03_r_5; 035qlx; 037mp6; 02rqxc; 03yvgp; 0329nn; ... >> query: (?x1833, 02vx4) <- position(?x1833, ?x63), team(?x5471, ?x1833), team(?x5471, ?x8326), team(?x5471, ?x5914), team(?x60, ?x1833), team(?x1898, ?x5914), teams(?x8956, ?x5914), colors(?x8326, ?x663), teams(?x10537, ?x1833) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07r78j sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.880 http://example.org/sports/sports_team/sport #15268-0gj9qxr PRED entity: 0gj9qxr PRED relation: film_crew_role PRED expected values: 09zzb8 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 33): 09zzb8 (0.84 #696, 0.81 #2638, 0.77 #913), 01vx2h (0.76 #706, 0.69 #191, 0.63 #1328), 0dxtw (0.62 #190, 0.59 #705, 0.57 #922), 01pvkk (0.39 #1329, 0.39 #707, 0.38 #192), 02rh1dz (0.37 #704, 0.30 #1326, 0.28 #335), 02ynfr (0.31 #711, 0.26 #1333, 0.24 #269), 015h31 (0.24 #446, 0.21 #920, 0.21 #1545), 0215hd (0.24 #272, 0.19 #2656, 0.18 #714), 0d2b38 (0.22 #721, 0.18 #1012, 0.18 #830), 089g0h (0.20 #715, 0.16 #1337, 0.16 #2657) >> Best rule #696 for best value: >> intensional similarity = 10 >> extensional distance = 47 >> proper extension: 04fzfj; >> query: (?x1552, 09zzb8) <- film_release_distribution_medium(?x1552, ?x81), genre(?x1552, ?x1013), genre(?x1552, ?x225), film_crew_role(?x1552, ?x1171), film_crew_role(?x1552, ?x468), ?x1013 = 06n90, ?x1171 = 09vw2b7, ?x225 = 02kdv5l, country(?x1552, ?x512), ?x468 = 02r96rf >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gj9qxr film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.837 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15267-087vnr5 PRED entity: 087vnr5 PRED relation: music PRED expected values: 04bpm6 => 74 concepts (41 used for prediction) PRED predicted values (max 10 best out of 66): 03975z (0.17 #166, 0.11 #376, 0.10 #587), 02g1jh (0.17 #128, 0.10 #549, 0.02 #3079), 03c_8t (0.11 #420, 0.07 #842, 0.02 #2106), 06fxnf (0.11 #279, 0.04 #3020, 0.03 #701), 0jn5l (0.11 #306, 0.03 #728, 0.02 #1150), 02g40r (0.11 #395, 0.03 #817), 04ls53 (0.10 #500, 0.02 #3030, 0.02 #2186), 0gv07g (0.10 #553), 02bh9 (0.07 #894, 0.04 #3212, 0.03 #4272), 018grr (0.07 #6756, 0.06 #4008, 0.06 #6121) >> Best rule #166 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 034qzw; 01shy7; 08952r; 05fm6m; >> query: (?x8492, 03975z) <- nominated_for(?x2101, ?x8492), genre(?x8492, ?x225), ?x2101 = 018grr, film(?x5019, ?x8492) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1080 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 119 *> proper extension: 0bmc4cm; *> query: (?x8492, 04bpm6) <- country(?x8492, ?x94), nominated_for(?x3190, ?x8492), film_regional_debut_venue(?x8492, ?x4356) *> conf = 0.02 ranks of expected_values: 56 EVAL 087vnr5 music 04bpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 74.000 41.000 0.167 http://example.org/film/film/music #15266-02swsm PRED entity: 02swsm PRED relation: artist PRED expected values: 015cxv 01323p 0bk1p 04d_mtq => 52 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1043): 0134s5 (0.50 #233, 0.44 #3521, 0.40 #1055), 01k23t (0.38 #3022, 0.20 #13709, 0.19 #15354), 07c0j (0.38 #3341, 0.25 #53, 0.20 #875), 01vtj38 (0.31 #3809, 0.25 #521, 0.23 #2987), 0qf3p (0.31 #3438, 0.25 #150, 0.20 #972), 0178kd (0.31 #3734, 0.25 #446, 0.20 #1268), 03xhj6 (0.31 #2770, 0.25 #3592, 0.25 #304), 01vxlbm (0.31 #2729, 0.18 #10128, 0.17 #13416), 0g824 (0.31 #2915, 0.17 #13602, 0.17 #15247), 01w60_p (0.31 #2582, 0.17 #14914, 0.16 #9981) >> Best rule #233 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 01cf93; 03qy3l; >> query: (?x12467, 0134s5) <- artist(?x12467, ?x3867), artist(?x12467, ?x2363), ?x2363 = 01hw6wq, category(?x12467, ?x134), nationality(?x3867, ?x1310), artists(?x1000, ?x3867) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3939 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 033hn8; 0g768; 041p3y; 05808s; *> query: (?x12467, 0bk1p) <- artist(?x12467, ?x2363), music(?x508, ?x2363), artist(?x9243, ?x2363), ?x9243 = 03qy3l, artists(?x597, ?x2363) *> conf = 0.25 ranks of expected_values: 14, 75, 857, 925 EVAL 02swsm artist 04d_mtq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 40.000 0.500 http://example.org/music/record_label/artist EVAL 02swsm artist 0bk1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 52.000 40.000 0.500 http://example.org/music/record_label/artist EVAL 02swsm artist 01323p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 52.000 40.000 0.500 http://example.org/music/record_label/artist EVAL 02swsm artist 015cxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 40.000 0.500 http://example.org/music/record_label/artist #15265-02773nt PRED entity: 02773nt PRED relation: profession PRED expected values: 03gjzk => 103 concepts (102 used for prediction) PRED predicted values (max 10 best out of 64): 03gjzk (0.83 #1803, 0.82 #1952, 0.82 #2101), 02hrh1q (0.79 #3590, 0.78 #5081, 0.77 #163), 01d_h8 (0.49 #1794, 0.48 #1645, 0.43 #6), 018gz8 (0.43 #17, 0.34 #166, 0.29 #315), 0cbd2 (0.31 #752, 0.29 #7, 0.28 #8347), 02krf9 (0.28 #2113, 0.28 #1964, 0.28 #8347), 0np9r (0.28 #8347, 0.27 #4322, 0.25 #12073), 02jknp (0.27 #4322, 0.25 #12073, 0.25 #12372), 09jwl (0.27 #4322, 0.25 #12073, 0.25 #12372), 0nbcg (0.27 #4322, 0.25 #12073, 0.25 #12372) >> Best rule #1803 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 07nznf; 0grwj; 0187y5; 04yj5z; 0bg539; 07s6tbm; 0162c8; 01n4f8; 0gz5hs; 01pcmd; ... >> query: (?x829, 03gjzk) <- gender(?x829, ?x231), award_nominee(?x881, ?x829), producer_type(?x829, ?x632) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02773nt profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 102.000 0.833 http://example.org/people/person/profession #15264-075wx7_ PRED entity: 075wx7_ PRED relation: film_crew_role PRED expected values: 02ynfr => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 26): 089g0h (0.63 #264, 0.59 #451, 0.14 #1610), 0dxtw (0.49 #726, 0.45 #257, 0.42 #413), 02_n3z (0.43 #249, 0.42 #436, 0.10 #718), 01pvkk (0.39 #103, 0.36 #227, 0.32 #727), 02rh1dz (0.25 #39, 0.19 #194, 0.18 #8), 02ynfr (0.22 #603, 0.22 #44, 0.22 #730), 033smt (0.19 #458, 0.18 #271, 0.10 #740), 015h31 (0.19 #255, 0.19 #724, 0.16 #442), 0263ycg (0.16 #263, 0.15 #450, 0.05 #732), 089fss (0.13 #253, 0.10 #440, 0.10 #502) >> Best rule #264 for best value: >> intensional similarity = 5 >> extensional distance = 99 >> proper extension: 04grkmd; 04ydr95; 0b7l4x; 07p12s; >> query: (?x1721, 089g0h) <- film_crew_role(?x1721, ?x4305), film_crew_role(?x1721, ?x137), ?x4305 = 0215hd, ?x137 = 09zzb8, film(?x561, ?x1721) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #603 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 167 *> proper extension: 0g5q34q; 0gh6j94; *> query: (?x1721, 02ynfr) <- film_crew_role(?x1721, ?x137), featured_film_locations(?x1721, ?x7468), films(?x7173, ?x1721), genre(?x1721, ?x53) *> conf = 0.22 ranks of expected_values: 6 EVAL 075wx7_ film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 108.000 108.000 0.634 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15263-051vz PRED entity: 051vz PRED relation: season PRED expected values: 0dx84s => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 7): 0dx84s (0.90 #348, 0.87 #179, 0.86 #158), 05kcgsf (0.58 #253, 0.57 #155, 0.53 #183), 04110b0 (0.43 #157, 0.37 #255, 0.36 #150), 02h7s73 (0.43 #159, 0.36 #337, 0.33 #180), 03c6s24 (0.36 #337, 0.32 #366, 0.30 #396), 03c74_8 (0.36 #337, 0.32 #366, 0.30 #396), 04n36qk (0.20 #21, 0.18 #374, 0.17 #42) >> Best rule #348 for best value: >> intensional similarity = 10 >> extensional distance = 29 >> proper extension: 07147; 03m1n; >> query: (?x2174, 0dx84s) <- position(?x2174, ?x2010), season(?x2174, ?x2406), school(?x2174, ?x3948), school(?x2174, ?x1884), draft(?x2174, ?x1161), student(?x3948, ?x1068), student(?x1884, ?x1815), major_field_of_study(?x1884, ?x1668), organization(?x5510, ?x1884), fraternities_and_sororities(?x3948, ?x4348) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051vz season 0dx84s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.903 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #15262-016khd PRED entity: 016khd PRED relation: actor! PRED expected values: 02r2j8 => 106 concepts (74 used for prediction) PRED predicted values (max 10 best out of 164): 0524b41 (0.12 #131, 0.02 #5687, 0.01 #1452), 03cv_gy (0.12 #94, 0.02 #1415, 0.01 #5650), 03ctqqf (0.12 #238), 03j63k (0.12 #134), 05rfst (0.10 #13252, 0.09 #10326, 0.09 #14313), 02rcwq0 (0.10 #13252, 0.09 #14313, 0.09 #5292), 0yx1m (0.10 #13252, 0.09 #14313, 0.09 #5292), 0sxkh (0.10 #13252, 0.09 #14313, 0.09 #5292), 02rx2m5 (0.10 #13252, 0.09 #14313, 0.09 #5292), 0c0yh4 (0.10 #13252, 0.09 #14313, 0.09 #5292) >> Best rule #131 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 07vc_9; 03h2d4; >> query: (?x851, 0524b41) <- film(?x851, ?x5964), film(?x851, ?x1038), ?x1038 = 05q96q6, film_crew_role(?x5964, ?x137) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016khd actor! 02r2j8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 74.000 0.125 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #15261-01g5kv PRED entity: 01g5kv PRED relation: profession PRED expected values: 0dxtg => 69 concepts (50 used for prediction) PRED predicted values (max 10 best out of 64): 0dxtg (0.64 #1188, 0.61 #2218, 0.61 #4570), 03gjzk (0.42 #454, 0.35 #5748, 0.31 #4571), 09jwl (0.31 #752, 0.28 #311, 0.28 #1046), 0cbd2 (0.27 #6035, 0.18 #594, 0.17 #888), 02krf9 (0.24 #1348, 0.22 #1643, 0.22 #1201), 0nbcg (0.21 #324, 0.20 #765, 0.19 #177), 016z4k (0.18 #739, 0.17 #1033, 0.15 #298), 0dz3r (0.17 #737, 0.16 #149, 0.15 #296), 0np9r (0.16 #460, 0.14 #901, 0.14 #754), 012t_z (0.15 #452, 0.14 #11, 0.11 #158) >> Best rule #1188 for best value: >> intensional similarity = 6 >> extensional distance = 284 >> proper extension: 03gm48; 06w33f8; 0j_c; 01ycck; 03xp8d5; 03nk3t; 0gv5c; 051wwp; 03cn92; 01d5vk; ... >> query: (?x13000, 0dxtg) <- profession(?x13000, ?x1032), profession(?x13000, ?x524), profession(?x13000, ?x319), ?x524 = 02jknp, ?x1032 = 02hrh1q, ?x319 = 01d_h8 >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01g5kv profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 50.000 0.636 http://example.org/people/person/profession #15260-02j4sk PRED entity: 02j4sk PRED relation: place_of_death PRED expected values: 027l4q => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 50): 04jpl (0.33 #396, 0.12 #1563, 0.12 #2540), 030qb3t (0.25 #1578, 0.19 #7430, 0.19 #1774), 027l4q (0.20 #333, 0.08 #1109, 0.07 #1499), 04vmp (0.15 #2641, 0.12 #1664, 0.07 #3032), 0k_p5 (0.12 #1840, 0.11 #2231, 0.08 #2621), 0k049 (0.12 #2732, 0.10 #7605, 0.10 #6242), 02_286 (0.11 #9560, 0.09 #9754, 0.08 #12484), 0fpzwf (0.09 #9937, 0.09 #1751, 0.08 #10327), 04ykg (0.09 #9937, 0.09 #1751, 0.08 #10327), 06_kh (0.08 #4294, 0.07 #6829, 0.07 #7024) >> Best rule #396 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0l99s; >> query: (?x10219, 04jpl) <- student(?x3948, ?x10219), award(?x10219, ?x2071), people(?x4322, ?x10219), sibling(?x6336, ?x10219) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #333 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0hsn_; *> query: (?x10219, 027l4q) <- student(?x3948, ?x10219), award(?x10219, ?x2071), religion(?x10219, ?x1624), ?x3948 = 025v3k *> conf = 0.20 ranks of expected_values: 3 EVAL 02j4sk place_of_death 027l4q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 173.000 173.000 0.333 http://example.org/people/deceased_person/place_of_death #15259-02825nf PRED entity: 02825nf PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #57, 0.82 #46, 0.82 #213), 02nxhr (0.11 #7, 0.07 #22, 0.06 #32), 07c52 (0.08 #8, 0.07 #38, 0.07 #69), 07z4p (0.06 #71, 0.05 #66, 0.04 #40) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 271 >> proper extension: 0kv2hv; 04tc1g; 05cj_j; 01_1pv; 03lrqw; 01f7kl; 03h3x5; 0bmpm; 014l6_; 0glnm; ... >> query: (?x7629, 029j_) <- film(?x541, ?x7629), music(?x7629, ?x12768), genre(?x7629, ?x258), ?x258 = 05p553 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02825nf film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15258-02w86hz PRED entity: 02w86hz PRED relation: edited_by PRED expected values: 02qfk4j => 105 concepts (70 used for prediction) PRED predicted values (max 10 best out of 16): 03q8ch (0.08 #464, 0.07 #130, 0.07 #703), 027pdrh (0.05 #216, 0.02 #610, 0.02 #790), 04cy8rb (0.05 #330, 0.05 #299, 0.03 #207), 02qggqc (0.05 #363, 0.04 #90, 0.04 #270), 04wp63 (0.04 #143, 0.04 #293, 0.03 #202), 0343h (0.04 #124, 0.03 #154, 0.03 #458), 06cv1 (0.02 #513), 0bn3jg (0.02 #295, 0.01 #1017, 0.01 #1288), 0bs1yy (0.02 #278, 0.01 #432, 0.01 #731), 03_gd (0.02 #271, 0.01 #425) >> Best rule #464 for best value: >> intensional similarity = 7 >> extensional distance = 104 >> proper extension: 03qcfvw; 034qmv; 03g90h; 01gc7; 047gn4y; 0m2kd; 0p_sc; 017gl1; 0p9lw; 01_mdl; ... >> query: (?x3742, 03q8ch) <- genre(?x3742, ?x811), film(?x12062, ?x3742), film_release_region(?x3742, ?x94), award(?x12062, ?x4687), titles(?x3741, ?x3742), ?x811 = 03k9fj, music(?x3742, ?x3890) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02w86hz edited_by 02qfk4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 70.000 0.075 http://example.org/film/film/edited_by #15257-01kt17 PRED entity: 01kt17 PRED relation: film PRED expected values: 0cwy47 => 88 concepts (56 used for prediction) PRED predicted values (max 10 best out of 764): 049xgc (0.24 #974, 0.05 #4558, 0.04 #6350), 0k0rf (0.20 #10753, 0.18 #17922), 0dr_4 (0.19 #247, 0.05 #55556, 0.04 #60933), 04vr_f (0.19 #171, 0.05 #55556, 0.04 #60933), 03shpq (0.10 #1449, 0.05 #5033, 0.04 #6825), 09p7fh (0.10 #406, 0.05 #55556, 0.03 #53763), 0661ql3 (0.10 #386, 0.04 #60933, 0.03 #100361), 011xg5 (0.10 #1435, 0.03 #53763, 0.03 #5019), 040_lv (0.10 #1049, 0.03 #6425, 0.03 #4633), 0bpm4yw (0.10 #725, 0.03 #4309, 0.01 #6101) >> Best rule #974 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 07vc_9; 0hvb2; 01y665; 0gy6z9; 01vxxb; 0bksh; 05xf75; >> query: (?x9256, 049xgc) <- award_nominee(?x9256, ?x3101), place_of_birth(?x9256, ?x9699), ?x3101 = 0dvmd >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #1933 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 02m30v; *> query: (?x9256, 0cwy47) <- spouse(?x6852, ?x9256), people(?x6720, ?x9256), risk_factors(?x6720, ?x231) *> conf = 0.04 ranks of expected_values: 209 EVAL 01kt17 film 0cwy47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 88.000 56.000 0.238 http://example.org/film/actor/film./film/performance/film #15256-01mvth PRED entity: 01mvth PRED relation: nationality PRED expected values: 09c7w0 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 80): 09c7w0 (0.75 #4108, 0.74 #3003, 0.74 #2303), 07ssc (0.40 #5913, 0.12 #816, 0.11 #1116), 0345h (0.20 #31, 0.02 #4238, 0.02 #4440), 02jx1 (0.10 #834, 0.10 #1134, 0.10 #7150), 03rk0 (0.09 #3551, 0.09 #3451, 0.09 #4253), 0d060g (0.07 #1308, 0.07 #2509, 0.06 #2009), 03_3d (0.07 #1608, 0.06 #2508, 0.06 #2008), 03rt9 (0.06 #4308, 0.04 #213, 0.03 #413), 027jk (0.06 #4308, 0.01 #4309), 05b7q (0.06 #4308, 0.01 #4309) >> Best rule #4108 for best value: >> intensional similarity = 2 >> extensional distance = 916 >> proper extension: 049tjg; 06jzh; 043q6n_; 01nzs7; 02wrhj; 0b79gfg; 027_tg; 05typm; 01bcq; 06lht1; ... >> query: (?x803, 09c7w0) <- nominated_for(?x803, ?x802), genre(?x802, ?x258) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mvth nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.755 http://example.org/people/person/nationality #15255-0127m7 PRED entity: 0127m7 PRED relation: award PRED expected values: 05zr6wv 05q5t0b => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 287): 07bdd_ (0.78 #40914, 0.77 #1971, 0.76 #2759), 05p1dby (0.78 #40914, 0.76 #2759, 0.74 #1576), 027c924 (0.76 #2759, 0.74 #1576, 0.74 #1970), 02qt02v (0.74 #1576, 0.74 #1970, 0.73 #2758), 05b1610 (0.47 #35, 0.22 #1611, 0.15 #35793), 04ljl_l (0.42 #3, 0.12 #1579, 0.07 #22810), 0gr51 (0.38 #485, 0.31 #3243, 0.17 #6388), 09sb52 (0.38 #5154, 0.34 #4761, 0.33 #24419), 02rdyk7 (0.31 #476, 0.22 #3234, 0.10 #4413), 0gr4k (0.29 #423, 0.26 #3181, 0.16 #9470) >> Best rule #40914 for best value: >> intensional similarity = 3 >> extensional distance = 1818 >> proper extension: 089tm; 01pfr3; 02mslq; 01vsxdm; 0hwd8; 0frsw; 016fmf; 01vrwfv; 0d193h; 0khth; ... >> query: (?x2451, ?x1312) <- award_winner(?x1312, ?x2451), award(?x6187, ?x1312), friend(?x262, ?x6187) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0cbm64; *> query: (?x2451, 05zr6wv) <- award_winner(?x1312, ?x2451), ?x1312 = 07cbcy, award_nominee(?x513, ?x2451) *> conf = 0.26 ranks of expected_values: 14, 56 EVAL 0127m7 award 05q5t0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 128.000 128.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0127m7 award 05zr6wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 128.000 128.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15254-0jgj7 PRED entity: 0jgj7 PRED relation: county! PRED expected values: 0rhp6 => 115 concepts (29 used for prediction) PRED predicted values (max 10 best out of 190): 0rhp6 (0.65 #2146, 0.62 #3992, 0.60 #3377), 0rrhp (0.25 #302, 0.07 #914, 0.03 #1221), 0ply0 (0.20 #349, 0.07 #655, 0.03 #962), 0rn0z (0.20 #536, 0.07 #842, 0.03 #1149), 0rk71 (0.07 #795, 0.03 #1102, 0.02 #1839), 0c5v2 (0.07 #882, 0.03 #1189, 0.02 #1839), 0rqf1 (0.07 #824, 0.03 #1131, 0.02 #1839), 0rql_ (0.07 #745, 0.03 #1052, 0.02 #1358), 0lhql (0.07 #668, 0.03 #975, 0.02 #1281), 0rh7t (0.03 #1003, 0.02 #1839, 0.02 #1309) >> Best rule #2146 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 0dbdy; 0121c1; 02m77; 028n3; 0dyjz; 052fbt; 03wxvk; 040hg8; 088cp; 04_xr8; ... >> query: (?x11257, ?x8005) <- contains(?x11257, ?x8005), second_level_divisions(?x94, ?x11257), category(?x8005, ?x134), time_zones(?x8005, ?x2674) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgj7 county! 0rhp6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 29.000 0.645 http://example.org/location/hud_county_place/county #15253-07k2mq PRED entity: 07k2mq PRED relation: genre PRED expected values: 0219x_ => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.80 #2587, 0.79 #3941, 0.79 #494), 01jfsb (0.50 #137, 0.38 #3585, 0.33 #4573), 02l7c8 (0.47 #2604, 0.43 #18, 0.37 #3466), 05p553 (0.43 #375, 0.43 #5, 0.42 #867), 03npn (0.38 #131, 0.09 #2347, 0.09 #2840), 0219x_ (0.37 #891, 0.33 #522, 0.21 #399), 03k9fj (0.37 #1983, 0.35 #2106, 0.29 #13), 02kdv5l (0.36 #1973, 0.33 #2096, 0.31 #1357), 04xvlr (0.33 #4067, 0.32 #4191, 0.31 #2464), 01hmnh (0.29 #20, 0.19 #1374, 0.17 #3837) >> Best rule #2587 for best value: >> intensional similarity = 3 >> extensional distance = 227 >> proper extension: 0dgq_kn; >> query: (?x4950, 07s9rl0) <- nominated_for(?x2478, ?x4950), award(?x7023, ?x2478), ?x7023 = 02jr26 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #891 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 07w8fz; *> query: (?x4950, 0219x_) <- nominated_for(?x2532, ?x4950), award_winner(?x4950, ?x2589), ?x2532 = 02x4wr9, film(?x2910, ?x4950) *> conf = 0.37 ranks of expected_values: 6 EVAL 07k2mq genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 89.000 89.000 0.803 http://example.org/film/film/genre #15252-03qcfvw PRED entity: 03qcfvw PRED relation: film_crew_role PRED expected values: 09zzb8 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 23): 09zzb8 (0.81 #266, 0.76 #765, 0.73 #931), 01pvkk (0.31 #274, 0.28 #1707, 0.27 #1006), 02ynfr (0.19 #777, 0.19 #943, 0.16 #278), 0215hd (0.16 #347, 0.16 #16, 0.13 #1013), 01xy5l_ (0.16 #11, 0.12 #342, 0.11 #210), 02rh1dz (0.15 #372, 0.15 #41, 0.14 #606), 0d2b38 (0.13 #23, 0.12 #354, 0.11 #1020), 089g0h (0.11 #1014, 0.11 #348, 0.11 #1382), 094hwz (0.11 #12, 0.09 #178, 0.08 #45), 02_n3z (0.11 #333, 0.08 #1265, 0.08 #999) >> Best rule #266 for best value: >> intensional similarity = 3 >> extensional distance = 199 >> proper extension: 03h_yy; 04gknr; 03t97y; 03s5lz; 0bh8yn3; 01b195; 04q00lw; 08gg47; 05m_jsg; 0bbw2z6; ... >> query: (?x103, 09zzb8) <- award_winner(?x103, ?x1017), film_crew_role(?x103, ?x2095), ?x2095 = 0dxtw >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qcfvw film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.811 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15251-081k8 PRED entity: 081k8 PRED relation: people! PRED expected values: 02w7gg => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 46): 041rx (0.23 #774, 0.18 #466, 0.17 #3007), 0x67 (0.20 #87, 0.15 #2397, 0.14 #5865), 07bch9 (0.20 #100, 0.10 #639, 0.10 #408), 063k3h (0.20 #108, 0.06 #647, 0.05 #416), 02w7gg (0.17 #4700, 0.16 #3467, 0.16 #3159), 02ctzb (0.14 #400, 0.03 #5947, 0.03 #6181), 0g6ff (0.11 #329, 0.05 #406, 0.04 #637), 033tf_ (0.11 #3934, 0.10 #4474, 0.09 #5321), 013xrm (0.10 #2253, 0.06 #4641, 0.06 #4795), 0dryh9k (0.08 #632, 0.05 #3943, 0.04 #4406) >> Best rule #774 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 021yw7; 076_74; 01xndd; 0fby2t; 01rlxt; 0n8bn; 01zwy; 015zql; 060pl5; >> query: (?x5004, 041rx) <- story_by(?x4668, ?x5004), film_release_region(?x4668, ?x1523), film_release_region(?x4668, ?x390), ?x390 = 0chghy, contains(?x1523, ?x682) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #4700 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 313 *> proper extension: 05fg2; 01w9mnm; *> query: (?x5004, 02w7gg) <- nationality(?x5004, ?x512), profession(?x5004, ?x2225), ?x512 = 07ssc *> conf = 0.17 ranks of expected_values: 5 EVAL 081k8 people! 02w7gg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 114.000 114.000 0.232 http://example.org/people/ethnicity/people #15250-0b_xm PRED entity: 0b_xm PRED relation: artists! PRED expected values: 05bt6j => 115 concepts (67 used for prediction) PRED predicted values (max 10 best out of 288): 02yv6b (0.64 #3494, 0.33 #3802, 0.33 #6583), 016clz (0.62 #14531, 0.47 #4944, 0.45 #4636), 064t9 (0.60 #9590, 0.59 #8666, 0.55 #9899), 05bt6j (0.50 #2200, 0.50 #1892, 0.49 #4365), 03lty (0.50 #3731, 0.49 #8987, 0.45 #11776), 06j6l (0.36 #9623, 0.35 #8699, 0.33 #47), 03_d0 (0.33 #12, 0.33 #14228, 0.24 #5263), 0gywn (0.33 #57, 0.32 #9633, 0.31 #8709), 0155w (0.33 #106, 0.32 #14013, 0.29 #1956), 025sc50 (0.33 #49, 0.30 #9625, 0.28 #8701) >> Best rule #3494 for best value: >> intensional similarity = 7 >> extensional distance = 20 >> proper extension: 095x_; >> query: (?x7653, 02yv6b) <- category(?x7653, ?x134), artists(?x2809, ?x7653), artists(?x1572, ?x7653), artists(?x1000, ?x7653), ?x1000 = 0xhtw, ?x2809 = 05w3f, ?x1572 = 06by7 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #2200 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 01p95y0; *> query: (?x7653, 05bt6j) <- artist(?x9243, ?x7653), artists(?x1000, ?x7653), ?x9243 = 03qy3l *> conf = 0.50 ranks of expected_values: 4 EVAL 0b_xm artists! 05bt6j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 67.000 0.636 http://example.org/music/genre/artists #15249-034lk7 PRED entity: 034lk7 PRED relation: contains! PRED expected values: 07z1m => 89 concepts (41 used for prediction) PRED predicted values (max 10 best out of 215): 0mpbj (0.73 #3585, 0.73 #2688, 0.72 #5377), 07z1m (0.69 #10752, 0.69 #10751, 0.66 #5378), 01n7q (0.17 #33217, 0.17 #34113, 0.17 #8140), 07ssc (0.15 #24213, 0.14 #26003, 0.14 #25108), 059rby (0.14 #8977, 0.14 #19, 0.13 #16142), 05k7sb (0.11 #5510, 0.11 #9090, 0.08 #22524), 02jx1 (0.11 #25163, 0.11 #24268, 0.11 #26058), 05fjf (0.10 #3959, 0.09 #16496, 0.08 #22765), 05kkh (0.08 #16131, 0.08 #904, 0.08 #8966), 04_1l0v (0.08 #5828, 0.08 #19259, 0.05 #7618) >> Best rule #3585 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 0rp46; 0rqyx; 0235l; 0l4vc; 0_lr1; 0s6jm; 0tt6k; 0sgtz; 0pc56; 0s9b_; ... >> query: (?x9689, ?x5998) <- contains(?x94, ?x9689), ?x94 = 09c7w0, administrative_division(?x9689, ?x5998), adjoins(?x9460, ?x5998) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #10752 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 259 *> proper extension: 09hzc; *> query: (?x9689, ?x94) <- administrative_division(?x9689, ?x5998), contains(?x94, ?x5998) *> conf = 0.69 ranks of expected_values: 2 EVAL 034lk7 contains! 07z1m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 41.000 0.732 http://example.org/location/location/contains #15248-02q0k7v PRED entity: 02q0k7v PRED relation: film_crew_role PRED expected values: 09zzb8 01vx2h 01xy5l_ => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.82 #436, 0.78 #374, 0.78 #94), 0215hd (0.75 #327, 0.74 #389, 0.63 #295), 01vx2h (0.58 #288, 0.52 #102, 0.50 #382), 01xy5l_ (0.53 #385, 0.51 #323, 0.48 #105), 02_n3z (0.36 #375, 0.36 #313, 0.31 #281), 01pvkk (0.29 #570, 0.28 #2141, 0.28 #1952), 015h31 (0.25 #286, 0.25 #1438, 0.19 #318), 033smt (0.25 #1438, 0.21 #302, 0.19 #116), 02rh1dz (0.25 #1438, 0.17 #225, 0.16 #287), 02vs3x5 (0.25 #1438, 0.12 #2890, 0.06 #611) >> Best rule #436 for best value: >> intensional similarity = 4 >> extensional distance = 137 >> proper extension: 0h95zbp; >> query: (?x7694, 09zzb8) <- film_release_region(?x7694, ?x94), genre(?x7694, ?x53), film_crew_role(?x7694, ?x3197), ?x3197 = 02ynfr >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4 EVAL 02q0k7v film_crew_role 01xy5l_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.820 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02q0k7v film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.820 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02q0k7v film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.820 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15247-0133_p PRED entity: 0133_p PRED relation: artists PRED expected values: 0kxbc => 63 concepts (30 used for prediction) PRED predicted values (max 10 best out of 986): 067mj (0.64 #9737, 0.54 #10808, 0.44 #11879), 0p76z (0.64 #10547, 0.54 #11618, 0.41 #4285), 01kcms4 (0.64 #10285, 0.44 #12427, 0.44 #2144), 014_lq (0.62 #11190, 0.25 #5839, 0.24 #7498), 02ndj5 (0.55 #10529, 0.46 #11600, 0.41 #4285), 01pny5 (0.55 #10686, 0.44 #12828, 0.44 #2144), 0qf11 (0.55 #10018, 0.44 #12160, 0.41 #13232), 02k5sc (0.50 #9276, 0.50 #6065, 0.44 #7134), 01vvycq (0.50 #5404, 0.44 #6473, 0.44 #2144), 01ydzx (0.50 #5961, 0.44 #7030, 0.40 #9172) >> Best rule #9737 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 0xhtw; 06by7; 01qzt1; 01fh36; 02yv6b; 016jny; >> query: (?x9935, 067mj) <- artists(?x9935, ?x8497), artists(?x9935, ?x211), parent_genre(?x2809, ?x9935), parent_genre(?x9935, ?x7440), ?x8497 = 01l_w0, role(?x211, ?x227), artists(?x7440, ?x2908), people(?x1050, ?x2908) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #4285 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 018ysx; *> query: (?x9935, ?x680) <- parent_genre(?x8386, ?x9935), parent_genre(?x9935, ?x10797), parent_genre(?x9935, ?x3319), artists(?x8386, ?x680), ?x10797 = 017371, artists(?x3319, ?x215) *> conf = 0.41 ranks of expected_values: 138 EVAL 0133_p artists 0kxbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 63.000 30.000 0.636 http://example.org/music/genre/artists #15246-02z3r8t PRED entity: 02z3r8t PRED relation: film! PRED expected values: 047hpm 06_bq1 => 98 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1013): 0bj9k (0.39 #10660, 0.20 #6526, 0.02 #47874), 04__f (0.35 #11705, 0.10 #7571, 0.01 #73743), 06cgy (0.20 #247, 0.17 #2314, 0.10 #6448), 0fgg4 (0.20 #875, 0.17 #2942, 0.01 #15344), 01gw4f (0.20 #855, 0.10 #7056, 0.06 #11190), 01r7t9 (0.20 #1866, 0.10 #8067, 0.03 #12201), 02t1cp (0.20 #719, 0.10 #6920, 0.03 #11054), 015c4g (0.20 #6973, 0.10 #11107, 0.03 #21442), 02yxwd (0.20 #4872, 0.05 #9006, 0.02 #81385), 04hpck (0.20 #167, 0.03 #10502, 0.03 #12569) >> Best rule #10660 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 015qsq; 0dj0m5; 02py4c8; 025scjj; 0bbgvp; >> query: (?x755, 0bj9k) <- language(?x755, ?x254), film(?x7780, ?x755), nominated_for(?x7780, ?x7016), ?x254 = 02h40lc, ?x7016 = 07g1sm >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #15696 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 80 *> proper extension: 0djb3vw; 0cnztc4; 0crh5_f; 04lqvly; 05zvzf3; *> query: (?x755, 06_bq1) <- film_crew_role(?x755, ?x468), language(?x755, ?x254), film_festivals(?x755, ?x9189), film(?x2156, ?x755), ?x468 = 02r96rf *> conf = 0.02 ranks of expected_values: 504 EVAL 02z3r8t film! 06_bq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 98.000 59.000 0.387 http://example.org/film/actor/film./film/performance/film EVAL 02z3r8t film! 047hpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 59.000 0.387 http://example.org/film/actor/film./film/performance/film #15245-03jj93 PRED entity: 03jj93 PRED relation: nationality PRED expected values: 02jx1 => 122 concepts (111 used for prediction) PRED predicted values (max 10 best out of 25): 09c7w0 (0.79 #302, 0.77 #502, 0.77 #702), 07ssc (0.77 #3314, 0.42 #9834, 0.39 #3313), 02jx1 (0.42 #9834, 0.39 #3313, 0.32 #834), 0dbdy (0.42 #9834, 0.39 #3313, 0.29 #9835), 06q1r (0.40 #77, 0.05 #878, 0.02 #3288), 0345h (0.37 #5922, 0.03 #432, 0.02 #632), 06mkj (0.37 #5922), 0j5g9 (0.20 #62, 0.02 #863, 0.01 #963), 0d060g (0.08 #808, 0.08 #107, 0.05 #708), 03rk0 (0.07 #6369, 0.07 #2251, 0.07 #7273) >> Best rule #302 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 01xv77; 0d608; >> query: (?x11651, 09c7w0) <- profession(?x11651, ?x987), participant(?x11651, ?x398), ?x987 = 0dxtg, award_winner(?x1770, ?x11651) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #9834 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2321 *> proper extension: 07m69t; *> query: (?x11651, ?x1758) <- location(?x11651, ?x14342), contains(?x1758, ?x14342), location_of_ceremony(?x566, ?x1758) *> conf = 0.42 ranks of expected_values: 3 EVAL 03jj93 nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 122.000 111.000 0.790 http://example.org/people/person/nationality #15244-0symg PRED entity: 0symg PRED relation: music PRED expected values: 0m2l9 => 82 concepts (21 used for prediction) PRED predicted values (max 10 best out of 55): 0csdzz (0.07 #398, 0.06 #187, 0.02 #3779), 0146pg (0.07 #2967, 0.06 #3602, 0.06 #1064), 0405l (0.06 #2533, 0.06 #3168, 0.06 #4226), 02cyfz (0.06 #34, 0.05 #2570, 0.05 #2781), 03h610 (0.06 #77, 0.04 #499, 0.03 #1342), 02bh9 (0.05 #1528, 0.03 #3854, 0.03 #51), 01tc9r (0.04 #909, 0.03 #3657, 0.02 #3022), 01cbt3 (0.04 #513, 0.03 #725, 0.03 #2413), 0150t6 (0.03 #1523, 0.03 #4061, 0.02 #3638), 03975z (0.03 #800, 0.03 #1220, 0.03 #166) >> Best rule #398 for best value: >> intensional similarity = 6 >> extensional distance = 40 >> proper extension: 0kv9d3; 043n0v_; 03cv_gy; 05znbh7; 017180; 0294mx; >> query: (?x11027, 0csdzz) <- genre(?x11027, ?x162), ?x162 = 04xvlr, nominated_for(?x11079, ?x11027), nominated_for(?x1063, ?x11027), category(?x11027, ?x134), ?x134 = 08mbj5d >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0symg music 0m2l9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 21.000 0.071 http://example.org/film/film/music #15243-074qgb PRED entity: 074qgb PRED relation: nominated_for PRED expected values: 019g8j => 61 concepts (18 used for prediction) PRED predicted values (max 10 best out of 135): 05nlzq (0.12 #3016, 0.02 #6260, 0.01 #7883), 01xdxy (0.12 #3032, 0.02 #11146, 0.01 #6276), 0dyb1 (0.12 #2081, 0.01 #10195), 01c22t (0.12 #1776), 01s81 (0.06 #2315, 0.03 #3937, 0.02 #7182), 0330r (0.06 #3037, 0.02 #12774, 0.02 #6281), 0kcn7 (0.06 #2009, 0.01 #8498), 0dr_4 (0.06 #1850, 0.01 #8339), 06fcqw (0.06 #2614), 0gmgwnv (0.06 #2602) >> Best rule #3016 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01g4zr; 0c94fn; 081nh; 05jcn8; 0h005; 0bjkpt; 01bzr4; 04jspq; 0250f; 03mstc; ... >> query: (?x13536, 05nlzq) <- award(?x13536, ?x4573), profession(?x13536, ?x1943), nationality(?x13536, ?x94), ?x4573 = 0gq_d >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 074qgb nominated_for 019g8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 18.000 0.125 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15242-01t110 PRED entity: 01t110 PRED relation: profession PRED expected values: 09jwl => 118 concepts (77 used for prediction) PRED predicted values (max 10 best out of 70): 09jwl (0.84 #456, 0.81 #1187, 0.79 #894), 0nbcg (0.58 #1200, 0.58 #907, 0.56 #615), 0dz3r (0.54 #732, 0.45 #586, 0.45 #1171), 01d_h8 (0.41 #2197, 0.34 #2051, 0.32 #2783), 03gjzk (0.37 #306, 0.24 #2206, 0.23 #4552), 0n1h (0.37 #741, 0.35 #595, 0.34 #157), 0dxtg (0.35 #305, 0.28 #9531, 0.28 #9677), 018gz8 (0.33 #308, 0.13 #2794, 0.12 #2940), 01c72t (0.29 #2361, 0.29 #1631, 0.29 #4999), 0kyk (0.28 #321, 0.13 #5591, 0.12 #5737) >> Best rule #456 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 0dt645q; >> query: (?x6461, 09jwl) <- profession(?x6461, ?x2659), profession(?x6461, ?x1032), ?x1032 = 02hrh1q, ?x2659 = 039v1 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01t110 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 77.000 0.844 http://example.org/people/person/profession #15241-07f8wg PRED entity: 07f8wg PRED relation: place_of_birth PRED expected values: 09bjv => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 70): 02_286 (0.31 #19, 0.21 #723, 0.12 #10579), 0dclg (0.08 #782, 0.04 #1486, 0.03 #5006), 01nl79 (0.08 #1245, 0.02 #6173, 0.02 #13213), 0cr3d (0.08 #94, 0.06 #3614, 0.06 #9950), 01531 (0.08 #105, 0.03 #8553, 0.03 #4329), 059rby (0.08 #7, 0.03 #2119, 0.02 #2823), 0dc95 (0.08 #86, 0.03 #2198, 0.01 #5718), 030qb3t (0.06 #13430, 0.06 #21175, 0.06 #4278), 01_d4 (0.05 #23299, 0.04 #26116, 0.04 #25412), 095w_ (0.04 #752, 0.04 #1456, 0.02 #7088) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 030pr; 043q6n_; 012vct; 02mc79; 030g9z; >> query: (?x519, 02_286) <- award_nominee(?x519, ?x541), produced_by(?x518, ?x519), ?x541 = 017s11 >> conf = 0.31 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07f8wg place_of_birth 09bjv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 126.000 0.308 http://example.org/people/person/place_of_birth #15240-01ydzx PRED entity: 01ydzx PRED relation: profession PRED expected values: 05z96 => 111 concepts (74 used for prediction) PRED predicted values (max 10 best out of 118): 0n1h (0.40 #294, 0.29 #10, 0.25 #862), 01d_h8 (0.33 #856, 0.24 #1282, 0.21 #6265), 0kyk (0.29 #4002, 0.17 #878, 0.14 #1588), 0fnpj (0.27 #622, 0.25 #906, 0.24 #1332), 04f2zj (0.27 #658, 0.15 #1368, 0.14 #90), 09lbv (0.22 #159, 0.13 #4279, 0.12 #3567), 03lgtv (0.22 #249, 0.05 #5115, 0.05 #2095), 0cbd2 (0.19 #3981, 0.12 #6981, 0.12 #5980), 01b30l (0.18 #618, 0.11 #192, 0.10 #334), 0dxtg (0.18 #5987, 0.17 #6988, 0.17 #3988) >> Best rule #294 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 01pbxb; 01wp8w7; 02fn5r; 01s21dg; 0pk41; >> query: (?x6774, 0n1h) <- role(?x6774, ?x227), profession(?x6774, ?x2659), artists(?x2823, ?x6774), ?x2659 = 039v1, ?x2823 = 02qdgx >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #889 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 10 *> proper extension: 02fybl; *> query: (?x6774, 05z96) <- role(?x6774, ?x227), profession(?x6774, ?x6476), profession(?x6774, ?x1614), profession(?x6774, ?x1183), ?x1614 = 01c72t, ?x1183 = 09jwl, ?x6476 = 025352 *> conf = 0.08 ranks of expected_values: 25 EVAL 01ydzx profession 05z96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 111.000 74.000 0.400 http://example.org/people/person/profession #15239-0fzyg PRED entity: 0fzyg PRED relation: films PRED expected values: 018js4 02gs6r 0fsw_7 0sxgv 04sh80 => 73 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1584): 095z4q (0.40 #1838, 0.10 #4884, 0.09 #5902), 02sg5v (0.36 #3547, 0.25 #4563, 0.25 #4054), 0fztbq (0.36 #3547, 0.25 #4563, 0.25 #4054), 0g5pv3 (0.36 #3547, 0.25 #4563, 0.25 #4054), 014kq6 (0.36 #3547, 0.24 #4055, 0.22 #5071), 0164qt (0.36 #3547, 0.24 #4055, 0.22 #5071), 02qrv7 (0.36 #3547, 0.24 #4055, 0.22 #5071), 0fsw_7 (0.36 #3547, 0.24 #4055, 0.22 #5071), 01kf4tt (0.36 #3547, 0.24 #4055, 0.22 #5071), 0g5pvv (0.36 #3547, 0.24 #4055, 0.22 #5071) >> Best rule #1838 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0jm_; >> query: (?x5954, 095z4q) <- films(?x5954, ?x8615), films(?x5954, ?x3643), film(?x2726, ?x8615), nominated_for(?x3643, ?x835), production_companies(?x8615, ?x2156), film(?x5869, ?x3643), ?x2156 = 01795t, language(?x8615, ?x254), genre(?x3643, ?x225), nominated_for(?x2726, ?x2336), award_nominee(?x450, ?x2726) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3547 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 6 *> proper extension: 018w0j; *> query: (?x5954, ?x835) <- films(?x5954, ?x8615), films(?x5954, ?x3643), film(?x968, ?x8615), nominated_for(?x3643, ?x2160), nominated_for(?x3643, ?x835), production_companies(?x8615, ?x2156), film(?x5869, ?x3643), film_release_distribution_medium(?x3643, ?x81), written_by(?x3643, ?x11598), film(?x2156, ?x148), film_crew_role(?x2160, ?x137), language(?x3643, ?x254) *> conf = 0.36 ranks of expected_values: 8, 392, 1019, 1030 EVAL 0fzyg films 04sh80 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 25.000 0.400 http://example.org/film/film_subject/films EVAL 0fzyg films 0sxgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 73.000 25.000 0.400 http://example.org/film/film_subject/films EVAL 0fzyg films 0fsw_7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 73.000 25.000 0.400 http://example.org/film/film_subject/films EVAL 0fzyg films 02gs6r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 25.000 0.400 http://example.org/film/film_subject/films EVAL 0fzyg films 018js4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 25.000 0.400 http://example.org/film/film_subject/films #15238-0661ql3 PRED entity: 0661ql3 PRED relation: film_release_region PRED expected values: 01ls2 05qhw 06npd 0k6nt 06qd3 07t21 06f32 0166b => 100 concepts (98 used for prediction) PRED predicted values (max 10 best out of 105): 05qhw (0.86 #260, 0.85 #761, 0.83 #886), 0k6nt (0.85 #1018, 0.85 #768, 0.84 #1143), 06qd3 (0.76 #777, 0.64 #276, 0.61 #902), 0ctw_b (0.61 #268, 0.61 #769, 0.59 #894), 06f32 (0.58 #795, 0.55 #920, 0.50 #1547), 01ls2 (0.55 #132, 0.48 #1260, 0.48 #1511), 06t8v (0.52 #805, 0.50 #304, 0.49 #1306), 07f1x (0.50 #339, 0.45 #213, 0.43 #965), 06npd (0.41 #765, 0.36 #264, 0.36 #890), 07t21 (0.31 #779, 0.30 #904, 0.25 #278) >> Best rule #260 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 0c3ybss; 09gdm7q; 04n52p6; 01fmys; 0dlngsd; 06fcqw; 04cppj; 07pd_j; 0fphf3v; 0ndsl1x; ... >> query: (?x2394, 05qhw) <- titles(?x3613, ?x2394), nominated_for(?x748, ?x2394), film_release_region(?x2394, ?x3749), ?x3749 = 03ryn >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 6, 9, 10, 28 EVAL 0661ql3 film_release_region 0166b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 100.000 98.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661ql3 film_release_region 06f32 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 98.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661ql3 film_release_region 07t21 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 100.000 98.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661ql3 film_release_region 06qd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 98.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661ql3 film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 98.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661ql3 film_release_region 06npd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 100.000 98.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661ql3 film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 98.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661ql3 film_release_region 01ls2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 98.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15237-019vsw PRED entity: 019vsw PRED relation: student PRED expected values: 04jwp => 139 concepts (107 used for prediction) PRED predicted values (max 10 best out of 980): 01vwbts (0.33 #2898, 0.20 #4986, 0.13 #15429), 02fx3c (0.33 #2657, 0.20 #4745, 0.07 #15188), 0cj2w (0.33 #3972, 0.20 #6060, 0.07 #16503), 09wj5 (0.33 #2167, 0.20 #4255, 0.07 #14698), 01r93l (0.33 #2800, 0.20 #4888, 0.07 #15331), 02wgln (0.33 #2382, 0.20 #4470, 0.07 #14913), 04shbh (0.33 #2227, 0.20 #4315, 0.07 #14758), 05cj4r (0.33 #2127, 0.20 #4215, 0.07 #14658), 02q42j_ (0.33 #3127, 0.20 #5215, 0.07 #15658), 03p9hl (0.33 #4163, 0.20 #6251, 0.07 #16694) >> Best rule #2898 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0m4yg; >> query: (?x9741, 01vwbts) <- state_province_region(?x9741, ?x512), student(?x9741, ?x12020), student(?x9741, ?x2424), category(?x9741, ?x134), ?x2424 = 01xsbh, profession(?x12020, ?x1032) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #32356 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 07vht; 0820xz; 0p5wz; 02kzfw; 01zn4y; 07vfz; 030w19; 05hf_5; *> query: (?x9741, 04jwp) <- school_type(?x9741, ?x8834), company(?x12076, ?x9741), contains(?x362, ?x9741) *> conf = 0.02 ranks of expected_values: 667 EVAL 019vsw student 04jwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 139.000 107.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #15236-0kfpm PRED entity: 0kfpm PRED relation: languages PRED expected values: 02h40lc => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 5): 02h40lc (0.91 #233, 0.90 #200, 0.90 #244), 03_9r (0.09 #158, 0.04 #444, 0.04 #576), 0t_2 (0.04 #325, 0.04 #127, 0.04 #347), 06nm1 (0.03 #324, 0.03 #148, 0.03 #159), 064_8sq (0.01 #447, 0.01 #458, 0.01 #469) >> Best rule #233 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 02_1q9; 06cs95; 027tbrc; 0557yqh; 09fc83; 0524b41; 02_1kl; 08bytj; 04sskp; 03nymk; ... >> query: (?x758, 02h40lc) <- nominated_for(?x4385, ?x758), actor(?x758, ?x3644), award_winner(?x2661, ?x4385), nominated_for(?x757, ?x758) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kfpm languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.908 http://example.org/tv/tv_program/languages #15235-01wbgdv PRED entity: 01wbgdv PRED relation: artists! PRED expected values: 06j6l => 121 concepts (65 used for prediction) PRED predicted values (max 10 best out of 238): 06j6l (0.57 #1578, 0.40 #2499, 0.36 #10462), 025sc50 (0.53 #2501, 0.30 #1580, 0.24 #8628), 05bt6j (0.37 #10457, 0.33 #41, 0.31 #960), 01fh36 (0.33 #83, 0.20 #1615, 0.14 #10499), 016clz (0.29 #4910, 0.29 #4297, 0.27 #924), 0xhtw (0.28 #3388, 0.27 #4308, 0.27 #4921), 02lnbg (0.26 #2509, 0.18 #2815, 0.17 #7050), 02k_kn (0.25 #63, 0.16 #1595, 0.15 #982), 0ggx5q (0.24 #2527, 0.18 #2833, 0.15 #8348), 05w3f (0.23 #1261, 0.17 #3715, 0.17 #4328) >> Best rule #1578 for best value: >> intensional similarity = 2 >> extensional distance = 81 >> proper extension: 053y0s; >> query: (?x1128, 06j6l) <- artists(?x1127, ?x1128), ?x1127 = 02x8m >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wbgdv artists! 06j6l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 65.000 0.566 http://example.org/music/genre/artists #15234-0dtfn PRED entity: 0dtfn PRED relation: production_companies PRED expected values: 0kx4m => 119 concepts (79 used for prediction) PRED predicted values (max 10 best out of 72): 05qd_ (0.26 #259, 0.17 #10, 0.14 #3180), 0g1rw (0.17 #8, 0.13 #257, 0.12 #424), 016tt2 (0.17 #4, 0.13 #253, 0.12 #919), 0kx4m (0.17 #9, 0.10 #341, 0.07 #674), 086k8 (0.13 #85, 0.12 #334, 0.10 #168), 01gb54 (0.12 #370, 0.09 #2371, 0.09 #3374), 01795t (0.11 #770, 0.08 #1604, 0.07 #521), 017s11 (0.10 #918, 0.08 #1085, 0.08 #1919), 030_1_ (0.10 #183, 0.09 #2350, 0.09 #3353), 016tw3 (0.09 #2179, 0.09 #4955, 0.08 #844) >> Best rule #259 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0gndh; >> query: (?x1386, 05qd_) <- nominated_for(?x1313, ?x1386), film(?x2916, ?x1386), ?x1313 = 0gs9p, film_art_direction_by(?x1386, ?x8719) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0bmpm; *> query: (?x1386, 0kx4m) <- nominated_for(?x198, ?x1386), film_release_distribution_medium(?x1386, ?x81), executive_produced_by(?x1386, ?x1387), film_art_direction_by(?x1386, ?x8719) *> conf = 0.17 ranks of expected_values: 4 EVAL 0dtfn production_companies 0kx4m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 119.000 79.000 0.263 http://example.org/film/film/production_companies #15233-01xlqd PRED entity: 01xlqd PRED relation: film! PRED expected values: 01kgg9 02d6n_ => 73 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1123): 0dzlk (0.73 #75057, 0.66 #29191, 0.64 #72970), 0f502 (0.73 #75057, 0.64 #72970, 0.63 #79230), 05dbf (0.25 #366, 0.06 #4537, 0.06 #33725), 049qx (0.25 #772, 0.06 #75058, 0.05 #72971), 02ck7w (0.25 #941, 0.05 #19701, 0.03 #28046), 0k269 (0.25 #612, 0.04 #13119, 0.04 #15203), 01nwwl (0.25 #504, 0.03 #23438, 0.02 #19264), 02zl4d (0.25 #1875, 0.02 #20635, 0.02 #28980), 04yj5z (0.25 #122, 0.01 #18882, 0.01 #20969), 05kwx2 (0.17 #3183, 0.12 #5268, 0.07 #15688) >> Best rule #75057 for best value: >> intensional similarity = 4 >> extensional distance = 611 >> proper extension: 02r_pp; >> query: (?x9832, ?x11475) <- nominated_for(?x11475, ?x9832), currency(?x9832, ?x170), participant(?x11475, ?x4394), genre(?x9832, ?x239) >> conf = 0.73 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01xlqd film! 02d6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 47.000 0.732 http://example.org/film/actor/film./film/performance/film EVAL 01xlqd film! 01kgg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 47.000 0.732 http://example.org/film/actor/film./film/performance/film #15232-02r2j8 PRED entity: 02r2j8 PRED relation: actor PRED expected values: 016khd => 101 concepts (93 used for prediction) PRED predicted values (max 10 best out of 672): 03_fk9 (0.42 #9295, 0.41 #7436, 0.40 #6506), 02tkzn (0.33 #453, 0.05 #9748, 0.05 #10677), 0lkr7 (0.33 #412, 0.05 #9707, 0.05 #10636), 01gw4f (0.33 #398, 0.05 #9693, 0.05 #10622), 049tjg (0.33 #21, 0.05 #9316, 0.05 #10245), 02fx3c (0.20 #2140, 0.20 #1211, 0.10 #3070), 016tbr (0.20 #2632, 0.20 #1703, 0.10 #3562), 08qxx9 (0.20 #2534, 0.20 #1605, 0.10 #3464), 05xf75 (0.20 #2520, 0.20 #1591, 0.10 #3450), 03w4sh (0.20 #2377, 0.20 #1448, 0.10 #3307) >> Best rule #9295 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 01h72l; 01fs__; >> query: (?x7928, ?x10650) <- actor(?x7928, ?x10663), language(?x7928, ?x254), nominated_for(?x10650, ?x7928), film(?x10663, ?x8193), genre(?x8193, ?x225) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02r2j8 actor 016khd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 93.000 0.423 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #15231-01gr00 PRED entity: 01gr00 PRED relation: time_zones PRED expected values: 02hcv8 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 8): 02hcv8 (0.67 #29, 0.60 #16, 0.57 #68), 02lcqs (0.20 #122, 0.17 #365, 0.16 #418), 02fqwt (0.17 #365, 0.16 #418, 0.16 #458), 02hczc (0.17 #365, 0.16 #418, 0.16 #458), 02lcrv (0.17 #365, 0.16 #418, 0.16 #458), 02llzg (0.07 #212, 0.07 #238, 0.07 #199), 03bdv (0.05 #97, 0.04 #84, 0.04 #149), 03plfd (0.01 #454, 0.01 #218, 0.01 #140) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01cx_; 0t_07; 01m7mv; >> query: (?x14449, 02hcv8) <- contains(?x2020, ?x14449), ?x2020 = 05k7sb, place_of_birth(?x3375, ?x14449) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gr00 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.667 http://example.org/location/location/time_zones #15230-038c0q PRED entity: 038c0q PRED relation: school PRED expected values: 078bz 0225bv => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1225): 01pl14 (0.67 #747, 0.57 #1700, 0.57 #985), 06pwq (0.50 #1686, 0.50 #1583, 0.50 #1109), 07vyf (0.50 #1860, 0.50 #1146, 0.50 #460), 06fq2 (0.50 #1187, 0.50 #605, 0.45 #1428), 09f2j (0.50 #912, 0.43 #1034, 0.43 #211), 05krk (0.50 #746, 0.43 #211, 0.36 #969), 04gd8j (0.50 #743, 0.07 #107, 0.03 #1461), 01qdhx (0.50 #743, 0.05 #214, 0.05 #106), 065y4w7 (0.43 #1825, 0.43 #1704, 0.43 #211), 07t90 (0.43 #211, 0.40 #685, 0.37 #315) >> Best rule #747 for best value: >> intensional similarity = 53 >> extensional distance = 4 >> proper extension: 0g3zpp; 03nt7j; >> query: (?x2569, 01pl14) <- draft(?x10837, ?x2569), draft(?x6128, ?x2569), draft(?x5154, ?x2569), draft(?x2820, ?x2569), draft(?x1347, ?x2569), school(?x2569, ?x10666), school(?x2569, ?x9620), school(?x2569, ?x4980), school(?x2569, ?x1011), student(?x4980, ?x7932), team(?x1348, ?x6128), school(?x2820, ?x11881), school(?x2820, ?x11318), school(?x2820, ?x10104), school(?x2820, ?x7596), school(?x2820, ?x7338), school(?x2820, ?x4293), school(?x2820, ?x1681), school(?x2820, ?x388), organization(?x346, ?x4980), ?x7596 = 012mzw, currency(?x11318, ?x170), school_type(?x4980, ?x3092), state_province_region(?x10104, ?x1426), organization(?x4980, ?x5487), major_field_of_study(?x4293, ?x1154), registering_agency(?x9620, ?x1982), major_field_of_study(?x1681, ?x1695), institution(?x865, ?x10104), school(?x5154, ?x6315), school(?x685, ?x388), institution(?x1368, ?x7338), contains(?x3778, ?x7338), contains(?x94, ?x1681), sport(?x5154, ?x4833), institution(?x7636, ?x1681), colors(?x5154, ?x1101), ?x1368 = 014mlp, award(?x7932, ?x3906), major_field_of_study(?x4980, ?x10046), colors(?x11881, ?x3189), team(?x8996, ?x1347), school_type(?x7338, ?x1507), ?x10046 = 041y2, ?x7636 = 01rr_d, ?x3092 = 05jxkf, school(?x10600, ?x10666), ?x1695 = 06ms6, ?x10600 = 04f4z1k, student(?x1681, ?x1580), teams(?x4090, ?x10837), citytown(?x4293, ?x1494), ?x1011 = 07w0v >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #211 for first EXPECTED value: *> intensional similarity = 51 *> extensional distance = 1 *> proper extension: 02qw1zx; *> query: (?x2569, ?x1087) <- draft(?x9760, ?x2569), draft(?x6128, ?x2569), school(?x2569, ?x10945), school(?x2569, ?x9620), school(?x2569, ?x6271), school(?x2569, ?x4980), school(?x2569, ?x1011), school(?x2569, ?x621), ?x4980 = 01n6r0, ?x10945 = 01jsk6, fraternities_and_sororities(?x621, ?x4348), ?x6271 = 015q1n, school(?x5773, ?x621), contains(?x1906, ?x621), team(?x13105, ?x9760), team(?x9070, ?x9760), colors(?x9760, ?x663), draft(?x9760, ?x8542), currency(?x9070, ?x170), citytown(?x1011, ?x3269), school_type(?x1011, ?x3092), type_of_union(?x13105, ?x566), major_field_of_study(?x621, ?x2981), major_field_of_study(?x1011, ?x947), ?x1906 = 04rrx, school(?x6128, ?x581), place_founded(?x7970, ?x3269), institution(?x4981, ?x621), category(?x1011, ?x134), school(?x8542, ?x1087), school(?x8542, ?x466), ?x3092 = 05jxkf, dog_breed(?x3269, ?x11363), location(?x287, ?x3269), school(?x9760, ?x735), school(?x8111, ?x1011), team(?x1348, ?x6128), ?x11363 = 01k3tq, ?x4981 = 03bwzr4, location(?x9070, ?x1719), student(?x1011, ?x1314), company(?x4486, ?x9620), colors(?x621, ?x332), student(?x621, ?x3853), sport(?x8111, ?x5063), major_field_of_study(?x1682, ?x947), ?x2981 = 02j62, team(?x2010, ?x8111), institution(?x734, ?x1011), ?x466 = 01pl14, profession(?x9070, ?x319) *> conf = 0.43 ranks of expected_values: 18, 45 EVAL 038c0q school 0225bv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 18.000 18.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 038c0q school 078bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 18.000 18.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #15229-02q3fdr PRED entity: 02q3fdr PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #76, 0.83 #112, 0.82 #122), 07c52 (0.21 #428, 0.20 #8, 0.20 #3), 02nxhr (0.21 #428, 0.08 #57, 0.07 #47), 07z4p (0.21 #428, 0.07 #50, 0.06 #55) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 05nlx4; >> query: (?x5936, 029j_) <- genre(?x5936, ?x811), category(?x5936, ?x134), ?x134 = 08mbj5d, ?x811 = 03k9fj, titles(?x252, ?x5936) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q3fdr film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.836 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15228-02zjd PRED entity: 02zjd PRED relation: religion PRED expected values: 0c8wxp => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 28): 0c8wxp (0.25 #141, 0.25 #96, 0.25 #6), 03_gx (0.25 #149, 0.25 #14, 0.23 #869), 0kpl (0.22 #1631, 0.21 #1901, 0.21 #1991), 0kq2 (0.17 #63, 0.14 #468, 0.12 #108), 0n2g (0.17 #58, 0.12 #1306, 0.11 #193), 092bf5 (0.12 #151, 0.09 #4727, 0.07 #826), 07w8f (0.12 #125, 0.02 #980, 0.02 #1025), 01lp8 (0.07 #856, 0.06 #991, 0.05 #451), 03j6c (0.05 #516, 0.04 #921, 0.04 #1552), 04pk9 (0.05 #515, 0.04 #965, 0.03 #650) >> Best rule #141 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0dfrq; >> query: (?x6163, 0c8wxp) <- student(?x6056, ?x6163), influenced_by(?x6163, ?x6370), ?x6370 = 0465_, profession(?x6163, ?x353) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zjd religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.250 http://example.org/people/person/religion #15227-02kxwk PRED entity: 02kxwk PRED relation: type_of_union PRED expected values: 04ztj => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #9, 0.74 #5, 0.72 #229), 01g63y (0.21 #6, 0.17 #2, 0.15 #30) >> Best rule #9 for best value: >> intensional similarity = 2 >> extensional distance = 262 >> proper extension: 06v8s0; 01c58j; 0bbxd3; 026xt5c; 06z9yh; 0168ql; >> query: (?x4367, 04ztj) <- profession(?x4367, ?x1943), ?x1943 = 02krf9 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02kxwk type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.742 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15226-01797x PRED entity: 01797x PRED relation: artists! PRED expected values: 02k_kn => 142 concepts (79 used for prediction) PRED predicted values (max 10 best out of 220): 06j6l (0.44 #2528, 0.36 #6253, 0.34 #10917), 0glt670 (0.40 #4071, 0.28 #10600, 0.23 #6246), 0gywn (0.39 #2538, 0.28 #6263, 0.27 #10617), 025sc50 (0.35 #6255, 0.33 #10919, 0.31 #4080), 03_d0 (0.30 #2493, 0.25 #1873, 0.23 #9638), 0ggx5q (0.29 #6284, 0.25 #10948, 0.21 #4109), 016clz (0.29 #9942, 0.29 #6521, 0.29 #7144), 0dl5d (0.29 #1880, 0.18 #6535, 0.17 #7158), 01fh36 (0.29 #1948, 0.15 #5361, 0.14 #6603), 02yv6b (0.28 #2580, 0.25 #1960, 0.19 #13763) >> Best rule #2528 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 01k5t_3; 0qdyf; 0134tg; 07mvp; 015cxv; 011z3g; 02z4b_8; >> query: (?x10396, 06j6l) <- award_winner(?x9945, ?x10396), artists(?x7440, ?x10396), ?x7440 = 0155w >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #6271 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 192 *> proper extension: 04gycf; *> query: (?x10396, 02k_kn) <- location(?x10396, ?x94), profession(?x10396, ?x220), artists(?x671, ?x10396), ?x671 = 064t9 *> conf = 0.18 ranks of expected_values: 19 EVAL 01797x artists! 02k_kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 142.000 79.000 0.438 http://example.org/music/genre/artists #15225-0b_4z PRED entity: 0b_4z PRED relation: student! PRED expected values: 065y4w7 => 112 concepts (81 used for prediction) PRED predicted values (max 10 best out of 153): 04b_46 (0.28 #1277, 0.26 #6527, 0.25 #7052), 065y4w7 (0.10 #16814, 0.07 #5789, 0.06 #8939), 017z88 (0.08 #16882, 0.05 #25808, 0.05 #1657), 0fr9jp (0.08 #344, 0.05 #9269, 0.04 #5069), 026gvfj (0.08 #111, 0.04 #2736, 0.04 #7986), 0cwx_ (0.08 #241, 0.03 #1291, 0.02 #1816), 01w3v (0.08 #15, 0.02 #1590, 0.02 #2115), 02301 (0.08 #74, 0.02 #2699, 0.02 #4799), 01nnsv (0.08 #185, 0.02 #5435, 0.02 #6485), 02zy1z (0.08 #494, 0.01 #1544, 0.01 #2069) >> Best rule #1277 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 012_53; 027r8p; 044mrh; >> query: (?x12462, 04b_46) <- student(?x7545, ?x12462), profession(?x12462, ?x1032), ?x7545 = 0bwfn, ?x1032 = 02hrh1q >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #16814 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 526 *> proper extension: 01v42g; *> query: (?x12462, 065y4w7) <- student(?x7545, ?x12462), award_nominee(?x7268, ?x12462), company(?x12147, ?x7545) *> conf = 0.10 ranks of expected_values: 2 EVAL 0b_4z student! 065y4w7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 81.000 0.282 http://example.org/education/educational_institution/students_graduates./education/education/student #15224-030s5g PRED entity: 030s5g PRED relation: place_of_death PRED expected values: 0k049 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 57): 030qb3t (0.18 #2161, 0.17 #1967, 0.16 #800), 02_286 (0.11 #13, 0.09 #1762, 0.09 #1373), 0f2wj (0.11 #12, 0.08 #207, 0.07 #790), 0k049 (0.11 #198, 0.10 #1169, 0.08 #2142), 0cr3d (0.07 #4088, 0.07 #4087, 0.07 #1945), 06_kh (0.07 #5, 0.05 #1754, 0.04 #3897), 0k_p5 (0.07 #88, 0.03 #5839, 0.02 #3980), 04jpl (0.04 #1952, 0.04 #2146, 0.04 #4095), 0d6lp (0.04 #46, 0.03 #5839, 0.01 #630), 01m1zk (0.04 #58, 0.03 #5839) >> Best rule #2161 for best value: >> intensional similarity = 3 >> extensional distance = 234 >> proper extension: 04bdlg; >> query: (?x11751, 030qb3t) <- people(?x4322, ?x11751), nominated_for(?x11751, ?x12766), nominated_for(?x601, ?x12766) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #198 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 030pr; 043q6n_; *> query: (?x11751, 0k049) <- produced_by(?x5509, ?x11751), film_sets_designed(?x12378, ?x5509), award_nominee(?x382, ?x11751) *> conf = 0.11 ranks of expected_values: 4 EVAL 030s5g place_of_death 0k049 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 86.000 0.178 http://example.org/people/deceased_person/place_of_death #15223-047hpm PRED entity: 047hpm PRED relation: film PRED expected values: 02z3r8t => 130 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1192): 01shy7 (0.12 #424, 0.11 #23714, 0.09 #43419), 01qn7n (0.11 #21498, 0.10 #28665, 0.09 #34039), 013q07 (0.11 #2148, 0.06 #7523, 0.05 #45143), 03bx2lk (0.10 #3767, 0.07 #18099, 0.06 #27057), 062zm5h (0.09 #859, 0.06 #9816, 0.05 #2650), 07vn_9 (0.09 #1685, 0.03 #44680, 0.02 #5267), 03bzjpm (0.08 #8483, 0.08 #3108, 0.05 #12065), 02v5_g (0.08 #2584, 0.05 #11541, 0.05 #13332), 03nfnx (0.08 #3196, 0.05 #4987, 0.05 #6779), 031hcx (0.08 #3067, 0.04 #20982, 0.04 #28148) >> Best rule #424 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 0162c8; 05bxwh; 0hqcy; 01yg9y; 01j590z; >> query: (?x2837, 01shy7) <- people(?x1050, ?x2837), participant(?x9781, ?x2837), ?x1050 = 041rx >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #108 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 0162c8; 05bxwh; 0hqcy; 01yg9y; 01j590z; *> query: (?x2837, 02z3r8t) <- people(?x1050, ?x2837), participant(?x9781, ?x2837), ?x1050 = 041rx *> conf = 0.06 ranks of expected_values: 19 EVAL 047hpm film 02z3r8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 130.000 123.000 0.118 http://example.org/film/actor/film./film/performance/film #15222-0gtt5fb PRED entity: 0gtt5fb PRED relation: genre PRED expected values: 07s9rl0 => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 81): 07s9rl0 (0.85 #2568, 0.69 #1, 0.69 #123), 05p553 (0.39 #493, 0.39 #1104, 0.37 #1839), 02kdv5l (0.34 #247, 0.33 #858, 0.32 #1590), 03k9fj (0.33 #257, 0.33 #868, 0.28 #379), 01jfsb (0.33 #1601, 0.31 #1357, 0.31 #258), 01hmnh (0.29 #263, 0.24 #874, 0.18 #1606), 0lsxr (0.27 #132, 0.21 #498, 0.21 #620), 06n90 (0.24 #259, 0.21 #870, 0.18 #381), 04xvlr (0.22 #124, 0.16 #2569, 0.15 #2), 060__y (0.19 #873, 0.17 #262, 0.15 #2585) >> Best rule #2568 for best value: >> intensional similarity = 3 >> extensional distance = 1188 >> proper extension: 0c0wvx; 02qjv1p; >> query: (?x5588, 07s9rl0) <- genre(?x5588, ?x1403), genre(?x3137, ?x1403), ?x3137 = 0htww >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gtt5fb genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.845 http://example.org/film/film/genre #15221-083chw PRED entity: 083chw PRED relation: student! PRED expected values: 07t90 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 63): 017j69 (0.25 #144, 0.03 #9558, 0.02 #26303), 07vyf (0.12 #137, 0.04 #1183), 02mj7c (0.12 #164, 0.01 #2779), 0trv (0.12 #316), 03ksy (0.08 #1151, 0.06 #628, 0.04 #14227), 09f2j (0.08 #1727, 0.05 #9572, 0.04 #26317), 01w5m (0.06 #9518, 0.04 #26263, 0.04 #14226), 017z88 (0.06 #604, 0.05 #9495, 0.04 #5311), 01rtm4 (0.06 #527, 0.04 #1050, 0.01 #2619), 01bm_ (0.06 #767, 0.04 #1290, 0.01 #9658) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0py5b; >> query: (?x275, 017j69) <- award_nominee(?x275, ?x221), student(?x10341, ?x275), ?x10341 = 02xwzh >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 083chw student! 07t90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #15220-06449 PRED entity: 06449 PRED relation: category PRED expected values: 08mbj5d => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #38, 0.82 #34, 0.82 #59) >> Best rule #38 for best value: >> intensional similarity = 3 >> extensional distance = 275 >> proper extension: 06lxn; >> query: (?x2940, 08mbj5d) <- artist(?x8721, ?x2940), artists(?x497, ?x2940), award_winner(?x4974, ?x2940) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06449 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.838 http://example.org/common/topic/webpage./common/webpage/category #15219-02t_99 PRED entity: 02t_99 PRED relation: type_of_union PRED expected values: 04ztj => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.95 #264, 0.94 #255, 0.94 #222) >> Best rule #264 for best value: >> intensional similarity = 3 >> extensional distance = 3024 >> proper extension: 09jrf; >> query: (?x4638, 04ztj) <- type_of_union(?x4638, ?x1873), type_of_union(?x5330, ?x1873), ?x5330 = 02f2p7 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02t_99 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.946 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15218-0697s PRED entity: 0697s PRED relation: adjoins! PRED expected values: 01z215 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 357): 01z215 (0.82 #44771, 0.81 #29047, 0.81 #28262), 03__y (0.21 #44772, 0.21 #45558, 0.05 #4124), 0j1z8 (0.21 #44772, 0.21 #45558, 0.03 #3152), 047yc (0.21 #44772, 0.21 #45558, 0.03 #3985), 0161c (0.21 #44772, 0.21 #45558, 0.02 #12560), 05l8y (0.21 #44772, 0.03 #4227, 0.03 #36120), 0d05q4 (0.21 #45558, 0.08 #4131, 0.05 #7272), 01z88t (0.21 #45558, 0.03 #3235, 0.03 #4021), 0697s (0.21 #45558, 0.03 #36120, 0.03 #34546), 06bnz (0.18 #88, 0.15 #873, 0.14 #3226) >> Best rule #44771 for best value: >> intensional similarity = 2 >> extensional distance = 761 >> proper extension: 0mmr1; 0135k2; 0n6mc; 0mvn6; 0drrw; >> query: (?x3016, ?x1781) <- adjoins(?x3016, ?x1781), adjoins(?x1781, ?x311) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0697s adjoins! 01z215 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.817 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #15217-061k5 PRED entity: 061k5 PRED relation: time_zones PRED expected values: 02llzg => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 10): 02llzg (0.75 #17, 0.65 #43, 0.41 #82), 02hcv8 (0.36 #148, 0.34 #226, 0.33 #356), 03bdv (0.18 #58, 0.18 #164, 0.15 #125), 02lcqs (0.18 #801, 0.17 #814, 0.16 #840), 02fqwt (0.16 #419, 0.15 #185, 0.15 #484), 03plfd (0.10 #129, 0.07 #181, 0.05 #350), 02hczc (0.06 #134, 0.05 #186, 0.04 #199), 052vwh (0.04 #38, 0.02 #742, 0.02 #756), 042g7t (0.02 #741, 0.02 #755, 0.01 #130), 0gsrz4 (0.02 #738, 0.02 #752, 0.01 #127) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0fhsz; >> query: (?x13218, 02llzg) <- contains(?x205, ?x13218), teams(?x13218, ?x3552), sport(?x3552, ?x471), ?x205 = 03rjj >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 061k5 time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.750 http://example.org/location/location/time_zones #15216-02xx5 PRED entity: 02xx5 PRED relation: seasonal_months! PRED expected values: 040fb => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 4): 05cw8 (0.81 #44, 0.78 #58, 0.74 #49), 040fb (0.81 #44, 0.78 #58, 0.74 #49), 040fv (0.81 #44, 0.78 #58, 0.74 #49), 02xx5 (0.78 #58, 0.75 #57, 0.74 #49) >> Best rule #44 for best value: >> intensional similarity = 95 >> extensional distance = 2 >> proper extension: 028kb; >> query: (?x4869, ?x2140) <- month(?x11237, ?x4869), month(?x9559, ?x4869), month(?x8977, ?x4869), month(?x8956, ?x4869), month(?x8174, ?x4869), month(?x6703, ?x4869), month(?x6494, ?x4869), month(?x6458, ?x4869), month(?x6054, ?x4869), month(?x5719, ?x4869), month(?x5036, ?x4869), month(?x4698, ?x4869), month(?x4627, ?x4869), month(?x3501, ?x4869), month(?x3373, ?x4869), month(?x3125, ?x4869), month(?x3052, ?x4869), month(?x3026, ?x4869), month(?x2645, ?x4869), month(?x2611, ?x4869), month(?x2277, ?x4869), month(?x1860, ?x4869), month(?x1658, ?x4869), month(?x1649, ?x4869), month(?x1523, ?x4869), month(?x659, ?x4869), ?x6494 = 02sn34, ?x3501 = 0f2v0, seasonal_months(?x7298, ?x4869), seasonal_months(?x4925, ?x4869), seasonal_months(?x4827, ?x4869), ?x3026 = 0cv3w, seasonal_months(?x4869, ?x2140), ?x4827 = 03_ly, ?x3125 = 0d6lp, ?x2645 = 03h64, month(?x9605, ?x7298), month(?x6357, ?x7298), month(?x4271, ?x7298), ?x4627 = 05qtj, ?x5719 = 0f2rq, ?x4925 = 0ll3, ?x1860 = 01_d4, ?x6357 = 02cft, ?x4698 = 056_y, ?x2611 = 02h6_6p, ?x4271 = 06wjf, ?x1649 = 01f62, ?x9605 = 02frhbc, ?x6703 = 0f04v, ?x659 = 02cl1, ?x9559 = 07dfk, ?x5036 = 06y57, ?x6054 = 0fn2g, ?x2277 = 013yq, teams(?x1658, ?x6179), citytown(?x1306, ?x1658), featured_film_locations(?x10515, ?x1658), featured_film_locations(?x6079, ?x1658), featured_film_locations(?x3306, ?x1658), ?x8956 = 0947l, locations(?x2686, ?x1658), place_of_birth(?x877, ?x1658), location(?x2662, ?x1658), location(?x483, ?x1658), administrative_division(?x1658, ?x1905), produced_by(?x3306, ?x10281), ?x3052 = 01cx_, team(?x60, ?x6179), ?x11237 = 03khn, ?x3373 = 0ply0, award(?x483, ?x247), current_club(?x9799, ?x6179), artists(?x482, ?x483), award_winner(?x139, ?x483), award_nominee(?x483, ?x1089), origin(?x442, ?x1658), time_zones(?x1658, ?x2674), ?x6458 = 08966, ?x8174 = 01lfy, film(?x382, ?x3306), origin(?x483, ?x1275), film_production_design_by(?x3306, ?x8844), contains(?x1658, ?x14707), ?x8977 = 02z0j, influenced_by(?x483, ?x1136), type_of_union(?x483, ?x566), profession(?x2662, ?x131), award_winner(?x486, ?x2662), award_nominee(?x2662, ?x217), film_regional_debut_venue(?x1283, ?x1658), ?x6079 = 05sy_5, ?x1523 = 030qb3t, film(?x96, ?x10515), award_winner(?x1584, ?x2662) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02xx5 seasonal_months! 040fb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 12.000 12.000 0.810 http://example.org/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months #15215-0fv4v PRED entity: 0fv4v PRED relation: country! PRED expected values: 0bynt => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 53): 0bynt (0.85 #1600, 0.85 #805, 0.85 #1282), 071t0 (0.71 #923, 0.58 #817, 0.57 #1294), 01cgz (0.63 #649, 0.62 #596, 0.61 #543), 01lb14 (0.51 #280, 0.51 #916, 0.48 #333), 06f41 (0.50 #915, 0.50 #332, 0.48 #385), 03hr1p (0.49 #924, 0.48 #341, 0.45 #394), 07jbh (0.46 #934, 0.46 #351, 0.45 #404), 06wrt (0.46 #334, 0.44 #917, 0.41 #387), 0194d (0.46 #364, 0.43 #417, 0.40 #947), 064vjs (0.44 #932, 0.35 #561, 0.35 #243) >> Best rule #1600 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 03f2w; >> query: (?x7360, 0bynt) <- organization(?x7360, ?x127), official_language(?x7360, ?x5607), country(?x668, ?x7360) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fv4v country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.851 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #15214-02qzh2 PRED entity: 02qzh2 PRED relation: cinematography PRED expected values: 05br10 => 59 concepts (44 used for prediction) PRED predicted values (max 10 best out of 37): 04qvl7 (0.12 #254, 0.06 #574, 0.02 #1214), 02vx4c2 (0.06 #287, 0.02 #1508, 0.01 #1827), 0280mv7 (0.06 #268, 0.01 #1100, 0.01 #1164), 05dppk (0.04 #323, 0.02 #707, 0.02 #1091), 0854hr (0.04 #336, 0.02 #720, 0.01 #399), 027t8fw (0.03 #476, 0.03 #411, 0.02 #1373), 05br10 (0.03 #499, 0.01 #1013, 0.01 #1139), 06qn87 (0.03 #413, 0.02 #928, 0.02 #1055), 043zg (0.03 #2433, 0.02 #2758, 0.02 #1983), 01rh0w (0.03 #2433, 0.02 #2758, 0.02 #1983) >> Best rule #254 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 06w99h3; 0dnvn3; 034qrh; 0b6tzs; 01jrbb; 0g54xkt; 0gbtbm; 0660b9b; 0dgrwqr; 05pxnmb; ... >> query: (?x4160, 04qvl7) <- film(?x9388, ?x4160), film(?x4112, ?x4160), location(?x4112, ?x739), ?x9388 = 0309lm >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #499 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 86 *> proper extension: 06zn1c; *> query: (?x4160, 05br10) <- nominated_for(?x1312, ?x4160), ?x1312 = 07cbcy, genre(?x4160, ?x53) *> conf = 0.03 ranks of expected_values: 7 EVAL 02qzh2 cinematography 05br10 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 59.000 44.000 0.125 http://example.org/film/film/cinematography #15213-0p_47 PRED entity: 0p_47 PRED relation: award PRED expected values: 02grdc 09qvc0 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 315): 026mg3 (0.70 #18221, 0.70 #17824, 0.67 #24165), 05pcn59 (0.39 #1266, 0.31 #474, 0.27 #2454), 05zr6wv (0.37 #412, 0.16 #1204, 0.15 #1996), 09sb52 (0.33 #435, 0.31 #1227, 0.28 #14694), 0gq9h (0.32 #8394, 0.32 #7998, 0.16 #6810), 05p09zm (0.28 #1308, 0.26 #2100, 0.25 #2496), 05ztrmj (0.27 #575, 0.16 #1367, 0.12 #2159), 0gr4k (0.25 #6766, 0.12 #7954, 0.12 #8350), 040njc (0.24 #8327, 0.24 #7931, 0.16 #6743), 0gr51 (0.23 #6831, 0.13 #8019, 0.13 #8415) >> Best rule #18221 for best value: >> intensional similarity = 2 >> extensional distance = 1324 >> proper extension: 04cy8rb; 0f830f; 08w7vj; 0dky9n; 01qkqwg; 0gsg7; 01ky2h; 0l56b; 03jvmp; 0cjdk; ... >> query: (?x3917, ?x341) <- award_winner(?x139, ?x3917), award_winner(?x341, ?x3917) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #19807 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1540 *> proper extension: 05d6kv; 01f2w0; 01fsyp; *> query: (?x3917, ?x247) <- award_winner(?x5766, ?x3917), ceremony(?x247, ?x5766) *> conf = 0.05 ranks of expected_values: 126, 204 EVAL 0p_47 award 09qvc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 75.000 75.000 0.699 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0p_47 award 02grdc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 75.000 75.000 0.699 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15212-019sc PRED entity: 019sc PRED relation: colors! PRED expected values: 01jv_6 0713r 0138mv 04mvk7 04l5b4 => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 883): 0j5m6 (0.50 #3001, 0.50 #2467, 0.50 #1933), 0cqt41 (0.50 #2682, 0.50 #1882, 0.46 #1065), 02896 (0.50 #2395, 0.50 #1861, 0.46 #1065), 05xvj (0.50 #2124, 0.50 #2070, 0.46 #2658), 04vn5 (0.50 #1701, 0.50 #1436, 0.46 #2658), 01yjl (0.50 #2124, 0.50 #1902, 0.46 #2658), 04l5d0 (0.50 #3079, 0.50 #2011, 0.40 #3611), 05gg4 (0.50 #2719, 0.50 #1919, 0.38 #2987), 0jnmj (0.50 #2969, 0.50 #2435, 0.33 #843), 0jmj7 (0.50 #2124, 0.46 #1065, 0.46 #2658) >> Best rule #3001 for best value: >> intensional similarity = 30 >> extensional distance = 6 >> proper extension: 06kqt3; >> query: (?x4557, 0j5m6) <- colors(?x10175, ?x4557), colors(?x10071, ?x4557), colors(?x9847, ?x4557), colors(?x6038, ?x4557), colors(?x3416, ?x4557), colors(?x8901, ?x4557), colors(?x4170, ?x4557), colors(?x684, ?x4557), school(?x8901, ?x10297), school(?x2569, ?x3416), ?x10297 = 02rv1w, season(?x8901, ?x2406), currency(?x10175, ?x170), team(?x180, ?x684), team(?x11323, ?x684), school_type(?x10175, ?x1044), major_field_of_study(?x10175, ?x3995), category(?x6038, ?x134), major_field_of_study(?x11252, ?x3995), major_field_of_study(?x2313, ?x3995), institution(?x620, ?x9847), currency(?x10071, ?x1099), ?x11252 = 017lvd, contains(?x362, ?x10071), sport(?x4170, ?x1083), student(?x3995, ?x1188), draft(?x8901, ?x1161), organization(?x4095, ?x10071), ?x2313 = 07wrz, school(?x2820, ?x9847) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1341 for first EXPECTED value: *> intensional similarity = 33 *> extensional distance = 2 *> proper extension: 0jc_p; *> query: (?x4557, 01jv_6) <- colors(?x11128, ?x4557), colors(?x10175, ?x4557), colors(?x3416, ?x4557), colors(?x1520, ?x4557), colors(?x8901, ?x4557), colors(?x2114, ?x4557), colors(?x1576, ?x4557), colors(?x684, ?x4557), school(?x8901, ?x10297), school(?x2569, ?x3416), ?x10297 = 02rv1w, season(?x8901, ?x10017), position(?x684, ?x180), organization(?x346, ?x10175), student(?x3416, ?x5030), category(?x10175, ?x134), currency(?x1520, ?x170), major_field_of_study(?x10175, ?x1668), institution(?x9054, ?x3416), institution(?x1526, ?x3416), contains(?x94, ?x11128), school_type(?x3416, ?x1507), school_type(?x1520, ?x1044), ?x10017 = 026fmqm, ?x1526 = 0bkj86, organization(?x3484, ?x1520), major_field_of_study(?x9054, ?x1527), institution(?x9054, ?x5581), ?x2114 = 01y49, ?x5581 = 037fqp, award_winner(?x968, ?x5030), teams(?x4499, ?x1576), team(?x2010, ?x8901) *> conf = 0.50 ranks of expected_values: 53, 169, 186, 216, 247 EVAL 019sc colors! 04l5b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 21.000 21.000 0.500 http://example.org/sports/sports_team/colors EVAL 019sc colors! 04mvk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 21.000 21.000 0.500 http://example.org/sports/sports_team/colors EVAL 019sc colors! 0138mv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 21.000 21.000 0.500 http://example.org/sports/sports_team/colors EVAL 019sc colors! 0713r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 21.000 21.000 0.500 http://example.org/sports/sports_team/colors EVAL 019sc colors! 01jv_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 21.000 21.000 0.500 http://example.org/sports/sports_team/colors #15211-0l14qv PRED entity: 0l14qv PRED relation: performance_role! PRED expected values: 01wg3q => 97 concepts (85 used for prediction) PRED predicted values (max 10 best out of 955): 02rn_bj (0.57 #2297, 0.50 #3188, 0.40 #3522), 01r0t_j (0.50 #1081, 0.36 #5301, 0.33 #1965), 01wxdn3 (0.50 #1423, 0.25 #1313, 0.25 #982), 01vrncs (0.43 #2439, 0.33 #7, 0.30 #3444), 04mky3 (0.40 #1766, 0.20 #1878, 0.18 #3763), 09hnb (0.33 #137, 0.30 #3130, 0.29 #2239), 0167v4 (0.33 #91, 0.29 #2413, 0.29 #2303), 01vsyjy (0.33 #292, 0.25 #1180, 0.25 #1069), 0l12d (0.33 #1895, 0.25 #1342, 0.25 #1232), 02qwg (0.33 #40, 0.25 #1037, 0.20 #1590) >> Best rule #2297 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 05r5c; >> query: (?x228, 02rn_bj) <- role(?x75, ?x228), role(?x74, ?x228), role(?x4052, ?x228), group(?x228, ?x1945), ?x4052 = 050z2, role(?x228, ?x214), instrumentalists(?x228, ?x140), performance_role(?x228, ?x2620), ?x1945 = 02_5x9 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1770 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 0l15bq; *> query: (?x228, ?x654) <- role(?x745, ?x228), role(?x3703, ?x228), role(?x3112, ?x228), role(?x3316, ?x228), role(?x1660, ?x228), ?x3112 = 0mbct, ?x1660 = 012x4t, ?x3703 = 02dlh2, artists(?x378, ?x3316), role(?x654, ?x745) *> conf = 0.02 ranks of expected_values: 396 EVAL 0l14qv performance_role! 01wg3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 97.000 85.000 0.571 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #15210-03tcnt PRED entity: 03tcnt PRED relation: award! PRED expected values: 01vn35l 0pkyh 0134s5 0g_g2 023p29 => 34 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2421): 0dw4g (0.81 #16729, 0.77 #43496, 0.77 #43495), 01kd57 (0.81 #16729, 0.77 #43496, 0.77 #43495), 0ggl02 (0.81 #16729, 0.77 #43496, 0.77 #43495), 07r1_ (0.64 #18770, 0.54 #22116, 0.50 #15424), 016l09 (0.64 #19482, 0.54 #22828, 0.50 #16136), 017959 (0.64 #19449, 0.54 #22795, 0.25 #16103), 0c9l1 (0.62 #16299, 0.55 #19645, 0.50 #2916), 01vs_v8 (0.60 #3921, 0.50 #13958, 0.46 #20650), 01wf86y (0.60 #5519, 0.27 #18902, 0.23 #22248), 0840vq (0.60 #4208, 0.14 #7554, 0.12 #10899) >> Best rule #16729 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 02f72n; 02f73p; 02f77l; 02f73b; 02f79n; >> query: (?x3103, ?x1566) <- award(?x9868, ?x3103), award(?x8490, ?x3103), award(?x646, ?x3103), ?x646 = 04rcr, award_winner(?x3103, ?x1566), instrumentalists(?x227, ?x8490), ?x9868 = 0134pk >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #7491 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 01bgqh; 01c9jp; 03qbh5; 02gdjb; 01ckrr; *> query: (?x3103, 0pkyh) <- award(?x4261, ?x3103), award(?x1751, ?x3103), ?x1751 = 05crg7, group(?x227, ?x4261), artist(?x8738, ?x4261) *> conf = 0.29 ranks of expected_values: 79, 129, 137, 173, 382 EVAL 03tcnt award! 023p29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 34.000 18.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03tcnt award! 0g_g2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 18.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03tcnt award! 0134s5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 34.000 18.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03tcnt award! 0pkyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 34.000 18.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03tcnt award! 01vn35l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 34.000 18.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15209-07f_t4 PRED entity: 07f_t4 PRED relation: nominated_for! PRED expected values: 05tk7y => 79 concepts (29 used for prediction) PRED predicted values (max 10 best out of 435): 08qxx9 (0.30 #65446, 0.28 #2337, 0.26 #39738), 05tk7y (0.30 #65446, 0.28 #2337, 0.26 #39738), 0fs9jn (0.28 #2337, 0.26 #39738, 0.26 #65445), 0jpdn (0.17 #23375, 0.15 #28050, 0.12 #30388), 0kk9v (0.13 #7851, 0.02 #26551, 0.01 #21875), 01795t (0.13 #7452, 0.01 #21476), 086k8 (0.12 #58, 0.09 #18700, 0.09 #2395), 05qd_ (0.12 #174, 0.09 #2511, 0.08 #7187), 06fxnf (0.12 #856, 0.09 #3193, 0.06 #7869), 0h32q (0.12 #963, 0.09 #3300, 0.04 #5640) >> Best rule #65446 for best value: >> intensional similarity = 4 >> extensional distance = 885 >> proper extension: 02d44q; 0gh8zks; 0hgnl3t; 07k2mq; 0372j5; >> query: (?x7672, ?x4015) <- film(?x4015, ?x7672), nominated_for(?x507, ?x7672), film_release_region(?x7672, ?x94), award_nominee(?x1194, ?x4015) >> conf = 0.30 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07f_t4 nominated_for! 05tk7y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 29.000 0.300 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15208-05dss7 PRED entity: 05dss7 PRED relation: executive_produced_by PRED expected values: 08gf93 => 104 concepts (77 used for prediction) PRED predicted values (max 10 best out of 112): 01pcmd (0.20 #56, 0.17 #309, 0.14 #562), 01twdk (0.15 #1128, 0.12 #1384, 0.06 #4666), 06q8hf (0.14 #673, 0.10 #2703, 0.09 #4216), 05hj_k (0.09 #3894, 0.08 #4399, 0.08 #2634), 02z2xdf (0.09 #5220, 0.08 #5976, 0.08 #7240), 04jspq (0.08 #1929, 0.08 #4704, 0.07 #5717), 079vf (0.08 #1780, 0.08 #1017, 0.06 #1273), 04pqqb (0.08 #876, 0.08 #1132, 0.06 #1388), 03c9pqt (0.08 #2277, 0.07 #2530, 0.06 #1773), 032v0v (0.08 #809, 0.02 #5616, 0.01 #8152) >> Best rule #56 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 011wtv; 01l_pn; 01y9jr; >> query: (?x6556, 01pcmd) <- crewmember(?x6556, ?x5664), film(?x4782, ?x6556), ?x4782 = 0bksh, music(?x6556, ?x460), film_release_distribution_medium(?x6556, ?x81), film_release_region(?x6556, ?x87) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05dss7 executive_produced_by 08gf93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 77.000 0.200 http://example.org/film/film/executive_produced_by #15207-09c7w0 PRED entity: 09c7w0 PRED relation: country! PRED expected values: 01mc11 0ygbf 09snz 027l4q 0vrmb 0ny57 0nbzp 0rydq => 188 concepts (175 used for prediction) PRED predicted values (max 10 best out of 1467): 06yxd (0.27 #14648, 0.26 #16240, 0.26 #19110), 0846v (0.27 #14648, 0.26 #16240, 0.26 #19110), 0hjy (0.27 #14648, 0.26 #16240, 0.26 #19110), 02_286 (0.27 #14648, 0.26 #16240, 0.26 #19110), 03s0w (0.27 #14648, 0.26 #16240, 0.26 #19110), 0h8d (0.27 #14648, 0.26 #16240, 0.26 #19110), 01n7q (0.23 #23254, 0.22 #20704, 0.22 #32813), 06mz5 (0.22 #20704, 0.22 #32813, 0.21 #24210), 0k9p4 (0.22 #20704, 0.22 #32813, 0.21 #24210), 0n1rj (0.22 #20704, 0.22 #32813, 0.21 #24210) >> Best rule #14648 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 07f1x; >> query: (?x94, ?x108) <- film_release_region(?x54, ?x94), country(?x108, ?x94), country(?x242, ?x94) >> conf = 0.27 => this is the best rule for 6 predicted values *> Best rule #20704 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 04gzd; 07ylj; 05qx1; 015qh; 07twz; *> query: (?x94, ?x95) <- film_release_region(?x1386, ?x94), ?x1386 = 0dtfn, contains(?x94, ?x95) *> conf = 0.22 ranks of expected_values: 26, 1088, 1224, 1264, 1362, 1438 EVAL 09c7w0 country! 0rydq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 188.000 175.000 0.268 http://example.org/base/biblioness/bibs_location/country EVAL 09c7w0 country! 0nbzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 188.000 175.000 0.268 http://example.org/base/biblioness/bibs_location/country EVAL 09c7w0 country! 0ny57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 188.000 175.000 0.268 http://example.org/base/biblioness/bibs_location/country EVAL 09c7w0 country! 0vrmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 188.000 175.000 0.268 http://example.org/base/biblioness/bibs_location/country EVAL 09c7w0 country! 027l4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 188.000 175.000 0.268 http://example.org/base/biblioness/bibs_location/country EVAL 09c7w0 country! 09snz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 188.000 175.000 0.268 http://example.org/base/biblioness/bibs_location/country EVAL 09c7w0 country! 0ygbf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 188.000 175.000 0.268 http://example.org/base/biblioness/bibs_location/country EVAL 09c7w0 country! 01mc11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 188.000 175.000 0.268 http://example.org/base/biblioness/bibs_location/country #15206-02pqgt8 PRED entity: 02pqgt8 PRED relation: type_of_union PRED expected values: 04ztj => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.74 #137, 0.74 #185, 0.73 #25), 01g63y (0.59 #385, 0.59 #422, 0.22 #22), 0jgjn (0.59 #385, 0.59 #422) >> Best rule #137 for best value: >> intensional similarity = 4 >> extensional distance = 715 >> proper extension: 01l1b90; 04rs03; 0168cl; 01g4zr; 044ntk; 01n4f8; 02fb1n; 01wg982; 0408np; 014q2g; ... >> query: (?x4190, 04ztj) <- gender(?x4190, ?x514), award(?x4190, ?x507), profession(?x4190, ?x319), ?x319 = 01d_h8 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pqgt8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.745 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15205-060c4 PRED entity: 060c4 PRED relation: jurisdiction_of_office PRED expected values: 05cgv 05qx1 01mjq 05sb1 0jgx 02k8k 01crd5 04v09 0jt3tjf 034m8 06s_2 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 530): 01n7q (0.36 #10900, 0.33 #11539, 0.33 #4187), 0ctw_b (0.33 #4178, 0.33 #3859, 0.33 #985), 04gqr (0.33 #1081, 0.33 #443, 0.30 #7466), 014tss (0.33 #4311, 0.33 #1118, 0.20 #7503), 03_3d (0.33 #964, 0.33 #645, 0.20 #7349), 0285m87 (0.33 #1184, 0.33 #865, 0.20 #7569), 07ssc (0.33 #4166, 0.33 #3847, 0.20 #7358), 059j2 (0.33 #995, 0.30 #7380, 0.20 #13774), 0d0vqn (0.33 #966, 0.30 #7351, 0.20 #13745), 081yw (0.33 #3962, 0.29 #12909, 0.29 #14186) >> Best rule #10900 for best value: >> intensional similarity = 9 >> extensional distance = 12 >> proper extension: 04syw; 0f6c3; 02079p; 01t7n9; 0fj45; >> query: (?x346, 01n7q) <- jurisdiction_of_office(?x346, ?x1536), jurisdiction_of_office(?x346, ?x789), jurisdiction_of_office(?x346, ?x756), basic_title(?x1157, ?x346), film_release_region(?x124, ?x1536), combatants(?x326, ?x1536), country(?x150, ?x756), film_release_region(?x303, ?x756), country(?x251, ?x789) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #169 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 0dq3c; *> query: (?x346, 02k8k) <- organization(?x346, ?x581), jurisdiction_of_office(?x346, ?x6428), basic_title(?x1157, ?x346), company(?x346, ?x127), ?x6428 = 0j4b, company(?x233, ?x581) *> conf = 0.33 ranks of expected_values: 17, 18, 45, 46, 50, 52, 53, 58, 59, 249, 359 EVAL 060c4 jurisdiction_of_office 06s_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060c4 jurisdiction_of_office 034m8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060c4 jurisdiction_of_office 0jt3tjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060c4 jurisdiction_of_office 04v09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060c4 jurisdiction_of_office 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060c4 jurisdiction_of_office 02k8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060c4 jurisdiction_of_office 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060c4 jurisdiction_of_office 05sb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060c4 jurisdiction_of_office 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060c4 jurisdiction_of_office 05qx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060c4 jurisdiction_of_office 05cgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 56.000 56.000 0.357 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #15204-06mmb PRED entity: 06mmb PRED relation: place_of_birth PRED expected values: 03l2n => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 73): 0106dv (0.33 #706, 0.33 #395, 0.27 #54233), 07b_l (0.27 #36628, 0.27 #54233, 0.27 #54232), 030qb3t (0.27 #36628, 0.27 #54233, 0.27 #54232), 059rby (0.27 #54233, 0.27 #54232, 0.27 #24655), 04jpl (0.20 #714, 0.15 #2123, 0.09 #1418), 01z56h (0.10 #1353, 0.09 #2057, 0.08 #2762), 01b8w_ (0.10 #1040, 0.09 #1744, 0.08 #2449), 01r32 (0.09 #1460), 02_286 (0.08 #35941, 0.07 #2838, 0.07 #54957), 0c4kv (0.07 #3338) >> Best rule #706 for best value: >> intensional similarity = 2 >> extensional distance = 4 >> proper extension: 053y0s; >> query: (?x2559, ?x10364) <- location(?x2559, ?x10364), ?x10364 = 0106dv >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06mmb place_of_birth 03l2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 86.000 0.333 http://example.org/people/person/place_of_birth #15203-02js_6 PRED entity: 02js_6 PRED relation: type_of_union PRED expected values: 04ztj => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #13, 0.86 #37, 0.86 #61), 01g63y (0.34 #6, 0.33 #50, 0.31 #42), 01bl8s (0.01 #27) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 129 >> proper extension: 0b478; >> query: (?x12359, 04ztj) <- nominated_for(?x12359, ?x1542), award_winner(?x12359, ?x5346), spouse(?x13307, ?x12359) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02js_6 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.870 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15202-02_n3z PRED entity: 02_n3z PRED relation: film_crew_role! PRED expected values: 03ckwzc 06_wqk4 05c46y6 047p7fr 05mrf_p 047vnkj 047csmy 02z2mr7 02z0f6l 07f_t4 01gglm 02mpyh 09rvwmy => 28 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1632): 05qbckf (0.80 #14438, 0.75 #13252, 0.71 #12066), 076xkps (0.80 #15235, 0.75 #14049, 0.71 #12863), 05m_jsg (0.80 #14665, 0.75 #13479, 0.67 #11106), 03cp4cn (0.80 #14968, 0.75 #13782, 0.67 #11409), 05pbl56 (0.75 #13205, 0.73 #15576, 0.70 #14391), 04jpg2p (0.75 #14026, 0.71 #12840, 0.67 #17581), 05szq8z (0.75 #13679, 0.70 #14865, 0.67 #11306), 05qbbfb (0.75 #13752, 0.70 #14938, 0.67 #11379), 07k8rt4 (0.75 #13544, 0.70 #14730, 0.67 #11171), 047vnkj (0.75 #13660, 0.70 #14846, 0.64 #16031) >> Best rule #14438 for best value: >> intensional similarity = 13 >> extensional distance = 8 >> proper extension: 01xy5l_; >> query: (?x281, 05qbckf) <- film_crew_role(?x5201, ?x281), film_crew_role(?x2085, ?x281), film_crew_role(?x2006, ?x281), film(?x981, ?x2006), nominated_for(?x384, ?x2085), film_release_distribution_medium(?x2006, ?x81), nominated_for(?x2006, ?x2869), genre(?x2085, ?x53), film_distribution_medium(?x2006, ?x627), ?x5201 = 05_5_22, nominated_for(?x669, ?x2006), ?x384 = 03hkv_r, genre(?x2006, ?x600) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #13660 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 6 *> proper extension: 01vx2h; *> query: (?x281, 047vnkj) <- film_crew_role(?x6081, ?x281), film_crew_role(?x5945, ?x281), film_crew_role(?x3714, ?x281), film_crew_role(?x2085, ?x281), film_crew_role(?x2006, ?x281), film(?x9797, ?x2006), nominated_for(?x384, ?x2085), film_release_distribution_medium(?x2006, ?x81), nominated_for(?x2006, ?x2869), ?x6081 = 027m5wv, film_distribution_medium(?x2006, ?x627), award(?x9797, ?x591), people(?x7322, ?x9797), film_festivals(?x3714, ?x9080), nominated_for(?x2086, ?x2085), ?x5945 = 05t0_2v *> conf = 0.75 ranks of expected_values: 10, 11, 23, 26, 30, 44, 53, 78, 209, 232, 322, 340, 373 EVAL 02_n3z film_crew_role! 09rvwmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 02mpyh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 01gglm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 07f_t4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 02z0f6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 02z2mr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 047csmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 047vnkj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 05mrf_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 047p7fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 05c46y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 06_wqk4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02_n3z film_crew_role! 03ckwzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 28.000 25.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15201-0ds3t5x PRED entity: 0ds3t5x PRED relation: film! PRED expected values: 0fqy4p => 71 concepts (59 used for prediction) PRED predicted values (max 10 best out of 72): 017s11 (0.50 #3, 0.16 #75, 0.13 #2855), 04cygb3 (0.49 #2925, 0.46 #2341, 0.46 #1972), 086k8 (0.25 #2, 0.19 #74, 0.18 #363), 016tt2 (0.25 #4, 0.15 #438, 0.14 #365), 024rgt (0.25 #20, 0.05 #92, 0.05 #527), 030_1m (0.25 #14, 0.05 #4096, 0.03 #375), 019v67 (0.25 #66, 0.05 #4096, 0.02 #138), 05qd_ (0.18 #81, 0.17 #516, 0.15 #663), 03xq0f (0.18 #77, 0.14 #221, 0.13 #512), 016tw3 (0.17 #2863, 0.15 #1541, 0.14 #2278) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 035s95; >> query: (?x385, 017s11) <- film(?x4154, ?x385), film_crew_role(?x385, ?x137), ?x4154 = 014g22 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #100 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 09rfh9; *> query: (?x385, 0fqy4p) <- nominated_for(?x3019, ?x385), film_release_region(?x385, ?x87), ?x3019 = 057xs89 *> conf = 0.02 ranks of expected_values: 63 EVAL 0ds3t5x film! 0fqy4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 71.000 59.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15200-02r1c18 PRED entity: 02r1c18 PRED relation: language PRED expected values: 03hkp => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 45): 064_8sq (0.25 #716, 0.17 #253, 0.16 #657), 06nm1 (0.17 #10, 0.15 #532, 0.14 #184), 04306rv (0.17 #4, 0.12 #236, 0.11 #699), 06b_j (0.13 #22, 0.08 #544, 0.08 #254), 02bjrlw (0.10 #696, 0.08 #233, 0.08 #175), 03_9r (0.09 #241, 0.07 #1400, 0.06 #879), 0653m (0.06 #243, 0.05 #1577, 0.05 #416), 012w70 (0.06 #244, 0.04 #591, 0.04 #302), 0jzc (0.06 #714, 0.05 #889, 0.04 #1120), 04h9h (0.05 #274, 0.05 #158, 0.04 #969) >> Best rule #716 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 02vl9ln; >> query: (?x1535, 064_8sq) <- country(?x1535, ?x789), country(?x1535, ?x94), ?x789 = 0f8l9c, service_location(?x127, ?x94) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 04sh80; *> query: (?x1535, 03hkp) <- edited_by(?x1535, ?x826), executive_produced_by(?x1535, ?x3568), titles(?x1316, ?x1535), film(?x1709, ?x1535) *> conf = 0.04 ranks of expected_values: 11 EVAL 02r1c18 language 03hkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 91.000 91.000 0.246 http://example.org/film/film/language #15199-0d63kt PRED entity: 0d63kt PRED relation: titles PRED expected values: 05hjnw => 45 concepts (9 used for prediction) PRED predicted values (max 10 best out of 1969): 05_5rjx (0.50 #5235, 0.40 #2107, 0.33 #544), 0209hj (0.42 #6343, 0.24 #7903, 0.23 #9467), 016z7s (0.42 #6540, 0.24 #8100, 0.23 #9664), 05hjnw (0.40 #3125, 0.40 #2284, 0.33 #1563), 093l8p (0.40 #2688, 0.33 #1125, 0.29 #4688), 01qbg5 (0.40 #2649, 0.33 #1086, 0.29 #4212), 02nczh (0.40 #2519, 0.33 #956, 0.29 #4082), 03hkch7 (0.40 #1999, 0.33 #436, 0.29 #3562), 02d413 (0.40 #1565, 0.33 #2, 0.29 #3128), 02s4l6 (0.40 #1872, 0.33 #309, 0.29 #3435) >> Best rule #5235 for best value: >> intensional similarity = 9 >> extensional distance = 6 >> proper extension: 02n4kr; 01jfsb; 0219x_; >> query: (?x11032, 05_5rjx) <- genre(?x9616, ?x11032), nominated_for(?x11577, ?x9616), nominated_for(?x1486, ?x9616), film_release_region(?x9616, ?x94), nominated_for(?x11577, ?x3981), location(?x11577, ?x108), film(?x1486, ?x1487), titles(?x11032, ?x414), ?x3981 = 047tsx3 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3125 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 07s9rl0; 02l7c8; *> query: (?x11032, ?x4939) <- genre(?x9616, ?x11032), genre(?x4939, ?x11032), nominated_for(?x11577, ?x9616), film_release_region(?x9616, ?x789), film_release_region(?x9616, ?x94), ?x11577 = 01gvxv, ?x789 = 0f8l9c, ?x4939 = 05hjnw, nationality(?x51, ?x94) *> conf = 0.40 ranks of expected_values: 4 EVAL 0d63kt titles 05hjnw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 45.000 9.000 0.500 http://example.org/media_common/netflix_genre/titles #15198-03_87 PRED entity: 03_87 PRED relation: influenced_by PRED expected values: 0gz_ 01tz6vs 04jwp => 166 concepts (63 used for prediction) PRED predicted values (max 10 best out of 478): 03hnd (0.38 #2214, 0.20 #3918, 0.18 #5189), 02lt8 (0.33 #115, 0.31 #9876, 0.29 #5210), 037jz (0.33 #203, 0.25 #1479, 0.23 #3174), 042q3 (0.33 #355, 0.23 #3326, 0.17 #13931), 0j3v (0.33 #58, 0.20 #8117, 0.19 #4727), 01tz6vs (0.33 #1868, 0.19 #4839, 0.13 #22063), 0d5_f (0.33 #122, 0.17 #1820, 0.13 #22063), 01hb6v (0.33 #491, 0.03 #8550, 0.03 #22061), 0282x (0.33 #592, 0.03 #8651, 0.02 #18668), 05qmj (0.29 #3583, 0.26 #8671, 0.25 #1034) >> Best rule #2214 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 01zwy; >> query: (?x6457, 03hnd) <- influenced_by(?x6457, ?x5435), place_of_burial(?x6457, ?x6458), location(?x6457, ?x1591), influenced_by(?x7861, ?x5435), ?x7861 = 06jcc >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1868 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 0c4y8; *> query: (?x6457, 01tz6vs) <- influenced_by(?x6457, ?x6975), influenced_by(?x6457, ?x4292), type_of_union(?x6457, ?x566), religion(?x6457, ?x1985), ?x4292 = 0zm1, location(?x6975, ?x1591) *> conf = 0.33 ranks of expected_values: 6, 47, 66 EVAL 03_87 influenced_by 04jwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 166.000 63.000 0.375 http://example.org/influence/influence_node/influenced_by EVAL 03_87 influenced_by 01tz6vs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 166.000 63.000 0.375 http://example.org/influence/influence_node/influenced_by EVAL 03_87 influenced_by 0gz_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 166.000 63.000 0.375 http://example.org/influence/influence_node/influenced_by #15197-0bg539 PRED entity: 0bg539 PRED relation: artist! PRED expected values: 012b30 => 145 concepts (100 used for prediction) PRED predicted values (max 10 best out of 107): 01trtc (0.50 #216, 0.14 #928, 0.10 #500), 01dtcb (0.25 #190, 0.12 #1613, 0.10 #1897), 01clyr (0.25 #176, 0.11 #4440, 0.10 #4014), 015_1q (0.21 #2011, 0.20 #4000, 0.18 #3147), 0n85g (0.20 #1061, 0.14 #2339, 0.14 #3901), 026s90 (0.20 #468, 0.09 #1465, 0.04 #3169), 03rhqg (0.16 #5133, 0.16 #3996, 0.15 #4422), 0181dw (0.15 #3170, 0.10 #4023, 0.10 #7150), 0k_kr (0.14 #2320, 0.09 #2746, 0.09 #2888), 011k1h (0.14 #4132, 0.10 #4416, 0.09 #5127) >> Best rule #216 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0gps0z; >> query: (?x1294, 01trtc) <- film(?x1294, ?x2350), ?x2350 = 0661m4p, student(?x4268, ?x1294), major_field_of_study(?x122, ?x4268) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1657 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 0lbj1; 03qd_; 01kx_81; 0pgjm; 03rl84; 01vsnff; 01vs_v8; 021bk; 06449; 01w02sy; ... *> query: (?x1294, 012b30) <- film(?x1294, ?x2350), role(?x1294, ?x227), nominated_for(?x1294, ?x12535), film_release_region(?x2350, ?x87) *> conf = 0.08 ranks of expected_values: 31 EVAL 0bg539 artist! 012b30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 145.000 100.000 0.500 http://example.org/music/record_label/artist #15196-02vxn PRED entity: 02vxn PRED relation: industry! PRED expected values: 030_1m 01gb54 031rq5 05cl8y 019v67 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 229): 0l8sx (0.38 #5553, 0.29 #2788, 0.27 #5364), 0sxdg (0.38 #5553, 0.29 #2853, 0.27 #4439), 09b3v (0.38 #5553, 0.29 #2808, 0.25 #1221), 02_l39 (0.38 #5553, 0.25 #1296, 0.14 #2883), 018_q8 (0.38 #5553, 0.25 #1253, 0.14 #2840), 049ql1 (0.38 #5553, 0.17 #2499, 0.14 #3096), 03xsby (0.38 #5553, 0.14 #2789, 0.11 #3979), 0338lq (0.38 #5553, 0.10 #2778), 086k8 (0.38 #5553, 0.10 #2778), 01bfjy (0.38 #5553) >> Best rule #5553 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 019mlh; >> query: (?x373, ?x3920) <- industry(?x7690, ?x373), industry(?x6969, ?x373), industry(?x166, ?x373), citytown(?x6969, ?x4801), child(?x3920, ?x166), state_province_region(?x7690, ?x1227), category(?x7690, ?x134) >> conf = 0.38 => this is the best rule for 12 predicted values *> Best rule #1064 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 04rlf; *> query: (?x373, 05cl8y) <- disciplines_or_subjects(?x277, ?x373), major_field_of_study(?x4955, ?x373), company(?x4309, ?x4955), student(?x4955, ?x123), industry(?x166, ?x373) *> conf = 0.25 ranks of expected_values: 37, 127, 130, 139, 206 EVAL 02vxn industry! 019v67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 73.000 73.000 0.385 http://example.org/business/business_operation/industry EVAL 02vxn industry! 05cl8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 73.000 73.000 0.385 http://example.org/business/business_operation/industry EVAL 02vxn industry! 031rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 73.000 73.000 0.385 http://example.org/business/business_operation/industry EVAL 02vxn industry! 01gb54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 73.000 73.000 0.385 http://example.org/business/business_operation/industry EVAL 02vxn industry! 030_1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 73.000 73.000 0.385 http://example.org/business/business_operation/industry #15195-01j590z PRED entity: 01j590z PRED relation: student! PRED expected values: 02gn8s => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 140): 0bwfn (0.12 #1329, 0.10 #6072, 0.07 #16612), 01w5m (0.09 #1159, 0.06 #5902, 0.06 #16442), 02g839 (0.07 #25, 0.07 #2660, 0.06 #3187), 01t0dy (0.07 #6014, 0.06 #1271, 0.02 #2325), 017z88 (0.06 #1136, 0.03 #13784, 0.03 #5879), 03ksy (0.05 #9065, 0.04 #23299, 0.03 #633), 0fr9jp (0.04 #2980, 0.04 #3507, 0.04 #4034), 02183k (0.04 #6428, 0.02 #3793, 0.02 #4847), 01d34b (0.04 #3945, 0.04 #256, 0.04 #4999), 09f2j (0.04 #7537, 0.04 #159, 0.03 #686) >> Best rule #1329 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 0hnlx; 01wj9y9; 0lrh; 06449; 01fs_4; 0cyhq; >> query: (?x8819, 0bwfn) <- location(?x8819, ?x6987), artists(?x1000, ?x8819), people(?x1050, ?x8819), ?x1050 = 041rx >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #778 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 03qd_; 04mn81; 01p0w_; *> query: (?x8819, 02gn8s) <- category(?x8819, ?x134), group(?x8819, ?x10813), role(?x8819, ?x1466), currency(?x8819, ?x170) *> conf = 0.03 ranks of expected_values: 29 EVAL 01j590z student! 02gn8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 167.000 167.000 0.118 http://example.org/education/educational_institution/students_graduates./education/education/student #15194-0c9k8 PRED entity: 0c9k8 PRED relation: music PRED expected values: 02ryx0 => 65 concepts (40 used for prediction) PRED predicted values (max 10 best out of 85): 09fb5 (0.10 #2941, 0.06 #7999, 0.06 #6728), 039x1k (0.10 #2941, 0.06 #7999, 0.06 #6728), 01csvq (0.10 #2941, 0.06 #7999, 0.06 #6728), 0gyx4 (0.10 #2941, 0.06 #7999, 0.06 #6728), 03_fk9 (0.10 #2941, 0.06 #7999, 0.06 #6728), 03wd5tk (0.10 #2941, 0.06 #6728, 0.06 #8209), 095zvfg (0.10 #2941, 0.06 #6728, 0.06 #8209), 0146pg (0.08 #10, 0.07 #431, 0.06 #1482), 04pf4r (0.08 #68, 0.02 #1540, 0.01 #6796), 0jn5l (0.08 #96, 0.02 #728) >> Best rule #2941 for best value: >> intensional similarity = 4 >> extensional distance = 577 >> proper extension: 0267wwv; 09rfpk; >> query: (?x2943, ?x406) <- nominated_for(?x406, ?x2943), language(?x2943, ?x254), music(?x2943, ?x5896), titles(?x53, ?x2943) >> conf = 0.10 => this is the best rule for 7 predicted values *> Best rule #109 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 032sl_; *> query: (?x2943, 02ryx0) <- film(?x1119, ?x2943), nominated_for(?x5894, ?x2943), genre(?x2943, ?x53), ?x1119 = 039bp *> conf = 0.08 ranks of expected_values: 11 EVAL 0c9k8 music 02ryx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 65.000 40.000 0.100 http://example.org/film/film/music #15193-0g5838s PRED entity: 0g5838s PRED relation: nominated_for! PRED expected values: 0fm3kw => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 191): 0fms83 (0.68 #5178, 0.66 #4942, 0.51 #3530), 0fm3kw (0.55 #423, 0.33 #188, 0.06 #1411), 0fm3nb (0.36 #450, 0.33 #215, 0.06 #1411), 05zvq6g (0.33 #47, 0.27 #282, 0.06 #1411), 09ly2r6 (0.33 #172, 0.27 #407, 0.06 #1411), 02x1dht (0.33 #43, 0.18 #278, 0.13 #748), 054knh (0.33 #192, 0.18 #427, 0.08 #1367), 0gs9p (0.32 #3357, 0.24 #4768, 0.23 #5005), 0gq9h (0.31 #3355, 0.27 #4766, 0.27 #1236), 019f4v (0.28 #3347, 0.22 #4758, 0.21 #523) >> Best rule #5178 for best value: >> intensional similarity = 2 >> extensional distance = 987 >> proper extension: 06mmr; >> query: (?x3076, ?x11083) <- award(?x3076, ?x11083), award(?x12856, ?x11083) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #423 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 04nnpw; *> query: (?x3076, 0fm3kw) <- nominated_for(?x4695, ?x3076), nominated_for(?x1441, ?x3076), ?x4695 = 0fm3b5, film_crew_role(?x3076, ?x468), award(?x396, ?x1441) *> conf = 0.55 ranks of expected_values: 2 EVAL 0g5838s nominated_for! 0fm3kw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 57.000 57.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15192-05hj_k PRED entity: 05hj_k PRED relation: people! PRED expected values: 07hwkr => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 43): 0xnvg (0.21 #321, 0.09 #1245, 0.08 #1707), 0x67 (0.20 #2474, 0.19 #2782, 0.12 #780), 041rx (0.20 #81, 0.19 #466, 0.16 #235), 02w7gg (0.20 #79, 0.10 #1388, 0.09 #695), 033tf_ (0.13 #1162, 0.12 #1085, 0.08 #777), 07bch9 (0.13 #639, 0.08 #1024, 0.07 #2025), 02ctzb (0.11 #246, 0.09 #631, 0.09 #400), 01qhm_ (0.11 #237, 0.09 #391, 0.07 #622), 07hwkr (0.09 #166, 0.08 #782, 0.08 #1475), 048z7l (0.09 #194, 0.05 #810, 0.05 #348) >> Best rule #321 for best value: >> intensional similarity = 2 >> extensional distance = 17 >> proper extension: 013qvn; 0m76b; >> query: (?x4060, 0xnvg) <- organizations_founded(?x4060, ?x3462), film(?x4060, ?x6205) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 9 *> proper extension: 04pg29; *> query: (?x4060, 07hwkr) <- organizations_founded(?x4060, ?x3462), producer_type(?x4060, ?x632) *> conf = 0.09 ranks of expected_values: 9 EVAL 05hj_k people! 07hwkr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 112.000 112.000 0.211 http://example.org/people/ethnicity/people #15191-02vsw1 PRED entity: 02vsw1 PRED relation: languages_spoken PRED expected values: 0t_2 => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 89): 02h40lc (0.83 #435, 0.81 #483, 0.56 #627), 0t_2 (0.50 #347, 0.44 #299, 0.43 #732), 06nm1 (0.33 #7, 0.27 #537, 0.25 #55), 0k0sv (0.33 #17, 0.25 #258, 0.25 #65), 02ztjwg (0.33 #25, 0.25 #266, 0.25 #73), 01wgr (0.33 #32, 0.25 #273, 0.25 #80), 0295r (0.33 #213, 0.25 #261, 0.25 #68), 0880p (0.33 #37, 0.25 #85, 0.20 #182), 03hkp (0.33 #11, 0.25 #59, 0.20 #156), 06b_j (0.33 #16, 0.25 #64, 0.20 #161) >> Best rule #435 for best value: >> intensional similarity = 12 >> extensional distance = 28 >> proper extension: 02w7gg; 033tf_; 09v5bdn; 03lmx1; 0d7wh; 03bkbh; 0dbxy; 013b6_; 078ds; 05l3g_; ... >> query: (?x11184, 02h40lc) <- languages_spoken(?x11184, ?x4442), languages(?x6957, ?x4442), languages(?x6440, ?x4442), language(?x6362, ?x4442), language(?x4772, ?x4442), language(?x4565, ?x4442), official_language(?x304, ?x4442), ?x4772 = 06kl78, ?x6957 = 03s9b, ?x4565 = 011wtv, ?x6362 = 03_gz8, ?x6440 = 0bdt8 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #347 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 18 *> proper extension: 02ctzb; *> query: (?x11184, 0t_2) <- people(?x11184, ?x2692), people(?x11184, ?x241), sibling(?x1286, ?x241), nationality(?x241, ?x94), languages(?x2692, ?x254), participant(?x241, ?x406), award_nominee(?x2692, ?x157), film(?x2692, ?x2090), profession(?x241, ?x319) *> conf = 0.50 ranks of expected_values: 2 EVAL 02vsw1 languages_spoken 0t_2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 28.000 28.000 0.833 http://example.org/people/ethnicity/languages_spoken #15190-03j24kf PRED entity: 03j24kf PRED relation: type_of_union PRED expected values: 04ztj => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.98 #73, 0.94 #328, 0.93 #286), 01bl8s (0.04 #23, 0.03 #32, 0.02 #14) >> Best rule #73 for best value: >> intensional similarity = 2 >> extensional distance = 226 >> proper extension: 0457w0; >> query: (?x4701, 04ztj) <- type_of_union(?x4701, ?x1873), location_of_ceremony(?x4701, ?x362) >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03j24kf type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.978 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15189-013tcv PRED entity: 013tcv PRED relation: nationality PRED expected values: 0chghy => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 28): 09c7w0 (0.75 #4612, 0.74 #6318, 0.74 #6920), 0chghy (0.39 #1002, 0.38 #5615, 0.33 #10635), 07ssc (0.39 #1002, 0.38 #5615, 0.18 #415), 0ctw_b (0.39 #1002, 0.02 #728, 0.01 #2232), 05fly (0.33 #10635, 0.01 #6016), 05nrg (0.33 #10635), 02jx1 (0.14 #433, 0.12 #4242, 0.11 #3942), 0d060g (0.09 #307, 0.06 #207, 0.06 #1311), 03rk0 (0.07 #4155, 0.07 #4959, 0.06 #3253), 03rt9 (0.04 #213, 0.04 #313, 0.02 #1517) >> Best rule #4612 for best value: >> intensional similarity = 3 >> extensional distance = 796 >> proper extension: 01ky2h; 0lzkm; 0f3zsq; >> query: (?x9281, 09c7w0) <- award_winner(?x472, ?x9281), gender(?x9281, ?x231), place_of_birth(?x9281, ?x5036) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1002 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 160 *> proper extension: 030pr; *> query: (?x9281, ?x94) <- film(?x9281, ?x308), award_nominee(?x9281, ?x10381), award(?x9281, ?x68), country(?x308, ?x94) *> conf = 0.39 ranks of expected_values: 2 EVAL 013tcv nationality 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.747 http://example.org/people/person/nationality #15188-07dnx PRED entity: 07dnx PRED relation: influenced_by! PRED expected values: 0453t 03f0324 => 120 concepts (56 used for prediction) PRED predicted values (max 10 best out of 402): 045bg (0.52 #3091, 0.33 #34, 0.20 #7679), 040db (0.40 #5170, 0.38 #6699, 0.25 #2621), 0nk72 (0.38 #3396, 0.27 #339, 0.15 #7984), 0683n (0.38 #5943, 0.35 #5433, 0.34 #6962), 01h2_6 (0.33 #489, 0.26 #1527, 0.21 #11219), 0399p (0.33 #327, 0.21 #4913, 0.21 #3384), 02yl42 (0.30 #2169, 0.24 #641, 0.22 #1150), 01hb6v (0.28 #7737, 0.17 #4678, 0.15 #2129), 013pp3 (0.28 #1238, 0.25 #2257, 0.24 #729), 0n6kf (0.28 #1207, 0.25 #2226, 0.24 #698) >> Best rule #3091 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 045m1_; >> query: (?x8768, 045bg) <- influenced_by(?x10110, ?x8768), influenced_by(?x10110, ?x8418), influenced_by(?x10110, ?x7250), ?x7250 = 03sbs, ?x8418 = 02ln1 >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #3252 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 045m1_; *> query: (?x8768, 03f0324) <- influenced_by(?x10110, ?x8768), influenced_by(?x10110, ?x8418), influenced_by(?x10110, ?x7250), ?x7250 = 03sbs, ?x8418 = 02ln1 *> conf = 0.17 ranks of expected_values: 35, 64 EVAL 07dnx influenced_by! 03f0324 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 120.000 56.000 0.517 http://example.org/influence/influence_node/influenced_by EVAL 07dnx influenced_by! 0453t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 120.000 56.000 0.517 http://example.org/influence/influence_node/influenced_by #15187-02fqrf PRED entity: 02fqrf PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 22): 0ch6mp2 (0.87 #36, 0.84 #579, 0.78 #730), 02rh1dz (0.32 #340, 0.29 #69, 0.27 #39), 01xy5l_ (0.29 #131, 0.25 #191, 0.16 #494), 0215hd (0.20 #44, 0.18 #134, 0.18 #74), 015h31 (0.20 #188, 0.18 #128, 0.16 #491), 089g0h (0.18 #135, 0.17 #195, 0.13 #45), 0d2b38 (0.18 #80, 0.15 #503, 0.15 #140), 089fss (0.13 #35, 0.12 #65, 0.10 #397), 02_n3z (0.12 #181, 0.12 #121, 0.12 #61), 094hwz (0.12 #132, 0.12 #72, 0.10 #192) >> Best rule #36 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 05nyqk; >> query: (?x3498, 0ch6mp2) <- film_crew_role(?x3498, ?x3305), film_crew_role(?x3498, ?x3197), nominated_for(?x154, ?x3498), ?x3305 = 04pyp5, ?x3197 = 02ynfr >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fqrf film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.867 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15186-04qz6n PRED entity: 04qz6n PRED relation: nominated_for PRED expected values: 0g60z => 87 concepts (50 used for prediction) PRED predicted values (max 10 best out of 439): 02_1q9 (0.51 #6490, 0.50 #4867, 0.49 #14600), 01q2nx (0.29 #9734, 0.26 #58388, 0.24 #47038), 06z8s_ (0.08 #81107, 0.02 #121), 0h03fhx (0.08 #81107, 0.02 #8824, 0.01 #21801), 05rfst (0.08 #81107, 0.01 #2513, 0.01 #4136), 063ykwt (0.08 #81107, 0.01 #2194, 0.01 #23284), 01hqk (0.08 #81107, 0.01 #660), 01bb9r (0.08 #81107, 0.01 #448), 09xbpt (0.08 #81107, 0.01 #43), 0gg5qcw (0.08 #81107) >> Best rule #6490 for best value: >> intensional similarity = 3 >> extensional distance = 347 >> proper extension: 01vvydl; 01dw4q; 016kjs; 01ztgm; 02lg9w; 0806vbn; 01trhmt; 01vx5w7; 0686zv; 044gyq; ... >> query: (?x7224, ?x416) <- award_winner(?x286, ?x7224), actor(?x416, ?x7224), award(?x7224, ?x1670) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #1662 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 242 *> proper extension: 062hgx; 06s6hs; 02lgfh; *> query: (?x7224, 0g60z) <- award_winner(?x286, ?x7224), actor(?x416, ?x7224), location(?x7224, ?x8969) *> conf = 0.03 ranks of expected_values: 38 EVAL 04qz6n nominated_for 0g60z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 87.000 50.000 0.506 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15185-0d9jr PRED entity: 0d9jr PRED relation: place_of_birth! PRED expected values: 045w_4 04xbr4 => 212 concepts (154 used for prediction) PRED predicted values (max 10 best out of 2134): 0fqjhm (0.40 #229321, 0.33 #148535, 0.33 #343972), 03xpsrx (0.40 #229321, 0.33 #148535, 0.33 #343972), 0227tr (0.40 #229321, 0.33 #148535, 0.33 #343972), 018y2s (0.40 #229321, 0.33 #148535, 0.33 #343972), 01m4yn (0.40 #229321, 0.33 #148535, 0.33 #343972), 0484q (0.40 #229321, 0.33 #148535, 0.33 #343972), 02r3zy (0.30 #224107, 0.29 #231928, 0.28 #278831), 01d1st (0.30 #224107, 0.29 #231928, 0.28 #278831), 0b1hw (0.28 #278831, 0.28 #179807, 0.28 #278832), 0d193h (0.28 #278831, 0.28 #179807, 0.28 #278832) >> Best rule #229321 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 0rp46; 01j8yr; 09b9m; 09snz; 0tt6k; 0rk71; 0rmby; 0s9b_; >> query: (?x5267, ?x1165) <- administrative_division(?x5267, ?x11525), category(?x5267, ?x134), location(?x1165, ?x5267) >> conf = 0.40 => this is the best rule for 6 predicted values No rule for expected values ranks of expected_values: EVAL 0d9jr place_of_birth! 04xbr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 212.000 154.000 0.400 http://example.org/people/person/place_of_birth EVAL 0d9jr place_of_birth! 045w_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 212.000 154.000 0.400 http://example.org/people/person/place_of_birth #15184-0194zl PRED entity: 0194zl PRED relation: titles! PRED expected values: 07ssc => 111 concepts (58 used for prediction) PRED predicted values (max 10 best out of 60): 07c52 (0.51 #1505, 0.11 #4180, 0.11 #3882), 07ssc (0.33 #303, 0.33 #107, 0.15 #598), 04xvlr (0.33 #101, 0.29 #3359, 0.29 #3), 01hmnh (0.32 #1502, 0.15 #1602, 0.14 #24), 01z4y (0.27 #228, 0.23 #2594, 0.21 #2299), 01jfsb (0.27 #214, 0.20 #508, 0.13 #4768), 024qqx (0.20 #273, 0.15 #567, 0.12 #1158), 02l7c8 (0.19 #1775, 0.18 #2267, 0.18 #589), 060__y (0.19 #1775, 0.18 #2267, 0.18 #589), 0c3351 (0.13 #244, 0.10 #538, 0.06 #637) >> Best rule #1505 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 01h72l; 08cx5g; 05fgr_; 06w7mlh; 07s8z_l; 06r1k; 03czz87; 01j95; >> query: (?x4963, 07c52) <- award_winner(?x4963, ?x5951), titles(?x2286, ?x4963), films(?x2286, ?x197) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #303 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0879bpq; 0gh65c5; *> query: (?x4963, 07ssc) <- film_crew_role(?x4963, ?x137), nominated_for(?x112, ?x4963), film(?x2938, ?x4963), ?x2938 = 01nwwl *> conf = 0.33 ranks of expected_values: 2 EVAL 0194zl titles! 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 58.000 0.512 http://example.org/media_common/netflix_genre/titles #15183-04n7njg PRED entity: 04n7njg PRED relation: profession PRED expected values: 0d2b38 => 163 concepts (62 used for prediction) PRED predicted values (max 10 best out of 58): 02hrh1q (0.86 #8658, 0.86 #8370, 0.84 #7649), 01d_h8 (0.58 #150, 0.56 #8362, 0.54 #5191), 02jknp (0.58 #6778, 0.52 #7355, 0.50 #5626), 018gz8 (0.56 #2608, 0.50 #2464, 0.50 #1456), 0cbd2 (0.33 #7, 0.30 #1015, 0.27 #1303), 0kyk (0.33 #170, 0.26 #602, 0.24 #458), 015h31 (0.33 #24, 0.25 #1032, 0.25 #168), 015cjr (0.29 #478, 0.21 #1774, 0.19 #1342), 01c72t (0.25 #165, 0.21 #885, 0.18 #309), 0nbcg (0.19 #8528, 0.14 #8240, 0.12 #7807) >> Best rule #8658 for best value: >> intensional similarity = 3 >> extensional distance = 364 >> proper extension: 03zqc1; >> query: (?x1182, 02hrh1q) <- actor(?x8554, ?x1182), category(?x8554, ?x134), genre(?x8554, ?x258) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #207 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 07d3x; *> query: (?x1182, 0d2b38) <- profession(?x1182, ?x987), gender(?x1182, ?x231), program_creator(?x8554, ?x1182), category(?x1182, ?x134) *> conf = 0.08 ranks of expected_values: 21 EVAL 04n7njg profession 0d2b38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 163.000 62.000 0.863 http://example.org/people/person/profession #15182-02ptczs PRED entity: 02ptczs PRED relation: featured_film_locations PRED expected values: 02_286 => 91 concepts (71 used for prediction) PRED predicted values (max 10 best out of 69): 02_286 (0.20 #260, 0.18 #1946, 0.18 #1706), 030qb3t (0.20 #279, 0.07 #3889, 0.07 #5818), 04jpl (0.10 #1935, 0.07 #731, 0.07 #4341), 0rh6k (0.07 #2408, 0.05 #482, 0.05 #964), 0h7h6 (0.06 #1247, 0.02 #3411, 0.02 #4857), 05kj_ (0.04 #1463, 0.03 #981, 0.02 #499), 03rjj (0.04 #487, 0.04 #728, 0.03 #969), 0b90_r (0.04 #485, 0.04 #726, 0.03 #967), 07b_l (0.04 #558, 0.04 #799, 0.03 #1040), 0dclg (0.04 #1739, 0.02 #2700, 0.02 #1257) >> Best rule #260 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02qr3k8; >> query: (?x9772, 02_286) <- film(?x9423, ?x9772), film(?x6993, ?x9772), ?x9423 = 02l101, nationality(?x6993, ?x94) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ptczs featured_film_locations 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 71.000 0.200 http://example.org/film/film/featured_film_locations #15181-03j0br4 PRED entity: 03j0br4 PRED relation: role PRED expected values: 02hnl => 178 concepts (155 used for prediction) PRED predicted values (max 10 best out of 110): 03bx0bm (0.35 #290, 0.33 #90, 0.26 #624), 0l14md (0.33 #74, 0.23 #540, 0.17 #1667), 02hnl (0.27 #563, 0.22 #134, 0.22 #97), 0342h (0.26 #605, 0.24 #1603, 0.24 #2866), 05148p4 (0.20 #1617, 0.20 #619, 0.16 #417), 026t6 (0.17 #1667, 0.17 #3793, 0.16 #4129), 05r5c (0.14 #609, 0.12 #1607, 0.11 #75), 05842k (0.12 #667, 0.12 #1666, 0.11 #3792), 02dlh2 (0.12 #667, 0.12 #1666, 0.11 #3792), 028tv0 (0.12 #614, 0.11 #1612, 0.10 #546) >> Best rule #290 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 07h5d; >> query: (?x2566, 03bx0bm) <- film(?x2566, ?x1769), award(?x2566, ?x1389), group(?x2566, ?x1271), location(?x2566, ?x1227) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #563 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 01pr_j6; 04f7c55; 0gs6vr; 032nl2; 04mky3; *> query: (?x2566, 02hnl) <- artists(?x505, ?x2566), gender(?x2566, ?x514), instrumentalists(?x212, ?x2566), ?x212 = 026t6 *> conf = 0.27 ranks of expected_values: 3 EVAL 03j0br4 role 02hnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 178.000 155.000 0.351 http://example.org/music/group_member/membership./music/group_membership/role #15180-0210f1 PRED entity: 0210f1 PRED relation: nationality PRED expected values: 09c7w0 => 132 concepts (123 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.90 #2807, 0.90 #3009, 0.89 #8446), 01n4w (0.38 #7133, 0.34 #9959, 0.33 #10467), 07ssc (0.29 #616, 0.27 #916, 0.24 #1016), 02jx1 (0.25 #1536, 0.15 #2137, 0.14 #1636), 06q1r (0.22 #177, 0.14 #678, 0.12 #1202), 03rk0 (0.13 #1248, 0.12 #1202, 0.08 #4561), 0f8l9c (0.13 #1224, 0.04 #1324, 0.03 #2929), 0d060g (0.12 #1202, 0.11 #107, 0.09 #307), 0hzlz (0.12 #1202, 0.03 #924, 0.03 #1024), 06m_5 (0.12 #1202, 0.02 #8948, 0.01 #1184) >> Best rule #2807 for best value: >> intensional similarity = 4 >> extensional distance = 410 >> proper extension: 01nqfh_; 03mz9r; 044mfr; 03xnq9_; 04f7c55; 01vw917; 013vdl; 05rx__; 06fc0b; 03txms; ... >> query: (?x7055, 09c7w0) <- place_of_birth(?x7055, ?x659), source(?x659, ?x958), administrative_division(?x659, ?x2982), location(?x2390, ?x659) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0210f1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 123.000 0.898 http://example.org/people/person/nationality #15179-01f8f7 PRED entity: 01f8f7 PRED relation: nominated_for! PRED expected values: 02z0dfh 09v51c2 => 135 concepts (95 used for prediction) PRED predicted values (max 10 best out of 196): 09v4bym (0.68 #20000, 0.68 #21178, 0.67 #19999), 0gq9h (0.46 #5469, 0.43 #6175, 0.42 #8530), 0gr0m (0.45 #5466, 0.44 #6172, 0.26 #7113), 019f4v (0.41 #5460, 0.37 #8521, 0.35 #6166), 0gq_v (0.40 #5426, 0.35 #6132, 0.31 #6367), 0gs9p (0.38 #8532, 0.34 #5471, 0.33 #7825), 0k611 (0.37 #5479, 0.35 #6185, 0.32 #8540), 0274v0r (0.36 #5643, 0.22 #20471, 0.20 #18116), 0p9sw (0.32 #5427, 0.29 #6133, 0.22 #8252), 0gqy2 (0.31 #5528, 0.28 #6234, 0.23 #8589) >> Best rule #20000 for best value: >> intensional similarity = 4 >> extensional distance = 943 >> proper extension: 02_1q9; 027tbrc; 0524b41; 02_1kl; 06qwh; 0fpxp; 04xbq3; 023ny6; 06qv_; >> query: (?x6788, ?x9377) <- award(?x6788, ?x9377), nominated_for(?x9377, ?x1625), nominated_for(?x4169, ?x6788), nominated_for(?x372, ?x6788) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #4467 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 116 *> proper extension: 0gtsx8c; 08hmch; 0g5pv3; 018nnz; 0gffmn8; 0bc1yhb; 027j9wd; 042fgh; 06_sc3; 08c6k9; *> query: (?x6788, ?x7215) <- prequel(?x6788, ?x6376), film(?x9809, ?x6376), country(?x6376, ?x2645), nominated_for(?x7215, ?x6376) *> conf = 0.27 ranks of expected_values: 18, 33 EVAL 01f8f7 nominated_for! 09v51c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 135.000 95.000 0.677 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01f8f7 nominated_for! 02z0dfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 135.000 95.000 0.677 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15178-07kbp5 PRED entity: 07kbp5 PRED relation: category PRED expected values: 08mbj5d => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.56 #12, 0.56 #13, 0.54 #8) >> Best rule #12 for best value: >> intensional similarity = 6 >> extensional distance = 85 >> proper extension: 01k6zy; >> query: (?x7892, 08mbj5d) <- position_s(?x7892, ?x1717), position(?x180, ?x1717), position(?x9172, ?x1717), position(?x3674, ?x1717), ?x3674 = 05tg3, ?x9172 = 06rpd >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07kbp5 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 21.000 21.000 0.563 http://example.org/common/topic/webpage./common/webpage/category #15177-05zppz PRED entity: 05zppz PRED relation: gender! PRED expected values: 01pbxb 01vvydl 06151l 0lbj1 04cy8rb 01vrx3g 09fqtq 033hqf 01nqfh_ 0lzb8 01kwld 032t2z 0146pg 08wq0g 02qggqc 0jf1b 034x61 016khd 0dky9n 01j5x6 030pr 02gvwz 01k5t_3 01yb09 0277470 01t6b4 05fg2 04y79_n 031zkw 043q6n_ 022_lg 01713c 02c4s 0157m 012x4t 01w923 09gffmz 02pb53 0c3kw 01dzz7 02wrhj 01c59k 0f1vrl 0241jw 016ywr 03mz9r 0784v1 045bs6 01vyp_ 02k6rq 06lgq8 015pxr 040db 0c3ns 0l56b 05b4rcb 01q415 01zmpg 05218gr 02xb2bt 0170s4 0738b8 04kj2v 05qsxy 01trhmt 07h1tr 0hskw 01qdjm 01zfmm 027l0b 0bt4r4 01nwwl 016srn 0qdyf 01q4qv 02j8nx 01vsykc 07qy0b 05jcn8 03gkn5 07cjqy 040_9 07lwsz 0blt6 07h1h5 07fvf1 0fx02 05_pkf 062dn7 0bkg4 0cj2nl 01dvtx 04pf4r 01m15br 050z2 034bs 0308kx 01wz01 02dth1 028qdb 044qx 08jbxf 03xp8d5 0gyx4 02mjf2 06msq2 076psv 01900g 02y_2y 033w9g 01vyv9 023l9y 0fhxv 0f7hc 0gct_ 0863x_ 03f0fnk 09qc1 0210hf 021yzs 0205dx 0b80__ 0ll3 051wwp 04w1j9 03xb2w 022g44 03flwk 01h8f 06j8wx 028r4y 03fbb6 048_p 04cr6qv 04_1nk 01bczm 034rd 0kxbc 0988cp 0c8hct 0cc63l 026y23w 02bgmr 03_wvl 06jrhz 02hhtj 01wgfp6 03q3sy 02q42j_ 0d02km 02r3cn 018y81 09cdxn 051z6rz 026yqrr 078g3l 02s2wq 0bvzp 03q43g 013t9y 049fgvm 01_k0d 043hg 01520h 0g69lg 01ydzx 05nzw6 07vfqj 02w5q6 0130sy 06sn8m 0dbc1s 041_y 02vqpx8 067xw 01v90t 0191h5 012vct 0gnbw 07h5d 0d608 087z12 06b_0 026f__m 0bl60p 02404v 0mdyn 048hf 01r4hry 06t8b 08jfkw 01m42d0 032md 078mgh 02hy9p 06sy4c 04fyhv 04_jsg 029pnn 0drc1 0l5yl 0164w8 06yrj6 06gn7r 057bc6m 03wjb7 0k57l 01w9mnm 0bkf72 07m69t 0154d7 01vn0t_ 023nlj 01wg3q 01vsy9_ 01d_h 05b49tt 01wj5hp 051m56 01f9zw 0cj2k3 06d6y 030dx5 01m7f5r 04z_x4v 0147jt 02zfdp 02x08c 01x53m 0c921 094tsh6 03975z 063tn 01npcy7 05myd2 01qklj 013sg6 0q1lp 033jj1 0qdwr 0gthm 0ccqd7 03z0l6 06f5j 0cymln 01xllf 01p7b6b 020hyj 044mvs 02jxsq 02j4sk 01nbq4 0bq4j6 04wp63 03qncl3 01w9k25 03xk1_ 069z_5 0cw67g 027z0pl 0bxy67 06w58f 024jwt 09myny 09fqd3 0534nr 045g4l 060pl5 02vtnf 07_m2 0kr7k 04j5fx 02d6n_ 0b_dh 015076 0k29f 0l9k1 0cj2w 0bqch 016z68 019gz 01lct6 01vzz1c 0276g40 01pj3h 04dyqk 01rzxl 01tsbmv 045931 030s5g 0420y 0n839 0835q 033071 01vh3r 03csqj4 01xsc9 07jmnh 0466k4 0h1q6 02js_6 01c65z 0pksh 02yy8 071jrc 01h2_6 026c0p 04kwbt 089kpp 03c9pqt 06s27s 03qhyn8 01fxfk 06w38l 079dy 02k76g 0p_r5 09xvf7 042fk 02zfg3 09jrf 03z_g7 074qgb 025_ql1 084x96 01k31p 01l3j 0443c 0w6w 0cfywh 03d63lb 03cxqp5 07q68q => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1151): 01bh6y (0.33 #694, 0.30 #876, 0.28 #880), 0227vl (0.33 #640, 0.30 #876, 0.28 #880), 0336mc (0.33 #639, 0.30 #876, 0.28 #880), 032wdd (0.33 #630, 0.30 #876, 0.28 #880), 01934k (0.33 #623, 0.30 #876, 0.28 #880), 0btpx (0.33 #620, 0.30 #876, 0.28 #880), 01933d (0.33 #602, 0.30 #876, 0.28 #880), 03zz8b (0.33 #552, 0.30 #876, 0.28 #880), 02ts3h (0.33 #541, 0.30 #876, 0.28 #880), 06mt91 (0.33 #517, 0.30 #876, 0.28 #880) >> Best rule #694 for best value: >> intensional similarity = 18 >> extensional distance = 1 >> proper extension: 02zsn; >> query: (?x231, 01bh6y) <- gender(?x10152, ?x231), gender(?x3974, ?x231), gender(?x3770, ?x231), gender(?x3419, ?x231), gender(?x1656, ?x231), gender(?x702, ?x231), gender(?x364, ?x231), risk_factors(?x1158, ?x231), award_winner(?x1079, ?x1656), award(?x1656, ?x1565), participant(?x932, ?x702), profession(?x702, ?x220), film(?x364, ?x2102), people(?x9428, ?x364), award_nominee(?x382, ?x3974), nationality(?x10152, ?x94), award_winner(?x3159, ?x3419), place_of_birth(?x3770, ?x362) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #876 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 02zsn; *> query: (?x231, ?x3159) <- gender(?x10152, ?x231), gender(?x3974, ?x231), gender(?x3770, ?x231), gender(?x3419, ?x231), gender(?x1656, ?x231), gender(?x702, ?x231), gender(?x364, ?x231), risk_factors(?x1158, ?x231), award_winner(?x1079, ?x1656), award(?x1656, ?x1565), participant(?x932, ?x702), profession(?x702, ?x220), film(?x364, ?x2102), people(?x9428, ?x364), award_nominee(?x382, ?x3974), nationality(?x10152, ?x94), award_winner(?x3159, ?x3419), place_of_birth(?x3770, ?x362) *> conf = 0.30 ranks of expected_values: 881, 884, 886, 887, 888, 889, 890, 891, 892, 894, 895, 896, 897, 899, 902, 903, 904, 905, 906, 912, 914, 916, 918, 919, 920, 923, 924, 925, 926, 927, 928, 930, 933, 935, 936, 940, 941, 942, 943, 944, 946, 947, 948, 949, 950, 951, 953, 954, 955, 956, 958, 960, 961, 963, 964, 965, 968, 969, 971, 973, 975, 976, 977, 978, 981, 982, 983, 984, 985, 986, 987, 988, 990, 992, 994, 996, 997, 998, 1002, 1003, 1004, 1005, 1010, 1011, 1015, 1016, 1018, 1021, 1023, 1024, 1025, 1026, 1028, 1029, 1030, 1031, 1035, 1039, 1041, 1043, 1052, 1053, 1054, 1055, 1057, 1060, 1061, 1063, 1064, 1069, 1070, 1071, 1073, 1074, 1075, 1078, 1079, 1080, 1082, 1083, 1085, 1086, 1087, 1089, 1090, 1091, 1092, 1093, 1098, 1100, 1101, 1102, 1103, 1104, 1107, 1109, 1110, 1113, 1116, 1117, 1122, 1123, 1126, 1128, 1131, 1132, 1133, 1135, 1136, 1138, 1139 EVAL 05zppz gender! 07q68q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03cxqp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03d63lb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0cfywh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0w6w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0443c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01l3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01k31p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 084x96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 025_ql1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 074qgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03z_g7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 09jrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02zfg3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 042fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 09xvf7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0p_r5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02k76g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 079dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06w38l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01fxfk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03qhyn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06s27s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03c9pqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 089kpp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04kwbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 026c0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01h2_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 071jrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02yy8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0pksh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01c65z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02js_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0h1q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0466k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07jmnh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01xsc9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03csqj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01vh3r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 033071 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0835q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0n839 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0420y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 030s5g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 045931 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01tsbmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01rzxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04dyqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01pj3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0276g40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01vzz1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01lct6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 019gz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 016z68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0bqch CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0cj2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0l9k1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0k29f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 015076 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0b_dh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02d6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04j5fx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0kr7k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07_m2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02vtnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 060pl5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 045g4l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0534nr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 09fqd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 09myny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 024jwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06w58f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0bxy67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 027z0pl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0cw67g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 069z_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03xk1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01w9k25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03qncl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04wp63 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0bq4j6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01nbq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02j4sk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02jxsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 044mvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 020hyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01p7b6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01xllf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0cymln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06f5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03z0l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0ccqd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0gthm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0qdwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 033jj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0q1lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 013sg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01qklj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 05myd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01npcy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 063tn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03975z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 094tsh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0c921 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01x53m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02x08c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02zfdp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0147jt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04z_x4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01m7f5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 030dx5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06d6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0cj2k3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01f9zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 051m56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01wj5hp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 05b49tt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01d_h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01vsy9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01wg3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 023nlj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01vn0t_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0154d7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07m69t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0bkf72 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01w9mnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0k57l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03wjb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 057bc6m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06gn7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06yrj6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0164w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0l5yl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0drc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 029pnn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04_jsg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04fyhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06sy4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02hy9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 078mgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 032md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01m42d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 08jfkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06t8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01r4hry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 048hf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0mdyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02404v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0bl60p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 026f__m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06b_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 087z12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0d608 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07h5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0gnbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 012vct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0191h5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01v90t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 067xw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02vqpx8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 041_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0dbc1s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06sn8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0130sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02w5q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07vfqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 05nzw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01ydzx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0g69lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01520h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 043hg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01_k0d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 049fgvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 013t9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03q43g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0bvzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02s2wq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 078g3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 026yqrr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 051z6rz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 09cdxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 018y81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02r3cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0d02km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02q42j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03q3sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01wgfp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02hhtj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06jrhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03_wvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02bgmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 026y23w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0cc63l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0c8hct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0988cp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0kxbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 034rd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01bczm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04_1nk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04cr6qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 048_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03fbb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 028r4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06j8wx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01h8f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03flwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 022g44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03xb2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04w1j9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 051wwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0ll3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0b80__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0205dx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 021yzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0210hf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 09qc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03f0fnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0863x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0gct_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0f7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0fhxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 023l9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01vyv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 033w9g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02y_2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01900g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 076psv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06msq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02mjf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0gyx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03xp8d5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 08jbxf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 044qx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 028qdb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02dth1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01wz01 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0308kx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 034bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 050z2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01m15br CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04pf4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01dvtx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0cj2nl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0bkg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 062dn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 05_pkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0fx02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07fvf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07h1h5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0blt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07lwsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 040_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07cjqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03gkn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 05jcn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07qy0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01vsykc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02j8nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01q4qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0qdyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 016srn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01nwwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0bt4r4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 027l0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01zfmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01qdjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0hskw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 07h1tr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01trhmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 05qsxy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04kj2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0738b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0170s4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02xb2bt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 05218gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01zmpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01q415 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 05b4rcb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0l56b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0c3ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 040db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 015pxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06lgq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02k6rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01vyp_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 045bs6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0784v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 03mz9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 016ywr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0241jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0f1vrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01c59k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02wrhj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01dzz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0c3kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02pb53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 09gffmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01w923 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0157m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02c4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01713c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 022_lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 043q6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 031zkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04y79_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 05fg2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01t6b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0277470 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01yb09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01k5t_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02gvwz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 030pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01j5x6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0dky9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 016khd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 034x61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0jf1b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 02qggqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 08wq0g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0146pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 032t2z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01kwld CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0lzb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01nqfh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 033hqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 09fqtq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01vrx3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 04cy8rb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 0lbj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 06151l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01vvydl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender EVAL 05zppz gender! 01pbxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 32.000 0.333 http://example.org/people/person/gender #15176-0jkhr PRED entity: 0jkhr PRED relation: major_field_of_study PRED expected values: 01lhf => 202 concepts (202 used for prediction) PRED predicted values (max 10 best out of 116): 02lp1 (0.58 #1550, 0.56 #1905, 0.56 #1075), 04_tv (0.50 #15, 0.19 #605, 0.16 #1908), 0g26h (0.49 #1578, 0.47 #2170, 0.44 #1933), 02_7t (0.44 #1599, 0.39 #1954, 0.32 #2191), 03g3w (0.43 #2392, 0.40 #10083, 0.38 #380), 0_jm (0.38 #645, 0.35 #1474, 0.33 #2659), 01540 (0.36 #1120, 0.35 #1950, 0.35 #1595), 05qjt (0.36 #3084, 0.34 #2374, 0.32 #1071), 04x_3 (0.35 #1918, 0.34 #1088, 0.33 #1563), 05qfh (0.30 #1097, 0.29 #1927, 0.27 #4291) >> Best rule #1550 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 08qnnv; >> query: (?x6856, 02lp1) <- institution(?x1200, ?x6856), ?x1200 = 016t_3, school(?x3089, ?x6856), school(?x1632, ?x6856) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #88 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 031hxk; *> query: (?x6856, 01lhf) <- colors(?x6856, ?x663), institution(?x865, ?x6856), ?x663 = 083jv, major_field_of_study(?x6856, ?x7403), ?x7403 = 06mnr *> conf = 0.25 ranks of expected_values: 28 EVAL 0jkhr major_field_of_study 01lhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 202.000 202.000 0.579 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #15175-011k_j PRED entity: 011k_j PRED relation: role! PRED expected values: 0130sy => 71 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1023): 032t2z (0.60 #1770, 0.57 #2360, 0.50 #600), 0ftps (0.60 #1792, 0.50 #622, 0.43 #2382), 01kx_81 (0.57 #2376, 0.50 #1201, 0.50 #616), 016h9b (0.50 #3306, 0.50 #1248, 0.50 #663), 0bg539 (0.50 #1203, 0.50 #618, 0.43 #2378), 04mx7s (0.50 #815, 0.43 #2575, 0.40 #1985), 0gcs9 (0.50 #668, 0.43 #2428, 0.40 #1838), 04kjrv (0.50 #777, 0.43 #2537, 0.40 #1947), 017g21 (0.50 #1373, 0.43 #2548, 0.33 #2253), 08n__5 (0.50 #750, 0.40 #1920, 0.33 #162) >> Best rule #1770 for best value: >> intensional similarity = 19 >> extensional distance = 3 >> proper extension: 013y1f; >> query: (?x4078, 032t2z) <- performance_role(?x4078, ?x3716), performance_role(?x4078, ?x212), ?x3716 = 03gvt, role(?x3171, ?x4078), role(?x4078, ?x227), role(?x1166, ?x4078), performance_role(?x1495, ?x4078), ?x212 = 026t6, ?x3171 = 0p3sf, role(?x7238, ?x1495), role(?x130, ?x1495), family(?x9219, ?x1495), group(?x1495, ?x997), role(?x1647, ?x1495), ?x1647 = 05ljv7, role(?x4078, ?x1466), role(?x642, ?x1495), gender(?x130, ?x231), ?x7238 = 0fq117k >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1356 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 2 *> proper extension: 0l14md; *> query: (?x4078, 0130sy) <- performance_role(?x4078, ?x3716), performance_role(?x4078, ?x1225), performance_role(?x4078, ?x212), ?x3716 = 03gvt, role(?x10989, ?x4078), role(?x3171, ?x4078), role(?x4078, ?x8172), role(?x4078, ?x894), role(?x4078, ?x615), role(?x1166, ?x4078), performance_role(?x1495, ?x4078), ?x212 = 026t6, artists(?x10207, ?x3171), ?x8172 = 06rvn, artists(?x10207, ?x9134), ?x1225 = 01qbl, ?x9134 = 01dhjz, role(?x1321, ?x615), ?x894 = 03m5k, profession(?x3171, ?x353), artist(?x8489, ?x3171), ?x10989 = 02s6sh *> conf = 0.25 ranks of expected_values: 72 EVAL 011k_j role! 0130sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 71.000 45.000 0.600 http://example.org/music/group_member/membership./music/group_membership/role #15174-023p29 PRED entity: 023p29 PRED relation: award PRED expected values: 03tcnt => 97 concepts (82 used for prediction) PRED predicted values (max 10 best out of 294): 01bgqh (0.78 #27690, 0.77 #22067, 0.76 #28093), 0c4z8 (0.67 #473, 0.23 #11303, 0.22 #13713), 02f5qb (0.50 #3363, 0.18 #10830, 0.18 #13238), 02f716 (0.44 #3384, 0.18 #25281, 0.18 #10830), 02f72n (0.42 #3353, 0.18 #10830, 0.18 #13238), 02qvyrt (0.40 #124, 0.09 #8948, 0.09 #10151), 0gq9h (0.39 #2084, 0.33 #2485, 0.32 #1683), 02f73b (0.39 #3494, 0.18 #10830, 0.18 #13238), 05pcn59 (0.38 #884, 0.31 #1285, 0.11 #6099), 02f72_ (0.37 #3437, 0.18 #25281, 0.18 #10830) >> Best rule #27690 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 01nrq5; 034bs; 06n9lt; 01t265; 0bkmf; 0f1jhc; 03j90; 02rf51g; >> query: (?x10209, ?x1827) <- award_winner(?x1827, ?x10209), ceremony(?x1827, ?x139), award(?x140, ?x1827) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #25281 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1470 *> proper extension: 01j53q; *> query: (?x10209, ?x3103) <- award_winner(?x10209, ?x8060), award_winner(?x3103, ?x8060), award_winner(?x2862, ?x10209) *> conf = 0.18 ranks of expected_values: 44 EVAL 023p29 award 03tcnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 97.000 82.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15173-03yxwq PRED entity: 03yxwq PRED relation: citytown PRED expected values: 0r00l => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 160): 030qb3t (0.60 #1502, 0.40 #1870, 0.33 #2607), 02_286 (0.50 #2226, 0.41 #5909, 0.39 #8121), 0f2wj (0.47 #7001, 0.46 #22475, 0.43 #22106), 0r00l (0.28 #28741, 0.25 #281, 0.24 #32802), 081yw (0.28 #28741, 0.24 #32802, 0.11 #2681), 0d9jr (0.28 #28741, 0.24 #32802, 0.11 #2697), 04jpl (0.25 #376, 0.11 #7008, 0.09 #19164), 0cc56 (0.20 #1125, 0.09 #3334, 0.07 #4439), 07dfk (0.15 #21950, 0.14 #22319, 0.14 #22688), 02dtg (0.14 #20631, 0.02 #21008, 0.01 #26536) >> Best rule #1502 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0c41qv; >> query: (?x6948, 030qb3t) <- child(?x382, ?x6948), ?x382 = 086k8, production_companies(?x9169, ?x6948), company(?x1855, ?x6948) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #28741 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 93 *> proper extension: 02rky4; 03hbbc; 06nfl; *> query: (?x6948, ?x4600) <- child(?x382, ?x6948), child(?x382, ?x13802), category(?x382, ?x134), citytown(?x13802, ?x4600) *> conf = 0.28 ranks of expected_values: 4 EVAL 03yxwq citytown 0r00l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 135.000 135.000 0.600 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #15172-02hzz PRED entity: 02hzz PRED relation: artists! PRED expected values: 0cx7f => 88 concepts (45 used for prediction) PRED predicted values (max 10 best out of 292): 05bt6j (0.84 #12604, 0.67 #1872, 0.58 #3405), 016clz (0.83 #9188, 0.82 #9496, 0.81 #2751), 064t9 (0.81 #11657, 0.79 #11353, 0.74 #3375), 025sc50 (0.77 #11692, 0.76 #11388, 0.42 #3410), 0xhtw (0.76 #10741, 0.68 #12269, 0.50 #1539), 06j6l (0.55 #11387, 0.55 #11691, 0.48 #3409), 0m0jc (0.50 #617, 0.40 #1226, 0.33 #313), 03lty (0.43 #12280, 0.40 #11339, 0.38 #10752), 02lnbg (0.42 #3418, 0.42 #11700, 0.42 #11396), 0glt670 (0.42 #11684, 0.40 #11380, 0.30 #11988) >> Best rule #12604 for best value: >> intensional similarity = 13 >> extensional distance = 120 >> proper extension: 01vv7sc; 01r9fv; 01vsnff; 0136pk; 0161sp; 01k98nm; 02qwg; 02s2wq; 01t110; 01ydzx; ... >> query: (?x8131, 05bt6j) <- artists(?x3243, ?x8131), artists(?x2491, ?x8131), origin(?x8131, ?x7919), artists(?x3243, ?x10326), artists(?x3243, ?x8058), artists(?x3243, ?x7536), artists(?x3243, ?x2319), ?x10326 = 01wwnh2, student(?x9479, ?x7536), artist(?x3887, ?x7536), group(?x75, ?x8058), ?x2319 = 0lccn, parent_genre(?x302, ?x2491) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #5952 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 57 *> proper extension: 0qmpd; *> query: (?x8131, 0cx7f) <- artists(?x7960, ?x8131), group(?x1750, ?x8131), group(?x227, ?x8131), group(?x5508, ?x8131), category(?x8131, ?x134), ?x227 = 0342h, ?x1750 = 02hnl *> conf = 0.22 ranks of expected_values: 54 EVAL 02hzz artists! 0cx7f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 88.000 45.000 0.844 http://example.org/music/genre/artists #15171-0q9t7 PRED entity: 0q9t7 PRED relation: influenced_by PRED expected values: 014z8v => 129 concepts (43 used for prediction) PRED predicted values (max 10 best out of 341): 0gzh (0.33 #1302, 0.11 #3912, 0.08 #3042), 014z8v (0.19 #5342, 0.16 #6647, 0.11 #8823), 01hmk9 (0.19 #5442, 0.16 #6747, 0.10 #8923), 015cbq (0.17 #1198, 0.11 #17404, 0.11 #18712), 01tz6vs (0.17 #1047, 0.11 #17404, 0.11 #18712), 0bwx3 (0.17 #1058, 0.11 #17404, 0.11 #18712), 0ph2w (0.17 #989, 0.08 #2729, 0.07 #5340), 012gq6 (0.17 #966, 0.08 #2706, 0.07 #3141), 052hl (0.17 #1079, 0.08 #2819, 0.07 #3254), 0p_pd (0.17 #878, 0.08 #2618, 0.07 #3053) >> Best rule #1302 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 051cc; >> query: (?x8388, 0gzh) <- student(?x5739, ?x8388), person(?x424, ?x8388), people(?x3591, ?x8388), influenced_by(?x8388, ?x986) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5342 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 02dlfh; *> query: (?x8388, 014z8v) <- film(?x8388, ?x2815), category(?x8388, ?x134), influenced_by(?x8388, ?x8389), award(?x8389, ?x8842) *> conf = 0.19 ranks of expected_values: 2 EVAL 0q9t7 influenced_by 014z8v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 43.000 0.333 http://example.org/influence/influence_node/influenced_by #15170-063t3j PRED entity: 063t3j PRED relation: award PRED expected values: 0c4z8 02f705 => 134 concepts (100 used for prediction) PRED predicted values (max 10 best out of 364): 02f6xy (0.82 #4412, 0.81 #22463, 0.79 #23267), 01by1l (0.67 #511, 0.60 #14150, 0.44 #3719), 01c99j (0.50 #625, 0.39 #4234, 0.32 #1828), 03qbh5 (0.50 #604, 0.37 #3812, 0.36 #2609), 0c4z8 (0.46 #3680, 0.45 #2477, 0.33 #472), 02f6ym (0.35 #1057, 0.33 #656, 0.31 #4265), 02f705 (0.35 #953, 0.29 #4964, 0.28 #4161), 01cky2 (0.35 #994, 0.18 #2598, 0.17 #4202), 09sb52 (0.35 #7260, 0.29 #5255, 0.26 #7662), 01ck6h (0.33 #2526, 0.31 #3729, 0.12 #10951) >> Best rule #4412 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 016qtt; 0cg9y; 01dw9z; 03h_0_z; 06p03s; >> query: (?x12565, ?x3926) <- award(?x12565, ?x1389), ?x1389 = 01c427, profession(?x12565, ?x220), award_winner(?x3926, ?x12565) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #3680 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 07s3vqk; 0lbj1; 01w61th; 01vrz41; 01wcp_g; 01sbf2; 015_30; 0j1yf; 0136pk; 02b25y; ... *> query: (?x12565, 0c4z8) <- award(?x12565, ?x1801), nationality(?x12565, ?x512), ?x1801 = 01c92g, artists(?x1572, ?x12565) *> conf = 0.46 ranks of expected_values: 5, 7 EVAL 063t3j award 02f705 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 134.000 100.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 063t3j award 0c4z8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 134.000 100.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15169-0gkr9q PRED entity: 0gkr9q PRED relation: nominated_for PRED expected values: 0ddd0gc 02rzdcp 0gvsh7l 01kt_j => 44 concepts (8 used for prediction) PRED predicted values (max 10 best out of 937): 04p5cr (0.79 #11042, 0.73 #11041, 0.56 #1004), 02rzdcp (0.71 #2063, 0.56 #486, 0.50 #5216), 0ddd0gc (0.62 #4925, 0.61 #3348, 0.53 #1772), 02k_4g (0.56 #105, 0.47 #1682, 0.35 #3258), 0gvsh7l (0.56 #1242, 0.35 #2819, 0.26 #4395), 063ykwt (0.47 #2142, 0.35 #3718, 0.33 #5295), 02qkq0 (0.44 #1039, 0.41 #2616, 0.30 #4192), 02md2d (0.44 #636, 0.41 #2213, 0.30 #3789), 030p35 (0.44 #711, 0.35 #2288, 0.26 #3864), 03_8kz (0.33 #1380, 0.29 #2957, 0.22 #4533) >> Best rule #11042 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 0fqnzts; >> query: (?x9640, ?x6482) <- award(?x368, ?x9640), award(?x6482, ?x9640), genre(?x6482, ?x53) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #2063 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 0bdw1g; 0fbvqf; 0cqh6z; 0bdx29; 04ldyx1; 09v7wsg; 02_3zj; 02xcb6n; *> query: (?x9640, 02rzdcp) <- nominated_for(?x9640, ?x8870), nominated_for(?x2071, ?x8870), nominated_for(?x435, ?x8870), ?x2071 = 0bdw6t, honored_for(?x762, ?x8870), ?x435 = 0bp_b2 *> conf = 0.71 ranks of expected_values: 2, 3, 5, 17 EVAL 0gkr9q nominated_for 01kt_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 44.000 8.000 0.793 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gkr9q nominated_for 0gvsh7l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 44.000 8.000 0.793 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gkr9q nominated_for 02rzdcp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 8.000 0.793 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gkr9q nominated_for 0ddd0gc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 8.000 0.793 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15168-03q45x PRED entity: 03q45x PRED relation: profession PRED expected values: 03gjzk => 105 concepts (75 used for prediction) PRED predicted values (max 10 best out of 53): 03gjzk (0.86 #2514, 0.85 #2808, 0.84 #1926), 0np9r (0.78 #460, 0.69 #1342, 0.63 #1195), 01d_h8 (0.56 #1035, 0.55 #5153, 0.51 #8977), 02jknp (0.43 #5155, 0.35 #8979, 0.31 #1037), 02krf9 (0.36 #1054, 0.33 #319, 0.32 #1496), 0cbd2 (0.31 #154, 0.27 #9266, 0.25 #8978), 015cjr (0.28 #636, 0.10 #1077, 0.09 #2107), 09jwl (0.22 #5017, 0.22 #4429, 0.21 #3399), 0kyk (0.17 #8999, 0.14 #5175, 0.12 #2087), 0nbcg (0.15 #1648, 0.14 #5030, 0.13 #4442) >> Best rule #2514 for best value: >> intensional similarity = 3 >> extensional distance = 214 >> proper extension: 0dbpyd; 06j0md; 02lf0c; 0d4fqn; 0415svh; 02773nt; 02773m2; 02778pf; 04wvhz; 0277470; ... >> query: (?x7795, 03gjzk) <- profession(?x7795, ?x987), program(?x7795, ?x3630), award_nominee(?x7795, ?x906) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q45x profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 75.000 0.856 http://example.org/people/person/profession #15167-09nqf PRED entity: 09nqf PRED relation: currency! PRED expected values: 01l1b90 014zcr 0fp_v1x 0bxtg 0c1pj 0146pg 01vvycq 058kqy 01hxs4 0134w7 01vrz41 0pz91 0bwh6 02zyy4 022769 01f7j9 01pgzn_ 0lx2l 02b25y 015z4j 0dvmd 01w02sy 015f7 04gycf 02vyw 062dn7 0cnl1c 02lymt 0c7xjb 01xzb6 01vswwx 02jq1 0167km 016jfw 018ygt 01my4f 057176 06_bq1 02z4b_8 04sry 0hfml 01kmd4 03q91d 03g5_y 02633g 01wmjkb 020hh3 0h7pj 01gct2 01kp_1t 01b9z4 04tnqn 02yygk 0cymln 01p8r8 044mvs 0pnf3 0hqly 02cg2v => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 1583): 01w5gg6 (0.33 #39, 0.20 #122, 0.20 #100), 02qwg (0.33 #29, 0.20 #112, 0.20 #90), 01kx_81 (0.33 #23, 0.20 #106, 0.20 #84), 0lbj1 (0.33 #22, 0.20 #105, 0.20 #83), 02y0dd (0.33 #40, 0.20 #123, 0.20 #101), 01vsyjy (0.33 #37, 0.20 #120, 0.20 #98), 018y81 (0.33 #36, 0.20 #119, 0.20 #97), 01bpnd (0.33 #35, 0.20 #118, 0.20 #96), 03j24kf (0.33 #34, 0.20 #117, 0.20 #95), 0qf11 (0.33 #33, 0.20 #116, 0.20 #94) >> Best rule #39 for best value: >> intensional similarity = 26 >> extensional distance = 1 >> proper extension: 01nv4h; >> query: (?x170, 01w5gg6) <- currency(?x47, ?x170), currency(?x9017, ?x170), currency(?x8072, ?x170), currency(?x7757, ?x170), currency(?x6543, ?x170), currency(?x2656, ?x170), currency(?x1724, ?x170), currency(?x1202, ?x170), currency(?x763, ?x170), currency(?x147, ?x170), currency(?x65, ?x170), currency(?x2980, ?x170), currency(?x99, ?x170), film_release_region(?x1724, ?x456), organization(?x346, ?x2980), written_by(?x1202, ?x8544), nominated_for(?x1053, ?x1724), featured_film_locations(?x763, ?x108), currency(?x918, ?x170), award(?x2656, ?x372), film_distribution_medium(?x8072, ?x81), film_crew_role(?x6543, ?x137), film(?x593, ?x9017), award(?x147, ?x3508), film_format(?x763, ?x909), film(?x8796, ?x7757) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 1 *> proper extension: 01nv4h; *> query: (?x170, ?x8568) <- currency(?x47, ?x170), currency(?x8072, ?x170), currency(?x7757, ?x170), currency(?x6543, ?x170), currency(?x5331, ?x170), currency(?x2656, ?x170), currency(?x1724, ?x170), currency(?x1202, ?x170), currency(?x763, ?x170), currency(?x147, ?x170), currency(?x65, ?x170), currency(?x2980, ?x170), currency(?x99, ?x170), film_release_region(?x1724, ?x456), organization(?x346, ?x2980), written_by(?x1202, ?x8544), nominated_for(?x1053, ?x1724), featured_film_locations(?x763, ?x108), currency(?x918, ?x170), film(?x8568, ?x5331), award(?x2656, ?x372), film_distribution_medium(?x8072, ?x81), film_crew_role(?x6543, ?x137), award(?x147, ?x3508), film_format(?x763, ?x909), film(?x8796, ?x7757) *> conf = 0.01 ranks of expected_values: 206, 270, 278, 284, 338, 341, 420, 484, 522, 567, 600, 620, 655, 668, 747, 950, 953, 1101, 1126, 1145, 1160, 1197, 1199, 1281, 1302, 1334, 1384, 1422, 1427, 1440, 1465, 1493, 1516, 1527, 1558, 1562 EVAL 09nqf currency! 02cg2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0hqly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0pnf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 044mvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01p8r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0cymln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02yygk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04tnqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01b9z4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01kp_1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01gct2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0h7pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 020hh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01wmjkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02633g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03g5_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03q91d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01kmd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0hfml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04sry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02z4b_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 06_bq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 057176 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01my4f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 018ygt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 016jfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0167km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02jq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01vswwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01xzb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0c7xjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02lymt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0cnl1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 062dn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02vyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04gycf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 015f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01w02sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0dvmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 015z4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02b25y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0lx2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01pgzn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01f7j9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 022769 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02zyy4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0bwh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0pz91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0134w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01hxs4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 058kqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01vvycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0146pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0c1pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0bxtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0fp_v1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 014zcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01l1b90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #15166-014knw PRED entity: 014knw PRED relation: genre PRED expected values: 082gq => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 98): 02l7c8 (0.48 #855, 0.44 #255, 0.41 #1935), 01g6gs (0.44 #860, 0.25 #980, 0.21 #1100), 01hmnh (0.43 #737, 0.25 #377, 0.20 #2538), 02kdv5l (0.36 #2763, 0.33 #2643, 0.30 #7578), 03k9fj (0.33 #2532, 0.27 #5906, 0.27 #6146), 0lsxr (0.26 #2168, 0.25 #2890, 0.21 #2649), 04xvlr (0.25 #361, 0.23 #2883, 0.21 #4692), 060__y (0.24 #856, 0.23 #2176, 0.18 #4106), 02xh1 (0.23 #567, 0.22 #327, 0.20 #87), 02n4kr (0.23 #487, 0.22 #247, 0.17 #1207) >> Best rule #855 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 0cq8nx; >> query: (?x9345, 02l7c8) <- nominated_for(?x591, ?x9345), ?x591 = 0f4x7, film_art_direction_by(?x9345, ?x4896), language(?x9345, ?x254) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 04wddl; *> query: (?x9345, 082gq) <- film_sets_designed(?x8401, ?x9345), film_release_region(?x9345, ?x87), written_by(?x9345, ?x9320), ?x8401 = 057bc6m *> conf = 0.20 ranks of expected_values: 13 EVAL 014knw genre 082gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 107.000 107.000 0.480 http://example.org/film/film/genre #15165-0m5s5 PRED entity: 0m5s5 PRED relation: nominated_for! PRED expected values: 0gr0m => 137 concepts (132 used for prediction) PRED predicted values (max 10 best out of 208): 0gq9h (0.46 #20542, 0.34 #3632, 0.29 #17443), 0gs9p (0.43 #20544, 0.35 #3634, 0.28 #2682), 019f4v (0.39 #20533, 0.30 #3623, 0.27 #2671), 0gq_v (0.39 #20500, 0.25 #258, 0.23 #3590), 0k611 (0.37 #20553, 0.34 #1977, 0.33 #2691), 0gr42 (0.34 #1517, 0.25 #1041, 0.17 #2945), 02g3v6 (0.33 #973, 0.24 #3829, 0.22 #4781), 0gqxm (0.33 #132, 0.14 #608, 0.13 #846), 040njc (0.32 #20487, 0.21 #12148, 0.19 #16912), 04dn09n (0.31 #1939, 0.30 #2653, 0.27 #20515) >> Best rule #20542 for best value: >> intensional similarity = 4 >> extensional distance = 583 >> proper extension: 04z_x4v; >> query: (?x10135, 0gq9h) <- nominated_for(?x1079, ?x10135), award(?x2168, ?x1079), ?x2168 = 0bx0l, award(?x669, ?x1079) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #20539 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 583 *> proper extension: 04z_x4v; *> query: (?x10135, 0gr0m) <- nominated_for(?x1079, ?x10135), award(?x2168, ?x1079), ?x2168 = 0bx0l, award(?x669, ?x1079) *> conf = 0.28 ranks of expected_values: 12 EVAL 0m5s5 nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 137.000 132.000 0.462 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15164-026m3y PRED entity: 026m3y PRED relation: category PRED expected values: 08mbj5d => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #21, 0.91 #53, 0.91 #45) >> Best rule #21 for best value: >> intensional similarity = 5 >> extensional distance = 46 >> proper extension: 0yl_j; >> query: (?x10432, 08mbj5d) <- currency(?x10432, ?x1099), citytown(?x10432, ?x362), ?x1099 = 01nv4h, place_of_birth(?x361, ?x362), location(?x374, ?x362) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026m3y category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 178.000 0.917 http://example.org/common/topic/webpage./common/webpage/category #15163-01wz3cx PRED entity: 01wz3cx PRED relation: type_of_union PRED expected values: 04ztj => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.85 #57, 0.81 #85, 0.80 #65), 01g63y (0.33 #106, 0.30 #134, 0.27 #150), 0jgjn (0.02 #76, 0.01 #88), 01bl8s (0.01 #87) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 012d40; 0f8pz; >> query: (?x1992, 04ztj) <- artists(?x671, ?x1992), award(?x1992, ?x4796), location_of_ceremony(?x1992, ?x739) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wz3cx type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.854 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15162-02ct_k PRED entity: 02ct_k PRED relation: type_of_union PRED expected values: 04ztj => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #65, 0.71 #97, 0.71 #89), 01g63y (0.15 #38, 0.14 #42, 0.14 #106) >> Best rule #65 for best value: >> intensional similarity = 3 >> extensional distance = 1048 >> proper extension: 0bymv; 049sb; 013rds; >> query: (?x9655, 04ztj) <- award_winner(?x678, ?x9655), film(?x9655, ?x6528), film_release_region(?x6528, ?x87) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ct_k type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.718 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15161-07g9f PRED entity: 07g9f PRED relation: genre PRED expected values: 0lsxr => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 74): 01z4y (0.39 #322, 0.37 #400, 0.36 #1710), 01z77k (0.33 #25, 0.14 #102, 0.13 #642), 04t36 (0.33 #5, 0.04 #545, 0.04 #159), 01t_vv (0.29 #337, 0.28 #415, 0.24 #1725), 0c4xc (0.26 #578, 0.25 #2276, 0.24 #1889), 01htzx (0.21 #1093, 0.19 #1632, 0.19 #553), 0hcr (0.19 #3949, 0.19 #864, 0.18 #1095), 01hmnh (0.19 #89, 0.17 #1631, 0.16 #1400), 06nbt (0.17 #866, 0.17 #1097, 0.16 #1405), 0vgkd (0.17 #548, 0.14 #1704, 0.14 #2323) >> Best rule #322 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 09dv8h; >> query: (?x10089, 01z4y) <- honored_for(?x8128, ?x10089), nominated_for(?x2554, ?x10089), program(?x2554, ?x50), titles(?x2008, ?x10089) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1858 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 96 *> proper extension: 04kzqz; 0n2bh; 02sqkh; 0h63q6t; *> query: (?x10089, 0lsxr) <- program(?x10215, ?x10089), award_winner(?x8238, ?x10215), award(?x10215, ?x4921) *> conf = 0.13 ranks of expected_values: 13 EVAL 07g9f genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 99.000 99.000 0.395 http://example.org/tv/tv_program/genre #15160-02yvct PRED entity: 02yvct PRED relation: genre PRED expected values: 082gq => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 108): 05p553 (0.74 #965, 0.71 #9392, 0.35 #6017), 03k9fj (0.61 #12284, 0.52 #13125, 0.50 #9388), 02l7c8 (0.39 #15, 0.38 #3261, 0.38 #2659), 02kdv5l (0.37 #122, 0.34 #2766, 0.33 #1204), 0lsxr (0.31 #729, 0.30 #1091, 0.24 #970), 04xvlr (0.31 #841, 0.26 #1443, 0.25 #601), 060__y (0.23 #616, 0.22 #2059, 0.22 #3863), 06n90 (0.22 #1575, 0.21 #1695, 0.21 #2176), 06nbt (0.21 #986, 0.07 #1107, 0.07 #25), 01hmnh (0.21 #1219, 0.20 #137, 0.18 #2781) >> Best rule #965 for best value: >> intensional similarity = 2 >> extensional distance = 68 >> proper extension: 0vgkd; >> query: (?x2189, 05p553) <- genre(?x2189, ?x809), ?x809 = 0vgkd >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #9418 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 815 *> proper extension: 0c0wvx; *> query: (?x2189, 082gq) <- genre(?x2189, ?x809), genre(?x3549, ?x809), ?x3549 = 017kct *> conf = 0.19 ranks of expected_values: 12 EVAL 02yvct genre 082gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 124.000 124.000 0.743 http://example.org/film/film/genre #15159-01w92 PRED entity: 01w92 PRED relation: film PRED expected values: 07j8r => 105 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1354): 02rb84n (0.43 #8260, 0.21 #11462, 0.20 #13063), 0dzz6g (0.33 #3759, 0.07 #13365, 0.06 #18168), 06kl78 (0.33 #3938, 0.07 #13544, 0.06 #18347), 0320fn (0.33 #3800, 0.07 #13406, 0.06 #18209), 0kvgtf (0.33 #3761, 0.07 #13367, 0.06 #18170), 04qw17 (0.33 #3460, 0.07 #13066, 0.06 #17869), 02rtqvb (0.33 #4791), 02r858_ (0.33 #4474), 03vyw8 (0.33 #4147), 06lpmt (0.33 #3821) >> Best rule #8260 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 05gnf; 03yxwq; >> query: (?x3487, 02rb84n) <- award_winner(?x10166, ?x3487), country(?x10166, ?x94), state_province_region(?x3487, ?x362) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #17979 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0jz9f; 054lpb6; 03xsby; 09b3v; *> query: (?x3487, 07j8r) <- award_nominee(?x3487, ?x2246), organization(?x4682, ?x3487), state_province_region(?x3487, ?x362) *> conf = 0.06 ranks of expected_values: 1005 EVAL 01w92 film 07j8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 105.000 79.000 0.429 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15158-0k54q PRED entity: 0k54q PRED relation: film! PRED expected values: 02gf_l 0935jw => 90 concepts (53 used for prediction) PRED predicted values (max 10 best out of 992): 084m3 (0.43 #17886, 0.25 #5446, 0.01 #69718), 01_rh4 (0.43 #17166, 0.03 #31679, 0.02 #35826), 0fby2t (0.33 #2827, 0.25 #6975, 0.17 #13194), 0dzf_ (0.33 #2883, 0.20 #9104, 0.07 #29837), 02gyl0 (0.33 #830, 0.11 #19490), 0mdqp (0.33 #2192, 0.10 #31219, 0.07 #20853), 0sw6g (0.33 #3475, 0.07 #22136, 0.05 #34576), 01q_ph (0.33 #2130, 0.06 #31157, 0.05 #24938), 01rcmg (0.33 #3542, 0.05 #40864, 0.04 #42937), 01nfys (0.33 #3641, 0.03 #30595, 0.03 #32668) >> Best rule #17886 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0m5s5; >> query: (?x5378, 084m3) <- language(?x5378, ?x254), film(?x8485, ?x5378), film(?x5601, ?x5378), genre(?x5378, ?x225), ?x8485 = 0f13b, produced_by(?x9185, ?x5601), gender(?x5601, ?x231) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #44806 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 93 *> proper extension: 0dr1c2; *> query: (?x5378, 02gf_l) <- film(?x5202, ?x5378), film(?x2100, ?x5378), genre(?x5378, ?x2540), ?x2540 = 0hcr, nationality(?x2100, ?x94), place_of_birth(?x5202, ?x7328), type_of_union(?x5202, ?x566) *> conf = 0.09 ranks of expected_values: 141, 858 EVAL 0k54q film! 0935jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 90.000 53.000 0.429 http://example.org/film/actor/film./film/performance/film EVAL 0k54q film! 02gf_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 90.000 53.000 0.429 http://example.org/film/actor/film./film/performance/film #15157-0jmj PRED entity: 0jmj PRED relation: award PRED expected values: 03ccq3s => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 254): 0fbvqf (0.70 #20186, 0.70 #21741, 0.70 #21352), 0f4x7 (0.33 #415, 0.11 #14388, 0.11 #11281), 09sdmz (0.31 #582, 0.07 #11448, 0.06 #14555), 099jhq (0.29 #404, 0.05 #11270, 0.05 #16), 02x4w6g (0.26 #492, 0.05 #11358, 0.05 #14465), 04kxsb (0.24 #503, 0.09 #11369, 0.08 #4773), 0cqhk0 (0.23 #33, 0.20 #2750, 0.19 #5855), 01bgqh (0.23 #1201, 0.18 #813, 0.17 #1590), 0gs9p (0.22 #3565, 0.16 #4342, 0.15 #8222), 01by1l (0.22 #1266, 0.18 #878, 0.17 #1655) >> Best rule #20186 for best value: >> intensional similarity = 3 >> extensional distance = 1571 >> proper extension: 04glx0; >> query: (?x4346, ?x783) <- award_nominee(?x1870, ?x4346), award_winner(?x783, ?x4346), award_nominee(?x100, ?x1870) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #20575 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1577 *> proper extension: 06jntd; *> query: (?x4346, ?x435) <- award_winner(?x337, ?x4346), nominated_for(?x435, ?x337) *> conf = 0.14 ranks of expected_values: 37 EVAL 0jmj award 03ccq3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 96.000 96.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15156-01jw67 PRED entity: 01jw67 PRED relation: films! PRED expected values: 0fx2s => 85 concepts (46 used for prediction) PRED predicted values (max 10 best out of 64): 02_h0 (0.20 #100, 0.05 #256, 0.03 #1513), 081pw (0.07 #1573, 0.06 #1890, 0.05 #1100), 0fzyg (0.07 #524, 0.06 #994, 0.06 #367), 0bxg3 (0.07 #863, 0.04 #1335, 0.01 #1650), 05489 (0.06 #365, 0.04 #1465, 0.04 #679), 06d4h (0.05 #983, 0.05 #43, 0.05 #1140), 018h2 (0.05 #178, 0.04 #1277, 0.03 #805), 04gb7 (0.05 #45, 0.04 #1773, 0.03 #2403), 03r8gp (0.05 #246, 0.02 #3078, 0.02 #1818), 01cgz (0.05 #19, 0.02 #3007, 0.02 #1589) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 01f8hf; 0j90s; >> query: (?x6222, 02_h0) <- nominated_for(?x1245, ?x6222), nominated_for(?x6222, ?x810), ?x1245 = 0gqwc, nominated_for(?x1622, ?x810) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1013 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 77 *> proper extension: 0m313; 0yyg4; 07gp9; 011yph; 01cssf; 0209hj; 01hp5; 061681; 05jzt3; 017gl1; ... *> query: (?x6222, 0fx2s) <- nominated_for(?x637, ?x6222), titles(?x53, ?x6222), ?x637 = 02r22gf, film_release_distribution_medium(?x6222, ?x81) *> conf = 0.04 ranks of expected_values: 16 EVAL 01jw67 films! 0fx2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 85.000 46.000 0.200 http://example.org/film/film_subject/films #15155-06cvj PRED entity: 06cvj PRED relation: genre! PRED expected values: 0m313 02y_lrp 05jf85 02_1sj 0pv2t 0272_vz 02pw_n 03bzyn4 0sxlb 0408m53 0322yj => 48 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1800): 02rv_dz (0.67 #10700, 0.62 #15932, 0.50 #3724), 0d8w2n (0.67 #12183, 0.62 #17415, 0.50 #5207), 02dr9j (0.67 #13428, 0.62 #18662, 0.50 #4707), 01hw5kk (0.67 #12864, 0.60 #7632, 0.50 #11119), 03s6l2 (0.67 #10547, 0.60 #7060, 0.50 #15779), 034qmv (0.67 #12221, 0.60 #6989, 0.50 #10476), 01s3vk (0.67 #11337, 0.50 #16569, 0.50 #6106), 0dq626 (0.67 #10512, 0.50 #15744, 0.50 #12257), 09y6pb (0.67 #11971, 0.50 #17203, 0.50 #4995), 021pqy (0.67 #11210, 0.50 #16442, 0.50 #4234) >> Best rule #10700 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 01t_vv; >> query: (?x239, 02rv_dz) <- genre(?x6244, ?x239), genre(?x6099, ?x239), genre(?x3071, ?x239), genre(?x2882, ?x239), ?x2882 = 03rz2b, genre(?x1631, ?x239), country(?x6244, ?x205), language(?x6244, ?x254), film(?x496, ?x3071), film_crew_role(?x6099, ?x137) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #10474 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 01t_vv; *> query: (?x239, 0m313) <- genre(?x6244, ?x239), genre(?x6099, ?x239), genre(?x3071, ?x239), genre(?x2882, ?x239), ?x2882 = 03rz2b, genre(?x1631, ?x239), country(?x6244, ?x205), language(?x6244, ?x254), film(?x496, ?x3071), film_crew_role(?x6099, ?x137) *> conf = 0.67 ranks of expected_values: 12, 15, 18, 27, 56, 306, 420, 421, 424, 600, 750 EVAL 06cvj genre! 0322yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 48.000 33.000 0.667 http://example.org/film/film/genre EVAL 06cvj genre! 0408m53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 33.000 0.667 http://example.org/film/film/genre EVAL 06cvj genre! 0sxlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 48.000 33.000 0.667 http://example.org/film/film/genre EVAL 06cvj genre! 03bzyn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 48.000 33.000 0.667 http://example.org/film/film/genre EVAL 06cvj genre! 02pw_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 48.000 33.000 0.667 http://example.org/film/film/genre EVAL 06cvj genre! 0272_vz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 33.000 0.667 http://example.org/film/film/genre EVAL 06cvj genre! 0pv2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 33.000 0.667 http://example.org/film/film/genre EVAL 06cvj genre! 02_1sj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 33.000 0.667 http://example.org/film/film/genre EVAL 06cvj genre! 05jf85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 33.000 0.667 http://example.org/film/film/genre EVAL 06cvj genre! 02y_lrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 33.000 0.667 http://example.org/film/film/genre EVAL 06cvj genre! 0m313 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 48.000 33.000 0.667 http://example.org/film/film/genre #15154-03ylxn PRED entity: 03ylxn PRED relation: current_club PRED expected values: 0mmd6 => 82 concepts (54 used for prediction) PRED predicted values (max 10 best out of 815): 0138mv (0.50 #508, 0.43 #2009, 0.33 #1084), 01w_d6 (0.43 #2009, 0.40 #739, 0.33 #164), 023zd7 (0.43 #2009, 0.40 #779, 0.25 #490), 080_y (0.43 #2009, 0.33 #1109, 0.33 #247), 050fh (0.43 #2009, 0.33 #184, 0.33 #41), 03x6m (0.43 #2009, 0.33 #217, 0.31 #1365), 0xbm (0.43 #2009, 0.33 #1024, 0.31 #1310), 0y54 (0.43 #2009, 0.33 #150, 0.25 #1298), 049dzz (0.43 #2009, 0.33 #236, 0.25 #522), 075q_ (0.43 #2009, 0.33 #147, 0.25 #433) >> Best rule #508 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 033nzk; >> query: (?x11225, 0138mv) <- position(?x11225, ?x63), current_club(?x11225, ?x10443), ?x10443 = 03j6_5, position(?x13464, ?x63), position(?x12184, ?x63), position(?x7662, ?x63), position(?x4972, ?x63), ?x7662 = 06vlk0, ?x12184 = 03zkr8, team(?x63, ?x676), ?x13464 = 04lhft, ?x4972 = 03d8m4 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #714 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 03zrc_; *> query: (?x11225, 0mmd6) <- team(?x1142, ?x11225), current_club(?x11225, ?x11421), current_club(?x11225, ?x9434), current_club(?x11225, ?x3383), team(?x63, ?x11225), ?x63 = 02sdk9v, team(?x203, ?x3383), ?x9434 = 02qhlm, team(?x12509, ?x11421) *> conf = 0.25 ranks of expected_values: 53 EVAL 03ylxn current_club 0mmd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 82.000 54.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #15153-0642ykh PRED entity: 0642ykh PRED relation: film_crew_role PRED expected values: 0dxtw => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.90 #2793, 0.84 #2855, 0.83 #1102), 0dxtw (0.57 #447, 0.55 #384, 0.52 #1327), 04pyp5 (0.27 #45, 0.25 #76, 0.18 #107), 02ynfr (0.24 #387, 0.24 #106, 0.23 #450), 0215hd (0.22 #15, 0.19 #1333, 0.18 #453), 015h31 (0.22 #8, 0.19 #351, 0.18 #288), 033smt (0.22 #23, 0.18 #55, 0.17 #86), 02_n3z (0.22 #1, 0.18 #33, 0.17 #64), 089g0h (0.22 #16, 0.17 #1334, 0.17 #454), 089fss (0.22 #5, 0.12 #99, 0.10 #192) >> Best rule #2793 for best value: >> intensional similarity = 7 >> extensional distance = 996 >> proper extension: 0170z3; 0ds35l9; 0m313; 07gp9; 0ddfwj1; 0dq626; 0gtv7pk; 03h_yy; 060v34; 0gx9rvq; ... >> query: (?x6826, 0ch6mp2) <- film_crew_role(?x6826, ?x2178), film_crew_role(?x7579, ?x2178), film_crew_role(?x3221, ?x2178), film_crew_role(?x3157, ?x2178), ?x3157 = 0ywrc, ?x7579 = 01k0vq, ?x3221 = 014nq4 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #447 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 125 *> proper extension: 09sh8k; 02y_lrp; 047gn4y; 0h1cdwq; 0bth54; 0pc62; 0fg04; 04fzfj; 08gsvw; 0b73_1d; ... *> query: (?x6826, 0dxtw) <- music(?x6826, ?x7027), currency(?x6826, ?x170), film_crew_role(?x6826, ?x2154), film_crew_role(?x6826, ?x137), ?x137 = 09zzb8, ?x2154 = 01vx2h *> conf = 0.57 ranks of expected_values: 2 EVAL 0642ykh film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 126.000 0.899 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15152-02phtzk PRED entity: 02phtzk PRED relation: nominated_for! PRED expected values: 02qvyrt => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 195): 0gq9h (0.52 #766, 0.45 #1236, 0.38 #1001), 0k611 (0.46 #776, 0.34 #1246, 0.32 #2891), 0gs9p (0.45 #1238, 0.41 #768, 0.33 #1943), 019f4v (0.43 #1229, 0.43 #759, 0.31 #2874), 0gq_v (0.42 #725, 0.34 #1195, 0.27 #960), 0gr0m (0.42 #763, 0.30 #1233, 0.26 #998), 0p9sw (0.41 #726, 0.28 #1196, 0.27 #961), 02qvyrt (0.38 #799, 0.24 #1034, 0.23 #1974), 040njc (0.36 #712, 0.34 #1182, 0.30 #2827), 02qyntr (0.34 #883, 0.26 #1353, 0.25 #2998) >> Best rule #766 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 0b2v79; 016z5x; 0p_sc; 01fmys; 05cvgl; 0jvt9; 011yr9; 0pd57; 0pd4f; 04v8h1; ... >> query: (?x4534, 0gq9h) <- nominated_for(?x1533, ?x4534), nominated_for(?x1079, ?x4534), film_release_region(?x4534, ?x94), ?x1079 = 0l8z1 >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #799 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 116 *> proper extension: 0b2v79; 016z5x; 0p_sc; 01fmys; 05cvgl; 0jvt9; 011yr9; 0pd57; 0pd4f; 04v8h1; ... *> query: (?x4534, 02qvyrt) <- nominated_for(?x1533, ?x4534), nominated_for(?x1079, ?x4534), film_release_region(?x4534, ?x94), ?x1079 = 0l8z1 *> conf = 0.38 ranks of expected_values: 8 EVAL 02phtzk nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 99.000 99.000 0.517 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15151-0126rp PRED entity: 0126rp PRED relation: film PRED expected values: 09xbpt => 145 concepts (103 used for prediction) PRED predicted values (max 10 best out of 848): 03q0r1 (0.29 #7771, 0.17 #2419, 0.11 #9555), 031778 (0.25 #316, 0.08 #2100, 0.06 #7452), 02754c9 (0.25 #1130, 0.08 #2914, 0.06 #8266), 01l_pn (0.25 #964, 0.07 #4532, 0.03 #24163), 034qmv (0.25 #15, 0.06 #5367, 0.03 #23214), 02lk60 (0.25 #788, 0.05 #15063, 0.03 #23987), 014kq6 (0.25 #346, 0.05 #14621, 0.03 #25329), 03nfnx (0.25 #1398, 0.04 #47792, 0.04 #22812), 03176f (0.25 #704, 0.04 #61371, 0.03 #14979), 02qydsh (0.25 #1493, 0.03 #24692, 0.03 #35399) >> Best rule #7771 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 01qklj; >> query: (?x2125, 03q0r1) <- film(?x2125, ?x4188), film(?x2125, ?x2933), film(?x65, ?x4188), ?x2933 = 0407yj_ >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #8967 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 01_x6v; 0gthm; 05g7q; *> query: (?x2125, 09xbpt) <- student(?x5846, ?x2125), influenced_by(?x2125, ?x3917), person(?x424, ?x2125), film(?x2125, ?x2287) *> conf = 0.05 ranks of expected_values: 218 EVAL 0126rp film 09xbpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 145.000 103.000 0.294 http://example.org/film/actor/film./film/performance/film #15150-01f1jy PRED entity: 01f1jy PRED relation: medal PRED expected values: 02lq5w 02lpp7 => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2): 02lpp7 (0.88 #50, 0.88 #46, 0.87 #44), 02lq5w (0.87 #51, 0.86 #32, 0.85 #39) >> Best rule #50 for best value: >> intensional similarity = 30 >> extensional distance = 39 >> proper extension: 06sks6; >> query: (?x1617, 02lpp7) <- olympics(?x304, ?x1617), film_release_region(?x11065, ?x304), film_release_region(?x7538, ?x304), film_release_region(?x5400, ?x304), film_release_region(?x4841, ?x304), film_release_region(?x4453, ?x304), film_release_region(?x4040, ?x304), film_release_region(?x2954, ?x304), film_release_region(?x2628, ?x304), film_release_region(?x2318, ?x304), film_release_region(?x607, ?x304), film_release_region(?x409, ?x304), organization(?x304, ?x1062), ?x4841 = 0k4fz, ?x607 = 02x3lt7, ?x409 = 0gtv7pk, medal(?x304, ?x422), contains(?x455, ?x304), olympics(?x304, ?x418), ?x2954 = 0crh5_f, ?x2628 = 06wbm8q, ?x1062 = 01rz1, ?x5400 = 0bhwhj, sports(?x1617, ?x453), film_release_region(?x499, ?x304), ?x11065 = 0n08r, written_by(?x4453, ?x7621), ?x2318 = 06v9_x, ?x4040 = 02mt51, ?x7538 = 035zr0 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01f1jy medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 23.000 0.878 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 01f1jy medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 23.000 0.878 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #15149-0544vh PRED entity: 0544vh PRED relation: current_club! PRED expected values: 033nzk => 65 concepts (60 used for prediction) PRED predicted values (max 10 best out of 29): 03ylxn (0.06 #25, 0.06 #355, 0.05 #115), 03yl2t (0.06 #814, 0.06 #1144, 0.05 #124), 03y_f8 (0.05 #633, 0.04 #363, 0.04 #903), 02ltg3 (0.05 #637, 0.04 #997, 0.03 #367), 033nzk (0.04 #362, 0.04 #632, 0.04 #692), 02s2lg (0.04 #996, 0.03 #636, 0.02 #1206), 03ys48 (0.04 #108, 0.04 #708, 0.04 #258), 03_44z (0.04 #149, 0.03 #299, 0.03 #329), 01_lhg (0.04 #1178, 0.04 #158, 0.03 #1418), 03xh50 (0.04 #162, 0.03 #432, 0.03 #492) >> Best rule #25 for best value: >> intensional similarity = 15 >> extensional distance = 62 >> proper extension: 019lwb; 03qx63; 025txtg; 0182r9; 01nd2c; 01kckd; 027pwl; 03m10r; 03fhm5; 02s2lg; ... >> query: (?x13792, 03ylxn) <- position(?x13792, ?x530), position(?x13792, ?x203), position(?x13792, ?x63), position(?x13792, ?x60), ?x203 = 0dgrmp, ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x60 = 02nzb8, position(?x13792, ?x203), team(?x530, ?x13792), team(?x203, ?x13792), position(?x13792, ?x63), position(?x13792, ?x60), position(?x13792, ?x530), team(?x63, ?x13792) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #362 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 87 *> proper extension: 0371rb; 041xyk; 03xzxb; 03ytj1; 011v3; 02b1k5; 07sqnh; 02gjt4; 02nt75; 01kkk4; ... *> query: (?x13792, 033nzk) <- position(?x13792, ?x530), position(?x13792, ?x203), position(?x13792, ?x63), position(?x13792, ?x60), ?x203 = 0dgrmp, ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x60 = 02nzb8, position(?x13792, ?x203), team(?x530, ?x13792), team(?x203, ?x13792), position(?x13792, ?x63), position(?x13792, ?x60), team(?x63, ?x13792) *> conf = 0.04 ranks of expected_values: 5 EVAL 0544vh current_club! 033nzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 65.000 60.000 0.062 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #15148-016ywb PRED entity: 016ywb PRED relation: genre PRED expected values: 082gq => 90 concepts (68 used for prediction) PRED predicted values (max 10 best out of 84): 05p553 (0.44 #7661, 0.37 #4179, 0.37 #4644), 02l7c8 (0.41 #131, 0.37 #827, 0.36 #15), 03k9fj (0.34 #590, 0.34 #1170, 0.33 #1054), 01jfsb (0.32 #11, 0.31 #591, 0.29 #5001), 01hmnh (0.28 #596, 0.26 #1060, 0.25 #1524), 06n90 (0.24 #592, 0.21 #1172, 0.21 #1520), 017fp (0.21 #826, 0.17 #2683, 0.14 #130), 0lsxr (0.20 #1863, 0.20 #2095, 0.18 #7), 082gq (0.19 #145, 0.18 #841, 0.16 #957), 03g3w (0.14 #835, 0.12 #139, 0.11 #2692) >> Best rule #7661 for best value: >> intensional similarity = 4 >> extensional distance = 1291 >> proper extension: 0gwf191; >> query: (?x7073, 05p553) <- film(?x488, ?x7073), genre(?x7073, ?x53), genre(?x3684, ?x53), ?x3684 = 06q8qh >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #145 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 103 *> proper extension: 03_wm6; 09rfpk; *> query: (?x7073, 082gq) <- country(?x7073, ?x512), genre(?x7073, ?x4757), genre(?x7073, ?x4088), ?x4088 = 04xvh5, titles(?x4757, ?x499) *> conf = 0.19 ranks of expected_values: 9 EVAL 016ywb genre 082gq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 90.000 68.000 0.437 http://example.org/film/film/genre #15147-03ffcz PRED entity: 03ffcz PRED relation: actor PRED expected values: 0dgskx => 122 concepts (66 used for prediction) PRED predicted values (max 10 best out of 845): 08_438 (0.33 #915, 0.17 #3706, 0.14 #5566), 0l6px (0.33 #182, 0.11 #7623, 0.11 #25120), 01tzm9 (0.33 #574, 0.11 #8015, 0.08 #10805), 013_vh (0.33 #309, 0.11 #7750, 0.08 #10540), 07hbxm (0.33 #174, 0.11 #7615, 0.08 #10405), 05cj4r (0.33 #956, 0.06 #13976, 0.05 #17699), 015qq1 (0.22 #8268, 0.17 #11058, 0.14 #5478), 016ggh (0.18 #10127, 0.14 #5477, 0.12 #7337), 024bbl (0.17 #3173, 0.14 #5033, 0.12 #14333), 016xk5 (0.17 #3350, 0.14 #5210, 0.11 #8930) >> Best rule #915 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0dl6fv; >> query: (?x6597, 08_438) <- film_release_distribution_medium(?x6597, ?x81), genre(?x6597, ?x307), country(?x6597, ?x390), actor(?x6597, ?x1739), actor(?x6597, ?x988), ?x988 = 01tspc6, award_winner(?x72, ?x1739) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #25120 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 61 *> proper extension: 02zv4b; *> query: (?x6597, ?x72) <- country_of_origin(?x6597, ?x512), actor(?x6597, ?x1739), actor(?x6597, ?x988), languages(?x6597, ?x254), ?x254 = 02h40lc, award_winner(?x72, ?x1739), spouse(?x988, ?x6612) *> conf = 0.11 ranks of expected_values: 96 EVAL 03ffcz actor 0dgskx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 122.000 66.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #15146-029k4p PRED entity: 029k4p PRED relation: titles! PRED expected values: 07yjb => 85 concepts (61 used for prediction) PRED predicted values (max 10 best out of 55): 01z4y (0.45 #649, 0.22 #1367, 0.21 #444), 07s9rl0 (0.39 #5541, 0.38 #1, 0.36 #4619), 01jfsb (0.27 #1452, 0.27 #736, 0.26 #1249), 04xvlr (0.26 #5544, 0.24 #105, 0.24 #207), 024qqx (0.23 #592, 0.21 #387, 0.14 #181), 0lsxr (0.22 #4721, 0.22 #2557, 0.22 #3382), 02kdv5l (0.22 #4721, 0.22 #2557, 0.21 #2045), 0fdjb (0.22 #4721, 0.22 #2557, 0.21 #2045), 06nbt (0.22 #4721, 0.22 #2557, 0.21 #2045), 01q03 (0.22 #4721, 0.22 #2557, 0.21 #2045) >> Best rule #649 for best value: >> intensional similarity = 4 >> extensional distance = 226 >> proper extension: 02y_lrp; 034qmv; 06w99h3; 027qgy; 047q2k1; 011yrp; 011yxg; 09xbpt; 07xtqq; 01k1k4; ... >> query: (?x4880, 01z4y) <- featured_film_locations(?x4880, ?x151), genre(?x4880, ?x258), film(?x286, ?x4880), ?x258 = 05p553 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #3352 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 614 *> proper extension: 07s3m4g; *> query: (?x4880, 07yjb) <- genre(?x4880, ?x604), genre(?x2081, ?x604), ?x2081 = 01j8wk *> conf = 0.01 ranks of expected_values: 52 EVAL 029k4p titles! 07yjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 85.000 61.000 0.452 http://example.org/media_common/netflix_genre/titles #15145-0r2bv PRED entity: 0r2bv PRED relation: place! PRED expected values: 0r2bv => 142 concepts (73 used for prediction) PRED predicted values (max 10 best out of 295): 0k9p4 (0.24 #7749, 0.22 #9300, 0.19 #9818), 0r2dp (0.24 #7749, 0.22 #9300, 0.19 #9818), 0r2l7 (0.24 #7749, 0.22 #9300, 0.19 #9818), 0d7k1z (0.22 #9300, 0.19 #9818, 0.19 #9817), 0h3lt (0.22 #9300, 0.19 #9818, 0.19 #9817), 0jbrr (0.22 #9300, 0.19 #9818, 0.19 #9817), 0r2kh (0.22 #9300, 0.19 #9818, 0.19 #9817), 0r2gj (0.22 #9300, 0.19 #9818, 0.19 #9817), 0r2bv (0.19 #28948, 0.15 #32053, 0.14 #28949), 0q_xk (0.07 #745, 0.05 #1262, 0.04 #1779) >> Best rule #7749 for best value: >> intensional similarity = 5 >> extensional distance = 49 >> proper extension: 01lxw6; >> query: (?x12153, ?x9417) <- contains(?x578, ?x12153), time_zones(?x12153, ?x2950), state(?x12153, ?x1227), county(?x9417, ?x578), contains(?x9417, ?x13079) >> conf = 0.24 => this is the best rule for 3 predicted values *> Best rule #28948 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 241 *> proper extension: 0194_r; 013hvr; 03xpx0; *> query: (?x12153, ?x10526) <- contains(?x578, ?x12153), contains(?x578, ?x10526), county(?x7152, ?x578), contains(?x1227, ?x578), time_zones(?x10526, ?x2950) *> conf = 0.19 ranks of expected_values: 9 EVAL 0r2bv place! 0r2bv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 142.000 73.000 0.238 http://example.org/location/hud_county_place/place #15144-0b_c7 PRED entity: 0b_c7 PRED relation: award PRED expected values: 0gq_v 02pqp12 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 295): 02qkk9_ (0.71 #29842, 0.71 #29438, 0.71 #30650), 040njc (0.38 #814, 0.37 #2426, 0.37 #2829), 02pqp12 (0.35 #472, 0.32 #2487, 0.32 #2890), 0gr4k (0.31 #4466, 0.27 #3660, 0.26 #6482), 09sb52 (0.31 #16163, 0.30 #15760, 0.30 #23423), 04dn09n (0.30 #4477, 0.27 #3671, 0.24 #2461), 0gr51 (0.30 #4532, 0.26 #3726, 0.25 #4129), 0gq9h (0.28 #4107, 0.28 #2494, 0.28 #4914), 03hkv_r (0.25 #4449, 0.23 #3643, 0.20 #6465), 02rdyk7 (0.24 #492, 0.22 #1701, 0.22 #2104) >> Best rule #29842 for best value: >> intensional similarity = 3 >> extensional distance = 1536 >> proper extension: 018p5f; 04qzm; 09jm8; 0c9l1; >> query: (?x1742, ?x5180) <- award_winner(?x5180, ?x1742), award(?x1742, ?x350), award_nominee(?x2724, ?x1742) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #472 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 0prjs; 0p51w; 0jw67; 012rng; 01twdk; 0cm89v; 01c6l; 045cq; 01_f_5; 03s9b; ... *> query: (?x1742, 02pqp12) <- film(?x1742, ?x4678), religion(?x1742, ?x1985), place_of_birth(?x1742, ?x9660) *> conf = 0.35 ranks of expected_values: 3, 26 EVAL 0b_c7 award 02pqp12 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 116.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0b_c7 award 0gq_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 116.000 116.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15143-01c_d PRED entity: 01c_d PRED relation: draft PRED expected values: 02qw1zx => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 17): 0g3zpp (0.86 #344, 0.84 #225, 0.80 #327), 05vsb7 (0.83 #292, 0.82 #190, 0.78 #599), 02qw1zx (0.78 #599, 0.76 #722, 0.72 #530), 02pq_rp (0.41 #571, 0.38 #417, 0.30 #658), 02z6872 (0.40 #172, 0.38 #572, 0.36 #418), 02r6gw6 (0.40 #172, 0.38 #575, 0.33 #421), 02pq_x5 (0.40 #172, 0.37 #578, 0.31 #424), 0f4vx0 (0.40 #172, 0.33 #590, 0.31 #521), 02x2khw (0.40 #172, 0.33 #567, 0.31 #413), 025tn92 (0.40 #172, 0.32 #591, 0.31 #522) >> Best rule #344 for best value: >> intensional similarity = 15 >> extensional distance = 27 >> proper extension: 0ws7; >> query: (?x8902, 0g3zpp) <- position(?x8902, ?x180), draft(?x8902, ?x4171), draft(?x8902, ?x3089), team(?x1114, ?x8902), school(?x8902, ?x388), ?x4171 = 092j54, draft(?x9172, ?x3089), draft(?x6976, ?x3089), draft(?x6645, ?x3089), draft(?x729, ?x3089), ?x6645 = 0wsr, ?x9172 = 06rpd, ?x729 = 05g3b, ?x6976 = 04vn5, school(?x3089, ?x466) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #599 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 61 *> proper extension: 0jmdb; 0jm3v; 0jmfv; 0jm2v; 0jmj7; 0jm6n; 0jm8l; 0jml5; 0jm4b; 0jmbv; ... *> query: (?x8902, ?x465) <- team(?x3113, ?x8902), team(?x3113, ?x6645), team(?x3113, ?x2574), team(?x3113, ?x2198), draft(?x8902, ?x3089), school(?x2574, ?x1011), school(?x8902, ?x388), colors(?x2198, ?x663), team(?x445, ?x8902), team(?x9586, ?x2574), ?x1011 = 07w0v, draft(?x6645, ?x465) *> conf = 0.78 ranks of expected_values: 3 EVAL 01c_d draft 02qw1zx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 66.000 0.862 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #15142-0d05w3 PRED entity: 0d05w3 PRED relation: exported_to! PRED expected values: 0853g => 260 concepts (167 used for prediction) PRED predicted values (max 10 best out of 110): 06q1r (0.39 #1551, 0.37 #1663, 0.37 #1607), 04sj3 (0.37 #3474, 0.31 #1222, 0.29 #1447), 0h3y (0.37 #3474, 0.29 #953, 0.24 #1401), 0ctw_b (0.37 #3474, 0.29 #627, 0.20 #516), 07dzf (0.37 #3474, 0.25 #148, 0.14 #1041), 016zwt (0.37 #3474, 0.20 #552, 0.20 #384), 07fsv (0.37 #3474, 0.14 #991, 0.12 #1214), 04hhv (0.37 #3474, 0.09 #2112, 0.06 #2956), 07t_x (0.37 #3474, 0.06 #1269, 0.06 #1494), 047t_ (0.29 #649, 0.25 #147, 0.20 #538) >> Best rule #1551 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 04w58; >> query: (?x2346, 06q1r) <- time_zones(?x2346, ?x11859), administrative_parent(?x206, ?x2346), participating_countries(?x418, ?x2346) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #556 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0130xz; *> query: (?x2346, 0853g) <- entity_involved(?x12844, ?x2346), taxonomy(?x2346, ?x939), ?x12844 = 02cnqk *> conf = 0.20 ranks of expected_values: 28 EVAL 0d05w3 exported_to! 0853g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 260.000 167.000 0.389 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #15141-07cyl PRED entity: 07cyl PRED relation: crewmember PRED expected values: 095zvfg => 96 concepts (69 used for prediction) PRED predicted values (max 10 best out of 36): 0284n42 (0.12 #417, 0.08 #782, 0.05 #463), 04ktcgn (0.11 #425, 0.08 #790, 0.08 #12), 0b79gfg (0.11 #431, 0.07 #796, 0.04 #843), 051z6rz (0.10 #442, 0.05 #807, 0.05 #305), 02xc1w4 (0.09 #440, 0.08 #27, 0.05 #805), 0z4s (0.08 #824, 0.05 #92, 0.04 #640), 04353 (0.08 #824, 0.05 #92, 0.04 #640), 0chw_ (0.08 #824, 0.05 #92, 0.04 #640), 04wp63 (0.08 #40, 0.06 #223, 0.05 #818), 0g9zcgx (0.08 #31, 0.05 #444, 0.05 #809) >> Best rule #417 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 05_5rjx; 01q2nx; 04y9mm8; 01j5ql; 0bw20; 0gy30w; >> query: (?x3471, 0284n42) <- genre(?x3471, ?x812), ?x812 = 01jfsb, film(?x450, ?x3471), crewmember(?x3471, ?x6232) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #449 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 102 *> proper extension: 05_5rjx; 01q2nx; 04y9mm8; 01j5ql; 0bw20; 0gy30w; *> query: (?x3471, 095zvfg) <- genre(?x3471, ?x812), ?x812 = 01jfsb, film(?x450, ?x3471), crewmember(?x3471, ?x6232) *> conf = 0.06 ranks of expected_values: 15 EVAL 07cyl crewmember 095zvfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 96.000 69.000 0.115 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #15140-01n6r0 PRED entity: 01n6r0 PRED relation: colors PRED expected values: 088fh => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.53 #62, 0.37 #902, 0.37 #882), 01g5v (0.28 #1004, 0.26 #1144, 0.26 #884), 01l849 (0.26 #261, 0.25 #881, 0.25 #901), 019sc (0.21 #48, 0.20 #188, 0.18 #1088), 06fvc (0.16 #1143, 0.15 #1083, 0.15 #1003), 036k5h (0.13 #426, 0.12 #86, 0.11 #106), 03wkwg (0.12 #135, 0.11 #155, 0.11 #115), 0jc_p (0.12 #65, 0.11 #185, 0.10 #225), 067z2v (0.11 #50, 0.09 #190, 0.07 #110), 088fh (0.09 #67, 0.05 #1007, 0.04 #1147) >> Best rule #62 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 01rr31; >> query: (?x4980, 083jv) <- colors(?x4980, ?x8047), currency(?x4980, ?x170), ?x8047 = 038hg >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 01rr31; *> query: (?x4980, 088fh) <- colors(?x4980, ?x8047), currency(?x4980, ?x170), ?x8047 = 038hg *> conf = 0.09 ranks of expected_values: 10 EVAL 01n6r0 colors 088fh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 109.000 109.000 0.529 http://example.org/education/educational_institution/colors #15139-09hrc PRED entity: 09hrc PRED relation: administrative_parent! PRED expected values: 016n7b => 229 concepts (134 used for prediction) PRED predicted values (max 10 best out of 677): 016n7b (0.55 #2970, 0.53 #6532, 0.50 #6531), 035yzw (0.55 #2970, 0.53 #6532, 0.50 #6531), 09hrc (0.25 #370, 0.20 #1560, 0.14 #11883), 070zc (0.25 #361, 0.20 #1551, 0.06 #10462), 017v_ (0.25 #66, 0.20 #1256, 0.06 #10167), 0156q (0.25 #65, 0.20 #1255, 0.06 #10166), 09hzw (0.25 #404, 0.20 #1594, 0.06 #10505), 07nf6 (0.25 #371, 0.20 #1561, 0.06 #10472), 06rf7 (0.25 #327, 0.20 #1517, 0.06 #10428), 09ksp (0.25 #261, 0.20 #1451, 0.06 #10362) >> Best rule #2970 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 09krp; >> query: (?x10765, ?x11790) <- adjoins(?x3623, ?x10765), administrative_parent(?x12642, ?x10765), contains(?x10765, ?x11790), ?x3623 = 04p0c >> conf = 0.55 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 09hrc administrative_parent! 016n7b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 229.000 134.000 0.545 http://example.org/base/aareas/schema/administrative_area/administrative_parent #15138-03nk3t PRED entity: 03nk3t PRED relation: film PRED expected values: 025rxjq => 100 concepts (36 used for prediction) PRED predicted values (max 10 best out of 365): 0g9lm2 (0.22 #18179, 0.11 #28098, 0.11 #19007), 07pd_j (0.08 #1408, 0.03 #3886), 05pdh86 (0.08 #1196, 0.03 #3674), 0bpx1k (0.08 #1065, 0.03 #3543), 04w7rn (0.08 #943, 0.03 #3421), 01c9d (0.06 #2464, 0.03 #3290, 0.03 #4942), 04fjzv (0.06 #2454, 0.03 #3280, 0.03 #4932), 0199wf (0.06 #2433, 0.03 #3259, 0.03 #4911), 0333t (0.06 #2412, 0.03 #3238, 0.03 #4890), 02qcr (0.06 #2373, 0.03 #3199, 0.03 #4851) >> Best rule #18179 for best value: >> intensional similarity = 3 >> extensional distance = 478 >> proper extension: 025p38; 01twdk; 03b78r; 01lqnff; 01c1px; 037q1z; 01nc3rh; 01b0k1; 072vj; >> query: (?x4472, ?x4359) <- award_winner(?x4359, ?x4472), profession(?x4472, ?x319), ?x319 = 01d_h8 >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03nk3t film 025rxjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 36.000 0.224 http://example.org/film/director/film #15137-0gtxj2q PRED entity: 0gtxj2q PRED relation: film_crew_role PRED expected values: 02r96rf => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 33): 09zzb8 (0.92 #1357, 0.81 #1215, 0.80 #678), 02r96rf (0.72 #110, 0.70 #287, 0.70 #466), 01vx2h (0.52 #614, 0.42 #81, 0.38 #294), 02rh1dz (0.33 #80, 0.25 #613, 0.20 #293), 0d2b38 (0.29 #95, 0.16 #166, 0.15 #308), 02ynfr (0.18 #1371, 0.17 #85, 0.17 #1731), 0215hd (0.14 #621, 0.14 #124, 0.14 #1734), 01xy5l_ (0.14 #119, 0.11 #616, 0.10 #1729), 015h31 (0.12 #79, 0.12 #612, 0.09 #3072), 033smt (0.12 #97, 0.09 #3072, 0.09 #3362) >> Best rule #1357 for best value: >> intensional similarity = 9 >> extensional distance = 665 >> proper extension: 07kb7vh; >> query: (?x4290, 09zzb8) <- language(?x4290, ?x254), film_crew_role(?x4290, ?x2095), ?x254 = 02h40lc, film(?x1914, ?x4290), country(?x4290, ?x94), film_crew_role(?x9599, ?x2095), film_crew_role(?x2168, ?x2095), ?x9599 = 07l450, ?x2168 = 0bx0l >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #110 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 27 *> proper extension: 03m5y9p; *> query: (?x4290, 02r96rf) <- language(?x4290, ?x254), film_crew_role(?x4290, ?x1171), ?x254 = 02h40lc, film(?x1914, ?x4290), country(?x4290, ?x94), genre(?x4290, ?x53), ?x1914 = 03xsby *> conf = 0.72 ranks of expected_values: 2 EVAL 0gtxj2q film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 95.000 0.918 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15136-0ct9_ PRED entity: 0ct9_ PRED relation: influenced_by PRED expected values: 03sbs => 177 concepts (74 used for prediction) PRED predicted values (max 10 best out of 356): 05qmj (0.56 #4053, 0.47 #16946, 0.47 #7917), 032l1 (0.53 #7384, 0.53 #10825, 0.50 #9535), 03_87 (0.48 #25128, 0.29 #10938, 0.27 #7497), 03sbs (0.47 #16976, 0.47 #7947, 0.44 #4083), 0gz_ (0.47 #16857, 0.33 #7828, 0.33 #3964), 06myp (0.44 #4233, 0.33 #369, 0.22 #17187), 0w6w (0.33 #4290, 0.33 #426, 0.27 #8154), 02lt8 (0.33 #7414, 0.31 #9565, 0.29 #10855), 015n8 (0.33 #4268, 0.28 #17161, 0.27 #8132), 0j3v (0.33 #58, 0.26 #13805, 0.22 #3922) >> Best rule #4053 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 01d494; 01dvtx; 0nk72; >> query: (?x8430, 05qmj) <- influenced_by(?x8430, ?x3993), influenced_by(?x8430, ?x2994), profession(?x2994, ?x353), ?x3993 = 099bk, interests(?x8430, ?x713), nationality(?x2994, ?x789) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #16976 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 07kb5; 04411; 0j3v; 052h3; 099bk; 0372p; 0lcx; 043s3; 0b78hw; 03_hd; ... *> query: (?x8430, 03sbs) <- influenced_by(?x8430, ?x2994), profession(?x2994, ?x353), influenced_by(?x2994, ?x5912), interests(?x8430, ?x713) *> conf = 0.47 ranks of expected_values: 4 EVAL 0ct9_ influenced_by 03sbs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 177.000 74.000 0.556 http://example.org/influence/influence_node/influenced_by #15135-09r4xx PRED entity: 09r4xx PRED relation: student PRED expected values: 034q3l => 107 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1585): 0405l (0.25 #1844, 0.15 #3925, 0.08 #12250), 028mc6 (0.25 #1609, 0.05 #3690, 0.04 #12015), 016tbr (0.25 #1734, 0.05 #3815, 0.04 #12140), 020_95 (0.25 #942, 0.05 #3023, 0.04 #5104), 01gv_f (0.25 #619, 0.05 #2700, 0.02 #8944), 0chsq (0.25 #61, 0.05 #2142, 0.02 #8386), 044ntk (0.25 #226, 0.05 #2307, 0.02 #8551), 073v6 (0.25 #523, 0.05 #2604, 0.02 #8848), 02sjf5 (0.25 #167, 0.05 #2248, 0.02 #8492), 0gcdzz (0.25 #204, 0.05 #2285, 0.02 #8529) >> Best rule #1844 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 017j69; 01s7j5; >> query: (?x4016, 0405l) <- student(?x4016, ?x9946), student(?x4016, ?x856), ?x856 = 02ndbd, state_province_region(?x4016, ?x335), nominated_for(?x9946, ?x9185), institution(?x13709, ?x4016) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #66618 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 210 *> proper extension: 01jpyb; *> query: (?x4016, ?x434) <- state_province_region(?x4016, ?x335), school_type(?x4016, ?x4017), citytown(?x4016, ?x3014), place_of_birth(?x5404, ?x3014), place_of_birth(?x434, ?x3014), award(?x5404, ?x154) *> conf = 0.02 ranks of expected_values: 1271 EVAL 09r4xx student 034q3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 107.000 32.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #15134-0283sdr PRED entity: 0283sdr PRED relation: category PRED expected values: 08mbj5d => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #18, 0.90 #25, 0.90 #24) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 227 >> proper extension: 0ym8f; 024y8p; 01w5m; 02gr81; 017j69; 01nnsv; 0gl5_; 01r3w7; 01jt2w; 07tjf; ... >> query: (?x10285, 08mbj5d) <- institution(?x1771, ?x10285), colors(?x10285, ?x332), ?x1771 = 019v9k, colors(?x580, ?x332) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0283sdr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.904 http://example.org/common/topic/webpage./common/webpage/category #15133-01slc PRED entity: 01slc PRED relation: team! PRED expected values: 02dwpf => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 47): 02dwn9 (0.83 #137, 0.83 #1541, 0.81 #1859), 02dwpf (0.83 #137, 0.83 #1541, 0.81 #1859), 02sdk9v (0.66 #3233, 0.58 #3093, 0.50 #3140), 02_j1w (0.64 #3237, 0.55 #3097, 0.47 #3052), 02nzb8 (0.63 #3232, 0.55 #3092, 0.46 #3002), 02rsl1 (0.59 #3001, 0.54 #3277, 0.54 #2497), 017drs (0.59 #3001, 0.54 #3277, 0.54 #2497), 02sddg (0.59 #3001, 0.54 #3277, 0.54 #2497), 049k4w (0.59 #3001, 0.54 #3277, 0.51 #3000), 01yvvn (0.59 #3001, 0.54 #3277, 0.51 #3000) >> Best rule #137 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 06x68; >> query: (?x7060, ?x261) <- school(?x7060, ?x581), season(?x7060, ?x9498), season(?x7060, ?x9192), season(?x7060, ?x3431), ?x9498 = 027pwzc, team(?x5727, ?x7060), team(?x4244, ?x7060), ?x3431 = 025ygqm, ?x5727 = 02wszf, draft(?x7060, ?x8786), ?x4244 = 028c_8, ?x8786 = 02pq_x5, position(?x7060, ?x261), ?x9192 = 04110b0, colors(?x7060, ?x4557) >> conf = 0.83 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01slc team! 02dwpf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.833 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #15132-081lh PRED entity: 081lh PRED relation: produced_by! PRED expected values: 011yrp => 119 concepts (109 used for prediction) PRED predicted values (max 10 best out of 244): 0sxns (0.38 #1896, 0.36 #8531, 0.35 #32217), 0jyx6 (0.38 #1896, 0.36 #8531, 0.35 #32217), 0bs5vty (0.38 #1896, 0.36 #8531, 0.35 #32217), 02qpt1w (0.38 #1896, 0.36 #8531, 0.35 #32217), 06gjk9 (0.38 #1896, 0.36 #8531, 0.35 #32217), 07bxqz (0.38 #1896, 0.36 #8531, 0.35 #32217), 0blpg (0.38 #1896, 0.36 #8531, 0.35 #32217), 05jf85 (0.38 #1896, 0.36 #8531, 0.35 #32217), 04t9c0 (0.38 #1896, 0.36 #8531, 0.35 #32217), 0421ng (0.38 #1896, 0.36 #8531, 0.35 #32217) >> Best rule #1896 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0144l1; 05whq_9; 0jw67; 04yt7; 0gyx4; 01gvxv; >> query: (?x986, ?x306) <- type_of_appearance(?x986, ?x3429), written_by(?x306, ?x986), award_winner(?x68, ?x986) >> conf = 0.38 => this is the best rule for 19 predicted values No rule for expected values ranks of expected_values: EVAL 081lh produced_by! 011yrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 109.000 0.375 http://example.org/film/film/produced_by #15131-02rchht PRED entity: 02rchht PRED relation: executive_produced_by! PRED expected values: 09k56b7 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 29): 08zrbl (0.09 #3726), 04k9y6 (0.09 #3726), 04cj79 (0.09 #3726), 0gyy53 (0.09 #3726), 01hvjx (0.09 #3726), 0416y94 (0.09 #3726), 0c0nhgv (0.09 #3726), 098s2w (0.08 #900), 02ppg1r (0.04 #2129, 0.02 #9580, 0.02 #14372), 025ts_z (0.01 #3664) >> Best rule #3726 for best value: >> intensional similarity = 3 >> extensional distance = 343 >> proper extension: 07mvp; >> query: (?x264, ?x1163) <- award_winner(?x264, ?x163), award_winner(?x277, ?x163), executive_produced_by(?x1163, ?x163) >> conf = 0.09 => this is the best rule for 7 predicted values No rule for expected values ranks of expected_values: EVAL 02rchht executive_produced_by! 09k56b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 96.000 0.093 http://example.org/film/film/executive_produced_by #15130-0jm9w PRED entity: 0jm9w PRED relation: school PRED expected values: 01qd_r => 108 concepts (76 used for prediction) PRED predicted values (max 10 best out of 380): 015q1n (0.50 #863, 0.45 #570, 0.44 #2195), 0j_sncb (0.50 #420, 0.45 #570, 0.33 #231), 0pspl (0.50 #431, 0.45 #570, 0.33 #242), 03tw2s (0.50 #492, 0.45 #570, 0.33 #303), 02mj7c (0.50 #461, 0.33 #272, 0.20 #4260), 01jt2w (0.50 #509, 0.33 #320, 0.17 #699), 0bx8pn (0.46 #3635, 0.37 #6504, 0.36 #3255), 065y4w7 (0.45 #570, 0.38 #4953, 0.33 #200), 01jsn5 (0.45 #570, 0.33 #789, 0.33 #221), 06pwq (0.45 #570, 0.33 #577, 0.33 #198) >> Best rule #863 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 0jmfb; 0jm6n; 0jm74; 0bwjj; >> query: (?x9995, 015q1n) <- sport(?x9995, ?x4833), draft(?x9995, ?x8133), position(?x9995, ?x6848), position(?x9995, ?x5755), position(?x9995, ?x4747), position(?x9995, ?x4570), teams(?x2879, ?x9995), ?x4570 = 03558l, colors(?x9995, ?x663), ?x6848 = 02_ssl, ?x5755 = 0355dz, ?x663 = 083jv, ?x4747 = 02sf_r >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #570 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 0jm3v; *> query: (?x9995, ?x581) <- team(?x1348, ?x9995), draft(?x9995, ?x8586), draft(?x9995, ?x8542), school(?x9995, ?x4296), ?x8542 = 09th87, team(?x13926, ?x9995), colors(?x9995, ?x332), ?x4296 = 07vyf, position(?x9995, ?x6848), school(?x8586, ?x581) *> conf = 0.45 ranks of expected_values: 20 EVAL 0jm9w school 01qd_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 108.000 76.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #15129-01cw51 PRED entity: 01cw51 PRED relation: ceremony PRED expected values: 01s695 01bx35 0466p0j => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 123): 0466p0j (0.91 #829, 0.59 #956, 0.53 #1210), 01s695 (0.80 #764, 0.54 #891, 0.50 #383), 01bx35 (0.78 #767, 0.56 #513, 0.54 #894), 0gx1673 (0.52 #869, 0.34 #996, 0.33 #107), 0bzn6_ (0.27 #3430, 0.21 #3685, 0.13 #936), 0drtv8 (0.27 #3430, 0.21 #3685, 0.04 #1200), 050yyb (0.21 #3685, 0.14 #921, 0.12 #1302), 059x66 (0.21 #3685, 0.13 #904, 0.11 #1285), 09306z (0.21 #3685, 0.13 #985, 0.11 #1366), 0bzknt (0.21 #3685, 0.12 #961, 0.10 #1342) >> Best rule #829 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0257yf; >> query: (?x2563, 0466p0j) <- award(?x6716, ?x2563), ceremony(?x2563, ?x2054), ?x2054 = 0gpjbt, award_nominee(?x6716, ?x1290) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 01cw51 ceremony 0466p0j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.915 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01cw51 ceremony 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.915 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01cw51 ceremony 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.915 http://example.org/award/award_category/winners./award/award_honor/ceremony #15128-0p3_y PRED entity: 0p3_y PRED relation: nominated_for! PRED expected values: 09pjnd => 119 concepts (27 used for prediction) PRED predicted values (max 10 best out of 610): 01ycfv (0.70 #28092, 0.67 #18733, 0.64 #21073), 04flrx (0.47 #16393, 0.02 #36474, 0.01 #43495), 03ktjq (0.38 #35115, 0.02 #34057, 0.02 #36399), 08d9z7 (0.38 #35115), 076psv (0.31 #15026, 0.15 #36090, 0.12 #33748), 0h7pj (0.31 #37457, 0.23 #42136, 0.21 #53843), 09y20 (0.31 #37457, 0.23 #42136, 0.21 #53843), 05v1sb (0.25 #14966, 0.07 #36030, 0.06 #33688), 0146pg (0.24 #25873, 0.15 #28213, 0.14 #30554), 016tt2 (0.22 #37456, 0.22 #35224, 0.20 #35114) >> Best rule #28092 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 05znxx; 02dr9j; >> query: (?x2498, ?x9408) <- nominated_for(?x6860, ?x2498), music(?x2498, ?x9408), ?x6860 = 018wdw, award_winner(?x1185, ?x9408), role(?x9408, ?x316) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #30758 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 40 *> proper extension: 02kfzz; 0h03fhx; *> query: (?x2498, 09pjnd) <- nominated_for(?x6860, ?x2498), music(?x2498, ?x9408), ?x6860 = 018wdw, award_winner(?x1185, ?x9408), award_winner(?x2238, ?x9408) *> conf = 0.02 ranks of expected_values: 338 EVAL 0p3_y nominated_for! 09pjnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 119.000 27.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15127-07hgm PRED entity: 07hgm PRED relation: influenced_by PRED expected values: 01vsy7t 06gcn => 130 concepts (59 used for prediction) PRED predicted values (max 10 best out of 360): 014z8v (0.43 #13977, 0.23 #10082, 0.18 #15708), 0p_47 (0.29 #14395, 0.17 #13963, 0.16 #10068), 081lh (0.25 #14308, 0.21 #9981, 0.17 #17776), 01hmk9 (0.24 #14075, 0.19 #10180, 0.18 #15806), 08433 (0.24 #6082, 0.24 #1318, 0.17 #6947), 014zfs (0.22 #17781, 0.22 #7384, 0.17 #21247), 012vd6 (0.22 #7526, 0.20 #11427, 0.19 #11859), 01k9lpl (0.22 #14164, 0.16 #10269, 0.13 #11135), 0ph2w (0.21 #10080, 0.11 #13975, 0.07 #14407), 0167xy (0.19 #2526, 0.17 #364, 0.08 #19057) >> Best rule #13977 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 02n9k; >> query: (?x9497, 014z8v) <- influenced_by(?x9497, ?x3316), profession(?x3316, ?x131), person(?x6864, ?x3316), people(?x2510, ?x3316) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #138 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 01vsqvs; *> query: (?x9497, 01vsy7t) <- artists(?x2491, ?x9497), origin(?x9497, ?x739), ?x2491 = 011j5x, influenced_by(?x9497, ?x3316) *> conf = 0.17 ranks of expected_values: 15 EVAL 07hgm influenced_by 06gcn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 59.000 0.426 http://example.org/influence/influence_node/influenced_by EVAL 07hgm influenced_by 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 130.000 59.000 0.426 http://example.org/influence/influence_node/influenced_by #15126-0xq63 PRED entity: 0xq63 PRED relation: county PRED expected values: 0n5d1 => 103 concepts (90 used for prediction) PRED predicted values (max 10 best out of 70): 0n5d1 (0.67 #6682, 0.52 #8454, 0.41 #9440), 05fjf (0.26 #6681, 0.22 #393, 0.20 #8453), 0nvt9 (0.08 #40, 0.02 #1418, 0.02 #631), 0cymp (0.08 #29, 0.02 #6119, 0.02 #1998), 0n5fz (0.07 #230, 0.06 #427, 0.02 #4160), 0n58p (0.07 #296, 0.06 #493, 0.01 #6584), 0kpys (0.06 #6300, 0.06 #7288, 0.05 #6497), 0n5gq (0.05 #234, 0.04 #431, 0.02 #1023), 0n5j_ (0.05 #197, 0.04 #394, 0.02 #3147), 0n5df (0.05 #233, 0.04 #430, 0.01 #3183) >> Best rule #6682 for best value: >> intensional similarity = 4 >> extensional distance = 203 >> proper extension: 0288zy; 02583l; 0dy04; 01q2sk; 01y9st; 02zd460; 02l1fn; 0lbp_; 07vfz; 02jmst; ... >> query: (?x6468, ?x10054) <- contains(?x10054, ?x6468), category(?x6468, ?x134), adjoins(?x10054, ?x6143), second_level_divisions(?x94, ?x10054) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xq63 county 0n5d1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 90.000 0.668 http://example.org/location/hud_county_place/county #15125-01hgwkr PRED entity: 01hgwkr PRED relation: profession PRED expected values: 016z4k => 122 concepts (120 used for prediction) PRED predicted values (max 10 best out of 83): 09jwl (0.76 #1054, 0.75 #2386, 0.71 #610), 016z4k (0.65 #1039, 0.60 #3, 0.57 #2519), 01d_h8 (0.54 #3409, 0.46 #6222, 0.43 #5185), 0nbcg (0.53 #4767, 0.50 #475, 0.50 #327), 039v1 (0.45 #184, 0.35 #1072, 0.30 #480), 0n1h (0.40 #11, 0.38 #307, 0.28 #2527), 03gjzk (0.38 #3418, 0.32 #6231, 0.30 #5194), 01c72t (0.36 #171, 0.36 #615, 0.34 #5944), 0dxtg (0.35 #3417, 0.32 #5193, 0.28 #6230), 0fnpj (0.27 #208, 0.25 #652, 0.22 #2576) >> Best rule #1054 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 03gr7w; 0p_47; 03mszl; >> query: (?x9442, 09jwl) <- award(?x9442, ?x7691), role(?x9442, ?x432), award(?x7258, ?x7691), ?x7258 = 05sq0m >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1039 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 03gr7w; 0p_47; 03mszl; *> query: (?x9442, 016z4k) <- award(?x9442, ?x7691), role(?x9442, ?x432), award(?x7258, ?x7691), ?x7258 = 05sq0m *> conf = 0.65 ranks of expected_values: 2 EVAL 01hgwkr profession 016z4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 120.000 0.765 http://example.org/people/person/profession #15124-03h_yfh PRED entity: 03h_yfh PRED relation: influenced_by! PRED expected values: 012vd6 => 135 concepts (61 used for prediction) PRED predicted values (max 10 best out of 223): 02kz_ (0.10 #1772, 0.06 #5902, 0.06 #5385), 086qd (0.07 #74, 0.02 #5752, 0.02 #5235), 05rx__ (0.07 #3923, 0.07 #4955, 0.05 #8572), 0ph2w (0.07 #3770, 0.06 #7385, 0.04 #4802), 048cl (0.07 #1848, 0.06 #5978, 0.06 #5461), 04hcw (0.07 #1838, 0.06 #5451, 0.04 #5968), 02wh0 (0.07 #2000, 0.04 #6130, 0.04 #5613), 014ps4 (0.07 #1860, 0.04 #5990, 0.04 #5473), 06myp (0.07 #1990, 0.04 #6120, 0.04 #5603), 06bng (0.07 #1894, 0.04 #6024, 0.04 #5507) >> Best rule #1772 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 099bk; >> query: (?x7803, 02kz_) <- type_of_union(?x7803, ?x566), organization(?x7803, ?x8603), gender(?x7803, ?x231), ?x231 = 05zppz >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #6931 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 62 *> proper extension: 02r3zy; 07c0j; 01wv9xn; 0249kn; 017j6; 0kr_t; 02cpp; 01q99h; 0178_w; 0ycp3; ... *> query: (?x7803, 012vd6) <- artist(?x3240, ?x7803), award(?x7803, ?x2139), artists(?x5905, ?x7803), ?x3240 = 017l96 *> conf = 0.02 ranks of expected_values: 188 EVAL 03h_yfh influenced_by! 012vd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 135.000 61.000 0.100 http://example.org/influence/influence_node/influenced_by #15123-01wb95 PRED entity: 01wb95 PRED relation: nominated_for! PRED expected values: 057bc6m => 71 concepts (19 used for prediction) PRED predicted values (max 10 best out of 645): 012vct (0.60 #14039, 0.60 #44462, 0.54 #39782), 0jgwf (0.60 #14039, 0.60 #44462, 0.54 #39782), 0161h5 (0.40 #35101, 0.33 #4681, 0.33 #4501), 043gj (0.40 #35101, 0.33 #23396, 0.32 #14038), 0chsq (0.33 #23396, 0.32 #14038, 0.29 #11699), 01mqnr (0.33 #23396, 0.32 #14038, 0.29 #11699), 0h1q6 (0.33 #23396, 0.32 #14038, 0.29 #11699), 0k525 (0.33 #23396, 0.32 #14038, 0.29 #11699), 0cgzj (0.33 #23396, 0.32 #14038, 0.29 #11699), 019l68 (0.33 #23396, 0.32 #14038, 0.29 #11699) >> Best rule #14039 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 0209hj; 0147sh; 0cwy47; 017gl1; 017gm7; 0168ls; 0k4kk; 0bm2g; 083skw; 019vhk; ... >> query: (?x3783, ?x7232) <- nominated_for(?x484, ?x3783), genre(?x3783, ?x4757), ?x4757 = 06l3bl, film(?x510, ?x3783), film(?x7232, ?x3783) >> conf = 0.60 => this is the best rule for 2 predicted values *> Best rule #11136 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 0m_mm; 01vw8k; 0pd6l; 011yr9; 0kvb6p; *> query: (?x3783, 057bc6m) <- nominated_for(?x2222, ?x3783), genre(?x3783, ?x4757), ?x4757 = 06l3bl, film(?x510, ?x3783), ?x2222 = 0gs96 *> conf = 0.06 ranks of expected_values: 92 EVAL 01wb95 nominated_for! 057bc6m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 71.000 19.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #15122-01m3x5p PRED entity: 01m3x5p PRED relation: award_winner! PRED expected values: 0lyb_ => 100 concepts (96 used for prediction) PRED predicted values (max 10 best out of 299): 02hgm4 (0.48 #431, 0.46 #862, 0.40 #23673), 01by1l (0.24 #544, 0.21 #3560, 0.19 #8293), 025m98 (0.19 #665, 0.07 #6262, 0.05 #3681), 025m8y (0.19 #3878, 0.14 #25399, 0.14 #24967), 0gqwc (0.19 #3878, 0.14 #25399, 0.14 #24967), 094qd5 (0.19 #3878, 0.14 #25399, 0.14 #24967), 02ppm4q (0.19 #3878, 0.14 #25399, 0.14 #24967), 09td7p (0.19 #3878, 0.14 #25399, 0.14 #24967), 0bdwft (0.19 #3878, 0.14 #25399, 0.14 #24967), 099t8j (0.19 #3878, 0.14 #25399, 0.14 #24967) >> Best rule #431 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0l99s; >> query: (?x4184, ?x2561) <- award(?x4184, ?x2561), sibling(?x1583, ?x4184), influenced_by(?x4184, ?x3378) >> conf = 0.48 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01m3x5p award_winner! 0lyb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 96.000 0.477 http://example.org/award/award_category/winners./award/award_honor/award_winner #15121-02v63m PRED entity: 02v63m PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.88 #21, 0.85 #11, 0.84 #16), 07z4p (0.44 #181, 0.02 #135, 0.02 #40), 02nxhr (0.04 #52, 0.04 #37, 0.03 #22), 07c52 (0.03 #108, 0.03 #148, 0.02 #133) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 236 >> proper extension: 0413cff; 09rfh9; >> query: (?x1184, 029j_) <- genre(?x1184, ?x812), featured_film_locations(?x1184, ?x9405), ?x812 = 01jfsb >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v63m film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15120-02kz_ PRED entity: 02kz_ PRED relation: award_winner! PRED expected values: 0ddd9 => 183 concepts (179 used for prediction) PRED predicted values (max 10 best out of 322): 0ddd9 (0.45 #1725, 0.42 #5606, 0.42 #7763), 0g9wd99 (0.45 #1725, 0.42 #5606, 0.42 #7763), 02v1m7 (0.33 #545, 0.25 #2701, 0.20 #3132), 01by1l (0.33 #544, 0.25 #113, 0.19 #5287), 02sp_v (0.33 #592, 0.17 #2748, 0.13 #3179), 02f72_ (0.33 #659, 0.17 #2815, 0.13 #3246), 02f71y (0.33 #612, 0.17 #2768, 0.13 #3199), 0c4z8 (0.33 #503, 0.15 #5246, 0.13 #7403), 01c92g (0.33 #529, 0.08 #2685, 0.08 #5272), 03x3wf (0.25 #65, 0.20 #3083, 0.17 #2652) >> Best rule #1725 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 073v6; 03f47xl; 041_y; >> query: (?x5336, ?x921) <- influenced_by(?x5336, ?x4292), influenced_by(?x117, ?x5336), ?x4292 = 0zm1, award(?x5336, ?x921) >> conf = 0.45 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 02kz_ award_winner! 0ddd9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 179.000 0.455 http://example.org/award/award_category/winners./award/award_honor/award_winner #15119-01wgcvn PRED entity: 01wgcvn PRED relation: award_winner! PRED expected values: 02wzl1d => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 136): 0hr3c8y (0.15 #430, 0.05 #1550, 0.04 #4490), 01c6qp (0.12 #159, 0.11 #299, 0.11 #2679), 01s695 (0.12 #143, 0.10 #2103, 0.10 #2663), 02cg41 (0.12 #265, 0.10 #2225, 0.10 #2785), 05pd94v (0.12 #142, 0.10 #2522, 0.10 #2102), 013b2h (0.12 #2740, 0.12 #2180, 0.11 #2600), 02rjjll (0.12 #2105, 0.11 #2665, 0.10 #985), 0jzphpx (0.11 #319, 0.09 #2139, 0.08 #1159), 092_25 (0.11 #492, 0.05 #772, 0.05 #912), 056878 (0.10 #2132, 0.08 #2692, 0.08 #2552) >> Best rule #430 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 043kzcr; 014g22; 01cwkq; >> query: (?x3756, 0hr3c8y) <- award_nominee(?x1871, ?x3756), award(?x3756, ?x704), ?x1871 = 02bkdn >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #1551 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 167 *> proper extension: 03d9v8; *> query: (?x3756, 02wzl1d) <- languages(?x3756, ?x254), award_winner(?x8347, ?x3756), film(?x3756, ?x857) *> conf = 0.04 ranks of expected_values: 52 EVAL 01wgcvn award_winner! 02wzl1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 86.000 86.000 0.152 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15118-01f2q5 PRED entity: 01f2q5 PRED relation: artist! PRED expected values: 0fb0v 073tm9 => 85 concepts (62 used for prediction) PRED predicted values (max 10 best out of 132): 03rhqg (0.38 #429, 0.25 #1395, 0.23 #1533), 01cl2y (0.27 #444, 0.11 #720, 0.10 #1686), 015_1q (0.26 #1261, 0.23 #985, 0.22 #1123), 017l96 (0.23 #156, 0.15 #432, 0.13 #1812), 06wcbk7 (0.22 #556, 0.20 #280, 0.17 #4), 073tm9 (0.22 #588, 0.20 #312, 0.08 #726), 01clyr (0.19 #723, 0.17 #33, 0.14 #1137), 0fb0v (0.17 #7, 0.15 #421, 0.11 #1387), 01cl0d (0.17 #54, 0.12 #1158, 0.10 #1020), 016ckq (0.17 #43, 0.11 #733, 0.10 #1009) >> Best rule #429 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 09prnq; 01vsy3q; 01wg25j; >> query: (?x11897, 03rhqg) <- artists(?x1572, ?x11897), artist(?x2241, ?x11897), ?x1572 = 06by7, ?x2241 = 02p11jq >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #588 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 01vvyd8; 07pzc; *> query: (?x11897, 073tm9) <- award(?x11897, ?x8705), artists(?x302, ?x11897), ?x8705 = 01c9dd *> conf = 0.22 ranks of expected_values: 6, 8 EVAL 01f2q5 artist! 073tm9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 85.000 62.000 0.385 http://example.org/music/record_label/artist EVAL 01f2q5 artist! 0fb0v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 62.000 0.385 http://example.org/music/record_label/artist #15117-0bfvw2 PRED entity: 0bfvw2 PRED relation: ceremony PRED expected values: 02q690_ => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 136): 02q690_ (0.60 #62, 0.29 #470, 0.22 #5035), 0hn821n (0.60 #126, 0.22 #5035, 0.22 #5172), 0gpjbt (0.48 #1116, 0.36 #2612, 0.32 #2885), 09n4nb (0.47 #1133, 0.35 #2629, 0.32 #2902), 0466p0j (0.46 #1160, 0.35 #2656, 0.31 #2929), 05pd94v (0.46 #1090, 0.33 #2586, 0.31 #2859), 02cg41 (0.46 #1209, 0.35 #2705, 0.31 #2978), 02rjjll (0.46 #1093, 0.34 #2589, 0.31 #2862), 056878 (0.46 #1119, 0.34 #2615, 0.31 #2888), 01c6qp (0.45 #1106, 0.33 #2602, 0.31 #2875) >> Best rule #62 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0bfvd4; 0bdwqv; >> query: (?x375, 02q690_) <- award(?x11577, ?x375), nominated_for(?x375, ?x9222), place_of_birth(?x11577, ?x2680), ?x9222 = 06zsk51 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bfvw2 ceremony 02q690_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.600 http://example.org/award/award_category/winners./award/award_honor/ceremony #15116-01s9vc PRED entity: 01s9vc PRED relation: crewmember PRED expected values: 015wc0 => 131 concepts (116 used for prediction) PRED predicted values (max 10 best out of 35): 02xc1w4 (0.25 #27, 0.07 #215, 0.07 #168), 02q9kqf (0.13 #171, 0.07 #599, 0.07 #218), 095zvfg (0.12 #512, 0.11 #132, 0.07 #795), 0284n42 (0.11 #98, 0.05 #334, 0.05 #1046), 0b79gfg (0.10 #681, 0.07 #823, 0.07 #634), 04ktcgn (0.08 #486, 0.04 #2053, 0.04 #1246), 0c94fn (0.07 #533, 0.05 #2289, 0.05 #437), 04wp63 (0.07 #611, 0.07 #230, 0.06 #752), 03m49ly (0.07 #887, 0.05 #3075, 0.04 #3509), 027y151 (0.07 #228, 0.05 #323, 0.05 #466) >> Best rule #27 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02qr3k8; >> query: (?x10404, 02xc1w4) <- genre(?x10404, ?x812), ?x812 = 01jfsb, language(?x10404, ?x254), film(?x4544, ?x10404), ?x4544 = 01q6bg >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01s9vc crewmember 015wc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 116.000 0.250 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #15115-01n_g9 PRED entity: 01n_g9 PRED relation: institution! PRED expected values: 019v9k => 146 concepts (104 used for prediction) PRED predicted values (max 10 best out of 19): 016t_3 (0.84 #79, 0.82 #156, 0.67 #195), 019v9k (0.78 #358, 0.74 #160, 0.72 #434), 07s6fsf (0.67 #193, 0.53 #135, 0.51 #252), 0bkj86 (0.61 #198, 0.55 #218, 0.52 #82), 013zdg (0.39 #197, 0.32 #256, 0.26 #432), 04zx3q1 (0.37 #214, 0.36 #78, 0.35 #429), 027f2w (0.36 #84, 0.35 #161, 0.32 #435), 0bjrnt (0.33 #1786, 0.29 #1288, 0.29 #1607), 071tyz (0.33 #1786, 0.29 #1288, 0.29 #1607), 01ysy9 (0.33 #1786, 0.29 #1288, 0.29 #1607) >> Best rule #79 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 06pwq; 01w3v; 071_8; 09f2j; >> query: (?x7716, 016t_3) <- institution(?x1368, ?x7716), currency(?x7716, ?x170), major_field_of_study(?x7716, ?x1682), ?x1682 = 02ky346, ?x1368 = 014mlp >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #358 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 93 *> proper extension: 02jyr8; *> query: (?x7716, 019v9k) <- institution(?x3437, ?x7716), institution(?x865, ?x7716), currency(?x7716, ?x170), ?x3437 = 02_xgp2, ?x865 = 02h4rq6 *> conf = 0.78 ranks of expected_values: 2 EVAL 01n_g9 institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 104.000 0.840 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15114-09gkx35 PRED entity: 09gkx35 PRED relation: film! PRED expected values: 03h2d4 => 84 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1107): 040rjq (0.12 #37498, 0.09 #41665, 0.09 #54164), 08qxx9 (0.10 #1521, 0.04 #18185, 0.04 #22351), 0c6qh (0.08 #414, 0.06 #8746, 0.04 #17078), 0c_gcr (0.08 #1647, 0.04 #18311, 0.04 #22477), 01wy5m (0.08 #860, 0.03 #17524, 0.02 #21690), 01nwwl (0.07 #8835, 0.02 #13001, 0.02 #23416), 02gf_l (0.07 #7518, 0.05 #13767, 0.04 #15850), 0h0wc (0.06 #2507, 0.05 #10839, 0.05 #19171), 044qx (0.06 #2816, 0.05 #11148, 0.03 #19480), 0j_c (0.06 #2493, 0.03 #10825, 0.03 #50407) >> Best rule #37498 for best value: >> intensional similarity = 4 >> extensional distance = 238 >> proper extension: 0170xl; >> query: (?x3603, ?x192) <- production_companies(?x3603, ?x9041), nominated_for(?x7147, ?x3603), film_release_distribution_medium(?x3603, ?x81), executive_produced_by(?x3603, ?x192) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #748 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 01d259; 03z9585; *> query: (?x3603, 03h2d4) <- genre(?x3603, ?x812), film_release_region(?x3603, ?x3277), ?x3277 = 06t8v, ?x812 = 01jfsb *> conf = 0.05 ranks of expected_values: 28 EVAL 09gkx35 film! 03h2d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 84.000 56.000 0.122 http://example.org/film/actor/film./film/performance/film #15113-07r1_ PRED entity: 07r1_ PRED relation: group! PRED expected values: 02hnl => 105 concepts (87 used for prediction) PRED predicted values (max 10 best out of 76): 02hnl (0.82 #2007, 0.79 #2955, 0.76 #1231), 018vs (0.72 #700, 0.72 #1216, 0.71 #786), 05r5c (0.38 #695, 0.33 #867, 0.33 #265), 03qjg (0.30 #1250, 0.30 #1508, 0.29 #820), 0l14qv (0.29 #779, 0.25 #2933, 0.25 #3449), 0mkg (0.25 #354, 0.21 #440, 0.11 #1214), 02snj9 (0.25 #399, 0.21 #485, 0.05 #3844), 07gql (0.25 #121, 0.20 #207, 0.14 #1153), 06ncr (0.22 #295, 0.20 #209, 0.17 #381), 0l14j_ (0.20 #222, 0.17 #394, 0.14 #480) >> Best rule #2007 for best value: >> intensional similarity = 5 >> extensional distance = 72 >> proper extension: 04r1t; 02t3ln; 03k3b; 08w4pm; 0jn38; 01shhf; 02cw1m; 0p76z; 0cfgd; 01518s; ... >> query: (?x7086, 02hnl) <- group(?x645, ?x7086), artists(?x1000, ?x7086), ?x1000 = 0xhtw, group(?x645, ?x9638), ?x9638 = 017959 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07r1_ group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 87.000 0.824 http://example.org/music/performance_role/regular_performances./music/group_membership/group #15112-03d49 PRED entity: 03d49 PRED relation: nutrient! PRED expected values: 061_f => 36 concepts (33 used for prediction) PRED predicted values (max 10 best out of 20): 061_f (0.94 #746, 0.93 #683, 0.93 #667), 04zpv (0.92 #776, 0.89 #732, 0.89 #726), 01nkt (0.90 #784, 0.90 #774, 0.89 #602), 0f25w9 (0.89 #25, 0.89 #472, 0.89 #51), 037ls6 (0.89 #25, 0.89 #472, 0.89 #51), 05z55 (0.89 #25, 0.89 #472, 0.89 #51), 0cxn2 (0.89 #25, 0.89 #472, 0.89 #51), 07j87 (0.89 #25, 0.89 #472, 0.89 #51), 0frq6 (0.89 #25, 0.89 #472, 0.89 #51), 0971v (0.89 #25, 0.89 #472, 0.89 #51) >> Best rule #746 for best value: >> intensional similarity = 134 >> extensional distance = 45 >> proper extension: 0466p20; >> query: (?x12868, 061_f) <- nutrient(?x6285, ?x12868), nutrient(?x6191, ?x12868), nutrient(?x6159, ?x12868), nutrient(?x4068, ?x12868), nutrient(?x2701, ?x12868), nutrient(?x1257, ?x12868), ?x2701 = 0hkxq, nutrient(?x1257, ?x13944), nutrient(?x1257, ?x12454), nutrient(?x1257, ?x11758), nutrient(?x1257, ?x11592), nutrient(?x1257, ?x11409), nutrient(?x1257, ?x11270), nutrient(?x1257, ?x10891), nutrient(?x1257, ?x10195), nutrient(?x1257, ?x10098), nutrient(?x1257, ?x9915), nutrient(?x1257, ?x9855), nutrient(?x1257, ?x9619), nutrient(?x1257, ?x9490), nutrient(?x1257, ?x9436), nutrient(?x1257, ?x9426), nutrient(?x1257, ?x9365), nutrient(?x1257, ?x8442), nutrient(?x1257, ?x8413), nutrient(?x1257, ?x8243), nutrient(?x1257, ?x7894), nutrient(?x1257, ?x7720), nutrient(?x1257, ?x7652), nutrient(?x1257, ?x7431), nutrient(?x1257, ?x7364), nutrient(?x1257, ?x7362), nutrient(?x1257, ?x7219), nutrient(?x1257, ?x7135), nutrient(?x1257, ?x6586), nutrient(?x1257, ?x6192), nutrient(?x1257, ?x6033), nutrient(?x1257, ?x6026), nutrient(?x1257, ?x5549), nutrient(?x1257, ?x5526), nutrient(?x1257, ?x5374), nutrient(?x1257, ?x5337), nutrient(?x1257, ?x4069), nutrient(?x1257, ?x3203), nutrient(?x1257, ?x2702), nutrient(?x1257, ?x2018), nutrient(?x1257, ?x1960), nutrient(?x1257, ?x1258), ?x9365 = 04k8n, ?x9915 = 025tkqy, ?x5526 = 09pbb, ?x7364 = 09gvd, ?x6285 = 01645p, ?x6192 = 06jry, ?x5549 = 025s7j4, ?x11409 = 0h1yf, nutrient(?x6191, ?x13126), nutrient(?x6191, ?x12902), nutrient(?x6191, ?x12481), nutrient(?x6191, ?x11784), nutrient(?x6191, ?x10709), nutrient(?x6191, ?x9949), nutrient(?x6191, ?x9840), nutrient(?x6191, ?x9795), nutrient(?x6191, ?x9708), nutrient(?x6191, ?x6286), nutrient(?x6191, ?x6160), nutrient(?x6191, ?x5010), nutrient(?x6191, ?x3469), nutrient(?x6191, ?x3264), ?x9795 = 05v_8y, ?x3264 = 0dcfv, ?x9855 = 0d9t0, ?x7219 = 0h1vg, ?x2702 = 0838f, ?x11758 = 0q01m, ?x5337 = 06x4c, ?x8243 = 014d7f, ?x9840 = 02p0tjr, ?x11592 = 025sf0_, ?x5010 = 0h1vz, ?x13944 = 0f4kp, ?x7720 = 025s7x6, ?x10098 = 0h1_c, ?x10709 = 0h1sz, ?x7135 = 025rsfk, ?x4069 = 0hqw8p_, ?x6586 = 05gh50, ?x1960 = 07hnp, ?x5374 = 025s0zp, ?x6033 = 04zjxcz, ?x10195 = 0hkwr, ?x9619 = 0h1tg, ?x13126 = 02kc_w5, nutrient(?x10612, ?x8442), nutrient(?x9732, ?x8442), nutrient(?x9005, ?x8442), nutrient(?x8298, ?x8442), nutrient(?x3468, ?x8442), nutrient(?x1959, ?x8442), ?x12454 = 025rw19, ?x7362 = 02kc5rj, ?x6286 = 02y_3rf, nutrient(?x9489, ?x9708), nutrient(?x4068, ?x14618), ?x7652 = 025s0s0, ?x9436 = 025sqz8, ?x6026 = 025sf8g, ?x9949 = 02kd0rh, ?x10612 = 0frq6, ?x11270 = 02kc008, ?x7431 = 09gwd, ?x11784 = 07zqy, ?x8413 = 02kc4sf, ?x7894 = 0f4hc, ?x12481 = 027g6p7, ?x9005 = 04zpv, ?x1959 = 0f25w9, ?x14618 = 02y_3rt, ?x2018 = 01sh2, ?x10891 = 0g5gq, ?x6160 = 041r51, ?x8298 = 037ls6, ?x9732 = 05z55, ?x12902 = 0fzjh, ?x3468 = 0cxn2, ?x3203 = 04kl74p, ?x3469 = 0h1zw, nutrient(?x6159, ?x13545), ?x1258 = 0h1wg, ?x9426 = 0h1yy, ?x9490 = 0h1sg, ?x13545 = 01w_3, ?x9489 = 07j87 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03d49 nutrient! 061_f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 33.000 0.936 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #15111-04344j PRED entity: 04344j PRED relation: colors PRED expected values: 083jv => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 20): 01g5v (0.58 #303, 0.31 #103, 0.28 #43), 083jv (0.37 #341, 0.37 #821, 0.36 #381), 019sc (0.18 #767, 0.18 #647, 0.18 #967), 06fvc (0.16 #762, 0.15 #962, 0.15 #342), 036k5h (0.15 #45, 0.10 #105, 0.09 #165), 0jc_p (0.10 #44, 0.10 #24, 0.09 #84), 038hg (0.10 #192, 0.10 #32, 0.09 #352), 04mkbj (0.10 #390, 0.09 #650, 0.09 #830), 03wkwg (0.08 #95, 0.08 #35, 0.07 #75), 01jnf1 (0.08 #11, 0.07 #1243, 0.07 #1121) >> Best rule #303 for best value: >> intensional similarity = 4 >> extensional distance = 207 >> proper extension: 02d9nr; >> query: (?x2970, 01g5v) <- contains(?x94, ?x2970), colors(?x2970, ?x332), colors(?x2959, ?x332), ?x2959 = 01swxv >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #341 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 220 *> proper extension: 01w_sh; *> query: (?x2970, 083jv) <- institution(?x865, ?x2970), colors(?x2970, ?x332), school_type(?x2970, ?x3205), ?x865 = 02h4rq6 *> conf = 0.37 ranks of expected_values: 2 EVAL 04344j colors 083jv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 124.000 0.584 http://example.org/education/educational_institution/colors #15110-01vs_v8 PRED entity: 01vs_v8 PRED relation: vacationer! PRED expected values: 030qb3t 06k5_ => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 87): 05qtj (0.28 #554, 0.19 #1643, 0.18 #796), 03gh4 (0.26 #1652, 0.17 #563, 0.17 #79), 0cv3w (0.25 #539, 0.19 #1628, 0.18 #781), 04jpl (0.11 #493, 0.09 #1582, 0.08 #9), 0b90_r (0.10 #1576, 0.06 #2425, 0.06 #1213), 0160w (0.08 #486, 0.08 #2, 0.05 #3029), 0d1qn (0.08 #54, 0.06 #538, 0.05 #1627), 03rjj (0.08 #4, 0.06 #488, 0.02 #730), 04swx (0.08 #118, 0.03 #602, 0.02 #1691), 0chghy (0.07 #1341, 0.07 #1583, 0.06 #2553) >> Best rule #554 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 01gq0b; >> query: (?x2237, 05qtj) <- spouse(?x5565, ?x2237), award_winner(?x154, ?x2237), vacationer(?x739, ?x2237) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #3061 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 01fwj8; 0b7t3p; *> query: (?x2237, 030qb3t) <- award_nominee(?x959, ?x2237), award_winner(?x2160, ?x2237), celebrity(?x1213, ?x2237) *> conf = 0.03 ranks of expected_values: 57 EVAL 01vs_v8 vacationer! 06k5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 154.000 0.278 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 01vs_v8 vacationer! 030qb3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 154.000 154.000 0.278 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #15109-0jdd PRED entity: 0jdd PRED relation: jurisdiction_of_office! PRED expected values: 0dq3c => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 21): 060bp (0.70 #309, 0.67 #749, 0.66 #199), 0f6c3 (0.60 #381, 0.48 #689, 0.43 #865), 09n5b9 (0.57 #385, 0.43 #429, 0.43 #693), 0fkvn (0.51 #377, 0.41 #685, 0.37 #861), 0pqc5 (0.43 #642, 0.36 #2339, 0.26 #1522), 0dq3c (0.36 #2135, 0.36 #2158, 0.24 #244), 0p5vf (0.36 #2135, 0.36 #2158, 0.21 #100), 09d6p2 (0.36 #2135, 0.36 #2158, 0.20 #30), 01zq91 (0.36 #2135, 0.36 #2158, 0.16 #2203), 0fj45 (0.36 #2135, 0.36 #2158, 0.16 #2203) >> Best rule #309 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 0n3g; >> query: (?x3352, 060bp) <- adjoins(?x6305, ?x3352), exported_to(?x94, ?x3352), adjoins(?x404, ?x6305) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2135 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 305 *> proper extension: 0xn7b; 01cz_1; 0mbf4; *> query: (?x3352, ?x182) <- adjoins(?x4073, ?x3352), jurisdiction_of_office(?x182, ?x4073) *> conf = 0.36 ranks of expected_values: 6 EVAL 0jdd jurisdiction_of_office! 0dq3c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 126.000 126.000 0.704 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #15108-03m_k0 PRED entity: 03m_k0 PRED relation: award_winner! PRED expected values: 09pnw5 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 133): 09pnw5 (0.44 #101, 0.17 #6538, 0.08 #796), 0gx_st (0.17 #6538, 0.16 #731, 0.15 #870), 05c1t6z (0.17 #6538, 0.14 #1265, 0.14 #848), 04n2r9h (0.17 #6538, 0.11 #44, 0.10 #183), 092_25 (0.17 #6538, 0.11 #70, 0.03 #3547), 0bxs_d (0.17 #6538, 0.10 #252, 0.09 #808), 0418154 (0.17 #6538, 0.06 #1357, 0.06 #523), 092c5f (0.17 #6538, 0.04 #3490, 0.04 #2099), 0g55tzk (0.17 #6538, 0.03 #2221, 0.03 #3612), 02q690_ (0.17 #759, 0.15 #481, 0.15 #898) >> Best rule #101 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0140t7; >> query: (?x3058, 09pnw5) <- award_winner(?x1084, ?x3058), award(?x3058, ?x68), ?x1084 = 02yw5r, profession(?x3058, ?x319) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03m_k0 award_winner! 09pnw5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.444 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15107-01vsy95 PRED entity: 01vsy95 PRED relation: location PRED expected values: 01cx_ => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 198): 04f_d (0.41 #29759, 0.41 #32978, 0.39 #33784), 01531 (0.33 #158, 0.03 #7396, 0.03 #962), 01_d4 (0.33 #102, 0.03 #906, 0.03 #1710), 0_vw8 (0.33 #786, 0.03 #1590, 0.03 #2394), 02_286 (0.16 #841, 0.16 #1645, 0.16 #4058), 030qb3t (0.11 #14560, 0.11 #10537, 0.10 #15364), 0cr3d (0.11 #10599, 0.07 #14622, 0.06 #28295), 01n7q (0.06 #867, 0.05 #1671, 0.05 #2475), 04lh6 (0.06 #1240, 0.05 #2044, 0.05 #2848), 0d9jr (0.06 #1073, 0.05 #1877, 0.05 #2681) >> Best rule #29759 for best value: >> intensional similarity = 3 >> extensional distance = 263 >> proper extension: 01syr4; >> query: (?x3374, ?x2017) <- origin(?x3374, ?x2017), nationality(?x3374, ?x94), category(?x3374, ?x134) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #10617 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 029_3; 02_0d2; *> query: (?x3374, 01cx_) <- award_nominee(?x487, ?x3374), student(?x4904, ?x3374), influenced_by(?x3374, ?x120) *> conf = 0.04 ranks of expected_values: 23 EVAL 01vsy95 location 01cx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 131.000 131.000 0.414 http://example.org/people/person/places_lived./people/place_lived/location #15106-02x_h0 PRED entity: 02x_h0 PRED relation: award_winner! PRED expected values: 09n4nb => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 106): 02rjjll (0.25 #5, 0.13 #1697, 0.12 #3671), 09n4nb (0.25 #48, 0.11 #1740, 0.10 #189), 019bk0 (0.25 #16, 0.11 #1708, 0.09 #721), 0466p0j (0.14 #1768, 0.12 #76, 0.10 #3742), 013b2h (0.14 #1772, 0.12 #4592, 0.12 #785), 05pd94v (0.13 #1694, 0.12 #2, 0.12 #3668), 02cg41 (0.12 #126, 0.11 #1818, 0.10 #3792), 01s695 (0.12 #3, 0.11 #3669, 0.10 #708), 056878 (0.12 #32, 0.10 #3698, 0.09 #4544), 0gx1673 (0.12 #120, 0.09 #1812, 0.07 #825) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02l840; 09qr6; 04xrx; 01w7nwm; 01w7nww; 01yzl2; >> query: (?x5479, 02rjjll) <- artist(?x4483, ?x5479), award_nominee(?x1818, ?x5479), ?x1818 = 0770cd >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 02l840; 09qr6; 04xrx; 01w7nwm; 01w7nww; 01yzl2; *> query: (?x5479, 09n4nb) <- artist(?x4483, ?x5479), award_nominee(?x1818, ?x5479), ?x1818 = 0770cd *> conf = 0.25 ranks of expected_values: 2 EVAL 02x_h0 award_winner! 09n4nb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 131.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15105-01vvydl PRED entity: 01vvydl PRED relation: profession PRED expected values: 0n1h => 140 concepts (118 used for prediction) PRED predicted values (max 10 best out of 85): 016z4k (0.52 #1443, 0.50 #1155, 0.49 #1731), 01d_h8 (0.43 #1589, 0.43 #869, 0.42 #3461), 0n1h (0.37 #1739, 0.35 #1451, 0.35 #1163), 03gjzk (0.36 #302, 0.34 #1598, 0.31 #878), 039v1 (0.33 #1186, 0.31 #4787, 0.28 #7959), 01c72t (0.32 #3190, 0.29 #7947, 0.29 #9100), 0dxtg (0.31 #301, 0.29 #1597, 0.28 #589), 02jknp (0.28 #295, 0.28 #583, 0.23 #1303), 0kyk (0.27 #460, 0.18 #3340, 0.18 #2908), 0fnpj (0.25 #2217, 0.18 #4810, 0.14 #3225) >> Best rule #1443 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 0411q; 05cljf; 0lbj1; 01q_ph; 0147dk; 02l840; 01wmxfs; 01vrncs; 016kjs; 07c0j; ... >> query: (?x140, 016z4k) <- participant(?x1410, ?x140), artist(?x2299, ?x140), award_winner(?x1827, ?x140) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1739 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 0lk90; 033wx9; 01wgxtl; 014q2g; 01vw20_; 01w02sy; 039bpc; 01q32bd; 01pfkw; 0c7xjb; ... *> query: (?x140, 0n1h) <- participant(?x1410, ?x140), artist(?x2299, ?x140), award_nominee(?x140, ?x527) *> conf = 0.37 ranks of expected_values: 3 EVAL 01vvydl profession 0n1h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 118.000 0.516 http://example.org/people/person/profession #15104-06s7rd PRED entity: 06s7rd PRED relation: profession PRED expected values: 016z4k => 137 concepts (91 used for prediction) PRED predicted values (max 10 best out of 72): 09jwl (0.74 #611, 0.72 #908, 0.72 #1205), 0nbcg (0.56 #624, 0.54 #179, 0.51 #327), 016z4k (0.51 #299, 0.48 #596, 0.48 #893), 01d_h8 (0.51 #1340, 0.41 #3860, 0.40 #4453), 03gjzk (0.31 #1349, 0.28 #4462, 0.27 #5649), 0dxtg (0.31 #3868, 0.30 #1348, 0.28 #4461), 01c72t (0.30 #6694, 0.29 #913, 0.28 #4917), 039v1 (0.26 #629, 0.24 #6707, 0.24 #4930), 0n1h (0.26 #307, 0.25 #2087, 0.24 #752), 02jknp (0.25 #1342, 0.20 #7, 0.20 #4010) >> Best rule #611 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 037hgm; >> query: (?x8253, 09jwl) <- award_nominee(?x8253, ?x527), currency(?x8253, ?x170), instrumentalists(?x316, ?x8253) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #299 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 01k3qj; 020_4z; *> query: (?x8253, 016z4k) <- artists(?x3319, ?x8253), ?x3319 = 06j6l, languages(?x8253, ?x254) *> conf = 0.51 ranks of expected_values: 3 EVAL 06s7rd profession 016z4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 137.000 91.000 0.742 http://example.org/people/person/profession #15103-0mhdz PRED entity: 0mhdz PRED relation: time_zones PRED expected values: 02lcqs => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 12): 02lcqs (0.93 #123, 0.91 #255, 0.87 #163), 02hcv8 (0.50 #566, 0.50 #539, 0.50 #592), 02llzg (0.22 #306, 0.21 #175, 0.17 #267), 02fqwt (0.18 #642, 0.18 #707, 0.17 #968), 02hczc (0.16 #1555, 0.14 #343, 0.10 #68), 02lcrv (0.16 #1555, 0.02 #178), 03plfd (0.07 #49, 0.06 #312, 0.06 #364), 03bdv (0.06 #360, 0.05 #334, 0.04 #438), 042g7t (0.05 #77, 0.04 #182, 0.03 #300), 05jphn (0.05 #79, 0.02 #157, 0.02 #184) >> Best rule #123 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 0l2l_; 0l2hf; 0l380; 0l34j; 0l2vz; 0l2v0; 0bxqq; 0l2lk; 0kq39; 0kpzy; ... >> query: (?x12143, 02lcqs) <- contains(?x1227, ?x12143), source(?x12143, ?x958), ?x958 = 0jbk9, ?x1227 = 01n7q, adjoins(?x6703, ?x12143) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mhdz time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.932 http://example.org/location/location/time_zones #15102-093h7p PRED entity: 093h7p PRED relation: film PRED expected values: 0jjy0 09dv8h => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1590): 0dq626 (0.50 #1631, 0.17 #3220, 0.12 #7987), 01_1pv (0.50 #1905, 0.17 #3494, 0.12 #8261), 0639bg (0.50 #2155, 0.12 #42911, 0.12 #42910), 027j9wd (0.50 #2512, 0.12 #42911, 0.12 #42910), 02bj22 (0.50 #2948, 0.12 #42911, 0.12 #42910), 0407yj_ (0.50 #2015, 0.12 #42911, 0.12 #42910), 0b6l1st (0.33 #1122, 0.25 #4300, 0.25 #2711), 035xwd (0.33 #100, 0.25 #3278, 0.19 #8045), 03mh_tp (0.33 #448, 0.25 #2037, 0.17 #3626), 0g0x9c (0.33 #1212, 0.25 #2801, 0.17 #4390) >> Best rule #1631 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03xq0f; 01795t; >> query: (?x10258, 0dq626) <- film(?x10258, ?x7757), film(?x10258, ?x5513), language(?x7757, ?x254), film(?x338, ?x7757), ?x5513 = 0d4htf >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6502 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 0c_j5d; 0kx4m; 0gsg7; 0kk9v; 04gvyp; 04rcl7; 02swsm; 02w_l9; *> query: (?x10258, 0jjy0) <- child(?x5636, ?x10258), child(?x3920, ?x10258), ?x3920 = 09b3v, award(?x5636, ?x3911), ?x3911 = 02x1z2s *> conf = 0.12 ranks of expected_values: 243, 371 EVAL 093h7p film 09dv8h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 64.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 093h7p film 0jjy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 64.000 64.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #15101-01nds PRED entity: 01nds PRED relation: industry PRED expected values: 01mw1 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 43): 01mw1 (0.80 #894, 0.74 #283, 0.71 #659), 02vxn (0.27 #2446, 0.23 #3198, 0.22 #3434), 03qh03g (0.18 #757, 0.16 #851, 0.16 #804), 01mf0 (0.17 #3244, 0.07 #1017, 0.07 #3527), 019z7b (0.17 #3244, 0.07 #3527, 0.04 #855), 06mbny (0.12 #216, 0.06 #263, 0.02 #592), 02jjt (0.12 #854, 0.12 #807, 0.12 #760), 04rlf (0.11 #860, 0.11 #813, 0.10 #766), 029g_vk (0.10 #857, 0.09 #1092, 0.09 #763), 0hz28 (0.09 #781, 0.08 #875, 0.08 #828) >> Best rule #894 for best value: >> intensional similarity = 9 >> extensional distance = 73 >> proper extension: 05925; 01tlrp; 01qvcr; >> query: (?x11304, 01mw1) <- industry(?x11304, ?x10022), industry(?x14420, ?x10022), industry(?x13935, ?x10022), industry(?x11273, ?x10022), industry(?x4878, ?x10022), ?x11273 = 027lf1, ?x13935 = 01scmq, ?x4878 = 01jx9, ?x14420 = 01yf92 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nds industry 01mw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.800 http://example.org/business/business_operation/industry #15100-01b65l PRED entity: 01b65l PRED relation: honored_for! PRED expected values: 0jt3qpk => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 86): 0jt3qpk (0.43 #155, 0.38 #276, 0.31 #639), 0gvstc3 (0.28 #995, 0.28 #1237, 0.27 #1358), 05c1t6z (0.26 #1100, 0.26 #3157, 0.24 #3399), 02q690_ (0.25 #3200, 0.24 #3442, 0.22 #2958), 03nnm4t (0.24 #1152, 0.21 #3209, 0.20 #3451), 0lp_cd3 (0.24 #1106, 0.19 #985, 0.17 #864), 0gx_st (0.13 #2934, 0.12 #3781, 0.11 #1119), 09pj68 (0.13 #937, 0.12 #1058, 0.11 #1179), 0hr3c8y (0.12 #974, 0.11 #1095, 0.10 #853), 03gwpw2 (0.11 #4482, 0.06 #2909, 0.05 #3151) >> Best rule #155 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0phrl; 0gj50; 01y6dz; >> query: (?x4114, 0jt3qpk) <- nominated_for(?x6853, ?x4114), nominated_for(?x2720, ?x4114), ?x6853 = 02p_04b, country_of_origin(?x4114, ?x94), award(?x415, ?x2720) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01b65l honored_for! 0jt3qpk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.429 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15099-06nr2h PRED entity: 06nr2h PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #27, 0.83 #65, 0.83 #87), 07c52 (0.08 #13, 0.08 #8, 0.03 #264), 07z4p (0.08 #10, 0.02 #266, 0.02 #250), 02nxhr (0.04 #55, 0.03 #366, 0.03 #236) >> Best rule #27 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 02ppg1r; 016z43; >> query: (?x4396, 029j_) <- genre(?x4396, ?x6674), film(?x879, ?x4396), nominated_for(?x375, ?x4396), award(?x4396, ?x3247), ?x6674 = 01t_vv >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06nr2h film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.836 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15098-01vsy7t PRED entity: 01vsy7t PRED relation: film PRED expected values: 01kjr0 => 118 concepts (109 used for prediction) PRED predicted values (max 10 best out of 516): 01d259 (0.14 #2775, 0.12 #4563, 0.11 #6351), 01lbcqx (0.12 #5024, 0.11 #6812, 0.10 #8600), 0f42nz (0.12 #4483, 0.11 #6271, 0.10 #8059), 0hv4t (0.12 #4756, 0.11 #6544, 0.10 #8332), 04vvh9 (0.12 #4175, 0.11 #5963, 0.10 #7751), 03m8y5 (0.10 #7560, 0.01 #21866, 0.01 #45111), 04zyhx (0.10 #7373, 0.01 #21679), 01jnc_ (0.10 #21237, 0.05 #15873, 0.04 #49846), 03kx49 (0.06 #15646, 0.02 #38891, 0.02 #26375), 07bzz7 (0.05 #13406, 0.05 #18770, 0.04 #20558) >> Best rule #2775 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 041rx; >> query: (?x4620, 01d259) <- split_to(?x7027, ?x4620), artists(?x4910, ?x7027) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #20757 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 92 *> proper extension: 01vs14j; 02qx69; 02bh9; 01y0y6; 016yzz; 02nfjp; 015p3p; 04bgy; 02fybl; 017l4; ... *> query: (?x4620, 01kjr0) <- gender(?x4620, ?x231), role(?x4620, ?x227), film(?x4620, ?x2644) *> conf = 0.02 ranks of expected_values: 70 EVAL 01vsy7t film 01kjr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 118.000 109.000 0.143 http://example.org/film/actor/film./film/performance/film #15097-05sfs PRED entity: 05sfs PRED relation: religion! PRED expected values: 016lh0 => 42 concepts (32 used for prediction) PRED predicted values (max 10 best out of 3712): 0mb5x (0.40 #3868, 0.33 #16580, 0.33 #12343), 04rfq (0.40 #3150, 0.33 #6327, 0.33 #2092), 0jcx (0.40 #3428, 0.33 #10842, 0.30 #17199), 0177g (0.40 #4079, 0.33 #1963, 0.22 #16791), 01chc7 (0.40 #3419, 0.33 #1303, 0.22 #16131), 040_9 (0.40 #3445, 0.33 #1329, 0.22 #16157), 099bk (0.40 #3490, 0.33 #1374, 0.22 #16202), 015p37 (0.40 #4073, 0.33 #1957, 0.22 #16785), 02kz_ (0.40 #3614, 0.33 #1498, 0.22 #16326), 014z8v (0.40 #3504, 0.33 #1388, 0.22 #16216) >> Best rule #3868 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 0kq2; >> query: (?x962, 0mb5x) <- religion(?x11396, ?x962), religion(?x4631, ?x962), nationality(?x11396, ?x512), participant(?x2194, ?x4631), award(?x4631, ?x401), ?x401 = 05zr6wv, award_winner(?x400, ?x4631), film(?x4631, ?x437), people(?x7322, ?x11396), award_nominee(?x91, ?x4631) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #4669 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 0flw86; *> query: (?x962, 016lh0) <- religion(?x4164, ?x962), religion(?x4105, ?x962), religion(?x3634, ?x962), religion(?x335, ?x962), religion(?x744, ?x962), district_represented(?x176, ?x4105), contains(?x4105, ?x2680), ?x3634 = 07b_l, religion(?x4105, ?x2591), country(?x4105, ?x94), ?x335 = 059rby, administrative_area_type(?x4164, ?x2792), ?x2591 = 0631_ *> conf = 0.17 ranks of expected_values: 606 EVAL 05sfs religion! 016lh0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 32.000 0.400 http://example.org/people/person/religion #15096-020hh3 PRED entity: 020hh3 PRED relation: profession PRED expected values: 01c72t => 154 concepts (107 used for prediction) PRED predicted values (max 10 best out of 75): 016z4k (0.68 #729, 0.56 #3, 0.55 #2616), 039v1 (0.48 #1196, 0.48 #1631, 0.45 #4681), 01c72t (0.46 #602, 0.38 #14701, 0.36 #1184), 01d_h8 (0.37 #7411, 0.35 #8719, 0.35 #6831), 0n1h (0.36 #737, 0.33 #1464, 0.33 #1319), 0fnpj (0.33 #57, 0.23 #783, 0.20 #347), 0dxtg (0.33 #3645, 0.33 #3497, 0.30 #4807), 02jknp (0.32 #9733, 0.21 #4946, 0.18 #7413), 03gjzk (0.27 #7420, 0.26 #8438, 0.26 #6840), 04f2zj (0.23 #674, 0.14 #819, 0.13 #1981) >> Best rule #729 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 06w2sn5; 02bc74; >> query: (?x8640, 016z4k) <- participant(?x8640, ?x1208), instrumentalists(?x1750, ?x8640), ?x1750 = 02hnl, artists(?x1000, ?x8640) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #602 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 01vw20_; 07g2v; 01wkmgb; *> query: (?x8640, 01c72t) <- participant(?x8640, ?x1208), type_of_union(?x8640, ?x566), artists(?x2249, ?x8640), profession(?x8640, ?x131), ?x2249 = 03lty *> conf = 0.46 ranks of expected_values: 3 EVAL 020hh3 profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 154.000 107.000 0.682 http://example.org/people/person/profession #15095-0jqp3 PRED entity: 0jqp3 PRED relation: film_release_region PRED expected values: 03_3d 02_286 05l64 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 115): 05r4w (0.81 #1970, 0.76 #2, 0.70 #330), 059j2 (0.79 #2005, 0.58 #365, 0.48 #37), 03_3d (0.76 #1975, 0.66 #335, 0.62 #7), 07ssc (0.76 #1987, 0.54 #347, 0.48 #19), 0345h (0.75 #2007, 0.48 #39, 0.30 #367), 015fr (0.70 #1989, 0.38 #21, 0.27 #349), 01znc_ (0.67 #2016, 0.52 #48, 0.39 #376), 05b4w (0.66 #2041, 0.39 #401, 0.33 #73), 0154j (0.66 #1973, 0.48 #5, 0.24 #333), 05qhw (0.64 #1985, 0.21 #7898, 0.19 #6748) >> Best rule #1970 for best value: >> intensional similarity = 3 >> extensional distance = 223 >> proper extension: 014lc_; 0ds35l9; 0g56t9t; 028_yv; 02vp1f_; 01gc7; 011yrp; 07gp9; 0ds3t5x; 0gtv7pk; ... >> query: (?x1069, 05r4w) <- film_release_region(?x1069, ?x390), nominated_for(?x112, ?x1069), ?x390 = 0chghy >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1975 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 223 *> proper extension: 014lc_; 0ds35l9; 0g56t9t; 028_yv; 02vp1f_; 01gc7; 011yrp; 07gp9; 0ds3t5x; 0gtv7pk; ... *> query: (?x1069, 03_3d) <- film_release_region(?x1069, ?x390), nominated_for(?x112, ?x1069), ?x390 = 0chghy *> conf = 0.76 ranks of expected_values: 3, 39 EVAL 0jqp3 film_release_region 05l64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 71.000 0.809 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jqp3 film_release_region 02_286 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 71.000 71.000 0.809 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jqp3 film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 71.000 0.809 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15094-04yt7 PRED entity: 04yt7 PRED relation: profession PRED expected values: 0cbd2 0dxtg 02hrh1q => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 73): 02hrh1q (0.90 #4459, 0.89 #9347, 0.88 #7126), 09jwl (0.80 #909, 0.45 #1353, 0.45 #1205), 0nbcg (0.55 #922, 0.39 #1218, 0.34 #773), 0dz3r (0.47 #892, 0.36 #594, 0.32 #1188), 016z4k (0.38 #894, 0.33 #1190, 0.32 #1338), 039v1 (0.37 #927, 0.17 #333, 0.15 #3296), 0dxtg (0.36 #162, 0.35 #458, 0.31 #2385), 01d_h8 (0.35 #302, 0.34 #3117, 0.34 #1785), 01c72t (0.33 #765, 0.32 #616, 0.25 #10813), 0np9r (0.33 #21, 0.22 #465, 0.15 #9353) >> Best rule #4459 for best value: >> intensional similarity = 3 >> extensional distance = 1190 >> proper extension: 01sl1q; 0184jc; 04bdxl; 06qgvf; 016qtt; 01vvydl; 012d40; 01k7d9; 0337vz; 07s3vqk; ... >> query: (?x4297, 02hrh1q) <- film(?x4297, ?x582), profession(?x4297, ?x1146), award_nominee(?x4987, ?x4297) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 14 EVAL 04yt7 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.900 http://example.org/people/person/profession EVAL 04yt7 profession 0dxtg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 88.000 0.900 http://example.org/people/person/profession EVAL 04yt7 profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 88.000 88.000 0.900 http://example.org/people/person/profession #15093-06kxk2 PRED entity: 06kxk2 PRED relation: profession PRED expected values: 02jknp => 126 concepts (57 used for prediction) PRED predicted values (max 10 best out of 62): 02hrh1q (0.69 #5342, 0.66 #8156, 0.63 #6527), 02jknp (0.68 #303, 0.63 #4744, 0.63 #4448), 03gjzk (0.42 #1642, 0.42 #1050, 0.40 #1790), 0cbd2 (0.38 #154, 0.30 #1634, 0.30 #1782), 01c72t (0.29 #23, 0.13 #3427, 0.13 #4612), 0kyk (0.20 #177, 0.17 #769, 0.13 #7432), 02krf9 (0.18 #1506, 0.18 #1802, 0.18 #4467), 018gz8 (0.18 #7419, 0.17 #7863, 0.17 #8307), 02hv44_ (0.18 #205, 0.16 #797, 0.11 #1241), 0nbcg (0.14 #31, 0.11 #1955, 0.11 #2991) >> Best rule #5342 for best value: >> intensional similarity = 4 >> extensional distance = 460 >> proper extension: 02yy8; 026c0p; 02m30v; >> query: (?x7130, 02hrh1q) <- profession(?x7130, ?x987), people(?x11307, ?x7130), profession(?x4036, ?x987), ?x4036 = 04m_zp >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #303 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 85 *> proper extension: 09ftwr; 02bfxb; 0hw1j; 03xp8d5; 03flwk; 0gn30; 01vb6z; 06pjs; 06l6nj; *> query: (?x7130, 02jknp) <- type_of_union(?x7130, ?x566), profession(?x7130, ?x319), award(?x7130, ?x1862), ?x1862 = 0gr51 *> conf = 0.68 ranks of expected_values: 2 EVAL 06kxk2 profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 57.000 0.693 http://example.org/people/person/profession #15092-04jwp PRED entity: 04jwp PRED relation: influenced_by! PRED expected values: 03_87 => 139 concepts (39 used for prediction) PRED predicted values (max 10 best out of 451): 06c44 (0.55 #13853, 0.03 #2816, 0.02 #6917), 0b78hw (0.22 #167, 0.17 #1191, 0.07 #17959), 0lrh (0.21 #3178, 0.16 #4204, 0.11 #6767), 03g5jw (0.21 #3117, 0.16 #4143, 0.08 #11327), 041mt (0.19 #3150, 0.15 #4176, 0.09 #5714), 0113sg (0.19 #12823, 0.18 #13854, 0.17 #7175), 0hqgp (0.19 #12823, 0.18 #13854, 0.12 #3587), 040db (0.18 #6738, 0.15 #3149, 0.14 #13415), 02wh0 (0.17 #961, 0.14 #1985, 0.13 #7111), 048cl (0.17 #811, 0.14 #1835, 0.09 #6961) >> Best rule #13853 for best value: >> intensional similarity = 5 >> extensional distance = 167 >> proper extension: 07scx; >> query: (?x5912, ?x6204) <- influenced_by(?x13248, ?x5912), influenced_by(?x2994, ?x5912), peers(?x11460, ?x13248), peers(?x6204, ?x11460), influenced_by(?x1236, ?x2994) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #17960 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 234 *> proper extension: 07c0j; 0bk1p; 04sd0; *> query: (?x5912, ?x5434) <- influenced_by(?x8659, ?x5912), influenced_by(?x5912, ?x1279), influenced_by(?x5434, ?x8659), influenced_by(?x118, ?x5434) *> conf = 0.13 ranks of expected_values: 19 EVAL 04jwp influenced_by! 03_87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 139.000 39.000 0.549 http://example.org/influence/influence_node/influenced_by #15091-0zcbl PRED entity: 0zcbl PRED relation: award_winner! PRED expected values: 03nnm4t => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 107): 0275n3y (0.12 #7832, 0.08 #70, 0.04 #880), 027hjff (0.12 #7832, 0.04 #862, 0.04 #322), 02q690_ (0.12 #7832, 0.03 #4380, 0.03 #2490), 09pj68 (0.12 #7832, 0.03 #235, 0.03 #910), 0fqpc7d (0.12 #7832, 0.03 #168, 0.02 #843), 09bymc (0.12 #7832, 0.02 #6325, 0.02 #925), 09p3h7 (0.08 #66, 0.03 #201, 0.03 #876), 0bxs_d (0.08 #109, 0.03 #244, 0.02 #2539), 0hn821n (0.08 #125, 0.03 #260, 0.01 #5390), 09q_6t (0.08 #8, 0.03 #818, 0.02 #953) >> Best rule #7832 for best value: >> intensional similarity = 3 >> extensional distance = 1424 >> proper extension: 01w92; 04glx0; 026v1z; >> query: (?x6980, ?x2245) <- award_nominee(?x8066, ?x6980), award_winner(?x451, ?x6980), award_winner(?x2245, ?x8066) >> conf = 0.12 => this is the best rule for 6 predicted values *> Best rule #204 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 04ns3gy; *> query: (?x6980, 03nnm4t) <- award_nominee(?x8066, ?x6980), student(?x122, ?x6980), ?x122 = 08815 *> conf = 0.03 ranks of expected_values: 41 EVAL 0zcbl award_winner! 03nnm4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 107.000 107.000 0.116 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15090-0cdbq PRED entity: 0cdbq PRED relation: official_language PRED expected values: 06b_j => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 44): 06b_j (0.33 #281, 0.25 #237, 0.25 #193), 02h40lc (0.31 #970, 0.29 #1014, 0.28 #1146), 064_8sq (0.25 #104, 0.17 #280, 0.15 #984), 06mp7 (0.25 #143, 0.17 #451, 0.11 #627), 032f6 (0.25 #172, 0.17 #436, 0.11 #656), 01gp_d (0.25 #160, 0.17 #468, 0.11 #644), 06nm1 (0.19 #2825, 0.19 #2913, 0.17 #3925), 02bjrlw (0.17 #353, 0.08 #925, 0.07 #3258), 04306rv (0.15 #929, 0.12 #3482, 0.11 #3350), 0jzc (0.15 #2522, 0.15 #2610, 0.12 #3007) >> Best rule #281 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 047lj; >> query: (?x4492, 06b_j) <- locations(?x7241, ?x4492), partially_contains(?x6956, ?x4492), partially_contains(?x455, ?x4492), ?x455 = 02j9z, participating_countries(?x2553, ?x4492), countries_within(?x6956, ?x252) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cdbq official_language 06b_j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.333 http://example.org/location/country/official_language #15089-04hk0w PRED entity: 04hk0w PRED relation: currency PRED expected values: 09nqf => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.84 #64, 0.84 #92, 0.82 #50), 02l6h (0.03 #39, 0.01 #270, 0.01 #102), 01nv4h (0.03 #86, 0.03 #226, 0.03 #282), 02gsvk (0.01 #132, 0.01 #202, 0.01 #209), 088n7 (0.01 #105) >> Best rule #64 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 016tvq; >> query: (?x12964, 09nqf) <- award_winner(?x12964, ?x10295), gender(?x10295, ?x231), profession(?x10295, ?x319), artists(?x4910, ?x10295), ?x319 = 01d_h8 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04hk0w currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.840 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #15088-02x8z_ PRED entity: 02x8z_ PRED relation: artists! PRED expected values: 016jhr => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 198): 01lyv (0.65 #345, 0.21 #3773, 0.20 #3150), 064t9 (0.46 #635, 0.40 #6871, 0.38 #3129), 0xhtw (0.33 #2198, 0.31 #2510, 0.19 #9369), 05bt6j (0.31 #43, 0.24 #2537, 0.24 #1289), 06j6l (0.31 #1606, 0.22 #6907, 0.21 #1294), 02w4v (0.30 #356, 0.16 #9042, 0.15 #2849), 016clz (0.26 #1873, 0.25 #627, 0.25 #2186), 017_qw (0.25 #997, 0.11 #1932, 0.10 #11282), 0mhfr (0.25 #335, 0.16 #9042, 0.11 #646), 0gywn (0.24 #1616, 0.19 #58, 0.16 #3175) >> Best rule #345 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0249kn; 0x3b7; 01jkqfz; >> query: (?x4528, 01lyv) <- award_nominee(?x1128, ?x4528), award(?x4528, ?x341), ?x341 = 026mg3 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #1882 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 165 *> proper extension: 0459z; *> query: (?x4528, 016jhr) <- instrumentalists(?x316, ?x4528), place_of_birth(?x4528, ?x10946), ?x316 = 05r5c *> conf = 0.02 ranks of expected_values: 112 EVAL 02x8z_ artists! 016jhr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 96.000 96.000 0.650 http://example.org/music/genre/artists #15087-03yl2t PRED entity: 03yl2t PRED relation: current_club PRED expected values: 0xbm => 93 concepts (71 used for prediction) PRED predicted values (max 10 best out of 186): 04ltf (0.43 #350, 0.24 #777, 0.20 #1203), 0138mv (0.33 #215, 0.25 #499, 0.17 #1210), 0kqbh (0.33 #275, 0.21 #417, 0.12 #559), 03gr14 (0.33 #114, 0.14 #398, 0.08 #825), 013xh7 (0.33 #117, 0.07 #401, 0.04 #828), 049msk (0.33 #109, 0.07 #393, 0.04 #820), 048gd_ (0.33 #69, 0.07 #353, 0.04 #780), 0cgwt8 (0.33 #38, 0.07 #322, 0.04 #749), 07r78j (0.33 #9, 0.07 #293, 0.04 #720), 0xbm (0.31 #442, 0.29 #300, 0.24 #727) >> Best rule #350 for best value: >> intensional similarity = 12 >> extensional distance = 12 >> proper extension: 035qgm; 02bh_v; 03dj48; 02pp1; 01352_; >> query: (?x1598, 04ltf) <- current_club(?x1598, ?x13115), current_club(?x1598, ?x3216), position(?x13115, ?x530), position(?x13115, ?x63), position(?x13115, ?x60), team(?x11781, ?x3216), ?x63 = 02sdk9v, ?x60 = 02nzb8, ?x530 = 02_j1w, sport(?x1598, ?x471), teams(?x6974, ?x1598), ?x11781 = 02y0dd >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #442 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 14 *> proper extension: 01l3vx; 02s2lg; 01_lhg; 02rqxc; 03_qj1; 03zrc_; 01l3wr; 03ylxn; 032jlh; 02w64f; *> query: (?x1598, 0xbm) <- current_club(?x1598, ?x13115), current_club(?x1598, ?x8750), current_club(?x1598, ?x3216), current_club(?x1598, ?x1297), position(?x13115, ?x530), position(?x13115, ?x63), position(?x13115, ?x60), team(?x2666, ?x3216), ?x63 = 02sdk9v, ?x60 = 02nzb8, ?x530 = 02_j1w, team(?x1142, ?x1598), colors(?x1297, ?x663), sport(?x8750, ?x471) *> conf = 0.31 ranks of expected_values: 10 EVAL 03yl2t current_club 0xbm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 93.000 71.000 0.429 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #15086-01pj7 PRED entity: 01pj7 PRED relation: partially_contains PRED expected values: 026zt => 132 concepts (90 used for prediction) PRED predicted values (max 10 best out of 30): 026zt (0.33 #24, 0.30 #64, 0.28 #2705), 0lcd (0.28 #2705, 0.19 #95, 0.16 #217), 0lm0n (0.13 #2689, 0.11 #2405, 0.09 #3171), 0cgm9 (0.11 #36, 0.07 #674, 0.05 #433), 09glw (0.10 #59, 0.07 #1022, 0.07 #1062), 0k3nk (0.08 #174, 0.08 #215, 0.08 #490), 06c6l (0.08 #191, 0.08 #232, 0.07 #389), 0p2n (0.08 #313, 0.07 #1893, 0.06 #2090), 065ky (0.06 #111, 0.06 #152, 0.04 #233), 05g56 (0.06 #109, 0.06 #150, 0.04 #231) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02psqkz; >> query: (?x1790, 026zt) <- combatants(?x1790, ?x8687), combatants(?x1790, ?x1892), ?x1892 = 02vzc, ?x8687 = 059z0 >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pj7 partially_contains 026zt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 90.000 0.333 http://example.org/location/location/partially_contains #15085-03hpkp PRED entity: 03hpkp PRED relation: major_field_of_study PRED expected values: 062z7 => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 114): 02j62 (0.51 #2368, 0.41 #14443, 0.38 #1752), 02lp1 (0.49 #2350, 0.41 #1734, 0.38 #4566), 03g3w (0.40 #2364, 0.33 #2487, 0.33 #1748), 062z7 (0.38 #2365, 0.32 #1749, 0.30 #3104), 05qjt (0.35 #2346, 0.29 #1730, 0.25 #5918), 02_7t (0.34 #1787, 0.28 #3142, 0.27 #3635), 01lj9 (0.32 #2378, 0.26 #1762, 0.23 #3117), 0_jm (0.31 #4736, 0.30 #4983, 0.29 #3874), 05qfh (0.30 #2374, 0.26 #1758, 0.26 #5946), 037mh8 (0.29 #2406, 0.22 #1790, 0.18 #2529) >> Best rule #2368 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 08815; 07wrz; 02dq8f; 01t38b; 02y9bj; 0gk7z; 0gl6x; 0mbwf; >> query: (?x10303, 02j62) <- citytown(?x10303, ?x9846), student(?x10303, ?x4528), major_field_of_study(?x10303, ?x1668), ?x1668 = 01mkq >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #2365 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 08815; 07wrz; 02dq8f; 01t38b; 02y9bj; 0gk7z; 0gl6x; 0mbwf; *> query: (?x10303, 062z7) <- citytown(?x10303, ?x9846), student(?x10303, ?x4528), major_field_of_study(?x10303, ?x1668), ?x1668 = 01mkq *> conf = 0.38 ranks of expected_values: 4 EVAL 03hpkp major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 201.000 201.000 0.506 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #15084-06bnz PRED entity: 06bnz PRED relation: film_release_region! PRED expected values: 01c22t 0gmcwlb 017gm7 07qg8v 04n52p6 08052t3 047svrl 0bmc4cm 05c26ss 0gyh2wm 0gtxj2q 06zn2v2 0hgnl3t 0gtt5fb 03mgx6z 0g9zljd 0gmd3k7 089j8p 0dgrwqr 02825nf 0ds2l81 05zvzf3 0fpgp26 => 138 concepts (129 used for prediction) PRED predicted values (max 10 best out of 1085): 017gm7 (0.83 #16378, 0.83 #10958, 0.82 #9874), 0dtfn (0.80 #10957, 0.78 #16377, 0.77 #18545), 0gmcwlb (0.80 #10955, 0.76 #9871, 0.76 #16375), 0fpgp26 (0.79 #19351, 0.73 #17183, 0.69 #11763), 067ghz (0.74 #10358, 0.71 #16862, 0.69 #11442), 03cw411 (0.71 #11199, 0.68 #16619, 0.68 #10115), 08052t3 (0.71 #11060, 0.68 #16480, 0.68 #9976), 05q4y12 (0.69 #2418, 0.66 #11090, 0.62 #10006), 0hgnl3t (0.69 #2614, 0.65 #10202, 0.63 #16706), 03mgx6z (0.69 #11437, 0.68 #16857, 0.68 #10353) >> Best rule #16378 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 034cm; >> query: (?x1603, 017gm7) <- service_location(?x555, ?x1603), contains(?x1603, ?x992), olympics(?x1603, ?x418) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 7, 9, 10, 11, 12, 19, 20, 21, 23, 24, 26, 28, 29, 36, 37, 45, 47, 57, 73, 112 EVAL 06bnz film_release_region! 0fpgp26 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 0ds2l81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 0dgrwqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 089j8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 0gmd3k7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 0g9zljd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 03mgx6z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 0gtt5fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 0hgnl3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 06zn2v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 0gtxj2q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 0gyh2wm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 0bmc4cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 047svrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 08052t3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 04n52p6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 07qg8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 017gm7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 0gmcwlb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06bnz film_release_region! 01c22t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 129.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15083-0kp2_ PRED entity: 0kp2_ PRED relation: influenced_by PRED expected values: 0ff2k => 172 concepts (74 used for prediction) PRED predicted values (max 10 best out of 329): 03_87 (0.38 #2800, 0.30 #11900, 0.15 #4099), 01v9724 (0.30 #4074, 0.25 #11875, 0.14 #3208), 032l1 (0.26 #3987, 0.25 #2688, 0.17 #11788), 081k8 (0.26 #4052, 0.25 #2753, 0.17 #11853), 058vp (0.25 #2782, 0.16 #11882, 0.12 #20799), 084w8 (0.19 #2601, 0.15 #3900, 0.10 #11701), 02kz_ (0.19 #2768, 0.12 #6234, 0.11 #4067), 073v6 (0.19 #2686, 0.06 #20798, 0.05 #21233), 040_t (0.19 #2795, 0.06 #20798, 0.05 #21233), 0379s (0.19 #3976, 0.12 #2677, 0.12 #20799) >> Best rule #2800 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01h2_6; >> query: (?x6795, 03_87) <- student(?x1103, ?x6795), religion(?x6795, ?x2694), influenced_by(?x6795, ?x4915), ?x4915 = 03f0324 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #24666 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 276 *> proper extension: 01xdf5; 0p_pd; 01zkxv; 01n5309; 0yfp; 0m77m; 045bg; 02p21g; 0b_c7; 034np8; ... *> query: (?x6795, 0ff2k) <- gender(?x6795, ?x231), influenced_by(?x6795, ?x4072), profession(?x6795, ?x987), award(?x6795, ?x9629) *> conf = 0.01 ranks of expected_values: 328 EVAL 0kp2_ influenced_by 0ff2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 172.000 74.000 0.375 http://example.org/influence/influence_node/influenced_by #15082-04mg6l PRED entity: 04mg6l PRED relation: film PRED expected values: 01r97z => 68 concepts (38 used for prediction) PRED predicted values (max 10 best out of 145): 0cz_ym (0.37 #44755, 0.37 #42964, 0.34 #62657), 0h95927 (0.10 #1327, 0.03 #53706, 0.03 #16112), 0b6tzs (0.10 #140, 0.03 #53706, 0.03 #21483), 07tlfx (0.10 #1610, 0.03 #16112, 0.03 #21483), 0466s8n (0.10 #1637), 093dqjy (0.05 #610, 0.03 #53706, 0.03 #16112), 02x2jl_ (0.05 #1755, 0.03 #53706, 0.03 #21483), 03m5y9p (0.05 #1421, 0.03 #53706, 0.03 #21483), 0btbyn (0.05 #662, 0.03 #53706, 0.03 #21483), 050gkf (0.05 #311, 0.03 #53706, 0.03 #21483) >> Best rule #44755 for best value: >> intensional similarity = 3 >> extensional distance = 1950 >> proper extension: 01nqfh_; 01m7f5r; 0q1lp; 03c9pqt; >> query: (?x5610, ?x1877) <- profession(?x5610, ?x1032), nationality(?x5610, ?x94), nominated_for(?x5610, ?x1877) >> conf = 0.37 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04mg6l film 01r97z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 38.000 0.372 http://example.org/film/actor/film./film/performance/film #15081-02_286 PRED entity: 02_286 PRED relation: location_of_ceremony! PRED expected values: 04shbh 0f7hc => 197 concepts (183 used for prediction) PRED predicted values (max 10 best out of 665): 02m30v (0.20 #1430, 0.20 #1191, 0.14 #2384), 01vzxld (0.11 #922, 0.08 #1876, 0.08 #1638), 0fgg4 (0.11 #828, 0.08 #1782, 0.08 #1544), 01chc7 (0.11 #786, 0.08 #1740, 0.08 #1502), 01cwcr (0.11 #878, 0.08 #1832, 0.06 #3265), 018yj6 (0.11 #901, 0.08 #1855, 0.06 #3288), 01htxr (0.11 #854, 0.08 #1808, 0.06 #3241), 014gf8 (0.11 #848, 0.08 #1802, 0.06 #3235), 06czyr (0.11 #847, 0.08 #1801, 0.06 #3234), 01fdc0 (0.11 #796, 0.08 #1750, 0.06 #3183) >> Best rule #1430 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 07751; 02fzs; >> query: (?x739, 02m30v) <- film_regional_debut_venue(?x2047, ?x739), vacationer(?x739, ?x444) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02_286 location_of_ceremony! 0f7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 197.000 183.000 0.200 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony EVAL 02_286 location_of_ceremony! 04shbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 197.000 183.000 0.200 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #15080-0cl_m PRED entity: 0cl_m PRED relation: politician! PRED expected values: 07wbk => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 8): 07wbk (0.29 #97, 0.16 #505, 0.11 #818), 02bb8j (0.25 #68, 0.02 #500, 0.01 #644), 0d075m (0.12 #507, 0.11 #675, 0.08 #387), 07wf9 (0.04 #294, 0.04 #318, 0.02 #342), 01f53 (0.01 #526, 0.01 #694), 07wgm (0.01 #518, 0.01 #686), 07wdw (0.01 #1090, 0.01 #1115), 0136kr (0.01 #707) >> Best rule #97 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 07t2k; >> query: (?x8651, 07wbk) <- gender(?x8651, ?x231), student(?x7127, ?x8651), company(?x8651, ?x466), ?x7127 = 07x4c >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cl_m politician! 07wbk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.286 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #15079-05qjt PRED entity: 05qjt PRED relation: major_field_of_study PRED expected values: 03g3w => 83 concepts (70 used for prediction) PRED predicted values (max 10 best out of 125): 0dc_v (0.87 #684, 0.82 #1712, 0.81 #5762), 02j62 (0.57 #1566, 0.50 #3012, 0.50 #2245), 05qfh (0.57 #1654, 0.50 #801, 0.40 #2673), 062z7 (0.50 #795, 0.44 #2926, 0.43 #1648), 05r79 (0.50 #1217, 0.40 #960, 0.33 #2066), 06ms6 (0.44 #2065, 0.43 #1639, 0.40 #2320), 05qjt (0.43 #1547, 0.40 #951, 0.33 #1293), 02822 (0.43 #1657, 0.33 #628, 0.25 #892), 03g3w (0.40 #967, 0.38 #2755, 0.33 #2925), 0fdys (0.40 #1060, 0.38 #1742, 0.33 #1233) >> Best rule #684 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 05qfh; >> query: (?x742, ?x1668) <- major_field_of_study(?x7618, ?x742), major_field_of_study(?x7066, ?x742), major_field_of_study(?x6919, ?x742), major_field_of_study(?x3439, ?x742), major_field_of_study(?x1391, ?x742), major_field_of_study(?x1668, ?x742), ?x3439 = 03ksy, company(?x346, ?x7618), ?x6919 = 017v3q, state_province_region(?x1391, ?x10632), ?x7066 = 0885n >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #967 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 02lp1; *> query: (?x742, 03g3w) <- major_field_of_study(?x10759, ?x742), major_field_of_study(?x7618, ?x742), major_field_of_study(?x5288, ?x742), ?x7618 = 01bk1y, child(?x10759, ?x2730), company(?x2127, ?x10759), major_field_of_study(?x742, ?x8221), student(?x2730, ?x434), school_type(?x5288, ?x3092), student(?x8221, ?x826), student(?x742, ?x3335) *> conf = 0.40 ranks of expected_values: 9 EVAL 05qjt major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 83.000 70.000 0.867 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #15078-05qgd9 PRED entity: 05qgd9 PRED relation: student PRED expected values: 09gffmz => 157 concepts (118 used for prediction) PRED predicted values (max 10 best out of 1136): 02lt8 (0.29 #676, 0.17 #4860, 0.14 #9045), 084w8 (0.14 #10, 0.11 #2102, 0.08 #4194), 02779r4 (0.14 #1162, 0.11 #3254, 0.08 #5346), 03r1pr (0.14 #461, 0.11 #2553, 0.08 #4645), 08chdb (0.14 #1757, 0.11 #3849, 0.08 #5941), 01w_10 (0.14 #1409, 0.11 #3501, 0.08 #5593), 0pz7h (0.14 #119, 0.11 #2211, 0.08 #4303), 08_hns (0.14 #1877, 0.08 #6061, 0.07 #10246), 07n39 (0.14 #1680, 0.08 #5864, 0.07 #10049), 06f5j (0.14 #1704, 0.08 #5888, 0.07 #10073) >> Best rule #676 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 07wf9; >> query: (?x12026, 02lt8) <- organizations_founded(?x5254, ?x12026), ?x5254 = 07cbs >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05qgd9 student 09gffmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 157.000 118.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #15077-0cnl80 PRED entity: 0cnl80 PRED relation: award_nominee PRED expected values: 043js => 67 concepts (27 used for prediction) PRED predicted values (max 10 best out of 696): 04t2l2 (0.81 #2334, 0.81 #34997, 0.81 #37332), 08wq0g (0.81 #2334, 0.81 #34997, 0.81 #37332), 0cnl09 (0.81 #2334, 0.81 #34997, 0.81 #37332), 08hsww (0.81 #2334, 0.81 #34997, 0.81 #37332), 0cj36c (0.81 #2334, 0.81 #34997, 0.81 #37332), 0cmt6q (0.81 #34997, 0.81 #37332, 0.81 #39665), 05p92jn (0.81 #34997, 0.81 #37332, 0.81 #39665), 0cnl80 (0.73 #46, 0.55 #2380, 0.37 #4713), 043js (0.60 #584, 0.37 #5251, 0.32 #2918), 06jnvs (0.37 #5552, 0.29 #56000, 0.27 #4667) >> Best rule #2334 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 08wq0g; 0cj36c; 05xpms; >> query: (?x274, ?x4719) <- award_winner(?x274, ?x7663), award_winner(?x4719, ?x274), ?x7663 = 04zkj5 >> conf = 0.81 => this is the best rule for 5 predicted values *> Best rule #584 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 08wq0g; 0cj36c; 05xpms; *> query: (?x274, 043js) <- award_winner(?x274, ?x7663), award_winner(?x4719, ?x274), ?x7663 = 04zkj5 *> conf = 0.60 ranks of expected_values: 9 EVAL 0cnl80 award_nominee 043js CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 67.000 27.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #15076-056878 PRED entity: 056878 PRED relation: award_winner PRED expected values: 0288fyj 01trhmt 018ndc 0p7h7 => 42 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1583): 0fpjd_g (0.67 #10701, 0.62 #15212, 0.60 #6205), 02fn5r (0.67 #21386, 0.60 #22890, 0.56 #28908), 02qwg (0.67 #7993, 0.50 #3496, 0.40 #24520), 02xs0q (0.62 #18538, 0.60 #17034, 0.57 #12523), 04ns3gy (0.57 #13288, 0.50 #17799, 0.38 #19303), 05bnq3j (0.57 #12697, 0.38 #18712, 0.31 #1498), 06fmdb (0.50 #15788, 0.50 #8278, 0.50 #3781), 0x3b7 (0.50 #14125, 0.50 #8121, 0.50 #3624), 02cx90 (0.50 #11141, 0.50 #5146, 0.47 #24669), 04rcr (0.50 #10573, 0.50 #7574, 0.40 #6077) >> Best rule #10701 for best value: >> intensional similarity = 23 >> extensional distance = 4 >> proper extension: 01c6qp; >> query: (?x2186, 0fpjd_g) <- award_winner(?x2186, ?x9493), award_winner(?x2186, ?x6383), award_winner(?x2186, ?x367), ceremony(?x12833, ?x2186), ceremony(?x12458, ?x2186), ceremony(?x5765, ?x2186), ceremony(?x4018, ?x2186), ceremony(?x3647, ?x2186), ceremony(?x3094, ?x2186), ceremony(?x567, ?x2186), ceremony(?x528, ?x2186), ?x567 = 01d38g, ?x4018 = 03qbh5, ?x367 = 01lmj3q, ?x528 = 02g3gj, ?x12833 = 0257pw, ?x12458 = 024_dt, ?x5765 = 024_fw, ?x3647 = 01c9jp, participant(?x6383, ?x2562), artist(?x2149, ?x9493), profession(?x9493, ?x131), ?x3094 = 026mff >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #10946 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 4 *> proper extension: 01c6qp; *> query: (?x2186, 018ndc) <- award_winner(?x2186, ?x9493), award_winner(?x2186, ?x6383), award_winner(?x2186, ?x367), ceremony(?x12833, ?x2186), ceremony(?x12458, ?x2186), ceremony(?x5765, ?x2186), ceremony(?x4018, ?x2186), ceremony(?x3647, ?x2186), ceremony(?x3094, ?x2186), ceremony(?x567, ?x2186), ceremony(?x528, ?x2186), ?x567 = 01d38g, ?x4018 = 03qbh5, ?x367 = 01lmj3q, ?x528 = 02g3gj, ?x12833 = 0257pw, ?x12458 = 024_dt, ?x5765 = 024_fw, ?x3647 = 01c9jp, participant(?x6383, ?x2562), artist(?x2149, ?x9493), profession(?x9493, ?x131), ?x3094 = 026mff *> conf = 0.33 ranks of expected_values: 60, 70, 138, 169 EVAL 056878 award_winner 0p7h7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 42.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 056878 award_winner 018ndc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 42.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 056878 award_winner 01trhmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 42.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 056878 award_winner 0288fyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 42.000 22.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15075-056vv PRED entity: 056vv PRED relation: participating_countries! PRED expected values: 0kbws => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 43): 0kbws (0.78 #138, 0.72 #302, 0.71 #2236), 09x3r (0.61 #12, 0.54 #135, 0.50 #94), 0lgxj (0.57 #152, 0.50 #111, 0.50 #29), 018ctl (0.55 #295, 0.51 #131, 0.50 #584), 09n48 (0.49 #126, 0.47 #290, 0.44 #3), 0sx8l (0.33 #14, 0.27 #137, 0.27 #260), 0blfl (0.28 #30, 0.27 #523, 0.23 #194), 016r9z (0.28 #22, 0.26 #309, 0.24 #145), 0c_tl (0.28 #24, 0.17 #311, 0.17 #106), 06sks6 (0.26 #535, 0.25 #1809, 0.24 #1603) >> Best rule #138 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 0160w; >> query: (?x2979, 0kbws) <- country(?x4045, ?x2979), ?x4045 = 06z6r, location_of_ceremony(?x566, ?x2979) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 056vv participating_countries! 0kbws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.784 http://example.org/olympics/olympic_games/participating_countries #15074-0fbx6 PRED entity: 0fbx6 PRED relation: award PRED expected values: 0gqwc => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 253): 02y_j8g (0.72 #28609, 0.71 #14699, 0.70 #16290), 0ck27z (0.14 #16289, 0.14 #6441, 0.14 #9222), 0gqwc (0.14 #16289, 0.14 #17086, 0.13 #24634), 0f4x7 (0.14 #16289, 0.14 #17086, 0.13 #24634), 04kxsb (0.14 #16289, 0.14 #17086, 0.13 #24634), 02z0dfh (0.14 #16289, 0.14 #17086, 0.13 #24634), 09qv_s (0.14 #16289, 0.14 #17086, 0.13 #24634), 0gqy2 (0.14 #16289, 0.13 #24634, 0.13 #25429), 05pcn59 (0.14 #16289, 0.13 #24634, 0.13 #25429), 02y_rq5 (0.14 #16289, 0.13 #24634, 0.13 #25429) >> Best rule #28609 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x4254, ?x1972) <- award_winner(?x1972, ?x4254), award(?x91, ?x1972) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #16289 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1566 *> proper extension: 026v1z; *> query: (?x4254, ?x401) <- award_nominee(?x4254, ?x2444), award_winner(?x1008, ?x4254), award(?x2444, ?x401) *> conf = 0.14 ranks of expected_values: 3 EVAL 0fbx6 award 0gqwc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 88.000 88.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15073-0407yfx PRED entity: 0407yfx PRED relation: film_release_region PRED expected values: 0k6nt 047yc 0345h 07t21 015qh 016wzw => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 117): 0345h (0.88 #408, 0.87 #536, 0.87 #664), 0k6nt (0.79 #1170, 0.79 #786, 0.78 #530), 015qh (0.76 #415, 0.75 #671, 0.62 #543), 016wzw (0.75 #686, 0.73 #430, 0.66 #814), 047yc (0.66 #660, 0.65 #404, 0.61 #788), 06f32 (0.66 #685, 0.63 #429, 0.59 #813), 06mzp (0.62 #526, 0.47 #782, 0.46 #654), 0h7x (0.56 #538, 0.42 #1819, 0.40 #794), 047lj (0.51 #519, 0.47 #391, 0.43 #647), 07t21 (0.49 #414, 0.44 #670, 0.41 #798) >> Best rule #408 for best value: >> intensional similarity = 6 >> extensional distance = 49 >> proper extension: 0g56t9t; 011yrp; 0g5qs2k; 0dscrwf; 0fpkhkz; 03qnvdl; 0bq8tmw; 0gvrws1; 0fpv_3_; 07x4qr; ... >> query: (?x2155, 0345h) <- film_release_region(?x2155, ?x3277), film_release_region(?x2155, ?x410), film_release_region(?x2155, ?x279), ?x410 = 01ls2, ?x3277 = 06t8v, ?x279 = 0d060g >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 10 EVAL 0407yfx film_release_region 016wzw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0407yfx film_release_region 015qh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0407yfx film_release_region 07t21 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 68.000 68.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0407yfx film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0407yfx film_release_region 047yc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0407yfx film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15072-0gwlfnb PRED entity: 0gwlfnb PRED relation: prequel PRED expected values: 03qcfvw => 93 concepts (25 used for prediction) PRED predicted values (max 10 best out of 50): 05zlld0 (0.04 #62, 0.02 #243, 0.02 #605), 06r2h (0.04 #160, 0.02 #341, 0.02 #522), 0d6_s (0.04 #170, 0.02 #351, 0.02 #532), 035w2k (0.04 #97, 0.02 #278, 0.02 #459), 014nq4 (0.04 #55), 0233bn (0.02 #1406), 0df2zx (0.02 #358, 0.02 #539), 03nfnx (0.02 #331, 0.02 #512), 02lk60 (0.02 #268, 0.02 #449), 0dr3sl (0.02 #227, 0.02 #408) >> Best rule #62 for best value: >> intensional similarity = 6 >> extensional distance = 26 >> proper extension: 015qsq; 0yx7h; >> query: (?x8891, 05zlld0) <- production_companies(?x8891, ?x902), country(?x8891, ?x94), genre(?x8891, ?x812), ?x812 = 01jfsb, ?x902 = 05qd_, film_release_distribution_medium(?x8891, ?x81) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gwlfnb prequel 03qcfvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 25.000 0.036 http://example.org/film/film/prequel #15071-011yfd PRED entity: 011yfd PRED relation: nominated_for! PRED expected values: 0f4x7 04kxsb => 88 concepts (51 used for prediction) PRED predicted values (max 10 best out of 245): 040njc (0.78 #1413, 0.51 #4000, 0.50 #1886), 02qt02v (0.74 #6579, 0.73 #5874, 0.70 #1641), 02pqp12 (0.74 #6579, 0.72 #1642, 0.72 #1463), 019f4v (0.73 #4046, 0.72 #1459, 0.64 #2870), 0gr0m (0.72 #1464, 0.46 #2875, 0.43 #526), 0f4x7 (0.68 #2609, 0.44 #1432, 0.43 #4019), 0k611 (0.67 #1476, 0.62 #4063, 0.54 #1949), 02qyntr (0.61 #1582, 0.50 #2055, 0.48 #1819), 03hl6lc (0.60 #5062, 0.29 #594, 0.24 #2709), 099c8n (0.59 #2639, 0.44 #1462, 0.44 #4992) >> Best rule #1413 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 017gl1; 01hv3t; >> query: (?x4000, 040njc) <- award(?x4000, ?x3233), award(?x4000, ?x1198), award_winner(?x3233, ?x2182), ?x2182 = 01f7j9, film(?x166, ?x4000), ?x1198 = 02pqp12 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2609 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 57 *> proper extension: 07w8fz; 0c38gj; *> query: (?x4000, 0f4x7) <- nominated_for(?x2853, ?x4000), country(?x4000, ?x205), ?x2853 = 09qv_s, film_release_region(?x3196, ?x205), country(?x5481, ?x205), ?x3196 = 084302 *> conf = 0.68 ranks of expected_values: 6, 13 EVAL 011yfd nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 88.000 51.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 011yfd nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 51.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15070-06t8v PRED entity: 06t8v PRED relation: member_states! PRED expected values: 085h1 059dn => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 12): 085h1 (0.96 #149, 0.90 #2, 0.88 #23), 018cqq (0.55 #22, 0.53 #10, 0.52 #1), 059dn (0.39 #3, 0.38 #12, 0.38 #33), 01rz1 (0.07 #348, 0.07 #182, 0.06 #214), 07t65 (0.07 #348, 0.07 #182, 0.06 #214), 02vk52z (0.07 #348, 0.07 #182, 0.06 #214), 041288 (0.07 #348), 0b6css (0.07 #348), 0gkjy (0.07 #348), 0j7v_ (0.07 #348) >> Best rule #149 for best value: >> intensional similarity = 2 >> extensional distance = 129 >> proper extension: 02jxk; >> query: (?x3277, 085h1) <- member_states(?x2106, ?x3277), jurisdiction_of_office(?x182, ?x3277) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 06t8v member_states! 059dn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 136.000 0.962 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 06t8v member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.962 http://example.org/user/ktrueman/default_domain/international_organization/member_states #15069-02jp2w PRED entity: 02jp2w PRED relation: instance_of_recurring_event! PRED expected values: 0cc8q3 0br1x_ => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 103): 018wrk (0.50 #389, 0.36 #97, 0.20 #194), 0gx1673 (0.50 #389, 0.33 #174, 0.20 #194), 0466p0j (0.50 #389, 0.33 #143, 0.20 #194), 0hhtgcw (0.50 #389, 0.20 #194, 0.17 #292), 0lk8j (0.50 #389, 0.20 #194, 0.17 #292), 0l6ny (0.50 #389, 0.20 #194, 0.17 #292), 02z6gky (0.50 #389, 0.17 #292, 0.17 #291), 0jhn7 (0.50 #389, 0.17 #292, 0.17 #291), 09n48 (0.50 #389, 0.17 #292, 0.17 #291), 04_m9gk (0.33 #383, 0.17 #480, 0.11 #576) >> Best rule #389 for best value: >> intensional similarity = 112 >> extensional distance = 1 >> proper extension: 018cvf; >> query: (?x10863, ?x418) <- instance_of_recurring_event(?x12162, ?x10863), instance_of_recurring_event(?x11210, ?x10863), instance_of_recurring_event(?x4803, ?x10863), instance_of_recurring_event(?x3797, ?x10863), locations(?x4803, ?x11669), locations(?x4803, ?x9445), locations(?x4803, ?x6084), locations(?x4803, ?x4733), locations(?x4803, ?x2622), locations(?x4803, ?x2017), locations(?x12162, ?x8993), locations(?x12162, ?x6703), locations(?x12162, ?x5381), locations(?x12162, ?x4499), locations(?x11210, ?x11848), locations(?x11210, ?x7996), locations(?x11210, ?x5267), locations(?x11210, ?x2277), locations(?x11210, ?x674), origin(?x1001, ?x5267), origin(?x1206, ?x2277), location(?x1165, ?x5267), contains(?x2277, ?x2497), location(?x287, ?x4733), teams(?x4499, ?x1576), place_of_birth(?x2061, ?x4733), place_of_birth(?x1887, ?x4499), citytown(?x2276, ?x2277), place_of_death(?x8508, ?x5267), contains(?x94, ?x5267), location(?x624, ?x2277), teams(?x2277, ?x2405), location_of_ceremony(?x566, ?x4733), month(?x5267, ?x6303), month(?x5267, ?x4925), month(?x5267, ?x1650), month(?x5267, ?x1459), place_of_birth(?x3058, ?x2277), location_of_ceremony(?x2012, ?x5267), locations(?x3797, ?x8263), locations(?x3797, ?x3373), citytown(?x3543, ?x5267), category(?x10863, ?x134), place_of_birth(?x4180, ?x11848), teams(?x5267, ?x3114), contains(?x3038, ?x2277), location(?x5809, ?x6703), featured_film_locations(?x2362, ?x2277), location(?x1896, ?x5381), contains(?x3350, ?x4499), place_of_death(?x2608, ?x4733), ?x1459 = 04w_7, time_zones(?x2622, ?x2950), contains(?x448, ?x11848), service_location(?x6315, ?x11848), location(?x9567, ?x8993), contains(?x4499, ?x331), location(?x1400, ?x2622), time_zones(?x4499, ?x2674), ?x6303 = 0lkm, place_of_birth(?x275, ?x5267), featured_film_locations(?x1015, ?x2017), origin(?x6854, ?x6703), citytown(?x6404, ?x6703), mode_of_transportation(?x2277, ?x8731), teams(?x2017, ?x1160), citytown(?x1635, ?x8993), ?x8731 = 01bjv, teams(?x5381, ?x1347), month(?x2277, ?x2255), origin(?x5906, ?x4499), place_of_birth(?x92, ?x2017), time_zones(?x2017, ?x1638), location(?x12194, ?x11669), featured_film_locations(?x5116, ?x4499), contains(?x726, ?x2622), location(?x396, ?x4499), location(?x329, ?x8263), contains(?x2256, ?x11669), capital(?x1024, ?x7996), citytown(?x2056, ?x8263), capital(?x2713, ?x8263), location(?x966, ?x7996), teams(?x9445, ?x12541), place_of_birth(?x427, ?x9445), location(?x5566, ?x3373), place_of_birth(?x4901, ?x5381), location(?x436, ?x674), contains(?x673, ?x674), origin(?x4842, ?x3373), ?x566 = 04ztj, citytown(?x7071, ?x5381), citytown(?x5780, ?x11669), featured_film_locations(?x641, ?x5267), contains(?x2623, ?x3373), ?x134 = 08mbj5d, contains(?x13509, ?x8993), citytown(?x2760, ?x674), origin(?x379, ?x4733), time_zones(?x674, ?x2088), teams(?x6084, ?x1639), place_of_birth(?x547, ?x6084), teams(?x7996, ?x6379), administrative_division(?x9445, ?x1755), ?x1650 = 06vkl, adjoins(?x6703, ?x3794), locations(?x418, ?x674), teams(?x674, ?x11420), place_of_death(?x3563, ?x2017), ?x4925 = 0ll3, location(?x5153, ?x6084), contains(?x4733, ?x8202) >> conf = 0.50 => this is the best rule for 9 predicted values No rule for expected values ranks of expected_values: EVAL 02jp2w instance_of_recurring_event! 0br1x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.500 http://example.org/time/event/instance_of_recurring_event EVAL 02jp2w instance_of_recurring_event! 0cc8q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.500 http://example.org/time/event/instance_of_recurring_event #15068-03gh4 PRED entity: 03gh4 PRED relation: location_of_ceremony! PRED expected values: 05j0wc => 207 concepts (170 used for prediction) PRED predicted values (max 10 best out of 281): 01fkxr (0.11 #1195, 0.09 #1443, 0.07 #1941), 01w23w (0.11 #1150, 0.09 #1398, 0.07 #1896), 034np8 (0.11 #1030, 0.09 #1278, 0.07 #1776), 01vzxld (0.11 #1208, 0.09 #1456, 0.07 #2452), 0fgg4 (0.11 #1109, 0.09 #1357, 0.05 #3346), 01chc7 (0.11 #1066, 0.09 #1314, 0.05 #3303), 0gdqy (0.08 #1708, 0.07 #2455, 0.04 #4195), 0c9c0 (0.08 #1554, 0.07 #2301, 0.04 #4041), 06wvj (0.08 #1547, 0.07 #2294, 0.04 #4034), 06x58 (0.08 #1529, 0.07 #2276, 0.04 #4016) >> Best rule #1195 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05c17; >> query: (?x6226, 01fkxr) <- film_release_region(?x11701, ?x6226), first_level_division_of(?x6226, ?x94), country(?x54, ?x94) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03gh4 location_of_ceremony! 05j0wc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 207.000 170.000 0.111 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #15067-0hr3g PRED entity: 0hr3g PRED relation: nationality PRED expected values: 0345h => 173 concepts (158 used for prediction) PRED predicted values (max 10 best out of 52): 09c7w0 (0.79 #12459, 0.79 #12863, 0.78 #11351), 0345h (0.54 #3508, 0.53 #1903, 0.40 #15692), 0h7x (0.40 #15692, 0.30 #335, 0.25 #435), 017v_ (0.35 #8733, 0.34 #10948, 0.34 #13571), 02h6_6p (0.25 #13266, 0.25 #12862, 0.24 #13165), 02jx1 (0.23 #1335, 0.20 #333, 0.20 #3541), 07ssc (0.20 #2820, 0.17 #3021, 0.17 #515), 06bnz (0.18 #742, 0.17 #441, 0.12 #842), 03rjj (0.18 #806, 0.16 #1007, 0.12 #706), 0f8l9c (0.11 #1224, 0.08 #522, 0.08 #3028) >> Best rule #12459 for best value: >> intensional similarity = 4 >> extensional distance = 1280 >> proper extension: 09hd6f; >> query: (?x9297, 09c7w0) <- place_of_birth(?x9297, ?x2611), gender(?x9297, ?x231), location(?x2610, ?x2611), time_zones(?x2611, ?x2864) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #3508 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 98 *> proper extension: 04h07s; 0q9t7; *> query: (?x9297, ?x1264) <- influenced_by(?x9297, ?x1211), nationality(?x1211, ?x1264), instrumentalists(?x316, ?x1211), profession(?x1211, ?x563) *> conf = 0.54 ranks of expected_values: 2 EVAL 0hr3g nationality 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 173.000 158.000 0.791 http://example.org/people/person/nationality #15066-0d0vqn PRED entity: 0d0vqn PRED relation: nationality! PRED expected values: 017l4 => 159 concepts (50 used for prediction) PRED predicted values (max 10 best out of 4433): 017l4 (0.44 #133864, 0.02 #202820), 0147dk (0.35 #133865, 0.34 #32450, 0.28 #64901), 01rzqj (0.35 #93295, 0.06 #9068, 0.05 #48675), 0cbkc (0.35 #93295, 0.06 #10916, 0.05 #48675), 0psss (0.35 #93295, 0.06 #9042, 0.05 #48675), 027zz (0.35 #93295, 0.06 #11538, 0.05 #19650), 01yf85 (0.35 #93295, 0.06 #10853, 0.05 #18965), 01271h (0.35 #93295, 0.06 #8947, 0.05 #17059), 07s93v (0.35 #93295, 0.06 #8530, 0.05 #16642), 0fvf9q (0.35 #93295, 0.06 #8139, 0.05 #16251) >> Best rule #133864 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 02psqkz; 0dv0z; 01s47p; 0gtzp; >> query: (?x304, ?x2083) <- capital(?x304, ?x5168), location(?x521, ?x5168), place_of_birth(?x2083, ?x5168) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d0vqn nationality! 017l4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 50.000 0.438 http://example.org/people/person/nationality #15065-0498y PRED entity: 0498y PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 19): 0fkvn (0.78 #130, 0.77 #67, 0.74 #214), 0pqc5 (0.60 #1160, 0.54 #1370, 0.53 #1496), 060c4 (0.54 #1431, 0.52 #1578, 0.52 #1704), 060bp (0.48 #1429, 0.45 #1576, 0.45 #1702), 0fkzq (0.30 #78, 0.24 #141, 0.24 #225), 01t7n9 (0.25 #17, 0.17 #80, 0.15 #227), 0789n (0.25 #9, 0.15 #219, 0.14 #282), 02079p (0.25 #10, 0.10 #73, 0.08 #619), 0dq3c (0.25 #2, 0.10 #1430, 0.09 #1703), 01gkgk (0.25 #6, 0.07 #216, 0.07 #48) >> Best rule #130 for best value: >> intensional similarity = 2 >> extensional distance = 43 >> proper extension: 0g0syc; >> query: (?x4061, 0fkvn) <- district_represented(?x3540, ?x4061), ?x3540 = 024tcq >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0498y jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.778 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #15064-04cw0j PRED entity: 04cw0j PRED relation: nationality PRED expected values: 09c7w0 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.79 #301, 0.79 #201, 0.74 #2102), 03rk0 (0.10 #2047, 0.07 #2348, 0.06 #647), 02jx1 (0.09 #2735, 0.09 #7241, 0.09 #2034), 07ssc (0.08 #2417, 0.08 #1916, 0.08 #416), 0d060g (0.05 #508, 0.04 #408, 0.04 #3209), 03gj2 (0.04 #727, 0.04 #927, 0.04 #827), 0345h (0.04 #331, 0.04 #432, 0.03 #732), 06npd (0.04 #421), 03rjj (0.03 #1706, 0.03 #1906, 0.03 #1506), 0h7x (0.02 #536, 0.01 #2437) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 03flwk; >> query: (?x3170, 09c7w0) <- award(?x3170, ?x3105), ?x3105 = 01l29r, place_of_birth(?x3170, ?x739) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cw0j nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.792 http://example.org/people/person/nationality #15063-01ckrr PRED entity: 01ckrr PRED relation: ceremony PRED expected values: 01mhwk => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 127): 05pd94v (0.87 #381, 0.75 #3407, 0.60 #759), 01mhwk (0.76 #412, 0.75 #3407, 0.54 #790), 0gx1673 (0.75 #3407, 0.51 #485, 0.35 #863), 03nnm4t (0.33 #2649, 0.33 #2650, 0.30 #1262), 0clfdj (0.33 #2649, 0.33 #2650, 0.30 #1262), 0bz6sb (0.33 #2649, 0.33 #2650, 0.30 #1262), 073h1t (0.33 #2649, 0.33 #2650, 0.27 #4165), 0418154 (0.33 #2649, 0.33 #2650, 0.27 #4165), 08pc1x (0.30 #1262, 0.27 #4165, 0.18 #379), 09p3h7 (0.30 #1262, 0.27 #4165, 0.18 #379) >> Best rule #381 for best value: >> intensional similarity = 5 >> extensional distance = 82 >> proper extension: 0257yf; 03q_g6; 03nl5k; 01c9d1; 0257pw; >> query: (?x4912, 05pd94v) <- ceremony(?x4912, ?x2054), award(?x1412, ?x4912), award(?x248, ?x4912), artists(?x302, ?x1412), ?x2054 = 0gpjbt >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #412 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 82 *> proper extension: 0257yf; 03q_g6; 03nl5k; 01c9d1; 0257pw; *> query: (?x4912, 01mhwk) <- ceremony(?x4912, ?x2054), award(?x1412, ?x4912), award(?x248, ?x4912), artists(?x302, ?x1412), ?x2054 = 0gpjbt *> conf = 0.76 ranks of expected_values: 2 EVAL 01ckrr ceremony 01mhwk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 45.000 45.000 0.869 http://example.org/award/award_category/winners./award/award_honor/ceremony #15062-04kl74p PRED entity: 04kl74p PRED relation: nutrient! PRED expected values: 0hkxq => 56 concepts (53 used for prediction) PRED predicted values (max 10 best out of 6): 0hkxq (0.90 #584, 0.89 #570, 0.88 #332), 05z55 (0.89 #305, 0.89 #301, 0.88 #332), 0971v (0.88 #332, 0.88 #85, 0.88 #63), 07j87 (0.88 #332, 0.88 #85, 0.88 #63), 06x4c (0.88 #332, 0.88 #85, 0.88 #63), 0dcfv (0.88 #332, 0.88 #85, 0.88 #63) >> Best rule #584 for best value: >> intensional similarity = 116 >> extensional distance = 27 >> proper extension: 0hkwr; >> query: (?x3203, 0hkxq) <- nutrient(?x10612, ?x3203), nutrient(?x8298, ?x3203), nutrient(?x7719, ?x3203), nutrient(?x7057, ?x3203), nutrient(?x6285, ?x3203), nutrient(?x4068, ?x3203), nutrient(?x3468, ?x3203), nutrient(?x1959, ?x3203), nutrient(?x1303, ?x3203), ?x7057 = 0fbdb, ?x1303 = 0fj52s, nutrient(?x1959, ?x12083), nutrient(?x1959, ?x11784), nutrient(?x1959, ?x11758), nutrient(?x1959, ?x11592), nutrient(?x1959, ?x11270), nutrient(?x1959, ?x10709), nutrient(?x1959, ?x10098), nutrient(?x1959, ?x9949), nutrient(?x1959, ?x9795), nutrient(?x1959, ?x9733), nutrient(?x1959, ?x9619), nutrient(?x1959, ?x9490), nutrient(?x1959, ?x9436), nutrient(?x1959, ?x9426), nutrient(?x1959, ?x9365), nutrient(?x1959, ?x8487), nutrient(?x1959, ?x8442), nutrient(?x1959, ?x8413), nutrient(?x1959, ?x7894), nutrient(?x1959, ?x7720), nutrient(?x1959, ?x7364), nutrient(?x1959, ?x7362), nutrient(?x1959, ?x7219), nutrient(?x1959, ?x7135), nutrient(?x1959, ?x6586), nutrient(?x1959, ?x6517), nutrient(?x1959, ?x6192), nutrient(?x1959, ?x6160), nutrient(?x1959, ?x6033), nutrient(?x1959, ?x6026), nutrient(?x1959, ?x5549), nutrient(?x1959, ?x5526), nutrient(?x1959, ?x5451), nutrient(?x1959, ?x5374), nutrient(?x1959, ?x5010), nutrient(?x1959, ?x4069), nutrient(?x1959, ?x3469), nutrient(?x1959, ?x2702), nutrient(?x1959, ?x1960), nutrient(?x1959, ?x1304), nutrient(?x1959, ?x1258), ?x7894 = 0f4hc, ?x6033 = 04zjxcz, ?x7362 = 02kc5rj, ?x3469 = 0h1zw, ?x8413 = 02kc4sf, ?x8487 = 014yzm, ?x9795 = 05v_8y, ?x5451 = 05wvs, ?x9619 = 0h1tg, ?x5526 = 09pbb, ?x10709 = 0h1sz, ?x4069 = 0hqw8p_, ?x9733 = 0h1tz, nutrient(?x10612, ?x13944), nutrient(?x10612, ?x13498), nutrient(?x10612, ?x12902), nutrient(?x10612, ?x10891), nutrient(?x10612, ?x3901), ?x6285 = 01645p, ?x12902 = 0fzjh, ?x3901 = 0466p20, ?x7720 = 025s7x6, nutrient(?x9732, ?x6517), nutrient(?x5373, ?x6517), ?x11270 = 02kc008, ?x13498 = 07q0m, ?x9365 = 04k8n, ?x11758 = 0q01m, ?x8442 = 02kcv4x, ?x9949 = 02kd0rh, ?x7135 = 025rsfk, ?x9436 = 025sqz8, ?x12083 = 01n78x, ?x11592 = 025sf0_, ?x6160 = 041r51, ?x1304 = 08lb68, ?x11784 = 07zqy, ?x1960 = 07hnp, ?x4068 = 0fbw6, ?x7364 = 09gvd, ?x9490 = 0h1sg, ?x5549 = 025s7j4, ?x5010 = 0h1vz, ?x10098 = 0h1_c, nutrient(?x7719, ?x6286), nutrient(?x7719, ?x3264), nutrient(?x7719, ?x2018), ?x9732 = 05z55, ?x6026 = 025sf8g, ?x8298 = 037ls6, ?x2018 = 01sh2, ?x9426 = 0h1yy, ?x1258 = 0h1wg, ?x2702 = 0838f, ?x5374 = 025s0zp, ?x6586 = 05gh50, ?x3264 = 0dcfv, ?x6286 = 02y_3rf, ?x10891 = 0g5gq, ?x5373 = 0971v, ?x7219 = 0h1vg, ?x13944 = 0f4kp, ?x3468 = 0cxn2, ?x6192 = 06jry >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04kl74p nutrient! 0hkxq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 53.000 0.897 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #15061-0hky PRED entity: 0hky PRED relation: influenced_by! PRED expected values: 06jcc => 169 concepts (65 used for prediction) PRED predicted values (max 10 best out of 458): 0683n (0.44 #3919, 0.19 #6485, 0.14 #1358), 07lp1 (0.43 #1437, 0.20 #4509, 0.18 #10158), 01vrncs (0.43 #1053, 0.12 #17464, 0.11 #9774), 05jm7 (0.33 #3210, 0.33 #2696, 0.30 #4234), 0j0pf (0.33 #3275, 0.33 #2761, 0.20 #5838), 04hcw (0.33 #286, 0.22 #3870, 0.12 #2332), 01vdrw (0.33 #4025, 0.19 #6591, 0.16 #8204), 013pp3 (0.33 #3804, 0.19 #6370, 0.14 #7908), 04z0g (0.33 #236, 0.12 #2282, 0.07 #14593), 03_87 (0.33 #3842, 0.10 #4353, 0.08 #32057) >> Best rule #3919 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 082mw; 03_dj; >> query: (?x6037, 0683n) <- profession(?x6037, ?x2225), influenced_by(?x4055, ?x6037), ?x4055 = 034bs, gender(?x6037, ?x231), ?x2225 = 0kyk >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #5944 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 056wb; *> query: (?x6037, 06jcc) <- influenced_by(?x6037, ?x3542), influenced_by(?x4895, ?x3542), influenced_by(?x3542, ?x6015), ?x4895 = 0klw, student(?x892, ?x6037) *> conf = 0.13 ranks of expected_values: 77 EVAL 0hky influenced_by! 06jcc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 169.000 65.000 0.444 http://example.org/influence/influence_node/influenced_by #15060-01qd_r PRED entity: 01qd_r PRED relation: institution! PRED expected values: 04zx3q1 014mlp 01rr_d => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 17): 014mlp (0.75 #240, 0.72 #259, 0.71 #317), 016t_3 (0.64 #58, 0.61 #76, 0.59 #112), 04zx3q1 (0.55 #57, 0.54 #75, 0.47 #111), 07s6fsf (0.49 #56, 0.48 #74, 0.45 #110), 027f2w (0.47 #62, 0.43 #80, 0.39 #98), 01rr_d (0.26 #31, 0.25 #86, 0.24 #122), 03mkk4 (0.26 #64, 0.25 #46, 0.24 #100), 028dcg (0.22 #52, 0.21 #33, 0.16 #178), 022h5x (0.21 #289, 0.19 #307, 0.19 #71), 0bjrnt (0.21 #24, 0.19 #79, 0.19 #61) >> Best rule #240 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 022xml; >> query: (?x7660, 014mlp) <- student(?x7660, ?x2390), major_field_of_study(?x7660, ?x373), fraternities_and_sororities(?x7660, ?x4348) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 6 EVAL 01qd_r institution! 01rr_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 82.000 0.752 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01qd_r institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.752 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01qd_r institution! 04zx3q1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.752 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15059-01xcr4 PRED entity: 01xcr4 PRED relation: student! PRED expected values: 01rtm4 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 233): 0bwfn (0.15 #1329, 0.11 #4492, 0.06 #2384), 04b_46 (0.15 #1281, 0.09 #4444, 0.06 #7606), 065y4w7 (0.15 #2650, 0.08 #1068, 0.07 #6866), 08815 (0.11 #10017, 0.08 #8963, 0.08 #11598), 0g8rj (0.09 #703, 0.06 #2285, 0.04 #3866), 0pspl (0.09 #636, 0.06 #2218, 0.03 #4853), 07x4c (0.09 #786, 0.04 #8165, 0.03 #1840), 017v3q (0.09 #772, 0.03 #1826, 0.03 #2354), 04rwx (0.09 #565, 0.03 #1619, 0.02 #3201), 07vyf (0.09 #665, 0.03 #2247, 0.02 #4355) >> Best rule #1329 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0br1w; >> query: (?x4259, 0bwfn) <- nationality(?x4259, ?x94), student(?x1368, ?x4259), program(?x4259, ?x4891) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #4221 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 02hsgn; 049gc; *> query: (?x4259, 01rtm4) <- nationality(?x4259, ?x94), student(?x1368, ?x4259), award_winner(?x3183, ?x4259) *> conf = 0.04 ranks of expected_values: 43 EVAL 01xcr4 student! 01rtm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 109.000 109.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #15058-0gpx6 PRED entity: 0gpx6 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #21, 0.84 #168, 0.84 #125), 07c52 (0.07 #13, 0.04 #316, 0.04 #296), 02nxhr (0.04 #239, 0.04 #169, 0.04 #290), 07z4p (0.04 #318, 0.03 #298, 0.03 #192) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 0456zg; >> query: (?x7735, 029j_) <- film_crew_role(?x7735, ?x137), nominated_for(?x5959, ?x7735), genre(?x7735, ?x6674), ?x6674 = 01t_vv >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gpx6 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #15057-0qdwr PRED entity: 0qdwr PRED relation: people! PRED expected values: 041rx => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 47): 041rx (0.99 #2666, 0.78 #2436, 0.71 #2895), 02w7gg (0.31 #2358, 0.29 #2893, 0.21 #2434), 0dryh9k (0.20 #928, 0.07 #2830, 0.07 #2983), 0x67 (0.18 #3588, 0.17 #5260, 0.17 #5488), 033tf_ (0.17 #767, 0.16 #1299, 0.15 #2821), 0xnvg (0.13 #773, 0.12 #1001, 0.09 #925), 048z7l (0.12 #267, 0.08 #2243, 0.06 #2701), 01qhm_ (0.12 #234, 0.07 #994, 0.06 #1754), 01p7s6 (0.12 #286, 0.03 #1046, 0.02 #1198), 07bch9 (0.10 #1771, 0.10 #1011, 0.10 #1087) >> Best rule #2666 for best value: >> intensional similarity = 4 >> extensional distance = 342 >> proper extension: 07nznf; 023tp8; 0c4f4; 04bs3j; 025vry; 03m8lq; 01vlj1g; 018db8; 03qd_; 058kqy; ... >> query: (?x9837, 041rx) <- people(?x8649, ?x9837), people(?x8649, ?x11251), profession(?x9837, ?x319), ?x11251 = 0m9c1 >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qdwr people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.985 http://example.org/people/ethnicity/people #15056-05qb8vx PRED entity: 05qb8vx PRED relation: award_winner PRED expected values: 04ls53 => 43 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1095): 0h0wc (0.45 #17312, 0.20 #6527, 0.15 #18855), 03_gd (0.42 #12325, 0.41 #30815, 0.30 #20028), 09pl3f (0.42 #12325, 0.21 #21570, 0.21 #23112), 09pl3s (0.42 #12325, 0.21 #21570, 0.21 #23112), 05mcjs (0.42 #12325, 0.20 #8707, 0.14 #11789), 0c6qh (0.41 #30815, 0.40 #18487, 0.30 #20028), 06r_by (0.41 #30815, 0.33 #2474, 0.30 #20028), 0693l (0.41 #30815, 0.30 #20028, 0.27 #7700), 07m9cm (0.41 #30815, 0.30 #20028, 0.27 #7700), 01q6bg (0.41 #30815, 0.30 #20028, 0.27 #7700) >> Best rule #17312 for best value: >> intensional similarity = 19 >> extensional distance = 9 >> proper extension: 0hhtgcw; >> query: (?x4224, 0h0wc) <- honored_for(?x4224, ?x2189), award_winner(?x4224, ?x5613), award_winner(?x144, ?x5613), film(?x8764, ?x2189), film(?x5940, ?x2189), film_release_region(?x2189, ?x2843), film_release_region(?x2189, ?x1475), film_release_region(?x2189, ?x151), ?x2843 = 016wzw, produced_by(?x146, ?x5940), spouse(?x3466, ?x8764), ?x1475 = 05qx1, film_crew_role(?x2189, ?x137), ?x151 = 0b90_r, award_nominee(?x902, ?x5940), place_of_birth(?x5613, ?x362), award_winner(?x401, ?x5940), location(?x5940, ?x1658), influenced_by(?x5940, ?x4988) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #30815 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 47 *> proper extension: 0bzkgg; 0bzk2h; 0fz2y7; 0bzknt; 0bzlrh; 0bzjvm; 0bzjgq; *> query: (?x4224, ?x5613) <- ceremony(?x2209, ?x4224), ceremony(?x1323, ?x4224), ?x1323 = 0gqz2, award(?x4152, ?x2209), award(?x3219, ?x2209), nominated_for(?x2209, ?x7834), nominated_for(?x2209, ?x7493), nominated_for(?x2209, ?x7225), nominated_for(?x2209, ?x2699), nominated_for(?x2209, ?x508), ?x2699 = 04t6fk, ?x7225 = 02mmwk, film_format(?x4152, ?x909), ?x508 = 0ds33, written_by(?x4152, ?x11705), film_release_region(?x3219, ?x94), honored_for(?x4224, ?x6900), ?x7493 = 0btpm6, film(?x3034, ?x7834), currency(?x4152, ?x170), award_winner(?x6900, ?x5613), country(?x4152, ?x1023) *> conf = 0.41 ranks of expected_values: 31 EVAL 05qb8vx award_winner 04ls53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 43.000 26.000 0.455 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15055-01n7q PRED entity: 01n7q PRED relation: state_province_region! PRED expected values: 04f525m 01q0kg 06xpp7 019rl6 0c41qv 01dtcb 041bnw 02rky4 02wbnv => 178 concepts (140 used for prediction) PRED predicted values (max 10 best out of 780): 01q0kg (0.35 #32696, 0.31 #43384, 0.29 #44015), 04p_hy (0.35 #32696, 0.31 #43384, 0.29 #44015), 01hhvg (0.35 #32696, 0.31 #43384, 0.29 #44015), 027xx3 (0.35 #32696, 0.31 #43384, 0.29 #44015), 02gnh0 (0.35 #32696, 0.31 #43384, 0.29 #44015), 07vht (0.35 #32696, 0.31 #43384, 0.29 #44015), 03b8c4 (0.35 #32696, 0.31 #43384, 0.29 #44015), 02zd460 (0.35 #32696, 0.29 #44015, 0.29 #54081), 0qcrj (0.35 #32696, 0.29 #44015, 0.23 #28287), 0jbrr (0.35 #32696, 0.29 #44015, 0.23 #28287) >> Best rule #32696 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 09cpb; >> query: (?x1227, ?x5475) <- contains(?x1227, ?x5475), state(?x581, ?x1227), category(?x5475, ?x134) >> conf = 0.35 => this is the best rule for 63 predicted values ranks of expected_values: 1, 66, 201 EVAL 01n7q state_province_region! 02wbnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 178.000 140.000 0.354 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 01n7q state_province_region! 02rky4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 178.000 140.000 0.354 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 01n7q state_province_region! 041bnw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 178.000 140.000 0.354 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 01n7q state_province_region! 01dtcb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 178.000 140.000 0.354 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 01n7q state_province_region! 0c41qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 178.000 140.000 0.354 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 01n7q state_province_region! 019rl6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 178.000 140.000 0.354 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 01n7q state_province_region! 06xpp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 178.000 140.000 0.354 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 01n7q state_province_region! 01q0kg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 140.000 0.354 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 01n7q state_province_region! 04f525m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 178.000 140.000 0.354 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #15054-015f7 PRED entity: 015f7 PRED relation: award_winner! PRED expected values: 02f5qb => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 322): 02f79n (0.44 #858, 0.40 #1287, 0.36 #2145), 05p09zm (0.44 #858, 0.40 #1287, 0.36 #2145), 05q8pss (0.44 #858, 0.40 #1287, 0.36 #2145), 02f716 (0.44 #858, 0.40 #1287, 0.36 #2145), 01c99j (0.44 #858, 0.40 #1287, 0.36 #2145), 02f72_ (0.44 #858, 0.40 #1287, 0.36 #2145), 02f777 (0.44 #858, 0.40 #1287, 0.36 #2145), 02f73p (0.44 #858, 0.40 #1287, 0.36 #2145), 02f71y (0.44 #858, 0.40 #1287, 0.36 #2145), 03qbnj (0.44 #858, 0.40 #1287, 0.36 #2145) >> Best rule #858 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01x1cn2; >> query: (?x3397, ?x1007) <- award(?x3397, ?x1007), award(?x3397, ?x154), influenced_by(?x3397, ?x4960), ?x154 = 05b4l5x >> conf = 0.44 => this is the best rule for 13 predicted values *> Best rule #1010 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 01wf86y; *> query: (?x3397, 02f5qb) <- award(?x3397, ?x3631), nominated_for(?x3397, ?x2084), ?x3631 = 02f73p *> conf = 0.25 ranks of expected_values: 15 EVAL 015f7 award_winner! 02f5qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 153.000 153.000 0.442 http://example.org/award/award_category/winners./award/award_honor/award_winner #15053-03ds3 PRED entity: 03ds3 PRED relation: location PRED expected values: 0r0m6 => 151 concepts (102 used for prediction) PRED predicted values (max 10 best out of 242): 0z1vw (0.33 #583, 0.03 #9417, 0.02 #10220), 02_286 (0.26 #72337, 0.23 #65106, 0.20 #73142), 01n7q (0.25 #865, 0.14 #2471, 0.14 #8093), 0cr3d (0.25 #1750, 0.12 #8978, 0.10 #72444), 0rh6k (0.25 #807, 0.07 #6429, 0.05 #4823), 0d6lp (0.25 #1773, 0.03 #72467, 0.03 #65236), 0xl08 (0.25 #1927, 0.03 #17186, 0.03 #9155), 0sf9_ (0.25 #1007), 030qb3t (0.23 #5704, 0.23 #4901, 0.22 #3294), 01cx_ (0.14 #2571, 0.05 #4981, 0.04 #5784) >> Best rule #583 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03h502k; >> query: (?x858, 0z1vw) <- film(?x858, ?x5721), ?x5721 = 01d259, music(?x2719, ?x858), participant(?x858, ?x3422) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3429 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 01yznp; 01wwvt2; 01mmslz; 015p3p; 028hc2; *> query: (?x858, 0r0m6) <- film(?x858, ?x9478), film(?x858, ?x5721), currency(?x5721, ?x170), film_release_region(?x5721, ?x87), ?x9478 = 0f8j13 *> conf = 0.11 ranks of expected_values: 14 EVAL 03ds3 location 0r0m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 151.000 102.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #15052-09d28z PRED entity: 09d28z PRED relation: award_winner PRED expected values: 081lh 0gs1_ => 38 concepts (10 used for prediction) PRED predicted values (max 10 best out of 880): 081lh (0.33 #187, 0.29 #5080, 0.20 #22032), 05kfs (0.33 #131, 0.29 #5024, 0.06 #9919), 0bwh6 (0.33 #264, 0.24 #5157, 0.08 #10052), 02vyw (0.33 #784, 0.24 #5677, 0.07 #8124), 06b_0 (0.33 #1659, 0.24 #6552, 0.07 #11447), 03_gd (0.33 #137, 0.24 #5030, 0.06 #9925), 0c921 (0.33 #1958, 0.24 #6851, 0.05 #9298), 01d8yn (0.33 #3249, 0.20 #22032, 0.19 #5696), 02bfxb (0.33 #3181, 0.20 #22032, 0.19 #5628), 01q415 (0.33 #2905, 0.20 #22032, 0.19 #5352) >> Best rule #187 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0gs9p; >> query: (?x8364, 081lh) <- award(?x5013, ?x8364), award(?x4216, ?x8364), ?x4216 = 0hfzr, award_winner(?x8364, ?x9313), nominated_for(?x112, ?x5013), ?x9313 = 04353 >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14 EVAL 09d28z award_winner 0gs1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 38.000 10.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 09d28z award_winner 081lh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 10.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #15051-04sntd PRED entity: 04sntd PRED relation: featured_film_locations PRED expected values: 052p7 => 86 concepts (73 used for prediction) PRED predicted values (max 10 best out of 89): 02_286 (0.38 #7203, 0.30 #8161, 0.29 #7681), 030qb3t (0.20 #754, 0.19 #7222, 0.15 #6984), 04jpl (0.18 #485, 0.17 #1921, 0.16 #7192), 0dclg (0.11 #52, 0.03 #1965, 0.02 #7714), 0vzm (0.11 #73, 0.02 #7257, 0.01 #8215), 0cc56 (0.11 #26), 06y57 (0.10 #2254, 0.07 #2493, 0.04 #3453), 05qtj (0.09 #333, 0.08 #2008, 0.04 #1050), 0q_xk (0.09 #390, 0.04 #2543, 0.03 #2065), 0d060g (0.09 #484, 0.04 #1201, 0.03 #1441) >> Best rule #7203 for best value: >> intensional similarity = 5 >> extensional distance = 375 >> proper extension: 02zk08; >> query: (?x2960, 02_286) <- featured_film_locations(?x2960, ?x108), film_release_region(?x2960, ?x94), place_of_birth(?x236, ?x108), month(?x108, ?x1459), location(?x2275, ?x108) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2448 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 44 *> proper extension: 07gp9; 0ddjy; 02mmwk; 02_nsc; 09v8clw; *> query: (?x2960, 052p7) <- film(?x643, ?x2960), language(?x2960, ?x5671), film_release_region(?x2960, ?x94), edited_by(?x2960, ?x5971), featured_film_locations(?x2960, ?x108) *> conf = 0.04 ranks of expected_values: 24 EVAL 04sntd featured_film_locations 052p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 86.000 73.000 0.379 http://example.org/film/film/featured_film_locations #15050-07z542 PRED entity: 07z542 PRED relation: award_winner! PRED expected values: 01mh_q => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 115): 02rjjll (0.19 #4449, 0.17 #10705, 0.16 #1533), 013b2h (0.19 #4449, 0.17 #10705, 0.15 #1885), 02cg41 (0.19 #4449, 0.17 #10705, 0.13 #10845), 01s695 (0.19 #4449, 0.17 #10705, 0.13 #10845), 01c6qp (0.19 #4449, 0.17 #10705, 0.13 #10845), 0466p0j (0.19 #4449, 0.17 #10705, 0.13 #10845), 01bx35 (0.19 #4449, 0.17 #10705, 0.13 #10845), 019bk0 (0.19 #4449, 0.17 #10705, 0.13 #10845), 01xqqp (0.19 #4449, 0.17 #10705, 0.13 #10845), 01mhwk (0.19 #4449, 0.17 #10705, 0.13 #10845) >> Best rule #4449 for best value: >> intensional similarity = 3 >> extensional distance = 384 >> proper extension: 0gsg7; 07k2d; 05s34b; >> query: (?x1524, ?x139) <- award_winner(?x1524, ?x506), award_winner(?x139, ?x506), category(?x1524, ?x134) >> conf = 0.19 => this is the best rule for 12 predicted values *> Best rule #504 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 93 *> proper extension: 0kzy0; 02whj; 01qvgl; 01vvpjj; 024dgj; 01vtqml; 03f0fnk; 01qgry; 01pbs9w; 01wvxw1; ... *> query: (?x1524, 01mh_q) <- award_winner(?x5123, ?x1524), role(?x1524, ?x214), award_winner(?x139, ?x1524) *> conf = 0.12 ranks of expected_values: 15 EVAL 07z542 award_winner! 01mh_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 118.000 118.000 0.193 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #15049-03z106 PRED entity: 03z106 PRED relation: language PRED expected values: 07zrf => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 40): 07zrf (0.33 #173, 0.25 #116, 0.05 #1043), 06nm1 (0.25 #238, 0.11 #1401, 0.10 #1632), 04306rv (0.22 #1045, 0.22 #1395, 0.13 #346), 01lqm (0.17 #227, 0.12 #170, 0.02 #1097), 02bjrlw (0.13 #1392, 0.11 #286, 0.11 #1681), 06b_j (0.12 #135, 0.12 #78, 0.12 #1062), 0jzc (0.12 #76, 0.07 #1060, 0.07 #767), 02hxcvy (0.12 #89, 0.04 #317, 0.03 #374), 06mp7 (0.12 #72, 0.03 #1406, 0.02 #588), 032f6 (0.12 #111, 0.02 #1734, 0.01 #1445) >> Best rule #173 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0sxmx; >> query: (?x3857, 07zrf) <- film(?x556, ?x3857), language(?x3857, ?x254), films(?x7455, ?x3857), ?x7455 = 07_nf, genre(?x3857, ?x53) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03z106 language 07zrf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.333 http://example.org/film/film/language #15048-01wqmm8 PRED entity: 01wqmm8 PRED relation: award PRED expected values: 02f705 02f6ym => 97 concepts (91 used for prediction) PRED predicted values (max 10 best out of 292): 09sb52 (0.34 #22101, 0.33 #14079, 0.28 #13275), 01by1l (0.34 #5726, 0.31 #8132, 0.29 #8934), 01bgqh (0.26 #5657, 0.26 #8063, 0.23 #3652), 02f6ym (0.26 #657, 0.26 #256, 0.13 #1459), 054ks3 (0.25 #3350, 0.18 #5756, 0.18 #3751), 03qbh5 (0.24 #3814, 0.24 #1408, 0.22 #5819), 0c4z8 (0.24 #3280, 0.23 #3681, 0.21 #5686), 03c7tr1 (0.21 #460, 0.16 #59, 0.11 #8480), 05b4l5x (0.21 #407, 0.16 #6, 0.08 #8427), 0gqz2 (0.19 #3289, 0.12 #6497, 0.12 #5695) >> Best rule #22101 for best value: >> intensional similarity = 3 >> extensional distance = 1163 >> proper extension: 06jzh; 0785v8; 04sx9_; 019_1h; 01v42g; 022769; 0f6_dy; 02xb2bt; 03q1vd; 050t68; ... >> query: (?x7553, 09sb52) <- award_nominee(?x2737, ?x7553), film(?x7553, ?x6499), award(?x7553, ?x1389) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #657 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 019pm_; 02fybl; *> query: (?x7553, 02f6ym) <- profession(?x7553, ?x4773), profession(?x7553, ?x2348), ?x2348 = 0nbcg, ?x4773 = 0d1pc, location(?x7553, ?x3148) *> conf = 0.26 ranks of expected_values: 4, 17 EVAL 01wqmm8 award 02f6ym CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 91.000 0.342 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01wqmm8 award 02f705 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 97.000 91.000 0.342 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15047-02yvct PRED entity: 02yvct PRED relation: films! PRED expected values: 012hw 05hyf => 135 concepts (35 used for prediction) PRED predicted values (max 10 best out of 66): 05qtj (0.25 #308, 0.25 #190, 0.11 #307), 07jq_ (0.12 #234, 0.06 #541, 0.03 #4677), 01w1sx (0.12 #89, 0.05 #702, 0.03 #4532), 01cgz (0.11 #479, 0.05 #631, 0.04 #1087), 07yjb (0.11 #371, 0.05 #676, 0.03 #1437), 0kbq (0.11 #411, 0.03 #1324, 0.03 #2857), 0156q (0.11 #307), 0345h (0.11 #307), 0d1w9 (0.10 #648, 0.06 #496, 0.06 #343), 05489 (0.07 #815, 0.05 #4493, 0.03 #3877) >> Best rule #308 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0bz3jx; >> query: (?x2189, ?x4627) <- featured_film_locations(?x2189, ?x4627), ?x4627 = 05qtj, film(?x815, ?x2189), language(?x2189, ?x90) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02yvct films! 05hyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 35.000 0.250 http://example.org/film/film_subject/films EVAL 02yvct films! 012hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 35.000 0.250 http://example.org/film/film_subject/films #15046-01x66d PRED entity: 01x66d PRED relation: profession PRED expected values: 01c72t => 143 concepts (78 used for prediction) PRED predicted values (max 10 best out of 84): 01c72t (0.86 #5031, 0.62 #1053, 0.59 #612), 09jwl (0.77 #7388, 0.76 #8271, 0.76 #901), 02hrh1q (0.67 #8119, 0.67 #7236, 0.67 #1484), 0nbcg (0.61 #6368, 0.55 #3268, 0.54 #5480), 016z4k (0.51 #885, 0.50 #8255, 0.49 #1179), 01d_h8 (0.39 #1475, 0.32 #740, 0.27 #2210), 039v1 (0.34 #6373, 0.33 #3273, 0.31 #6520), 0dxtg (0.33 #1483, 0.33 #13, 0.27 #7676), 0n1h (0.33 #11, 0.26 #4871, 0.22 #4134), 02jknp (0.33 #7, 0.24 #1477, 0.18 #2212) >> Best rule #5031 for best value: >> intensional similarity = 6 >> extensional distance = 314 >> proper extension: 0d0mbj; 02fybl; 0gv07g; 01rw116; 06zd1c; >> query: (?x1068, 01c72t) <- gender(?x1068, ?x231), profession(?x1068, ?x6565), profession(?x3869, ?x6565), profession(?x3171, ?x6565), ?x3171 = 0p3sf, ?x3869 = 06gd4 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x66d profession 01c72t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 78.000 0.864 http://example.org/people/person/profession #15045-02kzfw PRED entity: 02kzfw PRED relation: major_field_of_study PRED expected values: 02j62 => 137 concepts (129 used for prediction) PRED predicted values (max 10 best out of 119): 03g3w (0.58 #3107, 0.30 #6933, 0.30 #2366), 01mkq (0.57 #3218, 0.50 #631, 0.46 #754), 04rjg (0.42 #636, 0.35 #267, 0.35 #1867), 02j62 (0.42 #278, 0.40 #1878, 0.40 #893), 02lp1 (0.39 #628, 0.37 #2351, 0.34 #3215), 01lj9 (0.36 #780, 0.32 #288, 0.27 #534), 05qjt (0.32 #255, 0.28 #747, 0.27 #501), 062z7 (0.32 #3108, 0.32 #644, 0.27 #3231), 01tbp (0.31 #800, 0.29 #308, 0.29 #677), 05qfh (0.29 #653, 0.28 #899, 0.22 #1392) >> Best rule #3107 for best value: >> intensional similarity = 6 >> extensional distance = 202 >> proper extension: 02301; 0cchk3; 0373qg; 01mpwj; 01dbns; >> query: (?x6193, 03g3w) <- major_field_of_study(?x6193, ?x4321), major_field_of_study(?x2711, ?x4321), major_field_of_study(?x216, ?x4321), major_field_of_study(?x620, ?x4321), ?x216 = 05zjtn4, contains(?x94, ?x2711) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #278 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 29 *> proper extension: 02t4yc; 01zn4y; 02ldmw; *> query: (?x6193, 02j62) <- school_type(?x6193, ?x3092), ?x3092 = 05jxkf, currency(?x6193, ?x1099), company(?x3484, ?x6193), category(?x6193, ?x134) *> conf = 0.42 ranks of expected_values: 4 EVAL 02kzfw major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 137.000 129.000 0.583 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #15044-019r_1 PRED entity: 019r_1 PRED relation: nationality PRED expected values: 09c7w0 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 80): 09c7w0 (0.95 #3882, 0.95 #2886, 0.90 #991), 07ssc (0.51 #8000, 0.28 #9693, 0.25 #411), 03rk0 (0.33 #244, 0.26 #739, 0.17 #9724), 059rby (0.28 #11370), 0d060g (0.23 #7992, 0.20 #304, 0.17 #106), 02jx1 (0.21 #8018, 0.17 #132, 0.14 #1718), 0345h (0.20 #328, 0.19 #2517, 0.11 #1616), 0f8l9c (0.20 #319, 0.11 #8007, 0.06 #9700), 06m_5 (0.20 #83, 0.08 #578, 0.01 #2368), 0h7x (0.19 #1620, 0.10 #2722, 0.10 #2119) >> Best rule #3882 for best value: >> intensional similarity = 4 >> extensional distance = 202 >> proper extension: 0grrq8; >> query: (?x4724, 09c7w0) <- location(?x4724, ?x739), ?x739 = 02_286, student(?x11349, ?x4724), nationality(?x4724, ?x9006) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019r_1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.946 http://example.org/people/person/nationality #15043-062zm5h PRED entity: 062zm5h PRED relation: language PRED expected values: 06b_j => 93 concepts (89 used for prediction) PRED predicted values (max 10 best out of 36): 064_8sq (0.22 #199, 0.15 #374, 0.14 #2149), 04h9h (0.12 #337, 0.10 #279, 0.05 #454), 02bjrlw (0.11 #472, 0.10 #238, 0.08 #1064), 06nm1 (0.11 #897, 0.10 #721, 0.10 #247), 04306rv (0.09 #2132, 0.09 #1893, 0.09 #1008), 03_9r (0.08 #955, 0.07 #127, 0.07 #661), 06b_j (0.08 #791, 0.07 #1911, 0.07 #1145), 0653m (0.07 #1015, 0.05 #1900, 0.04 #722), 0t_2 (0.07 #131, 0.03 #250, 0.02 #308), 032f6 (0.06 #233, 0.03 #586, 0.02 #766) >> Best rule #199 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 017gm7; 0fpv_3_; 0j43swk; 0gyfp9c; 0gh65c5; 0j3d9tn; 0cc97st; 0gj96ln; 0gwjw0c; 0fpgp26; ... >> query: (?x5016, 064_8sq) <- film_release_region(?x5016, ?x3227), genre(?x5016, ?x225), nominated_for(?x298, ?x5016), ?x3227 = 0bjv6 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #791 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 138 *> proper extension: 03_wm6; *> query: (?x5016, 06b_j) <- genre(?x5016, ?x1013), country(?x5016, ?x94), ?x1013 = 06n90, film_crew_role(?x5016, ?x468) *> conf = 0.08 ranks of expected_values: 7 EVAL 062zm5h language 06b_j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 93.000 89.000 0.222 http://example.org/film/film/language #15042-0c1pj PRED entity: 0c1pj PRED relation: currency PRED expected values: 09nqf => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.38 #19, 0.37 #85, 0.36 #67), 0kz1h (0.15 #321, 0.12 #203), 01nv4h (0.01 #83) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 0dzlk; >> query: (?x556, 09nqf) <- nominated_for(?x556, ?x174), notable_people_with_this_condition(?x7374, ?x556), participant(?x262, ?x556) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c1pj currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.379 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #15041-02qnbs PRED entity: 02qnbs PRED relation: profession PRED expected values: 0196pc => 107 concepts (89 used for prediction) PRED predicted values (max 10 best out of 88): 02hrh1q (0.89 #9702, 0.88 #9851, 0.85 #10150), 0dxtg (0.86 #1652, 0.84 #2248, 0.84 #1205), 03gjzk (0.72 #1207, 0.70 #1654, 0.69 #1058), 0kyk (0.51 #5247, 0.49 #5993, 0.48 #5844), 09jwl (0.48 #6578, 0.17 #8962, 0.17 #9558), 01d_h8 (0.44 #6118, 0.42 #6416, 0.34 #1198), 02jknp (0.34 #6119, 0.31 #6417, 0.25 #305), 0nbcg (0.31 #6591, 0.11 #10019, 0.11 #10317), 02krf9 (0.30 #27, 0.23 #1368, 0.21 #1219), 0np9r (0.30 #21, 0.23 #1064, 0.16 #1213) >> Best rule #9702 for best value: >> intensional similarity = 6 >> extensional distance = 1787 >> proper extension: 01pgzn_; 06_bq1; >> query: (?x8339, 02hrh1q) <- profession(?x8339, ?x353), location(?x8339, ?x739), profession(?x4465, ?x353), profession(?x3083, ?x353), ?x3083 = 01pcrw, award_winner(?x11272, ?x4465) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #819 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 31 *> proper extension: 01gp_x; 02xs0q; 05pzdk; 07g7h2; 087qxp; 0brkwj; 02778tk; *> query: (?x8339, 0196pc) <- profession(?x8339, ?x353), gender(?x8339, ?x231), tv_program(?x8339, ?x8846), ?x231 = 05zppz, ?x353 = 0cbd2 *> conf = 0.15 ranks of expected_values: 15 EVAL 02qnbs profession 0196pc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 107.000 89.000 0.893 http://example.org/people/person/profession #15040-0cy__l PRED entity: 0cy__l PRED relation: written_by PRED expected values: 084w8 => 99 concepts (63 used for prediction) PRED predicted values (max 10 best out of 75): 0m9c1 (0.36 #7420, 0.36 #8096, 0.36 #5731), 030s5g (0.15 #8771, 0.15 #18232, 0.11 #14515), 026v_78 (0.11 #14515, 0.11 #8095, 0.11 #10120), 0cgbf (0.11 #14515, 0.11 #8095, 0.11 #10120), 086k8 (0.11 #10120, 0.10 #9108, 0.10 #1684), 0c921 (0.09 #623, 0.06 #1295, 0.03 #1632), 0m593 (0.08 #7082, 0.07 #6745, 0.07 #9446), 03thw4 (0.08 #1150, 0.05 #814, 0.04 #478), 027vps (0.06 #589, 0.05 #1261, 0.02 #2273), 0171lb (0.06 #466, 0.05 #1138, 0.02 #802) >> Best rule #7420 for best value: >> intensional similarity = 4 >> extensional distance = 415 >> proper extension: 02v8kmz; 09q5w2; 0qm8b; 0g3zrd; 07w8fz; 0ds2n; 02ht1k; 0cmc26r; 03176f; 0y_9q; ... >> query: (?x5509, ?x11251) <- nominated_for(?x382, ?x5509), film(?x11251, ?x5509), film_crew_role(?x5509, ?x137), nominated_for(?x601, ?x5509) >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cy__l written_by 084w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 63.000 0.361 http://example.org/film/film/written_by #15039-0dwt5 PRED entity: 0dwt5 PRED relation: role! PRED expected values: 0l1589 => 87 concepts (53 used for prediction) PRED predicted values (max 10 best out of 79): 04rzd (0.86 #153, 0.86 #2404, 0.85 #73), 013y1f (0.86 #153, 0.85 #73, 0.85 #1735), 07y_7 (0.86 #153, 0.85 #73, 0.85 #1735), 0gkd1 (0.86 #153, 0.85 #73, 0.85 #1735), 0xzly (0.86 #153, 0.85 #73, 0.85 #1735), 0dwtp (0.86 #153, 0.85 #73, 0.85 #1735), 01s0ps (0.86 #153, 0.85 #73, 0.85 #1735), 02qjv (0.86 #153, 0.85 #73, 0.85 #1735), 0gghm (0.86 #153, 0.85 #73, 0.85 #1735), 01rhl (0.86 #153, 0.85 #73, 0.85 #1735) >> Best rule #153 for best value: >> intensional similarity = 26 >> extensional distance = 1 >> proper extension: 026t6; >> query: (?x4769, ?x74) <- role(?x4769, ?x8172), role(?x4769, ?x3703), role(?x4769, ?x3296), role(?x4769, ?x2310), role(?x4769, ?x1432), role(?x4769, ?x960), role(?x4769, ?x745), role(?x4769, ?x614), role(?x4769, ?x314), role(?x4769, ?x74), ?x745 = 01vj9c, group(?x4769, ?x3516), ?x8172 = 06rvn, ?x614 = 0mkg, role(?x3296, ?x2459), role(?x227, ?x4769), ?x960 = 04q7r, role(?x3296, ?x11978), ?x11978 = 02hrlh, ?x2459 = 021bmf, instrumentalists(?x3296, ?x1399), ?x3703 = 02dlh2, ?x314 = 02sgy, ?x1432 = 0395lw, ?x2310 = 0gghm, role(?x565, ?x4769) >> conf = 0.86 => this is the best rule for 11 predicted values *> Best rule #157 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 1 *> proper extension: 026t6; *> query: (?x4769, ?x922) <- role(?x4769, ?x8172), role(?x4769, ?x3703), role(?x4769, ?x3296), role(?x4769, ?x2310), role(?x4769, ?x1432), role(?x4769, ?x960), role(?x4769, ?x745), role(?x4769, ?x614), role(?x4769, ?x314), ?x745 = 01vj9c, group(?x4769, ?x3516), ?x8172 = 06rvn, ?x614 = 0mkg, role(?x3296, ?x2459), role(?x227, ?x4769), ?x960 = 04q7r, role(?x3296, ?x11978), role(?x3296, ?x922), ?x11978 = 02hrlh, ?x2459 = 021bmf, instrumentalists(?x3296, ?x1399), ?x3703 = 02dlh2, ?x314 = 02sgy, ?x1432 = 0395lw, ?x2310 = 0gghm, role(?x565, ?x4769) *> conf = 0.72 ranks of expected_values: 26 EVAL 0dwt5 role! 0l1589 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 87.000 53.000 0.859 http://example.org/music/performance_role/track_performances./music/track_contribution/role #15038-0bl8l PRED entity: 0bl8l PRED relation: team! PRED expected values: 04bsx1 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 91): 0f1pyf (0.60 #244, 0.20 #354, 0.18 #1344), 071h5c (0.50 #200, 0.43 #750, 0.40 #310), 05s_c38 (0.43 #687, 0.29 #1787, 0.25 #137), 02d9k (0.42 #1437, 0.17 #447, 0.14 #1657), 02rnns (0.40 #374, 0.22 #1144, 0.18 #1254), 0g9zjp (0.40 #306, 0.18 #1406, 0.18 #1296), 0djvzd (0.40 #261, 0.18 #1361, 0.17 #7161), 04v68c (0.33 #547, 0.29 #877, 0.29 #657), 09r1j5 (0.33 #37, 0.22 #1137, 0.21 #1577), 09lhln (0.29 #1776, 0.25 #126, 0.22 #1006) >> Best rule #244 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 01cwm1; >> query: (?x5207, 0f1pyf) <- current_club(?x9926, ?x5207), position(?x5207, ?x203), position(?x5207, ?x63), sport(?x5207, ?x471), ?x203 = 0dgrmp, ?x63 = 02sdk9v, team(?x8598, ?x5207), team(?x530, ?x5207), ?x8598 = 07m69t, position(?x5207, ?x60) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7161 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 167 *> proper extension: 02b1lm; *> query: (?x5207, ?x5685) <- team(?x8598, ?x5207), team(?x8598, ?x348), nationality(?x8598, ?x94), team(?x8598, ?x7798), team(?x5685, ?x7798), sport(?x7798, ?x471), team(?x60, ?x7798), place_of_birth(?x8598, ?x1406) *> conf = 0.17 ranks of expected_values: 27 EVAL 0bl8l team! 04bsx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 102.000 102.000 0.600 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #15037-026p4q7 PRED entity: 026p4q7 PRED relation: nominated_for! PRED expected values: 0p9sw 0f4x7 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 177): 027b9ly (0.68 #4514, 0.68 #4103, 0.67 #6772), 027c924 (0.68 #4514, 0.68 #4103, 0.67 #6772), 0p9sw (0.47 #222, 0.45 #1657, 0.33 #837), 02r22gf (0.40 #229, 0.35 #2074, 0.33 #844), 0gr51 (0.37 #1290, 0.33 #2110, 0.33 #265), 027dtxw (0.37 #2055, 0.33 #210, 0.33 #1235), 02qyp19 (0.36 #1232, 0.33 #207, 0.26 #2052), 0gqy2 (0.36 #1324, 0.30 #2144, 0.29 #5632), 09sdmz (0.34 #1344, 0.33 #319, 0.21 #934), 02ppm4q (0.34 #1318, 0.32 #2138, 0.23 #8622) >> Best rule #4514 for best value: >> intensional similarity = 4 >> extensional distance = 339 >> proper extension: 0ds35l9; 03qcfvw; 0m313; 02y_lrp; 02vxq9m; 028_yv; 09m6kg; 01gc7; 011yxg; 07gp9; ... >> query: (?x2490, ?x143) <- film_crew_role(?x2490, ?x137), award(?x2490, ?x143), production_companies(?x2490, ?x902), nominated_for(?x198, ?x2490) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #222 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 07w8fz; 0298n7; *> query: (?x2490, 0p9sw) <- film_crew_role(?x2490, ?x137), nominated_for(?x2393, ?x2490), nominated_for(?x704, ?x2490), ?x704 = 09sb52, ?x2393 = 02x258x *> conf = 0.47 ranks of expected_values: 3, 17 EVAL 026p4q7 nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 123.000 123.000 0.680 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 026p4q7 nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 123.000 0.680 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15036-07db6x PRED entity: 07db6x PRED relation: profession PRED expected values: 01d_h8 => 124 concepts (88 used for prediction) PRED predicted values (max 10 best out of 76): 01d_h8 (0.87 #302, 0.81 #1338, 0.72 #5038), 02hrh1q (0.73 #8597, 0.70 #11113, 0.68 #8893), 03gjzk (0.49 #1494, 0.49 #6674, 0.48 #7118), 0cbd2 (0.40 #895, 0.39 #599, 0.38 #747), 09jwl (0.33 #18, 0.16 #11118, 0.16 #8602), 0nbcg (0.33 #31, 0.14 #2399, 0.13 #1215), 039v1 (0.33 #36, 0.04 #10692, 0.04 #12617), 02krf9 (0.27 #4614, 0.21 #5058, 0.20 #6686), 0kyk (0.23 #769, 0.18 #621, 0.17 #917), 018gz8 (0.19 #1496, 0.18 #6676, 0.18 #7120) >> Best rule #302 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0py5b; >> query: (?x13015, 01d_h8) <- award_nominee(?x13015, ?x574), gender(?x13015, ?x231), ?x574 = 016tt2 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07db6x profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 88.000 0.867 http://example.org/people/person/profession #15035-01q9b9 PRED entity: 01q9b9 PRED relation: type_of_union PRED expected values: 04ztj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.79 #21, 0.79 #9, 0.78 #81), 01bl8s (0.46 #341), 01g63y (0.16 #26, 0.16 #82, 0.15 #98), 0jgjn (0.02 #20, 0.01 #44) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 01l1ls; 01lct6; >> query: (?x7512, 04ztj) <- profession(?x7512, ?x319), award(?x7512, ?x594), ?x594 = 02grdc >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q9b9 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.791 http://example.org/people/person/spouse_s./people/marriage/type_of_union #15034-02301 PRED entity: 02301 PRED relation: institution! PRED expected values: 014mlp => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.79 #238, 0.78 #601, 0.77 #153), 02_xgp2 (0.73 #159, 0.59 #607, 0.57 #268), 016t_3 (0.64 #151, 0.56 #87, 0.56 #66), 03bwzr4 (0.59 #161, 0.54 #609, 0.47 #270), 013zdg (0.55 #155, 0.41 #264, 0.32 #240), 04zx3q1 (0.55 #150, 0.37 #235, 0.35 #259), 07s6fsf (0.50 #149, 0.41 #258, 0.41 #597), 027f2w (0.50 #156, 0.34 #241, 0.33 #29), 0bjrnt (0.33 #27, 0.32 #154, 0.28 #2457), 071tyz (0.33 #30, 0.28 #2457, 0.18 #2411) >> Best rule #238 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 01tntf; >> query: (?x2730, 014mlp) <- currency(?x2730, ?x170), major_field_of_study(?x2730, ?x3995), ?x3995 = 0fdys >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02301 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.789 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #15033-03x16f PRED entity: 03x16f PRED relation: location PRED expected values: 01m7mv => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 136): 030qb3t (0.33 #82, 0.29 #4894, 0.28 #4092), 0h7h6 (0.17 #89, 0.03 #7307, 0.02 #6505), 04n3l (0.17 #179, 0.02 #7397, 0.01 #10606), 0djd3 (0.17 #322, 0.01 #9144), 0sqgt (0.17 #529), 0cc56 (0.10 #3264, 0.06 #4868, 0.05 #4066), 0r0m6 (0.10 #3424, 0.05 #5028, 0.04 #4226), 04jpl (0.09 #4027, 0.08 #12048, 0.07 #4829), 0f2wj (0.07 #836, 0.06 #1638, 0.05 #2440), 0vzm (0.07 #974, 0.06 #1776, 0.05 #2578) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 02184q; >> query: (?x8746, 030qb3t) <- languages(?x8746, ?x254), actor(?x5852, ?x8746), ?x5852 = 024rwx >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03x16f location 01m7mv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 104.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #15032-01d4cb PRED entity: 01d4cb PRED relation: artists! PRED expected values: 02w4v => 107 concepts (88 used for prediction) PRED predicted values (max 10 best out of 212): 064t9 (0.74 #10063, 0.66 #3359, 0.65 #3055), 06j6l (0.68 #3394, 0.67 #3090, 0.33 #10098), 025sc50 (0.47 #3092, 0.47 #3396, 0.27 #10100), 08vlns (0.47 #1118, 0.04 #7207, 0.03 #9950), 03_d0 (0.42 #1533, 0.29 #316, 0.29 #11), 02lnbg (0.33 #972, 0.23 #3404, 0.21 #3100), 0glt670 (0.32 #3386, 0.32 #3082, 0.28 #10090), 02x8m (0.30 #3365, 0.29 #324, 0.26 #3061), 0mhfr (0.29 #2762, 0.08 #5504, 0.08 #5809), 05bt6j (0.29 #348, 0.28 #10093, 0.27 #957) >> Best rule #10063 for best value: >> intensional similarity = 3 >> extensional distance = 471 >> proper extension: 0m19t; 07_3qd; 01l_vgt; 03xhj6; 06nv27; 0123r4; 02vgh; 01kcms4; 08w4pm; 01l_w0; ... >> query: (?x9128, 064t9) <- artists(?x3928, ?x9128), artists(?x3928, ?x2614), ?x2614 = 04xrx >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #2782 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 03yf3z; 0249kn; 018ndc; 01cblr; 0134tg; 07mvp; 01jkqfz; 0mjn2; *> query: (?x9128, 02w4v) <- award(?x9128, ?x2379), artists(?x2664, ?x9128), ?x2664 = 01lyv *> conf = 0.27 ranks of expected_values: 12 EVAL 01d4cb artists! 02w4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 107.000 88.000 0.744 http://example.org/music/genre/artists #15031-0n1h PRED entity: 0n1h PRED relation: specialization_of! PRED expected values: 02ynfr => 53 concepts (50 used for prediction) PRED predicted values (max 10 best out of 129): 01xr66 (0.33 #138, 0.25 #542, 0.25 #441), 0np9r (0.33 #110, 0.25 #514, 0.25 #413), 0mbx4 (0.33 #196, 0.25 #600, 0.25 #499), 0g7nc (0.33 #187, 0.25 #591, 0.25 #490), 0w7c (0.33 #136, 0.25 #540, 0.25 #439), 021wpb (0.33 #127, 0.25 #531, 0.25 #430), 07s467s (0.25 #410, 0.12 #1016, 0.12 #1420), 09hljw (0.25 #504, 0.12 #1110, 0.06 #1514), 030pm0 (0.25 #492, 0.12 #1098, 0.06 #1502), 04syw (0.25 #423, 0.12 #1029, 0.06 #1433) >> Best rule #138 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 02hrh1q; >> query: (?x955, 01xr66) <- profession(?x7614, ?x955), profession(?x4080, ?x955), profession(?x3358, ?x955), profession(?x2138, ?x955), ?x7614 = 01s1zk, ?x2138 = 086qd, ?x3358 = 01n8gr, ?x4080 = 0dl567 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #929 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 04_tv; *> query: (?x955, 02ynfr) <- specialization_of(?x1183, ?x955), profession(?x10396, ?x1183), profession(?x2940, ?x1183), performance_role(?x2940, ?x1495), role(?x10396, ?x227) *> conf = 0.14 ranks of expected_values: 49 EVAL 0n1h specialization_of! 02ynfr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 53.000 50.000 0.333 http://example.org/people/profession/specialization_of #15030-05fjy PRED entity: 05fjy PRED relation: religion PRED expected values: 021_0p => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 23): 021_0p (0.66 #244, 0.56 #582, 0.55 #140), 03_gx (0.54 #240, 0.46 #188, 0.45 #136), 01s5nb (0.49 #249, 0.42 #93, 0.41 #145), 02t7t (0.42 #1639, 0.27 #143, 0.26 #169), 0flw86 (0.38 #1093, 0.38 #105, 0.38 #1431), 092bf5 (0.38 #111, 0.29 #241, 0.28 #813), 03j6c (0.25 #63, 0.25 #37, 0.10 #115), 0kpl (0.25 #55, 0.25 #29, 0.09 #2108), 07w8f (0.25 #72, 0.25 #46, 0.09 #2108), 072w0 (0.23 #562, 0.21 #198, 0.20 #536) >> Best rule #244 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 01bkb; >> query: (?x5575, 021_0p) <- country(?x5575, ?x94), religion(?x5575, ?x109), ?x109 = 01lp8 >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fjy religion 021_0p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.657 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #15029-02wvfxl PRED entity: 02wvfxl PRED relation: category PRED expected values: 08mbj5d => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.56 #38, 0.55 #34, 0.55 #37) >> Best rule #38 for best value: >> intensional similarity = 15 >> extensional distance = 82 >> proper extension: 013yq; 04n7ps6; >> query: (?x5472, 08mbj5d) <- team(?x180, ?x5472), position_s(?x7312, ?x180), position_s(?x6379, ?x180), position_s(?x5603, ?x180), position_s(?x4519, ?x180), position_s(?x1337, ?x180), position(?x2574, ?x180), position_s(?x1517, ?x180), position(?x180, ?x1240), ?x1240 = 023wyl, ?x1337 = 0ftf0f, ?x4519 = 084l5, ?x7312 = 0487_, ?x6379 = 0bjkk9, ?x5603 = 07kcvl >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wvfxl category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.560 http://example.org/common/topic/webpage./common/webpage/category #15028-05ty4m PRED entity: 05ty4m PRED relation: profession PRED expected values: 02jknp => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 75): 02jknp (0.88 #1747, 0.87 #442, 0.55 #2327), 09jwl (0.50 #741, 0.36 #4512, 0.36 #4367), 0cbd2 (0.46 #4792, 0.43 #5082, 0.43 #2616), 018gz8 (0.37 #1174, 0.36 #884, 0.34 #594), 0nbcg (0.35 #753, 0.30 #5222, 0.27 #4379), 016z4k (0.35 #729, 0.24 #5806, 0.24 #4355), 0kyk (0.31 #4812, 0.30 #5222, 0.29 #2636), 0dz3r (0.31 #727, 0.24 #4353, 0.24 #4498), 02hv44_ (0.30 #5222, 0.12 #2954, 0.11 #2664), 08z956 (0.30 #5222, 0.06 #75, 0.03 #220) >> Best rule #1747 for best value: >> intensional similarity = 2 >> extensional distance = 170 >> proper extension: 0162c8; >> query: (?x364, 02jknp) <- award_nominee(?x364, ?x237), film(?x364, ?x2695) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05ty4m profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.884 http://example.org/people/person/profession #15027-0170xl PRED entity: 0170xl PRED relation: genre PRED expected values: 060__y => 104 concepts (78 used for prediction) PRED predicted values (max 10 best out of 128): 05p553 (0.45 #699, 0.43 #3, 0.39 #1164), 060__y (0.38 #827, 0.29 #1642, 0.24 #2571), 02l7c8 (0.38 #3849, 0.37 #1641, 0.35 #826), 0lsxr (0.36 #240, 0.33 #124, 0.24 #1169), 01jfsb (0.33 #127, 0.32 #2915, 0.30 #3148), 03k9fj (0.33 #1869, 0.24 #6284, 0.24 #7792), 02kdv5l (0.30 #2905, 0.29 #6275, 0.27 #7667), 03g3w (0.23 #487, 0.20 #603, 0.20 #139), 082gq (0.22 #1306, 0.21 #1072, 0.20 #492), 04xvh5 (0.22 #1659, 0.17 #496, 0.12 #960) >> Best rule #699 for best value: >> intensional similarity = 2 >> extensional distance = 45 >> proper extension: 0cfhfz; 0b44shh; 047myg9; 05ft32; >> query: (?x11213, 05p553) <- nominated_for(?x2902, ?x11213), ?x2902 = 02x4sn8 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #827 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 64 *> proper extension: 083shs; 027qgy; 05jzt3; 048scx; 092vkg; 04vr_f; 02q5g1z; 09cr8; 0260bz; 026p4q7; ... *> query: (?x11213, 060__y) <- currency(?x11213, ?x170), nominated_for(?x2341, ?x11213), ?x2341 = 02x17s4, genre(?x11213, ?x53) *> conf = 0.38 ranks of expected_values: 2 EVAL 0170xl genre 060__y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 78.000 0.447 http://example.org/film/film/genre #15026-01vfqh PRED entity: 01vfqh PRED relation: award PRED expected values: 027b9k6 => 108 concepts (107 used for prediction) PRED predicted values (max 10 best out of 214): 0gq_v (0.27 #15548, 0.27 #15781, 0.24 #7194), 02rdyk7 (0.20 #765, 0.14 #997, 0.10 #533), 0l8z1 (0.20 #746, 0.14 #978, 0.10 #514), 09cm54 (0.20 #771, 0.14 #1003, 0.08 #2163), 02w9sd7 (0.20 #819, 0.14 #1051, 0.08 #2675), 0gs96 (0.18 #5888, 0.14 #3800, 0.10 #552), 0k611 (0.14 #1000, 0.12 #5872, 0.11 #3320), 02g3ft (0.14 #994, 0.10 #1226, 0.10 #762), 0gr0m (0.14 #5858, 0.12 #5162, 0.10 #754), 027c95y (0.13 #2204, 0.12 #2668, 0.11 #3132) >> Best rule #15548 for best value: >> intensional similarity = 3 >> extensional distance = 984 >> proper extension: 02_1q9; 02_1kl; 0fpxp; 097h2; 019g8j; 0147w8; >> query: (?x1331, ?x484) <- nominated_for(?x484, ?x1331), award(?x1331, ?x10597), award_winner(?x10597, ?x2595) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #841 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0g5qs2k; 017gl1; 0c0nhgv; 011yqc; 0bx0l; 03qnc6q; 03cw411; 0h63gl9; *> query: (?x1331, 027b9k6) <- film_release_region(?x1331, ?x172), honored_for(?x1331, ?x1330), ?x172 = 0154j, produced_by(?x1331, ?x826) *> conf = 0.10 ranks of expected_values: 20 EVAL 01vfqh award 027b9k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 108.000 107.000 0.270 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #15025-069nzr PRED entity: 069nzr PRED relation: profession PRED expected values: 02hrh1q => 108 concepts (107 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.90 #1511, 0.89 #11945, 0.88 #4193), 0dxtg (0.44 #312, 0.32 #2702, 0.28 #4490), 01d_h8 (0.34 #3886, 0.34 #5376, 0.33 #2694), 02jknp (0.26 #12975, 0.25 #12676, 0.25 #5819), 0np9r (0.26 #12975, 0.25 #12676, 0.25 #5819), 09jwl (0.26 #12975, 0.25 #12676, 0.25 #5819), 02krf9 (0.26 #12975, 0.25 #12676, 0.25 #5819), 0cbd2 (0.26 #12975, 0.25 #12676, 0.25 #5819), 01c72t (0.26 #12975, 0.25 #12676, 0.25 #5819), 018gz8 (0.15 #315, 0.13 #614, 0.13 #11947) >> Best rule #1511 for best value: >> intensional similarity = 3 >> extensional distance = 318 >> proper extension: 04bs3j; >> query: (?x5030, 02hrh1q) <- nominated_for(?x5030, ?x4607), languages(?x5030, ?x254), film(?x5030, ?x1012) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 069nzr profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 107.000 0.903 http://example.org/people/person/profession #15024-0d3k14 PRED entity: 0d3k14 PRED relation: student! PRED expected values: 0hsb3 => 228 concepts (228 used for prediction) PRED predicted values (max 10 best out of 298): 05qgd9 (0.50 #2024, 0.29 #6200, 0.20 #4112), 0g8rj (0.50 #1738, 0.29 #5914, 0.20 #3826), 01vc5m (0.25 #1659, 0.20 #3747, 0.19 #19841), 02bq1j (0.25 #7993, 0.14 #5383, 0.09 #14259), 065y4w7 (0.25 #2101, 0.11 #19854, 0.11 #10976), 021w0_ (0.25 #2407, 0.05 #11282, 0.05 #13893), 07tg4 (0.20 #3217, 0.17 #4783, 0.17 #4261), 02hmw9 (0.20 #3364, 0.17 #4930, 0.17 #4408), 014zws (0.20 #3458, 0.17 #4502, 0.08 #10245), 01wqg8 (0.20 #3452, 0.17 #4496, 0.03 #16505) >> Best rule #2024 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 06hx2; 0194xc; >> query: (?x11088, 05qgd9) <- sibling(?x11088, ?x6138), jurisdiction_of_office(?x11088, ?x94), student(?x581, ?x11088), ?x94 = 09c7w0 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #10644 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 063vn; *> query: (?x11088, 0hsb3) <- participant(?x11088, ?x543), place_of_death(?x11088, ?x5719), student(?x581, ?x11088) *> conf = 0.07 ranks of expected_values: 60 EVAL 0d3k14 student! 0hsb3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 228.000 228.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/student #15023-030pr PRED entity: 030pr PRED relation: nationality PRED expected values: 09c7w0 => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 101): 09c7w0 (0.86 #1585, 0.82 #13867, 0.79 #595), 02jx1 (0.16 #428, 0.14 #5974, 0.13 #7658), 07ssc (0.12 #4073, 0.11 #5956, 0.11 #4767), 0d060g (0.10 #1788, 0.08 #798, 0.08 #996), 03rk0 (0.09 #7176, 0.09 #7077, 0.08 #8860), 0f8l9c (0.08 #318, 0.07 #516, 0.05 #219), 05r7t (0.05 #275, 0.01 #1859), 06mkj (0.05 #244, 0.01 #1828), 03gj2 (0.04 #2005, 0.04 #1906, 0.02 #2302), 03spz (0.04 #858, 0.02 #1848, 0.02 #5017) >> Best rule #1585 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 02yy_j; >> query: (?x1134, 09c7w0) <- person(?x6093, ?x1134), student(?x7278, ?x1134), nationality(?x1134, ?x205) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030pr nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.857 http://example.org/people/person/nationality #15022-01qs54 PRED entity: 01qs54 PRED relation: location! PRED expected values: 018p4y => 109 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1453): 02mjmr (0.33 #501, 0.17 #8055, 0.07 #10574), 01p7yb (0.33 #47, 0.17 #7601, 0.07 #10120), 023kzp (0.33 #1216, 0.17 #8770, 0.07 #11289), 0405l (0.33 #2205, 0.17 #9759, 0.07 #12278), 0738b8 (0.33 #444, 0.17 #7998, 0.05 #25629), 046zh (0.33 #1064, 0.17 #8618, 0.04 #35260), 01s21dg (0.33 #964, 0.17 #8518, 0.03 #26149), 013w7j (0.33 #1250, 0.17 #8804, 0.03 #26435), 0h1mt (0.33 #188, 0.17 #7742, 0.03 #25373), 02lt8 (0.33 #796, 0.11 #23463, 0.08 #8350) >> Best rule #501 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02_286; >> query: (?x10062, 02mjmr) <- place_of_birth(?x1951, ?x10062), award_nominee(?x3604, ?x1951), ?x3604 = 03v3xp, administrative_parent(?x10062, ?x429) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #27508 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 0vmt; *> query: (?x10062, 018p4y) <- place_of_birth(?x1951, ?x10062), country(?x10062, ?x429), type_of_union(?x1951, ?x566), award_winner(?x472, ?x1951) *> conf = 0.03 ranks of expected_values: 1100 EVAL 01qs54 location! 018p4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 109.000 45.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #15021-07ss8_ PRED entity: 07ss8_ PRED relation: people! PRED expected values: 0x67 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 56): 0x67 (0.27 #10, 0.27 #549, 0.25 #780), 033tf_ (0.16 #84, 0.16 #392, 0.14 #1085), 041rx (0.15 #1082, 0.14 #1544, 0.14 #3007), 0xnvg (0.13 #90, 0.11 #244, 0.10 #167), 07bch9 (0.10 #177, 0.09 #254, 0.09 #408), 09vc4s (0.10 #86, 0.08 #625, 0.07 #1087), 02ctzb (0.08 #477, 0.07 #400, 0.06 #92), 07hwkr (0.08 #782, 0.07 #397, 0.06 #1783), 02w7gg (0.07 #1773, 0.07 #3852, 0.06 #3929), 01qhm_ (0.07 #237, 0.07 #1084, 0.06 #699) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0147dk; 01vvycq; 01w61th; 01vrt_c; 01v_pj6; 0j1yf; 01vs_v8; 01trhmt; 0840vq; 01wj18h; ... >> query: (?x2227, 0x67) <- award(?x2227, ?x8458), award_nominee(?x2227, ?x1125), ?x8458 = 02f777 >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ss8_ people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.267 http://example.org/people/ethnicity/people #15020-05cvgl PRED entity: 05cvgl PRED relation: films! PRED expected values: 081pw => 107 concepts (51 used for prediction) PRED predicted values (max 10 best out of 63): 0g1x2_ (0.06 #809, 0.05 #339, 0.05 #496), 05489 (0.06 #678, 0.05 #990, 0.04 #834), 0fzyg (0.06 #680, 0.03 #4770, 0.03 #2244), 0fx2s (0.06 #2420, 0.05 #1481, 0.05 #1638), 02_h0 (0.06 #1508, 0.06 #1665, 0.05 #1978), 06d4h (0.05 #512, 0.04 #3178, 0.04 #1921), 0bq3x (0.05 #2064, 0.04 #2536, 0.04 #2377), 07jq_ (0.04 #708, 0.03 #2745, 0.03 #2901), 03r8gp (0.04 #872, 0.03 #402, 0.03 #559), 081pw (0.04 #2350, 0.04 #2509, 0.04 #4719) >> Best rule #809 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 01c9d; >> query: (?x2734, 0g1x2_) <- film(?x5817, ?x2734), written_by(?x2734, ?x3954), nominated_for(?x601, ?x2734), ?x601 = 0gr4k >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #2350 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 138 *> proper extension: 02wyzmv; 0crs0b8; *> query: (?x2734, 081pw) <- film_release_region(?x2734, ?x94), film(?x488, ?x2734), genre(?x2734, ?x1509), ?x1509 = 060__y *> conf = 0.04 ranks of expected_values: 10 EVAL 05cvgl films! 081pw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 107.000 51.000 0.059 http://example.org/film/film_subject/films #15019-0k611 PRED entity: 0k611 PRED relation: nominated_for PRED expected values: 0jzw 035yn8 050gkf 0j43swk 011yn5 0bykpk 02jxbw 01jw67 0llcx 0y_pg 02k1pr 0bbgvp => 60 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1269): 07024 (0.81 #1383, 0.78 #11064, 0.77 #24902), 0jqj5 (0.81 #1383, 0.78 #11064, 0.77 #24902), 0bdjd (0.81 #1383, 0.78 #11064, 0.77 #24902), 0ccd3x (0.81 #1383, 0.78 #11064, 0.77 #24902), 0kcn7 (0.81 #1383, 0.78 #11064, 0.77 #24902), 0kb1g (0.81 #1383, 0.78 #11064, 0.77 #24902), 05qm9f (0.81 #1383, 0.78 #11064, 0.77 #24902), 0cq7tx (0.81 #1383, 0.78 #11064, 0.77 #24902), 083skw (0.81 #1383, 0.78 #11064, 0.77 #24902), 0k2sk (0.81 #1383, 0.78 #11064, 0.77 #24902) >> Best rule #1383 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0gr4k; >> query: (?x1703, ?x197) <- ceremony(?x1703, ?x4445), ?x4445 = 0c53zb, award(?x4047, ?x1703), award(?x197, ?x1703), nominated_for(?x1703, ?x144), ?x4047 = 07s846j >> conf = 0.81 => this is the best rule for 16 predicted values *> Best rule #4343 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0gqy2; *> query: (?x1703, 035yn8) <- ceremony(?x1703, ?x78), award(?x197, ?x1703), nominated_for(?x1703, ?x2189), nominated_for(?x1703, ?x592), ?x2189 = 02yvct, ?x592 = 0n0bp *> conf = 0.60 ranks of expected_values: 28, 58, 62, 87, 104, 112, 128, 160, 269, 324, 377, 396 EVAL 0k611 nominated_for 0bbgvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 02k1pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 0y_pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 0llcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 01jw67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 02jxbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 0bykpk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 011yn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 0j43swk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 050gkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 035yn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k611 nominated_for 0jzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 60.000 27.000 0.815 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15018-01vsy95 PRED entity: 01vsy95 PRED relation: profession PRED expected values: 09jwl => 129 concepts (128 used for prediction) PRED predicted values (max 10 best out of 71): 09jwl (0.84 #2536, 0.78 #464, 0.76 #760), 02hrh1q (0.71 #163, 0.66 #16604, 0.64 #16160), 0dz3r (0.55 #742, 0.50 #3259, 0.50 #1334), 0cbd2 (0.52 #3116, 0.46 #7855, 0.45 #7114), 0dxtg (0.47 #1642, 0.46 #1790, 0.40 #4011), 016z4k (0.47 #4149, 0.47 #5926, 0.46 #1484), 01d_h8 (0.43 #154, 0.38 #1782, 0.38 #1634), 01c72t (0.36 #173, 0.35 #2690, 0.35 #3430), 0kyk (0.35 #3140, 0.31 #7879, 0.31 #6990), 0n1h (0.32 #160, 0.24 #308, 0.24 #4157) >> Best rule #2536 for best value: >> intensional similarity = 3 >> extensional distance = 175 >> proper extension: 01cv3n; 01p45_v; 04d_mtq; 015196; >> query: (?x3374, 09jwl) <- group(?x3374, ?x3608), instrumentalists(?x2785, ?x3374), role(?x2785, ?x74) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vsy95 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 128.000 0.836 http://example.org/people/person/profession #15017-0d3k14 PRED entity: 0d3k14 PRED relation: place_of_birth PRED expected values: 0p9z5 => 246 concepts (246 used for prediction) PRED predicted values (max 10 best out of 274): 0rh6k (0.42 #45093, 0.42 #44389, 0.40 #26772), 05k7sb (0.40 #26772, 0.31 #45092, 0.30 #42977), 030qb3t (0.33 #54, 0.25 #7804, 0.19 #37395), 02_286 (0.25 #2131, 0.22 #31723, 0.19 #31018), 0235n9 (0.25 #2065, 0.17 #4883, 0.14 #6997), 03b12 (0.20 #10269, 0.14 #5337, 0.06 #27884), 01nl79 (0.20 #10403, 0.05 #47043, 0.03 #70301), 0hj6h (0.18 #11756, 0.14 #6121, 0.11 #27963), 0cr3d (0.17 #2910, 0.14 #5729, 0.12 #22637), 0cc56 (0.17 #2849, 0.09 #11303, 0.06 #19052) >> Best rule #45093 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 01gvxv; 018fwv; >> query: (?x11088, ?x108) <- location(?x11088, ?x2020), location(?x11088, ?x108), ?x108 = 0rh6k, state_province_region(?x1513, ?x2020) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #44387 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 0170vn; 03vrnh; 02tf1y; 01mxqyk; *> query: (?x11088, ?x9863) <- people(?x1446, ?x11088), sibling(?x11088, ?x9569), profession(?x11088, ?x353), place_of_birth(?x9569, ?x9863) *> conf = 0.15 ranks of expected_values: 13 EVAL 0d3k14 place_of_birth 0p9z5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 246.000 246.000 0.421 http://example.org/people/person/place_of_birth #15016-0d2ww PRED entity: 0d2ww PRED relation: profession! PRED expected values: 01jbx1 => 50 concepts (24 used for prediction) PRED predicted values (max 10 best out of 4232): 01nglk (0.74 #8481, 0.33 #3849, 0.30 #29303), 0b478 (0.74 #8481, 0.33 #1505, 0.30 #26959), 01wk7b7 (0.74 #8481, 0.33 #693, 0.25 #4933), 03lgg (0.60 #22802, 0.60 #10072, 0.56 #18559), 0159h6 (0.60 #8590, 0.56 #17077, 0.50 #25563), 029k55 (0.60 #12064, 0.56 #20551, 0.50 #29037), 01j7z7 (0.60 #10966, 0.50 #27939, 0.50 #23696), 03qd_ (0.60 #8674, 0.50 #25647, 0.50 #21404), 02g8h (0.60 #8546, 0.50 #25519, 0.50 #21276), 06crng (0.60 #10930, 0.50 #27903, 0.44 #19417) >> Best rule #8481 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0d1pc; >> query: (?x10485, ?x4685) <- profession(?x558, ?x10485), profession(?x380, ?x10485), ?x380 = 0m2wm, award(?x558, ?x154), location(?x558, ?x362), spouse(?x4685, ?x558) >> conf = 0.74 => this is the best rule for 3 predicted values *> Best rule #5222 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 0d1pc; *> query: (?x10485, 01jbx1) <- profession(?x558, ?x10485), profession(?x380, ?x10485), ?x380 = 0m2wm, award(?x558, ?x154), location(?x558, ?x362), spouse(?x4685, ?x558) *> conf = 0.50 ranks of expected_values: 59 EVAL 0d2ww profession! 01jbx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 50.000 24.000 0.741 http://example.org/people/person/profession #15015-08hmch PRED entity: 08hmch PRED relation: film_release_region PRED expected values: 059j2 05qx1 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 129): 059j2 (0.86 #2627, 0.85 #922, 0.84 #809), 05qx1 (0.58 #928, 0.57 #360, 0.56 #815), 07f1x (0.52 #419, 0.47 #987, 0.44 #874), 077qn (0.50 #280, 0.30 #2726, 0.29 #393), 05sb1 (0.42 #258, 0.33 #371, 0.30 #2726), 01crd5 (0.42 #314, 0.30 #2726, 0.19 #427), 0161c (0.33 #387, 0.33 #274, 0.25 #955), 04v3q (0.33 #238, 0.09 #2624, 0.08 #6483), 04g5k (0.30 #2726, 0.29 #404, 0.28 #972), 0d05q4 (0.30 #2726, 0.17 #281, 0.10 #394) >> Best rule #2627 for best value: >> intensional similarity = 5 >> extensional distance = 160 >> proper extension: 014lc_; 0g56t9t; 028_yv; 011yrp; 0ds3t5x; 0g5qs2k; 0fq27fp; 04969y; 0bwfwpj; 02d44q; ... >> query: (?x1035, 059j2) <- film_release_region(?x1035, ?x774), film_release_region(?x1035, ?x608), ?x774 = 06mzp, adjoins(?x608, ?x4120), jurisdiction_of_office(?x182, ?x608) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 08hmch film_release_region 05qx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.858 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08hmch film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.858 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #15014-0kr_t PRED entity: 0kr_t PRED relation: group! PRED expected values: 042v_gx 018vs => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 117): 05148p4 (0.73 #1282, 0.69 #1870, 0.69 #1618), 018vs (0.73 #1021, 0.72 #1105, 0.67 #264), 0l14md (0.70 #427, 0.68 #595, 0.67 #1016), 0l14qv (0.38 #257, 0.27 #1014, 0.26 #1098), 013y1f (0.33 #279, 0.13 #1878, 0.13 #1290), 01vj9c (0.27 #1864, 0.25 #1528, 0.23 #1612), 042v_gx (0.25 #92, 0.14 #260, 0.11 #176), 07gql (0.18 #35, 0.13 #371, 0.11 #203), 02fsn (0.17 #130, 0.07 #1937, 0.06 #1178), 06ncr (0.15 #1888, 0.13 #1552, 0.12 #1636) >> Best rule #1282 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 01vsxdm; 03t9sp; 017mbb; 016vj5; 011xhx; >> query: (?x5493, 05148p4) <- award(?x5493, ?x247), group(?x227, ?x5493), artists(?x1572, ?x5493), ?x1572 = 06by7 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1021 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 0167_s; 018gm9; 05xq9; 01j59b0; 06gcn; 0knhk; 01l_w0; 03q_w5; 09jvl; 03qkcn9; *> query: (?x5493, 018vs) <- group(?x645, ?x5493), group(?x316, ?x5493), ?x645 = 028tv0, instrumentalists(?x316, ?x115) *> conf = 0.73 ranks of expected_values: 2, 7 EVAL 0kr_t group! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.732 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0kr_t group! 042v_gx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 102.000 102.000 0.732 http://example.org/music/performance_role/regular_performances./music/group_membership/group #15013-06c0j PRED entity: 06c0j PRED relation: person! PRED expected values: 03hkch7 => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 62): 02v570 (0.40 #715, 0.40 #581, 0.33 #112), 04dsnp (0.40 #474, 0.33 #1144, 0.33 #72), 064q5v (0.38 #1108, 0.33 #1242, 0.33 #103), 06929s (0.33 #89, 0.29 #893, 0.25 #1094), 012jfb (0.33 #102, 0.20 #705, 0.20 #571), 043mk4y (0.33 #114, 0.20 #717, 0.20 #583), 0872p_c (0.29 #944, 0.20 #609, 0.18 #1547), 058kh7 (0.27 #4146, 0.20 #1466, 0.14 #3476), 02qr3k8 (0.25 #245, 0.09 #2657, 0.08 #3461), 053tj7 (0.25 #209, 0.07 #1884, 0.05 #5169) >> Best rule #715 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0c_md_; 014vk4; >> query: (?x12525, 02v570) <- basic_title(?x12525, ?x346), person(?x5400, ?x12525), award_winner(?x3846, ?x12525), student(?x1695, ?x12525), politician(?x8714, ?x12525) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #955 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 06c97; 034ls; *> query: (?x12525, 03hkch7) <- basic_title(?x12525, ?x346), person(?x5400, ?x12525), taxonomy(?x12525, ?x939), gender(?x12525, ?x231), company(?x12525, ?x94) *> conf = 0.14 ranks of expected_values: 19 EVAL 06c0j person! 03hkch7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 163.000 163.000 0.400 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #15012-03205_ PRED entity: 03205_ PRED relation: organization! PRED expected values: 060c4 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 18): 060c4 (0.87 #288, 0.85 #184, 0.85 #67), 0dq_5 (0.34 #529, 0.31 #451, 0.28 #503), 07xl34 (0.27 #661, 0.26 #11, 0.23 #726), 05k17c (0.16 #371, 0.14 #358, 0.12 #46), 0hm4q (0.05 #658, 0.05 #1009, 0.05 #1035), 05c0jwl (0.03 #902, 0.03 #915, 0.03 #551), 01t7n9 (0.02 #1342, 0.02 #1460), 0fkzq (0.02 #1342, 0.02 #1460), 09n5b9 (0.02 #1342, 0.02 #1460), 02079p (0.02 #1342, 0.02 #1460) >> Best rule #288 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 01j_cy; 07wlf; >> query: (?x11714, 060c4) <- country(?x11714, ?x94), colors(?x11714, ?x8632), ?x94 = 09c7w0, institution(?x1368, ?x11714) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03205_ organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.869 http://example.org/organization/role/leaders./organization/leadership/organization #15011-0bdwqv PRED entity: 0bdwqv PRED relation: award! PRED expected values: 05bnp0 0170s4 01s7zw 01rzqj 01nm3s 01zg98 01p4r3 027_sn 02vg0 0gt3p 0252fh 0gyy0 016kft 01vh18t 010xjr 0cgzj 039xcr 0hwqg => 46 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2909): 0z4s (0.80 #9820, 0.69 #36009, 0.69 #55656), 016ks_ (0.80 #9820, 0.69 #36009, 0.69 #55656), 03xkps (0.80 #9820, 0.69 #36009, 0.69 #55656), 01tcf7 (0.80 #9820, 0.69 #36009, 0.69 #55656), 023kzp (0.80 #9820, 0.69 #36009, 0.69 #39285), 015c4g (0.80 #9820, 0.69 #36009, 0.69 #39285), 01kt17 (0.80 #9820, 0.69 #36009, 0.69 #39285), 015gy7 (0.80 #9820, 0.69 #36009, 0.69 #39285), 0m0nq (0.80 #9820, 0.69 #36009, 0.69 #39285), 040z9 (0.80 #9820, 0.69 #36009, 0.69 #39285) >> Best rule #9820 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0f4x7; 0cqh46; 0bdw6t; 0bfvd4; 09sdmz; >> query: (?x3247, ?x450) <- award(?x6657, ?x3247), award(?x715, ?x3247), ?x6657 = 016kkx, award_winner(?x3247, ?x450) >> conf = 0.80 => this is the best rule for 11 predicted values *> Best rule #7154 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0f4x7; 0cqh46; 0bdw6t; 0bfvd4; 09sdmz; *> query: (?x3247, 0170s4) <- award(?x6657, ?x3247), award(?x715, ?x3247), ?x6657 = 016kkx, award_winner(?x3247, ?x450) *> conf = 0.43 ranks of expected_values: 28, 33, 36, 37, 58, 80, 256, 273, 289, 493, 528, 589, 626, 640, 2229, 2773, 2820, 2827 EVAL 0bdwqv award! 0hwqg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 039xcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 0cgzj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 010xjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 01vh18t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 016kft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 0gyy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 0252fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 0gt3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 02vg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 027_sn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 01p4r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 01zg98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 01nm3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 01rzqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 01s7zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 0170s4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwqv award! 05bnp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 19.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #15010-0fq117k PRED entity: 0fq117k PRED relation: instrumentalists! PRED expected values: 02hnl => 121 concepts (111 used for prediction) PRED predicted values (max 10 best out of 125): 05148p4 (0.50 #616, 0.48 #1555, 0.48 #1044), 03bx0bm (0.43 #1536, 0.39 #3337, 0.39 #3163), 01vj9c (0.43 #1536, 0.39 #3337, 0.39 #3163), 02hnl (0.40 #630, 0.27 #1313, 0.27 #1398), 013y1f (0.33 #427, 0.32 #683, 0.30 #3940), 0gkd1 (0.33 #427, 0.32 #683, 0.30 #3940), 0l14j_ (0.33 #427, 0.32 #683, 0.30 #3940), 04rzd (0.30 #633, 0.14 #1061, 0.13 #4540), 03gvt (0.25 #63, 0.20 #148, 0.15 #1002), 06ncr (0.25 #43, 0.20 #128, 0.15 #1579) >> Best rule #616 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 01gf5h; 01vrz41; 0gcs9; 0fhxv; 03j24kf; 01bczm; 02vr7; 0140t7; >> query: (?x7238, 05148p4) <- award(?x7238, ?x2322), award(?x7238, ?x2238), ?x2322 = 01ck6h, ?x2238 = 025m8l, role(?x7238, ?x227) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #630 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 01gf5h; 01vrz41; 0gcs9; 0fhxv; 03j24kf; 01bczm; 02vr7; 0140t7; *> query: (?x7238, 02hnl) <- award(?x7238, ?x2322), award(?x7238, ?x2238), ?x2322 = 01ck6h, ?x2238 = 025m8l, role(?x7238, ?x227) *> conf = 0.40 ranks of expected_values: 4 EVAL 0fq117k instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 121.000 111.000 0.500 http://example.org/music/instrument/instrumentalists #15009-03_3d PRED entity: 03_3d PRED relation: service_location! PRED expected values: 01c6k4 => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 185): 05b5c (0.55 #922, 0.33 #1459, 0.29 #1726), 01c6k4 (0.53 #1341, 0.50 #1608, 0.46 #1877), 0k9ts (0.45 #888, 0.29 #1291, 0.26 #1558), 0p4wb (0.40 #1344, 0.27 #807, 0.22 #1477), 064f29 (0.36 #857, 0.33 #1394, 0.31 #1126), 069b85 (0.36 #923, 0.27 #1460, 0.26 #1593), 01zpmq (0.36 #847, 0.27 #1384, 0.18 #1920), 01nn79 (0.36 #874, 0.21 #1678, 0.17 #1544), 018mxj (0.36 #1211, 0.33 #1345, 0.32 #1881), 04fv0k (0.36 #1285, 0.27 #1419, 0.22 #1552) >> Best rule #922 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 04jpl; >> query: (?x252, 05b5c) <- contains(?x252, ?x536), film_release_region(?x66, ?x252), country_of_origin(?x419, ?x252) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1341 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 13 *> proper extension: 09nm_; *> query: (?x252, 01c6k4) <- region(?x1315, ?x252), produced_by(?x1315, ?x1039) *> conf = 0.53 ranks of expected_values: 2 EVAL 03_3d service_location! 01c6k4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 161.000 161.000 0.545 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #15008-0mcl0 PRED entity: 0mcl0 PRED relation: genre PRED expected values: 03k9fj => 103 concepts (60 used for prediction) PRED predicted values (max 10 best out of 86): 05p553 (0.47 #822, 0.36 #6097, 0.35 #3871), 01jfsb (0.36 #2591, 0.33 #3880, 0.32 #1653), 02kdv5l (0.29 #4808, 0.28 #3869, 0.28 #3986), 03k9fj (0.25 #3996, 0.24 #6574, 0.23 #1535), 0lsxr (0.24 #2587, 0.21 #476, 0.19 #1767), 082gq (0.23 #28, 0.19 #379, 0.17 #730), 02n4kr (0.22 #2586, 0.14 #592, 0.13 #943), 04xvh5 (0.22 #734, 0.19 #383, 0.14 #266), 03g3w (0.21 #139, 0.20 #22, 0.13 #724), 03mqtr (0.18 #27, 0.17 #144, 0.11 #729) >> Best rule #822 for best value: >> intensional similarity = 4 >> extensional distance = 189 >> proper extension: 026p_bs; 058kh7; >> query: (?x3882, 05p553) <- genre(?x3882, ?x53), film(?x8041, ?x3882), film(?x5188, ?x3882), influenced_by(?x4066, ?x5188) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #3996 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 630 *> proper extension: 09gdm7q; 0cz8mkh; 0c8tkt; 02qhqz4; 09lcsj; 02w86hz; 05c26ss; 0gy2y8r; 062zjtt; 080lkt7; ... *> query: (?x3882, 03k9fj) <- genre(?x3882, ?x53), country(?x3882, ?x94), film_crew_role(?x3882, ?x6473), music(?x3882, ?x5251) *> conf = 0.25 ranks of expected_values: 4 EVAL 0mcl0 genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 60.000 0.471 http://example.org/film/film/genre #15007-02rv_dz PRED entity: 02rv_dz PRED relation: nominated_for! PRED expected values: 02x1dht 0gqwc => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 279): 0gr51 (0.71 #3827, 0.70 #968, 0.70 #3826), 03hj5vf (0.71 #3827, 0.70 #3826, 0.69 #4278), 019f4v (0.62 #3198, 0.61 #2523, 0.41 #4552), 0k611 (0.55 #2538, 0.53 #3213, 0.35 #4567), 02pqp12 (0.50 #2527, 0.43 #952, 0.40 #1627), 040njc (0.49 #2481, 0.46 #906, 0.45 #3156), 0gqwc (0.48 #5234, 0.32 #7035, 0.29 #8839), 02qyntr (0.47 #1743, 0.47 #2643, 0.46 #1068), 0gr4k (0.42 #3174, 0.39 #2499, 0.30 #9709), 0gq_v (0.41 #3167, 0.38 #4296, 0.38 #10378) >> Best rule #3827 for best value: >> intensional similarity = 3 >> extensional distance = 205 >> proper extension: 06mmr; >> query: (?x1531, ?x68) <- category(?x1531, ?x134), award(?x1531, ?x68), award(?x164, ?x68) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #5234 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 323 *> proper extension: 02n9bh; 0cvkv5; 0cq8nx; 0j8f09z; *> query: (?x1531, 0gqwc) <- nominated_for(?x68, ?x1531), nominated_for(?x68, ?x4197), nominated_for(?x68, ?x4067), ?x4197 = 01242_, ?x4067 = 02d478 *> conf = 0.48 ranks of expected_values: 7, 16 EVAL 02rv_dz nominated_for! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 96.000 96.000 0.712 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02rv_dz nominated_for! 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 96.000 96.000 0.712 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #15006-05pbl56 PRED entity: 05pbl56 PRED relation: film_crew_role PRED expected values: 02ynfr => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 22): 0dxtw (0.48 #107, 0.46 #133, 0.43 #7), 02rh1dz (0.43 #6, 0.23 #31, 0.15 #106), 01pvkk (0.36 #935, 0.32 #134, 0.30 #1513), 02_n3z (0.35 #101, 0.29 #1, 0.25 #76), 02ynfr (0.22 #86, 0.21 #11, 0.21 #36), 089fss (0.21 #5, 0.16 #105, 0.11 #80), 0ckd1 (0.21 #3, 0.07 #103, 0.04 #78), 04pyp5 (0.09 #37, 0.07 #12, 0.07 #62), 02vs3x5 (0.07 #15, 0.06 #40, 0.06 #491), 094hwz (0.07 #10, 0.05 #161, 0.05 #136) >> Best rule #107 for best value: >> intensional similarity = 5 >> extensional distance = 83 >> proper extension: 02qhqz4; 0b7l4x; >> query: (?x1595, 0dxtw) <- film_crew_role(?x1595, ?x7591), film_crew_role(?x1595, ?x1284), ?x1284 = 0ch6mp2, ?x7591 = 0d2b38, genre(?x1595, ?x225) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #86 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 79 *> proper extension: 05fgt1; 05zpghd; 0660b9b; 01gwk3; 034qbx; 04y9mm8; 02d003; 047vp1n; 076xkps; 047p798; ... *> query: (?x1595, 02ynfr) <- film_crew_role(?x1595, ?x2472), film_crew_role(?x1595, ?x1284), ?x1284 = 0ch6mp2, titles(?x812, ?x1595), ?x2472 = 01xy5l_ *> conf = 0.22 ranks of expected_values: 5 EVAL 05pbl56 film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 95.000 95.000 0.482 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #15005-058m5m4 PRED entity: 058m5m4 PRED relation: honored_for PRED expected values: 02rzdcp 03cv_gy 04p5cr 03cvvlg => 31 concepts (30 used for prediction) PRED predicted values (max 10 best out of 710): 02rzdcp (0.56 #3161, 0.50 #1975, 0.40 #5532), 0hz55 (0.46 #4442, 0.40 #1476, 0.38 #2665), 01q_y0 (0.38 #2512, 0.23 #4289, 0.20 #4880), 01j7mr (0.33 #6142, 0.33 #5548, 0.33 #4954), 08jgk1 (0.33 #4833, 0.33 #684, 0.31 #4242), 0b6tzs (0.33 #1829, 0.33 #643, 0.22 #3015), 08zrbl (0.33 #2244, 0.33 #1058, 0.22 #3430), 0cw3yd (0.33 #1942, 0.33 #756, 0.22 #3128), 0kfv9 (0.33 #698, 0.29 #6035, 0.25 #2479), 039cq4 (0.33 #5745, 0.27 #5151, 0.24 #6339) >> Best rule #3161 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 05c1t6z; 03nnm4t; >> query: (?x3609, 02rzdcp) <- award_winner(?x3609, ?x10138), award_winner(?x3609, ?x8612), award_winner(?x3609, ?x906), ?x906 = 0pz7h, ceremony(?x618, ?x3609), award_winner(?x5041, ?x10138), nominated_for(?x618, ?x10806), film_crew_role(?x10806, ?x137), profession(?x8612, ?x319), award_nominee(?x8612, ?x949), film(?x2473, ?x10806) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1, 29, 34, 80 EVAL 058m5m4 honored_for 03cvvlg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 31.000 30.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 058m5m4 honored_for 04p5cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 31.000 30.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 058m5m4 honored_for 03cv_gy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 31.000 30.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 058m5m4 honored_for 02rzdcp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 30.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #15004-07w4j PRED entity: 07w4j PRED relation: student PRED expected values: 0dgskx => 62 concepts (51 used for prediction) PRED predicted values (max 10 best out of 904): 0kh6b (0.07 #615, 0.02 #11076, 0.02 #2708), 0ff3y (0.04 #6254, 0.03 #10438, 0.03 #4162), 03ft8 (0.03 #4442, 0.02 #8626, 0.02 #19088), 042v2 (0.03 #5687, 0.02 #20333, 0.01 #34981), 0kn3g (0.03 #10037, 0.03 #1668, 0.01 #24684), 083chw (0.03 #8395, 0.02 #2119, 0.02 #4211), 09v6tz (0.03 #3435, 0.03 #5527, 0.02 #11803), 01hb6v (0.03 #18831, 0.02 #4591, 0.01 #17144), 013pp3 (0.03 #20924, 0.02 #17662, 0.02 #5109), 073v6 (0.03 #20924, 0.02 #6803, 0.02 #10987) >> Best rule #615 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 05bcl; >> query: (?x2196, 0kh6b) <- contains(?x10165, ?x2196), contains(?x512, ?x2196), place_of_death(?x10078, ?x10165), ?x512 = 07ssc >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07w4j student 0dgskx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 51.000 0.075 http://example.org/education/educational_institution/students_graduates./education/education/student #15003-0134s5 PRED entity: 0134s5 PRED relation: artists! PRED expected values: 0xhtw => 142 concepts (92 used for prediction) PRED predicted values (max 10 best out of 296): 0xhtw (0.79 #11539, 0.79 #3752, 0.77 #6557), 0155w (0.60 #1973, 0.37 #11006, 0.34 #11629), 017_qw (0.60 #11896, 0.51 #16884, 0.51 #17507), 064t9 (0.58 #12157, 0.58 #11224, 0.50 #23693), 016clz (0.52 #4675, 0.47 #3427, 0.46 #6233), 06j6l (0.50 #4094, 0.47 #10947, 0.44 #7211), 05bt6j (0.47 #14369, 0.45 #3154, 0.43 #10319), 05w3f (0.45 #11559, 0.28 #5640, 0.25 #2526), 05r6t (0.41 #3505, 0.35 #4441, 0.30 #5064), 0dl5d (0.40 #1575, 0.27 #8427, 0.26 #8739) >> Best rule #11539 for best value: >> intensional similarity = 7 >> extensional distance = 60 >> proper extension: 015196; >> query: (?x3420, 0xhtw) <- artists(?x7083, ?x3420), artists(?x378, ?x3420), artists(?x378, ?x9868), artists(?x378, ?x9762), ?x9868 = 0134pk, ?x9762 = 03f1zhf, ?x7083 = 02yv6b >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0134s5 artists! 0xhtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 92.000 0.790 http://example.org/music/genre/artists #15002-03bdkd PRED entity: 03bdkd PRED relation: produced_by PRED expected values: 01b9ck => 77 concepts (43 used for prediction) PRED predicted values (max 10 best out of 152): 01b9ck (0.14 #45, 0.07 #434, 0.03 #1210), 016tt2 (0.12 #8935, 0.11 #8545, 0.11 #10104), 02sj1x (0.12 #8935, 0.11 #8545, 0.11 #10104), 0cb77r (0.12 #4661, 0.12 #2329, 0.10 #4660), 0dqzkv (0.12 #2329, 0.10 #5050, 0.10 #388), 0flddp (0.10 #5050, 0.10 #388, 0.10 #389), 0cv9fc (0.07 #362, 0.03 #751, 0.02 #3495), 0fvf9q (0.07 #395, 0.03 #4668, 0.03 #3112), 02q_cc (0.06 #12443, 0.03 #422, 0.03 #4305), 06pj8 (0.06 #12443, 0.03 #4339, 0.03 #8223) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 011yxg; 05jzt3; 020fcn; 01f7gh; 050f0s; 06ybb1; 0ddt_; 0ds2n; 03n785; 011ysn; ... >> query: (?x10614, 01b9ck) <- nominated_for(?x3237, ?x10614), nominated_for(?x574, ?x10614), ?x574 = 016tt2, award_winner(?x951, ?x3237) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bdkd produced_by 01b9ck CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 43.000 0.143 http://example.org/film/film/produced_by #15001-0690dn PRED entity: 0690dn PRED relation: sport PRED expected values: 02vx4 => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.91 #137, 0.90 #119, 0.90 #128), 0z74 (0.49 #380, 0.48 #307, 0.27 #535), 0jm_ (0.09 #201, 0.08 #373, 0.08 #364), 018jz (0.09 #375, 0.09 #203, 0.08 #366), 03tmr (0.08 #371, 0.06 #362, 0.06 #344), 018w8 (0.05 #347, 0.05 #365, 0.04 #374), 039yzs (0.04 #169, 0.04 #368, 0.04 #178), 09xp_ (0.02 #177, 0.01 #204, 0.01 #367) >> Best rule #137 for best value: >> intensional similarity = 15 >> extensional distance = 72 >> proper extension: 05hc96; 04h4zx; 0dkb83; 0f6cl2; >> query: (?x6355, 02vx4) <- team(?x530, ?x6355), team(?x63, ?x6355), position(?x6355, ?x203), position(?x6355, ?x60), ?x60 = 02nzb8, ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x203 = 0dgrmp, colors(?x6355, ?x3189), colors(?x9911, ?x3189), colors(?x4531, ?x3189), colors(?x4220, ?x3189), ?x4220 = 01v3ht, school_type(?x9911, ?x1044), citytown(?x4531, ?x1036) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0690dn sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.905 http://example.org/sports/sports_team/sport #15000-018wdw PRED entity: 018wdw PRED relation: award! PRED expected values: 0299hs 01xq8v => 47 concepts (21 used for prediction) PRED predicted values (max 10 best out of 813): 017jd9 (0.60 #445, 0.50 #3448, 0.41 #8449), 0ywrc (0.60 #292, 0.50 #3295, 0.33 #7294), 0bx0l (0.60 #206, 0.40 #3209, 0.22 #7208), 0jyb4 (0.60 #622, 0.30 #3625, 0.21 #4626), 04v8x9 (0.50 #3038, 0.43 #4039, 0.40 #35), 0ccd3x (0.50 #3442, 0.40 #439, 0.39 #7441), 0hfzr (0.50 #3401, 0.40 #398, 0.32 #8402), 0mcl0 (0.50 #3368, 0.40 #365, 0.29 #4369), 01jc6q (0.50 #3014, 0.40 #11, 0.29 #4015), 0bs4r (0.40 #3603, 0.40 #600, 0.36 #4604) >> Best rule #445 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0p9sw; 0l8z1; 0k611; >> query: (?x6860, 017jd9) <- ceremony(?x6860, ?x3618), nominated_for(?x6860, ?x4607), ?x3618 = 0bzn6_, ?x4607 = 0h03fhx >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6762 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 0gr07; *> query: (?x6860, 01xq8v) <- ceremony(?x6860, ?x5761), ceremony(?x6860, ?x2082), ?x2082 = 0gmdkyy, category_of(?x6860, ?x3459), ?x5761 = 02ywhz *> conf = 0.12 ranks of expected_values: 269 EVAL 018wdw award! 01xq8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 47.000 21.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 018wdw award! 0299hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 21.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #14999-0gt_hv PRED entity: 0gt_hv PRED relation: interests! PRED expected values: 01d494 => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2029): 07kb5 (0.60 #320, 0.57 #479, 0.50 #168), 01bpn (0.60 #334, 0.50 #308, 0.50 #182), 039n1 (0.60 #347, 0.50 #195, 0.50 #154), 03sbs (0.50 #308, 0.50 #208, 0.50 #187), 048cl (0.50 #308, 0.50 #237, 0.44 #402), 01dvtx (0.50 #180, 0.50 #139, 0.43 #491), 0nk72 (0.50 #192, 0.50 #151, 0.43 #503), 0tfc (0.50 #308, 0.46 #215, 0.44 #402), 03_hd (0.50 #308, 0.46 #215, 0.38 #528), 04411 (0.50 #308, 0.44 #402, 0.37 #211) >> Best rule #320 for best value: >> intensional similarity = 38 >> extensional distance = 3 >> proper extension: 09xq9d; >> query: (?x14193, 07kb5) <- interests(?x12216, ?x14193), interests(?x4308, ?x14193), interests(?x4033, ?x14193), interests(?x1857, ?x14193), influenced_by(?x12216, ?x7250), influenced_by(?x12216, ?x6015), influenced_by(?x12216, ?x3712), ?x7250 = 03sbs, profession(?x4308, ?x353), place_of_death(?x12216, ?x362), people(?x1050, ?x12216), interests(?x12216, ?x1858), gender(?x12216, ?x231), religion(?x4308, ?x7131), nationality(?x4308, ?x94), influenced_by(?x2608, ?x4308), ?x3712 = 0gz_, location(?x4308, ?x2254), religion(?x1857, ?x2694), company(?x12216, ?x2396), influenced_by(?x1857, ?x3994), people(?x3715, ?x1857), student(?x4672, ?x4308), influenced_by(?x920, ?x4033), ?x6015 = 05qmj, influenced_by(?x2161, ?x1857), ?x94 = 09c7w0, ?x1858 = 05r79, student(?x892, ?x4033), profession(?x4033, ?x6630), influenced_by(?x4308, ?x3476), student(?x892, ?x5131), major_field_of_study(?x892, ?x3489), child(?x892, ?x893), contains(?x1310, ?x892), ?x3489 = 0193x, list(?x892, ?x2197), ?x5131 = 01tdnyh >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #211 for first EXPECTED value: *> intensional similarity = 42 *> extensional distance = 2 *> proper extension: 04s0m; *> query: (?x14193, ?x3341) <- interests(?x12216, ?x14193), interests(?x7296, ?x14193), interests(?x4308, ?x14193), interests(?x1857, ?x14193), influenced_by(?x12216, ?x9600), influenced_by(?x12216, ?x7250), influenced_by(?x12216, ?x3712), influenced_by(?x12216, ?x3335), ?x7250 = 03sbs, profession(?x4308, ?x353), place_of_death(?x12216, ?x362), people(?x1050, ?x12216), gender(?x12216, ?x231), religion(?x4308, ?x7131), nationality(?x4308, ?x94), influenced_by(?x10101, ?x4308), ?x3712 = 0gz_, location(?x4308, ?x2254), interests(?x4308, ?x6364), ?x1857 = 026lj, people(?x11490, ?x4308), ?x7296 = 04hcw, ?x94 = 09c7w0, student(?x4672, ?x4308), profession(?x12622, ?x353), religion(?x12216, ?x8140), company(?x3335, ?x5281), nationality(?x12216, ?x1355), ?x12622 = 02wlk, major_field_of_study(?x892, ?x6364), major_field_of_study(?x6364, ?x2605), people(?x7260, ?x10101), influenced_by(?x3341, ?x9600), influenced_by(?x1737, ?x9600), influenced_by(?x920, ?x9600), ?x920 = 04411, ?x1737 = 01d494, nationality(?x3335, ?x774), jurisdiction_of_office(?x346, ?x1355), film_release_region(?x66, ?x1355), organization(?x1355, ?x127), country(?x150, ?x1355) *> conf = 0.37 ranks of expected_values: 29 EVAL 0gt_hv interests! 01d494 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 14.000 14.000 0.600 http://example.org/user/alexander/philosophy/philosopher/interests #14998-070xg PRED entity: 070xg PRED relation: colors PRED expected values: 01g5v => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.83 #1706, 0.75 #327, 0.67 #1195), 019sc (0.67 #122, 0.50 #65, 0.43 #469), 06fvc (0.50 #252, 0.47 #290, 0.42 #214), 01l849 (0.37 #364, 0.36 #671, 0.35 #481), 01g5v (0.34 #1000, 0.31 #1331, 0.31 #134), 02rnmb (0.31 #134, 0.29 #1347, 0.29 #262), 067z2v (0.31 #134, 0.29 #1347, 0.27 #1054), 0jc_p (0.31 #134, 0.29 #1347, 0.27 #1054), 04d18d (0.31 #134, 0.29 #1347, 0.24 #135), 09ggk (0.24 #135, 0.23 #1348, 0.19 #1056) >> Best rule #1706 for best value: >> intensional similarity = 12 >> extensional distance = 264 >> proper extension: 026w398; >> query: (?x3114, 083jv) <- colors(?x3114, ?x5325), colors(?x6919, ?x5325), colors(?x5324, ?x5325), colors(?x9995, ?x5325), colors(?x9835, ?x5325), colors(?x8901, ?x5325), ?x5324 = 01jszm, ?x8901 = 07l4z, team(?x1348, ?x9995), major_field_of_study(?x6919, ?x742), ?x9835 = 02hqt6, student(?x6919, ?x2127) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1000 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 42 *> proper extension: 01453; 03y_f8; 0182r9; 01j95f; 0gxkm; 02b2np; 037mp6; 01xn7x1; 01rl_3; 011v3; ... *> query: (?x3114, 01g5v) <- teams(?x5267, ?x3114), team(?x1717, ?x3114), team(?x11323, ?x3114), team(?x1717, ?x9748), team(?x1717, ?x2277), contains(?x94, ?x2277), colors(?x3114, ?x5325), category(?x9748, ?x134), position(?x1717, ?x8329), ?x134 = 08mbj5d *> conf = 0.34 ranks of expected_values: 5 EVAL 070xg colors 01g5v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 98.000 0.835 http://example.org/sports/sports_team/colors #14997-025mb_ PRED entity: 025mb_ PRED relation: people! PRED expected values: 09vc4s => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 45): 0x67 (0.32 #759, 0.30 #1134, 0.30 #909), 041rx (0.26 #454, 0.24 #2329, 0.23 #2854), 033tf_ (0.15 #981, 0.13 #3006, 0.12 #756), 02w7gg (0.12 #452, 0.10 #3002, 0.10 #2927), 0xnvg (0.11 #987, 0.10 #762, 0.09 #1137), 07bch9 (0.10 #22, 0.08 #172, 0.07 #472), 065b6q (0.10 #3, 0.07 #228, 0.03 #303), 02ctzb (0.10 #14, 0.06 #914, 0.06 #1139), 07hwkr (0.09 #1511, 0.09 #461, 0.08 #1436), 0dryh9k (0.07 #1515, 0.05 #3390, 0.05 #5040) >> Best rule #759 for best value: >> intensional similarity = 3 >> extensional distance = 248 >> proper extension: 06jzh; 01z7_f; >> query: (?x9140, 0x67) <- people(?x1423, ?x9140), currency(?x9140, ?x170), award(?x9140, ?x594) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #158 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 03mv0b; 018qpb; *> query: (?x9140, 09vc4s) <- profession(?x9140, ?x1032), award_winner(?x8459, ?x9140), ?x8459 = 02py7pj *> conf = 0.05 ranks of expected_values: 12 EVAL 025mb_ people! 09vc4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 109.000 109.000 0.320 http://example.org/people/ethnicity/people #14996-0fhpv4 PRED entity: 0fhpv4 PRED relation: award! PRED expected values: 016szr 0csdzz => 49 concepts (7 used for prediction) PRED predicted values (max 10 best out of 2555): 02g40r (0.75 #13466, 0.75 #10099, 0.57 #3367), 06dv3 (0.40 #45, 0.36 #3412, 0.17 #20199), 03ym1 (0.40 #1670, 0.36 #5037, 0.17 #20199), 016k6x (0.40 #1445, 0.36 #4812, 0.12 #8179), 0blq0z (0.40 #715, 0.36 #4082, 0.12 #7449), 0c6qh (0.40 #661, 0.36 #4028, 0.10 #7395), 01wmxfs (0.40 #180, 0.36 #3547, 0.08 #6914), 0pmhf (0.40 #693, 0.36 #4060, 0.08 #7427), 02y_2y (0.40 #1273, 0.36 #4640, 0.07 #8007), 03mg35 (0.40 #491, 0.36 #3858, 0.07 #7225) >> Best rule #13466 for best value: >> intensional similarity = 5 >> extensional distance = 76 >> proper extension: 05qck; 0d085; >> query: (?x3889, ?x669) <- award_winner(?x3889, ?x3069), award_winner(?x3889, ?x669), profession(?x3069, ?x131), category(?x3069, ?x134), music(?x224, ?x3069) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #11501 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 05qck; 0d085; *> query: (?x3889, 016szr) <- award_winner(?x3889, ?x3069), profession(?x3069, ?x131), category(?x3069, ?x134), music(?x224, ?x3069) *> conf = 0.17 ranks of expected_values: 151, 658 EVAL 0fhpv4 award! 0csdzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 7.000 0.753 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fhpv4 award! 016szr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 49.000 7.000 0.753 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14995-0fhxv PRED entity: 0fhxv PRED relation: gender PRED expected values: 05zppz => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #27, 0.82 #49, 0.81 #61), 02zsn (0.50 #12, 0.47 #225, 0.46 #40) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 213 >> proper extension: 0f0y8; 03c7ln; 08wq0g; 07_3qd; 012zng; 02jg92; 01tp5bj; 01vv126; 03xl77; 0gkg6; ... >> query: (?x4646, 05zppz) <- category(?x4646, ?x134), ?x134 = 08mbj5d, role(?x4646, ?x315) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fhxv gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.842 http://example.org/people/person/gender #14994-03t5b6 PRED entity: 03t5b6 PRED relation: award! PRED expected values: 01wgxtl => 50 concepts (30 used for prediction) PRED predicted values (max 10 best out of 2429): 016kjs (0.78 #90756, 0.78 #53782, 0.76 #73948), 02l840 (0.78 #90756, 0.78 #53782, 0.76 #73948), 018n6m (0.78 #90756, 0.78 #53782, 0.76 #73948), 0gbwp (0.57 #7830, 0.33 #14550, 0.31 #17910), 01wgxtl (0.56 #14179, 0.50 #10819, 0.50 #739), 0126y2 (0.50 #10823, 0.50 #743, 0.46 #17543), 04mn81 (0.50 #10591, 0.50 #511, 0.44 #13951), 047sxrj (0.50 #606, 0.38 #10686, 0.33 #14046), 01f2q5 (0.50 #3170, 0.38 #13250, 0.33 #16610), 017j6 (0.50 #928, 0.29 #7648, 0.25 #11008) >> Best rule #90756 for best value: >> intensional similarity = 3 >> extensional distance = 228 >> proper extension: 0gq6s3; 02rdyk7; 0c_n9; 05zrvfd; 0262s1; 026fn29; >> query: (?x3978, ?x827) <- award(?x9167, ?x3978), artists(?x2937, ?x9167), award_winner(?x3978, ?x827) >> conf = 0.78 => this is the best rule for 3 predicted values *> Best rule #14179 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 02nhxf; 01by1l; 02v1m7; 03t5kl; 03t5n3; *> query: (?x3978, 01wgxtl) <- award(?x9167, ?x3978), award(?x140, ?x3978), ceremony(?x3978, ?x342), artist(?x8738, ?x9167), ?x140 = 01vvydl *> conf = 0.56 ranks of expected_values: 5 EVAL 03t5b6 award! 01wgxtl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 50.000 30.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14993-0194d PRED entity: 0194d PRED relation: country PRED expected values: 03_3d 0d0vqn 0f8l9c 03h64 03shp 0345_ => 25 concepts (23 used for prediction) PRED predicted values (max 10 best out of 274): 0f8l9c (0.88 #3256, 0.84 #2929, 0.81 #3421), 03_3d (0.86 #2600, 0.86 #2437, 0.82 #2112), 0d0vqn (0.81 #2918, 0.81 #2762, 0.78 #1950), 06c1y (0.79 #2455, 0.76 #1456, 0.67 #1967), 03h64 (0.78 #2107, 0.77 #1944, 0.76 #1780), 0jgd (0.76 #2759, 0.76 #1456, 0.75 #1783), 035qy (0.76 #1780, 0.76 #1456, 0.62 #1638), 03gj2 (0.76 #1780, 0.76 #1456, 0.60 #824), 07twz (0.76 #1780, 0.49 #648, 0.47 #2105), 082fr (0.76 #1780, 0.43 #1457, 0.40 #1014) >> Best rule #3256 for best value: >> intensional similarity = 75 >> extensional distance = 49 >> proper extension: 06zgc; >> query: (?x7108, 0f8l9c) <- country(?x7108, ?x8420), country(?x7108, ?x1122), country(?x7108, ?x429), film_release_region(?x8193, ?x1122), film_release_region(?x4514, ?x1122), film_release_region(?x3565, ?x1122), film_release_region(?x2050, ?x1122), film_release_region(?x1785, ?x1122), film_release_region(?x303, ?x1122), film_release_region(?x124, ?x1122), film_release_region(?x86, ?x1122), ?x86 = 0ds35l9, contains(?x1122, ?x3106), country(?x3554, ?x429), country(?x171, ?x429), film_release_region(?x8381, ?x429), film_release_region(?x7693, ?x429), film_release_region(?x7629, ?x429), film_release_region(?x7502, ?x429), film_release_region(?x6422, ?x429), film_release_region(?x6283, ?x429), film_release_region(?x5704, ?x429), film_release_region(?x5270, ?x429), film_release_region(?x4518, ?x429), film_release_region(?x4047, ?x429), film_release_region(?x3998, ?x429), film_release_region(?x3958, ?x429), film_release_region(?x3377, ?x429), film_release_region(?x2783, ?x429), film_release_region(?x1707, ?x429), film_release_region(?x1228, ?x429), film_release_region(?x1118, ?x429), film_release_region(?x1022, ?x429), ?x8381 = 0h2zvzr, capital(?x429, ?x6357), ?x3554 = 035d1m, ?x6422 = 02qk3fk, ?x6283 = 0gmd3k7, member_states(?x2106, ?x429), olympics(?x429, ?x391), ?x1228 = 05z_kps, second_level_divisions(?x429, ?x1788), country(?x1591, ?x429), ?x124 = 0g56t9t, ?x2050 = 01fmys, ?x4047 = 07s846j, ?x3958 = 0gyh2wm, ?x4518 = 0hgnl3t, ?x2783 = 0879bpq, ?x5704 = 0h95zbp, award(?x1118, ?x1313), ?x8193 = 03z9585, ?x3565 = 0cp0ph6, ?x5270 = 0bc1yhb, ?x4514 = 06tpmy, ?x7629 = 02825nf, nationality(?x294, ?x429), ?x1313 = 0gs9p, adjoins(?x4071, ?x429), ?x1785 = 0gj9tn5, titles(?x8280, ?x1118), film(?x241, ?x1022), ?x3998 = 0184tc, exported_to(?x4164, ?x1122), production_companies(?x1022, ?x10685), ?x171 = 0d1tm, ?x303 = 011yrp, ?x7693 = 0m63c, ?x1707 = 04n52p6, ?x3377 = 0gj8nq2, contains(?x429, ?x1789), country_of_origin(?x2447, ?x429), ?x7502 = 0233bn, countries_within(?x6956, ?x8420), language(?x1118, ?x254) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 37, 83 EVAL 0194d country 0345_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 25.000 23.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0194d country 03shp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 25.000 23.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0194d country 03h64 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 23.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0194d country 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 23.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0194d country 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 23.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0194d country 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 23.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #14992-0jsg0m PRED entity: 0jsg0m PRED relation: profession PRED expected values: 02krf9 => 123 concepts (83 used for prediction) PRED predicted values (max 10 best out of 68): 0dz3r (0.55 #290, 0.50 #2309, 0.50 #868), 01d_h8 (0.47 #4192, 0.40 #6070, 0.39 #6358), 01c72t (0.40 #10849, 0.34 #887, 0.30 #3341), 0dxtg (0.35 #156, 0.32 #4199, 0.31 #6077), 0cbd2 (0.35 #150, 0.20 #5926, 0.17 #4626), 03gjzk (0.30 #4200, 0.27 #6078, 0.27 #6366), 0kyk (0.30 #171, 0.15 #4214, 0.13 #1470), 0fnpj (0.28 #2652, 0.18 #344, 0.17 #1211), 0d1pc (0.23 #1489, 0.23 #1057, 0.20 #2065), 025352 (0.21 #488, 0.13 #921, 0.13 #2651) >> Best rule #290 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 028qdb; >> query: (?x7459, 0dz3r) <- artists(?x1572, ?x7459), artists(?x378, ?x7459), ?x378 = 07sbbz2, role(?x7459, ?x227), ?x1572 = 06by7 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #8106 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 463 *> proper extension: 03zqc1; *> query: (?x7459, 02krf9) <- participant(?x10738, ?x7459), type_of_union(?x7459, ?x566), profession(?x10738, ?x131) *> conf = 0.08 ranks of expected_values: 25 EVAL 0jsg0m profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 123.000 83.000 0.545 http://example.org/people/person/profession #14991-06449 PRED entity: 06449 PRED relation: religion PRED expected values: 092bf5 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 22): 0c8wxp (0.20 #366, 0.16 #1087, 0.16 #186), 0kpl (0.20 #55, 0.16 #1317, 0.15 #1407), 0kq2 (0.14 #153, 0.10 #108, 0.05 #1325), 03_gx (0.12 #1050, 0.12 #1321, 0.11 #1456), 019cr (0.08 #146, 0.04 #191, 0.03 #101), 0n2g (0.05 #148, 0.03 #103, 0.03 #1320), 0v53x (0.05 #164, 0.03 #119, 0.02 #209), 07x21 (0.05 #173, 0.03 #128), 0631_ (0.05 #143, 0.03 #98), 01lp8 (0.05 #406, 0.05 #181, 0.05 #361) >> Best rule #366 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 01vvydl; 06688p; 07s3vqk; 0147dk; 03f2_rc; 01vrncs; 0lk90; 018y2s; 01k5t_3; 05mt_q; ... >> query: (?x2940, 0c8wxp) <- artists(?x497, ?x2940), film(?x2940, ?x7432), place_of_birth(?x2940, ?x1705) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #286 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 93 *> proper extension: 0p5mw; 01_x6v; 0jfx1; 02qx69; 0p_47; 016yzz; 01_x6d; 026dx; 02nfjp; 02w670; ... *> query: (?x2940, 092bf5) <- nominated_for(?x2940, ?x414), profession(?x2940, ?x1183), role(?x2940, ?x316) *> conf = 0.05 ranks of expected_values: 11 EVAL 06449 religion 092bf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 109.000 109.000 0.203 http://example.org/people/person/religion #14990-06_9lg PRED entity: 06_9lg PRED relation: service_language PRED expected values: 03k50 => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 27): 064_8sq (0.42 #61, 0.32 #90, 0.32 #49), 03k50 (0.42 #61, 0.11 #403, 0.10 #182), 09bnf (0.42 #61), 09s02 (0.42 #61), 0688f (0.42 #61), 02hxcvy (0.42 #61), 055qm (0.42 #61), 0999q (0.42 #61), 07c9s (0.42 #61), 06nm1 (0.33 #5, 0.26 #66, 0.24 #45) >> Best rule #61 for best value: >> intensional similarity = 8 >> extensional distance = 23 >> proper extension: 0hm0k; 055z7; >> query: (?x10867, ?x13017) <- service_location(?x10867, ?x5384), organization(?x4682, ?x10867), contains(?x2146, ?x5384), location(?x5383, ?x5384), gender(?x5383, ?x231), contact_category(?x10867, ?x897), languages(?x5383, ?x13017), category(?x10867, ?x134) >> conf = 0.42 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 06_9lg service_language 03k50 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 44.000 0.425 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #14989-04j14qc PRED entity: 04j14qc PRED relation: featured_film_locations PRED expected values: 0f25y => 104 concepts (81 used for prediction) PRED predicted values (max 10 best out of 85): 04jpl (0.19 #11858, 0.16 #13041, 0.15 #12567), 030qb3t (0.18 #13070, 0.18 #11887, 0.17 #4538), 06y57 (0.17 #101, 0.07 #575, 0.06 #813), 0dc95 (0.17 #60, 0.02 #9541, 0.02 #5032), 0rh6k (0.10 #1896, 0.08 #3555, 0.08 #2133), 05kj_ (0.08 #730, 0.05 #492, 0.03 #3572), 05qtj (0.07 #568, 0.06 #806, 0.04 #2226), 080h2 (0.07 #3813, 0.06 #3340, 0.05 #14005), 0h7h6 (0.06 #3832, 0.05 #4306, 0.05 #516), 01_d4 (0.06 #9527, 0.06 #13078, 0.06 #5018) >> Best rule #11858 for best value: >> intensional similarity = 4 >> extensional distance = 401 >> proper extension: 0413cff; >> query: (?x8302, 04jpl) <- featured_film_locations(?x8302, ?x6960), titles(?x53, ?x8302), place_of_birth(?x1182, ?x6960), location_of_ceremony(?x3525, ?x6960) >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04j14qc featured_film_locations 0f25y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 81.000 0.191 http://example.org/film/film/featured_film_locations #14988-0fk0xk PRED entity: 0fk0xk PRED relation: honored_for PRED expected values: 0cwy47 => 39 concepts (28 used for prediction) PRED predicted values (max 10 best out of 687): 0bcndz (0.33 #692, 0.25 #2480, 0.20 #4172), 0k4kk (0.33 #693, 0.25 #2481, 0.10 #4768), 04954r (0.33 #814, 0.25 #2602, 0.07 #6779), 0m_mm (0.33 #650, 0.25 #2438, 0.07 #6615), 075cph (0.33 #738, 0.25 #2526, 0.07 #6703), 0h0wd9 (0.33 #1746, 0.20 #4172, 0.17 #4128), 0p9tm (0.33 #1657, 0.20 #4172, 0.17 #4039), 0286gm1 (0.33 #2169, 0.17 #3956, 0.17 #3360), 0cq806 (0.33 #2285, 0.17 #4072, 0.17 #3476), 01k7b0 (0.33 #408, 0.17 #3984, 0.16 #10738) >> Best rule #692 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 0fy6bh; >> query: (?x5723, 0bcndz) <- award_winner(?x5723, ?x7528), award_winner(?x5723, ?x4423), award_winner(?x5723, ?x2068), honored_for(?x5723, ?x2898), ceremony(?x6860, ?x5723), ?x4423 = 076psv, award_winner(?x4251, ?x7528), nominated_for(?x6860, ?x7692), nominated_for(?x6860, ?x2899), nominated_for(?x6860, ?x908), ?x2899 = 0ddt_, ?x2068 = 0gl88b, award_winner(?x1745, ?x7528), ?x908 = 01vksx, film_crew_role(?x7692, ?x1171), award(?x186, ?x6860) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10738 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 33 *> proper extension: 02yw5r; 059x66; 073hmq; 0bzm81; 02yv_b; 0gmdkyy; 0bvfqq; 02hn5v; 0bzkgg; 073h9x; ... *> query: (?x5723, ?x4841) <- award_winner(?x5723, ?x8401), award_winner(?x5723, ?x6261), award_winner(?x5723, ?x4423), honored_for(?x5723, ?x2898), ceremony(?x1972, ?x5723), ceremony(?x1307, ?x5723), ceremony(?x720, ?x5723), ceremony(?x77, ?x5723), award_winner(?x4251, ?x4423), ?x720 = 018wng, award_winner(?x951, ?x4423), award_nominee(?x199, ?x4423), award_winner(?x4841, ?x8401), ?x1307 = 0gq9h, ?x1972 = 0gqyl, ?x77 = 0gqng, film(?x6261, ?x3943) *> conf = 0.16 ranks of expected_values: 47 EVAL 0fk0xk honored_for 0cwy47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 39.000 28.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #14987-02jxrw PRED entity: 02jxrw PRED relation: language PRED expected values: 06nm1 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 41): 04306rv (0.30 #4, 0.24 #116, 0.14 #625), 06nm1 (0.18 #10, 0.18 #122, 0.12 #802), 02bjrlw (0.15 #113, 0.13 #622, 0.12 #1), 05qdh (0.14 #564, 0.09 #563, 0.02 #2883), 0jzc (0.11 #19, 0.08 #131, 0.06 #640), 03mqtr (0.09 #563, 0.02 #2883, 0.02 #1701), 04xvlr (0.09 #563, 0.02 #2883, 0.02 #1701), 0653m (0.05 #860, 0.05 #1258, 0.05 #67), 03_9r (0.05 #4929, 0.05 #858, 0.05 #1029), 032f6 (0.05 #109, 0.02 #446, 0.02 #390) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 02qrv7; 025n07; 0992d9; 01jr4j; 034hwx; 06bc59; 0by17xn; >> query: (?x10060, 04306rv) <- language(?x10060, ?x5671), film(?x1738, ?x10060), titles(?x162, ?x10060), ?x5671 = 06b_j >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 02qrv7; 025n07; 0992d9; 01jr4j; 034hwx; 06bc59; 0by17xn; *> query: (?x10060, 06nm1) <- language(?x10060, ?x5671), film(?x1738, ?x10060), titles(?x162, ?x10060), ?x5671 = 06b_j *> conf = 0.18 ranks of expected_values: 2 EVAL 02jxrw language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 115.000 0.304 http://example.org/film/film/language #14986-016z2j PRED entity: 016z2j PRED relation: people! PRED expected values: 09kr66 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 38): 0x67 (0.18 #3657, 0.18 #2635, 0.17 #1394), 02ctzb (0.14 #85, 0.14 #12, 0.12 #231), 09kr66 (0.14 #112, 0.14 #39, 0.12 #258), 0222qb (0.14 #113, 0.14 #40, 0.12 #259), 022dp5 (0.14 #119, 0.14 #46, 0.12 #265), 0d2by (0.12 #175, 0.01 #540), 01336l (0.12 #183), 0xnvg (0.10 #1032, 0.09 #375, 0.08 #667), 02w7gg (0.09 #3652, 0.09 #2630, 0.08 #1973), 07hwkr (0.07 #1031, 0.06 #3659, 0.06 #2637) >> Best rule #3657 for best value: >> intensional similarity = 3 >> extensional distance = 1248 >> proper extension: 0kzy0; 0cg9y; 016ntp; 02_4fn; 0fr7nt; 017_pb; 01nkxvx; 0c1jh; 03d8njj; 045n3p; >> query: (?x2373, 0x67) <- profession(?x2373, ?x220), award(?x2373, ?x112), people(?x1050, ?x2373) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #112 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 079vf; *> query: (?x2373, 09kr66) <- film(?x2373, ?x10515), ?x10515 = 0dnkmq, type_of_union(?x2373, ?x566) *> conf = 0.14 ranks of expected_values: 3 EVAL 016z2j people! 09kr66 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 109.000 0.180 http://example.org/people/ethnicity/people #14985-0bzknt PRED entity: 0bzknt PRED relation: honored_for PRED expected values: 0pd64 => 32 concepts (19 used for prediction) PRED predicted values (max 10 best out of 733): 035yn8 (0.18 #7761, 0.17 #4171, 0.16 #10747), 0pd64 (0.18 #7761, 0.17 #4171, 0.16 #10747), 0drnwh (0.18 #7761, 0.17 #4171, 0.16 #10747), 0hfzr (0.18 #7761, 0.17 #4171, 0.16 #10747), 0jqn5 (0.18 #7761, 0.17 #4171, 0.16 #10747), 0dtfn (0.18 #7761, 0.17 #4171, 0.16 #10747), 0jyb4 (0.18 #7761, 0.17 #4171, 0.16 #10747), 025rvx0 (0.18 #7761, 0.17 #4171, 0.16 #10747), 0322yj (0.18 #7761, 0.17 #4171, 0.16 #10747), 01flv_ (0.18 #7761, 0.17 #4171, 0.16 #10747) >> Best rule #7761 for best value: >> intensional similarity = 14 >> extensional distance = 48 >> proper extension: 0c53zb; 0fv89q; >> query: (?x5924, ?x1744) <- award_winner(?x5924, ?x1431), award_winner(?x5924, ?x1126), award_winner(?x5924, ?x1119), ceremony(?x1307, ?x5924), ceremony(?x720, ?x5924), ?x720 = 018wng, ?x1307 = 0gq9h, type_of_union(?x1431, ?x566), profession(?x1431, ?x319), profession(?x1126, ?x353), award_winner(?x1744, ?x1431), award_nominee(?x71, ?x1119), award(?x1126, ?x749), people(?x1446, ?x1126) >> conf = 0.18 => this is the best rule for 22 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0bzknt honored_for 0pd64 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 32.000 19.000 0.177 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #14984-0ck91 PRED entity: 0ck91 PRED relation: nationality PRED expected values: 02jx1 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 23): 03rk0 (0.15 #937, 0.11 #2525, 0.11 #2128), 02jx1 (0.12 #3902, 0.11 #4498, 0.11 #3008), 07ssc (0.09 #9639, 0.09 #4480, 0.09 #5570), 0d060g (0.09 #402, 0.08 #105, 0.07 #4173), 0chghy (0.08 #504, 0.05 #802, 0.04 #306), 0ctw_b (0.08 #125, 0.03 #422, 0.03 #521), 059j2 (0.08 #127, 0.03 #424, 0.02 #622), 03_3d (0.08 #104, 0.02 #599, 0.02 #3875), 03rt9 (0.08 #210, 0.07 #309, 0.03 #1399), 0f8l9c (0.08 #219, 0.04 #318, 0.03 #516) >> Best rule #937 for best value: >> intensional similarity = 4 >> extensional distance = 146 >> proper extension: 02hhtj; >> query: (?x11601, 03rk0) <- languages(?x11601, ?x254), profession(?x11601, ?x319), ?x319 = 01d_h8, nationality(?x11601, ?x94) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #3902 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1038 *> proper extension: 07qnf; 07_3qd; 0dhqyw; 01ly8d; *> query: (?x11601, 02jx1) <- nationality(?x11601, ?x94), category(?x11601, ?x134), ?x134 = 08mbj5d *> conf = 0.12 ranks of expected_values: 2 EVAL 0ck91 nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.149 http://example.org/people/person/nationality #14983-023kzp PRED entity: 023kzp PRED relation: award_nominee! PRED expected values: 05cx7x => 104 concepts (62 used for prediction) PRED predicted values (max 10 best out of 895): 023kzp (0.85 #5977, 0.84 #3673, 0.70 #1368), 0z4s (0.82 #59921, 0.81 #59920, 0.81 #122149), 04w391 (0.82 #59921, 0.81 #59920, 0.81 #122149), 02qgqt (0.82 #59921, 0.81 #59920, 0.81 #122149), 05cx7x (0.82 #59921, 0.81 #59920, 0.81 #122149), 0408np (0.82 #59921, 0.81 #59920, 0.81 #122149), 02wgln (0.82 #59921, 0.81 #59920, 0.81 #122149), 0hvb2 (0.82 #59921, 0.81 #59920, 0.81 #122149), 0flw6 (0.82 #59921, 0.81 #59920, 0.81 #122149), 01tfck (0.15 #122150, 0.13 #59922, 0.10 #5065) >> Best rule #5977 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 07h565; >> query: (?x5925, 023kzp) <- award_nominee(?x5834, ?x5925), ?x5834 = 01z7s_, film(?x5925, ?x1045) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #59921 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 415 *> proper extension: 01g969; *> query: (?x5925, ?x2844) <- award_nominee(?x5925, ?x2844), participant(?x516, ?x5925), award_nominee(?x2844, ?x4662) *> conf = 0.82 ranks of expected_values: 5 EVAL 023kzp award_nominee! 05cx7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 104.000 62.000 0.850 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14982-016jhr PRED entity: 016jhr PRED relation: artists PRED expected values: 02x8z_ => 58 concepts (25 used for prediction) PRED predicted values (max 10 best out of 970): 01s21dg (0.67 #3644, 0.27 #6447, 0.17 #6869), 0191h5 (0.58 #7094, 0.57 #10321, 0.57 #6019), 01vng3b (0.57 #5931, 0.50 #3781, 0.50 #2707), 06gd4 (0.57 #5708, 0.39 #13238, 0.37 #14312), 02cw1m (0.57 #5171, 0.33 #4096, 0.33 #874), 03k3b (0.57 #5008, 0.33 #711, 0.25 #2859), 0l8g0 (0.50 #7009, 0.50 #2710, 0.46 #9160), 0161sp (0.50 #2384, 0.43 #4533, 0.33 #236), 0m19t (0.50 #6475, 0.43 #9702, 0.31 #11853), 01gf5h (0.50 #3284, 0.43 #4359, 0.19 #17266) >> Best rule #3644 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02yv6b; >> query: (?x837, 01s21dg) <- parent_genre(?x2072, ?x837), artists(?x837, ?x9757), artists(?x837, ?x1165), artists(?x2542, ?x9757), ?x2542 = 03xnwz, ?x1165 = 018y2s >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5372 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 03lty; *> query: (?x837, ?x2392) <- parent_genre(?x13553, ?x837), artists(?x837, ?x9841), ?x13553 = 0b_6yv, group(?x2392, ?x9841) *> conf = 0.48 ranks of expected_values: 73 EVAL 016jhr artists 02x8z_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 58.000 25.000 0.667 http://example.org/music/genre/artists #14981-03fqv5 PRED entity: 03fqv5 PRED relation: profession PRED expected values: 02jknp => 115 concepts (67 used for prediction) PRED predicted values (max 10 best out of 62): 02jknp (0.89 #895, 0.88 #2375, 0.87 #1783), 02hrh1q (0.82 #5342, 0.79 #9784, 0.79 #5638), 03gjzk (0.46 #1642, 0.41 #3418, 0.41 #3862), 0cbd2 (0.37 #4891, 0.26 #3114, 0.22 #3262), 02krf9 (0.26 #1358, 0.23 #2394, 0.23 #1802), 018gz8 (0.18 #3124, 0.17 #5789, 0.17 #4605), 0kyk (0.18 #4914, 0.15 #3137, 0.13 #4321), 09jwl (0.17 #7123, 0.16 #7271, 0.16 #6383), 0np9r (0.11 #5793, 0.11 #4609, 0.10 #3128), 0nbcg (0.11 #7284, 0.10 #2103, 0.10 #7136) >> Best rule #895 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 0p51w; >> query: (?x12960, 02jknp) <- award_winner(?x289, ?x12960), film(?x12960, ?x7628), place_of_birth(?x12960, ?x3014), award(?x12960, ?x198) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03fqv5 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 67.000 0.885 http://example.org/people/person/profession #14980-02gr81 PRED entity: 02gr81 PRED relation: colors PRED expected values: 04d18d => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 20): 01l849 (0.29 #781, 0.29 #481, 0.29 #241), 01g5v (0.29 #283, 0.27 #2748, 0.27 #623), 019sc (0.24 #287, 0.19 #2212, 0.19 #2072), 06fvc (0.19 #122, 0.18 #102, 0.17 #1365), 09ggk (0.18 #116, 0.16 #136, 0.11 #76), 038hg (0.15 #212, 0.12 #32, 0.11 #1155), 0jc_p (0.12 #184, 0.12 #624, 0.11 #664), 04d18d (0.12 #199, 0.10 #1123, 0.10 #99), 036k5h (0.12 #385, 0.11 #465, 0.10 #1007), 03wkwg (0.12 #355, 0.12 #155, 0.11 #55) >> Best rule #781 for best value: >> intensional similarity = 6 >> extensional distance = 110 >> proper extension: 02zkz7; 02jztz; >> query: (?x4209, 01l849) <- school_type(?x4209, ?x4994), school_type(?x6548, ?x4994), school_type(?x1011, ?x4994), ?x1011 = 07w0v, school(?x260, ?x4209), major_field_of_study(?x6548, ?x1327) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #199 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 01jssp; 0j_sncb; 05zl0; 01qd_r; 01jt2w; 0187nd; 02q253; *> query: (?x4209, 04d18d) <- student(?x4209, ?x7828), school(?x2820, ?x4209), ?x2820 = 0jmj7, influenced_by(?x7828, ?x477), major_field_of_study(?x4209, ?x1154) *> conf = 0.12 ranks of expected_values: 8 EVAL 02gr81 colors 04d18d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 168.000 168.000 0.295 http://example.org/education/educational_institution/colors #14979-03f2w PRED entity: 03f2w PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.75 #118, 0.72 #394, 0.72 #624), 060bp (0.63 #507, 0.62 #208, 0.62 #392), 04syw (0.28 #191, 0.25 #76, 0.22 #283), 0p5vf (0.21 #243, 0.21 #220, 0.20 #59), 0dq3c (0.21 #117, 0.15 #347, 0.14 #393), 0789n (0.20 #33, 0.12 #10, 0.10 #148), 0fj45 (0.20 #204, 0.16 #273, 0.13 #296), 01zq91 (0.19 #222, 0.17 #84, 0.16 #245), 0377k9 (0.14 #154, 0.12 #16, 0.11 #223), 01_fjr (0.14 #156, 0.11 #225, 0.10 #248) >> Best rule #118 for best value: >> intensional similarity = 6 >> extensional distance = 22 >> proper extension: 027rn; 0jgd; 03_3d; 0chghy; 015fr; 0hzlz; 07ylj; 06qd3; 015qh; 01znc_; ... >> query: (?x11872, 060c4) <- country(?x6150, ?x11872), film_release_region(?x204, ?x11872), ?x6150 = 07_53, olympics(?x11872, ?x391), olympics(?x172, ?x391), sports(?x391, ?x171) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #507 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 114 *> proper extension: 0169t; 02khs; 01z215; 04w4s; 088q4; 07fj_; 03gyl; 07dzf; 088vb; 07t_x; ... *> query: (?x11872, 060bp) <- country(?x6733, ?x11872), organization(?x11872, ?x312), country(?x6733, ?x512), olympics(?x11872, ?x7775), ?x512 = 07ssc, sports(?x7775, ?x453) *> conf = 0.63 ranks of expected_values: 2 EVAL 03f2w jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 50.000 50.000 0.750 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #14978-09g0h PRED entity: 09g0h PRED relation: profession PRED expected values: 02jknp 02hrh1q => 82 concepts (67 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.88 #5646, 0.88 #591, 0.87 #5934), 0nbcg (0.67 #1183, 0.64 #750, 0.62 #1617), 0dxtg (0.64 #6509, 0.64 #2180, 0.57 #3914), 01d_h8 (0.56 #2028, 0.55 #1304, 0.51 #2172), 0dz3r (0.51 #1156, 0.49 #1879, 0.47 #1590), 016z4k (0.49 #1592, 0.46 #3181, 0.46 #869), 02jknp (0.49 #2174, 0.32 #6503, 0.30 #4918), 039v1 (0.43 #1766, 0.40 #34, 0.39 #2344), 01c72t (0.33 #1032, 0.33 #2910, 0.31 #3487), 018gz8 (0.33 #161, 0.31 #1315, 0.31 #2039) >> Best rule #5646 for best value: >> intensional similarity = 3 >> extensional distance = 1011 >> proper extension: 01h4rj; >> query: (?x11710, 02hrh1q) <- type_of_union(?x11710, ?x566), film(?x11710, ?x10651), executive_produced_by(?x10651, ?x4060) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 09g0h profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 67.000 0.877 http://example.org/people/person/profession EVAL 09g0h profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 82.000 67.000 0.877 http://example.org/people/person/profession #14977-05dbyt PRED entity: 05dbyt PRED relation: film PRED expected values: 05_5rjx => 106 concepts (63 used for prediction) PRED predicted values (max 10 best out of 456): 01dc0c (0.12 #1450, 0.09 #3236, 0.06 #5022), 01xq8v (0.12 #1344, 0.09 #3130, 0.06 #4916), 03vyw8 (0.12 #1049, 0.09 #2835, 0.06 #4621), 0260bz (0.12 #335, 0.09 #2121, 0.06 #3907), 083shs (0.12 #19, 0.06 #3591), 034qbx (0.12 #1160, 0.02 #6518), 0bpm4yw (0.12 #723, 0.01 #15014, 0.01 #13227), 0fphf3v (0.12 #1359, 0.01 #37088, 0.01 #54948), 0btpm6 (0.12 #1301, 0.01 #15592), 043tvp3 (0.12 #1209, 0.01 #15500) >> Best rule #1450 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01vlj1g; 03gm48; 055c8; 0219q; 01fx5l; 08k1lz; >> query: (?x8642, 01dc0c) <- film(?x8642, ?x6184), gender(?x8642, ?x231), ?x6184 = 02jxbw, profession(?x8642, ?x1032) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05dbyt film 05_5rjx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 63.000 0.125 http://example.org/film/actor/film./film/performance/film #14976-0194zl PRED entity: 0194zl PRED relation: country PRED expected values: 07ssc => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 34): 07ssc (0.50 #75, 0.50 #15, 0.46 #3268), 02jx1 (0.46 #3268, 0.44 #2971, 0.43 #3804), 0345h (0.13 #1330, 0.12 #1568, 0.11 #2105), 018h2 (0.12 #60, 0.06 #1662, 0.06 #3328), 017fp (0.12 #60, 0.06 #1662, 0.06 #3328), 0hn10 (0.12 #60, 0.06 #1662, 0.06 #3328), 07s9rl0 (0.12 #60, 0.06 #1662, 0.06 #3328), 0f8l9c (0.12 #196, 0.10 #1560, 0.09 #78), 0chghy (0.06 #11, 0.05 #308, 0.05 #605), 03_3d (0.06 #7, 0.05 #67, 0.04 #185) >> Best rule #75 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 03mh94; 0g9wdmc; 0879bpq; 0gh65c5; 024mxd; 02ywwy; >> query: (?x4963, 07ssc) <- film(?x2938, ?x4963), nominated_for(?x112, ?x4963), ?x2938 = 01nwwl >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0194zl country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.500 http://example.org/film/film/country #14975-02mj7c PRED entity: 02mj7c PRED relation: institution! PRED expected values: 02cq61 => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 19): 014mlp (0.77 #545, 0.75 #84, 0.73 #164), 019v9k (0.70 #87, 0.70 #548, 0.67 #428), 016t_3 (0.60 #82, 0.47 #262, 0.47 #282), 03bwzr4 (0.54 #92, 0.54 #553, 0.49 #372), 02_xgp2 (0.53 #90, 0.48 #551, 0.43 #210), 0bkj86 (0.46 #386, 0.42 #86, 0.40 #547), 04zx3q1 (0.33 #1, 0.26 #381, 0.25 #542), 027f2w (0.33 #8, 0.25 #48, 0.23 #208), 02cq61 (0.33 #16, 0.25 #56, 0.20 #36), 022h5x (0.23 #97, 0.19 #438, 0.19 #558) >> Best rule #545 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 01w3v; 01nnsv; 0ks67; 08qnnv; >> query: (?x5149, 014mlp) <- major_field_of_study(?x5149, ?x2606), student(?x5149, ?x194), school(?x799, ?x5149) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 07wrz; *> query: (?x5149, 02cq61) <- contains(?x1860, ?x5149), ?x1860 = 01_d4, student(?x5149, ?x194), state_province_region(?x5149, ?x3818) *> conf = 0.33 ranks of expected_values: 9 EVAL 02mj7c institution! 02cq61 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 176.000 176.000 0.771 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14974-026ps1 PRED entity: 026ps1 PRED relation: artists! PRED expected values: 01kcty => 154 concepts (152 used for prediction) PRED predicted values (max 10 best out of 244): 064t9 (0.57 #2519, 0.50 #640, 0.49 #10977), 06by7 (0.43 #14746, 0.43 #10672, 0.43 #19444), 06j6l (0.35 #1303, 0.32 #2556, 0.29 #6942), 016clz (0.33 #5, 0.23 #10655, 0.22 #19740), 025sc50 (0.32 #2558, 0.27 #6944, 0.26 #7257), 0glt670 (0.29 #7247, 0.27 #6934, 0.26 #12572), 0gywn (0.29 #1313, 0.26 #5699, 0.24 #4447), 0ggx5q (0.27 #2587, 0.23 #708, 0.18 #6973), 02lnbg (0.25 #2567, 0.23 #688, 0.18 #6953), 01lyv (0.24 #4422, 0.22 #7553, 0.22 #6300) >> Best rule #2519 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 0285c; 02wb6yq; 01vsy3q; 03f6fl0; 01sfmyk; 0130sy; 0gps0z; 020_4z; >> query: (?x506, 064t9) <- artist(?x11171, ?x506), location(?x506, ?x1523), languages(?x506, ?x254) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026ps1 artists! 01kcty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 152.000 0.570 http://example.org/music/genre/artists #14973-0c8hct PRED entity: 0c8hct PRED relation: profession PRED expected values: 01d_h8 015h31 => 88 concepts (75 used for prediction) PRED predicted values (max 10 best out of 69): 02hrh1q (0.88 #1453, 0.84 #9809, 0.80 #9953), 09jwl (0.80 #4771, 0.66 #594, 0.62 #1458), 0n1h (0.72 #587, 0.50 #155, 0.39 #1019), 01d_h8 (0.65 #6487, 0.64 #6631, 0.63 #726), 0nbcg (0.64 #4348, 0.38 #4781, 0.32 #1036), 03gjzk (0.50 #14, 0.38 #3038, 0.36 #4190), 015h31 (0.50 #24, 0.07 #4465, 0.06 #600), 0dz3r (0.47 #578, 0.41 #1010, 0.38 #146), 016z4k (0.41 #580, 0.31 #148, 0.29 #1012), 039v1 (0.28 #609, 0.25 #177, 0.20 #1041) >> Best rule #1453 for best value: >> intensional similarity = 7 >> extensional distance = 110 >> proper extension: 05mt_q; 07z542; 01vvpjj; 01trhmt; 0p3sf; 03f0vvr; 0137hn; 06z4wj; 01l3mk3; 03f7m4h; ... >> query: (?x5681, 02hrh1q) <- profession(?x5681, ?x1943), profession(?x5681, ?x1614), ?x1614 = 01c72t, profession(?x10423, ?x1943), profession(?x6693, ?x1943), ?x6693 = 049fgvm, ?x10423 = 01gc7h >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #6487 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1103 *> proper extension: 012d40; 0337vz; 06151l; 01l1b90; 06dv3; 0byfz; 042l3v; 032xhg; 02lfcm; 0m2l9; ... *> query: (?x5681, 01d_h8) <- profession(?x5681, ?x987), type_of_union(?x5681, ?x566), ?x566 = 04ztj, profession(?x1853, ?x987), ?x1853 = 052gzr *> conf = 0.65 ranks of expected_values: 4, 7 EVAL 0c8hct profession 015h31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 75.000 0.884 http://example.org/people/person/profession EVAL 0c8hct profession 01d_h8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 75.000 0.884 http://example.org/people/person/profession #14972-04gcyg PRED entity: 04gcyg PRED relation: genre PRED expected values: 03k9fj => 94 concepts (93 used for prediction) PRED predicted values (max 10 best out of 116): 02kdv5l (0.61 #1300, 0.58 #592, 0.54 #1418), 05p553 (0.50 #358, 0.39 #2128, 0.37 #594), 03k9fj (0.44 #1310, 0.42 #956, 0.42 #366), 01hmnh (0.42 #370, 0.33 #960, 0.29 #1078), 0lsxr (0.33 #2608, 0.27 #599, 0.22 #245), 04xvlr (0.33 #1, 0.17 #827, 0.16 #7097), 04xvh5 (0.33 #150, 0.11 #268, 0.08 #976), 06nbt (0.33 #24, 0.08 #378, 0.06 #9814), 0vgkd (0.33 #11, 0.06 #1899, 0.06 #9814), 073_6 (0.33 #83, 0.06 #9814, 0.04 #10998) >> Best rule #1300 for best value: >> intensional similarity = 3 >> extensional distance = 165 >> proper extension: 076xkdz; >> query: (?x7947, 02kdv5l) <- film_release_distribution_medium(?x7947, ?x81), genre(?x7947, ?x1013), ?x1013 = 06n90 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1310 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 165 *> proper extension: 076xkdz; *> query: (?x7947, 03k9fj) <- film_release_distribution_medium(?x7947, ?x81), genre(?x7947, ?x1013), ?x1013 = 06n90 *> conf = 0.44 ranks of expected_values: 3 EVAL 04gcyg genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 93.000 0.611 http://example.org/film/film/genre #14971-092bf5 PRED entity: 092bf5 PRED relation: religion! PRED expected values: 012d40 01t2h2 09hnb 0gdh5 06449 03tdlh => 50 concepts (44 used for prediction) PRED predicted values (max 10 best out of 3851): 019f2f (0.33 #174, 0.25 #1204, 0.20 #2234), 019_1h (0.33 #57, 0.25 #1087, 0.20 #2117), 02g9z1 (0.33 #957, 0.25 #1987, 0.20 #3017), 0m93 (0.33 #589, 0.25 #1619, 0.20 #2649), 079dy (0.33 #996, 0.25 #2026, 0.20 #3056), 049sb (0.33 #971, 0.25 #2001, 0.20 #3031), 03f4xvm (0.33 #357, 0.25 #1387, 0.20 #2417), 03_80b (0.33 #466, 0.25 #1496, 0.20 #2526), 01zmpg (0.33 #148, 0.25 #1178, 0.20 #2208), 01xwv7 (0.33 #848, 0.25 #1878, 0.20 #2908) >> Best rule #174 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 0flw86; >> query: (?x7422, 019f2f) <- religion(?x248, ?x7422), religion(?x3818, ?x7422), religion(?x1023, ?x7422), religion(?x335, ?x7422), religion(?x94, ?x7422), ?x3818 = 03v0t, ?x335 = 059rby, ?x94 = 09c7w0, film_release_region(?x66, ?x1023) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13408 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 05sfs; 058x5; 01y0s9; 03_gx; *> query: (?x7422, ?x230) <- religion(?x3118, ?x7422), religion(?x629, ?x7422), religion(?x3818, ?x7422), award_nominee(?x629, ?x230), location(?x3118, ?x252), district_represented(?x176, ?x3818) *> conf = 0.08 ranks of expected_values: 946, 2033, 2380, 2735, 2755, 2793 EVAL 092bf5 religion! 03tdlh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 44.000 0.333 http://example.org/people/person/religion EVAL 092bf5 religion! 06449 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 44.000 0.333 http://example.org/people/person/religion EVAL 092bf5 religion! 0gdh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 44.000 0.333 http://example.org/people/person/religion EVAL 092bf5 religion! 09hnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 44.000 0.333 http://example.org/people/person/religion EVAL 092bf5 religion! 01t2h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 44.000 0.333 http://example.org/people/person/religion EVAL 092bf5 religion! 012d40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 44.000 0.333 http://example.org/people/person/religion #14970-01wbg84 PRED entity: 01wbg84 PRED relation: people! PRED expected values: 0xnvg => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 35): 0x67 (0.33 #9, 0.16 #1380, 0.11 #85), 041rx (0.24 #766, 0.19 #80, 0.19 #1375), 013b6_ (0.17 #52, 0.04 #128, 0.04 #204), 01p7s6 (0.17 #58), 07bch9 (0.13 #98, 0.09 #250, 0.07 #326), 02ctzb (0.13 #90, 0.08 #242, 0.08 #166), 02w7gg (0.11 #1373, 0.08 #535, 0.08 #459), 07hwkr (0.10 #87, 0.07 #620, 0.07 #544), 0xnvg (0.08 #1383, 0.07 #774, 0.06 #88), 063k3h (0.07 #106, 0.04 #258, 0.04 #182) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 02lkcc; 02d45s; >> query: (?x368, 0x67) <- film(?x368, ?x3925), ?x3925 = 0435vm, award(?x368, ?x401) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1383 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 985 *> proper extension: 05m63c; 044ntk; 012s5j; 0qf3p; 0241wg; 01jbx1; 07g2v; 01s3kv; 01mwsnc; 0dfjb8; ... *> query: (?x368, 0xnvg) <- film(?x368, ?x3925), country(?x3925, ?x94), people(?x1446, ?x368) *> conf = 0.08 ranks of expected_values: 9 EVAL 01wbg84 people! 0xnvg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 91.000 91.000 0.333 http://example.org/people/ethnicity/people #14969-059j2 PRED entity: 059j2 PRED relation: exported_to! PRED expected values: 0h3y => 225 concepts (167 used for prediction) PRED predicted values (max 10 best out of 131): 04sj3 (0.40 #556, 0.36 #721, 0.33 #390), 0h3y (0.40 #509, 0.29 #398, 0.27 #674), 0jdd (0.33 #371, 0.25 #315, 0.24 #1594), 0ctw_b (0.33 #352, 0.25 #296, 0.22 #1408), 0n3g (0.33 #376, 0.25 #320, 0.20 #542), 0d060g (0.33 #342, 0.25 #286, 0.20 #508), 03_3d (0.33 #341, 0.25 #285, 0.20 #507), 0j4b (0.29 #436, 0.25 #325, 0.22 #492), 047t_ (0.25 #319, 0.19 #1988, 0.18 #706), 0f8l9c (0.25 #294, 0.17 #350, 0.14 #405) >> Best rule #556 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02jx1; >> query: (?x1229, 04sj3) <- olympics(?x1229, ?x391), second_level_divisions(?x1229, ?x3408), location(?x2580, ?x1229) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #509 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 02jx1; *> query: (?x1229, 0h3y) <- olympics(?x1229, ?x391), second_level_divisions(?x1229, ?x3408), location(?x2580, ?x1229) *> conf = 0.40 ranks of expected_values: 2 EVAL 059j2 exported_to! 0h3y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 225.000 167.000 0.400 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #14968-04w8f PRED entity: 04w8f PRED relation: olympics PRED expected values: 0jkvj => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 34): 0jdk_ (0.75 #191, 0.62 #670, 0.60 #327), 018ctl (0.71 #685, 0.65 #993, 0.49 #856), 09n48 (0.71 #685, 0.65 #993, 0.49 #856), 0swbd (0.71 #685, 0.65 #993, 0.31 #177), 0lgxj (0.67 #192, 0.42 #158, 0.38 #671), 0l6ny (0.67 #176, 0.41 #655, 0.38 #758), 0lbbj (0.67 #184, 0.36 #150, 0.33 #663), 0jkvj (0.67 #201, 0.33 #167, 0.33 #680), 0lbd9 (0.64 #196, 0.34 #332, 0.33 #778), 0ldqf (0.56 #200, 0.31 #166, 0.29 #679) >> Best rule #191 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 0chghy; 03_r3; ... >> query: (?x3357, 0jdk_) <- olympics(?x3357, ?x775), film_release_region(?x2340, ?x3357), ?x775 = 0l998 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #201 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 0chghy; 03_r3; ... *> query: (?x3357, 0jkvj) <- olympics(?x3357, ?x775), film_release_region(?x2340, ?x3357), ?x775 = 0l998 *> conf = 0.67 ranks of expected_values: 8 EVAL 04w8f olympics 0jkvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 80.000 80.000 0.750 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #14967-06pwq PRED entity: 06pwq PRED relation: institution! PRED expected values: 019v9k => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 12): 019v9k (0.77 #174, 0.72 #558, 0.71 #253), 02mjs7 (0.37 #504, 0.33 #238, 0.28 #1170), 03mkk4 (0.37 #504, 0.29 #189, 0.28 #95), 022h5x (0.37 #504, 0.28 #1170, 0.26 #182), 071tyz (0.37 #504, 0.28 #1170, 0.25 #29), 01ysy9 (0.37 #504, 0.28 #1170, 0.25 #38), 028dcg (0.37 #504, 0.28 #1170, 0.20 #194), 02cq61 (0.37 #504, 0.28 #1170, 0.20 #245), 02m4yg (0.37 #504, 0.28 #1170, 0.17 #244), 01kxxq (0.37 #504, 0.28 #1170, 0.08 #248) >> Best rule #174 for best value: >> intensional similarity = 2 >> extensional distance = 29 >> proper extension: 06mkj; 0d05w3; >> query: (?x581, 019v9k) <- school(?x1161, ?x581), organization(?x581, ?x5487) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06pwq institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.774 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14966-059lwy PRED entity: 059lwy PRED relation: honored_for! PRED expected values: 037xlx => 86 concepts (58 used for prediction) PRED predicted values (max 10 best out of 192): 06ybb1 (0.85 #3083, 0.85 #3082, 0.83 #3238), 02ny6g (0.85 #3083, 0.85 #3082, 0.82 #4006), 059lwy (0.69 #426, 0.44 #2311, 0.43 #1847), 037xlx (0.54 #397, 0.44 #2311, 0.43 #1847), 025s1wg (0.08 #5850, 0.07 #5696, 0.06 #5851), 0cf08 (0.08 #5850, 0.07 #5696, 0.06 #5851), 0kv2hv (0.07 #627, 0.06 #783, 0.05 #1244), 0fdv3 (0.07 #495, 0.06 #648, 0.05 #1265), 0f3m1 (0.07 #595, 0.06 #748, 0.04 #1365), 0dtfn (0.07 #485, 0.06 #638, 0.04 #1255) >> Best rule #3083 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 02xhpl; 0q9jk; >> query: (?x6746, ?x3639) <- nominated_for(?x102, ?x6746), honored_for(?x6746, ?x3639), honored_for(?x2749, ?x6746), nominated_for(?x3308, ?x3639) >> conf = 0.85 => this is the best rule for 2 predicted values *> Best rule #397 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 069q4f; 02ny6g; 074rg9; 037xlx; 01mszz; 03phtz; *> query: (?x6746, 037xlx) <- nominated_for(?x102, ?x6746), film(?x5338, ?x6746), honored_for(?x3330, ?x6746), ?x3330 = 0946bb *> conf = 0.54 ranks of expected_values: 4 EVAL 059lwy honored_for! 037xlx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 58.000 0.847 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #14965-01w3v PRED entity: 01w3v PRED relation: list PRED expected values: 09g7thr => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 5): 09g7thr (0.74 #114, 0.74 #107, 0.70 #135), 01ptsx (0.62 #202, 0.47 #97, 0.34 #405), 04k4rt (0.59 #201, 0.40 #96, 0.22 #404), 01pd60 (0.50 #203, 0.50 #41, 0.33 #63), 026cl_m (0.22 #74, 0.22 #52, 0.07 #165) >> Best rule #114 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 045c7b; >> query: (?x741, 09g7thr) <- company(?x265, ?x741), organization(?x741, ?x5487), company(?x4988, ?x741), currency(?x5487, ?x170) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w3v list 09g7thr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.737 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #14964-05tfm PRED entity: 05tfm PRED relation: draft PRED expected values: 02qw1zx => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 15): 02qw1zx (0.73 #544, 0.72 #529, 0.71 #514), 02rl201 (0.52 #679, 0.42 #785, 0.41 #573), 02x2khw (0.52 #678, 0.42 #784, 0.41 #572), 02pq_x5 (0.48 #689, 0.41 #583, 0.41 #693), 02z6872 (0.48 #683, 0.40 #789, 0.38 #820), 02pq_rp (0.45 #682, 0.42 #788, 0.40 #819), 02r6gw6 (0.45 #686, 0.41 #580, 0.40 #792), 047dpm0 (0.45 #691, 0.40 #797, 0.38 #828), 04f4z1k (0.45 #690, 0.40 #796, 0.38 #827), 0f4vx0 (0.36 #1030, 0.35 #1046, 0.34 #1137) >> Best rule #544 for best value: >> intensional similarity = 12 >> extensional distance = 24 >> proper extension: 06x76; >> query: (?x1576, 02qw1zx) <- position(?x1576, ?x1517), position(?x1576, ?x935), school(?x1576, ?x735), team(?x180, ?x1576), ?x935 = 06b1q, draft(?x1576, ?x465), position(?x4189, ?x1517), position(?x705, ?x1517), ?x705 = 07k53y, team(?x1517, ?x11061), ?x11061 = 06x76, ?x4189 = 026lg0s >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05tfm draft 02qw1zx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.731 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #14963-04j_gs PRED entity: 04j_gs PRED relation: producer_type PRED expected values: 0ckd1 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.41 #29, 0.35 #2, 0.33 #22) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 256 >> proper extension: 02p65p; 0bl2g; 04bs3j; 0415svh; 01wmxfs; 02773m2; 02778pf; 03h_9lg; 04wvhz; 0284gcb; ... >> query: (?x10512, 0ckd1) <- profession(?x10512, ?x1041), award_winner(?x1764, ?x10512), ?x1041 = 03gjzk >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04j_gs producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.407 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #14962-02x4wr9 PRED entity: 02x4wr9 PRED relation: award_winner PRED expected values: 06t8b => 53 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1451): 0gnbw (0.50 #13926, 0.04 #31174, 0.03 #41035), 014zcr (0.40 #2505, 0.32 #39426, 0.31 #34495), 021bk (0.39 #34496, 0.32 #39426, 0.31 #34495), 026dx (0.39 #34496, 0.31 #34495, 0.28 #54217), 05cgy8 (0.39 #34496, 0.31 #34495, 0.28 #54217), 06cv1 (0.39 #34496, 0.31 #34495, 0.28 #54217), 02qgqt (0.33 #4944, 0.25 #17, 0.17 #12336), 0sz28 (0.33 #5162, 0.25 #235, 0.17 #12554), 0flw6 (0.33 #5880, 0.25 #953, 0.17 #8343), 0gcs9 (0.33 #8033, 0.10 #32674, 0.03 #40071) >> Best rule #13926 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 03nqnk3; >> query: (?x2532, 0gnbw) <- award_winner(?x2532, ?x2533), award(?x276, ?x2532), award_winner(?x3196, ?x2533), ?x3196 = 084302 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #31277 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 131 *> proper extension: 0m7yy; 02wwsh8; 03ybrwc; 02vl9ln; 0468g4r; *> query: (?x2532, 06t8b) <- award_winner(?x2532, ?x2533), award(?x2840, ?x2532), nominated_for(?x384, ?x2840), award(?x2533, ?x68), written_by(?x3196, ?x2533) *> conf = 0.05 ranks of expected_values: 431 EVAL 02x4wr9 award_winner 06t8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 22.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #14961-02z4b_8 PRED entity: 02z4b_8 PRED relation: currency PRED expected values: 09nqf => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.27 #64, 0.25 #103, 0.24 #85), 01nv4h (0.20 #5, 0.11 #8, 0.11 #11) >> Best rule #64 for best value: >> intensional similarity = 3 >> extensional distance = 415 >> proper extension: 01pbxb; 016qtt; 01vvydl; 07s3vqk; 0197tq; 0411q; 05cljf; 0lbj1; 01vrx3g; 01lmj3q; ... >> query: (?x7115, 09nqf) <- award_nominee(?x1826, ?x7115), award(?x1826, ?x1232), artist(?x3265, ?x7115) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02z4b_8 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.271 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #14960-089j8p PRED entity: 089j8p PRED relation: film_release_region PRED expected values: 09c7w0 015fr 06bnz => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 127): 09c7w0 (0.92 #8263, 0.92 #8557, 0.82 #297), 015fr (0.85 #309, 0.80 #1487, 0.79 #603), 06bnz (0.80 #334, 0.77 #1512, 0.76 #628), 01znc_ (0.78 #330, 0.76 #183, 0.75 #624), 05v8c (0.78 #308, 0.71 #602, 0.65 #455), 01p1v (0.65 #341, 0.58 #635, 0.58 #194), 04gzd (0.62 #302, 0.55 #449, 0.55 #596), 06qd3 (0.61 #620, 0.59 #326, 0.57 #473), 09pmkv (0.61 #319, 0.53 #613, 0.52 #466), 015qh (0.59 #329, 0.54 #623, 0.54 #476) >> Best rule #8263 for best value: >> intensional similarity = 4 >> extensional distance = 1319 >> proper extension: 0900j5; >> query: (?x6446, 09c7w0) <- film_release_region(?x6446, ?x1264), film_release_region(?x1202, ?x1264), ?x1202 = 0gj8t_b, nationality(?x380, ?x1264) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 089j8p film_release_region 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.924 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 089j8p film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.924 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 089j8p film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.924 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14959-02lp1 PRED entity: 02lp1 PRED relation: major_field_of_study! PRED expected values: 02cttt 02zd460 01qgr3 050xpd => 64 concepts (28 used for prediction) PRED predicted values (max 10 best out of 559): 07wrz (0.70 #8157, 0.62 #8634, 0.60 #7681), 01mpwj (0.70 #8193, 0.53 #9624, 0.50 #4853), 07tds (0.70 #8228, 0.50 #5843, 0.50 #4888), 08815 (0.67 #5726, 0.60 #8111, 0.50 #4294), 02zd460 (0.60 #7770, 0.57 #9200, 0.57 #11114), 01j_9c (0.60 #5254, 0.57 #6210, 0.50 #6687), 08qnnv (0.60 #5426, 0.57 #6382, 0.50 #8289), 07tgn (0.60 #8121, 0.50 #4304, 0.47 #9552), 0373qt (0.60 #5519, 0.50 #5997, 0.43 #6475), 05mv4 (0.60 #8214, 0.50 #4397, 0.40 #9645) >> Best rule #8157 for best value: >> intensional similarity = 13 >> extensional distance = 8 >> proper extension: 05qjt; 01lj9; >> query: (?x1154, 07wrz) <- major_field_of_study(?x5750, ?x1154), major_field_of_study(?x5280, ?x1154), major_field_of_study(?x4293, ?x1154), major_field_of_study(?x4209, ?x1154), major_field_of_study(?x581, ?x1154), fraternities_and_sororities(?x5750, ?x3697), institution(?x1526, ?x5750), colors(?x4209, ?x663), major_field_of_study(?x1154, ?x1668), ?x5280 = 07vhb, ?x581 = 06pwq, ?x1526 = 0bkj86, state_province_region(?x4293, ?x3670) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #7770 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 8 *> proper extension: 0h5k; 04gb7; *> query: (?x1154, 02zd460) <- major_field_of_study(?x8191, ?x1154), major_field_of_study(?x5750, ?x1154), major_field_of_study(?x4750, ?x1154), ?x5750 = 01nnsv, student(?x1154, ?x8566), school(?x2574, ?x4750), currency(?x8191, ?x2244), major_field_of_study(?x4750, ?x7134), ?x7134 = 02_7t, colors(?x8191, ?x332) *> conf = 0.60 ranks of expected_values: 5, 91, 258, 371 EVAL 02lp1 major_field_of_study! 050xpd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 64.000 28.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02lp1 major_field_of_study! 01qgr3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 64.000 28.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02lp1 major_field_of_study! 02zd460 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 28.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02lp1 major_field_of_study! 02cttt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 28.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14958-02c9dj PRED entity: 02c9dj PRED relation: category PRED expected values: 08mbj5d => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #24, 0.90 #32, 0.90 #29) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 02zc7f; 03wv2g; >> query: (?x12863, 08mbj5d) <- contains(?x94, ?x12863), ?x94 = 09c7w0, organization(?x346, ?x12863), school(?x2820, ?x12863) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02c9dj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.915 http://example.org/common/topic/webpage./common/webpage/category #14957-0m123 PRED entity: 0m123 PRED relation: award PRED expected values: 0fbtbt => 102 concepts (92 used for prediction) PRED predicted values (max 10 best out of 187): 0fbvqf (0.51 #700, 0.50 #699, 0.50 #465), 0bdw6t (0.51 #700, 0.50 #699, 0.50 #465), 0cqh6z (0.50 #286, 0.36 #520, 0.33 #754), 0fbtbt (0.45 #1086, 0.43 #1320, 0.43 #1788), 0cjyzs (0.33 #81, 0.19 #3808, 0.17 #4272), 027gs1_ (0.33 #178, 0.19 #3441, 0.17 #4834), 0gkr9q (0.32 #1132, 0.30 #1366, 0.27 #1600), 0ck27z (0.32 #1004, 0.30 #1238, 0.27 #1706), 02xcb6n (0.29 #658, 0.27 #1593, 0.27 #892), 0cqhb3 (0.27 #1122, 0.27 #1590, 0.26 #1356) >> Best rule #700 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 01fx1l; >> query: (?x8627, ?x2071) <- award(?x8627, ?x686), award_winner(?x8627, ?x2062), nominated_for(?x2071, ?x8627), ?x686 = 0bdw1g, award(?x269, ?x2071) >> conf = 0.51 => this is the best rule for 2 predicted values *> Best rule #1086 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 0d7vtk; *> query: (?x8627, 0fbtbt) <- country_of_origin(?x8627, ?x94), nominated_for(?x783, ?x8627), ?x783 = 0fbvqf, titles(?x2008, ?x8627), ?x2008 = 07c52 *> conf = 0.45 ranks of expected_values: 4 EVAL 0m123 award 0fbtbt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 92.000 0.514 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #14956-01h7xx PRED entity: 01h7xx PRED relation: legislative_sessions! PRED expected values: 043djx => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 53): 01gtcq (0.87 #356, 0.87 #354, 0.86 #355), 01gtc0 (0.87 #356, 0.87 #354, 0.86 #355), 01gtbb (0.87 #356, 0.87 #354, 0.86 #355), 03rl1g (0.87 #356, 0.87 #354, 0.86 #355), 03rtmz (0.73 #848, 0.71 #959, 0.71 #1013), 060ny2 (0.73 #866, 0.64 #977, 0.62 #1206), 043djx (0.72 #301, 0.72 #298, 0.72 #297), 01h7xx (0.72 #301, 0.72 #298, 0.72 #297), 01gstn (0.72 #301, 0.72 #298, 0.72 #297), 01grr2 (0.72 #301, 0.72 #298, 0.72 #297) >> Best rule #356 for best value: >> intensional similarity = 40 >> extensional distance = 5 >> proper extension: 07p__7; >> query: (?x7944, ?x3669) <- district_represented(?x7944, ?x7058), district_represented(?x7944, ?x6895), district_represented(?x7944, ?x4061), district_represented(?x7944, ?x3818), district_represented(?x7944, ?x2623), district_represented(?x7944, ?x2020), district_represented(?x7944, ?x1906), district_represented(?x7944, ?x961), district_represented(?x7944, ?x728), ?x1906 = 04rrx, ?x4061 = 0498y, legislative_sessions(?x7944, ?x3669), legislative_sessions(?x7944, ?x2019), jurisdiction_of_office(?x900, ?x3818), contains(?x8260, ?x3818), state_province_region(?x13913, ?x3818), state_province_region(?x9071, ?x3818), contains(?x3818, ?x5867), ?x961 = 03s0w, religion(?x3818, ?x8249), location(?x1897, ?x3818), ?x8260 = 04_1l0v, ?x2020 = 05k7sb, partially_contains(?x3818, ?x4540), ?x7058 = 050ks, legislative_sessions(?x3669, ?x759), institution(?x3437, ?x9071), legislative_sessions(?x2860, ?x2019), currency(?x13913, ?x170), ?x728 = 059f4, ?x6895 = 05fjf, district_represented(?x3669, ?x335), major_field_of_study(?x9071, ?x1154), legislative_sessions(?x5401, ?x2019), ?x3437 = 02_xgp2, religion(?x3074, ?x8249), place_of_birth(?x510, ?x5867), ?x2623 = 02xry, organization(?x3484, ?x13913), fraternities_and_sororities(?x9071, ?x4348) >> conf = 0.87 => this is the best rule for 4 predicted values *> Best rule #301 for first EXPECTED value: *> intensional similarity = 47 *> extensional distance = 2 *> proper extension: 077g7n; 024tcq; *> query: (?x7944, ?x759) <- district_represented(?x7944, ?x7058), district_represented(?x7944, ?x4776), district_represented(?x7944, ?x4105), district_represented(?x7944, ?x4061), district_represented(?x7944, ?x3818), district_represented(?x7944, ?x3670), district_represented(?x7944, ?x2831), district_represented(?x7944, ?x2020), district_represented(?x7944, ?x1906), district_represented(?x7944, ?x1767), district_represented(?x7944, ?x1025), district_represented(?x7944, ?x728), district_represented(?x7944, ?x448), ?x1906 = 04rrx, ?x4061 = 0498y, legislative_sessions(?x7944, ?x10291), legislative_sessions(?x7944, ?x6021), legislative_sessions(?x7944, ?x5252), legislative_sessions(?x7944, ?x176), ?x3818 = 03v0t, legislative_sessions(?x5252, ?x759), district_represented(?x5252, ?x1755), district_represented(?x5252, ?x335), legislative_sessions(?x13086, ?x7944), ?x335 = 059rby, legislative_sessions(?x2860, ?x10291), legislative_sessions(?x7914, ?x6021), ?x1025 = 04ych, ?x2020 = 05k7sb, ?x448 = 03v1s, ?x7058 = 050ks, ?x4105 = 0824r, ?x3670 = 05tbn, ?x1767 = 04rrd, district_represented(?x176, ?x4754), district_represented(?x176, ?x3778), ?x4754 = 0g0syc, ?x728 = 059f4, ?x4776 = 06yxd, legislative_sessions(?x5742, ?x6021), ?x2831 = 0gyh, ?x3778 = 07h34, politician(?x8714, ?x13086), ?x1755 = 01x73, legislative_sessions(?x4787, ?x7914), student(?x2064, ?x13086), basic_title(?x13086, ?x900) *> conf = 0.72 ranks of expected_values: 7 EVAL 01h7xx legislative_sessions! 043djx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 29.000 29.000 0.866 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #14955-02mjs7 PRED entity: 02mjs7 PRED relation: institution PRED expected values: 07wrz 01nnsv 0hsb3 014zws 0lk0l 04gxp2 => 25 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2000): 01jt2w (0.82 #8655, 0.80 #8059, 0.71 #9851), 065y4w7 (0.81 #10156, 0.76 #10755, 0.75 #7169), 03ksy (0.80 #7866, 0.76 #10855, 0.75 #10256), 07wjk (0.80 #7820, 0.75 #10210, 0.75 #7223), 0bwfn (0.80 #8050, 0.75 #10440, 0.75 #7453), 0g8rj (0.80 #7946, 0.75 #7349, 0.73 #8542), 0gl5_ (0.80 #8023, 0.75 #7426, 0.73 #8619), 025v3k (0.80 #7883, 0.75 #7286, 0.73 #8479), 07tgn (0.80 #7769, 0.73 #8365, 0.71 #9561), 017j69 (0.80 #7911, 0.73 #8507, 0.69 #10301) >> Best rule #8655 for best value: >> intensional similarity = 30 >> extensional distance = 9 >> proper extension: 03mkk4; >> query: (?x1305, 01jt2w) <- institution(?x1305, ?x10100), institution(?x1305, ?x7787), institution(?x1305, ?x5288), institution(?x1305, ?x5178), institution(?x1305, ?x4672), institution(?x1305, ?x4365), institution(?x1305, ?x3424), institution(?x1305, ?x2999), institution(?x1305, ?x2948), company(?x1913, ?x5178), contains(?x512, ?x4365), ?x2948 = 0j_sncb, school_type(?x7787, ?x1044), state_province_region(?x5178, ?x1755), student(?x5178, ?x1620), school(?x2820, ?x4672), student(?x4672, ?x11956), currency(?x10100, ?x2244), ?x3424 = 01w5m, major_field_of_study(?x4672, ?x1527), state_province_region(?x7787, ?x335), ?x5288 = 02zd460, ?x1527 = 04_tv, profession(?x11956, ?x5805), currency(?x5178, ?x170), student(?x2999, ?x164), institution(?x1390, ?x4672), list(?x2999, ?x2197), institution(?x1390, ?x1098), ?x1098 = 0gkkf >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #10346 for first EXPECTED value: *> intensional similarity = 29 *> extensional distance = 14 *> proper extension: 028dcg; *> query: (?x1305, 01nnsv) <- institution(?x1305, ?x12293), institution(?x1305, ?x10100), institution(?x1305, ?x5178), institution(?x1305, ?x4365), institution(?x1305, ?x2948), institution(?x1305, ?x1681), company(?x1913, ?x5178), contains(?x512, ?x4365), institution(?x4981, ?x2948), institution(?x3437, ?x2948), student(?x5178, ?x1620), colors(?x10100, ?x332), major_field_of_study(?x2948, ?x4321), category(?x2948, ?x134), school_type(?x4365, ?x5931), ?x4981 = 03bwzr4, currency(?x12293, ?x7888), school(?x4469, ?x2948), ?x4469 = 043vc, ?x1681 = 07szy, currency(?x5178, ?x170), ?x134 = 08mbj5d, ?x3437 = 02_xgp2, major_field_of_study(?x13080, ?x4321), major_field_of_study(?x3021, ?x4321), ?x3021 = 027xx3, citytown(?x12293, ?x13174), student(?x2948, ?x129), ?x13080 = 01trxd *> conf = 0.62 ranks of expected_values: 41, 46, 47, 49, 165, 448 EVAL 02mjs7 institution 04gxp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 22.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02mjs7 institution 0lk0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 25.000 22.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02mjs7 institution 014zws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 25.000 22.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02mjs7 institution 0hsb3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 25.000 22.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02mjs7 institution 01nnsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 25.000 22.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02mjs7 institution 07wrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 25.000 22.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14954-0lm0n PRED entity: 0lm0n PRED relation: partially_contains! PRED expected values: 05kkh => 51 concepts (21 used for prediction) PRED predicted values (max 10 best out of 84): 03v0t (0.40 #423, 0.40 #347, 0.40 #273), 04ych (0.32 #1224, 0.30 #398, 0.30 #322), 0d060g (0.32 #1224, 0.28 #457, 0.28 #996), 04rrd (0.32 #1224, 0.28 #457, 0.28 #996), 0vbk (0.32 #1224, 0.28 #457, 0.28 #996), 04tgp (0.32 #1224, 0.28 #457, 0.28 #996), 05kkh (0.32 #1224, 0.28 #457, 0.28 #996), 02_286 (0.32 #1224, 0.28 #457, 0.28 #996), 0j3b (0.32 #1224, 0.28 #457, 0.28 #996), 0f8x_r (0.32 #1224, 0.28 #457, 0.28 #996) >> Best rule #423 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0fcgd; >> query: (?x10954, 03v0t) <- partially_contains(?x11542, ?x10954), partially_contains(?x1755, ?x10954), partially_contains(?x728, ?x10954), country(?x11542, ?x279), first_level_division_of(?x1755, ?x94), adjoins(?x728, ?x1144) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1224 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 01smm; *> query: (?x10954, ?x279) <- partially_contains(?x728, ?x10954), adjoins(?x279, ?x728), contains(?x8483, ?x728) *> conf = 0.32 ranks of expected_values: 7 EVAL 0lm0n partially_contains! 05kkh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 51.000 21.000 0.400 http://example.org/location/location/partially_contains #14953-026zlh9 PRED entity: 026zlh9 PRED relation: language PRED expected values: 02h40lc => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 32): 02h40lc (0.91 #240, 0.91 #836, 0.90 #299), 064_8sq (0.21 #319, 0.17 #974, 0.16 #556), 04306rv (0.13 #302, 0.11 #898, 0.11 #479), 06nm1 (0.11 #963, 0.10 #1791, 0.09 #1561), 02bjrlw (0.10 #1791, 0.09 #239, 0.08 #475), 06b_j (0.10 #1791, 0.07 #497, 0.07 #142), 0653m (0.10 #1791, 0.04 #665, 0.03 #1623), 0jzc (0.10 #1791, 0.04 #317, 0.04 #494), 03k50 (0.10 #1791, 0.03 #1021, 0.02 #1319), 06mp7 (0.10 #1791, 0.02 #194, 0.02 #254) >> Best rule #240 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 03j63k; >> query: (?x6133, 02h40lc) <- nominated_for(?x112, ?x6133), titles(?x512, ?x6133), ?x512 = 07ssc >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026zlh9 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.912 http://example.org/film/film/language #14952-06pj8 PRED entity: 06pj8 PRED relation: film PRED expected values: 04t6fk 0hfzr 04mcw4 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 199): 0hfzr (0.22 #25067, 0.21 #25876, 0.21 #25066), 0gy0l_ (0.22 #25067, 0.21 #25876, 0.21 #25066), 08xvpn (0.22 #25067, 0.21 #25876, 0.21 #25066), 01s7w3 (0.22 #25067, 0.21 #25876, 0.21 #25066), 0drnwh (0.22 #25067, 0.21 #25876, 0.21 #25066), 0h21v2 (0.22 #25067, 0.21 #25876, 0.21 #25066), 08fn5b (0.22 #25067, 0.21 #25876, 0.21 #25066), 0bwfwpj (0.22 #25067, 0.21 #25876, 0.21 #25066), 015g28 (0.21 #25876, 0.21 #25066, 0.09 #12938), 03n0cd (0.21 #25876, 0.21 #25066, 0.09 #12938) >> Best rule #25067 for best value: >> intensional similarity = 2 >> extensional distance = 348 >> proper extension: 024c1b; >> query: (?x2135, ?x1847) <- produced_by(?x1847, ?x2135), nominated_for(?x640, ?x1847) >> conf = 0.22 => this is the best rule for 8 predicted values ranks of expected_values: 1 EVAL 06pj8 film 04mcw4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 112.000 0.221 http://example.org/film/director/film EVAL 06pj8 film 0hfzr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.221 http://example.org/film/director/film EVAL 06pj8 film 04t6fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 112.000 0.221 http://example.org/film/director/film #14951-028d4v PRED entity: 028d4v PRED relation: film PRED expected values: 01fmys 04g73n 028kj0 => 109 concepts (77 used for prediction) PRED predicted values (max 10 best out of 708): 0888c3 (0.62 #3185, 0.62 #30264, 0.60 #44506), 0b76kw1 (0.62 #30264, 0.60 #44506, 0.59 #53407), 02825cv (0.33 #1132, 0.25 #2912, 0.04 #17153), 01k0xy (0.33 #1272, 0.12 #3052, 0.03 #64088), 01k0vq (0.33 #1306, 0.03 #64088, 0.03 #94353), 07pd_j (0.33 #1177, 0.03 #64088, 0.03 #94353), 048tv9 (0.25 #3170, 0.17 #1390, 0.03 #64088), 0b6m5fy (0.25 #2895, 0.17 #1115, 0.03 #64088), 0pdp8 (0.25 #2145, 0.03 #64088, 0.03 #94353), 0h1fktn (0.17 #961, 0.12 #2741, 0.07 #11642) >> Best rule #3185 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 051wwp; >> query: (?x2383, 0888c3) <- award_nominee(?x4107, ?x2383), award_nominee(?x806, ?x2383), ?x806 = 03qd_, ?x4107 = 073749 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3177 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 051wwp; *> query: (?x2383, 04g73n) <- award_nominee(?x4107, ?x2383), award_nominee(?x806, ?x2383), ?x806 = 03qd_, ?x4107 = 073749 *> conf = 0.12 ranks of expected_values: 39, 492 EVAL 028d4v film 028kj0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 77.000 0.625 http://example.org/film/actor/film./film/performance/film EVAL 028d4v film 04g73n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 109.000 77.000 0.625 http://example.org/film/actor/film./film/performance/film EVAL 028d4v film 01fmys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 109.000 77.000 0.625 http://example.org/film/actor/film./film/performance/film #14950-09lxtg PRED entity: 09lxtg PRED relation: organization PRED expected values: 02vk52z => 95 concepts (94 used for prediction) PRED predicted values (max 10 best out of 49): 02vk52z (0.85 #569, 0.84 #738, 0.84 #1058), 01rz1 (0.69 #23, 0.65 #65, 0.64 #2), 0b6css (0.59 #73, 0.46 #31, 0.45 #10), 04k4l (0.55 #5, 0.43 #215, 0.43 #47), 0_2v (0.54 #235, 0.47 #67, 0.41 #214), 018cqq (0.53 #74, 0.46 #32, 0.45 #11), 02jxk (0.46 #24, 0.41 #66, 0.36 #3), 0gkjy (0.34 #491, 0.32 #1659, 0.28 #744), 059dn (0.32 #1659, 0.29 #78, 0.27 #15), 085h1 (0.32 #1659, 0.03 #117, 0.03 #369) >> Best rule #569 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 027rn; 0160w; 0b90_r; 03_3d; 0h3y; 0d0vqn; 04gzd; 0chghy; 047lj; 01ls2; ... >> query: (?x4569, 02vk52z) <- country(?x1967, ?x4569), administrative_parent(?x4569, ?x551), ?x1967 = 01cgz >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09lxtg organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 94.000 0.845 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #14949-01j5sd PRED entity: 01j5sd PRED relation: special_performance_type PRED expected values: 01pb34 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.21 #23, 0.15 #28, 0.14 #50), 09_gdc (0.04 #27, 0.02 #17, 0.02 #90), 01kyvx (0.01 #573, 0.01 #557, 0.01 #541) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 079vf; 0d05fv; 0cm89v; 0d02km; >> query: (?x8269, 01pb34) <- produced_by(?x1002, ?x8269), religion(?x8269, ?x1985), film(?x8269, ?x394) >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j5sd special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.208 http://example.org/film/actor/film./film/performance/special_performance_type #14948-01hbgs PRED entity: 01hbgs PRED relation: risk_factors! PRED expected values: 0dcsx 0g02vk 014w_8 0lcdk => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 65): 068p_ (0.79 #277, 0.67 #196), 01n3bm (0.43 #183, 0.38 #263, 0.33 #304), 0k95h (0.40 #126, 0.38 #245, 0.33 #286), 0h1wz (0.40 #153, 0.25 #272, 0.25 #232), 097ns (0.40 #132, 0.25 #211, 0.14 #370), 01l2m3 (0.38 #246, 0.33 #287, 0.29 #166), 01bcp7 (0.33 #7, 0.29 #163, 0.25 #243), 0gwj (0.33 #38, 0.25 #116, 0.25 #77), 0g02vk (0.33 #23, 0.25 #101, 0.25 #62), 0h99n (0.33 #17, 0.25 #95, 0.25 #56) >> Best rule #277 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 012jc; >> query: (?x8524, ?x13845) <- risk_factors(?x13744, ?x8524), risk_factors(?x10199, ?x8524), risk_factors(?x5784, ?x8524), ?x10199 = 02k6hp, notable_people_with_this_condition(?x5784, ?x483), symptom_of(?x9509, ?x13744), notable_people_with_this_condition(?x13845, ?x483) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #23 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 05zppz; *> query: (?x8524, 0g02vk) <- risk_factors(?x10199, ?x8524), risk_factors(?x5784, ?x8524), risk_factors(?x4322, ?x8524), risk_factors(?x3680, ?x8524), ?x10199 = 02k6hp, ?x5784 = 02vrr, people(?x3680, ?x5840), ?x4322 = 0gk4g *> conf = 0.33 ranks of expected_values: 9, 13, 27, 35 EVAL 01hbgs risk_factors! 0lcdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 17.000 17.000 0.786 http://example.org/medicine/disease/risk_factors EVAL 01hbgs risk_factors! 014w_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 17.000 17.000 0.786 http://example.org/medicine/disease/risk_factors EVAL 01hbgs risk_factors! 0g02vk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 17.000 17.000 0.786 http://example.org/medicine/disease/risk_factors EVAL 01hbgs risk_factors! 0dcsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 17.000 17.000 0.786 http://example.org/medicine/disease/risk_factors #14947-017lqp PRED entity: 017lqp PRED relation: student! PRED expected values: 07wtc => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 92): 0bwfn (0.10 #3430, 0.09 #800, 0.09 #1852), 04b_46 (0.07 #226, 0.04 #752, 0.03 #3382), 03ksy (0.07 #1683, 0.04 #24301, 0.04 #7469), 065y4w7 (0.05 #1592, 0.04 #14, 0.04 #22632), 09f2j (0.05 #158, 0.04 #1736, 0.04 #684), 017z88 (0.04 #607, 0.04 #13757, 0.03 #3237), 01w5m (0.04 #24300, 0.03 #22722, 0.03 #3260), 08815 (0.04 #528, 0.03 #15256, 0.03 #2), 07tg4 (0.03 #611, 0.02 #24281, 0.02 #85), 0m4yg (0.03 #3520, 0.02 #7202, 0.02 #2468) >> Best rule #3430 for best value: >> intensional similarity = 3 >> extensional distance = 493 >> proper extension: 031x_3; >> query: (?x9406, 0bwfn) <- award_nominee(?x9406, ?x1126), people(?x743, ?x9406), student(?x2486, ?x9406) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 017lqp student! 07wtc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 85.000 0.099 http://example.org/education/educational_institution/students_graduates./education/education/student #14946-01wy6 PRED entity: 01wy6 PRED relation: instrumentalists PRED expected values: 0146pg => 77 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1026): 01sb5r (0.67 #4466, 0.60 #3862, 0.56 #8708), 01vw20_ (0.67 #4394, 0.60 #3790, 0.50 #6815), 032t2z (0.60 #3655, 0.56 #8501, 0.50 #9711), 01wmjkb (0.60 #10144, 0.43 #6509, 0.41 #18017), 0zjpz (0.53 #15228, 0.45 #12206, 0.45 #11601), 016s_5 (0.50 #6965, 0.50 #4544, 0.40 #9996), 0135xb (0.50 #10073, 0.44 #8863, 0.43 #6438), 03bnv (0.50 #4418, 0.44 #8660, 0.40 #15313), 0kxbc (0.50 #10009, 0.44 #8799, 0.38 #1811), 0473q (0.50 #4626, 0.43 #6443, 0.43 #5229) >> Best rule #4466 for best value: >> intensional similarity = 15 >> extensional distance = 4 >> proper extension: 02hnl; >> query: (?x2460, 01sb5r) <- role(?x2460, ?x4913), role(?x2460, ?x1332), role(?x2460, ?x227), role(?x3156, ?x2460), role(?x716, ?x2460), ?x227 = 0342h, instrumentalists(?x2460, ?x7027), nationality(?x7027, ?x1310), ?x716 = 018vs, ?x4913 = 03ndd, ?x1332 = 03qlv7, category(?x7027, ?x134), gender(?x7027, ?x231), role(?x228, ?x2460), role(?x2620, ?x3156) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1237 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 05r5c; *> query: (?x2460, 0146pg) <- role(?x2460, ?x1332), role(?x2460, ?x227), role(?x3161, ?x2460), role(?x2764, ?x2460), role(?x1831, ?x2460), ?x227 = 0342h, instrumentalists(?x2460, ?x7027), ?x7027 = 02g1jh, ?x2764 = 01s0ps, role(?x228, ?x2460), ?x3161 = 01v1d8, performance_role(?x120, ?x1332), role(?x1148, ?x1332), ?x1831 = 03t22m, role(?x2747, ?x1332) *> conf = 0.33 ranks of expected_values: 381 EVAL 01wy6 instrumentalists 0146pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 38.000 0.667 http://example.org/music/instrument/instrumentalists #14945-02cllz PRED entity: 02cllz PRED relation: film PRED expected values: 02qhlwd => 79 concepts (73 used for prediction) PRED predicted values (max 10 best out of 724): 020bv3 (0.80 #5649, 0.67 #7427, 0.56 #9205), 01vksx (0.62 #134, 0.05 #5468, 0.05 #7246), 072zl1 (0.25 #1271, 0.05 #6605, 0.05 #8383), 09v8clw (0.25 #1760, 0.01 #17763), 03_gz8 (0.25 #1116), 0prh7 (0.17 #2606, 0.03 #42676, 0.03 #76472), 011yg9 (0.15 #6355, 0.14 #8133, 0.07 #9911), 02c638 (0.15 #9224, 0.01 #14558), 0ddt_ (0.12 #467, 0.10 #5801, 0.10 #7579), 017jd9 (0.12 #772, 0.08 #2550, 0.03 #18553) >> Best rule #5649 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 01yhvv; 0993r; 020_95; 0djywgn; >> query: (?x2457, 020bv3) <- film(?x2457, ?x224), award_nominee(?x2457, ?x1550), ?x1550 = 05tk7y >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #693 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 6 *> proper extension: 01tsbmv; *> query: (?x2457, 02qhlwd) <- film(?x2457, ?x7207), ?x7207 = 03y0pn *> conf = 0.12 ranks of expected_values: 34 EVAL 02cllz film 02qhlwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 79.000 73.000 0.800 http://example.org/film/actor/film./film/performance/film #14944-0134wr PRED entity: 0134wr PRED relation: artists! PRED expected values: 05w3f => 88 concepts (52 used for prediction) PRED predicted values (max 10 best out of 282): 01fh36 (0.57 #1307, 0.20 #1001, 0.14 #2228), 016clz (0.51 #3070, 0.48 #2150, 0.39 #2457), 0gywn (0.45 #2814, 0.28 #10171, 0.27 #8334), 0xhtw (0.44 #3696, 0.40 #4310, 0.39 #5841), 03_d0 (0.40 #930, 0.29 #1236, 0.25 #318), 0dl5d (0.39 #3698, 0.38 #4312, 0.22 #5843), 025sc50 (0.32 #8326, 0.30 #8938, 0.28 #10163), 0155w (0.29 #4089, 0.22 #4703, 0.22 #5009), 05w3f (0.29 #1261, 0.24 #4330, 0.24 #3716), 02yv6b (0.29 #1319, 0.20 #4081, 0.17 #2547) >> Best rule #1307 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0163m1; >> query: (?x8078, 01fh36) <- group(?x74, ?x8078), award_nominee(?x8078, ?x4866), inductee(?x1091, ?x8078) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1261 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0163m1; *> query: (?x8078, 05w3f) <- group(?x74, ?x8078), award_nominee(?x8078, ?x4866), inductee(?x1091, ?x8078) *> conf = 0.29 ranks of expected_values: 9 EVAL 0134wr artists! 05w3f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 88.000 52.000 0.571 http://example.org/music/genre/artists #14943-037hz PRED entity: 037hz PRED relation: titles PRED expected values: 023vcd => 44 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1732): 03prz_ (0.60 #8659, 0.13 #18034, 0.08 #22720), 027pfg (0.60 #8848, 0.10 #18223, 0.06 #22909), 026p4q7 (0.60 #8155, 0.10 #17530, 0.06 #22216), 01719t (0.40 #8006, 0.25 #1758, 0.20 #4882), 06cm5 (0.40 #8715, 0.25 #2467, 0.20 #5591), 0296rz (0.40 #9227, 0.23 #18602, 0.13 #23288), 03h_yy (0.40 #7874, 0.19 #17249, 0.12 #21935), 01qbg5 (0.40 #8898, 0.16 #18273, 0.13 #22959), 07z6xs (0.40 #8569, 0.16 #17944, 0.10 #22630), 016z7s (0.40 #8096, 0.16 #17471, 0.10 #22157) >> Best rule #8659 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 07s9rl0; 04xvlr; 01hmnh; >> query: (?x14205, 03prz_) <- titles(?x14205, ?x8633), ?x8633 = 057__d >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #18596 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 29 *> proper extension: 015w9s; 07yjb; *> query: (?x14205, 023vcd) <- titles(?x14205, ?x8633), film(?x4564, ?x8633), currency(?x8633, ?x170), film_crew_role(?x8633, ?x1284), produced_by(?x8633, ?x6558), crewmember(?x8633, ?x1933), ?x1284 = 0ch6mp2 *> conf = 0.03 ranks of expected_values: 1128 EVAL 037hz titles 023vcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 23.000 0.600 http://example.org/media_common/netflix_genre/titles #14942-0hx4y PRED entity: 0hx4y PRED relation: film! PRED expected values: 03xq0f => 151 concepts (138 used for prediction) PRED predicted values (max 10 best out of 69): 03xq0f (0.84 #672, 0.84 #598, 0.58 #2235), 030_1_ (0.51 #4616, 0.50 #7244, 0.48 #3127), 05qd_ (0.29 #898, 0.28 #306, 0.23 #676), 086k8 (0.29 #225, 0.23 #2606, 0.21 #2157), 01795t (0.29 #91, 0.14 #536, 0.12 #980), 016tt2 (0.20 #893, 0.19 #1564, 0.18 #2159), 054g1r (0.19 #108, 0.10 #1965, 0.09 #2189), 01gb54 (0.18 #917, 0.16 #325, 0.14 #102), 0g1rw (0.15 #156, 0.10 #1119, 0.10 #1194), 017s11 (0.14 #1114, 0.14 #448, 0.14 #226) >> Best rule #672 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0522wp; >> query: (?x2878, 03xq0f) <- film_distribution_medium(?x2878, ?x2099), category(?x2878, ?x134), region(?x2878, ?x512) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hx4y film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 138.000 0.837 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #14941-0cqgl9 PRED entity: 0cqgl9 PRED relation: award_winner PRED expected values: 0h32q => 55 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1947): 0l6px (0.67 #7394, 0.67 #5415, 0.43 #2950), 01jw4r (0.43 #4301, 0.32 #2465, 0.32 #51760), 01jmv8 (0.40 #1846, 0.32 #2465, 0.32 #51760), 04wx2v (0.40 #1998, 0.17 #9392, 0.08 #6928), 03mp9s (0.40 #1533, 0.09 #41896, 0.08 #8927), 05dbf (0.37 #41895, 0.35 #9858, 0.34 #46827), 09zmys (0.37 #41895, 0.35 #9858, 0.34 #46827), 0lpjn (0.37 #41895, 0.34 #46827, 0.33 #5528), 01l9p (0.37 #41895, 0.34 #46827, 0.32 #19713), 01z5tr (0.37 #41895, 0.34 #46827, 0.32 #19713) >> Best rule #7394 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0bfvw2; 094qd5; 0cqh6z; 0bdwft; 0gqwc; 0ck27z; 0gqyl; 0bdx29; 09td7p; 02ppm4q; >> query: (?x3722, ?x2372) <- award(?x2372, ?x3722), ceremony(?x3722, ?x873), nominated_for(?x3722, ?x531), ?x2372 = 0l6px >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3435 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 01c427; *> query: (?x3722, 0h32q) <- award(?x3836, ?x3722), award(?x3756, ?x3722), ceremony(?x3722, ?x873), ?x3836 = 01gv_f, award_nominee(?x1871, ?x3756) *> conf = 0.29 ranks of expected_values: 52 EVAL 0cqgl9 award_winner 0h32q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 55.000 25.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #14940-0jvt9 PRED entity: 0jvt9 PRED relation: nominated_for! PRED expected values: 0gs96 => 104 concepts (103 used for prediction) PRED predicted values (max 10 best out of 224): 0k611 (0.68 #13474, 0.68 #13236, 0.67 #9214), 019f4v (0.64 #760, 0.58 #1233, 0.52 #1469), 0gs9p (0.60 #770, 0.54 #1243, 0.48 #1479), 040njc (0.45 #715, 0.42 #1188, 0.37 #1424), 0f4x7 (0.41 #732, 0.37 #1205, 0.33 #1441), 0gs96 (0.40 #2685, 0.31 #559, 0.22 #6937), 0gr4k (0.38 #497, 0.35 #1678, 0.34 #733), 0gr0m (0.36 #766, 0.36 #2656, 0.33 #2419), 04dn09n (0.36 #742, 0.34 #2395, 0.33 #34), 0gqy2 (0.36 #828, 0.33 #1301, 0.30 #1065) >> Best rule #13474 for best value: >> intensional similarity = 3 >> extensional distance = 989 >> proper extension: 02nf2c; 026njb5; 03j63k; 0m123; 097h2; 02_1ky; 019g8j; 0147w8; 0300ml; 02rq7nd; >> query: (?x3294, ?x1862) <- award(?x3294, ?x1862), nominated_for(?x484, ?x3294), nominated_for(?x1862, ?x69) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2685 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 225 *> proper extension: 04z_x4v; *> query: (?x3294, 0gs96) <- nominated_for(?x4526, ?x3294), nominated_for(?x484, ?x3294), ?x484 = 0gq_v *> conf = 0.40 ranks of expected_values: 6 EVAL 0jvt9 nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 104.000 103.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14939-01vvycq PRED entity: 01vvycq PRED relation: profession PRED expected values: 0dz3r => 138 concepts (113 used for prediction) PRED predicted values (max 10 best out of 82): 0dz3r (0.65 #707, 0.62 #989, 0.62 #284), 01c72t (0.58 #1146, 0.57 #2697, 0.55 #6371), 0n1h (0.47 #431, 0.46 #290, 0.29 #713), 03gjzk (0.43 #9899, 0.31 #12440, 0.29 #151), 025352 (0.40 #53, 0.16 #1886, 0.15 #335), 039v1 (0.39 #1299, 0.39 #8221, 0.39 #7937), 0d1pc (0.32 #890, 0.31 #12440, 0.27 #3430), 018gz8 (0.31 #12440, 0.15 #9901, 0.10 #14436), 09lbv (0.31 #12440, 0.11 #1847, 0.10 #8630), 0fnpj (0.29 #759, 0.27 #477, 0.23 #1041) >> Best rule #707 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 07s3vqk; 02l840; 01wcp_g; 012x4t; 01wwvc5; 0407f; 01w272y; 01lvcs1; 0gbwp; 0g824; ... >> query: (?x702, 0dz3r) <- award_winner(?x3835, ?x702), instrumentalists(?x227, ?x702), location(?x702, ?x5771), ?x3835 = 01cky2 >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vvycq profession 0dz3r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 113.000 0.647 http://example.org/people/person/profession #14938-09sh8k PRED entity: 09sh8k PRED relation: nominated_for! PRED expected values: 03g5_y => 88 concepts (35 used for prediction) PRED predicted values (max 10 best out of 663): 02xnjd (0.44 #51441, 0.34 #56121), 01yf85 (0.28 #51440, 0.26 #32732, 0.23 #56120), 058s44 (0.28 #51440, 0.26 #32732, 0.23 #56120), 014gf8 (0.28 #51440, 0.26 #32732, 0.23 #56120), 03gm48 (0.28 #51440, 0.26 #32732, 0.23 #56120), 07myb2 (0.28 #51440, 0.26 #32732, 0.23 #56120), 01sfmyk (0.28 #51440, 0.26 #32732, 0.23 #56120), 01r2c7 (0.22 #14026, 0.21 #4676, 0.19 #4677), 079vf (0.22 #14026, 0.21 #4676, 0.19 #4677), 0bs1yy (0.21 #65474, 0.21 #60798) >> Best rule #51441 for best value: >> intensional similarity = 4 >> extensional distance = 209 >> proper extension: 02pg45; >> query: (?x136, ?x7976) <- executive_produced_by(?x136, ?x96), film(?x965, ?x136), nominated_for(?x2325, ?x136), produced_by(?x136, ?x7976) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #58459 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 252 *> proper extension: 01vrwfv; 043qqt5; *> query: (?x136, ?x5788) <- nominated_for(?x9780, ?x136), category(?x136, ?x134), student(?x2486, ?x9780), award_nominee(?x9780, ?x5788) *> conf = 0.13 ranks of expected_values: 19 EVAL 09sh8k nominated_for! 03g5_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 88.000 35.000 0.442 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14937-02h7qr PRED entity: 02h7qr PRED relation: student PRED expected values: 05w88j => 114 concepts (97 used for prediction) PRED predicted values (max 10 best out of 584): 0gs7x (0.22 #1942, 0.18 #4035, 0.15 #8221), 017yfz (0.11 #689, 0.09 #2782, 0.08 #6968), 083q7 (0.11 #160, 0.09 #2253, 0.08 #6439), 013zyw (0.11 #1007, 0.09 #3100, 0.08 #7286), 03rqww (0.11 #1430, 0.09 #3523, 0.08 #7709), 02lp3c (0.11 #1084, 0.09 #3177, 0.08 #7363), 01rc4p (0.11 #1190, 0.09 #3283, 0.08 #7469), 04411 (0.11 #124, 0.09 #2217, 0.08 #6403), 01963w (0.11 #204, 0.09 #2297, 0.08 #6483), 044f7 (0.11 #973, 0.09 #3066, 0.08 #7252) >> Best rule #1942 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0352gk; 0123j6; 04fv0k; >> query: (?x7394, 0gs7x) <- category(?x7394, ?x134), state_province_region(?x7394, ?x1767), citytown(?x7394, ?x9850), ?x1767 = 04rrd >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02h7qr student 05w88j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 97.000 0.222 http://example.org/education/educational_institution/students_graduates./education/education/student #14936-06101p PRED entity: 06101p PRED relation: profession PRED expected values: 0cbd2 => 82 concepts (50 used for prediction) PRED predicted values (max 10 best out of 80): 0np9r (0.78 #1035, 0.33 #453, 0.29 #744), 018gz8 (0.50 #449, 0.42 #1031, 0.25 #740), 03gjzk (0.50 #1029, 0.39 #447, 0.38 #3211), 02krf9 (0.35 #604, 0.29 #750, 0.22 #2643), 09jwl (0.31 #6260, 0.25 #4955, 0.19 #888), 0kyk (0.28 #462, 0.25 #172, 0.17 #1334), 0nbcg (0.27 #4968, 0.15 #6273, 0.11 #901), 0cbd2 (0.25 #3205, 0.22 #4510, 0.18 #1023), 015h31 (0.22 #1042, 0.12 #170, 0.06 #460), 01c72t (0.19 #4960, 0.09 #6265, 0.08 #2182) >> Best rule #1035 for best value: >> intensional similarity = 7 >> extensional distance = 58 >> proper extension: 079vf; 05_k56; 01g4zr; 01c58j; 05jcn8; 0fby2t; 0c12h; 04jspq; 030g9z; 01x2tm8; ... >> query: (?x13074, 0np9r) <- profession(?x13074, ?x4725), profession(?x13074, ?x987), profession(?x13074, ?x319), ?x987 = 0dxtg, ?x319 = 01d_h8, profession(?x5715, ?x4725), ?x5715 = 03llf8 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3205 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 436 *> proper extension: 0l6qt; 01xdf5; 04t2l2; 02rchht; 083chw; 014zcr; 01vw87c; 02g8h; 05g8ky; 0h5f5n; ... *> query: (?x13074, 0cbd2) <- location(?x13074, ?x2645), gender(?x13074, ?x231), profession(?x13074, ?x987), nationality(?x13074, ?x2346), ?x987 = 0dxtg *> conf = 0.25 ranks of expected_values: 8 EVAL 06101p profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 82.000 50.000 0.783 http://example.org/people/person/profession #14935-014d7f PRED entity: 014d7f PRED relation: nutrient! PRED expected values: 0fj52s 0cxn2 033cnk => 43 concepts (14 used for prediction) PRED predicted values (max 10 best out of 78): 04zpv (0.90 #276, 0.90 #260, 0.90 #255), 0fjfh (0.90 #285, 0.90 #273, 0.90 #252), 0fj52s (0.90 #248, 0.90 #245, 0.90 #224), 05z55 (0.90 #223, 0.90 #212, 0.89 #184), 0hkxq (0.89 #147, 0.89 #25, 0.88 #146), 037ls6 (0.89 #147, 0.89 #25, 0.88 #167), 033cnk (0.89 #147, 0.89 #25, 0.88 #167), 0cxn2 (0.89 #147, 0.89 #25, 0.88 #167), 0frq6 (0.89 #25, 0.88 #167, 0.87 #53), 0f25w9 (0.89 #25, 0.88 #167, 0.87 #53) >> Best rule #276 for best value: >> intensional similarity = 123 >> extensional distance = 19 >> proper extension: 0h1zw; >> query: (?x8243, 04zpv) <- nutrient(?x7719, ?x8243), nutrient(?x7057, ?x8243), nutrient(?x6285, ?x8243), nutrient(?x6191, ?x8243), nutrient(?x6032, ?x8243), nutrient(?x5373, ?x8243), nutrient(?x4068, ?x8243), nutrient(?x3900, ?x8243), nutrient(?x1257, ?x8243), ?x3900 = 061_f, ?x7057 = 0fbdb, ?x6032 = 01nkt, nutrient(?x1257, ?x13944), nutrient(?x1257, ?x13498), nutrient(?x1257, ?x12454), nutrient(?x1257, ?x12083), nutrient(?x1257, ?x11758), nutrient(?x1257, ?x11592), nutrient(?x1257, ?x11409), nutrient(?x1257, ?x11270), nutrient(?x1257, ?x10891), nutrient(?x1257, ?x10098), nutrient(?x1257, ?x9915), nutrient(?x1257, ?x9855), nutrient(?x1257, ?x9733), nutrient(?x1257, ?x9619), nutrient(?x1257, ?x9490), nutrient(?x1257, ?x9436), nutrient(?x1257, ?x9426), nutrient(?x1257, ?x9365), nutrient(?x1257, ?x8487), nutrient(?x1257, ?x8442), nutrient(?x1257, ?x8413), nutrient(?x1257, ?x7894), nutrient(?x1257, ?x7720), nutrient(?x1257, ?x7652), nutrient(?x1257, ?x7431), nutrient(?x1257, ?x7364), nutrient(?x1257, ?x7362), nutrient(?x1257, ?x7219), nutrient(?x1257, ?x7135), nutrient(?x1257, ?x6586), nutrient(?x1257, ?x6192), nutrient(?x1257, ?x6033), nutrient(?x1257, ?x6026), nutrient(?x1257, ?x5549), nutrient(?x1257, ?x5526), nutrient(?x1257, ?x5451), nutrient(?x1257, ?x5374), nutrient(?x1257, ?x4069), nutrient(?x1257, ?x3203), nutrient(?x1257, ?x2702), nutrient(?x1257, ?x2018), nutrient(?x1257, ?x1960), nutrient(?x1257, ?x1258), ?x2018 = 01sh2, ?x6192 = 06jry, ?x7894 = 0f4hc, ?x5374 = 025s0zp, ?x4068 = 0fbw6, ?x7652 = 025s0s0, ?x9365 = 04k8n, ?x7720 = 025s7x6, ?x9733 = 0h1tz, ?x12083 = 01n78x, ?x7364 = 09gvd, ?x10098 = 0h1_c, ?x11758 = 0q01m, ?x10891 = 0g5gq, ?x3203 = 04kl74p, ?x6586 = 05gh50, ?x11409 = 0h1yf, ?x9619 = 0h1tg, ?x4069 = 0hqw8p_, ?x7135 = 025rsfk, ?x13944 = 0f4kp, ?x9915 = 025tkqy, ?x8413 = 02kc4sf, ?x11270 = 02kc008, ?x8442 = 02kcv4x, ?x6033 = 04zjxcz, ?x7431 = 09gwd, ?x2702 = 0838f, nutrient(?x10612, ?x5549), nutrient(?x9732, ?x5549), nutrient(?x9489, ?x5549), nutrient(?x8298, ?x5549), nutrient(?x6159, ?x5549), nutrient(?x5009, ?x5549), nutrient(?x3468, ?x5549), nutrient(?x2701, ?x5549), nutrient(?x1959, ?x5549), nutrient(?x1303, ?x5549), ?x10612 = 0frq6, ?x6159 = 033cnk, ?x6026 = 025sf8g, ?x7219 = 0h1vg, ?x9490 = 0h1sg, ?x11592 = 025sf0_, ?x5526 = 09pbb, ?x3468 = 0cxn2, ?x1303 = 0fj52s, ?x1258 = 0h1wg, ?x9436 = 025sqz8, ?x7719 = 0dj75, ?x9426 = 0h1yy, ?x1959 = 0f25w9, ?x5009 = 0fjfh, ?x9489 = 07j87, ?x8298 = 037ls6, ?x7362 = 02kc5rj, ?x8487 = 014yzm, ?x6285 = 01645p, ?x9732 = 05z55, ?x13498 = 07q0m, ?x1960 = 07hnp, ?x2701 = 0hkxq, ?x6191 = 014j1m, ?x5451 = 05wvs, ?x5373 = 0971v, ?x12454 = 025rw19, nutrient(?x4068, ?x9855), nutrient(?x2701, ?x9855) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #248 for first EXPECTED value: *> intensional similarity = 128 *> extensional distance = 19 *> proper extension: 02kcv4x; 014yzm; 07q0m; *> query: (?x8243, 0fj52s) <- nutrient(?x7719, ?x8243), nutrient(?x7057, ?x8243), nutrient(?x6285, ?x8243), nutrient(?x6191, ?x8243), nutrient(?x6032, ?x8243), nutrient(?x5373, ?x8243), nutrient(?x4068, ?x8243), nutrient(?x3900, ?x8243), nutrient(?x1257, ?x8243), ?x3900 = 061_f, nutrient(?x7057, ?x12902), nutrient(?x7057, ?x12868), nutrient(?x7057, ?x12454), nutrient(?x7057, ?x12083), nutrient(?x7057, ?x11758), nutrient(?x7057, ?x11592), nutrient(?x7057, ?x11409), nutrient(?x7057, ?x11270), nutrient(?x7057, ?x10891), nutrient(?x7057, ?x10709), nutrient(?x7057, ?x10195), nutrient(?x7057, ?x10098), nutrient(?x7057, ?x9949), nutrient(?x7057, ?x9915), nutrient(?x7057, ?x9840), nutrient(?x7057, ?x9795), nutrient(?x7057, ?x9733), nutrient(?x7057, ?x9619), nutrient(?x7057, ?x9490), nutrient(?x7057, ?x9426), nutrient(?x7057, ?x9365), nutrient(?x7057, ?x8413), nutrient(?x7057, ?x7894), nutrient(?x7057, ?x7720), nutrient(?x7057, ?x7652), nutrient(?x7057, ?x7431), nutrient(?x7057, ?x7364), nutrient(?x7057, ?x7362), nutrient(?x7057, ?x7219), nutrient(?x7057, ?x7135), nutrient(?x7057, ?x6586), nutrient(?x7057, ?x6286), nutrient(?x7057, ?x6192), nutrient(?x7057, ?x6160), nutrient(?x7057, ?x6026), nutrient(?x7057, ?x5549), nutrient(?x7057, ?x5526), nutrient(?x7057, ?x5451), nutrient(?x7057, ?x5374), nutrient(?x7057, ?x5010), nutrient(?x7057, ?x4069), nutrient(?x7057, ?x3901), nutrient(?x7057, ?x3469), nutrient(?x7057, ?x3203), nutrient(?x7057, ?x2702), nutrient(?x7057, ?x2018), nutrient(?x7057, ?x1960), nutrient(?x7057, ?x1304), nutrient(?x7057, ?x1258), ?x6191 = 014j1m, ?x1258 = 0h1wg, ?x5374 = 025s0zp, ?x12902 = 0fzjh, ?x9949 = 02kd0rh, ?x12454 = 025rw19, ?x9619 = 0h1tg, ?x5549 = 025s7j4, ?x7135 = 025rsfk, ?x12083 = 01n78x, ?x6160 = 041r51, ?x9426 = 0h1yy, ?x2702 = 0838f, ?x5010 = 0h1vz, ?x2018 = 01sh2, ?x9733 = 0h1tz, ?x10098 = 0h1_c, ?x6192 = 06jry, ?x7364 = 09gvd, ?x11592 = 025sf0_, ?x12868 = 03d49, ?x1257 = 09728, ?x10891 = 0g5gq, ?x5373 = 0971v, ?x7431 = 09gwd, ?x6285 = 01645p, ?x3203 = 04kl74p, ?x7219 = 0h1vg, ?x9490 = 0h1sg, ?x4068 = 0fbw6, ?x7720 = 025s7x6, ?x9365 = 04k8n, ?x9795 = 05v_8y, ?x7719 = 0dj75, ?x8413 = 02kc4sf, ?x6586 = 05gh50, ?x10195 = 0hkwr, ?x3469 = 0h1zw, ?x11758 = 0q01m, ?x7362 = 02kc5rj, ?x7894 = 0f4hc, ?x6026 = 025sf8g, ?x1960 = 07hnp, ?x11409 = 0h1yf, nutrient(?x10612, ?x3901), nutrient(?x9005, ?x3901), nutrient(?x2701, ?x3901), nutrient(?x9489, ?x9840), nutrient(?x6159, ?x9840), nutrient(?x3468, ?x9840), ?x2701 = 0hkxq, ?x1304 = 08lb68, ?x9489 = 07j87, ?x4069 = 0hqw8p_, ?x6286 = 02y_3rf, ?x5451 = 05wvs, nutrient(?x9732, ?x9915), nutrient(?x8298, ?x9915), ?x3468 = 0cxn2, ?x9732 = 05z55, ?x5526 = 09pbb, ?x10709 = 0h1sz, ?x9005 = 04zpv, ?x6032 = 01nkt, ?x6159 = 033cnk, ?x8298 = 037ls6, ?x7652 = 025s0s0, ?x10612 = 0frq6, ?x11270 = 02kc008 *> conf = 0.90 ranks of expected_values: 3, 7, 8 EVAL 014d7f nutrient! 033cnk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 43.000 14.000 0.905 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 014d7f nutrient! 0cxn2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 43.000 14.000 0.905 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 014d7f nutrient! 0fj52s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 43.000 14.000 0.905 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #14934-01jgkj2 PRED entity: 01jgkj2 PRED relation: award_nominee PRED expected values: 0flpy => 133 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1001): 06cc_1 (0.71 #74864, 0.03 #7129, 0.02 #14146), 01q3_2 (0.33 #74865, 0.06 #2058, 0.05 #4397), 02l840 (0.18 #160, 0.15 #23553, 0.09 #28233), 01wbgdv (0.18 #232, 0.08 #23625, 0.05 #2571), 0163kf (0.18 #2292, 0.07 #25685, 0.05 #4631), 01wcp_g (0.12 #289, 0.08 #25733, 0.08 #23682), 01vvyvk (0.12 #1054, 0.06 #24447, 0.04 #25734), 01vw37m (0.12 #1456, 0.06 #6134, 0.05 #24849), 02x_h0 (0.12 #1290, 0.05 #8307, 0.05 #15324), 02wwwv5 (0.12 #2043, 0.05 #4382, 0.04 #25734) >> Best rule #74864 for best value: >> intensional similarity = 3 >> extensional distance = 373 >> proper extension: 039cq4; >> query: (?x9176, ?x568) <- award_winner(?x568, ?x9176), award_winner(?x9731, ?x568), instrumentalists(?x212, ?x568) >> conf = 0.71 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jgkj2 award_nominee 0flpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 45.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14933-05xbx PRED entity: 05xbx PRED relation: award_winner! PRED expected values: 09gkdln => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 116): 09gkdln (0.23 #2484, 0.19 #4291, 0.18 #18490), 09bymc (0.23 #2483, 0.17 #537, 0.12 #3178), 03gwpw2 (0.18 #18490, 0.17 #20437, 0.15 #2373), 05c1t6z (0.17 #433, 0.12 #1267, 0.12 #850), 0fz0c2 (0.12 #1217, 0.10 #1773, 0.08 #2468), 0dznvw (0.12 #1246, 0.10 #1802, 0.08 #2497), 013b2h (0.12 #11894, 0.08 #13284, 0.08 #13841), 0fqpc7d (0.11 #4207, 0.11 #1427, 0.08 #2122), 0h_9252 (0.11 #1448, 0.08 #2421, 0.06 #8120), 01s695 (0.11 #11819, 0.08 #13209, 0.08 #13766) >> Best rule #2484 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0157m; >> query: (?x5007, 09gkdln) <- award_winner(?x3486, ?x5007), award_winner(?x2988, ?x5007), company(?x8314, ?x5007) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05xbx award_winner! 09gkdln CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 173.000 0.231 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14932-0ds35l9 PRED entity: 0ds35l9 PRED relation: film! PRED expected values: 01gw8b => 78 concepts (47 used for prediction) PRED predicted values (max 10 best out of 963): 09r9dp (0.46 #79023, 0.43 #89420, 0.41 #60308), 062ftr (0.14 #6238), 05ty4m (0.11 #83182, 0.11 #93580, 0.11 #72785), 0f5xn (0.08 #3048, 0.06 #969, 0.04 #13445), 0kszw (0.08 #420, 0.05 #4578, 0.03 #2499), 0f0kz (0.06 #4675, 0.06 #517, 0.04 #6755), 0gnbw (0.06 #1269, 0.03 #7507, 0.03 #22064), 03wy70 (0.06 #1286, 0.02 #13762, 0.02 #22081), 0tc7 (0.06 #395, 0.02 #21190, 0.01 #2474), 079vf (0.06 #2087, 0.05 #4166, 0.04 #8325) >> Best rule #79023 for best value: >> intensional similarity = 4 >> extensional distance = 741 >> proper extension: 014zwb; 09rvwmy; >> query: (?x86, ?x3789) <- film_crew_role(?x86, ?x137), nominated_for(?x4490, ?x86), nominated_for(?x3789, ?x86), people(?x2510, ?x4490) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #47827 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 523 *> proper extension: 06mr2s; *> query: (?x86, ?x2352) <- nominated_for(?x4490, ?x86), honored_for(?x1442, ?x86), award_nominee(?x4490, ?x2352) *> conf = 0.04 ranks of expected_values: 100 EVAL 0ds35l9 film! 01gw8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 78.000 47.000 0.465 http://example.org/film/actor/film./film/performance/film #14931-02hcv8 PRED entity: 02hcv8 PRED relation: time_zones! PRED expected values: 0rs6x 0fm9_ 0ydpd 02_286 0mw89 0fr59 03v_5 0mtdx 05k7sb 01ktz1 02xry 0mwh1 0tz1x 0m7d0 0ply0 071cn 0rj0z 0cymp 0t_gg 018dcy 0n5gq 0136jw 050ks 0f67f 0fr61 0n5yv 0jgk3 0zqq8 0v1xg 0l3n4 0jrxx 0rrwt 0p9z5 0jgm8 0p7vt 0f4y3 07l5z 0mpbx 0fvyg 0ff0x 0xms9 0fkhz 0n2kw 0n5_t 0mnlq 010z5n 013hvr 01qcx_ 0ycht 0vqcq 0mnyn 0njpq 0fbzp 0p9nv 01m24m 0yjvm 01dq0z 01m23s 01gr00 01t3h6 => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1719): 02_286 (0.81 #6041, 0.80 #1508, 0.68 #3777), 0zqq8 (0.81 #6041, 0.80 #1508, 0.61 #3776), 0lhql (0.81 #6041, 0.68 #3777, 0.67 #6042), 01m23s (0.81 #6041, 0.40 #8313, 0.25 #7554), 02zp1t (0.81 #6041, 0.40 #8313, 0.25 #7554), 0fvyg (0.81 #6041, 0.40 #8313, 0.25 #7554), 07l5z (0.81 #6041, 0.40 #8313, 0.25 #7554), 0_lr1 (0.81 #6041, 0.40 #8313, 0.25 #7554), 0136jw (0.81 #6041, 0.40 #8313, 0.25 #7554), 0ybkj (0.81 #6041, 0.40 #8313, 0.25 #7554) >> Best rule #6041 for best value: >> intensional similarity = 24 >> extensional distance = 4 >> proper extension: 02lcrv; >> query: (?x2674, ?x3373) <- time_zones(?x13293, ?x2674), time_zones(?x12390, ?x2674), time_zones(?x11903, ?x2674), time_zones(?x11803, ?x2674), time_zones(?x8055, ?x2674), time_zones(?x7460, ?x2674), time_zones(?x6491, ?x2674), time_zones(?x6050, ?x2674), time_zones(?x1906, ?x2674), time_zones(?x1110, ?x2674), time_zones(?x728, ?x2674), location(?x56, ?x11903), source(?x11803, ?x958), jurisdiction_of_office(?x1195, ?x11903), place_of_birth(?x1109, ?x1110), contains(?x6050, ?x4904), adjoins(?x8055, ?x12027), county(?x3373, ?x7460), location_of_ceremony(?x566, ?x6491), category(?x13293, ?x134), currency(?x12390, ?x170), origin(?x5637, ?x11903), district_represented(?x176, ?x728), religion(?x1906, ?x109) >> conf = 0.81 => this is the best rule for 23 predicted values ranks of expected_values: 1, 2, 4, 6, 7, 9, 12, 18, 19, 20, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 47, 48, 50, 57, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 270, 271, 274, 275, 278, 280, 283, 285, 288, 291, 294, 302, 325, 326, 339, 340, 372, 375, 376, 379, 381, 645, 1007, 1079 EVAL 02hcv8 time_zones! 01t3h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 01gr00 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 01m23s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 01dq0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0yjvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 01m24m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0p9nv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0fbzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0njpq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0mnyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0vqcq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0ycht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 01qcx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 013hvr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 010z5n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0mnlq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0n5_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0n2kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0fkhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0xms9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0ff0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0fvyg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0mpbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 07l5z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0f4y3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0p7vt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0jgm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0p9z5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0rrwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0jrxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0l3n4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0v1xg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0zqq8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0jgk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0n5yv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0fr61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0f67f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 050ks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0136jw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0n5gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 018dcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0t_gg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0cymp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0rj0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 071cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0ply0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0m7d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0tz1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0mwh1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 02xry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 01ktz1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 05k7sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0mtdx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 03v_5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0fr59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0mw89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0ydpd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0fm9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.809 http://example.org/location/location/time_zones EVAL 02hcv8 time_zones! 0rs6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 13.000 13.000 0.809 http://example.org/location/location/time_zones #14930-047dpm0 PRED entity: 047dpm0 PRED relation: school PRED expected values: 01j_06 021w0_ => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 662): 06pwq (0.50 #1289, 0.50 #681, 0.45 #1413), 07vyf (0.50 #1328, 0.46 #1816, 0.45 #1452), 01pl14 (0.50 #792, 0.38 #1651, 0.36 #1530), 01pq4w (0.50 #596, 0.38 #826, 0.35 #1636), 022lly (0.50 #699, 0.33 #468, 0.29 #1035), 0lyjf (0.46 #1699, 0.45 #1578, 0.40 #1273), 02pptm (0.44 #1032, 0.44 #1005, 0.40 #1400), 02rv1w (0.40 #557, 0.33 #556, 0.33 #321), 021w0_ (0.40 #557, 0.33 #313, 0.33 #198), 01tx9m (0.40 #557, 0.33 #511, 0.32 #903) >> Best rule #1289 for best value: >> intensional similarity = 63 >> extensional distance = 8 >> proper extension: 038981; >> query: (?x11905, 06pwq) <- draft(?x10279, ?x11905), draft(?x8894, ?x11905), draft(?x8111, ?x11905), draft(?x7725, ?x11905), draft(?x4208, ?x11905), draft(?x2405, ?x11905), draft(?x1823, ?x11905), draft(?x1632, ?x11905), draft(?x1160, ?x11905), school(?x7725, ?x12530), school(?x7725, ?x7716), school(?x7725, ?x331), draft(?x7725, ?x3334), ?x331 = 01jssp, team(?x261, ?x7725), school(?x8894, ?x12736), school(?x8894, ?x9131), school(?x8894, ?x8202), school(?x8894, ?x6455), ?x8202 = 06fq2, ?x9131 = 02pptm, currency(?x7716, ?x170), major_field_of_study(?x7716, ?x1682), teams(?x13949, ?x7725), school(?x8111, ?x5288), school(?x8111, ?x3777), school(?x8111, ?x2522), institution(?x4981, ?x7716), institution(?x1305, ?x7716), colors(?x7716, ?x4557), category(?x1160, ?x134), sport(?x4208, ?x5063), ?x12736 = 01stj9, ?x5288 = 02zd460, team(?x5727, ?x1160), contains(?x448, ?x2522), school(?x4208, ?x4209), ?x1682 = 02ky346, contains(?x3634, ?x7716), school(?x1160, ?x1428), school(?x1823, ?x10899), major_field_of_study(?x12530, ?x2981), colors(?x10279, ?x663), ?x448 = 03v1s, team(?x5412, ?x2405), school(?x10279, ?x4161), team(?x12323, ?x1632), student(?x2522, ?x4065), school(?x1632, ?x2171), colors(?x3777, ?x3315), ?x10899 = 01fsv9, ?x6455 = 026vcc, ?x4981 = 03bwzr4, ?x2981 = 02j62, school_type(?x3777, ?x1507), contains(?x6769, ?x12530), school(?x3334, ?x4599), organization(?x346, ?x12530), major_field_of_study(?x2522, ?x6756), profession(?x12323, ?x14261), ?x1305 = 02mjs7, gender(?x5412, ?x231), type_of_union(?x5412, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #557 for first EXPECTED value: *> intensional similarity = 75 *> extensional distance = 1 *> proper extension: 02x2khw; *> query: (?x11905, ?x6075) <- draft(?x8995, ?x11905), draft(?x8894, ?x11905), draft(?x8111, ?x11905), draft(?x7725, ?x11905), draft(?x6074, ?x11905), draft(?x4487, ?x11905), draft(?x4208, ?x11905), draft(?x2067, ?x11905), draft(?x1823, ?x11905), draft(?x1160, ?x11905), draft(?x1010, ?x11905), draft(?x700, ?x11905), draft(?x580, ?x11905), ?x7725 = 07l8x, ?x8111 = 07147, ?x8995 = 01d6g, ?x1823 = 01yhm, ?x8894 = 02d02, ?x580 = 05m_8, ?x4208 = 061xq, season(?x6074, ?x2406), draft(?x6074, ?x10600), draft(?x6074, ?x8786), draft(?x6074, ?x3334), draft(?x6074, ?x1633), ?x2406 = 03c6sl9, ?x10600 = 04f4z1k, team(?x11844, ?x6074), ?x1010 = 01d5z, colors(?x6074, ?x4557), colors(?x6074, ?x663), school(?x6074, ?x10297), school(?x6074, ?x6075), school(?x6074, ?x2948), team(?x8520, ?x6074), team(?x4244, ?x6074), ?x4244 = 028c_8, ?x4557 = 019sc, ?x4487 = 01ync, school(?x700, ?x9745), school(?x700, ?x4846), school(?x700, ?x1428), sport(?x700, ?x5063), season(?x700, ?x701), institution(?x4981, ?x10297), institution(?x1526, ?x10297), ?x1633 = 02rl201, ?x8786 = 02pq_x5, ?x663 = 083jv, ?x1428 = 01j_06, student(?x10297, ?x2451), ?x1160 = 049n7, teams(?x3501, ?x6074), institution(?x1771, ?x9745), organization(?x346, ?x6075), major_field_of_study(?x10297, ?x12158), major_field_of_study(?x10297, ?x5179), major_field_of_study(?x10297, ?x1668), currency(?x6075, ?x170), ?x12158 = 09s1f, ?x346 = 060c4, school(?x11905, ?x7338), ?x2948 = 0j_sncb, ?x1526 = 0bkj86, colors(?x10297, ?x3189), ?x2067 = 05g76, school_type(?x6075, ?x3092), category(?x10297, ?x134), ?x5179 = 04gb7, ?x4981 = 03bwzr4, ?x3334 = 02pq_rp, ?x1668 = 01mkq, ?x8520 = 01z9v6, school_type(?x4846, ?x1044), major_field_of_study(?x7338, ?x2601) *> conf = 0.40 ranks of expected_values: 9, 21 EVAL 047dpm0 school 021w0_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 17.000 17.000 0.500 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 047dpm0 school 01j_06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 17.000 17.000 0.500 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #14929-0m93 PRED entity: 0m93 PRED relation: influenced_by! PRED expected values: 015n8 => 116 concepts (36 used for prediction) PRED predicted values (max 10 best out of 350): 04hcw (0.60 #1825, 0.33 #1313, 0.25 #7982), 01hb6v (0.43 #2654, 0.29 #3167, 0.25 #6248), 02ln1 (0.40 #1879, 0.33 #1367, 0.29 #2903), 03j43 (0.40 #1603, 0.29 #2627, 0.19 #8787), 01d494 (0.40 #1587, 0.24 #9798, 0.24 #9286), 016dmx (0.40 #1869, 0.17 #7000, 0.15 #9053), 07dnx (0.40 #1898, 0.16 #12169, 0.15 #12683), 03jht (0.40 #1917, 0.15 #6535, 0.12 #8074), 06myp (0.40 #1976, 0.15 #6594, 0.12 #8133), 0bk5r (0.40 #1745, 0.15 #6363, 0.12 #7902) >> Best rule #1825 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0j3v; 0mj0c; 02wh0; >> query: (?x7341, 04hcw) <- interests(?x7341, ?x742), major_field_of_study(?x734, ?x742), major_field_of_study(?x9803, ?x742), major_field_of_study(?x3485, ?x742), ?x734 = 04zx3q1, ?x9803 = 02h659, ?x3485 = 01mpwj >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #9235 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 24 *> proper extension: 099bk; *> query: (?x7341, ?x3712) <- interests(?x7341, ?x742), influenced_by(?x4033, ?x7341), influenced_by(?x920, ?x4033), interests(?x4033, ?x713), influenced_by(?x4033, ?x3712), interests(?x920, ?x6978) *> conf = 0.19 ranks of expected_values: 68 EVAL 0m93 influenced_by! 015n8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 116.000 36.000 0.600 http://example.org/influence/influence_node/influenced_by #14928-031vy_ PRED entity: 031vy_ PRED relation: major_field_of_study PRED expected values: 02j62 => 170 concepts (158 used for prediction) PRED predicted values (max 10 best out of 118): 02j62 (0.61 #2259, 0.54 #1127, 0.42 #1033), 03g3w (0.54 #278, 0.42 #654, 0.39 #1029), 05qfh (0.46 #288, 0.42 #664, 0.29 #1039), 0fdys (0.46 #291, 0.38 #667, 0.33 #41), 037mh8 (0.46 #321, 0.25 #1072, 0.25 #697), 062z7 (0.46 #655, 0.38 #279, 0.33 #1911), 05qjt (0.42 #634, 0.38 #258, 0.29 #1135), 04rjg (0.40 #1148, 0.38 #271, 0.38 #1022), 01mkq (0.39 #1017, 0.38 #266, 0.34 #1268), 02lp1 (0.38 #262, 0.33 #638, 0.33 #1013) >> Best rule #2259 for best value: >> intensional similarity = 5 >> extensional distance = 145 >> proper extension: 02kth6; 03fmfs; 01cwdk; 01dthg; 01s7j5; 02mp0g; 02z6fs; 014d4v; >> query: (?x7574, ?x2981) <- institution(?x1368, ?x7574), contains(?x2146, ?x7574), student(?x7574, ?x10117), student(?x2981, ?x10117), major_field_of_study(?x7574, ?x8925) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031vy_ major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 158.000 0.605 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14927-069_0y PRED entity: 069_0y PRED relation: profession PRED expected values: 02jknp => 117 concepts (86 used for prediction) PRED predicted values (max 10 best out of 57): 02jknp (0.88 #1487, 0.88 #1191, 0.87 #1783), 02hrh1q (0.72 #7567, 0.69 #9936, 0.69 #11269), 0dxtg (0.71 #1197, 0.69 #2233, 0.69 #1493), 03gjzk (0.47 #6826, 0.43 #1347, 0.40 #2087), 0lgw7 (0.27 #9182, 0.26 #12738, 0.26 #12737), 0cbd2 (0.27 #2374, 0.16 #1190, 0.16 #2226), 02krf9 (0.25 #1211, 0.25 #1063, 0.23 #1951), 0np9r (0.23 #8610, 0.09 #6092, 0.09 #2982), 09jwl (0.17 #4312, 0.17 #11422, 0.16 #8164), 0kyk (0.15 #2398, 0.09 #10989, 0.09 #918) >> Best rule #1487 for best value: >> intensional similarity = 3 >> extensional distance = 234 >> proper extension: 0522wp; 01nr36; >> query: (?x7739, 02jknp) <- film(?x7739, ?x7502), award(?x7739, ?x7215), nominated_for(?x1864, ?x7502) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 069_0y profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 86.000 0.881 http://example.org/people/person/profession #14926-0cv72h PRED entity: 0cv72h PRED relation: location PRED expected values: 04tgp => 128 concepts (109 used for prediction) PRED predicted values (max 10 best out of 245): 0chrx (0.33 #404, 0.14 #2010, 0.09 #4416), 059rby (0.22 #3226, 0.12 #8039, 0.12 #7236), 02_286 (0.18 #64241, 0.18 #15284, 0.18 #46584), 0cr3d (0.18 #4157, 0.18 #7365, 0.17 #4959), 030qb3t (0.17 #18543, 0.17 #15330, 0.17 #24162), 0dclg (0.14 #1723, 0.14 #920, 0.10 #8942), 0yj9v (0.14 #1455, 0.12 #3060, 0.09 #4664), 0s9z_ (0.14 #1389, 0.12 #2994, 0.09 #4598), 01sn3 (0.14 #1018, 0.12 #2623, 0.08 #5029), 0z20d (0.14 #1178, 0.12 #2783, 0.08 #5189) >> Best rule #404 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0c_md_; >> query: (?x7064, 0chrx) <- award_winner(?x10746, ?x7064), athlete(?x1083, ?x7064), company(?x7064, ?x3658), profession(?x7064, ?x1032) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #65810 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1280 *> proper extension: 06lgq8; *> query: (?x7064, ?x94) <- place_of_birth(?x7064, ?x5090), student(?x2821, ?x7064), contains(?x94, ?x5090) *> conf = 0.04 ranks of expected_values: 61 EVAL 0cv72h location 04tgp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 128.000 109.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #14925-02v1m7 PRED entity: 02v1m7 PRED relation: award! PRED expected values: 09hnb 01vvyfh 0b_j2 01dpsv => 47 concepts (29 used for prediction) PRED predicted values (max 10 best out of 2662): 01wd9lv (0.77 #89790, 0.76 #69836, 0.74 #43225), 01vsy7t (0.77 #89790, 0.76 #69836, 0.72 #39898), 07mvp (0.77 #89790, 0.70 #89789, 0.67 #39895), 0l56b (0.76 #69836, 0.72 #39898, 0.72 #89791), 012x4t (0.71 #27010, 0.62 #30335, 0.61 #33660), 027zz (0.70 #89789, 0.67 #39895, 0.67 #59852), 01vvycq (0.62 #30067, 0.57 #26742, 0.56 #33392), 016pns (0.58 #24063, 0.57 #14091, 0.56 #20739), 01vrz41 (0.57 #26883, 0.56 #30208, 0.50 #33533), 0ffgh (0.57 #12024, 0.50 #35294, 0.50 #31969) >> Best rule #89790 for best value: >> intensional similarity = 4 >> extensional distance = 197 >> proper extension: 02kgb7; 0bqsk5; 02q3s; >> query: (?x2180, ?x4620) <- award_winner(?x2180, ?x4620), award_winner(?x2180, ?x1136), award_nominee(?x1136, ?x538), artists(?x671, ?x4620) >> conf = 0.77 => this is the best rule for 3 predicted values *> Best rule #4041 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 02f72n; 02f72_; *> query: (?x2180, 09hnb) <- award(?x10565, ?x2180), award(?x10209, ?x2180), award(?x2635, ?x2180), award(?x140, ?x2180), ?x2635 = 03fbc, award_nominee(?x527, ?x140), ?x10565 = 0c9l1, artist(?x2299, ?x10209) *> conf = 0.50 ranks of expected_values: 25, 44, 121, 375 EVAL 02v1m7 award! 01dpsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 29.000 0.770 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02v1m7 award! 0b_j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 47.000 29.000 0.770 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02v1m7 award! 01vvyfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 47.000 29.000 0.770 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02v1m7 award! 09hnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 47.000 29.000 0.770 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14924-0fphgb PRED entity: 0fphgb PRED relation: film! PRED expected values: 05myd2 => 97 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1127): 0h7pj (0.25 #1540, 0.05 #9853, 0.03 #34796), 0kszw (0.25 #418, 0.04 #39910, 0.03 #33674), 02lkcc (0.25 #242, 0.03 #43890, 0.02 #56362), 0gy6z9 (0.25 #566, 0.02 #8879, 0.02 #10957), 08x5c_ (0.25 #1946, 0.02 #10259, 0.02 #12337), 0693l (0.25 #528, 0.02 #8841, 0.02 #10919), 01g257 (0.25 #253, 0.02 #8566, 0.02 #10644), 01vs_v8 (0.25 #360, 0.02 #14907, 0.01 #54400), 0zcbl (0.25 #1217, 0.02 #17845, 0.02 #28236), 017khj (0.25 #985, 0.02 #46713, 0.01 #44633) >> Best rule #1540 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02pg45; >> query: (?x3619, 0h7pj) <- film(?x4764, ?x3619), film(?x1774, ?x3619), ?x4764 = 021yzs, film_release_distribution_medium(?x3619, ?x81) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fphgb film! 05myd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 32.000 0.250 http://example.org/film/actor/film./film/performance/film #14923-0fn8jc PRED entity: 0fn8jc PRED relation: languages PRED expected values: 02h40lc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.28 #822, 0.27 #900, 0.27 #80), 06nm1 (0.12 #625, 0.07 #45, 0.01 #357), 01r2l (0.12 #625, 0.07 #56), 064_8sq (0.03 #835, 0.03 #913, 0.03 #171), 03k50 (0.02 #2657, 0.02 #1565, 0.02 #1799), 02bjrlw (0.02 #352, 0.01 #430, 0.01 #1562), 07c9s (0.01 #1574, 0.01 #1808, 0.01 #833), 03_9r (0.01 #83) >> Best rule #822 for best value: >> intensional similarity = 3 >> extensional distance = 1128 >> proper extension: 017r2; 026lj; 01wj9y9; 03j0br4; 017yfz; 0dfjb8; 024zq; 04l19_; 023n39; 034ls; ... >> query: (?x7289, 02h40lc) <- profession(?x7289, ?x1032), location(?x7289, ?x1411), people(?x5606, ?x7289) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fn8jc languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.277 http://example.org/people/person/languages #14922-0gv5c PRED entity: 0gv5c PRED relation: written_by! PRED expected values: 0bbgvp => 127 concepts (30 used for prediction) PRED predicted values (max 10 best out of 509): 0bj25 (0.33 #553, 0.05 #14389, 0.01 #2515), 025scjj (0.33 #585, 0.01 #2547, 0.01 #4509), 01gvsn (0.08 #1279, 0.03 #2587, 0.02 #4549), 0cbl95 (0.08 #1307, 0.01 #1961, 0.01 #3269), 0djkrp (0.08 #1216, 0.01 #1870, 0.01 #3178), 06x77g (0.08 #1215, 0.01 #1869, 0.01 #3177), 016z7s (0.08 #785, 0.01 #1439, 0.01 #2747), 02z2mr7 (0.08 #1035, 0.01 #2997), 03wy8t (0.06 #2553, 0.03 #8439, 0.02 #4515), 0kb07 (0.05 #14389, 0.03 #2308, 0.01 #3616) >> Best rule #553 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 027vps; >> query: (?x4477, 0bj25) <- written_by(?x951, ?x4477), profession(?x4477, ?x319), ?x951 = 0cwy47, award(?x4477, ?x601) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gv5c written_by! 0bbgvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 30.000 0.333 http://example.org/film/film/written_by #14921-02wwwv5 PRED entity: 02wwwv5 PRED relation: origin PRED expected values: 0d0x8 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 64): 04jpl (0.13 #240, 0.08 #942, 0.07 #1176), 030qb3t (0.10 #267, 0.09 #1905, 0.09 #33), 02_286 (0.09 #1420, 0.09 #3058, 0.08 #3292), 0cr3d (0.09 #54, 0.03 #3330, 0.03 #1458), 0f2v0 (0.05 #537, 0.05 #303, 0.05 #771), 0k33p (0.05 #395, 0.04 #1331, 0.01 #3203), 09c7w0 (0.05 #1405, 0.04 #3043, 0.03 #3277), 02dtg (0.03 #1414, 0.03 #1882, 0.03 #3052), 01_d4 (0.03 #1443, 0.03 #39, 0.03 #3081), 0dclg (0.03 #1447, 0.02 #3085, 0.02 #2383) >> Best rule #240 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 03xhj6; 02vgh; 02hzz; 012vm6; >> query: (?x9623, 04jpl) <- artists(?x3243, ?x9623), ?x3243 = 0y3_8, origin(?x9623, ?x760) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02wwwv5 origin 0d0x8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 89.000 0.128 http://example.org/music/artist/origin #14920-01nqfh_ PRED entity: 01nqfh_ PRED relation: instrumentalists! PRED expected values: 02hnl => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 118): 026t6 (0.56 #3, 0.50 #84, 0.45 #489), 018vs (0.54 #173, 0.51 #1064, 0.48 #497), 02hnl (0.50 #680, 0.41 #518, 0.41 #437), 03qjg (0.38 #210, 0.34 #534, 0.27 #1101), 0l14qv (0.31 #491, 0.31 #167, 0.21 #1545), 0gkd1 (0.23 #237, 0.21 #561, 0.14 #480), 03gvt (0.20 #142, 0.11 #61, 0.09 #1845), 03f5mt (0.20 #159, 0.11 #78, 0.05 #1622), 042v_gx (0.15 #169, 0.14 #493, 0.10 #412), 04rzd (0.15 #197, 0.10 #521, 0.10 #440) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0fp_v1x; 01gg59; 04pf4r; 050z2; 09889g; 03h_fqv; 07zft; >> query: (?x562, 026t6) <- instrumentalists(?x315, ?x562), ?x315 = 0l14md, nationality(?x562, ?x94), music(?x1178, ?x562) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #680 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 03j0br4; *> query: (?x562, 02hnl) <- instrumentalists(?x315, ?x562), ?x315 = 0l14md, place_of_birth(?x562, ?x1523), profession(?x562, ?x563) *> conf = 0.50 ranks of expected_values: 3 EVAL 01nqfh_ instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 148.000 148.000 0.556 http://example.org/music/instrument/instrumentalists #14919-0cbgl PRED entity: 0cbgl PRED relation: influenced_by PRED expected values: 03f0324 01v_0b => 145 concepts (64 used for prediction) PRED predicted values (max 10 best out of 310): 03_87 (0.29 #1059, 0.18 #6651, 0.16 #1489), 081k8 (0.18 #2733, 0.16 #1443, 0.14 #1013), 05qmj (0.18 #2770, 0.13 #4921, 0.12 #6212), 03sbs (0.16 #4950, 0.16 #1509, 0.15 #6241), 01v9724 (0.16 #1465, 0.07 #15662, 0.07 #5767), 05gpy (0.16 #1485, 0.05 #6647, 0.05 #9227), 040_9 (0.14 #2677, 0.14 #957, 0.11 #1387), 084w8 (0.14 #2153, 0.14 #863, 0.11 #1293), 0j3v (0.14 #2640, 0.11 #6082, 0.09 #4791), 02zjd (0.14 #1053, 0.11 #1483, 0.05 #2343) >> Best rule #1059 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0kvnn; >> query: (?x14008, 03_87) <- gender(?x14008, ?x231), people(?x6821, ?x14008), ?x6821 = 06z5s, student(?x741, ?x14008) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #6601 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 034bs; 02yy8; 01h2_6; *> query: (?x14008, 03f0324) <- nationality(?x14008, ?x94), people(?x6821, ?x14008), influenced_by(?x14008, ?x117), student(?x741, ?x14008) *> conf = 0.11 ranks of expected_values: 31, 129 EVAL 0cbgl influenced_by 01v_0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 145.000 64.000 0.286 http://example.org/influence/influence_node/influenced_by EVAL 0cbgl influenced_by 03f0324 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 145.000 64.000 0.286 http://example.org/influence/influence_node/influenced_by #14918-09qvf4 PRED entity: 09qvf4 PRED relation: nominated_for PRED expected values: 0cs134 => 64 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1432): 030cx (0.81 #11077, 0.81 #11076, 0.79 #7909), 03ln8b (0.81 #11077, 0.81 #11076, 0.79 #7909), 01vnbh (0.81 #11077, 0.81 #11076, 0.79 #7909), 05c46y6 (0.73 #6719, 0.67 #8302, 0.16 #22553), 03cvvlg (0.64 #7584, 0.58 #9167, 0.13 #25004), 0ds3t5x (0.64 #6375, 0.58 #7958, 0.12 #23795), 095zlp (0.55 #6380, 0.50 #7963, 0.21 #15877), 01cmp9 (0.55 #7259, 0.50 #8842, 0.21 #16756), 011yg9 (0.55 #7241, 0.50 #8824, 0.18 #36419), 027r9t (0.55 #7414, 0.50 #8997, 0.12 #24834) >> Best rule #11077 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 0bp_b2; 0bdw1g; 09qv3c; 0cjyzs; 0bdx29; 0bfvd4; 0gkts9; 0fc9js; 0fbtbt; 09qrn4; ... >> query: (?x4225, ?x6341) <- ceremony(?x4225, ?x8238), ?x8238 = 0bxs_d, award(?x6341, ?x4225), nominated_for(?x4225, ?x631), nominated_for(?x693, ?x6341) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #4638 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0bsjcw; *> query: (?x4225, 0cs134) <- ceremony(?x4225, ?x8238), award(?x10660, ?x4225), ?x10660 = 01rs5p, award_winner(?x8238, ?x496) *> conf = 0.40 ranks of expected_values: 27 EVAL 09qvf4 nominated_for 0cs134 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 64.000 27.000 0.813 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14917-03qnvdl PRED entity: 03qnvdl PRED relation: film! PRED expected values: 02jsgf => 83 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1000): 0gn30 (0.15 #946, 0.07 #30043, 0.07 #5103), 014gf8 (0.12 #1007, 0.07 #5164, 0.06 #17633), 030_3z (0.11 #81061, 0.11 #31176, 0.11 #33255), 024bbl (0.08 #7072, 0.03 #23698, 0.03 #45728), 079vf (0.08 #8, 0.07 #4165, 0.05 #16634), 016ypb (0.08 #498, 0.07 #4655, 0.04 #42069), 086nl7 (0.08 #785, 0.07 #4942, 0.04 #17411), 07r1h (0.08 #1087, 0.04 #5244, 0.04 #11479), 03ym1 (0.08 #1011, 0.04 #5168, 0.03 #13481), 012d40 (0.08 #16, 0.04 #4173, 0.03 #16642) >> Best rule #946 for best value: >> intensional similarity = 6 >> extensional distance = 24 >> proper extension: 01_1hw; >> query: (?x1525, 0gn30) <- genre(?x1525, ?x225), produced_by(?x1525, ?x4552), ?x225 = 02kdv5l, prequel(?x1525, ?x763), film(?x548, ?x1525), location_of_ceremony(?x548, ?x739) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #4862 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 44 *> proper extension: 0140g4; 02sg5v; 0x25q; 014nq4; 02_sr1; 0292qb; 01jnc_; 048yqf; 025s1wg; *> query: (?x1525, 02jsgf) <- genre(?x1525, ?x225), produced_by(?x1525, ?x4552), ?x225 = 02kdv5l, prequel(?x1525, ?x763), film(?x548, ?x1525), country(?x1525, ?x94) *> conf = 0.04 ranks of expected_values: 32 EVAL 03qnvdl film! 02jsgf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 83.000 53.000 0.154 http://example.org/film/actor/film./film/performance/film #14916-0gnbw PRED entity: 0gnbw PRED relation: award_winner! PRED expected values: 02yw5r 092_25 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 132): 01s695 (0.10 #142, 0.04 #1949, 0.04 #6954), 019bk0 (0.10 #154, 0.04 #293, 0.04 #1961), 09qvms (0.06 #1124, 0.06 #1263, 0.05 #6256), 013b2h (0.06 #2024, 0.05 #1607, 0.04 #7029), 092c5f (0.05 #1125, 0.05 #6256, 0.05 #1264), 09g90vz (0.05 #1373, 0.04 #1234, 0.04 #1512), 092t4b (0.05 #1163, 0.05 #6256, 0.05 #1302), 0hr3c8y (0.05 #1121, 0.05 #6256, 0.04 #1260), 027hjff (0.05 #6256, 0.05 #1167, 0.04 #1306), 03gyp30 (0.05 #6256, 0.04 #1227, 0.04 #1366) >> Best rule #142 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 07s8r0; 015pkc; 032w8h; 01w7nww; 05slvm; 01fx2g; 029_l; 01fx5l; >> query: (?x7269, 01s695) <- award_nominee(?x7269, ?x1735), profession(?x7269, ?x319), ?x1735 = 01l9p >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #6256 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1373 *> proper extension: 018p5f; 09jm8; *> query: (?x7269, ?x873) <- award_winner(?x4091, ?x7269), award_nominee(?x1733, ?x7269), ceremony(?x4091, ?x873) *> conf = 0.05 ranks of expected_values: 13, 27 EVAL 0gnbw award_winner! 092_25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 102.000 102.000 0.100 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gnbw award_winner! 02yw5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 102.000 102.000 0.100 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14915-01kstn9 PRED entity: 01kstn9 PRED relation: artists! PRED expected values: 01lyv 0gg8l => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 183): 06by7 (0.54 #1909, 0.43 #6313, 0.42 #7570), 064t9 (0.45 #5676, 0.43 #6304, 0.42 #3786), 01lyv (0.37 #350, 0.33 #36, 0.28 #665), 06j6l (0.33 #50, 0.28 #679, 0.27 #5712), 0gywn (0.33 #60, 0.24 #689, 0.22 #3832), 0gg8l (0.30 #449, 0.16 #9746, 0.07 #3592), 0glt670 (0.25 #5705, 0.19 #3815, 0.17 #7590), 02x8m (0.25 #20, 0.24 #649, 0.16 #1906), 03_d0 (0.24 #641, 0.21 #12, 0.20 #1898), 025sc50 (0.23 #5714, 0.20 #3824, 0.18 #6342) >> Best rule #1909 for best value: >> intensional similarity = 2 >> extensional distance = 117 >> proper extension: 0c9d9; 0167_s; 01gx5f; 03xhj6; 0394y; 018gm9; 01mwsnc; 0k1bs; 02vgh; 0132k4; ... >> query: (?x3539, 06by7) <- artist(?x2931, ?x3539), ?x2931 = 03rhqg >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #350 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 01bpc9; 0137g1; 016z1t; 0kxbc; 01p0vf; 011vx3; 016s0m; 016j2t; 014v1q; *> query: (?x3539, 01lyv) <- award_winner(?x2139, ?x3539), instrumentalists(?x2048, ?x3539), ?x2048 = 018j2 *> conf = 0.37 ranks of expected_values: 3, 6 EVAL 01kstn9 artists! 0gg8l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.538 http://example.org/music/genre/artists EVAL 01kstn9 artists! 01lyv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 90.000 0.538 http://example.org/music/genre/artists #14914-0bzjvm PRED entity: 0bzjvm PRED relation: honored_for PRED expected values: 0jyb4 => 29 concepts (14 used for prediction) PRED predicted values (max 10 best out of 546): 09cr8 (0.10 #595, 0.08 #1191, 0.05 #596), 02fqxm (0.10 #595, 0.08 #1191, 0.05 #596), 09rvwmy (0.10 #595, 0.08 #1191, 0.05 #596), 0yx7h (0.10 #595, 0.08 #1191, 0.05 #596), 02rq8k8 (0.10 #595, 0.08 #1191), 01q_y0 (0.10 #595, 0.08 #1191), 01b7h8 (0.09 #1726), 07zhjj (0.09 #1689), 04p5cr (0.09 #1585), 0d68qy (0.09 #1342) >> Best rule #595 for best value: >> intensional similarity = 17 >> extensional distance = 27 >> proper extension: 073hkh; 0bzk8w; 02yw5r; 059x66; 0bzm81; 0dth6b; 0gmdkyy; 02hn5v; 0bzkgg; 0bzk2h; ... >> query: (?x7940, ?x407) <- ceremony(?x5409, ?x7940), ceremony(?x1972, ?x7940), ceremony(?x1862, ?x7940), ceremony(?x1313, ?x7940), ceremony(?x1245, ?x7940), ?x1245 = 0gqwc, ?x1313 = 0gs9p, award_winner(?x7940, ?x12287), award_winner(?x7940, ?x7946), ?x1862 = 0gr51, ?x1972 = 0gqyl, ?x5409 = 0gr07, award_winner(?x1132, ?x12287), award_winner(?x7946, ?x192), film(?x7946, ?x153), gender(?x12287, ?x514), nominated_for(?x7946, ?x407) >> conf = 0.10 => this is the best rule for 6 predicted values *> Best rule #1191 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 29 *> proper extension: 073h9x; 0fzrtf; *> query: (?x7940, ?x1820) <- ceremony(?x5409, ?x7940), ceremony(?x1972, ?x7940), ceremony(?x1862, ?x7940), ceremony(?x1313, ?x7940), ceremony(?x1245, ?x7940), ?x1245 = 0gqwc, ?x1313 = 0gs9p, award_winner(?x7940, ?x7946), ?x1862 = 0gr51, ?x1972 = 0gqyl, ?x5409 = 0gr07, award_winner(?x7946, ?x192), award_winner(?x834, ?x7946), honored_for(?x7940, ?x407), nominated_for(?x7946, ?x1820) *> conf = 0.08 ranks of expected_values: 19 EVAL 0bzjvm honored_for 0jyb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 29.000 14.000 0.096 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #14913-01wlt3k PRED entity: 01wlt3k PRED relation: award_nominee PRED expected values: 0837ql => 103 concepts (42 used for prediction) PRED predicted values (max 10 best out of 631): 026yqrr (0.81 #39699, 0.80 #16346, 0.80 #46705), 016kjs (0.81 #39699, 0.80 #16346, 0.80 #46705), 01wlt3k (0.50 #2232, 0.14 #81739, 0.07 #6902), 0837ql (0.40 #1139, 0.14 #81739, 0.10 #5809), 06mt91 (0.30 #1556, 0.14 #81739, 0.12 #6226), 01vsgrn (0.30 #1305, 0.14 #81739, 0.12 #5975), 01w9k25 (0.30 #2122, 0.14 #81739, 0.07 #6792), 05mt_q (0.30 #293, 0.14 #81739, 0.04 #4963), 02wwwv5 (0.30 #2040, 0.14 #81739, 0.03 #20721), 067nsm (0.20 #1504, 0.14 #81739, 0.07 #6174) >> Best rule #39699 for best value: >> intensional similarity = 3 >> extensional distance = 245 >> proper extension: 01dw9z; 04qzm; 0c9l1; 016ppr; >> query: (?x11371, ?x827) <- artists(?x2937, ?x11371), origin(?x11371, ?x12941), award_nominee(?x827, ?x11371) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #1139 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 026yqrr; *> query: (?x11371, 0837ql) <- artists(?x2937, ?x11371), award_nominee(?x11371, ?x5203), ?x5203 = 03f19q4 *> conf = 0.40 ranks of expected_values: 4 EVAL 01wlt3k award_nominee 0837ql CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 42.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14912-07x4c PRED entity: 07x4c PRED relation: major_field_of_study PRED expected values: 0jzc => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 106): 02lp1 (0.61 #1964, 0.52 #4043, 0.50 #1598), 01mkq (0.61 #4047, 0.44 #1968, 0.43 #1602), 062z7 (0.50 #394, 0.42 #638, 0.41 #2590), 0g26h (0.50 #43, 0.40 #165, 0.39 #1995), 0l5mz (0.50 #441, 0.33 #685, 0.16 #1783), 04rjg (0.49 #1972, 0.42 #630, 0.39 #1728), 01tbp (0.46 #2013, 0.30 #1647, 0.23 #1769), 04x_3 (0.43 #1612, 0.39 #1978, 0.25 #392), 03g3w (0.42 #1735, 0.41 #2467, 0.38 #393), 037mh8 (0.42 #679, 0.38 #435, 0.29 #2021) >> Best rule #1964 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 027xx3; >> query: (?x7127, 02lp1) <- student(?x7127, ?x9826), location(?x9826, ?x3764), major_field_of_study(?x7127, ?x1682), ?x1682 = 02ky346 >> conf = 0.61 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07x4c major_field_of_study 0jzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 111.000 0.610 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14911-0l76z PRED entity: 0l76z PRED relation: program! PRED expected values: 03ktjq 02tn0_ => 103 concepts (67 used for prediction) PRED predicted values (max 10 best out of 293): 0b1s_q (0.25 #246, 0.03 #995, 0.02 #1494), 04rtpt (0.24 #403, 0.14 #500, 0.08 #1401), 01pcdn (0.23 #4498, 0.22 #8503, 0.22 #6001), 01p85y (0.23 #4498, 0.22 #8503, 0.22 #6001), 03mdt (0.23 #4498, 0.22 #8503, 0.22 #6001), 015c2f (0.21 #1498, 0.19 #8000, 0.18 #5750), 05m9f9 (0.21 #1498, 0.19 #8000, 0.18 #5750), 09b0xs (0.21 #1498, 0.18 #5750, 0.18 #6000), 059j4x (0.21 #1498, 0.18 #5750, 0.18 #6000), 0bxtg (0.21 #1498, 0.18 #5750, 0.18 #6000) >> Best rule #246 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0n2bh; >> query: (?x4588, 0b1s_q) <- program(?x4589, ?x4588), nominated_for(?x3381, ?x4588), ?x4589 = 03fg0r >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1498 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 03y317; *> query: (?x4588, ?x496) <- program(?x7837, ?x4588), produced_by(?x253, ?x7837), award_winner(?x496, ?x7837) *> conf = 0.21 ranks of expected_values: 15, 88 EVAL 0l76z program! 02tn0_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 103.000 67.000 0.250 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/program EVAL 0l76z program! 03ktjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 103.000 67.000 0.250 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/program #14910-06pwq PRED entity: 06pwq PRED relation: student PRED expected values: 03gm48 04x4s2 0fgg4 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1374): 01n1gc (0.17 #6821, 0.10 #10965, 0.08 #13036), 0432cd (0.17 #7523, 0.10 #11667, 0.08 #13738), 0gs7x (0.17 #8135, 0.10 #12279, 0.08 #14350), 02hsgn (0.17 #7031, 0.10 #11175, 0.08 #13246), 03xx9l (0.17 #7527, 0.10 #11671, 0.08 #13742), 0683n (0.17 #7659, 0.10 #11803, 0.08 #13874), 07f7jp (0.17 #8174, 0.10 #12318, 0.08 #14389), 03fykz (0.17 #6966, 0.10 #11110, 0.08 #13181), 0641g8 (0.17 #7065, 0.10 #11209, 0.08 #13280), 0cbgl (0.10 #12423, 0.08 #8279, 0.08 #14494) >> Best rule #6821 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 06y3r; 023p29; 0n839; >> query: (?x581, 01n1gc) <- list(?x581, ?x2197), organizations_founded(?x581, ?x5487) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #37887 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 01jssp; 01wdl3; 0lfgr; 01s0_f; 01jswq; 0cchk3; 02q636; 01y8zd; 01dq5z; 025v3k; ... *> query: (?x581, 04x4s2) <- major_field_of_study(?x581, ?x6870), student(?x581, ?x1299), ?x6870 = 01540 *> conf = 0.03 ranks of expected_values: 372, 1313 EVAL 06pwq student 0fgg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 84.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06pwq student 04x4s2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 84.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 06pwq student 03gm48 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 84.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #14909-01dyvs PRED entity: 01dyvs PRED relation: genre PRED expected values: 04t2t => 66 concepts (59 used for prediction) PRED predicted values (max 10 best out of 87): 07s9rl0 (0.73 #4534, 0.58 #3696, 0.58 #1311), 024qqx (0.51 #3457, 0.48 #3935, 0.48 #5487), 05p553 (0.36 #2983, 0.34 #361, 0.34 #4657), 0lsxr (0.34 #1677, 0.33 #2630, 0.25 #366), 02l7c8 (0.33 #4547, 0.28 #3351, 0.26 #2159), 01hmnh (0.27 #730, 0.26 #373, 0.16 #3473), 02n4kr (0.23 #1676, 0.17 #2629, 0.12 #842), 060__y (0.16 #1325, 0.16 #1444, 0.14 #3352), 03npn (0.16 #1675, 0.14 #721, 0.10 #2628), 04xvlr (0.15 #3817, 0.15 #3339, 0.14 #3697) >> Best rule #4534 for best value: >> intensional similarity = 3 >> extensional distance = 1372 >> proper extension: 0fq27fp; 01h72l; >> query: (?x1808, 07s9rl0) <- genre(?x1808, ?x1013), genre(?x10555, ?x1013), ?x10555 = 06xkst >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #57 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0872p_c; 07cz2; 0x25q; 0gh65c5; 05zlld0; 017jd9; 047csmy; *> query: (?x1808, 04t2t) <- genre(?x1808, ?x225), film(?x2922, ?x1808), ?x2922 = 016ypb, film_format(?x1808, ?x909) *> conf = 0.11 ranks of expected_values: 15 EVAL 01dyvs genre 04t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 66.000 59.000 0.732 http://example.org/film/film/genre #14908-017v3q PRED entity: 017v3q PRED relation: category PRED expected values: 08mbj5d => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #20, 0.91 #18, 0.90 #4) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 170 >> proper extension: 02l1fn; >> query: (?x6919, 08mbj5d) <- institution(?x1368, ?x6919), school(?x12956, ?x6919), colors(?x6919, ?x332) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017v3q category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.919 http://example.org/common/topic/webpage./common/webpage/category #14907-01jft4 PRED entity: 01jft4 PRED relation: film! PRED expected values: 055c8 => 90 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1254): 02qgqt (0.22 #8341, 0.07 #6259, 0.05 #27069), 04l3_z (0.19 #58268, 0.17 #29132, 0.17 #52023), 01kb2j (0.16 #9231, 0.03 #23797, 0.03 #30040), 02qgyv (0.16 #8706, 0.03 #6624, 0.02 #33677), 027z0pl (0.13 #47861, 0.11 #2081, 0.11 #43699), 03h304l (0.13 #47861, 0.11 #2081, 0.11 #43699), 0pz91 (0.12 #2291, 0.04 #6451, 0.03 #29342), 01q_ph (0.12 #2137, 0.04 #31269, 0.03 #22945), 0mdqp (0.12 #2199, 0.02 #12601, 0.02 #23007), 016vg8 (0.12 #9153, 0.04 #2911, 0.02 #13313) >> Best rule #8341 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 0946bb; >> query: (?x7248, 02qgqt) <- film(?x5636, ?x7248), film(?x1324, ?x7248), film(?x1324, ?x9533), ?x9533 = 02b6n9 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #33835 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 298 *> proper extension: 0jdgr; 0gt1k; 0hr41p6; *> query: (?x7248, 055c8) <- nominated_for(?x1336, ?x7248), executive_produced_by(?x7248, ?x10430), nominated_for(?x2534, ?x7248), produced_by(?x511, ?x10430) *> conf = 0.02 ranks of expected_values: 628 EVAL 01jft4 film! 055c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 62.000 0.220 http://example.org/film/actor/film./film/performance/film #14906-04gcd1 PRED entity: 04gcd1 PRED relation: profession PRED expected values: 02hrh1q => 125 concepts (49 used for prediction) PRED predicted values (max 10 best out of 85): 02hrh1q (0.89 #6062, 0.87 #5773, 0.79 #4764), 03gjzk (0.49 #1453, 0.46 #589, 0.43 #733), 018gz8 (0.44 #1455, 0.19 #3903, 0.17 #5631), 09jwl (0.33 #16, 0.25 #160, 0.21 #4337), 08z956 (0.33 #74, 0.25 #218, 0.04 #1227), 02krf9 (0.25 #1608, 0.25 #1752, 0.24 #3048), 06q2q (0.25 #184, 0.06 #2777, 0.05 #3209), 05snw (0.25 #232, 0.06 #2825, 0.04 #1385), 01pxg (0.25 #273, 0.02 #2866, 0.01 #3298), 01sjmd (0.25 #274, 0.02 #2147, 0.01 #1283) >> Best rule #6062 for best value: >> intensional similarity = 3 >> extensional distance = 1059 >> proper extension: 05p606; >> query: (?x2295, 02hrh1q) <- film(?x2295, ?x1046), nationality(?x2295, ?x94), honored_for(?x2294, ?x1046) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gcd1 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 49.000 0.891 http://example.org/people/person/profession #14905-021w0_ PRED entity: 021w0_ PRED relation: contains! PRED expected values: 09c7w0 => 126 concepts (90 used for prediction) PRED predicted values (max 10 best out of 280): 09c7w0 (0.93 #73347, 0.84 #15209, 0.83 #29521), 030qb3t (0.65 #3678, 0.11 #4572, 0.09 #32300), 0kpys (0.34 #73343, 0.30 #73344, 0.27 #60819), 07ssc (0.26 #74271, 0.15 #79650, 0.14 #75168), 02jx1 (0.23 #36757, 0.22 #74325, 0.21 #75222), 02xry (0.16 #1950, 0.07 #10001, 0.06 #16262), 071vr (0.14 #379, 0.13 #3957, 0.08 #1273), 0mzy7 (0.14 #618, 0.08 #1512, 0.05 #2406), 0r2dp (0.14 #588, 0.08 #1482, 0.04 #3270), 05tbn (0.14 #223, 0.08 #36894, 0.07 #6483) >> Best rule #73347 for best value: >> intensional similarity = 3 >> extensional distance = 962 >> proper extension: 018mlg; >> query: (?x8851, 09c7w0) <- contains(?x1227, ?x8851), jurisdiction_of_office(?x900, ?x1227), place_of_burial(?x4943, ?x1227) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021w0_ contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 90.000 0.925 http://example.org/location/location/contains #14904-023zsh PRED entity: 023zsh PRED relation: location PRED expected values: 030qb3t => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 60): 0h7h6 (0.33 #90, 0.03 #13773, 0.02 #3310), 04n3l (0.33 #180, 0.01 #3400), 030qb3t (0.15 #6522, 0.13 #16984, 0.13 #12962), 04jpl (0.15 #2431, 0.12 #4041, 0.08 #821), 02_286 (0.14 #8891, 0.13 #7280, 0.13 #14526), 0n95v (0.11 #2203, 0.08 #1398, 0.01 #3008), 02jx1 (0.08 #875, 0.05 #1680, 0.04 #2485), 01n7q (0.08 #867, 0.05 #1672, 0.03 #3283), 0f2wj (0.08 #838, 0.05 #1643, 0.02 #3254), 06_kh (0.08 #815, 0.05 #1620, 0.02 #3231) >> Best rule #90 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0f276; >> query: (?x9780, 0h7h6) <- award_nominee(?x9780, ?x7872), ?x7872 = 03g5_y, film(?x9780, ?x136) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6522 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 641 *> proper extension: 01k5t_3; 01n4f8; *> query: (?x9780, 030qb3t) <- award_nominee(?x9780, ?x5788), film(?x9780, ?x136), people(?x6736, ?x9780) *> conf = 0.15 ranks of expected_values: 3 EVAL 023zsh location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 90.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #14903-07kh6f3 PRED entity: 07kh6f3 PRED relation: genre PRED expected values: 0lsxr => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 97): 04btyz (0.66 #1206, 0.53 #6266, 0.52 #5301), 02n4kr (0.52 #1093, 0.25 #8, 0.14 #730), 02kdv5l (0.47 #362, 0.43 #603, 0.39 #122), 0lsxr (0.45 #1094, 0.38 #9, 0.24 #490), 03k9fj (0.39 #613, 0.39 #132, 0.35 #372), 02l7c8 (0.38 #16, 0.37 #6766, 0.35 #738), 05p553 (0.35 #2173, 0.34 #4099, 0.32 #5184), 09blyk (0.28 #1116, 0.12 #31, 0.07 #512), 01hmnh (0.25 #618, 0.20 #377, 0.17 #137), 0vgkd (0.25 #11, 0.08 #733, 0.06 #854) >> Best rule #1206 for best value: >> intensional similarity = 4 >> extensional distance = 161 >> proper extension: 0cz8mkh; 0dh8v4; 05css_; 0gg5kmg; 02bg55; 0cf8qb; 078mm1; >> query: (?x3790, ?x9360) <- language(?x3790, ?x254), titles(?x9360, ?x3790), titles(?x812, ?x3790), ?x812 = 01jfsb >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #1094 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 161 *> proper extension: 0cz8mkh; 0dh8v4; 05css_; 0gg5kmg; 02bg55; 0cf8qb; 078mm1; *> query: (?x3790, 0lsxr) <- language(?x3790, ?x254), titles(?x812, ?x3790), ?x812 = 01jfsb *> conf = 0.45 ranks of expected_values: 4 EVAL 07kh6f3 genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 79.000 79.000 0.661 http://example.org/film/film/genre #14902-02kcz PRED entity: 02kcz PRED relation: organization PRED expected values: 0b6css => 179 concepts (153 used for prediction) PRED predicted values (max 10 best out of 16): 0b6css (0.66 #924, 0.65 #69, 0.62 #985), 0j7v_ (0.66 #924, 0.62 #985, 0.58 #1955), 04k4l (0.45 #84, 0.40 #164, 0.38 #124), 01rz1 (0.43 #41, 0.38 #101, 0.36 #81), 0_2v (0.33 #2549, 0.32 #83, 0.32 #2590), 018cqq (0.33 #2549, 0.32 #90, 0.32 #2590), 02jxk (0.33 #2549, 0.32 #2590, 0.32 #2695), 059dn (0.33 #2549, 0.32 #2590, 0.32 #2695), 085h1 (0.32 #2590, 0.32 #2695, 0.32 #2693), 034h1h (0.22 #2536, 0.22 #2577, 0.18 #2680) >> Best rule #924 for best value: >> intensional similarity = 4 >> extensional distance = 106 >> proper extension: 059z0; >> query: (?x7807, ?x127) <- taxonomy(?x7807, ?x939), adjoins(?x7807, ?x6974), organization(?x6974, ?x127), contains(?x2467, ?x7807) >> conf = 0.66 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 02kcz organization 0b6css CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 153.000 0.657 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #14901-01qqtr PRED entity: 01qqtr PRED relation: location PRED expected values: 030qb3t => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 62): 0f2wj (0.50 #20910, 0.50 #6435, 0.47 #31366), 02_286 (0.27 #37, 0.17 #4057, 0.16 #1645), 030qb3t (0.22 #1691, 0.22 #4103, 0.21 #8126), 0cc56 (0.09 #57, 0.05 #5686, 0.05 #8100), 0ccvx (0.09 #222, 0.03 #6657, 0.02 #14699), 01_d4 (0.09 #102, 0.03 #906, 0.02 #1710), 06_kh (0.09 #11, 0.02 #8054, 0.02 #4031), 0cr3d (0.09 #6580, 0.06 #8188, 0.05 #32315), 04jpl (0.05 #4037, 0.05 #3233, 0.05 #2429), 059rby (0.05 #1624, 0.04 #4036, 0.04 #9667) >> Best rule #20910 for best value: >> intensional similarity = 2 >> extensional distance = 1050 >> proper extension: 07h1q; 047g6; 01h2_6; 011zwl; >> query: (?x8966, ?x682) <- people(?x1050, ?x8966), place_of_birth(?x8966, ?x682) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1691 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 235 *> proper extension: 03zqc1; 019_1h; 022769; 02j9lm; 01438g; 02nwxc; 01bh6y; 02lyx4; *> query: (?x8966, 030qb3t) <- participant(?x8966, ?x4438), award_winner(?x8966, ?x91), award_nominee(?x157, ?x8966) *> conf = 0.22 ranks of expected_values: 3 EVAL 01qqtr location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 110.000 0.504 http://example.org/people/person/places_lived./people/place_lived/location #14900-0hvgt PRED entity: 0hvgt PRED relation: current_club! PRED expected values: 01352_ 02w64f => 110 concepts (68 used for prediction) PRED predicted values (max 10 best out of 34): 02ltg3 (0.53 #334, 0.50 #501, 0.47 #416), 03y_f8 (0.40 #140, 0.18 #859, 0.17 #975), 0cnk2q (0.33 #28, 0.25 #496, 0.18 #859), 02s2lg (0.30 #279, 0.20 #333, 0.20 #114), 02s9vc (0.27 #348, 0.25 #375, 0.24 #402), 02pp1 (0.20 #351, 0.20 #297, 0.20 #187), 02w64f (0.20 #355, 0.20 #136, 0.18 #859), 01l3wr (0.20 #158, 0.19 #376, 0.18 #859), 03d8m4 (0.20 #173, 0.18 #859, 0.17 #200), 03z8bw (0.20 #183, 0.18 #859, 0.17 #210) >> Best rule #334 for best value: >> intensional similarity = 7 >> extensional distance = 13 >> proper extension: 02gys2; 011v3; 02_lt; 06l22; 04ltf; 03x6m; 01634x; 01rly6; 0175rc; 0mmd6; >> query: (?x2428, 02ltg3) <- current_club(?x10501, ?x2428), team(?x63, ?x10501), team(?x60, ?x2428), current_club(?x10501, ?x6552), colors(?x2428, ?x3189), ?x6552 = 0y9j, team(?x2666, ?x2428) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #355 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 13 *> proper extension: 02gys2; 011v3; 02_lt; 06l22; 04ltf; 03x6m; 01634x; 01rly6; 0175rc; 0mmd6; *> query: (?x2428, 02w64f) <- current_club(?x10501, ?x2428), team(?x63, ?x10501), team(?x60, ?x2428), current_club(?x10501, ?x6552), colors(?x2428, ?x3189), ?x6552 = 0y9j, team(?x2666, ?x2428) *> conf = 0.20 ranks of expected_values: 7, 12 EVAL 0hvgt current_club! 02w64f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 110.000 68.000 0.533 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club EVAL 0hvgt current_club! 01352_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 110.000 68.000 0.533 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #14899-06rgq PRED entity: 06rgq PRED relation: profession PRED expected values: 01c72t => 144 concepts (142 used for prediction) PRED predicted values (max 10 best out of 92): 01c72t (0.56 #4628, 0.42 #2756, 0.36 #6790), 01d_h8 (0.40 #2884, 0.40 #3316, 0.37 #5910), 039v1 (0.40 #1328, 0.40 #176, 0.38 #8101), 0dxtg (0.30 #12842, 0.30 #10390, 0.29 #15866), 0cbd2 (0.29 #1733, 0.17 #5190, 0.17 #4181), 03gjzk (0.29 #7216, 0.25 #5918, 0.25 #2892), 0fnpj (0.25 #19170, 0.15 #8991, 0.14 #2792), 04f2zj (0.25 #19170, 0.12 #524, 0.10 #1676), 01d30f (0.25 #19170, 0.12 #498, 0.06 #1794), 09lbv (0.25 #19170, 0.08 #3184, 0.06 #8951) >> Best rule #4628 for best value: >> intensional similarity = 2 >> extensional distance = 162 >> proper extension: 07m4c; >> query: (?x8490, 01c72t) <- artists(?x302, ?x8490), music(?x6994, ?x8490) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06rgq profession 01c72t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 142.000 0.561 http://example.org/people/person/profession #14898-04bs3j PRED entity: 04bs3j PRED relation: category PRED expected values: 08mbj5d => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.66 #49, 0.50 #17, 0.48 #9) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 363 >> proper extension: 023l9y; >> query: (?x545, 08mbj5d) <- place_of_birth(?x545, ?x11937), profession(?x545, ?x1183), ?x1183 = 09jwl >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bs3j category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.663 http://example.org/common/topic/webpage./common/webpage/category #14897-025ts_z PRED entity: 025ts_z PRED relation: film_release_region PRED expected values: 0chghy 05qhw => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 186): 0f8l9c (0.91 #3064, 0.88 #3568, 0.88 #1883), 06mkj (0.84 #1921, 0.84 #3606, 0.83 #3102), 05r4w (0.82 #3543, 0.81 #5225, 0.80 #3039), 03rjj (0.81 #3043, 0.81 #678, 0.80 #3547), 0chghy (0.81 #3554, 0.77 #3050, 0.77 #1869), 07ssc (0.77 #3057, 0.77 #1876, 0.76 #841), 03h64 (0.75 #1933, 0.74 #3618, 0.74 #3114), 05qhw (0.75 #3055, 0.72 #1874, 0.65 #5241), 035qy (0.75 #3076, 0.70 #1895, 0.69 #3580), 01znc_ (0.71 #3589, 0.71 #3085, 0.67 #1904) >> Best rule #3064 for best value: >> intensional similarity = 4 >> extensional distance = 189 >> proper extension: 01jrbb; >> query: (?x8770, 0f8l9c) <- film(?x3366, ?x8770), nominated_for(?x880, ?x8770), film_release_region(?x8770, ?x1003), ?x1003 = 03gj2 >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #3554 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 221 *> proper extension: 04969y; 0gj9qxr; 040rmy; 043sct5; 0bhwhj; 07s3m4g; 0gh6j94; 0hz6mv2; 0j8f09z; 0bx_hnp; ... *> query: (?x8770, 0chghy) <- language(?x8770, ?x254), film_release_region(?x8770, ?x985), film_release_region(?x8770, ?x252), ?x252 = 03_3d, ?x985 = 0k6nt *> conf = 0.81 ranks of expected_values: 5, 8 EVAL 025ts_z film_release_region 05qhw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 025ts_z film_release_region 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14896-0bqxw PRED entity: 0bqxw PRED relation: institution! PRED expected values: 02h4rq6 019v9k => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 15): 02h4rq6 (0.78 #121, 0.76 #53, 0.75 #139), 019v9k (0.71 #73, 0.69 #56, 0.64 #107), 04zx3q1 (0.55 #103, 0.48 #52, 0.45 #69), 027f2w (0.47 #108, 0.42 #74, 0.41 #91), 0bjrnt (0.31 #55, 0.24 #89, 0.23 #72), 03mkk4 (0.28 #59, 0.27 #93, 0.26 #110), 01rr_d (0.21 #63, 0.20 #97, 0.18 #46), 02cq61 (0.17 #64, 0.13 #81, 0.10 #98), 028dcg (0.14 #65, 0.13 #116, 0.13 #133), 02mjs7 (0.14 #37, 0.13 #105, 0.13 #122) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 05krk; 01pl14; 06pwq; 01w3v; 0kz2w; 07szy; 0bx8pn; 07wrz; 07vht; 07tg4; ... >> query: (?x4338, 02h4rq6) <- major_field_of_study(?x4338, ?x1695), ?x1695 = 06ms6, institution(?x620, ?x4338) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0bqxw institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.782 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bqxw institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.782 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14895-01wmcbg PRED entity: 01wmcbg PRED relation: film PRED expected values: 0jqp3 => 96 concepts (55 used for prediction) PRED predicted values (max 10 best out of 450): 0pd57 (0.49 #12531, 0.45 #26852, 0.40 #35806), 0gwjw0c (0.20 #1213, 0.02 #6583), 0gy4k (0.20 #1711), 01s9vc (0.20 #1650), 01fwzk (0.20 #1500), 0f61tk (0.20 #1471), 0298n7 (0.20 #1349), 0294mx (0.20 #1270), 01jr4j (0.20 #1249), 0k7tq (0.20 #1182) >> Best rule #12531 for best value: >> intensional similarity = 3 >> extensional distance = 325 >> proper extension: 0p51w; 03bw6; >> query: (?x12809, ?x4179) <- award_winner(?x4179, ?x12809), religion(?x12809, ?x7300), nationality(?x12809, ?x94) >> conf = 0.49 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wmcbg film 0jqp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 55.000 0.488 http://example.org/film/actor/film./film/performance/film #14894-018wrk PRED entity: 018wrk PRED relation: olympics! PRED expected values: 0crlz => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 53): 07jjt (0.62 #381, 0.53 #327, 0.48 #641), 03hr1p (0.44 #384, 0.33 #644, 0.33 #330), 0crlz (0.44 #400, 0.33 #660, 0.33 #346), 018w8 (0.40 #342, 0.31 #396, 0.30 #602), 06z6r (0.39 #675, 0.39 #674, 0.38 #392), 07bs0 (0.39 #675, 0.39 #674, 0.35 #622), 01cgz (0.39 #675, 0.39 #674, 0.35 #622), 01hp22 (0.39 #675, 0.39 #674, 0.35 #622), 06f41 (0.39 #675, 0.39 #674, 0.35 #622), 04lgq (0.39 #675, 0.39 #674, 0.35 #622) >> Best rule #381 for best value: >> intensional similarity = 10 >> extensional distance = 14 >> proper extension: 0kbws; >> query: (?x358, 07jjt) <- olympics(?x512, ?x358), olympics(?x279, ?x358), olympics(?x94, ?x358), ?x279 = 0d060g, sports(?x358, ?x1967), medal(?x358, ?x422), ?x512 = 07ssc, ?x94 = 09c7w0, sports(?x775, ?x1967), ?x775 = 0l998 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #400 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 14 *> proper extension: 0kbws; *> query: (?x358, 0crlz) <- olympics(?x512, ?x358), olympics(?x279, ?x358), olympics(?x94, ?x358), ?x279 = 0d060g, sports(?x358, ?x1967), medal(?x358, ?x422), ?x512 = 07ssc, ?x94 = 09c7w0, sports(?x775, ?x1967), ?x775 = 0l998 *> conf = 0.44 ranks of expected_values: 3 EVAL 018wrk olympics! 0crlz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 51.000 0.625 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #14893-016khd PRED entity: 016khd PRED relation: film PRED expected values: 011xg5 => 103 concepts (75 used for prediction) PRED predicted values (max 10 best out of 708): 02rx2m5 (0.71 #3565, 0.60 #35644, 0.60 #57035), 0c0yh4 (0.71 #3565, 0.60 #35644, 0.60 #57035), 02rcwq0 (0.71 #3565, 0.60 #35644, 0.60 #57035), 04xbq3 (0.08 #37427, 0.05 #53468, 0.03 #40992), 01shy7 (0.06 #2205, 0.05 #7552, 0.05 #9334), 02qzh2 (0.06 #2474, 0.03 #6039, 0.02 #13167), 0blpg (0.06 #2437, 0.03 #655, 0.03 #13130), 03bx2lk (0.05 #184, 0.03 #1966, 0.03 #5531), 02cbhg (0.05 #1399, 0.03 #3181, 0.02 #6746), 026hh0m (0.05 #1624, 0.02 #8753, 0.02 #3406) >> Best rule #3565 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 01qscs; 01q_ph; 04wqr; 01rr9f; 01kwld; 09wj5; 03m8lq; 0mdqp; 01lbp; 03pmty; ... >> query: (?x851, ?x278) <- nominated_for(?x851, ?x278), award_winner(?x851, ?x1554), celebrity(?x851, ?x4414) >> conf = 0.71 => this is the best rule for 3 predicted values *> Best rule #4992 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 66 *> proper extension: 032t2z; 03hbzj; 016lh0; 06c97; 042d1; 0b22w; 02rsz0; 081t6; 05f0r8; *> query: (?x851, 011xg5) <- people(?x4195, ?x851), ?x4195 = 02ctzb *> conf = 0.01 ranks of expected_values: 570 EVAL 016khd film 011xg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 103.000 75.000 0.713 http://example.org/film/actor/film./film/performance/film #14892-025vry PRED entity: 025vry PRED relation: nationality PRED expected values: 09c7w0 => 163 concepts (118 used for prediction) PRED predicted values (max 10 best out of 38): 09c7w0 (0.86 #11136, 0.83 #4661, 0.82 #3271), 07ssc (0.41 #4576, 0.21 #213, 0.12 #1402), 01mjq (0.34 #11633), 03rk0 (0.29 #6697, 0.06 #9191, 0.06 #8692), 02jx1 (0.29 #231, 0.22 #496, 0.19 #1420), 0d060g (0.22 #496, 0.20 #4568, 0.06 #3376), 0345h (0.22 #496, 0.14 #626, 0.13 #328), 03rjj (0.22 #496, 0.14 #600, 0.09 #4566), 0chghy (0.22 #496, 0.07 #6662, 0.03 #3379), 03rt9 (0.22 #496, 0.06 #6665, 0.05 #4574) >> Best rule #11136 for best value: >> intensional similarity = 5 >> extensional distance = 1213 >> proper extension: 03qcq; 0dbpyd; 02bfmn; 02g8h; 05g8ky; 054_mz; 01rrwf6; 07lmxq; 044rvb; 02r_d4; ... >> query: (?x681, 09c7w0) <- nationality(?x681, ?x1355), profession(?x681, ?x563), gender(?x681, ?x231), place_of_birth(?x681, ?x14301), time_zones(?x14301, ?x2864) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025vry nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 118.000 0.863 http://example.org/people/person/nationality #14891-09ld6g PRED entity: 09ld6g PRED relation: type_of_union PRED expected values: 04ztj => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #77, 0.85 #105, 0.84 #97), 01g63y (0.40 #420, 0.36 #241, 0.36 #200), 01bl8s (0.19 #501, 0.02 #125, 0.01 #142) >> Best rule #77 for best value: >> intensional similarity = 7 >> extensional distance = 43 >> proper extension: 0151zx; >> query: (?x13716, 04ztj) <- languages(?x13716, ?x254), people(?x10199, ?x13716), people(?x10199, ?x8433), people(?x10199, ?x4808), risk_factors(?x10199, ?x231), peers(?x4808, ?x2239), influenced_by(?x576, ?x8433) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09ld6g type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.867 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14890-02kbtf PRED entity: 02kbtf PRED relation: school_type PRED expected values: 01_srz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 18): 05jxkf (0.52 #1171, 0.51 #1392, 0.50 #223), 01_srz (0.18 #1478, 0.18 #244, 0.10 #68), 01_9fk (0.18 #1478, 0.15 #419, 0.15 #463), 07tf8 (0.18 #1478, 0.15 #601, 0.14 #337), 01y64 (0.18 #1478, 0.07 #76, 0.07 #10), 02p0qmm (0.18 #1478, 0.04 #140, 0.04 #162), 04qbv (0.18 #1478, 0.04 #256, 0.04 #168), 04399 (0.18 #1478, 0.03 #188, 0.03 #254), 0m4mb (0.18 #1478, 0.02 #867, 0.02 #713), 06cs1 (0.18 #1478, 0.02 #246, 0.02 #730) >> Best rule #1171 for best value: >> intensional similarity = 3 >> extensional distance = 440 >> proper extension: 01b1pf; 03p7gb; 01dbns; 012gyf; 0jksm; >> query: (?x9307, 05jxkf) <- school_type(?x9307, ?x1044), institution(?x1368, ?x9307), organization(?x346, ?x9307) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1478 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 568 *> proper extension: 04s934; 01d34b; 01722w; 02xwzh; 0b6k40; 019vv1; 017hnw; 0d5fb; 031ns1; 01xcgf; *> query: (?x9307, ?x1962) <- school_type(?x9307, ?x3205), school_type(?x1103, ?x3205), country(?x1103, ?x94), school_type(?x1103, ?x1962) *> conf = 0.18 ranks of expected_values: 2 EVAL 02kbtf school_type 01_srz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.516 http://example.org/education/educational_institution/school_type #14889-0bbgvp PRED entity: 0bbgvp PRED relation: written_by PRED expected values: 0gv5c => 105 concepts (82 used for prediction) PRED predicted values (max 10 best out of 123): 015nvj (0.34 #21216, 0.33 #2355, 0.33 #2354), 0gv2r (0.34 #21216, 0.33 #2355, 0.33 #2354), 032v0v (0.25 #49, 0.03 #2404, 0.02 #7460), 076_74 (0.25 #114, 0.03 #2469, 0.02 #3815), 08hp53 (0.25 #53, 0.03 #2408, 0.02 #7464), 02fcs2 (0.17 #404, 0.07 #1413, 0.03 #2423), 0kb3n (0.14 #929, 0.05 #2948, 0.05 #3285), 0p50v (0.14 #926, 0.02 #5642, 0.02 #7328), 05drq5 (0.14 #711, 0.02 #2730, 0.02 #3067), 0hcvy (0.14 #986) >> Best rule #21216 for best value: >> intensional similarity = 6 >> extensional distance = 592 >> proper extension: 014nq4; 05_5rjx; 02tktw; 07p12s; >> query: (?x11998, ?x6713) <- genre(?x11998, ?x2605), genre(?x6219, ?x2605), genre(?x4167, ?x2605), ?x6219 = 05znbh7, ?x4167 = 08fn5b, film(?x6713, ?x11998) >> conf = 0.34 => this is the best rule for 2 predicted values *> Best rule #2834 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 59 *> proper extension: 016z9n; 021y7yw; 026p4q7; 012mrr; 03hmt9b; 0hfzr; 07cw4; 03bxp5; 01y9r2; 0y_pg; ... *> query: (?x11998, 0gv5c) <- nominated_for(?x1079, ?x11998), ?x1079 = 0l8z1, film(?x7385, ?x11998), genre(?x11998, ?x225), featured_film_locations(?x11998, ?x1879) *> conf = 0.02 ranks of expected_values: 73 EVAL 0bbgvp written_by 0gv5c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 105.000 82.000 0.338 http://example.org/film/film/written_by #14888-03clwtw PRED entity: 03clwtw PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #66, 0.83 #96, 0.83 #91), 07z4p (0.29 #563, 0.23 #492, 0.07 #75), 02nxhr (0.29 #563, 0.23 #492, 0.07 #142), 07c52 (0.29 #563, 0.23 #492, 0.05 #18) >> Best rule #66 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 03yvf2; >> query: (?x7145, 029j_) <- genre(?x7145, ?x812), executive_produced_by(?x7145, ?x4060), film_format(?x7145, ?x909), ?x812 = 01jfsb, production_companies(?x7145, ?x1478) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03clwtw film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #14887-02g8h PRED entity: 02g8h PRED relation: award PRED expected values: 0bdw6t => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 291): 05zr6wv (0.43 #17, 0.17 #2042, 0.17 #4472), 0gq9h (0.38 #6558, 0.36 #7369, 0.32 #5343), 040njc (0.30 #6488, 0.27 #7299, 0.27 #5273), 019bnn (0.29 #1484, 0.19 #7291, 0.10 #3914), 09sb52 (0.29 #446, 0.25 #15027, 0.23 #17457), 05pcn59 (0.29 #82, 0.18 #4537, 0.16 #13043), 07bdd_ (0.29 #66, 0.18 #6546, 0.17 #7357), 05zvj3m (0.29 #94, 0.15 #1714, 0.14 #499), 05ztrmj (0.29 #185, 0.12 #4640, 0.11 #3020), 0gr51 (0.22 #1721, 0.14 #5366, 0.14 #6581) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01vsn38; >> query: (?x318, 05zr6wv) <- film(?x318, ?x3639), nationality(?x318, ?x94), ?x3639 = 02ny6g >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #15097 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 954 *> proper extension: 0g476; *> query: (?x318, 0bdw6t) <- film(?x318, ?x3088), student(?x3922, ?x318), award(?x318, ?x2192) *> conf = 0.05 ranks of expected_values: 150 EVAL 02g8h award 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 86.000 86.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14886-06ncr PRED entity: 06ncr PRED relation: instrumentalists PRED expected values: 02b25y 015x1f 0132k4 => 83 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1013): 01sb5r (0.75 #17659, 0.71 #18238, 0.62 #13584), 01vw20_ (0.67 #17586, 0.60 #7116, 0.60 #6537), 016ntp (0.63 #7540, 0.60 #7128, 0.50 #3648), 01l4zqz (0.63 #7540, 0.42 #9283, 0.42 #9282), 018y81 (0.60 #7291, 0.57 #12522, 0.57 #11359), 01vvycq (0.60 #6990, 0.57 #12221, 0.57 #11058), 0473q (0.60 #7341, 0.57 #12572, 0.57 #11409), 03h_fqv (0.60 #7260, 0.57 #12491, 0.57 #11328), 048tgl (0.60 #7471, 0.57 #12702, 0.57 #11539), 0fhxv (0.60 #7217, 0.57 #11285, 0.50 #9541) >> Best rule #17659 for best value: >> intensional similarity = 15 >> extensional distance = 10 >> proper extension: 07brj; 04rzd; 018j2; >> query: (?x2309, 01sb5r) <- group(?x2309, ?x12810), group(?x2309, ?x10502), role(?x4583, ?x2309), role(?x1831, ?x2309), ?x4583 = 0bmnm, artists(?x2249, ?x12810), ?x2249 = 03lty, instrumentalists(?x2309, ?x10625), role(?x2785, ?x2309), role(?x212, ?x1831), ?x2785 = 0jtg0, artist(?x2193, ?x10502), award_winner(?x342, ?x10625), artists(?x9063, ?x10502), ?x9063 = 0cx7f >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #11157 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 5 *> proper extension: 0l14j_; *> query: (?x2309, 02b25y) <- group(?x2309, ?x12810), group(?x2309, ?x11107), group(?x2309, ?x4010), role(?x75, ?x2309), ?x12810 = 027kwc, role(?x74, ?x2309), ?x4010 = 0163m1, ?x11107 = 0pqp3, role(?x248, ?x2309), instrumentalists(?x2309, ?x9298), role(?x6208, ?x74), role(?x74, ?x885), ?x6208 = 07r4c, instrumentalists(?x74, ?x2765), award(?x9298, ?x2180) *> conf = 0.57 ranks of expected_values: 54, 141, 515 EVAL 06ncr instrumentalists 0132k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 83.000 37.000 0.750 http://example.org/music/instrument/instrumentalists EVAL 06ncr instrumentalists 015x1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 83.000 37.000 0.750 http://example.org/music/instrument/instrumentalists EVAL 06ncr instrumentalists 02b25y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 83.000 37.000 0.750 http://example.org/music/instrument/instrumentalists #14885-0glqh5_ PRED entity: 0glqh5_ PRED relation: genre PRED expected values: 02kdv5l => 69 concepts (68 used for prediction) PRED predicted values (max 10 best out of 133): 05p553 (0.40 #4, 0.39 #124, 0.37 #1566), 03k9fj (0.40 #11, 0.32 #251, 0.27 #491), 02kdv5l (0.40 #2, 0.29 #1925, 0.29 #2769), 02l7c8 (0.32 #2059, 0.28 #3625, 0.27 #2661), 02n4kr (0.30 #8, 0.13 #3256, 0.13 #728), 04xvlr (0.21 #2045, 0.19 #3249, 0.18 #4335), 01hmnh (0.20 #17, 0.19 #257, 0.16 #1699), 06n90 (0.20 #252, 0.16 #492, 0.16 #612), 060__y (0.18 #2060, 0.16 #3264, 0.15 #4350), 03q4nz (0.15 #378, 0.08 #2062, 0.06 #7220) >> Best rule #4 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 05p1tzf; 0661ql3; >> query: (?x5315, 05p553) <- film_release_region(?x5315, ?x4302), film_release_region(?x5315, ?x1499), film_release_region(?x5315, ?x87), ?x1499 = 01znc_, ?x87 = 05r4w, ?x4302 = 06vbd >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 05p1tzf; 0661ql3; *> query: (?x5315, 02kdv5l) <- film_release_region(?x5315, ?x4302), film_release_region(?x5315, ?x1499), film_release_region(?x5315, ?x87), ?x1499 = 01znc_, ?x87 = 05r4w, ?x4302 = 06vbd *> conf = 0.40 ranks of expected_values: 3 EVAL 0glqh5_ genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 68.000 0.400 http://example.org/film/film/genre #14884-01jvxb PRED entity: 01jvxb PRED relation: contains! PRED expected values: 02jx1 => 141 concepts (81 used for prediction) PRED predicted values (max 10 best out of 300): 02m__ (0.78 #70689, 0.67 #56370, 0.67 #18779), 02qkt (0.70 #34332, 0.49 #20913, 0.33 #45078), 09c7w0 (0.67 #54583, 0.66 #31306, 0.63 #25043), 02jx1 (0.58 #17074, 0.53 #72486, 0.53 #72485), 028n3 (0.53 #72486, 0.53 #72485, 0.51 #47420), 059rby (0.33 #2703, 0.17 #7175, 0.16 #8069), 0j0k (0.31 #34363, 0.27 #45109, 0.22 #52271), 0d060g (0.27 #14320, 0.26 #16108, 0.17 #21476), 0dg3n1 (0.27 #44886, 0.22 #52048), 0f8l9c (0.25 #1833, 0.13 #4516, 0.12 #5411) >> Best rule #70689 for best value: >> intensional similarity = 4 >> extensional distance = 328 >> proper extension: 05xb7q; 0b5hj5; 02m_41; 06b7s9; 0ch280; 0ym4t; 03q6zc; >> query: (?x7097, ?x5302) <- contains(?x455, ?x7097), institution(?x734, ?x7097), category(?x7097, ?x134), citytown(?x7097, ?x5302) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #17074 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 0yl_j; *> query: (?x7097, 02jx1) <- currency(?x7097, ?x1099), ?x1099 = 01nv4h, category(?x7097, ?x134), ?x134 = 08mbj5d *> conf = 0.58 ranks of expected_values: 4 EVAL 01jvxb contains! 02jx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 141.000 81.000 0.776 http://example.org/location/location/contains #14883-07gp9 PRED entity: 07gp9 PRED relation: production_companies PRED expected values: 02slt7 => 107 concepts (94 used for prediction) PRED predicted values (max 10 best out of 67): 016tt2 (0.40 #4, 0.14 #85, 0.10 #410), 016tw3 (0.20 #12, 0.14 #93, 0.12 #500), 054lpb6 (0.14 #910, 0.10 #1398, 0.09 #4256), 086k8 (0.14 #83, 0.13 #3425, 0.13 #5635), 05qd_ (0.12 #3271, 0.12 #3433, 0.12 #1148), 030_1_ (0.11 #259, 0.10 #1154, 0.08 #422), 024rgt (0.09 #105, 0.07 #186, 0.06 #1894), 0c_j5d (0.09 #249, 0.06 #412, 0.06 #1226), 017s11 (0.09 #572, 0.09 #2284, 0.09 #898), 01gb54 (0.09 #1907, 0.07 #1175, 0.07 #3298) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0dr_4; 0hwpz; >> query: (?x324, 016tt2) <- film(?x2387, ?x324), produced_by(?x324, ?x800), language(?x324, ?x254), ?x800 = 03_gd >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #191 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 0fpgp26; *> query: (?x324, 02slt7) <- film(?x2387, ?x324), prequel(?x324, ?x8370), film_release_region(?x324, ?x142), ?x142 = 0jgd *> conf = 0.04 ranks of expected_values: 36 EVAL 07gp9 production_companies 02slt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 107.000 94.000 0.400 http://example.org/film/film/production_companies #14882-034qzw PRED entity: 034qzw PRED relation: films! PRED expected values: 07c52 => 69 concepts (45 used for prediction) PRED predicted values (max 10 best out of 60): 0fx2s (0.14 #385, 0.04 #1014, 0.04 #1170), 07s2s (0.14 #411, 0.03 #881, 0.02 #4340), 07c52 (0.14 #332, 0.02 #2530, 0.02 #4419), 0g1x2_ (0.14 #339, 0.01 #3324, 0.01 #1907), 02_5h (0.09 #481), 0bq3x (0.08 #812, 0.04 #971, 0.04 #1440), 081pw (0.05 #628, 0.03 #1256, 0.03 #2828), 0fzyg (0.05 #679, 0.02 #5397, 0.02 #4610), 0j5ym (0.05 #750), 0gf28 (0.04 #469) >> Best rule #385 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01k1k4; 02c638; 04j13sx; 02mmwk; 025rxjq; >> query: (?x2102, 0fx2s) <- genre(?x2102, ?x258), film_crew_role(?x2102, ?x137), film(?x8491, ?x2102), ?x8491 = 01nr36 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #332 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 01k1k4; 02c638; 04j13sx; 02mmwk; 025rxjq; *> query: (?x2102, 07c52) <- genre(?x2102, ?x258), film_crew_role(?x2102, ?x137), film(?x8491, ?x2102), ?x8491 = 01nr36 *> conf = 0.14 ranks of expected_values: 3 EVAL 034qzw films! 07c52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 45.000 0.143 http://example.org/film/film_subject/films #14881-0bksh PRED entity: 0bksh PRED relation: profession PRED expected values: 0d1pc => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 67): 01d_h8 (0.43 #3413, 0.43 #1782, 0.43 #6), 0dxtg (0.43 #14, 0.29 #10827, 0.27 #9643), 016z4k (0.31 #7407, 0.31 #6814, 0.29 #4740), 0nbcg (0.31 #7407, 0.31 #6814, 0.29 #4740), 0dz3r (0.31 #7407, 0.31 #6814, 0.29 #4740), 0d1pc (0.31 #7407, 0.31 #6814, 0.29 #4740), 01c72t (0.31 #7407, 0.31 #6814, 0.29 #4740), 039v1 (0.31 #7407, 0.31 #6814, 0.29 #4740), 012t_z (0.31 #7407, 0.31 #6814, 0.29 #4740), 064xm0 (0.31 #7407, 0.31 #6814, 0.29 #4740) >> Best rule #3413 for best value: >> intensional similarity = 3 >> extensional distance = 219 >> proper extension: 06s6hs; 05g7q; >> query: (?x4782, 01d_h8) <- award_winner(?x4782, ?x2221), participant(?x1896, ?x4782), participant(?x1896, ?x1503) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #7407 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 603 *> proper extension: 01l_vgt; 02cg2v; *> query: (?x4782, ?x131) <- participant(?x7164, ?x4782), profession(?x7164, ?x131), film(?x7164, ?x3496) *> conf = 0.31 ranks of expected_values: 6 EVAL 0bksh profession 0d1pc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 112.000 112.000 0.434 http://example.org/people/person/profession #14880-0p9z5 PRED entity: 0p9z5 PRED relation: place_of_birth! PRED expected values: 0bqs56 06hx2 0d3k14 => 107 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1415): 01b3bp (0.20 #2605, 0.02 #36568, 0.01 #54856), 01v_0b (0.20 #2454, 0.02 #36417, 0.01 #54705), 03j0d (0.20 #2077, 0.02 #36040, 0.01 #54328), 0237jb (0.20 #1611, 0.02 #35574, 0.01 #53862), 01l9v7n (0.20 #602, 0.02 #34565, 0.01 #52853), 05g8ky (0.20 #39, 0.02 #34002, 0.01 #52290), 073v6 (0.14 #5223, 0.14 #15673, 0.10 #28736), 03d9wk (0.14 #5211, 0.08 #7826, 0.05 #15661), 0kbg6 (0.14 #5195, 0.08 #7810, 0.05 #15645), 09jd9 (0.14 #5189, 0.08 #7804, 0.05 #15639) >> Best rule #2605 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0_j_z; 0_jws; >> query: (?x9863, 01b3bp) <- category(?x9863, ?x134), contains(?x4990, ?x9863), adjoins(?x4990, ?x10893), ?x10893 = 0k3gw >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0p9z5 place_of_birth! 0d3k14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 30.000 0.200 http://example.org/people/person/place_of_birth EVAL 0p9z5 place_of_birth! 06hx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 30.000 0.200 http://example.org/people/person/place_of_birth EVAL 0p9z5 place_of_birth! 0bqs56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 30.000 0.200 http://example.org/people/person/place_of_birth #14879-024qwq PRED entity: 024qwq PRED relation: profession PRED expected values: 09jwl 029bkp => 133 concepts (112 used for prediction) PRED predicted values (max 10 best out of 76): 02hrh1q (0.75 #9211, 0.74 #9953, 0.73 #8765), 09jwl (0.73 #9364, 0.70 #2541, 0.70 #6992), 01c72t (0.58 #1209, 0.51 #1358, 0.42 #912), 016z4k (0.47 #1486, 0.47 #6233, 0.45 #2525), 01d_h8 (0.46 #15278, 0.33 #5, 0.29 #9202), 0dxtg (0.45 #15286, 0.44 #13, 0.26 #5354), 02jknp (0.44 #7, 0.27 #15280, 0.22 #5348), 015h31 (0.44 #28, 0.02 #4481, 0.02 #7298), 0np9r (0.33 #21, 0.13 #317, 0.10 #12924), 039v1 (0.30 #3003, 0.30 #2558, 0.27 #3598) >> Best rule #9211 for best value: >> intensional similarity = 3 >> extensional distance = 851 >> proper extension: 06jzh; 03f1zdw; 01v42g; 03q1vd; 01z7_f; 02nwxc; 031k24; 01wk3c; >> query: (?x9407, 02hrh1q) <- people(?x2510, ?x9407), award_nominee(?x5901, ?x9407), award(?x9407, ?x2322) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #9364 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 852 *> proper extension: 0739y; 01r4zfk; 01bbwp; 032qgs; *> query: (?x9407, 09jwl) <- profession(?x9407, ?x2348), profession(?x1715, ?x2348), ?x1715 = 04bpm6 *> conf = 0.73 ranks of expected_values: 2, 19 EVAL 024qwq profession 029bkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 133.000 112.000 0.750 http://example.org/people/person/profession EVAL 024qwq profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 112.000 0.750 http://example.org/people/person/profession #14878-0rj0z PRED entity: 0rj0z PRED relation: time_zones PRED expected values: 02hcv8 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.68 #107, 0.64 #860, 0.60 #287), 02lcqs (0.46 #44, 0.44 #83, 0.43 #31), 02hczc (0.22 #67, 0.21 #93, 0.20 #15), 02fqwt (0.19 #274, 0.18 #795, 0.18 #691), 02lcrv (0.16 #1633, 0.15 #1593, 0.15 #1579), 042g7t (0.15 #1500, 0.03 #428, 0.02 #480), 02llzg (0.10 #408, 0.10 #434, 0.10 #212), 03bdv (0.04 #1061, 0.04 #1087, 0.03 #201), 03plfd (0.02 #856, 0.02 #974, 0.02 #961), 0gsrz4 (0.01 #959, 0.01 #972, 0.01 #854) >> Best rule #107 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0jxgx; 0jrtv; 0jhz_; 0jgld; 0jgm8; 0jxh9; 0jgj7; 0jrjb; 0fxkr; 0jryt; ... >> query: (?x3892, 02hcv8) <- contains(?x2623, ?x3892), source(?x3892, ?x958), ?x958 = 0jbk9, ?x2623 = 02xry >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rj0z time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.684 http://example.org/location/location/time_zones #14877-0g2mbn PRED entity: 0g2mbn PRED relation: influenced_by PRED expected values: 01xdf5 => 116 concepts (57 used for prediction) PRED predicted values (max 10 best out of 108): 014zfs (0.12 #1336, 0.03 #2647, 0.02 #1773), 05rx__ (0.12 #1556), 0p_47 (0.07 #1856, 0.05 #3604, 0.03 #2730), 01j7rd (0.05 #1802, 0.05 #2676, 0.05 #3550), 014z8v (0.05 #1870, 0.05 #2744, 0.04 #3618), 01k9lpl (0.05 #2060, 0.04 #4245, 0.04 #2934), 01hmk9 (0.05 #1970, 0.04 #3718, 0.03 #4155), 01svq8 (0.05 #2175, 0.04 #3923, 0.01 #3049), 013tjc (0.05 #3000, 0.03 #4311, 0.02 #3874), 01wp_jm (0.04 #2091, 0.03 #4276, 0.02 #5587) >> Best rule #1336 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02t_v1; 02yplc; >> query: (?x5153, 014zfs) <- people(?x12951, ?x5153), film(?x5153, ?x1295), ?x1295 = 03s5lz >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #2628 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 013v5j; 01wkmgb; *> query: (?x5153, 01xdf5) <- nationality(?x5153, ?x94), profession(?x5153, ?x1032), person(?x3775, ?x5153) *> conf = 0.01 ranks of expected_values: 51 EVAL 0g2mbn influenced_by 01xdf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 116.000 57.000 0.125 http://example.org/influence/influence_node/influenced_by #14876-0gy7bj4 PRED entity: 0gy7bj4 PRED relation: film_release_region PRED expected values: 03rjj 047yc 03h64 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 104): 03h64 (0.88 #345, 0.79 #1353, 0.76 #1497), 03rjj (0.85 #435, 0.85 #1299, 0.84 #1443), 06t2t (0.81 #341, 0.73 #1349, 0.69 #1493), 05v8c (0.81 #300, 0.60 #1308, 0.59 #1452), 03spz (0.76 #1382, 0.74 #1526, 0.69 #374), 03rk0 (0.69 #336, 0.49 #1344, 0.47 #1488), 016wzw (0.69 #346, 0.49 #1354, 0.46 #1498), 047yc (0.62 #310, 0.54 #1318, 0.52 #1462), 0h7x (0.62 #316, 0.42 #2044, 0.38 #1324), 07f1x (0.56 #396, 0.36 #1404, 0.34 #1548) >> Best rule #345 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 02vxq9m; 017gl1; 011yqc; 02yvct; 0fpv_3_; 03qnc6q; 040b5k; 02fqrf; 07s846j; 0gwjw0c; ... >> query: (?x9839, 03h64) <- nominated_for(?x1243, ?x9839), film_release_region(?x9839, ?x1497), ?x1243 = 0gr0m, ?x1497 = 015qh >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 8 EVAL 0gy7bj4 film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gy7bj4 film_release_region 047yc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 81.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gy7bj4 film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14875-01fmys PRED entity: 01fmys PRED relation: film_release_region PRED expected values: 019rg5 0h7x 06t2t 04g5k => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 95): 06t2t (0.88 #284, 0.84 #529, 0.80 #162), 047yc (0.80 #138, 0.74 #505, 0.70 #260), 04gzd (0.79 #250, 0.74 #495, 0.70 #128), 05qx1 (0.75 #146, 0.61 #513, 0.61 #268), 047lj (0.70 #130, 0.54 #497, 0.48 #252), 03rj0 (0.70 #282, 0.65 #160, 0.65 #527), 01mjq (0.68 #515, 0.65 #148, 0.58 #270), 0d0kn (0.55 #156, 0.45 #278, 0.37 #523), 077qn (0.55 #306, 0.42 #551, 0.35 #184), 01crd5 (0.48 #340, 0.40 #218, 0.39 #585) >> Best rule #284 for best value: >> intensional similarity = 6 >> extensional distance = 31 >> proper extension: 0g56t9t; 0gtsx8c; 0g5qs2k; 087wc7n; 08hmch; 03bx2lk; 0bq8tmw; 0661m4p; 06wbm8q; 06ztvyx; ... >> query: (?x2050, 06t2t) <- film_release_region(?x2050, ?x1536), film_release_region(?x2050, ?x1471), film_release_region(?x2050, ?x512), ?x512 = 07ssc, ?x1471 = 07t21, ?x1536 = 06c1y >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 16, 19 EVAL 01fmys film_release_region 04g5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 75.000 75.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01fmys film_release_region 06t2t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01fmys film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 75.000 75.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01fmys film_release_region 019rg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 75.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14874-04v09 PRED entity: 04v09 PRED relation: jurisdiction_of_office! PRED expected values: 060c4 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.78 #421, 0.76 #641, 0.75 #223), 0pqc5 (0.49 #1440, 0.36 #1841, 0.36 #1885), 0f6c3 (0.37 #382, 0.28 #118, 0.26 #979), 0fkvn (0.35 #378, 0.28 #114, 0.24 #975), 09n5b9 (0.34 #386, 0.23 #983, 0.21 #672), 0dq3c (0.21 #68, 0.20 #24, 0.17 #222), 0p5vf (0.20 #35, 0.17 #101, 0.17 #57), 0377k9 (0.20 #38, 0.17 #60, 0.16 #1771), 01zq91 (0.20 #14, 0.16 #1771, 0.12 #367), 04syw (0.18 #579, 0.17 #601, 0.16 #205) >> Best rule #421 for best value: >> intensional similarity = 2 >> extensional distance = 118 >> proper extension: 05r7t; >> query: (?x9035, 060c4) <- country(?x2978, ?x9035), ?x2978 = 03_8r >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04v09 jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.775 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #14873-0r5y9 PRED entity: 0r5y9 PRED relation: place_of_birth! PRED expected values: 03lmzl => 116 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1955): 0fpzt5 (0.41 #60036, 0.37 #70477, 0.34 #88755), 02pjvc (0.25 #1191, 0.07 #6411, 0.07 #9021), 028qyn (0.11 #4785, 0.06 #73090, 0.06 #12615), 01l7cxq (0.11 #3809, 0.06 #73090, 0.06 #11639), 044mfr (0.11 #3763, 0.06 #73090, 0.06 #11593), 016szr (0.11 #3603, 0.06 #73090, 0.06 #11433), 02cyfz (0.11 #3013, 0.06 #73090, 0.06 #10843), 03rl84 (0.11 #2974, 0.06 #73090, 0.06 #10804), 0kj34 (0.11 #4499, 0.06 #73090, 0.02 #30601), 01qrbf (0.11 #4099, 0.06 #73090, 0.02 #30201) >> Best rule #60036 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 013jz2; 010t4v; 0s9z_; 0ycht; 0s3pw; 0b_cr; >> query: (?x6598, ?x8863) <- county(?x6598, ?x4577), place_of_birth(?x5943, ?x6598), category(?x6598, ?x134), location(?x8863, ?x6598) >> conf = 0.41 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r5y9 place_of_birth! 03lmzl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 80.000 0.415 http://example.org/people/person/place_of_birth #14872-0gry51 PRED entity: 0gry51 PRED relation: profession PRED expected values: 0dxtg => 55 concepts (28 used for prediction) PRED predicted values (max 10 best out of 59): 0dxtg (0.69 #600, 0.64 #159, 0.64 #894), 03gjzk (0.29 #3101, 0.29 #3543, 0.27 #895), 0cbd2 (0.22 #2947, 0.20 #594, 0.18 #741), 02krf9 (0.22 #907, 0.16 #172, 0.16 #3113), 018gz8 (0.16 #15, 0.14 #3103, 0.13 #3545), 0nbcg (0.15 #1354, 0.14 #1795, 0.09 #2824), 09jwl (0.14 #1782, 0.14 #3105, 0.13 #1341), 01c72t (0.13 #22, 0.12 #2816, 0.09 #1787), 0kyk (0.13 #616, 0.12 #763, 0.12 #2969), 0fj9f (0.12 #1524, 0.11 #1671, 0.07 #3730) >> Best rule #600 for best value: >> intensional similarity = 5 >> extensional distance = 198 >> proper extension: 057d89; 017r2; 03ft8; 014dq7; 0d4jl; 0bzyh; 02lt8; 02kz_; 0282x; 01v9724; ... >> query: (?x13488, 0dxtg) <- profession(?x13488, ?x319), gender(?x13488, ?x231), people(?x5801, ?x13488), profession(?x3692, ?x319), ?x3692 = 03kpvp >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gry51 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 28.000 0.685 http://example.org/people/person/profession #14871-0b76t12 PRED entity: 0b76t12 PRED relation: nominated_for! PRED expected values: 02x4sn8 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 201): 0gq9h (0.42 #1264, 0.41 #5825, 0.40 #2225), 0gs9p (0.40 #1266, 0.37 #5827, 0.35 #2227), 019f4v (0.37 #1255, 0.36 #5816, 0.35 #2216), 0k611 (0.35 #2236, 0.34 #1275, 0.32 #3916), 099c8n (0.32 #2219, 0.30 #3899, 0.25 #1738), 040njc (0.31 #2168, 0.31 #5768, 0.30 #1207), 0gq_v (0.31 #5781, 0.30 #1220, 0.27 #5061), 04dn09n (0.30 #1236, 0.27 #2197, 0.26 #5797), 02qyntr (0.28 #1382, 0.28 #2343, 0.25 #4023), 0gqyl (0.28 #1282, 0.24 #5843, 0.22 #5123) >> Best rule #1264 for best value: >> intensional similarity = 3 >> extensional distance = 177 >> proper extension: 011yfd; 05_61y; 05y0cr; >> query: (?x1861, 0gq9h) <- language(?x1861, ?x254), featured_film_locations(?x1861, ?x739), honored_for(?x2245, ?x1861) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1560 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 201 *> proper extension: 044g_k; 03twd6; 06gjk9; 064q5v; 03nsm5x; 01xbxn; 07ghq; 0g5qmbz; 072hx4; *> query: (?x1861, 02x4sn8) <- language(?x1861, ?x254), category(?x1861, ?x134), award_winner(?x1861, ?x91) *> conf = 0.06 ranks of expected_values: 113 EVAL 0b76t12 nominated_for! 02x4sn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 88.000 88.000 0.425 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14870-073hd1 PRED entity: 073hd1 PRED relation: ceremony! PRED expected values: 0p9sw 0gqyl => 31 concepts (29 used for prediction) PRED predicted values (max 10 best out of 341): 0p9sw (0.88 #3870, 0.87 #4110, 0.86 #4350), 0gqyl (0.87 #4161, 0.86 #3680, 0.86 #3440), 0gr42 (0.78 #1762, 0.77 #3929, 0.77 #4169), 0gqzz (0.75 #6993, 0.50 #764, 0.33 #280), 02x201b (0.75 #6993, 0.25 #655, 0.17 #1139), 0czp_ (0.75 #6993, 0.15 #5301, 0.15 #4288), 09td7p (0.33 #242, 0.33 #726, 0.23 #725), 099t8j (0.33 #242, 0.33 #726, 0.23 #725), 0cqgl9 (0.33 #242, 0.23 #725, 0.22 #2654), 09qvf4 (0.33 #242, 0.23 #725, 0.19 #3375) >> Best rule #3870 for best value: >> intensional similarity = 24 >> extensional distance = 55 >> proper extension: 073h9x; 0bz6sb; 02pgky2; 03tn9w; 0dznvw; >> query: (?x7105, 0p9sw) <- ceremony(?x1703, ?x7105), ceremony(?x1307, ?x7105), nominated_for(?x1703, ?x12679), nominated_for(?x1703, ?x9056), nominated_for(?x1703, ?x8551), nominated_for(?x1703, ?x6174), nominated_for(?x1703, ?x6137), nominated_for(?x1703, ?x5711), nominated_for(?x1703, ?x2168), nominated_for(?x1703, ?x1944), nominated_for(?x1703, ?x1452), nominated_for(?x1703, ?x697), ?x697 = 0209hj, ?x1452 = 0jqn5, ?x1307 = 0gq9h, ?x5711 = 0bl5c, award(?x707, ?x1703), ?x6137 = 06cm5, ?x1944 = 03hj3b3, ?x6174 = 0sxns, ?x2168 = 0bx0l, ?x9056 = 09sr0, ?x8551 = 03pc89, produced_by(?x12679, ?x5438) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 073hd1 ceremony! 0gqyl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 29.000 0.877 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073hd1 ceremony! 0p9sw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 29.000 0.877 http://example.org/award/award_category/winners./award/award_honor/ceremony #14869-02fm4d PRED entity: 02fm4d PRED relation: category_of PRED expected values: 0c4ys => 40 concepts (36 used for prediction) PRED predicted values (max 10 best out of 3): 0c4ys (0.93 #64, 0.92 #85, 0.92 #43), 0gcf2r (0.12 #214, 0.12 #235, 0.09 #277), 0g_w (0.09 #215, 0.08 #236, 0.07 #430) >> Best rule #64 for best value: >> intensional similarity = 6 >> extensional distance = 79 >> proper extension: 01c92g; 0257yf; 01c99j; 03t5n3; 0249fn; 03ncb2; 03nc9d; 023vrq; 0257__; >> query: (?x8505, 0c4ys) <- award(?x367, ?x8505), ceremony(?x8505, ?x2186), ceremony(?x8505, ?x139), ?x139 = 05pd94v, ceremony(?x2180, ?x2186), ?x2180 = 02v1m7 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fm4d category_of 0c4ys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 36.000 0.926 http://example.org/award/award_category/category_of #14868-06rny PRED entity: 06rny PRED relation: position PRED expected values: 08ns5s => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 23): 01r3hr (0.87 #262, 0.86 #166, 0.86 #271), 08ns5s (0.87 #239, 0.86 #207, 0.84 #298), 0b13yt (0.75 #263, 0.74 #165, 0.74 #156), 02vkdwz (0.70 #404, 0.63 #405, 0.61 #188), 0bgv8y (0.63 #405, 0.61 #188, 0.61 #273), 01snvb (0.63 #405, 0.36 #389, 0.29 #396), 03h42s4 (0.61 #188, 0.61 #273, 0.56 #509), 0bgv4g (0.61 #188, 0.61 #273, 0.54 #536), 05fyy5 (0.54 #536, 0.54 #452, 0.54 #382), 02sddg (0.18 #485, 0.14 #706, 0.14 #707) >> Best rule #262 for best value: >> intensional similarity = 11 >> extensional distance = 16 >> proper extension: 057xlyq; 025v26c; 0fbtm7; >> query: (?x5773, ?x180) <- position(?x5773, ?x2147), position(?x5773, ?x1717), position(?x5773, ?x180), team(?x10287, ?x5773), ?x2147 = 04nfpk, athlete(?x1083, ?x10287), team(?x935, ?x5773), position_s(?x5773, ?x1240), ?x1717 = 02g_6x, type_of_union(?x10287, ?x566), people(?x11321, ?x10287) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #239 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 13 *> proper extension: 01ct6; 084l5; *> query: (?x5773, 08ns5s) <- position(?x5773, ?x2147), team(?x10287, ?x5773), ?x2147 = 04nfpk, draft(?x5773, ?x685), nationality(?x10287, ?x94), position_s(?x5773, ?x935), colors(?x5773, ?x332), student(?x8427, ?x10287) *> conf = 0.87 ranks of expected_values: 2 EVAL 06rny position 08ns5s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.869 http://example.org/sports/sports_team/roster./american_football/football_roster_position/position #14867-0281y0 PRED entity: 0281y0 PRED relation: place_of_death! PRED expected values: 01hmk9 => 116 concepts (71 used for prediction) PRED predicted values (max 10 best out of 694): 02lt8 (0.14 #164, 0.02 #6187, 0.02 #9202), 08959 (0.14 #732, 0.02 #8262, 0.02 #9016), 0835q (0.14 #654, 0.02 #8184, 0.02 #8938), 03_nq (0.14 #460, 0.02 #7990, 0.02 #8744), 0c_jc (0.14 #265, 0.02 #7795, 0.02 #8549), 0dq2k (0.14 #246, 0.02 #7776, 0.02 #8530), 083pr (0.14 #63, 0.02 #7593, 0.02 #8347), 0jf1b (0.14 #21, 0.02 #7551, 0.02 #8305), 03cd1q (0.14 #623, 0.02 #8153, 0.02 #8907), 0bdlj (0.14 #338, 0.02 #7868, 0.02 #8622) >> Best rule #164 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0cc56; >> query: (?x7769, 02lt8) <- place_of_birth(?x4320, ?x7769), award_nominee(?x7189, ?x4320), contains(?x94, ?x7769), ?x7189 = 03wbzp >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0281y0 place_of_death! 01hmk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 71.000 0.143 http://example.org/people/deceased_person/place_of_death #14866-04j5fx PRED entity: 04j5fx PRED relation: film PRED expected values: 0dd6bf => 97 concepts (13 used for prediction) PRED predicted values (max 10 best out of 690): 026q3s3 (0.43 #3782, 0.16 #10938, 0.16 #9149), 0dd6bf (0.29 #4813, 0.25 #1235, 0.09 #11969), 0dh8v4 (0.29 #4519, 0.15 #8097, 0.14 #11675), 02z9hqn (0.25 #1918, 0.19 #5496, 0.15 #7285), 02gs6r (0.25 #915, 0.14 #4493, 0.07 #11649), 05t0zfv (0.25 #1631, 0.14 #5209, 0.05 #12365), 02z5x7l (0.25 #2997, 0.14 #11942, 0.12 #6575), 05pyrb (0.25 #2782, 0.07 #4571, 0.06 #6360), 0k54q (0.22 #13458, 0.02 #11669), 02qhqz4 (0.20 #12865, 0.02 #11076, 0.02 #18232) >> Best rule #3782 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0b9f7t; >> query: (?x11146, 026q3s3) <- film(?x11146, ?x9698), special_performance_type(?x11146, ?x296), genre(?x9698, ?x812), ?x812 = 01jfsb, actor(?x9698, ?x3788) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4813 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 0b9f7t; *> query: (?x11146, 0dd6bf) <- film(?x11146, ?x9698), special_performance_type(?x11146, ?x296), genre(?x9698, ?x812), ?x812 = 01jfsb, actor(?x9698, ?x3788) *> conf = 0.29 ranks of expected_values: 2 EVAL 04j5fx film 0dd6bf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 13.000 0.429 http://example.org/film/actor/film./film/performance/film #14865-0cp0ph6 PRED entity: 0cp0ph6 PRED relation: film! PRED expected values: 06pcz0 => 66 concepts (29 used for prediction) PRED predicted values (max 10 best out of 906): 0bksh (0.72 #16646, 0.71 #31212, 0.71 #14564), 0c6qh (0.20 #413, 0.08 #12896, 0.07 #14978), 0cj2k3 (0.16 #8323, 0.10 #24970, 0.10 #37458), 0cj2nl (0.16 #8323, 0.10 #24970, 0.10 #37458), 0f4vbz (0.15 #361, 0.06 #12844, 0.05 #14926), 05dbf (0.13 #364, 0.05 #12847, 0.04 #14929), 0169dl (0.10 #400, 0.07 #14965, 0.05 #12883), 0jfx1 (0.09 #12888, 0.08 #14970, 0.04 #29536), 07r1h (0.08 #1088, 0.03 #13571, 0.03 #15653), 014zcr (0.07 #36, 0.04 #12519, 0.03 #14601) >> Best rule #16646 for best value: >> intensional similarity = 4 >> extensional distance = 182 >> proper extension: 0gvsh7l; 06ys2; >> query: (?x3565, ?x4782) <- nominated_for(?x4782, ?x3565), celebrity(?x1896, ?x4782), vacationer(?x126, ?x4782), award(?x4782, ?x1007) >> conf = 0.72 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cp0ph6 film! 06pcz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 29.000 0.722 http://example.org/film/actor/film./film/performance/film #14864-07sqbl PRED entity: 07sqbl PRED relation: team! PRED expected values: 02nzb8 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 48): 02nzb8 (0.84 #1001, 0.84 #3399, 0.83 #2954), 03f0fp (0.55 #4054, 0.51 #4454, 0.51 #4304), 02md_2 (0.55 #4054, 0.51 #4454, 0.51 #4304), 02qvgy (0.43 #2651, 0.40 #3499, 0.39 #3802), 05b3ts (0.37 #251, 0.37 #250, 0.36 #4205), 01r3hr (0.37 #251, 0.37 #250, 0.36 #4205), 02g_6x (0.37 #251, 0.37 #250, 0.36 #4205), 04nfpk (0.37 #251, 0.37 #250, 0.36 #4205), 02qvl7 (0.19 #2753, 0.15 #320, 0.15 #219), 02g_7z (0.19 #2753, 0.14 #2074, 0.10 #1623) >> Best rule #1001 for best value: >> intensional similarity = 16 >> extensional distance = 69 >> proper extension: 0dkb83; >> query: (?x13480, ?x60) <- position(?x13480, ?x530), position(?x13480, ?x203), position(?x13480, ?x63), colors(?x13480, ?x1101), ?x530 = 02_j1w, ?x203 = 0dgrmp, position(?x13480, ?x60), ?x63 = 02sdk9v, colors(?x481, ?x1101), colors(?x12301, ?x1101), colors(?x10463, ?x1101), colors(?x662, ?x1101), ?x10463 = 032498, draft(?x662, ?x3334), ?x12301 = 0498yf, sport(?x13480, ?x471) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07sqbl team! 02nzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.841 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #14863-05kj_ PRED entity: 05kj_ PRED relation: religion PRED expected values: 01lp8 019cr 01s5nb => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 24): 01lp8 (0.80 #49, 0.78 #410, 0.77 #145), 019cr (0.80 #149, 0.76 #414, 0.70 #559), 03_gx (0.50 #56, 0.46 #417, 0.45 #200), 01s5nb (0.42 #206, 0.38 #423, 0.38 #399), 0flw86 (0.40 #50, 0.39 #1353, 0.38 #1135), 03j6c (0.10 #59, 0.09 #1555, 0.09 #1579), 0kq2 (0.08 #3585), 0kpl (0.04 #485, 0.03 #774, 0.03 #846), 04t_mf (0.04 #1559, 0.04 #1583, 0.03 #713), 0n2g (0.03 #126, 0.03 #174, 0.03 #1333) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01bkb; >> query: (?x726, 01lp8) <- religion(?x726, ?x962), country(?x726, ?x94), featured_film_locations(?x407, ?x726) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 05kj_ religion 01s5nb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 186.000 186.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05kj_ religion 019cr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05kj_ religion 01lp8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #14862-04wp2p PRED entity: 04wp2p PRED relation: written_by! PRED expected values: 06ybb1 => 84 concepts (60 used for prediction) PRED predicted values (max 10 best out of 29): 015g28 (0.37 #1987, 0.36 #1325, 0.35 #2649), 0cf8qb (0.37 #1987, 0.36 #1325, 0.35 #2649), 011xg5 (0.11 #540), 012mrr (0.11 #187), 0pvms (0.11 #165), 0c00zd0 (0.11 #104), 0pb33 (0.11 #89), 0bz3jx (0.02 #1768, 0.02 #1106, 0.02 #3092), 03wy8t (0.02 #1260, 0.01 #2584, 0.01 #1922), 084qpk (0.02 #1370, 0.01 #708, 0.01 #2032) >> Best rule #1987 for best value: >> intensional similarity = 3 >> extensional distance = 166 >> proper extension: 07nznf; 0q9kd; 02rchht; 0byfz; 05ty4m; 06cv1; 02lf0c; 0mdqp; 02ndbd; 030pr; ... >> query: (?x10589, ?x4037) <- award(?x10589, ?x384), award_nominee(?x7507, ?x10589), film(?x10589, ?x4037) >> conf = 0.37 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 04wp2p written_by! 06ybb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 60.000 0.367 http://example.org/film/film/written_by #14861-02x8z_ PRED entity: 02x8z_ PRED relation: award_winner! PRED expected values: 0jzphpx => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 104): 0gpjbt (0.18 #593, 0.14 #452, 0.12 #311), 02rjjll (0.14 #710, 0.12 #2825, 0.11 #4376), 01s695 (0.14 #708, 0.09 #1131, 0.09 #144), 019bk0 (0.14 #721, 0.09 #157, 0.08 #3259), 01bx35 (0.12 #712, 0.08 #5224, 0.07 #4378), 013b2h (0.11 #2900, 0.10 #3182, 0.10 #3323), 0jzphpx (0.11 #744, 0.07 #462, 0.06 #3987), 02cg41 (0.11 #690, 0.09 #972, 0.09 #2946), 05pd94v (0.11 #2822, 0.10 #707, 0.10 #4373), 09n4nb (0.10 #48, 0.10 #2868, 0.08 #4419) >> Best rule #593 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 03qd_; 01hw6wq; 04gycf; 0jn5l; 01dw_f; 01w5gg6; 04vrxh; 03dq9; 057xn_m; >> query: (?x4528, 0gpjbt) <- place_of_birth(?x4528, ?x10946), award_nominee(?x4528, ?x1128), group(?x4528, ?x4642) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #744 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 02lbrd; 0d9xq; 015xp4; 01n44c; 01pbs9w; 01wrcxr; 011z3g; 0135xb; 01vsy9_; 01fkxr; *> query: (?x4528, 0jzphpx) <- award_winner(?x4488, ?x4528), artists(?x505, ?x4528), ?x505 = 03_d0 *> conf = 0.11 ranks of expected_values: 7 EVAL 02x8z_ award_winner! 0jzphpx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 134.000 134.000 0.179 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14860-02dwj PRED entity: 02dwj PRED relation: featured_film_locations PRED expected values: 04jpl => 95 concepts (84 used for prediction) PRED predicted values (max 10 best out of 108): 02_286 (0.70 #1201, 0.40 #9985, 0.39 #2623), 030qb3t (0.23 #1220, 0.18 #4541, 0.16 #10004), 04jpl (0.23 #717, 0.22 #1901, 0.14 #2612), 080h2 (0.20 #260, 0.08 #496, 0.04 #12123), 0qr8z (0.20 #385, 0.08 #621, 0.02 #2990), 0l2hf (0.20 #314, 0.08 #550), 0rh6k (0.09 #2604, 0.08 #9966, 0.08 #2129), 01_d4 (0.07 #2175, 0.07 #2650, 0.07 #2888), 0345h (0.06 #1214, 0.04 #2161, 0.03 #2636), 052p7 (0.05 #2185, 0.03 #1238, 0.03 #10022) >> Best rule #1201 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 0d8w2n; >> query: (?x5228, 02_286) <- films(?x5954, ?x5228), featured_film_locations(?x5228, ?x2267), film_release_region(?x4668, ?x2267), ?x4668 = 0bh8x1y >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #717 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 08cx5g; *> query: (?x5228, 04jpl) <- titles(?x512, ?x5228), award_winner(?x5228, ?x2507), nominated_for(?x8460, ?x5228), ?x512 = 07ssc *> conf = 0.23 ranks of expected_values: 3 EVAL 02dwj featured_film_locations 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 84.000 0.701 http://example.org/film/film/featured_film_locations #14859-0j5g9 PRED entity: 0j5g9 PRED relation: nationality! PRED expected values: 023jq1 => 228 concepts (88 used for prediction) PRED predicted values (max 10 best out of 4419): 0h32q (0.63 #44533, 0.20 #9415, 0.20 #5367), 01c8v0 (0.60 #9277, 0.20 #5229, 0.20 #1181), 01pk8v (0.52 #149790, 0.40 #9787, 0.20 #5739), 0fb1q (0.52 #149790, 0.07 #33288, 0.06 #41385), 06s26c (0.52 #149790, 0.07 #35677, 0.06 #43774), 03dq9 (0.40 #11348, 0.36 #202422, 0.36 #226712), 015t7v (0.40 #9633, 0.36 #202422, 0.36 #226712), 0f0kz (0.40 #8955, 0.36 #202422, 0.36 #226712), 071ywj (0.40 #8946, 0.36 #202422, 0.36 #226712), 0djywgn (0.40 #10778, 0.36 #202422, 0.20 #6730) >> Best rule #44533 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 09c7w0; 0b90_r; 03rjj; 0d060g; 03rt9; 0f8l9c; 0345h; 035qy; 07t21; 06mkj; ... >> query: (?x4221, ?x4398) <- form_of_government(?x4221, ?x6065), nationality(?x6028, ?x4221), adjoins(?x4221, ?x1310), celebrity(?x4398, ?x6028) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #11180 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 07ssc; 02jx1; 06q1r; *> query: (?x4221, 023jq1) <- form_of_government(?x4221, ?x6065), state_province_region(?x4220, ?x4221), contains(?x4221, ?x9108), adjoins(?x4221, ?x1310) *> conf = 0.20 ranks of expected_values: 585 EVAL 0j5g9 nationality! 023jq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 228.000 88.000 0.630 http://example.org/people/person/nationality #14858-07ccs PRED entity: 07ccs PRED relation: school! PRED expected values: 05m_8 05g3b => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 87): 05m_8 (0.33 #3, 0.19 #1918, 0.17 #2266), 07147 (0.26 #671, 0.14 #497, 0.14 #149), 0512p (0.25 #13, 0.21 #100, 0.13 #622), 0jmm4 (0.17 #68, 0.14 #155, 0.11 #1026), 01yhm (0.17 #18, 0.13 #976, 0.12 #192), 01ync (0.17 #35, 0.12 #209, 0.10 #470), 0jmk7 (0.17 #84, 0.10 #519, 0.09 #693), 01slc (0.15 #401, 0.14 #1968, 0.12 #227), 07l8x (0.14 #496, 0.14 #148, 0.13 #670), 07l4z (0.14 #152, 0.13 #674, 0.11 #1980) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 03_c8p; >> query: (?x6333, 05m_8) <- organization(?x6333, ?x5487), citytown(?x6333, ?x1569), service_location(?x6333, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 53 EVAL 07ccs school! 05g3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 108.000 108.000 0.333 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 07ccs school! 05m_8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.333 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #14857-06zgc PRED entity: 06zgc PRED relation: sports! PRED expected values: 018ctl => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 40): 01f1kd (0.86 #137, 0.86 #846, 0.86 #845), 015l4k (0.86 #137, 0.86 #846, 0.86 #845), 01f1jf (0.86 #137, 0.86 #846, 0.86 #845), 01f1jy (0.86 #137, 0.86 #846, 0.86 #845), 0sxrz (0.79 #279, 0.72 #773, 0.59 #917), 06sks6 (0.77 #941, 0.71 #869, 0.66 #1045), 0jdk_ (0.76 #882, 0.74 #352, 0.74 #953), 018ctl (0.74 #352, 0.74 #953, 0.74 #525), 0jhn7 (0.73 #692, 0.73 #514, 0.70 #943), 0kbvb (0.73 #496, 0.70 #925, 0.62 #853) >> Best rule #137 for best value: >> intensional similarity = 47 >> extensional distance = 3 >> proper extension: 09_9n; >> query: (?x5177, ?x1617) <- sports(?x12388, ?x5177), sports(?x5176, ?x5177), sports(?x4424, ?x5177), sports(?x418, ?x5177), sports(?x1617, ?x5177), country(?x5177, ?x2346), country(?x5177, ?x304), country(?x5177, ?x279), country(?x5177, ?x205), ?x418 = 09n48, ?x4424 = 0blfl, ?x279 = 0d060g, ?x12388 = 015pkt, film_release_region(?x11074, ?x304), film_release_region(?x6661, ?x304), film_release_region(?x6247, ?x304), film_release_region(?x5849, ?x304), film_release_region(?x5588, ?x304), film_release_region(?x5509, ?x304), film_release_region(?x5496, ?x304), film_release_region(?x3491, ?x304), film_release_region(?x3287, ?x304), film_release_region(?x3252, ?x304), film_release_region(?x1724, ?x304), film_release_region(?x1602, ?x304), film_release_region(?x908, ?x304), film_release_region(?x633, ?x304), ?x3252 = 0gh8zks, ?x205 = 03rjj, ?x1602 = 0gxtknx, ?x6247 = 09v9mks, ?x5176 = 0sx92, ?x1724 = 02r8hh_, ?x5588 = 0gtt5fb, ?x5509 = 0cy__l, ?x6661 = 0k7tq, ?x3287 = 026njb5, ?x633 = 0c40vxk, ?x5496 = 07l50vn, ?x5849 = 02h22, ?x908 = 01vksx, ?x3491 = 0gtvpkw, nationality(?x2083, ?x304), ?x11074 = 0jqzt, partially_contains(?x2346, ?x8666), exported_to(?x2346, ?x291), participating_countries(?x784, ?x304) >> conf = 0.86 => this is the best rule for 4 predicted values *> Best rule #352 for first EXPECTED value: *> intensional similarity = 40 *> extensional distance = 5 *> proper extension: 09_94; *> query: (?x5177, ?x784) <- sports(?x5176, ?x5177), sports(?x418, ?x5177), sports(?x1617, ?x5177), country(?x5177, ?x1264), country(?x5177, ?x1023), country(?x5177, ?x390), ?x418 = 09n48, ?x1264 = 0345h, ?x5176 = 0sx92, olympics(?x5177, ?x784), combatants(?x326, ?x1023), nationality(?x226, ?x1023), service_location(?x555, ?x390), contains(?x1023, ?x2396), film_release_region(?x10048, ?x390), film_release_region(?x8292, ?x390), film_release_region(?x7693, ?x390), film_release_region(?x7246, ?x390), film_release_region(?x6168, ?x390), film_release_region(?x5400, ?x390), film_release_region(?x3745, ?x390), film_release_region(?x2037, ?x390), film_release_region(?x1315, ?x390), film_release_region(?x1080, ?x390), film_release_region(?x251, ?x390), ?x3745 = 03cw411, ?x1315 = 053tj7, ?x10048 = 09tcg4, ?x5400 = 0bhwhj, ?x555 = 01c6k4, contains(?x390, ?x901), ?x6168 = 0gj96ln, olympics(?x1023, ?x775), ?x7246 = 01srq2, ?x8292 = 0cmf0m0, ?x7693 = 0m63c, ?x1080 = 01c22t, ?x2037 = 0gvrws1, ?x251 = 02vp1f_, taxonomy(?x390, ?x939) *> conf = 0.74 ranks of expected_values: 8 EVAL 06zgc sports! 018ctl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 35.000 35.000 0.857 http://example.org/olympics/olympic_games/sports #14856-03262k PRED entity: 03262k PRED relation: team! PRED expected values: 07y9k => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 8): 07y9k (0.13 #76, 0.11 #100, 0.11 #4), 0355pl (0.11 #3, 0.11 #27, 0.11 #19), 0356lc (0.09 #370, 0.09 #361, 0.08 #57), 03zv9 (0.09 #370, 0.09 #361, 0.05 #98), 059yj (0.08 #341, 0.06 #391, 0.06 #399), 0h69c (0.04 #358, 0.04 #367, 0.04 #384), 01ddbl (0.03 #359, 0.03 #368, 0.02 #385), 021q23 (0.02 #369, 0.01 #410) >> Best rule #76 for best value: >> intensional similarity = 12 >> extensional distance = 67 >> proper extension: 04mnts; 032jlh; >> query: (?x13054, 07y9k) <- position(?x13054, ?x530), position(?x13054, ?x203), position(?x13054, ?x63), position(?x13054, ?x60), ?x203 = 0dgrmp, ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x60 = 02nzb8, sport(?x13054, ?x471), ?x471 = 02vx4, position(?x13054, ?x60), team(?x203, ?x13054) >> conf = 0.13 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03262k team! 07y9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.130 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #14855-04mz10g PRED entity: 04mz10g PRED relation: award PRED expected values: 0ck27z => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 222): 0ck27z (0.80 #1308, 0.70 #498, 0.67 #903), 09sb52 (0.28 #3281, 0.24 #18268, 0.24 #4901), 0cqhk0 (0.19 #1657, 0.18 #2062, 0.17 #2467), 05b1610 (0.17 #39, 0.03 #2874, 0.03 #3684), 02rdyk7 (0.17 #92, 0.03 #13862, 0.03 #17508), 0g9wd99 (0.17 #372), 04hddx (0.17 #368), 02tzwd (0.17 #361), 027x4ws (0.17 #321), 0c_dx (0.17 #277) >> Best rule #1308 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 05dtsb; 04wf_b; >> query: (?x1404, 0ck27z) <- award_nominee(?x1404, ?x2077), ?x2077 = 01541z, award(?x1404, ?x1921) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mz10g award 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14854-0q04f PRED entity: 0q04f PRED relation: profession! PRED expected values: 012t1 0hskw 022_q8 => 39 concepts (20 used for prediction) PRED predicted values (max 10 best out of 4178): 09yrh (0.76 #12640, 0.74 #12641, 0.50 #8427), 01hxs4 (0.76 #12640, 0.74 #12641, 0.50 #8427), 016kkx (0.76 #12640, 0.50 #6355, 0.33 #2143), 0dqcm (0.76 #12640, 0.33 #2979, 0.25 #11406), 01rrd4 (0.74 #12641, 0.50 #8427, 0.50 #6313), 02f1c (0.74 #12641, 0.50 #8427, 0.50 #7094), 01vb403 (0.74 #12641, 0.50 #8427, 0.50 #4771), 016tb7 (0.74 #12641, 0.50 #8427, 0.49 #16854), 01rr9f (0.74 #12641, 0.50 #8427, 0.49 #16854), 02pzc4 (0.74 #12641, 0.50 #8427, 0.33 #1002) >> Best rule #12640 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 0cbd2; >> query: (?x11804, ?x6657) <- profession(?x10663, ?x11804), profession(?x8423, ?x11804), profession(?x8382, ?x11804), profession(?x7077, ?x11804), profession(?x2875, ?x11804), ?x10663 = 01r9c_, ?x7077 = 016xk5, ?x8382 = 0mb5x, ?x2875 = 02645b, award_winner(?x8423, ?x1993), friend(?x6657, ?x8423) >> conf = 0.76 => this is the best rule for 4 predicted values *> Best rule #5010 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 01d_h8; *> query: (?x11804, 0hskw) <- profession(?x5370, ?x11804), profession(?x3637, ?x11804), profession(?x2483, ?x11804), profession(?x1645, ?x11804), ?x3637 = 09p06, award(?x2483, ?x1323), influenced_by(?x1645, ?x6457), ?x5370 = 016gkf, influenced_by(?x6534, ?x1645), award_winner(?x8275, ?x2483) *> conf = 0.50 ranks of expected_values: 574, 805, 988 EVAL 0q04f profession! 022_q8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 20.000 0.755 http://example.org/people/person/profession EVAL 0q04f profession! 0hskw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 20.000 0.755 http://example.org/people/person/profession EVAL 0q04f profession! 012t1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 20.000 0.755 http://example.org/people/person/profession #14853-06tp4h PRED entity: 06tp4h PRED relation: actor! PRED expected values: 0fkwzs => 165 concepts (155 used for prediction) PRED predicted values (max 10 best out of 145): 06r1k (0.17 #482, 0.12 #747, 0.11 #1013), 06qwh (0.17 #406, 0.12 #671, 0.11 #937), 030k94 (0.17 #312, 0.12 #577, 0.11 #843), 0gfzgl (0.12 #563, 0.11 #1094, 0.11 #829), 0199wf (0.12 #737, 0.11 #1003, 0.09 #1533), 026bfsh (0.12 #6197, 0.11 #6727, 0.08 #6462), 039cq4 (0.11 #1190, 0.10 #2251, 0.04 #5433), 0d7vtk (0.11 #1254, 0.06 #2050, 0.05 #2315), 0d68qy (0.11 #1098, 0.05 #2159, 0.03 #3750), 03_b1g (0.11 #1310, 0.05 #2371, 0.03 #3962) >> Best rule #482 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0d06m5; >> query: (?x6613, 06r1k) <- profession(?x6613, ?x7361), participant(?x6613, ?x5058), ?x7361 = 01xr66, category(?x6613, ?x134) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #8912 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 94 *> proper extension: 01bcq; 01bh6y; *> query: (?x6613, 0fkwzs) <- language(?x6613, ?x254), ?x254 = 02h40lc, film(?x6613, ?x2184), genre(?x2184, ?x53) *> conf = 0.01 ranks of expected_values: 134 EVAL 06tp4h actor! 0fkwzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 165.000 155.000 0.167 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #14852-03xhj6 PRED entity: 03xhj6 PRED relation: artists! PRED expected values: 02qm5j => 90 concepts (38 used for prediction) PRED predicted values (max 10 best out of 302): 016clz (0.96 #9773, 0.93 #7026, 0.87 #6412), 0xhtw (0.61 #3370, 0.52 #3979, 0.46 #8257), 08cyft (0.60 #3712, 0.50 #3103, 0.46 #6765), 0cx7f (0.57 #5624, 0.50 #1047, 0.33 #134), 05w3f (0.55 #2475, 0.54 #2781, 0.50 #11334), 011j5x (0.52 #7966, 0.37 #6133, 0.30 #5521), 0dl5d (0.50 #932, 0.36 #5509, 0.26 #8260), 0ggx5q (0.41 #4345, 0.40 #3733, 0.39 #4649), 025sc50 (0.41 #4318, 0.39 #4622, 0.38 #3097), 02lnbg (0.38 #4326, 0.38 #3105, 0.36 #4630) >> Best rule #9773 for best value: >> intensional similarity = 10 >> extensional distance = 102 >> proper extension: 01cv3n; 01tp5bj; 01vswwx; 02bgmr; 05y7hc; 048tgl; >> query: (?x4484, 016clz) <- artists(?x9342, ?x4484), artists(?x9342, ?x6234), artists(?x9342, ?x1001), artists(?x9342, ?x654), ?x1001 = 01gf5h, category(?x4484, ?x134), ?x134 = 08mbj5d, origin(?x4484, ?x1523), ?x6234 = 0l8g0, artist(?x2149, ?x654) >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #1062 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 06gcn; *> query: (?x4484, 02qm5j) <- artists(?x9342, ?x4484), artists(?x9342, ?x1206), artist(?x9224, ?x4484), artist(?x3265, ?x4484), ?x1206 = 01vrt_c, group(?x227, ?x4484), ?x9224 = 0n85g, artist(?x3265, ?x6990), ?x6990 = 0jbyg *> conf = 0.25 ranks of expected_values: 42 EVAL 03xhj6 artists! 02qm5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 90.000 38.000 0.962 http://example.org/music/genre/artists #14851-01b7h8 PRED entity: 01b7h8 PRED relation: program! PRED expected values: 058s57 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 110): 01yg9y (0.23 #212, 0.21 #295, 0.17 #128), 02yygk (0.22 #166, 0.19 #332, 0.19 #249), 01my_c (0.22 #166, 0.19 #332, 0.19 #249), 0b4rf3 (0.22 #166, 0.19 #332, 0.19 #249), 020ffd (0.17 #130, 0.15 #214, 0.14 #297), 01xcr4 (0.17 #116, 0.15 #200, 0.14 #283), 01xdf5 (0.17 #84, 0.15 #168, 0.11 #334), 01j7rd (0.17 #96, 0.15 #180, 0.11 #346), 015f7 (0.14 #165, 0.11 #355, 0.10 #520), 019f9z (0.14 #165, 0.07 #580, 0.06 #415) >> Best rule #212 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 072kp; 0275kr; 0ph24; >> query: (?x9788, 01yg9y) <- program(?x11519, ?x9788), program(?x2894, ?x9788), nominated_for(?x6937, ?x9788), profession(?x2894, ?x987), award_winner(?x1972, ?x11519) >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01b7h8 program! 058s57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 66.000 0.231 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #14850-0fq7dv_ PRED entity: 0fq7dv_ PRED relation: genre PRED expected values: 0219x_ => 70 concepts (67 used for prediction) PRED predicted values (max 10 best out of 109): 07s9rl0 (0.77 #5270, 0.68 #6089, 0.68 #703), 02kdv5l (0.58 #471, 0.44 #3047, 0.44 #354), 05p553 (0.49 #4102, 0.38 #1761, 0.35 #1175), 03k9fj (0.44 #361, 0.42 #478, 0.35 #3054), 06n90 (0.36 #479, 0.32 #362, 0.29 #128), 01hmnh (0.29 #133, 0.27 #1186, 0.26 #1303), 02l7c8 (0.27 #1418, 0.27 #2941, 0.27 #3175), 04xvh5 (0.22 #266, 0.16 #1436, 0.09 #2959), 060__y (0.21 #3059, 0.17 #1419, 0.17 #2942), 04xvlr (0.20 #2929, 0.16 #4919, 0.16 #1406) >> Best rule #5270 for best value: >> intensional similarity = 5 >> extensional distance = 1303 >> proper extension: 0gcrg; 016ztl; >> query: (?x1915, 07s9rl0) <- genre(?x1915, ?x812), genre(?x6556, ?x812), genre(?x5721, ?x812), ?x5721 = 01d259, film(?x8235, ?x6556) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #727 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 01kqq7; *> query: (?x1915, 0219x_) <- film(?x4214, ?x1915), country(?x1915, ?x94), currency(?x1915, ?x170), film_regional_debut_venue(?x1915, ?x3288) *> conf = 0.11 ranks of expected_values: 27 EVAL 0fq7dv_ genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 70.000 67.000 0.771 http://example.org/film/film/genre #14849-03x1s8 PRED entity: 03x1s8 PRED relation: contains! PRED expected values: 0gyh => 103 concepts (74 used for prediction) PRED predicted values (max 10 best out of 268): 0gyh (0.78 #2686, 0.72 #3581, 0.65 #1791), 07c5l (0.33 #39352), 01cx_ (0.33 #1987, 0.16 #2882, 0.08 #10934), 020d5 (0.25 #895, 0.23 #5370), 05k7sb (0.25 #1923, 0.11 #2818, 0.08 #6397), 01n7q (0.23 #12604, 0.17 #61801, 0.14 #11710), 07z1m (0.22 #91, 0.10 #987, 0.08 #1882), 0dzt9 (0.22 #543, 0.10 #1439, 0.08 #2334), 0cvw9 (0.22 #453, 0.03 #9401, 0.03 #11191), 07ssc (0.17 #63544, 0.17 #64438, 0.17 #65333) >> Best rule #2686 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 01l9vr; 02s838; >> query: (?x12126, ?x2831) <- contains(?x12384, ?x12126), contains(?x94, ?x12126), ?x94 = 09c7w0, capital(?x2831, ?x12384), religion(?x2831, ?x109) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x1s8 contains! 0gyh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 74.000 0.778 http://example.org/location/location/contains #14848-01y_rz PRED entity: 01y_rz PRED relation: instrumentalists! PRED expected values: 0342h 02bxd => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 111): 0342h (0.76 #87, 0.67 #421, 0.67 #4), 018vs (0.50 #429, 0.50 #95, 0.35 #929), 05148p4 (0.44 #437, 0.37 #186, 0.36 #853), 0l14md (0.39 #917, 0.39 #251, 0.37 #1001), 03qjg (0.23 #466, 0.21 #132, 0.18 #882), 0l14qv (0.17 #88, 0.14 #422, 0.10 #171), 018j2 (0.17 #120, 0.09 #454, 0.09 #954), 048j4l (0.17 #77, 0.03 #160, 0.03 #994), 04rzd (0.14 #202, 0.14 #119, 0.09 #953), 013y1f (0.09 #448, 0.07 #197, 0.06 #864) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 0285c; 0zjpz; 0gkg6; 01gx5f; 01vv6_6; 01w8n89; 0fpj4lx; 0bkg4; 01vsy3q; 01s7qqw; ... >> query: (?x10625, 0342h) <- artists(?x2249, ?x10625), profession(?x10625, ?x1359), ?x2249 = 03lty >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 101 EVAL 01y_rz instrumentalists! 02bxd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 127.000 127.000 0.758 http://example.org/music/instrument/instrumentalists EVAL 01y_rz instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.758 http://example.org/music/instrument/instrumentalists #14847-03q27t PRED entity: 03q27t PRED relation: award! PRED expected values: 01271h => 45 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2938): 0m2l9 (0.84 #27057, 0.81 #3382, 0.81 #23674), 01wwvt2 (0.84 #27057, 0.81 #3382, 0.81 #23674), 02whj (0.84 #27057, 0.81 #3382, 0.81 #23674), 0191h5 (0.81 #3382, 0.81 #23674, 0.80 #43975), 02qwg (0.60 #937, 0.22 #21229, 0.21 #3384), 0fhxv (0.43 #1350, 0.22 #21642, 0.22 #25024), 0lbj1 (0.43 #44, 0.20 #20336, 0.19 #23718), 01vrncs (0.40 #260, 0.21 #3384, 0.20 #27059), 0gcs9 (0.40 #820, 0.19 #21112, 0.17 #24494), 01vs_v8 (0.37 #585, 0.31 #20877, 0.27 #24259) >> Best rule #27057 for best value: >> intensional similarity = 5 >> extensional distance = 100 >> proper extension: 04ljl_l; >> query: (?x11456, ?x2392) <- award_winner(?x11456, ?x2392), award(?x4288, ?x11456), award_winner(?x1089, ?x4288), role(?x2392, ?x716), role(?x211, ?x716) >> conf = 0.84 => this is the best rule for 3 predicted values *> Best rule #817 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 33 *> proper extension: 05zkcn5; 02f72n; 02f73p; 02f6xy; 05q8pss; 02f72_; 02f79n; *> query: (?x11456, 01271h) <- award_winner(?x11456, ?x483), award(?x4288, ?x11456), role(?x4288, ?x432), award_nominee(?x4288, ?x7995), ?x7995 = 0pj8m *> conf = 0.09 ranks of expected_values: 274 EVAL 03q27t award! 01271h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 45.000 14.000 0.837 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14846-05v8c PRED entity: 05v8c PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.91 #7, 0.87 #89, 0.87 #24) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0697s; >> query: (?x550, 0hzc9wc) <- film_release_region(?x4352, ?x550), ?x4352 = 09v71cj, country(?x7195, ?x550) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v8c administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.913 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #14845-01bzw5 PRED entity: 01bzw5 PRED relation: student PRED expected values: 01jbx1 => 108 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1246): 01hbq0 (0.14 #4144, 0.12 #2055, 0.09 #6233), 015qq1 (0.12 #1889, 0.09 #3978, 0.06 #6067), 04t969 (0.12 #1278, 0.09 #3367, 0.06 #5456), 03ft8 (0.09 #2346, 0.06 #4435, 0.06 #257), 02cyfz (0.09 #2423, 0.06 #4512, 0.06 #334), 01pqy_ (0.09 #2986, 0.06 #5075, 0.06 #897), 01wwvt2 (0.09 #2454, 0.06 #4543, 0.06 #365), 023361 (0.09 #3540, 0.06 #5629, 0.06 #1451), 01l1ls (0.06 #5821, 0.06 #1643, 0.05 #3732), 09v6tz (0.06 #1339, 0.05 #3428, 0.04 #9695) >> Best rule #4144 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0kc6x; 0c41qv; >> query: (?x1276, 01hbq0) <- category(?x1276, ?x134), citytown(?x1276, ?x1523), ?x1523 = 030qb3t >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #19325 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 112 *> proper extension: 02l9wl; 05nrkb; 026036; 08tyb_; *> query: (?x1276, 01jbx1) <- student(?x1276, ?x10915), participant(?x10915, ?x2697), participant(?x10915, ?x1205) *> conf = 0.02 ranks of expected_values: 383 EVAL 01bzw5 student 01jbx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 108.000 102.000 0.136 http://example.org/education/educational_institution/students_graduates./education/education/student #14844-0prfz PRED entity: 0prfz PRED relation: people! PRED expected values: 041rx => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 55): 0x67 (0.38 #87, 0.21 #934, 0.19 #1858), 041rx (0.25 #4, 0.18 #312, 0.17 #851), 033tf_ (0.12 #7, 0.12 #700, 0.12 #2625), 0xnvg (0.08 #1168, 0.08 #706, 0.08 #783), 0d7wh (0.08 #479, 0.02 #2789, 0.02 #3405), 07hwkr (0.07 #166, 0.06 #1244, 0.06 #1475), 0dryh9k (0.07 #170, 0.04 #3404, 0.03 #8409), 0g48m4 (0.07 #236), 01qhm_ (0.07 #699, 0.06 #1007, 0.05 #776), 02w7gg (0.07 #4391, 0.07 #5315, 0.06 #5931) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01dwrc; >> query: (?x399, 0x67) <- category(?x399, ?x134), award_nominee(?x399, ?x6162), ?x6162 = 01w9wwg >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 02v60l; *> query: (?x399, 041rx) <- film(?x399, ?x1184), participant(?x398, ?x399), ?x1184 = 02v63m *> conf = 0.25 ranks of expected_values: 2 EVAL 0prfz people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 131.000 0.375 http://example.org/people/ethnicity/people #14843-0ym20 PRED entity: 0ym20 PRED relation: institution! PRED expected values: 019v9k => 173 concepts (126 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.84 #216, 0.81 #129, 0.77 #194), 019v9k (0.70 #716, 0.67 #328, 0.65 #199), 0bkj86 (0.61 #327, 0.59 #220, 0.58 #715), 03bwzr4 (0.56 #225, 0.54 #720, 0.53 #268), 016t_3 (0.53 #217, 0.53 #712, 0.49 #260), 04zx3q1 (0.44 #215, 0.38 #710, 0.37 #128), 027f2w (0.44 #222, 0.37 #135, 0.35 #2012), 07s6fsf (0.42 #709, 0.39 #321, 0.38 #214), 01rr_d (0.35 #2012, 0.25 #228, 0.21 #596), 02mjs7 (0.35 #2012, 0.19 #2239, 0.16 #586) >> Best rule #216 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 06pwq; 01w3v; 01w5m; 09f2j; 0c5x_; >> query: (?x14116, 02h4rq6) <- citytown(?x14116, ?x1841), major_field_of_study(?x14116, ?x8221), ?x8221 = 037mh8, state_province_region(?x14116, ?x2235), institution(?x1368, ?x14116) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #716 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 104 *> proper extension: 019q50; *> query: (?x14116, 019v9k) <- institution(?x3437, ?x14116), institution(?x1368, ?x14116), category(?x14116, ?x134), ?x3437 = 02_xgp2, ?x1368 = 014mlp *> conf = 0.70 ranks of expected_values: 2 EVAL 0ym20 institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 173.000 126.000 0.844 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14842-039d4 PRED entity: 039d4 PRED relation: colors PRED expected values: 01l849 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.89 #442, 0.50 #82, 0.49 #62), 04mkbj (0.47 #131, 0.08 #191, 0.08 #691), 01g5v (0.33 #204, 0.32 #384, 0.30 #644), 01l849 (0.25 #1261, 0.25 #461, 0.25 #1321), 019sc (0.19 #648, 0.19 #608, 0.19 #1108), 06fvc (0.19 #643, 0.18 #383, 0.18 #603), 0jc_p (0.11 #65, 0.10 #85, 0.07 #765), 036k5h (0.11 #166, 0.10 #206, 0.10 #1106), 03vtbc (0.10 #29, 0.10 #9, 0.06 #49), 02rnmb (0.10 #13, 0.05 #393, 0.05 #313) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 201 >> proper extension: 01d34b; 0d5fb; >> query: (?x9443, 083jv) <- colors(?x9443, ?x8047), colors(?x9358, ?x8047), ?x9358 = 0272vm >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1261 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 400 *> proper extension: 0173s9; *> query: (?x9443, 01l849) <- colors(?x9443, ?x8047), contains(?x1426, ?x9443), adjoins(?x108, ?x1426), jurisdiction_of_office(?x900, ?x1426) *> conf = 0.25 ranks of expected_values: 4 EVAL 039d4 colors 01l849 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 129.000 129.000 0.892 http://example.org/education/educational_institution/colors #14841-01grq1 PRED entity: 01grq1 PRED relation: legislative_sessions! PRED expected values: 07t58 => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 6): 07t58 (0.95 #143, 0.94 #149, 0.92 #119), 0x2sv (0.07 #176, 0.05 #169, 0.02 #163), 0h6dy (0.05 #177, 0.04 #170, 0.02 #164), 0l_j_ (0.04 #178, 0.02 #165, 0.02 #171), 0162kb (0.02 #166), 030p4s (0.02 #173, 0.02 #180) >> Best rule #143 for best value: >> intensional similarity = 38 >> extensional distance = 35 >> proper extension: 01gtc0; >> query: (?x11142, 07t58) <- district_represented(?x11142, ?x6895), district_represented(?x11142, ?x4754), district_represented(?x11142, ?x3670), district_represented(?x11142, ?x1767), district_represented(?x4821, ?x4754), district_represented(?x4787, ?x4754), district_represented(?x3766, ?x4754), district_represented(?x1829, ?x4754), district_represented(?x952, ?x4754), ?x3766 = 02gkzs, ?x4787 = 01grpq, ?x952 = 06f0dc, ?x4821 = 02bqm0, location(?x7585, ?x1767), location(?x4239, ?x1767), location(?x2913, ?x1767), featured_film_locations(?x5520, ?x1767), legislative_sessions(?x4437, ?x11142), contains(?x1767, ?x7394), ?x6895 = 05fjf, ?x1829 = 02bp37, district_represented(?x759, ?x1767), award_nominee(?x366, ?x4239), legislative_sessions(?x2860, ?x4437), language(?x5520, ?x254), artist(?x2931, ?x4239), profession(?x2913, ?x987), award_winner(?x2143, ?x2913), genre(?x5520, ?x2605), ?x3670 = 05tbn, production_companies(?x5520, ?x1914), jurisdiction_of_office(?x900, ?x1767), role(?x4239, ?x227), school_type(?x7394, ?x3092), religion(?x1767, ?x109), film_crew_role(?x5520, ?x137), film(?x7585, ?x1048), ?x759 = 043djx >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01grq1 legislative_sessions! 07t58 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.946 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #14840-04q827 PRED entity: 04q827 PRED relation: nominated_for! PRED expected values: 0dh73w => 94 concepts (26 used for prediction) PRED predicted values (max 10 best out of 920): 055c8 (0.79 #30382, 0.78 #51421, 0.78 #37395), 0bwh6 (0.46 #28045, 0.10 #60769, 0.07 #4943), 04ktcgn (0.37 #16354, 0.36 #21031, 0.35 #18693), 04t38b (0.23 #3331, 0.04 #5667, 0.04 #8003), 0146pg (0.20 #121, 0.11 #4793, 0.11 #7129), 04pqqb (0.20 #1077, 0.07 #5749, 0.07 #8085), 0829rj (0.15 #4494, 0.04 #6830, 0.04 #9166), 0l15n (0.15 #4522, 0.04 #6858, 0.04 #9194), 01gw4f (0.15 #3419, 0.04 #5755, 0.04 #8091), 086k8 (0.15 #16355, 0.11 #49084, 0.10 #58) >> Best rule #30382 for best value: >> intensional similarity = 3 >> extensional distance = 313 >> proper extension: 03rg2b; >> query: (?x10806, ?x3186) <- award_winner(?x10806, ?x3186), language(?x10806, ?x254), films(?x1967, ?x10806) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #10240 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 083shs; 09m6kg; 0ds3t5x; 05jzt3; 0b6tzs; 092vkg; 04vr_f; 0c0nhgv; 0pv3x; 0416y94; ... *> query: (?x10806, 0dh73w) <- award(?x10806, ?x618), nominated_for(?x2341, ?x10806), honored_for(?x2294, ?x10806), ?x2341 = 02x17s4 *> conf = 0.04 ranks of expected_values: 126 EVAL 04q827 nominated_for! 0dh73w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 94.000 26.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14839-059t6d PRED entity: 059t6d PRED relation: award PRED expected values: 0ck27z => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 221): 0ck27z (0.67 #498, 0.50 #93, 0.34 #6978), 09sb52 (0.54 #1256, 0.52 #851, 0.36 #13407), 0789_m (0.46 #1235, 0.06 #2855, 0.06 #3665), 0gqy2 (0.33 #1380, 0.11 #16771, 0.11 #13936), 0bdwqv (0.31 #1388, 0.09 #3818, 0.08 #7463), 0bfvd4 (0.23 #1330, 0.14 #925, 0.08 #7405), 0f4x7 (0.21 #1246, 0.09 #16637, 0.09 #13802), 04kxsb (0.21 #1341, 0.08 #12277, 0.07 #10252), 0cqhk0 (0.20 #5302, 0.19 #6922, 0.18 #5707), 05zr6wv (0.19 #827, 0.10 #3257, 0.10 #10143) >> Best rule #498 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02gvwz; 07fpm3; 06mnbn; 026g801; 02624g; 0356dp; >> query: (?x2708, 0ck27z) <- award_nominee(?x2708, ?x10577), award(?x2708, ?x1921), ?x10577 = 03rgvr >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059t6d award 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14838-03s0w PRED entity: 03s0w PRED relation: time_zones PRED expected values: 02fqwt => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 12): 02fqwt (0.65 #703, 0.64 #1421, 0.64 #1879), 02hczc (0.64 #1879, 0.60 #743, 0.24 #67), 02hcv8 (0.53 #29, 0.44 #1020, 0.43 #1059), 02lcqs (0.20 #840, 0.20 #213, 0.18 #1256), 02llzg (0.12 #982, 0.11 #787, 0.11 #485), 03bdv (0.07 #188, 0.07 #1427, 0.07 #1505), 0gsrz4 (0.06 #411, 0.05 #528, 0.05 #567), 03plfd (0.06 #962, 0.06 #1014, 0.06 #884), 042g7t (0.04 #544, 0.04 #557, 0.04 #622), 05jphn (0.04 #234, 0.02 #520, 0.02 #312) >> Best rule #703 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 0g14f; >> query: (?x961, ?x1638) <- contains(?x961, ?x10175), contains(?x961, ?x7343), time_zones(?x7343, ?x1638), major_field_of_study(?x10175, ?x1668) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s0w time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 182.000 0.645 http://example.org/location/location/time_zones #14837-027752 PRED entity: 027752 PRED relation: category PRED expected values: 08mbj5d => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14820, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027752 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #14836-0jrny PRED entity: 0jrny PRED relation: religion PRED expected values: 07w8f => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 18): 0c8wxp (0.95 #666, 0.44 #930, 0.44 #974), 02rsw (0.25 #23, 0.01 #1915, 0.01 #1783), 03_gx (0.15 #1906, 0.15 #1774, 0.13 #58), 0kpl (0.15 #1902, 0.15 #1770, 0.13 #54), 03j6c (0.07 #1912, 0.07 #1780, 0.05 #988), 04pk9 (0.07 #63, 0.03 #943, 0.03 #987), 0flw86 (0.05 #134, 0.05 #926, 0.05 #1762), 02vxy_ (0.05 #209), 01lp8 (0.05 #1761, 0.04 #1893, 0.04 #925), 0kq2 (0.05 #1777, 0.04 #1909, 0.03 #985) >> Best rule #666 for best value: >> intensional similarity = 3 >> extensional distance = 377 >> proper extension: 07kb5; 01pp3p; 01c6l; 01xyt7; 01pwz; 021r7r; 014ps4; 0dbb3; 01xwqn; 0hcvy; ... >> query: (?x3194, 0c8wxp) <- religion(?x3194, ?x7300), religion(?x6187, ?x7300), ?x6187 = 07r1h >> conf = 0.95 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jrny religion 07w8f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 120.000 0.953 http://example.org/people/person/religion #14835-0325pb PRED entity: 0325pb PRED relation: fraternities_and_sororities! PRED expected values: 01j_9c 01wdl3 024y8p 01ptt7 01swxv 07vyf 0g8rj 027ydt 01n_g9 02txdf 0dzst => 4 concepts (4 used for prediction) PRED predicted values (max 10 best out of 881): 06pwq (0.50 #63, 0.33 #3, 0.32 #54), 015zyd (0.50 #124, 0.25 #57, 0.20 #55), 02m0sc (0.50 #125, 0.07 #120), 01ky7c (0.33 #59, 0.33 #33, 0.25 #93), 01w5m (0.33 #58, 0.33 #20, 0.25 #80), 08815 (0.33 #1, 0.32 #54, 0.25 #61), 01j_9c (0.33 #2, 0.32 #54, 0.25 #62), 01j_cy (0.33 #10, 0.32 #54, 0.25 #70), 03ksy (0.33 #21, 0.32 #54, 0.25 #81), 01swxv (0.33 #19, 0.32 #54, 0.25 #79) >> Best rule #63 for best value: >> intensional similarity = 199 >> extensional distance = 2 >> proper extension: 04m8fy; >> query: (?x3697, 06pwq) <- fraternities_and_sororities(?x13736, ?x3697), fraternities_and_sororities(?x10297, ?x3697), fraternities_and_sororities(?x8120, ?x3697), fraternities_and_sororities(?x7338, ?x3697), fraternities_and_sororities(?x6953, ?x3697), fraternities_and_sororities(?x6912, ?x3697), fraternities_and_sororities(?x6637, ?x3697), fraternities_and_sororities(?x6315, ?x3697), fraternities_and_sororities(?x6280, ?x3697), fraternities_and_sororities(?x5750, ?x3697), fraternities_and_sororities(?x5158, ?x3697), fraternities_and_sororities(?x4955, ?x3697), fraternities_and_sororities(?x4599, ?x3697), fraternities_and_sororities(?x4211, ?x3697), fraternities_and_sororities(?x3779, ?x3697), fraternities_and_sororities(?x3149, ?x3697), fraternities_and_sororities(?x2970, ?x3697), fraternities_and_sororities(?x2171, ?x3697), fraternities_and_sororities(?x2034, ?x3697), fraternities_and_sororities(?x1440, ?x3697), fraternities_and_sororities(?x388, ?x3697), major_field_of_study(?x6280, ?x2605), student(?x6912, ?x8661), student(?x6912, ?x7598), student(?x6912, ?x4871), student(?x6912, ?x1564), school_type(?x2034, ?x3205), currency(?x6280, ?x170), award(?x4871, ?x746), award(?x4871, ?x601), category(?x2034, ?x134), citytown(?x4211, ?x9713), major_field_of_study(?x6637, ?x1668), student(?x2171, ?x3338), ?x2970 = 04344j, school(?x2067, ?x5750), profession(?x7598, ?x1943), profession(?x7598, ?x1041), profession(?x7598, ?x1032), profession(?x7598, ?x987), colors(?x8120, ?x663), institution(?x3386, ?x8120), institution(?x620, ?x8120), major_field_of_study(?x388, ?x4321), major_field_of_study(?x388, ?x4268), major_field_of_study(?x388, ?x3995), major_field_of_study(?x388, ?x2981), institution(?x1526, ?x6637), institution(?x1368, ?x6637), list(?x388, ?x2197), celebrity(?x2373, ?x1564), ?x1943 = 02krf9, award_nominee(?x91, ?x4871), award_winner(?x603, ?x4871), school_type(?x388, ?x3092), award_nominee(?x7598, ?x3224), nominated_for(?x1564, ?x4749), student(?x6315, ?x3520), award(?x1564, ?x154), ?x1368 = 014mlp, school(?x6074, ?x10297), major_field_of_study(?x6112, ?x2981), major_field_of_study(?x5486, ?x2981), major_field_of_study(?x4410, ?x2981), ?x3995 = 0fdys, award(?x7598, ?x693), ?x6112 = 095kp, student(?x5750, ?x1600), team(?x8110, ?x6074), ?x4321 = 0g26h, season(?x6074, ?x2406), profession(?x4871, ?x353), school(?x4856, ?x3779), school(?x4487, ?x3779), school(?x1438, ?x3779), country(?x3779, ?x94), colors(?x4211, ?x9778), ?x4268 = 02822, organization(?x346, ?x2034), draft(?x6074, ?x11905), draft(?x6074, ?x8786), draft(?x6074, ?x8499), draft(?x6074, ?x3334), school(?x387, ?x388), major_field_of_study(?x5750, ?x5179), ?x8499 = 02r6gw6, service_location(?x6315, ?x6316), ?x2197 = 09g7thr, ?x1041 = 03gjzk, time_zones(?x6637, ?x2950), major_field_of_study(?x4599, ?x10417), major_field_of_study(?x4599, ?x6870), major_field_of_study(?x4599, ?x3878), colors(?x5158, ?x4557), student(?x10297, ?x7244), ?x10417 = 01r4k, ?x1526 = 0bkj86, ?x746 = 04dn09n, colors(?x6074, ?x5325), major_field_of_study(?x6315, ?x947), institution(?x1200, ?x2171), ?x987 = 0dxtg, state_province_region(?x4599, ?x4600), team(?x2010, ?x6074), ?x3092 = 05jxkf, place_of_birth(?x2602, ?x6316), award_winner(?x11087, ?x1564), state_province_region(?x1440, ?x3818), student(?x6637, ?x395), executive_produced_by(?x9258, ?x4871), people(?x13131, ?x8661), ?x601 = 0gr4k, type_of_union(?x4871, ?x566), institution(?x1519, ?x5750), ?x663 = 083jv, colors(?x13736, ?x3315), ?x620 = 07s6fsf, ?x3334 = 02pq_rp, ?x1200 = 016t_3, institution(?x9054, ?x3149), major_field_of_study(?x2981, ?x1527), ?x11905 = 047dpm0, service_language(?x4211, ?x254), award_nominee(?x4871, ?x157), institution(?x3386, ?x9879), institution(?x3386, ?x5981), institution(?x3386, ?x2775), institution(?x3386, ?x2228), ?x2228 = 01s0_f, student(?x3386, ?x9503), student(?x3386, ?x123), ?x4487 = 01ync, company(?x3520, ?x10370), program_creator(?x2078, ?x7244), school(?x9937, ?x6953), school(?x7725, ?x6953), school(?x7643, ?x6953), ?x4410 = 017j69, award_winner(?x1336, ?x1564), state(?x5267, ?x4600), ?x8786 = 02pq_x5, award_nominee(?x8661, ?x1894), citytown(?x6953, ?x2624), film(?x1564, ?x518), time_zones(?x6316, ?x2674), student(?x4955, ?x11380), student(?x4955, ?x8433), student(?x4955, ?x5521), student(?x4955, ?x5197), student(?x4955, ?x4662), ?x123 = 05bnp0, ?x2775 = 078bz, major_field_of_study(?x3386, ?x373), ?x7725 = 07l8x, ?x9054 = 022h5x, award(?x8661, ?x1869), contains(?x3038, ?x13736), ?x9879 = 01pcj4, position(?x9937, ?x1348), school(?x465, ?x8120), award_winner(?x1323, ?x8661), major_field_of_study(?x7338, ?x10264), taxonomy(?x3878, ?x939), ?x11380 = 015qq1, ?x1668 = 01mkq, position(?x4856, ?x180), contains(?x4600, ?x1087), contains(?x177, ?x3149), ?x346 = 060c4, award_nominee(?x4662, ?x192), award_winner(?x7768, ?x5521), film(?x5197, ?x3752), ?x6870 = 01540, ?x1438 = 0512p, student(?x3878, ?x1309), citytown(?x6912, ?x3052), ?x5981 = 03bmmc, location_of_ceremony(?x5197, ?x6495), award_nominee(?x4703, ?x5521), colors(?x5750, ?x3189), school(?x4979, ?x4955), award_winner(?x3254, ?x8661), citytown(?x10297, ?x10526), student(?x1440, ?x6279), ?x1032 = 02hrh1q, participant(?x2443, ?x4662), profession(?x5521, ?x524), ?x5486 = 0g8rj, institution(?x11690, ?x4599), contains(?x3778, ?x7338), ?x9503 = 04ns3gy, award(?x8433, ?x3337), colors(?x11153, ?x4557), ?x11153 = 080_y, location(?x1600, ?x3014), state_province_region(?x8120, ?x4758), teams(?x659, ?x4856), location(?x1727, ?x4600), team(?x11323, ?x7643) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 195 *> extensional distance = 1 *> proper extension: 035tlh; *> query: (?x3697, 01j_9c) <- fraternities_and_sororities(?x12127, ?x3697), fraternities_and_sororities(?x11387, ?x3697), fraternities_and_sororities(?x8120, ?x3697), fraternities_and_sororities(?x7338, ?x3697), fraternities_and_sororities(?x7071, ?x3697), fraternities_and_sororities(?x6953, ?x3697), fraternities_and_sororities(?x6912, ?x3697), fraternities_and_sororities(?x6814, ?x3697), fraternities_and_sororities(?x6637, ?x3697), fraternities_and_sororities(?x6315, ?x3697), fraternities_and_sororities(?x6280, ?x3697), fraternities_and_sororities(?x6177, ?x3697), fraternities_and_sororities(?x5750, ?x3697), fraternities_and_sororities(?x4211, ?x3697), fraternities_and_sororities(?x3090, ?x3697), fraternities_and_sororities(?x2497, ?x3697), fraternities_and_sororities(?x2171, ?x3697), fraternities_and_sororities(?x2034, ?x3697), fraternities_and_sororities(?x1440, ?x3697), fraternities_and_sororities(?x388, ?x3697), major_field_of_study(?x6280, ?x2605), student(?x6912, ?x10754), student(?x6912, ?x8494), student(?x6912, ?x4871), student(?x6912, ?x2715), student(?x6912, ?x1564), state_province_region(?x11387, ?x2831), school_type(?x2034, ?x3205), currency(?x6280, ?x170), ?x2171 = 01jq34, major_field_of_study(?x8120, ?x4321), institution(?x4981, ?x8120), award_nominee(?x4872, ?x4871), award_nominee(?x3760, ?x4871), major_field_of_study(?x6637, ?x10705), major_field_of_study(?x6637, ?x6760), category(?x6637, ?x134), award_winner(?x68, ?x4871), ?x4872 = 02d42t, institution(?x734, ?x6315), major_field_of_study(?x6315, ?x10391), major_field_of_study(?x6315, ?x947), school(?x2568, ?x2497), major_field_of_study(?x11387, ?x9079), school(?x685, ?x2497), award_nominee(?x4871, ?x157), influenced_by(?x8494, ?x5434), student(?x5750, ?x5268), student(?x5750, ?x652), contains(?x94, ?x11387), school(?x3674, ?x6953), school(?x3658, ?x6953), school(?x1576, ?x6953), school(?x580, ?x6953), award(?x10754, ?x154), ?x9079 = 0l5mz, award_winner(?x7801, ?x10754), school(?x1115, ?x7071), colors(?x6953, ?x332), institution(?x1519, ?x5750), legislative_sessions(?x652, ?x6933), legislative_sessions(?x652, ?x6139), legislative_sessions(?x652, ?x5977), legislative_sessions(?x652, ?x4730), legislative_sessions(?x652, ?x653), legislative_sessions(?x652, ?x356), legislative_sessions(?x652, ?x355), student(?x6315, ?x4377), films(?x10705, ?x1331), ?x5977 = 06r713, ?x7801 = 01bn3l, gender(?x652, ?x231), profession(?x652, ?x5805), colors(?x6280, ?x5845), award_winner(?x5631, ?x652), organization(?x6637, ?x5487), major_field_of_study(?x7545, ?x10705), gender(?x5268, ?x514), school(?x8786, ?x4211), state_province_region(?x6637, ?x1227), citytown(?x4211, ?x9713), school_type(?x6953, ?x3092), award(?x4871, ?x384), colors(?x8120, ?x663), contains(?x3778, ?x6280), student(?x6280, ?x10696), ?x4981 = 03bwzr4, student(?x6953, ?x9232), student(?x6953, ?x5412), student(?x6637, ?x395), ?x653 = 070m6c, service_location(?x6315, ?x13405), major_field_of_study(?x6912, ?x12158), major_field_of_study(?x6912, ?x5614), ?x356 = 05l2z4, school(?x465, ?x6953), student(?x7071, ?x7992), school(?x387, ?x8120), award_winner(?x4377, ?x6170), ?x1115 = 01y3c, team(?x1517, ?x3674), ?x9232 = 03ywyk, colors(?x4211, ?x9778), ?x12158 = 09s1f, school(?x2067, ?x5750), ?x1227 = 01n7q, state_province_region(?x4211, ?x3634), ?x3090 = 01r3y2, student(?x2497, ?x11290), award_winner(?x603, ?x4871), major_field_of_study(?x9200, ?x5614), major_field_of_study(?x6545, ?x5614), major_field_of_study(?x4410, ?x5614), major_field_of_study(?x3424, ?x5614), ?x7545 = 0bwfn, service_language(?x6315, ?x254), ?x355 = 0495ys, school(?x1161, ?x6177), ?x4410 = 017j69, ?x1517 = 02g_6j, list(?x5750, ?x2197), ?x947 = 036hv, colors(?x7071, ?x3189), contains(?x448, ?x13405), state_province_region(?x8120, ?x4758), ?x6545 = 01ky7c, school_type(?x8120, ?x1507), ?x3760 = 03zg2x, ?x4730 = 02cg7g, type_of_union(?x5412, ?x566), award_nominee(?x690, ?x4377), ?x6139 = 060ny2, institution(?x8398, ?x4211), school_type(?x5750, ?x1044), ?x9200 = 0dzst, organization(?x346, ?x2034), citytown(?x6177, ?x10364), position(?x3658, ?x1792), institution(?x1390, ?x6177), ?x4321 = 0g26h, school(?x6462, ?x7338), ?x6933 = 024tkd, school(?x4487, ?x6177), location(?x5268, ?x1523), ?x10391 = 02jfc, contains(?x3634, ?x1569), contains(?x1025, ?x12127), ?x6462 = 09l0x9, ?x690 = 06n7h7, student(?x5614, ?x396), ?x580 = 05m_8, sport(?x3658, ?x1083), award_nominee(?x1244, ?x1564), featured_film_locations(?x945, ?x3634), location(?x11119, ?x13405), award_winner(?x594, ?x8494), school_type(?x12127, ?x1962), team(?x7749, ?x3674), religion(?x3634, ?x8249), religion(?x3634, ?x2769), languages(?x2715, ?x4442), team(?x11323, ?x3674), ?x1161 = 02x2khw, child(?x6315, ?x9525), organization(?x5510, ?x7338), major_field_of_study(?x373, ?x5614), major_field_of_study(?x5614, ?x3995), school(?x2114, ?x6814), ?x2568 = 0jmcb, ?x2114 = 01y49, school(?x729, ?x7338), jurisdiction_of_office(?x900, ?x3634), ?x3424 = 01w5m, ?x6170 = 02qssrm, major_field_of_study(?x2497, ?x5900), nominated_for(?x4377, ?x3075), ?x2769 = 019cr, student(?x4211, ?x8291), disciplines_or_subjects(?x850, ?x6760), award_winner(?x1230, ?x2715), major_field_of_study(?x5750, ?x1695), profession(?x10754, ?x319), colors(?x11387, ?x4557), teams(?x4499, ?x1576), nominated_for(?x4871, ?x6048), people(?x3591, ?x1564), location(?x56, ?x3634), nominated_for(?x1564, ?x4749), ?x231 = 05zppz, ?x1523 = 030qb3t, ?x8249 = 021_0p, ?x1440 = 017zq0, ?x5845 = 067z2v, ?x388 = 05krk, contains(?x8260, ?x3634) *> conf = 0.33 ranks of expected_values: 7, 10, 15, 37, 41, 43, 52, 56, 57, 89, 343 EVAL 0325pb fraternities_and_sororities! 0dzst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities EVAL 0325pb fraternities_and_sororities! 02txdf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities EVAL 0325pb fraternities_and_sororities! 01n_g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities EVAL 0325pb fraternities_and_sororities! 027ydt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities EVAL 0325pb fraternities_and_sororities! 0g8rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities EVAL 0325pb fraternities_and_sororities! 07vyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities EVAL 0325pb fraternities_and_sororities! 01swxv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities EVAL 0325pb fraternities_and_sororities! 01ptt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities EVAL 0325pb fraternities_and_sororities! 024y8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities EVAL 0325pb fraternities_and_sororities! 01wdl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities EVAL 0325pb fraternities_and_sororities! 01j_9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 4.000 4.000 0.500 http://example.org/education/university/fraternities_and_sororities #14834-061zc_ PRED entity: 061zc_ PRED relation: place_of_death PRED expected values: 04vmp => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 34): 030qb3t (0.23 #2354, 0.20 #1383, 0.16 #5857), 02_286 (0.11 #5848, 0.11 #6043, 0.10 #6433), 04vmp (0.10 #886, 0.09 #5361, 0.04 #2246), 0rj0z (0.10 #833, 0.08 #1027, 0.05 #1805), 029kpy (0.10 #2527, 0.08 #6225, 0.07 #6615), 0k049 (0.08 #975, 0.07 #5838, 0.07 #4285), 0r3w7 (0.08 #1149, 0.07 #1538, 0.05 #1732), 06_kh (0.07 #1366, 0.05 #2337, 0.05 #6035), 04jpl (0.07 #1368, 0.05 #2339, 0.03 #4289), 0k_p5 (0.07 #1449, 0.03 #2420, 0.02 #6118) >> Best rule #2354 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 0459z; >> query: (?x5568, 030qb3t) <- place_of_birth(?x5568, ?x7771), people(?x268, ?x5568), gender(?x5568, ?x231), ?x268 = 0qcr0 >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #886 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 01s7z0; *> query: (?x5568, 04vmp) <- profession(?x5568, ?x1032), profession(?x5568, ?x319), ?x1032 = 02hrh1q, politician(?x13990, ?x5568), ?x319 = 01d_h8 *> conf = 0.10 ranks of expected_values: 3 EVAL 061zc_ place_of_death 04vmp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 121.000 121.000 0.225 http://example.org/people/deceased_person/place_of_death #14833-0n8_m93 PRED entity: 0n8_m93 PRED relation: award_winner PRED expected values: 016yvw => 35 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1399): 06pj8 (0.60 #7989, 0.43 #9530, 0.19 #17235), 0169dl (0.50 #6489, 0.22 #12653, 0.17 #16933), 09fb5 (0.50 #6193, 0.22 #12357, 0.15 #15439), 02cyfz (0.50 #6461, 0.22 #12625, 0.12 #11085), 01d0fp (0.50 #6923, 0.22 #13087, 0.12 #11547), 03_gd (0.50 #6247, 0.22 #12411, 0.12 #10871), 0c4f4 (0.50 #6208, 0.22 #12372, 0.12 #10832), 018ygt (0.42 #20979, 0.33 #964, 0.25 #19441), 01713c (0.41 #20013, 0.22 #18474, 0.22 #20014), 03kpvp (0.41 #20013, 0.22 #18474, 0.22 #20014) >> Best rule #7989 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 09q_6t; >> query: (?x8407, 06pj8) <- award_winner(?x8407, ?x989), honored_for(?x8407, ?x6176), honored_for(?x8407, ?x186), award(?x6176, ?x2375), award(?x616, ?x2375), film(?x1018, ?x186), production_companies(?x6176, ?x1686), country(?x186, ?x94), ?x1018 = 04shbh, award_nominee(?x989, ?x968), award_winner(?x186, ?x185), award_nominee(?x1676, ?x989), ?x616 = 011yph, nominated_for(?x112, ?x186), participant(?x989, ?x287) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #18474 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 14 *> proper extension: 0dth6b; 073hd1; *> query: (?x8407, ?x185) <- award_winner(?x8407, ?x989), honored_for(?x8407, ?x6176), honored_for(?x8407, ?x186), award(?x6176, ?x2375), award(?x6176, ?x484), film(?x1018, ?x186), production_companies(?x6176, ?x1686), country(?x186, ?x94), nominated_for(?x112, ?x186), nominated_for(?x2375, ?x89), genre(?x6176, ?x53), sibling(?x875, ?x989), ?x484 = 0gq_v, nominated_for(?x185, ?x186), nominated_for(?x143, ?x6176) *> conf = 0.22 ranks of expected_values: 144 EVAL 0n8_m93 award_winner 016yvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 35.000 15.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14832-0chghy PRED entity: 0chghy PRED relation: country! PRED expected values: 02tqm5 04lqvly 05c9zr 05r3qc 06yykb => 222 concepts (118 used for prediction) PRED predicted values (max 10 best out of 1791): 01m13b (0.60 #19982, 0.41 #39821, 0.33 #21635), 02r79_h (0.53 #51253, 0.33 #6833, 0.22 #10139), 08hmch (0.53 #51253, 0.31 #176897, 0.23 #23147), 0dfw0 (0.53 #51253, 0.31 #176897, 0.11 #9037), 0fdv3 (0.53 #51253, 0.31 #176897, 0.11 #8534), 04gknr (0.53 #51253, 0.23 #15011, 0.22 #10051), 06gb1w (0.53 #51253, 0.23 #15550, 0.22 #10590), 03_gz8 (0.53 #51253, 0.23 #17569, 0.22 #7650), 09q5w2 (0.53 #51253, 0.23 #23147, 0.22 #8426), 05p09dd (0.53 #51253, 0.23 #23147, 0.22 #8970) >> Best rule #19982 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 02j71; >> query: (?x390, 01m13b) <- service_location(?x555, ?x390), ?x555 = 01c6k4, currency(?x390, ?x170) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #17126 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 02jxk; *> query: (?x390, 04lqvly) <- jurisdiction_of_office(?x10118, ?x390), member_states(?x7416, ?x390), ?x10118 = 0p5vf *> conf = 0.23 ranks of expected_values: 159, 381, 478, 984, 1451 EVAL 0chghy country! 06yykb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 222.000 118.000 0.600 http://example.org/film/film/country EVAL 0chghy country! 05r3qc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 222.000 118.000 0.600 http://example.org/film/film/country EVAL 0chghy country! 05c9zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 222.000 118.000 0.600 http://example.org/film/film/country EVAL 0chghy country! 04lqvly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 222.000 118.000 0.600 http://example.org/film/film/country EVAL 0chghy country! 02tqm5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 222.000 118.000 0.600 http://example.org/film/film/country #14831-06r3p2 PRED entity: 06r3p2 PRED relation: nationality PRED expected values: 09c7w0 => 87 concepts (86 used for prediction) PRED predicted values (max 10 best out of 19): 09c7w0 (0.82 #401, 0.79 #1102, 0.79 #1002), 02jx1 (0.13 #633, 0.10 #2836, 0.10 #2035), 0345h (0.12 #231, 0.04 #631, 0.02 #8238), 07ssc (0.11 #615, 0.09 #1816, 0.09 #2017), 03rk0 (0.06 #5952, 0.06 #5051, 0.06 #5852), 0d060g (0.04 #1508, 0.04 #5012, 0.04 #3311), 03rt9 (0.04 #613, 0.01 #1814, 0.01 #2015), 0f8l9c (0.04 #522, 0.02 #822, 0.02 #2224), 0chghy (0.03 #610, 0.02 #1811, 0.02 #2012), 03rjj (0.02 #605, 0.02 #3609, 0.02 #3509) >> Best rule #401 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0p_r5; >> query: (?x12630, 09c7w0) <- profession(?x12630, ?x1032), ?x1032 = 02hrh1q, film(?x12630, ?x5323), ?x5323 = 011yn5 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06r3p2 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 86.000 0.818 http://example.org/people/person/nationality #14830-017_cl PRED entity: 017_cl PRED relation: teams PRED expected values: 0j2jr => 77 concepts (77 used for prediction) No prediction ranks of expected_values: EVAL 017_cl teams 0j2jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.000 http://example.org/sports/sports_team_location/teams #14829-06n9lt PRED entity: 06n9lt PRED relation: written_by! PRED expected values: 09d38d => 67 concepts (39 used for prediction) PRED predicted values (max 10 best out of 165): 0kxf1 (0.08 #18484, 0.08 #20466, 0.08 #19805), 02ryz24 (0.03 #182, 0.01 #2822), 0c0nhgv (0.03 #68, 0.01 #2708), 0ds2n (0.03 #205), 04fzfj (0.03 #37), 03wy8t (0.03 #1256), 01lbcqx (0.03 #1206), 0gl3hr (0.03 #1085), 0g_zyp (0.02 #1259, 0.02 #599), 0291hr (0.02 #1198, 0.02 #538) >> Best rule #18484 for best value: >> intensional similarity = 3 >> extensional distance = 1315 >> proper extension: 05xbx; 05gnf; >> query: (?x5146, ?x3599) <- nominated_for(?x5146, ?x3599), produced_by(?x3599, ?x1172), nominated_for(?x746, ?x3599) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06n9lt written_by! 09d38d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 39.000 0.078 http://example.org/film/film/written_by #14828-09btt1 PRED entity: 09btt1 PRED relation: award_nominee PRED expected values: 048q6x => 77 concepts (17 used for prediction) PRED predicted values (max 10 best out of 565): 048q6x (0.81 #25682, 0.81 #25681, 0.80 #21011), 06vsbt (0.81 #25682, 0.81 #25681, 0.80 #21011), 044lyq (0.81 #25682, 0.81 #25681, 0.80 #21011), 09btt1 (0.67 #1063, 0.27 #32688, 0.27 #14008), 05lb30 (0.27 #32688, 0.27 #14008, 0.25 #7005), 03zqc1 (0.27 #32688, 0.27 #14008, 0.25 #7005), 04psyp (0.27 #32688, 0.27 #14008, 0.25 #7005), 05683p (0.27 #32688, 0.27 #14008, 0.25 #7005), 01dw4q (0.27 #32688, 0.27 #14008, 0.25 #7005), 035gjq (0.27 #32688, 0.27 #14008, 0.25 #7005) >> Best rule #25682 for best value: >> intensional similarity = 3 >> extensional distance = 1188 >> proper extension: 031rx9; 02z6l5f; 06hzsx; 051x52f; 04j_gs; >> query: (?x4508, ?x3789) <- award_nominee(?x3789, ?x4508), award_nominee(?x4618, ?x3789), award_winner(?x3609, ?x4508) >> conf = 0.81 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 09btt1 award_nominee 048q6x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 17.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14827-0ddjy PRED entity: 0ddjy PRED relation: film! PRED expected values: 0c0k1 => 138 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1334): 06rnl9 (0.65 #6241, 0.47 #72819, 0.46 #70739), 0146pg (0.65 #6241, 0.45 #137309, 0.44 #149788), 0b6mgp_ (0.65 #6241, 0.45 #137309, 0.44 #149788), 0bbxx9b (0.65 #6241, 0.45 #137309, 0.44 #149788), 09pjnd (0.47 #72819, 0.46 #70739, 0.46 #149787), 03h26tm (0.47 #72819, 0.46 #70739, 0.46 #149787), 02f_k_ (0.25 #3201, 0.17 #5282, 0.03 #7362), 0c0k1 (0.25 #1506, 0.11 #7747, 0.08 #18152), 01846t (0.25 #541, 0.11 #6782, 0.07 #8864), 0f5xn (0.25 #3049, 0.10 #17615, 0.06 #34265) >> Best rule #6241 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02v63m; >> query: (?x2366, ?x669) <- featured_film_locations(?x2366, ?x3677), film(?x6059, ?x2366), nominated_for(?x669, ?x2366), ?x6059 = 01tnbn >> conf = 0.65 => this is the best rule for 4 predicted values *> Best rule #1506 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0h21v2; *> query: (?x2366, 0c0k1) <- award_winner(?x2366, ?x1643), award(?x2366, ?x507), genre(?x2366, ?x225), ?x1643 = 09pjnd *> conf = 0.25 ranks of expected_values: 8 EVAL 0ddjy film! 0c0k1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 138.000 87.000 0.647 http://example.org/film/actor/film./film/performance/film #14826-07t21 PRED entity: 07t21 PRED relation: taxonomy PRED expected values: 04n6k => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.78 #24, 0.77 #19, 0.76 #82) >> Best rule #24 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 024pcx; >> query: (?x1471, 04n6k) <- adjoins(?x1471, ?x456), nationality(?x11075, ?x1471), profession(?x11075, ?x353) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07t21 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.784 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #14825-04g5k PRED entity: 04g5k PRED relation: medal PRED expected values: 02lpp7 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.79 #5, 0.78 #4, 0.77 #3) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 02_286; 030qb3t; >> query: (?x5482, 02lpp7) <- film_release_region(?x4111, ?x5482), contains(?x5482, ?x8212), ?x4111 = 0cmc26r >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04g5k medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.789 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #14824-02r8hh_ PRED entity: 02r8hh_ PRED relation: film_release_region PRED expected values: 05v8c 03rj0 05b4w => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 212): 05b4w (0.80 #901, 0.79 #1041, 0.79 #1182), 05v8c (0.77 #858, 0.76 #998, 0.73 #577), 03rj0 (0.73 #897, 0.73 #1037, 0.72 #1178), 0ctw_b (0.72 #1006, 0.71 #866, 0.70 #585), 015qh (0.69 #881, 0.69 #600, 0.66 #1021), 04gzd (0.66 #853, 0.66 #993, 0.64 #572), 01p1v (0.66 #1030, 0.65 #890, 0.61 #1171), 047yc (0.62 #869, 0.61 #588, 0.58 #1009), 016wzw (0.59 #903, 0.58 #1043, 0.56 #1184), 06qd3 (0.53 #596, 0.52 #877, 0.51 #1158) >> Best rule #901 for best value: >> intensional similarity = 8 >> extensional distance = 84 >> proper extension: 0gx1bnj; 0401sg; 08hmch; 0jjy0; 04zyhx; 0gj8nq2; 0gtvpkw; 05c26ss; 0gy2y8r; 0bc1yhb; ... >> query: (?x1724, 05b4w) <- film_release_region(?x1724, ?x2629), film_release_region(?x1724, ?x1499), film_release_region(?x1724, ?x456), film_release_region(?x1724, ?x205), ?x456 = 05qhw, ?x2629 = 06f32, ?x1499 = 01znc_, ?x205 = 03rjj >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 02r8hh_ film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.802 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02r8hh_ film_release_region 03rj0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.802 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02r8hh_ film_release_region 05v8c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.802 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14823-02ld6x PRED entity: 02ld6x PRED relation: award PRED expected values: 03hl6lc => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 296): 019f4v (0.59 #1643, 0.42 #5199, 0.41 #4014), 0gs9p (0.52 #1656, 0.43 #9558, 0.40 #7188), 04dn09n (0.48 #1622, 0.44 #3203, 0.27 #5968), 03hl6lc (0.46 #3332, 0.28 #1751, 0.23 #2937), 040njc (0.45 #1587, 0.38 #3958, 0.38 #5143), 0gr4k (0.45 #1611, 0.37 #3192, 0.29 #15044), 0gq9h (0.41 #1654, 0.38 #6395, 0.38 #7186), 02pqp12 (0.41 #1647, 0.28 #5203, 0.27 #4018), 03hkv_r (0.34 #1594, 0.23 #3175, 0.21 #5940), 09sb52 (0.30 #11891, 0.30 #23351, 0.30 #29277) >> Best rule #1643 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 0qf43; 021bk; 022wxh; 037d35; 0c12h; 05cgy8; 0405l; >> query: (?x2705, 019f4v) <- award(?x2705, ?x2532), film(?x2705, ?x4768), ?x2532 = 02x4wr9 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #3332 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 77 *> proper extension: 0h5f5n; 012t1; 0170vn; 05183k; 05ldnp; 02bfxb; 098n5; 0hw1j; 08vr94; 03thw4; ... *> query: (?x2705, 03hl6lc) <- award(?x2705, ?x1862), written_by(?x4651, ?x2705), ?x1862 = 0gr51 *> conf = 0.46 ranks of expected_values: 4 EVAL 02ld6x award 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 121.000 121.000 0.586 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14822-08k881 PRED entity: 08k881 PRED relation: film PRED expected values: 01qncf => 79 concepts (41 used for prediction) PRED predicted values (max 10 best out of 151): 0vjr (0.58 #16092, 0.53 #35755, 0.44 #30393), 062zm5h (0.05 #62563, 0.03 #30392, 0.03 #46478), 04n52p6 (0.05 #62563, 0.03 #30392, 0.03 #46478), 03h_yy (0.05 #62563, 0.03 #30392, 0.03 #46478), 01y9jr (0.05 #62563, 0.02 #5364), 01cmp9 (0.05 #62563, 0.02 #5364), 01rxyb (0.05 #62563, 0.02 #5364), 0dzlbx (0.05 #62563), 0d68qy (0.05 #62563), 02hct1 (0.05 #62563) >> Best rule #16092 for best value: >> intensional similarity = 3 >> extensional distance = 1049 >> proper extension: 07nznf; 0184jc; 04bdxl; 05vsxz; 05bnp0; 016qtt; 012d40; 07fq1y; 02qgqt; 0337vz; ... >> query: (?x5770, ?x5386) <- award_nominee(?x5770, ?x2307), nominated_for(?x5770, ?x5386), film(?x5770, ?x7187) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08k881 film 01qncf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 41.000 0.584 http://example.org/film/actor/film./film/performance/film #14821-07nnp_ PRED entity: 07nnp_ PRED relation: nominated_for! PRED expected values: 05q8pss => 88 concepts (75 used for prediction) PRED predicted values (max 10 best out of 203): 05p1dby (0.38 #1708, 0.33 #77, 0.29 #2640), 05q5t0b (0.33 #815, 0.12 #13518, 0.12 #13517), 0gq9h (0.28 #11476, 0.28 #7514, 0.26 #9845), 0f4x7 (0.27 #2819, 0.22 #5149, 0.19 #3984), 019f4v (0.26 #6806, 0.25 #5408, 0.24 #7505), 0gs9p (0.25 #11477, 0.24 #7515, 0.23 #3321), 09qwmm (0.25 #1889, 0.25 #1423, 0.17 #3287), 0gqwc (0.25 #1920, 0.25 #1454, 0.15 #5182), 054krc (0.25 #297, 0.20 #530, 0.18 #4025), 02x258x (0.25 #325, 0.20 #558, 0.14 #1257) >> Best rule #1708 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 08lr6s; 016z9n; 07vf5c; 04cbbz; 06bd5j; >> query: (?x12393, 05p1dby) <- genre(?x12393, ?x600), nominated_for(?x102, ?x12393), produced_by(?x12393, ?x8208), ?x8208 = 04fyhv >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2708 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 075wx7_; 06ybb1; 0ddt_; 02rq8k8; 033f8n; 0dfw0; 0cwfgz; 05b_gq; 02scbv; *> query: (?x12393, 05q8pss) <- genre(?x12393, ?x600), nominated_for(?x1105, ?x12393), nominated_for(?x1120, ?x12393), ?x1105 = 07bdd_ *> conf = 0.17 ranks of expected_values: 41 EVAL 07nnp_ nominated_for! 05q8pss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 88.000 75.000 0.375 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14820-016017 PRED entity: 016017 PRED relation: genre PRED expected values: 04xvh5 0bj8m2 => 96 concepts (70 used for prediction) PRED predicted values (max 10 best out of 95): 07s9rl0 (0.92 #7585, 0.81 #4386, 0.68 #2723), 09b3v (0.57 #237, 0.56 #3790, 0.56 #3909), 01jfsb (0.55 #3446, 0.46 #6053, 0.43 #6645), 02kdv5l (0.50 #1069, 0.49 #3437, 0.48 #2843), 03g3w (0.45 #732, 0.35 #1445, 0.34 #971), 02l7c8 (0.44 #6295, 0.38 #6531, 0.35 #2738), 04t36 (0.42 #243, 0.22 #361, 0.20 #124), 03bxz7 (0.40 #172, 0.12 #763, 0.12 #291), 01zhp (0.34 #430, 0.19 #1379, 0.14 #2206), 04xvlr (0.33 #2, 0.24 #711, 0.20 #120) >> Best rule #7585 for best value: >> intensional similarity = 5 >> extensional distance = 1088 >> proper extension: 0c0wvx; >> query: (?x11149, 07s9rl0) <- genre(?x11149, ?x8681), genre(?x3201, ?x8681), genre(?x1218, ?x8681), language(?x3201, ?x90), ?x1218 = 02prw4h >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #285 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 31 *> proper extension: 0jnwx; 0ddj0x; *> query: (?x11149, 0bj8m2) <- film_release_distribution_medium(?x11149, ?x81), film(?x8482, ?x11149), genre(?x11149, ?x8681), ?x8681 = 04rlf, profession(?x8482, ?x1032) *> conf = 0.18 ranks of expected_values: 22, 27 EVAL 016017 genre 0bj8m2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 96.000 70.000 0.923 http://example.org/film/film/genre EVAL 016017 genre 04xvh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 96.000 70.000 0.923 http://example.org/film/film/genre #14819-02tv80 PRED entity: 02tv80 PRED relation: film PRED expected values: 0jdgr => 94 concepts (42 used for prediction) PRED predicted values (max 10 best out of 192): 02yxbc (0.40 #1299), 011ysn (0.20 #566, 0.01 #5924), 08rr3p (0.20 #443, 0.01 #5801), 0bscw (0.20 #218, 0.01 #5576), 03b1l8 (0.20 #1382, 0.01 #50018), 0n08r (0.20 #1701, 0.01 #7059), 03cp4cn (0.20 #1104, 0.01 #6462), 016z43 (0.20 #1766), 03k8th (0.20 #1717), 07vn_9 (0.20 #1680) >> Best rule #1299 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 073w14; >> query: (?x6402, 02yxbc) <- profession(?x6402, ?x1032), film(?x6402, ?x8985), ?x8985 = 04x4nv, ?x1032 = 02hrh1q >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02tv80 film 0jdgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 42.000 0.400 http://example.org/film/actor/film./film/performance/film #14818-09lwrt PRED entity: 09lwrt PRED relation: group! PRED expected values: 05r5c => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 65): 01vj9c (0.27 #435, 0.26 #351, 0.18 #96), 0l14qv (0.25 #89, 0.23 #428, 0.22 #344), 05r5c (0.23 #430, 0.21 #346, 0.20 #91), 03qjg (0.23 #383, 0.22 #467, 0.22 #128), 04rzd (0.16 #112, 0.12 #367, 0.12 #451), 06ncr (0.14 #458, 0.14 #119, 0.13 #374), 07y_7 (0.14 #86, 0.11 #425, 0.10 #341), 0l14j_ (0.14 #132, 0.11 #471, 0.10 #387), 013y1f (0.13 #447, 0.13 #363, 0.08 #108), 042v_gx (0.10 #431, 0.08 #347, 0.08 #92) >> Best rule #435 for best value: >> intensional similarity = 2 >> extensional distance = 186 >> proper extension: 02_5x9; 01qqwp9; 02t3ln; 02mq_y; 0123r4; 015cxv; 0qmpd; 06br6t; >> query: (?x6699, 01vj9c) <- artists(?x302, ?x6699), group(?x227, ?x6699) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #430 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 186 *> proper extension: 02_5x9; 01qqwp9; 02t3ln; 02mq_y; 0123r4; 015cxv; 0qmpd; 06br6t; *> query: (?x6699, 05r5c) <- artists(?x302, ?x6699), group(?x227, ?x6699) *> conf = 0.23 ranks of expected_values: 3 EVAL 09lwrt group! 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 58.000 58.000 0.271 http://example.org/music/performance_role/regular_performances./music/group_membership/group #14817-07h34 PRED entity: 07h34 PRED relation: district_represented! PRED expected values: 01grpq 01gtcq 01grqd 01h7xx => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 33): 01h7xx (0.82 #60, 0.82 #127, 0.56 #160), 01gtcq (0.77 #119, 0.65 #52, 0.48 #926), 024tkd (0.70 #455, 0.65 #58, 0.64 #125), 02bn_p (0.67 #434, 0.65 #37, 0.64 #104), 02bp37 (0.61 #438, 0.59 #108, 0.54 #471), 01grqd (0.59 #120, 0.53 #53, 0.48 #926), 01grp0 (0.59 #126, 0.48 #926, 0.47 #59), 01grnp (0.59 #107, 0.48 #926, 0.47 #40), 02bqm0 (0.57 #448, 0.51 #151, 0.50 #580), 01grpq (0.55 #117, 0.48 #926, 0.47 #100) >> Best rule #60 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 04ych; >> query: (?x3778, 01h7xx) <- district_represented(?x2019, ?x3778), ?x2019 = 01gtbb, administrative_parent(?x3778, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6, 10 EVAL 07h34 district_represented! 01h7xx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.824 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07h34 district_represented! 01grqd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 158.000 158.000 0.824 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07h34 district_represented! 01gtcq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.824 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07h34 district_represented! 01grpq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 158.000 158.000 0.824 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #14816-025b5y PRED entity: 025b5y PRED relation: people! PRED expected values: 033tf_ => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 42): 041rx (0.24 #2895, 0.21 #4879, 0.19 #384), 033tf_ (0.18 #387, 0.16 #235, 0.15 #1375), 0x67 (0.17 #4885, 0.14 #162, 0.13 #238), 0xnvg (0.17 #393, 0.11 #925, 0.11 #849), 07bch9 (0.17 #98, 0.09 #858, 0.08 #554), 02g7sp (0.17 #94, 0.02 #398, 0.02 #854), 02ctzb (0.11 #243, 0.07 #471, 0.07 #547), 02w7gg (0.10 #306, 0.08 #838, 0.08 #1218), 09vc4s (0.10 #389, 0.06 #921, 0.06 #465), 01qhm_ (0.10 #386, 0.06 #918, 0.05 #842) >> Best rule #2895 for best value: >> intensional similarity = 2 >> extensional distance = 611 >> proper extension: 04rs03; 021sv1; 01cv3n; 08f3b1; 0hnlx; 019z7q; 02whj; 083p7; 083q7; 0k4gf; ... >> query: (?x5593, 041rx) <- religion(?x5593, ?x1363), people(?x5540, ?x5593) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #387 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 100 *> proper extension: 018qql; *> query: (?x5593, 033tf_) <- friend(?x5593, ?x849), people(?x5540, ?x5593) *> conf = 0.18 ranks of expected_values: 2 EVAL 025b5y people! 033tf_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 137.000 0.238 http://example.org/people/ethnicity/people #14815-0xjl2 PRED entity: 0xjl2 PRED relation: parent_genre PRED expected values: 01_qp_ => 58 concepts (41 used for prediction) PRED predicted values (max 10 best out of 292): 01243b (0.60 #1463, 0.47 #1784, 0.36 #1944), 03lty (0.46 #2254, 0.43 #973, 0.33 #335), 064t9 (0.40 #1768, 0.20 #1608, 0.18 #2570), 02x8m (0.33 #809, 0.33 #649, 0.33 #13), 06cqb (0.33 #797, 0.33 #637, 0.33 #1), 0827d (0.33 #2, 0.22 #1279, 0.17 #798), 01pfpt (0.33 #57, 0.20 #1655, 0.17 #853), 0190y4 (0.33 #110, 0.17 #906, 0.17 #746), 017371 (0.33 #579, 0.17 #898, 0.16 #3202), 0dl5d (0.30 #1451, 0.20 #1772, 0.17 #491) >> Best rule #1463 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 01gbcf; >> query: (?x3167, 01243b) <- parent_genre(?x3167, ?x5934), parent_genre(?x3167, ?x302), parent_genre(?x6349, ?x3167), ?x302 = 016clz, artists(?x5934, ?x4237), ?x4237 = 01w524f >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4983 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 163 *> proper extension: 02p4l6s; *> query: (?x3167, ?x3108) <- parent_genre(?x14058, ?x3167), parent_genre(?x14058, ?x3108) *> conf = 0.10 ranks of expected_values: 37 EVAL 0xjl2 parent_genre 01_qp_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 58.000 41.000 0.600 http://example.org/music/genre/parent_genre #14814-0cgs4 PRED entity: 0cgs4 PRED relation: capital PRED expected values: 096gm => 146 concepts (79 used for prediction) PRED predicted values (max 10 best out of 75): 056_y (0.13 #872, 0.09 #7426, 0.09 #7303), 01q0l (0.13 #891, 0.09 #7426, 0.09 #7303), 081m_ (0.10 #1258, 0.08 #526, 0.08 #771), 04llb (0.09 #7426, 0.09 #7303, 0.08 #542), 01b1nk (0.09 #7426, 0.09 #7303, 0.07 #963), 0fhsz (0.09 #7426, 0.09 #7303, 0.07 #927), 05qtj (0.09 #7426, 0.09 #7303, 0.07 #871), 0d34_ (0.09 #7426, 0.09 #7303, 0.07 #951), 02z0j (0.09 #7426, 0.09 #7303, 0.07 #892), 01f62 (0.09 #7426, 0.09 #7303, 0.07 #864) >> Best rule #872 for best value: >> intensional similarity = 2 >> extensional distance = 13 >> proper extension: 05kyr; 03l5m1; 017cw; 03f4n1; 026pz9s; 0cgm9; 01s47p; >> query: (?x14194, 056_y) <- entity_involved(?x9939, ?x14194), ?x9939 = 03jqfx >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #506 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0285m87; 05pq3_; 01rdm0; *> query: (?x14194, 096gm) <- contains(?x1536, ?x14194), entity_involved(?x9939, ?x14194), contains(?x455, ?x1536), partially_contains(?x1536, ?x10517) *> conf = 0.08 ranks of expected_values: 12 EVAL 0cgs4 capital 096gm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 146.000 79.000 0.133 http://example.org/location/country/capital #14813-01rcmg PRED entity: 01rcmg PRED relation: people! PRED expected values: 041rx => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 27): 041rx (0.17 #312, 0.14 #235, 0.13 #697), 033tf_ (0.12 #7, 0.09 #392, 0.08 #2086), 0x67 (0.10 #395, 0.10 #1396, 0.10 #2782), 02w7gg (0.08 #233, 0.07 #1234, 0.06 #1619), 01qhm_ (0.06 #83, 0.04 #160, 0.03 #1238), 03bkbh (0.06 #32, 0.04 #109, 0.03 #186), 065b6q (0.06 #3, 0.04 #80, 0.03 #157), 048z7l (0.06 #40, 0.03 #348, 0.03 #1734), 02ctzb (0.06 #15, 0.02 #400, 0.02 #92), 03lmx1 (0.06 #14, 0.01 #938, 0.01 #1015) >> Best rule #312 for best value: >> intensional similarity = 2 >> extensional distance = 310 >> proper extension: 0hskw; 01s7qqw; 0739y; 0k57l; 01k9lpl; 04rg6; 0dn44; 05b1062; 09ld6g; 03chx58; >> query: (?x8439, 041rx) <- profession(?x8439, ?x1146), ?x1146 = 018gz8 >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rcmg people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.167 http://example.org/people/ethnicity/people #14812-0319l PRED entity: 0319l PRED relation: role PRED expected values: 02w3w => 81 concepts (56 used for prediction) PRED predicted values (max 10 best out of 105): 03bx0bm (0.80 #2914, 0.80 #2090, 0.79 #1779), 013y1f (0.79 #1990, 0.77 #3435, 0.76 #2501), 07y_7 (0.76 #2475, 0.70 #3098, 0.70 #2996), 04rzd (0.75 #2925, 0.75 #2205, 0.71 #3342), 028tv0 (0.75 #2897, 0.71 #1142, 0.70 #3104), 01s0ps (0.73 #5466, 0.71 #1803, 0.66 #715), 02sgy (0.73 #5466, 0.69 #2375, 0.67 #1957), 042v_gx (0.71 #1853, 0.70 #1348, 0.66 #715), 0319l (0.71 #1780, 0.70 #1368, 0.64 #407), 02k84w (0.67 #1957, 0.66 #715, 0.65 #406) >> Best rule #2914 for best value: >> intensional similarity = 18 >> extensional distance = 18 >> proper extension: 03q5t; 07gql; 03ndd; >> query: (?x1472, 03bx0bm) <- instrumentalists(?x1472, ?x11947), role(?x1472, ?x2460), role(?x1472, ?x614), role(?x1472, ?x3991), group(?x1472, ?x997), ?x2460 = 01wy6, ?x614 = 0mkg, role(?x487, ?x1472), role(?x3991, ?x4917), role(?x3991, ?x2956), role(?x3991, ?x2297), role(?x5301, ?x3991), ?x4917 = 06w7v, role(?x1655, ?x3991), ?x1655 = 01hww_, ?x2297 = 051hrr, ?x5301 = 01vswwx, ?x2956 = 0myk8 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2040 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 12 *> proper extension: 01vj9c; *> query: (?x1472, 02w3w) <- role(?x1472, ?x2798), role(?x1472, ?x2309), role(?x1472, ?x1166), role(?x1472, ?x228), role(?x5815, ?x1472), role(?x925, ?x1472), group(?x1472, ?x997), ?x1166 = 05148p4, ?x2309 = 06ncr, award(?x5815, ?x2212), ?x228 = 0l14qv, role(?x3418, ?x1472), music(?x924, ?x925), role(?x3991, ?x3418), ?x3991 = 05842k, artists(?x505, ?x925), ?x2798 = 03qjg *> conf = 0.64 ranks of expected_values: 29 EVAL 0319l role 02w3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 81.000 56.000 0.800 http://example.org/music/performance_role/regular_performances./music/group_membership/role #14811-0gys2jp PRED entity: 0gys2jp PRED relation: genre PRED expected values: 082gq => 89 concepts (79 used for prediction) PRED predicted values (max 10 best out of 91): 02kdv5l (0.49 #244, 0.45 #728, 0.39 #607), 03k9fj (0.42 #738, 0.27 #254, 0.26 #1828), 01jfsb (0.41 #255, 0.34 #739, 0.33 #618), 02l7c8 (0.40 #380, 0.34 #1470, 0.32 #501), 05p553 (0.36 #3762, 0.35 #1578, 0.35 #973), 0lsxr (0.33 #130, 0.27 #251, 0.25 #9), 04xvlr (0.27 #1454, 0.26 #364, 0.18 #4247), 06n90 (0.25 #14, 0.22 #619, 0.18 #1104), 03q4nz (0.24 #262, 0.14 #383, 0.14 #504), 01hmnh (0.22 #745, 0.20 #624, 0.19 #1835) >> Best rule #244 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 05znbh7; >> query: (?x11701, 02kdv5l) <- language(?x11701, ?x2890), genre(?x11701, ?x53), film_release_distribution_medium(?x11701, ?x81), ?x2890 = 0653m >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 04t6fk; 02825kb; 048yqf; 0gldyz; *> query: (?x11701, 082gq) <- language(?x11701, ?x2164), film(?x1104, ?x11701), nominated_for(?x9086, ?x11701), ?x2164 = 03_9r *> conf = 0.18 ranks of expected_values: 11 EVAL 0gys2jp genre 082gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 89.000 79.000 0.486 http://example.org/film/film/genre #14810-02psqkz PRED entity: 02psqkz PRED relation: combatants! PRED expected values: 081pw 05nqz 025rzfc => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 93): 081pw (0.80 #507, 0.71 #395, 0.67 #282), 02h2z_ (0.67 #381, 0.64 #2705, 0.61 #2025), 01fc7p (0.67 #620, 0.33 #340, 0.23 #1631), 075k5 (0.64 #2705, 0.61 #2025, 0.61 #2704), 0gjw_ (0.50 #647, 0.50 #367, 0.23 #1658), 0c3mz (0.50 #372, 0.42 #652, 0.23 #1663), 03gqgt3 (0.39 #2298, 0.36 #2411, 0.36 #2582), 048n7 (0.33 #2383, 0.33 #2270, 0.33 #750), 01gjd0 (0.33 #734, 0.33 #341, 0.25 #621), 025rzfc (0.33 #303, 0.33 #22, 0.25 #753) >> Best rule #507 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 015qh; 07f1x; >> query: (?x3918, 081pw) <- combatants(?x1790, ?x3918), combatants(?x1497, ?x3918), ?x1790 = 01pj7, film_release_region(?x5092, ?x1497), film_release_region(?x4336, ?x1497), ?x4336 = 0bpm4yw, ?x5092 = 0gg5qcw, country(?x668, ?x1497) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10, 34 EVAL 02psqkz combatants! 025rzfc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 111.000 111.000 0.800 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 02psqkz combatants! 05nqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 111.000 111.000 0.800 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 02psqkz combatants! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.800 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #14809-06npd PRED entity: 06npd PRED relation: olympics PRED expected values: 0kbvb => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 40): 0jhn7 (0.81 #347, 0.75 #1030, 0.74 #387), 0kbvb (0.74 #208, 0.73 #328, 0.71 #930), 09n48 (0.70 #1887, 0.69 #3012, 0.69 #3011), 0kbvv (0.70 #1887, 0.69 #3012, 0.69 #3011), 0swff (0.70 #1887, 0.69 #3012, 0.69 #3011), 0swbd (0.70 #1887, 0.69 #3012, 0.69 #3011), 018ctl (0.70 #1887, 0.69 #3012, 0.69 #3011), 0lgxj (0.70 #228, 0.65 #348, 0.63 #388), 0l6m5 (0.67 #371, 0.65 #331, 0.64 #933), 0l6ny (0.65 #330, 0.57 #210, 0.56 #370) >> Best rule #347 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0154j; 015fr; 0k6nt; 035qy; 06qd3; 03spz; >> query: (?x756, 0jhn7) <- film_release_region(?x2714, ?x756), ?x2714 = 0kv238, country(?x150, ?x756), combatants(?x756, ?x1497) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #208 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 06sw9; 020p1; *> query: (?x756, 0kbvb) <- country(?x4310, ?x756), location_of_ceremony(?x566, ?x756), ?x4310 = 064vjs *> conf = 0.74 ranks of expected_values: 2 EVAL 06npd olympics 0kbvb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 200.000 200.000 0.808 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #14808-01svry PRED entity: 01svry PRED relation: genre PRED expected values: 02n4kr 06n90 => 83 concepts (60 used for prediction) PRED predicted values (max 10 best out of 138): 07s9rl0 (0.93 #4446, 0.75 #2283, 0.65 #3364), 05p553 (0.77 #605, 0.67 #125, 0.58 #1206), 02kdv5l (0.50 #1564, 0.50 #123, 0.48 #3126), 03k9fj (0.50 #252, 0.47 #372, 0.33 #132), 01hmnh (0.50 #258, 0.47 #378, 0.16 #1459), 06n90 (0.38 #253, 0.33 #133, 0.32 #373), 0lsxr (0.33 #3132, 0.31 #1570, 0.23 #1810), 02l7c8 (0.31 #4461, 0.29 #1096, 0.28 #3499), 04xvlr (0.26 #2284, 0.25 #4447, 0.20 #5529), 02n4kr (0.24 #1569, 0.23 #3131, 0.23 #488) >> Best rule #4446 for best value: >> intensional similarity = 5 >> extensional distance = 803 >> proper extension: 0bmc4cm; 0c5qvw; >> query: (?x6731, 07s9rl0) <- titles(?x571, ?x6731), language(?x6731, ?x254), genre(?x6731, ?x6452), genre(?x7243, ?x6452), ?x7243 = 0yzbg >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 0bth54; 05pbl56; 02vqhv0; 057lbk; 05pdh86; 047vnkj; 047csmy; 027j9wd; 05qbbfb; 05pdd86; ... *> query: (?x6731, 06n90) <- film_crew_role(?x6731, ?x8411), film_crew_role(?x6731, ?x5136), ?x5136 = 089g0h, ?x8411 = 033smt, film(?x1057, ?x6731), film_release_region(?x6731, ?x94) *> conf = 0.38 ranks of expected_values: 6, 10 EVAL 01svry genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 83.000 60.000 0.928 http://example.org/film/film/genre EVAL 01svry genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 83.000 60.000 0.928 http://example.org/film/film/genre #14807-05y5fw PRED entity: 05y5fw PRED relation: award PRED expected values: 0fbtbt 02xcb6n => 107 concepts (89 used for prediction) PRED predicted values (max 10 best out of 288): 0fbtbt (0.45 #1844, 0.41 #3053, 0.41 #2650), 0gq9h (0.40 #480, 0.33 #77, 0.18 #2092), 040njc (0.40 #411, 0.33 #8, 0.18 #8874), 0cqhb3 (0.40 #708, 0.06 #1111, 0.03 #5947), 0cjyzs (0.39 #2121, 0.38 #8569, 0.37 #6151), 09sb52 (0.36 #16564, 0.35 #14146, 0.31 #14549), 07bdd_ (0.33 #66, 0.09 #8529, 0.09 #8126), 0gr4k (0.29 #8899, 0.27 #10511, 0.20 #436), 0gr51 (0.27 #8966, 0.26 #10578, 0.15 #5339), 04dn09n (0.27 #8910, 0.25 #10522, 0.20 #447) >> Best rule #1844 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 04y8r; 0d7hg4; 0884hk; 070j61; 04s04; >> query: (?x5033, 0fbtbt) <- award_nominee(?x5033, ?x5034), producer_type(?x5033, ?x632), written_by(?x1035, ?x5033), place_of_birth(?x5033, ?x739) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1, 60 EVAL 05y5fw award 02xcb6n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 107.000 89.000 0.455 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05y5fw award 0fbtbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 89.000 0.455 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14806-03tps5 PRED entity: 03tps5 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 26): 09vw2b7 (0.68 #605, 0.68 #570, 0.67 #252), 01vx2h (0.50 #81, 0.45 #257, 0.37 #117), 0dxtw (0.39 #256, 0.38 #80, 0.37 #1210), 01pvkk (0.30 #258, 0.28 #1282, 0.27 #2669), 02ynfr (0.22 #262, 0.19 #615, 0.19 #580), 02rh1dz (0.21 #115, 0.18 #255, 0.17 #79), 015h31 (0.17 #78, 0.13 #254, 0.12 #2799), 0d2b38 (0.15 #271, 0.13 #166, 0.13 #131), 01xy5l_ (0.13 #120, 0.12 #225, 0.12 #2799), 089g0h (0.12 #89, 0.12 #2799, 0.11 #583) >> Best rule #605 for best value: >> intensional similarity = 4 >> extensional distance = 359 >> proper extension: 0dkv90; 0cvkv5; >> query: (?x4409, 09vw2b7) <- award_winner(?x4409, ?x398), film_crew_role(?x4409, ?x468), ?x468 = 02r96rf, genre(?x4409, ?x258) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03tps5 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.679 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14805-0chghy PRED entity: 0chghy PRED relation: country! PRED expected values: 0bynt 0w0d 0486tv 019tzd 09_b4 => 223 concepts (223 used for prediction) PRED predicted values (max 10 best out of 40): 0bynt (0.89 #1363, 0.88 #242, 0.88 #963), 0w0d (0.82 #532, 0.78 #388, 0.76 #516), 019tzd (0.75 #153, 0.74 #425, 0.70 #521), 02y8z (0.73 #534, 0.71 #422, 0.68 #630), 0486tv (0.61 #536, 0.59 #248, 0.57 #312), 02_5h (0.55 #531, 0.53 #243, 0.52 #387), 01yfj (0.50 #159, 0.35 #255, 0.32 #431), 09_b4 (0.45 #426, 0.44 #394, 0.42 #650), 03krj (0.42 #540, 0.41 #252, 0.38 #156), 09_9n (0.37 #397, 0.36 #525, 0.35 #253) >> Best rule #1363 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 06srk; >> query: (?x390, 0bynt) <- organization(?x390, ?x127), ?x127 = 02vk52z, country(?x901, ?x390) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 8 EVAL 0chghy country! 09_b4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 223.000 223.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0chghy country! 019tzd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 223.000 223.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0chghy country! 0486tv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 223.000 223.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0chghy country! 0w0d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 223.000 223.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0chghy country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 223.000 223.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #14804-04g9gd PRED entity: 04g9gd PRED relation: genre PRED expected values: 07s9rl0 01jfsb => 73 concepts (67 used for prediction) PRED predicted values (max 10 best out of 147): 07s9rl0 (0.96 #3548, 0.88 #2839, 0.77 #3076), 01jfsb (0.57 #1074, 0.51 #1665, 0.44 #1429), 02l7c8 (0.42 #486, 0.40 #250, 0.33 #3325), 06cvj (0.40 #239, 0.17 #475, 0.10 #3314), 03k9fj (0.40 #1664, 0.29 #1428, 0.29 #718), 04xvlr (0.31 #3077, 0.30 #592, 0.25 #120), 06n90 (0.27 #1666, 0.20 #248, 0.20 #838), 02n4kr (0.26 #1070, 0.15 #1543, 0.13 #5092), 01hmnh (0.25 #134, 0.22 #724, 0.20 #1197), 060__y (0.25 #133, 0.21 #3090, 0.20 #369) >> Best rule #3548 for best value: >> intensional similarity = 4 >> extensional distance = 734 >> proper extension: 0413cff; 0cp08zg; 0k20s; 0cbl95; >> query: (?x2418, 07s9rl0) <- film_release_region(?x2418, ?x94), genre(?x2418, ?x6887), genre(?x697, ?x6887), ?x697 = 0209hj >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04g9gd genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 67.000 0.955 http://example.org/film/film/genre EVAL 04g9gd genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 67.000 0.955 http://example.org/film/film/genre #14803-04vcdj PRED entity: 04vcdj PRED relation: location PRED expected values: 0f2s6 => 88 concepts (50 used for prediction) PRED predicted values (max 10 best out of 64): 01n4w (0.33 #153, 0.02 #956), 0f2s6 (0.33 #474), 0fr0t (0.33 #208), 02cl1 (0.33 #32), 02_286 (0.22 #22536, 0.19 #34587, 0.17 #10481), 030qb3t (0.18 #22582, 0.17 #1689, 0.16 #27402), 0cr3d (0.08 #22644, 0.08 #32285, 0.08 #27464), 04jpl (0.08 #20909, 0.07 #32157, 0.07 #27336), 07b_l (0.05 #16871, 0.01 #21079, 0.01 #22686), 09c7w0 (0.05 #16871) >> Best rule #153 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0f_y9; >> query: (?x13332, 01n4w) <- location(?x13332, ?x9767), profession(?x13332, ?x1032), ?x9767 = 0105y2 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #474 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 0f_y9; *> query: (?x13332, 0f2s6) <- location(?x13332, ?x9767), profession(?x13332, ?x1032), ?x9767 = 0105y2 *> conf = 0.33 ranks of expected_values: 2 EVAL 04vcdj location 0f2s6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 50.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #14802-04yqlk PRED entity: 04yqlk PRED relation: actor! PRED expected values: 02_1ky => 60 concepts (56 used for prediction) PRED predicted values (max 10 best out of 65): 033fqh (0.09 #2644, 0.08 #3702, 0.08 #10322), 026bfsh (0.04 #888, 0.02 #624, 0.02 #1152), 02_1q9 (0.03 #269, 0.02 #797, 0.01 #5), 0kfv9 (0.02 #291, 0.02 #819, 0.01 #555), 05f4vxd (0.02 #352, 0.02 #880, 0.01 #7146), 024rwx (0.02 #369, 0.02 #897, 0.01 #105), 0828jw (0.02 #368, 0.01 #896, 0.01 #1424), 0180mw (0.02 #383, 0.02 #911, 0.01 #1439), 0jwl2 (0.02 #336, 0.02 #864), 03ln8b (0.02 #295, 0.02 #823) >> Best rule #2644 for best value: >> intensional similarity = 2 >> extensional distance = 1275 >> proper extension: 01mqz0; 02wrhj; 01csrl; 05_pkf; 02yplc; 02y_2y; 021yzs; 013t9y; 039x1k; 06t8b; ... >> query: (?x4408, ?x2660) <- location(?x4408, ?x739), nominated_for(?x4408, ?x2660) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04yqlk actor! 02_1ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 56.000 0.088 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #14801-01b30l PRED entity: 01b30l PRED relation: profession! PRED expected values: 02jg92 => 58 concepts (13 used for prediction) PRED predicted values (max 10 best out of 4087): 02fybl (0.71 #19210, 0.60 #31875, 0.60 #14989), 0473q (0.71 #19235, 0.60 #15014, 0.53 #31900), 014q2g (0.71 #17697, 0.60 #13476, 0.53 #30362), 02cx90 (0.71 #18245, 0.60 #14024, 0.53 #30910), 03f1zhf (0.71 #20092, 0.60 #15871, 0.53 #32757), 0ddkf (0.71 #19105, 0.60 #14884, 0.50 #10663), 01ydzx (0.71 #19076, 0.60 #14855, 0.50 #10634), 01w02sy (0.71 #17803, 0.60 #13582, 0.50 #9361), 0l12d (0.71 #17331, 0.60 #13110, 0.50 #8889), 0161c2 (0.71 #17805, 0.60 #13584, 0.50 #9363) >> Best rule #19210 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0dz3r; >> query: (?x5917, 02fybl) <- profession(?x2492, ?x5917), profession(?x1660, ?x5917), ?x2492 = 01tp5bj, award(?x1660, ?x567), friend(?x1660, ?x105), role(?x1660, ?x212) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #9076 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0nbcg; *> query: (?x5917, 02jg92) <- profession(?x6626, ?x5917), profession(?x2492, ?x5917), profession(?x642, ?x5917), ?x2492 = 01tp5bj, specialization_of(?x5917, ?x1183), ?x642 = 032t2z, artists(?x505, ?x6626) *> conf = 0.50 ranks of expected_values: 193 EVAL 01b30l profession! 02jg92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 58.000 13.000 0.714 http://example.org/people/person/profession #14800-02qk2d5 PRED entity: 02qk2d5 PRED relation: team! PRED expected values: 0bzrxn 0b_770 0b_734 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 7): 0b_6lb (0.76 #173, 0.67 #280, 0.67 #273), 0f9rw9 (0.76 #173, 0.67 #192, 0.60 #163), 0b_770 (0.76 #173, 0.60 #171, 0.57 #219), 0bzrxn (0.76 #173, 0.50 #279, 0.50 #272), 0b_734 (0.76 #173, 0.50 #241, 0.50 #97), 0b_6h7 (0.76 #173, 0.50 #278, 0.50 #175), 0b_6jz (0.76 #173, 0.50 #174, 0.42 #277) >> Best rule #173 for best value: >> intensional similarity = 29 >> extensional distance = 3 >> proper extension: 026xxv_; >> query: (?x9576, ?x4803) <- team(?x12162, ?x9576), team(?x10673, ?x9576), team(?x10594, ?x9576), team(?x10441, ?x9576), team(?x8992, ?x9576), team(?x6002, ?x9576), team(?x5897, ?x9576), ?x6002 = 0cc8q3, ?x5897 = 0b_6rk, ?x10673 = 0b_6mr, team(?x12162, ?x6803), team(?x12162, ?x6003), team(?x12162, ?x5551), team(?x12162, ?x4938), team(?x12162, ?x4369), team(?x12162, ?x3798), team(?x12162, ?x2303), colors(?x9576, ?x9464), ?x4938 = 027yf83, ?x5551 = 02pjzvh, ?x3798 = 02ptzz0, ?x10594 = 0b_756, ?x6003 = 02py8_w, ?x10441 = 0b_71r, ?x6803 = 03by7wc, ?x8992 = 0b_6s7, locations(?x12162, ?x1719), ?x4369 = 02pqcfz, team(?x4803, ?x2303) >> conf = 0.76 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 4, 5 EVAL 02qk2d5 team! 0b_734 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 69.000 0.764 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02qk2d5 team! 0b_770 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 69.000 0.764 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02qk2d5 team! 0bzrxn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 69.000 0.764 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #14799-047sgz4 PRED entity: 047sgz4 PRED relation: award! PRED expected values: 03h610 09swkk => 63 concepts (21 used for prediction) PRED predicted values (max 10 best out of 3339): 0146pg (0.84 #3383, 0.81 #20296, 0.79 #40598), 01vvycq (0.64 #10296, 0.58 #13678, 0.33 #149), 05fnl9 (0.57 #7192, 0.09 #20723, 0.08 #27491), 01rzqj (0.57 #7694, 0.09 #21225, 0.08 #27993), 09r9dp (0.57 #7819, 0.07 #21350, 0.06 #28118), 01dw9z (0.50 #4106, 0.16 #24404, 0.14 #34555), 02l3_5 (0.50 #5734, 0.12 #22647, 0.11 #29415), 0161h5 (0.50 #6425, 0.11 #23338, 0.10 #19953), 01vs_v8 (0.47 #14115, 0.46 #10733, 0.30 #64876), 0jmj (0.43 #8004, 0.18 #21535, 0.16 #28303) >> Best rule #3383 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 025m8y; >> query: (?x2448, ?x669) <- award(?x3962, ?x2448), ?x3962 = 01vrkdt, ceremony(?x2448, ?x1265), award_winner(?x2448, ?x669), nominated_for(?x2448, ?x2447) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #38660 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 05qck; 0d085; *> query: (?x2448, 09swkk) <- award_winner(?x2448, ?x669), instrumentalists(?x316, ?x669), music(?x670, ?x669), award_winner(?x669, ?x4850) *> conf = 0.09 ranks of expected_values: 879, 2121 EVAL 047sgz4 award! 09swkk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 63.000 21.000 0.837 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 047sgz4 award! 03h610 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 21.000 0.837 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14798-016wvy PRED entity: 016wvy PRED relation: role PRED expected values: 07_l6 => 178 concepts (108 used for prediction) PRED predicted values (max 10 best out of 126): 05r5c (0.69 #941, 0.58 #2604, 0.54 #3642), 0342h (0.55 #5415, 0.50 #1143, 0.49 #8330), 02sgy (0.45 #1145, 0.45 #731, 0.40 #524), 07y_7 (0.41 #725, 0.32 #5515, 0.32 #7700), 018vs (0.40 #221, 0.33 #9464, 0.33 #311), 05842k (0.40 #285, 0.27 #1217, 0.27 #2255), 01vdm0 (0.37 #7522, 0.32 #5023, 0.29 #6272), 05148p4 (0.33 #9464, 0.33 #311, 0.32 #9463), 0l14qv (0.32 #7495, 0.25 #6245, 0.22 #4996), 042v_gx (0.30 #527, 0.28 #6769, 0.27 #7499) >> Best rule #941 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 01m7f5r; >> query: (?x10144, 05r5c) <- profession(?x10144, ?x131), student(?x6127, ?x10144), people(?x6736, ?x10144), music(?x6967, ?x10144), role(?x10144, ?x74) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #7595 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 217 *> proper extension: 06br6t; *> query: (?x10144, ?x75) <- role(?x10144, ?x74), artists(?x3734, ?x10144), role(?x1436, ?x74), role(?x75, ?x74), ?x1436 = 0xzly *> conf = 0.06 ranks of expected_values: 66 EVAL 016wvy role 07_l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 178.000 108.000 0.688 http://example.org/music/artist/track_contributions./music/track_contribution/role #14797-05728w1 PRED entity: 05728w1 PRED relation: award_nominee PRED expected values: 0fd6qb => 119 concepts (52 used for prediction) PRED predicted values (max 10 best out of 777): 0fd6qb (0.81 #107820, 0.81 #98442, 0.81 #114856), 058z1hb (0.81 #107820, 0.81 #98442, 0.81 #114856), 05v1sb (0.44 #44530, 0.38 #8020, 0.20 #107821), 0520r2x (0.44 #44530, 0.38 #7062, 0.16 #119547), 051ysmf (0.44 #44530, 0.25 #9359, 0.20 #7015), 0579tg2 (0.44 #44530, 0.25 #9342, 0.20 #107821), 05218gr (0.44 #44530, 0.25 #7524, 0.16 #119547), 04z_x4v (0.44 #44530, 0.25 #8992, 0.16 #119547), 05728w1 (0.44 #44530, 0.23 #117200, 0.16 #119547), 057bc6m (0.44 #44530, 0.20 #15922, 0.20 #107821) >> Best rule #107820 for best value: >> intensional similarity = 4 >> extensional distance = 1124 >> proper extension: 01vrkdt; 02b29; 08cn_n; 095zvfg; 033jj1; 06jz0; >> query: (?x2778, ?x10835) <- award_winner(?x4457, ?x2778), award_nominee(?x10835, ?x2778), nominated_for(?x2778, ?x2779), award_winner(?x10835, ?x11330) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 05728w1 award_nominee 0fd6qb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 52.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14796-0194zl PRED entity: 0194zl PRED relation: language PRED expected values: 02h40lc => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.91 #358, 0.90 #1317, 0.90 #1019), 04306rv (0.25 #5, 0.17 #123, 0.17 #64), 04h9h (0.25 #43, 0.17 #161, 0.17 #102), 06b_j (0.25 #23, 0.17 #82, 0.08 #141), 01r2l (0.25 #25, 0.17 #84, 0.01 #440), 03x42 (0.25 #50, 0.17 #109, 0.01 #465), 064_8sq (0.17 #140, 0.17 #555, 0.16 #496), 06nm1 (0.15 #247, 0.11 #544, 0.10 #188), 02bjrlw (0.08 #119, 0.08 #1138, 0.07 #654), 06mp7 (0.08 #134, 0.03 #431, 0.02 #490) >> Best rule #358 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 053tj7; >> query: (?x4963, 02h40lc) <- genre(?x4963, ?x1316), film_release_distribution_medium(?x4963, ?x81), ?x1316 = 017fp, film(?x166, ?x4963) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0194zl language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.908 http://example.org/film/film/language #14795-01p7b6b PRED entity: 01p7b6b PRED relation: instrumentalists! PRED expected values: 06ch55 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 52): 0342h (0.31 #2676, 0.25 #2409, 0.24 #1696), 05r5c (0.31 #365, 0.30 #1522, 0.30 #1700), 05148p4 (0.19 #1535, 0.19 #1624, 0.18 #1713), 018vs (0.12 #2596, 0.10 #2775, 0.10 #2685), 07gql (0.11 #400, 0.10 #489, 0.10 #756), 07y_7 (0.11 #180, 0.07 #892, 0.07 #536), 026t6 (0.10 #982, 0.08 #1071, 0.08 #1160), 03qjg (0.09 #2724, 0.09 #2457, 0.06 #2635), 02hnl (0.09 #2707, 0.08 #2440, 0.07 #1549), 0l14md (0.09 #542, 0.08 #2590, 0.08 #1165) >> Best rule #2676 for best value: >> intensional similarity = 5 >> extensional distance = 383 >> proper extension: 02mslq; 05crg7; 0dtd6; 03fbc; 03yf3z; 014hr0; 0249kn; 017j6; 02lbrd; 01l03w2; ... >> query: (?x10146, 0342h) <- award(?x10146, ?x7099), award(?x5391, ?x7099), award(?x4850, ?x7099), ?x4850 = 016szr, instrumentalists(?x212, ?x5391) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #707 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 07m4c; *> query: (?x10146, 06ch55) <- music(?x1255, ?x10146), nominated_for(?x1862, ?x1255), ?x1862 = 0gr51 *> conf = 0.06 ranks of expected_values: 14 EVAL 01p7b6b instrumentalists! 06ch55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 83.000 83.000 0.306 http://example.org/music/instrument/instrumentalists #14794-0395lw PRED entity: 0395lw PRED relation: performance_role! PRED expected values: 07_3qd => 87 concepts (56 used for prediction) PRED predicted values (max 10 best out of 989): 0f0y8 (0.60 #999, 0.60 #874, 0.38 #2512), 01vn35l (0.43 #2295, 0.33 #3053, 0.29 #5452), 02rn_bj (0.40 #1098, 0.38 #2864, 0.33 #1856), 01vrncs (0.40 #1136, 0.33 #1767, 0.33 #1641), 01vsy95 (0.40 #1169, 0.33 #1800, 0.33 #1674), 06h2w (0.33 #316, 0.20 #1445, 0.20 #1193), 01r0t_j (0.29 #2234, 0.29 #2109, 0.25 #471), 05qhnq (0.29 #2464, 0.29 #2339, 0.25 #2719), 04s5_s (0.29 #2507, 0.29 #2382, 0.25 #2762), 043c4j (0.29 #2345, 0.29 #2097, 0.22 #3103) >> Best rule #999 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 03t22m; >> query: (?x1432, ?x120) <- role(?x1332, ?x1432), role(?x1148, ?x1432), role(?x885, ?x1432), role(?x316, ?x1432), role(?x120, ?x1432), ?x316 = 05r5c, ?x1332 = 03qlv7, role(?x1432, ?x615), performance_role(?x3869, ?x1148), performance_role(?x10989, ?x1432), group(?x1432, ?x3516), role(?x75, ?x885), ?x120 = 0f0y8, role(?x317, ?x1148), role(?x885, ?x569), group(?x885, ?x5838) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2271 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 5 *> proper extension: 013y1f; *> query: (?x1432, 07_3qd) <- role(?x3716, ?x1432), role(?x3328, ?x1432), role(?x3161, ?x1432), role(?x2310, ?x1432), role(?x1969, ?x1432), role(?x1574, ?x1432), role(?x1332, ?x1432), role(?x885, ?x1432), ?x885 = 0dwtp, role(?x654, ?x3328), role(?x1432, ?x615), ?x1574 = 0l15bq, ?x1332 = 03qlv7, ?x3716 = 03gvt, ?x1969 = 04rzd, performance_role(?x1432, ?x1433), ?x2310 = 0gghm, ?x3161 = 01v1d8, role(?x120, ?x1432) *> conf = 0.29 ranks of expected_values: 15 EVAL 0395lw performance_role! 07_3qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 87.000 56.000 0.600 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #14793-07kjk7c PRED entity: 07kjk7c PRED relation: award! PRED expected values: 0p50v => 52 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2641): 04y8r (0.81 #16825, 0.80 #10095, 0.78 #33654), 02645b (0.44 #7515, 0.33 #4150, 0.05 #17610), 02ldv0 (0.33 #5253, 0.33 #1888, 0.13 #23557), 0fvf9q (0.33 #6753, 0.33 #3388, 0.13 #23557), 016gkf (0.33 #4935, 0.33 #1570, 0.11 #8300), 0127m7 (0.33 #7375, 0.33 #645, 0.11 #17470), 0bs8d (0.33 #8299, 0.33 #4934, 0.05 #18394), 05qd_ (0.33 #6931, 0.33 #3566, 0.05 #33855), 0jmj (0.33 #1228, 0.25 #11323, 0.23 #14688), 04cw0j (0.33 #4229, 0.22 #7594, 0.13 #23557) >> Best rule #16825 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 09v7wsg; >> query: (?x7850, ?x1367) <- award_winner(?x7850, ?x1367), award(?x293, ?x7850), ceremony(?x7850, ?x5585), ?x5585 = 03nnm4t >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #5754 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 01l78d; *> query: (?x7850, 0p50v) <- award_winner(?x7850, ?x10631), award(?x2733, ?x7850), ?x2733 = 0hskw, ?x10631 = 01nx_8 *> conf = 0.33 ranks of expected_values: 82 EVAL 07kjk7c award! 0p50v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 52.000 18.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14792-0cx7f PRED entity: 0cx7f PRED relation: artists PRED expected values: 0qf3p 02vgh 04kjrv 017g21 0326tc 02hzz 04mky3 01p0w_ => 62 concepts (30 used for prediction) PRED predicted values (max 10 best out of 992): 095x_ (0.71 #5785, 0.56 #6803, 0.50 #2728), 05563d (0.67 #4369, 0.50 #3350, 0.50 #2331), 067mj (0.67 #4171, 0.50 #3152, 0.50 #2133), 01dw_f (0.57 #5738, 0.50 #1663, 0.33 #6756), 08w4pm (0.57 #5783, 0.50 #1708, 0.33 #6801), 018gm9 (0.57 #5486, 0.50 #1411, 0.22 #6504), 01wj18h (0.56 #12483, 0.55 #11463, 0.33 #6367), 02cw1m (0.56 #6942, 0.50 #1849, 0.43 #5924), 03j24kf (0.56 #6507, 0.50 #3451, 0.33 #4470), 0jsg0m (0.56 #6745, 0.50 #3689, 0.33 #4708) >> Best rule #5785 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 05bt6j; 016ybr; 02qm5j; >> query: (?x9063, 095x_) <- artists(?x9063, ?x10257), artists(?x9063, ?x6469), artists(?x9063, ?x642), role(?x642, ?x228), instrumentalists(?x74, ?x642), ?x10257 = 01v0sxx, role(?x642, ?x1437), ?x6469 = 04bgy >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4757 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 03_d0; *> query: (?x9063, 0326tc) <- artists(?x9063, ?x10502), artists(?x9063, ?x6225), artists(?x9063, ?x642), ?x642 = 032t2z, profession(?x6225, ?x131), artist(?x6672, ?x10502), role(?x6225, ?x227) *> conf = 0.50 ranks of expected_values: 12, 129, 158, 196, 237, 270, 284, 289 EVAL 0cx7f artists 01p0w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 30.000 0.714 http://example.org/music/genre/artists EVAL 0cx7f artists 04mky3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 30.000 0.714 http://example.org/music/genre/artists EVAL 0cx7f artists 02hzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 30.000 0.714 http://example.org/music/genre/artists EVAL 0cx7f artists 0326tc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 62.000 30.000 0.714 http://example.org/music/genre/artists EVAL 0cx7f artists 017g21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 30.000 0.714 http://example.org/music/genre/artists EVAL 0cx7f artists 04kjrv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 30.000 0.714 http://example.org/music/genre/artists EVAL 0cx7f artists 02vgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 62.000 30.000 0.714 http://example.org/music/genre/artists EVAL 0cx7f artists 0qf3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 62.000 30.000 0.714 http://example.org/music/genre/artists #14791-0f8l9c PRED entity: 0f8l9c PRED relation: nationality! PRED expected values: 0fw2d3 0ct9_ 04093 014g9y => 242 concepts (148 used for prediction) PRED predicted values (max 10 best out of 4019): 06cgy (0.48 #391757, 0.18 #56351, 0.17 #16379), 03zyvw (0.48 #391757, 0.17 #17030, 0.14 #37016), 0f2df (0.48 #391757, 0.11 #44364, 0.09 #56356), 01j5sv (0.48 #391757, 0.09 #59338, 0.08 #67332), 01fwj8 (0.48 #391757, 0.06 #96360, 0.03 #192298), 04093 (0.48 #391757), 044kwr (0.29 #31441, 0.17 #19450, 0.14 #39436), 0gv2r (0.29 #30022, 0.17 #18031, 0.14 #38017), 01jpmpv (0.29 #28955, 0.17 #16964, 0.14 #36950), 0149xx (0.29 #29525, 0.17 #17534, 0.14 #37520) >> Best rule #391757 for best value: >> intensional similarity = 2 >> extensional distance = 55 >> proper extension: 01vfwd; 02bd41; >> query: (?x789, ?x598) <- geographic_distribution(?x6734, ?x789), people(?x6734, ?x598) >> conf = 0.48 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6, 56, 734, 3224 EVAL 0f8l9c nationality! 014g9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 242.000 148.000 0.482 http://example.org/people/person/nationality EVAL 0f8l9c nationality! 04093 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 242.000 148.000 0.482 http://example.org/people/person/nationality EVAL 0f8l9c nationality! 0ct9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 242.000 148.000 0.482 http://example.org/people/person/nationality EVAL 0f8l9c nationality! 0fw2d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 242.000 148.000 0.482 http://example.org/people/person/nationality #14790-0dq9wx PRED entity: 0dq9wx PRED relation: film PRED expected values: 026hxwx => 231 concepts (157 used for prediction) PRED predicted values (max 10 best out of 1211): 03l6q0 (0.33 #544, 0.05 #27424, 0.05 #32800), 01l_pn (0.17 #968, 0.15 #2760, 0.10 #27848), 02ryz24 (0.17 #469, 0.09 #5845, 0.05 #72149), 035s95 (0.17 #341, 0.08 #20053, 0.08 #2133), 04gv3db (0.17 #754, 0.06 #47346, 0.05 #22258), 0dt8xq (0.17 #872, 0.04 #9832, 0.03 #13416), 02_1sj (0.17 #80, 0.03 #10832, 0.02 #26960), 02j69w (0.17 #802, 0.03 #11554, 0.02 #27682), 020bv3 (0.17 #319, 0.02 #23615, 0.02 #28991), 026lgs (0.17 #940, 0.02 #27820, 0.02 #38572) >> Best rule #544 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0993r; >> query: (?x12047, 03l6q0) <- vacationer(?x4151, ?x12047), participant(?x12047, ?x9374), participant(?x12047, ?x8376), ?x4151 = 0r0m6 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8317 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 01vvycq; *> query: (?x12047, 026hxwx) <- celebrity(?x12047, ?x9374), participant(?x8376, ?x12047), participant(?x2352, ?x9374), category(?x9374, ?x134) *> conf = 0.04 ranks of expected_values: 380 EVAL 0dq9wx film 026hxwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 231.000 157.000 0.333 http://example.org/film/actor/film./film/performance/film #14789-0pqzh PRED entity: 0pqzh PRED relation: student! PRED expected values: 014mlp => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 15): 014mlp (0.22 #847, 0.22 #766, 0.20 #1127), 019v9k (0.20 #190, 0.11 #130, 0.09 #430), 0bkj86 (0.18 #369, 0.13 #389, 0.11 #790), 02_xgp2 (0.17 #394, 0.14 #374, 0.12 #674), 04zx3q1 (0.13 #762, 0.09 #362, 0.09 #783), 02h4rq6 (0.11 #123, 0.09 #383, 0.07 #781), 013zdg (0.11 #128, 0.06 #308, 0.05 #1129), 016t_3 (0.10 #164, 0.04 #764, 0.04 #464), 03bwzr4 (0.08 #255, 0.08 #275, 0.03 #635), 028dcg (0.08 #298, 0.07 #478, 0.07 #781) >> Best rule #847 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 01xyt7; >> query: (?x11404, 014mlp) <- company(?x11404, ?x13554), place_of_birth(?x11404, ?x3786), award_winner(?x3486, ?x11404), company(?x11696, ?x13554) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pqzh student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 200.000 200.000 0.224 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #14788-035xwd PRED entity: 035xwd PRED relation: films! PRED expected values: 0g1x2_ => 82 concepts (51 used for prediction) PRED predicted values (max 10 best out of 56): 081pw (0.10 #315, 0.07 #628, 0.04 #786), 04jjy (0.10 #319, 0.04 #790, 0.02 #2514), 0kcc7 (0.10 #456, 0.04 #927), 0mz2 (0.10 #454, 0.04 #925), 0hkt6 (0.10 #432, 0.04 #903), 018jz (0.10 #354, 0.04 #825), 07_m9_ (0.10 #350, 0.04 #821), 0cm2xh (0.08 #830, 0.04 #989, 0.03 #1301), 05489 (0.07 #521, 0.04 #1932, 0.03 #2089), 054yc0 (0.07 #603, 0.04 #1076, 0.02 #1232) >> Best rule #315 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0c0nhgv; >> query: (?x796, 081pw) <- film(?x2499, ?x796), ?x2499 = 0c6qh, genre(?x796, ?x53), written_by(?x796, ?x8950) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #4916 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 401 *> proper extension: 03kq98; *> query: (?x796, 0g1x2_) <- titles(?x53, ?x796), ?x53 = 07s9rl0 *> conf = 0.02 ranks of expected_values: 39 EVAL 035xwd films! 0g1x2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 82.000 51.000 0.100 http://example.org/film/film_subject/films #14787-05sns6 PRED entity: 05sns6 PRED relation: genre PRED expected values: 0vgkd => 109 concepts (93 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.71 #355, 0.71 #237, 0.69 #473), 03k9fj (0.57 #128, 0.43 #364, 0.43 #246), 01hmnh (0.57 #251, 0.43 #133, 0.29 #369), 04xvlr (0.40 #2, 0.20 #1064, 0.20 #2718), 03bxz7 (0.40 #53, 0.14 #289, 0.12 #11009), 0lsxr (0.30 #597, 0.23 #2605, 0.23 #1307), 06n90 (0.28 #3791, 0.23 #1311, 0.23 #8529), 0vgkd (0.25 #599, 0.14 #127, 0.12 #11009), 0219x_ (0.21 #378, 0.15 #1560, 0.15 #968), 0hn10 (0.20 #8, 0.12 #11009, 0.08 #1190) >> Best rule #355 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 02q8ms8; 02qk3fk; 07jqjx; >> query: (?x4269, 07s9rl0) <- film(?x963, ?x4269), ?x963 = 04f525m, featured_film_locations(?x4269, ?x739), country(?x4269, ?x94) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #599 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 06z8s_; *> query: (?x4269, 0vgkd) <- film_crew_role(?x4269, ?x137), film(?x2499, ?x4269), genre(?x4269, ?x225), ?x2499 = 0c6qh *> conf = 0.25 ranks of expected_values: 8 EVAL 05sns6 genre 0vgkd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 109.000 93.000 0.714 http://example.org/film/film/genre #14786-02_t6d PRED entity: 02_t6d PRED relation: sport PRED expected values: 02vx4 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 59): 02vx4 (0.93 #360, 0.93 #340, 0.93 #194), 0z74 (0.27 #986, 0.26 #976, 0.23 #966), 0jm_ (0.21 #490, 0.18 #555, 0.17 #575), 03tmr (0.15 #516, 0.14 #292, 0.13 #498), 09xp_ (0.13 #79, 0.10 #410, 0.04 #597), 018jz (0.13 #502, 0.13 #520, 0.13 #624), 018w8 (0.11 #501, 0.11 #519, 0.10 #623), 039yzs (0.03 #626, 0.03 #504, 0.03 #522), 09w1n (0.02 #358, 0.02 #348, 0.02 #183), 09f6b (0.02 #358, 0.02 #348, 0.02 #37) >> Best rule #360 for best value: >> intensional similarity = 12 >> extensional distance = 44 >> proper extension: 0264v8r; >> query: (?x10189, 02vx4) <- position(?x10189, ?x63), position(?x10189, ?x60), ?x60 = 02nzb8, teams(?x3040, ?x10189), ?x63 = 02sdk9v, team(?x530, ?x10189), team(?x203, ?x10189), adjoins(?x774, ?x3040), ?x530 = 02_j1w, team(?x203, ?x14073), position(?x470, ?x203), ?x14073 = 0h3c3g >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_t6d sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.935 http://example.org/sports/sports_team/sport #14785-05w88j PRED entity: 05w88j PRED relation: award_winner! PRED expected values: 07y_p6 0bxs_d => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 100): 013b2h (0.25 #80, 0.04 #1760, 0.04 #4560), 09q_6t (0.18 #7281, 0.16 #148, 0.02 #1128), 07y_p6 (0.18 #7281, 0.05 #238, 0.02 #378), 07z31v (0.18 #7281, 0.05 #171, 0.02 #2831), 0bxs_d (0.18 #7281, 0.01 #1795, 0.01 #4595), 0275n3y (0.11 #215, 0.03 #6515, 0.03 #1195), 09qftb (0.11 #253, 0.02 #1233, 0.02 #4593), 07y9ts (0.05 #208, 0.04 #348, 0.02 #1748), 09g90vz (0.05 #264, 0.03 #4604, 0.03 #6564), 02q690_ (0.05 #205, 0.03 #1745, 0.03 #4545) >> Best rule #80 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 02__ww; >> query: (?x9704, 013b2h) <- gender(?x9704, ?x231), award_nominee(?x9704, ?x6576), ?x6576 = 01vw917 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #7281 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1643 *> proper extension: 04f525m; 024rbz; 0cjdk; 027_tg; 09mfvx; 024rdh; 05gnf; 02jkkv; 01j7pt; 01zcrv; ... *> query: (?x9704, ?x747) <- nominated_for(?x9704, ?x782), award_winner(?x782, ?x1343), honored_for(?x747, ?x782) *> conf = 0.18 ranks of expected_values: 3, 5 EVAL 05w88j award_winner! 0bxs_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 101.000 101.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05w88j award_winner! 07y_p6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 101.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14784-034r25 PRED entity: 034r25 PRED relation: country PRED expected values: 03rt9 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 35): 0345h (0.42 #4627, 0.40 #4506, 0.36 #4871), 02jx1 (0.42 #4627, 0.40 #4506, 0.36 #4871), 0chghy (0.40 #4506, 0.36 #4871, 0.35 #2521), 0j5g9 (0.40 #4506, 0.36 #4871, 0.35 #2521), 0f8l9c (0.29 #77, 0.11 #1280, 0.10 #1340), 01mjq (0.29 #93, 0.06 #152, 0.05 #270), 06mkj (0.25 #39, 0.03 #394, 0.03 #1116), 03k9fj (0.07 #1140, 0.07 #3420, 0.06 #2221), 04xvlr (0.07 #1140, 0.07 #3420, 0.06 #2221), 03rjj (0.06 #124, 0.05 #664, 0.05 #785) >> Best rule #4627 for best value: >> intensional similarity = 4 >> extensional distance = 1526 >> proper extension: 0979n; >> query: (?x4452, ?x1264) <- language(?x4452, ?x254), film(?x4543, ?x4452), nationality(?x4543, ?x1264), award_winner(?x704, ?x4543) >> conf = 0.42 => this is the best rule for 2 predicted values *> Best rule #490 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 80 *> proper extension: 03ckwzc; 04gknr; 048scx; 0pv3x; 02prw4h; 0168ls; 02r8hh_; 020y73; 011ysn; 04vh83; ... *> query: (?x4452, 03rt9) <- language(?x4452, ?x254), film(?x1634, ?x4452), film_crew_role(?x4452, ?x468), genre(?x4452, ?x3515), ?x3515 = 082gq *> conf = 0.02 ranks of expected_values: 16 EVAL 034r25 country 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 88.000 88.000 0.415 http://example.org/film/film/country #14783-082fr PRED entity: 082fr PRED relation: medal PRED expected values: 02lq67 => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.87 #12, 0.86 #10, 0.81 #13) >> Best rule #12 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 0b90_r; 0d060g; 035qy; 01mjq; 03spz; >> query: (?x2984, 02lq67) <- film_release_region(?x4971, ?x2984), film_release_region(?x3566, ?x2984), language(?x4971, ?x254), ?x3566 = 04jpk2, film(?x269, ?x4971) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 082fr medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #14782-03m2fg PRED entity: 03m2fg PRED relation: religion PRED expected values: 03j6c => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 36): 03j6c (0.66 #156, 0.51 #201, 0.50 #337), 0c8wxp (0.29 #548, 0.20 #908, 0.20 #503), 019cr (0.27 #56, 0.11 #281, 0.10 #417), 0v53x (0.27 #74, 0.10 #29, 0.06 #299), 0flw86 (0.24 #92, 0.14 #137, 0.12 #454), 05sfs (0.20 #3, 0.18 #48, 0.09 #273), 07y1z (0.20 #43, 0.04 #313, 0.03 #449), 0631_ (0.13 #414, 0.13 #278, 0.11 #640), 0kpl (0.12 #371, 0.11 #235, 0.11 #687), 03_gx (0.10 #1321, 0.08 #1141, 0.08 #1824) >> Best rule #156 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0292l3; 040wdl; 02vmzp; 02xfrd; 061zc_; 02tq2r; 038b_x; 08hhm6; 02xgdv; 03vrnh; ... >> query: (?x7778, 03j6c) <- gender(?x7778, ?x231), profession(?x7778, ?x319), award(?x7778, ?x1937), ?x1937 = 03r8tl >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03m2fg religion 03j6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.655 http://example.org/people/person/religion #14781-0h32q PRED entity: 0h32q PRED relation: award PRED expected values: 02z0dfh 0cqgl9 => 136 concepts (115 used for prediction) PRED predicted values (max 10 best out of 292): 02y_j8g (0.73 #44956, 0.72 #4729, 0.70 #35091), 09sb52 (0.47 #434, 0.46 #25664, 0.37 #828), 0cqgl9 (0.46 #3731, 0.09 #5308, 0.07 #12797), 0gs9p (0.42 #7170, 0.14 #37462, 0.12 #45352), 019f4v (0.41 #7159, 0.14 #37462, 0.12 #45352), 040njc (0.39 #7101, 0.14 #37462, 0.13 #32326), 0gq9h (0.33 #7168, 0.15 #22543, 0.14 #75), 0gr51 (0.31 #7190, 0.14 #97, 0.11 #491), 094qd5 (0.30 #3590, 0.19 #4378, 0.17 #34694), 02pqp12 (0.28 #7162, 0.14 #37462, 0.12 #45352) >> Best rule #44956 for best value: >> intensional similarity = 3 >> extensional distance = 2234 >> proper extension: 0d4jl; 099ks0; 03j90; 090gpr; >> query: (?x4398, ?x2523) <- award_winner(?x2523, ?x4398), award(?x7352, ?x2523), award_winner(?x3392, ?x7352) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #3731 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 07fq1y; 01j5ts; 01csvq; 03d_w3h; 03knl; 0h1m9; 0blbxk; 030znt; 02jt1k; 09f0bj; ... *> query: (?x4398, 0cqgl9) <- film(?x4398, ?x80), award(?x4398, ?x1132), ?x1132 = 0bdwft *> conf = 0.46 ranks of expected_values: 3, 22 EVAL 0h32q award 0cqgl9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 136.000 115.000 0.729 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0h32q award 02z0dfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 136.000 115.000 0.729 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14780-0680x0 PRED entity: 0680x0 PRED relation: role! PRED expected values: 0kzy0 0p5mw 050z2 01x6v6 => 55 concepts (38 used for prediction) PRED predicted values (max 10 best out of 981): 050z2 (0.82 #10363, 0.80 #9444, 0.67 #4810), 023l9y (0.73 #10387, 0.70 #9468, 0.67 #4834), 0lzkm (0.73 #10347, 0.70 #9428, 0.50 #5260), 03j24kf (0.70 #9473, 0.64 #10392, 0.50 #5305), 0137g1 (0.67 #4739, 0.64 #10292, 0.60 #9373), 01w806h (0.67 #4763, 0.60 #9397, 0.55 #10316), 0133x7 (0.67 #5392, 0.50 #3540, 0.45 #10479), 01vs4ff (0.64 #10478, 0.60 #9559, 0.50 #9096), 0161sp (0.64 #10303, 0.60 #9384, 0.50 #7532), 01wxdn3 (0.64 #10583, 0.60 #9664, 0.50 #5496) >> Best rule #10363 for best value: >> intensional similarity = 25 >> extensional distance = 9 >> proper extension: 01vj9c; >> query: (?x3409, 050z2) <- role(?x3409, ?x9413), role(?x3409, ?x4975), role(?x3409, ?x3215), role(?x3409, ?x1495), role(?x3409, ?x745), role(?x3409, ?x75), role(?x4975, ?x2158), role(?x4975, ?x1466), ?x3215 = 0bxl5, ?x1495 = 013y1f, role(?x2888, ?x9413), role(?x315, ?x4975), ?x2888 = 02fsn, role(?x11402, ?x3409), role(?x4207, ?x3409), role(?x9413, ?x885), role(?x9413, ?x214), award_nominee(?x11402, ?x4476), ?x2158 = 01dnws, ?x4476 = 01vw20h, nationality(?x4207, ?x94), profession(?x4207, ?x131), ?x1466 = 03bx0bm, ?x75 = 07y_7, role(?x654, ?x745) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 80, 267, 678 EVAL 0680x0 role! 01x6v6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 38.000 0.818 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0680x0 role! 050z2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 38.000 0.818 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0680x0 role! 0p5mw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 55.000 38.000 0.818 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0680x0 role! 0kzy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 38.000 0.818 http://example.org/music/artist/track_contributions./music/track_contribution/role #14779-02f8lw PRED entity: 02f8lw PRED relation: profession PRED expected values: 02hrh1q => 151 concepts (150 used for prediction) PRED predicted values (max 10 best out of 97): 02hrh1q (0.90 #1952, 0.90 #1505, 0.90 #7468), 01d_h8 (0.43 #304, 0.36 #1795, 0.35 #2987), 0dxtg (0.34 #460, 0.31 #2994, 0.31 #3143), 09jwl (0.30 #2404, 0.29 #1659, 0.28 #2106), 03gjzk (0.30 #462, 0.25 #1059, 0.25 #761), 02jknp (0.29 #8355, 0.27 #305, 0.22 #14614), 0d1pc (0.26 #1989, 0.23 #1542, 0.20 #1095), 016z4k (0.21 #1644, 0.17 #750, 0.17 #302), 018gz8 (0.21 #3445, 0.20 #3893, 0.19 #2998), 0np9r (0.20 #916, 0.20 #21, 0.19 #1214) >> Best rule #1952 for best value: >> intensional similarity = 2 >> extensional distance = 78 >> proper extension: 0g476; 06r3p2; >> query: (?x3329, 02hrh1q) <- award(?x3329, ?x154), ?x154 = 05b4l5x >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02f8lw profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 150.000 0.900 http://example.org/people/person/profession #14778-07m77x PRED entity: 07m77x PRED relation: film PRED expected values: 08952r 027j9wd => 106 concepts (75 used for prediction) PRED predicted values (max 10 best out of 674): 05f4vxd (0.57 #46266, 0.46 #26690, 0.41 #8896), 01vnbh (0.46 #26690, 0.41 #8896, 0.41 #110341), 0bvn25 (0.22 #3608, 0.01 #21400, 0.01 #62337), 02825kb (0.17 #4778), 04gv3db (0.13 #4309, 0.03 #58727, 0.03 #129920), 034qzw (0.13 #3891, 0.03 #58727, 0.03 #129920), 07p62k (0.13 #3911, 0.01 #7469), 0872p_c (0.09 #175, 0.08 #1954, 0.03 #58727), 08fn5b (0.09 #693, 0.08 #2472, 0.03 #58727), 026hxwx (0.09 #1140, 0.08 #2919, 0.03 #58727) >> Best rule #46266 for best value: >> intensional similarity = 2 >> extensional distance = 939 >> proper extension: 012c6x; 057hz; 02nfjp; 098n_m; 02wk4d; 01wrcxr; 0jlv5; 0347db; 02mz_6; 0451j; ... >> query: (?x8896, ?x5060) <- award_winner(?x5060, ?x8896), film(?x8896, ?x1318) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4273 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 21 *> proper extension: 04s430; *> query: (?x8896, 08952r) <- film(?x8896, ?x6480), ?x6480 = 02825cv *> conf = 0.09 ranks of expected_values: 14, 600 EVAL 07m77x film 027j9wd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 106.000 75.000 0.574 http://example.org/film/actor/film./film/performance/film EVAL 07m77x film 08952r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 106.000 75.000 0.574 http://example.org/film/actor/film./film/performance/film #14777-0k269 PRED entity: 0k269 PRED relation: nationality PRED expected values: 07ssc => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 17): 09c7w0 (0.81 #1292, 0.77 #6145, 0.77 #4264), 02jx1 (0.60 #132, 0.34 #6838, 0.25 #33), 0d060g (0.34 #6838, 0.05 #1298, 0.04 #802), 0chghy (0.34 #6838, 0.03 #1202, 0.03 #1796), 07ssc (0.10 #413, 0.10 #1702, 0.09 #1900), 03rk0 (0.06 #4705, 0.06 #3318, 0.05 #8175), 0345h (0.03 #4690, 0.02 #1718, 0.02 #529), 03rjj (0.02 #1692, 0.02 #4664, 0.02 #3277), 0f8l9c (0.02 #3294, 0.02 #4681, 0.02 #8250), 03h64 (0.02 #351, 0.02 #451) >> Best rule #1292 for best value: >> intensional similarity = 2 >> extensional distance = 573 >> proper extension: 02w5q6; >> query: (?x3580, 09c7w0) <- participant(?x3580, ?x891), nationality(?x3580, ?x6401) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #413 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 01t2h2; 01v5h; *> query: (?x3580, 07ssc) <- film(?x3580, ?x308), award_nominee(?x3580, ?x489), location_of_ceremony(?x3580, ?x789) *> conf = 0.10 ranks of expected_values: 5 EVAL 0k269 nationality 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 87.000 0.810 http://example.org/people/person/nationality #14776-07tw_b PRED entity: 07tw_b PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #74, 0.84 #101, 0.83 #85), 02nxhr (0.24 #341, 0.04 #55, 0.04 #65), 07c52 (0.24 #341, 0.03 #28, 0.03 #18), 07z4p (0.24 #341, 0.03 #20, 0.03 #68) >> Best rule #74 for best value: >> intensional similarity = 4 >> extensional distance = 440 >> proper extension: 0413cff; >> query: (?x4110, 029j_) <- language(?x4110, ?x254), titles(?x307, ?x4110), featured_film_locations(?x4110, ?x739), citytown(?x166, ?x739) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tw_b film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.848 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #14775-0kc6 PRED entity: 0kc6 PRED relation: profession PRED expected values: 0n1h => 97 concepts (62 used for prediction) PRED predicted values (max 10 best out of 124): 0cbd2 (0.57 #1000, 0.57 #6, 0.56 #4835), 03gjzk (0.43 #154, 0.43 #12, 0.36 #864), 018gz8 (0.32 #2286, 0.32 #866, 0.29 #2996), 0n1h (0.30 #8384, 0.30 #8383, 0.26 #4261), 01l5t6 (0.30 #8384, 0.30 #8383, 0.26 #4261), 09jwl (0.29 #2998, 0.26 #2004, 0.24 #726), 015h31 (0.29 #24, 0.03 #6678, 0.03 #592), 02krf9 (0.27 #449, 0.14 #23, 0.11 #875), 05z96 (0.26 #747, 0.22 #321, 0.21 #1599), 02hv44_ (0.22 #335, 0.21 #193, 0.20 #3459) >> Best rule #1000 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 03g5jw; >> query: (?x10075, 0cbd2) <- influenced_by(?x10075, ?x1946), influenced_by(?x1946, ?x4292), ?x4292 = 0zm1 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #8384 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 494 *> proper extension: 0lhn5; *> query: (?x10075, ?x987) <- influenced_by(?x10075, ?x1946), profession(?x1946, ?x987) *> conf = 0.30 ranks of expected_values: 4 EVAL 0kc6 profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 62.000 0.571 http://example.org/people/person/profession #14774-0bdw1g PRED entity: 0bdw1g PRED relation: award! PRED expected values: 03j63k => 46 concepts (24 used for prediction) PRED predicted values (max 10 best out of 784): 0180mw (0.50 #667, 0.28 #17236, 0.27 #16221), 02k_4g (0.50 #70, 0.28 #17236, 0.27 #16221), 01j67j (0.50 #257, 0.27 #15206, 0.23 #2026), 03_8kz (0.50 #906, 0.07 #2933, 0.07 #1919), 0ddd0gc (0.28 #17236, 0.27 #16221, 0.27 #15206), 02rzdcp (0.28 #17236, 0.27 #16221, 0.27 #15206), 01g03q (0.28 #17236, 0.27 #16221, 0.27 #15206), 01xr2s (0.28 #17236, 0.27 #16221, 0.27 #15206), 0dsx3f (0.28 #17236, 0.27 #16221, 0.27 #15206), 02rcwq0 (0.28 #17236, 0.27 #16221, 0.27 #15206) >> Best rule #667 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0m7yy; >> query: (?x686, 0180mw) <- award_winner(?x686, ?x376), award(?x8627, ?x686), ?x8627 = 0m123 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #728 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0m7yy; *> query: (?x686, 03j63k) <- award_winner(?x686, ?x376), award(?x8627, ?x686), ?x8627 = 0m123 *> conf = 0.25 ranks of expected_values: 45 EVAL 0bdw1g award! 03j63k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 46.000 24.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #14773-046lt PRED entity: 046lt PRED relation: influenced_by PRED expected values: 0ph2w => 85 concepts (49 used for prediction) PRED predicted values (max 10 best out of 313): 01hmk9 (0.21 #4110, 0.19 #3246, 0.19 #3678), 0ph2w (0.18 #2711, 0.13 #4009, 0.07 #19038), 01s7qqw (0.18 #2755, 0.10 #4053, 0.07 #5784), 01wp_jm (0.17 #3367, 0.16 #3799, 0.08 #10287), 081lh (0.15 #3912, 0.11 #3048, 0.11 #3480), 01k9lpl (0.15 #4200, 0.11 #3336, 0.11 #3768), 014zfs (0.15 #3917, 0.08 #9973, 0.07 #12137), 0p_47 (0.14 #3134, 0.14 #3566, 0.13 #3998), 0127xk (0.14 #2979, 0.07 #19038, 0.06 #3413), 015cbq (0.14 #2920, 0.07 #19038, 0.05 #4218) >> Best rule #4110 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 081nh; 011_3s; 04x4s2; 012z8_; 01pjr7; 02pv_d; 0p_jc; >> query: (?x2942, 01hmk9) <- profession(?x2942, ?x353), influenced_by(?x692, ?x2942), currency(?x2942, ?x170) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2711 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 01nfys; 023w9s; *> query: (?x2942, 0ph2w) <- profession(?x2942, ?x353), influenced_by(?x692, ?x2942), participant(?x12255, ?x2942) *> conf = 0.18 ranks of expected_values: 2 EVAL 046lt influenced_by 0ph2w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 49.000 0.205 http://example.org/influence/influence_node/influenced_by #14772-013q07 PRED entity: 013q07 PRED relation: film! PRED expected values: 01pb34 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 3): 09_gdc (0.17 #7, 0.03 #51, 0.02 #23), 01pb34 (0.08 #18, 0.07 #24, 0.06 #72), 01kyvx (0.02 #377, 0.01 #387, 0.01 #463) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03nx8mj; 032sl_; 056xkh; >> query: (?x2218, 09_gdc) <- film(?x1986, ?x2218), film_crew_role(?x2218, ?x137), titles(?x2480, ?x2218), ?x1986 = 0gz5hs >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #18 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 08hmch; 0340hj; 03177r; 02xs6_; 0f4_2k; 01gwk3; 0cqr0q; 03k8th; *> query: (?x2218, 01pb34) <- film(?x71, ?x2218), films(?x11523, ?x2218), film_crew_role(?x2218, ?x137), prequel(?x2218, ?x408) *> conf = 0.08 ranks of expected_values: 2 EVAL 013q07 film! 01pb34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.167 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #14771-05mt_q PRED entity: 05mt_q PRED relation: award PRED expected values: 01by1l 02f72_ => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 315): 01by1l (0.48 #1297, 0.45 #2087, 0.31 #8802), 01bgqh (0.44 #43, 0.42 #1228, 0.40 #2018), 02f777 (0.44 #303, 0.19 #1488, 0.17 #2278), 01c92g (0.40 #1282, 0.36 #2072, 0.12 #8787), 0c4z8 (0.38 #1257, 0.36 #2047, 0.18 #4417), 02f79n (0.33 #333, 0.22 #2308, 0.19 #1518), 05zkcn5 (0.33 #21, 0.17 #1206, 0.16 #1996), 02f73b (0.28 #2255, 0.27 #1465, 0.22 #280), 03qbh5 (0.27 #1385, 0.24 #2175, 0.22 #8890), 09sb52 (0.27 #20186, 0.23 #19001, 0.21 #19791) >> Best rule #1297 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 01vvycq; 02l840; 01w61th; 01vrz41; 09qr6; 01wcp_g; 06w2sn5; 07ss8_; 09hnb; 01wwvc5; ... >> query: (?x1388, 01by1l) <- award(?x1388, ?x3926), ?x3926 = 02f6xy, artists(?x671, ?x1388), award_nominee(?x1388, ?x527) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12 EVAL 05mt_q award 02f72_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 94.000 94.000 0.481 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05mt_q award 01by1l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.481 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14770-02zl4d PRED entity: 02zl4d PRED relation: profession PRED expected values: 03gjzk => 94 concepts (79 used for prediction) PRED predicted values (max 10 best out of 45): 01d_h8 (0.38 #6, 0.36 #154, 0.33 #3410), 03gjzk (0.26 #9624, 0.26 #1642, 0.26 #10809), 0np9r (0.26 #9624, 0.26 #10809, 0.25 #20), 09jwl (0.26 #9624, 0.26 #10809, 0.19 #3866), 02jknp (0.24 #3412, 0.22 #4597, 0.21 #2820), 0cbd2 (0.15 #7260, 0.14 #7704, 0.14 #4744), 018gz8 (0.14 #1792, 0.13 #4012, 0.13 #4309), 0nbcg (0.13 #2251, 0.13 #3879, 0.12 #31), 0dz3r (0.13 #2222, 0.12 #3850, 0.12 #3258), 0kyk (0.12 #29, 0.10 #4766, 0.10 #5506) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 049qx; >> query: (?x11399, 01d_h8) <- award(?x11399, ?x14647), film(?x11399, ?x308), ?x308 = 011yxg >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #9624 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2092 *> proper extension: 025vldk; *> query: (?x11399, ?x319) <- award(?x11399, ?x14647), award_nominee(?x11399, ?x804), profession(?x804, ?x319) *> conf = 0.26 ranks of expected_values: 2 EVAL 02zl4d profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 79.000 0.375 http://example.org/people/person/profession #14769-02dr9j PRED entity: 02dr9j PRED relation: film_distribution_medium PRED expected values: 02nxhr => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.68 #24, 0.63 #69, 0.62 #19), 02nxhr (0.30 #21, 0.26 #66, 0.20 #16), 0dq6p (0.21 #17, 0.18 #67, 0.14 #22), 07z4p (0.02 #20, 0.01 #70) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 0gtsx8c; >> query: (?x7214, 0735l) <- film_crew_role(?x7214, ?x468), film(?x4337, ?x7214), ?x468 = 02r96rf, film_distribution_medium(?x7214, ?x81) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 109 *> proper extension: 0gtsx8c; *> query: (?x7214, 02nxhr) <- film_crew_role(?x7214, ?x468), film(?x4337, ?x7214), ?x468 = 02r96rf, film_distribution_medium(?x7214, ?x81) *> conf = 0.30 ranks of expected_values: 2 EVAL 02dr9j film_distribution_medium 02nxhr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.685 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #14768-01csvq PRED entity: 01csvq PRED relation: religion PRED expected values: 0kpl => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 14): 0c8wxp (0.39 #97, 0.39 #187, 0.23 #6), 0kpl (0.13 #236, 0.08 #326, 0.06 #56), 03_gx (0.13 #330, 0.10 #240, 0.08 #14), 01lp8 (0.05 #1, 0.03 #137, 0.02 #92), 0kq2 (0.04 #244, 0.02 #109, 0.02 #199), 092bf5 (0.04 #16, 0.03 #152, 0.03 #378), 03j6c (0.03 #1601, 0.02 #2233, 0.02 #1285), 0flw86 (0.02 #318, 0.02 #2078, 0.02 #1582), 0n2g (0.02 #13, 0.02 #239, 0.01 #375), 06nzl (0.01 #106, 0.01 #467, 0.01 #196) >> Best rule #97 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 02cg2v; >> query: (?x719, 0c8wxp) <- gender(?x719, ?x514), people(?x1446, ?x719), ?x1446 = 033tf_ >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #236 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 295 *> proper extension: 01p45_v; 04xjp; 0453t; 013v5j; 0gkg6; 0p3r8; 0hgqq; 081k8; 080r3; 06hmd; ... *> query: (?x719, 0kpl) <- profession(?x719, ?x2225), nationality(?x719, ?x94), ?x2225 = 0kyk *> conf = 0.13 ranks of expected_values: 2 EVAL 01csvq religion 0kpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.392 http://example.org/people/person/religion #14767-02_p5w PRED entity: 02_p5w PRED relation: film PRED expected values: 0k2sk 09v3jyg => 128 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1038): 0_7w6 (0.33 #2084, 0.33 #302, 0.29 #5649), 0fgrm (0.33 #2568, 0.14 #6133, 0.07 #22172), 099bhp (0.33 #1613, 0.14 #6960, 0.06 #14088), 0872p_c (0.33 #1957, 0.14 #5522, 0.06 #7304), 047csmy (0.33 #2693, 0.14 #6258, 0.06 #8040), 0cn_b8 (0.33 #2396, 0.14 #5961, 0.05 #11307), 0blpg (0.33 #654, 0.14 #6001, 0.04 #13129), 0k2sk (0.33 #1945, 0.14 #5510, 0.03 #21549), 07x4qr (0.33 #2186, 0.14 #5751, 0.03 #16443), 0g56t9t (0.33 #1792, 0.14 #5357, 0.02 #21396) >> Best rule #2084 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02gf_l; >> query: (?x3758, 0_7w6) <- film(?x3758, ?x11073), film(?x3758, ?x1893), actor(?x7325, ?x3758), ?x1893 = 0jnwx, ?x11073 = 01ry_x >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1945 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 02gf_l; *> query: (?x3758, 0k2sk) <- film(?x3758, ?x11073), film(?x3758, ?x1893), actor(?x7325, ?x3758), ?x1893 = 0jnwx, ?x11073 = 01ry_x *> conf = 0.33 ranks of expected_values: 8, 665 EVAL 02_p5w film 09v3jyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 128.000 68.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 02_p5w film 0k2sk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 128.000 68.000 0.333 http://example.org/film/actor/film./film/performance/film #14766-02mxw0 PRED entity: 02mxw0 PRED relation: film PRED expected values: 09q23x 051ys82 => 117 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1004): 042g97 (0.49 #12468, 0.40 #49876, 0.40 #26718), 024mxd (0.38 #4161, 0.33 #5942, 0.13 #53440), 0qmjd (0.29 #2992, 0.08 #8335), 01k0vq (0.17 #11994, 0.07 #10213, 0.01 #49402), 04y9mm8 (0.17 #11865, 0.02 #26115, 0.01 #18991), 02gpkt (0.17 #11991, 0.01 #51181), 02v8kmz (0.17 #27, 0.14 #1808, 0.08 #7151), 01xdxy (0.17 #1558, 0.13 #53440, 0.12 #67696), 02jkkv (0.17 #1545, 0.07 #10450, 0.03 #12231), 03q0r1 (0.17 #631, 0.03 #48725, 0.03 #50507) >> Best rule #12468 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 01t2h2; 04jb97; >> query: (?x2718, ?x12214) <- film(?x2718, ?x7425), award(?x2718, ?x2192), prequel(?x12214, ?x7425), film_release_region(?x7425, ?x94) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #29530 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 413 *> proper extension: 041mt; 06wvj; 0lgm5; 034bs; 01vsy3q; 03f6fl0; 081k8; 01gj8_; 0130sy; 0d0mbj; ... *> query: (?x2718, 051ys82) <- languages(?x2718, ?x254), location(?x2718, ?x1274), type_of_union(?x2718, ?x566) *> conf = 0.01 ranks of expected_values: 710, 764 EVAL 02mxw0 film 051ys82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 117.000 77.000 0.487 http://example.org/film/actor/film./film/performance/film EVAL 02mxw0 film 09q23x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 117.000 77.000 0.487 http://example.org/film/actor/film./film/performance/film #14765-0cw67g PRED entity: 0cw67g PRED relation: nationality PRED expected values: 09c7w0 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 86): 09c7w0 (0.84 #7016, 0.84 #2506, 0.79 #3611), 059rby (0.33 #10524), 02jx1 (0.11 #2438, 0.10 #5144, 0.10 #3242), 07ssc (0.11 #816, 0.10 #1016, 0.09 #2420), 0ctw_b (0.08 #327, 0.07 #528, 0.07 #728), 0d060g (0.07 #1108, 0.05 #3617, 0.05 #1008), 03rk0 (0.06 #10267, 0.05 #10670, 0.05 #10870), 0chghy (0.04 #611, 0.04 #811, 0.03 #110), 0f8l9c (0.03 #322, 0.02 #523, 0.02 #723), 09q17 (0.02 #6213, 0.02 #8719, 0.02 #8718) >> Best rule #7016 for best value: >> intensional similarity = 2 >> extensional distance = 1440 >> proper extension: 053y0s; 02qjj7; 014dq7; 01m65sp; 01w8n89; 0mj0c; 0kvnn; 0x3r3; 01386_; 034ls; ... >> query: (?x10416, 09c7w0) <- location(?x10416, ?x12820), source(?x12820, ?x958) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cw67g nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.843 http://example.org/people/person/nationality #14764-07kb5 PRED entity: 07kb5 PRED relation: influenced_by! PRED expected values: 0h25 => 104 concepts (37 used for prediction) PRED predicted values (max 10 best out of 357): 0j3v (0.67 #1610, 0.56 #2628, 0.55 #3649), 02wh0 (0.54 #6567, 0.45 #4014, 0.38 #6055), 0h25 (0.50 #3473, 0.50 #414, 0.40 #923), 047g6 (0.50 #2005, 0.40 #3533, 0.38 #6085), 0tfc (0.50 #2012, 0.40 #990, 0.33 #3030), 048cl (0.45 #3862, 0.44 #2841, 0.40 #3351), 039n1 (0.45 #3956, 0.33 #2935, 0.33 #1917), 01dvtx (0.44 #2697, 0.33 #1679, 0.29 #5759), 04z0g (0.40 #3294, 0.25 #235, 0.20 #744), 032l1 (0.36 #6242, 0.26 #6123, 0.20 #3058) >> Best rule #1610 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 05qmj; >> query: (?x712, 0j3v) <- influenced_by(?x7250, ?x712), influenced_by(?x6457, ?x712), influenced_by(?x4547, ?x712), ?x7250 = 03sbs, ?x4547 = 03_hd, type_of_union(?x6457, ?x566), influenced_by(?x118, ?x6457) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3473 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 052h3; 03sbs; 02wh0; 032r1; 0tfc; *> query: (?x712, 0h25) <- influenced_by(?x712, ?x6015), influenced_by(?x3711, ?x712), influenced_by(?x3711, ?x11500), interests(?x712, ?x713), ?x11500 = 0cpvcd, profession(?x3711, ?x353) *> conf = 0.50 ranks of expected_values: 3 EVAL 07kb5 influenced_by! 0h25 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 37.000 0.667 http://example.org/influence/influence_node/influenced_by #14763-02cvp8 PRED entity: 02cvp8 PRED relation: sibling PRED expected values: 030dx5 => 81 concepts (22 used for prediction) PRED predicted values (max 10 best out of 94): 030dx5 (0.86 #116, 0.81 #810, 0.77 #693), 0d3k14 (0.09 #98, 0.05 #329, 0.05 #560), 06hx2 (0.09 #54, 0.05 #285, 0.05 #516), 01n8_g (0.09 #18, 0.02 #711), 02cvp8 (0.05 #445, 0.03 #215), 02j4sk (0.05 #93, 0.03 #324, 0.02 #555), 0194xc (0.05 #88, 0.03 #319, 0.02 #550), 0h32q (0.05 #39, 0.03 #270, 0.02 #501), 0mj0c (0.05 #34, 0.03 #265, 0.02 #496), 02g5bf (0.05 #106, 0.02 #568, 0.01 #799) >> Best rule #116 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 03wd5tk; 0b5x23; >> query: (?x11256, ?x9015) <- nationality(?x11256, ?x94), sibling(?x9015, ?x11256), gender(?x11256, ?x231), people(?x268, ?x11256) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cvp8 sibling 030dx5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 22.000 0.862 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #14762-03mz9r PRED entity: 03mz9r PRED relation: gender PRED expected values: 05zppz => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #27, 0.82 #33, 0.81 #9), 02zsn (0.46 #138, 0.27 #78, 0.27 #80) >> Best rule #27 for best value: >> intensional similarity = 4 >> extensional distance = 206 >> proper extension: 019z7q; 04gcd1; 0bymv; 0lrh; 01h320; 08n9ng; 0d5_f; 0b78hw; 0534v; 0282x; ... >> query: (?x1875, 05zppz) <- profession(?x1875, ?x987), profession(?x1875, ?x353), ?x353 = 0cbd2, ?x987 = 0dxtg >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mz9r gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.865 http://example.org/people/person/gender #14761-01pcq3 PRED entity: 01pcq3 PRED relation: award PRED expected values: 0ck27z => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 260): 05ztrmj (0.36 #180, 0.16 #2574, 0.14 #2175), 04kxsb (0.32 #520, 0.23 #2116, 0.22 #1318), 05p09zm (0.30 #2912, 0.28 #2513, 0.28 #3710), 02w9sd7 (0.27 #166, 0.21 #565, 0.20 #2161), 0789_m (0.26 #417, 0.19 #1215, 0.15 #23145), 03c7tr1 (0.23 #2450, 0.21 #1652, 0.18 #2051), 05b4l5x (0.21 #2400, 0.19 #2799, 0.18 #3597), 0ck27z (0.21 #10063, 0.15 #23145, 0.13 #8467), 057xs89 (0.21 #555, 0.20 #2151, 0.18 #156), 099ck7 (0.21 #661, 0.16 #2257, 0.15 #23145) >> Best rule #180 for best value: >> intensional similarity = 2 >> extensional distance = 9 >> proper extension: 01pw2f1; >> query: (?x843, 05ztrmj) <- participant(?x793, ?x843), diet(?x843, ?x3130) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #10063 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 899 *> proper extension: 01nzs7; *> query: (?x843, 0ck27z) <- nominated_for(?x843, ?x3822), actor(?x3822, ?x1116) *> conf = 0.21 ranks of expected_values: 8 EVAL 01pcq3 award 0ck27z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 98.000 98.000 0.364 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14760-02v703 PRED entity: 02v703 PRED relation: ceremony PRED expected values: 01s695 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 124): 01s695 (0.80 #501, 0.50 #624, 0.43 #250), 092868 (0.50 #123, 0.50 #624, 0.43 #247), 08pc1x (0.50 #122, 0.50 #624, 0.43 #246), 073h1t (0.50 #624, 0.32 #373, 0.29 #499), 09k5jh7 (0.14 #320, 0.05 #445, 0.04 #1941), 0hr6lkl (0.14 #261, 0.05 #386, 0.04 #637), 0clfdj (0.14 #251, 0.05 #376, 0.03 #1872), 0n8_m93 (0.13 #727, 0.10 #1474, 0.10 #1599), 02yvhx (0.13 #690, 0.10 #1437, 0.09 #1562), 02hn5v (0.13 #657, 0.10 #1404, 0.09 #1529) >> Best rule #501 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 0257yf; 0257pw; >> query: (?x7594, 01s695) <- award(?x9220, ?x7594), award_winner(?x725, ?x9220), ceremony(?x7594, ?x139), ?x139 = 05pd94v >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v703 ceremony 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony #14759-012vct PRED entity: 012vct PRED relation: gender PRED expected values: 05zppz => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #7, 0.91 #31, 0.90 #23), 02zsn (0.55 #251, 0.46 #84, 0.46 #268) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 031bf1; 01qnfc; >> query: (?x7232, 05zppz) <- profession(?x7232, ?x2265), profession(?x7232, ?x319), ?x2265 = 0dgd_, ?x319 = 01d_h8 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012vct gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.912 http://example.org/people/person/gender #14758-0gzy02 PRED entity: 0gzy02 PRED relation: film! PRED expected values: 031y07 01m42d0 => 82 concepts (41 used for prediction) PRED predicted values (max 10 best out of 874): 01jpmpv (0.43 #68603, 0.42 #72763, 0.42 #60286), 06kxk2 (0.43 #68603, 0.42 #72763, 0.42 #60286), 041c4 (0.33 #895, 0.02 #7131, 0.01 #54946), 05sq84 (0.33 #236, 0.02 #12709, 0.01 #14788), 09y20 (0.33 #249, 0.02 #14801, 0.02 #54300), 06ltr (0.33 #946, 0.02 #54997, 0.01 #40445), 0l6px (0.33 #387, 0.01 #12860, 0.01 #6623), 013_vh (0.33 #662), 065jlv (0.33 #313), 0134w7 (0.33 #161) >> Best rule #68603 for best value: >> intensional similarity = 4 >> extensional distance = 873 >> proper extension: 0g5qs2k; 069q4f; 07nt8p; 016z9n; 07sp4l; 02tqm5; 0cmc26r; 08sfxj; 0kvgnq; 05q7874; ... >> query: (?x327, ?x3483) <- genre(?x327, ?x53), film(?x9797, ?x327), award_winner(?x327, ?x3483), award_nominee(?x9797, ?x262) >> conf = 0.43 => this is the best rule for 2 predicted values *> Best rule #13489 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 201 *> proper extension: 06mmr; *> query: (?x327, 031y07) <- award(?x327, ?x1443), nominated_for(?x1443, ?x522), ?x522 = 01h7bb, award(?x84, ?x1443) *> conf = 0.01 ranks of expected_values: 520 EVAL 0gzy02 film! 01m42d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 41.000 0.435 http://example.org/film/actor/film./film/performance/film EVAL 0gzy02 film! 031y07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 82.000 41.000 0.435 http://example.org/film/actor/film./film/performance/film #14757-0175tv PRED entity: 0175tv PRED relation: sport PRED expected values: 02vx4 => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.88 #93, 0.88 #120, 0.87 #295), 0z74 (0.49 #146, 0.49 #358, 0.49 #348), 03tmr (0.49 #146, 0.45 #46, 0.44 #212), 0jm_ (0.20 #58, 0.17 #67, 0.15 #251), 018jz (0.13 #253, 0.12 #244, 0.12 #316), 018w8 (0.12 #252, 0.10 #243, 0.10 #59), 039yzs (0.05 #246, 0.05 #255, 0.03 #365), 09xp_ (0.03 #61, 0.02 #70, 0.01 #236) >> Best rule #93 for best value: >> intensional similarity = 13 >> extensional distance = 75 >> proper extension: 0lhp1; 03mqj_; 0gxkm; 017znw; 0196bp; 02_lt; 04ltf; 031zp2; 04mvk7; 03j6_5; ... >> query: (?x8899, 02vx4) <- position(?x8899, ?x530), position(?x8899, ?x60), colors(?x8899, ?x663), teams(?x3622, ?x8899), ?x530 = 02_j1w, position(?x14267, ?x60), position(?x12419, ?x60), position(?x4805, ?x60), team(?x60, ?x10463), ?x14267 = 06vv_6, ?x12419 = 057pq5, ?x10463 = 032498, ?x4805 = 02rqxc >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0175tv sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.883 http://example.org/sports/sports_team/sport #14756-04ns3gy PRED entity: 04ns3gy PRED relation: award_winner! PRED expected values: 07z31v => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 107): 09pnw5 (0.14 #98, 0.12 #6257, 0.06 #1050), 03gyp30 (0.14 #112, 0.12 #6257, 0.05 #2560), 07y_p6 (0.14 #93, 0.12 #6257, 0.04 #1045), 0bx6zs (0.14 #122, 0.12 #6257, 0.04 #1074), 02wzl1d (0.14 #11, 0.05 #691, 0.03 #147), 0hndn2q (0.14 #37, 0.04 #989, 0.04 #717), 0bxs_d (0.14 #110, 0.03 #1062, 0.01 #4054), 0275n3y (0.13 #342, 0.08 #478, 0.04 #2654), 092_25 (0.12 #6257, 0.07 #68, 0.02 #3740), 09gkdln (0.09 #389, 0.05 #525, 0.04 #661) >> Best rule #98 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0sx5w; >> query: (?x9503, 09pnw5) <- gender(?x9503, ?x514), student(?x1368, ?x9503), producer_type(?x9503, ?x632) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #982 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 130 *> proper extension: 0bg539; 02bwc7; 03f0r5w; 05y5fw; 01rlxt; 046mxj; 08n__5; 07g7h2; 02238b; 013pk3; ... *> query: (?x9503, 07z31v) <- award_nominee(?x9503, ?x4328), type_of_union(?x9503, ?x566), producer_type(?x9503, ?x632) *> conf = 0.05 ranks of expected_values: 31 EVAL 04ns3gy award_winner! 07z31v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 88.000 88.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14755-04f0xq PRED entity: 04f0xq PRED relation: place_founded PRED expected values: 059rby => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 43): 02_286 (0.12 #10, 0.11 #401, 0.10 #270), 01_d4 (0.12 #16, 0.08 #407, 0.07 #146), 0y1rf (0.07 #246, 0.05 #640, 0.05 #573), 06pwq (0.05 #1056, 0.05 #463, 0.05 #529), 0d6lp (0.05 #677, 0.04 #22, 0.04 #941), 09c7w0 (0.04 #1, 0.03 #131, 0.03 #391), 0d9jr (0.04 #36, 0.03 #296, 0.03 #361), 030qb3t (0.04 #13, 0.03 #404, 0.03 #2053), 0r6c4 (0.04 #127, 0.03 #257, 0.03 #322), 01sn3 (0.04 #94, 0.03 #224, 0.03 #289) >> Best rule #10 for best value: >> intensional similarity = 7 >> extensional distance = 23 >> proper extension: 0537b; >> query: (?x7471, 02_286) <- list(?x7471, ?x8915), list(?x7471, ?x7472), list(?x7471, ?x5997), ?x7472 = 01ptsx, ?x5997 = 04k4rt, company(?x265, ?x7471), ?x8915 = 01pd60 >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04f0xq place_founded 059rby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 67.000 0.120 http://example.org/organization/organization/place_founded #14754-0hvjr PRED entity: 0hvjr PRED relation: colors PRED expected values: 083jv => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 17): 083jv (0.98 #1261, 0.98 #1014, 0.98 #995), 06fvc (0.51 #1376, 0.44 #596, 0.43 #1511), 01g5v (0.36 #1531, 0.36 #1512, 0.34 #864), 019sc (0.33 #8, 0.29 #373, 0.29 #1888), 0jc_p (0.29 #82, 0.23 #1900, 0.16 #1507), 04d18d (0.26 #1756, 0.18 #1858, 0.16 #77), 067z2v (0.26 #1756, 0.15 #1759, 0.15 #1757), 01l849 (0.23 #1900, 0.18 #1858, 0.16 #1680), 03vtbc (0.23 #1900, 0.07 #1496, 0.06 #641), 038hg (0.18 #1858, 0.16 #77, 0.16 #1507) >> Best rule #1261 for best value: >> intensional similarity = 10 >> extensional distance = 127 >> proper extension: 0b256b; 03x6w8; >> query: (?x3216, 083jv) <- position(?x3216, ?x203), team(?x63, ?x3216), colors(?x3216, ?x8271), colors(?x7247, ?x8271), colors(?x5229, ?x8271), ?x5229 = 07l2m, ?x7247 = 04991x, colors(?x817, ?x8271), position(?x10112, ?x203), ?x10112 = 01kc4s >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hvjr colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.984 http://example.org/sports/sports_team/colors #14753-05_61y PRED entity: 05_61y PRED relation: featured_film_locations PRED expected values: 01crd5 => 130 concepts (70 used for prediction) PRED predicted values (max 10 best out of 112): 02_286 (0.43 #7687, 0.43 #7927, 0.40 #1936), 0cv3w (0.33 #69, 0.17 #786, 0.10 #1986), 030qb3t (0.18 #7706, 0.18 #7946, 0.17 #755), 04jpl (0.17 #725, 0.15 #7676, 0.15 #7916), 02dtg (0.17 #728, 0.09 #1207, 0.06 #1687), 0d6hn (0.17 #893, 0.09 #1372, 0.06 #1852), 0nqv1 (0.17 #889, 0.09 #1368, 0.06 #1848), 0b1t1 (0.17 #882, 0.09 #1361, 0.06 #1841), 01b8w_ (0.17 #874, 0.09 #1353, 0.06 #1833), 04f_d (0.17 #766, 0.09 #1245, 0.06 #1725) >> Best rule #7687 for best value: >> intensional similarity = 6 >> extensional distance = 128 >> proper extension: 02rv_dz; 02mt51; 0hv81; >> query: (?x6767, 02_286) <- genre(?x6767, ?x1014), honored_for(?x3173, ?x6767), featured_film_locations(?x6767, ?x108), month(?x108, ?x1459), citytown(?x127, ?x108), location(?x236, ?x108) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05_61y featured_film_locations 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 70.000 0.431 http://example.org/film/film/featured_film_locations #14752-05p9_ql PRED entity: 05p9_ql PRED relation: languages PRED expected values: 02h40lc => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.91 #68, 0.89 #35, 0.89 #134), 0t_2 (0.04 #17, 0.04 #28, 0.03 #149), 06nm1 (0.04 #16, 0.04 #27, 0.03 #38), 03_9r (0.04 #279, 0.04 #323, 0.04 #312), 064_8sq (0.02 #216, 0.01 #73, 0.01 #249), 02bv9 (0.01 #20, 0.01 #31, 0.01 #218), 04306rv (0.01 #14, 0.01 #25, 0.01 #212), 02bjrlw (0.01 #12, 0.01 #23, 0.01 #210) >> Best rule #68 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 06w7mlh; 03cf9ly; >> query: (?x7317, 02h40lc) <- nominated_for(?x2965, ?x7317), titles(?x2008, ?x7317), genre(?x7317, ?x53) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05p9_ql languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.905 http://example.org/tv/tv_program/languages #14751-02q0v8n PRED entity: 02q0v8n PRED relation: film! PRED expected values: 03pp73 => 85 concepts (61 used for prediction) PRED predicted values (max 10 best out of 781): 023361 (0.46 #4166, 0.46 #68737, 0.46 #41659), 0z4s (0.20 #68, 0.04 #4234, 0.03 #6317), 06cgy (0.20 #251, 0.04 #4417, 0.03 #10665), 01nm3s (0.20 #691, 0.03 #122897, 0.02 #29852), 05p606 (0.20 #1915, 0.03 #3998), 012q4n (0.20 #1140, 0.02 #9472, 0.01 #24052), 0d608 (0.20 #1307, 0.02 #26302, 0.02 #5473), 0dzf_ (0.20 #812, 0.02 #23724, 0.02 #79962), 0k525 (0.20 #1845, 0.02 #33091, 0.02 #37256), 016ks_ (0.20 #787, 0.02 #13284, 0.02 #11201) >> Best rule #4166 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 070fnm; >> query: (?x9069, ?x8374) <- film(?x902, ?x9069), genre(?x9069, ?x4205), nominated_for(?x8374, ?x9069), ?x4205 = 0c3351 >> conf = 0.46 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02q0v8n film! 03pp73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 61.000 0.464 http://example.org/film/actor/film./film/performance/film #14750-0mz73 PRED entity: 0mz73 PRED relation: film PRED expected values: 07z6xs => 127 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1017): 04q827 (0.65 #33956, 0.46 #25019, 0.45 #32168), 0h3k3f (0.33 #1487, 0.01 #100082), 03b1sb (0.08 #3289, 0.03 #8650, 0.02 #5076), 027gy0k (0.08 #2935, 0.02 #4722), 0b7l4x (0.08 #2827, 0.01 #27846, 0.01 #33208), 0crd8q6 (0.08 #3418, 0.01 #19501, 0.01 #40948), 02qr3k8 (0.08 #8436, 0.05 #4862, 0.03 #142976), 09lxv9 (0.07 #5078, 0.05 #8652, 0.04 #3291), 0bz3jx (0.07 #4712, 0.05 #8286, 0.04 #2925), 0ds3t5x (0.07 #3628, 0.02 #51880, 0.02 #48306) >> Best rule #33956 for best value: >> intensional similarity = 3 >> extensional distance = 358 >> proper extension: 01rr9f; 01j5x6; 02_hj4; 03xmy1; 01pgzn_; 01w02sy; 04gycf; 06wm0z; 01pqy_; 0gn30; ... >> query: (?x7831, ?x10806) <- location(?x7831, ?x1523), participant(?x3101, ?x7831), nominated_for(?x7831, ?x10806) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0mz73 film 07z6xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 82.000 0.651 http://example.org/film/actor/film./film/performance/film #14749-0lkr7 PRED entity: 0lkr7 PRED relation: film PRED expected values: 016y_f => 109 concepts (49 used for prediction) PRED predicted values (max 10 best out of 568): 0431v3 (0.52 #26732, 0.49 #57035, 0.48 #28515), 03lvwp (0.20 #1040, 0.02 #15297, 0.01 #17079), 04h41v (0.20 #1028), 0fphf3v (0.09 #3139, 0.02 #17395, 0.02 #13831), 0bvn25 (0.05 #7179, 0.04 #5397, 0.03 #10743), 034qzw (0.05 #5681, 0.04 #7463, 0.04 #9245), 03q0r1 (0.05 #5983, 0.04 #7765, 0.04 #4201), 03bx2lk (0.05 #5532, 0.04 #7314, 0.03 #9096), 01shy7 (0.04 #9335, 0.04 #7553, 0.04 #5771), 02825cv (0.04 #11829, 0.03 #8265, 0.03 #6483) >> Best rule #26732 for best value: >> intensional similarity = 4 >> extensional distance = 581 >> proper extension: 0p51w; 09d5d5; 071jv5; >> query: (?x4992, ?x3496) <- people(?x3584, ?x4992), award(?x4992, ?x451), location(?x4992, ?x1523), nominated_for(?x4992, ?x3496) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #2528 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 0j3v; 02wh0; 01h2_6; *> query: (?x4992, 016y_f) <- people(?x5540, ?x4992), location(?x4992, ?x1523), ?x5540 = 013xrm *> conf = 0.03 ranks of expected_values: 70 EVAL 0lkr7 film 016y_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 109.000 49.000 0.525 http://example.org/film/actor/film./film/performance/film #14748-016l09 PRED entity: 016l09 PRED relation: award PRED expected values: 02sp_v => 91 concepts (74 used for prediction) PRED predicted values (max 10 best out of 246): 01c4_6 (0.81 #8297, 0.81 #15015, 0.80 #28467), 03tcnt (0.59 #1745, 0.53 #6486, 0.39 #4906), 054ks3 (0.58 #15157, 0.30 #3303, 0.29 #14760), 01bgqh (0.56 #3204, 0.43 #43, 0.37 #14661), 01c427 (0.53 #12333, 0.24 #2851, 0.22 #3641), 02v1m7 (0.47 #1693, 0.40 #2879, 0.34 #3669), 01d38t (0.47 #1902, 0.40 #1507, 0.34 #5063), 02x4wb (0.39 #5090, 0.27 #1534, 0.22 #6670), 03qbh5 (0.33 #3362, 0.31 #991, 0.26 #14819), 026mfs (0.33 #10402, 0.26 #3290, 0.14 #6055) >> Best rule #8297 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 06lxn; >> query: (?x9791, ?x1565) <- group(?x227, ?x9791), artists(?x302, ?x9791), artist(?x382, ?x9791), award_winner(?x1565, ?x9791) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #4507 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 01czx; 013w8y; *> query: (?x9791, 02sp_v) <- award_winner(?x486, ?x9791), artists(?x302, ?x9791), artist(?x382, ?x9791), award(?x9791, ?x2139), group(?x227, ?x9791) *> conf = 0.22 ranks of expected_values: 25 EVAL 016l09 award 02sp_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 91.000 74.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14747-042fk PRED entity: 042fk PRED relation: legislative_sessions PRED expected values: 01gtcq => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 48): 06f0dc (0.46 #791, 0.41 #561, 0.39 #745), 024tkd (0.42 #818, 0.39 #772, 0.35 #588), 07p__7 (0.42 #790, 0.35 #560, 0.35 #744), 01h7xx (0.41 #829, 0.12 #592, 0.09 #776), 043djx (0.41 #829, 0.12 #559, 0.09 #743), 01gsvp (0.41 #829, 0.09 #768, 0.08 #814), 01gsvb (0.41 #829, 0.07 #455, 0.06 #501), 01gtcq (0.41 #829, 0.06 #488, 0.06 #580), 03rl1g (0.41 #829, 0.06 #553, 0.04 #737), 01gt99 (0.41 #829, 0.04 #780, 0.04 #826) >> Best rule #791 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 021sv1; >> query: (?x13098, 06f0dc) <- basic_title(?x13098, ?x346), legislative_sessions(?x13098, ?x2019), legislative_sessions(?x759, ?x2019), district_represented(?x2019, ?x177) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #829 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 021sv1; *> query: (?x13098, ?x759) <- basic_title(?x13098, ?x346), legislative_sessions(?x13098, ?x2019), legislative_sessions(?x759, ?x2019), district_represented(?x2019, ?x177) *> conf = 0.41 ranks of expected_values: 8 EVAL 042fk legislative_sessions 01gtcq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 189.000 189.000 0.458 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #14746-033srr PRED entity: 033srr PRED relation: film_crew_role PRED expected values: 09vw2b7 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 31): 09vw2b7 (0.76 #525, 0.73 #820, 0.73 #1217), 02rh1dz (0.50 #40, 0.26 #200, 0.25 #8), 02ynfr (0.50 #44, 0.24 #531, 0.21 #1223), 01xy5l_ (0.33 #74, 0.14 #235, 0.13 #1088), 089fss (0.25 #101, 0.13 #3409, 0.13 #3408), 0d2b38 (0.21 #247, 0.19 #771, 0.19 #869), 0215hd (0.17 #79, 0.16 #240, 0.16 #143), 089g0h (0.14 #241, 0.13 #765, 0.13 #3409), 02vs3x5 (0.13 #3409, 0.13 #3408, 0.12 #116), 0ckd1 (0.13 #3409, 0.13 #3408, 0.12 #99) >> Best rule #525 for best value: >> intensional similarity = 6 >> extensional distance = 143 >> proper extension: 01d2v1; >> query: (?x3990, 09vw2b7) <- genre(?x3990, ?x225), film(?x1194, ?x3990), country(?x3990, ?x94), film_crew_role(?x3990, ?x137), featured_film_locations(?x3990, ?x108), ?x225 = 02kdv5l >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033srr film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.759 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14745-04snp2 PRED entity: 04snp2 PRED relation: profession PRED expected values: 03gjzk => 149 concepts (118 used for prediction) PRED predicted values (max 10 best out of 71): 0dxtg (0.93 #1642, 0.92 #1346, 0.87 #5047), 03gjzk (0.89 #1792, 0.87 #3272, 0.87 #2976), 01d_h8 (0.86 #10816, 0.86 #10520, 0.86 #8891), 02hrh1q (0.74 #14673, 0.73 #12156, 0.71 #11712), 02jknp (0.64 #2376, 0.60 #2820, 0.58 #1636), 0kyk (0.44 #3139, 0.36 #10248, 0.36 #10099), 02krf9 (0.33 #1804, 0.33 #619, 0.33 #7727), 0np9r (0.29 #11571, 0.27 #761, 0.19 #3870), 018gz8 (0.21 #3866, 0.21 #5051, 0.21 #4755), 0n1h (0.18 #11117, 0.13 #16436, 0.09 #899) >> Best rule #1642 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 079vf; 0mdqp; 03_gd; 012t1; 05183k; 07s93v; 052gzr; 01f7j9; 0184dt; 02l5rm; ... >> query: (?x4238, 0dxtg) <- produced_by(?x9636, ?x4238), story_by(?x9169, ?x4238), student(?x3424, ?x4238), language(?x9636, ?x254) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1792 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 0dbpyd; 0c4f4; 04n7njg; 01j7rd; 01pcmd; 015pxr; 02_2v2; 0h53p1; 0bt4r4; 01jbx1; ... *> query: (?x4238, 03gjzk) <- student(?x3424, ?x4238), program(?x4238, ?x5810), place_of_birth(?x4238, ?x4074), place_of_death(?x12521, ?x4074) *> conf = 0.89 ranks of expected_values: 2 EVAL 04snp2 profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 149.000 118.000 0.930 http://example.org/people/person/profession #14744-09889g PRED entity: 09889g PRED relation: artists! PRED expected values: 06by7 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 234): 06by7 (0.71 #933, 0.58 #6407, 0.53 #7623), 017_qw (0.51 #12223, 0.40 #13743, 0.37 #11614), 01fh36 (0.50 #81, 0.09 #4643, 0.09 #3730), 0glt670 (0.43 #648, 0.38 #4602, 0.33 #3081), 0xhtw (0.35 #7619, 0.30 #6403, 0.20 #8228), 016clz (0.34 #6391, 0.26 #7607, 0.23 #20078), 0y3_8 (0.29 #958, 0.19 #3087, 0.18 #2478), 02w4v (0.29 #955, 0.13 #4300, 0.12 #2475), 02ny8t (0.29 #1040, 0.12 #2560, 0.07 #4385), 02k_kn (0.27 #7663, 0.19 #4623, 0.14 #973) >> Best rule #933 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01p45_v; >> query: (?x4960, 06by7) <- company(?x4960, ?x7448), artists(?x3061, ?x4960), ?x3061 = 05bt6j >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09889g artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.714 http://example.org/music/genre/artists #14743-042v_gx PRED entity: 042v_gx PRED relation: role! PRED expected values: 01wy6 0myk8 => 74 concepts (61 used for prediction) PRED predicted values (max 10 best out of 62): 0jtg0 (0.83 #864, 0.83 #863, 0.83 #227), 03qjg (0.83 #864, 0.83 #863, 0.83 #227), 01v1d8 (0.83 #864, 0.83 #863, 0.83 #227), 07y_7 (0.83 #864, 0.83 #863, 0.83 #227), 0g2dz (0.83 #864, 0.83 #863, 0.83 #227), 02w3w (0.83 #864, 0.83 #863, 0.83 #227), 01hww_ (0.83 #864, 0.83 #863, 0.83 #227), 01wy6 (0.83 #864, 0.83 #863, 0.83 #227), 03qlv7 (0.83 #864, 0.83 #863, 0.83 #227), 07_l6 (0.83 #864, 0.83 #863, 0.83 #227) >> Best rule #864 for best value: >> intensional similarity = 12 >> extensional distance = 7 >> proper extension: 0979zs; >> query: (?x432, ?x4311) <- role(?x432, ?x4311), role(?x432, ?x1969), role(?x432, ?x227), role(?x7987, ?x432), role(?x7112, ?x432), ?x7112 = 0133x7, ?x227 = 0342h, group(?x1969, ?x9589), role(?x366, ?x1969), ?x9589 = 02cw1m, instrumentalists(?x1969, ?x1001), artist(?x2299, ?x7987) >> conf = 0.83 => this is the best rule for 16 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 8, 42 EVAL 042v_gx role! 0myk8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 74.000 61.000 0.834 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 042v_gx role! 01wy6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 74.000 61.000 0.834 http://example.org/music/performance_role/track_performances./music/track_contribution/role #14742-01m13b PRED entity: 01m13b PRED relation: film! PRED expected values: 02t__l => 75 concepts (37 used for prediction) PRED predicted values (max 10 best out of 939): 04k25 (0.40 #43716, 0.36 #70783, 0.34 #60372), 03jj93 (0.12 #1896, 0.10 #3977, 0.09 #6058), 037w7r (0.10 #3667, 0.09 #5748, 0.06 #1586), 053xw6 (0.09 #5417, 0.06 #1255, 0.05 #3336), 019f2f (0.08 #6682, 0.05 #8763, 0.01 #15008), 02gf_l (0.06 #13758, 0.02 #20004, 0.01 #32494), 02_p5w (0.06 #13135), 02d4ct (0.06 #391, 0.05 #2472, 0.05 #4553), 05kwx2 (0.06 #1098, 0.05 #3179, 0.05 #5260), 0h5g_ (0.06 #74, 0.05 #2155, 0.05 #4236) >> Best rule #43716 for best value: >> intensional similarity = 3 >> extensional distance = 581 >> proper extension: 02zk08; >> query: (?x1009, ?x2671) <- production_companies(?x1009, ?x3331), award_winner(?x1009, ?x2671), nominated_for(?x8888, ?x1009) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01m13b film! 02t__l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 37.000 0.401 http://example.org/film/actor/film./film/performance/film #14741-07vfj PRED entity: 07vfj PRED relation: institution! PRED expected values: 02h4rq6 016t_3 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 15): 02h4rq6 (0.73 #36, 0.71 #104, 0.71 #53), 016t_3 (0.70 #37, 0.67 #54, 0.63 #20), 04zx3q1 (0.46 #52, 0.45 #35, 0.44 #18), 013zdg (0.36 #39, 0.33 #56, 0.32 #22), 022h5x (0.28 #813, 0.23 #48, 0.21 #65), 01rr_d (0.28 #813, 0.20 #28, 0.17 #165), 071tyz (0.28 #813, 0.13 #108, 0.08 #160), 02m4yg (0.28 #813, 0.11 #44, 0.10 #61), 01ysy9 (0.28 #813, 0.07 #50, 0.06 #255), 01gkg3 (0.28 #813, 0.05 #1437, 0.02 #316) >> Best rule #36 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 059ss; >> query: (?x3813, 02h4rq6) <- organization(?x3813, ?x5487), contains(?x94, ?x3813), second_level_divisions(?x94, ?x321) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07vfj institution! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07vfj institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14740-0c_dx PRED entity: 0c_dx PRED relation: award! PRED expected values: 0bv7t => 71 concepts (46 used for prediction) PRED predicted values (max 10 best out of 2974): 02kz_ (0.79 #74359, 0.77 #81125, 0.75 #3378), 084w8 (0.75 #3378, 0.74 #84507, 0.71 #23660), 073v6 (0.75 #3378, 0.74 #84507, 0.71 #23660), 04107 (0.75 #3378, 0.74 #84507, 0.71 #23660), 01h320 (0.75 #3378, 0.74 #84507, 0.71 #23660), 0lfbm (0.55 #12125, 0.38 #8743, 0.03 #110152), 01dzz7 (0.54 #37633, 0.53 #17352, 0.50 #20732), 01k56k (0.53 #20185, 0.50 #23565, 0.48 #30330), 02y49 (0.53 #19475, 0.45 #22855, 0.43 #26236), 048_p (0.50 #38808, 0.47 #18527, 0.45 #21907) >> Best rule #74359 for best value: >> intensional similarity = 6 >> extensional distance = 58 >> proper extension: 01bgqh; >> query: (?x7111, ?x5336) <- award_winner(?x7111, ?x5336), influenced_by(?x5335, ?x5336), profession(?x5336, ?x987), influenced_by(?x5336, ?x3336), participant(?x1607, ?x5336), influenced_by(?x2609, ?x5335) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #64222 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 30 *> proper extension: 03y8cbv; *> query: (?x7111, ?x1752) <- award_winner(?x7111, ?x2248), disciplines_or_subjects(?x7111, ?x6647), disciplines_or_subjects(?x7111, ?x5864), genre(?x972, ?x6647), disciplines_or_subjects(?x11579, ?x5864), award(?x2248, ?x384), award(?x1752, ?x11579) *> conf = 0.10 ranks of expected_values: 655 EVAL 0c_dx award! 0bv7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 46.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14739-02n1gr PRED entity: 02n1gr PRED relation: award PRED expected values: 03r8tl => 142 concepts (133 used for prediction) PRED predicted values (max 10 best out of 361): 03r8tl (0.44 #1320, 0.38 #2940, 0.33 #5370), 03r8v_ (0.37 #5607, 0.09 #19785, 0.08 #12898), 09sb52 (0.34 #34065, 0.27 #23939, 0.26 #26369), 05b4l5x (0.20 #1626, 0.20 #6, 0.18 #13777), 0f4x7 (0.20 #4891, 0.16 #21094, 0.14 #14612), 05zr6wv (0.20 #4877, 0.15 #15004, 0.15 #6902), 0gqwc (0.20 #1695, 0.14 #13846, 0.14 #26403), 03c7tr1 (0.20 #59, 0.12 #27602, 0.12 #8969), 024fz9 (0.20 #211, 0.10 #1831, 0.08 #2641), 02x4x18 (0.20 #1754, 0.10 #26462, 0.09 #26057) >> Best rule #1320 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02qy3py; 04xfb; >> query: (?x9039, 03r8tl) <- type_of_union(?x9039, ?x566), nationality(?x9039, ?x2146), politician(?x11946, ?x9039), ?x2146 = 03rk0 >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02n1gr award 03r8tl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 133.000 0.444 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14738-02183k PRED entity: 02183k PRED relation: institution! PRED expected values: 019v9k => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 16): 019v9k (0.80 #57, 0.77 #23, 0.74 #125), 016t_3 (0.62 #88, 0.55 #173, 0.54 #20), 02_xgp2 (0.59 #128, 0.56 #94, 0.54 #351), 07s6fsf (0.55 #52, 0.46 #18, 0.46 #120), 04zx3q1 (0.33 #206, 0.32 #121, 0.31 #70), 027f2w (0.31 #126, 0.28 #211, 0.25 #92), 013zdg (0.28 #124, 0.27 #90, 0.27 #175), 028dcg (0.27 #82, 0.15 #184, 0.15 #65), 02mjs7 (0.15 #21, 0.13 #72, 0.13 #208), 02cq61 (0.12 #81, 0.10 #217, 0.08 #30) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 05ftw3; >> query: (?x3416, 019v9k) <- institution(?x9054, ?x3416), student(?x3416, ?x8114), ?x9054 = 022h5x, profession(?x8114, ?x1359) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02183k institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14737-0g02vk PRED entity: 0g02vk PRED relation: risk_factors PRED expected values: 01hbgs => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 87): 0jpmt (0.69 #1499, 0.53 #1660, 0.44 #2037), 01hbgs (0.68 #2039, 0.62 #1229, 0.60 #391), 0fltx (0.60 #297, 0.40 #401, 0.33 #1135), 0c58k (0.47 #1661, 0.42 #1124, 0.40 #390), 0k95h (0.43 #847, 0.40 #324, 0.33 #637), 02zsn (0.38 #1370, 0.33 #1853, 0.33 #1102), 012jc (0.35 #1614, 0.31 #1235, 0.29 #1347), 02ctzb (0.33 #895, 0.25 #164, 0.21 #2300), 02y0js (0.33 #55, 0.20 #474, 0.20 #422), 09d11 (0.33 #66, 0.20 #485, 0.20 #433) >> Best rule #1499 for best value: >> intensional similarity = 8 >> extensional distance = 14 >> proper extension: 0hg45; >> query: (?x9933, 0jpmt) <- symptom_of(?x4905, ?x9933), risk_factors(?x9933, ?x231), risk_factors(?x14098, ?x231), risk_factors(?x6720, ?x231), risk_factors(?x5118, ?x231), ?x14098 = 01k9gb, ?x5118 = 01bcp7, people(?x6720, ?x457) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2039 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 23 *> proper extension: 0146bp; 0h3bn; *> query: (?x9933, 01hbgs) <- symptom_of(?x4905, ?x9933), risk_factors(?x9933, ?x231), risk_factors(?x14098, ?x231), risk_factors(?x10199, ?x231), risk_factors(?x5118, ?x231), ?x14098 = 01k9gb, ?x10199 = 02k6hp, people(?x5118, ?x5119) *> conf = 0.68 ranks of expected_values: 2 EVAL 0g02vk risk_factors 01hbgs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.688 http://example.org/medicine/disease/risk_factors #14736-0b_6zk PRED entity: 0b_6zk PRED relation: locations PRED expected values: 0dyl9 0qpsn => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 577): 0kcw2 (0.50 #2432, 0.38 #2258, 0.36 #9651), 0f2r6 (0.40 #715, 0.37 #3508, 0.36 #9651), 030qb3t (0.38 #4046, 0.36 #9651, 0.35 #8943), 0fr0t (0.38 #2165, 0.36 #9651, 0.35 #8943), 0fsb8 (0.37 #3621, 0.36 #9651, 0.35 #8943), 0156q (0.36 #4572, 0.35 #4746, 0.31 #5795), 029cr (0.36 #9651, 0.35 #8943, 0.35 #3719), 0_vn7 (0.36 #9651, 0.35 #8943, 0.33 #255), 0vzm (0.36 #9651, 0.35 #8943, 0.33 #1459), 010h9y (0.36 #9651, 0.35 #8943, 0.33 #153) >> Best rule #2432 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 0b_6v_; 0b_6lb; >> query: (?x3797, 0kcw2) <- team(?x3797, ?x9983), team(?x3797, ?x9975), locations(?x3797, ?x5719), locations(?x3797, ?x5259), locations(?x3797, ?x2879), team(?x6802, ?x9975), ?x5259 = 0d9y6, administrative_division(?x5719, ?x11836), ?x9983 = 02q4ntp, ?x6802 = 0br1x_, place_of_birth(?x2580, ?x2879) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2367 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 6 *> proper extension: 0b_6v_; 0b_6lb; *> query: (?x3797, 0dyl9) <- team(?x3797, ?x9983), team(?x3797, ?x9975), locations(?x3797, ?x5719), locations(?x3797, ?x5259), locations(?x3797, ?x2879), team(?x6802, ?x9975), ?x5259 = 0d9y6, administrative_division(?x5719, ?x11836), ?x9983 = 02q4ntp, ?x6802 = 0br1x_, place_of_birth(?x2580, ?x2879) *> conf = 0.25 ranks of expected_values: 38, 39 EVAL 0b_6zk locations 0qpsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 74.000 74.000 0.500 http://example.org/time/event/locations EVAL 0b_6zk locations 0dyl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 74.000 74.000 0.500 http://example.org/time/event/locations #14735-01w9mnm PRED entity: 01w9mnm PRED relation: artists! PRED expected values: 0fd3y => 115 concepts (47 used for prediction) PRED predicted values (max 10 best out of 282): 08jyyk (0.67 #992, 0.45 #3466, 0.43 #1301), 06by7 (0.61 #7444, 0.60 #329, 0.57 #1254), 03lty (0.61 #13342, 0.40 #336, 0.29 #1261), 064t9 (0.60 #2483, 0.56 #7435, 0.50 #7743), 09nwwf (0.60 #446, 0.43 #1371, 0.29 #3227), 01738f (0.60 #425, 0.43 #1350, 0.21 #3206), 0xhtw (0.50 #3414, 0.49 #13330, 0.31 #12709), 0dl5d (0.50 #2181, 0.43 #1252, 0.41 #3417), 05bt6j (0.50 #2515, 0.41 #3442, 0.37 #4371), 05w3f (0.45 #3436, 0.27 #10831, 0.21 #7422) >> Best rule #992 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 023l9y; >> query: (?x8539, 08jyyk) <- artists(?x9063, ?x8539), role(?x8539, ?x1437), role(?x8539, ?x745), ?x745 = 01vj9c, ?x9063 = 0cx7f, ?x1437 = 01vdm0 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #935 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 023l9y; *> query: (?x8539, 0fd3y) <- artists(?x9063, ?x8539), role(?x8539, ?x1437), role(?x8539, ?x745), ?x745 = 01vj9c, ?x9063 = 0cx7f, ?x1437 = 01vdm0 *> conf = 0.33 ranks of expected_values: 23 EVAL 01w9mnm artists! 0fd3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 115.000 47.000 0.667 http://example.org/music/genre/artists #14734-0280061 PRED entity: 0280061 PRED relation: category PRED expected values: 08mbj5d => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.41 #5, 0.34 #16, 0.32 #6) >> Best rule #5 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 026njb5; 0bs8s1p; >> query: (?x7204, 08mbj5d) <- film_release_region(?x7204, ?x94), film_release_distribution_medium(?x7204, ?x81), nominated_for(?x5886, ?x7204), genre(?x7204, ?x53), ?x94 = 09c7w0, ?x5886 = 0fq9zdv >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0280061 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.412 http://example.org/common/topic/webpage./common/webpage/category #14733-017n9 PRED entity: 017n9 PRED relation: story_by PRED expected values: 05qzv => 89 concepts (69 used for prediction) PRED predicted values (max 10 best out of 70): 02nygk (0.09 #211, 0.04 #1076, 0.02 #2374), 011s9r (0.06 #198, 0.04 #1279, 0.03 #2145), 01y8d4 (0.06 #137, 0.04 #1218, 0.03 #2084), 079vf (0.05 #1083, 0.04 #1949, 0.02 #2597), 046_v (0.04 #1254, 0.03 #2120, 0.01 #605), 0343h (0.03 #667, 0.02 #2397, 0.02 #1099), 07nznf (0.03 #1, 0.03 #1082, 0.01 #1948), 07rd7 (0.03 #72, 0.01 #288, 0.01 #2667), 03cdg (0.03 #191, 0.01 #840), 02q4mt (0.03 #197) >> Best rule #211 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 09xbpt; 06z8s_; 01_mdl; 044g_k; 09txzv; 035yn8; 050xxm; 01dyvs; 02stbw; 0cq7kw; ... >> query: (?x11685, 02nygk) <- production_companies(?x11685, ?x382), language(?x11685, ?x254), category(?x11685, ?x134), ?x382 = 086k8 >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 017n9 story_by 05qzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 69.000 0.091 http://example.org/film/film/story_by #14732-032xky PRED entity: 032xky PRED relation: written_by PRED expected values: 06z9yh => 91 concepts (48 used for prediction) PRED predicted values (max 10 best out of 56): 0p__8 (0.38 #1868), 0jt90f5 (0.28 #5394, 0.18 #15893, 0.17 #739), 03s9b (0.20 #547, 0.08 #2572, 0.07 #3922), 04m_zp (0.17 #798, 0.07 #4173, 0.05 #4846), 09zw90 (0.12 #1668, 0.11 #2343, 0.07 #3018), 03_gd (0.12 #1369, 0.11 #2044, 0.07 #2719), 027j79k (0.12 #1976), 03jldb (0.12 #1732), 01pjr7 (0.11 #2254, 0.08 #6302, 0.07 #2929), 0d608 (0.11 #2249, 0.07 #2924, 0.07 #3599) >> Best rule #1868 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01k1k4; 013q07; 03nx8mj; 013q0p; 032sl_; 0gfzfj; >> query: (?x11699, 0p__8) <- genre(?x11699, ?x53), film(?x1986, ?x11699), country(?x11699, ?x94), ?x1986 = 0gz5hs >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 032xky written_by 06z9yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 48.000 0.375 http://example.org/film/film/written_by #14731-07t21 PRED entity: 07t21 PRED relation: countries_spoken_in! PRED expected values: 06b_j => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 51): 02h40lc (0.36 #3486, 0.36 #4890, 0.35 #4422), 06nm1 (0.32 #318, 0.24 #578, 0.21 #1150), 0jzc (0.21 #482, 0.19 #534, 0.19 #1314), 064_8sq (0.19 #796, 0.19 #2772, 0.19 #4904), 06b_j (0.18 #121, 0.14 #277, 0.09 #745), 04306rv (0.16 #316, 0.14 #836, 0.14 #524), 02bjrlw (0.16 #313, 0.12 #1145, 0.11 #677), 02hwhyv (0.11 #700, 0.11 #544, 0.11 #336), 02hxcvy (0.11 #548, 0.11 #340, 0.10 #652), 01r2l (0.11 #539, 0.11 #331, 0.08 #643) >> Best rule #3486 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 0g8bw; 088q1s; >> query: (?x1471, 02h40lc) <- countries_spoken_in(?x8650, ?x1471), form_of_government(?x1471, ?x1926), official_language(?x1003, ?x8650) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #121 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 013m4v; *> query: (?x1471, 06b_j) <- adjoins(?x1471, ?x456), partially_contains(?x11687, ?x1471), contains(?x455, ?x1471) *> conf = 0.18 ranks of expected_values: 5 EVAL 07t21 countries_spoken_in! 06b_j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 152.000 152.000 0.363 http://example.org/language/human_language/countries_spoken_in #14730-02756j PRED entity: 02756j PRED relation: profession PRED expected values: 02hrh1q => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 76): 02hrh1q (0.88 #8267, 0.88 #3165, 0.88 #6467), 01d_h8 (0.39 #456, 0.37 #756, 0.36 #1806), 09jwl (0.36 #6152, 0.25 #20, 0.21 #2570), 02jknp (0.33 #458, 0.32 #758, 0.30 #3759), 0dxtg (0.31 #6916, 0.31 #9166, 0.30 #10516), 03gjzk (0.25 #1816, 0.24 #4217, 0.24 #1516), 0nbcg (0.25 #33, 0.22 #633, 0.13 #3333), 0cbd2 (0.25 #7, 0.17 #2557, 0.17 #1657), 016z4k (0.25 #4, 0.16 #3004, 0.14 #1654), 0kyk (0.25 #31, 0.15 #931, 0.14 #1681) >> Best rule #8267 for best value: >> intensional similarity = 3 >> extensional distance = 915 >> proper extension: 01fwj8; 01wxyx1; 03xnq9_; 05yh_t; 01vw917; 06tp4h; 07mz77; 09r_wb; 013bd1; 01xllf; ... >> query: (?x6312, 02hrh1q) <- people(?x5025, ?x6312), nationality(?x6312, ?x2146), film(?x6312, ?x257) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02756j profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.883 http://example.org/people/person/profession #14729-0fz0c2 PRED entity: 0fz0c2 PRED relation: award_winner PRED expected values: 0g1rw 081nh => 34 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1055): 081nh (0.60 #3410, 0.44 #11087, 0.40 #8016), 076psv (0.50 #5289, 0.31 #11430, 0.25 #6823), 02cqbx (0.40 #3945, 0.33 #2410, 0.30 #8551), 07zhd7 (0.40 #4570, 0.30 #9176, 0.25 #7640), 01vsps (0.38 #18429), 0c0tzp (0.38 #6112, 0.33 #3041, 0.25 #7646), 0h005 (0.33 #2249, 0.33 #713, 0.25 #6854), 02sj1x (0.33 #524, 0.25 #11272, 0.25 #6665), 02wb6d (0.33 #2573, 0.25 #7178, 0.20 #8714), 0gl88b (0.33 #1822, 0.25 #4893, 0.20 #3357) >> Best rule #3410 for best value: >> intensional similarity = 21 >> extensional distance = 3 >> proper extension: 0c53vt; 0fv89q; >> query: (?x7589, 081nh) <- honored_for(?x7589, ?x5183), ceremony(?x4573, ?x7589), ceremony(?x2209, ?x7589), ceremony(?x1313, ?x7589), ceremony(?x591, ?x7589), ?x2209 = 0gr42, ?x1313 = 0gs9p, award_winner(?x7589, ?x2449), award_winner(?x7589, ?x200), award_nominee(?x200, ?x2304), instance_of_recurring_event(?x7589, ?x3459), film_sets_designed(?x200, ?x3986), ?x4573 = 0gq_d, nominated_for(?x2449, ?x785), award_nominee(?x2801, ?x2449), film_art_direction_by(?x2721, ?x2449), ?x591 = 0f4x7, produced_by(?x785, ?x9928), music(?x785, ?x8275), profession(?x2449, ?x1078), award(?x2449, ?x484) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 243 EVAL 0fz0c2 award_winner 081nh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 22.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0fz0c2 award_winner 0g1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 22.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14728-0512p PRED entity: 0512p PRED relation: season PRED expected values: 03c6sl9 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 6): 03c6sl9 (0.91 #165, 0.90 #159, 0.90 #115), 04110b0 (0.50 #21, 0.47 #177, 0.42 #81), 02h7s73 (0.50 #22, 0.47 #177, 0.42 #82), 03c6s24 (0.47 #177, 0.33 #83, 0.33 #23), 03c74_8 (0.47 #177, 0.33 #20, 0.25 #110), 04n36qk (0.47 #177, 0.17 #30, 0.16 #151) >> Best rule #165 for best value: >> intensional similarity = 11 >> extensional distance = 30 >> proper extension: 02__x; >> query: (?x1438, 03c6sl9) <- season(?x1438, ?x701), school(?x1438, ?x581), major_field_of_study(?x581, ?x8221), currency(?x581, ?x170), institution(?x3437, ?x581), taxonomy(?x8221, ?x939), ?x3437 = 02_xgp2, school(?x1161, ?x581), student(?x581, ?x1299), major_field_of_study(?x4672, ?x8221), ?x4672 = 07tds >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0512p season 03c6sl9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.906 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #14727-0rkkv PRED entity: 0rkkv PRED relation: time_zones PRED expected values: 02fqwt => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.54 #3, 0.50 #16, 0.45 #42), 02lcqs (0.30 #70, 0.30 #83, 0.28 #161), 02fqwt (0.19 #105, 0.18 #417, 0.18 #66), 02hczc (0.13 #41, 0.11 #54, 0.10 #106), 02llzg (0.13 #329, 0.12 #303, 0.12 #342), 03bdv (0.04 #357, 0.04 #435, 0.04 #383), 03plfd (0.02 #439, 0.02 #361, 0.02 #374), 02lcrv (0.01 #163), 042g7t (0.01 #297) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0rh7t; 0rn8q; 0rhp6; 0rrwt; 0rj4g; 0rmwd; >> query: (?x5124, 02hcv8) <- category(?x5124, ?x134), state(?x5124, ?x2623), source(?x5124, ?x958), ?x2623 = 02xry >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #105 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 124 *> proper extension: 0cb4j; 0vmt; 01n7q; 07z1m; 04rrx; 07srw; 06btq; 081mh; 0498y; 0824r; ... *> query: (?x5124, 02fqwt) <- location_of_ceremony(?x566, ?x5124), contains(?x2623, ?x5124), contains(?x94, ?x5124), ?x94 = 09c7w0, jurisdiction_of_office(?x900, ?x2623) *> conf = 0.19 ranks of expected_values: 3 EVAL 0rkkv time_zones 02fqwt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.538 http://example.org/location/location/time_zones #14726-0329r5 PRED entity: 0329r5 PRED relation: teams! PRED expected values: 07twz => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 89): 06qd3 (0.33 #316, 0.25 #1397, 0.12 #1667), 05r4w (0.33 #812, 0.25 #1082, 0.07 #2164), 077qn (0.33 #113, 0.09 #2005, 0.07 #2276), 01p1v (0.33 #602, 0.09 #1954, 0.07 #2225), 01nln (0.25 #1508, 0.12 #1778, 0.05 #2591), 05v10 (0.25 #1133, 0.05 #2486, 0.05 #2757), 0k6nt (0.12 #1648, 0.09 #1919, 0.07 #2190), 059j2 (0.12 #1659, 0.09 #1930, 0.07 #2201), 06mkj (0.12 #1688, 0.05 #2501, 0.05 #2772), 0f8l9c (0.12 #1645, 0.05 #2458, 0.05 #2999) >> Best rule #316 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 03zrhb; >> query: (?x7616, 06qd3) <- team(?x203, ?x7616), team(?x60, ?x7616), ?x60 = 02nzb8, current_club(?x7616, ?x10196), current_club(?x7616, ?x5027), current_club(?x7616, ?x4094), sport(?x7616, ?x471), ?x4094 = 01gjlw, ?x203 = 0dgrmp, position(?x7616, ?x530), team(?x5471, ?x5027), colors(?x10196, ?x663) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0329r5 teams! 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 70.000 0.333 http://example.org/sports/sports_team_location/teams #14725-0lv1x PRED entity: 0lv1x PRED relation: olympics! PRED expected values: 02vzc => 66 concepts (58 used for prediction) PRED predicted values (max 10 best out of 328): 02vzc (0.87 #3429, 0.84 #2754, 0.80 #3205), 06q1r (0.80 #2148, 0.67 #453, 0.57 #5430), 015fr (0.68 #2732, 0.65 #3183, 0.60 #464), 06qd3 (0.64 #1604, 0.60 #474, 0.53 #2397), 03shp (0.63 #2780, 0.60 #3231, 0.60 #512), 0b90_r (0.61 #3400, 0.60 #457, 0.60 #344), 06c1y (0.60 #479, 0.60 #366, 0.58 #2747), 015qh (0.60 #477, 0.47 #2745, 0.47 #2400), 06mkj (0.60 #491, 0.47 #2759, 0.45 #3210), 03rk0 (0.60 #490, 0.40 #377, 0.33 #2413) >> Best rule #3429 for best value: >> intensional similarity = 14 >> extensional distance = 21 >> proper extension: 0c_tl; >> query: (?x2043, 02vzc) <- olympics(?x205, ?x2043), sports(?x2043, ?x4045), sports(?x2043, ?x2044), film_release_region(?x5564, ?x205), film_release_region(?x1108, ?x205), film_release_region(?x607, ?x205), country(?x150, ?x205), featured_film_locations(?x787, ?x205), ?x1108 = 0jjy0, ?x607 = 02x3lt7, film_crew_role(?x5564, ?x137), country(?x2044, ?x87), ?x4045 = 06z6r, nationality(?x101, ?x205) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lv1x olympics! 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 58.000 0.870 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #14724-01vv6_6 PRED entity: 01vv6_6 PRED relation: nationality PRED expected values: 02jx1 => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 76): 02jx1 (0.80 #13033, 0.78 #5866, 0.78 #11237), 07ssc (0.80 #13033, 0.39 #13034, 0.34 #9447), 04jpl (0.39 #13034, 0.34 #9447, 0.34 #12234), 0g14f (0.28 #12335), 0d060g (0.16 #402, 0.12 #601, 0.10 #502), 03rk0 (0.09 #3918, 0.08 #8498, 0.08 #8697), 035qy (0.06 #132, 0.05 #5168, 0.04 #2977), 06q1r (0.06 #5842, 0.05 #472, 0.05 #5168), 03_3d (0.06 #2387, 0.06 #203, 0.05 #302), 0chghy (0.06 #207, 0.05 #5168, 0.04 #2977) >> Best rule #13033 for best value: >> intensional similarity = 3 >> extensional distance = 2046 >> proper extension: 05fh2; >> query: (?x3472, ?x1310) <- place_of_birth(?x3472, ?x6764), contains(?x1310, ?x6764), nationality(?x57, ?x1310) >> conf = 0.80 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01vv6_6 nationality 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.797 http://example.org/people/person/nationality #14723-0ldqf PRED entity: 0ldqf PRED relation: sports PRED expected values: 06wrt 06z6r => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 47): 03fyrh (0.87 #77, 0.86 #193, 0.86 #349), 0486tv (0.87 #77, 0.86 #193, 0.86 #349), 06wrt (0.85 #434, 0.83 #1013, 0.82 #319), 06z6r (0.84 #1102, 0.83 #1023, 0.80 #289), 07_53 (0.75 #142, 0.67 #180, 0.65 #721), 03krj (0.67 #186, 0.64 #342, 0.62 #457), 03_8r (0.56 #167, 0.55 #323, 0.46 #78), 06z68 (0.50 #251, 0.50 #136, 0.46 #78), 019w9j (0.50 #249, 0.46 #78, 0.42 #465), 0w0d (0.46 #78, 0.42 #465, 0.41 #702) >> Best rule #77 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 0kbvb; 0jhn7; 0lgxj; >> query: (?x7441, ?x171) <- sports(?x7441, ?x4833), sports(?x7441, ?x171), olympics(?x2267, ?x7441), olympics(?x1497, ?x7441), olympics(?x1003, ?x7441), olympics(?x429, ?x7441), ?x4833 = 018w8, ?x1003 = 03gj2, sports(?x7441, ?x779), ?x1497 = 015qh, ?x429 = 03rt9, film_release_region(?x5827, ?x2267), country(?x2266, ?x2267), ?x5827 = 0ggbfwf >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #434 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 11 *> proper extension: 0l6ny; 09x3r; *> query: (?x7441, 06wrt) <- sports(?x7441, ?x4833), olympics(?x1003, ?x7441), ?x4833 = 018w8, film_release_region(?x8193, ?x1003), film_release_region(?x6603, ?x1003), film_release_region(?x6520, ?x1003), film_release_region(?x5992, ?x1003), ?x8193 = 03z9585, ?x5992 = 0g5q34q, ?x6603 = 094g2z, ?x6520 = 02bg55, country(?x1037, ?x1003) *> conf = 0.85 ranks of expected_values: 3, 4 EVAL 0ldqf sports 06z6r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 59.000 59.000 0.871 http://example.org/olympics/olympic_games/sports EVAL 0ldqf sports 06wrt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 59.000 59.000 0.871 http://example.org/olympics/olympic_games/sports #14722-0h10vt PRED entity: 0h10vt PRED relation: award_winner PRED expected values: 01pj5q => 94 concepts (39 used for prediction) PRED predicted values (max 10 best out of 479): 016khd (0.82 #62570, 0.82 #43317, 0.82 #33689), 01swck (0.82 #62570, 0.82 #43317, 0.82 #33689), 03zz8b (0.82 #62570, 0.82 #43317, 0.82 #33689), 01pj5q (0.50 #6033, 0.14 #38500, 0.02 #9240), 0h10vt (0.43 #6229, 0.33 #4626, 0.14 #38500), 013knm (0.20 #2201, 0.03 #11823, 0.03 #23056), 01438g (0.20 #2103, 0.02 #11725, 0.02 #14935), 011zd3 (0.20 #1959, 0.02 #11581, 0.02 #14791), 02__7n (0.20 #2780, 0.02 #12402, 0.02 #15612), 02qgyv (0.20 #1966, 0.01 #11588, 0.01 #13193) >> Best rule #62570 for best value: >> intensional similarity = 3 >> extensional distance = 1326 >> proper extension: 01p5yn; 05s34b; >> query: (?x9561, ?x851) <- award_winner(?x9561, ?x2531), award_winner(?x851, ?x9561), nominated_for(?x2531, ?x1508) >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #6033 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 030hcs; *> query: (?x9561, 01pj5q) <- award_winner(?x9561, ?x4173), award(?x9561, ?x704), ?x4173 = 01wz01 *> conf = 0.50 ranks of expected_values: 4 EVAL 0h10vt award_winner 01pj5q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 39.000 0.821 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #14721-01jzyx PRED entity: 01jzyx PRED relation: contains! PRED expected values: 01sn04 => 192 concepts (149 used for prediction) PRED predicted values (max 10 best out of 225): 0ccvx (0.77 #86667, 0.76 #99172, 0.76 #15189), 01cx_ (0.62 #1982, 0.20 #14489, 0.05 #94900), 05k7sb (0.46 #1918, 0.24 #14425, 0.05 #3707), 07b_l (0.22 #1114, 0.20 #2903, 0.15 #3796), 04jpl (0.19 #94726, 0.05 #67025, 0.05 #3597), 05tbn (0.16 #26131, 0.16 #27917, 0.15 #22558), 01n7q (0.15 #39387, 0.14 #58148, 0.13 #55467), 01x73 (0.15 #14407, 0.03 #22449, 0.03 #25128), 02jx1 (0.14 #126065, 0.14 #126960, 0.13 #85857), 07z1m (0.14 #6345, 0.13 #7239, 0.10 #14384) >> Best rule #86667 for best value: >> intensional similarity = 4 >> extensional distance = 236 >> proper extension: 0p5wz; 095kp; >> query: (?x5426, ?x4253) <- currency(?x5426, ?x170), organization(?x346, ?x5426), contains(?x94, ?x5426), citytown(?x5426, ?x4253) >> conf = 0.77 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jzyx contains! 01sn04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 192.000 149.000 0.773 http://example.org/location/location/contains #14720-03xhj6 PRED entity: 03xhj6 PRED relation: artist! PRED expected values: 030jj7 => 78 concepts (74 used for prediction) PRED predicted values (max 10 best out of 128): 0fb0v (0.70 #2515, 0.33 #137, 0.17 #401), 01cl2y (0.50 #290, 0.33 #26, 0.19 #686), 01w40h (0.39 #3326, 0.27 #2137, 0.19 #1873), 033hn8 (0.37 #5035, 0.31 #672, 0.24 #1597), 011k1h (0.36 #5031, 0.25 #2385, 0.22 #2253), 01clyr (0.33 #29, 0.25 #293, 0.22 #4787), 03qy3l (0.33 #55, 0.25 #319, 0.11 #1243), 013x0b (0.33 #133, 0.17 #397, 0.11 #793), 0229rs (0.25 #279, 0.17 #411, 0.12 #675), 01cl0d (0.25 #313, 0.13 #4807, 0.12 #709) >> Best rule #2515 for best value: >> intensional similarity = 5 >> extensional distance = 74 >> proper extension: 05_swj; >> query: (?x4484, 0fb0v) <- artist(?x382, ?x4484), artists(?x474, ?x4484), parent_genre(?x474, ?x3915), parent_genre(?x3232, ?x474), award(?x382, ?x500) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2510 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 63 *> proper extension: 01q_ph; *> query: (?x4484, ?x2299) <- artist(?x9224, ?x4484), artist(?x2931, ?x4484), ?x9224 = 0n85g, artist(?x2931, ?x9287), artist(?x2931, ?x6996), artist(?x2931, ?x4790), ?x4790 = 01kph_c, artist(?x2299, ?x9287), instrumentalists(?x716, ?x6996) *> conf = 0.06 ranks of expected_values: 84 EVAL 03xhj6 artist! 030jj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 78.000 74.000 0.697 http://example.org/music/record_label/artist #14719-01wxyx1 PRED entity: 01wxyx1 PRED relation: film PRED expected values: 011wtv => 128 concepts (108 used for prediction) PRED predicted values (max 10 best out of 955): 011yth (0.33 #297, 0.12 #5643, 0.11 #7425), 0b44shh (0.33 #877, 0.12 #6223, 0.11 #8005), 078sj4 (0.33 #451, 0.12 #5797, 0.11 #7579), 033srr (0.33 #654, 0.12 #6000, 0.11 #7782), 09m6kg (0.33 #31, 0.12 #5377, 0.11 #7159), 0dgq_kn (0.33 #1035, 0.12 #6381, 0.11 #8163), 015bpl (0.33 #1385, 0.12 #6731, 0.11 #8513), 027r7k (0.33 #1715, 0.12 #7061, 0.11 #8843), 047myg9 (0.33 #1123, 0.12 #6469, 0.11 #8251), 0cq7tx (0.33 #733, 0.12 #6079, 0.11 #7861) >> Best rule #297 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01ycbq; >> query: (?x2108, 011yth) <- film(?x2108, ?x6148), film(?x2108, ?x4127), ?x4127 = 049mql, ?x6148 = 04pmnt >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6113 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 03h_fqv; 012q4n; *> query: (?x2108, 011wtv) <- film(?x2108, ?x4127), film(?x2108, ?x1481), ?x1481 = 02r79_h, production_companies(?x4127, ?x382) *> conf = 0.12 ranks of expected_values: 78 EVAL 01wxyx1 film 011wtv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 128.000 108.000 0.333 http://example.org/film/actor/film./film/performance/film #14718-0558_1 PRED entity: 0558_1 PRED relation: colors PRED expected values: 083jv 09ggk => 223 concepts (223 used for prediction) PRED predicted values (max 10 best out of 21): 083jv (0.44 #149, 0.38 #170, 0.35 #1703), 01l849 (0.43 #64, 0.39 #358, 0.35 #421), 01g5v (0.33 #151, 0.32 #991, 0.29 #928), 03wkwg (0.29 #100, 0.15 #184, 0.13 #268), 019sc (0.22 #848, 0.21 #638, 0.18 #1709), 036k5h (0.20 #258, 0.19 #300, 0.19 #279), 09ggk (0.20 #269, 0.17 #59, 0.17 #38), 06fvc (0.18 #990, 0.18 #549, 0.17 #843), 038hg (0.17 #55, 0.17 #34, 0.14 #97), 02rnmb (0.15 #182, 0.12 #308, 0.08 #518) >> Best rule #149 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 07w0v; >> query: (?x11688, 083jv) <- state_province_region(?x11688, ?x3634), currency(?x11688, ?x170), category(?x11688, ?x134), ?x3634 = 07b_l, ?x134 = 08mbj5d >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 0558_1 colors 09ggk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 223.000 223.000 0.444 http://example.org/education/educational_institution/colors EVAL 0558_1 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 223.000 223.000 0.444 http://example.org/education/educational_institution/colors #14717-04s1zr PRED entity: 04s1zr PRED relation: music PRED expected values: 0fpjyd => 80 concepts (64 used for prediction) PRED predicted values (max 10 best out of 53): 0gv07g (0.20 #132), 01tc9r (0.06 #275, 0.03 #697, 0.03 #907), 01x1fq (0.06 #385, 0.02 #2073, 0.02 #1017), 02jxmr (0.05 #706, 0.05 #1338, 0.04 #495), 0146pg (0.05 #1063, 0.05 #1697, 0.04 #431), 02bh9 (0.04 #3222, 0.04 #683, 0.04 #2372), 02cyfz (0.04 #244, 0.03 #1932, 0.03 #5742), 023361 (0.04 #360, 0.03 #571, 0.02 #6911), 015wc0 (0.04 #386, 0.03 #597, 0.02 #1018), 0150t6 (0.04 #4274, 0.03 #5542, 0.03 #5964) >> Best rule #132 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 034qrh; >> query: (?x11332, 0gv07g) <- film(?x13847, ?x11332), film_release_distribution_medium(?x11332, ?x81), nominated_for(?x2252, ?x11332), genre(?x11332, ?x600), ?x13847 = 0378zn >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04s1zr music 0fpjyd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 64.000 0.200 http://example.org/film/film/music #14716-0b_72t PRED entity: 0b_72t PRED relation: team PRED expected values: 02pzy52 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 8): 02pzy52 (0.76 #151, 0.70 #175, 0.67 #159), 02ptzz0 (0.60 #17, 0.52 #201, 0.50 #153), 02pjzvh (0.59 #259, 0.53 #107, 0.53 #147), 02pyyld (0.50 #72, 0.50 #48, 0.50 #16), 0263cyj (0.50 #69, 0.50 #29, 0.47 #109), 03d555l (0.50 #10, 0.40 #66, 0.39 #202), 03d5m8w (0.38 #46, 0.35 #174, 0.34 #262), 02r2qt7 (0.38 #44, 0.33 #4, 0.31 #260) >> Best rule #151 for best value: >> intensional similarity = 10 >> extensional distance = 15 >> proper extension: 0b_6zk; 0b_6jz; 0br1xn; 0b_75k; 0bzrxn; 0b_6s7; 0b_6xf; >> query: (?x7042, 02pzy52) <- team(?x7042, ?x12370), team(?x7042, ?x8728), team(?x4570, ?x12370), ?x8728 = 026xxv_, locations(?x7042, ?x3269), team(?x9956, ?x12370), team(?x9146, ?x12370), sport(?x12370, ?x12913), ?x9956 = 0bzrsh, ?x9146 = 0b_6qj >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_72t team 02pzy52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.765 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #14715-05k7sb PRED entity: 05k7sb PRED relation: location! PRED expected values: 05fyss 01nr63 => 120 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1793): 01797x (0.25 #7072, 0.25 #4576, 0.17 #17056), 04z0g (0.25 #6163, 0.25 #3667, 0.17 #16147), 099d4 (0.25 #7336, 0.25 #4840, 0.17 #17320), 03f1zdw (0.25 #5196, 0.25 #2700, 0.17 #15180), 032r1 (0.25 #7286, 0.25 #4790, 0.17 #17270), 02756j (0.25 #6261, 0.25 #3765, 0.17 #16245), 0b78hw (0.25 #5837, 0.25 #3341, 0.17 #15821), 01zwy (0.25 #6703, 0.25 #4207, 0.17 #16687), 099p5 (0.25 #6878, 0.25 #4382, 0.17 #16862), 06crk (0.25 #6270, 0.25 #3774, 0.17 #16254) >> Best rule #7072 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0k3jq; >> query: (?x2020, 01797x) <- contains(?x94, ?x2020), contains(?x2020, ?x12099), ?x12099 = 0d739 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #16204 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 03kxzm; *> query: (?x2020, 05fyss) <- contains(?x94, ?x2020), contains(?x2020, ?x10563), ?x10563 = 02s838 *> conf = 0.17 ranks of expected_values: 62 EVAL 05k7sb location! 01nr63 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 97.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location EVAL 05k7sb location! 05fyss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 120.000 97.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #14714-06wbm8q PRED entity: 06wbm8q PRED relation: film! PRED expected values: 05qd_ => 95 concepts (65 used for prediction) PRED predicted values (max 10 best out of 53): 056ws9 (0.69 #1358, 0.55 #1282, 0.51 #980), 01795t (0.44 #395, 0.17 #94, 0.15 #320), 054g1r (0.33 #412, 0.11 #1693, 0.10 #337), 05qd_ (0.28 #9, 0.20 #160, 0.19 #913), 03xq0f (0.23 #307, 0.23 #81, 0.21 #609), 086k8 (0.20 #1208, 0.18 #1133, 0.18 #2186), 016tt2 (0.17 #230, 0.17 #80, 0.17 #608), 016tw3 (0.17 #162, 0.15 #2496, 0.14 #4465), 017s11 (0.15 #907, 0.12 #1812, 0.12 #3697), 01gb54 (0.14 #180, 0.13 #406, 0.09 #1084) >> Best rule #1358 for best value: >> intensional similarity = 4 >> extensional distance = 213 >> proper extension: 0524b41; >> query: (?x2628, ?x5970) <- nominated_for(?x1723, ?x2628), award(?x2628, ?x3911), nominated_for(?x5970, ?x2628), production_companies(?x1904, ?x5970) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 07s8z_l; *> query: (?x2628, 05qd_) <- award_winner(?x2628, ?x5970), company(?x1491, ?x5970), currency(?x5970, ?x170) *> conf = 0.28 ranks of expected_values: 4 EVAL 06wbm8q film! 05qd_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 95.000 65.000 0.692 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #14713-094xh PRED entity: 094xh PRED relation: profession PRED expected values: 0nbcg => 225 concepts (165 used for prediction) PRED predicted values (max 10 best out of 89): 02hrh1q (0.85 #6167, 0.85 #5867, 0.84 #6767), 09jwl (0.82 #8575, 0.81 #2570, 0.81 #9025), 0nbcg (0.64 #1533, 0.64 #1383, 0.59 #8436), 016z4k (0.60 #4054, 0.50 #9460, 0.50 #3454), 01d_h8 (0.50 #156, 0.43 #3306, 0.37 #7959), 0dz3r (0.46 #12315, 0.45 #8405, 0.44 #8557), 039v1 (0.45 #1538, 0.44 #5440, 0.41 #8593), 01c72t (0.40 #325, 0.35 #6903, 0.34 #5402), 09lbv (0.35 #6903, 0.34 #5402, 0.18 #1521), 029bkp (0.35 #6903, 0.34 #5402, 0.12 #950) >> Best rule #6167 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 03cvfg; >> query: (?x5312, 02hrh1q) <- student(?x8427, ?x5312), celebrity(?x5312, ?x5285), award_nominee(?x2639, ?x5285) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1533 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 02fybl; *> query: (?x5312, 0nbcg) <- celebrity(?x5285, ?x5312), location(?x5312, ?x4577), role(?x5312, ?x1166), adjoins(?x3125, ?x4577) *> conf = 0.64 ranks of expected_values: 3 EVAL 094xh profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 225.000 165.000 0.849 http://example.org/people/person/profession #14712-02vxq9m PRED entity: 02vxq9m PRED relation: nominated_for! PRED expected values: 02r22gf => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 213): 099c8n (0.78 #501, 0.40 #277, 0.36 #950), 0k611 (0.77 #293, 0.51 #517, 0.35 #966), 0gq9h (0.67 #506, 0.60 #282, 0.42 #955), 018wdw (0.67 #3138, 0.67 #4037, 0.67 #4486), 019f4v (0.57 #274, 0.44 #498, 0.40 #947), 0gs9p (0.55 #508, 0.47 #284, 0.37 #957), 04dn09n (0.53 #256, 0.49 #480, 0.35 #929), 0gqy2 (0.53 #336, 0.47 #1009, 0.31 #560), 040njc (0.51 #454, 0.47 #230, 0.41 #903), 02r22gf (0.50 #249, 0.40 #25, 0.31 #473) >> Best rule #501 for best value: >> intensional similarity = 2 >> extensional distance = 53 >> proper extension: 0b73_1d; 026p4q7; 02vqsll; 03hmt9b; 0dgq_kn; >> query: (?x186, 099c8n) <- nominated_for(?x277, ?x186), ?x277 = 0f_nbyh >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #249 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 020fcn; 0jqj5; 035_2h; 049xgc; 01y9r2; *> query: (?x186, 02r22gf) <- nominated_for(?x500, ?x186), nominated_for(?x112, ?x186), ?x112 = 027dtxw, ?x500 = 0p9sw *> conf = 0.50 ranks of expected_values: 10 EVAL 02vxq9m nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 52.000 52.000 0.782 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14711-07bch9 PRED entity: 07bch9 PRED relation: people PRED expected values: 0n6f8 0453t 0d06m5 01vsyjy 0gt3p 01vsn38 => 29 concepts (7 used for prediction) PRED predicted values (max 10 best out of 4037): 0hwqz (0.33 #798, 0.12 #2452, 0.11 #4104), 01fkxr (0.33 #1243, 0.12 #2897, 0.11 #4549), 016kkx (0.33 #880, 0.12 #2534, 0.11 #4186), 0lkr7 (0.33 #677, 0.12 #7287, 0.10 #5635), 01vwllw (0.33 #411, 0.11 #3717, 0.09 #7021), 0197tq (0.33 #21, 0.11 #3327, 0.07 #4979), 0261x8t (0.33 #928, 0.09 #9191, 0.07 #10845), 0dn3n (0.33 #389, 0.08 #1654, 0.05 #3695), 01hcj2 (0.33 #1286, 0.07 #1653, 0.05 #4592), 06rgq (0.33 #1149, 0.07 #1653, 0.05 #4455) >> Best rule #798 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 07hwkr; >> query: (?x5741, 0hwqz) <- people(?x5741, ?x9276), people(?x5741, ?x5097), people(?x5741, ?x509), participant(?x3422, ?x9276), participant(?x3034, ?x9276), profession(?x9276, ?x319), nominated_for(?x5097, ?x5066), gender(?x509, ?x514), ?x5066 = 07bwr >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #442 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 07hwkr; *> query: (?x5741, 0d06m5) <- people(?x5741, ?x9276), people(?x5741, ?x5097), people(?x5741, ?x509), participant(?x3422, ?x9276), participant(?x3034, ?x9276), profession(?x9276, ?x319), nominated_for(?x5097, ?x5066), gender(?x509, ?x514), ?x5066 = 07bwr *> conf = 0.33 ranks of expected_values: 69, 514, 798, 2043, 2732, 3707 EVAL 07bch9 people 01vsn38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 29.000 7.000 0.333 http://example.org/people/ethnicity/people EVAL 07bch9 people 0gt3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 29.000 7.000 0.333 http://example.org/people/ethnicity/people EVAL 07bch9 people 01vsyjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 29.000 7.000 0.333 http://example.org/people/ethnicity/people EVAL 07bch9 people 0d06m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 29.000 7.000 0.333 http://example.org/people/ethnicity/people EVAL 07bch9 people 0453t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 29.000 7.000 0.333 http://example.org/people/ethnicity/people EVAL 07bch9 people 0n6f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 7.000 0.333 http://example.org/people/ethnicity/people #14710-0dn8b PRED entity: 0dn8b PRED relation: adjoins PRED expected values: 0cv0r => 144 concepts (48 used for prediction) PRED predicted values (max 10 best out of 407): 0cc1v (0.81 #36356, 0.80 #15467, 0.80 #29389), 0cc07 (0.33 #2951, 0.30 #3724, 0.25 #1405), 0mnlq (0.33 #2975, 0.30 #3748, 0.17 #4521), 07z1m (0.33 #79, 0.25 #1625, 0.08 #10904), 0rh6k (0.33 #3, 0.25 #1549, 0.03 #10828), 0bx9y (0.25 #1237, 0.22 #2783, 0.20 #3556), 0dn8b (0.25 #1406, 0.11 #2952, 0.10 #3725), 0d060g (0.25 #1556, 0.06 #10835, 0.05 #19344), 0j3b (0.25 #1606, 0.05 #10885, 0.04 #13978), 0mnz0 (0.25 #1445, 0.04 #6083) >> Best rule #36356 for best value: >> intensional similarity = 4 >> extensional distance = 215 >> proper extension: 0f4y_; 0mm0p; 0drr3; 09dfcj; 0l2mg; 0n4z2; 0mrf1; >> query: (?x12275, ?x10601) <- time_zones(?x12275, ?x2674), source(?x12275, ?x958), adjoins(?x10601, ?x12275), currency(?x12275, ?x170) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #6183 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 25 *> proper extension: 09kvv; 01jq34; 0352gk; 027rqbx; 02h7qr; 0sxgh; 0txhf; *> query: (?x12275, ?x14070) <- contains(?x6845, ?x12275), contains(?x6845, ?x14070), contains(?x6845, ?x13133), contains(?x6845, ?x12680), ?x12680 = 0cv1w, ?x13133 = 0fr5p *> conf = 0.02 ranks of expected_values: 211 EVAL 0dn8b adjoins 0cv0r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 144.000 48.000 0.806 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #14709-01rtm4 PRED entity: 01rtm4 PRED relation: institution! PRED expected values: 014mlp => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 24): 02_xgp2 (0.78 #38, 0.51 #158, 0.51 #182), 0bkj86 (0.78 #34, 0.51 #154, 0.44 #178), 03bwzr4 (0.78 #40, 0.45 #160, 0.44 #184), 014mlp (0.77 #199, 0.74 #345, 0.71 #418), 02h4rq6 (0.67 #28, 0.66 #1233, 0.66 #680), 016t_3 (0.67 #29, 0.45 #681, 0.44 #368), 04zx3q1 (0.56 #27, 0.41 #147, 0.35 #366), 027f2w (0.56 #35, 0.33 #10, 0.27 #179), 07s6fsf (0.44 #26, 0.33 #678, 0.33 #1), 028dcg (0.44 #45, 0.33 #20, 0.32 #93) >> Best rule #38 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 065y4w7; 0j_sncb; 03ksy; 01n6r0; 01lhdt; 0bwfn; >> query: (?x263, 02_xgp2) <- student(?x263, ?x5468), colors(?x263, ?x663), institution(?x1771, ?x263), film_festivals(?x5468, ?x11231) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #199 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 06bw5; *> query: (?x263, 014mlp) <- student(?x263, ?x10617), currency(?x263, ?x170), major_field_of_study(?x263, ?x254), award(?x10617, ?x704) *> conf = 0.77 ranks of expected_values: 4 EVAL 01rtm4 institution! 014mlp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 118.000 118.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14708-01jw67 PRED entity: 01jw67 PRED relation: nominated_for! PRED expected values: 0k611 02y_rq5 => 107 concepts (103 used for prediction) PRED predicted values (max 10 best out of 211): 0k611 (0.67 #1010, 0.58 #4536, 0.43 #2890), 0gq9h (0.59 #999, 0.56 #2879, 0.56 #294), 0gq_v (0.59 #960, 0.51 #2840, 0.49 #3075), 04dn09n (0.56 #269, 0.44 #3324, 0.41 #2149), 0gqyl (0.56 #312, 0.42 #2427, 0.37 #1252), 0f4x7 (0.56 #260, 0.40 #2375, 0.38 #2845), 02qyntr (0.53 #4643, 0.35 #2292, 0.30 #1117), 0l8z1 (0.52 #989, 0.30 #4515, 0.30 #2164), 019f4v (0.49 #2166, 0.48 #991, 0.44 #2871), 02qvyrt (0.46 #4560, 0.37 #1034, 0.33 #1269) >> Best rule #1010 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 02k1pr; >> query: (?x6222, 0k611) <- nominated_for(?x500, ?x6222), honored_for(?x810, ?x6222), film(?x2173, ?x6222), ?x500 = 0p9sw >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 40 EVAL 01jw67 nominated_for! 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 107.000 103.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01jw67 nominated_for! 0k611 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 103.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14707-020ddc PRED entity: 020ddc PRED relation: major_field_of_study PRED expected values: 06ms6 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 121): 01mkq (0.56 #515, 0.53 #3265, 0.52 #2390), 02j62 (0.50 #531, 0.47 #2156, 0.46 #3406), 02lp1 (0.50 #511, 0.45 #3261, 0.44 #2386), 062z7 (0.50 #528, 0.40 #1028, 0.38 #2903), 04rjg (0.47 #520, 0.44 #2395, 0.44 #2895), 04x_3 (0.47 #526, 0.33 #1151, 0.32 #776), 03g3w (0.47 #2152, 0.43 #1152, 0.42 #1027), 0g26h (0.44 #544, 0.39 #3920, 0.35 #6674), 05qjt (0.40 #882, 0.35 #757, 0.34 #3382), 02_7t (0.39 #567, 0.29 #3943, 0.28 #2192) >> Best rule #515 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 02t4yc; 029qzx; >> query: (?x8822, 01mkq) <- company(?x346, ?x8822), contains(?x94, ?x8822), student(?x8822, ?x459), school(?x7399, ?x8822) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #892 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 0d5fb; *> query: (?x8822, 06ms6) <- company(?x346, ?x8822), colors(?x8822, ?x3189), colors(?x8822, ?x663), ?x663 = 083jv, colors(?x387, ?x3189) *> conf = 0.28 ranks of expected_values: 21 EVAL 020ddc major_field_of_study 06ms6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 121.000 121.000 0.556 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14706-01yqqv PRED entity: 01yqqv PRED relation: school! PRED expected values: 06x68 => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 92): 05m_8 (0.14 #5026, 0.12 #4747, 0.12 #4561), 01slc (0.14 #244, 0.11 #58, 0.08 #430), 05g76 (0.11 #21, 0.07 #207, 0.07 #2346), 03m1n (0.11 #85, 0.07 #271, 0.06 #5108), 01xvb (0.11 #13, 0.07 #199, 0.03 #385), 0bwjj (0.11 #75, 0.06 #5098, 0.06 #1191), 07l4z (0.10 #4628, 0.10 #4814, 0.09 #4349), 0713r (0.10 #5059, 0.09 #4315, 0.08 #4594), 01yjl (0.10 #5053, 0.09 #4309, 0.09 #2355), 06x68 (0.09 #4286, 0.09 #4565, 0.09 #4751) >> Best rule #5026 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 0frm7n; >> query: (?x9522, 05m_8) <- school(?x2820, ?x9522), school(?x2820, ?x9131), ?x9131 = 02pptm >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #4286 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 125 *> proper extension: 02zc7f; *> query: (?x9522, 06x68) <- currency(?x9522, ?x170), colors(?x9522, ?x663), school(?x2820, ?x9522), contains(?x94, ?x9522) *> conf = 0.09 ranks of expected_values: 10 EVAL 01yqqv school! 06x68 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 181.000 181.000 0.145 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #14705-04rrd PRED entity: 04rrd PRED relation: religion PRED expected values: 05sfs 01y0s9 => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 21): 05sfs (0.83 #386, 0.83 #314, 0.82 #146), 01y0s9 (0.66 #316, 0.65 #388, 0.62 #196), 0flw86 (0.39 #1297, 0.37 #3074, 0.37 #1826), 072w0 (0.37 #3074, 0.37 #3123, 0.26 #158), 02t7t (0.28 #324, 0.27 #396, 0.27 #444), 03j6c (0.25 #34, 0.09 #1835, 0.09 #1571), 0kpl (0.25 #29, 0.03 #917, 0.03 #1061), 07w8f (0.25 #42, 0.02 #234, 0.02 #810), 04t_mf (0.04 #1840, 0.01 #1311), 0n2g (0.03 #1831, 0.03 #150, 0.03 #126) >> Best rule #386 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05kr_; >> query: (?x1767, 05sfs) <- district_represented(?x176, ?x1767), religion(?x1767, ?x109), state_province_region(?x1768, ?x1767) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04rrd religion 01y0s9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.833 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 04rrd religion 05sfs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.833 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #14704-0bdx29 PRED entity: 0bdx29 PRED relation: award! PRED expected values: 0g60z => 41 concepts (23 used for prediction) PRED predicted values (max 10 best out of 771): 0g60z (0.42 #7097, 0.42 #5068, 0.42 #6082), 080dwhx (0.42 #7097, 0.42 #5068, 0.42 #6082), 030k94 (0.42 #7097, 0.42 #5068, 0.42 #6082), 01rf57 (0.42 #7097, 0.42 #5068, 0.42 #6082), 03d34x8 (0.42 #7097, 0.42 #5068, 0.42 #6082), 02rzdcp (0.42 #7097, 0.42 #5068, 0.42 #6082), 039c26 (0.42 #7097, 0.42 #5068, 0.42 #6082), 01g03q (0.42 #7097, 0.42 #5068, 0.42 #6082), 017f3m (0.42 #7097, 0.42 #5068, 0.40 #8111), 02md2d (0.42 #7097, 0.42 #5068, 0.40 #8111) >> Best rule #7097 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 0262s1; >> query: (?x2041, ?x2009) <- nominated_for(?x2041, ?x3303), nominated_for(?x2041, ?x2009), program(?x3727, ?x2009), award(?x931, ?x2041), actor(?x3303, ?x818) >> conf = 0.42 => this is the best rule for 12 predicted values ranks of expected_values: 1 EVAL 0bdx29 award! 0g60z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 23.000 0.417 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #14703-0hhggmy PRED entity: 0hhggmy PRED relation: currency PRED expected values: 09nqf => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.85 #260, 0.84 #218, 0.84 #281), 01nv4h (0.51 #477, 0.26 #662, 0.13 #633), 02l6h (0.51 #477, 0.26 #662, 0.13 #633), 02gsvk (0.26 #662, 0.12 #692, 0.12 #684), 088n7 (0.13 #633, 0.13 #625, 0.12 #692), 0kz1h (0.13 #633, 0.13 #625, 0.12 #692), 0ptk_ (0.13 #633, 0.13 #625, 0.12 #692) >> Best rule #260 for best value: >> intensional similarity = 11 >> extensional distance = 180 >> proper extension: 0fdv3; >> query: (?x8580, 09nqf) <- film_crew_role(?x8580, ?x2095), film_crew_role(?x8580, ?x1171), ?x1171 = 09vw2b7, genre(?x8580, ?x604), ?x2095 = 0dxtw, genre(?x8063, ?x604), genre(?x1283, ?x604), genre(?x186, ?x604), ?x186 = 02vxq9m, production_companies(?x8063, ?x382), ?x1283 = 0cnztc4 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hhggmy currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.846 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #14702-032r1 PRED entity: 032r1 PRED relation: influenced_by! PRED expected values: 052h3 => 151 concepts (87 used for prediction) PRED predicted values (max 10 best out of 424): 07h1q (0.38 #1423, 0.33 #1932, 0.23 #2443), 0399p (0.30 #6444, 0.28 #8994, 0.26 #8485), 01d494 (0.29 #4131, 0.23 #8211, 0.22 #8720), 0dzkq (0.29 #8286, 0.25 #8795, 0.20 #6245), 045bg (0.26 #8196, 0.22 #8705, 0.16 #16858), 032r1 (0.25 #1488, 0.22 #1997, 0.19 #9139), 07dnx (0.25 #1376, 0.22 #1885, 0.15 #2396), 041jlr (0.25 #1375, 0.22 #1884, 0.15 #2395), 0nk72 (0.23 #8497, 0.17 #9006, 0.10 #17159), 0459z (0.22 #1987, 0.12 #1478, 0.11 #5050) >> Best rule #1423 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 032md; >> query: (?x11837, 07h1q) <- location(?x11837, ?x863), ?x863 = 0fhp9, religion(?x11837, ?x1985), influenced_by(?x3864, ?x11837) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #8805 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 07h1q; *> query: (?x11837, 052h3) <- interests(?x11837, ?x1858), influenced_by(?x3864, ?x11837), gender(?x11837, ?x231) *> conf = 0.11 ranks of expected_values: 62 EVAL 032r1 influenced_by! 052h3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 151.000 87.000 0.375 http://example.org/influence/influence_node/influenced_by #14701-04l59s PRED entity: 04l59s PRED relation: colors PRED expected values: 083jv 06fvc 01g5v => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.97 #756, 0.97 #800, 0.80 #197), 019sc (0.90 #617, 0.90 #569, 0.83 #146), 01g5v (0.87 #591, 0.34 #821, 0.32 #783), 01l849 (0.67 #495, 0.57 #78, 0.55 #118), 06fvc (0.60 #61, 0.55 #101, 0.53 #217), 06kqt3 (0.57 #78, 0.55 #118, 0.31 #39), 0jc_p (0.31 #39, 0.30 #391, 0.27 #651), 0680m7 (0.31 #39, 0.30 #391, 0.25 #778), 088fh (0.31 #39, 0.30 #391, 0.25 #778), 02rnmb (0.31 #39, 0.24 #350, 0.23 #393) >> Best rule #756 for best value: >> intensional similarity = 25 >> extensional distance = 187 >> proper extension: 04088s0; 026xxv_; 0263cyj; 03dkx; >> query: (?x14123, 083jv) <- sport(?x14123, ?x453), colors(?x14123, ?x5325), sport(?x14258, ?x453), sport(?x3723, ?x453), team(?x2918, ?x14258), colors(?x14258, ?x1101), colors(?x12629, ?x5325), colors(?x9995, ?x5325), colors(?x8228, ?x5325), colors(?x7060, ?x5325), colors(?x6074, ?x5325), colors(?x3723, ?x4557), ?x8228 = 0jmcv, ?x9995 = 0jm9w, teams(?x2474, ?x14258), ?x4557 = 019sc, position_s(?x12629, ?x2312), season(?x7060, ?x701), school(?x7060, ?x581), ?x6074 = 02__x, colors(?x1609, ?x5325), position(?x7060, ?x261), ?x581 = 06pwq, ?x2312 = 02qpbqj, draft(?x7060, ?x1161) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 5 EVAL 04l59s colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 49.000 49.000 0.974 http://example.org/sports/sports_team/colors EVAL 04l59s colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 49.000 49.000 0.974 http://example.org/sports/sports_team/colors EVAL 04l59s colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.974 http://example.org/sports/sports_team/colors #14700-09thp87 PRED entity: 09thp87 PRED relation: award PRED expected values: 0p9sw => 84 concepts (69 used for prediction) PRED predicted values (max 10 best out of 231): 0p9sw (0.46 #1238, 0.40 #428, 0.38 #833), 09sb52 (0.34 #6521, 0.33 #40, 0.33 #2470), 0gqy2 (0.33 #165, 0.15 #15803, 0.10 #3000), 04kxsb (0.33 #126, 0.15 #15803, 0.10 #2556), 09sdmz (0.33 #207, 0.15 #15803, 0.07 #2637), 057xs89 (0.33 #161, 0.15 #15803, 0.06 #2591), 05pcn59 (0.33 #81, 0.12 #2511, 0.12 #2916), 0f4x7 (0.33 #31, 0.11 #2461, 0.10 #2866), 027dtxw (0.33 #4, 0.07 #2839, 0.07 #2434), 09qv_s (0.33 #152, 0.06 #2582, 0.06 #2987) >> Best rule #1238 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 04cy8rb; 0284n42; 03h26tm; 021yc7p; 06rnl9; 0b79gfg; 0bbxx9b; 0b6mgp_; 02q9kqf; 0g9zcgx; ... >> query: (?x8415, 0p9sw) <- crewmember(?x4742, ?x8415), film_crew_role(?x4742, ?x1284), ?x1284 = 0ch6mp2 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09thp87 award 0p9sw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 69.000 0.457 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14699-02cg7g PRED entity: 02cg7g PRED relation: district_represented PRED expected values: 0gyh => 38 concepts (34 used for prediction) PRED predicted values (max 10 best out of 823): 0gyh (0.91 #284, 0.87 #141, 0.86 #1141), 059f4 (0.91 #284, 0.87 #141, 0.84 #1440), 01n7q (0.91 #284, 0.87 #141, 0.84 #189), 05mph (0.91 #284, 0.87 #141, 0.84 #189), 07z1m (0.91 #284, 0.87 #141, 0.84 #189), 04ykg (0.91 #284, 0.87 #141, 0.84 #189), 02xry (0.91 #284, 0.87 #141, 0.84 #189), 06btq (0.91 #284, 0.87 #141, 0.84 #189), 06yxd (0.91 #284, 0.87 #141, 0.84 #189), 07b_l (0.91 #284, 0.87 #141, 0.84 #189) >> Best rule #284 for best value: >> intensional similarity = 53 >> extensional distance = 1 >> proper extension: 077g7n; >> query: (?x4730, ?x726) <- district_represented(?x4730, ?x6895), district_represented(?x4730, ?x4622), district_represented(?x4730, ?x2020), district_represented(?x4730, ?x1906), district_represented(?x4730, ?x1767), district_represented(?x4730, ?x1138), district_represented(?x4730, ?x448), district_represented(?x4730, ?x335), legislative_sessions(?x4730, ?x5339), legislative_sessions(?x4730, ?x3540), legislative_sessions(?x4730, ?x2976), legislative_sessions(?x4730, ?x2861), legislative_sessions(?x4730, ?x1830), legislative_sessions(?x4730, ?x1028), ?x3540 = 024tcq, legislative_sessions(?x2860, ?x4730), ?x1830 = 03z5xd, legislative_sessions(?x6728, ?x4730), legislative_sessions(?x1137, ?x4730), legislative_sessions(?x1027, ?x4730), ?x2860 = 0b3wk, ?x448 = 03v1s, district_represented(?x1027, ?x6521), district_represented(?x1027, ?x3908), district_represented(?x1027, ?x3634), district_represented(?x1027, ?x3086), district_represented(?x1027, ?x2831), district_represented(?x1027, ?x726), district_represented(?x1027, ?x177), ?x2976 = 03rtmz, legislative_sessions(?x11605, ?x1027), legislative_sessions(?x6742, ?x1027), ?x4622 = 04tgp, ?x1906 = 04rrx, ?x2861 = 03tcbx, ?x3634 = 07b_l, ?x1138 = 059_c, legislative_sessions(?x7961, ?x4730), ?x2831 = 0gyh, ?x1137 = 02bqn1, ?x335 = 059rby, ?x1767 = 04rrd, ?x3908 = 04ly1, ?x5339 = 02glc4, ?x1028 = 032ft5, ?x6895 = 05fjf, ?x11605 = 024_vw, ?x6728 = 070mff, ?x3086 = 0846v, ?x6521 = 05mph, ?x177 = 05kkh, ?x2020 = 05k7sb, ?x6742 = 06bss >> conf = 0.91 => this is the best rule for 29 predicted values ranks of expected_values: 1 EVAL 02cg7g district_represented 0gyh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 34.000 0.907 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #14698-02bh9 PRED entity: 02bh9 PRED relation: award PRED expected values: 02qvyrt => 120 concepts (98 used for prediction) PRED predicted values (max 10 best out of 290): 0gqz2 (0.48 #3680, 0.37 #6080, 0.32 #4880), 054ks3 (0.43 #3739, 0.30 #6139, 0.29 #539), 09sb52 (0.40 #8841, 0.38 #18442, 0.38 #18842), 01by1l (0.40 #5310, 0.35 #3710, 0.31 #12110), 02qvyrt (0.38 #4924, 0.36 #5724, 0.34 #6124), 02x17c2 (0.38 #3816, 0.31 #1016, 0.27 #2216), 0c4z8 (0.37 #3671, 0.25 #5271, 0.23 #12071), 05p09zm (0.32 #1321, 0.21 #2521, 0.14 #13321), 02wh75 (0.31 #809, 0.29 #409, 0.22 #2009), 01bgqh (0.30 #7643, 0.28 #2443, 0.25 #1243) >> Best rule #3680 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 01gf5h; 01wbgdv; 02r4qs; 02v3yy; 01w272y; 0412f5y; 01s21dg; 03q2t9; 013423; 02pt7h_; ... >> query: (?x3410, 0gqz2) <- award_nominee(?x6380, ?x3410), award(?x3410, ?x2238), ?x2238 = 025m8l >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #4924 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 07qy0b; 09swkk; *> query: (?x3410, 02qvyrt) <- award_nominee(?x6380, ?x3410), music(?x4967, ?x3410), film_crew_role(?x4967, ?x468) *> conf = 0.38 ranks of expected_values: 5 EVAL 02bh9 award 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 120.000 98.000 0.476 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14697-01qrb2 PRED entity: 01qrb2 PRED relation: school! PRED expected values: 04wmvz => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 94): 06x68 (0.20 #7, 0.14 #193, 0.08 #844), 05m_8 (0.16 #2893, 0.15 #1587, 0.14 #840), 01yjl (0.14 #216, 0.14 #123, 0.11 #774), 07147 (0.14 #253, 0.14 #160, 0.10 #346), 06wpc (0.14 #250, 0.14 #157, 0.10 #343), 051vz (0.14 #767, 0.13 #2913, 0.11 #1607), 01yhm (0.14 #764, 0.13 #1604, 0.13 #1138), 0jmm4 (0.14 #817, 0.13 #1191, 0.13 #1098), 0jmnl (0.14 #278, 0.11 #1117, 0.09 #464), 01y3c (0.14 #197, 0.09 #383, 0.07 #755) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 016tw3; >> query: (?x9525, 06x68) <- featured_film_locations(?x6740, ?x9525), organization(?x346, ?x9525), child(?x6315, ?x9525), citytown(?x9525, ?x12953) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1849 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 08tyb_; *> query: (?x9525, 04wmvz) <- student(?x9525, ?x9526), citytown(?x9525, ?x12953), participant(?x9526, ?x3210), award_winner(?x749, ?x3210) *> conf = 0.09 ranks of expected_values: 26 EVAL 01qrb2 school! 04wmvz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 171.000 171.000 0.200 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #14696-05pq9 PRED entity: 05pq9 PRED relation: student! PRED expected values: 01n951 => 99 concepts (82 used for prediction) PRED predicted values (max 10 best out of 171): 0bwfn (0.15 #274, 0.09 #800, 0.08 #19211), 01722w (0.13 #830, 0.08 #304, 0.04 #1356), 015nl4 (0.13 #593, 0.04 #15848, 0.03 #9536), 017z88 (0.12 #1134, 0.06 #3238, 0.04 #5343), 02g839 (0.12 #1077, 0.04 #3181, 0.03 #2129), 065y4w7 (0.11 #1592, 0.08 #2644, 0.07 #6853), 03ksy (0.09 #5892, 0.06 #9574, 0.05 #10100), 01qd_r (0.08 #1332, 0.03 #3436, 0.02 #5541), 017rbx (0.08 #341, 0.04 #867, 0.04 #1393), 0yjf0 (0.08 #48, 0.04 #574, 0.04 #1100) >> Best rule #274 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 02fgm7; >> query: (?x2483, 0bwfn) <- award_winner(?x1323, ?x2483), profession(?x2483, ?x11804), award_winner(?x2483, ?x2875), ?x11804 = 0q04f >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05pq9 student! 01n951 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 82.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #14695-0f4dx2 PRED entity: 0f4dx2 PRED relation: type_of_union PRED expected values: 04ztj => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #85, 0.71 #13, 0.71 #9), 01g63y (0.14 #102, 0.14 #98, 0.12 #2) >> Best rule #85 for best value: >> intensional similarity = 2 >> extensional distance = 1101 >> proper extension: 05typm; 01ry0f; 02756j; 01h4rj; 015qq1; 03k545; 081hvm; >> query: (?x3273, 04ztj) <- student(?x4599, ?x3273), film(?x3273, ?x945) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f4dx2 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.721 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14694-0yzvw PRED entity: 0yzvw PRED relation: film_release_region PRED expected values: 09c7w0 03_3d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 234): 09c7w0 (0.93 #6246, 0.93 #3781, 0.93 #3617), 03rjj (0.85 #663, 0.81 #1811, 0.80 #1647), 0345h (0.83 #1842, 0.81 #694, 0.77 #1186), 07ssc (0.80 #1167, 0.79 #1823, 0.79 #675), 03_3d (0.79 #665, 0.78 #1649, 0.76 #1157), 03h64 (0.78 #1879, 0.75 #2208, 0.73 #1715), 015fr (0.76 #1825, 0.72 #2154, 0.69 #1661), 05qhw (0.76 #1821, 0.74 #673, 0.68 #2150), 035qy (0.73 #1844, 0.71 #696, 0.68 #2173), 05b4w (0.71 #1876, 0.67 #2205, 0.65 #1712) >> Best rule #6246 for best value: >> intensional similarity = 4 >> extensional distance = 853 >> proper extension: 087wc7n; 03nqnnk; 063y9fp; >> query: (?x2151, 09c7w0) <- film(?x5363, ?x2151), people(?x1050, ?x5363), award_winner(?x748, ?x5363), film_release_region(?x2151, ?x87) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 0yzvw film_release_region 03_3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 94.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0yzvw film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14693-0j80w PRED entity: 0j80w PRED relation: nominated_for! PRED expected values: 0gr4k 054krc => 91 concepts (82 used for prediction) PRED predicted values (max 10 best out of 183): 0gs9p (0.66 #12515, 0.65 #9209, 0.65 #12278), 0k611 (0.53 #1723, 0.39 #779, 0.37 #1487), 0gs96 (0.42 #1504, 0.40 #1268, 0.39 #796), 0gr0m (0.40 #1710, 0.38 #1238, 0.37 #766), 0gr4k (0.37 #4748, 0.33 #25, 0.28 #5929), 040njc (0.35 #4730, 0.32 #2131, 0.32 #1423), 0l8z1 (0.34 #1703, 0.33 #1467, 0.32 #759), 0f4x7 (0.33 #24, 0.31 #260, 0.29 #5928), 099c8n (0.31 #291, 0.26 #2179, 0.23 #4778), 04dn09n (0.31 #4757, 0.29 #1686, 0.26 #5938) >> Best rule #12515 for best value: >> intensional similarity = 1 >> extensional distance = 1025 >> proper extension: 0lcdk; 0542n; 087z2; >> query: (?x4927, ?x1313) <- award(?x4927, ?x1313) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #4748 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 540 *> proper extension: 02d44q; 0hgnl3t; *> query: (?x4927, 0gr4k) <- nominated_for(?x1307, ?x4927), award(?x6629, ?x1307), ?x6629 = 013t9y *> conf = 0.37 ranks of expected_values: 5, 13 EVAL 0j80w nominated_for! 054krc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 91.000 82.000 0.657 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0j80w nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 82.000 0.657 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14692-0bfp0l PRED entity: 0bfp0l PRED relation: artist PRED expected values: 0bhvtc => 30 concepts (11 used for prediction) PRED predicted values (max 10 best out of 835): 03xhj6 (0.38 #2801, 0.33 #1967, 0.33 #1135), 02vr7 (0.38 #3106, 0.33 #2272, 0.33 #1440), 01wx756 (0.38 #3286, 0.33 #2452, 0.33 #1620), 09hnb (0.38 #2659, 0.33 #1825, 0.33 #993), 089tm (0.38 #2513, 0.33 #1679, 0.33 #847), 0cg9y (0.38 #2632, 0.33 #966, 0.13 #4298), 01wg25j (0.38 #3115, 0.33 #2281, 0.12 #3948), 01vsy7t (0.38 #2814, 0.33 #1148, 0.12 #4480), 0565cz (0.38 #2688, 0.33 #1022, 0.11 #3521), 016dsy (0.38 #2776, 0.33 #1110, 0.10 #6109) >> Best rule #2801 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 01w40h; 011k11; 0181dw; 03qx_f; >> query: (?x13449, 03xhj6) <- artist(?x13449, ?x11514), artist(?x13449, ?x8799), artist(?x13449, ?x4044), artist(?x13449, ?x3419), ?x8799 = 02f1c, award_nominee(?x158, ?x4044), artists(?x505, ?x11514), gender(?x11514, ?x231), award_winner(?x3419, ?x2124), performance_role(?x11514, ?x14165) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1664 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 015_1q; *> query: (?x13449, ?x158) <- artist(?x13449, ?x11514), artist(?x13449, ?x8799), artist(?x13449, ?x4044), artist(?x13449, ?x3419), ?x8799 = 02f1c, award_nominee(?x158, ?x4044), artists(?x505, ?x11514), ?x3419 = 03cfjg, profession(?x4044, ?x220), award(?x4044, ?x159) *> conf = 0.14 ranks of expected_values: 272 EVAL 0bfp0l artist 0bhvtc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 30.000 11.000 0.375 http://example.org/music/record_label/artist #14691-02yv6b PRED entity: 02yv6b PRED relation: parent_genre! PRED expected values: 018lg0 => 56 concepts (29 used for prediction) PRED predicted values (max 10 best out of 307): 0133_p (0.50 #1183, 0.33 #656, 0.29 #2239), 0dn16 (0.44 #2916, 0.33 #11, 0.25 #802), 0173b0 (0.44 #2794, 0.20 #528, 0.20 #527), 01h0kx (0.33 #1445, 0.33 #390, 0.33 #127), 06cp5 (0.33 #2714, 0.33 #1391, 0.33 #336), 05w3f (0.33 #2405, 0.33 #557, 0.33 #292), 0y3_8 (0.33 #2943, 0.33 #301, 0.33 #38), 018ysx (0.33 #734, 0.33 #469, 0.29 #2317), 059kh (0.33 #303, 0.33 #40, 0.25 #831), 015pdg (0.33 #535, 0.33 #270, 0.25 #1062) >> Best rule #1183 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 016jny; >> query: (?x7083, 0133_p) <- artists(?x7083, ?x9246), artists(?x7083, ?x5623), artists(?x7083, ?x4741), ?x9246 = 0pk41, ?x5623 = 01vsyg9, location(?x4741, ?x739), participant(?x4741, ?x4536), vacationer(?x126, ?x4741) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #528 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 06by7; *> query: (?x7083, ?x505) <- artists(?x7083, ?x8391), artists(?x7083, ?x7193), artists(?x7083, ?x4957), artists(?x7083, ?x4029), artists(?x505, ?x7193), ?x8391 = 01693z, ?x4957 = 0g_g2, ?x4029 = 01c8v0 *> conf = 0.20 ranks of expected_values: 84 EVAL 02yv6b parent_genre! 018lg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 56.000 29.000 0.500 http://example.org/music/genre/parent_genre #14690-089kpp PRED entity: 089kpp PRED relation: gender PRED expected values: 05zppz => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #9, 0.89 #39, 0.89 #43), 02zsn (0.29 #183, 0.28 #91, 0.28 #161) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 07s3vqk; 03kwtb; 01kv4mb; 0150t6; 03bnv; 01r6jt2; 01gg59; 09889g; 01vsgrn; 01bczm; ... >> query: (?x12768, 05zppz) <- music(?x3093, ?x12768), place_of_birth(?x12768, ?x13174), category(?x12768, ?x134), award_nominee(?x12768, ?x523) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 089kpp gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.897 http://example.org/people/person/gender #14689-01699 PRED entity: 01699 PRED relation: form_of_government PRED expected values: 06cx9 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 6): 06cx9 (0.53 #13, 0.46 #7, 0.46 #31), 01fpfn (0.42 #45, 0.38 #81, 0.37 #87), 01d9r3 (0.34 #35, 0.33 #95, 0.33 #41), 018wl5 (0.30 #50, 0.28 #56, 0.28 #68), 01q20 (0.26 #58, 0.25 #52, 0.24 #214), 026wp (0.06 #84, 0.06 #48, 0.06 #30) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 07p7g; >> query: (?x6431, 06cx9) <- contains(?x2467, ?x6431), administrative_parent(?x6431, ?x551), ?x2467 = 0dg3n1 >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01699 form_of_government 06cx9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.533 http://example.org/location/country/form_of_government #14688-01n_g9 PRED entity: 01n_g9 PRED relation: major_field_of_study PRED expected values: 04x_3 => 143 concepts (139 used for prediction) PRED predicted values (max 10 best out of 112): 01mkq (0.48 #1920, 0.48 #1800, 0.45 #2874), 02j62 (0.48 #981, 0.40 #2888, 0.37 #1100), 0g26h (0.46 #1113, 0.45 #1351, 0.39 #1470), 062z7 (0.36 #1931, 0.32 #1811, 0.31 #7894), 03g3w (0.35 #857, 0.35 #977, 0.34 #2884), 0_jm (0.33 #57, 0.29 #2321, 0.26 #1367), 01tbp (0.33 #1965, 0.31 #3396, 0.31 #2680), 04sh3 (0.30 #3410, 0.24 #2933, 0.21 #1979), 04x_3 (0.29 #2644, 0.29 #1929, 0.28 #1809), 01540 (0.29 #1846, 0.28 #1966, 0.26 #2681) >> Best rule #1920 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 01jssp; 07szy; 0bthb; 01bvw5; 0f1nl; 01wdj_; 0j_sncb; 01swxv; 027xx3; 01q460; ... >> query: (?x7716, 01mkq) <- major_field_of_study(?x7716, ?x1154), institution(?x865, ?x7716), ?x1154 = 02lp1, colors(?x7716, ?x4557) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2644 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 137 *> proper extension: 07xpm; 01jsn5; 07x4c; 012lzr; 01bzs9; *> query: (?x7716, 04x_3) <- major_field_of_study(?x7716, ?x1154), institution(?x865, ?x7716), major_field_of_study(?x5357, ?x1154), ?x5357 = 02d_zc *> conf = 0.29 ranks of expected_values: 9 EVAL 01n_g9 major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 143.000 139.000 0.481 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14687-0272_vz PRED entity: 0272_vz PRED relation: language PRED expected values: 0349s => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 48): 04h9h (0.20 #42, 0.09 #158, 0.04 #391), 05f_3 (0.20 #26, 0.03 #5639, 0.02 #200), 064_8sq (0.15 #195, 0.14 #79, 0.14 #370), 07zrf (0.14 #60, 0.03 #5639, 0.01 #411), 04306rv (0.12 #294, 0.11 #120, 0.09 #1349), 06nm1 (0.11 #1355, 0.11 #242, 0.11 #828), 02bjrlw (0.09 #291, 0.07 #175, 0.06 #1171), 06b_j (0.06 #138, 0.05 #840, 0.05 #958), 0jzc (0.06 #135, 0.03 #896, 0.03 #955), 03_9r (0.06 #1354, 0.05 #1588, 0.05 #767) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 06q8qh; >> query: (?x4501, 04h9h) <- film(?x2551, ?x4501), country(?x4501, ?x94), film_format(?x4501, ?x6392), ?x2551 = 0h0wc >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #5639 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1874 *> proper extension: 0vgkd; 0c0wvx; *> query: (?x4501, ?x254) <- genre(?x4501, ?x258), genre(?x6614, ?x258), language(?x6614, ?x254) *> conf = 0.03 ranks of expected_values: 33 EVAL 0272_vz language 0349s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 98.000 98.000 0.200 http://example.org/film/film/language #14686-0565cz PRED entity: 0565cz PRED relation: role PRED expected values: 07_l6 06w7v => 98 concepts (47 used for prediction) PRED predicted values (max 10 best out of 120): 018vs (0.53 #609, 0.50 #712, 0.38 #1399), 042v_gx (0.50 #307, 0.41 #1010, 0.38 #1307), 01vdm0 (0.50 #130, 0.30 #834, 0.28 #1430), 05r5c (0.42 #504, 0.40 #2709, 0.40 #1406), 02fsn (0.38 #1399, 0.34 #1900, 0.33 #1899), 01dnws (0.38 #1399, 0.34 #1900, 0.33 #1899), 0myk8 (0.38 #1399, 0.34 #1900, 0.33 #1899), 01bns_ (0.38 #1399, 0.33 #1899, 0.33 #1499), 01vj9c (0.32 #611, 0.30 #714, 0.30 #314), 0l14qv (0.32 #502, 0.23 #808, 0.20 #5) >> Best rule #609 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 01yznp; >> query: (?x2964, 018vs) <- instrumentalists(?x2888, ?x2964), instrumentalists(?x2048, ?x2964), ?x2888 = 02fsn, gender(?x2964, ?x231), role(?x2048, ?x75) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #84 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0p8h0; *> query: (?x2964, 06w7v) <- artist(?x10504, ?x2964), artist(?x3265, ?x2964), ?x10504 = 03qx_f, ?x3265 = 015_1q, category(?x2964, ?x134) *> conf = 0.20 ranks of expected_values: 16, 92 EVAL 0565cz role 06w7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 98.000 47.000 0.526 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0565cz role 07_l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 98.000 47.000 0.526 http://example.org/music/artist/track_contributions./music/track_contribution/role #14685-03mp9s PRED entity: 03mp9s PRED relation: actor! PRED expected values: 026bfsh => 75 concepts (70 used for prediction) PRED predicted values (max 10 best out of 84): 02zv4b (0.11 #290, 0.09 #555, 0.02 #820), 0h7t36 (0.11 #2654, 0.10 #3453, 0.10 #3719), 05hjnw (0.11 #2654, 0.10 #3453, 0.10 #3719), 0dgst_d (0.11 #2654, 0.10 #3453, 0.10 #3719), 04b_jc (0.08 #11709, 0.08 #15160, 0.08 #11443), 04xbq3 (0.06 #447, 0.04 #712, 0.04 #977), 017f3m (0.06 #351, 0.04 #616, 0.02 #881), 02pqs8l (0.06 #325, 0.04 #590, 0.01 #2980), 06zsk51 (0.06 #448, 0.04 #713), 08bytj (0.04 #677) >> Best rule #290 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 02s2ft; 03m8lq; 016khd; 05k2s_; 0dlglj; 049k07; 02l4pj; 026_w57; 03zg2x; 02xv8m; ... >> query: (?x6977, 02zv4b) <- award_nominee(?x91, ?x6977), award_winner(?x3760, ?x6977), ?x91 = 04bdxl >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #1423 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 377 *> proper extension: 02yy8; 02m30v; *> query: (?x6977, 026bfsh) <- profession(?x6977, ?x1032), spouse(?x2443, ?x6977) *> conf = 0.02 ranks of expected_values: 45 EVAL 03mp9s actor! 026bfsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 75.000 70.000 0.111 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #14684-01pbwwl PRED entity: 01pbwwl PRED relation: place_of_birth PRED expected values: 0n96z => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 53): 04jpl (0.11 #7049, 0.03 #3529, 0.02 #8459), 02_286 (0.10 #4948, 0.09 #3540, 0.08 #18331), 01_d4 (0.07 #2882, 0.05 #2178, 0.04 #1474), 030qb3t (0.05 #3575, 0.04 #35271, 0.03 #17661), 0cr3d (0.04 #1502, 0.04 #798, 0.03 #5727), 0cc56 (0.04 #737, 0.02 #4962, 0.02 #33), 0rh6k (0.03 #1410, 0.01 #9862, 0.01 #6339), 0d6lp (0.02 #2930, 0.02 #5043, 0.01 #11383), 06_kh (0.02 #1413, 0.01 #5, 0.01 #3526), 0chrx (0.02 #305, 0.02 #1713, 0.01 #5234) >> Best rule #7049 for best value: >> intensional similarity = 2 >> extensional distance = 394 >> proper extension: 0784v1; 07m69t; >> query: (?x10547, 04jpl) <- nationality(?x10547, ?x1310), ?x1310 = 02jx1 >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pbwwl place_of_birth 0n96z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.114 http://example.org/people/person/place_of_birth #14683-0gry51 PRED entity: 0gry51 PRED relation: award PRED expected values: 0gr07 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 424): 0gs9p (0.31 #892, 0.28 #486, 0.17 #5360), 040njc (0.27 #820, 0.26 #414, 0.17 #4476), 0gqy2 (0.24 #166, 0.16 #1385, 0.16 #1791), 019f4v (0.23 #879, 0.22 #473, 0.16 #5347), 0gq9h (0.21 #890, 0.17 #484, 0.15 #5358), 09sb52 (0.13 #8976, 0.13 #9382, 0.13 #9788), 0f4x7 (0.13 #437, 0.12 #843, 0.09 #31), 0gr51 (0.13 #5381, 0.12 #4569, 0.10 #913), 02pqp12 (0.11 #477, 0.10 #883, 0.10 #5351), 0gr4k (0.11 #5313, 0.11 #4501, 0.09 #2064) >> Best rule #892 for best value: >> intensional similarity = 9 >> extensional distance = 46 >> proper extension: 012vct; 0l9k1; 04dyqk; >> query: (?x13488, 0gs9p) <- profession(?x13488, ?x1032), profession(?x13488, ?x524), profession(?x13488, ?x319), ?x319 = 01d_h8, ?x524 = 02jknp, ?x1032 = 02hrh1q, people(?x5801, ?x13488), people(?x5801, ?x1606), award_winner(?x2192, ?x1606) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #3495 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 271 *> proper extension: 03_gd; 03qd_; 081nh; 04g865; 019pm_; 01vw26l; 07rd7; 021r7r; 095x_; 01vl17; ... *> query: (?x13488, 0gr07) <- profession(?x13488, ?x1032), profession(?x13488, ?x524), profession(?x13488, ?x319), ?x319 = 01d_h8, ?x524 = 02jknp, gender(?x13488, ?x231), profession(?x4620, ?x1032), profession(?x1128, ?x1032), ?x1128 = 01wbgdv, ?x4620 = 01vsy7t *> conf = 0.02 ranks of expected_values: 111 EVAL 0gry51 award 0gr07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 42.000 42.000 0.312 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14682-0180mw PRED entity: 0180mw PRED relation: nominated_for! PRED expected values: 0k269 05yh_t 023kzp 02bj6k => 88 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1524): 0210hf (0.80 #23166, 0.80 #41697, 0.79 #141292), 07qcbw (0.60 #6948, 0.58 #13899, 0.54 #20849), 02pjvc (0.58 #44013, 0.57 #46329, 0.46 #71804), 02jt1k (0.58 #44013, 0.57 #46329, 0.46 #71804), 014zcr (0.58 #44013, 0.57 #46329, 0.46 #71804), 027r8p (0.58 #44013, 0.57 #46329, 0.46 #71804), 01wk7b7 (0.58 #44013, 0.57 #46329, 0.46 #71804), 02q3bb (0.58 #44013, 0.57 #46329, 0.46 #71804), 03q5dr (0.58 #44013, 0.57 #46329, 0.46 #71804), 04yj5z (0.58 #44013, 0.57 #46329, 0.46 #71804) >> Best rule #23166 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 0g60z; 080dwhx; 0kfpm; 0cwrr; 0358x_; 019nnl; 0ddd0gc; 0kfv9; 0584r4; 03d34x8; ... >> query: (?x6482, ?x426) <- honored_for(?x1265, ?x6482), award_winner(?x6482, ?x426), producer_type(?x6482, ?x632) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #115814 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 523 *> proper extension: 075cph; 0fsw_7; 01kf5lf; 042g97; *> query: (?x6482, ?x286) <- honored_for(?x1265, ?x6482), nominated_for(?x6481, ?x6482), award_nominee(?x6481, ?x286) *> conf = 0.09 ranks of expected_values: 110, 118, 119, 985 EVAL 0180mw nominated_for! 02bj6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 88.000 63.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0180mw nominated_for! 023kzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 88.000 63.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0180mw nominated_for! 05yh_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 88.000 63.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0180mw nominated_for! 0k269 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 88.000 63.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14681-01nrq5 PRED entity: 01nrq5 PRED relation: film PRED expected values: 02sfnv => 139 concepts (90 used for prediction) PRED predicted values (max 10 best out of 749): 0sw0q (0.60 #1790, 0.60 #134222, 0.59 #105586), 01f39b (0.25 #979, 0.09 #15296, 0.09 #13506), 0jvt9 (0.25 #539, 0.09 #13066, 0.08 #2329), 07x4qr (0.25 #404, 0.08 #7562, 0.07 #3983), 0j90s (0.25 #1236, 0.02 #33448, 0.01 #22714), 0n0bp (0.25 #81, 0.01 #21559), 034fl9 (0.19 #16107, 0.18 #17897, 0.18 #14317), 02c7k4 (0.15 #2893, 0.13 #4682, 0.05 #19000), 016dj8 (0.15 #2904, 0.13 #4693, 0.03 #19011), 016z43 (0.15 #3559, 0.13 #5348, 0.03 #19666) >> Best rule #1790 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01gn36; 0cf2h; >> query: (?x3261, ?x9098) <- award_winner(?x870, ?x3261), nominated_for(?x3261, ?x9098), film(?x3261, ?x6214), ?x6214 = 0k5fg >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #11637 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 022q4j; *> query: (?x3261, 02sfnv) <- people(?x4659, ?x3261), film(?x3261, ?x1734), influenced_by(?x7717, ?x3261) *> conf = 0.06 ranks of expected_values: 85 EVAL 01nrq5 film 02sfnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 139.000 90.000 0.600 http://example.org/film/actor/film./film/performance/film #14680-0g2lq PRED entity: 0g2lq PRED relation: film PRED expected values: 0hv8w => 119 concepts (83 used for prediction) PRED predicted values (max 10 best out of 916): 011yd2 (0.64 #43013, 0.62 #43014, 0.59 #118279), 04ynx7 (0.64 #43013, 0.59 #118279, 0.59 #96778), 02vqsll (0.64 #43013, 0.59 #118279, 0.59 #96778), 065dc4 (0.64 #43013, 0.59 #118279, 0.59 #96778), 01xvjb (0.64 #43013, 0.59 #118279, 0.59 #96778), 02hct1 (0.64 #43013, 0.59 #118279, 0.59 #96778), 09m6kg (0.62 #43014, 0.57 #87817, 0.52 #5376), 016zfm (0.62 #43014, 0.57 #87817, 0.45 #78856), 02kk_c (0.62 #43014, 0.57 #87817, 0.45 #78856), 050gkf (0.52 #5376, 0.47 #7169, 0.35 #10754) >> Best rule #43013 for best value: >> intensional similarity = 3 >> extensional distance = 298 >> proper extension: 02wb6yq; >> query: (?x7837, ?x2215) <- nominated_for(?x7837, ?x2215), participant(?x7837, ?x496), award_winner(?x253, ?x7837) >> conf = 0.64 => this is the best rule for 6 predicted values No rule for expected values ranks of expected_values: EVAL 0g2lq film 0hv8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 83.000 0.644 http://example.org/film/actor/film./film/performance/film #14679-0bdxs5 PRED entity: 0bdxs5 PRED relation: artists! PRED expected values: 01lyv => 160 concepts (88 used for prediction) PRED predicted values (max 10 best out of 202): 025sc50 (0.46 #3088, 0.33 #1567, 0.33 #655), 06j6l (0.44 #3086, 0.40 #653, 0.36 #1565), 01lyv (0.42 #336, 0.25 #944, 0.18 #3073), 0glt670 (0.36 #1559, 0.35 #6121, 0.33 #2167), 016cjb (0.33 #376, 0.25 #984, 0.08 #13456), 017_qw (0.32 #10400, 0.16 #5533, 0.15 #6749), 0gywn (0.29 #3096, 0.28 #13439, 0.27 #14047), 016clz (0.28 #16430, 0.28 #17647, 0.27 #1829), 0xhtw (0.28 #16440, 0.27 #17657, 0.19 #26480), 0155w (0.25 #407, 0.19 #1015, 0.17 #16528) >> Best rule #3088 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 0kzy0; 03t852; 03d2k; 03f0qd7; >> query: (?x8693, 025sc50) <- artists(?x671, ?x8693), languages(?x8693, ?x254), ?x671 = 064t9 >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #336 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0147jt; *> query: (?x8693, 01lyv) <- location(?x8693, ?x4978), ?x4978 = 05jbn, artists(?x482, ?x8693) *> conf = 0.42 ranks of expected_values: 3 EVAL 0bdxs5 artists! 01lyv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 160.000 88.000 0.456 http://example.org/music/genre/artists #14678-01jzyx PRED entity: 01jzyx PRED relation: major_field_of_study PRED expected values: 0g4gr => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 121): 062z7 (0.52 #403, 0.41 #528, 0.36 #4504), 0g26h (0.48 #418, 0.43 #293, 0.42 #168), 01mkq (0.47 #4491, 0.43 #390, 0.41 #5982), 02lp1 (0.45 #4487, 0.41 #5978, 0.38 #386), 03g3w (0.43 #10835, 0.33 #6864, 0.33 #402), 05qjt (0.43 #382, 0.36 #507, 0.27 #6844), 04rjg (0.43 #395, 0.36 #270, 0.33 #6857), 01540 (0.38 #437, 0.36 #562, 0.25 #4538), 0fdys (0.33 #414, 0.33 #164, 0.29 #289), 0_jm (0.32 #4039, 0.31 #7641, 0.30 #5406) >> Best rule #403 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 03ksy; 08qnnv; >> query: (?x5426, 062z7) <- institution(?x620, ?x5426), service_language(?x5426, ?x254), currency(?x5426, ?x170), student(?x5426, ?x9156) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #5874 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 112 *> proper extension: 0frm7n; *> query: (?x5426, 0g4gr) <- school(?x8786, ?x5426), draft(?x260, ?x8786) *> conf = 0.18 ranks of expected_values: 46 EVAL 01jzyx major_field_of_study 0g4gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 179.000 179.000 0.524 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14677-02slt7 PRED entity: 02slt7 PRED relation: production_companies! PRED expected values: 07gp9 023g6w => 63 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1815): 0k54q (0.50 #1738, 0.40 #2873, 0.20 #5143), 0gj9qxr (0.40 #3570, 0.33 #163, 0.14 #5840), 035zr0 (0.40 #4232, 0.33 #825, 0.14 #6502), 0f4_2k (0.40 #2929, 0.25 #1794, 0.09 #10870), 087pfc (0.38 #8922, 0.25 #12324, 0.18 #22546), 050gkf (0.36 #10425, 0.31 #13833, 0.29 #14968), 0cz_ym (0.36 #10415, 0.31 #13823, 0.29 #14958), 02qr69m (0.36 #10478, 0.31 #13886, 0.29 #15021), 09m6kg (0.36 #10236, 0.31 #13644, 0.29 #14779), 02vqsll (0.36 #10542, 0.31 #13950, 0.29 #15085) >> Best rule #1738 for best value: >> intensional similarity = 17 >> extensional distance = 2 >> proper extension: 03rwz3; >> query: (?x3331, 0k54q) <- production_companies(?x8471, ?x3331), production_companies(?x6121, ?x3331), production_companies(?x2903, ?x3331), production_companies(?x1421, ?x3331), films(?x326, ?x6121), produced_by(?x6121, ?x7670), film_crew_role(?x6121, ?x2178), film_crew_role(?x6121, ?x1171), ?x1171 = 09vw2b7, service_location(?x3331, ?x789), nominated_for(?x277, ?x2903), film_release_region(?x1421, ?x87), film_crew_role(?x4315, ?x2178), film_crew_role(?x1463, ?x2178), ?x1463 = 0gtvrv3, ?x4315 = 0sxkh, language(?x8471, ?x254) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 03sb38; *> query: (?x3331, 07gp9) <- production_companies(?x8176, ?x3331), production_companies(?x6121, ?x3331), production_companies(?x1421, ?x3331), ?x6121 = 064lsn, film_release_region(?x1421, ?x1174), genre(?x1421, ?x53), nominated_for(?x8888, ?x1421), film_release_region(?x8176, ?x3277), ?x3277 = 06t8v, language(?x8176, ?x254), film_release_region(?x3423, ?x1174), film_release_region(?x3377, ?x1174), ?x3377 = 0gj8nq2, film_regional_debut_venue(?x8176, ?x4903), ?x3423 = 09g7vfw *> conf = 0.33 ranks of expected_values: 32, 1421 EVAL 02slt7 production_companies! 023g6w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 63.000 29.000 0.500 http://example.org/film/film/production_companies EVAL 02slt7 production_companies! 07gp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 63.000 29.000 0.500 http://example.org/film/film/production_companies #14676-01bb1c PRED entity: 01bb1c PRED relation: award_winner PRED expected values: 07zl1 => 57 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1582): 07w21 (0.64 #12398, 0.57 #9933, 0.54 #14865), 01k56k (0.50 #9795, 0.50 #7329, 0.50 #4865), 0fpzt5 (0.50 #6827, 0.36 #14223, 0.33 #9293), 04r68 (0.50 #6076, 0.36 #13472, 0.33 #8542), 05jm7 (0.50 #5764, 0.33 #8230, 0.29 #10695), 04mhl (0.45 #13310, 0.33 #5914, 0.33 #986), 03rx9 (0.45 #14382, 0.33 #6986, 0.33 #2058), 02y49 (0.45 #14232, 0.33 #6836, 0.31 #16699), 0g5ff (0.43 #11210, 0.33 #8745, 0.25 #3815), 0c3kw (0.36 #12677, 0.33 #353, 0.33 #17256) >> Best rule #12398 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 040vk98; 02662b; 0262zm; 02664f; 0262yt; >> query: (?x14213, 07w21) <- award_winner(?x14213, ?x7828), award_winner(?x14213, ?x7055), influenced_by(?x7828, ?x6810), influenced_by(?x7828, ?x6055), disciplines_or_subjects(?x14213, ?x1013), ?x7055 = 0210f1, ?x6055 = 0g5ff, award(?x6810, ?x921) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #7087 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 01yz0x; 0262x6; *> query: (?x14213, 07zl1) <- award_winner(?x14213, ?x7828), award_winner(?x14213, ?x7055), influenced_by(?x7828, ?x6810), influenced_by(?x7828, ?x6055), disciplines_or_subjects(?x14213, ?x1013), ?x7055 = 0210f1, ?x6055 = 0g5ff, ?x6810 = 037jz *> conf = 0.33 ranks of expected_values: 17 EVAL 01bb1c award_winner 07zl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 57.000 35.000 0.636 http://example.org/award/award_category/winners./award/award_honor/award_winner #14675-06439y PRED entity: 06439y PRED relation: school PRED expected values: 0bx8pn 0j_sncb => 15 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1360): 06pwq (0.78 #1423, 0.50 #1202, 0.41 #1529), 07w0v (0.75 #1318, 0.40 #985, 0.38 #1206), 01pl14 (0.62 #1312, 0.50 #866, 0.38 #1200), 065y4w7 (0.50 #1317, 0.50 #1205, 0.50 #871), 05krk (0.50 #1311, 0.50 #865, 0.33 #647), 01jsk6 (0.50 #959, 0.40 #1072, 0.38 #1405), 01pq4w (0.50 #899, 0.40 #1012, 0.38 #1122), 0j_sncb (0.50 #890, 0.38 #1113, 0.33 #787), 01n6r0 (0.50 #915, 0.33 #483, 0.33 #431), 0lyjf (0.50 #1358, 0.33 #694, 0.33 #587) >> Best rule #1423 for best value: >> intensional similarity = 66 >> extensional distance = 7 >> proper extension: 02x2khw; 02pq_rp; 02z6872; 02pq_x5; 04f4z1k; >> query: (?x12852, 06pwq) <- school(?x12852, ?x8706), school(?x12852, ?x2497), draft(?x11420, ?x12852), draft(?x9760, ?x12852), draft(?x5154, ?x12852), draft(?x4571, ?x12852), school(?x5154, ?x6315), school(?x5154, ?x2775), team(?x1348, ?x9760), team(?x13105, ?x9760), team(?x4834, ?x9760), sport(?x5154, ?x4833), team(?x8996, ?x5154), school(?x9760, ?x7660), school(?x9760, ?x7202), institution(?x1771, ?x7660), major_field_of_study(?x7660, ?x2314), major_field_of_study(?x7660, ?x1668), list(?x7660, ?x2197), student(?x8706, ?x1817), teams(?x3052, ?x9760), major_field_of_study(?x8706, ?x3489), major_field_of_study(?x8706, ?x947), ?x1771 = 019v9k, school(?x8901, ?x8706), school(?x2174, ?x8706), school(?x684, ?x8706), colors(?x9760, ?x663), ?x2174 = 051vz, citytown(?x8706, ?x4419), institution(?x1200, ?x8706), school(?x11420, ?x8120), school(?x11420, ?x3948), ?x3948 = 025v3k, ?x947 = 036hv, contains(?x94, ?x7660), ?x2314 = 0h5k, currency(?x8706, ?x170), student(?x7660, ?x2390), team(?x5755, ?x5154), school(?x4571, ?x6856), ?x3489 = 0193x, ?x8901 = 07l4z, contains(?x2277, ?x2497), colors(?x7202, ?x3364), student(?x6315, ?x1400), colors(?x2497, ?x332), school(?x4170, ?x8120), service_location(?x6315, ?x551), profession(?x13105, ?x1581), school(?x1010, ?x2497), major_field_of_study(?x2775, ?x1154), ?x1668 = 01mkq, category(?x7660, ?x134), gender(?x4834, ?x231), state_province_region(?x8120, ?x4758), student(?x2775, ?x7382), ?x4170 = 05l71, people(?x1816, ?x1817), school(?x465, ?x8120), draft(?x684, ?x685), major_field_of_study(?x7202, ?x6756), ?x6856 = 0jkhr, position_s(?x684, ?x180), organization(?x346, ?x7202), ?x7382 = 04t969 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #890 for first EXPECTED value: *> intensional similarity = 65 *> extensional distance = 2 *> proper extension: 02qw1zx; *> query: (?x12852, 0j_sncb) <- school(?x12852, ?x9847), school(?x12852, ?x6271), school(?x12852, ?x4599), school(?x12852, ?x2399), school(?x12852, ?x1087), school(?x12852, ?x331), draft(?x9760, ?x12852), draft(?x5154, ?x12852), draft(?x1578, ?x12852), draft(?x799, ?x12852), school(?x5154, ?x2775), team(?x1348, ?x9760), team(?x13105, ?x9760), team(?x9070, ?x9760), sport(?x5154, ?x4833), team(?x8996, ?x5154), school(?x9760, ?x7660), school(?x9760, ?x7202), school(?x9760, ?x6763), ?x7660 = 01qd_r, ?x6271 = 015q1n, company(?x4486, ?x1578), team(?x5755, ?x5154), ?x331 = 01jssp, category(?x1578, ?x134), colors(?x799, ?x3315), major_field_of_study(?x6763, ?x6870), institution(?x1200, ?x2775), organization(?x9847, ?x5487), contains(?x94, ?x1087), student(?x6763, ?x426), organization(?x5510, ?x9847), team(?x5582, ?x1578), colors(?x5154, ?x8271), colors(?x5154, ?x663), student(?x2775, ?x1447), location(?x13105, ?x4776), ?x3315 = 0jc_p, student(?x9847, ?x12123), ?x1200 = 016t_3, colors(?x1087, ?x3189), ?x6870 = 01540, major_field_of_study(?x2775, ?x10046), major_field_of_study(?x2775, ?x5615), school_type(?x2399, ?x1507), ?x1507 = 01_9fk, school(?x1578, ?x1783), film(?x9070, ?x1728), currency(?x7202, ?x170), ?x10046 = 041y2, profession(?x9070, ?x319), organization(?x346, ?x6763), ?x5615 = 011s0, state_province_region(?x7202, ?x3670), major_field_of_study(?x2399, ?x1527), teams(?x739, ?x799), school_type(?x2775, ?x1044), nominated_for(?x12123, ?x3820), gender(?x13105, ?x231), list(?x4599, ?x2197), company(?x2998, ?x4599), ?x663 = 083jv, colors(?x817, ?x8271), ?x170 = 09nqf, major_field_of_study(?x4599, ?x742) *> conf = 0.50 ranks of expected_values: 8, 12 EVAL 06439y school 0j_sncb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 15.000 15.000 0.778 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 06439y school 0bx8pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 15.000 15.000 0.778 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #14674-016ynj PRED entity: 016ynj PRED relation: people! PRED expected values: 0gk4g => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 36): 0gk4g (0.19 #604, 0.18 #934, 0.13 #1000), 0dq9p (0.12 #611, 0.11 #941, 0.07 #1007), 04p3w (0.10 #935, 0.08 #605, 0.05 #77), 0qcr0 (0.09 #793, 0.09 #727, 0.08 #661), 02y0js (0.05 #1322, 0.05 #596, 0.05 #926), 0m32h (0.05 #617, 0.04 #947, 0.04 #683), 02k6hp (0.05 #961, 0.05 #631, 0.04 #1027), 02knxx (0.04 #1022, 0.04 #758, 0.03 #824), 01_qc_ (0.04 #622, 0.03 #952, 0.02 #1348), 0dcsx (0.03 #939, 0.03 #609, 0.03 #1005) >> Best rule #604 for best value: >> intensional similarity = 4 >> extensional distance = 151 >> proper extension: 02knnd; 0ly5n; 0cf_h9; 03llf8; 04bgy; 029cpw; 013qvn; 022q4j; 02784z; 0223g8; ... >> query: (?x8301, 0gk4g) <- nationality(?x8301, ?x390), film(?x8301, ?x6013), gender(?x8301, ?x231), place_of_death(?x8301, ?x191) >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016ynj people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.190 http://example.org/people/cause_of_death/people #14673-0gmcwlb PRED entity: 0gmcwlb PRED relation: film_crew_role PRED expected values: 09zzb8 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.79 #724, 0.72 #1884, 0.70 #688), 02r96rf (0.71 #727, 0.68 #400, 0.65 #691), 09vw2b7 (0.70 #731, 0.61 #659, 0.61 #803), 0dxtw (0.42 #735, 0.38 #663, 0.38 #807), 0215hd (0.22 #127, 0.19 #597, 0.19 #742), 02ynfr (0.20 #52, 0.17 #703, 0.16 #739), 04pyp5 (0.20 #53, 0.09 #197, 0.07 #125), 089g0h (0.17 #92, 0.14 #598, 0.13 #128), 02rh1dz (0.17 #83, 0.14 #734, 0.11 #806), 02_n3z (0.17 #74, 0.13 #725, 0.12 #653) >> Best rule #724 for best value: >> intensional similarity = 4 >> extensional distance = 203 >> proper extension: 02v63m; 01j8wk; 07bwr; 01q2nx; 0gs973; 0415ggl; 047gpsd; 047rkcm; 08984j; 0bw20; ... >> query: (?x1370, 09zzb8) <- film(?x4004, ?x1370), featured_film_locations(?x1370, ?x1523), executive_produced_by(?x1370, ?x4060), film_crew_role(?x1370, ?x1284) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gmcwlb film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.785 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14672-0gj9tn5 PRED entity: 0gj9tn5 PRED relation: film_release_region PRED expected values: 0f8l9c 06c1y 02vzc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 134): 0f8l9c (0.91 #1488, 0.90 #1891, 0.90 #2427), 02vzc (0.81 #1513, 0.81 #2184, 0.81 #1379), 016wzw (0.57 #1389, 0.53 #1523, 0.43 #1926), 047yc (0.54 #1358, 0.52 #1492, 0.42 #1895), 06qd3 (0.51 #1367, 0.50 #965, 0.49 #1501), 06mzp (0.51 #1353, 0.45 #1487, 0.44 #1621), 05qx1 (0.46 #1370, 0.41 #1504, 0.32 #1907), 077qn (0.44 #203, 0.31 #1409, 0.29 #1543), 06c1y (0.43 #1372, 0.39 #1506, 0.33 #32), 0h7x (0.42 #2169, 0.39 #1364, 0.39 #962) >> Best rule #1488 for best value: >> intensional similarity = 4 >> extensional distance = 192 >> proper extension: 0cnztc4; >> query: (?x1785, 0f8l9c) <- film_release_region(?x1785, ?x2645), film_release_region(?x1785, ?x151), ?x151 = 0b90_r, ?x2645 = 03h64 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 9 EVAL 0gj9tn5 film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.912 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gj9tn5 film_release_region 06c1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 72.000 72.000 0.912 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gj9tn5 film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.912 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14671-0g7k2g PRED entity: 0g7k2g PRED relation: location PRED expected values: 02h6_6p => 157 concepts (122 used for prediction) PRED predicted values (max 10 best out of 370): 0h7x (0.67 #1603, 0.62 #32049, 0.59 #15223), 0345h (0.67 #1603, 0.62 #32049, 0.59 #15223), 0f8l9c (0.67 #1603, 0.62 #32049, 0.59 #15223), 030qb3t (0.46 #18511, 0.41 #37738, 0.30 #48955), 0fhp9 (0.33 #844, 0.28 #5652, 0.14 #3248), 04jpl (0.33 #17, 0.19 #19246, 0.19 #68114), 02_286 (0.29 #37692, 0.28 #84946, 0.24 #90554), 06c62 (0.26 #9149, 0.12 #11551, 0.07 #3540), 094jv (0.17 #894, 0.14 #1696, 0.07 #3298), 0b1mf (0.17 #1543, 0.08 #3146, 0.07 #3947) >> Best rule #1603 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 031x_3; >> query: (?x8600, ?x789) <- nationality(?x8600, ?x205), origin(?x8600, ?x789), artists(?x888, ?x8600), ?x888 = 05lls >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #18559 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 0jfx1; *> query: (?x8600, 02h6_6p) <- instrumentalists(?x316, ?x8600), location(?x8600, ?x10537), locations(?x3110, ?x10537), administrative_division(?x10537, ?x9230) *> conf = 0.03 ranks of expected_values: 155 EVAL 0g7k2g location 02h6_6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 157.000 122.000 0.667 http://example.org/people/person/places_lived./people/place_lived/location #14670-07nxnw PRED entity: 07nxnw PRED relation: film_format PRED expected values: 017fx5 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.27 #6, 0.20 #1, 0.20 #16), 0cj16 (0.13 #138, 0.12 #228, 0.11 #273), 017fx5 (0.08 #19, 0.07 #24, 0.07 #70) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 05pbl56; 049mql; 0bbw2z6; 098s2w; 01qbg5; 0d6_s; >> query: (?x6881, 07fb8_) <- nominated_for(?x2258, ?x6881), film(?x450, ?x6881), genre(?x6881, ?x225), ?x2258 = 0f4vbz >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #19 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 04lqvly; 0dkv90; 0581vn8; *> query: (?x6881, 017fx5) <- nominated_for(?x902, ?x6881), film_crew_role(?x6881, ?x1966), ?x1966 = 015h31, nominated_for(?x1723, ?x6881) *> conf = 0.08 ranks of expected_values: 3 EVAL 07nxnw film_format 017fx5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 91.000 0.273 http://example.org/film/film/film_format #14669-0gg5qcw PRED entity: 0gg5qcw PRED relation: nominated_for PRED expected values: 0bmhvpr => 72 concepts (32 used for prediction) PRED predicted values (max 10 best out of 87): 01hqhm (0.04 #311, 0.02 #563, 0.01 #815), 0fy34l (0.04 #308, 0.02 #560, 0.01 #812), 0y_hb (0.04 #435, 0.02 #687), 06fpsx (0.04 #466, 0.01 #970), 0209xj (0.04 #267, 0.01 #771), 0gg5qcw (0.03 #5055, 0.02 #6074, 0.01 #5311), 029k4p (0.03 #5055, 0.02 #6074, 0.01 #5311), 06_x996 (0.03 #5055, 0.02 #6074, 0.01 #5311), 07w8fz (0.03 #5055, 0.02 #6074, 0.01 #5311), 01bb9r (0.03 #5055, 0.02 #6074, 0.01 #5311) >> Best rule #311 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 02q56mk; 05vxdh; 0sxlb; >> query: (?x5092, 01hqhm) <- film(?x157, ?x5092), nominated_for(?x1180, ?x5092), ?x1180 = 02n9nmz, film_format(?x5092, ?x6392) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #8100 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1193 *> proper extension: 09fb5; 0cwrr; 01h1bf; 03y3bp7; 02kk_c; 04glx0; 05gnf; 028k2x; 05fgr_; 05sy0cv; ... *> query: (?x5092, ?x3133) <- nominated_for(?x2590, ?x5092), award_nominee(?x2590, ?x969), film(?x2590, ?x3133) *> conf = 0.01 ranks of expected_values: 78 EVAL 0gg5qcw nominated_for 0bmhvpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 72.000 32.000 0.043 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #14668-0mpbx PRED entity: 0mpbx PRED relation: time_zones PRED expected values: 02hcv8 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.86 #81, 0.57 #1095, 0.55 #1394), 03bdv (0.41 #45, 0.37 #58, 0.21 #136), 02lcqs (0.38 #31, 0.34 #317, 0.33 #213), 02fqwt (0.29 #261, 0.29 #1, 0.19 #560), 02hczc (0.26 #262, 0.26 #236, 0.23 #249), 02llzg (0.21 #134, 0.18 #225, 0.16 #56), 02lcrv (0.07 #20, 0.05 #267, 0.04 #124), 03plfd (0.05 #361, 0.04 #1115, 0.04 #1128), 052vwh (0.04 #233, 0.04 #376, 0.03 #168), 042g7t (0.04 #128, 0.02 #687, 0.02 #765) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 0mp08; 0mp36; 0mnwd; 0mnrb; >> query: (?x11240, 02hcv8) <- contains(?x1426, ?x11240), currency(?x11240, ?x170), ?x1426 = 07z1m >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mpbx time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.857 http://example.org/location/location/time_zones #14667-027jk PRED entity: 027jk PRED relation: participating_countries! PRED expected values: 0kbws => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 43): 0kbws (0.71 #3060, 0.70 #877, 0.70 #1164), 018ctl (0.52 #623, 0.50 #1199, 0.46 #665), 09n48 (0.52 #618, 0.46 #1194, 0.43 #1152), 0lgxj (0.42 #1220, 0.42 #644, 0.40 #686), 09x3r (0.42 #627, 0.37 #258, 0.36 #669), 0sx8l (0.35 #629, 0.28 #712, 0.28 #794), 0blfl (0.35 #645, 0.28 #728, 0.26 #810), 06sks6 (0.25 #2469, 0.25 #2263, 0.23 #271), 016r9z (0.25 #637, 0.25 #761, 0.24 #679), 0c_tl (0.23 #270, 0.19 #188, 0.17 #639) >> Best rule #3060 for best value: >> intensional similarity = 3 >> extensional distance = 190 >> proper extension: 027rn; 0160w; 027nb; 0d0vqn; 04gzd; 047lj; 01ls2; 03_r3; 05v8c; 06npd; ... >> query: (?x8558, 0kbws) <- organization(?x8558, ?x127), jurisdiction_of_office(?x182, ?x8558), country(?x1121, ?x8558) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027jk participating_countries! 0kbws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.708 http://example.org/olympics/olympic_games/participating_countries #14666-01hq1 PRED entity: 01hq1 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #11, 0.82 #49, 0.82 #27), 02nxhr (0.06 #7, 0.06 #134, 0.05 #207), 07c52 (0.03 #41, 0.03 #274, 0.03 #321), 07z4p (0.03 #276, 0.02 #323, 0.02 #256) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0140g4; 07cyl; 05pdh86; 04x4vj; 0sxmx; 047wh1; 0bt4g; >> query: (?x7881, 029j_) <- films(?x5011, ?x7881), film(?x4314, ?x7881), nominated_for(?x71, ?x7881), prequel(?x339, ?x7881) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hq1 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.833 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #14665-0g9yrw PRED entity: 0g9yrw PRED relation: music PRED expected values: 01cbt3 => 64 concepts (42 used for prediction) PRED predicted values (max 10 best out of 81): 01nr36 (0.17 #4440, 0.15 #4652, 0.15 #5710), 01r4hry (0.17 #4440, 0.15 #4652, 0.13 #6135), 01l1rw (0.17 #103, 0.06 #313, 0.05 #525), 01hw6wq (0.17 #38, 0.03 #2576, 0.02 #3632), 07j8kh (0.17 #101, 0.02 #1370, 0.02 #3273), 019x62 (0.17 #130, 0.01 #1610, 0.01 #2035), 03h610 (0.12 #287, 0.07 #710, 0.06 #922), 02bh9 (0.12 #1320, 0.06 #2167, 0.04 #6187), 0bs1yy (0.10 #1314, 0.06 #255, 0.03 #678), 023361 (0.09 #572, 0.05 #1843, 0.05 #1630) >> Best rule #4440 for best value: >> intensional similarity = 4 >> extensional distance = 384 >> proper extension: 03y3bp7; 01b7h8; 0gxsh4; >> query: (?x4032, ?x8491) <- nominated_for(?x8491, ?x4032), award(?x8491, ?x102), category(?x8491, ?x134), student(?x4955, ?x8491) >> conf = 0.17 => this is the best rule for 2 predicted values *> Best rule #1784 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 03qcfvw; 02y_lrp; 034qmv; 0140g4; 08lr6s; 04ddm4; 02x3lt7; 0dj0m5; 06_wqk4; 0kv2hv; ... *> query: (?x4032, 01cbt3) <- nominated_for(?x102, ?x4032), film(?x5427, ?x4032), ?x102 = 04ljl_l, nominated_for(?x7856, ?x4032) *> conf = 0.03 ranks of expected_values: 42 EVAL 0g9yrw music 01cbt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 64.000 42.000 0.168 http://example.org/film/film/music #14664-028qdb PRED entity: 028qdb PRED relation: artists! PRED expected values: 016jny => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 222): 064t9 (0.50 #13, 0.41 #6900, 0.41 #7212), 0155w (0.37 #1046, 0.17 #3862, 0.16 #1671), 02yv6b (0.32 #1038, 0.16 #3854, 0.15 #3228), 01lyv (0.31 #972, 0.19 #2536, 0.18 #5667), 016clz (0.29 #5012, 0.29 #3759, 0.26 #3133), 0xhtw (0.28 #3771, 0.27 #5024, 0.27 #3145), 05bt6j (0.25 #1294, 0.24 #356, 0.23 #1920), 03_d0 (0.24 #323, 0.21 #4705, 0.21 #949), 0glt670 (0.22 #7552, 0.21 #6928, 0.21 #7240), 05w3f (0.21 #976, 0.17 #3792, 0.15 #3166) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 086qd; 01cwhp; 01wwvd2; >> query: (?x4206, 064t9) <- award_winner(?x4574, ?x4206), award(?x4206, ?x2139), ?x4574 = 02dbp7 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #418 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 19 *> proper extension: 0c9d9; 032t2z; 0zjpz; 01wy61y; 01l4g5; 0kp2_; 0135xb; 017l4; 01p95y0; *> query: (?x4206, 016jny) <- instrumentalists(?x614, ?x4206), ?x614 = 0mkg *> conf = 0.14 ranks of expected_values: 22 EVAL 028qdb artists! 016jny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 74.000 74.000 0.500 http://example.org/music/genre/artists #14663-06g2d1 PRED entity: 06g2d1 PRED relation: nationality PRED expected values: 09c7w0 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 19): 09c7w0 (0.82 #401, 0.74 #1606, 0.74 #1406), 07ssc (0.30 #7219, 0.16 #15, 0.09 #818), 0f8l9c (0.30 #7219, 0.02 #523, 0.02 #624), 02jx1 (0.12 #333, 0.11 #4044, 0.10 #936), 03rk0 (0.10 #1951, 0.10 #1851, 0.07 #1149), 0chghy (0.06 #10, 0.03 #1213, 0.02 #1314), 0j5g9 (0.06 #62), 06mkj (0.06 #47), 0d060g (0.05 #2515, 0.05 #2314, 0.05 #2213), 03rjj (0.03 #5, 0.02 #1810, 0.02 #1910) >> Best rule #401 for best value: >> intensional similarity = 2 >> extensional distance = 349 >> proper extension: 012v1t; >> query: (?x6085, 09c7w0) <- location(?x6085, ?x739), ?x739 = 02_286 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06g2d1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.818 http://example.org/people/person/nationality #14662-0glb5 PRED entity: 0glb5 PRED relation: time_zones PRED expected values: 02llzg => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 12): 02llzg (0.62 #134, 0.56 #161, 0.50 #69), 02hcv8 (0.37 #864, 0.34 #851, 0.34 #786), 02lcqs (0.23 #619, 0.19 #723, 0.18 #736), 02fqwt (0.17 #302, 0.16 #354, 0.16 #315), 02hczc (0.13 #434, 0.13 #394, 0.13 #447), 03bdv (0.10 #281, 0.10 #176, 0.09 #841), 042g7t (0.06 #233, 0.04 #194, 0.02 #612), 03plfd (0.04 #337, 0.04 #232, 0.04 #611), 052vwh (0.04 #418, 0.02 #522, 0.02 #535), 0gsrz4 (0.03 #596, 0.03 #752, 0.03 #687) >> Best rule #134 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01c6rd; >> query: (?x7383, 02llzg) <- contains(?x789, ?x7383), ?x789 = 0f8l9c, first_level_division_of(?x7383, ?x789), administrative_parent(?x7383, ?x789) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0glb5 time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.625 http://example.org/location/location/time_zones #14661-0h96g PRED entity: 0h96g PRED relation: award PRED expected values: 0bdwft 0bsjcw => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 280): 09sb52 (0.37 #2447, 0.26 #24503, 0.26 #6056), 094qd5 (0.30 #2451, 0.14 #21655, 0.09 #8867), 02z0dfh (0.30 #2481, 0.07 #7694, 0.07 #8897), 0bdwft (0.28 #2475, 0.11 #871, 0.09 #8891), 09td7p (0.26 #2526, 0.05 #8942, 0.05 #7739), 05zr6wv (0.25 #418, 0.13 #4428, 0.13 #2824), 09qrn4 (0.25 #638, 0.11 #1039, 0.08 #1440), 099t8j (0.24 #2546, 0.06 #1744, 0.05 #2145), 0bfvw2 (0.22 #2421, 0.08 #7634, 0.08 #8837), 02x4x18 (0.19 #2538, 0.07 #8954, 0.07 #7751) >> Best rule #2447 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 02lxj_; 02rmxx; 02l0sf; 039x1k; 01kgg9; >> query: (?x4771, 09sb52) <- award(?x4771, ?x1972), type_of_union(?x4771, ?x566), ?x1972 = 0gqyl, profession(?x4771, ?x1032) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #2475 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 92 *> proper extension: 02lxj_; 02rmxx; 02l0sf; 039x1k; 01kgg9; *> query: (?x4771, 0bdwft) <- award(?x4771, ?x1972), type_of_union(?x4771, ?x566), ?x1972 = 0gqyl, profession(?x4771, ?x1032) *> conf = 0.28 ranks of expected_values: 4, 55 EVAL 0h96g award 0bsjcw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 126.000 126.000 0.372 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0h96g award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 126.000 126.000 0.372 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14660-01bczm PRED entity: 01bczm PRED relation: artists! PRED expected values: 016clz 05bt6j => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 236): 017_qw (0.74 #1910, 0.55 #3451, 0.43 #2218), 05bt6j (0.40 #1582, 0.36 #658, 0.33 #966), 016clz (0.35 #3701, 0.31 #3085, 0.30 #312), 06j6l (0.32 #1587, 0.30 #355, 0.29 #7758), 0gywn (0.27 #1597, 0.21 #7768, 0.21 #8076), 059kh (0.27 #664, 0.20 #356, 0.12 #972), 03lty (0.27 #642, 0.19 #1258, 0.19 #3107), 02x8m (0.27 #633, 0.19 #10179, 0.17 #1249), 0glt670 (0.25 #8058, 0.24 #7750, 0.20 #347), 025sc50 (0.24 #7760, 0.24 #8068, 0.21 #1589) >> Best rule #1910 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 03c_8t; >> query: (?x5550, 017_qw) <- music(?x4623, ?x5550), nominated_for(?x1549, ?x4623), nominated_for(?x6334, ?x4623) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1582 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 01sbf2; 03f0vvr; 0167km; 01vtmw6; 0135xb; 02p68d; 03f7m4h; 01f9zw; 01bmlb; 02bc74; *> query: (?x5550, 05bt6j) <- profession(?x5550, ?x220), award(?x5550, ?x1801), ?x1801 = 01c92g *> conf = 0.40 ranks of expected_values: 2, 3 EVAL 01bczm artists! 05bt6j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.742 http://example.org/music/genre/artists EVAL 01bczm artists! 016clz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.742 http://example.org/music/genre/artists #14659-02r22gf PRED entity: 02r22gf PRED relation: nominated_for PRED expected values: 02vxq9m 0jqn5 091z_p 016z7s 02q6gfp 02n9bh 0ctb4g 02ll45 0jqj5 09tkzy 04x4gw => 53 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1694): 0ch26b_ (0.78 #16369, 0.77 #16370, 0.77 #16368), 011yqc (0.78 #16369, 0.77 #16370, 0.77 #16368), 07024 (0.78 #16369, 0.77 #16370, 0.77 #16368), 042y1c (0.78 #16369, 0.77 #16370, 0.77 #16368), 011yl_ (0.78 #16369, 0.77 #16370, 0.77 #16368), 0661ql3 (0.78 #16369, 0.77 #16370, 0.77 #16368), 01gc7 (0.78 #16369, 0.77 #16370, 0.77 #16368), 0404j37 (0.78 #16369, 0.77 #16370, 0.77 #16368), 08720 (0.78 #16369, 0.77 #16370, 0.77 #16368), 03b1l8 (0.78 #16369, 0.77 #16370, 0.77 #16368) >> Best rule #16369 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 0fqnzts; >> query: (?x637, ?x1916) <- award(?x1916, ?x637), ceremony(?x637, ?x2032), nominated_for(?x277, ?x1916) >> conf = 0.78 => this is the best rule for 11 predicted values *> Best rule #6120 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 02x17c2; *> query: (?x637, 0jqn5) <- award_winner(?x637, ?x1585), crewmember(?x1386, ?x1585), ceremony(?x637, ?x2032), nominated_for(?x198, ?x1386) *> conf = 0.55 ranks of expected_values: 17, 21, 23, 30, 44, 82, 101, 124, 131, 471, 624 EVAL 02r22gf nominated_for 04x4gw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r22gf nominated_for 09tkzy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r22gf nominated_for 0jqj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r22gf nominated_for 02ll45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r22gf nominated_for 0ctb4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r22gf nominated_for 02n9bh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r22gf nominated_for 02q6gfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r22gf nominated_for 016z7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r22gf nominated_for 091z_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r22gf nominated_for 0jqn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r22gf nominated_for 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 53.000 16.000 0.777 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14658-02vgh PRED entity: 02vgh PRED relation: group! PRED expected values: 02hnl => 91 concepts (66 used for prediction) PRED predicted values (max 10 best out of 117): 02hnl (0.77 #1949, 0.77 #1219, 0.77 #1302), 028tv0 (0.50 #169, 0.37 #1287, 0.37 #1204), 05r5c (0.50 #165, 0.27 #1444, 0.26 #1527), 03qjg (0.50 #202, 0.26 #1527, 0.23 #1967), 0mkg (0.40 #86, 0.10 #160, 0.10 #167), 04rzd (0.30 #187, 0.21 #240, 0.20 #106), 01vj9c (0.28 #1205, 0.28 #1288, 0.28 #1935), 013y1f (0.27 #1444, 0.21 #240, 0.20 #182), 0l14j_ (0.27 #1444, 0.21 #240, 0.20 #125), 01s0ps (0.27 #1444, 0.21 #240, 0.20 #119) >> Best rule #1949 for best value: >> intensional similarity = 6 >> extensional distance = 182 >> proper extension: 01wv9xn; 04r1t; 02r1tx7; 0dm5l; 05563d; 07yg2; 05xq9; 0kr_t; 0bpk2; 015srx; ... >> query: (?x6986, 02hnl) <- group(?x432, ?x6986), role(?x212, ?x432), role(?x432, ?x2785), ?x2785 = 0jtg0, role(?x7210, ?x432), ?x7210 = 05qhnq >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vgh group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 66.000 0.772 http://example.org/music/performance_role/regular_performances./music/group_membership/group #14657-0gd9k PRED entity: 0gd9k PRED relation: type_of_union PRED expected values: 04ztj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.83 #49, 0.82 #105, 0.82 #97), 01g63y (0.21 #14, 0.15 #66, 0.15 #194), 01bl8s (0.03 #43, 0.03 #47, 0.02 #55) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 01r216; >> query: (?x7984, 04ztj) <- written_by(?x7494, ?x7984), producer_type(?x7984, ?x632), film_release_region(?x7494, ?x142) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gd9k type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.833 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14656-06pjs PRED entity: 06pjs PRED relation: student! PRED expected values: 07c52 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 34): 02822 (0.24 #459, 0.18 #337, 0.09 #826), 03qsdpk (0.13 #464, 0.10 #342, 0.08 #831), 0w7c (0.11 #470, 0.06 #348, 0.04 #837), 0fdys (0.06 #457, 0.06 #824, 0.05 #335), 02h40lc (0.06 #432, 0.03 #310, 0.03 #62), 01zc2w (0.05 #476, 0.04 #354, 0.04 #843), 03g3w (0.05 #816, 0.04 #449, 0.02 #632), 04rlf (0.05 #169, 0.03 #475, 0.03 #46), 0mg1w (0.04 #473, 0.02 #351, 0.01 #1206), 062z7 (0.04 #817, 0.03 #511, 0.02 #695) >> Best rule #459 for best value: >> intensional similarity = 3 >> extensional distance = 120 >> proper extension: 02lg9w; >> query: (?x9153, 02822) <- film(?x9153, ?x1941), award(?x9153, ?x746), student(?x373, ?x9153) >> conf = 0.24 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06pjs student! 07c52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 100.000 0.238 http://example.org/education/field_of_study/students_majoring./education/education/student #14655-03hhd3 PRED entity: 03hhd3 PRED relation: film PRED expected values: 03h3x5 0dc_ms => 111 concepts (82 used for prediction) PRED predicted values (max 10 best out of 728): 04f6hhm (0.09 #30268, 0.08 #23142, 0.08 #37393), 024rwx (0.09 #30268, 0.08 #23142, 0.08 #37393), 09lxv9 (0.05 #5057, 0.04 #1497, 0.04 #6837), 02v5_g (0.05 #4346, 0.04 #6126, 0.04 #2566), 04x4vj (0.05 #4328, 0.04 #6108, 0.02 #13228), 01633c (0.04 #1319, 0.03 #3099, 0.02 #4879), 0fphf3v (0.04 #1353, 0.01 #3133, 0.01 #58331), 02qydsh (0.04 #6830, 0.03 #5050, 0.03 #3270), 03z20c (0.04 #2252, 0.03 #4032, 0.03 #12932), 0g7pm1 (0.04 #2977, 0.03 #4757, 0.03 #6537) >> Best rule #30268 for best value: >> intensional similarity = 3 >> extensional distance = 577 >> proper extension: 0lzb8; 02wrhj; 045bs6; 01csrl; 0241wg; 030x48; 02y_2y; 01gw4f; 01m42d0; 0154d7; ... >> query: (?x8587, ?x5852) <- type_of_union(?x8587, ?x566), actor(?x5852, ?x8587), film(?x8587, ?x66) >> conf = 0.09 => this is the best rule for 2 predicted values *> Best rule #2927 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 0jsw9l; *> query: (?x8587, 0dc_ms) <- student(?x8398, ?x8587), student(?x5614, ?x8587), nominated_for(?x8587, ?x251) *> conf = 0.03 ranks of expected_values: 51 EVAL 03hhd3 film 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 111.000 82.000 0.089 http://example.org/film/actor/film./film/performance/film EVAL 03hhd3 film 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 82.000 0.089 http://example.org/film/actor/film./film/performance/film #14654-01l_pn PRED entity: 01l_pn PRED relation: film! PRED expected values: 030vnj 0h7pj => 90 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1073): 017s11 (0.44 #60031, 0.42 #51748, 0.42 #111785), 05zh9c (0.44 #60031, 0.42 #51748, 0.42 #43466), 01m7f5r (0.42 #51748, 0.42 #111785, 0.42 #43466), 012d40 (0.18 #2086, 0.17 #16, 0.07 #16574), 02zyy4 (0.18 #2340, 0.17 #270, 0.03 #4410), 02p5hf (0.18 #3824, 0.17 #1754), 01xv77 (0.18 #3160, 0.17 #1090), 01q_ph (0.17 #57, 0.09 #2127, 0.04 #16615), 0p_pd (0.17 #54, 0.09 #2124, 0.04 #16612), 01x_d8 (0.17 #1068, 0.09 #3138, 0.04 #4140) >> Best rule #60031 for best value: >> intensional similarity = 3 >> extensional distance = 774 >> proper extension: 01h72l; >> query: (?x5608, ?x4784) <- language(?x5608, ?x254), award_winner(?x5608, ?x4784), award_nominee(?x2136, ?x4784) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #1440 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 04g9gd; 01f6x7; *> query: (?x5608, 030vnj) <- film(?x2307, ?x5608), ?x2307 = 011zd3, produced_by(?x5608, ?x4784) *> conf = 0.17 ranks of expected_values: 12, 169 EVAL 01l_pn film! 0h7pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 90.000 56.000 0.443 http://example.org/film/actor/film./film/performance/film EVAL 01l_pn film! 030vnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 90.000 56.000 0.443 http://example.org/film/actor/film./film/performance/film #14653-09sh8k PRED entity: 09sh8k PRED relation: language PRED expected values: 02h40lc => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 40): 02h40lc (0.96 #1241, 0.95 #1184, 0.95 #2835), 06nm1 (0.23 #122, 0.21 #178, 0.14 #234), 064_8sq (0.19 #412, 0.17 #468, 0.16 #300), 04306rv (0.15 #117, 0.14 #173, 0.11 #1244), 06b_j (0.15 #133, 0.14 #189, 0.11 #245), 02bjrlw (0.11 #225, 0.09 #57, 0.08 #281), 02hxc3j (0.10 #3636, 0.04 #231), 05zjd (0.10 #3636, 0.02 #639, 0.02 #415), 02hxcvy (0.10 #3636, 0.02 #985, 0.02 #1213), 032f6 (0.10 #3636, 0.02 #1007, 0.02 #1859) >> Best rule #1241 for best value: >> intensional similarity = 4 >> extensional distance = 322 >> proper extension: 0979n; >> query: (?x136, 02h40lc) <- language(?x136, ?x2164), country(?x136, ?x512), film(?x965, ?x136), ?x512 = 07ssc >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09sh8k language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.957 http://example.org/film/film/language #14652-04ktcgn PRED entity: 04ktcgn PRED relation: award PRED expected values: 018wdw => 105 concepts (90 used for prediction) PRED predicted values (max 10 best out of 272): 09sb52 (0.33 #39, 0.26 #6910, 0.24 #5698), 057xs89 (0.33 #160, 0.15 #15357, 0.14 #23442), 01by1l (0.24 #3343, 0.13 #15468, 0.12 #515), 018wng (0.21 #848, 0.06 #4042, 0.05 #21825), 0gq_v (0.19 #1638, 0.16 #4851, 0.16 #1234), 01bgqh (0.18 #3273, 0.12 #445, 0.11 #15398), 02r0csl (0.17 #5, 0.16 #4851, 0.15 #15357), 0gr42 (0.17 #923, 0.16 #4851, 0.15 #15357), 0gq9h (0.17 #884, 0.16 #4851, 0.15 #15357), 0gqxm (0.17 #179, 0.16 #4851, 0.15 #15357) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01l2fn; 02cllz; >> query: (?x1983, 09sb52) <- award_winner(?x667, ?x1983), nominated_for(?x1983, ?x7207), ?x7207 = 03y0pn >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4851 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 927 *> proper extension: 0hm0k; *> query: (?x1983, ?x298) <- award_winner(?x1597, ?x1983), award_winner(?x5653, ?x1983), award(?x1597, ?x298) *> conf = 0.16 ranks of expected_values: 29 EVAL 04ktcgn award 018wdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 105.000 90.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14651-01l3wr PRED entity: 01l3wr PRED relation: teams! PRED expected values: 0345h => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 82): 077qn (0.33 #113, 0.25 #923, 0.25 #653), 0k6nt (0.33 #297, 0.25 #1107, 0.08 #4621), 06mzp (0.25 #833, 0.14 #2725, 0.08 #4617), 05r4w (0.25 #1081, 0.14 #2703, 0.08 #4325), 0b90_r (0.25 #544, 0.12 #3516, 0.07 #5139), 0qb1z (0.20 #1704, 0.17 #1974, 0.14 #2245), 02h6_6p (0.17 #1969, 0.12 #3321, 0.12 #3051), 017w_ (0.14 #2423, 0.12 #3234, 0.09 #4045), 02jx1 (0.14 #2744, 0.12 #3554, 0.07 #5177), 02cl1 (0.14 #2451, 0.09 #3803, 0.05 #5695) >> Best rule #113 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 03y_f8; >> query: (?x10788, 077qn) <- current_club(?x10788, ?x12030), current_club(?x10788, ?x10248), current_club(?x10788, ?x9338), current_club(?x10788, ?x2096), position(?x10788, ?x60), team(?x8594, ?x10788), ?x10248 = 049dzz, colors(?x12030, ?x663), ?x2096 = 0371rb, sport(?x9338, ?x471) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01l3wr teams! 0345h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 79.000 0.333 http://example.org/sports/sports_team_location/teams #14650-0428bc PRED entity: 0428bc PRED relation: film PRED expected values: 03sxd2 => 113 concepts (71 used for prediction) PRED predicted values (max 10 best out of 424): 04954r (0.06 #11339, 0.02 #5978, 0.02 #32783), 01shy7 (0.05 #2211, 0.03 #9359, 0.03 #25442), 0jvt9 (0.05 #540, 0.05 #11262, 0.04 #13049), 0k5fg (0.05 #1090, 0.03 #8238, 0.02 #11812), 027rpym (0.05 #834, 0.02 #11556, 0.02 #7982), 0168ls (0.05 #242, 0.02 #10964, 0.02 #7390), 03rg2b (0.04 #11815, 0.02 #6454, 0.02 #13602), 01f39b (0.04 #11700, 0.02 #13487, 0.02 #20635), 04v89z (0.03 #6779, 0.03 #12140, 0.03 #4992), 0f42nz (0.03 #25926, 0.03 #42009, 0.02 #38435) >> Best rule #11339 for best value: >> intensional similarity = 3 >> extensional distance = 166 >> proper extension: 069z_5; >> query: (?x9977, 04954r) <- nationality(?x9977, ?x94), place_of_death(?x9977, ?x739), film(?x9977, ?x4024) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0428bc film 03sxd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 71.000 0.060 http://example.org/film/actor/film./film/performance/film #14649-01vvyfh PRED entity: 01vvyfh PRED relation: nationality PRED expected values: 07ssc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.84 #694, 0.83 #1, 0.82 #3764), 07ssc (0.33 #3283, 0.32 #2589, 0.19 #411), 0345h (0.10 #1813, 0.08 #1615, 0.07 #823), 03rk0 (0.07 #4402, 0.07 #5095, 0.06 #3610), 0f8l9c (0.06 #1804, 0.05 #1606, 0.04 #2893), 0d060g (0.05 #502, 0.05 #403, 0.05 #1195), 0h7x (0.05 #1816, 0.04 #1618, 0.03 #2905), 03_3d (0.05 #105, 0.02 #204, 0.02 #4858), 04jpl (0.03 #3070), 06bnz (0.03 #1822, 0.02 #1624, 0.02 #832) >> Best rule #694 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 0xnc3; >> query: (?x3929, 09c7w0) <- nationality(?x3929, ?x1310), celebrities_impersonated(?x8145, ?x3929), gender(?x3929, ?x231) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #3283 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 394 *> proper extension: 0784v1; 07m69t; *> query: (?x3929, 07ssc) <- nationality(?x3929, ?x1310), ?x1310 = 02jx1 *> conf = 0.33 ranks of expected_values: 2 EVAL 01vvyfh nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.835 http://example.org/people/person/nationality #14648-04fkg4 PRED entity: 04fkg4 PRED relation: type_of_union PRED expected values: 04ztj => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.83 #25, 0.82 #17, 0.80 #29), 01g63y (0.29 #206, 0.15 #38, 0.14 #46), 0jgjn (0.29 #206), 01bl8s (0.02 #39, 0.01 #27) >> Best rule #25 for best value: >> intensional similarity = 6 >> extensional distance = 70 >> proper extension: 012d40; 0m2l9; 01vvycq; 09byk; 0l12d; 016_mj; 016sp_; 021yw7; 01vvyfh; 03f0r5w; ... >> query: (?x11002, 04ztj) <- profession(?x11002, ?x524), profession(?x11002, ?x319), ?x524 = 02jknp, gender(?x11002, ?x231), category(?x11002, ?x134), ?x319 = 01d_h8 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04fkg4 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.833 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14647-0d87hc PRED entity: 0d87hc PRED relation: production_companies PRED expected values: 016tw3 => 96 concepts (91 used for prediction) PRED predicted values (max 10 best out of 58): 016tw3 (0.37 #2421, 0.30 #4359, 0.30 #6786), 056ws9 (0.33 #44, 0.20 #125, 0.11 #287), 01gb54 (0.20 #117, 0.14 #198, 0.11 #279), 086k8 (0.15 #3631, 0.13 #4844, 0.12 #569), 016tt2 (0.14 #166, 0.11 #247, 0.11 #3633), 046b0s (0.14 #185, 0.11 #266, 0.05 #509), 0c41qv (0.14 #215, 0.11 #296, 0.03 #3602), 031rq5 (0.14 #204, 0.11 #285, 0.03 #447), 05nn2c (0.14 #189, 0.01 #432, 0.01 #351), 017s11 (0.13 #408, 0.13 #327, 0.10 #3632) >> Best rule #2421 for best value: >> intensional similarity = 3 >> extensional distance = 522 >> proper extension: 0522wp; >> query: (?x10274, ?x1104) <- film(?x1104, ?x10274), film(?x1104, ?x4656), ?x4656 = 02lk60 >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d87hc production_companies 016tw3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 91.000 0.367 http://example.org/film/film/production_companies #14646-0wr_s PRED entity: 0wr_s PRED relation: place! PRED expected values: 0wr_s => 66 concepts (32 used for prediction) PRED predicted values (max 10 best out of 8): 0wqwj (0.09 #475, 0.03 #990), 0wq36 (0.09 #460, 0.03 #975), 043yj (0.09 #417, 0.03 #932), 0ws0h (0.09 #205, 0.03 #720), 0wq3z (0.09 #120, 0.03 #635), 0wp9b (0.09 #29, 0.03 #544), 0xgpv (0.09 #484), 0qkyj (0.09 #370) >> Best rule #475 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0wp9b; 0qkyj; 0yx74; 0xgpv; >> query: (?x13807, 0wqwj) <- source(?x13807, ?x958), contains(?x4622, ?x13807), ?x4622 = 04tgp, ?x958 = 0jbk9 >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0wr_s place! 0wr_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 32.000 0.091 http://example.org/location/hud_county_place/place #14645-01d34b PRED entity: 01d34b PRED relation: citytown PRED expected values: 02_286 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 127): 02_286 (0.59 #6286, 0.57 #3702, 0.33 #15), 030qb3t (0.25 #397, 0.05 #1870, 0.05 #22149), 01nl79 (0.25 #674, 0.02 #1778, 0.01 #2884), 0d6lp (0.20 #806, 0.02 #17765, 0.02 #22190), 094jv (0.20 #771, 0.02 #4459, 0.01 #8885), 0978r (0.14 #1180, 0.04 #20722, 0.03 #17771), 019fh (0.14 #1185, 0.03 #26184, 0.01 #3767), 0dclg (0.14 #1147, 0.01 #7051, 0.01 #23270), 0cr3d (0.11 #3686, 0.06 #16222, 0.06 #18804), 01531 (0.07 #6271, 0.06 #16222, 0.06 #18804) >> Best rule #6286 for best value: >> intensional similarity = 2 >> extensional distance = 118 >> proper extension: 087c7; 0l8sx; 02bh8z; 027kmrb; 095kp; 04htfd; 07l1c; 018_q8; 0sxdg; 01dfb6; ... >> query: (?x7075, 02_286) <- state_province_region(?x7075, ?x335), ?x335 = 059rby >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d34b citytown 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.592 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #14644-02f4s3 PRED entity: 02f4s3 PRED relation: major_field_of_study PRED expected values: 041y2 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 118): 04rjg (0.63 #2095, 0.47 #874, 0.46 #1118), 02lp1 (0.48 #988, 0.47 #866, 0.45 #1110), 0g26h (0.45 #2729, 0.39 #1263, 0.36 #653), 062z7 (0.42 #1004, 0.40 #4184, 0.39 #882), 03g3w (0.40 #881, 0.40 #1125, 0.39 #4794), 02_7t (0.33 #675, 0.28 #1285, 0.25 #1041), 05qjt (0.33 #1106, 0.30 #862, 0.30 #4775), 05qfh (0.31 #1012, 0.29 #890, 0.29 #1134), 01540 (0.29 #915, 0.28 #1159, 0.27 #3481), 01lj9 (0.29 #894, 0.28 #1138, 0.25 #1016) >> Best rule #2095 for best value: >> intensional similarity = 4 >> extensional distance = 158 >> proper extension: 019dwp; 0ylvj; 0yls9; 01bzs9; 02_jjm; 01nn7r; >> query: (?x9676, 04rjg) <- student(?x9676, ?x2259), major_field_of_study(?x9676, ?x1668), major_field_of_study(?x3044, ?x1668), ?x3044 = 01c333 >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #78 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0fr9jp; *> query: (?x9676, 041y2) <- student(?x9676, ?x11878), student(?x9676, ?x10051), nominated_for(?x10051, ?x83), ?x83 = 014_x2, gender(?x11878, ?x231) *> conf = 0.20 ranks of expected_values: 17 EVAL 02f4s3 major_field_of_study 041y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 130.000 130.000 0.631 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14643-0fv4v PRED entity: 0fv4v PRED relation: form_of_government PRED expected values: 06cx9 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 5): 06cx9 (0.76 #96, 0.57 #31, 0.53 #41), 01fpfn (0.42 #88, 0.42 #258, 0.42 #173), 018wl5 (0.34 #152, 0.30 #172, 0.30 #257), 01q20 (0.25 #174, 0.25 #299, 0.25 #9), 026wp (0.12 #55, 0.07 #95, 0.07 #130) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 02wm6l; >> query: (?x7360, 06cx9) <- form_of_government(?x7360, ?x6377), form_of_government(?x9455, ?x6377), ?x9455 = 0jt3tjf >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fv4v form_of_government 06cx9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.758 http://example.org/location/country/form_of_government #14642-0j0k PRED entity: 0j0k PRED relation: contains PRED expected values: 05v8c 0jhd => 104 concepts (72 used for prediction) PRED predicted values (max 10 best out of 2842): 04vmp (0.73 #106858, 0.39 #72204, 0.29 #176177), 0fnb4 (0.73 #106858, 0.39 #72204, 0.29 #176177), 09pmkv (0.69 #11551, 0.61 #155958, 0.61 #153070), 07bxhl (0.69 #11551, 0.61 #155958, 0.61 #153070), 0jdd (0.69 #11551, 0.61 #155958, 0.61 #153070), 03_3d (0.69 #11551, 0.60 #205057, 0.25 #2907), 05v8c (0.69 #11551, 0.48 #80868, 0.48 #205056), 06k5_ (0.61 #155958, 0.61 #153070, 0.60 #205057), 0f1_p (0.61 #155958, 0.61 #153070, 0.60 #205057), 075mb (0.61 #155958, 0.61 #153070, 0.60 #205057) >> Best rule #106858 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 02frhbc; >> query: (?x6956, ?x13165) <- contains(?x6956, ?x2645), contains(?x6956, ?x1961), featured_film_locations(?x7493, ?x2645), citytown(?x1961, ?x13165) >> conf = 0.73 => this is the best rule for 2 predicted values *> Best rule #11551 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 02j9z; 0dg3n1; 059g4; *> query: (?x6956, ?x252) <- contains(?x6956, ?x311), locations(?x326, ?x6956), countries_within(?x6956, ?x252) *> conf = 0.69 ranks of expected_values: 7, 11 EVAL 0j0k contains 0jhd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 104.000 72.000 0.734 http://example.org/location/location/contains EVAL 0j0k contains 05v8c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 104.000 72.000 0.734 http://example.org/location/location/contains #14641-038723 PRED entity: 038723 PRED relation: people PRED expected values: 01wk7b7 09yrh 044zvm => 31 concepts (13 used for prediction) PRED predicted values (max 10 best out of 2352): 01twdk (0.33 #670, 0.27 #2389, 0.25 #4106), 01pk3z (0.33 #784, 0.22 #5938, 0.16 #9373), 0311wg (0.33 #290, 0.19 #8879, 0.18 #2009), 0478__m (0.33 #644, 0.18 #2363, 0.17 #4080), 03_vx9 (0.33 #121, 0.18 #1840, 0.17 #3557), 01ksr1 (0.33 #446, 0.18 #2165, 0.17 #3882), 058ncz (0.33 #65, 0.18 #1784, 0.17 #3501), 0227tr (0.33 #334, 0.17 #5488, 0.12 #8923), 04nw9 (0.33 #193, 0.17 #5347, 0.09 #8782), 018ygt (0.33 #889, 0.17 #6043, 0.09 #9478) >> Best rule #670 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 06v41q; >> query: (?x13213, 01twdk) <- people(?x13213, ?x9140), people(?x13213, ?x1092), people(?x13213, ?x906), ?x1092 = 02whj, languages_spoken(?x13213, ?x3592), profession(?x9140, ?x1032), award_winner(?x944, ?x906), award_winner(?x2016, ?x906), award_nominee(?x237, ?x906), award_nominee(?x3308, ?x9140) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5788 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 16 *> proper extension: 09vc4s; 02g7sp; 025rpb0; *> query: (?x13213, 09yrh) <- people(?x13213, ?x12470), people(?x13213, ?x1092), instrumentalists(?x227, ?x1092), ?x227 = 0342h, type_of_union(?x12470, ?x566), film(?x12470, ?x2812), gender(?x12470, ?x514), religion(?x1092, ?x2694), location(?x1092, ?x1705) *> conf = 0.11 ranks of expected_values: 130, 1877 EVAL 038723 people 044zvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 13.000 0.333 http://example.org/people/ethnicity/people EVAL 038723 people 09yrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 31.000 13.000 0.333 http://example.org/people/ethnicity/people EVAL 038723 people 01wk7b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 13.000 0.333 http://example.org/people/ethnicity/people #14640-0cjcbg PRED entity: 0cjcbg PRED relation: nominated_for PRED expected values: 05h95s => 56 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1558): 0ddd0gc (0.38 #4983, 0.27 #6578, 0.24 #8174), 0fkwzs (0.33 #1284, 0.05 #4475, 0.02 #7665), 02rzdcp (0.28 #5277, 0.22 #6872, 0.19 #8468), 0d68qy (0.28 #5152, 0.20 #6747, 0.17 #8343), 01bv8b (0.28 #5174, 0.20 #6769, 0.17 #8365), 05f4vxd (0.28 #5577, 0.20 #7172, 0.17 #8768), 026p4q7 (0.28 #9933, 0.18 #17919, 0.17 #22711), 0q9jk (0.28 #23950, 0.23 #15962, 0.22 #6029), 03_8kz (0.28 #23950, 0.23 #15962, 0.12 #6183), 0kfpm (0.28 #23950, 0.23 #15962, 0.12 #4888) >> Best rule #4983 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 09v7wsg; >> query: (?x11272, 0ddd0gc) <- ceremony(?x11272, ?x1265), award(?x9787, ?x11272), genre(?x9787, ?x258), ?x1265 = 05c1t6z >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cjcbg nominated_for 05h95s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 18.000 0.375 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14639-04ych PRED entity: 04ych PRED relation: contains! PRED expected values: 04_1l0v => 250 concepts (163 used for prediction) PRED predicted values (max 10 best out of 314): 04_1l0v (0.83 #22852, 0.82 #27329, 0.79 #14782), 02qkt (0.34 #98049, 0.30 #126734, 0.30 #132111), 07ssc (0.27 #109362, 0.22 #49315, 0.20 #8061), 02jx1 (0.27 #109362, 0.16 #49370, 0.16 #76269), 04ych (0.23 #144320, 0.05 #72658, 0.04 #41281), 0nf3h (0.23 #144320, 0.03 #12932, 0.01 #53259), 02v3m7 (0.23 #144320, 0.02 #73262, 0.01 #43907), 0ndh6 (0.23 #144320, 0.02 #31147, 0.01 #57136), 06wxw (0.23 #144320, 0.02 #53765), 013gwb (0.23 #144320) >> Best rule #22852 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 0gj4fx; >> query: (?x1025, 04_1l0v) <- district_represented(?x6021, ?x1025), district_represented(?x845, ?x1025), ?x845 = 07p__7, legislative_sessions(?x6021, ?x3973) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ych contains! 04_1l0v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 250.000 163.000 0.826 http://example.org/location/location/contains #14638-05bnp0 PRED entity: 05bnp0 PRED relation: award PRED expected values: 05pcn59 04kxsb 0bdwqv => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 285): 04kxsb (0.52 #1695, 0.25 #513, 0.15 #2089), 05pcn59 (0.50 #471, 0.27 #865, 0.21 #5199), 02w9sd7 (0.37 #1738, 0.14 #18125, 0.14 #33101), 0gqy2 (0.34 #1732, 0.16 #2126, 0.14 #18125), 0bfvd4 (0.33 #109, 0.18 #1685, 0.09 #1291), 0cqhb3 (0.33 #295, 0.04 #2659, 0.03 #2265), 09sdmz (0.29 #1773, 0.25 #591, 0.14 #18125), 027dtxw (0.28 #1579, 0.07 #1973, 0.07 #12611), 057xs89 (0.25 #546, 0.21 #1728, 0.09 #2122), 05zr6wv (0.25 #410, 0.20 #1592, 0.14 #6714) >> Best rule #1695 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0cj2w; >> query: (?x123, 04kxsb) <- award(?x123, ?x591), award_nominee(?x1733, ?x123), ?x591 = 0f4x7 >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 21 EVAL 05bnp0 award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 120.000 120.000 0.522 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05bnp0 award 04kxsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.522 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05bnp0 award 05pcn59 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.522 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14637-0nlh7 PRED entity: 0nlh7 PRED relation: citytown! PRED expected values: 01k3s2 => 176 concepts (75 used for prediction) PRED predicted values (max 10 best out of 602): 0mbwf (0.25 #601, 0.14 #1410, 0.03 #11122), 07wm6 (0.14 #1500, 0.10 #2309, 0.07 #4737), 0trv (0.12 #10940, 0.08 #2847, 0.07 #4465), 0jpkw (0.10 #2113, 0.02 #12635, 0.01 #35318), 01dq0z (0.10 #2369, 0.02 #23426, 0.01 #33955), 011kn2 (0.10 #2356, 0.02 #23413, 0.01 #33942), 01nds (0.09 #7050, 0.07 #3814, 0.06 #22442), 05cl8y (0.08 #9317, 0.08 #2843, 0.06 #22280), 03_c8p (0.08 #9478, 0.04 #15957, 0.04 #16767), 02kj7g (0.08 #3174, 0.07 #4792, 0.07 #3983) >> Best rule #601 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01k3s2; >> query: (?x10718, 0mbwf) <- contains(?x10063, ?x10718), contains(?x279, ?x10718), ?x279 = 0d060g, ?x10063 = 0j95, category(?x10718, ?x134) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0nlh7 citytown! 01k3s2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 176.000 75.000 0.250 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #14636-088cp PRED entity: 088cp PRED relation: place_of_birth! PRED expected values: 0lpjn => 154 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1576): 066yfh (0.25 #5047, 0.06 #31148, 0.04 #38978), 02g40r (0.25 #4789, 0.06 #30890, 0.04 #38720), 02q42j_ (0.25 #3838, 0.06 #29939, 0.04 #37769), 04_1nk (0.25 #3744, 0.06 #29845, 0.04 #37675), 02pq9yv (0.25 #3289, 0.06 #29390, 0.04 #37220), 02d42t (0.12 #6221, 0.09 #11441, 0.09 #8831), 0dsb_yy (0.12 #6215, 0.09 #11435, 0.09 #8825), 03dq9 (0.12 #7368, 0.09 #9978, 0.08 #17809), 01k47c (0.12 #7104, 0.09 #9714, 0.08 #17545), 016dmx (0.12 #6962, 0.09 #9572, 0.08 #17403) >> Best rule #5047 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 02j9z; >> query: (?x12600, 066yfh) <- contains(?x12600, ?x11583), ?x11583 = 014d4v >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 088cp place_of_birth! 0lpjn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 83.000 0.250 http://example.org/people/person/place_of_birth #14635-0dwt5 PRED entity: 0dwt5 PRED relation: role! PRED expected values: 050z2 => 95 concepts (59 used for prediction) PRED predicted values (max 10 best out of 985): 0770cd (0.71 #8876, 0.56 #14436, 0.50 #19545), 04s5_s (0.71 #9252, 0.50 #12495, 0.44 #14812), 050z2 (0.62 #12231, 0.62 #9912, 0.62 #22909), 01vn35l (0.62 #12170, 0.57 #8927, 0.44 #14487), 01vsyjy (0.62 #12358, 0.57 #9115, 0.44 #14675), 01wxdn3 (0.62 #10131, 0.50 #13376, 0.50 #12450), 01vs4ff (0.62 #12343, 0.50 #10952, 0.50 #3073), 01vsnff (0.62 #10743, 0.43 #22812, 0.38 #12134), 0326tc (0.60 #4041, 0.57 #9143, 0.57 #7750), 0m_v0 (0.60 #3862, 0.57 #8964, 0.57 #7571) >> Best rule #8876 for best value: >> intensional similarity = 21 >> extensional distance = 5 >> proper extension: 03q5t; 0342h; >> query: (?x4769, 0770cd) <- role(?x4913, ?x4769), role(?x3328, ?x4769), role(?x2944, ?x4769), role(?x2206, ?x4769), role(?x885, ?x4769), role(?x614, ?x4769), role(?x4425, ?x4769), ?x614 = 0mkg, ?x4425 = 0979zs, family(?x2206, ?x2620), role(?x4769, ?x868), ?x2944 = 0l14j_, role(?x2377, ?x3328), group(?x4769, ?x3516), role(?x645, ?x2206), ?x2377 = 01bns_, ?x4913 = 03ndd, role(?x565, ?x4769), role(?x4769, ?x2460), ?x885 = 0dwtp, instrumentalists(?x2206, ?x669) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #12231 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 6 *> proper extension: 01vj9c; *> query: (?x4769, 050z2) <- role(?x4913, ?x4769), role(?x3328, ?x4769), role(?x2944, ?x4769), role(?x2206, ?x4769), role(?x614, ?x4769), role(?x4425, ?x4769), ?x614 = 0mkg, ?x4425 = 0979zs, family(?x2206, ?x2620), role(?x4769, ?x868), ?x2944 = 0l14j_, role(?x2377, ?x3328), group(?x4769, ?x3516), role(?x645, ?x2206), ?x2377 = 01bns_, instrumentalists(?x2206, ?x2392), role(?x1292, ?x4913), ?x2392 = 01wwvt2, ?x3516 = 05563d, group(?x2206, ?x1751) *> conf = 0.62 ranks of expected_values: 3 EVAL 0dwt5 role! 050z2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #14634-06l6nj PRED entity: 06l6nj PRED relation: nationality PRED expected values: 09c7w0 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.79 #4606, 0.79 #8106, 0.76 #501), 07ssc (0.16 #415, 0.11 #2916, 0.11 #1315), 02jx1 (0.11 #2033, 0.11 #2133, 0.11 #5438), 0d060g (0.06 #807, 0.06 #907, 0.05 #707), 03rk0 (0.05 #12751, 0.05 #11551, 0.05 #7651), 0ctw_b (0.04 #327, 0.03 #427, 0.03 #727), 0h7x (0.04 #335, 0.03 #435, 0.02 #635), 01mjq (0.04 #340), 03rt9 (0.04 #613, 0.03 #1013, 0.02 #1413), 0345h (0.03 #3935, 0.03 #2631, 0.03 #431) >> Best rule #4606 for best value: >> intensional similarity = 3 >> extensional distance = 849 >> proper extension: 03mz9r; 07_grx; 0564mx; 019n7x; >> query: (?x10964, 09c7w0) <- award_nominee(?x10964, ?x1300), student(?x3136, ?x10964), currency(?x3136, ?x170) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06l6nj nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.794 http://example.org/people/person/nationality #14633-0gr0m PRED entity: 0gr0m PRED relation: nominated_for PRED expected values: 0n0bp 0fgpvf 05dy7p 019vhk 0b_5d 0cc5qkt 0jsqk 01cmp9 07jnt 0pd64 09tkzy 02p86pb 0yx_w 07tlfx 0m5s5 015gm8 => 50 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1306): 0y_9q (0.78 #24054, 0.78 #28303, 0.78 #28302), 07024 (0.78 #24054, 0.78 #28303, 0.78 #28302), 0kbf1 (0.78 #24054, 0.78 #28303, 0.78 #28302), 0p9rz (0.78 #24054, 0.78 #28303, 0.78 #28302), 0glbqt (0.78 #24054, 0.78 #28303, 0.78 #28302), 015gm8 (0.78 #24054, 0.78 #28303, 0.78 #28302), 01gc7 (0.78 #24054, 0.78 #28303, 0.78 #28302), 012mrr (0.78 #24054, 0.78 #28303, 0.78 #28302), 04mzf8 (0.78 #24054, 0.78 #28303, 0.78 #28302), 04j4tx (0.78 #24054, 0.78 #28303, 0.78 #28302) >> Best rule #24054 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 06196; >> query: (?x1243, ?x9452) <- award(?x9452, ?x1243), award(?x185, ?x1243), ceremony(?x1243, ?x78), award_winner(?x9452, ?x1384) >> conf = 0.78 => this is the best rule for 17 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6, 23, 28, 31, 32, 46, 48, 50, 85, 94, 125, 173, 253, 259, 394, 549 EVAL 0gr0m nominated_for 015gm8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 0m5s5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 07tlfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 0yx_w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 02p86pb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 09tkzy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 0pd64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 07jnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 01cmp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 0jsqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 0cc5qkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 0b_5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 019vhk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 05dy7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 0fgpvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr0m nominated_for 0n0bp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 50.000 24.000 0.781 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14632-01x9_8 PRED entity: 01x9_8 PRED relation: profession PRED expected values: 0d1pc => 93 concepts (73 used for prediction) PRED predicted values (max 10 best out of 52): 02jknp (0.47 #6182, 0.35 #1919, 0.25 #448), 0dxtg (0.46 #6188, 0.44 #307, 0.44 #160), 09jwl (0.39 #5605, 0.37 #6635, 0.37 #7958), 0nbcg (0.29 #5617, 0.27 #6647, 0.27 #5911), 03gjzk (0.29 #1926, 0.29 #6189, 0.26 #10000), 016z4k (0.28 #2357, 0.27 #5591, 0.26 #6621), 0dz3r (0.26 #5589, 0.25 #5883, 0.24 #6030), 018gz8 (0.19 #1928, 0.15 #899, 0.15 #3398), 02krf9 (0.17 #466, 0.14 #6200, 0.09 #8113), 0d1pc (0.17 #2843, 0.16 #2402, 0.15 #2696) >> Best rule #6182 for best value: >> intensional similarity = 4 >> extensional distance = 610 >> proper extension: 042l3v; 054_mz; 02pp_q_; 042rnl; 0415svh; 02ndbd; 02lk1s; 06pk8; 067jsf; 01g4zr; ... >> query: (?x8875, 02jknp) <- profession(?x8875, ?x1032), profession(?x8875, ?x319), ?x1032 = 02hrh1q, ?x319 = 01d_h8 >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #2843 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 312 *> proper extension: 01xyt7; *> query: (?x8875, 0d1pc) <- participant(?x1987, ?x8875), people(?x3591, ?x8875), award_nominee(?x221, ?x1987) *> conf = 0.17 ranks of expected_values: 10 EVAL 01x9_8 profession 0d1pc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 93.000 73.000 0.466 http://example.org/people/person/profession #14631-025s89p PRED entity: 025s89p PRED relation: genre! PRED expected values: 020qr4 028k2x 0170k0 => 35 concepts (19 used for prediction) PRED predicted values (max 10 best out of 267): 0584r4 (0.71 #2577, 0.50 #2293, 0.50 #1443), 06y_n (0.71 #2749, 0.50 #2465, 0.50 #1615), 03y3bp7 (0.71 #2595, 0.50 #1461, 0.40 #2028), 028k2x (0.62 #2973, 0.60 #1839, 0.50 #1273), 0170k0 (0.62 #3004, 0.60 #1870, 0.50 #1304), 020qr4 (0.60 #3123, 0.60 #1988, 0.60 #1704), 06r1k (0.60 #1924, 0.50 #3058, 0.50 #1358), 043qqt5 (0.60 #2216, 0.50 #3066, 0.50 #1649), 0cwrr (0.60 #1998, 0.50 #1431, 0.40 #3133), 0vhm (0.60 #2072, 0.50 #1505, 0.40 #1788) >> Best rule #2577 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 01z4y; 06nbt; >> query: (?x10159, 0584r4) <- genre(?x9340, ?x10159), genre(?x1395, ?x10159), languages(?x9340, ?x254), ?x254 = 02h40lc, nominated_for(?x3263, ?x9340), genre(?x9340, ?x811), program(?x1762, ?x9340), actor(?x9340, ?x3758), ?x811 = 03k9fj, ?x1395 = 019nnl >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2973 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 6 *> proper extension: 02n4kr; *> query: (?x10159, 028k2x) <- genre(?x11454, ?x10159), genre(?x9340, ?x10159), genre(?x5938, ?x10159), genre(?x5852, ?x10159), ?x9340 = 05nlzq, program_creator(?x5852, ?x5832), genre(?x5938, ?x258), actor(?x5852, ?x8587), program(?x11453, ?x11454), actor(?x5938, ?x478), country_of_origin(?x5938, ?x94), actor(?x11454, ?x9238), profession(?x8587, ?x1032), ?x258 = 05p553, film(?x8587, ?x66) *> conf = 0.62 ranks of expected_values: 4, 5, 6 EVAL 025s89p genre! 0170k0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 35.000 19.000 0.714 http://example.org/tv/tv_program/genre EVAL 025s89p genre! 028k2x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 35.000 19.000 0.714 http://example.org/tv/tv_program/genre EVAL 025s89p genre! 020qr4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 35.000 19.000 0.714 http://example.org/tv/tv_program/genre #14630-01grp0 PRED entity: 01grp0 PRED relation: district_represented PRED expected values: 026mj => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 461): 026mj (0.93 #1609, 0.91 #541, 0.90 #969), 07h34 (0.91 #541, 0.90 #969, 0.87 #434), 05kkh (0.91 #541, 0.90 #969, 0.87 #434), 0g0syc (0.91 #541, 0.90 #969, 0.87 #434), 03v1s (0.91 #541, 0.84 #1623, 0.79 #809), 0gyh (0.91 #541, 0.79 #809, 0.75 #1319), 04tgp (0.91 #541, 0.79 #809, 0.74 #1654), 04ych (0.91 #541, 0.79 #809, 0.71 #1629), 050ks (0.91 #541, 0.79 #809, 0.60 #634), 03v0t (0.91 #541, 0.79 #809, 0.56 #1353) >> Best rule #1609 for best value: >> intensional similarity = 42 >> extensional distance = 28 >> proper extension: 01gstn; 01gst9; >> query: (?x7715, 026mj) <- district_represented(?x7715, ?x4776), district_represented(?x7715, ?x4061), district_represented(?x7715, ?x2713), district_represented(?x7715, ?x728), district_represented(?x7715, ?x335), ?x4776 = 06yxd, currency(?x4061, ?x170), location(?x13698, ?x4061), location(?x7258, ?x4061), location(?x3842, ?x4061), location(?x117, ?x4061), district_represented(?x7714, ?x4061), district_represented(?x5339, ?x4061), district_represented(?x4437, ?x4061), district_represented(?x3973, ?x4061), district_represented(?x1754, ?x4061), district_represented(?x1137, ?x4061), district_represented(?x1027, ?x4061), administrative_parent(?x4061, ?x94), ?x2713 = 06btq, contains(?x4061, ?x2175), ?x728 = 059f4, ?x5339 = 02glc4, ?x3973 = 01gssm, partially_contains(?x4061, ?x10954), time_zones(?x4061, ?x1638), influenced_by(?x6698, ?x117), ?x335 = 059rby, influenced_by(?x117, ?x118), ?x7714 = 01grr2, ?x10954 = 0lm0n, religion(?x4061, ?x109), jurisdiction_of_office(?x13698, ?x3818), ?x4437 = 01gsrl, jurisdiction_of_office(?x3959, ?x4061), ?x1754 = 01grnp, award_nominee(?x3842, ?x5831), award_nominee(?x2300, ?x7258), ?x1137 = 02bqn1, ?x1027 = 02bn_p, profession(?x13698, ?x3342), ?x5831 = 0dyztm >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01grp0 district_represented 026mj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.933 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #14629-02d9k PRED entity: 02d9k PRED relation: profession PRED expected values: 01445t => 156 concepts (80 used for prediction) PRED predicted values (max 10 best out of 80): 01445t (0.53 #1191, 0.38 #1629, 0.35 #10953), 01d_h8 (0.53 #3072, 0.46 #3364, 0.44 #2196), 0dxtg (0.47 #3080, 0.38 #2204, 0.37 #8338), 03gjzk (0.38 #3081, 0.32 #8485, 0.28 #3519), 0kyk (0.37 #11686, 0.37 #11685, 0.37 #8324), 012t_z (0.37 #11686, 0.37 #11685, 0.37 #8324), 01c979 (0.37 #11686, 0.37 #11685, 0.37 #8324), 0dz3r (0.35 #10953, 0.25 #294, 0.23 #4089), 01c72t (0.35 #10953, 0.25 #316, 0.23 #4089), 016z4k (0.35 #10953, 0.23 #4089, 0.21 #3362) >> Best rule #1191 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 08gwzt; 06qjgc; 0234pg; >> query: (?x1898, 01445t) <- team(?x1898, ?x1899), profession(?x1898, ?x1032), nationality(?x1898, ?x512), currency(?x1898, ?x170) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d9k profession 01445t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 80.000 0.529 http://example.org/people/person/profession #14628-0645k5 PRED entity: 0645k5 PRED relation: film_release_region PRED expected values: 0jgd 03rt9 05v8c 0k6nt => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 106): 0k6nt (0.89 #297, 0.81 #853, 0.81 #714), 0jgd (0.86 #698, 0.86 #281, 0.85 #1393), 03rt9 (0.74 #706, 0.73 #845, 0.71 #289), 05v8c (0.73 #569, 0.73 #708, 0.72 #847), 0ctw_b (0.72 #854, 0.71 #576, 0.70 #993), 016wzw (0.69 #331, 0.64 #609, 0.63 #887), 01p1v (0.63 #1014, 0.58 #1292, 0.56 #736), 03rk0 (0.58 #601, 0.56 #879, 0.55 #1018), 01ls2 (0.51 #982, 0.50 #1260, 0.49 #287), 09pmkv (0.50 #578, 0.49 #856, 0.49 #300) >> Best rule #297 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0ds35l9; 01vksx; 0c0nhgv; 047msdk; 0gmcwlb; 0dtfn; 011yqc; 04jkpgv; 04w7rn; 02r8hh_; ... >> query: (?x2896, 0k6nt) <- film_release_region(?x2896, ?x3277), ?x3277 = 06t8v, currency(?x2896, ?x170), award(?x2896, ?x6463) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0645k5 film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0645k5 film_release_region 05v8c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0645k5 film_release_region 03rt9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0645k5 film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14627-01d5vk PRED entity: 01d5vk PRED relation: nationality PRED expected values: 09c7w0 => 67 concepts (51 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.75 #501, 0.73 #2204, 0.72 #2404), 02jx1 (0.33 #33, 0.13 #1033, 0.12 #333), 07ssc (0.20 #115, 0.14 #715, 0.11 #1015), 03rk0 (0.12 #646, 0.09 #1949, 0.09 #2149), 0f8l9c (0.12 #2804, 0.05 #322, 0.04 #422), 0d0vqn (0.12 #2804, 0.04 #4212, 0.04 #3809), 0d060g (0.06 #207, 0.05 #307, 0.05 #507), 03rjj (0.05 #305, 0.03 #605, 0.02 #1908), 0h7x (0.04 #4212, 0.04 #3809, 0.03 #4111), 082fr (0.04 #4212, 0.04 #3809, 0.03 #4111) >> Best rule #501 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 02dh86; 0b478; 026w_gk; 0382m4; 05szp; 01my4f; 079kdz; 04glr5h; 03c6vl; >> query: (?x7342, 09c7w0) <- profession(?x7342, ?x1041), spouse(?x7342, ?x8355), ?x1041 = 03gjzk >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d5vk nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 51.000 0.750 http://example.org/people/person/nationality #14626-027dtxw PRED entity: 027dtxw PRED relation: nominated_for PRED expected values: 04qw17 016z7s 012mrr 0mcl0 017jd9 015qqg 02lxrv 0b4lkx 03cvvlg 0422v0 => 53 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1495): 0y_9q (0.82 #6681, 0.45 #8162, 0.29 #9642), 03hmt9b (0.73 #6469, 0.45 #7950, 0.43 #4988), 0pv3x (0.73 #6066, 0.45 #7547, 0.35 #9027), 017gl1 (0.73 #6040, 0.43 #4559, 0.41 #9001), 02cbhg (0.73 #7056, 0.36 #8537, 0.29 #5575), 03hkch7 (0.71 #4859, 0.60 #1899, 0.59 #9301), 0b6tzs (0.67 #26669, 0.65 #29630, 0.65 #8999), 0_92w (0.67 #26669, 0.65 #29630, 0.64 #29631), 01flv_ (0.67 #26669, 0.65 #29630, 0.64 #29631), 01qz5 (0.67 #26669, 0.65 #29630, 0.64 #29631) >> Best rule #6681 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 040njc; 02r22gf; 0l8z1; 019f4v; 02pqp12; 054krc; 02qvyrt; 02qyntr; >> query: (?x112, 0y_9q) <- nominated_for(?x112, ?x12430), nominated_for(?x112, ?x9432), film_release_region(?x9432, ?x87), ?x12430 = 034hzj, award(?x92, ?x112) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #7641 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 0gs9p; 0gr51; *> query: (?x112, 04qw17) <- nominated_for(?x112, ?x9432), nominated_for(?x112, ?x4347), film_release_region(?x9432, ?x87), ?x4347 = 04smdd, genre(?x9432, ?x53) *> conf = 0.64 ranks of expected_values: 26, 29, 53, 60, 72, 76, 104, 132, 141, 263 EVAL 027dtxw nominated_for 0422v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 53.000 24.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027dtxw nominated_for 03cvvlg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 53.000 24.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027dtxw nominated_for 0b4lkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 53.000 24.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027dtxw nominated_for 02lxrv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 53.000 24.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027dtxw nominated_for 015qqg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 53.000 24.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027dtxw nominated_for 017jd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 53.000 24.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027dtxw nominated_for 0mcl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 53.000 24.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027dtxw nominated_for 012mrr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 53.000 24.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027dtxw nominated_for 016z7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 53.000 24.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027dtxw nominated_for 04qw17 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 53.000 24.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14625-034qmv PRED entity: 034qmv PRED relation: film! PRED expected values: 01nwwl 0pkr1 0n839 => 80 concepts (53 used for prediction) PRED predicted values (max 10 best out of 818): 03h2d4 (0.25 #746, 0.02 #13184, 0.01 #11111), 016k6x (0.25 #888, 0.01 #40278), 02_p5w (0.14 #6863, 0.02 #13082, 0.01 #29666), 015wnl (0.12 #4794, 0.11 #2721, 0.10 #8940), 04wp3s (0.11 #3046, 0.08 #5119, 0.05 #9265), 0652ty (0.11 #3901, 0.08 #5974, 0.05 #10120), 03rl84 (0.11 #2396, 0.05 #8615, 0.04 #4469), 0436kgz (0.11 #3234, 0.05 #9453, 0.04 #5307), 01v3vp (0.10 #6927, 0.01 #25584, 0.01 #23511), 0127m7 (0.08 #4551, 0.05 #2478, 0.05 #8697) >> Best rule #746 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02r2j8; >> query: (?x148, 03h2d4) <- titles(?x811, ?x148), genre(?x148, ?x53), film(?x13385, ?x148), ?x13385 = 08_438 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2574 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 043sct5; *> query: (?x148, 01nwwl) <- titles(?x811, ?x148), genre(?x148, ?x8280), film_crew_role(?x148, ?x468), ?x8280 = 0hfjk *> conf = 0.05 ranks of expected_values: 35 EVAL 034qmv film! 0n839 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 53.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 034qmv film! 0pkr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 53.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 034qmv film! 01nwwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 80.000 53.000 0.250 http://example.org/film/actor/film./film/performance/film #14624-0h3y PRED entity: 0h3y PRED relation: country! PRED expected values: 02y8z 03hr1p 07_53 => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 49): 06wrt (0.75 #57, 0.73 #192, 0.68 #507), 03hr1p (0.69 #62, 0.65 #512, 0.64 #197), 07jbh (0.69 #71, 0.65 #521, 0.62 #611), 0w0d (0.69 #55, 0.65 #505, 0.60 #730), 02y8z (0.69 #59, 0.59 #554, 0.59 #509), 064vjs (0.65 #519, 0.64 #204, 0.62 #69), 019tzd (0.64 #212, 0.62 #77, 0.62 #527), 07bs0 (0.64 #191, 0.57 #506, 0.53 #1451), 07rlg (0.64 #181, 0.56 #46, 0.51 #496), 01sgl (0.62 #81, 0.59 #531, 0.59 #711) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 04wlh; >> query: (?x291, 06wrt) <- exported_to(?x87, ?x291), administrative_parent(?x291, ?x551), location(?x2162, ?x291) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #62 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 04wlh; *> query: (?x291, 03hr1p) <- exported_to(?x87, ?x291), administrative_parent(?x291, ?x551), location(?x2162, ?x291) *> conf = 0.69 ranks of expected_values: 2, 5, 33 EVAL 0h3y country! 07_53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 199.000 199.000 0.750 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0h3y country! 03hr1p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 199.000 199.000 0.750 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0h3y country! 02y8z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 199.000 199.000 0.750 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #14623-03cvvlg PRED entity: 03cvvlg PRED relation: music PRED expected values: 01tc9r => 73 concepts (52 used for prediction) PRED predicted values (max 10 best out of 83): 0146pg (0.09 #1485, 0.08 #1272, 0.08 #1700), 01tc9r (0.07 #65, 0.07 #696, 0.06 #906), 07v4dm (0.07 #193, 0.06 #824, 0.05 #1244), 02bh9 (0.07 #51, 0.05 #682, 0.04 #472), 0h0wc (0.06 #7189, 0.06 #10152, 0.06 #9304), 0499lc (0.06 #7189, 0.06 #10152, 0.06 #9304), 0gjvqm (0.06 #7189, 0.06 #10152, 0.06 #9304), 0b25vg (0.06 #7189, 0.06 #9304, 0.05 #2752), 03h610 (0.05 #1339, 0.05 #1552, 0.05 #77), 0bwh6 (0.05 #22, 0.03 #232, 0.02 #653) >> Best rule #1485 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 02x8fs; 08sk8l; >> query: (?x8438, 0146pg) <- written_by(?x8438, ?x4666), film(?x3193, ?x8438), cinematography(?x8438, ?x185), award_nominee(?x3193, ?x843) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #65 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 0170xl; *> query: (?x8438, 01tc9r) <- nominated_for(?x1162, ?x8438), nominated_for(?x384, ?x8438), genre(?x8438, ?x53), ?x384 = 03hkv_r, ?x1162 = 099c8n *> conf = 0.07 ranks of expected_values: 2 EVAL 03cvvlg music 01tc9r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 52.000 0.086 http://example.org/film/film/music #14622-09sr0 PRED entity: 09sr0 PRED relation: film! PRED expected values: 040z9 => 72 concepts (25 used for prediction) PRED predicted values (max 10 best out of 758): 0bn3jg (0.42 #43487, 0.41 #49699, 0.41 #16568), 021yc7p (0.42 #43487, 0.41 #49699, 0.41 #16568), 05kfs (0.42 #43487, 0.41 #16568, 0.31 #51772), 06r_by (0.42 #43487, 0.41 #16568, 0.31 #51772), 0146pg (0.41 #49699, 0.37 #49700, 0.31 #10355), 04pqqb (0.41 #49699, 0.31 #10355, 0.29 #4142), 03w4sh (0.13 #1143), 0gd_b_ (0.13 #518), 048hf (0.11 #1363, 0.05 #26920, 0.02 #7577), 01wb8bs (0.09 #682) >> Best rule #43487 for best value: >> intensional similarity = 4 >> extensional distance = 766 >> proper extension: 0564x; >> query: (?x9056, ?x777) <- country(?x9056, ?x94), award_winner(?x9056, ?x777), genre(?x9056, ?x53), film(?x382, ?x9056) >> conf = 0.42 => this is the best rule for 4 predicted values *> Best rule #13714 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 222 *> proper extension: 027ct7c; *> query: (?x9056, 040z9) <- nominated_for(?x1313, ?x9056), language(?x9056, ?x254), ?x1313 = 0gs9p, ?x254 = 02h40lc *> conf = 0.01 ranks of expected_values: 637 EVAL 09sr0 film! 040z9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 25.000 0.417 http://example.org/film/actor/film./film/performance/film #14621-03cvfg PRED entity: 03cvfg PRED relation: award_winner! PRED expected values: 037vqt => 79 concepts (65 used for prediction) PRED predicted values (max 10 best out of 248): 05pcn59 (0.25 #82, 0.13 #513, 0.09 #2668), 0ck27z (0.17 #11304, 0.05 #15614, 0.05 #12597), 02f6xy (0.17 #199, 0.10 #2785, 0.09 #630), 05p09zm (0.17 #125, 0.09 #556, 0.09 #987), 09sb52 (0.12 #2627, 0.11 #1765, 0.10 #3920), 0m7yy (0.12 #11391, 0.05 #17425, 0.03 #23029), 0gqwc (0.10 #2661, 0.09 #3954, 0.07 #1799), 0f4x7 (0.09 #11242, 0.06 #893, 0.05 #1324), 03c7tr1 (0.09 #3938, 0.08 #59, 0.05 #6527), 07cbcy (0.09 #941, 0.08 #79, 0.04 #510) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0170s4; >> query: (?x1580, 05pcn59) <- notable_people_with_this_condition(?x8318, ?x1580), ?x8318 = 0h99n, award_winner(?x10746, ?x1580) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03cvfg award_winner! 037vqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 65.000 0.250 http://example.org/award/award_category/winners./award/award_honor/award_winner #14620-0gvvf4j PRED entity: 0gvvf4j PRED relation: film_release_region PRED expected values: 015fr 02vzc 06t2t 03h64 => 88 concepts (64 used for prediction) PRED predicted values (max 10 best out of 156): 0f8l9c (0.93 #960, 0.90 #3145, 0.89 #1116), 03rjj (0.92 #1567, 0.91 #1099, 0.85 #2035), 0jgd (0.89 #941, 0.82 #1565, 0.82 #1097), 03h64 (0.89 #1630, 0.88 #1162, 0.85 #2567), 05b4w (0.87 #222, 0.84 #1628, 0.82 #1160), 01ls2 (0.87 #169, 0.55 #1107, 0.53 #1575), 01p1v (0.87 #210, 0.50 #1616, 0.50 #1148), 06mkj (0.86 #2088, 0.85 #1620, 0.85 #996), 0chghy (0.85 #1573, 0.84 #1105, 0.82 #2041), 0345h (0.84 #2064, 0.82 #1596, 0.82 #1128) >> Best rule #960 for best value: >> intensional similarity = 6 >> extensional distance = 53 >> proper extension: 0cp0ph6; >> query: (?x7678, 0f8l9c) <- nominated_for(?x3817, ?x7678), film_release_region(?x7678, ?x94), film_release_region(?x7678, ?x87), ?x94 = 09c7w0, executive_produced_by(?x7678, ?x4060), ?x87 = 05r4w >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1630 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 0g56t9t; 047msdk; 0bx0l; 0bh8tgs; 0gvvm6l; 04fjzv; *> query: (?x7678, 03h64) <- nominated_for(?x3817, ?x7678), film_release_region(?x7678, ?x2629), production_companies(?x7678, ?x3462), ?x2629 = 06f32 *> conf = 0.89 ranks of expected_values: 4, 11, 13, 14 EVAL 0gvvf4j film_release_region 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 64.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvvf4j film_release_region 06t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 88.000 64.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvvf4j film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 88.000 64.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvvf4j film_release_region 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 88.000 64.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14619-09r9dp PRED entity: 09r9dp PRED relation: type_of_union PRED expected values: 01g63y => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.80 #9, 0.77 #1, 0.76 #5), 01g63y (0.45 #318, 0.16 #26, 0.15 #30) >> Best rule #9 for best value: >> intensional similarity = 2 >> extensional distance = 165 >> proper extension: 012v1t; >> query: (?x3789, 04ztj) <- student(?x1368, ?x3789), location(?x3789, ?x1523) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #318 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2101 *> proper extension: 01zmpg; 01dq9q; 03csqj4; *> query: (?x3789, ?x566) <- award_nominee(?x3789, ?x3051), type_of_union(?x3051, ?x566) *> conf = 0.45 ranks of expected_values: 2 EVAL 09r9dp type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.802 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14618-014d4v PRED entity: 014d4v PRED relation: contains! PRED expected values: 07ssc => 70 concepts (61 used for prediction) PRED predicted values (max 10 best out of 282): 07ssc (0.64 #21468, 0.64 #924, 0.57 #8068), 09c7w0 (0.64 #37524, 0.62 #23228, 0.61 #26803), 0125q1 (0.31 #46455, 0.31 #43775, 0.28 #54502), 04jpl (0.22 #3594, 0.16 #4487, 0.16 #21459), 0978r (0.20 #3777, 0.16 #4670, 0.15 #11814), 0d060g (0.18 #1799, 0.13 #9836, 0.13 #10729), 01n7q (0.16 #77, 0.12 #1863, 0.09 #49215), 0dg3n1 (0.15 #8190, 0.14 #9083, 0.03 #1046), 05l5n (0.13 #3692, 0.12 #1013, 0.09 #11729), 01w0v (0.13 #3778, 0.09 #11815, 0.07 #4671) >> Best rule #21468 for best value: >> intensional similarity = 2 >> extensional distance = 219 >> proper extension: 022_6; 0crjn65; 0121c1; 0nccd; 0fgj2; 013bqg; 01t21q; 0n9dn; 017_4z; 02ly_; ... >> query: (?x11583, 07ssc) <- contains(?x1310, ?x11583), ?x1310 = 02jx1 >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014d4v contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 61.000 0.643 http://example.org/location/location/contains #14617-08htt0 PRED entity: 08htt0 PRED relation: colors PRED expected values: 0jc_p => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.39 #458, 0.39 #477, 0.38 #838), 07plts (0.33 #17, 0.09 #1483, 0.03 #169), 019sc (0.32 #101, 0.18 #766, 0.18 #842), 01g5v (0.28 #573, 0.26 #744, 0.26 #98), 01l849 (0.26 #533, 0.25 #742, 0.25 #761), 038hg (0.11 #87, 0.09 #543, 0.09 #847), 04mkbj (0.09 #484, 0.09 #465, 0.09 #1483), 036k5h (0.09 #1483, 0.09 #575, 0.08 #461), 0jc_p (0.09 #1483, 0.07 #745, 0.07 #536), 09ggk (0.09 #1483, 0.06 #737, 0.06 #851) >> Best rule #458 for best value: >> intensional similarity = 4 >> extensional distance = 257 >> proper extension: 02g839; 031q3w; 0204jh; 04s934; 02zc7f; 02gn8s; 01d34b; 01qwb5; 04gd8j; 02xwzh; ... >> query: (?x12869, 083jv) <- student(?x12869, ?x6263), colors(?x12869, ?x1101), award_nominee(?x6263, ?x237), award_nominee(?x2657, ?x6263) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1483 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 652 *> proper extension: 086x3; *> query: (?x12869, ?x663) <- state_province_region(?x12869, ?x335), state_province_region(?x7133, ?x335), location(?x101, ?x335), colors(?x7133, ?x663) *> conf = 0.09 ranks of expected_values: 9 EVAL 08htt0 colors 0jc_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 109.000 109.000 0.390 http://example.org/education/educational_institution/colors #14616-016yr0 PRED entity: 016yr0 PRED relation: profession PRED expected values: 0np9r => 93 concepts (67 used for prediction) PRED predicted values (max 10 best out of 44): 03gjzk (0.58 #13, 0.33 #1035, 0.32 #1765), 0dxtg (0.56 #12, 0.30 #1764, 0.29 #1034), 0np9r (0.21 #1625, 0.20 #1479, 0.18 #19), 018gz8 (0.20 #15, 0.14 #3811, 0.14 #2205), 09jwl (0.17 #4543, 0.17 #9070, 0.16 #8340), 0cbd2 (0.14 #6, 0.12 #1904, 0.12 #2488), 0d1pc (0.14 #486, 0.13 #194, 0.11 #340), 0nbcg (0.12 #4555, 0.11 #5285, 0.11 #5870), 0dz3r (0.11 #4528, 0.11 #5843, 0.11 #5258), 0kyk (0.10 #1925, 0.10 #2509, 0.10 #4845) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 0c8hct; >> query: (?x4327, 03gjzk) <- profession(?x4327, ?x1943), ?x1943 = 02krf9, location(?x4327, ?x7769) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1625 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 808 *> proper extension: 06v8s0; 0c7ct; 01m65sp; 01vv6_6; 059xvg; 081jbk; 044mfr; 066l3y; 09fp45; 01kymm; ... *> query: (?x4327, 0np9r) <- profession(?x4327, ?x319), gender(?x4327, ?x231), actor(?x8870, ?x4327) *> conf = 0.21 ranks of expected_values: 3 EVAL 016yr0 profession 0np9r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 67.000 0.583 http://example.org/people/person/profession #14615-059j1m PRED entity: 059j1m PRED relation: profession PRED expected values: 018gz8 => 74 concepts (36 used for prediction) PRED predicted values (max 10 best out of 49): 03gjzk (0.45 #611, 0.25 #4025, 0.23 #3890), 09jwl (0.40 #317, 0.37 #2403, 0.37 #1658), 0dxtg (0.39 #610, 0.29 #3293, 0.27 #1504), 01d_h8 (0.34 #602, 0.33 #1496, 0.33 #155), 0nbcg (0.28 #330, 0.27 #2416, 0.26 #926), 016z4k (0.27 #302, 0.24 #898, 0.24 #749), 02krf9 (0.25 #4025, 0.16 #623, 0.09 #3306), 0np9r (0.25 #4025, 0.15 #5239, 0.14 #4344), 018gz8 (0.25 #4025, 0.14 #166, 0.13 #4340), 0kyk (0.25 #4025, 0.10 #179, 0.10 #1222) >> Best rule #611 for best value: >> intensional similarity = 3 >> extensional distance = 577 >> proper extension: 0dbpyd; 0fvf9q; 06j0md; 06gp3f; 01r42_g; 03ckxdg; 050023; 04lgymt; 02pp_q_; 0d4fqn; ... >> query: (?x8440, 03gjzk) <- award_nominee(?x8440, ?x2101), gender(?x8440, ?x231), producer_type(?x2101, ?x632) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #4025 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1673 *> proper extension: 012t1; 08t7nz; *> query: (?x8440, ?x319) <- award_nominee(?x237, ?x8440), nominated_for(?x8440, ?x2102), profession(?x237, ?x319) *> conf = 0.25 ranks of expected_values: 9 EVAL 059j1m profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 74.000 36.000 0.451 http://example.org/people/person/profession #14614-018z_c PRED entity: 018z_c PRED relation: type_of_union PRED expected values: 01g63y => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1): 01g63y (0.32 #16, 0.31 #13, 0.31 #25) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 04shbh; >> query: (?x4414, 01g63y) <- nominated_for(?x4414, ?x631), celebrity(?x4414, ?x851), award(?x851, ?x591) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018z_c type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.315 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14613-05qbckf PRED entity: 05qbckf PRED relation: film! PRED expected values: 04wp3s 02m501 => 89 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1259): 0bq2g (0.73 #85200, 0.72 #70651, 0.72 #108055), 04ktcgn (0.53 #4155, 0.48 #43628, 0.48 #31162), 070yzk (0.18 #66494), 01wbg84 (0.17 #46, 0.06 #2123, 0.04 #22898), 079vf (0.15 #10389, 0.14 #16621, 0.10 #47786), 046_v (0.15 #10389, 0.14 #16621, 0.10 #47786), 04zd4m (0.15 #10389, 0.14 #16621, 0.10 #47786), 01twdk (0.13 #68572, 0.11 #39473, 0.10 #58178), 016ypb (0.11 #2575, 0.08 #10887, 0.08 #17119), 07swvb (0.11 #2771, 0.06 #6928, 0.05 #13160) >> Best rule #85200 for best value: >> intensional similarity = 4 >> extensional distance = 906 >> proper extension: 05jyb2; >> query: (?x1956, ?x3705) <- nominated_for(?x2209, ?x1956), film_release_distribution_medium(?x1956, ?x81), nominated_for(?x3705, ?x1956), film(?x3705, ?x695) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #23824 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 100 *> proper extension: 0gtsx8c; *> query: (?x1956, 04wp3s) <- film_release_region(?x1956, ?x344), film(?x8163, ?x1956), ?x344 = 04gzd, currency(?x8163, ?x170) *> conf = 0.02 ranks of expected_values: 750, 936 EVAL 05qbckf film! 02m501 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 60.000 0.727 http://example.org/film/actor/film./film/performance/film EVAL 05qbckf film! 04wp3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 60.000 0.727 http://example.org/film/actor/film./film/performance/film #14612-014zfs PRED entity: 014zfs PRED relation: profession PRED expected values: 0dxtg => 94 concepts (93 used for prediction) PRED predicted values (max 10 best out of 82): 0dxtg (0.80 #872, 0.79 #2446, 0.76 #1015), 0cbd2 (0.54 #3727, 0.52 #3584, 0.52 #4586), 02jknp (0.43 #1009, 0.41 #3578, 0.33 #4437), 0nbcg (0.41 #7614, 0.39 #6898, 0.35 #2172), 0np9r (0.41 #3578, 0.33 #4437, 0.33 #1019), 025352 (0.41 #3578, 0.30 #7159, 0.26 #11739), 05sxg2 (0.41 #3578, 0.30 #7159, 0.04 #1146), 0gl2ny2 (0.37 #2781, 0.34 #3210, 0.01 #12659), 0dz3r (0.35 #7590, 0.32 #6874, 0.28 #8449), 02krf9 (0.33 #4437, 0.29 #6012, 0.26 #7324) >> Best rule #872 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 02g8h; 03g5_y; >> query: (?x1145, 0dxtg) <- influenced_by(?x1145, ?x4112), award(?x1145, ?x688), ?x4112 = 014z8v >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014zfs profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 93.000 0.800 http://example.org/people/person/profession #14611-050xpd PRED entity: 050xpd PRED relation: major_field_of_study PRED expected values: 02lp1 => 151 concepts (125 used for prediction) PRED predicted values (max 10 best out of 122): 0jjw (0.58 #1940, 0.09 #397, 0.07 #5580), 03g3w (0.50 #27, 0.47 #391, 0.38 #148), 037mh8 (0.43 #430, 0.29 #551, 0.24 #1762), 01mkq (0.43 #1712, 0.40 #1349, 0.38 #137), 02lp1 (0.42 #1345, 0.32 #376, 0.31 #3768), 04rjg (0.40 #869, 0.38 #385, 0.37 #1717), 04gb7 (0.33 #42, 0.25 #163, 0.23 #406), 05qjt (0.31 #1704, 0.31 #1341, 0.29 #1825), 05qfh (0.31 #520, 0.28 #399, 0.24 #1731), 01tbp (0.30 #1391, 0.20 #906, 0.18 #3087) >> Best rule #1940 for best value: >> intensional similarity = 6 >> extensional distance = 130 >> proper extension: 0194_r; >> query: (?x11975, ?x3440) <- contains(?x2146, ?x11975), student(?x11975, ?x12927), student(?x11975, ?x11976), location(?x12927, ?x2446), student(?x3440, ?x12927), profession(?x11976, ?x319) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1345 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 105 *> proper extension: 03v6t; 049dk; 07vht; 01y17m; 027ydt; 0ylsr; 06b19; 04bbpm; 02x9g_; 019q50; ... *> query: (?x11975, 02lp1) <- contains(?x2146, ?x11975), institution(?x1771, ?x11975), institution(?x1200, ?x11975), ?x1771 = 019v9k, ?x1200 = 016t_3 *> conf = 0.42 ranks of expected_values: 5 EVAL 050xpd major_field_of_study 02lp1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 151.000 125.000 0.581 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14610-073hgx PRED entity: 073hgx PRED relation: honored_for PRED expected values: 01hp5 => 35 concepts (16 used for prediction) PRED predicted values (max 10 best out of 852): 02d413 (0.25 #1, 0.14 #590, 0.08 #2951), 0hx4y (0.25 #168, 0.14 #757, 0.08 #2951), 0_7w6 (0.25 #112, 0.14 #701, 0.08 #2951), 0hfzr (0.25 #252, 0.14 #841, 0.06 #1434), 0_b9f (0.25 #282, 0.14 #871, 0.06 #1464), 05znxx (0.25 #307, 0.14 #896, 0.06 #1489), 016z43 (0.25 #583, 0.14 #1172, 0.06 #1765), 09sr0 (0.25 #502, 0.14 #1091, 0.06 #1684), 07cyl (0.25 #203, 0.14 #792, 0.06 #1385), 0_816 (0.25 #190, 0.14 #779, 0.06 #1372) >> Best rule #1 for best value: >> intensional similarity = 16 >> extensional distance = 2 >> proper extension: 073h1t; 073h9x; >> query: (?x7038, 02d413) <- ceremony(?x5409, ?x7038), ceremony(?x601, ?x7038), honored_for(?x7038, ?x7073), honored_for(?x7038, ?x2151), honored_for(?x7038, ?x1425), ?x5409 = 0gr07, award_winner(?x7038, ?x2870), film(?x5495, ?x7073), ?x601 = 0gr4k, ?x2870 = 06rnl9, film_release_region(?x2151, ?x87), language(?x2151, ?x254), award_winner(?x5495, ?x628), nominated_for(?x9163, ?x1425), award_nominee(?x1286, ?x5495), gender(?x5495, ?x231) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 073hgx honored_for 01hp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 35.000 16.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #14609-0f2pf9 PRED entity: 0f2pf9 PRED relation: partially_contains! PRED expected values: 0498y => 87 concepts (27 used for prediction) PRED predicted values (max 10 best out of 99): 0f8l9c (0.43 #496, 0.17 #980, 0.13 #1174), 03v0t (0.40 #342, 0.35 #771, 0.33 #149), 0498y (0.40 #345, 0.33 #152, 0.29 #441), 05tbn (0.40 #340, 0.33 #147, 0.29 #436), 081mh (0.40 #331, 0.33 #138, 0.29 #427), 05kkh (0.35 #771, 0.33 #99, 0.28 #966), 01_d4 (0.35 #771, 0.28 #966), 01n7q (0.29 #407, 0.20 #696, 0.20 #599), 02j9z (0.26 #1168, 0.21 #877, 0.21 #1265), 0fb_1 (0.25 #283, 0.07 #862, 0.05 #1153) >> Best rule #496 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0lm0n; 02v3m7; 05g56; 065ky; >> query: (?x14493, 0f8l9c) <- partially_contains(?x448, ?x14493), contains(?x14493, ?x10845), religion(?x448, ?x109) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #345 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 04yf_; 02cgp8; *> query: (?x14493, 0498y) <- contains(?x94, ?x14493), partially_contains(?x448, ?x14493), district_represented(?x6021, ?x448), ?x6021 = 01gsvp *> conf = 0.40 ranks of expected_values: 3 EVAL 0f2pf9 partially_contains! 0498y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 27.000 0.429 http://example.org/location/location/partially_contains #14608-017dpj PRED entity: 017dpj PRED relation: award_winner! PRED expected values: 0m7yy => 135 concepts (113 used for prediction) PRED predicted values (max 10 best out of 256): 0ck27z (0.41 #10454, 0.11 #19944, 0.08 #18219), 0gr51 (0.37 #1297, 0.36 #45311, 0.36 #7340), 0m7yy (0.25 #179, 0.20 #1476, 0.19 #18558), 027gs1_ (0.19 #18558, 0.18 #21577, 0.18 #21578), 09qrn4 (0.19 #18558, 0.18 #21577, 0.18 #21578), 09qj50 (0.19 #18558, 0.18 #21577, 0.18 #21578), 09qs08 (0.19 #18558, 0.18 #21577, 0.18 #21578), 09qv3c (0.19 #18558, 0.18 #21577, 0.18 #21578), 0cc8l6d (0.17 #171, 0.08 #2764, 0.05 #1468), 0fbtbt (0.16 #8432, 0.13 #3254, 0.13 #11455) >> Best rule #10454 for best value: >> intensional similarity = 3 >> extensional distance = 153 >> proper extension: 0f721s; 0gsg7; 09d5h; 03mdt; 027_tg; 05gnf; >> query: (?x10506, 0ck27z) <- award_winner(?x12533, ?x10506), nominated_for(?x2041, ?x12533), ?x2041 = 0bdx29 >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #179 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0grwj; 014zfs; 01pcmd; 03j24kf; 044f7; 01zlh5; 019pkm; 03mstc; 01l1ls; 0488g9; *> query: (?x10506, 0m7yy) <- profession(?x10506, ?x319), location(?x10506, ?x961), producer_type(?x10506, ?x632), inductee(?x9953, ?x10506) *> conf = 0.25 ranks of expected_values: 3 EVAL 017dpj award_winner! 0m7yy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 135.000 113.000 0.413 http://example.org/award/award_category/winners./award/award_honor/award_winner #14607-02snj9 PRED entity: 02snj9 PRED relation: role PRED expected values: 018vs 018j2 0cfdd => 71 concepts (54 used for prediction) PRED predicted values (max 10 best out of 137): 018vs (0.89 #4345, 0.88 #5013, 0.85 #3493), 03qjg (0.88 #3144, 0.85 #2091, 0.82 #1809), 0l14j_ (0.87 #3196, 0.85 #2141, 0.85 #93), 02hnl (0.85 #2141, 0.85 #93, 0.85 #561), 07y_7 (0.85 #2141, 0.85 #93, 0.85 #561), 0395lw (0.85 #2141, 0.85 #93, 0.85 #561), 07gql (0.84 #2560, 0.79 #2181, 0.73 #1801), 07xzm (0.83 #1872, 0.73 #2251, 0.73 #1778), 026t6 (0.80 #96, 0.79 #284, 0.78 #2713), 026g73 (0.80 #96, 0.79 #284, 0.75 #1024) >> Best rule #4345 for best value: >> intensional similarity = 18 >> extensional distance = 45 >> proper extension: 02pprs; 0395lw; 01kcd; 02k856; 011k_j; >> query: (?x3214, 018vs) <- role(?x3214, ?x3215), role(?x3214, ?x432), group(?x3214, ?x498), role(?x75, ?x3214), role(?x217, ?x3215), role(?x3967, ?x3215), role(?x3716, ?x3215), group(?x432, ?x442), role(?x432, ?x7772), role(?x2237, ?x432), role(?x1338, ?x432), ?x3716 = 03gvt, ?x3967 = 01p970, instrumentalists(?x432, ?x133), ?x7772 = 0j862, ?x1338 = 09qr6, performance_role(?x3214, ?x3703), nominated_for(?x2237, ?x408) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 19, 33 EVAL 02snj9 role 0cfdd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 71.000 54.000 0.894 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 02snj9 role 018j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 71.000 54.000 0.894 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 02snj9 role 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 54.000 0.894 http://example.org/music/performance_role/regular_performances./music/group_membership/role #14606-0c1jh PRED entity: 0c1jh PRED relation: place_of_death PRED expected values: 07_pf => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 59): 04jpl (0.13 #1755, 0.12 #2921, 0.12 #1949), 05qtj (0.12 #5504, 0.11 #4920, 0.11 #3754), 0jbrr (0.11 #375), 02_286 (0.11 #2732, 0.10 #790, 0.09 #3509), 030qb3t (0.09 #3518, 0.09 #3324, 0.08 #1188), 0cpyv (0.08 #456, 0.07 #650, 0.07 #3758), 06pr6 (0.08 #493, 0.07 #687, 0.03 #2824), 09b93 (0.08 #549, 0.07 #743, 0.02 #4822), 0d33k (0.08 #552, 0.07 #746, 0.01 #9718), 05l64 (0.08 #540, 0.07 #734) >> Best rule #1755 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 03g5jw; >> query: (?x9508, 04jpl) <- peers(?x587, ?x9508), influenced_by(?x9508, ?x1279), award(?x9508, ?x14436), influenced_by(?x587, ?x3541) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c1jh place_of_death 07_pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 123.000 0.129 http://example.org/people/deceased_person/place_of_death #14605-016fjj PRED entity: 016fjj PRED relation: award_nominee! PRED expected values: 07ddz9 => 119 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1092): 0gx_p (0.81 #13964, 0.81 #44222, 0.81 #18620), 0dvmd (0.25 #46551, 0.19 #88446, 0.18 #79136), 0h0wc (0.25 #46551, 0.19 #88446, 0.18 #79136), 0151w_ (0.25 #46551, 0.19 #88446, 0.18 #79136), 016fjj (0.25 #46551, 0.19 #88446, 0.18 #79136), 017s11 (0.25 #46551, 0.18 #79136, 0.16 #137322), 05br10 (0.25 #46551, 0.18 #79136, 0.16 #137322), 025hwq (0.25 #46551, 0.18 #79136, 0.16 #137322), 03m49ly (0.25 #46551, 0.18 #79136, 0.16 #137322), 03mfqm (0.25 #46551, 0.18 #79136, 0.16 #137322) >> Best rule #13964 for best value: >> intensional similarity = 3 >> extensional distance = 250 >> proper extension: 028q6; 02lf0c; 03ldxq; 016kjs; 02pkpfs; 0fpjd_g; 02lxj_; 05d8vw; 0443y3; 01f7j9; ... >> query: (?x3701, ?x3756) <- religion(?x3701, ?x1624), student(?x3394, ?x3701), award_nominee(?x3701, ?x3756) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #88446 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 728 *> proper extension: 07xr3w; 01m7f5r; 01b0k1; *> query: (?x3701, ?x989) <- nominated_for(?x3701, ?x4709), people(?x1423, ?x3701), film(?x989, ?x4709) *> conf = 0.19 ranks of expected_values: 18 EVAL 016fjj award_nominee! 07ddz9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 119.000 63.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14604-02b0y3 PRED entity: 02b0y3 PRED relation: position PRED expected values: 0dgrmp => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 4): 02_j1w (0.82 #190, 0.82 #189, 0.81 #47), 0dgrmp (0.81 #134, 0.81 #122, 0.81 #94), 02md_2 (0.50 #238, 0.44 #78, 0.36 #153), 02qvgy (0.44 #78) >> Best rule #190 for best value: >> intensional similarity = 12 >> extensional distance = 445 >> proper extension: 0lhp1; 0g701n; 04n1hqz; 08036w; 04hzfz; 01yxbw; 041n28; 0586wl; 0gd70t; 05hywl; ... >> query: (?x5403, ?x60) <- position(?x5403, ?x530), position(?x5403, ?x63), position(?x5403, ?x60), ?x530 = 02_j1w, ?x63 = 02sdk9v, team(?x60, ?x13580), team(?x60, ?x10553), team(?x60, ?x5433), ?x10553 = 0425yz, position(?x1026, ?x60), ?x5433 = 0449sw, ?x13580 = 01_1kk >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #134 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 214 *> proper extension: 01whvs; 047fwlg; 03n5v; 04_bfq; 02b171; *> query: (?x5403, ?x203) <- team(?x9411, ?x5403), position(?x5403, ?x12598), position(?x5403, ?x60), team(?x9411, ?x6892), ?x60 = 02nzb8, team(?x7622, ?x6892), team(?x203, ?x6892), sport(?x6892, ?x471), ?x471 = 02vx4, team(?x12598, ?x4364) *> conf = 0.81 ranks of expected_values: 2 EVAL 02b0y3 position 0dgrmp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 44.000 0.819 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #14603-01mwsnc PRED entity: 01mwsnc PRED relation: profession PRED expected values: 0dz3r 02hrh1q => 164 concepts (85 used for prediction) PRED predicted values (max 10 best out of 84): 02hrh1q (0.93 #3205, 0.87 #4803, 0.87 #3350), 016z4k (0.66 #1746, 0.62 #149, 0.57 #3776), 01c72t (0.62 #10949, 0.39 #1473, 0.36 #312), 0dz3r (0.59 #873, 0.55 #4502, 0.55 #4210), 01d_h8 (0.45 #296, 0.43 #586, 0.38 #151), 0dxtg (0.35 #3059, 0.33 #2334, 0.32 #2044), 02jknp (0.29 #2330, 0.27 #1895, 0.26 #2040), 03gjzk (0.27 #304, 0.22 #4949, 0.20 #3061), 0kyk (0.24 #2060, 0.21 #2350, 0.21 #608), 025352 (0.24 #927, 0.18 #346, 0.14 #1507) >> Best rule #3205 for best value: >> intensional similarity = 5 >> extensional distance = 79 >> proper extension: 07fq1y; 05cj4r; 09fb5; 0159h6; 014x77; 09wj5; 08w7vj; 032_jg; 01j5x6; 01sp81; ... >> query: (?x4918, 02hrh1q) <- film(?x4918, ?x1619), profession(?x4918, ?x655), type_of_union(?x4918, ?x566), people(?x743, ?x4918), ?x743 = 02w7gg >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 01mwsnc profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 85.000 0.926 http://example.org/people/person/profession EVAL 01mwsnc profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 164.000 85.000 0.926 http://example.org/people/person/profession #14602-02v5_g PRED entity: 02v5_g PRED relation: genre PRED expected values: 02n4kr => 67 concepts (50 used for prediction) PRED predicted values (max 10 best out of 87): 07s9rl0 (0.64 #1045, 0.63 #465, 0.61 #1858), 02kdv5l (0.44 #699, 0.35 #583, 0.33 #119), 03k9fj (0.40 #243, 0.36 #591, 0.22 #1984), 04xvlr (0.38 #350, 0.16 #4411, 0.16 #3947), 0lsxr (0.34 #704, 0.26 #472, 0.23 #356), 02l7c8 (0.33 #131, 0.27 #4424, 0.26 #1059), 01hmnh (0.28 #597, 0.15 #2802, 0.15 #1990), 02n4kr (0.25 #703, 0.23 #355, 0.13 #471), 0hfjk (0.22 #176, 0.03 #4817, 0.03 #5165), 04t2t (0.22 #171, 0.02 #751, 0.02 #1331) >> Best rule #1045 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 0564x; >> query: (?x4663, 07s9rl0) <- written_by(?x4663, ?x10407), genre(?x4663, ?x258), award_winner(?x4663, ?x413) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #703 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 334 *> proper extension: 064n1pz; 0bmc4cm; *> query: (?x4663, 02n4kr) <- nominated_for(?x3064, ?x4663), genre(?x4663, ?x812), ?x812 = 01jfsb *> conf = 0.25 ranks of expected_values: 8 EVAL 02v5_g genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 67.000 50.000 0.637 http://example.org/film/film/genre #14601-04s9n PRED entity: 04s9n PRED relation: religion PRED expected values: 0flw86 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 25): 03_gx (0.30 #1103, 0.28 #2281, 0.28 #1330), 0c8wxp (0.20 #142, 0.13 #4355, 0.13 #3946), 0kpl (0.18 #146, 0.16 #1552, 0.15 #2323), 0flw86 (0.12 #1907, 0.02 #3577, 0.02 #3669), 06pq6 (0.12 #1907), 01lp8 (0.11 #46, 0.05 #91, 0.05 #364), 019cr (0.11 #56, 0.02 #1735, 0.02 #1054), 0kq2 (0.08 #154, 0.06 #1560, 0.05 #2150), 03j6c (0.07 #520, 0.05 #3596, 0.04 #157), 0n2g (0.06 #1874, 0.06 #738, 0.05 #1374) >> Best rule #1103 for best value: >> intensional similarity = 6 >> extensional distance = 173 >> proper extension: 03_0p; >> query: (?x10688, 03_gx) <- gender(?x10688, ?x231), ?x231 = 05zppz, people(?x12262, ?x10688), type_of_union(?x10688, ?x566), ?x566 = 04ztj, combatants(?x11531, ?x12262) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #1907 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 259 *> proper extension: 07c0j; 048xh; 07hgm; 073_6; 04sd0; *> query: (?x10688, ?x492) <- influenced_by(?x13020, ?x10688), influenced_by(?x7341, ?x10688), type_of_union(?x13020, ?x566), religion(?x7341, ?x492), gender(?x7341, ?x231), profession(?x7341, ?x3801), nationality(?x7341, ?x3730) *> conf = 0.12 ranks of expected_values: 4 EVAL 04s9n religion 0flw86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 145.000 0.303 http://example.org/people/person/religion #14600-02j3d4 PRED entity: 02j3d4 PRED relation: profession PRED expected values: 09jwl => 101 concepts (100 used for prediction) PRED predicted values (max 10 best out of 54): 09jwl (0.73 #909, 0.72 #2246, 0.70 #1949), 02hrh1q (0.66 #9951, 0.65 #10395, 0.64 #13356), 0nbcg (0.50 #2258, 0.48 #921, 0.47 #3742), 016z4k (0.49 #2231, 0.47 #894, 0.45 #1191), 0dz3r (0.42 #298, 0.41 #892, 0.41 #2822), 039v1 (0.30 #5197, 0.29 #36, 0.28 #926), 0n1h (0.30 #5197, 0.29 #11, 0.28 #6829), 05vyk (0.30 #5197, 0.28 #6829, 0.26 #11862), 029bkp (0.30 #5197, 0.28 #6829, 0.26 #11862), 01b30l (0.30 #5197, 0.28 #6829, 0.26 #11862) >> Best rule #909 for best value: >> intensional similarity = 3 >> extensional distance = 251 >> proper extension: 0dm5l; 094xh; >> query: (?x4568, 09jwl) <- award(?x4568, ?x159), artist(?x5666, ?x4568), role(?x4568, ?x227) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02j3d4 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 100.000 0.731 http://example.org/people/person/profession #14599-0178rl PRED entity: 0178rl PRED relation: award_winner! PRED expected values: 059x66 0jzphpx => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 119): 059x66 (0.25 #4003, 0.17 #8697, 0.16 #4970), 01c6qp (0.16 #4970, 0.10 #9527, 0.10 #8974), 0466p0j (0.16 #4970, 0.10 #9527, 0.10 #8974), 01s695 (0.16 #4970, 0.10 #9527, 0.10 #8974), 01mhwk (0.16 #4970, 0.10 #9527, 0.10 #8974), 0jzphpx (0.16 #4970, 0.10 #9527, 0.10 #8974), 01xqqp (0.16 #4970, 0.10 #9527, 0.10 #8974), 0gx1673 (0.16 #4970, 0.10 #9527, 0.10 #8974), 0fqpc7d (0.16 #4970, 0.10 #9527, 0.10 #8974), 0h_9252 (0.16 #4970, 0.10 #9527, 0.10 #8974) >> Best rule #4003 for best value: >> intensional similarity = 3 >> extensional distance = 973 >> proper extension: 02wrhj; >> query: (?x5223, ?x1449) <- nominated_for(?x5223, ?x7693), award_winner(?x725, ?x5223), honored_for(?x1449, ?x7693) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 0178rl award_winner! 0jzphpx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 96.000 96.000 0.254 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0178rl award_winner! 059x66 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.254 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14598-018wdw PRED entity: 018wdw PRED relation: nominated_for PRED expected values: 01hr1 0bth54 0c38gj 08phg9 01xq8v 034b6k => 55 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1396): 02qm_f (0.78 #27450, 0.77 #22877, 0.77 #24402), 0k2sk (0.78 #27450, 0.77 #22877, 0.77 #24402), 02vxq9m (0.78 #27450, 0.77 #22877, 0.77 #24402), 07024 (0.78 #27450, 0.77 #22877, 0.77 #24402), 012mrr (0.78 #27450, 0.77 #22877, 0.77 #24402), 0f4yh (0.78 #27450, 0.77 #22877, 0.77 #24402), 0pb33 (0.78 #27450, 0.77 #22877, 0.77 #24402), 01kf5lf (0.77 #22877, 0.77 #24402, 0.77 #27449), 017jd9 (0.60 #2196, 0.58 #9818, 0.54 #11345), 017gl1 (0.60 #1649, 0.55 #9271, 0.51 #10798) >> Best rule #27450 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 0fqnzts; >> query: (?x6860, ?x2770) <- award(?x2770, ?x6860), ceremony(?x6860, ?x78), nominated_for(?x298, ?x2770) >> conf = 0.78 => this is the best rule for 7 predicted values *> Best rule #1594 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0p9sw; 02hsq3m; 0gr42; *> query: (?x6860, 0bth54) <- award(?x7214, ?x6860), nominated_for(?x6860, ?x155), ceremony(?x6860, ?x78), ?x7214 = 02dr9j *> conf = 0.60 ranks of expected_values: 12, 13, 360, 373, 384, 416 EVAL 018wdw nominated_for 034b6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 20.000 0.775 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 018wdw nominated_for 01xq8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 20.000 0.775 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 018wdw nominated_for 08phg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 55.000 20.000 0.775 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 018wdw nominated_for 0c38gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 20.000 0.775 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 018wdw nominated_for 0bth54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 55.000 20.000 0.775 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 018wdw nominated_for 01hr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 20.000 0.775 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14597-0gm2_0 PRED entity: 0gm2_0 PRED relation: genre PRED expected values: 0lsxr => 92 concepts (82 used for prediction) PRED predicted values (max 10 best out of 88): 017fp (0.91 #248, 0.21 #3995, 0.10 #1070), 05p553 (0.59 #6233, 0.50 #4, 0.39 #708), 02kdv5l (0.55 #4114, 0.49 #353, 0.30 #2703), 03k9fj (0.37 #6240, 0.28 #4123, 0.23 #6357), 04xvlr (0.35 #235, 0.21 #3995, 0.21 #469), 0lsxr (0.33 #359, 0.26 #4120, 0.21 #3995), 03bxz7 (0.32 #287, 0.10 #3578, 0.10 #1109), 02l7c8 (0.30 #3540, 0.29 #5890, 0.29 #3421), 06n90 (0.26 #4124, 0.21 #3995, 0.20 #363), 0gf28 (0.25 #62, 0.21 #3995, 0.14 #179) >> Best rule #248 for best value: >> intensional similarity = 3 >> extensional distance = 122 >> proper extension: 0413cff; >> query: (?x9744, 017fp) <- genre(?x9744, ?x9360), titles(?x9360, ?x7765), ?x7765 = 0hvvf >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #359 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 279 *> proper extension: 01771z; 0900j5; 0yx7h; 0c9t0y; *> query: (?x9744, 0lsxr) <- nominated_for(?x166, ?x9744), currency(?x9744, ?x170), genre(?x9744, ?x812), ?x812 = 01jfsb *> conf = 0.33 ranks of expected_values: 6 EVAL 0gm2_0 genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 92.000 82.000 0.911 http://example.org/film/film/genre #14596-01rcmg PRED entity: 01rcmg PRED relation: award_winner! PRED expected values: 0bxs_d => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 126): 03nnm4t (0.20 #1185, 0.20 #1046, 0.19 #629), 0fqpc7d (0.19 #592, 0.15 #1148, 0.15 #1009), 092_25 (0.15 #349, 0.11 #71, 0.07 #488), 0g55tzk (0.14 #552, 0.12 #691, 0.11 #135), 0418154 (0.12 #662, 0.11 #106, 0.10 #1218), 05c1t6z (0.12 #571, 0.10 #1127, 0.10 #988), 09g90vz (0.12 #678, 0.10 #1234, 0.10 #1095), 09gkdln (0.12 #676, 0.10 #1232, 0.10 #1093), 058m5m4 (0.12 #611, 0.10 #1167, 0.10 #1028), 092t4b (0.11 #52, 0.09 #1442, 0.07 #1998) >> Best rule #1185 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 01bcq; >> query: (?x8439, 03nnm4t) <- award_winner(?x5296, ?x8439), language(?x8439, ?x254), award(?x8439, ?x678) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #113 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 01x6jd; *> query: (?x8439, 0bxs_d) <- film(?x8439, ?x2893), award_nominee(?x8439, ?x3446), ?x2893 = 01jrbb *> conf = 0.11 ranks of expected_values: 15 EVAL 01rcmg award_winner! 0bxs_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 117.000 117.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14595-0m66w PRED entity: 0m66w PRED relation: location PRED expected values: 01snm => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 174): 030qb3t (0.30 #54487, 0.26 #37685, 0.25 #6480), 04jpl (0.10 #54424, 0.07 #93634, 0.06 #24020), 01cx_ (0.09 #960, 0.06 #2560, 0.05 #3360), 0cr3d (0.09 #30547, 0.07 #93759, 0.06 #35347), 01531 (0.08 #1755, 0.04 #30560, 0.04 #17758), 0r0m6 (0.08 #5015, 0.03 #18618, 0.03 #6615), 01n7q (0.07 #6460, 0.06 #4860, 0.05 #10461), 059rby (0.06 #54423, 0.06 #816, 0.05 #16), 0k049 (0.06 #1608, 0.05 #8, 0.03 #6408), 0dclg (0.06 #1714, 0.03 #914, 0.03 #2514) >> Best rule #54487 for best value: >> intensional similarity = 3 >> extensional distance = 730 >> proper extension: 018fwv; >> query: (?x5889, 030qb3t) <- film(?x5889, ?x2586), location(?x5889, ?x739), film_release_region(?x204, ?x739) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #317 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 0721cy; 01tj34; 05y5fw; *> query: (?x5889, 01snm) <- program(?x5889, ?x4588), award(?x5889, ?x678), spouse(?x722, ?x5889) *> conf = 0.05 ranks of expected_values: 19 EVAL 0m66w location 01snm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 141.000 141.000 0.299 http://example.org/people/person/places_lived./people/place_lived/location #14594-03gh4 PRED entity: 03gh4 PRED relation: vacationer PRED expected values: 0151w_ 0lx2l 0dn3n 046zh 022q32 => 188 concepts (140 used for prediction) PRED predicted values (max 10 best out of 380): 0261x8t (0.50 #254, 0.20 #538, 0.17 #822), 01yf85 (0.50 #267, 0.12 #3964, 0.12 #1119), 04fzk (0.25 #924, 0.25 #214, 0.12 #1066), 0151w_ (0.25 #870, 0.25 #160, 0.12 #1012), 0dq9wx (0.25 #1135, 0.25 #283, 0.12 #993), 026c1 (0.25 #1025, 0.25 #883, 0.11 #3442), 0bbf1f (0.25 #1044, 0.25 #902, 0.11 #3461), 04xrx (0.25 #1039, 0.25 #897, 0.09 #4597), 07r1h (0.25 #1098, 0.25 #956, 0.07 #4798), 01wxyx1 (0.25 #171, 0.18 #1165, 0.07 #1308) >> Best rule #254 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0b90_r; 0f2v0; >> query: (?x6226, 0261x8t) <- vacationer(?x6226, ?x6613), vacationer(?x6226, ?x3281), ?x6613 = 06tp4h, award_winner(?x995, ?x3281) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #870 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02_286; 02jx1; 0156q; 0cv3w; 0fgj2; 05qtj; *> query: (?x6226, 0151w_) <- vacationer(?x6226, ?x6613), vacationer(?x6226, ?x4628), artists(?x3061, ?x6613), ?x4628 = 016fnb *> conf = 0.25 ranks of expected_values: 4, 81, 89, 101 EVAL 03gh4 vacationer 022q32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 188.000 140.000 0.500 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 03gh4 vacationer 046zh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 188.000 140.000 0.500 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 03gh4 vacationer 0dn3n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 188.000 140.000 0.500 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 03gh4 vacationer 0lx2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 188.000 140.000 0.500 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 03gh4 vacationer 0151w_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 188.000 140.000 0.500 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #14593-01qwb5 PRED entity: 01qwb5 PRED relation: school_type PRED expected values: 01_9fk 01rs41 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 21): 01rs41 (0.51 #293, 0.51 #341, 0.47 #245), 05pcjw (0.50 #217, 0.48 #145, 0.48 #265), 05jxkf (0.45 #1876, 0.43 #52, 0.42 #1996), 07tf8 (0.22 #57, 0.21 #105, 0.18 #129), 01_9fk (0.18 #26, 0.17 #98, 0.17 #2), 01_srz (0.12 #339, 0.10 #219, 0.09 #147), 02p0qmm (0.07 #538, 0.04 #1066, 0.03 #1426), 01y64 (0.07 #492, 0.05 #348, 0.05 #372), 04qbv (0.06 #328, 0.05 #280, 0.05 #400), 02dk5q (0.04 #463, 0.04 #367, 0.04 #487) >> Best rule #293 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 0l0wv; >> query: (?x7939, 01rs41) <- major_field_of_study(?x7939, ?x1695), colors(?x7939, ?x5845), currency(?x7939, ?x170), ?x170 = 09nqf >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 01qwb5 school_type 01rs41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.506 http://example.org/education/educational_institution/school_type EVAL 01qwb5 school_type 01_9fk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 141.000 141.000 0.506 http://example.org/education/educational_institution/school_type #14592-041r51 PRED entity: 041r51 PRED relation: nutrient! PRED expected values: 09728 0fj52s 0dj75 => 56 concepts (53 used for prediction) PRED predicted values (max 10 best out of 5): 0fj52s (0.92 #456, 0.92 #448, 0.91 #615), 09728 (0.89 #121, 0.89 #54, 0.89 #46), 0dj75 (0.89 #121, 0.89 #54, 0.89 #46), 06x4c (0.89 #121, 0.89 #54, 0.89 #46), 0dcfv (0.89 #121, 0.89 #54, 0.89 #46) >> Best rule #456 for best value: >> intensional similarity = 117 >> extensional distance = 24 >> proper extension: 06x4c; >> query: (?x6160, ?x1303) <- nutrient(?x9732, ?x6160), nutrient(?x8298, ?x6160), nutrient(?x7057, ?x6160), nutrient(?x6285, ?x6160), nutrient(?x6191, ?x6160), nutrient(?x6159, ?x6160), nutrient(?x5373, ?x6160), nutrient(?x4068, ?x6160), nutrient(?x3900, ?x6160), nutrient(?x2701, ?x6160), nutrient(?x1959, ?x6160), ?x8298 = 037ls6, nutrient(?x9732, ?x14210), nutrient(?x9732, ?x13944), nutrient(?x9732, ?x13545), nutrient(?x9732, ?x12902), nutrient(?x9732, ?x12083), nutrient(?x9732, ?x11758), nutrient(?x9732, ?x11409), nutrient(?x9732, ?x11270), nutrient(?x9732, ?x10891), nutrient(?x9732, ?x10709), nutrient(?x9732, ?x10098), nutrient(?x9732, ?x9949), nutrient(?x9732, ?x9915), nutrient(?x9732, ?x9733), nutrient(?x9732, ?x9490), nutrient(?x9732, ?x9436), nutrient(?x9732, ?x9365), nutrient(?x9732, ?x8413), nutrient(?x9732, ?x7720), nutrient(?x9732, ?x7652), nutrient(?x9732, ?x7364), nutrient(?x9732, ?x7362), nutrient(?x9732, ?x7219), nutrient(?x9732, ?x6033), nutrient(?x9732, ?x5549), nutrient(?x9732, ?x5526), nutrient(?x9732, ?x5451), nutrient(?x9732, ?x5374), nutrient(?x9732, ?x5010), nutrient(?x9732, ?x2702), nutrient(?x9732, ?x2018), nutrient(?x9732, ?x1960), nutrient(?x9732, ?x1304), nutrient(?x9732, ?x1258), ?x5549 = 025s7j4, ?x3900 = 061_f, ?x5526 = 09pbb, ?x2702 = 0838f, ?x11758 = 0q01m, ?x4068 = 0fbw6, ?x1258 = 0h1wg, ?x2701 = 0hkxq, ?x2018 = 01sh2, ?x10709 = 0h1sz, ?x9733 = 0h1tz, ?x10098 = 0h1_c, nutrient(?x1959, ?x13126), nutrient(?x1959, ?x11784), nutrient(?x1959, ?x9840), nutrient(?x1959, ?x9795), nutrient(?x1959, ?x9619), nutrient(?x1959, ?x6192), nutrient(?x1959, ?x6026), nutrient(?x1959, ?x4069), nutrient(?x1959, ?x3203), ?x14210 = 0f4k5, ?x9840 = 02p0tjr, ?x13126 = 02kc_w5, ?x9795 = 05v_8y, ?x11409 = 0h1yf, ?x6159 = 033cnk, ?x9436 = 025sqz8, ?x5010 = 0h1vz, ?x6033 = 04zjxcz, ?x9619 = 0h1tg, ?x6026 = 025sf8g, ?x13545 = 01w_3, ?x8413 = 02kc4sf, nutrient(?x7719, ?x5374), nutrient(?x1303, ?x5374), nutrient(?x1257, ?x5374), ?x12083 = 01n78x, nutrient(?x5373, ?x10453), ?x6192 = 06jry, ?x3203 = 04kl74p, ?x13944 = 0f4kp, ?x9490 = 0h1sg, ?x9915 = 025tkqy, ?x7219 = 0h1vg, ?x10891 = 0g5gq, ?x11784 = 07zqy, ?x7719 = 0dj75, ?x10453 = 075pwf, ?x9365 = 04k8n, ?x1303 = 0fj52s, nutrient(?x6285, ?x12868), nutrient(?x6285, ?x12481), nutrient(?x6285, ?x9855), ?x5451 = 05wvs, ?x12868 = 03d49, ?x1960 = 07hnp, ?x7652 = 025s0s0, ?x11270 = 02kc008, ?x6191 = 014j1m, ?x7057 = 0fbdb, ?x9949 = 02kd0rh, ?x1257 = 09728, ?x7720 = 025s7x6, ?x12481 = 027g6p7, ?x7364 = 09gvd, ?x1304 = 08lb68, ?x12902 = 0fzjh, ?x9855 = 0d9t0, ?x4069 = 0hqw8p_, ?x7362 = 02kc5rj >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 041r51 nutrient! 0dj75 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 53.000 0.923 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 041r51 nutrient! 0fj52s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 53.000 0.923 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 041r51 nutrient! 09728 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 53.000 0.923 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #14591-05qtj PRED entity: 05qtj PRED relation: place_of_death! PRED expected values: 058vp => 291 concepts (236 used for prediction) PRED predicted values (max 10 best out of 849): 02sdx (0.20 #2048, 0.05 #17533, 0.04 #21220), 02lj6p (0.20 #1891, 0.05 #17376, 0.04 #21063), 07csf4 (0.20 #1524, 0.05 #17009, 0.04 #20696), 01y8d4 (0.17 #2599, 0.14 #4074, 0.03 #29145), 0465_ (0.14 #3978, 0.11 #6926, 0.06 #12824), 03qhyn8 (0.14 #4374, 0.11 #7322, 0.06 #13220), 058w5 (0.14 #4161, 0.11 #7109, 0.06 #13007), 034q3l (0.14 #4114, 0.11 #7062, 0.06 #12960), 012201 (0.14 #4099, 0.11 #7047, 0.06 #12945), 09qc1 (0.14 #3886, 0.11 #6834, 0.06 #12732) >> Best rule #2048 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0rp46; >> query: (?x4627, 02sdx) <- location(?x5336, ?x4627), location(?x1236, ?x4627), ?x5336 = 02kz_, influenced_by(?x1236, ?x2240) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #75995 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 0136jw; 0t6hk; 079yb; 09b93; 09hzc; 0161jj; *> query: (?x4627, ?x1279) <- place_of_death(?x4265, ?x4627), influenced_by(?x4265, ?x1279), influenced_by(?x1029, ?x4265) *> conf = 0.03 ranks of expected_values: 611 EVAL 05qtj place_of_death! 058vp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 291.000 236.000 0.200 http://example.org/people/deceased_person/place_of_death #14590-02_jjm PRED entity: 02_jjm PRED relation: organization! PRED expected values: 0hm4q => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 11): 060c4 (0.70 #615, 0.64 #198, 0.64 #876), 07xl34 (0.38 #51, 0.38 #455, 0.37 #129), 0dq_5 (0.24 #948, 0.23 #1039, 0.22 #766), 05k17c (0.21 #47, 0.20 #294, 0.20 #73), 0hm4q (0.18 #21, 0.17 #113, 0.16 #139), 05c0jwl (0.11 #149, 0.08 #188, 0.06 #162), 0fkvn (0.04 #1553, 0.03 #1698, 0.03 #1842), 0f6c3 (0.04 #1883, 0.03 #1869, 0.03 #1962), 060bp (0.04 #1883, 0.03 #1869, 0.03 #1962), 08jcfy (0.04 #156, 0.03 #52, 0.03 #169) >> Best rule #615 for best value: >> intensional similarity = 5 >> extensional distance = 272 >> proper extension: 031n8c; 0352gk; 02jztz; 03wv2g; >> query: (?x12475, 060c4) <- contains(?x7184, ?x12475), school_type(?x12475, ?x3092), state_province_region(?x10223, ?x7184), jurisdiction_of_office(?x900, ?x7184), ?x900 = 0fkvn >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 04swd; *> query: (?x12475, 0hm4q) <- contains(?x7184, ?x12475), contains(?x1603, ?x12475), jurisdiction_of_office(?x900, ?x7184), contains(?x7184, ?x10223), ?x1603 = 06bnz, ?x10223 = 02_gzx *> conf = 0.18 ranks of expected_values: 5 EVAL 02_jjm organization! 0hm4q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 188.000 188.000 0.704 http://example.org/organization/role/leaders./organization/leadership/organization #14589-028fjr PRED entity: 028fjr PRED relation: company PRED expected values: 0300cp 03qbm => 32 concepts (26 used for prediction) PRED predicted values (max 10 best out of 793): 060ppp (0.97 #341, 0.80 #2302, 0.67 #1961), 0300cp (0.97 #341, 0.70 #2102, 0.67 #1761), 019rl6 (0.97 #341, 0.70 #2214, 0.67 #1873), 02r5dz (0.97 #341, 0.70 #2124, 0.67 #1783), 07xyn1 (0.97 #341, 0.70 #2240, 0.67 #1899), 087c7 (0.97 #341, 0.70 #2058, 0.60 #1033), 01npw8 (0.97 #341, 0.67 #2007, 0.60 #2348), 0cv9b (0.97 #341, 0.67 #1739, 0.60 #713), 01_4lx (0.97 #341, 0.67 #1952, 0.60 #926), 03s7h (0.97 #341, 0.62 #3683, 0.61 #5397) >> Best rule #341 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 0krdk; >> query: (?x14601, ?x502) <- company(?x14601, ?x10699), company(?x14601, ?x6896), ?x6896 = 07l1c, currency(?x10699, ?x170), place_founded(?x10699, ?x1860), company(?x4792, ?x10699), ?x1860 = 01_d4, company(?x4792, ?x12471), company(?x4792, ?x12452), company(?x4792, ?x9923), company(?x4792, ?x502), ?x9923 = 05th69, ?x12471 = 01npw8, ?x12452 = 0vlf, organization(?x4682, ?x10699) >> conf = 0.97 => this is the best rule for 45 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 105 EVAL 028fjr company 03qbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 32.000 26.000 0.974 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 028fjr company 0300cp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 32.000 26.000 0.974 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #14588-04jpl PRED entity: 04jpl PRED relation: film_regional_debut_venue! PRED expected values: 0jyb4 => 211 concepts (211 used for prediction) PRED predicted values (max 10 best out of 250): 0crh5_f (0.36 #1143, 0.31 #1870, 0.30 #781), 0hv81 (0.27 #1198, 0.25 #291, 0.23 #1925), 01sby_ (0.25 #278, 0.20 #823, 0.18 #1185), 0b44shh (0.25 #275, 0.20 #820, 0.18 #1182), 0blpg (0.25 #253, 0.20 #798, 0.18 #1160), 0dgrwqr (0.25 #322, 0.20 #867, 0.10 #1048), 0267wwv (0.25 #352, 0.10 #1078, 0.10 #897), 0cbn7c (0.25 #329, 0.10 #1055, 0.10 #874), 01jwxx (0.25 #270, 0.10 #996, 0.10 #815), 0hmm7 (0.25 #222, 0.10 #948, 0.10 #767) >> Best rule #1143 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 02cl1; 01f62; 01ly5m; 0fhzf; >> query: (?x362, 0crh5_f) <- location(?x361, ?x362), featured_film_locations(?x136, ?x362), film_regional_debut_venue(?x1597, ?x362) >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04jpl film_regional_debut_venue! 0jyb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 211.000 211.000 0.364 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #14587-05p1qyh PRED entity: 05p1qyh PRED relation: film! PRED expected values: 057176 => 77 concepts (46 used for prediction) PRED predicted values (max 10 best out of 985): 057176 (0.60 #93623, 0.60 #60330, 0.59 #89460), 0p8r1 (0.14 #6823, 0.04 #13063, 0.03 #25548), 01vsn38 (0.12 #1853, 0.06 #8093, 0.03 #12253), 015rkw (0.12 #282, 0.05 #89461, 0.05 #95706), 016zp5 (0.12 #976, 0.03 #9296, 0.03 #5136), 07mz77 (0.12 #1417, 0.03 #9737, 0.01 #5577), 01c65z (0.12 #1980, 0.03 #8220, 0.01 #14460), 04t2l2 (0.12 #28, 0.02 #76973, 0.02 #68652), 0147dk (0.12 #81, 0.02 #29207, 0.02 #27127), 015wnl (0.12 #647, 0.02 #13127, 0.02 #38093) >> Best rule #93623 for best value: >> intensional similarity = 4 >> extensional distance = 1185 >> proper extension: 09fb5; 04glx0; 05sy0cv; 0gxsh4; 06ys2; >> query: (?x2362, ?x397) <- nominated_for(?x397, ?x2362), film(?x397, ?x696), gender(?x397, ?x231), award_winner(?x834, ?x397) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05p1qyh film! 057176 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 46.000 0.602 http://example.org/film/actor/film./film/performance/film #14586-0k419 PRED entity: 0k419 PRED relation: language PRED expected values: 02bjrlw => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 44): 064_8sq (0.36 #419, 0.20 #649, 0.14 #134), 02bjrlw (0.21 #400, 0.09 #744, 0.08 #630), 06b_j (0.16 #420, 0.07 #650, 0.05 #1221), 06nm1 (0.13 #408, 0.11 #123, 0.10 #237), 0jzc (0.08 #417, 0.04 #532, 0.04 #474), 03_9r (0.06 #407, 0.05 #3376, 0.05 #464), 0653m (0.06 #524, 0.05 #466, 0.04 #753), 04h9h (0.05 #670, 0.05 #440, 0.03 #841), 012w70 (0.05 #410, 0.04 #467, 0.04 #525), 06mp7 (0.05 #413, 0.03 #643, 0.01 #1099) >> Best rule #419 for best value: >> intensional similarity = 2 >> extensional distance = 127 >> proper extension: 0bs8hvm; >> query: (?x10435, 064_8sq) <- language(?x10435, ?x732), ?x732 = 04306rv >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #400 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 127 *> proper extension: 0bs8hvm; *> query: (?x10435, 02bjrlw) <- language(?x10435, ?x732), ?x732 = 04306rv *> conf = 0.21 ranks of expected_values: 2 EVAL 0k419 language 02bjrlw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.357 http://example.org/film/film/language #14585-01wd02c PRED entity: 01wd02c PRED relation: influenced_by PRED expected values: 028p0 03f70xs 037jz => 218 concepts (105 used for prediction) PRED predicted values (max 10 best out of 362): 06kb_ (0.40 #1018, 0.25 #5760, 0.20 #6190), 02lt8 (0.33 #118, 0.25 #5722, 0.23 #7013), 03_dj (0.33 #408, 0.20 #1270, 0.18 #7303), 02wh0 (0.33 #379, 0.20 #1241, 0.18 #9862), 05gpy (0.33 #196, 0.20 #1058, 0.18 #7091), 06hmd (0.33 #167, 0.15 #12065, 0.12 #1893), 03sbs (0.29 #4103, 0.16 #19598, 0.15 #9702), 015n8 (0.29 #4290, 0.15 #9889, 0.12 #12902), 07c37 (0.29 #4069, 0.07 #19564, 0.05 #8190), 02mpb (0.25 #6297, 0.25 #5867, 0.20 #1125) >> Best rule #1018 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 040db; >> query: (?x6796, 06kb_) <- people(?x11563, ?x6796), influenced_by(?x3858, ?x6796), ?x3858 = 05jm7, award_winner(?x9629, ?x6796) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #931 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 040db; *> query: (?x6796, 03f70xs) <- people(?x11563, ?x6796), influenced_by(?x3858, ?x6796), ?x3858 = 05jm7, award_winner(?x9629, ?x6796) *> conf = 0.20 ranks of expected_values: 18, 24, 33 EVAL 01wd02c influenced_by 037jz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 218.000 105.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 01wd02c influenced_by 03f70xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 218.000 105.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 01wd02c influenced_by 028p0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 218.000 105.000 0.400 http://example.org/influence/influence_node/influenced_by #14584-027xq5 PRED entity: 027xq5 PRED relation: major_field_of_study PRED expected values: 02csf => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 115): 06ms6 (0.67 #2498, 0.27 #2126, 0.26 #2002), 0h5k (0.67 #519, 0.26 #1140, 0.25 #147), 02j62 (0.56 #4501, 0.56 #527, 0.52 #1148), 01mkq (0.56 #511, 0.53 #1380, 0.48 #1132), 0fdys (0.56 #536, 0.31 #2521, 0.30 #1157), 04sh3 (0.56 #3553, 0.25 #325, 0.22 #2310), 04rlf (0.50 #443, 0.45 #1684, 0.30 #691), 04rjg (0.45 #2501, 0.44 #516, 0.40 #2005), 03g3w (0.44 #523, 0.43 #2012, 0.41 #2260), 037mh8 (0.44 #565, 0.31 #2054, 0.29 #1682) >> Best rule #2498 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 01_s9q; 02ldkf; 015wy_; 02zr0z; 01dq0z; >> query: (?x13781, 06ms6) <- citytown(?x13781, ?x4030), major_field_of_study(?x13781, ?x373), student(?x373, ?x6677), ?x6677 = 03l3ln >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2730 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 59 *> proper extension: 0bjqh; 01hb1t; 02607j; 02gr81; 026vcc; 01jq0j; 02nvg1; *> query: (?x13781, ?x1695) <- citytown(?x13781, ?x4030), major_field_of_study(?x13781, ?x373), student(?x373, ?x1294), industry(?x166, ?x373), major_field_of_study(?x1695, ?x373) *> conf = 0.24 ranks of expected_values: 41 EVAL 027xq5 major_field_of_study 02csf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 135.000 135.000 0.672 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14583-03jn4 PRED entity: 03jn4 PRED relation: contains! PRED expected values: 017jq => 129 concepts (72 used for prediction) PRED predicted values (max 10 best out of 282): 02jx1 (0.93 #42129, 0.89 #43024, 0.83 #10815), 09c7w0 (0.85 #26834, 0.79 #60852, 0.75 #40257), 01n7q (0.51 #14382, 0.41 #18854, 0.39 #21538), 036wy (0.40 #1658, 0.23 #7023, 0.20 #7917), 01_c4 (0.25 #521, 0.05 #59056, 0.05 #6780), 0j5g9 (0.21 #54580, 0.06 #11883, 0.06 #12777), 05bcl (0.21 #54580, 0.05 #59056, 0.03 #59058), 0dg3n1 (0.20 #3729, 0.17 #23403, 0.15 #25192), 02qkt (0.20 #3921, 0.14 #58506, 0.08 #56715), 02j9z (0.20 #3603, 0.12 #58188, 0.11 #23277) >> Best rule #42129 for best value: >> intensional similarity = 5 >> extensional distance = 151 >> proper extension: 022_6; 0crjn65; 01k8q5; 0121c1; 0nccd; 0c_zj; 04p3c; 0fgj2; 013bqg; 01ykl0; ... >> query: (?x13367, 02jx1) <- contains(?x512, ?x13367), contains(?x362, ?x13367), ?x512 = 07ssc, featured_film_locations(?x136, ?x362), location(?x361, ?x362) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #4208 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 03rj0; 04v09; 03t1s; 0c1xm; *> query: (?x13367, 017jq) <- featured_film_locations(?x5228, ?x13367), time_zones(?x13367, ?x5327), contains(?x362, ?x13367), ?x5327 = 03bdv *> conf = 0.10 ranks of expected_values: 37 EVAL 03jn4 contains! 017jq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 129.000 72.000 0.928 http://example.org/location/location/contains #14582-05n6sq PRED entity: 05n6sq PRED relation: film! PRED expected values: 0blq0z 04mhxx => 119 concepts (79 used for prediction) PRED predicted values (max 10 best out of 991): 05mkhs (0.60 #24986, 0.48 #95801, 0.48 #99968), 012q4n (0.22 #1138, 0.03 #15713, 0.03 #19877), 027zz (0.13 #45812, 0.12 #68722, 0.12 #58307), 01wy5m (0.11 #859, 0.06 #124960, 0.04 #124959), 0154qm (0.11 #561, 0.06 #124960, 0.04 #124959), 01yfm8 (0.11 #1294, 0.06 #124960, 0.04 #124959), 01438g (0.11 #523, 0.06 #124960, 0.04 #124959), 0psss (0.11 #560, 0.06 #124960, 0.04 #124959), 055c8 (0.11 #542, 0.05 #2624, 0.03 #10953), 02js6_ (0.11 #447, 0.04 #124959, 0.04 #108298) >> Best rule #24986 for best value: >> intensional similarity = 4 >> extensional distance = 172 >> proper extension: 0gfzgl; 0cskb; >> query: (?x6343, ?x3816) <- nominated_for(?x3816, ?x6343), category(?x6343, ?x134), titles(?x53, ?x6343), participant(?x3816, ?x1410) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05n6sq film! 04mhxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 79.000 0.596 http://example.org/film/actor/film./film/performance/film EVAL 05n6sq film! 0blq0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 79.000 0.596 http://example.org/film/actor/film./film/performance/film #14581-05ccxr PRED entity: 05ccxr PRED relation: student! PRED expected values: 01pcj4 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 92): 09f2j (0.50 #159, 0.07 #1740, 0.07 #2267), 017z88 (0.25 #82, 0.24 #1136, 0.08 #2190), 02htv6 (0.25 #991, 0.07 #1518, 0.02 #2572), 05p7tx (0.25 #838, 0.02 #1365, 0.01 #2419), 065y4w7 (0.07 #1068, 0.06 #4230, 0.05 #6338), 02g839 (0.07 #1079, 0.04 #8457, 0.04 #3187), 04sylm (0.07 #1130, 0.03 #2184, 0.03 #3765), 017rbx (0.07 #1396, 0.03 #10882, 0.03 #5612), 0bwfn (0.05 #3437, 0.05 #28209, 0.05 #25046), 02cw8s (0.04 #1124, 0.03 #6394, 0.03 #4286) >> Best rule #159 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0146pg; 016szr; >> query: (?x8730, 09f2j) <- artists(?x3597, ?x8730), award_winner(?x8730, ?x7556), ?x7556 = 01vttb9 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5639 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 117 *> proper extension: 01m7f5r; 01p7b6b; *> query: (?x8730, 01pcj4) <- nationality(?x8730, ?x94), music(?x2519, ?x8730), place_of_birth(?x8730, ?x2850) *> conf = 0.03 ranks of expected_values: 16 EVAL 05ccxr student! 01pcj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 118.000 118.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/student #14580-07bxqz PRED entity: 07bxqz PRED relation: genre PRED expected values: 0219x_ => 75 concepts (52 used for prediction) PRED predicted values (max 10 best out of 90): 01z4y (0.61 #5368, 0.61 #3579, 0.57 #2504), 02l7c8 (0.50 #15, 0.37 #5144, 0.37 #968), 06cvj (0.50 #2, 0.23 #955, 0.21 #477), 082gq (0.39 #1103, 0.15 #864, 0.14 #507), 01jfsb (0.39 #607, 0.38 #249, 0.36 #1441), 02kdv5l (0.38 #239, 0.35 #597, 0.30 #1431), 0219x_ (0.38 #26, 0.21 #477, 0.11 #5155), 0lsxr (0.35 #245, 0.29 #603, 0.22 #126), 04xvh5 (0.28 #1107, 0.19 #511, 0.11 #153), 01t_vv (0.25 #54, 0.21 #477, 0.15 #1007) >> Best rule #5368 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 02xhwm; >> query: (?x11417, ?x2480) <- titles(?x2480, ?x11417), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0blpg; *> query: (?x11417, 0219x_) <- film(?x986, ?x11417), produced_by(?x11417, ?x4562), ?x986 = 081lh, genre(?x11417, ?x53) *> conf = 0.38 ranks of expected_values: 7 EVAL 07bxqz genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 75.000 52.000 0.612 http://example.org/film/film/genre #14579-0h0yt PRED entity: 0h0yt PRED relation: award PRED expected values: 04mqgr => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 274): 099tbz (0.71 #19849, 0.70 #14581, 0.67 #29170), 05p09zm (0.25 #934, 0.09 #5794, 0.08 #7009), 05pcn59 (0.24 #891, 0.15 #21471, 0.12 #5751), 094qd5 (0.22 #44, 0.18 #16607, 0.18 #20255), 02ppm4q (0.22 #157, 0.18 #16607, 0.18 #20255), 01by1l (0.22 #3352, 0.14 #1327, 0.12 #922), 0gqwc (0.19 #74, 0.18 #16607, 0.18 #20255), 09qwmm (0.19 #34, 0.15 #21471, 0.15 #27548), 0ck27z (0.18 #16607, 0.18 #20255, 0.15 #21471), 0gqyl (0.18 #16607, 0.18 #20255, 0.15 #21471) >> Best rule #19849 for best value: >> intensional similarity = 3 >> extensional distance = 1530 >> proper extension: 04qvl7; 012t1; 0b_c7; 01dzz7; 01795t; 01ycck; 07b3r9; 016bx2; 031rq5; 02qlkc3; ... >> query: (?x7746, ?x995) <- award_nominee(?x1951, ?x7746), gender(?x1951, ?x514), award_winner(?x995, ?x7746) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1370 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 111 *> proper extension: 0d193h; 0b1zz; 0838y; *> query: (?x7746, 04mqgr) <- influenced_by(?x7746, ?x2608), award_winner(?x472, ?x7746) *> conf = 0.03 ranks of expected_values: 200 EVAL 0h0yt award 04mqgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 91.000 91.000 0.707 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14578-02qggqc PRED entity: 02qggqc PRED relation: edited_by! PRED expected values: 05c9zr => 90 concepts (43 used for prediction) PRED predicted values (max 10 best out of 155): 0hwpz (0.12 #889, 0.10 #1041, 0.08 #1193), 02704ff (0.12 #858, 0.10 #1010, 0.08 #1162), 07bwr (0.12 #851, 0.10 #1003, 0.08 #1155), 05fgt1 (0.12 #808, 0.10 #960, 0.08 #1112), 02r1c18 (0.12 #791, 0.10 #943, 0.08 #1095), 01vfqh (0.12 #788, 0.10 #940, 0.08 #1092), 0b6tzs (0.12 #781, 0.10 #933, 0.08 #1085), 0f4yh (0.10 #976, 0.08 #1128, 0.08 #520), 0dnqr (0.10 #969, 0.08 #1121, 0.08 #513), 0dfw0 (0.10 #998, 0.08 #1150, 0.07 #1302) >> Best rule #889 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 052gzr; 0kft; >> query: (?x707, 0hwpz) <- edited_by(?x5890, ?x707), edited_by(?x5135, ?x707), edited_by(?x485, ?x707), nominated_for(?x1033, ?x5890), film_crew_role(?x485, ?x137), films(?x11523, ?x5135) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02qggqc edited_by! 05c9zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 43.000 0.118 http://example.org/film/film/edited_by #14577-02pzz3p PRED entity: 02pzz3p PRED relation: award! PRED expected values: 01vhb0 01kb2j => 61 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2722): 01vhb0 (0.79 #16913, 0.74 #16912, 0.72 #20297), 01kb2j (0.74 #16912, 0.69 #20296, 0.50 #1493), 01p7yb (0.67 #70, 0.07 #23753, 0.07 #27135), 03mp9s (0.67 #2028, 0.07 #25711, 0.07 #22325), 0159h6 (0.50 #99, 0.10 #23782, 0.10 #27164), 0f4vbz (0.50 #586, 0.09 #24269, 0.09 #27651), 0154qm (0.50 #903, 0.09 #24586, 0.09 #27968), 01hkhq (0.50 #663, 0.09 #27728, 0.08 #31111), 020_95 (0.50 #1606, 0.09 #28671, 0.08 #32054), 05dbf (0.50 #589, 0.09 #27654, 0.08 #20886) >> Best rule #16913 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 0d085; >> query: (?x2773, ?x2308) <- award_winner(?x2773, ?x2308), location(?x2308, ?x7919), category(?x2308, ?x134), participant(?x3917, ?x2308) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02pzz3p award! 01kb2j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 14.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02pzz3p award! 01vhb0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 14.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14576-05ty4m PRED entity: 05ty4m PRED relation: influenced_by PRED expected values: 046lt => 125 concepts (96 used for prediction) PRED predicted values (max 10 best out of 316): 014z8v (0.15 #118, 0.12 #4399, 0.11 #7824), 0ff2k (0.15 #393, 0.07 #821, 0.06 #1248), 03f0324 (0.14 #577, 0.08 #18126, 0.08 #13415), 01hmk9 (0.12 #7922, 0.11 #3640, 0.10 #5354), 081k8 (0.11 #17702, 0.08 #14276, 0.08 #27551), 03_87 (0.10 #14323, 0.09 #17749, 0.09 #13466), 032l1 (0.10 #17637, 0.08 #27486, 0.08 #21912), 014zfs (0.09 #3448, 0.08 #7730, 0.08 #4305), 05qmj (0.09 #17739, 0.07 #25871, 0.07 #22443), 02wh0 (0.09 #17925, 0.07 #27774, 0.07 #26057) >> Best rule #118 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0mdqp; 06pj8; 015pxr; 01_x6v; 0j_c; 02ld6x; 0693l; 07rd7; 0gd9k; 06pjs; ... >> query: (?x364, 014z8v) <- participant(?x364, ?x237), film(?x364, ?x2695), influenced_by(?x364, ?x986) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #4356 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 0sx5w; 01t_wfl; *> query: (?x364, 046lt) <- award(?x364, ?x277), influenced_by(?x364, ?x986), participant(?x237, ?x364) *> conf = 0.04 ranks of expected_values: 97 EVAL 05ty4m influenced_by 046lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 125.000 96.000 0.154 http://example.org/influence/influence_node/influenced_by #14575-07tds PRED entity: 07tds PRED relation: service_language PRED expected values: 02h40lc => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 18): 02h40lc (0.93 #971, 0.93 #929, 0.92 #845), 06nm1 (0.15 #849, 0.14 #828, 0.14 #765), 064_8sq (0.14 #937, 0.12 #536, 0.12 #1042), 04306rv (0.09 #930, 0.09 #529, 0.09 #1035), 03_9r (0.08 #173, 0.05 #764, 0.05 #974), 05zjd (0.08 #181, 0.04 #307, 0.04 #835), 0c_v2 (0.08 #176, 0.01 #767, 0.01 #830), 01r2l (0.06 #939, 0.05 #538, 0.05 #834), 02bv9 (0.05 #541, 0.04 #942, 0.03 #562), 01jb8r (0.04 #547, 0.03 #568, 0.02 #780) >> Best rule #971 for best value: >> intensional similarity = 2 >> extensional distance = 108 >> proper extension: 084l5; >> query: (?x4672, 02h40lc) <- service_location(?x4672, ?x94), ?x94 = 09c7w0 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tds service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.927 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #14574-05gc0h PRED entity: 05gc0h PRED relation: languages PRED expected values: 0999q => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 13): 03k50 (0.22 #4, 0.16 #43, 0.03 #238), 02h40lc (0.20 #80, 0.18 #197, 0.16 #1250), 07c9s (0.13 #13, 0.09 #52, 0.02 #91), 0999q (0.06 #23, 0.05 #62), 09s02 (0.05 #36, 0.03 #75), 055qm (0.04 #24, 0.02 #63), 09bnf (0.03 #39, 0.03 #78), 01c7y (0.03 #31, 0.02 #70), 02hxcvy (0.03 #26, 0.02 #65), 064_8sq (0.02 #15, 0.02 #93, 0.02 #54) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 0cfywh; >> query: (?x7999, 03k50) <- type_of_union(?x7999, ?x566), ?x566 = 04ztj, nationality(?x7999, ?x2146), ?x2146 = 03rk0 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #23 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 145 *> proper extension: 0cfywh; *> query: (?x7999, 0999q) <- type_of_union(?x7999, ?x566), ?x566 = 04ztj, nationality(?x7999, ?x2146), ?x2146 = 03rk0 *> conf = 0.06 ranks of expected_values: 4 EVAL 05gc0h languages 0999q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 69.000 0.224 http://example.org/people/person/languages #14573-0326tc PRED entity: 0326tc PRED relation: artists! PRED expected values: 0fd3y 07lnk 0cx7f 03ckfl9 => 149 concepts (60 used for prediction) PRED predicted values (max 10 best out of 280): 06by7 (0.71 #2118, 0.65 #3019, 0.63 #3319), 0cx7f (0.60 #430, 0.46 #2829, 0.42 #5532), 016jny (0.60 #397, 0.24 #2496, 0.23 #4295), 02qm5j (0.60 #444, 0.08 #2543, 0.08 #5546), 0m0jc (0.54 #9325, 0.51 #8426, 0.37 #8725), 05bt6j (0.47 #2138, 0.33 #3339, 0.33 #40), 03_d0 (0.44 #17145, 0.29 #2109, 0.23 #2709), 0xhtw (0.42 #2714, 0.40 #2414, 0.31 #5417), 029fbr (0.40 #472, 0.12 #2871, 0.10 #1370), 0m0fw (0.40 #357, 0.10 #1255, 0.09 #1556) >> Best rule #2118 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 09mq4m; >> query: (?x7972, 06by7) <- instrumentalists(?x1166, ?x7972), instrumentalists(?x716, ?x7972), artists(?x671, ?x7972), ?x671 = 064t9, ?x716 = 018vs, ?x1166 = 05148p4 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #430 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 07yg2; *> query: (?x7972, 0cx7f) <- artists(?x13686, ?x7972), artists(?x5379, ?x7972), artists(?x1380, ?x7972), ?x5379 = 08jyyk, parent_genre(?x301, ?x1380), ?x13686 = 052smk *> conf = 0.60 ranks of expected_values: 2, 24, 32, 139 EVAL 0326tc artists! 03ckfl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 149.000 60.000 0.706 http://example.org/music/genre/artists EVAL 0326tc artists! 0cx7f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 149.000 60.000 0.706 http://example.org/music/genre/artists EVAL 0326tc artists! 07lnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 149.000 60.000 0.706 http://example.org/music/genre/artists EVAL 0326tc artists! 0fd3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 149.000 60.000 0.706 http://example.org/music/genre/artists #14572-0mfj2 PRED entity: 0mfj2 PRED relation: profession PRED expected values: 0dxtg => 95 concepts (80 used for prediction) PRED predicted values (max 10 best out of 69): 0dxtg (0.72 #5754, 0.71 #749, 0.60 #308), 01d_h8 (0.67 #5893, 0.55 #2358, 0.50 #8101), 02jknp (0.34 #5895, 0.32 #5748, 0.30 #2360), 0np9r (0.30 #754, 0.28 #5759, 0.28 #1930), 09jwl (0.29 #1046, 0.27 #2516, 0.27 #1340), 0cbd2 (0.26 #1183, 0.23 #1624, 0.20 #742), 02krf9 (0.24 #760, 0.18 #6770, 0.15 #2230), 0dgd_ (0.18 #6770, 0.05 #5475, 0.04 #4442), 021wpb (0.18 #6770, 0.01 #933, 0.01 #1521), 07s467s (0.18 #6770) >> Best rule #5754 for best value: >> intensional similarity = 5 >> extensional distance = 1330 >> proper extension: 06v8s0; 0dbpyd; 06j0md; 03ckxdg; 026dcvf; 042rnl; 02l840; 02773m2; 0265v21; 01pr_j6; ... >> query: (?x8858, 0dxtg) <- profession(?x8858, ?x1146), profession(?x12975, ?x1146), profession(?x71, ?x1146), ?x71 = 0q9kd, ?x12975 = 0p_r5 >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mfj2 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 80.000 0.718 http://example.org/people/person/profession #14571-0btpm6 PRED entity: 0btpm6 PRED relation: film_crew_role PRED expected values: 09vw2b7 01vx2h => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 28): 09vw2b7 (0.78 #346, 0.73 #380, 0.69 #278), 01vx2h (0.55 #350, 0.53 #214, 0.47 #384), 01pvkk (0.45 #249, 0.33 #45, 0.31 #283), 0215hd (0.44 #52, 0.22 #86, 0.22 #358), 02ynfr (0.38 #15, 0.27 #321, 0.22 #287), 015h31 (0.22 #42, 0.20 #382, 0.20 #348), 089g0h (0.22 #53, 0.20 #359, 0.17 #393), 01xy5l_ (0.22 #47, 0.16 #353, 0.15 #387), 02_n3z (0.22 #35, 0.14 #273, 0.13 #307), 033smt (0.22 #60, 0.11 #94, 0.11 #230) >> Best rule #346 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 01gglm; >> query: (?x7493, 09vw2b7) <- film_format(?x7493, ?x6392), film_release_distribution_medium(?x7493, ?x81), story_by(?x7493, ?x2533), film_crew_role(?x7493, ?x137) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0btpm6 film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.782 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0btpm6 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.782 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14570-06pjs PRED entity: 06pjs PRED relation: award_winner! PRED expected values: 02q690_ => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 137): 0dznvw (0.25 #135, 0.15 #417, 0.11 #276), 0bzkvd (0.25 #114, 0.11 #255, 0.08 #396), 02hn5v (0.25 #42, 0.11 #183, 0.08 #324), 0d__c3 (0.25 #125, 0.11 #266, 0.08 #407), 05hmp6 (0.25 #87, 0.11 #228, 0.08 #369), 0c53vt (0.25 #112, 0.11 #253, 0.08 #394), 0c53zb (0.25 #61, 0.11 #202, 0.08 #343), 0fzrhn (0.25 #138, 0.11 #279, 0.08 #420), 0fv89q (0.25 #123, 0.11 #264, 0.08 #405), 05c1t6z (0.17 #10435, 0.11 #438, 0.10 #14526) >> Best rule #135 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 081nh; >> query: (?x9153, 0dznvw) <- award(?x9153, ?x3617), ?x3617 = 0gvx_, currency(?x9153, ?x170) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #10435 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1134 *> proper extension: 04cy8rb; 0f721s; 0gsg7; 09d5h; 03jvmp; 0g5lhl7; 01w92; 01p5yn; 05xbx; 07fzq3; ... *> query: (?x9153, ?x1193) <- award_winner(?x2551, ?x9153), award_winner(?x5398, ?x9153), award_winner(?x1193, ?x2551) *> conf = 0.17 ranks of expected_values: 12 EVAL 06pjs award_winner! 02q690_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 124.000 124.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14569-0372p PRED entity: 0372p PRED relation: influenced_by! PRED expected values: 040db 03sbs => 149 concepts (66 used for prediction) PRED predicted values (max 10 best out of 447): 048cl (0.60 #3874, 0.60 #1318, 0.50 #807), 047g6 (0.60 #1496, 0.30 #4052, 0.29 #3030), 045bg (0.53 #27075, 0.43 #2591, 0.36 #8722), 0dzkq (0.53 #27075, 0.43 #2679, 0.26 #8299), 0424m (0.53 #27075, 0.25 #247, 0.17 #13790), 0c1jh (0.53 #27075, 0.20 #1916, 0.09 #19916), 034bs (0.53 #27075, 0.15 #11903, 0.14 #16495), 060_7 (0.53 #27075, 0.13 #25027, 0.09 #19916), 03_js (0.53 #27075, 0.13 #25027), 0j3v (0.50 #3655, 0.40 #1099, 0.27 #4168) >> Best rule #3874 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 07kb5; >> query: (?x3994, 048cl) <- influenced_by(?x9600, ?x3994), influenced_by(?x3994, ?x712), influenced_by(?x11499, ?x9600), ?x11499 = 06jkm, religion(?x712, ?x1985) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3862 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 07kb5; *> query: (?x3994, 03sbs) <- influenced_by(?x9600, ?x3994), influenced_by(?x3994, ?x712), influenced_by(?x11499, ?x9600), ?x11499 = 06jkm, religion(?x712, ?x1985) *> conf = 0.50 ranks of expected_values: 12, 18 EVAL 0372p influenced_by! 03sbs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 149.000 66.000 0.600 http://example.org/influence/influence_node/influenced_by EVAL 0372p influenced_by! 040db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 149.000 66.000 0.600 http://example.org/influence/influence_node/influenced_by #14568-052vwh PRED entity: 052vwh PRED relation: time_zones! PRED expected values: 014vm4 => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 1525): 0d060g (0.75 #5026, 0.67 #6280, 0.57 #3775), 09c7w0 (0.71 #3767, 0.50 #5018, 0.44 #6272), 06bnz (0.56 #3752, 0.54 #3751, 0.50 #3753), 03rk0 (0.56 #3752, 0.54 #3751, 0.50 #3753), 05sb1 (0.56 #3752, 0.54 #3751, 0.50 #3753), 05b7q (0.56 #3752, 0.54 #3751, 0.50 #3753), 047lj (0.56 #3752, 0.54 #3751, 0.50 #3753), 016zwt (0.56 #3752, 0.54 #3751, 0.50 #3753), 0jdd (0.56 #3752, 0.54 #3751, 0.50 #3753), 04xn_ (0.56 #3752, 0.54 #3751, 0.50 #3753) >> Best rule #5026 for best value: >> intensional similarity = 76 >> extensional distance = 6 >> proper extension: 02fqwt; 02hczc; 02hcv8; 02lcqs; 042g7t; 05jphn; >> query: (?x11859, 0d060g) <- time_zones(?x12999, ?x11859), time_zones(?x2346, ?x11859), service_location(?x9517, ?x2346), participating_countries(?x418, ?x2346), contains(?x2346, ?x2645), country(?x10585, ?x2346), country(?x6941, ?x2346), country(?x4673, ?x2346), country(?x3598, ?x2346), country(?x2315, ?x2346), country(?x2266, ?x2346), country(?x1175, ?x2346), film_release_region(?x7693, ?x2346), film_release_region(?x7393, ?x2346), film_release_region(?x7009, ?x2346), film_release_region(?x5315, ?x2346), film_release_region(?x4998, ?x2346), film_release_region(?x3854, ?x2346), film_release_region(?x3498, ?x2346), film_release_region(?x2155, ?x2346), film_release_region(?x1518, ?x2346), film_release_region(?x1263, ?x2346), film_release_region(?x1035, ?x2346), film_release_region(?x781, ?x2346), film_release_region(?x385, ?x2346), film_release_region(?x324, ?x2346), ?x3498 = 02fqrf, ?x3854 = 03q0r1, combatants(?x5114, ?x2346), ?x3598 = 03rbzn, country(?x8105, ?x2346), olympics(?x2346, ?x3110), ?x3110 = 0kbvv, adjoins(?x2146, ?x2346), country(?x5782, ?x2346), ?x385 = 0ds3t5x, exported_to(?x2346, ?x6305), exported_to(?x2346, ?x5457), ?x324 = 07gp9, ?x2266 = 01lb14, ?x1263 = 0dgst_d, countries_spoken_in(?x254, ?x5457), ?x1175 = 02_5h, ?x7009 = 0bs8s1p, citytown(?x9309, ?x12999), form_of_government(?x5457, ?x48), ?x6941 = 02y74, organization(?x2346, ?x127), ?x4673 = 07jbh, adjoins(?x608, ?x5457), ?x5315 = 0glqh5_, olympics(?x2346, ?x452), ?x2155 = 0407yfx, nominated_for(?x637, ?x7693), ?x10585 = 01gqfm, currency(?x2346, ?x170), time_zones(?x5457, ?x6582), capital(?x6305, ?x13440), geographic_distribution(?x5590, ?x6305), ?x2315 = 06wrt, ?x781 = 0gkz15s, nationality(?x9813, ?x2346), ?x1035 = 08hmch, ?x4998 = 0dzlbx, film_release_distribution_medium(?x7693, ?x81), film(?x9813, ?x3640), film(?x722, ?x7693), ?x1518 = 04w7rn, category(?x12999, ?x134), genre(?x5782, ?x53), medal(?x2346, ?x422), ?x7393 = 02vz6dn, jurisdiction_of_office(?x265, ?x2346), combatants(?x1140, ?x2346), award(?x5782, ?x507), film_release_region(?x80, ?x2146) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3765 for first EXPECTED value: *> intensional similarity = 81 *> extensional distance = 2 *> proper extension: 0gsrz4; *> query: (?x11859, ?x2645) <- time_zones(?x2346, ?x11859), service_location(?x9517, ?x2346), participating_countries(?x418, ?x2346), contains(?x2346, ?x2645), country(?x3641, ?x2346), country(?x3015, ?x2346), country(?x2885, ?x2346), country(?x2044, ?x2346), country(?x1967, ?x2346), country(?x1037, ?x2346), country(?x150, ?x2346), countries_spoken_in(?x3271, ?x2346), taxonomy(?x2346, ?x939), adjoins(?x2346, ?x3352), adjoins(?x2346, ?x2146), adjoins(?x2346, ?x404), sports(?x6464, ?x3015), sports(?x1608, ?x3015), country(?x3015, ?x7833), country(?x3015, ?x7747), country(?x3015, ?x5482), country(?x3015, ?x5114), country(?x3015, ?x4743), country(?x3015, ?x2290), country(?x3015, ?x1892), country(?x3015, ?x1558), country(?x3015, ?x1355), country(?x3015, ?x1174), country(?x3015, ?x512), country(?x3015, ?x410), country(?x3015, ?x344), country(?x3015, ?x311), country(?x3015, ?x291), country(?x3015, ?x151), ?x1174 = 047yc, ?x1355 = 0h7x, ?x291 = 0h3y, currency(?x9517, ?x170), ?x7833 = 0jdx, olympics(?x2346, ?x2630), olympics(?x2346, ?x452), country(?x3641, ?x4521), country(?x3641, ?x1499), organization(?x2346, ?x127), ?x344 = 04gzd, ?x1892 = 02vzc, ?x4521 = 07fj_, citytown(?x9517, ?x5962), ?x2885 = 07jjt, ?x1967 = 01cgz, ?x311 = 0j1z8, ?x5482 = 04g5k, country(?x3411, ?x2146), contains(?x2146, ?x1391), sports(?x7051, ?x2044), ?x151 = 0b90_r, ?x939 = 04n6k, ?x6464 = 0lbd9, ?x410 = 01ls2, service_language(?x9517, ?x732), company(?x265, ?x9517), ?x512 = 07ssc, ?x7747 = 07f1x, ?x4743 = 03spz, country(?x150, ?x9072), ?x9072 = 04vs9, administrative_parent(?x2146, ?x551), ?x1608 = 09x3r, medal(?x2630, ?x422), language(?x148, ?x3271), ?x1558 = 01mjq, countries_spoken_in(?x403, ?x404), ?x5114 = 05vz3zq, ?x2290 = 02wt0, olympics(?x1037, ?x1277), countries_within(?x6956, ?x3352), sports(?x452, ?x2752), ?x7051 = 018ljb, jurisdiction_of_office(?x346, ?x2346), olympics(?x2884, ?x418), ?x1499 = 01znc_ *> conf = 0.34 ranks of expected_values: 36 EVAL 052vwh time_zones! 014vm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 8.000 8.000 0.750 http://example.org/location/location/time_zones #14567-0hv7l PRED entity: 0hv7l PRED relation: place_of_birth! PRED expected values: 044mrh => 148 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1245): 064nh4k (0.33 #2735, 0.25 #10574, 0.25 #7961), 02rn_bj (0.33 #1737, 0.12 #20028, 0.08 #33094), 05zh9c (0.33 #982, 0.12 #19273, 0.08 #32339), 01d8yn (0.33 #725, 0.12 #19016, 0.08 #32082), 02cx72 (0.33 #718, 0.12 #19009, 0.08 #32075), 0146pg (0.33 #97, 0.12 #18388, 0.08 #31454), 07vfqj (0.25 #6641, 0.03 #49649, 0.02 #98098), 045gzq (0.14 #18275, 0.12 #23501, 0.11 #28727), 018fwv (0.14 #18259, 0.12 #23485, 0.11 #28711), 018417 (0.14 #18218, 0.12 #23444, 0.11 #28670) >> Best rule #2735 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0hsqf; >> query: (?x14401, 064nh4k) <- category(?x14401, ?x134), ?x134 = 08mbj5d, teams(?x14401, ?x5953), administrative_parent(?x14401, ?x1453), ?x1453 = 06qd3, position(?x5953, ?x63) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hv7l place_of_birth! 044mrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 57.000 0.333 http://example.org/people/person/place_of_birth #14566-057hz PRED entity: 057hz PRED relation: award PRED expected values: 094qd5 03c7tr1 => 131 concepts (110 used for prediction) PRED predicted values (max 10 best out of 321): 0bfvw2 (0.76 #25746, 0.73 #40636, 0.71 #31381), 09qj50 (0.76 #25746, 0.73 #40636, 0.71 #31381), 02py7pj (0.76 #25746, 0.71 #31381, 0.71 #40635), 02qkk9_ (0.76 #25746, 0.71 #31381, 0.71 #40635), 09sb52 (0.34 #12511, 0.32 #10499, 0.30 #16936), 03tk6z (0.33 #212, 0.20 #615, 0.12 #1419), 02lp0w (0.33 #248, 0.20 #651, 0.08 #44260), 024dzn (0.33 #326, 0.20 #729, 0.08 #44260), 024fz9 (0.33 #207, 0.20 #610, 0.06 #1414), 0f4x7 (0.29 #1238, 0.16 #8881, 0.14 #6870) >> Best rule #25746 for best value: >> intensional similarity = 3 >> extensional distance = 1006 >> proper extension: 03mv0b; >> query: (?x3644, ?x757) <- location(?x3644, ?x1227), award_winner(?x757, ?x3644), ceremony(?x757, ?x1265) >> conf = 0.76 => this is the best rule for 4 predicted values *> Best rule #9311 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 276 *> proper extension: 01l_vgt; *> query: (?x3644, 03c7tr1) <- participant(?x4397, ?x3644), gender(?x3644, ?x514), ?x514 = 02zsn *> conf = 0.17 ranks of expected_values: 22, 34 EVAL 057hz award 03c7tr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 131.000 110.000 0.760 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 057hz award 094qd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 131.000 110.000 0.760 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14565-037cr1 PRED entity: 037cr1 PRED relation: currency PRED expected values: 09nqf => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.86 #36, 0.84 #106, 0.83 #120), 01nv4h (0.06 #128, 0.04 #212, 0.03 #191), 0kz1h (0.05 #47), 02l6h (0.03 #46, 0.02 #214, 0.01 #319), 088n7 (0.01 #133, 0.01 #168), 02gsvk (0.01 #195) >> Best rule #36 for best value: >> intensional similarity = 6 >> extensional distance = 26 >> proper extension: 0872p_c; >> query: (?x10260, 09nqf) <- film_crew_role(?x10260, ?x2848), film_crew_role(?x10260, ?x468), ?x468 = 02r96rf, genre(?x10260, ?x811), film(?x2279, ?x10260), ?x2848 = 094hwz >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 037cr1 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.857 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #14564-0345h PRED entity: 0345h PRED relation: adjoins! PRED expected values: 0h7x => 206 concepts (152 used for prediction) PRED predicted values (max 10 best out of 508): 06mzp (0.83 #45069, 0.83 #107286, 0.82 #115833), 04p0c (0.33 #4835, 0.33 #3283, 0.33 #3110), 09hzw (0.33 #4464, 0.33 #3110, 0.33 #2908), 09krp (0.33 #5039, 0.33 #4263, 0.33 #3110), 03hrz (0.33 #4031, 0.33 #3110, 0.33 #923), 017wh (0.33 #4196, 0.33 #3420, 0.33 #3110), 070zc (0.33 #3614, 0.33 #3110, 0.33 #2834), 09ksp (0.33 #4245, 0.33 #3110, 0.33 #1913), 09hrc (0.33 #5186, 0.33 #4410, 0.33 #3110), 017v_ (0.33 #4749, 0.33 #3110, 0.11 #102618) >> Best rule #45069 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 05kj_; 059f4; 0cc56; 04rrd; 05rgl; 07srw; 0dc95; 0cr3d; 07b_l; 05tbn; ... >> query: (?x1264, ?x456) <- contains(?x1264, ?x196), featured_film_locations(?x1470, ?x1264), adjoins(?x1264, ?x456) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #7062 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 022dp5; 012f86; *> query: (?x1264, 0h7x) <- split_to(?x5540, ?x1264), people(?x5540, ?x380) *> conf = 0.14 ranks of expected_values: 29 EVAL 0345h adjoins! 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 206.000 152.000 0.832 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #14563-01sxdy PRED entity: 01sxdy PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #36, 0.82 #46, 0.82 #92), 02nxhr (0.21 #384, 0.07 #2, 0.04 #7), 07c52 (0.21 #384, 0.05 #3, 0.04 #53), 07z4p (0.21 #384, 0.04 #10, 0.03 #136) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 290 >> proper extension: 03t97y; 034r25; 08sk8l; 02nx2k; 08c6k9; 0n_hp; 0b2km_; 023vcd; 07p12s; 076tw54; >> query: (?x3681, 029j_) <- film(?x1690, ?x3681), film_crew_role(?x3681, ?x2095), ?x2095 = 0dxtw, production_companies(?x3681, ?x382) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sxdy film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #14562-05vz3zq PRED entity: 05vz3zq PRED relation: participating_countries! PRED expected values: 0sx8l => 236 concepts (236 used for prediction) PRED predicted values (max 10 best out of 43): 018ctl (0.78 #808, 0.75 #1489, 0.73 #568), 0kbws (0.78 #815, 0.73 #575, 0.73 #6046), 09x3r (0.73 #572, 0.72 #812, 0.67 #2379), 09n48 (0.61 #2126, 0.61 #803, 0.60 #563), 0sx8l (0.61 #814, 0.60 #574, 0.56 #2137), 0blfl (0.50 #829, 0.44 #229, 0.42 #2152), 016r9z (0.46 #1503, 0.44 #1624, 0.42 #2187), 06sks6 (0.44 #225, 0.40 #65, 0.35 #745), 0c_tl (0.40 #64, 0.33 #1505, 0.33 #824), 0l6mp (0.29 #2327, 0.29 #2326, 0.28 #2408) >> Best rule #808 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 0d060g; 0f8l9c; 059j2; >> query: (?x5114, 018ctl) <- combatants(?x279, ?x5114), combatants(?x172, ?x5114), combatants(?x151, ?x5114), ?x172 = 0154j, ?x151 = 0b90_r, film_release_region(?x66, ?x279) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #814 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 16 *> proper extension: 0d060g; 0f8l9c; 059j2; *> query: (?x5114, 0sx8l) <- combatants(?x279, ?x5114), combatants(?x172, ?x5114), combatants(?x151, ?x5114), ?x172 = 0154j, ?x151 = 0b90_r, film_release_region(?x66, ?x279) *> conf = 0.61 ranks of expected_values: 5 EVAL 05vz3zq participating_countries! 0sx8l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 236.000 236.000 0.778 http://example.org/olympics/olympic_games/participating_countries #14561-0cp0t91 PRED entity: 0cp0t91 PRED relation: genre PRED expected values: 03k9fj => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 103): 03k9fj (0.62 #256, 0.60 #12, 0.50 #134), 01hmnh (0.60 #19, 0.50 #141, 0.38 #263), 05p553 (0.50 #492, 0.44 #736, 0.42 #858), 02kdv5l (0.47 #368, 0.41 #734, 0.37 #2564), 01jfsb (0.38 #745, 0.36 #2697, 0.36 #3431), 02l7c8 (0.33 #383, 0.31 #1969, 0.26 #4048), 060__y (0.27 #384, 0.25 #262, 0.19 #750), 06n90 (0.27 #380, 0.19 #1478, 0.19 #1356), 0lsxr (0.26 #619, 0.20 #3795, 0.20 #3427), 02b5_l (0.25 #294, 0.20 #50, 0.17 #172) >> Best rule #256 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 02rx2m5; 09bw4_; 03c7twt; >> query: (?x8471, 03k9fj) <- film(?x4046, ?x8471), film_release_region(?x8471, ?x1353), ?x4046 = 07swvb, film_release_region(?x5644, ?x1353), ?x5644 = 0dll_t2 >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cp0t91 genre 03k9fj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.625 http://example.org/film/film/genre #14560-04w7rn PRED entity: 04w7rn PRED relation: nominated_for! PRED expected values: 0gq_v => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 204): 0p9sw (0.54 #493, 0.43 #21, 0.35 #1201), 019f4v (0.53 #1233, 0.34 #4774, 0.33 #6898), 0gq9h (0.50 #534, 0.47 #1242, 0.36 #4783), 02r22gf (0.43 #28, 0.29 #500, 0.27 #1208), 0k611 (0.42 #545, 0.41 #1253, 0.31 #4794), 0gs9p (0.42 #536, 0.39 #1244, 0.33 #6909), 040njc (0.41 #1187, 0.29 #7, 0.25 #6852), 02qyntr (0.39 #1358, 0.29 #178, 0.25 #2538), 0l8z1 (0.39 #1231, 0.23 #3355, 0.21 #523), 0gq_v (0.38 #492, 0.33 #1200, 0.29 #20) >> Best rule #493 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0cq8nx; >> query: (?x1518, 0p9sw) <- nominated_for(?x2549, ?x1518), nominated_for(?x2209, ?x1518), ?x2209 = 0gr42, film(?x2549, ?x54) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #492 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0cq8nx; *> query: (?x1518, 0gq_v) <- nominated_for(?x2549, ?x1518), nominated_for(?x2209, ?x1518), ?x2209 = 0gr42, film(?x2549, ?x54) *> conf = 0.38 ranks of expected_values: 10 EVAL 04w7rn nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 95.000 95.000 0.542 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14559-0h7h6 PRED entity: 0h7h6 PRED relation: month PRED expected values: 040fb 028kb => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 2): 028kb (0.89 #110, 0.88 #132, 0.88 #72), 040fb (0.88 #63, 0.83 #55, 0.82 #131) >> Best rule #110 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0g6xq; >> query: (?x1658, 028kb) <- month(?x1658, ?x6303), month(?x1658, ?x3270), ?x3270 = 05cw8, ?x6303 = 0lkm >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0h7h6 month 028kb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.889 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0h7h6 month 040fb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.889 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #14558-01vj9c PRED entity: 01vj9c PRED relation: role! PRED expected values: 02hrlh => 83 concepts (73 used for prediction) PRED predicted values (max 10 best out of 81): 0g2dz (0.86 #2351, 0.82 #1895, 0.80 #1368), 0l14md (0.84 #3017, 0.83 #3324, 0.83 #1420), 02hnl (0.83 #1420, 0.81 #4522, 0.79 #1795), 0dwtp (0.83 #1420, 0.81 #4522, 0.79 #1795), 07gql (0.83 #1420, 0.81 #4522, 0.79 #1795), 02w3w (0.83 #1420, 0.81 #4522, 0.79 #1795), 04rzd (0.83 #1420, 0.81 #4522, 0.79 #1795), 03qjg (0.83 #1420, 0.81 #4522, 0.79 #1795), 0dq630k (0.83 #1420, 0.81 #4522, 0.79 #1795), 0239kh (0.83 #1420, 0.81 #4522, 0.79 #1795) >> Best rule #2351 for best value: >> intensional similarity = 12 >> extensional distance = 12 >> proper extension: 07gql; >> query: (?x745, 0g2dz) <- role(?x1472, ?x745), role(?x211, ?x745), role(?x1004, ?x745), role(?x745, ?x4583), role(?x745, ?x2460), ?x1472 = 0319l, group(?x745, ?x498), performance_role(?x5356, ?x745), role(?x1997, ?x4583), role(?x4583, ?x1437), instrumentalists(?x2460, ?x680), role(?x74, ?x2460) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #895 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 03bx0bm; *> query: (?x745, ?x74) <- role(?x2944, ?x745), role(?x2888, ?x745), role(?x6225, ?x745), role(?x10989, ?x745), role(?x745, ?x212), group(?x745, ?x498), ?x6225 = 01vng3b, ?x2944 = 0l14j_, role(?x5356, ?x2888), role(?x74, ?x2888), artist(?x10504, ?x10989) *> conf = 0.56 ranks of expected_values: 78 EVAL 01vj9c role! 02hrlh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 83.000 73.000 0.857 http://example.org/music/performance_role/regular_performances./music/group_membership/role #14557-0lpjn PRED entity: 0lpjn PRED relation: profession PRED expected values: 02hrh1q => 92 concepts (91 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.89 #1065, 0.89 #2412, 0.89 #915), 01d_h8 (0.55 #306, 0.54 #906, 0.52 #456), 03gjzk (0.38 #466, 0.38 #316, 0.33 #616), 0dxtg (0.36 #464, 0.36 #314, 0.35 #14), 02jknp (0.30 #308, 0.30 #458, 0.29 #1208), 0np9r (0.29 #1820, 0.28 #2120, 0.27 #7762), 09jwl (0.27 #7762, 0.23 #470, 0.21 #620), 018gz8 (0.27 #7762, 0.21 #468, 0.20 #618), 0cbd2 (0.27 #7762, 0.18 #2553, 0.16 #2255), 016z4k (0.27 #7762, 0.13 #2848, 0.10 #7766) >> Best rule #1065 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 01x72k; >> query: (?x2805, 02hrh1q) <- special_performance_type(?x2805, ?x4832), award(?x2805, ?x618), nominated_for(?x2805, ?x144) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lpjn profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 91.000 0.893 http://example.org/people/person/profession #14556-02l_7y PRED entity: 02l_7y PRED relation: type_of_union PRED expected values: 04ztj => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.75 #37, 0.70 #77, 0.70 #73), 01g63y (0.26 #321, 0.25 #474, 0.20 #479), 01bl8s (0.26 #321, 0.20 #479, 0.03 #19), 0jgjn (0.25 #474, 0.20 #479) >> Best rule #37 for best value: >> intensional similarity = 5 >> extensional distance = 83 >> proper extension: 01p45_v; 01m65sp; 02bh9; 04gycf; 01vswwx; 0jn5l; 016jfw; 0bkf4; 095x_; 020hh3; ... >> query: (?x7172, 04ztj) <- role(?x7172, ?x432), artists(?x505, ?x7172), group(?x7172, ?x4461), location(?x7172, ?x362), role(?x75, ?x432) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02l_7y type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.753 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14555-073h1t PRED entity: 073h1t PRED relation: award_winner PRED expected values: 01pcq3 01kp66 0133sq => 50 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1713): 0h0wc (0.80 #28019, 0.38 #24945, 0.27 #26482), 04t2l2 (0.50 #12308, 0.40 #16919, 0.33 #21530), 03wd5tk (0.50 #16265, 0.40 #19337, 0.33 #7047), 0127m7 (0.40 #18776, 0.33 #6486, 0.25 #15704), 03h26tm (0.33 #3192, 0.33 #1655, 0.33 #121), 09fb5 (0.33 #21551, 0.33 #7721, 0.27 #26164), 01gbn6 (0.33 #10537, 0.33 #2858, 0.25 #13610), 0cw67g (0.33 #1402, 0.27 #27523, 0.25 #32135), 018ygt (0.33 #10171, 0.27 #28616, 0.25 #25542), 02ryx0 (0.33 #2445, 0.26 #10750, 0.18 #12283) >> Best rule #28019 for best value: >> intensional similarity = 15 >> extensional distance = 13 >> proper extension: 0gmdkyy; 09k5jh7; >> query: (?x1998, 0h0wc) <- award_winner(?x1998, ?x6546), award_winner(?x1998, ?x4702), ceremony(?x77, ?x1998), award(?x4702, ?x4091), film(?x4702, ?x3684), nominated_for(?x6546, ?x908), award_nominee(?x6546, ?x10416), award_nominee(?x4702, ?x2352), ?x3684 = 06q8qh, participant(?x1338, ?x2352), participant(?x2352, ?x400), nominated_for(?x4091, ?x9533), nominated_for(?x4091, ?x2177), ?x9533 = 02b6n9, ?x2177 = 0f4_l >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #7579 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 073hd1; *> query: (?x1998, 0133sq) <- award_winner(?x1998, ?x6546), award_winner(?x1998, ?x4702), ceremony(?x77, ?x1998), award(?x4702, ?x112), film(?x4702, ?x9222), film(?x4702, ?x9056), film(?x4702, ?x945), nominated_for(?x6546, ?x908), award_nominee(?x6546, ?x10416), award_nominee(?x4702, ?x521), instance_of_recurring_event(?x1998, ?x3459), ?x9056 = 09sr0, genre(?x945, ?x604), featured_film_locations(?x9222, ?x3634), crewmember(?x781, ?x6546) *> conf = 0.33 ranks of expected_values: 52, 423, 1622 EVAL 073h1t award_winner 0133sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 50.000 27.000 0.800 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 073h1t award_winner 01kp66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 27.000 0.800 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 073h1t award_winner 01pcq3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 27.000 0.800 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14554-01xsc9 PRED entity: 01xsc9 PRED relation: gender PRED expected values: 05zppz => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #29, 0.83 #60, 0.76 #15), 02zsn (0.38 #18, 0.30 #91, 0.30 #165) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 015vql; >> query: (?x12020, 05zppz) <- award(?x12020, ?x112), profession(?x12020, ?x1032), ?x112 = 027dtxw, type_of_union(?x12020, ?x566) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xsc9 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.908 http://example.org/people/person/gender #14553-061dn_ PRED entity: 061dn_ PRED relation: film PRED expected values: 0ddfwj1 04dsnp 047vp1n 02vz6dn => 116 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1571): 05fgr_ (0.62 #9391, 0.61 #20348, 0.04 #70440), 04z257 (0.59 #32873, 0.58 #21914, 0.58 #10957), 0gvvf4j (0.59 #32873, 0.58 #21914, 0.58 #10957), 0dgst_d (0.59 #32873, 0.58 #21914, 0.58 #10957), 09qljs (0.59 #32873, 0.58 #21914, 0.58 #10957), 03mh_tp (0.33 #8268, 0.31 #3573, 0.29 #16095), 0k0rf (0.33 #2347, 0.31 #3912, 0.19 #14869), 02rb84n (0.31 #3379, 0.25 #1814, 0.24 #15901), 035s95 (0.29 #9689, 0.26 #19080, 0.24 #15950), 0dgq_kn (0.27 #8739, 0.23 #4044, 0.19 #16566) >> Best rule #9391 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0hm0k; >> query: (?x3462, ?x7651) <- award_winner(?x163, ?x3462), award_winner(?x7651, ?x3462), industry(?x3462, ?x373) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2693 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 086k8; 017s11; 0g1rw; 016tw3; 017jv5; 09d5h; 03mdt; 01gb54; 05xbx; 05gnf; *> query: (?x3462, 02vz6dn) <- award_winner(?x163, ?x3462), film(?x3462, ?x814), category(?x3462, ?x134) *> conf = 0.08 ranks of expected_values: 416, 1230, 1233 EVAL 061dn_ film 02vz6dn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 116.000 89.000 0.620 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 061dn_ film 047vp1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 89.000 0.620 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 061dn_ film 04dsnp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 89.000 0.620 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 061dn_ film 0ddfwj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 89.000 0.620 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #14552-0d87hc PRED entity: 0d87hc PRED relation: film_crew_role PRED expected values: 0dxtw => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 27): 0dxtw (0.51 #77, 0.41 #2094, 0.39 #1442), 01vx2h (0.42 #351, 0.35 #2095, 0.33 #249), 02ynfr (0.22 #82, 0.18 #903, 0.18 #2099), 02rh1dz (0.20 #76, 0.17 #349, 0.14 #144), 0215hd (0.18 #256, 0.15 #222, 0.15 #2102), 0d2b38 (0.16 #92, 0.13 #3564, 0.12 #24), 01xy5l_ (0.14 #217, 0.13 #3564, 0.12 #12), 02_n3z (0.14 #240, 0.13 #3564, 0.11 #206), 089g0h (0.13 #3564, 0.12 #18, 0.12 #257), 015h31 (0.13 #3564, 0.11 #75, 0.10 #212) >> Best rule #77 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0d90m; 05p1tzf; 01rxyb; 01pj_5; 012s1d; 01gwk3; 02825kb; 03k8th; >> query: (?x10274, 0dxtw) <- titles(?x2480, ?x10274), featured_film_locations(?x10274, ?x191), prequel(?x2494, ?x10274), film_crew_role(?x10274, ?x137) >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d87hc film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.511 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14551-0gjcrrw PRED entity: 0gjcrrw PRED relation: film! PRED expected values: 016tt2 => 63 concepts (44 used for prediction) PRED predicted values (max 10 best out of 49): 017s11 (0.33 #77, 0.16 #447, 0.16 #891), 081bls (0.33 #114, 0.02 #1448, 0.01 #2115), 05qd_ (0.27 #379, 0.19 #1713, 0.15 #1268), 054g1r (0.25 #256, 0.25 #182, 0.20 #404), 020h2v (0.25 #266, 0.25 #192, 0.10 #340), 01gb54 (0.25 #250, 0.13 #398, 0.13 #472), 025jfl (0.25 #154, 0.10 #302, 0.07 #376), 016tw3 (0.20 #381, 0.17 #1047, 0.15 #2533), 086k8 (0.20 #1186, 0.20 #1261, 0.19 #1706), 03xq0f (0.14 #597, 0.14 #671, 0.14 #745) >> Best rule #77 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03cyslc; >> query: (?x3830, 017s11) <- film(?x5620, ?x3830), ?x5620 = 05drr9, film_crew_role(?x3830, ?x1078), film_release_region(?x3830, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1708 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 588 *> proper extension: 04cf_l; 0c5qvw; *> query: (?x3830, 016tt2) <- production_companies(?x3830, ?x3462), genre(?x3830, ?x53), film(?x3462, ?x814), company(?x4060, ?x3462) *> conf = 0.13 ranks of expected_values: 12 EVAL 0gjcrrw film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 63.000 44.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #14550-01xllf PRED entity: 01xllf PRED relation: gender PRED expected values: 05zppz => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.82 #3, 0.81 #23, 0.81 #51), 02zsn (0.46 #124, 0.46 #57, 0.30 #26) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 58 >> proper extension: 03pvt; 03mv0b; >> query: (?x10126, 05zppz) <- profession(?x10126, ?x1032), profession(?x10126, ?x319), ?x319 = 01d_h8, ?x1032 = 02hrh1q, special_performance_type(?x10126, ?x3558) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xllf gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.817 http://example.org/people/person/gender #14549-02pv_d PRED entity: 02pv_d PRED relation: award PRED expected values: 0gs9p 026mmy => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 287): 02x17s4 (0.78 #40306, 0.77 #7906, 0.76 #40703), 0gs9p (0.73 #468, 0.64 #71, 0.42 #3630), 02x4sn8 (0.72 #44657, 0.72 #44656, 0.70 #40305), 09d28z (0.72 #44657, 0.72 #44656, 0.70 #40305), 0gr51 (0.50 #488, 0.45 #91, 0.38 #12342), 02pqp12 (0.45 #63, 0.41 #460, 0.32 #3622), 02qyp19 (0.45 #1, 0.21 #7510, 0.19 #795), 02x1dht (0.45 #49, 0.14 #843, 0.14 #7558), 03hl6lc (0.36 #169, 0.27 #566, 0.23 #7678), 09sb52 (0.34 #23743, 0.29 #17028, 0.29 #16633) >> Best rule #40306 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 07s3vqk; 0411q; 05cljf; 01vrx3g; 0m2l9; 06cc_1; 01zkxv; 04rcr; 0kzy0; 01n5309; ... >> query: (?x8070, ?x601) <- award_winner(?x601, ?x8070), ceremony(?x601, ?x78), award(?x164, ?x601) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #468 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 0kr5_; 05drq5; 0h1p; 06pj8; 02fcs2; 09ftwr; 02l5rm; 01f8ld; 02kxbx3; 0bzyh; ... *> query: (?x8070, 0gs9p) <- award_winner(?x8364, ?x8070), student(?x581, ?x8070), ?x8364 = 09d28z *> conf = 0.73 ranks of expected_values: 2, 54 EVAL 02pv_d award 026mmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 140.000 140.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02pv_d award 0gs9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 140.000 140.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14548-02qhqz4 PRED entity: 02qhqz4 PRED relation: currency PRED expected values: 09nqf => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.87 #57, 0.82 #78, 0.79 #148), 02gsvk (0.10 #69, 0.02 #132, 0.02 #146), 088n7 (0.08 #35, 0.07 #70, 0.01 #77), 02l6h (0.03 #67, 0.03 #144, 0.03 #130), 0kz1h (0.03 #68, 0.01 #75), 01nv4h (0.03 #128, 0.03 #142, 0.02 #86) >> Best rule #57 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 0b6l1st; 07f_t4; >> query: (?x2153, 09nqf) <- film_release_distribution_medium(?x2153, ?x81), film(?x1735, ?x2153), genre(?x2153, ?x6888), film_crew_role(?x2153, ?x468), ?x6888 = 04pbhw >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qhqz4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.872 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #14547-01x0sy PRED entity: 01x0sy PRED relation: nationality PRED expected values: 09c7w0 => 129 concepts (103 used for prediction) PRED predicted values (max 10 best out of 55): 09c7w0 (0.90 #5309, 0.88 #5209, 0.88 #4305), 01n7q (0.34 #7415, 0.33 #9923, 0.32 #9221), 0154j (0.30 #10328, 0.03 #6312), 0f0z_ (0.24 #8418), 03s5t (0.24 #8418), 03_3d (0.11 #3308, 0.08 #1206, 0.08 #2007), 02jx1 (0.11 #3636, 0.10 #7448, 0.10 #10057), 0d060g (0.09 #3309, 0.07 #2708, 0.07 #2308), 07ssc (0.08 #1715, 0.08 #1916, 0.08 #3718), 07t21 (0.08 #137, 0.07 #237) >> Best rule #5309 for best value: >> intensional similarity = 4 >> extensional distance = 737 >> proper extension: 05218gr; >> query: (?x9471, 09c7w0) <- place_of_birth(?x9471, ?x12025), county(?x12025, ?x9887), contains(?x94, ?x12025), time_zones(?x12025, ?x2950) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x0sy nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 103.000 0.904 http://example.org/people/person/nationality #14546-01y9xg PRED entity: 01y9xg PRED relation: gender PRED expected values: 02zsn => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #3, 0.71 #1, 0.71 #151), 02zsn (0.46 #177, 0.35 #16, 0.32 #10) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 015grj; >> query: (?x3733, 05zppz) <- award_winner(?x5030, ?x3733), award_winner(?x968, ?x3733), ?x5030 = 069nzr, film(?x968, ?x1120) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #177 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4064 *> proper extension: 0jrg; *> query: (?x3733, ?x231) <- nationality(?x3733, ?x94), nationality(?x7310, ?x94), gender(?x7310, ?x231) *> conf = 0.46 ranks of expected_values: 2 EVAL 01y9xg gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.778 http://example.org/people/person/gender #14545-04wgh PRED entity: 04wgh PRED relation: olympics PRED expected values: 018ctl => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 39): 06sks6 (0.92 #2603, 0.91 #3114, 0.88 #3583), 0kbvv (0.50 #532, 0.44 #2093, 0.42 #1039), 018ctl (0.47 #515, 0.42 #1022, 0.40 #2076), 0swbd (0.47 #518, 0.34 #1025, 0.33 #1064), 0jdk_ (0.43 #533, 0.39 #2384, 0.39 #2305), 09n48 (0.43 #511, 0.38 #2072, 0.35 #1564), 0jhn7 (0.39 #2384, 0.39 #2305, 0.39 #2737), 0l6mp (0.39 #2384, 0.39 #2305, 0.39 #2737), 0l6m5 (0.39 #2384, 0.39 #2305, 0.39 #2737), 0lbd9 (0.39 #2384, 0.39 #2305, 0.39 #2737) >> Best rule #2603 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 01z88t; 07bxhl; 04gqr; 01p1b; 06v36; 01c4pv; 0fv4v; 07f5x; 0jt3tjf; 06tgw; >> query: (?x1273, 06sks6) <- olympics(?x1273, ?x778), country(?x471, ?x1273), countries_within(?x2467, ?x1273) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 012m_; *> query: (?x1273, 018ctl) <- nationality(?x5544, ?x1273), contains(?x1273, ?x14462), jurisdiction_of_office(?x1195, ?x14462) *> conf = 0.47 ranks of expected_values: 3 EVAL 04wgh olympics 018ctl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 126.000 0.917 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #14544-04g7x PRED entity: 04g7x PRED relation: major_field_of_study! PRED expected values: 01w5m 07tds => 62 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1403): 01w5m (0.76 #14471, 0.75 #15049, 0.71 #2983), 07wrz (0.71 #2930, 0.67 #2356, 0.62 #5799), 05mv4 (0.71 #3007, 0.67 #2433, 0.60 #4727), 09f2j (0.70 #4759, 0.67 #8781, 0.67 #7632), 07szy (0.70 #4628, 0.67 #7501, 0.65 #6927), 07tds (0.67 #2455, 0.60 #4749, 0.59 #6473), 0885n (0.67 #2564, 0.57 #3138, 0.40 #4858), 07vhb (0.60 #4773, 0.57 #3053, 0.50 #2479), 07t90 (0.60 #4747, 0.53 #6471, 0.48 #8769), 0bwfn (0.60 #8324, 0.52 #10049, 0.50 #4877) >> Best rule #14471 for best value: >> intensional similarity = 11 >> extensional distance = 44 >> proper extension: 05b6c; >> query: (?x8962, 01w5m) <- major_field_of_study(?x1368, ?x8962), major_field_of_study(?x122, ?x8962), student(?x122, ?x3768), institution(?x1368, ?x13670), institution(?x1368, ?x4780), institution(?x1368, ?x2838), ?x3768 = 01n1gc, student(?x1368, ?x164), ?x13670 = 01dq0z, currency(?x2838, ?x170), ?x4780 = 017cy9 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 04g7x major_field_of_study! 07tds CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 30.000 0.761 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04g7x major_field_of_study! 01w5m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 30.000 0.761 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14543-05l64 PRED entity: 05l64 PRED relation: mode_of_transportation PRED expected values: 07jdr => 228 concepts (228 used for prediction) PRED predicted values (max 10 best out of 3): 07jdr (0.78 #136, 0.78 #103, 0.78 #124), 0k4j (0.08 #119, 0.05 #38, 0.04 #65), 06d_3 (0.07 #69, 0.06 #102, 0.05 #120) >> Best rule #136 for best value: >> intensional similarity = 6 >> extensional distance = 39 >> proper extension: 02cl1; 06t2t; 02h6_6p; 01lfy; >> query: (?x11197, 07jdr) <- month(?x11197, ?x4869), month(?x11197, ?x3270), mode_of_transportation(?x11197, ?x6665), ?x4869 = 02xx5, ?x3270 = 05cw8, contains(?x2513, ?x11197) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05l64 mode_of_transportation 07jdr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 228.000 228.000 0.780 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #14542-0fw2f PRED entity: 0fw2f PRED relation: category PRED expected values: 08mbj5d => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #2, 0.79 #6, 0.78 #8) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 0_3cs; 01mc11; 0mb2b; 0_565; >> query: (?x13400, 08mbj5d) <- county(?x13400, ?x13477), administrative_division(?x13400, ?x2977), time_zones(?x13400, ?x2674) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fw2f category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.796 http://example.org/common/topic/webpage./common/webpage/category #14541-08966 PRED entity: 08966 PRED relation: category PRED expected values: 08mbj5d => 243 concepts (243 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.76 #83, 0.76 #50, 0.76 #84) >> Best rule #83 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 02dtg; 0ydpd; 0f2r6; 0fvvz; 0f2w0; 03v_5; 05l5n; 099ty; 0mp3l; 0pmp2; ... >> query: (?x6458, 08mbj5d) <- location(?x14208, ?x6458), citytown(?x9227, ?x6458), administrative_division(?x6458, ?x7406), contains(?x774, ?x7406) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08966 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 243.000 243.000 0.762 http://example.org/common/topic/webpage./common/webpage/category #14540-070j61 PRED entity: 070j61 PRED relation: award PRED expected values: 05f4m9q 03ccq3s => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 278): 027gs1_ (0.71 #22116, 0.71 #23727, 0.70 #22519), 0cjcbg (0.43 #363, 0.04 #8806, 0.04 #10013), 0gq9h (0.38 #880, 0.36 #7715, 0.34 #10530), 0gs9p (0.37 #10130, 0.29 #882, 0.19 #2491), 0fbtbt (0.35 #8673, 0.34 #7467, 0.33 #8271), 019f4v (0.33 #10117, 0.21 #2478, 0.20 #4086), 040njc (0.32 #10060, 0.27 #10462, 0.26 #7647), 02q1tc5 (0.31 #5373, 0.29 #5775, 0.26 #6177), 09sb52 (0.30 #12102, 0.28 #14112, 0.25 #14514), 04dn09n (0.30 #2456, 0.25 #1249, 0.18 #10095) >> Best rule #22116 for best value: >> intensional similarity = 3 >> extensional distance = 1210 >> proper extension: 01t2h2; 09d5h; 03wpmd; 017vkx; 01kgxf; 02rk45; 03dbww; >> query: (?x7611, ?x2022) <- award_winner(?x2548, ?x7611), award_nominee(?x7611, ?x3381), award_winner(?x2022, ?x7611) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1219 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 0mm1q; *> query: (?x7611, 05f4m9q) <- nationality(?x7611, ?x94), award(?x7611, ?x688), ?x688 = 05b1610 *> conf = 0.25 ranks of expected_values: 16, 38 EVAL 070j61 award 03ccq3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 119.000 119.000 0.709 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 070j61 award 05f4m9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 119.000 119.000 0.709 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14539-0pkyh PRED entity: 0pkyh PRED relation: instrumentalists! PRED expected values: 0l14md => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 77): 05r5c (0.50 #352, 0.49 #2510, 0.49 #785), 018j2 (0.43 #38, 0.15 #3192, 0.12 #3712), 05148p4 (0.41 #279, 0.39 #2523, 0.38 #798), 018vs (0.33 #2515, 0.33 #357, 0.32 #790), 02hnl (0.30 #292, 0.29 #378, 0.23 #120), 026t6 (0.17 #780, 0.11 #5011, 0.11 #4059), 0l14md (0.16 #265, 0.15 #351, 0.14 #784), 0l14qv (0.15 #3192, 0.15 #782, 0.14 #263), 04rzd (0.15 #3192, 0.14 #209, 0.14 #37), 06w7v (0.15 #3192, 0.12 #3712, 0.09 #329) >> Best rule #352 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 02fybl; >> query: (?x2930, 05r5c) <- participant(?x6208, ?x2930), role(?x2930, ?x2798), profession(?x2930, ?x220) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #265 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 01j4ls; 0892sx; 02qx69; *> query: (?x2930, 0l14md) <- participant(?x6208, ?x2930), role(?x2930, ?x2798), award(?x2930, ?x724) *> conf = 0.16 ranks of expected_values: 7 EVAL 0pkyh instrumentalists! 0l14md CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 119.000 119.000 0.500 http://example.org/music/instrument/instrumentalists #14538-02_nsc PRED entity: 02_nsc PRED relation: genre PRED expected values: 02kdv5l => 75 concepts (60 used for prediction) PRED predicted values (max 10 best out of 88): 0ltv (0.72 #3280, 0.59 #3279, 0.59 #1578), 05p553 (0.43 #6447, 0.42 #611, 0.42 #490), 02kdv5l (0.38 #4862, 0.30 #7052, 0.28 #245), 01jfsb (0.31 #2320, 0.31 #7063, 0.31 #2684), 03k9fj (0.30 #4872, 0.27 #255, 0.24 #7062), 04xvlr (0.25 #3524, 0.23 #365, 0.23 #2064), 0lsxr (0.23 #3776, 0.20 #9, 0.19 #131), 06cvj (0.23 #489, 0.23 #610, 0.18 #367), 060__y (0.22 #381, 0.19 #17, 0.18 #1473), 01hmnh (0.18 #261, 0.16 #18, 0.16 #4878) >> Best rule #3280 for best value: >> intensional similarity = 4 >> extensional distance = 821 >> proper extension: 06cs95; 03kq98; 01q_y0; 039c26; 0372j5; >> query: (?x9642, ?x1403) <- nominated_for(?x500, ?x9642), nominated_for(?x666, ?x9642), titles(?x1403, ?x9642), genre(?x83, ?x1403) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #4862 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1097 *> proper extension: 0ckrgs; 0192hw; 0k54q; 0bh72t; 02r9p0c; 06_sc3; 05dfy_; 063y9fp; 03gyvwg; 0h63q6t; *> query: (?x9642, 02kdv5l) <- genre(?x9642, ?x1403), language(?x9642, ?x254), genre(?x3979, ?x1403), ?x3979 = 01vw8k *> conf = 0.38 ranks of expected_values: 3 EVAL 02_nsc genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 75.000 60.000 0.716 http://example.org/film/film/genre #14537-0p9nv PRED entity: 0p9nv PRED relation: time_zones PRED expected values: 02hcv8 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.57 #16, 0.56 #42, 0.50 #3), 02fqwt (0.29 #27, 0.26 #118, 0.21 #248), 02lcqs (0.19 #200, 0.18 #187, 0.18 #499), 02llzg (0.18 #82, 0.16 #95, 0.16 #134), 02hczc (0.16 #1353, 0.11 #210, 0.11 #223), 042g7t (0.16 #1353, 0.09 #115, 0.07 #102), 02lcrv (0.16 #1353, 0.02 #98, 0.02 #111), 03bdv (0.07 #58, 0.06 #396, 0.05 #357), 052vwh (0.06 #90, 0.04 #103, 0.04 #116), 03plfd (0.06 #88, 0.03 #140, 0.02 #166) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0smfm; 0sq2v; >> query: (?x13405, 02hcv8) <- contains(?x94, ?x13405), service_location(?x6315, ?x13405), ?x6315 = 08qnnv, source(?x13405, ?x958) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p9nv time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.571 http://example.org/location/location/time_zones #14536-01gbn6 PRED entity: 01gbn6 PRED relation: film PRED expected values: 07phbc 0fzm0g => 98 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1178): 01kff7 (0.69 #8916, 0.66 #21400, 0.65 #37449), 06q8qh (0.69 #8916, 0.66 #21400, 0.65 #37449), 035_2h (0.14 #917, 0.02 #2700, 0.02 #4483), 04kkz8 (0.14 #142, 0.02 #3708, 0.02 #5491), 016017 (0.14 #1707, 0.02 #5273, 0.01 #8839), 02q7yfq (0.09 #2985, 0.02 #6551, 0.01 #26168), 02qr3k8 (0.07 #1287, 0.06 #8419, 0.04 #4853), 02ny6g (0.07 #599, 0.04 #2382, 0.01 #9515), 011yth (0.07 #298, 0.04 #3864, 0.03 #7430), 040_lv (0.07 #1046, 0.04 #4612, 0.02 #11745) >> Best rule #8916 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 02nb2s; 03gm48; 0j582; 02t_zq; 015rhv; 02mxw0; 0gr36; 016k6x; 016gkf; 016z51; ... >> query: (?x9526, ?x582) <- award(?x9526, ?x3247), nominated_for(?x9526, ?x582), ?x3247 = 0bdwqv >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #3558 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 0309jm; *> query: (?x9526, 0fzm0g) <- award(?x9526, ?x2325), religion(?x9526, ?x1985), ?x2325 = 05p09zm *> conf = 0.02 ranks of expected_values: 308, 1071 EVAL 01gbn6 film 0fzm0g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 98.000 66.000 0.688 http://example.org/film/actor/film./film/performance/film EVAL 01gbn6 film 07phbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 98.000 66.000 0.688 http://example.org/film/actor/film./film/performance/film #14535-0d02km PRED entity: 0d02km PRED relation: actor! PRED expected values: 0d7vtk => 102 concepts (73 used for prediction) PRED predicted values (max 10 best out of 126): 08phg9 (0.13 #9793, 0.11 #529, 0.11 #5291), 0cc7hmk (0.13 #9793, 0.11 #529, 0.11 #5291), 0bbm7r (0.10 #373, 0.01 #15893, 0.01 #902), 02qr46y (0.10 #499, 0.01 #15893), 06cs95 (0.10 #271, 0.01 #3445, 0.01 #8473), 02gl58 (0.10 #469), 021gzd (0.10 #404), 01s81 (0.10 #339), 026bfsh (0.07 #890, 0.05 #4330, 0.05 #3535), 0828jw (0.05 #898, 0.04 #3543, 0.03 #6190) >> Best rule #9793 for best value: >> intensional similarity = 3 >> extensional distance = 947 >> proper extension: 01q415; 01ycck; >> query: (?x5999, ?x1868) <- award_winner(?x1868, ?x5999), award_nominee(?x1867, ?x5999), film(?x1867, ?x1219) >> conf = 0.13 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0d02km actor! 0d7vtk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 73.000 0.128 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #14534-04rjg PRED entity: 04rjg PRED relation: major_field_of_study! PRED expected values: 03g3w 0jjw => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 111): 03g3w (0.84 #4541, 0.84 #2694, 0.83 #2539), 01lj9 (0.84 #4541, 0.82 #4706, 0.82 #4703), 0jjw (0.84 #4541, 0.82 #4706, 0.82 #4703), 04rjg (0.50 #854, 0.38 #1625, 0.38 #1240), 0fdys (0.38 #1254, 0.33 #868, 0.33 #408), 02_7t (0.33 #274, 0.33 #196, 0.33 #44), 064_8sq (0.33 #875, 0.33 #338, 0.33 #183), 01zc2w (0.33 #892, 0.33 #432, 0.33 #124), 062z7 (0.33 #1784, 0.33 #401, 0.33 #17), 02822 (0.33 #410, 0.33 #102, 0.31 #1564) >> Best rule #4541 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 0j0k; >> query: (?x2014, ?x2981) <- major_field_of_study(?x2014, ?x2981), major_field_of_study(?x1327, ?x2014), major_field_of_study(?x2981, ?x7070), taxonomy(?x7070, ?x939) >> conf = 0.84 => this is the best rule for 3 predicted values ranks of expected_values: 1, 3 EVAL 04rjg major_field_of_study! 0jjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 95.000 0.841 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study EVAL 04rjg major_field_of_study! 03g3w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.841 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #14533-01lly5 PRED entity: 01lly5 PRED relation: profession PRED expected values: 02hrh1q => 121 concepts (66 used for prediction) PRED predicted values (max 10 best out of 52): 02hrh1q (0.92 #4160, 0.92 #4456, 0.91 #8748), 0dxtg (0.78 #8155, 0.65 #9045, 0.52 #1790), 03gjzk (0.47 #1200, 0.42 #1792, 0.31 #8157), 02jknp (0.45 #9039, 0.35 #8149, 0.19 #6521), 01d_h8 (0.43 #9037, 0.40 #8147, 0.38 #154), 0dz3r (0.23 #150, 0.10 #7107, 0.09 #3555), 09jwl (0.20 #3572, 0.19 #7124, 0.15 #167), 0cbd2 (0.18 #8148, 0.17 #1783, 0.16 #9038), 0nbcg (0.15 #179, 0.11 #7136, 0.10 #8024), 0n1h (0.15 #160, 0.06 #7117, 0.05 #3565) >> Best rule #4160 for best value: >> intensional similarity = 4 >> extensional distance = 570 >> proper extension: 01r42_g; 02pkpfs; 04mz10g; 01l1sq; 01bpc9; 01541z; 0443y3; 0783m_; 0277990; 07sgfsl; ... >> query: (?x3785, 02hrh1q) <- actor(?x11454, ?x3785), titles(?x7712, ?x11454), profession(?x3785, ?x1146), nationality(?x3785, ?x94) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lly5 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 66.000 0.925 http://example.org/people/person/profession #14532-01wbsdz PRED entity: 01wbsdz PRED relation: nationality PRED expected values: 09c7w0 => 134 concepts (131 used for prediction) PRED predicted values (max 10 best out of 28): 09c7w0 (0.80 #301, 0.76 #5918, 0.75 #5217), 0345h (0.37 #10825, 0.03 #631, 0.03 #1734), 05kkh (0.27 #11929, 0.23 #4412), 0d060g (0.20 #7, 0.07 #2311, 0.07 #2111), 02jx1 (0.18 #1536, 0.17 #1837, 0.17 #4747), 07ssc (0.10 #1518, 0.10 #1819, 0.09 #4729), 01ls2 (0.06 #411, 0.04 #511, 0.01 #812), 03rk0 (0.06 #12478, 0.05 #12678, 0.05 #12778), 01smm (0.05 #1804, 0.05 #3407, 0.04 #3006), 0chghy (0.04 #1212, 0.03 #3016, 0.03 #3417) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 01vrncs; 016dsy; >> query: (?x5882, 09c7w0) <- artist(?x3265, ?x5882), ?x3265 = 015_1q, participant(?x5882, ?x2227), participant(?x5882, ?x6144) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wbsdz nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 131.000 0.800 http://example.org/people/person/nationality #14531-02l424 PRED entity: 02l424 PRED relation: school_type PRED expected values: 01rs41 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 18): 01rs41 (0.95 #1340, 0.58 #695, 0.55 #764), 05jxkf (0.67 #902, 0.56 #1132, 0.55 #1063), 05pcjw (0.62 #208, 0.54 #415, 0.52 #277), 01_9fk (0.39 #324, 0.34 #900, 0.28 #1199), 07tf8 (0.36 #331, 0.24 #907, 0.24 #1114), 02p0qmm (0.14 #33, 0.10 #194, 0.09 #240), 01jlsn (0.14 #39, 0.08 #2811, 0.05 #1121), 0m4mb (0.14 #34, 0.05 #1116, 0.05 #218), 01_srz (0.13 #693, 0.11 #417, 0.10 #924), 01y64 (0.11 #81, 0.07 #702, 0.06 #794) >> Best rule #1340 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 020yvh; >> query: (?x9620, 01rs41) <- school_type(?x9620, ?x11041), school_type(?x8715, ?x11041), ?x8715 = 01wv24 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02l424 school_type 01rs41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.945 http://example.org/education/educational_institution/school_type #14530-046488 PRED entity: 046488 PRED relation: film! PRED expected values: 018db8 => 75 concepts (46 used for prediction) PRED predicted values (max 10 best out of 825): 017jv5 (0.48 #58353, 0.47 #10420, 0.45 #43767), 0bj9k (0.11 #328, 0.04 #4496, 0.02 #2412), 05nzw6 (0.11 #1193, 0.02 #5361, 0.02 #26204), 014zcr (0.07 #36, 0.05 #33349, 0.04 #4204), 0h0wc (0.07 #424, 0.02 #46275, 0.02 #25435), 0sw6g (0.07 #1406, 0.02 #9742, 0.02 #11826), 01ycbq (0.07 #327, 0.02 #4495, 0.02 #29506), 04gc65 (0.07 #1976, 0.02 #6144, 0.01 #29071), 0gpprt (0.07 #1525, 0.02 #5693, 0.01 #59879), 02kxwk (0.07 #765, 0.02 #11185, 0.02 #9101) >> Best rule #58353 for best value: >> intensional similarity = 5 >> extensional distance = 756 >> proper extension: 08hmch; 0bh8yn3; 0gydcp7; 05m_jsg; 02vrgnr; 0bbw2z6; 0ggbfwf; 027j9wd; 03rg2b; 0456zg; >> query: (?x4993, ?x192) <- nominated_for(?x4992, ?x4993), nominated_for(?x192, ?x4993), film(?x4992, ?x2886), genre(?x4993, ?x53), participant(?x3585, ?x4992) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #33349 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 435 *> proper extension: 06mmr; *> query: (?x4993, ?x100) <- honored_for(?x8762, ?x4993), award_winner(?x4993, ?x192), award_nominee(?x192, ?x100) *> conf = 0.05 ranks of expected_values: 74 EVAL 046488 film! 018db8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 75.000 46.000 0.480 http://example.org/film/actor/film./film/performance/film #14529-01fchy PRED entity: 01fchy PRED relation: artists! PRED expected values: 0172rj 0xv2x => 74 concepts (29 used for prediction) PRED predicted values (max 10 best out of 271): 06by7 (0.93 #7065, 0.62 #8595, 0.57 #2777), 0m0jc (0.85 #3381, 0.45 #1841, 0.43 #1535), 05w3f (0.74 #3720, 0.20 #4333, 0.13 #5250), 0xv2x (0.67 #1373, 0.67 #1067, 0.64 #1984), 05r6t (0.64 #4069, 0.47 #2836, 0.33 #3145), 064t9 (0.57 #8280, 0.56 #8586, 0.52 #2462), 011j5x (0.47 #2787, 0.36 #2172, 0.35 #3096), 0dl5d (0.45 #3702, 0.29 #4315, 0.23 #3372), 0xhtw (0.43 #3699, 0.33 #4312, 0.25 #7060), 05bt6j (0.42 #3108, 0.39 #2799, 0.35 #4339) >> Best rule #7065 for best value: >> intensional similarity = 9 >> extensional distance = 417 >> proper extension: 01pbxb; 053y0s; 07s3vqk; 01vrx3g; 089tm; 01t_xp_; 032nwy; 02mslq; 01nqfh_; 06cc_1; ... >> query: (?x9706, 06by7) <- artists(?x7220, ?x9706), artists(?x3370, ?x9706), parent_genre(?x2439, ?x7220), artists(?x3370, ?x4658), artists(?x3370, ?x4628), artists(?x3370, ?x1732), ?x4628 = 016fnb, ?x4658 = 018gm9, ?x1732 = 03t9sp >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1373 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 03h502k; *> query: (?x9706, 0xv2x) <- artists(?x7220, ?x9706), artists(?x3753, ?x9706), ?x7220 = 0mmp3, ?x3753 = 01_bkd, artist(?x12666, ?x9706) *> conf = 0.67 ranks of expected_values: 4, 39 EVAL 01fchy artists! 0xv2x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 29.000 0.928 http://example.org/music/genre/artists EVAL 01fchy artists! 0172rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 74.000 29.000 0.928 http://example.org/music/genre/artists #14528-0fk0xk PRED entity: 0fk0xk PRED relation: award_winner PRED expected values: 01dvms 03csqj4 => 35 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1114): 03csqj4 (0.53 #26127, 0.25 #6105, 0.09 #1540), 037q1z (0.53 #26127, 0.14 #24589, 0.12 #32277), 0bs8d (0.53 #26127, 0.09 #1540, 0.03 #16201), 0b_dy (0.53 #26127, 0.02 #10758, 0.02 #1539), 01dvms (0.53 #26127, 0.02 #10758, 0.01 #18439), 076lxv (0.50 #6240, 0.33 #3169, 0.29 #10851), 012vct (0.50 #7219, 0.33 #4148, 0.25 #5683), 0c0tzp (0.33 #4585, 0.33 #3049, 0.29 #12267), 081nh (0.33 #3417, 0.26 #20318, 0.26 #18780), 09r9m7 (0.33 #3978, 0.25 #7049, 0.25 #5513) >> Best rule #26127 for best value: >> intensional similarity = 14 >> extensional distance = 37 >> proper extension: 0bzmt8; >> query: (?x5723, ?x3139) <- award_winner(?x5723, ?x4423), award_winner(?x5723, ?x2068), award_winner(?x5723, ?x1852), ceremony(?x3617, ?x5723), ?x3617 = 0gvx_, honored_for(?x5723, ?x5980), nominated_for(?x2068, ?x951), award_nominee(?x2068, ?x2069), type_of_union(?x1852, ?x566), award(?x1852, ?x1079), award_winner(?x2924, ?x4423), place_of_death(?x4423, ?x1523), award_winner(?x5980, ?x3139), award_winner(?x12389, ?x2068) >> conf = 0.53 => this is the best rule for 5 predicted values ranks of expected_values: 1, 5 EVAL 0fk0xk award_winner 03csqj4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 21.000 0.529 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0fk0xk award_winner 01dvms CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 35.000 21.000 0.529 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14527-013yq PRED entity: 013yq PRED relation: dog_breed PRED expected values: 0km5c => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 1): 0km5c (0.81 #2, 0.55 #9, 0.52 #4) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0dyl9; >> query: (?x2277, 0km5c) <- administrative_division(?x2277, ?x3038), featured_film_locations(?x2362, ?x2277), dog_breed(?x2277, ?x3095) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013yq dog_breed 0km5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.812 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #14526-03nkts PRED entity: 03nkts PRED relation: place_of_birth PRED expected values: 04pry => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 67): 02_286 (0.07 #16922, 0.07 #31012, 0.07 #42983), 030qb3t (0.05 #4982, 0.05 #16957, 0.04 #54), 02dtg (0.04 #10, 0.03 #1418, 0.02 #2826), 01_d4 (0.04 #2178, 0.04 #5698, 0.04 #31059), 0cr3d (0.03 #9248, 0.03 #31087, 0.03 #16997), 0nbrp (0.02 #532, 0.02 #1940, 0.02 #1236), 01cx_ (0.02 #109, 0.02 #1517, 0.01 #3629), 013nv_ (0.02 #381, 0.02 #1085, 0.01 #1789), 0fpzwf (0.02 #206, 0.02 #910, 0.01 #1614), 0s5cg (0.02 #181, 0.02 #885, 0.01 #1589) >> Best rule #16922 for best value: >> intensional similarity = 3 >> extensional distance = 1525 >> proper extension: 02qggqc; 0b79gfg; 0b6yp2; 03q8ch; 03bw6; 0bn3jg; >> query: (?x6397, 02_286) <- nominated_for(?x6397, ?x3784), nationality(?x6397, ?x94), ?x94 = 09c7w0 >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #1240 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 02qjj7; *> query: (?x6397, 04pry) <- profession(?x6397, ?x1383), participant(?x6844, ?x6397), ?x1383 = 0np9r *> conf = 0.02 ranks of expected_values: 31 EVAL 03nkts place_of_birth 04pry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 81.000 81.000 0.071 http://example.org/people/person/place_of_birth #14525-01s73z PRED entity: 01s73z PRED relation: industry PRED expected values: 029g_vk => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 44): 03qh03g (0.63 #2838, 0.43 #245, 0.42 #870), 029g_vk (0.63 #2838, 0.18 #828, 0.17 #203), 02vxn (0.40 #1350, 0.33 #1398, 0.33 #194), 0hz28 (0.30 #606, 0.29 #270, 0.25 #895), 0sydc (0.29 #273, 0.25 #898, 0.23 #996), 02jjt (0.29 #248, 0.22 #440, 0.21 #1885), 01mw1 (0.25 #337, 0.23 #2646, 0.22 #433), 020mfr (0.22 #449, 0.21 #2662, 0.20 #2758), 019z7b (0.18 #826, 0.12 #345, 0.12 #1164), 0191_7 (0.18 #809, 0.09 #2013, 0.08 #2637) >> Best rule #2838 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 039cpd; >> query: (?x5108, ?x2271) <- child(?x5108, ?x1104), child(?x10957, ?x1104), industry(?x10957, ?x2271), category(?x1104, ?x134) >> conf = 0.63 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01s73z industry 029g_vk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 167.000 167.000 0.634 http://example.org/business/business_operation/industry #14524-0dq3c PRED entity: 0dq3c PRED relation: jurisdiction_of_office PRED expected values: 0jdd 0165v => 47 concepts (38 used for prediction) PRED predicted values (max 10 best out of 723): 0f8l9c (0.57 #5337, 0.50 #13309, 0.47 #14193), 03rk0 (0.50 #3627, 0.38 #7614, 0.34 #16374), 03gj2 (0.43 #5346, 0.40 #10218, 0.40 #4457), 0hzlz (0.43 #5338, 0.38 #7553, 0.36 #13310), 03rj0 (0.43 #5404, 0.38 #7619, 0.36 #13376), 035qy (0.43 #5366, 0.38 #7581, 0.36 #13338), 03shp (0.43 #5465, 0.38 #7680, 0.33 #1044), 0ctw_b (0.43 #5790, 0.38 #7562, 0.33 #14203), 06mkj (0.43 #5843, 0.38 #7615, 0.33 #979), 035yg (0.43 #6059, 0.38 #7831, 0.30 #10488) >> Best rule #5337 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 0fkvn; 0789n; 0p5vf; 01zq91; >> query: (?x265, 0f8l9c) <- jurisdiction_of_office(?x265, ?x583), jurisdiction_of_office(?x265, ?x550), company(?x265, ?x266), film_release_region(?x2783, ?x583), film_release_region(?x2656, ?x583), film_release_region(?x1701, ?x583), country(?x150, ?x583), film_release_region(?x280, ?x550), ?x2656 = 03qnc6q, country(?x5969, ?x550), countries_within(?x6956, ?x550), ?x2783 = 0879bpq, ?x1701 = 0bh8yn3 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #16374 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 19 *> proper extension: 09n5b9; *> query: (?x265, ?x2843) <- jurisdiction_of_office(?x265, ?x583), film_release_region(?x5588, ?x583), film_release_region(?x5070, ?x583), film_release_region(?x1552, ?x583), film_release_region(?x1118, ?x583), adjoins(?x583, ?x2843), production_companies(?x1552, ?x7303), film(?x9281, ?x5588), film(?x1365, ?x1118), nominated_for(?x298, ?x5070) *> conf = 0.34 ranks of expected_values: 27, 40 EVAL 0dq3c jurisdiction_of_office 0165v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 47.000 38.000 0.571 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0dq3c jurisdiction_of_office 0jdd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 47.000 38.000 0.571 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #14523-038czx PRED entity: 038czx PRED relation: registering_agency PRED expected values: 03z19 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.45 #11, 0.28 #20, 0.28 #16) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 02_2kg; 04ycjk; >> query: (?x6955, 03z19) <- contains(?x94, ?x6955), ?x94 = 09c7w0, school_type(?x6955, ?x3205), ?x3205 = 01rs41 >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 038czx registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.450 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #14522-01wj5hp PRED entity: 01wj5hp PRED relation: artist! PRED expected values: 0181dw 02p4jf0 => 93 concepts (67 used for prediction) PRED predicted values (max 10 best out of 106): 073tm9 (0.26 #318, 0.22 #177, 0.06 #2010), 015_1q (0.22 #302, 0.21 #866, 0.20 #5248), 0g768 (0.22 #178, 0.15 #319, 0.12 #2152), 011k1h (0.22 #151, 0.13 #574, 0.11 #1279), 01trtc (0.17 #214, 0.15 #355, 0.10 #919), 06wcbk7 (0.17 #145, 0.15 #286, 0.04 #850), 04fcjt (0.17 #171, 0.11 #312, 0.05 #594), 04gm7n (0.17 #239, 0.08 #662, 0.03 #521), 0181dw (0.16 #1311, 0.15 #1029, 0.14 #1593), 033hn8 (0.16 #437, 0.12 #4110, 0.11 #1283) >> Best rule #318 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0288fyj; >> query: (?x8829, 073tm9) <- award(?x8829, ?x9295), award_nominee(?x827, ?x8829), category(?x8829, ?x134), ?x827 = 02l840 >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #1311 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 094xh; *> query: (?x8829, 0181dw) <- award(?x8829, ?x9295), languages(?x8829, ?x254), category(?x8829, ?x134), artists(?x2937, ?x8829) *> conf = 0.16 ranks of expected_values: 9, 21 EVAL 01wj5hp artist! 02p4jf0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 93.000 67.000 0.259 http://example.org/music/record_label/artist EVAL 01wj5hp artist! 0181dw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 93.000 67.000 0.259 http://example.org/music/record_label/artist #14521-01dpdh PRED entity: 01dpdh PRED relation: ceremony PRED expected values: 05pd94v 01s695 01bx35 01mhwk 01xqqp 02cg41 => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 130): 02cg41 (0.71 #115, 0.58 #245, 0.49 #505), 01mhwk (0.71 #35, 0.52 #165, 0.46 #425), 05pd94v (0.59 #132, 0.57 #2, 0.51 #392), 01s695 (0.57 #3, 0.54 #133, 0.46 #393), 01bx35 (0.54 #136, 0.47 #396, 0.45 #266), 01xqqp (0.49 #215, 0.44 #475, 0.43 #85), 0gx1673 (0.34 #239, 0.30 #499, 0.29 #369), 05c1t6z (0.18 #274, 0.15 #664, 0.13 #1315), 02q690_ (0.16 #318, 0.14 #708, 0.12 #1359), 0bzm81 (0.16 #149, 0.14 #279, 0.10 #1580) >> Best rule #115 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 026mg3; 01c9f2; 026mfs; 01dk00; 02fm4d; >> query: (?x2430, 02cg41) <- award_winner(?x2430, ?x2638), ?x2638 = 02fn5r, ceremony(?x2430, ?x2186), ?x2186 = 056878 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6 EVAL 01dpdh ceremony 02cg41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01dpdh ceremony 01xqqp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01dpdh ceremony 01mhwk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01dpdh ceremony 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01dpdh ceremony 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01dpdh ceremony 05pd94v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony #14520-02qdyj PRED entity: 02qdyj PRED relation: category PRED expected values: 08mbj5d => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #104, 0.86 #97, 0.86 #96) >> Best rule #104 for best value: >> intensional similarity = 4 >> extensional distance = 209 >> proper extension: 01j_9c; 01j_cy; 0q19t; 022lly; 022jr5; 0lvng; 0187nd; 07w6r; 019tfm; >> query: (?x6141, 08mbj5d) <- citytown(?x6141, ?x2474), state(?x2474, ?x6842), country(?x2474, ?x279), organization(?x4682, ?x6141) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qdyj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 194.000 0.896 http://example.org/common/topic/webpage./common/webpage/category #14519-03l3jy PRED entity: 03l3jy PRED relation: film PRED expected values: 01k0vq => 81 concepts (43 used for prediction) PRED predicted values (max 10 best out of 210): 09cr8 (0.03 #62380, 0.03 #60594, 0.03 #7129), 0cc7hmk (0.03 #62380, 0.03 #60594, 0.03 #7129), 0djlxb (0.03 #62380, 0.03 #60594, 0.03 #7129), 0bth54 (0.03 #62380, 0.03 #60594, 0.03 #7129), 017jd9 (0.03 #62380, 0.03 #7129, 0.03 #26732), 017gm7 (0.03 #62380, 0.03 #7129, 0.03 #26732), 02r858_ (0.03 #62380, 0.03 #7129, 0.03 #26732), 048scx (0.03 #62380, 0.03 #7129, 0.03 #26732), 01vksx (0.03 #62380, 0.03 #7129, 0.03 #26732), 01qz5 (0.03 #62380, 0.03 #7129, 0.03 #26732) >> Best rule #62380 for best value: >> intensional similarity = 3 >> extensional distance = 1362 >> proper extension: 0cjdk; 014l4w; 03yxwq; 018_q8; 03lpbx; 04qb6g; >> query: (?x4389, ?x1820) <- award_winner(?x4389, ?x1596), award_winner(?x1596, ?x192), award_winner(?x1820, ?x192) >> conf = 0.03 => this is the best rule for 39 predicted values *> Best rule #8440 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 529 *> proper extension: 024jwt; 02js_6; *> query: (?x4389, 01k0vq) <- award_winner(?x4389, ?x1596), film(?x4389, ?x141), location(?x4389, ?x1523) *> conf = 0.01 ranks of expected_values: 147 EVAL 03l3jy film 01k0vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 81.000 43.000 0.035 http://example.org/film/actor/film./film/performance/film #14518-04ynx7 PRED entity: 04ynx7 PRED relation: film_crew_role PRED expected values: 09zzb8 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.77 #106, 0.77 #1029, 0.76 #814), 0dxtw (0.55 #150, 0.52 #220, 0.51 #255), 01vx2h (0.50 #116, 0.47 #221, 0.46 #256), 02ynfr (0.36 #85, 0.29 #15, 0.21 #367), 02rh1dz (0.31 #114, 0.29 #79, 0.28 #219), 0215hd (0.29 #88, 0.16 #123, 0.16 #831), 0d2b38 (0.21 #95, 0.14 #130, 0.13 #377), 089g0h (0.21 #89, 0.14 #54, 0.13 #124), 01xy5l_ (0.16 #118, 0.14 #83, 0.14 #48), 04pyp5 (0.14 #86, 0.14 #51, 0.14 #16) >> Best rule #106 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 07gp9; 0dnvn3; 0ds33; 0bth54; 01cssf; 0dqytn; 04fzfj; 04gknr; 0bwfwpj; 08hmch; ... >> query: (?x9872, 09zzb8) <- genre(?x9872, ?x812), ?x812 = 01jfsb, crewmember(?x9872, ?x2887), film(?x541, ?x9872), produced_by(?x9872, ?x1039) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ynx7 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.771 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14517-048hf PRED entity: 048hf PRED relation: film PRED expected values: 0191n => 98 concepts (73 used for prediction) PRED predicted values (max 10 best out of 586): 03ln8b (0.64 #35744, 0.56 #30382, 0.47 #85790), 07gbf (0.64 #35744, 0.56 #30382, 0.47 #85790), 06r2h (0.14 #1514, 0.12 #3301, 0.08 #6876), 08fn5b (0.12 #2482, 0.08 #6057, 0.07 #695), 01q2nx (0.08 #6273, 0.07 #911, 0.06 #8060), 090s_0 (0.07 #37, 0.06 #1824, 0.05 #3612), 03nqnnk (0.07 #1022, 0.06 #2809, 0.05 #4597), 07024 (0.07 #481, 0.06 #2268, 0.05 #4056), 05f4_n0 (0.07 #713, 0.06 #2500, 0.05 #4288), 0c57yj (0.07 #639, 0.06 #2426, 0.05 #4214) >> Best rule #35744 for best value: >> intensional similarity = 2 >> extensional distance = 441 >> proper extension: 01t07j; 02x2t07; >> query: (?x7842, ?x2078) <- participant(?x7842, ?x1397), nominated_for(?x7842, ?x2078) >> conf = 0.64 => this is the best rule for 2 predicted values *> Best rule #13373 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 197 *> proper extension: 02qjj7; 02wb6yq; 02jq1; 02s58t; 03f1zhf; 01wkmgb; 01q8fxx; *> query: (?x7842, 0191n) <- languages(?x7842, ?x254), participant(?x7842, ?x1397) *> conf = 0.02 ranks of expected_values: 368 EVAL 048hf film 0191n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 98.000 73.000 0.638 http://example.org/film/actor/film./film/performance/film #14516-03xmy1 PRED entity: 03xmy1 PRED relation: languages PRED expected values: 02h40lc => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 25): 02h40lc (0.90 #1226, 0.89 #1262, 0.88 #1046), 06nm1 (0.25 #40, 0.07 #473, 0.07 #1514), 064_8sq (0.23 #374, 0.21 #482, 0.14 #1058), 03k50 (0.12 #471, 0.11 #363, 0.08 #1588), 07c9s (0.09 #480, 0.07 #372, 0.04 #1597), 05zjd (0.08 #52, 0.01 #485), 0t_2 (0.07 #368, 0.06 #476, 0.04 #296), 06b_j (0.07 #1514, 0.04 #86, 0.02 #303), 07zrf (0.07 #1514), 0999q (0.04 #489, 0.04 #381, 0.02 #1606) >> Best rule #1226 for best value: >> intensional similarity = 4 >> extensional distance = 282 >> proper extension: 03h40_7; >> query: (?x1888, 02h40lc) <- languages(?x1888, ?x732), nominated_for(?x1888, ?x2749), film(?x8568, ?x2749), language(?x148, ?x732) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xmy1 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.898 http://example.org/people/person/languages #14515-0h95zbp PRED entity: 0h95zbp PRED relation: produced_by PRED expected values: 05nn4k => 91 concepts (67 used for prediction) PRED predicted values (max 10 best out of 155): 08hp53 (0.12 #1225, 0.12 #838, 0.10 #2000), 016dmx (0.12 #1450, 0.10 #2225, 0.08 #676), 01t6b4 (0.11 #3529, 0.09 #4692, 0.09 #5080), 05nn4k (0.10 #17823, 0.06 #1333, 0.06 #946), 0fvf9q (0.10 #17823, 0.05 #2718, 0.04 #3105), 04pqqb (0.10 #178, 0.08 #566, 0.06 #1340), 05prs8 (0.10 #54, 0.07 #6254, 0.07 #8190), 02tn0_ (0.10 #328, 0.05 #1877, 0.03 #2652), 04flrx (0.10 #218, 0.05 #1767, 0.02 #3704), 020trj (0.10 #204, 0.05 #1753, 0.02 #3690) >> Best rule #1225 for best value: >> intensional similarity = 14 >> extensional distance = 15 >> proper extension: 0fdv3; 03whyr; >> query: (?x5704, 08hp53) <- genre(?x5704, ?x1013), genre(?x5704, ?x812), genre(?x5704, ?x225), film_crew_role(?x5704, ?x3197), film_crew_role(?x5704, ?x1171), film_crew_role(?x5704, ?x468), ?x1013 = 06n90, ?x1171 = 09vw2b7, ?x468 = 02r96rf, ?x3197 = 02ynfr, ?x225 = 02kdv5l, titles(?x812, ?x80), genre(?x11565, ?x812), ?x11565 = 0270k40 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #17823 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 381 *> proper extension: 01gglm; *> query: (?x5704, ?x4660) <- production_companies(?x5704, ?x3323), production_companies(?x4132, ?x3323), genre(?x4132, ?x811), country(?x4132, ?x94), currency(?x4132, ?x170), film_crew_role(?x5704, ?x468), ?x468 = 02r96rf, company(?x4660, ?x3323), crewmember(?x4132, ?x3879) *> conf = 0.10 ranks of expected_values: 4 EVAL 0h95zbp produced_by 05nn4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 91.000 67.000 0.118 http://example.org/film/film/produced_by #14514-0gmd3k7 PRED entity: 0gmd3k7 PRED relation: film! PRED expected values: 0kjrx => 118 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1400): 05kfs (0.30 #14557, 0.22 #151830, 0.21 #18720), 01f6zc (0.21 #5100, 0.09 #13419, 0.07 #17579), 01nm3s (0.20 #688, 0.14 #11085, 0.10 #15245), 0prfz (0.20 #55, 0.08 #2134, 0.06 #6293), 01y665 (0.20 #518, 0.08 #2597, 0.06 #83192), 05bnp0 (0.20 #13, 0.07 #16650, 0.06 #83192), 02k4gv (0.20 #981, 0.06 #9299, 0.01 #63373), 0sz28 (0.20 #192, 0.06 #83192, 0.05 #99833), 028k57 (0.20 #789, 0.04 #13266, 0.03 #17426), 01vwllw (0.20 #547, 0.02 #37985, 0.02 #44223) >> Best rule #14557 for best value: >> intensional similarity = 6 >> extensional distance = 21 >> proper extension: 05vc35; >> query: (?x6283, ?x777) <- genre(?x6283, ?x53), film(?x3558, ?x6283), film_release_distribution_medium(?x6283, ?x81), film(?x395, ?x6283), ?x81 = 029j_, written_by(?x6283, ?x777) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #24301 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 35 *> proper extension: 01hqk; 01qdmh; 0419kt; *> query: (?x6283, 0kjrx) <- genre(?x6283, ?x812), genre(?x6283, ?x53), film_crew_role(?x6283, ?x2178), film_crew_role(?x6283, ?x1171), film(?x777, ?x6283), ?x1171 = 09vw2b7, ?x812 = 01jfsb, ?x2178 = 01pvkk, genre(?x273, ?x53) *> conf = 0.08 ranks of expected_values: 63 EVAL 0gmd3k7 film! 0kjrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 118.000 82.000 0.296 http://example.org/film/actor/film./film/performance/film #14513-05tbn PRED entity: 05tbn PRED relation: state_province_region! PRED expected values: 01prf3 => 179 concepts (150 used for prediction) PRED predicted values (max 10 best out of 776): 015y3j (0.81 #5088, 0.81 #10175, 0.81 #11629), 06rkfs (0.81 #5088, 0.81 #10175, 0.81 #11629), 02s8qk (0.81 #10175, 0.81 #11629, 0.60 #27630), 07tds (0.81 #10175, 0.81 #11629, 0.60 #27630), 0_565 (0.26 #63997, 0.24 #5087, 0.23 #11628), 0_24q (0.26 #63997, 0.24 #5087, 0.23 #11628), 068p2 (0.26 #63997, 0.24 #5087, 0.23 #11628), 04x8mj (0.26 #63997, 0.24 #5087, 0.23 #11628), 0l4vc (0.26 #63997, 0.24 #5087, 0.23 #11628), 0zlt9 (0.26 #63997, 0.24 #5087, 0.23 #11628) >> Best rule #5088 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 03gh4; >> query: (?x3670, ?x8943) <- contains(?x3670, ?x8943), district_represented(?x176, ?x3670), country(?x8943, ?x94) >> conf = 0.81 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 05tbn state_province_region! 01prf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 179.000 150.000 0.814 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #14512-06rzwx PRED entity: 06rzwx PRED relation: genre PRED expected values: 02kdv5l => 124 concepts (111 used for prediction) PRED predicted values (max 10 best out of 102): 04t2t (0.79 #6938, 0.79 #7058, 0.74 #7297), 02kdv5l (0.64 #8016, 0.50 #2511, 0.50 #3106), 01hmnh (0.37 #2526, 0.35 #3121, 0.33 #3002), 05p553 (0.34 #10412, 0.34 #3708, 0.34 #10292), 02l7c8 (0.33 #134, 0.32 #2643, 0.29 #7790), 04xvlr (0.32 #599, 0.23 #1915, 0.22 #6819), 03g3w (0.30 #622, 0.16 #1938, 0.10 #4807), 0lsxr (0.29 #9, 0.26 #8023, 0.26 #6827), 04t36 (0.28 #125, 0.13 #1442, 0.12 #965), 06n90 (0.27 #2997, 0.25 #3116, 0.23 #2521) >> Best rule #6938 for best value: >> intensional similarity = 6 >> extensional distance = 573 >> proper extension: 02qjv1p; >> query: (?x7114, ?x7160) <- titles(?x7160, ?x7114), genre(?x4038, ?x7160), genre(?x763, ?x7160), ?x763 = 061681, genre(?x7114, ?x53), film_sets_designed(?x8814, ?x4038) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #8016 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 686 *> proper extension: 0dr1c2; *> query: (?x7114, 02kdv5l) <- film(?x2587, ?x7114), genre(?x7114, ?x812), genre(?x8605, ?x812), genre(?x103, ?x812), ?x8605 = 01jmyj, ?x103 = 03qcfvw *> conf = 0.64 ranks of expected_values: 2 EVAL 06rzwx genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 111.000 0.792 http://example.org/film/film/genre #14511-01mgw PRED entity: 01mgw PRED relation: film_release_region PRED expected values: 0d060g 0f8l9c 0hzlz 047yc 01znc_ 06t8v 03ryn => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 162): 0f8l9c (0.91 #2453, 0.90 #1880, 0.81 #4752), 05r4w (0.79 #1005, 0.79 #2438, 0.77 #1865), 0d060g (0.69 #1008, 0.66 #1868, 0.66 #2441), 01znc_ (0.67 #2469, 0.67 #1896, 0.66 #1036), 06bnz (0.66 #1040, 0.61 #2473, 0.60 #1900), 03rt9 (0.57 #1875, 0.56 #2448, 0.52 #1015), 0d05w3 (0.53 #3873, 0.51 #2723, 0.51 #2293), 06t8v (0.45 #1069, 0.36 #1785, 0.33 #1929), 047yc (0.42 #2458, 0.41 #1025, 0.40 #1885), 015qh (0.41 #1035, 0.40 #1895, 0.40 #2468) >> Best rule #2453 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 03bx2lk; 0gtvrv3; 06v9_x; 0crc2cp; 0gffmn8; 09v9mks; 049w1q; >> query: (?x7554, 0f8l9c) <- film_release_region(?x7554, ?x1355), film_release_region(?x7554, ?x512), ?x512 = 07ssc, ?x1355 = 0h7x >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 8, 9, 15, 19 EVAL 01mgw film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 97.000 97.000 0.911 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mgw film_release_region 06t8v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 97.000 0.911 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mgw film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.911 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mgw film_release_region 047yc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 97.000 0.911 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mgw film_release_region 0hzlz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 97.000 97.000 0.911 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mgw film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.911 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mgw film_release_region 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.911 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14510-050f0s PRED entity: 050f0s PRED relation: region PRED expected values: 07ssc => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 15): 07ssc (0.88 #191, 0.60 #237, 0.58 #260), 09c7w0 (0.09 #325, 0.09 #301, 0.05 #71), 01z4y (0.02 #326), 059j2 (0.02 #79, 0.01 #102, 0.01 #148), 0d060g (0.02 #74, 0.01 #143), 06t2t (0.01 #109), 02vzc (0.01 #106), 06bnz (0.01 #105), 06qd3 (0.01 #104), 0345h (0.01 #103) >> Best rule #191 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 0cp08zg; >> query: (?x1965, 07ssc) <- film_distribution_medium(?x1965, ?x2099), nominated_for(?x574, ?x1965), ?x2099 = 0735l >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050f0s region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.881 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #14509-0gjm7 PRED entity: 0gjm7 PRED relation: taxonomy PRED expected values: 04n6k => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.33 #1, 0.25 #3, 0.25 #2) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02ky346; >> query: (?x13774, 04n6k) <- profession(?x5609, ?x13774), specialization_of(?x13774, ?x8498), ?x8498 = 09j9h, ?x5609 = 034rd >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gjm7 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.333 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #14508-0db94w PRED entity: 0db94w PRED relation: film_release_region PRED expected values: 015fr => 87 concepts (86 used for prediction) PRED predicted values (max 10 best out of 194): 03rjj (0.91 #2041, 0.91 #1459, 0.89 #2186), 015fr (0.81 #741, 0.81 #596, 0.80 #451), 03spz (0.81 #1542, 0.81 #960, 0.77 #2124), 06mzp (0.77 #746, 0.77 #601, 0.75 #456), 06bnz (0.73 #1494, 0.72 #477, 0.72 #622), 03rj0 (0.73 #782, 0.72 #637, 0.70 #492), 0h7x (0.62 #757, 0.62 #612, 0.57 #467), 015qh (0.60 #908, 0.55 #618, 0.54 #763), 05qx1 (0.60 #907, 0.55 #472, 0.54 #1489), 04gzd (0.60 #881, 0.53 #446, 0.51 #591) >> Best rule #2041 for best value: >> intensional similarity = 12 >> extensional distance = 85 >> proper extension: 03bx2lk; >> query: (?x4446, 03rjj) <- film_release_region(?x4446, ?x1453), film_release_region(?x4446, ?x1003), film_release_region(?x4446, ?x429), film_release_region(?x4446, ?x172), ?x429 = 03rt9, ?x1453 = 06qd3, genre(?x4446, ?x571), ?x172 = 0154j, film_release_region(?x3377, ?x1003), film_release_region(?x2656, ?x1003), ?x2656 = 03qnc6q, ?x3377 = 0gj8nq2 >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #741 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 46 *> proper extension: 02d44q; *> query: (?x4446, 015fr) <- film_release_region(?x4446, ?x2316), film_release_region(?x4446, ?x2152), film_release_region(?x4446, ?x1558), film_release_region(?x4446, ?x1453), film_release_region(?x4446, ?x429), ?x429 = 03rt9, ?x1453 = 06qd3, film_crew_role(?x4446, ?x468), ?x2152 = 06mkj, ?x1558 = 01mjq, film_release_region(?x5089, ?x2316), ?x5089 = 0bh8tgs *> conf = 0.81 ranks of expected_values: 2 EVAL 0db94w film_release_region 015fr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 86.000 0.908 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14507-0d06m5 PRED entity: 0d06m5 PRED relation: basic_title PRED expected values: 01dz7z => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 17): 01gkgk (0.55 #261, 0.55 #142, 0.50 #159), 060c4 (0.36 #564, 0.36 #513, 0.33 #37), 0fkx3 (0.33 #51, 0.02 #544, 0.02 #527), 0fkvn (0.27 #514, 0.23 #446, 0.22 #565), 060bp (0.25 #69, 0.24 #273, 0.17 #443), 02079p (0.25 #78, 0.09 #146, 0.09 #299), 0f6c3 (0.17 #110, 0.04 #297, 0.04 #314), 0dq3c (0.14 #274, 0.14 #631, 0.11 #444), 0p5vf (0.14 #283, 0.13 #453, 0.07 #249), 01q24l (0.14 #284, 0.10 #131, 0.09 #148) >> Best rule #261 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 01mvpv; >> query: (?x3445, 01gkgk) <- legislative_sessions(?x3445, ?x6933), district_represented(?x6933, ?x4622), ?x4622 = 04tgp >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #220 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 0cj2w; *> query: (?x3445, 01dz7z) <- profession(?x3445, ?x3342), inductee(?x13697, ?x3445), spouse(?x3445, ?x1620) *> conf = 0.07 ranks of expected_values: 12 EVAL 0d06m5 basic_title 01dz7z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 155.000 155.000 0.550 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #14506-05qkp PRED entity: 05qkp PRED relation: country! PRED expected values: 07gyv => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 54): 071t0 (0.75 #454, 0.61 #616, 0.60 #1210), 01lb14 (0.54 #447, 0.50 #15, 0.48 #501), 07gyv (0.52 #439, 0.48 #331, 0.46 #493), 06f41 (0.50 #446, 0.41 #500, 0.41 #986), 03hr1p (0.49 #455, 0.44 #509, 0.40 #995), 07jbh (0.49 #465, 0.42 #357, 0.40 #519), 0w0d (0.48 #444, 0.41 #606, 0.40 #498), 06wrt (0.44 #448, 0.37 #502, 0.36 #2864), 064vjs (0.43 #463, 0.36 #2864, 0.35 #3352), 0486tv (0.43 #471, 0.36 #363, 0.35 #1173) >> Best rule #454 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 01mk6; >> query: (?x3120, 071t0) <- adjoins(?x3120, ?x390), combatants(?x326, ?x390), olympics(?x3120, ?x2966) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #439 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 01mk6; *> query: (?x3120, 07gyv) <- adjoins(?x3120, ?x390), combatants(?x326, ?x390), olympics(?x3120, ?x2966) *> conf = 0.52 ranks of expected_values: 3 EVAL 05qkp country! 07gyv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 103.000 0.748 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #14505-03ynwqj PRED entity: 03ynwqj PRED relation: music PRED expected values: 0gv07g => 72 concepts (32 used for prediction) PRED predicted values (max 10 best out of 40): 02g40r (0.33 #185), 03h610 (0.25 #497, 0.03 #1343, 0.03 #1767), 02jxkw (0.12 #352, 0.04 #772, 0.03 #2045), 02bh9 (0.12 #471, 0.03 #1528, 0.03 #893), 04pf4r (0.12 #278, 0.02 #1971, 0.02 #4093), 05_pkf (0.12 #271, 0.02 #903, 0.01 #1538), 089kpp (0.12 #414, 0.01 #1046, 0.01 #1681), 07q1v4 (0.12 #225, 0.01 #857, 0.01 #2979), 01hw6wq (0.12 #248), 06fxnf (0.07 #699, 0.03 #1546, 0.02 #1972) >> Best rule #185 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 07s846j; >> query: (?x8625, 02g40r) <- country(?x8625, ?x94), film(?x8146, ?x8625), film(?x1942, ?x8625), ?x8146 = 078jnn, award_winner(?x4760, ?x1942) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03ynwqj music 0gv07g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 32.000 0.333 http://example.org/film/film/music #14504-0162b PRED entity: 0162b PRED relation: religion PRED expected values: 0flw86 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 31): 0c8wxp (0.92 #533, 0.68 #1721, 0.61 #2018), 051kv (0.82 #532, 0.57 #1720, 0.47 #2017), 0631_ (0.82 #535, 0.56 #1723, 0.45 #2020), 019cr (0.79 #538, 0.56 #1726, 0.46 #2023), 04pk9 (0.79 #545, 0.52 #1733, 0.42 #2030), 05sfs (0.77 #530, 0.56 #1718, 0.47 #2015), 05w5d (0.77 #548, 0.51 #1736, 0.42 #2033), 01y0s9 (0.62 #536, 0.39 #1724, 0.32 #2021), 021_0p (0.62 #544, 0.39 #1732, 0.32 #2029), 0flw86 (0.57 #166, 0.48 #100, 0.45 #67) >> Best rule #533 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 05kr_; >> query: (?x10457, 0c8wxp) <- capital(?x10457, ?x13165), taxonomy(?x10457, ?x939), religion(?x10457, ?x109) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 07ytt; *> query: (?x10457, 0flw86) <- contains(?x6304, ?x10457), capital(?x10457, ?x13165), religion(?x10457, ?x109) *> conf = 0.57 ranks of expected_values: 10 EVAL 0162b religion 0flw86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 138.000 138.000 0.923 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #14503-0brkwj PRED entity: 0brkwj PRED relation: award_winner PRED expected values: 06jrhz => 83 concepts (33 used for prediction) PRED predicted values (max 10 best out of 610): 01xndd (0.80 #678, 0.60 #38611, 0.54 #41830), 0h5jg5 (0.60 #38611, 0.60 #49875, 0.54 #41830), 08q3s0 (0.60 #49875, 0.53 #27350, 0.53 #51484), 0brkwj (0.30 #1281, 0.22 #8046, 0.20 #38612), 06jrhz (0.30 #990, 0.20 #38612, 0.06 #2599), 01rzqj (0.22 #8046, 0.10 #552, 0.04 #2161), 059j4x (0.22 #8046, 0.10 #1579, 0.02 #3188), 04wvhz (0.22 #8046, 0.05 #4979, 0.03 #6587), 07lwsz (0.22 #8046, 0.01 #2192, 0.01 #5411), 047cqr (0.22 #8046) >> Best rule #678 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06jrhz; >> query: (?x8094, 01xndd) <- award_winner(?x10340, ?x8094), award_nominee(?x2650, ?x8094), ?x10340 = 09hd6f >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #990 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 06jrhz; *> query: (?x8094, 06jrhz) <- award_winner(?x10340, ?x8094), award_nominee(?x2650, ?x8094), ?x10340 = 09hd6f *> conf = 0.30 ranks of expected_values: 5 EVAL 0brkwj award_winner 06jrhz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 83.000 33.000 0.800 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #14502-023nlj PRED entity: 023nlj PRED relation: gender PRED expected values: 05zppz => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #29, 0.72 #87, 0.71 #189), 02zsn (0.38 #36, 0.36 #42, 0.33 #16) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 0dv1hh; 09m465; >> query: (?x8749, 05zppz) <- sibling(?x8749, ?x5246), nationality(?x5246, ?x94), film_release_region(?x54, ?x94) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023nlj gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.728 http://example.org/people/person/gender #14501-05vz3zq PRED entity: 05vz3zq PRED relation: combatants! PRED expected values: 07ssc => 218 concepts (139 used for prediction) PRED predicted values (max 10 best out of 348): 0345h (0.84 #851, 0.83 #4144, 0.83 #2927), 0hzlz (0.84 #851, 0.83 #2927, 0.83 #5052), 0d05w3 (0.84 #851, 0.83 #2927, 0.83 #5052), 07ssc (0.73 #1770, 0.71 #606, 0.64 #2930), 05vz3zq (0.55 #1802, 0.45 #2962, 0.44 #885), 06qd3 (0.50 #12, 0.31 #5053, 0.31 #2380), 0g8bw (0.50 #39, 0.31 #5053, 0.31 #2380), 0k6nt (0.50 #8, 0.31 #5053, 0.31 #2380), 02psqkz (0.43 #148, 0.38 #819, 0.32 #1433), 0bq0p9 (0.42 #305, 0.38 #856, 0.32 #1469) >> Best rule #851 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0d05w3; >> query: (?x5114, ?x94) <- combatants(?x5114, ?x94), olympics(?x5114, ?x391), taxonomy(?x5114, ?x939), entity_involved(?x5352, ?x5114) >> conf = 0.84 => this is the best rule for 3 predicted values *> Best rule #1770 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 06v9sf; 02psqkz; 059z0; *> query: (?x5114, 07ssc) <- combatants(?x5114, ?x1229), combatants(?x5114, ?x1023), combatants(?x326, ?x5114), ?x1229 = 059j2, featured_film_locations(?x522, ?x1023) *> conf = 0.73 ranks of expected_values: 4 EVAL 05vz3zq combatants! 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 218.000 139.000 0.837 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #14500-02ylg6 PRED entity: 02ylg6 PRED relation: genre PRED expected values: 02l7c8 => 100 concepts (99 used for prediction) PRED predicted values (max 10 best out of 99): 01jfsb (0.44 #11, 0.36 #852, 0.34 #611), 02l7c8 (0.42 #7479, 0.32 #1337, 0.30 #2660), 02kdv5l (0.34 #843, 0.31 #3247, 0.29 #122), 03k9fj (0.31 #1091, 0.30 #851, 0.30 #490), 01hmnh (0.25 #257, 0.24 #377, 0.21 #3262), 0lsxr (0.22 #247, 0.19 #2652, 0.18 #3974), 06n90 (0.21 #492, 0.18 #853, 0.17 #1454), 04xvlr (0.20 #7465, 0.20 #2646, 0.19 #5054), 060__y (0.19 #7480, 0.18 #616, 0.18 #2181), 02n4kr (0.18 #606, 0.17 #6, 0.16 #486) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 02vxq9m; 0c40vxk; 08hmch; 0jjy0; 011yqc; 03qnvdl; 02yvct; 023gxx; 0gvs1kt; 0gj8nq2; ... >> query: (?x5347, 01jfsb) <- film_release_region(?x5347, ?x4743), film_crew_role(?x5347, ?x137), ?x4743 = 03spz, cinematography(?x5347, ?x7384) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #7479 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1085 *> proper extension: 04svwx; *> query: (?x5347, 02l7c8) <- genre(?x5347, ?x239), genre(?x9364, ?x239), ?x9364 = 04xg2f *> conf = 0.42 ranks of expected_values: 2 EVAL 02ylg6 genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 99.000 0.444 http://example.org/film/film/genre #14499-0bv8h2 PRED entity: 0bv8h2 PRED relation: nominated_for! PRED expected values: 07cbcy => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 183): 0gq9h (0.33 #304, 0.24 #16147, 0.23 #8464), 0f4x7 (0.33 #266, 0.19 #19926, 0.19 #21368), 03hl6lc (0.33 #373, 0.17 #613, 0.14 #133), 0gr51 (0.33 #320, 0.12 #16163, 0.12 #4400), 04dn09n (0.29 #36, 0.17 #516, 0.15 #6276), 02r22gf (0.29 #29, 0.16 #1469, 0.15 #1229), 0gqwc (0.29 #62, 0.13 #16145, 0.11 #16385), 07h0cl (0.29 #130, 0.01 #1330), 019f4v (0.22 #295, 0.22 #7735, 0.22 #6295), 0gs9p (0.22 #306, 0.22 #8466, 0.21 #7746) >> Best rule #304 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 024hbv; >> query: (?x3595, 0gq9h) <- nominated_for(?x4563, ?x3595), ?x4563 = 0dzf_, nominated_for(?x3458, ?x3595), award(?x2871, ?x3458) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #19926 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1443 *> proper extension: 0c3xpwy; *> query: (?x3595, ?x102) <- nominated_for(?x4563, ?x3595), award(?x4563, ?x102), type_of_union(?x4563, ?x566) *> conf = 0.19 ranks of expected_values: 19 EVAL 0bv8h2 nominated_for! 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 101.000 101.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14498-07l4z PRED entity: 07l4z PRED relation: sport PRED expected values: 018jz => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 59): 018jz (0.82 #159, 0.80 #140, 0.79 #324), 02vx4 (0.56 #684, 0.56 #493, 0.55 #636), 0jm_ (0.44 #120, 0.41 #385, 0.38 #75), 018w8 (0.40 #40, 0.36 #167, 0.33 #112), 03tmr (0.17 #550, 0.14 #447, 0.13 #474), 039yzs (0.14 #537, 0.11 #154, 0.10 #565), 06f3l (0.11 #154, 0.08 #181), 0z74 (0.11 #154, 0.07 #336, 0.03 #363), 09xp_ (0.11 #154, 0.04 #574, 0.02 #452), 037hz (0.01 #671) >> Best rule #159 for best value: >> intensional similarity = 15 >> extensional distance = 9 >> proper extension: 051wf; >> query: (?x8901, 018jz) <- season(?x8901, ?x9267), season(?x8901, ?x8529), team(?x12323, ?x8901), ?x8529 = 025ygws, school(?x8901, ?x8706), season(?x8894, ?x9267), season(?x1632, ?x9267), student(?x8706, ?x8160), draft(?x8901, ?x1161), ?x8894 = 02d02, participant(?x8160, ?x1094), ?x1632 = 0cqt41, influenced_by(?x8160, ?x13118), profession(?x8160, ?x319), major_field_of_study(?x8706, ?x947) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07l4z sport 018jz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.818 http://example.org/sports/sports_team/sport #14497-0g55tzk PRED entity: 0g55tzk PRED relation: ceremony! PRED expected values: 09qwmm 0ck27z 03m73lj => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 300): 0f4x7 (0.53 #1527, 0.49 #6797, 0.46 #7048), 0gqy2 (0.53 #6891, 0.50 #7142, 0.48 #6640), 0gq_d (0.51 #6927, 0.50 #1657, 0.49 #7178), 0k611 (0.51 #6842, 0.50 #1572, 0.48 #7093), 0gqwc (0.51 #6829, 0.50 #1559, 0.48 #7080), 018wng (0.50 #1535, 0.49 #6805, 0.47 #5301), 0gqyl (0.50 #1580, 0.49 #6850, 0.47 #7101), 0gvx_ (0.50 #6906, 0.47 #7157, 0.45 #5402), 0gqz2 (0.50 #1562, 0.46 #6832, 0.43 #7083), 0gq_v (0.50 #1521, 0.43 #6791, 0.41 #7042) >> Best rule #1527 for best value: >> intensional similarity = 14 >> extensional distance = 28 >> proper extension: 0bzk8w; 0bzkgg; 0bzmt8; >> query: (?x11738, 0f4x7) <- award_winner(?x11738, ?x9924), award_winner(?x11738, ?x2531), honored_for(?x11738, ?x1994), honored_for(?x11738, ?x1988), film(?x9924, ?x3035), film(?x2531, ?x485), location(?x9924, ?x760), award_nominee(?x2531, ?x844), genre(?x1988, ?x53), film_release_region(?x1988, ?x789), nominated_for(?x1132, ?x1994), nominated_for(?x1995, ?x1994), film_regional_debut_venue(?x1988, ?x10083), ?x789 = 0f8l9c >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #776 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 10 *> proper extension: 0hr3c8y; 0drtv8; 03nnm4t; 0275n3y; 0418154; 03gyp30; 09g90vz; *> query: (?x11738, 09qwmm) <- award_winner(?x11738, ?x6324), award_winner(?x11738, ?x2531), honored_for(?x11738, ?x1813), film(?x2531, ?x485), languages(?x2531, ?x254), award(?x2531, ?x1972), ?x1972 = 0gqyl, ?x6324 = 018ygt, ceremony(?x678, ?x11738), award_nominee(?x2531, ?x844) *> conf = 0.33 ranks of expected_values: 28, 29, 35 EVAL 0g55tzk ceremony! 03m73lj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 32.000 32.000 0.533 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0g55tzk ceremony! 0ck27z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 32.000 32.000 0.533 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0g55tzk ceremony! 09qwmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 32.000 32.000 0.533 http://example.org/award/award_category/winners./award/award_honor/ceremony #14496-01dhjz PRED entity: 01dhjz PRED relation: role PRED expected values: 0dwvl => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 121): 0342h (0.40 #1947, 0.38 #2153, 0.38 #2050), 03q5t (0.32 #2251, 0.24 #3699, 0.24 #3389), 06ch55 (0.32 #2251, 0.24 #3389, 0.23 #3698), 01vdm0 (0.27 #2387, 0.27 #2490, 0.27 #2284), 02sgy (0.25 #1949, 0.24 #2155, 0.23 #2672), 042v_gx (0.23 #1951, 0.21 #2260, 0.20 #2674), 01s0ps (0.20 #61, 0.08 #2003, 0.08 #2209), 018vs (0.18 #2059, 0.17 #1035, 0.17 #1956), 01vj9c (0.17 #1037, 0.16 #1958, 0.16 #2681), 026t6 (0.17 #2048, 0.16 #1024, 0.16 #2668) >> Best rule #1947 for best value: >> intensional similarity = 3 >> extensional distance = 340 >> proper extension: 01vsxdm; 0dm5l; 016lj_; >> query: (?x9134, 0342h) <- role(?x9134, ?x316), artists(?x119, ?x9134), artist(?x6474, ?x9134) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2457 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 413 *> proper extension: 0p5mw; 01_x6v; 0jfx1; 02qx69; 01y0y6; 01r6jt2; 0p_47; 016yzz; 03k0yw; 01_x6d; ... *> query: (?x9134, ?x212) <- gender(?x9134, ?x231), role(?x9134, ?x1831), profession(?x9134, ?x1614), role(?x212, ?x1831) *> conf = 0.04 ranks of expected_values: 93 EVAL 01dhjz role 0dwvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 76.000 76.000 0.398 http://example.org/music/artist/track_contributions./music/track_contribution/role #14495-01vs5c PRED entity: 01vs5c PRED relation: organization! PRED expected values: 060c4 => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.81 #444, 0.80 #561, 0.80 #626), 07xl34 (0.28 #219, 0.25 #362, 0.25 #648), 0dq_5 (0.26 #1114, 0.22 #1270, 0.22 #906), 0hm4q (0.09 #762, 0.09 #346, 0.08 #424), 05k17c (0.09 #1229, 0.08 #1489, 0.08 #1424), 05c0jwl (0.05 #1175, 0.05 #590, 0.04 #1227), 0f6c3 (0.03 #1652, 0.02 #2124, 0.02 #1926), 0fkvn (0.03 #1652, 0.02 #2124, 0.02 #1926), 09n5b9 (0.03 #1652, 0.02 #1926, 0.02 #1940), 08jcfy (0.03 #623, 0.02 #1234, 0.02 #1377) >> Best rule #444 for best value: >> intensional similarity = 5 >> extensional distance = 76 >> proper extension: 01hhvg; 01y17m; 037njl; 04p_hy; 015fsv; 0177sq; >> query: (?x5621, 060c4) <- currency(?x5621, ?x170), contains(?x94, ?x5621), school(?x8111, ?x5621), major_field_of_study(?x5621, ?x254), position(?x8111, ?x5727) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vs5c organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.808 http://example.org/organization/role/leaders./organization/leadership/organization #14494-0d_84 PRED entity: 0d_84 PRED relation: film PRED expected values: 02y_lrp => 142 concepts (124 used for prediction) PRED predicted values (max 10 best out of 942): 0dp7wt (0.77 #10683, 0.69 #33824, 0.66 #78328), 07g1sm (0.33 #1228, 0.17 #6568, 0.15 #11911), 044g_k (0.33 #206, 0.17 #5546, 0.08 #10889), 0d_wms (0.33 #632, 0.17 #5972, 0.08 #11315), 0dj0m5 (0.33 #95, 0.17 #5435, 0.08 #10778), 0bbgvp (0.33 #1754, 0.17 #7094, 0.08 #12437), 0bmhn (0.33 #1615, 0.17 #6955, 0.08 #12298), 025scjj (0.33 #1565, 0.17 #6905, 0.08 #12248), 0m_q0 (0.33 #743, 0.17 #6083, 0.08 #11426), 097zcz (0.33 #707, 0.17 #6047, 0.08 #11390) >> Best rule #10683 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0dw4g; >> query: (?x338, ?x4089) <- award(?x338, ?x102), nominated_for(?x338, ?x4089), nominated_for(?x338, ?x339), ?x339 = 01hr1 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #19597 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 111 *> proper extension: 01vng3b; 01lz4tf; *> query: (?x338, 02y_lrp) <- participant(?x5798, ?x338), artists(?x2937, ?x5798), participant(?x5798, ?x7909) *> conf = 0.03 ranks of expected_values: 358 EVAL 0d_84 film 02y_lrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 142.000 124.000 0.767 http://example.org/film/actor/film./film/performance/film #14493-031rq5 PRED entity: 031rq5 PRED relation: industry PRED expected values: 02vxn => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 21): 02vxn (0.53 #915, 0.52 #1203, 0.51 #1299), 07c52 (0.25 #4, 0.12 #100, 0.03 #772), 04rlf (0.17 #350, 0.11 #206, 0.09 #1071), 0g4gr (0.14 #55, 0.11 #199, 0.09 #247), 01mw1 (0.14 #1586, 0.14 #865, 0.11 #193), 020mfr (0.13 #1602, 0.09 #1699, 0.09 #1554), 02jjt (0.12 #104, 0.12 #1634, 0.11 #200), 011s0 (0.12 #106), 03qh03g (0.12 #1634, 0.08 #437, 0.06 #918), 0h6dj (0.12 #1634, 0.04 #754, 0.01 #1379) >> Best rule #915 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 025jfl; 054lpb6; 03xsby; 09b3v; 031rx9; >> query: (?x5908, 02vxn) <- production_companies(?x11996, ?x5908), country(?x11996, ?x94), award_nominee(?x5908, ?x541) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031rq5 industry 02vxn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.528 http://example.org/business/business_operation/industry #14492-07hyk PRED entity: 07hyk PRED relation: basic_title PRED expected values: 060c4 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 17): 060c4 (0.64 #402, 0.50 #178, 0.50 #114), 0789n (0.20 #56, 0.20 #600, 0.19 #689), 01gkgk (0.19 #689, 0.15 #596, 0.15 #628), 0fkzq (0.19 #689, 0.12 #124, 0.12 #108), 02079p (0.19 #689, 0.04 #409, 0.04 #633), 0f6c3 (0.19 #689, 0.03 #566, 0.02 #630), 01t7n9 (0.19 #689, 0.02 #637, 0.02 #669), 09n5b9 (0.19 #689), 060bp (0.12 #657, 0.11 #673, 0.09 #625), 0fj45 (0.12 #78, 0.05 #350, 0.05 #382) >> Best rule #402 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 0g4gr; 037mh8; >> query: (?x10888, 060c4) <- gender(?x10888, ?x231), ?x231 = 05zppz, taxonomy(?x10888, ?x939) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07hyk basic_title 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.640 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #14491-01x53m PRED entity: 01x53m PRED relation: gender PRED expected values: 05zppz => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #98, 0.87 #90, 0.87 #112), 02zsn (0.57 #4, 0.55 #247, 0.33 #160) >> Best rule #98 for best value: >> intensional similarity = 4 >> extensional distance = 242 >> proper extension: 0j3v; 0dzkq; 02ln1; 047g6; >> query: (?x9173, 05zppz) <- influenced_by(?x9173, ?x118), student(?x9861, ?x9173), major_field_of_study(?x9861, ?x373), category(?x9861, ?x134) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x53m gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.877 http://example.org/people/person/gender #14490-054knh PRED entity: 054knh PRED relation: nominated_for PRED expected values: 0gpx6 => 50 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1825): 09gq0x5 (0.82 #20525, 0.62 #11166, 0.60 #14286), 0k20s (0.77 #31189, 0.73 #6237, 0.65 #43671), 05y0cr (0.77 #31189, 0.73 #6237, 0.65 #43671), 0gy0l_ (0.77 #31189, 0.73 #6237, 0.65 #43671), 0gpx6 (0.77 #31189, 0.73 #6237, 0.65 #43671), 04nl83 (0.77 #31189, 0.73 #6237, 0.65 #43671), 0gmcwlb (0.75 #11096, 0.68 #20455, 0.60 #18896), 0y_9q (0.75 #11732, 0.59 #21091, 0.50 #14852), 097zcz (0.75 #11550, 0.50 #8432, 0.36 #20909), 0ccck7 (0.75 #12402, 0.33 #9284, 0.33 #6165) >> Best rule #20525 for best value: >> intensional similarity = 9 >> extensional distance = 20 >> proper extension: 0p9sw; 0gqxm; >> query: (?x7965, 09gq0x5) <- nominated_for(?x7965, ?x2903), nominated_for(?x7965, ?x1425), nominated_for(?x7965, ?x697), award_winner(?x2903, ?x9754), nominated_for(?x846, ?x2903), film_release_distribution_medium(?x1425, ?x81), honored_for(?x7038, ?x1425), award(?x2903, ?x1107), ?x697 = 0209hj >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #31189 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 83 *> proper extension: 02sp_v; *> query: (?x7965, ?x534) <- nominated_for(?x7965, ?x8646), nominated_for(?x7965, ?x8188), nominated_for(?x7965, ?x6529), ceremony(?x7965, ?x747), film_release_region(?x8646, ?x87), award(?x534, ?x7965), award(?x8188, ?x112), film_crew_role(?x6529, ?x137) *> conf = 0.77 ranks of expected_values: 5 EVAL 054knh nominated_for 0gpx6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 50.000 29.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14489-0gg9_5q PRED entity: 0gg9_5q PRED relation: executive_produced_by! PRED expected values: 0cc5mcj 0kv238 047rkcm 04jplwp => 130 concepts (27 used for prediction) PRED predicted values (max 10 best out of 499): 026mfbr (0.33 #30, 0.09 #4146, 0.08 #2621), 0bt4g (0.23 #2485, 0.23 #1966, 0.20 #930), 0mbql (0.23 #2441, 0.23 #1922, 0.20 #886), 01f7kl (0.23 #2204, 0.23 #1685, 0.20 #649), 01pj_5 (0.20 #761, 0.15 #2316, 0.15 #1797), 03clwtw (0.20 #907, 0.15 #2462, 0.15 #1943), 049xgc (0.20 #832, 0.15 #2387, 0.15 #1868), 0fsd9t (0.20 #975, 0.15 #2530, 0.15 #2011), 0gwjw0c (0.20 #895, 0.15 #2450, 0.15 #1931), 0234j5 (0.20 #957, 0.15 #2512, 0.15 #1993) >> Best rule #30 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 018grr; >> query: (?x3744, 026mfbr) <- executive_produced_by(?x5001, ?x3744), executive_produced_by(?x4304, ?x3744), ?x4304 = 08952r, nominated_for(?x112, ?x5001), written_by(?x5001, ?x3751), profession(?x3744, ?x319) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #940 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 0m593; *> query: (?x3744, 04jplwp) <- executive_produced_by(?x4304, ?x3744), award_winner(?x4304, ?x2101), profession(?x3744, ?x319), organizations_founded(?x3744, ?x1478), place_of_birth(?x3744, ?x1523) *> conf = 0.10 ranks of expected_values: 57, 81, 150, 171 EVAL 0gg9_5q executive_produced_by! 04jplwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 130.000 27.000 0.333 http://example.org/film/film/executive_produced_by EVAL 0gg9_5q executive_produced_by! 047rkcm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 130.000 27.000 0.333 http://example.org/film/film/executive_produced_by EVAL 0gg9_5q executive_produced_by! 0kv238 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 130.000 27.000 0.333 http://example.org/film/film/executive_produced_by EVAL 0gg9_5q executive_produced_by! 0cc5mcj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 130.000 27.000 0.333 http://example.org/film/film/executive_produced_by #14488-02hhtj PRED entity: 02hhtj PRED relation: gender PRED expected values: 05zppz => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #23, 0.81 #171, 0.76 #165), 02zsn (0.53 #52, 0.51 #34, 0.51 #18) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 0f87jy; >> query: (?x5881, 05zppz) <- profession(?x5881, ?x1383), profession(?x5881, ?x1041), ?x1383 = 0np9r, ?x1041 = 03gjzk >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hhtj gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.847 http://example.org/people/person/gender #14487-0x0d PRED entity: 0x0d PRED relation: school PRED expected values: 01jq0j => 81 concepts (66 used for prediction) PRED predicted values (max 10 best out of 249): 06fq2 (0.64 #3966, 0.54 #4916, 0.40 #706), 065y4w7 (0.55 #3838, 0.50 #4598, 0.50 #1149), 0j_sncb (0.50 #2330, 0.50 #1180, 0.40 #419), 01dzg0 (0.50 #2263, 0.45 #3417, 0.43 #1688), 012vwb (0.50 #242, 0.45 #3691, 0.40 #622), 01jq0j (0.45 #4138, 0.43 #2024, 0.40 #2989), 01tx9m (0.43 #1624, 0.40 #1053, 0.40 #482), 03tw2s (0.43 #1826, 0.30 #2983, 0.27 #3751), 021w0_ (0.40 #3207, 0.40 #1097, 0.38 #2243), 02rv1w (0.40 #732, 0.38 #2453, 0.33 #1303) >> Best rule #3966 for best value: >> intensional similarity = 21 >> extensional distance = 9 >> proper extension: 05g76; >> query: (?x10939, 06fq2) <- team(?x8520, ?x10939), team(?x5727, ?x10939), team(?x8520, ?x8995), team(?x8520, ?x7357), team(?x8520, ?x6348), team(?x8520, ?x1010), ?x6348 = 021f30, season(?x10939, ?x11501), season(?x10939, ?x8517), season(?x10939, ?x2406), teams(?x4419, ?x10939), sport(?x10939, ?x5063), ?x8517 = 0285r5d, ?x1010 = 01d5z, draft(?x10939, ?x4779), ?x4779 = 02z6872, ?x11501 = 027mvrc, ?x8995 = 01d6g, ?x5727 = 02wszf, ?x7357 = 04mjl, ?x2406 = 03c6sl9 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #4138 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 9 *> proper extension: 01ypc; *> query: (?x10939, 01jq0j) <- team(?x8520, ?x10939), team(?x8520, ?x11919), team(?x8520, ?x11361), team(?x8520, ?x8111), team(?x8520, ?x6348), team(?x8520, ?x6074), team(?x8520, ?x3333), team(?x8520, ?x700), team(?x8520, ?x662), ?x6348 = 021f30, season(?x10939, ?x3431), colors(?x10939, ?x4557), ?x6074 = 02__x, ?x11361 = 03m1n, school(?x10939, ?x581), ?x662 = 03lpp_, team(?x12826, ?x10939), ?x8111 = 07147, ?x3431 = 025ygqm, ?x11919 = 04b5l3, ?x700 = 06x68, ?x3333 = 01yjl *> conf = 0.45 ranks of expected_values: 6 EVAL 0x0d school 01jq0j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 66.000 0.636 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #14486-03gr7w PRED entity: 03gr7w PRED relation: artists! PRED expected values: 06by7 => 116 concepts (115 used for prediction) PRED predicted values (max 10 best out of 197): 06by7 (0.61 #334, 0.53 #1578, 0.50 #645), 064t9 (0.42 #8426, 0.41 #9360, 0.39 #9672), 02w4v (0.39 #356, 0.22 #44, 0.16 #1600), 03jsvl (0.39 #479, 0.06 #1101, 0.04 #1723), 0155w (0.38 #731, 0.23 #1664, 0.18 #3533), 0xhtw (0.34 #640, 0.25 #1573, 0.20 #3442), 03_d0 (0.34 #634, 0.24 #945, 0.20 #6553), 01fh36 (0.34 #711, 0.11 #1644, 0.10 #6630), 06j6l (0.31 #982, 0.24 #8461, 0.24 #9395), 016clz (0.30 #1561, 0.23 #3430, 0.22 #939) >> Best rule #334 for best value: >> intensional similarity = 2 >> extensional distance = 16 >> proper extension: 07m4c; >> query: (?x1795, 06by7) <- artists(?x12215, ?x1795), ?x12215 = 09n5t_ >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03gr7w artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 115.000 0.611 http://example.org/music/genre/artists #14485-0fb7sd PRED entity: 0fb7sd PRED relation: film_crew_role PRED expected values: 09zzb8 02ynfr => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 34): 09zzb8 (0.83 #316, 0.81 #1700, 0.80 #106), 01vx2h (0.50 #150, 0.41 #1709, 0.40 #2315), 01pvkk (0.41 #221, 0.40 #396, 0.40 #291), 02ynfr (0.27 #155, 0.25 #15, 0.23 #85), 0d2b38 (0.27 #130, 0.25 #25, 0.16 #165), 01xy5l_ (0.25 #13, 0.20 #118, 0.15 #48), 015h31 (0.25 #8, 0.20 #113, 0.14 #925), 089g0h (0.25 #19, 0.20 #124, 0.14 #1324), 02rh1dz (0.22 #926, 0.20 #149, 0.20 #819), 0215hd (0.20 #123, 0.17 #1323, 0.17 #18) >> Best rule #316 for best value: >> intensional similarity = 5 >> extensional distance = 84 >> proper extension: 01gglm; >> query: (?x4967, 09zzb8) <- films(?x9677, ?x4967), executive_produced_by(?x4967, ?x3744), film(?x92, ?x4967), film_crew_role(?x4967, ?x1284), ?x1284 = 0ch6mp2 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0fb7sd film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 98.000 0.826 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0fb7sd film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.826 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14484-072hx4 PRED entity: 072hx4 PRED relation: film_release_region PRED expected values: 03_3d 047lj 05qhw 035qy 01mjq 02vzc => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 163): 03_3d (0.90 #869, 0.89 #1013, 0.85 #1157), 035qy (0.89 #317, 0.88 #2192, 0.84 #1038), 02vzc (0.89 #332, 0.87 #909, 0.86 #1053), 05qhw (0.86 #1021, 0.85 #1165, 0.85 #877), 047yc (0.78 #311, 0.67 #888, 0.61 #1032), 047lj (0.78 #297, 0.54 #874, 0.52 #1018), 01p1v (0.72 #333, 0.50 #2208, 0.47 #3794), 0ctw_b (0.69 #885, 0.68 #1029, 0.60 #1173), 03rk0 (0.67 #337, 0.54 #914, 0.50 #1058), 01ls2 (0.67 #298, 0.51 #2173, 0.50 #1019) >> Best rule #869 for best value: >> intensional similarity = 10 >> extensional distance = 37 >> proper extension: 08hmch; 024mpp; 02rmd_2; 047vnkj; 02qk3fk; 07s3m4g; 03z9585; 0ndsl1x; 07jqjx; >> query: (?x11839, 03_3d) <- film_release_region(?x11839, ?x1603), film_release_region(?x11839, ?x1355), film_release_region(?x11839, ?x985), film_release_region(?x11839, ?x512), film_release_region(?x11839, ?x429), ?x1603 = 06bnz, ?x1355 = 0h7x, ?x985 = 0k6nt, ?x429 = 03rt9, ?x512 = 07ssc >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 14 EVAL 072hx4 film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 072hx4 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 98.000 98.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 072hx4 film_release_region 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 072hx4 film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 072hx4 film_release_region 047lj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 072hx4 film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14483-0g0x9c PRED entity: 0g0x9c PRED relation: film! PRED expected values: 032xhg => 75 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1029): 015pkc (0.31 #2353, 0.08 #4429, 0.05 #74710), 05bnp0 (0.23 #2088, 0.03 #35292, 0.02 #8315), 01f7dd (0.23 #3280, 0.03 #26107, 0.02 #36484), 01h8f (0.23 #3001, 0.02 #11303), 04fzk (0.23 #2779, 0.02 #13156, 0.01 #33908), 0169dl (0.17 #10775, 0.09 #398, 0.05 #17000), 07fq1y (0.15 #2092, 0.09 #17), 07myb2 (0.15 #5936, 0.04 #10087, 0.03 #16312), 01yf85 (0.15 #5655, 0.04 #9806, 0.03 #16031), 046qq (0.15 #4890, 0.03 #6965, 0.02 #11116) >> Best rule #2353 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 05b_gq; >> query: (?x7844, 015pkc) <- film(?x11813, ?x7844), film(?x7381, ?x7844), gender(?x7381, ?x231), award_winner(?x237, ?x7381), ?x11813 = 0716t2 >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #4215 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0581vn8; *> query: (?x7844, 032xhg) <- featured_film_locations(?x7844, ?x2254), film_crew_role(?x7844, ?x632), ?x632 = 0ckd1, film(?x2156, ?x7844) *> conf = 0.08 ranks of expected_values: 142 EVAL 0g0x9c film! 032xhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 75.000 41.000 0.308 http://example.org/film/actor/film./film/performance/film #14482-0_7w6 PRED entity: 0_7w6 PRED relation: list PRED expected values: 05glt => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.15 #121, 0.12 #191, 0.12 #23) >> Best rule #121 for best value: >> intensional similarity = 4 >> extensional distance = 357 >> proper extension: 05_61y; >> query: (?x1919, 05glt) <- genre(?x1919, ?x307), country(?x1919, ?x94), ?x94 = 09c7w0, honored_for(?x3254, ?x1919) >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_7w6 list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.153 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #14481-07g2v PRED entity: 07g2v PRED relation: artists! PRED expected values: 05jg58 05jt_ => 209 concepts (157 used for prediction) PRED predicted values (max 10 best out of 289): 064t9 (0.74 #6713, 0.70 #6409, 0.68 #20103), 0glt670 (0.67 #952, 0.35 #10698, 0.33 #6740), 06by7 (0.60 #20111, 0.60 #38075, 0.59 #8850), 02lnbg (0.59 #6757, 0.44 #6453, 0.39 #8582), 016clz (0.54 #12490, 0.50 #4266, 0.50 #1829), 0ggx5q (0.52 #6777, 0.50 #4949, 0.46 #10431), 025sc50 (0.44 #6750, 0.33 #6446, 0.32 #21664), 06j6l (0.41 #6748, 0.35 #20138, 0.33 #17098), 0y3_8 (0.38 #9488, 0.34 #10401, 0.33 #8267), 05bt6j (0.36 #15875, 0.33 #9179, 0.33 #17093) >> Best rule #6713 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 01wgcvn; >> query: (?x3422, 064t9) <- artists(?x474, ?x3422), participant(?x777, ?x3422), film(?x3422, ?x2288), vacationer(?x6226, ?x3422) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #2859 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 01wt4wc; 01j590z; 017mbb; *> query: (?x3422, 05jt_) <- role(?x3422, ?x315), artists(?x11040, ?x3422), ?x11040 = 0173b0 *> conf = 0.18 ranks of expected_values: 53, 55 EVAL 07g2v artists! 05jt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 209.000 157.000 0.741 http://example.org/music/genre/artists EVAL 07g2v artists! 05jg58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 209.000 157.000 0.741 http://example.org/music/genre/artists #14480-0g9zcgx PRED entity: 0g9zcgx PRED relation: place_of_birth PRED expected values: 0r3wm => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 36): 0mpbx (0.25 #442, 0.03 #1146, 0.03 #1850), 01jr6 (0.25 #143, 0.02 #2256, 0.02 #2962), 030qb3t (0.11 #4282, 0.06 #758, 0.05 #1462), 02_286 (0.08 #723, 0.08 #1427, 0.07 #2132), 01_d4 (0.04 #4294, 0.03 #8518, 0.03 #6406), 0cr3d (0.03 #8546, 0.03 #50090, 0.03 #60651), 0qc7l (0.03 #1359, 0.03 #2063, 0.02 #2768), 0ynfz (0.03 #1050, 0.03 #1754, 0.02 #2459), 0d6lp (0.03 #818, 0.03 #1522, 0.02 #2227), 0k_q_ (0.03 #787, 0.03 #1491, 0.02 #2196) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 051z6rz; >> query: (?x6546, 0mpbx) <- nominated_for(?x6546, ?x7881), crewmember(?x781, ?x6546), ?x7881 = 01hq1 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g9zcgx place_of_birth 0r3wm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.250 http://example.org/people/person/place_of_birth #14479-05pcn59 PRED entity: 05pcn59 PRED relation: award_winner PRED expected values: 03n08b 02jtjz => 52 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1449): 01gq0b (0.40 #7745, 0.29 #56467, 0.29 #56465), 02bkdn (0.40 #7742, 0.12 #12653, 0.07 #15107), 01jw4r (0.40 #9191, 0.09 #23918, 0.08 #26374), 07s8r0 (0.40 #7688, 0.09 #58921, 0.08 #31911), 01dy7j (0.40 #7995, 0.03 #22722, 0.03 #25178), 03d_w3h (0.40 #7538, 0.03 #22265, 0.03 #24721), 026r8q (0.33 #4066, 0.29 #56467, 0.29 #56465), 0993r (0.33 #644, 0.29 #56467, 0.29 #56465), 0mdqp (0.33 #9952, 0.29 #56467, 0.29 #56465), 01qscs (0.33 #2513, 0.27 #14785, 0.17 #9876) >> Best rule #7745 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0bdw1g; 0cqh6z; >> query: (?x1336, 01gq0b) <- nominated_for(?x1336, ?x7248), award(?x376, ?x1336), ?x376 = 023tp8, titles(?x1510, ?x7248) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #56467 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 227 *> proper extension: 026mg3; 02g3v6; 0gs96; 02x258x; 047sgz4; 02x2gy0; 02qwdhq; 02q1tc5; 02pz3j5; 0gqxm; ... *> query: (?x1336, ?x3580) <- nominated_for(?x1336, ?x144), award(?x3580, ?x1336), award(?x376, ?x1336), profession(?x376, ?x1032) *> conf = 0.29 ranks of expected_values: 117, 282 EVAL 05pcn59 award_winner 02jtjz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 52.000 25.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 05pcn59 award_winner 03n08b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 52.000 25.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #14478-019fv4 PRED entity: 019fv4 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.73 #21, 0.68 #29, 0.64 #173), 0jgjn (0.14 #565, 0.07 #24, 0.04 #88), 01g63y (0.07 #22, 0.05 #30, 0.04 #78), 01bl8s (0.01 #227) >> Best rule #21 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 04jpl; 02_286; 0cr3d; 02sn34; 02z0j; 07dfk; 09bkv; 0cy07; >> query: (?x12642, 04ztj) <- contains(?x1264, ?x12642), place_of_birth(?x5600, ?x12642), citytown(?x11790, ?x12642), location(?x5600, ?x362), ?x362 = 04jpl >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019fv4 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 188.000 0.733 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #14477-01f7kl PRED entity: 01f7kl PRED relation: genre PRED expected values: 06n90 => 77 concepts (62 used for prediction) PRED predicted values (max 10 best out of 95): 07s9rl0 (0.98 #4887, 0.74 #597, 0.69 #716), 01jfsb (0.49 #4779, 0.32 #2036, 0.30 #3708), 02kdv5l (0.47 #4770, 0.32 #1313, 0.31 #956), 09kqc (0.40 #238, 0.33 #596, 0.33 #477), 01hmnh (0.33 #256, 0.33 #18, 0.25 #1328), 03npn (0.33 #365, 0.09 #4774, 0.09 #1317), 02l7c8 (0.32 #1921, 0.30 #1445, 0.30 #4902), 04xvlr (0.23 #2383, 0.22 #4769, 0.21 #717), 0lsxr (0.22 #4776, 0.21 #724, 0.21 #2870), 06cvj (0.21 #1909, 0.18 #2861, 0.11 #1671) >> Best rule #4887 for best value: >> intensional similarity = 5 >> extensional distance = 1021 >> proper extension: 0fq27fp; >> query: (?x2470, 07s9rl0) <- genre(?x2470, ?x8280), genre(?x10931, ?x8280), genre(?x3537, ?x8280), film(?x4800, ?x3537), ?x10931 = 06pyc2 >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #132 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 01pvxl; 0bt4g; 01f7jt; *> query: (?x2470, 06n90) <- film(?x9700, ?x2470), ?x9700 = 01l_yg, film_distribution_medium(?x2470, ?x81), currency(?x2470, ?x170), ?x170 = 09nqf *> conf = 0.20 ranks of expected_values: 11 EVAL 01f7kl genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 77.000 62.000 0.983 http://example.org/film/film/genre #14476-0pkgt PRED entity: 0pkgt PRED relation: category PRED expected values: 08mbj5d => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.77 #20, 0.74 #21, 0.74 #27) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 487 >> proper extension: 025vry; 01qkqwg; 01ky2h; 0c_mvb; 01lcxbb; 01wz_ml; 0lzkm; 01vsy3q; 0191h5; 0lsw9; ... >> query: (?x11238, 08mbj5d) <- award_winner(?x1232, ?x11238), profession(?x11238, ?x1614), artists(?x10332, ?x11238) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pkgt category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.771 http://example.org/common/topic/webpage./common/webpage/category #14475-04vs9 PRED entity: 04vs9 PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.87 #29, 0.87 #15, 0.87 #30) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 05br2; 06s_2; 04hvw; >> query: (?x9072, 0hzc9wc) <- official_language(?x9072, ?x254), jurisdiction_of_office(?x182, ?x9072), country(?x150, ?x9072) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04vs9 administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.874 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #14474-033x5p PRED entity: 033x5p PRED relation: colors PRED expected values: 019sc => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.43 #122, 0.38 #1642, 0.38 #82), 01g5v (0.37 #84, 0.33 #124, 0.32 #304), 01l849 (0.27 #21, 0.27 #1321, 0.26 #561), 019sc (0.18 #87, 0.18 #1407, 0.18 #1947), 06fvc (0.17 #183, 0.16 #1943, 0.16 #263), 036k5h (0.12 #205, 0.12 #25, 0.10 #265), 04mkbj (0.11 #130, 0.10 #310, 0.10 #350), 038hg (0.09 #52, 0.09 #672, 0.08 #1192), 01jnf1 (0.08 #31, 0.07 #551, 0.06 #671), 03wkwg (0.08 #35, 0.07 #935, 0.07 #95) >> Best rule #122 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 015zyd; 01rtm4; 0kz2w; 01k2wn; 04rwx; 0bthb; 02s62q; 07w3r; 07wrz; 017z88; ... >> query: (?x4363, 083jv) <- organization(?x346, ?x4363), student(?x4363, ?x158), institution(?x865, ?x4363), currency(?x4363, ?x170) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 06jk5_; 02897w; 037njl; 01_s9q; 02km0m; 02l1fn; 0hpv3; 01tntf; 03818y; 030w19; *> query: (?x4363, 019sc) <- organization(?x346, ?x4363), currency(?x4363, ?x170), institution(?x1368, ?x4363), ?x1368 = 014mlp *> conf = 0.18 ranks of expected_values: 4 EVAL 033x5p colors 019sc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 163.000 163.000 0.429 http://example.org/education/educational_institution/colors #14473-04qk12 PRED entity: 04qk12 PRED relation: honored_for! PRED expected values: 09bymc => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 87): 02glmx (0.33 #68, 0.02 #678, 0.02 #2020), 092c5f (0.33 #10, 0.02 #1962, 0.02 #2084), 09p30_ (0.09 #316, 0.07 #438, 0.03 #926), 02hn5v (0.09 #277, 0.07 #399, 0.03 #887), 02cg41 (0.09 #354, 0.01 #1086, 0.01 #2062), 04110lv (0.07 #583, 0.07 #461, 0.04 #705), 09bymc (0.07 #593, 0.04 #715, 0.04 #959), 0drtv8 (0.07 #543, 0.04 #665, 0.03 #1031), 0bvhz9 (0.07 #602, 0.04 #1090, 0.04 #2188), 09p2r9 (0.07 #567, 0.03 #2763, 0.03 #811) >> Best rule #68 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02cbhg; >> query: (?x8555, 02glmx) <- film_crew_role(?x8555, ?x137), award(?x8555, ?x2489), executive_produced_by(?x8555, ?x12252), film(?x374, ?x8555), ?x374 = 05cj4r >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 25 *> proper extension: 0164qt; 014kq6; *> query: (?x8555, 09bymc) <- film_crew_role(?x8555, ?x3305), film_crew_role(?x8555, ?x137), award(?x8555, ?x2489), currency(?x8555, ?x170), ?x137 = 09zzb8, ?x3305 = 04pyp5 *> conf = 0.07 ranks of expected_values: 7 EVAL 04qk12 honored_for! 09bymc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 109.000 109.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #14472-02h758 PRED entity: 02h758 PRED relation: category PRED expected values: 08mbj5d => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #71, 0.84 #70, 0.84 #67) >> Best rule #71 for best value: >> intensional similarity = 7 >> extensional distance = 780 >> proper extension: 0gkkf; 07tl0; 01k8q5; 018m5q; 0c_zj; 01sjz_; 0d07s; 01f2xy; 0677j; 01clyb; ... >> query: (?x12690, ?x134) <- organization(?x4682, ?x12690), organization(?x4682, ?x13035), organization(?x4682, ?x5666), organization(?x4682, ?x5108), category(?x5666, ?x134), citytown(?x13035, ?x1156), company(?x265, ?x5108) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02h758 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.840 http://example.org/common/topic/webpage./common/webpage/category #14471-02r6c_ PRED entity: 02r6c_ PRED relation: participant PRED expected values: 05dbf => 131 concepts (64 used for prediction) PRED predicted values (max 10 best out of 151): 0gx_p (0.18 #2996, 0.11 #3639, 0.11 #2353), 013w7j (0.12 #1699, 0.05 #4914, 0.02 #6843), 03f19q4 (0.12 #1643, 0.05 #4858, 0.02 #6787), 0837ql (0.12 #1625, 0.05 #4840, 0.02 #6769), 0c6qh (0.09 #2738, 0.06 #3381, 0.05 #4024), 0pmhf (0.09 #2747, 0.06 #3390, 0.05 #4033), 01vhrz (0.09 #3140, 0.06 #3783, 0.05 #4426), 046zh (0.09 #2932, 0.06 #3575, 0.04 #6147), 023v4_ (0.09 #2917, 0.06 #3560, 0.04 #6132), 0210hf (0.09 #2906, 0.06 #3549, 0.02 #6121) >> Best rule #2996 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0343h; 06pj8; 0gn30; 017c87; 0bq4j6; >> query: (?x8812, 0gx_p) <- student(?x865, ?x8812), film(?x8812, ?x2121), award(?x8812, ?x68), place_of_birth(?x8812, ?x11743) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #18158 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 157 *> proper extension: 04t2l2; 0byfz; 014zcr; 0h5f5n; 01q_ph; 0159h6; 0bxtg; 06cv1; 03f2_rc; 0c1pj; ... *> query: (?x8812, 05dbf) <- award(?x8812, ?x68), student(?x5778, ?x8812), award_winner(?x372, ?x8812), written_by(?x2121, ?x8812) *> conf = 0.01 ranks of expected_values: 113 EVAL 02r6c_ participant 05dbf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 131.000 64.000 0.182 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #14470-04fzk PRED entity: 04fzk PRED relation: religion PRED expected values: 04pk9 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 17): 0c8wxp (0.26 #726, 0.26 #816, 0.24 #906), 0kpl (0.12 #10, 0.07 #2170, 0.04 #1495), 0n2g (0.12 #13, 0.02 #1183, 0.02 #2173), 03_gx (0.10 #59, 0.08 #2309, 0.08 #4201), 092bf5 (0.09 #196, 0.05 #781, 0.04 #376), 0kq2 (0.06 #198, 0.04 #378, 0.03 #558), 01lp8 (0.04 #91, 0.03 #361, 0.03 #901), 019cr (0.04 #191, 0.03 #821, 0.03 #731), 06nzl (0.03 #1005, 0.03 #240, 0.03 #285), 03j6c (0.02 #381, 0.02 #4208, 0.02 #561) >> Best rule #726 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 01l1b90; 0m2wm; 01q7cb_; 01yhvv; 01vv126; 033wx9; 01pcrw; 039bpc; 05mkhs; 02hhtj; ... >> query: (?x4106, 0c8wxp) <- participant(?x4106, ?x1733), participant(?x1896, ?x4106), participant(?x1733, ?x2763) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #1145 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 226 *> proper extension: 0d05fv; 0d02km; *> query: (?x4106, 04pk9) <- award_winner(?x1490, ?x4106), participant(?x629, ?x4106), award_winner(?x629, ?x628) *> conf = 0.01 ranks of expected_values: 13 EVAL 04fzk religion 04pk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 123.000 123.000 0.263 http://example.org/people/person/religion #14469-015bwt PRED entity: 015bwt PRED relation: artists! PRED expected values: 06j6l => 103 concepts (42 used for prediction) PRED predicted values (max 10 best out of 236): 06j6l (0.73 #5275, 0.70 #355, 0.55 #2812), 06by7 (0.69 #2171, 0.61 #3401, 0.58 #3710), 02lnbg (0.60 #364, 0.41 #2821, 0.39 #1899), 0ggx5q (0.50 #77, 0.41 #2841, 0.40 #384), 05bt6j (0.50 #2192, 0.35 #6500, 0.30 #2807), 01fm07 (0.50 #1046, 0.20 #1967, 0.17 #2889), 0dn16 (0.50 #16, 0.09 #630, 0.07 #2165), 016clz (0.43 #3384, 0.39 #3693, 0.38 #4002), 0xhtw (0.40 #3705, 0.39 #4014, 0.38 #3396), 02vjzr (0.30 #441, 0.20 #2898, 0.18 #748) >> Best rule #5275 for best value: >> intensional similarity = 3 >> extensional distance = 245 >> proper extension: 03xnq9_; 0dhqyw; 0djc3s; >> query: (?x11455, 06j6l) <- artists(?x14382, ?x11455), artists(?x14382, ?x11667), ?x11667 = 01v27pl >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015bwt artists! 06j6l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 42.000 0.729 http://example.org/music/genre/artists #14468-0420y PRED entity: 0420y PRED relation: influenced_by! PRED expected values: 045bg => 185 concepts (79 used for prediction) PRED predicted values (max 10 best out of 431): 04hcw (0.58 #4808, 0.25 #1289, 0.12 #32283), 0dw6b (0.40 #1855, 0.12 #32283, 0.12 #36811), 026lj (0.33 #10061, 0.33 #57, 0.30 #22678), 07dnx (0.33 #4879, 0.25 #3874, 0.20 #4377), 0jcx (0.33 #118, 0.25 #4644, 0.17 #10062), 06jkm (0.33 #450, 0.25 #1457, 0.12 #32283), 0bk5r (0.33 #4732, 0.25 #1213, 0.12 #32283), 06myp (0.33 #4955, 0.20 #1938, 0.12 #32283), 03jht (0.33 #4898, 0.12 #3893, 0.12 #32283), 0j3v (0.25 #4605, 0.25 #1086, 0.20 #1588) >> Best rule #4808 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0d0mbj; >> query: (?x11830, 04hcw) <- location(?x11830, ?x4627), influenced_by(?x5254, ?x11830), profession(?x5254, ?x7998), ?x7998 = 01d30f, religion(?x11830, ?x1985) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1042 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 03sbs; *> query: (?x11830, 045bg) <- influenced_by(?x10654, ?x11830), influenced_by(?x9600, ?x11830), nationality(?x11830, ?x774), ?x10654 = 042q3, ?x9600 = 039n1 *> conf = 0.25 ranks of expected_values: 15 EVAL 0420y influenced_by! 045bg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 185.000 79.000 0.583 http://example.org/influence/influence_node/influenced_by #14467-01grnp PRED entity: 01grnp PRED relation: legislative_sessions! PRED expected values: 042d1 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 33): 0rlz (0.65 #141, 0.50 #179, 0.42 #629), 03_nq (0.65 #141, 0.42 #629, 0.42 #628), 0fd_1 (0.65 #141, 0.42 #629, 0.42 #628), 042d1 (0.65 #141, 0.42 #629, 0.42 #628), 021sv1 (0.42 #838, 0.40 #867, 0.39 #903), 0226cw (0.42 #853, 0.40 #882, 0.39 #918), 02hy5d (0.40 #856, 0.38 #885, 0.37 #921), 024_vw (0.40 #831, 0.38 #861, 0.36 #890), 0194xc (0.36 #857, 0.34 #886, 0.34 #922), 0bymv (0.35 #809, 0.33 #115, 0.30 #747) >> Best rule #141 for best value: >> intensional similarity = 47 >> extensional distance = 1 >> proper extension: 07p__7; >> query: (?x1754, ?x7891) <- district_represented(?x1754, ?x7518), district_represented(?x1754, ?x7405), district_represented(?x1754, ?x6895), district_represented(?x1754, ?x4776), district_represented(?x1754, ?x4061), district_represented(?x1754, ?x3038), district_represented(?x1754, ?x2713), district_represented(?x1754, ?x2020), district_represented(?x1754, ?x1767), district_represented(?x1754, ?x1755), district_represented(?x1754, ?x760), district_represented(?x1754, ?x728), district_represented(?x1754, ?x335), ?x1755 = 01x73, legislative_sessions(?x11142, ?x1754), legislative_sessions(?x7715, ?x1754), legislative_sessions(?x5256, ?x1754), ?x760 = 05fkf, ?x7518 = 026mj, ?x6895 = 05fjf, legislative_sessions(?x4665, ?x1754), district_represented(?x7715, ?x3670), ?x7405 = 07_f2, ?x2713 = 06btq, ?x728 = 059f4, ?x1767 = 04rrd, ?x335 = 059rby, legislative_sessions(?x5978, ?x1754), legislative_sessions(?x11142, ?x3973), ?x4061 = 0498y, ?x4776 = 06yxd, legislative_sessions(?x3973, ?x2712), legislative_sessions(?x7891, ?x5256), ?x4665 = 07t58, ?x3670 = 05tbn, district_represented(?x11142, ?x3778), influenced_by(?x5978, ?x5254), influenced_by(?x5978, ?x1857), ?x3038 = 0d0x8, profession(?x5978, ?x2225), ?x2020 = 05k7sb, gender(?x5254, ?x231), district_represented(?x3973, ?x177), influenced_by(?x1857, ?x3994), interests(?x1857, ?x14193), ?x14193 = 0gt_hv, peers(?x5254, ?x8991) >> conf = 0.65 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 01grnp legislative_sessions! 042d1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 33.000 33.000 0.647 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #14466-01rgn3 PRED entity: 01rgn3 PRED relation: major_field_of_study PRED expected values: 064_8sq => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 118): 01mkq (0.68 #984, 0.64 #1589, 0.58 #1952), 03g3w (0.60 #1961, 0.54 #993, 0.51 #1598), 062z7 (0.52 #1962, 0.43 #3293, 0.43 #994), 05qjt (0.48 #1944, 0.45 #1581, 0.43 #976), 0fdys (0.46 #1005, 0.42 #1973, 0.36 #1610), 02lp1 (0.45 #1585, 0.43 #980, 0.39 #3400), 05qfh (0.44 #1970, 0.39 #1002, 0.37 #2575), 01lj9 (0.43 #1006, 0.43 #1611, 0.37 #1974), 04x_3 (0.36 #1597, 0.36 #992, 0.27 #1960), 0g26h (0.34 #1614, 0.33 #404, 0.29 #1009) >> Best rule #984 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 07tgn; 01mpwj; 05zl0; 07tk7; 01hc1j; >> query: (?x8216, 01mkq) <- major_field_of_study(?x8216, ?x8221), major_field_of_study(?x8216, ?x2014), ?x2014 = 04rjg, organization(?x346, ?x8216), ?x8221 = 037mh8 >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #16951 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 581 *> proper extension: 05zjtn4; 0ymbl; 0gkkf; 0ym8f; 01wdl3; 0277jc; 01bzw5; 01ngz1; 07tl0; 01j_06; ... *> query: (?x8216, ?x254) <- major_field_of_study(?x8216, ?x8221), major_field_of_study(?x8216, ?x2014), major_field_of_study(?x734, ?x2014), major_field_of_study(?x8221, ?x254) *> conf = 0.13 ranks of expected_values: 49 EVAL 01rgn3 major_field_of_study 064_8sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 188.000 188.000 0.679 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14465-07vc_9 PRED entity: 07vc_9 PRED relation: film PRED expected values: 08hmch => 101 concepts (62 used for prediction) PRED predicted values (max 10 best out of 607): 019vhk (0.29 #460, 0.04 #103249, 0.03 #8901), 0hfzr (0.29 #701, 0.01 #6041), 0gydcp7 (0.29 #329, 0.01 #5669), 05f4_n0 (0.29 #710), 085bd1 (0.29 #450), 0661ql3 (0.14 #384, 0.04 #103249, 0.03 #8901), 01cssf (0.14 #89, 0.04 #103249, 0.03 #8901), 026p4q7 (0.14 #397, 0.03 #8901, 0.03 #72985), 058kh7 (0.14 #1572, 0.03 #8901, 0.03 #72985), 0prhz (0.14 #792, 0.03 #8901, 0.03 #72985) >> Best rule #460 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 018swb; >> query: (?x1286, 019vhk) <- film(?x1286, ?x5458), nationality(?x1286, ?x94), ?x5458 = 05szq8z >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #5492 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 183 *> proper extension: 01gvr1; 0157m; 01dw9z; 01w02sy; 0ddkf; 02tc5y; *> query: (?x1286, 08hmch) <- award_nominee(?x2353, ?x1286), film(?x1286, ?x1038), spouse(?x8346, ?x1286) *> conf = 0.01 ranks of expected_values: 582 EVAL 07vc_9 film 08hmch CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 101.000 62.000 0.286 http://example.org/film/actor/film./film/performance/film #14464-02zccd PRED entity: 02zccd PRED relation: major_field_of_study PRED expected values: 0jjw => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 111): 02j62 (0.43 #531, 0.34 #781, 0.33 #6537), 02lp1 (0.41 #512, 0.29 #1389, 0.26 #2014), 04rjg (0.41 #520, 0.28 #1397, 0.24 #3772), 062z7 (0.35 #528, 0.24 #6534, 0.23 #7785), 03g3w (0.33 #527, 0.25 #6533, 0.25 #902), 05qjt (0.28 #508, 0.22 #1385, 0.21 #2010), 05qfh (0.28 #537, 0.18 #912, 0.18 #1414), 0g26h (0.26 #544, 0.22 #169, 0.22 #44), 01540 (0.25 #563, 0.16 #938, 0.16 #2065), 01lj9 (0.25 #541, 0.16 #3543, 0.16 #2043) >> Best rule #531 for best value: >> intensional similarity = 2 >> extensional distance = 136 >> proper extension: 03bwzr4; >> query: (?x3172, 02j62) <- major_field_of_study(?x3172, ?x1668), ?x1668 = 01mkq >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #10013 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 557 *> proper extension: 04rkkv; *> query: (?x3172, ?x373) <- institution(?x8398, ?x3172), major_field_of_study(?x8398, ?x8681), major_field_of_study(?x8398, ?x373), ?x8681 = 04rlf *> conf = 0.06 ranks of expected_values: 56 EVAL 02zccd major_field_of_study 0jjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 132.000 132.000 0.428 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14463-0h953 PRED entity: 0h953 PRED relation: profession PRED expected values: 09jwl => 110 concepts (26 used for prediction) PRED predicted values (max 10 best out of 75): 0dxtg (0.59 #605, 0.42 #1345, 0.34 #2233), 0cbd2 (0.44 #2374, 0.43 #2670, 0.41 #2226), 02jknp (0.36 #599, 0.29 #155, 0.25 #3851), 03gjzk (0.34 #606, 0.25 #3851, 0.25 #3850), 0kyk (0.29 #2397, 0.28 #2693, 0.28 #2249), 018gz8 (0.27 #608, 0.25 #3851, 0.25 #3850), 0np9r (0.25 #3851, 0.25 #3850, 0.11 #168), 025352 (0.25 #3851, 0.25 #3850, 0.05 #1391), 05sxg2 (0.25 #3851, 0.25 #3850, 0.03 #297), 09jwl (0.21 #1350, 0.21 #610, 0.17 #314) >> Best rule #605 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 07c0j; >> query: (?x8450, 0dxtg) <- award_winner(?x537, ?x8450), nominated_for(?x8450, ?x5212), influenced_by(?x6771, ?x8450) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #1350 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 165 *> proper extension: 05xq9; 01kcms4; 07m4c; 0167xy; 04sd0; *> query: (?x8450, 09jwl) <- influenced_by(?x6771, ?x8450), award_nominee(?x4060, ?x6771), award_winner(?x1480, ?x6771) *> conf = 0.21 ranks of expected_values: 10 EVAL 0h953 profession 09jwl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 110.000 26.000 0.589 http://example.org/people/person/profession #14462-0jc_p PRED entity: 0jc_p PRED relation: colors! PRED expected values: 0jnrk 02d02 0byq0v => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 471): 03lpp_ (0.60 #2463, 0.50 #3519, 0.46 #1755), 0j5m6 (0.60 #2197, 0.50 #3253, 0.46 #1755), 01y3c (0.60 #2122, 0.46 #1755, 0.43 #4234), 07l4z (0.60 #2281, 0.46 #1755, 0.43 #4393), 0jm9w (0.60 #2666, 0.46 #1755, 0.41 #1754), 051vz (0.60 #2489, 0.46 #1755, 0.41 #1754), 04b5l3 (0.60 #2726, 0.46 #1755, 0.41 #1754), 02896 (0.60 #2108, 0.46 #1755, 0.41 #1754), 04l5d0 (0.50 #5117, 0.50 #4765, 0.50 #3709), 01ct6 (0.50 #3520, 0.50 #3169, 0.46 #1755) >> Best rule #2463 for best value: >> intensional similarity = 35 >> extensional distance = 3 >> proper extension: 02rnmb; >> query: (?x3315, 03lpp_) <- colors(?x12795, ?x3315), colors(?x11881, ?x3315), colors(?x8565, ?x3315), colors(?x7920, ?x3315), colors(?x8697, ?x3315), colors(?x7499, ?x3315), colors(?x2067, ?x3315), institution(?x4981, ?x12795), organization(?x346, ?x11881), colors(?x11881, ?x3189), ?x3189 = 01g5v, major_field_of_study(?x12795, ?x11820), major_field_of_study(?x12795, ?x4321), category(?x11881, ?x134), currency(?x11881, ?x170), team(?x7907, ?x8697), colors(?x7499, ?x4557), draft(?x2067, ?x1161), ?x4981 = 03bwzr4, student(?x7920, ?x6338), sport(?x8697, ?x471), position(?x7499, ?x2010), colors(?x3723, ?x4557), ?x3723 = 0hn6d, ?x4321 = 0g26h, colors(?x546, ?x4557), season(?x2067, ?x701), ?x1161 = 02x2khw, film(?x6338, ?x4167), team(?x63, ?x8697), ?x4167 = 08fn5b, school(?x2067, ?x1276), ?x11820 = 0w7s, team(?x1696, ?x8697), contains(?x94, ?x8565) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1755 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 3 *> proper extension: 06kqt3; *> query: (?x3315, ?x260) <- colors(?x12795, ?x3315), colors(?x11881, ?x3315), colors(?x7920, ?x3315), colors(?x8697, ?x3315), colors(?x7499, ?x3315), colors(?x2067, ?x3315), colors(?x934, ?x3315), institution(?x4981, ?x12795), organization(?x346, ?x11881), colors(?x11881, ?x3189), ?x3189 = 01g5v, major_field_of_study(?x12795, ?x1668), category(?x11881, ?x134), currency(?x11881, ?x170), team(?x7907, ?x8697), colors(?x7499, ?x4557), draft(?x2067, ?x1161), ?x4981 = 03bwzr4, student(?x7920, ?x5558), sport(?x8697, ?x471), position(?x7499, ?x2010), colors(?x260, ?x4557), ?x7907 = 0841zn, colors(?x546, ?x4557), company(?x1907, ?x2067), team(?x1177, ?x934), teams(?x739, ?x2067), position(?x8697, ?x60), position(?x2067, ?x261), ?x1161 = 02x2khw *> conf = 0.46 ranks of expected_values: 41, 302, 305 EVAL 0jc_p colors! 0byq0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.600 http://example.org/sports/sports_team/colors EVAL 0jc_p colors! 02d02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 20.000 20.000 0.600 http://example.org/sports/sports_team/colors EVAL 0jc_p colors! 0jnrk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.600 http://example.org/sports/sports_team/colors #14461-0jgwf PRED entity: 0jgwf PRED relation: place_of_birth PRED expected values: 02gw_w => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 104): 04jpl (0.34 #41572, 0.33 #705, 0.32 #35934), 0dprg (0.33 #356, 0.04 #1765, 0.03 #3878), 02_286 (0.15 #724, 0.12 #24678, 0.10 #9178), 01_d4 (0.12 #2179, 0.08 #8520, 0.08 #4996), 01jr6 (0.08 #2256, 0.05 #5073), 0c630 (0.08 #1093, 0.04 #3205), 0tgcy (0.08 #1089, 0.04 #3201), 0rtv (0.08 #711, 0.04 #2823), 030qb3t (0.07 #3576, 0.05 #7098, 0.05 #8508), 0cc56 (0.06 #4259, 0.04 #2146, 0.04 #12010) >> Best rule #41572 for best value: >> intensional similarity = 4 >> extensional distance = 1138 >> proper extension: 07s8r0; 012x4t; 0170s4; 01nwwl; 02mjf2; 0gnbw; 03q45x; 0cw67g; 01pj3h; >> query: (?x8645, ?x362) <- nominated_for(?x8645, ?x2168), location(?x8645, ?x362), place_of_birth(?x4020, ?x362), nominated_for(?x4020, ?x1904) >> conf = 0.34 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jgwf place_of_birth 02gw_w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 121.000 0.335 http://example.org/people/person/place_of_birth #14460-01jr4j PRED entity: 01jr4j PRED relation: genre PRED expected values: 07s9rl0 => 108 concepts (50 used for prediction) PRED predicted values (max 10 best out of 118): 07s9rl0 (0.83 #2820, 0.77 #4233, 0.76 #4584), 02kdv5l (0.66 #1175, 0.42 #3175, 0.41 #5058), 09blyk (0.57 #264, 0.50 #147, 0.45 #615), 05p553 (0.51 #2583, 0.51 #2470, 0.38 #5055), 02l7c8 (0.40 #1304, 0.38 #5055, 0.38 #3777), 0vgkd (0.38 #5055, 0.37 #2114, 0.37 #1996), 03npn (0.38 #5055, 0.36 #4115, 0.36 #4819), 01585b (0.38 #5055, 0.36 #4115, 0.36 #4819), 01q03 (0.38 #5055, 0.36 #4115, 0.36 #4819), 0vjs6 (0.37 #2114, 0.37 #1996, 0.35 #937) >> Best rule #2820 for best value: >> intensional similarity = 8 >> extensional distance = 111 >> proper extension: 035bcl; >> query: (?x7149, 07s9rl0) <- language(?x7149, ?x254), genre(?x7149, ?x1509), genre(?x7149, ?x600), genre(?x4216, ?x1509), genre(?x2932, ?x1509), ?x2932 = 0gyy53, award(?x4216, ?x112), ?x600 = 02n4kr >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jr4j genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 50.000 0.832 http://example.org/film/film/genre #14459-0gkr9q PRED entity: 0gkr9q PRED relation: ceremony PRED expected values: 07z31v 07y9ts => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 132): 03nnm4t (0.71 #466, 0.50 #334, 0.40 #730), 0gpjbt (0.54 #1480, 0.51 #1744, 0.50 #2008), 09n4nb (0.53 #1497, 0.50 #1761, 0.49 #2025), 05pd94v (0.52 #1454, 0.49 #1982, 0.49 #1718), 0466p0j (0.52 #1524, 0.49 #2052, 0.49 #1788), 056878 (0.52 #1483, 0.49 #1747, 0.48 #2011), 02rjjll (0.52 #1457, 0.49 #1721, 0.48 #1985), 02cg41 (0.51 #1571, 0.48 #1835, 0.48 #2099), 01c6qp (0.51 #1470, 0.48 #1734, 0.47 #1998), 019bk0 (0.48 #1467, 0.45 #1731, 0.45 #1995) >> Best rule #466 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 09v7wsg; >> query: (?x9640, 03nnm4t) <- nominated_for(?x9640, ?x6322), award_winner(?x6322, ?x5030), category_of(?x9640, ?x2758), program(?x1039, ?x6322) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #196 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0bdw1g; 0bdw6t; 0cqhb3; *> query: (?x9640, 07y9ts) <- nominated_for(?x9640, ?x6322), nominated_for(?x9640, ?x337), ?x6322 = 0dsx3f, award(?x368, ?x9640), ?x337 = 0g60z *> conf = 0.40 ranks of expected_values: 19, 20 EVAL 0gkr9q ceremony 07y9ts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 53.000 53.000 0.710 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gkr9q ceremony 07z31v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 53.000 53.000 0.710 http://example.org/award/award_category/winners./award/award_honor/ceremony #14458-01vrnsk PRED entity: 01vrnsk PRED relation: artist! PRED expected values: 017l96 => 115 concepts (89 used for prediction) PRED predicted values (max 10 best out of 98): 033hn8 (0.33 #147, 0.19 #1211, 0.17 #1344), 03rhqg (0.28 #2410, 0.22 #2543, 0.21 #548), 01cl0d (0.25 #50, 0.21 #1114, 0.16 #582), 0n85g (0.25 #58, 0.17 #1122, 0.16 #590), 011k11 (0.25 #33, 0.16 #565, 0.13 #2427), 0mzkr (0.25 #25, 0.11 #2419, 0.11 #557), 06x2ww (0.25 #44, 0.08 #1108, 0.07 #1773), 015_1q (0.22 #5341, 0.22 #3878, 0.21 #5075), 01dtcb (0.21 #2436, 0.17 #2569, 0.15 #308), 03qy3l (0.21 #591, 0.14 #857, 0.13 #2453) >> Best rule #147 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 03qd_; 07s6prs; >> query: (?x6947, 033hn8) <- award_winner(?x6947, ?x1089), instrumentalists(?x212, ?x6947), actor(?x4275, ?x6947) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #285 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 04yt7; *> query: (?x6947, 017l96) <- type_of_appearance(?x6947, ?x3429), ?x3429 = 01jdpf, group(?x6947, ?x1136) *> conf = 0.15 ranks of expected_values: 15 EVAL 01vrnsk artist! 017l96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 115.000 89.000 0.333 http://example.org/music/record_label/artist #14457-021sv1 PRED entity: 021sv1 PRED relation: legislative_sessions PRED expected values: 03z5xd 03tcbx => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 27): 077g7n (0.50 #83, 0.38 #29, 0.33 #56), 03tcbx (0.44 #61, 0.40 #88, 0.38 #34), 03z5xd (0.40 #86, 0.33 #59, 0.25 #32), 01gtc0 (0.14 #256, 0.11 #283, 0.10 #310), 05rrw9 (0.10 #135, 0.07 #297, 0.07 #324), 01gsvp (0.10 #259, 0.07 #286, 0.07 #313), 01h7xx (0.10 #263, 0.07 #290, 0.07 #317), 043djx (0.10 #246, 0.07 #273, 0.07 #300), 01gtcc (0.10 #252, 0.07 #279, 0.07 #306), 01gtbb (0.10 #249, 0.07 #276, 0.07 #303) >> Best rule #83 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0226cw; >> query: (?x652, 077g7n) <- legislative_sessions(?x652, ?x3766), legislative_sessions(?x652, ?x1027), district_represented(?x1027, ?x2020), ?x2020 = 05k7sb, ?x3766 = 02gkzs >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #61 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0bymv; 0d3qd0; 016lh0; 06bss; 02hy5d; 0194xc; 024_vw; *> query: (?x652, 03tcbx) <- legislative_sessions(?x652, ?x6933), legislative_sessions(?x652, ?x1027), ?x1027 = 02bn_p, student(?x5750, ?x652), ?x6933 = 024tkd *> conf = 0.44 ranks of expected_values: 2, 3 EVAL 021sv1 legislative_sessions 03tcbx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 171.000 171.000 0.500 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 021sv1 legislative_sessions 03z5xd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 171.000 171.000 0.500 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #14456-016z2j PRED entity: 016z2j PRED relation: nominated_for PRED expected values: 07w8fz => 107 concepts (50 used for prediction) PRED predicted values (max 10 best out of 306): 05qbckf (0.27 #79022, 0.26 #62891, 0.25 #30639), 07vn_9 (0.27 #79022, 0.26 #62891, 0.25 #30639), 0dnkmq (0.27 #79022, 0.26 #62891, 0.25 #30639), 0bc1yhb (0.27 #79022, 0.26 #62891, 0.25 #30639), 059rc (0.27 #79022, 0.26 #62891, 0.25 #30639), 02q56mk (0.27 #79022, 0.26 #62891, 0.25 #30639), 0d68qy (0.14 #371, 0.03 #6820, 0.03 #8433), 01cmp9 (0.14 #947, 0.02 #23524, 0.01 #42874), 01l_pn (0.14 #876, 0.02 #69345, 0.02 #2488), 01rxyb (0.14 #661, 0.02 #3886, 0.01 #7110) >> Best rule #79022 for best value: >> intensional similarity = 2 >> extensional distance = 1155 >> proper extension: 01h4rj; >> query: (?x2373, ?x557) <- film(?x2373, ?x557), award_winner(?x2375, ?x2373) >> conf = 0.27 => this is the best rule for 6 predicted values *> Best rule #2081 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 023tp8; 01qscs; 01q_ph; 0159h6; 04wqr; 01rr9f; 06cv1; 01kwld; 09wj5; 01vvycq; ... *> query: (?x2373, 07w8fz) <- profession(?x2373, ?x220), award_nominee(?x969, ?x2373), celebrity(?x1564, ?x2373) *> conf = 0.02 ranks of expected_values: 139 EVAL 016z2j nominated_for 07w8fz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 107.000 50.000 0.266 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14455-0197tq PRED entity: 0197tq PRED relation: profession PRED expected values: 09jwl => 162 concepts (153 used for prediction) PRED predicted values (max 10 best out of 76): 09jwl (0.72 #1193, 0.71 #3252, 0.69 #4579), 0nbcg (0.49 #4592, 0.49 #1206, 0.48 #2088), 0dz3r (0.45 #4859, 0.44 #2354, 0.44 #2207), 01d_h8 (0.39 #152, 0.38 #887, 0.37 #1475), 01c72t (0.33 #5026, 0.32 #3698, 0.32 #2080), 0fnpj (0.33 #59, 0.28 #17377, 0.26 #15171), 0dxtg (0.31 #1776, 0.29 #1482, 0.28 #894), 039v1 (0.29 #329, 0.29 #4597, 0.28 #4006), 029bkp (0.28 #17377, 0.26 #15171, 0.25 #20760), 0kyk (0.28 #17377, 0.26 #15171, 0.25 #20760) >> Best rule #1193 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 0132k4; 021r7r; >> query: (?x217, 09jwl) <- origin(?x217, ?x739), role(?x217, ?x314), people(?x3584, ?x217) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0197tq profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 153.000 0.721 http://example.org/people/person/profession #14454-01z8f0 PRED entity: 01z8f0 PRED relation: contains! PRED expected values: 02jx1 => 102 concepts (48 used for prediction) PRED predicted values (max 10 best out of 275): 02jx1 (0.80 #7154, 0.67 #7153, 0.67 #6344), 0d6br (0.80 #7154, 0.42 #21468, 0.42 #21467), 09c7w0 (0.74 #39374, 0.71 #31322, 0.65 #32216), 02qkt (0.42 #21468, 0.42 #21467, 0.30 #27736), 0345h (0.21 #19758, 0.20 #10814, 0.07 #14392), 0dbdy (0.20 #1009, 0.15 #42055, 0.06 #1903), 09cpb (0.20 #1510, 0.15 #42055, 0.06 #2404), 013p59 (0.20 #1700, 0.15 #42055, 0.06 #2594), 03rk0 (0.17 #10869, 0.15 #19813, 0.08 #30558), 036wy (0.15 #42055, 0.14 #7022, 0.05 #11497) >> Best rule #7154 for best value: >> intensional similarity = 7 >> extensional distance = 49 >> proper extension: 0nccd; 0n9dn; 01m4pc; 01n244; 0m75g; 01n4nd; 02z2lj; 071zb; 0d6yv; 01fbb3; ... >> query: (?x9390, ?x7736) <- contains(?x2199, ?x9390), contains(?x512, ?x9390), ?x512 = 07ssc, administrative_parent(?x11875, ?x2199), contains(?x7736, ?x2199), contains(?x1310, ?x2199), ?x1310 = 02jx1 >> conf = 0.80 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01z8f0 contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 48.000 0.802 http://example.org/location/location/contains #14453-01w9wwg PRED entity: 01w9wwg PRED relation: award_winner! PRED expected values: 0466p0j => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 96): 056878 (0.33 #32, 0.10 #11141, 0.10 #11424), 02rjjll (0.17 #1274, 0.17 #5, 0.15 #569), 013b2h (0.17 #80, 0.12 #785, 0.11 #1349), 0466p0j (0.17 #76, 0.11 #1345, 0.10 #11141), 019bk0 (0.17 #16, 0.11 #1285, 0.10 #11141), 0gpjbt (0.17 #29, 0.10 #11141, 0.10 #11424), 09qvms (0.17 #13, 0.10 #11141, 0.04 #7768), 04n2r9h (0.17 #45, 0.02 #3429, 0.02 #468), 01s695 (0.14 #1272, 0.10 #11141, 0.09 #1977), 01c6qp (0.12 #1288, 0.11 #1993, 0.07 #2557) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 02x_h0; 01vw37m; 0g824; 0ffgh; >> query: (?x6162, 056878) <- award(?x6162, ?x528), award_nominee(?x6162, ?x2562), ?x2562 = 01trhmt >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 02x_h0; 01vw37m; 0g824; 0ffgh; *> query: (?x6162, 0466p0j) <- award(?x6162, ?x528), award_nominee(?x6162, ?x2562), ?x2562 = 01trhmt *> conf = 0.17 ranks of expected_values: 4 EVAL 01w9wwg award_winner! 0466p0j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 105.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14452-02frhbc PRED entity: 02frhbc PRED relation: location! PRED expected values: 098n_m => 224 concepts (153 used for prediction) PRED predicted values (max 10 best out of 2263): 03xgm3 (0.50 #52681, 0.48 #117906, 0.48 #117905), 02vnpv (0.31 #32614, 0.29 #280989, 0.29 #198200), 023kzp (0.22 #6231, 0.14 #1214, 0.12 #26300), 0gl88b (0.17 #5387, 0.14 #370, 0.12 #12913), 0sx5w (0.17 #9661, 0.14 #2135, 0.11 #7152), 01q_ph (0.17 #5067, 0.14 #50, 0.10 #22628), 02mjmr (0.17 #5518, 0.14 #501, 0.10 #18061), 0pyww (0.17 #5996, 0.10 #18539, 0.09 #26065), 01s21dg (0.17 #5979, 0.09 #26048, 0.08 #38593), 0jsg0m (0.17 #6510, 0.09 #26579, 0.08 #14036) >> Best rule #52681 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 01mgsn; >> query: (?x9605, ?x1400) <- place_of_birth(?x1400, ?x9605), county_seat(?x11062, ?x9605), country(?x9605, ?x94), state(?x9605, ?x726) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02frhbc location! 098n_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 224.000 153.000 0.505 http://example.org/people/person/places_lived./people/place_lived/location #14451-03w6sj PRED entity: 03w6sj PRED relation: combatants PRED expected values: 088q1s => 62 concepts (45 used for prediction) PRED predicted values (max 10 best out of 271): 07ssc (0.91 #3819, 0.87 #3557, 0.85 #4209), 027qpc (0.57 #853, 0.57 #723, 0.50 #462), 0chghy (0.57 #2238, 0.50 #3402, 0.50 #3284), 059z0 (0.55 #3141, 0.50 #128, 0.44 #1051), 01h3dj (0.50 #466, 0.43 #2300, 0.43 #857), 0cdbq (0.50 #451, 0.26 #4517, 0.25 #1761), 0f8l9c (0.43 #803, 0.36 #3292, 0.35 #2360), 02k54 (0.43 #655, 0.27 #1316, 0.25 #3011), 0hzlz (0.42 #3672, 0.40 #3270, 0.40 #4588), 03rk0 (0.42 #3672, 0.40 #3270, 0.40 #4588) >> Best rule #3819 for best value: >> intensional similarity = 12 >> extensional distance = 30 >> proper extension: 081pw; 0d06vc; 0gfq9; 0cmc2; 031x2; 0cm2xh; 01h6pn; 0py8j; 086m1; 0cbvg; ... >> query: (?x11436, 07ssc) <- combatants(?x11436, ?x5738), combatants(?x11431, ?x5738), combatants(?x5738, ?x512), capital(?x5738, ?x8751), ?x11431 = 0cwt70, organization(?x5738, ?x4230), country(?x1156, ?x512), film_release_region(?x1386, ?x512), contains(?x512, ?x362), ?x1386 = 0dtfn, nationality(?x11470, ?x512), ?x11470 = 03k545 >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #487 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 4 *> proper extension: 06k75; 08qz1l; *> query: (?x11436, 088q1s) <- combatants(?x11436, ?x5738), combatants(?x11436, ?x3918), combatants(?x11431, ?x5738), combatants(?x3278, ?x5738), combatants(?x5738, ?x512), combatants(?x5738, ?x390), capital(?x5738, ?x8751), ?x11431 = 0cwt70, organization(?x5738, ?x4230), ?x512 = 07ssc, ?x3278 = 0dl4z, locations(?x11436, ?x608), ?x390 = 0chghy, ?x3918 = 02psqkz, featured_film_locations(?x2203, ?x8751) *> conf = 0.33 ranks of expected_values: 21 EVAL 03w6sj combatants 088q1s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 62.000 45.000 0.906 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #14450-01gw8b PRED entity: 01gw8b PRED relation: film PRED expected values: 0ds35l9 => 113 concepts (66 used for prediction) PRED predicted values (max 10 best out of 770): 0jym0 (0.09 #325), 083shs (0.09 #19), 034fl9 (0.08 #59143, 0.07 #69898, 0.06 #78859), 02qr3k8 (0.06 #4875, 0.05 #1291, 0.03 #3083), 03nqnnk (0.06 #2817, 0.05 #4609, 0.02 #40453), 06_wqk4 (0.05 #127, 0.04 #1919, 0.03 #5504), 0blpg (0.05 #657, 0.04 #2449, 0.03 #4241), 08r4x3 (0.05 #154, 0.04 #7324, 0.04 #9116), 01gglm (0.05 #1407, 0.03 #34051, 0.03 #34052), 02qmsr (0.05 #407, 0.03 #2199, 0.03 #3991) >> Best rule #325 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0htlr; 03z509; 0hwbd; 01gvyp; 02l3_5; 01j851; 01l1ls; 05ggt_; >> query: (?x10617, 0jym0) <- award(?x10617, ?x3184), type_of_union(?x10617, ?x566), student(?x263, ?x10617), ?x3184 = 0gkts9 >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01gw8b film 0ds35l9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 66.000 0.091 http://example.org/film/actor/film./film/performance/film #14449-01dkpb PRED entity: 01dkpb PRED relation: people! PRED expected values: 0dcsx => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 46): 0gk4g (0.26 #1050, 0.22 #1115, 0.21 #920), 01l2m3 (0.20 #81, 0.11 #536, 0.10 #601), 0qcr0 (0.12 #261, 0.12 #1561, 0.12 #2731), 02y0js (0.12 #262, 0.12 #847, 0.11 #977), 04p3w (0.12 #271, 0.10 #791, 0.10 #141), 02knxx (0.11 #551, 0.10 #616, 0.05 #2761), 032s66 (0.10 #178, 0.10 #113, 0.08 #438), 051_y (0.10 #177, 0.10 #112, 0.08 #437), 01mtqf (0.10 #134, 0.10 #69, 0.07 #849), 019dmc (0.10 #634, 0.10 #114, 0.07 #894) >> Best rule #1050 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 03h_yfh; 011lpr; >> query: (?x10648, 0gk4g) <- place_of_death(?x10648, ?x1523), celebrities_impersonated(?x3649, ?x10648), people(?x6260, ?x10648) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #145 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 04nw9; 02cqbx; *> query: (?x10648, 0dcsx) <- religion(?x10648, ?x2260), award_nominee(?x10648, ?x5869), place_of_death(?x10648, ?x1523), ?x1523 = 030qb3t *> conf = 0.10 ranks of expected_values: 12 EVAL 01dkpb people! 0dcsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 131.000 131.000 0.255 http://example.org/people/cause_of_death/people #14448-0h5g_ PRED entity: 0h5g_ PRED relation: award_nominee! PRED expected values: 02cllz => 114 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1072): 01l2fn (0.84 #13906, 0.82 #44036, 0.82 #20859), 02cllz (0.84 #13906, 0.82 #44036, 0.82 #20859), 0djywgn (0.84 #13906, 0.82 #44036, 0.82 #20859), 0h5g_ (0.59 #11671, 0.17 #122861, 0.14 #88087), 0170qf (0.21 #18541, 0.17 #122861, 0.14 #88088), 01rr9f (0.21 #18541, 0.14 #88088, 0.11 #111269), 057176 (0.20 #143721, 0.17 #122861, 0.14 #88087), 01pcq3 (0.20 #143721, 0.17 #122861, 0.14 #88087), 0blbxk (0.20 #143721, 0.17 #122861, 0.14 #88087), 0171cm (0.20 #143721, 0.17 #122861, 0.14 #88087) >> Best rule #13906 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 05vsxz; 02qgqt; 02zq43; 01pcq3; 0sz28; 03f1zdw; 01yhvv; 09y20; 05tk7y; 07hbxm; ... >> query: (?x489, ?x100) <- film(?x489, ?x1330), award_nominee(?x489, ?x5454), award_nominee(?x489, ?x100), ?x5454 = 020_95 >> conf = 0.84 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0h5g_ award_nominee! 02cllz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 63.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14447-01pxcf PRED entity: 01pxcf PRED relation: educational_institution! PRED expected values: 01pxcf => 81 concepts (77 used for prediction) PRED predicted values (max 10 best out of 105): 017lvd (0.12 #956, 0.04 #1495, 0.03 #2034), 01bvw5 (0.12 #588, 0.04 #1127, 0.03 #1666), 01csqg (0.12 #922), 019q50 (0.12 #896), 0hsb3 (0.04 #1272, 0.03 #1811, 0.03 #2889), 0kw4j (0.04 #1179, 0.03 #1718, 0.03 #2796), 0g8rj (0.04 #1241, 0.03 #1780, 0.01 #3397), 07wrz (0.04 #1135, 0.03 #1674, 0.01 #3291), 09kvv (0.04 #1115, 0.03 #1654, 0.01 #3271), 08815 (0.04 #1080, 0.03 #1619, 0.01 #3236) >> Best rule #956 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 019q50; >> query: (?x12051, 017lvd) <- institution(?x734, ?x12051), school_type(?x12051, ?x4722), ?x4722 = 047951 >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pxcf educational_institution! 01pxcf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 77.000 0.125 http://example.org/education/educational_institution_campus/educational_institution #14446-028rk PRED entity: 028rk PRED relation: basic_title PRED expected values: 060c4 => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.64 #742, 0.64 #291, 0.57 #381), 0fkvn (0.50 #382, 0.45 #292, 0.43 #112), 0dq3c (0.43 #92, 0.31 #326, 0.28 #741), 01gkgk (0.38 #168, 0.29 #114, 0.18 #1231), 0789n (0.21 #433, 0.14 #388, 0.13 #424), 01dz7z (0.21 #433, 0.06 #1658, 0.04 #1785), 07t3gd (0.21 #433, 0.06 #1658, 0.04 #1785), 04n1q6 (0.21 #433, 0.06 #1658, 0.04 #1785), 021q1c (0.21 #433, 0.06 #1658, 0.04 #1785), 09d6p2 (0.20 #243, 0.13 #405, 0.12 #496) >> Best rule #742 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 0g4gr; 037mh8; >> query: (?x2663, 060c4) <- gender(?x2663, ?x231), ?x231 = 05zppz, taxonomy(?x2663, ?x939) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028rk basic_title 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 180.000 180.000 0.640 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #14445-0dr_4 PRED entity: 0dr_4 PRED relation: nominated_for! PRED expected values: 01y665 => 95 concepts (34 used for prediction) PRED predicted values (max 10 best out of 957): 092ys_y (0.78 #48778, 0.77 #58072, 0.77 #76656), 016tt2 (0.78 #48778, 0.77 #58072, 0.77 #76656), 01d0fp (0.78 #48778, 0.77 #58072, 0.77 #76656), 04ktcgn (0.78 #48778, 0.77 #58072, 0.77 #76656), 0bxtg (0.64 #84, 0.03 #34925, 0.02 #51185), 04cy8rb (0.41 #11612, 0.41 #18580, 0.41 #27872), 01y665 (0.28 #32518, 0.25 #34841, 0.22 #27873), 023zsh (0.28 #32518, 0.25 #34841, 0.22 #27873), 017149 (0.24 #93, 0.13 #53424, 0.02 #9382), 0146pg (0.19 #2443, 0.15 #9410, 0.11 #16378) >> Best rule #48778 for best value: >> intensional similarity = 4 >> extensional distance = 378 >> proper extension: 08sfxj; >> query: (?x1597, ?x574) <- genre(?x1597, ?x53), award_winner(?x1597, ?x574), ?x53 = 07s9rl0, film_crew_role(?x1597, ?x468) >> conf = 0.78 => this is the best rule for 4 predicted values *> Best rule #32518 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 167 *> proper extension: 035xwd; 05p3738; 03sxd2; 04grkmd; 04cv9m; 02z2mr7; 0fsd9t; *> query: (?x1597, ?x800) <- genre(?x1597, ?x162), genre(?x1597, ?x53), ?x53 = 07s9rl0, film(?x800, ?x1597), ?x162 = 04xvlr *> conf = 0.28 ranks of expected_values: 7 EVAL 0dr_4 nominated_for! 01y665 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 95.000 34.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14444-08fbnx PRED entity: 08fbnx PRED relation: language PRED expected values: 03_9r => 89 concepts (88 used for prediction) PRED predicted values (max 10 best out of 49): 03_9r (0.48 #1057, 0.23 #591, 0.23 #883), 06nm1 (0.29 #184, 0.24 #300, 0.19 #768), 03k50 (0.19 #531, 0.13 #706, 0.10 #356), 05zjd (0.18 #4109, 0.17 #25, 0.12 #83), 04306rv (0.18 #294, 0.17 #4, 0.14 #178), 064_8sq (0.17 #21, 0.15 #369, 0.15 #779), 012w70 (0.17 #12, 0.15 #360, 0.14 #186), 0653m (0.17 #11, 0.13 #534, 0.12 #69), 0459q4 (0.17 #36, 0.12 #94, 0.10 #384), 06mp7 (0.17 #15, 0.12 #73, 0.10 #363) >> Best rule #1057 for best value: >> intensional similarity = 2 >> extensional distance = 67 >> proper extension: 05hd32; >> query: (?x4770, 03_9r) <- country(?x4770, ?x252), ?x252 = 03_3d >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08fbnx language 03_9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 88.000 0.478 http://example.org/film/film/language #14443-0cj2nl PRED entity: 0cj2nl PRED relation: award_winner! PRED expected values: 027n06w => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 98): 027n06w (0.17 #7616, 0.17 #73, 0.12 #919), 0gx_st (0.17 #7616, 0.17 #37, 0.11 #8041), 05c1t6z (0.17 #7616, 0.11 #861, 0.08 #579), 02q690_ (0.17 #7616, 0.11 #8041, 0.10 #8324), 03gyp30 (0.17 #7616, 0.11 #8041, 0.10 #8324), 027hjff (0.17 #7616, 0.11 #8041, 0.10 #8324), 09pj68 (0.17 #7616, 0.11 #8041, 0.10 #8324), 03gt46z (0.17 #63, 0.11 #8041, 0.10 #8324), 07y_p6 (0.17 #98, 0.11 #8041, 0.10 #8324), 02wzl1d (0.11 #8041, 0.10 #8324, 0.10 #8889) >> Best rule #7616 for best value: >> intensional similarity = 2 >> extensional distance = 1699 >> proper extension: 09mfvx; 0kcdl; 0kc9f; >> query: (?x3896, ?x1265) <- nominated_for(?x3896, ?x1631), honored_for(?x1265, ?x1631) >> conf = 0.17 => this is the best rule for 7 predicted values ranks of expected_values: 1 EVAL 0cj2nl award_winner! 027n06w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.174 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14442-08nz99 PRED entity: 08nz99 PRED relation: award_nominee PRED expected values: 04511f => 111 concepts (66 used for prediction) PRED predicted values (max 10 best out of 887): 04511f (0.80 #30439, 0.80 #91323, 0.79 #63225), 08nz99 (0.50 #4581, 0.46 #96006, 0.33 #2239), 03ft8 (0.46 #96006, 0.23 #70252, 0.22 #126443), 018dnt (0.33 #142838, 0.06 #63223, 0.05 #60879), 01541z (0.33 #142838, 0.02 #124545, 0.02 #145624), 0fb1q (0.33 #142838, 0.02 #47544, 0.01 #59253), 0l_dv (0.33 #142838), 0jbp0 (0.33 #142838), 0n8bn (0.33 #142838), 0m32_ (0.33 #142838) >> Best rule #30439 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 06j0md; 06pj8; 09b0xs; 098n5; 07lwsz; 03y9ccy; 01d8yn; 04x4s2; 01xndd; 08qvhv; ... >> query: (?x11373, ?x4299) <- program_creator(?x13070, ?x11373), award_nominee(?x5431, ?x11373), award_nominee(?x4299, ?x11373), actor(?x13070, ?x585), story_by(?x66, ?x5431) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08nz99 award_nominee 04511f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 66.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14441-091n7z PRED entity: 091n7z PRED relation: profession PRED expected values: 02hrh1q => 138 concepts (93 used for prediction) PRED predicted values (max 10 best out of 86): 02hrh1q (0.97 #12689, 0.93 #12988, 0.86 #4789), 0dxtg (0.80 #11644, 0.50 #461, 0.41 #2400), 016z4k (0.72 #10591, 0.33 #2092, 0.29 #8803), 01d_h8 (0.53 #2392, 0.46 #11636, 0.36 #9699), 09jwl (0.53 #10607, 0.39 #4774, 0.37 #11054), 0kyk (0.46 #7040, 0.14 #5997, 0.13 #6593), 02krf9 (0.39 #4774, 0.18 #2860, 0.17 #6292), 02jknp (0.38 #11638, 0.20 #6273, 0.18 #13130), 03gjzk (0.35 #9709, 0.35 #5983, 0.35 #11646), 018gz8 (0.33 #5985, 0.28 #7177, 0.28 #6879) >> Best rule #12689 for best value: >> intensional similarity = 7 >> extensional distance = 682 >> proper extension: 0cnl80; 04wtx1; 049g_xj; 02d4ct; 07sgfsl; 01_j71; 01k5zk; 027r8p; 031ydm; 045w_4; ... >> query: (?x12811, 02hrh1q) <- gender(?x12811, ?x514), ?x514 = 02zsn, profession(?x12811, ?x1383), profession(?x11992, ?x1383), profession(?x11152, ?x1383), ?x11992 = 01pgk0, ?x11152 = 06cl2w >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 091n7z profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 93.000 0.972 http://example.org/people/person/profession #14440-02d413 PRED entity: 02d413 PRED relation: films! PRED expected values: 0fx2s => 101 concepts (53 used for prediction) PRED predicted values (max 10 best out of 71): 06d4h (0.20 #43, 0.06 #4118, 0.05 #2543), 02_h0 (0.08 #411, 0.06 #256, 0.06 #721), 081pw (0.08 #2503, 0.07 #4078, 0.07 #2031), 07c52 (0.06 #487, 0.03 #1266, 0.03 #4095), 0fx2s (0.06 #2572, 0.05 #1318, 0.05 #2100), 05489 (0.05 #2079, 0.05 #2551, 0.04 #1297), 0fzyg (0.05 #2081, 0.05 #4128, 0.05 #2553), 07_nf (0.05 #533, 0.04 #1624, 0.03 #1468), 0kbq (0.05 #571, 0.03 #1350, 0.03 #2132), 0bq3x (0.04 #2058, 0.04 #2530, 0.04 #4105) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 06pyc2; >> query: (?x69, 06d4h) <- film(?x1250, ?x69), nominated_for(?x2596, ?x69), film_release_distribution_medium(?x69, ?x81), ?x1250 = 01tcf7 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2572 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 331 *> proper extension: 05f67hw; *> query: (?x69, 0fx2s) <- film_release_region(?x69, ?x94), films(?x5179, ?x69), ?x94 = 09c7w0 *> conf = 0.06 ranks of expected_values: 5 EVAL 02d413 films! 0fx2s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 53.000 0.200 http://example.org/film/film_subject/films #14439-0ggbhy7 PRED entity: 0ggbhy7 PRED relation: nominated_for! PRED expected values: 0f4x7 02pqp12 => 76 concepts (69 used for prediction) PRED predicted values (max 10 best out of 215): 0gq9h (0.42 #2578, 0.38 #2807, 0.35 #1433), 0gs9p (0.38 #2580, 0.32 #2809, 0.31 #1435), 019f4v (0.37 #2570, 0.33 #2799, 0.31 #1425), 0k611 (0.32 #2589, 0.31 #1444, 0.29 #2818), 0gq_v (0.32 #2538, 0.27 #2767, 0.25 #3912), 0gqy2 (0.28 #2637, 0.26 #1492, 0.25 #2866), 0f4x7 (0.27 #2544, 0.25 #1399, 0.23 #2773), 04dn09n (0.27 #2552, 0.25 #2781, 0.24 #1407), 0gr0m (0.25 #2575, 0.22 #2804, 0.22 #1430), 0p9sw (0.25 #2539, 0.24 #1394, 0.22 #2768) >> Best rule #2578 for best value: >> intensional similarity = 4 >> extensional distance = 439 >> proper extension: 04xbq3; >> query: (?x3012, 0gq9h) <- film(?x874, ?x3012), nominated_for(?x1414, ?x3012), nominated_for(?x198, ?x3012), honored_for(?x2032, ?x3012) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #2544 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 439 *> proper extension: 04xbq3; *> query: (?x3012, 0f4x7) <- film(?x874, ?x3012), nominated_for(?x1414, ?x3012), nominated_for(?x198, ?x3012), honored_for(?x2032, ?x3012) *> conf = 0.27 ranks of expected_values: 7, 14 EVAL 0ggbhy7 nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 76.000 69.000 0.424 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0ggbhy7 nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 76.000 69.000 0.424 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14438-03f1d47 PRED entity: 03f1d47 PRED relation: artists! PRED expected values: 012yc => 85 concepts (27 used for prediction) PRED predicted values (max 10 best out of 180): 064t9 (0.72 #6507, 0.69 #2177, 0.69 #3105), 06j6l (0.70 #2213, 0.70 #3141, 0.68 #3450), 06by7 (0.56 #7135, 0.49 #6516, 0.44 #6825), 02x8m (0.30 #3420, 0.29 #3111, 0.27 #2183), 05bt6j (0.29 #6847, 0.27 #7157, 0.27 #6538), 03_d0 (0.28 #3103, 0.27 #2175, 0.27 #3412), 0155w (0.25 #2889, 0.23 #3507, 0.22 #3198), 02lnbg (0.25 #3149, 0.23 #4077, 0.23 #3458), 0ggx5q (0.25 #2860, 0.24 #2241, 0.23 #3169), 02vjzr (0.24 #752, 0.20 #2297, 0.18 #2916) >> Best rule #6507 for best value: >> intensional similarity = 3 >> extensional distance = 487 >> proper extension: 0kvnn; 04mky3; >> query: (?x4983, 064t9) <- artists(?x3928, ?x4983), artists(?x3928, ?x1181), ?x1181 = 0b68vs >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #5565 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 286 *> proper extension: 03rwz3; 05mxw33; *> query: (?x4983, ?x3319) <- award_nominee(?x4983, ?x3175), artists(?x3319, ?x3175), artists(?x2937, ?x3175), ?x2937 = 0glt670 *> conf = 0.18 ranks of expected_values: 20 EVAL 03f1d47 artists! 012yc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 85.000 27.000 0.720 http://example.org/music/genre/artists #14437-0443v1 PRED entity: 0443v1 PRED relation: film! PRED expected values: 033w9g 031v3p => 99 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1090): 03d_w3h (0.50 #108203, 0.44 #64510, 0.44 #81155), 01gp_x (0.20 #2082, 0.17 #2081, 0.16 #6246), 027hnjh (0.20 #2082, 0.17 #2081, 0.16 #6246), 0dvld (0.13 #3143, 0.08 #9388, 0.02 #65571), 0c0k1 (0.13 #1509, 0.05 #5673, 0.03 #22322), 0jbp0 (0.13 #1759, 0.04 #14251, 0.03 #8006), 0k2mxq (0.11 #10410, 0.09 #4164, 0.06 #83237), 0bl2g (0.10 #8382, 0.02 #10465, 0.02 #60404), 015pkc (0.10 #2361, 0.06 #8606, 0.03 #29419), 01vy_v8 (0.10 #734, 0.04 #4898, 0.03 #6981) >> Best rule #108203 for best value: >> intensional similarity = 4 >> extensional distance = 1010 >> proper extension: 04glx0; >> query: (?x11615, ?x3583) <- nominated_for(?x3583, ?x11615), award_nominee(?x3583, ?x5995), location(?x3583, ?x1131), place_of_death(?x1047, ?x1131) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #108205 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1010 *> proper extension: 04glx0; *> query: (?x11615, ?x5995) <- nominated_for(?x3583, ?x11615), award_nominee(?x3583, ?x5995), location(?x3583, ?x1131), place_of_death(?x1047, ?x1131) *> conf = 0.06 ranks of expected_values: 55 EVAL 0443v1 film! 031v3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 99.000 52.000 0.503 http://example.org/film/actor/film./film/performance/film EVAL 0443v1 film! 033w9g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 52.000 0.503 http://example.org/film/actor/film./film/performance/film #14436-05kkh PRED entity: 05kkh PRED relation: district_represented! PRED expected values: 077g7n 01gsvp 070mff 01gtdd => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 35): 077g7n (0.89 #248, 0.89 #213, 0.88 #528), 070mff (0.81 #550, 0.78 #270, 0.78 #235), 02bp37 (0.61 #218, 0.60 #533, 0.58 #183), 02bqm0 (0.57 #474, 0.57 #229, 0.56 #544), 02bqmq (0.55 #1156, 0.55 #467, 0.54 #222), 01gtdd (0.55 #1156, 0.48 #239, 0.44 #554), 02bqn1 (0.55 #1156, 0.45 #461, 0.44 #531), 02cg7g (0.55 #1156, 0.43 #226, 0.43 #471), 02gkzs (0.55 #1156, 0.41 #225, 0.41 #470), 01gsvp (0.55 #1156, 0.41 #233, 0.38 #548) >> Best rule #248 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0h5qxv; >> query: (?x177, 077g7n) <- jurisdiction_of_office(?x900, ?x177), first_level_division_of(?x177, ?x94), district_represented(?x176, ?x177) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6, 10 EVAL 05kkh district_represented! 01gtdd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 124.000 124.000 0.891 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05kkh district_represented! 070mff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.891 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05kkh district_represented! 01gsvp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 124.000 124.000 0.891 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05kkh district_represented! 077g7n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.891 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #14435-0d1w9 PRED entity: 0d1w9 PRED relation: films PRED expected values: 0170z3 => 59 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1260): 019vhk (0.40 #1171, 0.17 #2208, 0.14 #2725), 0hfzr (0.33 #1759, 0.33 #724, 0.29 #2796), 0p3_y (0.33 #120, 0.20 #1158, 0.17 #2195), 06rzwx (0.33 #354, 0.20 #1392, 0.17 #2429), 02q0k7v (0.33 #387, 0.20 #1425, 0.17 #2462), 0btpm6 (0.33 #376, 0.20 #1414, 0.17 #2451), 02v570 (0.33 #373, 0.20 #1411, 0.17 #2448), 0cf8qb (0.33 #389, 0.20 #1427, 0.17 #2464), 0f4_2k (0.33 #288, 0.20 #1326, 0.17 #2363), 07j8r (0.33 #123, 0.20 #1161, 0.17 #2198) >> Best rule #1171 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0kbq; 0ktds; >> query: (?x4450, 019vhk) <- films(?x4450, ?x1002), films(?x4450, ?x407), nominated_for(?x112, ?x407), written_by(?x1002, ?x6356), nominated_for(?x230, ?x407), film(?x1850, ?x407), genre(?x407, ?x53), film(?x2122, ?x1002), film(?x71, ?x407), ?x2122 = 018swb, film_crew_role(?x1002, ?x2154) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #4146 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 17 *> proper extension: 04xfb; *> query: (?x4450, 0170z3) <- films(?x4450, ?x1002), films(?x4450, ?x407), nominated_for(?x112, ?x407), film_production_design_by(?x407, ?x12933), film_release_region(?x1002, ?x87), nominated_for(?x1307, ?x1002), produced_by(?x407, ?x7946), ?x1307 = 0gq9h, film(?x6356, ?x1002), film(?x71, ?x407) *> conf = 0.05 ranks of expected_values: 210 EVAL 0d1w9 films 0170z3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 59.000 14.000 0.400 http://example.org/film/film_subject/films #14434-0k5g9 PRED entity: 0k5g9 PRED relation: featured_film_locations PRED expected values: 035p3 => 86 concepts (79 used for prediction) PRED predicted values (max 10 best out of 96): 02_286 (0.32 #1220, 0.29 #5779, 0.27 #7698), 030qb3t (0.16 #278, 0.14 #1239, 0.14 #759), 04jpl (0.14 #7687, 0.11 #1209, 0.09 #5768), 01n6r0 (0.08 #101, 0.02 #580, 0.02 #1301), 01sn3 (0.08 #87, 0.02 #807, 0.02 #1047), 0fvvz (0.08 #32, 0.02 #1232), 0rh6k (0.07 #5760, 0.05 #240, 0.05 #7679), 080h2 (0.06 #5783, 0.03 #7702, 0.03 #9864), 0cv3w (0.05 #790, 0.03 #1750, 0.02 #5829), 01_d4 (0.05 #286, 0.05 #1007, 0.04 #526) >> Best rule #1220 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 0192hw; >> query: (?x2717, 02_286) <- featured_film_locations(?x2717, ?x3125), film_release_region(?x2717, ?x87), film_festivals(?x2717, ?x5415) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #5991 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 294 *> proper extension: 035xwd; 047qxs; 03whyr; *> query: (?x2717, 035p3) <- production_companies(?x2717, ?x902), music(?x2717, ?x9946), featured_film_locations(?x2717, ?x3125) *> conf = 0.03 ranks of expected_values: 20 EVAL 0k5g9 featured_film_locations 035p3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 86.000 79.000 0.317 http://example.org/film/film/featured_film_locations #14433-010bxh PRED entity: 010bxh PRED relation: contains! PRED expected values: 09c7w0 => 106 concepts (37 used for prediction) PRED predicted values (max 10 best out of 121): 09c7w0 (0.86 #28678, 0.79 #31372, 0.77 #32269), 04_1l0v (0.40 #17013, 0.35 #30470, 0.31 #19703), 02jx1 (0.38 #16203, 0.31 #18894, 0.25 #10830), 01n7q (0.25 #10821, 0.23 #18885, 0.19 #21576), 07ssc (0.25 #16148, 0.20 #18839, 0.16 #23322), 05tbn (0.12 #16339, 0.10 #10966, 0.08 #21721), 0d060g (0.12 #18820, 0.10 #21511, 0.08 #27791), 0mr_8 (0.09 #3407, 0.06 #1617, 0.06 #5197), 0mskq (0.09 #675, 0.06 #1570, 0.06 #2465), 0msck (0.09 #878, 0.06 #1773, 0.06 #2668) >> Best rule #28678 for best value: >> intensional similarity = 6 >> extensional distance = 1040 >> proper extension: 0nv99; >> query: (?x7282, 09c7w0) <- contains(?x3634, ?x7282), contains(?x3634, ?x10465), contains(?x3634, ?x8952), time_zones(?x10465, ?x1638), ?x1638 = 02fqwt, source(?x8952, ?x958) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 010bxh contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 37.000 0.856 http://example.org/location/location/contains #14432-059f4 PRED entity: 059f4 PRED relation: contains PRED expected values: 0n5y4 => 204 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2877): 01m9f1 (0.83 #61649, 0.81 #76328, 0.80 #79264), 0nbzp (0.83 #61649, 0.81 #76328, 0.80 #79264), 02bqy (0.80 #79264, 0.67 #184956, 0.51 #117427), 0n5y4 (0.67 #32292, 0.64 #105685, 0.62 #49906), 0n5_g (0.67 #32292, 0.64 #105685, 0.62 #49906), 0mpfn (0.67 #32292, 0.64 #105685, 0.62 #49906), 0k3ll (0.67 #32292, 0.64 #105685, 0.62 #49906), 0nm6z (0.67 #32292, 0.64 #105685, 0.62 #49906), 0nm3n (0.67 #32292, 0.64 #105685, 0.62 #49906), 0k3k1 (0.67 #32292, 0.64 #105685, 0.62 #49906) >> Best rule #61649 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 0d9rp; >> query: (?x728, ?x7600) <- contains(?x728, ?x5088), adjoins(?x728, ?x279), state(?x7600, ?x728), country(?x728, ?x94) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #32292 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 06btq; 0gj4fx; *> query: (?x728, ?x7954) <- contains(?x728, ?x12433), adjoins(?x279, ?x728), district_represented(?x176, ?x728), adjoins(?x7954, ?x12433) *> conf = 0.67 ranks of expected_values: 4 EVAL 059f4 contains 0n5y4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 204.000 94.000 0.834 http://example.org/location/location/contains #14431-029bkp PRED entity: 029bkp PRED relation: specialization_of PRED expected values: 028kk_ => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 40): 09jwl (0.50 #136, 0.31 #264, 0.30 #232), 0n1h (0.33 #4, 0.25 #69, 0.17 #197), 0cbd2 (0.10 #290, 0.09 #258, 0.08 #455), 06q2q (0.08 #858, 0.08 #1256, 0.07 #1289), 02hrh1q (0.06 #295, 0.05 #391, 0.05 #425), 01c979 (0.05 #575, 0.04 #739, 0.04 #868), 015cjr (0.04 #666, 0.04 #763, 0.03 #1027), 02jknp (0.04 #227, 0.02 #291, 0.02 #323), 014ktf (0.04 #252, 0.02 #1236, 0.02 #481), 04_tv (0.03 #956, 0.03 #1020, 0.03 #988) >> Best rule #136 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 01c72t; 0nbcg; 0fnpj; >> query: (?x4654, 09jwl) <- profession(?x2698, ?x4654), profession(?x215, ?x4654), award(?x215, ?x2563), nationality(?x215, ?x94), ?x2563 = 01cw51, ?x2698 = 09hnb, artists(?x378, ?x215) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #844 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 72 *> proper extension: 01bs9f; *> query: (?x4654, ?x220) <- profession(?x7262, ?x4654), profession(?x215, ?x4654), award_winner(?x215, ?x217), profession(?x215, ?x220), award_winner(?x1088, ?x7262), religion(?x7262, ?x109) *> conf = 0.02 ranks of expected_values: 36 EVAL 029bkp specialization_of 028kk_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 41.000 41.000 0.500 http://example.org/people/profession/specialization_of #14430-0jm5b PRED entity: 0jm5b PRED relation: draft PRED expected values: 09th87 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 16): 06439y (0.81 #592, 0.78 #1110, 0.71 #932), 09th87 (0.80 #284, 0.78 #1110, 0.76 #588), 092j54 (0.48 #827, 0.34 #1021, 0.33 #594), 05vsb7 (0.46 #820, 0.33 #594, 0.30 #949), 09l0x9 (0.46 #829, 0.32 #1023, 0.30 #958), 0g3zpp (0.46 #821, 0.30 #1015, 0.30 #950), 03nt7j (0.38 #825, 0.34 #857, 0.31 #970), 02qw1zx (0.33 #824, 0.22 #953, 0.21 #1098), 02pq_rp (0.33 #594, 0.30 #955, 0.28 #1100), 02pq_x5 (0.33 #594, 0.30 #977, 0.26 #961) >> Best rule #592 for best value: >> intensional similarity = 10 >> extensional distance = 19 >> proper extension: 0jmnl; >> query: (?x11805, 06439y) <- school(?x11805, ?x9131), draft(?x11805, ?x2569), ?x2569 = 038c0q, school(?x465, ?x9131), contains(?x94, ?x9131), ?x94 = 09c7w0, school(?x729, ?x9131), major_field_of_study(?x9131, ?x2601), position_s(?x729, ?x180), draft(?x729, ?x685) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #284 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 8 *> proper extension: 0jmcb; *> query: (?x11805, 09th87) <- position(?x11805, ?x6848), position(?x11805, ?x5755), school(?x11805, ?x6644), sport(?x11805, ?x4833), draft(?x11805, ?x8586), draft(?x11805, ?x8133), ?x6848 = 02_ssl, ?x8586 = 038981, school_type(?x6644, ?x3092), school_type(?x6644, ?x1507), ?x8133 = 025tn92, ?x3092 = 05jxkf, ?x5755 = 0355dz, ?x1507 = 01_9fk *> conf = 0.80 ranks of expected_values: 2 EVAL 0jm5b draft 09th87 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.810 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #14429-0mlzk PRED entity: 0mlzk PRED relation: adjoins! PRED expected values: 0mmpz => 157 concepts (93 used for prediction) PRED predicted values (max 10 best out of 502): 0mmpz (0.84 #52472, 0.84 #5477, 0.83 #7825), 0mmrd (0.33 #769, 0.25 #27400, 0.24 #60307), 0mlzk (0.25 #63442, 0.25 #68139, 0.25 #32099), 0mmr1 (0.25 #63442, 0.25 #68139, 0.25 #32099), 0mlyj (0.25 #63442, 0.25 #68139, 0.24 #50119), 05rgl (0.22 #885, 0.09 #4798, 0.07 #8711), 0l2xl (0.13 #1935, 0.09 #5065, 0.07 #6631), 0d6lp (0.13 #1727, 0.07 #4857, 0.06 #6423), 05kj_ (0.11 #815, 0.09 #4728, 0.07 #8641), 03s5t (0.11 #920, 0.07 #4833, 0.05 #8746) >> Best rule #52472 for best value: >> intensional similarity = 5 >> extensional distance = 190 >> proper extension: 01279v; >> query: (?x11569, ?x11525) <- adjoins(?x11366, ?x11569), adjoins(?x11569, ?x11525), adjoins(?x14148, ?x11366), administrative_division(?x11367, ?x11366), second_level_divisions(?x94, ?x11366) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mlzk adjoins! 0mmpz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 93.000 0.836 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #14428-02hrlh PRED entity: 02hrlh PRED relation: role! PRED expected values: 011lvx => 38 concepts (33 used for prediction) PRED predicted values (max 10 best out of 899): 01vs4ff (0.67 #2696, 0.57 #3173, 0.50 #4140), 050z2 (0.60 #5745, 0.60 #5462, 0.60 #4021), 04bpm6 (0.60 #3903, 0.57 #2936, 0.53 #5344), 0137g1 (0.60 #3950, 0.57 #2983, 0.50 #3468), 03ryks (0.60 #4137, 0.50 #3655, 0.50 #1267), 05qhnq (0.53 #5588, 0.50 #4147, 0.50 #2703), 082brv (0.50 #4103, 0.50 #1713, 0.50 #1431), 018x3 (0.50 #4089, 0.50 #2645, 0.50 #2173), 0lzkm (0.50 #4005, 0.50 #1615, 0.50 #1135), 0161sp (0.50 #2517, 0.50 #2045, 0.50 #1091) >> Best rule #2696 for best value: >> intensional similarity = 39 >> extensional distance = 4 >> proper extension: 02fsn; >> query: (?x11978, 01vs4ff) <- role(?x8014, ?x11978), role(?x5417, ?x11978), role(?x3296, ?x11978), role(?x1969, ?x11978), role(?x1437, ?x11978), role(?x314, ?x11978), role(?x227, ?x11978), ?x1969 = 04rzd, role(?x11978, ?x3991), role(?x11978, ?x3418), ?x5417 = 02w3w, ?x1437 = 01vdm0, ?x3296 = 07_l6, ?x3991 = 05842k, ?x314 = 02sgy, ?x227 = 0342h, role(?x1750, ?x3418), role(?x228, ?x3418), role(?x214, ?x3418), ?x228 = 0l14qv, role(?x745, ?x3418), role(?x3418, ?x614), role(?x6039, ?x8014), role(?x2798, ?x8014), role(?x1495, ?x8014), role(?x432, ?x8014), group(?x11978, ?x1945), role(?x925, ?x3418), role(?x3657, ?x8014), ?x3657 = 01w8n89, role(?x3418, ?x316), ?x432 = 042v_gx, ?x925 = 07q1v4, ?x1495 = 013y1f, ?x6039 = 05kms, ?x2798 = 03qjg, instrumentalists(?x214, ?x215), role(?x214, ?x1166), ?x1750 = 02hnl >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1437 for first EXPECTED value: *> intensional similarity = 44 *> extensional distance = 2 *> proper extension: 05842k; *> query: (?x11978, ?x211) <- role(?x5417, ?x11978), role(?x3296, ?x11978), role(?x1969, ?x11978), role(?x1437, ?x11978), ?x1969 = 04rzd, role(?x11978, ?x3991), role(?x11978, ?x3418), ?x5417 = 02w3w, ?x1437 = 01vdm0, ?x3296 = 07_l6, role(?x8921, ?x3991), role(?x6626, ?x3991), role(?x6049, ?x3991), role(?x4741, ?x3991), role(?x1181, ?x3991), role(?x460, ?x3991), role(?x211, ?x3991), role(?x3991, ?x3703), role(?x3991, ?x1663), role(?x3991, ?x1267), role(?x3991, ?x894), role(?x3991, ?x716), role(?x3991, ?x214), ?x4741 = 01s21dg, ?x214 = 02pprs, ?x1663 = 01w4dy, ?x6049 = 082brv, ?x3418 = 02w4b, ?x3703 = 02dlh2, role(?x2957, ?x3991), role(?x1662, ?x3991), role(?x316, ?x3991), ?x2957 = 01v8y9, award_nominee(?x5298, ?x6626), ?x1662 = 02bxd, ?x8921 = 016s0m, ?x316 = 05r5c, ?x716 = 018vs, ?x1181 = 0b68vs, ?x894 = 03m5k, location(?x460, ?x461), nationality(?x460, ?x94), group(?x1267, ?x9228), ?x9228 = 0cbm64 *> conf = 0.28 ranks of expected_values: 164 EVAL 02hrlh role! 011lvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 38.000 33.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #14427-07s9rl0 PRED entity: 07s9rl0 PRED relation: genre! PRED expected values: 017f3m 0hz55 0828jw 04p5cr 04glx0 02qkq0 0dk0dj 03y317 03_8kz 0300ml => 60 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1228): 0l76z (0.60 #2591, 0.40 #2077, 0.33 #1060), 045qmr (0.60 #2470, 0.38 #3834, 0.33 #1110), 0828jw (0.60 #2949, 0.33 #734, 0.33 #565), 028k2x (0.60 #2976, 0.33 #423, 0.29 #3480), 09g_31 (0.60 #2991, 0.33 #438, 0.29 #3495), 02648p (0.60 #2927, 0.33 #374, 0.25 #1729), 09kn9 (0.60 #2903, 0.33 #350, 0.25 #1705), 014gjp (0.60 #2116, 0.33 #1099, 0.20 #2802), 0584r4 (0.60 #2046, 0.33 #1029, 0.20 #2560), 019nnl (0.60 #2042, 0.33 #1025, 0.20 #2556) >> Best rule #2591 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 06cvj; >> query: (?x53, 0l76z) <- genre(?x10446, ?x53), genre(?x7538, ?x53), genre(?x238, ?x53), film(?x2646, ?x7538), language(?x10446, ?x254), ?x238 = 027qgy >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2949 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 01htzx; *> query: (?x53, 0828jw) <- genre(?x11549, ?x53), genre(?x4063, ?x53), genre(?x623, ?x53), ?x4063 = 08cx5g, genre(?x11549, ?x225), nominated_for(?x593, ?x623) *> conf = 0.60 ranks of expected_values: 3, 14, 17, 28, 49, 50, 145, 1134, 1213 EVAL 07s9rl0 genre! 0300ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 60.000 47.000 0.600 http://example.org/tv/tv_program/genre EVAL 07s9rl0 genre! 03_8kz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 47.000 0.600 http://example.org/tv/tv_program/genre EVAL 07s9rl0 genre! 03y317 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 47.000 0.600 http://example.org/tv/tv_program/genre EVAL 07s9rl0 genre! 0dk0dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 60.000 47.000 0.600 http://example.org/tv/tv_program/genre EVAL 07s9rl0 genre! 02qkq0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 47.000 0.600 http://example.org/tv/tv_program/genre EVAL 07s9rl0 genre! 04glx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 60.000 47.000 0.600 http://example.org/tv/tv_program/genre EVAL 07s9rl0 genre! 04p5cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 60.000 47.000 0.600 http://example.org/tv/tv_program/genre EVAL 07s9rl0 genre! 0828jw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 47.000 0.600 http://example.org/tv/tv_program/genre EVAL 07s9rl0 genre! 0hz55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 60.000 47.000 0.600 http://example.org/tv/tv_program/genre EVAL 07s9rl0 genre! 017f3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 60.000 47.000 0.600 http://example.org/tv/tv_program/genre #14426-0bvfqq PRED entity: 0bvfqq PRED relation: ceremony! PRED expected values: 0gr07 => 32 concepts (31 used for prediction) PRED predicted values (max 10 best out of 368): 0gr07 (0.80 #2053, 0.80 #862, 0.77 #624), 02r22gf (0.48 #1427, 0.47 #1189, 0.47 #1904), 02hsq3m (0.48 #1427, 0.47 #1189, 0.47 #1904), 02r0csl (0.48 #1427, 0.47 #1189, 0.47 #1904), 02g3v6 (0.48 #1427, 0.47 #1189, 0.47 #1904), 02x2gy0 (0.48 #1427, 0.47 #1189, 0.47 #1904), 09qv_s (0.31 #1428, 0.25 #1666, 0.23 #2142), 09qwmm (0.31 #1428, 0.25 #1666, 0.23 #2142), 09td7p (0.31 #1428, 0.25 #1666, 0.23 #2142), 019f4v (0.31 #1428, 0.25 #1666, 0.23 #2142) >> Best rule #2053 for best value: >> intensional similarity = 17 >> extensional distance = 33 >> proper extension: 02yw5r; 0bzkgg; 0bzk2h; 0bz6sb; 0ftlxj; 0bzknt; 02pgky2; 05q7cj; 0c4hnm; >> query: (?x2210, 0gr07) <- award_winner(?x2210, ?x5653), award_winner(?x2210, ?x157), ceremony(?x3066, ?x2210), ceremony(?x1972, ?x2210), ceremony(?x1243, ?x2210), ceremony(?x500, ?x2210), ?x3066 = 0gqy2, ?x1972 = 0gqyl, honored_for(?x2210, ?x861), award_nominee(?x4393, ?x5653), ?x1243 = 0gr0m, award_winner(?x972, ?x5653), ?x500 = 0p9sw, nominated_for(?x4393, ?x324), award_nominee(?x92, ?x157), film(?x157, ?x8438), nominated_for(?x384, ?x8438) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bvfqq ceremony! 0gr07 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony #14425-07_f2 PRED entity: 07_f2 PRED relation: religion PRED expected values: 0c8wxp => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 26): 0c8wxp (0.90 #396, 0.90 #284, 0.84 #312), 0631_ (0.90 #286, 0.86 #398, 0.86 #314), 04pk9 (0.86 #70, 0.83 #295, 0.80 #407), 05w5d (0.81 #298, 0.80 #326, 0.78 #410), 021_0p (0.65 #294, 0.65 #97, 0.62 #153), 03_gx (0.49 #402, 0.49 #318, 0.48 #290), 058x5 (0.42 #86, 0.41 #58, 0.40 #283), 0flw86 (0.40 #2501, 0.38 #1210, 0.38 #1575), 092bf5 (0.40 #2501, 0.37 #2726, 0.29 #319), 03j6c (0.09 #1842, 0.09 #1224, 0.09 #1449) >> Best rule #396 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0rh6k; 07ssc; >> query: (?x7405, 0c8wxp) <- jurisdiction_of_office(?x900, ?x7405), state_province_region(?x1476, ?x7405), religion(?x7405, ?x109) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07_f2 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 188.000 0.902 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #14424-01t8sr PRED entity: 01t8sr PRED relation: major_field_of_study PRED expected values: 04_tv => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 118): 01mkq (0.43 #520, 0.40 #646, 0.37 #772), 02lp1 (0.41 #642, 0.40 #516, 0.39 #768), 0g26h (0.39 #675, 0.38 #801, 0.35 #1809), 02j62 (0.39 #536, 0.38 #662, 0.37 #788), 062z7 (0.39 #533, 0.34 #659, 0.31 #1163), 04rjg (0.37 #525, 0.30 #651, 0.30 #777), 03g3w (0.34 #532, 0.26 #1288, 0.25 #3934), 0_jm (0.33 #691, 0.30 #1447, 0.29 #1573), 01lj9 (0.31 #546, 0.22 #1176, 0.22 #672), 05qfh (0.31 #542, 0.21 #920, 0.20 #1298) >> Best rule #520 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 01d34b; 02xwzh; >> query: (?x1506, 01mkq) <- student(?x1506, ?x105), colors(?x1506, ?x663), participant(?x105, ?x4767) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #771 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 98 *> proper extension: 0frm7n; *> query: (?x1506, 04_tv) <- category(?x1506, ?x134), school(?x1883, ?x1506), ?x134 = 08mbj5d *> conf = 0.12 ranks of expected_values: 36 EVAL 01t8sr major_field_of_study 04_tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 132.000 132.000 0.433 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14423-0ds2n PRED entity: 0ds2n PRED relation: film_crew_role PRED expected values: 020xn5 0dxtw => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 28): 0dxtw (0.40 #858, 0.39 #372, 0.39 #675), 02rh1dz (0.17 #8, 0.12 #371, 0.11 #461), 0215hd (0.15 #378, 0.14 #773, 0.14 #468), 01xy5l_ (0.12 #374, 0.11 #769, 0.10 #677), 02_n3z (0.09 #364, 0.09 #2341, 0.09 #454), 015h31 (0.09 #2341, 0.09 #826, 0.08 #7), 04pyp5 (0.09 #2341, 0.08 #862, 0.07 #135), 089fss (0.09 #2341, 0.07 #368, 0.07 #458), 094hwz (0.09 #2341, 0.06 #12, 0.04 #104), 033smt (0.09 #2341, 0.06 #22, 0.05 #475) >> Best rule #858 for best value: >> intensional similarity = 3 >> extensional distance = 847 >> proper extension: 09v42sf; >> query: (?x3218, 0dxtw) <- film(?x434, ?x3218), film_crew_role(?x3218, ?x137), ?x137 = 09zzb8 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14 EVAL 0ds2n film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.396 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ds2n film_crew_role 020xn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 78.000 78.000 0.396 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14422-01v9724 PRED entity: 01v9724 PRED relation: influenced_by PRED expected values: 04xjp 081k8 => 116 concepts (34 used for prediction) PRED predicted values (max 10 best out of 293): 05qmj (0.38 #1073, 0.33 #2385, 0.33 #1510), 01v9724 (0.33 #2370, 0.33 #1495, 0.31 #3248), 04xjp (0.33 #1373, 0.25 #2248, 0.25 #936), 081k8 (0.30 #1910, 0.26 #4104, 0.22 #1473), 03sbs (0.26 #4170, 0.22 #1539, 0.17 #2414), 042q3 (0.26 #4312, 0.17 #2995, 0.17 #2556), 0gz_ (0.26 #4051, 0.17 #2295, 0.15 #9723), 0dw6b (0.25 #1172, 0.22 #1609, 0.20 #2046), 0113sg (0.25 #1259, 0.22 #1696, 0.17 #2571), 02lt8 (0.25 #121, 0.20 #1874, 0.20 #560) >> Best rule #1073 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0bt23; >> query: (?x5435, 05qmj) <- influenced_by(?x4055, ?x5435), gender(?x5435, ?x231), ?x4055 = 034bs, influenced_by(?x5435, ?x7024), people(?x1158, ?x5435) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1373 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0379s; 032l1; 03hnd; 01tz6vs; 03_87; 04jvt; 032r1; *> query: (?x5435, 04xjp) <- influenced_by(?x4055, ?x5435), gender(?x5435, ?x231), ?x4055 = 034bs, influenced_by(?x5435, ?x7024), nationality(?x5435, ?x1310) *> conf = 0.33 ranks of expected_values: 3, 4 EVAL 01v9724 influenced_by 081k8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 34.000 0.375 http://example.org/influence/influence_node/influenced_by EVAL 01v9724 influenced_by 04xjp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 34.000 0.375 http://example.org/influence/influence_node/influenced_by #14421-0dq2k PRED entity: 0dq2k PRED relation: student! PRED expected values: 08815 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 212): 01cyd5 (0.33 #52, 0.04 #6891, 0.02 #15834), 03ksy (0.28 #3262, 0.26 #6418, 0.25 #4840), 014zws (0.22 #1908, 0.11 #3487, 0.10 #5065), 01mpwj (0.17 #3263, 0.15 #7471, 0.15 #4841), 0g8rj (0.17 #701, 0.12 #1227, 0.11 #2280), 07x4c (0.17 #784, 0.12 #1310, 0.11 #2363), 017v3q (0.17 #770, 0.11 #3401, 0.11 #1822), 0dzbl (0.17 #1026, 0.11 #2605, 0.10 #3131), 02mw6c (0.17 #955, 0.11 #2534, 0.10 #3060), 01w5m (0.13 #15360, 0.09 #13782, 0.09 #17990) >> Best rule #52 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01mvpv; >> query: (?x5401, 01cyd5) <- profession(?x5401, ?x3342), legislative_sessions(?x5401, ?x759), ?x759 = 043djx, basic_title(?x5401, ?x265) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8946 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 012v1t; *> query: (?x5401, 08815) <- people(?x4195, ?x5401), basic_title(?x5401, ?x265), jurisdiction_of_office(?x265, ?x94) *> conf = 0.11 ranks of expected_values: 19 EVAL 0dq2k student! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 135.000 135.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #14420-02sgy PRED entity: 02sgy PRED relation: role PRED expected values: 0xzly => 97 concepts (72 used for prediction) PRED predicted values (max 10 best out of 55): 0mkg (0.83 #1263, 0.83 #881, 0.83 #735), 0gkd1 (0.83 #769, 0.83 #484, 0.83 #732), 02hnl (0.83 #484, 0.83 #2194, 0.83 #732), 07brj (0.83 #484, 0.83 #732, 0.82 #829), 026g73 (0.83 #484, 0.83 #732, 0.82 #829), 011_6p (0.83 #484, 0.83 #732, 0.82 #829), 0dwsp (0.83 #484, 0.83 #732, 0.82 #829), 0xzly (0.83 #484, 0.83 #732, 0.82 #829), 01679d (0.83 #484, 0.83 #732, 0.82 #829), 023r2x (0.83 #484, 0.83 #732, 0.82 #829) >> Best rule #1263 for best value: >> intensional similarity = 13 >> extensional distance = 16 >> proper extension: 07y_7; 0l14qv; 0l14md; 07xzm; 0g2dz; 02k84w; 04rzd; 0gkd1; >> query: (?x314, 0mkg) <- performance_role(?x212, ?x314), role(?x565, ?x314), role(?x4769, ?x314), role(?x1437, ?x314), role(?x885, ?x314), role(?x7112, ?x314), role(?x217, ?x314), profession(?x7112, ?x220), ?x885 = 0dwtp, ?x4769 = 0dwt5, ?x1437 = 01vdm0, artists(?x302, ?x217), award(?x217, ?x724) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #484 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 5 *> proper extension: 04q7r; *> query: (?x314, ?x74) <- role(?x3991, ?x314), role(?x3161, ?x314), role(?x74, ?x314), ?x3161 = 01v1d8, group(?x314, ?x5303), ?x5303 = 02mq_y, instrumentalists(?x314, ?x133), role(?x214, ?x3991), role(?x7084, ?x3991), role(?x2963, ?x3991), ?x7084 = 01vs4ff, ?x2963 = 0gcs9 *> conf = 0.83 ranks of expected_values: 8 EVAL 02sgy role 0xzly CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 97.000 72.000 0.833 http://example.org/music/performance_role/track_performances./music/track_contribution/role #14419-01jnf1 PRED entity: 01jnf1 PRED relation: colors! PRED expected values: 015q1n 08qnnv 02mzg9 => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1042): 021996 (0.60 #4086, 0.55 #6657, 0.38 #5041), 02607j (0.60 #3894, 0.55 #6657, 0.38 #4849), 0yls9 (0.55 #6657, 0.50 #2588, 0.40 #5916), 0bsnm (0.55 #6657, 0.50 #3129, 0.40 #4078), 016ndm (0.55 #6657, 0.50 #3442, 0.40 #3917), 01jq34 (0.55 #6657, 0.50 #4811, 0.40 #3856), 02vnp2 (0.55 #6657, 0.50 #5090, 0.40 #4135), 017j69 (0.55 #6657, 0.50 #3455, 0.33 #2035), 0gdm1 (0.55 #6657, 0.50 #3543, 0.33 #2123), 01bm_ (0.55 #6657, 0.50 #2609, 0.33 #2138) >> Best rule #4086 for best value: >> intensional similarity = 39 >> extensional distance = 3 >> proper extension: 01l849; >> query: (?x7203, 021996) <- colors(?x6955, ?x7203), colors(?x5621, ?x7203), colors(?x2760, ?x7203), country(?x2760, ?x94), list(?x2760, ?x2197), school(?x6089, ?x2760), colors(?x9165, ?x7203), school(?x12852, ?x2760), institution(?x4981, ?x2760), institution(?x1200, ?x2760), institution(?x620, ?x2760), draft(?x660, ?x12852), major_field_of_study(?x2760, ?x2981), sport(?x6089, ?x4833), currency(?x5621, ?x170), contains(?x4061, ?x6955), school(?x685, ?x5621), ?x4981 = 03bwzr4, student(?x2760, ?x1934), school_type(?x2760, ?x3092), ?x620 = 07s6fsf, ?x2197 = 09g7thr, major_field_of_study(?x9947, ?x2981), major_field_of_study(?x6417, ?x2981), major_field_of_study(?x4599, ?x2981), major_field_of_study(?x2980, ?x2981), ?x9947 = 01kvrz, major_field_of_study(?x1527, ?x2981), major_field_of_study(?x1390, ?x2981), ?x2980 = 02q636, team(?x12339, ?x6089), company(?x3520, ?x5621), ?x4061 = 0498y, ?x6417 = 01t0dy, school(?x260, ?x5621), ?x4599 = 07t90, ?x1200 = 016t_3, team(?x2247, ?x9165), ?x2247 = 01_9c1 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1626 for first EXPECTED value: *> intensional similarity = 37 *> extensional distance = 1 *> proper extension: 01g5v; *> query: (?x7203, 015q1n) <- colors(?x12699, ?x7203), colors(?x6955, ?x7203), colors(?x5621, ?x7203), colors(?x2760, ?x7203), country(?x2760, ?x94), list(?x2760, ?x2197), school(?x6089, ?x2760), colors(?x8528, ?x7203), school(?x12852, ?x2760), institution(?x4981, ?x2760), draft(?x660, ?x12852), major_field_of_study(?x2760, ?x2981), sport(?x6089, ?x4833), currency(?x5621, ?x170), contains(?x4061, ?x6955), school(?x685, ?x5621), ?x4981 = 03bwzr4, student(?x2760, ?x1934), school_type(?x2760, ?x3092), ?x12699 = 03b8c4, major_field_of_study(?x8943, ?x2981), major_field_of_study(?x6814, ?x2981), major_field_of_study(?x6056, ?x2981), major_field_of_study(?x1768, ?x2981), major_field_of_study(?x735, ?x2981), major_field_of_study(?x2981, ?x1527), ?x8943 = 0qlnr, ?x6814 = 03tw2s, ?x6056 = 05zl0, draft(?x6089, ?x2569), ?x94 = 09c7w0, school(?x260, ?x5621), student(?x6955, ?x2718), ?x735 = 065y4w7, ?x1768 = 09kvv, institution(?x1390, ?x5621), ?x260 = 01ypc *> conf = 0.33 ranks of expected_values: 268, 271, 494 EVAL 01jnf1 colors! 02mzg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 01jnf1 colors! 08qnnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 01jnf1 colors! 015q1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 21.000 21.000 0.600 http://example.org/education/educational_institution/colors #14418-05tcx0 PRED entity: 05tcx0 PRED relation: parent_genre PRED expected values: 016jny => 41 concepts (27 used for prediction) PRED predicted values (max 10 best out of 210): 06by7 (0.50 #504, 0.48 #1483, 0.47 #1155), 016clz (0.40 #1143, 0.39 #1307, 0.25 #1635), 03lty (0.33 #19, 0.30 #2308, 0.26 #1977), 011j5x (0.33 #22, 0.18 #1653, 0.17 #510), 05bt6j (0.22 #843, 0.20 #1005, 0.13 #1168), 06j6l (0.21 #1827, 0.10 #2984, 0.08 #2820), 0dl5d (0.20 #1154, 0.17 #1318, 0.07 #1646), 0jmwg (0.20 #1214, 0.13 #3278, 0.13 #1378), 0xhtw (0.20 #2454, 0.10 #2137, 0.10 #2302), 0gywn (0.19 #1834, 0.09 #2991, 0.07 #1507) >> Best rule #504 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 06by7; 05bt6j; 07v64s; >> query: (?x13412, 06by7) <- artists(?x13412, ?x7781), artists(?x13412, ?x4791), group(?x2048, ?x4791), group(?x614, ?x4791), group(?x316, ?x4791), ?x7781 = 089pg7, parent_genre(?x13412, ?x2996), ?x316 = 05r5c, ?x614 = 0mkg, ?x2048 = 018j2 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1863 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 66 *> proper extension: 0jf1v; 012x7b; *> query: (?x13412, 016jny) <- parent_genre(?x13412, ?x2996), artists(?x2996, ?x6049), parent_genre(?x9935, ?x2996), role(?x6049, ?x314), role(?x6049, ?x227), ?x9935 = 0133_p *> conf = 0.15 ranks of expected_values: 18 EVAL 05tcx0 parent_genre 016jny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 41.000 27.000 0.500 http://example.org/music/genre/parent_genre #14417-09qwmm PRED entity: 09qwmm PRED relation: nominated_for PRED expected values: 0416y94 016z9n 02q6gfp 07k2mq 03_gz8 02jxrw => 55 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1499): 026p4q7 (0.70 #11059, 0.69 #4933, 0.67 #3401), 017gl1 (0.69 #4719, 0.67 #3187, 0.56 #10845), 011yl_ (0.69 #6629, 0.63 #9693, 0.60 #2036), 049xgc (0.69 #6963, 0.60 #2370, 0.53 #10027), 07w8fz (0.69 #6559, 0.60 #1966, 0.53 #9623), 0gmgwnv (0.67 #11643, 0.67 #3985, 0.62 #5517), 011yqc (0.67 #3261, 0.63 #10919, 0.62 #4793), 03hmt9b (0.63 #11290, 0.53 #3632, 0.50 #6695), 05hjnw (0.63 #11453, 0.40 #3795, 0.38 #5327), 09gq0x5 (0.62 #4835, 0.60 #3303, 0.59 #10961) >> Best rule #11059 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 03hkv_r; 0gr4k; 04dn09n; 099tbz; 02n9nmz; 02pqp12; 0k611; 0gqyl; 09td7p; 02ppm4q; >> query: (?x618, 026p4q7) <- nominated_for(?x618, ?x6679), nominated_for(?x618, ?x414), award_winner(?x618, ?x396), ?x414 = 095zlp, category(?x6679, ?x134) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #328 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 02y_rq5; 02x4x18; *> query: (?x618, 02q6gfp) <- nominated_for(?x618, ?x7283), award_winner(?x618, ?x1735), ?x1735 = 01l9p, ?x7283 = 0294mx *> conf = 0.40 ranks of expected_values: 117, 127, 136, 249, 313, 695 EVAL 09qwmm nominated_for 02jxrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 28.000 0.704 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qwmm nominated_for 03_gz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 28.000 0.704 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qwmm nominated_for 07k2mq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 55.000 28.000 0.704 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qwmm nominated_for 02q6gfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 55.000 28.000 0.704 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qwmm nominated_for 016z9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 28.000 0.704 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qwmm nominated_for 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 28.000 0.704 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14416-01vrncs PRED entity: 01vrncs PRED relation: location PRED expected values: 0fpzwf => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 260): 030qb3t (0.24 #8094, 0.24 #25720, 0.21 #36938), 02dtg (0.11 #2427, 0.07 #3228, 0.06 #4030), 02jx1 (0.08 #871, 0.04 #24105, 0.04 #12889), 013yq (0.08 #919, 0.04 #25756, 0.04 #1720), 07b_l (0.08 #987, 0.02 #17011, 0.01 #20216), 0cr3d (0.08 #24179, 0.07 #31391, 0.07 #33795), 04jpl (0.07 #36873, 0.06 #8029, 0.06 #12035), 07h34 (0.07 #2598, 0.07 #3399, 0.06 #4201), 01n7q (0.07 #2465, 0.07 #3266, 0.05 #32912), 094jv (0.07 #1694, 0.05 #7303, 0.03 #3296) >> Best rule #8094 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 01pcvn; >> query: (?x1089, 030qb3t) <- profession(?x1089, ?x131), religion(?x1089, ?x109), celebrity(?x1992, ?x1089) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #1081 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 094xh; *> query: (?x1089, 0fpzwf) <- celebrity(?x1089, ?x1992), artists(?x378, ?x1089), role(?x1089, ?x227) *> conf = 0.04 ranks of expected_values: 35 EVAL 01vrncs location 0fpzwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 138.000 138.000 0.242 http://example.org/people/person/places_lived./people/place_lived/location #14415-02_nsc PRED entity: 02_nsc PRED relation: film! PRED expected values: 05dbf => 75 concepts (46 used for prediction) PRED predicted values (max 10 best out of 838): 094tsh6 (0.42 #89428, 0.42 #89427, 0.41 #49916), 0284n42 (0.42 #89428, 0.42 #89427, 0.41 #49916), 0j_c (0.25 #410, 0.03 #8728, 0.03 #6648), 034q3l (0.25 #1527, 0.02 #9845, 0.01 #7765), 01hkck (0.25 #1860, 0.01 #8098), 016dgz (0.25 #1798, 0.01 #8036), 0hw1j (0.21 #20800, 0.18 #14558), 02tn0_ (0.16 #18720), 01t6b4 (0.15 #8318, 0.12 #6238, 0.11 #45756), 0zcbl (0.14 #3298, 0.04 #5377, 0.03 #7457) >> Best rule #89428 for best value: >> intensional similarity = 3 >> extensional distance = 1057 >> proper extension: 0cvkv5; 06zn1c; >> query: (?x9642, ?x666) <- genre(?x9642, ?x53), nominated_for(?x666, ?x9642), currency(?x9642, ?x170) >> conf = 0.42 => this is the best rule for 2 predicted values *> Best rule #2445 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0f40w; *> query: (?x9642, 05dbf) <- genre(?x9642, ?x1403), film(?x6187, ?x9642), ?x1403 = 02l7c8, ?x6187 = 07r1h *> conf = 0.14 ranks of expected_values: 12 EVAL 02_nsc film! 05dbf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 75.000 46.000 0.422 http://example.org/film/actor/film./film/performance/film #14414-0gdqy PRED entity: 0gdqy PRED relation: influenced_by! PRED expected values: 0n6kf => 138 concepts (42 used for prediction) PRED predicted values (max 10 best out of 257): 032md (0.20 #6718, 0.18 #7237, 0.16 #7758), 06mn7 (0.13 #7240, 0.13 #7239, 0.11 #6720), 022_lg (0.13 #7240, 0.13 #7239, 0.11 #6720), 02yl42 (0.08 #135, 0.07 #652, 0.06 #3236), 01hc9_ (0.08 #364, 0.07 #881, 0.05 #1914), 0cbgl (0.08 #514, 0.07 #1031, 0.05 #2064), 040rjq (0.08 #487, 0.07 #1004, 0.05 #2037), 0821j (0.08 #358, 0.07 #875, 0.01 #11736), 0n6kf (0.08 #3293, 0.07 #6393, 0.05 #6912), 0b78hw (0.08 #6369, 0.06 #6888, 0.06 #7408) >> Best rule #6718 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 0167xy; >> query: (?x10354, ?x8043) <- peers(?x10354, ?x8043), influenced_by(?x4353, ?x8043), award_winner(?x198, ?x4353) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3293 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 48 *> proper extension: 01w5n51; *> query: (?x10354, 0n6kf) <- award(?x10354, ?x1587), peers(?x10354, ?x8043) *> conf = 0.08 ranks of expected_values: 9 EVAL 0gdqy influenced_by! 0n6kf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 138.000 42.000 0.195 http://example.org/influence/influence_node/influenced_by #14413-0dgr5xp PRED entity: 0dgr5xp PRED relation: award! PRED expected values: 0451j => 41 concepts (13 used for prediction) PRED predicted values (max 10 best out of 2315): 042rnl (0.77 #30455, 0.69 #37225, 0.69 #40610), 06b_0 (0.40 #2232, 0.06 #5616, 0.06 #25918), 03nk3t (0.40 #1286, 0.06 #4670, 0.04 #24972), 022_q8 (0.40 #1657, 0.04 #25343, 0.04 #28729), 01g1lp (0.40 #2283, 0.04 #5667, 0.04 #25969), 054bt3 (0.40 #1523, 0.04 #4907, 0.03 #8292), 014zcr (0.20 #51, 0.14 #27123, 0.13 #23737), 02kxbx3 (0.20 #989, 0.09 #24675, 0.09 #28061), 026670 (0.20 #2779, 0.08 #6163, 0.08 #26465), 0qf43 (0.20 #50, 0.08 #3434, 0.07 #23736) >> Best rule #30455 for best value: >> intensional similarity = 5 >> extensional distance = 185 >> proper extension: 01lj_c; >> query: (?x8117, ?x754) <- award_winner(?x8117, ?x754), award(?x147, ?x8117), film(?x147, ?x148), award_winner(?x401, ?x147), location_of_ceremony(?x147, ?x1523) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #27071 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 182 *> proper extension: 02rdxsh; 099c8n; 09tqxt; 03m73lj; 02qysm0; 054knh; 02qwzkm; *> query: (?x8117, ?x7610) <- nominated_for(?x8117, ?x9175), titles(?x2346, ?x9175), nominated_for(?x7610, ?x9175), film_crew_role(?x9175, ?x137) *> conf = 0.12 ranks of expected_values: 84 EVAL 0dgr5xp award! 0451j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 41.000 13.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14412-0gvstc3 PRED entity: 0gvstc3 PRED relation: honored_for PRED expected values: 04kzqz 05lfwd => 29 concepts (23 used for prediction) PRED predicted values (max 10 best out of 705): 0d68qy (0.76 #8771, 0.72 #9346, 0.67 #6473), 08jgk1 (0.50 #4688, 0.50 #2384, 0.43 #5264), 03nt59 (0.50 #4376, 0.50 #3224, 0.33 #2076), 05lfwd (0.50 #4934, 0.40 #3780, 0.33 #911), 02gl58 (0.50 #3403, 0.33 #4555, 0.33 #1682), 027tbrc (0.50 #3017, 0.33 #4169, 0.33 #1296), 06y_n (0.50 #3386, 0.33 #4538, 0.33 #1091), 01b_lz (0.43 #5369, 0.40 #5942, 0.40 #3639), 0kfv9 (0.43 #5278, 0.40 #5851, 0.33 #1826), 0431v3 (0.40 #3770, 0.33 #4924, 0.33 #1475) >> Best rule #8771 for best value: >> intensional similarity = 13 >> extensional distance = 15 >> proper extension: 02wzl1d; 0fqpc7d; 058m5m4; 0418154; >> query: (?x2213, 0d68qy) <- ceremony(?x2071, ?x2213), award_winner(?x2213, ?x2320), award_winner(?x2071, ?x965), nominated_for(?x2320, ?x1847), award(?x269, ?x2071), honored_for(?x2213, ?x6884), honored_for(?x2213, ?x6439), nominated_for(?x906, ?x6884), ?x906 = 0pz7h, genre(?x6439, ?x258), award_winner(?x6884, ?x8139), nominated_for(?x2071, ?x337), program(?x65, ?x6439) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #4934 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 4 *> proper extension: 0drtv8; *> query: (?x2213, 05lfwd) <- ceremony(?x8660, ?x2213), ceremony(?x2071, ?x2213), award_winner(?x2213, ?x2320), award_winner(?x2071, ?x3651), nominated_for(?x2320, ?x1847), award(?x4346, ?x2071), honored_for(?x2213, ?x6439), ?x6439 = 04p5cr, award(?x782, ?x2071), nominated_for(?x2071, ?x337), award(?x10160, ?x8660), ?x4346 = 0jmj, type_of_union(?x2320, ?x566), profession(?x10160, ?x524), award(?x3651, ?x458) *> conf = 0.50 ranks of expected_values: 4, 53 EVAL 0gvstc3 honored_for 05lfwd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 29.000 23.000 0.765 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0gvstc3 honored_for 04kzqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 29.000 23.000 0.765 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #14411-0n5gb PRED entity: 0n5gb PRED relation: adjoins! PRED expected values: 0n5kc => 106 concepts (49 used for prediction) PRED predicted values (max 10 best out of 392): 0m2lt (0.82 #5473, 0.82 #5474, 0.81 #28192), 0n5kc (0.82 #5473, 0.81 #28192, 0.81 #8605), 0fr59 (0.40 #859, 0.24 #28194, 0.24 #21140), 0mwzv (0.40 #1013, 0.02 #7272, 0.02 #8055), 0n5gb (0.33 #291, 0.25 #21922, 0.25 #22706), 0n5hw (0.33 #632, 0.25 #21922, 0.25 #22706), 0n5jm (0.33 #384, 0.25 #21922, 0.24 #28194), 0n5by (0.33 #728, 0.24 #28194, 0.24 #21140), 0fxyd (0.26 #23490, 0.25 #21922, 0.25 #22706), 0mwvq (0.26 #23490, 0.25 #21922, 0.25 #22706) >> Best rule #5473 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 0s3pw; >> query: (?x6478, ?x6490) <- adjoins(?x6478, ?x6490), adjoins(?x6478, ?x2832), source(?x6478, ?x958), location(?x3307, ?x2832) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0n5gb adjoins! 0n5kc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 49.000 0.822 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #14410-05cljf PRED entity: 05cljf PRED relation: instrumentalists! PRED expected values: 02sgy 04rzd 0jtg0 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 113): 02hnl (0.47 #192, 0.29 #1408, 0.19 #436), 05148p4 (0.46 #1395, 0.38 #179, 0.38 #990), 03qjg (0.29 #208, 0.23 #1424, 0.19 #1181), 018j2 (0.16 #1412, 0.11 #114, 0.10 #2141), 04rzd (0.12 #1411, 0.11 #2140, 0.10 #2950), 0l14qv (0.12 #1383, 0.11 #816, 0.10 #411), 06ncr (0.11 #201, 0.08 #1255, 0.08 #769), 03gvt (0.11 #140, 0.09 #222, 0.07 #790), 013y1f (0.10 #1405, 0.06 #5703, 0.06 #5378), 06w7v (0.09 #1445, 0.07 #1202, 0.06 #473) >> Best rule #192 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0bg539; 01vs14j; 04gycf; 0ph2w; 018y81; 04bgy; 02jxkw; 03mszl; 020hh3; 01y_rz; ... >> query: (?x226, 02hnl) <- type_of_union(?x226, ?x566), instrumentalists(?x212, ?x226), ?x212 = 026t6 >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1411 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 139 *> proper extension: 03c7ln; 0lbj1; 0c9d9; 01vrx3g; 032t2z; 0kzy0; 01vvycq; 03f5spx; 01gf5h; 01vv7sc; ... *> query: (?x226, 04rzd) <- artist(?x3240, ?x226), profession(?x226, ?x220), instrumentalists(?x716, ?x226), ?x716 = 018vs *> conf = 0.12 ranks of expected_values: 5, 24, 27 EVAL 05cljf instrumentalists! 0jtg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 165.000 165.000 0.467 http://example.org/music/instrument/instrumentalists EVAL 05cljf instrumentalists! 04rzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 165.000 165.000 0.467 http://example.org/music/instrument/instrumentalists EVAL 05cljf instrumentalists! 02sgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 165.000 165.000 0.467 http://example.org/music/instrument/instrumentalists #14409-06bnz PRED entity: 06bnz PRED relation: combatants PRED expected values: 0f8l9c => 176 concepts (119 used for prediction) PRED predicted values (max 10 best out of 321): 09c7w0 (0.84 #5643, 0.84 #3347, 0.83 #1515), 07ssc (0.84 #5643, 0.84 #3347, 0.83 #1515), 03rjj (0.84 #5643, 0.84 #3347, 0.83 #1515), 0d060g (0.84 #5643, 0.84 #3347, 0.83 #1515), 0f8l9c (0.65 #1450, 0.52 #3888, 0.52 #1677), 05qhw (0.50 #1444, 0.42 #1671, 0.40 #1905), 01mk6 (0.50 #1489, 0.42 #1716, 0.40 #275), 06f32 (0.46 #1468, 0.43 #1929, 0.43 #4337), 059z0 (0.43 #4337, 0.40 #205, 0.33 #662), 05b4w (0.43 #4337, 0.40 #253, 0.29 #1928) >> Best rule #5643 for best value: >> intensional similarity = 2 >> extensional distance = 78 >> proper extension: 01s47p; >> query: (?x1603, ?x512) <- combatants(?x512, ?x1603), nationality(?x111, ?x512) >> conf = 0.84 => this is the best rule for 4 predicted values *> Best rule #1450 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0bq0p9; 03b79; *> query: (?x1603, 0f8l9c) <- combatants(?x512, ?x1603), nationality(?x889, ?x1603), ?x512 = 07ssc *> conf = 0.65 ranks of expected_values: 5 EVAL 06bnz combatants 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 176.000 119.000 0.840 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #14408-02wyzmv PRED entity: 02wyzmv PRED relation: film! PRED expected values: 05cj4r => 139 concepts (64 used for prediction) PRED predicted values (max 10 best out of 818): 0zcbl (0.33 #7471, 0.12 #15803, 0.12 #13720), 02fx3c (0.33 #2692, 0.12 #15190, 0.12 #13107), 057_yx (0.33 #1842, 0.07 #26839, 0.07 #24756), 0309lm (0.33 #1608, 0.07 #26605, 0.07 #24522), 01yfm8 (0.33 #5460, 0.03 #45045, 0.02 #67964), 02dbn2 (0.33 #5022, 0.02 #67526, 0.01 #69609), 01ypsj (0.33 #5846), 048hf (0.33 #5535), 0171cm (0.20 #8756, 0.12 #27504, 0.12 #12922), 03ym1 (0.20 #9345, 0.06 #28093, 0.06 #32261) >> Best rule #7471 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 03cv_gy; >> query: (?x6704, 0zcbl) <- film_release_distribution_medium(?x6704, ?x81), genre(?x6704, ?x53), titles(?x162, ?x6704), program(?x2776, ?x6704), country_of_origin(?x6704, ?x512), ?x162 = 04xvlr >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #43799 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 34 *> proper extension: 0m2kd; 020fcn; 0ctb4g; 0prh7; 07z6xs; 0fjyzt; 02w9k1c; 057__d; *> query: (?x6704, 05cj4r) <- genre(?x6704, ?x1509), film_crew_role(?x6704, ?x1284), titles(?x162, ?x6704), ?x1509 = 060__y, ?x162 = 04xvlr, ?x1284 = 0ch6mp2 *> conf = 0.03 ranks of expected_values: 253 EVAL 02wyzmv film! 05cj4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 139.000 64.000 0.333 http://example.org/film/actor/film./film/performance/film #14407-054bt3 PRED entity: 054bt3 PRED relation: place_of_birth PRED expected values: 01w2dq => 121 concepts (117 used for prediction) PRED predicted values (max 10 best out of 103): 02_286 (0.19 #2135, 0.17 #3545, 0.15 #2840), 04jpl (0.16 #4944, 0.12 #11995, 0.11 #1419), 0b_yz (0.08 #1138, 0.03 #1844, 0.03 #5369), 0206v5 (0.08 #1092, 0.03 #1798, 0.02 #5323), 0cr3d (0.07 #94, 0.04 #19839, 0.04 #26884), 088cp (0.07 #546, 0.03 #1957, 0.02 #5482), 01b8w_ (0.07 #334, 0.02 #12321, 0.02 #5976), 0853g (0.07 #471, 0.02 #1882, 0.01 #2587), 0c499 (0.07 #610, 0.02 #2021), 04f_d (0.07 #73) >> Best rule #2135 for best value: >> intensional similarity = 5 >> extensional distance = 66 >> proper extension: 030pr; >> query: (?x5192, 02_286) <- nationality(?x5192, ?x512), people(?x743, ?x5192), award(?x5192, ?x9171), film(?x5192, ?x6005), student(?x892, ?x5192) >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 054bt3 place_of_birth 01w2dq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 117.000 0.191 http://example.org/people/person/place_of_birth #14406-0cqhmg PRED entity: 0cqhmg PRED relation: ceremony PRED expected values: 09g90vz => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 132): 05c1t6z (0.75 #276, 0.71 #538, 0.70 #144), 02q690_ (0.75 #323, 0.71 #585, 0.60 #191), 09g90vz (0.71 #510, 0.23 #658, 0.22 #1576), 0gvstc3 (0.67 #295, 0.60 #163, 0.58 #557), 0gx_st (0.58 #298, 0.54 #560, 0.50 #166), 03nnm4t (0.58 #593, 0.50 #331, 0.50 #67), 0hn821n (0.50 #122, 0.42 #648, 0.42 #386), 0bx6zs (0.50 #118, 0.38 #644, 0.25 #382), 07y_p6 (0.50 #90, 0.33 #616, 0.22 #1576), 0bxs_d (0.42 #633, 0.38 #107, 0.33 #371) >> Best rule #276 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0cqhk0; 09qj50; 09qv3c; 0cjyzs; 047sgz4; 09qs08; 03ccq3s; 09qvf4; 04ldyx1; 027gs1_; >> query: (?x11179, 05c1t6z) <- nominated_for(?x11179, ?x2528), ?x2528 = 0d68qy, award_winner(?x11179, ?x495), award(?x444, ?x11179) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #510 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 02py7pj; *> query: (?x11179, 09g90vz) <- award_winner(?x11179, ?x495), ceremony(?x11179, ?x873), ?x873 = 0hr3c8y *> conf = 0.71 ranks of expected_values: 3 EVAL 0cqhmg ceremony 09g90vz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 41.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony #14405-02pg45 PRED entity: 02pg45 PRED relation: film_release_region PRED expected values: 09c7w0 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 105): 09c7w0 (0.69 #898, 0.67 #4479, 0.67 #4838), 03rjj (0.42 #10569, 0.36 #10570, 0.32 #10750), 02jx1 (0.32 #10750, 0.32 #11110, 0.31 #11111), 0d0vqn (0.21 #3235, 0.21 #9326, 0.21 #10762), 0f8l9c (0.21 #10782, 0.21 #11322, 0.20 #2718), 06mkj (0.20 #2224, 0.20 #76, 0.20 #1866), 02vzc (0.20 #70, 0.19 #3293, 0.18 #9384), 0k6nt (0.20 #36, 0.19 #3259, 0.18 #9350), 07ssc (0.20 #24, 0.19 #2710, 0.19 #9338), 05qhw (0.20 #22, 0.19 #1454, 0.16 #2708) >> Best rule #898 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 03m5y9p; >> query: (?x5358, 09c7w0) <- film(?x2237, ?x5358), film(?x1564, ?x5358), award_winner(?x11087, ?x1564), program(?x1564, ?x631), participant(?x702, ?x2237) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pg45 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.691 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14404-04zpv PRED entity: 04zpv PRED relation: nutrient PRED expected values: 025s7j4 0f4l5 => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 34): 025s7j4 (0.78 #90, 0.76 #73, 0.75 #16), 07zqy (0.78 #90, 0.76 #73, 0.75 #16), 0hkwr (0.78 #90, 0.76 #73, 0.75 #16), 08lb68 (0.78 #90, 0.76 #73, 0.75 #16), 0d9t0 (0.78 #90, 0.76 #73, 0.75 #16), 03d49 (0.78 #90, 0.76 #73, 0.75 #16), 0dcfv (0.78 #90, 0.76 #73, 0.75 #16), 014d7f (0.78 #90, 0.76 #73, 0.75 #16), 061xhr (0.78 #90, 0.76 #73, 0.75 #16), 02y_3rt (0.78 #90, 0.76 #73, 0.75 #16) >> Best rule #90 for best value: >> intensional similarity = 126 >> extensional distance = 1 >> proper extension: 0fjfh; >> query: (?x9005, ?x5549) <- nutrient(?x9005, ?x13944), nutrient(?x9005, ?x13498), nutrient(?x9005, ?x13126), nutrient(?x9005, ?x12902), nutrient(?x9005, ?x12454), nutrient(?x9005, ?x12083), nutrient(?x9005, ?x11758), nutrient(?x9005, ?x11592), nutrient(?x9005, ?x11409), nutrient(?x9005, ?x11270), nutrient(?x9005, ?x10891), nutrient(?x9005, ?x10709), nutrient(?x9005, ?x10098), nutrient(?x9005, ?x9949), nutrient(?x9005, ?x9915), nutrient(?x9005, ?x9733), nutrient(?x9005, ?x9619), nutrient(?x9005, ?x9490), nutrient(?x9005, ?x9436), nutrient(?x9005, ?x9426), nutrient(?x9005, ?x9365), nutrient(?x9005, ?x8487), nutrient(?x9005, ?x8413), nutrient(?x9005, ?x7720), nutrient(?x9005, ?x7652), nutrient(?x9005, ?x7431), nutrient(?x9005, ?x7364), nutrient(?x9005, ?x7362), nutrient(?x9005, ?x7219), nutrient(?x9005, ?x7135), nutrient(?x9005, ?x6586), nutrient(?x9005, ?x6286), nutrient(?x9005, ?x6192), nutrient(?x9005, ?x6160), nutrient(?x9005, ?x6033), nutrient(?x9005, ?x6026), nutrient(?x9005, ?x5526), nutrient(?x9005, ?x5374), nutrient(?x9005, ?x5337), nutrient(?x9005, ?x5010), nutrient(?x9005, ?x4069), nutrient(?x9005, ?x3469), nutrient(?x9005, ?x3203), nutrient(?x9005, ?x2702), nutrient(?x9005, ?x2018), nutrient(?x9005, ?x1960), nutrient(?x9005, ?x1258), ?x13944 = 0f4kp, ?x5337 = 06x4c, ?x12454 = 025rw19, ?x8487 = 014yzm, ?x9426 = 0h1yy, ?x9490 = 0h1sg, ?x7135 = 025rsfk, ?x6286 = 02y_3rf, ?x9365 = 04k8n, ?x5010 = 0h1vz, ?x7431 = 09gwd, ?x9436 = 025sqz8, ?x6026 = 025sf8g, ?x5526 = 09pbb, ?x7652 = 025s0s0, ?x5374 = 025s0zp, ?x7362 = 02kc5rj, ?x2702 = 0838f, ?x12902 = 0fzjh, ?x9619 = 0h1tg, nutrient(?x10612, ?x9915), nutrient(?x9732, ?x9915), nutrient(?x9489, ?x9915), nutrient(?x8298, ?x9915), nutrient(?x7719, ?x9915), nutrient(?x7057, ?x9915), nutrient(?x6285, ?x9915), nutrient(?x6159, ?x9915), nutrient(?x6032, ?x9915), nutrient(?x5373, ?x9915), nutrient(?x4068, ?x9915), nutrient(?x3900, ?x9915), nutrient(?x3468, ?x9915), nutrient(?x3264, ?x9915), nutrient(?x2701, ?x9915), nutrient(?x1303, ?x9915), ?x6159 = 033cnk, ?x4068 = 0fbw6, ?x6285 = 01645p, ?x7057 = 0fbdb, ?x10709 = 0h1sz, ?x8298 = 037ls6, ?x11270 = 02kc008, ?x10098 = 0h1_c, ?x9489 = 07j87, ?x7364 = 09gvd, ?x11592 = 025sf0_, ?x3469 = 0h1zw, nutrient(?x1959, ?x13126), ?x6586 = 05gh50, ?x1959 = 0f25w9, ?x9732 = 05z55, ?x8413 = 02kc4sf, ?x10891 = 0g5gq, ?x6032 = 01nkt, ?x6160 = 041r51, ?x3203 = 04kl74p, ?x7720 = 025s7x6, ?x3468 = 0cxn2, ?x11409 = 0h1yf, ?x2701 = 0hkxq, ?x12083 = 01n78x, ?x6192 = 06jry, ?x1960 = 07hnp, ?x6033 = 04zjxcz, ?x1258 = 0h1wg, ?x5373 = 0971v, ?x3900 = 061_f, ?x7719 = 0dj75, ?x7219 = 0h1vg, ?x9733 = 0h1tz, ?x1303 = 0fj52s, ?x10612 = 0frq6, ?x4069 = 0hqw8p_, ?x13498 = 07q0m, ?x11758 = 0q01m, nutrient(?x3264, ?x5549), ?x9949 = 02kd0rh, ?x2018 = 01sh2 >> conf = 0.78 => this is the best rule for 14 predicted values ranks of expected_values: 1, 12 EVAL 04zpv nutrient 0f4l5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 22.000 22.000 0.778 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 04zpv nutrient 025s7j4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.778 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #14403-0k8z PRED entity: 0k8z PRED relation: industry PRED expected values: 07c1v => 254 concepts (254 used for prediction) PRED predicted values (max 10 best out of 44): 01mw1 (0.86 #4141, 0.78 #6212, 0.75 #7202), 020mfr (0.66 #4156, 0.58 #6227, 0.45 #7217), 02vxn (0.60 #2702, 0.55 #1712, 0.50 #2252), 029g_vk (0.39 #2305, 0.25 #595, 0.25 #460), 0h6dj (0.33 #392, 0.20 #1112, 0.17 #1202), 01zhp (0.33 #107, 0.17 #1142, 0.12 #1547), 02jjt (0.29 #1313, 0.27 #2978, 0.25 #4598), 07c1v (0.25 #580, 0.25 #9904, 0.18 #7292), 0hz28 (0.25 #569, 0.25 #9904, 0.18 #7292), 06xw2 (0.25 #574, 0.25 #9904, 0.18 #7292) >> Best rule #4141 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 049vhf; >> query: (?x3793, 01mw1) <- industry(?x3793, ?x12987), industry(?x4267, ?x12987), place_founded(?x3793, ?x3794), ?x4267 = 08t9df >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #580 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 06q07; *> query: (?x3793, 07c1v) <- organization(?x4682, ?x3793), industry(?x3793, ?x5078), service_language(?x3793, ?x254), ?x5078 = 019z7b *> conf = 0.25 ranks of expected_values: 8 EVAL 0k8z industry 07c1v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 254.000 254.000 0.857 http://example.org/business/business_operation/industry #14402-0c7xjb PRED entity: 0c7xjb PRED relation: gender PRED expected values: 02zsn => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #79, 0.80 #75, 0.79 #71), 02zsn (0.69 #4, 0.65 #24, 0.53 #28) >> Best rule #79 for best value: >> intensional similarity = 1 >> extensional distance = 243 >> proper extension: 0ct9_; >> query: (?x4819, 05zppz) <- company(?x4819, ?x3265) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 03f2_rc; 01x1cn2; 047c9l; 014vk4; *> query: (?x4819, 02zsn) <- award(?x4819, ?x154), artists(?x671, ?x4819), ?x154 = 05b4l5x *> conf = 0.69 ranks of expected_values: 2 EVAL 0c7xjb gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.800 http://example.org/people/person/gender #14401-053rxgm PRED entity: 053rxgm PRED relation: film! PRED expected values: 0h7pj 063g7l => 78 concepts (38 used for prediction) PRED predicted values (max 10 best out of 756): 01nqfh_ (0.48 #47812, 0.45 #2079, 0.43 #54050), 07swvb (0.20 #698, 0.17 #2777, 0.03 #11091), 0dqmt0 (0.12 #10393, 0.11 #43652), 01l2fn (0.10 #263, 0.08 #2342, 0.03 #6499), 0c_gcr (0.10 #1643, 0.08 #3722, 0.03 #9957), 0fby2t (0.10 #754, 0.08 #2833, 0.03 #9068), 01r93l (0.10 #748, 0.08 #2827, 0.03 #13219), 0jlv5 (0.10 #1180, 0.08 #3259, 0.03 #5337), 038rzr (0.10 #469, 0.08 #2548, 0.03 #4626), 062dn7 (0.10 #662, 0.08 #2741, 0.03 #11055) >> Best rule #47812 for best value: >> intensional similarity = 5 >> extensional distance = 685 >> proper extension: 01fs__; >> query: (?x1178, ?x562) <- language(?x1178, ?x254), ?x254 = 02h40lc, nominated_for(?x5338, ?x1178), nominated_for(?x562, ?x1178), participant(?x5338, ?x2387) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1541 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 0g5qs2k; 01fmys; 04f52jw; 040b5k; 05pdh86; 06tpmy; 07jqjx; 0gy7bj4; *> query: (?x1178, 0h7pj) <- film_release_region(?x1178, ?x8958), film_release_region(?x1178, ?x1003), ?x1003 = 03gj2, ?x8958 = 01ppq, nominated_for(?x562, ?x1178) *> conf = 0.10 ranks of expected_values: 11, 294 EVAL 053rxgm film! 063g7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 78.000 38.000 0.481 http://example.org/film/actor/film./film/performance/film EVAL 053rxgm film! 0h7pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 78.000 38.000 0.481 http://example.org/film/actor/film./film/performance/film #14400-02ld6x PRED entity: 02ld6x PRED relation: profession PRED expected values: 0dxtg => 114 concepts (88 used for prediction) PRED predicted values (max 10 best out of 77): 0dxtg (0.85 #11, 0.82 #3223, 0.81 #3077), 03gjzk (0.53 #450, 0.53 #596, 0.47 #1180), 018gz8 (0.38 #306, 0.34 #2058, 0.34 #1328), 0kyk (0.36 #2217, 0.33 #3969, 0.33 #3531), 09jwl (0.29 #1038, 0.29 #892, 0.26 #1622), 02krf9 (0.27 #2360, 0.24 #462, 0.23 #24), 0nbcg (0.23 #905, 0.22 #1051, 0.18 #321), 01c72t (0.20 #897, 0.19 #1043, 0.12 #1627), 0n1h (0.17 #885, 0.17 #1031, 0.15 #301), 0dz3r (0.17 #878, 0.17 #1024, 0.12 #7304) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0k_mt; >> query: (?x2705, 0dxtg) <- profession(?x2705, ?x319), influenced_by(?x5351, ?x2705), film(?x2705, ?x4768) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ld6x profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 88.000 0.846 http://example.org/people/person/profession #14399-0dfw0 PRED entity: 0dfw0 PRED relation: genre PRED expected values: 06n90 => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 102): 07s9rl0 (0.73 #1771, 0.65 #12886, 0.62 #11344), 01jfsb (0.59 #2254, 0.56 #3790, 0.50 #720), 024qqx (0.53 #11462, 0.52 #12410, 0.49 #13714), 05p553 (0.46 #6498, 0.46 #4372, 0.39 #2128), 06n90 (0.44 #131, 0.38 #485, 0.36 #249), 04xvlr (0.42 #1772, 0.21 #946, 0.20 #1182), 04pbhw (0.33 #173, 0.33 #55, 0.25 #527), 0lsxr (0.33 #1661, 0.29 #2369, 0.27 #717), 060__y (0.23 #1786, 0.22 #16, 0.22 #1314), 04xvh5 (0.23 #1803, 0.11 #977, 0.10 #1213) >> Best rule #1771 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 0b76d_m; 0g5qs2k; 01242_; 072zl1; 0gy7bj4; >> query: (?x4902, 07s9rl0) <- nominated_for(?x574, ?x4902), nominated_for(?x2489, ?x4902), ?x2489 = 02x2gy0, award_winner(?x902, ?x574) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #131 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 042fgh; *> query: (?x4902, 06n90) <- prequel(?x1812, ?x4902), honored_for(?x1386, ?x4902), featured_film_locations(?x4902, ?x5036), film(?x574, ?x4902) *> conf = 0.44 ranks of expected_values: 5 EVAL 0dfw0 genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 152.000 152.000 0.731 http://example.org/film/film/genre #14398-05xpms PRED entity: 05xpms PRED relation: award_nominee PRED expected values: 0cmt6q => 103 concepts (53 used for prediction) PRED predicted values (max 10 best out of 852): 04t2l2 (0.83 #4662, 0.81 #46627, 0.80 #72268), 04zkj5 (0.83 #4662, 0.81 #46627, 0.80 #72268), 05xpms (0.79 #4313, 0.75 #6645, 0.73 #1982), 05l0j5 (0.71 #4047, 0.60 #6379, 0.55 #1716), 0cmt6q (0.64 #1487, 0.60 #6150, 0.57 #3818), 043js (0.57 #2913, 0.55 #5245, 0.55 #582), 05p92jn (0.55 #6173, 0.50 #3841, 0.36 #1510), 027cxsm (0.38 #9665, 0.29 #6994, 0.22 #23312), 0cj2nl (0.35 #10208, 0.29 #6994, 0.28 #74601), 0cj2t3 (0.35 #9978, 0.29 #6994, 0.28 #74601) >> Best rule #4662 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0bt4r4; >> query: (?x9272, ?x274) <- award_winner(?x4332, ?x9272), award_winner(?x274, ?x9272), award_winner(?x237, ?x9272), ?x4332 = 0cnl1c, ?x237 = 04t2l2 >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #1487 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 04t2l2; 072bb1; 0bt7ws; 0cnl1c; 060j8b; 0cms7f; 0cl0bk; 0cj36c; 04zkj5; *> query: (?x9272, 0cmt6q) <- award_winner(?x4333, ?x9272), actor(?x1631, ?x9272), ?x4333 = 0cnl09 *> conf = 0.64 ranks of expected_values: 5 EVAL 05xpms award_nominee 0cmt6q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 103.000 53.000 0.832 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14397-016qtt PRED entity: 016qtt PRED relation: people! PRED expected values: 063k3h => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 45): 0x67 (0.51 #314, 0.45 #542, 0.31 #1074), 041rx (0.37 #156, 0.24 #5096, 0.22 #3652), 07hwkr (0.25 #12, 0.07 #1076, 0.06 #5104), 033tf_ (0.14 #2439, 0.13 #3351, 0.13 #3047), 063k3h (0.12 #30, 0.03 #334, 0.02 #182), 02w7gg (0.11 #3042, 0.11 #154, 0.11 #2434), 0xnvg (0.11 #469, 0.08 #3053, 0.08 #2445), 02ctzb (0.07 #91, 0.05 #5107, 0.04 #4271), 048z7l (0.07 #191, 0.05 #115, 0.05 #495), 0dryh9k (0.05 #4272, 0.05 #4424, 0.05 #5564) >> Best rule #314 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 031x_3; >> query: (?x133, 0x67) <- people(?x5741, ?x133), artist(?x2931, ?x133), award_winner(?x102, ?x133) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 03xnq9_; *> query: (?x133, 063k3h) <- profession(?x133, ?x220), film(?x133, ?x607), ?x607 = 02x3lt7 *> conf = 0.12 ranks of expected_values: 5 EVAL 016qtt people! 063k3h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 104.000 104.000 0.511 http://example.org/people/ethnicity/people #14396-0r02m PRED entity: 0r02m PRED relation: contains PRED expected values: 03hdz8 => 138 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1913): 019n9w (0.78 #156153, 0.75 #11787, 0.71 #103120), 03hdz8 (0.78 #156153, 0.75 #11787, 0.71 #103120), 01n7q (0.24 #194447, 0.20 #185610, 0.02 #11788), 0r02m (0.24 #194447, 0.04 #162045, 0.03 #159100), 09c7w0 (0.24 #194447, 0.02 #11788), 0fnmz (0.12 #420, 0.10 #3367, 0.07 #9261), 021w0_ (0.12 #1262, 0.10 #4209, 0.07 #10103), 05cwl_ (0.12 #739, 0.10 #3686, 0.07 #9580), 01bzw5 (0.12 #134, 0.10 #3081, 0.07 #8975), 06b7s9 (0.12 #2113, 0.10 #5060, 0.07 #10954) >> Best rule #156153 for best value: >> intensional similarity = 4 >> extensional distance = 201 >> proper extension: 0fngy; >> query: (?x13255, ?x7178) <- citytown(?x7178, ?x13255), category(?x7178, ?x134), school_type(?x7178, ?x1044), contains(?x94, ?x7178) >> conf = 0.78 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0r02m contains 03hdz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 82.000 0.776 http://example.org/location/location/contains #14395-07wrz PRED entity: 07wrz PRED relation: institution! PRED expected values: 07s6fsf 02mjs7 014mlp 0bjrnt 019v9k => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 15): 014mlp (0.75 #836, 0.69 #547, 0.69 #1223), 019v9k (0.70 #838, 0.64 #405, 0.63 #855), 07s6fsf (0.52 #851, 0.46 #401, 0.43 #65), 0bjrnt (0.31 #404, 0.29 #68, 0.22 #596), 028dcg (0.30 #140, 0.29 #76, 0.18 #300), 03mkk4 (0.29 #71, 0.28 #407, 0.20 #840), 01rr_d (0.29 #75, 0.23 #411, 0.21 #603), 02mjs7 (0.29 #66, 0.18 #402, 0.15 #194), 022h5x (0.21 #863, 0.19 #846, 0.15 #413), 071tyz (0.14 #70, 0.10 #406, 0.08 #598) >> Best rule #836 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 022xml; >> query: (?x2313, 014mlp) <- student(?x2313, ?x920), major_field_of_study(?x2313, ?x742), fraternities_and_sororities(?x2313, ?x4348) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 8 EVAL 07wrz institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.752 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07wrz institution! 0bjrnt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.752 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07wrz institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.752 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07wrz institution! 02mjs7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 135.000 135.000 0.752 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07wrz institution! 07s6fsf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.752 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14394-0sz28 PRED entity: 0sz28 PRED relation: religion PRED expected values: 0kq2 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 15): 0c8wxp (0.25 #51, 0.25 #6, 0.23 #727), 0kpl (0.25 #55, 0.06 #2406, 0.05 #3309), 03_gx (0.08 #2410, 0.08 #2772, 0.08 #1593), 01lp8 (0.07 #91, 0.03 #136, 0.03 #316), 03j6c (0.06 #471, 0.05 #1873, 0.04 #336), 0kq2 (0.06 #288, 0.03 #108, 0.03 #153), 092bf5 (0.05 #241, 0.04 #646, 0.03 #106), 06nzl (0.04 #510, 0.03 #600, 0.02 #375), 051kv (0.04 #275, 0.03 #320, 0.02 #455), 019cr (0.03 #101, 0.03 #146, 0.03 #326) >> Best rule #51 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01yhvv; >> query: (?x1208, 0c8wxp) <- award_nominee(?x5454, ?x1208), participant(?x1554, ?x1208), ?x5454 = 020_95 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #288 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 51 *> proper extension: 02x8mt; *> query: (?x1208, 0kq2) <- student(?x9865, ?x1208), sibling(?x1208, ?x13442) *> conf = 0.06 ranks of expected_values: 6 EVAL 0sz28 religion 0kq2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 126.000 126.000 0.250 http://example.org/people/person/religion #14393-07h0cl PRED entity: 07h0cl PRED relation: award! PRED expected values: 0psss 02f2p7 => 46 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2221): 0154qm (0.62 #53991, 0.56 #43871, 0.52 #26995), 0h0wc (0.62 #53991, 0.56 #43871, 0.52 #26995), 02kxwk (0.48 #14735, 0.07 #11359, 0.06 #65358), 0l6px (0.45 #14118, 0.06 #64741, 0.06 #68116), 02mqc4 (0.45 #14667, 0.05 #65290, 0.05 #68665), 01jw4r (0.42 #15979, 0.07 #12603, 0.06 #53099), 0lpjn (0.39 #14264, 0.11 #10888, 0.07 #51384), 0h32q (0.39 #14749, 0.06 #24872, 0.05 #31623), 028knk (0.35 #14023, 0.15 #10647, 0.08 #51143), 02jsgf (0.35 #14649, 0.11 #11273, 0.08 #51769) >> Best rule #53991 for best value: >> intensional similarity = 4 >> extensional distance = 164 >> proper extension: 03ybrwc; 02vl9ln; >> query: (?x3245, ?x2551) <- award_winner(?x3245, ?x2551), award(?x4742, ?x3245), film(?x166, ?x4742), nominated_for(?x843, ?x4742) >> conf = 0.62 => this is the best rule for 2 predicted values *> Best rule #15055 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 02grdc; 03nqnk3; 0bsjcw; 0bb57s; 02lp0w; *> query: (?x3245, 02f2p7) <- award(?x8674, ?x3245), award_winner(?x375, ?x8674), nominated_for(?x8674, ?x1330), ?x375 = 0bfvw2 *> conf = 0.06 ranks of expected_values: 720 EVAL 07h0cl award! 02f2p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 22.000 0.625 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07h0cl award! 0psss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 22.000 0.625 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14392-02lz1s PRED entity: 02lz1s PRED relation: award PRED expected values: 0c4z8 054ks3 => 92 concepts (46 used for prediction) PRED predicted values (max 10 best out of 262): 054ks3 (0.53 #1349, 0.25 #1752, 0.23 #2155), 054krc (0.43 #1698, 0.41 #2101, 0.37 #2504), 02qvyrt (0.35 #1737, 0.33 #2140, 0.29 #2543), 0c4z8 (0.35 #1280, 0.19 #877, 0.18 #1683), 0gq_v (0.33 #425, 0.33 #22, 0.05 #9272), 025m8l (0.33 #1326, 0.13 #1729, 0.12 #2132), 09sb52 (0.31 #5684, 0.29 #4072, 0.24 #6087), 025m8y (0.23 #1710, 0.20 #2113, 0.19 #1307), 02x17c2 (0.21 #1427, 0.15 #1830, 0.13 #2233), 01by1l (0.18 #1319, 0.17 #4544, 0.12 #1722) >> Best rule #1349 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 01pbxb; >> query: (?x1852, 054ks3) <- profession(?x1852, ?x1614), award(?x1852, ?x1323), ?x1323 = 0gqz2 >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 02lz1s award 054ks3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 46.000 0.534 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02lz1s award 0c4z8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 46.000 0.534 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14391-0dq9wx PRED entity: 0dq9wx PRED relation: profession PRED expected values: 0kyk 01c979 => 229 concepts (143 used for prediction) PRED predicted values (max 10 best out of 80): 01d_h8 (0.48 #11185, 0.46 #155, 0.44 #6), 0d1pc (0.46 #200, 0.35 #14761, 0.35 #19534), 09jwl (0.39 #14481, 0.39 #15078, 0.38 #13436), 015cjr (0.35 #14761, 0.35 #19534, 0.34 #10433), 03gkb0 (0.35 #14761, 0.35 #19534, 0.34 #10433), 0kyk (0.35 #14761, 0.35 #19534, 0.34 #10433), 01c979 (0.35 #14761, 0.35 #19534, 0.34 #10433), 0dz3r (0.35 #10733, 0.33 #14464, 0.33 #15061), 0nbcg (0.34 #3459, 0.30 #15091, 0.30 #2267), 03gjzk (0.32 #11194, 0.32 #611, 0.30 #15372) >> Best rule #11185 for best value: >> intensional similarity = 4 >> extensional distance = 165 >> proper extension: 0738b8; 049fgvm; 0154d7; 01rzxl; >> query: (?x12047, 01d_h8) <- location(?x12047, ?x1523), currency(?x12047, ?x170), time_zones(?x1523, ?x2950), place_of_death(?x457, ?x1523) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #14761 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 228 *> proper extension: 04qmr; *> query: (?x12047, ?x2225) <- category(?x12047, ?x134), participant(?x10777, ?x12047), participant(?x56, ?x10777), profession(?x10777, ?x2225) *> conf = 0.35 ranks of expected_values: 6, 7 EVAL 0dq9wx profession 01c979 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 229.000 143.000 0.479 http://example.org/people/person/profession EVAL 0dq9wx profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 229.000 143.000 0.479 http://example.org/people/person/profession #14390-0fzjh PRED entity: 0fzjh PRED relation: nutrient! PRED expected values: 09728 0f25w9 => 53 concepts (51 used for prediction) PRED predicted values (max 10 best out of 7): 0f25w9 (0.90 #327, 0.90 #305, 0.90 #74), 09728 (0.90 #74, 0.89 #26, 0.89 #35), 06x4c (0.90 #74, 0.89 #26, 0.89 #35), 0dcfv (0.90 #74, 0.89 #26, 0.89 #35), 04k8n (0.04 #387), 05wvs (0.04 #387), 01sh2 (0.04 #387) >> Best rule #327 for best value: >> intensional similarity = 112 >> extensional distance = 19 >> proper extension: 02kc_w5; >> query: (?x12902, 0f25w9) <- nutrient(?x10612, ?x12902), nutrient(?x9732, ?x12902), nutrient(?x9005, ?x12902), nutrient(?x8298, ?x12902), nutrient(?x7719, ?x12902), nutrient(?x6191, ?x12902), nutrient(?x6159, ?x12902), nutrient(?x5373, ?x12902), nutrient(?x5009, ?x12902), nutrient(?x3900, ?x12902), nutrient(?x2701, ?x12902), nutrient(?x1303, ?x12902), ?x6159 = 033cnk, ?x9005 = 04zpv, nutrient(?x5009, ?x13498), nutrient(?x5009, ?x12454), nutrient(?x5009, ?x11758), nutrient(?x5009, ?x11592), nutrient(?x5009, ?x11409), nutrient(?x5009, ?x11270), nutrient(?x5009, ?x10891), nutrient(?x5009, ?x10098), nutrient(?x5009, ?x9949), nutrient(?x5009, ?x9795), nutrient(?x5009, ?x9733), nutrient(?x5009, ?x9619), nutrient(?x5009, ?x9490), nutrient(?x5009, ?x9426), nutrient(?x5009, ?x9365), nutrient(?x5009, ?x8487), nutrient(?x5009, ?x8442), nutrient(?x5009, ?x8413), nutrient(?x5009, ?x7894), nutrient(?x5009, ?x7720), nutrient(?x5009, ?x7652), nutrient(?x5009, ?x7431), nutrient(?x5009, ?x7364), nutrient(?x5009, ?x7135), nutrient(?x5009, ?x6586), nutrient(?x5009, ?x6286), nutrient(?x5009, ?x6192), nutrient(?x5009, ?x6160), nutrient(?x5009, ?x6026), nutrient(?x5009, ?x5451), nutrient(?x5009, ?x5337), nutrient(?x5009, ?x5010), nutrient(?x5009, ?x4069), nutrient(?x5009, ?x3469), nutrient(?x5009, ?x3203), nutrient(?x5009, ?x2702), nutrient(?x5009, ?x2018), nutrient(?x5009, ?x1960), nutrient(?x5009, ?x1258), ?x10891 = 0g5gq, ?x11592 = 025sf0_, ?x8413 = 02kc4sf, ?x9490 = 0h1sg, ?x5010 = 0h1vz, ?x13498 = 07q0m, ?x7894 = 0f4hc, ?x5451 = 05wvs, ?x7720 = 025s7x6, ?x5373 = 0971v, ?x6160 = 041r51, nutrient(?x9732, ?x14210), nutrient(?x9732, ?x13545), nutrient(?x9732, ?x9915), ?x5337 = 06x4c, ?x12454 = 025rw19, ?x2702 = 0838f, ?x6026 = 025sf8g, ?x11409 = 0h1yf, ?x11758 = 0q01m, ?x6192 = 06jry, ?x10098 = 0h1_c, ?x14210 = 0f4k5, ?x9365 = 04k8n, ?x3203 = 04kl74p, ?x4069 = 0hqw8p_, ?x1258 = 0h1wg, ?x11270 = 02kc008, ?x2018 = 01sh2, ?x8298 = 037ls6, ?x9915 = 025tkqy, ?x2701 = 0hkxq, nutrient(?x1303, ?x9840), nutrient(?x1303, ?x3264), ?x8487 = 014yzm, ?x6191 = 014j1m, ?x3469 = 0h1zw, nutrient(?x3900, ?x12481), nutrient(?x3900, ?x3901), ?x3264 = 0dcfv, ?x6586 = 05gh50, ?x12481 = 027g6p7, ?x1960 = 07hnp, ?x7135 = 025rsfk, ?x9795 = 05v_8y, ?x9619 = 0h1tg, ?x7652 = 025s0s0, ?x9949 = 02kd0rh, ?x13545 = 01w_3, ?x9733 = 0h1tz, ?x6286 = 02y_3rf, ?x9426 = 0h1yy, ?x10612 = 0frq6, ?x8442 = 02kcv4x, ?x7719 = 0dj75, ?x7431 = 09gwd, ?x7364 = 09gvd, ?x9840 = 02p0tjr, ?x3901 = 0466p20 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0fzjh nutrient! 0f25w9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 51.000 0.905 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fzjh nutrient! 09728 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 51.000 0.905 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #14389-06y57 PRED entity: 06y57 PRED relation: location! PRED expected values: 0c1pj 01_p6t 016ynj => 215 concepts (151 used for prediction) PRED predicted values (max 10 best out of 2300): 03h_9lg (0.54 #132645, 0.47 #327879, 0.47 #375425), 013tcv (0.54 #132645, 0.47 #327879, 0.47 #375425), 02404v (0.54 #132645, 0.47 #327879, 0.47 #375425), 06lgq8 (0.54 #132645, 0.47 #327879, 0.47 #375425), 0151ns (0.50 #83, 0.18 #7594, 0.18 #15101), 01797x (0.50 #2079, 0.15 #14595, 0.12 #17097), 01cyjx (0.50 #1367, 0.12 #16385, 0.12 #38912), 03d_w3h (0.50 #149, 0.12 #15167, 0.11 #40197), 02gyl0 (0.50 #938, 0.12 #15956, 0.10 #53499), 0150t6 (0.50 #581, 0.12 #15599, 0.09 #8092) >> Best rule #132645 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 02m__; 03kjh; >> query: (?x5036, ?x844) <- country(?x5036, ?x390), place_of_birth(?x844, ?x5036), capital(?x8506, ?x5036) >> conf = 0.54 => this is the best rule for 4 predicted values *> Best rule #2503 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 04jpl; 030qb3t; *> query: (?x5036, ?x815) <- location(?x5446, ?x5036), location(?x4544, ?x5036), place_of_birth(?x844, ?x5036), award_nominee(?x4544, ?x815), ?x5446 = 03hh89 *> conf = 0.10 ranks of expected_values: 536 EVAL 06y57 location! 016ynj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 151.000 0.539 http://example.org/people/person/places_lived./people/place_lived/location EVAL 06y57 location! 01_p6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 215.000 151.000 0.539 http://example.org/people/person/places_lived./people/place_lived/location EVAL 06y57 location! 0c1pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 215.000 151.000 0.539 http://example.org/people/person/places_lived./people/place_lived/location #14388-0h3lt PRED entity: 0h3lt PRED relation: category PRED expected values: 08mbj5d => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #57, 0.78 #84, 0.78 #2) >> Best rule #57 for best value: >> intensional similarity = 2 >> extensional distance = 251 >> proper extension: 01mc11; 01m1_t; 0_565; 0_j_z; 0t_48; 031sn; 0nqph; >> query: (?x6047, 08mbj5d) <- county(?x6047, ?x578), time_zones(?x6047, ?x2950) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h3lt category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.779 http://example.org/common/topic/webpage./common/webpage/category #14387-08c9b0 PRED entity: 08c9b0 PRED relation: music! PRED expected values: 03bx2lk => 111 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1374): 03_gz8 (0.10 #647, 0.03 #4663, 0.01 #5667), 011yxg (0.10 #24, 0.01 #4040, 0.01 #5044), 0dnkmq (0.10 #937, 0.01 #4953, 0.01 #16065), 035zr0 (0.10 #737, 0.01 #4753, 0.01 #16065), 03xf_m (0.10 #637, 0.01 #4653, 0.01 #16065), 020bv3 (0.10 #194, 0.01 #4210, 0.01 #16065), 01cssf (0.10 #49, 0.01 #4065, 0.01 #16065), 049w1q (0.10 #954, 0.01 #4970), 0b2km_ (0.10 #906, 0.01 #4922), 0dgpwnk (0.10 #333, 0.01 #4349) >> Best rule #647 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01wdcxk; >> query: (?x4911, 03_gz8) <- gender(?x4911, ?x231), nationality(?x4911, ?x6401), artists(?x4910, ?x4911), ?x6401 = 06q1r >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #2122 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0fhxv; *> query: (?x4911, 03bx2lk) <- film(?x4911, ?x2155), award(?x4911, ?x1443), ?x1443 = 054krc, profession(?x4911, ?x987) *> conf = 0.08 ranks of expected_values: 80 EVAL 08c9b0 music! 03bx2lk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 111.000 37.000 0.100 http://example.org/film/film/music #14386-04jwly PRED entity: 04jwly PRED relation: nominated_for! PRED expected values: 0l8z1 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 186): 02x4wr9 (0.71 #681, 0.69 #1813, 0.68 #12227), 02z1nbg (0.71 #681, 0.69 #1813, 0.68 #12227), 027571b (0.71 #681, 0.69 #1813, 0.68 #12227), 027b9k6 (0.71 #681, 0.69 #1813, 0.68 #12227), 09d28z (0.71 #681, 0.69 #1813, 0.68 #12227), 094qd5 (0.60 #263, 0.48 #35, 0.47 #1394), 0gq9h (0.60 #1870, 0.52 #57, 0.50 #1416), 0gqy2 (0.51 #3507, 0.28 #1471, 0.27 #1925), 0k611 (0.49 #1879, 0.37 #3461, 0.31 #1425), 0gs9p (0.48 #1872, 0.44 #3454, 0.44 #1418) >> Best rule #681 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 026njb5; >> query: (?x2833, ?x1254) <- language(?x2833, ?x254), award(?x2833, ?x1254), film_format(?x2833, ?x6392), executive_produced_by(?x2833, ?x286) >> conf = 0.71 => this is the best rule for 5 predicted values *> Best rule #1863 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 162 *> proper extension: 01br2w; 01hr1; 05z7c; 02qr69m; 04t6fk; 0k4f3; 0kb57; 084302; 011ysn; 02fqrf; ... *> query: (?x2833, 0l8z1) <- language(?x2833, ?x254), nominated_for(?x1414, ?x2833), nominated_for(?x1243, ?x2833), ?x1243 = 0gr0m *> conf = 0.35 ranks of expected_values: 16 EVAL 04jwly nominated_for! 0l8z1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 112.000 112.000 0.706 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14385-0407yj_ PRED entity: 0407yj_ PRED relation: film_distribution_medium PRED expected values: 0735l => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.84 #71, 0.83 #59, 0.18 #17), 029j_ (0.15 #55, 0.13 #67, 0.13 #49), 02nxhr (0.12 #68, 0.12 #56, 0.11 #38), 0dq6p (0.10 #57, 0.08 #69, 0.05 #51) >> Best rule #71 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 0522wp; >> query: (?x2933, 0735l) <- film(?x609, ?x2933), ?x609 = 03xq0f, region(?x2933, ?x512) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0407yj_ film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.842 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #14384-0c38gj PRED entity: 0c38gj PRED relation: film! PRED expected values: 0fgg4 => 82 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1000): 017r13 (0.25 #1112, 0.03 #34429, 0.03 #21936), 016ywr (0.25 #299, 0.03 #16958, 0.03 #19041), 014x77 (0.25 #92, 0.03 #10503, 0.02 #37574), 0175wg (0.25 #1021, 0.02 #21845, 0.01 #11432), 01wk3c (0.25 #1821), 01g969 (0.25 #1673), 04g3p5 (0.18 #60388, 0.11 #14576, 0.11 #87464), 05strv (0.11 #87464, 0.10 #47894, 0.10 #79132), 02pq9yv (0.11 #87464, 0.10 #47894, 0.10 #79132), 02ck7w (0.10 #3022, 0.05 #13433, 0.04 #15516) >> Best rule #1112 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 070g7; >> query: (?x4633, 017r13) <- genre(?x4633, ?x811), ?x811 = 03k9fj, country(?x4633, ?x94), film(?x10866, ?x4633), ?x10866 = 02d45s >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #13376 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 0c8tkt; 0gjc4d3; 0184tc; 08sk8l; 0mbql; *> query: (?x4633, 0fgg4) <- genre(?x4633, ?x811), ?x811 = 03k9fj, film_crew_role(?x4633, ?x137), written_by(?x4633, ?x4634) *> conf = 0.02 ranks of expected_values: 356 EVAL 0c38gj film! 0fgg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 82.000 49.000 0.250 http://example.org/film/actor/film./film/performance/film #14383-017z49 PRED entity: 017z49 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #31, 0.84 #102, 0.82 #82), 07z4p (0.24 #81, 0.10 #30, 0.09 #5), 02nxhr (0.24 #81, 0.09 #2, 0.06 #37), 07c52 (0.14 #3, 0.08 #53, 0.07 #28) >> Best rule #31 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 01gglm; >> query: (?x3482, 029j_) <- award_winner(?x3482, ?x2533), language(?x3482, ?x254), film_format(?x3482, ?x6392), currency(?x3482, ?x170) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017z49 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #14382-0k2cb PRED entity: 0k2cb PRED relation: nominated_for! PRED expected values: 02r0csl => 105 concepts (98 used for prediction) PRED predicted values (max 10 best out of 211): 0gs96 (0.68 #14555, 0.67 #10913, 0.67 #5910), 0gr51 (0.45 #297, 0.25 #1432, 0.22 #6207), 0k611 (0.42 #973, 0.33 #1427, 0.33 #5065), 019f4v (0.41 #1412, 0.40 #731, 0.40 #958), 099c8n (0.41 #961, 0.35 #280, 0.23 #3459), 04dn09n (0.40 #1395, 0.40 #260, 0.36 #941), 0gs9p (0.40 #1418, 0.40 #5056, 0.40 #964), 0gr0m (0.40 #281, 0.31 #1416, 0.30 #962), 09qwmm (0.37 #1387, 0.22 #22054, 0.21 #16604), 0gqy2 (0.35 #339, 0.31 #1020, 0.31 #1474) >> Best rule #14555 for best value: >> intensional similarity = 3 >> extensional distance = 975 >> proper extension: 02_1q9; 027tbrc; 0524b41; 02_1kl; 03j63k; 0fpxp; 097h2; 023ny6; 019g8j; 0147w8; ... >> query: (?x4751, ?x384) <- award(?x4751, ?x384), nominated_for(?x749, ?x4751), award(?x164, ?x384) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1140 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 034r25; *> query: (?x4751, 02r0csl) <- film(?x6278, ?x4751), genre(?x4751, ?x4088), ?x4088 = 04xvh5 *> conf = 0.18 ranks of expected_values: 60 EVAL 0k2cb nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 105.000 98.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14381-01f8hf PRED entity: 01f8hf PRED relation: nominated_for! PRED expected values: 0p9sw => 96 concepts (86 used for prediction) PRED predicted values (max 10 best out of 211): 02hsq3m (0.68 #13771, 0.68 #14006, 0.67 #7700), 02g3ft (0.68 #13771, 0.68 #14006, 0.67 #7700), 0262s1 (0.68 #13771, 0.68 #14006, 0.67 #7700), 0p9sw (0.62 #252, 0.40 #1885, 0.32 #4452), 0gq9h (0.54 #292, 0.49 #4492, 0.37 #7525), 019f4v (0.50 #284, 0.45 #4484, 0.32 #6350), 0gs9p (0.50 #294, 0.44 #4494, 0.34 #7527), 040njc (0.42 #239, 0.35 #4439, 0.27 #7472), 0gr0m (0.42 #290, 0.32 #4490, 0.26 #4956), 054krc (0.38 #299, 0.30 #4965, 0.25 #3966) >> Best rule #13771 for best value: >> intensional similarity = 4 >> extensional distance = 937 >> proper extension: 06w7mlh; >> query: (?x4680, ?x2209) <- nominated_for(?x2214, ?x4680), award(?x4680, ?x2209), nominated_for(?x2209, ?x324), award(?x382, ?x2209) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #252 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 06mmr; *> query: (?x4680, 0p9sw) <- honored_for(?x3579, ?x4680), award_winner(?x4680, ?x800), award(?x4680, ?x2209), ?x2209 = 0gr42 *> conf = 0.62 ranks of expected_values: 4 EVAL 01f8hf nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 86.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14380-0dgpwnk PRED entity: 0dgpwnk PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #131, 0.83 #46, 0.83 #91), 02nxhr (0.10 #7, 0.06 #27, 0.06 #32), 07c52 (0.10 #103, 0.08 #78, 0.08 #68), 07z4p (0.08 #20, 0.07 #105, 0.06 #90) >> Best rule #131 for best value: >> intensional similarity = 4 >> extensional distance = 540 >> proper extension: 0bz3jx; 05_61y; 032xky; >> query: (?x3453, 029j_) <- country(?x3453, ?x429), featured_film_locations(?x3453, ?x739), genre(?x3453, ?x258), origin(?x217, ?x739) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dgpwnk film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.834 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #14379-03z19 PRED entity: 03z19 PRED relation: company! PRED expected values: 027qb1 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 21): 05_wyz (0.33 #66, 0.25 #1297, 0.25 #444), 0142rn (0.33 #74, 0.25 #1305, 0.25 #452), 060c4 (0.33 #712, 0.25 #428, 0.22 #1519), 0dq_5 (0.29 #774, 0.25 #302, 0.21 #2008), 0krdk (0.25 #291, 0.14 #1997, 0.14 #857), 01yc02 (0.25 #293, 0.14 #859, 0.14 #765), 0dq3c (0.20 #1612, 0.14 #2182, 0.14 #1041), 033smt (0.17 #692, 0.14 #1070, 0.14 #787), 02y6fz (0.17 #686, 0.14 #1064, 0.14 #781), 014l7h (0.14 #2209, 0.14 #1068, 0.13 #2304) >> Best rule #66 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 0jbk9; >> query: (?x1982, 05_wyz) <- category(?x1982, ?x134), ?x134 = 08mbj5d, organizations_founded(?x13698, ?x1982), people(?x12333, ?x13698), location(?x13698, ?x4061), nationality(?x13698, ?x94), entity_involved(?x11988, ?x13698), basic_title(?x13698, ?x346), gender(?x13698, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03z19 company! 027qb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 102.000 0.333 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #14378-01vvlyt PRED entity: 01vvlyt PRED relation: award PRED expected values: 0c4z8 => 96 concepts (69 used for prediction) PRED predicted values (max 10 best out of 273): 0c4z8 (0.34 #2055, 0.25 #864, 0.22 #2452), 01c99j (0.34 #1809, 0.20 #2206, 0.19 #618), 09sb52 (0.28 #14730, 0.24 #10760, 0.21 #5995), 02f6ym (0.26 #1841, 0.21 #7544, 0.20 #650), 01c92g (0.23 #2080, 0.21 #7544, 0.20 #889), 03c7tr1 (0.22 #454, 0.09 #10777, 0.08 #6012), 03qbnj (0.21 #2213, 0.15 #27002, 0.14 #3404), 02f5qb (0.21 #7544, 0.19 #2138, 0.19 #22235), 02f72n (0.21 #7544, 0.19 #22235, 0.18 #143), 02f73b (0.21 #7544, 0.19 #22235, 0.18 #24221) >> Best rule #2055 for best value: >> intensional similarity = 3 >> extensional distance = 180 >> proper extension: 015cxv; >> query: (?x5405, 0c4z8) <- award(?x5405, ?x3835), award(?x6659, ?x3835), ?x6659 = 01vw_dv >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vvlyt award 0c4z8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 69.000 0.341 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14377-09n48 PRED entity: 09n48 PRED relation: olympics! PRED expected values: 0ctw_b => 58 concepts (48 used for prediction) PRED predicted values (max 10 best out of 271): 06qd3 (0.77 #666, 0.72 #664, 0.53 #830), 015qh (0.77 #666, 0.62 #693, 0.53 #830), 0345h (0.75 #686, 0.72 #664, 0.70 #831), 0f8l9c (0.72 #664, 0.70 #831, 0.67 #665), 035qy (0.72 #664, 0.67 #665, 0.53 #830), 01znc_ (0.72 #664, 0.67 #665, 0.53 #830), 0b90_r (0.70 #831, 0.67 #665, 0.53 #830), 0jgd (0.70 #831, 0.67 #665, 0.53 #830), 015fr (0.70 #831, 0.67 #665, 0.53 #830), 0jgx (0.67 #665, 0.53 #830, 0.50 #562) >> Best rule #666 for best value: >> intensional similarity = 14 >> extensional distance = 2 >> proper extension: 06sks6; >> query: (?x418, ?x94) <- locations(?x418, ?x674), participating_countries(?x418, ?x3855), participating_countries(?x418, ?x2346), participating_countries(?x418, ?x2267), film_release_region(?x6270, ?x3855), ?x6270 = 0g9zljd, olympics(?x94, ?x418), olympics(?x453, ?x418), nationality(?x6406, ?x3855), ?x2346 = 0d05w3, film_release_region(?x7693, ?x2267), film_release_region(?x1498, ?x2267), ?x7693 = 0m63c, ?x1498 = 04jkpgv >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #514 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 06sks6; *> query: (?x418, 0ctw_b) <- locations(?x418, ?x674), participating_countries(?x418, ?x3855), participating_countries(?x418, ?x2346), participating_countries(?x418, ?x2267), film_release_region(?x6270, ?x3855), ?x6270 = 0g9zljd, olympics(?x94, ?x418), olympics(?x453, ?x418), nationality(?x6406, ?x3855), ?x2346 = 0d05w3, film_release_region(?x7693, ?x2267), film_release_region(?x1498, ?x2267), ?x7693 = 0m63c, ?x1498 = 04jkpgv *> conf = 0.50 ranks of expected_values: 31 EVAL 09n48 olympics! 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 58.000 48.000 0.768 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #14376-01y49 PRED entity: 01y49 PRED relation: school PRED expected values: 0hd7j => 88 concepts (69 used for prediction) PRED predicted values (max 10 best out of 355): 0lyjf (0.60 #2910, 0.57 #2720, 0.57 #830), 07w0v (0.33 #580, 0.28 #7430, 0.27 #8958), 01pl14 (0.33 #574, 0.25 #1138, 0.25 #5), 0hd7j (0.33 #636, 0.25 #1200, 0.25 #67), 01jq0j (0.33 #7534, 0.28 #3143, 0.25 #3525), 01jsk6 (0.29 #926, 0.25 #169, 0.17 #738), 01qgr3 (0.29 #877, 0.20 #500, 0.20 #308), 01vs5c (0.27 #4068, 0.24 #5400, 0.24 #3687), 01jq34 (0.25 #1347, 0.25 #25, 0.20 #405), 0g8rj (0.25 #1218, 0.23 #1975, 0.22 #1596) >> Best rule #2910 for best value: >> intensional similarity = 14 >> extensional distance = 13 >> proper extension: 05g49; >> query: (?x2114, 0lyjf) <- draft(?x2114, ?x3089), draft(?x2114, ?x1883), position(?x2114, ?x1717), position(?x2114, ?x1114), position(?x2114, ?x180), ?x1114 = 047g8h, ?x1717 = 02g_6x, ?x3089 = 03nt7j, draft(?x4469, ?x1883), school(?x1883, ?x331), ?x4469 = 043vc, position_s(?x2114, ?x1240), position(?x1115, ?x180), team(?x180, ?x179) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #636 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 070xg; 0wsr; 0487_; 06rpd; *> query: (?x2114, 0hd7j) <- draft(?x2114, ?x685), position(?x2114, ?x7079), position(?x2114, ?x1114), ?x1114 = 047g8h, teams(?x6555, ?x2114), position_s(?x2114, ?x2147), ?x2147 = 04nfpk, team(?x2312, ?x2114), school(?x2114, ?x735), ?x7079 = 08ns5s, place_of_birth(?x1913, ?x6555), ?x2312 = 02qpbqj *> conf = 0.33 ranks of expected_values: 4 EVAL 01y49 school 0hd7j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 69.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #14375-04__f PRED entity: 04__f PRED relation: location PRED expected values: 0psxp => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 172): 030qb3t (0.21 #8103, 0.17 #25756, 0.15 #49020), 02_286 (0.18 #50578, 0.17 #48974, 0.17 #25710), 0cc56 (0.14 #859, 0.12 #1661, 0.06 #3265), 0k049 (0.14 #810, 0.04 #2414, 0.03 #4820), 06pvr (0.14 #937, 0.02 #3343), 04rrd (0.14 #900, 0.01 #8118), 0947l (0.14 #1230), 0cr3d (0.08 #2551, 0.08 #1749, 0.07 #3353), 01n7q (0.08 #1667, 0.04 #2469, 0.04 #3271), 059rby (0.08 #1620, 0.04 #25689, 0.04 #32908) >> Best rule #8103 for best value: >> intensional similarity = 2 >> extensional distance = 153 >> proper extension: 01npcy7; >> query: (?x7958, 030qb3t) <- place_of_birth(?x7958, ?x8451), participant(?x7958, ?x1149) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2695 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 46 *> proper extension: 019n7x; *> query: (?x7958, 0psxp) <- award_nominee(?x1126, ?x7958), celebrities_impersonated(?x3649, ?x7958) *> conf = 0.02 ranks of expected_values: 60 EVAL 04__f location 0psxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 134.000 134.000 0.206 http://example.org/people/person/places_lived./people/place_lived/location #14374-0194xc PRED entity: 0194xc PRED relation: legislative_sessions PRED expected values: 06f0dc 04gp1d 02glc4 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 30): 06f0dc (0.52 #244, 0.48 #304, 0.48 #214), 070mff (0.40 #319, 0.39 #259, 0.35 #229), 02bp37 (0.35 #246, 0.32 #306, 0.30 #216), 02glc4 (0.26 #257, 0.26 #227, 0.24 #317), 077g7n (0.24 #302, 0.22 #242, 0.17 #212), 04gp1d (0.17 #250, 0.17 #220, 0.16 #310), 01gtc0 (0.17 #74, 0.12 #314, 0.09 #224), 05rrw9 (0.17 #90, 0.10 #60, 0.04 #240), 01gsvp (0.17 #78, 0.08 #318, 0.04 #258), 01gtcc (0.17 #69, 0.08 #309, 0.04 #219) >> Best rule #244 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0rlz; 012v1t; >> query: (?x9569, 06f0dc) <- legislative_sessions(?x9569, ?x355), gender(?x9569, ?x231), jurisdiction_of_office(?x9569, ?x94) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 6 EVAL 0194xc legislative_sessions 02glc4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 119.000 0.522 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 0194xc legislative_sessions 04gp1d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 119.000 119.000 0.522 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 0194xc legislative_sessions 06f0dc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.522 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #14373-0mp08 PRED entity: 0mp08 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 142 concepts (81 used for prediction) PRED predicted values (max 10 best out of 13): 09c7w0 (0.79 #92, 0.79 #81, 0.77 #483), 07z1m (0.17 #12, 0.14 #35, 0.08 #253), 04_1l0v (0.15 #667, 0.02 #902), 07ssc (0.14 #29, 0.04 #217, 0.04 #119), 020d5 (0.11 #139, 0.10 #151, 0.06 #187), 0d060g (0.11 #774, 0.03 #130, 0.03 #142), 0694j (0.11 #774), 0160w (0.11 #774), 059j2 (0.03 #249, 0.03 #135, 0.03 #147), 0f8l9c (0.03 #134, 0.03 #146, 0.02 #182) >> Best rule #92 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0mnlq; >> query: (?x11716, 09c7w0) <- contains(?x1426, ?x11716), ?x1426 = 07z1m, source(?x11716, ?x958), ?x958 = 0jbk9 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mp08 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 81.000 0.792 http://example.org/location/country/second_level_divisions #14372-02_p8v PRED entity: 02_p8v PRED relation: film PRED expected values: 01hp5 04jpg2p => 116 concepts (80 used for prediction) PRED predicted values (max 10 best out of 492): 04165w (0.33 #3095, 0.33 #1312, 0.17 #4878), 04jpg2p (0.33 #1457, 0.30 #8589, 0.08 #41014), 09g7vfw (0.33 #550, 0.30 #7682, 0.08 #41014), 0ds11z (0.33 #63, 0.20 #7195), 03177r (0.33 #2244, 0.17 #4027, 0.02 #45041), 02z3r8t (0.33 #1888, 0.17 #3671, 0.01 #94629), 0glbqt (0.33 #3439, 0.17 #5222, 0.01 #23052), 0p_rk (0.33 #3133, 0.17 #4916), 0prh7 (0.33 #2614, 0.17 #4397), 01vw8k (0.33 #2432, 0.17 #4215) >> Best rule #3095 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 015q43; >> query: (?x5188, 04165w) <- film(?x5188, ?x6133), ?x6133 = 026zlh9, gender(?x5188, ?x231), people(?x5042, ?x5188) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1457 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 0jfx1; *> query: (?x5188, 04jpg2p) <- film(?x5188, ?x6133), film(?x5188, ?x1797), ?x1797 = 050xxm, film(?x5043, ?x6133), ?x5043 = 015q43 *> conf = 0.33 ranks of expected_values: 2, 47 EVAL 02_p8v film 04jpg2p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 80.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 02_p8v film 01hp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 116.000 80.000 0.333 http://example.org/film/actor/film./film/performance/film #14371-010p3 PRED entity: 010p3 PRED relation: film PRED expected values: 0cc97st => 104 concepts (66 used for prediction) PRED predicted values (max 10 best out of 773): 0p9lw (0.29 #146, 0.13 #7314, 0.12 #9106), 0prrm (0.18 #6238, 0.14 #862, 0.08 #66307), 0f2sx4 (0.18 #6763, 0.11 #3179, 0.07 #19308), 03x7hd (0.14 #562, 0.09 #4146, 0.07 #7730), 05t54s (0.14 #1201, 0.07 #8369, 0.06 #10161), 02v63m (0.14 #177, 0.07 #7345, 0.06 #9137), 091xrc (0.14 #1770, 0.07 #8938, 0.06 #10730), 0gd92 (0.14 #1308, 0.07 #8476, 0.06 #10268), 08k40m (0.14 #485, 0.07 #7653, 0.06 #9445), 01719t (0.12 #9191, 0.09 #3815, 0.08 #14567) >> Best rule #146 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 048wrb; >> query: (?x8196, 0p9lw) <- profession(?x8196, ?x8709), profession(?x8196, ?x1146), ?x8709 = 08z956, religion(?x8196, ?x2694), ?x1146 = 018gz8 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #17117 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 05xq9; *> query: (?x8196, 0cc97st) <- influenced_by(?x8196, ?x4112), religion(?x4112, ?x1985), film(?x4112, ?x994), award_winner(?x2431, ?x4112) *> conf = 0.02 ranks of expected_values: 346 EVAL 010p3 film 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 104.000 66.000 0.286 http://example.org/film/actor/film./film/performance/film #14370-09gb9xh PRED entity: 09gb9xh PRED relation: nationality PRED expected values: 09c7w0 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 14): 09c7w0 (0.78 #701, 0.77 #601, 0.76 #1001), 0d060g (0.37 #3302, 0.07 #307, 0.05 #7), 02jx1 (0.10 #2733, 0.10 #533, 0.09 #2333), 07ssc (0.08 #2715, 0.08 #515, 0.08 #2816), 03_3d (0.06 #306, 0.01 #906, 0.01 #6212), 03rk0 (0.05 #7652, 0.05 #7753, 0.04 #946), 0chghy (0.02 #510, 0.02 #3612, 0.02 #1310), 03rjj (0.02 #5008, 0.02 #6711, 0.02 #3607), 0215n (0.02 #5605, 0.02 #5304, 0.02 #2801), 0146mv (0.02 #5304, 0.02 #2801, 0.02 #6206) >> Best rule #701 for best value: >> intensional similarity = 3 >> extensional distance = 840 >> proper extension: 049tjg; 04bs3j; 0lzb8; 012c6x; 0htlr; 03gm48; 0f0p0; 04hpck; 01sxq9; 04n7njg; ... >> query: (?x11788, 09c7w0) <- nominated_for(?x11788, ?x7657), award_winner(?x7657, ?x129), actor(?x7657, ?x1986) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09gb9xh nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.776 http://example.org/people/person/nationality #14369-011zd3 PRED entity: 011zd3 PRED relation: award PRED expected values: 0cqhk0 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 241): 0cqhk0 (0.72 #30335, 0.71 #12770, 0.70 #14766), 05pcn59 (0.50 #478, 0.23 #1276, 0.21 #1675), 05ztrmj (0.29 #979, 0.29 #580, 0.14 #19954), 05p09zm (0.29 #521, 0.18 #1319, 0.17 #122), 07cbcy (0.29 #475, 0.17 #76, 0.13 #27937), 03c7tr1 (0.29 #455, 0.15 #1253, 0.13 #2051), 05zvj3m (0.29 #490, 0.14 #889, 0.13 #27937), 05q5t0b (0.21 #559, 0.07 #958, 0.03 #1357), 0ck27z (0.17 #90, 0.16 #8868, 0.16 #10065), 09qv_s (0.17 #149, 0.14 #548, 0.14 #19954) >> Best rule #30335 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 099ks0; >> query: (?x2307, ?x678) <- award_winner(?x678, ?x2307), award(?x237, ?x678) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011zd3 award 0cqhk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14368-09swkk PRED entity: 09swkk PRED relation: award PRED expected values: 047sgz4 => 125 concepts (89 used for prediction) PRED predicted values (max 10 best out of 295): 02x17c2 (0.76 #11151, 0.76 #13540, 0.74 #1991), 09sb52 (0.50 #6811, 0.30 #23536, 0.30 #14775), 0gqz2 (0.44 #1273, 0.43 #875, 0.42 #1671), 054ks3 (0.39 #139, 0.36 #1333, 0.33 #935), 025m8y (0.39 #97, 0.33 #893, 0.27 #495), 01by1l (0.34 #7280, 0.33 #7678, 0.32 #8872), 0c4z8 (0.26 #3256, 0.24 #7240, 0.24 #8832), 01bgqh (0.26 #8009, 0.25 #3229, 0.24 #13583), 025m8l (0.24 #3303, 0.16 #1311, 0.15 #515), 03qbh5 (0.22 #8167, 0.20 #8565, 0.20 #13741) >> Best rule #11151 for best value: >> intensional similarity = 3 >> extensional distance = 338 >> proper extension: 0c_mvb; 01jllg1; 06lxn; >> query: (?x4940, ?x884) <- award_winner(?x4940, ?x11182), artists(?x4910, ?x4940), award_winner(?x884, ?x4940) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #924 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 0p5mw; 04zwjd; 01l9v7n; 03975z; 01x1fq; 0csdzz; 01njxvw; 07v4dm; *> query: (?x4940, 047sgz4) <- music(?x4541, ?x4940), award(?x4940, ?x1443), ?x1443 = 054krc *> conf = 0.03 ranks of expected_values: 271 EVAL 09swkk award 047sgz4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 125.000 89.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14367-03j149k PRED entity: 03j149k PRED relation: origin PRED expected values: 0y62n => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 69): 02zp1t (0.28 #6824, 0.22 #4704, 0.21 #4940), 030qb3t (0.12 #738, 0.12 #973, 0.12 #1208), 0cr3d (0.12 #55, 0.11 #290, 0.05 #995), 01_d4 (0.12 #39, 0.07 #274, 0.03 #4979), 01531 (0.07 #295, 0.03 #765, 0.03 #1000), 04jpl (0.07 #4946, 0.05 #3063, 0.05 #946), 01sn3 (0.06 #77, 0.04 #312, 0.01 #2192), 0k9p4 (0.06 #156, 0.04 #391, 0.01 #861), 0281y0 (0.06 #127, 0.04 #362, 0.01 #832), 06wxw (0.06 #82, 0.04 #317, 0.01 #787) >> Best rule #6824 for best value: >> intensional similarity = 2 >> extensional distance = 498 >> proper extension: 03c_8t; >> query: (?x8124, ?x13062) <- place_of_birth(?x8124, ?x13062), artists(?x2937, ?x8124) >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03j149k origin 0y62n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 113.000 0.280 http://example.org/music/artist/origin #14366-0296y PRED entity: 0296y PRED relation: artists PRED expected values: 016lj_ => 69 concepts (18 used for prediction) PRED predicted values (max 10 best out of 900): 01w8n89 (0.60 #4640, 0.59 #14374, 0.58 #12214), 01vsxdm (0.57 #5510, 0.50 #6591, 0.33 #102), 0fpj4lx (0.57 #5732, 0.46 #10057, 0.42 #6813), 01386_ (0.57 #5988, 0.44 #6489, 0.42 #13558), 020_4z (0.57 #6346, 0.42 #7427, 0.38 #10671), 048tgl (0.57 #6319, 0.42 #7400, 0.38 #10644), 014_lq (0.57 #5889, 0.42 #6970, 0.33 #481), 01j59b0 (0.57 #5878, 0.42 #6959, 0.33 #470), 0150jk (0.57 #5456, 0.42 #6537, 0.33 #48), 0pkyh (0.57 #5648, 0.42 #6729, 0.33 #240) >> Best rule #4640 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 03_d0; 0hdf8; >> query: (?x6350, 01w8n89) <- parent_genre(?x9486, ?x6350), parent_genre(?x8011, ?x6350), artists(?x6350, ?x1955), ?x9486 = 05g7tj, artists(?x8011, ?x2073), group(?x227, ?x2073), location(?x1955, ?x5867) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4155 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 02z7f3; *> query: (?x6350, 016lj_) <- parent_genre(?x14445, ?x6350), parent_genre(?x9486, ?x6350), parent_genre(?x8011, ?x6350), artists(?x6350, ?x764), parent_genre(?x9486, ?x2249), ?x2249 = 03lty, artists(?x8011, ?x2073), ?x14445 = 066d03 *> conf = 0.50 ranks of expected_values: 14 EVAL 0296y artists 016lj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 69.000 18.000 0.600 http://example.org/music/genre/artists #14365-06qm3 PRED entity: 06qm3 PRED relation: genre! PRED expected values: 0c57yj => 46 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1869): 0d87hc (0.78 #1859, 0.76 #7437, 0.73 #18589), 0k5fg (0.78 #1859, 0.76 #7437, 0.73 #18589), 01sxly (0.78 #1859, 0.76 #7437, 0.73 #18589), 09g8vhw (0.78 #1859, 0.76 #7437, 0.73 #18589), 0c8tkt (0.78 #1859, 0.76 #7437, 0.73 #18589), 02jkkv (0.62 #16471, 0.60 #9037, 0.43 #12753), 05sns6 (0.62 #15600, 0.60 #8166, 0.43 #11882), 03s6l2 (0.62 #14958, 0.40 #9382, 0.40 #7524), 01pgp6 (0.60 #9588, 0.60 #7730, 0.50 #13306), 04yc76 (0.60 #9750, 0.60 #7892, 0.50 #13468) >> Best rule #1859 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 02l7c8; >> query: (?x4150, ?x582) <- genre(?x8457, ?x4150), genre(?x6603, ?x4150), genre(?x5066, ?x4150), ?x8457 = 034xyf, language(?x5066, ?x254), film(?x157, ?x5066), titles(?x4150, ?x582), film_crew_role(?x5066, ?x137), ?x6603 = 094g2z >> conf = 0.78 => this is the best rule for 5 predicted values *> Best rule #8092 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 06cvj; *> query: (?x4150, 0c57yj) <- genre(?x8457, ?x4150), genre(?x5066, ?x4150), ?x8457 = 034xyf, language(?x5066, ?x254), film(?x368, ?x5066), ?x254 = 02h40lc, currency(?x5066, ?x170), ?x368 = 01wbg84 *> conf = 0.60 ranks of expected_values: 68 EVAL 06qm3 genre! 0c57yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 46.000 23.000 0.784 http://example.org/film/film/genre #14364-03tps5 PRED entity: 03tps5 PRED relation: film! PRED expected values: 01bcq => 71 concepts (29 used for prediction) PRED predicted values (max 10 best out of 704): 016szr (0.43 #56156, 0.36 #6238, 0.36 #31192), 06cgy (0.25 #246, 0.11 #51996, 0.02 #39758), 01bcq (0.25 #870), 01zfmm (0.14 #29111), 01wy5m (0.12 #855, 0.04 #2934, 0.02 #13330), 0pz91 (0.12 #207, 0.03 #8524, 0.02 #18922), 01vsn38 (0.12 #1852, 0.03 #12248, 0.03 #10169), 0lx2l (0.12 #416, 0.03 #8733, 0.02 #4574), 0738b8 (0.12 #400, 0.02 #12875, 0.02 #6638), 0pgm3 (0.12 #2002, 0.02 #14477, 0.02 #6160) >> Best rule #56156 for best value: >> intensional similarity = 4 >> extensional distance = 939 >> proper extension: 0d90m; 02vxq9m; 0b2v79; 01jc6q; 011yrp; 016z5x; 08720; 04jwjq; 0dj0m5; 02py4c8; ... >> query: (?x4409, ?x4850) <- genre(?x4409, ?x258), film(?x794, ?x4409), nominated_for(?x794, ?x437), award_winner(?x4409, ?x4850) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #870 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 09g8vhw; *> query: (?x4409, 01bcq) <- genre(?x4409, ?x258), film(?x794, ?x4409), ?x794 = 0mdqp, award(?x4409, ?x1691) *> conf = 0.25 ranks of expected_values: 3 EVAL 03tps5 film! 01bcq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 29.000 0.427 http://example.org/film/actor/film./film/performance/film #14363-0n5dt PRED entity: 0n5dt PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 125 concepts (39 used for prediction) PRED predicted values (max 10 best out of 10): 09c7w0 (0.88 #109, 0.88 #199, 0.88 #210), 05fjf (0.11 #273, 0.09 #472, 0.08 #120), 05tbn (0.09 #472, 0.03 #172), 0n5dt (0.06 #354, 0.03 #234, 0.01 #502), 07ssc (0.04 #18, 0.02 #91, 0.02 #422), 02jx1 (0.04 #374, 0.04 #311, 0.04 #482), 03rt9 (0.03 #277), 0f8l9c (0.01 #227), 0d060g (0.01 #394, 0.01 #291), 03rjj (0.01 #304) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 0mp3l; 0mmzt; 0mndw; 0mn8t; 034lk7; 0dzt9; 0fwc0; 0nn83; 0mm_4; 0mp08; ... >> query: (?x12221, 09c7w0) <- time_zones(?x12221, ?x2674), county(?x1214, ?x12221), contains(?x6895, ?x12221), ?x2674 = 02hcv8 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n5dt second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 39.000 0.883 http://example.org/location/country/second_level_divisions #14362-01w2dq PRED entity: 01w2dq PRED relation: place_of_birth! PRED expected values: 054bt3 => 79 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1434): 025t9b (0.11 #761, 0.04 #3373, 0.02 #8600), 0fvt2 (0.11 #2278, 0.04 #4890, 0.02 #10117), 02465 (0.11 #2273, 0.04 #4885, 0.02 #10112), 07rhpg (0.11 #1649, 0.04 #4261, 0.02 #9488), 01vrnsk (0.11 #1436, 0.04 #4048, 0.02 #9275), 03bnv (0.11 #638, 0.04 #3250, 0.02 #8477), 03ym1 (0.11 #1169, 0.02 #6394, 0.02 #16846), 01tp5bj (0.11 #465, 0.02 #5690, 0.01 #23978), 0525b (0.11 #2369, 0.02 #18046, 0.02 #15433), 08_83x (0.11 #1065, 0.02 #14129, 0.02 #11516) >> Best rule #761 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 018h8j; 04682_; >> query: (?x12884, 025t9b) <- contains(?x1758, ?x12884), contains(?x1310, ?x12884), contains(?x512, ?x12884), ?x1310 = 02jx1, ?x512 = 07ssc, ?x1758 = 0dbdy >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w2dq place_of_birth! 054bt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 33.000 0.111 http://example.org/people/person/place_of_birth #14361-0tz1j PRED entity: 0tz1j PRED relation: contains! PRED expected values: 05k7sb => 96 concepts (56 used for prediction) PRED predicted values (max 10 best out of 213): 05k7sb (0.85 #16999, 0.77 #11628, 0.74 #24159), 01n7q (0.23 #20656, 0.23 #19760, 0.21 #11705), 07ssc (0.17 #6292, 0.16 #33139, 0.15 #47452), 02jx1 (0.14 #6347, 0.11 #47507, 0.11 #33194), 059rby (0.13 #32233, 0.13 #34022, 0.11 #42968), 0kpys (0.13 #11808, 0.11 #19863, 0.06 #20759), 05fjf (0.12 #11106, 0.12 #12001, 0.10 #7528), 07z1m (0.09 #10824, 0.06 #21565, 0.06 #7246), 05kkh (0.09 #32222, 0.08 #34011, 0.07 #42957), 0k3k1 (0.09 #13419, 0.06 #48316, 0.03 #11229) >> Best rule #16999 for best value: >> intensional similarity = 4 >> extensional distance = 143 >> proper extension: 02qjb7z; >> query: (?x12919, ?x2020) <- administrative_division(?x12919, ?x6905), contains(?x94, ?x12919), contains(?x2020, ?x6905), district_represented(?x176, ?x2020) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tz1j contains! 05k7sb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 56.000 0.848 http://example.org/location/location/contains #14360-0gsy3b PRED entity: 0gsy3b PRED relation: genre! PRED expected values: 0963mq => 29 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1862): 034xyf (0.67 #7052, 0.62 #10764, 0.50 #5196), 01pgp6 (0.67 #5859, 0.50 #9571, 0.50 #4003), 09cxm4 (0.57 #8890, 0.50 #5178, 0.50 #3323), 0291ck (0.57 #9034, 0.50 #3467, 0.33 #1612), 02t_h3 (0.57 #9243, 0.38 #11099, 0.33 #7387), 027s39y (0.50 #6237, 0.50 #4381, 0.50 #2526), 0d87hc (0.50 #7262, 0.50 #5406, 0.50 #3551), 065zlr (0.50 #5980, 0.50 #4124, 0.50 #2269), 0yx1m (0.50 #10747, 0.50 #7035, 0.50 #5179), 02jkkv (0.50 #10879, 0.50 #7167, 0.50 #5311) >> Best rule #7052 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 06cvj; 011ys5; >> query: (?x12008, 034xyf) <- genre(?x9383, ?x12008), genre(?x4130, ?x12008), genre(?x2754, ?x12008), ?x2754 = 04yc76, currency(?x9383, ?x170), ?x170 = 09nqf, film(?x3022, ?x9383), film_crew_role(?x4130, ?x137), film(?x986, ?x4130), award(?x9383, ?x3209), featured_film_locations(?x4130, ?x739) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 05p553; *> query: (?x12008, 0963mq) <- genre(?x9383, ?x12008), genre(?x7702, ?x12008), genre(?x7579, ?x12008), genre(?x7501, ?x12008), genre(?x4130, ?x12008), genre(?x2754, ?x12008), genre(?x770, ?x12008), ?x2754 = 04yc76, ?x9383 = 0p7pw, ?x770 = 01r97z, ?x7501 = 0gd92, ?x4130 = 06lpmt, ?x7702 = 06fpsx, currency(?x7579, ?x170), film(?x1460, ?x7579) *> conf = 0.33 ranks of expected_values: 413 EVAL 0gsy3b genre! 0963mq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 15.000 0.667 http://example.org/film/film/genre #14359-09sb52 PRED entity: 09sb52 PRED relation: award_winner PRED expected values: 07rhpg => 43 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1701): 0gyy0 (0.50 #1716, 0.40 #6423, 0.04 #11133), 01ycbq (0.40 #5092, 0.36 #37672, 0.32 #9416), 0p_pd (0.40 #2407, 0.20 #4760, 0.08 #9470), 0pmhf (0.36 #37672, 0.35 #28252, 0.32 #9416), 0h10vt (0.36 #37672, 0.35 #28252, 0.32 #9416), 06cgy (0.36 #37672, 0.35 #28252, 0.32 #9416), 0237fw (0.36 #37672, 0.35 #28252, 0.32 #9416), 01wy5m (0.36 #37672, 0.35 #28252, 0.32 #9416), 014v6f (0.36 #37672, 0.35 #28252, 0.32 #9416), 02tr7d (0.36 #37672, 0.35 #28252, 0.32 #9416) >> Best rule #1716 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0bs0bh; 0gqy2; >> query: (?x704, 0gyy0) <- award_winner(?x704, ?x968), award(?x851, ?x704), ?x968 = 015grj, ?x851 = 016khd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #23543 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 186 *> proper extension: 02qt02v; *> query: (?x704, ?x2654) <- award_winner(?x704, ?x2556), nominated_for(?x704, ?x86), award_winner(?x2556, ?x2654), award_winner(?x6612, ?x2556) *> conf = 0.13 ranks of expected_values: 429 EVAL 09sb52 award_winner 07rhpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 21.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #14358-05rgl PRED entity: 05rgl PRED relation: featured_film_locations! PRED expected values: 02z9rr => 158 concepts (151 used for prediction) PRED predicted values (max 10 best out of 678): 061681 (0.25 #4464, 0.13 #22868, 0.08 #21396), 092vkg (0.20 #3014, 0.11 #5222, 0.08 #5958), 042zrm (0.20 #3540, 0.08 #6484, 0.07 #8692), 01rxyb (0.20 #3259, 0.08 #6203, 0.07 #8411), 01svry (0.20 #3445, 0.08 #6389, 0.07 #8597), 04cppj (0.20 #3433, 0.08 #6377, 0.07 #8585), 02nczh (0.20 #3424, 0.08 #6368, 0.07 #8576), 02j69w (0.20 #3287, 0.08 #6231, 0.07 #8439), 04tqtl (0.20 #3169, 0.08 #6113, 0.07 #8321), 02f6g5 (0.17 #3804, 0.05 #21472, 0.05 #12639) >> Best rule #4464 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 07_pf; >> query: (?x1879, 061681) <- locations(?x8303, ?x1879), entity_involved(?x8303, ?x1528), featured_film_locations(?x11998, ?x1879) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05rgl featured_film_locations! 02z9rr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 151.000 0.250 http://example.org/film/film/featured_film_locations #14357-02183k PRED entity: 02183k PRED relation: school! PRED expected values: 06x68 0jmj7 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 126): 0jmj7 (0.66 #2465, 0.66 #3366, 0.63 #2104), 05m_8 (0.22 #363, 0.19 #1177, 0.18 #996), 051vz (0.16 #383, 0.16 #1016, 0.12 #2190), 01d5z (0.16 #370, 0.13 #1184, 0.12 #1003), 07l8x (0.16 #154, 0.14 #1057, 0.13 #424), 01y3v (0.15 #386, 0.12 #1265, 0.11 #2076), 01slc (0.14 #1230, 0.14 #2223, 0.13 #2133), 07l4z (0.13 #1061, 0.11 #2235, 0.11 #428), 07147 (0.13 #335, 0.13 #425, 0.12 #1058), 04wmvz (0.13 #347, 0.12 #1070, 0.11 #437) >> Best rule #2465 for best value: >> intensional similarity = 3 >> extensional distance = 129 >> proper extension: 02zkz7; >> query: (?x3416, 0jmj7) <- school(?x2198, ?x3416), currency(?x3416, ?x170), school_type(?x3416, ?x1507) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1, 63 EVAL 02183k school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.656 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 02183k school! 06x68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 145.000 145.000 0.656 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #14356-0mw_q PRED entity: 0mw_q PRED relation: source PRED expected values: 0jbk9 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #8, 0.92 #9, 0.92 #16) >> Best rule #8 for best value: >> intensional similarity = 5 >> extensional distance = 148 >> proper extension: 0mn0v; 0njcw; >> query: (?x13653, 0jbk9) <- currency(?x13653, ?x170), time_zones(?x13653, ?x2674), ?x170 = 09nqf, ?x2674 = 02hcv8, second_level_divisions(?x94, ?x13653) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mw_q source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.940 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #14355-03h0byn PRED entity: 03h0byn PRED relation: film! PRED expected values: 09l3p => 121 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1027): 053xw6 (0.25 #1252, 0.06 #5411, 0.02 #15807), 0c6qh (0.25 #413, 0.05 #48245, 0.04 #85271), 02xs5v (0.25 #1405, 0.04 #13881, 0.03 #24281), 09wj5 (0.25 #101, 0.03 #22977, 0.03 #25056), 02gvwz (0.25 #188, 0.02 #18904, 0.02 #8506), 015q43 (0.25 #901, 0.02 #19617, 0.01 #5060), 0336mc (0.25 #1518, 0.02 #13994, 0.02 #20234), 03hh89 (0.25 #963, 0.01 #5122, 0.01 #9281), 0356dp (0.25 #1745, 0.01 #5904, 0.01 #30859), 0h0jz (0.25 #39, 0.01 #4198, 0.01 #12515) >> Best rule #1252 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02phtzk; >> query: (?x11022, 053xw6) <- titles(?x53, ?x11022), written_by(?x11022, ?x5033), produced_by(?x11022, ?x3223), ?x5033 = 05y5fw >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #48579 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 411 *> proper extension: 0h1fktn; *> query: (?x11022, 09l3p) <- titles(?x53, ?x11022), film(?x1733, ?x11022), participant(?x1733, ?x2763), vacationer(?x6226, ?x1733) *> conf = 0.03 ranks of expected_values: 79 EVAL 03h0byn film! 09l3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 121.000 71.000 0.250 http://example.org/film/actor/film./film/performance/film #14354-0d9_96 PRED entity: 0d9_96 PRED relation: award_nominee! PRED expected values: 021yw7 => 76 concepts (40 used for prediction) PRED predicted values (max 10 best out of 853): 03jldb (0.81 #81675, 0.81 #79341, 0.80 #39676), 021yw7 (0.81 #81675, 0.81 #79341, 0.80 #39676), 027j79k (0.81 #81675, 0.81 #79341, 0.80 #39676), 03mg35 (0.60 #2739), 0gpprt (0.53 #4253, 0.01 #20589), 0bsb4j (0.53 #2899), 03pmty (0.53 #2530), 031k24 (0.47 #4123, 0.01 #34464, 0.01 #46132), 02d4ct (0.47 #2839, 0.01 #77513, 0.01 #79847), 0736qr (0.47 #4645) >> Best rule #81675 for best value: >> intensional similarity = 3 >> extensional distance = 1978 >> proper extension: 02zq43; 0b05xm; 03kpvp; 073749; 05v1sb; 05f7snc; 08_83x; 05qhnq; 02fgm7; 01jllg1; ... >> query: (?x3339, ?x1537) <- award_nominee(?x3340, ?x3339), gender(?x3339, ?x231), award_nominee(?x3339, ?x1537) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0d9_96 award_nominee! 021yw7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 40.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14353-0gmcwlb PRED entity: 0gmcwlb PRED relation: film_release_region PRED expected values: 03rjj 0j1z8 01ls2 06c1y 06bnz => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 177): 06bnz (0.90 #432, 0.88 #1363, 0.86 #964), 03rjj (0.85 #1067, 0.85 #1333, 0.85 #402), 03rt9 (0.83 #143, 0.73 #1207, 0.73 #808), 09pmkv (0.74 #951, 0.71 #20, 0.70 #818), 01ls2 (0.74 #939, 0.71 #8, 0.70 #407), 06c1y (0.72 #962, 0.71 #31, 0.61 #164), 01mjq (0.71 #32, 0.67 #165, 0.55 #2427), 07f1x (0.71 #97, 0.58 #1028, 0.49 #895), 03rj0 (0.70 #844, 0.64 #2441, 0.63 #1509), 05qx1 (0.67 #162, 0.65 #428, 0.62 #1359) >> Best rule #432 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 03z9585; >> query: (?x1370, 06bnz) <- film_release_region(?x1370, ?x3855), film_release_region(?x1370, ?x3749), film_release_region(?x1370, ?x279), ?x3749 = 03ryn, ?x279 = 0d060g, ?x3855 = 0jgx >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 6, 15 EVAL 0gmcwlb film_release_region 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gmcwlb film_release_region 06c1y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 85.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gmcwlb film_release_region 01ls2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 85.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gmcwlb film_release_region 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 85.000 85.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gmcwlb film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14352-0c1pj PRED entity: 0c1pj PRED relation: location PRED expected values: 06y57 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 195): 0dclg (0.25 #117, 0.06 #2529, 0.06 #9767), 030qb3t (0.20 #9733, 0.19 #36267, 0.19 #33051), 02_286 (0.18 #18533, 0.16 #12904, 0.15 #14512), 0cr3d (0.14 #4970, 0.09 #5774, 0.08 #6578), 01_d4 (0.09 #5731, 0.08 #6535, 0.08 #906), 0cc56 (0.09 #9707, 0.06 #13728, 0.05 #25789), 04jpl (0.09 #9667, 0.04 #36201, 0.04 #48265), 01m1_d (0.08 #1480, 0.06 #3088, 0.06 #3893), 0b1t1 (0.08 #1277, 0.05 #4494, 0.05 #5298), 04f_d (0.08 #912, 0.05 #4129, 0.05 #4933) >> Best rule #117 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01mkn_d; >> query: (?x556, 0dclg) <- award_winner(?x299, ?x556), nominated_for(?x556, ?x174), ?x174 = 01br2w >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #21968 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 193 *> proper extension: 03bw6; *> query: (?x556, 06y57) <- award_winner(?x401, ?x556), film(?x556, ?x174), nominated_for(?x556, ?x1185) *> conf = 0.02 ranks of expected_values: 121 EVAL 0c1pj location 06y57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 114.000 114.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #14351-02rzdcp PRED entity: 02rzdcp PRED relation: honored_for! PRED expected values: 03gwpw2 058m5m4 03gt46z 09v0p2c => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 76): 0bzjvm (0.33 #90, 0.33 #1727, 0.08 #6101), 03gt46z (0.33 #1727, 0.08 #6101, 0.08 #6100), 058m5m4 (0.33 #1727, 0.08 #6101, 0.08 #6100), 09v0p2c (0.33 #1727, 0.08 #6101, 0.08 #6100), 02q690_ (0.26 #625, 0.25 #1085, 0.24 #855), 0lp_cd3 (0.17 #130, 0.16 #820, 0.16 #1050), 0gx_st (0.11 #1061, 0.11 #601, 0.10 #831), 0bxs_d (0.10 #669, 0.09 #899, 0.08 #1129), 0jt3qpk (0.09 #145, 0.08 #835, 0.08 #375), 09pj68 (0.09 #200, 0.08 #315, 0.07 #660) >> Best rule #90 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0ptx_; >> query: (?x3310, 0bzjvm) <- nominated_for(?x4948, ?x3310), ?x4948 = 02d6cy, award(?x3310, ?x1670) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1727 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 112 *> proper extension: 03ffcz; *> query: (?x3310, ?x3609) <- languages(?x3310, ?x254), award_winner(?x3310, ?x3051), award_winner(?x3609, ?x3051) *> conf = 0.33 ranks of expected_values: 2, 3, 4, 29 EVAL 02rzdcp honored_for! 09v0p2c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 02rzdcp honored_for! 03gt46z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 02rzdcp honored_for! 058m5m4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 02rzdcp honored_for! 03gwpw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 68.000 68.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #14350-02vqpx8 PRED entity: 02vqpx8 PRED relation: profession PRED expected values: 0dxtg => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 61): 0dxtg (0.84 #1336, 0.83 #1042, 0.83 #307), 02hrh1q (0.80 #3101, 0.76 #3542, 0.76 #4277), 01d_h8 (0.54 #594, 0.51 #447, 0.48 #3240), 02krf9 (0.32 #467, 0.32 #1202, 0.30 #1790), 018gz8 (0.31 #163, 0.19 #3250, 0.18 #1339), 02jknp (0.28 #595, 0.26 #448, 0.26 #3241), 0kyk (0.26 #10291, 0.25 #10880, 0.17 #29), 012t_z (0.26 #10291, 0.25 #10880, 0.06 #12), 01d30f (0.26 #10291, 0.25 #10880, 0.02 #2275), 09jwl (0.23 #4428, 0.21 #6780, 0.18 #3987) >> Best rule #1336 for best value: >> intensional similarity = 2 >> extensional distance = 184 >> proper extension: 04bs3j; 04n7njg; 02jm0n; 03m_k0; 04snp2; 04h07s; 054187; 04pg29; 023qfd; 01r4zfk; ... >> query: (?x7043, 0dxtg) <- profession(?x7043, ?x353), tv_program(?x7043, ?x1653) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vqpx8 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.839 http://example.org/people/person/profession #14349-0p3r8 PRED entity: 0p3r8 PRED relation: artists! PRED expected values: 05bt6j 059kh => 176 concepts (97 used for prediction) PRED predicted values (max 10 best out of 254): 016clz (0.79 #2455, 0.76 #9813, 0.70 #4910), 03lty (0.68 #7383, 0.65 #4933, 0.65 #8608), 0xhtw (0.64 #7372, 0.53 #8597, 0.50 #4922), 025sc50 (0.50 #4649, 0.45 #1888, 0.40 #50), 09nwwf (0.50 #2582, 0.33 #745, 0.30 #1358), 059kh (0.50 #969, 0.25 #9551, 0.20 #10165), 05bt6j (0.44 #4028, 0.40 #43, 0.32 #10465), 0gywn (0.40 #57, 0.36 #1895, 0.30 #4656), 0glt670 (0.40 #4640, 0.34 #14451, 0.33 #961), 06j6l (0.40 #48, 0.31 #14458, 0.30 #4647) >> Best rule #2455 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 04qzm; 0jg77; >> query: (?x3754, 016clz) <- artists(?x5934, ?x3754), artists(?x3753, ?x3754), ?x3753 = 01_bkd, parent_genre(?x7960, ?x5934), ?x7960 = 05y8n7 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #969 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 03xl77; 0kxbc; 0135xb; 01dw_f; *> query: (?x3754, 059kh) <- artists(?x8386, ?x3754), artists(?x5934, ?x3754), nationality(?x3754, ?x1310), ?x5934 = 05r6t, profession(?x3754, ?x1032), ?x8386 = 016ybr *> conf = 0.50 ranks of expected_values: 6, 7 EVAL 0p3r8 artists! 059kh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 176.000 97.000 0.786 http://example.org/music/genre/artists EVAL 0p3r8 artists! 05bt6j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 176.000 97.000 0.786 http://example.org/music/genre/artists #14348-0hr3c8y PRED entity: 0hr3c8y PRED relation: award_winner PRED expected values: 027dtv3 01gvr1 057hz => 25 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1587): 01z7_f (0.54 #6073, 0.36 #7591, 0.33 #6072), 026v437 (0.54 #6073, 0.36 #7591, 0.33 #3988), 02lfcm (0.54 #6073, 0.36 #7591, 0.33 #4606), 02lfl4 (0.54 #6073, 0.36 #7591, 0.33 #4661), 021_rm (0.54 #6073, 0.36 #7591, 0.33 #4693), 01r42_g (0.54 #6073, 0.36 #7591, 0.33 #4592), 02lg9w (0.54 #6073, 0.36 #7591, 0.33 #4775), 027dtv3 (0.54 #6073, 0.36 #7591, 0.32 #7589), 01dy7j (0.54 #6073, 0.36 #7591, 0.32 #7589), 02lg3y (0.54 #6073, 0.36 #7591, 0.32 #7589) >> Best rule #6073 for best value: >> intensional similarity = 18 >> extensional distance = 1 >> proper extension: 03gyp30; >> query: (?x873, ?x369) <- award_winner(?x873, ?x7959), award_winner(?x873, ?x6324), award_winner(?x873, ?x3842), award_winner(?x873, ?x368), nationality(?x7959, ?x94), award_nominee(?x157, ?x3842), ?x6324 = 018ygt, award_winner(?x1670, ?x3842), award_nominee(?x368, ?x4328), award_nominee(?x368, ?x369), honored_for(?x873, ?x385), location(?x7959, ?x4253), profession(?x3842, ?x1032), ?x4328 = 01z7_f, type_of_union(?x3842, ?x1873), film(?x3842, ?x667), nominated_for(?x368, ?x1849), ?x4253 = 0ccvx >> conf = 0.54 => this is the best rule for 11 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 8, 124, 853 EVAL 0hr3c8y award_winner 057hz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 25.000 19.000 0.542 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0hr3c8y award_winner 01gvr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 25.000 19.000 0.542 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0hr3c8y award_winner 027dtv3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 25.000 19.000 0.542 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14347-01fh9 PRED entity: 01fh9 PRED relation: film PRED expected values: 05fgt1 => 110 concepts (68 used for prediction) PRED predicted values (max 10 best out of 539): 0344gc (0.58 #33899, 0.57 #32114, 0.49 #39253), 01jzyf (0.58 #33899, 0.57 #32114, 0.39 #37468), 04s1zr (0.19 #5353, 0.01 #32047), 04gv3db (0.09 #751, 0.02 #18592, 0.02 #20376), 0888c3 (0.09 #1412, 0.02 #4980, 0.01 #6765), 02ht1k (0.09 #628, 0.01 #4196, 0.01 #52373), 02stbw (0.09 #381, 0.01 #3949), 01shy7 (0.05 #9342, 0.03 #25398, 0.03 #7558), 016z9n (0.04 #367, 0.04 #112417, 0.01 #52112), 0jzw (0.04 #118, 0.04 #112417, 0.01 #32232) >> Best rule #33899 for best value: >> intensional similarity = 3 >> extensional distance = 765 >> proper extension: 01kkx2; >> query: (?x1979, ?x898) <- award_winner(?x384, ?x1979), film(?x1979, ?x508), award_winner(?x898, ?x1979) >> conf = 0.58 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01fh9 film 05fgt1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 68.000 0.579 http://example.org/film/actor/film./film/performance/film #14346-08sfxj PRED entity: 08sfxj PRED relation: film! PRED expected values: 03czrpj 09v3hq_ => 132 concepts (125 used for prediction) PRED predicted values (max 10 best out of 77): 03xq0f (0.61 #1796, 0.25 #4, 0.14 #2018), 016tw3 (0.33 #158, 0.29 #84, 0.20 #1280), 05qd_ (0.25 #8, 0.15 #602, 0.15 #1800), 0jz9f (0.21 #297, 0.17 #820, 0.13 #971), 017s11 (0.19 #523, 0.18 #449, 0.14 #2092), 01795t (0.17 #1062, 0.09 #2106, 0.08 #2629), 086k8 (0.17 #5690, 0.17 #746, 0.16 #8179), 054g1r (0.15 #703, 0.15 #1079, 0.08 #1603), 024rbz (0.14 #85, 0.12 #1357, 0.11 #159), 01gb54 (0.14 #1523, 0.09 #2491, 0.08 #622) >> Best rule #1796 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0522wp; >> query: (?x5152, 03xq0f) <- category(?x5152, ?x134), film(?x574, ?x5152), film(?x574, ?x5070), ?x5070 = 0dt8xq >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1257 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 38 *> proper extension: 0qmd5; 03cv_gy; 01qbg5; 09sr0; *> query: (?x5152, 09v3hq_) <- titles(?x1316, ?x5152), titles(?x162, ?x5152), ?x162 = 04xvlr, ?x1316 = 017fp, genre(?x5152, ?x1403), film_release_distribution_medium(?x5152, ?x81) *> conf = 0.03 ranks of expected_values: 51, 67 EVAL 08sfxj film! 09v3hq_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 132.000 125.000 0.610 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 08sfxj film! 03czrpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 132.000 125.000 0.610 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #14345-08fn5b PRED entity: 08fn5b PRED relation: film_crew_role PRED expected values: 01vx2h => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 27): 01vx2h (0.42 #214, 0.41 #1378, 0.39 #521), 01pvkk (0.32 #522, 0.31 #693, 0.30 #1379), 02rh1dz (0.23 #42, 0.21 #213, 0.18 #110), 0d2b38 (0.19 #126, 0.16 #160, 0.16 #194), 02ynfr (0.19 #355, 0.18 #219, 0.18 #868), 015h31 (0.16 #212, 0.13 #143, 0.12 #109), 0215hd (0.16 #2150, 0.14 #2286, 0.14 #2669), 089fss (0.14 #39, 0.08 #1374, 0.08 #2138), 094hwz (0.13 #149, 0.12 #183, 0.07 #115), 089g0h (0.12 #2151, 0.12 #2670, 0.11 #2287) >> Best rule #214 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 063zky; >> query: (?x4167, 01vx2h) <- film_crew_role(?x4167, ?x137), film(?x3051, ?x4167), prequel(?x9133, ?x4167), production_companies(?x4167, ?x382) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08fn5b film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.424 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14344-07vjm PRED entity: 07vjm PRED relation: major_field_of_study PRED expected values: 02j62 01540 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 99): 02lp1 (0.75 #11, 0.70 #576, 0.64 #1367), 04rjg (0.63 #584, 0.56 #1375, 0.55 #1262), 02j62 (0.62 #27, 0.60 #592, 0.58 #1383), 01tbp (0.62 #54, 0.43 #619, 0.42 #1636), 0fdys (0.50 #34, 0.47 #599, 0.40 #260), 037mh8 (0.47 #627, 0.37 #1644, 0.36 #1418), 04x_3 (0.44 #1380, 0.43 #589, 0.42 #1606), 0g26h (0.43 #602, 0.41 #3653, 0.41 #3766), 02_7t (0.38 #59, 0.35 #285, 0.31 #1415), 02ky346 (0.38 #15, 0.33 #580, 0.31 #1371) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 07vht; >> query: (?x6637, 02lp1) <- organization(?x6637, ?x5487), institution(?x734, ?x6637), time_zones(?x6637, ?x2950) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 07vht; *> query: (?x6637, 02j62) <- organization(?x6637, ?x5487), institution(?x734, ?x6637), time_zones(?x6637, ?x2950) *> conf = 0.62 ranks of expected_values: 3, 13 EVAL 07vjm major_field_of_study 01540 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 136.000 136.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07vjm major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 136.000 136.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14343-05y8n7 PRED entity: 05y8n7 PRED relation: parent_genre PRED expected values: 059kh => 49 concepts (43 used for prediction) PRED predicted values (max 10 best out of 237): 06by7 (0.84 #4368, 0.65 #2099, 0.62 #2422), 016clz (0.75 #1285, 0.43 #1445, 0.39 #2249), 05bt6j (0.50 #507, 0.33 #29, 0.25 #667), 03lty (0.46 #4533, 0.33 #178, 0.28 #4854), 0jmwg (0.38 #713, 0.33 #874, 0.33 #234), 011j5x (0.33 #181, 0.27 #1601, 0.26 #2105), 03p7rp (0.33 #107, 0.25 #585, 0.12 #745), 018ysx (0.27 #1601, 0.22 #2566, 0.20 #2243), 064t9 (0.25 #649, 0.25 #489, 0.25 #329), 059kh (0.25 #672, 0.25 #352, 0.24 #4023) >> Best rule #4368 for best value: >> intensional similarity = 9 >> extensional distance = 68 >> proper extension: 05hs4r; 0m0jc; 015pdg; 064t9; 0xhtw; 061fhg; 01756d; 0mhfr; 05bt6j; 01qzt1; ... >> query: (?x7960, 06by7) <- parent_genre(?x7960, ?x5934), parent_genre(?x7960, ?x2996), artists(?x5934, ?x2408), parent_genre(?x2996, ?x302), artists(?x2996, ?x5768), ?x5768 = 02bgmr, parent_genre(?x10366, ?x5934), ?x10366 = 0621cs, written_by(?x10651, ?x2408) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #672 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 6 *> proper extension: 03xnwz; *> query: (?x7960, 059kh) <- parent_genre(?x7960, ?x12498), parent_genre(?x7960, ?x5934), parent_genre(?x7960, ?x2996), ?x2996 = 01243b, artists(?x7960, ?x6035), ?x5934 = 05r6t, celebrity(?x6035, ?x10777), artists(?x12498, ?x317), parent_genre(?x3243, ?x12498), ?x3243 = 0y3_8 *> conf = 0.25 ranks of expected_values: 10 EVAL 05y8n7 parent_genre 059kh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 49.000 43.000 0.843 http://example.org/music/genre/parent_genre #14342-0bqxw PRED entity: 0bqxw PRED relation: category PRED expected values: 08mbj5d => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #52, 0.90 #55, 0.90 #54) >> Best rule #52 for best value: >> intensional similarity = 3 >> extensional distance = 272 >> proper extension: 02zkz7; >> query: (?x4338, 08mbj5d) <- organization(?x346, ?x4338), ?x346 = 060c4, currency(?x4338, ?x170) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bqxw category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.905 http://example.org/common/topic/webpage./common/webpage/category #14341-08304 PRED entity: 08304 PRED relation: profession PRED expected values: 0mn6 => 116 concepts (70 used for prediction) PRED predicted values (max 10 best out of 93): 01d_h8 (0.71 #2051, 0.69 #1612, 0.67 #1758), 09jwl (0.67 #9517, 0.50 #2355, 0.49 #3816), 02hrh1q (0.63 #10096, 0.59 #3519, 0.59 #4250), 0dxtg (0.53 #2057, 0.53 #1764, 0.48 #4395), 0kyk (0.47 #3096, 0.45 #1197, 0.45 #905), 0nbcg (0.42 #3829, 0.39 #2368, 0.36 #9530), 016z4k (0.41 #3802, 0.34 #1318, 0.33 #9503), 02jknp (0.39 #2052, 0.36 #1759, 0.33 #7), 0dz3r (0.37 #2339, 0.35 #3800, 0.30 #1316), 0dgd_ (0.33 #30, 0.08 #760, 0.07 #2075) >> Best rule #2051 for best value: >> intensional similarity = 5 >> extensional distance = 85 >> proper extension: 07s3vqk; 0m2l9; 04wqr; 0hnlx; 019z7q; 01vrncs; 014zfs; 0343h; 01wp8w7; 01n4f8; ... >> query: (?x6512, 01d_h8) <- gender(?x6512, ?x231), influenced_by(?x477, ?x6512), profession(?x6512, ?x2472), nationality(?x6512, ?x512), film_crew_role(?x136, ?x2472) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3360 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 142 *> proper extension: 0j_c; 033cw; *> query: (?x6512, ?x2225) <- gender(?x6512, ?x231), influenced_by(?x11554, ?x6512), profession(?x6512, ?x353), profession(?x11554, ?x2225), peers(?x11554, ?x3542) *> conf = 0.30 ranks of expected_values: 15 EVAL 08304 profession 0mn6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 116.000 70.000 0.713 http://example.org/people/person/profession #14340-01yhvv PRED entity: 01yhvv PRED relation: nominated_for PRED expected values: 0hz55 => 84 concepts (27 used for prediction) PRED predicted values (max 10 best out of 210): 020bv3 (0.68 #1917, 0.67 #295, 0.33 #11358), 060v34 (0.33 #11358, 0.33 #12981, 0.30 #29215), 03y0pn (0.11 #1121, 0.11 #2743), 011yg9 (0.11 #2557, 0.06 #935), 02py4c8 (0.07 #43824, 0.06 #99, 0.05 #1721), 03cv_gy (0.07 #43824, 0.05 #2471), 0124k9 (0.07 #43824, 0.01 #8333), 0828jw (0.07 #43824, 0.01 #43113), 024hbv (0.07 #43824), 06qv_ (0.07 #43824) >> Best rule #1917 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 05vsxz; 0m2wm; 0159h6; 0h5g_; 03f1zdw; 09y20; 05tk7y; 01l2fn; 07hbxm; 02cllz; ... >> query: (?x1410, 020bv3) <- award_nominee(?x9236, ?x1410), award_nominee(?x5743, ?x1410), ?x9236 = 02fz3w, ?x5743 = 0175wg >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #43824 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 790 *> proper extension: 01sl1q; 0q9kd; 0184jc; 04bdxl; 06qgvf; 01vvydl; 012d40; 07fq1y; 02qgqt; 0fvf9q; ... *> query: (?x1410, ?x10234) <- award_nominee(?x9236, ?x1410), location(?x1410, ?x1411), actor(?x10234, ?x9236) *> conf = 0.07 ranks of expected_values: 17 EVAL 01yhvv nominated_for 0hz55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 84.000 27.000 0.684 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14339-06rjp PRED entity: 06rjp PRED relation: major_field_of_study PRED expected values: 05qfh => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 122): 04rjg (0.57 #21, 0.50 #2205, 0.40 #505), 03g3w (0.57 #28, 0.41 #5004, 0.38 #2212), 02j62 (0.54 #31, 0.47 #2215, 0.44 #636), 01lj9 (0.51 #41, 0.28 #525, 0.28 #404), 02lp1 (0.51 #496, 0.51 #375, 0.46 #12), 01mkq (0.51 #500, 0.49 #379, 0.49 #16), 02_7t (0.47 #549, 0.43 #65, 0.43 #428), 05qfh (0.43 #37, 0.31 #2221, 0.30 #521), 01540 (0.37 #61, 0.32 #545, 0.32 #424), 0fdys (0.37 #40, 0.30 #524, 0.26 #1738) >> Best rule #21 for best value: >> intensional similarity = 6 >> extensional distance = 33 >> proper extension: 0hsb3; 01jt2w; >> query: (?x11555, 04rjg) <- institution(?x1519, ?x11555), institution(?x1200, ?x11555), major_field_of_study(?x11555, ?x2606), ?x1200 = 016t_3, student(?x11555, ?x6957), ?x1519 = 013zdg >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 33 *> proper extension: 0hsb3; 01jt2w; *> query: (?x11555, 05qfh) <- institution(?x1519, ?x11555), institution(?x1200, ?x11555), major_field_of_study(?x11555, ?x2606), ?x1200 = 016t_3, student(?x11555, ?x6957), ?x1519 = 013zdg *> conf = 0.43 ranks of expected_values: 8 EVAL 06rjp major_field_of_study 05qfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 159.000 159.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14338-09pl3s PRED entity: 09pl3s PRED relation: location PRED expected values: 07b_l => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 136): 02_286 (0.32 #32123, 0.18 #56993, 0.18 #9661), 030qb3t (0.23 #32169, 0.15 #52227, 0.15 #36182), 04jpl (0.12 #23279, 0.11 #32103, 0.10 #34510), 01531 (0.06 #2563, 0.04 #9782, 0.03 #36257), 01cx_ (0.06 #964, 0.05 #1766, 0.04 #2568), 027l4q (0.06 #1298, 0.05 #2100, 0.04 #2902), 0cr3d (0.06 #8165, 0.06 #68334, 0.06 #54695), 059rby (0.06 #32102, 0.04 #14454, 0.04 #12047), 0rh6k (0.06 #23267, 0.04 #806, 0.03 #1608), 0h7h6 (0.05 #2495, 0.04 #34583, 0.03 #8110) >> Best rule #32123 for best value: >> intensional similarity = 3 >> extensional distance = 624 >> proper extension: 03j43; >> query: (?x2442, 02_286) <- award_winner(?x688, ?x2442), location(?x2442, ?x279), film_release_region(?x66, ?x279) >> conf = 0.32 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09pl3s location 07b_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 136.000 0.323 http://example.org/people/person/places_lived./people/place_lived/location #14337-0bm02 PRED entity: 0bm02 PRED relation: role! PRED expected values: 02dlh2 => 51 concepts (41 used for prediction) PRED predicted values (max 10 best out of 110): 01vdm0 (0.87 #444, 0.85 #788, 0.85 #671), 0dwtp (0.87 #444, 0.85 #788, 0.85 #671), 0342h (0.82 #1367, 0.81 #1020, 0.80 #1483), 0239kh (0.82 #1367, 0.81 #1020, 0.80 #1483), 018vs (0.82 #1367, 0.81 #1020, 0.80 #1483), 018j2 (0.80 #1069, 0.78 #1299, 0.77 #1184), 06w7v (0.80 #994, 0.77 #336, 0.75 #766), 01vj9c (0.79 #1841, 0.78 #1271, 0.78 #1504), 0bxl5 (0.78 #864, 0.77 #1253, 0.77 #1208), 0l14md (0.78 #804, 0.77 #336, 0.74 #337) >> Best rule #444 for best value: >> intensional similarity = 28 >> extensional distance = 2 >> proper extension: 01vj9c; >> query: (?x1268, ?x228) <- role(?x1268, ?x5480), role(?x1268, ?x1437), role(?x1268, ?x228), role(?x1268, ?x212), role(?x1268, ?x1433), role(?x1268, ?x227), ?x212 = 026t6, ?x1433 = 0239kh, ?x5480 = 01w4c9, role(?x12743, ?x1437), role(?x11633, ?x1437), role(?x9830, ?x1437), role(?x4052, ?x1437), role(?x3399, ?x1437), role(?x2698, ?x1437), role(?x1818, ?x1437), ?x227 = 0342h, instrumentalists(?x1437, ?x226), ?x1818 = 0770cd, ?x11633 = 01ww_vs, role(?x315, ?x1437), role(?x1437, ?x74), ?x3399 = 01gx5f, ?x9830 = 01m7pwq, student(?x6894, ?x12743), ?x4052 = 050z2, award_winner(?x486, ?x2698), award_nominee(?x217, ?x2698) >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #336 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x1268, ?x894) <- role(?x1268, ?x5480), role(?x1268, ?x2888), role(?x1268, ?x1437), role(?x1268, ?x1432), role(?x1268, ?x1267), role(?x1268, ?x1225), role(?x1268, ?x212), role(?x1268, ?x1433), ?x212 = 026t6, ?x1433 = 0239kh, ?x5480 = 01w4c9, ?x1437 = 01vdm0, role(?x1225, ?x5676), role(?x1225, ?x4078), role(?x1225, ?x1750), role(?x1225, ?x1495), role(?x1225, ?x894), role(?x1225, ?x569), ?x1432 = 0395lw, role(?x1660, ?x1268), ?x1267 = 07brj, ?x569 = 07c6l, performance_role(?x1817, ?x1225), ?x5676 = 0151b0, ?x4078 = 011k_j, ?x1750 = 02hnl, ?x2888 = 02fsn, ?x1495 = 013y1f, instrumentalists(?x1225, ?x8311), role(?x1089, ?x894) *> conf = 0.77 ranks of expected_values: 17 EVAL 0bm02 role! 02dlh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 51.000 41.000 0.872 http://example.org/music/performance_role/track_performances./music/track_contribution/role #14336-057lbk PRED entity: 057lbk PRED relation: currency PRED expected values: 09nqf => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.93 #141, 0.90 #260, 0.89 #64), 01nv4h (0.06 #51, 0.05 #79, 0.05 #93), 088n7 (0.05 #91, 0.02 #112, 0.02 #133), 02l6h (0.03 #53, 0.02 #683, 0.01 #326), 02gsvk (0.01 #440) >> Best rule #141 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 05_5_22; 07p12s; 09rvwmy; >> query: (?x4378, 09nqf) <- produced_by(?x4378, ?x7976), film_crew_role(?x4378, ?x2472), ?x2472 = 01xy5l_ >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 057lbk currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.929 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #14335-03j149k PRED entity: 03j149k PRED relation: profession PRED expected values: 02hrh1q => 115 concepts (113 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.89 #14, 0.89 #12228, 0.88 #5315), 09jwl (0.58 #3845, 0.56 #5171, 0.55 #460), 01d_h8 (0.46 #887, 0.45 #593, 0.44 #1034), 016z4k (0.38 #3829, 0.37 #1326, 0.37 #1474), 03gjzk (0.30 #1927, 0.30 #603, 0.29 #1044), 0dxtg (0.29 #895, 0.28 #601, 0.27 #1925), 01c72t (0.28 #5176, 0.24 #1347, 0.24 #2230), 0n1h (0.25 #158, 0.23 #452, 0.23 #305), 039v1 (0.21 #3862, 0.19 #5188, 0.17 #4599), 02jknp (0.20 #595, 0.19 #1919, 0.19 #742) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 049tjg; >> query: (?x8124, 02hrh1q) <- location(?x8124, ?x739), ?x739 = 02_286, actor(?x1653, ?x8124) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03j149k profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 113.000 0.893 http://example.org/people/person/profession #14334-07bcn PRED entity: 07bcn PRED relation: time_zones PRED expected values: 02lcqs => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 12): 02lcqs (0.77 #109, 0.76 #161, 0.74 #379), 02hcv8 (0.45 #1189, 0.44 #421, 0.43 #1631), 02fqwt (0.29 #170, 0.28 #27, 0.27 #66), 02llzg (0.24 #56, 0.22 #43, 0.19 #251), 02hczc (0.18 #952, 0.16 #1746, 0.11 #171), 042g7t (0.18 #952, 0.16 #1746, 0.03 #219), 02lcrv (0.18 #952, 0.16 #1746, 0.02 #59), 03plfd (0.09 #441, 0.06 #636, 0.06 #701), 03bdv (0.07 #866, 0.06 #1023, 0.06 #971), 052vwh (0.07 #64, 0.07 #51, 0.02 #638) >> Best rule #109 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0r540; 0r4z7; 0mhdz; >> query: (?x5893, 02lcqs) <- state(?x5893, ?x1227), ?x1227 = 01n7q, contains(?x94, ?x5893) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07bcn time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.769 http://example.org/location/location/time_zones #14333-014zws PRED entity: 014zws PRED relation: institution! PRED expected values: 02mjs7 014mlp => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 20): 019v9k (0.85 #50, 0.74 #92, 0.70 #178), 02h4rq6 (0.78 #45, 0.69 #87, 0.64 #173), 014mlp (0.74 #47, 0.71 #89, 0.71 #110), 0bkj86 (0.63 #49, 0.60 #91, 0.55 #112), 02_xgp2 (0.61 #117, 0.60 #96, 0.59 #54), 03bwzr4 (0.56 #56, 0.53 #119, 0.51 #141), 04zx3q1 (0.52 #44, 0.40 #86, 0.36 #172), 027f2w (0.52 #51, 0.37 #93, 0.37 #114), 02mjs7 (0.37 #46, 0.36 #152, 0.35 #195), 07s6fsf (0.37 #43, 0.32 #106, 0.31 #85) >> Best rule #50 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 07vk2; 0pspl; 02w6bq; 050xpd; 01pj48; >> query: (?x9045, 019v9k) <- institution(?x7636, ?x9045), institution(?x1200, ?x9045), ?x7636 = 01rr_d, student(?x9045, ?x4771), ?x1200 = 016t_3 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 07vk2; 0pspl; 02w6bq; 050xpd; 01pj48; *> query: (?x9045, 014mlp) <- institution(?x7636, ?x9045), institution(?x1200, ?x9045), ?x7636 = 01rr_d, student(?x9045, ?x4771), ?x1200 = 016t_3 *> conf = 0.74 ranks of expected_values: 3, 9 EVAL 014zws institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 114.000 0.852 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014zws institution! 02mjs7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 114.000 114.000 0.852 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14332-062zjtt PRED entity: 062zjtt PRED relation: film_crew_role PRED expected values: 01xy5l_ => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 30): 01pvkk (0.43 #231, 0.41 #263, 0.39 #295), 01xy5l_ (0.31 #74, 0.21 #297, 0.21 #329), 015h31 (0.27 #135, 0.23 #326, 0.23 #71), 02ynfr (0.26 #524, 0.25 #589, 0.25 #331), 0215hd (0.19 #79, 0.18 #3625, 0.17 #1669), 094hwz (0.18 #3625, 0.16 #170, 0.16 #106), 089g0h (0.18 #3625, 0.14 #1670, 0.14 #335), 033smt (0.18 #3625, 0.13 #3306, 0.13 #3402), 02_n3z (0.18 #3625, 0.13 #3306, 0.13 #3402), 05smlt (0.18 #3625, 0.13 #3306, 0.13 #3402) >> Best rule #231 for best value: >> intensional similarity = 7 >> extensional distance = 56 >> proper extension: 0ds33; 0pc62; 01kff7; 031t2d; 05p1qyh; 0661ql3; 0cc5mcj; 08052t3; 0ddt_; 07024; ... >> query: (?x4273, 01pvkk) <- film_crew_role(?x4273, ?x2091), film_crew_role(?x4273, ?x1171), genre(?x4273, ?x225), film(?x450, ?x4273), ?x2091 = 02rh1dz, ?x225 = 02kdv5l, ?x1171 = 09vw2b7 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #74 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 24 *> proper extension: 09sh8k; 0czyxs; 017gl1; 0dtfn; 024l2y; 04n52p6; 014nq4; 024mpp; 0125xq; 04mcw4; ... *> query: (?x4273, 01xy5l_) <- film_crew_role(?x4273, ?x2091), genre(?x4273, ?x225), film(?x450, ?x4273), ?x2091 = 02rh1dz, ?x225 = 02kdv5l, story_by(?x4273, ?x96) *> conf = 0.31 ranks of expected_values: 2 EVAL 062zjtt film_crew_role 01xy5l_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 120.000 0.431 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14331-01v90t PRED entity: 01v90t PRED relation: nationality PRED expected values: 02jx1 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 83): 09c7w0 (0.85 #1887, 0.79 #5861, 0.78 #2185), 02jx1 (0.78 #4075, 0.40 #1620, 0.39 #2315), 0h924 (0.37 #496, 0.34 #4076), 03rk0 (0.24 #4816, 0.08 #1831, 0.08 #2129), 0f8l9c (0.14 #21, 0.08 #4792, 0.08 #517), 05b4w (0.14 #50, 0.03 #446, 0.02 #546), 0345h (0.09 #4801, 0.08 #724, 0.08 #526), 03rjj (0.08 #1390, 0.08 #4776, 0.03 #1092), 03_3d (0.08 #1390, 0.05 #4777, 0.04 #502), 0d0vqn (0.08 #1390, 0.04 #306, 0.03 #1092) >> Best rule #1887 for best value: >> intensional similarity = 4 >> extensional distance = 300 >> proper extension: 07fzq3; >> query: (?x7209, 09c7w0) <- place_of_death(?x7209, ?x11072), nationality(?x7209, ?x512), award_winner(?x1033, ?x7209), country_of_origin(?x293, ?x512) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #4075 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 670 *> proper extension: 075wq; *> query: (?x7209, ?x512) <- place_of_death(?x7209, ?x11072), contains(?x512, ?x11072), nationality(?x111, ?x512) *> conf = 0.78 ranks of expected_values: 2 EVAL 01v90t nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 106.000 0.851 http://example.org/people/person/nationality #14330-02lfns PRED entity: 02lfns PRED relation: award_winner! PRED expected values: 092_25 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 99): 0g55tzk (0.58 #276, 0.33 #136, 0.07 #416), 03gyp30 (0.57 #396, 0.08 #256, 0.04 #816), 092_25 (0.57 #351, 0.03 #771, 0.02 #911), 0g5b0q5 (0.33 #19, 0.08 #159, 0.07 #299), 09pnw5 (0.21 #382, 0.02 #1642, 0.02 #2202), 07y_p6 (0.14 #377, 0.02 #657, 0.01 #797), 092t4b (0.08 #191, 0.05 #751, 0.04 #891), 0clfdj (0.08 #144, 0.04 #704, 0.03 #844), 0gx_st (0.08 #176, 0.03 #596, 0.02 #1436), 09p30_ (0.07 #364, 0.02 #784, 0.02 #1484) >> Best rule #276 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 03w1v2; 08w7vj; 02tr7d; 0fx0mw; 03yj_0n; 0dyztm; 02l6dy; 02sb1w; 040981l; >> query: (?x1169, 0g55tzk) <- award_nominee(?x1169, ?x560), ?x560 = 0f830f >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #351 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 01r42_g; 02lfcm; 02lfl4; 021_rm; 02lg9w; 02lf70; 01dy7j; 03zyvw; 01z7_f; 02lgfh; ... *> query: (?x1169, 092_25) <- award_nominee(?x1169, ?x4401), award_nominee(?x1169, ?x560), ?x4401 = 02lg3y, award_nominee(?x560, ?x3224) *> conf = 0.57 ranks of expected_values: 3 EVAL 02lfns award_winner! 092_25 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.583 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14329-011zd3 PRED entity: 011zd3 PRED relation: student! PRED expected values: 0bwfn => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 69): 013807 (0.25 #409, 0.10 #934), 065y4w7 (0.25 #14, 0.04 #19441, 0.04 #24168), 017j69 (0.12 #1194, 0.02 #12744, 0.02 #11694), 0ks67 (0.12 #1238, 0.01 #3863), 025v3k (0.10 #644, 0.01 #19021), 0bsnm (0.10 #824), 023znp (0.10 #643), 0bwfn (0.09 #11299, 0.08 #19701, 0.08 #12874), 017z88 (0.06 #1131, 0.05 #11106, 0.04 #19508), 05nrkb (0.06 #1398, 0.03 #3498, 0.02 #5598) >> Best rule #409 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 02p5hf; >> query: (?x2307, 013807) <- film(?x2307, ?x4375), student(?x1681, ?x2307), ?x4375 = 01rxyb >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #11299 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 604 *> proper extension: 015zql; *> query: (?x2307, 0bwfn) <- award_winner(?x2221, ?x2307), award_winner(?x678, ?x2307), student(?x1681, ?x2307) *> conf = 0.09 ranks of expected_values: 8 EVAL 011zd3 student! 0bwfn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 94.000 94.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #14328-0n839 PRED entity: 0n839 PRED relation: film PRED expected values: 034qmv => 70 concepts (53 used for prediction) PRED predicted values (max 10 best out of 770): 08gsvw (0.60 #7274, 0.40 #9064, 0.02 #37704), 02vxq9m (0.40 #8970, 0.40 #7180, 0.01 #59094), 014kq6 (0.40 #7505, 0.20 #9295, 0.06 #14665), 01v1ln (0.40 #8389, 0.20 #10179, 0.02 #74624), 01npcx (0.40 #8124, 0.20 #9914, 0.01 #60038), 026hh0m (0.40 #10580, 0.20 #8790), 01hqk (0.33 #722, 0.11 #18622, 0.07 #23992), 032016 (0.33 #503, 0.07 #13033, 0.06 #18403), 034qmv (0.33 #15, 0.07 #12545, 0.06 #17915), 085wqm (0.33 #1649, 0.07 #14179, 0.06 #19549) >> Best rule #7274 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0lpjn; 03kpvp; 059_gf; >> query: (?x11949, 08gsvw) <- profession(?x11949, ?x1032), film(?x11949, ?x3693), ?x3693 = 03r0g9 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 0tc7; *> query: (?x11949, 034qmv) <- profession(?x11949, ?x7841), list(?x11949, ?x5160), ?x7841 = 025sppp *> conf = 0.33 ranks of expected_values: 9 EVAL 0n839 film 034qmv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 70.000 53.000 0.600 http://example.org/film/actor/film./film/performance/film #14327-013pk3 PRED entity: 013pk3 PRED relation: student! PRED expected values: 0dzbl => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 113): 0bwfn (0.08 #21314, 0.08 #27628, 0.08 #28154), 03ksy (0.06 #9573, 0.06 #3787, 0.06 #9047), 065y4w7 (0.05 #8430, 0.05 #3696, 0.05 #8956), 017z88 (0.05 #21122, 0.04 #27436, 0.04 #27962), 01w5m (0.04 #18514, 0.04 #39030, 0.03 #40082), 09f2j (0.04 #4366, 0.04 #3314, 0.03 #27512), 04b_46 (0.04 #3908, 0.03 #7590, 0.03 #8642), 05nrkb (0.04 #2978, 0.03 #6660, 0.02 #5082), 07tgn (0.04 #6855, 0.04 #18427, 0.03 #5803), 017j69 (0.04 #144, 0.03 #670, 0.02 #5404) >> Best rule #21314 for best value: >> intensional similarity = 3 >> extensional distance = 689 >> proper extension: 023361; 03s2y9; >> query: (?x7638, 0bwfn) <- profession(?x7638, ?x987), award_winner(?x3609, ?x7638), student(?x2999, ?x7638) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 013pk3 student! 0dzbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 108.000 0.084 http://example.org/education/educational_institution/students_graduates./education/education/student #14326-0fpxp PRED entity: 0fpxp PRED relation: country_of_origin PRED expected values: 09c7w0 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 7): 09c7w0 (0.93 #166, 0.92 #188, 0.92 #177), 03_3d (0.24 #157, 0.13 #113, 0.09 #124), 07ssc (0.14 #97, 0.14 #31, 0.14 #64), 0d060g (0.03 #312, 0.03 #279, 0.03 #323), 03rt9 (0.02 #118, 0.02 #129, 0.01 #162), 02jx1 (0.01 #297, 0.01 #341, 0.01 #352), 05v8c (0.01 #340, 0.01 #351) >> Best rule #166 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 0h95b81; 01b7h8; >> query: (?x7904, 09c7w0) <- genre(?x7904, ?x258), producer_type(?x7904, ?x632), program(?x6678, ?x7904), nominated_for(?x10694, ?x7904) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fpxp country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.933 http://example.org/tv/tv_program/country_of_origin #14325-012w70 PRED entity: 012w70 PRED relation: language! PRED expected values: 033g4d 01f8gz 01v1ln 02825nf => 51 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1806): 043n0v_ (0.78 #15335, 0.77 #13631, 0.76 #13632), 01f8gz (0.78 #15335, 0.77 #13631, 0.76 #13632), 0dckvs (0.78 #15335, 0.77 #13631, 0.76 #13632), 0g5qmbz (0.50 #16813, 0.43 #20222, 0.33 #1477), 05sy_5 (0.50 #4406, 0.33 #2704, 0.33 #999), 01l_pn (0.50 #4322, 0.33 #2620, 0.33 #915), 0bz3jx (0.50 #4486, 0.33 #2784, 0.33 #1079), 01f6x7 (0.50 #4278, 0.33 #2576, 0.33 #871), 0gys2jp (0.50 #6774, 0.33 #3368, 0.29 #13591), 065ym0c (0.50 #4940, 0.33 #1533, 0.29 #13461) >> Best rule #15335 for best value: >> intensional similarity = 9 >> extensional distance = 6 >> proper extension: 07c9s; >> query: (?x3271, ?x467) <- titles(?x3271, ?x6788), titles(?x3271, ?x467), language(?x10446, ?x3271), language(?x6219, ?x3271), nominated_for(?x9377, ?x6219), ?x9377 = 09v4bym, film(?x6709, ?x6788), country(?x6788, ?x205), award(?x10446, ?x3508) >> conf = 0.78 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 155, 156, 1741 EVAL 012w70 language! 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 12.000 0.775 http://example.org/film/film/language EVAL 012w70 language! 01v1ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 12.000 0.775 http://example.org/film/film/language EVAL 012w70 language! 01f8gz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 51.000 12.000 0.775 http://example.org/film/film/language EVAL 012w70 language! 033g4d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 12.000 0.775 http://example.org/film/film/language #14324-047cx PRED entity: 047cx PRED relation: group! PRED expected values: 02sgy 05r5c => 97 concepts (61 used for prediction) PRED predicted values (max 10 best out of 110): 028tv0 (0.40 #801, 0.38 #1307, 0.38 #1452), 05r5c (0.40 #220, 0.38 #796, 0.28 #1302), 013y1f (0.40 #238, 0.27 #814, 0.25 #94), 03qjg (0.36 #829, 0.33 #1335, 0.33 #1480), 01vj9c (0.30 #1308, 0.29 #1453, 0.28 #1742), 02snj9 (0.20 #260, 0.14 #1516, 0.14 #2174), 07kc_ (0.20 #158, 0.14 #1516, 0.14 #2174), 01v1d8 (0.20 #259, 0.14 #1516, 0.14 #2174), 02sgy (0.20 #219, 0.14 #1516, 0.14 #2174), 02qjv (0.20 #231, 0.14 #1516, 0.14 #2174) >> Best rule #801 for best value: >> intensional similarity = 5 >> extensional distance = 43 >> proper extension: 0123r4; >> query: (?x4783, 028tv0) <- group(?x228, ?x4783), group(?x75, ?x4783), ?x228 = 0l14qv, artists(?x2249, ?x4783), role(?x74, ?x75) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #220 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 06br6t; *> query: (?x4783, 05r5c) <- group(?x228, ?x4783), ?x228 = 0l14qv, artists(?x11746, ?x4783), ?x11746 = 03w94xt *> conf = 0.40 ranks of expected_values: 2, 9 EVAL 047cx group! 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 61.000 0.400 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 047cx group! 02sgy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 97.000 61.000 0.400 http://example.org/music/performance_role/regular_performances./music/group_membership/group #14323-0k0q73t PRED entity: 0k0q73t PRED relation: program_creator PRED expected values: 01w92 => 63 concepts (16 used for prediction) PRED predicted values (max 10 best out of 61): 0438pz (0.33 #86, 0.25 #299, 0.03 #1156), 01pfkw (0.25 #370, 0.14 #476, 0.12 #582), 0bvg70 (0.19 #591, 0.15 #913, 0.14 #485), 02kmx6 (0.19 #587, 0.15 #909, 0.14 #481), 01my_c (0.15 #925, 0.14 #497, 0.12 #603), 01jbx1 (0.07 #456, 0.06 #562, 0.05 #884), 01my4f (0.07 #1141, 0.03 #1468, 0.03 #1250), 03ft8 (0.07 #1085, 0.03 #1633, 0.01 #1412), 01t6b4 (0.06 #541, 0.05 #863), 05gp3x (0.06 #704, 0.05 #1025, 0.03 #1136) >> Best rule #86 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 01b_lz; >> query: (?x12265, 0438pz) <- country_of_origin(?x12265, ?x512), actor(?x12265, ?x7601), award(?x7601, ?x10169), award(?x7601, ?x6287), ?x6287 = 02f75t, profession(?x7601, ?x131), ?x10169 = 02f79n, languages(?x12265, ?x254), award_nominee(?x1128, ?x7601), origin(?x7601, ?x12107) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k0q73t program_creator 01w92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 16.000 0.333 http://example.org/tv/tv_program/program_creator #14322-012v1t PRED entity: 012v1t PRED relation: profession PRED expected values: 0fj9f => 149 concepts (131 used for prediction) PRED predicted values (max 10 best out of 115): 0fj9f (0.91 #2010, 0.88 #3961, 0.88 #1409), 02hrh1q (0.90 #2720, 0.83 #10684, 0.83 #9783), 04gc2 (0.57 #2147, 0.56 #2297, 0.56 #1547), 0dxtg (0.33 #3769, 0.26 #5872, 0.25 #14), 01d_h8 (0.31 #8874, 0.30 #9174, 0.29 #9024), 0kyk (0.29 #3786, 0.27 #4987, 0.25 #31), 09jwl (0.29 #170, 0.16 #14890, 0.15 #18642), 0nbcg (0.29 #183, 0.11 #14903, 0.11 #13702), 01c72t (0.29 #175, 0.09 #19524, 0.08 #18923), 016m9h (0.27 #582, 0.25 #431, 0.25 #131) >> Best rule #2010 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 063vn; 03f5vvx; 06c97; 014vk4; >> query: (?x5932, 0fj9f) <- gender(?x5932, ?x514), student(?x1368, ?x5932), basic_title(?x5932, ?x2358), politician(?x8714, ?x5932) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012v1t profession 0fj9f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 131.000 0.909 http://example.org/people/person/profession #14321-01v1d8 PRED entity: 01v1d8 PRED relation: group PRED expected values: 02_5x9 => 90 concepts (44 used for prediction) PRED predicted values (max 10 best out of 604): 02vnpv (0.75 #5826, 0.75 #4723, 0.69 #4906), 017_hq (0.71 #2526, 0.69 #542, 0.61 #181), 014pg1 (0.71 #2472, 0.61 #181, 0.56 #6510), 02dw1_ (0.69 #542, 0.64 #5187, 0.62 #5738), 05563d (0.69 #542, 0.64 #4053, 0.61 #181), 07mvp (0.69 #542, 0.61 #6300, 0.61 #181), 01q99h (0.69 #542, 0.61 #181, 0.60 #1343), 0134wr (0.69 #542, 0.61 #181, 0.57 #2474), 07m4c (0.69 #542, 0.61 #181, 0.57 #2094), 01fchy (0.69 #542, 0.61 #181, 0.57 #2502) >> Best rule #5826 for best value: >> intensional similarity = 16 >> extensional distance = 14 >> proper extension: 02hnl; >> query: (?x3161, 02vnpv) <- role(?x3161, ?x2297), role(?x3161, ?x1267), role(?x8172, ?x1267), role(?x5990, ?x1267), role(?x4583, ?x1267), role(?x2459, ?x1267), role(?x9830, ?x1267), group(?x3161, ?x3682), role(?x2306, ?x3161), ?x2297 = 051hrr, ?x5990 = 0192l, ?x2459 = 021bmf, ?x4583 = 0bmnm, ?x9830 = 01m7pwq, ?x8172 = 06rvn, instrumentalists(?x3161, ?x140) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #542 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 2 *> proper extension: 0342h; *> query: (?x3161, ?x997) <- role(?x3161, ?x4917), role(?x3161, ?x2048), role(?x3161, ?x1267), role(?x3161, ?x1148), role(?x3161, ?x316), ?x1267 = 07brj, role(?x645, ?x3161), role(?x432, ?x3161), ?x316 = 05r5c, group(?x3161, ?x3682), instrumentalists(?x3161, ?x140), ?x645 = 028tv0, instrumentalists(?x2048, ?x7398), ?x7398 = 011vx3, ?x4917 = 06w7v, role(?x2048, ?x75), ?x1148 = 02qjv, group(?x2048, ?x997) *> conf = 0.69 ranks of expected_values: 13 EVAL 01v1d8 group 02_5x9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 90.000 44.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group #14320-02rjjll PRED entity: 02rjjll PRED relation: award_winner PRED expected values: 0137n0 017j6 01v6480 => 32 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1695): 0hl3d (0.57 #17989, 0.55 #14997, 0.50 #12007), 05pdbs (0.57 #10631, 0.50 #9133, 0.33 #13628), 06fmdb (0.56 #14248, 0.55 #15742, 0.54 #17237), 0x3b7 (0.55 #15584, 0.50 #18576, 0.50 #12594), 0fpjd_g (0.50 #9179, 0.46 #16663, 0.45 #15168), 0dw4g (0.50 #8311, 0.43 #18788, 0.38 #12806), 01s21dg (0.50 #9696, 0.43 #11194, 0.33 #5203), 01dwrc (0.50 #8347, 0.33 #5350, 0.33 #3855), 0dvqq (0.50 #7804, 0.33 #4807, 0.33 #3312), 0163kf (0.50 #8936, 0.33 #5939, 0.29 #11930) >> Best rule #17989 for best value: >> intensional similarity = 14 >> extensional distance = 12 >> proper extension: 0jzphpx; 01mh_q; >> query: (?x486, 0hl3d) <- ceremony(?x11048, ?x486), ceremony(?x9828, ?x486), ceremony(?x247, ?x486), award_winner(?x486, ?x9791), award_winner(?x486, ?x1128), ?x11048 = 03nl5k, ?x247 = 02wh75, group(?x227, ?x9791), award(?x8999, ?x9828), ceremony(?x9828, ?x2704), ?x8999 = 0bk1p, artist(?x382, ?x9791), ?x2704 = 01mhwk, performance_role(?x1128, ?x1466) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #8979 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 09n4nb; *> query: (?x486, ?x2929) <- ceremony(?x11048, ?x486), ceremony(?x6739, ?x486), ceremony(?x2703, ?x486), ceremony(?x1389, ?x486), award_winner(?x486, ?x7865), award_winner(?x486, ?x2930), award_winner(?x486, ?x1795), ?x11048 = 03nl5k, ?x7865 = 02k5sc, award(?x236, ?x6739), ?x2703 = 0257w4, ?x1389 = 01c427, ceremony(?x6739, ?x5656), award_winner(?x2929, ?x2930), instrumentalists(?x227, ?x1795), artist(?x3006, ?x2930), ?x5656 = 0466p0j *> conf = 0.26 ranks of expected_values: 213, 307, 469 EVAL 02rjjll award_winner 01v6480 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 23.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02rjjll award_winner 017j6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 32.000 23.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02rjjll award_winner 0137n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 32.000 23.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14319-01jswq PRED entity: 01jswq PRED relation: campuses! PRED expected values: 01jswq => 138 concepts (54 used for prediction) PRED predicted values (max 10 best out of 198): 01k2wn (0.33 #19, 0.25 #565, 0.11 #2732), 01mpwj (0.25 #641, 0.14 #1187, 0.11 #2280), 01jsn5 (0.11 #2245, 0.02 #6072, 0.02 #8806), 03kmyy (0.11 #2529), 01rgn3 (0.11 #2478), 02bq1j (0.11 #2348), 09krm_ (0.11 #2343), 01y9st (0.07 #3443, 0.03 #4536, 0.03 #2733), 01stj9 (0.05 #4324, 0.01 #9790, 0.01 #10884), 017v3q (0.05 #4061, 0.01 #10621) >> Best rule #19 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01k2wn; >> query: (?x2711, 01k2wn) <- contains(?x8483, ?x2711), organization(?x346, ?x2711), ?x8483 = 059g4, category(?x2711, ?x134), ?x346 = 060c4 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2733 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 09krm_; *> query: (?x2711, ?x151) <- contains(?x8483, ?x2711), organization(?x346, ?x2711), school_type(?x2711, ?x3092), contains(?x8483, ?x1103), contains(?x8483, ?x151), ?x1103 = 01k2wn *> conf = 0.03 ranks of expected_values: 41 EVAL 01jswq campuses! 01jswq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 138.000 54.000 0.333 http://example.org/education/educational_institution/campuses #14318-0d6484 PRED entity: 0d6484 PRED relation: award PRED expected values: 0f_nbyh => 113 concepts (95 used for prediction) PRED predicted values (max 10 best out of 260): 05p1dby (0.60 #510, 0.52 #914, 0.38 #2530), 09sb52 (0.39 #11354, 0.31 #13778, 0.31 #10142), 040njc (0.31 #6877, 0.29 #8493, 0.28 #5665), 0gs9p (0.25 #78, 0.22 #6947, 0.20 #8563), 019f4v (0.25 #66, 0.21 #6935, 0.20 #8551), 04dn09n (0.25 #44, 0.19 #2829, 0.17 #17778), 0gr4k (0.25 #33, 0.19 #2829, 0.17 #17778), 0gr51 (0.25 #99, 0.19 #2829, 0.17 #17778), 0fbtbt (0.25 #232, 0.18 #1444, 0.13 #37988), 0f_nbyh (0.25 #10, 0.16 #5667, 0.15 #2030) >> Best rule #510 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 034f0d; >> query: (?x9743, 05p1dby) <- award(?x9743, ?x1105), award_winner(?x2920, ?x9743), ?x1105 = 07bdd_ >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 03xp8d5; *> query: (?x9743, 0f_nbyh) <- award_nominee(?x6698, ?x9743), ?x6698 = 01vb6z, profession(?x9743, ?x319) *> conf = 0.25 ranks of expected_values: 10 EVAL 0d6484 award 0f_nbyh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 113.000 95.000 0.597 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14317-05v_8y PRED entity: 05v_8y PRED relation: nutrient! PRED expected values: 01nkt => 51 concepts (50 used for prediction) PRED predicted values (max 10 best out of 15): 01nkt (0.95 #767, 0.95 #755, 0.93 #799), 05z55 (0.90 #34, 0.89 #356, 0.89 #348), 07j87 (0.90 #34, 0.89 #220, 0.89 #54), 0cxn2 (0.90 #34, 0.89 #220, 0.89 #54), 037ls6 (0.90 #34, 0.89 #220, 0.89 #54), 09728 (0.90 #34, 0.89 #220, 0.89 #54), 06x4c (0.90 #34, 0.89 #220, 0.89 #54), 0dcfv (0.90 #34, 0.89 #220, 0.89 #54), 01sh2 (0.03 #603, 0.02 #566, 0.02 #134), 025rw19 (0.03 #603, 0.01 #654) >> Best rule #767 for best value: >> intensional similarity = 125 >> extensional distance = 35 >> proper extension: 01w_3; 0f4k5; >> query: (?x9795, ?x6032) <- nutrient(?x10612, ?x9795), nutrient(?x9005, ?x9795), nutrient(?x7719, ?x9795), nutrient(?x6285, ?x9795), nutrient(?x6159, ?x9795), nutrient(?x5373, ?x9795), nutrient(?x4068, ?x9795), ?x10612 = 0frq6, nutrient(?x6285, ?x13944), nutrient(?x6285, ?x13498), nutrient(?x6285, ?x12902), nutrient(?x6285, ?x12454), nutrient(?x6285, ?x11758), nutrient(?x6285, ?x11592), nutrient(?x6285, ?x11409), nutrient(?x6285, ?x11270), nutrient(?x6285, ?x10891), nutrient(?x6285, ?x10709), nutrient(?x6285, ?x10195), nutrient(?x6285, ?x10098), nutrient(?x6285, ?x9915), nutrient(?x6285, ?x9840), nutrient(?x6285, ?x9733), nutrient(?x6285, ?x9490), nutrient(?x6285, ?x9426), nutrient(?x6285, ?x8487), nutrient(?x6285, ?x8442), nutrient(?x6285, ?x8413), nutrient(?x6285, ?x7894), nutrient(?x6285, ?x7720), nutrient(?x6285, ?x7652), nutrient(?x6285, ?x7364), nutrient(?x6285, ?x7362), nutrient(?x6285, ?x7219), nutrient(?x6285, ?x6586), nutrient(?x6285, ?x6286), nutrient(?x6285, ?x6160), nutrient(?x6285, ?x6033), nutrient(?x6285, ?x6026), nutrient(?x6285, ?x5526), nutrient(?x6285, ?x5451), nutrient(?x6285, ?x5374), nutrient(?x6285, ?x5010), nutrient(?x6285, ?x4069), nutrient(?x6285, ?x3901), nutrient(?x6285, ?x3469), nutrient(?x6285, ?x3203), nutrient(?x6285, ?x2702), nutrient(?x6285, ?x1960), nutrient(?x6285, ?x1304), nutrient(?x6285, ?x1258), ?x9915 = 025tkqy, ?x6026 = 025sf8g, ?x7652 = 025s0s0, nutrient(?x4068, ?x12083), nutrient(?x4068, ?x9436), nutrient(?x4068, ?x7431), nutrient(?x4068, ?x7135), nutrient(?x4068, ?x6192), nutrient(?x4068, ?x5337), nutrient(?x4068, ?x3264), nutrient(?x4068, ?x2018), ?x3264 = 0dcfv, ?x13944 = 0f4kp, ?x1960 = 07hnp, ?x11758 = 0q01m, ?x4069 = 0hqw8p_, ?x5010 = 0h1vz, ?x2018 = 01sh2, ?x7720 = 025s7x6, ?x11592 = 025sf0_, ?x3901 = 0466p20, ?x10098 = 0h1_c, ?x5451 = 05wvs, ?x7364 = 09gvd, ?x11270 = 02kc008, ?x1258 = 0h1wg, ?x5374 = 025s0zp, ?x6033 = 04zjxcz, ?x7135 = 025rsfk, ?x9426 = 0h1yy, ?x9490 = 0h1sg, ?x8413 = 02kc4sf, ?x12454 = 025rw19, nutrient(?x9489, ?x10195), nutrient(?x8298, ?x10195), nutrient(?x3468, ?x10195), nutrient(?x1257, ?x10195), ?x7219 = 0h1vg, ?x3469 = 0h1zw, ?x6286 = 02y_3rf, ?x6192 = 06jry, ?x9005 = 04zpv, ?x8487 = 014yzm, ?x3203 = 04kl74p, ?x9436 = 025sqz8, ?x11409 = 0h1yf, ?x9489 = 07j87, ?x6159 = 033cnk, ?x5373 = 0971v, ?x2702 = 0838f, ?x9733 = 0h1tz, ?x12902 = 0fzjh, ?x10709 = 0h1sz, ?x8298 = 037ls6, ?x1257 = 09728, ?x10891 = 0g5gq, nutrient(?x9732, ?x1304), nutrient(?x6032, ?x1304), ?x13498 = 07q0m, ?x6032 = 01nkt, ?x5337 = 06x4c, ?x7431 = 09gwd, ?x8442 = 02kcv4x, ?x5526 = 09pbb, ?x6586 = 05gh50, ?x7894 = 0f4hc, ?x9732 = 05z55, ?x12083 = 01n78x, nutrient(?x7719, ?x13126), ?x9840 = 02p0tjr, ?x7362 = 02kc5rj, ?x6160 = 041r51, ?x3468 = 0cxn2, ?x13126 = 02kc_w5 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v_8y nutrient! 01nkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 50.000 0.946 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #14316-0d0vj4 PRED entity: 0d0vj4 PRED relation: people! PRED expected values: 02ctzb => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 58): 02ctzb (0.50 #785, 0.50 #246, 0.44 #323), 063k3h (0.43 #185, 0.40 #108, 0.38 #262), 041rx (0.30 #1467, 0.28 #1698, 0.21 #2468), 07bch9 (0.28 #1024, 0.25 #3026, 0.25 #1640), 033tf_ (0.25 #623, 0.16 #4088, 0.15 #2856), 07hwkr (0.20 #89, 0.18 #474, 0.17 #1090), 048z7l (0.17 #1503, 0.17 #656, 0.14 #1734), 0x67 (0.17 #1088, 0.16 #4708, 0.15 #1396), 09vc4s (0.17 #625, 0.14 #163, 0.11 #317), 0222qb (0.16 #7486, 0.04 #7530, 0.03 #6444) >> Best rule #785 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 06c97; 042kg; >> query: (?x966, 02ctzb) <- person(?x4312, ?x966), company(?x966, ?x12122), film_release_region(?x4312, ?x94), politician(?x1912, ?x966) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d0vj4 people! 02ctzb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.500 http://example.org/people/ethnicity/people #14315-04vr_f PRED entity: 04vr_f PRED relation: featured_film_locations PRED expected values: 01cx_ => 108 concepts (81 used for prediction) PRED predicted values (max 10 best out of 46): 02_286 (0.18 #3631, 0.17 #4113, 0.17 #3872), 0h7h6 (0.09 #283, 0.03 #18325, 0.02 #6064), 06y57 (0.08 #583, 0.05 #1305, 0.03 #1064), 030qb3t (0.08 #6543, 0.07 #1241, 0.07 #5097), 04jpl (0.07 #5067, 0.07 #5308, 0.06 #9164), 01_d4 (0.05 #767, 0.04 #1490, 0.04 #1730), 0rh6k (0.05 #962, 0.04 #5300, 0.04 #8915), 035p3 (0.04 #713, 0.02 #1435, 0.01 #3121), 01cx_ (0.04 #551, 0.02 #2235, 0.01 #3200), 080h2 (0.03 #3635, 0.03 #3876, 0.03 #4359) >> Best rule #3631 for best value: >> intensional similarity = 3 >> extensional distance = 228 >> proper extension: 0gtv7pk; 0dtw1x; 0c8tkt; 0d_2fb; 0crh5_f; 0ckrgs; 09lcsj; 0fb7sd; 0gbfn9; 02bg55; ... >> query: (?x1135, 02_286) <- production_companies(?x1135, ?x382), category(?x1135, ?x134), film_crew_role(?x1135, ?x137) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #551 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 02fn5r; *> query: (?x1135, 01cx_) <- nominated_for(?x1135, ?x9701), category(?x1135, ?x134), nominated_for(?x68, ?x9701) *> conf = 0.04 ranks of expected_values: 9 EVAL 04vr_f featured_film_locations 01cx_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 108.000 81.000 0.178 http://example.org/film/film/featured_film_locations #14314-016ksk PRED entity: 016ksk PRED relation: profession PRED expected values: 03gjzk => 94 concepts (70 used for prediction) PRED predicted values (max 10 best out of 80): 09jwl (0.65 #1322, 0.65 #1902, 0.64 #2048), 0nbcg (0.61 #899, 0.56 #1335, 0.55 #1915), 03gjzk (0.47 #1173, 0.42 #591, 0.41 #1027), 016z4k (0.44 #1310, 0.43 #2471, 0.41 #4078), 01445t (0.42 #19, 0.28 #745, 0.26 #454), 0gl2ny2 (0.40 #1516, 0.19 #209, 0.07 #499), 0n1h (0.32 #7276, 0.31 #1316, 0.26 #1896), 018gz8 (0.29 #1175, 0.27 #593, 0.24 #1029), 01c72t (0.28 #3803, 0.28 #5406, 0.26 #4241), 039v1 (0.26 #1920, 0.26 #2066, 0.23 #1340) >> Best rule #1322 for best value: >> intensional similarity = 5 >> extensional distance = 102 >> proper extension: 0136g9; 06g2d1; 02fybl; >> query: (?x3707, 09jwl) <- profession(?x3707, ?x1032), profession(?x3707, ?x131), location(?x3707, ?x7519), ?x131 = 0dz3r, ?x1032 = 02hrh1q >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #1173 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 01v3s2_; 02w5q6; 0mdyn; 024t0y; *> query: (?x3707, 03gjzk) <- profession(?x3707, ?x1032), ?x1032 = 02hrh1q, program(?x3707, ?x2583) *> conf = 0.47 ranks of expected_values: 3 EVAL 016ksk profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 70.000 0.654 http://example.org/people/person/profession #14313-017ztv PRED entity: 017ztv PRED relation: major_field_of_study PRED expected values: 05qjt => 214 concepts (214 used for prediction) PRED predicted values (max 10 best out of 128): 04rjg (0.65 #778, 0.56 #1409, 0.54 #904), 05qjt (0.63 #260, 0.52 #386, 0.46 #134), 01mkq (0.62 #899, 0.60 #2034, 0.58 #773), 02j62 (0.62 #410, 0.61 #789, 0.54 #2050), 02lp1 (0.60 #1400, 0.53 #1778, 0.53 #264), 03g3w (0.55 #785, 0.54 #154, 0.49 #2046), 062z7 (0.54 #155, 0.53 #281, 0.52 #407), 01lj9 (0.54 #168, 0.53 #294, 0.48 #420), 037mh8 (0.54 #197, 0.47 #323, 0.44 #576), 0fdys (0.54 #167, 0.47 #293, 0.39 #798) >> Best rule #778 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 08815; 065y4w7; 07tgn; 07w0v; 07szy; 09kvv; 0bx8pn; 07wrz; 0f1nl; 0j_sncb; ... >> query: (?x9028, 04rjg) <- institution(?x734, ?x9028), company(?x3131, ?x9028), contains(?x985, ?x9028), student(?x9028, ?x10870), ?x734 = 04zx3q1 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #260 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 02hcxm; *> query: (?x9028, 05qjt) <- company(?x3131, ?x9028), company(?x10913, ?x9028), company(?x3131, ?x11768), ?x11768 = 01hc1j *> conf = 0.63 ranks of expected_values: 2 EVAL 017ztv major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 214.000 214.000 0.645 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14312-04v9wn PRED entity: 04v9wn PRED relation: colors PRED expected values: 083jv => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.81 #761, 0.79 #274, 0.63 #545), 06fvc (0.56 #183, 0.53 #147, 0.39 #672), 019sc (0.29 #459, 0.28 #913, 0.27 #550), 0jc_p (0.16 #814, 0.15 #235, 0.14 #294), 088fh (0.16 #814, 0.15 #235, 0.14 #6), 01l849 (0.16 #814, 0.15 #235, 0.13 #925), 036k5h (0.16 #814, 0.15 #235, 0.13 #925), 06kqt3 (0.16 #814, 0.15 #235, 0.13 #925), 04d18d (0.16 #814, 0.15 #235, 0.13 #925), 04mkbj (0.16 #814, 0.15 #235, 0.11 #272) >> Best rule #761 for best value: >> intensional similarity = 11 >> extensional distance = 271 >> proper extension: 01ypc; 01jv_6; 01y49; 01ync; 027yf83; 025v1sx; 01lpx8; 04n7ps6; 02r7lqg; 04wmvz; ... >> query: (?x8400, 083jv) <- colors(?x8400, ?x3189), colors(?x12742, ?x3189), colors(?x11474, ?x3189), colors(?x10993, ?x3189), ?x11474 = 03bnd9, colors(?x12039, ?x3189), colors(?x9254, ?x3189), ?x9254 = 03ys48, ?x12039 = 026l1lq, ?x10993 = 01fy2s, ?x12742 = 032r4n >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04v9wn colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.813 http://example.org/sports/sports_team/colors #14311-02bqm0 PRED entity: 02bqm0 PRED relation: legislative_sessions PRED expected values: 07p__7 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 37): 07p__7 (0.85 #1001, 0.85 #161, 0.85 #160), 04gp1d (0.85 #161, 0.85 #160, 0.84 #511), 02glc4 (0.85 #161, 0.85 #160, 0.84 #511), 02bqm0 (0.81 #683, 0.80 #657, 0.80 #531), 043djx (0.62 #436, 0.40 #517, 0.39 #82), 03rl1g (0.62 #436, 0.40 #517, 0.39 #82), 01gsvb (0.43 #1071, 0.40 #517, 0.39 #82), 01gsvp (0.40 #517, 0.40 #1065, 0.39 #82), 01gstn (0.40 #517, 0.39 #82, 0.39 #281), 01grrf (0.40 #517, 0.39 #82, 0.39 #281) >> Best rule #1001 for best value: >> intensional similarity = 33 >> extensional distance = 26 >> proper extension: 01gtc0; 01gsvb; >> query: (?x4821, ?x653) <- legislative_sessions(?x653, ?x4821), legislative_sessions(?x355, ?x4821), district_represented(?x4821, ?x3670), district_represented(?x4821, ?x2049), district_represented(?x4821, ?x1767), religion(?x1767, ?x7422), contains(?x1767, ?x1396), district_represented(?x3973, ?x1767), district_represented(?x2712, ?x1767), district_represented(?x1754, ?x1767), ?x3670 = 05tbn, location(?x6157, ?x1767), location(?x5925, ?x1767), category(?x2049, ?x134), jurisdiction_of_office(?x900, ?x1767), ?x2712 = 01gst_, ?x7422 = 092bf5, country(?x1767, ?x94), adjoins(?x2049, ?x1351), award_nominee(?x5925, ?x157), award(?x5925, ?x435), location(?x5925, ?x1860), ?x1860 = 01_d4, district_represented(?x653, ?x2623), ?x1754 = 01grnp, legislative_sessions(?x652, ?x355), profession(?x6157, ?x987), ?x987 = 0dxtg, ?x2623 = 02xry, award_winner(?x54, ?x6157), state_province_region(?x3379, ?x1767), award_winner(?x3247, ?x5925), ?x3973 = 01gssm >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bqm0 legislative_sessions 07p__7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.847 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #14310-03czrpj PRED entity: 03czrpj PRED relation: film PRED expected values: 08sfxj => 61 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1852): 03clwtw (0.33 #1114, 0.25 #4306, 0.24 #5902), 035s95 (0.33 #303, 0.25 #14670, 0.19 #3495), 05n6sq (0.33 #1001, 0.15 #15368, 0.06 #4193), 01cycq (0.33 #1216, 0.15 #15583, 0.06 #28353), 0295sy (0.33 #855, 0.15 #15222, 0.06 #27992), 0184tc (0.33 #591, 0.15 #14958, 0.06 #27728), 02x2jl_ (0.33 #1565, 0.12 #4757, 0.12 #6353), 07jqjx (0.33 #1409, 0.12 #4601, 0.12 #6197), 02gpkt (0.33 #1172, 0.10 #15539, 0.06 #4364), 0992d9 (0.33 #882, 0.10 #15249, 0.06 #4074) >> Best rule #1114 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 030_1m; >> query: (?x3713, 03clwtw) <- film(?x3713, ?x4038), film(?x3713, ?x2685), ?x4038 = 02_sr1, film_crew_role(?x2685, ?x137), film_release_region(?x2685, ?x87), film(?x5699, ?x2685) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3991 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 0jz9f; 086k8; 017s11; 016tt2; 025jfl; 0g1rw; 05qd_; 04f525m; 016tw3; 017jv5; ... *> query: (?x3713, 08sfxj) <- film(?x3713, ?x4038), award_winner(?x8762, ?x3713), produced_by(?x4038, ?x10715), featured_film_locations(?x4038, ?x1523), film_crew_role(?x4038, ?x137) *> conf = 0.06 ranks of expected_values: 930 EVAL 03czrpj film 08sfxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 20.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #14309-01k3qj PRED entity: 01k3qj PRED relation: artists! PRED expected values: 064t9 06by7 02lnbg => 139 concepts (58 used for prediction) PRED predicted values (max 10 best out of 260): 064t9 (0.79 #2115, 0.63 #15345, 0.55 #3017), 0xv2x (0.62 #745, 0.33 #144, 0.09 #1504), 02v2lh (0.57 #514, 0.14 #1416, 0.09 #1504), 06by7 (0.57 #3025, 0.48 #2424, 0.47 #7533), 03_d0 (0.52 #8126, 0.28 #3015, 0.23 #4819), 08cyft (0.49 #1856, 0.32 #1254, 0.23 #653), 0gywn (0.47 #3059, 0.46 #2157, 0.31 #3361), 08jyyk (0.39 #1265, 0.23 #664, 0.19 #1867), 02lnbg (0.38 #2158, 0.31 #956, 0.26 #4263), 01_bkd (0.38 #651, 0.09 #1504, 0.07 #1854) >> Best rule #2115 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 015mrk; >> query: (?x7578, 064t9) <- profession(?x7578, ?x1614), artists(?x3562, ?x7578), instrumentalists(?x227, ?x7578), ?x3562 = 025sc50 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 9 EVAL 01k3qj artists! 02lnbg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 139.000 58.000 0.788 http://example.org/music/genre/artists EVAL 01k3qj artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 139.000 58.000 0.788 http://example.org/music/genre/artists EVAL 01k3qj artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 58.000 0.788 http://example.org/music/genre/artists #14308-07ffjc PRED entity: 07ffjc PRED relation: artists PRED expected values: 0143q0 => 71 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1150): 01jcxwp (0.68 #13638, 0.56 #11469, 0.56 #10385), 01j59b0 (0.59 #17803, 0.46 #15637, 0.42 #22138), 03q_w5 (0.56 #7453, 0.50 #3122, 0.33 #11785), 0285c (0.53 #8803, 0.50 #2306, 0.44 #6637), 01tv3x2 (0.50 #2760, 0.44 #7091, 0.36 #8174), 01wt4wc (0.50 #2896, 0.33 #11559, 0.33 #10475), 02y7sr (0.50 #2958, 0.33 #7289, 0.33 #6206), 0b1zz (0.50 #2708, 0.33 #7039, 0.33 #5956), 012zng (0.50 #2298, 0.33 #6629, 0.33 #5546), 03sww (0.50 #2607, 0.33 #11270, 0.33 #1524) >> Best rule #13638 for best value: >> intensional similarity = 8 >> extensional distance = 17 >> proper extension: 07bbw; >> query: (?x8481, 01jcxwp) <- parent_genre(?x8481, ?x5934), artists(?x8481, ?x3875), group(?x227, ?x3875), artists(?x7124, ?x3875), ?x7124 = 01hcvm, artists(?x5934, ?x248), ?x227 = 0342h, artist(?x8738, ?x3875) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2753 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 0jmwg; *> query: (?x8481, 0143q0) <- parent_genre(?x8481, ?x5934), artists(?x8481, ?x10938), artists(?x8481, ?x8272), artists(?x8481, ?x3875), ?x3875 = 0mgcr, ?x8272 = 01mr2g6, group(?x1750, ?x10938), group(?x645, ?x10938), group(?x315, ?x10938), ?x315 = 0l14md, ?x1750 = 02hnl, ?x645 = 028tv0 *> conf = 0.25 ranks of expected_values: 153 EVAL 07ffjc artists 0143q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 71.000 34.000 0.684 http://example.org/music/genre/artists #14307-072zl1 PRED entity: 072zl1 PRED relation: nominated_for! PRED expected values: 0gs96 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 177): 019f4v (0.54 #514, 0.44 #745, 0.42 #1900), 0gr4k (0.51 #487, 0.32 #1411, 0.28 #1180), 0gq9h (0.49 #1907, 0.49 #521, 0.45 #752), 0k611 (0.49 #531, 0.46 #762, 0.35 #1917), 054krc (0.49 #527, 0.46 #758, 0.23 #1913), 0gs96 (0.49 #547, 0.41 #1933, 0.30 #778), 0gs9p (0.43 #523, 0.39 #1909, 0.38 #754), 0p9sw (0.43 #481, 0.38 #712, 0.32 #1867), 02qvyrt (0.42 #785, 0.40 #554, 0.19 #1940), 040njc (0.42 #699, 0.31 #468, 0.29 #1854) >> Best rule #514 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 01q7h2; >> query: (?x7320, 019f4v) <- genre(?x7320, ?x1509), ?x1509 = 060__y, nominated_for(?x1079, ?x7320), ?x1079 = 0l8z1 >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #547 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 01q7h2; *> query: (?x7320, 0gs96) <- genre(?x7320, ?x1509), ?x1509 = 060__y, nominated_for(?x1079, ?x7320), ?x1079 = 0l8z1 *> conf = 0.49 ranks of expected_values: 6 EVAL 072zl1 nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 67.000 0.543 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14306-07tgn PRED entity: 07tgn PRED relation: major_field_of_study PRED expected values: 06nm1 0l5mz => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 108): 02lp1 (0.45 #3191, 0.35 #2766, 0.34 #1070), 0g26h (0.40 #670, 0.38 #564, 0.28 #1094), 0fdys (0.38 #561, 0.31 #1091, 0.31 #2363), 02h40lc (0.38 #534, 0.30 #640, 0.28 #1064), 05qfh (0.32 #2360, 0.31 #1088, 0.31 #2254), 02_7t (0.30 #689, 0.25 #583, 0.24 #3234), 02jfc (0.30 #704, 0.25 #598, 0.17 #1128), 01tbp (0.28 #1109, 0.25 #579, 0.24 #3230), 0dc_v (0.28 #1095, 0.25 #565, 0.20 #671), 04x_3 (0.28 #1081, 0.25 #3202, 0.23 #2353) >> Best rule #3191 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 05x_5; >> query: (?x892, 02lp1) <- student(?x892, ?x164), major_field_of_study(?x892, ?x1668), ?x1668 = 01mkq >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1120 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 09c7w0; *> query: (?x892, 0l5mz) <- contains(?x1310, ?x892), company(?x10818, ?x892), company(?x3970, ?x892) *> conf = 0.24 ranks of expected_values: 16, 98 EVAL 07tgn major_field_of_study 0l5mz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 101.000 101.000 0.455 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07tgn major_field_of_study 06nm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 101.000 101.000 0.455 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14305-0r785 PRED entity: 0r785 PRED relation: source PRED expected values: 0jbk9 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #30, 0.94 #29, 0.94 #33) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 130 >> proper extension: 0l4vc; 0ycht; 0mnyn; >> query: (?x11708, ?x958) <- county(?x11708, ?x7369), category(?x11708, ?x134), source(?x7369, ?x958), location(?x4285, ?x11708) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r785 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.939 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #14304-0170pk PRED entity: 0170pk PRED relation: film PRED expected values: 05nlx4 05pt0l 04z4j2 => 103 concepts (44 used for prediction) PRED predicted values (max 10 best out of 522): 04x4vj (0.29 #763, 0.01 #29148, 0.01 #25600), 0m9p3 (0.26 #3931, 0.01 #7479, 0.01 #37258), 017jd9 (0.22 #2543, 0.04 #6091, 0.03 #9639), 011yxg (0.22 #1815, 0.01 #37258), 02nx2k (0.22 #2977), 031778 (0.14 #311, 0.11 #2085, 0.03 #42583), 0ndwt2w (0.14 #990, 0.11 #2764, 0.02 #6312), 01sxly (0.14 #77, 0.11 #1851, 0.01 #37258), 08c4yn (0.14 #1725, 0.11 #3499), 05650n (0.14 #1002, 0.11 #2776) >> Best rule #763 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 03dpqd; >> query: (?x1738, 04x4vj) <- award_winner(?x112, ?x1738), film(?x1738, ?x4396), ?x4396 = 06nr2h >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3388 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 06cl2w; *> query: (?x1738, 04z4j2) <- award_nominee(?x72, ?x1738), film(?x1738, ?x2847), ?x2847 = 05fcbk7 *> conf = 0.11 ranks of expected_values: 65 EVAL 0170pk film 04z4j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 103.000 44.000 0.286 http://example.org/film/actor/film./film/performance/film EVAL 0170pk film 05pt0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 44.000 0.286 http://example.org/film/actor/film./film/performance/film EVAL 0170pk film 05nlx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 44.000 0.286 http://example.org/film/actor/film./film/performance/film #14303-01pkhw PRED entity: 01pkhw PRED relation: student! PRED expected values: 015nl4 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 117): 0bwfn (0.25 #275, 0.08 #1856, 0.06 #15031), 015nl4 (0.24 #594, 0.04 #10080, 0.04 #16931), 04b_46 (0.12 #227, 0.10 #1808, 0.04 #1281), 08815 (0.06 #5799, 0.06 #1583, 0.06 #6853), 09f2j (0.05 #5956, 0.04 #1213, 0.04 #1740), 0m4yg (0.05 #892, 0.02 #6689, 0.02 #10378), 018sg9 (0.05 #998), 07w6r (0.05 #995), 015wy_ (0.05 #982), 0ymb6 (0.05 #819) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 062dn7; 07swvb; 07ncs0; 0z05l; 0fthdk; >> query: (?x4053, 0bwfn) <- award(?x4053, ?x112), film(?x4053, ?x4464), ?x4464 = 05pdh86 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #594 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 19 *> proper extension: 07m69t; *> query: (?x4053, 015nl4) <- nationality(?x4053, ?x4221), ?x4221 = 0j5g9 *> conf = 0.24 ranks of expected_values: 2 EVAL 01pkhw student! 015nl4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #14302-019r_1 PRED entity: 019r_1 PRED relation: profession PRED expected values: 02hrh1q => 159 concepts (62 used for prediction) PRED predicted values (max 10 best out of 105): 02hrh1q (0.82 #2028, 0.77 #4044, 0.77 #8222), 0np9r (0.58 #1459, 0.54 #2179, 0.50 #2611), 03gjzk (0.57 #589, 0.48 #1453, 0.46 #733), 0cbd2 (0.33 #6, 0.32 #7351, 0.29 #438), 0kyk (0.33 #27, 0.21 #1755, 0.20 #2475), 02krf9 (0.32 #1465, 0.31 #2185, 0.29 #2617), 018gz8 (0.27 #1743, 0.23 #5487, 0.19 #1311), 0196pc (0.26 #1510, 0.23 #2230, 0.21 #2662), 01c72t (0.22 #1030, 0.17 #886, 0.14 #454), 0nbcg (0.22 #893, 0.12 #7951, 0.11 #4349) >> Best rule #2028 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 02g8h; 0151ns; 04yj5z; 01w61th; 0hvb2; 02v406; 0g2mbn; 0127s7; 01x209s; 04x1_w; ... >> query: (?x4724, 02hrh1q) <- location(?x4724, ?x739), nationality(?x4724, ?x9006), official_language(?x9006, ?x403), ?x739 = 02_286 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019r_1 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 62.000 0.824 http://example.org/people/person/profession #14301-09qycb PRED entity: 09qycb PRED relation: film! PRED expected values: 051wwp => 71 concepts (39 used for prediction) PRED predicted values (max 10 best out of 756): 08z39v (0.41 #58099, 0.38 #76778, 0.36 #16597), 0ff3y (0.33 #2075, 0.08 #24899), 0p8r1 (0.33 #584, 0.06 #18672, 0.04 #15106), 01rcmg (0.33 #1467, 0.06 #18672, 0.02 #15989), 013cr (0.33 #225, 0.05 #2300, 0.05 #4374), 01f7dd (0.33 #1206, 0.03 #9505, 0.03 #3281), 02bkdn (0.33 #298, 0.03 #2373, 0.02 #4447), 0170pk (0.33 #280, 0.02 #6503, 0.02 #10654), 0309lm (0.33 #1601, 0.02 #11975, 0.02 #32725), 01x6jd (0.33 #1929) >> Best rule #58099 for best value: >> intensional similarity = 5 >> extensional distance = 1014 >> proper extension: 0d6b7; 05dy7p; 02n9bh; 02phtzk; 027ct7c; 012jfb; 064lsn; 03q8xj; 02wk7b; 04cf_l; ... >> query: (?x10349, ?x968) <- country(?x10349, ?x94), genre(?x10349, ?x53), nominated_for(?x11873, ?x10349), nominated_for(?x968, ?x10349), award_winner(?x11873, ?x1314) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #2948 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 37 *> proper extension: 01hvjx; *> query: (?x10349, 051wwp) <- film(?x968, ?x10349), genre(?x10349, ?x2700), genre(?x10349, ?x53), ?x2700 = 06nbt, titles(?x53, ?x54) *> conf = 0.03 ranks of expected_values: 175 EVAL 09qycb film! 051wwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 71.000 39.000 0.411 http://example.org/film/actor/film./film/performance/film #14300-04hvw PRED entity: 04hvw PRED relation: form_of_government PRED expected values: 01fpfn => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 4): 01fpfn (0.47 #46, 0.45 #94, 0.43 #22), 06cx9 (0.45 #93, 0.45 #53, 0.43 #45), 01d9r3 (0.39 #23, 0.36 #55, 0.35 #95), 026wp (0.09 #4, 0.07 #20, 0.07 #96) >> Best rule #46 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 0j11; >> query: (?x11774, 01fpfn) <- administrative_parent(?x11774, ?x551), form_of_government(?x11774, ?x1926), adjustment_currency(?x11774, ?x170) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04hvw form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.467 http://example.org/location/country/form_of_government #14299-0408np PRED entity: 0408np PRED relation: film PRED expected values: 011ysn => 92 concepts (56 used for prediction) PRED predicted values (max 10 best out of 767): 02_1sj (0.33 #80, 0.02 #3656, 0.01 #5444), 09xbpt (0.18 #1835, 0.03 #67945, 0.03 #87615), 06z8s_ (0.18 #1918, 0.03 #67945, 0.03 #87615), 01qncf (0.18 #2147, 0.03 #89404, 0.03 #87615), 03s6l2 (0.18 #1871, 0.02 #5447, 0.02 #3659), 0418wg (0.18 #2189, 0.02 #3977, 0.02 #25433), 05sy_5 (0.18 #2843, 0.02 #4631, 0.02 #9995), 02704ff (0.18 #2771, 0.02 #4559, 0.01 #6347), 034hwx (0.18 #3337, 0.01 #6913, 0.01 #10489), 08r4x3 (0.09 #1942, 0.05 #5518, 0.04 #9094) >> Best rule #80 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01n4f8; >> query: (?x2692, 02_1sj) <- profession(?x2692, ?x319), award_nominee(?x157, ?x2692), participant(?x2692, ?x1207), ?x1207 = 02lnhv >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2354 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01qscs; *> query: (?x2692, 011ysn) <- profession(?x2692, ?x319), award_nominee(?x192, ?x2692), participant(?x2692, ?x1207), ?x192 = 02p65p *> conf = 0.09 ranks of expected_values: 17 EVAL 0408np film 011ysn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 92.000 56.000 0.333 http://example.org/film/actor/film./film/performance/film #14298-07ww5 PRED entity: 07ww5 PRED relation: country! PRED expected values: 0bynt 071t0 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 56): 0bynt (0.86 #2029, 0.85 #2757, 0.85 #2982), 071t0 (0.86 #1146, 0.83 #1370, 0.82 #809), 06f41 (0.75 #1138, 0.75 #801, 0.70 #1754), 01lb14 (0.75 #1139, 0.75 #1923, 0.74 #1307), 03hr1p (0.75 #1147, 0.74 #1315, 0.71 #1371), 0194d (0.74 #1339, 0.72 #1171, 0.71 #1395), 03_8r (0.72 #1145, 0.72 #1761, 0.71 #1929), 07jbh (0.71 #820, 0.67 #1157, 0.67 #315), 0w0d (0.67 #1135, 0.66 #1751, 0.64 #1359), 01sgl (0.67 #326, 0.64 #551, 0.59 #1280) >> Best rule #2029 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 02k54; 01n6c; >> query: (?x1317, 0bynt) <- country(?x4045, ?x1317), time_zones(?x1317, ?x11506), country(?x4045, ?x9730), ?x9730 = 01p8s >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07ww5 country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.865 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07ww5 country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.865 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #14297-049fgvm PRED entity: 049fgvm PRED relation: location PRED expected values: 04sqj => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 179): 0rh6k (0.70 #53776, 0.55 #12836, 0.49 #37716), 02_286 (0.23 #56219, 0.19 #44981, 0.19 #839), 030qb3t (0.19 #45027, 0.19 #11312, 0.19 #1687), 0cr3d (0.11 #144, 0.09 #1748, 0.09 #56326), 013yq (0.07 #2524, 0.07 #920, 0.06 #1722), 04jpl (0.06 #44961, 0.05 #7236, 0.05 #61820), 0cc56 (0.06 #9682, 0.06 #11286, 0.06 #4068), 01531 (0.06 #4168, 0.05 #11386, 0.05 #157), 01n7q (0.06 #1667, 0.06 #8084, 0.05 #2469), 059rby (0.06 #1620, 0.05 #2422, 0.05 #16) >> Best rule #53776 for best value: >> intensional similarity = 3 >> extensional distance = 1387 >> proper extension: 0f1pyf; 07m69t; 02x8kk; 02x8mt; 011zwl; >> query: (?x6693, ?x108) <- nationality(?x6693, ?x94), place_of_birth(?x6693, ?x108), location(?x6693, ?x2020) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 049fgvm location 04sqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 106.000 0.703 http://example.org/people/person/places_lived./people/place_lived/location #14296-05zrvfd PRED entity: 05zrvfd PRED relation: award_winner PRED expected values: 049g_xj => 39 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1107): 0151w_ (0.38 #2668, 0.07 #5142, 0.06 #10093), 0d6d2 (0.33 #1777, 0.07 #6728, 0.05 #4254), 0byfz (0.33 #39, 0.06 #4990, 0.05 #2516), 0171cm (0.33 #540, 0.04 #5491, 0.03 #10442), 0l786 (0.33 #1587, 0.04 #6538, 0.03 #11489), 016gr2 (0.33 #239, 0.04 #5190, 0.02 #20048), 0f6_x (0.33 #791, 0.04 #5742, 0.02 #28034), 0341n5 (0.33 #2151, 0.03 #7102, 0.02 #12053), 01kt17 (0.33 #1969, 0.03 #6920, 0.02 #29212), 03pp73 (0.33 #1164, 0.02 #6115, 0.01 #20973) >> Best rule #2668 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 02rdxsh; 099c8n; >> query: (?x2115, 0151w_) <- nominated_for(?x2115, ?x9250), nominated_for(?x2115, ?x3035), ?x3035 = 0j43swk, genre(?x9250, ?x53) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #9902 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 185 *> proper extension: 05ztjjw; 09tqxt; 03m73lj; 02qt02v; 02qysm0; 054knh; 02qwzkm; *> query: (?x2115, ?x2516) <- nominated_for(?x2115, ?x3035), film_release_region(?x3035, ?x87), award_winner(?x3035, ?x2516) *> conf = 0.07 ranks of expected_values: 124 EVAL 05zrvfd award_winner 049g_xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 39.000 13.000 0.381 http://example.org/award/award_category/winners./award/award_honor/award_winner #14295-0s4sj PRED entity: 0s4sj PRED relation: contains! PRED expected values: 0nvt9 => 82 concepts (52 used for prediction) PRED predicted values (max 10 best out of 181): 0l3kx (0.50 #659, 0.30 #3340, 0.25 #1554), 0nvrd (0.38 #1023, 0.33 #894, 0.33 #128), 04_1l0v (0.33 #8936, 0.13 #37998, 0.13 #38892), 01n7q (0.28 #11696, 0.24 #5438, 0.23 #13484), 0nvt9 (0.18 #895, 0.13 #3575, 0.11 #30394), 0nv6n (0.18 #895, 0.13 #3575, 0.11 #30394), 02xry (0.14 #39498, 0.09 #6416, 0.07 #14462), 0kpys (0.13 #6434, 0.08 #5541, 0.07 #39516), 07ssc (0.13 #41157, 0.13 #42944, 0.08 #42051), 059rby (0.12 #6273, 0.10 #44719, 0.07 #23258) >> Best rule #659 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0s5cg; 0s9b_; >> query: (?x14478, 0l3kx) <- county(?x14478, ?x10134), contains(?x94, ?x14478), adjoins(?x10134, ?x1963), ?x94 = 09c7w0, ?x1963 = 0nvrd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #895 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 0s5cg; 0s9b_; *> query: (?x14478, ?x6410) <- county(?x14478, ?x10134), contains(?x94, ?x14478), adjoins(?x10134, ?x6410), adjoins(?x10134, ?x1963), ?x94 = 09c7w0, ?x1963 = 0nvrd *> conf = 0.18 ranks of expected_values: 5 EVAL 0s4sj contains! 0nvt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 82.000 52.000 0.500 http://example.org/location/location/contains #14294-04mlmx PRED entity: 04mlmx PRED relation: film PRED expected values: 042fgh => 145 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1103): 042fgh (0.67 #4866, 0.50 #3078, 0.25 #1290), 0m9p3 (0.38 #9328, 0.11 #3963, 0.02 #27210), 01jwxx (0.33 #2635, 0.25 #847, 0.22 #4423), 0c_j9x (0.27 #9314, 0.01 #80845, 0.01 #59387), 017kct (0.25 #582, 0.17 #2370, 0.15 #9523), 0cq806 (0.25 #1493, 0.17 #3281, 0.11 #5069), 0p4v_ (0.25 #473, 0.17 #2261, 0.11 #4049), 019vhk (0.25 #462, 0.05 #5826, 0.04 #23709), 0gh65c5 (0.25 #596, 0.05 #5960, 0.03 #11325), 0639bg (0.25 #633, 0.05 #5997, 0.03 #11362) >> Best rule #4866 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 012c6x; 039bp; >> query: (?x8222, 042fgh) <- film(?x8222, ?x1072), profession(?x8222, ?x1032), ?x1072 = 01_mdl, nationality(?x8222, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mlmx film 042fgh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 117.000 0.667 http://example.org/film/actor/film./film/performance/film #14293-0127ps PRED entity: 0127ps PRED relation: film_crew_role PRED expected values: 09zzb8 02r96rf => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.82 #402, 0.66 #835, 0.66 #601), 09zzb8 (0.76 #832, 0.75 #598, 0.74 #432), 02rh1dz (0.21 #408, 0.15 #374, 0.13 #2972), 015h31 (0.19 #407, 0.13 #2972, 0.10 #373), 01xy5l_ (0.18 #12, 0.16 #410, 0.13 #2972), 0215hd (0.18 #414, 0.14 #1482, 0.14 #346), 0d2b38 (0.18 #421, 0.13 #387, 0.13 #2972), 089g0h (0.17 #415, 0.13 #2972, 0.12 #1483), 033smt (0.13 #2972, 0.10 #423, 0.06 #389), 02_n3z (0.13 #2972, 0.10 #400, 0.09 #2) >> Best rule #402 for best value: >> intensional similarity = 4 >> extensional distance = 317 >> proper extension: 0dscrwf; 0ch26b_; 0by1wkq; 02qhqz4; 04f52jw; 0879bpq; 0g5838s; 02ctc6; 0gvs1kt; 09g7vfw; ... >> query: (?x5948, 02r96rf) <- film(?x556, ?x5948), film(?x382, ?x5948), film_crew_role(?x5948, ?x2154), ?x2154 = 01vx2h >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0127ps film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.815 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0127ps film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.815 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14292-0gzy02 PRED entity: 0gzy02 PRED relation: production_companies PRED expected values: 017s11 => 73 concepts (51 used for prediction) PRED predicted values (max 10 best out of 51): 016tw3 (0.37 #414, 0.34 #166, 0.33 #661), 017s11 (0.37 #414, 0.34 #166, 0.33 #661), 05qd_ (0.16 #259, 0.13 #506, 0.12 #1664), 086k8 (0.13 #2, 0.13 #84, 0.12 #1656), 016tt2 (0.11 #170, 0.10 #4, 0.09 #335), 030_1_ (0.08 #99, 0.08 #348, 0.07 #17), 0g1rw (0.08 #174, 0.06 #586, 0.05 #1662), 01gb54 (0.06 #1691, 0.06 #368, 0.06 #119), 054lpb6 (0.06 #3239, 0.06 #2826, 0.05 #429), 024rgt (0.04 #1678, 0.04 #2835, 0.04 #3248) >> Best rule #414 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 02h22; >> query: (?x327, ?x541) <- nominated_for(?x500, ?x327), ?x500 = 0p9sw, language(?x327, ?x254), film(?x541, ?x327) >> conf = 0.37 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0gzy02 production_companies 017s11 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 51.000 0.365 http://example.org/film/film/production_companies #14291-02qlp4 PRED entity: 02qlp4 PRED relation: film! PRED expected values: 04g2mkf => 71 concepts (40 used for prediction) PRED predicted values (max 10 best out of 46): 024rgt (0.63 #300, 0.13 #299, 0.07 #150), 03rwz3 (0.33 #43, 0.14 #117, 0.05 #193), 030_1m (0.33 #13, 0.04 #834, 0.03 #163), 017s11 (0.29 #77, 0.14 #601, 0.13 #1046), 086k8 (0.19 #226, 0.17 #1715, 0.17 #1194), 016tt2 (0.16 #154, 0.13 #1196, 0.13 #1792), 016tw3 (0.15 #1053, 0.14 #608, 0.14 #310), 054g1r (0.14 #108, 0.07 #184, 0.06 #1374), 024rbz (0.14 #85, 0.07 #311, 0.04 #1351), 032dg7 (0.14 #121, 0.02 #1611, 0.02 #1685) >> Best rule #300 for best value: >> intensional similarity = 4 >> extensional distance = 210 >> proper extension: 04bp0l; >> query: (?x10902, ?x2549) <- nominated_for(?x2549, ?x10902), film(?x2549, ?x54), award_winner(?x2022, ?x2549), state_province_region(?x2549, ?x1227) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #366 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 261 *> proper extension: 0gj9qxr; 026njb5; *> query: (?x10902, 04g2mkf) <- genre(?x10902, ?x811), country(?x10902, ?x512), film_crew_role(?x10902, ?x137), ?x512 = 07ssc *> conf = 0.02 ranks of expected_values: 32 EVAL 02qlp4 film! 04g2mkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 71.000 40.000 0.633 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #14290-01x0sy PRED entity: 01x0sy PRED relation: actor! PRED expected values: 07vqnc => 119 concepts (94 used for prediction) PRED predicted values (max 10 best out of 169): 019nnl (0.43 #1336, 0.02 #7141, 0.01 #10039), 07vqnc (0.33 #223, 0.07 #1540, 0.06 #2069), 0gfzfj (0.29 #1317, 0.14 #1316, 0.09 #5014), 0jwl2 (0.25 #599, 0.25 #336, 0.15 #1655), 0ctzf1 (0.25 #661, 0.25 #398, 0.10 #1981), 014gjp (0.25 #668, 0.25 #405, 0.05 #1724), 02648p (0.25 #593, 0.25 #330, 0.05 #1649), 05nlzq (0.25 #709, 0.10 #2029, 0.07 #3349), 05f7w84 (0.19 #1952, 0.14 #3008, 0.14 #3536), 09g_31 (0.19 #2011, 0.14 #3067, 0.12 #3331) >> Best rule #1336 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 03jldb; 0gz5hs; 027xbpw; 021yw7; 023v4_; >> query: (?x9471, 019nnl) <- film(?x9471, ?x10942), ?x10942 = 0gfzfj, profession(?x9471, ?x1032) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #223 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 067hq2; *> query: (?x9471, 07vqnc) <- film(?x9471, ?x10942), film(?x9471, ?x10327), ?x10942 = 0gfzfj, ?x10327 = 03vfr_, profession(?x9471, ?x1032) *> conf = 0.33 ranks of expected_values: 2 EVAL 01x0sy actor! 07vqnc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 94.000 0.429 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #14289-047cx PRED entity: 047cx PRED relation: group! PRED expected values: 018gkb => 104 concepts (45 used for prediction) PRED predicted values (max 10 best out of 271): 01vs4ff (0.20 #522, 0.03 #5302, 0.03 #3507), 016wvy (0.20 #576, 0.03 #3561, 0.03 #4158), 03f0fnk (0.20 #485, 0.03 #3470, 0.03 #4067), 01vs4f3 (0.20 #559, 0.03 #3544, 0.03 #4141), 017g21 (0.20 #532, 0.03 #3517, 0.03 #4114), 01gx5f (0.20 #459, 0.03 #3444, 0.03 #4041), 01nn6c (0.20 #454, 0.03 #3439, 0.03 #4036), 01w02sy (0.20 #450, 0.03 #3435, 0.03 #4032), 01wwvt2 (0.17 #835, 0.11 #1628, 0.08 #2224), 0191h5 (0.17 #926, 0.11 #1719, 0.08 #2315) >> Best rule #522 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 07yg2; >> query: (?x4783, 01vs4ff) <- artists(?x11746, ?x4783), origin(?x4783, ?x1310), group(?x2944, ?x4783), role(?x74, ?x2944), ?x11746 = 03w94xt >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 047cx group! 018gkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 45.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group #14288-04g61 PRED entity: 04g61 PRED relation: form_of_government PRED expected values: 01q20 => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 3): 01q20 (0.50 #5, 0.43 #47, 0.34 #53), 06cx9 (0.41 #256, 0.40 #205, 0.39 #346), 01d9r3 (0.39 #201, 0.38 #207, 0.38 #258) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 068cn; >> query: (?x5274, 01q20) <- adjoins(?x5274, ?x789), ?x789 = 0f8l9c, currency(?x5274, ?x170) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04g61 form_of_government 01q20 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.500 http://example.org/location/country/form_of_government #14287-0253b6 PRED entity: 0253b6 PRED relation: type_of_union PRED expected values: 04ztj => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.86 #25, 0.86 #45, 0.84 #37), 01g63y (0.30 #26, 0.28 #30, 0.27 #38) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 226 >> proper extension: 0cj2w; >> query: (?x3645, 04ztj) <- profession(?x3645, ?x1032), spouse(?x11113, ?x3645), award_nominee(?x2387, ?x3645) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0253b6 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.864 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14286-0kszw PRED entity: 0kszw PRED relation: nominated_for PRED expected values: 0ds11z 09gq0x5 => 111 concepts (54 used for prediction) PRED predicted values (max 10 best out of 630): 011ywj (0.37 #4518, 0.02 #38868, 0.02 #45010), 0ds11z (0.36 #6476, 0.35 #21048, 0.32 #17809), 0prhz (0.36 #6476, 0.35 #21048, 0.32 #17809), 03hxsv (0.36 #6476, 0.35 #21048, 0.32 #17809), 04t9c0 (0.36 #6476, 0.35 #21048, 0.32 #17809), 027pfg (0.36 #6476, 0.35 #21048, 0.32 #17809), 043tvp3 (0.36 #6476, 0.35 #21048, 0.32 #17809), 09g7vfw (0.36 #6476, 0.35 #21048, 0.32 #17809), 04ltlj (0.36 #6476, 0.35 #21048, 0.32 #17809), 03yvf2 (0.36 #6476, 0.35 #21048, 0.32 #17809) >> Best rule #4518 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 05cj4r; 09fqtq; 016gr2; 02tr7d; 0170pk; 06t61y; 065jlv; 02k6rq; 015gw6; 0l6px; ... >> query: (?x2531, 011ywj) <- award_winner(?x1739, ?x2531), ?x1739 = 015rkw, film(?x2531, ?x485) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #6476 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 012_53; *> query: (?x2531, ?x485) <- film(?x2531, ?x485), friend(?x2444, ?x2531), type_of_union(?x2531, ?x1873) *> conf = 0.36 ranks of expected_values: 2, 37 EVAL 0kszw nominated_for 09gq0x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 111.000 54.000 0.367 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0kszw nominated_for 0ds11z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 54.000 0.367 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14285-04l5b4 PRED entity: 04l5b4 PRED relation: position PRED expected values: 02qvzf => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 48): 02qvzf (0.91 #109, 0.90 #105, 0.88 #73), 02qvgy (0.65 #132, 0.60 #40, 0.55 #52), 02qvkj (0.60 #40, 0.55 #52, 0.55 #142), 02_j1w (0.08 #12, 0.06 #95, 0.06 #91), 0dgrmp (0.08 #12, 0.06 #95, 0.06 #91), 02sdk9v (0.08 #12, 0.06 #95, 0.06 #91), 02nzb8 (0.08 #12, 0.06 #95, 0.06 #91), 02sddg (0.08 #12, 0.06 #95, 0.06 #91), 03f0fp (0.08 #12, 0.06 #95, 0.06 #91), 02dwpf (0.08 #12, 0.06 #95, 0.06 #91) >> Best rule #109 for best value: >> intensional similarity = 25 >> extensional distance = 26 >> proper extension: 0jnpc; 0j86l; >> query: (?x13629, ?x3724) <- position(?x13629, ?x5234), position(?x13629, ?x2918), ?x5234 = 02qvdc, team(?x3724, ?x13629), ?x2918 = 02qvl7, position(?x13661, ?x3724), position(?x13326, ?x3724), position(?x12977, ?x3724), position(?x10755, ?x3724), position(?x10644, ?x3724), position(?x10142, ?x3724), position(?x9515, ?x3724), position(?x8037, ?x3724), ?x13661 = 0jnr3, ?x8037 = 0jnrk, team(?x3724, ?x14124), team(?x3724, ?x14015), ?x9515 = 0j2zj, ?x13326 = 0hm2b, ?x10755 = 0jbqf, ?x14124 = 04l590, teams(?x10141, ?x10142), ?x14015 = 0jnlm, ?x12977 = 0jnkr, ?x10644 = 0jnnx >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04l5b4 position 02qvzf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.911 http://example.org/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position #14284-0prrm PRED entity: 0prrm PRED relation: film_format PRED expected values: 07fb8_ => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.32 #11, 0.29 #16, 0.20 #6), 0cj16 (0.10 #100, 0.10 #111, 0.10 #147), 017fx5 (0.05 #30, 0.05 #35, 0.04 #40) >> Best rule #11 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 09xbpt; 061681; 0b73_1d; 06z8s_; 0p9lw; 04vr_f; 03twd6; 07yk1xz; 01b195; 0418wg; ... >> query: (?x5024, 07fb8_) <- film(?x2559, ?x5024), film(?x2422, ?x5024), ?x2422 = 0169dl, country(?x5024, ?x94), award_nominee(?x380, ?x2559) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0prrm film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.318 http://example.org/film/film/film_format #14283-0dw3l PRED entity: 0dw3l PRED relation: artists! PRED expected values: 02x8m 08jyyk => 133 concepts (49 used for prediction) PRED predicted values (max 10 best out of 250): 0xhtw (0.67 #17, 0.52 #1862, 0.50 #324), 03lty (0.64 #1873, 0.58 #28, 0.57 #335), 0dl5d (0.58 #20, 0.20 #1557, 0.20 #2481), 064t9 (0.39 #10176, 0.38 #12023, 0.37 #14800), 05jt_ (0.33 #1969, 0.31 #1046, 0.21 #431), 02t8gf (0.33 #1986, 0.25 #141, 0.21 #448), 08jyyk (0.33 #683, 0.25 #68, 0.14 #375), 05w3f (0.33 #653, 0.17 #4962, 0.16 #6810), 02x8m (0.33 #634, 0.17 #1248, 0.11 #3713), 02yv6b (0.30 #1636, 0.20 #714, 0.17 #5023) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01t_xp_; 0187x8; 0jn38; 01shhf; 02ndj5; >> query: (?x8048, 0xhtw) <- artist(?x10426, ?x8048), category(?x8048, ?x134), artists(?x5436, ?x8048), ?x5436 = 0hdf8 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #683 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 01gf5h; 01wwvt2; 0161sp; 01gx5f; 01ttg5; 01vsy3q; 019f9z; 0191h5; 01lz4tf; 0xsk8; ... *> query: (?x8048, 08jyyk) <- profession(?x8048, ?x655), type_of_union(?x8048, ?x566), place_of_birth(?x8048, ?x7519), artists(?x9013, ?x8048), ?x9013 = 09nwwf *> conf = 0.33 ranks of expected_values: 7, 9 EVAL 0dw3l artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 133.000 49.000 0.667 http://example.org/music/genre/artists EVAL 0dw3l artists! 02x8m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 133.000 49.000 0.667 http://example.org/music/genre/artists #14282-06rmdr PRED entity: 06rmdr PRED relation: costume_design_by PRED expected values: 06w33f8 => 62 concepts (32 used for prediction) PRED predicted values (max 10 best out of 17): 0bytfv (0.03 #68, 0.03 #96, 0.02 #497), 03mfqm (0.02 #330, 0.02 #859, 0.02 #803), 02mxbd (0.02 #273, 0.02 #329, 0.01 #715), 02w0dc0 (0.02 #1, 0.02 #201, 0.01 #143), 02pqgt8 (0.02 #41, 0.02 #268, 0.01 #797), 02cqbx (0.02 #16, 0.01 #744, 0.01 #73), 0b80__ (0.02 #44), 0c6g29 (0.02 #36), 03gt0c5 (0.02 #27), 02h1rt (0.02 #14) >> Best rule #68 for best value: >> intensional similarity = 4 >> extensional distance = 237 >> proper extension: 06w99h3; 06wzvr; 02hxhz; 0gmcwlb; 0407yfx; 03kxj2; 0h1v19; 085bd1; 076tq0z; 0dr3sl; ... >> query: (?x1769, 0bytfv) <- genre(?x1769, ?x258), film(?x1784, ?x1769), ?x258 = 05p553, award(?x1769, ?x688) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06rmdr costume_design_by 06w33f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 32.000 0.025 http://example.org/film/film/costume_design_by #14281-059_c PRED entity: 059_c PRED relation: district_represented! PRED expected values: 02bp37 => 217 concepts (217 used for prediction) PRED predicted values (max 10 best out of 46): 02bp37 (0.86 #100, 0.83 #146, 0.61 #330), 03rl1g (0.70 #139, 0.58 #875, 0.57 #93), 03rtmz (0.67 #105, 0.61 #151, 0.56 #921), 043djx (0.65 #143, 0.62 #97, 0.58 #879), 02glc4 (0.62 #117, 0.57 #163, 0.56 #921), 03tcbx (0.62 #104, 0.56 #921, 0.52 #150), 01h7xx (0.57 #172, 0.53 #356, 0.52 #126), 03ww_x (0.56 #921, 0.38 #96, 0.35 #142), 03z5xd (0.56 #921, 0.38 #101, 0.35 #147), 032ft5 (0.56 #921, 0.22 #144, 0.19 #98) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 059rby; 04rrx; 05k7sb; 07srw; 05tbn; 04tgp; >> query: (?x1138, 02bp37) <- religion(?x1138, ?x109), district_represented(?x4730, ?x1138), adjoins(?x1138, ?x726), ?x4730 = 02cg7g >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059_c district_represented! 02bp37 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 217.000 217.000 0.857 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #14280-0gkz15s PRED entity: 0gkz15s PRED relation: genre PRED expected values: 07s9rl0 => 66 concepts (65 used for prediction) PRED predicted values (max 10 best out of 85): 07s9rl0 (0.61 #1683, 0.60 #361, 0.60 #3486), 01hmnh (0.50 #137, 0.29 #737, 0.28 #1458), 01jfsb (0.45 #1453, 0.45 #1573, 0.45 #732), 05p553 (0.41 #484, 0.38 #604, 0.36 #2888), 02l7c8 (0.27 #5544, 0.26 #2899, 0.26 #5424), 0hcr (0.25 #23, 0.24 #503, 0.23 #623), 02n4kr (0.25 #8, 0.24 #488, 0.20 #608), 03g3w (0.25 #24, 0.18 #264, 0.06 #3028), 060__y (0.25 #136, 0.16 #1818, 0.16 #2178), 01zhp (0.25 #77, 0.12 #557, 0.10 #677) >> Best rule #1683 for best value: >> intensional similarity = 3 >> extensional distance = 227 >> proper extension: 02vxq9m; 01vksx; 01f7gh; 0b1y_2; 0gyfp9c; 0gh65c5; 04vvh9; 024lff; 0jymd; 0n04r; ... >> query: (?x781, 07s9rl0) <- film_release_region(?x781, ?x87), film_format(?x781, ?x10390), nominated_for(?x298, ?x781) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gkz15s genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 65.000 0.607 http://example.org/film/film/genre #14279-09hnb PRED entity: 09hnb PRED relation: nationality PRED expected values: 09c7w0 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 18): 09c7w0 (0.74 #4803, 0.72 #6203, 0.71 #3102), 0d060g (0.37 #2801, 0.33 #8705, 0.29 #7), 02jx1 (0.37 #2801, 0.33 #8705, 0.23 #433), 07ssc (0.37 #2801, 0.33 #8705, 0.14 #15), 0f8l9c (0.06 #222, 0.03 #1622, 0.02 #1022), 03rk0 (0.05 #8550, 0.05 #9453, 0.05 #9654), 0345h (0.03 #231, 0.02 #3232, 0.02 #2231), 03rjj (0.03 #205, 0.02 #405, 0.02 #5307), 0jgd (0.03 #202, 0.01 #902, 0.01 #602), 06q1r (0.03 #477, 0.02 #777, 0.02 #1177) >> Best rule #4803 for best value: >> intensional similarity = 3 >> extensional distance = 1055 >> proper extension: 08ff1k; 02b29; 06jz0; 02drd3; >> query: (?x2698, 09c7w0) <- award_nominee(?x2698, ?x217), award(?x2698, ?x1079), student(?x2767, ?x2698) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09hnb nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.735 http://example.org/people/person/nationality #14278-03tbg6 PRED entity: 03tbg6 PRED relation: film! PRED expected values: 033jj1 => 81 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1104): 01mkn_d (0.43 #16595, 0.41 #93360, 0.41 #99585), 04fhn_ (0.25 #677, 0.20 #2751, 0.09 #8973), 01w23w (0.25 #1157, 0.20 #3231, 0.09 #9453), 041c4 (0.25 #889, 0.20 #2963, 0.07 #17484), 0f7hc (0.25 #825, 0.20 #2899, 0.03 #17420), 079vf (0.25 #8, 0.20 #2082, 0.03 #20752), 05np4c (0.25 #571, 0.20 #2645, 0.03 #21315), 04yywz (0.25 #19, 0.20 #2093, 0.02 #26985), 02dth1 (0.25 #719, 0.20 #2793, 0.02 #13165), 01twdk (0.25 #838, 0.20 #2912, 0.02 #13284) >> Best rule #16595 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 0140g4; 0jzw; 026390q; 0k4kk; 01jzyf; 015whm; 0h6r5; 011yr9; 04cbbz; 0gw7p; ... >> query: (?x10455, ?x6664) <- honored_for(?x5135, ?x10455), films(?x12273, ?x10455), nominated_for(?x6664, ?x10455), film(?x478, ?x10455) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03tbg6 film! 033jj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 51.000 0.427 http://example.org/film/actor/film./film/performance/film #14277-0cbv4g PRED entity: 0cbv4g PRED relation: category PRED expected values: 08mbj5d => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.31 #18, 0.30 #8, 0.29 #38) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 286 >> proper extension: 02n9bh; 02wk7b; >> query: (?x5293, 08mbj5d) <- film_crew_role(?x5293, ?x137), genre(?x5293, ?x258), nominated_for(?x143, ?x5293), ?x258 = 05p553 >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cbv4g category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.306 http://example.org/common/topic/webpage./common/webpage/category #14276-047vp1n PRED entity: 047vp1n PRED relation: genre PRED expected values: 05p553 => 110 concepts (51 used for prediction) PRED predicted values (max 10 best out of 134): 05p553 (0.94 #2470, 0.90 #1296, 0.90 #943), 01z4y (0.68 #1764, 0.52 #2820, 0.45 #4704), 06n90 (0.62 #5071, 0.17 #4362, 0.17 #1540), 01jfsb (0.50 #11, 0.39 #5896, 0.35 #5070), 02kdv5l (0.46 #5061, 0.37 #5887, 0.32 #4352), 0lsxr (0.35 #831, 0.29 #714, 0.24 #2357), 02m4t (0.33 #183, 0.02 #2063, 0.02 #2180), 03k9fj (0.30 #5069, 0.27 #1538, 0.24 #4360), 060__y (0.28 #1072, 0.26 #1190, 0.25 #1661), 0556j8 (0.27 #394, 0.12 #2508, 0.12 #864) >> Best rule #2470 for best value: >> intensional similarity = 7 >> extensional distance = 70 >> proper extension: 01sxly; 07h9gp; 029k4p; 02x8fs; 0yyn5; 0277j40; 02mc5v; 0bz6sq; 0ckt6; >> query: (?x7314, 05p553) <- genre(?x7314, ?x239), featured_film_locations(?x7314, ?x739), genre(?x5966, ?x239), genre(?x857, ?x239), ?x5966 = 04h41v, executive_produced_by(?x7314, ?x4060), ?x857 = 06_wqk4 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047vp1n genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 51.000 0.944 http://example.org/film/film/genre #14275-0fphf3v PRED entity: 0fphf3v PRED relation: genre PRED expected values: 05p553 => 67 concepts (66 used for prediction) PRED predicted values (max 10 best out of 86): 07s9rl0 (0.67 #6361, 0.60 #1711, 0.57 #4771), 01z4y (0.54 #1833, 0.53 #1587, 0.52 #4402), 02l7c8 (0.50 #17, 0.31 #6377, 0.28 #4787), 02kdv5l (0.41 #125, 0.32 #857, 0.32 #613), 05p553 (0.40 #2814, 0.39 #126, 0.37 #2447), 01jfsb (0.33 #135, 0.32 #867, 0.31 #623), 03k9fj (0.33 #134, 0.32 #256, 0.28 #378), 0lsxr (0.25 #9, 0.21 #619, 0.20 #863), 04xvlr (0.25 #2, 0.17 #1712, 0.16 #3792), 017fp (0.25 #16, 0.10 #1726, 0.08 #4663) >> Best rule #6361 for best value: >> intensional similarity = 3 >> extensional distance = 1489 >> proper extension: 0vgkd; >> query: (?x7832, 07s9rl0) <- genre(?x7832, ?x239), genre(?x2961, ?x239), ?x2961 = 047p7fr >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2814 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 861 *> proper extension: 0qm8b; 0gzlb9; 0dl6fv; 058kh7; *> query: (?x7832, 05p553) <- film(?x7831, ?x7832), award_nominee(?x7831, ?x1365), currency(?x7831, ?x170) *> conf = 0.40 ranks of expected_values: 5 EVAL 0fphf3v genre 05p553 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 67.000 66.000 0.675 http://example.org/film/film/genre #14274-03mdt PRED entity: 03mdt PRED relation: service_language PRED expected values: 02h40lc => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 23): 02h40lc (0.93 #1629, 0.92 #1755, 0.92 #1734), 064_8sq (0.20 #274, 0.16 #317, 0.13 #1595), 04306rv (0.20 #274, 0.16 #317, 0.09 #1588), 06nm1 (0.20 #274, 0.15 #1188, 0.14 #1591), 02bv9 (0.20 #274, 0.08 #951, 0.05 #887), 02bjrlw (0.20 #274, 0.08 #951, 0.05 #887), 0880p (0.16 #317), 03hkp (0.16 #317), 03_9r (0.05 #1187, 0.05 #1548, 0.05 #1590), 01r2l (0.05 #1555, 0.05 #1681, 0.05 #1702) >> Best rule #1629 for best value: >> intensional similarity = 2 >> extensional distance = 108 >> proper extension: 05krk; 04rwx; 011k1h; 01xdn1; 0cchk3; 02607j; 03ksy; 0178g; 0221g_; 03d6fyn; ... >> query: (?x3381, 02h40lc) <- service_location(?x3381, ?x94), ?x94 = 09c7w0 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mdt service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.927 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #14273-011zdm PRED entity: 011zdm PRED relation: symptom_of! PRED expected values: 01cdt5 => 43 concepts (42 used for prediction) PRED predicted values (max 10 best out of 67): 01j6t0 (0.90 #989, 0.83 #729, 0.82 #675), 02tfl8 (0.62 #101, 0.54 #523, 0.54 #522), 0hgxh (0.62 #101, 0.54 #523, 0.54 #522), 0f3kl (0.62 #101, 0.50 #312, 0.46 #316), 01cdt5 (0.55 #321, 0.50 #464, 0.50 #323), 098s1 (0.55 #321, 0.50 #323, 0.46 #316), 0j5fv (0.50 #323, 0.50 #303, 0.46 #316), 01pf6 (0.50 #464, 0.50 #323, 0.46 #316), 0k95h (0.50 #323, 0.46 #316, 0.43 #372), 0hg45 (0.50 #464, 0.43 #573, 0.40 #907) >> Best rule #989 for best value: >> intensional similarity = 26 >> extensional distance = 28 >> proper extension: 04p3w; 02bft; 0c58k; 0g02vk; 0hg45; 0lcdk; 072hv; 0fltx; 0146bp; >> query: (?x9119, 01j6t0) <- symptom_of(?x10717, ?x9119), symptom_of(?x9118, ?x9119), symptom_of(?x10717, ?x14228), symptom_of(?x10717, ?x13744), symptom_of(?x10717, ?x13131), symptom_of(?x10717, ?x10480), symptom_of(?x10717, ?x10199), symptom_of(?x10717, ?x9898), symptom_of(?x10717, ?x4291), risk_factors(?x9119, ?x7007), ?x9898 = 09jg8, people(?x13744, ?x4204), ?x4204 = 02dth1, risk_factors(?x13744, ?x8524), ?x8524 = 01hbgs, risk_factors(?x9510, ?x14228), ?x10199 = 02k6hp, ?x10480 = 0h1n9, ?x4291 = 07jwr, symptom_of(?x9118, ?x7260), symptom_of(?x9118, ?x7006), ?x13131 = 0d19y2, people(?x14228, ?x5912), ?x7006 = 02psvcf, people(?x7260, ?x1737), risk_factors(?x7260, ?x2510) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #321 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 4 *> proper extension: 0d19y2; *> query: (?x9119, ?x13487) <- symptom_of(?x10717, ?x9119), symptom_of(?x9509, ?x9119), symptom_of(?x9438, ?x9119), ?x10717 = 0cjf0, ?x9438 = 012qjw, symptom_of(?x9509, ?x14562), symptom_of(?x9509, ?x11659), symptom_of(?x9509, ?x11126), symptom_of(?x9509, ?x11064), symptom_of(?x9509, ?x10480), symptom_of(?x9509, ?x6781), symptom_of(?x9509, ?x4322), ?x11659 = 072hv, ?x14562 = 087z2, ?x6781 = 035482, ?x10480 = 0h1n9, people(?x11064, ?x120), risk_factors(?x11064, ?x8023), risk_factors(?x11126, ?x11678), people(?x4322, ?x12334), ?x12334 = 02h48, ?x8023 = 0jpmt, ?x11678 = 0fltx, symptom_of(?x13487, ?x11126) *> conf = 0.55 ranks of expected_values: 5 EVAL 011zdm symptom_of! 01cdt5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 43.000 42.000 0.900 http://example.org/medicine/symptom/symptom_of #14272-0jnl5 PRED entity: 0jnl5 PRED relation: teams! PRED expected values: 0rh6k => 30 concepts (26 used for prediction) PRED predicted values (max 10 best out of 53): 02_286 (0.05 #22, 0.05 #3249, 0.05 #2998), 0dclg (0.05 #72, 0.05 #3048, 0.04 #342), 02dtg (0.05 #16, 0.04 #286, 0.04 #556), 0hptm (0.05 #144, 0.04 #414, 0.04 #684), 052p7 (0.05 #76, 0.04 #346, 0.04 #616), 0694j (0.05 #154, 0.04 #424, 0.04 #694), 068p2 (0.05 #123, 0.04 #393, 0.04 #663), 019fh (0.05 #106, 0.04 #376, 0.04 #646), 02cl1 (0.05 #20, 0.04 #290, 0.04 #560), 0n1rj (0.05 #142, 0.04 #412, 0.04 #682) >> Best rule #22 for best value: >> intensional similarity = 9 >> extensional distance = 18 >> proper extension: 0c41y70; 0hn6d; 0j5m6; 0b6p3qf; 0jnrk; 02fp3; 0jnnx; 0jbqf; 030ykh; 0hmt3; ... >> query: (?x13608, 02_286) <- sport(?x13608, ?x453), position(?x13608, ?x5234), ?x453 = 03tmr, team(?x3724, ?x13608), team(?x2918, ?x13608), ?x2918 = 02qvl7, ?x5234 = 02qvdc, ?x3724 = 02qvzf, team(?x5234, ?x13608) >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #3526 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 103 *> proper extension: 0jmdb; 03lpp_; 06x68; 01d5z; 049n7; 0512p; 0jm2v; 0cqt41; 01yhm; 04913k; ... *> query: (?x13608, 0rh6k) <- sport(?x13608, ?x453), team(?x2918, ?x13608), team(?x2918, ?x12757), team(?x2918, ?x10941), team(?x2918, ?x10034), team(?x2918, ?x5233), colors(?x5233, ?x5325), company(?x6010, ?x10034), colors(?x10034, ?x4557), colors(?x10941, ?x8271), ?x5325 = 03vtbc, ?x8271 = 02rnmb, teams(?x739, ?x12757), ?x739 = 02_286, sports(?x7775, ?x453), sports(?x4424, ?x453), country(?x453, ?x2188), adjoins(?x344, ?x2188), olympics(?x205, ?x7775), participating_countries(?x4424, ?x142) *> conf = 0.02 ranks of expected_values: 35 EVAL 0jnl5 teams! 0rh6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 30.000 26.000 0.050 http://example.org/sports/sports_team_location/teams #14271-025rxjq PRED entity: 025rxjq PRED relation: film! PRED expected values: 03nk3t => 74 concepts (45 used for prediction) PRED predicted values (max 10 best out of 51): 0b13g7 (0.23 #3864, 0.22 #4141, 0.21 #2484), 01gkmx (0.12 #828, 0.09 #3866, 0.09 #3865), 01nr36 (0.12 #828, 0.03 #3037, 0.03 #827), 01pjr7 (0.12 #828, 0.03 #827, 0.02 #6620), 0gcs9 (0.09 #3866, 0.09 #3865, 0.09 #4142), 01vsn38 (0.09 #3866, 0.09 #3865, 0.09 #4142), 081lh (0.03 #578, 0.02 #2510, 0.02 #3063), 01zfmm (0.03 #627, 0.03 #75, 0.01 #903), 04jspq (0.03 #159), 03xp8d5 (0.03 #108) >> Best rule #3864 for best value: >> intensional similarity = 3 >> extensional distance = 764 >> proper extension: 0d7vtk; >> query: (?x7819, ?x3568) <- produced_by(?x7819, ?x3568), nominated_for(?x11233, ?x7819), award(?x11233, ?x401) >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 025rxjq film! 03nk3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 45.000 0.226 http://example.org/film/director/film #14270-03z9585 PRED entity: 03z9585 PRED relation: cinematography PRED expected values: 08mhyd => 63 concepts (54 used for prediction) PRED predicted values (max 10 best out of 33): 08mhyd (0.25 #32, 0.06 #347, 0.02 #1232), 0f3zsq (0.17 #113, 0.08 #239, 0.02 #1060), 03rqww (0.07 #294, 0.05 #546, 0.04 #609), 02vx4c2 (0.06 #349, 0.03 #728, 0.02 #1364), 027t8fw (0.06 #409, 0.02 #725, 0.02 #1361), 06r_by (0.03 #654, 0.02 #717, 0.02 #1353), 04qvl7 (0.03 #442, 0.03 #505, 0.02 #1264), 06t8b (0.03 #482, 0.03 #545, 0.02 #608), 03cx282 (0.02 #899, 0.02 #1026, 0.01 #647), 069_0y (0.02 #984, 0.01 #1110, 0.01 #857) >> Best rule #32 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0dgq_kn; >> query: (?x8193, 08mhyd) <- film(?x10663, ?x8193), genre(?x8193, ?x225), film_crew_role(?x8193, ?x137), ?x10663 = 01r9c_, genre(?x1688, ?x225), ?x1688 = 024l2y >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03z9585 cinematography 08mhyd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 54.000 0.250 http://example.org/film/film/cinematography #14269-0jgk3 PRED entity: 0jgk3 PRED relation: time_zones PRED expected values: 02hcv8 => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.89 #522, 0.83 #170, 0.74 #718), 02fqwt (0.50 #1, 0.30 #118, 0.29 #144), 02lcqs (0.35 #70, 0.30 #527, 0.25 #135), 02hczc (0.25 #2, 0.20 #132, 0.17 #28), 042g7t (0.25 #11, 0.03 #415, 0.02 #402), 02lcrv (0.25 #7, 0.02 #555, 0.01 #685), 02llzg (0.09 #997, 0.08 #735, 0.07 #826), 03bdv (0.04 #1172, 0.04 #999, 0.04 #1158), 03plfd (0.03 #1029, 0.02 #1216, 0.02 #1189), 0gsrz4 (0.02 #1435, 0.02 #1318, 0.02 #1448) >> Best rule #522 for best value: >> intensional similarity = 4 >> extensional distance = 114 >> proper extension: 0cc1v; 0nt4s; >> query: (?x8219, ?x2674) <- contains(?x8219, ?x6194), source(?x6194, ?x958), currency(?x8219, ?x170), time_zones(?x6194, ?x2674) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgk3 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.888 http://example.org/location/location/time_zones #14268-099tbz PRED entity: 099tbz PRED relation: nominated_for PRED expected values: 0h3xztt 020bv3 09tqkv2 01gkp1 => 44 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1303): 01cmp9 (0.79 #8655, 0.71 #13958, 0.70 #13957), 011yqc (0.79 #7952, 0.50 #199, 0.33 #4850), 03hmt9b (0.71 #8324, 0.50 #571, 0.33 #5222), 0pv3x (0.71 #7907, 0.25 #154, 0.21 #9456), 049xgc (0.64 #8595, 0.50 #5493, 0.50 #2392), 05hjnw (0.64 #8489, 0.50 #6937, 0.50 #736), 0m313 (0.64 #7764, 0.33 #6212, 0.33 #4662), 011yg9 (0.64 #8637, 0.25 #3984, 0.25 #2434), 0ctb4g (0.64 #8237, 0.25 #484, 0.17 #11337), 0bdjd (0.64 #8842, 0.19 #10391, 0.18 #11942) >> Best rule #8655 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 099c8n; >> query: (?x995, 01cmp9) <- nominated_for(?x995, ?x4086), nominated_for(?x995, ?x414), ?x414 = 095zlp, ?x4086 = 06_x996 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #4933 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 03hl6lc; *> query: (?x995, 09tqkv2) <- award(?x192, ?x995), nominated_for(?x995, ?x2742), nominated_for(?x995, ?x238), ?x238 = 027qgy, film(?x609, ?x2742) *> conf = 0.50 ranks of expected_values: 30, 195, 268, 507 EVAL 099tbz nominated_for 01gkp1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 44.000 19.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099tbz nominated_for 09tqkv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 44.000 19.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099tbz nominated_for 020bv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 19.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099tbz nominated_for 0h3xztt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 44.000 19.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14267-03nt59 PRED entity: 03nt59 PRED relation: honored_for! PRED expected values: 05c1t6z => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 104): 05c1t6z (0.39 #1308, 0.28 #1663, 0.28 #1427), 07y_p6 (0.33 #315, 0.10 #1614, 0.08 #1968), 0gvstc3 (0.33 #1324, 0.25 #262, 0.24 #1679), 0lp_cd3 (0.25 #1314, 0.25 #252, 0.18 #1669), 059x66 (0.25 #12, 0.20 #130, 0.02 #6857), 0bxs_d (0.25 #332, 0.11 #1631, 0.10 #1985), 0275n3y (0.18 #414, 0.11 #1004, 0.11 #532), 09pj68 (0.18 #440, 0.08 #794, 0.08 #1030), 027hjff (0.18 #399, 0.08 #871, 0.06 #1343), 09p30_ (0.17 #304, 0.09 #658, 0.07 #1603) >> Best rule #1308 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 0gpjbt; >> query: (?x6070, 05c1t6z) <- honored_for(?x5585, ?x6070), award_winner(?x5585, ?x236), honored_for(?x5585, ?x6884), ?x6884 = 039cq4 >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03nt59 honored_for! 05c1t6z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.388 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #14266-064_8sq PRED entity: 064_8sq PRED relation: service_language! PRED expected values: 01nn79 055z7 07zl6m => 94 concepts (68 used for prediction) PRED predicted values (max 10 best out of 160): 07zl6m (0.40 #637, 0.40 #253, 0.33 #125), 0dmtp (0.40 #185, 0.33 #57, 0.25 #1086), 045c7b (0.40 #554, 0.33 #42, 0.25 #1071), 0z07 (0.40 #608, 0.33 #96, 0.25 #1383), 055z7 (0.40 #629, 0.33 #117, 0.20 #501), 0plw (0.40 #627, 0.33 #115, 0.20 #499), 06p8m (0.40 #613, 0.33 #101, 0.20 #485), 01yx7f (0.40 #600, 0.33 #88, 0.20 #472), 01dfb6 (0.40 #595, 0.33 #83, 0.20 #467), 077w0b (0.40 #574, 0.33 #62, 0.20 #446) >> Best rule #637 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0jzc; >> query: (?x5607, 07zl6m) <- languages(?x380, ?x5607), official_language(?x7871, ?x5607), countries_spoken_in(?x5607, ?x183), language(?x4541, ?x5607), ?x4541 = 08nvyr, adjoins(?x5457, ?x7871) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 15 EVAL 064_8sq service_language! 07zl6m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 68.000 0.400 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 064_8sq service_language! 055z7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 68.000 0.400 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 064_8sq service_language! 01nn79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 94.000 68.000 0.400 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #14265-02fp82 PRED entity: 02fp82 PRED relation: program PRED expected values: 015w8_ 028k2x => 36 concepts (30 used for prediction) PRED predicted values (max 10 best out of 328): 015w8_ (0.62 #5672, 0.40 #3695, 0.38 #6659), 020qr4 (0.51 #736, 0.44 #1728, 0.34 #7404), 025x1t (0.51 #736, 0.40 #3695, 0.38 #6659), 0170k0 (0.51 #736, 0.37 #5424, 0.35 #6909), 028k2x (0.51 #736, 0.35 #7403, 0.33 #125), 03g9xj (0.51 #736, 0.35 #7403, 0.33 #171), 03y317 (0.51 #736, 0.35 #7403, 0.33 #155), 0cskb (0.51 #736, 0.35 #7403, 0.33 #177), 01f3p_ (0.51 #736, 0.35 #7403, 0.33 #44), 0123qq (0.51 #736, 0.35 #7403, 0.33 #199) >> Best rule #5672 for best value: >> intensional similarity = 8 >> extensional distance = 22 >> proper extension: 03jl0_; >> query: (?x14105, ?x3144) <- program(?x14105, ?x5219), category(?x14105, ?x134), program_creator(?x5219, ?x3145), ?x134 = 08mbj5d, program_creator(?x3144, ?x3145), country_of_origin(?x5219, ?x94), country(?x108, ?x94), nationality(?x51, ?x94) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 02fp82 program 028k2x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 36.000 30.000 0.615 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program EVAL 02fp82 program 015w8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 30.000 0.615 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #14264-015196 PRED entity: 015196 PRED relation: profession PRED expected values: 039v1 => 93 concepts (39 used for prediction) PRED predicted values (max 10 best out of 121): 039v1 (0.61 #1186, 0.56 #1475, 0.54 #1908), 0dxtg (0.59 #3340, 0.34 #1600, 0.31 #3918), 01c72t (0.58 #309, 0.38 #886, 0.31 #453), 01d_h8 (0.47 #4632, 0.45 #4921, 0.43 #3332), 0cbd2 (0.38 #3333, 0.36 #1593, 0.13 #725), 03gjzk (0.32 #4930, 0.30 #4641, 0.30 #3341), 025352 (0.29 #343, 0.12 #55, 0.12 #920), 0d1pc (0.25 #46, 0.15 #766, 0.11 #2356), 02jknp (0.24 #3334, 0.21 #4923, 0.20 #4634), 018gz8 (0.23 #3343, 0.20 #1603, 0.16 #3776) >> Best rule #1186 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 02fybl; >> query: (?x10756, 039v1) <- role(?x10756, ?x227), profession(?x10756, ?x2348), ?x227 = 0342h, ?x2348 = 0nbcg >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015196 profession 039v1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 39.000 0.608 http://example.org/people/person/profession #14263-02wh75 PRED entity: 02wh75 PRED relation: award! PRED expected values: 04rcr 018y2s 0dvqq 0161sp 01bczm 01p0w_ => 56 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2692): 0gcs9 (0.82 #23380, 0.81 #50105, 0.81 #56786), 0191h5 (0.82 #23380, 0.81 #50105, 0.81 #56786), 01vs_v8 (0.68 #20614, 0.53 #17274, 0.50 #13933), 02z4b_8 (0.68 #22083, 0.50 #8722, 0.40 #18743), 0dvqq (0.67 #17320, 0.50 #13979, 0.50 #10639), 07r1_ (0.62 #12052, 0.53 #18733, 0.50 #15392), 017959 (0.53 #19412, 0.50 #12731, 0.40 #16071), 06mj4 (0.53 #19001, 0.40 #15660, 0.38 #12320), 0478__m (0.53 #21349, 0.50 #11328, 0.47 #18009), 01wf86y (0.53 #22202, 0.50 #8841, 0.33 #5502) >> Best rule #23380 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 02f705; 02f5qb; 02f716; 02f71y; 02f6ym; 02f73b; >> query: (?x247, ?x2963) <- award(?x7121, ?x247), award(?x2796, ?x247), award_winner(?x247, ?x2963), instrumentalists(?x716, ?x7121), ?x2796 = 0gdh5, artist(?x382, ?x7121) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #17320 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 13 *> proper extension: 02f6yz; *> query: (?x247, 0dvqq) <- award(?x7121, ?x247), award(?x2796, ?x247), award(?x1060, ?x247), award_winner(?x247, ?x2963), instrumentalists(?x716, ?x7121), ?x1060 = 02r3zy, award_winner(?x1480, ?x2796) *> conf = 0.67 ranks of expected_values: 5, 222, 227, 313, 357, 420 EVAL 02wh75 award! 01p0w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 27.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02wh75 award! 01bczm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 27.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02wh75 award! 0161sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 27.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02wh75 award! 0dvqq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 56.000 27.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02wh75 award! 018y2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 27.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02wh75 award! 04rcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 56.000 27.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14262-04lgymt PRED entity: 04lgymt PRED relation: award PRED expected values: 01bgqh => 68 concepts (45 used for prediction) PRED predicted values (max 10 best out of 246): 01bgqh (0.60 #846, 0.53 #2856, 0.37 #1650), 09sb52 (0.54 #1246, 0.52 #2050, 0.32 #5267), 02f777 (0.40 #1113, 0.37 #1917, 0.30 #2721), 02f5qb (0.40 #155, 0.30 #959, 0.26 #3371), 02f716 (0.40 #176, 0.30 #980, 0.23 #3392), 02v1m7 (0.40 #112, 0.30 #916, 0.22 #4825), 02f72n (0.40 #145, 0.30 #949, 0.21 #1753), 03t5kl (0.40 #226, 0.22 #4825, 0.20 #6434), 02f71y (0.40 #986, 0.22 #2594, 0.21 #1790), 01c99j (0.40 #1029, 0.22 #2637, 0.21 #1833) >> Best rule #846 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01wf86y; >> query: (?x527, 01bgqh) <- award(?x527, ?x2139), award(?x527, ?x528), ?x528 = 02g3gj, ?x2139 = 01by1l >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04lgymt award 01bgqh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 45.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14261-0chghy PRED entity: 0chghy PRED relation: olympics PRED expected values: 0ldqf 0jkvj => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 19): 0kbvb (0.81 #384, 0.78 #518, 0.75 #1153), 0swff (0.73 #535, 0.73 #709, 0.71 #824), 0ldqf (0.70 #128, 0.64 #185, 0.56 #395), 0sx8l (0.60 #286, 0.59 #670, 0.58 #979), 0c_tl (0.60 #286, 0.59 #670, 0.58 #979), 0jkvj (0.56 #396, 0.56 #281, 0.55 #665), 018qb4 (0.50 #276, 0.50 #124, 0.45 #181), 01f1kd (0.50 #130, 0.47 #496, 0.45 #187), 0sx92 (0.50 #123, 0.45 #180, 0.34 #1381), 018ljb (0.50 #278, 0.41 #393, 0.40 #126) >> Best rule #384 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 014tss; >> query: (?x390, 0kbvb) <- nationality(?x72, ?x390), combatants(?x390, ?x94), country(?x308, ?x390) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 02jx1; *> query: (?x390, 0ldqf) <- country_of_origin(?x6793, ?x390), country(?x308, ?x390), contains(?x390, ?x901) *> conf = 0.70 ranks of expected_values: 3, 6 EVAL 0chghy olympics 0jkvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 194.000 194.000 0.815 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0chghy olympics 0ldqf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 194.000 194.000 0.815 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #14260-05925 PRED entity: 05925 PRED relation: child! PRED expected values: 01n073 => 96 concepts (69 used for prediction) PRED predicted values (max 10 best out of 50): 09b3v (0.14 #451, 0.14 #365, 0.14 #537), 0kx4m (0.11 #8, 0.10 #91, 0.09 #260), 049ql1 (0.11 #69, 0.10 #152, 0.09 #321), 03d6fyn (0.11 #30, 0.10 #113, 0.09 #282), 025txrl (0.11 #71, 0.10 #154, 0.09 #323), 0l8sx (0.10 #96, 0.09 #179, 0.07 #1038), 027lf1 (0.10 #148, 0.09 #231, 0.04 #661), 01rt2z (0.10 #147, 0.09 #230, 0.04 #660), 0fnmz (0.09 #1389, 0.08 #1472, 0.07 #1901), 01dtcb (0.07 #507, 0.03 #593, 0.03 #940) >> Best rule #451 for best value: >> intensional similarity = 8 >> extensional distance = 19 >> proper extension: 02bm1v; 01rt2z; >> query: (?x5112, 09b3v) <- category(?x5112, ?x134), state_province_region(?x5112, ?x1227), ?x1227 = 01n7q, industry(?x5112, ?x245), ?x134 = 08mbj5d, industry(?x6230, ?x245), child(?x7793, ?x6230), company(?x4467, ?x6230) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 20 *> proper extension: 056ws9; *> query: (?x5112, ?x7793) <- category(?x5112, ?x134), state_province_region(?x5112, ?x1227), ?x1227 = 01n7q, industry(?x5112, ?x245), ?x134 = 08mbj5d, industry(?x10312, ?x245), industry(?x6230, ?x245), child(?x7793, ?x6230), organization(?x4682, ?x10312) *> conf = 0.03 ranks of expected_values: 23 EVAL 05925 child! 01n073 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 96.000 69.000 0.143 http://example.org/organization/organization/child./organization/organization_relationship/child #14259-01bcq PRED entity: 01bcq PRED relation: award_winner! PRED expected values: 0gx_st => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 116): 07y_p6 (0.26 #1077, 0.05 #937, 0.04 #1497), 09pj68 (0.21 #1084, 0.08 #384, 0.04 #2624), 03nnm4t (0.16 #1053, 0.11 #1333, 0.08 #2453), 0gx_st (0.16 #1017, 0.05 #877, 0.04 #1437), 02q690_ (0.16 #1045, 0.04 #2585, 0.04 #1325), 07z31v (0.16 #1011, 0.04 #1291, 0.02 #5771), 0bq_mx (0.16 #1112, 0.01 #5872), 092t4b (0.15 #332, 0.11 #1312, 0.10 #2572), 092c5f (0.15 #294, 0.07 #1274, 0.06 #4354), 01mh_q (0.15 #508, 0.07 #648, 0.06 #1908) >> Best rule #1077 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 09hd6f; >> query: (?x4919, 07y_p6) <- gender(?x4919, ?x514), award_winner(?x5296, ?x4919), ?x5296 = 07y9ts >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #1017 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 09hd6f; *> query: (?x4919, 0gx_st) <- gender(?x4919, ?x514), award_winner(?x5296, ?x4919), ?x5296 = 07y9ts *> conf = 0.16 ranks of expected_values: 4 EVAL 01bcq award_winner! 0gx_st CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 126.000 126.000 0.263 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14258-02kc008 PRED entity: 02kc008 PRED relation: nutrient! PRED expected values: 033cnk => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 9): 033cnk (0.91 #420, 0.90 #411, 0.89 #283), 06x4c (0.89 #121, 0.88 #64, 0.88 #269), 0dcfv (0.89 #121, 0.88 #64, 0.88 #269), 025rw19 (0.02 #72, 0.01 #255), 025tkqy (0.02 #72, 0.01 #255), 06jry (0.02 #72, 0.01 #255), 025s7j4 (0.02 #72, 0.01 #255), 01sh2 (0.02 #72, 0.01 #255), 014d7f (0.01 #255) >> Best rule #420 for best value: >> intensional similarity = 117 >> extensional distance = 30 >> proper extension: 075pwf; >> query: (?x11270, 033cnk) <- nutrient(?x10612, ?x11270), nutrient(?x9005, ?x11270), nutrient(?x8298, ?x11270), nutrient(?x7719, ?x11270), nutrient(?x6032, ?x11270), nutrient(?x5373, ?x11270), nutrient(?x3900, ?x11270), nutrient(?x3468, ?x11270), nutrient(?x7719, ?x13498), nutrient(?x7719, ?x13126), nutrient(?x7719, ?x12902), nutrient(?x7719, ?x12868), nutrient(?x7719, ?x11758), nutrient(?x7719, ?x10709), nutrient(?x7719, ?x9915), nutrient(?x7719, ?x9855), nutrient(?x7719, ?x9840), nutrient(?x7719, ?x9795), nutrient(?x7719, ?x9733), nutrient(?x7719, ?x9619), nutrient(?x7719, ?x9490), nutrient(?x7719, ?x9436), nutrient(?x7719, ?x9426), nutrient(?x7719, ?x9365), nutrient(?x7719, ?x8487), nutrient(?x7719, ?x8442), nutrient(?x7719, ?x8413), nutrient(?x7719, ?x7720), nutrient(?x7719, ?x7652), nutrient(?x7719, ?x7431), nutrient(?x7719, ?x7364), nutrient(?x7719, ?x7362), nutrient(?x7719, ?x7219), nutrient(?x7719, ?x7135), nutrient(?x7719, ?x6586), nutrient(?x7719, ?x6192), nutrient(?x7719, ?x6026), nutrient(?x7719, ?x5526), nutrient(?x7719, ?x5374), nutrient(?x7719, ?x5337), nutrient(?x7719, ?x5010), nutrient(?x7719, ?x4069), nutrient(?x7719, ?x3469), nutrient(?x7719, ?x3264), nutrient(?x7719, ?x3203), nutrient(?x7719, ?x2702), nutrient(?x7719, ?x2018), nutrient(?x7719, ?x1960), ?x9855 = 0d9t0, ?x10709 = 0h1sz, ?x9619 = 0h1tg, ?x9426 = 0h1yy, ?x13126 = 02kc_w5, ?x6032 = 01nkt, ?x6192 = 06jry, ?x3203 = 04kl74p, ?x7362 = 02kc5rj, nutrient(?x3900, ?x11409), nutrient(?x3900, ?x10891), nutrient(?x3900, ?x10098), nutrient(?x3900, ?x9949), nutrient(?x3900, ?x6160), nutrient(?x3900, ?x6033), nutrient(?x3900, ?x5451), nutrient(?x3900, ?x3901), ?x5010 = 0h1vz, ?x3264 = 0dcfv, ?x6160 = 041r51, ?x4069 = 0hqw8p_, ?x3901 = 0466p20, ?x8413 = 02kc4sf, ?x2702 = 0838f, ?x6033 = 04zjxcz, nutrient(?x10612, ?x14210), nutrient(?x10612, ?x13545), nutrient(?x10612, ?x12336), nutrient(?x10612, ?x6517), ?x9490 = 0h1sg, ?x9005 = 04zpv, ?x7431 = 09gwd, ?x5373 = 0971v, ?x8298 = 037ls6, ?x8442 = 02kcv4x, ?x7135 = 025rsfk, ?x9840 = 02p0tjr, ?x7219 = 0h1vg, ?x12868 = 03d49, ?x9795 = 05v_8y, ?x5451 = 05wvs, ?x7652 = 025s0s0, ?x3468 = 0cxn2, ?x9436 = 025sqz8, ?x9733 = 0h1tz, ?x9365 = 04k8n, ?x5337 = 06x4c, ?x12336 = 0f4l5, ?x6026 = 025sf8g, ?x13498 = 07q0m, ?x13545 = 01w_3, ?x2018 = 01sh2, ?x1960 = 07hnp, ?x12902 = 0fzjh, ?x5374 = 025s0zp, ?x9949 = 02kd0rh, ?x11758 = 0q01m, ?x10098 = 0h1_c, ?x6586 = 05gh50, ?x8487 = 014yzm, ?x5526 = 09pbb, ?x6517 = 02kd8zw, ?x7720 = 025s7x6, ?x11409 = 0h1yf, ?x14210 = 0f4k5, ?x10891 = 0g5gq, ?x9915 = 025tkqy, ?x7364 = 09gvd, ?x3469 = 0h1zw >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02kc008 nutrient! 033cnk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.906 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #14257-01cv3n PRED entity: 01cv3n PRED relation: location PRED expected values: 030qb3t => 153 concepts (117 used for prediction) PRED predicted values (max 10 best out of 263): 01x73 (0.42 #40225, 0.42 #18503, 0.41 #45857), 030qb3t (0.19 #7323, 0.15 #28242, 0.15 #4910), 02_286 (0.18 #28196, 0.14 #8081, 0.14 #7277), 0r02m (0.12 #714, 0.03 #4736, 0.03 #6346), 01m3b7 (0.12 #644, 0.01 #11906), 049kw (0.12 #540, 0.01 #11802), 0h3lt (0.12 #295, 0.01 #11557), 0gdk0 (0.12 #361, 0.01 #13231), 04rrd (0.11 #902, 0.02 #16185, 0.01 #21016), 0cr3d (0.10 #10602, 0.09 #1753, 0.06 #15427) >> Best rule #40225 for best value: >> intensional similarity = 4 >> extensional distance = 284 >> proper extension: 01k5t_3; 01trhmt; 01d_h; 01vzz1c; >> query: (?x680, ?x1755) <- category(?x680, ?x134), artists(?x302, ?x680), profession(?x680, ?x1032), origin(?x680, ?x1755) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #7323 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 01qn8k; 03q5dr; 044mvs; *> query: (?x680, 030qb3t) <- people(?x1816, ?x680), ?x1816 = 09vc4s, profession(?x680, ?x1032) *> conf = 0.19 ranks of expected_values: 2 EVAL 01cv3n location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 153.000 117.000 0.421 http://example.org/people/person/places_lived./people/place_lived/location #14256-01w3lzq PRED entity: 01w3lzq PRED relation: award PRED expected values: 01c4_6 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 331): 01bgqh (0.36 #2864, 0.32 #3672, 0.29 #2461), 01by1l (0.35 #5757, 0.33 #3742, 0.32 #2934), 0c4z8 (0.30 #3701, 0.27 #7731, 0.26 #3298), 01ck6h (0.29 #2541, 0.16 #3349, 0.15 #3752), 02nhxf (0.29 #502, 0.11 #5743, 0.11 #3728), 01d38g (0.25 #2849, 0.14 #431, 0.11 #2446), 03qbh5 (0.24 #2624, 0.20 #3835, 0.18 #3027), 01c99j (0.24 #10303, 0.18 #3855, 0.18 #12721), 0f4x7 (0.24 #1240, 0.07 #18169, 0.07 #39127), 04kxsb (0.24 #1336, 0.07 #7383, 0.06 #18265) >> Best rule #2864 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0lhn5; >> query: (?x5057, 01bgqh) <- influenced_by(?x5057, ?x9497), award_winner(?x8994, ?x9497), award(?x9497, ?x2634), origin(?x9497, ?x739) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1299 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 02c4s; *> query: (?x5057, 01c4_6) <- profession(?x5057, ?x131), people(?x5056, ?x5057), ?x5056 = 02g7sp *> conf = 0.12 ranks of expected_values: 63 EVAL 01w3lzq award 01c4_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 117.000 117.000 0.364 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14255-01f8gz PRED entity: 01f8gz PRED relation: language PRED expected values: 012w70 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 40): 012w70 (0.69 #56, 0.34 #55, 0.17 #9), 064_8sq (0.35 #571, 0.19 #18, 0.16 #515), 03h64 (0.34 #55, 0.08 #440, 0.04 #552), 06nm1 (0.30 #561, 0.12 #450, 0.11 #8), 04306rv (0.24 #557, 0.16 #118, 0.12 #446), 02bjrlw (0.16 #554, 0.12 #223, 0.12 #332), 02hxcvy (0.13 #30, 0.13 #88, 0.05 #415), 0459q4 (0.11 #33, 0.08 #91, 0.05 #418), 06b_j (0.08 #461, 0.07 #903, 0.07 #572), 0jzc (0.06 #569, 0.04 #623, 0.04 #292) >> Best rule #56 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 0crh5_f; 043sct5; 0bs8hvm; >> query: (?x1625, ?x3271) <- titles(?x3271, ?x1625), genre(?x1625, ?x53), film_release_region(?x1625, ?x94), language(?x148, ?x3271) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f8gz language 012w70 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.694 http://example.org/film/film/language #14254-012201 PRED entity: 012201 PRED relation: music! PRED expected values: 0b85mm => 127 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1000): 03hjv97 (0.07 #20202, 0.04 #2086, 0.02 #3096), 0140g4 (0.07 #20202, 0.03 #14, 0.02 #2034), 0jzw (0.07 #20202, 0.03 #70, 0.02 #2090), 07cyl (0.07 #20202, 0.03 #338, 0.02 #2358), 0dtfn (0.07 #20202, 0.03 #130, 0.02 #2150), 01_mdl (0.07 #20202, 0.03 #98, 0.02 #2118), 03s5lz (0.07 #20202, 0.03 #121, 0.02 #2141), 0y_yw (0.07 #20202, 0.03 #619, 0.02 #2639), 0168ls (0.07 #20202, 0.03 #151, 0.02 #2171), 097zcz (0.07 #20202, 0.03 #425, 0.02 #2445) >> Best rule #20202 for best value: >> intensional similarity = 3 >> extensional distance = 191 >> proper extension: 02qfhb; >> query: (?x8476, ?x188) <- music(?x6213, ?x8476), nominated_for(?x4867, ?x6213), nominated_for(?x4867, ?x188) >> conf = 0.07 => this is the best rule for 73 predicted values No rule for expected values ranks of expected_values: EVAL 012201 music! 0b85mm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 79.000 0.075 http://example.org/film/film/music #14253-059g4 PRED entity: 059g4 PRED relation: contains PRED expected values: 0lm0n 01gc8c => 153 concepts (76 used for prediction) PRED predicted values (max 10 best out of 2887): 09c7w0 (0.81 #23341, 0.79 #8753, 0.72 #8752), 02k8k (0.81 #23341, 0.79 #8753, 0.72 #8752), 0d04z6 (0.81 #23341, 0.79 #8753, 0.33 #649), 027rn (0.81 #23341, 0.79 #8753, 0.33 #1), 06s0l (0.81 #23341, 0.79 #8753, 0.33 #952), 06s6l (0.81 #23341, 0.79 #8753, 0.33 #225), 03_r3 (0.81 #23341, 0.79 #8753, 0.33 #37), 035yg (0.81 #23341, 0.79 #8753, 0.33 #1256), 0l3h (0.81 #23341, 0.79 #8753, 0.33 #716), 03gyl (0.81 #23341, 0.79 #8753, 0.33 #593) >> Best rule #23341 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0j0k; >> query: (?x8483, ?x47) <- countries_within(?x8483, ?x47), contains(?x8483, ?x10440), time_zones(?x10440, ?x2674) >> conf = 0.81 => this is the best rule for 14 predicted values *> Best rule #140055 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 01mk6; *> query: (?x8483, ?x481) <- contains(?x8483, ?x279), film_release_region(?x1535, ?x8483), contains(?x279, ?x481) *> conf = 0.64 ranks of expected_values: 55 EVAL 059g4 contains 01gc8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 153.000 76.000 0.805 http://example.org/location/location/contains EVAL 059g4 contains 0lm0n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 153.000 76.000 0.805 http://example.org/location/location/contains #14252-012z8_ PRED entity: 012z8_ PRED relation: artist! PRED expected values: 03gfvsz => 110 concepts (94 used for prediction) PRED predicted values (max 10 best out of 4): 03gfvsz (0.15 #43, 0.12 #91, 0.11 #1), 01fjfv (0.07 #2, 0.06 #44, 0.04 #276), 04y652m (0.04 #70, 0.04 #46, 0.03 #88), 04rqd (0.04 #47, 0.04 #279, 0.02 #386) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 01wv9xn; 01czx; 0134s5; 07yg2; 0394y; 0134tg; 0dw4g; 07h76; 07bzp; 08w4pm; ... >> query: (?x4576, 03gfvsz) <- artists(?x505, ?x4576), inductee(?x1091, ?x4576), ?x1091 = 0g2c8 >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012z8_ artist! 03gfvsz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 94.000 0.148 http://example.org/broadcast/content/artist #14251-06x4l_ PRED entity: 06x4l_ PRED relation: location PRED expected values: 0fpzwf => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 150): 02_286 (0.12 #36220, 0.11 #54719, 0.11 #51500), 030qb3t (0.09 #55570, 0.09 #8928, 0.09 #53961), 07b_l (0.07 #187, 0.01 #2599, 0.01 #3403), 0cr3d (0.06 #949, 0.05 #1753, 0.05 #3361), 05fkf (0.04 #38, 0.02 #8883, 0.01 #12903), 04jpl (0.04 #2429, 0.04 #7253, 0.04 #8862), 0h7h6 (0.04 #894, 0.03 #3306, 0.02 #90), 0r0m6 (0.04 #1022, 0.03 #3434, 0.02 #1826), 059rby (0.03 #1624, 0.03 #33786, 0.02 #49870), 03pzf (0.03 #2133, 0.03 #2937, 0.02 #1329) >> Best rule #36220 for best value: >> intensional similarity = 2 >> extensional distance = 1170 >> proper extension: 0443c; >> query: (?x2862, 02_286) <- award_winner(?x1232, ?x2862), student(?x3439, ?x2862) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06x4l_ location 0fpzwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 94.000 0.118 http://example.org/people/person/places_lived./people/place_lived/location #14250-0cwfgz PRED entity: 0cwfgz PRED relation: honored_for! PRED expected values: 07b1gq => 106 concepts (31 used for prediction) PRED predicted values (max 10 best out of 209): 02scbv (0.85 #2490, 0.85 #2489, 0.84 #1554), 0q9sg (0.85 #2490, 0.85 #2489, 0.84 #1554), 0946bb (0.85 #2490, 0.85 #2489, 0.84 #1554), 0cwfgz (0.75 #107, 0.67 #418, 0.67 #263), 07b1gq (0.67 #372, 0.67 #217, 0.59 #2800), 0dfw0 (0.06 #1170, 0.06 #3267, 0.05 #1326), 07gghl (0.06 #1204, 0.05 #1360, 0.05 #1672), 0fdv3 (0.06 #1123, 0.05 #1279, 0.05 #1591), 0kv2hv (0.06 #1102, 0.05 #1258, 0.05 #1570), 024mxd (0.06 #1149, 0.05 #1617, 0.05 #1461) >> Best rule #2490 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 02xhpl; 0q9jk; >> query: (?x6206, ?x5731) <- honored_for(?x6206, ?x5731), nominated_for(?x102, ?x6206), nominated_for(?x1561, ?x5731), honored_for(?x3639, ?x6206) >> conf = 0.85 => this is the best rule for 3 predicted values *> Best rule #372 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 069q4f; *> query: (?x6206, 07b1gq) <- music(?x6206, ?x3134), genre(?x6206, ?x53), honored_for(?x6206, ?x3330), ?x3330 = 0946bb *> conf = 0.67 ranks of expected_values: 5 EVAL 0cwfgz honored_for! 07b1gq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 106.000 31.000 0.850 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #14249-04gzd PRED entity: 04gzd PRED relation: country! PRED expected values: 01dys 01z27 06z6r 018w8 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 42): 06z6r (0.87 #314, 0.85 #146, 0.84 #818), 0w0d (0.75 #53, 0.75 #11, 0.68 #221), 02y8z (0.75 #15, 0.71 #99, 0.63 #225), 07gyv (0.75 #49, 0.65 #91, 0.64 #7), 07jbh (0.74 #106, 0.71 #64, 0.71 #22), 07bs0 (0.71 #96, 0.64 #54, 0.64 #12), 019tzd (0.68 #113, 0.64 #29, 0.61 #71), 01z27 (0.65 #97, 0.62 #139, 0.59 #307), 01gqfm (0.64 #80, 0.64 #38, 0.56 #164), 07rlg (0.61 #43, 0.56 #127, 0.54 #211) >> Best rule #314 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 06sff; >> query: (?x344, 06z6r) <- olympics(?x344, ?x784), country(?x766, ?x344), ?x784 = 018ctl >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8, 19, 36 EVAL 04gzd country! 018w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 113.000 113.000 0.870 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 04gzd country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.870 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 04gzd country! 01z27 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 113.000 113.000 0.870 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 04gzd country! 01dys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 113.000 113.000 0.870 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #14248-0jbp0 PRED entity: 0jbp0 PRED relation: film PRED expected values: 014lc_ => 93 concepts (57 used for prediction) PRED predicted values (max 10 best out of 827): 06qv_ (0.60 #78331, 0.40 #87233, 0.36 #97915), 06nr2h (0.15 #2509, 0.04 #4289, 0.04 #7849), 0cc7hmk (0.15 #2071, 0.04 #7411, 0.02 #12751), 01y9r2 (0.10 #3120, 0.04 #8460, 0.01 #13800), 05tgks (0.10 #2708, 0.02 #8048, 0.01 #13388), 09p4w8 (0.10 #2605, 0.02 #7945, 0.01 #13285), 078sj4 (0.10 #2231, 0.02 #7571, 0.01 #32492), 028kj0 (0.10 #3439, 0.02 #8779, 0.01 #23019), 0283_zv (0.10 #2063, 0.02 #7403), 02qr3k8 (0.06 #8403, 0.03 #13743, 0.02 #33324) >> Best rule #78331 for best value: >> intensional similarity = 3 >> extensional distance = 882 >> proper extension: 04wqr; 0f830f; 025p38; 08w7vj; 0bz5v2; 049k07; 02k6rq; 04smkr; 05wjnt; 06mmb; ... >> query: (?x10398, ?x10661) <- award_winner(?x5948, ?x10398), film(?x10398, ?x97), nominated_for(?x10398, ?x10661) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #64088 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 782 *> proper extension: 02wrhj; 01ry0f; 018fwv; *> query: (?x10398, 014lc_) <- actor(?x10661, ?x10398), film(?x10398, ?x2475), language(?x2475, ?x90) *> conf = 0.01 ranks of expected_values: 504 EVAL 0jbp0 film 014lc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 93.000 57.000 0.596 http://example.org/film/actor/film./film/performance/film #14247-060pl5 PRED entity: 060pl5 PRED relation: gender PRED expected values: 05zppz => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #15, 0.88 #9, 0.88 #19), 02zsn (0.46 #107, 0.46 #155, 0.46 #152) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 03j90; >> query: (?x10917, 05zppz) <- story_by(?x5713, ?x10917), award_winner(?x8059, ?x10917), award(?x1259, ?x8059) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 060pl5 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.895 http://example.org/people/person/gender #14246-0dc95 PRED entity: 0dc95 PRED relation: teams PRED expected values: 0jmj7 => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 256): 06rny (0.25 #103, 0.05 #1536, 0.04 #1894), 0713r (0.25 #76, 0.05 #1509, 0.04 #1867), 0x0d (0.11 #616, 0.05 #1691, 0.04 #2049), 0jm6n (0.08 #801, 0.03 #3666, 0.03 #4024), 05g3v (0.08 #755, 0.03 #3620, 0.03 #3978), 01k8vh (0.08 #982, 0.03 #3847, 0.02 #9217), 01y3c (0.08 #735, 0.03 #3600, 0.02 #8970), 01d6g (0.08 #907, 0.03 #4130, 0.02 #5562), 0ckf6 (0.08 #1033, 0.03 #4256, 0.02 #5330), 01z1r (0.08 #865, 0.03 #4088, 0.02 #5162) >> Best rule #103 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0r771; >> query: (?x2552, 06rny) <- location(?x496, ?x2552), category(?x2552, ?x134), featured_film_locations(?x12720, ?x2552), ?x12720 = 02fqxm >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dc95 teams 0jmj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 192.000 192.000 0.250 http://example.org/sports/sports_team_location/teams #14245-02gyl0 PRED entity: 02gyl0 PRED relation: profession PRED expected values: 02hrh1q 01d30f => 136 concepts (78 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.92 #2218, 0.91 #2659, 0.91 #3688), 01c72t (0.56 #2815, 0.32 #5020, 0.28 #1492), 0nbcg (0.51 #1059, 0.51 #1206, 0.51 #1500), 01d_h8 (0.48 #3239, 0.39 #5591, 0.34 #7061), 0dxtg (0.40 #3246, 0.34 #747, 0.34 #600), 02jknp (0.40 #3241, 0.30 #5593, 0.26 #742), 0n1h (0.38 #2363, 0.20 #1040, 0.20 #452), 016z4k (0.38 #1032, 0.37 #1179, 0.37 #1473), 0cbd2 (0.27 #594, 0.20 #153, 0.18 #1770), 039v1 (0.24 #1505, 0.24 #1211, 0.22 #2387) >> Best rule #2218 for best value: >> intensional similarity = 3 >> extensional distance = 384 >> proper extension: 02zq43; 01j5x6; 02jyhv; 04bbv7; 038nv6; >> query: (?x4655, 02hrh1q) <- people(?x1050, ?x4655), actor(?x416, ?x4655), profession(?x4655, ?x131) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 55 EVAL 02gyl0 profession 01d30f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 136.000 78.000 0.920 http://example.org/people/person/profession EVAL 02gyl0 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 78.000 0.920 http://example.org/people/person/profession #14244-02sh8y PRED entity: 02sh8y PRED relation: student! PRED expected values: 041y2 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 27): 02822 (0.06 #965, 0.05 #776, 0.05 #466), 062z7 (0.05 #519, 0.03 #643, 0.02 #581), 05qjt (0.05 #502, 0.02 #626), 03qsdpk (0.04 #970, 0.03 #347, 0.03 #781), 03g3w (0.04 #518, 0.02 #955, 0.02 #766), 036hv (0.04 #505, 0.02 #629), 01zc2w (0.03 #982, 0.02 #793, 0.02 #919), 04rlf (0.03 #420, 0.01 #792, 0.01 #544), 0fdys (0.02 #774, 0.02 #900, 0.02 #963), 04rjg (0.02 #511, 0.01 #635) >> Best rule #965 for best value: >> intensional similarity = 4 >> extensional distance = 251 >> proper extension: 04cw0j; >> query: (?x5813, 02822) <- student(?x9847, ?x5813), gender(?x5813, ?x231), languages(?x5813, ?x254), ?x254 = 02h40lc >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02sh8y student! 041y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 124.000 0.055 http://example.org/education/field_of_study/students_majoring./education/education/student #14243-07g2b PRED entity: 07g2b PRED relation: profession PRED expected values: 02hv44_ => 164 concepts (160 used for prediction) PRED predicted values (max 10 best out of 91): 02hrh1q (0.74 #1633, 0.71 #15165, 0.68 #19723), 0kyk (0.62 #1355, 0.55 #1208, 0.55 #1060), 01c72t (0.61 #2084, 0.56 #759, 0.50 #465), 09jwl (0.56 #754, 0.50 #460, 0.50 #19), 0dxtg (0.54 #2220, 0.53 #4133, 0.53 #1632), 01d_h8 (0.53 #1625, 0.50 #1919, 0.50 #1478), 0d8qb (0.48 #883, 0.20 #1178, 0.18 #1256), 02jknp (0.44 #1479, 0.35 #5739, 0.30 #4274), 02hv44_ (0.42 #3736, 0.35 #5739, 0.33 #1823), 0nbcg (0.38 #473, 0.35 #5739, 0.35 #2092) >> Best rule #1633 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 04wqr; 049gc; >> query: (?x587, 02hrh1q) <- influenced_by(?x8382, ?x587), influenced_by(?x118, ?x587), languages(?x118, ?x254), award_winner(?x3849, ?x587), nominated_for(?x8382, ?x2519) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #3736 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 0jz9f; 025jfl; *> query: (?x587, 02hv44_) <- award(?x587, ?x7606), award(?x6866, ?x7606), ?x6866 = 03m9c8 *> conf = 0.42 ranks of expected_values: 9 EVAL 07g2b profession 02hv44_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 164.000 160.000 0.737 http://example.org/people/person/profession #14242-0gmgwnv PRED entity: 0gmgwnv PRED relation: currency PRED expected values: 09nqf => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.79 #134, 0.78 #176, 0.78 #106), 02l6h (0.03 #88, 0.03 #102, 0.02 #53), 01nv4h (0.03 #93, 0.03 #100, 0.02 #107), 0kz1h (0.02 #40), 088n7 (0.01 #49), 02gsvk (0.01 #195, 0.01 #202, 0.01 #209) >> Best rule #134 for best value: >> intensional similarity = 3 >> extensional distance = 401 >> proper extension: 05css_; >> query: (?x6176, 09nqf) <- music(?x6176, ?x669), film(?x949, ?x6176), friend(?x949, ?x950) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gmgwnv currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.792 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #14241-0lphb PRED entity: 0lphb PRED relation: locations! PRED expected values: 0b_6pv => 152 concepts (137 used for prediction) PRED predicted values (max 10 best out of 112): 0b_6jz (0.22 #2516, 0.16 #3763, 0.15 #2764), 0b_6rk (0.19 #1659, 0.18 #667, 0.17 #2776), 0b_75k (0.19 #2531, 0.19 #1290, 0.13 #3778), 0bzrsh (0.19 #2560, 0.15 #2808, 0.14 #1939), 0b_6qj (0.17 #2549, 0.15 #1680, 0.14 #2797), 0b_6xf (0.17 #2584, 0.15 #1343, 0.12 #2088), 0b_6v_ (0.17 #2546, 0.13 #933, 0.12 #1305), 0b_6pv (0.16 #2561, 0.15 #700, 0.15 #2809), 0b_6zk (0.16 #2512, 0.14 #2760, 0.13 #1643), 0b_6x2 (0.16 #2515, 0.12 #1894, 0.12 #2763) >> Best rule #2516 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 0pc6x; 010016; 0qpsn; >> query: (?x6952, 0b_6jz) <- contains(?x94, ?x6952), locations(?x11210, ?x6952), team(?x11210, ?x2303) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #2561 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 0pc6x; 010016; 0qpsn; *> query: (?x6952, 0b_6pv) <- contains(?x94, ?x6952), locations(?x11210, ?x6952), team(?x11210, ?x2303) *> conf = 0.16 ranks of expected_values: 8 EVAL 0lphb locations! 0b_6pv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 152.000 137.000 0.219 http://example.org/time/event/locations #14240-09v8clw PRED entity: 09v8clw PRED relation: honored_for! PRED expected values: 0hhtgcw => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 91): 0bzm81 (0.09 #16, 0.08 #138, 0.01 #748), 02yw5r (0.09 #8, 0.08 #130, 0.01 #740), 0hhtgcw (0.08 #195, 0.05 #1171, 0.04 #1415), 0hr6lkl (0.08 #134, 0.03 #1110, 0.03 #1354), 0gmdkyy (0.08 #146, 0.02 #268, 0.01 #512), 0hndn2q (0.08 #154, 0.02 #1008, 0.01 #5888), 0h_cssd (0.08 #144, 0.01 #510, 0.01 #2706), 0275n3y (0.08 #430, 0.05 #674, 0.05 #1162), 02jp5r (0.07 #302, 0.03 #546, 0.03 #1400), 0bvhz9 (0.04 #724, 0.04 #1212, 0.03 #1822) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 03wjm2; >> query: (?x12423, 0bzm81) <- film(?x1469, ?x12423), ?x1469 = 05sq84, film(?x2156, ?x12423), film_release_distribution_medium(?x12423, ?x81) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #195 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 04x4gw; *> query: (?x12423, 0hhtgcw) <- film(?x1469, ?x12423), ?x1469 = 05sq84, film(?x2156, ?x12423), genre(?x12423, ?x225) *> conf = 0.08 ranks of expected_values: 3 EVAL 09v8clw honored_for! 0hhtgcw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 89.000 0.091 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #14239-0ws7 PRED entity: 0ws7 PRED relation: team! PRED expected values: 059yj => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 8): 059yj (0.89 #69, 0.88 #53, 0.85 #133), 0h69c (0.20 #223, 0.18 #359, 0.17 #327), 021q23 (0.15 #192, 0.03 #168, 0.02 #425), 0355pl (0.09 #380, 0.08 #420, 0.07 #364), 07y9k (0.09 #525, 0.08 #541, 0.05 #365), 01ddbl (0.05 #224, 0.04 #512, 0.04 #408), 0356lc (0.05 #538, 0.05 #522, 0.02 #546), 03zv9 (0.05 #539, 0.04 #515, 0.04 #531) >> Best rule #69 for best value: >> intensional similarity = 10 >> extensional distance = 16 >> proper extension: 05tg3; >> query: (?x7078, 059yj) <- position(?x7078, ?x1717), position(?x7078, ?x1240), ?x1240 = 023wyl, ?x1717 = 02g_6x, position(?x7078, ?x1114), position_s(?x7078, ?x2247), draft(?x7078, ?x465), ?x2247 = 01_9c1, position_s(?x1114, ?x706), position_s(?x1516, ?x1114) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ws7 team! 059yj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.889 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #14238-07ssc PRED entity: 07ssc PRED relation: country! PRED expected values: 01_c4 01rwf_ => 244 concepts (213 used for prediction) PRED predicted values (max 10 best out of 601): 04jpl (0.67 #4959, 0.38 #15178, 0.32 #4337), 034cm (0.67 #4959, 0.32 #4337, 0.20 #23853), 0j5g9 (0.50 #14867, 0.46 #33476, 0.45 #30682), 0978r (0.38 #15178, 0.32 #4337, 0.31 #43090), 0h924 (0.38 #15178, 0.32 #4337, 0.31 #43090), 0127c4 (0.38 #15178, 0.32 #4337, 0.31 #43090), 0n048 (0.38 #15178, 0.32 #4337, 0.31 #43090), 036wy (0.38 #15178, 0.32 #4337, 0.25 #2095), 03lrc (0.38 #15178, 0.32 #4337, 0.20 #23853), 013t2y (0.32 #4337, 0.25 #2075, 0.25 #1765) >> Best rule #4959 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01mzwp; >> query: (?x512, ?x362) <- contains(?x512, ?x362), combatants(?x512, ?x94), taxonomy(?x362, ?x939) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #4337 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 06mx8; *> query: (?x512, ?x362) <- contains(?x512, ?x362), region(?x54, ?x512), titles(?x512, ?x144) *> conf = 0.32 ranks of expected_values: 49, 553 EVAL 07ssc country! 01rwf_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 244.000 213.000 0.671 http://example.org/location/administrative_division/country EVAL 07ssc country! 01_c4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 244.000 213.000 0.671 http://example.org/location/administrative_division/country #14237-02g2wv PRED entity: 02g2wv PRED relation: award! PRED expected values: 0dzlbx => 48 concepts (21 used for prediction) PRED predicted values (max 10 best out of 780): 08zrbl (0.22 #18370, 0.22 #16328, 0.22 #13266), 07cz2 (0.22 #18370, 0.22 #16328, 0.22 #13266), 07f_t4 (0.22 #18370, 0.22 #16328, 0.22 #13266), 08720 (0.22 #18370, 0.22 #16328, 0.22 #13266), 03k8th (0.22 #18370, 0.22 #16328, 0.22 #13266), 016dj8 (0.22 #18370, 0.22 #16328, 0.22 #13266), 035bcl (0.22 #18370, 0.22 #16328, 0.22 #13266), 03r0g9 (0.22 #18370, 0.22 #16328, 0.22 #13266), 05qbckf (0.22 #18370, 0.22 #16328, 0.22 #13266), 0443v1 (0.22 #18370, 0.22 #16328, 0.22 #13266) >> Best rule #18370 for best value: >> intensional similarity = 4 >> extensional distance = 221 >> proper extension: 02py_sj; >> query: (?x5734, ?x9017) <- award(?x6994, ?x5734), nominated_for(?x5734, ?x9017), nominated_for(?x6211, ?x6994), nominated_for(?x2774, ?x9017) >> conf = 0.22 => this is the best rule for 12 predicted values No rule for expected values ranks of expected_values: EVAL 02g2wv award! 0dzlbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 21.000 0.223 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #14236-047vnkj PRED entity: 047vnkj PRED relation: film_release_region PRED expected values: 05r4w 09pmkv 06c1y => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 152): 09c7w0 (0.92 #3495, 0.92 #10483, 0.92 #9522), 05r4w (0.84 #966, 0.82 #1206, 0.78 #242), 09pmkv (0.59 #257, 0.43 #981, 0.33 #1221), 01p1v (0.57 #997, 0.49 #1237, 0.44 #273), 01ls2 (0.53 #973, 0.51 #249, 0.44 #1213), 06c1y (0.48 #266, 0.42 #990, 0.34 #1230), 07f1x (0.46 #323, 0.39 #1047, 0.35 #1287), 0hzlz (0.41 #254, 0.26 #1218, 0.26 #978), 077qn (0.37 #1022, 0.27 #1262, 0.24 #298), 02_286 (0.30 #13, 0.14 #133, 0.05 #1217) >> Best rule #3495 for best value: >> intensional similarity = 4 >> extensional distance = 504 >> proper extension: 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 034qmv; 0g22z; 018js4; 02vxq9m; 0b2v79; ... >> query: (?x5271, 09c7w0) <- film_release_region(?x5271, ?x142), film(?x100, ?x5271), genre(?x5271, ?x53), featured_film_locations(?x5271, ?x108) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #966 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 139 *> proper extension: 014lc_; 0b76d_m; 0gx1bnj; 0h1cdwq; 0g5qs2k; 0dscrwf; 0c40vxk; 0gx9rvq; 0gkz15s; 087wc7n; ... *> query: (?x5271, 05r4w) <- film_release_region(?x5271, ?x1174), film(?x100, ?x5271), genre(?x5271, ?x53), ?x1174 = 047yc *> conf = 0.84 ranks of expected_values: 2, 3, 6 EVAL 047vnkj film_release_region 06c1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 103.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047vnkj film_release_region 09pmkv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047vnkj film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14235-01spm PRED entity: 01spm PRED relation: religion! PRED expected values: 0cmpn 070c93 => 30 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1509): 032l1 (0.50 #1320, 0.25 #3457, 0.22 #17038), 01_x6d (0.38 #2496, 0.22 #5694, 0.20 #14208), 082_p (0.33 #746, 0.25 #3947, 0.25 #1810), 01lwx (0.33 #985, 0.25 #4186, 0.25 #3116), 0mb5x (0.33 #7090, 0.25 #9223, 0.23 #11353), 034bs (0.33 #329, 0.25 #2460, 0.22 #6723), 040_t (0.33 #530, 0.22 #17038, 0.15 #9593), 0d1_f (0.33 #263, 0.14 #13044, 0.12 #3464), 07g2b (0.33 #35, 0.14 #9590, 0.12 #3236), 084nh (0.33 #937, 0.14 #9590, 0.12 #4138) >> Best rule #1320 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 02rxj; >> query: (?x13975, 032l1) <- religion(?x5004, ?x13975), influenced_by(?x11097, ?x5004), influenced_by(?x7861, ?x5004), influenced_by(?x6457, ?x5004), influenced_by(?x3325, ?x5004), place_of_birth(?x5004, ?x8828), ?x11097 = 02wh0, religion(?x7861, ?x13061), gender(?x5004, ?x231), ?x3325 = 073v6, ?x6457 = 03_87 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #10657 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 11 *> proper extension: 05g9_; *> query: (?x13975, ?x111) <- religion(?x512, ?x13975), film_release_region(?x607, ?x512), country(?x136, ?x512), contains(?x512, ?x362), ?x607 = 02x3lt7, nationality(?x647, ?x512), nationality(?x111, ?x512), award(?x647, ?x384) *> conf = 0.01 ranks of expected_values: 1473 EVAL 01spm religion! 070c93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 23.000 0.500 http://example.org/people/person/religion EVAL 01spm religion! 0cmpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 23.000 0.500 http://example.org/people/person/religion #14234-01s7ns PRED entity: 01s7ns PRED relation: artist! PRED expected values: 064r9cb => 112 concepts (77 used for prediction) PRED predicted values (max 10 best out of 109): 043g7l (0.44 #172, 0.20 #312, 0.20 #32), 033hn8 (0.33 #154, 0.20 #14, 0.15 #1134), 03mp8k (0.33 #207, 0.20 #67, 0.15 #1187), 015_1q (0.22 #160, 0.20 #20, 0.20 #6186), 03rhqg (0.20 #16, 0.16 #1416, 0.15 #6182), 0181dw (0.20 #42, 0.13 #1582, 0.12 #1162), 02p11jq (0.20 #13, 0.08 #1413, 0.08 #713), 07gqbk (0.20 #98, 0.03 #1218, 0.03 #518), 041bnw (0.20 #69, 0.03 #3433, 0.02 #4413), 011k1h (0.17 #430, 0.15 #1130, 0.12 #1550) >> Best rule #172 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01w61th; 01wbl_r; 01wj18h; 01w58n3; 03c602; >> query: (?x11026, 043g7l) <- location(?x11026, ?x3501), artists(?x12082, ?x11026), ?x12082 = 08vlns >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1084 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 0197tq; 025xt8y; 03f5spx; 01vv7sc; 018y2s; 0l12d; 012zng; 04mn81; 01wsl7c; 05qw5; ... *> query: (?x11026, 064r9cb) <- location(?x11026, ?x3501), artists(?x302, ?x11026), ?x302 = 016clz *> conf = 0.03 ranks of expected_values: 52 EVAL 01s7ns artist! 064r9cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 112.000 77.000 0.444 http://example.org/music/record_label/artist #14233-02c7k4 PRED entity: 02c7k4 PRED relation: nominated_for! PRED expected values: 02hsq3m => 67 concepts (58 used for prediction) PRED predicted values (max 10 best out of 274): 09tqxt (0.68 #2873, 0.67 #4314, 0.66 #6469), 02g3v6 (0.50 #978, 0.18 #1217, 0.08 #2414), 0l8z1 (0.33 #53, 0.30 #1966, 0.25 #531), 0gq_v (0.33 #976, 0.29 #1933, 0.23 #2412), 07bdd_ (0.33 #293, 0.09 #2686, 0.07 #6765), 05f4m9q (0.33 #251, 0.09 #2644, 0.07 #6723), 05b4l5x (0.33 #245, 0.07 #1201, 0.07 #5751), 07cbcy (0.33 #304, 0.07 #4858, 0.07 #10127), 0gq9h (0.32 #1977, 0.31 #3657, 0.31 #2456), 0k611 (0.28 #1987, 0.25 #2466, 0.23 #3187) >> Best rule #2873 for best value: >> intensional similarity = 4 >> extensional distance = 477 >> proper extension: 04glx0; >> query: (?x6256, ?x1723) <- nominated_for(?x4850, ?x6256), award(?x6256, ?x1723), award(?x4850, ?x1079), category(?x4850, ?x134) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1225 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 189 *> proper extension: 0fdv3; 05wp1p; 0184tc; 0dgq80b; *> query: (?x6256, 02hsq3m) <- film(?x2825, ?x6256), genre(?x6256, ?x1510), religion(?x2825, ?x2694), ?x1510 = 01hmnh *> conf = 0.18 ranks of expected_values: 34 EVAL 02c7k4 nominated_for! 02hsq3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 67.000 58.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14232-02hxhz PRED entity: 02hxhz PRED relation: film_release_region PRED expected values: 09c7w0 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 121): 09c7w0 (0.79 #901, 0.77 #721, 0.77 #1081), 0f8l9c (0.33 #1827, 0.32 #3083, 0.30 #1110), 059j2 (0.33 #1840, 0.32 #3096, 0.30 #2199), 0d0vqn (0.32 #3063, 0.31 #1807, 0.30 #2525), 06mkj (0.31 #1871, 0.31 #3127, 0.30 #2589), 03gj2 (0.31 #1832, 0.29 #3088, 0.28 #2550), 0jgd (0.31 #1800, 0.28 #2518, 0.28 #2159), 07ssc (0.30 #1819, 0.29 #3075, 0.29 #2693), 0345h (0.30 #1842, 0.28 #3098, 0.28 #2560), 02vzc (0.30 #1865, 0.27 #3121, 0.27 #1148) >> Best rule #901 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 0ds35l9; 011yxg; 0czyxs; 01k1k4; 0ds11z; 01vksx; 0bwfwpj; 0872p_c; 053rxgm; 02rv_dz; ... >> query: (?x821, 09c7w0) <- nominated_for(?x350, ?x821), region(?x821, ?x512), genre(?x821, ?x258), produced_by(?x821, ?x1335) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hxhz film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.792 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14231-02l7c8 PRED entity: 02l7c8 PRED relation: titles PRED expected values: 0456zg => 40 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1577): 09m6kg (0.39 #21450, 0.39 #18391, 0.39 #12258), 01qbg5 (0.39 #21450, 0.39 #18391, 0.39 #12258), 02z0f6l (0.39 #21450, 0.39 #18391, 0.39 #12258), 0fjyzt (0.39 #21450, 0.39 #18391, 0.39 #12258), 0dr_4 (0.39 #21450, 0.39 #18391, 0.39 #12258), 02cbhg (0.39 #21450, 0.39 #18391, 0.39 #12258), 03lvwp (0.39 #21450, 0.39 #18391, 0.39 #12258), 02__34 (0.39 #21450, 0.39 #18391, 0.39 #12258), 0bdjd (0.39 #21450, 0.39 #18391, 0.39 #12258), 043n1r5 (0.39 #21450, 0.39 #18391, 0.39 #12258) >> Best rule #21450 for best value: >> intensional similarity = 7 >> extensional distance = 32 >> proper extension: 0hn10; 017fp; 0219x_; 0glj9q; 015w9s; 01chg; 06qm3; 06l3bl; 0h9qh; 04t2t; ... >> query: (?x1403, ?x1402) <- genre(?x11348, ?x1403), genre(?x7941, ?x1403), genre(?x1402, ?x1403), titles(?x1403, ?x308), award_winner(?x7941, ?x3808), nominated_for(?x1107, ?x11348), award(?x1402, ?x289) >> conf = 0.39 => this is the best rule for 239 predicted values *> Best rule #18390 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 16 *> proper extension: 04xvlr; 03npn; 02n4kr; 01jfsb; 0jtdp; 01hmnh; 03mqtr; 09blyk; 0c3351; 09q17; ... *> query: (?x1403, ?x1066) <- genre(?x7941, ?x1403), genre(?x1066, ?x1403), titles(?x1403, ?x308), award_winner(?x7941, ?x3808), genre(?x2078, ?x1403), film(?x1065, ?x1066) *> conf = 0.37 ranks of expected_values: 403 EVAL 02l7c8 titles 0456zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 40.000 17.000 0.390 http://example.org/media_common/netflix_genre/titles #14230-0czp_ PRED entity: 0czp_ PRED relation: ceremony PRED expected values: 0fz20l => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 131): 0bzm81 (0.89 #284, 0.88 #153, 0.83 #22), 0n8_m93 (0.89 #373, 0.88 #242, 0.83 #111), 0bvfqq (0.89 #295, 0.88 #164, 0.83 #33), 02yxh9 (0.89 #359, 0.88 #228, 0.83 #97), 0bc773 (0.89 #315, 0.88 #184, 0.83 #53), 02yw5r (0.89 #274, 0.88 #143, 0.83 #12), 05q7cj (0.89 #353, 0.88 #222, 0.83 #91), 04110lv (0.88 #236, 0.83 #367, 0.83 #105), 0fzrtf (0.88 #191, 0.83 #322, 0.83 #60), 02yvhx (0.88 #206, 0.83 #337, 0.76 #5504) >> Best rule #284 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 018wng; >> query: (?x8153, 0bzm81) <- ceremony(?x8153, ?x9400), ceremony(?x8153, ?x6323), ?x6323 = 05hmp6, award(?x1172, ?x8153), ceremony(?x2222, ?x9400), ?x2222 = 0gs96 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #52 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 10 *> proper extension: 0gq_d; *> query: (?x8153, 0fz20l) <- ceremony(?x8153, ?x9400), ceremony(?x8153, ?x7589), ceremony(?x8153, ?x6323), ?x6323 = 05hmp6, award(?x1172, ?x8153), ?x9400 = 0d__c3, ?x7589 = 0fz0c2 *> conf = 0.83 ranks of expected_values: 27 EVAL 0czp_ ceremony 0fz20l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 58.000 58.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony #14229-095x_ PRED entity: 095x_ PRED relation: role PRED expected values: 03bx0bm => 112 concepts (70 used for prediction) PRED predicted values (max 10 best out of 128): 03bx0bm (0.42 #1184, 0.41 #1315, 0.40 #1575), 05148p4 (0.27 #1635, 0.27 #1618, 0.26 #1570), 018vs (0.27 #1618, 0.23 #272, 0.20 #1566), 05r5c (0.27 #1618, 0.20 #389, 0.20 #388), 02hnl (0.27 #1618, 0.20 #389, 0.20 #388), 06ncr (0.27 #1618, 0.20 #389, 0.20 #388), 07kc_ (0.27 #1618, 0.20 #389, 0.20 #388), 03f5mt (0.27 #1618, 0.20 #389, 0.20 #388), 01vdm0 (0.20 #1684, 0.20 #1683, 0.11 #2138), 05842k (0.20 #1684, 0.20 #1683, 0.11 #2138) >> Best rule #1184 for best value: >> intensional similarity = 5 >> extensional distance = 117 >> proper extension: 01wbgdv; 02dbp7; 03lgg; 02lvtb; 01vzz1c; 01mbwlb; >> query: (?x8035, 03bx0bm) <- artists(?x671, ?x8035), profession(?x8035, ?x131), location(?x8035, ?x2254), role(?x8035, ?x227), category(?x8035, ?x134) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 095x_ role 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 70.000 0.420 http://example.org/music/group_member/membership./music/group_membership/role #14228-0d05q4 PRED entity: 0d05q4 PRED relation: location! PRED expected values: 01pqy_ => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1266): 09yrh (0.14 #914, 0.06 #10990, 0.06 #66408), 03rl84 (0.09 #2881, 0.08 #5400, 0.07 #25552), 03hzl42 (0.08 #5937, 0.07 #899, 0.07 #8456), 0prfz (0.08 #5087, 0.07 #49, 0.06 #12644), 032r1 (0.07 #2316, 0.06 #14911, 0.05 #19949), 05d1y (0.07 #1682, 0.06 #11758, 0.05 #21834), 01w02sy (0.07 #596, 0.06 #13191, 0.05 #30824), 0465_ (0.07 #1297, 0.04 #6335, 0.04 #279619), 0227tr (0.07 #480, 0.04 #5518, 0.04 #279619), 0134w7 (0.07 #163, 0.04 #5201, 0.04 #279619) >> Best rule #914 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 05qhw; 0f8l9c; >> query: (?x4092, 09yrh) <- combatants(?x13022, ?x4092), countries_spoken_in(?x5359, ?x4092), ?x13022 = 03gqgt3 >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d05q4 location! 01pqy_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 126.000 0.143 http://example.org/people/person/places_lived./people/place_lived/location #14227-01ty7ll PRED entity: 01ty7ll PRED relation: nationality PRED expected values: 09c7w0 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 128): 09c7w0 (0.89 #1805, 0.83 #301, 0.83 #1102), 07ssc (0.52 #5740, 0.50 #5034, 0.10 #7871), 02jx1 (0.52 #5740, 0.50 #5034, 0.10 #2440), 0ndh6 (0.33 #9976, 0.32 #8967, 0.25 #8664), 04ych (0.33 #9976, 0.32 #8967, 0.25 #8664), 01n7q (0.32 #7152, 0.31 #6950, 0.01 #7958), 0345h (0.18 #231, 0.06 #931, 0.05 #2842), 03rk0 (0.10 #6996, 0.08 #7501, 0.08 #7902), 06bnz (0.09 #241, 0.03 #841, 0.02 #2750), 0h7x (0.09 #235, 0.02 #6680, 0.02 #7389) >> Best rule #1805 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 09h_q; 05d1y; >> query: (?x543, 09c7w0) <- place_of_death(?x543, ?x242), location(?x543, ?x4356), profession(?x543, ?x1032), dog_breed(?x4356, ?x1706) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ty7ll nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.894 http://example.org/people/person/nationality #14226-02x201b PRED entity: 02x201b PRED relation: award! PRED expected values: 0146pg 01vvycq 02bn75 => 51 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2977): 02zft0 (0.78 #6739, 0.77 #64022, 0.68 #13477), 01l3mk3 (0.71 #2300, 0.50 #5669, 0.22 #3369), 0146pg (0.71 #144, 0.50 #3513, 0.22 #3369), 01x6v6 (0.57 #1964, 0.50 #5333, 0.16 #8703), 02cyfz (0.57 #576, 0.50 #3945, 0.16 #7315), 01tc9r (0.57 #1091, 0.43 #4460, 0.12 #7830), 02jxkw (0.57 #2228, 0.43 #5597, 0.08 #8967), 01wd9lv (0.57 #1862, 0.36 #5231, 0.25 #11970), 05mt6w (0.57 #2093, 0.36 #5462, 0.08 #8832), 020fgy (0.57 #2625, 0.29 #5994, 0.22 #3369) >> Best rule #6739 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 025m8l; 026mfs; 054ks3; 099vwn; 02x17c2; 025m98; 026mmy; >> query: (?x7099, ?x6011) <- award(?x4850, ?x7099), award(?x999, ?x7099), ?x4850 = 016szr, award_winner(?x1869, ?x999), award_nominee(?x999, ?x3170), award_winner(?x7099, ?x6011) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 0l8z1; 0gqz2; 054krc; 025m8y; 02gdjb; *> query: (?x7099, 0146pg) <- award(?x4850, ?x7099), award(?x1940, ?x7099), award(?x999, ?x7099), ?x4850 = 016szr, award_winner(?x1869, ?x999), award_nominee(?x999, ?x3170), ?x1940 = 04zwjd *> conf = 0.71 ranks of expected_values: 3, 36, 45 EVAL 02x201b award! 02bn75 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 51.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x201b award! 01vvycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 51.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x201b award! 0146pg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14225-01wmxfs PRED entity: 01wmxfs PRED relation: award PRED expected values: 099jhq 09sb52 031b3h => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 258): 0f4x7 (0.70 #28420, 0.70 #26082, 0.70 #24133), 01d38g (0.70 #28420, 0.70 #26082, 0.70 #24133), 027b9j5 (0.70 #28420, 0.70 #26082, 0.70 #24133), 027c95y (0.70 #28420, 0.70 #26082, 0.70 #24133), 09sb52 (0.44 #40, 0.34 #7825, 0.34 #21056), 05p09zm (0.31 #121, 0.16 #6738, 0.16 #8295), 057xs89 (0.31 #154, 0.13 #26472, 0.13 #31145), 04ljl_l (0.28 #3, 0.09 #5841, 0.09 #781), 01by1l (0.27 #9840, 0.21 #1666, 0.20 #14511), 05zr6wv (0.25 #16, 0.18 #794, 0.17 #2739) >> Best rule #28420 for best value: >> intensional similarity = 2 >> extensional distance = 1587 >> proper extension: 04glx0; >> query: (?x828, ?x567) <- award_winner(?x567, ?x828), award_nominee(?x193, ?x828) >> conf = 0.70 => this is the best rule for 4 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 015076; *> query: (?x828, 09sb52) <- participant(?x91, ?x828), award(?x828, ?x3209), ?x3209 = 02w9sd7 *> conf = 0.44 ranks of expected_values: 5, 14, 40 EVAL 01wmxfs award 031b3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 106.000 106.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01wmxfs award 09sb52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 106.000 106.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01wmxfs award 099jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 106.000 106.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14224-0btpm6 PRED entity: 0btpm6 PRED relation: prequel PRED expected values: 02fqrf => 82 concepts (43 used for prediction) PRED predicted values (max 10 best out of 29): 0btpm6 (0.09 #137, 0.08 #499, 0.08 #318), 01gwk3 (0.09 #110), 031778 (0.08 #219, 0.01 #762), 06bc59 (0.08 #344), 033qdy (0.08 #296), 07cyl (0.08 #239), 017kz7 (0.03 #687, 0.01 #1050), 0fdv3 (0.03 #577, 0.01 #940), 014lc_ (0.03 #544, 0.01 #725), 048vhl (0.03 #701) >> Best rule #137 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 023p7l; 025rvx0; >> query: (?x7493, 0btpm6) <- film(?x4835, ?x7493), ?x4835 = 01wy5m, film_release_region(?x7493, ?x87) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0btpm6 prequel 02fqrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 43.000 0.091 http://example.org/film/film/prequel #14223-0b_c7 PRED entity: 0b_c7 PRED relation: nominated_for PRED expected values: 01cssf => 99 concepts (39 used for prediction) PRED predicted values (max 10 best out of 426): 0p9rz (0.77 #63239, 0.60 #14593, 0.57 #16215), 0cz_ym (0.20 #274, 0.07 #3516, 0.03 #6759), 09q5w2 (0.20 #153, 0.03 #3395, 0.02 #6638), 084qpk (0.20 #1735, 0.03 #3356, 0.02 #6599), 04f6df0 (0.20 #1248, 0.01 #9354), 05z43v (0.20 #1211, 0.01 #9317), 02xs6_ (0.20 #782, 0.01 #8888), 063ykwt (0.20 #573, 0.01 #8679), 04kzqz (0.20 #294, 0.01 #8400), 0qm8b (0.20 #225, 0.01 #8331) >> Best rule #63239 for best value: >> intensional similarity = 3 >> extensional distance = 930 >> proper extension: 0hm0k; >> query: (?x1742, ?x9261) <- award_winner(?x9261, ?x1742), titles(?x4757, ?x9261), genre(?x573, ?x4757) >> conf = 0.77 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0b_c7 nominated_for 01cssf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 39.000 0.774 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14222-048n7 PRED entity: 048n7 PRED relation: combatants PRED expected values: 07t65 0ctw_b => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 303): 0d060g (0.50 #681, 0.46 #113, 0.41 #1142), 088q1s (0.50 #681, 0.41 #1142, 0.39 #4599), 01k31p (0.50 #681, 0.41 #1142, 0.39 #4599), 03_lf (0.50 #681, 0.41 #1142, 0.39 #4599), 04xzm (0.50 #681, 0.41 #1142, 0.39 #4599), 0c_jc (0.50 #681, 0.41 #1142, 0.39 #4599), 0ctw_b (0.46 #113, 0.33 #700, 0.33 #356), 015qh (0.46 #113, 0.33 #132, 0.33 #19), 06f32 (0.46 #113, 0.33 #375, 0.33 #35), 05qhw (0.46 #113, 0.33 #121, 0.33 #8) >> Best rule #681 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 0727h; >> query: (?x9203, ?x279) <- entity_involved(?x9203, ?x390), entity_involved(?x9203, ?x279), locations(?x9203, ?x9954), film_release_region(?x542, ?x390), combatants(?x1603, ?x390), ?x542 = 0djb3vw, ?x1603 = 06bnz, country(?x150, ?x390) >> conf = 0.50 => this is the best rule for 6 predicted values *> Best rule #113 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 081pw; *> query: (?x9203, ?x279) <- combatants(?x9203, ?x5738), combatants(?x9203, ?x1499), films(?x9203, ?x8551), combatants(?x279, ?x5738), nominated_for(?x1254, ?x8551), ?x1254 = 02z0dfh, film_release_region(?x4024, ?x1499), country(?x668, ?x1499), ?x4024 = 0n04r *> conf = 0.46 ranks of expected_values: 7, 130 EVAL 048n7 combatants 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 75.000 75.000 0.500 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 048n7 combatants 07t65 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 75.000 75.000 0.500 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #14221-03t95n PRED entity: 03t95n PRED relation: film_crew_role PRED expected values: 02r96rf => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.81 #764, 0.75 #525, 0.67 #559), 01pvkk (0.32 #771, 0.31 #1050, 0.31 #668), 02rh1dz (0.23 #9, 0.22 #111, 0.21 #770), 02ynfr (0.22 #775, 0.21 #536, 0.19 #1054), 015h31 (0.18 #769, 0.17 #425, 0.17 #355), 0215hd (0.18 #778, 0.15 #17, 0.15 #675), 0d2b38 (0.16 #785, 0.15 #126, 0.15 #231), 01xy5l_ (0.15 #773, 0.13 #738, 0.13 #1052), 089g0h (0.15 #779, 0.13 #1058, 0.12 #744), 094hwz (0.12 #47, 0.12 #2696, 0.11 #220) >> Best rule #764 for best value: >> intensional similarity = 4 >> extensional distance = 334 >> proper extension: 01xbxn; >> query: (?x6615, 02r96rf) <- language(?x6615, ?x254), genre(?x6615, ?x225), film_crew_role(?x6615, ?x2154), ?x2154 = 01vx2h >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t95n film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.812 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14220-0dzlbx PRED entity: 0dzlbx PRED relation: story_by PRED expected values: 046_v => 102 concepts (76 used for prediction) PRED predicted values (max 10 best out of 127): 02xnjd (0.19 #3647, 0.07 #3862, 0.05 #7304), 01twdk (0.19 #3647, 0.07 #3862, 0.05 #7304), 04hw4b (0.12 #122, 0.07 #336, 0.05 #1193), 0184dt (0.12 #35, 0.07 #249, 0.05 #1106), 0ky1 (0.12 #173, 0.02 #1244, 0.02 #3605), 01rlxt (0.12 #94, 0.01 #1594, 0.01 #2022), 0n8bn (0.12 #120, 0.01 #1620), 079ws (0.07 #343, 0.05 #771, 0.04 #986), 040_9 (0.07 #268, 0.05 #696, 0.04 #911), 0jpdn (0.07 #368, 0.03 #3586, 0.02 #2082) >> Best rule #3647 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 0kv2hv; 02s4l6; 0bmpm; 0b2qtl; 09fc83; 0277j40; 043h78; >> query: (?x4998, ?x4731) <- nominated_for(?x298, ?x4998), executive_produced_by(?x4998, ?x4731), story_by(?x4998, ?x96), genre(?x4998, ?x225) >> conf = 0.19 => this is the best rule for 2 predicted values *> Best rule #599 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 04svwx; *> query: (?x4998, 046_v) <- country(?x4998, ?x94), genre(?x4998, ?x225), person(?x4998, ?x96), ?x225 = 02kdv5l *> conf = 0.06 ranks of expected_values: 13 EVAL 0dzlbx story_by 046_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 102.000 76.000 0.193 http://example.org/film/film/story_by #14219-02t_vx PRED entity: 02t_vx PRED relation: award_nominee PRED expected values: 01vyv9 034zc0 => 69 concepts (28 used for prediction) PRED predicted values (max 10 best out of 812): 02ct_k (0.83 #2331, 0.81 #65262, 0.81 #30298), 02p7_k (0.83 #2331, 0.81 #30298, 0.81 #18642), 01vyv9 (0.83 #2331, 0.81 #30298, 0.81 #18642), 034zc0 (0.79 #1358, 0.24 #20974, 0.17 #65263), 02t_vx (0.71 #1752, 0.24 #20974, 0.17 #65263), 07h565 (0.24 #20974, 0.17 #65263, 0.10 #30299), 02yxwd (0.24 #20974, 0.17 #65263, 0.07 #984), 0gnbw (0.24 #20974, 0.17 #65263, 0.07 #1641), 022g44 (0.24 #20974, 0.17 #65263, 0.07 #1174), 0hvb2 (0.24 #20974, 0.17 #65263, 0.04 #23702) >> Best rule #2331 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 050t68; 03q95r; 0739z6; >> query: (?x7923, ?x3660) <- award_nominee(?x7923, ?x4137), ?x4137 = 0410cp, award_nominee(?x3660, ?x7923) >> conf = 0.83 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 4 EVAL 02t_vx award_nominee 034zc0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 28.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 02t_vx award_nominee 01vyv9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 28.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14218-01tt27 PRED entity: 01tt27 PRED relation: category PRED expected values: 08mbj5d => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.64 #53, 0.64 #52, 0.64 #59) >> Best rule #53 for best value: >> intensional similarity = 22 >> extensional distance = 158 >> proper extension: 0gvbw; 0168nq; 04f0xq; >> query: (?x10021, ?x134) <- industry(?x10021, ?x10022), industry(?x11304, ?x10022), industry(?x11273, ?x10022), industry(?x11070, ?x10022), industry(?x6717, ?x10022), industry(?x4878, ?x10022), industry(?x3253, ?x10022), currency(?x11273, ?x170), category(?x11304, ?x134), service_location(?x11070, ?x94), contact_category(?x11070, ?x897), company(?x4682, ?x3253), company(?x346, ?x3253), ?x170 = 09nqf, state_province_region(?x4878, ?x1227), ?x4682 = 0dq_5, citytown(?x6717, ?x3125), service_language(?x6717, ?x90), service_location(?x6717, ?x205), ?x346 = 060c4, citytown(?x4878, ?x5783), ?x94 = 09c7w0 >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tt27 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 32.000 0.637 http://example.org/common/topic/webpage./common/webpage/category #14217-0k_q_ PRED entity: 0k_q_ PRED relation: location_of_ceremony! PRED expected values: 01933d => 178 concepts (141 used for prediction) PRED predicted values (max 10 best out of 188): 0h7pj (0.25 #456, 0.08 #2742, 0.03 #6548), 03f6fl0 (0.25 #377, 0.05 #4690, 0.03 #6975), 01vzxld (0.25 #475, 0.05 #4788, 0.03 #7833), 01vv6xv (0.25 #490, 0.03 #7848, 0.01 #9373), 015p37 (0.25 #484, 0.03 #7842, 0.01 #9367), 017b2p (0.25 #458, 0.03 #7816, 0.01 #9341), 0dx_q (0.25 #436, 0.03 #7794, 0.01 #9319), 0gnbw (0.25 #427, 0.03 #7785, 0.01 #9310), 0gs1_ (0.25 #414, 0.03 #7772, 0.01 #9297), 0lh0c (0.25 #406, 0.03 #7764, 0.01 #9289) >> Best rule #456 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0cv3w; 0r00l; >> query: (?x2495, 0h7pj) <- place_of_birth(?x2103, ?x2495), featured_film_locations(?x3482, ?x2495), ?x3482 = 017z49, location(?x4647, ?x2495) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #6533 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 0r4xt; 0r4wn; 0r4z7; 02fzs; *> query: (?x2495, 01933d) <- contains(?x1227, ?x2495), location_of_ceremony(?x566, ?x2495), ?x1227 = 01n7q *> conf = 0.03 ranks of expected_values: 130 EVAL 0k_q_ location_of_ceremony! 01933d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 178.000 141.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #14216-0pvms PRED entity: 0pvms PRED relation: film! PRED expected values: 03fbb6 => 114 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1207): 01j5sd (0.25 #3518, 0.03 #51277, 0.02 #57508), 014v6f (0.17 #964, 0.12 #3040, 0.05 #17575), 015grj (0.17 #154, 0.12 #2230, 0.04 #29069), 07r1h (0.17 #1085, 0.12 #3161, 0.02 #11466), 0341n5 (0.17 #1745, 0.12 #3821), 07r_dg (0.17 #1714, 0.12 #3790), 02t_st (0.17 #1284, 0.05 #9589, 0.03 #28276), 01_p6t (0.17 #1018, 0.04 #5171, 0.02 #11399), 0686zv (0.17 #519, 0.04 #15053, 0.03 #27511), 013knm (0.17 #632, 0.04 #6861, 0.02 #11013) >> Best rule #3518 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 016fyc; 051zy_b; 091rc5; 0sxgv; >> query: (?x2565, 01j5sd) <- film_release_distribution_medium(?x2565, ?x81), genre(?x2565, ?x162), film(?x2564, ?x2565), film(?x1424, ?x2565), ?x81 = 029j_, ?x2564 = 02lf1j, award_nominee(?x1424, ?x230) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #29069 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 67 *> proper extension: 01ln5z; 0pc62; 0cwy47; 092vkg; 033g4d; 02c6d; 0jqn5; 05p3738; 075wx7_; 01pgp6; ... *> query: (?x2565, ?x968) <- film_release_distribution_medium(?x2565, ?x81), film_release_region(?x2565, ?x94), film(?x1676, ?x2565), film_crew_role(?x2565, ?x137), award_nominee(?x1676, ?x968), language(?x2565, ?x254) *> conf = 0.04 ranks of expected_values: 374 EVAL 0pvms film! 03fbb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 114.000 82.000 0.250 http://example.org/film/actor/film./film/performance/film #14215-035yn8 PRED entity: 035yn8 PRED relation: language PRED expected values: 04306rv 0jzc 064_8sq => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 33): 064_8sq (0.15 #1395, 0.14 #714, 0.14 #995), 06nm1 (0.12 #703, 0.12 #871, 0.11 #984), 02bjrlw (0.10 #58, 0.08 #1772, 0.07 #1713), 04306rv (0.10 #865, 0.10 #697, 0.09 #1775), 06b_j (0.08 #883, 0.08 #715, 0.06 #996), 03_9r (0.06 #870, 0.05 #758, 0.05 #3142), 0jzc (0.05 #712, 0.05 #880, 0.04 #1224), 0653m (0.04 #704, 0.04 #648, 0.04 #184), 012w70 (0.04 #649, 0.03 #127, 0.03 #873), 0t_2 (0.04 #186, 0.04 #244, 0.03 #128) >> Best rule #1395 for best value: >> intensional similarity = 4 >> extensional distance = 553 >> proper extension: 04z_x4v; >> query: (?x1744, 064_8sq) <- nominated_for(?x500, ?x1744), nominated_for(?x500, ?x4559), category_of(?x500, ?x3459), ?x4559 = 0ccd3x >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 7 EVAL 035yn8 language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.146 http://example.org/film/film/language EVAL 035yn8 language 0jzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 86.000 0.146 http://example.org/film/film/language EVAL 035yn8 language 04306rv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.146 http://example.org/film/film/language #14214-02k8k PRED entity: 02k8k PRED relation: jurisdiction_of_office! PRED expected values: 060c4 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 41): 060c4 (0.98 #1322, 0.85 #508, 0.73 #1058), 060bp (0.71 #67, 0.65 #287, 0.65 #1057), 0f6c3 (0.51 #447, 0.36 #1151, 0.28 #1767), 0pqc5 (0.50 #268, 0.39 #1236, 0.37 #1258), 0fkvn (0.46 #443, 0.34 #2184, 0.33 #1147), 09n5b9 (0.43 #451, 0.30 #1155, 0.25 #1661), 0p5vf (0.24 #34, 0.17 #100, 0.17 #122), 04syw (0.16 #1304, 0.16 #1348, 0.16 #2602), 0377k9 (0.16 #2602, 0.14 #81, 0.12 #37), 01zq91 (0.16 #2602, 0.12 #36, 0.12 #476) >> Best rule #1322 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 0cx2r; >> query: (?x6691, 060c4) <- jurisdiction_of_office(?x265, ?x6691), company(?x265, ?x6092), ?x6092 = 0hm0k >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02k8k jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.980 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #14213-01f6x7 PRED entity: 01f6x7 PRED relation: genre PRED expected values: 02kdv5l => 122 concepts (97 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.84 #1, 0.79 #3372, 0.75 #233), 02kdv5l (0.75 #699, 0.68 #351, 0.57 #583), 06n90 (0.43 #4197, 0.39 #708, 0.25 #2450), 01hmnh (0.34 #2455, 0.32 #4202, 0.30 #365), 0lsxr (0.34 #2214, 0.31 #125, 0.25 #473), 03q4nz (0.31 #134, 0.07 #250, 0.05 #8510), 02l7c8 (0.29 #1523, 0.28 #3386, 0.28 #9324), 04xvlr (0.27 #3373, 0.19 #118, 0.19 #10364), 02n4kr (0.25 #2213, 0.13 #10137, 0.13 #10370), 060__y (0.21 #3387, 0.16 #16, 0.16 #10378) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 03kg2v; 02q87z6; 01qb559; >> query: (?x5313, 07s9rl0) <- film_release_region(?x5313, ?x94), production_companies(?x5313, ?x7980), film_crew_role(?x5313, ?x137), ?x7980 = 020h2v >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #699 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 95 *> proper extension: 02vw1w2; *> query: (?x5313, 02kdv5l) <- genre(?x5313, ?x812), genre(?x5313, ?x811), ?x811 = 03k9fj, film(?x147, ?x5313), ?x812 = 01jfsb *> conf = 0.75 ranks of expected_values: 2 EVAL 01f6x7 genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 97.000 0.842 http://example.org/film/film/genre #14212-0dm5l PRED entity: 0dm5l PRED relation: category PRED expected values: 08mbj5d => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.93 #20, 0.92 #14, 0.92 #18) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 013v5j; 09nhvw; 0f8grf; >> query: (?x2854, 08mbj5d) <- artists(?x5876, ?x2854), origin(?x2854, ?x362), artist(?x2149, ?x2854), ?x5876 = 0ggx5q >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dm5l category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.932 http://example.org/common/topic/webpage./common/webpage/category #14211-0y1rf PRED entity: 0y1rf PRED relation: state PRED expected values: 059rby => 158 concepts (151 used for prediction) PRED predicted values (max 10 best out of 70): 059rby (0.42 #8118, 0.23 #7258, 0.05 #1877), 01x73 (0.42 #8118, 0.04 #2321, 0.04 #2492), 01n7q (0.35 #184, 0.22 #524, 0.20 #1549), 09c7w0 (0.21 #2217, 0.19 #5461, 0.16 #1108), 0fc2c (0.19 #2644, 0.19 #2985, 0.18 #1194), 07b_l (0.11 #719, 0.11 #294, 0.11 #124), 05tbn (0.11 #40, 0.07 #1915, 0.04 #2769), 07h34 (0.11 #41, 0.04 #807, 0.04 #1321), 05mph (0.11 #55, 0.03 #1845, 0.02 #2101), 02xry (0.06 #1135, 0.05 #1902, 0.05 #1647) >> Best rule #8118 for best value: >> intensional similarity = 2 >> extensional distance = 266 >> proper extension: 01xhb_; >> query: (?x11086, ?x335) <- citytown(?x502, ?x11086), state_province_region(?x502, ?x335) >> conf = 0.42 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0y1rf state 059rby CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 151.000 0.420 http://example.org/base/biblioness/bibs_location/state #14210-077rj PRED entity: 077rj PRED relation: split_to PRED expected values: 01pfr3 => 97 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1): 01jpmpv (0.01 #1191) >> Best rule #1191 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 0dhqyw; >> query: (?x5896, 01jpmpv) <- gender(?x5896, ?x231), artists(?x307, ?x5896), titles(?x307, ?x11356), music(?x11356, ?x7168) >> conf = 0.01 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 077rj split_to 01pfr3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 30.000 0.014 http://example.org/dataworld/gardening_hint/split_to #14209-01p1v PRED entity: 01p1v PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.89 #83, 0.89 #73, 0.86 #45) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 05rznz; >> query: (?x1917, 0hzc9wc) <- organization(?x1917, ?x127), official_language(?x1917, ?x2502), administrative_parent(?x1917, ?x551) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p1v administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 170.000 0.888 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #14208-06mkj PRED entity: 06mkj PRED relation: film_release_region! PRED expected values: 0b4lkx => 212 concepts (96 used for prediction) PRED predicted values (max 10 best out of 472): 025ts_z (0.33 #633, 0.33 #114, 0.25 #504), 05fcbk7 (0.33 #556, 0.33 #37, 0.25 #427), 02psgq (0.33 #77, 0.25 #725, 0.25 #467), 03nqnnk (0.33 #87, 0.25 #735, 0.25 #477), 0299hs (0.33 #45, 0.25 #435, 0.17 #1729), 02bg8v (0.33 #18, 0.25 #408, 0.17 #537), 047p7fr (0.33 #40, 0.25 #430, 0.17 #559), 05z7c (0.33 #23, 0.25 #413, 0.17 #542), 048tv9 (0.33 #110, 0.25 #500, 0.17 #629), 02z0f6l (0.33 #96, 0.25 #486, 0.17 #615) >> Best rule #633 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0v74; >> query: (?x2152, 025ts_z) <- combatants(?x2391, ?x2152), ?x2391 = 0d06vc, entity_involved(?x9939, ?x2152) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06mkj film_release_region! 0b4lkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 212.000 96.000 0.333 http://example.org/film/film/runtime./film/film_cut/film_release_region #14207-0gj50 PRED entity: 0gj50 PRED relation: award PRED expected values: 02p_7cr => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 176): 02p_04b (0.78 #231, 0.67 #629, 0.64 #922), 02p_7cr (0.78 #231, 0.64 #922, 0.56 #485), 02pzz3p (0.78 #231, 0.64 #922, 0.46 #2995), 0m7yy (0.46 #5655, 0.44 #5885, 0.39 #1512), 0ck27z (0.22 #1454, 0.18 #1685, 0.18 #2836), 027gs1_ (0.19 #1099, 0.17 #1559, 0.16 #5932), 0cjyzs (0.18 #5837, 0.17 #5607, 0.15 #6989), 0gkr9q (0.17 #1580, 0.16 #1811, 0.16 #2962), 09qj50 (0.16 #5792, 0.15 #5562, 0.13 #6944), 0fbtbt (0.15 #5677, 0.15 #1534, 0.14 #1765) >> Best rule #231 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01b66d; >> query: (?x4011, ?x588) <- nominated_for(?x438, ?x4011), tv_program(?x4146, ?x4011), ?x4146 = 0g28b1, nominated_for(?x588, ?x4011) >> conf = 0.78 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0gj50 award 02p_7cr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.778 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #14206-01ym8l PRED entity: 01ym8l PRED relation: industry PRED expected values: 0sydc => 226 concepts (226 used for prediction) PRED predicted values (max 10 best out of 48): 02vxn (0.59 #4326, 0.56 #4562, 0.54 #4704), 03qh03g (0.47 #428, 0.38 #616, 0.28 #2872), 04rlf (0.41 #1329, 0.16 #4337, 0.15 #4573), 01mw1 (0.33 #6914, 0.25 #7760, 0.24 #4184), 020mfr (0.27 #6929, 0.22 #4199, 0.20 #7775), 0hz28 (0.25 #640, 0.24 #4608, 0.21 #452), 0sydc (0.25 #643, 0.24 #4608, 0.16 #455), 02h400t (0.25 #307, 0.19 #354, 0.14 #871), 01mf0 (0.24 #4608, 0.20 #77, 0.12 #2004), 0jzgd (0.24 #4608, 0.14 #135, 0.11 #182) >> Best rule #4326 for best value: >> intensional similarity = 4 >> extensional distance = 85 >> proper extension: 027jw0c; 02x2097; 01dv21; 018tnx; >> query: (?x7151, 02vxn) <- industry(?x7151, ?x6575), major_field_of_study(?x2948, ?x6575), major_field_of_study(?x6575, ?x2606), major_field_of_study(?x1368, ?x6575) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #643 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0g5lhl7; 0206k5; *> query: (?x7151, 0sydc) <- industry(?x7151, ?x6575), industry(?x1908, ?x6575), company(?x346, ?x7151), ?x1908 = 0l8sx *> conf = 0.25 ranks of expected_values: 7 EVAL 01ym8l industry 0sydc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 226.000 226.000 0.586 http://example.org/business/business_operation/industry #14205-0784v1 PRED entity: 0784v1 PRED relation: gender PRED expected values: 05zppz => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #39, 0.91 #47, 0.91 #27), 02zsn (0.46 #147, 0.25 #14, 0.24 #50) >> Best rule #39 for best value: >> intensional similarity = 6 >> extensional distance = 69 >> proper extension: 0c_md_; >> query: (?x1935, 05zppz) <- athlete(?x471, ?x1935), place_of_birth(?x1935, ?x11783), sport(?x8387, ?x471), team(?x60, ?x8387), athlete(?x471, ?x2387), film(?x2387, ?x148) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0784v1 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.915 http://example.org/people/person/gender #14204-0cc56 PRED entity: 0cc56 PRED relation: place_of_death! PRED expected values: 019z7q => 126 concepts (113 used for prediction) PRED predicted values (max 10 best out of 798): 04dyqk (0.25 #1378, 0.01 #17829, 0.01 #20818), 07tvwy (0.25 #1355, 0.01 #17806, 0.01 #20795), 029m83 (0.25 #1127, 0.01 #17578, 0.01 #20567), 03fvqg (0.25 #816, 0.01 #17267, 0.01 #20256), 02x2t07 (0.14 #45648, 0.10 #48640, 0.06 #3739), 01l3mk3 (0.14 #45648, 0.10 #48640, 0.06 #3739), 01vrlqd (0.14 #45648, 0.06 #3739, 0.05 #4103), 02sjf5 (0.14 #45648, 0.06 #3739, 0.05 #3774), 01gzm2 (0.14 #45648, 0.06 #3739, 0.05 #3794), 01jrs46 (0.14 #45648, 0.06 #3739, 0.05 #4256) >> Best rule #1378 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 027l4q; >> query: (?x1131, 04dyqk) <- location(?x8587, ?x1131), location(?x8375, ?x1131), student(?x5614, ?x8587), ?x8375 = 0q9zc >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cc56 place_of_death! 019z7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 113.000 0.250 http://example.org/people/deceased_person/place_of_death #14203-02xlf PRED entity: 02xlf PRED relation: genre! PRED expected values: 017gm7 031786 => 65 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1877): 034qmv (0.60 #13045, 0.50 #5597, 0.50 #3736), 01kff7 (0.60 #13248, 0.50 #3939, 0.33 #217), 01f6x7 (0.60 #13973, 0.50 #4664, 0.33 #942), 01bl7g (0.60 #14001, 0.50 #4692, 0.33 #970), 043sct5 (0.60 #13793, 0.50 #4484, 0.33 #762), 01zfzb (0.60 #13977, 0.50 #4668, 0.33 #946), 06w99h3 (0.60 #13057, 0.50 #3748, 0.33 #26), 0p9tm (0.60 #14434, 0.50 #5125, 0.33 #1403), 027pfg (0.50 #6839, 0.50 #4978, 0.40 #14287), 0436yk (0.50 #3984, 0.40 #13293, 0.33 #18877) >> Best rule #13045 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0hfjk; >> query: (?x6647, 034qmv) <- genre(?x4610, ?x6647), genre(?x2470, ?x6647), ?x2470 = 01f7kl, nominated_for(?x1194, ?x4610), film_release_region(?x4610, ?x87), award(?x4610, ?x198), honored_for(?x1193, ?x4610) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #5803 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 060__y; *> query: (?x6647, 017gm7) <- genre(?x4998, ?x6647), genre(?x4610, ?x6647), genre(?x2470, ?x6647), ?x4610 = 017jd9, film(?x2182, ?x2470), currency(?x2470, ?x170), film_release_region(?x4998, ?x87) *> conf = 0.50 ranks of expected_values: 114, 146 EVAL 02xlf genre! 031786 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 65.000 57.000 0.600 http://example.org/film/film/genre EVAL 02xlf genre! 017gm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 65.000 57.000 0.600 http://example.org/film/film/genre #14202-01k_mc PRED entity: 01k_mc PRED relation: artist! PRED expected values: 0n85g => 101 concepts (65 used for prediction) PRED predicted values (max 10 best out of 96): 03mp8k (0.33 #67, 0.14 #207, 0.14 #627), 043g7l (0.33 #31, 0.14 #171, 0.10 #1293), 015_1q (0.23 #2124, 0.20 #1281, 0.19 #1561), 016ckq (0.17 #43, 0.14 #183, 0.13 #603), 06q07 (0.17 #50, 0.14 #190, 0.01 #470), 0181dw (0.17 #42, 0.14 #602, 0.12 #1164), 01f_3w (0.17 #34, 0.07 #594, 0.06 #314), 03rhqg (0.15 #575, 0.14 #1137, 0.14 #1277), 0g768 (0.15 #597, 0.13 #1159, 0.13 #1299), 01cl0d (0.14 #195, 0.05 #615, 0.05 #4129) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0j1yf; 01wv9p; 09889g; 01bmlb; >> query: (?x5904, 03mp8k) <- actor(?x5529, ?x5904), award(?x5904, ?x1801), ?x1801 = 01c92g >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #623 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 108 *> proper extension: 017yfz; 01ydzx; 03f3_p3; 01wg25j; 020_4z; 01pgk0; 0ql36; *> query: (?x5904, 0n85g) <- profession(?x5904, ?x131), artists(?x3928, ?x5904), ?x3928 = 0gywn *> conf = 0.12 ranks of expected_values: 11 EVAL 01k_mc artist! 0n85g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 101.000 65.000 0.333 http://example.org/music/record_label/artist #14201-08n__5 PRED entity: 08n__5 PRED relation: award PRED expected values: 03ccq3s => 152 concepts (124 used for prediction) PRED predicted values (max 10 best out of 291): 054ks3 (0.51 #6572, 0.31 #6170, 0.21 #2552), 01bgqh (0.38 #6073, 0.23 #17731, 0.22 #17329), 0fbtbt (0.37 #5858, 0.35 #4652, 0.32 #5054), 01by1l (0.34 #6141, 0.29 #2523, 0.29 #17799), 054krc (0.33 #6519, 0.20 #6117, 0.11 #17775), 0gkvb7 (0.31 #3645, 0.25 #1233, 0.12 #9273), 0c4z8 (0.29 #6504, 0.23 #10926, 0.23 #6102), 0l8z1 (0.28 #6496, 0.14 #6094, 0.08 #17752), 0fc9js (0.28 #3832, 0.20 #1420, 0.14 #10666), 09sb52 (0.27 #31803, 0.24 #43463, 0.23 #41855) >> Best rule #6572 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 01vs14j; 02r4qs; 05pq9; 02v3yy; 01vvdm; 01jrvr6; 07hgkd; 029h45; 077rj; 018gqj; ... >> query: (?x5820, 054ks3) <- type_of_union(?x5820, ?x566), award(?x5820, ?x1323), profession(?x5820, ?x1032), ?x1323 = 0gqz2 >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #4217 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 03jl0_; 0d_rw; *> query: (?x5820, 03ccq3s) <- program_creator(?x12535, ?x5820), genre(?x12535, ?x2480), titles(?x2480, ?x9441), ?x9441 = 02qdrjx *> conf = 0.24 ranks of expected_values: 14 EVAL 08n__5 award 03ccq3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 152.000 124.000 0.506 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14200-024hbv PRED entity: 024hbv PRED relation: actor PRED expected values: 0807ml => 86 concepts (59 used for prediction) PRED predicted values (max 10 best out of 869): 011_3s (0.73 #21373, 0.39 #9297, 0.38 #25089), 01hmb_ (0.73 #21373, 0.39 #9297, 0.38 #25089), 030znt (0.73 #21373, 0.39 #9297, 0.37 #17655), 02b29 (0.40 #1861, 0.39 #6507, 0.36 #6506), 05gnf (0.40 #1861, 0.39 #9297, 0.37 #17655), 0dzf_ (0.39 #9297, 0.38 #25089, 0.37 #17655), 01v80y (0.39 #9297, 0.38 #25089, 0.37 #17655), 03pmty (0.20 #79, 0.02 #6587, 0.02 #12162), 01ft2l (0.20 #285, 0.02 #1216, 0.02 #8653), 0c3p7 (0.20 #503, 0.02 #2364, 0.02 #3292) >> Best rule #21373 for best value: >> intensional similarity = 4 >> extensional distance = 152 >> proper extension: 0123qq; 0gxsh4; >> query: (?x12105, ?x3267) <- actor(?x12105, ?x965), genre(?x12105, ?x53), nominated_for(?x3267, ?x12105), actor(?x5047, ?x3267) >> conf = 0.73 => this is the best rule for 3 predicted values *> Best rule #1440 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 07s8z_l; 01j95; *> query: (?x12105, 0807ml) <- program(?x8231, ?x12105), genre(?x12105, ?x53), titles(?x2008, ?x12105), award_winner(?x12105, ?x965) *> conf = 0.01 ranks of expected_values: 615 EVAL 024hbv actor 0807ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 86.000 59.000 0.731 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #14199-03lvwp PRED entity: 03lvwp PRED relation: genre PRED expected values: 060__y => 91 concepts (78 used for prediction) PRED predicted values (max 10 best out of 87): 01jfsb (0.45 #132, 0.38 #252, 0.32 #3151), 05p553 (0.37 #9278, 0.35 #7958, 0.34 #5071), 02kdv5l (0.33 #482, 0.30 #3140, 0.28 #4585), 03k9fj (0.28 #492, 0.21 #7366, 0.21 #3873), 0lsxr (0.26 #128, 0.21 #852, 0.20 #1094), 060__y (0.22 #2432, 0.20 #860, 0.20 #617), 01hmnh (0.21 #7012, 0.19 #498, 0.16 #9292), 082gq (0.19 #1962, 0.19 #995, 0.18 #1841), 017fp (0.16 #15, 0.14 #2431, 0.11 #3275), 04xvh5 (0.16 #34, 0.13 #274, 0.12 #2450) >> Best rule #132 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 03z9585; >> query: (?x6020, 01jfsb) <- genre(?x6020, ?x53), language(?x6020, ?x90), ?x90 = 02bjrlw, produced_by(?x6020, ?x5527) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2432 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 582 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x6020, 060__y) <- titles(?x53, ?x6020), titles(?x53, ?x1863), ?x1863 = 04qw17 *> conf = 0.22 ranks of expected_values: 6 EVAL 03lvwp genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 91.000 78.000 0.452 http://example.org/film/film/genre #14198-01k3s2 PRED entity: 01k3s2 PRED relation: school_type PRED expected values: 05jxkf => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.73 #100, 0.64 #196, 0.53 #76), 05pcjw (0.32 #73, 0.25 #361, 0.24 #1563), 01rs41 (0.30 #1567, 0.25 #125, 0.25 #1399), 07tf8 (0.22 #297, 0.21 #81, 0.20 #369), 01_9fk (0.17 #122, 0.13 #578, 0.13 #1251), 02p0qmm (0.10 #2214, 0.10 #1853, 0.08 #418), 04qbv (0.10 #2214, 0.10 #1853, 0.08 #136), 01_srz (0.10 #2214, 0.10 #1853, 0.06 #1252), 04399 (0.10 #2214, 0.10 #1853, 0.03 #855), 06cs1 (0.10 #2214, 0.10 #1853, 0.02 #174) >> Best rule #100 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 05gm16l; >> query: (?x4342, 05jxkf) <- category(?x4342, ?x134), ?x134 = 08mbj5d, contains(?x279, ?x4342), ?x279 = 0d060g, colors(?x4342, ?x332) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k3s2 school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.727 http://example.org/education/educational_institution/school_type #14197-076689 PRED entity: 076689 PRED relation: film PRED expected values: 083skw => 128 concepts (71 used for prediction) PRED predicted values (max 10 best out of 932): 03rg2b (0.12 #1093, 0.07 #13623, 0.02 #29734), 0bbgly (0.12 #1738, 0.03 #14268, 0.01 #25009), 0gndh (0.12 #1333, 0.03 #13863), 0872p_c (0.11 #9124, 0.07 #7334, 0.07 #18074), 0jvt9 (0.10 #13069, 0.03 #29180, 0.03 #30970), 04tng0 (0.09 #3058, 0.03 #13798, 0.01 #24539), 0bmhn (0.09 #3415, 0.01 #14155), 0cbn7c (0.09 #3160, 0.01 #13900), 0kbf1 (0.09 #2695, 0.01 #13435), 035yn8 (0.09 #2058, 0.01 #12798) >> Best rule #1093 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0bc71w; 03g62; >> query: (?x11606, 03rg2b) <- student(?x741, ?x11606), type_of_union(?x11606, ?x566), place_of_death(?x11606, ?x5895), ?x741 = 01w3v >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 076689 film 083skw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 71.000 0.125 http://example.org/film/actor/film./film/performance/film #14196-016z9n PRED entity: 016z9n PRED relation: nominated_for! PRED expected values: 02ch1w => 45 concepts (23 used for prediction) PRED predicted values (max 10 best out of 372): 06r_by (0.38 #9288, 0.03 #5962, 0.02 #8284), 04fyhv (0.37 #20899), 02zfdp (0.33 #1886, 0.10 #11611, 0.07 #46447), 02rf1y (0.33 #1182, 0.10 #11611, 0.07 #46447), 05th8t (0.33 #546, 0.10 #11611, 0.07 #46447), 03mcwq3 (0.33 #524, 0.10 #11611, 0.07 #46447), 0f6_dy (0.33 #426, 0.10 #11611, 0.07 #46447), 05ml_s (0.33 #145, 0.10 #11611, 0.07 #46447), 02bfmn (0.33 #31, 0.10 #11611, 0.07 #46447), 0cv9fc (0.33 #2201) >> Best rule #9288 for best value: >> intensional similarity = 2 >> extensional distance = 314 >> proper extension: 0bs8hvm; >> query: (?x2336, ?x6062) <- country(?x2336, ?x94), cinematography(?x2336, ?x6062) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #41798 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1232 *> proper extension: 0170z3; 02d413; 0b76d_m; 014_x2; 0ds35l9; 015qsq; 0d90m; 03qcfvw; 0m313; 02y_lrp; ... *> query: (?x2336, ?x5840) <- film(?x5840, ?x2336), titles(?x53, ?x2336), award_nominee(?x4103, ?x5840) *> conf = 0.26 ranks of expected_values: 15 EVAL 016z9n nominated_for! 02ch1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 45.000 23.000 0.382 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14195-02grdc PRED entity: 02grdc PRED relation: ceremony PRED expected values: 019bk0 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 125): 01bx35 (0.54 #754, 0.31 #1629, 0.30 #1754), 019bk0 (0.54 #763, 0.30 #1638, 0.30 #1763), 0bzm81 (0.33 #18, 0.27 #3754, 0.27 #3628), 0bzjvm (0.33 #97, 0.27 #3754, 0.27 #3628), 0bzmt8 (0.33 #85, 0.27 #3754, 0.27 #3628), 0fk0xk (0.33 #68, 0.27 #3754, 0.27 #3628), 0bz6sb (0.33 #55, 0.27 #3754, 0.27 #3628), 0fz0c2 (0.33 #92, 0.27 #3754, 0.27 #3628), 0bc773 (0.33 #45, 0.25 #420, 0.21 #4005), 03tn9w (0.33 #81, 0.25 #456, 0.21 #4005) >> Best rule #754 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 07n52; 02xzd9; >> query: (?x594, 01bx35) <- category_of(?x594, ?x2421), category_of(?x1088, ?x2421), disciplines_or_subjects(?x1088, ?x8681) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #763 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 125 *> proper extension: 07n52; 02xzd9; *> query: (?x594, 019bk0) <- category_of(?x594, ?x2421), category_of(?x1088, ?x2421), disciplines_or_subjects(?x1088, ?x8681) *> conf = 0.54 ranks of expected_values: 2 EVAL 02grdc ceremony 019bk0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 42.000 42.000 0.543 http://example.org/award/award_category/winners./award/award_honor/ceremony #14194-02w5q6 PRED entity: 02w5q6 PRED relation: person! PRED expected values: 043q4d => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 5): 043q4d (0.81 #16, 0.43 #23, 0.37 #171), 026h21_ (0.20 #7, 0.14 #28, 0.10 #63), 02k13d (0.15 #59, 0.13 #75, 0.12 #172), 0c5lg (0.12 #20, 0.08 #62, 0.06 #166), 09jwl (0.01 #161, 0.01 #170) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 016qtt; 02h9_l; >> query: (?x6817, 043q4d) <- category(?x6817, ?x134), program(?x6817, ?x2583), ?x2583 = 06hwzy >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02w5q6 person! 043q4d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.812 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #14193-02nxhr PRED entity: 02nxhr PRED relation: film_release_distribution_medium! PRED expected values: 04hwbq => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 1949): 0g5qs2k (0.80 #5836, 0.79 #7322, 0.78 #4393), 0ds33 (0.80 #5836, 0.78 #4393, 0.77 #5848), 02ny6g (0.80 #5836, 0.78 #4393, 0.77 #5848), 0q9sg (0.80 #5836, 0.78 #4393, 0.77 #5848), 037xlx (0.80 #5836, 0.78 #4393, 0.77 #5848), 02scbv (0.80 #5836, 0.78 #4393, 0.77 #5848), 0cwfgz (0.80 #5836, 0.78 #4393, 0.77 #5848), 059lwy (0.80 #5836, 0.78 #4393, 0.77 #5848), 0140g4 (0.80 #5836, 0.78 #4393, 0.77 #5848), 06c0ns (0.80 #5836, 0.78 #4393, 0.77 #5848) >> Best rule #5836 for best value: >> intensional similarity = 100 >> extensional distance = 2 >> proper extension: 07z4p; >> query: (?x627, ?x188) <- film_release_distribution_medium(?x12648, ?x627), film_release_distribution_medium(?x7822, ?x627), film_release_distribution_medium(?x5767, ?x627), film_release_distribution_medium(?x5388, ?x627), film_release_distribution_medium(?x3565, ?x627), film_release_distribution_medium(?x1077, ?x627), film_distribution_medium(?x9213, ?x627), film_distribution_medium(?x6119, ?x627), film_distribution_medium(?x409, ?x627), film_distribution_medium(?x97, ?x627), honored_for(?x2525, ?x1077), genre(?x1077, ?x4088), genre(?x11416, ?x4088), genre(?x8711, ?x4088), genre(?x6184, ?x4088), genre(?x5950, ?x4088), genre(?x4504, ?x4088), genre(?x3759, ?x4088), genre(?x144, ?x4088), genre(?x493, ?x4088), award_winner(?x1077, ?x262), film_release_region(?x3565, ?x344), film_release_region(?x3565, ?x304), film_release_region(?x3565, ?x252), film_release_region(?x3565, ?x172), film_release_region(?x3565, ?x142), ?x493 = 080dwhx, nominated_for(?x2209, ?x1077), nominated_for(?x1243, ?x1077), executive_produced_by(?x3565, ?x3896), honored_for(?x12648, ?x188), film_crew_role(?x1077, ?x137), award(?x1077, ?x591), film_release_region(?x409, ?x1023), ?x6184 = 02jxbw, ?x4504 = 0cq7kw, award(?x185, ?x1243), film(?x96, ?x97), award_winner(?x2209, ?x788), ?x344 = 04gzd, ?x142 = 0jgd, ?x144 = 0m313, award(?x1392, ?x2209), ?x172 = 0154j, ?x8711 = 0kvb6p, film(?x12765, ?x9213), ?x5950 = 011yg9, film(?x274, ?x3565), award(?x7822, ?x102), nominated_for(?x338, ?x7822), film_release_region(?x10475, ?x252), film_release_region(?x8370, ?x252), film_release_region(?x8162, ?x252), film_release_region(?x6446, ?x252), film_release_region(?x6175, ?x252), film_release_region(?x3886, ?x252), film_release_region(?x3784, ?x252), film_release_region(?x204, ?x252), ?x10475 = 047p798, country(?x2885, ?x252), ?x8162 = 0bs8ndx, country(?x596, ?x252), film(?x629, ?x5767), film_crew_role(?x5767, ?x2154), jurisdiction_of_office(?x182, ?x252), ?x3886 = 0198b6, ?x304 = 0d0vqn, ?x8370 = 07ghq, administrative_parent(?x536, ?x252), ?x2885 = 07jjt, genre(?x97, ?x812), organization(?x252, ?x127), nationality(?x256, ?x252), currency(?x252, ?x170), nominated_for(?x1336, ?x6119), ?x11416 = 033dbw, ?x137 = 09zzb8, ?x3759 = 023p7l, nominated_for(?x154, ?x5388), country_of_origin(?x419, ?x252), film(?x2803, ?x1077), olympics(?x252, ?x418), ?x6175 = 0gg5kmg, film(?x382, ?x5388), ?x204 = 028_yv, olympics(?x252, ?x452), executive_produced_by(?x5388, ?x8503), combatants(?x613, ?x1023), film_format(?x9213, ?x909), featured_film_locations(?x97, ?x739), exported_to(?x4164, ?x252), people(?x913, ?x12765), administrative_area_type(?x252, ?x2792), film_release_region(?x3226, ?x1023), film_release_region(?x2093, ?x1023), ?x3784 = 0bmhvpr, ?x3226 = 0gyfp9c, medal(?x252, ?x422), ?x6446 = 089j8p, ?x2093 = 0gydcp7 >> conf = 0.80 => this is the best rule for 19 predicted values *> Best rule #5848 for first EXPECTED value: *> intensional similarity = 101 *> extensional distance = 2 *> proper extension: 07z4p; *> query: (?x627, ?x186) <- film_release_distribution_medium(?x12648, ?x627), film_release_distribution_medium(?x7822, ?x627), film_release_distribution_medium(?x5767, ?x627), film_release_distribution_medium(?x5388, ?x627), film_release_distribution_medium(?x3565, ?x627), film_release_distribution_medium(?x1077, ?x627), film_distribution_medium(?x9213, ?x627), film_distribution_medium(?x6119, ?x627), film_distribution_medium(?x409, ?x627), film_distribution_medium(?x97, ?x627), honored_for(?x2525, ?x1077), genre(?x1077, ?x4088), genre(?x11416, ?x4088), genre(?x8711, ?x4088), genre(?x6184, ?x4088), genre(?x5950, ?x4088), genre(?x4504, ?x4088), genre(?x3759, ?x4088), genre(?x144, ?x4088), genre(?x493, ?x4088), award_winner(?x1077, ?x262), film_release_region(?x3565, ?x344), film_release_region(?x3565, ?x304), film_release_region(?x3565, ?x252), film_release_region(?x3565, ?x172), film_release_region(?x3565, ?x142), ?x493 = 080dwhx, nominated_for(?x2209, ?x1077), nominated_for(?x1243, ?x1077), executive_produced_by(?x3565, ?x3896), honored_for(?x12648, ?x188), film_crew_role(?x1077, ?x137), award(?x1077, ?x591), film_release_region(?x409, ?x1023), ?x6184 = 02jxbw, ?x4504 = 0cq7kw, nominated_for(?x1243, ?x186), award(?x185, ?x1243), film(?x96, ?x97), award_winner(?x2209, ?x788), ?x344 = 04gzd, ?x142 = 0jgd, ?x144 = 0m313, award(?x1392, ?x2209), ?x172 = 0154j, ?x8711 = 0kvb6p, film(?x12765, ?x9213), ?x5950 = 011yg9, film(?x274, ?x3565), award(?x7822, ?x102), nominated_for(?x338, ?x7822), film_release_region(?x10475, ?x252), film_release_region(?x8370, ?x252), film_release_region(?x8162, ?x252), film_release_region(?x6446, ?x252), film_release_region(?x6175, ?x252), film_release_region(?x3886, ?x252), film_release_region(?x3784, ?x252), film_release_region(?x204, ?x252), ?x10475 = 047p798, country(?x2885, ?x252), ?x8162 = 0bs8ndx, country(?x596, ?x252), film(?x629, ?x5767), film_crew_role(?x5767, ?x2154), jurisdiction_of_office(?x182, ?x252), ?x3886 = 0198b6, ?x304 = 0d0vqn, ?x8370 = 07ghq, administrative_parent(?x536, ?x252), ?x2885 = 07jjt, genre(?x97, ?x812), organization(?x252, ?x127), nationality(?x256, ?x252), currency(?x252, ?x170), nominated_for(?x1336, ?x6119), ?x11416 = 033dbw, ?x137 = 09zzb8, ?x3759 = 023p7l, nominated_for(?x154, ?x5388), country_of_origin(?x419, ?x252), film(?x2803, ?x1077), olympics(?x252, ?x418), ?x6175 = 0gg5kmg, film(?x382, ?x5388), ?x204 = 028_yv, olympics(?x252, ?x452), executive_produced_by(?x5388, ?x8503), combatants(?x613, ?x1023), film_format(?x9213, ?x909), featured_film_locations(?x97, ?x739), exported_to(?x4164, ?x252), people(?x913, ?x12765), administrative_area_type(?x252, ?x2792), film_release_region(?x3226, ?x1023), film_release_region(?x2093, ?x1023), ?x3784 = 0bmhvpr, ?x3226 = 0gyfp9c, medal(?x252, ?x422), ?x6446 = 089j8p, ?x2093 = 0gydcp7 *> conf = 0.77 ranks of expected_values: 1007 EVAL 02nxhr film_release_distribution_medium! 04hwbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 6.000 6.000 0.801 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #14192-077w0b PRED entity: 077w0b PRED relation: service_location PRED expected values: 0d060g 02j71 09lxtg => 157 concepts (134 used for prediction) PRED predicted values (max 10 best out of 160): 02j71 (0.40 #106, 0.30 #384, 0.28 #848), 06mkj (0.40 #124, 0.20 #402, 0.12 #216), 0k6nt (0.40 #110, 0.10 #388, 0.03 #2340), 0d060g (0.34 #839, 0.33 #1209, 0.33 #1117), 03h64 (0.30 #3162, 0.12 #224, 0.11 #317), 0chghy (0.30 #379, 0.20 #101, 0.19 #843), 02vzc (0.20 #399, 0.20 #121, 0.12 #213), 059g4 (0.20 #428, 0.20 #150, 0.12 #242), 059j2 (0.20 #114, 0.10 #392, 0.09 #856), 06t2t (0.20 #125, 0.10 #403, 0.09 #590) >> Best rule #106 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 018mxj; 064f29; >> query: (?x7177, 02j71) <- service_language(?x7177, ?x2502), industry(?x7177, ?x6575), service_location(?x7177, ?x205), ?x205 = 03rjj, ?x2502 = 06nm1 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 077w0b service_location 09lxtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 157.000 134.000 0.400 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 077w0b service_location 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 134.000 0.400 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 077w0b service_location 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 157.000 134.000 0.400 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #14191-0738y5 PRED entity: 0738y5 PRED relation: award PRED expected values: 0b6k___ => 89 concepts (81 used for prediction) PRED predicted values (max 10 best out of 326): 0gr0m (0.44 #3728, 0.02 #5758, 0.02 #22815), 02rdyk7 (0.42 #92, 0.41 #1310, 0.29 #904), 03r8tl (0.38 #511, 0.25 #1729, 0.24 #2135), 03rbj2 (0.36 #1849, 0.31 #631, 0.28 #3067), 0gs9p (0.36 #892, 0.27 #1298, 0.18 #4952), 05f4m9q (0.33 #13, 0.21 #825, 0.14 #1231), 02pqp12 (0.23 #1289, 0.17 #71, 0.16 #4943), 02x4wr9 (0.23 #1355, 0.17 #137, 0.14 #949), 0gr4k (0.21 #845, 0.18 #1251, 0.14 #7342), 040njc (0.19 #6911, 0.17 #5692, 0.16 #4880) >> Best rule #3728 for best value: >> intensional similarity = 7 >> extensional distance = 87 >> proper extension: 09cdxn; >> query: (?x9506, 0gr0m) <- profession(?x9506, ?x2265), profession(?x9506, ?x987), ?x2265 = 0dgd_, profession(?x10841, ?x987), profession(?x4589, ?x987), award_nominee(?x3381, ?x4589), gender(?x10841, ?x231) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #3062 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 72 *> proper extension: 01qx13; *> query: (?x9506, 0b6k___) <- gender(?x9506, ?x231), ?x231 = 05zppz, nationality(?x9506, ?x2146), ?x2146 = 03rk0, place_of_birth(?x9506, ?x13551) *> conf = 0.18 ranks of expected_values: 14 EVAL 0738y5 award 0b6k___ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 89.000 81.000 0.438 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14190-0306bt PRED entity: 0306bt PRED relation: award_nominee! PRED expected values: 0306ds 03061d => 108 concepts (49 used for prediction) PRED predicted values (max 10 best out of 781): 0306ds (0.82 #23345, 0.81 #107403, 0.81 #114410), 03061d (0.82 #23345, 0.81 #107403, 0.81 #114410), 0hwbd (0.78 #18676, 0.77 #95725, 0.76 #77042), 0306bt (0.30 #39687, 0.28 #46692, 0.16 #114411), 043js (0.30 #39687, 0.28 #46692, 0.03 #9920), 07k2p6 (0.30 #39687, 0.28 #46692), 030znt (0.28 #46692, 0.03 #16618, 0.02 #21287), 021vwt (0.28 #46692, 0.02 #9687, 0.01 #37700), 018z_c (0.28 #46692, 0.01 #10376), 07ddz9 (0.28 #46692, 0.01 #2113, 0.01 #4447) >> Best rule #23345 for best value: >> intensional similarity = 3 >> extensional distance = 504 >> proper extension: 06lj1m; 02g0mx; 04g4n; 015pvh; 057xn_m; 01dpsv; >> query: (?x9670, ?x1397) <- award_nominee(?x9670, ?x1397), gender(?x9670, ?x514), ?x514 = 02zsn >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2 EVAL 0306bt award_nominee! 03061d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 49.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 0306bt award_nominee! 0306ds CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 49.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14189-01gln9 PRED entity: 01gln9 PRED relation: contains! PRED expected values: 02jx1 => 163 concepts (60 used for prediction) PRED predicted values (max 10 best out of 282): 0jcg8 (0.77 #21517, 0.75 #46615, 0.72 #20620), 09c7w0 (0.72 #11660, 0.69 #13452, 0.65 #26899), 02jx1 (0.71 #24207, 0.68 #17931, 0.67 #23311), 02qkt (0.51 #16483, 0.47 #17380, 0.42 #22760), 04_1l0v (0.46 #20173, 0.44 #21070, 0.35 #10316), 059rby (0.30 #20640, 0.28 #28709, 0.14 #49322), 04jpl (0.29 #1813, 0.20 #22, 0.18 #51117), 01n7q (0.28 #28766, 0.27 #3663, 0.16 #14422), 02j9z (0.28 #15269, 0.22 #16165, 0.22 #22442), 06pvr (0.27 #3751, 0.16 #14510, 0.14 #6444) >> Best rule #21517 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 0f63n; 0drs7; 0ff0x; 0fplv; 0drrw; >> query: (?x12711, ?x1976) <- adjoins(?x11117, ?x12711), contains(?x512, ?x12711), administrative_parent(?x11117, ?x1976), place_of_death(?x5797, ?x1976) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #24207 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 95 *> proper extension: 0g251; 0bdg5; 0195j0; 013wf1; 0619_; 01zrs_; 01ngx6; 0dj0x; 0gyvgw; *> query: (?x12711, ?x1310) <- place_of_birth(?x7211, ?x12711), contains(?x512, ?x12711), ?x512 = 07ssc, nationality(?x7211, ?x1310) *> conf = 0.71 ranks of expected_values: 3 EVAL 01gln9 contains! 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 163.000 60.000 0.766 http://example.org/location/location/contains #14188-02pptm PRED entity: 02pptm PRED relation: major_field_of_study PRED expected values: 02j62 0g26h => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 111): 01mkq (0.63 #631, 0.60 #139, 0.54 #385), 02j62 (0.59 #646, 0.58 #769, 0.55 #277), 02lp1 (0.52 #1119, 0.46 #381, 0.44 #750), 02_7t (0.50 #190, 0.45 #313, 0.43 #67), 04rjg (0.48 #636, 0.45 #267, 0.40 #1128), 062z7 (0.46 #397, 0.44 #766, 0.38 #889), 0_jm (0.46 #429, 0.33 #798, 0.32 #1413), 05qfh (0.44 #652, 0.38 #406, 0.36 #775), 0g26h (0.43 #1520, 0.43 #1028, 0.41 #5211), 01tbp (0.42 #800, 0.38 #431, 0.33 #677) >> Best rule #631 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 08815; 01w3v; 03v6t; 0f102; 07tds; 02zd460; 0gl5_; 01bm_; >> query: (?x9131, 01mkq) <- major_field_of_study(?x9131, ?x2601), ?x2601 = 04x_3, school(?x580, ?x9131), citytown(?x9131, ?x10904) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 08815; 01w3v; 03v6t; 0f102; 07tds; 02zd460; 0gl5_; 01bm_; *> query: (?x9131, 02j62) <- major_field_of_study(?x9131, ?x2601), ?x2601 = 04x_3, school(?x580, ?x9131), citytown(?x9131, ?x10904) *> conf = 0.59 ranks of expected_values: 2, 9 EVAL 02pptm major_field_of_study 0g26h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 142.000 142.000 0.630 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02pptm major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 142.000 142.000 0.630 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14187-0dwr4 PRED entity: 0dwr4 PRED relation: instrumentalists PRED expected values: 01p0vf => 74 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1162): 01nn3m (0.71 #623, 0.60 #3732, 0.59 #8723), 01wsl7c (0.71 #623, 0.60 #3732, 0.59 #8723), 05qhnq (0.71 #623, 0.60 #3732, 0.59 #8723), 01vsy7t (0.71 #623, 0.60 #3732, 0.59 #8723), 01nkxvx (0.71 #623, 0.60 #3732, 0.59 #8723), 01271h (0.67 #4521, 0.50 #9511, 0.50 #1407), 0bg539 (0.63 #3108, 0.60 #622, 0.59 #620), 0135xb (0.62 #9750, 0.59 #620, 0.50 #4760), 02vr7 (0.60 #3580, 0.59 #620, 0.57 #8572), 01vrnsk (0.59 #620, 0.53 #621, 0.50 #9735) >> Best rule #623 for best value: >> intensional similarity = 23 >> extensional distance = 1 >> proper extension: 0342h; >> query: (?x2059, ?x1997) <- role(?x2059, ?x716), role(?x2059, ?x615), role(?x3409, ?x2059), role(?x2798, ?x2059), role(?x1969, ?x2059), role(?x868, ?x2059), role(?x745, ?x2059), role(?x314, ?x2059), ?x615 = 0dwsp, ?x868 = 0dwvl, role(?x2059, ?x432), ?x1969 = 04rzd, ?x3409 = 0680x0, ?x716 = 018vs, ?x432 = 042v_gx, ?x314 = 02sgy, performance_role(?x212, ?x2059), ?x745 = 01vj9c, role(?x1294, ?x2059), role(?x1997, ?x2059), ?x2798 = 03qjg, role(?x211, ?x212), instrumentalists(?x212, ?x226) >> conf = 0.71 => this is the best rule for 5 predicted values *> Best rule #620 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x2059, ?x226) <- role(?x2059, ?x716), role(?x2059, ?x615), role(?x3409, ?x2059), role(?x2798, ?x2059), role(?x1969, ?x2059), role(?x868, ?x2059), role(?x745, ?x2059), role(?x314, ?x2059), ?x615 = 0dwsp, ?x868 = 0dwvl, role(?x2059, ?x432), ?x1969 = 04rzd, ?x3409 = 0680x0, ?x716 = 018vs, ?x432 = 042v_gx, ?x314 = 02sgy, performance_role(?x212, ?x2059), ?x745 = 01vj9c, role(?x1294, ?x2059), role(?x1997, ?x2059), ?x2798 = 03qjg, role(?x211, ?x212), instrumentalists(?x212, ?x226) *> conf = 0.59 ranks of expected_values: 12 EVAL 0dwr4 instrumentalists 01p0vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 74.000 42.000 0.711 http://example.org/music/instrument/instrumentalists #14186-01jv1z PRED entity: 01jv1z PRED relation: artist PRED expected values: 017lb_ => 52 concepts (20 used for prediction) PRED predicted values (max 10 best out of 964): 0ycp3 (0.50 #4633, 0.50 #3803, 0.33 #5464), 01323p (0.50 #4705, 0.50 #3875, 0.33 #556), 01vwyqp (0.50 #4362, 0.50 #3532, 0.33 #213), 0565cz (0.50 #3508, 0.33 #4338, 0.33 #189), 0bk1p (0.50 #4809, 0.33 #660, 0.25 #3979), 0kr_t (0.50 #4540, 0.33 #391, 0.25 #3710), 07zft (0.50 #2307, 0.33 #1479, 0.17 #5629), 01vrnsk (0.50 #2148, 0.16 #13772, 0.11 #7957), 01304j (0.50 #2422, 0.09 #16606, 0.08 #9061), 01vw8mh (0.33 #6151, 0.33 #5323, 0.33 #4492) >> Best rule #4633 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 011k1h; 01dtcb; >> query: (?x1543, 0ycp3) <- artist(?x1543, ?x7570), artist(?x1543, ?x1338), group(?x7570, ?x8226), ?x1338 = 09qr6, film(?x7570, ?x9755), award_nominee(?x7088, ?x7570), artists(?x283, ?x7570), instrumentalists(?x315, ?x7570), film(?x5636, ?x9755) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3319 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 01cf93; *> query: (?x1543, ?x8226) <- artist(?x1543, ?x7570), artist(?x1543, ?x2363), artist(?x1543, ?x1338), group(?x7570, ?x8226), instrumentalists(?x316, ?x1338), participant(?x1338, ?x4394), profession(?x1338, ?x131), artists(?x671, ?x1338), ?x2363 = 01hw6wq, ?x316 = 05r5c *> conf = 0.33 ranks of expected_values: 55 EVAL 01jv1z artist 017lb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 52.000 20.000 0.500 http://example.org/music/record_label/artist #14185-02h40lc PRED entity: 02h40lc PRED relation: language! PRED expected values: 02d413 03qcfvw 0g56t9t 09sh8k 02y_lrp 0140g4 02_fm2 02v8kmz 047gn4y 0ddfwj1 0ds3t5x 0g5qs2k 016z5x 0fq27fp 0cpllql 0c40vxk 0209hj 0gkz15s 09q5w2 0gjk1d 07g_0c 069q4f 0416y94 02rqwhl 07qg8v 0qm8b 0bq8tmw 072x7s 0bh8yn3 09gq0x5 0b76kw1 09tqkv2 023p33 064n1pz 034qzw 0bm2g 0407yfx 07nt8p 01_1pv 020y73 0g3zrd 016z9n 0d_2fb 0yyts 02qr69m 0fpmrm3 047svrl 01771z 0cc846d 05q4y12 0cw3yd 01jrbb 03kg2v 0p7qm 01bb9r 04sntd 0bmpm 07sp4l 07w8fz 0ds2n 02tqm5 0299hs 0p_qr 04grkmd 0900j5 06q8qh 0kvgtf 0yx7h 02ht1k 047fjjr 0cmc26r 049mql 03176f 0243cq 0fz3b1 03wbqc4 02rmd_2 0pd4f 0125xq 0db94w 0q9sg 021pqy 0c38gj 04v8h1 043t8t 019kyn 01qvz8 013q0p 0c1sgd3 06t6dz 0dln8jk 0194zl 08sfxj 0j3d9tn 0dl9_4 0ptxj 0gs973 0y_9q 01zfzb 026lgs 02prwdh 0cc97st 0415ggl 0kvgnq 03mgx6z 016ky6 0ggbfwf 011ypx 02q87z6 04h41v 06__m6 0dgq_kn 0g5pvv 05q7874 026zlh9 05r3qc 01chpn 03hxsv 05n6sq 03n3gl 0cmdwwg 0404j37 027gy0k 047bynf 0drnwh 05ft32 03p2xc 0c9t0y 05nlx4 0bdjd 07xvf 02v570 01qb559 05fm6m 0bcp9b 02825nf 0bt4g 0298n7 0cbn7c 0f2sx4 0b4lkx 02qkwl 03nfnx 04g73n 0888c3 04gp58p 0yx1m 015ynm 011ywj 02cbg0 03pc89 0gzlb9 02mpyh 0h3k3f 01xvjb 0ndsl1x 05nyqk 0353tm 0m3gy 0g5qmbz 058kh7 0dc7hc 0fh2v5 048yqf 02gqm3 01sbv9 09rfh9 0dgq80b 06zn1c 01k5y0 0ckrnn 06cgf 023cjg 03h0byn 0jqzt 0gy4k 01d2v1 02wtp6 025twgt 07bxqz 01f69m 03cffvv 016yxn 0322yj 07ykkx5 04q01mn => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 458): 043t8t (0.76 #1119, 0.50 #924, 0.44 #2667), 0g5qmbz (0.57 #2090, 0.56 #2841, 0.56 #2715), 0pd4f (0.56 #2786, 0.43 #2035, 0.40 #1414), 01sbv9 (0.44 #2722, 0.43 #2097, 0.40 #1228), 072x7s (0.44 #2756, 0.40 #1384, 0.40 #1136), 03_wm6 (0.43 #2068, 0.38 #2319, 0.38 #2193), 011yfd (0.40 #1288, 0.33 #45, 0.29 #2033), 03mgx6z (0.40 #1184, 0.33 #190, 0.24 #1988), 03n3gl (0.40 #1194, 0.33 #200, 0.22 #2814), 0322yj (0.40 #1490, 0.33 #248, 0.22 #2862) >> Best rule #1119 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 02w7gg; >> query: (?x254, ?x4651) <- split_to(?x254, ?x9057), language(?x7293, ?x9057), language(?x4651, ?x9057), nominated_for(?x7215, ?x7293), film_release_region(?x7293, ?x94), film(?x1864, ?x7293) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 8, 9, 10, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 37, 40, 42, 44, 46, 48, 50, 54, 56, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 142, 145, 146, 149, 194, 195, 375, 381, 383, 384, 385, 386, 387, 388, 395, 396, 397, 402, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 438, 439, 440, 441, 442, 443, 444 EVAL 02h40lc language! 04q01mn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 07ykkx5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0322yj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 016yxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03cffvv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01f69m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 07bxqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 025twgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02wtp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01d2v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0gy4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0jqzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03h0byn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 023cjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 06cgf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0ckrnn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01k5y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 06zn1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0dgq80b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 09rfh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01sbv9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02gqm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 048yqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0fh2v5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0dc7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 058kh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0g5qmbz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0m3gy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0353tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 05nyqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0ndsl1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01xvjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0h3k3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02mpyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0gzlb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03pc89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02cbg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 011ywj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 015ynm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0yx1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 04gp58p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0888c3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 04g73n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03nfnx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02qkwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0b4lkx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0f2sx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0cbn7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0298n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0bt4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0bcp9b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 05fm6m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01qb559 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02v570 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 07xvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0bdjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 05nlx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0c9t0y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03p2xc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 05ft32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0drnwh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 047bynf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 027gy0k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0cmdwwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03n3gl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 05n6sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03hxsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01chpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 05r3qc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 026zlh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 05q7874 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0g5pvv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0dgq_kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 06__m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 04h41v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02q87z6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 011ypx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0ggbfwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 016ky6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03mgx6z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0kvgnq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0415ggl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02prwdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 026lgs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01zfzb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0y_9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0gs973 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0ptxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0dl9_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0j3d9tn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 08sfxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0194zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0dln8jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 06t6dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0c1sgd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 013q0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01qvz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 019kyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 043t8t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 04v8h1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0c38gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 021pqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0q9sg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0db94w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0125xq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0pd4f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02rmd_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03wbqc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0fz3b1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0243cq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03176f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 049mql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0cmc26r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 047fjjr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02ht1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0yx7h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0kvgtf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 06q8qh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0900j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 04grkmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0p_qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0299hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02tqm5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0ds2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 07w8fz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 07sp4l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0bmpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 04sntd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01bb9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0p7qm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03kg2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01jrbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0cw3yd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 05q4y12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0cc846d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01771z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 047svrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0fpmrm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02qr69m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0yyts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0d_2fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 016z9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0g3zrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 020y73 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 01_1pv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 07nt8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0407yfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0bm2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 034qzw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 064n1pz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 023p33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 09tqkv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0b76kw1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 09gq0x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0bh8yn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 072x7s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0bq8tmw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0qm8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 07qg8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02rqwhl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0416y94 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 069q4f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 07g_0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0gjk1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 09q5w2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0209hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0c40vxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0cpllql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0fq27fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 016z5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0g5qs2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0ds3t5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0ddfwj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 047gn4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02v8kmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02_fm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0140g4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02y_lrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 09sh8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 0g56t9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 03qcfvw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 77.000 0.765 http://example.org/film/film/language EVAL 02h40lc language! 02d413 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 77.000 0.765 http://example.org/film/film/language #14184-0cc5mcj PRED entity: 0cc5mcj PRED relation: film_crew_role PRED expected values: 01vx2h => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 28): 01vx2h (0.57 #112, 0.43 #146, 0.43 #283), 0dxtw (0.40 #8, 0.40 #626, 0.40 #557), 01pvkk (0.27 #1393, 0.27 #113, 0.27 #977), 015h31 (0.20 #7, 0.16 #144, 0.13 #178), 04pyp5 (0.20 #15, 0.10 #49, 0.09 #83), 01xy5l_ (0.20 #183, 0.19 #149, 0.13 #286), 02ynfr (0.19 #151, 0.19 #981, 0.18 #528), 0215hd (0.19 #154, 0.17 #188, 0.15 #291), 0d2b38 (0.17 #195, 0.14 #161, 0.12 #746), 089g0h (0.14 #155, 0.13 #189, 0.11 #985) >> Best rule #112 for best value: >> intensional similarity = 6 >> extensional distance = 28 >> proper extension: 0cz8mkh; 0661ql3; 0crc2cp; 0gh65c5; 0c3xw46; 05pdh86; 0dll_t2; 0fpgp26; 0by17xn; 072hx4; >> query: (?x2441, 01vx2h) <- film_release_region(?x2441, ?x3855), film_release_region(?x2441, ?x1499), film_release_region(?x2441, ?x512), ?x512 = 07ssc, ?x1499 = 01znc_, ?x3855 = 0jgx >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cc5mcj film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.567 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14183-05g3b PRED entity: 05g3b PRED relation: school PRED expected values: 07ccs => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 187): 065y4w7 (0.50 #2633, 0.40 #383, 0.38 #1323), 05krk (0.50 #4, 0.40 #378, 0.33 #1692), 01rc6f (0.40 #506, 0.25 #881, 0.25 #132), 06pwq (0.38 #756, 0.27 #3192, 0.18 #8632), 015q1n (0.33 #1790, 0.27 #3287, 0.14 #3849), 07vyf (0.33 #1749, 0.27 #3246, 0.14 #3808), 01jq0j (0.30 #2740, 0.25 #865, 0.25 #116), 01vs5c (0.30 #2712, 0.25 #1402, 0.23 #7024), 07w0v (0.30 #2636, 0.23 #8637, 0.21 #6948), 07ccs (0.27 #3289, 0.22 #1792, 0.12 #1043) >> Best rule #2633 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 05g3v; 070xg; >> query: (?x729, 065y4w7) <- position_s(?x729, ?x180), category(?x729, ?x134), ?x180 = 01r3hr, school(?x729, ?x1681) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3289 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 049n7; 01ync; *> query: (?x729, 07ccs) <- company(?x935, ?x729), school(?x729, ?x1681), team(?x180, ?x729) *> conf = 0.27 ranks of expected_values: 10 EVAL 05g3b school 07ccs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 81.000 81.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #14182-059ts PRED entity: 059ts PRED relation: district_represented! PRED expected values: 034_7s => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 55): 077g7n (0.78 #609, 0.78 #719, 0.77 #774), 070m6c (0.76 #611, 0.74 #776, 0.74 #721), 06f0dc (0.75 #614, 0.74 #724, 0.73 #779), 07p__7 (0.75 #613, 0.74 #723, 0.73 #778), 070mff (0.72 #590, 0.71 #810, 0.71 #755), 024tcq (0.69 #736, 0.69 #626, 0.68 #791), 034_7s (0.67 #274, 0.67 #219, 0.57 #164), 024tkd (0.60 #757, 0.59 #647, 0.58 #812), 02bn_p (0.59 #725, 0.57 #615, 0.56 #780), 02bp37 (0.52 #729, 0.50 #784, 0.49 #839) >> Best rule #609 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 059f4; 05fkf; 05fhy; 059_c; 01x73; 05k7sb; 06btq; 03s5t; 0gyh; 07b_l; ... >> query: (?x10544, 077g7n) <- district_represented(?x10543, ?x10544), adjoins(?x7468, ?x10544), first_level_division_of(?x10544, ?x279), legislative_sessions(?x8776, ?x10543) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #274 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 05j49; *> query: (?x10544, 034_7s) <- district_represented(?x10543, ?x10544), country(?x10544, ?x279), ?x279 = 0d060g, ?x10543 = 03h_f4 *> conf = 0.67 ranks of expected_values: 7 EVAL 059ts district_represented! 034_7s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 142.000 142.000 0.784 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #14181-016fnb PRED entity: 016fnb PRED relation: award PRED expected values: 01by1l 02f77y => 116 concepts (97 used for prediction) PRED predicted values (max 10 best out of 290): 02f71y (0.78 #9835, 0.78 #16522, 0.78 #12982), 05p09zm (0.50 #122, 0.24 #4843, 0.24 #4450), 01cky2 (0.42 #585, 0.13 #2946, 0.11 #9238), 01by1l (0.36 #1292, 0.36 #2865, 0.36 #7977), 05pcn59 (0.33 #79, 0.30 #4407, 0.30 #4800), 02f716 (0.33 #568, 0.27 #962, 0.17 #8041), 0f4x7 (0.33 #31, 0.18 #3542, 0.18 #15341), 0gqy2 (0.33 #162, 0.18 #3542, 0.18 #15341), 09qv_s (0.33 #150, 0.18 #3542, 0.18 #15341), 09sdmz (0.33 #201, 0.18 #3542, 0.18 #15341) >> Best rule #9835 for best value: >> intensional similarity = 3 >> extensional distance = 330 >> proper extension: 01sbf2; 01dw9z; 04qzm; 09jm8; 0c9l1; 03x82v; >> query: (?x4628, ?x3488) <- award_winner(?x3488, ?x4628), artist(?x5021, ?x4628), award_nominee(?x140, ?x4628) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1292 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 53 *> proper extension: 07h5d; *> query: (?x4628, 01by1l) <- award_winner(?x3488, ?x4628), location(?x4628, ?x9417), group(?x4628, ?x2723) *> conf = 0.36 ranks of expected_values: 4, 104 EVAL 016fnb award 02f77y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 116.000 97.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 016fnb award 01by1l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 97.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14180-02wwmhc PRED entity: 02wwmhc PRED relation: language PRED expected values: 06nm1 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 41): 064_8sq (0.27 #79, 0.19 #1018, 0.17 #372), 06nm1 (0.19 #244, 0.18 #68, 0.15 #1007), 04306rv (0.18 #62, 0.11 #120, 0.10 #1001), 02bjrlw (0.12 #352, 0.09 #59, 0.09 #880), 04h9h (0.10 #393, 0.09 #100, 0.06 #921), 06b_j (0.10 #1604, 0.09 #1311, 0.08 #1545), 0c_v2 (0.09 #74, 0.05 #132, 0.02 #367), 06mp7 (0.09 #73, 0.05 #190, 0.02 #425), 02hwyss (0.09 #99, 0.05 #216, 0.02 #392), 0653m (0.06 #597, 0.05 #1886, 0.05 #127) >> Best rule #79 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0c_j9x; 02q_4ph; >> query: (?x10778, 064_8sq) <- story_by(?x10778, ?x117), costume_design_by(?x10778, ?x6327), nominated_for(?x12423, ?x10778), film(?x8796, ?x10778) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #244 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 090s_0; 0p9rz; *> query: (?x10778, 06nm1) <- story_by(?x10778, ?x117), costume_design_by(?x10778, ?x6327), influenced_by(?x6698, ?x117), language(?x10778, ?x254) *> conf = 0.19 ranks of expected_values: 2 EVAL 02wwmhc language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.273 http://example.org/film/film/language #14179-0b_j2 PRED entity: 0b_j2 PRED relation: artists! PRED expected values: 01wtlq => 102 concepts (79 used for prediction) PRED predicted values (max 10 best out of 238): 06j6l (0.50 #44, 0.40 #1571, 0.33 #5853), 03lty (0.47 #1247, 0.18 #1858, 0.15 #6447), 025sc50 (0.44 #46, 0.41 #963, 0.30 #5855), 0glt670 (0.41 #954, 0.39 #37, 0.23 #4927), 0gywn (0.38 #1581, 0.33 #54, 0.28 #971), 02w4v (0.36 #652, 0.26 #1567, 0.19 #1873), 02yv6b (0.32 #1622, 0.25 #1317, 0.24 #1928), 016clz (0.31 #12852, 0.27 #1838, 0.25 #6427), 01lyv (0.28 #1558, 0.25 #643, 0.24 #1864), 0ggx5q (0.28 #74, 0.24 #991, 0.22 #5883) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 02qtywd; >> query: (?x6626, 06j6l) <- award(?x6626, ?x1827), ?x1827 = 02nhxf, instrumentalists(?x227, ?x6626) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3376 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 230 *> proper extension: 02fybl; 01m7f5r; *> query: (?x6626, 01wtlq) <- nationality(?x6626, ?x94), location(?x6626, ?x3014), role(?x6626, ?x74) *> conf = 0.01 ranks of expected_values: 225 EVAL 0b_j2 artists! 01wtlq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 102.000 79.000 0.500 http://example.org/music/genre/artists #14178-0c3ybss PRED entity: 0c3ybss PRED relation: titles! PRED expected values: 09blyk => 62 concepts (44 used for prediction) PRED predicted values (max 10 best out of 70): 07s9rl0 (0.53 #1432, 0.38 #1947, 0.37 #2457), 04xvlr (0.36 #1435, 0.26 #1950, 0.25 #2358), 07ssc (0.28 #1224, 0.28 #1129, 0.18 #1430), 01jfsb (0.23 #1450, 0.20 #1945, 0.18 #1740), 01z4y (0.20 #3521, 0.18 #4344, 0.17 #339), 01hmnh (0.17 #533, 0.14 #634, 0.13 #1972), 07yjb (0.15 #174, 0.12 #276, 0.05 #73), 09blyk (0.14 #46, 0.10 #249, 0.08 #1477), 024qqx (0.14 #688, 0.12 #587, 0.08 #485), 0d060g (0.13 #1222, 0.13 #1221, 0.11 #1429) >> Best rule #1432 for best value: >> intensional similarity = 4 >> extensional distance = 722 >> proper extension: 015qsq; 0sxg4; 083shs; 0140g4; 0c0yh4; 0yyg4; 011yxg; 03h_yy; 050r1z; 0dj0m5; ... >> query: (?x249, 07s9rl0) <- film(?x8626, ?x249), titles(?x571, ?x249), genre(?x4089, ?x571), ?x4089 = 02kfzz >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 02v63m; 03t79f; 012kyx; 03cwwl; *> query: (?x249, 09blyk) <- film(?x8626, ?x249), titles(?x571, ?x249), ?x571 = 03npn, nominated_for(?x5446, ?x249) *> conf = 0.14 ranks of expected_values: 8 EVAL 0c3ybss titles! 09blyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 62.000 44.000 0.533 http://example.org/media_common/netflix_genre/titles #14177-020h2v PRED entity: 020h2v PRED relation: award_nominee! PRED expected values: 016tt2 => 112 concepts (78 used for prediction) PRED predicted values (max 10 best out of 939): 016tt2 (0.82 #107118, 0.81 #165353, 0.81 #167683), 043zg (0.25 #17557, 0.25 #8243, 0.02 #150303), 017s11 (0.25 #104895, 0.22 #123528, 0.18 #135175), 01rh0w (0.25 #7282, 0.22 #160693, 0.12 #16596), 014zcr (0.25 #7031, 0.12 #16345, 0.04 #167730), 0169dl (0.25 #7499, 0.12 #16813, 0.03 #168198), 0lpjn (0.25 #7607, 0.12 #16921, 0.02 #170635), 0171cm (0.25 #7535, 0.12 #16849, 0.02 #170563), 0dzf_ (0.25 #8063, 0.12 #17377, 0.02 #86160), 0170pk (0.25 #7347, 0.12 #16661, 0.02 #170375) >> Best rule #107118 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 0181hw; >> query: (?x7980, ?x574) <- award_nominee(?x7980, ?x3920), award_nominee(?x7980, ?x574), production_companies(?x148, ?x3920), place_founded(?x3920, ?x1523) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 020h2v award_nominee! 016tt2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 78.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14176-01_njt PRED entity: 01_njt PRED relation: student! PRED expected values: 0778_3 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 82): 0bwfn (0.11 #275, 0.08 #1329, 0.08 #802), 04b_46 (0.11 #227, 0.04 #754, 0.04 #1808), 01p79b (0.11 #290, 0.02 #1871), 07vyf (0.11 #138, 0.02 #1719), 01w5m (0.06 #1159, 0.02 #3267, 0.02 #47551), 065y4w7 (0.06 #1595, 0.03 #17935, 0.03 #5812), 026gvfj (0.04 #638, 0.03 #2219, 0.02 #4328), 07tg4 (0.04 #613, 0.02 #4830, 0.02 #1140), 08815 (0.04 #529, 0.02 #8962, 0.02 #1056), 033gn8 (0.04 #905, 0.02 #1959, 0.01 #9338) >> Best rule #275 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 034x61; 066m4g; 09f0bj; 05np4c; 08yx9q; 06s6hs; 0k2mxq; >> query: (?x8167, 0bwfn) <- award_nominee(?x5599, ?x8167), award_nominee(?x1677, ?x8167), ?x5599 = 06czyr, award_nominee(?x450, ?x1677) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01_njt student! 0778_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 105.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #14175-05bnq8 PRED entity: 05bnq8 PRED relation: citytown PRED expected values: 01qh7 => 142 concepts (105 used for prediction) PRED predicted values (max 10 best out of 255): 01qh7 (0.33 #62, 0.24 #4122, 0.21 #3018), 01cx_ (0.29 #5970, 0.28 #4127, 0.21 #3023), 02_286 (0.24 #9604, 0.23 #24363, 0.22 #25102), 0mzww (0.14 #895, 0.05 #3482, 0.03 #5692), 06wxw (0.14 #834, 0.05 #3421, 0.03 #5631), 0h7h6 (0.11 #14386, 0.09 #23610, 0.08 #14017), 0fpzwf (0.11 #1232, 0.10 #1603, 0.08 #1974), 01nl79 (0.11 #1410, 0.01 #8788, 0.01 #9526), 03v_5 (0.10 #1512, 0.08 #1883, 0.05 #3728), 052p7 (0.10 #1523, 0.08 #1894, 0.05 #3739) >> Best rule #62 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 04rwx; >> query: (?x9827, 01qh7) <- institution(?x1200, ?x9827), major_field_of_study(?x9827, ?x3878), school_type(?x9827, ?x1044), student(?x9827, ?x12580), student(?x9827, ?x11596), ?x12580 = 03xds, ?x1200 = 016t_3, location(?x11596, ?x1227), award_winner(?x13257, ?x11596) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05bnq8 citytown 01qh7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 105.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #14174-03_js PRED entity: 03_js PRED relation: nationality PRED expected values: 09c7w0 => 191 concepts (155 used for prediction) PRED predicted values (max 10 best out of 62): 09c7w0 (0.89 #9272, 0.88 #9071, 0.86 #15131), 05k7sb (0.51 #12702, 0.38 #4221, 0.34 #3818), 0f8l9c (0.33 #223, 0.26 #10278, 0.14 #15633), 07ssc (0.30 #1419, 0.27 #2222, 0.26 #10278), 029jpy (0.27 #14625), 02jx1 (0.26 #10278, 0.21 #6171, 0.17 #534), 0d060g (0.26 #10278, 0.12 #3422, 0.11 #1008), 03rk0 (0.26 #10278, 0.11 #7191, 0.11 #6484), 0h3y (0.26 #10278, 0.04 #3826, 0.02 #6346), 05v8c (0.26 #10278, 0.01 #6761) >> Best rule #9272 for best value: >> intensional similarity = 3 >> extensional distance = 213 >> proper extension: 04cy8rb; 09hd6f; >> query: (?x8991, 09c7w0) <- place_of_birth(?x8991, ?x4989), source(?x4989, ?x958), currency(?x4989, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_js nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 155.000 0.893 http://example.org/people/person/nationality #14173-0myn8 PRED entity: 0myn8 PRED relation: adjoins PRED expected values: 0n228 => 178 concepts (93 used for prediction) PRED predicted values (max 10 best out of 432): 0n228 (0.82 #63254, 0.82 #65571, 0.82 #60940), 0myn8 (0.26 #50911, 0.26 #29308, 0.25 #35482), 0n24p (0.26 #50911, 0.26 #29308, 0.25 #35482), 0n1v8 (0.26 #50911, 0.26 #29308, 0.25 #35482), 0mwxl (0.26 #50911, 0.26 #29308, 0.25 #35483), 0n1tx (0.26 #50911, 0.25 #35482, 0.25 #35483), 0n2q0 (0.26 #50911, 0.25 #35482, 0.25 #35483), 0m7fm (0.25 #70, 0.02 #11636, 0.02 #15492), 0n2sh (0.15 #1312, 0.14 #2083, 0.08 #2854), 0myhb (0.08 #1149, 0.07 #1920, 0.06 #2691) >> Best rule #63254 for best value: >> intensional similarity = 4 >> extensional distance = 313 >> proper extension: 0rh6k; 02dtg; 02_286; 0wh3; 01_d4; 0dclg; 0dc95; 0f04c; 013m43; 0r679; ... >> query: (?x10235, ?x12554) <- adjoins(?x12554, ?x10235), time_zones(?x12554, ?x2674), contains(?x177, ?x12554), source(?x10235, ?x958) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0myn8 adjoins 0n228 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 93.000 0.819 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #14172-025mb_ PRED entity: 025mb_ PRED relation: inductee! PRED expected values: 06szd3 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 3): 06szd3 (0.05 #345, 0.03 #182, 0.02 #290), 0g2c8 (0.04 #109, 0.03 #461, 0.03 #836), 0qjfl (0.02 #201, 0.02 #219, 0.01 #138) >> Best rule #345 for best value: >> intensional similarity = 3 >> extensional distance = 517 >> proper extension: 0dbpyd; 02rchht; 0h5f5n; 02lf0c; 0d4fqn; 08f3b1; 0415svh; 02773m2; 02778pf; 02q_cc; ... >> query: (?x9140, 06szd3) <- award(?x9140, ?x594), profession(?x9140, ?x1041), ?x1041 = 03gjzk >> conf = 0.05 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025mb_ inductee! 06szd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.052 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #14171-016k62 PRED entity: 016k62 PRED relation: person! PRED expected values: 09jwl => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 7): 043q4d (0.03 #24, 0.03 #233, 0.03 #210), 02k13d (0.03 #25, 0.01 #85, 0.01 #92), 09jwl (0.02 #53, 0.02 #46, 0.02 #61), 0c5lg (0.02 #58, 0.02 #66), 026h21_ (0.01 #75), 029bkp (0.01 #72), 05ll37 (0.01 #80) >> Best rule #24 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0bxfmk; 07cbs; 013zyw; 04z0g; 0f7fy; 01rc4p; 04hcw; 0b22w; 05xd_v; >> query: (?x5151, 043q4d) <- profession(?x5151, ?x7998), ?x7998 = 01d30f, nationality(?x5151, ?x94) >> conf = 0.03 => this is the best rule for 1 predicted values *> Best rule #53 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 05qhnq; 01hrqc; *> query: (?x5151, 09jwl) <- award_nominee(?x5125, ?x5151), profession(?x5151, ?x563), performance_role(?x5151, ?x75), artists(?x505, ?x5151) *> conf = 0.02 ranks of expected_values: 3 EVAL 016k62 person! 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 117.000 0.033 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #14170-06_9lg PRED entity: 06_9lg PRED relation: service_location PRED expected values: 0cw51 0fk98 => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 117): 09c7w0 (0.94 #1968, 0.92 #2247, 0.92 #1555), 0d060g (0.76 #436, 0.72 #720, 0.71 #935), 07ssc (0.74 #515, 0.50 #374, 0.42 #873), 03rk0 (0.50 #71, 0.40 #285, 0.33 #72), 03h64 (0.44 #331, 0.40 #403, 0.14 #473), 0chghy (0.40 #370, 0.36 #795, 0.34 #440), 0f8l9c (0.33 #236, 0.33 #94, 0.33 #22), 0345h (0.33 #100, 0.33 #28, 0.32 #527), 059rby (0.33 #82, 0.33 #10, 0.17 #153), 07c98 (0.33 #72, 0.25 #143, 0.23 #358) >> Best rule #1968 for best value: >> intensional similarity = 13 >> extensional distance = 91 >> proper extension: 087c7; 016tt2; 0cchk3; 045c7b; 01tx9m; 07l1c; 077w0b; 03rwz3; 0z90c; 01b39j; ... >> query: (?x10867, 09c7w0) <- service_location(?x10867, ?x11801), service_location(?x10867, ?x7412), service_location(?x10867, ?x4335), contains(?x2146, ?x4335), location(?x10579, ?x4335), organization(?x4682, ?x10867), category(?x10579, ?x134), profession(?x10579, ?x1032), location(?x12204, ?x11801), place_of_death(?x2145, ?x7412), location(?x1806, ?x7412), place_of_birth(?x491, ?x7412), film(?x12204, ?x5247) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #855 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 37 *> proper extension: 01_qgp; 013fn; *> query: (?x10867, ?x14117) <- service_location(?x10867, ?x11801), service_location(?x10867, ?x4335), contains(?x9305, ?x4335), location(?x13250, ?x4335), contains(?x11801, ?x11800), contains(?x9305, ?x14117), contact_category(?x10867, ?x897), featured_film_locations(?x257, ?x11801), profession(?x13250, ?x1032) *> conf = 0.02 ranks of expected_values: 96, 107 EVAL 06_9lg service_location 0fk98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 51.000 51.000 0.935 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 06_9lg service_location 0cw51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 51.000 51.000 0.935 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #14169-036hf4 PRED entity: 036hf4 PRED relation: award PRED expected values: 05pcn59 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 273): 09sb52 (0.45 #27781, 0.39 #28585, 0.36 #35017), 05pcn59 (0.29 #2896, 0.28 #2494, 0.27 #886), 05p09zm (0.28 #1329, 0.25 #2937, 0.25 #1731), 05b4l5x (0.27 #810, 0.16 #2016, 0.16 #3222), 0ck27z (0.25 #93, 0.16 #35069, 0.15 #29843), 01bgqh (0.25 #43, 0.13 #1249, 0.13 #53872), 01by1l (0.25 #111, 0.13 #53872, 0.12 #11367), 0c4z8 (0.25 #72, 0.13 #53872, 0.11 #38193), 01cky2 (0.25 #193, 0.13 #53872, 0.11 #38193), 03t5n3 (0.25 #248, 0.13 #53872, 0.11 #38193) >> Best rule #27781 for best value: >> intensional similarity = 3 >> extensional distance = 554 >> proper extension: 02bwc7; 01wbsdz; >> query: (?x9084, 09sb52) <- location(?x9084, ?x191), award_nominee(?x3308, ?x9084), participant(?x989, ?x3308) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2896 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 032xhg; 034x61; 01l2fn; 0l12d; 018grr; 0c6g1l; 01cwhp; 01trhmt; 0pmhf; 0154qm; ... *> query: (?x9084, 05pcn59) <- location(?x9084, ?x191), award_nominee(?x399, ?x9084), vacationer(?x151, ?x9084) *> conf = 0.29 ranks of expected_values: 2 EVAL 036hf4 award 05pcn59 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 146.000 0.446 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14168-0g8st4 PRED entity: 0g8st4 PRED relation: actor! PRED expected values: 0524b41 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 150): 0cfhfz (0.23 #529, 0.10 #5552, 0.09 #4495), 039c26 (0.14 #313, 0.07 #3437, 0.02 #1106), 02k_4g (0.14 #278, 0.07 #3437, 0.01 #10844), 08bytj (0.10 #410, 0.07 #3437, 0.01 #10844), 080dwhx (0.10 #270, 0.01 #1327, 0.01 #1063), 0g60z (0.07 #4, 0.07 #3437, 0.02 #8462), 01b66t (0.07 #82, 0.07 #3437, 0.02 #8462), 0gvsh7l (0.07 #155, 0.07 #3437, 0.02 #8462), 09fc83 (0.07 #90, 0.07 #3437, 0.01 #1147), 0fkwzs (0.07 #161, 0.07 #3437) >> Best rule #529 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 07lmxq; 0h1nt; 01k8rb; 045c66; 07s8r0; 043kzcr; 05th8t; 0347xl; 03yj_0n; 0cjsxp; ... >> query: (?x6708, ?x2973) <- award_nominee(?x6708, ?x2615), ?x2615 = 0306ds, nominated_for(?x6708, ?x2973) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #923 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 394 *> proper extension: 0784v1; 07m69t; *> query: (?x6708, 0524b41) <- nationality(?x6708, ?x1310), ?x1310 = 02jx1 *> conf = 0.01 ranks of expected_values: 88 EVAL 0g8st4 actor! 0524b41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 67.000 67.000 0.226 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #14167-01w03jv PRED entity: 01w03jv PRED relation: politician! PRED expected values: 07k5l => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 20): 0d075m (0.31 #317, 0.22 #463, 0.21 #100), 07wbk (0.21 #461, 0.17 #315, 0.17 #49), 02245 (0.16 #116, 0.04 #381, 0.04 #430), 014j0w (0.11 #122, 0.05 #73, 0.04 #387), 01fml (0.11 #102, 0.03 #199, 0.03 #367), 07wf9 (0.07 #320, 0.06 #466, 0.01 #368), 049tb (0.05 #105, 0.03 #322, 0.02 #468), 0_00 (0.05 #109, 0.02 #472, 0.02 #326), 01fpdh (0.05 #120, 0.02 #337, 0.01 #385), 02189 (0.05 #112, 0.02 #329, 0.01 #377) >> Best rule #317 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 08f3b1; 0d0vj4; 083q7; 016hvl; 028p0; 0bwh6; 01_4z; 02c4s; 0203v; 0tc7; ... >> query: (?x12619, 0d075m) <- profession(?x12619, ?x5805), location(?x12619, ?x3125), nationality(?x12619, ?x94), ?x5805 = 0fj9f >> conf = 0.31 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w03jv politician! 07k5l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 137.000 0.310 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #14166-01q_y0 PRED entity: 01q_y0 PRED relation: nominated_for! PRED expected values: 0c3p7 => 89 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1019): 06yrj6 (0.79 #116308, 0.78 #25584, 0.78 #79091), 02773m2 (0.71 #25585, 0.66 #46520, 0.62 #39540), 02wk_43 (0.33 #2018, 0.21 #46521, 0.17 #72111), 03wh8pq (0.33 #1906, 0.21 #46521, 0.17 #72111), 02bvt (0.33 #1064, 0.21 #46521, 0.17 #72111), 03wh8kl (0.33 #1527, 0.21 #46521, 0.17 #72111), 0crx5w (0.33 #305, 0.21 #46521, 0.17 #2630), 04cl1 (0.33 #1037, 0.17 #3362, 0.06 #8014), 026_dcw (0.33 #774, 0.17 #3099, 0.06 #7751), 0170s4 (0.33 #492, 0.17 #2817, 0.06 #7469) >> Best rule #116308 for best value: >> intensional similarity = 3 >> extensional distance = 488 >> proper extension: 07s8z_l; >> query: (?x2293, ?x9815) <- honored_for(?x1112, ?x2293), award_winner(?x2293, ?x9815), award(?x9815, ?x678) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #22311 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 113 *> proper extension: 06hwzy; 025ljp; *> query: (?x2293, 0c3p7) <- honored_for(?x1193, ?x2293), ceremony(?x618, ?x1193), genre(?x2293, ?x258) *> conf = 0.02 ranks of expected_values: 551 EVAL 01q_y0 nominated_for! 0c3p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 89.000 51.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14165-013zdg PRED entity: 013zdg PRED relation: student PRED expected values: 012gx2 => 24 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1061): 014vk4 (0.43 #2991, 0.33 #686, 0.33 #456), 04z0g (0.33 #1048, 0.33 #588, 0.33 #358), 0b78hw (0.33 #1020, 0.33 #560, 0.33 #330), 06g4_ (0.33 #1133, 0.33 #902, 0.33 #443), 01tdnyh (0.33 #572, 0.33 #342, 0.29 #2877), 06y7d (0.33 #684, 0.33 #454, 0.29 #2989), 0969fd (0.33 #668, 0.33 #438, 0.29 #2973), 059y0 (0.33 #1132, 0.33 #901, 0.29 #2516), 01mr2g6 (0.33 #857, 0.33 #398, 0.29 #2933), 083q7 (0.33 #720, 0.33 #261, 0.29 #2796) >> Best rule #2991 for best value: >> intensional similarity = 27 >> extensional distance = 5 >> proper extension: 016t_3; >> query: (?x1519, 014vk4) <- institution(?x1519, ?x12737), institution(?x1519, ?x9200), institution(?x1519, ?x6912), institution(?x1519, ?x6019), institution(?x1519, ?x5178), institution(?x1519, ?x4410), institution(?x1519, ?x1087), institution(?x1519, ?x581), ?x581 = 06pwq, ?x6912 = 0gl5_, institution(?x734, ?x9200), major_field_of_study(?x1519, ?x5179), school(?x2820, ?x9200), company(?x3131, ?x6019), contains(?x1755, ?x5178), currency(?x9200, ?x170), ?x734 = 04zx3q1, student(?x5178, ?x1620), school_type(?x6019, ?x3205), ?x5179 = 04gb7, category(?x9200, ?x134), ?x4410 = 017j69, major_field_of_study(?x9200, ?x2605), citytown(?x12737, ?x1275), ?x2605 = 03g3w, currency(?x12737, ?x2244), colors(?x1087, ?x663) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #360 for first EXPECTED value: *> intensional similarity = 32 *> extensional distance = 1 *> proper extension: 014mlp; *> query: (?x1519, 012gx2) <- institution(?x1519, ?x13961), institution(?x1519, ?x13913), institution(?x1519, ?x9200), institution(?x1519, ?x6912), institution(?x1519, ?x6019), institution(?x1519, ?x5807), institution(?x1519, ?x5750), institution(?x1519, ?x4846), institution(?x1519, ?x2775), institution(?x1519, ?x1087), institution(?x1519, ?x741), institution(?x1519, ?x581), contains(?x94, ?x13913), contains(?x3908, ?x13961), ?x581 = 06pwq, ?x6912 = 0gl5_, ?x6019 = 021s9n, school_type(?x9200, ?x1044), ?x5750 = 01nnsv, major_field_of_study(?x9200, ?x6760), organization(?x3484, ?x13961), ?x741 = 01w3v, ?x4846 = 037njl, student(?x9200, ?x4137), ?x1087 = 01b1mj, citytown(?x9200, ?x6158), school(?x2820, ?x9200), ?x5807 = 0ks67, ?x94 = 09c7w0, ?x2775 = 078bz, ?x6760 = 0w7c, school_type(?x13913, ?x3205) *> conf = 0.33 ranks of expected_values: 46 EVAL 013zdg student 012gx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 24.000 22.000 0.429 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #14164-05bt6j PRED entity: 05bt6j PRED relation: parent_genre! PRED expected values: 016ybr => 65 concepts (41 used for prediction) PRED predicted values (max 10 best out of 305): 03xnwz (0.57 #2591, 0.50 #2077, 0.44 #3619), 01h0kx (0.50 #636, 0.43 #2689, 0.40 #1148), 0bt7w (0.50 #2135, 0.43 #2649, 0.33 #3677), 0grjmv (0.50 #626, 0.40 #1138, 0.40 #882), 0xjl2 (0.43 #2601, 0.33 #3629, 0.33 #2087), 01243b (0.43 #2599, 0.33 #2085, 0.33 #1315), 06cp5 (0.40 #838, 0.33 #3921, 0.33 #2378), 01ym9b (0.40 #805, 0.33 #2345, 0.33 #37), 06hzq3 (0.40 #1133, 0.33 #3702, 0.29 #2674), 017371 (0.40 #1163, 0.25 #651, 0.22 #3990) >> Best rule #2591 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 011j5x; >> query: (?x3061, 03xnwz) <- artists(?x3061, ?x11700), artists(?x3061, ?x6854), artists(?x3061, ?x883), ?x11700 = 017_hq, award(?x883, ?x724), location(?x883, ?x739), artist(?x2190, ?x6854) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #100 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 064t9; *> query: (?x3061, 016ybr) <- artists(?x3061, ?x8490), artists(?x3061, ?x6854), artists(?x3061, ?x6639), artists(?x3061, ?x4080), artists(?x3061, ?x2226), ?x6854 = 0178_w, ?x6639 = 0137hn, ?x2226 = 09k2t1, ?x8490 = 06rgq, ?x4080 = 0dl567 *> conf = 0.33 ranks of expected_values: 16 EVAL 05bt6j parent_genre! 016ybr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 65.000 41.000 0.571 http://example.org/music/genre/parent_genre #14163-0cp0t91 PRED entity: 0cp0t91 PRED relation: film_release_region PRED expected values: 05r4w 03gj2 03spz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 233): 0f8l9c (0.94 #1084, 0.91 #3520, 0.90 #4583), 06mkj (0.89 #1121, 0.88 #3557, 0.87 #4318), 05r4w (0.86 #3503, 0.86 #4566, 0.85 #4415), 03gj2 (0.85 #1239, 0.84 #4587, 0.84 #4436), 06t2t (0.85 #365, 0.77 #213, 0.77 #3562), 0b90_r (0.85 #156, 0.80 #308, 0.75 #2746), 03_3d (0.84 #1071, 0.81 #1526, 0.78 #1222), 01znc_ (0.79 #1256, 0.77 #3541, 0.76 #1105), 01p1v (0.77 #203, 0.65 #355, 0.52 #3552), 03spz (0.75 #1308, 0.71 #3593, 0.71 #2834) >> Best rule #1084 for best value: >> intensional similarity = 5 >> extensional distance = 60 >> proper extension: 0gffmn8; >> query: (?x8471, 0f8l9c) <- language(?x8471, ?x254), film_release_region(?x8471, ?x2513), executive_produced_by(?x8471, ?x3662), ?x2513 = 05b4w, film(?x1414, ?x8471) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #3503 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 157 *> proper extension: 0g4vmj8; *> query: (?x8471, 05r4w) <- language(?x8471, ?x254), film_crew_role(?x8471, ?x137), film_release_region(?x8471, ?x2645), film_release_region(?x8471, ?x1603), ?x1603 = 06bnz, ?x2645 = 03h64 *> conf = 0.86 ranks of expected_values: 3, 4, 10 EVAL 0cp0t91 film_release_region 03spz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 111.000 111.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cp0t91 film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 111.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cp0t91 film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 111.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14162-0b7l4x PRED entity: 0b7l4x PRED relation: film! PRED expected values: 0c6g1l 05bpg3 => 84 concepts (45 used for prediction) PRED predicted values (max 10 best out of 972): 08vr94 (0.20 #673, 0.05 #33193, 0.04 #62233), 044rvb (0.13 #101, 0.07 #2175, 0.03 #8397), 086nl7 (0.13 #783, 0.05 #33193, 0.05 #2857), 0fby2t (0.13 #751, 0.05 #17347, 0.05 #2825), 02wycg2 (0.13 #702, 0.05 #2776, 0.02 #17298), 0mdqp (0.13 #117, 0.03 #20862, 0.02 #31234), 0315q3 (0.13 #820, 0.02 #17416, 0.01 #31937), 02_0d2 (0.13 #1171), 079vf (0.11 #68456, 0.11 #53935, 0.11 #72605), 02xnjd (0.11 #68456, 0.11 #53935, 0.11 #72605) >> Best rule #673 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 02ntb8; 02825kb; 02x2jl_; >> query: (?x6009, 08vr94) <- language(?x6009, ?x254), ?x254 = 02h40lc, film(?x436, ?x6009), country(?x6009, ?x94), ?x436 = 032xhg >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #957 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 02ntb8; 02825kb; 02x2jl_; *> query: (?x6009, 05bpg3) <- language(?x6009, ?x254), ?x254 = 02h40lc, film(?x436, ?x6009), country(?x6009, ?x94), ?x436 = 032xhg *> conf = 0.07 ranks of expected_values: 75 EVAL 0b7l4x film! 05bpg3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 84.000 45.000 0.200 http://example.org/film/actor/film./film/performance/film EVAL 0b7l4x film! 0c6g1l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 45.000 0.200 http://example.org/film/actor/film./film/performance/film #14161-04353 PRED entity: 04353 PRED relation: produced_by! PRED expected values: 02d413 => 101 concepts (31 used for prediction) PRED predicted values (max 10 best out of 226): 07cyl (0.40 #6605, 0.34 #8495, 0.34 #7550), 03shpq (0.34 #8495, 0.34 #7550, 0.33 #5661), 02d413 (0.34 #8495, 0.34 #7550, 0.33 #5661), 03hj5lq (0.11 #579, 0.04 #1522, 0.04 #2465), 0gy0l_ (0.06 #3639, 0.03 #4582, 0.01 #5527), 08fn5b (0.06 #3203, 0.03 #4146, 0.01 #5091), 01f8f7 (0.04 #1591, 0.04 #2534), 0ct5zc (0.04 #1148, 0.04 #2091), 09tcg4 (0.04 #1805, 0.03 #3691, 0.02 #4634), 07vf5c (0.04 #1324, 0.03 #3210, 0.02 #4153) >> Best rule #6605 for best value: >> intensional similarity = 4 >> extensional distance = 179 >> proper extension: 04dyqk; >> query: (?x9313, ?x3471) <- nationality(?x9313, ?x94), ?x94 = 09c7w0, award_winner(?x3471, ?x9313), produced_by(?x2565, ?x9313) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #8495 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 234 *> proper extension: 05ty4m; 0m32_; 01c6l; 037d35; 0py5b; *> query: (?x9313, ?x69) <- nominated_for(?x9313, ?x3471), award(?x9313, ?x198), film(?x9313, ?x69) *> conf = 0.34 ranks of expected_values: 3 EVAL 04353 produced_by! 02d413 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 31.000 0.400 http://example.org/film/film/produced_by #14160-05z775 PRED entity: 05z775 PRED relation: student! PRED expected values: 015q1n => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 78): 078bz (0.10 #77, 0.07 #604, 0.03 #1131), 018t8f (0.10 #341, 0.04 #868, 0.03 #1395), 0ks67 (0.10 #189, 0.02 #5459, 0.02 #3878), 026gvfj (0.06 #1165, 0.05 #1692, 0.04 #2746), 0bwfn (0.06 #4491, 0.05 #1856, 0.05 #7653), 09f2j (0.05 #1740, 0.04 #4375, 0.04 #4902), 04b_46 (0.05 #4443, 0.04 #4970, 0.03 #3916), 01jszm (0.04 #700, 0.03 #1227, 0.03 #1754), 02bpy_ (0.04 #965, 0.03 #2019, 0.02 #2546), 01w5m (0.04 #632, 0.02 #4848, 0.02 #24347) >> Best rule #77 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 044_7j; >> query: (?x11435, 078bz) <- profession(?x11435, ?x1032), nationality(?x11435, ?x94), actor(?x8628, ?x11435), ?x8628 = 09g_31 >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #4428 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 79 *> proper extension: 02vntj; *> query: (?x11435, 015q1n) <- profession(?x11435, ?x1032), film(?x11435, ?x6216), ?x1032 = 02hrh1q, language(?x11435, ?x254) *> conf = 0.01 ranks of expected_values: 64 EVAL 05z775 student! 015q1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 75.000 75.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student #14159-016z2j PRED entity: 016z2j PRED relation: award PRED expected values: 09sb52 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 260): 04kxsb (0.72 #2766, 0.71 #21730, 0.70 #24891), 027b9j5 (0.72 #2766, 0.71 #21730, 0.70 #24891), 09sb52 (0.39 #431, 0.35 #2011, 0.34 #15049), 0cjyzs (0.33 #101, 0.07 #4052, 0.06 #17879), 05pcn59 (0.27 #471, 0.24 #2051, 0.23 #2446), 05p09zm (0.23 #514, 0.21 #909, 0.20 #1699), 03c7tr1 (0.23 #448, 0.18 #2028, 0.16 #1633), 094qd5 (0.20 #435, 0.15 #2015, 0.13 #39510), 01by1l (0.20 #10775, 0.19 #14725, 0.16 #4058), 0ck27z (0.20 #17075, 0.15 #31212, 0.14 #22212) >> Best rule #2766 for best value: >> intensional similarity = 2 >> extensional distance = 157 >> proper extension: 0yxl; >> query: (?x2373, ?x2375) <- participant(?x10473, ?x2373), award_winner(?x2375, ?x2373) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #431 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 81 *> proper extension: 014x77; 03_vx9; 04shbh; 0prjs; 03xmy1; 01pqy_; 07jrjb; *> query: (?x2373, 09sb52) <- celebrity(?x2373, ?x1564), award_winner(?x518, ?x2373) *> conf = 0.39 ranks of expected_values: 3 EVAL 016z2j award 09sb52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 119.000 0.718 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14158-01xqw PRED entity: 01xqw PRED relation: family PRED expected values: 0l14_3 => 56 concepts (51 used for prediction) PRED predicted values (max 10 best out of 125): 0fx80y (0.39 #524, 0.35 #638, 0.27 #255), 05148p4 (0.33 #108, 0.25 #296, 0.25 #269), 0342h (0.22 #209, 0.20 #28, 0.18 #235), 0l14md (0.20 #51, 0.20 #30, 0.16 #924), 026t6 (0.20 #27, 0.12 #154, 0.11 #181), 01kcd (0.20 #67, 0.09 #248, 0.09 #491), 02qjv (0.17 #107, 0.12 #160, 0.11 #187), 0859_ (0.17 #121, 0.06 #393, 0.04 #497), 085jw (0.16 #441, 0.08 #307, 0.07 #785), 0l14_3 (0.11 #230, 0.11 #203, 0.09 #256) >> Best rule #524 for best value: >> intensional similarity = 10 >> extensional distance = 21 >> proper extension: 01w4dy; >> query: (?x4311, 0fx80y) <- role(?x2923, ?x4311), role(?x74, ?x4311), role(?x228, ?x2923), role(?x4311, ?x3991), role(?x4311, ?x960), ?x960 = 04q7r, ?x3991 = 05842k, role(?x1663, ?x4311), family(?x4311, ?x10811), role(?x74, ?x868) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #230 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 7 *> proper extension: 07y_7; 02sgy; 05r5c; 02k84w; 01dnws; *> query: (?x4311, 0l14_3) <- role(?x2923, ?x4311), role(?x2048, ?x4311), role(?x315, ?x4311), ?x2923 = 02k856, ?x315 = 0l14md, family(?x4311, ?x10811), ?x2048 = 018j2, role(?x1399, ?x4311), award_nominee(?x1399, ?x158), role(?x4311, ?x212) *> conf = 0.11 ranks of expected_values: 10 EVAL 01xqw family 0l14_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 56.000 51.000 0.391 http://example.org/music/instrument/family #14157-07q1m PRED entity: 07q1m PRED relation: country PRED expected values: 0f8l9c => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 139): 0f8l9c (0.50 #18, 0.11 #77, 0.10 #314), 0345h (0.12 #85, 0.12 #2287, 0.11 #1394), 0d060g (0.08 #8, 0.07 #4350, 0.05 #67), 06mkj (0.08 #39, 0.07 #4350, 0.02 #2477), 05qhw (0.08 #15, 0.07 #4350, 0.01 #3272), 017fp (0.07 #2140, 0.07 #2261, 0.07 #2260), 07s9rl0 (0.07 #2140, 0.07 #2261, 0.07 #2260), 03_3d (0.07 #4350, 0.06 #1730, 0.05 #900), 0chghy (0.07 #4350, 0.05 #905, 0.04 #1735), 03rjj (0.07 #4350, 0.04 #65, 0.03 #2919) >> Best rule #18 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01d259; 04h4c9; >> query: (?x5646, 0f8l9c) <- music(?x5646, ?x9891), genre(?x5646, ?x811), ?x9891 = 01x1fq >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07q1m country 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.500 http://example.org/film/film/country #14156-0z05l PRED entity: 0z05l PRED relation: award_winner! PRED expected values: 01s695 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 83): 01s695 (0.17 #4372, 0.13 #3, 0.09 #144), 0hr3c8y (0.17 #4372, 0.07 #10, 0.04 #997), 058m5m4 (0.17 #4372, 0.07 #55, 0.04 #760), 0hndn2q (0.17 #4372, 0.07 #40, 0.02 #1591), 0418154 (0.17 #4372, 0.07 #108, 0.02 #954), 01bx35 (0.13 #7, 0.09 #148, 0.09 #430), 02cg41 (0.13 #126, 0.09 #408, 0.09 #549), 01mhwk (0.13 #41, 0.06 #182, 0.05 #323), 013b2h (0.12 #503, 0.12 #362, 0.10 #221), 02rjjll (0.10 #287, 0.10 #428, 0.09 #146) >> Best rule #4372 for best value: >> intensional similarity = 3 >> extensional distance = 1364 >> proper extension: 0f721s; 0gsg7; 01p5yn; 0hm0k; 0283xx2; 01j53q; 01zcrv; 05s34b; >> query: (?x7069, ?x342) <- award_winner(?x4184, ?x7069), award_winner(?x5172, ?x4184), award_winner(?x342, ?x4184) >> conf = 0.17 => this is the best rule for 5 predicted values ranks of expected_values: 1 EVAL 0z05l award_winner! 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.170 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14155-03xkps PRED entity: 03xkps PRED relation: award_winner! PRED expected values: 0bfvd4 => 110 concepts (107 used for prediction) PRED predicted values (max 10 best out of 205): 09sb52 (0.37 #23179, 0.37 #22320, 0.37 #20173), 0bfvd4 (0.37 #23179, 0.37 #22320, 0.37 #20173), 0cqh46 (0.37 #23179, 0.37 #22320, 0.37 #20173), 0bp_b2 (0.37 #23179, 0.37 #22320, 0.37 #20173), 0ck27z (0.22 #3524, 0.20 #4811, 0.20 #3953), 0cqhk0 (0.17 #3469, 0.14 #4756, 0.13 #3898), 099tbz (0.14 #57, 0.10 #486, 0.07 #1344), 02w9sd7 (0.14 #166, 0.10 #595, 0.05 #1453), 05zr6wv (0.14 #18, 0.10 #447, 0.04 #1735), 024fz9 (0.14 #205, 0.10 #634) >> Best rule #23179 for best value: >> intensional similarity = 2 >> extensional distance = 1462 >> proper extension: 0khth; 014l4w; 07mvp; 04k05; 014g91; 07k2d; >> query: (?x3808, ?x435) <- award_winner(?x9972, ?x3808), award(?x3808, ?x435) >> conf = 0.37 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 03xkps award_winner! 0bfvd4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 107.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #14154-0gyx4 PRED entity: 0gyx4 PRED relation: gender PRED expected values: 05zppz => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #77, 0.87 #71, 0.87 #65), 02zsn (0.55 #143, 0.52 #20, 0.50 #38) >> Best rule #77 for best value: >> intensional similarity = 2 >> extensional distance = 290 >> proper extension: 0qf43; 019z7q; 04l3_z; 08433; 012t1; 016hvl; 03ft8; 01q415; 04gcd1; 0jt90f5; ... >> query: (?x4397, 05zppz) <- written_by(?x2943, ?x4397), nominated_for(?x406, ?x2943) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gyx4 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.866 http://example.org/people/person/gender #14153-011yrp PRED entity: 011yrp PRED relation: film_release_region PRED expected values: 0f8l9c 07f1x => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 133): 09c7w0 (0.93 #5857, 0.93 #5126, 0.92 #8197), 0f8l9c (0.91 #602, 0.90 #2502, 0.89 #3235), 0b90_r (0.79 #442, 0.74 #2488, 0.64 #3221), 06t2t (0.77 #491, 0.69 #2537, 0.57 #3270), 03rt9 (0.73 #450, 0.67 #2496, 0.58 #3229), 04gzd (0.71 #446, 0.47 #2492, 0.42 #592), 03spz (0.69 #524, 0.68 #2570, 0.58 #3303), 015qh (0.67 #472, 0.44 #2518, 0.42 #618), 01p1v (0.65 #482, 0.46 #2528, 0.37 #3261), 047yc (0.60 #461, 0.44 #2507, 0.42 #607) >> Best rule #5857 for best value: >> intensional similarity = 3 >> extensional distance = 610 >> proper extension: 047q2k1; 016z5x; 06krf3; 0k4kk; 0gxfz; 011ydl; 0kxf1; 097zcz; 09fn1w; 0q9sg; ... >> query: (?x303, 09c7w0) <- award(?x303, ?x77), film_release_region(?x303, ?x87), nominated_for(?x68, ?x303) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #602 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 51 *> proper extension: 026p_bs; 08hmch; 0m491; 024mpp; 0blpg; 047vnkj; 03mgx6z; 0mbql; 06rzwx; 03z9585; *> query: (?x303, 0f8l9c) <- film_release_region(?x303, ?x1355), film_release_region(?x303, ?x985), ?x985 = 0k6nt, written_by(?x303, ?x6426), ?x1355 = 0h7x *> conf = 0.91 ranks of expected_values: 2, 19 EVAL 011yrp film_release_region 07f1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 88.000 88.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 011yrp film_release_region 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14152-047s_cr PRED entity: 047s_cr PRED relation: languages PRED expected values: 03k50 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 15): 03k50 (0.47 #82, 0.42 #121, 0.38 #160), 02h40lc (0.35 #80, 0.30 #275, 0.29 #1055), 07c9s (0.25 #13, 0.21 #208, 0.21 #52), 09s02 (0.15 #192, 0.12 #153, 0.12 #114), 02hxcvy (0.12 #26, 0.07 #65, 0.07 #260), 0999q (0.10 #218, 0.09 #764, 0.09 #179), 09bnf (0.07 #78, 0.04 #780, 0.04 #975), 01c7y (0.06 #109, 0.05 #226, 0.04 #1485), 055qm (0.06 #609, 0.05 #453, 0.04 #492), 0121sr (0.04 #1485, 0.04 #345, 0.03 #462) >> Best rule #82 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 06gn7r; >> query: (?x12681, 03k50) <- location(?x12681, ?x5384), people(?x5025, ?x12681), ?x5025 = 0dryh9k, profession(?x12681, ?x1146), profession(?x2564, ?x1146), ?x2564 = 02lf1j >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047s_cr languages 03k50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.471 http://example.org/people/person/languages #14151-0f2v0 PRED entity: 0f2v0 PRED relation: vacationer PRED expected values: 058s57 01wgxtl 02b9g4 => 204 concepts (179 used for prediction) PRED predicted values (max 10 best out of 305): 016fnb (0.50 #97, 0.21 #1242, 0.17 #2062), 0bbf1f (0.25 #59, 0.21 #1204, 0.17 #1860), 019pm_ (0.25 #57, 0.17 #384, 0.12 #2186), 02mjf2 (0.25 #94, 0.17 #421, 0.09 #3042), 01gq0b (0.25 #194, 0.17 #357, 0.07 #6263), 05r5w (0.25 #71, 0.16 #1216, 0.15 #3019), 01xyt7 (0.25 #119, 0.16 #1264, 0.13 #2084), 04xrx (0.25 #52, 0.16 #1197, 0.13 #1853), 02d9k (0.25 #33, 0.16 #1178, 0.13 #1834), 0151w_ (0.25 #19, 0.15 #674, 0.12 #837) >> Best rule #97 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02_286; 05qtj; >> query: (?x3501, 016fnb) <- mode_of_transportation(?x3501, ?x4272), location(?x4476, ?x3501), vacationer(?x3501, ?x4106), ?x4106 = 04fzk >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #10498 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 01mc11; 06dfg; 04swx; *> query: (?x3501, ?x56) <- vacationer(?x3501, ?x8716), vacationer(?x3501, ?x2108), place_of_birth(?x8716, ?x12250), participant(?x2108, ?x56) *> conf = 0.05 ranks of expected_values: 107 EVAL 0f2v0 vacationer 02b9g4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 204.000 179.000 0.500 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 0f2v0 vacationer 01wgxtl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 179.000 0.500 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 0f2v0 vacationer 058s57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 179.000 0.500 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #14150-0cycc PRED entity: 0cycc PRED relation: symptom_of! PRED expected values: 0cjf0 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 56): 01j6t0 (0.84 #1195, 0.73 #1171, 0.66 #1265), 0cjf0 (0.50 #589, 0.50 #355, 0.43 #871), 0gxb2 (0.50 #252, 0.32 #433, 0.30 #419), 012qjw (0.40 #418, 0.39 #268, 0.38 #324), 01cdt5 (0.38 #362, 0.36 #526, 0.35 #961), 0brgy (0.34 #1473, 0.33 #30, 0.32 #1270), 02tfl8 (0.34 #1473, 0.33 #24, 0.32 #433), 0f3kl (0.34 #1473, 0.33 #41, 0.32 #433), 08g5q7 (0.32 #433, 0.20 #1215, 0.17 #1395), 01pf6 (0.32 #433, 0.20 #1215, 0.17 #1395) >> Best rule #1195 for best value: >> intensional similarity = 7 >> extensional distance = 36 >> proper extension: 01g2q; >> query: (?x13231, 01j6t0) <- symptom_of(?x6780, ?x13231), symptom_of(?x6780, ?x11392), symptom_of(?x6780, ?x11307), symptom_of(?x6780, ?x6656), ?x6656 = 03p41, people(?x11307, ?x6745), ?x11392 = 0lcdk >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #589 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 12 *> proper extension: 087z2; *> query: (?x13231, 0cjf0) <- symptom_of(?x6780, ?x13231), ?x6780 = 0j5fv *> conf = 0.50 ranks of expected_values: 2 EVAL 0cycc symptom_of! 0cjf0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 79.000 0.842 http://example.org/medicine/symptom/symptom_of #14149-0hsqf PRED entity: 0hsqf PRED relation: citytown! PRED expected values: 01nds => 197 concepts (148 used for prediction) PRED predicted values (max 10 best out of 616): 01bvw5 (0.52 #92670, 0.47 #73329, 0.29 #4026), 01nds (0.12 #3794, 0.11 #19103, 0.09 #10243), 0dn_w (0.12 #3989, 0.09 #4795, 0.07 #6408), 01z_jj (0.12 #3966, 0.09 #4772, 0.07 #6385), 07k5l (0.12 #3964, 0.09 #4770, 0.07 #6383), 0c0sl (0.12 #3939, 0.09 #4745, 0.07 #6358), 01kcmr (0.12 #3932, 0.09 #4738, 0.07 #6351), 07w42 (0.12 #3910, 0.09 #4716, 0.07 #6329), 03f2fw (0.12 #3751, 0.09 #4557, 0.07 #6170), 02vnp2 (0.12 #3704, 0.09 #4510, 0.07 #6123) >> Best rule #92670 for best value: >> intensional similarity = 2 >> extensional distance = 236 >> proper extension: 0t015; 0_7z2; 0_ytw; 013cz2; 013hxv; 0j8p6; 0d23k; 0fvwg; 0mnm2; 0_lr1; ... >> query: (?x9310, ?x2079) <- citytown(?x8121, ?x9310), contains(?x9310, ?x2079) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #3794 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 07ssc; *> query: (?x9310, 01nds) <- place_of_birth(?x823, ?x9310), teams(?x9310, ?x4006), origin(?x11667, ?x9310), state_province_region(?x2079, ?x9310) *> conf = 0.12 ranks of expected_values: 2 EVAL 0hsqf citytown! 01nds CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 197.000 148.000 0.516 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #14148-078g3l PRED entity: 078g3l PRED relation: place_of_birth PRED expected values: 0rh6k => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 23): 0cr3d (0.27 #94, 0.04 #7136, 0.04 #6432), 01531 (0.08 #105, 0.02 #7851, 0.02 #5739), 0yc7f (0.08 #279), 02_286 (0.08 #9173, 0.07 #8469, 0.07 #19737), 030qb3t (0.04 #3574, 0.04 #758, 0.04 #5688), 0rh6k (0.04 #2, 0.01 #9156, 0.01 #6340), 094jv (0.04 #61, 0.01 #9215), 0d_kd (0.04 #538), 013_gg (0.04 #472), 0xl08 (0.04 #241) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 03mdt; >> query: (?x6299, 0cr3d) <- nominated_for(?x6299, ?x1849), award(?x6299, ?x435), ?x1849 = 0kfv9 >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 03mdt; *> query: (?x6299, 0rh6k) <- nominated_for(?x6299, ?x1849), award(?x6299, ?x435), ?x1849 = 0kfv9 *> conf = 0.04 ranks of expected_values: 6 EVAL 078g3l place_of_birth 0rh6k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 67.000 0.269 http://example.org/people/person/place_of_birth #14147-015bpl PRED entity: 015bpl PRED relation: film! PRED expected values: 01_rh4 => 62 concepts (30 used for prediction) PRED predicted values (max 10 best out of 771): 02lkcc (0.10 #243, 0.05 #8555, 0.05 #10633), 03wy70 (0.10 #1287, 0.04 #9599, 0.04 #11677), 079vf (0.09 #12476, 0.09 #4164, 0.06 #6242), 03kpvp (0.08 #2711, 0.06 #15180, 0.06 #17259), 0lpjn (0.08 #2557, 0.05 #15026, 0.05 #8791), 04shbh (0.07 #6400, 0.07 #2244, 0.04 #14713), 0c6qh (0.07 #6648, 0.03 #41977, 0.03 #23274), 0f4vbz (0.07 #2440, 0.05 #8674, 0.05 #10752), 016ypb (0.07 #499, 0.06 #6733, 0.05 #8811), 01sl1q (0.07 #1, 0.05 #2079, 0.04 #14548) >> Best rule #243 for best value: >> intensional similarity = 8 >> extensional distance = 28 >> proper extension: 03_wm6; >> query: (?x7989, 02lkcc) <- genre(?x7989, ?x1013), genre(?x7989, ?x812), genre(?x7989, ?x811), ?x812 = 01jfsb, ?x1013 = 06n90, ?x811 = 03k9fj, currency(?x7989, ?x170), film(?x902, ?x7989) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #580 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 28 *> proper extension: 03_wm6; *> query: (?x7989, 01_rh4) <- genre(?x7989, ?x1013), genre(?x7989, ?x812), genre(?x7989, ?x811), ?x812 = 01jfsb, ?x1013 = 06n90, ?x811 = 03k9fj, currency(?x7989, ?x170), film(?x902, ?x7989) *> conf = 0.03 ranks of expected_values: 143 EVAL 015bpl film! 01_rh4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 62.000 30.000 0.100 http://example.org/film/actor/film./film/performance/film #14146-03f6fl0 PRED entity: 03f6fl0 PRED relation: profession PRED expected values: 01c72t 0fnpj => 159 concepts (103 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.86 #6749, 0.85 #5459, 0.82 #3160), 01c72t (0.50 #593, 0.43 #10784, 0.40 #6184), 0fnpj (0.40 #55, 0.29 #1485, 0.22 #6218), 01d_h8 (0.29 #5451, 0.28 #9760, 0.26 #6741), 01445t (0.29 #163, 0.02 #6328, 0.01 #7472), 03gjzk (0.24 #5460, 0.21 #6033, 0.20 #4310), 0dxtg (0.24 #5458, 0.21 #12642, 0.21 #9767), 09lbv (0.20 #3596, 0.10 #1304, 0.10 #875), 02jknp (0.20 #6, 0.19 #5453, 0.18 #9762), 05vyk (0.20 #89, 0.19 #6252, 0.17 #2521) >> Best rule #6749 for best value: >> intensional similarity = 4 >> extensional distance = 204 >> proper extension: 0byfz; 014x77; 03m8lq; 04nw9; 01713c; 01t2h2; 01f7j9; 0f4vbz; 06w6_; 01fdc0; ... >> query: (?x4977, 02hrh1q) <- location_of_ceremony(?x4977, ?x3026), profession(?x4977, ?x220), profession(?x10461, ?x220), ?x10461 = 01vvybv >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 01lvcs1; *> query: (?x4977, 01c72t) <- artists(?x302, ?x4977), profession(?x4977, ?x4654), location(?x4977, ?x3501), instrumentalists(?x227, ?x4977), ?x4654 = 029bkp *> conf = 0.50 ranks of expected_values: 2, 3 EVAL 03f6fl0 profession 0fnpj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 159.000 103.000 0.859 http://example.org/people/person/profession EVAL 03f6fl0 profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 159.000 103.000 0.859 http://example.org/people/person/profession #14145-0947l PRED entity: 0947l PRED relation: location! PRED expected values: 044mvs => 257 concepts (139 used for prediction) PRED predicted values (max 10 best out of 2446): 03bxh (0.40 #3666, 0.22 #11208, 0.20 #21266), 0bdxs5 (0.30 #16847, 0.14 #26905, 0.14 #24391), 03d6q (0.28 #30172, 0.15 #93037, 0.14 #289203), 023kzp (0.24 #36417, 0.18 #41446, 0.17 #46476), 01qq_lp (0.22 #10818, 0.20 #20876, 0.20 #18361), 01q_ph (0.21 #25192, 0.21 #22678, 0.20 #15134), 021mlp (0.20 #331955, 0.20 #271592, 0.19 #178540), 09bxq9 (0.20 #16641, 0.20 #14127, 0.20 #4071), 0gl88b (0.20 #15455, 0.18 #35571, 0.14 #25513), 073749 (0.20 #15887, 0.14 #25945, 0.14 #23431) >> Best rule #3666 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 096g3; 031y2; >> query: (?x8956, 03bxh) <- administrative_division(?x8956, ?x9792), place_of_birth(?x10520, ?x8956), contains(?x205, ?x8956), ?x205 = 03rjj >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #47322 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 02k54; *> query: (?x8956, 044mvs) <- vacationer(?x8956, ?x2237), citytown(?x5695, ?x8956), award_winner(?x2160, ?x2237), profession(?x2237, ?x319) *> conf = 0.13 ranks of expected_values: 74 EVAL 0947l location! 044mvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 257.000 139.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #14144-05ldxl PRED entity: 05ldxl PRED relation: costume_design_by PRED expected values: 02pqgt8 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 19): 02mxbd (0.05 #73, 0.03 #101, 0.02 #355), 0bytfv (0.04 #39, 0.03 #293, 0.03 #349), 02cqbx (0.04 #44, 0.02 #1009, 0.02 #922), 03y1mlp (0.03 #86, 0.02 #58, 0.02 #880), 02vkvcz (0.02 #78), 03mfqm (0.02 #271, 0.02 #612, 0.02 #1011), 03qhyn8 (0.02 #54, 0.01 #110), 05x2t7 (0.02 #34, 0.01 #259), 026lyl4 (0.02 #51), 0gl88b (0.02 #33) >> Best rule #73 for best value: >> intensional similarity = 2 >> extensional distance = 134 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x8258, 02mxbd) <- titles(?x512, ?x8258), ?x512 = 07ssc >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #68 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 134 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x8258, 02pqgt8) <- titles(?x512, ?x8258), ?x512 = 07ssc *> conf = 0.01 ranks of expected_values: 13 EVAL 05ldxl costume_design_by 02pqgt8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 78.000 78.000 0.051 http://example.org/film/film/costume_design_by #14143-024pcx PRED entity: 024pcx PRED relation: jurisdiction_of_office! PRED expected values: 0p5vf => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 20): 060bp (0.96 #2311, 0.82 #505, 0.72 #1051), 060c4 (0.78 #654, 0.74 #1221, 0.74 #1914), 0789n (0.43 #261, 0.21 #576, 0.20 #135), 0pqc5 (0.41 #2294, 0.36 #3050, 0.36 #3071), 0p5vf (0.38 #389, 0.33 #11, 0.31 #410), 0fj45 (0.34 #1068, 0.24 #522, 0.17 #564), 0fkvn (0.31 #361, 0.23 #2818, 0.22 #2797), 0f6c3 (0.31 #364, 0.22 #2821, 0.22 #2800), 01zq91 (0.29 #286, 0.29 #265, 0.28 #538), 09n5b9 (0.23 #367, 0.20 #2803, 0.20 #2824) >> Best rule #2311 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 04fh3; >> query: (?x9328, 060bp) <- adjoins(?x1611, ?x9328), jurisdiction_of_office(?x3119, ?x9328), jurisdiction_of_office(?x3119, ?x3683), ?x3683 = 0161c >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #389 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 03rt9; 0h7x; 084n_; *> query: (?x9328, 0p5vf) <- adjoins(?x1611, ?x9328), contains(?x455, ?x9328), nationality(?x5249, ?x9328), organizations_founded(?x5249, ?x5250), student(?x892, ?x5249) *> conf = 0.38 ranks of expected_values: 5 EVAL 024pcx jurisdiction_of_office! 0p5vf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 184.000 184.000 0.962 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #14142-012gyf PRED entity: 012gyf PRED relation: colors PRED expected values: 01l849 => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.67 #2124, 0.65 #1523, 0.45 #22), 01l849 (0.50 #1161, 0.33 #901, 0.32 #1382), 06fvc (0.42 #63, 0.32 #223, 0.31 #163), 019sc (0.27 #407, 0.25 #67, 0.24 #187), 036k5h (0.18 #465, 0.18 #225, 0.17 #65), 088fh (0.18 #26, 0.11 #1181, 0.10 #2503), 038hg (0.17 #52, 0.14 #252, 0.12 #852), 067z2v (0.15 #349, 0.14 #89, 0.12 #129), 01jnf1 (0.12 #191, 0.11 #1181, 0.10 #2503), 04mkbj (0.11 #1652, 0.11 #1181, 0.10 #2503) >> Best rule #2124 for best value: >> intensional similarity = 7 >> extensional distance = 217 >> proper extension: 0173s9; 02kj7g; >> query: (?x12620, 083jv) <- colors(?x12620, ?x3189), colors(?x10178, ?x3189), colors(?x918, ?x3189), school_type(?x12620, ?x3092), colors(?x387, ?x3189), ?x10178 = 01tntf, ?x918 = 02cttt >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1161 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 127 *> proper extension: 03zw80; 03x1s8; *> query: (?x12620, 01l849) <- category(?x12620, ?x134), citytown(?x12620, ?x8181), colors(?x12620, ?x3189), colors(?x4369, ?x3189), ?x4369 = 02pqcfz *> conf = 0.50 ranks of expected_values: 2 EVAL 012gyf colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 188.000 188.000 0.671 http://example.org/education/educational_institution/colors #14141-018dnt PRED entity: 018dnt PRED relation: type_of_union PRED expected values: 04ztj => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #137, 0.71 #101, 0.71 #145), 01g63y (0.44 #349, 0.25 #2, 0.15 #50) >> Best rule #137 for best value: >> intensional similarity = 4 >> extensional distance = 807 >> proper extension: 04bdxl; 04yywz; 07s3vqk; 02bfmn; 01j5ts; 06dv3; 02g8h; 0d_84; 01p7yb; 0prfz; ... >> query: (?x585, 04ztj) <- place_of_birth(?x585, ?x6357), film(?x585, ?x586), location(?x585, ?x1591), nominated_for(?x143, ?x586) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018dnt type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.716 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14140-02qx69 PRED entity: 02qx69 PRED relation: film PRED expected values: 02stbw => 99 concepts (80 used for prediction) PRED predicted values (max 10 best out of 447): 0h1fktn (0.30 #4536, 0.03 #64263, 0.03 #99967), 01shy7 (0.25 #422, 0.12 #2207, 0.05 #7562), 02_1sj (0.25 #80, 0.05 #3650, 0.03 #64263), 07024 (0.25 #480, 0.01 #45106, 0.01 #20115), 016z5x (0.25 #70, 0.01 #32200, 0.01 #19705), 07vn_9 (0.25 #1679, 0.01 #21314), 080dfr7 (0.25 #1661), 03cwwl (0.25 #1607), 06_sc3 (0.25 #1416), 048xyn (0.25 #1104) >> Best rule #4536 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 03qd_; 05b__vr; 064nh4k; 0pgjm; 07sgfvl; 05ztm4r; 0806vbn; 07s6prs; 021bk; 0783m_; ... >> query: (?x3258, 0h1fktn) <- award_nominee(?x3258, ?x8896), award(?x3258, ?x1691), ?x8896 = 07m77x >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #3952 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 03qd_; 05b__vr; 064nh4k; 0pgjm; 07sgfvl; 05ztm4r; 0806vbn; 07s6prs; 021bk; 0783m_; ... *> query: (?x3258, 02stbw) <- award_nominee(?x3258, ?x8896), award(?x3258, ?x1691), ?x8896 = 07m77x *> conf = 0.15 ranks of expected_values: 15 EVAL 02qx69 film 02stbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 99.000 80.000 0.300 http://example.org/film/actor/film./film/performance/film #14139-039g82 PRED entity: 039g82 PRED relation: film PRED expected values: 0kbwb => 95 concepts (76 used for prediction) PRED predicted values (max 10 best out of 302): 05zr0xl (0.59 #34003, 0.57 #39373, 0.46 #19687), 023vcd (0.33 #1637, 0.03 #109166, 0.03 #110956), 0crd8q6 (0.33 #1632, 0.03 #109166, 0.03 #110956), 02_1q9 (0.08 #8948, 0.07 #7158, 0.06 #37583), 0h1fktn (0.06 #4547, 0.02 #6337, 0.02 #11707), 030k94 (0.05 #62637), 03bx2lk (0.03 #1974, 0.02 #5553, 0.02 #32398), 016z9n (0.03 #2158, 0.02 #11107, 0.02 #7527), 06gb1w (0.03 #2522, 0.02 #4311, 0.02 #6101), 0g7pm1 (0.03 #2991, 0.02 #4780, 0.01 #6570) >> Best rule #34003 for best value: >> intensional similarity = 3 >> extensional distance = 882 >> proper extension: 025p38; 05wjnt; 05hdf; 0c01c; 0glmv; 01nrq5; 01twdk; 0n8bn; 06_bq1; 01lqnff; ... >> query: (?x1784, ?x8533) <- award_winner(?x8533, ?x1784), film(?x1784, ?x5713), film_release_region(?x5713, ?x87) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 039g82 film 0kbwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 76.000 0.595 http://example.org/film/actor/film./film/performance/film #14138-01vyv9 PRED entity: 01vyv9 PRED relation: award_nominee PRED expected values: 057_yx => 138 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1159): 057_yx (0.83 #11511, 0.78 #4530, 0.76 #16165), 01vyv9 (0.56 #10376, 0.56 #3395, 0.54 #8049), 0bxtg (0.21 #125669, 0.17 #90, 0.09 #4744), 05hj0n (0.21 #125669, 0.17 #100, 0.09 #4754), 04954 (0.21 #125669, 0.17 #1677, 0.09 #6331), 02bkdn (0.21 #125669, 0.11 #9707, 0.10 #14361), 030znt (0.21 #125669, 0.08 #7261, 0.06 #11915), 046m59 (0.21 #125669, 0.08 #8256, 0.06 #12910), 02ldv0 (0.21 #125669, 0.08 #8467, 0.06 #13121), 01s7zw (0.21 #125669, 0.08 #7536, 0.06 #12190) >> Best rule #11511 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 02jsgf; 015vq_; 014g22; 042z_g; 034zc0; 02ch1w; 02t_vx; 02ct_k; 057_yx; >> query: (?x4553, 057_yx) <- award_nominee(?x4463, ?x4553), award_nominee(?x4137, ?x4553), ?x4137 = 0410cp, ?x4463 = 016ks_ >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vyv9 award_nominee 057_yx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 61.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14137-0qmhk PRED entity: 0qmhk PRED relation: language PRED expected values: 06nm1 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 39): 064_8sq (0.26 #195, 0.18 #315, 0.18 #79), 06b_j (0.26 #196, 0.10 #138, 0.09 #316), 04306rv (0.19 #178, 0.10 #942, 0.09 #1235), 0jzc (0.19 #193, 0.07 #545, 0.06 #77), 06nm1 (0.11 #656, 0.10 #1006, 0.10 #2006), 02hwyss (0.10 #157, 0.06 #99, 0.05 #335), 071fb (0.07 #191, 0.02 #311, 0.02 #369), 02bjrlw (0.07 #647, 0.07 #939, 0.07 #705), 03_9r (0.06 #1534, 0.06 #1240, 0.06 #3185), 07zrf (0.06 #60, 0.05 #118, 0.04 #176) >> Best rule #195 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0413cff; >> query: (?x5515, 064_8sq) <- country(?x5515, ?x94), titles(?x11405, ?x5515), genre(?x5515, ?x53), ?x11405 = 02qfv5d >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #656 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 145 *> proper extension: 01hvjx; 01svry; 0bxsk; 04sh80; *> query: (?x5515, 06nm1) <- film(?x3177, ?x5515), film(?x7391, ?x5515), music(?x5515, ?x8849), executive_produced_by(?x5515, ?x10522) *> conf = 0.11 ranks of expected_values: 5 EVAL 0qmhk language 06nm1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 97.000 0.259 http://example.org/film/film/language #14136-01lmj3q PRED entity: 01lmj3q PRED relation: type_of_union PRED expected values: 04ztj => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.66 #297, 0.66 #281, 0.66 #289), 01g63y (0.12 #18, 0.11 #166, 0.11 #250) >> Best rule #297 for best value: >> intensional similarity = 2 >> extensional distance = 2194 >> proper extension: 025vry; 067jsf; 083q7; 016hvl; 05fg2; 03cvfg; 0203v; 0bymv; 02m7r; 04b19t; ... >> query: (?x367, 04ztj) <- profession(?x367, ?x131), award_winner(?x341, ?x367) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lmj3q type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.664 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14135-025m98 PRED entity: 025m98 PRED relation: ceremony PRED expected values: 05pd94v 056878 01mh_q => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 126): 01mh_q (0.89 #329, 0.72 #455, 0.61 #708), 056878 (0.85 #278, 0.77 #404, 0.66 #657), 05pd94v (0.81 #254, 0.76 #380, 0.67 #633), 0bzknt (0.46 #1515, 0.38 #1894, 0.37 #2527), 05c1t6z (0.46 #1515, 0.38 #1894, 0.37 #2527), 0bzjvm (0.46 #1515, 0.38 #1894, 0.37 #2527), 073h1t (0.46 #1515, 0.38 #1894, 0.37 #2527), 0bzmt8 (0.46 #1515, 0.38 #1894, 0.37 #2527), 03tn9w (0.46 #1515, 0.38 #1894, 0.37 #2527), 09pj68 (0.46 #1515, 0.38 #1894, 0.37 #2527) >> Best rule #329 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 0257yf; >> query: (?x5123, 01mh_q) <- award(?x158, ?x5123), ceremony(?x5123, ?x1480), ?x1480 = 01c6qp >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 025m98 ceremony 01mh_q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 025m98 ceremony 056878 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 025m98 ceremony 05pd94v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony #14134-0693l PRED entity: 0693l PRED relation: tv_program PRED expected values: 01kt_j => 148 concepts (138 used for prediction) PRED predicted values (max 10 best out of 26): 039cq4 (0.08 #397, 0.07 #571, 0.06 #1532), 01kt_j (0.04 #3852, 0.04 #4290, 0.03 #10328), 02yvct (0.04 #8484, 0.04 #9362, 0.04 #9187), 0f4_l (0.04 #8484, 0.04 #9362, 0.04 #9187), 019nnl (0.03 #181, 0.01 #1138, 0.01 #3505), 03nymk (0.03 #231), 0gwjw0c (0.03 #10328, 0.02 #10680, 0.02 #10768), 01rxyb (0.03 #10328, 0.02 #10680, 0.02 #10768), 07c72 (0.03 #370, 0.02 #544, 0.02 #3607), 0ph24 (0.03 #429, 0.02 #603) >> Best rule #397 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 0lrh; 018x3; >> query: (?x3117, 039cq4) <- award(?x3117, ?x68), influenced_by(?x12392, ?x3117), participant(?x3118, ?x3117) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #3852 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 281 *> proper extension: 039crh; 0q1lp; 01507p; 0bkq_8; *> query: (?x3117, ?x10595) <- people(?x1446, ?x3117), nominated_for(?x3117, ?x10595), program(?x2062, ?x10595) *> conf = 0.04 ranks of expected_values: 2 EVAL 0693l tv_program 01kt_j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 148.000 138.000 0.077 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #14133-09c7w0 PRED entity: 09c7w0 PRED relation: place_of_birth! PRED expected values: 055sjw => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 1568): 0gpprt (0.33 #4419, 0.02 #105876, 0.02 #155300), 0bqdvt (0.33 #3516, 0.02 #104973, 0.02 #154397), 04z0g (0.33 #366807, 0.29 #299168, 0.28 #223722), 099p5 (0.33 #366807, 0.29 #299168, 0.28 #223722), 06crk (0.33 #366807, 0.29 #299168, 0.28 #223722), 032r1 (0.33 #366807, 0.29 #299168, 0.28 #223722), 01zwy (0.33 #366807, 0.29 #299168, 0.28 #223722), 0b78hw (0.33 #366807, 0.29 #299168, 0.28 #223722), 099d4 (0.33 #366807, 0.29 #299168, 0.28 #223722), 01797x (0.33 #366807, 0.29 #299168, 0.28 #223722) >> Best rule #4419 for best value: >> intensional similarity = 2 >> extensional distance = 1 >> proper extension: 05fjf; >> query: (?x94, 0gpprt) <- contains(?x94, ?x1428), ?x1428 = 01j_06 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09c7w0 place_of_birth! 055sjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 152.000 152.000 0.333 http://example.org/people/person/place_of_birth #14132-0m_mm PRED entity: 0m_mm PRED relation: nominated_for! PRED expected values: 0gr4k => 55 concepts (47 used for prediction) PRED predicted values (max 10 best out of 225): 0l8z1 (0.43 #517, 0.32 #752, 0.31 #985), 0gr0m (0.39 #992, 0.39 #759, 0.29 #524), 0gqy2 (0.34 #350, 0.26 #818, 0.26 #2682), 054krc (0.33 #532, 0.21 #767, 0.21 #1000), 0p9sw (0.32 #486, 0.28 #721, 0.27 #954), 040njc (0.29 #941, 0.29 #708, 0.25 #3738), 0gr4k (0.28 #2590, 0.27 #959, 0.27 #726), 02r0csl (0.27 #939, 0.27 #706, 0.12 #3036), 0gqwc (0.27 #760, 0.26 #993, 0.21 #3790), 02qvyrt (0.25 #1025, 0.25 #792, 0.22 #557) >> Best rule #517 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 01vrwfv; >> query: (?x984, 0l8z1) <- nominated_for(?x3483, ?x984), award(?x3483, ?x2585), music(?x327, ?x3483), ?x2585 = 054ks3 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2590 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 322 *> proper extension: 0123qq; *> query: (?x984, 0gr4k) <- nominated_for(?x3483, ?x984), place_of_death(?x3483, ?x362), nationality(?x3483, ?x94) *> conf = 0.28 ranks of expected_values: 7 EVAL 0m_mm nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 55.000 47.000 0.434 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14131-03nkts PRED entity: 03nkts PRED relation: profession PRED expected values: 02jknp => 78 concepts (65 used for prediction) PRED predicted values (max 10 best out of 48): 0dxtg (0.47 #2219, 0.28 #6043, 0.27 #5896), 02jknp (0.46 #2213, 0.26 #7060, 0.25 #7797), 03gjzk (0.32 #2220, 0.26 #7060, 0.25 #7797), 02krf9 (0.26 #7060, 0.25 #7797, 0.14 #2231), 09jwl (0.23 #900, 0.17 #2077, 0.16 #6342), 018gz8 (0.21 #16, 0.19 #310, 0.15 #457), 0d1pc (0.14 #1519, 0.14 #1372, 0.14 #1078), 0nbcg (0.12 #912, 0.11 #6354, 0.11 #2972), 016z4k (0.11 #886, 0.11 #1327, 0.11 #1180), 0dz3r (0.11 #884, 0.11 #1178, 0.10 #6326) >> Best rule #2219 for best value: >> intensional similarity = 3 >> extensional distance = 751 >> proper extension: 05g8ky; 04rs03; 019z7q; 067jsf; 01g4zr; 022_lg; 01p45_v; 0ksf29; 06w33f8; 01c58j; ... >> query: (?x6397, 0dxtg) <- profession(?x6397, ?x319), type_of_union(?x6397, ?x566), ?x319 = 01d_h8 >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #2213 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 751 *> proper extension: 05g8ky; 04rs03; 019z7q; 067jsf; 01g4zr; 022_lg; 01p45_v; 0ksf29; 06w33f8; 01c58j; ... *> query: (?x6397, 02jknp) <- profession(?x6397, ?x319), type_of_union(?x6397, ?x566), ?x319 = 01d_h8 *> conf = 0.46 ranks of expected_values: 2 EVAL 03nkts profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 65.000 0.474 http://example.org/people/person/profession #14130-02yv6b PRED entity: 02yv6b PRED relation: artists PRED expected values: 01vrt_c 03g5jw 014q2g 01vsl3_ 0pkyh 0qdyf 0134tg 081wh1 017f4y 01518s => 55 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1021): 01gf5h (0.67 #4053, 0.50 #8040, 0.50 #7043), 04bgy (0.67 #4531, 0.50 #8518, 0.50 #5527), 01r9fv (0.67 #4094, 0.50 #8081, 0.50 #5090), 0j6cj (0.67 #5658, 0.50 #8649, 0.50 #4662), 02cw1m (0.67 #4802, 0.50 #8789, 0.33 #5798), 0bkg4 (0.67 #4302, 0.50 #8289, 0.33 #5298), 018y81 (0.67 #4497, 0.50 #8484, 0.33 #5493), 0qdyf (0.67 #5232, 0.40 #8223, 0.33 #4236), 027kwc (0.67 #4965, 0.40 #8952, 0.33 #5961), 0150jk (0.67 #4038, 0.40 #8025, 0.33 #5034) >> Best rule #4053 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 0xhtw; 06by7; 03lty; 05bt6j; >> query: (?x7083, 01gf5h) <- artists(?x7083, ?x12791), artists(?x7083, ?x8921), artists(?x7083, ?x8579), artists(?x7083, ?x4957), award_winner(?x247, ?x8921), artist(?x2149, ?x8579), role(?x8921, ?x314), ?x12791 = 01pny5, award(?x8921, ?x724), ?x4957 = 0g_g2 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5232 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 01fh36; *> query: (?x7083, 0qdyf) <- artists(?x7083, ?x12791), artists(?x7083, ?x8921), artists(?x7083, ?x8579), artists(?x7083, ?x7437), award_winner(?x247, ?x8921), artist(?x2149, ?x8579), role(?x8921, ?x314), ?x12791 = 01pny5, award(?x8921, ?x724), ?x7437 = 021r7r *> conf = 0.67 ranks of expected_values: 8, 41, 46, 62, 66, 80, 98, 297, 516, 629 EVAL 02yv6b artists 01518s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 55.000 23.000 0.667 http://example.org/music/genre/artists EVAL 02yv6b artists 017f4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 23.000 0.667 http://example.org/music/genre/artists EVAL 02yv6b artists 081wh1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 23.000 0.667 http://example.org/music/genre/artists EVAL 02yv6b artists 0134tg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 55.000 23.000 0.667 http://example.org/music/genre/artists EVAL 02yv6b artists 0qdyf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 55.000 23.000 0.667 http://example.org/music/genre/artists EVAL 02yv6b artists 0pkyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 55.000 23.000 0.667 http://example.org/music/genre/artists EVAL 02yv6b artists 01vsl3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 55.000 23.000 0.667 http://example.org/music/genre/artists EVAL 02yv6b artists 014q2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 55.000 23.000 0.667 http://example.org/music/genre/artists EVAL 02yv6b artists 03g5jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 23.000 0.667 http://example.org/music/genre/artists EVAL 02yv6b artists 01vrt_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 55.000 23.000 0.667 http://example.org/music/genre/artists #14129-05cgv PRED entity: 05cgv PRED relation: country! PRED expected values: 035d1m => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 50): 071t0 (0.79 #1923, 0.78 #1823, 0.78 #822), 03_8r (0.77 #1572, 0.75 #1822, 0.73 #671), 01lb14 (0.71 #464, 0.61 #414, 0.60 #1014), 06wrt (0.68 #465, 0.64 #415, 0.62 #765), 06f41 (0.66 #663, 0.63 #813, 0.62 #1013), 0194d (0.65 #493, 0.57 #843, 0.57 #693), 0w0d (0.65 #461, 0.55 #761, 0.55 #811), 03hr1p (0.62 #473, 0.58 #1924, 0.58 #1023), 064vjs (0.58 #1030, 0.56 #480, 0.51 #1480), 02vx4 (0.58 #105, 0.47 #155, 0.44 #455) >> Best rule #1923 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 06c62; >> query: (?x1241, 071t0) <- contains(?x1241, ?x9125), film_release_region(?x2050, ?x1241), olympics(?x1241, ?x778) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #476 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 012m_; *> query: (?x1241, 035d1m) <- contains(?x1241, ?x9125), location(?x1222, ?x1241), nationality(?x1935, ?x1241) *> conf = 0.47 ranks of expected_values: 21 EVAL 05cgv country! 035d1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 171.000 171.000 0.787 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #14128-03q43g PRED entity: 03q43g PRED relation: influenced_by PRED expected values: 0p_47 => 77 concepts (25 used for prediction) PRED predicted values (max 10 best out of 264): 05rx__ (0.33 #1117, 0.06 #2427, 0.05 #1990), 014z8v (0.22 #3178, 0.11 #6670, 0.11 #4486), 04sd0 (0.20 #854, 0.05 #3474, 0.04 #4782), 041c4 (0.20 #592, 0.02 #3212, 0.01 #4520), 0d608 (0.20 #674, 0.01 #3294), 01hmk9 (0.17 #3278, 0.11 #4586, 0.09 #6770), 0p_47 (0.17 #980, 0.16 #3164, 0.10 #1853), 014zfs (0.17 #897, 0.12 #3081, 0.08 #4389), 01svq8 (0.17 #1298, 0.10 #2171, 0.06 #3482), 0p_pd (0.17 #880, 0.07 #3064, 0.04 #4372) >> Best rule #1117 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0h5g_; 0pz7h; 034np8; 01h1b; >> query: (?x6569, 05rx__) <- influenced_by(?x6569, ?x11357), film(?x6569, ?x814), language(?x6569, ?x254) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #980 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0h5g_; 0pz7h; 034np8; 01h1b; *> query: (?x6569, 0p_47) <- influenced_by(?x6569, ?x11357), film(?x6569, ?x814), language(?x6569, ?x254) *> conf = 0.17 ranks of expected_values: 7 EVAL 03q43g influenced_by 0p_47 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 25.000 0.333 http://example.org/influence/influence_node/influenced_by #14127-01zwy PRED entity: 01zwy PRED relation: influenced_by PRED expected values: 0klw => 135 concepts (86 used for prediction) PRED predicted values (max 10 best out of 301): 03sbs (0.14 #1529, 0.08 #12410, 0.07 #12845), 05qmj (0.14 #1499, 0.07 #12380, 0.06 #1064), 0gz_ (0.12 #1409, 0.07 #12725, 0.07 #12290), 03_87 (0.12 #203, 0.07 #12825, 0.07 #1944), 03f0324 (0.11 #2764, 0.07 #12774, 0.07 #17124), 039n1 (0.11 #1632, 0.06 #1197, 0.04 #5116), 0j3v (0.11 #1367, 0.06 #61, 0.06 #13118), 081k8 (0.10 #13213, 0.08 #12778, 0.07 #19738), 02wh0 (0.09 #383, 0.09 #1689, 0.06 #13005), 0420y (0.09 #1710, 0.06 #404, 0.04 #13461) >> Best rule #1529 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 01d494; 01c58j; 0bzyh; 03v40v; 039n1; >> query: (?x8508, 03sbs) <- gender(?x8508, ?x231), place_of_death(?x8508, ?x5267), company(?x8508, ?x741) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #21324 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 515 *> proper extension: 02pb2bp; 05xq9; 0lhn5; 01kcms4; 070b4; 02m4t; 0167xy; 04sd0; 01d5g; 0chnf; ... *> query: (?x8508, ?x5334) <- influenced_by(?x8508, ?x8210), influenced_by(?x5334, ?x8210) *> conf = 0.05 ranks of expected_values: 43 EVAL 01zwy influenced_by 0klw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 135.000 86.000 0.143 http://example.org/influence/influence_node/influenced_by #14126-05183k PRED entity: 05183k PRED relation: written_by! PRED expected values: 0bh8x1y 09zf_q => 97 concepts (66 used for prediction) PRED predicted values (max 10 best out of 467): 02vxq9m (0.44 #2622, 0.11 #20321, 0.10 #20320), 0gmgwnv (0.44 #2622, 0.08 #16388, 0.08 #15731), 01gglm (0.38 #3933, 0.19 #7866, 0.01 #2488), 06w99h3 (0.38 #3933, 0.19 #7866), 0bh8x1y (0.14 #11799, 0.12 #8523, 0.11 #5900), 050f0s (0.07 #2737, 0.03 #8638, 0.03 #6015), 072x7s (0.06 #754, 0.02 #2064, 0.01 #4031), 03tn80 (0.06 #988, 0.02 #2954, 0.02 #1643), 03cvvlg (0.06 #1195, 0.02 #1850, 0.01 #2505), 0yxm1 (0.06 #952, 0.02 #1607, 0.01 #2262) >> Best rule #2622 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 0qf43; 01q415; 01q4qv; 085pr; 0c12h; 03hy3g; 02b29; 06kxk2; 02hfp_; 01nr36; ... >> query: (?x1532, ?x186) <- award(?x1532, ?x746), nominated_for(?x1532, ?x186), ?x746 = 04dn09n >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #11799 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 334 *> proper extension: 024c1b; *> query: (?x1532, ?x4668) <- produced_by(?x4668, ?x1532), film_release_region(?x4668, ?x87) *> conf = 0.14 ranks of expected_values: 5, 67 EVAL 05183k written_by! 09zf_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 97.000 66.000 0.445 http://example.org/film/film/written_by EVAL 05183k written_by! 0bh8x1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 66.000 0.445 http://example.org/film/film/written_by #14125-0kj34 PRED entity: 0kj34 PRED relation: artists! PRED expected values: 05w3f => 173 concepts (93 used for prediction) PRED predicted values (max 10 best out of 273): 06by7 (0.77 #23162, 0.71 #1871, 0.64 #3105), 0glt670 (0.47 #2506, 0.43 #2198, 0.41 #15468), 06j6l (0.43 #2204, 0.38 #7140, 0.33 #3748), 059kh (0.40 #48, 0.30 #1281, 0.24 #4057), 05w3f (0.40 #39, 0.26 #4048, 0.23 #3122), 01lyv (0.40 #343, 0.24 #7436, 0.23 #4970), 0cx7f (0.40 #137, 0.18 #3220, 0.18 #4146), 016clz (0.39 #9564, 0.39 #5557, 0.36 #4940), 0xhtw (0.36 #4952, 0.35 #5569, 0.31 #12355), 025sc50 (0.36 #7142, 0.34 #7759, 0.29 #1898) >> Best rule #23162 for best value: >> intensional similarity = 4 >> extensional distance = 339 >> proper extension: 053y0s; 032nwy; 01pr_j6; 01wdqrx; 01vs14j; 01qvgl; 02r4qs; 01p45_v; 04bpm6; 01qkqwg; ... >> query: (?x9087, 06by7) <- gender(?x9087, ?x231), artists(?x7329, ?x9087), parent_genre(?x10306, ?x7329), ?x10306 = 09jw2 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #39 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 047cx; 06gcn; *> query: (?x9087, 05w3f) <- artist(?x5744, ?x9087), artist(?x4483, ?x9087), origin(?x9087, ?x362), ?x5744 = 01clyr, ?x4483 = 0mzkr, artists(?x671, ?x9087) *> conf = 0.40 ranks of expected_values: 5 EVAL 0kj34 artists! 05w3f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 173.000 93.000 0.765 http://example.org/music/genre/artists #14124-05bcl PRED entity: 05bcl PRED relation: official_language PRED expected values: 02h40lc => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.59 #1379, 0.50 #88, 0.40 #131), 083tk (0.25 #113, 0.20 #156, 0.06 #1016), 064_8sq (0.19 #575, 0.17 #4403, 0.17 #4231), 04306rv (0.17 #994, 0.15 #1167, 0.14 #693), 06nm1 (0.16 #3019, 0.15 #610, 0.15 #3922), 0jzc (0.14 #960, 0.14 #1348, 0.12 #4229), 0cjk9 (0.10 #692, 0.08 #434, 0.03 #1854), 0k0sb (0.10 #729, 0.05 #1074, 0.05 #1117), 0k0sv (0.10 #706, 0.03 #2255, 0.03 #2384), 02bv9 (0.09 #1398, 0.09 #924, 0.06 #580) >> Best rule #1379 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 02psqkz; 06jnv; 0c4b8; 088q1s; >> query: (?x4071, 02h40lc) <- form_of_government(?x4071, ?x6065), ?x6065 = 01q20, official_language(?x4071, ?x12326) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05bcl official_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.587 http://example.org/location/country/official_language #14123-0r0f7 PRED entity: 0r0f7 PRED relation: place_of_birth! PRED expected values: 0kzy0 => 140 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1970): 0p_47 (0.35 #67905, 0.34 #62682, 0.34 #114923), 09yrh (0.17 #923, 0.05 #8758, 0.01 #112311), 033db3 (0.17 #2599, 0.05 #10434), 02tv80 (0.17 #1318, 0.05 #9153), 01z0rcq (0.17 #671, 0.05 #8506), 01hcj2 (0.08 #7207, 0.05 #9819, 0.01 #22878), 04kr63w (0.08 #6349, 0.05 #8961, 0.01 #22020), 0b1q7c (0.08 #7510, 0.05 #10122, 0.01 #23181), 01sbhvd (0.08 #7492, 0.05 #10104, 0.01 #23163), 02p5hf (0.08 #7379, 0.05 #9991, 0.01 #23050) >> Best rule #67905 for best value: >> intensional similarity = 5 >> extensional distance = 174 >> proper extension: 0f2tj; >> query: (?x8618, ?x3917) <- place_of_birth(?x3291, ?x8618), place_of_birth(?x1953, ?x8618), location(?x3917, ?x8618), gender(?x3291, ?x514), instrumentalists(?x316, ?x1953) >> conf = 0.35 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r0f7 place_of_birth! 0kzy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 53.000 0.349 http://example.org/people/person/place_of_birth #14122-07cn2c PRED entity: 07cn2c PRED relation: location PRED expected values: 052p7 => 85 concepts (81 used for prediction) PRED predicted values (max 10 best out of 69): 030qb3t (0.21 #83, 0.18 #1691, 0.17 #887), 02_286 (0.13 #4864, 0.13 #15318, 0.12 #7276), 059rby (0.11 #1624, 0.05 #16, 0.04 #820), 04jpl (0.08 #821, 0.07 #1625, 0.05 #17), 01n7q (0.05 #63, 0.04 #867, 0.04 #1671), 01cx_ (0.05 #163, 0.04 #967, 0.04 #1771), 0f2wj (0.05 #34, 0.04 #838, 0.04 #1642), 02cft (0.05 #307, 0.04 #1111, 0.04 #1915), 02xry (0.05 #133, 0.04 #937, 0.04 #1741), 01smm (0.05 #312, 0.04 #1116, 0.02 #1920) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 05dxl5; >> query: (?x4134, 030qb3t) <- award_nominee(?x4134, ?x1657), nominated_for(?x4134, ?x8277), actor(?x297, ?x4134) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07cn2c location 052p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 81.000 0.211 http://example.org/people/person/places_lived./people/place_lived/location #14121-05k8m5 PRED entity: 05k8m5 PRED relation: artist PRED expected values: 01t8399 => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 05k8m5 artist 01t8399 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/music/record_label/artist #14120-03q_g6 PRED entity: 03q_g6 PRED relation: ceremony PRED expected values: 01c6qp => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 125): 09n4nb (0.94 #428, 0.94 #299, 0.93 #170), 01c6qp (0.86 #401, 0.86 #143, 0.85 #272), 01mh_q (0.85 #466, 0.84 #208, 0.84 #337), 01mhwk (0.82 #421, 0.81 #292, 0.80 #163), 01xqqp (0.74 #344, 0.72 #473, 0.71 #215), 0jzphpx (0.71 #290, 0.69 #419, 0.68 #161), 0bzm81 (0.16 #1178, 0.09 #2855, 0.09 #2984), 0n8_m93 (0.16 #1269, 0.09 #2946, 0.09 #3075), 02yxh9 (0.15 #1252, 0.09 #2929, 0.09 #3058), 0bc773 (0.15 #1208, 0.09 #2885, 0.09 #3014) >> Best rule #428 for best value: >> intensional similarity = 6 >> extensional distance = 63 >> proper extension: 02h3d1; 031b3h; 0257__; >> query: (?x6090, 09n4nb) <- ceremony(?x6090, ?x5656), ceremony(?x6090, ?x2054), ceremony(?x6090, ?x725), ?x5656 = 0466p0j, ?x725 = 01bx35, award_winner(?x2054, ?x367) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #401 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 63 *> proper extension: 02h3d1; 031b3h; 0257__; *> query: (?x6090, 01c6qp) <- ceremony(?x6090, ?x5656), ceremony(?x6090, ?x2054), ceremony(?x6090, ?x725), ?x5656 = 0466p0j, ?x725 = 01bx35, award_winner(?x2054, ?x367) *> conf = 0.86 ranks of expected_values: 2 EVAL 03q_g6 ceremony 01c6qp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 41.000 0.938 http://example.org/award/award_category/winners./award/award_honor/ceremony #14119-07tlfx PRED entity: 07tlfx PRED relation: genre PRED expected values: 0lsxr => 123 concepts (121 used for prediction) PRED predicted values (max 10 best out of 99): 04xvlr (0.72 #9197, 0.59 #9196, 0.55 #596), 02kdv5l (0.44 #1194, 0.44 #2, 0.37 #2986), 01jfsb (0.44 #1205, 0.40 #2997, 0.39 #2877), 05p553 (0.37 #720, 0.36 #3945, 0.36 #3587), 03bxz7 (0.35 #1366, 0.31 #412, 0.14 #3159), 02l7c8 (0.34 #3120, 0.32 #2164, 0.32 #4558), 03k9fj (0.28 #728, 0.27 #250, 0.25 #1920), 060__y (0.25 #3121, 0.19 #136, 0.16 #9093), 0lsxr (0.25 #844, 0.24 #485, 0.22 #9), 06n90 (0.23 #730, 0.23 #1206, 0.19 #2998) >> Best rule #9197 for best value: >> intensional similarity = 4 >> extensional distance = 821 >> proper extension: 06cs95; >> query: (?x9978, ?x162) <- nominated_for(?x875, ?x9978), titles(?x162, ?x9978), nominated_for(?x451, ?x9978), genre(?x144, ?x162) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #844 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 05pbl56; 01hqhm; 025n07; 011yn5; 03cp4cn; 095z4q; 0bxsk; 07kdkfj; 01bn3l; 02mpyh; ... *> query: (?x9978, 0lsxr) <- executive_produced_by(?x9978, ?x3880), cinematography(?x9978, ?x185), film_release_distribution_medium(?x9978, ?x81), genre(?x9978, ?x53) *> conf = 0.25 ranks of expected_values: 9 EVAL 07tlfx genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 123.000 121.000 0.716 http://example.org/film/film/genre #14118-05qtj PRED entity: 05qtj PRED relation: place_of_birth! PRED expected values: 04dqdk => 261 concepts (207 used for prediction) PRED predicted values (max 10 best out of 2229): 03j43 (0.43 #44057, 0.37 #44056, 0.34 #326603), 07c37 (0.43 #44057, 0.37 #44056, 0.34 #326603), 02kz_ (0.37 #44056, 0.34 #326603, 0.33 #399188), 0420y (0.37 #44056, 0.34 #326603, 0.33 #399188), 06c44 (0.37 #44056, 0.34 #326603, 0.33 #399188), 09h_q (0.37 #44056, 0.34 #326603, 0.33 #399188), 06whf (0.37 #44056, 0.34 #326603, 0.33 #399188), 0d5_f (0.37 #44056, 0.34 #326603, 0.33 #399188), 07ym0 (0.37 #44056, 0.34 #326603, 0.33 #399188), 01_f_5 (0.37 #44056, 0.34 #326603, 0.33 #399188) >> Best rule #44057 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0rtv; 03pbf; 01k4f; 07gdw; 0d7_n; >> query: (?x4627, ?x1236) <- location(?x1236, ?x4627), place_of_birth(?x771, ?x4627), interests(?x1236, ?x713) >> conf = 0.43 => this is the best rule for 2 predicted values *> Best rule #44056 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0rtv; 03pbf; 01k4f; 07gdw; 0d7_n; *> query: (?x4627, ?x598) <- location(?x1236, ?x4627), location(?x598, ?x4627), place_of_birth(?x771, ?x4627), interests(?x1236, ?x713) *> conf = 0.37 ranks of expected_values: 19 EVAL 05qtj place_of_birth! 04dqdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 261.000 207.000 0.429 http://example.org/people/person/place_of_birth #14117-0kp2_ PRED entity: 0kp2_ PRED relation: student! PRED expected values: 04s934 => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 328): 0bwfn (0.29 #50779, 0.17 #13424, 0.14 #3956), 025v3k (0.25 #119, 0.06 #5379, 0.02 #11165), 07wjk (0.25 #62, 0.03 #22683, 0.03 #17420), 08815 (0.23 #18413, 0.14 #26305, 0.12 #24201), 0fnmz (0.20 #626, 0.05 #6938, 0.03 #8516), 07wrz (0.17 #7425, 0.14 #1639, 0.14 #10055), 01w5m (0.16 #18515, 0.15 #11150, 0.10 #15884), 017z88 (0.14 #2185, 0.13 #50586, 0.12 #2711), 065y4w7 (0.14 #2118, 0.12 #2644, 0.09 #17898), 07tgn (0.14 #4225, 0.12 #17901, 0.10 #20006) >> Best rule #50779 for best value: >> intensional similarity = 4 >> extensional distance = 392 >> proper extension: 0308kx; 031x_3; >> query: (?x6795, 0bwfn) <- student(?x1103, ?x6795), award(?x6795, ?x9629), currency(?x1103, ?x170), organization(?x346, ?x1103) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #6527 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 09rp4r_; 08yx9q; 0fqjks; *> query: (?x6795, 04s934) <- award(?x6795, ?x9629), place_of_birth(?x6795, ?x3125), award_winner(?x9629, ?x6796), ?x3125 = 0d6lp *> conf = 0.05 ranks of expected_values: 67 EVAL 0kp2_ student! 04s934 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 183.000 183.000 0.287 http://example.org/education/educational_institution/students_graduates./education/education/student #14116-07_3qd PRED entity: 07_3qd PRED relation: performance_role PRED expected values: 0395lw => 131 concepts (78 used for prediction) PRED predicted values (max 10 best out of 123): 03bx0bm (0.46 #422, 0.45 #583, 0.43 #463), 026t6 (0.21 #970, 0.21 #808, 0.20 #569), 0l14qv (0.19 #727, 0.18 #768, 0.17 #87), 0342h (0.17 #488, 0.17 #450, 0.15 #764), 03gvt (0.17 #278, 0.06 #756, 0.06 #797), 04rzd (0.17 #102, 0.06 #264, 0.05 #447), 07y_7 (0.17 #84, 0.06 #246, 0.02 #969), 05r5c (0.16 #573, 0.11 #251, 0.10 #812), 018vs (0.15 #764, 0.14 #847, 0.14 #846), 02sgy (0.15 #764, 0.14 #847, 0.14 #846) >> Best rule #422 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 016ggh; >> query: (?x1260, 03bx0bm) <- nationality(?x1260, ?x1310), performance_role(?x1260, ?x1433), ?x1310 = 02jx1, performance_role(?x1433, ?x212), role(?x1433, ?x227) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #462 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 01vrncs; 014q2g; 01gx5f; 01vsyg9; 01vsyjy; 04s5_s; *> query: (?x1260, 0395lw) <- nationality(?x1260, ?x1310), performance_role(?x1260, ?x315), role(?x1260, ?x227), artist(?x4868, ?x1260), ?x227 = 0342h *> conf = 0.07 ranks of expected_values: 20 EVAL 07_3qd performance_role 0395lw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 131.000 78.000 0.464 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #14115-03bx0bm PRED entity: 03bx0bm PRED relation: role PRED expected values: 01xqw => 80 concepts (58 used for prediction) PRED predicted values (max 10 best out of 102): 03bx0bm (0.84 #1817, 0.82 #2598, 0.82 #2052), 02sgy (0.83 #347, 0.83 #346, 0.83 #810), 0l1589 (0.83 #347, 0.83 #346, 0.83 #810), 011k_j (0.83 #347, 0.83 #346, 0.83 #810), 02w3w (0.83 #347, 0.83 #346, 0.83 #810), 026g73 (0.83 #347, 0.83 #346, 0.83 #810), 01vdm0 (0.83 #347, 0.83 #346, 0.83 #810), 01qzyz (0.83 #347, 0.83 #346, 0.83 #810), 01xqw (0.75 #1259, 0.71 #797, 0.68 #2272), 07kc_ (0.73 #1575, 0.67 #2212, 0.62 #2706) >> Best rule #1817 for best value: >> intensional similarity = 11 >> extensional distance = 17 >> proper extension: 02pprs; 0gghm; >> query: (?x1466, 03bx0bm) <- role(?x6380, ?x1466), group(?x1466, ?x442), role(?x1466, ?x7033), role(?x1466, ?x4616), role(?x1466, ?x4583), role(?x314, ?x1466), award_winner(?x724, ?x6380), ?x4583 = 0bmnm, instrumentalists(?x7033, ?x2987), role(?x1181, ?x4616), role(?x4616, ?x868) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1259 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 10 *> proper extension: 011_6p; *> query: (?x1466, 01xqw) <- performance_role(?x248, ?x1466), role(?x4917, ?x1466), role(?x2048, ?x1466), role(?x645, ?x1466), role(?x74, ?x1466), group(?x1466, ?x442), ?x74 = 03q5t, role(?x8143, ?x645), ?x8143 = 01wvxw1, role(?x211, ?x4917), ?x2048 = 018j2, role(?x614, ?x4917), group(?x645, ?x1136) *> conf = 0.75 ranks of expected_values: 9 EVAL 03bx0bm role 01xqw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 80.000 58.000 0.842 http://example.org/music/performance_role/regular_performances./music/group_membership/role #14114-025rxjq PRED entity: 025rxjq PRED relation: nominated_for! PRED expected values: 02n9nmz => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 200): 02x4w6g (0.33 #88, 0.25 #328, 0.22 #16089), 03hj5vf (0.33 #126, 0.25 #366, 0.06 #606), 0gq9h (0.29 #543, 0.29 #4623, 0.28 #5583), 0gs9p (0.25 #4625, 0.23 #5585, 0.20 #3185), 09tqxt (0.25 #316, 0.06 #1276, 0.05 #1036), 0gqzz (0.25 #290, 0.05 #1250, 0.04 #1010), 0drtkx (0.25 #437, 0.03 #1397, 0.02 #1157), 019f4v (0.25 #4614, 0.22 #5574, 0.21 #774), 099c8n (0.24 #777, 0.18 #2217, 0.16 #4617), 0l8z1 (0.23 #1252, 0.15 #772, 0.15 #1012) >> Best rule #88 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0dr_9t7; >> query: (?x7819, 02x4w6g) <- nominated_for(?x11233, ?x7819), genre(?x7819, ?x53), ?x11233 = 01vsn38, ?x53 = 07s9rl0 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #16089 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1545 *> proper extension: 09fb5; 0cwrr; 0n2bh; 01vrwfv; 01h1bf; 03y3bp7; 02sqkh; 02kk_c; 0c3xpwy; 04glx0; ... *> query: (?x7819, ?x1312) <- nominated_for(?x11233, ?x7819), award(?x11233, ?x1312), profession(?x11233, ?x131), nominated_for(?x1312, ?x188) *> conf = 0.22 ranks of expected_values: 13 EVAL 025rxjq nominated_for! 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 82.000 82.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14113-0qxzd PRED entity: 0qxzd PRED relation: contains! PRED expected values: 01n7q => 125 concepts (60 used for prediction) PRED predicted values (max 10 best out of 230): 01n7q (0.97 #48458, 0.93 #13513, 0.79 #27758), 0kpzy (0.81 #51967, 0.80 #43889, 0.80 #47481), 0d060g (0.62 #22388, 0.61 #21493, 0.56 #25966), 0l2vz (0.43 #1168, 0.20 #2066, 0.14 #4755), 04_1l0v (0.31 #33582, 0.21 #35374, 0.13 #30896), 0vmt (0.31 #2740, 0.29 #6327, 0.29 #3637), 0m27n (0.31 #3091, 0.29 #3988, 0.27 #5780), 0kpys (0.25 #7350, 0.24 #12716, 0.20 #13615), 0kq1l (0.20 #2208, 0.20 #416, 0.14 #4897), 059rby (0.15 #2705, 0.13 #5394, 0.12 #6292) >> Best rule #48458 for best value: >> intensional similarity = 5 >> extensional distance = 168 >> proper extension: 0288zy; 01hhvg; 01bzw5; 033q4k; 027xx3; 02bb47; 0fnmz; 01f1r4; 020923; 01q0kg; ... >> query: (?x12770, 01n7q) <- contains(?x2632, ?x12770), contains(?x2632, ?x6815), contains(?x2632, ?x4413), ?x4413 = 0gjcy, ?x6815 = 0kpzy >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qxzd contains! 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 60.000 0.971 http://example.org/location/location/contains #14112-023slg PRED entity: 023slg PRED relation: nationality PRED expected values: 09c7w0 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 44): 09c7w0 (0.78 #5314, 0.75 #5010, 0.75 #3905), 02jx1 (0.32 #833, 0.29 #433, 0.28 #1233), 07ssc (0.25 #115, 0.18 #815, 0.18 #615), 03rt9 (0.14 #413, 0.05 #813, 0.02 #913), 0ctw_b (0.14 #427, 0.02 #1027, 0.02 #8823), 0d0vqn (0.09 #609, 0.06 #709, 0.02 #8823), 03rk0 (0.06 #6567, 0.06 #6467, 0.06 #6667), 0d060g (0.05 #1508, 0.05 #5320, 0.04 #3911), 0y1rf (0.04 #1602, 0.03 #1501), 0f8l9c (0.03 #3926, 0.03 #4427, 0.02 #4327) >> Best rule #5314 for best value: >> intensional similarity = 2 >> extensional distance = 1546 >> proper extension: 0784v1; >> query: (?x11916, 09c7w0) <- place_of_birth(?x11916, ?x3052), time_zones(?x3052, ?x2674) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023slg nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.780 http://example.org/people/person/nationality #14111-01_30_ PRED entity: 01_30_ PRED relation: industry PRED expected values: 01mw1 => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 38): 01mw1 (0.80 #96, 0.79 #143, 0.75 #192), 019z7b (0.59 #95, 0.56 #1538, 0.41 #383), 02vxn (0.41 #1058, 0.40 #1106, 0.34 #1347), 01mf0 (0.24 #912, 0.23 #1345, 0.23 #1152), 02jjt (0.22 #391, 0.22 #439, 0.20 #583), 03qh03g (0.20 #821, 0.19 #965, 0.19 #917), 029g_vk (0.14 #490, 0.13 #682, 0.12 #779), 04rlf (0.14 #541, 0.12 #926, 0.09 #1359), 0hz28 (0.11 #364, 0.08 #1229, 0.07 #1326), 02h400t (0.09 #72, 0.04 #889, 0.03 #504) >> Best rule #96 for best value: >> intensional similarity = 39 >> extensional distance = 73 >> proper extension: 08t9df; 05925; 02qdyj; 073tm9; 04sv4; 01dycg; 01swdw; 01tlrp; 02mdty; 01qvcr; ... >> query: (?x14783, 01mw1) <- industry(?x14783, ?x10022), industry(?x14538, ?x10022), industry(?x14458, ?x10022), industry(?x14420, ?x10022), industry(?x14118, ?x10022), industry(?x13750, ?x10022), industry(?x13723, ?x10022), industry(?x13714, ?x10022), industry(?x11720, ?x10022), industry(?x11325, ?x10022), industry(?x11273, ?x10022), industry(?x11071, ?x10022), industry(?x11070, ?x10022), industry(?x10646, ?x10022), industry(?x6717, ?x10022), industry(?x4683, ?x10022), industry(?x3147, ?x10022), industry(?x244, ?x10022), ?x13723 = 0260p2, ?x14420 = 01yf92, state_province_region(?x11071, ?x1227), child(?x9077, ?x3147), ?x14118 = 026wmz6, place_founded(?x10646, ?x11773), country(?x3147, ?x94), child(?x13291, ?x11071), ?x13714 = 06zl7g, ?x11070 = 02brqp, ?x14538 = 06nfl, ?x13750 = 04kqk, service_location(?x6717, ?x205), ?x11325 = 0225z1, service_language(?x6717, ?x90), ?x11720 = 049vhf, ?x244 = 0dwl2, ?x4683 = 04vgq5, citytown(?x11071, ?x9559), category(?x14458, ?x134), ?x11273 = 027lf1 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_30_ industry 01mw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.800 http://example.org/business/business_operation/industry #14110-018ljb PRED entity: 018ljb PRED relation: medal PRED expected values: 02lq5w 02lpp7 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 2): 02lpp7 (0.88 #77, 0.88 #63, 0.87 #42), 02lq5w (0.88 #62, 0.86 #51, 0.83 #76) >> Best rule #77 for best value: >> intensional similarity = 16 >> extensional distance = 40 >> proper extension: 09n48; 0sx7r; 0sx8l; 0blfl; 0sx92; 019n8z; 0124ld; 01f1kd; 015l4k; 01f1jf; >> query: (?x7051, 02lpp7) <- sports(?x7051, ?x2867), olympics(?x1557, ?x7051), sports(?x358, ?x2867), country(?x2867, ?x3730), country(?x2867, ?x2645), country(?x2867, ?x205), country(?x2867, ?x172), countries_spoken_in(?x5359, ?x3730), ?x172 = 0154j, film_release_region(?x9345, ?x2645), film_release_region(?x4441, ?x2645), ?x205 = 03rjj, country(?x1135, ?x2645), ?x9345 = 014knw, contains(?x6304, ?x3730), ?x4441 = 0125xq >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 018ljb medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.881 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 018ljb medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.881 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #14109-04xm_ PRED entity: 04xm_ PRED relation: influenced_by! PRED expected values: 0cpvcd => 161 concepts (63 used for prediction) PRED predicted values (max 10 best out of 406): 047g6 (0.50 #479, 0.44 #993, 0.33 #2020), 0b78hw (0.50 #167, 0.22 #1708, 0.12 #7872), 02wh0 (0.44 #964, 0.25 #450, 0.24 #8155), 045bg (0.44 #550, 0.25 #36, 0.24 #1063), 0nk72 (0.44 #855, 0.25 #341, 0.24 #1368), 0j3v (0.44 #594, 0.25 #80, 0.22 #1621), 0h25 (0.44 #1961, 0.25 #420, 0.15 #3504), 043s3 (0.44 #667, 0.11 #1694, 0.09 #7705), 0372p (0.44 #664, 0.11 #1691, 0.05 #7855), 048cl (0.33 #813, 0.25 #299, 0.22 #1840) >> Best rule #479 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03sbs; >> query: (?x10111, 047g6) <- influenced_by(?x11837, ?x10111), influenced_by(?x10110, ?x10111), influenced_by(?x10111, ?x2240), ?x11837 = 032r1, ?x10110 = 07h1q >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #463 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 03sbs; *> query: (?x10111, 0cpvcd) <- influenced_by(?x11837, ?x10111), influenced_by(?x10110, ?x10111), influenced_by(?x10111, ?x2240), ?x11837 = 032r1, ?x10110 = 07h1q *> conf = 0.25 ranks of expected_values: 29 EVAL 04xm_ influenced_by! 0cpvcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 161.000 63.000 0.500 http://example.org/influence/influence_node/influenced_by #14108-0lfyx PRED entity: 0lfyx PRED relation: contains! PRED expected values: 0kvt9 => 77 concepts (31 used for prediction) PRED predicted values (max 10 best out of 145): 06pvr (0.20 #1952, 0.19 #164, 0.15 #1058), 0kpys (0.13 #1073, 0.12 #3755, 0.12 #179), 05kj_ (0.13 #4511, 0.13 #5405, 0.03 #17930), 02jx1 (0.11 #22451, 0.03 #10816, 0.03 #11710), 030qb3t (0.11 #3675, 0.09 #2781, 0.04 #993), 081yw (0.10 #4747, 0.09 #5641, 0.03 #1170), 059rby (0.09 #16118, 0.09 #17014, 0.08 #18804), 0cb4j (0.09 #34, 0.07 #928, 0.05 #1822), 07ssc (0.08 #22397, 0.04 #10762, 0.04 #11656), 04_1l0v (0.08 #10285, 0.05 #26395, 0.04 #11180) >> Best rule #1952 for best value: >> intensional similarity = 5 >> extensional distance = 96 >> proper extension: 0qzhw; >> query: (?x13689, 06pvr) <- contains(?x1227, ?x13689), contains(?x94, ?x13689), source(?x13689, ?x958), ?x1227 = 01n7q, film_release_region(?x54, ?x94) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #545 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 56 *> proper extension: 0r6ff; *> query: (?x13689, 0kvt9) <- contains(?x1227, ?x13689), contains(?x94, ?x13689), source(?x13689, ?x958), ?x1227 = 01n7q, ?x94 = 09c7w0 *> conf = 0.02 ranks of expected_values: 62 EVAL 0lfyx contains! 0kvt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 77.000 31.000 0.204 http://example.org/location/location/contains #14107-01_6dw PRED entity: 01_6dw PRED relation: profession PRED expected values: 0kyk => 97 concepts (95 used for prediction) PRED predicted values (max 10 best out of 73): 02hrh1q (0.79 #4571, 0.76 #3101, 0.73 #3395), 01c72t (0.69 #611, 0.11 #464, 0.11 #170), 03gjzk (0.68 #1484, 0.67 #1190, 0.38 #1925), 01d_h8 (0.64 #1917, 0.54 #741, 0.36 #888), 02jknp (0.55 #1918, 0.27 #742, 0.25 #3683), 025352 (0.53 #646, 0.30 #2794, 0.04 #793), 0nbcg (0.44 #619, 0.15 #913, 0.12 #1060), 0kyk (0.37 #1646, 0.32 #2234, 0.31 #2087), 05sxg2 (0.33 #442, 0.33 #148, 0.20 #1), 05z96 (0.30 #2794, 0.16 #1659, 0.13 #2541) >> Best rule #4571 for best value: >> intensional similarity = 3 >> extensional distance = 1226 >> proper extension: 04yywz; 02g8h; 0d_84; 041h0; 02nb2s; 02rgz4; 0151ns; 025p38; 0htlr; 019z7q; ... >> query: (?x6534, 02hrh1q) <- nominated_for(?x6534, ?x715), profession(?x6534, ?x353), location(?x6534, ?x11498) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1646 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 223 *> proper extension: 0chnf; 0716b6; 055yr; *> query: (?x6534, 0kyk) <- influenced_by(?x6534, ?x1645), peers(?x12592, ?x1645) *> conf = 0.37 ranks of expected_values: 8 EVAL 01_6dw profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 97.000 95.000 0.794 http://example.org/people/person/profession #14106-04__f PRED entity: 04__f PRED relation: film PRED expected values: 01_mdl 0gl3hr => 124 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1154): 026y3cf (0.41 #12470, 0.38 #162109, 0.38 #130036), 026bfsh (0.16 #8907, 0.11 #3563, 0.07 #92625), 04954r (0.10 #4177, 0.06 #32678, 0.04 #614), 03kx49 (0.10 #4900, 0.06 #15588, 0.05 #10244), 027fwmt (0.08 #1586, 0.06 #5149, 0.05 #6930), 0c8tkt (0.08 #265, 0.06 #3828, 0.04 #21643), 01f39b (0.07 #8099, 0.07 #2755, 0.05 #33038), 02qr3k8 (0.07 #8409, 0.06 #4847, 0.05 #13754), 03rg2b (0.07 #8214, 0.05 #33153, 0.03 #65213), 0jvt9 (0.07 #7662, 0.04 #57536, 0.04 #32601) >> Best rule #12470 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 04wqr; 0djywgn; 01nfys; >> query: (?x7958, ?x689) <- influenced_by(?x11884, ?x7958), nominated_for(?x7958, ?x689), award_nominee(?x7958, ?x1126) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #32223 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 94 *> proper extension: 0d9kl; 057ph; 0dng4; *> query: (?x7958, 01_mdl) <- celebrities_impersonated(?x3649, ?x7958), ?x3649 = 03m6t5 *> conf = 0.02 ranks of expected_values: 483, 905 EVAL 04__f film 0gl3hr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 92.000 0.415 http://example.org/film/actor/film./film/performance/film EVAL 04__f film 01_mdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 124.000 92.000 0.415 http://example.org/film/actor/film./film/performance/film #14105-0l6px PRED entity: 0l6px PRED relation: award PRED expected values: 0fq9zcx => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 239): 09sb52 (0.83 #432, 0.72 #36089, 0.71 #824), 099tbz (0.72 #36089, 0.71 #16469, 0.70 #18041), 0bb57s (0.72 #36089, 0.71 #16469, 0.70 #18041), 0gqy2 (0.25 #937, 0.14 #5641, 0.12 #12308), 027dtxw (0.21 #788, 0.19 #23534, 0.19 #19218), 09qwmm (0.19 #23534, 0.19 #19218, 0.19 #17646), 0789_m (0.19 #23534, 0.19 #19218, 0.19 #17646), 09qvc0 (0.19 #23534, 0.19 #19218, 0.19 #17646), 0fq9zdn (0.19 #23534, 0.19 #19218, 0.19 #17646), 05zvq6g (0.19 #23534, 0.19 #19218, 0.19 #17646) >> Best rule #432 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 05cj4r; 09fqtq; 01sp81; 02tr7d; 06t61y; 065jlv; 02k6rq; 01hkhq; 01ksr1; 02l4pj; ... >> query: (?x2372, 09sb52) <- award_winner(?x2173, ?x2372), award_nominee(?x374, ?x2372), ?x2173 = 015gw6 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #18040 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1255 *> proper extension: 024y6w; *> query: (?x2372, ?x704) <- award_winner(?x2372, ?x7746), award(?x7746, ?x704), award_winner(?x375, ?x2372) *> conf = 0.16 ranks of expected_values: 26 EVAL 0l6px award 0fq9zcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 102.000 102.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14104-0175wg PRED entity: 0175wg PRED relation: profession PRED expected values: 02hrh1q => 61 concepts (60 used for prediction) PRED predicted values (max 10 best out of 48): 02hrh1q (0.88 #611, 0.88 #3147, 0.87 #3296), 01d_h8 (0.30 #3436, 0.29 #2541, 0.28 #6420), 03gjzk (0.30 #2834, 0.27 #3729, 0.26 #5818), 0np9r (0.30 #2834, 0.27 #3729, 0.26 #5818), 0dxtg (0.27 #3729, 0.26 #4937, 0.26 #5818), 018gz8 (0.27 #3729, 0.26 #5818, 0.25 #7459), 0kyk (0.27 #3729, 0.26 #5818, 0.25 #7459), 09j9h (0.27 #3729, 0.26 #5818, 0.25 #7459), 0d2ww (0.26 #5818, 0.25 #2386, 0.20 #90), 0cbd2 (0.20 #7, 0.15 #4930, 0.14 #6868) >> Best rule #611 for best value: >> intensional similarity = 3 >> extensional distance = 681 >> proper extension: 030x48; 013bd1; >> query: (?x5743, 02hrh1q) <- film(?x5743, ?x5290), genre(?x5290, ?x1509), ?x1509 = 060__y >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0175wg profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 60.000 0.881 http://example.org/people/person/profession #14103-02whj PRED entity: 02whj PRED relation: influenced_by! PRED expected values: 01mxt_ => 180 concepts (69 used for prediction) PRED predicted values (max 10 best out of 244): 03g5jw (0.15 #4690, 0.12 #4174, 0.10 #6239), 0ph2w (0.14 #5836, 0.14 #2221, 0.09 #14093), 0gcs9 (0.14 #111, 0.08 #4757, 0.08 #4241), 017yfz (0.12 #3774, 0.04 #4806, 0.02 #22881), 01vvyfh (0.12 #4274, 0.10 #6339, 0.08 #4790), 0167xy (0.12 #5081, 0.08 #4565, 0.07 #5598), 05rx__ (0.11 #1342, 0.10 #3407, 0.09 #1858), 0m2l9 (0.11 #1045, 0.07 #2077, 0.05 #3110), 015f7 (0.11 #1156, 0.05 #3221, 0.04 #4770), 01trhmt (0.11 #1124, 0.05 #3189, 0.04 #4738) >> Best rule #4690 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0jfx1; 0p_47; >> query: (?x1092, 03g5jw) <- role(?x1092, ?x227), instrumentalists(?x315, ?x1092), location(?x1092, ?x1705), influenced_by(?x1092, ?x8080) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02whj influenced_by! 01mxt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 69.000 0.154 http://example.org/influence/influence_node/influenced_by #14102-04f_d PRED entity: 04f_d PRED relation: place PRED expected values: 04f_d => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 208): 030qb3t (0.07 #30, 0.06 #545, 0.03 #1060), 0dclg (0.07 #43, 0.06 #558, 0.03 #1073), 06_kh (0.07 #5, 0.06 #520, 0.01 #12880), 0vzm (0.07 #71, 0.03 #1101, 0.03 #16996), 0ckhc (0.07 #368), 02j3w (0.07 #101), 04ych (0.07 #61813, 0.04 #27297), 0f2w0 (0.07 #61813, 0.03 #16996, 0.03 #33480), 04f_d (0.07 #61813, 0.03 #16996, 0.03 #33480), 0z53k (0.07 #61813, 0.01 #15815) >> Best rule #30 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 06_kh; 0ccvx; 02j3w; 0ckhc; 016722; 0430_; 0cy07; >> query: (?x2017, 030qb3t) <- place_of_birth(?x92, ?x2017), film(?x92, ?x755), ?x755 = 02z3r8t >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #61813 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 803 *> proper extension: 0290rb; 0k_s5; 01c6zg; 01fv4z; 015q02; *> query: (?x2017, ?x1025) <- location(?x6934, ?x2017), location(?x6934, ?x1025) *> conf = 0.07 ranks of expected_values: 9 EVAL 04f_d place 04f_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 128.000 128.000 0.071 http://example.org/location/hud_county_place/place #14101-038hg PRED entity: 038hg PRED relation: colors! PRED expected values: 03mqj_ 019m9h 03x6w8 01k6zy => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 955): 0j5m6 (0.85 #2347, 0.50 #3783, 0.50 #2777), 04l5d0 (0.85 #2347, 0.50 #4225, 0.50 #2884), 01ct6 (0.85 #2347, 0.50 #2690, 0.50 #1683), 01y3c (0.85 #2347, 0.50 #1695, 0.49 #1005), 05tg3 (0.85 #2347, 0.50 #1736, 0.49 #1005), 02__x (0.85 #2347, 0.50 #1789, 0.49 #1005), 02c_4 (0.85 #2347, 0.50 #1824, 0.49 #1005), 01ync (0.85 #2347, 0.50 #1750, 0.49 #1005), 0jm9w (0.85 #2347, 0.50 #1555, 0.43 #3568), 07l4z (0.85 #2347, 0.50 #1856, 0.40 #2192) >> Best rule #2347 for best value: >> intensional similarity = 30 >> extensional distance = 3 >> proper extension: 0jc_p; >> query: (?x8047, ?x662) <- colors(?x10576, ?x8047), colors(?x10341, ?x8047), colors(?x9249, ?x8047), colors(?x6919, ?x8047), colors(?x4824, ?x8047), colors(?x4390, ?x8047), student(?x6919, ?x5254), school(?x12956, ?x6919), institution(?x620, ?x9249), colors(?x4148, ?x8047), contains(?x94, ?x9249), colors(?x6919, ?x332), student(?x10341, ?x275), citytown(?x4824, ?x3014), category(?x10341, ?x134), currency(?x6919, ?x170), list(?x10576, ?x2197), major_field_of_study(?x4390, ?x10518), team(?x6523, ?x4148), student(?x4390, ?x1857), team(?x60, ?x4148), school_type(?x6919, ?x3092), ?x3092 = 05jxkf, major_field_of_study(?x10576, ?x1154), major_field_of_study(?x6919, ?x2314), basic_title(?x5254, ?x265), school_type(?x4824, ?x4017), ?x10518 = 034ns, student(?x10576, ?x3539), colors(?x662, ?x332) >> conf = 0.85 => this is the best rule for 56 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 53, 71, 245, 250 EVAL 038hg colors! 01k6zy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 20.000 20.000 0.845 http://example.org/sports/sports_team/colors EVAL 038hg colors! 03x6w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 20.000 20.000 0.845 http://example.org/sports/sports_team/colors EVAL 038hg colors! 019m9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.845 http://example.org/sports/sports_team/colors EVAL 038hg colors! 03mqj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.845 http://example.org/sports/sports_team/colors #14100-09pmkv PRED entity: 09pmkv PRED relation: country! PRED expected values: 07gyv 06wrt 071t0 => 195 concepts (195 used for prediction) PRED predicted values (max 10 best out of 50): 071t0 (0.89 #70, 0.88 #1320, 0.84 #1470), 03_8r (0.87 #119, 0.82 #1069, 0.76 #669), 01lb14 (0.87 #114, 0.76 #564, 0.71 #1314), 06f41 (0.87 #113, 0.76 #713, 0.74 #663), 06wrt (0.87 #115, 0.72 #65, 0.68 #665), 01cgz (0.79 #2612, 0.78 #62, 0.71 #712), 07jbh (0.74 #130, 0.68 #680, 0.67 #1330), 07gyv (0.74 #106, 0.66 #356, 0.65 #1056), 07rlg (0.70 #101, 0.58 #1, 0.57 #301), 01hp22 (0.65 #107, 0.61 #57, 0.58 #7) >> Best rule #70 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01ls2; 07t21; >> query: (?x1122, 071t0) <- film_release_region(?x5826, ?x1122), film_release_region(?x1904, ?x1122), award(?x5826, ?x7215), ?x1904 = 09146g >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 8 EVAL 09pmkv country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 195.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09pmkv country! 06wrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 195.000 195.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09pmkv country! 07gyv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 195.000 195.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #14099-07_s4b PRED entity: 07_s4b PRED relation: profession PRED expected values: 03gjzk => 72 concepts (46 used for prediction) PRED predicted values (max 10 best out of 48): 03gjzk (0.85 #1050, 0.84 #754, 0.83 #902), 02hrh1q (0.71 #2233, 0.69 #5491, 0.68 #2085), 01d_h8 (0.68 #302, 0.67 #598, 0.65 #450), 02jknp (0.57 #303, 0.55 #599, 0.52 #451), 02krf9 (0.30 #766, 0.30 #914, 0.29 #1062), 0kyk (0.27 #1657, 0.20 #2989, 0.15 #1509), 0np9r (0.25 #3405, 0.25 #3554, 0.14 #20), 015h31 (0.25 #3405, 0.25 #3554, 0.14 #27), 0196pc (0.25 #3405, 0.25 #3554, 0.14 #73), 018gz8 (0.19 #1496, 0.17 #1940, 0.17 #1792) >> Best rule #1050 for best value: >> intensional similarity = 2 >> extensional distance = 249 >> proper extension: 0f1vrl; >> query: (?x2952, 03gjzk) <- profession(?x2952, ?x353), program(?x2952, ?x3180) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07_s4b profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 46.000 0.849 http://example.org/people/person/profession #14098-011yth PRED entity: 011yth PRED relation: film_format PRED expected values: 07fb8_ => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.40 #6, 0.20 #1, 0.19 #41), 0cj16 (0.20 #3, 0.13 #124, 0.12 #23), 017fx5 (0.05 #44, 0.04 #84, 0.03 #222) >> Best rule #6 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 056xkh; >> query: (?x1910, 07fb8_) <- film(?x3581, ?x1910), film(?x1909, ?x1910), ?x1909 = 0mj1l, produced_by(?x1910, ?x2332), participant(?x2280, ?x3581) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011yth film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.400 http://example.org/film/film/film_format #14097-0gtsx8c PRED entity: 0gtsx8c PRED relation: film_release_region PRED expected values: 03rjj 05b4w 04g5k => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 125): 03rjj (0.90 #1363, 0.89 #819, 0.88 #1227), 03_3d (0.86 #1229, 0.82 #957, 0.79 #685), 09pmkv (0.83 #427, 0.51 #971, 0.46 #835), 05b4w (0.83 #863, 0.83 #1407, 0.81 #999), 047yc (0.67 #428, 0.66 #1380, 0.66 #972), 06qd3 (0.63 #1253, 0.59 #981, 0.56 #2749), 06t8v (0.61 #875, 0.59 #1419, 0.56 #1011), 0h7x (0.59 #1250, 0.44 #1386, 0.43 #842), 0hzlz (0.58 #423, 0.33 #1239, 0.31 #4355), 05qx1 (0.50 #439, 0.50 #983, 0.48 #1391) >> Best rule #1363 for best value: >> intensional similarity = 6 >> extensional distance = 102 >> proper extension: 087wc7n; 03bx2lk; 0fq7dv_; 01fmys; 0gffmn8; 0fpgp26; >> query: (?x141, 03rjj) <- film(?x1460, ?x141), film_release_region(?x141, ?x1497), film_release_region(?x141, ?x512), ?x512 = 07ssc, ?x1497 = 015qh, language(?x141, ?x254) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 59 EVAL 0gtsx8c film_release_region 04g5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 79.000 79.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gtsx8c film_release_region 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 79.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gtsx8c film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14096-077qn PRED entity: 077qn PRED relation: film_release_region! PRED expected values: 0by1wkq 0fpv_3_ 0gz6b6g 0gyfp9c 026njb5 07cyl 017jd9 0dr89x 02qyv3h 0gvvm6l 0j8f09z => 185 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1245): 08hmch (0.89 #18782, 0.85 #7577, 0.81 #20027), 043tvp3 (0.89 #19537, 0.79 #8332, 0.75 #17047), 04f52jw (0.88 #7772, 0.78 #18977, 0.77 #16487), 017jd9 (0.87 #19223, 0.85 #8018, 0.82 #16733), 03nm_fh (0.83 #20479, 0.82 #19234, 0.76 #8029), 0fpv_3_ (0.82 #7726, 0.80 #16441, 0.78 #18931), 0dzlbx (0.82 #8073, 0.76 #19278, 0.75 #16788), 047vnkj (0.82 #19325, 0.76 #8120, 0.70 #16835), 017gm7 (0.82 #18821, 0.74 #7616, 0.73 #16331), 0dtfn (0.79 #7615, 0.72 #20065, 0.71 #18820) >> Best rule #18782 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 05r4w; 06npd; 0f8l9c; >> query: (?x4059, 08hmch) <- film_release_region(?x3784, ?x4059), olympics(?x4059, ?x784), ?x3784 = 0bmhvpr >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #19223 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 05r4w; 06npd; 0f8l9c; *> query: (?x4059, 017jd9) <- film_release_region(?x3784, ?x4059), olympics(?x4059, ?x784), ?x3784 = 0bmhvpr *> conf = 0.87 ranks of expected_values: 4, 6, 33, 99, 107, 114, 135, 143, 185, 186, 200 EVAL 077qn film_release_region! 0j8f09z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077qn film_release_region! 0gvvm6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077qn film_release_region! 02qyv3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077qn film_release_region! 0dr89x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077qn film_release_region! 017jd9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077qn film_release_region! 07cyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077qn film_release_region! 026njb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077qn film_release_region! 0gyfp9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077qn film_release_region! 0gz6b6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077qn film_release_region! 0fpv_3_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077qn film_release_region! 0by1wkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 185.000 73.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14095-09gq0x5 PRED entity: 09gq0x5 PRED relation: nominated_for! PRED expected values: 0kszw => 83 concepts (45 used for prediction) PRED predicted values (max 10 best out of 544): 05qd_ (0.19 #18642, 0.18 #20973, 0.13 #41949), 03rwz3 (0.19 #18642, 0.18 #20973, 0.13 #41949), 01gb54 (0.18 #8002, 0.09 #17323, 0.08 #19654), 0151w_ (0.17 #2529, 0.16 #88567, 0.13 #58265), 01y665 (0.17 #2972, 0.12 #5302, 0.06 #7632), 058frd (0.17 #3663, 0.12 #5993, 0.06 #8323), 0170qf (0.16 #88567, 0.13 #58265, 0.11 #456), 02l4rh (0.16 #88567, 0.13 #58265, 0.11 #1515), 01ksr1 (0.16 #88567, 0.13 #58265, 0.09 #16311), 0lpjn (0.16 #88567, 0.13 #58265, 0.09 #16311) >> Best rule #18642 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0btyf5z; 0fy34l; 0h63gl9; 01y9r2; >> query: (?x1813, ?x902) <- nominated_for(?x451, ?x1813), ?x451 = 099jhq, film(?x902, ?x1813), nominated_for(?x72, ?x1813) >> conf = 0.19 => this is the best rule for 2 predicted values *> Best rule #88567 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 799 *> proper extension: 028k2x; 03d17dg; 06r1k; 025x1t; 0gxsh4; *> query: (?x1813, ?x374) <- award_winner(?x1813, ?x1223), award(?x1223, ?x112), award_winner(?x1223, ?x374) *> conf = 0.16 ranks of expected_values: 12 EVAL 09gq0x5 nominated_for! 0kszw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 83.000 45.000 0.186 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #14094-0n2m7 PRED entity: 0n2m7 PRED relation: currency PRED expected values: 09nqf => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.87 #71, 0.87 #70, 0.86 #79) >> Best rule #71 for best value: >> intensional similarity = 5 >> extensional distance = 216 >> proper extension: 0d6lp; 0mwxl; 0m24v; 0ms6_; 0mww2; 0j_1v; 0l2nd; 0mw_q; >> query: (?x7916, ?x170) <- time_zones(?x7916, ?x2674), adjoins(?x7915, ?x7916), currency(?x7915, ?x170), second_level_divisions(?x94, ?x7916), ?x94 = 09c7w0 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n2m7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.872 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #14093-03rjj PRED entity: 03rjj PRED relation: film_release_region! PRED expected values: 023g6w => 214 concepts (214 used for prediction) PRED predicted values (max 10 best out of 474): 043h78 (0.18 #1077, 0.16 #2440, 0.13 #3395), 025ts_z (0.18 #1075, 0.13 #1212, 0.10 #937), 05fcbk7 (0.18 #993, 0.13 #1130, 0.10 #855), 03cyslc (0.18 #1053, 0.13 #1190, 0.08 #2416), 08sfxj (0.18 #1031, 0.13 #1168, 0.08 #2394), 05c46y6 (0.18 #989, 0.13 #1126, 0.08 #2352), 03nqnnk (0.18 #1044, 0.10 #906, 0.08 #2407), 0299hs (0.16 #2364, 0.14 #3047, 0.13 #1138), 0dkv90 (0.16 #2430, 0.13 #3522, 0.13 #1204), 03z106 (0.16 #2374, 0.13 #3329, 0.12 #1421) >> Best rule #1077 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 09c7w0; 0d060g; 03rt9; 05qhw; 07ssc; 0f8l9c; 059j2; 0h7x; 06mkj; >> query: (?x205, 043h78) <- film_release_region(?x66, ?x205), second_level_divisions(?x205, ?x7191), contains(?x205, ?x1356) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #1210 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 04q_g; *> query: (?x205, 023g6w) <- contains(?x205, ?x9660), currency(?x205, ?x170), location_of_ceremony(?x2582, ?x9660) *> conf = 0.07 ranks of expected_values: 119 EVAL 03rjj film_release_region! 023g6w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 214.000 214.000 0.182 http://example.org/film/film/runtime./film/film_cut/film_release_region #14092-03s9b PRED entity: 03s9b PRED relation: influenced_by! PRED expected values: 041jlr => 145 concepts (53 used for prediction) PRED predicted values (max 10 best out of 904): 05ty4m (0.14 #1033, 0.11 #24647, 0.10 #5139), 0bqs56 (0.14 #1275, 0.11 #24647, 0.09 #7946), 01xwv7 (0.14 #1449, 0.11 #24647, 0.09 #7094), 016_mj (0.14 #1080, 0.11 #24647, 0.08 #1593), 01xwqn (0.14 #1468, 0.11 #24647, 0.08 #1981), 01j7rd (0.14 #1097, 0.11 #24647, 0.08 #1610), 015pxr (0.14 #1100, 0.11 #24647, 0.08 #1613), 049fgvm (0.14 #1291, 0.11 #24647, 0.08 #1804), 01wp_jm (0.14 #1432, 0.11 #24647, 0.08 #1945), 05g8ky (0.14 #1032, 0.11 #24647, 0.08 #1545) >> Best rule #1033 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 05whq_9; 04yt7; >> query: (?x6957, 05ty4m) <- student(?x11555, ?x6957), type_of_appearance(?x6957, ?x3429), written_by(?x6345, ?x6957) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #9084 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 147 *> proper extension: 04sd0; *> query: (?x6957, 041jlr) <- influenced_by(?x3527, ?x6957), nationality(?x3527, ?x94), award_winner(?x7846, ?x3527) *> conf = 0.05 ranks of expected_values: 64 EVAL 03s9b influenced_by! 041jlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 145.000 53.000 0.143 http://example.org/influence/influence_node/influenced_by #14091-0bl60p PRED entity: 0bl60p PRED relation: profession PRED expected values: 02hrh1q => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 53): 02hrh1q (0.88 #4367, 0.88 #915, 0.88 #6317), 03gjzk (0.33 #1516, 0.25 #3601, 0.21 #7519), 0dxtg (0.31 #614, 0.30 #1514, 0.25 #10968), 01d_h8 (0.30 #2556, 0.30 #606, 0.29 #906), 0cbd2 (0.26 #3902, 0.25 #3601, 0.22 #457), 0kyk (0.26 #3902, 0.25 #3601, 0.22 #481), 09jwl (0.26 #3902, 0.25 #3601, 0.18 #770), 0dz3r (0.26 #3902, 0.25 #3601, 0.12 #1052), 0nbcg (0.26 #3902, 0.25 #3601, 0.11 #1083), 016z4k (0.26 #3902, 0.25 #3601, 0.10 #1054) >> Best rule #4367 for best value: >> intensional similarity = 3 >> extensional distance = 1269 >> proper extension: 01ty7ll; 01nrq5; 01mt1fy; 06_bq1; 0f14q; 014zn0; 033071; 05m7zg; >> query: (?x7730, 02hrh1q) <- location(?x7730, ?x108), film(?x7730, ?x5890), nominated_for(?x1033, ?x5890) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bl60p profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.881 http://example.org/people/person/profession #14090-023tp8 PRED entity: 023tp8 PRED relation: sibling PRED expected values: 02v60l => 121 concepts (66 used for prediction) PRED predicted values (max 10 best out of 91): 01wk51 (0.82 #233, 0.82 #1401, 0.78 #1052), 04d_mtq (0.11 #211, 0.04 #328, 0.03 #913), 04cr6qv (0.05 #164, 0.04 #281, 0.02 #1332), 032_jg (0.05 #123, 0.04 #240, 0.02 #474), 01pllx (0.05 #193, 0.04 #310, 0.02 #895), 01z7s_ (0.05 #166, 0.04 #283, 0.02 #868), 01fwpt (0.05 #144, 0.04 #261, 0.02 #846), 06hx2 (0.05 #170, 0.03 #755, 0.02 #1338), 023nlj (0.05 #190, 0.03 #424, 0.02 #541), 033wx9 (0.05 #139, 0.03 #373, 0.02 #607) >> Best rule #233 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 026_dq6; >> query: (?x376, ?x7617) <- profession(?x376, ?x1032), celebrity(?x376, ?x3056), sibling(?x7617, ?x376) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #273 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 02x8kk; 02x8mt; *> query: (?x376, 02v60l) <- place_of_birth(?x376, ?x1860), sibling(?x7617, ?x376), adjoins(?x448, ?x1860) *> conf = 0.04 ranks of expected_values: 27 EVAL 023tp8 sibling 02v60l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 121.000 66.000 0.818 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #14089-084w8 PRED entity: 084w8 PRED relation: influenced_by PRED expected values: 0379s 032l1 01v9724 => 134 concepts (63 used for prediction) PRED predicted values (max 10 best out of 318): 032l1 (0.58 #2222, 0.38 #1370, 0.28 #4349), 0l99s (0.40 #645, 0.25 #219, 0.12 #1501), 084w8 (0.38 #1285, 0.25 #3, 0.20 #4264), 042q3 (0.37 #6749, 0.33 #9305, 0.22 #2064), 03f0324 (0.36 #3133, 0.25 #2282, 0.25 #1430), 0379s (0.33 #2211, 0.25 #77, 0.20 #4338), 01v9724 (0.32 #4433, 0.25 #2306, 0.20 #1025), 0j3v (0.29 #2618, 0.25 #6452, 0.22 #9008), 03sbs (0.27 #6608, 0.25 #9164, 0.22 #1923), 02kz_ (0.25 #1448, 0.25 #166, 0.20 #592) >> Best rule #2222 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0gd5z; 051cc; >> query: (?x118, 032l1) <- influenced_by(?x118, ?x5434), student(?x4603, ?x118), ?x5434 = 01tz6vs, nationality(?x118, ?x94) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 7 EVAL 084w8 influenced_by 01v9724 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 134.000 63.000 0.583 http://example.org/influence/influence_node/influenced_by EVAL 084w8 influenced_by 032l1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 63.000 0.583 http://example.org/influence/influence_node/influenced_by EVAL 084w8 influenced_by 0379s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 134.000 63.000 0.583 http://example.org/influence/influence_node/influenced_by #14088-06kqt3 PRED entity: 06kqt3 PRED relation: colors! PRED expected values: 02896 => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 917): 01yhm (0.50 #1828, 0.50 #749, 0.47 #1436), 01ct6 (0.50 #2524, 0.50 #1445, 0.43 #3603), 0jmk7 (0.50 #2813, 0.50 #1734, 0.43 #3892), 04l5d0 (0.50 #2006, 0.50 #1285, 0.40 #4163), 03lpp_ (0.50 #1805, 0.50 #726, 0.40 #3962), 05gg4 (0.50 #1878, 0.50 #1157, 0.33 #5112), 04wmvz (0.50 #2031, 0.50 #952, 0.33 #4907), 01yjl (0.50 #1852, 0.50 #1131, 0.33 #54), 03y_f8 (0.50 #1812, 0.50 #1091, 0.33 #14), 01_1kk (0.50 #2137, 0.50 #1416, 0.33 #339) >> Best rule #1828 for best value: >> intensional similarity = 30 >> extensional distance = 2 >> proper extension: 06fvc; >> query: (?x12067, 01yhm) <- colors(?x11185, ?x12067), colors(?x5868, ?x12067), colors(?x2150, ?x12067), colors(?x1506, ?x12067), colors(?x13860, ?x12067), colors(?x3298, ?x12067), colors(?x529, ?x12067), category(?x2150, ?x134), sport(?x13860, ?x453), school(?x1883, ?x1506), student(?x1506, ?x105), school_type(?x1506, ?x1507), state_province_region(?x2150, ?x6521), school_type(?x5868, ?x3205), major_field_of_study(?x2150, ?x7134), currency(?x2150, ?x170), ?x7134 = 02_7t, position(?x13860, ?x2918), registering_agency(?x2150, ?x1982), student(?x2150, ?x5133), country(?x11185, ?x94), institution(?x1368, ?x5868), nominated_for(?x105, ?x8837), profession(?x105, ?x106), ?x3298 = 0jnmj, award_nominee(?x105, ?x4060), participant(?x105, ?x2451), participant(?x105, ?x4767), award(?x105, ?x1972), teams(?x14083, ?x529) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1081 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 2 *> proper extension: 01g5v; *> query: (?x12067, 02896) <- colors(?x10899, ?x12067), colors(?x2150, ?x12067), colors(?x1506, ?x12067), colors(?x13860, ?x12067), colors(?x13166, ?x12067), category(?x2150, ?x134), sport(?x13860, ?x453), school(?x1883, ?x1506), student(?x1506, ?x105), school_type(?x1506, ?x1507), state_province_region(?x2150, ?x6521), major_field_of_study(?x2150, ?x7134), currency(?x2150, ?x170), ?x7134 = 02_7t, ?x13166 = 0j6tr, contains(?x94, ?x1506), contains(?x859, ?x2150), service_language(?x10899, ?x254), ?x254 = 02h40lc, school_type(?x2150, ?x1044), school(?x1823, ?x10899), teams(?x4356, ?x13860), team(?x2010, ?x1823), draft(?x1823, ?x1161), season(?x1823, ?x701), organization(?x346, ?x2150) *> conf = 0.50 ranks of expected_values: 29 EVAL 06kqt3 colors! 02896 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 19.000 19.000 0.500 http://example.org/sports/sports_team/colors #14087-0d05w3 PRED entity: 0d05w3 PRED relation: countries_spoken_in! PRED expected values: 02h40lc 05zjd => 238 concepts (238 used for prediction) PRED predicted values (max 10 best out of 60): 0653m (0.61 #3874, 0.61 #2600, 0.60 #4140), 02h40lc (0.53 #7171, 0.33 #904, 0.31 #6268), 064_8sq (0.27 #7186, 0.20 #2935, 0.18 #3837), 0jzc (0.22 #2880, 0.21 #2137, 0.21 #2721), 02bjrlw (0.20 #903, 0.19 #1540, 0.13 #2494), 06nm1 (0.20 #326, 0.19 #6753, 0.17 #1334), 0h407 (0.20 #362, 0.08 #840, 0.07 #946), 083tk (0.20 #347, 0.08 #825, 0.07 #931), 04306rv (0.20 #2817, 0.18 #2498, 0.18 #3082), 012v8 (0.14 #516, 0.12 #2161, 0.10 #2692) >> Best rule #3874 for best value: >> intensional similarity = 2 >> extensional distance = 54 >> proper extension: 0366c; >> query: (?x2346, ?x2890) <- time_zones(?x2346, ?x11859), official_language(?x2346, ?x2890) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #7171 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 123 *> proper extension: 0h44w; *> query: (?x2346, 02h40lc) <- countries_spoken_in(?x5974, ?x2346), major_field_of_study(?x1368, ?x5974) *> conf = 0.53 ranks of expected_values: 2, 18 EVAL 0d05w3 countries_spoken_in! 05zjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 238.000 238.000 0.614 http://example.org/language/human_language/countries_spoken_in EVAL 0d05w3 countries_spoken_in! 02h40lc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 238.000 238.000 0.614 http://example.org/language/human_language/countries_spoken_in #14086-0fr0t PRED entity: 0fr0t PRED relation: locations! PRED expected values: 0b_6_l => 222 concepts (193 used for prediction) PRED predicted values (max 10 best out of 105): 0bzrxn (0.24 #798, 0.17 #1542, 0.15 #1170), 0b_6rk (0.23 #1037, 0.21 #2029, 0.19 #789), 0b_6jz (0.22 #4253, 0.17 #6741, 0.15 #1026), 0b_75k (0.20 #1660, 0.18 #4267, 0.18 #2032), 0b_6lb (0.19 #1067, 0.18 #1935, 0.17 #3175), 0b_6v_ (0.19 #1056, 0.17 #4283, 0.14 #64), 0b_6mr (0.19 #3683, 0.16 #4553, 0.14 #2193), 0b_6xf (0.18 #4322, 0.17 #3203, 0.17 #1715), 0b_6q5 (0.17 #1456, 0.16 #4560, 0.15 #1084), 0bzrsh (0.17 #4296, 0.17 #1689, 0.15 #1069) >> Best rule #798 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0lhql; >> query: (?x3983, 0bzrxn) <- location_of_ceremony(?x566, ?x3983), place_of_death(?x3891, ?x3983), category(?x3983, ?x134), locations(?x4368, ?x3983) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #4570 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 01lxw6; *> query: (?x3983, 0b_6_l) <- contains(?x94, ?x3983), category(?x3983, ?x134), locations(?x4368, ?x3983) *> conf = 0.15 ranks of expected_values: 12 EVAL 0fr0t locations! 0b_6_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 222.000 193.000 0.238 http://example.org/time/event/locations #14085-065y4w7 PRED entity: 065y4w7 PRED relation: major_field_of_study PRED expected values: 062z7 03nfmq 02822 0_jm 0mg1w => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 91): 062z7 (0.58 #225, 0.35 #1551, 0.35 #2471), 01540 (0.50 #253, 0.30 #1579, 0.25 #2499), 05qfh (0.50 #232, 0.29 #1558, 0.28 #2478), 03g3w (0.44 #428, 0.40 #1550, 0.35 #1960), 04x_3 (0.42 #223, 0.24 #2061, 0.24 #1447), 05qjt (0.35 #1535, 0.29 #1945, 0.28 #2455), 01bt59 (0.33 #266, 0.19 #470, 0.17 #1082), 01zc2w (0.33 #263, 0.19 #467, 0.14 #1589), 037mh8 (0.32 #1585, 0.26 #2403, 0.25 #2505), 06ms6 (0.31 #419, 0.22 #1031, 0.22 #1541) >> Best rule #225 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 07szy; 0f1nl; 0j_sncb; 07vyf; 04hgpt; 07ccs; 0gl5_; 012mzw; 01qd_r; 027ybp; >> query: (?x735, 062z7) <- school(?x580, ?x735), major_field_of_study(?x735, ?x11820), ?x11820 = 0w7s >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1, 13, 20, 21, 32 EVAL 065y4w7 major_field_of_study 0mg1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 110.000 110.000 0.583 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 065y4w7 major_field_of_study 0_jm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 110.000 110.000 0.583 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 065y4w7 major_field_of_study 02822 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 110.000 110.000 0.583 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 065y4w7 major_field_of_study 03nfmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 110.000 110.000 0.583 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 065y4w7 major_field_of_study 062z7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.583 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14084-01vrx3g PRED entity: 01vrx3g PRED relation: gender PRED expected values: 05zppz => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 3): 05zppz (0.88 #17, 0.86 #15, 0.86 #51), 02zsn (0.35 #56, 0.31 #187, 0.30 #137), 0jpmt (0.12 #135) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 09g0h; >> query: (?x366, 05zppz) <- role(?x366, ?x1750), role(?x366, ?x227), ?x1750 = 02hnl, instrumentalists(?x227, ?x115) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrx3g gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.875 http://example.org/people/person/gender #14083-02661h PRED entity: 02661h PRED relation: film PRED expected values: 0bscw 02q7fl9 016dj8 => 142 concepts (98 used for prediction) PRED predicted values (max 10 best out of 768): 0266s9 (0.57 #115793, 0.50 #37415, 0.45 #69481), 017jd9 (0.44 #6114, 0.03 #63125, 0.03 #36404), 017gl1 (0.32 #5486, 0.07 #3705, 0.03 #35776), 01c22t (0.25 #1946, 0.07 #3727, 0.03 #10853), 07bwr (0.25 #2641, 0.07 #4422, 0.01 #38275), 0f2sx4 (0.25 #3158, 0.07 #4939, 0.01 #47699), 0422v0 (0.25 #3556, 0.01 #55222, 0.01 #23154), 0c0zq (0.25 #1555), 017gm7 (0.24 #5553, 0.07 #3772, 0.02 #62564), 0ndwt2w (0.16 #6335, 0.07 #4554, 0.02 #36625) >> Best rule #115793 for best value: >> intensional similarity = 3 >> extensional distance = 860 >> proper extension: 01mqz0; 02pb53; 027l0b; 01w02sy; 0347xl; 062dn7; 05slvm; 01fwf1; 01xv77; 0gx_p; ... >> query: (?x8022, ?x11806) <- place_of_birth(?x8022, ?x12567), film(?x8022, ?x1331), nominated_for(?x8022, ?x11806) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4668 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0gs6vr; 0436kgz; 01cwcr; *> query: (?x8022, 016dj8) <- film(?x8022, ?x11066), ?x11066 = 025s1wg, gender(?x8022, ?x231) *> conf = 0.07 ranks of expected_values: 83, 471 EVAL 02661h film 016dj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 142.000 98.000 0.572 http://example.org/film/actor/film./film/performance/film EVAL 02661h film 02q7fl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 142.000 98.000 0.572 http://example.org/film/actor/film./film/performance/film EVAL 02661h film 0bscw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 98.000 0.572 http://example.org/film/actor/film./film/performance/film #14082-01ck6h PRED entity: 01ck6h PRED relation: ceremony PRED expected values: 019bk0 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 132): 0466p0j (0.75 #468, 0.61 #996, 0.60 #204), 0gpjbt (0.70 #422, 0.64 #950, 0.61 #1082), 09n4nb (0.70 #440, 0.64 #968, 0.60 #176), 056878 (0.70 #425, 0.61 #953, 0.58 #1085), 05pd94v (0.65 #398, 0.61 #926, 0.60 #134), 02rjjll (0.65 #400, 0.61 #928, 0.58 #1060), 019bk0 (0.57 #938, 0.55 #410, 0.54 #1070), 0jzphpx (0.55 #432, 0.48 #960, 0.45 #1092), 0gx1673 (0.50 #508, 0.40 #244, 0.36 #772), 073h1t (0.26 #3829, 0.13 #1080, 0.11 #288) >> Best rule #468 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 01d38g; 01bgqh; 01c4_6; 025m8y; 025m8l; 054ks3; 025mb9; 03qbh5; 03tk6z; 099vwn; ... >> query: (?x2322, 0466p0j) <- award_winner(?x2322, ?x6626), award(?x4701, ?x2322), ?x4701 = 03j24kf, role(?x6626, ?x74), profession(?x6626, ?x131) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #938 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 120 *> proper extension: 02qkk9_; *> query: (?x2322, 019bk0) <- award_winner(?x2322, ?x6626), ceremony(?x2322, ?x342), instrumentalists(?x227, ?x6626), profession(?x6626, ?x131), artists(?x505, ?x6626) *> conf = 0.57 ranks of expected_values: 7 EVAL 01ck6h ceremony 019bk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 42.000 42.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony #14081-06v8s0 PRED entity: 06v8s0 PRED relation: place_of_birth PRED expected values: 030qb3t => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 106): 030qb3t (0.25 #54, 0.14 #1464, 0.12 #2169), 02_286 (0.25 #19, 0.11 #3543, 0.11 #6362), 0_g_6 (0.17 #1090, 0.14 #1795, 0.12 #3204), 02dtg (0.17 #715, 0.12 #2829, 0.11 #4238), 0z18v (0.17 #1334, 0.12 #3448, 0.02 #5562), 0r00l (0.14 #1897, 0.12 #2602, 0.02 #5420), 04f_d (0.12 #2188, 0.03 #9941, 0.03 #10645), 02frhbc (0.12 #3181, 0.02 #5295, 0.02 #6000), 0sf9_ (0.11 #3666, 0.02 #7190, 0.02 #10010), 0kc40 (0.11 #3998) >> Best rule #54 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 016yr0; >> query: (?x51, 030qb3t) <- nationality(?x51, ?x94), profession(?x51, ?x1943), language(?x51, ?x254), actor(?x50, ?x51), ?x1943 = 02krf9 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06v8s0 place_of_birth 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.250 http://example.org/people/person/place_of_birth #14080-01bmlb PRED entity: 01bmlb PRED relation: award PRED expected values: 08_vwq => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 328): 03x3wf (0.76 #33376, 0.75 #30963, 0.72 #20909), 0c4z8 (0.60 #873, 0.14 #30629, 0.14 #8112), 01d38g (0.40 #831, 0.33 #2037, 0.11 #18120), 01by1l (0.40 #913, 0.27 #2119, 0.23 #18202), 03qbh5 (0.40 #1007, 0.27 #2213, 0.15 #3017), 01ck6h (0.40 #923, 0.10 #4140, 0.10 #2933), 025m8y (0.40 #900, 0.09 #3313, 0.08 #8139), 02f777 (0.33 #2318, 0.20 #1112, 0.08 #60334), 054ky1 (0.30 #1714, 0.25 #508, 0.20 #1312), 0f4x7 (0.30 #12093, 0.24 #14907, 0.20 #1638) >> Best rule #33376 for best value: >> intensional similarity = 3 >> extensional distance = 692 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01vsxdm; 0frsw; 03fbc; 01vrwfv; 0163m1; 0hvbj; 01fmz6; ... >> query: (?x10411, ?x1088) <- award_winner(?x1088, ?x10411), award(?x10411, ?x102), category(?x10411, ?x134) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #12332 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 01wskg; *> query: (?x10411, 08_vwq) <- film(?x10411, ?x2506), place_of_death(?x10411, ?x191), nominated_for(?x10411, ?x6080) *> conf = 0.05 ranks of expected_values: 189 EVAL 01bmlb award 08_vwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 165.000 165.000 0.762 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #14079-057176 PRED entity: 057176 PRED relation: currency PRED expected values: 09nqf => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.23 #1, 0.20 #28, 0.20 #10) >> Best rule #1 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 01dwrc; >> query: (?x6979, 09nqf) <- award_nominee(?x1896, ?x6979), ?x1896 = 0j1yf >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 057176 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.231 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #14078-034gxk PRED entity: 034gxk PRED relation: parent_genre PRED expected values: 0dl5d => 50 concepts (37 used for prediction) PRED predicted values (max 10 best out of 150): 06by7 (0.71 #348, 0.40 #849, 0.33 #1019), 05r6t (0.62 #555, 0.48 #1058, 0.44 #720), 011j5x (0.38 #522, 0.33 #22, 0.22 #831), 03lty (0.33 #19, 0.25 #519, 0.16 #1191), 07sbbz2 (0.33 #169, 0.14 #337, 0.06 #670), 02l96k (0.33 #236, 0.14 #404, 0.06 #737), 01243b (0.29 #1032, 0.16 #2038, 0.15 #2206), 09jw2 (0.25 #603, 0.22 #768, 0.20 #936), 016clz (0.19 #1169, 0.19 #1007, 0.14 #332), 0jmwg (0.19 #1080, 0.14 #332, 0.12 #577) >> Best rule #348 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 0dl5d; 01fh36; >> query: (?x13908, 06by7) <- artists(?x13908, ?x8215), artists(?x13908, ?x6471), artists(?x13908, ?x5512), artists(?x2491, ?x6471), artists(?x302, ?x6471), ?x302 = 016clz, ?x2491 = 011j5x, ?x5512 = 02jqjm, profession(?x8215, ?x131) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1018 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 19 *> proper extension: 0190_q; 01cbwl; 01hcvm; 0pm85; *> query: (?x13908, 0dl5d) <- artists(?x13908, ?x6471), artists(?x13908, ?x5512), artists(?x10933, ?x6471), artists(?x2491, ?x6471), artists(?x302, ?x6471), ?x302 = 016clz, ?x2491 = 011j5x, ?x10933 = 03p7rp, award(?x5512, ?x724) *> conf = 0.10 ranks of expected_values: 37 EVAL 034gxk parent_genre 0dl5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 50.000 37.000 0.714 http://example.org/music/genre/parent_genre #14077-0bxtg PRED entity: 0bxtg PRED relation: currency PRED expected values: 09nqf => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.53 #19, 0.49 #37, 0.43 #16), 01nv4h (0.04 #26, 0.02 #74, 0.02 #47) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 02xp18; >> query: (?x496, 09nqf) <- film(?x496, ?x69), program(?x496, ?x4881), producer_type(?x496, ?x632) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bxtg currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.526 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #14076-0h25 PRED entity: 0h25 PRED relation: type_of_union PRED expected values: 04ztj => 221 concepts (221 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.84 #237, 0.81 #297, 0.80 #373), 01g63y (0.14 #602, 0.14 #74, 0.14 #610), 01bl8s (0.03 #71, 0.03 #31, 0.03 #175) >> Best rule #237 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 012vct; 030s5g; 06w38l; >> query: (?x10500, 04ztj) <- profession(?x10500, ?x987), ?x987 = 0dxtg, people(?x10199, ?x10500) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h25 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 221.000 221.000 0.839 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14075-028hc2 PRED entity: 028hc2 PRED relation: artists! PRED expected values: 01lyv => 112 concepts (53 used for prediction) PRED predicted values (max 10 best out of 245): 01lyv (0.67 #34, 0.53 #5580, 0.24 #5888), 064t9 (0.62 #937, 0.60 #1245, 0.60 #629), 06by7 (0.54 #5568, 0.43 #8958, 0.43 #9266), 0glt670 (0.42 #657, 0.40 #349, 0.37 #3122), 02lnbg (0.36 #984, 0.34 #676, 0.34 #368), 06j6l (0.35 #665, 0.35 #973, 0.34 #357), 05bt6j (0.35 #968, 0.31 #1584, 0.28 #1892), 025sc50 (0.34 #1283, 0.34 #667, 0.34 #359), 02w4v (0.32 #5591, 0.21 #15101, 0.15 #4051), 0ggx5q (0.31 #1004, 0.27 #696, 0.26 #1620) >> Best rule #34 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01fkxr; 016j2t; >> query: (?x7477, 01lyv) <- artist(?x5726, ?x7477), award_winner(?x1361, ?x7477), ?x5726 = 030jj7 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028hc2 artists! 01lyv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 53.000 0.667 http://example.org/music/genre/artists #14074-06mzp PRED entity: 06mzp PRED relation: contains PRED expected values: 01y888 => 226 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2883): 0345h (0.55 #49815, 0.44 #90853, 0.03 #158400), 06mzp (0.55 #49815, 0.03 #158342, 0.03 #79197), 0h7x (0.55 #49815, 0.03 #158417, 0.03 #79272), 04j53 (0.55 #49815, 0.03 #79496, 0.02 #240342), 019xz9 (0.55 #143615, 0.08 #20460, 0.08 #14600), 03902 (0.55 #143615), 05qtj (0.44 #90853, 0.08 #9387, 0.08 #18177), 02h6_6p (0.44 #90853, 0.08 #17887, 0.08 #12027), 01f08r (0.44 #90853, 0.06 #23922, 0.05 #47366), 0ljsz (0.44 #90853, 0.02 #151206, 0.01 #168791) >> Best rule #49815 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 06mx8; >> query: (?x774, ?x1264) <- region(?x1315, ?x774), contains(?x774, ?x7461), contains(?x1264, ?x7461) >> conf = 0.55 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 06mzp contains 01y888 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 226.000 103.000 0.553 http://example.org/location/location/contains #14073-03lrls PRED entity: 03lrls PRED relation: major_field_of_study! PRED expected values: 03ksy => 38 concepts (24 used for prediction) PRED predicted values (max 10 best out of 625): 03ksy (0.73 #2470, 0.67 #3057, 0.67 #1881), 01w5m (0.71 #1293, 0.67 #1880, 0.57 #3056), 05zl0 (0.67 #818, 0.60 #2582, 0.50 #231), 08815 (0.67 #589, 0.57 #2940, 0.57 #1177), 01mpwj (0.67 #2471, 0.57 #1295, 0.53 #1882), 02bqy (0.67 #2556, 0.57 #1380, 0.53 #1967), 06pwq (0.57 #1188, 0.57 #3543, 0.53 #1775), 07wjk (0.57 #1241, 0.53 #1828, 0.47 #2417), 07t90 (0.57 #1341, 0.53 #1928, 0.39 #3696), 0bwfn (0.53 #2062, 0.52 #4418, 0.52 #5594) >> Best rule #2470 for best value: >> intensional similarity = 12 >> extensional distance = 13 >> proper extension: 05qt0; >> query: (?x13501, 03ksy) <- major_field_of_study(?x10393, ?x13501), major_field_of_study(?x7971, ?x13501), major_field_of_study(?x892, ?x13501), ?x892 = 07tgn, category(?x7971, ?x134), student(?x7971, ?x11797), major_field_of_study(?x1526, ?x13501), major_field_of_study(?x1368, ?x13501), ?x1526 = 0bkj86, ?x1368 = 014mlp, currency(?x10393, ?x1099), citytown(?x7971, ?x1841) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03lrls major_field_of_study! 03ksy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 24.000 0.733 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #14072-09rwjly PRED entity: 09rwjly PRED relation: film_festivals! PRED expected values: 019vhk => 86 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1311): 07l4zhn (0.50 #1239, 0.40 #1462, 0.33 #123), 05zvzf3 (0.50 #1301, 0.40 #1524, 0.33 #185), 047p798 (0.40 #1547, 0.25 #1324, 0.22 #2222), 05q7874 (0.33 #139, 0.25 #1255, 0.25 #1032), 0ddfwj1 (0.33 #455, 0.25 #1798, 0.19 #6957), 03c_cxn (0.33 #337, 0.12 #7062, 0.12 #6610), 09gq0x5 (0.33 #485, 0.12 #6987, 0.12 #6535), 0cp08zg (0.33 #620, 0.12 #7122, 0.12 #6670), 09v42sf (0.33 #657, 0.12 #7159, 0.12 #6707), 0404j37 (0.33 #373, 0.12 #1712, 0.11 #2162) >> Best rule #1239 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 04_m9gk; >> query: (?x9080, 07l4zhn) <- film_festivals(?x6005, ?x9080), film_festivals(?x3714, ?x9080), nominated_for(?x6005, ?x857), nominated_for(?x601, ?x3714), region(?x6005, ?x512), genre(?x3714, ?x812), film(?x8394, ?x6005), film(?x875, ?x6005), film_crew_role(?x6005, ?x1171), ?x8394 = 05d6q1, country(?x6005, ?x279) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #671 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 0bmj62v; *> query: (?x9080, ?x280) <- film_festivals(?x6005, ?x9080), film_festivals(?x2812, ?x9080), film_crew_role(?x6005, ?x13719), film_crew_role(?x6005, ?x1284), film_release_distribution_medium(?x6005, ?x81), ?x1284 = 0ch6mp2, film(?x8394, ?x6005), profession(?x147, ?x13719), film_crew_role(?x280, ?x13719), ?x8394 = 05d6q1, genre(?x6005, ?x53), ?x2812 = 0cw3yd *> conf = 0.02 ranks of expected_values: 962 EVAL 09rwjly film_festivals! 019vhk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 37.000 0.500 http://example.org/film/film/film_festivals #14071-01rrd4 PRED entity: 01rrd4 PRED relation: type_of_union PRED expected values: 04ztj => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #169, 0.74 #201, 0.74 #117), 01g63y (0.21 #122, 0.20 #110, 0.20 #146) >> Best rule #169 for best value: >> intensional similarity = 3 >> extensional distance = 722 >> proper extension: 01g4zr; 01t07j; 081_zm; 0d4jl; 03hnd; 0lcx; 04511f; 03nk3t; 0c5tl; 0hky; ... >> query: (?x6515, 04ztj) <- profession(?x6515, ?x319), award(?x6515, ?x678), religion(?x6515, ?x1985) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rrd4 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.744 http://example.org/people/person/spouse_s./people/marriage/type_of_union #14070-092kgw PRED entity: 092kgw PRED relation: produced_by! PRED expected values: 035xwd => 68 concepts (35 used for prediction) PRED predicted values (max 10 best out of 446): 04vr_f (0.39 #4693, 0.22 #9386, 0.16 #17832), 0g9lm2 (0.39 #4693, 0.22 #9386, 0.16 #17832), 0h1x5f (0.39 #4693, 0.22 #9386, 0.13 #939), 03cp4cn (0.06 #599, 0.03 #6571, 0.02 #2477), 07tlfx (0.06 #854, 0.03 #6571), 03n0cd (0.06 #792, 0.03 #6571), 033pf1 (0.06 #750, 0.03 #6571), 02_06s (0.06 #664, 0.03 #6571), 0gg5kmg (0.06 #590, 0.03 #6571), 01vw8k (0.06 #347, 0.03 #6571) >> Best rule #4693 for best value: >> intensional similarity = 3 >> extensional distance = 336 >> proper extension: 07nznf; 0q9kd; 0grwj; 016qtt; 0fvf9q; 04t2l2; 06dv3; 014zcr; 02g8h; 042l3v; ... >> query: (?x5527, ?x1135) <- nominated_for(?x5527, ?x1135), profession(?x5527, ?x319), produced_by(?x343, ?x5527) >> conf = 0.39 => this is the best rule for 3 predicted values *> Best rule #6571 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 377 *> proper extension: 024c1b; *> query: (?x5527, ?x288) <- produced_by(?x10173, ?x5527), film(?x6916, ?x10173), film(?x6916, ?x288) *> conf = 0.03 ranks of expected_values: 368 EVAL 092kgw produced_by! 035xwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 68.000 35.000 0.391 http://example.org/film/film/produced_by #14069-01r9nk PRED entity: 01r9nk PRED relation: contains! PRED expected values: 0chghy => 112 concepts (24 used for prediction) PRED predicted values (max 10 best out of 146): 0chghy (0.96 #8066, 0.96 #6275, 0.94 #17026), 09c7w0 (0.86 #14342, 0.85 #6278, 0.84 #5382), 05fly (0.50 #2258, 0.23 #3155, 0.03 #4947), 05nrg (0.50 #1793, 0.20 #897, 0.20 #896), 02jx1 (0.32 #3675, 0.29 #4570, 0.15 #15321), 07ssc (0.30 #3620, 0.29 #4515, 0.09 #12580), 0g39h (0.20 #3227, 0.17 #2330, 0.04 #895), 02xry (0.19 #3751, 0.05 #10021, 0.05 #10918), 0d060g (0.18 #4496, 0.08 #7183, 0.08 #5392), 01b8jj (0.17 #2462, 0.04 #895, 0.03 #1792) >> Best rule #8066 for best value: >> intensional similarity = 7 >> extensional distance = 565 >> proper extension: 01c0cc; 04rwx; 07wjk; 021s9n; 01s7j5; 021996; 014zws; 01trxd; 043q2z; 01stzp; ... >> query: (?x14636, ?x390) <- category(?x14636, ?x134), contains(?x12125, ?x14636), contains(?x8823, ?x14636), country(?x12125, ?x390), service_location(?x5919, ?x8823), origin(?x6368, ?x8823), adjoins(?x12125, ?x12854) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r9nk contains! 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 24.000 0.963 http://example.org/location/location/contains #14068-016_mj PRED entity: 016_mj PRED relation: people! PRED expected values: 0x67 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 50): 041rx (0.33 #312, 0.24 #851, 0.20 #81), 0x67 (0.33 #318, 0.21 #395, 0.20 #472), 033tf_ (0.23 #546, 0.23 #777, 0.16 #469), 0xnvg (0.21 #398, 0.16 #475, 0.14 #1014), 02ctzb (0.12 #477, 0.11 #169, 0.11 #400), 07hwkr (0.12 #551, 0.05 #1090, 0.05 #3939), 09vc4s (0.11 #394, 0.08 #471, 0.08 #1934), 07bch9 (0.10 #2102, 0.09 #793, 0.09 #2487), 048z7l (0.08 #887, 0.07 #1195, 0.07 #810), 013b6_ (0.08 #900, 0.05 #438, 0.05 #1208) >> Best rule #312 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01k9lpl; >> query: (?x1835, 041rx) <- type_of_union(?x1835, ?x566), influenced_by(?x10560, ?x1835), ?x10560 = 01xwv7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #318 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 01k9lpl; *> query: (?x1835, 0x67) <- type_of_union(?x1835, ?x566), influenced_by(?x10560, ?x1835), ?x10560 = 01xwv7 *> conf = 0.33 ranks of expected_values: 2 EVAL 016_mj people! 0x67 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.333 http://example.org/people/ethnicity/people #14067-0cgfb PRED entity: 0cgfb PRED relation: profession PRED expected values: 0nbcg => 145 concepts (103 used for prediction) PRED predicted values (max 10 best out of 92): 0dxtg (0.63 #4219, 0.41 #883, 0.40 #12191), 01d_h8 (0.60 #2326, 0.55 #1021, 0.50 #1456), 09jwl (0.58 #9158, 0.56 #12355, 0.56 #13521), 016z4k (0.54 #3050, 0.51 #2470, 0.50 #4500), 0dz3r (0.50 #4498, 0.46 #3048, 0.42 #2468), 03gjzk (0.50 #14, 0.40 #12191, 0.38 #1464), 0nbcg (0.48 #2496, 0.46 #4526, 0.44 #9170), 02jknp (0.40 #12191, 0.37 #12045, 0.36 #4214), 02krf9 (0.40 #12191, 0.37 #12045, 0.31 #10448), 01xr66 (0.33 #5513, 0.12 #352, 0.10 #1222) >> Best rule #4219 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 022_lg; 03ft8; 052gzr; 02dh86; 0b478; 02778yp; 016bx2; 096hm; 03d1y3; 04glr5h; ... >> query: (?x11098, 0dxtg) <- profession(?x11098, ?x2225), spouse(?x1898, ?x11098), profession(?x7167, ?x2225), ?x7167 = 01wd9vs >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #2496 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 84 *> proper extension: 01vvydl; 0lbj1; 018y2s; 01vrz41; 0137n0; 058s57; 01trhmt; 014q2g; 01vx5w7; 0161c2; ... *> query: (?x11098, 0nbcg) <- film(?x11098, ?x2644), profession(?x11098, ?x967), participant(?x286, ?x11098), artists(?x671, ?x11098) *> conf = 0.48 ranks of expected_values: 7 EVAL 0cgfb profession 0nbcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 145.000 103.000 0.632 http://example.org/people/person/profession #14066-0845v PRED entity: 0845v PRED relation: combatants PRED expected values: 017v_ 06mkj => 56 concepts (44 used for prediction) PRED predicted values (max 10 best out of 366): 09c7w0 (0.72 #2502, 0.65 #2755, 0.40 #4159), 07ssc (0.68 #2874, 0.64 #2621, 0.60 #4170), 0f8l9c (0.62 #1765, 0.25 #1141, 0.22 #387), 03x1x (0.60 #214, 0.30 #582, 0.27 #959), 0cdbq (0.50 #58, 0.46 #1303, 0.38 #1552), 01tdpv (0.50 #101, 0.40 #591, 0.38 #1346), 01m41_ (0.50 #97, 0.31 #1342, 0.25 #1591), 025ndl (0.43 #278, 0.33 #401, 0.33 #121), 0chghy (0.40 #2510, 0.38 #1756, 0.35 #2763), 0dbxy (0.40 #211, 0.27 #956, 0.27 #829) >> Best rule #2502 for best value: >> intensional similarity = 9 >> extensional distance = 23 >> proper extension: 081pw; 01gjd0; 0d06vc; 0gfq9; 0cm2xh; 06k75; 07_nf; 022840; 01y998; 086m1; ... >> query: (?x1777, 09c7w0) <- combatants(?x1777, ?x13256), combatants(?x1777, ?x6371), locations(?x1777, ?x455), entity_involved(?x9798, ?x13256), combatants(?x1679, ?x13256), jurisdiction_of_office(?x182, ?x6371), split_to(?x6371, ?x512), entity_involved(?x7734, ?x6371), nationality(?x1328, ?x6371) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #121 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 0dr7s; 01hwkn; *> query: (?x1777, ?x1679) <- combatants(?x1777, ?x13430), combatants(?x1777, ?x13256), combatants(?x1777, ?x8949), combatants(?x1777, ?x6371), combatants(?x1777, ?x4493), locations(?x1777, ?x455), entity_involved(?x9798, ?x13256), combatants(?x1679, ?x13256), ?x6371 = 014tss, ?x13430 = 040vgd, ?x4493 = 01k6y1, ?x8949 = 0dv0z *> conf = 0.33 ranks of expected_values: 14, 20 EVAL 0845v combatants 06mkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 56.000 44.000 0.720 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 0845v combatants 017v_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 56.000 44.000 0.720 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #14065-04mkbj PRED entity: 04mkbj PRED relation: colors! PRED expected values: 01y8zd 0b1xl 0trv 09k9d0 => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1301): 01jq34 (0.60 #2285, 0.38 #3627, 0.33 #2733), 02vnp2 (0.60 #2550, 0.38 #3892, 0.33 #2998), 07lx1s (0.50 #2713, 0.43 #3160, 0.40 #2265), 065r8g (0.50 #2751, 0.43 #3198, 0.40 #2303), 0gl6x (0.50 #3907, 0.40 #2565, 0.33 #3013), 07x4c (0.50 #2014, 0.40 #2463, 0.33 #2911), 02mw6c (0.50 #2159, 0.40 #2608, 0.33 #3056), 0vkl2 (0.50 #2124, 0.33 #783, 0.20 #2573), 0gl6f (0.40 #2488, 0.38 #3830, 0.33 #2936), 021996 (0.40 #2505, 0.38 #3847, 0.33 #1609) >> Best rule #2285 for best value: >> intensional similarity = 33 >> extensional distance = 3 >> proper extension: 019sc; >> query: (?x7179, 01jq34) <- colors(?x11807, ?x7179), colors(?x8191, ?x7179), colors(?x7178, ?x7179), colors(?x6434, ?x7179), colors(?x5321, ?x7179), colors(?x4293, ?x7179), state_province_region(?x5321, ?x9559), colors(?x6537, ?x7179), school_type(?x5321, ?x1044), category(?x5321, ?x134), major_field_of_study(?x11807, ?x3995), organization(?x346, ?x11807), currency(?x7178, ?x170), school_type(?x7178, ?x3205), major_field_of_study(?x6434, ?x12158), major_field_of_study(?x4293, ?x6870), institution(?x865, ?x6434), ?x8191 = 0bsnm, ?x12158 = 09s1f, ?x3995 = 0fdys, contains(?x94, ?x4293), school_type(?x6434, ?x4994), ?x1044 = 05pcjw, contains(?x252, ?x5321), place_founded(?x3636, ?x9559), location(?x2306, ?x9559), film_release_region(?x903, ?x9559), contains(?x335, ?x6434), contains(?x1906, ?x11807), place_of_birth(?x256, ?x9559), position(?x6537, ?x60), currency(?x5321, ?x12281), ?x6870 = 01540 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2379 for first EXPECTED value: *> intensional similarity = 33 *> extensional distance = 3 *> proper extension: 019sc; *> query: (?x7179, 0b1xl) <- colors(?x11807, ?x7179), colors(?x8191, ?x7179), colors(?x7178, ?x7179), colors(?x6434, ?x7179), colors(?x5321, ?x7179), colors(?x4293, ?x7179), state_province_region(?x5321, ?x9559), colors(?x6537, ?x7179), school_type(?x5321, ?x1044), category(?x5321, ?x134), major_field_of_study(?x11807, ?x3995), organization(?x346, ?x11807), currency(?x7178, ?x170), school_type(?x7178, ?x3205), major_field_of_study(?x6434, ?x12158), major_field_of_study(?x4293, ?x6870), institution(?x865, ?x6434), ?x8191 = 0bsnm, ?x12158 = 09s1f, ?x3995 = 0fdys, contains(?x94, ?x4293), school_type(?x6434, ?x4994), ?x1044 = 05pcjw, contains(?x252, ?x5321), place_founded(?x3636, ?x9559), location(?x2306, ?x9559), film_release_region(?x903, ?x9559), contains(?x335, ?x6434), contains(?x1906, ?x11807), place_of_birth(?x256, ?x9559), position(?x6537, ?x60), currency(?x5321, ?x12281), ?x6870 = 01540 *> conf = 0.40 ranks of expected_values: 36, 189, 296, 377 EVAL 04mkbj colors! 09k9d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.600 http://example.org/education/educational_institution/colors EVAL 04mkbj colors! 0trv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 17.000 17.000 0.600 http://example.org/education/educational_institution/colors EVAL 04mkbj colors! 0b1xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 17.000 17.000 0.600 http://example.org/education/educational_institution/colors EVAL 04mkbj colors! 01y8zd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.600 http://example.org/education/educational_institution/colors #14064-03qd_ PRED entity: 03qd_ PRED relation: profession PRED expected values: 02hrh1q => 113 concepts (69 used for prediction) PRED predicted values (max 10 best out of 59): 02hrh1q (0.90 #7208, 0.89 #4527, 0.88 #5514), 01c72t (0.59 #1569, 0.57 #864, 0.55 #1992), 0nbcg (0.55 #2281, 0.55 #1717, 0.54 #2423), 0dz3r (0.45 #1694, 0.44 #2258, 0.43 #2400), 016z4k (0.42 #3673, 0.41 #3391, 0.41 #3955), 039v1 (0.37 #1722, 0.36 #2286, 0.36 #2428), 0cbd2 (0.24 #1416, 0.22 #5790, 0.21 #1134), 02krf9 (0.23 #444, 0.21 #3126, 0.20 #162), 01c8w0 (0.23 #1981, 0.22 #1558, 0.21 #1276), 0n1h (0.17 #3397, 0.17 #3961, 0.17 #3679) >> Best rule #7208 for best value: >> intensional similarity = 3 >> extensional distance = 1223 >> proper extension: 05bp8g; 0yfp; 01n4f8; 058s57; 036c_0; 0136pk; 03mcwq3; 0161c2; 07z1_q; 011hdn; ... >> query: (?x806, 02hrh1q) <- film(?x806, ?x590), place_of_birth(?x806, ?x1523), profession(?x806, ?x319) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qd_ profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 69.000 0.900 http://example.org/people/person/profession #14063-023kzp PRED entity: 023kzp PRED relation: location PRED expected values: 0d0x8 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 166): 0cr3d (0.10 #49564, 0.08 #55144, 0.07 #61523), 04jpl (0.09 #51035, 0.08 #6394, 0.08 #55020), 0rh6k (0.06 #2395, 0.03 #4, 0.03 #49427), 059rby (0.06 #1610, 0.06 #16, 0.05 #7190), 0ccvx (0.06 #217, 0.05 #2608, 0.03 #49640), 0f2wj (0.06 #34, 0.03 #7208, 0.03 #831), 0498y (0.06 #208, 0.01 #1005, 0.01 #6585), 09c7w0 (0.06 #61382, 0.03 #2394, 0.01 #19132), 0cc56 (0.05 #49479, 0.05 #4839, 0.05 #15200), 01n7q (0.04 #7236, 0.04 #5642, 0.04 #49485) >> Best rule #49564 for best value: >> intensional similarity = 2 >> extensional distance = 1333 >> proper extension: 0466k4; >> query: (?x5925, 0cr3d) <- location(?x5925, ?x1860), adjoins(?x1860, ?x5037) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 023kzp location 0d0x8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.099 http://example.org/people/person/places_lived./people/place_lived/location #14062-084x96 PRED entity: 084x96 PRED relation: gender PRED expected values: 05zppz => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.76 #157, 0.73 #61, 0.72 #142), 02zsn (0.52 #144, 0.51 #24, 0.50 #8) >> Best rule #157 for best value: >> intensional similarity = 4 >> extensional distance = 2346 >> proper extension: 06j0md; 0qf43; 050023; 026dcvf; 07f8wg; 02lf0c; 032t2z; 04r7jc; 06y9c2; 03_gd; ... >> query: (?x13638, 05zppz) <- nationality(?x13638, ?x94), profession(?x13638, ?x1383), profession(?x806, ?x1383), ?x806 = 03qd_ >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 084x96 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.764 http://example.org/people/person/gender #14061-047vp1n PRED entity: 047vp1n PRED relation: production_companies PRED expected values: 032j_n => 71 concepts (51 used for prediction) PRED predicted values (max 10 best out of 61): 05qd_ (0.40 #338, 0.33 #10, 0.11 #174), 01gb54 (0.33 #37, 0.11 #201, 0.07 #694), 016tt2 (0.22 #250, 0.10 #332, 0.07 #2225), 0c41qv (0.22 #301, 0.05 #1535, 0.04 #1041), 086k8 (0.20 #330, 0.12 #988, 0.11 #1482), 016tw3 (0.14 #94, 0.12 #1492, 0.11 #2233), 024rgt (0.14 #106, 0.06 #517, 0.05 #599), 0g1rw (0.14 #90, 0.04 #2229, 0.03 #1571), 054lpb6 (0.12 #754, 0.12 #672, 0.12 #836), 01795t (0.11 #267, 0.11 #185, 0.04 #1749) >> Best rule #338 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0bl1_; >> query: (?x7314, 05qd_) <- genre(?x7314, ?x53), film(?x11741, ?x7314), language(?x7314, ?x5607), countries_spoken_in(?x5607, ?x172), ?x11741 = 045931 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #483 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 02zk08; *> query: (?x7314, 032j_n) <- genre(?x7314, ?x6452), ?x6452 = 02b5_l, film_release_region(?x7314, ?x94), titles(?x2480, ?x7314), ?x94 = 09c7w0 *> conf = 0.10 ranks of expected_values: 14 EVAL 047vp1n production_companies 032j_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 71.000 51.000 0.400 http://example.org/film/film/production_companies #14060-03rjj PRED entity: 03rjj PRED relation: medal PRED expected values: 02lq67 => 241 concepts (241 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.89 #53, 0.85 #47, 0.85 #46) >> Best rule #53 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 047lj; >> query: (?x205, 02lq67) <- country(?x150, ?x205), film_release_region(?x2714, ?x205), ?x2714 = 0kv238 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rjj medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 241.000 241.000 0.886 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #14059-01crd5 PRED entity: 01crd5 PRED relation: olympics PRED expected values: 0jdk_ => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 40): 0jdk_ (0.78 #546, 0.73 #426, 0.65 #746), 0jhn7 (0.76 #547, 0.75 #427, 0.66 #507), 0kbvb (0.72 #527, 0.68 #127, 0.67 #87), 0l6m5 (0.67 #90, 0.64 #130, 0.61 #410), 0l98s (0.56 #85, 0.50 #125, 0.40 #285), 0l6ny (0.54 #529, 0.50 #409, 0.48 #489), 0l998 (0.54 #126, 0.50 #86, 0.43 #286), 0l6mp (0.50 #538, 0.50 #138, 0.50 #98), 0jkvj (0.50 #116, 0.43 #156, 0.41 #556), 0l6vl (0.50 #82, 0.43 #122, 0.34 #282) >> Best rule #546 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0b90_r; 03rjj; 0chghy; 03_r3; 03rt9; 015fr; 06npd; 06mzp; 0k6nt; 0ctw_b; ... >> query: (?x8593, 0jdk_) <- country(?x7108, ?x8593), ?x7108 = 0194d, film_release_region(?x124, ?x8593) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01crd5 olympics 0jdk_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.783 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #14058-01c65z PRED entity: 01c65z PRED relation: spouse! PRED expected values: 014g22 => 115 concepts (64 used for prediction) PRED predicted values (max 10 best out of 41): 014g22 (0.82 #5220, 0.80 #6427, 0.80 #4012), 01pcq3 (0.09 #827, 0.06 #1228), 033_1p (0.04 #1957, 0.01 #2759, 0.01 #3160), 03p9hl (0.04 #2003, 0.01 #2805), 03crmd (0.04 #1967, 0.01 #2769), 017gxw (0.04 #1799, 0.01 #2601), 018z_c (0.04 #1774, 0.01 #2576), 01fwk3 (0.04 #1702, 0.01 #2504), 023tp8 (0.04 #1610, 0.01 #2412), 0cgfb (0.03 #2375) >> Best rule #5220 for best value: >> intensional similarity = 4 >> extensional distance = 241 >> proper extension: 03zqc1; 0gm34; >> query: (?x12448, ?x4154) <- film(?x12448, ?x3532), award(?x12448, ?x3209), nationality(?x12448, ?x512), spouse(?x12448, ?x4154) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c65z spouse! 014g22 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 64.000 0.820 http://example.org/people/person/spouse_s./people/marriage/spouse #14057-018m5q PRED entity: 018m5q PRED relation: contains! PRED expected values: 07ssc => 150 concepts (69 used for prediction) PRED predicted values (max 10 best out of 277): 07ssc (0.77 #45577, 0.73 #48290, 0.62 #1818), 09c7w0 (0.71 #34857, 0.71 #37536, 0.67 #49155), 04jpl (0.67 #20574, 0.30 #5381, 0.29 #8953), 059rby (0.61 #29512, 0.53 #33087, 0.38 #42915), 0d060g (0.29 #9838, 0.22 #16095, 0.15 #19670), 05tbn (0.22 #43116, 0.08 #32394, 0.08 #35075), 05l5n (0.20 #2800, 0.19 #4586, 0.17 #10839), 01n7q (0.19 #4543, 0.14 #32251, 0.09 #34039), 02xry (0.15 #43057, 0.02 #37695, 0.02 #31441), 07tgn (0.14 #53, 0.12 #1839, 0.10 #2732) >> Best rule #45577 for best value: >> intensional similarity = 4 >> extensional distance = 340 >> proper extension: 026036; >> query: (?x3671, ?x512) <- state_province_region(?x3671, ?x3302), school_type(?x3671, ?x5931), country(?x3302, ?x512), contains(?x3302, ?x1369) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018m5q contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 69.000 0.766 http://example.org/location/location/contains #14056-07vyf PRED entity: 07vyf PRED relation: school! PRED expected values: 0jm4v => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 84): 05m_8 (0.21 #252, 0.20 #501, 0.19 #916), 0jmm4 (0.21 #315, 0.11 #1329, 0.11 #481), 051vz (0.18 #267, 0.16 #516, 0.15 #350), 0jmnl (0.18 #331, 0.11 #1329, 0.11 #497), 02d02 (0.18 #311, 0.11 #1329, 0.11 #1413), 05g49 (0.18 #288, 0.09 #454, 0.08 #537), 0cqt41 (0.14 #264, 0.11 #1329, 0.11 #1413), 0bwjj (0.14 #316, 0.11 #1329, 0.11 #1413), 0jm74 (0.14 #302, 0.11 #1329, 0.11 #1413), 05xvj (0.14 #327, 0.09 #825, 0.09 #1074) >> Best rule #252 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 06mkj; 0d05w3; >> query: (?x4296, 05m_8) <- school(?x1161, ?x4296), organization(?x4296, ?x5487), contains(?x94, ?x4296) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #1329 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 109 *> proper extension: 0frm7n; *> query: (?x4296, ?x1347) <- school(?x4979, ?x4296), school(?x700, ?x4296), draft(?x1347, ?x4979) *> conf = 0.11 ranks of expected_values: 66 EVAL 07vyf school! 0jm4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 119.000 119.000 0.214 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #14055-02vr3gz PRED entity: 02vr3gz PRED relation: film_release_region PRED expected values: 04gzd 07ylj => 116 concepts (112 used for prediction) PRED predicted values (max 10 best out of 205): 03f2w (0.85 #1085, 0.83 #1222, 0.83 #679), 03spz (0.85 #2789, 0.85 #755, 0.83 #890), 04gzd (0.83 #820, 0.75 #685, 0.74 #2719), 047yc (0.75 #697, 0.74 #832, 0.74 #1240), 015qh (0.74 #842, 0.70 #707, 0.64 #1791), 016wzw (0.74 #861, 0.66 #1269, 0.66 #2219), 07ylj (0.70 #834, 0.50 #699, 0.43 #1377), 0ctw_b (0.67 #15, 0.65 #829, 0.65 #694), 01p1v (0.65 #2749, 0.61 #850, 0.59 #2208), 06f32 (0.64 #182, 0.61 #860, 0.55 #2759) >> Best rule #1085 for best value: >> intensional similarity = 7 >> extensional distance = 23 >> proper extension: 02x3lt7; 02d44q; 0dtfn; 0fpkhkz; 04w7rn; 02r8hh_; 035yn8; 0407yfx; 02yvct; 0hx4y; ... >> query: (?x3757, ?x11872) <- film_release_region(?x3757, ?x1499), film_release_region(?x3757, ?x789), ?x1499 = 01znc_, category(?x3757, ?x134), country(?x3757, ?x11872), award(?x3757, ?x533), ?x789 = 0f8l9c >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #820 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 21 *> proper extension: 053rxgm; 0ch26b_; 017jd9; 0dc_ms; *> query: (?x3757, 04gzd) <- film_release_region(?x3757, ?x2146), film_release_region(?x3757, ?x1892), film_release_region(?x3757, ?x1499), film_release_region(?x3757, ?x1003), film_release_region(?x3757, ?x756), ?x1499 = 01znc_, ?x1003 = 03gj2, ?x756 = 06npd, ?x1892 = 02vzc, ?x2146 = 03rk0 *> conf = 0.83 ranks of expected_values: 3, 7 EVAL 02vr3gz film_release_region 07ylj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 116.000 112.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vr3gz film_release_region 04gzd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 112.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14054-0y1rf PRED entity: 0y1rf PRED relation: contains! PRED expected values: 059rby => 159 concepts (143 used for prediction) PRED predicted values (max 10 best out of 355): 0fc2c (0.77 #26861, 0.76 #67158, 0.74 #82382), 059rby (0.77 #77903, 0.66 #64470, 0.59 #29548), 01x73 (0.59 #29548, 0.14 #8171, 0.06 #21602), 01n7q (0.44 #1867, 0.36 #6343, 0.35 #19774), 06pvr (0.26 #1060, 0.16 #1955, 0.15 #6431), 04_1l0v (0.26 #22833, 0.25 #55964, 0.21 #68505), 02qkt (0.18 #97948, 0.17 #102424, 0.15 #75562), 07ssc (0.18 #124491, 0.16 #125386, 0.15 #101214), 03rk0 (0.14 #7297, 0.06 #69086, 0.06 #57440), 02jx1 (0.13 #126336, 0.11 #101269, 0.11 #124546) >> Best rule #26861 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 0fvvg; >> query: (?x11086, ?x7281) <- administrative_division(?x11086, ?x7281), time_zones(?x11086, ?x2674), ?x2674 = 02hcv8 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #77903 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 246 *> proper extension: 0ybkj; 0235l; 0zrlp; 0s6g4; 0c5v2; 01m24m; 0s4sj; *> query: (?x11086, ?x335) <- county(?x11086, ?x7281), adjoins(?x7281, ?x7387), contains(?x335, ?x7281) *> conf = 0.77 ranks of expected_values: 2 EVAL 0y1rf contains! 059rby CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 159.000 143.000 0.765 http://example.org/location/location/contains #14053-01_sz1 PRED entity: 01_sz1 PRED relation: parent_genre PRED expected values: 05r6t => 72 concepts (40 used for prediction) PRED predicted values (max 10 best out of 251): 06by7 (0.84 #3437, 0.59 #3926, 0.58 #4091), 05r6t (0.60 #3147, 0.51 #3636, 0.33 #53), 064t9 (0.50 #497, 0.40 #659, 0.33 #11), 017371 (0.50 #429, 0.33 #267, 0.14 #1402), 01243b (0.38 #2142, 0.33 #29, 0.32 #3123), 05bt6j (0.33 #30, 0.25 #516, 0.20 #678), 0xhtw (0.33 #175, 0.25 #337, 0.16 #2126), 05c6073 (0.33 #931, 0.16 #5882, 0.11 #2234), 01pfpt (0.33 #869, 0.16 #5882, 0.11 #2172), 0glt670 (0.30 #3611, 0.18 #1814, 0.17 #2959) >> Best rule #3437 for best value: >> intensional similarity = 9 >> extensional distance = 68 >> proper extension: 028cl7; 017ht; >> query: (?x5911, 06by7) <- parent_genre(?x5911, ?x3370), artists(?x3370, ?x5745), artists(?x3370, ?x4646), artists(?x3370, ?x2723), artists(?x3370, ?x1974), ?x1974 = 0136p1, ?x5745 = 01l87db, ?x2723 = 016fmf, ?x4646 = 0fhxv >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #3147 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 66 *> proper extension: 01nd9f; *> query: (?x5911, 05r6t) <- parent_genre(?x5911, ?x3370), artists(?x3370, ?x2492), artists(?x3370, ?x1974), award_winner(?x528, ?x1974), ?x2492 = 01tp5bj *> conf = 0.60 ranks of expected_values: 2 EVAL 01_sz1 parent_genre 05r6t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 40.000 0.843 http://example.org/music/genre/parent_genre #14052-0428bc PRED entity: 0428bc PRED relation: student! PRED expected values: 026036 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 119): 0bwfn (0.40 #274, 0.09 #12874, 0.08 #14449), 01nnsv (0.20 #184), 017hnw (0.08 #1032, 0.07 #1557, 0.02 #3132), 015nl4 (0.08 #592, 0.05 #17917, 0.05 #21595), 02l9wl (0.08 #776, 0.05 #1301, 0.04 #1826), 06182p (0.08 #822, 0.05 #1347, 0.03 #6072), 017z88 (0.07 #7431, 0.06 #12681, 0.05 #14256), 065y4w7 (0.06 #7364, 0.05 #2639, 0.05 #1064), 08815 (0.05 #1052, 0.05 #7352, 0.04 #527), 0dy04 (0.05 #1121, 0.04 #596, 0.02 #1646) >> Best rule #274 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0203v; >> query: (?x9977, 0bwfn) <- student(?x2730, ?x9977), ?x2730 = 02301, religion(?x9977, ?x1985) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0428bc student! 026036 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.400 http://example.org/education/educational_institution/students_graduates./education/education/student #14051-0jmk7 PRED entity: 0jmk7 PRED relation: school PRED expected values: 06mkj 0d05w3 012vwb => 86 concepts (85 used for prediction) PRED predicted values (max 10 best out of 372): 015q1n (0.33 #825, 0.28 #3556, 0.25 #1554), 06fq2 (0.33 #128, 0.13 #7413, 0.11 #6318), 01jpyb (0.33 #102, 0.12 #1559, 0.12 #3561), 01rc6f (0.33 #127, 0.09 #7412, 0.08 #6317), 01jszm (0.33 #77, 0.06 #2080, 0.05 #5644), 065y4w7 (0.27 #7293, 0.26 #6198, 0.17 #736), 01jsn5 (0.25 #1483, 0.25 #754, 0.20 #3485), 0dzst (0.25 #1598, 0.25 #869, 0.20 #1233), 01jsk6 (0.25 #892, 0.20 #1256, 0.19 #2531), 07vyf (0.25 #785, 0.20 #1149, 0.19 #1514) >> Best rule #825 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 04cxw5b; >> query: (?x12141, 015q1n) <- team(?x1348, ?x12141), draft(?x12141, ?x8133), ?x8133 = 025tn92, colors(?x12141, ?x332) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7333 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 91 *> proper extension: 01ypc; 02896; 05m_8; 03lpp_; 01ct6; 06x68; 05g3b; 01d5z; 01y3c; 049n7; ... *> query: (?x12141, 012vwb) <- team(?x1348, ?x12141), draft(?x12141, ?x2569) *> conf = 0.12 ranks of expected_values: 46 EVAL 0jmk7 school 012vwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 86.000 85.000 0.333 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmk7 school 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 85.000 0.333 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmk7 school 06mkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 85.000 0.333 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #14050-058vp PRED entity: 058vp PRED relation: place_of_death PRED expected values: 05qtj => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 92): 08966 (0.25 #287, 0.22 #676, 0.12 #481), 06pr6 (0.25 #299, 0.11 #688, 0.08 #1272), 06c62 (0.25 #101, 0.02 #8264, 0.01 #16144), 05qtj (0.22 #2205, 0.17 #2593, 0.15 #2787), 04jpl (0.12 #1174, 0.12 #784, 0.11 #590), 02z0j (0.12 #514, 0.04 #1098, 0.04 #1293), 0g251 (0.12 #510, 0.02 #7773, 0.01 #11473), 02_286 (0.11 #1765, 0.11 #1960, 0.10 #1375), 07g0_ (0.11 #722, 0.01 #3250), 0cpyv (0.08 #1235, 0.04 #1040, 0.03 #10890) >> Best rule #287 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 032l1; 03_87; >> query: (?x5612, 08966) <- religion(?x5612, ?x7131), influenced_by(?x9982, ?x5612), influenced_by(?x5262, ?x5612), ?x9982 = 05qzv, nationality(?x5612, ?x789), ?x5262 = 080r3 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2205 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 011zf2; 0d3f83; *> query: (?x5612, 05qtj) <- nationality(?x5612, ?x789), ?x789 = 0f8l9c, gender(?x5612, ?x231), ?x231 = 05zppz *> conf = 0.22 ranks of expected_values: 4 EVAL 058vp place_of_death 05qtj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 110.000 0.250 http://example.org/people/deceased_person/place_of_death #14049-05szq8z PRED entity: 05szq8z PRED relation: titles! PRED expected values: 03k9fj => 56 concepts (35 used for prediction) PRED predicted values (max 10 best out of 57): 07s9rl0 (0.33 #3197, 0.28 #2886, 0.26 #720), 07ssc (0.29 #822, 0.29 #729, 0.13 #821), 04xvlr (0.22 #723, 0.22 #3200, 0.19 #2889), 01z4y (0.20 #3231, 0.20 #546, 0.19 #650), 01jfsb (0.19 #1975, 0.18 #1666, 0.17 #2286), 024qqx (0.19 #80, 0.17 #1213, 0.16 #1006), 03k9fj (0.16 #223, 0.13 #120, 0.10 #1152), 02kdv5l (0.15 #2057, 0.15 #2471, 0.14 #2058), 0jdm8 (0.15 #2057, 0.15 #2471, 0.14 #1748), 09c7w0 (0.13 #821, 0.07 #2883, 0.07 #2882) >> Best rule #3197 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 07s8z_l; 02xhwm; >> query: (?x5458, 07s9rl0) <- titles(?x1510, ?x5458), genre(?x419, ?x1510) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #223 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 81 *> proper extension: 0dr1c2; *> query: (?x5458, 03k9fj) <- genre(?x5458, ?x1510), genre(?x5458, ?x225), ?x225 = 02kdv5l, film(?x489, ?x5458), ?x1510 = 01hmnh *> conf = 0.16 ranks of expected_values: 7 EVAL 05szq8z titles! 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 56.000 35.000 0.328 http://example.org/media_common/netflix_genre/titles #14048-0dn3n PRED entity: 0dn3n PRED relation: award_nominee PRED expected values: 028knk => 109 concepts (62 used for prediction) PRED predicted values (max 10 best out of 840): 0bxtg (0.81 #86645, 0.81 #107720, 0.81 #84302), 028knk (0.81 #107720, 0.81 #84302, 0.81 #14052), 0dn3n (0.23 #133476, 0.19 #37473, 0.15 #96015), 086k8 (0.23 #133476, 0.03 #88988, 0.02 #70317), 01gkmx (0.19 #37473, 0.15 #96015, 0.14 #1990), 0f7h2v (0.19 #37473, 0.15 #96015, 0.02 #12321), 02sb1w (0.19 #37473, 0.15 #96015, 0.01 #83431), 030xr_ (0.19 #37473, 0.15 #96015, 0.01 #13706), 0161h5 (0.19 #37473, 0.15 #96015), 01xwv7 (0.19 #37473, 0.15 #96015) >> Best rule #86645 for best value: >> intensional similarity = 3 >> extensional distance = 981 >> proper extension: 07lmxq; 01v3s2_; 038g2x; 03kpvp; 02cgb8; 0d02km; 01mkn_d; 01my_c; 09dv0sz; 02fgm7; ... >> query: (?x3070, ?x7609) <- nominated_for(?x3070, ?x83), award_nominee(?x7609, ?x3070), participant(?x4777, ?x7609) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #107720 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1140 *> proper extension: 0275_pj; 01gp_x; 06rnl9; 07qy0b; 0b05xm; 0fqyzz; 05v1sb; 02pt6k_; 03fykz; 08_83x; ... *> query: (?x3070, ?x496) <- nominated_for(?x3070, ?x83), award_nominee(?x496, ?x3070), type_of_union(?x3070, ?x566) *> conf = 0.81 ranks of expected_values: 2 EVAL 0dn3n award_nominee 028knk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 62.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14047-01_c4 PRED entity: 01_c4 PRED relation: time_zones PRED expected values: 03bdv => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 12): 03bdv (0.65 #717, 0.61 #586, 0.60 #822), 02hcv8 (0.48 #1165, 0.48 #1621, 0.48 #1191), 02llzg (0.31 #199, 0.23 #329, 0.14 #1596), 02fqwt (0.28 #287, 0.26 #352, 0.25 #456), 02lcqs (0.23 #1571, 0.23 #343, 0.21 #382), 02hczc (0.22 #262, 0.21 #483, 0.20 #496), 042g7t (0.07 #414, 0.04 #453, 0.03 #597), 02lcrv (0.06 #202, 0.03 #332, 0.02 #358), 03plfd (0.06 #1250, 0.05 #1315, 0.05 #1302), 052vwh (0.05 #337, 0.05 #1135, 0.04 #860) >> Best rule #717 for best value: >> intensional similarity = 5 >> extensional distance = 93 >> proper extension: 0crjn65; >> query: (?x9491, ?x5327) <- administrative_parent(?x9491, ?x12774), contains(?x12774, ?x13588), state_province_region(?x13052, ?x12774), contains(?x512, ?x12774), time_zones(?x13588, ?x5327) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_c4 time_zones 03bdv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.653 http://example.org/location/location/time_zones #14046-0nhr5 PRED entity: 0nhr5 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 153 concepts (65 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.88 #214, 0.88 #135, 0.87 #323), 0vzm (0.26 #573), 04ych (0.26 #573), 04ykg (0.19 #249, 0.12 #463, 0.10 #547), 0nhmw (0.19 #249), 05mph (0.02 #617), 0vbk (0.02 #617), 04tgp (0.02 #617), 05fky (0.02 #617), 0824r (0.02 #617) >> Best rule #214 for best value: >> intensional similarity = 4 >> extensional distance = 166 >> proper extension: 0mrf1; >> query: (?x12942, 09c7w0) <- currency(?x12942, ?x170), source(?x12942, ?x958), adjoins(?x10566, ?x12942), county_seat(?x10566, ?x7328) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nhr5 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 65.000 0.881 http://example.org/location/country/second_level_divisions #14045-02fgmn PRED entity: 02fgmn PRED relation: split_to! PRED expected values: 05ws7 => 34 concepts (21 used for prediction) PRED predicted values (max 10 best out of 10): 01rbb (0.33 #192, 0.14 #291, 0.12 #389), 02822 (0.33 #46, 0.14 #244, 0.12 #342), 01z4y (0.08 #423, 0.07 #521, 0.07 #619), 0btmb (0.02 #1373, 0.02 #2093), 02fgmn (0.01 #888, 0.01 #987), 0c031k6 (0.01 #888, 0.01 #987), 01jfsb (0.01 #888, 0.01 #987), 0lsxr (0.01 #888, 0.01 #987), 02n4kr (0.01 #888, 0.01 #987), 07s9rl0 (0.01 #888, 0.01 #987) >> Best rule #192 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 0lsxr; >> query: (?x12176, 01rbb) <- genre(?x14197, ?x12176), genre(?x12324, ?x12176), genre(?x12105, ?x12176), genre(?x11336, ?x12176), genre(?x10595, ?x12176), genre(?x10234, ?x12176), genre(?x3326, ?x12176), ?x10595 = 01kt_j, ?x10234 = 053x8hr, ?x12105 = 024hbv, ?x3326 = 01b_lz, program(?x10016, ?x14197), ?x11336 = 0qmk5, ?x12324 = 0300ml, languages(?x14197, ?x254), service_location(?x10016, ?x5036), ?x254 = 02h40lc >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02fgmn split_to! 05ws7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 21.000 0.333 http://example.org/dataworld/gardening_hint/split_to #14044-0gr42 PRED entity: 0gr42 PRED relation: ceremony PRED expected values: 073h1t 0fz2y7 0bzknt 073hgx 0c4hx0 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 89): 0bzjvm (0.88 #693, 0.56 #515, 0.56 #426), 0bzjgq (0.88 #697, 0.56 #519, 0.44 #430), 0bzlrh (0.82 #689, 0.56 #511, 0.44 #422), 0bzkgg (0.82 #655, 0.56 #477, 0.44 #388), 073hd1 (0.76 #687, 0.56 #420, 0.44 #509), 0bzm__ (0.76 #680, 0.56 #413, 0.44 #502), 0bzk2h (0.76 #659, 0.56 #481, 0.44 #392), 0bzk8w (0.76 #628, 0.44 #361, 0.33 #450), 0bzknt (0.71 #676, 0.56 #498, 0.33 #409), 073h1t (0.71 #642, 0.44 #464, 0.44 #375) >> Best rule #693 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x2209, 0bzjvm) <- ceremony(?x2209, ?x7144), ceremony(?x2209, ?x5053), ?x7144 = 02yxh9, ?x5053 = 0dthsy, award_winner(?x2209, ?x788) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #676 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 018wng; 0gq_d; 0gr07; *> query: (?x2209, 0bzknt) <- ceremony(?x2209, ?x7144), ceremony(?x2209, ?x5053), ?x7144 = 02yxh9, ?x5053 = 0dthsy, award_winner(?x2209, ?x788) *> conf = 0.71 ranks of expected_values: 9, 10, 11, 12, 13 EVAL 0gr42 ceremony 0c4hx0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 57.000 57.000 0.882 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr42 ceremony 073hgx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 57.000 57.000 0.882 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr42 ceremony 0bzknt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 57.000 57.000 0.882 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr42 ceremony 0fz2y7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 57.000 57.000 0.882 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr42 ceremony 073h1t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 57.000 57.000 0.882 http://example.org/award/award_category/winners./award/award_honor/ceremony #14043-02pqcfz PRED entity: 02pqcfz PRED relation: team! PRED expected values: 0br1xn 0b_6rk 0b_6qj 0b_71r 0bqthy => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 17): 0b_77q (0.80 #446, 0.73 #362, 0.71 #520), 0bqthy (0.73 #457, 0.71 #506, 0.69 #409), 0b_6rk (0.73 #448, 0.68 #217, 0.65 #497), 0b_756 (0.71 #249, 0.68 #217, 0.67 #200), 0b_71r (0.69 #404, 0.68 #217, 0.67 #452), 0b_6lb (0.68 #217, 0.67 #450, 0.67 #209), 0bzrsh (0.68 #217, 0.67 #451, 0.67 #186), 0b_6q5 (0.68 #217, 0.67 #215, 0.67 #191), 0f9rw9 (0.68 #217, 0.60 #347, 0.60 #154), 0b_6mr (0.68 #217, 0.60 #454, 0.60 #153) >> Best rule #446 for best value: >> intensional similarity = 13 >> extensional distance = 13 >> proper extension: 04088s0; >> query: (?x4369, 0b_77q) <- team(?x12162, ?x4369), team(?x7042, ?x4369), locations(?x12162, ?x8993), team(?x12162, ?x10846), team(?x12162, ?x9576), team(?x12162, ?x6803), team(?x12162, ?x2303), ?x7042 = 0b_72t, ?x10846 = 02pzy52, ?x6803 = 03by7wc, ?x8993 = 0fsb8, ?x9576 = 02qk2d5, ?x2303 = 02plv57 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #457 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 13 *> proper extension: 04088s0; *> query: (?x4369, 0bqthy) <- team(?x12162, ?x4369), team(?x7042, ?x4369), locations(?x12162, ?x8993), team(?x12162, ?x10846), team(?x12162, ?x9576), team(?x12162, ?x6803), team(?x12162, ?x2303), ?x7042 = 0b_72t, ?x10846 = 02pzy52, ?x6803 = 03by7wc, ?x8993 = 0fsb8, ?x9576 = 02qk2d5, ?x2303 = 02plv57 *> conf = 0.73 ranks of expected_values: 2, 3, 5, 11, 12 EVAL 02pqcfz team! 0bqthy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02pqcfz team! 0b_71r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 102.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02pqcfz team! 0b_6qj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 102.000 102.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02pqcfz team! 0b_6rk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02pqcfz team! 0br1xn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 102.000 102.000 0.800 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #14042-036jp8 PRED entity: 036jp8 PRED relation: people! PRED expected values: 013xrm => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 48): 041rx (0.35 #1296, 0.32 #1448, 0.31 #4184), 033tf_ (0.33 #235, 0.33 #7, 0.30 #1603), 02ctzb (0.33 #15, 0.20 #1079, 0.20 #167), 0x67 (0.31 #2974, 0.25 #390, 0.24 #2822), 07bch9 (0.25 #1923, 0.21 #2759, 0.14 #2303), 0xnvg (0.25 #89, 0.20 #469, 0.18 #1457), 09vc4s (0.25 #85, 0.10 #465, 0.08 #1909), 01qhm_ (0.25 #82, 0.10 #462, 0.06 #1222), 0g8_vp (0.25 #98, 0.10 #478, 0.03 #1998), 0g48m4 (0.25 #81, 0.10 #461) >> Best rule #1296 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 01rzqj; 01t94_1; 03mstc; 0161h5; 02v2jy; >> query: (?x6336, 041rx) <- profession(?x6336, ?x1041), people(?x13796, ?x6336), inductee(?x11145, ?x6336), ?x1041 = 03gjzk >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #2224 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 01j5ts; 023tp8; 049qx; *> query: (?x6336, 013xrm) <- profession(?x6336, ?x319), people(?x13796, ?x6336), award(?x6336, ?x2071), sibling(?x10219, ?x6336) *> conf = 0.05 ranks of expected_values: 32 EVAL 036jp8 people! 013xrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 171.000 171.000 0.350 http://example.org/people/ethnicity/people #14041-0byfz PRED entity: 0byfz PRED relation: award_winner! PRED expected values: 0fzrhn => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 135): 02jp5r (0.33 #69, 0.06 #351, 0.04 #774), 09pnw5 (0.19 #385, 0.17 #103, 0.05 #2077), 0bzkgg (0.17 #11704, 0.05 #1313, 0.04 #3005), 09g90vz (0.17 #1534, 0.08 #3226, 0.06 #7033), 09qftb (0.17 #113, 0.14 #254, 0.12 #395), 09p2r9 (0.17 #93, 0.12 #798, 0.04 #2772), 019bk0 (0.17 #16, 0.07 #2836, 0.06 #8476), 0n8_m93 (0.17 #118, 0.06 #400, 0.03 #4348), 0bz6sb (0.14 #205, 0.06 #346, 0.06 #1051), 09pj68 (0.14 #246, 0.04 #4476, 0.02 #5745) >> Best rule #69 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0151w_; 07r1h; >> query: (?x269, 02jp5r) <- award(?x269, ?x1312), ?x1312 = 07cbcy, person(?x7480, ?x269) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #702 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 02yy8; *> query: (?x269, 0fzrhn) <- people(?x268, ?x269), student(?x7021, ?x269), spouse(?x269, ?x11571) *> conf = 0.06 ranks of expected_values: 50 EVAL 0byfz award_winner! 0fzrhn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 125.000 125.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14040-01ln5z PRED entity: 01ln5z PRED relation: nominated_for! PRED expected values: 02hsq3m => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 221): 0gq9h (0.29 #2691, 0.27 #2930, 0.25 #3408), 02hsq3m (0.24 #3135, 0.24 #1223, 0.23 #6481), 05ztjjw (0.24 #3117, 0.22 #488, 0.22 #10), 0gs9p (0.22 #2693, 0.20 #2932, 0.20 #3410), 0k611 (0.22 #2702, 0.19 #2941, 0.19 #3419), 099c8n (0.20 #5792, 0.20 #5075, 0.18 #7943), 057xs89 (0.20 #1315, 0.20 #3227, 0.18 #1554), 0gr42 (0.19 #328, 0.19 #567, 0.18 #3196), 02r22gf (0.19 #23904, 0.18 #3134, 0.17 #3612), 019f4v (0.19 #13676, 0.19 #11047, 0.18 #11286) >> Best rule #2691 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 0bykpk; >> query: (?x549, 0gq9h) <- genre(?x549, ?x811), film(?x1104, ?x549), nominated_for(?x9391, ?x549), film_release_region(?x549, ?x512) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3135 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 85 *> proper extension: 0140g4; 01hr1; 0c_j9x; 0dnqr; 0dyb1; 011yr9; 0bmssv; 0q9sg; 02v5_g; 013q0p; ... *> query: (?x549, 02hsq3m) <- titles(?x6154, ?x549), nominated_for(?x9391, ?x549), prequel(?x1074, ?x549), nominated_for(?x500, ?x549) *> conf = 0.24 ranks of expected_values: 2 EVAL 01ln5z nominated_for! 02hsq3m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.289 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14039-0gy3w PRED entity: 0gy3w PRED relation: category PRED expected values: 08mbj5d => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #26, 0.90 #18, 0.90 #49) >> Best rule #26 for best value: >> intensional similarity = 5 >> extensional distance = 214 >> proper extension: 01pl14; 02w2bc; 01hhvg; 07w3r; 02q636; 02fy0z; 012fvq; 09hgk; 01jzyx; 01xrlm; ... >> query: (?x7576, 08mbj5d) <- institution(?x620, ?x7576), major_field_of_study(?x7576, ?x2606), currency(?x7576, ?x170), major_field_of_study(?x3178, ?x2606), ?x3178 = 01vc5m >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gy3w category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.912 http://example.org/common/topic/webpage./common/webpage/category #14038-02v3m7 PRED entity: 02v3m7 PRED relation: contains PRED expected values: 014b6c => 96 concepts (40 used for prediction) PRED predicted values (max 10 best out of 2676): 0sc6p (0.80 #76626, 0.79 #76625, 0.77 #82515), 0sgxg (0.80 #76626, 0.79 #76625, 0.77 #82515), 0sgtz (0.79 #76625, 0.77 #82515, 0.76 #103124), 06wxw (0.77 #82515, 0.76 #103124, 0.66 #117846), 03v0t (0.68 #61883, 0.56 #117845, 0.41 #109013), 04ych (0.68 #61883, 0.56 #117845, 0.41 #109013), 014b6c (0.62 #79571, 0.62 #88402, 0.61 #114900), 02v3m7 (0.56 #117845, 0.41 #109013, 0.33 #106069), 0s69k (0.42 #35597, 0.33 #238, 0.29 #59177), 0jpn8 (0.35 #61882, 0.33 #1321, 0.25 #36680) >> Best rule #76626 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 029jpy; >> query: (?x11703, ?x13584) <- time_zones(?x11703, ?x1638), contains(?x11703, ?x10877), second_level_divisions(?x94, ?x10877), county(?x13584, ?x10877), contains(?x10877, ?x10876) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #79571 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 33 *> proper extension: 0604m; *> query: (?x11703, ?x13921) <- contains(?x11703, ?x11877), adjoins(?x11877, ?x13921), adjoins(?x11877, ?x4356), location(?x543, ?x4356), place_of_birth(?x587, ?x4356), locations(?x358, ?x4356) *> conf = 0.62 ranks of expected_values: 7 EVAL 02v3m7 contains 014b6c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 96.000 40.000 0.801 http://example.org/location/location/contains #14037-01_0f7 PRED entity: 01_0f7 PRED relation: language PRED expected values: 0jzc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 51): 064_8sq (0.20 #131, 0.13 #2117, 0.13 #2688), 0t_2 (0.20 #67, 0.01 #916, 0.01 #689), 097kp (0.20 #106), 04306rv (0.17 #115, 0.11 #1133, 0.11 #227), 06b_j (0.10 #244, 0.09 #301, 0.09 #415), 0653m (0.07 #121, 0.04 #1478, 0.04 #1707), 0jzc (0.07 #241, 0.07 #412, 0.06 #298), 03_9r (0.06 #120, 0.06 #686, 0.06 #176), 07zrf (0.05 #113, 0.02 #340, 0.01 #1131), 04h9h (0.04 #492, 0.04 #264, 0.03 #321) >> Best rule #131 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 03cv_gy; 02qjv1p; >> query: (?x6531, 064_8sq) <- nominated_for(?x629, ?x6531), genre(?x6531, ?x3515), award_winner(?x6531, ?x4019), ?x3515 = 082gq >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 193 *> proper extension: 0m313; 01br2w; 09m6kg; 0bvn25; 0dq626; 0czyxs; 01k1k4; 095zlp; 02_1sj; 0pc62; ... *> query: (?x6531, 0jzc) <- film_crew_role(?x6531, ?x1171), films(?x7727, ?x6531), ?x1171 = 09vw2b7, language(?x6531, ?x90) *> conf = 0.07 ranks of expected_values: 7 EVAL 01_0f7 language 0jzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 91.000 91.000 0.200 http://example.org/film/film/language #14036-0gpx6 PRED entity: 0gpx6 PRED relation: nominated_for! PRED expected values: 02wkmx 054knh => 98 concepts (87 used for prediction) PRED predicted values (max 10 best out of 209): 054knh (0.68 #14803, 0.67 #232, 0.67 #13876), 099c8n (0.50 #286, 0.27 #1442, 0.24 #3291), 0gq9h (0.44 #5837, 0.43 #4451, 0.42 #4913), 04dn09n (0.42 #265, 0.28 #4425, 0.27 #5811), 0gs9p (0.39 #5839, 0.38 #6301, 0.38 #4915), 019f4v (0.39 #5829, 0.37 #4443, 0.36 #6291), 0k611 (0.34 #4462, 0.33 #5848, 0.32 #4924), 0gr0m (0.33 #288, 0.27 #4448, 0.27 #5834), 0gqxm (0.33 #129, 0.17 #8557, 0.17 #8556), 0fm3h2 (0.33 #220, 0.12 #20125, 0.12 #15500) >> Best rule #14803 for best value: >> intensional similarity = 3 >> extensional distance = 989 >> proper extension: 0c0yh4; 085bd1; 02c7k4; 034fl9; 02_1ky; 02rq7nd; >> query: (?x7735, ?x10597) <- award(?x7735, ?x10597), nominated_for(?x10597, ?x5347), nominated_for(?x68, ?x7735) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1, 120 EVAL 0gpx6 nominated_for! 054knh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 87.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gpx6 nominated_for! 02wkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 98.000 87.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14035-02dq8f PRED entity: 02dq8f PRED relation: institution! PRED expected values: 01gkg3 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 20): 02_xgp2 (0.71 #199, 0.67 #66, 0.55 #850), 03bwzr4 (0.62 #68, 0.62 #201, 0.55 #852), 0bkj86 (0.57 #195, 0.54 #119, 0.52 #62), 027f2w (0.38 #196, 0.38 #63, 0.24 #847), 013zdg (0.30 #194, 0.30 #118, 0.26 #61), 022h5x (0.30 #131, 0.19 #2053, 0.18 #2156), 0bjrnt (0.24 #193, 0.21 #174, 0.21 #60), 03mkk4 (0.24 #198, 0.19 #2053, 0.18 #2156), 01rr_d (0.23 #204, 0.22 #71, 0.19 #2053), 028dcg (0.19 #2053, 0.18 #2156, 0.18 #53) >> Best rule #199 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 017cy9; 01vmv_; >> query: (?x4889, 02_xgp2) <- institution(?x734, ?x4889), ?x734 = 04zx3q1, major_field_of_study(?x4889, ?x1668), student(?x4889, ?x7731) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3000 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2634 *> proper extension: 0hb37; *> query: (?x4889, ?x1368) <- contains(?x94, ?x4889), contains(?x94, ?x11128), institution(?x1368, ?x11128) *> conf = 0.05 ranks of expected_values: 17 EVAL 02dq8f institution! 01gkg3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 158.000 158.000 0.709 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14034-0p51w PRED entity: 0p51w PRED relation: profession PRED expected values: 02jknp => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 102): 02jknp (0.90 #2858, 0.89 #6758, 0.89 #3008), 01d_h8 (0.87 #6156, 0.86 #10356, 0.85 #10956), 02hrh1q (0.83 #15015, 0.81 #10215, 0.78 #15615), 0dxtg (0.74 #3164, 0.74 #5114, 0.72 #2864), 03gjzk (0.42 #10966, 0.41 #10366, 0.41 #9616), 0cbd2 (0.30 #4357, 0.29 #3457, 0.28 #1207), 02krf9 (0.29 #2578, 0.25 #6778, 0.24 #3178), 0kyk (0.23 #4981, 0.23 #1981, 0.21 #4381), 01c72t (0.23 #1075, 0.19 #3625, 0.16 #1225), 09jwl (0.20 #16370, 0.20 #17421, 0.20 #16220) >> Best rule #2858 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 0qf43; 0c1pj; 0kr5_; 02kxbwx; 05drq5; 01f7j9; 04y8r; 0b_7k; 06chf; 02l5rm; ... >> query: (?x2800, 02jknp) <- award(?x2800, ?x1198), ?x1198 = 02pqp12, gender(?x2800, ?x231) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p51w profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 201.000 201.000 0.897 http://example.org/people/person/profession #14033-03h3x5 PRED entity: 03h3x5 PRED relation: music PRED expected values: 01l9v7n => 80 concepts (39 used for prediction) PRED predicted values (max 10 best out of 91): 06fxnf (0.13 #274, 0.03 #897, 0.03 #689), 086k8 (0.11 #4997, 0.10 #6246, 0.10 #7701), 0150t6 (0.10 #252, 0.08 #1289, 0.06 #459), 016szr (0.10 #286, 0.02 #4868, 0.02 #1323), 02fgpf (0.08 #236, 0.01 #443, 0.01 #6067), 02jxkw (0.07 #347, 0.04 #762, 0.04 #970), 04pf4r (0.07 #273, 0.04 #688, 0.04 #1310), 02rgz4 (0.07 #212), 02bh9 (0.06 #4211, 0.05 #672, 0.05 #3793), 03h610 (0.06 #489, 0.04 #1529, 0.03 #2154) >> Best rule #274 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 016ztl; >> query: (?x2642, 06fxnf) <- music(?x2642, ?x669), genre(?x2642, ?x2540), ?x2540 = 0hcr >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03h3x5 music 01l9v7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 39.000 0.127 http://example.org/film/film/music #14032-02lj6p PRED entity: 02lj6p PRED relation: award_nominee PRED expected values: 0c6qh => 115 concepts (52 used for prediction) PRED predicted values (max 10 best out of 752): 0169dl (0.82 #95944, 0.81 #9361, 0.81 #100628), 032_jg (0.81 #9361, 0.81 #100628, 0.81 #109992), 02xwgr (0.81 #9361, 0.81 #109992, 0.81 #121697), 02lj6p (0.46 #1889, 0.21 #109993), 0c6qh (0.31 #541, 0.26 #95945, 0.21 #109993), 042xrr (0.26 #95945, 0.21 #109993, 0.08 #1090), 0gy6z9 (0.26 #95945, 0.15 #744, 0.07 #100629), 0151w_ (0.26 #95945, 0.15 #205, 0.07 #100629), 01r93l (0.26 #95945, 0.15 #996, 0.01 #92257), 0bsb4j (0.26 #95945, 0.08 #567, 0.07 #100629) >> Best rule #95944 for best value: >> intensional similarity = 3 >> extensional distance = 1014 >> proper extension: 092kgw; >> query: (?x8619, ?x5246) <- award_nominee(?x5246, ?x8619), participant(?x5246, ?x489), award_winner(?x5246, ?x4294) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #541 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 014zcr; 032_jg; 0169dl; 0c6qh; 0b_dy; 02xwgr; 046zh; 028r4y; 044lyq; 0h7pj; ... *> query: (?x8619, 0c6qh) <- award_nominee(?x192, ?x8619), film(?x8619, ?x2500), ?x2500 = 0418wg *> conf = 0.31 ranks of expected_values: 5 EVAL 02lj6p award_nominee 0c6qh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 115.000 52.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #14031-0yxm1 PRED entity: 0yxm1 PRED relation: nominated_for! PRED expected values: 02ppm4q => 106 concepts (103 used for prediction) PRED predicted values (max 10 best out of 220): 0gr51 (0.67 #13936, 0.67 #6034, 0.67 #7660), 0gqwc (0.67 #13936, 0.67 #6034, 0.67 #7660), 019f4v (0.60 #284, 0.35 #516, 0.34 #5853), 0f4x7 (0.60 #257, 0.28 #489, 0.26 #1185), 040njc (0.37 #470, 0.34 #1166, 0.33 #1398), 099c8n (0.36 #1215, 0.36 #1447, 0.32 #519), 02x1dht (0.36 #1434, 0.35 #1202, 0.30 #506), 02qyntr (0.35 #638, 0.34 #1334, 0.31 #1566), 02pqp12 (0.34 #1217, 0.33 #521, 0.30 #1449), 0k611 (0.29 #5871, 0.28 #4710, 0.27 #7497) >> Best rule #13936 for best value: >> intensional similarity = 4 >> extensional distance = 928 >> proper extension: 07bz5; >> query: (?x4460, ?x1972) <- nominated_for(?x3056, ?x4460), award(?x4460, ?x1972), award(?x91, ?x1972), award_winner(?x1972, ?x1559) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #1272 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 86 *> proper extension: 02d44q; *> query: (?x4460, 02ppm4q) <- film(?x3056, ?x4460), nominated_for(?x3435, ?x4460), titles(?x1403, ?x4460), ?x3435 = 03hl6lc *> conf = 0.24 ranks of expected_values: 15 EVAL 0yxm1 nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 106.000 103.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #14030-0jz9f PRED entity: 0jz9f PRED relation: film PRED expected values: 0dsvzh 0f4_l 0gyh2wm 05v38p 02pw_n 04b_jc 07tj4c => 137 concepts (108 used for prediction) PRED predicted values (max 10 best out of 1553): 049xgc (0.70 #18343, 0.69 #39743, 0.67 #42802), 03vyw8 (0.70 #18343, 0.65 #42804, 0.64 #39744), 03p2xc (0.70 #18343, 0.65 #42804, 0.64 #39744), 0bnzd (0.69 #39743, 0.67 #42802, 0.67 #42803), 02754c9 (0.69 #39743, 0.67 #42802, 0.67 #39742), 01dvbd (0.40 #422, 0.14 #17235, 0.12 #8064), 0ds3t5x (0.40 #41, 0.12 #7683, 0.10 #3098), 02mc5v (0.40 #1194, 0.11 #25986, 0.10 #4251), 0prrm (0.40 #732, 0.11 #25986, 0.10 #3789), 05zlld0 (0.40 #524, 0.11 #26510, 0.11 #11223) >> Best rule #18343 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 06q07; >> query: (?x166, ?x1224) <- production_companies(?x1224, ?x166), child(?x166, ?x10884), film(?x574, ?x1224) >> conf = 0.70 => this is the best rule for 3 predicted values *> Best rule #1003 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 01gb54; 054g1r; 032j_n; *> query: (?x166, 02pw_n) <- film(?x166, ?x10060), film(?x166, ?x3612), language(?x10060, ?x254), ?x3612 = 04z257 *> conf = 0.20 ranks of expected_values: 142, 332, 333, 1052, 1395, 1547 EVAL 0jz9f film 07tj4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 137.000 108.000 0.704 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0jz9f film 04b_jc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 108.000 0.704 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0jz9f film 02pw_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 137.000 108.000 0.704 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0jz9f film 05v38p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 137.000 108.000 0.704 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0jz9f film 0gyh2wm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 137.000 108.000 0.704 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0jz9f film 0f4_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 137.000 108.000 0.704 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0jz9f film 0dsvzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 137.000 108.000 0.704 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #14029-03m3nzf PRED entity: 03m3nzf PRED relation: film PRED expected values: 0fqt1ns => 115 concepts (77 used for prediction) PRED predicted values (max 10 best out of 352): 0f42nz (0.25 #907, 0.20 #2694, 0.06 #8055), 052_mn (0.25 #1401, 0.02 #8549), 026hxwx (0.25 #1146), 044g_k (0.25 #208), 030z4z (0.20 #3261, 0.04 #6835, 0.04 #8622), 04jwjq (0.20 #1879, 0.04 #7240, 0.02 #5453), 01p3ty (0.20 #2204, 0.04 #7565, 0.02 #5778), 03bx2lk (0.20 #3758, 0.03 #14480, 0.03 #9119), 05qbbfb (0.20 #4622, 0.02 #6409, 0.02 #8196), 0h3xztt (0.20 #3745) >> Best rule #907 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03x31g; >> query: (?x9136, 0f42nz) <- film(?x9136, ?x10842), film(?x9136, ?x8381), film_release_region(?x8381, ?x94), ?x10842 = 08g_jw >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03m3nzf film 0fqt1ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 77.000 0.250 http://example.org/film/actor/film./film/performance/film #14028-026n9h3 PRED entity: 026n9h3 PRED relation: tv_program PRED expected values: 02_1kl => 79 concepts (43 used for prediction) PRED predicted values (max 10 best out of 47): 0gj50 (0.50 #28, 0.06 #113, 0.03 #283), 0358x_ (0.31 #5, 0.03 #90, 0.02 #260), 01b66t (0.25 #34, 0.05 #119, 0.02 #289), 039cq4 (0.15 #131, 0.03 #301, 0.03 #473), 01y6dz (0.12 #42, 0.02 #127, 0.01 #297), 02_1kl (0.12 #341, 0.08 #427, 0.03 #770), 07c72 (0.07 #104, 0.01 #274, 0.01 #360), 0phrl (0.06 #23, 0.03 #108, 0.01 #278), 0170k0 (0.06 #57, 0.02 #142), 02_1rq (0.06 #2, 0.02 #87) >> Best rule #28 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0265vcb; 02760sl; >> query: (?x6970, 0gj50) <- award_winner(?x6970, ?x1281), ?x1281 = 04wtx1, tv_program(?x6970, ?x2829) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #341 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 471 *> proper extension: 02wrhj; 0p_jc; *> query: (?x6970, ?x3104) <- nominated_for(?x6970, ?x3104), nominated_for(?x6970, ?x2829), honored_for(?x2751, ?x2829), tv_program(?x1056, ?x2829) *> conf = 0.12 ranks of expected_values: 6 EVAL 026n9h3 tv_program 02_1kl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 43.000 0.500 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #14027-0gx_st PRED entity: 0gx_st PRED relation: ceremony! PRED expected values: 0bp_b2 => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 285): 0cqh6z (0.67 #1935, 0.60 #1226, 0.57 #2171), 09qv_s (0.67 #1994, 0.60 #1285, 0.57 #2230), 0cqhmg (0.67 #2110, 0.60 #1401, 0.57 #2346), 09sb52 (0.67 #1918, 0.60 #1209, 0.57 #2154), 0cqh46 (0.67 #1925, 0.60 #1216, 0.57 #2161), 09td7p (0.67 #1971, 0.60 #1262, 0.57 #2207), 09sdmz (0.67 #2027, 0.60 #1318, 0.57 #2263), 02py7pj (0.67 #2086, 0.60 #1377, 0.57 #2322), 0cqhb3 (0.67 #2082, 0.60 #1373, 0.57 #2318), 0bp_b2 (0.64 #2611, 0.60 #1666, 0.50 #956) >> Best rule #1935 for best value: >> intensional similarity = 16 >> extensional distance = 4 >> proper extension: 092t4b; >> query: (?x2292, 0cqh6z) <- award_winner(?x2292, ?x9503), award_winner(?x2292, ?x3763), award_winner(?x2292, ?x1669), ?x1669 = 02tr7d, ceremony(?x5235, ?x2292), award_winner(?x493, ?x3763), award(?x8036, ?x5235), award(?x7138, ?x5235), award(?x6360, ?x5235), award(?x2293, ?x5235), honored_for(?x2292, ?x1631), profession(?x9503, ?x319), ?x7138 = 0l786, type_of_union(?x8036, ?x566), film(?x8036, ?x1046), award_nominee(?x6360, ?x368) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2611 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 9 *> proper extension: 0lp_cd3; 056878; *> query: (?x2292, 0bp_b2) <- award_winner(?x2292, ?x9503), award_winner(?x2292, ?x5410), award_winner(?x2292, ?x3763), award_winner(?x2292, ?x2127), award_winner(?x2292, ?x1669), award_nominee(?x6359, ?x1669), award_nominee(?x4702, ?x1669), award_nominee(?x2372, ?x1669), award_nominee(?x1169, ?x1669), ?x2127 = 01j7rd, company(?x9503, ?x1908), ceremony(?x375, ?x2292), award_winner(?x493, ?x6359), award_winner(?x873, ?x1169), film(?x5410, ?x1586), student(?x3439, ?x4702), award_nominee(?x374, ?x2372), award_winner(?x2415, ?x4702), award_winner(?x1849, ?x3763) *> conf = 0.64 ranks of expected_values: 10 EVAL 0gx_st ceremony! 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 29.000 29.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony #14026-01l47f5 PRED entity: 01l47f5 PRED relation: instrumentalists! PRED expected values: 0342h 05148p4 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 119): 0342h (0.76 #344, 0.63 #514, 0.62 #684), 05r5c (0.49 #348, 0.47 #1283, 0.46 #2646), 05148p4 (0.43 #360, 0.31 #2744, 0.31 #1295), 018j2 (0.25 #37, 0.14 #377, 0.14 #1616), 02fsn (0.25 #50, 0.14 #1616, 0.13 #2553), 0gghm (0.25 #44, 0.04 #129, 0.03 #2810), 0l14md (0.19 #347, 0.14 #262, 0.12 #1282), 0l14qv (0.14 #345, 0.09 #430, 0.09 #515), 07y_7 (0.14 #1616, 0.13 #2553, 0.09 #1702), 07_l6 (0.14 #1616, 0.13 #2553, 0.09 #1702) >> Best rule #344 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 03c7ln; 0c9d9; 032t2z; 01cv3n; 0274ck; 01w923; 012zng; 0zjpz; 09prnq; 01zmpg; ... >> query: (?x6467, 0342h) <- instrumentalists(?x716, ?x6467), profession(?x6467, ?x131), ?x716 = 018vs >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01l47f5 instrumentalists! 05148p4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 79.000 0.759 http://example.org/music/instrument/instrumentalists EVAL 01l47f5 instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.759 http://example.org/music/instrument/instrumentalists #14025-04fzk PRED entity: 04fzk PRED relation: nationality PRED expected values: 09c7w0 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 25): 09c7w0 (0.86 #1193, 0.85 #496, 0.83 #4974), 02jx1 (0.52 #6167, 0.40 #32, 0.15 #627), 0d060g (0.52 #6167, 0.08 #502, 0.06 #1199), 0f8l9c (0.20 #22, 0.02 #8771, 0.02 #3012), 07ssc (0.10 #213, 0.10 #312, 0.09 #8764), 03rk0 (0.06 #8794, 0.06 #8695, 0.05 #11374), 0j5g9 (0.05 #259, 0.05 #358, 0.04 #457), 03rt9 (0.05 #211, 0.05 #310, 0.04 #409), 0ctw_b (0.04 #423, 0.03 #622, 0.02 #721), 0chghy (0.03 #1403, 0.03 #1702, 0.03 #2099) >> Best rule #1193 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 02jg92; >> query: (?x4106, 09c7w0) <- location(?x4106, ?x1523), nationality(?x4106, ?x1264), participant(?x4106, ?x3536) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04fzk nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.862 http://example.org/people/person/nationality #14024-03b_fm5 PRED entity: 03b_fm5 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 99 concepts (97 used for prediction) PRED predicted values (max 10 best out of 29): 09vw2b7 (0.76 #6, 0.64 #74, 0.63 #1784), 02r96rf (0.70 #1781, 0.69 #3, 0.64 #2058), 01vx2h (0.38 #11, 0.34 #79, 0.33 #1789), 0dxtw (0.36 #78, 0.36 #2237, 0.36 #215), 01pvkk (0.33 #80, 0.29 #217, 0.28 #1654), 0215hd (0.21 #17, 0.13 #222, 0.12 #1659), 089g0h (0.14 #18, 0.11 #1319, 0.11 #1660), 015h31 (0.14 #8, 0.09 #179, 0.09 #3195), 02rh1dz (0.12 #77, 0.11 #180, 0.10 #2849), 02_n3z (0.11 #69, 0.10 #2849, 0.10 #1) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 02qr69m; >> query: (?x4745, 09vw2b7) <- production_companies(?x4745, ?x1478), written_by(?x4745, ?x1736), language(?x4745, ?x254), ?x1478 = 054lpb6 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03b_fm5 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 97.000 0.759 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #14023-0b1s_q PRED entity: 0b1s_q PRED relation: profession PRED expected values: 03gjzk => 107 concepts (105 used for prediction) PRED predicted values (max 10 best out of 144): 03gjzk (0.85 #4038, 0.85 #2846, 0.84 #4485), 02hrh1q (0.80 #14320, 0.79 #7613, 0.78 #8954), 01d_h8 (0.70 #2688, 0.66 #3284, 0.62 #155), 02jknp (0.47 #5074, 0.45 #8054, 0.45 #2690), 02krf9 (0.38 #325, 0.33 #3007, 0.33 #3156), 018gz8 (0.33 #2699, 0.32 #3295, 0.28 #613), 0cbd2 (0.28 #1050, 0.27 #7, 0.26 #6861), 09jwl (0.23 #5383, 0.18 #9853, 0.18 #9704), 0np9r (0.22 #2703, 0.19 #3299, 0.18 #170), 0kyk (0.20 #30, 0.15 #6884, 0.15 #6437) >> Best rule #4038 for best value: >> intensional similarity = 4 >> extensional distance = 150 >> proper extension: 0b05xm; >> query: (?x12754, 03gjzk) <- profession(?x12754, ?x987), ?x987 = 0dxtg, program(?x12754, ?x4535), gender(?x12754, ?x231) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b1s_q profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 105.000 0.849 http://example.org/people/person/profession #14022-029cpw PRED entity: 029cpw PRED relation: actor! PRED expected values: 0ctzf1 => 121 concepts (44 used for prediction) PRED predicted values (max 10 best out of 165): 01f39b (0.33 #101, 0.25 #886, 0.09 #4541), 0ctzf1 (0.31 #2225, 0.29 #1442, 0.14 #263), 03k99c (0.29 #1548, 0.20 #1287, 0.14 #263), 09fc83 (0.25 #1658, 0.07 #2963, 0.05 #3485), 015pnb (0.20 #1289, 0.11 #2072, 0.07 #2594), 0k54q (0.20 #262, 0.05 #1047, 0.03 #6008), 01kf4tt (0.20 #262, 0.05 #1047, 0.03 #6008), 026y3cf (0.15 #3642, 0.14 #3120, 0.12 #1815), 025x1t (0.14 #1528, 0.14 #263, 0.05 #3877), 01h72l (0.14 #1346, 0.14 #263, 0.03 #5784) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01bmlb; >> query: (?x7001, 01f39b) <- film(?x7001, ?x2506), ?x2506 = 01kf4tt, actor(?x3144, ?x7001) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2225 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 027km64; 01d_4t; 08jtv5; *> query: (?x7001, 0ctzf1) <- film(?x7001, ?x5378), film(?x7001, ?x2506), genre(?x2506, ?x225), film(?x1850, ?x2506), language(?x2506, ?x254), ?x5378 = 0k54q *> conf = 0.31 ranks of expected_values: 2 EVAL 029cpw actor! 0ctzf1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 44.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #14021-017149 PRED entity: 017149 PRED relation: profession PRED expected values: 02hrh1q => 87 concepts (46 used for prediction) PRED predicted values (max 10 best out of 42): 02hrh1q (0.89 #1345, 0.89 #1789, 0.88 #309), 01d_h8 (0.36 #302, 0.34 #2226, 0.30 #2966), 03gjzk (0.28 #6811, 0.28 #6810, 0.24 #162), 0np9r (0.28 #6811, 0.28 #6810, 0.21 #1796), 02krf9 (0.28 #6811, 0.28 #6810, 0.19 #174), 09jwl (0.28 #6811, 0.28 #6810, 0.19 #1646), 05z96 (0.17 #42, 0.03 #3594, 0.03 #5962), 0lgw7 (0.17 #47), 0cbd2 (0.15 #4299, 0.14 #4447, 0.13 #6075), 018gz8 (0.13 #1792, 0.13 #1052, 0.12 #3716) >> Best rule #1345 for best value: >> intensional similarity = 3 >> extensional distance = 727 >> proper extension: 079vf; >> query: (?x525, 02hrh1q) <- award_winner(?x525, ?x496), film(?x525, ?x414), profession(?x525, ?x524) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017149 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 46.000 0.890 http://example.org/people/person/profession #14020-05bmq PRED entity: 05bmq PRED relation: country! PRED expected values: 03fyrh 0486tv 019tzd => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 53): 06z6r (0.87 #453, 0.87 #400, 0.84 #612), 03_8r (0.76 #445, 0.76 #392, 0.70 #1081), 06f41 (0.67 #438, 0.67 #385, 0.57 #67), 0w0d (0.57 #65, 0.56 #436, 0.56 #383), 0194d (0.57 #99, 0.53 #470, 0.53 #417), 06wrt (0.56 #439, 0.56 #386, 0.47 #333), 064vjs (0.56 #454, 0.56 #401, 0.47 #348), 07jbh (0.54 #456, 0.54 #403, 0.50 #350), 07gyv (0.53 #431, 0.53 #378, 0.48 #696), 0486tv (0.53 #462, 0.51 #409, 0.42 #1098) >> Best rule #453 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 05r7t; >> query: (?x9458, 06z6r) <- jurisdiction_of_office(?x182, ?x9458), olympics(?x9458, ?x3971), ?x3971 = 0jhn7, olympics(?x9458, ?x2966) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #462 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 05r7t; *> query: (?x9458, 0486tv) <- jurisdiction_of_office(?x182, ?x9458), olympics(?x9458, ?x3971), ?x3971 = 0jhn7, olympics(?x9458, ?x2966) *> conf = 0.53 ranks of expected_values: 10, 11, 19 EVAL 05bmq country! 019tzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 111.000 111.000 0.871 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 05bmq country! 0486tv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 111.000 111.000 0.871 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 05bmq country! 03fyrh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 111.000 111.000 0.871 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #14019-04_jsg PRED entity: 04_jsg PRED relation: artists! PRED expected values: 0jmwg => 96 concepts (50 used for prediction) PRED predicted values (max 10 best out of 276): 0dl5d (0.73 #326, 0.21 #9209, 0.21 #10742), 064t9 (0.64 #1238, 0.51 #3383, 0.49 #2463), 05bt6j (0.55 #1573, 0.52 #1267, 0.36 #654), 06by7 (0.52 #1553, 0.51 #5540, 0.51 #6153), 05w3f (0.45 #343, 0.27 #3099, 0.23 #4327), 01lyv (0.40 #2790, 0.36 #3711, 0.23 #5246), 08jyyk (0.36 #373, 0.18 #679, 0.16 #4665), 0xhtw (0.33 #2160, 0.30 #5535, 0.27 #323), 059kh (0.33 #48, 0.17 #1579, 0.16 #11404), 03_d0 (0.33 #11367, 0.18 #3688, 0.17 #5223) >> Best rule #326 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0qmpd; >> query: (?x8215, 0dl5d) <- artists(?x2808, ?x8215), category(?x8215, ?x134), ?x2808 = 0190_q >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #9209 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 349 *> proper extension: 01jrz5j; 0770cd; 02fgpf; 02zmh5; 0412f5y; 0415mzy; 03xnq9_; 026yqrr; 01vs73g; 01x1fq; ... *> query: (?x8215, ?x283) <- artists(?x2491, ?x8215), profession(?x8215, ?x2348), ?x2348 = 0nbcg, parent_genre(?x2491, ?x283) *> conf = 0.21 ranks of expected_values: 25 EVAL 04_jsg artists! 0jmwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 96.000 50.000 0.727 http://example.org/music/genre/artists #14018-038_3y PRED entity: 038_3y PRED relation: teams! PRED expected values: 088q4 => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 125): 0ctw_b (0.14 #29, 0.12 #569, 0.12 #299), 03rk0 (0.14 #66, 0.12 #606, 0.12 #336), 0chghy (0.14 #10, 0.12 #550, 0.12 #280), 0261m (0.14 #196, 0.12 #736, 0.12 #466), 06m_5 (0.14 #173, 0.12 #713, 0.12 #443), 0j5g9 (0.12 #658, 0.04 #1928, 0.03 #2479), 02jx1 (0.12 #582, 0.04 #1928, 0.03 #2479), 030qb3t (0.05 #1688, 0.02 #2804, 0.01 #4476), 0m75g (0.05 #2087, 0.02 #3468, 0.01 #3188), 03pzf (0.05 #1019) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02fbb5; 098knd; 020wyp; 024nj1; 023fxp; >> query: (?x13752, 0ctw_b) <- sport(?x13752, ?x12682), ?x12682 = 09xp_, team(?x13559, ?x13752), ?x13559 = 021q23 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #3131 for first EXPECTED value: *> intensional similarity = 52 *> extensional distance = 94 *> proper extension: 0223bl; 03y_f8; 075q_; 04b4yg; 03yl2t; 0182r9; 0j2pg; 01l3vx; 044l47; 02rytm; ... *> query: (?x13752, 088q4) <- sport(?x13752, ?x12682), sport(?x14441, ?x12682), sport(?x14238, ?x12682), sport(?x13932, ?x12682), sport(?x13358, ?x12682), sport(?x12490, ?x12682), sport(?x10085, ?x12682), country(?x12682, ?x512), colors(?x13358, ?x8047), team(?x13559, ?x14441), teams(?x390, ?x13358), colors(?x14238, ?x3189), ?x3189 = 01g5v, sports(?x2553, ?x12682), ?x8047 = 038hg, colors(?x12490, ?x7179), ?x512 = 07ssc, teams(?x1023, ?x10085), colors(?x10085, ?x4557), colors(?x10085, ?x663), teams(?x8420, ?x14441), athlete(?x12682, ?x10562), athlete(?x12682, ?x4895), ?x663 = 083jv, award(?x4895, ?x575), award_nominee(?x4895, ?x4353), type_of_union(?x4895, ?x566), teams(?x9729, ?x12490), profession(?x4895, ?x353), gender(?x4895, ?x231), film(?x4895, ?x4699), teams(?x2146, ?x14238), ?x7179 = 04mkbj, religion(?x4895, ?x2694), teams(?x4221, ?x13932), ?x566 = 04ztj, ?x4221 = 0j5g9, place_of_birth(?x10562, ?x6885), profession(?x10562, ?x987), nationality(?x10562, ?x6401), ?x231 = 05zppz, ?x2146 = 03rk0, ?x2553 = 016r9z, ?x4557 = 019sc, student(?x4390, ?x10562), location(?x10562, ?x6885), nationality(?x4895, ?x8420), team(?x13559, ?x13358), award_nominee(?x4353, ?x4895), nominated_for(?x4895, ?x4699), type_of_union(?x10562, ?x566), team(?x13559, ?x13752) *> conf = 0.01 ranks of expected_values: 119 EVAL 038_3y teams! 088q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 20.000 20.000 0.143 http://example.org/sports/sports_team_location/teams #14017-05c5z8j PRED entity: 05c5z8j PRED relation: film_release_region PRED expected values: 09c7w0 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 120): 09c7w0 (0.80 #182, 0.77 #362, 0.75 #1080), 0345h (0.60 #47, 0.39 #1483, 0.38 #1842), 05r4w (0.60 #2, 0.38 #1438, 0.37 #1797), 0d060g (0.60 #11, 0.36 #1447, 0.34 #1806), 05qhw (0.60 #22, 0.35 #560, 0.33 #1458), 07ssc (0.51 #1616, 0.45 #6276, 0.44 #4661), 03rjj (0.50 #8, 0.39 #1444, 0.37 #1803), 015fr (0.50 #26, 0.36 #1462, 0.33 #1821), 03_3d (0.50 #10, 0.35 #548, 0.34 #189), 03gj2 (0.50 #37, 0.34 #216, 0.31 #396) >> Best rule #182 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 0ds35l9; 0d90m; 03qcfvw; 02hxhz; 01qb5d; 08hmch; 0872p_c; 0dtfn; 02f6g5; 0f4_l; ... >> query: (?x4329, 09c7w0) <- executive_produced_by(?x4329, ?x4857), film_crew_role(?x4329, ?x281), region(?x4329, ?x512) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05c5z8j film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.795 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #14016-05sy2k_ PRED entity: 05sy2k_ PRED relation: genre PRED expected values: 01t_vv => 64 concepts (52 used for prediction) PRED predicted values (max 10 best out of 169): 01jfsb (0.82 #491, 0.62 #570, 0.57 #170), 03k9fj (0.60 #889, 0.40 #808, 0.39 #648), 01htzx (0.55 #893, 0.46 #732, 0.42 #652), 05p553 (0.49 #1679, 0.48 #1919, 0.47 #1838), 0hcr (0.40 #814, 0.32 #654, 0.25 #895), 01z4y (0.38 #1052, 0.38 #973, 0.38 #1132), 02n4kr (0.36 #487, 0.33 #566, 0.33 #87), 0vgkd (0.33 #9, 0.17 #1208, 0.15 #1126), 0c3351 (0.29 #183, 0.23 #504, 0.21 #422), 0c4xc (0.28 #1714, 0.26 #1873, 0.26 #2113) >> Best rule #491 for best value: >> intensional similarity = 7 >> extensional distance = 20 >> proper extension: 03d34x8; 0gxr1c; >> query: (?x3848, 01jfsb) <- genre(?x3848, ?x6277), genre(?x7366, ?x6277), genre(?x485, ?x6277), ?x7366 = 01g3gq, film(?x609, ?x485), nominated_for(?x143, ?x485), film(?x2444, ?x485) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1067 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 58 *> proper extension: 03gvm3t; 05fgr_; 07s8z_l; 02xhwm; *> query: (?x3848, 01t_vv) <- genre(?x3848, ?x571), program(?x8817, ?x3848), titles(?x571, ?x11209), category(?x3848, ?x134), nominated_for(?x4353, ?x11209) *> conf = 0.25 ranks of expected_values: 13 EVAL 05sy2k_ genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 64.000 52.000 0.818 http://example.org/tv/tv_program/genre #14015-03h502k PRED entity: 03h502k PRED relation: profession PRED expected values: 0dz3r 02hrh1q => 162 concepts (108 used for prediction) PRED predicted values (max 10 best out of 90): 02hrh1q (0.92 #7684, 0.91 #11098, 0.90 #7258), 01d_h8 (0.76 #5404, 0.56 #1993, 0.52 #11519), 0dz3r (0.58 #3413, 0.56 #1990, 0.51 #5259), 0dxtg (0.55 #5835, 0.47 #5409, 0.41 #3705), 039v1 (0.48 #3442, 0.40 #3157, 0.40 #1877), 03gjzk (0.39 #5411, 0.38 #11526, 0.31 #3991), 0kyk (0.33 #3720, 0.27 #5850, 0.27 #1729), 0fnpj (0.33 #1900, 0.25 #2042, 0.21 #1332), 01c8w0 (0.27 #7537, 0.27 #6542, 0.25 #9384), 02hv44_ (0.25 #3746, 0.19 #2749, 0.08 #3604) >> Best rule #7684 for best value: >> intensional similarity = 4 >> extensional distance = 126 >> proper extension: 06c0j; >> query: (?x5126, 02hrh1q) <- participant(?x6844, ?x5126), profession(?x5126, ?x220), religion(?x5126, ?x2694), participant(?x2258, ?x5126) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 03h502k profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 108.000 0.922 http://example.org/people/person/profession EVAL 03h502k profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 162.000 108.000 0.922 http://example.org/people/person/profession #14014-04gknr PRED entity: 04gknr PRED relation: film! PRED expected values: 022_lg => 105 concepts (78 used for prediction) PRED predicted values (max 10 best out of 147): 02hfp_ (0.33 #191, 0.03 #740, 0.03 #1015), 03v1w7 (0.24 #11594, 0.23 #11593, 0.21 #15996), 0fvf9q (0.24 #11594, 0.23 #11593, 0.21 #15996), 03cvv4 (0.13 #17924, 0.12 #9936, 0.12 #1649), 06chf (0.06 #627, 0.06 #902, 0.04 #1177), 06pj8 (0.05 #1422, 0.04 #1973, 0.04 #2251), 04g3p5 (0.05 #388, 0.05 #663, 0.05 #938), 0j_c (0.04 #5300, 0.04 #4744, 0.04 #5022), 02kxbx3 (0.04 #1736, 0.03 #3390, 0.02 #2012), 0jrqq (0.04 #1742, 0.01 #2018, 0.01 #4774) >> Best rule #191 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02mpyh; >> query: (?x924, 02hfp_) <- film_crew_role(?x924, ?x137), genre(?x924, ?x53), film(?x1324, ?x924), crewmember(?x924, ?x666), ?x1324 = 04t7ts >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04gknr film! 022_lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 78.000 0.333 http://example.org/film/director/film #14013-0gsg7 PRED entity: 0gsg7 PRED relation: program PRED expected values: 01b9w3 => 202 concepts (179 used for prediction) PRED predicted values (max 10 best out of 210): 0124k9 (0.71 #4502, 0.70 #5937, 0.70 #4093), 05sy0cv (0.69 #4503, 0.65 #3684, 0.63 #1635), 034fl9 (0.33 #1759, 0.33 #1554, 0.20 #1350), 01b66d (0.33 #1665, 0.33 #1460, 0.20 #1256), 043qqt5 (0.33 #1596, 0.23 #3644, 0.20 #4874), 01hn_t (0.23 #10694, 0.20 #4756, 0.16 #7220), 015w8_ (0.20 #1257, 0.20 #1053, 0.20 #644), 07s8z_l (0.20 #1378, 0.20 #1174, 0.20 #765), 0d_rw (0.20 #1420, 0.20 #1216, 0.20 #807), 034vds (0.20 #1413, 0.20 #1209, 0.20 #800) >> Best rule #4502 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 01nzs7; 027_tg; 01j7pt; 0kctd; >> query: (?x1762, ?x782) <- nominated_for(?x1762, ?x782), award_winner(?x687, ?x1762), program(?x1762, ?x50) >> conf = 0.71 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gsg7 program 01b9w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 202.000 179.000 0.706 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #14012-0gpprt PRED entity: 0gpprt PRED relation: award_winner! PRED expected values: 04n2r9h => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 115): 0hndn2q (0.07 #40, 0.07 #181, 0.05 #322), 0n8_m93 (0.06 #118, 0.05 #259, 0.04 #400), 02wzl1d (0.05 #152, 0.04 #293, 0.04 #434), 02glmx (0.04 #81, 0.04 #222, 0.04 #363), 09p2r9 (0.04 #93, 0.04 #234, 0.03 #516), 0bzkvd (0.04 #114, 0.04 #255, 0.03 #396), 09q_6t (0.04 #149, 0.03 #8, 0.03 #290), 09qvms (0.04 #1282, 0.04 #3256, 0.04 #2128), 02q690_ (0.04 #1052, 0.03 #206, 0.03 #3308), 013b2h (0.03 #3323, 0.03 #5861, 0.03 #5297) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 0kr5_; 0prjs; 05whq_9; 034bgm; 0m32_; 03tf_h; 0jw67; 09p06; 01twdk; 013zyw; ... >> query: (?x8783, 0hndn2q) <- film(?x8783, ?x7012), film(?x8783, ?x10732), films(?x3530, ?x7012) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #750 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 221 *> proper extension: 0l6qt; 0h5f5n; 06cv1; 01wl38s; 04r7jc; 0jf1b; 02kxbwx; 05m883; 05drq5; 0136g9; ... *> query: (?x8783, 04n2r9h) <- award_nominee(?x8783, ?x286), award(?x8783, ?x68), written_by(?x10732, ?x8783) *> conf = 0.01 ranks of expected_values: 98 EVAL 0gpprt award_winner! 04n2r9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 93.000 93.000 0.072 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #14011-0djywgn PRED entity: 0djywgn PRED relation: profession PRED expected values: 02hrh1q => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 81): 02hrh1q (0.89 #8396, 0.87 #5603, 0.87 #5897), 01d_h8 (0.78 #2506, 0.77 #1623, 0.75 #2800), 09jwl (0.77 #1047, 0.69 #4578, 0.67 #5166), 0cbd2 (0.60 #889, 0.48 #3243, 0.48 #3537), 0nbcg (0.59 #1060, 0.46 #4591, 0.45 #5179), 03gjzk (0.48 #1632, 0.45 #2809, 0.44 #2515), 0kyk (0.47 #323, 0.38 #911, 0.38 #764), 0dz3r (0.46 #1031, 0.41 #4562, 0.40 #5150), 02jknp (0.40 #1331, 0.39 #2508, 0.39 #1625), 0np9r (0.36 #167, 0.28 #3697, 0.25 #9706) >> Best rule #8396 for best value: >> intensional similarity = 2 >> extensional distance = 2012 >> proper extension: 027l0b; 01hkck; 045931; 033071; >> query: (?x8566, 02hrh1q) <- film(?x8566, ?x2029), profession(?x8566, ?x987) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0djywgn profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.892 http://example.org/people/person/profession #14010-0lhn5 PRED entity: 0lhn5 PRED relation: contains! PRED expected values: 0d0x8 => 174 concepts (166 used for prediction) PRED predicted values (max 10 best out of 252): 0d0x8 (0.84 #45671, 0.83 #69850, 0.82 #85067), 0n_2q (0.64 #50148, 0.64 #68057, 0.61 #42984), 01n7q (0.21 #7240, 0.20 #16195, 0.20 #77), 07ssc (0.18 #71671, 0.18 #86888, 0.17 #94049), 0kpys (0.18 #7343, 0.13 #5551, 0.11 #13611), 04_1l0v (0.16 #77461, 0.14 #82831, 0.13 #79251), 059_c (0.13 #70, 0.11 #1861, 0.09 #5441), 0vmt (0.13 #54, 0.07 #16172, 0.06 #10798), 02jx1 (0.13 #34114, 0.13 #142442, 0.12 #94104), 0chghy (0.11 #1813, 0.09 #5393, 0.09 #4498) >> Best rule #45671 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 01tlmw; 0yc84; 0dhdp; 0xkq4; 0fvxz; 0fvvz; 0r7fy; 04f_d; 099ty; 019k6n; ... >> query: (?x5211, ?x3038) <- place_of_birth(?x4735, ?x5211), state(?x5211, ?x3038), country(?x5211, ?x94), category(?x5211, ?x134) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lhn5 contains! 0d0x8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 166.000 0.844 http://example.org/location/location/contains #14009-06tw8 PRED entity: 06tw8 PRED relation: jurisdiction_of_office! PRED expected values: 09d6p2 => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 20): 060bp (0.76 #993, 0.76 #316, 0.74 #719), 0f6c3 (0.62 #1271, 0.62 #1229, 0.61 #1313), 09n5b9 (0.59 #1275, 0.58 #1317, 0.57 #1233), 0fkvn (0.53 #1267, 0.52 #1309, 0.51 #1225), 0pqc5 (0.50 #3078, 0.49 #3120, 0.46 #3205), 0377k9 (0.48 #2043, 0.42 #2402, 0.38 #3202), 01_fjr (0.48 #2043, 0.42 #2402, 0.38 #3202), 09d6p2 (0.48 #2043, 0.42 #2402, 0.38 #3202), 02079p (0.48 #2043, 0.42 #2402, 0.36 #3245), 04syw (0.43 #110, 0.33 #765, 0.30 #934) >> Best rule #993 for best value: >> intensional similarity = 5 >> extensional distance = 56 >> proper extension: 02jx1; >> query: (?x5457, 060bp) <- adjoins(?x5457, ?x13717), time_zones(?x5457, ?x6582), form_of_government(?x13717, ?x48), country(?x1121, ?x5457), administrative_parent(?x13717, ?x551) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #2043 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 113 *> proper extension: 0rh6k; 05kkh; 059rby; 03v1s; 05kj_; 05fkf; 0vmt; 05fhy; 04ych; 059_c; ... *> query: (?x5457, ?x182) <- adjoins(?x5457, ?x4121), time_zones(?x5457, ?x6582), jurisdiction_of_office(?x265, ?x5457), contains(?x2467, ?x5457), jurisdiction_of_office(?x182, ?x4121) *> conf = 0.48 ranks of expected_values: 8 EVAL 06tw8 jurisdiction_of_office! 09d6p2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 187.000 187.000 0.759 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #14008-01gq0b PRED entity: 01gq0b PRED relation: location PRED expected values: 0cc56 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 133): 04jpl (0.33 #821, 0.07 #9651, 0.06 #8863), 030qb3t (0.27 #2495, 0.25 #4103, 0.24 #4908), 02_286 (0.22 #2449, 0.17 #16122, 0.17 #18534), 0cr3d (0.15 #1753, 0.06 #8187, 0.05 #31509), 059rby (0.11 #16, 0.08 #820, 0.05 #17709), 0r0m6 (0.11 #218, 0.06 #3434, 0.04 #6652), 0k049 (0.11 #8, 0.05 #2420, 0.04 #4028), 0d6lp (0.11 #168, 0.03 #2580, 0.03 #6602), 02xry (0.11 #133, 0.03 #2545, 0.02 #3349), 01_d4 (0.11 #102, 0.02 #8144, 0.02 #3318) >> Best rule #821 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01v42g; >> query: (?x1890, 04jpl) <- film(?x1890, ?x10509), award(?x1890, ?x401), ?x10509 = 09fqgj >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #861 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 01v42g; *> query: (?x1890, 0cc56) <- film(?x1890, ?x10509), award(?x1890, ?x401), ?x10509 = 09fqgj *> conf = 0.08 ranks of expected_values: 14 EVAL 01gq0b location 0cc56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 107.000 107.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #14007-016vqk PRED entity: 016vqk PRED relation: role PRED expected values: 02hnl => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 100): 0342h (0.21 #593, 0.21 #1707, 0.20 #200), 03bx0bm (0.18 #611, 0.18 #1725, 0.17 #1528), 06ncr (0.15 #1571, 0.15 #2035, 0.15 #1768), 057cc (0.15 #1571, 0.15 #1768, 0.14 #2034), 05148p4 (0.11 #1720, 0.11 #1918, 0.11 #1523), 018vs (0.08 #1519, 0.08 #1716, 0.07 #1914), 02hnl (0.07 #1535, 0.07 #1998, 0.07 #1076), 028tv0 (0.07 #1715, 0.06 #1518, 0.06 #1913), 03qjg (0.06 #303, 0.05 #1548, 0.04 #1745), 0l14md (0.05 #1908, 0.05 #1976, 0.05 #596) >> Best rule #593 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 06y9c2; 01p45_v; 01vv126; 02wb6yq; 0bkg4; 04f7c55; 02r3cn; 018y81; 04bgy; 0gs6vr; ... >> query: (?x9008, 0342h) <- profession(?x9008, ?x220), artists(?x3061, ?x9008), ?x3061 = 05bt6j >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #1535 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 413 *> proper extension: 03llf8; 09g0h; *> query: (?x9008, 02hnl) <- profession(?x9008, ?x220), instrumentalists(?x316, ?x9008), type_of_union(?x9008, ?x566) *> conf = 0.07 ranks of expected_values: 7 EVAL 016vqk role 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 108.000 108.000 0.213 http://example.org/music/group_member/membership./music/group_membership/role #14006-0mx5p PRED entity: 0mx5p PRED relation: source PRED expected values: 0jbk9 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #69, 0.92 #68, 0.92 #60) >> Best rule #69 for best value: >> intensional similarity = 4 >> extensional distance = 260 >> proper extension: 0m2gk; 0n5_g; 0k3ll; 0mws3; 0n5y4; 0nm8n; 0f4zv; >> query: (?x12499, ?x958) <- adjoins(?x1939, ?x12499), second_level_divisions(?x94, ?x12499), time_zones(?x1939, ?x2950), source(?x1939, ?x958) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mx5p source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.924 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #14005-0872p_c PRED entity: 0872p_c PRED relation: prequel PRED expected values: 047csmy => 64 concepts (28 used for prediction) PRED predicted values (max 10 best out of 65): 05zlld0 (0.25 #62, 0.20 #243, 0.04 #424), 0dln8jk (0.04 #3084, 0.04 #3085, 0.04 #2719), 07kb7vh (0.04 #3084, 0.04 #3085, 0.04 #2719), 09146g (0.04 #398, 0.02 #1304, 0.01 #1848), 035w2k (0.04 #459), 0dnqr (0.04 #412), 013q0p (0.02 #634, 0.02 #815, 0.01 #2084), 0fdv3 (0.02 #577, 0.02 #940, 0.01 #1665), 0bmssv (0.02 #619, 0.02 #1163, 0.01 #2069), 06w839_ (0.02 #596, 0.02 #1321, 0.01 #1684) >> Best rule #62 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05zlld0; 047csmy; >> query: (?x1173, 05zlld0) <- film(?x11091, ?x1173), ?x11091 = 03dn9v, film_release_region(?x1173, ?x87), nominated_for(?x848, ?x1173) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0872p_c prequel 047csmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 28.000 0.250 http://example.org/film/film/prequel #14004-0gwjw0c PRED entity: 0gwjw0c PRED relation: honored_for! PRED expected values: 0n8_m93 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 63): 09gkdln (0.08 #4271, 0.08 #106, 0.07 #350), 0n8_m93 (0.08 #4271, 0.08 #103, 0.07 #347), 02hn5v (0.08 #4271, 0.08 #33, 0.07 #277), 0275n3y (0.08 #4271, 0.08 #186, 0.03 #1162), 0bvhz9 (0.08 #4271, 0.08 #236, 0.03 #724), 05c1t6z (0.08 #4271, 0.03 #1109, 0.02 #621), 05zksls (0.08 #4271, 0.03 #638, 0.02 #1126), 09g90vz (0.08 #4271, 0.02 #718, 0.02 #1206), 09k5jh7 (0.08 #4271, 0.02 #1169, 0.01 #1413), 09p2r9 (0.08 #4271, 0.02 #1177, 0.01 #1421) >> Best rule #4271 for best value: >> intensional similarity = 3 >> extensional distance = 1276 >> proper extension: 0n2bh; 08cx5g; 02kk_c; 0c3xpwy; 03d17dg; 04bp0l; >> query: (?x6886, ?x2220) <- nominated_for(?x8740, ?x6886), award_winner(?x112, ?x8740), award_winner(?x2220, ?x8740) >> conf = 0.08 => this is the best rule for 17 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0gwjw0c honored_for! 0n8_m93 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.084 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #14003-01n244 PRED entity: 01n244 PRED relation: place_of_birth! PRED expected values: 0pkyh => 60 concepts (35 used for prediction) PRED predicted values (max 10 best out of 113): 037q1z (0.23 #18294, 0.20 #1984, 0.14 #4597), 02508x (0.23 #18294, 0.20 #1110, 0.14 #3723), 0k29f (0.23 #18294, 0.20 #2288, 0.14 #4901), 026y23w (0.23 #18294, 0.20 #1183, 0.14 #3796), 0bz5v2 (0.23 #18294, 0.20 #167, 0.14 #2780), 081k8 (0.14 #3642, 0.02 #8869, 0.02 #14096), 0473q (0.08 #20908, 0.06 #10454, 0.04 #7840), 01vtqml (0.08 #20908, 0.06 #10454, 0.04 #7840), 01nn6c (0.08 #20908, 0.06 #10454, 0.04 #7840), 01vn35l (0.08 #20908, 0.06 #10454, 0.04 #7840) >> Best rule #18294 for best value: >> intensional similarity = 6 >> extensional distance = 63 >> proper extension: 0dhdp; 01ykl0; 02m__; 01b_d4; 01zn4y; 05vw7; 01z8f0; 0h30_; 0grd7; 015cj9; ... >> query: (?x6992, ?x1040) <- contains(?x6991, ?x6992), contains(?x512, ?x6992), second_level_divisions(?x1310, ?x6991), contains(?x6991, ?x9878), ?x512 = 07ssc, place_of_birth(?x1040, ?x9878) >> conf = 0.23 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 01n244 place_of_birth! 0pkyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 35.000 0.232 http://example.org/people/person/place_of_birth #14002-061_f PRED entity: 061_f PRED relation: nutrient PRED expected values: 0838f 0dcfv 025rsfk 01n78x 03d49 => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 33): 0838f (0.74 #143, 0.67 #311, 0.67 #292), 025rsfk (0.74 #143, 0.50 #313, 0.50 #295), 01n78x (0.74 #143, 0.50 #306, 0.50 #297), 03d49 (0.74 #143, 0.50 #299, 0.50 #278), 0dcfv (0.74 #143, 0.33 #305, 0.33 #293), 02kc_w5 (0.74 #143, 0.33 #223, 0.33 #209), 01w_3 (0.74 #143, 0.33 #224, 0.33 #182), 0f4k5 (0.74 #143, 0.33 #225, 0.33 #183), 075pwf (0.74 #143, 0.33 #219, 0.33 #177), 0f4l5 (0.74 #143, 0.33 #152, 0.33 #123) >> Best rule #143 for best value: >> intensional similarity = 140 >> extensional distance = 1 >> proper extension: 01645p; >> query: (?x3900, ?x7135) <- nutrient(?x3900, ?x13944), nutrient(?x3900, ?x13498), nutrient(?x3900, ?x12902), nutrient(?x3900, ?x12481), nutrient(?x3900, ?x12454), nutrient(?x3900, ?x11784), nutrient(?x3900, ?x11758), nutrient(?x3900, ?x11592), nutrient(?x3900, ?x11409), nutrient(?x3900, ?x11270), nutrient(?x3900, ?x10891), nutrient(?x3900, ?x10709), nutrient(?x3900, ?x10195), nutrient(?x3900, ?x10098), nutrient(?x3900, ?x9949), nutrient(?x3900, ?x9915), nutrient(?x3900, ?x9855), nutrient(?x3900, ?x9840), nutrient(?x3900, ?x9795), nutrient(?x3900, ?x9733), nutrient(?x3900, ?x9619), nutrient(?x3900, ?x9490), nutrient(?x3900, ?x9426), nutrient(?x3900, ?x9365), nutrient(?x3900, ?x8442), nutrient(?x3900, ?x8413), nutrient(?x3900, ?x8243), nutrient(?x3900, ?x7894), nutrient(?x3900, ?x7652), nutrient(?x3900, ?x7364), nutrient(?x3900, ?x7362), nutrient(?x3900, ?x7219), nutrient(?x3900, ?x6586), nutrient(?x3900, ?x6286), nutrient(?x3900, ?x6192), nutrient(?x3900, ?x6160), nutrient(?x3900, ?x6033), nutrient(?x3900, ?x6026), nutrient(?x3900, ?x5549), nutrient(?x3900, ?x5526), nutrient(?x3900, ?x5451), nutrient(?x3900, ?x5374), nutrient(?x3900, ?x5010), nutrient(?x3900, ?x4069), nutrient(?x3900, ?x3901), nutrient(?x3900, ?x3469), nutrient(?x3900, ?x3203), nutrient(?x3900, ?x2018), nutrient(?x3900, ?x1960), nutrient(?x3900, ?x1304), nutrient(?x3900, ?x1258), ?x10709 = 0h1sz, ?x7362 = 02kc5rj, taxonomy(?x2018, ?x939), ?x9733 = 0h1tz, nutrient(?x9732, ?x2018), nutrient(?x9489, ?x2018), nutrient(?x9005, ?x2018), nutrient(?x8298, ?x2018), nutrient(?x7719, ?x2018), nutrient(?x7057, ?x2018), nutrient(?x6191, ?x2018), nutrient(?x6159, ?x2018), nutrient(?x5009, ?x2018), nutrient(?x4068, ?x2018), nutrient(?x3468, ?x2018), nutrient(?x1303, ?x2018), nutrient(?x1257, ?x2018), ?x12902 = 0fzjh, ?x6033 = 04zjxcz, ?x5009 = 0fjfh, ?x8442 = 02kcv4x, ?x6586 = 05gh50, ?x8298 = 037ls6, ?x5526 = 09pbb, ?x6191 = 014j1m, ?x1304 = 08lb68, ?x9915 = 025tkqy, ?x8243 = 014d7f, ?x1303 = 0fj52s, ?x11758 = 0q01m, ?x6159 = 033cnk, ?x7364 = 09gvd, ?x5010 = 0h1vz, ?x5549 = 025s7j4, ?x3901 = 0466p20, ?x6286 = 02y_3rf, ?x7219 = 0h1vg, ?x11784 = 07zqy, ?x939 = 04n6k, ?x9840 = 02p0tjr, ?x13498 = 07q0m, ?x5374 = 025s0zp, ?x9365 = 04k8n, ?x9949 = 02kd0rh, ?x7719 = 0dj75, ?x7057 = 0fbdb, ?x12481 = 027g6p7, nutrient(?x10612, ?x6192), nutrient(?x6032, ?x6192), nutrient(?x5373, ?x6192), nutrient(?x1959, ?x6192), ?x1257 = 09728, ?x3468 = 0cxn2, ?x13944 = 0f4kp, ?x9795 = 05v_8y, ?x9490 = 0h1sg, ?x1959 = 0f25w9, ?x9619 = 0h1tg, ?x3469 = 0h1zw, ?x6026 = 025sf8g, ?x7894 = 0f4hc, ?x8413 = 02kc4sf, ?x11270 = 02kc008, ?x11592 = 025sf0_, ?x9732 = 05z55, nutrient(?x9489, ?x12083), nutrient(?x9489, ?x7135), nutrient(?x9489, ?x2702), ?x1960 = 07hnp, ?x6032 = 01nkt, ?x12083 = 01n78x, ?x1258 = 0h1wg, ?x4068 = 0fbw6, ?x10195 = 0hkwr, ?x6160 = 041r51, ?x9855 = 0d9t0, ?x2702 = 0838f, ?x4069 = 0hqw8p_, ?x9426 = 0h1yy, ?x5451 = 05wvs, ?x12454 = 025rw19, ?x10098 = 0h1_c, ?x10891 = 0g5gq, ?x7652 = 025s0s0, ?x3203 = 04kl74p, ?x9005 = 04zpv, ?x10612 = 0frq6, ?x5373 = 0971v, ?x11409 = 0h1yf >> conf = 0.74 => this is the best rule for 13 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 061_f nutrient 03d49 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.736 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 061_f nutrient 01n78x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.736 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 061_f nutrient 025rsfk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.736 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 061_f nutrient 0dcfv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.736 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 061_f nutrient 0838f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.736 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #14001-089fss PRED entity: 089fss PRED relation: profession! PRED expected values: 0584j4n 053j4w4 034qt_ => 42 concepts (27 used for prediction) PRED predicted values (max 10 best out of 4163): 071dcs (0.33 #4897, 0.33 #680, 0.25 #42852), 026xt5c (0.33 #12115, 0.33 #7898, 0.20 #37418), 07s9tsr (0.33 #12379, 0.33 #8162, 0.20 #37682), 01pw9v (0.33 #11564, 0.33 #3130, 0.19 #53738), 06chf (0.33 #9257, 0.33 #823, 0.07 #80958), 07hhnl (0.33 #5785, 0.28 #92790, 0.28 #84353), 0fmqp6 (0.33 #10671, 0.28 #92790, 0.28 #84353), 0py5b (0.33 #3980, 0.28 #92790, 0.28 #84353), 04z_x4v (0.33 #11415, 0.28 #92790, 0.28 #84353), 03gyh_z (0.33 #9493, 0.28 #92790, 0.28 #84353) >> Best rule #4897 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 02ynfr; >> query: (?x1078, 071dcs) <- profession(?x12933, ?x1078), film_crew_role(?x6081, ?x1078), film_crew_role(?x4786, ?x1078), film_crew_role(?x1488, ?x1078), ?x4786 = 0bbw2z6, ?x1488 = 01719t, genre(?x6081, ?x53), ?x12933 = 03wdsbz >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #92790 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 75 *> proper extension: 014ktf; *> query: (?x1078, ?x2068) <- profession(?x12378, ?x1078), profession(?x2449, ?x1078), award_nominee(?x2068, ?x12378), nationality(?x2449, ?x429), nominated_for(?x12378, ?x4280), award_winner(?x10362, ?x12378) *> conf = 0.28 ranks of expected_values: 1018, 1026 EVAL 089fss profession! 034qt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 27.000 0.333 http://example.org/people/person/profession EVAL 089fss profession! 053j4w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 27.000 0.333 http://example.org/people/person/profession EVAL 089fss profession! 0584j4n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 27.000 0.333 http://example.org/people/person/profession #14000-02vs3x5 PRED entity: 02vs3x5 PRED relation: film_crew_role! PRED expected values: 04jwly 047p7fr 07cyl 0qf2t 03wh49y 02q87z6 => 23 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1822): 04jpg2p (0.78 #12014, 0.67 #9570, 0.67 #8350), 02mpyh (0.78 #12015, 0.67 #9571, 0.67 #7130), 0g0x9c (0.78 #11946, 0.67 #9502, 0.63 #1221), 01gwk3 (0.73 #13001, 0.71 #10557, 0.67 #11780), 011yn5 (0.73 #12864, 0.67 #11643, 0.67 #9199), 02d003 (0.73 #13074, 0.67 #9409, 0.67 #6968), 02yvct (0.73 #12465, 0.67 #6359, 0.64 #13687), 0416y94 (0.73 #12351, 0.67 #8686, 0.60 #5025), 0bth54 (0.71 #13487, 0.71 #9821, 0.67 #11044), 09sh8k (0.71 #13440, 0.71 #9774, 0.64 #12218) >> Best rule #12014 for best value: >> intensional similarity = 24 >> extensional distance = 7 >> proper extension: 0215hd; 089g0h; >> query: (?x6473, 04jpg2p) <- film_crew_role(?x6076, ?x6473), film_crew_role(?x5736, ?x6473), film_crew_role(?x5293, ?x6473), film_crew_role(?x4596, ?x6473), film_crew_role(?x3882, ?x6473), film_crew_role(?x3283, ?x6473), film_crew_role(?x2036, ?x6473), category(?x6076, ?x134), nominated_for(?x1564, ?x6076), nominated_for(?x462, ?x6076), produced_by(?x3882, ?x8041), honored_for(?x7884, ?x3882), genre(?x3882, ?x53), crewmember(?x5736, ?x12092), award(?x3882, ?x484), award(?x8041, ?x198), film(?x3282, ?x3283), language(?x6076, ?x254), film(?x8041, ?x2323), gender(?x8041, ?x231), film_format(?x2036, ?x909), ?x5293 = 0cbv4g, award_winner(?x4596, ?x950), film(?x788, ?x5736) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #11344 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 7 *> proper extension: 0215hd; 089g0h; *> query: (?x6473, 047p7fr) <- film_crew_role(?x6076, ?x6473), film_crew_role(?x5736, ?x6473), film_crew_role(?x5293, ?x6473), film_crew_role(?x4596, ?x6473), film_crew_role(?x3882, ?x6473), film_crew_role(?x3283, ?x6473), film_crew_role(?x2036, ?x6473), category(?x6076, ?x134), nominated_for(?x1564, ?x6076), nominated_for(?x462, ?x6076), produced_by(?x3882, ?x8041), honored_for(?x7884, ?x3882), genre(?x3882, ?x53), crewmember(?x5736, ?x12092), award(?x3882, ?x484), award(?x8041, ?x198), film(?x3282, ?x3283), language(?x6076, ?x254), film(?x8041, ?x2323), gender(?x8041, ?x231), film_format(?x2036, ?x909), ?x5293 = 0cbv4g, award_winner(?x4596, ?x950), film(?x788, ?x5736) *> conf = 0.67 ranks of expected_values: 109, 142, 428, 976, 1186, 1508 EVAL 02vs3x5 film_crew_role! 02q87z6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 23.000 16.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02vs3x5 film_crew_role! 03wh49y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 16.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02vs3x5 film_crew_role! 0qf2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 16.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02vs3x5 film_crew_role! 07cyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 16.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02vs3x5 film_crew_role! 047p7fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 23.000 16.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02vs3x5 film_crew_role! 04jwly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 23.000 16.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13999-0ftlkg PRED entity: 0ftlkg PRED relation: honored_for PRED expected values: 02psgq => 38 concepts (27 used for prediction) PRED predicted values (max 10 best out of 884): 05f4vxd (0.33 #302, 0.25 #1501, 0.25 #901), 02yvct (0.33 #133, 0.25 #732, 0.12 #1332), 0404j37 (0.33 #393, 0.25 #992, 0.12 #1592), 083skw (0.33 #153, 0.25 #752, 0.12 #1352), 02q52q (0.33 #99, 0.25 #698, 0.12 #1298), 04jplwp (0.33 #464, 0.25 #1063, 0.12 #1663), 0fhzwl (0.33 #500, 0.25 #1099, 0.12 #1699), 05c46y6 (0.33 #160, 0.25 #759, 0.12 #1359), 064r97z (0.33 #339, 0.25 #938, 0.12 #1538), 03d34x8 (0.33 #115, 0.25 #714, 0.12 #1314) >> Best rule #302 for best value: >> intensional similarity = 18 >> extensional distance = 1 >> proper extension: 09gkdln; >> query: (?x1821, 05f4vxd) <- award_winner(?x1821, ?x3931), ?x3931 = 01qq_lp, ceremony(?x2209, ?x1821), ceremony(?x1243, ?x1821), ceremony(?x591, ?x1821), award(?x382, ?x2209), nominated_for(?x1243, ?x3745), nominated_for(?x1243, ?x3430), nominated_for(?x1243, ?x174), award(?x185, ?x1243), film_release_region(?x3745, ?x2316), ?x2316 = 06t2t, titles(?x53, ?x3745), film_crew_role(?x174, ?x137), award(?x327, ?x2209), ?x3430 = 0ctb4g, award_winner(?x591, ?x157), nominated_for(?x591, ?x54) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ftlkg honored_for 02psgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 38.000 27.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #13998-011yqc PRED entity: 011yqc PRED relation: titles! PRED expected values: 01jfsb => 86 concepts (32 used for prediction) PRED predicted values (max 10 best out of 51): 07ssc (0.25 #8, 0.22 #716, 0.15 #209), 01z4y (0.19 #2963, 0.18 #2252, 0.17 #1950), 0lsxr (0.19 #201, 0.19 #200, 0.18 #1816), 024qqx (0.16 #1589, 0.11 #1186, 0.11 #684), 01jfsb (0.15 #117, 0.15 #218, 0.13 #1934), 07c52 (0.13 #1843, 0.11 #2449, 0.10 #2246), 01hmnh (0.13 #326, 0.09 #2345, 0.08 #24), 017fp (0.11 #729, 0.11 #1735, 0.10 #121), 03mqtr (0.09 #951, 0.07 #1455, 0.07 #1554), 0hfjk (0.08 #77, 0.07 #177, 0.03 #985) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 017gl1; 0_92w; 0jqn5; 03qnc6q; 0j43swk; 064lsn; 0g9zljd; 03np63f; 0gvvm6l; 05ldxl; >> query: (?x1496, 07ssc) <- film_release_region(?x1496, ?x172), nominated_for(?x1063, ?x1496), ?x1063 = 02rdxsh, ?x172 = 0154j >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #117 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 014zwb; *> query: (?x1496, 01jfsb) <- nominated_for(?x382, ?x1496), genre(?x1496, ?x53), ?x382 = 086k8 *> conf = 0.15 ranks of expected_values: 5 EVAL 011yqc titles! 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 32.000 0.250 http://example.org/media_common/netflix_genre/titles #13997-03kdl PRED entity: 03kdl PRED relation: basic_title PRED expected values: 060c4 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 18): 060c4 (0.68 #201, 0.64 #147, 0.64 #327), 0fkvn (0.57 #148, 0.40 #184, 0.40 #40), 0dq3c (0.28 #326, 0.27 #92, 0.26 #200), 0789n (0.25 #118, 0.25 #82, 0.21 #154), 060bp (0.21 #127, 0.12 #55, 0.12 #524), 01gkgk (0.20 #186, 0.14 #691, 0.14 #529), 01q24l (0.14 #139, 0.12 #85, 0.09 #373), 02079p (0.12 #83, 0.09 #101, 0.07 #155), 01dz7z (0.12 #433, 0.05 #867, 0.04 #848), 0fkzq (0.09 #104, 0.05 #212, 0.04 #338) >> Best rule #201 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 07hyk; >> query: (?x3615, 060c4) <- type_of_union(?x3615, ?x566), profession(?x3615, ?x8498), taxonomy(?x3615, ?x939), gender(?x3615, ?x231) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03kdl basic_title 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.684 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #13996-01bk1y PRED entity: 01bk1y PRED relation: institution! PRED expected values: 0bkj86 => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 19): 016t_3 (0.86 #22, 0.71 #42, 0.64 #329), 014mlp (0.79 #24, 0.78 #331, 0.71 #44), 0bkj86 (0.71 #27, 0.67 #47, 0.63 #162), 04zx3q1 (0.50 #21, 0.47 #156, 0.42 #41), 027f2w (0.46 #48, 0.36 #28, 0.35 #163), 013zdg (0.46 #46, 0.36 #26, 0.33 #333), 022h5x (0.34 #2005, 0.29 #56, 0.29 #36), 071tyz (0.34 #2005, 0.18 #2441, 0.12 #164), 01ysy9 (0.34 #2005, 0.18 #2441, 0.06 #135), 01gkg3 (0.34 #2005, 0.05 #3242, 0.04 #282) >> Best rule #22 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0gl5_; >> query: (?x7618, 016t_3) <- major_field_of_study(?x7618, ?x7134), major_field_of_study(?x7618, ?x2601), ?x7134 = 02_7t, student(?x7618, ?x6486), ?x2601 = 04x_3 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 0gl5_; *> query: (?x7618, 0bkj86) <- major_field_of_study(?x7618, ?x7134), major_field_of_study(?x7618, ?x2601), ?x7134 = 02_7t, student(?x7618, ?x6486), ?x2601 = 04x_3 *> conf = 0.71 ranks of expected_values: 3 EVAL 01bk1y institution! 0bkj86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 177.000 177.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13995-04fv0k PRED entity: 04fv0k PRED relation: company! PRED expected values: 060c4 => 212 concepts (212 used for prediction) PRED predicted values (max 10 best out of 33): 060c4 (0.79 #4846, 0.77 #2956, 0.75 #2792), 01kr6k (0.33 #432, 0.31 #678, 0.29 #1088), 02211by (0.33 #290, 0.27 #823, 0.25 #331), 0142rn (0.27 #3818, 0.20 #5628, 0.20 #882), 02y6fz (0.25 #142, 0.22 #388, 0.20 #880), 09lq2c (0.16 #1010, 0.13 #3901, 0.12 #6125), 04192r (0.14 #1924, 0.13 #2047, 0.13 #856), 01rk91 (0.13 #862, 0.13 #3901, 0.12 #6125), 06hpx2 (0.13 #3901, 0.12 #6125, 0.11 #6581), 05k17c (0.13 #3901, 0.12 #6125, 0.11 #6581) >> Best rule #4846 for best value: >> intensional similarity = 2 >> extensional distance = 165 >> proper extension: 059j2; >> query: (?x9517, 060c4) <- company(?x5161, ?x9517), basic_title(?x3341, ?x5161) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04fv0k company! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 212.000 0.790 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #13994-0n1rj PRED entity: 0n1rj PRED relation: category PRED expected values: 08mbj5d => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #82, 0.83 #55, 0.83 #71) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 0rp46; 01j8yr; 0tr3p; 0tt6k; 0rmby; 0r785; 0s9b_; 0mnwd; >> query: (?x6084, 08mbj5d) <- county_seat(?x13776, ?x6084), location(?x5153, ?x6084), contains(?x2623, ?x13776) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n1rj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 220.000 220.000 0.836 http://example.org/common/topic/webpage./common/webpage/category #13993-02mpb PRED entity: 02mpb PRED relation: place_of_birth PRED expected values: 01_d4 => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 159): 0214m4 (0.33 #296, 0.09 #3112, 0.05 #5225), 02_286 (0.19 #7062, 0.19 #7767, 0.18 #11291), 0d6lp (0.17 #1522, 0.17 #818, 0.09 #3635), 02m77 (0.17 #1660, 0.17 #956, 0.09 #3773), 0f8j6 (0.17 #2103, 0.17 #1399, 0.09 #4216), 04vmp (0.17 #972, 0.09 #3789, 0.05 #5197), 0tz54 (0.17 #1902, 0.09 #4015, 0.04 #6832), 0vzm (0.14 #4342, 0.03 #16318, 0.01 #42387), 01cx_ (0.12 #2221, 0.09 #3630, 0.02 #17718), 0qkcb (0.12 #2404, 0.03 #7335, 0.02 #10155) >> Best rule #296 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 03hnd; >> query: (?x8210, 0214m4) <- story_by(?x9524, ?x8210), influenced_by(?x13125, ?x8210), influenced_by(?x8433, ?x8210), ?x8433 = 06bng, ?x13125 = 02xyl >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4291 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 033cw; *> query: (?x8210, 01_d4) <- influenced_by(?x5334, ?x8210), gender(?x8210, ?x231), award(?x8210, ?x1375), ?x1375 = 0262zm *> conf = 0.07 ranks of expected_values: 18 EVAL 02mpb place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 157.000 157.000 0.333 http://example.org/people/person/place_of_birth #13992-05bnp0 PRED entity: 05bnp0 PRED relation: award_nominee! PRED expected values: 0sz28 => 92 concepts (29 used for prediction) PRED predicted values (max 10 best out of 713): 0sz28 (0.81 #20947, 0.81 #65172, 0.81 #34915), 0149xx (0.23 #1206, 0.06 #3534), 02tr7d (0.20 #65174, 0.04 #30606, 0.03 #35262), 06dv3 (0.20 #65174, 0.03 #18659, 0.03 #20988), 01kwsg (0.20 #65174, 0.03 #17405, 0.02 #33701), 03zz8b (0.20 #65174, 0.02 #6303, 0.02 #8630), 01qscs (0.20 #65174, 0.02 #21010, 0.01 #55924), 0170s4 (0.20 #65174, 0.02 #16806, 0.01 #19133), 0c9c0 (0.20 #65174, 0.01 #14580), 0h10vt (0.20 #65174, 0.01 #20647) >> Best rule #20947 for best value: >> intensional similarity = 3 >> extensional distance = 414 >> proper extension: 07c0j; 04qmr; 0cbm64; >> query: (?x123, ?x1208) <- participant(?x1017, ?x123), award_nominee(?x123, ?x1208), award(?x123, ?x102) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05bnp0 award_nominee! 0sz28 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 29.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13991-02h22 PRED entity: 02h22 PRED relation: film_release_region PRED expected values: 06qd3 02vzc => 118 concepts (110 used for prediction) PRED predicted values (max 10 best out of 332): 0b90_r (0.90 #322, 0.86 #2712, 0.86 #1917), 035qy (0.90 #4178, 0.90 #2744, 0.90 #2108), 01znc_ (0.88 #2594, 0.86 #1958, 0.86 #1480), 05qhw (0.87 #3681, 0.87 #4158, 0.87 #2565), 03spz (0.86 #2013, 0.85 #1055, 0.82 #2808), 06t2t (0.86 #1502, 0.85 #2775, 0.83 #1980), 015fr (0.86 #1453, 0.84 #2726, 0.84 #2090), 03rt9 (0.83 #810, 0.79 #1450, 0.70 #970), 06bnz (0.82 #1962, 0.80 #2757, 0.79 #3714), 0d060g (0.81 #2238, 0.81 #3672, 0.80 #2715) >> Best rule #322 for best value: >> intensional similarity = 9 >> extensional distance = 18 >> proper extension: 0ggbfwf; >> query: (?x5849, 0b90_r) <- genre(?x5849, ?x53), film_release_region(?x5849, ?x2843), film_release_region(?x5849, ?x2645), film_release_region(?x5849, ?x789), ?x2645 = 03h64, ?x2843 = 016wzw, ?x789 = 0f8l9c, film(?x541, ?x5849), film_festivals(?x5849, ?x13969) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2605 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 82 *> proper extension: 08052t3; 0bq6ntw; *> query: (?x5849, 02vzc) <- genre(?x5849, ?x53), film_release_region(?x5849, ?x2843), film_release_region(?x5849, ?x2645), film_release_region(?x5849, ?x789), film_release_region(?x5849, ?x390), ?x2645 = 03h64, ?x2843 = 016wzw, ?x789 = 0f8l9c, film(?x541, ?x5849), ?x390 = 0chghy *> conf = 0.81 ranks of expected_values: 11, 18 EVAL 02h22 film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 118.000 110.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02h22 film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 118.000 110.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13990-029k55 PRED entity: 029k55 PRED relation: profession PRED expected values: 01d_h8 => 76 concepts (48 used for prediction) PRED predicted values (max 10 best out of 94): 01d_h8 (0.68 #1294, 0.67 #1150, 0.67 #1580), 03gjzk (0.56 #4020, 0.43 #728, 0.39 #299), 02krf9 (0.39 #308, 0.37 #451, 0.22 #22), 0cbd2 (0.26 #6732, 0.26 #293, 0.19 #722), 015h31 (0.25 #452, 0.11 #23, 0.04 #6296), 0dz3r (0.22 #5010, 0.08 #5439, 0.08 #5296), 0nbcg (0.14 #5034, 0.10 #5463, 0.10 #5320), 01c72t (0.13 #591, 0.09 #305, 0.08 #5027), 0d1pc (0.11 #45, 0.09 #3335, 0.09 #3049), 0196pc (0.11 #68, 0.06 #497, 0.04 #6296) >> Best rule #1294 for best value: >> intensional similarity = 5 >> extensional distance = 644 >> proper extension: 0jf1b; 022_lg; 09gffmz; 0c3ns; 0hskw; 01zfmm; 03xp8d5; 09qc1; 051z6rz; 013t9y; ... >> query: (?x10909, 01d_h8) <- profession(?x10909, ?x1383), profession(?x10909, ?x524), ?x524 = 02jknp, profession(?x1942, ?x1383), ?x1942 = 07ymr5 >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 029k55 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 48.000 0.683 http://example.org/people/person/profession #13989-0mjn2 PRED entity: 0mjn2 PRED relation: artists! PRED expected values: 015pdg => 91 concepts (51 used for prediction) PRED predicted values (max 10 best out of 258): 064t9 (0.72 #6197, 0.59 #2487, 0.57 #14845), 03lty (0.62 #10228, 0.33 #5273, 0.33 #5585), 0dl5d (0.59 #9913, 0.28 #4649, 0.25 #5578), 05bt6j (0.55 #2515, 0.54 #971, 0.53 #6225), 016clz (0.49 #12986, 0.49 #10827, 0.43 #8041), 03_d0 (0.38 #4025, 0.36 #2485, 0.28 #13609), 06j6l (0.36 #2520, 0.35 #4368, 0.32 #6230), 02yv6b (0.35 #3803, 0.31 #1955, 0.27 #1335), 01243b (0.33 #660, 0.33 #41, 0.25 #349), 03xnwz (0.33 #651, 0.33 #32, 0.25 #340) >> Best rule #6197 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 07_3qd; 03f6fl0; 0232lm; >> query: (?x10263, 064t9) <- artists(?x5300, ?x10263), artist(?x3887, ?x10263), ?x5300 = 02k_kn >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #318 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0178_w; *> query: (?x10263, 015pdg) <- group(?x2048, ?x10263), group(?x1886, ?x10263), group(?x432, ?x10263), ?x2048 = 018j2, role(?x211, ?x432), role(?x432, ?x75), ?x1886 = 02k84w *> conf = 0.25 ranks of expected_values: 30 EVAL 0mjn2 artists! 015pdg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 91.000 51.000 0.720 http://example.org/music/genre/artists #13988-0c9l1 PRED entity: 0c9l1 PRED relation: award_winner! PRED expected values: 02x4wb => 106 concepts (78 used for prediction) PRED predicted values (max 10 best out of 243): 02x4wb (0.41 #19756, 0.39 #29203, 0.38 #16749), 01d38t (0.41 #19756, 0.39 #29203, 0.38 #16749), 02f73p (0.38 #16749, 0.37 #30064, 0.37 #29202), 02f716 (0.38 #16749, 0.37 #30064, 0.37 #29202), 02f72_ (0.38 #16749, 0.37 #30064, 0.37 #29202), 02f72n (0.38 #16749, 0.37 #30064, 0.37 #29202), 02f5qb (0.38 #16749, 0.37 #30064, 0.37 #29202), 01by1l (0.23 #5697, 0.21 #10849, 0.20 #974), 09sb52 (0.19 #12064, 0.13 #21517, 0.13 #10348), 01c4_6 (0.19 #2239, 0.05 #6533, 0.05 #2669) >> Best rule #19756 for best value: >> intensional similarity = 4 >> extensional distance = 525 >> proper extension: 05ty4m; 01vlj1g; 02qgyv; 027xbpw; 013_vh; 076_74; 01vd7hn; 02xwq9; 03_0p; 02qmncd; ... >> query: (?x10565, ?x11068) <- award(?x10565, ?x11068), award_nominee(?x2181, ?x10565), ceremony(?x11068, ?x5766), ?x5766 = 013b2h >> conf = 0.41 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0c9l1 award_winner! 02x4wb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 78.000 0.407 http://example.org/award/award_category/winners./award/award_honor/award_winner #13987-01y9pk PRED entity: 01y9pk PRED relation: category PRED expected values: 08mbj5d => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #37, 0.90 #54, 0.89 #59) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 185 >> proper extension: 03pmfw; >> query: (?x2243, 08mbj5d) <- organization(?x346, ?x2243), citytown(?x2243, ?x3877), ?x346 = 060c4, state_province_region(?x2243, ?x1905) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y9pk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.904 http://example.org/common/topic/webpage./common/webpage/category #13986-02f705 PRED entity: 02f705 PRED relation: award! PRED expected values: 02l840 086qd 047sxrj 01vsykc 01wqmm8 063t3j => 36 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2185): 0134pk (0.75 #29471, 0.73 #26132, 0.60 #16117), 02z4b_8 (0.71 #18730, 0.55 #22068, 0.50 #32084), 016s0m (0.71 #19227, 0.44 #32581, 0.40 #12550), 04xrx (0.71 #17382, 0.36 #20720, 0.33 #691), 0g824 (0.70 #66777, 0.70 #63435, 0.69 #73462), 016l09 (0.60 #16096, 0.58 #29450, 0.55 #26111), 07r1_ (0.60 #15381, 0.55 #25396, 0.50 #28735), 01bczm (0.60 #14966, 0.55 #24981, 0.50 #28320), 01xzb6 (0.60 #14874, 0.55 #21551, 0.43 #18213), 0gbwp (0.60 #14452, 0.53 #34483, 0.45 #24467) >> Best rule #29471 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 02f6yz; >> query: (?x2855, 0134pk) <- award(?x6835, ?x2855), award(?x3773, ?x2855), award(?x475, ?x2855), ?x475 = 01pfr3, artists(?x505, ?x3773), award_nominee(?x6835, ?x140) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #20209 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 02g3gj; 01bgqh; 0c4z8; 02f5qb; 056jm_; *> query: (?x2855, 02l840) <- award(?x7581, ?x2855), award(?x475, ?x2855), ?x7581 = 01wf86y, artists(?x302, ?x475), group(?x227, ?x475) *> conf = 0.45 ranks of expected_values: 39, 50, 106, 108, 109, 149 EVAL 02f705 award! 063t3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 36.000 22.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f705 award! 01wqmm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 36.000 22.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f705 award! 01vsykc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 36.000 22.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f705 award! 047sxrj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 36.000 22.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f705 award! 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 36.000 22.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f705 award! 02l840 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 36.000 22.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13985-0gyx4 PRED entity: 0gyx4 PRED relation: participant PRED expected values: 03knl => 129 concepts (102 used for prediction) PRED predicted values (max 10 best out of 388): 03knl (0.80 #41748, 0.80 #30182, 0.80 #43675), 014zcr (0.17 #18, 0.11 #7722, 0.09 #10292), 01vs_v8 (0.13 #1927, 0.12 #8347, 0.11 #25688), 01817f (0.13 #1927, 0.12 #8347, 0.10 #18624), 01tj34 (0.13 #1927, 0.12 #8347, 0.10 #18624), 01q8fxx (0.13 #1927, 0.12 #8347, 0.10 #18624), 0dx_q (0.13 #1927, 0.12 #8347, 0.10 #18624), 0k8y7 (0.13 #1927, 0.11 #25688, 0.10 #34038), 01vtj38 (0.13 #1927, 0.10 #34038, 0.10 #34037), 03lvyj (0.13 #1927, 0.10 #34038, 0.10 #34037) >> Best rule #41748 for best value: >> intensional similarity = 2 >> extensional distance = 540 >> proper extension: 05ty4m; 0mj1l; 019g40; 02wgln; 014q2g; 0lrh; 01wyy_; 01q32bd; 0164nb; 05txrz; ... >> query: (?x4397, ?x971) <- award(?x4397, ?x350), participant(?x971, ?x4397) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gyx4 participant 03knl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 102.000 0.802 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #13984-055c8 PRED entity: 055c8 PRED relation: type_of_union PRED expected values: 04ztj => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #29, 0.73 #145, 0.72 #37), 01g63y (0.33 #10, 0.17 #186, 0.17 #34) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 06y3r; 024t0y; >> query: (?x3186, 04ztj) <- currency(?x3186, ?x170), ?x170 = 09nqf, executive_produced_by(?x2203, ?x3186) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 055c8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.741 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13983-07_3qd PRED entity: 07_3qd PRED relation: instrumentalists! PRED expected values: 05148p4 => 143 concepts (111 used for prediction) PRED predicted values (max 10 best out of 126): 05r5c (0.71 #1140, 0.60 #183, 0.53 #5966), 05148p4 (0.60 #195, 0.54 #1152, 0.53 #717), 01vj9c (0.44 #2006, 0.43 #4812, 0.43 #4201), 02hnl (0.40 #208, 0.32 #1165, 0.31 #643), 02fsn (0.40 #2354, 0.33 #89, 0.33 #51), 0l14md (0.40 #3149, 0.39 #2888, 0.37 #3419), 013y1f (0.40 #3149, 0.39 #2888, 0.37 #3419), 02dlh2 (0.39 #2888, 0.37 #3419, 0.32 #2887), 03qjg (0.38 #660, 0.32 #1182, 0.29 #487), 01xqw (0.33 #67, 0.06 #264, 0.06 #852) >> Best rule #1140 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 016jfw; 095x_; >> query: (?x1260, 05r5c) <- nationality(?x1260, ?x1310), role(?x1260, ?x716), artists(?x5300, ?x1260), ?x5300 = 02k_kn, group(?x716, ?x379) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #195 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0326tc; *> query: (?x1260, 05148p4) <- nationality(?x1260, ?x1310), role(?x1260, ?x2888), role(?x1260, ?x1574), performance_role(?x1260, ?x315), ?x1574 = 0l15bq, role(?x2888, ?x74) *> conf = 0.60 ranks of expected_values: 2 EVAL 07_3qd instrumentalists! 05148p4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 111.000 0.714 http://example.org/music/instrument/instrumentalists #13982-02wlk PRED entity: 02wlk PRED relation: people! PRED expected values: 07hwkr => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 45): 041rx (0.26 #2930, 0.25 #3161, 0.22 #1621), 0d2by (0.25 #110, 0.20 #341, 0.14 #418), 07hwkr (0.25 #166, 0.09 #2707, 0.08 #705), 09vc4s (0.25 #9, 0.03 #5477, 0.03 #5786), 01g7zj (0.25 #52), 0x67 (0.21 #3244, 0.20 #934, 0.19 #472), 02w7gg (0.20 #233, 0.11 #2851, 0.08 #695), 07bch9 (0.14 #408, 0.06 #2949, 0.06 #485), 033tf_ (0.12 #5475, 0.12 #4858, 0.12 #5784), 02g7sp (0.12 #557, 0.10 #634, 0.06 #942) >> Best rule #2930 for best value: >> intensional similarity = 4 >> extensional distance = 126 >> proper extension: 01l3j; >> query: (?x12622, 041rx) <- nationality(?x12622, ?x94), ?x94 = 09c7w0, place_of_death(?x12622, ?x4419), religion(?x12622, ?x13061) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 06jcc; 099p5; *> query: (?x12622, 07hwkr) <- award_winner(?x13257, ?x12622), location(?x12622, ?x938), profession(?x12622, ?x353), religion(?x12622, ?x13061), ?x13061 = 07w8f *> conf = 0.25 ranks of expected_values: 3 EVAL 02wlk people! 07hwkr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 165.000 165.000 0.258 http://example.org/people/ethnicity/people #13981-06myp PRED entity: 06myp PRED relation: people! PRED expected values: 013b6_ => 167 concepts (75 used for prediction) PRED predicted values (max 10 best out of 51): 013xrm (0.36 #551, 0.16 #2679, 0.15 #3363), 02ctzb (0.29 #318, 0.10 #470, 0.09 #1230), 0x67 (0.14 #4113, 0.12 #4493, 0.10 #1605), 033tf_ (0.14 #310, 0.14 #1754, 0.11 #2362), 07bch9 (0.14 #630, 0.13 #706, 0.11 #4506), 0d7wh (0.14 #320, 0.11 #928, 0.10 #472), 013b6_ (0.14 #356, 0.10 #508, 0.09 #584), 0g5y6 (0.14 #416, 0.09 #3912, 0.06 #2392), 07hwkr (0.14 #1531, 0.08 #2063, 0.08 #2139), 06v41q (0.14 #332, 0.07 #636, 0.07 #712) >> Best rule #551 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 03j43; 048cl; 039n1; >> query: (?x10895, 013xrm) <- gender(?x10895, ?x231), influenced_by(?x10895, ?x2240), influenced_by(?x3428, ?x10895), ?x3428 = 0dzkq >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #356 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02whj; 01zwy; 0d3k14; *> query: (?x10895, 013b6_) <- place_of_burial(?x10895, ?x14321), influenced_by(?x10895, ?x2240), religion(?x10895, ?x2694), people(?x1050, ?x10895) *> conf = 0.14 ranks of expected_values: 7 EVAL 06myp people! 013b6_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 167.000 75.000 0.364 http://example.org/people/ethnicity/people #13980-033p3_ PRED entity: 033p3_ PRED relation: gender PRED expected values: 02zsn => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.82 #4, 0.64 #30, 0.51 #16), 05zppz (0.72 #235, 0.72 #135, 0.72 #223) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0f4vbz; 019pm_; 0bbf1f; 0bx_q; 0127s7; 01skmp; 01vtj38; 0kjrx; 0227vl; >> query: (?x10325, 02zsn) <- participant(?x10325, ?x8159), special_performance_type(?x8159, ?x4832), award_winner(?x1972, ?x10325), executive_produced_by(?x6213, ?x8159) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033p3_ gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.818 http://example.org/people/person/gender #13979-0prh7 PRED entity: 0prh7 PRED relation: nominated_for! PRED expected values: 02x2gy0 => 75 concepts (70 used for prediction) PRED predicted values (max 10 best out of 210): 0gq9h (0.68 #295, 0.51 #2176, 0.51 #3586), 0gr0m (0.67 #3583, 0.54 #292, 0.42 #2173), 019f4v (0.63 #287, 0.41 #2168, 0.41 #2403), 0gs9p (0.61 #297, 0.41 #3588, 0.39 #2413), 0k611 (0.54 #306, 0.44 #2187, 0.44 #3597), 040njc (0.54 #242, 0.39 #1182, 0.34 #2123), 0gqyl (0.44 #313, 0.27 #2429, 0.25 #2194), 054krc (0.43 #2183, 0.34 #302, 0.29 #3593), 0p9sw (0.41 #2136, 0.36 #3546, 0.29 #255), 0f4x7 (0.39 #260, 0.28 #2376, 0.24 #6372) >> Best rule #295 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 0c_j9x; 09p7fh; 04wddl; >> query: (?x4874, 0gq9h) <- nominated_for(?x2222, ?x4874), nominated_for(?x601, ?x4874), film(?x156, ?x4874), ?x2222 = 0gs96, ?x601 = 0gr4k >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2449 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 146 *> proper extension: 0g5qs2k; 0c9k8; 03hkch7; 0pd6l; 0m_h6; *> query: (?x4874, 02x2gy0) <- nominated_for(?x2222, ?x4874), nominated_for(?x601, ?x4874), film(?x156, ?x4874), ?x2222 = 0gs96, ceremony(?x601, ?x78) *> conf = 0.20 ranks of expected_values: 33 EVAL 0prh7 nominated_for! 02x2gy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 75.000 70.000 0.683 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13978-04x4nv PRED entity: 04x4nv PRED relation: film! PRED expected values: 01p4r3 01mqc_ => 71 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1133): 01mqnr (0.41 #6241, 0.36 #16642, 0.02 #20155), 03_fk9 (0.41 #6241, 0.36 #16642), 0cdf37 (0.41 #6241, 0.36 #16642), 02pqgt8 (0.41 #6241, 0.36 #16642), 0p8r1 (0.10 #17229, 0.09 #8908, 0.02 #25550), 0343h (0.09 #27047), 03kpvp (0.09 #11035, 0.08 #634, 0.07 #4794), 0lpjn (0.08 #4640, 0.07 #480, 0.07 #10881), 0z4s (0.06 #6309, 0.03 #22951, 0.03 #20870), 02gf_l (0.06 #9588, 0.05 #17909, 0.01 #26230) >> Best rule #6241 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 02vxq9m; 0140g4; 016fyc; 04v8x9; 01sxly; 0n0bp; 08720; 03hjv97; 08gsvw; 0pv2t; ... >> query: (?x8985, ?x4190) <- film(?x4103, ?x8985), film(?x788, ?x8985), ?x788 = 0g1rw, nominated_for(?x4190, ?x8985) >> conf = 0.41 => this is the best rule for 4 predicted values *> Best rule #1305 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 69 *> proper extension: 02sg5v; 0299hs; 07kb7vh; 03clwtw; *> query: (?x8985, 01mqc_) <- film(?x4103, ?x8985), film(?x788, ?x8985), ?x788 = 0g1rw, currency(?x8985, ?x170) *> conf = 0.01 ranks of expected_values: 530 EVAL 04x4nv film! 01mqc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 13.000 0.408 http://example.org/film/actor/film./film/performance/film EVAL 04x4nv film! 01p4r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 13.000 0.408 http://example.org/film/actor/film./film/performance/film #13977-041288 PRED entity: 041288 PRED relation: organization! PRED expected values: 02khs 06s6l 01699 06srk 04sj3 035yg 0hdx8 04vjh => 52 concepts (15 used for prediction) PRED predicted values (max 10 best out of 270): 04sj3 (0.70 #1015, 0.65 #1269, 0.60 #720), 02k54 (0.70 #1015, 0.60 #534, 0.50 #788), 04vjh (0.70 #1015, 0.60 #734, 0.50 #988), 01699 (0.70 #1015, 0.60 #678, 0.50 #932), 02khs (0.70 #1015, 0.60 #570, 0.50 #824), 0chghy (0.70 #1015, 0.50 #1031, 0.50 #777), 06srk (0.70 #1015, 0.50 #962, 0.50 #455), 01rxw (0.70 #1015, 0.50 #938, 0.50 #431), 04gqr (0.70 #1015, 0.50 #889, 0.50 #382), 0h3y (0.70 #1015, 0.50 #771, 0.50 #264) >> Best rule #1015 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0gkjy; >> query: (?x9102, ?x583) <- organization(?x9459, ?x9102), organization(?x8742, ?x9102), organization(?x910, ?x9102), ?x8742 = 04wlh, ?x910 = 019rg5, adjoins(?x9459, ?x583), country(?x1121, ?x9459), citytown(?x9102, ?x4826) >> conf = 0.70 => this is the best rule for 20 predicted values ranks of expected_values: 1, 3, 4, 5, 7, 27, 111, 112 EVAL 041288 organization! 04vjh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 15.000 0.698 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 041288 organization! 0hdx8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 52.000 15.000 0.698 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 041288 organization! 035yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 52.000 15.000 0.698 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 041288 organization! 04sj3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 15.000 0.698 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 041288 organization! 06srk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 52.000 15.000 0.698 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 041288 organization! 01699 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 15.000 0.698 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 041288 organization! 06s6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 52.000 15.000 0.698 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 041288 organization! 02khs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 15.000 0.698 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #13976-01l5t6 PRED entity: 01l5t6 PRED relation: profession! PRED expected values: 040db 058vp => 39 concepts (11 used for prediction) PRED predicted values (max 10 best out of 4146): 0399p (0.60 #8486, 0.52 #21210, 0.45 #21207), 02ln1 (0.60 #8486, 0.52 #21210, 0.45 #21207), 039n1 (0.60 #8486, 0.52 #21210, 0.45 #21207), 02wh0 (0.60 #8486, 0.52 #21210, 0.45 #21207), 099bk (0.60 #8486, 0.52 #21210, 0.45 #21207), 03j43 (0.60 #8486, 0.52 #21210, 0.45 #21207), 0jcx (0.60 #8486, 0.45 #21207, 0.40 #25455), 0b78hw (0.60 #8486, 0.45 #21207, 0.40 #25455), 0424m (0.60 #8486, 0.45 #21207, 0.40 #25455), 040db (0.60 #8486, 0.45 #21207, 0.40 #25455) >> Best rule #8486 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 02hrh1q; >> query: (?x12779, ?x2161) <- profession(?x12258, ?x12779), profession(?x11335, ?x12779), profession(?x1857, ?x12779), nationality(?x12258, ?x94), gender(?x12258, ?x231), ?x11335 = 0bqch, award_winner(?x12587, ?x12258), location(?x12258, ?x4627), ?x4627 = 05qtj, ?x94 = 09c7w0, influenced_by(?x2161, ?x1857) >> conf = 0.60 => this is the best rule for 21 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 10, 2769 EVAL 01l5t6 profession! 058vp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 11.000 0.598 http://example.org/people/person/profession EVAL 01l5t6 profession! 040db CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 39.000 11.000 0.598 http://example.org/people/person/profession #13975-01w9wwg PRED entity: 01w9wwg PRED relation: film PRED expected values: 056xkh => 106 concepts (89 used for prediction) PRED predicted values (max 10 best out of 533): 08hmch (0.63 #66232, 0.42 #128886, 0.42 #119934), 07bzz7 (0.30 #2681, 0.30 #891, 0.25 #6261), 02847m9 (0.20 #2039, 0.17 #5619, 0.10 #249), 01jnc_ (0.10 #14099, 0.07 #17679, 0.06 #23049), 02825cv (0.10 #2934, 0.10 #1144, 0.08 #6514), 02cbhg (0.10 #3194, 0.10 #1404, 0.08 #6774), 01f39b (0.10 #2770, 0.10 #980, 0.08 #6350), 0353xq (0.10 #2716, 0.10 #926, 0.08 #6296), 0jdgr (0.10 #2186, 0.10 #396, 0.08 #5766), 015x74 (0.10 #2077, 0.10 #287, 0.08 #5657) >> Best rule #66232 for best value: >> intensional similarity = 2 >> extensional distance = 420 >> proper extension: 01pnn3; 012x2b; >> query: (?x6162, ?x1035) <- participant(?x6162, ?x827), nominated_for(?x6162, ?x1035) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #17710 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 92 *> proper extension: 01tszq; *> query: (?x6162, 056xkh) <- location(?x6162, ?x2949), film(?x6162, ?x6798), instrumentalists(?x716, ?x6162) *> conf = 0.04 ranks of expected_values: 91 EVAL 01w9wwg film 056xkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 106.000 89.000 0.629 http://example.org/film/actor/film./film/performance/film #13974-0mws3 PRED entity: 0mws3 PRED relation: contains! PRED expected values: 05tbn => 127 concepts (72 used for prediction) PRED predicted values (max 10 best out of 115): 05tbn (0.79 #3597, 0.79 #2923, 0.66 #63859), 026mj (0.66 #63859, 0.66 #40480, 0.61 #44079), 05fjf (0.66 #63859, 0.60 #17996, 0.58 #53964), 09c7w0 (0.42 #8093, 0.42 #9890, 0.39 #16202), 0mws3 (0.36 #43179, 0.33 #30593, 0.31 #36882), 04_1l0v (0.36 #5851, 0.30 #16650, 0.29 #10338), 02qkt (0.29 #12037, 0.29 #15643, 0.28 #13840), 01n7q (0.24 #4575, 0.19 #3675, 0.19 #10867), 059rby (0.18 #45895, 0.18 #46795, 0.13 #59387), 0j0k (0.15 #13871, 0.15 #12068, 0.15 #15674) >> Best rule #3597 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 0zqq8; >> query: (?x9539, ?x3670) <- source(?x9539, ?x958), contains(?x9539, ?x3650), ?x958 = 0jbk9, contains(?x3670, ?x3650), ?x3670 = 05tbn >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mws3 contains! 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 72.000 0.789 http://example.org/location/location/contains #13973-0g8rj PRED entity: 0g8rj PRED relation: institution! PRED expected values: 03mkk4 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 13): 028dcg (0.40 #87, 0.30 #152, 0.22 #269), 0bjrnt (0.31 #249, 0.24 #340, 0.22 #379), 03mkk4 (0.29 #251, 0.27 #342, 0.24 #486), 01rr_d (0.27 #385, 0.26 #307, 0.25 #268), 022h5x (0.24 #466, 0.20 #88, 0.20 #544), 02mjs7 (0.22 #261, 0.22 #1595, 0.20 #248), 02m4yg (0.20 #7, 0.10 #384, 0.09 #254), 071tyz (0.17 #263, 0.14 #380, 0.12 #511), 01ysy9 (0.08 #389, 0.07 #90, 0.06 #520), 01kxxq (0.03 #271, 0.03 #310, 0.02 #1042) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 07szy; 017j69; 019dwp; >> query: (?x5486, 028dcg) <- school(?x465, ?x5486), ?x465 = 05vsb7, student(?x5486, ?x118) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #251 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 0d06m5; 0d05fv; *> query: (?x5486, 03mkk4) <- category(?x5486, ?x134), list(?x5486, ?x2197), organization(?x5486, ?x5487) *> conf = 0.29 ranks of expected_values: 3 EVAL 0g8rj institution! 03mkk4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 136.000 136.000 0.400 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13972-01kph_c PRED entity: 01kph_c PRED relation: category PRED expected values: 08mbj5d => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #21, 0.82 #26, 0.82 #22) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 502 >> proper extension: 02fgpf; 015mrk; 0412f5y; 082brv; 01kymm; 0123r4; 01hrqc; 01f9zw; 01wwnh2; 01vs8ng; >> query: (?x4790, 08mbj5d) <- artists(?x1928, ?x4790), artists(?x1928, ?x2906), ?x2906 = 0249kn >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kph_c category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.829 http://example.org/common/topic/webpage./common/webpage/category #13971-0d7vtk PRED entity: 0d7vtk PRED relation: actor PRED expected values: 0d02km => 101 concepts (71 used for prediction) PRED predicted values (max 10 best out of 874): 04snp2 (0.36 #33337, 0.35 #42590, 0.34 #46292), 05gnf (0.36 #33337, 0.35 #42590, 0.34 #46292), 01qr1_ (0.33 #281, 0.25 #2131, 0.07 #9536), 055c8 (0.33 #250, 0.14 #5802, 0.10 #6727), 03zyvw (0.33 #295, 0.07 #15108, 0.04 #13254), 031ydm (0.33 #341, 0.05 #11450, 0.04 #12375), 0k2mxq (0.33 #490, 0.05 #11599, 0.04 #13449), 04qz6n (0.33 #566, 0.04 #13525, 0.04 #15379), 08m4c8 (0.33 #149, 0.04 #13108, 0.04 #14962), 0443y3 (0.33 #166, 0.03 #22389, 0.02 #42756) >> Best rule #33337 for best value: >> intensional similarity = 5 >> extensional distance = 126 >> proper extension: 02qjv1p; >> query: (?x9636, ?x4238) <- nominated_for(?x783, ?x9636), country_of_origin(?x9636, ?x94), nominated_for(?x4238, ?x9636), languages(?x9636, ?x254), genre(?x9636, ?x53) >> conf = 0.36 => this is the best rule for 2 predicted values *> Best rule #12525 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0dk0dj; *> query: (?x9636, 0d02km) <- country_of_origin(?x9636, ?x94), program(?x6678, ?x9636), genre(?x9636, ?x1510), ?x1510 = 01hmnh *> conf = 0.04 ranks of expected_values: 289 EVAL 0d7vtk actor 0d02km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 101.000 71.000 0.358 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #13970-016z43 PRED entity: 016z43 PRED relation: film! PRED expected values: 03h_fqv => 62 concepts (24 used for prediction) PRED predicted values (max 10 best out of 766): 03975z (0.54 #4159, 0.39 #45755, 0.36 #27033), 012d40 (0.20 #16, 0.11 #2095, 0.10 #6254), 07h5d (0.14 #45756, 0.11 #27034), 01kwsg (0.10 #837, 0.08 #2916, 0.08 #7075), 01wbg84 (0.10 #47, 0.08 #2126, 0.06 #8364), 0147dk (0.10 #82, 0.06 #8399, 0.06 #2161), 02js_6 (0.10 #1971, 0.06 #10288, 0.06 #4050), 0q9kd (0.10 #4, 0.06 #2083, 0.05 #6242), 0738b8 (0.10 #404, 0.06 #2483, 0.05 #6642), 02f_k_ (0.10 #1121, 0.06 #3200, 0.05 #7359) >> Best rule #4159 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 069q4f; 033f8n; 0dln8jk; 01y9jr; 06c0ns; 03ynwqj; >> query: (?x12401, ?x4380) <- genre(?x12401, ?x5231), nominated_for(?x4380, ?x12401), ?x5231 = 0556j8, film_release_distribution_medium(?x12401, ?x81) >> conf = 0.54 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016z43 film! 03h_fqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 24.000 0.541 http://example.org/film/actor/film./film/performance/film #13969-018db8 PRED entity: 018db8 PRED relation: profession PRED expected values: 01d_h8 064xm0 => 94 concepts (93 used for prediction) PRED predicted values (max 10 best out of 64): 0nbcg (0.42 #5617, 0.31 #2382, 0.31 #2676), 01d_h8 (0.41 #2505, 0.38 #1770, 0.38 #3093), 016z4k (0.35 #5591, 0.28 #2650, 0.23 #2356), 0dz3r (0.34 #5589, 0.31 #2648, 0.25 #2354), 0dxtg (0.33 #5293, 0.27 #455, 0.27 #8101), 018gz8 (0.33 #5293, 0.25 #16, 0.14 #4867), 02krf9 (0.33 #5293, 0.25 #25, 0.09 #8112), 02jknp (0.33 #5293, 0.24 #7066, 0.21 #8095), 0np9r (0.33 #5293, 0.14 #10899, 0.14 #6048), 0kyk (0.33 #5293, 0.11 #1057, 0.10 #7674) >> Best rule #5617 for best value: >> intensional similarity = 2 >> extensional distance = 621 >> proper extension: 032t2z; 01w923; 0bkg4; 023l9y; 018y81; 01ydzx; 03wjb7; >> query: (?x793, 0nbcg) <- profession(?x793, ?x1183), ?x1183 = 09jwl >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #2505 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 270 *> proper extension: 01r42_g; 08m4c8; *> query: (?x793, 01d_h8) <- participant(?x794, ?x793), award_winner(?x2929, ?x793), award_nominee(?x192, ?x793) *> conf = 0.41 ranks of expected_values: 2, 32 EVAL 018db8 profession 064xm0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 94.000 93.000 0.416 http://example.org/people/person/profession EVAL 018db8 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 93.000 0.416 http://example.org/people/person/profession #13968-06gn7r PRED entity: 06gn7r PRED relation: student! PRED expected values: 014mlp => 212 concepts (189 used for prediction) PRED predicted values (max 10 best out of 23): 014mlp (0.36 #1306, 0.35 #266, 0.34 #466), 019v9k (0.29 #270, 0.18 #350, 0.15 #390), 0bkj86 (0.18 #269, 0.14 #469, 0.14 #349), 02h4rq6 (0.14 #463, 0.12 #243, 0.10 #143), 02_xgp2 (0.14 #354, 0.12 #394, 0.10 #874), 016t_3 (0.14 #344, 0.12 #384, 0.09 #804), 04zx3q1 (0.08 #382, 0.07 #842, 0.07 #862), 028dcg (0.06 #678, 0.06 #1438, 0.06 #1238), 03mkk4 (0.06 #673, 0.06 #273, 0.06 #873), 071tyz (0.05 #352, 0.04 #1542, 0.04 #392) >> Best rule #1306 for best value: >> intensional similarity = 5 >> extensional distance = 149 >> proper extension: 06y9c2; 06n7h7; 03ldxq; 0d0vj4; 02dh86; 01k165; 049_zz; 01v3bn; 0bkg4; 01mr2g6; ... >> query: (?x8296, 014mlp) <- type_of_union(?x8296, ?x566), ?x566 = 04ztj, student(?x11870, ?x8296), student(?x3995, ?x8296), profession(?x8296, ?x524) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06gn7r student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 189.000 0.364 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #13967-041h0 PRED entity: 041h0 PRED relation: religion PRED expected values: 0c8wxp => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 30): 0c8wxp (0.28 #501, 0.22 #1222, 0.21 #1629), 03_gx (0.26 #1908, 0.21 #914, 0.20 #779), 01lp8 (0.25 #1, 0.20 #91, 0.09 #181), 0kq2 (0.20 #108, 0.10 #1777, 0.08 #2319), 0n2g (0.20 #103, 0.09 #1772, 0.08 #2314), 0kpl (0.18 #1904, 0.18 #3572, 0.18 #2626), 092bf5 (0.12 #151, 0.08 #601, 0.07 #781), 051kv (0.12 #140, 0.03 #455, 0.03 #545), 06nzl (0.06 #510, 0.06 #1819, 0.06 #961), 07w8f (0.05 #305, 0.02 #2336, 0.01 #2561) >> Best rule #501 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 02dth1; >> query: (?x477, 0c8wxp) <- place_of_birth(?x477, ?x13319), friend(?x477, ?x6796), profession(?x6796, ?x353), influenced_by(?x6796, ?x3542) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 041h0 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.281 http://example.org/people/person/religion #13966-0cwx_ PRED entity: 0cwx_ PRED relation: institution! PRED expected values: 02h4rq6 014mlp 019v9k => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 14): 02h4rq6 (0.81 #177, 0.78 #209, 0.75 #436), 014mlp (0.78 #179, 0.73 #115, 0.71 #438), 0bkj86 (0.75 #133, 0.64 #181, 0.60 #101), 019v9k (0.71 #214, 0.67 #441, 0.67 #311), 027f2w (0.62 #135, 0.44 #183, 0.43 #103), 013zdg (0.42 #180, 0.34 #132, 0.33 #164), 01rr_d (0.30 #1215, 0.28 #1433, 0.25 #219), 022h5x (0.30 #1215, 0.28 #1433, 0.19 #189), 02m4yg (0.30 #1215, 0.28 #1433, 0.12 #42), 01ysy9 (0.30 #1215, 0.28 #1433, 0.07 #223) >> Best rule #177 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 01nmgc; >> query: (?x6894, 02h4rq6) <- institution(?x620, ?x6894), list(?x6894, ?x2197), ?x620 = 07s6fsf >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 0cwx_ institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 122.000 0.806 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0cwx_ institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.806 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0cwx_ institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.806 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13965-02sgy PRED entity: 02sgy PRED relation: instrumentalists PRED expected values: 05cljf 0161c2 => 92 concepts (50 used for prediction) PRED predicted values (max 10 best out of 3792): 01sb5r (0.78 #11875, 0.67 #6363, 0.67 #5752), 02fn5r (0.71 #1222, 0.67 #11025, 0.62 #11024), 01vsy3q (0.71 #1222, 0.67 #11025, 0.62 #11024), 082brv (0.71 #1222, 0.67 #11025, 0.62 #11024), 0l12d (0.71 #1222, 0.67 #11025, 0.62 #11024), 01271h (0.71 #1222, 0.67 #11025, 0.62 #11024), 0197tq (0.71 #1222, 0.67 #11025, 0.62 #11024), 01kd57 (0.71 #1222, 0.67 #11025, 0.62 #11024), 06x4l_ (0.71 #1222, 0.67 #11025, 0.62 #11024), 01vsnff (0.71 #1222, 0.67 #11025, 0.62 #11024) >> Best rule #11875 for best value: >> intensional similarity = 12 >> extensional distance = 7 >> proper extension: 06w7v; >> query: (?x314, 01sb5r) <- role(?x314, ?x1969), instrumentalists(?x314, ?x133), role(?x6877, ?x314), role(?x3740, ?x314), role(?x2862, ?x314), role(?x614, ?x314), ?x2862 = 06x4l_, ?x1969 = 04rzd, artists(?x302, ?x3740), group(?x314, ?x442), role(?x615, ?x314), spouse(?x6059, ?x6877) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #5524 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 0l14md; *> query: (?x314, 05cljf) <- role(?x314, ?x4078), role(?x314, ?x1969), instrumentalists(?x314, ?x133), role(?x3740, ?x314), role(?x2862, ?x314), role(?x614, ?x314), ?x2862 = 06x4l_, ?x1969 = 04rzd, artists(?x302, ?x3740), group(?x314, ?x442), role(?x615, ?x314), ?x4078 = 011k_j *> conf = 0.67 ranks of expected_values: 101, 211 EVAL 02sgy instrumentalists 0161c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 92.000 50.000 0.778 http://example.org/music/instrument/instrumentalists EVAL 02sgy instrumentalists 05cljf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 92.000 50.000 0.778 http://example.org/music/instrument/instrumentalists #13964-015cbq PRED entity: 015cbq PRED relation: influenced_by! PRED expected values: 052hl => 117 concepts (63 used for prediction) PRED predicted values (max 10 best out of 378): 01xwv7 (0.10 #423, 0.07 #5044, 0.07 #8123), 016_mj (0.10 #54, 0.07 #5189, 0.06 #4675), 01xdf5 (0.10 #3, 0.07 #5138, 0.04 #4109), 049fgvm (0.10 #264, 0.06 #4885, 0.04 #4370), 0lx2l (0.10 #89, 0.05 #5224, 0.04 #4195), 02633g (0.10 #318, 0.05 #5453, 0.04 #4939), 03g5_y (0.10 #312, 0.04 #4418, 0.04 #4933), 0q5hw (0.10 #102, 0.04 #4723, 0.04 #1129), 01hmk9 (0.10 #283, 0.04 #1310, 0.03 #2336), 0f7hc (0.10 #184, 0.04 #1211, 0.03 #4805) >> Best rule #423 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0frmb1; >> query: (?x9716, 01xwv7) <- athlete(?x14205, ?x9716), inductee(?x9953, ?x9716), type_of_union(?x9716, ?x566) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #21561 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 436 *> proper extension: 0d6b7; 04r1t; 0fpzzp; 085j0; 07scx; 055yr; *> query: (?x9716, ?x2283) <- influenced_by(?x4066, ?x9716), influenced_by(?x4066, ?x2283) *> conf = 0.07 ranks of expected_values: 25 EVAL 015cbq influenced_by! 052hl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 117.000 63.000 0.100 http://example.org/influence/influence_node/influenced_by #13963-04d5v9 PRED entity: 04d5v9 PRED relation: citytown PRED expected values: 02_286 => 102 concepts (39 used for prediction) PRED predicted values (max 10 best out of 44): 0cc56 (0.71 #11828, 0.52 #1478, 0.30 #6280), 059rby (0.71 #11828, 0.30 #11827, 0.24 #6279), 02_286 (0.30 #1123, 0.29 #753, 0.27 #1863), 09c7w0 (0.30 #11827, 0.24 #6279, 0.21 #14049), 030qb3t (0.21 #1136, 0.04 #2247, 0.03 #398), 0rh6k (0.07 #1109, 0.02 #2220, 0.02 #3325), 01qh7 (0.06 #432, 0.06 #2281, 0.03 #2649), 0f2nf (0.06 #578, 0.06 #2427, 0.02 #3532), 0dclg (0.06 #412, 0.04 #2261, 0.02 #3366), 0psxp (0.06 #501, 0.04 #2350) >> Best rule #11828 for best value: >> intensional similarity = 3 >> extensional distance = 370 >> proper extension: 0277jc; 02301; 0373qg; 01q2sk; 05t7c1; 01y888; 01bcwk; 015cz0; 057bxr; 080z7; ... >> query: (?x3665, ?x335) <- contains(?x335, ?x3665), institution(?x2636, ?x3665), citytown(?x6896, ?x335) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #1123 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 41 *> proper extension: 0f2wj; 0f94t; 0284jb; 0cr3d; 0ccvx; 0r04p; 0l1pj; 0y62n; *> query: (?x3665, 02_286) <- contains(?x1131, ?x3665), contains(?x94, ?x3665), ?x94 = 09c7w0, location(?x4816, ?x1131), ?x4816 = 0pyww *> conf = 0.30 ranks of expected_values: 3 EVAL 04d5v9 citytown 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 39.000 0.709 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #13962-0l14qv PRED entity: 0l14qv PRED relation: group PRED expected values: 0bpk2 => 86 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1050): 0b_xm (0.75 #2844, 0.57 #2402, 0.55 #4308), 03c3yf (0.71 #2254, 0.67 #1670, 0.60 #3868), 06nv27 (0.71 #2223, 0.67 #3547, 0.60 #3691), 0mjn2 (0.71 #2288, 0.67 #3612, 0.60 #3756), 012x1l (0.71 #2311, 0.60 #1146, 0.50 #3779), 01w5n51 (0.71 #2255, 0.60 #1090, 0.50 #3723), 0838y (0.71 #2245, 0.60 #1080, 0.50 #3713), 07h76 (0.71 #2237, 0.60 #1072, 0.50 #3705), 033s6 (0.71 #2279, 0.60 #1114, 0.50 #3747), 01lf293 (0.71 #2267, 0.50 #1683, 0.44 #3591) >> Best rule #2844 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 05r5c; >> query: (?x228, 0b_xm) <- role(?x1495, ?x228), role(?x1148, ?x228), role(?x642, ?x228), role(?x2460, ?x1148), performance_role(?x1225, ?x228), role(?x3442, ?x228), role(?x74, ?x228), group(?x1495, ?x997), role(?x211, ?x1495), ?x3442 = 0m_v0, ?x2460 = 01wy6 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2231 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 02hnl; 03qjg; *> query: (?x228, 0bpk2) <- role(?x1495, ?x228), role(?x1148, ?x228), role(?x642, ?x228), role(?x1332, ?x1148), performance_role(?x1225, ?x228), ?x1495 = 013y1f, group(?x228, ?x5385), role(?x74, ?x228), instrumentalists(?x228, ?x140), role(?x130, ?x228), ?x5385 = 0134tg *> conf = 0.43 ranks of expected_values: 99 EVAL 0l14qv group 0bpk2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 86.000 50.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group #13961-0jq2r PRED entity: 0jq2r PRED relation: actor PRED expected values: 041c4 => 83 concepts (38 used for prediction) PRED predicted values (max 10 best out of 792): 01pgzn_ (0.20 #1112, 0.13 #3909, 0.13 #2977), 048hf (0.20 #1547, 0.12 #5276, 0.07 #4344), 026zvx7 (0.20 #1140, 0.07 #3937, 0.07 #3005), 06b0d2 (0.20 #1020, 0.07 #3817, 0.07 #2885), 03x16f (0.20 #1607, 0.07 #4404, 0.07 #3472), 04mhxx (0.20 #1692, 0.07 #4489, 0.07 #3557), 04vmqg (0.20 #1688, 0.07 #4485, 0.07 #3553), 01k53x (0.20 #1660, 0.07 #4457, 0.07 #3525), 03_2td (0.20 #1641, 0.07 #4438, 0.07 #3506), 06hgym (0.20 #1582, 0.07 #4379, 0.07 #3447) >> Best rule #1112 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 01b9w3; >> query: (?x8057, 01pgzn_) <- genre(?x8057, ?x8805), genre(?x8057, ?x2480), genre(?x8057, ?x258), ?x258 = 05p553, titles(?x512, ?x8057), ?x8805 = 06q7n, languages(?x8057, ?x254), ?x2480 = 01z4y >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jq2r actor 041c4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 38.000 0.200 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #13960-02p0qmm PRED entity: 02p0qmm PRED relation: school_type! PRED expected values: 019n9w => 69 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1556): 01pl14 (0.50 #1700, 0.33 #7353, 0.29 #9615), 02km0m (0.50 #1368, 0.30 #5324, 0.25 #7586), 02ldkf (0.50 #2144, 0.25 #7797, 0.21 #10059), 07vyf (0.50 #1837, 0.25 #7490, 0.21 #9752), 0f102 (0.50 #1766, 0.25 #7419, 0.21 #9681), 049dk (0.50 #1737, 0.25 #7390, 0.21 #9652), 01j_cy (0.50 #1732, 0.25 #7385, 0.21 #9647), 07w0v (0.50 #1711, 0.25 #7364, 0.21 #9626), 03fgm (0.50 #1532, 0.25 #7750, 0.21 #10012), 012mzw (0.50 #1418, 0.25 #7636, 0.21 #9898) >> Best rule #1700 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 05jxkf; 07tf8; >> query: (?x5931, 01pl14) <- school_type(?x11614, ?x5931), school_type(?x1513, ?x5931), school_type(?x1369, ?x5931), category(?x1369, ?x134), institution(?x1368, ?x1369), ?x134 = 08mbj5d, currency(?x1513, ?x170), organization(?x346, ?x1513), student(?x11614, ?x2648), major_field_of_study(?x11614, ?x1668), currency(?x1369, ?x1099), ?x2648 = 034bgm >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1449 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 05pcjw; 01rs41; *> query: (?x5931, 019n9w) <- school_type(?x11614, ?x5931), school_type(?x1513, ?x5931), school_type(?x1369, ?x5931), category(?x1369, ?x134), institution(?x1526, ?x1369), ?x134 = 08mbj5d, ?x1513 = 017d77, currency(?x1369, ?x1099), major_field_of_study(?x11614, ?x1668), student(?x11614, ?x2397), major_field_of_study(?x1526, ?x254), student(?x1526, ?x476) *> conf = 0.25 ranks of expected_values: 343 EVAL 02p0qmm school_type! 019n9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 64.000 0.500 http://example.org/education/educational_institution/school_type #13959-0qpjt PRED entity: 0qpjt PRED relation: place PRED expected values: 0qpjt => 65 concepts (34 used for prediction) PRED predicted values (max 10 best out of 24): 0qplq (0.22 #1548, 0.02 #383), 0qpsn (0.22 #1548, 0.02 #377), 0qpqn (0.22 #1548, 0.02 #245), 0qpn9 (0.22 #1548, 0.02 #190), 0d35y (0.22 #1548, 0.02 #103), 0qpjt (0.22 #1548), 0m27n (0.05 #3094), 0x1y7 (0.02 #510), 060wq (0.02 #501), 0fw3f (0.02 #404) >> Best rule #1548 for best value: >> intensional similarity = 5 >> extensional distance = 146 >> proper extension: 0xddr; 0r6ff; 0s6g4; 01m23s; 0sc6p; 0s4sj; >> query: (?x9010, ?x4419) <- contains(?x7409, ?x9010), contains(?x94, ?x9010), source(?x9010, ?x958), ?x94 = 09c7w0, county(?x4419, ?x7409) >> conf = 0.22 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 0qpjt place 0qpjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 65.000 34.000 0.218 http://example.org/location/hud_county_place/place #13958-013pk3 PRED entity: 013pk3 PRED relation: film PRED expected values: 0bz6sq => 98 concepts (73 used for prediction) PRED predicted values (max 10 best out of 831): 04p5cr (0.34 #110927, 0.06 #41151, 0.04 #100192), 01shy7 (0.07 #7581, 0.04 #5792, 0.04 #23684), 013q07 (0.07 #3936, 0.05 #5725, 0.04 #9303), 017jd9 (0.05 #20461, 0.03 #11514, 0.02 #50875), 02qzh2 (0.05 #4272, 0.05 #6061, 0.03 #9639), 03nfnx (0.05 #6770, 0.04 #4981, 0.03 #10348), 031hcx (0.04 #4852, 0.04 #6641, 0.03 #31689), 0prrm (0.04 #4439, 0.04 #6228, 0.03 #16963), 02v5_g (0.04 #4370, 0.04 #6159, 0.03 #9737), 0b3n61 (0.04 #4937, 0.04 #6726, 0.02 #24618) >> Best rule #110927 for best value: >> intensional similarity = 2 >> extensional distance = 1558 >> proper extension: 01j7pt; 01zcrv; 0kctd; 0kcd5; 0kc9f; >> query: (?x7638, ?x6439) <- award_winner(?x8250, ?x7638), nominated_for(?x7638, ?x6439) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #1515 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 26 *> proper extension: 03xhj6; 0jn38; *> query: (?x7638, 0bz6sq) <- artist(?x382, ?x7638), ?x382 = 086k8 *> conf = 0.04 ranks of expected_values: 18 EVAL 013pk3 film 0bz6sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 98.000 73.000 0.343 http://example.org/film/actor/film./film/performance/film #13957-067sqt PRED entity: 067sqt PRED relation: gender PRED expected values: 02zsn => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #61, 0.81 #89, 0.81 #83), 02zsn (0.44 #10, 0.38 #24, 0.38 #12) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 502 >> proper extension: 079vf; 03gm48; 01g4zr; 02lnhv; 0c_mvb; 05whq_9; 01f8ld; 03m_k0; 09p06; 03pvt; ... >> query: (?x11782, 05zppz) <- profession(?x11782, ?x319), ?x319 = 01d_h8, student(?x5145, ?x11782) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 02mjmr; 0d06m5; *> query: (?x11782, 02zsn) <- student(?x5614, ?x11782), award(?x11782, ?x1670), participant(?x11782, ?x2697) *> conf = 0.44 ranks of expected_values: 2 EVAL 067sqt gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 106.000 0.808 http://example.org/people/person/gender #13956-03f0vvr PRED entity: 03f0vvr PRED relation: artists! PRED expected values: 06by7 => 115 concepts (46 used for prediction) PRED predicted values (max 10 best out of 238): 06by7 (0.81 #9405, 0.75 #10344, 0.74 #2524), 064t9 (0.78 #638, 0.76 #950, 0.73 #326), 0xhtw (0.52 #2519, 0.40 #18, 0.35 #9400), 06j6l (0.47 #362, 0.40 #50, 0.39 #674), 05bt6j (0.44 #669, 0.40 #357, 0.38 #981), 05w3f (0.40 #40, 0.38 #2541, 0.16 #1915), 07sbbz2 (0.40 #8, 0.25 #2509, 0.24 #1257), 03_d0 (0.33 #1575, 0.33 #1261, 0.26 #1887), 059kh (0.33 #987, 0.28 #675, 0.20 #363), 0gywn (0.28 #684, 0.27 #372, 0.24 #996) >> Best rule #9405 for best value: >> intensional similarity = 5 >> extensional distance = 479 >> proper extension: 02t3ln; >> query: (?x4798, 06by7) <- artists(?x7083, ?x4798), artists(?x7083, ?x9074), artists(?x7083, ?x6225), ?x9074 = 01k47c, ?x6225 = 01vng3b >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f0vvr artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 46.000 0.809 http://example.org/music/genre/artists #13955-0146pg PRED entity: 0146pg PRED relation: award_winner! PRED expected values: 02qvyrt => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 320): 0gqz2 (0.43 #504, 0.37 #31810, 0.36 #24598), 0c4z8 (0.43 #495, 0.20 #71, 0.11 #4736), 054ks3 (0.37 #31810, 0.36 #24598, 0.36 #2121), 025m8l (0.37 #31810, 0.36 #24598, 0.36 #2121), 01by1l (0.37 #31810, 0.36 #24598, 0.36 #2121), 0g9wd99 (0.37 #31810, 0.36 #24598, 0.36 #2121), 025mb9 (0.29 #622, 0.20 #198, 0.07 #3167), 02qvyrt (0.20 #125, 0.19 #2670, 0.16 #4366), 01bgqh (0.14 #467, 0.12 #3436, 0.10 #3012), 0ck27z (0.14 #939, 0.09 #24689, 0.09 #23840) >> Best rule #504 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 0ddkf; >> query: (?x669, 0gqz2) <- award_winner(?x5949, ?x669), ?x5949 = 02ryx0 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #125 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 05ccxr; 01pbwwl; *> query: (?x669, 02qvyrt) <- music(?x670, ?x669), award_winner(?x669, ?x4951), ?x4951 = 02lfp4 *> conf = 0.20 ranks of expected_values: 8 EVAL 0146pg award_winner! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 116.000 116.000 0.429 http://example.org/award/award_category/winners./award/award_honor/award_winner #13954-01fx5l PRED entity: 01fx5l PRED relation: award_nominee PRED expected values: 032w8h => 98 concepts (48 used for prediction) PRED predicted values (max 10 best out of 601): 01fx5l (0.38 #1458, 0.05 #107527, 0.03 #102852), 032w8h (0.31 #369, 0.16 #100514, 0.02 #2706), 0h0wc (0.30 #70125, 0.04 #5227, 0.03 #102852), 0bl2g (0.30 #70125, 0.02 #4741), 03fqv5 (0.30 #70125), 085pr (0.30 #70125), 01tcf7 (0.30 #70125), 02l840 (0.23 #160, 0.02 #56257, 0.02 #46906), 01w7nwm (0.23 #710), 0770cd (0.23 #387) >> Best rule #1458 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01f2q5; >> query: (?x6282, 01fx5l) <- award_nominee(?x6282, ?x3176), award(?x6282, ?x375), ?x3176 = 01w7nww >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #369 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 01f2q5; *> query: (?x6282, 032w8h) <- award_nominee(?x6282, ?x3176), award(?x6282, ?x375), ?x3176 = 01w7nww *> conf = 0.31 ranks of expected_values: 2 EVAL 01fx5l award_nominee 032w8h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 48.000 0.385 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13953-0lrh PRED entity: 0lrh PRED relation: type_of_union PRED expected values: 01g63y => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.82 #185, 0.82 #261, 0.80 #77), 01g63y (0.28 #38, 0.26 #54, 0.20 #6), 01bl8s (0.10 #7, 0.08 #19, 0.05 #35) >> Best rule #185 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 0jf1b; 0gl88b; 0l5yl; 015076; >> query: (?x2845, 04ztj) <- people(?x1050, ?x2845), people(?x9898, ?x2845), award(?x2845, ?x1901), location(?x2845, ?x739) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 016m5c; *> query: (?x2845, 01g63y) <- peers(?x2845, ?x1029), artists(?x10872, ?x2845), award(?x2845, ?x1901) *> conf = 0.28 ranks of expected_values: 2 EVAL 0lrh type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 180.000 180.000 0.825 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13952-01y8cr PRED entity: 01y8cr PRED relation: award PRED expected values: 0bdw6t => 121 concepts (99 used for prediction) PRED predicted values (max 10 best out of 285): 02py7pj (0.70 #37596, 0.68 #21421, 0.68 #29101), 09sb52 (0.50 #1657, 0.33 #4889, 0.32 #13780), 0f4x7 (0.36 #435, 0.30 #4879, 0.28 #5688), 09sdmz (0.33 #5054, 0.30 #5863, 0.30 #6672), 027dtxw (0.32 #4852, 0.32 #5661, 0.31 #6470), 02x73k6 (0.32 #4909, 0.29 #5718, 0.29 #6527), 0bdwqv (0.30 #5020, 0.29 #5425, 0.28 #5829), 0ck27z (0.26 #1709, 0.13 #13832, 0.13 #15045), 0gq9h (0.24 #14626, 0.16 #2098, 0.14 #4522), 099jhq (0.24 #4867, 0.21 #5676, 0.21 #6485) >> Best rule #37596 for best value: >> intensional similarity = 4 >> extensional distance = 1897 >> proper extension: 0f6lx; 06lxn; >> query: (?x4279, ?x8459) <- award_winner(?x8459, ?x4279), award_winner(?x2192, ?x4279), award(?x72, ?x2192), ceremony(?x2192, ?x1265) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4151 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 104 *> proper extension: 033hqf; 026c0p; *> query: (?x4279, 0bdw6t) <- people(?x9771, ?x4279), student(?x5149, ?x4279), film(?x4279, ?x3294) *> conf = 0.11 ranks of expected_values: 38 EVAL 01y8cr award 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 121.000 99.000 0.702 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13951-0262s1 PRED entity: 0262s1 PRED relation: award! PRED expected values: 0dtfn => 69 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1183): 07cz2 (0.50 #2274, 0.36 #23095, 0.27 #29123), 017jd9 (0.50 #2464, 0.13 #11501, 0.10 #14515), 02dr9j (0.50 #2726, 0.04 #29124), 06qw_ (0.42 #23096, 0.39 #29126, 0.36 #23095), 0661ql3 (0.40 #2240, 0.05 #11277, 0.04 #29124), 017gm7 (0.40 #2133, 0.04 #29124), 026lgs (0.36 #23095, 0.29 #3012, 0.27 #29123), 06r2_ (0.36 #23095, 0.29 #3012, 0.27 #29123), 04zl8 (0.36 #23095, 0.29 #3012, 0.27 #29123), 06r2h (0.36 #23095, 0.27 #29123, 0.27 #29122) >> Best rule #2274 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 05ztjjw; 0p9sw; 02hsq3m; 02g3ft; 0gr42; 063y_ky; 0gqxm; 018wdw; >> query: (?x10747, 07cz2) <- nominated_for(?x10747, ?x5317), award(?x324, ?x10747), film_release_region(?x5317, ?x789), ?x789 = 0f8l9c, ?x324 = 07gp9 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2132 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 05ztjjw; 0p9sw; 02hsq3m; 02g3ft; 0gr42; 063y_ky; 0gqxm; 018wdw; *> query: (?x10747, 0dtfn) <- nominated_for(?x10747, ?x5317), award(?x324, ?x10747), film_release_region(?x5317, ?x789), ?x789 = 0f8l9c, ?x324 = 07gp9 *> conf = 0.10 ranks of expected_values: 150 EVAL 0262s1 award! 0dtfn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 69.000 29.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13950-0bxqq PRED entity: 0bxqq PRED relation: adjoins PRED expected values: 0l34j => 153 concepts (75 used for prediction) PRED predicted values (max 10 best out of 457): 09c7w0 (0.45 #50828, 0.02 #6930, 0.02 #6160), 01n7q (0.45 #50828, 0.02 #6991, 0.02 #10839), 0n6mc (0.33 #1286, 0.25 #40048, 0.24 #41586), 0l2mg (0.33 #639, 0.25 #40048, 0.24 #41586), 0bxqq (0.25 #47747, 0.25 #37739, 0.25 #40048), 0l2hf (0.25 #47747, 0.25 #37739, 0.25 #40048), 0l34j (0.25 #47747, 0.25 #37739, 0.25 #40048), 0kpzy (0.25 #37739, 0.25 #40048, 0.24 #41586), 0kq0q (0.25 #37739, 0.25 #40048, 0.24 #41586), 0kq39 (0.09 #1829, 0.08 #3369, 0.04 #10299) >> Best rule #50828 for best value: >> intensional similarity = 4 >> extensional distance = 309 >> proper extension: 0c82s; >> query: (?x5892, ?x94) <- contains(?x1227, ?x5892), adjoins(?x5892, ?x9582), administrative_division(?x11934, ?x9582), contains(?x94, ?x11934) >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #47747 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 295 *> proper extension: 035p3; *> query: (?x5892, ?x3677) <- adjoins(?x7520, ?x5892), currency(?x7520, ?x170), adjoins(?x7520, ?x3677) *> conf = 0.25 ranks of expected_values: 7 EVAL 0bxqq adjoins 0l34j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 153.000 75.000 0.448 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #13949-03lmzl PRED entity: 03lmzl PRED relation: place_of_birth PRED expected values: 0r5y9 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 72): 0cr3d (0.18 #94, 0.06 #4319, 0.04 #12768), 02_286 (0.12 #723, 0.10 #4244, 0.09 #19), 01531 (0.10 #2921, 0.02 #21228, 0.02 #10667), 030qb3t (0.09 #54, 0.07 #5687, 0.06 #758), 0y1rf (0.09 #434, 0.02 #3250), 049kw (0.09 #427), 05r7t (0.09 #240), 068p2 (0.09 #162), 01_d4 (0.07 #2882, 0.03 #59923, 0.03 #770), 03l2n (0.06 #873, 0.03 #4394, 0.01 #6506) >> Best rule #94 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01j5x6; >> query: (?x8871, 0cr3d) <- award_nominee(?x11861, ?x8871), actor(?x8870, ?x8871), film(?x8871, ?x2441), ?x8870 = 0fhzwl >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03lmzl place_of_birth 0r5y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 98.000 0.182 http://example.org/people/person/place_of_birth #13948-018p4y PRED entity: 018p4y PRED relation: profession PRED expected values: 07s467s => 132 concepts (131 used for prediction) PRED predicted values (max 10 best out of 73): 09jwl (0.41 #2682, 0.39 #906, 0.39 #4015), 0dxtg (0.36 #605, 0.29 #2381, 0.28 #8006), 02jknp (0.33 #599, 0.27 #747, 0.26 #2819), 016z4k (0.30 #892, 0.30 #2668, 0.27 #4001), 0kyk (0.30 #473, 0.25 #177, 0.16 #1213), 0nbcg (0.28 #4028, 0.27 #2695, 0.27 #9208), 03gjzk (0.28 #606, 0.24 #8747, 0.24 #8007), 0dz3r (0.27 #3999, 0.26 #2666, 0.24 #890), 0cbd2 (0.23 #450, 0.18 #4151, 0.17 #302), 0n1h (0.19 #899, 0.16 #2675, 0.14 #4008) >> Best rule #2682 for best value: >> intensional similarity = 3 >> extensional distance = 401 >> proper extension: 07q1v4; 02whj; 01bpc9; 01ky2h; 0gd5z; 01wz_ml; 02yl42; 050z2; 036px; 0d3qd0; ... >> query: (?x11879, 09jwl) <- category(?x11879, ?x134), award_winner(?x102, ?x11879), location(?x11879, ?x1310) >> conf = 0.41 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018p4y profession 07s467s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 131.000 0.409 http://example.org/people/person/profession #13947-0sngf PRED entity: 0sngf PRED relation: location! PRED expected values: 04fhn_ => 111 concepts (90 used for prediction) PRED predicted values (max 10 best out of 617): 029m83 (0.07 #1619, 0.06 #4138, 0.02 #6657), 01vv6xv (0.07 #2263, 0.06 #4782, 0.02 #7301), 03f68r6 (0.07 #2212, 0.06 #4731, 0.02 #7250), 01rw116 (0.07 #2191, 0.06 #4710), 0q1lp (0.07 #1961, 0.06 #4480), 09889g (0.06 #3530), 01y8cr (0.06 #3360), 055c8 (0.06 #3126), 013v5j (0.06 #2928), 02sjf5 (0.04 #10278, 0.04 #12797, 0.03 #5240) >> Best rule #1619 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 012mzw; 03bnd9; >> query: (?x13663, 029m83) <- contains(?x448, ?x13663), contains(?x94, ?x13663), ?x448 = 03v1s, ?x94 = 09c7w0 >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #10843 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 76 *> proper extension: 0dclg; 0n1rj; 0jpy_; *> query: (?x13663, 04fhn_) <- source(?x13663, ?x958), time_zones(?x13663, ?x2674), ?x2674 = 02hcv8, ?x958 = 0jbk9, state(?x13663, ?x448) *> conf = 0.01 ranks of expected_values: 415 EVAL 0sngf location! 04fhn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 111.000 90.000 0.067 http://example.org/people/person/places_lived./people/place_lived/location #13946-030qb3t PRED entity: 030qb3t PRED relation: place_founded! PRED expected values: 05qd_ => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 128): 04htfd (0.10 #348, 0.10 #243, 0.09 #559), 024rgt (0.10 #216, 0.09 #426, 0.08 #738), 025txrl (0.10 #394, 0.09 #605, 0.08 #738), 01_4mn (0.10 #307, 0.09 #517, 0.07 #1468), 0dq23 (0.10 #296, 0.09 #506, 0.06 #718), 01ynvx (0.10 #294, 0.09 #504, 0.06 #716), 01hlwv (0.10 #283, 0.09 #493, 0.06 #705), 032j_n (0.10 #271, 0.09 #481, 0.06 #693), 01dfb6 (0.10 #263, 0.09 #473, 0.06 #685), 043g7l (0.10 #237, 0.09 #447, 0.06 #659) >> Best rule #348 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 022b_; >> query: (?x1523, 04htfd) <- films(?x1523, ?x6103), jurisdiction_of_office(?x1195, ?x1523) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 030qb3t place_founded! 05qd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 149.000 149.000 0.100 http://example.org/organization/organization/place_founded #13945-02r6mf PRED entity: 02r6mf PRED relation: parent_genre! PRED expected values: 052smk => 70 concepts (42 used for prediction) PRED predicted values (max 10 best out of 291): 0g_bh (0.43 #905, 0.40 #374, 0.40 #108), 06cp5 (0.43 #1138, 0.24 #1405, 0.13 #2472), 0ccxx6 (0.40 #485, 0.29 #1016, 0.20 #219), 01243b (0.29 #1099, 0.12 #1366, 0.07 #1901), 0y3_8 (0.27 #1370, 0.21 #1103, 0.11 #1905), 0xjl2 (0.21 #1101, 0.20 #38, 0.15 #1368), 01h0kx (0.21 #1192, 0.15 #1459, 0.09 #4930), 05jt_ (0.21 #1165, 0.09 #1967, 0.09 #1432), 0b_6yv (0.21 #1278, 0.09 #1545, 0.08 #5067), 016clz (0.21 #1068, 0.09 #1335, 0.07 #1870) >> Best rule #905 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 08jyyk; >> query: (?x14132, 0g_bh) <- artists(?x14132, ?x5494), artists(?x14132, ?x2354), ?x5494 = 018x3, parent_genre(?x14132, ?x5640), nominated_for(?x2354, ?x1444), gender(?x2354, ?x231), award_nominee(?x2354, ?x3667) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #487 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 0fd3y; *> query: (?x14132, 052smk) <- artists(?x14132, ?x5494), artists(?x14132, ?x2354), ?x5494 = 018x3, parent_genre(?x14132, ?x5640), nominated_for(?x2354, ?x1444), people(?x743, ?x2354), produced_by(?x1444, ?x6690) *> conf = 0.20 ranks of expected_values: 27 EVAL 02r6mf parent_genre! 052smk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 70.000 42.000 0.429 http://example.org/music/genre/parent_genre #13944-0jnwx PRED entity: 0jnwx PRED relation: award PRED expected values: 05zvj3m => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 205): 0drtkx (0.29 #420, 0.17 #886, 0.13 #1119), 02qvyrt (0.28 #1399, 0.27 #4668, 0.26 #3269), 05zvj3m (0.28 #1399, 0.27 #4668, 0.26 #3269), 02hsq3m (0.28 #1399, 0.27 #4668, 0.26 #3269), 0p9sw (0.28 #1399, 0.27 #4668, 0.26 #3269), 018wdw (0.28 #1399, 0.27 #4668, 0.26 #3269), 05ztjjw (0.28 #1399, 0.27 #4668, 0.26 #3269), 09tqxt (0.28 #773, 0.21 #307, 0.11 #1474), 0gqz2 (0.26 #5135, 0.15 #16343, 0.14 #14241), 05q8pss (0.26 #5135, 0.15 #16343, 0.14 #14241) >> Best rule #420 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 023p7l; >> query: (?x1893, 0drtkx) <- production_companies(?x1893, ?x2156), award_winner(?x1893, ?x1894), film_release_region(?x1893, ?x94), ?x2156 = 01795t >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1399 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 83 *> proper extension: 011ypx; 03lfd_; *> query: (?x1893, ?x500) <- nominated_for(?x500, ?x1893), nominated_for(?x298, ?x1893), genre(?x1893, ?x258), film(?x3758, ?x1893), ?x298 = 05ztjjw *> conf = 0.28 ranks of expected_values: 3 EVAL 0jnwx award 05zvj3m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 105.000 0.286 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13943-03m5k PRED entity: 03m5k PRED relation: role! PRED expected values: 01kx_81 => 75 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1004): 04bpm6 (0.71 #8036, 0.71 #7566, 0.67 #7098), 0lzkm (0.71 #8135, 0.71 #7665, 0.55 #10011), 01vs4ff (0.71 #7797, 0.64 #10143, 0.57 #8733), 03j24kf (0.71 #7710, 0.57 #8180, 0.45 #10056), 01wxdn3 (0.67 #4621, 0.64 #10251, 0.57 #8841), 03ryks (0.67 #7326, 0.60 #13890, 0.54 #11544), 0326tc (0.67 #5030, 0.57 #7841, 0.55 #10187), 023l9y (0.57 #8641, 0.57 #8175, 0.57 #7705), 01wp8w7 (0.57 #8490, 0.57 #7554, 0.50 #4270), 0161sp (0.57 #8092, 0.57 #7622, 0.45 #9968) >> Best rule #8036 for best value: >> intensional similarity = 24 >> extensional distance = 5 >> proper extension: 02qjv; >> query: (?x894, 04bpm6) <- role(?x894, ?x3215), role(?x894, ?x2158), role(?x894, ?x2059), role(?x894, ?x1437), role(?x894, ?x75), role(?x894, ?x6039), role(?x894, ?x1267), role(?x894, ?x1166), ?x1437 = 01vdm0, role(?x10802, ?x6039), role(?x6039, ?x645), ?x2059 = 0dwr4, ?x75 = 07y_7, role(?x3296, ?x894), role(?x6838, ?x894), role(?x4052, ?x894), ?x1166 = 05148p4, ?x3215 = 0bxl5, artists(?x284, ?x4052), place_of_birth(?x4052, ?x11072), role(?x1267, ?x212), artist(?x3265, ?x6838), ?x2158 = 01dnws, instrumentalists(?x6039, ?x3030) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #8473 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 5 *> proper extension: 02sgy; 03qjg; *> query: (?x894, 01kx_81) <- role(?x894, ?x4583), role(?x894, ?x2158), role(?x894, ?x2059), role(?x894, ?x1969), role(?x894, ?x1437), role(?x894, ?x75), role(?x894, ?x6039), ?x1437 = 01vdm0, role(?x10802, ?x6039), role(?x6039, ?x6801), role(?x6039, ?x2460), ?x2059 = 0dwr4, ?x75 = 07y_7, instrumentalists(?x894, ?x2584), ?x2158 = 01dnws, ?x1969 = 04rzd, ?x2460 = 01wy6, ?x6801 = 01c3q, award_winner(?x2584, ?x2461), ?x4583 = 0bmnm, role(?x212, ?x894), role(?x6039, ?x214), artist(?x5891, ?x2584) *> conf = 0.29 ranks of expected_values: 192 EVAL 03m5k role! 01kx_81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 75.000 42.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #13942-0bm2g PRED entity: 0bm2g PRED relation: nominated_for! PRED expected values: 0gqwc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 195): 0gqy2 (0.69 #232, 0.66 #6939, 0.66 #3700), 0gq_v (0.48 #19, 0.37 #713, 0.37 #251), 0gr0m (0.44 #56, 0.30 #3293, 0.29 #750), 04kxsb (0.40 #1244, 0.25 #88, 0.24 #3325), 02qyntr (0.40 #173, 0.29 #3410, 0.26 #1329), 04dn09n (0.39 #1188, 0.35 #3269, 0.31 #32), 02r22gf (0.38 #25, 0.16 #3262, 0.15 #1412), 0l8z1 (0.35 #49, 0.22 #3286, 0.20 #281), 054krc (0.33 #64, 0.19 #11795, 0.19 #14800), 02pqp12 (0.30 #3292, 0.29 #55, 0.27 #1211) >> Best rule #232 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 06mmr; >> query: (?x2112, ?x601) <- award(?x2112, ?x601), award(?x2112, ?x500), ?x500 = 0p9sw, honored_for(?x4445, ?x2112) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1213 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 177 *> proper extension: 0hgnl3t; *> query: (?x2112, 0gqwc) <- film(?x3017, ?x2112), nominated_for(?x591, ?x2112), ?x591 = 0f4x7 *> conf = 0.25 ranks of expected_values: 15 EVAL 0bm2g nominated_for! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 72.000 72.000 0.691 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13941-029d_ PRED entity: 029d_ PRED relation: institution! PRED expected values: 02h4rq6 => 185 concepts (118 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.82 #104, 0.81 #290, 0.80 #84), 014mlp (0.78 #45, 0.74 #700, 0.73 #639), 019v9k (0.72 #973, 0.70 #296, 0.68 #359), 0bkj86 (0.64 #171, 0.56 #48, 0.50 #150), 02_xgp2 (0.60 #482, 0.60 #976, 0.57 #175), 04zx3q1 (0.57 #165, 0.44 #42, 0.40 #472), 013zdg (0.43 #149, 0.36 #170, 0.35 #272), 028dcg (0.30 #99, 0.29 #181, 0.27 #119), 03mkk4 (0.25 #10, 0.22 #361, 0.21 #481), 0bjrnt (0.25 #271, 0.21 #169, 0.19 #293) >> Best rule #104 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 025v3k; >> query: (?x5055, 02h4rq6) <- contact_category(?x5055, ?x897), organization(?x346, ?x5055), ?x346 = 060c4, major_field_of_study(?x5055, ?x1154) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 029d_ institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 185.000 118.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13940-02qnhk1 PRED entity: 02qnhk1 PRED relation: type_of_union PRED expected values: 04ztj => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.76 #17, 0.75 #21, 0.74 #25), 01g63y (0.25 #134, 0.21 #53, 0.19 #188), 0jgjn (0.25 #134, 0.21 #53, 0.19 #188), 01bl8s (0.21 #53, 0.19 #188, 0.19 #183) >> Best rule #17 for best value: >> intensional similarity = 6 >> extensional distance = 477 >> proper extension: 05ty4m; 01vvycq; 02lk1s; 03gm48; 0sz28; 0126rp; 0jfx1; 0127m7; 0j_c; 09ftwr; ... >> query: (?x14496, 04ztj) <- profession(?x14496, ?x1032), profession(?x14496, ?x319), gender(?x14496, ?x231), ?x1032 = 02hrh1q, ?x231 = 05zppz, ?x319 = 01d_h8 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qnhk1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.760 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13939-020hh3 PRED entity: 020hh3 PRED relation: currency PRED expected values: 09nqf => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.45 #130, 0.44 #52, 0.43 #10), 01nv4h (0.22 #23, 0.14 #8, 0.12 #41) >> Best rule #130 for best value: >> intensional similarity = 4 >> extensional distance = 212 >> proper extension: 01sl1q; 01wmxfs; 0134w7; 01nczg; 07ymr5; 01wxyx1; 01wk7b7; 02js6_; 0dvmd; 01jbx1; ... >> query: (?x8640, 09nqf) <- profession(?x8640, ?x131), participant(?x8640, ?x1208), gender(?x8640, ?x231), category(?x8640, ?x134) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 020hh3 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.453 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #13938-03f_jk PRED entity: 03f_jk PRED relation: category PRED expected values: 08mbj5d => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14819, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f_jk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #13937-01wvxw1 PRED entity: 01wvxw1 PRED relation: category PRED expected values: 08mbj5d => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.87 #22, 0.85 #33, 0.83 #28) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 01vvydl; 0147dk; 01vrncs; 016kjs; 014zfs; 018y2s; 01qvgl; 06w2sn5; 058s57; 0j1yf; ... >> query: (?x8143, 08mbj5d) <- place_of_birth(?x8143, ?x12250), participant(?x219, ?x8143), artists(?x283, ?x8143), award_winner(?x3313, ?x8143) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wvxw1 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.868 http://example.org/common/topic/webpage./common/webpage/category #13936-0bt4g PRED entity: 0bt4g PRED relation: language PRED expected values: 02h40lc => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.89 #654, 0.89 #1132, 0.88 #2025), 064_8sq (0.17 #1628, 0.16 #912, 0.16 #1270), 04306rv (0.17 #5, 0.13 #539, 0.13 #182), 06nm1 (0.13 #425, 0.12 #663, 0.11 #1081), 02bjrlw (0.10 #60, 0.08 #891, 0.08 #535), 03hkp (0.08 #15, 0.03 #192, 0.03 #549), 05qqm (0.08 #41, 0.02 #575, 0.02 #218), 06b_j (0.08 #437, 0.07 #141, 0.07 #1271), 03_9r (0.06 #424, 0.06 #3581, 0.06 #544), 012w70 (0.06 #427, 0.03 #72, 0.03 #725) >> Best rule #654 for best value: >> intensional similarity = 3 >> extensional distance = 235 >> proper extension: 0gtsx8c; 047svrl; >> query: (?x7692, 02h40lc) <- executive_produced_by(?x7692, ?x2135), film_release_region(?x7692, ?x94), award_winner(?x198, ?x2135) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bt4g language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.890 http://example.org/film/film/language #13935-07rd7 PRED entity: 07rd7 PRED relation: film PRED expected values: 0661m4p => 131 concepts (95 used for prediction) PRED predicted values (max 10 best out of 950): 050xxm (0.62 #8963, 0.56 #16135, 0.49 #111150), 0g56t9t (0.62 #8963, 0.49 #111150, 0.43 #111149), 04jpg2p (0.62 #8963, 0.49 #111150, 0.43 #111149), 09lxv9 (0.62 #8963, 0.49 #111150, 0.43 #111149), 0ds11z (0.62 #8963, 0.49 #111150, 0.43 #111149), 027pfg (0.62 #8963, 0.49 #111150, 0.43 #111149), 04pk1f (0.62 #8963, 0.49 #111150, 0.43 #111149), 02lxrv (0.62 #8963, 0.43 #111149, 0.41 #147000), 01s3vk (0.40 #5378, 0.17 #34067, 0.16 #12548), 01hq1 (0.40 #5378, 0.17 #34067, 0.16 #12548) >> Best rule #8963 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 041h0; 03_vx9; 0456xp; 0j582; 01wk7b7; 0jrny; 02wb6yq; 09qh1; 01wc7p; 01wrcxr; ... >> query: (?x4314, ?x124) <- nationality(?x4314, ?x94), nominated_for(?x4314, ?x124), friend(?x4314, ?x2444) >> conf = 0.62 => this is the best rule for 8 predicted values *> Best rule #2168 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 0gn30; 0cj2w; *> query: (?x4314, 0661m4p) <- spouse(?x4314, ?x2531), type_of_union(?x4314, ?x566), written_by(?x124, ?x4314) *> conf = 0.03 ranks of expected_values: 137 EVAL 07rd7 film 0661m4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 131.000 95.000 0.616 http://example.org/film/actor/film./film/performance/film #13934-0349s PRED entity: 0349s PRED relation: language! PRED expected values: 0gzy02 0dnqr 03cw411 0272_vz => 45 concepts (7 used for prediction) PRED predicted values (max 10 best out of 1837): 014kq6 (0.86 #3445, 0.55 #12064, 0.53 #12065), 0d1qmz (0.86 #3445, 0.55 #12064, 0.53 #12065), 0fsw_7 (0.86 #3445, 0.55 #12064, 0.53 #12065), 02qzh2 (0.86 #3445, 0.55 #12064, 0.53 #12065), 0164qt (0.86 #3445, 0.55 #12064, 0.53 #12065), 02n72k (0.86 #3445, 0.53 #12065, 0.33 #4547), 02qrv7 (0.86 #3445, 0.53 #12065, 0.33 #181), 01kf4tt (0.86 #3445, 0.53 #12065, 0.33 #382), 0fztbq (0.86 #3445, 0.53 #12065, 0.33 #1640), 025twgt (0.86 #3445, 0.53 #12065, 0.12 #8552) >> Best rule #3445 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 04h9h; >> query: (?x11590, ?x835) <- language(?x7207, ?x11590), language(?x1744, ?x11590), language(?x1262, ?x11590), languages_spoken(?x11067, ?x11590), ?x1744 = 035yn8, official_language(?x1353, ?x11590), region(?x7207, ?x512), film_crew_role(?x7207, ?x468), film_release_distribution_medium(?x7207, ?x81), nominated_for(?x835, ?x1262), genre(?x7207, ?x811) >> conf = 0.86 => this is the best rule for 13 predicted values *> Best rule #2305 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 04h9h; *> query: (?x11590, 03cw411) <- language(?x7207, ?x11590), language(?x1744, ?x11590), language(?x1262, ?x11590), languages_spoken(?x11067, ?x11590), ?x1744 = 035yn8, official_language(?x1353, ?x11590), region(?x7207, ?x512), film_crew_role(?x7207, ?x468), film_release_distribution_medium(?x7207, ?x81), nominated_for(?x835, ?x1262), genre(?x7207, ?x811) *> conf = 0.50 ranks of expected_values: 30, 538, 539, 1270 EVAL 0349s language! 0272_vz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 45.000 7.000 0.860 http://example.org/film/film/language EVAL 0349s language! 03cw411 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 45.000 7.000 0.860 http://example.org/film/film/language EVAL 0349s language! 0dnqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 7.000 0.860 http://example.org/film/film/language EVAL 0349s language! 0gzy02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 7.000 0.860 http://example.org/film/film/language #13933-0m63c PRED entity: 0m63c PRED relation: award PRED expected values: 054krc => 82 concepts (75 used for prediction) PRED predicted values (max 10 best out of 242): 02qvyrt (0.33 #1410, 0.28 #5395, 0.27 #470), 054krc (0.33 #1410, 0.28 #5395, 0.27 #470), 02r22gf (0.33 #1410, 0.28 #5395, 0.27 #470), 057xs89 (0.33 #1410, 0.28 #5395, 0.27 #470), 0g_w (0.33 #1410, 0.28 #5395, 0.27 #470), 0m7yy (0.23 #1306, 0.13 #2011, 0.09 #366), 040njc (0.20 #7, 0.14 #2121, 0.12 #1417), 027dtxw (0.20 #4, 0.09 #239, 0.09 #1414), 02ppm4q (0.20 #115, 0.06 #13137, 0.06 #2229), 0gq9h (0.19 #1472, 0.15 #2176, 0.11 #1237) >> Best rule #1410 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 0m123; >> query: (?x7693, ?x637) <- nominated_for(?x637, ?x7693), award_winner(?x7693, ?x2156), film(?x2156, ?x148) >> conf = 0.33 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0m63c award 054krc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 75.000 0.334 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13932-03y1mlp PRED entity: 03y1mlp PRED relation: place_of_birth PRED expected values: 0j5g9 => 96 concepts (34 used for prediction) PRED predicted values (max 10 best out of 50): 02jx1 (0.20 #45, 0.01 #2861), 0fg6k (0.20 #360), 03h64 (0.15 #2202, 0.04 #1498, 0.04 #794), 04jpl (0.09 #11279, 0.07 #6349, 0.07 #3529), 02_286 (0.07 #8474, 0.07 #7065, 0.07 #9178), 01_d4 (0.07 #1474, 0.07 #770, 0.03 #12747), 05qtj (0.07 #1575, 0.07 #871, 0.03 #12143), 030qb3t (0.06 #4280, 0.04 #7100, 0.04 #15554), 01914 (0.04 #2116), 0kf9p (0.04 #1858, 0.04 #1154, 0.02 #2562) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0lpjn; 04_1nk; 02g1jh; >> query: (?x1500, 02jx1) <- nominated_for(?x1500, ?x3693), award(?x1500, ?x507), profession(?x1500, ?x7630), ?x3693 = 03r0g9 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03y1mlp place_of_birth 0j5g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 34.000 0.200 http://example.org/people/person/place_of_birth #13931-0k4fz PRED entity: 0k4fz PRED relation: film_release_region PRED expected values: 0jgd => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 198): 09c7w0 (0.92 #10593, 0.92 #9952, 0.92 #15567), 059j2 (0.88 #4848, 0.85 #6615, 0.83 #1639), 0345h (0.87 #198, 0.85 #4850, 0.84 #5011), 0jgd (0.86 #1608, 0.82 #6584, 0.80 #165), 03gj2 (0.85 #6607, 0.83 #1631, 0.73 #188), 01znc_ (0.79 #1650, 0.75 #6626, 0.67 #1810), 0d060g (0.77 #6588, 0.76 #1612, 0.76 #4821), 05b4w (0.76 #1674, 0.75 #6650, 0.73 #231), 015fr (0.76 #6598, 0.75 #4831, 0.74 #4992), 0154j (0.76 #6586, 0.75 #4819, 0.70 #4980) >> Best rule #10593 for best value: >> intensional similarity = 4 >> extensional distance = 690 >> proper extension: 03s6l2; 03s5lz; 0pdp8; 08gg47; 05m_jsg; 019kyn; 064q5v; 03t95n; 023g6w; 0ndsl1x; ... >> query: (?x4841, 09c7w0) <- film_release_region(?x4841, ?x4737), award_winner(?x4841, ?x2716), country(?x471, ?x4737), adjoins(?x4737, ?x583) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1608 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 03twd6; *> query: (?x4841, 0jgd) <- film_release_region(?x4841, ?x4737), award_winner(?x4841, ?x2716), ?x4737 = 07twz *> conf = 0.86 ranks of expected_values: 4 EVAL 0k4fz film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 119.000 119.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13930-044lbv PRED entity: 044lbv PRED relation: team! PRED expected values: 0356lc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 8): 07y9k (0.43 #28, 0.40 #12, 0.39 #20), 0356lc (0.25 #41, 0.24 #57, 0.24 #33), 021q23 (0.20 #16, 0.11 #24, 0.10 #32), 0355pl (0.14 #115, 0.10 #163, 0.10 #155), 059yj (0.10 #293, 0.08 #381, 0.07 #413), 0h69c (0.08 #294, 0.07 #382, 0.06 #414), 03zv9 (0.07 #162, 0.07 #234, 0.06 #274), 01ddbl (0.02 #447, 0.02 #455, 0.02 #463) >> Best rule #28 for best value: >> intensional similarity = 8 >> extensional distance = 19 >> proper extension: 02fbb5; 024nj1; >> query: (?x9967, 07y9k) <- teams(?x6428, ?x9967), adjoins(?x1144, ?x6428), participating_countries(?x1931, ?x6428), exported_to(?x87, ?x6428), administrative_area_type(?x6428, ?x2792), sport(?x9967, ?x471), country(?x1121, ?x6428), official_language(?x6428, ?x6753) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #41 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 30 *> proper extension: 03yl2t; 03ytj1; 04r7f2; *> query: (?x9967, 0356lc) <- team(?x530, ?x9967), team(?x203, ?x9967), team(?x63, ?x9967), team(?x60, ?x9967), ?x63 = 02sdk9v, ?x530 = 02_j1w, teams(?x6428, ?x9967), ?x60 = 02nzb8, ?x203 = 0dgrmp, country(?x1121, ?x6428), jurisdiction_of_office(?x265, ?x6428), adjoins(?x1144, ?x6428) *> conf = 0.25 ranks of expected_values: 2 EVAL 044lbv team! 0356lc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.429 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #13929-0mdyn PRED entity: 0mdyn PRED relation: location PRED expected values: 0281y0 => 138 concepts (116 used for prediction) PRED predicted values (max 10 best out of 294): 02_286 (0.50 #841, 0.22 #12101, 0.22 #8078), 030qb3t (0.32 #8124, 0.28 #13755, 0.26 #20994), 01x73 (0.20 #96, 0.02 #19398, 0.02 #10551), 0r0m6 (0.17 #3435, 0.06 #6651, 0.05 #8259), 01531 (0.13 #4983, 0.08 #5787, 0.08 #3375), 04lh6 (0.12 #1240, 0.07 #5261, 0.07 #4457), 02jx1 (0.12 #875, 0.05 #10526, 0.05 #12135), 013yq (0.12 #923, 0.04 #16204, 0.04 #17008), 02dtg (0.12 #828, 0.04 #10479, 0.02 #8065), 010rvx (0.12 #1562, 0.02 #12822, 0.01 #16039) >> Best rule #841 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01f5q5; >> query: (?x7836, 02_286) <- participant(?x7836, ?x4960), profession(?x7836, ?x1032), ?x1032 = 02hrh1q, ?x4960 = 09889g >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0mdyn location 0281y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 116.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #13928-02q87z6 PRED entity: 02q87z6 PRED relation: language PRED expected values: 02h40lc => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 62): 02h40lc (0.91 #894, 0.90 #1549, 0.89 #954), 04h9h (0.20 #43, 0.06 #399, 0.05 #520), 05zjd (0.20 #26, 0.03 #3584, 0.03 #3644), 064_8sq (0.18 #438, 0.17 #81, 0.14 #854), 06nm1 (0.17 #70, 0.10 #963, 0.10 #784), 03_9r (0.17 #69, 0.07 #723, 0.05 #2512), 04306rv (0.13 #600, 0.13 #421, 0.09 #1017), 02bjrlw (0.10 #417, 0.09 #596, 0.08 #357), 06b_j (0.08 #439, 0.06 #1035, 0.06 #796), 0653m (0.06 #548, 0.04 #964, 0.04 #1500) >> Best rule #894 for best value: >> intensional similarity = 3 >> extensional distance = 305 >> proper extension: 0gx9rvq; 0jjy0; 0m491; 03m8y5; 03l6q0; 0gj8nq2; 03z106; 01jwxx; 02x8fs; 047vnkj; ... >> query: (?x5964, 02h40lc) <- film(?x494, ?x5964), country(?x5964, ?x94), cinematography(?x5964, ?x185) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q87z6 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.906 http://example.org/film/film/language #13927-047p7fr PRED entity: 047p7fr PRED relation: film_crew_role PRED expected values: 02_n3z 0dxtw 02vs3x5 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 24): 01vx2h (0.53 #562, 0.50 #358, 0.50 #914), 0dxtw (0.47 #2903, 0.47 #357, 0.44 #1151), 015h31 (0.43 #151, 0.21 #559, 0.21 #355), 01pvkk (0.38 #2964, 0.38 #2993, 0.33 #475), 02_n3z (0.37 #906, 0.36 #996, 0.33 #437), 0263ycg (0.29 #130, 0.29 #14, 0.18 #363), 033smt (0.21 #166, 0.21 #370, 0.16 #457), 02rh1dz (0.20 #1150, 0.18 #1267, 0.17 #1534), 020xn5 (0.17 #558, 0.15 #354, 0.14 #121), 02vs3x5 (0.15 #105, 0.14 #280, 0.12 #222) >> Best rule #562 for best value: >> intensional similarity = 7 >> extensional distance = 51 >> proper extension: 0g3zrd; 0415ggl; 05q7874; >> query: (?x2961, 01vx2h) <- film_crew_role(?x2961, ?x5136), film_crew_role(?x2961, ?x2472), ?x5136 = 089g0h, film(?x3713, ?x2961), ?x2472 = 01xy5l_, genre(?x2961, ?x53), film(?x1550, ?x2961) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #2903 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 852 *> proper extension: 03h_yy; 09p35z; 0963mq; 09cr8; 050gkf; 0kvgxk; 021y7yw; 014zwb; 05_5rjx; 01242_; ... *> query: (?x2961, 0dxtw) <- film_crew_role(?x2961, ?x468), language(?x2961, ?x254), film_crew_role(?x9154, ?x468), film_crew_role(?x8690, ?x468), film_crew_role(?x3055, ?x468), ?x3055 = 0x25q, ?x9154 = 01s7w3, ?x8690 = 027x7z5 *> conf = 0.47 ranks of expected_values: 2, 5, 10 EVAL 047p7fr film_crew_role 02vs3x5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 139.000 139.000 0.528 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 047p7fr film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 139.000 139.000 0.528 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 047p7fr film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 139.000 139.000 0.528 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13926-02jsgf PRED entity: 02jsgf PRED relation: film PRED expected values: 03qnvdl => 87 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1078): 016z9n (0.71 #3940, 0.70 #2155, 0.59 #5725), 029zqn (0.51 #26783, 0.51 #26784, 0.50 #1786), 0kvgxk (0.51 #26783, 0.51 #26784, 0.46 #85712), 03ntbmw (0.29 #1766, 0.14 #5337, 0.12 #7122), 01q7h2 (0.20 #3358, 0.14 #5143, 0.14 #1572), 0c9k8 (0.20 #2271, 0.14 #4056, 0.12 #5841), 0260bz (0.14 #3906, 0.14 #335, 0.12 #5691), 095zlp (0.14 #60, 0.10 #1846, 0.07 #3631), 0gmblvq (0.14 #674, 0.10 #2460, 0.07 #4245), 01hv3t (0.14 #1292, 0.10 #3078, 0.07 #4863) >> Best rule #3940 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0410cp; >> query: (?x4103, 016z9n) <- award_nominee(?x11100, ?x4103), ?x11100 = 057_yx, award(?x4103, ?x375) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #16307 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 02qgqt; 02p65p; 03x3qv; 01qscs; 0p_pd; 09fb5; 0187y5; 04bd8y; 0sz28; 01yb09; ... *> query: (?x4103, 03qnvdl) <- award_nominee(?x11100, ?x4103), film(?x11100, ?x6036), ?x6036 = 040_lv *> conf = 0.01 ranks of expected_values: 1023 EVAL 02jsgf film 03qnvdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 87.000 54.000 0.714 http://example.org/film/actor/film./film/performance/film #13925-02sjf5 PRED entity: 02sjf5 PRED relation: award PRED expected values: 02x73k6 0ck27z => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 245): 0ck27z (0.40 #92, 0.34 #8092, 0.29 #9692), 09sb52 (0.36 #17640, 0.28 #16440, 0.27 #8040), 0gkts9 (0.30 #167, 0.16 #24001, 0.13 #34003), 0gqyl (0.30 #105, 0.13 #34003, 0.12 #29202), 0fbtbt (0.20 #231, 0.16 #24001, 0.12 #36804), 0fbvqf (0.20 #47, 0.13 #34003, 0.12 #36804), 0bfvw2 (0.20 #15, 0.13 #34003, 0.12 #29202), 05b4l5x (0.20 #6, 0.09 #406, 0.07 #4806), 0cqhk0 (0.19 #8037, 0.19 #5237, 0.17 #437), 0gq_v (0.18 #2822, 0.08 #4022, 0.07 #6422) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06r3p2; >> query: (?x1204, 0ck27z) <- nominated_for(?x1204, ?x4898), award(?x1204, ?x693), ?x4898 = 017f3m >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 90 EVAL 02sjf5 award 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02sjf5 award 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 112.000 112.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13924-02ch1w PRED entity: 02ch1w PRED relation: nominated_for PRED expected values: 016z9n => 91 concepts (51 used for prediction) PRED predicted values (max 10 best out of 318): 016z9n (0.38 #1961, 0.25 #37250, 0.24 #42110), 02c7k4 (0.25 #37250, 0.24 #42110, 0.22 #22673), 0291hr (0.25 #37250, 0.24 #42110, 0.22 #22673), 03kx49 (0.25 #37250, 0.24 #42110, 0.22 #22673), 095zlp (0.19 #1672, 0.02 #68025, 0.02 #66403), 011yd2 (0.11 #1948, 0.07 #72892, 0.02 #68025), 09m6kg (0.11 #1649, 0.07 #72892), 02k_4g (0.09 #108, 0.07 #72892, 0.05 #1727), 03ln8b (0.09 #302, 0.03 #22975, 0.03 #1921), 049xgc (0.09 #886, 0.03 #2505, 0.01 #15461) >> Best rule #1961 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 0525b; >> query: (?x5840, 016z9n) <- award_nominee(?x525, ?x5840), ?x525 = 017149, award(?x5840, ?x704) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ch1w nominated_for 016z9n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 51.000 0.378 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #13923-0chghy PRED entity: 0chghy PRED relation: religion PRED expected values: 0c8wxp => 227 concepts (227 used for prediction) PRED predicted values (max 10 best out of 33): 0c8wxp (0.69 #3767, 0.61 #4230, 0.60 #269), 01lp8 (0.63 #3763, 0.61 #4226, 0.58 #925), 05sfs (0.54 #3764, 0.47 #4227, 0.42 #926), 051kv (0.54 #3766, 0.46 #4229, 0.42 #928), 019cr (0.53 #3772, 0.45 #4235, 0.38 #934), 0631_ (0.53 #3769, 0.44 #4232, 0.38 #931), 04pk9 (0.49 #3778, 0.41 #4241, 0.31 #940), 05w5d (0.48 #3782, 0.41 #4245, 0.35 #944), 03_gx (0.45 #375, 0.40 #507, 0.35 #3774), 021_0p (0.37 #3777, 0.31 #4240, 0.23 #939) >> Best rule #3767 for best value: >> intensional similarity = 2 >> extensional distance = 81 >> proper extension: 019fv4; >> query: (?x390, 0c8wxp) <- contains(?x390, ?x901), religion(?x390, ?x492) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0chghy religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 227.000 227.000 0.687 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #13922-02q7fl9 PRED entity: 02q7fl9 PRED relation: titles! PRED expected values: 01z4y => 116 concepts (80 used for prediction) PRED predicted values (max 10 best out of 73): 07s9rl0 (0.67 #103, 0.57 #308, 0.45 #2255), 01z4y (0.58 #547, 0.33 #36, 0.31 #1162), 05mrx8 (0.44 #204, 0.32 #409, 0.25 #1536), 0cshrf (0.44 #204, 0.32 #409, 0.25 #1536), 01t_vv (0.44 #204, 0.32 #409, 0.25 #1536), 060__y (0.44 #204, 0.32 #409, 0.25 #1536), 05p553 (0.44 #204, 0.32 #409, 0.25 #1536), 04xvlr (0.35 #2258, 0.29 #719, 0.29 #311), 024qqx (0.29 #693, 0.19 #1000, 0.17 #2130), 07ssc (0.25 #521, 0.17 #112, 0.14 #317) >> Best rule #103 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 019vhk; 0c9k8; 0p_qr; 0pv54; >> query: (?x5976, 07s9rl0) <- genre(?x5976, ?x3506), costume_design_by(?x5976, ?x3685), award(?x5976, ?x451), ?x3506 = 03mqtr >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #547 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 03p2xc; *> query: (?x5976, 01z4y) <- genre(?x5976, ?x7217), film(?x1726, ?x5976), film(?x1104, ?x5976), ?x7217 = 0cshrf *> conf = 0.58 ranks of expected_values: 2 EVAL 02q7fl9 titles! 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 80.000 0.667 http://example.org/media_common/netflix_genre/titles #13921-02825nf PRED entity: 02825nf PRED relation: film! PRED expected values: 05mlqj 0378zn => 85 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1064): 05bnp0 (0.67 #99781, 0.64 #101862, 0.46 #27019), 051wwp (0.50 #873, 0.11 #2952, 0.04 #29970), 089kpp (0.40 #2079, 0.03 #43645, 0.02 #87306), 0fby2t (0.36 #4910, 0.09 #6988, 0.04 #9067), 04t2l2 (0.27 #4185, 0.03 #33281, 0.02 #37437), 07m77x (0.25 #1539, 0.18 #5696, 0.02 #47262), 060j8b (0.25 #1102, 0.11 #3181, 0.04 #9416), 028k57 (0.25 #788, 0.11 #2867, 0.02 #7023), 015wnl (0.25 #649, 0.03 #19354, 0.03 #15198), 048lv (0.25 #221, 0.03 #29318, 0.02 #8535) >> Best rule #99781 for best value: >> intensional similarity = 3 >> extensional distance = 855 >> proper extension: 01f3p_; 0clpml; >> query: (?x7629, ?x123) <- nominated_for(?x123, ?x7629), participant(?x1017, ?x123), film(?x123, ?x1219) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #22385 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 130 *> proper extension: 0gx1bnj; 0jjy0; 0gj8t_b; 0gydcp7; 07f_7h; 0gj8nq2; 080nwsb; 0blpg; 09v71cj; 06tpmy; ... *> query: (?x7629, 05mlqj) <- film_release_region(?x7629, ?x1264), film(?x3013, ?x7629), produced_by(?x7629, ?x364), ?x1264 = 0345h *> conf = 0.02 ranks of expected_values: 695 EVAL 02825nf film! 0378zn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 49.000 0.665 http://example.org/film/actor/film./film/performance/film EVAL 02825nf film! 05mlqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 85.000 49.000 0.665 http://example.org/film/actor/film./film/performance/film #13920-0dr89x PRED entity: 0dr89x PRED relation: film_release_region PRED expected values: 0chghy 05qhw 077qn => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 125): 05qhw (0.84 #171, 0.79 #795, 0.79 #483), 0chghy (0.83 #790, 0.82 #1883, 0.80 #1103), 015fr (0.83 #798, 0.80 #174, 0.79 #486), 0154j (0.77 #784, 0.77 #1097, 0.76 #1877), 0b90_r (0.77 #783, 0.72 #1096, 0.70 #1876), 03spz (0.76 #252, 0.72 #876, 0.69 #1189), 05b4w (0.73 #844, 0.70 #1937, 0.70 #1157), 06t2t (0.72 #217, 0.70 #841, 0.69 #1154), 01p1v (0.69 #51, 0.52 #207, 0.49 #831), 03rt9 (0.65 #794, 0.65 #1107, 0.62 #1887) >> Best rule #171 for best value: >> intensional similarity = 6 >> extensional distance = 23 >> proper extension: 0gtvrv3; >> query: (?x5688, 05qhw) <- film_release_region(?x5688, ?x3699), film_release_region(?x5688, ?x304), film_release_region(?x5688, ?x205), ?x3699 = 012wgb, ?x304 = 0d0vqn, ?x205 = 03rjj >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 30 EVAL 0dr89x film_release_region 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 81.000 81.000 0.840 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dr89x film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.840 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dr89x film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.840 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13919-0166v PRED entity: 0166v PRED relation: geographic_distribution! PRED expected values: 04mvp8 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 35): 0d29z (0.20 #261, 0.19 #221, 0.19 #861), 071x0k (0.15 #603, 0.14 #723, 0.14 #843), 04mvp8 (0.14 #1121, 0.12 #114, 0.11 #874), 01rv7x (0.07 #102, 0.06 #62, 0.05 #622), 0g6ff (0.07 #610, 0.06 #1090, 0.06 #730), 0g48m4 (0.06 #1762, 0.06 #2202, 0.05 #1962), 01xhh5 (0.05 #620, 0.05 #740, 0.04 #940), 06mvq (0.03 #138, 0.03 #178, 0.03 #218), 013b6_ (0.03 #147, 0.03 #427, 0.03 #507), 012f86 (0.03 #432, 0.03 #512, 0.02 #632) >> Best rule #261 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 06w92; >> query: (?x4421, 0d29z) <- currency(?x4421, ?x170), taxonomy(?x4421, ?x939), ?x939 = 04n6k >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1121 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 142 *> proper extension: 059g4; *> query: (?x4421, ?x9148) <- adjoins(?x792, ?x4421), contains(?x2467, ?x4421), geographic_distribution(?x9148, ?x792) *> conf = 0.14 ranks of expected_values: 3 EVAL 0166v geographic_distribution! 04mvp8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 105.000 0.196 http://example.org/people/ethnicity/geographic_distribution #13918-01njxvw PRED entity: 01njxvw PRED relation: profession PRED expected values: 01c72t => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.82 #3176, 0.79 #6483, 0.79 #6633), 01c72t (0.68 #775, 0.65 #1076, 0.64 #475), 09jwl (0.60 #5283, 0.58 #620, 0.55 #5434), 01c8w0 (0.55 #459, 0.36 #1511, 0.33 #1060), 01d_h8 (0.50 #156, 0.39 #3167, 0.36 #4518), 0nbcg (0.44 #5296, 0.42 #5748, 0.41 #6350), 016z4k (0.40 #5267, 0.38 #5418, 0.34 #1205), 0dz3r (0.39 #5265, 0.38 #5416, 0.36 #1956), 0gl2ny2 (0.30 #369, 0.04 #8341, 0.01 #15251), 0dxtg (0.29 #9339, 0.28 #9189, 0.28 #7534) >> Best rule #3176 for best value: >> intensional similarity = 4 >> extensional distance = 175 >> proper extension: 0lzb8; 0jf1b; 015pxr; 027l0b; 06b_0; 01cwkq; 01lct6; >> query: (?x10949, 02hrh1q) <- nominated_for(?x10949, ?x1415), award(?x10949, ?x1079), category(?x10949, ?x134), people(?x1575, ?x10949) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #775 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 09hnb; 017l4; 0bk1p; *> query: (?x10949, 01c72t) <- artists(?x4910, ?x10949), award(?x10949, ?x2379), ?x2379 = 02qvyrt *> conf = 0.68 ranks of expected_values: 2 EVAL 01njxvw profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.819 http://example.org/people/person/profession #13917-0ltv PRED entity: 0ltv PRED relation: athlete PRED expected values: 01qklj => 58 concepts (26 used for prediction) PRED predicted values (max 10 best out of 100): 02hg53 (0.04 #3108, 0.02 #2398), 054c1 (0.04 #3106, 0.02 #2396), 02m501 (0.02 #2373, 0.02 #3083, 0.02 #3368), 02cg2v (0.02 #2419, 0.02 #3129), 04g9sq (0.02 #2418, 0.02 #3128), 095nx (0.02 #2416, 0.02 #3126), 02lm0t (0.02 #2410, 0.02 #3120), 01jz6d (0.02 #2409, 0.02 #3119), 02_nkp (0.02 #2407, 0.02 #3117), 049sb (0.02 #2402, 0.02 #3112) >> Best rule #3108 for best value: >> intensional similarity = 8 >> extensional distance = 51 >> proper extension: 018jz; >> query: (?x10308, 02hg53) <- titles(?x10308, ?x8955), titles(?x10308, ?x6510), country(?x8955, ?x512), music(?x6510, ?x4727), production_companies(?x8955, ?x2156), film(?x2156, ?x148), produced_by(?x8955, ?x3568), language(?x8955, ?x254) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ltv athlete 01qklj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 26.000 0.038 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #13916-033x5p PRED entity: 033x5p PRED relation: school! PRED expected values: 04f4z1k => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 20): 0f4vx0 (0.20 #331, 0.18 #611, 0.18 #711), 02qw1zx (0.16 #705, 0.15 #505, 0.14 #865), 025tn92 (0.14 #613, 0.13 #713, 0.12 #333), 05vsb7 (0.13 #701, 0.11 #561, 0.10 #861), 02x2khw (0.13 #23, 0.09 #643, 0.09 #703), 092j54 (0.13 #569, 0.13 #709, 0.12 #649), 0g3zpp (0.13 #62, 0.10 #702, 0.09 #562), 09l0x9 (0.12 #712, 0.12 #572, 0.11 #332), 02pq_x5 (0.12 #17, 0.11 #337, 0.11 #657), 03nt7j (0.11 #67, 0.11 #707, 0.10 #647) >> Best rule #331 for best value: >> intensional similarity = 4 >> extensional distance = 94 >> proper extension: 03wv2g; >> query: (?x4363, 0f4vx0) <- currency(?x4363, ?x170), school(?x2820, ?x4363), ?x2820 = 0jmj7, contains(?x1227, ?x4363) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 04wlz2; 01pl14; 06pwq; 07w3r; 07wlf; 01swxv; 02fgdx; 01pq4w; 0221g_; 037njl; ... *> query: (?x4363, 04f4z1k) <- currency(?x4363, ?x170), country(?x4363, ?x94), ?x94 = 09c7w0, school(?x1160, ?x4363) *> conf = 0.10 ranks of expected_values: 12 EVAL 033x5p school! 04f4z1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 181.000 181.000 0.198 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #13915-01400v PRED entity: 01400v PRED relation: major_field_of_study! PRED expected values: 016t_3 02_xgp2 => 59 concepts (33 used for prediction) PRED predicted values (max 10 best out of 20): 016t_3 (0.83 #150, 0.81 #270, 0.80 #211), 02h4rq6 (0.83 #149, 0.80 #210, 0.80 #189), 02_xgp2 (0.83 #157, 0.79 #178, 0.78 #340), 04zx3q1 (0.75 #148, 0.71 #105, 0.70 #124), 027f2w (0.70 #124, 0.58 #61, 0.52 #103), 02m4yg (0.70 #124, 0.58 #61, 0.52 #103), 01rr_d (0.70 #124, 0.58 #61, 0.52 #103), 02cq61 (0.70 #124, 0.58 #61, 0.52 #103), 0bjrnt (0.69 #40, 0.60 #89, 0.58 #61), 07s6fsf (0.58 #61, 0.52 #103, 0.50 #146) >> Best rule #150 for best value: >> intensional similarity = 11 >> extensional distance = 10 >> proper extension: 06ms6; 04rjg; 04x_3; 0fdys; >> query: (?x12035, 016t_3) <- major_field_of_study(?x9443, ?x12035), major_field_of_study(?x9200, ?x12035), major_field_of_study(?x6127, ?x12035), major_field_of_study(?x1682, ?x12035), ?x9200 = 0dzst, contains(?x1426, ?x9443), student(?x6127, ?x2143), institution(?x865, ?x9443), profession(?x2143, ?x319), award_nominee(?x237, ?x2143), ?x1426 = 07z1m >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01400v major_field_of_study! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 59.000 33.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 01400v major_field_of_study! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 33.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #13914-01wd02c PRED entity: 01wd02c PRED relation: religion PRED expected values: 0n2g => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 33): 03_gx (0.63 #3975, 0.27 #3138, 0.26 #541), 0c8wxp (0.40 #2557, 0.37 #5817, 0.37 #5465), 0kpl (0.35 #449, 0.34 #3134, 0.34 #3662), 0flw86 (0.19 #3963, 0.09 #5328, 0.06 #4976), 0n2g (0.18 #320, 0.17 #980, 0.15 #1156), 0kq2 (0.16 #1733, 0.16 #1337, 0.14 #501), 092bf5 (0.12 #59, 0.11 #411, 0.09 #5328), 051kv (0.12 #48, 0.09 #5328, 0.05 #444), 03j6c (0.10 #2396, 0.10 #460, 0.09 #2572), 04pk9 (0.09 #5328, 0.07 #767, 0.07 #723) >> Best rule #3975 for best value: >> intensional similarity = 3 >> extensional distance = 259 >> proper extension: 01w3v; 0mcf4; >> query: (?x6796, 03_gx) <- religion(?x6796, ?x109), religion(?x4815, ?x109), ?x4815 = 05kyr >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #320 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 0j3v; 0372p; 01dvtx; 043s3; 07c37; 02ln1; 06jkm; 047g6; *> query: (?x6796, 0n2g) <- influenced_by(?x6796, ?x6015), student(?x892, ?x6796), ?x6015 = 05qmj, influenced_by(?x3858, ?x6796) *> conf = 0.18 ranks of expected_values: 5 EVAL 01wd02c religion 0n2g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 190.000 190.000 0.632 http://example.org/people/person/religion #13913-01vsksr PRED entity: 01vsksr PRED relation: artists! PRED expected values: 03_d0 => 122 concepts (54 used for prediction) PRED predicted values (max 10 best out of 296): 0xhtw (0.66 #4960, 0.50 #5578, 0.38 #8670), 064t9 (0.58 #12992, 0.55 #2797, 0.48 #9901), 05bt6j (0.41 #2209, 0.31 #3753, 0.25 #9931), 01lyv (0.40 #34, 0.36 #343, 0.20 #10231), 0dl5d (0.39 #4963, 0.36 #5581, 0.22 #8673), 06cqb (0.36 #312, 0.30 #3, 0.11 #6800), 0gywn (0.30 #6854, 0.30 #57, 0.27 #366), 016clz (0.29 #7419, 0.26 #7732, 0.26 #8658), 08jyyk (0.29 #5628, 0.26 #8720, 0.23 #5010), 0cx7f (0.27 #5698, 0.23 #5080, 0.16 #8790) >> Best rule #4960 for best value: >> intensional similarity = 6 >> extensional distance = 62 >> proper extension: 067mj; 05563d; 07yg2; 0394y; 047cx; 06nv27; 07bzp; 07r1_; 0b_xm; 03k3b; ... >> query: (?x6351, 0xhtw) <- artists(?x7440, ?x6351), artists(?x2809, ?x6351), ?x2809 = 05w3f, artists(?x7440, ?x5623), parent_genre(?x482, ?x7440), ?x5623 = 01vsyg9 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #309 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 07mvp; *> query: (?x6351, ?x505) <- artists(?x12513, ?x6351), artists(?x7440, ?x6351), artists(?x3319, ?x6351), artists(?x2809, ?x6351), ?x2809 = 05w3f, ?x7440 = 0155w, ?x3319 = 06j6l, parent_genre(?x12513, ?x505) *> conf = 0.24 ranks of expected_values: 12 EVAL 01vsksr artists! 03_d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 122.000 54.000 0.656 http://example.org/music/genre/artists #13912-056vv PRED entity: 056vv PRED relation: olympics PRED expected values: 0kbws => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 39): 09n48 (0.69 #42, 0.47 #120, 0.44 #3), 0kbws (0.62 #208, 0.61 #559, 0.60 #13), 0kbvb (0.57 #202, 0.56 #7, 0.56 #280), 0kbvv (0.57 #219, 0.55 #63, 0.52 #24), 0jdk_ (0.48 #64, 0.48 #25, 0.35 #298), 0swbd (0.48 #49, 0.44 #10, 0.30 #244), 0swff (0.41 #61, 0.36 #22, 0.23 #256), 0sxrz (0.40 #20, 0.31 #59, 0.23 #254), 0l6m5 (0.34 #48, 0.24 #9, 0.20 #87), 0jhn7 (0.32 #26, 0.31 #299, 0.31 #65) >> Best rule #42 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 0j1z8; 02wt0; >> query: (?x2979, 09n48) <- currency(?x2979, ?x170), film_release_region(?x5877, ?x2979), ?x5877 = 02qyv3h >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #208 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 05c74; *> query: (?x2979, 0kbws) <- currency(?x2979, ?x170), film_release_region(?x1150, ?x2979), ?x1150 = 0h3xztt *> conf = 0.62 ranks of expected_values: 2 EVAL 056vv olympics 0kbws CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.690 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #13911-02j9z PRED entity: 02j9z PRED relation: locations! PRED expected values: 081pw 0cm2xh 02kxg_ 05t2fh4 => 153 concepts (129 used for prediction) PRED predicted values (max 10 best out of 128): 03jqfx (0.62 #1631, 0.24 #1511, 0.19 #1392), 081pw (0.40 #239, 0.33 #1195, 0.33 #120), 01gqg3 (0.40 #319, 0.20 #557, 0.15 #2471), 0jnh (0.20 #326, 0.15 #3321, 0.10 #1641), 086m1 (0.20 #301, 0.14 #1377, 0.14 #1138), 0cm2xh (0.20 #281, 0.12 #639, 0.11 #758), 09r3f (0.20 #336, 0.07 #574, 0.06 #694), 06k75 (0.15 #6046, 0.13 #8926, 0.12 #7729), 07_nf (0.15 #3291, 0.07 #534, 0.06 #654), 05t2fh4 (0.14 #1426, 0.14 #1187, 0.14 #1784) >> Best rule #1631 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 07l75; 0cgs4; >> query: (?x455, 03jqfx) <- locations(?x10206, ?x455), entity_involved(?x10206, ?x9602), ?x9602 = 0285m87 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #239 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0j0k; *> query: (?x455, 081pw) <- contains(?x455, ?x1003), film_release_region(?x66, ?x1003), countries_within(?x455, ?x87) *> conf = 0.40 ranks of expected_values: 2, 6, 10, 58 EVAL 02j9z locations! 05t2fh4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 153.000 129.000 0.619 http://example.org/time/event/locations EVAL 02j9z locations! 02kxg_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 153.000 129.000 0.619 http://example.org/time/event/locations EVAL 02j9z locations! 0cm2xh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 153.000 129.000 0.619 http://example.org/time/event/locations EVAL 02j9z locations! 081pw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 153.000 129.000 0.619 http://example.org/time/event/locations #13910-056878 PRED entity: 056878 PRED relation: ceremony! PRED expected values: 01c92g 0257yf 01c99j 025m98 03t5n3 0249fn 03ncb2 03nc9d 023vrq 0257__ => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 195): 01c99j (0.86 #3712, 0.83 #3351, 0.78 #3892), 0249fn (0.86 #3724, 0.83 #3363, 0.78 #3904), 023vrq (0.79 #3755, 0.77 #2513, 0.75 #3394), 0257__ (0.78 #3228, 0.77 #2513, 0.77 #3589), 025m98 (0.77 #2513, 0.72 #3895, 0.71 #3715), 03ncb2 (0.77 #2513, 0.72 #3930, 0.71 #3750), 0257yf (0.77 #2513, 0.69 #3505, 0.67 #3144), 03nc9d (0.77 #2513, 0.67 #3393, 0.67 #3213), 01c92g (0.77 #2513, 0.67 #3291, 0.64 #3652), 031b91 (0.77 #2513, 0.64 #3761, 0.62 #3581) >> Best rule #3712 for best value: >> intensional similarity = 21 >> extensional distance = 12 >> proper extension: 01s695; >> query: (?x2186, 01c99j) <- award_winner(?x2186, ?x12102), award_winner(?x2186, ?x3426), award_winner(?x2186, ?x2698), award_winner(?x2186, ?x217), award_winner(?x2186, ?x215), ceremony(?x8505, ?x2186), ceremony(?x2563, ?x2186), ceremony(?x1584, ?x2186), ?x1584 = 02gx2k, award(?x215, ?x537), artist(?x2039, ?x215), artists(?x505, ?x2698), award_winner(?x487, ?x2698), artist(?x4868, ?x2698), location_of_ceremony(?x3426, ?x4061), artists(?x302, ?x217), award_nominee(?x217, ?x1060), award_winner(?x1128, ?x215), nationality(?x12102, ?x94), ?x8505 = 02fm4d, award(?x250, ?x2563) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 13 EVAL 056878 ceremony! 0257__ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 056878 ceremony! 023vrq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 056878 ceremony! 03nc9d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 056878 ceremony! 03ncb2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 056878 ceremony! 0249fn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 056878 ceremony! 03t5n3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 40.000 40.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 056878 ceremony! 025m98 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 056878 ceremony! 01c99j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 056878 ceremony! 0257yf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 056878 ceremony! 01c92g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony #13909-0fr59 PRED entity: 0fr59 PRED relation: time_zones PRED expected values: 02hcv8 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.89 #42, 0.87 #474, 0.85 #314), 02lcqs (0.33 #70, 0.26 #96, 0.26 #109), 02fqwt (0.22 #157, 0.18 #184, 0.17 #131), 02hczc (0.15 #158, 0.12 #185, 0.11 #198), 02llzg (0.11 #331, 0.11 #398, 0.11 #384), 03bdv (0.04 #1129, 0.04 #1116, 0.04 #1156), 03plfd (0.04 #180, 0.03 #153, 0.03 #404), 042g7t (0.03 #154, 0.03 #181, 0.02 #141), 0gsrz4 (0.02 #918, 0.02 #972, 0.02 #986), 02lcrv (0.01 #137, 0.01 #150, 0.01 #163) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0bxbr; 0bxbb; 0fvwg; 0pc6x; 0ttxp; 0dhml; 0txrs; >> query: (?x1396, 02hcv8) <- contains(?x1767, ?x1396), ?x1767 = 04rrd, source(?x1396, ?x958), ?x958 = 0jbk9 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fr59 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.895 http://example.org/location/location/time_zones #13908-025t9b PRED entity: 025t9b PRED relation: film PRED expected values: 0ds33 031786 => 94 concepts (63 used for prediction) PRED predicted values (max 10 best out of 707): 011ywj (0.12 #1430, 0.03 #4993, 0.02 #17464), 04nm0n0 (0.12 #12472, 0.12 #8908, 0.12 #10690), 03177r (0.06 #462, 0.03 #4025, 0.01 #36088), 0dl6fv (0.06 #1482), 09fqgj (0.05 #1652, 0.04 #5215, 0.02 #3433), 0879bpq (0.05 #447, 0.03 #4010, 0.01 #23606), 034r25 (0.05 #742, 0.03 #90839, 0.03 #94402), 0jwmp (0.05 #547, 0.02 #4110, 0.01 #5891), 0bz6sq (0.05 #1509, 0.01 #5072), 0g83dv (0.05 #67685, 0.04 #112215, 0.03 #90839) >> Best rule #1430 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 05jm7; 0473q; 01wk7ql; >> query: (?x3872, 011ywj) <- award_nominee(?x3872, ?x539), people(?x743, ?x3872), ?x743 = 02w7gg >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #1270 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 05jm7; 0473q; 01wk7ql; *> query: (?x3872, 031786) <- award_nominee(?x3872, ?x539), people(?x743, ?x3872), ?x743 = 02w7gg *> conf = 0.04 ranks of expected_values: 35, 118 EVAL 025t9b film 031786 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 94.000 63.000 0.124 http://example.org/film/actor/film./film/performance/film EVAL 025t9b film 0ds33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 94.000 63.000 0.124 http://example.org/film/actor/film./film/performance/film #13907-0h14ln PRED entity: 0h14ln PRED relation: country PRED expected values: 0345h => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 66): 07ssc (0.44 #424, 0.43 #660, 0.42 #542), 0345h (0.36 #26, 0.33 #1494, 0.27 #434), 03rk0 (0.32 #154, 0.06 #4224, 0.03 #1506), 0ctw_b (0.24 #1704, 0.18 #138, 0.07 #22), 03rjj (0.24 #1704, 0.18 #532, 0.17 #414), 0d060g (0.24 #1704, 0.14 #8, 0.11 #357), 0d0vqn (0.24 #1704, 0.08 #359, 0.07 #10), 0k6nt (0.24 #1704, 0.07 #20, 0.06 #4224), 05b4w (0.24 #1704, 0.07 #42, 0.06 #4224), 01mjq (0.24 #1704, 0.06 #4224, 0.05 #150) >> Best rule #424 for best value: >> intensional similarity = 6 >> extensional distance = 61 >> proper extension: 02psgq; 0b85mm; >> query: (?x9292, 07ssc) <- film(?x398, ?x9292), titles(?x53, ?x9292), nominated_for(?x6165, ?x9292), country(?x9292, ?x789), ?x789 = 0f8l9c, film_crew_role(?x9292, ?x137) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 0g5q34q; *> query: (?x9292, 0345h) <- films(?x3530, ?x9292), genre(?x9292, ?x1626), film_crew_role(?x9292, ?x137), film(?x963, ?x9292), ?x1626 = 03q4nz *> conf = 0.36 ranks of expected_values: 2 EVAL 0h14ln country 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.444 http://example.org/film/film/country #13906-03lvwp PRED entity: 03lvwp PRED relation: film! PRED expected values: 030hbp => 90 concepts (33 used for prediction) PRED predicted values (max 10 best out of 476): 092kgw (0.11 #24908), 09l3p (0.08 #12455, 0.07 #31137, 0.05 #53965), 023v4_ (0.08 #12455, 0.07 #31137, 0.05 #53965), 0gcdzz (0.08 #12455, 0.05 #53965, 0.04 #4151), 049dyj (0.08 #12455, 0.05 #53965, 0.04 #4151), 0170pk (0.06 #281, 0.05 #2356, 0.05 #4432), 0lpjn (0.06 #477, 0.04 #2552, 0.04 #4628), 0154qm (0.06 #560, 0.04 #2635, 0.04 #4711), 030hbp (0.05 #53965, 0.04 #4151, 0.03 #12456), 086sj (0.05 #53965, 0.04 #4151, 0.03 #43588) >> Best rule #24908 for best value: >> intensional similarity = 4 >> extensional distance = 574 >> proper extension: 05f67hw; >> query: (?x6020, ?x5527) <- film_release_region(?x6020, ?x94), produced_by(?x6020, ?x5527), award_nominee(?x7571, ?x5527), nominated_for(?x7571, ?x7299) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #53965 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 737 *> proper extension: 01h72l; 028k2x; 07s8z_l; 06r1k; *> query: (?x6020, ?x1065) <- award_winner(?x6020, ?x2216), titles(?x53, ?x6020), award_nominee(?x1065, ?x2216), nominated_for(?x2216, ?x5561) *> conf = 0.05 ranks of expected_values: 9 EVAL 03lvwp film! 030hbp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 90.000 33.000 0.111 http://example.org/film/actor/film./film/performance/film #13905-0p0cw PRED entity: 0p0cw PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 171 concepts (94 used for prediction) PRED predicted values (max 10 best out of 7): 09c7w0 (0.91 #165, 0.90 #242, 0.90 #153), 04_1l0v (0.29 #1007, 0.24 #1193, 0.21 #1224), 01n4w (0.11 #662, 0.11 #177, 0.11 #289), 03rjj (0.04 #142, 0.02 #327, 0.02 #533), 02jx1 (0.02 #874, 0.02 #990, 0.01 #965), 03rt9 (0.02 #505, 0.02 #748, 0.02 #894), 0f8l9c (0.01 #468, 0.01 #508) >> Best rule #165 for best value: >> intensional similarity = 5 >> extensional distance = 90 >> proper extension: 0mw89; 0mwh1; 0m7d0; 0cymp; 0n5gq; 0l3n4; 0jrxx; 0jgm8; 0mpbx; 0njpq; >> query: (?x4516, 09c7w0) <- adjoins(?x7010, ?x4516), currency(?x4516, ?x170), source(?x4516, ?x958), contains(?x2982, ?x4516), county_seat(?x4516, ?x3987) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p0cw second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 94.000 0.913 http://example.org/location/country/second_level_divisions #13904-027qq9b PRED entity: 027qq9b PRED relation: nominated_for PRED expected values: 0147w8 => 46 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1047): 02_1kl (0.89 #4775, 0.84 #1591, 0.79 #11145), 0147w8 (0.40 #4735, 0.38 #7919, 0.29 #6327), 034fl9 (0.38 #7703, 0.29 #6111, 0.25 #1335), 0ddd0gc (0.28 #8154, 0.27 #14531, 0.27 #9748), 01g03q (0.24 #9331, 0.23 #10925, 0.22 #14115), 02rzdcp (0.22 #8448, 0.22 #14825, 0.21 #10042), 0kfv9 (0.22 #8219, 0.22 #14596, 0.21 #9813), 01bv8b (0.22 #8346, 0.21 #9940, 0.20 #13130), 0d68qy (0.22 #8324, 0.21 #9918, 0.20 #13108), 05f4vxd (0.20 #15121, 0.20 #8744, 0.19 #10338) >> Best rule #4775 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 02pzxlw; >> query: (?x4115, ?x416) <- award(?x7175, ?x4115), award(?x2829, ?x4115), award(?x416, ?x4115), ?x7175 = 02_1kl, nominated_for(?x4115, ?x1280), ?x2829 = 01b64v, award(?x1056, ?x4115) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #4735 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 02pzxlw; *> query: (?x4115, 0147w8) <- award(?x7175, ?x4115), award(?x2829, ?x4115), ?x7175 = 02_1kl, nominated_for(?x4115, ?x1280), ?x2829 = 01b64v, award(?x1056, ?x4115) *> conf = 0.40 ranks of expected_values: 2 EVAL 027qq9b nominated_for 0147w8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 13.000 0.889 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13903-0443xn PRED entity: 0443xn PRED relation: student! PRED expected values: 07w0v => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 66): 05zl0 (0.12 #202, 0.01 #20559, 0.01 #10543), 0bwfn (0.06 #275, 0.05 #4491, 0.05 #7126), 09f2j (0.06 #159, 0.04 #4902, 0.03 #3321), 04b_46 (0.06 #227, 0.02 #2862, 0.02 #7078), 08815 (0.06 #2, 0.02 #15290, 0.02 #19506), 053mhx (0.06 #295, 0.02 #3984, 0.02 #3457), 0lyjf (0.06 #157, 0.02 #1211, 0.01 #684), 02mj7c (0.06 #165, 0.01 #20559, 0.01 #10543), 01p79b (0.06 #290, 0.01 #20559, 0.01 #10543), 07wjk (0.06 #63, 0.01 #20559, 0.01 #10543) >> Best rule #202 for best value: >> intensional similarity = 2 >> extensional distance = 14 >> proper extension: 0cjdk; >> query: (?x13236, 05zl0) <- nominated_for(?x13236, ?x10089), ?x10089 = 07g9f >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #1601 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 158 *> proper extension: 04_jsg; 02yy8; 02m30v; *> query: (?x13236, 07w0v) <- nationality(?x13236, ?x94), ?x94 = 09c7w0, location_of_ceremony(?x13236, ?x6226) *> conf = 0.01 ranks of expected_values: 33 EVAL 0443xn student! 07w0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 89.000 89.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #13902-02yvct PRED entity: 02yvct PRED relation: executive_produced_by PRED expected values: 06q8hf => 108 concepts (75 used for prediction) PRED predicted values (max 10 best out of 114): 06pj8 (0.09 #307, 0.06 #4335, 0.06 #4841), 04jspq (0.09 #653, 0.08 #905, 0.04 #3674), 01twdk (0.07 #1119, 0.05 #364, 0.03 #1621), 06q8hf (0.05 #4698, 0.05 #2683, 0.04 #1675), 02qzjj (0.05 #235, 0.05 #487, 0.02 #4515), 04pqqb (0.04 #1625, 0.02 #368, 0.02 #1123), 02z6l5f (0.04 #2131, 0.04 #3138, 0.03 #117), 0fvf9q (0.04 #2020, 0.03 #2523, 0.03 #3027), 0343h (0.04 #4322, 0.04 #4828, 0.04 #4574), 079vf (0.04 #3275, 0.03 #4282, 0.03 #4788) >> Best rule #307 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 053tj7; 0crh5_f; >> query: (?x2189, 06pj8) <- film_release_region(?x2189, ?x1264), film_release_region(?x2189, ?x512), ?x1264 = 0345h, ?x512 = 07ssc, film_distribution_medium(?x2189, ?x81) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #4698 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 195 *> proper extension: 0c3xpwy; *> query: (?x2189, 06q8hf) <- nominated_for(?x1585, ?x2189), nominated_for(?x1585, ?x9056), film(?x1244, ?x9056), crewmember(?x599, ?x1585) *> conf = 0.05 ranks of expected_values: 4 EVAL 02yvct executive_produced_by 06q8hf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 75.000 0.091 http://example.org/film/film/executive_produced_by #13901-0141kz PRED entity: 0141kz PRED relation: film PRED expected values: 0209hj => 114 concepts (78 used for prediction) PRED predicted values (max 10 best out of 725): 01fx6y (0.59 #101947, 0.59 #37561, 0.59 #109100), 0c_j9x (0.10 #374, 0.02 #2162, 0.01 #5739), 026bfsh (0.08 #35772, 0.08 #25038, 0.08 #32195), 03177r (0.07 #465, 0.02 #7618, 0.02 #9406), 02ywwy (0.06 #3232, 0.03 #1444, 0.01 #8597), 049xgc (0.06 #2758, 0.03 #970, 0.01 #22429), 095zlp (0.06 #1848, 0.02 #17942, 0.02 #16153), 02b6n9 (0.06 #3359, 0.01 #6936, 0.01 #8724), 03shpq (0.06 #3233, 0.01 #28272, 0.01 #37217), 06fqlk (0.06 #2929) >> Best rule #101947 for best value: >> intensional similarity = 3 >> extensional distance = 1270 >> proper extension: 044mz_; 07nznf; 02s2ft; 05vsxz; 05bnp0; 016qtt; 01k7d9; 02p65p; 0337vz; 0byfz; ... >> query: (?x9808, ?x6669) <- gender(?x9808, ?x231), film(?x9808, ?x3549), nominated_for(?x9808, ?x6669) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #100 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 04rvy8; 049l7; *> query: (?x9808, 0209hj) <- gender(?x9808, ?x231), award(?x9808, ?x2523), ?x2523 = 03nqnk3 *> conf = 0.03 ranks of expected_values: 117 EVAL 0141kz film 0209hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 114.000 78.000 0.594 http://example.org/film/actor/film./film/performance/film #13900-040nwr PRED entity: 040nwr PRED relation: religion PRED expected values: 03j6c => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 23): 03j6c (0.53 #66, 0.32 #651, 0.27 #832), 0c8wxp (0.31 #6, 0.29 #276, 0.25 #546), 0flw86 (0.13 #47, 0.10 #632, 0.10 #92), 03_gx (0.09 #419, 0.09 #374, 0.08 #329), 01lp8 (0.08 #181, 0.08 #1, 0.07 #271), 092bf5 (0.07 #151, 0.04 #691, 0.04 #241), 0kpl (0.06 #730, 0.06 #190, 0.06 #911), 0kq2 (0.04 #198, 0.02 #513, 0.02 #1641), 06nzl (0.03 #330, 0.02 #420, 0.02 #195), 06yyp (0.03 #652, 0.02 #112, 0.02 #833) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 02756j; >> query: (?x12675, 03j6c) <- place_of_birth(?x12675, ?x13121), film(?x12675, ?x5247), type_of_union(?x12675, ?x566), ?x5247 = 0f42nz >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 040nwr religion 03j6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.533 http://example.org/people/person/religion #13899-0sxns PRED entity: 0sxns PRED relation: film! PRED expected values: 0pyww => 67 concepts (23 used for prediction) PRED predicted values (max 10 best out of 520): 0gnbw (0.42 #43576, 0.41 #41501, 0.40 #43577), 0grrq8 (0.42 #43576, 0.41 #41501, 0.40 #43577), 014zcr (0.11 #37, 0.02 #10408, 0.02 #8333), 0c6qh (0.11 #414, 0.02 #23236, 0.02 #14934), 0p_47 (0.11 #675, 0.01 #11046), 016yzz (0.11 #686), 09fb5 (0.06 #58, 0.04 #2132, 0.02 #31182), 0mdqp (0.06 #119, 0.03 #10490, 0.02 #37471), 015c4g (0.06 #781, 0.03 #33199, 0.03 #2855), 014g22 (0.06 #719, 0.03 #33199, 0.01 #47727) >> Best rule #43576 for best value: >> intensional similarity = 3 >> extensional distance = 958 >> proper extension: 04969y; 04m1bm; 05dy7p; 02rb607; 01h72l; 02n9bh; 02phtzk; 0bhwhj; 027ct7c; 02q3fdr; ... >> query: (?x6174, ?x986) <- award_winner(?x6174, ?x986), genre(?x6174, ?x53), nominated_for(?x6525, ?x6174) >> conf = 0.42 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0sxns film! 0pyww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 23.000 0.416 http://example.org/film/actor/film./film/performance/film #13898-036k5h PRED entity: 036k5h PRED relation: colors! PRED expected values: 04913k => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 366): 0ws7 (0.60 #3006, 0.50 #3722, 0.38 #4080), 02d02 (0.50 #2694, 0.47 #1437, 0.47 #1436), 07l8f (0.50 #2642, 0.47 #1437, 0.47 #1436), 06x68 (0.50 #2522, 0.45 #2871, 0.33 #3239), 04vn5 (0.50 #2645, 0.45 #2871, 0.33 #3720), 0jmcv (0.50 #2672, 0.45 #2871, 0.33 #3389), 01ct6 (0.50 #2163, 0.40 #2880, 0.38 #3954), 0jnm_ (0.50 #3745, 0.40 #3029, 0.36 #5177), 01wx_y (0.50 #3762, 0.40 #3046, 0.33 #1250), 0132_h (0.50 #3734, 0.40 #3018, 0.33 #1222) >> Best rule #3006 for best value: >> intensional similarity = 41 >> extensional distance = 3 >> proper extension: 019sc; >> query: (?x3364, 0ws7) <- colors(?x6894, ?x3364), institution(?x2759, ?x6894), colors(?x1632, ?x3364), major_field_of_study(?x6894, ?x4268), major_field_of_study(?x6894, ?x3995), major_field_of_study(?x6894, ?x1668), student(?x6894, ?x7189), student(?x6894, ?x5668), student(?x6894, ?x4697), currency(?x6894, ?x170), category(?x6894, ?x134), major_field_of_study(?x4268, ?x3490), student(?x4268, ?x906), major_field_of_study(?x4599, ?x4268), major_field_of_study(?x546, ?x4268), ?x3490 = 05qfh, award_nominee(?x4697, ?x3051), organization(?x346, ?x6894), major_field_of_study(?x2008, ?x4268), ?x546 = 01j_9c, ?x3995 = 0fdys, profession(?x5668, ?x319), major_field_of_study(?x11614, ?x1668), major_field_of_study(?x5807, ?x1668), major_field_of_study(?x4889, ?x1668), major_field_of_study(?x2775, ?x1668), ?x11614 = 07tk7, ?x5807 = 0ks67, executive_produced_by(?x2833, ?x7189), ?x2775 = 078bz, contains(?x4499, ?x6894), award_winner(?x3609, ?x4697), jurisdiction_of_office(?x1195, ?x4499), institution(?x2759, ?x14116), institution(?x2759, ?x13424), ?x13424 = 0yldt, ?x14116 = 0ym20, producer_type(?x7189, ?x632), major_field_of_study(?x865, ?x1668), ?x4599 = 07t90, ?x4889 = 02dq8f >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2903 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 3 *> proper extension: 019sc; *> query: (?x3364, 04913k) <- colors(?x6894, ?x3364), institution(?x2759, ?x6894), colors(?x1632, ?x3364), major_field_of_study(?x6894, ?x4268), major_field_of_study(?x6894, ?x3995), major_field_of_study(?x6894, ?x1668), student(?x6894, ?x7189), student(?x6894, ?x5668), student(?x6894, ?x4697), currency(?x6894, ?x170), category(?x6894, ?x134), major_field_of_study(?x4268, ?x3490), student(?x4268, ?x906), major_field_of_study(?x4599, ?x4268), major_field_of_study(?x546, ?x4268), ?x3490 = 05qfh, award_nominee(?x4697, ?x3051), organization(?x346, ?x6894), major_field_of_study(?x2008, ?x4268), ?x546 = 01j_9c, ?x3995 = 0fdys, profession(?x5668, ?x319), major_field_of_study(?x11614, ?x1668), major_field_of_study(?x5807, ?x1668), major_field_of_study(?x4889, ?x1668), major_field_of_study(?x2775, ?x1668), ?x11614 = 07tk7, ?x5807 = 0ks67, executive_produced_by(?x2833, ?x7189), ?x2775 = 078bz, contains(?x4499, ?x6894), award_winner(?x3609, ?x4697), jurisdiction_of_office(?x1195, ?x4499), institution(?x2759, ?x14116), institution(?x2759, ?x13424), ?x13424 = 0yldt, ?x14116 = 0ym20, producer_type(?x7189, ?x632), major_field_of_study(?x865, ?x1668), ?x4599 = 07t90, ?x4889 = 02dq8f *> conf = 0.40 ranks of expected_values: 72 EVAL 036k5h colors! 04913k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 21.000 21.000 0.600 http://example.org/sports/sports_team/colors #13897-01f1p9 PRED entity: 01f1p9 PRED relation: parent_genre PRED expected values: 03lty => 57 concepts (35 used for prediction) PRED predicted values (max 10 best out of 175): 06by7 (0.83 #1818, 0.50 #178, 0.46 #1654), 03lty (0.45 #979, 0.45 #835, 0.40 #1472), 05r6t (0.33 #55, 0.27 #1857, 0.26 #3827), 0xhtw (0.25 #338, 0.25 #175, 0.22 #651), 02yv6b (0.25 #228, 0.12 #391, 0.11 #554), 07sbbz2 (0.25 #167, 0.12 #330, 0.11 #493), 0pm85 (0.25 #261, 0.12 #424, 0.11 #587), 02l96k (0.25 #234, 0.12 #397, 0.11 #560), 016clz (0.21 #1806, 0.18 #2621, 0.10 #3776), 07bbw (0.21 #1144, 0.12 #408, 0.11 #571) >> Best rule #1818 for best value: >> intensional similarity = 8 >> extensional distance = 69 >> proper extension: 01gbcf; 01h0kx; 018ysx; 028cl7; 017ht; >> query: (?x13024, 06by7) <- parent_genre(?x13024, ?x12241), artists(?x12241, ?x9103), artists(?x12241, ?x6699), artists(?x12241, ?x5227), ?x6699 = 09lwrt, ?x5227 = 01j59b0, parent_genre(?x12241, ?x1572), instrumentalists(?x227, ?x9103) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #979 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 45 *> proper extension: 02yw1c; 066d03; *> query: (?x13024, ?x2249) <- artists(?x13024, ?x3024), artists(?x5436, ?x3024), artists(?x2249, ?x3024), ?x2249 = 03lty, artist(?x441, ?x3024), category(?x3024, ?x134), ?x5436 = 0hdf8, parent_genre(?x13024, ?x12241) *> conf = 0.45 ranks of expected_values: 2 EVAL 01f1p9 parent_genre 03lty CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 57.000 35.000 0.831 http://example.org/music/genre/parent_genre #13896-012wgb PRED entity: 012wgb PRED relation: taxonomy PRED expected values: 04n6k => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.79 #38, 0.78 #29, 0.76 #19) >> Best rule #38 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 078lk; >> query: (?x3699, 04n6k) <- contains(?x3699, ?x429), administrative_parent(?x429, ?x551), vacationer(?x3699, ?x1017) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012wgb taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 189.000 189.000 0.786 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #13895-018zvb PRED entity: 018zvb PRED relation: award PRED expected values: 0g9wd99 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 306): 0g9wd99 (0.21 #4839, 0.15 #21925, 0.13 #3621), 040vk98 (0.19 #4089, 0.13 #3683, 0.13 #3277), 0gr51 (0.19 #14311, 0.19 #11875, 0.17 #7409), 0gr4k (0.18 #4499, 0.17 #15055, 0.17 #15461), 0ddd9 (0.17 #2492, 0.15 #21925, 0.11 #6958), 04dn09n (0.17 #7352, 0.15 #4510, 0.14 #15066), 01by1l (0.17 #925, 0.12 #13917, 0.11 #43556), 03qbh5 (0.17 #1019, 0.10 #12387, 0.10 #1425), 01ckcd (0.17 #1150, 0.10 #1556, 0.09 #14142), 054ks3 (0.17 #955, 0.10 #1361, 0.08 #43586) >> Best rule #4839 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 041h0; 0184dt; 0kb3n; >> query: (?x10871, 0g9wd99) <- influenced_by(?x10871, ?x117), location(?x10871, ?x1426), story_by(?x6030, ?x10871), people(?x3584, ?x10871) >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018zvb award 0g9wd99 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.212 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13894-041td_ PRED entity: 041td_ PRED relation: nominated_for! PRED expected values: 0gr51 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 208): 02r0csl (0.77 #6014, 0.68 #9952, 0.67 #9488), 0gs96 (0.77 #6014, 0.68 #9952, 0.67 #9488), 0gq9h (0.40 #291, 0.38 #2371, 0.38 #3064), 019f4v (0.40 #282, 0.33 #2593, 0.33 #3055), 02hsq3m (0.40 #258, 0.33 #951, 0.16 #720), 0k611 (0.40 #301, 0.29 #2381, 0.29 #3074), 0gr0m (0.40 #288, 0.28 #981, 0.22 #4217), 040njc (0.40 #236, 0.27 #3009, 0.26 #2547), 0p9sw (0.40 #249, 0.26 #942, 0.22 #1866), 02pqp12 (0.40 #287, 0.25 #4392, 0.22 #19202) >> Best rule #6014 for best value: >> intensional similarity = 3 >> extensional distance = 662 >> proper extension: 07bz5; >> query: (?x6272, ?x143) <- nominated_for(?x1500, ?x6272), award(?x6272, ?x143), ceremony(?x143, ?x2032) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #2617 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 387 *> proper extension: 07s8z_l; *> query: (?x6272, 0gr51) <- award_winner(?x6272, ?x1500), titles(?x162, ?x6272), honored_for(?x1084, ?x6272) *> conf = 0.20 ranks of expected_values: 31 EVAL 041td_ nominated_for! 0gr51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 97.000 97.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13893-05z96 PRED entity: 05z96 PRED relation: profession! PRED expected values: 0152cw 0465_ 01ydzx 04qsdh 01vh096 0fpzt5 0c4y8 0h336 03j90 06jkm 04d2yp 03_dj => 58 concepts (34 used for prediction) PRED predicted values (max 10 best out of 4193): 02cx90 (0.67 #34613, 0.67 #26294, 0.50 #17976), 01k_n63 (0.67 #35631, 0.67 #27312, 0.50 #18994), 01vw8mh (0.67 #34802, 0.50 #26483, 0.50 #18165), 01vsy7t (0.67 #34722, 0.50 #26403, 0.50 #18085), 01271h (0.67 #25807, 0.50 #34126, 0.50 #17489), 0ddkf (0.67 #35462, 0.50 #27143, 0.50 #18825), 0161c2 (0.67 #34177, 0.50 #25858, 0.50 #17540), 01w02sy (0.67 #34175, 0.50 #25856, 0.50 #17538), 01j6mff (0.67 #36342, 0.50 #28023, 0.50 #19705), 06rgq (0.67 #36016, 0.50 #27697, 0.50 #19379) >> Best rule #34613 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0dz3r; 02hrh1q; >> query: (?x3746, 02cx90) <- profession(?x9167, ?x3746), profession(?x7679, ?x3746), profession(?x5126, ?x3746), ?x9167 = 07pzc, split_to(?x1467, ?x5126), religion(?x5126, ?x2694), nationality(?x7679, ?x429) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #27114 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 016z4k; 09jwl; 01c72t; 0nbcg; *> query: (?x3746, 01ydzx) <- specialization_of(?x3746, ?x353), profession(?x8389, ?x3746), profession(?x5126, ?x3746), profession(?x5040, ?x3746), ?x5126 = 03h502k, influenced_by(?x5334, ?x5040), award(?x8389, ?x8842) *> conf = 0.67 ranks of expected_values: 30, 70, 138, 139, 337, 338, 363, 430, 439, 759, 1509, 1588 EVAL 05z96 profession! 03_dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 04d2yp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 06jkm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 03j90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 0h336 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 0c4y8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 0fpzt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 01vh096 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 04qsdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 01ydzx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 0465_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 34.000 0.667 http://example.org/people/person/profession EVAL 05z96 profession! 0152cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 34.000 0.667 http://example.org/people/person/profession #13892-01vsyg9 PRED entity: 01vsyg9 PRED relation: artists! PRED expected values: 07sbbz2 => 108 concepts (61 used for prediction) PRED predicted values (max 10 best out of 213): 06by7 (0.86 #13037, 0.67 #330, 0.67 #21), 06j6l (0.56 #48, 0.45 #10269, 0.44 #976), 0gywn (0.52 #58, 0.37 #1605, 0.36 #986), 064t9 (0.51 #14892, 0.48 #13, 0.47 #10234), 02w4v (0.33 #44, 0.26 #662, 0.24 #1900), 05bt6j (0.30 #352, 0.28 #13059, 0.27 #14922), 05w3f (0.30 #346, 0.20 #3749, 0.18 #3439), 016clz (0.29 #5266, 0.29 #5576, 0.27 #7439), 07sbbz2 (0.29 #1555, 0.27 #317, 0.26 #8), 01lyv (0.28 #961, 0.27 #342, 0.26 #651) >> Best rule #13037 for best value: >> intensional similarity = 3 >> extensional distance = 450 >> proper extension: 01jqr_5; 05vzw3; 02mq_y; 0415mzy; 026yqrr; 04bbv7; 0djc3s; >> query: (?x5623, 06by7) <- artists(?x7440, ?x5623), artists(?x7440, ?x1556), ?x1556 = 03qmj9 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1555 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 0dbb3; *> query: (?x5623, 07sbbz2) <- artists(?x7440, ?x5623), ?x7440 = 0155w, location(?x5623, ?x362) *> conf = 0.29 ranks of expected_values: 9 EVAL 01vsyg9 artists! 07sbbz2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 108.000 61.000 0.861 http://example.org/music/genre/artists #13891-0gldyz PRED entity: 0gldyz PRED relation: nominated_for! PRED expected values: 05ztjjw => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 206): 0gq9h (0.38 #303, 0.28 #4844, 0.26 #8429), 0gqy2 (0.38 #363, 0.22 #2514, 0.19 #4904), 0k611 (0.33 #313, 0.23 #2464, 0.22 #4854), 0f4x7 (0.33 #265, 0.19 #4806, 0.19 #2416), 019f4v (0.29 #294, 0.25 #4835, 0.24 #2445), 03hj5vf (0.29 #604, 0.10 #843, 0.05 #3472), 0gs9p (0.26 #4846, 0.23 #2456, 0.23 #8431), 02x1z2s (0.25 #1578, 0.17 #1100, 0.07 #4207), 099c8n (0.24 #297, 0.22 #1731, 0.19 #1970), 05pcn59 (0.21 #9800, 0.19 #14821, 0.19 #16256) >> Best rule #303 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 01cgz; >> query: (?x10459, 0gq9h) <- films(?x1175, ?x10459), olympics(?x1175, ?x418), sports(?x452, ?x1175), country(?x1175, ?x94) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1922 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 229 *> proper extension: 01b64v; 01b66d; 01j7mr; 0gj50; 01b65l; 030cx; 01b66t; 05_z42; 0fkwzs; 043qqt5; ... *> query: (?x10459, 05ztjjw) <- award_winner(?x10459, ?x7205), category(?x10459, ?x134), nominated_for(?x1691, ?x10459), nominated_for(?x2101, ?x10459) *> conf = 0.10 ranks of expected_values: 55 EVAL 0gldyz nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 75.000 75.000 0.381 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13890-01sn3 PRED entity: 01sn3 PRED relation: featured_film_locations! PRED expected values: 062zm5h => 179 concepts (143 used for prediction) PRED predicted values (max 10 best out of 777): 0bl1_ (0.30 #3283, 0.08 #6958, 0.06 #17248), 01_1hw (0.25 #1348, 0.07 #7964, 0.06 #10169), 0jyx6 (0.25 #811, 0.07 #7427, 0.06 #9632), 061681 (0.15 #6663, 0.12 #10338, 0.11 #16218), 0dnkmq (0.15 #7304, 0.12 #10244, 0.07 #8039), 04dsnp (0.13 #14767, 0.12 #6682, 0.11 #25057), 03s6l2 (0.12 #6655, 0.08 #18415, 0.06 #16945), 047csmy (0.12 #7010, 0.08 #13625, 0.07 #7745), 09sh8k (0.12 #6622, 0.07 #7357, 0.06 #16177), 0btpm6 (0.12 #7163, 0.06 #9368, 0.06 #17453) >> Best rule #3283 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01f07x; >> query: (?x4090, 0bl1_) <- contains(?x6689, ?x4090), featured_film_locations(?x1511, ?x4090), location_of_ceremony(?x566, ?x6689), currency(?x6689, ?x170) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #6984 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 04wgh; *> query: (?x4090, 062zm5h) <- contains(?x177, ?x4090), featured_film_locations(?x1511, ?x4090), vacationer(?x4090, ?x5657), teams(?x4090, ?x1239) *> conf = 0.04 ranks of expected_values: 287 EVAL 01sn3 featured_film_locations! 062zm5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 179.000 143.000 0.300 http://example.org/film/film/featured_film_locations #13889-070w7s PRED entity: 070w7s PRED relation: nationality PRED expected values: 09c7w0 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 22): 09c7w0 (0.88 #301, 0.86 #603, 0.85 #502), 0k3hn (0.33 #7215), 05k7sb (0.33 #7215), 02jx1 (0.09 #7248, 0.09 #7549, 0.09 #6244), 07ssc (0.08 #5326, 0.08 #3621, 0.07 #3922), 01m7mv (0.05 #401, 0.04 #602), 03spz (0.05 #167), 03rk0 (0.05 #7461, 0.05 #7562, 0.04 #4454), 0d060g (0.04 #508, 0.04 #307, 0.04 #4415), 0chghy (0.02 #3917, 0.02 #2616, 0.02 #4520) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 01wyzyl; 03d_zl4; 02qnbs; 02gnj2; 0p_r5; >> query: (?x2811, 09c7w0) <- tv_program(?x2811, ?x4721), award_winner(?x4721, ?x415), place_of_birth(?x2811, ?x13533) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 070w7s nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.876 http://example.org/people/person/nationality #13888-05xb7q PRED entity: 05xb7q PRED relation: campuses PRED expected values: 05xb7q => 142 concepts (47 used for prediction) PRED predicted values (max 10 best out of 222): 05ftw3 (0.14 #329), 03x83_ (0.14 #131), 07wlf (0.06 #614, 0.03 #1160, 0.02 #1706), 07w0v (0.06 #563, 0.03 #1109, 0.02 #1655), 02nq10 (0.06 #884, 0.03 #1430, 0.02 #3614), 0f102 (0.06 #612, 0.03 #1158, 0.02 #3342), 07vyf (0.06 #672, 0.03 #1218, 0.02 #3402), 02ldkf (0.06 #968, 0.02 #2606, 0.02 #3698), 02482c (0.06 #863, 0.02 #3047, 0.02 #3593), 0lwyk (0.06 #828, 0.02 #3558) >> Best rule #329 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0hj6h; >> query: (?x5968, 05ftw3) <- contains(?x5967, ?x5968), category(?x5968, ?x134), ?x134 = 08mbj5d, contains(?x2236, ?x5967), ?x2236 = 05sb1 >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05xb7q campuses 05xb7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 47.000 0.143 http://example.org/education/educational_institution/campuses #13887-03r0g9 PRED entity: 03r0g9 PRED relation: featured_film_locations PRED expected values: 07_pf => 81 concepts (70 used for prediction) PRED predicted values (max 10 best out of 93): 03rjj (0.33 #5, 0.03 #242, 0.01 #716), 02_286 (0.32 #1678, 0.32 #3341, 0.17 #1441), 030qb3t (0.14 #1696, 0.13 #3359, 0.08 #3837), 0rh6k (0.07 #1661, 0.06 #3324, 0.04 #1898), 01_d4 (0.04 #3367, 0.03 #1704, 0.03 #755), 0dclg (0.04 #287, 0.02 #1710, 0.02 #3373), 03gh4 (0.04 #586, 0.02 #3435, 0.02 #1772), 080h2 (0.03 #1682, 0.03 #2395, 0.03 #3345), 0h7h6 (0.03 #1700, 0.03 #277, 0.03 #1463), 04lh6 (0.03 #8564, 0.03 #12378) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 08gsvw; >> query: (?x3693, 03rjj) <- country(?x3693, ?x94), ?x94 = 09c7w0, nominated_for(?x1018, ?x3693), ?x1018 = 04shbh >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #880 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 95 *> proper extension: 048rn; *> query: (?x3693, 07_pf) <- produced_by(?x3693, ?x3692), film_release_distribution_medium(?x3693, ?x81), costume_design_by(?x3693, ?x1500), film(?x1018, ?x3693) *> conf = 0.01 ranks of expected_values: 82 EVAL 03r0g9 featured_film_locations 07_pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 81.000 70.000 0.333 http://example.org/film/film/featured_film_locations #13886-07wbk PRED entity: 07wbk PRED relation: category PRED expected values: 08mbj5d => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #144, 0.82 #143, 0.82 #122) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 606 >> proper extension: 06klyh; >> query: (?x1912, ?x134) <- citytown(?x1912, ?x108), contains(?x108, ?x11680), category(?x11680, ?x134) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07wbk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.819 http://example.org/common/topic/webpage./common/webpage/category #13885-0hqgp PRED entity: 0hqgp PRED relation: profession PRED expected values: 05vyk => 122 concepts (94 used for prediction) PRED predicted values (max 10 best out of 110): 02hrh1q (0.79 #8757, 0.79 #9498, 0.79 #9796), 09jwl (0.71 #13662, 0.69 #9949, 0.69 #10097), 05z96 (0.67 #338, 0.33 #11115, 0.31 #10670), 0cbd2 (0.56 #303, 0.52 #5488, 0.52 #6082), 0nbcg (0.52 #1365, 0.49 #12037, 0.48 #10109), 05vyk (0.46 #538, 0.33 #11115, 0.31 #10670), 01c8w0 (0.44 #749, 0.31 #453, 0.30 #6973), 0dxtg (0.43 #9646, 0.42 #10387, 0.40 #10239), 0dz3r (0.42 #12007, 0.39 #6818, 0.36 #13344), 016z4k (0.42 #12009, 0.39 #9933, 0.38 #10674) >> Best rule #8757 for best value: >> intensional similarity = 4 >> extensional distance = 295 >> proper extension: 01rr9f; 012cph; 02_hj4; 03xmy1; 0311wg; 01pgzn_; 043js; 01dw9z; 0cjsxp; 01mt1fy; ... >> query: (?x11460, 02hrh1q) <- profession(?x11460, ?x1614), type_of_union(?x11460, ?x1873), gender(?x11460, ?x231), ?x1873 = 01g63y >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #538 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0c73z; *> query: (?x11460, 05vyk) <- profession(?x11460, ?x1614), influenced_by(?x11460, ?x3774), artists(?x888, ?x11460), ?x888 = 05lls *> conf = 0.46 ranks of expected_values: 6 EVAL 0hqgp profession 05vyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 94.000 0.795 http://example.org/people/person/profession #13884-023361 PRED entity: 023361 PRED relation: nationality PRED expected values: 09c7w0 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 84): 09c7w0 (0.82 #8117, 0.76 #2806, 0.75 #2406), 07zrf (0.37 #7816), 02jx1 (0.19 #2338, 0.18 #2938, 0.18 #2538), 07ssc (0.16 #215, 0.12 #4526, 0.12 #1316), 03rk0 (0.13 #946, 0.06 #7962, 0.05 #11272), 0345h (0.11 #1432, 0.07 #2636, 0.07 #3940), 03rjj (0.10 #105, 0.04 #905, 0.03 #1301), 0chghy (0.10 #110, 0.03 #1301, 0.03 #310), 03spz (0.10 #167, 0.03 #1301, 0.02 #3675), 0ctw_b (0.10 #127, 0.03 #1301, 0.01 #3435) >> Best rule #8117 for best value: >> intensional similarity = 2 >> extensional distance = 1187 >> proper extension: 07m69t; >> query: (?x8374, 09c7w0) <- place_of_birth(?x8374, ?x6987), state(?x6987, ?x1227) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023361 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.822 http://example.org/people/person/nationality #13883-02r79_h PRED entity: 02r79_h PRED relation: genre PRED expected values: 03k9fj => 93 concepts (91 used for prediction) PRED predicted values (max 10 best out of 103): 07s9rl0 (0.75 #961, 0.71 #2525, 0.71 #721), 02kdv5l (0.61 #1083, 0.40 #243, 0.36 #3729), 03k9fj (0.48 #492, 0.43 #1092, 0.42 #1935), 01jfsb (0.47 #253, 0.45 #1093, 0.39 #373), 02l7c8 (0.38 #856, 0.35 #976, 0.32 #2540), 060__y (0.36 #1940, 0.22 #977, 0.19 #3623), 05p553 (0.35 #6142, 0.34 #6863, 0.34 #6983), 01g6gs (0.23 #140, 0.10 #10948, 0.06 #9627), 04xvlr (0.22 #3608, 0.22 #962, 0.19 #1925), 0lsxr (0.20 #9, 0.20 #1571, 0.20 #609) >> Best rule #961 for best value: >> intensional similarity = 3 >> extensional distance = 207 >> proper extension: 06mmr; >> query: (?x1481, 07s9rl0) <- award(?x1481, ?x2489), nominated_for(?x2489, ?x522), ?x522 = 01h7bb >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #492 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 06zn1c; *> query: (?x1481, 03k9fj) <- award(?x1481, ?x2489), genre(?x1481, ?x1510), ?x1510 = 01hmnh, nominated_for(?x2489, ?x80) *> conf = 0.48 ranks of expected_values: 3 EVAL 02r79_h genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 91.000 0.751 http://example.org/film/film/genre #13882-02pptm PRED entity: 02pptm PRED relation: category PRED expected values: 08mbj5d => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #26, 0.92 #28, 0.91 #7) >> Best rule #26 for best value: >> intensional similarity = 4 >> extensional distance = 135 >> proper extension: 03wv2g; >> query: (?x9131, 08mbj5d) <- contains(?x94, ?x9131), ?x94 = 09c7w0, colors(?x9131, ?x3189), school(?x580, ?x9131) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pptm category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.920 http://example.org/common/topic/webpage./common/webpage/category #13881-0jgd PRED entity: 0jgd PRED relation: film_release_region! PRED expected values: 05zy2cy 03c7twt => 227 concepts (146 used for prediction) PRED predicted values (max 10 best out of 406): 0299hs (0.18 #539, 0.17 #288, 0.16 #2159), 02psgq (0.18 #569, 0.17 #318, 0.14 #444), 03nqnnk (0.17 #327, 0.14 #453, 0.12 #578), 04nnpw (0.17 #309, 0.10 #1432, 0.09 #1806), 085bd1 (0.17 #279, 0.10 #1402, 0.09 #1776), 0dkv90 (0.16 #2220, 0.14 #1472, 0.13 #1846), 045j3w (0.14 #410, 0.12 #2155, 0.10 #1407), 03cyslc (0.14 #462, 0.11 #961, 0.10 #1459), 08sfxj (0.14 #442, 0.11 #941, 0.10 #1439), 05c46y6 (0.14 #404, 0.11 #903, 0.10 #1401) >> Best rule #539 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 015qh; >> query: (?x142, 0299hs) <- film_release_region(?x7832, ?x142), film_release_region(?x1022, ?x142), country(?x471, ?x142), ?x7832 = 0fphf3v, ?x1022 = 0crfwmx >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #367 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0k6nt; 082fr; *> query: (?x142, 03c7twt) <- film_release_region(?x3425, ?x142), country(?x471, ?x142), ?x3425 = 0qm9n *> conf = 0.08 ranks of expected_values: 45, 72 EVAL 0jgd film_release_region! 03c7twt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 227.000 146.000 0.176 http://example.org/film/film/runtime./film/film_cut/film_release_region EVAL 0jgd film_release_region! 05zy2cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 227.000 146.000 0.176 http://example.org/film/film/runtime./film/film_cut/film_release_region #13880-0gqrb PRED entity: 0gqrb PRED relation: award_winner! PRED expected values: 0fy6bh => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 137): 0fy6bh (0.50 #47, 0.05 #611, 0.04 #1457), 0c53zb (0.38 #61, 0.12 #202, 0.09 #343), 0fk0xk (0.38 #78, 0.09 #642, 0.06 #783), 026kq4q (0.18 #328, 0.07 #469, 0.05 #610), 0d__c3 (0.12 #266, 0.12 #125, 0.07 #1958), 05hmp6 (0.12 #228, 0.12 #87, 0.07 #1920), 0fv89q (0.12 #264, 0.12 #123, 0.04 #1251), 0bzkvd (0.12 #255, 0.09 #396, 0.05 #1524), 0dznvw (0.12 #276, 0.05 #3096, 0.04 #1968), 0c53vt (0.12 #253, 0.05 #1945, 0.04 #2932) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01b9ck; 0gl88b; 02sj1x; 03thw4; 076psv; 0579tg2; >> query: (?x10914, 0fy6bh) <- nationality(?x10914, ?x774), award_winner(?x1745, ?x10914), award_winner(?x591, ?x10914), ?x1745 = 0bcndz >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gqrb award_winner! 0fy6bh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13879-0163r3 PRED entity: 0163r3 PRED relation: profession PRED expected values: 0dz3r => 126 concepts (78 used for prediction) PRED predicted values (max 10 best out of 81): 01c72t (0.48 #1309, 0.41 #737, 0.35 #307), 0dz3r (0.45 #5441, 0.44 #5012, 0.43 #6587), 01d_h8 (0.36 #1007, 0.33 #1150, 0.33 #1722), 03gjzk (0.33 #1159, 0.32 #1016, 0.30 #2017), 039v1 (0.33 #5043, 0.32 #5472, 0.30 #462), 0dxtg (0.30 #2160, 0.29 #1873, 0.28 #2303), 0n1h (0.25 #1442, 0.24 #3017, 0.22 #6452), 02jknp (0.24 #4158, 0.23 #1724, 0.23 #2154), 018gz8 (0.24 #16, 0.17 #1161, 0.14 #7318), 025352 (0.22 #1343, 0.13 #1629, 0.11 #6210) >> Best rule #1309 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 01pbxb; 0lbj1; 01vw87c; 0m2l9; 03f2_rc; 0146pg; 01vrncs; 0lgsq; 01vrz41; 0137n0; ... >> query: (?x6716, 01c72t) <- profession(?x6716, ?x220), award(?x6716, ?x1323), ?x1323 = 0gqz2 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #5441 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 329 *> proper extension: 05qhnq; *> query: (?x6716, 0dz3r) <- profession(?x6716, ?x220), artist(?x2039, ?x6716), role(?x6716, ?x316) *> conf = 0.45 ranks of expected_values: 2 EVAL 0163r3 profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 78.000 0.476 http://example.org/people/person/profession #13878-02hwhyv PRED entity: 02hwhyv PRED relation: languages_spoken! PRED expected values: 01336l => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 72): 07hwkr (0.67 #793, 0.61 #1645, 0.60 #1077), 03w9bjf (0.58 #1326, 0.26 #2534, 0.25 #190), 02vsw1 (0.56 #684, 0.50 #1110, 0.50 #1039), 04gfy7 (0.50 #1337, 0.33 #2545, 0.23 #3397), 04czx7 (0.44 #920, 0.43 #423, 0.25 #1346), 059_w (0.43 #311, 0.40 #240, 0.33 #808), 0x67 (0.40 #223, 0.33 #791, 0.29 #294), 071x0k (0.40 #221, 0.29 #363, 0.29 #292), 09zyn5 (0.33 #705, 0.30 #1131, 0.30 #1060), 0d2by (0.33 #882, 0.29 #385, 0.25 #1308) >> Best rule #793 for best value: >> intensional similarity = 13 >> extensional distance = 7 >> proper extension: 02hxc3j; 0t_2; 0k0sb; >> query: (?x7926, 07hwkr) <- language(?x4446, ?x7926), film_release_region(?x4446, ?x4737), film_release_region(?x4446, ?x985), film_release_region(?x4446, ?x279), film_regional_debut_venue(?x4446, ?x6601), ?x985 = 0k6nt, ?x4737 = 07twz, languages_spoken(?x8088, ?x7926), film_release_distribution_medium(?x4446, ?x81), film(?x7821, ?x4446), ?x279 = 0d060g, genre(?x4446, ?x571), film(?x2972, ?x4446) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 03_9r; *> query: (?x7926, 01336l) <- language(?x2160, ?x7926), service_language(?x1492, ?x7926), service_language(?x555, ?x7926), languages(?x147, ?x7926), ?x555 = 01c6k4, titles(?x7926, ?x4430), film_crew_role(?x2160, ?x137), nominated_for(?x154, ?x2160), ?x1492 = 0cv9b, film(?x541, ?x2160), nominated_for(?x2160, ?x835) *> conf = 0.33 ranks of expected_values: 13 EVAL 02hwhyv languages_spoken! 01336l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 70.000 70.000 0.667 http://example.org/people/ethnicity/languages_spoken #13877-0h6sv PRED entity: 0h6sv PRED relation: artists! PRED expected values: 01wqlc => 148 concepts (62 used for prediction) PRED predicted values (max 10 best out of 233): 06by7 (0.97 #9615, 0.48 #4663, 0.45 #12092), 0ggq0m (0.94 #7129, 0.56 #941, 0.44 #1250), 064t9 (0.50 #6511, 0.46 #9607, 0.45 #8988), 05w3f (0.38 #658, 0.20 #39, 0.16 #4680), 03_d0 (0.35 #1558, 0.30 #5272, 0.28 #7128), 0dl5d (0.31 #639, 0.20 #20, 0.16 #4661), 0xhtw (0.31 #636, 0.19 #17044, 0.18 #17355), 01wqlc (0.29 #384, 0.14 #5644, 0.12 #1312), 05bt6j (0.28 #9638, 0.22 #4686, 0.20 #15832), 016clz (0.25 #15792, 0.23 #624, 0.22 #18272) >> Best rule #9615 for best value: >> intensional similarity = 5 >> extensional distance = 121 >> proper extension: 0kzy0; 01gf5h; 04bpm6; 02zmh5; 0144l1; 01vsnff; 0136pk; 02qlg7s; 01w724; 0pkyh; ... >> query: (?x13167, 06by7) <- nationality(?x13167, ?x1310), artists(?x9137, ?x13167), role(?x13167, ?x316), award(?x13167, ?x2324), films(?x9137, ?x4276) >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #384 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 02r38; *> query: (?x13167, 01wqlc) <- nationality(?x13167, ?x1310), artists(?x9137, ?x13167), ?x9137 = 0d6n1, role(?x13167, ?x316), ?x316 = 05r5c *> conf = 0.29 ranks of expected_values: 8 EVAL 0h6sv artists! 01wqlc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 148.000 62.000 0.967 http://example.org/music/genre/artists #13876-025rvx0 PRED entity: 025rvx0 PRED relation: executive_produced_by PRED expected values: 030_3z => 80 concepts (23 used for prediction) PRED predicted values (max 10 best out of 67): 05prs8 (0.11 #45), 05hj_k (0.09 #859, 0.08 #1869, 0.07 #1111), 0343h (0.07 #1307, 0.01 #3077), 012d40 (0.06 #259), 03q8ch (0.06 #1519, 0.02 #2528, 0.02 #5567), 030_3z (0.06 #108, 0.02 #1373, 0.01 #2638), 01f7j9 (0.06 #58), 06q8hf (0.05 #2190, 0.04 #1938, 0.04 #1686), 03c9pqt (0.05 #755, 0.03 #502, 0.02 #1008), 079vf (0.05 #510, 0.01 #1267) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 015g28; >> query: (?x5795, 05prs8) <- film(?x794, ?x5795), nominated_for(?x2135, ?x5795), ?x2135 = 06pj8, genre(?x5795, ?x53) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #108 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 015g28; *> query: (?x5795, 030_3z) <- film(?x794, ?x5795), nominated_for(?x2135, ?x5795), ?x2135 = 06pj8, genre(?x5795, ?x53) *> conf = 0.06 ranks of expected_values: 6 EVAL 025rvx0 executive_produced_by 030_3z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 80.000 23.000 0.111 http://example.org/film/film/executive_produced_by #13875-024lff PRED entity: 024lff PRED relation: film_crew_role PRED expected values: 09zzb8 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.78 #1906, 0.75 #1319, 0.74 #2003), 04pyp5 (0.30 #142, 0.29 #46, 0.11 #559), 02rh1dz (0.25 #554, 0.21 #1327, 0.20 #682), 02ynfr (0.23 #558, 0.23 #1073, 0.21 #718), 015h31 (0.20 #1326, 0.18 #297, 0.18 #1391), 02_n3z (0.20 #130, 0.14 #98, 0.14 #34), 033smt (0.20 #152, 0.14 #56, 0.14 #313), 089fss (0.20 #134, 0.09 #1066, 0.09 #1324), 0215hd (0.18 #1399, 0.18 #1921, 0.15 #1334), 0d2b38 (0.17 #1405, 0.17 #727, 0.17 #631) >> Best rule #1906 for best value: >> intensional similarity = 5 >> extensional distance = 402 >> proper extension: 0fq27fp; >> query: (?x3700, 09zzb8) <- film_crew_role(?x3700, ?x1171), film_crew_role(?x3700, ?x468), film_release_region(?x3700, ?x94), ?x1171 = 09vw2b7, ?x468 = 02r96rf >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024lff film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.780 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13874-0134wr PRED entity: 0134wr PRED relation: group! PRED expected values: 016h9b => 104 concepts (53 used for prediction) PRED predicted values (max 10 best out of 59): 06cc_1 (0.03 #407, 0.03 #2608, 0.03 #2807), 02p2zq (0.03 #539, 0.03 #738, 0.02 #938), 01304j (0.03 #587, 0.03 #786, 0.02 #986), 01vswwx (0.03 #497, 0.03 #696, 0.02 #896), 01vswx5 (0.03 #495, 0.03 #694, 0.02 #894), 01vs14j (0.03 #419, 0.03 #618, 0.02 #818), 01p95y0 (0.03 #578, 0.03 #777, 0.02 #1177), 01vsyjy (0.03 #533, 0.03 #732, 0.02 #1132), 01vsyg9 (0.03 #504, 0.03 #703, 0.02 #1103), 01mwsnc (0.03 #490, 0.03 #689, 0.02 #1089) >> Best rule #407 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 01czx; 07yg2; 0394y; 015srx; 08w4pm; 033s6; 01v0sxx; 0jltp; 012x1l; 0p8h0; >> query: (?x8078, 06cc_1) <- inductee(?x1091, ?x8078), artists(?x671, ?x8078), group(?x74, ?x8078) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0134wr group! 016h9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 53.000 0.033 http://example.org/music/group_member/membership./music/group_membership/group #13873-09m6kg PRED entity: 09m6kg PRED relation: film_crew_role PRED expected values: 02r96rf => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 20): 02r96rf (0.64 #979, 0.64 #947, 0.63 #1105), 01vx2h (0.31 #1112, 0.30 #954, 0.30 #986), 01pvkk (0.28 #1113, 0.28 #546, 0.28 #1271), 02ynfr (0.18 #14, 0.16 #1117, 0.15 #959), 089g0h (0.12 #1120, 0.09 #1497, 0.09 #1278), 01xy5l_ (0.11 #1115, 0.11 #12, 0.09 #989), 02rh1dz (0.10 #1111, 0.10 #953, 0.10 #985), 015h31 (0.09 #606, 0.08 #1110, 0.08 #952), 04pyp5 (0.08 #46, 0.07 #15, 0.06 #551), 089fss (0.07 #1108, 0.06 #950, 0.06 #982) >> Best rule #979 for best value: >> intensional similarity = 2 >> extensional distance = 833 >> proper extension: 0gtsx8c; >> query: (?x253, 02r96rf) <- production_companies(?x253, ?x1104), film_crew_role(?x253, ?x137) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09m6kg film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.644 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13872-01s7zw PRED entity: 01s7zw PRED relation: award PRED expected values: 0bdwqv => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 255): 0ck27z (0.26 #2515, 0.21 #4131, 0.21 #4939), 01by1l (0.24 #111, 0.09 #9403, 0.09 #12636), 01bgqh (0.17 #42, 0.07 #9334, 0.06 #12567), 09sdmz (0.16 #11717, 0.15 #23437, 0.15 #24650), 09td7p (0.16 #11717, 0.15 #23437, 0.15 #24650), 0f4x7 (0.16 #11717, 0.15 #23437, 0.15 #24650), 0gqwc (0.16 #11717, 0.15 #23437, 0.15 #24650), 094qd5 (0.16 #11717, 0.15 #23437, 0.15 #24650), 0bfvd4 (0.16 #11717, 0.15 #23437, 0.15 #24650), 09qv_s (0.16 #11717, 0.15 #23437, 0.15 #24650) >> Best rule #2515 for best value: >> intensional similarity = 2 >> extensional distance = 721 >> proper extension: 0131kb; >> query: (?x2557, 0ck27z) <- award(?x2557, ?x704), actor(?x7756, ?x2557) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #17375 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1695 *> proper extension: 0gfmc_; *> query: (?x2557, ?x704) <- award_nominee(?x2557, ?x7530), nominated_for(?x2557, ?x1597), award(?x7530, ?x704) *> conf = 0.12 ranks of expected_values: 71 EVAL 01s7zw award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 88.000 88.000 0.261 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13871-04gfy7 PRED entity: 04gfy7 PRED relation: people PRED expected values: 0jrqq => 31 concepts (28 used for prediction) PRED predicted values (max 10 best out of 3541): 0g824 (0.50 #2624, 0.43 #7794, 0.33 #6071), 0227tr (0.43 #7230, 0.33 #5507, 0.25 #2060), 04nw9 (0.43 #7090, 0.33 #5367, 0.25 #1920), 01pk3z (0.33 #5958, 0.29 #7681, 0.27 #11129), 01l4zqz (0.33 #5341, 0.29 #7064, 0.25 #1894), 01twdk (0.33 #5845, 0.29 #7568, 0.17 #16189), 016zdd (0.33 #6663, 0.29 #8386, 0.09 #39667), 0q5hw (0.33 #377, 0.05 #14168, 0.02 #27966), 046zh (0.29 #7640, 0.25 #2470, 0.20 #11088), 016z2j (0.29 #7199, 0.25 #2029, 0.17 #15820) >> Best rule #2624 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 0x67; 07bch9; >> query: (?x12951, 0g824) <- languages_spoken(?x12951, ?x1882), people(?x12951, ?x3924), people(?x12951, ?x2296), people(?x12951, ?x217), ?x2296 = 0311wg, artists(?x302, ?x217), award(?x217, ?x724), origin(?x217, ?x739), geographic_distribution(?x12951, ?x94), award_nominee(?x3924, ?x274) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #10344 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 10 *> proper extension: 04mvp8; *> query: (?x12951, ?x3129) <- languages_spoken(?x12951, ?x8531), people(?x12951, ?x6677), people(?x12951, ?x2296), award(?x2296, ?x1670), languages(?x3129, ?x8531), gender(?x2296, ?x231), politician(?x8714, ?x6677), geographic_distribution(?x12951, ?x94) *> conf = 0.05 ranks of expected_values: 1128 EVAL 04gfy7 people 0jrqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 28.000 0.500 http://example.org/people/ethnicity/people #13870-0mdyn PRED entity: 0mdyn PRED relation: people! PRED expected values: 041rx => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 63): 041rx (0.63 #774, 0.18 #81, 0.17 #3396), 0x67 (0.20 #395, 0.15 #1551, 0.15 #1474), 033tf_ (0.15 #1702, 0.14 #1008, 0.13 #1779), 02rbdlq (0.12 #1, 0.01 #848, 0.01 #1079), 0xnvg (0.11 #321, 0.10 #1708, 0.09 #1245), 07hwkr (0.10 #320, 0.09 #1090, 0.09 #859), 07bch9 (0.10 #331, 0.08 #716, 0.07 #870), 09vc4s (0.09 #471, 0.07 #933, 0.07 #317), 0g5y6 (0.08 #807, 0.02 #1886, 0.02 #345), 0dryh9k (0.08 #1402, 0.06 #1865, 0.04 #4102) >> Best rule #774 for best value: >> intensional similarity = 2 >> extensional distance = 164 >> proper extension: 07h1q; 015c1b; >> query: (?x7836, 041rx) <- religion(?x7836, ?x7131), ?x7131 = 03_gx >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mdyn people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.627 http://example.org/people/ethnicity/people #13869-03n0pv PRED entity: 03n0pv PRED relation: type_of_union PRED expected values: 04ztj => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.78 #5, 0.75 #85, 0.72 #105), 01g63y (0.11 #86, 0.11 #166, 0.11 #34), 0jgjn (0.01 #16) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 01gw4f; >> query: (?x11729, 04ztj) <- nominated_for(?x11729, ?x2640), sibling(?x2641, ?x11729), profession(?x11729, ?x987) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03n0pv type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.776 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13868-01dbns PRED entity: 01dbns PRED relation: major_field_of_study PRED expected values: 05qjt => 128 concepts (113 used for prediction) PRED predicted values (max 10 best out of 118): 037mh8 (0.74 #2688, 0.67 #1853, 0.57 #424), 02j62 (0.72 #1815, 0.71 #386, 0.70 #6843), 02h40lc (0.72 #1791, 0.71 #362, 0.67 #243), 01mkq (0.71 #373, 0.69 #1206, 0.68 #6228), 05qfh (0.70 #2775, 0.61 #1821, 0.43 #392), 04g7x (0.67 #310, 0.57 #429, 0.39 #1858), 02lp1 (0.58 #6345, 0.57 #370, 0.54 #1203), 062z7 (0.57 #1335, 0.57 #6479, 0.54 #1216), 01lj9 (0.56 #1825, 0.46 #1229, 0.45 #5533), 0fdys (0.50 #1824, 0.50 #276, 0.47 #1585) >> Best rule #2688 for best value: >> intensional similarity = 12 >> extensional distance = 37 >> proper extension: 04rwx; 07w4j; 07wrz; 02301; 07tds; 09f2j; 015cz0; 017v71; 0cwx_; 01rgn3; ... >> query: (?x7950, 037mh8) <- major_field_of_study(?x7950, ?x9111), major_field_of_study(?x7950, ?x2014), ?x2014 = 04rjg, major_field_of_study(?x13316, ?x9111), major_field_of_study(?x6127, ?x9111), major_field_of_study(?x1675, ?x9111), interests(?x7341, ?x9111), ?x13316 = 01stzp, ?x1675 = 01j_cy, ?x7341 = 0m93, major_field_of_study(?x734, ?x9111), ?x6127 = 0gjv_ >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1795 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 16 *> proper extension: 01w3v; 01k2wn; 07szy; 01mpwj; 05mv4; 0bqxw; 02zd460; 0g8rj; 02bqy; 05zl0; ... *> query: (?x7950, 05qjt) <- major_field_of_study(?x7950, ?x11038), major_field_of_study(?x7950, ?x9111), major_field_of_study(?x7950, ?x2014), ?x2014 = 04rjg, major_field_of_study(?x13316, ?x9111), major_field_of_study(?x5754, ?x9111), major_field_of_study(?x1675, ?x9111), interests(?x7341, ?x9111), ?x13316 = 01stzp, ?x1675 = 01j_cy, language(?x174, ?x11038), institution(?x1200, ?x5754), ?x1200 = 016t_3 *> conf = 0.50 ranks of expected_values: 11 EVAL 01dbns major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 128.000 113.000 0.744 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13867-0bdw6t PRED entity: 0bdw6t PRED relation: award! PRED expected values: 014zcr 02g8h 014zfs 01y8cr 01mt1fy 022yb4 0428bc => 55 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2703): 069nzr (0.79 #39885, 0.78 #26587, 0.77 #39884), 035kl6 (0.79 #39885, 0.78 #26587, 0.77 #39884), 0347xz (0.79 #39885, 0.78 #26587, 0.77 #39884), 015grj (0.75 #10191, 0.71 #6868, 0.50 #3544), 018ygt (0.62 #11791, 0.57 #8468, 0.50 #5144), 03ym1 (0.57 #8298, 0.53 #18267, 0.50 #11621), 02bj6k (0.57 #8926, 0.50 #12249, 0.50 #5602), 016k6x (0.53 #18048, 0.50 #11402, 0.50 #4755), 0bj9k (0.53 #17132, 0.50 #3839, 0.38 #10486), 0d6d2 (0.53 #18947, 0.50 #5654, 0.29 #8978) >> Best rule #39885 for best value: >> intensional similarity = 3 >> extensional distance = 136 >> proper extension: 02r0d0; >> query: (?x2071, ?x3575) <- award_winner(?x2071, ?x3575), student(?x9844, ?x3575), category_of(?x2071, ?x2758) >> conf = 0.79 => this is the best rule for 3 predicted values *> Best rule #6698 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 027dtxw; 0bp_b2; 09qvc0; 0gqy2; 09sdmz; *> query: (?x2071, 014zcr) <- award(?x8179, ?x2071), nominated_for(?x2071, ?x337), ?x8179 = 01mqnr, ceremony(?x2071, ?x1265) *> conf = 0.43 ranks of expected_values: 56, 114, 443, 527, 1274, 1407 EVAL 0bdw6t award! 0428bc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 27.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdw6t award! 022yb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 55.000 27.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdw6t award! 01mt1fy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 27.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdw6t award! 01y8cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 27.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdw6t award! 014zfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 27.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdw6t award! 02g8h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 27.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdw6t award! 014zcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 55.000 27.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13866-0837ql PRED entity: 0837ql PRED relation: artists! PRED expected values: 0glt670 06j6l => 132 concepts (130 used for prediction) PRED predicted values (max 10 best out of 229): 06by7 (0.56 #19504, 0.50 #2803, 0.50 #4040), 0glt670 (0.51 #2514, 0.47 #2205, 0.24 #12103), 06j6l (0.42 #2522, 0.40 #2213, 0.31 #50), 02lnbg (0.28 #2532, 0.28 #2223, 0.25 #1605), 05bt6j (0.28 #1590, 0.27 #7466, 0.25 #2826), 0gywn (0.28 #368, 0.23 #59, 0.21 #12120), 016clz (0.25 #1550, 0.24 #4023, 0.24 #5260), 01lyv (0.24 #4980, 0.22 #8694, 0.21 #7456), 0xhtw (0.22 #326, 0.22 #4035, 0.20 #13314), 03_d0 (0.20 #4957, 0.20 #19494, 0.18 #6812) >> Best rule #19504 for best value: >> intensional similarity = 3 >> extensional distance = 695 >> proper extension: 0123r4; 03_gx; 0h08p; >> query: (?x4836, 06by7) <- artists(?x3562, ?x4836), artists(?x3562, ?x6651), ?x6651 = 019f9z >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2514 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 86 *> proper extension: 01vzz1c; *> query: (?x4836, 0glt670) <- origin(?x4836, ?x94), currency(?x4836, ?x170), artist(?x5836, ?x4836) *> conf = 0.51 ranks of expected_values: 2, 3 EVAL 0837ql artists! 06j6l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 130.000 0.558 http://example.org/music/genre/artists EVAL 0837ql artists! 0glt670 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 130.000 0.558 http://example.org/music/genre/artists #13865-04njml PRED entity: 04njml PRED relation: award! PRED expected values: 06rrzn 02f1c => 57 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2637): 01vrlqd (0.79 #59993, 0.40 #12250, 0.33 #8918), 01c7qd (0.79 #59993, 0.33 #2755, 0.29 #16084), 06rrzn (0.79 #59993, 0.33 #1865, 0.22 #16662), 0h5f5n (0.67 #16722, 0.08 #66719, 0.05 #76719), 053ksp (0.67 #19529, 0.05 #22862, 0.05 #69526), 09v6tz (0.56 #18884, 0.20 #39994, 0.17 #63326), 05183k (0.56 #17037, 0.20 #39994, 0.17 #63326), 05ldnp (0.56 #17549, 0.10 #67546, 0.08 #27547), 0184jw (0.56 #18900, 0.08 #25565, 0.08 #28898), 0qf43 (0.56 #16713, 0.08 #23378, 0.08 #66710) >> Best rule #59993 for best value: >> intensional similarity = 5 >> extensional distance = 138 >> proper extension: 027b9j5; >> query: (?x1869, ?x6518) <- award(?x10547, ?x1869), award(?x2390, ?x1869), celebrities_impersonated(?x3649, ?x10547), award_winner(?x1869, ?x6518), award_winner(?x163, ?x2390) >> conf = 0.79 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 350 EVAL 04njml award! 02f1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 57.000 27.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04njml award! 06rrzn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 57.000 27.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13864-01dq5z PRED entity: 01dq5z PRED relation: student PRED expected values: 03q95r => 142 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1499): 064jjy (0.12 #1429, 0.11 #3522, 0.07 #5615), 01_xtx (0.11 #6909, 0.04 #27841, 0.04 #11095), 0p8jf (0.11 #6757, 0.03 #19317, 0.03 #21410), 04t969 (0.10 #9653, 0.04 #18026, 0.04 #32678), 01cv3n (0.10 #8461, 0.04 #16834, 0.04 #31486), 02p2zq (0.10 #9678, 0.03 #15958, 0.03 #18051), 01sb5r (0.10 #9060, 0.03 #17433, 0.03 #32085), 01mvjl0 (0.10 #9425, 0.03 #17798, 0.03 #32450), 03c6v3 (0.07 #12294, 0.03 #20668, 0.03 #22761), 01nczg (0.07 #10710, 0.03 #19084, 0.03 #21177) >> Best rule #1429 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 013f9v; 0fpzwf; 0h1k6; 0w9hk; >> query: (?x3204, 064jjy) <- contains(?x1274, ?x3204), contains(?x94, ?x3204), category(?x3204, ?x134), ?x1274 = 04ykg, ?x94 = 09c7w0 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #4951 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0nht0; 0nh1v; 0ngy8; 0nhmw; 0nhr5; *> query: (?x3204, 03q95r) <- contains(?x1274, ?x3204), contains(?x94, ?x3204), ?x1274 = 04ykg, adjoins(?x151, ?x94) *> conf = 0.07 ranks of expected_values: 20 EVAL 01dq5z student 03q95r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 142.000 75.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #13863-02vrgnr PRED entity: 02vrgnr PRED relation: written_by PRED expected values: 03y9ccy => 78 concepts (67 used for prediction) PRED predicted values (max 10 best out of 103): 064jjy (0.30 #7755, 0.29 #7417, 0.02 #14839), 04y8r (0.25 #7079, 0.21 #7078, 0.15 #5055), 0147dk (0.21 #7078, 0.17 #13151, 0.15 #5055), 06dkzt (0.21 #7078, 0.15 #5055, 0.15 #13827), 02vyw (0.08 #1115, 0.07 #1452, 0.06 #1789), 0kb3n (0.08 #1268, 0.07 #1605, 0.06 #1942), 0343h (0.07 #381, 0.04 #1055, 0.02 #4087), 081lh (0.06 #704, 0.05 #1715, 0.03 #30), 02mt4k (0.06 #830, 0.04 #1167, 0.03 #1504), 02kxbwx (0.06 #697, 0.02 #6764, 0.02 #2717) >> Best rule #7755 for best value: >> intensional similarity = 3 >> extensional distance = 503 >> proper extension: 016z43; >> query: (?x4621, ?x8235) <- music(?x4621, ?x4020), film(?x8235, ?x4621), genre(?x4621, ?x53) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02vrgnr written_by 03y9ccy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 67.000 0.295 http://example.org/film/film/written_by #13862-02pzc4 PRED entity: 02pzc4 PRED relation: profession PRED expected values: 01c72t => 98 concepts (72 used for prediction) PRED predicted values (max 10 best out of 59): 0nbcg (0.63 #322, 0.52 #176, 0.51 #2813), 016z4k (0.51 #296, 0.43 #2787, 0.39 #4546), 0dz3r (0.48 #294, 0.44 #879, 0.41 #1612), 039v1 (0.32 #2818, 0.30 #181, 0.30 #8490), 01d_h8 (0.30 #8490, 0.29 #4402, 0.29 #3670), 01c72t (0.30 #8490, 0.29 #2218, 0.28 #3832), 0fnpj (0.30 #8490, 0.28 #9222, 0.20 #58), 04f2zj (0.30 #8490, 0.28 #9222, 0.13 #94), 05vyk (0.30 #8490, 0.28 #9222, 0.13 #92), 0kyk (0.30 #8490, 0.28 #9222, 0.10 #10418) >> Best rule #322 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 01wbz9; 0bkg4; 04bgy; 01tv3x2; 095x_; 020hh3; 01vxqyl; 0232lm; 0167v4; 023322; ... >> query: (?x3375, 0nbcg) <- artists(?x505, ?x3375), instrumentalists(?x1750, ?x3375), ?x1750 = 02hnl >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #8490 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1399 *> proper extension: 079vf; 079ws; 01y8d4; 02c0mv; 023jq1; 04f9r2; 0blgl; 011s9r; 08f3yq; *> query: (?x3375, ?x1614) <- award_winner(?x3375, ?x1563), profession(?x1563, ?x1614), profession(?x3375, ?x1032) *> conf = 0.30 ranks of expected_values: 6 EVAL 02pzc4 profession 01c72t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 98.000 72.000 0.634 http://example.org/people/person/profession #13861-0gp5l6 PRED entity: 0gp5l6 PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 21): 060bp (0.57 #254, 0.54 #185, 0.38 #507), 060c4 (0.54 #256, 0.51 #187, 0.43 #509), 0pqc5 (0.50 #166, 0.43 #235, 0.39 #327), 0fkvn (0.25 #119, 0.17 #303, 0.14 #27), 01zq91 (0.19 #268, 0.14 #199, 0.06 #521), 0p5vf (0.16 #266, 0.14 #197, 0.11 #105), 0f6c3 (0.16 #100, 0.12 #307, 0.09 #744), 0789n (0.16 #102, 0.06 #332, 0.05 #263), 04syw (0.14 #191, 0.14 #260, 0.07 #513), 01_fjr (0.14 #271, 0.11 #202, 0.04 #524) >> Best rule #254 for best value: >> intensional similarity = 2 >> extensional distance = 35 >> proper extension: 05r4w; 0f8l9c; >> query: (?x11889, 060bp) <- film_release_region(?x7538, ?x11889), ?x7538 = 035zr0 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 0rh6k; 01914; 059rby; 04jpl; 02dtg; 0f2wj; 05ksh; 030qb3t; 0156q; 0h7h6; ... *> query: (?x11889, 0pqc5) <- citytown(?x11823, ?x11889), film_release_region(?x2644, ?x11889), country(?x11889, ?x252) *> conf = 0.50 ranks of expected_values: 3 EVAL 0gp5l6 jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 64.000 0.568 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #13860-0214m4 PRED entity: 0214m4 PRED relation: contains! PRED expected values: 048kw => 99 concepts (55 used for prediction) PRED predicted values (max 10 best out of 232): 09c7w0 (0.63 #33992, 0.53 #41145, 0.51 #25939), 04jpl (0.17 #2704, 0.16 #4492, 0.14 #3598), 0345h (0.15 #40328, 0.11 #9916, 0.09 #13494), 03rk0 (0.13 #40382, 0.06 #23388, 0.06 #27860), 0dbdy (0.11 #114, 0.09 #1008, 0.08 #1902), 01n7q (0.10 #34066, 0.10 #47479, 0.10 #32276), 059rby (0.10 #16116, 0.09 #14327, 0.09 #20588), 0f8l9c (0.10 #40293, 0.03 #13459, 0.02 #8987), 036wy (0.10 #3446, 0.07 #7022, 0.07 #5234), 04_1l0v (0.09 #37120, 0.09 #27280, 0.08 #39802) >> Best rule #33992 for best value: >> intensional similarity = 4 >> extensional distance = 423 >> proper extension: 0195pd; >> query: (?x8251, 09c7w0) <- contains(?x512, ?x8251), place_of_birth(?x1674, ?x8251), country_of_origin(?x293, ?x512), nationality(?x111, ?x512) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #25727 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 254 *> proper extension: 0zc6f; 07w4j; 0p5wz; 05bcl; 0143hl; 0j5g9; 034cm; 0jmxb; 0hsb3; 01zkhk; ... *> query: (?x8251, 048kw) <- contains(?x1310, ?x8251), contains(?x512, ?x8251), ?x512 = 07ssc, nationality(?x57, ?x1310) *> conf = 0.02 ranks of expected_values: 116 EVAL 0214m4 contains! 048kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 99.000 55.000 0.631 http://example.org/location/location/contains #13859-02wrhj PRED entity: 02wrhj PRED relation: actor! PRED expected values: 015w8_ => 138 concepts (113 used for prediction) PRED predicted values (max 10 best out of 148): 0jwl2 (0.56 #858, 0.02 #6108, 0.02 #4531), 06y_n (0.40 #195, 0.01 #3866, 0.01 #4916), 01j7mr (0.25 #1365, 0.20 #1102, 0.03 #2413), 019g8j (0.22 #1013, 0.01 #3898, 0.01 #4686), 015w8_ (0.22 #831, 0.01 #6344, 0.01 #10549), 039c26 (0.17 #572, 0.17 #310, 0.03 #3719), 02sqkh (0.17 #603, 0.17 #341, 0.01 #6115), 02zv4b (0.17 #549, 0.01 #3434, 0.01 #7899), 05f7w84 (0.11 #891, 0.05 #1678, 0.05 #1940), 07c72 (0.11 #833, 0.03 #4985, 0.02 #7659) >> Best rule #858 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01yh3y; 0sw6y; >> query: (?x1765, 0jwl2) <- film(?x1765, ?x10072), actor(?x2555, ?x1765), nationality(?x1765, ?x279), ?x10072 = 099bhp >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #831 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01yh3y; 0sw6y; *> query: (?x1765, 015w8_) <- film(?x1765, ?x10072), actor(?x2555, ?x1765), nationality(?x1765, ?x279), ?x10072 = 099bhp *> conf = 0.22 ranks of expected_values: 5 EVAL 02wrhj actor! 015w8_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 113.000 0.556 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #13858-01nhgd PRED entity: 01nhgd PRED relation: organization! PRED expected values: 060c4 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 15): 060c4 (0.78 #171, 0.78 #211, 0.77 #41), 07xl34 (0.25 #193, 0.22 #753, 0.21 #233), 0dq_5 (0.25 #660, 0.25 #621, 0.23 #764), 05k17c (0.11 #372, 0.10 #320, 0.10 #619), 01t7n9 (0.07 #209, 0.07 #1043, 0.03 #1435), 0fkzq (0.07 #209, 0.07 #1043, 0.03 #1435), 0789n (0.07 #209, 0.07 #1043, 0.03 #1435), 0f6c3 (0.07 #209, 0.07 #1043, 0.03 #1435), 0fkvn (0.07 #209, 0.07 #1043, 0.03 #1435), 01q24l (0.07 #209, 0.07 #1043, 0.03 #1618) >> Best rule #171 for best value: >> intensional similarity = 5 >> extensional distance = 85 >> proper extension: 02zc7f; >> query: (?x13680, 060c4) <- currency(?x13680, ?x170), citytown(?x13680, ?x4090), contains(?x177, ?x13680), school(?x2820, ?x13680), ?x170 = 09nqf >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nhgd organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.782 http://example.org/organization/role/leaders./organization/leadership/organization #13857-01tnbn PRED entity: 01tnbn PRED relation: gender PRED expected values: 02zsn => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #23, 0.80 #76, 0.75 #174), 02zsn (0.55 #6, 0.50 #30, 0.50 #20) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 079vf; 0p_pd; 01wl38s; 019z7q; 02r34n; 016_mj; 0126rp; 01j7rd; 0144l1; 0bymv; ... >> query: (?x6059, 05zppz) <- profession(?x6059, ?x987), person(?x424, ?x6059), ?x987 = 0dxtg >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01rh0w; 01pw2f1; 0j1yf; 03rl84; 0lx2l; 01z0rcq; 07r1h; 05vk_d; 0227vl; *> query: (?x6059, 02zsn) <- profession(?x6059, ?x353), participant(?x2934, ?x6059), notable_people_with_this_condition(?x7374, ?x6059), participant(?x6059, ?x6877) *> conf = 0.55 ranks of expected_values: 2 EVAL 01tnbn gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 140.000 140.000 0.827 http://example.org/people/person/gender #13856-035xwd PRED entity: 035xwd PRED relation: currency PRED expected values: 09nqf => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.82 #50, 0.81 #71, 0.79 #43), 02l6h (0.11 #484, 0.08 #18, 0.01 #158), 01nv4h (0.11 #484, 0.03 #30, 0.02 #156), 088n7 (0.11 #484), 02gsvk (0.11 #484), 0kz1h (0.11 #484), 0ptk_ (0.11 #484) >> Best rule #50 for best value: >> intensional similarity = 5 >> extensional distance = 273 >> proper extension: 03t97y; 02qmsr; 0hv4t; 0cqr0q; >> query: (?x796, 09nqf) <- film(?x1690, ?x796), country(?x796, ?x94), profession(?x1690, ?x220), celebrity(?x1690, ?x2422), featured_film_locations(?x796, ?x739) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035xwd currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.818 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #13855-034bgm PRED entity: 034bgm PRED relation: produced_by! PRED expected values: 03cyslc => 96 concepts (66 used for prediction) PRED predicted values (max 10 best out of 468): 05pdh86 (0.46 #5674, 0.45 #12299, 0.44 #10407), 0bpx1k (0.46 #5674, 0.45 #12299, 0.44 #10407), 04w7rn (0.46 #5674, 0.45 #12299, 0.44 #10407), 05sns6 (0.15 #3782, 0.02 #35937, 0.02 #34990), 0h03fhx (0.08 #425, 0.05 #1371, 0.03 #2316), 09p35z (0.08 #69, 0.05 #1015, 0.03 #1960), 03h3x5 (0.08 #232, 0.05 #1178, 0.03 #2123), 015g28 (0.08 #354, 0.05 #1300, 0.03 #2245), 084qpk (0.08 #71, 0.03 #1962, 0.02 #1017), 01k0xy (0.05 #4728, 0.05 #7566, 0.05 #7565) >> Best rule #5674 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 03g62; >> query: (?x2648, ?x1518) <- produced_by(?x6684, ?x2648), student(?x2999, ?x2648), film(?x2648, ?x1518) >> conf = 0.46 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 034bgm produced_by! 03cyslc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 66.000 0.461 http://example.org/film/film/produced_by #13854-01gw4f PRED entity: 01gw4f PRED relation: award PRED expected values: 0gqyl 01l78d => 106 concepts (83 used for prediction) PRED predicted values (max 10 best out of 257): 09sb52 (0.69 #10896, 0.61 #442, 0.52 #1649), 0gqyl (0.68 #504, 0.55 #1711, 0.34 #5329), 094qd5 (0.65 #446, 0.39 #1653, 0.21 #5271), 02ppm4q (0.58 #556, 0.38 #1763, 0.24 #5381), 02y_rq5 (0.52 #494, 0.24 #1701, 0.15 #5319), 09qwmm (0.48 #435, 0.29 #1642, 0.16 #5260), 02x4x18 (0.42 #532, 0.27 #1739, 0.16 #5357), 0bdwft (0.42 #469, 0.24 #1676, 0.16 #5294), 0cqgl9 (0.42 #592, 0.23 #1799, 0.13 #5417), 099t8j (0.35 #540, 0.29 #1747, 0.18 #5365) >> Best rule #10896 for best value: >> intensional similarity = 4 >> extensional distance = 573 >> proper extension: 01x0yrt; 01dpsv; >> query: (?x4867, 09sb52) <- award(?x4867, ?x1254), film(?x4867, ?x188), award(?x1244, ?x1254), ?x1244 = 0h1nt >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #504 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 29 *> proper extension: 01j5ts; 01p7yb; 0h1nt; 0gjvqm; 028knk; 0l6px; 043kzcr; 0h0wc; 019f2f; 0lpjn; ... *> query: (?x4867, 0gqyl) <- award(?x4867, ?x1254), award(?x4867, ?x1245), ?x1254 = 02z0dfh, ?x1245 = 0gqwc, nominated_for(?x4867, ?x188) *> conf = 0.68 ranks of expected_values: 2, 214 EVAL 01gw4f award 01l78d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 106.000 83.000 0.692 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01gw4f award 0gqyl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 83.000 0.692 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13853-04g61 PRED entity: 04g61 PRED relation: combatants! PRED expected values: 01h6pn => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 63): 081pw (0.74 #442, 0.52 #1514, 0.50 #884), 07j9n (0.38 #1227, 0.25 #92, 0.22 #848), 0845v (0.38 #67, 0.26 #1202, 0.20 #256), 01gqg3 (0.38 #93, 0.21 #1228, 0.20 #282), 01h6pn (0.35 #454, 0.18 #1085, 0.18 #896), 01hwkn (0.31 #1247, 0.17 #1814, 0.16 #1940), 0k4y6 (0.31 #1222, 0.15 #3089, 0.14 #3469), 0cm2xh (0.26 #453, 0.23 #1147, 0.21 #1777), 06k75 (0.26 #1151, 0.25 #1781, 0.24 #1907), 0dr7s (0.26 #1246, 0.08 #1813, 0.08 #1939) >> Best rule #442 for best value: >> intensional similarity = 2 >> extensional distance = 21 >> proper extension: 01h3dj; >> query: (?x5274, 081pw) <- combatants(?x5274, ?x172), ?x172 = 0154j >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #454 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 21 *> proper extension: 01h3dj; *> query: (?x5274, 01h6pn) <- combatants(?x5274, ?x172), ?x172 = 0154j *> conf = 0.35 ranks of expected_values: 5 EVAL 04g61 combatants! 01h6pn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 167.000 167.000 0.739 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #13852-03jvmp PRED entity: 03jvmp PRED relation: nominated_for PRED expected values: 0f4k49 043mk4y 02rlj20 => 97 concepts (63 used for prediction) PRED predicted values (max 10 best out of 678): 0b76kw1 (0.81 #96758, 0.80 #88690, 0.79 #80625), 01l_pn (0.25 #879, 0.04 #15383, 0.03 #16995), 02f6g5 (0.25 #257, 0.03 #42186, 0.03 #43799), 01y9jr (0.25 #1045, 0.01 #42974, 0.01 #44587), 02krdz (0.25 #518, 0.01 #42447, 0.01 #44060), 0h3mh3q (0.25 #1402, 0.01 #46557), 0124k9 (0.22 #8278, 0.15 #101598, 0.15 #79012), 0m313 (0.20 #3235, 0.06 #11293, 0.03 #16128), 09gq0x5 (0.20 #3483, 0.05 #99982, 0.03 #16376), 011ywj (0.20 #4496, 0.04 #14166, 0.02 #67382) >> Best rule #96758 for best value: >> intensional similarity = 3 >> extensional distance = 1240 >> proper extension: 04n7njg; 0br1w; >> query: (?x2246, ?x715) <- award_winner(?x715, ?x2246), nominated_for(?x2246, ?x4396), honored_for(?x5296, ?x4396) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #12893 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 15 *> proper extension: 04gvyp; 02swsm; 02w_l9; *> query: (?x2246, ?x715) <- child(?x3381, ?x2246), titles(?x3381, ?x715) *> conf = 0.02 ranks of expected_values: 493, 495 EVAL 03jvmp nominated_for 02rlj20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 97.000 63.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 03jvmp nominated_for 043mk4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 63.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 03jvmp nominated_for 0f4k49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 97.000 63.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #13851-0n5y4 PRED entity: 0n5y4 PRED relation: contains! PRED expected values: 059f4 => 110 concepts (54 used for prediction) PRED predicted values (max 10 best out of 104): 029jpy (0.79 #16179, 0.62 #13483, 0.62 #44950), 059g4 (0.79 #16179, 0.62 #13483, 0.62 #44950), 09c7w0 (0.79 #16179, 0.62 #48547, 0.61 #7189), 04_1l0v (0.79 #16179, 0.48 #3149, 0.43 #8539), 059f4 (0.76 #28763, 0.73 #31460, 0.71 #14382), 0gj4fx (0.63 #26966, 0.62 #13483, 0.62 #44950), 0n5y4 (0.42 #31461, 0.42 #48548, 0.42 #28764), 050ks (0.25 #1285, 0.03 #8475, 0.03 #45849), 01n7q (0.22 #25244, 0.17 #9066, 0.15 #16258), 07c5l (0.20 #2193, 0.03 #23765, 0.03 #32756) >> Best rule #16179 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 0kvwh; >> query: (?x10162, ?x94) <- administrative_division(?x6188, ?x10162), capital(?x728, ?x6188), contains(?x94, ?x728), administrative_division(?x6188, ?x728) >> conf = 0.79 => this is the best rule for 4 predicted values *> Best rule #28763 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 155 *> proper extension: 02ly_; 028n3; 0124jj; *> query: (?x10162, ?x728) <- administrative_division(?x6188, ?x10162), contains(?x728, ?x6188), state_province_region(?x2064, ?x728), currency(?x728, ?x170) *> conf = 0.76 ranks of expected_values: 5 EVAL 0n5y4 contains! 059f4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 110.000 54.000 0.791 http://example.org/location/location/contains #13850-04wqr PRED entity: 04wqr PRED relation: award_winner! PRED expected values: 02kgb7 => 130 concepts (117 used for prediction) PRED predicted values (max 10 best out of 315): 0bdw6t (0.25 #975, 0.06 #4863, 0.03 #5295), 05qck (0.21 #2353, 0.15 #39748, 0.15 #40615), 02py7pj (0.20 #1604, 0.15 #39748, 0.15 #40615), 054ky1 (0.20 #1406, 0.13 #7022, 0.12 #3566), 09sb52 (0.18 #9113, 0.17 #27689, 0.13 #28553), 0gqy2 (0.18 #595, 0.11 #4915, 0.10 #5347), 0gqwc (0.17 #3531, 0.12 #1803, 0.12 #5691), 02z1nbg (0.17 #1923, 0.12 #3651, 0.09 #4083), 0f4x7 (0.16 #6943, 0.15 #40614, 0.15 #39748), 04ljl_l (0.15 #40614, 0.15 #39748, 0.15 #40615) >> Best rule #975 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0bmh4; 0k8y7; 09889g; 03n6r; 05xpv; 0mfj2; 01h4rj; 02p5hf; 01kkx2; 0py5b; >> query: (?x509, 0bdw6t) <- film(?x509, ?x2779), place_of_burial(?x509, ?x3153), languages(?x509, ?x254) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #6809 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 66 *> proper extension: 024y6w; *> query: (?x509, 02kgb7) <- award_winner(?x509, ?x510), profession(?x509, ?x4773), ?x4773 = 0d1pc *> conf = 0.01 ranks of expected_values: 276 EVAL 04wqr award_winner! 02kgb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 130.000 117.000 0.250 http://example.org/award/award_category/winners./award/award_honor/award_winner #13849-03mnn0 PRED entity: 03mnn0 PRED relation: featured_film_locations PRED expected values: 02dtg => 103 concepts (73 used for prediction) PRED predicted values (max 10 best out of 75): 02_286 (0.50 #260, 0.29 #2666, 0.26 #3627), 04jpl (0.20 #9, 0.16 #3136, 0.15 #2414), 030qb3t (0.20 #39, 0.15 #2444, 0.14 #2685), 02dtg (0.20 #12, 0.11 #1214, 0.09 #1934), 0cv3w (0.20 #70, 0.11 #1272, 0.09 #1992), 0d6hn (0.20 #177, 0.11 #1379, 0.09 #2099), 0nqv1 (0.20 #173, 0.11 #1375, 0.09 #2095), 0b1t1 (0.20 #166, 0.11 #1368, 0.09 #2088), 01b8w_ (0.20 #158, 0.11 #1360, 0.09 #2080), 04f_d (0.20 #50, 0.11 #1252, 0.09 #1972) >> Best rule #260 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0bh8tgs; 0g9z_32; >> query: (?x6125, 02_286) <- language(?x6125, ?x254), person(?x6125, ?x8143), film_release_distribution_medium(?x6125, ?x81), film_crew_role(?x6125, ?x137), award_winner(?x2186, ?x8143), vacationer(?x4627, ?x8143) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 012jfb; *> query: (?x6125, 02dtg) <- titles(?x1014, ?x6125), film_release_distribution_medium(?x6125, ?x81), ?x1014 = 0jtdp, genre(?x6125, ?x8681), person(?x6125, ?x965), major_field_of_study(?x865, ?x8681) *> conf = 0.20 ranks of expected_values: 4 EVAL 03mnn0 featured_film_locations 02dtg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 73.000 0.500 http://example.org/film/film/featured_film_locations #13848-01dycg PRED entity: 01dycg PRED relation: child PRED expected values: 07rfp => 214 concepts (190 used for prediction) PRED predicted values (max 10 best out of 231): 0mzkr (0.33 #40, 0.29 #2258, 0.25 #550), 03_c8p (0.25 #626, 0.17 #1650, 0.17 #1308), 01jx9 (0.22 #3457, 0.20 #4309, 0.20 #3969), 0dwcl (0.22 #3552, 0.20 #4404, 0.20 #4064), 01qszl (0.22 #3577, 0.20 #4429, 0.17 #1870), 05gnf (0.20 #4153, 0.20 #3812, 0.20 #1082), 016tw3 (0.20 #4104, 0.20 #3763, 0.17 #4784), 031rq5 (0.20 #4145, 0.17 #4825, 0.17 #4655), 017s11 (0.20 #4096, 0.17 #4776, 0.17 #4606), 011k1h (0.20 #1041, 0.17 #1553, 0.12 #3089) >> Best rule #40 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 04fc6c; >> query: (?x9558, 0mzkr) <- service_language(?x9558, ?x254), contact_category(?x9558, ?x897), artist(?x9558, ?x10039), ?x897 = 03w5xm, artists(?x1572, ?x10039), ?x1572 = 06by7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10079 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 09tlc8; *> query: (?x9558, ?x14538) <- child(?x9558, ?x11325), industry(?x9558, ?x245), industry(?x14538, ?x245), place_founded(?x14538, ?x9559), industry(?x11325, ?x10022) *> conf = 0.02 ranks of expected_values: 165 EVAL 01dycg child 07rfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 214.000 190.000 0.333 http://example.org/organization/organization/child./organization/organization_relationship/child #13847-08d9z7 PRED entity: 08d9z7 PRED relation: produced_by! PRED expected values: 0dc_ms 0d6_s => 99 concepts (64 used for prediction) PRED predicted values (max 10 best out of 453): 02z9rr (0.39 #20692, 0.25 #1671, 0.16 #38575), 0cwfgz (0.39 #20692, 0.11 #38574, 0.11 #32928), 03kg2v (0.33 #259, 0.17 #2139, 0.07 #3079), 0ds33 (0.33 #42, 0.02 #3802, 0.01 #5683), 0pc62 (0.33 #57, 0.02 #3817, 0.01 #6639), 09v8clw (0.33 #930, 0.01 #4690), 02_nsc (0.33 #837, 0.01 #4597), 032sl_ (0.33 #824, 0.01 #4584), 01qb559 (0.33 #700, 0.01 #4460), 07bx6 (0.33 #699, 0.01 #4459) >> Best rule #20692 for best value: >> intensional similarity = 3 >> extensional distance = 324 >> proper extension: 02g8h; 0kr5_; 0n6f8; 0ksf29; 015npr; 034bgm; 04g865; 0p51w; 03tf_h; 01f8ld; ... >> query: (?x7848, ?x6206) <- produced_by(?x383, ?x7848), award(?x7848, ?x1105), nominated_for(?x7848, ?x6206) >> conf = 0.39 => this is the best rule for 2 predicted values *> Best rule #2749 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 030_1_; *> query: (?x7848, 0d6_s) <- award_nominee(?x1104, ?x7848), ?x1104 = 016tw3, award(?x7848, ?x1105) *> conf = 0.08 ranks of expected_values: 70 EVAL 08d9z7 produced_by! 0d6_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 99.000 64.000 0.395 http://example.org/film/film/produced_by EVAL 08d9z7 produced_by! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 64.000 0.395 http://example.org/film/film/produced_by #13846-0cm89v PRED entity: 0cm89v PRED relation: profession PRED expected values: 02jknp => 136 concepts (77 used for prediction) PRED predicted values (max 10 best out of 67): 02jknp (0.90 #1458, 0.90 #5519, 0.88 #3489), 0cbd2 (0.36 #3923, 0.34 #4793, 0.25 #6), 018gz8 (0.33 #5381, 0.30 #14, 0.28 #4656), 0kyk (0.25 #4813, 0.21 #3943, 0.16 #2057), 0np9r (0.20 #5385, 0.18 #1034, 0.17 #163), 09jwl (0.16 #4223, 0.16 #10459, 0.15 #7993), 0d1pc (0.15 #2949, 0.11 #4109, 0.10 #2513), 01c72t (0.13 #7998, 0.09 #9013, 0.08 #5243), 0nbcg (0.11 #10471, 0.10 #4235, 0.10 #3220), 0dgd_ (0.11 #317, 0.10 #463, 0.10 #608) >> Best rule #1458 for best value: >> intensional similarity = 4 >> extensional distance = 119 >> proper extension: 042l3v; 0bxtg; 06cv1; 0kr5_; 02ndbd; 06pk8; 02r5w9; 01_vfy; 01_x6v; 0bsb4j; ... >> query: (?x4900, 02jknp) <- film(?x4900, ?x2558), profession(?x4900, ?x1032), type_of_union(?x4900, ?x566), ?x1032 = 02hrh1q >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cm89v profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 77.000 0.901 http://example.org/people/person/profession #13845-043n0v_ PRED entity: 043n0v_ PRED relation: genre PRED expected values: 07s9rl0 02p0szs => 98 concepts (62 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.94 #5619, 0.91 #3745, 0.83 #118), 01jfsb (0.66 #2938, 0.52 #1532, 0.50 #12), 03k9fj (0.65 #245, 0.46 #1531, 0.41 #1647), 012w70 (0.58 #1520, 0.56 #1985, 0.56 #5501), 03h64 (0.58 #1520, 0.56 #1985, 0.56 #5501), 03_9r (0.58 #1520, 0.56 #1985, 0.56 #5501), 01hmnh (0.57 #251, 0.19 #1537, 0.19 #485), 05p553 (0.53 #822, 0.50 #3, 0.36 #2105), 02l7c8 (0.38 #132, 0.38 #7034, 0.37 #5163), 0lsxr (0.33 #8, 0.27 #2934, 0.23 #1644) >> Best rule #5619 for best value: >> intensional similarity = 5 >> extensional distance = 1063 >> proper extension: 0fq27fp; 04grkmd; 02v5xg; 058kh7; >> query: (?x5038, 07s9rl0) <- genre(?x5038, ?x6887), genre(?x6362, ?x6887), genre(?x1968, ?x6887), ?x1968 = 050gkf, ?x6362 = 03_gz8 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 22 EVAL 043n0v_ genre 02p0szs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 98.000 62.000 0.945 http://example.org/film/film/genre EVAL 043n0v_ genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 62.000 0.945 http://example.org/film/film/genre #13844-03r0g9 PRED entity: 03r0g9 PRED relation: nominated_for! PRED expected values: 01gf5h => 94 concepts (43 used for prediction) PRED predicted values (max 10 best out of 707): 05ldnp (0.60 #74703, 0.50 #60693, 0.50 #77038), 0d5wn3 (0.31 #979, 0.11 #7978, 0.10 #21979), 059_gf (0.30 #56022, 0.27 #74702, 0.25 #58359), 0n839 (0.27 #74702, 0.25 #58359, 0.22 #23334), 0170qf (0.26 #5125, 0.08 #459, 0.02 #30797), 0fx02 (0.23 #56024, 0.20 #49017, 0.18 #53687), 013_vh (0.23 #814, 0.09 #7813, 0.07 #10146), 0b_dy (0.21 #5330), 03mfqm (0.16 #3716, 0.07 #31721, 0.03 #17716), 0jfx1 (0.16 #2835, 0.05 #30840, 0.05 #19168) >> Best rule #74703 for best value: >> intensional similarity = 4 >> extensional distance = 390 >> proper extension: 02pg45; >> query: (?x3693, ?x3260) <- currency(?x3693, ?x170), nominated_for(?x637, ?x3693), film(?x1018, ?x3693), written_by(?x3693, ?x3260) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03r0g9 nominated_for! 01gf5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 43.000 0.596 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #13843-0chghy PRED entity: 0chghy PRED relation: film_release_region! PRED expected values: 02vxq9m 04ddm4 0401sg 04969y 0_b3d 02r1c18 02yvct 0fpv_3_ 0661m4p 085ccd 03qnc6q 0f4m2z 0g5879y 0hx4y 0g5838s 0jwmp 0qm9n 0gtvpkw 0cp0ph6 0gh65c5 0cmc26r 0sxkh 02xbyr 02ylg6 0gbfn9 0dr89x 0gl02yg 0ggbfwf 077q8x 02825cv 03y0pn 0bdjd 0btpm6 01cm8w 0h95927 025ts_z 023vcd 0dgq80b 0gy4k => 210 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1073): 02vxq9m (0.90 #20958, 0.79 #29932, 0.77 #12979), 0661m4p (0.87 #21134, 0.75 #7173, 0.73 #13155), 0cmc26r (0.86 #6341, 0.81 #7338, 0.75 #3349), 02r1c18 (0.86 #6108, 0.75 #7105, 0.75 #3116), 0fpv_3_ (0.84 #21132, 0.79 #6174, 0.75 #7171), 0btpm6 (0.81 #7693, 0.79 #6696, 0.77 #21654), 0hx4y (0.81 #7223, 0.79 #6226, 0.62 #3234), 03qnc6q (0.81 #21159, 0.79 #6201, 0.77 #13180), 02yvct (0.81 #21120, 0.75 #7159, 0.75 #3170), 0c0nhgv (0.81 #21038, 0.69 #7077, 0.64 #20040) >> Best rule #20958 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 05v8c; >> query: (?x390, 02vxq9m) <- film_release_region(?x5315, ?x390), nationality(?x72, ?x390), ?x5315 = 0glqh5_ >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 14, 15, 18, 19, 20, 21, 23, 26, 28, 29, 32, 33, 38, 46, 81, 85, 86, 89, 92, 94, 96, 104, 106, 108, 126, 129, 134, 254, 261 EVAL 0chghy film_release_region! 0gy4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0dgq80b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 023vcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 025ts_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0h95927 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 01cm8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0btpm6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0bdjd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 03y0pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 02825cv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 077q8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0ggbfwf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0gl02yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0dr89x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0gbfn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 02ylg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 02xbyr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0sxkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0cmc26r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0gh65c5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0cp0ph6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0gtvpkw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0qm9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0jwmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0g5838s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0hx4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0g5879y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0f4m2z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 03qnc6q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 085ccd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0661m4p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0fpv_3_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 02yvct CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 02r1c18 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0_b3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 04969y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 0401sg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 04ddm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0chghy film_release_region! 02vxq9m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 125.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13842-013nws PRED entity: 013nws PRED relation: contains! PRED expected values: 09c7w0 => 114 concepts (18 used for prediction) PRED predicted values (max 10 best out of 134): 09c7w0 (0.68 #9856, 0.65 #7168, 0.64 #5375), 04rrx (0.67 #4602, 0.04 #14464, 0.03 #9082), 04_1l0v (0.30 #13440, 0.22 #15234, 0.20 #14337), 059rby (0.28 #11665, 0.09 #14357, 0.03 #8975), 03v1s (0.25 #6293, 0.04 #5398, 0.03 #9852), 03s0w (0.21 #6325, 0.08 #5430, 0.05 #7223), 01n7q (0.18 #14415, 0.13 #10826, 0.13 #9931), 0d060g (0.17 #16131, 0.06 #8968, 0.06 #10761), 0498y (0.16 #6514, 0.03 #9852, 0.02 #7412), 05tbn (0.16 #11869, 0.06 #14561, 0.03 #10077) >> Best rule #9856 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x9453, 09c7w0) <- category(?x9453, ?x134), ?x134 = 08mbj5d, place(?x9453, ?x9453) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013nws contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 18.000 0.679 http://example.org/location/location/contains #13841-04gqr PRED entity: 04gqr PRED relation: organization PRED expected values: 0b6css => 122 concepts (115 used for prediction) PRED predicted values (max 10 best out of 49): 041288 (0.59 #396, 0.53 #36, 0.51 #318), 0b6css (0.59 #30, 0.55 #51, 0.54 #390), 0_2v (0.54 #66, 0.51 #171, 0.50 #129), 04k4l (0.51 #318, 0.45 #4, 0.38 #618), 018cqq (0.46 #73, 0.33 #178, 0.32 #1737), 01rz1 (0.45 #1, 0.43 #64, 0.41 #552), 0j7v_ (0.32 #1737, 0.29 #26, 0.25 #131), 02jxk (0.32 #1737, 0.29 #65, 0.27 #2), 085h1 (0.32 #1737, 0.16 #2251, 0.03 #95), 059dn (0.32 #1737, 0.14 #77, 0.10 #56) >> Best rule #396 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 05hf_5; >> query: (?x4120, 041288) <- contains(?x2467, ?x4120), contains(?x2467, ?x12331), ?x12331 = 0fnyc >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 05g2v; *> query: (?x4120, 0b6css) <- contains(?x2467, ?x4120), ?x2467 = 0dg3n1, contains(?x4120, ?x13844) *> conf = 0.59 ranks of expected_values: 2 EVAL 04gqr organization 0b6css CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 115.000 0.590 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #13840-04wddl PRED entity: 04wddl PRED relation: language PRED expected values: 064_8sq => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 35): 064_8sq (0.23 #195, 0.16 #834, 0.15 #1068), 04306rv (0.21 #178, 0.14 #410, 0.12 #294), 02bjrlw (0.12 #175, 0.09 #407, 0.08 #466), 06nm1 (0.10 #823, 0.10 #184, 0.09 #1057), 06b_j (0.08 #196, 0.07 #835, 0.07 #894), 03_9r (0.08 #183, 0.05 #2931, 0.05 #2636), 04h9h (0.06 #216, 0.04 #507, 0.04 #914), 06mp7 (0.06 #189, 0.01 #1121, 0.01 #305), 0jzc (0.05 #251, 0.03 #1125, 0.03 #2056), 0653m (0.05 #417, 0.04 #185, 0.03 #766) >> Best rule #195 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 0m313; 011yxg; 0209hj; 0pv3x; 0gmcwlb; 0dtfn; 0168ls; 0dr_4; 0bcndz; 042y1c; ... >> query: (?x9183, 064_8sq) <- nominated_for(?x2716, ?x9183), honored_for(?x8015, ?x9183), award(?x9183, ?x2222), ?x2222 = 0gs96 >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wddl language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.229 http://example.org/film/film/language #13839-05_k56 PRED entity: 05_k56 PRED relation: award PRED expected values: 02x1dht => 113 concepts (97 used for prediction) PRED predicted values (max 10 best out of 282): 09sb52 (0.40 #10014, 0.35 #10413, 0.32 #19591), 04dn09n (0.38 #2834, 0.31 #6426, 0.28 #2036), 0gs9p (0.27 #6461, 0.26 #2869, 0.21 #9653), 0gq9h (0.26 #1271, 0.24 #2867, 0.23 #4463), 03hkv_r (0.26 #2807, 0.25 #6399, 0.23 #2009), 019f4v (0.24 #2856, 0.24 #6448, 0.20 #9640), 03hl6lc (0.24 #2967, 0.23 #6559, 0.22 #2568), 02n9nmz (0.22 #2859, 0.21 #6451, 0.19 #3657), 02pqp12 (0.20 #2860, 0.18 #6452, 0.15 #67), 02x17s4 (0.20 #2914, 0.18 #2116, 0.18 #2515) >> Best rule #10014 for best value: >> intensional similarity = 3 >> extensional distance = 432 >> proper extension: 02wr2r; >> query: (?x1052, 09sb52) <- award_winner(?x1052, ?x3456), film(?x1052, ?x9487), crewmember(?x9487, ?x1933) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #24741 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1465 *> proper extension: 01qkqwg; 07sgfsl; 02vyh; 035_2h; 02x0bdb; 039cq4; 0191h5; 03gvpk; 01jllg1; 051m56; ... *> query: (?x1052, ?x601) <- award_winner(?x1052, ?x6682), nationality(?x6682, ?x94), award(?x6682, ?x601) *> conf = 0.15 ranks of expected_values: 16 EVAL 05_k56 award 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 113.000 97.000 0.403 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13838-01p85y PRED entity: 01p85y PRED relation: student! PRED expected values: 06182p => 112 concepts (94 used for prediction) PRED predicted values (max 10 best out of 146): 0bwfn (0.11 #275, 0.10 #1327, 0.09 #15006), 015nl4 (0.07 #9011, 0.06 #6380, 0.05 #6906), 04b_46 (0.07 #227, 0.07 #1279, 0.05 #753), 017j69 (0.07 #145, 0.06 #1197, 0.04 #5405), 05bjp6 (0.07 #415, 0.02 #941, 0.02 #1467), 01bm_ (0.07 #246, 0.02 #1298, 0.02 #6559), 065y4w7 (0.06 #3696, 0.05 #18429, 0.04 #16324), 07tg4 (0.06 #9030, 0.04 #9556, 0.03 #12187), 017z88 (0.05 #6395, 0.05 #14287, 0.05 #1134), 03ksy (0.05 #8523, 0.04 #9576, 0.04 #26414) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 03c5bz; >> query: (?x8741, 0bwfn) <- award(?x8741, ?x678), ?x678 = 0cqhk0, participant(?x1984, ?x8741) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #6610 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 271 *> proper extension: 09dv0sz; *> query: (?x8741, 06182p) <- award_nominee(?x8741, ?x4775), participant(?x3917, ?x4775), student(?x8021, ?x8741) *> conf = 0.03 ranks of expected_values: 29 EVAL 01p85y student! 06182p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 112.000 94.000 0.107 http://example.org/education/educational_institution/students_graduates./education/education/student #13837-07_fl PRED entity: 07_fl PRED relation: location! PRED expected values: 01j5ts => 81 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1662): 09fb5 (0.20 #2566, 0.17 #7596, 0.15 #51), 0q9kd (0.20 #2517, 0.17 #7547, 0.15 #2), 014v6f (0.20 #3632, 0.17 #8662, 0.15 #1117), 073749 (0.13 #3317, 0.11 #8347, 0.08 #15890), 039crh (0.13 #3399, 0.11 #8429, 0.08 #884), 0c6qh (0.13 #2976, 0.11 #8006, 0.08 #461), 0lkr7 (0.13 #3530, 0.11 #8560, 0.08 #1015), 01zg98 (0.13 #3348, 0.11 #8378, 0.08 #833), 016tb7 (0.13 #3226, 0.11 #8256, 0.08 #711), 02d9k (0.13 #2849, 0.11 #7879, 0.08 #334) >> Best rule #2566 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0k049; 0f2wj; 030qb3t; 0r0m6; 0k_p5; 0281y0; 0nbwf; 0r0f7; 0281rp; 0r15k; ... >> query: (?x11352, 09fb5) <- location(?x2762, ?x11352), contains(?x2949, ?x11352), ?x2949 = 0kpys, place_of_death(?x4284, ?x11352) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #10085 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 0r1yc; 0r540; 0f04c; 0r04p; 0281rb; 0r8c8; 0l1pj; 0r3tb; 0r3w7; *> query: (?x11352, 01j5ts) <- place_of_death(?x4284, ?x11352), contains(?x1227, ?x11352), ?x1227 = 01n7q *> conf = 0.03 ranks of expected_values: 492 EVAL 07_fl location! 01j5ts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 81.000 17.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location #13836-06lxn PRED entity: 06lxn PRED relation: artist! PRED expected values: 03x9yr => 114 concepts (83 used for prediction) PRED predicted values (max 10 best out of 117): 03rhqg (0.67 #154, 0.53 #571, 0.50 #6969), 0g768 (0.42 #6991, 0.27 #315, 0.18 #2539), 01cszh (0.39 #4320, 0.12 #1400, 0.11 #2512), 015_1q (0.38 #992, 0.31 #2521, 0.29 #2104), 01t04r (0.27 #343, 0.27 #4375, 0.20 #1455), 011k1h (0.27 #287, 0.24 #1399, 0.24 #565), 017l96 (0.26 #713, 0.25 #852, 0.23 #1130), 01cl2y (0.24 #1420, 0.18 #308, 0.18 #586), 086k8 (0.23 #4311, 0.09 #1113, 0.08 #3059), 01w40h (0.20 #28, 0.19 #1001, 0.17 #1835) >> Best rule #154 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01vs_v8; 01vsykc; 0p7h7; 01hgwkr; >> query: (?x13511, 03rhqg) <- award_winner(?x13511, ?x8423), artist(?x12666, ?x13511), award_winner(?x3375, ?x13511), ?x12666 = 02y21l >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #413 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0dm5l; 07yg2; 02cpp; 013rfk; 033s6; 07hgm; 01fchy; 012x1l; 0fsyx; *> query: (?x13511, 03x9yr) <- artist(?x12666, ?x13511), ?x12666 = 02y21l, group(?x227, ?x13511) *> conf = 0.09 ranks of expected_values: 48 EVAL 06lxn artist! 03x9yr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 114.000 83.000 0.667 http://example.org/music/record_label/artist #13835-02r9p0c PRED entity: 02r9p0c PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 6): 029j_ (0.83 #161, 0.83 #286, 0.83 #246), 02nxhr (0.20 #484, 0.20 #438, 0.19 #392), 0735l (0.20 #484, 0.20 #438, 0.19 #392), 07c52 (0.20 #484, 0.20 #438, 0.18 #63), 07z4p (0.20 #484, 0.20 #438, 0.07 #60), 0dq6p (0.19 #392) >> Best rule #161 for best value: >> intensional similarity = 11 >> extensional distance = 57 >> proper extension: 0gj8t_b; 0yyts; 02stbw; 04pk1f; 01bn3l; 09cxm4; >> query: (?x6999, 029j_) <- film(?x382, ?x6999), film(?x13156, ?x6999), genre(?x6999, ?x10185), language(?x6999, ?x254), genre(?x3457, ?x10185), genre(?x2512, ?x10185), genre(?x1847, ?x10185), ?x3457 = 03x7hd, ?x2512 = 07x4qr, ?x382 = 086k8, production_companies(?x1847, ?x902) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r9p0c film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.831 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #13834-0ff2k PRED entity: 0ff2k PRED relation: profession PRED expected values: 0dxtg 0kyk => 146 concepts (87 used for prediction) PRED predicted values (max 10 best out of 111): 02hrh1q (0.82 #11939, 0.79 #12387, 0.78 #9555), 0dxtg (0.82 #9703, 0.81 #4187, 0.81 #12236), 01d_h8 (0.66 #9696, 0.63 #12229, 0.63 #900), 02jknp (0.63 #1646, 0.58 #901, 0.57 #305), 03gjzk (0.47 #909, 0.42 #1654, 0.38 #12238), 0kyk (0.46 #3756, 0.46 #2861, 0.44 #2563), 018gz8 (0.43 #315, 0.33 #17, 0.30 #8515), 015h31 (0.39 #4473, 0.30 #3428, 0.27 #4025), 020xn5 (0.39 #4473, 0.30 #3428, 0.27 #4025), 01nxfc (0.39 #4473, 0.30 #3428, 0.27 #4025) >> Best rule #11939 for best value: >> intensional similarity = 3 >> extensional distance = 328 >> proper extension: 04bdxl; 01rr9f; 06jzh; 04bs3j; 0151ns; 0456xp; 04shbh; 07vc_9; 03rl84; 02fb1n; ... >> query: (?x11598, 02hrh1q) <- award(?x11598, ?x10747), spouse(?x4349, ?x11598), nominated_for(?x10747, ?x2770) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #9703 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 238 *> proper extension: 05ty4m; 01wl38s; 02lk1s; 04l3_z; 08433; 03jldb; 032w8h; 01wg982; 0d7hg4; 03xpf_7; ... *> query: (?x11598, 0dxtg) <- award(?x11598, ?x10747), type_of_union(?x11598, ?x566), profession(?x11598, ?x353), written_by(?x3643, ?x11598) *> conf = 0.82 ranks of expected_values: 2, 6 EVAL 0ff2k profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 146.000 87.000 0.821 http://example.org/people/person/profession EVAL 0ff2k profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 87.000 0.821 http://example.org/people/person/profession #13833-032016 PRED entity: 032016 PRED relation: genre PRED expected values: 02kdv5l 05p553 => 60 concepts (55 used for prediction) PRED predicted values (max 10 best out of 98): 07s9rl0 (0.81 #3365, 0.62 #5170, 0.62 #1201), 02kdv5l (0.70 #2043, 0.50 #2403, 0.49 #843), 01jfsb (0.64 #2173, 0.52 #2413, 0.41 #373), 05p553 (0.62 #2285, 0.51 #3009, 0.38 #965), 02l7c8 (0.38 #3743, 0.30 #977, 0.29 #1337), 060__y (0.38 #258, 0.33 #18, 0.17 #3744), 02n4kr (0.33 #9, 0.17 #2169, 0.15 #609), 06n90 (0.27 #854, 0.26 #1094, 0.25 #1454), 0lsxr (0.22 #2170, 0.21 #2410, 0.20 #610), 0hcr (0.19 #504, 0.19 #864, 0.16 #1104) >> Best rule #3365 for best value: >> intensional similarity = 4 >> extensional distance = 1148 >> proper extension: 0cnztc4; 0crh5_f; 0413cff; 0h95zbp; 0g5q34q; 0gh6j94; 015qy1; 0d8w2n; >> query: (?x3059, 07s9rl0) <- genre(?x3059, ?x811), language(?x3059, ?x254), genre(?x3292, ?x811), ?x3292 = 0gvs1kt >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #2043 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 650 *> proper extension: 04svwx; *> query: (?x3059, 02kdv5l) <- genre(?x3059, ?x811), genre(?x6615, ?x811), genre(?x5320, ?x811), ?x5320 = 01zfzb, ?x6615 = 03t95n *> conf = 0.70 ranks of expected_values: 2, 4 EVAL 032016 genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 55.000 0.806 http://example.org/film/film/genre EVAL 032016 genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 60.000 55.000 0.806 http://example.org/film/film/genre #13832-0n6mc PRED entity: 0n6mc PRED relation: currency PRED expected values: 09nqf => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.86 #62, 0.86 #61, 0.86 #29) >> Best rule #62 for best value: >> intensional similarity = 5 >> extensional distance = 272 >> proper extension: 0mrf1; >> query: (?x10702, ?x170) <- adjoins(?x12341, ?x10702), adjoins(?x7697, ?x10702), currency(?x12341, ?x170), time_zones(?x12341, ?x2950), source(?x7697, ?x958) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n6mc currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.858 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #13831-024y6w PRED entity: 024y6w PRED relation: people! PRED expected values: 08q1tg => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 26): 0gk4g (0.22 #10, 0.08 #340, 0.06 #76), 01_qc_ (0.11 #28, 0.03 #226, 0.02 #490), 01l2m3 (0.11 #16, 0.02 #346), 0dq9p (0.06 #347, 0.06 #83, 0.03 #1007), 0c58k (0.06 #96, 0.02 #294, 0.02 #360), 01psyx (0.06 #111, 0.02 #639, 0.02 #375), 02y0js (0.06 #68, 0.02 #332, 0.02 #464), 0m32h (0.06 #89, 0.02 #353), 0dcsx (0.06 #81, 0.02 #345), 02k6hp (0.06 #367, 0.02 #829, 0.02 #4329) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05r5w; 01n44c; 0cgbf; >> query: (?x8371, 0gk4g) <- award_winner(?x9628, ?x8371), profession(?x8371, ?x10649), ?x10649 = 01p5_g >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 024y6w people! 08q1tg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 126.000 0.222 http://example.org/people/cause_of_death/people #13830-0h7h6 PRED entity: 0h7h6 PRED relation: location! PRED expected values: 0mfj2 => 268 concepts (171 used for prediction) PRED predicted values (max 10 best out of 2319): 01tc9r (0.46 #325473, 0.46 #223598, 0.46 #173905), 0h53p1 (0.46 #325473, 0.46 #223598, 0.46 #173905), 0dky9n (0.46 #325473, 0.46 #223598, 0.46 #173905), 031v3p (0.46 #325473, 0.46 #223598, 0.46 #173905), 01vrx35 (0.46 #325473, 0.46 #223598, 0.46 #173905), 09fp45 (0.46 #325473, 0.46 #223598, 0.46 #173905), 023kzp (0.33 #8648, 0.27 #16102, 0.25 #18586), 0151ns (0.33 #7535, 0.27 #14989, 0.25 #17473), 01s7ns (0.33 #2171, 0.17 #19561, 0.13 #22045), 09n70c (0.33 #2037, 0.08 #19427, 0.07 #21911) >> Best rule #325473 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 0hknf; >> query: (?x1658, ?x877) <- place_of_birth(?x11596, ?x1658), place_of_birth(?x877, ?x1658), teams(?x1658, ?x6179), location(?x11596, ?x1227) >> conf = 0.46 => this is the best rule for 6 predicted values No rule for expected values ranks of expected_values: EVAL 0h7h6 location! 0mfj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 268.000 171.000 0.463 http://example.org/people/person/places_lived./people/place_lived/location #13829-02pprs PRED entity: 02pprs PRED relation: role! PRED expected values: 01vsy7t => 92 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1134): 050z2 (0.71 #10038, 0.71 #9567, 0.67 #10976), 0137g1 (0.71 #9497, 0.53 #15134, 0.53 #14664), 03ryks (0.67 #8275, 0.60 #6396, 0.47 #14384), 016ntp (0.67 #8585, 0.40 #5298, 0.38 #10464), 023l9y (0.60 #16173, 0.60 #13823, 0.58 #15229), 04bpm6 (0.57 #9450, 0.55 #12269, 0.50 #16031), 082brv (0.57 #9649, 0.50 #10590, 0.50 #8241), 06x4l_ (0.57 #9975, 0.50 #8096, 0.50 #4339), 05qhnq (0.57 #10164, 0.50 #4528, 0.42 #14860), 02s6sh (0.57 #9818, 0.41 #18276, 0.37 #15455) >> Best rule #10038 for best value: >> intensional similarity = 20 >> extensional distance = 5 >> proper extension: 013y1f; >> query: (?x214, 050z2) <- role(?x2460, ?x214), role(?x1524, ?x214), role(?x9413, ?x214), role(?x5926, ?x214), role(?x2888, ?x214), role(?x2309, ?x214), ?x9413 = 07m2y, ?x2888 = 02fsn, role(?x5926, ?x6449), role(?x5926, ?x1969), role(?x5926, ?x894), award_winner(?x1524, ?x158), role(?x5926, ?x75), ?x894 = 03m5k, ?x2309 = 06ncr, ?x1969 = 04rzd, ?x6449 = 014zz1, group(?x2460, ?x3516), instrumentalists(?x2460, ?x680), instrumentalists(?x5926, ?x140) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3493 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 0342h; *> query: (?x214, 01vsy7t) <- role(?x3239, ?x214), role(?x1332, ?x214), role(?x1166, ?x214), role(?x1524, ?x214), role(?x9413, ?x214), role(?x868, ?x214), gender(?x1524, ?x231), ?x868 = 0dwvl, ?x3239 = 03qmg1, ?x1166 = 05148p4, role(?x214, ?x75), award_winner(?x158, ?x1524), role(?x120, ?x214), role(?x212, ?x9413), instrumentalists(?x9413, ?x2945), artists(?x5985, ?x1524), ?x1332 = 03qlv7 *> conf = 0.50 ranks of expected_values: 30 EVAL 02pprs role! 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 92.000 55.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #13828-0sz28 PRED entity: 0sz28 PRED relation: award_nominee PRED expected values: 05bnp0 => 132 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1289): 05bnp0 (0.82 #11660, 0.82 #16325, 0.81 #6995), 04zn7g (0.36 #11661, 0.27 #9328, 0.24 #4663), 0sz28 (0.30 #248, 0.02 #63217, 0.02 #25903), 02vntj (0.20 #979, 0.03 #5642, 0.02 #14972), 016tw3 (0.20 #225, 0.03 #65527, 0.01 #18884), 06cgy (0.15 #44310, 0.15 #32652, 0.15 #74631), 01pllx (0.15 #44310, 0.15 #32652, 0.15 #74631), 020hh3 (0.15 #44310, 0.15 #32652, 0.15 #74631), 0169dl (0.14 #2853, 0.12 #5185, 0.07 #7518), 06jzh (0.13 #27988, 0.12 #25655, 0.01 #121366) >> Best rule #11660 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 07qy0b; >> query: (?x1208, ?x123) <- award_nominee(?x123, ?x1208), sibling(?x1208, ?x13442), profession(?x1208, ?x319) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sz28 award_nominee 05bnp0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 65.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13827-05f2jk PRED entity: 05f2jk PRED relation: place_of_death PRED expected values: 0f2wj => 85 concepts (83 used for prediction) PRED predicted values (max 10 best out of 23): 030qb3t (0.20 #22, 0.17 #216, 0.13 #800), 02_286 (0.08 #791, 0.06 #1763, 0.05 #401), 04swd (0.04 #1092, 0.04 #898, 0.03 #1287), 0k049 (0.04 #781, 0.03 #975, 0.02 #7203), 02h6_6p (0.03 #425, 0.02 #620, 0.01 #1204), 04jpl (0.03 #979, 0.03 #1174, 0.02 #1368), 0f2wj (0.03 #984, 0.02 #790, 0.02 #3125), 06_kh (0.03 #1172, 0.02 #1366, 0.02 #783), 05qtj (0.02 #1619, 0.01 #4150, 0.01 #2204), 0fhp9 (0.02 #597, 0.01 #3127, 0.01 #3516) >> Best rule #22 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 01l1rw; >> query: (?x10541, 030qb3t) <- profession(?x10541, ?x1032), profession(?x10541, ?x563), artists(?x4910, ?x10541), ?x563 = 01c8w0, ?x1032 = 02hrh1q, gender(?x10541, ?x231) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #984 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 74 *> proper extension: 02ryx0; *> query: (?x10541, 0f2wj) <- profession(?x10541, ?x1032), profession(?x10541, ?x563), ?x563 = 01c8w0, profession(?x4836, ?x1032), profession(?x2871, ?x1032), nationality(?x4836, ?x94), award_nominee(?x929, ?x2871) *> conf = 0.03 ranks of expected_values: 7 EVAL 05f2jk place_of_death 0f2wj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 83.000 0.200 http://example.org/people/deceased_person/place_of_death #13826-01f1r4 PRED entity: 01f1r4 PRED relation: student PRED expected values: 0kjgl => 104 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1302): 02cyfz (0.14 #333, 0.05 #2423, 0.05 #4513), 01pqy_ (0.14 #897, 0.05 #2987, 0.05 #5077), 01wwvt2 (0.14 #364, 0.05 #2454, 0.05 #4544), 01_rh4 (0.14 #534, 0.05 #2624, 0.05 #4714), 04t969 (0.07 #1279, 0.05 #3369, 0.04 #7549), 0ff3y (0.07 #2067, 0.05 #4157, 0.04 #8337), 01hbq0 (0.07 #2056, 0.05 #4146, 0.04 #8326), 024y6w (0.07 #1451, 0.05 #3541, 0.04 #7721), 028r4y (0.07 #947, 0.05 #3037, 0.04 #7217), 01mr2g6 (0.07 #1434, 0.05 #3524, 0.04 #7704) >> Best rule #333 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 04m_kpx; 09xzd; >> query: (?x4099, 02cyfz) <- category(?x4099, ?x134), ?x134 = 08mbj5d, split_to(?x4099, ?x4099) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #50168 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 243 *> proper extension: 02l9wl; *> query: (?x4099, ?x7761) <- student(?x4099, ?x7762), award_winner(?x2490, ?x7762), award_winner(?x7762, ?x7761) *> conf = 0.02 ranks of expected_values: 845 EVAL 01f1r4 student 0kjgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 104.000 101.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #13825-01vvpjj PRED entity: 01vvpjj PRED relation: artists! PRED expected values: 0827d 03vyh => 125 concepts (71 used for prediction) PRED predicted values (max 10 best out of 224): 064t9 (0.64 #2192, 0.61 #325, 0.57 #1258), 0ggq0m (0.55 #3125, 0.10 #324, 0.09 #12157), 06by7 (0.53 #4069, 0.52 #4381, 0.52 #334), 0dl5d (0.44 #3133, 0.15 #3755, 0.13 #7806), 05bt6j (0.42 #356, 0.28 #1601, 0.28 #1289), 06j6l (0.30 #2228, 0.28 #10638, 0.24 #8458), 0xhtw (0.29 #3752, 0.26 #4376, 0.25 #7803), 025sc50 (0.29 #2230, 0.25 #1919, 0.23 #10640), 01lyv (0.29 #346, 0.20 #4706, 0.20 #1279), 016clz (0.27 #7790, 0.25 #4363, 0.25 #3739) >> Best rule #2192 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 012d40; 07ss8_; 039bpc; 01vzxld; >> query: (?x2440, 064t9) <- award(?x2440, ?x1323), category(?x2440, ?x134), languages(?x2440, ?x254), artists(?x2439, ?x2440) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #19000 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 817 *> proper extension: 0m19t; 07qnf; 04r1t; 0167_s; 02r1tx7; 05563d; 07yg2; 03xhj6; 0394y; 018gm9; ... *> query: (?x2440, ?x497) <- artists(?x2439, ?x2440), parent_genre(?x2439, ?x497), artist(?x1954, ?x2440) *> conf = 0.22 ranks of expected_values: 20, 99 EVAL 01vvpjj artists! 03vyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 125.000 71.000 0.640 http://example.org/music/genre/artists EVAL 01vvpjj artists! 0827d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 125.000 71.000 0.640 http://example.org/music/genre/artists #13824-01qqwn PRED entity: 01qqwn PRED relation: symptom_of! PRED expected values: 01j6t0 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 45): 01j6t0 (0.76 #873, 0.69 #243, 0.67 #332), 012qjw (0.50 #31, 0.43 #162, 0.42 #475), 02tfl8 (0.46 #467, 0.33 #331, 0.33 #196), 0brgy (0.44 #203, 0.43 #161, 0.38 #474), 0f3kl (0.42 #482, 0.29 #488, 0.29 #169), 01cdt5 (0.40 #234, 0.40 #62, 0.37 #594), 0j5fv (0.33 #93, 0.31 #247, 0.29 #472), 01pf6 (0.33 #16, 0.29 #488, 0.23 #921), 0hg45 (0.33 #14, 0.23 #921, 0.23 #919), 04kllm9 (0.29 #488, 0.27 #109, 0.08 #416) >> Best rule #873 for best value: >> intensional similarity = 7 >> extensional distance = 40 >> proper extension: 01g2q; >> query: (?x13744, 01j6t0) <- symptom_of(?x9509, ?x13744), symptom_of(?x9509, ?x14096), symptom_of(?x9509, ?x10480), symptom_of(?x9509, ?x4322), ?x4322 = 0gk4g, risk_factors(?x14096, ?x8524), ?x10480 = 0h1n9 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qqwn symptom_of! 01j6t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.762 http://example.org/medicine/symptom/symptom_of #13823-034m8 PRED entity: 034m8 PRED relation: olympics PRED expected values: 06sks6 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 41): 06sks6 (0.88 #1671, 0.87 #969, 0.86 #1095), 0kbws (0.60 #55, 0.55 #493, 0.55 #465), 0kbvb (0.48 #48, 0.41 #130, 0.40 #787), 0kbvv (0.45 #67, 0.37 #806, 0.37 #149), 09n48 (0.44 #44, 0.33 #783, 0.33 #126), 0jdk_ (0.42 #68, 0.33 #150, 0.31 #807), 018ctl (0.40 #49, 0.34 #788, 0.34 #459), 0l6vl (0.38 #1194, 0.18 #1852, 0.16 #84), 0swbd (0.34 #52, 0.27 #93, 0.27 #462), 0jhn7 (0.31 #69, 0.25 #151, 0.23 #274) >> Best rule #1671 for best value: >> intensional similarity = 3 >> extensional distance = 158 >> proper extension: 027nb; 01n6c; 02lx0; 047t_; 06ryl; 05tr7; 06s0l; 06s9y; 04ty8; 01nqj; ... >> query: (?x9459, 06sks6) <- organization(?x9459, ?x127), jurisdiction_of_office(?x182, ?x9459), ?x127 = 02vk52z >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034m8 olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.875 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #13822-018sg9 PRED entity: 018sg9 PRED relation: major_field_of_study PRED expected values: 04x_3 => 229 concepts (229 used for prediction) PRED predicted values (max 10 best out of 120): 02h40lc (0.71 #865, 0.38 #127, 0.31 #1234), 05qjt (0.62 #622, 0.60 #376, 0.54 #1237), 01lj9 (0.62 #655, 0.54 #1270, 0.48 #1516), 02j62 (0.62 #1261, 0.55 #2741, 0.55 #1507), 03g3w (0.60 #396, 0.54 #519, 0.53 #2737), 01mkq (0.60 #384, 0.52 #630, 0.51 #5679), 04rjg (0.58 #1250, 0.54 #512, 0.52 #635), 037mh8 (0.54 #561, 0.50 #438, 0.50 #192), 062z7 (0.50 #1258, 0.45 #1504, 0.39 #5692), 04x_3 (0.50 #887, 0.38 #641, 0.31 #1256) >> Best rule #865 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 049dk; 02x9g_; >> query: (?x12276, 02h40lc) <- category(?x12276, ?x134), school_type(?x12276, ?x3092), major_field_of_study(?x12276, ?x732), company(?x3484, ?x12276), language(?x148, ?x732) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #887 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 049dk; 02x9g_; *> query: (?x12276, 04x_3) <- category(?x12276, ?x134), school_type(?x12276, ?x3092), major_field_of_study(?x12276, ?x732), company(?x3484, ?x12276), language(?x148, ?x732) *> conf = 0.50 ranks of expected_values: 10 EVAL 018sg9 major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 229.000 229.000 0.708 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13821-0gx1bnj PRED entity: 0gx1bnj PRED relation: film_release_region PRED expected values: 0jgd 03rt9 03gj2 01pj7 03spz => 59 concepts (56 used for prediction) PRED predicted values (max 10 best out of 166): 09c7w0 (0.93 #6253, 0.92 #6405, 0.92 #6711), 06mkj (0.91 #1120, 0.89 #816, 0.88 #968), 03gj2 (0.89 #785, 0.86 #1089, 0.83 #937), 0jgd (0.89 #1527, 0.88 #1221, 0.88 #1374), 0k6nt (0.86 #1393, 0.84 #1240, 0.83 #1546), 02vzc (0.86 #963, 0.83 #1420, 0.83 #1881), 03_3d (0.84 #767, 0.83 #1071, 0.80 #1376), 09pmkv (0.82 #1370, 0.80 #1677, 0.80 #1831), 05b4w (0.81 #1127, 0.80 #975, 0.79 #823), 03spz (0.81 #1462, 0.80 #1615, 0.80 #1005) >> Best rule #6253 for best value: >> intensional similarity = 10 >> extensional distance = 1309 >> proper extension: 0bh72t; 015qy1; >> query: (?x343, 09c7w0) <- film_release_region(?x343, ?x172), film_release_region(?x7502, ?x172), film_release_region(?x6492, ?x172), film_release_region(?x6321, ?x172), film_release_region(?x3986, ?x172), ?x6321 = 0gg8z1f, ?x7502 = 0233bn, olympics(?x172, ?x418), ?x6492 = 0ds6bmk, genre(?x3986, ?x53) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #785 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 71 *> proper extension: 0c40vxk; 0gkz15s; 01vksx; 0872p_c; 0jqn5; 0bh8yn3; 06wbm8q; 04pk1f; 0cmf0m0; *> query: (?x343, 03gj2) <- film_release_region(?x343, ?x2645), film_release_region(?x343, ?x1264), film_release_region(?x343, ?x1174), film_release_region(?x343, ?x172), film_release_region(?x343, ?x151), ?x172 = 0154j, ?x2645 = 03h64, ?x151 = 0b90_r, ?x1174 = 047yc, genre(?x343, ?x53), ?x1264 = 0345h *> conf = 0.89 ranks of expected_values: 3, 4, 10, 11, 24 EVAL 0gx1bnj film_release_region 03spz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 59.000 56.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gx1bnj film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 59.000 56.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gx1bnj film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 59.000 56.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gx1bnj film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 59.000 56.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gx1bnj film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 59.000 56.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13820-0g9zljd PRED entity: 0g9zljd PRED relation: film_release_region PRED expected values: 03_3d 06qd3 06bnz 01pj7 03rk0 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 171): 07ssc (0.88 #695, 0.85 #422, 0.81 #968), 03_3d (0.84 #686, 0.81 #959, 0.81 #1781), 03rt9 (0.78 #420, 0.73 #693, 0.67 #1924), 06bnz (0.78 #443, 0.66 #716, 0.65 #2220), 01p1v (0.67 #450, 0.46 #2227, 0.45 #1954), 03rk0 (0.67 #454, 0.43 #1958, 0.42 #2231), 06qd3 (0.64 #711, 0.56 #438, 0.55 #984), 05v8c (0.63 #423, 0.62 #696, 0.56 #1791), 04gzd (0.63 #415, 0.48 #2192, 0.48 #688), 01ls2 (0.56 #418, 0.45 #691, 0.43 #1922) >> Best rule #695 for best value: >> intensional similarity = 6 >> extensional distance = 54 >> proper extension: 011yrp; 0g5qs2k; 05p1tzf; 02x3lt7; 04969y; 01vksx; 02d44q; 01c22t; 053rxgm; 04hwbq; ... >> query: (?x6270, 07ssc) <- film_release_region(?x6270, ?x2267), film_release_region(?x6270, ?x1355), film(?x609, ?x6270), nominated_for(?x77, ?x6270), ?x1355 = 0h7x, ?x2267 = 03rj0 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #686 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 54 *> proper extension: 011yrp; 0g5qs2k; 05p1tzf; 02x3lt7; 04969y; 01vksx; 02d44q; 01c22t; 053rxgm; 04hwbq; ... *> query: (?x6270, 03_3d) <- film_release_region(?x6270, ?x2267), film_release_region(?x6270, ?x1355), film(?x609, ?x6270), nominated_for(?x77, ?x6270), ?x1355 = 0h7x, ?x2267 = 03rj0 *> conf = 0.84 ranks of expected_values: 2, 4, 6, 7, 16 EVAL 0g9zljd film_release_region 03rk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 74.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g9zljd film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 74.000 74.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g9zljd film_release_region 06bnz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 74.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g9zljd film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 74.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g9zljd film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13819-0g54xkt PRED entity: 0g54xkt PRED relation: genre PRED expected values: 07s9rl0 => 67 concepts (49 used for prediction) PRED predicted values (max 10 best out of 90): 07s9rl0 (0.96 #4453, 0.73 #1683, 0.73 #843), 05p553 (0.46 #245, 0.44 #365, 0.38 #3132), 02kdv5l (0.41 #1445, 0.34 #965, 0.32 #2527), 01jfsb (0.41 #1455, 0.35 #2537, 0.34 #1936), 02l7c8 (0.34 #497, 0.33 #859, 0.30 #137), 04xvlr (0.29 #482, 0.27 #844, 0.26 #603), 0219x_ (0.27 #268, 0.22 #388, 0.14 #870), 03k9fj (0.26 #1454, 0.23 #974, 0.21 #5431), 01t_vv (0.23 #295, 0.20 #415, 0.13 #897), 017fp (0.22 #496, 0.21 #858, 0.20 #738) >> Best rule #4453 for best value: >> intensional similarity = 5 >> extensional distance = 1042 >> proper extension: 0413cff; >> query: (?x3222, 07s9rl0) <- genre(?x3222, ?x6887), genre(?x5001, ?x6887), genre(?x2376, ?x6887), ?x2376 = 042y1c, ?x5001 = 09q23x >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g54xkt genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 49.000 0.964 http://example.org/film/film/genre #13818-0162v PRED entity: 0162v PRED relation: member_states! PRED expected values: 085h1 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 13): 085h1 (0.76 #124, 0.75 #23, 0.74 #75), 018cqq (0.40 #18, 0.35 #74, 0.34 #70), 02jxk (0.22 #134, 0.21 #69, 0.21 #73), 059dn (0.21 #76, 0.21 #137, 0.20 #20), 041288 (0.10 #138, 0.07 #349, 0.06 #215), 0j7v_ (0.10 #138, 0.07 #349, 0.06 #215), 07t65 (0.10 #138, 0.07 #349, 0.06 #215), 02vk52z (0.10 #138, 0.07 #349, 0.06 #215), 0b6css (0.07 #349), 0gkjy (0.07 #349) >> Best rule #124 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 03rj0; 04w8f; 04g5k; 0jhd; >> query: (?x1957, 085h1) <- contains(?x8882, ?x1957), medal(?x1957, ?x422), currency(?x1957, ?x170) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0162v member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.758 http://example.org/user/ktrueman/default_domain/international_organization/member_states #13817-0d4htf PRED entity: 0d4htf PRED relation: region PRED expected values: 07ssc => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 13): 07ssc (0.59 #168, 0.55 #191, 0.14 #98), 09c7w0 (0.04 #839, 0.03 #186, 0.01 #278), 06t2t (0.04 #839, 0.01 #201), 02vzc (0.04 #839, 0.01 #198), 06bnz (0.04 #839, 0.01 #197), 06qd3 (0.04 #839, 0.01 #196), 0345h (0.04 #839, 0.01 #195), 059j2 (0.04 #839, 0.01 #194), 0f8l9c (0.04 #839, 0.01 #193), 06mzp (0.04 #839, 0.01 #192) >> Best rule #168 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 07sc6nw; 01fmys; 06ztvyx; 0cp0ph6; 09v71cj; 01qxc7; 01pj_5; 0dln8jk; 0b44shh; 027j9wd; ... >> query: (?x5513, 07ssc) <- genre(?x5513, ?x258), ?x258 = 05p553, film_distribution_medium(?x5513, ?x81), film(?x848, ?x5513) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d4htf region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.589 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #13816-04y8r PRED entity: 04y8r PRED relation: award_winner! PRED expected values: 09p2r9 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 136): 0bvhz9 (0.17 #5500, 0.04 #130, 0.02 #271), 02q690_ (0.11 #911, 0.02 #4295, 0.02 #4154), 05c1t6z (0.10 #861, 0.04 #9026, 0.03 #4245), 0gx_st (0.08 #883, 0.04 #9026, 0.04 #37), 03nnm4t (0.08 #920, 0.04 #9026, 0.03 #1907), 02wzl1d (0.08 #152, 0.07 #11, 0.07 #293), 0gvstc3 (0.07 #880, 0.04 #9026, 0.02 #4123), 0hndn2q (0.07 #40, 0.06 #181, 0.05 #322), 02yxh9 (0.07 #101, 0.06 #242, 0.05 #383), 0hr6lkl (0.07 #17, 0.06 #158, 0.05 #299) >> Best rule #5500 for best value: >> intensional similarity = 3 >> extensional distance = 1344 >> proper extension: 035_2h; 01j53q; >> query: (?x2332, ?x9921) <- award_winner(?x3528, ?x2332), award_nominee(?x2135, ?x3528), award_winner(?x9921, ?x3528) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #234 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 015v3r; 014g9y; *> query: (?x2332, 09p2r9) <- award(?x2332, ?x384), ?x384 = 03hkv_r, award_nominee(?x2332, ?x3528) *> conf = 0.04 ranks of expected_values: 42 EVAL 04y8r award_winner! 09p2r9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 100.000 100.000 0.172 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13815-02sg5v PRED entity: 02sg5v PRED relation: nominated_for! PRED expected values: 03c7tr1 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 191): 0gq9h (0.42 #4643, 0.37 #5366, 0.32 #10910), 0k611 (0.40 #3449, 0.36 #4172, 0.25 #557), 019f4v (0.40 #4634, 0.34 #10901, 0.33 #3429), 0p9sw (0.35 #6288, 0.33 #3395, 0.25 #6047), 0f4x7 (0.33 #4605, 0.25 #1231, 0.22 #10631), 0gq_v (0.30 #4599, 0.28 #8215, 0.27 #10866), 04dn09n (0.30 #4615, 0.25 #1241, 0.24 #4133), 0gqyl (0.30 #4661, 0.25 #1287, 0.19 #10928), 0gqy2 (0.27 #3017, 0.25 #1330, 0.24 #4222), 02x73k6 (0.27 #2942, 0.25 #1255, 0.23 #3665) >> Best rule #4643 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 03f7xg; >> query: (?x836, 0gq9h) <- film(?x2538, ?x836), production_companies(?x836, ?x788), written_by(?x836, ?x3692), genre(?x836, ?x225), film_production_design_by(?x836, ?x5532) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #2217 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 02gqm3; *> query: (?x836, 03c7tr1) <- film(?x2538, ?x836), genre(?x836, ?x5104), ?x5104 = 0bkbm, cinematography(?x836, ?x13360), country(?x836, ?x94) *> conf = 0.12 ranks of expected_values: 60 EVAL 02sg5v nominated_for! 03c7tr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 141.000 141.000 0.425 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13814-01l1sq PRED entity: 01l1sq PRED relation: award_nominee! PRED expected values: 02lg3y => 133 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1072): 021_rm (0.83 #2320, 0.81 #185568, 0.81 #190209), 059gkk (0.83 #2320, 0.81 #185568, 0.81 #190209), 01b9z4 (0.81 #185568, 0.81 #190209, 0.81 #106704), 01wbg84 (0.81 #185568, 0.81 #190209, 0.81 #106704), 01l1sq (0.72 #7294, 0.72 #4975, 0.60 #335), 02lg3y (0.61 #5663, 0.60 #1023, 0.56 #7982), 06dn58 (0.40 #127580, 0.11 #8670, 0.11 #6351), 02tr7d (0.28 #192531, 0.21 #55674, 0.20 #339), 03yj_0n (0.28 #192531, 0.21 #55674, 0.20 #805), 08w7vj (0.28 #192531, 0.21 #55674, 0.20 #168) >> Best rule #2320 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01r42_g; >> query: (?x1652, ?x369) <- award_winner(?x1652, ?x2965), award_winner(?x1652, ?x446), award_winner(?x1652, ?x369), ?x446 = 0436f4, ?x2965 = 01dy7j >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #5663 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 02lg3y; *> query: (?x1652, 02lg3y) <- award_nominee(?x1651, ?x1652), ?x1651 = 02lg9w, award_winner(?x1670, ?x1652) *> conf = 0.61 ranks of expected_values: 6 EVAL 01l1sq award_nominee! 02lg3y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 133.000 83.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13813-0m40d PRED entity: 0m40d PRED relation: artists PRED expected values: 02lvtb 03kts => 38 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1133): 04r1t (0.62 #1198, 0.30 #3328, 0.14 #132), 01vrncs (0.50 #1132, 0.30 #3262, 0.14 #66), 016j2t (0.50 #1916, 0.19 #4046, 0.14 #850), 0197tq (0.43 #12, 0.27 #4263, 0.25 #1078), 07s3vqk (0.43 #11, 0.26 #3207, 0.25 #1077), 01pq5j7 (0.43 #462, 0.24 #9607, 0.19 #9609), 0127s7 (0.43 #532, 0.19 #9609, 0.17 #20275), 02jq1 (0.38 #1550, 0.33 #3680, 0.14 #484), 01vw20_ (0.38 #1309, 0.30 #3439, 0.17 #7715), 018ndc (0.38 #1316, 0.30 #3446, 0.08 #9859) >> Best rule #1198 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 06cqb; 0mhfr; 01lyv; 0155w; 016zgj; 026g51; >> query: (?x9427, 04r1t) <- artists(?x9427, ?x3017), artists(?x9427, ?x1270), ?x1270 = 0137n0, award(?x3017, ?x4018), ?x4018 = 03qbh5, award_winner(?x1088, ?x3017), award_winner(?x2704, ?x3017) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #452 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 03_d0; 08lpkq; 01gjw; 015y_n; *> query: (?x9427, 02lvtb) <- artists(?x9427, ?x10559), artists(?x9427, ?x1270), ?x10559 = 0dbb3, award_winner(?x2420, ?x1270), artist(?x4483, ?x1270), award(?x1270, ?x724) *> conf = 0.14 ranks of expected_values: 331, 407 EVAL 0m40d artists 03kts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 38.000 21.000 0.625 http://example.org/music/genre/artists EVAL 0m40d artists 02lvtb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 38.000 21.000 0.625 http://example.org/music/genre/artists #13812-047wh1 PRED entity: 047wh1 PRED relation: cinematography PRED expected values: 03cx282 => 96 concepts (76 used for prediction) PRED predicted values (max 10 best out of 42): 0f3zf_ (0.25 #66, 0.02 #703, 0.02 #1342), 04qvl7 (0.10 #253, 0.07 #379, 0.05 #571), 0854hr (0.10 #271, 0.04 #782, 0.03 #1041), 03v1w7 (0.05 #636, 0.04 #1912, 0.03 #700), 0chsq (0.05 #636, 0.04 #1912, 0.03 #700), 016tt2 (0.05 #636, 0.04 #1912, 0.03 #828), 02vx4c2 (0.05 #223, 0.02 #670, 0.02 #1184), 0bqytm (0.05 #269, 0.03 #653, 0.03 #780), 05br10 (0.05 #306, 0.02 #1140, 0.02 #817), 07mb57 (0.05 #264, 0.01 #1225, 0.01 #582) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05b_gq; >> query: (?x5135, 0f3zf_) <- award(?x5135, ?x2022), film(?x1522, ?x5135), nominated_for(?x10455, ?x5135), ?x2022 = 05p1dby >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1229 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 201 *> proper extension: 048rn; *> query: (?x5135, 03cx282) <- genre(?x5135, ?x225), film(?x1522, ?x5135), costume_design_by(?x5135, ?x6327) *> conf = 0.01 ranks of expected_values: 32 EVAL 047wh1 cinematography 03cx282 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 96.000 76.000 0.250 http://example.org/film/film/cinematography #13811-0345h PRED entity: 0345h PRED relation: combatants PRED expected values: 07ssc => 193 concepts (164 used for prediction) PRED predicted values (max 10 best out of 311): 07ssc (0.84 #5243, 0.84 #5767, 0.83 #5242), 0hzlz (0.84 #5243, 0.84 #5767, 0.83 #5242), 05vz3zq (0.84 #5243, 0.84 #5767, 0.83 #5241), 0345h (0.50 #894, 0.48 #969, 0.43 #1116), 05qhw (0.48 #960, 0.46 #885, 0.42 #1401), 01mk6 (0.48 #1005, 0.42 #1446, 0.42 #930), 06f32 (0.44 #983, 0.43 #1130, 0.42 #908), 02psqkz (0.41 #620, 0.33 #1428, 0.33 #2534), 059z0 (0.36 #643, 0.33 #2557, 0.30 #3759), 05b4w (0.31 #907, 0.31 #392, 0.30 #3759) >> Best rule #5243 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 0432mrk; >> query: (?x1264, ?x1603) <- combatants(?x1603, ?x1264), combatants(?x583, ?x1264), nationality(?x889, ?x1603), country(?x511, ?x583) >> conf = 0.84 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0345h combatants 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 164.000 0.837 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #13810-071cn PRED entity: 071cn PRED relation: time_zones PRED expected values: 02hcv8 => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.75 #912, 0.74 #417, 0.47 #706), 02fqwt (0.29 #600, 0.29 #92, 0.28 #157), 02lcqs (0.21 #265, 0.20 #643, 0.20 #448), 02hczc (0.20 #15, 0.16 #2135, 0.14 #601), 02llzg (0.17 #251, 0.16 #277, 0.15 #342), 02lcrv (0.16 #2135, 0.01 #215, 0.01 #241), 03bdv (0.15 #579, 0.12 #501, 0.10 #488), 03plfd (0.07 #348, 0.07 #257, 0.07 #283), 042g7t (0.04 #323, 0.03 #441, 0.03 #297), 052vwh (0.02 #376, 0.02 #741, 0.02 #989) >> Best rule #912 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 0jq27; >> query: (?x3786, ?x2674) <- administrative_division(?x3786, ?x8854), time_zones(?x8854, ?x2674), contains(?x335, ?x8854) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 071cn time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 171.000 0.749 http://example.org/location/location/time_zones #13809-01nzs7 PRED entity: 01nzs7 PRED relation: program PRED expected values: 02r2j8 02rhwjr => 113 concepts (34 used for prediction) PRED predicted values (max 10 best out of 256): 02gl58 (0.73 #725, 0.69 #1450, 0.35 #2657), 04glx0 (0.40 #585, 0.29 #827, 0.25 #1309), 097h2 (0.30 #636, 0.21 #878, 0.20 #1119), 017dcd (0.30 #484, 0.19 #1208, 0.15 #3623), 017dbx (0.30 #707, 0.19 #1431, 0.15 #3623), 043qqt5 (0.20 #683, 0.19 #1407, 0.14 #925), 01fs__ (0.20 #600, 0.15 #3623, 0.15 #3865), 0q9jk (0.20 #616, 0.15 #3623, 0.15 #3865), 070ltt (0.20 #669, 0.14 #911, 0.13 #1152), 034fl9 (0.20 #634, 0.14 #876, 0.13 #1117) >> Best rule #725 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 03lpbx; >> query: (?x1648, ?x10284) <- program(?x1648, ?x9082), program(?x1648, ?x1843), program(?x1762, ?x9082), genre(?x1843, ?x811), award_winner(?x10284, ?x1648), ?x1762 = 0gsg7 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #3857 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 01y67v; 01w92; 0g4c1t; 01y81r; 02qbjm; 01w5gp; 07zlqp; 03jl0_; 0b275x; 017vb_; ... *> query: (?x1648, 02rhwjr) <- program(?x1648, ?x1843), genre(?x1843, ?x811), languages(?x1843, ?x254) *> conf = 0.02 ranks of expected_values: 245 EVAL 01nzs7 program 02rhwjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 113.000 34.000 0.732 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program EVAL 01nzs7 program 02r2j8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 34.000 0.732 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #13808-01l4g5 PRED entity: 01l4g5 PRED relation: nationality PRED expected values: 0d060g => 118 concepts (113 used for prediction) PRED predicted values (max 10 best out of 49): 09c7w0 (0.85 #4404, 0.79 #8010, 0.79 #6605), 02jx1 (0.33 #33, 0.29 #533, 0.27 #1633), 07ssc (0.33 #115, 0.24 #715, 0.12 #2015), 05bcl (0.33 #160, 0.05 #760, 0.03 #8009), 0d060g (0.28 #7507, 0.25 #8812, 0.24 #8511), 0pmq2 (0.28 #7507, 0.25 #8812, 0.24 #8511), 03_3d (0.14 #706, 0.06 #506, 0.05 #606), 03rk0 (0.12 #546, 0.06 #9758, 0.06 #9858), 03rjj (0.10 #7006, 0.03 #8009, 0.02 #1305), 0h7x (0.10 #7006, 0.03 #8009, 0.02 #3236) >> Best rule #4404 for best value: >> intensional similarity = 5 >> extensional distance = 358 >> proper extension: 02cg2v; >> query: (?x4855, 09c7w0) <- student(?x12737, ?x4855), major_field_of_study(?x12737, ?x4321), contains(?x279, ?x12737), ?x4321 = 0g26h, organization(?x346, ?x12737) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #7507 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 885 *> proper extension: 04shbh; 06lgq8; 0p51w; 03q95r; 081hvm; *> query: (?x4855, ?x279) <- award(?x4855, ?x8929), student(?x12737, ?x4855), category(?x12737, ?x134), contains(?x279, ?x12737), currency(?x12737, ?x2244) *> conf = 0.28 ranks of expected_values: 5 EVAL 01l4g5 nationality 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 118.000 113.000 0.847 http://example.org/people/person/nationality #13807-0f2wj PRED entity: 0f2wj PRED relation: location! PRED expected values: 032wdd => 117 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2095): 04kr63w (0.74 #162186, 0.70 #229551, 0.48 #154699), 04jspq (0.48 #154699, 0.47 #217073, 0.47 #232048), 01sbhvd (0.48 #154699, 0.47 #217073, 0.47 #232048), 0g476 (0.48 #154699, 0.47 #217073, 0.47 #232048), 03lvyj (0.48 #154699, 0.47 #217073, 0.47 #232048), 01qqtr (0.48 #154699, 0.47 #217073, 0.47 #232048), 0b1q7c (0.48 #154699, 0.47 #217073, 0.47 #232048), 07b3r9 (0.48 #154699, 0.47 #217073, 0.47 #244526), 01ttg5 (0.45 #32435, 0.42 #87326, 0.30 #134736), 016jll (0.30 #134736, 0.30 #159690, 0.29 #87325) >> Best rule #162186 for best value: >> intensional similarity = 3 >> extensional distance = 194 >> proper extension: 01c1nm; >> query: (?x682, ?x10445) <- place_of_birth(?x10445, ?x682), location_of_ceremony(?x566, ?x682), location(?x10445, ?x739) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #6740 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0k049; 06_kh; 0284jb; 0r0m6; 05jbn; 0c_m3; 0f2rq; 0r15k; 02d6c; 0y1rf; *> query: (?x682, 032wdd) <- place_of_death(?x5601, ?x682), location(?x794, ?x682), celebrities_impersonated(?x3649, ?x5601) *> conf = 0.06 ranks of expected_values: 401 EVAL 0f2wj location! 032wdd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 117.000 110.000 0.737 http://example.org/people/person/places_lived./people/place_lived/location #13806-06y57 PRED entity: 06y57 PRED relation: citytown! PRED expected values: 012lzr 06p8m => 198 concepts (163 used for prediction) PRED predicted values (max 10 best out of 706): 07vk2 (0.52 #102437, 0.21 #121798, 0.16 #54034), 03_c8p (0.50 #1381, 0.17 #4606, 0.11 #19932), 01bfjy (0.50 #1598, 0.04 #20149, 0.04 #19343), 02b07b (0.50 #1568, 0.04 #20119, 0.04 #19313), 026f5s (0.50 #1473, 0.04 #20024, 0.04 #19218), 05b0f7 (0.50 #1459, 0.04 #20010, 0.04 #19204), 075znj (0.50 #1408, 0.04 #19959, 0.04 #19153), 06q07 (0.50 #1203, 0.04 #19754, 0.04 #18948), 081g_l (0.50 #972, 0.04 #19523, 0.04 #18717), 01zn4y (0.33 #320, 0.01 #62417, 0.01 #64835) >> Best rule #102437 for best value: >> intensional similarity = 3 >> extensional distance = 211 >> proper extension: 07sb1; >> query: (?x5036, ?x2013) <- citytown(?x10312, ?x5036), contains(?x5036, ?x2013), organization(?x4682, ?x10312) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #12681 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 0jgd; 02k54; 04v3q; 04wgh; *> query: (?x5036, 06p8m) <- locations(?x3729, ?x5036), film_release_region(?x1035, ?x5036), featured_film_locations(?x308, ?x5036) *> conf = 0.05 ranks of expected_values: 323, 495 EVAL 06y57 citytown! 06p8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 198.000 163.000 0.518 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 06y57 citytown! 012lzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 198.000 163.000 0.518 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #13805-04z0g PRED entity: 04z0g PRED relation: company PRED expected values: 025v3k => 115 concepts (95 used for prediction) PRED predicted values (max 10 best out of 136): 03ksy (0.33 #238, 0.29 #428, 0.25 #48), 01w3v (0.25 #15, 0.17 #205, 0.14 #395), 03hdz8 (0.25 #107, 0.17 #297, 0.14 #487), 0bwfn (0.17 #303, 0.14 #493, 0.11 #684), 07tg4 (0.17 #233, 0.14 #423, 0.11 #614), 05zl0 (0.16 #2563, 0.13 #3897, 0.07 #2753), 09c7w0 (0.12 #2854, 0.12 #2664, 0.11 #4760), 01jpqb (0.11 #709, 0.07 #1089, 0.05 #1469), 01k2wn (0.11 #1350, 0.05 #2492, 0.05 #2682), 07wh1 (0.10 #4176, 0.06 #4938, 0.05 #2652) >> Best rule #238 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01tdnyh; 06y7d; >> query: (?x5790, 03ksy) <- profession(?x5790, ?x3802), ?x3802 = 06q2q, student(?x2014, ?x5790), student(?x1368, ?x5790), company(?x5790, ?x2313) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04z0g company 025v3k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 95.000 0.333 http://example.org/people/person/employment_history./business/employment_tenure/company #13804-0bzn6_ PRED entity: 0bzn6_ PRED relation: ceremony! PRED expected values: 0gr0m 0gqwc 0gs9p 0gqxm 0gr07 => 37 concepts (36 used for prediction) PRED predicted values (max 10 best out of 358): 0gqwc (0.90 #4186, 0.89 #4917, 0.89 #4430), 0gs9p (0.88 #4187, 0.85 #4431, 0.84 #2970), 0gr07 (0.81 #4291, 0.79 #3074, 0.77 #4535), 0gr0m (0.79 #4429, 0.79 #3454, 0.79 #3211), 0gqxm (0.54 #3523, 0.47 #3037, 0.46 #2794), 054krc (0.45 #1706, 0.43 #1220, 0.39 #1705), 04dn09n (0.45 #1706, 0.43 #1220, 0.39 #1705), 02n9nmz (0.45 #1706, 0.43 #1220, 0.39 #1705), 02r22gf (0.45 #1706, 0.43 #1220, 0.39 #1705), 02qyntr (0.45 #1706, 0.43 #1220, 0.39 #1705) >> Best rule #4186 for best value: >> intensional similarity = 15 >> extensional distance = 46 >> proper extension: 02hn5v; 0bzk2h; 0fz20l; 0c4hgj; 073hd1; 0fz0c2; 09306z; 0c4hnm; 073h5b; >> query: (?x3618, 0gqwc) <- honored_for(?x3618, ?x5519), ceremony(?x7099, ?x3618), award_winner(?x3618, ?x4232), award_winner(?x3618, ?x1660), genre(?x5519, ?x3506), award(?x4232, ?x1243), award(?x5949, ?x7099), ?x5949 = 02ryx0, award_nominee(?x1660, ?x521), award(?x5519, ?x2915), titles(?x3506, ?x240), award(?x8188, ?x7099), artists(?x505, ?x1660), nominated_for(?x6514, ?x5519), award_winner(?x2915, ?x157) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 0bzn6_ ceremony! 0gr07 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 36.000 0.896 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzn6_ ceremony! 0gqxm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 36.000 0.896 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzn6_ ceremony! 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 36.000 0.896 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzn6_ ceremony! 0gqwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 36.000 0.896 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzn6_ ceremony! 0gr0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 36.000 0.896 http://example.org/award/award_category/winners./award/award_honor/ceremony #13803-0lsw9 PRED entity: 0lsw9 PRED relation: artists! PRED expected values: 0827d 08jyyk => 87 concepts (28 used for prediction) PRED predicted values (max 10 best out of 210): 064t9 (0.48 #1820, 0.45 #2719, 0.43 #3619), 06by7 (0.42 #5132, 0.41 #6937, 0.41 #4228), 0glt670 (0.33 #1848, 0.27 #2747, 0.20 #3947), 06j6l (0.33 #2155, 0.30 #1856, 0.28 #2755), 08jyyk (0.32 #66, 0.27 #2407, 0.21 #5412), 0cx7f (0.32 #134, 0.14 #733, 0.12 #1035), 016clz (0.30 #603, 0.28 #905, 0.28 #1208), 0gywn (0.28 #2165, 0.27 #1866, 0.22 #2765), 0155w (0.27 #2407, 0.21 #2212, 0.21 #5412), 05bt6j (0.27 #2407, 0.21 #5412, 0.19 #6959) >> Best rule #1820 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 03zz8b; >> query: (?x7706, 064t9) <- profession(?x7706, ?x131), award_winner(?x12729, ?x7706), people(?x1050, ?x7706), origin(?x7706, ?x739) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #66 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 03c7ln; 02whj; 0l12d; 01w923; 06k02; 01vsl3_; 0p3sf; 0lzkm; 01gg59; 050z2; ... *> query: (?x7706, 08jyyk) <- artists(?x10290, ?x7706), nationality(?x7706, ?x94), instrumentalists(?x315, ?x7706), ?x10290 = 03ckfl9 *> conf = 0.32 ranks of expected_values: 5, 42 EVAL 0lsw9 artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 28.000 0.482 http://example.org/music/genre/artists EVAL 0lsw9 artists! 0827d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 87.000 28.000 0.482 http://example.org/music/genre/artists #13802-0693l PRED entity: 0693l PRED relation: participant PRED expected values: 06cv1 => 118 concepts (71 used for prediction) PRED predicted values (max 10 best out of 274): 014zcr (0.11 #2583, 0.09 #3866, 0.04 #7715), 05ty4m (0.09 #3869, 0.04 #9000, 0.03 #12207), 01phtd (0.09 #22456, 0.09 #29525, 0.09 #29524), 02f2dn (0.09 #22456, 0.09 #29525, 0.09 #29524), 0c6qh (0.09 #2731, 0.07 #4014, 0.06 #1448), 0gx_p (0.09 #2988, 0.06 #4271, 0.04 #8120), 01_f_5 (0.09 #29525, 0.09 #29524, 0.09 #23099), 0mbw0 (0.09 #29525, 0.09 #29524, 0.09 #23099), 03359d (0.09 #29525, 0.09 #29524, 0.09 #23099), 09yrh (0.08 #5446, 0.04 #16994, 0.04 #22129) >> Best rule #2583 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0q9kd; 014zcr; 05ty4m; 0c1pj; 0j_c; 021yw7; 043gj; 0mm1q; 084m3; 0g2lq; ... >> query: (?x3117, 014zcr) <- film(?x3117, ?x814), profession(?x3117, ?x319), participant(?x3117, ?x3118) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #2602 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 0q9kd; 014zcr; 05ty4m; 0c1pj; 0j_c; 021yw7; 043gj; 0mm1q; 084m3; 0g2lq; ... *> query: (?x3117, 06cv1) <- film(?x3117, ?x814), profession(?x3117, ?x319), participant(?x3117, ?x3118) *> conf = 0.02 ranks of expected_values: 101 EVAL 0693l participant 06cv1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 118.000 71.000 0.109 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #13801-01tgwv PRED entity: 01tgwv PRED relation: disciplines_or_subjects PRED expected values: 06n90 02xlf => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 37): 02xlf (0.75 #60, 0.53 #246, 0.51 #209), 01hmnh (0.62 #47, 0.40 #196, 0.39 #233), 06n90 (0.50 #44, 0.38 #81, 0.34 #193), 02vxn (0.38 #484, 0.38 #521, 0.38 #559), 05qgc (0.21 #558, 0.19 #184, 0.12 #147), 014dfn (0.21 #558, 0.17 #1052, 0.12 #100), 0707q (0.21 #558, 0.17 #1052, 0.12 #179), 0j7v_ (0.21 #558, 0.17 #1052, 0.12 #94), 08_lx0 (0.21 #558, 0.17 #1052, 0.12 #149), 0l67h (0.21 #558, 0.17 #1052, 0.06 #219) >> Best rule #60 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 02662b; 01yz0x; 0262yt; 0262x6; 040_9s0; >> query: (?x11263, 02xlf) <- award(?x2485, ?x11263), award(?x576, ?x11263), ?x576 = 01zkxv, influenced_by(?x2485, ?x3336), student(?x2327, ?x2485), type_of_union(?x2485, ?x566), ?x566 = 04ztj >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01tgwv disciplines_or_subjects 02xlf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.750 http://example.org/award/award_category/disciplines_or_subjects EVAL 01tgwv disciplines_or_subjects 06n90 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 43.000 43.000 0.750 http://example.org/award/award_category/disciplines_or_subjects #13800-016z9n PRED entity: 016z9n PRED relation: currency PRED expected values: 09nqf => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.82 #8, 0.78 #99, 0.78 #36), 01nv4h (0.02 #23, 0.02 #30, 0.02 #191), 02l6h (0.01 #235), 02gsvk (0.01 #83) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0gmd3k7; >> query: (?x2336, 09nqf) <- film(?x777, ?x2336), film(?x450, ?x2336), ?x777 = 05kfs >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016z9n currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.818 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #13799-046p9 PRED entity: 046p9 PRED relation: award_winner! PRED expected values: 02f5qb => 74 concepts (53 used for prediction) PRED predicted values (max 10 best out of 244): 01by1l (0.49 #4835, 0.21 #542, 0.17 #2259), 02f716 (0.40 #1288, 0.39 #12450, 0.38 #20180), 02f5qb (0.40 #1288, 0.39 #12450, 0.38 #20180), 03qbnj (0.40 #1288, 0.39 #12450, 0.38 #20180), 02f72_ (0.40 #1288, 0.39 #12450, 0.38 #20180), 02f71y (0.40 #1288, 0.39 #12450, 0.38 #20180), 02f73b (0.40 #1288, 0.39 #12450, 0.38 #20180), 02f705 (0.40 #1288, 0.39 #12450, 0.38 #20180), 0c4z8 (0.20 #930, 0.19 #1360, 0.12 #2218), 01cky2 (0.20 #1047, 0.11 #1906, 0.10 #2335) >> Best rule #4835 for best value: >> intensional similarity = 5 >> extensional distance = 164 >> proper extension: 0l56b; >> query: (?x8156, 01by1l) <- award_winner(?x3647, ?x8156), award(?x8913, ?x3647), award(?x7865, ?x3647), ?x8913 = 013w8y, artists(?x302, ?x7865) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1288 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 23 *> proper extension: 025xt8y; 01wcp_g; 03g5jw; 01cwhp; 018pj3; 0892sx; 018ndc; 01wj18h; 01w272y; 01vxlbm; ... *> query: (?x8156, ?x2634) <- artists(?x302, ?x8156), artist(?x9492, ?x8156), artist(?x5666, ?x8156), award(?x8156, ?x2634), ?x5666 = 043g7l, ?x9492 = 03mp8k *> conf = 0.40 ranks of expected_values: 3 EVAL 046p9 award_winner! 02f5qb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 53.000 0.488 http://example.org/award/award_category/winners./award/award_honor/award_winner #13798-04rcr PRED entity: 04rcr PRED relation: origin PRED expected values: 030qb3t => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 88): 0d9jr (0.50 #98, 0.12 #1986, 0.10 #2458), 04jpl (0.40 #714, 0.33 #1186, 0.25 #1894), 02_286 (0.25 #488, 0.20 #960, 0.17 #1196), 030qb3t (0.25 #506, 0.14 #1450, 0.12 #2158), 09c7w0 (0.25 #1, 0.08 #2833, 0.06 #1889), 0k33p (0.20 #871, 0.17 #1343, 0.14 #1579), 04lh6 (0.20 #858, 0.17 #1330, 0.06 #2038), 0fpzwf (0.20 #1048, 0.05 #2700, 0.01 #6476), 07_fl (0.14 #1602, 0.06 #2310, 0.05 #2546), 0c_m3 (0.12 #1753, 0.06 #3877, 0.03 #3405) >> Best rule #98 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0d193h; 0b1hw; >> query: (?x646, 0d9jr) <- influenced_by(?x5329, ?x646), group(?x227, ?x646), award(?x646, ?x9462), ?x9462 = 01d38t >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #506 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0lsw9; *> query: (?x646, 030qb3t) <- award_winner(?x4912, ?x646), artists(?x10930, ?x646), ?x10930 = 0jrv_ *> conf = 0.25 ranks of expected_values: 4 EVAL 04rcr origin 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 115.000 0.500 http://example.org/music/artist/origin #13797-0d4fqn PRED entity: 0d4fqn PRED relation: nationality PRED expected values: 09c7w0 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 19): 09c7w0 (0.83 #401, 0.83 #301, 0.81 #201), 07ssc (0.33 #4204, 0.11 #15, 0.08 #3317), 02jx1 (0.09 #1435, 0.09 #2135, 0.09 #5537), 03rk0 (0.06 #4850, 0.06 #5250, 0.05 #5550), 0d060g (0.05 #907, 0.04 #1409, 0.04 #1007), 0ctw_b (0.03 #127, 0.02 #227, 0.01 #327), 0chghy (0.02 #1412, 0.02 #2212, 0.02 #2012), 03rjj (0.02 #2507, 0.02 #1507, 0.02 #2007), 07c52 (0.02 #3703), 0345h (0.02 #5335, 0.02 #5635, 0.01 #5836) >> Best rule #401 for best value: >> intensional similarity = 2 >> extensional distance = 184 >> proper extension: 02k76g; >> query: (?x636, 09c7w0) <- tv_program(?x636, ?x3180), profession(?x636, ?x1032) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d4fqn nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.828 http://example.org/people/person/nationality #13796-0g2mbn PRED entity: 0g2mbn PRED relation: location PRED expected values: 020d8d => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 118): 04vmp (0.70 #36911, 0.47 #52957, 0.42 #55364), 030qb3t (0.19 #82, 0.17 #2491, 0.15 #32177), 06y57 (0.10 #255, 0.01 #2664, 0.01 #3466), 0cc56 (0.07 #2465, 0.04 #3267, 0.04 #5673), 0cvw9 (0.07 #2000, 0.01 #12432), 04jpl (0.06 #2426, 0.06 #8041, 0.05 #14460), 0cr3d (0.06 #946, 0.06 #36252, 0.06 #2553), 059rby (0.06 #2425, 0.05 #3227, 0.05 #818), 0ccvx (0.06 #2630, 0.05 #1023, 0.04 #3432), 0c8tk (0.05 #1829) >> Best rule #36911 for best value: >> intensional similarity = 3 >> extensional distance = 1483 >> proper extension: 0c3kw; 0f1vrl; 040db; 07h1h5; 05_pkf; 03f0fnk; 04r68; 03flwk; 048_p; 0c8hct; ... >> query: (?x5153, ?x7412) <- profession(?x5153, ?x1032), place_of_birth(?x5153, ?x7412), location(?x5153, ?x739) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g2mbn location 020d8d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 82.000 0.697 http://example.org/people/person/places_lived./people/place_lived/location #13795-04xbq3 PRED entity: 04xbq3 PRED relation: person PRED expected values: 0p_47 => 95 concepts (72 used for prediction) PRED predicted values (max 10 best out of 5): 02h8hr (0.02 #2179, 0.02 #2367, 0.02 #2555), 051cc (0.01 #4118), 01zlh5 (0.01 #4116), 02rmxx (0.01 #4078), 09b6zr (0.01 #4052) >> Best rule #2179 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 07hpv3; 024rwx; 0dr1c2; 0ctzf1; 04svwx; 02rhwjr; 04hs7d; 045nc5; >> query: (?x9188, 02h8hr) <- genre(?x9188, ?x2605), program(?x5007, ?x9188), languages(?x9188, ?x3592), major_field_of_study(?x122, ?x2605) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04xbq3 person 0p_47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 72.000 0.024 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #13794-07h9gp PRED entity: 07h9gp PRED relation: film! PRED expected values: 01pllx => 84 concepts (38 used for prediction) PRED predicted values (max 10 best out of 623): 01lbp (0.70 #14533, 0.67 #70594, 0.65 #60212), 030g9z (0.17 #1570, 0.11 #3646, 0.09 #16610), 02qgyv (0.17 #384, 0.11 #2460, 0.02 #8688), 0p_pd (0.17 #54, 0.11 #2130, 0.02 #4205), 0c7lcx (0.17 #511, 0.11 #2587, 0.02 #8815), 02114t (0.17 #635, 0.11 #2711, 0.02 #27626), 03fbb6 (0.17 #976, 0.11 #3052, 0.01 #32120), 016ks_ (0.17 #783, 0.11 #2859, 0.01 #9087), 07rzf (0.17 #1876, 0.11 #3952, 0.01 #16410), 0g2mbn (0.17 #918, 0.11 #2994, 0.01 #21680) >> Best rule #14533 for best value: >> intensional similarity = 3 >> extensional distance = 278 >> proper extension: 0gfzgl; 0431v3; 06dfz1; 07zhjj; >> query: (?x1728, ?x932) <- titles(?x2480, ?x1728), nominated_for(?x932, ?x1728), celebrity(?x932, ?x702) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #3617 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0640y35; *> query: (?x1728, 01pllx) <- film(?x5541, ?x1728), country(?x1728, ?x94), genre(?x1728, ?x258), ?x5541 = 01pk3z *> conf = 0.11 ranks of expected_values: 39 EVAL 07h9gp film! 01pllx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 84.000 38.000 0.699 http://example.org/film/actor/film./film/performance/film #13793-024dgj PRED entity: 024dgj PRED relation: profession PRED expected values: 0nbcg => 177 concepts (173 used for prediction) PRED predicted values (max 10 best out of 83): 02hrh1q (0.90 #6587, 0.89 #4105, 0.89 #3959), 016z4k (0.79 #1464, 0.70 #1610, 0.59 #2195), 0nbcg (0.59 #12746, 0.55 #615, 0.54 #9527), 01d_h8 (0.55 #2928, 0.54 #7747, 0.53 #7455), 0n1h (0.47 #1472, 0.40 #1618, 0.37 #2203), 01c72t (0.47 #1338, 0.34 #12446, 0.31 #9519), 03gjzk (0.39 #1915, 0.37 #7465, 0.36 #7757), 0dxtg (0.37 #12290, 0.34 #7463, 0.33 #7755), 0d1pc (0.36 #8766, 0.32 #3264, 0.31 #1072), 0fnpj (0.36 #8766, 0.26 #1520, 0.22 #5464) >> Best rule #6587 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 09fb5; 06x58; 01gbbz; 01vwllw; 0bqs56; 0fthdk; >> query: (?x3503, 02hrh1q) <- award_winner(?x5656, ?x3503), participant(?x3503, ?x4106), participant(?x3503, ?x3553), award(?x3503, ?x247) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #12746 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 262 *> proper extension: 01gx5f; *> query: (?x3503, 0nbcg) <- artists(?x302, ?x3503), profession(?x3503, ?x131), role(?x3503, ?x227) *> conf = 0.59 ranks of expected_values: 3 EVAL 024dgj profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 177.000 173.000 0.903 http://example.org/people/person/profession #13792-063hp4 PRED entity: 063hp4 PRED relation: film_release_region PRED expected values: 09c7w0 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 120): 09c7w0 (0.71 #3232, 0.70 #3591, 0.70 #9338), 0f8l9c (0.50 #571, 0.35 #211, 0.27 #930), 06mkj (0.50 #615, 0.30 #255, 0.29 #974), 02vzc (0.46 #609, 0.31 #968, 0.30 #249), 03h64 (0.43 #627, 0.25 #986, 0.22 #267), 03rjj (0.39 #547, 0.35 #187, 0.29 #906), 0k6nt (0.39 #575, 0.31 #934, 0.30 #215), 082fr (0.39 #631, 0.26 #271, 0.15 #451), 05r4w (0.39 #541, 0.24 #900, 0.23 #12024), 0d0vqn (0.36 #551, 0.33 #910, 0.26 #12034) >> Best rule #3232 for best value: >> intensional similarity = 3 >> extensional distance = 185 >> proper extension: 07gp9; 01vksx; 0jqn5; 0cc5mcj; 08rr3p; 0b1y_2; 04fv5b; 0284b56; 05nlx4; 02mpyh; ... >> query: (?x6722, 09c7w0) <- nominated_for(?x198, ?x6722), crewmember(?x6722, ?x10164), titles(?x307, ?x6722) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 063hp4 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.711 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13791-09f2j PRED entity: 09f2j PRED relation: institution! PRED expected values: 03mkk4 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 10): 03mkk4 (0.36 #471, 0.35 #43, 0.29 #113), 022h5x (0.36 #471, 0.26 #78, 0.22 #322), 02m4yg (0.36 #471, 0.25 #46, 0.14 #739), 071tyz (0.36 #471, 0.15 #42, 0.14 #739), 01ysy9 (0.36 #471, 0.14 #1213, 0.10 #80), 01kxxq (0.36 #471, 0.14 #1213, 0.05 #455), 01gkg3 (0.36 #471, 0.03 #105, 0.01 #803), 02cq61 (0.27 #26, 0.14 #739, 0.14 #288), 0g26h (0.02 #184, 0.02 #234, 0.01 #244), 0bpgx (0.01 #302, 0.01 #335) >> Best rule #471 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 01rr31; >> query: (?x4955, ?x865) <- major_field_of_study(?x4955, ?x8925), major_field_of_study(?x4955, ?x2606), ?x2606 = 062z7, major_field_of_study(?x865, ?x8925) >> conf = 0.36 => this is the best rule for 7 predicted values ranks of expected_values: 1 EVAL 09f2j institution! 03mkk4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.357 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13790-05v1sb PRED entity: 05v1sb PRED relation: nationality PRED expected values: 09c7w0 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.79 #1502, 0.79 #1402, 0.79 #301), 059j2 (0.37 #3709), 07ssc (0.19 #815, 0.18 #915, 0.15 #1015), 06bnz (0.14 #341, 0.10 #841, 0.09 #941), 03gj2 (0.12 #626, 0.10 #726, 0.07 #1026), 02jx1 (0.10 #3540, 0.10 #1133, 0.10 #8047), 03rjj (0.10 #805, 0.09 #905, 0.07 #305), 0345h (0.09 #131, 0.08 #231, 0.07 #531), 0d060g (0.09 #107, 0.08 #207, 0.06 #607), 03rt9 (0.08 #213, 0.07 #513, 0.07 #413) >> Best rule #1502 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 01y8d4; 03gvpk; 06y3r; >> query: (?x4251, 09c7w0) <- award_winner(?x200, ?x4251), place_of_death(?x4251, ?x5895), award_winner(?x199, ?x200) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v1sb nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.788 http://example.org/people/person/nationality #13789-02cg2v PRED entity: 02cg2v PRED relation: currency PRED expected values: 09nqf => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.75 #4, 0.54 #7, 0.52 #40), 01nv4h (0.04 #38, 0.02 #65, 0.02 #71), 02l6h (0.02 #66, 0.02 #75, 0.01 #87) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02qjj7; 054c1; 01g0jn; >> query: (?x13931, 09nqf) <- participant(?x3054, ?x13931), team(?x13931, ?x5483), student(?x5288, ?x13931), draft(?x5483, ?x2569) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cg2v currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.750 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #13788-022fj_ PRED entity: 022fj_ PRED relation: student PRED expected values: 05fyss => 178 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1564): 02vyw (0.07 #2676, 0.04 #13146, 0.04 #4770), 01ry0f (0.07 #2921, 0.04 #5015, 0.04 #34335), 06pwf6 (0.07 #2557, 0.04 #4651, 0.03 #6745), 019g65 (0.07 #3842, 0.04 #5936, 0.03 #8030), 094xh (0.07 #3014, 0.04 #5108, 0.03 #7202), 02xnjd (0.07 #3473, 0.04 #5567, 0.02 #13943), 05mc99 (0.07 #3412, 0.04 #5506, 0.02 #13882), 02ch1w (0.07 #3118, 0.04 #5212, 0.02 #13588), 016fjj (0.07 #2688, 0.04 #4782, 0.02 #13158), 05f7snc (0.07 #2923, 0.04 #5017, 0.02 #21771) >> Best rule #2676 for best value: >> intensional similarity = 7 >> extensional distance = 12 >> proper extension: 08l_c1; >> query: (?x9022, 02vyw) <- category(?x9022, ?x134), colors(?x9022, ?x3189), colors(?x9022, ?x332), school_type(?x9022, ?x3092), ?x3189 = 01g5v, citytown(?x9022, ?x3501), ?x332 = 01l849 >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 022fj_ student 05fyss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 178.000 71.000 0.071 http://example.org/education/educational_institution/students_graduates./education/education/student #13787-01wd9lv PRED entity: 01wd9lv PRED relation: actor! PRED expected values: 026bfsh => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 95): 025ljp (0.33 #1327, 0.29 #1061, 0.15 #2388), 08xvpn (0.08 #24422, 0.07 #27076, 0.07 #27075), 027fwmt (0.08 #24422, 0.07 #27076, 0.07 #27075), 026bfsh (0.07 #1954, 0.06 #1689, 0.05 #4345), 019nnl (0.06 #1080, 0.05 #814, 0.02 #5064), 0330r (0.06 #1250, 0.01 #3374), 03y3bp7 (0.05 #839, 0.03 #1105, 0.01 #3495), 02xhwm (0.05 #1011, 0.03 #1277), 03r0rq (0.05 #1000, 0.03 #1266), 097h2 (0.05 #974, 0.03 #1240) >> Best rule #1327 for best value: >> intensional similarity = 2 >> extensional distance = 30 >> proper extension: 025504; >> query: (?x6382, ?x9668) <- program(?x6382, ?x9668), category(?x6382, ?x134) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1954 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 79 *> proper extension: 02fybl; *> query: (?x6382, 026bfsh) <- origin(?x6382, ?x739), participant(?x3083, ?x6382) *> conf = 0.07 ranks of expected_values: 4 EVAL 01wd9lv actor! 026bfsh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 115.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #13786-02h4rq6 PRED entity: 02h4rq6 PRED relation: institution PRED expected values: 022xml 0j_sncb 02fy0z 04chyn 0p5wz 01pq4w 07vfj 03x33n 02cbvn 02rg_4 07vyf 0bqxw 029d_ 02dj3 0b1xl 02zd2b 06bw5 025rcc 0gjv_ 015q1n 03tw2s 0cwx_ 017v3q 01jvxb 02sjgpq 01dbns 01q7q2 014xf6 0k__z 03gn1x 01hr11 011xy1 03l78j 0jpn8 01_r9k 01l8t8 02hwww 05hf_5 02jx_v 0jksm => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 335): 0j_sncb (0.71 #4156, 0.67 #5110, 0.64 #5746), 07vfj (0.71 #4174, 0.62 #4811, 0.60 #2905), 0b1xl (0.62 #4838, 0.60 #2932, 0.45 #3171), 0bqxw (0.62 #4828, 0.57 #4191, 0.50 #6098), 0gjv_ (0.62 #5494, 0.57 #4223, 0.56 #6447), 015q1n (0.60 #2958, 0.57 #4227, 0.50 #4864), 026vcc (0.60 #2960, 0.57 #4229, 0.50 #4866), 011xy1 (0.60 #3015, 0.50 #4921, 0.45 #3171), 0bsnm (0.60 #3322, 0.50 #3639, 0.45 #3171), 07wtc (0.60 #3101, 0.45 #3171, 0.43 #4759) >> Best rule #4156 for best value: >> intensional similarity = 23 >> extensional distance = 5 >> proper extension: 027f2w; >> query: (?x865, 0j_sncb) <- major_field_of_study(?x865, ?x742), institution(?x865, ?x11768), institution(?x865, ?x9607), institution(?x865, ?x8797), institution(?x865, ?x5866), institution(?x865, ?x5750), institution(?x865, ?x4410), institution(?x865, ?x1884), institution(?x865, ?x331), ?x331 = 01jssp, ?x11768 = 01hc1j, contains(?x94, ?x8797), major_field_of_study(?x12877, ?x742), student(?x865, ?x1117), currency(?x9607, ?x170), school(?x2820, ?x8797), ?x5750 = 01nnsv, student(?x4410, ?x510), organization(?x346, ?x8797), citytown(?x5866, ?x2277), ?x1884 = 0bx8pn, organization(?x4095, ?x12877), ?x170 = 09nqf >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 8, 11, 12, 13, 16, 19, 20, 21, 24, 34, 35, 39, 46, 47, 51, 56, 57, 61, 75, 80, 89, 103, 105, 126, 130, 140, 141, 153, 165, 216, 235, 236, 252 EVAL 02h4rq6 institution 0jksm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 02jx_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 05hf_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 02hwww CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 01l8t8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 01_r9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 0jpn8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 03l78j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 011xy1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 01hr11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 03gn1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 0k__z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 014xf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 01q7q2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 01dbns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 02sjgpq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 01jvxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 017v3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 0cwx_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 03tw2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 015q1n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 0gjv_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 025rcc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 06bw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 02zd2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 0b1xl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 02dj3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 029d_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 0bqxw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 07vyf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 02rg_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 02cbvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 03x33n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 07vfj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 01pq4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 0p5wz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 04chyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 02fy0z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 0j_sncb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02h4rq6 institution 022xml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 25.000 25.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13785-05q_dw PRED entity: 05q_dw PRED relation: film! PRED expected values: 09v3hq_ => 79 concepts (44 used for prediction) PRED predicted values (max 10 best out of 50): 03rwz3 (0.48 #458, 0.47 #1830, 0.45 #1677), 017s11 (0.40 #3, 0.12 #615, 0.12 #766), 0jz9f (0.20 #1, 0.08 #77, 0.07 #537), 086k8 (0.16 #383, 0.16 #1756, 0.15 #1603), 016tw3 (0.15 #1383, 0.14 #1612, 0.14 #1002), 016tt2 (0.14 #385, 0.12 #1376, 0.12 #1605), 05qd_ (0.14 #390, 0.13 #1610, 0.13 #1000), 03xq0f (0.12 #157, 0.10 #310, 0.10 #233), 024rbz (0.10 #88, 0.04 #240, 0.04 #164), 025jfl (0.09 #311, 0.08 #82, 0.07 #234) >> Best rule #458 for best value: >> intensional similarity = 3 >> extensional distance = 368 >> proper extension: 0299hs; 02qkwl; 09rfh9; >> query: (?x5157, ?x7526) <- featured_film_locations(?x5157, ?x739), nominated_for(?x899, ?x5157), production_companies(?x5157, ?x7526) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #215 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 182 *> proper extension: 0gs973; 0353tm; *> query: (?x5157, 09v3hq_) <- featured_film_locations(?x5157, ?x739), category(?x5157, ?x134), genre(?x5157, ?x53) *> conf = 0.01 ranks of expected_values: 47 EVAL 05q_dw film! 09v3hq_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 79.000 44.000 0.482 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #13784-01jc6q PRED entity: 01jc6q PRED relation: film_format PRED expected values: 0cj16 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 4): 07fb8_ (0.16 #43, 0.16 #49, 0.16 #80), 0cj16 (0.11 #372, 0.10 #337, 0.10 #24), 017fx5 (0.03 #194, 0.03 #199, 0.03 #291), 01dc60 (0.01 #15, 0.01 #99) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 225 >> proper extension: 03wbqc4; >> query: (?x197, 07fb8_) <- films(?x2286, ?x197), genre(?x197, ?x53), currency(?x197, ?x170), produced_by(?x197, ?x767) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #372 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1319 *> proper extension: 0ckr7s; 0gx1bnj; 0dtw1x; 026p_bs; 0crfwmx; 0cnztc4; 053tj7; 026q3s3; 04zyhx; 0cz8mkh; ... *> query: (?x197, 0cj16) <- film_release_region(?x197, ?x94), genre(?x197, ?x53) *> conf = 0.11 ranks of expected_values: 2 EVAL 01jc6q film_format 0cj16 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.159 http://example.org/film/film/film_format #13783-04vh83 PRED entity: 04vh83 PRED relation: language PRED expected values: 04h9h => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 31): 064_8sq (0.18 #495, 0.16 #198, 0.15 #788), 04306rv (0.17 #478, 0.13 #181, 0.12 #62), 0jzc (0.10 #493, 0.06 #77, 0.05 #669), 06nm1 (0.09 #1015, 0.09 #1371, 0.09 #956), 06b_j (0.09 #496, 0.07 #672, 0.07 #1383), 02bjrlw (0.09 #533, 0.08 #888, 0.07 #357), 03_9r (0.07 #659, 0.06 #1370, 0.06 #483), 04h9h (0.06 #160, 0.06 #100, 0.05 #219), 0653m (0.04 #485, 0.04 #1372, 0.04 #1193), 07zrf (0.04 #476, 0.02 #652, 0.01 #534) >> Best rule #495 for best value: >> intensional similarity = 5 >> extensional distance = 155 >> proper extension: 0c0wvx; >> query: (?x3514, 064_8sq) <- genre(?x3514, ?x4757), genre(?x3514, ?x3515), ?x3515 = 082gq, genre(?x7789, ?x4757), ?x7789 = 0dkv90 >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #160 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 45 *> proper extension: 0kxf1; 0gy4k; *> query: (?x3514, 04h9h) <- film_release_region(?x3514, ?x2984), film_release_region(?x3514, ?x2152), ?x2984 = 082fr, nominated_for(?x198, ?x3514), ?x2152 = 06mkj *> conf = 0.06 ranks of expected_values: 8 EVAL 04vh83 language 04h9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 70.000 70.000 0.178 http://example.org/film/film/language #13782-070c93 PRED entity: 070c93 PRED relation: gender PRED expected values: 02zsn => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #9, 0.73 #5, 0.73 #15), 02zsn (0.46 #153, 0.46 #183, 0.46 #166) >> Best rule #9 for best value: >> intensional similarity = 5 >> extensional distance = 205 >> proper extension: 0dhqyw; 0frpd5; 02qfk4j; >> query: (?x12230, 05zppz) <- nationality(?x12230, ?x6307), nationality(?x12230, ?x2146), ?x2146 = 03rk0, country(?x668, ?x6307), contains(?x6304, ?x6307) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2892 *> proper extension: 05v1sb; 03_hd; 01sg7_; 02qny_; *> query: (?x12230, ?x231) <- profession(?x12230, ?x1032), type_of_union(?x12230, ?x566), profession(?x5620, ?x1032), gender(?x5620, ?x231) *> conf = 0.46 ranks of expected_values: 2 EVAL 070c93 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.739 http://example.org/people/person/gender #13781-02fj8n PRED entity: 02fj8n PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #101, 0.85 #187, 0.85 #162), 02nxhr (0.33 #2, 0.20 #12, 0.12 #37), 07z4p (0.07 #141, 0.04 #256, 0.04 #65), 07c52 (0.04 #43, 0.04 #48, 0.04 #58) >> Best rule #101 for best value: >> intensional similarity = 6 >> extensional distance = 65 >> proper extension: 0pc62; 04gknr; 0963mq; 0872p_c; 01kff7; 0jqn5; 02rv_dz; 029zqn; 09cr8; 08rr3p; ... >> query: (?x7463, 029j_) <- film_crew_role(?x7463, ?x2178), film(?x8587, ?x7463), titles(?x7173, ?x7463), ?x2178 = 01pvkk, location(?x8587, ?x739), student(?x8398, ?x8587) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fj8n film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #13780-052p7 PRED entity: 052p7 PRED relation: featured_film_locations! PRED expected values: 04sntd 01ffx4 => 223 concepts (179 used for prediction) PRED predicted values (max 10 best out of 725): 0192hw (0.21 #12489, 0.09 #7441, 0.09 #8162), 02q0v8n (0.20 #2063, 0.20 #1341, 0.08 #9996), 02bg55 (0.20 #1923, 0.20 #1201, 0.08 #9856), 03l6q0 (0.20 #1679, 0.20 #957, 0.08 #9612), 061681 (0.20 #767, 0.15 #11585, 0.14 #5094), 0dnkmq (0.20 #2119, 0.14 #5724, 0.10 #13658), 09sh8k (0.20 #1449, 0.14 #5054, 0.10 #12988), 0872p_c (0.20 #798, 0.13 #14501, 0.11 #11616), 0ds2n (0.20 #947, 0.11 #11765, 0.10 #13208), 033srr (0.20 #996, 0.11 #11814, 0.10 #13257) >> Best rule #12489 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 01bkb; 0jdtt; >> query: (?x2474, 0192hw) <- contains(?x6842, ?x2474), location_of_ceremony(?x566, ?x2474), featured_film_locations(?x5992, ?x2474), film_regional_debut_venue(?x5992, ?x6601) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #935 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 017j4q; *> query: (?x2474, 04sntd) <- contains(?x6842, ?x2474), place_of_birth(?x4345, ?x2474), film(?x4345, ?x2490), ?x2490 = 026p4q7 *> conf = 0.20 ranks of expected_values: 35, 584 EVAL 052p7 featured_film_locations! 01ffx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 223.000 179.000 0.207 http://example.org/film/film/featured_film_locations EVAL 052p7 featured_film_locations! 04sntd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 223.000 179.000 0.207 http://example.org/film/film/featured_film_locations #13779-01svry PRED entity: 01svry PRED relation: executive_produced_by PRED expected values: 06q8hf => 94 concepts (66 used for prediction) PRED predicted values (max 10 best out of 124): 02lfcm (0.25 #14), 0z4s (0.17 #268), 06q8hf (0.15 #3446, 0.09 #4454, 0.08 #2943), 06cv1 (0.14 #252, 0.11 #2273, 0.06 #1767), 06pj8 (0.09 #2075, 0.07 #3335, 0.05 #1570), 0glyyw (0.08 #693, 0.06 #2965, 0.05 #4476), 03p01x (0.06 #6060, 0.06 #9085, 0.05 #1768), 01trhmt (0.06 #253, 0.03 #1514, 0.03 #4540), 01j7rd (0.06 #253, 0.03 #4540, 0.03 #4541), 06x58 (0.06 #253, 0.03 #4540, 0.03 #4541) >> Best rule #14 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03sxd2; 0_816; >> query: (?x6731, 02lfcm) <- film_release_distribution_medium(?x6731, ?x81), genre(?x6731, ?x571), produced_by(?x6731, ?x523), film(?x1057, ?x6731), ?x1057 = 01sxq9 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3446 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 330 *> proper extension: 0hr41p6; *> query: (?x6731, 06q8hf) <- genre(?x6731, ?x571), executive_produced_by(?x6731, ?x4060), award_nominee(?x105, ?x4060), produced_by(?x144, ?x4060) *> conf = 0.15 ranks of expected_values: 3 EVAL 01svry executive_produced_by 06q8hf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 66.000 0.250 http://example.org/film/film/executive_produced_by #13778-01nxzv PRED entity: 01nxzv PRED relation: award PRED expected values: 09qvc0 => 117 concepts (101 used for prediction) PRED predicted values (max 10 best out of 292): 09sb52 (0.72 #40757, 0.71 #21794, 0.71 #30269), 099tbz (0.27 #57, 0.19 #23410, 0.14 #6455), 0ck27z (0.27 #92, 0.15 #16638, 0.15 #21079), 05pcn59 (0.23 #887, 0.20 #2098, 0.19 #1694), 09td7p (0.20 #121, 0.19 #23410, 0.14 #6455), 0gqyl (0.20 #105, 0.14 #6455, 0.13 #39949), 0f4x7 (0.19 #23410, 0.14 #6455, 0.14 #836), 02w9sd7 (0.19 #23410, 0.14 #6455, 0.13 #171), 02z0dfh (0.19 #23410, 0.14 #6455, 0.13 #75), 03qgjwc (0.19 #23410, 0.14 #6455, 0.13 #184) >> Best rule #40757 for best value: >> intensional similarity = 3 >> extensional distance = 2320 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01vsxdm; 01wv9xn; 0frsw; 016fmf; 01vrwfv; 0134s5; 02lbrd; ... >> query: (?x11808, ?x6878) <- award_winner(?x6878, ?x11808), award(?x9815, ?x6878), award_winner(?x678, ?x9815) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #39949 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2230 *> proper extension: 054_mz; 07s6tbm; 0162c8; 0mj1l; 05b4rcb; 03jjzf; 087v17; 0f5mdz; 03m49ly; 09h4b5; ... *> query: (?x11808, ?x678) <- award_nominee(?x7268, ?x11808), award_nominee(?x11808, ?x1119), award(?x7268, ?x678) *> conf = 0.13 ranks of expected_values: 46 EVAL 01nxzv award 09qvc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 117.000 101.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13777-048yqf PRED entity: 048yqf PRED relation: production_companies PRED expected values: 0c_j5d => 79 concepts (65 used for prediction) PRED predicted values (max 10 best out of 59): 016tt2 (0.33 #1482, 0.31 #3126, 0.29 #987), 03xq0f (0.33 #1482, 0.31 #3126, 0.29 #987), 016tw3 (0.13 #341, 0.11 #999, 0.10 #1822), 054lpb6 (0.12 #1332, 0.11 #1414, 0.09 #1825), 0c_j5d (0.11 #335, 0.07 #170, 0.06 #746), 046b0s (0.11 #353, 0.06 #24, 0.06 #764), 086k8 (0.11 #989, 0.11 #1648, 0.10 #413), 05qd_ (0.09 #2149, 0.09 #2313, 0.08 #997), 01gb54 (0.08 #860, 0.08 #1848, 0.08 #614), 024rgt (0.08 #107, 0.07 #354, 0.06 #436) >> Best rule #1482 for best value: >> intensional similarity = 4 >> extensional distance = 243 >> proper extension: 09qljs; >> query: (?x9914, ?x574) <- executive_produced_by(?x9914, ?x96), genre(?x9914, ?x225), produced_by(?x9914, ?x4854), film(?x574, ?x9914) >> conf = 0.33 => this is the best rule for 2 predicted values *> Best rule #335 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 0gtsx8c; *> query: (?x9914, 0c_j5d) <- film_crew_role(?x9914, ?x137), executive_produced_by(?x9914, ?x96), prequel(?x9914, ?x8787), film(?x1019, ?x9914) *> conf = 0.11 ranks of expected_values: 5 EVAL 048yqf production_companies 0c_j5d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 65.000 0.326 http://example.org/film/film/production_companies #13776-01wf86y PRED entity: 01wf86y PRED relation: type_of_union PRED expected values: 04ztj => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.76 #53, 0.74 #57, 0.73 #85), 01g63y (0.39 #361, 0.26 #42, 0.20 #6), 0jgjn (0.05 #40, 0.03 #56, 0.03 #68), 01bl8s (0.01 #99) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0lbj1; 06cc_1; 0152cw; 01vrz41; 010hn; 02w4fkq; 01vw20_; 0161c2; 01vw26l; 01vwbts; ... >> query: (?x7581, 04ztj) <- artist(?x6672, ?x7581), instrumentalists(?x227, ?x7581), artists(?x671, ?x7581), award(?x7581, ?x528) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wf86y type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.757 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13775-02r771y PRED entity: 02r771y PRED relation: category_of PRED expected values: 02r771y => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 18): 0c4ys (0.33 #451, 0.33 #429, 0.27 #472), 0gcf2r (0.11 #409, 0.08 #515, 0.08 #494), 0g_w (0.06 #495, 0.06 #410, 0.06 #622), 04jhhng (0.05 #60, 0.04 #82, 0.04 #104), 01b8bn (0.05 #51, 0.04 #73, 0.04 #95), 01ppdy (0.05 #50, 0.04 #72, 0.04 #94), 05x2s (0.04 #79, 0.04 #101, 0.04 #123), 07n52 (0.03 #193), 02v1ws (0.01 #334), 02r0d0 (0.01 #333) >> Best rule #451 for best value: >> intensional similarity = 4 >> extensional distance = 243 >> proper extension: 02pr67; >> query: (?x14221, 0c4ys) <- award_winner(?x14221, ?x12156), award_winner(?x12156, ?x1367), category(?x12156, ?x134), award_nominee(?x3568, ?x1367) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02r771y category_of 02r771y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 36.000 36.000 0.327 http://example.org/award/award_category/category_of #13774-0gx1l PRED entity: 0gx1l PRED relation: location! PRED expected values: 0c6qh => 134 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2422): 0136pk (0.46 #85583, 0.40 #110753, 0.32 #15101), 09fb5 (0.40 #2567, 0.22 #5083, 0.09 #20186), 09yrh (0.40 #3429, 0.12 #13496, 0.11 #31119), 02t__3 (0.40 #3738, 0.12 #13805, 0.11 #16324), 01797x (0.40 #4608, 0.12 #14675, 0.11 #17194), 0c6qh (0.40 #2976, 0.11 #5492, 0.11 #33184), 03d9v8 (0.40 #4371, 0.11 #6887, 0.06 #14438), 01fh9 (0.40 #2868, 0.11 #15454, 0.09 #30558), 0c01c (0.40 #2990, 0.06 #13057, 0.06 #30680), 09889g (0.40 #3526, 0.06 #13593, 0.06 #31216) >> Best rule #85583 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 0mn0v; >> query: (?x10687, ?x2321) <- location(?x975, ?x10687), origin(?x2321, ?x10687), award_nominee(?x2964, ?x2321), instrumentalists(?x227, ?x2321) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #2976 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 09c7w0; *> query: (?x10687, 0c6qh) <- location(?x975, ?x10687), location_of_ceremony(?x566, ?x10687), contains(?x10687, ?x1276), ?x1276 = 01bzw5 *> conf = 0.40 ranks of expected_values: 6 EVAL 0gx1l location! 0c6qh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 134.000 86.000 0.457 http://example.org/people/person/places_lived./people/place_lived/location #13773-02rsl1 PRED entity: 02rsl1 PRED relation: team PRED expected values: 049n7 => 26 concepts (22 used for prediction) PRED predicted values (max 10 best out of 996): 05g76 (0.80 #10629, 0.79 #7728, 0.79 #5794), 06wpc (0.71 #6146, 0.71 #14505, 0.71 #14504), 01d6g (0.71 #14505, 0.71 #14504, 0.71 #15473), 07147 (0.71 #14505, 0.71 #14504, 0.71 #15473), 0cqt41 (0.71 #14505, 0.71 #14504, 0.71 #15473), 04wmvz (0.71 #14505, 0.71 #14504, 0.71 #15473), 07l4z (0.71 #14505, 0.71 #14504, 0.71 #15473), 01d5z (0.71 #14505, 0.71 #14504, 0.71 #15473), 04mjl (0.71 #14505, 0.71 #14504, 0.71 #15473), 051vz (0.71 #14505, 0.71 #14504, 0.71 #15473) >> Best rule #10629 for best value: >> intensional similarity = 33 >> extensional distance = 11 >> proper extension: 01yvvn; >> query: (?x7724, ?x4243) <- position(?x4243, ?x7724), position(?x580, ?x7724), team(?x2010, ?x580), school(?x580, ?x8706), school(?x580, ?x3439), school(?x580, ?x2175), school(?x580, ?x388), student(?x3439, ?x562), service_location(?x3439, ?x94), major_field_of_study(?x3439, ?x10391), major_field_of_study(?x3439, ?x3995), major_field_of_study(?x3439, ?x2605), ?x3995 = 0fdys, teams(?x3125, ?x4243), season(?x4243, ?x10017), institution(?x7636, ?x3439), institution(?x1390, ?x3439), ?x8706 = 0trv, ?x1390 = 0bjrnt, school(?x4171, ?x2175), contains(?x4061, ?x2175), citytown(?x388, ?x6453), company(?x346, ?x3439), colors(?x4243, ?x332), draft(?x580, ?x1161), ?x10391 = 02jfc, team(?x10434, ?x4243), school(?x685, ?x388), colors(?x388, ?x3364), ?x7636 = 01rr_d, company(?x5796, ?x3439), ?x2605 = 03g3w, ?x10017 = 026fmqm >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #14505 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 18 *> proper extension: 02qvkj; *> query: (?x7724, ?x1010) <- team(?x7724, ?x4243), team(?x7724, ?x2405), team(?x4244, ?x2405), team(?x11844, ?x2405), team(?x4244, ?x1010), sport(?x4243, ?x5063), colors(?x2405, ?x8271), colors(?x4243, ?x3315), colors(?x4243, ?x332), colors(?x10838, ?x3315), colors(?x7338, ?x3315), colors(?x8186, ?x3315), colors(?x934, ?x3315), ?x934 = 01jv_6, ?x10838 = 016sd3, ?x7338 = 01qgr3, athlete(?x5063, ?x5412), colors(?x14402, ?x8271), colors(?x2574, ?x8271), colors(?x817, ?x8271), olympics(?x5063, ?x778), ?x14402 = 03dkx, ?x2574 = 01y3v, country(?x5063, ?x94), ?x8186 = 0jnm_, colors(?x2821, ?x332), ?x2821 = 0cchk3 *> conf = 0.71 ranks of expected_values: 13 EVAL 02rsl1 team 049n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 26.000 22.000 0.798 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #13772-0584j4n PRED entity: 0584j4n PRED relation: place_of_birth PRED expected values: 05tbn => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 49): 01_d4 (0.25 #66, 0.05 #2884, 0.04 #3588), 0r62v (0.17 #731, 0.04 #1435, 0.04 #2140), 0ftyc (0.17 #889, 0.04 #1593, 0.04 #2298), 02_286 (0.13 #4246, 0.13 #4951, 0.10 #3541), 030qb3t (0.08 #4227, 0.08 #3576, 0.08 #7048), 0cr3d (0.07 #2912, 0.03 #12783, 0.03 #7848), 01sn3 (0.05 #2967, 0.04 #1558, 0.04 #2263), 04swd (0.05 #3134, 0.01 #3838, 0.01 #26780), 01cx_ (0.05 #2927, 0.01 #3631, 0.01 #26780), 0kv5t (0.04 #2026, 0.04 #2731, 0.02 #3435) >> Best rule #66 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 05218gr; >> query: (?x4897, 01_d4) <- nationality(?x4897, ?x94), award_nominee(?x6766, ?x4897), ?x6766 = 07fzq3 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0584j4n place_of_birth 05tbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 58.000 0.250 http://example.org/people/person/place_of_birth #13771-0fbtbt PRED entity: 0fbtbt PRED relation: award! PRED expected values: 0m123 => 45 concepts (27 used for prediction) PRED predicted values (max 10 best out of 854): 0g60z (0.55 #7076, 0.55 #6092, 0.44 #5082), 0hz55 (0.44 #5548, 0.38 #7075, 0.33 #1505), 0hfzr (0.38 #7489, 0.10 #17599, 0.09 #22664), 0c0zq (0.38 #7972, 0.09 #18082, 0.08 #23147), 0209hj (0.38 #7138, 0.09 #17248, 0.08 #20285), 07xtqq (0.38 #7108, 0.08 #17218, 0.07 #18230), 0cq806 (0.38 #7927, 0.05 #18037, 0.05 #21074), 01fx1l (0.38 #7075, 0.33 #5614, 0.33 #4603), 0ddd0gc (0.38 #7075, 0.33 #1144, 0.33 #133), 063ykwt (0.38 #7075, 0.33 #4415, 0.33 #1383) >> Best rule #7076 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 09v7wsg; >> query: (?x4921, ?x337) <- nominated_for(?x4921, ?x1849), nominated_for(?x4921, ?x337), ?x1849 = 0kfv9, award_winner(?x4921, ?x1039), ?x337 = 0g60z >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #4884 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0bdw1g; 0fbvqf; 02xcb6n; *> query: (?x4921, 0m123) <- award(?x496, ?x4921), award(?x11250, ?x4921), ?x11250 = 01cvtf, award_nominee(?x495, ?x496), nominated_for(?x496, ?x69) *> conf = 0.33 ranks of expected_values: 30 EVAL 0fbtbt award! 0m123 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 45.000 27.000 0.545 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13770-0k7tq PRED entity: 0k7tq PRED relation: film_release_region PRED expected values: 0k6nt 059j2 03f2w => 137 concepts (136 used for prediction) PRED predicted values (max 10 best out of 162): 059j2 (0.85 #3939, 0.85 #7029, 0.83 #6055), 03rjj (0.84 #3257, 0.82 #1144, 0.82 #3908), 0154j (0.82 #3907, 0.80 #3256, 0.77 #6997), 07ssc (0.82 #3269, 0.79 #1156, 0.78 #3920), 0345h (0.81 #7031, 0.81 #6057, 0.80 #3941), 0k6nt (0.81 #4092, 0.80 #4746, 0.79 #6208), 03h64 (0.80 #3976, 0.75 #7066, 0.75 #6092), 0b90_r (0.79 #3906, 0.70 #6996, 0.69 #3255), 0jgd (0.79 #3254, 0.79 #1141, 0.78 #6021), 03gj2 (0.78 #6047, 0.78 #3931, 0.78 #7021) >> Best rule #3939 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 0j43swk; >> query: (?x6661, 059j2) <- film(?x382, ?x6661), film_release_region(?x6661, ?x1353), produced_by(?x6661, ?x2465), ?x1353 = 035qy >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 97 EVAL 0k7tq film_release_region 03f2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 137.000 136.000 0.855 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k7tq film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 136.000 0.855 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k7tq film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 137.000 136.000 0.855 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13769-0127s7 PRED entity: 0127s7 PRED relation: type_of_union PRED expected values: 04ztj => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.74 #209, 0.74 #325, 0.74 #341), 01g63y (0.26 #14, 0.23 #158, 0.22 #34), 0jgjn (0.04 #8, 0.02 #48) >> Best rule #209 for best value: >> intensional similarity = 3 >> extensional distance = 334 >> proper extension: 0drdv; >> query: (?x5906, 04ztj) <- award_nominee(?x5906, ?x883), award_winner(?x1389, ?x5906), religion(?x5906, ?x1985) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0127s7 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.741 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13768-0315rp PRED entity: 0315rp PRED relation: film_distribution_medium PRED expected values: 0735l => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 6): 0735l (0.13 #35, 0.13 #71, 0.12 #59), 029j_ (0.11 #55, 0.11 #1, 0.11 #37), 0dq6p (0.08 #3, 0.07 #57, 0.06 #9), 02nxhr (0.06 #68, 0.06 #14, 0.05 #2), 07z4p (0.01 #42, 0.01 #48), 07c52 (0.01 #22) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 0353tm; >> query: (?x8397, 0735l) <- film(?x609, ?x8397), story_by(?x8397, ?x6045), featured_film_locations(?x8397, ?x6226) >> conf = 0.13 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0315rp film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.131 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #13767-016lmg PRED entity: 016lmg PRED relation: artists! PRED expected values: 07gxw 041738 => 52 concepts (21 used for prediction) PRED predicted values (max 10 best out of 219): 06by7 (0.67 #619, 0.52 #1219, 0.48 #2419), 059kh (0.65 #2147, 0.59 #1246, 0.56 #945), 05r6t (0.47 #1578, 0.43 #1878, 0.36 #977), 064t9 (0.41 #3909, 0.41 #3010, 0.38 #4808), 05bt6j (0.41 #1240, 0.33 #2141, 0.32 #939), 0y3_8 (0.33 #345, 0.33 #46, 0.28 #943), 0xjl2 (0.33 #343, 0.28 #941, 0.23 #1542), 07gxw (0.33 #55, 0.22 #5096, 0.21 #6299), 08jyyk (0.33 #365, 0.20 #2464, 0.16 #1864), 08s6r6 (0.33 #255, 0.16 #1152, 0.10 #1753) >> Best rule #619 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0lbj1; 01wl38s; 01vs14j; 0l12d; 01vswx5; 01vswwx; 0dw4g; 02bgmr; 01dw_f; 02vr7; >> query: (?x8199, 06by7) <- artists(?x2491, ?x8199), ?x2491 = 011j5x, award(?x8199, ?x1565), award_nominee(?x8199, ?x5618) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 02hzz; *> query: (?x8199, 07gxw) <- artists(?x5911, ?x8199), artists(?x2491, ?x8199), ?x2491 = 011j5x, artist(?x2023, ?x8199), ?x5911 = 01_sz1 *> conf = 0.33 ranks of expected_values: 8, 75 EVAL 016lmg artists! 041738 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 52.000 21.000 0.667 http://example.org/music/genre/artists EVAL 016lmg artists! 07gxw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 52.000 21.000 0.667 http://example.org/music/genre/artists #13766-04mvp8 PRED entity: 04mvp8 PRED relation: people PRED expected values: 05gc0h 02hkw6 => 32 concepts (16 used for prediction) PRED predicted values (max 10 best out of 4095): 01zp33 (0.33 #6213, 0.33 #2765, 0.12 #4487), 04v7k2 (0.33 #3415, 0.25 #5137, 0.22 #6863), 02n1p5 (0.33 #2740, 0.25 #4462, 0.22 #6188), 0gp_x9 (0.33 #3072, 0.25 #4794, 0.22 #6520), 05d7rk (0.33 #1732, 0.25 #3454, 0.22 #5180), 03vrnh (0.33 #2768, 0.22 #6216, 0.12 #4490), 08d6bd (0.33 #2633, 0.22 #6081, 0.12 #4355), 05g3ss (0.33 #3357, 0.22 #6805, 0.12 #5079), 0276g40 (0.33 #3289, 0.22 #6737, 0.12 #5011), 0241wg (0.33 #2146, 0.22 #5594, 0.12 #3868) >> Best rule #6213 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 03kbr; >> query: (?x13008, 01zp33) <- people(?x13008, ?x13550), people(?x13008, ?x11285), people(?x13008, ?x9506), profession(?x13550, ?x319), profession(?x11285, ?x3342), gender(?x9506, ?x231), place_of_birth(?x13550, ?x13551), location(?x11285, ?x6250), nationality(?x13550, ?x2146), religion(?x11285, ?x492), ?x2146 = 03rk0 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8616 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 8 *> proper extension: 0g6ff; 03ts0c; 01xhh5; 0d29z; 01rv7x; 013b6_; 01p7s6; 012f86; *> query: (?x13008, ?x10688) <- geographic_distribution(?x13008, ?x3016), geographic_distribution(?x13008, ?x1781), countries_spoken_in(?x5359, ?x1781), nationality(?x10688, ?x1781), olympics(?x3016, ?x1081), member_states(?x7695, ?x3016), film_release_region(?x1012, ?x3016), locations(?x11047, ?x1781), ?x1012 = 0bwfwpj, administrative_parent(?x1781, ?x551), adjoins(?x1781, ?x1174) *> conf = 0.08 ranks of expected_values: 311, 347 EVAL 04mvp8 people 02hkw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 32.000 16.000 0.333 http://example.org/people/ethnicity/people EVAL 04mvp8 people 05gc0h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 32.000 16.000 0.333 http://example.org/people/ethnicity/people #13765-0nm6z PRED entity: 0nm6z PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 139 concepts (65 used for prediction) PRED predicted values (max 10 best out of 12): 09c7w0 (0.86 #45, 0.85 #103, 0.85 #136), 04_1l0v (0.37 #270, 0.37 #241, 0.31 #447), 029jpy (0.37 #270, 0.37 #241, 0.31 #447), 07ssc (0.14 #180, 0.12 #193, 0.09 #168), 050ks (0.12 #567, 0.09 #161, 0.08 #757), 02jx1 (0.11 #442, 0.09 #133, 0.09 #530), 059j2 (0.08 #263, 0.07 #222, 0.06 #236), 03rjj (0.08 #189, 0.07 #230, 0.07 #216), 0f8l9c (0.05 #235, 0.04 #221, 0.03 #440), 03rt9 (0.03 #557, 0.03 #82, 0.02 #246) >> Best rule #45 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 0mk7z; 0n5yv; 0n5_g; 0mx0f; 0n4yq; 0mk1z; 0n4z2; 0k3j0; 0mk59; >> query: (?x7954, 09c7w0) <- administrative_parent(?x7954, ?x7058), adjoins(?x7330, ?x7954), contains(?x7058, ?x3044), currency(?x7058, ?x170), district_represented(?x176, ?x7058) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nm6z second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 65.000 0.861 http://example.org/location/country/second_level_divisions #13764-0mn8t PRED entity: 0mn8t PRED relation: source PRED expected values: 0jbk9 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.88 #46, 0.87 #55, 0.87 #45) >> Best rule #46 for best value: >> intensional similarity = 4 >> extensional distance = 364 >> proper extension: 0ml25; 0nh0f; 0mmr1; 0mm0p; 0nvd8; 0nh57; 0mlzk; 0l2mg; 0n4z2; 0mrf1; ... >> query: (?x7689, 0jbk9) <- currency(?x7689, ?x170), time_zones(?x7689, ?x2674), time_zones(?x1705, ?x2674), dog_breed(?x1705, ?x1706) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mn8t source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.877 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #13763-06fc0b PRED entity: 06fc0b PRED relation: film PRED expected values: 0cbv4g => 107 concepts (105 used for prediction) PRED predicted values (max 10 best out of 801): 03nt59 (0.69 #12532, 0.69 #21484, 0.64 #60866), 049mql (0.29 #684, 0.22 #4264, 0.03 #11425), 02r79_h (0.22 #3809, 0.14 #229, 0.02 #10970), 02p86pb (0.14 #1522, 0.12 #3312, 0.09 #6892), 0gvrws1 (0.14 #321, 0.11 #3901, 0.03 #11062), 0gxtknx (0.14 #248, 0.11 #3828, 0.01 #14570), 0crd8q6 (0.14 #1633, 0.11 #5213, 0.01 #62499), 07p12s (0.14 #1676, 0.11 #5256), 048vhl (0.14 #1495, 0.11 #5075), 08nhfc1 (0.14 #1324, 0.11 #4904) >> Best rule #12532 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 03knl; 01w9wwg; >> query: (?x7823, ?x6070) <- people(?x1446, ?x7823), participant(?x2352, ?x7823), nominated_for(?x7823, ?x6070) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #11659 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 118 *> proper extension: 03knl; 01w9wwg; *> query: (?x7823, 0cbv4g) <- people(?x1446, ?x7823), participant(?x2352, ?x7823), nominated_for(?x7823, ?x6070) *> conf = 0.02 ranks of expected_values: 292 EVAL 06fc0b film 0cbv4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 107.000 105.000 0.688 http://example.org/film/actor/film./film/performance/film #13762-09txzv PRED entity: 09txzv PRED relation: film_crew_role PRED expected values: 033smt 014kbl => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 25): 09zzb8 (0.80 #601, 0.75 #87, 0.74 #857), 01vx2h (0.75 #94, 0.39 #179, 0.38 #123), 01xy5l_ (0.33 #126, 0.25 #97, 0.15 #401), 01pvkk (0.28 #893, 0.28 #1459, 0.28 #1092), 033smt (0.27 #106, 0.22 #135, 0.15 #401), 02rh1dz (0.24 #93, 0.15 #401, 0.15 #178), 02ynfr (0.20 #612, 0.18 #155, 0.18 #868), 0263ycg (0.15 #401, 0.13 #129, 0.10 #100), 0ckd1 (0.15 #401, 0.13 #118, 0.06 #89), 089fss (0.15 #401, 0.09 #120, 0.08 #605) >> Best rule #601 for best value: >> intensional similarity = 5 >> extensional distance = 580 >> proper extension: 0d90m; 03qcfvw; 09sh8k; 0gtsx8c; 0m313; 02y_lrp; 034qmv; 083shs; 09m6kg; 03g90h; ... >> query: (?x1644, 09zzb8) <- film_crew_role(?x1644, ?x1284), film_crew_role(?x1644, ?x1171), ?x1284 = 0ch6mp2, film(?x92, ?x1644), ?x1171 = 09vw2b7 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #106 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 61 *> proper extension: 04lqvly; *> query: (?x1644, 033smt) <- film_crew_role(?x1644, ?x1966), film_crew_role(?x1644, ?x1284), ?x1284 = 0ch6mp2, ?x1966 = 015h31, genre(?x1644, ?x53) *> conf = 0.27 ranks of expected_values: 5, 16 EVAL 09txzv film_crew_role 014kbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 78.000 78.000 0.804 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09txzv film_crew_role 033smt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 78.000 0.804 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13761-0r8c8 PRED entity: 0r8c8 PRED relation: category PRED expected values: 08mbj5d => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #27, 0.81 #16, 0.80 #13) >> Best rule #27 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 0s3y5; >> query: (?x6367, 08mbj5d) <- place_of_death(?x10439, ?x6367), source(?x6367, ?x958), profession(?x10439, ?x353), nationality(?x10439, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r8c8 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.821 http://example.org/common/topic/webpage./common/webpage/category #13760-035dk PRED entity: 035dk PRED relation: adjoins PRED expected values: 0fv4v => 107 concepts (101 used for prediction) PRED predicted values (max 10 best out of 390): 0fv4v (0.82 #77934, 0.82 #4628, 0.82 #52441), 04v09 (0.40 #1176, 0.22 #66346, 0.22 #31620), 05cc1 (0.40 #1063, 0.16 #77935, 0.10 #7234), 03676 (0.22 #66346, 0.22 #31620, 0.22 #77936), 035dk (0.22 #66346, 0.22 #31620, 0.22 #77936), 04hzj (0.22 #31620, 0.22 #77936, 0.22 #54760), 0164v (0.22 #31620, 0.22 #77936, 0.22 #54760), 0h3y (0.20 #781, 0.16 #77935, 0.10 #6952), 06s_2 (0.20 #1266, 0.16 #77935, 0.05 #28531), 06srk (0.20 #1143, 0.16 #77935, 0.05 #23903) >> Best rule #77934 for best value: >> intensional similarity = 3 >> extensional distance = 747 >> proper extension: 01914; 0fhp9; 080h2; 0cc56; 025ndl; 0mxcf; 01531; 0d22f; 0clz7; 0jxgx; ... >> query: (?x2051, ?x7360) <- adjoins(?x7360, ?x2051), adjoins(?x9035, ?x7360), adjoins(?x291, ?x9035) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035dk adjoins 0fv4v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 101.000 0.824 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #13759-0137n0 PRED entity: 0137n0 PRED relation: award_winner! PRED expected values: 02rjjll => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 88): 013b2h (0.22 #79, 0.13 #1759, 0.13 #919), 056878 (0.22 #31, 0.10 #1711, 0.10 #871), 0gpjbt (0.17 #28, 0.10 #1708, 0.09 #868), 02rjjll (0.13 #285, 0.12 #1685, 0.11 #845), 0466p0j (0.12 #1755, 0.11 #75, 0.09 #915), 01c6qp (0.12 #1698, 0.11 #18, 0.11 #858), 05pd94v (0.12 #1682, 0.11 #2, 0.10 #842), 01s695 (0.11 #1683, 0.11 #3, 0.10 #843), 02cg41 (0.11 #125, 0.11 #1805, 0.10 #965), 01mh_q (0.11 #88, 0.09 #1768, 0.08 #368) >> Best rule #79 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01cblr; 017959; >> query: (?x1270, 013b2h) <- artists(?x9750, ?x1270), award(?x1270, ?x724), ?x9750 = 016zgj >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #285 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 232 *> proper extension: 01vd7hn; 03_0p; *> query: (?x1270, 02rjjll) <- gender(?x1270, ?x231), award_nominee(?x1270, ?x217), role(?x1270, ?x432) *> conf = 0.13 ranks of expected_values: 4 EVAL 0137n0 award_winner! 02rjjll CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 104.000 104.000 0.222 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13758-04kzqz PRED entity: 04kzqz PRED relation: genre PRED expected values: 060__y => 96 concepts (71 used for prediction) PRED predicted values (max 10 best out of 89): 02l7c8 (0.67 #6174, 0.67 #6411, 0.45 #2380), 06l3bl (0.57 #5328, 0.57 #5208, 0.54 #2484), 03k9fj (0.54 #6881, 0.30 #364, 0.28 #1547), 01hmnh (0.40 #371, 0.37 #6888, 0.24 #1554), 017fp (0.38 #5461, 0.18 #4748, 0.18 #4866), 01jfsb (0.36 #720, 0.26 #6288, 0.26 #8185), 05p553 (0.34 #8176, 0.33 #238, 0.33 #6161), 060__y (0.32 #6175, 0.30 #4750, 0.29 #2144), 0hcr (0.22 #259, 0.20 #377, 0.17 #613), 0lsxr (0.22 #5335, 0.21 #5929, 0.20 #5215) >> Best rule #6174 for best value: >> intensional similarity = 4 >> extensional distance = 348 >> proper extension: 02y_lrp; 09m6kg; 02x3lt7; 05jzt3; 0kv2hv; 0jyx6; 0j_tw; 065zlr; 09p7fh; 0k5g9; ... >> query: (?x2026, 02l7c8) <- genre(?x2026, ?x4088), music(?x2026, ?x3805), genre(?x9261, ?x4088), ?x9261 = 0p9rz >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #6175 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 348 *> proper extension: 02y_lrp; 09m6kg; 02x3lt7; 05jzt3; 0kv2hv; 0jyx6; 0j_tw; 065zlr; 09p7fh; 0k5g9; ... *> query: (?x2026, 060__y) <- genre(?x2026, ?x4088), music(?x2026, ?x3805), genre(?x9261, ?x4088), ?x9261 = 0p9rz *> conf = 0.32 ranks of expected_values: 8 EVAL 04kzqz genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 96.000 71.000 0.671 http://example.org/film/film/genre #13757-05fg2 PRED entity: 05fg2 PRED relation: type_of_union PRED expected values: 04ztj => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.80 #109, 0.80 #33, 0.78 #149), 01g63y (0.25 #289, 0.21 #58, 0.19 #38), 0jgjn (0.25 #289), 01bl8s (0.04 #51, 0.03 #55, 0.02 #63) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 133 >> proper extension: 04m_kpx; >> query: (?x1309, 04ztj) <- student(?x1771, ?x1309), student(?x3878, ?x1309), profession(?x1309, ?x8368), major_field_of_study(?x1771, ?x90) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fg2 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.800 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13756-0h5k PRED entity: 0h5k PRED relation: major_field_of_study! PRED expected values: 09kvv 07tds => 62 concepts (32 used for prediction) PRED predicted values (max 10 best out of 648): 08815 (0.80 #5623, 0.67 #2811, 0.63 #12376), 02zd460 (0.73 #9180, 0.60 #5800, 0.60 #2425), 025v3k (0.67 #2933, 0.62 #5183, 0.57 #4058), 07tds (0.67 #2963, 0.60 #5775, 0.57 #4088), 05zl0 (0.64 #8082, 0.60 #9213, 0.50 #3583), 07wjk (0.62 #5117, 0.50 #5679, 0.50 #2867), 0bwfn (0.60 #5900, 0.57 #10407, 0.57 #11530), 07tg4 (0.60 #2329, 0.50 #2892, 0.43 #4017), 07vhb (0.60 #2423, 0.50 #5236, 0.40 #5798), 0ks67 (0.60 #2442, 0.33 #3005, 0.31 #7503) >> Best rule #5623 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 01mkq; 0pf2; >> query: (?x2314, 08815) <- major_field_of_study(?x6637, ?x2314), major_field_of_study(?x2013, ?x2314), taxonomy(?x2314, ?x939), major_field_of_study(?x4268, ?x2314), institution(?x1368, ?x2013), student(?x4268, ?x9415), major_field_of_study(?x122, ?x4268), location(?x9415, ?x739), ?x1368 = 014mlp, ?x6637 = 07vjm >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2963 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 03nfmq; *> query: (?x2314, 07tds) <- major_field_of_study(?x13206, ?x2314), major_field_of_study(?x3424, ?x2314), major_field_of_study(?x2013, ?x2314), student(?x2314, ?x8812), student(?x2314, ?x8272), ?x3424 = 01w5m, category(?x13206, ?x134), organization(?x4095, ?x13206), nominated_for(?x8812, ?x2121), ?x2013 = 07vk2, major_field_of_study(?x734, ?x2314), profession(?x8272, ?x131) *> conf = 0.67 ranks of expected_values: 4, 68 EVAL 0h5k major_field_of_study! 07tds CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 62.000 32.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0h5k major_field_of_study! 09kvv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 62.000 32.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13755-0r540 PRED entity: 0r540 PRED relation: location! PRED expected values: 0993r => 76 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1080): 01m15br (0.44 #45357, 0.44 #47877, 0.44 #37797), 05218gr (0.11 #5039, 0.11 #10079, 0.11 #12599), 0k8y7 (0.06 #844, 0.03 #5884, 0.03 #8403), 086sj (0.06 #808, 0.03 #5848, 0.02 #3327), 02whj (0.06 #182, 0.03 #7741, 0.02 #2701), 0bwh6 (0.06 #234, 0.01 #2753, 0.01 #5274), 02t901 (0.06 #2431, 0.01 #4950), 073749 (0.05 #3322, 0.04 #10882, 0.04 #13403), 0pyww (0.05 #3501, 0.04 #11061, 0.04 #8541), 01vsy3q (0.04 #11071, 0.04 #13592, 0.04 #3511) >> Best rule #45357 for best value: >> intensional similarity = 3 >> extensional distance = 398 >> proper extension: 01vskn; 05d49; >> query: (?x1990, ?x4044) <- place_of_birth(?x4044, ?x1990), category(?x1990, ?x134), profession(?x4044, ?x220) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r540 location! 0993r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 24.000 0.441 http://example.org/people/person/places_lived./people/place_lived/location #13754-065z3_x PRED entity: 065z3_x PRED relation: film_crew_role PRED expected values: 02r96rf 09vw2b7 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 20): 02r96rf (0.78 #127, 0.68 #563, 0.67 #1214), 09vw2b7 (0.75 #131, 0.69 #443, 0.68 #505), 0dxtw (0.43 #509, 0.43 #447, 0.43 #571), 01vx2h (0.41 #136, 0.36 #510, 0.36 #74), 089fss (0.24 #130, 0.13 #3433, 0.10 #442), 015h31 (0.19 #71, 0.17 #40, 0.13 #3433), 02rh1dz (0.15 #134, 0.13 #3433, 0.13 #165), 02_n3z (0.13 #3433, 0.11 #437, 0.10 #499), 04pyp5 (0.13 #3433, 0.10 #139, 0.08 #108), 094hwz (0.13 #3433, 0.09 #76, 0.06 #169) >> Best rule #127 for best value: >> intensional similarity = 4 >> extensional distance = 163 >> proper extension: 0h1cdwq; 02qyv3h; 03z9585; 0ds2l81; 08c6k9; >> query: (?x2386, 02r96rf) <- film(?x1405, ?x2386), country(?x2386, ?x94), film_crew_role(?x2386, ?x3197), ?x3197 = 02ynfr >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 065z3_x film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.776 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 065z3_x film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.776 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13753-0m2l9 PRED entity: 0m2l9 PRED relation: award_nominee! PRED expected values: 03bnv => 123 concepts (64 used for prediction) PRED predicted values (max 10 best out of 867): 03bnv (0.82 #74691, 0.82 #79360, 0.82 #70022), 02qwg (0.82 #74691, 0.82 #79360, 0.82 #70022), 0x3b7 (0.26 #12650, 0.25 #7982, 0.16 #19650), 03yf3z (0.21 #2908, 0.02 #70597, 0.01 #75266), 01vd7hn (0.20 #7949, 0.19 #12617, 0.13 #19617), 0249kn (0.20 #7653, 0.15 #12321, 0.13 #19321), 02p2zq (0.20 #1686, 0.07 #4019), 01mvjl0 (0.20 #1405, 0.07 #3738), 01v0fn1 (0.20 #1248, 0.02 #33921, 0.02 #36255), 05mt6w (0.20 #1630, 0.02 #34303, 0.02 #36637) >> Best rule #74691 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 01zkxv; 01n5309; 03g5jw; 0b_c7; 015pxr; 0c9c0; 0c2dl; 0p__8; 06q5t7; 01q9b9; ... >> query: (?x483, ?x1089) <- influenced_by(?x483, ?x1136), award_nominee(?x483, ?x1089), award_winner(?x247, ?x483) >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0m2l9 award_nominee! 03bnv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 64.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13752-086qd PRED entity: 086qd PRED relation: award_winner! PRED expected values: 01s695 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 111): 01s695 (0.22 #423, 0.19 #143, 0.18 #283), 09p30_ (0.20 #84, 0.06 #224, 0.03 #1484), 09p3h7 (0.20 #70, 0.06 #210, 0.03 #4130), 09gkdln (0.20 #121, 0.06 #261, 0.03 #12301), 02hn5v (0.20 #41, 0.06 #181, 0.03 #321), 05zksls (0.20 #35, 0.04 #1295, 0.02 #3255), 02cg41 (0.19 #265, 0.15 #545, 0.13 #405), 01bx35 (0.19 #147, 0.15 #427, 0.10 #287), 02rjjll (0.16 #565, 0.13 #2945, 0.11 #2105), 05pd94v (0.15 #422, 0.13 #282, 0.12 #142) >> Best rule #423 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 0dvqq; 02k5sc; 04qzm; >> query: (?x2138, 01s695) <- award_nominee(?x2451, ?x2138), artist(?x6672, ?x2138), award_winner(?x462, ?x2138) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 086qd award_winner! 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.220 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13751-0pyg6 PRED entity: 0pyg6 PRED relation: award_winner! PRED expected values: 0fqpc7d => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 118): 09qvms (0.57 #432, 0.07 #1272, 0.05 #5052), 0275n3y (0.33 #74, 0.10 #10502, 0.07 #634), 09bymc (0.33 #120, 0.02 #4600, 0.02 #2360), 013b2h (0.20 #359, 0.14 #2179, 0.10 #3719), 01c6qp (0.20 #298, 0.10 #4358, 0.09 #2678), 0hhtgcw (0.14 #645, 0.10 #1625, 0.07 #785), 02rjjll (0.14 #2105, 0.10 #4345, 0.10 #3645), 056878 (0.14 #2131, 0.07 #4791, 0.07 #4371), 0jt3qpk (0.14 #882, 0.08 #1862, 0.07 #602), 0lp_cd3 (0.14 #862, 0.08 #1842, 0.07 #1702) >> Best rule #432 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 038g2x; >> query: (?x2194, 09qvms) <- award_winner(?x2078, ?x2194), profession(?x2194, ?x131), ?x2078 = 03ln8b >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #595 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 02d9k; *> query: (?x2194, 0fqpc7d) <- location(?x2194, ?x191), ?x191 = 0k049, participant(?x11924, ?x2194) *> conf = 0.07 ranks of expected_values: 46 EVAL 0pyg6 award_winner! 0fqpc7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 96.000 96.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13750-04sskp PRED entity: 04sskp PRED relation: genre PRED expected values: 0dz8b => 68 concepts (66 used for prediction) PRED predicted values (max 10 best out of 97): 07s9rl0 (0.93 #6147, 0.92 #6269, 0.83 #6391), 03k9fj (0.81 #5670, 0.40 #135, 0.40 #13), 05p553 (0.62 #5906, 0.61 #5784, 0.34 #7127), 017fp (0.54 #4439, 0.15 #1976, 0.14 #2590), 01hmnh (0.46 #1468, 0.43 #733, 0.40 #1223), 02kdv5l (0.42 #5660, 0.27 #5904, 0.26 #5782), 04xvlr (0.41 #1839, 0.40 #2207, 0.39 #2329), 02l7c8 (0.41 #1855, 0.40 #1977, 0.40 #2345), 060__y (0.30 #2592, 0.29 #1978, 0.29 #2224), 01jfsb (0.27 #7136, 0.26 #7014, 0.26 #7868) >> Best rule #6147 for best value: >> intensional similarity = 5 >> extensional distance = 1081 >> proper extension: 0170z3; 02d413; 0b76d_m; 014_x2; 034qmv; 0g22z; 018js4; 0sxg4; 083shs; 0140g4; ... >> query: (?x8062, 07s9rl0) <- genre(?x8062, ?x4088), genre(?x6531, ?x4088), genre(?x2932, ?x4088), ?x2932 = 0gyy53, ?x6531 = 01_0f7 >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #2242 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 97 *> proper extension: 0m313; 01gc7; 011yxg; 08lr6s; 095zlp; 01h7bb; 050r1z; 0fgpvf; 0fg04; 0147sh; ... *> query: (?x8062, 0dz8b) <- genre(?x8062, ?x4088), ?x4088 = 04xvh5, language(?x8062, ?x254), film(?x731, ?x8062) *> conf = 0.02 ranks of expected_values: 82 EVAL 04sskp genre 0dz8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 68.000 66.000 0.929 http://example.org/film/film/genre #13749-0889d PRED entity: 0889d PRED relation: category PRED expected values: 08mbj5d => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.71 #14, 0.69 #86, 0.69 #41) >> Best rule #14 for best value: >> intensional similarity = 7 >> extensional distance = 26 >> proper extension: 01q0l; >> query: (?x13391, 08mbj5d) <- capital(?x3855, ?x13391), contains(?x6304, ?x3855), nationality(?x6406, ?x3855), location(?x6406, ?x461), contains(?x6304, ?x1780), ?x1780 = 01z88t, gender(?x6406, ?x231) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0889d category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.714 http://example.org/common/topic/webpage./common/webpage/category #13748-03t5kl PRED entity: 03t5kl PRED relation: award! PRED expected values: 07ss8_ 01wyz92 018n6m 01yzl2 016ppr => 40 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2241): 04n2vgk (0.80 #40185, 0.80 #43533, 0.79 #13393), 01vw_dv (0.80 #40185, 0.80 #43533, 0.79 #13393), 01vs_v8 (0.62 #10623, 0.27 #13972, 0.22 #24021), 0478__m (0.52 #11359, 0.18 #6696, 0.17 #8011), 0gbwp (0.50 #7799, 0.48 #11147, 0.22 #14496), 018n6m (0.50 #8028, 0.43 #11376, 0.33 #1331), 01vvlyt (0.50 #8269, 0.33 #4920, 0.14 #11617), 01wwvc5 (0.50 #7429, 0.29 #10777, 0.20 #26794), 01w9wwg (0.50 #8480, 0.20 #26794, 0.18 #6696), 04xrx (0.50 #7391, 0.18 #6696, 0.14 #10739) >> Best rule #40185 for best value: >> intensional similarity = 5 >> extensional distance = 169 >> proper extension: 026mg3; 05zvq6g; 0l8z1; 02g8mp; 02nbqh; 047sgz4; 02hgm4; 02681xs; 0fhpv4; 025m98; ... >> query: (?x4837, ?x6659) <- award(?x5536, ?x4837), award(?x4836, ?x4837), award_winner(?x4837, ?x6659), artists(?x671, ?x4836), origin(?x5536, ?x479) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #8028 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 01d38g; 01by1l; 01cky2; 03t5n3; *> query: (?x4837, 018n6m) <- award(?x4836, ?x4837), award_winner(?x4837, ?x6659), ?x4836 = 0837ql, category_of(?x4837, ?x2421) *> conf = 0.50 ranks of expected_values: 6, 110, 175, 214, 235 EVAL 03t5kl award! 016ppr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 40.000 17.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03t5kl award! 01yzl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 40.000 17.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03t5kl award! 018n6m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 40.000 17.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03t5kl award! 01wyz92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 40.000 17.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03t5kl award! 07ss8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 40.000 17.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13747-04shbh PRED entity: 04shbh PRED relation: film PRED expected values: 05ch98 => 119 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1044): 08gsvw (0.73 #51654, 0.66 #51653, 0.65 #55217), 0clpml (0.66 #51653, 0.65 #55217, 0.56 #37403), 05k4my (0.33 #1643, 0.17 #3424, 0.08 #8768), 01npcx (0.33 #2740, 0.17 #4521, 0.06 #8084), 014kq6 (0.33 #2123, 0.17 #3904, 0.05 #7467), 01v1ln (0.33 #3004, 0.17 #4785, 0.05 #8348), 078sj4 (0.33 #450, 0.17 #2231, 0.03 #7575), 0gg5qcw (0.33 #870, 0.17 #2651, 0.03 #7995), 03shpq (0.33 #1440, 0.17 #3221, 0.02 #44187), 03m5y9p (0.33 #1413, 0.17 #3194, 0.02 #6757) >> Best rule #51654 for best value: >> intensional similarity = 3 >> extensional distance = 351 >> proper extension: 07c0j; 04qmr; >> query: (?x1018, ?x3693) <- nominated_for(?x1018, ?x3693), participant(?x5665, ?x1018), film(?x2805, ?x3693) >> conf = 0.73 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04shbh film 05ch98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 66.000 0.726 http://example.org/film/actor/film./film/performance/film #13746-01fx5l PRED entity: 01fx5l PRED relation: nominated_for PRED expected values: 01b_lz => 65 concepts (46 used for prediction) PRED predicted values (max 10 best out of 183): 0gy0n (0.25 #9724, 0.25 #11346, 0.22 #8102), 043tvp3 (0.25 #9724, 0.25 #11346, 0.22 #8102), 083shs (0.25 #9724, 0.25 #11346, 0.22 #8102), 02jxbw (0.25 #9724, 0.25 #11346, 0.22 #8102), 03cvvlg (0.20 #1288, 0.05 #2908), 0jyx6 (0.20 #157, 0.05 #1777), 03shpq (0.20 #1290, 0.03 #2910), 0bcp9b (0.20 #1177, 0.03 #2797), 08s6mr (0.20 #1175, 0.03 #2795), 01chpn (0.20 #1007, 0.03 #2627) >> Best rule #9724 for best value: >> intensional similarity = 3 >> extensional distance = 755 >> proper extension: 04bdxl; 06qgvf; 02bfmn; 0l8v5; 054_mz; 04wqr; 01rr9f; 07lmxq; 044rvb; 02r_d4; ... >> query: (?x6282, ?x167) <- place_of_birth(?x6282, ?x3052), film(?x6282, ?x167), award_nominee(?x1733, ?x6282) >> conf = 0.25 => this is the best rule for 4 predicted values *> Best rule #2121 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 35 *> proper extension: 06r3p2; *> query: (?x6282, 01b_lz) <- award(?x6282, ?x3989), ?x3989 = 0bsjcw *> conf = 0.08 ranks of expected_values: 42 EVAL 01fx5l nominated_for 01b_lz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 65.000 46.000 0.253 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #13745-06y57 PRED entity: 06y57 PRED relation: month PRED expected values: 06vkl => 197 concepts (197 used for prediction) PRED predicted values (max 10 best out of 1): 06vkl (0.88 #47, 0.86 #43, 0.86 #71) >> Best rule #47 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0fn2g; >> query: (?x5036, 06vkl) <- mode_of_transportation(?x5036, ?x6665), featured_film_locations(?x308, ?x5036), month(?x5036, ?x1459) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06y57 month 06vkl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 197.000 197.000 0.879 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #13744-016ywr PRED entity: 016ywr PRED relation: gender PRED expected values: 05zppz => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #11, 0.72 #182, 0.71 #190), 02zsn (0.53 #67, 0.38 #14, 0.33 #4) >> Best rule #11 for best value: >> intensional similarity = 2 >> extensional distance = 141 >> proper extension: 012c6j; 034cj9; >> query: (?x1867, 05zppz) <- award(?x1867, ?x591), ?x591 = 0f4x7 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016ywr gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.909 http://example.org/people/person/gender #13743-08n9ng PRED entity: 08n9ng PRED relation: place_of_birth PRED expected values: 01d26y => 69 concepts (68 used for prediction) PRED predicted values (max 10 best out of 74): 080h2 (0.29 #705, 0.29 #30, 0.28 #16212), 0h7h6 (0.12 #3582, 0.12 #4990, 0.12 #1468), 02_286 (0.11 #724, 0.08 #15526, 0.07 #5655), 0rh6k (0.11 #707, 0.03 #5638, 0.02 #6343), 0nlh7 (0.07 #414, 0.04 #1824, 0.04 #3938), 01vqq1 (0.07 #423, 0.03 #1833, 0.02 #3242), 0d9jr (0.07 #194), 03h64 (0.07 #90), 052p7 (0.07 #3606, 0.06 #1492, 0.06 #2901), 0cr3d (0.06 #15601, 0.05 #799, 0.03 #21239) >> Best rule #705 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 036hf4; >> query: (?x3668, ?x1036) <- gender(?x3668, ?x231), ?x231 = 05zppz, location(?x3668, ?x1036), ?x1036 = 080h2 >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08n9ng place_of_birth 01d26y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 68.000 0.286 http://example.org/people/person/place_of_birth #13742-01ycbq PRED entity: 01ycbq PRED relation: location PRED expected values: 052p7 => 128 concepts (126 used for prediction) PRED predicted values (max 10 best out of 84): 02_286 (0.32 #28947, 0.29 #40189, 0.22 #840), 030qb3t (0.25 #28993, 0.22 #40235, 0.19 #7311), 04jpl (0.14 #17, 0.12 #28927, 0.11 #40169), 01cx_ (0.14 #162, 0.04 #29072, 0.03 #63610), 0f2wj (0.14 #34, 0.03 #4852, 0.02 #10474), 0k049 (0.14 #8, 0.02 #40160, 0.02 #4826), 01x73 (0.14 #95, 0.01 #14551, 0.01 #22581), 01tlmw (0.14 #25), 0cc56 (0.14 #6481, 0.04 #63505, 0.04 #22543), 01531 (0.11 #6581, 0.03 #34688, 0.03 #35491) >> Best rule #28947 for best value: >> intensional similarity = 2 >> extensional distance = 1091 >> proper extension: 0274ck; 0j3v; 07h1h5; 08n9ng; 0kvnn; 03f0324; 0f1pyf; 06hx2; 09wlpl; 06hgj; ... >> query: (?x2033, 02_286) <- location(?x2033, ?x1658), month(?x1658, ?x1459) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #4141 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 185 *> proper extension: 06v8s0; 066l3y; 09fp45; 01kymm; 01kwh5j; 06czxq; 084x96; 07glc4; *> query: (?x2033, 052p7) <- profession(?x2033, ?x1383), actor(?x11482, ?x2033), ?x1383 = 0np9r *> conf = 0.02 ranks of expected_values: 51 EVAL 01ycbq location 052p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 128.000 126.000 0.320 http://example.org/people/person/places_lived./people/place_lived/location #13741-0k1jg PRED entity: 0k1jg PRED relation: currency PRED expected values: 09nqf => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.84 #11, 0.83 #16, 0.83 #10) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 315 >> proper extension: 0cb4j; 0jcgs; 0mx4_; 0mxcf; 0mx6c; 0ml25; 0kpys; 0d22f; 0d6lp; 0l2l_; ... >> query: (?x14026, 09nqf) <- second_level_divisions(?x94, ?x14026), time_zones(?x14026, ?x2674), ?x94 = 09c7w0 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k1jg currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.842 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #13740-0sz28 PRED entity: 0sz28 PRED relation: award_winner! PRED expected values: 02glmx => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 126): 09pnw5 (0.19 #239, 0.10 #101, 0.06 #653), 03gyp30 (0.16 #9661, 0.10 #14495, 0.10 #14356), 0fqpc7d (0.16 #9661, 0.10 #14495, 0.10 #14356), 092c5f (0.16 #9661, 0.10 #14495, 0.10 #14356), 02yv_b (0.16 #9661, 0.10 #14495, 0.10 #14356), 02glmx (0.16 #9661, 0.10 #14495, 0.10 #14356), 07z31v (0.16 #9661, 0.10 #14495, 0.10 #14356), 0418154 (0.16 #9661, 0.10 #14495, 0.10 #14356), 0g5b0q5 (0.16 #9661, 0.10 #14495, 0.10 #14356), 03nnm4t (0.16 #9661, 0.10 #14495, 0.10 #14356) >> Best rule #239 for best value: >> intensional similarity = 2 >> extensional distance = 14 >> proper extension: 01wk51; >> query: (?x1208, 09pnw5) <- sibling(?x1208, ?x13442), produced_by(?x1866, ?x1208) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #9661 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1119 *> proper extension: 012ljv; 0411q; 0284n42; 01sbf2; 0157m; 01q415; 06k02; 010hn; 01dw9z; 0k8y7; ... *> query: (?x1208, ?x1819) <- award_nominee(?x395, ?x1208), award_winner(?x1442, ?x1208), award_winner(?x1819, ?x395) *> conf = 0.16 ranks of expected_values: 6 EVAL 0sz28 award_winner! 02glmx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 131.000 131.000 0.188 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13739-01jrbv PRED entity: 01jrbv PRED relation: nominated_for! PRED expected values: 03hkv_r => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 202): 0gq9h (0.33 #1477, 0.32 #2185, 0.32 #1005), 019f4v (0.30 #996, 0.30 #1468, 0.27 #2176), 0gs9p (0.29 #1007, 0.29 #1479, 0.28 #2187), 0k611 (0.27 #1487, 0.25 #1015, 0.24 #2195), 04dn09n (0.24 #33, 0.22 #1449, 0.22 #977), 0p9sw (0.24 #19, 0.22 #1435, 0.21 #963), 0f4x7 (0.24 #24, 0.20 #2148, 0.20 #11803), 0gqyl (0.24 #78, 0.19 #13221, 0.19 #786), 0gqwc (0.23 #767, 0.16 #2183, 0.16 #4543), 0gs96 (0.23 #796, 0.15 #2212, 0.15 #1976) >> Best rule #1477 for best value: >> intensional similarity = 3 >> extensional distance = 370 >> proper extension: 0bx_hnp; >> query: (?x3404, 0gq9h) <- film_release_region(?x3404, ?x94), award_winner(?x3404, ?x4563), film(?x2733, ?x3404) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13221 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1588 *> proper extension: 01tspc6; 06g60w; 04z_x4v; 0clpml; *> query: (?x3404, ?x375) <- nominated_for(?x5504, ?x3404), award(?x5504, ?x375) *> conf = 0.19 ranks of expected_values: 40 EVAL 01jrbv nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 66.000 66.000 0.325 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13738-0gqng PRED entity: 0gqng PRED relation: ceremony PRED expected values: 050yyb 0ftlxj 02glmx 0bzknt 0c4hgj 073hgx => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 92): 050yyb (0.87 #210, 0.83 #763, 0.82 #579), 02glmx (0.82 #513, 0.78 #789, 0.76 #605), 0c4hgj (0.80 #244, 0.75 #4235, 0.72 #797), 073hgx (0.76 #524, 0.75 #4235, 0.73 #247), 0fv89q (0.75 #4235, 0.73 #262, 0.72 #815), 0c53vt (0.75 #4235, 0.73 #254, 0.72 #807), 0fzrhn (0.75 #4235, 0.73 #273, 0.61 #826), 0d__c3 (0.75 #4235, 0.71 #541, 0.67 #909), 0ftlxj (0.75 #4235, 0.71 #597, 0.67 #781), 0fz20l (0.75 #4235, 0.67 #772, 0.67 #219) >> Best rule #210 for best value: >> intensional similarity = 7 >> extensional distance = 13 >> proper extension: 018wng; >> query: (?x77, 050yyb) <- ceremony(?x77, ?x9899), ceremony(?x77, ?x4598), ceremony(?x77, ?x3618), award(?x1872, ?x77), ?x9899 = 0c4hnm, ?x3618 = 0bzn6_, award_winner(?x4598, ?x1852) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 9, 11 EVAL 0gqng ceremony 073hgx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqng ceremony 0c4hgj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqng ceremony 0bzknt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 63.000 63.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqng ceremony 02glmx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqng ceremony 0ftlxj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 63.000 63.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqng ceremony 050yyb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony #13737-09qrn4 PRED entity: 09qrn4 PRED relation: award_winner PRED expected values: 0338g8 => 51 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2296): 01ycbq (0.60 #2880, 0.12 #5345, 0.07 #34518), 01nm3s (0.40 #4930, 0.40 #2465, 0.40 #872), 016gr2 (0.40 #237, 0.20 #2702, 0.12 #5167), 026rm_y (0.40 #4323, 0.20 #1858, 0.12 #6788), 0341n5 (0.40 #2140, 0.20 #4605, 0.12 #7070), 06cgy (0.40 #2772, 0.20 #5237, 0.07 #17561), 0pmhf (0.40 #3012, 0.20 #5477, 0.06 #17801), 0sw6g (0.40 #1746, 0.08 #6676, 0.07 #34518), 055c8 (0.40 #3153, 0.08 #5618, 0.06 #17942), 01tcf7 (0.40 #2708, 0.08 #5173, 0.05 #17497) >> Best rule #2880 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0789_m; 02x73k6; 0gqy2; >> query: (?x5235, 01ycbq) <- award(?x6324, ?x5235), award(?x4004, ?x5235), film(?x4004, ?x4518), ?x4518 = 0hgnl3t, ?x6324 = 018ygt >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #34517 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 166 *> proper extension: 0262yt; *> query: (?x5235, ?x4004) <- award(?x8619, ?x5235), award(?x4004, ?x5235), award_nominee(?x4004, ?x968), place_of_death(?x8619, ?x1860), ceremony(?x5235, ?x1265) *> conf = 0.33 ranks of expected_values: 36 EVAL 09qrn4 award_winner 0338g8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 51.000 16.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #13736-07y9w5 PRED entity: 07y9w5 PRED relation: genre PRED expected values: 05p553 => 82 concepts (62 used for prediction) PRED predicted values (max 10 best out of 96): 03k9fj (0.70 #732, 0.62 #612, 0.58 #3735), 07s9rl0 (0.65 #121, 0.62 #841, 0.60 #3604), 01jfsb (0.64 #973, 0.63 #1213, 0.62 #373), 05p553 (0.57 #725, 0.42 #605, 0.41 #1446), 02kdv5l (0.47 #4569, 0.43 #483, 0.36 #3726), 0lsxr (0.45 #9, 0.44 #369, 0.39 #969), 01zhp (0.34 #797, 0.25 #677, 0.09 #77), 02l7c8 (0.27 #497, 0.27 #6508, 0.27 #4462), 082gq (0.25 #150, 0.21 #270, 0.11 #1351), 09blyk (0.25 #391, 0.21 #1231, 0.18 #991) >> Best rule #732 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 01h72l; >> query: (?x1477, 03k9fj) <- genre(?x1477, ?x2540), nominated_for(?x2135, ?x1477), language(?x1477, ?x254), ?x2540 = 0hcr >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #725 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 01h72l; *> query: (?x1477, 05p553) <- genre(?x1477, ?x2540), nominated_for(?x2135, ?x1477), language(?x1477, ?x254), ?x2540 = 0hcr *> conf = 0.57 ranks of expected_values: 4 EVAL 07y9w5 genre 05p553 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 62.000 0.698 http://example.org/film/film/genre #13735-047sxrj PRED entity: 047sxrj PRED relation: award_nominee PRED expected values: 0412f5y => 100 concepts (39 used for prediction) PRED predicted values (max 10 best out of 784): 0412f5y (0.82 #25711, 0.81 #53761, 0.80 #65447), 02l840 (0.55 #159, 0.18 #88824, 0.13 #18858), 01vw20h (0.40 #1057, 0.18 #88824, 0.11 #19756), 01pfkw (0.32 #4675, 0.07 #18699, 0.05 #1038), 05szp (0.32 #4675, 0.07 #18699, 0.04 #3865), 02b9g4 (0.32 #4675, 0.07 #18699, 0.02 #6272), 01my_c (0.32 #4675, 0.04 #6252, 0.04 #3914), 02yygk (0.32 #4675, 0.02 #6765, 0.02 #4427), 0b4rf3 (0.32 #4675, 0.02 #4594, 0.02 #11606), 047sxrj (0.30 #497, 0.07 #18699, 0.03 #19196) >> Best rule #25711 for best value: >> intensional similarity = 3 >> extensional distance = 215 >> proper extension: 01qkqwg; 01vw917; 0191h5; >> query: (?x2334, ?x527) <- artists(?x671, ?x2334), people(?x2510, ?x2334), award_nominee(?x527, ?x2334) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047sxrj award_nominee 0412f5y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 39.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13734-02lxj_ PRED entity: 02lxj_ PRED relation: award PRED expected values: 09sb52 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 263): 09sb52 (0.78 #445, 0.60 #41, 0.36 #15393), 05pcn59 (0.28 #1294, 0.22 #486, 0.21 #3314), 0gqwc (0.22 #479, 0.20 #75, 0.17 #2499), 02ppm4q (0.22 #560, 0.20 #156, 0.13 #44443), 09qvf4 (0.22 #614, 0.20 #210, 0.13 #44443), 099t8j (0.22 #544, 0.20 #140, 0.13 #44443), 0fbvqf (0.22 #452, 0.20 #48, 0.03 #15804), 05zr6wv (0.22 #421, 0.17 #3249, 0.16 #3653), 05b4l5x (0.20 #6, 0.17 #2430, 0.13 #44443), 02z0dfh (0.20 #76, 0.13 #44443, 0.11 #1288) >> Best rule #445 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 0dvmd; 015t7v; 017khj; 0dvld; >> query: (?x1623, 09sb52) <- award_nominee(?x4366, ?x1623), ?x4366 = 01vxxb >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lxj_ award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13733-0dcsx PRED entity: 0dcsx PRED relation: risk_factors PRED expected values: 01hbgs => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 84): 01hbgs (0.92 #2016, 0.60 #228, 0.60 #177), 05zppz (0.62 #1942, 0.60 #205, 0.60 #155), 0jpmt (0.60 #227, 0.50 #363, 0.50 #362), 0fltx (0.60 #238, 0.50 #712, 0.44 #607), 012jc (0.50 #363, 0.50 #362, 0.36 #924), 0432mrk (0.33 #41, 0.26 #730, 0.23 #891), 02ctzb (0.33 #6, 0.22 #526, 0.13 #3224), 0f0gt_ (0.33 #46, 0.12 #3272, 0.11 #566), 0x67 (0.32 #1412, 0.28 #731, 0.25 #57), 02y0js (0.32 #1412, 0.28 #731, 0.20 #107) >> Best rule #2016 for best value: >> intensional similarity = 6 >> extensional distance = 23 >> proper extension: 0146bp; 0h3bn; >> query: (?x5801, 01hbgs) <- risk_factors(?x5801, ?x5802), risk_factors(?x5801, ?x514), risk_factors(?x5784, ?x5802), risk_factors(?x13632, ?x514), ?x5784 = 02vrr, ?x13632 = 06g7c >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dcsx risk_factors 01hbgs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.920 http://example.org/medicine/disease/risk_factors #13732-03hl6lc PRED entity: 03hl6lc PRED relation: nominated_for PRED expected values: 05jf85 01sxly 0fh694 09k56b7 047d21r 08nvyr 0sxmx 03mz5b 02pw_n 06fpsx 0y_pg 01xvjb => 53 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1430): 02mt51 (0.77 #8992, 0.68 #2993, 0.67 #19495), 01gc7 (0.77 #8992, 0.68 #2993, 0.67 #19495), 0bs5vty (0.77 #8992, 0.68 #2993, 0.67 #19495), 0c0zq (0.77 #8992, 0.68 #2993, 0.67 #19495), 0sxns (0.77 #8992, 0.68 #2993, 0.67 #19495), 020fcn (0.70 #1650, 0.24 #29988, 0.23 #13495), 0fpv_3_ (0.70 #1808, 0.24 #4807, 0.17 #7806), 017gl1 (0.60 #1617, 0.33 #4616, 0.24 #29988), 0btpm6 (0.60 #2561, 0.24 #29988, 0.24 #5560), 0ch26b_ (0.60 #1749, 0.23 #4748, 0.23 #13495) >> Best rule #8992 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 06196; >> query: (?x3435, ?x144) <- award_winner(?x3435, ?x826), award(?x144, ?x3435), award(?x237, ?x3435), ceremony(?x3435, ?x4617) >> conf = 0.77 => this is the best rule for 5 predicted values *> Best rule #650 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 02x17c2; 0bm70b; *> query: (?x3435, 08nvyr) <- award_winner(?x3435, ?x6673), award(?x9281, ?x3435), award_winner(?x2213, ?x6673), ?x9281 = 013tcv *> conf = 0.44 ranks of expected_values: 21, 48, 49, 120, 123, 150, 185, 269, 492, 497, 1069, 1191 EVAL 03hl6lc nominated_for 01xvjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 0y_pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 06fpsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 02pw_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 03mz5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 0sxmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 08nvyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 047d21r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 09k56b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 0fh694 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 01sxly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hl6lc nominated_for 05jf85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13731-0btpm6 PRED entity: 0btpm6 PRED relation: film_release_region PRED expected values: 03rjj 0chghy 047lj 0f8l9c 0ctw_b 0h7x 05qx1 01znc_ 06f32 0jgx => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 124): 0f8l9c (0.90 #138, 0.90 #388, 0.87 #13), 03rjj (0.89 #128, 0.88 #378, 0.83 #3), 0chghy (0.83 #382, 0.83 #132, 0.77 #7), 01znc_ (0.80 #27, 0.74 #152, 0.73 #402), 0ctw_b (0.70 #141, 0.70 #391, 0.63 #16), 05qx1 (0.67 #26, 0.48 #401, 0.47 #151), 047lj (0.67 #8, 0.46 #133, 0.44 #383), 06f32 (0.62 #169, 0.58 #419, 0.57 #44), 01crd5 (0.60 #97, 0.27 #472, 0.26 #222), 0d0kn (0.40 #36, 0.25 #161, 0.24 #411) >> Best rule #138 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 0gtsx8c; 087wc7n; 08hmch; 0gj8t_b; 03bx2lk; 04zyhx; 0661m4p; 0gffmn8; 0gjc4d3; 0gj8nq2; ... >> query: (?x7493, 0f8l9c) <- film_release_region(?x7493, ?x1536), ?x1536 = 06c1y, language(?x7493, ?x254) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 11, 12 EVAL 0btpm6 film_release_region 0jgx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 80.000 80.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0btpm6 film_release_region 06f32 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0btpm6 film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0btpm6 film_release_region 05qx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0btpm6 film_release_region 0h7x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 80.000 80.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0btpm6 film_release_region 0ctw_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0btpm6 film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0btpm6 film_release_region 047lj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0btpm6 film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0btpm6 film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13730-08966 PRED entity: 08966 PRED relation: citytown! PRED expected values: 01tpvt => 231 concepts (152 used for prediction) PRED predicted values (max 10 best out of 651): 01tpvt (0.75 #42842, 0.71 #24249, 0.64 #33142), 085h1 (0.33 #379, 0.04 #30288, 0.03 #31904), 04k4l (0.33 #177, 0.04 #30086, 0.03 #31702), 01y888 (0.33 #164, 0.04 #30073, 0.03 #31689), 0277jc (0.25 #1661, 0.20 #3277, 0.17 #4086), 01nds (0.22 #25633, 0.18 #19167, 0.15 #20784), 0ch280 (0.20 #3182, 0.11 #7226, 0.10 #8034), 051pnv (0.20 #3167, 0.11 #7211, 0.10 #8019), 07w0v (0.20 #2458, 0.11 #6502, 0.10 #7310), 03_c8p (0.15 #10275, 0.14 #11083, 0.13 #12700) >> Best rule #42842 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 0pmq2; >> query: (?x6458, ?x6811) <- capital(?x7406, ?x6458), contains(?x774, ?x6458), contains(?x7406, ?x9227), location(?x14208, ?x6458), contains(?x6458, ?x6811) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08966 citytown! 01tpvt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 231.000 152.000 0.753 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #13729-018w8 PRED entity: 018w8 PRED relation: sport! PRED expected values: 0jm3v 0jmfv 0jmj7 082wbh 0jmm4 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 440): 026dqjm (0.33 #369, 0.12 #5649), 02pzy52 (0.33 #298, 0.12 #5578), 02q4ntp (0.33 #270, 0.12 #5550), 03d5m8w (0.33 #269, 0.12 #5549), 026wlnm (0.33 #264, 0.12 #5544), 03y9p40 (0.33 #261, 0.12 #5541), 02qk2d5 (0.33 #255, 0.12 #5535), 0263cyj (0.33 #233, 0.12 #5513), 026xxv_ (0.33 #217, 0.12 #5497), 091tgz (0.33 #208, 0.12 #5488) >> Best rule #369 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 039yzs; >> query: (?x4833, 026dqjm) <- sport(?x9760, ?x4833), sport(?x8079, ?x4833), sport(?x2398, ?x4833), position(?x2398, ?x1348), colors(?x8079, ?x332), team(?x4834, ?x9760) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018w8 sport! 0jmm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 59.000 0.333 http://example.org/sports/sports_team/sport EVAL 018w8 sport! 082wbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 59.000 0.333 http://example.org/sports/sports_team/sport EVAL 018w8 sport! 0jmj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 59.000 0.333 http://example.org/sports/sports_team/sport EVAL 018w8 sport! 0jmfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 59.000 0.333 http://example.org/sports/sports_team/sport EVAL 018w8 sport! 0jm3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 59.000 0.333 http://example.org/sports/sports_team/sport #13728-06c62 PRED entity: 06c62 PRED relation: capital! PRED expected values: 02psqkz 06cmp => 247 concepts (223 used for prediction) PRED predicted values (max 10 best out of 132): 04q_g (0.56 #5603, 0.49 #6406, 0.49 #9345), 0f8l9c (0.20 #286, 0.14 #552, 0.07 #9211), 0gtzp (0.20 #399, 0.14 #665, 0.07 #1467), 02psqkz (0.17 #1263, 0.14 #729, 0.06 #4199), 03f4n1 (0.17 #1327, 0.14 #793, 0.04 #8939), 020d5 (0.17 #1305, 0.09 #4241, 0.04 #11195), 03pn9 (0.14 #582, 0.08 #1249, 0.03 #4185), 05qhw (0.14 #545, 0.08 #1212, 0.03 #4148), 01m41_ (0.14 #792, 0.08 #1326, 0.03 #4395), 084n_ (0.14 #649, 0.07 #1451, 0.05 #2117) >> Best rule #5603 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0ftlx; 01l63; 0c499; >> query: (?x6959, ?x205) <- place_of_birth(?x10650, ?x6959), contains(?x205, ?x6959), capital(?x8845, ?x6959), nominated_for(?x10650, ?x810) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1263 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0d6nx; *> query: (?x6959, 02psqkz) <- contains(?x205, ?x6959), administrative_division(?x6959, ?x12784), capital(?x8845, ?x6959) *> conf = 0.17 ranks of expected_values: 4, 20 EVAL 06c62 capital! 06cmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 247.000 223.000 0.560 http://example.org/location/country/capital EVAL 06c62 capital! 02psqkz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 247.000 223.000 0.560 http://example.org/location/country/capital #13727-07c9s PRED entity: 07c9s PRED relation: languages_spoken! PRED expected values: 078ds => 60 concepts (25 used for prediction) PRED predicted values (max 10 best out of 71): 07hwkr (0.62 #900, 0.62 #832, 0.56 #1241), 078vc (0.57 #793, 0.50 #315, 0.50 #247), 04czx7 (0.57 #680, 0.25 #407, 0.25 #339), 0d2by (0.57 #644, 0.25 #371, 0.25 #303), 02vsw1 (0.50 #933, 0.50 #865, 0.36 #1001), 0dryh9k (0.50 #220, 0.33 #83, 0.29 #766), 059_w (0.40 #505, 0.25 #915, 0.25 #847), 0c41n (0.40 #546, 0.25 #409, 0.25 #341), 0fk3s (0.40 #540, 0.25 #403, 0.25 #335), 03x1x (0.40 #528, 0.25 #391, 0.25 #323) >> Best rule #900 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 03_9r; >> query: (?x5121, 07hwkr) <- languages(?x9994, ?x5121), languages(?x9506, ?x5121), languages(?x8097, ?x5121), titles(?x5121, ?x3742), language(?x2340, ?x5121), gender(?x8097, ?x514), student(?x9399, ?x8097), profession(?x9506, ?x524), student(?x2605, ?x9994), official_language(?x2316, ?x5121), place_of_birth(?x8097, ?x4335) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #389 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 055qm; *> query: (?x5121, 078ds) <- languages(?x8530, ?x5121), languages(?x8097, ?x5121), languages(?x7295, ?x5121), ?x8530 = 02wmbg, spouse(?x7295, ?x9039), place_of_birth(?x8097, ?x4335), religion(?x7295, ?x492), award(?x7295, ?x10156), profession(?x7295, ?x1032) *> conf = 0.25 ranks of expected_values: 26 EVAL 07c9s languages_spoken! 078ds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 60.000 25.000 0.625 http://example.org/people/ethnicity/languages_spoken #13726-0c5v2 PRED entity: 0c5v2 PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 20): 0pqc5 (0.38 #166, 0.31 #236, 0.31 #212), 0f6c3 (0.26 #192, 0.16 #355, 0.14 #378), 09n5b9 (0.24 #196, 0.15 #359, 0.13 #382), 0fkvn (0.21 #188, 0.14 #351, 0.14 #328), 01q24l (0.13 #129, 0.08 #175, 0.07 #315), 060c4 (0.09 #902, 0.09 #603, 0.09 #649), 060bp (0.08 #624, 0.08 #647, 0.08 #601), 0fkzq (0.07 #201, 0.05 #364, 0.04 #387), 0p5vf (0.04 #475, 0.03 #360, 0.02 #613), 0789n (0.04 #194, 0.03 #357, 0.03 #472) >> Best rule #166 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 0f04v; >> query: (?x13119, 0pqc5) <- country(?x13119, ?x94), ?x94 = 09c7w0, citytown(?x9066, ?x13119) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c5v2 jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.375 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #13725-04cl1 PRED entity: 04cl1 PRED relation: award_winner! PRED expected values: 0gx_st 0hndn2q => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 110): 0gx_st (0.25 #4172, 0.20 #3754, 0.17 #7232), 05c1t6z (0.25 #4172, 0.20 #3754, 0.17 #7232), 0275n3y (0.25 #4172, 0.20 #3754, 0.17 #7232), 0gvstc3 (0.25 #4172, 0.20 #3754, 0.17 #7232), 0bx6zs (0.25 #4172, 0.20 #3754, 0.17 #7232), 013b2h (0.10 #2024, 0.07 #3275, 0.06 #2858), 05pd94v (0.09 #1948, 0.06 #2782, 0.05 #3199), 02q690_ (0.09 #342, 0.09 #620, 0.06 #64), 02rjjll (0.09 #1951, 0.06 #2785, 0.05 #3202), 01s695 (0.09 #1949, 0.06 #2783, 0.05 #3200) >> Best rule #4172 for best value: >> intensional similarity = 3 >> extensional distance = 973 >> proper extension: 0dky9n; 0gsg7; 02wrhj; 0cjdk; 027_tg; 05gnf; >> query: (?x4676, ?x1265) <- nominated_for(?x4676, ?x3180), honored_for(?x1265, ?x3180), award_winner(?x2126, ?x4676) >> conf = 0.25 => this is the best rule for 5 predicted values ranks of expected_values: 1, 34 EVAL 04cl1 award_winner! 0hndn2q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 103.000 103.000 0.254 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 04cl1 award_winner! 0gx_st CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.254 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13724-015882 PRED entity: 015882 PRED relation: instrumentalists! PRED expected values: 0l14md => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 74): 05r5c (0.55 #450, 0.50 #1512, 0.49 #1336), 05148p4 (0.41 #374, 0.36 #816, 0.34 #3022), 0l14jd (0.35 #442, 0.33 #353, 0.28 #885), 0j210 (0.35 #442, 0.33 #353, 0.28 #885), 0l15bq (0.35 #442, 0.33 #353, 0.28 #885), 03bx0bm (0.35 #442, 0.33 #353, 0.28 #885), 01qbl (0.35 #442, 0.33 #353, 0.28 #885), 018vs (0.34 #3014, 0.33 #189, 0.32 #986), 07xzm (0.25 #22, 0.17 #110, 0.13 #464), 07y_7 (0.25 #2, 0.10 #444, 0.08 #709) >> Best rule #450 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 01jqr_5; 017yfz; 01sxd1; 03f3_p3; >> query: (?x1817, 05r5c) <- artists(?x3108, ?x1817), people(?x1816, ?x1817), ?x3108 = 02w4v >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #892 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 82 *> proper extension: 01p45_v; 095x_; 032nl2; 0232lm; 01pny5; *> query: (?x1817, 0l14md) <- artists(?x3061, ?x1817), location(?x1817, ?x938), ?x3061 = 05bt6j *> conf = 0.14 ranks of expected_values: 18 EVAL 015882 instrumentalists! 0l14md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 137.000 137.000 0.548 http://example.org/music/instrument/instrumentalists #13723-01yzhn PRED entity: 01yzhn PRED relation: film PRED expected values: 0y_yw => 139 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1006): 02x3lt7 (0.20 #83, 0.14 #5457, 0.14 #3666), 02ryz24 (0.20 #468, 0.14 #4051, 0.11 #25543), 026wlxw (0.20 #1419, 0.14 #5002, 0.10 #8584), 0bvn25 (0.20 #49, 0.14 #3632, 0.10 #7214), 02qydsh (0.20 #1500, 0.14 #5083, 0.10 #8665), 0q9b0 (0.20 #1274, 0.14 #4857, 0.09 #10230), 05fm6m (0.17 #3112, 0.10 #38935, 0.06 #35353), 02ny6g (0.17 #2392, 0.05 #25676, 0.02 #68667), 0fpgp26 (0.17 #3329, 0.05 #26613, 0.01 #78562), 02c7k4 (0.17 #2895, 0.03 #38718, 0.03 #42300) >> Best rule #83 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 029_3; >> query: (?x10592, 02x3lt7) <- person(?x3480, ?x10592), participant(?x7605, ?x10592), location(?x10592, ?x335), special_performance_type(?x10592, ?x4832) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #24345 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0p_pd; 094xh; 03vrnh; 03d9v8; 0djtky; *> query: (?x10592, 0y_yw) <- location(?x10592, ?x1227), ?x1227 = 01n7q, languages(?x10592, ?x254) *> conf = 0.06 ranks of expected_values: 114 EVAL 01yzhn film 0y_yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 139.000 119.000 0.200 http://example.org/film/actor/film./film/performance/film #13722-0lbj1 PRED entity: 0lbj1 PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #49, 0.85 #41, 0.83 #37), 02zsn (0.55 #2, 0.37 #86, 0.36 #80) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 201 >> proper extension: 01tp5bj; 01m65sp; 082brv; 018y81; 0326tc; 095x_; 020hh3; 03f1zhf; 048tgl; >> query: (?x248, 05zppz) <- profession(?x248, ?x131), role(?x248, ?x716), role(?x248, ?x227) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lbj1 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.867 http://example.org/people/person/gender #13721-0gx1bnj PRED entity: 0gx1bnj PRED relation: film_crew_role PRED expected values: 0dxtw => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 32): 0ch6mp2 (0.90 #1289, 0.80 #1804, 0.80 #447), 02r96rf (0.82 #514, 0.82 #624, 0.82 #550), 09vw2b7 (0.70 #628, 0.70 #518, 0.70 #446), 0dxtw (0.51 #451, 0.50 #559, 0.50 #633), 02ynfr (0.32 #856, 0.22 #637, 0.22 #527), 0215hd (0.23 #859, 0.20 #458, 0.19 #566), 02rh1dz (0.21 #632, 0.21 #522, 0.21 #558), 0d2b38 (0.20 #866, 0.18 #465, 0.17 #647), 015h31 (0.19 #557, 0.19 #631, 0.19 #521), 089g0h (0.18 #860, 0.17 #459, 0.16 #641) >> Best rule #1289 for best value: >> intensional similarity = 8 >> extensional distance = 924 >> proper extension: 05dy7p; 02n9bh; 04lqvly; 03_wm6; 03xj05; >> query: (?x343, 0ch6mp2) <- film_crew_role(?x343, ?x2178), language(?x343, ?x254), film_crew_role(?x9900, ?x2178), film_crew_role(?x9429, ?x2178), film_crew_role(?x1074, ?x2178), ?x9900 = 0qmfk, ?x9429 = 032sl_, ?x1074 = 03t97y >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #451 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 274 *> proper extension: 0k4d7; 014nq4; *> query: (?x343, 0dxtw) <- film_crew_role(?x343, ?x2154), language(?x343, ?x254), film(?x237, ?x343), ?x2154 = 01vx2h, film_release_distribution_medium(?x343, ?x81), ?x81 = 029j_ *> conf = 0.51 ranks of expected_values: 4 EVAL 0gx1bnj film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 75.000 0.898 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13720-0djkrp PRED entity: 0djkrp PRED relation: nominated_for! PRED expected values: 0fq9zdn => 91 concepts (84 used for prediction) PRED predicted values (max 10 best out of 224): 0gq9h (0.41 #4128, 0.39 #2693, 0.34 #4367), 0gqwc (0.38 #4365, 0.24 #3170, 0.21 #1735), 0gs9p (0.34 #2695, 0.33 #4130, 0.30 #4369), 0gq_v (0.32 #4084, 0.30 #2649, 0.21 #6235), 02r0csl (0.32 #483, 0.32 #244, 0.14 #3113), 0fq9zdv (0.32 #649, 0.26 #410, 0.10 #12431), 019f4v (0.30 #4119, 0.29 #2684, 0.27 #4358), 0gqyl (0.29 #4385, 0.27 #3190, 0.26 #799), 0789_m (0.29 #2869, 0.19 #19847, 0.04 #20087), 0gr4k (0.29 #4091, 0.29 #2656, 0.25 #4330) >> Best rule #4128 for best value: >> intensional similarity = 4 >> extensional distance = 309 >> proper extension: 0123qq; >> query: (?x9145, 0gq9h) <- nominated_for(?x1469, ?x9145), people(?x1468, ?x1469), profession(?x1469, ?x1032), gender(?x1469, ?x231) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #524 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 0gh8zks; *> query: (?x9145, 0fq9zdn) <- production_companies(?x9145, ?x9041), ?x9041 = 05mgj0, country(?x9145, ?x512), film(?x1469, ?x9145) *> conf = 0.21 ranks of expected_values: 30 EVAL 0djkrp nominated_for! 0fq9zdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 91.000 84.000 0.412 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13719-02kd0rh PRED entity: 02kd0rh PRED relation: nutrient! PRED expected values: 09728 0971v 0dj75 037ls6 => 52 concepts (49 used for prediction) PRED predicted values (max 10 best out of 14): 037ls6 (0.89 #521, 0.89 #512, 0.88 #161), 09728 (0.88 #161, 0.88 #79, 0.88 #36), 0971v (0.88 #161, 0.88 #79, 0.88 #36), 0fbw6 (0.88 #161, 0.88 #79, 0.88 #36), 0dj75 (0.88 #161, 0.88 #79, 0.88 #36), 06x4c (0.88 #161, 0.88 #79, 0.88 #36), 0dcfv (0.88 #161, 0.88 #79, 0.88 #36), 01sh2 (0.03 #388, 0.02 #762), 025rw19 (0.02 #762), 025tkqy (0.02 #762) >> Best rule #521 for best value: >> intensional similarity = 119 >> extensional distance = 25 >> proper extension: 0hkwr; >> query: (?x9949, ?x8298) <- nutrient(?x10612, ?x9949), nutrient(?x9732, ?x9949), nutrient(?x9489, ?x9949), nutrient(?x7057, ?x9949), nutrient(?x6285, ?x9949), nutrient(?x6191, ?x9949), nutrient(?x6159, ?x9949), nutrient(?x5009, ?x9949), nutrient(?x3900, ?x9949), nutrient(?x3468, ?x9949), nutrient(?x2701, ?x9949), nutrient(?x1959, ?x9949), nutrient(?x1303, ?x9949), ?x9489 = 07j87, nutrient(?x9732, ?x13498), nutrient(?x9732, ?x12902), nutrient(?x9732, ?x12454), nutrient(?x9732, ?x12083), nutrient(?x9732, ?x11758), nutrient(?x9732, ?x11592), nutrient(?x9732, ?x11409), nutrient(?x9732, ?x11270), nutrient(?x9732, ?x10891), nutrient(?x9732, ?x10709), nutrient(?x9732, ?x10098), nutrient(?x9732, ?x9915), nutrient(?x9732, ?x9708), nutrient(?x9732, ?x9490), nutrient(?x9732, ?x9436), nutrient(?x9732, ?x9426), nutrient(?x9732, ?x8442), nutrient(?x9732, ?x8413), nutrient(?x9732, ?x7894), nutrient(?x9732, ?x7652), nutrient(?x9732, ?x7364), nutrient(?x9732, ?x7362), nutrient(?x9732, ?x7135), nutrient(?x9732, ?x6586), nutrient(?x9732, ?x6160), nutrient(?x9732, ?x6033), nutrient(?x9732, ?x5549), nutrient(?x9732, ?x5526), nutrient(?x9732, ?x5451), nutrient(?x9732, ?x5010), nutrient(?x9732, ?x2702), nutrient(?x9732, ?x2018), nutrient(?x9732, ?x1960), nutrient(?x9732, ?x1258), ?x8442 = 02kcv4x, ?x12902 = 0fzjh, ?x12454 = 025rw19, ?x3900 = 061_f, ?x11592 = 025sf0_, nutrient(?x10612, ?x13126), nutrient(?x10612, ?x9840), nutrient(?x10612, ?x9795), nutrient(?x10612, ?x8487), nutrient(?x10612, ?x7431), nutrient(?x10612, ?x6192), nutrient(?x10612, ?x3901), ?x10891 = 0g5gq, ?x7652 = 025s0s0, ?x11270 = 02kc008, ?x1303 = 0fj52s, nutrient(?x6159, ?x12868), nutrient(?x6159, ?x9855), nutrient(?x6159, ?x5337), nutrient(?x6159, ?x4069), nutrient(?x6159, ?x3264), ?x6191 = 014j1m, ?x9708 = 061xhr, ?x9915 = 025tkqy, ?x9436 = 025sqz8, ?x7431 = 09gwd, ?x6160 = 041r51, ?x11758 = 0q01m, ?x7894 = 0f4hc, ?x5337 = 06x4c, ?x6586 = 05gh50, ?x13126 = 02kc_w5, ?x6285 = 01645p, ?x9795 = 05v_8y, ?x5451 = 05wvs, ?x1258 = 0h1wg, ?x10098 = 0h1_c, ?x9426 = 0h1yy, ?x7364 = 09gvd, ?x8413 = 02kc4sf, ?x6192 = 06jry, ?x12083 = 01n78x, ?x11409 = 0h1yf, ?x4069 = 0hqw8p_, ?x5549 = 025s7j4, ?x7057 = 0fbdb, ?x9840 = 02p0tjr, ?x7135 = 025rsfk, ?x8487 = 014yzm, ?x5010 = 0h1vz, ?x2701 = 0hkxq, ?x1960 = 07hnp, ?x3901 = 0466p20, ?x6033 = 04zjxcz, ?x7362 = 02kc5rj, ?x13498 = 07q0m, nutrient(?x8298, ?x9490), nutrient(?x7719, ?x9490), ?x2702 = 0838f, ?x5526 = 09pbb, ?x5009 = 0fjfh, ?x3468 = 0cxn2, ?x9855 = 0d9t0, ?x7719 = 0dj75, nutrient(?x1959, ?x11784), ?x10709 = 0h1sz, ?x2018 = 01sh2, ?x12868 = 03d49, ?x11784 = 07zqy, ?x8298 = 037ls6, ?x3264 = 0dcfv >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5 EVAL 02kd0rh nutrient! 037ls6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 49.000 0.889 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 02kd0rh nutrient! 0dj75 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 49.000 0.889 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 02kd0rh nutrient! 0971v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 49.000 0.889 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 02kd0rh nutrient! 09728 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 49.000 0.889 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #13718-0gzy02 PRED entity: 0gzy02 PRED relation: language PRED expected values: 0349s => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 32): 064_8sq (0.16 #1684, 0.16 #934, 0.15 #477), 06nm1 (0.15 #9, 0.11 #66, 0.11 #923), 06b_j (0.12 #21, 0.08 #78, 0.08 #478), 02bjrlw (0.10 #115, 0.10 #58, 0.09 #1606), 03_9r (0.08 #1325, 0.07 #1440, 0.06 #122), 0jzc (0.06 #75, 0.04 #532, 0.04 #189), 0653m (0.05 #1327, 0.05 #1442, 0.04 #867), 03hkp (0.04 #13, 0.03 #70, 0.02 #184), 01r2l (0.04 #23, 0.01 #1340), 02002f (0.04 #29) >> Best rule #1684 for best value: >> intensional similarity = 4 >> extensional distance = 445 >> proper extension: 07l50vn; >> query: (?x327, 064_8sq) <- award(?x327, ?x2209), nominated_for(?x500, ?x327), ceremony(?x2209, ?x78), film_release_distribution_medium(?x327, ?x81) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #1533 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 428 *> proper extension: 047p798; *> query: (?x327, 0349s) <- films(?x326, ?x327), film(?x2167, ?x327), award_winner(?x2060, ?x2167), award_winner(?x3029, ?x2167) *> conf = 0.02 ranks of expected_values: 20 EVAL 0gzy02 language 0349s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 102.000 102.000 0.161 http://example.org/film/film/language #13717-0ds3t5x PRED entity: 0ds3t5x PRED relation: award PRED expected values: 027cyf7 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 169): 0ck27z (0.33 #67, 0.20 #519, 0.12 #13350), 0bdw1g (0.33 #30, 0.20 #482, 0.12 #13350), 0cqh6z (0.33 #52, 0.20 #504, 0.12 #13350), 0m7yy (0.33 #123, 0.20 #575, 0.11 #12896), 0gkr9q (0.33 #193, 0.20 #645, 0.02 #4038), 0fbvqf (0.33 #36, 0.20 #488, 0.02 #6368), 0cqhb3 (0.33 #183, 0.20 #635, 0.02 #4028), 094qd5 (0.27 #4298, 0.26 #3845, 0.25 #260), 0gqwc (0.27 #4298, 0.26 #3845, 0.25 #284), 03hkv_r (0.27 #4298, 0.26 #3845, 0.25 #240) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0g60z; >> query: (?x385, 0ck27z) <- nominated_for(?x1871, ?x385), ?x1871 = 02bkdn, honored_for(?x2655, ?x385) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1718 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 145 *> proper extension: 026p_bs; 0k0rf; 0g5pvv; 02n72k; 01jr4j; 025twgf; 0fztbq; *> query: (?x385, 027cyf7) <- film_release_region(?x385, ?x87), film(?x4564, ?x385), nominated_for(?x2655, ?x385) *> conf = 0.01 ranks of expected_values: 144 EVAL 0ds3t5x award 027cyf7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 80.000 80.000 0.333 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13716-0f721s PRED entity: 0f721s PRED relation: program PRED expected values: 07zhjj => 104 concepts (59 used for prediction) PRED predicted values (max 10 best out of 248): 06y_n (0.20 #2625, 0.12 #3886, 0.11 #4201), 0330r (0.17 #739, 0.14 #1366, 0.12 #1522), 0170k0 (0.15 #2607, 0.09 #3868, 0.09 #4183), 03y3bp7 (0.11 #1596, 0.03 #3798, 0.03 #4113), 0vhm (0.10 #2567, 0.06 #3828, 0.06 #4143), 025x1t (0.10 #2645, 0.06 #3906, 0.06 #4221), 015w8_ (0.10 #2539, 0.06 #3800, 0.06 #4115), 06r1k (0.10 #2641, 0.06 #3902, 0.06 #4217), 028k2x (0.10 #2594, 0.06 #3855, 0.06 #4170), 0l76z (0.09 #4450, 0.09 #4607, 0.08 #4922) >> Best rule #2625 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0dbpyd; 06pj8; 01_x6v; 071dcs; 09b0xs; 07fvf1; 06jnvs; 02cm2m; 03fykz; 086nl7; ... >> query: (?x1394, 06y_n) <- program(?x1394, ?x1395), genre(?x1395, ?x2540), ?x2540 = 0hcr, award_winner(?x1762, ?x1394) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #6070 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 158 *> proper extension: 0d1mp3; *> query: (?x1394, 07zhjj) <- program(?x1394, ?x1395), producer_type(?x1395, ?x632), honored_for(?x2213, ?x1395), nominated_for(?x1537, ?x1395) *> conf = 0.01 ranks of expected_values: 96 EVAL 0f721s program 07zhjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 104.000 59.000 0.200 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/program #13715-018ndc PRED entity: 018ndc PRED relation: award_winner! PRED expected values: 056878 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 101): 013b2h (0.50 #213, 0.41 #624, 0.23 #350), 02rjjll (0.23 #826, 0.14 #2744, 0.14 #3155), 056878 (0.22 #439, 0.21 #850, 0.15 #302), 0466p0j (0.19 #1031, 0.18 #894, 0.17 #7129), 09n4nb (0.18 #866, 0.17 #181, 0.14 #2784), 0gx1673 (0.17 #7129, 0.17 #6991, 0.13 #938), 01bx35 (0.17 #143, 0.14 #965, 0.12 #3157), 01xqqp (0.17 #229, 0.14 #640, 0.09 #3243), 01mhwk (0.17 #174, 0.12 #996, 0.09 #4010), 02yv_b (0.17 #159, 0.08 #296, 0.06 #433) >> Best rule #213 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 018pj3; 0x3b7; >> query: (?x3109, 013b2h) <- award(?x3109, ?x724), artists(?x9750, ?x3109), artists(?x3108, ?x3109), ?x9750 = 016zgj, ?x3108 = 02w4v >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #439 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 01bpc9; 017xm3; 028hc2; *> query: (?x3109, 056878) <- award(?x3109, ?x724), artists(?x9750, ?x3109), ?x9750 = 016zgj *> conf = 0.22 ranks of expected_values: 3 EVAL 018ndc award_winner! 056878 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13714-0153nq PRED entity: 0153nq PRED relation: artists! PRED expected values: 06by7 03lty 0190xp => 99 concepts (88 used for prediction) PRED predicted values (max 10 best out of 279): 06by7 (0.85 #25195, 0.77 #25825, 0.75 #1910), 064t9 (0.59 #26132, 0.57 #2532, 0.54 #2217), 03lty (0.57 #18594, 0.50 #16075, 0.49 #13870), 016clz (0.42 #12901, 0.42 #14160, 0.41 #11955), 0gywn (0.40 #374, 0.26 #6665, 0.26 #3521), 03_d0 (0.40 #326, 0.26 #4731, 0.25 #1900), 0dl5d (0.38 #962, 0.32 #22676, 0.29 #3166), 05w3f (0.36 #4129, 0.27 #6329, 0.26 #13880), 05bt6j (0.36 #11052, 0.35 #3191, 0.35 #7906), 025sc50 (0.32 #6657, 0.26 #4456, 0.25 #11374) >> Best rule #25195 for best value: >> intensional similarity = 6 >> extensional distance = 454 >> proper extension: 053y0s; 01nqfh_; 04bpm6; 04zwjd; 028qdb; 01pbs9w; 01d4cb; >> query: (?x13578, 06by7) <- artists(?x1000, ?x13578), artists(?x1000, ?x10671), artists(?x1000, ?x3930), parent_genre(?x1380, ?x1000), ?x10671 = 04k05, ?x3930 = 01svw8n >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 101 EVAL 0153nq artists! 0190xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 99.000 88.000 0.853 http://example.org/music/genre/artists EVAL 0153nq artists! 03lty CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 88.000 0.853 http://example.org/music/genre/artists EVAL 0153nq artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 88.000 0.853 http://example.org/music/genre/artists #13713-0bb57s PRED entity: 0bb57s PRED relation: award! PRED expected values: 01hkhq 02tk74 => 51 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2476): 0l6px (0.83 #3334, 0.80 #16670, 0.80 #20005), 01dbk6 (0.83 #3334, 0.80 #16670, 0.80 #20005), 01jw4r (0.69 #19124, 0.46 #15790, 0.40 #9122), 0h0wc (0.60 #7342, 0.50 #674, 0.44 #17344), 028knk (0.60 #7187, 0.50 #519, 0.38 #13855), 0154qm (0.54 #14229, 0.50 #893, 0.44 #17563), 01hkhq (0.50 #10659, 0.50 #657, 0.44 #17327), 01w1kyf (0.50 #4805, 0.50 #1471, 0.23 #14807), 03f2_rc (0.50 #3448, 0.50 #114, 0.15 #13450), 043kzcr (0.50 #661, 0.46 #13997, 0.44 #17331) >> Best rule #3334 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 094qd5; 0gqwc; >> query: (?x5455, ?x156) <- ceremony(?x5455, ?x4141), award_winner(?x5455, ?x6852), award_winner(?x5455, ?x156), award(?x374, ?x5455), ?x6852 = 0lfbm >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #10659 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 027b9k6; *> query: (?x5455, 01hkhq) <- award_winner(?x5455, ?x4165), award(?x4398, ?x5455), ?x4165 = 02mqc4, award_winner(?x375, ?x4398) *> conf = 0.50 ranks of expected_values: 7, 1074 EVAL 0bb57s award! 02tk74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 20.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bb57s award! 01hkhq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 51.000 20.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13712-02d6n_ PRED entity: 02d6n_ PRED relation: film PRED expected values: 07gp9 01f8hf 01xlqd => 134 concepts (82 used for prediction) PRED predicted values (max 10 best out of 997): 01qb559 (0.50 #1303, 0.14 #8455, 0.09 #13819), 01f8hf (0.40 #4373), 09wnnb (0.29 #8776, 0.25 #1624, 0.20 #3412), 0ptdz (0.25 #1756, 0.14 #10696, 0.14 #8908), 03bx2lk (0.25 #184, 0.14 #7336, 0.11 #10912), 02qhqz4 (0.25 #343, 0.14 #7495, 0.09 #12859), 01rnly (0.25 #1569, 0.14 #8721, 0.09 #14085), 02_fz3 (0.25 #1382, 0.14 #8534, 0.09 #13898), 06yykb (0.25 #1387, 0.14 #8539, 0.09 #13903), 01chpn (0.25 #1111, 0.14 #8263, 0.09 #13627) >> Best rule #1303 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01xllf; >> query: (?x11220, 01qb559) <- special_performance_type(?x11220, ?x4832), film(?x11220, ?x7757), film(?x11220, ?x814), film(?x8796, ?x7757), ?x814 = 084qpk >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4373 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 02zhkz; *> query: (?x11220, 01f8hf) <- place_of_birth(?x11220, ?x11221), film(?x11220, ?x8370), film(?x11220, ?x7482), ?x8370 = 07ghq, film_crew_role(?x7482, ?x137) *> conf = 0.40 ranks of expected_values: 2, 992 EVAL 02d6n_ film 01xlqd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 82.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 02d6n_ film 01f8hf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 134.000 82.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 02d6n_ film 07gp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 134.000 82.000 0.500 http://example.org/film/actor/film./film/performance/film #13711-06t8b PRED entity: 06t8b PRED relation: award PRED expected values: 019f4v 02x4wr9 => 117 concepts (93 used for prediction) PRED predicted values (max 10 best out of 327): 0gs9p (0.85 #3605, 0.82 #2804, 0.80 #2002), 02rdyk7 (0.85 #3605, 0.82 #2804, 0.80 #2002), 02wkmx (0.85 #3605, 0.82 #2804, 0.80 #2002), 027c924 (0.85 #3605, 0.82 #2804, 0.80 #2002), 02w_6xj (0.85 #3605, 0.82 #2804, 0.80 #2002), 09d28z (0.85 #3605, 0.82 #2804, 0.80 #2002), 0gr0m (0.78 #2474, 0.77 #1672, 0.76 #4476), 019f4v (0.54 #8471, 0.49 #11271, 0.44 #12873), 040njc (0.51 #8413, 0.43 #11213, 0.40 #12015), 0k611 (0.39 #2896, 0.16 #17615, 0.14 #30830) >> Best rule #3605 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 06g60w; 03cx282; 03ctv8m; 087yty; 09cdxn; 026v_78; 027t8fw; 0cqh57; 069_0y; 03_fk9; ... >> query: (?x7903, ?x289) <- award(?x7903, ?x68), cinematography(?x633, ?x7903), award_winner(?x289, ?x7903), film_release_region(?x633, ?x87) >> conf = 0.85 => this is the best rule for 6 predicted values *> Best rule #8471 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 144 *> proper extension: 025y9fn; 0htcn; *> query: (?x7903, 019f4v) <- award(?x7903, ?x68), film(?x7903, ?x1820), honored_for(?x1819, ?x1820) *> conf = 0.54 ranks of expected_values: 8, 19 EVAL 06t8b award 02x4wr9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 117.000 93.000 0.851 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 06t8b award 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 117.000 93.000 0.851 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13710-07gxw PRED entity: 07gxw PRED relation: artists PRED expected values: 03bxwtd 01w9wwg 048xh 016lmg => 66 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1033): 011z3g (0.62 #10243, 0.60 #7028, 0.55 #14531), 03f5spx (0.62 #9700, 0.60 #6485, 0.44 #11843), 0gdh5 (0.62 #9862, 0.60 #6647, 0.44 #12005), 01vxlbm (0.62 #9981, 0.60 #6766, 0.33 #12124), 01vs_v8 (0.62 #9807, 0.40 #6592, 0.34 #7501), 0bdxs5 (0.62 #10436, 0.40 #7221, 0.33 #12579), 0lk90 (0.62 #9711, 0.40 #6496, 0.33 #11854), 03y82t6 (0.62 #10063, 0.40 #6848, 0.33 #12206), 01_ztw (0.62 #10149, 0.40 #6934, 0.33 #12292), 02zmh5 (0.62 #9794, 0.40 #6579, 0.33 #11937) >> Best rule #10243 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 02lnbg; 0ggx5q; >> query: (?x3915, 011z3g) <- artists(?x3915, ?x7331), artists(?x3915, ?x1732), artists(?x3915, ?x475), ?x7331 = 01vtj38, category(?x475, ?x134), ?x1732 = 03t9sp, artists(?x671, ?x475), artists(?x474, ?x475), ?x671 = 064t9, ?x474 = 0m0jc >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #8250 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 011j5x; 01_sz1; 05c6073; *> query: (?x3915, 016lmg) <- parent_genre(?x474, ?x3915), artists(?x3915, ?x8636), artists(?x3915, ?x8131), artists(?x3915, ?x7865), artists(?x14374, ?x8636), ?x14374 = 01g_bs, group(?x227, ?x7865), origin(?x7865, ?x1523), ?x8131 = 02hzz *> conf = 0.60 ranks of expected_values: 35, 71, 316, 779 EVAL 07gxw artists 016lmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 66.000 16.000 0.625 http://example.org/music/genre/artists EVAL 07gxw artists 048xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 16.000 0.625 http://example.org/music/genre/artists EVAL 07gxw artists 01w9wwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 66.000 16.000 0.625 http://example.org/music/genre/artists EVAL 07gxw artists 03bxwtd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 66.000 16.000 0.625 http://example.org/music/genre/artists #13709-027rfxc PRED entity: 027rfxc PRED relation: edited_by! PRED expected values: 0gy4k => 65 concepts (35 used for prediction) PRED predicted values (max 10 best out of 174): 03wy8t (0.25 #164, 0.08 #512, 0.06 #860), 03cp4cn (0.25 #118, 0.08 #466, 0.06 #814), 02fttd (0.25 #88, 0.08 #436, 0.06 #784), 016y_f (0.25 #84, 0.08 #432, 0.06 #780), 0h6r5 (0.25 #74, 0.08 #422, 0.06 #770), 019vhk (0.25 #53, 0.08 #401, 0.06 #749), 05dy7p (0.25 #48, 0.08 #396, 0.06 #744), 0ch26b_ (0.25 #40, 0.08 #388, 0.06 #736), 04vr_f (0.25 #25, 0.08 #373, 0.06 #721), 04x4gw (0.25 #171, 0.06 #867, 0.05 #1041) >> Best rule #164 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 027pdrh; >> query: (?x8469, 03wy8t) <- nationality(?x8469, ?x94), gender(?x8469, ?x514), edited_by(?x951, ?x8469), ?x514 = 02zsn >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027rfxc edited_by! 0gy4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 35.000 0.250 http://example.org/film/film/edited_by #13708-018nnz PRED entity: 018nnz PRED relation: film! PRED expected values: 0725ny => 77 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1376): 01mylz (0.50 #6112, 0.25 #1947, 0.08 #10275), 0p8r1 (0.50 #4750, 0.10 #52627, 0.07 #46381), 013tjc (0.50 #5987, 0.06 #14313, 0.05 #43454), 019vgs (0.50 #4825, 0.06 #13151, 0.04 #42292), 01rs5p (0.50 #5958, 0.06 #14284, 0.04 #43425), 02t1dv (0.33 #8282, 0.33 #4119, 0.17 #18689), 0bxtg (0.33 #4242, 0.06 #41709, 0.06 #18812), 085q5 (0.33 #5885, 0.06 #14211, 0.03 #53762), 07663r (0.33 #6186, 0.06 #14512, 0.02 #43653), 0169dl (0.28 #12893, 0.23 #44116, 0.19 #10812) >> Best rule #6112 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0dyb1; 08s6mr; 01xdxy; >> query: (?x1807, 01mylz) <- film(?x4376, ?x1807), genre(?x1807, ?x2540), language(?x1807, ?x254), award_nominee(?x6693, ?x4376), nominated_for(?x1807, ?x1808), ?x2540 = 0hcr >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1446 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0crfwmx; 05sw5b; *> query: (?x1807, 0725ny) <- film(?x4376, ?x1807), film(?x2818, ?x1807), ?x4376 = 062hgx, genre(?x1807, ?x53), award_nominee(?x2818, ?x6444), award_nominee(?x6444, ?x368), nationality(?x6444, ?x94) *> conf = 0.25 ranks of expected_values: 12 EVAL 018nnz film! 0725ny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 77.000 37.000 0.500 http://example.org/film/actor/film./film/performance/film #13707-02h8p8 PRED entity: 02h8p8 PRED relation: sport PRED expected values: 018jz => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 9): 018jz (0.80 #187, 0.79 #178, 0.79 #159), 02vx4 (0.71 #264, 0.68 #274, 0.67 #362), 0jm_ (0.34 #246, 0.20 #295, 0.19 #305), 018w8 (0.32 #257, 0.32 #237, 0.25 #195), 039yzs (0.26 #88, 0.22 #79, 0.16 #170), 03tmr (0.24 #303, 0.23 #293, 0.12 #332), 09xp_ (0.12 #332, 0.10 #233, 0.10 #418), 06f3l (0.12 #332, 0.10 #233, 0.10 #418), 0z74 (0.10 #233, 0.10 #418, 0.10 #408) >> Best rule #187 for best value: >> intensional similarity = 34 >> extensional distance = 33 >> proper extension: 03m1n; >> query: (?x12925, 018jz) <- team(?x4244, ?x12925), position(?x12925, ?x8520), position(?x12925, ?x2010), team(?x8520, ?x13260), team(?x8520, ?x11673), team(?x8520, ?x11361), team(?x8520, ?x10279), team(?x8520, ?x8894), team(?x8520, ?x7357), team(?x8520, ?x6823), team(?x8520, ?x6074), team(?x8520, ?x2174), team(?x8520, ?x2011), team(?x8520, ?x1823), team(?x8520, ?x1160), team(?x8520, ?x662), position(?x7725, ?x8520), position(?x7499, ?x8520), ?x1160 = 049n7, ?x7357 = 04mjl, ?x7725 = 07l8x, ?x1823 = 01yhm, ?x13260 = 03qrh9, ?x2174 = 051vz, ?x6823 = 07l8f, ?x2010 = 02lyr4, ?x7499 = 0132_h, ?x2011 = 04913k, ?x662 = 03lpp_, ?x11673 = 02gtm4, ?x11361 = 03m1n, ?x6074 = 02__x, ?x8894 = 02d02, ?x10279 = 04wmvz >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02h8p8 sport 018jz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.800 http://example.org/sports/sports_team/sport #13706-0gfsq9 PRED entity: 0gfsq9 PRED relation: film_crew_role PRED expected values: 09vw2b7 0dxtw => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 28): 09vw2b7 (0.70 #428, 0.67 #216, 0.66 #1176), 0dxtw (0.40 #1180, 0.38 #432, 0.38 #857), 01pvkk (0.29 #81, 0.28 #858, 0.28 #1000), 02ynfr (0.25 #50, 0.22 #1772, 0.19 #225), 04pyp5 (0.25 #51, 0.07 #226, 0.07 #403), 0215hd (0.22 #1772, 0.15 #440, 0.14 #829), 0d2b38 (0.22 #1772, 0.15 #447, 0.14 #235), 089g0h (0.22 #1772, 0.15 #229, 0.13 #441), 01xy5l_ (0.22 #1772, 0.14 #223, 0.13 #435), 02_n3z (0.22 #1772, 0.13 #211, 0.10 #812) >> Best rule #428 for best value: >> intensional similarity = 4 >> extensional distance = 205 >> proper extension: 01gglm; >> query: (?x2772, 09vw2b7) <- nominated_for(?x521, ?x2772), film_format(?x2772, ?x909), film_crew_role(?x2772, ?x137), currency(?x2772, ?x170) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0gfsq9 film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.700 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0gfsq9 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.700 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13705-0dgst_d PRED entity: 0dgst_d PRED relation: nominated_for! PRED expected values: 0gqy2 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 213): 099c8n (0.47 #515, 0.38 #977, 0.28 #284), 0gq9h (0.44 #520, 0.41 #3061, 0.38 #4910), 040njc (0.37 #467, 0.30 #929, 0.27 #3008), 04dn09n (0.37 #494, 0.30 #3035, 0.28 #3728), 0gs9p (0.37 #3063, 0.35 #522, 0.32 #3756), 019f4v (0.35 #512, 0.33 #4902, 0.32 #3053), 0gr4k (0.33 #3027, 0.29 #3720, 0.22 #948), 02y_rq5 (0.33 #531, 0.32 #3072, 0.29 #993), 0k611 (0.30 #530, 0.29 #4920, 0.26 #3071), 02qyntr (0.30 #635, 0.21 #5025, 0.21 #1097) >> Best rule #515 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 09tqkv2; 01242_; 0194zl; 02nczh; 04lhc4; 04gp58p; 0cvkv5; 04xg2f; 0c0zq; 04q827; >> query: (?x1263, 099c8n) <- nominated_for(?x618, ?x1263), ?x618 = 09qwmm, honored_for(?x2515, ?x1263), genre(?x1263, ?x3312) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #3119 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 186 *> proper extension: 011yxg; 0pv2t; 04mzf8; 0416y94; 0sxfd; 02rqwhl; 070fnm; 09p7fh; 06g77c; 083skw; ... *> query: (?x1263, 0gqy2) <- nominated_for(?x618, ?x1263), nominated_for(?x618, ?x5688), ?x5688 = 0dr89x, award(?x396, ?x618) *> conf = 0.25 ranks of expected_values: 21 EVAL 0dgst_d nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 58.000 58.000 0.465 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13704-02txdf PRED entity: 02txdf PRED relation: fraternities_and_sororities PRED expected values: 0325pb => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 0325pb (0.58 #15, 0.48 #3, 0.31 #25), 04m8fy (0.04 #16, 0.04 #22, 0.03 #44) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 02hp6p; >> query: (?x8789, 0325pb) <- contains(?x94, ?x8789), colors(?x8789, ?x332), fraternities_and_sororities(?x8789, ?x4348) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02txdf fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.584 http://example.org/education/university/fraternities_and_sororities #13703-07jdr PRED entity: 07jdr PRED relation: mode_of_transportation! PRED expected values: 01_d4 0dclg 03hrz 06y57 08966 06c62 02frhbc 05l64 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 132): 0978r (0.60 #153, 0.40 #127, 0.33 #31), 03hrz (0.53 #171, 0.40 #150, 0.40 #124), 05l64 (0.53 #171, 0.40 #165, 0.40 #139), 08966 (0.53 #171, 0.40 #157, 0.40 #131), 06y57 (0.53 #171, 0.40 #156, 0.40 #130), 0cv3w (0.53 #171, 0.40 #151, 0.40 #125), 01_d4 (0.53 #171, 0.40 #148, 0.40 #122), 03czqs (0.53 #171, 0.40 #164, 0.33 #18), 0dclg (0.53 #171, 0.33 #27, 0.20 #149), 06c62 (0.53 #171, 0.33 #12, 0.20 #158) >> Best rule #153 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 06d_3; >> query: (?x4272, 0978r) <- mode_of_transportation(?x2316, ?x4272), mode_of_transportation(?x1658, ?x4272), mode_of_transportation(?x1458, ?x4272), mode_of_transportation(?x206, ?x4272), month(?x1458, ?x7298), ?x7298 = 04wzr, category(?x206, ?x134), location(?x11928, ?x1658), profession(?x11928, ?x353), place_of_birth(?x877, ?x1658), ?x134 = 08mbj5d, teams(?x2316, ?x10636) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 3 *> proper extension: 06d_3; *> query: (?x4272, ?x1860) <- mode_of_transportation(?x2316, ?x4272), mode_of_transportation(?x1658, ?x4272), mode_of_transportation(?x1458, ?x4272), mode_of_transportation(?x206, ?x4272), month(?x1458, ?x7298), month(?x1458, ?x3270), ?x7298 = 04wzr, category(?x206, ?x134), location(?x11928, ?x1658), month(?x1860, ?x3270), profession(?x11928, ?x353), place_of_birth(?x877, ?x1658), ?x134 = 08mbj5d, teams(?x2316, ?x10636) *> conf = 0.53 ranks of expected_values: 2, 3, 4, 5, 7, 9, 10, 12 EVAL 07jdr mode_of_transportation! 05l64 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 07jdr mode_of_transportation! 02frhbc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 67.000 67.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 07jdr mode_of_transportation! 06c62 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 67.000 67.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 07jdr mode_of_transportation! 08966 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 07jdr mode_of_transportation! 06y57 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 07jdr mode_of_transportation! 03hrz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 07jdr mode_of_transportation! 0dclg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 67.000 67.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 07jdr mode_of_transportation! 01_d4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 67.000 67.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #13702-0178rl PRED entity: 0178rl PRED relation: award PRED expected values: 025m8l => 92 concepts (71 used for prediction) PRED predicted values (max 10 best out of 311): 0gqz2 (0.77 #8320, 0.77 #8319, 0.77 #23792), 01by1l (0.42 #507, 0.30 #5260, 0.27 #8033), 025m8l (0.42 #514, 0.19 #3961, 0.18 #21409), 09sb52 (0.34 #3209, 0.33 #8361, 0.27 #11531), 01bgqh (0.31 #439, 0.24 #5192, 0.23 #7965), 099vwn (0.29 #606, 0.06 #3774, 0.06 #6548), 0gr51 (0.26 #892, 0.19 #2080, 0.14 #2872), 03qbh5 (0.25 #596, 0.17 #4953, 0.16 #5745), 01ck6h (0.23 #517, 0.16 #15852, 0.15 #21806), 0gr4k (0.23 #825, 0.19 #2013, 0.14 #2805) >> Best rule #8320 for best value: >> intensional similarity = 2 >> extensional distance = 591 >> proper extension: 06lxn; >> query: (?x5223, ?x2874) <- artists(?x10332, ?x5223), award_winner(?x2874, ?x5223) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #514 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 081wh1; *> query: (?x5223, 025m8l) <- award(?x5223, ?x4481), ?x4481 = 02x17c2, category(?x5223, ?x134) *> conf = 0.42 ranks of expected_values: 3 EVAL 0178rl award 025m8l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 71.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13701-03ln8b PRED entity: 03ln8b PRED relation: actor PRED expected values: 02lg3y => 78 concepts (69 used for prediction) PRED predicted values (max 10 best out of 844): 05lb87 (0.40 #918, 0.39 #7344, 0.37 #16526), 05lb30 (0.40 #918, 0.39 #7344, 0.37 #16526), 01wb8bs (0.40 #918, 0.39 #7344, 0.37 #16526), 0308kx (0.40 #918, 0.39 #7344, 0.37 #16526), 038g2x (0.40 #918, 0.39 #7344, 0.37 #16526), 035gjq (0.40 #918, 0.39 #7344, 0.37 #16526), 058ncz (0.40 #918, 0.39 #7344, 0.37 #16526), 0pyg6 (0.40 #918, 0.39 #7344, 0.37 #16526), 0gsg7 (0.40 #918, 0.39 #7344, 0.37 #16526), 04pg29 (0.40 #918, 0.39 #7344, 0.37 #16526) >> Best rule #918 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 07gbf; >> query: (?x2078, ?x515) <- actor(?x2078, ?x7842), ?x7842 = 048hf, nominated_for(?x515, ?x2078), genre(?x2078, ?x53) >> conf = 0.40 => this is the best rule for 17 predicted values *> Best rule #3097 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 71 *> proper extension: 06mr2s; *> query: (?x2078, 02lg3y) <- actor(?x2078, ?x7842), producer_type(?x2078, ?x632), award_nominee(?x7842, ?x1094), honored_for(?x1112, ?x2078) *> conf = 0.01 ranks of expected_values: 358 EVAL 03ln8b actor 02lg3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 78.000 69.000 0.400 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #13700-07jbh PRED entity: 07jbh PRED relation: country PRED expected values: 01ls2 01pj7 06mkj 01nln 05b7q => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 438): 02vzc (0.88 #1631, 0.81 #594, 0.79 #593), 0h3y (0.88 #1631, 0.81 #594, 0.77 #591), 02wt0 (0.88 #1631, 0.77 #591, 0.76 #2229), 015qh (0.81 #594, 0.79 #593, 0.78 #2100), 06mkj (0.81 #594, 0.79 #593, 0.77 #591), 0ctw_b (0.81 #594, 0.79 #593, 0.77 #591), 05sb1 (0.81 #594, 0.79 #593, 0.77 #591), 0hzlz (0.81 #594, 0.79 #593, 0.77 #591), 01pj7 (0.81 #594, 0.79 #593, 0.77 #591), 03spz (0.81 #594, 0.79 #593, 0.77 #591) >> Best rule #1631 for best value: >> intensional similarity = 50 >> extensional distance = 3 >> proper extension: 06f41; >> query: (?x4673, ?x2290) <- country(?x4673, ?x5147), country(?x4673, ?x2291), country(?x4673, ?x2146), country(?x4673, ?x1355), country(?x4673, ?x1273), country(?x4673, ?x1264), country(?x4673, ?x789), olympics(?x4673, ?x3729), ?x789 = 0f8l9c, ?x2146 = 03rk0, country(?x3309, ?x1273), currency(?x1273, ?x170), countries_spoken_in(?x5359, ?x1273), adjoins(?x2290, ?x2291), film_release_region(?x10404, ?x1355), film_release_region(?x7554, ?x1355), film_release_region(?x7538, ?x1355), film_release_region(?x7114, ?x1355), film_release_region(?x1470, ?x1355), film_release_region(?x785, ?x1355), film_release_region(?x650, ?x1355), countries_spoken_in(?x732, ?x1355), nationality(?x681, ?x1355), ?x650 = 026p_bs, ?x10404 = 01s9vc, ?x785 = 03hjv97, olympics(?x1355, ?x1277), country(?x5989, ?x1355), country(?x2631, ?x1355), ?x3729 = 0jdk_, ?x5147 = 0d04z6, official_language(?x2290, ?x254), jurisdiction_of_office(?x346, ?x1355), administrative_parent(?x863, ?x1355), ?x1277 = 0swbd, adjoins(?x291, ?x1273), countries_within(?x2467, ?x1273), ?x5989 = 019tzd, ?x1264 = 0345h, organization(?x2291, ?x127), ?x7114 = 06rzwx, ?x2631 = 01z27, second_level_divisions(?x1355, ?x5368), ?x3309 = 09w1n, vacationer(?x2290, ?x6187), contains(?x2290, ?x13607), administrative_parent(?x2290, ?x551), ?x1470 = 03twd6, ?x7538 = 035zr0, ?x7554 = 01mgw >> conf = 0.88 => this is the best rule for 3 predicted values *> Best rule #594 for first EXPECTED value: *> intensional similarity = 59 *> extensional distance = 1 *> proper extension: 06z6r; *> query: (?x4673, ?x126) <- country(?x4673, ?x11052), country(?x4673, ?x8593), country(?x4673, ?x8558), country(?x4673, ?x7413), country(?x4673, ?x5274), country(?x4673, ?x5147), country(?x4673, ?x2316), country(?x4673, ?x2146), country(?x4673, ?x1917), country(?x4673, ?x1536), country(?x4673, ?x1273), country(?x4673, ?x1241), country(?x4673, ?x1229), country(?x4673, ?x789), country(?x4673, ?x756), country(?x4673, ?x583), country(?x4673, ?x512), country(?x4673, ?x404), country(?x4673, ?x94), olympics(?x4673, ?x1931), olympics(?x4673, ?x778), ?x789 = 0f8l9c, ?x2146 = 03rk0, ?x1273 = 04wgh, ?x11052 = 04ty8, ?x8558 = 027jk, ?x7413 = 04hqz, ?x1229 = 059j2, ?x5274 = 04g61, olympics(?x4302, ?x778), olympics(?x126, ?x778), ?x404 = 047lj, sports(?x778, ?x7687), sports(?x778, ?x6150), sports(?x778, ?x3659), sports(?x778, ?x3015), ?x512 = 07ssc, ?x756 = 06npd, ?x3015 = 071t0, ?x1536 = 06c1y, ?x1917 = 01p1v, olympics(?x4737, ?x778), ?x583 = 015fr, ?x3659 = 0dwxr, ?x1241 = 05cgv, ?x7687 = 03krj, ?x6150 = 07_53, adjustment_currency(?x4302, ?x170), official_language(?x4302, ?x5359), film_release_region(?x559, ?x4302), ?x2316 = 06t2t, ?x8593 = 01crd5, organization(?x4302, ?x127), medal(?x4302, ?x422), ?x94 = 09c7w0, ?x1931 = 0kbws, contains(?x4302, ?x13593), combatants(?x7419, ?x4302), ?x5147 = 0d04z6 *> conf = 0.81 ranks of expected_values: 5, 9, 14, 25, 53 EVAL 07jbh country 05b7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 28.000 28.000 0.881 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07jbh country 01nln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 28.000 28.000 0.881 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07jbh country 06mkj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 28.000 28.000 0.881 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07jbh country 01pj7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 28.000 28.000 0.881 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07jbh country 01ls2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 28.000 28.000 0.881 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #13699-0hjy PRED entity: 0hjy PRED relation: jurisdiction_of_office! PRED expected values: 02079p => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 22): 09n5b9 (0.86 #475, 0.85 #137, 0.81 #748), 060c4 (0.55 #2316, 0.52 #2190, 0.52 #2295), 060bp (0.48 #2314, 0.47 #2188, 0.46 #2293), 0pqc5 (0.36 #3157, 0.30 #2632, 0.30 #216), 01t7n9 (0.33 #59, 0.33 #17, 0.23 #102), 0dq3c (0.33 #2, 0.31 #65, 0.10 #192), 0789n (0.33 #8, 0.25 #50, 0.19 #135), 02079p (0.33 #9, 0.15 #72, 0.08 #789), 01gkgk (0.33 #5, 0.11 #26, 0.09 #554), 0fkzq (0.26 #795, 0.26 #142, 0.24 #480) >> Best rule #475 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0g0syc; >> query: (?x953, 09n5b9) <- district_represented(?x3540, ?x953), district_represented(?x605, ?x953), ?x605 = 077g7n, ?x3540 = 024tcq >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 09c7w0; *> query: (?x953, 02079p) <- contains(?x953, ?x13425), contains(?x94, ?x953), ?x13425 = 0l_n1 *> conf = 0.33 ranks of expected_values: 8 EVAL 0hjy jurisdiction_of_office! 02079p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 188.000 188.000 0.857 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #13698-0c9c0 PRED entity: 0c9c0 PRED relation: profession PRED expected values: 01d_h8 => 131 concepts (130 used for prediction) PRED predicted values (max 10 best out of 79): 01d_h8 (0.84 #6201, 0.77 #3609, 0.67 #4329), 02jknp (0.59 #4762, 0.55 #5338, 0.55 #5770), 02krf9 (0.45 #1873, 0.41 #1462, 0.39 #1606), 0kyk (0.45 #1873, 0.33 #6076, 0.31 #2042), 09jwl (0.45 #1873, 0.30 #11956, 0.24 #2176), 0nbcg (0.45 #1873, 0.30 #11956, 0.17 #2188), 0d1pc (0.45 #1873, 0.30 #11956, 0.16 #3793), 01p5_g (0.45 #1873, 0.04 #1814, 0.02 #2536), 0n1h (0.30 #11956, 0.10 #4910, 0.08 #6926), 016z4k (0.22 #148, 0.13 #6919, 0.12 #7495) >> Best rule #6201 for best value: >> intensional similarity = 2 >> extensional distance = 385 >> proper extension: 024c1b; >> query: (?x2790, 01d_h8) <- produced_by(?x7311, ?x2790), genre(?x7311, ?x258) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c9c0 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 130.000 0.837 http://example.org/people/person/profession #13697-01vsl3_ PRED entity: 01vsl3_ PRED relation: influenced_by PRED expected values: 041mt => 129 concepts (65 used for prediction) PRED predicted values (max 10 best out of 323): 01nz1q6 (0.22 #5654, 0.07 #2609, 0.04 #1739), 07n39 (0.22 #5654, 0.07 #2609, 0.03 #870), 012vd6 (0.21 #1472, 0.09 #6691, 0.05 #168), 03sbs (0.15 #5441, 0.07 #15003, 0.06 #9351), 02wh0 (0.12 #5601, 0.11 #9511, 0.06 #24300), 032l1 (0.11 #9218, 0.11 #24353, 0.09 #5308), 03_87 (0.11 #9331, 0.11 #24353, 0.08 #5421), 081k8 (0.11 #24353, 0.11 #9285, 0.10 #14937), 08433 (0.11 #24353, 0.09 #6544, 0.07 #2195), 037jz (0.11 #24353, 0.08 #5429, 0.07 #9339) >> Best rule #5654 for best value: >> intensional similarity = 2 >> extensional distance = 86 >> proper extension: 0chnf; 0fpzzp; >> query: (?x2799, ?x9826) <- peers(?x2799, ?x9826), influenced_by(?x2799, ?x2845) >> conf = 0.22 => this is the best rule for 2 predicted values *> Best rule #24353 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 464 *> proper extension: 02m4t; 01d5g; 0716b6; 055yr; *> query: (?x2799, ?x5434) <- influenced_by(?x2799, ?x8383), influenced_by(?x8383, ?x5434) *> conf = 0.11 ranks of expected_values: 12 EVAL 01vsl3_ influenced_by 041mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 129.000 65.000 0.220 http://example.org/influence/influence_node/influenced_by #13696-02tf1y PRED entity: 02tf1y PRED relation: sibling! PRED expected values: 02_l96 => 107 concepts (62 used for prediction) PRED predicted values (max 10 best out of 103): 030g9z (0.83 #2217, 0.83 #2216, 0.81 #2333), 02_l96 (0.83 #2217, 0.83 #2216, 0.81 #2333), 02mc79 (0.83 #2217, 0.83 #2216, 0.81 #2333), 0gbwp (0.14 #36, 0.10 #502, 0.10 #153), 01zmpg (0.14 #17, 0.10 #134, 0.06 #716), 013v5j (0.14 #18, 0.10 #135, 0.05 #366), 0p_r5 (0.14 #112, 0.10 #229, 0.05 #578), 01kvqc (0.14 #13, 0.10 #130, 0.03 #712), 01gw4f (0.14 #45, 0.10 #162, 0.02 #1093), 02tf1y (0.11 #423, 0.07 #657, 0.06 #774) >> Best rule #2217 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 0dv1hh; 09m465; >> query: (?x8897, ?x8071) <- sibling(?x8897, ?x8071), gender(?x8897, ?x231), nationality(?x8071, ?x94) >> conf = 0.83 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02tf1y sibling! 02_l96 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 62.000 0.832 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #13695-0cy__l PRED entity: 0cy__l PRED relation: film_format PRED expected values: 0cj16 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 4): 0cj16 (0.19 #59, 0.17 #3, 0.14 #54), 07fb8_ (0.16 #88, 0.16 #145, 0.16 #37), 01dc60 (0.07 #5, 0.02 #10, 0.02 #31), 017fx5 (0.05 #55, 0.05 #60, 0.04 #65) >> Best rule #59 for best value: >> intensional similarity = 3 >> extensional distance = 235 >> proper extension: 0bmc4cm; >> query: (?x5509, 0cj16) <- nominated_for(?x601, ?x5509), film_release_region(?x5509, ?x1892), ?x1892 = 02vzc >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cy__l film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.186 http://example.org/film/film/film_format #13694-0ftqr PRED entity: 0ftqr PRED relation: instrumentalists! PRED expected values: 01vdm0 => 117 concepts (73 used for prediction) PRED predicted values (max 10 best out of 125): 0342h (0.71 #1199, 0.70 #942, 0.68 #2484), 05148p4 (0.68 #360, 0.43 #105, 0.42 #701), 018vs (0.60 #12, 0.43 #97, 0.41 #352), 01xqw (0.40 #66, 0.29 #151, 0.12 #491), 0l14qv (0.32 #345, 0.29 #90, 0.19 #430), 02hnl (0.32 #373, 0.21 #1228, 0.20 #1315), 03qjg (0.27 #389, 0.21 #987, 0.20 #49), 04rzd (0.27 #376, 0.20 #36, 0.14 #121), 03gvt (0.27 #403, 0.11 #1368, 0.09 #2567), 0l14_3 (0.21 #852, 0.04 #1110, 0.03 #4196) >> Best rule #1199 for best value: >> intensional similarity = 5 >> extensional distance = 192 >> proper extension: 01wdqrx; >> query: (?x10039, 0342h) <- artists(?x1572, ?x10039), category(?x10039, ?x134), ?x1572 = 06by7, instrumentalists(?x1495, ?x10039), role(?x227, ?x1495) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #452 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 02rgz4; 02ck1; 082db; 0kn3g; 02r38; *> query: (?x10039, 01vdm0) <- artists(?x9645, ?x10039), parent_genre(?x9645, ?x597), instrumentalists(?x75, ?x10039), ?x75 = 07y_7 *> conf = 0.06 ranks of expected_values: 56 EVAL 0ftqr instrumentalists! 01vdm0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 117.000 73.000 0.706 http://example.org/music/instrument/instrumentalists #13693-047d21r PRED entity: 047d21r PRED relation: executive_produced_by PRED expected values: 06q8hf => 89 concepts (49 used for prediction) PRED predicted values (max 10 best out of 104): 06q8hf (0.12 #666, 0.06 #416, 0.05 #917), 02pq9yv (0.08 #85, 0.01 #585, 0.01 #836), 05zrx3v (0.08 #206, 0.01 #706, 0.01 #1208), 059x0w (0.08 #202), 06t8b (0.06 #423, 0.03 #924, 0.01 #673), 02z6l5f (0.04 #1370, 0.04 #1621, 0.02 #4879), 02q42j_ (0.04 #887, 0.02 #4898, 0.01 #2892), 0b13g7 (0.04 #837, 0.01 #4848, 0.01 #1590), 0glyyw (0.03 #2694, 0.03 #1692, 0.03 #2944), 079vf (0.03 #1255, 0.03 #1506, 0.02 #3009) >> Best rule #666 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 02d44q; >> query: (?x3743, 06q8hf) <- film_crew_role(?x3743, ?x137), nominated_for(?x1307, ?x3743), nominated_for(?x704, ?x3743), ?x704 = 09sb52, award(?x71, ?x1307) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047d21r executive_produced_by 06q8hf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 49.000 0.119 http://example.org/film/film/executive_produced_by #13692-0133_p PRED entity: 0133_p PRED relation: parent_genre PRED expected values: 06by7 => 69 concepts (48 used for prediction) PRED predicted values (max 10 best out of 288): 06by7 (0.88 #3176, 0.79 #3333, 0.76 #2540), 02x8m (0.52 #1908, 0.29 #1435, 0.29 #331), 064t9 (0.42 #1116, 0.40 #644, 0.33 #1590), 016clz (0.33 #479, 0.15 #5065, 0.14 #4908), 0glt670 (0.32 #1922, 0.14 #1449, 0.14 #1291), 03lty (0.30 #3652, 0.18 #5563, 0.15 #6679), 05bt6j (0.30 #662, 0.17 #1134, 0.13 #3977), 026z9 (0.29 #366, 0.25 #1154, 0.24 #1943), 05r6t (0.27 #3685, 0.25 #2576, 0.24 #2893), 03_d0 (0.25 #956, 0.17 #1588, 0.16 #1903) >> Best rule #3176 for best value: >> intensional similarity = 9 >> extensional distance = 65 >> proper extension: 028cl7; 017ht; >> query: (?x9935, 06by7) <- parent_genre(?x9935, ?x7329), artists(?x7329, ?x8497), artists(?x7329, ?x1992), artists(?x7329, ?x997), artists(?x7329, ?x483), ?x997 = 07qnf, ?x483 = 0m2l9, ?x1992 = 01wz3cx, ?x8497 = 01l_w0 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0133_p parent_genre 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 48.000 0.881 http://example.org/music/genre/parent_genre #13691-03cprft PRED entity: 03cprft PRED relation: people! PRED expected values: 0gg4h => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 31): 0gk4g (0.25 #10, 0.22 #76, 0.16 #538), 0gg4h (0.12 #36, 0.11 #102, 0.03 #366), 01l2m3 (0.11 #82, 0.06 #16, 0.03 #346), 0dq9p (0.09 #545, 0.07 #1403, 0.07 #809), 04p3w (0.08 #539, 0.05 #1067, 0.05 #1265), 0qcr0 (0.07 #529, 0.06 #661, 0.06 #1321), 08g5q7 (0.06 #42, 0.06 #108, 0.02 #372), 0c58k (0.06 #30, 0.06 #96, 0.01 #360), 035482 (0.06 #24, 0.06 #90), 01psyx (0.06 #45, 0.02 #903, 0.02 #1101) >> Best rule #10 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 02vmzp; 0674cw; 0cvbb9q; 02xgdv; 0k0q8q; 087_wh; 03fw4y; 01k6nm; 0cfz_z; 0cct7p; ... >> query: (?x13646, 0gk4g) <- gender(?x13646, ?x231), profession(?x13646, ?x1032), place_of_death(?x13646, ?x7412), nationality(?x13646, ?x2146), ?x7412 = 04vmp >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #36 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 02vmzp; 0674cw; 0cvbb9q; 02xgdv; 0k0q8q; 087_wh; 03fw4y; 01k6nm; 0cfz_z; 0cct7p; ... *> query: (?x13646, 0gg4h) <- gender(?x13646, ?x231), profession(?x13646, ?x1032), place_of_death(?x13646, ?x7412), nationality(?x13646, ?x2146), ?x7412 = 04vmp *> conf = 0.12 ranks of expected_values: 2 EVAL 03cprft people! 0gg4h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.250 http://example.org/people/cause_of_death/people #13690-017gl1 PRED entity: 017gl1 PRED relation: executive_produced_by PRED expected values: 06q8hf => 58 concepts (44 used for prediction) PRED predicted values (max 10 best out of 73): 06q8hf (0.15 #417, 0.09 #3439, 0.05 #2178), 04jspq (0.09 #150, 0.07 #653, 0.03 #1660), 0js9s (0.06 #4534, 0.02 #3525, 0.02 #6307), 02bfxb (0.06 #4534, 0.02 #10095, 0.02 #9085), 079vf (0.05 #2266, 0.04 #3275, 0.02 #2517), 06pj8 (0.05 #3328, 0.04 #1313, 0.03 #810), 0glyyw (0.04 #3461, 0.04 #188, 0.04 #439), 021lby (0.04 #64, 0.04 #315, 0.01 #567), 0343h (0.04 #42, 0.03 #2306, 0.02 #3315), 02xnjd (0.04 #175, 0.02 #2439, 0.02 #2690) >> Best rule #417 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 03cf9ly; >> query: (?x972, 06q8hf) <- nominated_for(?x2728, ?x972), award_winner(?x2728, ?x5661), ?x5661 = 03ym1 >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017gl1 executive_produced_by 06q8hf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 44.000 0.154 http://example.org/film/film/executive_produced_by #13689-01wj9y9 PRED entity: 01wj9y9 PRED relation: place_of_death PRED expected values: 030qb3t => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 63): 030qb3t (0.31 #1574, 0.22 #3903, 0.20 #4873), 02_286 (0.15 #4282, 0.12 #1371, 0.11 #789), 0k049 (0.14 #1555, 0.11 #4854, 0.11 #4272), 06_kh (0.10 #975, 0.09 #1945, 0.08 #3886), 04jpl (0.10 #2141, 0.05 #7579, 0.05 #4276), 0f2wj (0.06 #1952, 0.05 #4281, 0.04 #9720), 05qtj (0.06 #6471, 0.05 #1034, 0.05 #7442), 0ftvg (0.05 #917, 0.04 #1499, 0.02 #2469), 0c_m3 (0.05 #858, 0.04 #1440, 0.02 #2605), 0lhql (0.05 #835, 0.04 #1417, 0.02 #3164) >> Best rule #1574 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 01l3j; >> query: (?x2283, 030qb3t) <- people(?x6260, ?x2283), celebrities_impersonated(?x3649, ?x2283), religion(?x2283, ?x7131) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wj9y9 place_of_death 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.310 http://example.org/people/deceased_person/place_of_death #13688-01n4f8 PRED entity: 01n4f8 PRED relation: religion PRED expected values: 0kq2 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 26): 0kpl (0.33 #273, 0.33 #493, 0.23 #53), 03_gx (0.26 #277, 0.25 #497, 0.21 #1643), 0kq2 (0.11 #281, 0.10 #501, 0.07 #589), 0n2g (0.09 #496, 0.06 #584, 0.05 #1510), 03j6c (0.08 #2227, 0.07 #1917, 0.07 #1340), 019cr (0.07 #10, 0.06 #230, 0.04 #362), 0v53x (0.07 #28, 0.04 #248, 0.03 #292), 05sfs (0.07 #3, 0.03 #223, 0.02 #267), 051kv (0.07 #5, 0.03 #1858, 0.02 #2212), 06nzl (0.07 #14, 0.02 #146, 0.02 #1600) >> Best rule #273 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 040_9; 02w5q6; 067xw; >> query: (?x1725, 0kpl) <- profession(?x1725, ?x2225), ?x2225 = 0kyk, religion(?x1725, ?x1985) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #281 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 127 *> proper extension: 040_9; 02w5q6; 067xw; *> query: (?x1725, 0kq2) <- profession(?x1725, ?x2225), ?x2225 = 0kyk, religion(?x1725, ?x1985) *> conf = 0.11 ranks of expected_values: 3 EVAL 01n4f8 religion 0kq2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 95.000 0.333 http://example.org/people/person/religion #13687-07pd_j PRED entity: 07pd_j PRED relation: nominated_for! PRED expected values: 05zvj3m => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 179): 0gq9h (0.28 #3664, 0.27 #4144, 0.26 #6305), 019f4v (0.25 #3655, 0.24 #4135, 0.23 #2935), 099vwn (0.25 #5522, 0.19 #12723, 0.19 #14644), 09sb52 (0.25 #5522, 0.08 #3155, 0.08 #2915), 0gs9p (0.24 #3666, 0.23 #6307, 0.22 #6547), 02g3v6 (0.22 #501, 0.18 #741, 0.08 #1221), 02hsq3m (0.22 #509, 0.16 #749, 0.15 #989), 0k611 (0.21 #3675, 0.21 #4155, 0.20 #6316), 0gq_v (0.20 #3620, 0.20 #4100, 0.20 #6261), 04dn09n (0.19 #3636, 0.19 #2916, 0.19 #3876) >> Best rule #3664 for best value: >> intensional similarity = 3 >> extensional distance = 665 >> proper extension: 03czz87; >> query: (?x6684, 0gq9h) <- award_winner(?x6684, ?x6066), titles(?x2480, ?x6684), award_winner(?x5459, ?x6066) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #12723 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1419 *> proper extension: 0c5qvw; 0hr41p6; *> query: (?x6684, ?x1336) <- genre(?x6684, ?x1403), nominated_for(?x1460, ?x6684), award(?x1460, ?x1336) *> conf = 0.19 ranks of expected_values: 11 EVAL 07pd_j nominated_for! 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 72.000 72.000 0.279 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13686-04ly1 PRED entity: 04ly1 PRED relation: district_represented! PRED expected values: 01gtcc 01gtc0 01gtcq 01gsvp 01gst9 01gsvb => 208 concepts (208 used for prediction) PRED predicted values (max 10 best out of 40): 01gtc0 (0.84 #300, 0.83 #260, 0.44 #540), 01gsvb (0.84 #312, 0.83 #272, 0.42 #552), 01gsvp (0.80 #305, 0.79 #265, 0.40 #545), 01gtcc (0.76 #292, 0.75 #252, 0.42 #532), 01gtcq (0.76 #301, 0.75 #261, 0.42 #541), 01gst9 (0.72 #307, 0.71 #267, 0.38 #747), 01gsrl (0.71 #255, 0.68 #295, 0.35 #735), 01grpc (0.67 #258, 0.64 #298, 0.33 #738), 01grr2 (0.67 #269, 0.64 #309, 0.33 #749), 01grq1 (0.62 #277, 0.60 #317, 0.31 #757) >> Best rule #300 for best value: >> intensional similarity = 2 >> extensional distance = 23 >> proper extension: 03v1s; >> query: (?x3908, 01gtc0) <- district_represented(?x10803, ?x3908), ?x10803 = 01gt99 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6 EVAL 04ly1 district_represented! 01gsvb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 208.000 208.000 0.840 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04ly1 district_represented! 01gst9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 208.000 208.000 0.840 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04ly1 district_represented! 01gsvp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 208.000 208.000 0.840 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04ly1 district_represented! 01gtcq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 208.000 208.000 0.840 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04ly1 district_represented! 01gtc0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 208.000 208.000 0.840 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04ly1 district_represented! 01gtcc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 208.000 208.000 0.840 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #13685-0gv07g PRED entity: 0gv07g PRED relation: student! PRED expected values: 017z88 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 127): 01jq34 (0.15 #57, 0.05 #583, 0.05 #1109), 017z88 (0.11 #5342, 0.10 #9551, 0.10 #4816), 03ksy (0.10 #632, 0.08 #106, 0.07 #15362), 01mpwj (0.10 #633, 0.08 #107, 0.05 #1159), 08815 (0.10 #528, 0.05 #1054, 0.04 #15258), 09f2j (0.09 #3315, 0.07 #3841, 0.06 #6998), 0bwfn (0.08 #20792, 0.08 #35525, 0.08 #274), 06pwq (0.08 #12, 0.05 #538, 0.05 #1064), 0gdm1 (0.08 #230, 0.05 #756, 0.05 #1282), 01_r9k (0.08 #379, 0.05 #905, 0.05 #1431) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 05gp3x; 02vqpx8; >> query: (?x7205, 01jq34) <- award_winner(?x437, ?x7205), place_of_birth(?x7205, ?x108), ?x108 = 0rh6k, profession(?x7205, ?x1614) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #5342 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 04ls53; 02z81h; 019x62; 012201; 01njxvw; *> query: (?x7205, 017z88) <- music(?x437, ?x7205), film_release_region(?x437, ?x94), award_winner(?x2886, ?x7205), titles(?x2480, ?x437) *> conf = 0.11 ranks of expected_values: 2 EVAL 0gv07g student! 017z88 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 142.000 142.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #13684-04sylm PRED entity: 04sylm PRED relation: institution! PRED expected values: 016t_3 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 22): 014mlp (0.68 #261, 0.67 #1393, 0.66 #561), 02h4rq6 (0.64 #973, 0.62 #1390, 0.61 #1228), 016t_3 (0.50 #166, 0.46 #375, 0.43 #813), 02_xgp2 (0.43 #174, 0.43 #1422, 0.42 #1560), 03bwzr4 (0.43 #176, 0.40 #1054, 0.38 #984), 013zdg (0.37 #494, 0.27 #170, 0.26 #124), 04zx3q1 (0.34 #164, 0.33 #25, 0.29 #373), 07s6fsf (0.34 #579, 0.32 #487, 0.30 #279), 028dcg (0.33 #19, 0.21 #88, 0.19 #597), 027f2w (0.30 #171, 0.26 #495, 0.22 #380) >> Best rule #261 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 02zcz3; 019_6d; >> query: (?x2767, 014mlp) <- citytown(?x2767, ?x739), country(?x2767, ?x94), ?x94 = 09c7w0, currency(?x2767, ?x170) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 015wy_; *> query: (?x2767, 016t_3) <- citytown(?x2767, ?x739), student(?x2767, ?x4013), type_of_union(?x4013, ?x566), music(?x3943, ?x4013) *> conf = 0.50 ranks of expected_values: 3 EVAL 04sylm institution! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 142.000 142.000 0.679 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13683-08966 PRED entity: 08966 PRED relation: time_zones PRED expected values: 02llzg => 246 concepts (246 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.38 #757, 0.38 #94, 0.36 #1174), 02llzg (0.38 #108, 0.33 #186, 0.33 #69), 02lcqs (0.33 #122, 0.25 #200, 0.24 #291), 02fqwt (0.33 #131, 0.24 #469, 0.24 #378), 03bdv (0.27 #396, 0.21 #526, 0.21 #539), 02hczc (0.14 #587, 0.11 #132, 0.11 #119), 03plfd (0.12 #114, 0.10 #660, 0.10 #1337), 042g7t (0.11 #141, 0.11 #128, 0.06 #1143), 02lcrv (0.11 #137, 0.11 #124, 0.05 #345), 052vwh (0.09 #155, 0.08 #194, 0.08 #233) >> Best rule #757 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0t015; 0f2nf; 0tzt_; >> query: (?x6458, 02hcv8) <- contains(?x6458, ?x6811), country(?x6458, ?x774), administrative_division(?x6458, ?x7406), student(?x6811, ?x3335) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #108 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 03rjj; 03_3d; 0f8l9c; 02vzc; 05b4w; *> query: (?x6458, 02llzg) <- location_of_ceremony(?x566, ?x6458), administrative_parent(?x6458, ?x7406), film_release_region(?x3392, ?x6458), ?x3392 = 0jwmp *> conf = 0.38 ranks of expected_values: 2 EVAL 08966 time_zones 02llzg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 246.000 246.000 0.383 http://example.org/location/location/time_zones #13682-0f4_l PRED entity: 0f4_l PRED relation: genre PRED expected values: 0219x_ => 83 concepts (75 used for prediction) PRED predicted values (max 10 best out of 107): 05p553 (0.84 #3131, 0.74 #365, 0.45 #125), 07s9rl0 (0.80 #482, 0.77 #963, 0.75 #1323), 03k9fj (0.35 #853, 0.35 #733, 0.31 #612), 02kdv5l (0.31 #845, 0.31 #725, 0.28 #4692), 02l7c8 (0.31 #3141, 0.30 #1217, 0.30 #977), 04xvlr (0.31 #242, 0.23 #1204, 0.22 #1324), 01hmnh (0.31 #739, 0.27 #859, 0.22 #618), 03bxz7 (0.24 #536, 0.12 #7460, 0.12 #1377), 0219x_ (0.23 #146, 0.20 #386, 0.19 #3152), 06nbt (0.21 #385, 0.09 #4930, 0.06 #3151) >> Best rule #3131 for best value: >> intensional similarity = 3 >> extensional distance = 692 >> proper extension: 04svwx; >> query: (?x2177, 05p553) <- genre(?x2177, ?x809), genre(?x6036, ?x809), ?x6036 = 040_lv >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #146 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 03bx2lk; 02_qt; 0435vm; 0bz3jx; 047vp1n; 0cp0t91; 03wy8t; *> query: (?x2177, 0219x_) <- film(?x368, ?x2177), language(?x2177, ?x254), ?x368 = 01wbg84 *> conf = 0.23 ranks of expected_values: 9 EVAL 0f4_l genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 83.000 75.000 0.840 http://example.org/film/film/genre #13681-0fx02 PRED entity: 0fx02 PRED relation: gender PRED expected values: 05zppz => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 5): 05zppz (0.93 #25, 0.92 #77, 0.92 #99), 02zsn (0.46 #240, 0.46 #276, 0.46 #249), 0fltx (0.13 #141), 01hbgs (0.13 #141), 0c58k (0.13 #141) >> Best rule #25 for best value: >> intensional similarity = 6 >> extensional distance = 25 >> proper extension: 01pjr7; >> query: (?x3686, 05zppz) <- story_by(?x2506, ?x3686), story_by(?x836, ?x3686), featured_film_locations(?x836, ?x2552), film(?x2538, ?x836), prequel(?x2506, ?x11120), written_by(?x836, ?x3692) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fx02 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.926 http://example.org/people/person/gender #13680-016cjb PRED entity: 016cjb PRED relation: artists PRED expected values: 01vrncs 015xp4 0147jt => 58 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1213): 0f0y8 (0.75 #12516, 0.67 #9387, 0.21 #18783), 0407f (0.71 #10694, 0.50 #5480, 0.50 #4438), 01kx_81 (0.71 #10510, 0.50 #7380, 0.50 #4254), 020_4z (0.71 #11333, 0.50 #5077, 0.33 #8203), 01wd9lv (0.71 #10979, 0.33 #1595, 0.33 #552), 012x03 (0.71 #11439, 0.33 #2055, 0.33 #1012), 02vwckw (0.67 #9066, 0.57 #12195, 0.50 #5938), 0415mzy (0.67 #8823, 0.57 #11952, 0.33 #482), 01vx5w7 (0.67 #8569, 0.57 #11698, 0.33 #1043), 03f0qd7 (0.67 #9319, 0.57 #12448, 0.25 #6191) >> Best rule #12516 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 01lxd4; >> query: (?x5717, 0f0y8) <- artists(?x5717, ?x11621), artists(?x5717, ?x5718), profession(?x11621, ?x131), award_winner(?x2139, ?x11621), ?x5718 = 024zq, award(?x140, ?x2139) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #10869 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 07sbbz2; 02x8m; 0gywn; *> query: (?x5717, 015xp4) <- parent_genre(?x5355, ?x5717), artists(?x5717, ?x9528), artists(?x5717, ?x8362), artists(?x5717, ?x6651), award(?x9528, ?x4018), ?x8362 = 01wg25j, ?x6651 = 019f9z *> conf = 0.57 ranks of expected_values: 25, 159, 650 EVAL 016cjb artists 0147jt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 21.000 0.750 http://example.org/music/genre/artists EVAL 016cjb artists 015xp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 58.000 21.000 0.750 http://example.org/music/genre/artists EVAL 016cjb artists 01vrncs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 58.000 21.000 0.750 http://example.org/music/genre/artists #13679-0ct9_ PRED entity: 0ct9_ PRED relation: nationality PRED expected values: 0f8l9c => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 67): 09c7w0 (0.77 #8531, 0.76 #3006, 0.76 #8129), 0f8l9c (0.50 #122, 0.47 #1223, 0.44 #4615), 0h3y (0.44 #4615, 0.37 #3407, 0.24 #8833), 0345h (0.42 #531, 0.33 #1032, 0.29 #331), 07ssc (0.29 #315, 0.29 #215, 0.25 #12247), 03rk0 (0.26 #10389, 0.09 #14400, 0.08 #14500), 0h7x (0.25 #12247, 0.18 #4013, 0.18 #2138), 0jgd (0.25 #12247, 0.18 #4013, 0.07 #1003), 03rt9 (0.24 #3710, 0.14 #313, 0.14 #213), 03shp (0.24 #3710, 0.14 #256, 0.03 #9940) >> Best rule #8531 for best value: >> intensional similarity = 6 >> extensional distance = 132 >> proper extension: 01vvydl; 0gbwp; 0c7xjb; >> query: (?x8430, 09c7w0) <- profession(?x8430, ?x353), company(?x8430, ?x5288), profession(?x5660, ?x353), profession(?x523, ?x353), ?x5660 = 02rmxx, ?x523 = 06cv1 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #122 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 045bg; 05wh0sh; 0dzkq; *> query: (?x8430, 0f8l9c) <- influenced_by(?x1737, ?x8430), influenced_by(?x8430, ?x9600), influenced_by(?x8430, ?x7509), influenced_by(?x8430, ?x1236), ?x9600 = 039n1, interests(?x1236, ?x713), ?x7509 = 048cl *> conf = 0.50 ranks of expected_values: 2 EVAL 0ct9_ nationality 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 160.000 0.769 http://example.org/people/person/nationality #13678-0175tv PRED entity: 0175tv PRED relation: current_club! PRED expected values: 02s9vc => 61 concepts (30 used for prediction) PRED predicted values (max 10 best out of 29): 02ltg3 (0.25 #7, 0.12 #38, 0.11 #70), 01l3wr (0.25 #24, 0.12 #55, 0.11 #87), 03ylxn (0.17 #121, 0.13 #245, 0.06 #367), 03yl2t (0.17 #100, 0.13 #224, 0.04 #886), 03_qrp (0.12 #46, 0.11 #78, 0.08 #173), 032jlh (0.11 #90, 0.08 #123, 0.08 #185), 01_lhg (0.11 #71, 0.08 #166, 0.04 #290), 02s9vc (0.10 #395, 0.06 #612, 0.05 #425), 03y_f8 (0.09 #345, 0.08 #161, 0.07 #406), 03z8bw (0.08 #148, 0.08 #303, 0.07 #333) >> Best rule #7 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 0fvly; >> query: (?x8899, 02ltg3) <- colors(?x8899, ?x4557), colors(?x8899, ?x663), team(?x63, ?x8899), ?x663 = 083jv, ?x63 = 02sdk9v, position(?x8899, ?x3299), teams(?x3622, ?x8899), ?x4557 = 019sc, team(?x3299, ?x3298), contains(?x2984, ?x3622), location(?x5600, ?x3622), film_release_region(?x6100, ?x2984) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #395 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 39 *> proper extension: 086x3; *> query: (?x8899, 02s9vc) <- teams(?x3622, ?x8899), origin(?x7868, ?x3622), contains(?x1264, ?x3622), artist(?x2149, ?x7868), artists(?x2249, ?x7868), artists(?x1000, ?x7868), category(?x7868, ?x134), ?x2249 = 03lty, ?x1000 = 0xhtw, place_of_birth(?x8404, ?x3622) *> conf = 0.10 ranks of expected_values: 8 EVAL 0175tv current_club! 02s9vc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 61.000 30.000 0.250 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #13677-0bxs_d PRED entity: 0bxs_d PRED relation: ceremony! PRED expected values: 0bdw6t => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 297): 0bdw6t (0.82 #3529, 0.75 #4023, 0.75 #3037), 0bdwft (0.67 #2268, 0.55 #3499, 0.50 #3007), 02xcb6n (0.67 #2427, 0.55 #3658, 0.50 #3166), 0bdwqv (0.67 #2342, 0.55 #3573, 0.50 #3081), 09qj50 (0.67 #2254, 0.50 #2993, 0.49 #3700), 09v7wsg (0.67 #2393, 0.49 #3700, 0.42 #4118), 0gqy2 (0.59 #6783, 0.51 #4801, 0.48 #5047), 0gq_d (0.57 #6818, 0.51 #4836, 0.48 #5082), 0gqwc (0.57 #6722, 0.51 #4740, 0.48 #4986), 0k611 (0.57 #6735, 0.51 #4753, 0.48 #4999) >> Best rule #3529 for best value: >> intensional similarity = 19 >> extensional distance = 9 >> proper extension: 07y_p6; >> query: (?x8238, 0bdw6t) <- ceremony(?x7510, ?x8238), ceremony(?x5235, ?x8238), ceremony(?x2041, ?x8238), nominated_for(?x7510, ?x9951), nominated_for(?x7510, ?x6726), nominated_for(?x7510, ?x5060), nominated_for(?x7510, ?x4517), award_winner(?x8238, ?x7611), award(?x201, ?x7510), award_nominee(?x2548, ?x7611), ?x2041 = 0bdx29, location(?x7611, ?x1523), ?x5060 = 05f4vxd, award(?x6726, ?x2603), ?x4517 = 01s81, award_winner(?x6726, ?x5105), ?x5235 = 09qrn4, ?x9951 = 023ny6, award_winner(?x2022, ?x7611) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bxs_d ceremony! 0bdw6t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 32.000 0.818 http://example.org/award/award_category/winners./award/award_honor/ceremony #13676-0h53p1 PRED entity: 0h53p1 PRED relation: award_winner! PRED expected values: 0d7hg4 => 96 concepts (50 used for prediction) PRED predicted values (max 10 best out of 547): 09hd6f (0.82 #60964, 0.82 #62568, 0.82 #57754), 01xndd (0.82 #60964, 0.82 #62568, 0.82 #57754), 08q3s0 (0.82 #60964, 0.82 #62568, 0.82 #57754), 0h5jg5 (0.82 #60964, 0.82 #62568, 0.82 #57754), 08xwck (0.53 #51336, 0.53 #52941, 0.52 #41708), 02tn0_ (0.53 #51336, 0.53 #52941, 0.52 #41708), 06chf (0.53 #51336, 0.53 #52941, 0.52 #41708), 07nznf (0.53 #51336, 0.53 #52941, 0.52 #41708), 013pk3 (0.53 #51336, 0.52 #41708, 0.49 #35291), 0cjdk (0.33 #408) >> Best rule #60964 for best value: >> intensional similarity = 3 >> extensional distance = 1335 >> proper extension: 0khth; 04k05; >> query: (?x2802, ?x4035) <- award_winner(?x8337, ?x2802), award_winner(?x2802, ?x4035), award_winner(?x7573, ?x8337) >> conf = 0.82 => this is the best rule for 4 predicted values *> Best rule #59360 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1332 *> proper extension: 01sbf2; 03sww; 09h_q; 05b2f_k; 0hsmh; *> query: (?x2802, ?x2650) <- gender(?x2802, ?x231), award_winner(?x2802, ?x4022), award_nominee(?x2650, ?x4022) *> conf = 0.16 ranks of expected_values: 15 EVAL 0h53p1 award_winner! 0d7hg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 96.000 50.000 0.820 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #13675-0ftlxj PRED entity: 0ftlxj PRED relation: ceremony! PRED expected values: 0gqng 0gvx_ => 36 concepts (34 used for prediction) PRED predicted values (max 10 best out of 324): 0gvx_ (0.89 #4317, 0.88 #4071, 0.88 #3083), 0gqz2 (0.88 #1038, 0.88 #544, 0.87 #298), 0gq9h (0.88 #2516, 0.87 #1775, 0.86 #4244), 0gr4k (0.87 #1972, 0.87 #1744, 0.83 #2979), 0gr51 (0.87 #1791, 0.85 #3768, 0.85 #3026), 0gs9p (0.86 #2269, 0.86 #2023, 0.85 #2765), 0gqng (0.86 #2220, 0.86 #1974, 0.85 #2716), 018wdw (0.79 #1404, 0.76 #1158, 0.75 #910), 0gqxm (0.75 #8396, 0.67 #366, 0.65 #1106), 0gqzz (0.75 #8396, 0.47 #285, 0.44 #531) >> Best rule #4317 for best value: >> intensional similarity = 21 >> extensional distance = 63 >> proper extension: 073h9x; 0bz6sb; 02pgky2; 03tn9w; 0dznvw; >> query: (?x5369, 0gvx_) <- ceremony(?x1703, ?x5369), honored_for(?x5369, ?x1547), nominated_for(?x1703, ?x9185), nominated_for(?x1703, ?x6111), nominated_for(?x1703, ?x3003), nominated_for(?x1703, ?x2215), nominated_for(?x1703, ?x1002), nominated_for(?x1703, ?x299), award_winner(?x5369, ?x1671), ceremony(?x1703, ?x9899), ceremony(?x1703, ?x1998), ?x299 = 01gc7, ?x6111 = 0ptx_, ?x9185 = 01lsl, ?x1002 = 0_b3d, ?x3003 = 0bmpm, ?x2215 = 011yd2, award(?x707, ?x1703), ?x1998 = 073h1t, award(?x763, ?x1703), ?x9899 = 0c4hnm >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 0ftlxj ceremony! 0gvx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 34.000 0.892 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0ftlxj ceremony! 0gqng CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 36.000 34.000 0.892 http://example.org/award/award_category/winners./award/award_honor/ceremony #13674-01wp8w7 PRED entity: 01wp8w7 PRED relation: influenced_by PRED expected values: 0f6lx => 118 concepts (53 used for prediction) PRED predicted values (max 10 best out of 300): 08433 (0.14 #20, 0.12 #1765, 0.10 #1329), 0lrh (0.14 #74, 0.08 #1383, 0.07 #946), 02jq1 (0.14 #179, 0.07 #1924, 0.06 #1488), 01vsy3q (0.14 #149, 0.06 #1458, 0.05 #1021), 012vd6 (0.13 #1913, 0.05 #168, 0.04 #5843), 032l1 (0.12 #7946, 0.10 #14934, 0.10 #2270), 03_87 (0.12 #8060, 0.11 #6750, 0.09 #11554), 081k8 (0.11 #8013, 0.09 #11507, 0.09 #6703), 03f0324 (0.11 #6699, 0.08 #14997, 0.08 #8882), 0gcs9 (0.11 #1309, 0.10 #2618, 0.07 #3927) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 017mbb; >> query: (?x1521, 08433) <- award(?x1521, ?x2322), influenced_by(?x1521, ?x215), role(?x1521, ?x1466) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #310 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 017mbb; *> query: (?x1521, 0f6lx) <- award(?x1521, ?x2322), influenced_by(?x1521, ?x215), role(?x1521, ?x1466) *> conf = 0.05 ranks of expected_values: 73 EVAL 01wp8w7 influenced_by 0f6lx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 118.000 53.000 0.136 http://example.org/influence/influence_node/influenced_by #13673-01l_vgt PRED entity: 01l_vgt PRED relation: artist! PRED expected values: 03mp8k => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 126): 015_1q (0.33 #1261, 0.28 #2227, 0.25 #295), 0n85g (0.33 #61, 0.25 #199, 0.20 #1303), 011k1h (0.33 #9, 0.25 #147, 0.20 #699), 01txts (0.33 #82, 0.25 #220, 0.13 #3452), 017l96 (0.30 #708, 0.30 #570, 0.29 #1122), 0g768 (0.29 #1554, 0.18 #4317, 0.17 #5560), 01q940 (0.27 #4884, 0.13 #3452, 0.12 #2761), 01cf93 (0.25 #332, 0.10 #608, 0.07 #1850), 025t8bv (0.25 #335, 0.03 #2267, 0.02 #5307), 0mcf4 (0.25 #333, 0.03 #12354, 0.02 #5305) >> Best rule #1261 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 015882; 01wz3cx; 01svw8n; 04cr6qv; 01pgk0; >> query: (?x3382, 015_1q) <- artist(?x1543, ?x3382), participant(?x4620, ?x3382), instrumentalists(?x227, ?x4620), award_winner(?x4620, ?x1136) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #755 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 016jfw; *> query: (?x3382, 03mp8k) <- type_of_union(?x3382, ?x566), artists(?x5792, ?x3382), artists(?x1572, ?x3382), ?x1572 = 06by7, ?x5792 = 026z9, nationality(?x3382, ?x512) *> conf = 0.20 ranks of expected_values: 13 EVAL 01l_vgt artist! 03mp8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 158.000 158.000 0.333 http://example.org/music/record_label/artist #13672-09td7p PRED entity: 09td7p PRED relation: ceremony PRED expected values: 058m5m4 => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 131): 05c1t6z (0.50 #1191, 0.43 #798, 0.25 #274), 0gvstc3 (0.50 #1210, 0.43 #817, 0.25 #293), 03nnm4t (0.50 #1247, 0.43 #854, 0.25 #330), 0gx_st (0.50 #1213, 0.43 #820, 0.25 #296), 0bzm81 (0.50 #674, 0.36 #1329, 0.33 #1460), 0n8_m93 (0.50 #765, 0.36 #1420, 0.33 #1551), 02yvhx (0.50 #726, 0.36 #1381, 0.33 #1512), 0bvfqq (0.50 #685, 0.36 #1340, 0.33 #1471), 02yxh9 (0.50 #749, 0.36 #1404, 0.33 #1535), 0bc773 (0.50 #705, 0.36 #1360, 0.33 #1491) >> Best rule #1191 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0cqh6z; 0ck27z; 0bdx29; >> query: (?x2257, 05c1t6z) <- nominated_for(?x2257, ?x86), award(?x4165, ?x2257), ceremony(?x2257, ?x873), ?x4165 = 02mqc4 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1230 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0cqh6z; 0ck27z; 0bdx29; *> query: (?x2257, 058m5m4) <- nominated_for(?x2257, ?x86), award(?x4165, ?x2257), ceremony(?x2257, ?x873), ?x4165 = 02mqc4 *> conf = 0.30 ranks of expected_values: 85 EVAL 09td7p ceremony 058m5m4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 55.000 55.000 0.500 http://example.org/award/award_category/winners./award/award_honor/ceremony #13671-02k_kn PRED entity: 02k_kn PRED relation: artists PRED expected values: 0bqsy 02jqjm 01797x 020jqv 02s6sh 0dzlk => 52 concepts (34 used for prediction) PRED predicted values (max 10 best out of 958): 01vtj38 (0.67 #5667, 0.62 #10721, 0.50 #9710), 0127s7 (0.67 #5559, 0.62 #10613, 0.50 #6569), 09889g (0.67 #5474, 0.54 #10528, 0.50 #9517), 0gbwp (0.67 #5379, 0.50 #6389, 0.50 #2348), 049qx (0.67 #5411, 0.50 #6421, 0.50 #2380), 0136p1 (0.67 #5188, 0.50 #6198, 0.50 #2157), 02zmh5 (0.67 #5198, 0.50 #3178, 0.50 #2167), 01x1cn2 (0.67 #5237, 0.50 #6247, 0.50 #2206), 0bqsy (0.67 #5387, 0.50 #2356, 0.50 #1346), 02yygk (0.67 #5896, 0.50 #2865, 0.50 #1855) >> Best rule #5667 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 025sc50; 02lnbg; >> query: (?x5300, 01vtj38) <- artists(?x5300, ?x11026), artists(?x5300, ?x9731), artists(?x5300, ?x9228), artists(?x5300, ?x1338), artists(?x5300, ?x1260), instrumentalists(?x227, ?x9228), ?x1338 = 09qr6, ?x11026 = 01s7ns, role(?x1260, ?x314), award(?x9731, ?x1232) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5387 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 025sc50; 02lnbg; *> query: (?x5300, 0bqsy) <- artists(?x5300, ?x11026), artists(?x5300, ?x9731), artists(?x5300, ?x9228), artists(?x5300, ?x1338), artists(?x5300, ?x1260), instrumentalists(?x227, ?x9228), ?x1338 = 09qr6, ?x11026 = 01s7ns, role(?x1260, ?x314), award(?x9731, ?x1232) *> conf = 0.67 ranks of expected_values: 9, 103, 353, 534, 546, 602 EVAL 02k_kn artists 0dzlk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 34.000 0.667 http://example.org/music/genre/artists EVAL 02k_kn artists 02s6sh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 34.000 0.667 http://example.org/music/genre/artists EVAL 02k_kn artists 020jqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 34.000 0.667 http://example.org/music/genre/artists EVAL 02k_kn artists 01797x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 52.000 34.000 0.667 http://example.org/music/genre/artists EVAL 02k_kn artists 02jqjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 34.000 0.667 http://example.org/music/genre/artists EVAL 02k_kn artists 0bqsy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 52.000 34.000 0.667 http://example.org/music/genre/artists #13670-01gq0b PRED entity: 01gq0b PRED relation: language PRED expected values: 02h40lc => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 1): 02h40lc (0.09 #4, 0.07 #22, 0.07 #7) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 0n6f8; 05r5w; 0mm1q; 01g0jn; >> query: (?x1890, 02h40lc) <- celebrity(?x1890, ?x1815), award_winner(?x686, ?x1890), vacationer(?x205, ?x1890) >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gq0b language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.088 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #13669-09p7fh PRED entity: 09p7fh PRED relation: nominated_for! PRED expected values: 02pqp12 => 82 concepts (75 used for prediction) PRED predicted values (max 10 best out of 213): 027571b (0.68 #10549, 0.66 #10548, 0.66 #9171), 0gq9h (0.64 #973, 0.45 #5558, 0.41 #1202), 019f4v (0.60 #965, 0.36 #5550, 0.35 #1194), 0gs9p (0.59 #975, 0.39 #5560, 0.34 #1204), 02pqp12 (0.58 #970, 0.26 #741, 0.21 #5555), 0f4x7 (0.38 #940, 0.27 #1169, 0.26 #5525), 0gr0m (0.35 #971, 0.31 #1200, 0.30 #284), 0p9sw (0.32 #19, 0.27 #1164, 0.23 #935), 0gqy2 (0.30 #1030, 0.30 #1259, 0.30 #5615), 099c8n (0.30 #968, 0.23 #1656, 0.22 #5553) >> Best rule #10549 for best value: >> intensional similarity = 3 >> extensional distance = 989 >> proper extension: 0g60z; 02_1q9; 080dwhx; 02_1rq; 03kq98; 072kp; 039fgy; 0kfpm; 02k_4g; 02nf2c; ... >> query: (?x2519, ?x637) <- award(?x2519, ?x637), nominated_for(?x198, ?x2519), nominated_for(?x637, ?x288) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #970 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 154 *> proper extension: 0m313; 01jc6q; 09m6kg; 0ds3t5x; 016fyc; 07xtqq; 095zlp; 04v8x9; 01sxly; 0n0bp; ... *> query: (?x2519, 02pqp12) <- genre(?x2519, ?x162), titles(?x512, ?x2519), nominated_for(?x198, ?x2519), ?x198 = 040njc *> conf = 0.58 ranks of expected_values: 5 EVAL 09p7fh nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 82.000 75.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13668-026zlh9 PRED entity: 026zlh9 PRED relation: music PRED expected values: 012ky3 => 66 concepts (43 used for prediction) PRED predicted values (max 10 best out of 61): 0146pg (0.10 #10, 0.05 #220, 0.04 #852), 02cyfz (0.10 #34, 0.05 #7601, 0.03 #455), 08c9b0 (0.10 #83, 0.03 #715, 0.02 #925), 01m5m5b (0.10 #188, 0.02 #1030, 0.01 #1240), 020fgy (0.10 #164, 0.02 #585, 0.02 #2060), 02ryx0 (0.10 #110, 0.02 #531, 0.01 #1162), 02g40r (0.10 #185), 0mb5x (0.08 #4852, 0.07 #3164, 0.07 #2952), 02_p8v (0.08 #4852, 0.07 #3164, 0.07 #2952), 01xsbh (0.08 #4852, 0.07 #3164, 0.07 #2952) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02z3r8t; >> query: (?x6133, 0146pg) <- genre(?x6133, ?x1403), film(?x5043, ?x6133), ?x5043 = 015q43 >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026zlh9 music 012ky3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 43.000 0.100 http://example.org/film/film/music #13667-02zdwq PRED entity: 02zdwq PRED relation: contact_category! PRED expected values: 02slt7 01pq4w 05w3y 02lv2v 01bvx1 0hkqn => 47 concepts (36 used for prediction) PRED predicted values (max 10 best out of 82): 026db_ (0.50 #494, 0.33 #78), 013fn (0.50 #485, 0.33 #69), 069vt (0.50 #472, 0.33 #56), 04fc6c (0.50 #470, 0.33 #54), 01b39j (0.50 #457, 0.33 #41), 077w0b (0.50 #448, 0.33 #32), 07l1c (0.50 #445, 0.33 #29), 0j47s (0.50 #443, 0.33 #27), 0226k3 (0.33 #82, 0.25 #498), 06rfy5 (0.33 #81, 0.25 #497) >> Best rule #494 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 014dgf; >> query: (?x6046, 026db_) <- contact_category(?x11636, ?x6046), contact_category(?x11051, ?x6046), contact_category(?x7633, ?x6046), contact_category(?x5108, ?x6046), ?x5108 = 01s73z, company(?x265, ?x11636), ?x265 = 0dq3c, service_language(?x11636, ?x254), service_location(?x11636, ?x252), ?x11051 = 07_dn, ?x7633 = 0z90c >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 03w5xm; *> query: (?x6046, 0hkqn) <- contact_category(?x11636, ?x6046), contact_category(?x5108, ?x6046), contact_category(?x5072, ?x6046), contact_category(?x3578, ?x6046), contact_category(?x3230, ?x6046), contact_category(?x581, ?x6046), ?x5108 = 01s73z, ?x11636 = 03s7h, ?x3230 = 03mnk, ?x3578 = 08z129, school(?x580, ?x581), student(?x581, ?x1299), ?x5072 = 045c7b, company(?x233, ?x581) *> conf = 0.33 ranks of expected_values: 21, 24, 44, 61, 64 EVAL 02zdwq contact_category! 0hkqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 47.000 36.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 02zdwq contact_category! 01bvx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 47.000 36.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 02zdwq contact_category! 02lv2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 47.000 36.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 02zdwq contact_category! 05w3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 36.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 02zdwq contact_category! 01pq4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 47.000 36.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 02zdwq contact_category! 02slt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 47.000 36.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #13666-0gtgp6 PRED entity: 0gtgp6 PRED relation: athlete! PRED expected values: 02vx4 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 5): 02vx4 (0.89 #122, 0.89 #152, 0.88 #132), 0jm_ (0.20 #43, 0.19 #173, 0.17 #143), 018w8 (0.13 #186, 0.13 #206, 0.13 #196), 018jz (0.10 #47, 0.08 #87, 0.07 #207), 03tmr (0.02 #181, 0.02 #191, 0.02 #201) >> Best rule #122 for best value: >> intensional similarity = 7 >> extensional distance = 61 >> proper extension: 07nv3_; 02qny_; >> query: (?x8576, 02vx4) <- team(?x8576, ?x4148), teams(?x6885, ?x4148), team(?x6523, ?x4148), gender(?x8576, ?x231), team(?x60, ?x4148), team(?x6523, ?x1697), nationality(?x6523, ?x1310) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gtgp6 athlete! 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.889 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #13665-027n06w PRED entity: 027n06w PRED relation: honored_for PRED expected values: 0kfv9 0h1x5f => 28 concepts (27 used for prediction) PRED predicted values (max 10 best out of 653): 03nt59 (0.60 #959, 0.50 #1555, 0.25 #2150), 0d68qy (0.50 #150, 0.40 #745, 0.38 #1936), 01j7mr (0.50 #214, 0.40 #809, 0.38 #2000), 01b_lz (0.50 #199, 0.40 #794, 0.38 #1985), 0hz55 (0.50 #294, 0.38 #2080, 0.20 #889), 039cq4 (0.50 #412, 0.26 #2795, 0.25 #2198), 07zhjj (0.50 #498, 0.25 #2284, 0.22 #2881), 06mr2s (0.50 #284, 0.25 #2070, 0.22 #2667), 01vnbh (0.50 #318, 0.25 #2104, 0.20 #3296), 01b7h8 (0.50 #535, 0.25 #2321, 0.20 #1130) >> Best rule #959 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 02wzl1d; 0gx_st; 02q690_; >> query: (?x5469, 03nt59) <- award_winner(?x5469, ?x9011), award_winner(?x5469, ?x6868), award_winner(?x5469, ?x4719), award_winner(?x5469, ?x2143), honored_for(?x5469, ?x1135), ?x2143 = 015pxr, gender(?x6868, ?x514), award_winner(?x6868, ?x2176), profession(?x4719, ?x353), award_winner(?x4719, ?x3852), award_winner(?x4719, ?x237), ceremony(?x384, ?x5469), ?x237 = 04t2l2, award_nominee(?x1630, ?x4719), tv_program(?x9011, ?x5808), languages(?x3852, ?x12394) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4170 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 24 *> proper extension: 05pd94v; 0gx1673; *> query: (?x5469, ?x4721) <- award_winner(?x5469, ?x8229), award_winner(?x5469, ?x7696), award_winner(?x5469, ?x415), award_nominee(?x3880, ?x8229), student(?x918, ?x3880), award_winner(?x2811, ?x415), producer_type(?x3880, ?x632), award_winner(?x4760, ?x8229), award_nominee(?x237, ?x7696), award_nominee(?x7696, ?x1630), ?x4760 = 02q690_, tv_program(?x415, ?x4721), nominated_for(?x2811, ?x416) *> conf = 0.36 ranks of expected_values: 20, 115 EVAL 027n06w honored_for 0h1x5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 28.000 27.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 027n06w honored_for 0kfv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 28.000 27.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #13664-02vz6dn PRED entity: 02vz6dn PRED relation: film! PRED expected values: 061dn_ => 90 concepts (74 used for prediction) PRED predicted values (max 10 best out of 73): 016tw3 (0.54 #812, 0.16 #2492, 0.15 #1688), 054g1r (0.33 #33, 0.12 #179, 0.08 #909), 01795t (0.33 #16, 0.11 #1476, 0.10 #1038), 05qd_ (0.25 #80, 0.18 #1175, 0.16 #445), 016tt2 (0.18 #441, 0.18 #733, 0.18 #222), 017s11 (0.17 #1243, 0.17 #294, 0.15 #513), 020h2v (0.17 #43, 0.11 #554, 0.08 #335), 024rgt (0.17 #91, 0.09 #164, 0.06 #748), 01gb54 (0.17 #100, 0.08 #392, 0.08 #1195), 025tlyv (0.17 #57, 0.07 #860, 0.03 #203) >> Best rule #812 for best value: >> intensional similarity = 7 >> extensional distance = 85 >> proper extension: 053tj7; 0hz6mv2; >> query: (?x7393, 016tw3) <- film_release_region(?x7393, ?x985), film(?x382, ?x7393), ?x985 = 0k6nt, film(?x382, ?x11996), film(?x382, ?x6438), ?x11996 = 03s9kp, genre(?x6438, ?x53) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #22 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 09wnnb; *> query: (?x7393, 061dn_) <- film_release_region(?x7393, ?x1558), film(?x8764, ?x7393), ?x8764 = 0336mc, film_release_region(?x5318, ?x1558), genre(?x7393, ?x53), ?x5318 = 0353xq *> conf = 0.17 ranks of expected_values: 11 EVAL 02vz6dn film! 061dn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 90.000 74.000 0.540 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #13663-0mzkr PRED entity: 0mzkr PRED relation: artist PRED expected values: 0zjpz 0qf3p 03f1d47 0jg77 => 134 concepts (47 used for prediction) PRED predicted values (max 10 best out of 829): 0qf3p (0.71 #6453, 0.50 #1718, 0.27 #10395), 02f1c (0.60 #4555, 0.50 #5346, 0.43 #7712), 01w524f (0.50 #2641, 0.43 #7376, 0.40 #4219), 02qwg (0.50 #1795, 0.43 #6530, 0.18 #9467), 01vsy7t (0.50 #5037, 0.40 #4246, 0.33 #8979), 01wp8w7 (0.50 #2437, 0.33 #70, 0.29 #6383), 01k_n63 (0.50 #2865, 0.33 #498, 0.14 #6811), 0zjpz (0.50 #2462, 0.33 #95, 0.09 #11045), 01dpsv (0.50 #3141, 0.33 #774, 0.08 #18913), 01vtj38 (0.50 #2074, 0.29 #6809, 0.27 #10751) >> Best rule #6453 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 01cl2y; 01clyr; >> query: (?x4483, 0qf3p) <- artist(?x4483, ?x7620), artist(?x4483, ?x4082), artist(?x4483, ?x498), participant(?x4082, ?x3403), artists(?x6173, ?x498), ?x7620 = 06gcn, parent_genre(?x2809, ?x6173) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8, 535, 597 EVAL 0mzkr artist 0jg77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 134.000 47.000 0.714 http://example.org/music/record_label/artist EVAL 0mzkr artist 03f1d47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 134.000 47.000 0.714 http://example.org/music/record_label/artist EVAL 0mzkr artist 0qf3p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 47.000 0.714 http://example.org/music/record_label/artist EVAL 0mzkr artist 0zjpz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 134.000 47.000 0.714 http://example.org/music/record_label/artist #13662-02wgbb PRED entity: 02wgbb PRED relation: film! PRED expected values: 059xnf => 64 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1033): 016tw3 (0.48 #54063, 0.47 #49904, 0.47 #56142), 03n08b (0.33 #235, 0.04 #29342, 0.03 #8553), 073749 (0.33 #708, 0.03 #29815, 0.03 #31893), 02g87m (0.33 #234, 0.02 #29341, 0.02 #31419), 049k07 (0.33 #285, 0.02 #29392, 0.02 #31470), 0c01c (0.33 #429, 0.01 #29536, 0.01 #31614), 0c5vh (0.24 #16633, 0.20 #6237, 0.20 #12476), 028k57 (0.22 #790, 0.04 #29897, 0.03 #31975), 06mmb (0.22 #428, 0.02 #29535, 0.02 #31613), 032wdd (0.22 #1503, 0.01 #35344, 0.01 #27028) >> Best rule #54063 for best value: >> intensional similarity = 4 >> extensional distance = 674 >> proper extension: 0bby9p5; 05n6sq; >> query: (?x7800, ?x1104) <- film(?x4046, ?x7800), nominated_for(?x1104, ?x7800), currency(?x4046, ?x170), award_nominee(?x91, ?x4046) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #13713 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 034qmv; 08lr6s; 060v34; 0pc62; 04fzfj; 026n4h6; 075wx7_; 07p62k; 05h43ls; 03n785; ... *> query: (?x7800, 059xnf) <- nominated_for(?x2022, ?x7800), language(?x7800, ?x254), country(?x7800, ?x94), ?x2022 = 05p1dby *> conf = 0.02 ranks of expected_values: 677 EVAL 02wgbb film! 059xnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 35.000 0.477 http://example.org/film/actor/film./film/performance/film #13661-07_3qd PRED entity: 07_3qd PRED relation: role PRED expected values: 05842k => 165 concepts (89 used for prediction) PRED predicted values (max 10 best out of 119): 05r5c (0.54 #393, 0.50 #6565, 0.50 #872), 013y1f (0.52 #483, 0.50 #1732, 0.45 #962), 02dlh2 (0.52 #483, 0.50 #1732, 0.45 #962), 0l14md (0.52 #483, 0.45 #962, 0.42 #291), 018j2 (0.50 #235, 0.31 #426, 0.15 #905), 01hww_ (0.43 #2221, 0.35 #1833, 0.33 #3094), 01vdm0 (0.42 #892, 0.38 #222, 0.35 #5433), 026t6 (0.40 #3, 0.36 #486, 0.33 #965), 03gvt (0.40 #69, 0.25 #264, 0.23 #934), 05842k (0.38 #265, 0.33 #168, 0.33 #1705) >> Best rule #393 for best value: >> intensional similarity = 7 >> extensional distance = 11 >> proper extension: 0m_v0; >> query: (?x1260, 05r5c) <- role(?x1260, ?x745), role(?x1260, ?x716), role(?x1260, ?x314), ?x716 = 018vs, performance_role(?x1260, ?x315), ?x314 = 02sgy, role(?x745, ?x75) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #265 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 04bpm6; *> query: (?x1260, 05842k) <- role(?x1260, ?x745), role(?x1260, ?x716), role(?x1260, ?x314), ?x716 = 018vs, performance_role(?x1260, ?x315), ?x314 = 02sgy, ?x745 = 01vj9c *> conf = 0.38 ranks of expected_values: 10 EVAL 07_3qd role 05842k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 165.000 89.000 0.538 http://example.org/music/artist/track_contributions./music/track_contribution/role #13660-0kbg6 PRED entity: 0kbg6 PRED relation: nationality PRED expected values: 07ssc => 69 concepts (63 used for prediction) PRED predicted values (max 10 best out of 14): 07ssc (0.13 #14, 0.13 #212, 0.12 #609), 02jx1 (0.12 #1420, 0.11 #32, 0.10 #726), 03rk0 (0.10 #1136, 0.06 #540, 0.06 #5118), 0d060g (0.07 #1989, 0.05 #2586, 0.05 #3185), 0345h (0.06 #923, 0.06 #824, 0.05 #1022), 0f8l9c (0.03 #914, 0.03 #815, 0.03 #1013), 03rt9 (0.02 #607, 0.02 #706, 0.02 #905), 0h7x (0.02 #927, 0.02 #828, 0.02 #1026), 03rjj (0.02 #1095, 0.02 #1987, 0.02 #5770), 06q1r (0.02 #472, 0.02 #2357, 0.02 #2656) >> Best rule #14 for best value: >> intensional similarity = 2 >> extensional distance = 156 >> proper extension: 07c0j; 03g5jw; 0d193h; 0lhn5; 014_lq; 0dw4g; 0b1zz; 0838y; 07r1_; 01kcms4; ... >> query: (?x13701, 07ssc) <- category(?x13701, ?x134), influenced_by(?x13701, ?x5040) >> conf = 0.13 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kbg6 nationality 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 63.000 0.127 http://example.org/people/person/nationality #13659-01ync PRED entity: 01ync PRED relation: school PRED expected values: 01qgr3 => 86 concepts (72 used for prediction) PRED predicted values (max 10 best out of 664): 07w0v (0.43 #4493, 0.43 #4307, 0.40 #5043), 065y4w7 (0.43 #4116, 0.40 #4864, 0.38 #4304), 0bx8pn (0.43 #1704, 0.40 #395, 0.33 #2078), 0lyjf (0.43 #1753, 0.37 #5116, 0.35 #6428), 06fq2 (0.40 #4988, 0.38 #4428, 0.36 #2934), 01vs5c (0.40 #646, 0.36 #2889, 0.33 #4383), 02pptm (0.40 #702, 0.33 #2010, 0.33 #1263), 0jkhr (0.40 #482, 0.29 #1791, 0.25 #296), 09f2j (0.35 #4556, 0.33 #2501, 0.33 #74), 012vwb (0.35 #4533, 0.33 #1918, 0.29 #3225) >> Best rule #4493 for best value: >> intensional similarity = 8 >> extensional distance = 21 >> proper extension: 01d5z; 0cqt41; >> query: (?x4487, 07w0v) <- school(?x4487, ?x2948), draft(?x4487, ?x4779), draft(?x4487, ?x1161), season(?x4487, ?x2406), ?x1161 = 02x2khw, major_field_of_study(?x2948, ?x1154), school(?x4779, ?x388), institution(?x620, ?x2948) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4415 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 19 *> proper extension: 06wpc; 04wmvz; *> query: (?x4487, 01qgr3) <- school(?x4487, ?x3779), draft(?x4487, ?x4779), draft(?x4487, ?x3334), ?x4779 = 02z6872, ?x3334 = 02pq_rp, position(?x4487, ?x2010), season(?x4487, ?x2406), contains(?x94, ?x3779), currency(?x3779, ?x170) *> conf = 0.24 ranks of expected_values: 36 EVAL 01ync school 01qgr3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 86.000 72.000 0.435 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #13658-0ddd0gc PRED entity: 0ddd0gc PRED relation: country_of_origin PRED expected values: 07ssc => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 11): 09c7w0 (0.92 #34, 0.91 #56, 0.87 #100), 02jx1 (0.75 #122, 0.45 #354, 0.02 #177), 02k54 (0.75 #122), 07ssc (0.45 #354, 0.17 #9, 0.11 #175), 06q1r (0.45 #354), 03rt9 (0.45 #354), 03_3d (0.09 #312, 0.08 #169, 0.08 #357), 0d060g (0.04 #170, 0.03 #181, 0.03 #313), 0d0vqn (0.02 #16, 0.02 #27, 0.01 #38), 03rjj (0.02 #13, 0.02 #24, 0.01 #35) >> Best rule #34 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 01j95; >> query: (?x1434, 09c7w0) <- program_creator(?x1434, ?x6673), award_winner(?x1434, ?x2372), award(?x2372, ?x375) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #354 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 256 *> proper extension: 017dcd; 07ng9k; 063zky; 05x72k; 0dr1c2; 031kyy; 08cl7s; 01lk02; 02v5xg; 03d3ht; ... *> query: (?x1434, ?x429) <- actor(?x1434, ?x1191), nationality(?x1191, ?x429) *> conf = 0.45 ranks of expected_values: 4 EVAL 0ddd0gc country_of_origin 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 73.000 73.000 0.917 http://example.org/tv/tv_program/country_of_origin #13657-02r3zy PRED entity: 02r3zy PRED relation: group! PRED expected values: 0mkg => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 117): 0l14md (0.63 #499, 0.62 #89, 0.61 #335), 0l14qv (0.39 #333, 0.37 #497, 0.24 #1071), 04rzd (0.35 #519, 0.26 #355, 0.12 #1093), 042v_gx (0.26 #336, 0.19 #500, 0.13 #172), 018j2 (0.21 #520, 0.09 #356, 0.08 #1094), 07y_7 (0.19 #494, 0.13 #330, 0.11 #1068), 02k84w (0.17 #354, 0.16 #518, 0.07 #1233), 06ncr (0.17 #444, 0.16 #526, 0.15 #1100), 0mkg (0.16 #502, 0.13 #338, 0.11 #10), 07brj (0.16 #509, 0.13 #345, 0.11 #17) >> Best rule #499 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 07qnf; 02r1tx7; 0394y; 02mq_y; 02dw1_; 012vm6; 03qkcn9; >> query: (?x1060, 0l14md) <- group(?x2798, ?x1060), group(?x1466, ?x1060), ?x2798 = 03qjg, artists(?x302, ?x1060), role(?x115, ?x1466) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #502 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 41 *> proper extension: 07qnf; 02r1tx7; 0394y; 02mq_y; 02dw1_; 012vm6; 03qkcn9; *> query: (?x1060, 0mkg) <- group(?x2798, ?x1060), group(?x1466, ?x1060), ?x2798 = 03qjg, artists(?x302, ?x1060), role(?x115, ?x1466) *> conf = 0.16 ranks of expected_values: 9 EVAL 02r3zy group! 0mkg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 72.000 72.000 0.628 http://example.org/music/performance_role/regular_performances./music/group_membership/group #13656-0n5kc PRED entity: 0n5kc PRED relation: adjoins PRED expected values: 0n5gb => 140 concepts (50 used for prediction) PRED predicted values (max 10 best out of 400): 0n5jm (0.83 #15452, 0.83 #36312, 0.82 #33993), 0n5j7 (0.83 #36312, 0.82 #33993, 0.82 #36310), 0n5gb (0.83 #36312, 0.82 #33993, 0.82 #36310), 0n5kc (0.40 #2716, 0.40 #1944, 0.33 #1173), 0m2kw (0.40 #2158, 0.33 #1387, 0.29 #4635), 0n5dt (0.33 #627, 0.29 #4635, 0.26 #8495), 0n5fl (0.33 #74, 0.25 #11582, 0.15 #3936), 0n57k (0.33 #413, 0.25 #11582, 0.15 #13132), 0mwh1 (0.33 #134, 0.25 #11582, 0.15 #13132), 0fxyd (0.33 #207, 0.25 #11582, 0.15 #13132) >> Best rule #15452 for best value: >> intensional similarity = 5 >> extensional distance = 182 >> proper extension: 0d0kn; 03rk0; 05sb1; 075mb; 01ly5m; 0jdd; 0f1_p; 07bxhl; 03shp; 0jgx; ... >> query: (?x8766, ?x8276) <- adjoins(?x8276, ?x8766), adjoins(?x8276, ?x10054), origin(?x6613, ?x10054), contains(?x10054, ?x6468), contains(?x6895, ?x8276) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #36312 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 287 *> proper extension: 02dtg; 0wh3; 01_d4; 0dc95; 0f04c; 013m43; 0r679; 01sn3; 0d35y; 0r5wt; ... *> query: (?x8766, ?x8276) <- adjoins(?x8276, ?x8766), time_zones(?x8766, ?x2674), source(?x8766, ?x958), contains(?x6895, ?x8276) *> conf = 0.83 ranks of expected_values: 3 EVAL 0n5kc adjoins 0n5gb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 50.000 0.828 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #13655-0164nb PRED entity: 0164nb PRED relation: languages PRED expected values: 02h40lc => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 16): 02h40lc (0.46 #432, 0.44 #471, 0.40 #588), 0t_2 (0.07 #87, 0.04 #282, 0.01 #439), 03_9r (0.07 #2732, 0.01 #317, 0.01 #395), 06mp7 (0.06 #128, 0.05 #206, 0.01 #323), 064_8sq (0.04 #874, 0.04 #913, 0.03 #718), 03k50 (0.03 #512, 0.03 #590, 0.03 #355), 07c9s (0.03 #521, 0.03 #599, 0.02 #755), 0999q (0.03 #609, 0.02 #531, 0.02 #765), 04306rv (0.02 #550, 0.01 #667, 0.01 #823), 09s02 (0.02 #622, 0.02 #778, 0.02 #544) >> Best rule #432 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 03zqc1; >> query: (?x3817, 02h40lc) <- student(?x1771, ?x3817), film(?x3817, ?x7678), type_of_union(?x3817, ?x566) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0164nb languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.464 http://example.org/people/person/languages #13654-01qbg5 PRED entity: 01qbg5 PRED relation: music PRED expected values: 01m3b1t => 84 concepts (45 used for prediction) PRED predicted values (max 10 best out of 89): 01d_h (0.20 #156, 0.02 #787), 01m3b1t (0.20 #136, 0.01 #3301, 0.01 #3722), 02fgpf (0.12 #872, 0.04 #1925, 0.04 #2347), 0gv07g (0.10 #342, 0.01 #7100, 0.01 #1184), 016szr (0.09 #923, 0.05 #1976, 0.04 #2398), 0146pg (0.07 #1273, 0.07 #2115, 0.06 #2537), 0f4vbz (0.07 #6119, 0.07 #6543, 0.07 #6118), 0hwbd (0.07 #6119, 0.07 #6543, 0.07 #6118), 0306bt (0.07 #6119, 0.07 #6543, 0.07 #6118), 03mdt (0.06 #6544, 0.06 #8664, 0.06 #8663) >> Best rule #156 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 095zlp; 0963mq; 0294mx; >> query: (?x7319, 01d_h) <- country(?x7319, ?x94), titles(?x942, ?x7319), ?x942 = 04jjy, films(?x2286, ?x7319) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #136 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 095zlp; 0963mq; 0294mx; *> query: (?x7319, 01m3b1t) <- country(?x7319, ?x94), titles(?x942, ?x7319), ?x942 = 04jjy, films(?x2286, ?x7319) *> conf = 0.20 ranks of expected_values: 2 EVAL 01qbg5 music 01m3b1t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 45.000 0.200 http://example.org/film/film/music #13653-035qgm PRED entity: 035qgm PRED relation: team! PRED expected values: 07y9k => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 8): 07y9k (0.60 #196, 0.58 #268, 0.57 #116), 0355pl (0.33 #235, 0.22 #371, 0.22 #443), 0356lc (0.33 #153, 0.20 #481, 0.19 #281), 03zv9 (0.21 #186, 0.15 #106, 0.14 #306), 059yj (0.14 #397, 0.13 #533, 0.12 #429), 0h69c (0.12 #398, 0.11 #534, 0.10 #430), 021q23 (0.07 #152, 0.02 #728, 0.01 #808), 01ddbl (0.02 #807, 0.02 #815, 0.02 #823) >> Best rule #196 for best value: >> intensional similarity = 7 >> extensional distance = 18 >> proper extension: 0329gm; 03b6j8; >> query: (?x9542, 07y9k) <- teams(?x1353, ?x9542), team(?x60, ?x9542), film_release_region(?x7538, ?x1353), film_release_region(?x6886, ?x1353), organization(?x1353, ?x127), ?x7538 = 035zr0, ?x6886 = 0gwjw0c >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035qgm team! 07y9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.600 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #13652-0892sx PRED entity: 0892sx PRED relation: artist! PRED expected values: 0181dw => 126 concepts (90 used for prediction) PRED predicted values (max 10 best out of 113): 033hn8 (0.24 #428, 0.13 #2774, 0.12 #3603), 017l96 (0.22 #157, 0.16 #847, 0.15 #2089), 0181dw (0.19 #455, 0.11 #5287, 0.11 #41), 03rhqg (0.17 #154, 0.17 #4710, 0.16 #4019), 011k1h (0.17 #148, 0.16 #424, 0.14 #2080), 0fb0v (0.17 #7, 0.11 #283, 0.09 #1387), 03qx_f (0.17 #71, 0.07 #347, 0.04 #623), 0n85g (0.16 #338, 0.09 #1442, 0.08 #4756), 01w40h (0.13 #304, 0.10 #994, 0.09 #5136), 0g768 (0.13 #1830, 0.13 #4730, 0.12 #7914) >> Best rule #428 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 08w4pm; >> query: (?x2690, 033hn8) <- artist(?x5666, ?x2690), artists(?x302, ?x2690), ?x5666 = 043g7l >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #455 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 08w4pm; *> query: (?x2690, 0181dw) <- artist(?x5666, ?x2690), artists(?x302, ?x2690), ?x5666 = 043g7l *> conf = 0.19 ranks of expected_values: 3 EVAL 0892sx artist! 0181dw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 90.000 0.241 http://example.org/music/record_label/artist #13651-0k2sk PRED entity: 0k2sk PRED relation: genre PRED expected values: 02kdv5l 01hmnh => 80 concepts (77 used for prediction) PRED predicted values (max 10 best out of 103): 07s9rl0 (0.65 #697, 0.63 #1278, 0.62 #3143), 01z4y (0.62 #4886, 0.61 #5237, 0.61 #5470), 02kdv5l (0.48 #3494, 0.42 #4423, 0.38 #119), 03k9fj (0.38 #125, 0.38 #9, 0.34 #589), 01jfsb (0.38 #126, 0.34 #938, 0.31 #1404), 02l7c8 (0.33 #2107, 0.31 #710, 0.30 #2457), 01hmnh (0.29 #596, 0.25 #132, 0.22 #944), 06nbt (0.25 #371, 0.06 #4887, 0.06 #2116), 06qm3 (0.25 #34, 0.06 #4887, 0.05 #8144), 0hfjk (0.25 #60, 0.06 #4887, 0.05 #8144) >> Best rule #697 for best value: >> intensional similarity = 4 >> extensional distance = 224 >> proper extension: 05jf85; 016z5x; 01kff7; 04n52p6; 0gxfz; 0170th; 07w8fz; 097zcz; 02x8fs; 0ch3qr1; ... >> query: (?x1076, 07s9rl0) <- genre(?x1076, ?x258), film(?x1802, ?x1076), cinematography(?x1076, ?x1075), award_winner(?x1076, ?x930) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #3494 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 921 *> proper extension: 0g56t9t; 047q2k1; 06wzvr; 0gx1bnj; 0ddfwj1; 04ddm4; 0209xj; 061681; 0gkz15s; 087wc7n; ... *> query: (?x1076, 02kdv5l) <- genre(?x1076, ?x8467), film(?x1802, ?x1076), genre(?x8214, ?x8467), ?x8214 = 026wlxw *> conf = 0.48 ranks of expected_values: 3, 7 EVAL 0k2sk genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 80.000 77.000 0.655 http://example.org/film/film/genre EVAL 0k2sk genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 80.000 77.000 0.655 http://example.org/film/film/genre #13650-015z4j PRED entity: 015z4j PRED relation: participant! PRED expected values: 01vx5w7 => 126 concepts (74 used for prediction) PRED predicted values (max 10 best out of 395): 01vx5w7 (0.83 #36833, 0.81 #24126, 0.81 #27938), 0227vl (0.83 #36833, 0.81 #24126, 0.81 #27938), 01trhmt (0.17 #813, 0.06 #16678, 0.04 #20490), 01ggc9 (0.17 #1218, 0.06 #1853, 0.04 #3123), 0bbf1f (0.17 #840, 0.05 #14165, 0.04 #18612), 01l47f5 (0.17 #1063, 0.04 #3603, 0.03 #4871), 01nczg (0.17 #740, 0.04 #3280, 0.03 #4548), 01g0jn (0.17 #1257, 0.04 #3797, 0.02 #6967), 01pk8v (0.17 #1003, 0.03 #16868, 0.02 #12421), 014g_s (0.17 #1234, 0.02 #6944, 0.02 #7578) >> Best rule #36833 for best value: >> intensional similarity = 4 >> extensional distance = 428 >> proper extension: 07nznf; 0411q; 04nw9; 019g40; 09f0bj; 0hskw; 022wxh; 08hsww; 0d02km; 0gd9k; ... >> query: (?x3020, ?x2925) <- nationality(?x3020, ?x94), participant(?x3020, ?x2925), ?x94 = 09c7w0, participant(?x4775, ?x3020) >> conf = 0.83 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 015z4j participant! 01vx5w7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 74.000 0.827 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #13649-0db94w PRED entity: 0db94w PRED relation: language PRED expected values: 02h40lc => 97 concepts (84 used for prediction) PRED predicted values (max 10 best out of 57): 02h40lc (0.94 #2629, 0.94 #2569, 0.94 #2689), 03_9r (0.56 #3468, 0.48 #1203, 0.48 #3346), 03115z (0.56 #3468, 0.48 #3346, 0.45 #4010), 064_8sq (0.23 #439, 0.20 #557, 0.17 #22), 06b_j (0.17 #23, 0.10 #1517, 0.09 #2052), 097kp (0.17 #52, 0.08 #469, 0.07 #587), 02bjrlw (0.15 #418, 0.13 #536, 0.12 #657), 06nm1 (0.14 #70, 0.13 #1505, 0.12 #1801), 05zjd (0.14 #85, 0.10 #324, 0.08 #443), 04306rv (0.11 #124, 0.11 #1975, 0.10 #3109) >> Best rule #2629 for best value: >> intensional similarity = 7 >> extensional distance = 398 >> proper extension: 015qsq; 03t97y; 0bscw; 05p3738; 02qzmz6; 0bpbhm; 074rg9; 05znbh7; 02nx2k; 02z9rr; ... >> query: (?x4446, 02h40lc) <- genre(?x4446, ?x812), country(?x4446, ?x252), language(?x4446, ?x7926), ?x812 = 01jfsb, film_release_region(?x5139, ?x252), country(?x150, ?x252), ?x5139 = 07bzz7 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0db94w language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 84.000 0.938 http://example.org/film/film/language #13648-01jt2w PRED entity: 01jt2w PRED relation: school! PRED expected values: 09th87 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 20): 0f4vx0 (0.67 #11, 0.21 #291, 0.18 #431), 025tn92 (0.50 #13, 0.15 #622, 0.15 #723), 02qw1zx (0.33 #5, 0.18 #165, 0.13 #365), 03nt7j (0.27 #87, 0.10 #367, 0.10 #247), 02pq_x5 (0.18 #97, 0.17 #17, 0.15 #297), 092j54 (0.18 #89, 0.17 #9, 0.13 #289), 09l0x9 (0.18 #92, 0.15 #292, 0.13 #432), 05vsb7 (0.18 #81, 0.10 #703, 0.10 #281), 02x2khw (0.17 #3, 0.15 #622, 0.15 #723), 06439y (0.17 #20, 0.15 #622, 0.15 #723) >> Best rule #11 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0pspl; >> query: (?x7707, 0f4vx0) <- school(?x799, ?x7707), institution(?x620, ?x7707), ?x799 = 0jm3v, student(?x7707, ?x1145), colors(?x7707, ?x663) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #622 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 152 *> proper extension: 02zkz7; *> query: (?x7707, ?x1161) <- currency(?x7707, ?x170), school(?x1010, ?x7707), school(?x1010, ?x1884), student(?x1884, ?x1815), draft(?x1010, ?x1161) *> conf = 0.15 ranks of expected_values: 14 EVAL 01jt2w school! 09th87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 119.000 119.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #13647-016_mj PRED entity: 016_mj PRED relation: influenced_by! PRED expected values: 02xfj0 => 123 concepts (109 used for prediction) PRED predicted values (max 10 best out of 283): 05rx__ (0.19 #2892, 0.04 #10122, 0.03 #24574), 016_mj (0.14 #2637, 0.07 #24780, 0.07 #3670), 01xwqn (0.14 #3026, 0.07 #4059, 0.07 #9224), 01j7rd (0.14 #2654, 0.07 #3687, 0.06 #4204), 01s7qqw (0.14 #2793, 0.06 #4343, 0.06 #4860), 0bqs56 (0.14 #2833, 0.06 #4383, 0.06 #4900), 05ty4m (0.12 #523, 0.12 #7, 0.10 #2589), 01n5309 (0.12 #535, 0.12 #19, 0.10 #2601), 07ymr5 (0.12 #579, 0.12 #63, 0.02 #5230), 01wp_jm (0.10 #2991, 0.07 #24780, 0.05 #28909) >> Best rule #2892 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 02jq1; >> query: (?x1835, 05rx__) <- participant(?x1835, ?x2237), influenced_by(?x10560, ?x1835), nationality(?x1835, ?x94) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #28909 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 515 *> proper extension: 07kb5; 017r2; 026lj; 02pb2bp; 0mj0c; 03_hd; 07c37; 04jwp; 04093; 02y49; ... *> query: (?x1835, ?x236) <- influenced_by(?x1835, ?x3917), influenced_by(?x236, ?x3917) *> conf = 0.05 ranks of expected_values: 71 EVAL 016_mj influenced_by! 02xfj0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 123.000 109.000 0.190 http://example.org/influence/influence_node/influenced_by #13646-08d6bd PRED entity: 08d6bd PRED relation: profession PRED expected values: 02hrh1q => 181 concepts (70 used for prediction) PRED predicted values (max 10 best out of 93): 02hrh1q (0.86 #3863, 0.85 #9934, 0.83 #4011), 0dxtg (0.49 #8895, 0.48 #5934, 0.47 #10082), 02jknp (0.48 #8889, 0.47 #10076, 0.42 #3856), 04gc2 (0.33 #3298, 0.31 #2854, 0.31 #3150), 03gjzk (0.32 #10084, 0.32 #8897, 0.30 #5936), 0kyk (0.29 #622, 0.27 #1510, 0.24 #3435), 080ntlp (0.29 #231, 0.20 #379, 0.16 #823), 0cbd2 (0.28 #4299, 0.21 #598, 0.21 #3411), 09jwl (0.20 #5200, 0.19 #5496, 0.18 #9049), 015cjr (0.19 #1086, 0.13 #2566, 0.13 #3455) >> Best rule #3863 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0hskw; >> query: (?x6442, 02hrh1q) <- languages(?x6442, ?x1882), profession(?x6442, ?x319), award_winner(?x4687, ?x6442), ?x319 = 01d_h8 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08d6bd profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 70.000 0.858 http://example.org/people/person/profession #13645-02g8h PRED entity: 02g8h PRED relation: religion PRED expected values: 0c8wxp => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 24): 0c8wxp (0.27 #276, 0.22 #1132, 0.21 #907), 0kpl (0.21 #370, 0.17 #1496, 0.16 #686), 03_gx (0.21 #104, 0.20 #149, 0.14 #374), 0kq2 (0.10 #378, 0.06 #1279, 0.05 #739), 03j6c (0.08 #66, 0.05 #246, 0.05 #201), 092bf5 (0.05 #376, 0.04 #466, 0.03 #646), 01lp8 (0.04 #46, 0.03 #316, 0.03 #406), 019cr (0.04 #56, 0.02 #236, 0.02 #191), 0v53x (0.04 #74, 0.02 #254, 0.02 #209), 06nzl (0.04 #60, 0.02 #240, 0.02 #195) >> Best rule #276 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 01t07j; 0pkyh; 04cbtrw; 0ph2w; 02kz_; 03f3yfj; 0d3k14; >> query: (?x318, 0c8wxp) <- influenced_by(?x318, ?x4112), gender(?x318, ?x231), participant(?x2647, ?x318) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02g8h religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.267 http://example.org/people/person/religion #13644-012bk PRED entity: 012bk PRED relation: student! PRED expected values: 015cz0 01hc1j => 116 concepts (114 used for prediction) PRED predicted values (max 10 best out of 159): 07tgn (0.29 #1071, 0.25 #17, 0.08 #4233), 0h6rm (0.25 #144, 0.14 #1198, 0.11 #1725), 0f11p (0.25 #515, 0.14 #1569, 0.04 #4731), 02w6bq (0.17 #951, 0.07 #3059, 0.06 #3586), 03ksy (0.16 #7485, 0.14 #9066, 0.14 #1160), 08815 (0.14 #6327, 0.14 #3691, 0.12 #11070), 07x4c (0.14 #6057, 0.12 #12381, 0.12 #8165), 0hsb3 (0.14 #1262, 0.08 #2316, 0.04 #4424), 0ymcz (0.14 #1468, 0.07 #3049, 0.06 #3576), 07wjk (0.14 #1117, 0.04 #4279, 0.04 #4806) >> Best rule #1071 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 063vn; >> query: (?x8437, 07tgn) <- religion(?x8437, ?x7131), basic_title(?x8437, ?x182), ?x182 = 060bp, gender(?x8437, ?x231), nationality(?x8437, ?x4743), profession(?x8437, ?x5805) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2558 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 070m12; *> query: (?x8437, 01hc1j) <- nationality(?x8437, ?x4743), ?x4743 = 03spz, gender(?x8437, ?x231), gender(?x3811, ?x231), nominated_for(?x3811, ?x3137) *> conf = 0.08 ranks of expected_values: 23, 24 EVAL 012bk student! 01hc1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 116.000 114.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 012bk student! 015cz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 116.000 114.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #13643-0l_v1 PRED entity: 0l_v1 PRED relation: administrative_parent PRED expected values: 0hjy => 57 concepts (33 used for prediction) PRED predicted values (max 10 best out of 15): 0vmt (0.16 #20, 0.12 #157, 0.08 #294), 09c7w0 (0.10 #686, 0.09 #4406, 0.09 #824), 02j71 (0.09 #4418), 0gyh (0.08 #52, 0.05 #189, 0.04 #326), 059rby (0.07 #556, 0.05 #694, 0.05 #832), 0hjy (0.07 #158, 0.03 #21, 0.02 #295), 07z1m (0.05 #31, 0.05 #168, 0.03 #305), 02xry (0.03 #49, 0.02 #186, 0.01 #323), 0846v (0.02 #192), 06q1r (0.01 #1992, 0.01 #2129) >> Best rule #20 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 0cb4j; 0d6lp; 0l2q3; 0m24v; 0kwmc; >> query: (?x14180, 0vmt) <- currency(?x14180, ?x170), second_level_divisions(?x94, ?x14180), ?x170 = 09nqf, ?x94 = 09c7w0, contains(?x94, ?x14180) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #158 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 0l2l_; 06wxw; 0g_wn2; 0235l; 0kvt9; 0fwc0; 0l_n1; 0k1jg; 0mk59; *> query: (?x14180, 0hjy) <- contains(?x94, ?x14180), source(?x14180, ?x958), ?x958 = 0jbk9, ?x94 = 09c7w0, second_level_divisions(?x94, ?x14180) *> conf = 0.07 ranks of expected_values: 6 EVAL 0l_v1 administrative_parent 0hjy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 57.000 33.000 0.162 http://example.org/base/aareas/schema/administrative_area/administrative_parent #13642-027m67 PRED entity: 027m67 PRED relation: language PRED expected values: 03115z => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 36): 064_8sq (0.25 #18, 0.15 #1307, 0.15 #608), 0c_v2 (0.25 #14, 0.11 #68, 0.03 #175), 0459q4 (0.19 #140, 0.11 #86, 0.03 #193), 03_9r (0.16 #117, 0.06 #63, 0.05 #1029), 06nm1 (0.15 #331, 0.14 #118, 0.11 #171), 03115z (0.14 #141, 0.11 #87, 0.02 #729), 02bjrlw (0.13 #162, 0.11 #55, 0.09 #697), 04306rv (0.11 #165, 0.11 #58, 0.10 #594), 04h9h (0.11 #92, 0.05 #146, 0.04 #949), 03k50 (0.08 #116, 0.06 #62, 0.03 #1351) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0233bn; >> query: (?x7293, 064_8sq) <- nominated_for(?x7740, ?x7293), film_release_region(?x7293, ?x1003), ?x1003 = 03gj2, ?x7740 = 02404v >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #141 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 0bz3jx; *> query: (?x7293, 03115z) <- film_release_distribution_medium(?x7293, ?x81), language(?x7293, ?x2890), ?x2890 = 0653m *> conf = 0.14 ranks of expected_values: 6 EVAL 027m67 language 03115z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 88.000 0.250 http://example.org/film/film/language #13641-01hmnh PRED entity: 01hmnh PRED relation: genre! PRED expected values: 0k2sk 07x4qr 0gj8nq2 027s39y 032zq6 017jd9 05t0_2v 05qbbfb 09gdh6k 01xq8v 09lxv9 0symg 09v8clw => 89 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1666): 0639bg (0.81 #19479, 0.81 #19478, 0.80 #21105), 0125xq (0.81 #19479, 0.81 #19478, 0.80 #21105), 0jnwx (0.81 #19479, 0.81 #19478, 0.80 #21105), 011ydl (0.81 #19479, 0.81 #19478, 0.80 #21105), 0kcn7 (0.81 #19479, 0.81 #19478, 0.80 #21105), 01z452 (0.81 #19479, 0.81 #19478, 0.80 #21105), 014_x2 (0.81 #19479, 0.81 #19478, 0.80 #21105), 01c22t (0.81 #19479, 0.81 #19478, 0.80 #21105), 0b9rdk (0.81 #19479, 0.81 #19478, 0.80 #21105), 04gv3db (0.81 #19479, 0.81 #19478, 0.80 #21105) >> Best rule #19479 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 02l7c8; >> query: (?x1510, ?x306) <- titles(?x1510, ?x306), genre(?x9514, ?x1510), genre(?x297, ?x1510), tv_program(?x5061, ?x9514), genre(?x306, ?x53), ?x297 = 0ckr7s >> conf = 0.81 => this is the best rule for 30 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 17, 124, 187, 297, 380, 390, 594, 611, 627, 736, 793, 1143, 1449 EVAL 01hmnh genre! 09v8clw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 0symg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 09lxv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 01xq8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 09gdh6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 05qbbfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 05t0_2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 017jd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 032zq6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 027s39y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 0gj8nq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 07x4qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 89.000 45.000 0.815 http://example.org/film/film/genre EVAL 01hmnh genre! 0k2sk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 89.000 45.000 0.815 http://example.org/film/film/genre #13640-0840vq PRED entity: 0840vq PRED relation: artists! PRED expected values: 02lnbg 0ggx5q 02w6s3 => 122 concepts (72 used for prediction) PRED predicted values (max 10 best out of 217): 0m0jc (0.67 #924, 0.43 #314, 0.33 #619), 0ggx5q (0.56 #686, 0.47 #991, 0.42 #1296), 06by7 (0.55 #2768, 0.52 #2158, 0.51 #2463), 05bt6j (0.46 #1262, 0.45 #1872, 0.29 #10414), 025sc50 (0.44 #658, 0.43 #353, 0.42 #1268), 0gywn (0.44 #665, 0.26 #2191, 0.26 #2496), 02lnbg (0.43 #361, 0.42 #1276, 0.34 #1886), 029h7y (0.43 #344, 0.21 #21973, 0.11 #649), 06j6l (0.33 #656, 0.33 #10418, 0.33 #4622), 026z9 (0.33 #685, 0.21 #21973, 0.17 #1295) >> Best rule #924 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 05vzw3; >> query: (?x3187, 0m0jc) <- award(?x3187, ?x8331), artists(?x671, ?x3187), ?x8331 = 056jm_ >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #686 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0c7ct; 0136p1; 01x1cn2; 01vsykc; 07g2v; 01vxlbm; 01wgfp6; *> query: (?x3187, 0ggx5q) <- type_of_union(?x3187, ?x566), artist(?x2149, ?x3187), artists(?x7267, ?x3187), ?x7267 = 03mb9 *> conf = 0.56 ranks of expected_values: 2, 7, 25 EVAL 0840vq artists! 02w6s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 122.000 72.000 0.667 http://example.org/music/genre/artists EVAL 0840vq artists! 0ggx5q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 72.000 0.667 http://example.org/music/genre/artists EVAL 0840vq artists! 02lnbg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 72.000 0.667 http://example.org/music/genre/artists #13639-02hkv5 PRED entity: 02hkv5 PRED relation: profession PRED expected values: 02hrh1q => 116 concepts (66 used for prediction) PRED predicted values (max 10 best out of 77): 02hrh1q (0.85 #9344, 0.84 #2235, 0.83 #1791), 0fj9f (0.64 #794, 0.56 #943, 0.55 #498), 02jknp (0.52 #599, 0.50 #1636, 0.46 #4152), 0dxtg (0.51 #6528, 0.49 #3122, 0.49 #4158), 03gjzk (0.35 #6530, 0.34 #6678, 0.32 #4160), 012t_z (0.33 #12, 0.06 #2381, 0.05 #6675), 0cbd2 (0.24 #6817, 0.22 #598, 0.20 #154), 09jwl (0.24 #1204, 0.21 #1056, 0.19 #6238), 0np9r (0.20 #6536, 0.20 #169, 0.19 #6832), 0d1pc (0.20 #198, 0.18 #346, 0.12 #1383) >> Best rule #9344 for best value: >> intensional similarity = 5 >> extensional distance = 1408 >> proper extension: 0184jc; 04bdxl; 06qgvf; 0grwj; 01vvydl; 07fq1y; 02qgqt; 04yywz; 06688p; 02bfmn; ... >> query: (?x11285, 02hrh1q) <- location(?x11285, ?x6250), profession(?x11285, ?x319), type_of_union(?x11285, ?x566), profession(?x12689, ?x319), ?x12689 = 0btj0 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hkv5 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 66.000 0.851 http://example.org/people/person/profession #13638-044k8 PRED entity: 044k8 PRED relation: artists! PRED expected values: 02w4v => 199 concepts (122 used for prediction) PRED predicted values (max 10 best out of 262): 064t9 (0.67 #1549, 0.65 #7690, 0.65 #5541), 0glt670 (0.59 #7715, 0.57 #3109, 0.55 #5566), 03lty (0.53 #15073, 0.43 #17223, 0.21 #24568), 01fbr2 (0.50 #679, 0.23 #1293, 0.14 #8048), 0cx6f (0.50 #795, 0.15 #1409, 0.14 #8164), 016clz (0.46 #3382, 0.35 #6146, 0.32 #3690), 025sc50 (0.42 #6804, 0.42 #5576, 0.41 #7725), 06j6l (0.42 #6802, 0.38 #7416, 0.37 #5267), 05bt6j (0.40 #349, 0.38 #1270, 0.35 #7411), 0ggx5q (0.40 #384, 0.38 #1612, 0.32 #5604) >> Best rule #1549 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 0pyg6; >> query: (?x4608, 064t9) <- artists(?x1000, ?x4608), participant(?x4608, ?x3403), religion(?x4608, ?x8249) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #964 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 04nw9; 044f7; 01t9qj_; *> query: (?x4608, 02w4v) <- gender(?x4608, ?x514), award_winner(?x4609, ?x4608), inductee(?x1091, ?x4608), place_of_death(?x4608, ?x1523) *> conf = 0.25 ranks of expected_values: 21 EVAL 044k8 artists! 02w4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 199.000 122.000 0.667 http://example.org/music/genre/artists #13637-083shs PRED entity: 083shs PRED relation: nominated_for! PRED expected values: 03hkv_r => 64 concepts (59 used for prediction) PRED predicted values (max 10 best out of 221): 0gr4k (0.68 #2071, 0.66 #5988, 0.66 #6680), 09sdmz (0.68 #2071, 0.66 #5988, 0.66 #6680), 019f4v (0.36 #1890, 0.33 #1660, 0.30 #2121), 040njc (0.30 #1846, 0.26 #1616, 0.24 #2077), 054krc (0.27 #1443, 0.20 #11747, 0.20 #11746), 0f4x7 (0.26 #1864, 0.24 #1634, 0.21 #2095), 0p9sw (0.25 #19, 0.24 #1399, 0.23 #1859), 0gr0m (0.25 #1435, 0.24 #1895, 0.22 #1665), 0gqyl (0.23 #1913, 0.21 #1683, 0.20 #11747), 05pcn59 (0.23 #2992, 0.20 #11747, 0.20 #11746) >> Best rule #2071 for best value: >> intensional similarity = 4 >> extensional distance = 408 >> proper extension: 06mmr; >> query: (?x167, ?x601) <- award(?x167, ?x601), award_winner(?x167, ?x8656), honored_for(?x1084, ?x167), type_of_union(?x8656, ?x566) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #244 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 035xwd; *> query: (?x167, 03hkv_r) <- genre(?x167, ?x7223), ?x7223 = 01j1n2, film_crew_role(?x167, ?x137) *> conf = 0.21 ranks of expected_values: 15 EVAL 083shs nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 64.000 59.000 0.680 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13636-01795t PRED entity: 01795t PRED relation: production_companies! PRED expected values: 02c7k4 => 121 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1135): 0639bg (0.45 #24591, 0.44 #8944, 0.43 #20117), 091rc5 (0.45 #24591, 0.44 #8944, 0.43 #20117), 015ynm (0.45 #24591, 0.44 #8944, 0.43 #20117), 0241y7 (0.45 #24591, 0.44 #8944, 0.43 #20117), 01vksx (0.37 #13414, 0.35 #22356, 0.34 #25711), 01ry_x (0.37 #13414, 0.35 #22356, 0.34 #25711), 0g0x9c (0.37 #13414, 0.35 #22356, 0.34 #25711), 03y0pn (0.37 #13414, 0.35 #22356, 0.34 #25711), 0243cq (0.37 #13414, 0.35 #22356, 0.34 #25711), 065z3_x (0.37 #13414, 0.35 #22356, 0.34 #25711) >> Best rule #24591 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 04rcl7; 02x2097; >> query: (?x2156, ?x1080) <- nominated_for(?x2156, ?x1080), production_companies(?x5646, ?x2156), music(?x5646, ?x9891) >> conf = 0.45 => this is the best rule for 4 predicted values *> Best rule #7393 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 04gvyp; 02swsm; *> query: (?x2156, 02c7k4) <- child(?x3920, ?x2156), ?x3920 = 09b3v *> conf = 0.06 ranks of expected_values: 359 EVAL 01795t production_companies! 02c7k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 121.000 86.000 0.447 http://example.org/film/film/production_companies #13635-07fj_ PRED entity: 07fj_ PRED relation: adjoins! PRED expected values: 0h3y => 133 concepts (114 used for prediction) PRED predicted values (max 10 best out of 473): 0h3y (0.82 #19582, 0.82 #88544, 0.81 #51697), 02j9z (0.33 #26, 0.06 #14122, 0.03 #32924), 0d05q4 (0.26 #1775, 0.07 #2557, 0.06 #23705), 06tw8 (0.22 #83055, 0.22 #76002, 0.13 #12783), 05cc1 (0.22 #83055, 0.22 #76002, 0.10 #12829), 04v09 (0.22 #83055, 0.22 #76002, 0.09 #1983), 02k54 (0.22 #83055, 0.22 #76002, 0.09 #1600), 01p1b (0.22 #83055, 0.22 #76002, 0.08 #12737), 07fj_ (0.22 #83055, 0.22 #76002, 0.06 #28201), 04wgh (0.22 #83055, 0.22 #76002, 0.06 #28201) >> Best rule #19582 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 082pc; >> query: (?x4521, ?x291) <- contains(?x2467, ?x4521), adjoins(?x4521, ?x291), locations(?x12789, ?x4521) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07fj_ adjoins! 0h3y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 114.000 0.825 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #13634-024fxq PRED entity: 024fxq PRED relation: ceremony PRED expected values: 0jzphpx 0466p0j 01mh_q => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 128): 0466p0j (0.93 #194, 0.91 #322, 0.88 #578), 01mh_q (0.87 #589, 0.86 #333, 0.86 #461), 01xqqp (0.78 #340, 0.77 #212, 0.75 #468), 0jzphpx (0.77 #159, 0.76 #287, 0.74 #415), 0gx1673 (0.52 #1004, 0.51 #876, 0.50 #748), 02yxh9 (0.42 #2049, 0.42 #2178, 0.18 #1625), 05q7cj (0.42 #2049, 0.42 #2178, 0.17 #1619), 073h9x (0.42 #2049, 0.42 #2178, 0.17 #1576), 059x66 (0.42 #2049, 0.42 #2178, 0.16 #1549), 073h5b (0.42 #2049, 0.42 #2178, 0.16 #1657) >> Best rule #194 for best value: >> intensional similarity = 8 >> extensional distance = 54 >> proper extension: 02wh75; 026mg3; 01d38g; 01c4_6; 02gx2k; 01c92g; 02nhxf; 025m8y; 01by1l; 02v1m7; ... >> query: (?x12701, 0466p0j) <- award(?x3069, ?x12701), ceremony(?x12701, ?x5766), ceremony(?x12701, ?x725), ceremony(?x12701, ?x486), ?x5766 = 013b2h, ?x725 = 01bx35, ?x486 = 02rjjll, location(?x3069, ?x362) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 024fxq ceremony 01mh_q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.929 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 024fxq ceremony 0466p0j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.929 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 024fxq ceremony 0jzphpx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 44.000 0.929 http://example.org/award/award_category/winners./award/award_honor/ceremony #13633-03n52j PRED entity: 03n52j PRED relation: profession PRED expected values: 02hrh1q => 122 concepts (77 used for prediction) PRED predicted values (max 10 best out of 74): 02hrh1q (0.98 #9257, 0.96 #9555, 0.94 #9406), 09jwl (0.55 #6429, 0.42 #5535, 0.42 #616), 0dxtg (0.54 #14, 0.36 #2547, 0.33 #3293), 03gjzk (0.46 #16, 0.43 #463, 0.35 #314), 018gz8 (0.38 #18, 0.28 #3148, 0.28 #6427), 01d_h8 (0.35 #4031, 0.33 #453, 0.33 #1198), 0nbcg (0.33 #1373, 0.28 #5994, 0.27 #5249), 016z4k (0.32 #600, 0.27 #5966, 0.26 #5221), 0dz3r (0.26 #1343, 0.24 #5964, 0.23 #5517), 02jknp (0.25 #3436, 0.22 #10889, 0.21 #902) >> Best rule #9257 for best value: >> intensional similarity = 4 >> extensional distance = 1494 >> proper extension: 0l6qt; 01r42_g; 058ncz; 01wl38s; 0d4fqn; 02773m2; 026dg51; 019z7q; 01hxs4; 02qflgv; ... >> query: (?x5397, 02hrh1q) <- profession(?x5397, ?x1383), nominated_for(?x5397, ?x4223), profession(?x12375, ?x1383), ?x12375 = 03cz9_ >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03n52j profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 77.000 0.975 http://example.org/people/person/profession #13632-0fm3kw PRED entity: 0fm3kw PRED relation: disciplines_or_subjects PRED expected values: 02vxn => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 26): 02vxn (0.77 #457, 0.73 #533, 0.68 #495), 04g51 (0.43 #633, 0.40 #671, 0.09 #2127), 02xlf (0.22 #635, 0.21 #673, 0.05 #2129), 01hmnh (0.16 #658, 0.15 #620, 0.04 #2114), 06n90 (0.15 #655, 0.14 #617, 0.04 #2111), 02jknp (0.14 #78, 0.12 #534, 0.12 #116), 05hgj (0.11 #634, 0.10 #672, 0.03 #2128), 0dwly (0.06 #636, 0.06 #674), 01mkq (0.05 #353, 0.05 #391), 0jtdp (0.05 #502, 0.02 #540, 0.02 #656) >> Best rule #457 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0dt49; >> query: (?x7774, 02vxn) <- award(?x534, ?x7774), disciplines_or_subjects(?x7774, ?x6760), student(?x6760, ?x665) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fm3kw disciplines_or_subjects 02vxn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.767 http://example.org/award/award_category/disciplines_or_subjects #13631-03dn9v PRED entity: 03dn9v PRED relation: film PRED expected values: 099bhp => 115 concepts (95 used for prediction) PRED predicted values (max 10 best out of 696): 01l_pn (0.22 #2752, 0.02 #11693, 0.02 #13482), 0dq626 (0.22 #1840), 01hvjx (0.20 #374, 0.04 #9315, 0.01 #25410), 04sh80 (0.20 #1747, 0.03 #8900, 0.02 #10688), 03nfnx (0.20 #1401, 0.02 #12130, 0.01 #15707), 07xtqq (0.20 #57, 0.02 #8998, 0.01 #12575), 06r2_ (0.20 #576, 0.01 #11305), 015bpl (0.20 #1388, 0.01 #30001, 0.01 #31790), 08phg9 (0.20 #884, 0.01 #81356, 0.01 #92084), 0m5s5 (0.20 #1625, 0.01 #26661) >> Best rule #2752 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 046qq; 01swck; 04hxyv; >> query: (?x11091, 01l_pn) <- location(?x11091, ?x2474), gender(?x11091, ?x231), film(?x11091, ?x3748), ?x3748 = 05zlld0 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #35596 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 336 *> proper extension: 01kwh5j; 07q68q; *> query: (?x11091, 099bhp) <- profession(?x11091, ?x1383), nationality(?x11091, ?x94), ?x1383 = 0np9r *> conf = 0.02 ranks of expected_values: 400 EVAL 03dn9v film 099bhp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 115.000 95.000 0.222 http://example.org/film/actor/film./film/performance/film #13630-03cvvlg PRED entity: 03cvvlg PRED relation: nominated_for! PRED expected values: 027dtxw 09sb52 => 111 concepts (88 used for prediction) PRED predicted values (max 10 best out of 216): 0gq9h (0.71 #946, 0.58 #1169, 0.54 #2730), 0gs9p (0.63 #948, 0.57 #1171, 0.51 #2732), 019f4v (0.58 #940, 0.54 #1163, 0.46 #2724), 0k611 (0.46 #1179, 0.44 #956, 0.36 #2740), 0gq_v (0.46 #910, 0.36 #1133, 0.34 #2694), 0f4x7 (0.44 #916, 0.42 #1139, 0.37 #3146), 040njc (0.42 #1122, 0.40 #1345, 0.36 #2683), 0gr0m (0.34 #1168, 0.34 #945, 0.30 #722), 02qyntr (0.34 #1280, 0.32 #1503, 0.28 #2841), 02pqp12 (0.33 #1390, 0.31 #1167, 0.27 #2728) >> Best rule #946 for best value: >> intensional similarity = 5 >> extensional distance = 57 >> proper extension: 0jym0; 0c9k8; >> query: (?x8438, 0gq9h) <- nominated_for(?x3066, ?x8438), nominated_for(?x1972, ?x8438), ?x1972 = 0gqyl, nominated_for(?x1253, ?x8438), ?x3066 = 0gqy2 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1369 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 0ds35l9; 0m313; 095zlp; 0b73_1d; 0fh694; 0_b3d; 092vkg; 0jyx6; 0pv3x; 0gmcwlb; ... *> query: (?x8438, 09sb52) <- nominated_for(?x1972, ?x8438), ?x1972 = 0gqyl, film_crew_role(?x8438, ?x137) *> conf = 0.32 ranks of expected_values: 12, 14 EVAL 03cvvlg nominated_for! 09sb52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 111.000 88.000 0.712 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03cvvlg nominated_for! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 111.000 88.000 0.712 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13629-02j9z PRED entity: 02j9z PRED relation: geographic_distribution! PRED expected values: 06j2v => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 39): 0d29z (0.50 #461, 0.38 #381, 0.36 #261), 04mvp8 (0.29 #634, 0.29 #474, 0.27 #274), 071x0k (0.29 #1084, 0.29 #443, 0.27 #243), 0g48m4 (0.24 #1604, 0.16 #1684, 0.15 #2084), 01rv7x (0.21 #462, 0.18 #262, 0.18 #622), 0432mrk (0.18 #318, 0.18 #238, 0.17 #358), 03bkbh (0.17 #176, 0.09 #256, 0.09 #216), 0g6ff (0.14 #1011, 0.14 #450, 0.12 #490), 013b6_ (0.14 #1108, 0.14 #467, 0.12 #627), 012f86 (0.14 #472, 0.10 #1033, 0.09 #272) >> Best rule #461 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 06bnz; >> query: (?x455, 0d29z) <- contains(?x455, ?x7833), location_of_ceremony(?x566, ?x455), taxonomy(?x7833, ?x939) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2239 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 0n3g; 0ckhc; *> query: (?x455, 06j2v) <- vacationer(?x455, ?x4046), adjoins(?x2467, ?x455), contains(?x2467, ?x291) *> conf = 0.02 ranks of expected_values: 37 EVAL 02j9z geographic_distribution! 06j2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 150.000 150.000 0.500 http://example.org/people/ethnicity/geographic_distribution #13628-026l37 PRED entity: 026l37 PRED relation: profession PRED expected values: 01d_h8 0dxtg => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 49): 01d_h8 (0.42 #1182, 0.42 #888, 0.40 #1035), 0dxtg (0.29 #2220, 0.29 #2368, 0.29 #161), 02krf9 (0.27 #2354, 0.17 #25, 0.11 #1348), 0d1pc (0.27 #2354, 0.09 #343, 0.07 #490), 0d8qb (0.27 #2354, 0.09 #372, 0.07 #519), 09jwl (0.25 #1636, 0.25 #1489, 0.24 #900), 0dz3r (0.22 #884, 0.21 #1473, 0.21 #1620), 02jknp (0.21 #4126, 0.20 #3685, 0.20 #1184), 0nbcg (0.20 #1648, 0.19 #1206, 0.19 #1501), 018gz8 (0.17 #16, 0.15 #1045, 0.15 #1487) >> Best rule #1182 for best value: >> intensional similarity = 2 >> extensional distance = 335 >> proper extension: 0d1_f; 02_nkp; >> query: (?x4580, 01d_h8) <- type_of_union(?x4580, ?x566), currency(?x4580, ?x170) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 026l37 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.424 http://example.org/people/person/profession EVAL 026l37 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.424 http://example.org/people/person/profession #13627-053y4h PRED entity: 053y4h PRED relation: location PRED expected values: 030qb3t => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 122): 013kcv (0.70 #24903, 0.48 #16868, 0.43 #24099), 02_286 (0.69 #4051, 0.19 #8069, 0.19 #32169), 030qb3t (0.22 #885, 0.16 #4901, 0.16 #1688), 01n7q (0.13 #3274, 0.11 #4077, 0.06 #865), 04jpl (0.11 #1622, 0.07 #32149, 0.06 #37774), 0cr3d (0.07 #8177, 0.07 #32277, 0.06 #947), 0cc56 (0.07 #4071, 0.05 #5678, 0.05 #4875), 02xry (0.05 #1738, 0.05 #3344, 0.01 #5754), 0f2wj (0.05 #1639, 0.03 #836, 0.02 #5655), 05k7sb (0.05 #3320, 0.01 #4123, 0.01 #20992) >> Best rule #24903 for best value: >> intensional similarity = 3 >> extensional distance = 1483 >> proper extension: 0f1vrl; 01wz_ml; 07h1h5; 0c8hct; 01pwz; 01lz4tf; 04m2zj; 04093; 04hqbbz; 05yvfd; ... >> query: (?x5133, ?x859) <- location(?x5133, ?x335), profession(?x5133, ?x1032), place_of_birth(?x5133, ?x859) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #885 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 03lpd0; *> query: (?x5133, 030qb3t) <- award(?x5133, ?x2603), gender(?x5133, ?x514), ?x2603 = 09qs08 *> conf = 0.22 ranks of expected_values: 3 EVAL 053y4h location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 82.000 0.697 http://example.org/people/person/places_lived./people/place_lived/location #13626-011wtv PRED entity: 011wtv PRED relation: featured_film_locations PRED expected values: 0rh6k => 90 concepts (58 used for prediction) PRED predicted values (max 10 best out of 68): 02_286 (0.26 #1941, 0.18 #1221, 0.18 #4109), 030qb3t (0.19 #520, 0.17 #39, 0.08 #5329), 052p7 (0.17 #298, 0.05 #779, 0.02 #2943), 0rh6k (0.09 #2403, 0.08 #1922, 0.08 #1682), 0d6lp (0.08 #312, 0.06 #1273, 0.03 #4401), 04jpl (0.08 #2894, 0.08 #2411, 0.08 #5540), 080h2 (0.07 #1945, 0.05 #4113, 0.03 #2426), 03rjj (0.05 #967, 0.05 #1447, 0.04 #1687), 06y57 (0.04 #1304, 0.03 #3712, 0.03 #3952), 0dc95 (0.04 #1262, 0.02 #1502, 0.02 #1742) >> Best rule #1941 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 03g90h; 0jjy0; 05pdd86; 01gwk3; 0gfzfj; 04sh80; 03wjm2; >> query: (?x4565, 02_286) <- genre(?x4565, ?x1013), produced_by(?x4565, ?x1533), ?x1013 = 06n90, film(?x1051, ?x4565) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #2403 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 148 *> proper extension: 07s3m4g; *> query: (?x4565, 0rh6k) <- genre(?x4565, ?x600), film_release_distribution_medium(?x4565, ?x81), ?x600 = 02n4kr *> conf = 0.09 ranks of expected_values: 4 EVAL 011wtv featured_film_locations 0rh6k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 58.000 0.257 http://example.org/film/film/featured_film_locations #13625-0sxlb PRED entity: 0sxlb PRED relation: genre PRED expected values: 06cvj => 82 concepts (48 used for prediction) PRED predicted values (max 10 best out of 89): 01z4y (0.48 #5578, 0.47 #1859, 0.46 #2208), 03k9fj (0.47 #3843, 0.47 #8, 0.23 #5352), 01jfsb (0.31 #9, 0.29 #2334, 0.28 #5353), 06cvj (0.27 #582, 0.25 #1279, 0.25 #1627), 082gq (0.21 #142, 0.21 #258, 0.17 #374), 01hmnh (0.20 #3848, 0.18 #13, 0.16 #5357), 04xvlr (0.20 #697, 0.20 #3139, 0.19 #1743), 03bxz7 (0.15 #399, 0.13 #167, 0.13 #283), 01t_vv (0.14 #746, 0.13 #630, 0.12 #4117), 06n90 (0.14 #3845, 0.11 #5354, 0.09 #1519) >> Best rule #5578 for best value: >> intensional similarity = 3 >> extensional distance = 1051 >> proper extension: 0gfzgl; 01f3p_; 07wqr6; 03g9xj; 0cskb; >> query: (?x9761, ?x2480) <- nominated_for(?x406, ?x9761), titles(?x2480, ?x9761), location(?x406, ?x191) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #582 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 197 *> proper extension: 02z3r8t; 0c00zd0; 0j_tw; 05fgt1; 065zlr; 05q4y12; 014zwb; 014nq4; 0gtvpkw; 0c57yj; ... *> query: (?x9761, 06cvj) <- genre(?x9761, ?x1403), ?x1403 = 02l7c8, film(?x6239, ?x9761), film(?x241, ?x9761) *> conf = 0.27 ranks of expected_values: 4 EVAL 0sxlb genre 06cvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 48.000 0.479 http://example.org/film/film/genre #13624-0bbvr84 PRED entity: 0bbvr84 PRED relation: award_winner! PRED expected values: 09g90vz => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 103): 09g90vz (0.67 #123, 0.62 #263, 0.28 #4061), 02wzl1d (0.28 #4061, 0.17 #4483, 0.16 #4202), 03nnm4t (0.28 #4061, 0.06 #353, 0.04 #1473), 05c1t6z (0.28 #4061, 0.05 #1135, 0.04 #1415), 0gvstc3 (0.28 #4061, 0.04 #1154, 0.03 #3254), 0418154 (0.28 #4061, 0.02 #3747, 0.02 #3327), 05zksls (0.28 #4061, 0.02 #3675, 0.02 #1435), 09qvms (0.17 #4483, 0.16 #4202, 0.10 #9665), 03gyp30 (0.07 #816, 0.07 #956, 0.05 #1096), 0hr3c8y (0.06 #850, 0.06 #710, 0.05 #990) >> Best rule #123 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 05lb87; 08pth9; 09btt1; 0d810y; >> query: (?x10138, 09g90vz) <- award(?x10138, ?x1670), award_winner(?x5215, ?x10138), ?x5215 = 07s95_l >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bbvr84 award_winner! 09g90vz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13623-03c_cxn PRED entity: 03c_cxn PRED relation: film_crew_role PRED expected values: 09zzb8 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 32): 09zzb8 (0.68 #196, 0.61 #863, 0.60 #627), 0ch6mp2 (0.67 #87, 0.65 #204, 0.64 #322), 02r96rf (0.55 #630, 0.54 #984, 0.53 #435), 09vw2b7 (0.52 #870, 0.49 #1146, 0.49 #1263), 01pvkk (0.37 #93, 0.28 #210, 0.27 #54), 0dxtw (0.27 #993, 0.26 #2176, 0.25 #3826), 0215hd (0.27 #100, 0.21 #453, 0.20 #648), 01vx2h (0.26 #994, 0.24 #2177, 0.22 #4025), 089g0h (0.23 #101, 0.18 #2359, 0.14 #649), 0d2b38 (0.20 #107, 0.18 #2359, 0.13 #655) >> Best rule #196 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 0dq626; 09p35z; 0bs5f0b; >> query: (?x5107, 09zzb8) <- film_release_distribution_medium(?x5107, ?x81), genre(?x5107, ?x6674), genre(?x5107, ?x1403), ?x6674 = 01t_vv, ?x1403 = 02l7c8 >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03c_cxn film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.675 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13622-01dtcb PRED entity: 01dtcb PRED relation: state_province_region PRED expected values: 01n7q => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 55): 059rby (0.75 #4203, 0.66 #5930, 0.43 #2473), 01n7q (0.59 #6438, 0.36 #2487, 0.35 #3722), 0kpys (0.25 #14719, 0.23 #20848, 0.23 #21471), 0gx1l (0.23 #20848, 0.23 #21471, 0.15 #20347), 09c7w0 (0.23 #20848, 0.23 #21471, 0.15 #20347), 030qb3t (0.15 #20347, 0.04 #15342, 0.03 #15341), 02_286 (0.15 #20347, 0.03 #15341, 0.03 #15468), 0cc56 (0.15 #20347), 0f2wj (0.15 #20347), 0d060g (0.15 #20347) >> Best rule #4203 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 0sxdg; 01_4lx; >> query: (?x7793, 059rby) <- citytown(?x7793, ?x1523), citytown(?x7793, ?x739), child(?x7793, ?x4483), ?x739 = 02_286, place_of_birth(?x338, ?x1523) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6438 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 01bzw5; 06xpp7; 0k9ctht; 05cwl_; 06nzl; 06182p; 05q2c; 01nds; 06kknt; 06b7s9; ... *> query: (?x7793, 01n7q) <- citytown(?x7793, ?x1523), citytown(?x7793, ?x242), ?x1523 = 030qb3t, contains(?x94, ?x242), place_of_death(?x200, ?x242) *> conf = 0.59 ranks of expected_values: 2 EVAL 01dtcb state_province_region 01n7q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 174.000 174.000 0.750 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #13621-01f9zw PRED entity: 01f9zw PRED relation: gender PRED expected values: 05zppz => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #3, 0.81 #1, 0.81 #51), 02zsn (0.41 #82, 0.39 #34, 0.38 #70) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01wmxfs; >> query: (?x8856, 05zppz) <- award(?x8856, ?x2563), profession(?x8856, ?x2348), ?x2348 = 0nbcg, ?x2563 = 01cw51 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f9zw gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.833 http://example.org/people/person/gender #13620-012g92 PRED entity: 012g92 PRED relation: gender PRED expected values: 02zsn => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #101, 0.72 #153, 0.71 #179), 02zsn (0.30 #14, 0.28 #48, 0.28 #128) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 1653 >> proper extension: 0f1vrl; 07nv3_; 017yfz; 01wy61y; 01_k1z; 0c8hct; 01pwz; 01lz4tf; 021r7r; 04093; ... >> query: (?x12218, 05zppz) <- type_of_union(?x12218, ?x566), ?x566 = 04ztj, place_of_birth(?x12218, ?x4627) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 492 *> proper extension: 08849; *> query: (?x12218, 02zsn) <- type_of_union(?x12218, ?x566), award_winner(?x8888, ?x12218), participant(?x5665, ?x8888) *> conf = 0.30 ranks of expected_values: 2 EVAL 012g92 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.720 http://example.org/people/person/gender #13619-0chw_ PRED entity: 0chw_ PRED relation: languages PRED expected values: 02bjrlw => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 14): 02h40lc (0.90 #382, 0.86 #534, 0.32 #116), 03k50 (0.07 #536, 0.03 #384, 0.02 #498), 04306rv (0.06 #2395, 0.06 #2472, 0.03 #383), 06nm1 (0.06 #2395, 0.06 #2472, 0.03 #386), 06b_j (0.06 #2395, 0.06 #2472, 0.01 #547), 02bjrlw (0.06 #381, 0.06 #77, 0.05 #533), 07c9s (0.04 #545, 0.01 #1153, 0.01 #2217), 0999q (0.02 #554), 09s02 (0.02 #567), 0t_2 (0.02 #389, 0.02 #9, 0.02 #275) >> Best rule #382 for best value: >> intensional similarity = 3 >> extensional distance = 267 >> proper extension: 03_wpf; >> query: (?x9033, 02h40lc) <- award_winner(?x618, ?x9033), film(?x9033, ?x3471), languages(?x9033, ?x5607) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #381 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 267 *> proper extension: 03_wpf; *> query: (?x9033, 02bjrlw) <- award_winner(?x618, ?x9033), film(?x9033, ?x3471), languages(?x9033, ?x5607) *> conf = 0.06 ranks of expected_values: 6 EVAL 0chw_ languages 02bjrlw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 102.000 102.000 0.903 http://example.org/people/person/languages #13618-01bjv PRED entity: 01bjv PRED relation: mode_of_transportation! PRED expected values: 05ywg 0ply0 02cft 0f04v => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 229): 0853g (0.60 #71, 0.40 #52, 0.33 #33), 0k9p4 (0.60 #67, 0.40 #48, 0.33 #29), 0fq8f (0.60 #58, 0.40 #39, 0.33 #20), 02sn34 (0.40 #63, 0.40 #44, 0.33 #25), 02cft (0.40 #62, 0.40 #43, 0.33 #24), 0f04v (0.40 #64, 0.40 #45, 0.33 #26), 02h6_6p (0.40 #59, 0.40 #40, 0.33 #21), 0171b8 (0.40 #53, 0.33 #17, 0.20 #72), 03czqs (0.40 #51, 0.33 #15, 0.20 #70), 0ply0 (0.33 #23, 0.20 #61, 0.20 #42) >> Best rule #71 for best value: >> intensional similarity = 45 >> extensional distance = 3 >> proper extension: 0k4j; >> query: (?x8731, 0853g) <- mode_of_transportation(?x5036, ?x8731), mode_of_transportation(?x4826, ?x8731), mode_of_transportation(?x4698, ?x8731), mode_of_transportation(?x3501, ?x8731), mode_of_transportation(?x3269, ?x8731), mode_of_transportation(?x2254, ?x8731), location(?x5299, ?x5036), location(?x2179, ?x5036), category(?x4826, ?x134), location(?x8395, ?x3501), contains(?x8506, ?x5036), place_of_birth(?x5342, ?x4698), featured_film_locations(?x308, ?x5036), capital(?x2152, ?x4698), origin(?x4995, ?x3501), location(?x818, ?x3269), citytown(?x2106, ?x4826), award_winner(?x2179, ?x4383), titles(?x162, ?x308), locations(?x5897, ?x3269), place_of_birth(?x2691, ?x4826), ?x162 = 04xvlr, place_of_birth(?x91, ?x3501), country(?x308, ?x94), contains(?x4698, ?x7377), genre(?x308, ?x53), location(?x120, ?x2254), award(?x2179, ?x68), award_nominee(?x1384, ?x5299), place_of_birth(?x487, ?x2254), film(?x5299, ?x1868), contains(?x172, ?x4826), contains(?x2254, ?x4262), featured_film_locations(?x69, ?x2254), origin(?x226, ?x5036), film(?x804, ?x308), award_winner(?x3274, ?x5299), award_nominee(?x100, ?x818), profession(?x8395, ?x1581), origin(?x3176, ?x2254), locations(?x12451, ?x3501), jurisdiction_of_office(?x900, ?x8506), place_of_birth(?x844, ?x5036), location_of_ceremony(?x566, ?x4698), nominated_for(?x818, ?x1877) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #62 for first EXPECTED value: *> intensional similarity = 45 *> extensional distance = 3 *> proper extension: 0k4j; *> query: (?x8731, 02cft) <- mode_of_transportation(?x5036, ?x8731), mode_of_transportation(?x4826, ?x8731), mode_of_transportation(?x4698, ?x8731), mode_of_transportation(?x3501, ?x8731), mode_of_transportation(?x3269, ?x8731), mode_of_transportation(?x2254, ?x8731), location(?x5299, ?x5036), location(?x2179, ?x5036), category(?x4826, ?x134), location(?x8395, ?x3501), contains(?x8506, ?x5036), place_of_birth(?x5342, ?x4698), featured_film_locations(?x308, ?x5036), capital(?x2152, ?x4698), origin(?x4995, ?x3501), location(?x818, ?x3269), citytown(?x2106, ?x4826), award_winner(?x2179, ?x4383), titles(?x162, ?x308), locations(?x5897, ?x3269), place_of_birth(?x2691, ?x4826), ?x162 = 04xvlr, place_of_birth(?x91, ?x3501), country(?x308, ?x94), contains(?x4698, ?x7377), genre(?x308, ?x53), location(?x120, ?x2254), award(?x2179, ?x68), award_nominee(?x1384, ?x5299), place_of_birth(?x487, ?x2254), film(?x5299, ?x1868), contains(?x172, ?x4826), contains(?x2254, ?x4262), featured_film_locations(?x69, ?x2254), origin(?x226, ?x5036), film(?x804, ?x308), award_winner(?x3274, ?x5299), award_nominee(?x100, ?x818), profession(?x8395, ?x1581), origin(?x3176, ?x2254), locations(?x12451, ?x3501), jurisdiction_of_office(?x900, ?x8506), place_of_birth(?x844, ?x5036), location_of_ceremony(?x566, ?x4698), nominated_for(?x818, ?x1877) *> conf = 0.40 ranks of expected_values: 5, 6, 10, 12 EVAL 01bjv mode_of_transportation! 0f04v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 5.000 5.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 01bjv mode_of_transportation! 02cft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 5.000 5.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 01bjv mode_of_transportation! 0ply0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 5.000 5.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 01bjv mode_of_transportation! 05ywg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 5.000 5.000 0.600 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #13617-07c0j PRED entity: 07c0j PRED relation: award_winner! PRED expected values: 01c427 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 324): 01bgqh (0.38 #3846, 0.37 #3418, 0.37 #46139), 01ckcd (0.38 #3846, 0.37 #3418, 0.37 #46139), 02f72n (0.38 #3846, 0.37 #3418, 0.37 #46139), 0l8z1 (0.27 #13308, 0.23 #15443, 0.19 #14589), 054ks3 (0.25 #140, 0.18 #995, 0.15 #13384), 01ck6h (0.25 #121, 0.18 #976, 0.09 #2684), 01ck6v (0.25 #267, 0.18 #1122, 0.09 #2830), 0c4z8 (0.25 #72, 0.12 #927, 0.12 #7337), 025m8y (0.25 #13344, 0.21 #14625, 0.20 #15479), 054krc (0.25 #13332, 0.21 #15467, 0.16 #14613) >> Best rule #3846 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 0sx5w; >> query: (?x1136, ?x724) <- influenced_by(?x483, ?x1136), award(?x1136, ?x724), participant(?x1089, ?x1136) >> conf = 0.38 => this is the best rule for 3 predicted values *> Best rule #7350 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 67 *> proper extension: 01kd57; 01w20rx; *> query: (?x1136, 01c427) <- artists(?x3061, ?x1136), award_winner(?x6487, ?x1136), ?x3061 = 05bt6j *> conf = 0.09 ranks of expected_values: 54 EVAL 07c0j award_winner! 01c427 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 126.000 126.000 0.375 http://example.org/award/award_category/winners./award/award_honor/award_winner #13616-01kph_c PRED entity: 01kph_c PRED relation: award PRED expected values: 01c92g => 149 concepts (120 used for prediction) PRED predicted values (max 10 best out of 315): 01by1l (0.46 #6945, 0.41 #5739, 0.36 #3327), 01bgqh (0.38 #6876, 0.36 #5670, 0.29 #13711), 03qbh5 (0.30 #607, 0.22 #5833, 0.21 #3421), 03tcnt (0.30 #568, 0.18 #41413, 0.17 #28143), 054ks3 (0.30 #5769, 0.27 #6975, 0.19 #6573), 01ckrr (0.27 #633, 0.18 #41413, 0.15 #1437), 0gqz2 (0.26 #5707, 0.25 #6913, 0.18 #41413), 09sb52 (0.26 #16925, 0.24 #27780, 0.24 #15317), 02sp_v (0.25 #162, 0.18 #41413, 0.09 #3378), 01ck6h (0.24 #523, 0.18 #41413, 0.17 #28143) >> Best rule #6945 for best value: >> intensional similarity = 4 >> extensional distance = 239 >> proper extension: 0c_drn; >> query: (?x4790, 01by1l) <- award_winner(?x9828, ?x4790), award(?x2963, ?x9828), ?x2963 = 0gcs9, ceremony(?x9828, ?x139) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #5724 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 221 *> proper extension: 06cv1; 04k25; 012gq6; 01_k71; 014dm6; 05q9g1; 01pbwwl; *> query: (?x4790, 01c92g) <- award_winner(?x9828, ?x4790), award(?x2963, ?x9828), ?x2963 = 0gcs9, profession(?x4790, ?x220) *> conf = 0.18 ranks of expected_values: 19 EVAL 01kph_c award 01c92g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 149.000 120.000 0.461 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13615-0948xk PRED entity: 0948xk PRED relation: profession PRED expected values: 04gc2 => 169 concepts (131 used for prediction) PRED predicted values (max 10 best out of 109): 09jwl (0.81 #10831, 0.79 #9369, 0.76 #13167), 02hrh1q (0.81 #12286, 0.70 #11410, 0.68 #14768), 0cbd2 (0.76 #15492, 0.66 #16807, 0.60 #3806), 0dz3r (0.76 #13003, 0.56 #13733, 0.49 #9351), 0nbcg (0.62 #9382, 0.58 #10844, 0.54 #13034), 016z4k (0.61 #9353, 0.59 #13735, 0.55 #11983), 04gc2 (0.49 #6615, 0.43 #3987, 0.41 #1170), 0kyk (0.45 #3830, 0.41 #1170, 0.31 #1755), 016fly (0.41 #1170, 0.33 #658, 0.31 #1755), 012t_z (0.41 #1170, 0.31 #1755, 0.29 #1036) >> Best rule #10831 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 05qhnq; >> query: (?x9680, 09jwl) <- category(?x9680, ?x134), profession(?x9680, ?x2659), ?x2659 = 039v1 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #6615 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 08f3b1; 083p7; 0157m; 083pr; 063vn; 0bymv; 01k165; 0d06m5; 03f5vvx; 0d05fv; ... *> query: (?x9680, 04gc2) <- religion(?x9680, ?x1985), student(?x892, ?x9680), politician(?x9679, ?x9680), basic_title(?x9680, ?x182) *> conf = 0.49 ranks of expected_values: 7 EVAL 0948xk profession 04gc2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 169.000 131.000 0.812 http://example.org/people/person/profession #13614-01400v PRED entity: 01400v PRED relation: major_field_of_study! PRED expected values: 06pwq 07szy 02kbtf => 65 concepts (44 used for prediction) PRED predicted values (max 10 best out of 664): 09f2j (0.67 #7262, 0.59 #6670, 0.57 #9028), 01w3v (0.67 #7099, 0.56 #1782, 0.50 #14183), 07wjk (0.67 #1834, 0.50 #1761, 0.50 #1244), 07szy (0.62 #1220, 0.61 #7127, 0.60 #8847), 01w5m (0.62 #1295, 0.61 #17817, 0.57 #14286), 08815 (0.62 #1178, 0.53 #7079, 0.53 #6493), 02zd460 (0.61 #7278, 0.55 #14362, 0.52 #19659), 06pwq (0.59 #6504, 0.58 #11819, 0.57 #602), 01mpwj (0.56 #1886, 0.50 #7203, 0.50 #1296), 02bqy (0.56 #1972, 0.50 #8467, 0.50 #7289) >> Best rule #7262 for best value: >> intensional similarity = 11 >> extensional distance = 16 >> proper extension: 03nfmq; >> query: (?x12035, 09f2j) <- major_field_of_study(?x6127, ?x12035), major_field_of_study(?x6056, ?x12035), ?x6056 = 05zl0, major_field_of_study(?x1368, ?x12035), ?x1368 = 014mlp, student(?x6127, ?x3867), currency(?x6127, ?x1099), institution(?x7636, ?x6127), instrumentalists(?x227, ?x3867), ?x7636 = 01rr_d, currency(?x1098, ?x1099) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1220 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 6 *> proper extension: 03g3w; 05qfh; 0fdys; 04gb7; 0_jm; *> query: (?x12035, 07szy) <- major_field_of_study(?x12035, ?x9111), major_field_of_study(?x12035, ?x2606), major_field_of_study(?x12035, ?x1682), major_field_of_study(?x1771, ?x12035), major_field_of_study(?x1368, ?x12035), ?x2606 = 062z7, ?x1771 = 019v9k, major_field_of_study(?x2895, ?x1682), major_field_of_study(?x734, ?x1682), major_field_of_study(?x2327, ?x9111), ?x1368 = 014mlp, student(?x1682, ?x4026), ?x2895 = 0l2tk, ?x2327 = 07wjk *> conf = 0.62 ranks of expected_values: 4, 8, 184 EVAL 01400v major_field_of_study! 02kbtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 65.000 44.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01400v major_field_of_study! 07szy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 65.000 44.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01400v major_field_of_study! 06pwq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 65.000 44.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13613-09g7vfw PRED entity: 09g7vfw PRED relation: costume_design_by PRED expected values: 03mfqm => 98 concepts (85 used for prediction) PRED predicted values (max 10 best out of 13): 02h1rt (0.12 #42, 0.04 #182, 0.04 #98), 03mfqm (0.07 #186, 0.05 #130, 0.05 #779), 03gt0c5 (0.06 #83, 0.04 #195, 0.04 #111), 03qhyn8 (0.06 #82, 0.02 #307, 0.02 #279), 0bytfv (0.05 #123, 0.04 #320, 0.04 #208), 03y1mlp (0.03 #537, 0.03 #339, 0.03 #904), 02mxbd (0.03 #495, 0.02 #891, 0.02 #242), 02cqbx (0.03 #156, 0.02 #1652, 0.02 #1764), 02w0dc0 (0.02 #1326, 0.01 #1045, 0.01 #1382), 02pqgt8 (0.01 #1816, 0.01 #914, 0.01 #1732) >> Best rule #42 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0gkz15s; 04f52jw; >> query: (?x3423, 02h1rt) <- film_release_region(?x3423, ?x1264), film_release_region(?x3423, ?x311), ?x1264 = 0345h, genre(?x3423, ?x258), crewmember(?x3423, ?x3782), film_release_distribution_medium(?x3423, ?x81), ?x311 = 0j1z8 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #186 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 44 *> proper extension: 0ds11z; 0ds33; 048scx; 01kff7; 065dc4; 0bw20; 02vjp3; 01cycq; 09lxv9; *> query: (?x3423, 03mfqm) <- film_crew_role(?x3423, ?x2091), film_crew_role(?x3423, ?x468), film_crew_role(?x3423, ?x137), ?x468 = 02r96rf, crewmember(?x3423, ?x3782), ?x2091 = 02rh1dz, ?x137 = 09zzb8, film(?x891, ?x3423) *> conf = 0.07 ranks of expected_values: 2 EVAL 09g7vfw costume_design_by 03mfqm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 85.000 0.125 http://example.org/film/film/costume_design_by #13612-0992d9 PRED entity: 0992d9 PRED relation: film! PRED expected values: 01l9p 07yp0f => 75 concepts (34 used for prediction) PRED predicted values (max 10 best out of 943): 01mylz (0.40 #6104, 0.01 #20662, 0.01 #26901), 044rvb (0.33 #102, 0.02 #14661, 0.02 #16740), 0161h5 (0.33 #1824, 0.02 #10144), 02_p5w (0.33 #646, 0.02 #19363, 0.02 #13126), 01gkmx (0.33 #1584, 0.02 #20301, 0.02 #16143), 04fzk (0.33 #708, 0.02 #19425, 0.02 #15267), 01l_yg (0.33 #1656), 01ccr8 (0.33 #1465), 02k4b2 (0.33 #934), 0dn3n (0.33 #523) >> Best rule #6104 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0gjcrrw; 06gb1w; 01xdxy; >> query: (?x5730, 01mylz) <- film(?x4676, ?x5730), film(?x1554, ?x5730), ?x4676 = 04cl1, film_crew_role(?x5730, ?x137), award_nominee(?x1554, ?x400), participant(?x286, ?x1554) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0992d9 film! 07yp0f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 34.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 0992d9 film! 01l9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 34.000 0.400 http://example.org/film/actor/film./film/performance/film #13611-05kwx2 PRED entity: 05kwx2 PRED relation: people! PRED expected values: 02w7gg => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 26): 02w7gg (0.50 #2, 0.23 #310, 0.09 #541), 041rx (0.30 #235, 0.20 #4, 0.11 #1468), 0g8_vp (0.18 #99, 0.01 #561, 0.01 #1486), 0x67 (0.15 #241, 0.10 #472, 0.10 #1474), 0dryh9k (0.11 #170, 0.02 #5715, 0.02 #1557), 03lmx1 (0.10 #14, 0.09 #91, 0.02 #553), 0d7wh (0.10 #17, 0.06 #325, 0.02 #865), 07bch9 (0.09 #100, 0.03 #2950, 0.03 #3335), 033tf_ (0.08 #392, 0.08 #469, 0.07 #238), 048z7l (0.07 #271, 0.02 #1581, 0.02 #3891) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06ltr; 03jj93; >> query: (?x6227, 02w7gg) <- profession(?x6227, ?x1032), film(?x6227, ?x6332), ?x6332 = 03hxsv >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05kwx2 people! 02w7gg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.500 http://example.org/people/ethnicity/people #13610-0cmdwwg PRED entity: 0cmdwwg PRED relation: film_crew_role PRED expected values: 02r96rf => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 24): 02r96rf (0.58 #152, 0.56 #226, 0.54 #300), 09zzb8 (0.58 #260, 0.58 #297, 0.56 #409), 09vw2b7 (0.48 #640, 0.48 #566, 0.43 #529), 01pvkk (0.27 #421, 0.26 #272, 0.25 #309), 01vx2h (0.25 #160, 0.24 #570, 0.24 #644), 02ynfr (0.17 #17, 0.13 #575, 0.12 #649), 0215hd (0.15 #205, 0.14 #94, 0.14 #353), 089g0h (0.12 #206, 0.10 #354, 0.10 #95), 02rh1dz (0.11 #159, 0.09 #233, 0.08 #569), 02_n3z (0.10 #76, 0.09 #113, 0.07 #187) >> Best rule #152 for best value: >> intensional similarity = 5 >> extensional distance = 140 >> proper extension: 0b76d_m; 0g56t9t; 02vxq9m; 011yrp; 07gp9; 0ds3t5x; 0m2kd; 0dckvs; 0g5qs2k; 0dscrwf; ... >> query: (?x6394, 02r96rf) <- nominated_for(?x3435, ?x6394), film_release_region(?x6394, ?x279), film_release_region(?x6394, ?x142), ?x279 = 0d060g, ?x142 = 0jgd >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cmdwwg film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.585 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13609-03k1vm PRED entity: 03k1vm PRED relation: student! PRED expected values: 01hx2t => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 98): 01jq34 (0.25 #584, 0.05 #2692, 0.04 #3219), 015zyd (0.22 #1582, 0.12 #3690, 0.01 #13703), 01qd_r (0.22 #1862, 0.04 #3970, 0.02 #4497), 01w3v (0.17 #1069, 0.02 #4758, 0.02 #4231), 02yr3z (0.17 #1296, 0.01 #4985), 02dq8f (0.17 #1210, 0.01 #4899), 02g839 (0.11 #1606, 0.08 #3714, 0.01 #8457), 07vjm (0.11 #1809, 0.04 #3917), 026vcc (0.11 #1801, 0.04 #3909), 02s62q (0.11 #1633) >> Best rule #584 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 044f7; >> query: (?x11326, 01jq34) <- place_of_death(?x11326, ?x5895), profession(?x11326, ?x1383), ?x1383 = 0np9r, special_performance_type(?x11326, ?x9609) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03k1vm student! 01hx2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 83.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #13608-015fsv PRED entity: 015fsv PRED relation: student PRED expected values: 06pwf6 => 104 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1187): 025j1t (0.18 #3158, 0.14 #1063, 0.03 #9440), 035kl6 (0.14 #1821, 0.09 #3916, 0.02 #16480), 03fnyk (0.14 #1959, 0.09 #4054, 0.02 #16618), 037d35 (0.14 #1056, 0.09 #3151, 0.02 #9433), 02hy5d (0.14 #1610, 0.09 #3705, 0.01 #16269), 01d_4t (0.14 #1540, 0.09 #3635, 0.01 #16199), 025b3k (0.14 #1651, 0.09 #3746), 033w9g (0.09 #2867, 0.04 #4961, 0.03 #11243), 019vgs (0.09 #2723, 0.03 #6911, 0.02 #15287), 04hw4b (0.09 #3329, 0.02 #17987, 0.02 #20081) >> Best rule #3158 for best value: >> intensional similarity = 2 >> extensional distance = 9 >> proper extension: 03x_k5m; 01_bp; >> query: (?x9249, 025j1t) <- state_province_region(?x9249, ?x1906), ?x1906 = 04rrx >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #6746 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 02cttt; 01wdl3; 07szy; 0bx8pn; 0bqxw; 03x83_; 02zd460; 037fqp; 050xpd; 01dq0z; *> query: (?x9249, 06pwf6) <- school_type(?x9249, ?x3092), institution(?x9054, ?x9249), contains(?x94, ?x9249), ?x9054 = 022h5x *> conf = 0.06 ranks of expected_values: 21 EVAL 015fsv student 06pwf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 104.000 84.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student #13607-080dwhx PRED entity: 080dwhx PRED relation: nominated_for! PRED expected values: 026v437 => 78 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1126): 026v437 (0.80 #6948, 0.79 #32423, 0.79 #129709), 0c6qh (0.36 #2825, 0.14 #74113, 0.13 #85698), 01hkhq (0.17 #81062, 0.17 #64845, 0.15 #122756), 016gr2 (0.17 #81062, 0.17 #64845, 0.15 #122756), 03n_7k (0.17 #81062, 0.17 #64845, 0.15 #122756), 051wwp (0.17 #81062, 0.17 #64845, 0.15 #122756), 02qgqt (0.17 #81062, 0.17 #64845, 0.15 #122756), 0h0yt (0.17 #81062, 0.17 #64845, 0.15 #122756), 02l4rh (0.17 #81062, 0.17 #64845, 0.15 #122756), 0755wz (0.17 #81062, 0.17 #64845, 0.15 #122756) >> Best rule #6948 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 01j95; >> query: (?x493, ?x494) <- category(?x493, ?x134), award_winner(?x493, ?x494), program(?x3293, ?x493) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 080dwhx nominated_for! 026v437 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 61.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #13606-018ygt PRED entity: 018ygt PRED relation: actor! PRED expected values: 01hn_t => 112 concepts (78 used for prediction) PRED predicted values (max 10 best out of 138): 08jgk1 (0.20 #22, 0.02 #6100, 0.01 #15905), 02py4c8 (0.20 #12, 0.02 #540, 0.01 #5297), 04vr_f (0.12 #10063, 0.10 #6610, 0.10 #11124), 07nxvj (0.12 #10063, 0.10 #11124, 0.09 #12720), 049xgc (0.10 #6610, 0.07 #13252, 0.06 #12985), 01q_y0 (0.10 #6610, 0.07 #13252, 0.06 #12985), 0vjr (0.07 #14311, 0.03 #15373, 0.02 #1414), 039cq4 (0.07 #14311, 0.03 #15373, 0.02 #1184), 0g60z (0.07 #14311, 0.03 #15373, 0.01 #15905), 034fl9 (0.07 #14311, 0.01 #15905) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0h0wc; 0p_47; 060j8b; >> query: (?x6324, 08jgk1) <- award_winner(?x406, ?x6324), film(?x6324, ?x2846), ?x2846 = 076tq0z >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018ygt actor! 01hn_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 78.000 0.200 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #13605-04sntd PRED entity: 04sntd PRED relation: country PRED expected values: 03_3d => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 169): 03rjj (0.50 #120, 0.33 #63, 0.11 #293), 0d05w3 (0.33 #97, 0.25 #154, 0.11 #327), 03h64 (0.33 #100, 0.25 #157, 0.06 #330), 0d060g (0.33 #8, 0.20 #179, 0.15 #352), 01jfsb (0.32 #1848, 0.14 #1847, 0.07 #3694), 0mw1j (0.27 #2484), 04_1l0v (0.27 #2484), 06yxd (0.27 #2484), 03mqtr (0.14 #1847, 0.07 #3694, 0.07 #3232), 0rh6k (0.12 #229, 0.05 #402, 0.04 #749) >> Best rule #120 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 02754c9; >> query: (?x2960, 03rjj) <- country(?x2960, ?x1264), country(?x2960, ?x789), ?x1264 = 0345h, film(?x3078, ?x2960), costume_design_by(?x2960, ?x771), ?x789 = 0f8l9c, award_winner(?x264, ?x3078) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #351 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 25 *> proper extension: 01gwk3; 080dfr7; *> query: (?x2960, 03_3d) <- country(?x2960, ?x1264), country(?x2960, ?x512), ?x1264 = 0345h, film(?x4965, ?x2960), ?x512 = 07ssc, award_nominee(?x4965, ?x56), featured_film_locations(?x2960, ?x108) *> conf = 0.11 ranks of expected_values: 12 EVAL 04sntd country 03_3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 83.000 83.000 0.500 http://example.org/film/film/country #13604-0k4p0 PRED entity: 0k4p0 PRED relation: award PRED expected values: 02r0csl => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 170): 0bdw1g (0.30 #31, 0.01 #10716, 0.01 #10948), 0gs9p (0.30 #529, 0.26 #1627, 0.25 #465), 0gq9h (0.27 #527, 0.26 #1627, 0.25 #465), 0k611 (0.26 #538, 0.26 #1627, 0.25 #465), 094qd5 (0.26 #1627, 0.25 #465, 0.25 #11615), 019f4v (0.26 #1627, 0.25 #465, 0.25 #11615), 0gr51 (0.26 #1627, 0.25 #465, 0.25 #11615), 0gr0m (0.26 #1627, 0.25 #465, 0.25 #11615), 0f4x7 (0.26 #1627, 0.25 #465, 0.25 #11615), 0p9sw (0.26 #1627, 0.25 #465, 0.25 #11615) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 0300ml; >> query: (?x5712, 0bdw1g) <- award(?x5712, ?x1254), award_winner(?x1254, ?x1641), ?x1641 = 07s8r0 >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #11382 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x5712, ?x601) <- award(?x5712, ?x1254), award(?x2742, ?x1254), award(?x2742, ?x601) *> conf = 0.05 ranks of expected_values: 92 EVAL 0k4p0 award 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 80.000 80.000 0.298 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13603-04mkft PRED entity: 04mkft PRED relation: child! PRED expected values: 0l8sx => 111 concepts (110 used for prediction) PRED predicted values (max 10 best out of 167): 0l8sx (0.78 #1247, 0.62 #5644, 0.60 #4309), 03rwz3 (0.62 #5644, 0.60 #4309, 0.57 #4724), 016tt2 (0.62 #5644, 0.60 #4309, 0.57 #4724), 03phgz (0.62 #5644, 0.60 #4309, 0.57 #4724), 02bh8z (0.41 #2005, 0.21 #4083, 0.17 #847), 09b3v (0.38 #3416, 0.38 #1097, 0.36 #3835), 01dtcb (0.30 #4101, 0.21 #4769, 0.08 #783), 018_q8 (0.23 #3179, 0.06 #1192, 0.05 #1358), 049ql1 (0.22 #3292, 0.18 #1220, 0.15 #2709), 0fnmz (0.22 #4332, 0.15 #4662, 0.12 #5246) >> Best rule #1247 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 01p5yn; >> query: (?x5854, 0l8sx) <- child(?x382, ?x5854), child(?x382, ?x5908), child(?x382, ?x2548), ?x2548 = 046b0s, ?x5908 = 031rq5 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mkft child! 0l8sx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 110.000 0.778 http://example.org/organization/organization/child./organization/organization_relationship/child #13602-06gbnc PRED entity: 06gbnc PRED relation: people PRED expected values: 05b2f_k 01bbwp => 30 concepts (13 used for prediction) PRED predicted values (max 10 best out of 3694): 01rrd4 (0.40 #6047, 0.33 #7760, 0.25 #4334), 01vwllw (0.40 #5571, 0.33 #7284, 0.25 #12418), 07r1h (0.40 #6004, 0.33 #7717, 0.25 #12851), 046zh (0.40 #5880, 0.33 #7593, 0.19 #14440), 0127m7 (0.40 #5450, 0.33 #7163, 0.17 #12297), 01_ztw (0.40 #5924, 0.33 #7637, 0.17 #12771), 018ygt (0.40 #6026, 0.33 #7739, 0.17 #12873), 08vr94 (0.40 #5676, 0.33 #7389, 0.17 #12523), 06qgvf (0.40 #5145, 0.33 #6858, 0.17 #11992), 044mvs (0.40 #6557, 0.33 #8270, 0.17 #13404) >> Best rule #6047 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 033tf_; 09vc4s; >> query: (?x6736, 01rrd4) <- people(?x6736, ?x12584), people(?x6736, ?x450), nominated_for(?x12584, ?x3111), gender(?x12584, ?x231), film(?x450, ?x518), award_nominee(?x450, ?x5834), award_winner(?x7573, ?x450), ?x5834 = 01z7s_, award_winner(?x2499, ?x450) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #22258 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 44 *> proper extension: 012f86; *> query: (?x6736, ?x1159) <- people(?x6736, ?x12584), people(?x6736, ?x11797), nationality(?x12584, ?x512), profession(?x11797, ?x7397), profession(?x11797, ?x353), profession(?x9738, ?x7397), profession(?x1159, ?x7397), ?x353 = 0cbd2, ?x9738 = 03rx9, gender(?x11797, ?x231) *> conf = 0.01 ranks of expected_values: 2297, 2403 EVAL 06gbnc people 01bbwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 13.000 0.400 http://example.org/people/ethnicity/people EVAL 06gbnc people 05b2f_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 13.000 0.400 http://example.org/people/ethnicity/people #13601-05qhw PRED entity: 05qhw PRED relation: second_level_divisions PRED expected values: 071g6 => 208 concepts (125 used for prediction) PRED predicted values (max 10 best out of 621): 0d1yn (0.17 #651, 0.06 #1639, 0.04 #4113), 0fhmf (0.17 #578, 0.06 #1566, 0.04 #4040), 0fhp9 (0.17 #504, 0.06 #1492, 0.04 #3966), 0bd67 (0.16 #9396, 0.15 #6923, 0.12 #20278), 0mzg2 (0.16 #9396, 0.15 #6923, 0.12 #20278), 04cwcdb (0.16 #9396, 0.15 #6923, 0.12 #20278), 01yl6n (0.16 #9396, 0.15 #6923, 0.12 #20278), 0bdd_ (0.16 #9396, 0.15 #6923, 0.12 #20278), 0bld8 (0.08 #34639, 0.06 #1980, 0.06 #7912), 055hc (0.08 #34639, 0.06 #1980, 0.06 #7912) >> Best rule #651 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 070zc; >> query: (?x456, 0d1yn) <- adjoins(?x456, ?x1558), adjoins(?x456, ?x1264), ?x1558 = 01mjq, country(?x136, ?x1264) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #40578 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 04ykz; *> query: (?x456, ?x205) <- taxonomy(?x456, ?x939), time_zones(?x456, ?x2864), time_zones(?x205, ?x2864) *> conf = 0.01 ranks of expected_values: 515 EVAL 05qhw second_level_divisions 071g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 208.000 125.000 0.167 http://example.org/location/country/second_level_divisions #13600-013w7j PRED entity: 013w7j PRED relation: location PRED expected values: 04n3l => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 277): 0f94t (0.68 #16807, 0.53 #39217, 0.50 #69624), 030qb3t (0.44 #4082, 0.32 #3282, 0.27 #66504), 0cr3d (0.15 #143, 0.10 #8143, 0.09 #943), 01n7q (0.14 #862, 0.08 #4062, 0.06 #4862), 02jx1 (0.09 #870, 0.08 #70, 0.05 #3270), 01531 (0.09 #955, 0.04 #67377, 0.03 #9757), 0cc56 (0.08 #4056, 0.06 #18464, 0.06 #13660), 0f2tj (0.08 #326, 0.05 #39216, 0.04 #1926), 02dtg (0.08 #24, 0.05 #824, 0.04 #1624), 0dclg (0.08 #116, 0.04 #1716, 0.04 #2516) >> Best rule #16807 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 01p47r; >> query: (?x6151, ?x1005) <- participant(?x1017, ?x6151), location(?x6151, ?x739), place_of_birth(?x6151, ?x1005) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #39216 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 352 *> proper extension: 01xyt7; *> query: (?x6151, ?x2850) <- participant(?x6151, ?x4657), place_of_birth(?x6151, ?x1005), location(?x4657, ?x2850) *> conf = 0.05 ranks of expected_values: 27 EVAL 013w7j location 04n3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 122.000 122.000 0.677 http://example.org/people/person/places_lived./people/place_lived/location #13599-06vkl PRED entity: 06vkl PRED relation: month! PRED expected values: 0fhp9 080h2 02h6_6p 0ply0 06y57 02cft 03902 03khn => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1340): 06y57 (0.91 #57, 0.89 #118, 0.88 #71), 02h6_6p (0.91 #57, 0.89 #118, 0.88 #71), 03khn (0.91 #57, 0.89 #118, 0.88 #71), 0fhp9 (0.91 #57, 0.89 #118, 0.88 #71), 080h2 (0.91 #57, 0.89 #118, 0.88 #71), 03902 (0.91 #57, 0.89 #118, 0.88 #71), 02cft (0.91 #57, 0.89 #118, 0.88 #71), 0ply0 (0.91 #57, 0.89 #118, 0.88 #71), 03czqs (0.91 #57, 0.89 #118, 0.88 #71), 01l3k6 (0.33 #20, 0.01 #21) >> Best rule #57 for best value: >> intensional similarity = 104 >> extensional distance = 1 >> proper extension: 04wzr; >> query: (?x1650, ?x863) <- month(?x11197, ?x1650), month(?x10143, ?x1650), month(?x9605, ?x1650), month(?x9559, ?x1650), month(?x8977, ?x1650), month(?x8956, ?x1650), month(?x8602, ?x1650), month(?x8252, ?x1650), month(?x8174, ?x1650), month(?x6960, ?x1650), month(?x6959, ?x1650), month(?x6703, ?x1650), month(?x6494, ?x1650), month(?x6054, ?x1650), month(?x5168, ?x1650), month(?x4826, ?x1650), month(?x4627, ?x1650), month(?x4298, ?x1650), month(?x3501, ?x1650), month(?x3269, ?x1650), month(?x3125, ?x1650), month(?x3052, ?x1650), month(?x2645, ?x1650), month(?x2474, ?x1650), month(?x2277, ?x1650), month(?x2254, ?x1650), month(?x1658, ?x1650), month(?x1649, ?x1650), month(?x1646, ?x1650), month(?x1523, ?x1650), month(?x1458, ?x1650), month(?x739, ?x1650), month(?x659, ?x1650), month(?x206, ?x1650), month(?x108, ?x1650), seasonal_months(?x1650, ?x9905), seasonal_months(?x1650, ?x4925), seasonal_months(?x1650, ?x4869), seasonal_months(?x1650, ?x2255), seasonal_months(?x1650, ?x2140), ?x6960 = 071vr, seasonal_months(?x3107, ?x1650), seasonal_months(?x1459, ?x1650), ?x3125 = 0d6lp, ?x3501 = 0f2v0, ?x3269 = 0vzm, ?x1649 = 01f62, ?x108 = 0rh6k, ?x2254 = 0dclg, ?x8602 = 0chgzm, ?x4627 = 05qtj, ?x8252 = 0k3p, ?x1458 = 05ywg, ?x11197 = 05l64, ?x206 = 01914, ?x1658 = 0h7h6, ?x4925 = 0ll3, ?x6703 = 0f04v, ?x2277 = 013yq, ?x1459 = 04w_7, ?x2140 = 040fb, ?x6494 = 02sn34, ?x3107 = 05lf_, ?x6054 = 0fn2g, ?x1646 = 0156q, ?x8956 = 0947l, ?x10143 = 0h3tv, month(?x10610, ?x9905), month(?x6357, ?x9905), month(?x2611, ?x9905), month(?x863, ?x9905), ?x8174 = 01lfy, ?x739 = 02_286, ?x8977 = 02z0j, ?x2645 = 03h64, ?x4869 = 02xx5, ?x9559 = 07dfk, ?x6959 = 06c62, ?x1523 = 030qb3t, ?x2611 = 02h6_6p, ?x2255 = 040fv, mode_of_transportation(?x4826, ?x4272), category(?x4298, ?x134), ?x6357 = 02cft, citytown(?x5280, ?x4298), state(?x4298, ?x1227), location(?x8452, ?x4298), country(?x4826, ?x172), ?x2474 = 052p7, ?x5168 = 06mxs, citytown(?x1151, ?x3052), capital(?x2020, ?x3052), place_of_birth(?x1987, ?x3052), contains(?x3052, ?x1809), location(?x1322, ?x3052), nominated_for(?x1987, ?x4021), ?x9605 = 02frhbc, county_seat(?x7309, ?x3052), award_nominee(?x221, ?x1987), citytown(?x2106, ?x4826), ?x10610 = 03902, film(?x1987, ?x4392), ?x659 = 02cl1, dog_breed(?x3052, ?x1706) >> conf = 0.91 => this is the best rule for 9 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8 EVAL 06vkl month! 03khn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.911 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 06vkl month! 03902 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.911 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 06vkl month! 02cft CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.911 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 06vkl month! 06y57 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.911 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 06vkl month! 0ply0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.911 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 06vkl month! 02h6_6p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.911 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 06vkl month! 080h2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.911 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 06vkl month! 0fhp9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.911 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #13598-02w6s3 PRED entity: 02w6s3 PRED relation: artists PRED expected values: 03t9sp 06k02 0840vq => 87 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1070): 0191h5 (0.75 #11493, 0.74 #12575, 0.50 #4987), 03t9sp (0.70 #18552, 0.50 #1208, 0.38 #14215), 01w8n89 (0.67 #2484, 0.57 #5735, 0.40 #11159), 016lmg (0.59 #15933, 0.54 #14850, 0.44 #17017), 0m19t (0.50 #16286, 0.50 #1112, 0.40 #10869), 03fbc (0.50 #4540, 0.50 #1289, 0.39 #12128), 01v_pj6 (0.50 #1203, 0.44 #8790, 0.41 #15293), 04n2vgk (0.50 #1946, 0.44 #9533, 0.38 #8448), 0qdyf (0.50 #2430, 0.43 #5681, 0.33 #4599), 011z3g (0.50 #1690, 0.40 #21202, 0.28 #19034) >> Best rule #11493 for best value: >> intensional similarity = 10 >> extensional distance = 18 >> proper extension: 016clz; 0fd3y; 0dl5d; 06by7; 01243b; 0m0fw; 08jyyk; 01fh36; 0mmp3; 02l96k; ... >> query: (?x13782, 0191h5) <- artists(?x13782, ?x8332), parent_genre(?x8847, ?x13782), artists(?x3916, ?x8332), artists(?x2809, ?x8332), artists(?x474, ?x8332), ?x2809 = 05w3f, ?x3916 = 08cyft, ?x474 = 0m0jc, parent_genre(?x13782, ?x2439), award_winner(?x486, ?x8332) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #18552 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 44 *> proper extension: 0fxgg9; 01ym9b; 025sc50; 02lnbg; 0k0r0n7; 07s72n; 05lwjc; 01hyfj; 01rthc; 06lq2g; ... *> query: (?x13782, 03t9sp) <- artists(?x13782, ?x8332), award(?x8332, ?x8458), ?x8458 = 02f777, artists(?x3915, ?x8332), artist(?x7681, ?x8332), origin(?x8332, ?x8771), ?x3915 = 07gxw *> conf = 0.70 ranks of expected_values: 2, 42, 55 EVAL 02w6s3 artists 0840vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 87.000 27.000 0.750 http://example.org/music/genre/artists EVAL 02w6s3 artists 06k02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 87.000 27.000 0.750 http://example.org/music/genre/artists EVAL 02w6s3 artists 03t9sp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 27.000 0.750 http://example.org/music/genre/artists #13597-01n5309 PRED entity: 01n5309 PRED relation: type_of_union PRED expected values: 04ztj => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.89 #25, 0.87 #9, 0.86 #37), 01g63y (0.28 #30, 0.24 #22, 0.22 #50) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 016gr2; 03fbb6; 02xs5v; >> query: (?x692, 04ztj) <- award(?x692, ?x693), award_nominee(?x1422, ?x692), ?x693 = 09qvc0 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n5309 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.889 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13596-03t5n3 PRED entity: 03t5n3 PRED relation: ceremony PRED expected values: 056878 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 133): 056878 (0.87 #1226, 0.85 #1359, 0.85 #1625), 01c6qp (0.84 #1613, 0.77 #1347, 0.77 #1214), 019bk0 (0.80 #1610, 0.79 #1344, 0.79 #1211), 01mh_q (0.80 #1679, 0.79 #1413, 0.79 #1280), 01bx35 (0.80 #1601, 0.79 #1202, 0.77 #1335), 01s695 (0.78 #1598, 0.75 #1332, 0.75 #3060), 013b2h (0.78 #1671, 0.75 #3060, 0.74 #1272), 01mhwk (0.77 #1634, 0.75 #3060, 0.74 #1235), 01xqqp (0.75 #3060, 0.71 #1686, 0.64 #1287), 0jzphpx (0.75 #3060, 0.66 #1632, 0.57 #1233) >> Best rule #1226 for best value: >> intensional similarity = 6 >> extensional distance = 45 >> proper extension: 0257pw; >> query: (?x5799, 056878) <- ceremony(?x5799, ?x8500), award(?x8185, ?x5799), award(?x3977, ?x5799), ?x8500 = 0gx1673, artists(?x671, ?x8185), location(?x3977, ?x1860) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t5n3 ceremony 056878 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.872 http://example.org/award/award_category/winners./award/award_honor/ceremony #13595-0g4gr PRED entity: 0g4gr PRED relation: major_field_of_study! PRED expected values: 0fnmz 01h8rk 01jzyx 095kp 0trv => 70 concepts (43 used for prediction) PRED predicted values (max 10 best out of 645): 01w5m (0.67 #10179, 0.64 #11297, 0.60 #9060), 07vhb (0.64 #11364, 0.25 #1854, 0.23 #18643), 08815 (0.60 #8953, 0.50 #10072, 0.50 #5597), 09f2j (0.58 #12472, 0.57 #11351, 0.55 #18630), 07szy (0.57 #18507, 0.50 #11228, 0.50 #8991), 0bwfn (0.57 #11468, 0.56 #15387, 0.50 #9231), 02bqy (0.57 #11376, 0.50 #1866, 0.40 #9139), 06pwq (0.55 #18480, 0.50 #11201, 0.48 #15120), 03ksy (0.53 #18577, 0.52 #15217, 0.50 #11298), 07tgn (0.50 #11204, 0.50 #8967, 0.50 #1694) >> Best rule #10179 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 0l14jd; >> query: (?x3213, 01w5m) <- student(?x3213, ?x1065), people(?x3584, ?x1065), languages_spoken(?x3584, ?x5814), languages_spoken(?x3584, ?x732), ?x732 = 04306rv, ?x5814 = 0k0sv, profession(?x1065, ?x353) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #11517 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 12 *> proper extension: 0299ct; *> query: (?x3213, 0trv) <- major_field_of_study(?x8120, ?x3213), major_field_of_study(?x6953, ?x3213), major_field_of_study(?x5807, ?x3213), major_field_of_study(?x3213, ?x4321), major_field_of_study(?x620, ?x3213), contains(?x94, ?x6953), ?x5807 = 0ks67, currency(?x8120, ?x170) *> conf = 0.36 ranks of expected_values: 50, 97, 133, 134, 164 EVAL 0g4gr major_field_of_study! 0trv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 70.000 43.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g4gr major_field_of_study! 095kp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 70.000 43.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g4gr major_field_of_study! 01jzyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 70.000 43.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g4gr major_field_of_study! 01h8rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 70.000 43.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g4gr major_field_of_study! 0fnmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 70.000 43.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13594-0g476 PRED entity: 0g476 PRED relation: nationality PRED expected values: 09c7w0 => 154 concepts (152 used for prediction) PRED predicted values (max 10 best out of 32): 09c7w0 (0.88 #1601, 0.86 #701, 0.84 #10913), 02jx1 (0.15 #433, 0.13 #2633, 0.12 #4333), 07ssc (0.11 #115, 0.11 #5318, 0.10 #2615), 0chghy (0.11 #110, 0.05 #410, 0.03 #1010), 0j5g9 (0.11 #162, 0.05 #462), 01xbgx (0.11 #281, 0.02 #1181, 0.02 #1581), 03rk0 (0.10 #4547, 0.08 #7151, 0.08 #8555), 07b_l (0.10 #8108), 07z1m (0.10 #8108), 059rby (0.10 #8108) >> Best rule #1601 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 07bsj; >> query: (?x9963, 09c7w0) <- type_of_union(?x9963, ?x566), actor(?x5529, ?x9963), participant(?x10001, ?x9963) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g476 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 152.000 0.882 http://example.org/people/person/nationality #13593-06nm1 PRED entity: 06nm1 PRED relation: titles PRED expected values: 02vr3gz => 81 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1981): 0296rz (0.33 #15427, 0.20 #38793, 0.17 #40349), 04q24zv (0.33 #3504, 0.20 #14404, 0.11 #37770), 016z7s (0.33 #14300, 0.14 #37666, 0.12 #39222), 0209hj (0.33 #14104, 0.14 #37470, 0.12 #39026), 02vr3gz (0.33 #3632, 0.13 #14532, 0.06 #62312), 0qmd5 (0.33 #14450, 0.11 #37816, 0.11 #42486), 01qbg5 (0.27 #15099, 0.17 #40021, 0.15 #43135), 041td_ (0.27 #14946, 0.17 #38312, 0.12 #39868), 0ywrc (0.27 #14455, 0.15 #39377, 0.14 #37821), 03h_yy (0.27 #14079, 0.14 #37445, 0.13 #42115) >> Best rule #15427 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 07s9rl0; 04xvlr; 07ssc; 0hn10; 01jfsb; 017fp; 01hmnh; 01z4y; 0djd22; 03mqtr; ... >> query: (?x2502, 0296rz) <- titles(?x2502, ?x7735), award(?x7735, ?x1198), genre(?x7735, ?x53), music(?x7735, ?x10949), ?x1198 = 02pqp12, film(?x382, ?x7735) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3632 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 06mkj; *> query: (?x2502, 02vr3gz) <- titles(?x2502, ?x8496), titles(?x2502, ?x7735), titles(?x2502, ?x5496), ?x7735 = 0gpx6, ?x8496 = 0cvkv5, film_release_region(?x5496, ?x87), genre(?x5496, ?x53), ?x53 = 07s9rl0 *> conf = 0.33 ranks of expected_values: 5 EVAL 06nm1 titles 02vr3gz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 81.000 41.000 0.333 http://example.org/media_common/netflix_genre/titles #13592-08h79x PRED entity: 08h79x PRED relation: nationality PRED expected values: 09c7w0 => 134 concepts (111 used for prediction) PRED predicted values (max 10 best out of 66): 09c7w0 (0.83 #9733, 0.82 #10031, 0.78 #7738), 02jx1 (0.32 #3207, 0.20 #627, 0.19 #726), 07ssc (0.30 #3189, 0.29 #6561, 0.16 #1998), 0f8l9c (0.25 #120, 0.23 #1210, 0.08 #7939), 0d060g (0.20 #205, 0.17 #304, 0.13 #6554), 03rk0 (0.18 #6592, 0.08 #7939, 0.07 #3220), 0345h (0.17 #1219, 0.06 #6577, 0.05 #3205), 03rjj (0.15 #1194, 0.10 #897, 0.08 #401), 03_3d (0.10 #898, 0.05 #8639, 0.05 #8537), 0h7x (0.09 #1223, 0.05 #8639, 0.05 #8537) >> Best rule #9733 for best value: >> intensional similarity = 3 >> extensional distance = 1731 >> proper extension: 07_m2; >> query: (?x7333, 09c7w0) <- nationality(?x7333, ?x291), student(?x741, ?x7333), currency(?x291, ?x170) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08h79x nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 111.000 0.828 http://example.org/people/person/nationality #13591-02snj9 PRED entity: 02snj9 PRED relation: role! PRED expected values: 017g21 => 84 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1067): 016h9b (0.67 #3267, 0.62 #4726, 0.62 #6184), 0473q (0.67 #3389, 0.62 #4268, 0.60 #3096), 01mxnvc (0.67 #3461, 0.62 #4340, 0.60 #3168), 04mx7s (0.67 #3418, 0.60 #3125, 0.54 #6335), 01v_pj6 (0.67 #3233, 0.60 #2940, 0.50 #4112), 01vs4ff (0.62 #4257, 0.50 #3378, 0.40 #3085), 03bxwtd (0.60 #2981, 0.60 #2690, 0.60 #2400), 01vsnff (0.60 #2951, 0.60 #2370, 0.50 #4123), 01vng3b (0.60 #3067, 0.60 #2486, 0.50 #4239), 01s7qqw (0.60 #3043, 0.50 #3336, 0.50 #2171) >> Best rule #3267 for best value: >> intensional similarity = 20 >> extensional distance = 4 >> proper extension: 02hnl; >> query: (?x3214, 016h9b) <- performance_role(?x4471, ?x3214), performance_role(?x2944, ?x3214), role(?x2888, ?x3214), role(?x3214, ?x960), role(?x3214, ?x316), group(?x3214, ?x9706), group(?x3214, ?x6471), ?x6471 = 0143q0, ?x2888 = 02fsn, role(?x4471, ?x1433), role(?x214, ?x4471), artists(?x302, ?x9706), role(?x4311, ?x960), role(?x642, ?x3214), ?x4311 = 01xqw, role(?x74, ?x2944), role(?x868, ?x316), instrumentalists(?x316, ?x115), role(?x483, ?x316), ?x868 = 0dwvl >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2807 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 3 *> proper extension: 042v_gx; *> query: (?x3214, 017g21) <- performance_role(?x1574, ?x3214), role(?x645, ?x3214), role(?x314, ?x3214), role(?x3214, ?x316), group(?x3214, ?x10043), group(?x3214, ?x6471), ?x645 = 028tv0, artists(?x302, ?x6471), award(?x6471, ?x6126), role(?x211, ?x1574), ?x6126 = 02f77l, instrumentalists(?x3214, ?x2945), ?x314 = 02sgy, role(?x1574, ?x1886), ?x10043 = 0fb2l, ?x1886 = 02k84w, role(?x642, ?x3214) *> conf = 0.60 ranks of expected_values: 20 EVAL 02snj9 role! 017g21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 84.000 37.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role #13590-01s695 PRED entity: 01s695 PRED relation: ceremony! PRED expected values: 02g3gj 01c9f2 01ckbq 01c92g 025m8l 01dpdh 01cw51 02flpc 02flpq 02v703 03nc9d => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 244): 02v703 (0.83 #3330, 0.82 #3144, 0.80 #1872), 01cky2 (0.82 #3102, 0.80 #1872, 0.80 #2916), 02g3gj (0.82 #3005, 0.80 #1872, 0.80 #2819), 03qpp9 (0.82 #3164, 0.80 #1872, 0.80 #2978), 01c9f2 (0.80 #1872, 0.78 #2671, 0.75 #4671), 025m8l (0.80 #1872, 0.78 #2691, 0.75 #4671), 03nc9d (0.80 #1872, 0.75 #4671, 0.75 #6163), 01cw51 (0.80 #1872, 0.75 #4671, 0.75 #6163), 02flpq (0.80 #1872, 0.75 #4671, 0.75 #6163), 02flpc (0.80 #1872, 0.75 #4671, 0.75 #6163) >> Best rule #3330 for best value: >> intensional similarity = 23 >> extensional distance = 10 >> proper extension: 0gx1673; >> query: (?x342, 02v703) <- award_winner(?x342, ?x2237), award_winner(?x342, ?x646), ceremony(?x12819, ?x342), ceremony(?x1827, ?x342), ceremony(?x1565, ?x342), ?x12819 = 0257__, award(?x140, ?x1827), profession(?x2237, ?x524), ?x1565 = 01c4_6, celebrity(?x1213, ?x2237), group(?x227, ?x646), profession(?x14072, ?x524), profession(?x10259, ?x524), profession(?x3707, ?x524), profession(?x3117, ?x524), profession(?x989, ?x524), profession(?x635, ?x524), ?x10259 = 016tbr, ?x14072 = 02r0st6, ?x3707 = 016ksk, ?x3117 = 0693l, ?x635 = 02pp_q_, ?x989 = 0151w_ >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14 EVAL 01s695 ceremony! 03nc9d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01s695 ceremony! 02v703 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01s695 ceremony! 02flpq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01s695 ceremony! 02flpc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01s695 ceremony! 01cw51 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01s695 ceremony! 01dpdh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01s695 ceremony! 025m8l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01s695 ceremony! 01c92g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01s695 ceremony! 01ckbq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01s695 ceremony! 01c9f2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01s695 ceremony! 02g3gj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 35.000 35.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony #13589-083shs PRED entity: 083shs PRED relation: film_crew_role PRED expected values: 0dxtw => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 23): 0dxtw (0.43 #115, 0.36 #255, 0.35 #887), 01vx2h (0.40 #116, 0.33 #256, 0.31 #888), 01pvkk (0.28 #889, 0.28 #503, 0.28 #1345), 02ynfr (0.20 #121, 0.17 #261, 0.16 #156), 0d2b38 (0.15 #25, 0.14 #131, 0.12 #166), 0215hd (0.14 #124, 0.14 #896, 0.13 #159), 089g0h (0.13 #125, 0.11 #897, 0.11 #265), 02rh1dz (0.13 #114, 0.11 #219, 0.10 #254), 01xy5l_ (0.12 #119, 0.11 #891, 0.11 #259), 015h31 (0.12 #113, 0.09 #253, 0.09 #885) >> Best rule #115 for best value: >> intensional similarity = 3 >> extensional distance = 233 >> proper extension: 0cnztc4; 0crh5_f; 0gh6j94; >> query: (?x167, 0dxtw) <- film_crew_role(?x167, ?x468), film_format(?x167, ?x909), ?x468 = 02r96rf >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 083shs film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.430 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13588-06tp4h PRED entity: 06tp4h PRED relation: instrumentalists! PRED expected values: 05r5c => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 65): 0342h (0.50 #1073, 0.42 #4366, 0.42 #4811), 05r5c (0.39 #1077, 0.35 #3213, 0.35 #3391), 05148p4 (0.31 #2336, 0.26 #1802, 0.24 #8748), 018vs (0.20 #7761, 0.20 #8117, 0.19 #8740), 02hnl (0.18 #1104, 0.15 #4219, 0.14 #5554), 0l14md (0.18 #1076, 0.14 #3212, 0.13 #3479), 03qjg (0.14 #4414, 0.12 #5571, 0.12 #3257), 0l14qv (0.13 #1786, 0.11 #3121, 0.10 #1875), 026t6 (0.12 #2317, 0.11 #5521, 0.10 #4186), 06ncr (0.11 #1114, 0.06 #5653, 0.06 #1648) >> Best rule #1073 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 01vwyqp; 01w524f; 01w3lzq; >> query: (?x6613, 0342h) <- religion(?x6613, ?x7131), artists(?x3061, ?x6613), nationality(?x6613, ?x94), ?x3061 = 05bt6j >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1077 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 01vwyqp; 01w524f; 01w3lzq; *> query: (?x6613, 05r5c) <- religion(?x6613, ?x7131), artists(?x3061, ?x6613), nationality(?x6613, ?x94), ?x3061 = 05bt6j *> conf = 0.39 ranks of expected_values: 2 EVAL 06tp4h instrumentalists! 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 151.000 151.000 0.500 http://example.org/music/instrument/instrumentalists #13587-041p3y PRED entity: 041p3y PRED relation: artist PRED expected values: 016376 => 34 concepts (9 used for prediction) PRED predicted values (max 10 best out of 1066): 01lcxbb (0.50 #3549, 0.40 #2717, 0.33 #1054), 01q99h (0.50 #2099, 0.38 #3324, 0.22 #4595), 046p9 (0.50 #2252, 0.38 #3324, 0.22 #4748), 019g40 (0.50 #1764, 0.38 #3324, 0.20 #2596), 017lb_ (0.50 #2260, 0.38 #3324, 0.17 #4756), 04dqdk (0.50 #1730, 0.38 #3324, 0.17 #4226), 0c9l1 (0.50 #2399, 0.38 #3324, 0.11 #4895), 01s560x (0.50 #2411, 0.33 #749, 0.28 #4907), 01wj18h (0.50 #1871, 0.22 #4367, 0.20 #2703), 0136p1 (0.50 #1767, 0.20 #2599, 0.17 #4263) >> Best rule #3549 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 0mcf4; >> query: (?x10727, 01lcxbb) <- artist(?x10727, ?x10802), artist(?x10727, ?x5718), artist(?x10727, ?x3358), artist(?x10727, ?x2906), ?x5718 = 024zq, award_nominee(?x366, ?x2906), award(?x2906, ?x3094), award(?x2906, ?x341), role(?x10802, ?x227), artists(?x671, ?x2906), type_of_union(?x3358, ?x566), ceremony(?x341, ?x2431), ?x2431 = 0jzphpx, award_winner(?x3094, ?x1270) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3324 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 3 *> proper extension: 015_1q; 01w40h; *> query: (?x10727, ?x1270) <- artist(?x10727, ?x10802), artist(?x10727, ?x5718), artist(?x10727, ?x2906), ?x5718 = 024zq, award_nominee(?x366, ?x2906), award(?x2906, ?x341), role(?x10802, ?x6039), group(?x2321, ?x2906), artist(?x12017, ?x2906), artists(?x671, ?x10802), group(?x614, ?x2906), role(?x75, ?x6039), artist(?x12017, ?x1270) *> conf = 0.38 ranks of expected_values: 52 EVAL 041p3y artist 016376 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 34.000 9.000 0.500 http://example.org/music/record_label/artist #13586-0dck27 PRED entity: 0dck27 PRED relation: type_of_union PRED expected values: 04ztj => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.73 #65, 0.72 #37, 0.70 #81), 01g63y (0.19 #373, 0.17 #54, 0.15 #94) >> Best rule #65 for best value: >> intensional similarity = 3 >> extensional distance = 438 >> proper extension: 06gn7r; 02jxsq; 01hkck; >> query: (?x2110, 04ztj) <- place_of_death(?x2110, ?x1523), profession(?x2110, ?x7630), award(?x2110, ?x2222) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dck27 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.732 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13585-0jrgr PRED entity: 0jrgr PRED relation: parent_genre PRED expected values: 016jny => 43 concepts (33 used for prediction) PRED predicted values (max 10 best out of 223): 06by7 (0.67 #497, 0.65 #1947, 0.63 #2108), 05r6t (0.58 #1500, 0.55 #1661, 0.50 #1822), 01dqhq (0.33 #48, 0.25 #208, 0.20 #368), 0hdf8 (0.33 #44, 0.25 #204, 0.20 #364), 0ggq0m (0.33 #10, 0.25 #170, 0.20 #330), 0296y (0.25 #217, 0.20 #377, 0.16 #859), 0xjl2 (0.25 #190, 0.20 #350, 0.03 #1478), 0xhtw (0.24 #977, 0.19 #1300, 0.18 #655), 0jrv_ (0.22 #586, 0.18 #642, 0.16 #907), 0glt670 (0.22 #508, 0.18 #2932, 0.11 #4717) >> Best rule #497 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 016clz; 01_bkd; 06cp5; 05jt_; 03fpx; 0ccxx6; 028cl7; >> query: (?x11537, 06by7) <- parent_genre(?x11537, ?x5580), parent_genre(?x11537, ?x3108), parent_genre(?x11537, ?x2249), ?x2249 = 03lty, parent_genre(?x5580, ?x10930), artists(?x3108, ?x2807), artists(?x3108, ?x1997), artists(?x3108, ?x1089), ?x1997 = 01wsl7c, ?x2807 = 03h_fk5, location(?x1089, ?x739) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #388 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 0jf1v; *> query: (?x11537, 016jny) <- parent_genre(?x11537, ?x5580), parent_genre(?x11537, ?x3108), parent_genre(?x11537, ?x2249), ?x2249 = 03lty, ?x5580 = 01jwt, artists(?x3108, ?x6877), artists(?x3108, ?x1720), award(?x6877, ?x724), nationality(?x1720, ?x94) *> conf = 0.20 ranks of expected_values: 12 EVAL 0jrgr parent_genre 016jny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 43.000 33.000 0.667 http://example.org/music/genre/parent_genre #13584-02cft PRED entity: 02cft PRED relation: place_of_birth! PRED expected values: 072twv 05np2 => 208 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1371): 01w02sy (0.37 #98992, 0.36 #195371, 0.36 #119835), 04x1_w (0.37 #98992, 0.36 #195371, 0.36 #119835), 059t6d (0.37 #98992, 0.36 #195371, 0.36 #119835), 0h5g_ (0.37 #98992, 0.36 #195371, 0.36 #119835), 045c66 (0.37 #98992, 0.36 #195371, 0.36 #119835), 0f6_x (0.37 #98992, 0.36 #195371, 0.36 #119835), 01j5sd (0.37 #98992, 0.36 #195371, 0.36 #119835), 03hy3g (0.37 #98992, 0.36 #195371, 0.36 #119835), 016z68 (0.37 #98992, 0.36 #195371, 0.36 #119835), 01nd6v (0.25 #2600, 0.14 #15626, 0.08 #33862) >> Best rule #98992 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0cvw9; 04swd; 0430_; >> query: (?x6357, ?x489) <- capital(?x429, ?x6357), location(?x489, ?x6357), place_of_birth(?x585, ?x6357), contains(?x6357, ?x8694) >> conf = 0.37 => this is the best rule for 9 predicted values *> Best rule #171932 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 09bkv; 0fttg; *> query: (?x6357, ?x294) <- capital(?x429, ?x6357), contains(?x3699, ?x6357), place_of_birth(?x585, ?x6357), nationality(?x294, ?x429) *> conf = 0.05 ranks of expected_values: 728, 731 EVAL 02cft place_of_birth! 05np2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 208.000 75.000 0.366 http://example.org/people/person/place_of_birth EVAL 02cft place_of_birth! 072twv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 208.000 75.000 0.366 http://example.org/people/person/place_of_birth #13583-03t22m PRED entity: 03t22m PRED relation: group PRED expected values: 01lf293 => 80 concepts (56 used for prediction) PRED predicted values (max 10 best out of 555): 01lf293 (0.67 #2361, 0.43 #5378, 0.43 #3117), 02vnpv (0.64 #8271, 0.61 #9595, 0.60 #7324), 05563d (0.64 #4546, 0.62 #6056, 0.57 #3038), 02dw1_ (0.57 #3069, 0.56 #5899, 0.55 #7232), 07mvp (0.57 #8191, 0.53 #5723, 0.50 #7244), 02t3ln (0.57 #3050, 0.50 #1732, 0.50 #1168), 048xh (0.57 #3094, 0.50 #1776, 0.40 #1120), 0khth (0.57 #3048, 0.40 #1120, 0.38 #6256), 0bpk2 (0.57 #3067, 0.40 #1120, 0.33 #809), 047cx (0.56 #6067, 0.50 #4744, 0.44 #3994) >> Best rule #2361 for best value: >> intensional similarity = 18 >> extensional distance = 4 >> proper extension: 07c6l; 07gql; >> query: (?x1831, 01lf293) <- instrumentalists(?x1831, ?x2242), role(?x9413, ?x1831), role(?x315, ?x1831), role(?x1831, ?x3239), role(?x547, ?x1831), ?x9413 = 07m2y, artists(?x302, ?x2242), role(?x2242, ?x1147), instrumentalists(?x315, ?x7581), instrumentalists(?x315, ?x2987), ?x2987 = 01vw20_, ?x7581 = 01wf86y, ?x3239 = 03qmg1, group(?x315, ?x8226), role(?x315, ?x2798), ?x8226 = 017lb_, ?x2798 = 03qjg, profession(?x2242, ?x131) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t22m group 01lf293 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 56.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group #13582-0dc7hc PRED entity: 0dc7hc PRED relation: film! PRED expected values: 041c4 => 82 concepts (44 used for prediction) PRED predicted values (max 10 best out of 737): 017s11 (0.51 #6239, 0.47 #37442, 0.46 #62406), 01trf3 (0.25 #728, 0.02 #2807, 0.01 #6967), 052hl (0.25 #1189, 0.01 #28231, 0.01 #9507), 096hm (0.19 #45764, 0.19 #43684), 0h0wc (0.12 #423, 0.05 #4582, 0.04 #10820), 028k57 (0.12 #789, 0.04 #2868, 0.02 #7028), 0170qf (0.12 #366, 0.03 #4525, 0.02 #10763), 01vy_v8 (0.12 #731, 0.03 #9049, 0.02 #2810), 0jbp0 (0.12 #1758, 0.03 #3837, 0.03 #28800), 02gf_l (0.12 #1267, 0.02 #17903, 0.02 #22065) >> Best rule #6239 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 0c0nhgv; 0gxtknx; 0cw3yd; 0gl02yg; 0h2zvzr; 0g57wgv; >> query: (?x9774, ?x541) <- film(?x1867, ?x9774), nominated_for(?x541, ?x9774), film_crew_role(?x9774, ?x137), film_festivals(?x9774, ?x3831) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #27935 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 400 *> proper extension: 01hr1; 034qrh; 01sxly; 0hmr4; 084qpk; 02sg5v; 0kv2hv; 04tc1g; 03cvwkr; 018f8; ... *> query: (?x9774, 041c4) <- film(?x3917, ?x9774), film(?x541, ?x9774), country(?x9774, ?x94), influenced_by(?x3917, ?x5208) *> conf = 0.03 ranks of expected_values: 101 EVAL 0dc7hc film! 041c4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 82.000 44.000 0.507 http://example.org/film/actor/film./film/performance/film #13581-0hnjt PRED entity: 0hnjt PRED relation: award_winner! PRED expected values: 0d085 => 140 concepts (136 used for prediction) PRED predicted values (max 10 best out of 245): 01l78d (0.39 #6900, 0.39 #6899, 0.38 #8194), 054ky1 (0.25 #109, 0.05 #11753, 0.05 #7871), 027c95y (0.25 #157, 0.03 #19995, 0.03 #1452), 0gs9p (0.16 #11723, 0.04 #31560, 0.04 #30265), 0gq9h (0.14 #11721, 0.03 #6544, 0.02 #31991), 019f4v (0.13 #11710, 0.04 #6533, 0.04 #7828), 040njc (0.13 #11652, 0.03 #1303, 0.03 #30194), 04njml (0.12 #532, 0.11 #964, 0.05 #3120), 0c_dx (0.12 #705, 0.03 #1569, 0.02 #2431), 027c924 (0.10 #11655, 0.03 #30197, 0.03 #31492) >> Best rule #6900 for best value: >> intensional similarity = 4 >> extensional distance = 213 >> proper extension: 028q6; 041h0; 032nwy; 0chsq; 08433; 012t1; 01g4zr; 02sjf5; 01tcf7; 09dt7; ... >> query: (?x4738, ?x921) <- place_of_death(?x4738, ?x3052), student(?x3439, ?x4738), award(?x4738, ?x921), award_winner(?x921, ?x118) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #4561 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 159 *> proper extension: 042d1; *> query: (?x4738, 0d085) <- place_of_death(?x4738, ?x3052), student(?x6056, ?x4738), major_field_of_study(?x6056, ?x254), currency(?x6056, ?x170) *> conf = 0.03 ranks of expected_values: 58 EVAL 0hnjt award_winner! 0d085 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 140.000 136.000 0.391 http://example.org/award/award_category/winners./award/award_honor/award_winner #13580-06nm1 PRED entity: 06nm1 PRED relation: countries_spoken_in PRED expected values: 035hm => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 223): 06mkj (0.70 #1400, 0.65 #4668, 0.63 #3267), 01p8s (0.70 #1400, 0.65 #4668, 0.63 #3267), 0697s (0.60 #685, 0.50 #219, 0.38 #1618), 01ppq (0.50 #290, 0.40 #756, 0.38 #1689), 04hhv (0.50 #280, 0.40 #746, 0.33 #1367), 01nln (0.50 #269, 0.40 #735, 0.33 #1356), 03_xj (0.50 #253, 0.40 #719, 0.33 #1340), 07z5n (0.50 #208, 0.40 #674, 0.33 #1295), 027nb (0.50 #159, 0.40 #625, 0.33 #1246), 06mzp (0.50 #1105, 0.38 #1573, 0.33 #1261) >> Best rule #1400 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0295r; >> query: (?x2502, ?x4954) <- languages(?x5283, ?x2502), official_language(?x4954, ?x2502), ?x5283 = 01ps2h8, languages_spoken(?x1571, ?x2502), language(?x89, ?x2502), olympics(?x4954, ?x2966), country(?x668, ?x4954) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #138 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x2502, 035hm) <- service_language(?x234, ?x2502), languages(?x419, ?x2502), language(?x6269, ?x2502), language(?x5576, ?x2502), language(?x1045, ?x2502), official_language(?x47, ?x2502), ?x6269 = 0286gm1, ?x1045 = 08r4x3, ?x5576 = 0gbfn9 *> conf = 0.33 ranks of expected_values: 47 EVAL 06nm1 countries_spoken_in 035hm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 75.000 75.000 0.705 http://example.org/language/human_language/countries_spoken_in #13579-0j8hd PRED entity: 0j8hd PRED relation: people PRED expected values: 02kmx6 018qpb => 64 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1272): 0137hn (0.60 #7831, 0.50 #10580, 0.17 #29845), 01hcj2 (0.50 #24062, 0.50 #5496, 0.50 #5495), 0dzlk (0.50 #24062, 0.50 #5496, 0.50 #5495), 016dsy (0.50 #5496, 0.50 #5495, 0.33 #17180), 027r8p (0.50 #5495, 0.33 #2058, 0.31 #11678), 049qx (0.50 #5495, 0.33 #2058, 0.31 #11678), 0chsq (0.40 #6880, 0.33 #11005, 0.33 #1385), 014zn0 (0.33 #11591, 0.33 #1971, 0.25 #6095), 01m42d0 (0.33 #3783, 0.33 #1035, 0.25 #6531), 01xcqc (0.33 #2792, 0.33 #1419, 0.25 #4856) >> Best rule #7831 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 03m3vr6; >> query: (?x11990, 0137hn) <- people(?x11990, ?x3533), nationality(?x3533, ?x1310), ?x1310 = 02jx1, sibling(?x3533, ?x4398), award_nominee(?x3533, ?x1738), award(?x3533, ?x5455), category(?x3533, ?x134), award_winner(?x5455, ?x2372) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #34376 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 20 *> proper extension: 02knxx; 08nhwb; *> query: (?x11990, ?x156) <- people(?x11990, ?x3533), nationality(?x3533, ?x1310), ?x1310 = 02jx1, award(?x3533, ?x2880), award(?x156, ?x2880), ceremony(?x2880, ?x2032), award(?x253, ?x2880) *> conf = 0.02 ranks of expected_values: 939 EVAL 0j8hd people 018qpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 53.000 0.600 http://example.org/people/cause_of_death/people EVAL 0j8hd people 02kmx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 53.000 0.600 http://example.org/people/cause_of_death/people #13578-0gkz15s PRED entity: 0gkz15s PRED relation: film_crew_role PRED expected values: 09zzb8 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.71 #567, 0.67 #112, 0.67 #337), 09vw2b7 (0.63 #573, 0.60 #156, 0.60 #118), 01vx2h (0.50 #12, 0.47 #161, 0.45 #199), 0dxtw (0.46 #235, 0.43 #577, 0.42 #347), 01pvkk (0.30 #162, 0.29 #124, 0.28 #579), 02ynfr (0.23 #128, 0.17 #583, 0.17 #353), 02rh1dz (0.22 #47, 0.20 #84, 0.18 #576), 0d2b38 (0.15 #27, 0.14 #101, 0.12 #176), 01xy5l_ (0.15 #15, 0.09 #581, 0.09 #657), 089fss (0.13 #43, 0.12 #80, 0.09 #193) >> Best rule #567 for best value: >> intensional similarity = 3 >> extensional distance = 311 >> proper extension: 03ckwzc; 0963mq; 0c00zd0; 01j8wk; 014zwb; 0c57yj; 05_5rjx; 07bwr; 01q2nx; 02tktw; ... >> query: (?x781, 09zzb8) <- genre(?x781, ?x225), film(?x2415, ?x781), crewmember(?x781, ?x6546) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gkz15s film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.709 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13577-06vbd PRED entity: 06vbd PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.88 #6, 0.87 #81, 0.87 #78) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 05b7q; >> query: (?x4302, 0hzc9wc) <- contains(?x6304, ?x4302), combatants(?x4302, ?x608), teams(?x4302, ?x3060) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06vbd administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.880 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #13576-01vswwx PRED entity: 01vswwx PRED relation: religion PRED expected values: 0c8wxp => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 22): 0c8wxp (0.42 #2338, 0.35 #2030, 0.35 #3306), 03_gx (0.18 #3313, 0.17 #2345, 0.15 #3842), 0kpl (0.17 #2034, 0.15 #3839, 0.15 #3310), 0flw86 (0.09 #310, 0.05 #2026, 0.05 #3302), 01lp8 (0.08 #529, 0.08 #705, 0.08 #133), 03j6c (0.07 #3849, 0.06 #3320, 0.05 #2044), 092bf5 (0.06 #763, 0.06 #631, 0.05 #147), 019cr (0.05 #143, 0.05 #231, 0.04 #539), 0kq2 (0.05 #2349, 0.05 #2041, 0.04 #3846), 06nzl (0.04 #806, 0.03 #454, 0.03 #498) >> Best rule #2338 for best value: >> intensional similarity = 3 >> extensional distance = 357 >> proper extension: 049k07; 02xbw2; 0fqyzz; 03h2d4; 013ybx; >> query: (?x5301, 0c8wxp) <- award_nominee(?x5301, ?x954), type_of_union(?x5301, ?x566), religion(?x5301, ?x4641) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vswwx religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.418 http://example.org/people/person/religion #13575-0bscw PRED entity: 0bscw PRED relation: produced_by PRED expected values: 0q9kd => 91 concepts (62 used for prediction) PRED predicted values (max 10 best out of 138): 016dmx (0.25 #673, 0.02 #10364, 0.02 #8423), 03s2y9 (0.25 #748), 0kvrb (0.16 #16276, 0.16 #14726, 0.16 #13565), 03mfqm (0.16 #16276, 0.16 #14726, 0.16 #13565), 0fvf9q (0.08 #1938, 0.06 #3874, 0.05 #1552), 02vyw (0.07 #1282, 0.05 #1669, 0.04 #2442), 05hj_k (0.07 #914, 0.02 #2073, 0.02 #2460), 06pj8 (0.06 #6262, 0.04 #7813, 0.04 #11309), 02q_cc (0.05 #6228, 0.04 #11275, 0.04 #7779), 05prs8 (0.05 #1213, 0.03 #2373, 0.03 #7800) >> Best rule #673 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01f39b; >> query: (?x1444, 016dmx) <- film(?x4587, ?x1444), genre(?x1444, ?x53), ?x4587 = 015d3h, produced_by(?x1444, ?x6690) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1161 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 026p_bs; *> query: (?x1444, 0q9kd) <- honored_for(?x7432, ?x1444), language(?x1444, ?x254), produced_by(?x1444, ?x6690), music(?x1444, ?x2354) *> conf = 0.02 ranks of expected_values: 100 EVAL 0bscw produced_by 0q9kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 91.000 62.000 0.250 http://example.org/film/film/produced_by #13574-01r9md PRED entity: 01r9md PRED relation: category PRED expected values: 08mbj5d => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.36 #2, 0.32 #17, 0.32 #112) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 03f1zdw; 02756j; >> query: (?x13173, 08mbj5d) <- location(?x13173, ?x1023), film(?x13173, ?x4502), country(?x972, ?x1023), combatants(?x94, ?x1023) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r9md category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.357 http://example.org/common/topic/webpage./common/webpage/category #13573-03f0qd7 PRED entity: 03f0qd7 PRED relation: artists! PRED expected values: 01d_s5 01y2mq => 153 concepts (88 used for prediction) PRED predicted values (max 10 best out of 224): 025sc50 (0.73 #5488, 0.56 #1561, 0.56 #7300), 0m0jc (0.64 #3936, 0.20 #311, 0.12 #12998), 06j6l (0.52 #5486, 0.40 #351, 0.39 #7298), 06by7 (0.52 #13011, 0.50 #1532, 0.43 #23591), 05bt6j (0.39 #5481, 0.34 #13033, 0.25 #16962), 0gywn (0.38 #7307, 0.31 #9421, 0.31 #12745), 0y3_8 (0.27 #3975, 0.27 #5485, 0.21 #21755), 016clz (0.27 #3932, 0.23 #23574, 0.23 #11483), 01lyv (0.27 #12118, 0.20 #16953, 0.20 #6982), 036jv (0.25 #1395, 0.25 #1093, 0.19 #1999) >> Best rule #5488 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 03f5spx; 0lk90; 09qr6; 02zmh5; 09k2t1; 07ss8_; 013v5j; 01x1cn2; 0gdh5; 03bxwtd; ... >> query: (?x11709, 025sc50) <- profession(?x11709, ?x131), artists(?x3996, ?x11709), nationality(?x11709, ?x94), ?x3996 = 02lnbg >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1743 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 01pfkw; *> query: (?x11709, 01y2mq) <- company(?x11709, ?x14145), artist(?x7089, ?x11709), award(?x11709, ?x6416), languages(?x11709, ?x254) *> conf = 0.06 ranks of expected_values: 72, 96 EVAL 03f0qd7 artists! 01y2mq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 153.000 88.000 0.731 http://example.org/music/genre/artists EVAL 03f0qd7 artists! 01d_s5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 153.000 88.000 0.731 http://example.org/music/genre/artists #13572-014hdb PRED entity: 014hdb PRED relation: award PRED expected values: 09v51c2 => 118 concepts (76 used for prediction) PRED predicted values (max 10 best out of 296): 02rdyk7 (0.73 #29479, 0.72 #19381, 0.72 #19380), 0789r6 (0.73 #29479, 0.72 #19381, 0.72 #19380), 05h5nb8 (0.73 #29479, 0.72 #19381, 0.72 #19380), 02pqp12 (0.67 #473, 0.28 #4103, 0.26 #2491), 0gs9p (0.56 #482, 0.43 #4112, 0.39 #7749), 040njc (0.44 #410, 0.38 #3234, 0.37 #4040), 02x4wr9 (0.44 #539, 0.17 #3766, 0.16 #2557), 07bdd_ (0.42 #1679, 0.17 #6118, 0.16 #10154), 019f4v (0.41 #4099, 0.38 #3293, 0.36 #7736), 0gq9h (0.39 #4917, 0.38 #5725, 0.38 #5321) >> Best rule #29479 for best value: >> intensional similarity = 4 >> extensional distance = 2043 >> proper extension: 089tm; 01pfr3; 02mslq; 02r3zy; 01v0sx2; 03g5jw; 01wv9xn; 0dvqq; 0frsw; 03fbc; ... >> query: (?x10271, ?x13075) <- award_winner(?x13075, ?x10271), award(?x10271, ?x77), ceremony(?x77, ?x78), award(?x286, ?x13075) >> conf = 0.73 => this is the best rule for 3 predicted values *> Best rule #6053 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 218 *> proper extension: 0grrq8; 059x0w; *> query: (?x10271, ?x143) <- award_winner(?x1587, ?x10271), produced_by(?x2889, ?x10271), film_release_region(?x2889, ?x94), nominated_for(?x143, ?x2889) *> conf = 0.18 ranks of expected_values: 36 EVAL 014hdb award 09v51c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 118.000 76.000 0.729 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13571-01sb5r PRED entity: 01sb5r PRED relation: artists! PRED expected values: 016jny => 138 concepts (64 used for prediction) PRED predicted values (max 10 best out of 241): 0155w (0.67 #727, 0.50 #3524, 0.46 #1038), 01lyv (0.60 #4073, 0.23 #964, 0.23 #13085), 064t9 (0.53 #6539, 0.50 #7472, 0.49 #19292), 05w3f (0.46 #968, 0.36 #3454, 0.24 #4388), 08jyyk (0.46 #999, 0.23 #1310, 0.18 #4419), 06j6l (0.44 #668, 0.32 #7506, 0.29 #1912), 0glt670 (0.35 #7498, 0.21 #19318, 0.21 #18073), 03_d0 (0.33 #632, 0.25 #1876, 0.23 #1254), 0126t5 (0.33 #706, 0.10 #2262, 0.08 #1328), 05bt6j (0.31 #4083, 0.31 #974, 0.30 #17765) >> Best rule #727 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0134tg; >> query: (?x4140, 0155w) <- artist(?x2039, ?x4140), artists(?x12114, ?x4140), ?x12114 = 0175yg >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1036 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 01kd57; *> query: (?x4140, 016jny) <- instrumentalists(?x4917, ?x4140), profession(?x4140, ?x220), ?x220 = 016z4k, ?x4917 = 06w7v *> conf = 0.31 ranks of expected_values: 12 EVAL 01sb5r artists! 016jny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 138.000 64.000 0.667 http://example.org/music/genre/artists #13570-01l0__ PRED entity: 01l0__ PRED relation: team! PRED expected values: 0d3mlc => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 73): 09r1j5 (0.33 #30, 0.22 #923, 0.20 #181), 0d3mlc (0.33 #145, 0.20 #220, 0.18 #448), 054kmq (0.33 #68, 0.20 #219, 0.17 #516), 08b0cj (0.33 #402, 0.08 #1380, 0.07 #1456), 09j028 (0.21 #1094, 0.20 #201, 0.17 #498), 09l9xt (0.20 #246, 0.17 #319, 0.15 #745), 0dhrqx (0.20 #192, 0.17 #489, 0.14 #564), 07nvmx (0.18 #448, 0.18 #447, 0.18 #446), 0bn9sc (0.18 #448, 0.18 #447, 0.18 #446), 071pf2 (0.18 #448, 0.18 #447, 0.18 #446) >> Best rule #30 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 03yfh3; >> query: (?x10557, 09r1j5) <- current_club(?x7294, ?x10557), current_club(?x59, ?x10557), position(?x10557, ?x530), position(?x10557, ?x63), ?x7294 = 03xh50, team(?x60, ?x10557), ?x63 = 02sdk9v, ?x60 = 02nzb8, ?x530 = 02_j1w, team(?x6873, ?x59), team(?x3031, ?x59), team(?x6873, ?x13172), athlete(?x471, ?x3031), profession(?x6873, ?x7623), gender(?x3031, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #145 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 049f05; *> query: (?x10557, 0d3mlc) <- current_club(?x7294, ?x10557), current_club(?x59, ?x10557), position(?x10557, ?x530), position(?x10557, ?x63), ?x7294 = 03xh50, team(?x60, ?x10557), ?x63 = 02sdk9v, ?x60 = 02nzb8, ?x530 = 02_j1w, team(?x6873, ?x59), nationality(?x6873, ?x390), sport(?x59, ?x471), gender(?x6873, ?x231), ?x471 = 02vx4 *> conf = 0.33 ranks of expected_values: 2 EVAL 01l0__ team! 0d3mlc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.333 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #13569-01fx4k PRED entity: 01fx4k PRED relation: award PRED expected values: 02z1nbg => 83 concepts (78 used for prediction) PRED predicted values (max 10 best out of 226): 094qd5 (0.26 #1870, 0.25 #11674, 0.25 #468), 0gr0m (0.26 #1870, 0.25 #11674, 0.25 #468), 019f4v (0.26 #1870, 0.25 #11674, 0.25 #468), 040njc (0.26 #1870, 0.25 #11674, 0.25 #468), 0gr4k (0.26 #1870, 0.25 #11674, 0.25 #468), 02y_rq5 (0.26 #1870, 0.25 #11674, 0.25 #468), 02qvyrt (0.26 #1870, 0.25 #11674, 0.25 #468), 02pqp12 (0.26 #1870, 0.25 #11674, 0.25 #468), 04kxsb (0.26 #1870, 0.25 #11674, 0.25 #468), 0gq9h (0.16 #1698, 0.11 #6131, 0.10 #6597) >> Best rule #1870 for best value: >> intensional similarity = 5 >> extensional distance = 127 >> proper extension: 02vxq9m; 07gp9; 0bth54; 0fg04; 0b6tzs; 0cwy47; 017gl1; 0_92w; 0gmcwlb; 0jqn5; ... >> query: (?x10049, ?x198) <- film_release_region(?x10049, ?x94), nominated_for(?x1243, ?x10049), nominated_for(?x198, ?x10049), film(?x2173, ?x10049), ?x1243 = 0gr0m >> conf = 0.26 => this is the best rule for 9 predicted values *> Best rule #16576 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1470 *> proper extension: 0275kr; 04bp0l; *> query: (?x10049, ?x1716) <- nominated_for(?x8307, ?x10049), award_winner(?x1716, ?x8307), award(?x718, ?x1716) *> conf = 0.14 ranks of expected_values: 15 EVAL 01fx4k award 02z1nbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 83.000 78.000 0.257 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13568-02khs PRED entity: 02khs PRED relation: olympics PRED expected values: 06sks6 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 43): 06sks6 (0.89 #230, 0.88 #847, 0.88 #2166), 0kbws (0.57 #260, 0.54 #1001, 0.53 #1414), 0kbvv (0.51 #272, 0.38 #478, 0.36 #1260), 0kbvb (0.46 #288, 0.40 #171, 0.39 #1029), 09n48 (0.45 #249, 0.33 #414, 0.33 #373), 018ctl (0.43 #254, 0.35 #460, 0.33 #502), 0swbd (0.41 #257, 0.28 #381, 0.28 #175), 0jdk_ (0.37 #273, 0.30 #191, 0.29 #479), 0jhn7 (0.30 #192, 0.25 #274, 0.24 #151), 0swff (0.27 #269, 0.21 #187, 0.20 #393) >> Best rule #230 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 04vs9; >> query: (?x1756, 06sks6) <- organization(?x1756, ?x4753), ?x4753 = 0gkjy, country(?x1121, ?x1756) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02khs olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.891 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #13567-04x4gj PRED entity: 04x4gj PRED relation: country_of_origin PRED expected values: 09c7w0 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 131): 09c7w0 (0.91 #367, 0.90 #344, 0.89 #435), 07ssc (0.54 #604, 0.17 #9, 0.14 #592), 03_3d (0.28 #161, 0.24 #287, 0.23 #116), 0d060g (0.20 #71, 0.19 #140, 0.18 #83), 0d0vqn (0.14 #592, 0.14 #753, 0.14 #446), 03rjj (0.14 #592, 0.14 #753, 0.14 #446), 02jx1 (0.14 #592, 0.14 #753, 0.14 #446), 03rt9 (0.14 #592, 0.14 #753, 0.14 #446), 04jpl (0.14 #592, 0.14 #753, 0.14 #446), 05v8c (0.14 #592, 0.14 #446, 0.03 #294) >> Best rule #367 for best value: >> intensional similarity = 10 >> extensional distance = 54 >> proper extension: 01hn_t; >> query: (?x12434, 09c7w0) <- tv_program(?x11404, ?x12434), genre(?x12434, ?x1013), genre(?x11174, ?x1013), genre(?x5054, ?x1013), genre(?x1490, ?x1013), ?x11174 = 01d2v1, award_winner(?x1490, ?x2671), film_release_region(?x1490, ?x87), award(?x1490, ?x1063), film_crew_role(?x5054, ?x137) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04x4gj country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.911 http://example.org/tv/tv_program/country_of_origin #13566-081nh PRED entity: 081nh PRED relation: program_creator! PRED expected values: 05631 => 117 concepts (83 used for prediction) PRED predicted values (max 10 best out of 25): 0b005 (0.04 #659, 0.03 #900, 0.01 #1500), 025x1t (0.03 #830, 0.02 #1191, 0.02 #1311), 0k4d7 (0.03 #3009, 0.02 #2887, 0.02 #7578), 0kcn7 (0.03 #3009, 0.02 #8183, 0.01 #7337), 05b6s5j (0.02 #1192, 0.02 #1312), 070ltt (0.02 #1182, 0.02 #1302), 01yb1y (0.02 #1160, 0.02 #1280), 03cf9ly (0.02 #2878), 03ln8b (0.02 #1217), 06y_n (0.01 #1414, 0.01 #2618, 0.01 #2858) >> Best rule #659 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 01pjr7; >> query: (?x2426, 0b005) <- profession(?x2426, ?x319), produced_by(?x2425, ?x2426), influenced_by(?x1855, ?x2426) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 081nh program_creator! 05631 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 83.000 0.036 http://example.org/tv/tv_program/program_creator #13565-01wsl7c PRED entity: 01wsl7c PRED relation: people! PRED expected values: 03lmx1 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 52): 03lmx1 (0.44 #14, 0.32 #245, 0.31 #553), 0x67 (0.23 #1935, 0.21 #3244, 0.21 #1165), 02w7gg (0.18 #2081, 0.18 #1234, 0.17 #310), 041rx (0.15 #1082, 0.15 #7705, 0.14 #8630), 07hwkr (0.13 #320, 0.08 #397, 0.07 #89), 0dryh9k (0.09 #6100, 0.09 #7178, 0.06 #8951), 09vc4s (0.09 #317, 0.03 #1703, 0.03 #1395), 01g7zj (0.09 #360, 0.02 #1900, 0.02 #1361), 0xnvg (0.08 #398, 0.05 #5250, 0.04 #9256), 0d7wh (0.07 #1249, 0.07 #94, 0.06 #2096) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 011zwl; >> query: (?x1997, 03lmx1) <- place_of_birth(?x1997, ?x6885), student(?x13827, ?x1997), ?x6885 = 02m77, gender(?x1997, ?x514) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wsl7c people! 03lmx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.444 http://example.org/people/ethnicity/people #13564-0gry51 PRED entity: 0gry51 PRED relation: place_of_burial PRED expected values: 018mrd => 47 concepts (43 used for prediction) PRED predicted values (max 10 best out of 10): 018mmj (0.08 #11, 0.05 #321, 0.04 #352), 018mm4 (0.06 #40, 0.03 #288, 0.03 #102), 018mmw (0.03 #17, 0.01 #389, 0.01 #358), 01f38z (0.02 #60, 0.02 #91, 0.01 #122), 018mlg (0.02 #55, 0.02 #117, 0.01 #272), 01n7q (0.02 #97, 0.01 #314, 0.01 #252), 018mrd (0.01 #23, 0.01 #116, 0.01 #302), 0bvqq (0.01 #12), 09c7w0 (0.01 #1), 0nb1s (0.01 #123) >> Best rule #11 for best value: >> intensional similarity = 5 >> extensional distance = 73 >> proper extension: 0jf1b; 022g44; 012vct; 0k57l; 0l9k1; 04dyqk; >> query: (?x13488, 018mmj) <- profession(?x13488, ?x1032), profession(?x13488, ?x524), ?x1032 = 02hrh1q, ?x524 = 02jknp, people(?x5801, ?x13488) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #23 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 73 *> proper extension: 0jf1b; 022g44; 012vct; 0k57l; 0l9k1; 04dyqk; *> query: (?x13488, 018mrd) <- profession(?x13488, ?x1032), profession(?x13488, ?x524), ?x1032 = 02hrh1q, ?x524 = 02jknp, people(?x5801, ?x13488) *> conf = 0.01 ranks of expected_values: 7 EVAL 0gry51 place_of_burial 018mrd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 47.000 43.000 0.080 http://example.org/people/deceased_person/place_of_burial #13563-013jz2 PRED entity: 013jz2 PRED relation: source PRED expected values: 0jbk9 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #22, 0.92 #17, 0.91 #28) >> Best rule #22 for best value: >> intensional similarity = 2 >> extensional distance = 302 >> proper extension: 01m24m; >> query: (?x1629, 0jbk9) <- contains(?x94, ?x1629), county(?x1629, ?x10235) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013jz2 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.918 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #13562-06myp PRED entity: 06myp PRED relation: profession PRED expected values: 05t4q => 215 concepts (151 used for prediction) PRED predicted values (max 10 best out of 99): 02hrh1q (0.84 #13103, 0.83 #13250, 0.83 #12956), 0cbd2 (0.72 #5888, 0.71 #1036, 0.69 #6770), 0dxtg (0.50 #2366, 0.48 #13837, 0.48 #7218), 05z96 (0.50 #3132, 0.39 #4896, 0.35 #5925), 0kyk (0.47 #2530, 0.46 #2236, 0.43 #5912), 05snw (0.43 #1269, 0.20 #15000, 0.09 #5092), 0h9c (0.43 #1221, 0.08 #2250, 0.06 #19706), 01c72t (0.40 #1936, 0.38 #1348, 0.31 #2671), 05t4q (0.40 #7646, 0.38 #20148, 0.37 #17500), 01d_h8 (0.36 #7210, 0.36 #16035, 0.36 #2358) >> Best rule #13103 for best value: >> intensional similarity = 4 >> extensional distance = 266 >> proper extension: 059t6d; 0259r0; 01vzxmq; 053xw6; 03crmd; 0404wqb; >> query: (?x10895, 02hrh1q) <- location(?x10895, ?x863), profession(?x10895, ?x3802), languages(?x10895, ?x254), month(?x863, ?x1459) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #7646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 113 *> proper extension: 0177g; 0168ql; *> query: (?x10895, ?x6630) <- profession(?x10895, ?x13995), religion(?x10895, ?x2694), specialization_of(?x13995, ?x6630), people(?x6821, ?x10895) *> conf = 0.40 ranks of expected_values: 9 EVAL 06myp profession 05t4q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 215.000 151.000 0.836 http://example.org/people/person/profession #13561-04crrxr PRED entity: 04crrxr PRED relation: place_of_birth PRED expected values: 02_286 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 69): 0cr3d (0.20 #94, 0.05 #3614, 0.04 #798), 05fkf (0.20 #20), 013yq (0.11 #783, 0.02 #5008, 0.02 #5712), 02_286 (0.07 #48601, 0.07 #12694, 0.07 #49306), 01_d4 (0.05 #2882, 0.05 #2178, 0.04 #8515), 030qb3t (0.05 #10616, 0.04 #9912, 0.04 #48636), 0rh6k (0.04 #706, 0.04 #2818, 0.03 #7747), 0dclg (0.04 #782, 0.02 #5711, 0.02 #7119), 05qtj (0.04 #871, 0.01 #5096), 0t_07 (0.04 #1152, 0.01 #1856) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 05qsxy; >> query: (?x5447, 0cr3d) <- award_winner(?x2127, ?x5447), student(?x1884, ?x5447), ?x1884 = 0bx8pn >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #48601 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2491 *> proper extension: 0h1_w; 019y64; 01d494; 0b6yp2; 015wfg; 05typm; 01ry0f; 04107; 03lh3v; 094xh; ... *> query: (?x5447, 02_286) <- gender(?x5447, ?x231), nationality(?x5447, ?x94), ?x94 = 09c7w0 *> conf = 0.07 ranks of expected_values: 4 EVAL 04crrxr place_of_birth 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 83.000 0.200 http://example.org/people/person/place_of_birth #13560-032j_n PRED entity: 032j_n PRED relation: film PRED expected values: 01pj_5 047vp1n => 120 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1693): 0gvvf4j (0.67 #31647, 0.64 #17405, 0.59 #7911), 02rb84n (0.36 #1834, 0.27 #4998, 0.21 #12910), 03mh_tp (0.36 #2030, 0.25 #6776, 0.19 #41589), 01dvbd (0.36 #2024, 0.25 #6770, 0.19 #8353), 035s95 (0.33 #5047, 0.27 #1883, 0.26 #12959), 0h14ln (0.27 #2946, 0.25 #7692, 0.16 #28263), 0k0rf (0.27 #2370, 0.25 #7116, 0.13 #5534), 047gpsd (0.27 #2638, 0.20 #5802, 0.19 #7384), 0jqj5 (0.27 #2369, 0.19 #7115, 0.16 #15027), 05b6rdt (0.27 #2555, 0.19 #7301, 0.13 #5719) >> Best rule #31647 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 0kx4m; 046b0s; 0kk9v; 031rq5; 056ws9; 04rcl7; >> query: (?x10884, ?x4696) <- production_companies(?x1184, ?x10884), child(?x166, ?x10884), nominated_for(?x10884, ?x4696) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2256 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 054g1r; *> query: (?x10884, 01pj_5) <- film(?x10884, ?x10651), film(?x12566, ?x10651), ?x12566 = 04xhwn *> conf = 0.09 ranks of expected_values: 918, 1404 EVAL 032j_n film 047vp1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 120.000 99.000 0.672 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 032j_n film 01pj_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 120.000 99.000 0.672 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #13559-0r0m6 PRED entity: 0r0m6 PRED relation: location! PRED expected values: 03ds3 03gr7w 046zh 0h7pj => 140 concepts (100 used for prediction) PRED predicted values (max 10 best out of 3375): 02t__3 (0.46 #179365, 0.44 #226681, 0.44 #77239), 01vsy3q (0.15 #3461, 0.11 #13428, 0.10 #5953), 023kzp (0.13 #6175, 0.12 #3683, 0.11 #16143), 073749 (0.13 #8259, 0.12 #3275, 0.11 #15735), 03r1pr (0.13 #74748, 0.13 #67273, 0.12 #4982), 015qyf (0.13 #74748, 0.13 #67273, 0.12 #4982), 01vsps (0.13 #74748, 0.13 #67273, 0.12 #4982), 01xcqc (0.13 #74748, 0.13 #67273, 0.12 #4982), 02zyq6 (0.13 #74748, 0.13 #67273, 0.12 #4982), 0pyww (0.12 #3451, 0.11 #13418, 0.10 #20894) >> Best rule #179365 for best value: >> intensional similarity = 3 >> extensional distance = 250 >> proper extension: 01km6_; 0r6ff; 011wdm; 0kdqw; 0hx5f; 03kjh; >> query: (?x4151, ?x5979) <- contains(?x94, ?x4151), citytown(?x6455, ?x4151), place_of_birth(?x5979, ?x4151) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #4278 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0r1yc; *> query: (?x4151, 0h7pj) <- featured_film_locations(?x951, ?x4151), vacationer(?x4151, ?x445), place_of_death(?x1606, ?x4151) *> conf = 0.08 ranks of expected_values: 74, 274, 1798, 3203 EVAL 0r0m6 location! 0h7pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 140.000 100.000 0.457 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0r0m6 location! 046zh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 140.000 100.000 0.457 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0r0m6 location! 03gr7w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 100.000 0.457 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0r0m6 location! 03ds3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 140.000 100.000 0.457 http://example.org/people/person/places_lived./people/place_lived/location #13558-045g4l PRED entity: 045g4l PRED relation: gender PRED expected values: 05zppz => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #32, 0.85 #110, 0.84 #112), 02zsn (0.46 #198, 0.27 #109, 0.26 #189) >> Best rule #32 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 01jqr_5; 03pm9; 053yx; 0lgm5; 09qh1; 012j8z; 03_f0; 024qwq; 04mby; 03bdm4; ... >> query: (?x10901, 05zppz) <- location(?x10901, ?x2850), people(?x1158, ?x10901), profession(?x10901, ?x1146), ?x1158 = 02y0js >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045g4l gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.897 http://example.org/people/person/gender #13557-02sf_r PRED entity: 02sf_r PRED relation: position! PRED expected values: 02plv57 => 20 concepts (18 used for prediction) PRED predicted values (max 10 best out of 82): 0jm4b (0.80 #115, 0.75 #76, 0.62 #143), 0jmgb (0.80 #115, 0.75 #76, 0.43 #111), 0jmjr (0.80 #115, 0.75 #76, 0.43 #108), 02pqcfz (0.50 #79, 0.43 #98, 0.33 #118), 02r2qt7 (0.50 #86, 0.43 #105, 0.33 #125), 02ptzz0 (0.50 #78, 0.33 #59, 0.33 #2), 03d5m8w (0.50 #90, 0.33 #71, 0.33 #14), 027yf83 (0.50 #81, 0.33 #62, 0.29 #100), 02qk2d5 (0.33 #68, 0.33 #29, 0.29 #106), 02py8_w (0.33 #65, 0.33 #26, 0.29 #103) >> Best rule #115 for best value: >> intensional similarity = 29 >> extensional distance = 5 >> proper extension: 0619m3; >> query: (?x4747, ?x5756) <- position(?x9760, ?x4747), position(?x8528, ?x4747), position(?x4571, ?x4747), position(?x1578, ?x4747), position(?x660, ?x4747), team(?x13045, ?x8528), team(?x6802, ?x8528), team(?x4937, ?x8528), school(?x9760, ?x2895), ?x13045 = 0bqthy, school(?x660, ?x2775), sport(?x660, ?x4833), colors(?x8528, ?x7203), teams(?x6769, ?x4571), team(?x4937, ?x8728), draft(?x9760, ?x12852), draft(?x9760, ?x8586), major_field_of_study(?x2895, ?x4321), locations(?x4937, ?x2504), ?x4321 = 0g26h, school(?x4571, ?x546), team(?x4747, ?x5756), ?x8728 = 026xxv_, ?x1578 = 0jm2v, student(?x2775, ?x1447), ?x12852 = 06439y, ?x8586 = 038981, ?x6802 = 0br1x_, state_province_region(?x2775, ?x335) >> conf = 0.80 => this is the best rule for 3 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 45 *> extensional distance = 1 *> proper extension: 01pv51; *> query: (?x4747, 02plv57) <- position(?x12141, ?x4747), position(?x11805, ?x4747), position(?x11420, ?x4747), position(?x9995, ?x4747), position(?x9760, ?x4747), position(?x8528, ?x4747), position(?x8228, ?x4747), position(?x7158, ?x4747), position(?x6128, ?x4747), position(?x6089, ?x4747), position(?x5419, ?x4747), position(?x5154, ?x4747), position(?x4571, ?x4747), position(?x2820, ?x4747), team(?x10673, ?x8528), team(?x8527, ?x8528), team(?x7378, ?x8528), team(?x7042, ?x8528), team(?x6002, ?x8528), team(?x2302, ?x8528), ?x9760 = 0bwjj, ?x7378 = 0bzrxn, colors(?x8528, ?x3189), team(?x6848, ?x8528), ?x6128 = 0jm64, ?x8228 = 0jmcv, ?x5154 = 0jm8l, ?x8527 = 0b_6v_, ?x11805 = 0jm5b, ?x5419 = 0jmmn, ?x4571 = 0jm6n, ?x12141 = 0jmk7, ?x6002 = 0cc8q3, locations(?x10673, ?x8263), ?x7042 = 0b_72t, ?x11420 = 0jmhr, school(?x7158, ?x2775), ?x6089 = 0jmbv, team(?x2302, ?x4938), ?x9995 = 0jm9w, ?x8263 = 0c1d0, ?x4938 = 027yf83, team(?x8996, ?x7158), ?x2775 = 078bz, ?x2820 = 0jmj7 *> conf = 0.33 ranks of expected_values: 17 EVAL 02sf_r position! 02plv57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 20.000 18.000 0.800 http://example.org/sports/sports_team/roster./basketball/basketball_roster_position/position #13556-01mpwj PRED entity: 01mpwj PRED relation: major_field_of_study PRED expected values: 036nz => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 109): 02lp1 (0.57 #230, 0.40 #4966, 0.39 #2540), 02ky346 (0.57 #233, 0.36 #453, 0.28 #783), 03nfmq (0.57 #250, 0.36 #470, 0.25 #1020), 062z7 (0.48 #793, 0.47 #2553, 0.43 #1673), 0dc_v (0.43 #254, 0.33 #144, 0.27 #474), 04x_3 (0.43 #242, 0.29 #2552, 0.28 #792), 01tbp (0.43 #270, 0.28 #820, 0.27 #490), 02jfc (0.33 #181, 0.32 #841, 0.29 #291), 0h5k (0.33 #129, 0.29 #239, 0.25 #2549), 036nz (0.33 #166, 0.15 #936, 0.14 #276) >> Best rule #230 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 03y7ml; >> query: (?x3485, 02lp1) <- company(?x5796, ?x3485), influenced_by(?x5796, ?x13698), organizations_founded(?x13698, ?x1912), jurisdiction_of_office(?x13698, ?x94) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 05qgd9; *> query: (?x3485, 036nz) <- student(?x3485, ?x6138), student(?x3485, ?x879), major_field_of_study(?x3485, ?x254), institution(?x865, ?x3485), people(?x5741, ?x879), ?x6138 = 06hx2 *> conf = 0.33 ranks of expected_values: 10 EVAL 01mpwj major_field_of_study 036nz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 127.000 127.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13555-02fqrf PRED entity: 02fqrf PRED relation: prequel! PRED expected values: 0btpm6 => 95 concepts (29 used for prediction) PRED predicted values (max 10 best out of 60): 0bpm4yw (0.07 #74, 0.04 #254, 0.02 #434), 03y0pn (0.07 #122, 0.04 #302, 0.02 #482), 0bc1yhb (0.07 #94, 0.01 #1534), 0dln8jk (0.07 #86, 0.01 #1526), 03nfnx (0.04 #318, 0.02 #1758, 0.02 #498), 047csmy (0.04 #275, 0.02 #455, 0.02 #635), 02lk60 (0.04 #265, 0.02 #445, 0.02 #985), 0gffmn8 (0.04 #239, 0.02 #419, 0.02 #959), 05qbckf (0.02 #403, 0.02 #583, 0.02 #763), 0315rp (0.02 #503, 0.02 #683, 0.02 #863) >> Best rule #74 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0gwjw0c; 0m63c; >> query: (?x3498, 0bpm4yw) <- film_release_region(?x3498, ?x2267), nominated_for(?x3019, ?x3498), ?x2267 = 03rj0, ?x3019 = 057xs89 >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02fqrf prequel! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 29.000 0.071 http://example.org/film/film/prequel #13554-0d_rw PRED entity: 0d_rw PRED relation: genre PRED expected values: 01tz3c => 83 concepts (50 used for prediction) PRED predicted values (max 10 best out of 138): 01jfsb (0.62 #1388, 0.50 #173, 0.40 #418), 05p553 (0.61 #2194, 0.39 #1867, 0.38 #1140), 01htzx (0.54 #906, 0.44 #746, 0.38 #2366), 01z4y (0.46 #2204, 0.29 #3108, 0.24 #1150), 0hcr (0.45 #1878, 0.29 #2205, 0.28 #2537), 0fdjb (0.40 #842, 0.40 #437, 0.38 #517), 03k9fj (0.38 #902, 0.31 #2362, 0.26 #1872), 0c4xc (0.32 #2229, 0.20 #3133, 0.14 #1175), 09n3wz (0.25 #552, 0.25 #227, 0.22 #797), 0lsxr (0.25 #170, 0.21 #1385, 0.20 #820) >> Best rule #1388 for best value: >> intensional similarity = 8 >> extensional distance = 22 >> proper extension: 01kt_j; >> query: (?x13221, 01jfsb) <- genre(?x13221, ?x571), program(?x2062, ?x13221), genre(?x6235, ?x571), genre(?x1889, ?x571), genre(?x1074, ?x571), ?x6235 = 05b6rdt, ?x1889 = 028cg00, ?x1074 = 03t97y >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #530 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 06w7mlh; *> query: (?x13221, 01tz3c) <- genre(?x13221, ?x1013), genre(?x13221, ?x600), genre(?x13221, ?x571), program(?x2062, ?x13221), ?x571 = 03npn, ?x1013 = 06n90, titles(?x600, ?x394), genre(?x1108, ?x600), ?x1108 = 0jjy0 *> conf = 0.12 ranks of expected_values: 27 EVAL 0d_rw genre 01tz3c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 83.000 50.000 0.625 http://example.org/tv/tv_program/genre #13553-0dzlbx PRED entity: 0dzlbx PRED relation: film_release_region PRED expected values: 06mzp 0k6nt 047yc => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 183): 0k6nt (0.80 #4052, 0.79 #392, 0.79 #1652), 0ctw_b (0.71 #1401, 0.62 #1653, 0.55 #393), 01p1v (0.68 #1419, 0.65 #1671, 0.55 #411), 047yc (0.67 #1655, 0.67 #395, 0.60 #1403), 015qh (0.62 #1662, 0.59 #1410, 0.56 #780), 01mjq (0.59 #656, 0.59 #1664, 0.58 #782), 06mzp (0.59 #767, 0.58 #641, 0.52 #2154), 06t8v (0.55 #1690, 0.52 #1438, 0.49 #808), 047lj (0.47 #762, 0.46 #1644, 0.45 #384), 077qn (0.45 #1448, 0.43 #1700, 0.28 #4417) >> Best rule #4052 for best value: >> intensional similarity = 4 >> extensional distance = 311 >> proper extension: 0ddj0x; 09v42sf; 0b85mm; >> query: (?x4998, 0k6nt) <- film(?x3553, ?x4998), film_release_region(?x4998, ?x1229), ?x1229 = 059j2, gender(?x3553, ?x514) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 7 EVAL 0dzlbx film_release_region 047yc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 116.000 0.796 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dzlbx film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.796 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dzlbx film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 116.000 116.000 0.796 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13552-019pm_ PRED entity: 019pm_ PRED relation: profession PRED expected values: 02hrh1q => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.92 #7989, 0.91 #4363, 0.91 #1753), 0dxtg (0.61 #9293, 0.53 #5812, 0.53 #6538), 09jwl (0.33 #2192, 0.30 #6381, 0.24 #3497), 0dz3r (0.30 #6381, 0.22 #3482, 0.18 #2177), 016z4k (0.30 #6381, 0.21 #2179, 0.16 #729), 0cbd2 (0.30 #6381, 0.14 #9287, 0.12 #6532), 0kyk (0.30 #6381, 0.11 #2203, 0.10 #3508), 01c72t (0.30 #6381, 0.09 #11043, 0.09 #10463), 04f2zj (0.30 #6381, 0.04 #1253, 0.02 #2268), 01p5_g (0.30 #6381, 0.03 #812, 0.03 #957) >> Best rule #7989 for best value: >> intensional similarity = 3 >> extensional distance = 523 >> proper extension: 02_01w; >> query: (?x2763, 02hrh1q) <- participant(?x2763, ?x1733), profession(?x2763, ?x319), film(?x2763, ?x351) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019pm_ profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.916 http://example.org/people/person/profession #13551-0258dh PRED entity: 0258dh PRED relation: nominated_for! PRED expected values: 07cbcy 063y_ky => 102 concepts (95 used for prediction) PRED predicted values (max 10 best out of 229): 05f4m9q (0.40 #11, 0.28 #5149, 0.26 #5384), 07cbcy (0.30 #60, 0.28 #5149, 0.26 #5384), 05b4l5x (0.30 #6, 0.19 #708, 0.18 #474), 0gq9h (0.30 #1229, 0.26 #12230, 0.25 #1463), 05p09zm (0.28 #5149, 0.26 #5384, 0.25 #5385), 0p9sw (0.28 #5149, 0.26 #5384, 0.25 #5385), 054krc (0.28 #5149, 0.26 #5384, 0.25 #5385), 05p1dby (0.28 #5149, 0.26 #5384, 0.25 #5385), 0gr0m (0.28 #5149, 0.26 #5384, 0.25 #5385), 04ljl_l (0.28 #5149, 0.26 #5384, 0.25 #5385) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 032016; 02x6dqb; 03459x; 0g9yrw; 0125xq; 0bz6sq; >> query: (?x7354, 05f4m9q) <- production_companies(?x7354, ?x382), featured_film_locations(?x7354, ?x191), nominated_for(?x3064, ?x7354), ?x3064 = 05q5t0b >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #60 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 032016; 02x6dqb; 03459x; 0g9yrw; 0125xq; 0bz6sq; *> query: (?x7354, 07cbcy) <- production_companies(?x7354, ?x382), featured_film_locations(?x7354, ?x191), nominated_for(?x3064, ?x7354), ?x3064 = 05q5t0b *> conf = 0.30 ranks of expected_values: 2, 52 EVAL 0258dh nominated_for! 063y_ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 102.000 95.000 0.400 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0258dh nominated_for! 07cbcy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 95.000 0.400 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13550-02d44q PRED entity: 02d44q PRED relation: film_release_region PRED expected values: 09c7w0 05b4w 07dfk => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 158): 09c7w0 (0.92 #6187, 0.92 #6046, 0.92 #6327), 03h64 (0.78 #1034, 0.77 #1174, 0.75 #754), 05b4w (0.75 #752, 0.73 #1172, 0.73 #1032), 06bnz (0.71 #1154, 0.70 #734, 0.70 #1014), 06t2t (0.67 #1029, 0.66 #1169, 0.65 #749), 05v8c (0.61 #992, 0.60 #712, 0.59 #1132), 04gzd (0.53 #987, 0.52 #1127, 0.50 #707), 047yc (0.47 #1001, 0.47 #1141, 0.46 #721), 015qh (0.47 #1150, 0.46 #1010, 0.44 #730), 06t8v (0.46 #1186, 0.45 #766, 0.44 #1046) >> Best rule #6187 for best value: >> intensional similarity = 3 >> extensional distance = 1325 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; 018js4; ... >> query: (?x1071, 09c7w0) <- film_release_region(?x1071, ?x456), film_release_region(?x6215, ?x456), ?x6215 = 0jyb4 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 95 EVAL 02d44q film_release_region 07dfk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 62.000 62.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02d44q film_release_region 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 62.000 62.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02d44q film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13549-0z1vw PRED entity: 0z1vw PRED relation: location! PRED expected values: 03f1d47 => 83 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1411): 01vsy3q (0.10 #6025, 0.10 #3508, 0.09 #11059), 03d9v8 (0.10 #4372, 0.09 #11923, 0.07 #6889), 01nz1q6 (0.10 #4702, 0.07 #7219, 0.06 #12253), 0pyww (0.10 #981, 0.06 #11049, 0.05 #3498), 02sjf5 (0.10 #201, 0.05 #17820, 0.04 #15303), 015v3r (0.10 #599, 0.05 #3116, 0.04 #10667), 05gp3x (0.10 #1238, 0.05 #3755, 0.03 #6272), 0d06m5 (0.10 #660, 0.05 #3177, 0.03 #5694), 09b6zr (0.10 #820, 0.05 #3337, 0.03 #5854), 01pbxb (0.10 #7, 0.01 #32728, 0.01 #37762) >> Best rule #6025 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 04jpl; 02m77; 07dfk; >> query: (?x11595, 01vsy3q) <- category(?x11595, ?x134), location(?x1157, ?x11595), contains(?x94, ?x11595), place_founded(?x11323, ?x11595) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0z1vw location! 03f1d47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 35.000 0.100 http://example.org/people/person/places_lived./people/place_lived/location #13548-01z28b PRED entity: 01z28b PRED relation: teams PRED expected values: 0lmm3 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 106): 0jmk7 (0.33 #303), 0jnq8 (0.33 #229), 0jmjr (0.33 #222), 04mjl (0.33 #156), 02pqcfz (0.33 #82), 04112r (0.33 #51), 07k53y (0.33 #12), 038zh6 (0.20 #2150, 0.08 #2510, 0.06 #4310), 014nzp (0.12 #1379, 0.08 #2459, 0.06 #3179), 0cqt41 (0.12 #1110, 0.06 #2910, 0.04 #3630) >> Best rule #303 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 030qb3t; >> query: (?x12597, 0jmk7) <- time_zones(?x12597, ?x5327), location(?x7870, ?x12597), contains(?x512, ?x12597), ?x7870 = 01lqnff >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01z28b teams 0lmm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 145.000 145.000 0.333 http://example.org/sports/sports_team_location/teams #13547-01nm3s PRED entity: 01nm3s PRED relation: award PRED expected values: 0cqh46 0bdwqv => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 275): 02x8n1n (0.27 #922, 0.19 #13671, 0.18 #17693), 05zr6wv (0.20 #1625, 0.17 #1223, 0.17 #419), 0ck27z (0.19 #6925, 0.18 #895, 0.16 #4111), 0gq9h (0.19 #13671, 0.18 #18096, 0.18 #880), 0bdw6t (0.19 #13671, 0.18 #18096, 0.18 #17693), 05p09zm (0.19 #13671, 0.18 #18096, 0.18 #17693), 040njc (0.19 #13671, 0.18 #18096, 0.18 #17693), 02pqp12 (0.19 #13671, 0.18 #18096, 0.18 #17693), 04dn09n (0.19 #13671, 0.18 #18096, 0.18 #17693), 07cbcy (0.19 #13671, 0.18 #18096, 0.18 #17693) >> Best rule #922 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 015grj; 03pmty; 03mcwq3; 0f7h2v; 069nzr; 0807ml; 02779r4; >> query: (?x4004, 02x8n1n) <- award_nominee(?x1676, ?x4004), film(?x4004, ?x1080), ?x1676 = 05fnl9 >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #14878 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1419 *> proper extension: 0gv2r; 09h_q; 016ghw; *> query: (?x4004, ?x112) <- award_winner(?x4004, ?x968), profession(?x4004, ?x1032), award(?x968, ?x112) *> conf = 0.15 ranks of expected_values: 46, 131 EVAL 01nm3s award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 92.000 92.000 0.273 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01nm3s award 0cqh46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 92.000 92.000 0.273 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13546-0qf3p PRED entity: 0qf3p PRED relation: award PRED expected values: 02f79n => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 317): 05pcn59 (0.50 #892, 0.44 #4132, 0.33 #487), 09sb52 (0.41 #19076, 0.33 #25961, 0.33 #446), 01by1l (0.35 #11858, 0.32 #34538, 0.30 #6188), 07cbcy (0.33 #484, 0.31 #2509, 0.25 #889), 01bgqh (0.33 #43, 0.30 #6118, 0.28 #11788), 05p09zm (0.33 #530, 0.27 #16730, 0.26 #9845), 02f73b (0.33 #288, 0.26 #6363, 0.19 #7578), 04kxsb (0.33 #532, 0.25 #937, 0.23 #2557), 05zr6wv (0.33 #422, 0.25 #827, 0.21 #16622), 0gq9h (0.33 #483, 0.25 #888, 0.15 #2508) >> Best rule #892 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01jfrg; >> query: (?x2600, 05pcn59) <- location_of_ceremony(?x2600, ?x8569), ?x8569 = 07fr_, profession(?x2600, ?x1183) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6418 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 01vw87c; 0lk90; 01vrt_c; 01l1sq; 0285c; 01vsykc; 0gy6z9; 01wv9p; 01vw20h; 03j24kf; ... *> query: (?x2600, 02f79n) <- location_of_ceremony(?x2600, ?x8569), artist(?x2149, ?x2600), currency(?x2600, ?x1099) *> conf = 0.17 ranks of expected_values: 54 EVAL 0qf3p award 02f79n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 186.000 186.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13545-0nbcg PRED entity: 0nbcg PRED relation: profession! PRED expected values: 05mt_q 01wp8w7 01sbf2 01ky2h 01vvpjj 01trhmt 053yx 0gcs9 02v3yy 015f7 01wz_ml 024dgj 06gd4 0dl567 01m3x5p 028qdb 01vw20h 02x8z_ 0478__m 016fnb 06_6j3 016z1t 01s21dg 02nfjp 01vswx5 094xh 01vsyg9 0kxbc 02bgmr 09r9m7 01bpnd 01wgfp6 01mvjl0 025cn2 01vng3b 01l47f5 04b7xr 01vrnsk 01vrx35 017l4 0147jt 01kp_1t 012ycy 02pt27 0cgfb 01vsn38 03n0pv 01dpsv 0ql36 01pny5 => 52 concepts (28 used for prediction) PRED predicted values (max 10 best out of 3767): 01wp8w7 (0.67 #42390, 0.62 #57672, 0.60 #30929), 01vsl3_ (0.67 #42749, 0.62 #54210, 0.60 #27468), 02qwg (0.67 #42914, 0.61 #61126, 0.60 #27633), 01s21dg (0.67 #43374, 0.60 #28093, 0.50 #58656), 01l47f5 (0.67 #43895, 0.60 #28614, 0.50 #59177), 0bqsy (0.67 #43136, 0.60 #27855, 0.50 #58418), 01sb5r (0.67 #43158, 0.60 #27877, 0.50 #58440), 0274ck (0.67 #42190, 0.40 #30729, 0.40 #26909), 02rn_bj (0.67 #44444, 0.40 #32983, 0.40 #29163), 01vv6xv (0.67 #45350, 0.40 #33889, 0.40 #30069) >> Best rule #42390 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 039v1; >> query: (?x2348, 01wp8w7) <- profession(?x8819, ?x2348), profession(?x7164, ?x2348), profession(?x5405, ?x2348), profession(?x3770, ?x2348), ?x5405 = 01vvlyt, ?x7164 = 02fybl, award_nominee(?x3770, ?x6311), ?x8819 = 01j590z >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 5, 115, 121, 130, 131, 133, 135, 156, 162, 165, 166, 167, 168, 169, 170, 203, 311, 316, 319, 338, 350, 355, 364, 414, 451, 466, 521, 562, 572, 590, 596, 612, 626, 634, 651, 1145, 1153, 1166, 1324, 1439, 1443, 1480, 1893, 2869, 2870, 2896 EVAL 0nbcg profession! 01pny5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 0ql36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01dpsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 03n0pv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01vsn38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 0cgfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 02pt27 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 012ycy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01kp_1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 0147jt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 017l4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01vrx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01vrnsk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 04b7xr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01l47f5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01vng3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 025cn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01mvjl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01wgfp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01bpnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 09r9m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 02bgmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 0kxbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01vsyg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 094xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01vswx5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 02nfjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01s21dg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 016z1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 06_6j3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 016fnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 0478__m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 02x8z_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01vw20h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 028qdb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01m3x5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 0dl567 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 06gd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 024dgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01wz_ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 015f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 02v3yy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 0gcs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 053yx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01trhmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01vvpjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01ky2h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01sbf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 01wp8w7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 28.000 0.667 http://example.org/people/person/profession EVAL 0nbcg profession! 05mt_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 28.000 0.667 http://example.org/people/person/profession #13544-01n7q PRED entity: 01n7q PRED relation: religion PRED expected values: 04pk9 => 198 concepts (198 used for prediction) PRED predicted values (max 10 best out of 26): 04pk9 (0.77 #445, 0.74 #745, 0.69 #930), 0flw86 (0.43 #300, 0.40 #346, 0.39 #1713), 01s5nb (0.43 #450, 0.39 #750, 0.38 #935), 02t7t (0.29 #264, 0.24 #748, 0.22 #933), 03j6c (0.25 #55, 0.17 #170, 0.12 #354), 0kpl (0.25 #49, 0.17 #164, 0.11 #210), 07w8f (0.25 #63, 0.17 #178, 0.11 #224), 0b06q (0.11 #214, 0.08 #237, 0.06 #283), 013b6_ (0.10 #2961, 0.08 #3123, 0.07 #3469), 0kq2 (0.10 #2961, 0.08 #3123, 0.07 #3469) >> Best rule #445 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0g0syc; >> query: (?x1227, 04pk9) <- district_represented(?x3540, ?x1227), district_represented(?x1027, ?x1227), ?x1027 = 02bn_p, ?x3540 = 024tcq >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n7q religion 04pk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 198.000 0.771 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #13543-0f8grf PRED entity: 0f8grf PRED relation: actor! PRED expected values: 06xkst => 122 concepts (79 used for prediction) PRED predicted values (max 10 best out of 172): 0dr1c2 (0.56 #7131, 0.07 #1976, 0.06 #3033), 03d3ht (0.36 #2036, 0.24 #4149, 0.24 #3093), 05631 (0.33 #257, 0.25 #785, 0.25 #521), 01lk02 (0.21 #2012, 0.19 #4125, 0.12 #3069), 08cl7s (0.21 #2002, 0.18 #3059, 0.14 #4115), 02v5xg (0.21 #2018, 0.12 #3075, 0.11 #1226), 031kyy (0.19 #4112, 0.14 #1999, 0.12 #3056), 026bfsh (0.19 #3794, 0.13 #2473, 0.09 #11192), 04svwx (0.14 #4198, 0.14 #2085, 0.12 #3142), 07ng9k (0.14 #1866, 0.12 #2923, 0.11 #1074) >> Best rule #7131 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 0f0p0; 02tkzn; 01x0sy; >> query: (?x12753, ?x6839) <- gender(?x12753, ?x231), ?x231 = 05zppz, film(?x12753, ?x6839), genre(?x6839, ?x53) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2056 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 01kwh5j; *> query: (?x12753, 06xkst) <- category(?x12753, ?x134), ?x134 = 08mbj5d, nationality(?x12753, ?x252), ?x252 = 03_3d, actor(?x13557, ?x12753) *> conf = 0.07 ranks of expected_values: 18 EVAL 0f8grf actor! 06xkst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 122.000 79.000 0.558 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #13542-04sh3 PRED entity: 04sh3 PRED relation: major_field_of_study! PRED expected values: 09f2j 0g8rj 01qgr3 01wqg8 01vg0s => 78 concepts (39 used for prediction) PRED predicted values (max 10 best out of 619): 07tg4 (0.78 #7042, 0.78 #6579, 0.50 #9289), 07wrz (0.78 #6554, 0.60 #7097, 0.60 #2220), 01k2wn (0.71 #3811, 0.50 #9228, 0.50 #7061), 01jswq (0.71 #4943, 0.44 #6025, 0.40 #2776), 01w3v (0.67 #9220, 0.67 #6510, 0.67 #5969), 07szy (0.67 #15212, 0.67 #9246, 0.67 #6536), 02zd460 (0.67 #9924, 0.67 #8836, 0.64 #10465), 09f2j (0.67 #9909, 0.60 #7198, 0.58 #8821), 01mpwj (0.67 #6604, 0.60 #2270, 0.58 #9314), 07tds (0.67 #6646, 0.60 #2312, 0.58 #9356) >> Best rule #7042 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 0dc_v; >> query: (?x9111, ?x2999) <- major_field_of_study(?x9111, ?x2605), major_field_of_study(?x11693, ?x9111), major_field_of_study(?x8427, ?x9111), disciplines_or_subjects(?x12628, ?x9111), category(?x11693, ?x134), time_zones(?x8427, ?x2950), major_field_of_study(?x5167, ?x2605), major_field_of_study(?x2999, ?x2605), contains(?x94, ?x8427), ?x5167 = 015cz0, ?x2999 = 07tg4 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #9909 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 10 *> proper extension: 0h5k; 062z7; 0fdys; 02822; 037mh8; *> query: (?x9111, 09f2j) <- major_field_of_study(?x9111, ?x2605), major_field_of_study(?x735, ?x9111), major_field_of_study(?x2981, ?x9111), major_field_of_study(?x734, ?x9111), ?x2605 = 03g3w, student(?x735, ?x1643), school(?x2174, ?x735), ?x2174 = 051vz, company(?x3484, ?x735), award_winner(?x2366, ?x1643) *> conf = 0.67 ranks of expected_values: 8, 29, 101, 383, 595 EVAL 04sh3 major_field_of_study! 01vg0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 78.000 39.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04sh3 major_field_of_study! 01wqg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 78.000 39.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04sh3 major_field_of_study! 01qgr3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 78.000 39.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04sh3 major_field_of_study! 0g8rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 78.000 39.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04sh3 major_field_of_study! 09f2j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 78.000 39.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13541-07c37 PRED entity: 07c37 PRED relation: interests PRED expected values: 05r79 => 207 concepts (196 used for prediction) PRED predicted values (max 10 best out of 16): 04s0m (0.50 #92, 0.50 #27, 0.50 #10), 02jcc (0.50 #1, 0.42 #131, 0.40 #50), 05qt0 (0.40 #41, 0.32 #17, 0.25 #82), 05r79 (0.33 #69, 0.32 #17, 0.25 #134), 09xq9d (0.33 #89, 0.20 #56, 0.09 #265), 0gt_hv (0.32 #17, 0.25 #82, 0.25 #16), 04rjg (0.32 #17, 0.25 #82, 0.20 #422), 0x0w (0.25 #143, 0.25 #30, 0.20 #62), 06ms6 (0.17 #68, 0.07 #826, 0.03 #619), 037mh8 (0.17 #91, 0.07 #826, 0.02 #275) >> Best rule #92 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 07kb5; 0m93; >> query: (?x5797, 04s0m) <- influenced_by(?x11830, ?x5797), influenced_by(?x4033, ?x5797), influenced_by(?x5797, ?x3712), ?x4033 = 043s3, gender(?x5797, ?x231), religion(?x11830, ?x1985) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #69 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 04411; 043s3; 03_hd; 048cl; *> query: (?x5797, 05r79) <- influenced_by(?x5796, ?x5797), influenced_by(?x4033, ?x5797), influenced_by(?x5797, ?x3712), place_of_death(?x5797, ?x1976), interests(?x4033, ?x713), ?x5796 = 0x3r3 *> conf = 0.33 ranks of expected_values: 4 EVAL 07c37 interests 05r79 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 207.000 196.000 0.500 http://example.org/user/alexander/philosophy/philosopher/interests #13540-0408m53 PRED entity: 0408m53 PRED relation: genre PRED expected values: 06cvj => 100 concepts (60 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.67 #2046, 0.62 #6267, 0.61 #121), 01z4y (0.62 #6387, 0.58 #1323, 0.53 #4458), 06cvj (0.52 #846, 0.28 #124, 0.25 #2049), 02kdv5l (0.44 #243, 0.38 #965, 0.33 #2652), 01jfsb (0.39 #975, 0.36 #2178, 0.36 #2662), 03k9fj (0.32 #734, 0.27 #974, 0.24 #1335), 01t_vv (0.28 #175, 0.21 #897, 0.11 #1739), 0219x_ (0.22 #147, 0.12 #1229, 0.11 #1350), 01hmnh (0.20 #740, 0.17 #2183, 0.17 #5923), 0lsxr (0.20 #2174, 0.19 #2899, 0.19 #2778) >> Best rule #2046 for best value: >> intensional similarity = 3 >> extensional distance = 401 >> proper extension: 0cks1m; >> query: (?x10395, 07s9rl0) <- film(?x541, ?x10395), genre(?x10395, ?x1403), ?x1403 = 02l7c8 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #846 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 194 *> proper extension: 04svwx; *> query: (?x10395, 06cvj) <- genre(?x10395, ?x1403), genre(?x10395, ?x258), ?x258 = 05p553, ?x1403 = 02l7c8 *> conf = 0.52 ranks of expected_values: 3 EVAL 0408m53 genre 06cvj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 100.000 60.000 0.667 http://example.org/film/film/genre #13539-0jm3b PRED entity: 0jm3b PRED relation: school PRED expected values: 01jsn5 => 125 concepts (118 used for prediction) PRED predicted values (max 10 best out of 230): 065y4w7 (0.34 #9084, 0.29 #764, 0.27 #13067), 015q1n (0.32 #3127, 0.29 #2560, 0.26 #4640), 01jq0j (0.29 #3897, 0.29 #873, 0.28 #9193), 01ptt7 (0.29 #782, 0.26 #3050, 0.17 #4942), 09f2j (0.29 #832, 0.18 #12188, 0.17 #4992), 05krk (0.29 #760, 0.17 #11163, 0.16 #3028), 0f1nl (0.29 #785, 0.16 #3053, 0.14 #4188), 01rc6f (0.29 #890, 0.14 #4103, 0.13 #11351), 01jssp (0.29 #758, 0.13 #9266, 0.12 #14577), 01jsn5 (0.26 #3052, 0.17 #4565, 0.17 #1162) >> Best rule #9084 for best value: >> intensional similarity = 6 >> extensional distance = 45 >> proper extension: 01ypc; 01ct6; 084l5; 06wpc; 04wmvz; >> query: (?x9931, 065y4w7) <- team(?x13926, ?x9931), school(?x9931, ?x581), draft(?x9931, ?x8133), team(?x1348, ?x9931), colors(?x9931, ?x663), school(?x8133, ?x466) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #3052 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 17 *> proper extension: 0jmcb; 0jmmn; *> query: (?x9931, 01jsn5) <- position(?x9931, ?x1348), draft(?x9931, ?x12852), draft(?x9931, ?x8133), team(?x4570, ?x9931), school(?x9931, ?x581), ?x12852 = 06439y, ?x8133 = 025tn92 *> conf = 0.26 ranks of expected_values: 10 EVAL 0jm3b school 01jsn5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 125.000 118.000 0.340 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #13538-07j94 PRED entity: 07j94 PRED relation: film! PRED expected values: 01pb34 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 3): 09_gdc (0.33 #2, 0.05 #27, 0.02 #73), 01pb34 (0.04 #94, 0.04 #84, 0.04 #236), 01kyvx (0.02 #300, 0.02 #400, 0.02 #435) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02x8fs; >> query: (?x4530, 09_gdc) <- genre(?x4530, ?x6277), award_winner(?x4530, ?x3873), cinematography(?x4530, ?x10542), ?x6277 = 0fdjb >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #94 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 492 *> proper extension: 047svrl; *> query: (?x4530, 01pb34) <- film(?x2279, ?x4530), music(?x4530, ?x4428), film(?x3873, ?x4530) *> conf = 0.04 ranks of expected_values: 2 EVAL 07j94 film! 01pb34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.333 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #13537-09rp4r_ PRED entity: 09rp4r_ PRED relation: nominated_for PRED expected values: 07xtqq => 101 concepts (37 used for prediction) PRED predicted values (max 10 best out of 597): 02mpyh (0.33 #1306, 0.25 #3237, 0.25 #6477), 02cbhg (0.33 #1250, 0.09 #42100, 0.03 #2868), 0kvgnq (0.25 #3237, 0.25 #6477, 0.24 #4857), 072x7s (0.25 #3237, 0.25 #6477, 0.24 #4857), 07xtqq (0.25 #3237, 0.25 #6477, 0.24 #4857), 024lt6 (0.25 #3237, 0.25 #6477, 0.24 #4857), 01n30p (0.25 #3237, 0.25 #6477, 0.24 #4857), 0sxkh (0.25 #3237, 0.25 #6477, 0.24 #4857), 01b195 (0.25 #3237, 0.25 #6477, 0.24 #4857), 0bw20 (0.25 #3237, 0.25 #6477, 0.24 #4857) >> Best rule #1306 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 012ljv; 0bytfv; 0d5wn3; 08mhyd; >> query: (?x1622, 02mpyh) <- nominated_for(?x1622, ?x1199), ?x1199 = 0pv3x, award(?x1622, ?x500) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3237 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 03hpr; *> query: (?x1622, ?x407) <- crewmember(?x407, ?x1622), award_nominee(?x1622, ?x3879), gender(?x1622, ?x231) *> conf = 0.25 ranks of expected_values: 5 EVAL 09rp4r_ nominated_for 07xtqq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 37.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #13536-0168t PRED entity: 0168t PRED relation: participating_countries! PRED expected values: 09n48 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 40): 09n48 (0.58 #403, 0.51 #803, 0.50 #563), 018ctl (0.50 #568, 0.48 #808, 0.45 #408), 0lgxj (0.44 #548, 0.42 #428, 0.42 #468), 09x3r (0.39 #412, 0.38 #292, 0.38 #572), 0blfl (0.33 #589, 0.33 #429, 0.30 #829), 0sx8l (0.33 #414, 0.31 #454, 0.30 #814), 06sks6 (0.25 #1161, 0.16 #744, 0.15 #424), 016r9z (0.24 #421, 0.24 #581, 0.22 #461), 0c_tl (0.15 #823, 0.14 #583, 0.12 #703), 0sxrz (0.12 #2484, 0.11 #180, 0.06 #420) >> Best rule #403 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0f8l9c; >> query: (?x11553, 09n48) <- currency(?x11553, ?x170), participating_countries(?x1931, ?x11553), form_of_government(?x11553, ?x1926), time_zones(?x11553, ?x11506) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0168t participating_countries! 09n48 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.576 http://example.org/olympics/olympic_games/participating_countries #13535-05m_8 PRED entity: 05m_8 PRED relation: school PRED expected values: 01hhvg 02gr81 07ccs => 173 concepts (128 used for prediction) PRED predicted values (max 10 best out of 331): 07w0v (0.50 #1377, 0.41 #2233, 0.39 #2575), 06fq2 (0.35 #4232, 0.33 #2688, 0.33 #1661), 0f1nl (0.33 #24, 0.30 #1221, 0.25 #537), 012vwb (0.33 #2611, 0.29 #2269, 0.26 #2782), 01pl14 (0.33 #1543, 0.29 #1886, 0.27 #2057), 01jt2w (0.33 #116, 0.25 #629, 0.20 #3424), 01j_06 (0.33 #13, 0.22 #5152, 0.20 #3424), 07vyf (0.33 #53, 0.20 #3424, 0.20 #1421), 01hx2t (0.33 #126, 0.20 #3424, 0.11 #3252), 01h8rk (0.33 #69, 0.20 #3424, 0.10 #1266) >> Best rule #1377 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0fsb_6; 02yjk8; >> query: (?x580, 07w0v) <- category(?x580, ?x134), teams(?x2552, ?x580), team(?x2010, ?x580), colors(?x580, ?x332), jurisdiction_of_office(?x1195, ?x2552), adjoins(?x2552, ?x3125) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3424 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 21 *> proper extension: 057xlyq; *> query: (?x580, ?x1276) <- category(?x580, ?x134), team(?x12323, ?x580), team(?x2010, ?x580), team(?x12323, ?x2067), sport(?x2067, ?x5063), school(?x2067, ?x1276) *> conf = 0.20 ranks of expected_values: 25, 46, 107 EVAL 05m_8 school 07ccs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 173.000 128.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 05m_8 school 02gr81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 173.000 128.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 05m_8 school 01hhvg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 173.000 128.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #13534-0blq0z PRED entity: 0blq0z PRED relation: award_winner! PRED expected values: 09r9dp => 88 concepts (32 used for prediction) PRED predicted values (max 10 best out of 482): 06bzwt (0.82 #48070, 0.82 #48069, 0.82 #49675), 0151w_ (0.82 #48070, 0.82 #48069, 0.82 #49675), 09r9dp (0.82 #48070, 0.82 #48069, 0.82 #49675), 0c35b1 (0.82 #48070, 0.82 #48069, 0.82 #22431), 058s44 (0.52 #51278, 0.50 #19228, 0.50 #17624), 035rnz (0.52 #51278, 0.50 #19228, 0.50 #17624), 02114t (0.52 #51278, 0.50 #19228, 0.50 #17624), 016z2j (0.52 #51278, 0.50 #19228, 0.45 #35250), 015rkw (0.29 #38460, 0.28 #48071, 0.19 #49676), 0dgskx (0.29 #38460, 0.28 #48071, 0.19 #49676) >> Best rule #48070 for best value: >> intensional similarity = 3 >> extensional distance = 1145 >> proper extension: 0l12d; 09d5h; 0h1p; 03wpmd; 071dcs; 09pl3s; 0cjdk; 09swkk; 079ws; 01c1px; ... >> query: (?x2670, ?x72) <- award_winner(?x2670, ?x72), award_winner(?x72, ?x1222), nominated_for(?x2670, ?x5016) >> conf = 0.82 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0blq0z award_winner! 09r9dp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 88.000 32.000 0.820 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #13533-0d0vqn PRED entity: 0d0vqn PRED relation: combatants! PRED expected values: 0flry => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 62): 07j9n (0.67 #95, 0.62 #30, 0.23 #225), 0845v (0.56 #69, 0.50 #4, 0.10 #4035), 081pw (0.48 #456, 0.41 #716, 0.35 #2277), 03gqgt3 (0.46 #251, 0.41 #771, 0.38 #576), 0cm2xh (0.43 #272, 0.38 #207, 0.29 #532), 0flry (0.38 #40, 0.33 #105, 0.18 #170), 01hwkn (0.38 #50, 0.33 #115, 0.14 #4081), 048n7 (0.36 #154, 0.36 #284, 0.33 #544), 018w0j (0.36 #296, 0.31 #231, 0.24 #556), 0jnh (0.33 #103, 0.25 #38, 0.06 #7189) >> Best rule #95 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 043870; >> query: (?x304, 07j9n) <- combatants(?x10176, ?x304), ?x10176 = 01gqg3 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0dv0z; *> query: (?x304, 0flry) <- capital(?x304, ?x5168), combatants(?x10176, ?x304), ?x10176 = 01gqg3 *> conf = 0.38 ranks of expected_values: 6 EVAL 0d0vqn combatants! 0flry CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 199.000 199.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #13532-01ysy9 PRED entity: 01ysy9 PRED relation: major_field_of_study PRED expected values: 04_tv 0l5mz => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 135): 02_7t (0.81 #526, 0.75 #1109, 0.74 #105), 0g4gr (0.81 #526, 0.75 #1079, 0.74 #105), 02h40lc (0.81 #526, 0.75 #1056, 0.74 #105), 0g26h (0.81 #526, 0.74 #105, 0.71 #667), 01lj9 (0.81 #526, 0.74 #105, 0.67 #559), 09s1f (0.81 #526, 0.74 #105, 0.66 #736), 041y2 (0.81 #526, 0.74 #105, 0.66 #736), 05qfh (0.75 #1297, 0.75 #1084, 0.74 #105), 062z7 (0.75 #1291, 0.75 #1078, 0.74 #105), 0l5mz (0.75 #1330, 0.75 #1117, 0.74 #105) >> Best rule #526 for best value: >> intensional similarity = 27 >> extensional distance = 3 >> proper extension: 016t_3; >> query: (?x11690, ?x254) <- major_field_of_study(?x11690, ?x10391), major_field_of_study(?x11690, ?x10380), major_field_of_study(?x11690, ?x6760), major_field_of_study(?x11690, ?x1154), institution(?x11690, ?x10832), institution(?x11690, ?x9847), institution(?x11690, ?x5621), ?x10380 = 02stgt, ?x10832 = 014jyk, ?x1154 = 02lp1, disciplines_or_subjects(?x850, ?x6760), major_field_of_study(?x6760, ?x10332), major_field_of_study(?x5733, ?x6760), major_field_of_study(?x2486, ?x6760), school(?x1438, ?x5621), student(?x5621, ?x525), school(?x465, ?x5621), ?x1438 = 0512p, student(?x2486, ?x111), ?x10391 = 02jfc, currency(?x5621, ?x170), ?x5733 = 03zj9, student(?x6760, ?x665), school(?x9172, ?x9847), major_field_of_study(?x9847, ?x254), state_province_region(?x9847, ?x1025), ?x9172 = 06rpd >> conf = 0.81 => this is the best rule for 7 predicted values *> Best rule #1330 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 10 *> proper extension: 0bjrnt; *> query: (?x11690, 0l5mz) <- major_field_of_study(?x11690, ?x10417), major_field_of_study(?x11690, ?x10380), major_field_of_study(?x11690, ?x3878), institution(?x11690, ?x7660), major_field_of_study(?x5679, ?x10417), major_field_of_study(?x5288, ?x10417), major_field_of_study(?x3779, ?x10417), major_field_of_study(?x122, ?x10417), major_field_of_study(?x11415, ?x10380), ?x122 = 08815, major_field_of_study(?x10380, ?x2606), student(?x3878, ?x1309), currency(?x7660, ?x170), contains(?x94, ?x7660), category(?x7660, ?x134), major_field_of_study(?x7660, ?x6870), ?x5288 = 02zd460, state_province_region(?x5679, ?x3474), major_field_of_study(?x11516, ?x3878), major_field_of_study(?x481, ?x3878), contains(?x4198, ?x11516), ?x6870 = 01540, school(?x1438, ?x3779), institution(?x620, ?x3779), ?x11415 = 02j416, ?x481 = 052nd *> conf = 0.75 ranks of expected_values: 10, 33 EVAL 01ysy9 major_field_of_study 0l5mz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 20.000 20.000 0.810 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 01ysy9 major_field_of_study 04_tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 20.000 20.000 0.810 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #13531-033jkj PRED entity: 033jkj PRED relation: location PRED expected values: 0fvzg => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 256): 02_286 (0.25 #46517, 0.24 #57735, 0.23 #837), 059rby (0.16 #16, 0.08 #4823, 0.08 #7228), 0k049 (0.12 #809, 0.05 #8, 0.03 #6419), 0rd6b (0.11 #526, 0.04 #2929, 0.04 #4532), 0cc56 (0.09 #6467, 0.08 #857, 0.06 #5665), 0rh6k (0.09 #8017, 0.08 #3209, 0.07 #8818), 04jpl (0.09 #57716, 0.08 #46498, 0.07 #33674), 0cr3d (0.08 #8155, 0.08 #7354, 0.07 #69863), 01n7q (0.08 #4068, 0.05 #8075, 0.04 #29711), 01cx_ (0.06 #4166, 0.05 #160, 0.05 #5769) >> Best rule #46517 for best value: >> intensional similarity = 3 >> extensional distance = 792 >> proper extension: 05ty4m; 02zq43; 01rr9f; 02lk1s; 01j5x6; 0bz5v2; 0blbxk; 02_hj4; 01w02sy; 035rnz; ... >> query: (?x4411, 02_286) <- location(?x4411, ?x682), place_of_death(?x199, ?x682), award_nominee(?x9815, ?x4411) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 033jkj location 0fvzg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 118.000 0.246 http://example.org/people/person/places_lived./people/place_lived/location #13530-0m9p3 PRED entity: 0m9p3 PRED relation: genre PRED expected values: 082gq => 80 concepts (74 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.90 #4535, 0.76 #1909, 0.76 #4059), 07ssc (0.51 #1788, 0.49 #5850, 0.48 #5730), 03k9fj (0.50 #11, 0.33 #4785, 0.20 #8007), 02l7c8 (0.45 #849, 0.41 #1088, 0.41 #135), 01jfsb (0.43 #4786, 0.37 #5862, 0.35 #369), 05p553 (0.34 #2630, 0.33 #7999, 0.33 #2749), 01g6gs (0.32 #1092, 0.30 #1211, 0.30 #1330), 04xvh5 (0.32 #271, 0.24 #152, 0.20 #390), 017fp (0.32 #253, 0.18 #134, 0.17 #1072), 06n90 (0.25 #13, 0.19 #4787, 0.17 #1072) >> Best rule #4535 for best value: >> intensional similarity = 4 >> extensional distance = 1027 >> proper extension: 04m1bm; 02rb607; 040rmy; 0crh5_f; 0bmc4cm; 026njb5; 016kz1; 04lqvlr; 04lqvly; 011yfd; ... >> query: (?x2423, 07s9rl0) <- language(?x2423, ?x254), genre(?x2423, ?x1509), genre(?x8555, ?x1509), ?x8555 = 04qk12 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2535 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 526 *> proper extension: 01cgz; *> query: (?x2423, 082gq) <- films(?x326, ?x2423), films(?x326, ?x3496), film(?x286, ?x3496) *> conf = 0.19 ranks of expected_values: 14 EVAL 0m9p3 genre 082gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 80.000 74.000 0.901 http://example.org/film/film/genre #13529-07x4c PRED entity: 07x4c PRED relation: institution! PRED expected values: 019v9k => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 24): 019v9k (0.80 #348, 0.72 #298, 0.71 #424), 016t_3 (0.75 #418, 0.75 #342, 0.66 #292), 014mlp (0.71 #420, 0.71 #764, 0.69 #546), 03bwzr4 (0.71 #430, 0.70 #354, 0.59 #381), 02_xgp2 (0.62 #428, 0.62 #352, 0.55 #302), 07s6fsf (0.52 #290, 0.42 #760, 0.35 #416), 04zx3q1 (0.49 #785, 0.44 #366, 0.44 #444), 02m4yg (0.49 #785, 0.44 #366, 0.44 #444), 01ysy9 (0.49 #785, 0.44 #366, 0.44 #444), 01gkg3 (0.49 #785, 0.44 #366, 0.44 #444) >> Best rule #348 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 03v6t; 01jpyb; 01xvlc; >> query: (?x7127, 019v9k) <- major_field_of_study(?x7127, ?x12760), major_field_of_study(?x7127, ?x1682), ?x1682 = 02ky346, major_field_of_study(?x865, ?x12760), contains(?x94, ?x7127) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07x4c institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13528-0fpxp PRED entity: 0fpxp PRED relation: program! PRED expected values: 01yznp => 74 concepts (57 used for prediction) PRED predicted values (max 10 best out of 95): 01gn36 (0.35 #192, 0.35 #191, 0.33 #95), 013sg6 (0.35 #192, 0.35 #191, 0.33 #95), 02r_d4 (0.35 #192, 0.35 #191, 0.33 #95), 0fb1q (0.35 #192, 0.35 #191, 0.33 #95), 02y0yt (0.35 #192, 0.35 #191, 0.33 #95), 015rhv (0.35 #192, 0.35 #191, 0.33 #95), 0163t3 (0.20 #76, 0.14 #172, 0.02 #555), 0gps0z (0.20 #80, 0.14 #176, 0.01 #653), 01pfkw (0.20 #43, 0.14 #139, 0.01 #616), 03fwln (0.20 #86, 0.14 #182) >> Best rule #192 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 06mr2s; 03czz87; >> query: (?x7904, ?x4554) <- actor(?x7904, ?x4554), genre(?x7904, ?x12120), ?x12120 = 03fpg, profession(?x4554, ?x955) >> conf = 0.35 => this is the best rule for 6 predicted values *> Best rule #578 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 96 *> proper extension: 06hwzy; 07s8z_l; *> query: (?x7904, 01yznp) <- program(?x6678, ?x7904), producer_type(?x7904, ?x632), award_winner(?x1762, ?x6678) *> conf = 0.01 ranks of expected_values: 93 EVAL 0fpxp program! 01yznp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 74.000 57.000 0.353 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #13527-05fnl9 PRED entity: 05fnl9 PRED relation: film PRED expected values: 07024 05c26ss 0g4vmj8 => 80 concepts (72 used for prediction) PRED predicted values (max 10 best out of 590): 03d34x8 (0.59 #58911, 0.46 #8922, 0.46 #19638), 05zr0xl (0.59 #58911, 0.46 #8922, 0.46 #19638), 09cr8 (0.14 #283, 0.10 #2068, 0.02 #21708), 01719t (0.14 #230, 0.03 #53556, 0.01 #5583), 032sl_ (0.14 #1556, 0.03 #53556, 0.01 #5125), 01rwyq (0.14 #547, 0.03 #53556, 0.01 #4116), 0jqkh (0.14 #1326, 0.03 #53556), 0dgrwqr (0.14 #1301, 0.03 #53556), 03p2xc (0.14 #1241, 0.03 #53556), 016fyc (0.14 #56, 0.03 #53556) >> Best rule #58911 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 01pnn3; 0cwtm; >> query: (?x1676, ?x2009) <- nominated_for(?x1676, ?x2009), film(?x1676, ?x1202), profession(?x1676, ?x353) >> conf = 0.59 => this is the best rule for 2 predicted values *> Best rule #2263 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 8 *> proper extension: 04h07s; 01jfr3y; *> query: (?x1676, 07024) <- film(?x1676, ?x1202), ?x1202 = 0gj8t_b *> conf = 0.10 ranks of expected_values: 18, 70 EVAL 05fnl9 film 0g4vmj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 72.000 0.586 http://example.org/film/actor/film./film/performance/film EVAL 05fnl9 film 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 80.000 72.000 0.586 http://example.org/film/actor/film./film/performance/film EVAL 05fnl9 film 07024 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 80.000 72.000 0.586 http://example.org/film/actor/film./film/performance/film #13526-099flj PRED entity: 099flj PRED relation: award! PRED expected values: 03_vx9 => 54 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2401): 02ply6j (0.79 #50790, 0.78 #44018, 0.78 #40631), 014zcr (0.50 #10206, 0.33 #3437, 0.33 #52), 02kxbx3 (0.50 #11144, 0.33 #14530, 0.20 #7759), 0184dt (0.50 #10829, 0.20 #7444, 0.15 #14215), 02kxbwx (0.43 #10334, 0.33 #13720, 0.20 #6949), 02ld6x (0.43 #10885, 0.20 #7500, 0.09 #44749), 0184jw (0.37 #15815, 0.13 #19201, 0.12 #25970), 05ldnp (0.36 #11053, 0.30 #14439, 0.20 #7668), 0bsb4j (0.36 #10850, 0.22 #14236, 0.20 #7465), 022wxh (0.36 #11382, 0.06 #45246, 0.06 #24923) >> Best rule #50790 for best value: >> intensional similarity = 3 >> extensional distance = 120 >> proper extension: 026mg3; 0gkvb7; 0cqhk0; 0bdw1g; 09qvc0; 047byns; 0cqh6z; 0ck27z; 0bdx29; 0bdw6t; ... >> query: (?x11466, ?x7123) <- nominated_for(?x11466, ?x825), ceremony(?x11466, ?x1442), award_winner(?x11466, ?x7123) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #27082 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 84 *> proper extension: 04ldyx1; *> query: (?x11466, ?x1596) <- nominated_for(?x11466, ?x11022), ceremony(?x11466, ?x1442), award_winner(?x11466, ?x7123), film(?x1596, ?x11022) *> conf = 0.07 ranks of expected_values: 747 EVAL 099flj award! 03_vx9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 21.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13525-015t7v PRED entity: 015t7v PRED relation: award_nominee! PRED expected values: 0dvmd => 95 concepts (39 used for prediction) PRED predicted values (max 10 best out of 748): 016ypb (0.81 #80988, 0.81 #27762, 0.81 #80987), 03ym1 (0.81 #80988, 0.81 #27762, 0.81 #80987), 02gvwz (0.81 #80988, 0.81 #27762, 0.81 #80987), 01y665 (0.81 #80988, 0.81 #27762, 0.81 #80987), 017khj (0.81 #80988, 0.81 #27762, 0.81 #80987), 0f0kz (0.81 #80988, 0.81 #27762, 0.81 #80987), 015t7v (0.54 #8115, 0.25 #57850, 0.22 #87930), 0dvmd (0.27 #7627, 0.17 #50905, 0.15 #90246), 01r93l (0.27 #7925, 0.17 #50905, 0.15 #90246), 01pk8v (0.27 #8210, 0.17 #50905, 0.15 #90246) >> Best rule #80988 for best value: >> intensional similarity = 3 >> extensional distance = 1044 >> proper extension: 076df9; 0dbb3; 0fvt2; >> query: (?x4999, ?x9817) <- location(?x4999, ?x2997), award_nominee(?x4999, ?x9817), film(?x9817, ?x915) >> conf = 0.81 => this is the best rule for 6 predicted values *> Best rule #7627 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 07vc_9; 0f0kz; 0jmj; 01pk8v; *> query: (?x4999, 0dvmd) <- film(?x4999, ?x1392), award_nominee(?x5495, ?x4999), ?x5495 = 016zp5 *> conf = 0.27 ranks of expected_values: 8 EVAL 015t7v award_nominee! 0dvmd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 95.000 39.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13524-0gs96 PRED entity: 0gs96 PRED relation: award! PRED expected values: 04v8x9 => 52 concepts (32 used for prediction) PRED predicted values (max 10 best out of 818): 0hfzr (0.50 #1373, 0.36 #2344, 0.28 #5259), 03hmt9b (0.45 #2322, 0.27 #3294, 0.25 #1351), 0bx0l (0.45 #2150, 0.25 #1179, 0.22 #5065), 04v8x9 (0.40 #2951, 0.38 #1008, 0.36 #1979), 01jc6q (0.40 #2926, 0.33 #4869, 0.28 #3897), 0bmhn (0.40 #3806, 0.33 #5749, 0.28 #4777), 0y_9q (0.38 #1493, 0.33 #3436, 0.28 #5379), 09gq0x5 (0.38 #1136, 0.27 #29159, 0.25 #25269), 0404j37 (0.38 #1615, 0.27 #3558, 0.25 #643), 0b_5d (0.38 #1258, 0.27 #3201, 0.22 #5144) >> Best rule #1373 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02wwsh8; >> query: (?x2222, 0hfzr) <- award(?x8984, ?x2222), award(?x8769, ?x2222), ?x8769 = 0bj25, nominated_for(?x269, ?x8984) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2951 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 13 *> proper extension: 018wng; 0gq_d; *> query: (?x2222, 04v8x9) <- ceremony(?x2222, ?x9058), ceremony(?x2222, ?x8407), ceremony(?x2222, ?x8150), ?x8407 = 0n8_m93, ?x8150 = 0bzkvd, ?x9058 = 0fv89q *> conf = 0.40 ranks of expected_values: 4 EVAL 0gs96 award! 04v8x9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 52.000 32.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13523-0pswc PRED entity: 0pswc PRED relation: origin! PRED expected values: 01nkxvx => 127 concepts (55 used for prediction) PRED predicted values (max 10 best out of 387): 01304j (0.21 #11918, 0.20 #19686, 0.11 #18653), 01d1st (0.18 #2372, 0.14 #2888, 0.11 #5477), 01q99h (0.18 #2342, 0.14 #2858, 0.10 #785), 0892sx (0.18 #1132, 0.11 #4757, 0.11 #5275), 01vtj38 (0.15 #2072, 0.11 #2071, 0.10 #5178), 0170s4 (0.15 #2072, 0.11 #2071, 0.05 #9330), 06s7rd (0.12 #4499, 0.10 #876, 0.09 #2433), 06nv27 (0.10 #735, 0.09 #1773, 0.09 #1254), 02lfp4 (0.10 #725, 0.09 #1763, 0.09 #1244), 0dm5l (0.10 #624, 0.09 #1662, 0.09 #1143) >> Best rule #11918 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0281y0; >> query: (?x11561, ?x11186) <- location(?x11186, ?x11561), artists(?x1127, ?x11186), role(?x11186, ?x227), ?x1127 = 02x8m >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pswc origin! 01nkxvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 55.000 0.212 http://example.org/music/artist/origin #13522-0r2bv PRED entity: 0r2bv PRED relation: place PRED expected values: 0r2bv => 119 concepts (55 used for prediction) PRED predicted values (max 10 best out of 309): 0jbrr (0.25 #16538, 0.25 #16539, 0.23 #11882), 0d7k1z (0.25 #16538, 0.25 #16539, 0.23 #11882), 0k9p4 (0.25 #16538, 0.25 #16539, 0.23 #11882), 0r2dp (0.25 #16538, 0.25 #16539, 0.23 #11882), 0r2gj (0.25 #16538, 0.25 #16539, 0.23 #11882), 0r2kh (0.25 #16538, 0.25 #16539, 0.23 #11882), 0h3lt (0.25 #16538, 0.25 #16539, 0.23 #11882), 0r2l7 (0.25 #16538, 0.25 #16539, 0.23 #11882), 0r2bv (0.05 #27417, 0.05 #27416, 0.05 #25865), 0q_xk (0.03 #3838, 0.03 #3323, 0.02 #4353) >> Best rule #16538 for best value: >> intensional similarity = 5 >> extensional distance = 171 >> proper extension: 01m24m; >> query: (?x12153, ?x8317) <- contains(?x578, ?x12153), county(?x8317, ?x578), category(?x12153, ?x134), contains(?x1227, ?x8317), source(?x12153, ?x958) >> conf = 0.25 => this is the best rule for 8 predicted values *> Best rule #27417 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 351 *> proper extension: 0jbs5; *> query: (?x12153, ?x5783) <- contains(?x578, ?x12153), contains(?x578, ?x5783), currency(?x578, ?x170) *> conf = 0.05 ranks of expected_values: 9 EVAL 0r2bv place 0r2bv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 119.000 55.000 0.252 http://example.org/location/hud_county_place/place #13521-02bh8z PRED entity: 02bh8z PRED relation: child PRED expected values: 041bnw => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 231): 01dtcb (0.40 #391, 0.12 #3492, 0.10 #5608), 05b0f7 (0.33 #120, 0.14 #1096, 0.11 #2079), 024rbz (0.20 #335, 0.18 #2134, 0.08 #2621), 0dwcl (0.20 #457, 0.17 #2743, 0.14 #6001), 01jx9 (0.20 #366, 0.17 #2652, 0.14 #5910), 015_1q (0.20 #352, 0.17 #514, 0.09 #2476), 033hn8 (0.20 #345, 0.17 #507, 0.09 #2469), 016tw3 (0.20 #334, 0.15 #5551, 0.12 #3597), 03sb38 (0.20 #387, 0.12 #3650, 0.11 #1528), 025txrl (0.20 #439, 0.10 #5656, 0.10 #5983) >> Best rule #391 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 016tw3; 049ql1; >> query: (?x3887, 01dtcb) <- child(?x3887, ?x10992), industry(?x3887, ?x3368), artist(?x10992, ?x7865), ?x7865 = 02k5sc >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02bh8z child 041bnw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 200.000 200.000 0.400 http://example.org/organization/organization/child./organization/organization_relationship/child #13520-01wdqrx PRED entity: 01wdqrx PRED relation: artist! PRED expected values: 0n85g => 108 concepts (81 used for prediction) PRED predicted values (max 10 best out of 100): 015_1q (0.21 #162, 0.18 #2438, 0.18 #2581), 03rhqg (0.18 #158, 0.14 #584, 0.14 #300), 0g768 (0.14 #322, 0.13 #38, 0.12 #4162), 033hn8 (0.12 #4138, 0.12 #298, 0.12 #4280), 0n85g (0.11 #64, 0.07 #3052, 0.07 #3762), 03mp8k (0.11 #494, 0.09 #352, 0.08 #2200), 0181dw (0.11 #185, 0.11 #327, 0.11 #611), 02p11jq (0.11 #155, 0.10 #13, 0.08 #2145), 043g7l (0.11 #458, 0.08 #316, 0.08 #2164), 01clyr (0.10 #34, 0.07 #4727, 0.06 #5154) >> Best rule #162 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 02lbrd; 011z3g; 013w8y; >> query: (?x1282, 015_1q) <- award_winner(?x2054, ?x1282), artists(?x505, ?x1282), ?x505 = 03_d0 >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #64 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 094xh; *> query: (?x1282, 0n85g) <- role(?x1282, ?x212), artists(?x7440, ?x1282), ?x7440 = 0155w *> conf = 0.11 ranks of expected_values: 5 EVAL 01wdqrx artist! 0n85g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 81.000 0.211 http://example.org/music/record_label/artist #13519-02bh9 PRED entity: 02bh9 PRED relation: category PRED expected values: 08mbj5d => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #22, 0.80 #21, 0.80 #26) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 293 >> proper extension: 0fpj4lx; 01kymm; 01386_; 03wjb7; 01tpl1p; 01mxnvc; 02s6sh; 01vzz1c; 04mky3; >> query: (?x3410, 08mbj5d) <- artists(?x302, ?x3410), profession(?x3410, ?x220), ?x220 = 016z4k >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bh9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.847 http://example.org/common/topic/webpage./common/webpage/category #13518-01cmp9 PRED entity: 01cmp9 PRED relation: language PRED expected values: 02ztjwg => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 41): 04306rv (0.21 #178, 0.12 #471, 0.11 #1876), 06nm1 (0.20 #68, 0.12 #767, 0.12 #1231), 0295r (0.20 #86), 064_8sq (0.18 #1068, 0.17 #195, 0.16 #488), 02bjrlw (0.10 #175, 0.08 #1635, 0.08 #526), 0653m (0.10 #301, 0.07 #360, 0.07 #185), 0jzc (0.08 #309, 0.05 #718, 0.05 #368), 06b_j (0.07 #779, 0.07 #721, 0.07 #605), 03_9r (0.07 #183, 0.07 #125, 0.06 #592), 04h9h (0.07 #216, 0.07 #158, 0.04 #683) >> Best rule #178 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0299hs; >> query: (?x6048, 04306rv) <- nominated_for(?x500, ?x6048), nominated_for(?x143, ?x6048), ?x143 = 02r0csl, ?x500 = 0p9sw >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01cmp9 language 02ztjwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 98.000 0.207 http://example.org/film/film/language #13517-014g22 PRED entity: 014g22 PRED relation: spouse PRED expected values: 01c65z => 73 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1): 016khd (0.05 #26) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 02p65p; 017149; 05gml8; 07s8r0; 021vwt; 03q1vd; 02jsgf; 0410cp; 018z_c; 016ks_; ... >> query: (?x4154, 016khd) <- award_nominee(?x3932, ?x4154), ?x3932 = 050t68, award_nominee(?x4154, ?x1244) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 014g22 spouse 01c65z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 38.000 0.045 http://example.org/people/person/spouse_s./people/marriage/spouse #13516-017cy9 PRED entity: 017cy9 PRED relation: major_field_of_study PRED expected values: 0fdys 04gb7 => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 99): 03g3w (0.45 #247, 0.38 #135, 0.34 #1704), 0g26h (0.38 #1270, 0.35 #1942, 0.34 #598), 04rjg (0.34 #1697, 0.27 #689, 0.27 #2145), 04gb7 (0.30 #263, 0.27 #151, 0.16 #1720), 0_jm (0.29 #613, 0.28 #1285, 0.27 #1957), 0fdys (0.28 #257, 0.23 #145, 0.23 #1714), 02_7t (0.27 #1291, 0.25 #619, 0.24 #1963), 04x_3 (0.24 #583, 0.24 #1255, 0.20 #1927), 01lj9 (0.23 #1715, 0.23 #819, 0.23 #371), 01540 (0.22 #615, 0.20 #1287, 0.19 #1959) >> Best rule #247 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 08815; 052nd; 06pwq; 065y4w7; 07tl0; 05f7s1; 07szy; 07vk2; 07wjk; 07vht; ... >> query: (?x4780, 03g3w) <- institution(?x7636, ?x4780), ?x7636 = 01rr_d, major_field_of_study(?x4780, ?x254) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #263 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 08815; 052nd; 06pwq; 065y4w7; 07tl0; 05f7s1; 07szy; 07vk2; 07wjk; 07vht; ... *> query: (?x4780, 04gb7) <- institution(?x7636, ?x4780), ?x7636 = 01rr_d, major_field_of_study(?x4780, ?x254) *> conf = 0.30 ranks of expected_values: 4, 6 EVAL 017cy9 major_field_of_study 04gb7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 60.000 60.000 0.450 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 017cy9 major_field_of_study 0fdys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 60.000 60.000 0.450 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13515-01wbgdv PRED entity: 01wbgdv PRED relation: gender PRED expected values: 02zsn => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #5, 0.85 #69, 0.83 #17), 02zsn (0.34 #16, 0.33 #58, 0.32 #26) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01wl38s; 01kx_81; 0407f; 018dyl; 01vswx5; 01vswwx; 0bkf4; >> query: (?x1128, 05zppz) <- person(?x6125, ?x1128), award_nominee(?x1128, ?x215), role(?x1128, ?x1466) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 059m45; 03v0vd; 0fvt2; *> query: (?x1128, 02zsn) <- award_nominee(?x9070, ?x1128), award(?x9070, ?x567), athlete(?x4833, ?x9070) *> conf = 0.34 ranks of expected_values: 2 EVAL 01wbgdv gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 143.000 0.889 http://example.org/people/person/gender #13514-017s11 PRED entity: 017s11 PRED relation: award_nominee PRED expected values: 0g1rw 06rq2l => 120 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1074): 07f8wg (0.80 #183183, 0.77 #190140, 0.77 #185502), 031rq5 (0.80 #183183, 0.77 #190140, 0.77 #185502), 017s11 (0.22 #23289, 0.20 #14016, 0.20 #11698), 020h2v (0.18 #22626, 0.15 #34221, 0.15 #31903), 0343h (0.18 #190141, 0.13 #23471, 0.12 #25791), 01gb54 (0.18 #190141, 0.12 #33535, 0.12 #31217), 0dbpwb (0.18 #190141, 0.12 #31779, 0.11 #53331), 06rq2l (0.18 #190141, 0.11 #53331, 0.10 #141442), 03xb2w (0.18 #190141, 0.11 #53331, 0.10 #141442), 0320jz (0.18 #190141, 0.11 #53331, 0.10 #141442) >> Best rule #183183 for best value: >> intensional similarity = 3 >> extensional distance = 1208 >> proper extension: 0411q; 015rmq; 0157m; 010hn; 01kph_c; 01lvzbl; 04glx0; 04n65n; 081l_; 08xz51; ... >> query: (?x541, ?x574) <- award_winner(?x163, ?x541), award_winner(?x1105, ?x541), award_nominee(?x574, ?x541) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #190141 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1251 *> proper extension: 07g2b; 036px; 0pmw9; 044k8; 016kkx; 03d0ns; 015zql; 0gdqy; 0kc8y; 014g91; ... *> query: (?x541, ?x164) <- award_winner(?x3170, ?x541), award_winner(?x1105, ?x541), award_nominee(?x164, ?x3170) *> conf = 0.18 ranks of expected_values: 8, 144 EVAL 017s11 award_nominee 06rq2l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 120.000 86.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 017s11 award_nominee 0g1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 120.000 86.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13513-08d6bd PRED entity: 08d6bd PRED relation: location PRED expected values: 09f07 => 144 concepts (111 used for prediction) PRED predicted values (max 10 best out of 224): 030qb3t (0.25 #36189, 0.24 #28162, 0.23 #8104), 02_286 (0.24 #65830, 0.21 #2443, 0.21 #13674), 0r62v (0.20 #47, 0.11 #849, 0.09 #1651), 0sb1r (0.20 #207, 0.11 #1009, 0.09 #1811), 055vr (0.14 #31290, 0.11 #1420, 0.09 #2222), 01c1nm (0.14 #31290), 0cr3d (0.12 #4155, 0.10 #3353, 0.09 #5759), 0c8tk (0.11 #1028, 0.09 #1830, 0.05 #17873), 0d6lp (0.11 #970, 0.09 #1772, 0.04 #14607), 0yyh (0.11 #1401, 0.09 #2203, 0.02 #26271) >> Best rule #36189 for best value: >> intensional similarity = 4 >> extensional distance = 265 >> proper extension: 04shbh; 022769; 094xh; 0b5x23; >> query: (?x6442, 030qb3t) <- place_of_birth(?x6442, ?x11914), award(?x6442, ?x4687), languages(?x6442, ?x1882), location(?x6442, ?x7412) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #18244 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 99 *> proper extension: 0cfywh; *> query: (?x6442, 09f07) <- place_of_birth(?x6442, ?x11914), nationality(?x6442, ?x2146), ?x2146 = 03rk0 *> conf = 0.02 ranks of expected_values: 120 EVAL 08d6bd location 09f07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 144.000 111.000 0.251 http://example.org/people/person/places_lived./people/place_lived/location #13512-0jmmn PRED entity: 0jmmn PRED relation: school PRED expected values: 0225bv => 50 concepts (48 used for prediction) PRED predicted values (max 10 best out of 654): 078bz (0.60 #35, 0.43 #223, 0.25 #1935), 0bx8pn (0.50 #969, 0.35 #2113, 0.35 #2679), 065y4w7 (0.43 #197, 0.35 #7010, 0.35 #6829), 06pwq (0.42 #953, 0.29 #2097, 0.27 #1332), 0f1nl (0.40 #30, 0.29 #218, 0.27 #1547), 01j_cy (0.40 #18, 0.29 #206, 0.25 #775), 01n6r0 (0.40 #77, 0.29 #265, 0.18 #2167), 0jkhr (0.40 #111, 0.29 #299, 0.17 #2846), 07w0v (0.37 #6832, 0.17 #2846, 0.17 #7786), 0dzst (0.33 #1092, 0.30 #2802, 0.29 #334) >> Best rule #35 for best value: >> intensional similarity = 24 >> extensional distance = 3 >> proper extension: 0jmdb; 0jmj7; 0jm7n; >> query: (?x5419, 078bz) <- team(?x6848, ?x5419), team(?x4570, ?x5419), ?x6848 = 02_ssl, school(?x5419, ?x5807), ?x4570 = 03558l, major_field_of_study(?x5807, ?x10046), major_field_of_study(?x5807, ?x4100), major_field_of_study(?x5807, ?x2014), major_field_of_study(?x5807, ?x1154), institution(?x4981, ?x5807), institution(?x1771, ?x5807), ?x4981 = 03bwzr4, ?x1771 = 019v9k, ?x2014 = 04rjg, ?x10046 = 041y2, student(?x5807, ?x5335), category(?x5807, ?x134), ?x1154 = 02lp1, award(?x5335, ?x575), citytown(?x5807, ?x6142), ?x134 = 08mbj5d, influenced_by(?x2609, ?x5335), spouse(?x5335, ?x13793), ?x4100 = 01lj9 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #177 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 3 *> proper extension: 0jmdb; 0jmj7; 0jm7n; *> query: (?x5419, 0225bv) <- team(?x6848, ?x5419), team(?x4570, ?x5419), ?x6848 = 02_ssl, school(?x5419, ?x5807), ?x4570 = 03558l, major_field_of_study(?x5807, ?x10046), major_field_of_study(?x5807, ?x4100), major_field_of_study(?x5807, ?x2014), major_field_of_study(?x5807, ?x1154), institution(?x4981, ?x5807), institution(?x1771, ?x5807), ?x4981 = 03bwzr4, ?x1771 = 019v9k, ?x2014 = 04rjg, ?x10046 = 041y2, student(?x5807, ?x5335), category(?x5807, ?x134), ?x1154 = 02lp1, award(?x5335, ?x575), citytown(?x5807, ?x6142), ?x134 = 08mbj5d, influenced_by(?x2609, ?x5335), spouse(?x5335, ?x13793), ?x4100 = 01lj9 *> conf = 0.20 ranks of expected_values: 40 EVAL 0jmmn school 0225bv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 50.000 48.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #13511-015qq1 PRED entity: 015qq1 PRED relation: place_of_burial PRED expected values: 018mmw => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 11): 018mm4 (0.11 #229, 0.07 #417, 0.07 #261), 018mmj (0.08 #168, 0.06 #356, 0.06 #702), 018mmw (0.05 #111, 0.03 #79, 0.03 #425), 01n7q (0.03 #349, 0.02 #161, 0.01 #695), 0lbp_ (0.03 #78, 0.01 #393, 0.01 #771), 01f38z (0.03 #249, 0.02 #281, 0.02 #186), 018mrd (0.02 #368, 0.02 #180, 0.01 #243), 018mlg (0.02 #181, 0.02 #369, 0.01 #432), 0nb1s (0.02 #187, 0.02 #375, 0.01 #785), 016h5l (0.02 #188, 0.01 #251) >> Best rule #229 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 08433; 07csf4; 04y9dk; 03fvqg; 015gw6; 01_vfy; 09p06; 036jb; 016z51; 034zc0; ... >> query: (?x11380, 018mm4) <- film(?x11380, ?x3524), award(?x11380, ?x435), place_of_death(?x11380, ?x1523), student(?x581, ?x11380) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #111 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 01vq3nl; *> query: (?x11380, 018mmw) <- actor(?x3725, ?x11380), gender(?x11380, ?x231), place_of_death(?x11380, ?x1523) *> conf = 0.05 ranks of expected_values: 3 EVAL 015qq1 place_of_burial 018mmw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 99.000 0.108 http://example.org/people/deceased_person/place_of_burial #13510-021npv PRED entity: 021npv PRED relation: film PRED expected values: 04kzqz => 111 concepts (47 used for prediction) PRED predicted values (max 10 best out of 992): 01s81 (0.68 #12511, 0.38 #55407, 0.38 #67921), 02qr3k8 (0.16 #3076, 0.08 #10224, 0.08 #8437), 02_1sj (0.14 #80, 0.03 #12591, 0.03 #10803), 06gb1w (0.14 #733, 0.03 #4308, 0.03 #2521), 0gkz15s (0.14 #112, 0.03 #1900, 0.02 #7261), 035s95 (0.14 #341, 0.03 #2129, 0.02 #28937), 0gmblvq (0.14 #674, 0.03 #2462, 0.01 #6036), 0170th (0.14 #444, 0.03 #2232, 0.01 #5806), 03mh_tp (0.14 #508, 0.02 #13019, 0.02 #11231), 011ysn (0.14 #566, 0.02 #13077, 0.02 #11289) >> Best rule #12511 for best value: >> intensional similarity = 4 >> extensional distance = 119 >> proper extension: 0p_pd; 0c9c0; 01fdc0; 0f502; 014gf8; 01jfrg; 0436kgz; 05cx7x; 02byfd; 01bmlb; ... >> query: (?x12123, ?x4517) <- film(?x12123, ?x2394), award(?x12123, ?x693), location_of_ceremony(?x12123, ?x13006), nominated_for(?x12123, ?x4517) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #7467 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 91 *> proper extension: 0184jc; 02s2ft; 02qgqt; 02p65p; 0h0jz; 02g8h; 0z4s; 017149; 01vlj1g; 01yk13; ... *> query: (?x12123, 04kzqz) <- film(?x12123, ?x2394), award(?x12123, ?x2192), ?x2192 = 0bfvd4, nationality(?x12123, ?x94) *> conf = 0.01 ranks of expected_values: 959 EVAL 021npv film 04kzqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 111.000 47.000 0.679 http://example.org/film/actor/film./film/performance/film #13509-042xh PRED entity: 042xh PRED relation: student! PRED expected values: 01jvxb => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 231): 0lk0l (0.33 #491, 0.02 #15774, 0.01 #21044), 01w5m (0.20 #1686, 0.13 #3267, 0.11 #11172), 07tgn (0.17 #544, 0.11 #8976, 0.10 #6868), 0ylvj (0.17 #728, 0.04 #3890, 0.03 #4417), 07vjm (0.14 #1282, 0.13 #3390, 0.07 #7606), 065y4w7 (0.14 #2122, 0.11 #2649, 0.09 #5811), 02zd460 (0.14 #1224, 0.10 #1751, 0.03 #4386), 09r4xx (0.14 #2231, 0.05 #2758, 0.03 #5393), 01_k7f (0.14 #1430, 0.03 #6173, 0.03 #7227), 06thjt (0.11 #4087, 0.05 #3033, 0.03 #4614) >> Best rule #491 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0dz46; >> query: (?x13644, 0lk0l) <- location(?x13644, ?x362), award(?x13644, ?x11084), ?x11084 = 02tzwd, influenced_by(?x13644, ?x2343) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5528 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 03kpvp; *> query: (?x13644, 01jvxb) <- story_by(?x4235, ?x13644), film(?x981, ?x4235), profession(?x13644, ?x319), film_production_design_by(?x4235, ?x4449) *> conf = 0.03 ranks of expected_values: 104 EVAL 042xh student! 01jvxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 157.000 157.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #13508-080dwhx PRED entity: 080dwhx PRED relation: honored_for! PRED expected values: 0g55tzk => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 78): 02q690_ (0.18 #752, 0.15 #1103, 0.14 #635), 05c1t6z (0.18 #361, 0.16 #10, 0.15 #1063), 03nnm4t (0.17 #59, 0.14 #293, 0.14 #527), 0gx_st (0.11 #3511, 0.10 #3863, 0.10 #377), 0275n3y (0.11 #3511, 0.10 #3863, 0.08 #5035), 04n2r9h (0.11 #3511, 0.10 #3863, 0.08 #5035), 07z31v (0.11 #3511, 0.10 #3863, 0.08 #5035), 05zksls (0.11 #3511, 0.10 #3863, 0.08 #5035), 092t4b (0.11 #3511, 0.10 #3863, 0.08 #5035), 0g55tzk (0.11 #3511, 0.10 #3863, 0.08 #5035) >> Best rule #752 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 0b6m5fy; 03_8kz; 0147w8; >> query: (?x493, 02q690_) <- actor(?x493, ?x368), nominated_for(?x2041, ?x493), award(?x493, ?x1670) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #3511 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 844 *> proper extension: 07bz5; *> query: (?x493, ?x472) <- nominated_for(?x1669, ?x493), award(?x493, ?x1670), award_winner(?x472, ?x1669) *> conf = 0.11 ranks of expected_values: 10 EVAL 080dwhx honored_for! 0g55tzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 55.000 55.000 0.184 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #13507-06krf3 PRED entity: 06krf3 PRED relation: films! PRED expected values: 0cgbf => 85 concepts (30 used for prediction) PRED predicted values (max 10 best out of 70): 07c52 (0.14 #20, 0.05 #490, 0.02 #2547), 06c97 (0.14 #48), 02_h0 (0.07 #100, 0.05 #570, 0.04 #1042), 081pw (0.07 #3, 0.04 #1579, 0.04 #2055), 04gb7 (0.07 #45, 0.03 #987, 0.02 #1939), 0fzyg (0.07 #54, 0.02 #2106, 0.02 #2581), 07wh1 (0.07 #122), 0htp (0.07 #121), 0mzj_ (0.07 #115), 05f4p (0.07 #95) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 02vqsll; 015g28; 0sxgv; 0y_hb; 04lhc4; 043mk4y; 0b4lkx; >> query: (?x1006, 07c52) <- music(?x1006, ?x7556), award(?x1006, ?x102), genre(?x1006, ?x10122), ?x10122 = 01f9r0 >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06krf3 films! 0cgbf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 30.000 0.143 http://example.org/film/film_subject/films #13506-0dn3n PRED entity: 0dn3n PRED relation: vacationer! PRED expected values: 03gh4 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 37): 05qtj (0.10 #72, 0.09 #197, 0.06 #2706), 0cv3w (0.08 #57, 0.07 #182, 0.06 #807), 03gh4 (0.08 #206, 0.08 #2715, 0.08 #831), 0b90_r (0.04 #3, 0.04 #2637, 0.04 #2260), 04jpl (0.04 #134, 0.04 #9, 0.03 #1135), 0f2v0 (0.03 #2697, 0.03 #1816, 0.03 #2320), 02_286 (0.03 #140, 0.03 #15, 0.02 #2649), 0r0m6 (0.03 #194, 0.02 #2703, 0.02 #69), 06c62 (0.03 #2721, 0.02 #2344, 0.02 #1840), 0160w (0.02 #2636, 0.02 #2, 0.02 #2761) >> Best rule #72 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 033wx9; 0hskw; 03d0ns; >> query: (?x3070, 05qtj) <- spouse(?x3070, ?x8147), award(?x3070, ?x401), participant(?x496, ?x3070) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #206 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 149 *> proper extension: 0zjpz; 01pcvn; 02r3cn; 01lz4tf; 01npcy7; 022q32; *> query: (?x3070, 03gh4) <- spouse(?x3070, ?x8147), profession(?x3070, ?x1032), participant(?x496, ?x3070) *> conf = 0.08 ranks of expected_values: 3 EVAL 0dn3n vacationer! 03gh4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 117.000 0.097 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #13505-05m63c PRED entity: 05m63c PRED relation: profession PRED expected values: 012t_z => 108 concepts (82 used for prediction) PRED predicted values (max 10 best out of 63): 01d_h8 (0.60 #154, 0.57 #598, 0.34 #1932), 0nbcg (0.50 #476, 0.43 #772, 0.17 #1662), 03gjzk (0.40 #163, 0.30 #4591, 0.29 #607), 0dxtg (0.30 #4591, 0.28 #9787, 0.28 #10231), 02jknp (0.30 #4591, 0.20 #156, 0.19 #2526), 015cjr (0.30 #4591, 0.20 #346, 0.17 #494), 02krf9 (0.30 #4591, 0.20 #323, 0.09 #7405), 0np9r (0.30 #4591, 0.15 #12015, 0.14 #8758), 018gz8 (0.30 #4591, 0.15 #8162, 0.14 #8310), 0cbd2 (0.20 #303, 0.15 #11704, 0.14 #7708) >> Best rule #154 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 06x58; 046zh; >> query: (?x287, 01d_h8) <- location(?x287, ?x3269), participant(?x286, ?x287), ?x286 = 014zcr, people(?x1446, ?x287) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1939 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 173 *> proper extension: 0337vz; 01bczm; *> query: (?x287, 012t_z) <- type_of_union(?x287, ?x566), profession(?x287, ?x1032), participant(?x287, ?x989) *> conf = 0.05 ranks of expected_values: 23 EVAL 05m63c profession 012t_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 108.000 82.000 0.600 http://example.org/people/person/profession #13504-0fqy4p PRED entity: 0fqy4p PRED relation: film PRED expected values: 0ds3t5x 02rx2m5 => 93 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1611): 03mh_tp (0.26 #8372, 0.26 #17883, 0.23 #13127), 035s95 (0.26 #14565, 0.21 #8225, 0.19 #32006), 0c3zjn7 (0.25 #848, 0.22 #2433, 0.11 #8773), 02_fz3 (0.25 #1223, 0.22 #2808, 0.11 #9148), 04x4vj (0.25 #690, 0.22 #2275, 0.11 #8615), 02rrfzf (0.24 #6822, 0.21 #8407, 0.20 #9992), 01dvbd (0.19 #11536, 0.18 #6781, 0.17 #16292), 0h14ln (0.19 #12461, 0.18 #7706, 0.17 #17217), 08k40m (0.18 #13106, 0.16 #8351, 0.13 #28962), 047gpsd (0.18 #7398, 0.17 #15323, 0.16 #8983) >> Best rule #8372 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0kx4m; 046b0s; 0kk9v; 056ws9; 04rcl7; >> query: (?x4533, 03mh_tp) <- nominated_for(?x4533, ?x1753), child(?x902, ?x4533), industry(?x4533, ?x373), production_companies(?x4534, ?x4533) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #44 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 04wvhz; 0bgrsl; 05mvd62; 016dmx; *> query: (?x4533, 0ds3t5x) <- nominated_for(?x4533, ?x5400), award_nominee(?x4533, ?x4564), ?x4564 = 01gb54, award_winner(?x5400, ?x1152) *> conf = 0.12 ranks of expected_values: 63 EVAL 0fqy4p film 02rx2m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 74.000 0.263 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0fqy4p film 0ds3t5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 93.000 74.000 0.263 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #13503-071t0 PRED entity: 071t0 PRED relation: sports! PRED expected values: 0lbd9 => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 24): 0jhn7 (0.88 #254, 0.84 #278, 0.83 #92), 0sxrz (0.88 #254, 0.84 #278, 0.83 #92), 0lbbj (0.88 #254, 0.83 #92, 0.81 #253), 0lbd9 (0.84 #278, 0.83 #92, 0.81 #253), 06sks6 (0.83 #92, 0.81 #253, 0.80 #138), 0kbws (0.83 #92, 0.81 #253, 0.80 #22), 0lv1x (0.67 #308, 0.60 #478, 0.60 #238), 018wrk (0.56 #396, 0.50 #472, 0.44 #421), 09n48 (0.40 #495, 0.38 #394, 0.38 #419), 0swbd (0.40 #495, 0.38 #394, 0.38 #419) >> Best rule #254 for best value: >> intensional similarity = 41 >> extensional distance = 3 >> proper extension: 01hp22; >> query: (?x3015, ?x867) <- country(?x3015, ?x9458), country(?x3015, ?x3550), country(?x3015, ?x3016), country(?x3015, ?x2236), country(?x3015, ?x1892), country(?x3015, ?x1203), country(?x3015, ?x792), country(?x3015, ?x512), country(?x3015, ?x410), country(?x3015, ?x344), sports(?x2966, ?x3015), sports(?x1608, ?x3015), sports(?x867, ?x3015), form_of_government(?x9458, ?x48), ?x1892 = 02vzc, ?x512 = 07ssc, ?x1608 = 09x3r, ?x2966 = 06sks6, film_release_region(?x7693, ?x2236), film_release_region(?x3377, ?x2236), film_release_region(?x781, ?x2236), film_release_region(?x280, ?x2236), ?x344 = 04gzd, ?x1203 = 07ylj, ?x410 = 01ls2, ?x3377 = 0gj8nq2, ?x781 = 0gkz15s, countries_spoken_in(?x254, ?x9458), administrative_area_type(?x3550, ?x2792), ?x254 = 02h40lc, sports(?x867, ?x4876), member_states(?x7695, ?x792), ?x4876 = 0d1t3, contains(?x6304, ?x3016), ?x7693 = 0m63c, organization(?x2236, ?x127), film_release_region(?x80, ?x792), olympics(?x421, ?x867), country(?x5967, ?x2236), currency(?x9458, ?x170), ?x280 = 03g90h >> conf = 0.88 => this is the best rule for 3 predicted values *> Best rule #278 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 3 *> proper extension: 03_8r; *> query: (?x3015, ?x1608) <- country(?x3015, ?x9458), country(?x3015, ?x7709), country(?x3015, ?x3550), country(?x3015, ?x3277), country(?x3015, ?x2188), country(?x3015, ?x1892), country(?x3015, ?x1497), country(?x3015, ?x1453), country(?x3015, ?x1353), country(?x3015, ?x1264), country(?x3015, ?x512), country(?x3015, ?x410), country(?x3015, ?x344), country(?x3015, ?x304), sports(?x1608, ?x3015), sports(?x867, ?x3015), sports(?x584, ?x3015), form_of_government(?x9458, ?x48), ?x1892 = 02vzc, ?x512 = 07ssc, ?x1453 = 06qd3, countries_within(?x2467, ?x9458), sports(?x2134, ?x3015), ?x1497 = 015qh, ?x584 = 0l98s, ?x1264 = 0345h, sports(?x1608, ?x766), ?x3277 = 06t8v, ?x766 = 01hp22, ?x410 = 01ls2, ?x2188 = 0163v, organization(?x9458, ?x127), ?x304 = 0d0vqn, film_release_region(?x1150, ?x7709), countries_spoken_in(?x254, ?x9458), adjoins(?x7709, ?x1144), ?x1353 = 035qy, olympics(?x421, ?x867), region(?x2627, ?x3550), ?x1144 = 0j3b, ?x344 = 04gzd *> conf = 0.84 ranks of expected_values: 4 EVAL 071t0 sports! 0lbd9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 37.000 37.000 0.875 http://example.org/user/jg/default_domain/olympic_games/sports #13502-0gjk1d PRED entity: 0gjk1d PRED relation: film! PRED expected values: 01vsn38 => 120 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1071): 01nr36 (0.71 #70719, 0.46 #18717, 0.45 #10399), 01vwllw (0.30 #76960, 0.28 #99845, 0.07 #2627), 016ggh (0.25 #1867, 0.04 #10186, 0.04 #14345), 046chh (0.21 #3263, 0.14 #7423, 0.12 #1184), 016xh5 (0.15 #9401, 0.14 #7321, 0.14 #3161), 01wbg84 (0.14 #2126, 0.12 #47, 0.11 #4206), 02fz3w (0.14 #7820, 0.11 #11980, 0.07 #9900), 012x2b (0.14 #7877, 0.11 #12037, 0.07 #14116), 02cllz (0.14 #6648, 0.11 #10808, 0.06 #23286), 05vsxz (0.14 #2088, 0.10 #6248, 0.07 #8328) >> Best rule #70719 for best value: >> intensional similarity = 3 >> extensional distance = 377 >> proper extension: 0jdr0; >> query: (?x1209, ?x1208) <- films(?x12672, ?x1209), nominated_for(?x1208, ?x1209), film(?x1208, ?x1724) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3932 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 07bwr; 03p2xc; *> query: (?x1209, 01vsn38) <- production_companies(?x1209, ?x6554), ?x6554 = 02j_j0, film(?x8491, ?x1209), country(?x1209, ?x94) *> conf = 0.07 ranks of expected_values: 126 EVAL 0gjk1d film! 01vsn38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 120.000 54.000 0.714 http://example.org/film/actor/film./film/performance/film #13501-02jt1k PRED entity: 02jt1k PRED relation: award PRED expected values: 0gqwc => 124 concepts (87 used for prediction) PRED predicted values (max 10 best out of 266): 09sb52 (0.38 #3677, 0.38 #41, 0.34 #849), 05pcn59 (0.38 #81, 0.33 #485, 0.12 #3717), 0ck27z (0.32 #11808, 0.30 #9384, 0.29 #900), 05zr6wv (0.25 #17, 0.22 #421, 0.12 #3653), 02x4x18 (0.25 #133, 0.22 #537, 0.11 #7405), 05ztrmj (0.25 #185, 0.22 #589, 0.08 #21012), 0cqhk0 (0.19 #6905, 0.19 #9329, 0.18 #11753), 0gqyl (0.17 #7377, 0.14 #11417, 0.13 #11013), 099ck7 (0.17 #1075, 0.12 #267, 0.11 #671), 0gqwc (0.17 #7346, 0.15 #11386, 0.12 #74) >> Best rule #3677 for best value: >> intensional similarity = 5 >> extensional distance = 118 >> proper extension: 05m63c; 0mm1q; 01lqnff; >> query: (?x1700, 09sb52) <- film(?x1700, ?x5418), film(?x1700, ?x4269), film(?x2499, ?x4269), ?x2499 = 0c6qh, film_release_region(?x5418, ?x87) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #7346 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 290 *> proper extension: 015mrk; 01w3lzq; 01pq5j7; 06czyr; 02rmxx; 01l79yc; 0b7t3p; 0kt64b; 07bsj; 01vdrw; ... *> query: (?x1700, 0gqwc) <- type_of_union(?x1700, ?x566), people(?x2510, ?x1700), gender(?x1700, ?x514), ?x514 = 02zsn *> conf = 0.17 ranks of expected_values: 10 EVAL 02jt1k award 0gqwc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 124.000 87.000 0.383 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13500-02wgk1 PRED entity: 02wgk1 PRED relation: nominated_for! PRED expected values: 02hsq3m => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 200): 02g3ft (0.69 #1873, 0.67 #6322, 0.66 #7259), 0gq9h (0.38 #6148, 0.35 #6851, 0.34 #5679), 0gs9p (0.34 #6150, 0.31 #6853, 0.30 #5681), 02hsq3m (0.33 #729, 0.31 #963, 0.21 #1197), 019f4v (0.31 #6139, 0.29 #5670, 0.28 #5904), 0gq_v (0.28 #6809, 0.27 #6106, 0.25 #5637), 0k611 (0.28 #6159, 0.27 #6862, 0.25 #5690), 040njc (0.26 #6094, 0.24 #5625, 0.24 #5859), 0gr4k (0.25 #6815, 0.24 #6112, 0.20 #7284), 04dn09n (0.24 #6120, 0.21 #6823, 0.21 #5651) >> Best rule #1873 for best value: >> intensional similarity = 3 >> extensional distance = 189 >> proper extension: 05h95s; 06mmr; >> query: (?x4502, ?x1429) <- award_winner(?x4502, ?x12894), award(?x4502, ?x1429), category(?x4502, ?x134) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #729 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 0cnztc4; 0crh5_f; 04nlb94; *> query: (?x4502, 02hsq3m) <- film_format(?x4502, ?x909), film_crew_role(?x4502, ?x137), film_distribution_medium(?x4502, ?x81) *> conf = 0.33 ranks of expected_values: 4 EVAL 02wgk1 nominated_for! 02hsq3m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 88.000 0.692 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13499-019rl6 PRED entity: 019rl6 PRED relation: state_province_region PRED expected values: 01n7q => 139 concepts (121 used for prediction) PRED predicted values (max 10 best out of 66): 01n7q (0.87 #2845, 0.86 #6319, 0.63 #4460), 059rby (0.52 #2725, 0.43 #3470, 0.40 #4088), 09c7w0 (0.34 #8426, 0.33 #4209, 0.31 #11020), 0l2xl (0.34 #8426, 0.33 #4209, 0.31 #11020), 06pvr (0.33 #4209, 0.31 #11020, 0.31 #7184), 081yw (0.25 #185, 0.12 #5079, 0.10 #12015), 05fly (0.17 #84, 0.12 #5079, 0.10 #12015), 03v0t (0.14 #672, 0.12 #795, 0.11 #8225), 0d0x8 (0.11 #6733, 0.06 #11808, 0.05 #5496), 05k7sb (0.11 #11795, 0.10 #12046, 0.08 #8203) >> Best rule #2845 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 01p7x7; >> query: (?x7218, ?x1227) <- currency(?x7218, ?x170), citytown(?x7218, ?x11315), adjoins(?x11315, ?x3794), place_of_birth(?x9586, ?x11315), state(?x11315, ?x1227) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019rl6 state_province_region 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 121.000 0.870 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #13498-0137g1 PRED entity: 0137g1 PRED relation: award_winner! PRED expected values: 01s695 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 116): 02rjjll (0.33 #5, 0.17 #565, 0.16 #1545), 019bk0 (0.33 #16, 0.14 #1556, 0.13 #576), 013b2h (0.25 #220, 0.19 #500, 0.19 #360), 056878 (0.25 #312, 0.17 #172, 0.15 #732), 0gpjbt (0.19 #309, 0.12 #3109, 0.12 #729), 0bz6sb (0.17 #624, 0.07 #1324, 0.03 #1184), 02cg41 (0.17 #265, 0.16 #1665, 0.12 #405), 0466p0j (0.17 #216, 0.12 #356, 0.12 #3016), 01s695 (0.15 #843, 0.13 #2943, 0.13 #3083), 05pd94v (0.14 #982, 0.14 #3082, 0.14 #2942) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01w7nwm; >> query: (?x2784, 02rjjll) <- role(?x2784, ?x1574), artists(?x9630, ?x2784), ?x1574 = 0l15bq, ?x9630 = 012yc >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #843 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 06w2sn5; 01wz3cx; 01vxqyl; *> query: (?x2784, 01s695) <- artists(?x302, ?x2784), instrumentalists(?x227, ?x2784), award(?x2784, ?x1565), celebrity(?x2784, ?x4126) *> conf = 0.15 ranks of expected_values: 9 EVAL 0137g1 award_winner! 01s695 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 137.000 137.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13497-01jc6q PRED entity: 01jc6q PRED relation: nominated_for! PRED expected values: 02x73k6 02y_rq5 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 172): 0gs9p (0.66 #9170, 0.66 #8480, 0.66 #9630), 0gq_v (0.66 #9170, 0.66 #8480, 0.66 #9630), 02x73k6 (0.66 #9170, 0.66 #8480, 0.66 #9630), 094qd5 (0.66 #9170, 0.66 #8480, 0.66 #9630), 027c924 (0.66 #9170, 0.66 #8480, 0.66 #9630), 0f4x7 (0.41 #711, 0.40 #940, 0.38 #24), 0gr51 (0.35 #70, 0.33 #986, 0.31 #757), 0gqyl (0.34 #759, 0.33 #1218, 0.33 #72), 0l8z1 (0.34 #2341, 0.29 #278, 0.24 #2570), 04kxsb (0.33 #88, 0.25 #1004, 0.24 #775) >> Best rule #9170 for best value: >> intensional similarity = 3 >> extensional distance = 962 >> proper extension: 0cwrr; 04glx0; 05fgr_; 05sy0cv; 06w7mlh; >> query: (?x197, ?x198) <- nominated_for(?x1034, ?x197), award(?x197, ?x198), award(?x1034, ?x693) >> conf = 0.66 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 24 EVAL 01jc6q nominated_for! 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 69.000 69.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01jc6q nominated_for! 02x73k6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 69.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13496-07zmj PRED entity: 07zmj PRED relation: film_regional_debut_venue! PRED expected values: 017z49 0n08r => 31 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1760): 0crh5_f (0.55 #2317, 0.50 #573, 0.30 #1969), 01s9vc (0.50 #511, 0.40 #2081, 0.36 #2603), 0g5q34q (0.50 #279, 0.33 #1322, 0.33 #451), 09v42sf (0.50 #341, 0.33 #513, 0.33 #167), 0fq7dv_ (0.50 #210, 0.33 #382, 0.33 #36), 07k2mq (0.50 #257, 0.33 #429, 0.33 #83), 05z_kps (0.50 #189, 0.33 #361, 0.33 #15), 01mgw (0.50 #308, 0.33 #480, 0.33 #134), 0djb3vw (0.50 #181, 0.33 #353, 0.33 #7), 011yrp (0.50 #178, 0.33 #350, 0.33 #4) >> Best rule #2317 for best value: >> intensional similarity = 25 >> extensional distance = 9 >> proper extension: 01_d4; >> query: (?x13344, 0crh5_f) <- film_regional_debut_venue(?x4024, ?x13344), film_regional_debut_venue(?x4009, ?x13344), film_regional_debut_venue(?x204, ?x13344), film(?x541, ?x4024), film_release_distribution_medium(?x204, ?x2008), ?x2008 = 07c52, film_release_region(?x204, ?x6959), film_release_region(?x204, ?x2152), film_release_region(?x204, ?x94), ?x94 = 09c7w0, film_crew_role(?x204, ?x137), titles(?x53, ?x204), language(?x4009, ?x254), vacationer(?x6959, ?x444), month(?x6959, ?x1459), place_of_death(?x4732, ?x6959), film_festivals(?x4024, ?x13969), film_release_region(?x6620, ?x2152), film_release_region(?x2783, ?x2152), film_release_region(?x2676, ?x2152), film_release_region(?x633, ?x2152), ?x633 = 0c40vxk, ?x6620 = 0mbql, ?x2783 = 0879bpq, ?x2676 = 0f4m2z >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 4 *> proper extension: 02_286; *> query: (?x13344, 0n08r) <- film_regional_debut_venue(?x4841, ?x13344), film_regional_debut_venue(?x4024, ?x13344), film_regional_debut_venue(?x204, ?x13344), film(?x541, ?x4024), film(?x6259, ?x204), film_format(?x4024, ?x6392), genre(?x4024, ?x600), film_release_region(?x204, ?x429), film_release_region(?x204, ?x252), award_nominee(?x6259, ?x931), ?x429 = 03rt9, award_nominee(?x4858, ?x6259), ?x252 = 03_3d, nominated_for(?x198, ?x4024), award(?x4841, ?x1443), film(?x7091, ?x4841), list(?x4841, ?x3004), nominated_for(?x9163, ?x204), production_companies(?x204, ?x7339) *> conf = 0.33 ranks of expected_values: 19, 430 EVAL 07zmj film_regional_debut_venue! 0n08r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 31.000 17.000 0.545 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 07zmj film_regional_debut_venue! 017z49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 17.000 0.545 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #13495-0f4vbz PRED entity: 0f4vbz PRED relation: nationality PRED expected values: 09c7w0 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 41): 09c7w0 (0.85 #11106, 0.77 #1984, 0.77 #1885), 03_3d (0.37 #8030, 0.35 #8823, 0.14 #800), 07ssc (0.37 #8030, 0.35 #8823, 0.12 #1502), 0345h (0.37 #8030, 0.35 #8823, 0.08 #31), 03rjj (0.37 #8030, 0.35 #8823, 0.04 #403), 0f8l9c (0.37 #8030, 0.35 #8823, 0.02 #2302), 059j2 (0.37 #8030, 0.35 #8823), 03rk0 (0.16 #840, 0.15 #642, 0.06 #11151), 0d060g (0.14 #106, 0.08 #7, 0.05 #2386), 02jx1 (0.14 #1520, 0.12 #331, 0.12 #530) >> Best rule #11106 for best value: >> intensional similarity = 2 >> extensional distance = 3233 >> proper extension: 07qnf; 06s27s; 01nvdc; 03cxqp5; >> query: (?x2258, 09c7w0) <- nationality(?x2258, ?x7748), countries_within(?x6956, ?x7748) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f4vbz nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.850 http://example.org/people/person/nationality #13494-084m3 PRED entity: 084m3 PRED relation: film PRED expected values: 034qrh => 114 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1129): 030k94 (0.59 #117977, 0.59 #121553, 0.58 #98317), 02k_4g (0.59 #117977, 0.59 #121553, 0.58 #98317), 06r2_ (0.59 #117977, 0.59 #121553, 0.55 #39319), 01lv85 (0.59 #117977, 0.59 #121553, 0.55 #39319), 02qzh2 (0.15 #4267, 0.04 #14991, 0.04 #7841), 03lrht (0.15 #3831, 0.03 #14555, 0.02 #7405), 01xq8v (0.15 #4921, 0.03 #19219, 0.02 #10282), 01v1ln (0.15 #4803, 0.02 #8377, 0.02 #11951), 033qdy (0.12 #1176, 0.08 #4750, 0.01 #19048), 0209hj (0.12 #99, 0.08 #3673) >> Best rule #117977 for best value: >> intensional similarity = 3 >> extensional distance = 1273 >> proper extension: 02wrhj; 02zrv7; 04mlh8; 0418ft; 065d1h; >> query: (?x7489, ?x782) <- nationality(?x7489, ?x279), film(?x7489, ?x592), nominated_for(?x7489, ?x782) >> conf = 0.59 => this is the best rule for 4 predicted values *> Best rule #14361 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 03b78r; *> query: (?x7489, 034qrh) <- nationality(?x7489, ?x279), special_performance_type(?x7489, ?x4832), award_nominee(?x7489, ?x1290) *> conf = 0.04 ranks of expected_values: 223 EVAL 084m3 film 034qrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 114.000 87.000 0.587 http://example.org/film/actor/film./film/performance/film #13493-05qx1 PRED entity: 05qx1 PRED relation: jurisdiction_of_office! PRED expected values: 060c4 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.72 #624, 0.71 #141, 0.71 #463), 060bp (0.70 #116, 0.69 #208, 0.63 #47), 0f6c3 (0.37 #514, 0.34 #675, 0.31 #744), 09n5b9 (0.33 #518, 0.30 #679, 0.26 #748), 0fkvn (0.32 #510, 0.30 #671, 0.28 #740), 0p5vf (0.23 #59, 0.20 #128, 0.15 #151), 01zq91 (0.23 #61, 0.17 #130, 0.13 #153), 0dq3c (0.22 #71, 0.17 #186, 0.17 #25), 04syw (0.20 #444, 0.19 #99, 0.17 #720), 01_fjr (0.17 #64, 0.15 #133, 0.09 #179) >> Best rule #624 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 03pn9; >> query: (?x1475, 060c4) <- countries_spoken_in(?x2502, ?x1475), adjoins(?x1475, ?x9730), country(?x668, ?x9730) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qx1 jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.716 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #13492-0jrqq PRED entity: 0jrqq PRED relation: profession PRED expected values: 02jknp => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 73): 02jknp (0.88 #3074, 0.81 #444, 0.62 #882), 03gjzk (0.49 #888, 0.40 #4540, 0.39 #4394), 09jwl (0.41 #6004, 0.40 #4252, 0.40 #5712), 018gz8 (0.31 #890, 0.18 #2059, 0.17 #1036), 016z4k (0.30 #1611, 0.29 #5992, 0.28 #5700), 0nbcg (0.29 #6017, 0.29 #4265, 0.27 #5725), 0np9r (0.29 #2355, 0.27 #1186, 0.11 #2063), 0dz3r (0.26 #4238, 0.26 #5990, 0.26 #5698), 0kyk (0.26 #903, 0.18 #319, 0.14 #3533), 02krf9 (0.23 #3092, 0.16 #3968, 0.15 #900) >> Best rule #3074 for best value: >> intensional similarity = 2 >> extensional distance = 270 >> proper extension: 0cm89v; 0454s1; 032md; 0k_mt; >> query: (?x3873, 02jknp) <- profession(?x3873, ?x319), film(?x3873, ?x1108) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jrqq profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.875 http://example.org/people/person/profession #13491-0jdr0 PRED entity: 0jdr0 PRED relation: genre PRED expected values: 01jfsb 09blyk => 103 concepts (81 used for prediction) PRED predicted values (max 10 best out of 98): 01jfsb (0.78 #481, 0.78 #364, 0.60 #246), 09blyk (0.44 #382, 0.40 #499, 0.17 #147), 02kdv5l (0.40 #236, 0.36 #1876, 0.33 #471), 082gq (0.40 #263, 0.19 #4133, 0.18 #1786), 03k9fj (0.37 #597, 0.36 #714, 0.34 #1885), 0lsxr (0.37 #360, 0.31 #477, 0.23 #3409), 05p553 (0.34 #4928, 0.33 #5163, 0.33 #7389), 02l7c8 (0.34 #1538, 0.33 #1187, 0.32 #953), 04xvlr (0.27 #235, 0.23 #3402, 0.21 #2931), 01hmnh (0.26 #1305, 0.26 #1422, 0.25 #1891) >> Best rule #481 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 05css_; >> query: (?x9349, 01jfsb) <- genre(?x9349, ?x4205), country(?x9349, ?x512), film(?x5601, ?x9349), ?x4205 = 0c3351 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0jdr0 genre 09blyk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 81.000 0.778 http://example.org/film/film/genre EVAL 0jdr0 genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 81.000 0.778 http://example.org/film/film/genre #13490-0cw67g PRED entity: 0cw67g PRED relation: gender PRED expected values: 05zppz => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #17, 0.78 #11, 0.77 #7), 02zsn (0.53 #230, 0.51 #157, 0.33 #38) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 035_2h; >> query: (?x10416, 05zppz) <- award_winner(?x6546, ?x10416), crewmember(?x908, ?x6546), film_release_region(?x908, ?x87) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cw67g gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.776 http://example.org/people/person/gender #13489-01jfnvd PRED entity: 01jfnvd PRED relation: place_of_birth PRED expected values: 02dtg => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 117): 0vm4s (0.34 #69043, 0.33 #80319, 0.32 #69749), 02_286 (0.11 #61297, 0.11 #64830, 0.11 #62712), 0vm5t (0.06 #65517, 0.06 #63399), 0vg8x (0.06 #65517, 0.06 #63399), 0vm39 (0.06 #65517, 0.06 #63399), 01_d4 (0.05 #39513, 0.05 #771, 0.04 #43739), 0cr3d (0.05 #40950, 0.04 #41654, 0.04 #38131), 01531 (0.05 #810, 0.03 #1514, 0.03 #2218), 03b12 (0.05 #1112, 0.03 #1816, 0.03 #2520), 04jpl (0.05 #713, 0.03 #1417, 0.03 #2121) >> Best rule #69043 for best value: >> intensional similarity = 4 >> extensional distance = 1257 >> proper extension: 01r42_g; 0f830f; 083p7; 083q7; 02knnd; 01pw2f1; 01pl9g; 0203v; 03ft8; 01hb6v; ... >> query: (?x8344, ?x7321) <- location(?x8344, ?x7321), nationality(?x8344, ?x94), source(?x7321, ?x958), place_of_birth(?x5217, ?x7321) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #5643 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 0pgjm; *> query: (?x8344, 02dtg) <- group(?x8344, ?x10263), role(?x8344, ?x227), award_nominee(?x8344, ?x5285), type_of_union(?x8344, ?x566) *> conf = 0.02 ranks of expected_values: 55 EVAL 01jfnvd place_of_birth 02dtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 147.000 147.000 0.342 http://example.org/people/person/place_of_birth #13488-09zmys PRED entity: 09zmys PRED relation: award PRED expected values: 0gkts9 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 294): 09sb52 (0.41 #6409, 0.37 #11584, 0.33 #438), 05pcn59 (0.39 #477, 0.31 #6448, 0.28 #2865), 0gqyl (0.31 #1694, 0.17 #500, 0.16 #4480), 0bb57s (0.31 #1831, 0.05 #23723, 0.05 #18549), 05zr6wv (0.28 #414, 0.18 #2802, 0.18 #11560), 02ppm4q (0.27 #1745, 0.11 #11697, 0.09 #27863), 05p09zm (0.27 #6490, 0.26 #8082, 0.25 #10073), 094qd5 (0.25 #1636, 0.19 #840, 0.17 #442), 05b4l5x (0.22 #7967, 0.22 #9958, 0.20 #6375), 09qwmm (0.22 #829, 0.22 #431, 0.15 #1625) >> Best rule #6409 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 0hqcy; >> query: (?x5521, 09sb52) <- participant(?x8143, ?x5521), nominated_for(?x5521, ?x7768), film(?x2789, ?x7768) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1757 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 0bw6y; *> query: (?x5521, 0gkts9) <- award(?x5521, ?x1132), ?x1132 = 0bdwft, student(?x3564, ?x5521) *> conf = 0.19 ranks of expected_values: 14 EVAL 09zmys award 0gkts9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 129.000 129.000 0.409 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13487-015pdg PRED entity: 015pdg PRED relation: artists PRED expected values: 03c3yf 0mjn2 => 49 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1044): 0gcs9 (0.71 #2364, 0.60 #1300, 0.47 #5560), 07m4c (0.60 #1727, 0.57 #2791, 0.44 #3855), 01kph_c (0.60 #1480, 0.44 #3608, 0.43 #2544), 0zjpz (0.56 #3326, 0.33 #5458, 0.31 #6521), 018ndc (0.53 #5569, 0.50 #6632, 0.43 #2373), 0249kn (0.50 #6615, 0.47 #5552, 0.40 #1292), 01vrncs (0.47 #5386, 0.44 #3254, 0.44 #6449), 0x3b7 (0.47 #5689, 0.44 #6752, 0.43 #2493), 01shhf (0.46 #5113, 0.31 #5323, 0.22 #4047), 0326tc (0.46 #4969, 0.15 #12425, 0.14 #14559) >> Best rule #2364 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 05hs4r; 03jsvl; >> query: (?x482, 0gcs9) <- artists(?x482, ?x5452), artists(?x482, ?x1929), artists(?x482, ?x1795), artists(?x482, ?x115), artist(?x441, ?x1929), group(?x227, ?x1929), ?x115 = 01pbxb, ?x5452 = 016s_5, award_nominee(?x1413, ?x1795), origin(?x1929, ?x7930) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3040 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 5 *> proper extension: 05hs4r; 03jsvl; *> query: (?x482, 0mjn2) <- artists(?x482, ?x5452), artists(?x482, ?x1929), artists(?x482, ?x1795), artists(?x482, ?x115), artist(?x441, ?x1929), group(?x227, ?x1929), ?x115 = 01pbxb, ?x5452 = 016s_5, award_nominee(?x1413, ?x1795), origin(?x1929, ?x7930) *> conf = 0.43 ranks of expected_values: 25, 34 EVAL 015pdg artists 0mjn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 49.000 20.000 0.714 http://example.org/music/genre/artists EVAL 015pdg artists 03c3yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 49.000 20.000 0.714 http://example.org/music/genre/artists #13486-04hqz PRED entity: 04hqz PRED relation: location_of_ceremony! PRED expected values: 04ztj => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.50 #113, 0.50 #33, 0.49 #41), 01g63y (0.06 #30, 0.05 #14, 0.05 #10), 0jgjn (0.03 #76, 0.03 #32, 0.03 #36), 01bl8s (0.01 #115) >> Best rule #113 for best value: >> intensional similarity = 2 >> extensional distance = 84 >> proper extension: 02p3my; >> query: (?x7413, 04ztj) <- service_location(?x8082, ?x7413), organization(?x346, ?x8082) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04hqz location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.500 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #13485-02g3ft PRED entity: 02g3ft PRED relation: award! PRED expected values: 0bwh6 => 44 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2221): 06pj8 (0.71 #33817, 0.71 #13520, 0.68 #74395), 01twdk (0.71 #33817, 0.71 #13520, 0.68 #74395), 0343h (0.71 #33817, 0.71 #13520, 0.68 #74395), 06chf (0.71 #33817, 0.71 #13520, 0.68 #74395), 03_2y (0.71 #33817, 0.71 #13520, 0.68 #74395), 02vyw (0.71 #33817, 0.71 #13520, 0.68 #74395), 052hl (0.71 #33817, 0.71 #13520, 0.68 #74395), 02qzjj (0.71 #33817, 0.71 #13520, 0.68 #74395), 032v0v (0.71 #33817, 0.71 #13520, 0.68 #74395), 0js9s (0.71 #33817, 0.71 #13520, 0.68 #74395) >> Best rule #33817 for best value: >> intensional similarity = 6 >> extensional distance = 153 >> proper extension: 02v1m7; 025m98; 0dgr5xp; 09v1lrz; >> query: (?x1429, ?x65) <- award_winner(?x1429, ?x65), nominated_for(?x1429, ?x8965), nominated_for(?x1429, ?x5418), film_crew_role(?x5418, ?x137), music(?x5418, ?x7701), film(?x496, ?x8965) >> conf = 0.71 => this is the best rule for 15 predicted values *> Best rule #10470 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 26 *> proper extension: 02qyp19; 027dtxw; 0f_nbyh; 02wkmx; 0p9sw; 02g3v6; 09sb52; 04dn09n; 02z13jg; 099tbz; ... *> query: (?x1429, 0bwh6) <- award_winner(?x1429, ?x65), nominated_for(?x1429, ?x8965), nominated_for(?x1429, ?x2189), featured_film_locations(?x8965, ?x739), ?x2189 = 02yvct, award(?x97, ?x1429) *> conf = 0.14 ranks of expected_values: 153 EVAL 02g3ft award! 0bwh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 44.000 22.000 0.712 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13484-0fb2l PRED entity: 0fb2l PRED relation: group! PRED expected values: 02hnl => 78 concepts (59 used for prediction) PRED predicted values (max 10 best out of 118): 02hnl (0.82 #1357, 0.80 #691, 0.78 #2454), 03qjg (0.50 #710, 0.41 #1376, 0.38 #2427), 01vj9c (0.50 #261, 0.36 #1342, 0.33 #11), 028tv0 (0.40 #675, 0.36 #2438, 0.36 #3360), 0l14j_ (0.38 #2427, 0.30 #714, 0.25 #299), 05r5c (0.38 #2427, 0.23 #3356, 0.23 #2434), 0g2dz (0.38 #2427, 0.17 #3434, 0.17 #1415), 026t6 (0.38 #2427, 0.17 #3434, 0.17 #1415), 01wy6 (0.38 #2427, 0.17 #3434, 0.11 #3435), 05kms (0.38 #2427, 0.17 #3434, 0.11 #3435) >> Best rule #1357 for best value: >> intensional similarity = 10 >> extensional distance = 20 >> proper extension: 02t3ln; >> query: (?x10043, 02hnl) <- artists(?x12800, ?x10043), artists(?x12800, ?x5543), artists(?x12800, ?x4387), ?x4387 = 0kvnn, ?x5543 = 01kd57, group(?x315, ?x10043), role(?x74, ?x315), instrumentalists(?x315, ?x4595), ?x4595 = 023l9y, performance_role(?x315, ?x1225) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fb2l group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 59.000 0.818 http://example.org/music/performance_role/regular_performances./music/group_membership/group #13483-06w99h3 PRED entity: 06w99h3 PRED relation: genre PRED expected values: 01t_vv => 95 concepts (79 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.75 #7927, 0.75 #3424, 0.70 #2952), 01jfsb (0.51 #1900, 0.38 #2136, 0.35 #4143), 0jxy (0.49 #280, 0.03 #4648, 0.03 #2051), 01hmnh (0.43 #135, 0.41 #253, 0.32 #2024), 01zhp (0.43 #193, 0.33 #311, 0.05 #2082), 02l7c8 (0.42 #487, 0.42 #5568, 0.38 #6873), 06n90 (0.38 #248, 0.27 #1901, 0.26 #2019), 01t_vv (0.34 #525, 0.17 #2414, 0.11 #53), 06cvj (0.29 #475, 0.20 #2364, 0.12 #357), 0lsxr (0.24 #1897, 0.20 #2841, 0.18 #3903) >> Best rule #7927 for best value: >> intensional similarity = 4 >> extensional distance = 1092 >> proper extension: 05dy7p; 05jyb2; 04lqvly; 0cq8nx; 03xj05; 0c5qvw; >> query: (?x224, 07s9rl0) <- genre(?x224, ?x258), nominated_for(?x1053, ?x224), genre(?x5984, ?x258), ?x5984 = 0n1s0 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #525 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 111 *> proper extension: 027ct7c; 0hr41p6; *> query: (?x224, 01t_vv) <- genre(?x224, ?x258), nominated_for(?x1053, ?x224), ?x258 = 05p553, honored_for(?x1442, ?x224) *> conf = 0.34 ranks of expected_values: 8 EVAL 06w99h3 genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 95.000 79.000 0.754 http://example.org/film/film/genre #13482-09fqgj PRED entity: 09fqgj PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.78 #477, 0.76 #545, 0.74 #613), 0dxtw (0.55 #480, 0.54 #548, 0.53 #446), 02rh1dz (0.31 #479, 0.30 #547, 0.29 #513), 02ynfr (0.21 #484, 0.20 #620, 0.20 #518), 0d2b38 (0.20 #731, 0.20 #528, 0.18 #663), 0215hd (0.18 #521, 0.16 #656, 0.16 #487), 04pyp5 (0.16 #148, 0.16 #114, 0.12 #1691), 02vs3x5 (0.16 #88, 0.12 #1691, 0.12 #189), 089g0h (0.16 #488, 0.15 #522, 0.14 #657), 01xy5l_ (0.15 #516, 0.15 #618, 0.12 #651) >> Best rule #477 for best value: >> intensional similarity = 6 >> extensional distance = 107 >> proper extension: 047gn4y; 02qm_f; 085ccd; 05zy2cy; 06r2_; 09gb_4p; 0c3z0; 08sk8l; 063fh9; 037cr1; ... >> query: (?x10509, 0ch6mp2) <- film_crew_role(?x10509, ?x1171), genre(?x10509, ?x811), ?x811 = 03k9fj, ?x1171 = 09vw2b7, film(?x294, ?x10509), production_companies(?x10509, ?x902) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09fqgj film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.780 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13481-0f4yh PRED entity: 0f4yh PRED relation: currency PRED expected values: 09nqf => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.89 #36, 0.83 #176, 0.83 #162), 01nv4h (0.03 #268, 0.03 #212, 0.02 #226), 02l6h (0.02 #137, 0.02 #102, 0.02 #53), 02gsvk (0.02 #307, 0.02 #174, 0.01 #188), 088n7 (0.01 #112) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 01b195; >> query: (?x3535, 09nqf) <- language(?x3535, ?x254), nominated_for(?x669, ?x3535), nominated_for(?x2947, ?x3535), film_distribution_medium(?x2947, ?x81) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f4yh currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.889 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #13480-020hh3 PRED entity: 020hh3 PRED relation: athlete! PRED expected values: 07bs0 => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 4): 02vx4 (0.05 #1094, 0.02 #1425, 0.02 #1486), 0jm_ (0.03 #253, 0.02 #303, 0.02 #333), 037hz (0.02 #260, 0.02 #270, 0.01 #300), 018w8 (0.02 #256) >> Best rule #1094 for best value: >> intensional similarity = 5 >> extensional distance = 934 >> proper extension: 04gtq43; >> query: (?x8640, 02vx4) <- nationality(?x8640, ?x985), film_release_region(?x8025, ?x985), film_release_region(?x5271, ?x985), ?x8025 = 03nsm5x, ?x5271 = 047vnkj >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 020hh3 athlete! 07bs0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 155.000 0.046 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #13479-0mtl5 PRED entity: 0mtl5 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 127 concepts (79 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.87 #378, 0.86 #330, 0.85 #282), 07h34 (0.19 #451, 0.19 #56, 0.13 #940), 0mtl5 (0.19 #451, 0.07 #426, 0.06 #818), 02jx1 (0.09 #422, 0.09 #203, 0.06 #841), 03rjj (0.03 #245, 0.02 #343, 0.02 #415), 03rt9 (0.02 #515, 0.02 #735, 0.01 #822), 05jbn (0.02 #646), 0f8l9c (0.02 #567, 0.02 #580, 0.02 #250), 07ssc (0.02 #737, 0.01 #838, 0.01 #878) >> Best rule #378 for best value: >> intensional similarity = 4 >> extensional distance = 149 >> proper extension: 0235l; 0n6mc; >> query: (?x13824, 09c7w0) <- county_seat(?x13824, ?x4978), contains(?x3778, ?x13824), source(?x13824, ?x958), ?x958 = 0jbk9 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mtl5 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 79.000 0.868 http://example.org/location/country/second_level_divisions #13478-03j1p2n PRED entity: 03j1p2n PRED relation: profession PRED expected values: 09jwl => 118 concepts (97 used for prediction) PRED predicted values (max 10 best out of 56): 09jwl (0.74 #1798, 0.73 #3428, 0.73 #1946), 02hrh1q (0.70 #754, 0.69 #13357, 0.68 #2089), 0nbcg (0.57 #1810, 0.56 #1958, 0.51 #3440), 039v1 (0.50 #1815, 0.49 #1963, 0.45 #36), 016z4k (0.50 #299, 0.48 #1782, 0.46 #1930), 025352 (0.36 #59, 0.16 #2282, 0.10 #503), 01d_h8 (0.36 #1041, 0.36 #745, 0.33 #8907), 0dxtg (0.29 #10839, 0.29 #11875, 0.28 #10987), 03gjzk (0.24 #13358, 0.24 #9361, 0.24 #9657), 02jknp (0.24 #8909, 0.23 #10833, 0.22 #11869) >> Best rule #1798 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 01tp5bj; 082brv; 0326tc; 095x_; 04m2zj; 06br6t; 03f1zhf; >> query: (?x7859, 09jwl) <- role(?x7859, ?x227), ?x227 = 0342h, artists(?x497, ?x7859) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03j1p2n profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 97.000 0.738 http://example.org/people/person/profession #13477-0lbfv PRED entity: 0lbfv PRED relation: contains! PRED expected values: 02jx1 => 89 concepts (75 used for prediction) PRED predicted values (max 10 best out of 273): 09c7w0 (0.64 #3579, 0.64 #50091, 0.64 #4473), 02jx1 (0.60 #1874, 0.55 #21549, 0.32 #60827), 02qkt (0.14 #57592), 0345h (0.12 #55536, 0.05 #64490, 0.04 #65384), 02j9z (0.12 #57274, 0.03 #26857, 0.03 #28645), 03rk0 (0.11 #55591, 0.06 #63650, 0.03 #64545), 03rjj (0.11 #55465, 0.03 #64419, 0.03 #65313), 01n7q (0.10 #17069, 0.09 #19752, 0.09 #26012), 059rby (0.10 #914, 0.09 #2702, 0.09 #4490), 0j0k (0.09 #57623, 0.02 #10211, 0.01 #20052) >> Best rule #3579 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 0ylsr; >> query: (?x6505, 09c7w0) <- company(?x5652, ?x6505), colors(?x6505, ?x3189), category(?x6505, ?x134) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #1874 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 04jpl; 0dhdp; 0fm2_; 05l5n; 09tlh; 04p3c; 01ykl0; 02m__; 0c5_3; 02m77; ... *> query: (?x6505, 02jx1) <- contains(?x512, ?x6505), category(?x6505, ?x134), ?x512 = 07ssc *> conf = 0.60 ranks of expected_values: 2 EVAL 0lbfv contains! 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 75.000 0.644 http://example.org/location/location/contains #13476-05sb1 PRED entity: 05sb1 PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.87 #24, 0.87 #31, 0.87 #54) >> Best rule #24 for best value: >> intensional similarity = 3 >> extensional distance = 92 >> proper extension: 04thp; >> query: (?x2236, 0hzc9wc) <- administrative_parent(?x2236, ?x551), currency(?x2236, ?x170), official_language(?x2236, ?x254) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05sb1 administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.872 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #13475-0f4m2z PRED entity: 0f4m2z PRED relation: film_release_region PRED expected values: 0d0vqn 0chghy => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 149): 0d0vqn (0.91 #2679, 0.90 #2365, 0.90 #2051), 0chghy (0.84 #2056, 0.80 #2370, 0.80 #2684), 03h64 (0.82 #1486, 0.80 #2114, 0.78 #541), 015fr (0.78 #2063, 0.72 #2377, 0.71 #490), 0b90_r (0.75 #2048, 0.64 #475, 0.64 #2676), 0d060g (0.73 #2050, 0.72 #477, 0.67 #2364), 03spz (0.70 #569, 0.69 #2142, 0.58 #2456), 06bnz (0.69 #2092, 0.66 #519, 0.60 #2406), 06t2t (0.66 #2109, 0.60 #536, 0.58 #2423), 03rj0 (0.64 #534, 0.63 #2107, 0.54 #1479) >> Best rule #2679 for best value: >> intensional similarity = 6 >> extensional distance = 270 >> proper extension: 0sxkh; 08tq4x; 05ft32; 025ts_z; 0jdr0; 0b85mm; >> query: (?x2676, 0d0vqn) <- film_release_region(?x2676, ?x1892), film_release_region(?x2676, ?x1229), film_release_region(?x2676, ?x172), ?x1892 = 02vzc, ?x1229 = 059j2, olympics(?x172, ?x391) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0f4m2z film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.908 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f4m2z film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.908 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13474-04093 PRED entity: 04093 PRED relation: nationality PRED expected values: 0f8l9c => 130 concepts (101 used for prediction) PRED predicted values (max 10 best out of 41): 0f8l9c (0.82 #9932, 0.58 #10034, 0.26 #9127), 09c7w0 (0.79 #9228, 0.78 #9630, 0.77 #10035), 07ssc (0.45 #301, 0.44 #316, 0.38 #215), 06m_5 (0.45 #301, 0.29 #4713, 0.27 #6414), 02jx1 (0.38 #133, 0.33 #334, 0.25 #233), 06q1r (0.24 #7522, 0.13 #802, 0.06 #778), 03rt9 (0.12 #213, 0.12 #113, 0.11 #314), 0h7x (0.08 #636, 0.05 #1839, 0.04 #4146), 0345h (0.07 #5644, 0.07 #6847, 0.07 #934), 03rk0 (0.06 #8971, 0.06 #9173, 0.06 #8769) >> Best rule #9932 for best value: >> intensional similarity = 4 >> extensional distance = 1367 >> proper extension: 0dky9n; 06lgq8; 05fh2; >> query: (?x8699, ?x789) <- place_of_birth(?x8699, ?x14322), contains(?x789, ?x14322), location_of_ceremony(?x566, ?x14322), country(?x251, ?x789) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04093 nationality 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 101.000 0.823 http://example.org/people/person/nationality #13473-06y57 PRED entity: 06y57 PRED relation: mode_of_transportation PRED expected values: 07jdr => 204 concepts (204 used for prediction) PRED predicted values (max 10 best out of 3): 07jdr (0.80 #121, 0.80 #139, 0.79 #76), 0k4j (0.06 #167, 0.05 #101, 0.04 #227), 06d_3 (0.06 #168, 0.04 #228, 0.03 #81) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 02h6_6p; 0l0mk; >> query: (?x5036, 07jdr) <- location(?x3281, ?x5036), month(?x5036, ?x1459), award_nominee(?x3281, ?x230) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06y57 mode_of_transportation 07jdr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 204.000 204.000 0.805 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #13472-01wdtv PRED entity: 01wdtv PRED relation: artist PRED expected values: 07c0j 03f0fnk 01n44c => 37 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1114): 01wg25j (0.50 #2288, 0.40 #3122, 0.38 #4788), 0qf11 (0.50 #1967, 0.40 #2801, 0.33 #1132), 0knhk (0.50 #2236, 0.40 #3070, 0.29 #3903), 0178kd (0.50 #2119, 0.40 #2953, 0.29 #3786), 024zq (0.50 #2079, 0.40 #2913, 0.29 #3746), 01vsyg9 (0.50 #2074, 0.40 #2908, 0.29 #3741), 0pkyh (0.50 #1856, 0.40 #2690, 0.29 #3523), 01t_xp_ (0.50 #1687, 0.40 #2521, 0.29 #3354), 0pyg6 (0.50 #1789, 0.40 #2623, 0.29 #3456), 0411q (0.43 #3344, 0.40 #2511, 0.33 #8) >> Best rule #2288 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 01w40h; 0g768; >> query: (?x13569, 01wg25j) <- artist(?x13569, ?x6947), artist(?x13569, ?x6207), artist(?x13569, ?x4182), artist(?x13569, ?x1092), ?x4182 = 07yg2, award(?x1092, ?x2322), award_winner(?x2704, ?x1092), role(?x1092, ?x227), instrumentalists(?x315, ?x1092), ?x6947 = 01vrnsk, ?x2704 = 01mhwk, award_winner(?x1660, ?x6207) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6675 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 21 *> proper extension: 0gh4g0; 02p11jq; 015_1q; 0mzkr; 043g7l; 0181hw; 0n85g; 03qy3l; 03mp8k; 041p3y; *> query: (?x13569, ?x10144) <- artist(?x13569, ?x4182), artists(?x2809, ?x4182), group(?x10144, ?x4182), group(?x745, ?x4182), group(?x75, ?x4182), ?x75 = 07y_7, role(?x2309, ?x745), role(?x654, ?x745), artist(?x3050, ?x10144), ?x2309 = 06ncr, role(?x645, ?x745) *> conf = 0.37 ranks of expected_values: 24, 155, 515 EVAL 01wdtv artist 01n44c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 21.000 0.500 http://example.org/music/record_label/artist EVAL 01wdtv artist 03f0fnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 37.000 21.000 0.500 http://example.org/music/record_label/artist EVAL 01wdtv artist 07c0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 37.000 21.000 0.500 http://example.org/music/record_label/artist #13471-07cz2 PRED entity: 07cz2 PRED relation: production_companies PRED expected values: 05h4t7 => 84 concepts (67 used for prediction) PRED predicted values (max 10 best out of 59): 086k8 (0.35 #330, 0.34 #1238, 0.34 #660), 05qd_ (0.29 #10, 0.14 #422, 0.12 #2404), 016tt2 (0.15 #581, 0.14 #828, 0.12 #251), 017s11 (0.14 #3, 0.08 #2397, 0.07 #4304), 016tw3 (0.12 #1745, 0.11 #176, 0.11 #3157), 0kx4m (0.10 #339, 0.08 #256, 0.05 #1494), 054lpb6 (0.09 #1748, 0.09 #3160, 0.06 #4316), 01795t (0.08 #1507, 0.08 #104, 0.07 #1260), 01gb54 (0.08 #779, 0.08 #1192, 0.07 #697), 09b3v (0.08 #114, 0.07 #1270, 0.04 #1517) >> Best rule #330 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 076xkdz; 080dfr7; >> query: (?x2770, ?x382) <- genre(?x2770, ?x1013), film(?x382, ?x2770), award(?x2770, ?x298), ?x1013 = 06n90 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1746 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 319 *> proper extension: 0ddcbd5; 08c6k9; *> query: (?x2770, 05h4t7) <- genre(?x2770, ?x812), film(?x2922, ?x2770), production_companies(?x2770, ?x2548), ?x812 = 01jfsb *> conf = 0.04 ranks of expected_values: 28 EVAL 07cz2 production_companies 05h4t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 84.000 67.000 0.347 http://example.org/film/film/production_companies #13470-0yfp PRED entity: 0yfp PRED relation: student! PRED expected values: 07szy => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 176): 0bwfn (0.12 #275, 0.11 #1329, 0.10 #7654), 01rtm4 (0.12 #4, 0.01 #4221, 0.01 #2112), 07tds (0.12 #149, 0.01 #5947), 02cttt (0.12 #19), 08815 (0.11 #529, 0.07 #8436, 0.07 #10017), 01w5m (0.09 #1159, 0.08 #2740, 0.07 #2213), 065y4w7 (0.07 #2122, 0.06 #3177, 0.05 #7393), 04b_46 (0.07 #754, 0.06 #2862, 0.05 #4444), 015nl4 (0.07 #594, 0.05 #3757, 0.04 #9555), 033gn8 (0.07 #905, 0.02 #4595, 0.02 #1432) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 026g4l_; 0b7xl8; >> query: (?x973, 0bwfn) <- place_of_birth(?x973, ?x1005), award_nominee(?x3170, ?x973), ?x3170 = 04cw0j >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #4257 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 133 *> proper extension: 04m_kpx; *> query: (?x973, 07szy) <- profession(?x973, ?x1032), student(?x1771, ?x973), ?x1032 = 02hrh1q *> conf = 0.03 ranks of expected_values: 53 EVAL 0yfp student! 07szy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 137.000 137.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #13469-017371 PRED entity: 017371 PRED relation: artists PRED expected values: 01p9hgt 03q_w5 => 68 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1284): 02z4b_8 (0.71 #6023, 0.67 #10333, 0.50 #8177), 01vtj38 (0.71 #6048, 0.56 #10358, 0.50 #3891), 025ldg (0.71 #5761, 0.56 #10071, 0.50 #3604), 01vzxld (0.71 #6306, 0.56 #10616, 0.50 #4149), 01vrz41 (0.71 #5471, 0.56 #9781, 0.50 #3314), 0x3b7 (0.61 #6468, 0.50 #3606, 0.50 #2528), 01vrx3g (0.61 #6468, 0.50 #3254, 0.44 #9721), 0137n0 (0.61 #6468, 0.50 #2239, 0.43 #6552), 03cfjg (0.61 #6468, 0.50 #3529, 0.38 #7840), 016h4r (0.61 #6468, 0.50 #3539, 0.38 #7850) >> Best rule #6023 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 064t9; 05bt6j; 06j6l; >> query: (?x10797, 02z4b_8) <- artists(?x10797, ?x5442), artists(?x10797, ?x2807), parent_genre(?x10797, ?x1572), ?x5442 = 02jq1, type_of_union(?x2807, ?x566), influenced_by(?x483, ?x2807), company(?x2807, ?x10370), award_nominee(?x2807, ?x366) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3334 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 01lyv; *> query: (?x10797, 01p9hgt) <- artists(?x10797, ?x8708), artists(?x10797, ?x5442), artists(?x10797, ?x2807), artists(?x10797, ?x1398), parent_genre(?x10797, ?x1572), ?x5442 = 02jq1, ?x2807 = 03h_fk5, type_of_union(?x8708, ?x1873), profession(?x1398, ?x220) *> conf = 0.50 ranks of expected_values: 75, 600 EVAL 017371 artists 03q_w5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 37.000 0.714 http://example.org/music/genre/artists EVAL 017371 artists 01p9hgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 68.000 37.000 0.714 http://example.org/music/genre/artists #13468-0ds35l9 PRED entity: 0ds35l9 PRED relation: genre PRED expected values: 02l7c8 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 107): 07s9rl0 (0.71 #1222, 0.69 #977, 0.64 #2198), 01z4y (0.61 #4397, 0.55 #5130, 0.50 #2320), 03k9fj (0.38 #256, 0.35 #134, 0.30 #12), 02l7c8 (0.33 #1482, 0.32 #1238, 0.31 #993), 02kdv5l (0.33 #247, 0.31 #125, 0.29 #2689), 01hmnh (0.31 #141, 0.28 #263, 0.16 #19), 01jfsb (0.30 #3309, 0.30 #2699, 0.29 #379), 06cvj (0.21 #1469, 0.08 #4278, 0.08 #5011), 06n90 (0.19 #258, 0.19 #380, 0.17 #136), 04xvlr (0.19 #1100, 0.19 #978, 0.18 #1223) >> Best rule #1222 for best value: >> intensional similarity = 3 >> extensional distance = 476 >> proper extension: 07gp9; 0jzw; 01vksx; 0jqn5; 09k56b7; 0jym0; 01hqhm; 0c_j9x; 0yyts; 02qr69m; ... >> query: (?x86, 07s9rl0) <- genre(?x86, ?x258), nominated_for(?x68, ?x86), honored_for(?x1442, ?x86) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1482 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 527 *> proper extension: 04svwx; *> query: (?x86, 02l7c8) <- genre(?x86, ?x258), country(?x86, ?x94), ?x258 = 05p553 *> conf = 0.33 ranks of expected_values: 4 EVAL 0ds35l9 genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 57.000 57.000 0.707 http://example.org/film/film/genre #13467-0fy6bh PRED entity: 0fy6bh PRED relation: instance_of_recurring_event PRED expected values: 0g_w => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 3): 0g_w (0.92 #204, 0.90 #244, 0.87 #220), 0c4ys (0.21 #266, 0.12 #337, 0.11 #346), 0gcf2r (0.11 #283, 0.10 #328, 0.09 #251) >> Best rule #204 for best value: >> intensional similarity = 12 >> extensional distance = 49 >> proper extension: 073hkh; 073hmq; 0dth6b; 073h1t; 02hn5v; 0bz6sb; 0bzknt; 0bzm__; 0bzmt8; 09306z; ... >> query: (?x3029, 0g_w) <- award_winner(?x3029, ?x2449), award_winner(?x3029, ?x2068), honored_for(?x3029, ?x2368), award_nominee(?x2069, ?x2068), ceremony(?x1243, ?x3029), language(?x2368, ?x254), titles(?x600, ?x2368), nominated_for(?x2068, ?x951), ?x1243 = 0gr0m, award_nominee(?x2801, ?x2449), place_of_birth(?x2068, ?x3052), gender(?x2449, ?x231) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fy6bh instance_of_recurring_event 0g_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.922 http://example.org/time/event/instance_of_recurring_event #13466-02plv57 PRED entity: 02plv57 PRED relation: team! PRED expected values: 02sf_r 02_ssl => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 51): 03558l (0.88 #1869, 0.82 #1711, 0.82 #1289), 01pv51 (0.84 #1892, 0.84 #1850, 0.82 #1734), 02_ssl (0.80 #1875, 0.79 #1735, 0.78 #1893), 02sf_r (0.80 #1870, 0.79 #1735, 0.78 #1893), 0619m3 (0.79 #1735, 0.78 #1893, 0.72 #2265), 0ctt4z (0.79 #1735, 0.78 #1893, 0.62 #905), 0355dz (0.79 #1735, 0.65 #1715, 0.62 #1662), 02sdk9v (0.61 #1895, 0.59 #2001, 0.57 #3993), 02nzb8 (0.55 #1894, 0.53 #3992, 0.53 #1788), 02_j1w (0.53 #3997, 0.52 #4051, 0.52 #1899) >> Best rule #1869 for best value: >> intensional similarity = 7 >> extensional distance = 23 >> proper extension: 0jmmn; 0jml5; 0jm64; >> query: (?x2303, 03558l) <- position(?x2303, ?x1348), ?x1348 = 01pv51, sport(?x2303, ?x12913), sport(?x9576, ?x12913), team(?x4570, ?x9576), colors(?x9576, ?x9464), teams(?x3983, ?x9576) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1875 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 23 *> proper extension: 0jmmn; 0jml5; 0jm64; *> query: (?x2303, 02_ssl) <- position(?x2303, ?x1348), ?x1348 = 01pv51, sport(?x2303, ?x12913), sport(?x9576, ?x12913), team(?x4570, ?x9576), colors(?x9576, ?x9464), teams(?x3983, ?x9576) *> conf = 0.80 ranks of expected_values: 3, 4 EVAL 02plv57 team! 02_ssl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 79.000 0.880 http://example.org/sports/sports_position/players./sports/sports_team_roster/team EVAL 02plv57 team! 02sf_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 79.000 0.880 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #13465-0196pc PRED entity: 0196pc PRED relation: profession! PRED expected values: 01g4zr 02qnbs 05vtbl 09hd6f 011w20 => 66 concepts (27 used for prediction) PRED predicted values (max 10 best out of 4101): 05vtbl (0.71 #32777, 0.50 #15955, 0.33 #66431), 021yw7 (0.71 #30550, 0.42 #64204, 0.36 #59997), 01_x6v (0.71 #30122, 0.42 #63776, 0.36 #59569), 01_x6d (0.71 #30858, 0.29 #64512, 0.25 #14036), 052hl (0.57 #31640, 0.50 #14818, 0.41 #61087), 0b57p6 (0.57 #33153, 0.50 #16331, 0.33 #7916), 02t_8z (0.57 #33123, 0.50 #16301, 0.33 #7886), 079vf (0.57 #29454, 0.50 #12632, 0.33 #4217), 04gcd1 (0.57 #30091, 0.50 #13269, 0.33 #4854), 01c58j (0.57 #29955, 0.42 #71510, 0.27 #59402) >> Best rule #32777 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 01d_h8; 03gjzk; 0np9r; 015h31; >> query: (?x8310, 05vtbl) <- profession(?x11463, ?x8310), profession(?x10439, ?x8310), profession(?x8101, ?x8310), profession(?x7180, ?x8310), story_by(?x136, ?x10439), place_of_death(?x11463, ?x11708), location(?x10439, ?x1131), ?x8101 = 04s04, religion(?x7180, ?x7131) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 28, 625, 642, 2690 EVAL 0196pc profession! 011w20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 66.000 27.000 0.714 http://example.org/people/person/profession EVAL 0196pc profession! 09hd6f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 27.000 0.714 http://example.org/people/person/profession EVAL 0196pc profession! 05vtbl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 27.000 0.714 http://example.org/people/person/profession EVAL 0196pc profession! 02qnbs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 66.000 27.000 0.714 http://example.org/people/person/profession EVAL 0196pc profession! 01g4zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 66.000 27.000 0.714 http://example.org/people/person/profession #13464-03lvyj PRED entity: 03lvyj PRED relation: student! PRED expected values: 06xpp7 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 95): 015nl4 (0.08 #67, 0.03 #11661, 0.03 #24836), 01rtm4 (0.08 #4, 0.03 #531, 0.03 #7909), 0187nd (0.08 #366, 0.03 #893, 0.02 #1420), 011xy1 (0.08 #318, 0.02 #2426), 0bwfn (0.07 #8180, 0.07 #17139, 0.06 #1856), 0lyjf (0.06 #1738, 0.05 #2792, 0.04 #1211), 06xpp7 (0.06 #704, 0.02 #5974, 0.02 #6501), 01w3v (0.06 #542, 0.02 #1069, 0.02 #1596), 01w5m (0.05 #16969, 0.04 #8010, 0.03 #4848), 026gvfj (0.04 #1165, 0.04 #5908, 0.04 #6435) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 02m30v; >> query: (?x8626, 015nl4) <- spouse(?x8626, ?x10445), location_of_ceremony(?x8626, ?x1755), people(?x14284, ?x10445) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #704 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 08ff1k; 05_2h8; 08h79x; 03d1y3; 01kt17; 01nz1q6; 01hdht; 013ybx; 02vkvcz; *> query: (?x8626, 06xpp7) <- spouse(?x8626, ?x10445), gender(?x8626, ?x514), people(?x14284, ?x10445) *> conf = 0.06 ranks of expected_values: 7 EVAL 03lvyj student! 06xpp7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 127.000 127.000 0.077 http://example.org/education/educational_institution/students_graduates./education/education/student #13463-01gssz PRED entity: 01gssz PRED relation: district_represented PRED expected values: 05fkf => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 803): 05fkf (0.94 #352, 0.91 #566, 0.90 #513), 04ych (0.94 #352, 0.91 #566, 0.90 #513), 04tgp (0.94 #352, 0.91 #566, 0.90 #513), 0gyh (0.94 #352, 0.91 #566, 0.90 #513), 03v0t (0.94 #352, 0.91 #566, 0.90 #513), 050ks (0.94 #352, 0.91 #566, 0.90 #513), 0vbk (0.83 #507, 0.81 #506, 0.81 #767), 04rrx (0.83 #507, 0.81 #506, 0.81 #767), 03s0w (0.83 #507, 0.81 #506, 0.81 #767), 02xry (0.83 #507, 0.81 #506, 0.81 #767) >> Best rule #352 for best value: >> intensional similarity = 49 >> extensional distance = 2 >> proper extension: 043djx; >> query: (?x9702, ?x1025) <- district_represented(?x9702, ?x7405), district_represented(?x9702, ?x6895), district_represented(?x9702, ?x4061), district_represented(?x9702, ?x3908), district_represented(?x9702, ?x3778), district_represented(?x9702, ?x3670), district_represented(?x9702, ?x3038), district_represented(?x9702, ?x2713), district_represented(?x9702, ?x2020), district_represented(?x9702, ?x1755), district_represented(?x9702, ?x1426), district_represented(?x9702, ?x728), district_represented(?x9702, ?x177), ?x728 = 059f4, legislative_sessions(?x9702, ?x7973), legislative_sessions(?x9702, ?x6021), legislative_sessions(?x9702, ?x5005), legislative_sessions(?x2712, ?x9702), ?x177 = 05kkh, ?x2713 = 06btq, ?x3778 = 07h34, district_represented(?x6021, ?x7058), district_represented(?x6021, ?x3818), district_represented(?x6021, ?x1025), district_represented(?x6021, ?x760), legislative_sessions(?x2860, ?x6021), ?x3038 = 0d0x8, ?x7058 = 050ks, legislative_sessions(?x7944, ?x6021), legislative_sessions(?x5252, ?x6021), legislative_sessions(?x5006, ?x6021), ?x3670 = 05tbn, ?x7944 = 01h7xx, ?x5252 = 01gtcq, ?x7973 = 01gsvb, ?x2712 = 01gst_, ?x760 = 05fkf, ?x2860 = 0b3wk, ?x4061 = 0498y, ?x2020 = 05k7sb, ?x5006 = 01gtc0, ?x6895 = 05fjf, ?x1755 = 01x73, legislative_sessions(?x5005, ?x10291), ?x1426 = 07z1m, ?x3908 = 04ly1, ?x3818 = 03v0t, legislative_sessions(?x5742, ?x6021), ?x7405 = 07_f2 >> conf = 0.94 => this is the best rule for 6 predicted values ranks of expected_values: 1 EVAL 01gssz district_represented 05fkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.940 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #13462-09fqd3 PRED entity: 09fqd3 PRED relation: place_of_birth PRED expected values: 0ytph => 50 concepts (42 used for prediction) PRED predicted values (max 10 best out of 41): 0n1rj (0.42 #3527, 0.40 #5643, 0.35 #4233), 01_d4 (0.17 #66, 0.05 #2888, 0.05 #3594), 04vmp (0.17 #268), 02_286 (0.16 #5663, 0.13 #7076, 0.13 #8487), 0jj6k (0.09 #5644, 0.06 #4234, 0.02 #6350), 0cr3d (0.08 #7857, 0.08 #8562, 0.06 #11386), 01531 (0.05 #5043, 0.02 #1517, 0.02 #811), 01cx_ (0.05 #2931, 0.04 #3637, 0.03 #5047), 0hptm (0.04 #3047, 0.02 #5163, 0.01 #7988), 030qb3t (0.04 #20519, 0.04 #21224, 0.03 #21929) >> Best rule #3527 for best value: >> intensional similarity = 4 >> extensional distance = 350 >> proper extension: 094xh; 05mlqj; 0436zq; 0739z6; >> query: (?x10723, ?x6084) <- location(?x10723, ?x6084), place_of_birth(?x547, ?x6084), county_seat(?x13776, ?x6084), jurisdiction_of_office(?x1195, ?x6084) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09fqd3 place_of_birth 0ytph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 42.000 0.422 http://example.org/people/person/place_of_birth #13461-0dqmt0 PRED entity: 0dqmt0 PRED relation: people! PRED expected values: 018s6c => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 33): 041rx (0.17 #1236, 0.15 #466, 0.15 #1621), 02w7gg (0.15 #695, 0.08 #618, 0.07 #387), 0x67 (0.09 #2243, 0.09 #2166, 0.09 #2859), 0d7wh (0.06 #710, 0.04 #94, 0.02 #402), 0xnvg (0.06 #629, 0.06 #783, 0.05 #860), 033tf_ (0.05 #1393, 0.05 #623, 0.05 #84), 07bch9 (0.05 #100, 0.03 #408, 0.02 #2641), 03bkbh (0.04 #725, 0.02 #417, 0.02 #109), 03lmx1 (0.04 #707), 01qhm_ (0.03 #160, 0.02 #468, 0.02 #1315) >> Best rule #1236 for best value: >> intensional similarity = 3 >> extensional distance = 270 >> proper extension: 037q1z; 01b0k1; >> query: (?x7146, 041rx) <- produced_by(?x3217, ?x7146), place_of_birth(?x7146, ?x13948), genre(?x3217, ?x225) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dqmt0 people! 018s6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 82.000 0.165 http://example.org/people/ethnicity/people #13460-04gcyg PRED entity: 04gcyg PRED relation: country PRED expected values: 09c7w0 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 110): 09c7w0 (0.86 #674, 0.83 #919, 0.82 #980), 07ssc (0.29 #1240, 0.29 #139, 0.28 #1056), 03h64 (0.25 #47, 0.17 #108, 0.14 #169), 0345h (0.18 #333, 0.17 #89, 0.14 #150), 0ctw_b (0.18 #329, 0.04 #1494, 0.04 #1185), 0f8l9c (0.10 #1613, 0.08 #3290, 0.08 #2793), 0d060g (0.08 #1109, 0.07 #1357, 0.06 #1232), 09blyk (0.07 #2460, 0.06 #5683, 0.06 #5682), 01jfsb (0.07 #2460, 0.06 #5683, 0.06 #5682), 03_3d (0.06 #313, 0.05 #3278, 0.04 #1417) >> Best rule #674 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 04v8x9; 0bcndz; 01_1pv; 0kcn7; 0p7qm; 02x6dqb; 0kxf1; 097zcz; 0cqnss; 014kkm; ... >> query: (?x7947, 09c7w0) <- film_sets_designed(?x8814, ?x7947), film(?x879, ?x7947), production_companies(?x7947, ?x788), award_nominee(?x8814, ?x5894) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gcyg country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.857 http://example.org/film/film/country #13459-0f7hc PRED entity: 0f7hc PRED relation: influenced_by PRED expected values: 0427y => 131 concepts (99 used for prediction) PRED predicted values (max 10 best out of 368): 05rx__ (0.17 #242, 0.04 #4147, 0.04 #5449), 014z8v (0.16 #3592, 0.13 #18221, 0.12 #3157), 03_87 (0.13 #10177, 0.12 #2804, 0.12 #12781), 01k9lpl (0.13 #18221, 0.11 #3779, 0.11 #3344), 0l5yl (0.13 #18221, 0.05 #3739, 0.05 #3304), 052hl (0.13 #18221, 0.05 #4113, 0.05 #5415), 01wj9y9 (0.13 #18221, 0.04 #5701, 0.04 #3097), 01gn36 (0.13 #18221, 0.04 #4475, 0.04 #3607), 03hnd (0.12 #2701, 0.09 #7907, 0.08 #10074), 032l1 (0.12 #12668, 0.10 #17875, 0.10 #7897) >> Best rule #242 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0378zn; >> query: (?x4657, 05rx__) <- film(?x4657, ?x6628), nationality(?x4657, ?x94), ?x6628 = 0bxxzb >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #3793 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 01s7qqw; 01kcms4; 01w9ph_; 070b4; 01wp_jm; 0h25; 0167xy; 06g4_; 04sd0; *> query: (?x4657, 0427y) <- influenced_by(?x1835, ?x4657), influenced_by(?x4657, ?x1145), category(?x4657, ?x134) *> conf = 0.03 ranks of expected_values: 184 EVAL 0f7hc influenced_by 0427y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 131.000 99.000 0.167 http://example.org/influence/influence_node/influenced_by #13458-01yfm8 PRED entity: 01yfm8 PRED relation: profession PRED expected values: 02hrh1q 09jwl => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 43): 02hrh1q (0.88 #6127, 0.87 #3295, 0.87 #3743), 0dxtg (0.62 #312, 0.29 #5083, 0.28 #1058), 018gz8 (0.62 #316, 0.23 #1062, 0.14 #1211), 03gjzk (0.62 #314, 0.23 #5532, 0.23 #3446), 01d_h8 (0.50 #304, 0.38 #603, 0.38 #453), 02jknp (0.25 #455, 0.24 #3587, 0.22 #2393), 09jwl (0.17 #4940, 0.17 #1958, 0.16 #4195), 016z4k (0.14 #153, 0.12 #451, 0.10 #4179), 02hv44_ (0.14 #207, 0.12 #505, 0.03 #9746), 01c72t (0.12 #471, 0.12 #322, 0.08 #4646) >> Best rule #6127 for best value: >> intensional similarity = 2 >> extensional distance = 1675 >> proper extension: 0cm89v; 05d1dy; 01s0l0; 01nbq4; 033p3_; 03_fk9; 07rn0z; 06r3p2; 013rds; >> query: (?x7401, 02hrh1q) <- film(?x7401, ?x924), award_winner(?x924, ?x9972) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 01yfm8 profession 09jwl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 74.000 74.000 0.881 http://example.org/people/person/profession EVAL 01yfm8 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.881 http://example.org/people/person/profession #13457-01hb1t PRED entity: 01hb1t PRED relation: organization! PRED expected values: 060c4 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 18): 060c4 (0.75 #262, 0.71 #288, 0.71 #145), 0dq_5 (0.34 #87, 0.30 #74, 0.24 #100), 07xl34 (0.21 #24, 0.20 #219, 0.20 #726), 05k17c (0.15 #7, 0.12 #449, 0.12 #46), 0hm4q (0.09 #138, 0.06 #502, 0.05 #892), 05c0jwl (0.05 #213, 0.04 #681, 0.04 #135), 0dq3c (0.02 #1252, 0.02 #1526, 0.01 #66), 01t7n9 (0.02 #1252, 0.02 #1526), 0fkzq (0.02 #1252, 0.02 #1526), 09n5b9 (0.02 #1252, 0.02 #1526) >> Best rule #262 for best value: >> intensional similarity = 4 >> extensional distance = 268 >> proper extension: 01rgdw; 0k9wp; 039d4; 01tntf; 0325dj; 03b8c4; 01gwck; 0p7tb; >> query: (?x3123, 060c4) <- contains(?x94, ?x3123), institution(?x1200, ?x3123), ?x94 = 09c7w0, colors(?x3123, ?x332) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hb1t organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.748 http://example.org/organization/role/leaders./organization/leadership/organization #13456-03f47xl PRED entity: 03f47xl PRED relation: influenced_by! PRED expected values: 0n6kf 07lp1 => 138 concepts (64 used for prediction) PRED predicted values (max 10 best out of 469): 06hmd (0.43 #1231, 0.33 #2243, 0.33 #216), 040db (0.33 #2102, 0.33 #75, 0.29 #1090), 05jm7 (0.33 #2163, 0.33 #136, 0.29 #1151), 02kz_ (0.33 #218, 0.29 #1233, 0.22 #2245), 0ldd (0.33 #493, 0.29 #1508, 0.11 #2520), 07d3x (0.33 #391, 0.22 #2418, 0.14 #1406), 0nk72 (0.33 #332, 0.14 #1347, 0.11 #2359), 04xfb (0.33 #328, 0.14 #1343, 0.11 #2355), 03f0324 (0.33 #192, 0.14 #1207, 0.11 #2219), 07g2b (0.33 #17, 0.14 #1032, 0.11 #2044) >> Best rule #1231 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 040db; 03vrp; 06kb_; 0ky1; >> query: (?x6504, 06hmd) <- influenced_by(?x6504, ?x6400), ?x6400 = 06lbp, influenced_by(?x3279, ?x6504), profession(?x3279, ?x987) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4461 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 0dzkq; *> query: (?x6504, 07lp1) <- influenced_by(?x6504, ?x6457), influenced_by(?x6504, ?x6400), location_of_ceremony(?x6400, ?x3125), ?x6457 = 03_87 *> conf = 0.21 ranks of expected_values: 26, 37 EVAL 03f47xl influenced_by! 07lp1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 138.000 64.000 0.429 http://example.org/influence/influence_node/influenced_by EVAL 03f47xl influenced_by! 0n6kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 138.000 64.000 0.429 http://example.org/influence/influence_node/influenced_by #13455-086qd PRED entity: 086qd PRED relation: place_of_death PRED expected values: 0k049 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 31): 030qb3t (0.13 #22, 0.05 #3323, 0.05 #3517), 02_286 (0.07 #790, 0.07 #1179, 0.07 #1373), 0k049 (0.07 #3, 0.03 #780, 0.03 #1363), 05qtj (0.04 #3753, 0.04 #4725, 0.03 #5889), 04jpl (0.04 #3696, 0.03 #4668, 0.03 #5832), 0fhp9 (0.04 #209, 0.03 #2345, 0.03 #791), 06_kh (0.04 #394, 0.03 #782, 0.03 #1365), 0hptm (0.02 #2331, 0.02 #17092, 0.02 #4078), 0n9dn (0.02 #274, 0.02 #468, 0.02 #662), 0ftvg (0.02 #336, 0.02 #724, 0.01 #918) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 02l3_5; 0pz04; 01l1ls; >> query: (?x2138, 030qb3t) <- award_winner(?x537, ?x2138), place_of_birth(?x2138, ?x6253), ?x537 = 0gkvb7 >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 02l3_5; 0pz04; 01l1ls; *> query: (?x2138, 0k049) <- award_winner(?x537, ?x2138), place_of_birth(?x2138, ?x6253), ?x537 = 0gkvb7 *> conf = 0.07 ranks of expected_values: 3 EVAL 086qd place_of_death 0k049 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 110.000 0.133 http://example.org/people/deceased_person/place_of_death #13454-04n2vgk PRED entity: 04n2vgk PRED relation: award PRED expected values: 03t5n3 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 308): 03t5kl (0.72 #39708, 0.67 #40912, 0.67 #36496), 01bgqh (0.62 #443, 0.32 #42, 0.29 #7662), 02f79n (0.46 #740, 0.09 #4349, 0.08 #6354), 01by1l (0.42 #513, 0.36 #112, 0.35 #3320), 03qbh5 (0.42 #604, 0.25 #3010, 0.23 #203), 054ks3 (0.42 #543, 0.23 #4152, 0.21 #3350), 0c4z8 (0.42 #472, 0.22 #10498, 0.22 #4081), 09sb52 (0.40 #18488, 0.28 #23701, 0.27 #6457), 02f6xy (0.38 #600, 0.27 #199, 0.17 #3006), 02f73p (0.38 #587, 0.18 #186, 0.14 #988) >> Best rule #39708 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 099ks0; >> query: (?x9262, ?x4837) <- award_winner(?x4837, ?x9262), award(?x140, ?x4837) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #28073 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1519 *> proper extension: 0f721s; 0cjdk; 01_8w2; 01p5yn; 018_q8; 0gsgr; 05s34b; *> query: (?x9262, ?x4837) <- award_winner(?x9262, ?x3930), award_winner(?x4837, ?x3930) *> conf = 0.18 ranks of expected_values: 33 EVAL 04n2vgk award 03t5n3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 113.000 113.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13453-031v3p PRED entity: 031v3p PRED relation: film PRED expected values: 0443v1 => 121 concepts (91 used for prediction) PRED predicted values (max 10 best out of 863): 07g9f (0.49 #23300, 0.49 #17921, 0.49 #19714), 0443v1 (0.12 #3539, 0.05 #89623, 0.04 #21507), 08mg_b (0.08 #1124, 0.06 #4708, 0.03 #8292), 04k9y6 (0.08 #1044, 0.06 #4628, 0.02 #18965), 0fpgp26 (0.08 #1539, 0.06 #5123, 0.02 #24839), 01ffx4 (0.08 #522, 0.06 #4106, 0.01 #7690), 05q4y12 (0.08 #451, 0.06 #4035, 0.01 #7619), 0g56t9t (0.08 #10, 0.06 #3594, 0.01 #7178), 09txzv (0.08 #254, 0.06 #3838, 0.01 #9214), 02ht1k (0.08 #631, 0.06 #4215, 0.01 #81291) >> Best rule #23300 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 0jsw9l; 07rzf; >> query: (?x12417, ?x10089) <- nominated_for(?x12417, ?x10089), student(?x8221, ?x12417), gender(?x12417, ?x231) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #3539 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 0cjdk; *> query: (?x12417, 0443v1) <- nominated_for(?x12417, ?x10089), ?x10089 = 07g9f *> conf = 0.12 ranks of expected_values: 2 EVAL 031v3p film 0443v1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 91.000 0.494 http://example.org/film/actor/film./film/performance/film #13452-06x8y PRED entity: 06x8y PRED relation: language! PRED expected values: 0g83dv => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1715): 09gdm7q (0.83 #5214, 0.33 #164, 0.27 #14106), 012kyx (0.40 #8087, 0.29 #11573, 0.25 #13321), 0f4_2k (0.40 #7952, 0.25 #21891, 0.25 #4466), 0g5qmbz (0.40 #8470, 0.25 #29377, 0.24 #24152), 0gh6j94 (0.40 #8247, 0.25 #4761, 0.23 #16960), 03z9585 (0.40 #8325, 0.23 #17038, 0.20 #25751), 0bbw2z6 (0.40 #7750, 0.23 #16463, 0.20 #9493), 014kq6 (0.40 #7295, 0.21 #17750, 0.21 #21234), 034xyf (0.40 #8355, 0.20 #10098, 0.19 #27521), 0mbql (0.40 #8094, 0.20 #9837, 0.15 #16807) >> Best rule #5214 for best value: >> intensional similarity = 43 >> extensional distance = 2 >> proper extension: 05qqm; >> query: (?x14139, ?x1170) <- countries_spoken_in(?x14139, ?x6435), capital(?x6435, ?x13196), film_release_region(?x11313, ?x6435), film_release_region(?x9194, ?x6435), film_release_region(?x5704, ?x6435), film_release_region(?x4336, ?x6435), film_release_region(?x2350, ?x6435), film_release_region(?x1498, ?x6435), film_release_region(?x1228, ?x6435), film_release_region(?x511, ?x6435), ?x511 = 0dscrwf, ?x1228 = 05z_kps, contains(?x455, ?x6435), country(?x4673, ?x6435), country(?x4045, ?x6435), country(?x1967, ?x6435), ?x4336 = 0bpm4yw, countries_spoken_in(?x13473, ?x6435), form_of_government(?x6435, ?x48), ?x4673 = 07jbh, administrative_parent(?x6435, ?x551), organization(?x6435, ?x127), ?x11313 = 0by17xn, adjoins(?x6435, ?x2979), ?x2350 = 0661m4p, administrative_area_type(?x6435, ?x2792), ?x9194 = 0fpgp26, ?x1967 = 01cgz, ?x2792 = 0hzc9wc, language(?x1170, ?x13473), adjoins(?x1790, ?x6435), film_release_region(?x1498, ?x2843), film_release_region(?x1498, ?x512), film_release_region(?x1498, ?x172), nominated_for(?x2671, ?x1498), ?x2843 = 016wzw, locations(?x14661, ?x6435), ?x172 = 0154j, ?x4045 = 06z6r, ?x5704 = 0h95zbp, ?x512 = 07ssc, currency(?x6435, ?x170), film_regional_debut_venue(?x1498, ?x11147) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #18085 for first EXPECTED value: *> intensional similarity = 38 *> extensional distance = 12 *> proper extension: 0k0sv; 01wgr; *> query: (?x14139, 0g83dv) <- countries_spoken_in(?x14139, ?x6435), capital(?x6435, ?x13196), film_release_region(?x9501, ?x6435), film_release_region(?x9194, ?x6435), film_release_region(?x4607, ?x6435), film_release_region(?x4336, ?x6435), film_release_region(?x2037, ?x6435), film_release_region(?x1228, ?x6435), film_release_region(?x511, ?x6435), film_festivals(?x9501, ?x2686), olympics(?x6435, ?x2966), genre(?x9501, ?x1014), language(?x9501, ?x90), ?x9194 = 0fpgp26, organization(?x6435, ?x312), ?x1228 = 05z_kps, contains(?x6304, ?x6435), film_release_region(?x9501, ?x1892), film_release_region(?x9501, ?x1353), ?x1892 = 02vzc, ?x2686 = 0gg7gsl, ?x1353 = 035qy, film(?x1676, ?x2037), country(?x1121, ?x6435), genre(?x2037, ?x225), produced_by(?x2037, ?x1417), film_regional_debut_venue(?x9501, ?x5416), film_crew_role(?x511, ?x468), film_release_region(?x511, ?x1497), ?x6304 = 02qkt, production_companies(?x511, ?x7690), ?x312 = 07t65, nominated_for(?x3560, ?x511), ?x4607 = 0h03fhx, ?x4336 = 0bpm4yw, film(?x5910, ?x511), ?x1497 = 015qh, contains(?x6435, ?x14782) *> conf = 0.07 ranks of expected_values: 1313 EVAL 06x8y language! 0g83dv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 30.000 0.833 http://example.org/film/film/language #13451-02lq10 PRED entity: 02lq10 PRED relation: actor! PRED expected values: 02gjrc => 110 concepts (71 used for prediction) PRED predicted values (max 10 best out of 80): 05sy0cv (0.41 #6106, 0.39 #5840, 0.34 #8497), 02pqs8l (0.22 #590, 0.09 #1918, 0.07 #2449), 05f4vxd (0.14 #1947, 0.05 #619, 0.03 #5397), 06cs95 (0.08 #7, 0.03 #802), 02rzdcp (0.06 #1908, 0.01 #5358), 0kfv9 (0.03 #4273, 0.03 #5335, 0.02 #5069), 02py4c8 (0.03 #1337, 0.03 #542, 0.02 #1072), 06qv_ (0.03 #1536, 0.03 #741, 0.02 #1803), 0n2bh (0.03 #1357, 0.02 #1624, 0.02 #2155), 080dwhx (0.03 #536, 0.02 #5314, 0.02 #4252) >> Best rule #6106 for best value: >> intensional similarity = 4 >> extensional distance = 478 >> proper extension: 0gcdzz; 03bx_5q; 03xp8d5; 05f7snc; 08nz99; >> query: (?x2217, ?x8837) <- profession(?x2217, ?x2225), student(?x892, ?x2217), nominated_for(?x2217, ?x8837), languages(?x8837, ?x254) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1287 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 01zkxv; 0m77m; 0144l1; 040_9; 05jm7; 081k8; 02508x; 01_k0d; 08c7cz; 016dmx; ... *> query: (?x2217, 02gjrc) <- profession(?x2217, ?x2225), nationality(?x2217, ?x512), ?x512 = 07ssc, ?x2225 = 0kyk *> conf = 0.02 ranks of expected_values: 34 EVAL 02lq10 actor! 02gjrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 110.000 71.000 0.407 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #13450-0h7dd PRED entity: 0h7dd PRED relation: location PRED expected values: 0zdfp => 144 concepts (128 used for prediction) PRED predicted values (max 10 best out of 194): 02_286 (0.35 #55421, 0.32 #58632, 0.31 #57829), 01cx_ (0.33 #1767, 0.09 #9794, 0.04 #55547), 030qb3t (0.33 #29778, 0.29 #55467, 0.28 #57875), 0r3tq (0.25 #544, 0.06 #2950, 0.06 #3752), 01_d4 (0.22 #1706, 0.06 #9733, 0.04 #58697), 0r3w7 (0.21 #4011, 0.20 #23275, 0.19 #4814), 0mq17 (0.18 #10434, 0.04 #59398, 0.04 #58595), 05mph (0.12 #319, 0.03 #2725, 0.03 #3527), 0cc56 (0.12 #9688, 0.12 #3265, 0.11 #4068), 0cr3d (0.12 #3353, 0.11 #4156, 0.11 #65161) >> Best rule #55421 for best value: >> intensional similarity = 4 >> extensional distance = 713 >> proper extension: 06jzh; 0785v8; 03f1zdw; 022769; 01hkhq; 03yf3z; 01438g; 01z7_f; 0grrq8; 05typm; ... >> query: (?x6017, 02_286) <- award(?x6017, ?x1245), location(?x6017, ?x9713), gender(?x6017, ?x514), dog_breed(?x9713, ?x1706) >> conf = 0.35 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0h7dd location 0zdfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 128.000 0.354 http://example.org/people/person/places_lived./people/place_lived/location #13449-05567m PRED entity: 05567m PRED relation: film! PRED expected values: 01n1gc => 108 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1227): 0p_pd (0.17 #2136, 0.04 #45862, 0.04 #60442), 016ypb (0.16 #4662, 0.11 #8827, 0.05 #19237), 01tsbmv (0.12 #1899, 0.08 #18556, 0.05 #12310), 01vh3r (0.12 #1934, 0.07 #10263, 0.05 #14427), 02xs5v (0.12 #1406, 0.07 #13899, 0.05 #20145), 01v42g (0.12 #203, 0.06 #27270, 0.05 #12696), 03h_9lg (0.12 #133, 0.05 #10544, 0.05 #12626), 01k53x (0.12 #1637, 0.05 #14130, 0.04 #9966), 06mnps (0.12 #567, 0.05 #13060, 0.04 #8896), 09y20 (0.11 #8577, 0.11 #4412, 0.06 #37726) >> Best rule #2136 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 03lrqw; >> query: (?x9303, 0p_pd) <- titles(?x2480, ?x9303), titles(?x1510, ?x9303), nominated_for(?x507, ?x9303), ?x1510 = 01hmnh, ?x2480 = 01z4y >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #2729 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 03lrqw; *> query: (?x9303, 01n1gc) <- titles(?x2480, ?x9303), titles(?x1510, ?x9303), nominated_for(?x507, ?x9303), ?x1510 = 01hmnh, ?x2480 = 01z4y *> conf = 0.06 ranks of expected_values: 181 EVAL 05567m film! 01n1gc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 108.000 33.000 0.167 http://example.org/film/actor/film./film/performance/film #13448-087pfc PRED entity: 087pfc PRED relation: film_crew_role PRED expected values: 09zzb8 02r96rf => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 26): 09zzb8 (0.70 #1125, 0.70 #560, 0.69 #1498), 02r96rf (0.70 #78, 0.69 #563, 0.65 #152), 01pvkk (0.45 #50, 0.33 #13, 0.28 #572), 0dxtw (0.43 #570, 0.36 #1135, 0.33 #11), 01vx2h (0.39 #86, 0.38 #571, 0.36 #271), 02rh1dz (0.33 #10, 0.25 #787, 0.22 #84), 0d2b38 (0.30 #101, 0.25 #787, 0.13 #175), 01xy5l_ (0.25 #787, 0.22 #89, 0.12 #237), 02ynfr (0.25 #787, 0.20 #387, 0.19 #576), 089g0h (0.25 #787, 0.17 #95, 0.13 #580) >> Best rule #1125 for best value: >> intensional similarity = 3 >> extensional distance = 816 >> proper extension: 07kb7vh; 01gglm; >> query: (?x9174, 09zzb8) <- film(?x1335, ?x9174), film_crew_role(?x9174, ?x1171), production_companies(?x9174, ?x541) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 087pfc film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.704 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 087pfc film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.704 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13447-0cc7hmk PRED entity: 0cc7hmk PRED relation: film_release_region PRED expected values: 03rjj 03_3d 06t2t => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 119): 09c7w0 (0.93 #3388, 0.93 #3978, 0.92 #9732), 03rjj (0.89 #151, 0.88 #886, 0.87 #1474), 03_3d (0.86 #300, 0.78 #1623, 0.78 #1476), 0k6nt (0.85 #1196, 0.84 #902, 0.83 #1490), 02vzc (0.83 #1220, 0.83 #338, 0.83 #1367), 05b4w (0.79 #939, 0.72 #1233, 0.70 #1527), 06t2t (0.75 #936, 0.68 #1230, 0.65 #1083), 06qd3 (0.74 #324, 0.66 #1206, 0.62 #1647), 03rj0 (0.71 #1228, 0.66 #1522, 0.65 #1375), 05v8c (0.67 #307, 0.65 #1189, 0.63 #895) >> Best rule #3388 for best value: >> intensional similarity = 4 >> extensional distance = 539 >> proper extension: 014lc_; 0g56t9t; 09sh8k; 0m313; 0g22z; 018js4; 01br2w; 01jc6q; 028_yv; 027qgy; ... >> query: (?x1868, 09c7w0) <- produced_by(?x1868, ?x5999), film_release_region(?x1868, ?x87), nominated_for(?x1867, ?x1868), genre(?x1868, ?x53) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #151 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 0b76d_m; 0ds35l9; 0djb3vw; 0jqn5; 02r1c18; 0gxtknx; 0gydcp7; 0j_tw; 0bx0l; 01shy7; ... *> query: (?x1868, 03rjj) <- produced_by(?x1868, ?x5999), film_release_region(?x1868, ?x1229), film_festivals(?x1868, ?x6828), ?x1229 = 059j2 *> conf = 0.89 ranks of expected_values: 2, 3, 7 EVAL 0cc7hmk film_release_region 06t2t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 86.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc7hmk film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc7hmk film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13446-017m2y PRED entity: 017m2y PRED relation: student! PRED expected values: 09f2j => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 108): 01w5m (0.33 #105, 0.04 #2740, 0.04 #3267), 05nrkb (0.25 #876, 0.07 #2984, 0.05 #1930), 05zl0 (0.17 #1256, 0.03 #8107, 0.02 #10215), 01rtm4 (0.07 #2112, 0.06 #3693, 0.02 #5801), 0bwfn (0.06 #3437, 0.06 #9761, 0.06 #5018), 026gvfj (0.06 #3273, 0.04 #9070, 0.04 #6962), 065y4w7 (0.05 #1595, 0.04 #13189, 0.04 #10554), 08815 (0.05 #2110, 0.04 #7907, 0.02 #36892), 05bjp6 (0.04 #3578, 0.02 #2524, 0.02 #9902), 09f2j (0.04 #4902, 0.04 #10699, 0.04 #13334) >> Best rule #105 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0bbf1f; >> query: (?x9276, 01w5m) <- nationality(?x9276, ?x94), participant(?x9276, ?x6187), participant(?x9276, ?x8160), ?x6187 = 07r1h >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4902 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 0159h6; 01hkhq; 01dvms; 07q0g5; *> query: (?x9276, 09f2j) <- nationality(?x9276, ?x94), spouse(?x9276, ?x1970), participant(?x3422, ?x9276), actor(?x8695, ?x9276) *> conf = 0.04 ranks of expected_values: 10 EVAL 017m2y student! 09f2j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 126.000 126.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #13445-035wcs PRED entity: 035wcs PRED relation: artists PRED expected values: 01lqf49 => 65 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1180): 0bqsy (0.67 #3588, 0.54 #5745, 0.50 #6824), 01vtj38 (0.67 #3890, 0.50 #1735, 0.47 #8206), 0127s7 (0.67 #3768, 0.50 #1613, 0.47 #8084), 02vwckw (0.67 #3983, 0.50 #1828, 0.46 #6140), 047sxrj (0.67 #3411, 0.50 #1256, 0.41 #5387), 0dt1cm (0.67 #3985, 0.50 #1830, 0.41 #8301), 09qr6 (0.67 #3323, 0.50 #1168, 0.41 #7639), 07ss8_ (0.67 #3399, 0.50 #1244, 0.41 #7715), 0gbwp (0.67 #3580, 0.50 #1425, 0.40 #4657), 01w9wwg (0.67 #3784, 0.50 #1629, 0.40 #4861) >> Best rule #3588 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 0glt670; 02lnbg; >> query: (?x12482, 0bqsy) <- artists(?x12482, ?x11709), artists(?x12482, ?x6835), artists(?x12482, ?x4593), artists(?x12482, ?x2925), ?x6835 = 06mt91, ?x11709 = 03f0qd7, ?x2925 = 01vx5w7, artist(?x10426, ?x4593), award_winner(?x528, ?x4593), languages(?x4593, ?x254) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #8362 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 15 *> proper extension: 06cqb; 0m0jc; 02c8d7; 05bt6j; 0y3_8; 06j6l; 025sc50; 02k_kn; 07s72n; 0190yn; *> query: (?x12482, 01lqf49) <- artists(?x12482, ?x11709), artists(?x12482, ?x6835), ?x6835 = 06mt91, artists(?x3916, ?x11709), artists(?x671, ?x11709), ?x671 = 064t9, ?x3916 = 08cyft, award_nominee(?x11709, ?x8253), award(?x11709, ?x6416) *> conf = 0.12 ranks of expected_values: 587 EVAL 035wcs artists 01lqf49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 25.000 0.667 http://example.org/music/genre/artists #13444-0x25q PRED entity: 0x25q PRED relation: category PRED expected values: 08mbj5d => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.37 #3, 0.32 #9, 0.30 #6) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 04glx0; >> query: (?x3055, 08mbj5d) <- nominated_for(?x6917, ?x3055), location(?x6917, ?x1658), ?x1658 = 0h7h6, nationality(?x6917, ?x94) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0x25q category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.368 http://example.org/common/topic/webpage./common/webpage/category #13443-064jjy PRED entity: 064jjy PRED relation: award PRED expected values: 0gkr9q => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 271): 0gs9p (0.41 #5318, 0.37 #6930, 0.37 #6124), 019f4v (0.38 #5305, 0.34 #6111, 0.33 #4096), 040njc (0.37 #5247, 0.33 #6053, 0.33 #6859), 0gq9h (0.35 #8137, 0.30 #5316, 0.28 #4107), 0cjyzs (0.34 #6553, 0.32 #7359, 0.31 #2523), 09sb52 (0.30 #16564, 0.27 #19385, 0.27 #12131), 0gr4k (0.28 #5674, 0.23 #4062, 0.21 #6883), 0gr51 (0.26 #4129, 0.25 #5741, 0.25 #502), 02f72_ (0.25 #229, 0.18 #1035, 0.02 #2244), 07bdd_ (0.25 #468, 0.16 #8125, 0.15 #8464) >> Best rule #5318 for best value: >> intensional similarity = 3 >> extensional distance = 213 >> proper extension: 01t07j; 01q4qv; 015njf; 0534v; 01r_t_; 0flddp; 0522wp; 0454s1; 025jbj; 0dh1n_; ... >> query: (?x8235, 0gs9p) <- profession(?x8235, ?x319), film(?x8235, ?x4621), type_of_union(?x8235, ?x566) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #6780 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 242 *> proper extension: 0f721s; *> query: (?x8235, 0gkr9q) <- program(?x8235, ?x6322), nominated_for(?x5618, ?x6322) *> conf = 0.06 ranks of expected_values: 107 EVAL 064jjy award 0gkr9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 119.000 119.000 0.409 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13442-07xtqq PRED entity: 07xtqq PRED relation: genre PRED expected values: 0219x_ => 88 concepts (51 used for prediction) PRED predicted values (max 10 best out of 81): 0djd22 (0.50 #2581), 02l7c8 (0.43 #250, 0.38 #836, 0.32 #1773), 02kdv5l (0.36 #939, 0.32 #1173, 0.28 #2348), 01jfsb (0.34 #949, 0.34 #1183, 0.32 #2827), 0219x_ (0.33 #143, 0.33 #26, 0.25 #260), 03k9fj (0.30 #948, 0.30 #1182, 0.20 #362), 04xvlr (0.30 #469, 0.26 #586, 0.24 #1523), 06cvj (0.28 #237, 0.25 #823, 0.17 #120), 082gq (0.23 #1552, 0.19 #2140, 0.19 #2022), 01hmnh (0.22 #954, 0.22 #1188, 0.15 #2363) >> Best rule #2581 for best value: >> intensional similarity = 3 >> extensional distance = 561 >> proper extension: 02_1sj; 0hmr4; 02z3r8t; 035xwd; 03ckwzc; 0963mq; 05q96q6; 03t97y; 0jjy0; 07sc6nw; ... >> query: (?x407, ?x3155) <- film(?x71, ?x407), featured_film_locations(?x407, ?x726), titles(?x3155, ?x407) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #143 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0sxfd; 01h18v; 04b_jc; *> query: (?x407, 0219x_) <- nominated_for(?x230, ?x407), genre(?x407, ?x13957), ?x13957 = 0q9mp, language(?x407, ?x254) *> conf = 0.33 ranks of expected_values: 5 EVAL 07xtqq genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 88.000 51.000 0.504 http://example.org/film/film/genre #13441-01j2xj PRED entity: 01j2xj PRED relation: award PRED expected values: 0gs9p => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 287): 027b9ly (0.70 #36599, 0.70 #23724, 0.70 #21309), 02w_6xj (0.70 #36599, 0.70 #23724, 0.70 #21309), 0gs9p (0.37 #8521, 0.37 #7717, 0.35 #2490), 09sb52 (0.35 #2855, 0.33 #2051, 0.32 #4464), 0gq9h (0.34 #10127, 0.34 #4097, 0.33 #76), 0gr4k (0.33 #33, 0.23 #2445, 0.21 #7672), 01l78d (0.33 #288, 0.18 #24529, 0.11 #690), 03hl6lc (0.33 #177, 0.18 #2589, 0.15 #4198), 03hkv_r (0.33 #16, 0.13 #2428, 0.12 #4037), 02x17s4 (0.33 #123, 0.13 #32978, 0.12 #35794) >> Best rule #36599 for best value: >> intensional similarity = 2 >> extensional distance = 2276 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01vsxdm; 01wv9xn; 01795t; 0frsw; 03fbc; 01vrwfv; 014_lq; ... >> query: (?x4922, ?x1107) <- award_winner(?x1107, ?x4922), award(?x4922, ?x1198) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #8521 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 270 *> proper extension: 0m32_; 037d35; 0454s1; *> query: (?x4922, 0gs9p) <- profession(?x4922, ?x319), film(?x4922, ?x186) *> conf = 0.37 ranks of expected_values: 3 EVAL 01j2xj award 0gs9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 113.000 0.701 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13440-0hvbj PRED entity: 0hvbj PRED relation: artist! PRED expected values: 043g7l => 75 concepts (55 used for prediction) PRED predicted values (max 10 best out of 121): 015_1q (0.35 #2939, 0.25 #159, 0.25 #20), 016ckq (0.27 #598, 0.25 #459, 0.25 #320), 033hn8 (0.27 #570, 0.25 #14, 0.16 #2099), 03rhqg (0.27 #572, 0.24 #2935, 0.21 #1684), 01cszh (0.25 #428, 0.25 #289, 0.25 #150), 011k11 (0.25 #452, 0.25 #313, 0.25 #174), 02p4jf0 (0.25 #491, 0.25 #352, 0.25 #213), 01sqd7 (0.25 #476, 0.25 #337, 0.25 #198), 02swsm (0.25 #232, 0.25 #93, 0.12 #1622), 043g7l (0.19 #866, 0.16 #1561, 0.15 #1839) >> Best rule #2939 for best value: >> intensional similarity = 4 >> extensional distance = 289 >> proper extension: 01lcxbb; 013rds; >> query: (?x4842, 015_1q) <- award_winner(?x4416, ?x4842), artist(?x7089, ?x4842), artist(?x7089, ?x4484), ?x4484 = 03xhj6 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #866 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 01ky2h; *> query: (?x4842, 043g7l) <- award_winner(?x4416, ?x4842), artist(?x7089, ?x4842), ?x7089 = 0181dw, artists(?x671, ?x4842) *> conf = 0.19 ranks of expected_values: 10 EVAL 0hvbj artist! 043g7l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 75.000 55.000 0.347 http://example.org/music/record_label/artist #13439-0lzkm PRED entity: 0lzkm PRED relation: role PRED expected values: 013y1f 0bxl5 => 143 concepts (70 used for prediction) PRED predicted values (max 10 best out of 116): 013y1f (0.38 #1133, 0.34 #2053, 0.25 #1041), 05148p4 (0.28 #1121, 0.26 #2041, 0.25 #293), 07brj (0.27 #848, 0.25 #388, 0.25 #112), 026t6 (0.27 #831, 0.25 #95, 0.24 #5091), 0l15bq (0.25 #398, 0.25 #122, 0.21 #1042), 0gkd1 (0.25 #177, 0.24 #1189, 0.13 #729), 01v1d8 (0.25 #153, 0.20 #797, 0.12 #1073), 0dwt5 (0.25 #74, 0.14 #1178, 0.12 #442), 0dwsp (0.25 #101, 0.13 #837, 0.13 #745), 0bxl5 (0.25 #338, 0.13 #798, 0.10 #2086) >> Best rule #1133 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 0l12d; 0770cd; 01vn35l; 0m_v0; 01lvcs1; 050z2; 01sb5r; 023l9y; 01vsy7t; 01s21dg; ... >> query: (?x3735, 013y1f) <- artists(?x2996, ?x3735), role(?x3735, ?x228), role(?x3735, ?x227), ?x228 = 0l14qv, ?x227 = 0342h, profession(?x3735, ?x131) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 0lzkm role 0bxl5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 143.000 70.000 0.379 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0lzkm role 013y1f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 70.000 0.379 http://example.org/music/artist/track_contributions./music/track_contribution/role #13438-01vs14j PRED entity: 01vs14j PRED relation: award PRED expected values: 02f79n => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 269): 01by1l (0.41 #1728, 0.33 #5768, 0.31 #3344), 09sb52 (0.34 #28725, 0.33 #26705, 0.24 #849), 01bgqh (0.30 #5699, 0.25 #3679, 0.23 #19031), 04kxsb (0.29 #934, 0.15 #2550, 0.12 #4570), 05q8pss (0.29 #213, 0.18 #1425, 0.13 #35149), 01c4_6 (0.28 #2109, 0.16 #2917, 0.14 #89), 03qbh5 (0.27 #5861, 0.24 #3437, 0.22 #1821), 02f72_ (0.24 #2249, 0.14 #5885, 0.14 #3057), 02x17c2 (0.24 #623, 0.19 #1835, 0.14 #5875), 01c92g (0.24 #3733, 0.18 #5753, 0.17 #9793) >> Best rule #1728 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 07s6prs; 04mn81; 011hdn; 03y82t6; >> query: (?x1321, 01by1l) <- person(?x10796, ?x1321), award_nominee(?x1321, ?x5170), instrumentalists(?x212, ?x1321) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #342 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0dw4g; *> query: (?x1321, 02f79n) <- person(?x10796, ?x1321), award_nominee(?x1321, ?x5170), ?x10796 = 0dtzkt *> conf = 0.14 ranks of expected_values: 35 EVAL 01vs14j award 02f79n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 110.000 110.000 0.407 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13437-0gyh PRED entity: 0gyh PRED relation: contains PRED expected values: 0q74c 03x1s8 => 151 concepts (100 used for prediction) PRED predicted values (max 10 best out of 2765): 0kwgs (0.85 #114205, 0.85 #43920, 0.84 #93705), 0kcrd (0.85 #114205, 0.85 #43920, 0.84 #93705), 0gyh (0.50 #108346, 0.48 #5856, 0.48 #2928), 09c7w0 (0.50 #108346, 0.48 #5856, 0.48 #2928), 01qwb5 (0.47 #49775, 0.47 #152278, 0.25 #4035), 0ch280 (0.25 #5544, 0.25 #2616, 0.08 #11400), 07w0v (0.25 #3024, 0.25 #96, 0.08 #8880), 0sngf (0.25 #5487, 0.25 #2559, 0.08 #11343), 0sqc8 (0.25 #4780, 0.25 #1852, 0.08 #10636), 0sn4f (0.25 #4015, 0.25 #1087, 0.08 #9871) >> Best rule #114205 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 0b90_r; 03rjj; 03_3d; 015fr; 02_286; 0hzlz; 09pmkv; 059j2; 02jx1; 035qy; ... >> query: (?x2831, ?x8350) <- contains(?x2831, ?x1201), administrative_parent(?x8350, ?x2831), adjoins(?x2831, ?x2623) >> conf = 0.85 => this is the best rule for 2 predicted values *> Best rule #4980 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2 *> proper extension: 0lphb; *> query: (?x2831, 03x1s8) <- contains(?x2831, ?x1845), ?x1845 = 02jyr8 *> conf = 0.25 ranks of expected_values: 791, 832 EVAL 0gyh contains 03x1s8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 151.000 100.000 0.848 http://example.org/location/location/contains EVAL 0gyh contains 0q74c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 151.000 100.000 0.848 http://example.org/location/location/contains #13436-01v_0b PRED entity: 01v_0b PRED relation: award PRED expected values: 040vk98 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 304): 0bqsk5 (0.77 #15728, 0.71 #39924, 0.70 #44765), 04hddx (0.40 #1173, 0.25 #2786, 0.17 #4399), 0gqz2 (0.35 #15405, 0.08 #33550, 0.07 #37179), 040vk98 (0.33 #29, 0.27 #1642, 0.20 #1239), 040_9s0 (0.33 #317, 0.27 #3140, 0.20 #1527), 09sb52 (0.33 #444, 0.21 #21819, 0.19 #28268), 02664f (0.33 #219, 0.19 #2638, 0.09 #1832), 01yz0x (0.33 #177, 0.18 #3000, 0.18 #1790), 0265vt (0.33 #325, 0.13 #17263, 0.12 #2744), 0262x6 (0.33 #316, 0.12 #2735, 0.10 #17254) >> Best rule #15728 for best value: >> intensional similarity = 5 >> extensional distance = 199 >> proper extension: 02mslq; 01vd7hn; 025cn2; 0c_drn; 02pt7h_; 0134wr; 01wyq0w; 01m5m5b; >> query: (?x12382, ?x12729) <- award_winner(?x12729, ?x12382), award_winner(?x11471, ?x12382), gender(?x12382, ?x231), award(?x669, ?x11471), ?x669 = 0146pg >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #29 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 09dt7; *> query: (?x12382, 040vk98) <- location(?x12382, ?x9341), location(?x12382, ?x8093), ?x9341 = 0f25y, contains(?x94, ?x8093), influenced_by(?x12382, ?x118) *> conf = 0.33 ranks of expected_values: 4 EVAL 01v_0b award 040vk98 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 145.000 0.766 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13435-01rs41 PRED entity: 01rs41 PRED relation: school_type! PRED expected values: 01rtm4 03p7gb 01j_5k 02yr3z 01qwb5 02l424 01pcj4 01tntf 06mvyf 01n4w_ 01qdhx => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1383): 01pl14 (0.50 #4451, 0.44 #5784, 0.36 #6229), 037njl (0.50 #3691, 0.33 #7247, 0.33 #6801), 02ldkf (0.50 #4795, 0.33 #6128, 0.33 #1240), 07w0v (0.50 #4463, 0.33 #5796, 0.33 #908), 0c5x_ (0.50 #4689, 0.33 #6022, 0.33 #1134), 01rc6f (0.50 #4686, 0.33 #6019, 0.33 #1131), 07vyf (0.50 #4560, 0.33 #5893, 0.33 #1005), 0f102 (0.50 #4507, 0.33 #5840, 0.33 #952), 049dk (0.50 #4485, 0.33 #5818, 0.33 #930), 01j_cy (0.50 #4481, 0.33 #5814, 0.33 #926) >> Best rule #4451 for best value: >> intensional similarity = 23 >> extensional distance = 2 >> proper extension: 01_9fk; 05jxkf; >> query: (?x3205, 01pl14) <- school_type(?x7202, ?x3205), school_type(?x6455, ?x3205), school_type(?x5324, ?x3205), school_type(?x2064, ?x3205), school_type(?x1103, ?x3205), school_type(?x1087, ?x3205), currency(?x7202, ?x170), colors(?x6455, ?x3315), school(?x5419, ?x1087), institution(?x620, ?x6455), major_field_of_study(?x7202, ?x2981), category(?x7202, ?x134), contains(?x94, ?x1087), institution(?x865, ?x5324), company(?x265, ?x2064), school(?x8542, ?x1087), student(?x1103, ?x1340), ?x5419 = 0jmmn, organization(?x346, ?x7202), ?x3315 = 0jc_p, major_field_of_study(?x5324, ?x2605), school(?x2820, ?x6455), company(?x1159, ?x1103) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2529 for first EXPECTED value: *> intensional similarity = 32 *> extensional distance = 1 *> proper extension: 05pcjw; *> query: (?x3205, 01pcj4) <- school_type(?x11688, ?x3205), school_type(?x11654, ?x3205), school_type(?x8538, ?x3205), school_type(?x7447, ?x3205), school_type(?x7202, ?x3205), school_type(?x7178, ?x3205), school_type(?x6019, ?x3205), school_type(?x5178, ?x3205), school_type(?x3182, ?x3205), school_type(?x2767, ?x3205), school_type(?x1924, ?x3205), school_type(?x1520, ?x3205), school_type(?x1513, ?x3205), ?x7202 = 02bhj4, ?x5178 = 02bq1j, ?x8538 = 026ssfj, state_province_region(?x6019, ?x1227), ?x1520 = 07lx1s, contains(?x94, ?x11688), ?x7178 = 03hdz8, major_field_of_study(?x11688, ?x6756), major_field_of_study(?x6019, ?x2606), major_field_of_study(?x7447, ?x742), ?x11654 = 02hp6p, ?x742 = 05qjt, organization(?x3484, ?x6019), ?x1513 = 017d77, ?x3182 = 02ccqg, institution(?x8398, ?x1924), institution(?x865, ?x7447), ?x8398 = 028dcg, ?x2767 = 04sylm *> conf = 0.33 ranks of expected_values: 68, 87, 112, 115, 137, 395, 406, 431, 486, 536, 578 EVAL 01rs41 school_type! 01qdhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01rs41 school_type! 01n4w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01rs41 school_type! 06mvyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01rs41 school_type! 01tntf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01rs41 school_type! 01pcj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01rs41 school_type! 02l424 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01rs41 school_type! 01qwb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01rs41 school_type! 02yr3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01rs41 school_type! 01j_5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01rs41 school_type! 03p7gb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type EVAL 01rs41 school_type! 01rtm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 22.000 0.500 http://example.org/education/educational_institution/school_type #13434-080h2 PRED entity: 080h2 PRED relation: featured_film_locations! PRED expected values: 0bmssv 0dqcs3 => 234 concepts (130 used for prediction) PRED predicted values (max 10 best out of 721): 061681 (0.50 #754, 0.33 #45, 0.21 #4299), 04dsnp (0.50 #772, 0.16 #19917, 0.15 #14243), 0cc846d (0.50 #901, 0.14 #4446, 0.09 #20755), 072x7s (0.50 #816, 0.13 #10033, 0.11 #14287), 05f4_n0 (0.50 #1007, 0.09 #2425, 0.09 #10224), 047bynf (0.50 #1187, 0.09 #2605, 0.09 #10404), 01bl7g (0.50 #1103, 0.09 #2521, 0.09 #10320), 05c26ss (0.50 #968, 0.09 #2386, 0.09 #10185), 095zlp (0.50 #735, 0.09 #2153, 0.09 #9952), 033srr (0.33 #269, 0.25 #978, 0.19 #8068) >> Best rule #754 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04jpl; 02_286; >> query: (?x1036, 061681) <- featured_film_locations(?x136, ?x1036), vacationer(?x1036, ?x1093), place_of_birth(?x3466, ?x1036), ?x136 = 09sh8k >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #999 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 04jpl; 02_286; *> query: (?x1036, 0bmssv) <- featured_film_locations(?x136, ?x1036), vacationer(?x1036, ?x1093), place_of_birth(?x3466, ?x1036), ?x136 = 09sh8k *> conf = 0.25 ranks of expected_values: 193 EVAL 080h2 featured_film_locations! 0dqcs3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 234.000 130.000 0.500 http://example.org/film/film/featured_film_locations EVAL 080h2 featured_film_locations! 0bmssv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 234.000 130.000 0.500 http://example.org/film/film/featured_film_locations #13433-02vyh PRED entity: 02vyh PRED relation: award PRED expected values: 0gq9h => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 215): 07bdd_ (0.71 #1284, 0.63 #2909, 0.54 #2503), 05p1dby (0.59 #1326, 0.50 #2545, 0.48 #2951), 0gq9h (0.50 #484, 0.38 #2109, 0.33 #78), 02x1z2s (0.33 #201, 0.26 #3044, 0.25 #607), 0gq_d (0.33 #224, 0.25 #630, 0.24 #1442), 018wng (0.33 #42, 0.25 #448, 0.19 #2885), 0gr42 (0.33 #117, 0.25 #523, 0.18 #1335), 0gr07 (0.33 #245, 0.25 #651, 0.16 #3494), 0p9sw (0.33 #23, 0.25 #429, 0.15 #2866), 09sb52 (0.31 #14256, 0.31 #8164, 0.29 #15474) >> Best rule #1284 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 05gnf; >> query: (?x4077, 07bdd_) <- award_winner(?x6488, ?x4077), produced_by(?x3311, ?x6488), award_winner(?x2060, ?x6488), state_province_region(?x4077, ?x1426) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #484 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0m593; *> query: (?x4077, 0gq9h) <- award_winner(?x6857, ?x4077), award_winner(?x6488, ?x4077), ?x6488 = 03_bcg, award_nominee(?x6857, ?x382), place_of_birth(?x6857, ?x9699) *> conf = 0.50 ranks of expected_values: 3 EVAL 02vyh award 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 85.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13432-02r_d4 PRED entity: 02r_d4 PRED relation: languages PRED expected values: 02h40lc => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 13): 02h40lc (0.46 #120, 0.45 #199, 0.30 #239), 06b_j (0.07 #2306), 03k50 (0.04 #82, 0.03 #943, 0.03 #319), 064_8sq (0.03 #369, 0.03 #1306, 0.03 #1579), 0999q (0.02 #101, 0.02 #260, 0.02 #338), 02bjrlw (0.02 #79, 0.02 #119, 0.02 #198), 07c9s (0.02 #328, 0.02 #250, 0.01 #991), 09s02 (0.02 #351, 0.02 #273), 0w7c (0.02 #237), 04306rv (0.01 #357, 0.01 #81, 0.01 #121) >> Best rule #120 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 05bnp0; 01p7yb; 0168cl; 06n7h7; 03ldxq; 05gml8; 05ml_s; 01yk13; 0pz7h; 016kjs; ... >> query: (?x665, 02h40lc) <- award_nominee(?x436, ?x665), student(?x10910, ?x665), student(?x6760, ?x665) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r_d4 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.465 http://example.org/people/person/languages #13431-0m25p PRED entity: 0m25p PRED relation: currency PRED expected values: 09nqf => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.87 #5, 0.82 #31, 0.82 #30) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0l_q9; 0g_wn2; 0kcrd; 0fw4v; 0p2rj; 0nlqq; >> query: (?x7659, 09nqf) <- source(?x7659, ?x958), administrative_parent(?x7659, ?x938), time_zones(?x7659, ?x2088), contains(?x938, ?x3983) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m25p currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.867 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #13430-03bw6 PRED entity: 03bw6 PRED relation: profession PRED expected values: 02jknp => 181 concepts (180 used for prediction) PRED predicted values (max 10 best out of 95): 02jknp (0.90 #4808, 0.89 #3608, 0.88 #6008), 01d_h8 (0.84 #2556, 0.82 #5406, 0.82 #2106), 02hrh1q (0.80 #9465, 0.78 #13816, 0.78 #13515), 0dxtg (0.78 #5414, 0.75 #5564, 0.72 #6014), 03gjzk (0.42 #5416, 0.41 #2566, 0.41 #3316), 02krf9 (0.31 #2578, 0.26 #4828, 0.26 #5428), 0kyk (0.29 #1231, 0.20 #5281, 0.20 #31), 0cbd2 (0.27 #5257, 0.27 #6757, 0.26 #6457), 018gz8 (0.24 #1518, 0.14 #5268, 0.14 #24007), 0dgd_ (0.20 #482, 0.15 #632, 0.14 #24007) >> Best rule #4808 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 0522wp; >> query: (?x7257, 02jknp) <- film(?x7257, ?x4504), gender(?x7257, ?x231), category(?x4504, ?x134), nominated_for(?x198, ?x4504) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bw6 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 180.000 0.897 http://example.org/people/person/profession #13429-03f7jfh PRED entity: 03f7jfh PRED relation: artists! PRED expected values: 012yc => 134 concepts (85 used for prediction) PRED predicted values (max 10 best out of 237): 064t9 (0.84 #14064, 0.52 #1575, 0.52 #6261), 01flzq (0.59 #1057, 0.22 #1369, 0.17 #120), 06by7 (0.56 #648, 0.45 #2209, 0.44 #14699), 016clz (0.50 #5, 0.38 #1566, 0.27 #317), 02x8m (0.50 #20, 0.22 #22172, 0.21 #14675), 025sc50 (0.33 #1613, 0.33 #1301, 0.31 #677), 06j6l (0.33 #50, 0.31 #14100, 0.29 #8482), 012yc (0.33 #152, 0.28 #1401, 0.24 #1089), 09nwwf (0.33 #139, 0.11 #1388, 0.05 #4825), 036jv (0.29 #1132, 0.11 #1444, 0.06 #21547) >> Best rule #14064 for best value: >> intensional similarity = 3 >> extensional distance = 416 >> proper extension: 0123r4; >> query: (?x8874, 064t9) <- artists(?x2937, ?x8874), artists(?x2937, ?x2732), ?x2732 = 01wgxtl >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #152 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 01yzl2; 02k5sc; 01f2q5; *> query: (?x8874, 012yc) <- artist(?x1124, ?x8874), artists(?x12070, ?x8874), ?x12070 = 01f9y_ *> conf = 0.33 ranks of expected_values: 8 EVAL 03f7jfh artists! 012yc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 134.000 85.000 0.842 http://example.org/music/genre/artists #13428-0msck PRED entity: 0msck PRED relation: contains! PRED expected values: 07b_l => 144 concepts (77 used for prediction) PRED predicted values (max 10 best out of 110): 07b_l (0.57 #46694, 0.50 #60164, 0.37 #44897), 09c7w0 (0.56 #17956, 0.48 #22443, 0.47 #20648), 04_1l0v (0.45 #18404, 0.45 #21096, 0.37 #15711), 0msck (0.37 #44897, 0.35 #56571, 0.23 #62858), 03v0t (0.23 #11905, 0.23 #7415, 0.20 #2028), 01n7q (0.21 #45874, 0.21 #9055, 0.19 #29704), 0824r (0.17 #14615, 0.07 #12821, 0.07 #64906), 03s0w (0.16 #955, 0.10 #12627, 0.10 #1853), 05fjf (0.15 #9351, 0.15 #10250, 0.12 #6658), 059rby (0.15 #66468, 0.12 #6304, 0.12 #42221) >> Best rule #46694 for best value: >> intensional similarity = 5 >> extensional distance = 134 >> proper extension: 02hrh0_; 0tlq9; >> query: (?x14231, ?x3634) <- source(?x14231, ?x958), contains(?x14231, ?x10465), ?x958 = 0jbk9, contains(?x3634, ?x10465), featured_film_locations(?x945, ?x3634) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0msck contains! 07b_l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 77.000 0.575 http://example.org/location/location/contains #13427-0df92l PRED entity: 0df92l PRED relation: genre PRED expected values: 02l7c8 => 88 concepts (84 used for prediction) PRED predicted values (max 10 best out of 98): 01jfsb (0.50 #1183, 0.37 #6353, 0.32 #2121), 0653m (0.49 #5754, 0.49 #6342, 0.49 #6694), 03h64 (0.49 #5754, 0.49 #6342, 0.49 #6694), 02l7c8 (0.39 #483, 0.36 #1069, 0.36 #951), 03k9fj (0.38 #1182, 0.22 #361, 0.21 #829), 0lsxr (0.37 #124, 0.32 #358, 0.27 #7), 05p553 (0.36 #704, 0.33 #5874, 0.33 #5756), 06n90 (0.29 #1184, 0.14 #3058, 0.13 #1301), 03g3w (0.20 #23, 0.16 #140, 0.11 #491), 04xvh5 (0.20 #501, 0.15 #1087, 0.13 #969) >> Best rule #1183 for best value: >> intensional similarity = 4 >> extensional distance = 383 >> proper extension: 03t97y; 03twd6; 0436yk; 04sntd; 0ddcbd5; 034r25; 08fbnx; 02x8fs; 03z9585; 08c6k9; ... >> query: (?x5782, 01jfsb) <- language(?x5782, ?x2890), country(?x5782, ?x2346), genre(?x5782, ?x225), ?x225 = 02kdv5l >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #483 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 011yxg; 0ds11z; 0n0bp; 0b73_1d; 029zqn; 0260bz; 02s4l6; 026p4q7; 019vhk; 0_816; ... *> query: (?x5782, 02l7c8) <- language(?x5782, ?x2890), nominated_for(?x2222, ?x5782), produced_by(?x5782, ?x10271), ?x2222 = 0gs96 *> conf = 0.39 ranks of expected_values: 4 EVAL 0df92l genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 84.000 0.504 http://example.org/film/film/genre #13426-0d9v9q PRED entity: 0d9v9q PRED relation: nationality PRED expected values: 0162v => 124 concepts (95 used for prediction) PRED predicted values (max 10 best out of 64): 09c7w0 (0.88 #6523, 0.82 #8808, 0.80 #7118), 0n048 (0.41 #2564, 0.41 #6225, 0.40 #5630), 0ctw_b (0.33 #418, 0.12 #712, 0.11 #810), 03rk0 (0.29 #5476, 0.16 #5178, 0.07 #8355), 03_r3 (0.25 #110, 0.20 #208, 0.02 #2477), 0chghy (0.25 #108, 0.15 #500, 0.12 #1680), 034m8 (0.20 #287, 0.08 #581, 0.05 #2754), 06q1r (0.18 #3827, 0.13 #3332, 0.11 #467), 014kj2 (0.18 #2762, 0.16 #3059, 0.16 #2861), 016wrq (0.18 #2762, 0.16 #3059, 0.16 #2861) >> Best rule #6523 for best value: >> intensional similarity = 5 >> extensional distance = 1220 >> proper extension: 078mgh; 020hyj; 04dyqk; >> query: (?x7212, 09c7w0) <- profession(?x7212, ?x7623), place_of_birth(?x7212, ?x7213), nationality(?x7212, ?x512), location(?x1194, ?x7213), time_zones(?x7213, ?x5327) >> conf = 0.88 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d9v9q nationality 0162v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 95.000 0.877 http://example.org/people/person/nationality #13425-0161c PRED entity: 0161c PRED relation: film_release_region! PRED expected values: 0gd0c7x 0dc_ms 03z9585 0hhggmy => 146 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1302): 04f52jw (0.94 #11955, 0.85 #13248, 0.85 #26179), 0gd0c7x (0.88 #29971, 0.88 #11866, 0.87 #26090), 047vnkj (0.88 #13605, 0.88 #12312, 0.85 #26536), 0dzlbx (0.88 #12265, 0.85 #13558, 0.80 #26489), 03nsm5x (0.88 #12650, 0.76 #26874, 0.73 #30755), 08hmch (0.87 #25975, 0.87 #29856, 0.85 #24682), 05pdh86 (0.85 #13477, 0.83 #30289, 0.83 #26408), 04hwbq (0.85 #13070, 0.79 #29882, 0.78 #26001), 03nm_fh (0.85 #26444, 0.85 #30325, 0.81 #12220), 0fpgp26 (0.84 #12751, 0.83 #26975, 0.82 #14044) >> Best rule #11955 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 05r4w; >> query: (?x3683, 04f52jw) <- film_release_region(?x3981, ?x3683), film_release_region(?x3748, ?x3683), ?x3748 = 05zlld0, olympics(?x3683, ?x1931), ?x3981 = 047tsx3 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #29971 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 02k54; 015qh; 04g5k; *> query: (?x3683, 0gd0c7x) <- film_release_region(?x3748, ?x3683), ?x3748 = 05zlld0, olympics(?x3683, ?x1931) *> conf = 0.88 ranks of expected_values: 2, 47, 56, 88 EVAL 0161c film_release_region! 0hhggmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 146.000 85.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0161c film_release_region! 03z9585 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 146.000 85.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0161c film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 146.000 85.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0161c film_release_region! 0gd0c7x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 85.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13424-0drc1 PRED entity: 0drc1 PRED relation: gender PRED expected values: 05zppz => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #33, 0.92 #19, 0.91 #21), 02zsn (0.38 #52, 0.35 #64, 0.34 #60) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 03ds3; 05cgy8; 02mz_6; 0csdzz; >> query: (?x8275, 05zppz) <- award_winner(?x1869, ?x8275), music(?x785, ?x8275), profession(?x8275, ?x106) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0drc1 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.923 http://example.org/people/person/gender #13423-0bmc4cm PRED entity: 0bmc4cm PRED relation: film_release_region PRED expected values: 09c7w0 06bnz 06t2t 03h64 => 118 concepts (88 used for prediction) PRED predicted values (max 10 best out of 206): 09c7w0 (0.93 #11238, 0.92 #12527, 0.92 #12689), 0f8l9c (0.90 #4192, 0.90 #3551, 0.89 #5634), 0k6nt (0.89 #4678, 0.87 #3555, 0.86 #3235), 03h64 (0.89 #1996, 0.86 #4724, 0.83 #5684), 05r4w (0.88 #5615, 0.88 #4655, 0.87 #4815), 035qy (0.88 #3565, 0.86 #4206, 0.86 #3245), 03gj2 (0.87 #4679, 0.86 #3236, 0.85 #5960), 015fr (0.86 #1941, 0.85 #4187, 0.84 #1780), 0b90_r (0.86 #1930, 0.82 #5618, 0.81 #3215), 01znc_ (0.86 #1968, 0.80 #4856, 0.79 #4696) >> Best rule #11238 for best value: >> intensional similarity = 8 >> extensional distance = 786 >> proper extension: 02qpt1w; 0h7t36; >> query: (?x3135, 09c7w0) <- film_release_distribution_medium(?x3135, ?x81), film_release_region(?x3135, ?x4743), film_release_region(?x3135, ?x774), olympics(?x774, ?x358), titles(?x2164, ?x3135), film_release_region(?x4336, ?x4743), ?x4336 = 0bpm4yw, country(?x1121, ?x4743) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 11, 14 EVAL 0bmc4cm film_release_region 03h64 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 118.000 88.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmc4cm film_release_region 06t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 118.000 88.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmc4cm film_release_region 06bnz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 118.000 88.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmc4cm film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 88.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13422-0hgnl3t PRED entity: 0hgnl3t PRED relation: film! PRED expected values: 02p65p => 83 concepts (41 used for prediction) PRED predicted values (max 10 best out of 704): 0bbxx9b (0.46 #49941, 0.45 #18725, 0.44 #20806), 027rwmr (0.46 #49941, 0.45 #18725, 0.44 #20806), 06jzh (0.18 #88, 0.01 #6328, 0.01 #52110), 01jw4r (0.18 #1494, 0.01 #11895, 0.01 #9815), 01f7j9 (0.15 #43698, 0.15 #27052, 0.15 #37454), 05prs8 (0.13 #27051, 0.12 #41617, 0.12 #58266), 0170pk (0.09 #282, 0.05 #21088, 0.02 #48142), 014zcr (0.09 #37, 0.04 #14600, 0.03 #29169), 0bxtg (0.09 #77, 0.04 #14640, 0.03 #29209), 05nzw6 (0.09 #1192, 0.03 #15755, 0.02 #11593) >> Best rule #49941 for best value: >> intensional similarity = 4 >> extensional distance = 493 >> proper extension: 03h_yy; 048scx; 02rqwhl; 048htn; 0gyy53; 07tw_b; 06lpmt; 043t8t; 02ntb8; 05q7874; ... >> query: (?x4518, ?x929) <- film_crew_role(?x4518, ?x1171), film(?x902, ?x4518), nominated_for(?x929, ?x4518), ?x1171 = 09vw2b7 >> conf = 0.46 => this is the best rule for 2 predicted values *> Best rule #4181 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 132 *> proper extension: 0h95zbp; 0gh6j94; *> query: (?x4518, 02p65p) <- film_release_region(?x4518, ?x789), film_release_region(?x4518, ?x344), ?x789 = 0f8l9c, ?x344 = 04gzd *> conf = 0.01 ranks of expected_values: 390 EVAL 0hgnl3t film! 02p65p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 83.000 41.000 0.462 http://example.org/film/actor/film./film/performance/film #13421-0p50v PRED entity: 0p50v PRED relation: nationality PRED expected values: 02jx1 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 50): 09c7w0 (0.82 #9219, 0.81 #7436, 0.76 #5056), 02jx1 (0.39 #2706, 0.37 #1319, 0.33 #32), 03rjj (0.38 #2080, 0.07 #3270, 0.04 #6049), 0f8l9c (0.25 #120, 0.07 #3270, 0.07 #1011), 02k54 (0.12 #116), 03rt9 (0.10 #508, 0.09 #409, 0.07 #1003), 0h7x (0.09 #430, 0.02 #628, 0.02 #1519), 0d060g (0.08 #601, 0.08 #898, 0.08 #700), 03rk0 (0.08 #3116, 0.08 #4108, 0.08 #4306), 0345h (0.07 #3270, 0.06 #426, 0.04 #6049) >> Best rule #9219 for best value: >> intensional similarity = 3 >> extensional distance = 2547 >> proper extension: 01pbxb; 07cjqy; 03xb2w; 02bgmr; 03q43g; 087z12; 023nlj; 01d_h; 02x08c; 05myd2; ... >> query: (?x8268, 09c7w0) <- nationality(?x8268, ?x512), award(?x8268, ?x601), region(?x54, ?x512) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #2706 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 332 *> proper extension: 07m69t; *> query: (?x8268, 02jx1) <- nationality(?x8268, ?x512), ?x512 = 07ssc *> conf = 0.39 ranks of expected_values: 2 EVAL 0p50v nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.821 http://example.org/people/person/nationality #13420-029k4p PRED entity: 029k4p PRED relation: film! PRED expected values: 03vrv9 => 96 concepts (43 used for prediction) PRED predicted values (max 10 best out of 658): 0693l (0.19 #37419, 0.18 #27027, 0.16 #12475), 054_mz (0.12 #22869, 0.11 #70673, 0.11 #56125), 06cv1 (0.12 #22869, 0.11 #16633, 0.10 #10395), 0pz91 (0.10 #211, 0.09 #2289, 0.03 #8527), 0gn30 (0.08 #945, 0.06 #3023, 0.03 #17578), 01fyzy (0.06 #1059, 0.06 #3137, 0.02 #9375), 02_l96 (0.06 #904, 0.06 #2982, 0.01 #8316), 03xb2w (0.06 #878, 0.04 #2956, 0.01 #9194), 0jfx1 (0.06 #4561, 0.02 #33667, 0.02 #17038), 016_mj (0.06 #2372, 0.04 #294, 0.01 #31477) >> Best rule #37419 for best value: >> intensional similarity = 3 >> extensional distance = 559 >> proper extension: 02qrv7; 035s95; 014nq4; 0d_wms; 016ztl; 02tktw; 01lbcqx; 0gfzfj; 03d8jd1; 04sh80; >> query: (?x4880, ?x3117) <- film(?x166, ?x4880), written_by(?x4880, ?x3117), genre(?x4880, ?x225) >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 029k4p film! 03vrv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 43.000 0.189 http://example.org/film/actor/film./film/performance/film #13419-02pp_q_ PRED entity: 02pp_q_ PRED relation: award PRED expected values: 0cjyzs => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 260): 0fc9js (0.77 #14219, 0.72 #17067, 0.72 #16660), 0cjyzs (0.67 #107, 0.50 #1325, 0.44 #919), 03ccq3s (0.44 #200, 0.29 #1418, 0.28 #1012), 0gr51 (0.30 #2537, 0.23 #4161, 0.23 #5788), 0gr4k (0.29 #1657, 0.28 #2469, 0.26 #4093), 0cjcbg (0.28 #1179, 0.13 #22756, 0.11 #367), 04dn09n (0.27 #2480, 0.22 #4104, 0.22 #5731), 0gqy2 (0.26 #572, 0.13 #4632, 0.11 #5039), 0gs9p (0.25 #1704, 0.21 #4140, 0.19 #2516), 040njc (0.25 #1632, 0.19 #4068, 0.16 #2850) >> Best rule #14219 for best value: >> intensional similarity = 3 >> extensional distance = 1373 >> proper extension: 024rdh; 018p5f; 09jm8; >> query: (?x635, ?x4386) <- award_winner(?x4386, ?x635), award_nominee(?x636, ?x635), ceremony(?x4386, ?x1265) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #107 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0721cy; *> query: (?x635, 0cjyzs) <- award_nominee(?x7943, ?x635), award_nominee(?x636, ?x635), ?x636 = 0d4fqn, location(?x7943, ?x12443) *> conf = 0.67 ranks of expected_values: 2 EVAL 02pp_q_ award 0cjyzs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 75.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13418-078bz PRED entity: 078bz PRED relation: school! PRED expected values: 038c0q => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 18): 02qw1zx (0.25 #113, 0.23 #329, 0.22 #365), 092j54 (0.25 #117, 0.16 #333, 0.15 #369), 03nt7j (0.20 #115, 0.19 #61, 0.15 #331), 09l0x9 (0.20 #119, 0.17 #47, 0.16 #371), 05vsb7 (0.18 #325, 0.17 #361, 0.16 #55), 02pq_x5 (0.17 #51, 0.15 #123, 0.15 #339), 09th87 (0.16 #67, 0.15 #121, 0.12 #337), 02pq_rp (0.16 #62, 0.15 #116, 0.10 #332), 0g3zpp (0.15 #110, 0.13 #326, 0.12 #362), 06439y (0.14 #126, 0.12 #378, 0.12 #342) >> Best rule #113 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 02t4yc; >> query: (?x2775, 02qw1zx) <- student(?x2775, ?x1447), fraternities_and_sororities(?x2775, ?x4348), school(?x4979, ?x2775) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #330 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 97 *> proper extension: 02jyr8; *> query: (?x2775, 038c0q) <- institution(?x620, ?x2775), contains(?x94, ?x2775), school(?x4979, ?x2775) *> conf = 0.13 ranks of expected_values: 12 EVAL 078bz school! 038c0q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 111.000 111.000 0.254 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #13417-056xkh PRED entity: 056xkh PRED relation: film_crew_role PRED expected values: 09zzb8 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 33): 09zzb8 (0.78 #211, 0.75 #494, 0.75 #282), 01vx2h (0.50 #186, 0.50 #151, 0.38 #327), 0dxtw (0.50 #80, 0.48 #291, 0.47 #185), 0d2b38 (0.23 #200, 0.22 #165, 0.17 #341), 0215hd (0.22 #193, 0.20 #158, 0.16 #334), 089g0h (0.20 #194, 0.18 #159, 0.16 #335), 015h31 (0.20 #183, 0.18 #148, 0.12 #2173), 02ynfr (0.19 #190, 0.18 #155, 0.18 #331), 02rh1dz (0.19 #184, 0.18 #149, 0.14 #325), 01xy5l_ (0.17 #188, 0.16 #329, 0.15 #153) >> Best rule #211 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 04lqvlr; 08j7lh; >> query: (?x9858, 09zzb8) <- film_format(?x9858, ?x909), film_release_region(?x9858, ?x94), film_crew_role(?x9858, ?x2178), ?x2178 = 01pvkk >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 056xkh film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.779 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13416-083qy7 PRED entity: 083qy7 PRED relation: nationality PRED expected values: 0b90_r => 95 concepts (44 used for prediction) PRED predicted values (max 10 best out of 50): 09c7w0 (0.97 #2084, 0.88 #4183, 0.84 #4283), 0b90_r (0.61 #3180, 0.47 #2083, 0.46 #3380), 07ssc (0.58 #2695, 0.41 #3195, 0.35 #3898), 0345h (0.55 #1915, 0.12 #2711, 0.09 #3211), 02jx1 (0.51 #2215, 0.38 #2016, 0.27 #1122), 03_3d (0.34 #1890, 0.07 #1989, 0.06 #3186), 03rjj (0.33 #5, 0.08 #3185, 0.07 #1988), 01p1v (0.33 #44, 0.04 #539, 0.03 #638), 0chghy (0.29 #109, 0.19 #505, 0.17 #604), 0d060g (0.18 #3187, 0.16 #3890, 0.10 #3487) >> Best rule #2084 for best value: >> intensional similarity = 6 >> extensional distance = 259 >> proper extension: 07s8r0; 01q415; 0170s4; 015f7; 014g22; 02mjf2; 0c7xjb; 051m56; 0q1lp; 01rzxl; ... >> query: (?x2666, 09c7w0) <- location(?x2666, ?x5851), category(?x5851, ?x134), time_zones(?x5851, ?x1638), nationality(?x2666, ?x2152), ?x1638 = 02fqwt, film_release_region(?x66, ?x2152) >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #3180 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 769 *> proper extension: 07c37; *> query: (?x2666, ?x151) <- location(?x2666, ?x5851), gender(?x2666, ?x231), teams(?x5851, ?x8265), time_zones(?x5851, ?x1638), contains(?x151, ?x5851), film_release_region(?x66, ?x151) *> conf = 0.61 ranks of expected_values: 2 EVAL 083qy7 nationality 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 44.000 0.966 http://example.org/people/person/nationality #13415-041b4j PRED entity: 041b4j PRED relation: religion PRED expected values: 03_gx => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 12): 03_gx (0.29 #239, 0.08 #1696, 0.08 #919), 0c8wxp (0.17 #368, 0.16 #186, 0.16 #1183), 0kpl (0.12 #10, 0.07 #235, 0.07 #100), 03j6c (0.03 #1794, 0.03 #1427, 0.03 #1930), 092bf5 (0.03 #151, 0.02 #241, 0.02 #1514), 0n2g (0.03 #103, 0.02 #148, 0.01 #421), 0flw86 (0.02 #2363, 0.02 #1546, 0.02 #2726), 0kq2 (0.02 #243, 0.02 #923, 0.02 #3285), 01lp8 (0.02 #1636, 0.02 #136, 0.02 #1545), 04pk9 (0.01 #110, 0.01 #200, 0.01 #337) >> Best rule #239 for best value: >> intensional similarity = 3 >> extensional distance = 330 >> proper extension: 02ln1; >> query: (?x9615, 03_gx) <- people(?x1050, ?x9615), nationality(?x9615, ?x94), ?x1050 = 041rx >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 041b4j religion 03_gx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.292 http://example.org/people/person/religion #13414-04ykg PRED entity: 04ykg PRED relation: location! PRED expected values: 0frmb1 => 196 concepts (137 used for prediction) PRED predicted values (max 10 best out of 3375): 0d7hg4 (0.47 #122569, 0.46 #270149, 0.46 #190108), 032r1 (0.25 #2298, 0.07 #12304, 0.06 #14805), 01797x (0.25 #2078, 0.06 #19588, 0.04 #84622), 099d4 (0.25 #2348, 0.06 #7350, 0.04 #12354), 04z0g (0.25 #1171, 0.04 #78710, 0.04 #76208), 01zwy (0.25 #1710, 0.03 #14217, 0.03 #79249), 0b78hw (0.25 #846, 0.03 #13353, 0.03 #78385), 06crk (0.25 #1280, 0.03 #13787, 0.03 #78819), 099p5 (0.25 #1885, 0.03 #14392, 0.03 #79424), 03f1zdw (0.25 #208, 0.03 #12715, 0.03 #75245) >> Best rule #122569 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 016v46; 020d8d; 015cj9; 01z53w; 0q34g; 0gqfy; 025569; 0c9cw; 01v8c; >> query: (?x1274, ?x2650) <- place_of_birth(?x2650, ?x1274), contains(?x8260, ?x1274), administrative_parent(?x1274, ?x94) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #7504 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0hv7l; *> query: (?x1274, ?x51) <- teams(?x1274, ?x10690), administrative_parent(?x1274, ?x94), nationality(?x51, ?x94) *> conf = 0.02 ranks of expected_values: 2389 EVAL 04ykg location! 0frmb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 137.000 0.473 http://example.org/people/person/places_lived./people/place_lived/location #13413-020xn5 PRED entity: 020xn5 PRED relation: film_crew_role! PRED expected values: 0ds2n 0dzlbx => 58 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1256): 057lbk (0.75 #19384, 0.75 #18128, 0.67 #8079), 0bth54 (0.75 #18899, 0.71 #10106, 0.71 #8850), 076xkps (0.75 #19922, 0.71 #11129, 0.67 #18666), 09sh8k (0.75 #18849, 0.71 #10056, 0.67 #17593), 0ct2tf5 (0.75 #19952, 0.67 #18696, 0.67 #8647), 0270k40 (0.75 #20071, 0.67 #8766, 0.58 #18815), 07f_t4 (0.75 #19803, 0.67 #8498, 0.50 #18547), 05qbckf (0.71 #10283, 0.71 #9027, 0.67 #19076), 01gwk3 (0.71 #10865, 0.67 #19658, 0.58 #18402), 05zpghd (0.71 #10743, 0.67 #8231, 0.58 #18280) >> Best rule #19384 for best value: >> intensional similarity = 8 >> extensional distance = 10 >> proper extension: 02r96rf; 02rh1dz; 0dxtw; 01vx2h; 033smt; >> query: (?x1776, 057lbk) <- film_crew_role(?x6053, ?x1776), film_crew_role(?x5277, ?x1776), film_crew_role(?x5113, ?x1776), ?x6053 = 05qbbfb, ?x5277 = 047csmy, film(?x237, ?x5113), film_release_distribution_medium(?x5113, ?x81), executive_produced_by(?x5113, ?x2790) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #19472 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 10 *> proper extension: 02r96rf; 02rh1dz; 0dxtw; 01vx2h; 033smt; *> query: (?x1776, 0dzlbx) <- film_crew_role(?x6053, ?x1776), film_crew_role(?x5277, ?x1776), film_crew_role(?x5113, ?x1776), ?x6053 = 05qbbfb, ?x5277 = 047csmy, film(?x237, ?x5113), film_release_distribution_medium(?x5113, ?x81), executive_produced_by(?x5113, ?x2790) *> conf = 0.58 ranks of expected_values: 69, 103 EVAL 020xn5 film_crew_role! 0dzlbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 58.000 26.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 020xn5 film_crew_role! 0ds2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 58.000 26.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13412-01_4z PRED entity: 01_4z PRED relation: entity_involved! PRED expected values: 0j5ym => 217 concepts (217 used for prediction) PRED predicted values (max 10 best out of 68): 0gfq9 (0.50 #1286, 0.22 #1921, 0.20 #390), 02h2z_ (0.40 #434, 0.12 #818, 0.11 #1138), 048n7 (0.33 #469, 0.25 #789, 0.25 #149), 01hwkn (0.33 #48, 0.14 #688, 0.12 #880), 07_nf (0.33 #4742, 0.25 #721, 0.22 #5264), 0cm2xh (0.30 #1291, 0.14 #2956, 0.12 #843), 01h6pn (0.25 #140, 0.22 #5264, 0.22 #4809), 0chhs (0.25 #189, 0.22 #5264, 0.22 #4809), 02kxg_ (0.25 #161, 0.22 #5264, 0.22 #4809), 02kxjx (0.25 #109, 0.16 #2990, 0.14 #557) >> Best rule #1286 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0cdbq; 0193qj; 01h3dj; 01hnp; 012m_; >> query: (?x1528, 0gfq9) <- entity_involved(?x9351, ?x1528), entity_involved(?x8303, ?x1528), films(?x9351, ?x2402), combatants(?x8303, ?x6465), ?x6465 = 0193qj >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1921 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 02mjmr; *> query: (?x1528, ?x326) <- basic_title(?x1528, ?x346), ?x346 = 060c4, entity_involved(?x8303, ?x1528), locations(?x8303, ?x1879), adjoins(?x1879, ?x94), locations(?x326, ?x1879) *> conf = 0.22 ranks of expected_values: 13 EVAL 01_4z entity_involved! 0j5ym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 217.000 217.000 0.500 http://example.org/base/culturalevent/event/entity_involved #13411-0g54xkt PRED entity: 0g54xkt PRED relation: film! PRED expected values: 01xcfy => 65 concepts (30 used for prediction) PRED predicted values (max 10 best out of 732): 0dvmd (0.73 #14574, 0.67 #33324, 0.66 #10409), 0c6qh (0.16 #415, 0.08 #10411, 0.04 #12907), 0g2lq (0.14 #2083, 0.11 #35407, 0.11 #41654), 0bwh6 (0.14 #2083, 0.11 #35407, 0.11 #41654), 04wvhz (0.14 #2083, 0.11 #35407, 0.11 #41654), 0154qm (0.14 #562, 0.07 #22908, 0.07 #20824), 014zcr (0.14 #37, 0.03 #12529, 0.03 #8364), 0bxtg (0.12 #77, 0.05 #2160, 0.03 #14653), 0169dl (0.08 #10411, 0.07 #22908, 0.07 #402), 01s7zw (0.08 #10411, 0.07 #22908, 0.07 #20824) >> Best rule #14574 for best value: >> intensional similarity = 4 >> extensional distance = 359 >> proper extension: 0gfzgl; 0clpml; >> query: (?x3222, ?x3101) <- nominated_for(?x3101, ?x3222), award_winner(?x638, ?x3101), participant(?x709, ?x3101), participant(?x1017, ?x3101) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #12985 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 359 *> proper extension: 0gfzgl; 0clpml; *> query: (?x3222, 01xcfy) <- nominated_for(?x3101, ?x3222), award_winner(?x638, ?x3101), participant(?x709, ?x3101), participant(?x1017, ?x3101) *> conf = 0.01 ranks of expected_values: 463 EVAL 0g54xkt film! 01xcfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 30.000 0.730 http://example.org/film/actor/film./film/performance/film #13410-01mc11 PRED entity: 01mc11 PRED relation: country PRED expected values: 09c7w0 => 88 concepts (31 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.77 #695, 0.77 #609, 0.72 #1997), 059rby (0.20 #2697, 0.19 #954, 0.18 #1127), 0dc3_ (0.19 #954, 0.18 #1127, 0.17 #1474), 01mc11 (0.16 #1907, 0.06 #2256, 0.04 #432), 07ssc (0.07 #1836, 0.03 #2538, 0.03 #1230), 0d060g (0.06 #787, 0.04 #876, 0.04 #963), 059j2 (0.05 #2025, 0.01 #809, 0.01 #2639), 03rk0 (0.04 #2655, 0.01 #2568, 0.01 #914), 02jx1 (0.04 #1853, 0.02 #2555, 0.01 #901), 0345h (0.03 #2027, 0.02 #2554, 0.01 #1852) >> Best rule #695 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 0l_q9; >> query: (?x1096, 09c7w0) <- time_zones(?x1096, ?x2674), county_seat(?x6136, ?x1096), state(?x1096, ?x335), district_represented(?x355, ?x335) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mc11 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 31.000 0.773 http://example.org/base/biblioness/bibs_location/country #13409-01vxlbm PRED entity: 01vxlbm PRED relation: artists! PRED expected values: 02x8m 0glt670 06j6l => 171 concepts (79 used for prediction) PRED predicted values (max 10 best out of 234): 0glt670 (0.64 #338, 0.61 #638, 0.56 #6038), 06j6l (0.59 #6045, 0.46 #4545, 0.36 #345), 05bt6j (0.32 #6642, 0.29 #18348, 0.29 #4541), 026z9 (0.30 #71, 0.15 #4571, 0.10 #6071), 0155w (0.30 #1598, 0.15 #18405, 0.14 #11199), 02x8m (0.27 #317, 0.22 #1517, 0.21 #6017), 02w4v (0.27 #342, 0.14 #942, 0.12 #6643), 0827d (0.27 #304, 0.11 #604, 0.05 #18611), 01lyv (0.24 #932, 0.18 #11133, 0.17 #15036), 02k_kn (0.24 #959, 0.15 #6059, 0.14 #4559) >> Best rule #338 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 01vs73g; 01lqf49; >> query: (?x3894, 0glt670) <- artists(?x3562, ?x3894), artists(?x2936, ?x3894), award(?x3894, ?x12835), ?x3562 = 025sc50, ?x2936 = 029h7y >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6 EVAL 01vxlbm artists! 06j6l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 79.000 0.636 http://example.org/music/genre/artists EVAL 01vxlbm artists! 0glt670 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 79.000 0.636 http://example.org/music/genre/artists EVAL 01vxlbm artists! 02x8m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 171.000 79.000 0.636 http://example.org/music/genre/artists #13408-0f2wj PRED entity: 0f2wj PRED relation: place_of_birth! PRED expected values: 0dvmd => 107 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2172): 0mdqp (0.35 #210864, 0.33 #239494, 0.33 #236891), 01cwkq (0.35 #210864, 0.33 #239494, 0.33 #236891), 033jkj (0.35 #210864, 0.33 #239494, 0.33 #236891), 01mqc_ (0.35 #210864, 0.33 #239494, 0.33 #236891), 048hf (0.35 #210864, 0.33 #239494, 0.33 #236891), 01cyjx (0.35 #210864, 0.33 #239494, 0.33 #236891), 0h0yt (0.35 #210864, 0.33 #239494, 0.33 #236891), 0g2lq (0.35 #210864, 0.33 #239494, 0.33 #236891), 050zr4 (0.35 #210864, 0.33 #239494, 0.33 #236891), 01rcmg (0.35 #210864, 0.33 #239494, 0.33 #236891) >> Best rule #210864 for best value: >> intensional similarity = 3 >> extensional distance = 335 >> proper extension: 01914; 0f04v; 0x335; 02p3my; >> query: (?x682, ?x794) <- place_of_birth(?x10445, ?x682), location(?x794, ?x682), film(?x10445, ?x4375) >> conf = 0.35 => this is the best rule for 22 predicted values No rule for expected values ranks of expected_values: EVAL 0f2wj place_of_birth! 0dvmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 93.000 0.349 http://example.org/people/person/place_of_birth #13407-0ycp3 PRED entity: 0ycp3 PRED relation: group! PRED expected values: 02y7sr => 83 concepts (43 used for prediction) PRED predicted values (max 10 best out of 167): 0285c (0.11 #28, 0.11 #826, 0.10 #1025), 048tgl (0.11 #2970, 0.08 #2373, 0.07 #3567), 01vrnsk (0.11 #920, 0.03 #3512, 0.03 #2318), 01vrx3g (0.11 #804, 0.02 #4394, 0.01 #3994), 01gx5f (0.10 #261, 0.05 #859, 0.05 #1058), 01w02sy (0.10 #252, 0.05 #850, 0.05 #1049), 04mx7s (0.10 #357, 0.05 #955, 0.05 #1154), 01vs4ff (0.10 #525, 0.05 #923, 0.03 #1721), 01sb5r (0.10 #479, 0.05 #877, 0.03 #2076), 01nn3m (0.10 #597, 0.05 #995, 0.03 #2194) >> Best rule #28 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0fpj4lx; 0p3r8; >> query: (?x6876, 0285c) <- artists(?x3753, ?x6876), ?x3753 = 01_bkd, artist(?x3240, ?x6876), artist(?x3240, ?x4675), ?x4675 = 026spg >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #958 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 0123r4; *> query: (?x6876, 02y7sr) <- group(?x716, ?x6876), group(?x316, ?x6876), ?x716 = 018vs, group(?x2392, ?x6876), ?x316 = 05r5c, artists(?x302, ?x6876) *> conf = 0.05 ranks of expected_values: 47 EVAL 0ycp3 group! 02y7sr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 83.000 43.000 0.111 http://example.org/music/group_member/membership./music/group_membership/group #13406-01n44c PRED entity: 01n44c PRED relation: type_of_union PRED expected values: 04ztj => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.81 #29, 0.78 #289, 0.77 #321), 01g63y (0.41 #325, 0.33 #374, 0.16 #38), 01bl8s (0.41 #325, 0.01 #91), 0jgjn (0.01 #120) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01fl3; 0qf11; 013qvn; 03h_yfh; 0dbb3; >> query: (?x5181, 04ztj) <- artists(?x9427, ?x5181), artist(?x7089, ?x5181), ?x9427 = 0m40d >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n44c type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.810 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13405-0jwmp PRED entity: 0jwmp PRED relation: written_by PRED expected values: 07h07 => 70 concepts (45 used for prediction) PRED predicted values (max 10 best out of 110): 04pqqb (0.14 #5700), 016zp5 (0.08 #6371, 0.06 #13090, 0.06 #14772), 0693l (0.05 #91, 0.04 #427, 0.03 #765), 02vyw (0.05 #104, 0.04 #440, 0.01 #778), 0kb3n (0.05 #256, 0.04 #592, 0.01 #930), 07s93v (0.05 #47, 0.04 #383, 0.01 #1056), 01pjr7 (0.05 #230, 0.04 #566), 037d35 (0.05 #186, 0.04 #522), 04y8r (0.05 #66, 0.04 #402), 06cv1 (0.05 #12, 0.04 #348) >> Best rule #5700 for best value: >> intensional similarity = 3 >> extensional distance = 648 >> proper extension: 04cf_l; >> query: (?x3392, ?x4854) <- currency(?x3392, ?x170), produced_by(?x3392, ?x4854), country(?x3392, ?x512) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jwmp written_by 07h07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 45.000 0.141 http://example.org/film/film/written_by #13404-0mwk9 PRED entity: 0mwk9 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 119 concepts (36 used for prediction) PRED predicted values (max 10 best out of 7): 09c7w0 (0.90 #128, 0.90 #165, 0.88 #90), 05tbn (0.33 #114, 0.31 #438, 0.26 #278), 0mwk9 (0.33 #114, 0.26 #278, 0.26 #177), 0f8l9c (0.02 #285, 0.02 #406, 0.02 #299), 03rt9 (0.02 #388, 0.02 #282, 0.02 #296), 03rjj (0.02 #280, 0.02 #294, 0.02 #307), 02jx1 (0.02 #314, 0.01 #420, 0.01 #448) >> Best rule #128 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 0mw89; 0m7d0; 0mpbx; 0njpq; >> query: (?x12296, 09c7w0) <- contains(?x3670, ?x12296), source(?x12296, ?x958), adjoins(?x10767, ?x12296), county_seat(?x12296, ?x12295) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwk9 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 36.000 0.901 http://example.org/location/country/second_level_divisions #13403-099jhq PRED entity: 099jhq PRED relation: award! PRED expected values: 017149 01wmxfs 013cr 03n_7k 0169dl 04wp3s => 44 concepts (7 used for prediction) PRED predicted values (max 10 best out of 1989): 0jmj (0.64 #7881, 0.43 #11217, 0.40 #1207), 03ym1 (0.60 #1646, 0.57 #11656, 0.45 #8320), 01f7dd (0.60 #1969, 0.56 #5306, 0.27 #8643), 048lv (0.60 #328, 0.55 #7002, 0.44 #3665), 017149 (0.60 #107, 0.55 #6781, 0.44 #3444), 02ldv0 (0.60 #1862, 0.45 #8536, 0.33 #5199), 015c4g (0.60 #1239, 0.44 #4576, 0.36 #7913), 02s2ft (0.60 #8, 0.44 #3345, 0.36 #6682), 0187y5 (0.60 #144, 0.44 #3481, 0.27 #6818), 0flw6 (0.60 #1196, 0.36 #11206, 0.33 #4533) >> Best rule #7881 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 08_vwq; >> query: (?x451, 0jmj) <- award(?x10866, ?x451), award(?x2033, ?x451), award_nominee(?x10866, ?x262), ?x2033 = 01ycbq >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #107 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 09sb52; 0gqy2; 09sdmz; *> query: (?x451, 017149) <- award(?x2189, ?x451), nominated_for(?x451, ?x186), ?x2189 = 02yvct, award(?x450, ?x451), ?x450 = 0z4s *> conf = 0.60 ranks of expected_values: 5, 35, 42, 49, 119, 555 EVAL 099jhq award! 04wp3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 7.000 0.636 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099jhq award! 0169dl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 44.000 7.000 0.636 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099jhq award! 03n_7k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 44.000 7.000 0.636 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099jhq award! 013cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 44.000 7.000 0.636 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099jhq award! 01wmxfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 44.000 7.000 0.636 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099jhq award! 017149 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 44.000 7.000 0.636 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13402-06ybb1 PRED entity: 06ybb1 PRED relation: honored_for PRED expected values: 07b1gq => 123 concepts (76 used for prediction) PRED predicted values (max 10 best out of 157): 059lwy (0.85 #4948, 0.85 #4483, 0.85 #4638), 0140g4 (0.85 #4948, 0.85 #4483, 0.85 #4638), 07b1gq (0.62 #374, 0.62 #220, 0.58 #4947), 06ybb1 (0.58 #4947, 0.57 #5573, 0.57 #5572), 08984j (0.41 #1698, 0.16 #6809, 0.14 #7118), 0cf08 (0.41 #1698, 0.14 #7118, 0.03 #7583), 0bxxzb (0.08 #581, 0.06 #1198, 0.06 #1353), 05pxnmb (0.08 #595, 0.06 #1830, 0.05 #2139), 07gghl (0.08 #580, 0.04 #1197, 0.04 #1352), 0ddt_ (0.06 #1759, 0.05 #2068, 0.05 #832) >> Best rule #4948 for best value: >> intensional similarity = 4 >> extensional distance = 144 >> proper extension: 0d_wms; 02vrgnr; 0bbw2z6; 042fgh; 025twgf; >> query: (?x2165, ?x188) <- honored_for(?x2165, ?x1311), honored_for(?x188, ?x2165), film(?x4667, ?x1311), honored_for(?x1311, ?x3640) >> conf = 0.85 => this is the best rule for 2 predicted values *> Best rule #374 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 01771z; 02ny6g; 07b1gq; 074rg9; 037xlx; 0cwfgz; 059lwy; 06c0ns; *> query: (?x2165, 07b1gq) <- honored_for(?x2165, ?x5441), honored_for(?x188, ?x2165), nominated_for(?x102, ?x2165), ?x5441 = 04cbbz *> conf = 0.62 ranks of expected_values: 3 EVAL 06ybb1 honored_for 07b1gq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 76.000 0.852 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #13401-058dm9 PRED entity: 058dm9 PRED relation: position PRED expected values: 02_j1w => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 5): 02nzb8 (0.86 #165, 0.82 #142, 0.82 #135), 02_j1w (0.82 #126, 0.81 #177, 0.81 #176), 03f0fp (0.53 #170, 0.43 #198, 0.31 #223), 02md_2 (0.53 #170, 0.43 #198, 0.31 #223), 02qvgy (0.43 #198) >> Best rule #165 for best value: >> intensional similarity = 27 >> extensional distance = 692 >> proper extension: 02b1zs; >> query: (?x8954, 02nzb8) <- position(?x8954, ?x203), position(?x13580, ?x203), position(?x12905, ?x203), position(?x11379, ?x203), position(?x10788, ?x203), position(?x10557, ?x203), position(?x10066, ?x203), position(?x7674, ?x203), position(?x6526, ?x203), position(?x5344, ?x203), position(?x5292, ?x203), position(?x4972, ?x203), ?x11379 = 046vvc, ?x4972 = 03d8m4, ?x12905 = 0230rx, position(?x8826, ?x203), position(?x993, ?x203), ?x993 = 03mqj_, ?x7674 = 01jdxj, ?x5292 = 04zw9hs, ?x10066 = 02rjz5, ?x10557 = 01l0__, ?x6526 = 03c0t9, team(?x60, ?x13580), ?x5344 = 07sqm1, ?x10788 = 01l3wr, ?x8826 = 03x6w8 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #126 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 315 *> proper extension: 0cnk2q; 0c9cp0; 093g7v; 03j79x; 0dwz3t; 04knvh; 02b0zt; 03ytp3; 051ghn; 0520y3; ... *> query: (?x8954, ?x530) <- team(?x203, ?x8954), team(?x63, ?x8954), team(?x60, ?x8954), ?x63 = 02sdk9v, ?x60 = 02nzb8, position(?x8954, ?x530), ?x203 = 0dgrmp, position(?x13259, ?x530), position(?x9092, ?x530), position(?x8953, ?x530), position(?x8698, ?x530), position(?x6524, ?x530), position(?x6179, ?x530), position(?x5082, ?x530), ?x8698 = 02b14q, ?x9092 = 02b18l, position(?x12748, ?x530), position(?x12706, ?x530), position(?x5403, ?x530), ?x12706 = 03j0ss, ?x8953 = 046k81, ?x6524 = 01cwq9, ?x5082 = 0586wl, ?x12748 = 05hz6_, ?x13259 = 04mrjs, ?x6179 = 0cgwt8, ?x5403 = 02b0y3 *> conf = 0.82 ranks of expected_values: 2 EVAL 058dm9 position 02_j1w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 32.000 32.000 0.865 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #13400-0fsv2 PRED entity: 0fsv2 PRED relation: featured_film_locations! PRED expected values: 0jqp3 0h1x5f => 198 concepts (42 used for prediction) PRED predicted values (max 10 best out of 725): 0btbyn (0.22 #284, 0.11 #1018, 0.06 #5422), 0192hw (0.19 #9777, 0.11 #233, 0.08 #1701), 09fc83 (0.17 #6988, 0.15 #1849, 0.15 #2583), 04dsnp (0.15 #21354, 0.14 #6673, 0.13 #4470), 02rx2m5 (0.14 #3065, 0.11 #129, 0.06 #21417), 02fqxm (0.12 #5869, 0.07 #10275, 0.07 #4401), 01lsl (0.11 #7238, 0.10 #5035, 0.10 #4301), 0473rc (0.11 #7061, 0.10 #4858, 0.10 #8529), 03s6l2 (0.11 #39, 0.07 #2975, 0.06 #5177), 0jqp3 (0.11 #70, 0.06 #5208, 0.06 #804) >> Best rule #284 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 07vyf; >> query: (?x13739, 0btbyn) <- category(?x13739, ?x134), ?x134 = 08mbj5d, contains(?x938, ?x13739), ?x938 = 0vmt >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #70 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 07vyf; *> query: (?x13739, 0jqp3) <- category(?x13739, ?x134), ?x134 = 08mbj5d, contains(?x938, ?x13739), ?x938 = 0vmt *> conf = 0.11 ranks of expected_values: 10 EVAL 0fsv2 featured_film_locations! 0h1x5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 198.000 42.000 0.222 http://example.org/film/film/featured_film_locations EVAL 0fsv2 featured_film_locations! 0jqp3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 198.000 42.000 0.222 http://example.org/film/film/featured_film_locations #13399-071jv5 PRED entity: 071jv5 PRED relation: influenced_by! PRED expected values: 0dh73w => 120 concepts (59 used for prediction) PRED predicted values (max 10 best out of 95): 045bg (0.09 #2618), 02wh0 (0.08 #3034, 0.02 #2518), 07h1q (0.08 #2992), 0ct9_ (0.08 #2927), 0399p (0.08 #2912), 0dzkq (0.08 #2708), 047g6 (0.06 #3064), 03cdg (0.06 #3050), 07dnx (0.06 #2945), 0nk72 (0.06 #2924) >> Best rule #2618 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 06y9c2; 099bk; 07_m9_; 09l9xt; 02lmk; 03sbs; 02ln1; 01kx1j; 039n1; 06vnh2; ... >> query: (?x12186, 045bg) <- gender(?x12186, ?x231), nationality(?x12186, ?x1264), ?x1264 = 0345h >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 071jv5 influenced_by! 0dh73w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 59.000 0.091 http://example.org/influence/influence_node/influenced_by #13398-06wm0z PRED entity: 06wm0z PRED relation: film PRED expected values: 047bynf => 113 concepts (93 used for prediction) PRED predicted values (max 10 best out of 968): 03q0r1 (0.29 #637, 0.02 #14941, 0.01 #63217), 01hq1 (0.29 #1371, 0.02 #15675, 0.01 #31767), 06_wqk4 (0.14 #126, 0.07 #10854, 0.06 #3702), 01flv_ (0.14 #1066, 0.05 #73309, 0.04 #15370), 01qvz8 (0.14 #806, 0.05 #73309, 0.03 #160926), 0f4_l (0.14 #349, 0.05 #73309, 0.03 #160926), 0jqj5 (0.14 #886, 0.05 #73309, 0.03 #160926), 0fg04 (0.14 #101, 0.05 #73309, 0.03 #160926), 0p_tz (0.14 #1192, 0.05 #73309, 0.03 #160926), 03l6q0 (0.14 #543, 0.03 #5907, 0.03 #20211) >> Best rule #637 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01j5ws; 016fjj; 03yrkt; 07ddz9; >> query: (?x5058, 03q0r1) <- award_nominee(?x5058, ?x6278), profession(?x5058, ?x1032), ?x6278 = 0gx_p >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06wm0z film 047bynf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 93.000 0.286 http://example.org/film/actor/film./film/performance/film #13397-01b8w_ PRED entity: 01b8w_ PRED relation: location! PRED expected values: 0c9c0 => 119 concepts (50 used for prediction) PRED predicted values (max 10 best out of 2118): 071ywj (0.51 #75418, 0.51 #95537, 0.47 #93021), 0cj2w (0.48 #110631, 0.47 #93021, 0.47 #72901), 01ww_vs (0.48 #110631, 0.47 #93021, 0.47 #72901), 0151ns (0.20 #84, 0.09 #5114, 0.06 #17681), 01vsy3q (0.20 #990, 0.08 #13560, 0.07 #18587), 01cyjx (0.20 #1378, 0.08 #13948, 0.07 #18975), 07r4c (0.20 #1258, 0.06 #6288, 0.06 #13828), 03hh89 (0.20 #1111, 0.06 #6141, 0.06 #13681), 01797x (0.20 #2089, 0.06 #7119, 0.06 #14659), 03d_w3h (0.20 #150, 0.06 #5180, 0.06 #12720) >> Best rule #75418 for best value: >> intensional similarity = 5 >> extensional distance = 118 >> proper extension: 0281rb; 0fdpd; >> query: (?x9042, ?x4713) <- place_of_birth(?x6122, ?x9042), place_of_birth(?x4713, ?x9042), participant(?x2647, ?x6122), award_winner(?x3567, ?x6122), film(?x4713, ?x4159) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #5560 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 32 *> proper extension: 035dk; 088q4; 04v09; *> query: (?x9042, 0c9c0) <- featured_film_locations(?x1015, ?x9042), person(?x1015, ?x1620) *> conf = 0.06 ranks of expected_values: 239 EVAL 01b8w_ location! 0c9c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 119.000 50.000 0.509 http://example.org/people/person/places_lived./people/place_lived/location #13396-04cf_l PRED entity: 04cf_l PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #31, 0.83 #87, 0.82 #36), 07c52 (0.21 #339, 0.06 #28, 0.05 #18), 02nxhr (0.21 #339, 0.06 #78, 0.06 #68), 07z4p (0.21 #339, 0.03 #45, 0.02 #277) >> Best rule #31 for best value: >> intensional similarity = 5 >> extensional distance = 74 >> proper extension: 034qmv; 06w99h3; 01k1k4; 03s6l2; 06z8s_; 02qm_f; 033g4d; 01kff7; 02pjc1h; 0bq8tmw; ... >> query: (?x8608, 029j_) <- nominated_for(?x902, ?x8608), genre(?x8608, ?x258), genre(?x8608, ?x225), ?x225 = 02kdv5l, ?x258 = 05p553 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cf_l film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #13395-04hzj PRED entity: 04hzj PRED relation: organization PRED expected values: 04k4l => 128 concepts (120 used for prediction) PRED predicted values (max 10 best out of 48): 034h1h (0.81 #123, 0.73 #161, 0.21 #1330), 0j7v_ (0.56 #1167, 0.56 #670, 0.56 #459), 085h1 (0.56 #1167, 0.56 #670, 0.56 #459), 0_2v (0.45 #213, 0.33 #576, 0.32 #385), 01rz1 (0.43 #345, 0.41 #402, 0.36 #211), 04k4l (0.36 #386, 0.33 #539, 0.32 #253), 018cqq (0.33 #28, 0.33 #219, 0.30 #66), 02jxk (0.22 #21, 0.20 #212, 0.19 #613), 059dn (0.22 #32, 0.17 #51, 0.15 #70), 03mbdx_ (0.10 #172, 0.01 #1535, 0.01 #1575) >> Best rule #123 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 08815; 01j_9c; 065y4w7; 07w0v; 04rwx; 07szy; 09kvv; 0bx8pn; 03rj0; 07wrz; ... >> query: (?x7037, 034h1h) <- contains(?x2467, ?x7037), organization(?x7037, ?x127), company(?x346, ?x7037) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #386 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 0bq0p9; 0c4b8; *> query: (?x7037, 04k4l) <- capital(?x7037, ?x14212), organization(?x7037, ?x127), form_of_government(?x7037, ?x48) *> conf = 0.36 ranks of expected_values: 6 EVAL 04hzj organization 04k4l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 128.000 120.000 0.806 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #13394-09s5q8 PRED entity: 09s5q8 PRED relation: student PRED expected values: 0g2mbn => 138 concepts (59 used for prediction) PRED predicted values (max 10 best out of 964): 03ywyk (0.20 #1589, 0.09 #3682, 0.07 #5775), 0hwbd (0.10 #1022, 0.09 #3115, 0.07 #5208), 03qcq (0.10 #9, 0.04 #2102, 0.04 #4195), 0ckm4x (0.10 #2001, 0.04 #4094, 0.04 #6187), 030b93 (0.10 #1235, 0.04 #3328, 0.04 #5421), 03n69x (0.10 #939, 0.04 #3032, 0.04 #5125), 03m_k0 (0.10 #488, 0.04 #2581, 0.04 #4674), 0187y5 (0.10 #93, 0.04 #2186, 0.04 #4279), 03ft8 (0.10 #257, 0.04 #8630, 0.04 #12816), 01w7nwm (0.10 #506, 0.04 #8879, 0.03 #15158) >> Best rule #1589 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01jygk; >> query: (?x6083, 03ywyk) <- state_province_region(?x6083, ?x2623), ?x2623 = 02xry, category(?x6083, ?x134), ?x134 = 08mbj5d >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #18839 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 01pl14; 01wdl3; 01t8sr; 078bz; 02183k; 0pspl; 025v3k; 01vs5c; 021w0_; 01jpqb; ... *> query: (?x6083, ?x5153) <- school(?x1883, ?x6083), school(?x387, ?x6083), citytown(?x6083, ?x6084), location(?x5153, ?x6084) *> conf = 0.05 ranks of expected_values: 32 EVAL 09s5q8 student 0g2mbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 138.000 59.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #13393-03_8r PRED entity: 03_8r PRED relation: country PRED expected values: 0jgd 0d060g 0j1z8 05v8c 06mzp 019rg5 0ctw_b 04w58 077qn 0d05q4 02k8k 02k1b 04sj3 034m8 01n8qg => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 274): 0d060g (0.88 #2605, 0.88 #2514, 0.88 #2329), 0jgd (0.87 #1768, 0.83 #1953, 0.80 #185), 06mzp (0.85 #184, 0.80 #185, 0.67 #1402), 09lxtg (0.85 #184, 0.80 #185, 0.60 #878), 0ctw_b (0.80 #185, 0.78 #1405, 0.69 #834), 05b4w (0.80 #185, 0.71 #1234, 0.67 #2228), 0k6nt (0.80 #185, 0.71 #1221, 0.67 #1033), 0j1z8 (0.80 #185, 0.69 #834, 0.65 #371), 05cgv (0.80 #185, 0.67 #1135, 0.65 #371), 019rg5 (0.80 #185, 0.67 #1129, 0.62 #1312) >> Best rule #2605 for best value: >> intensional similarity = 45 >> extensional distance = 50 >> proper extension: 018jz; >> query: (?x2978, 0d060g) <- country(?x2978, ?x3951), country(?x2978, ?x3749), country(?x2978, ?x3720), country(?x2978, ?x2267), country(?x2978, ?x1790), country(?x2978, ?x1577), country(?x2978, ?x1499), film_release_region(?x2155, ?x3749), film_release_region(?x1035, ?x3749), film_release_region(?x249, ?x3749), form_of_government(?x1577, ?x48), religion(?x3749, ?x492), olympics(?x3749, ?x778), form_of_government(?x3749, ?x4763), film_release_region(?x7678, ?x1499), film_release_region(?x7629, ?x1499), film_release_region(?x6235, ?x1499), film_release_region(?x4422, ?x1499), film_release_region(?x3498, ?x1499), film_release_region(?x2655, ?x1499), film_release_region(?x1803, ?x1499), olympics(?x1499, ?x3110), sports(?x391, ?x2978), olympics(?x1499, ?x2134), contains(?x6304, ?x3951), currency(?x1577, ?x170), ?x7678 = 0gvvf4j, ?x2655 = 0fpmrm3, ?x3498 = 02fqrf, ?x2134 = 0blg2, countries_within(?x455, ?x2267), organization(?x2267, ?x312), ?x4422 = 06zn2v2, ?x249 = 0c3ybss, ?x2155 = 0407yfx, ?x1803 = 0g9wdmc, olympics(?x1790, ?x418), teams(?x3720, ?x9799), ?x7629 = 02825nf, ?x1035 = 08hmch, countries_spoken_in(?x254, ?x3951), film_release_region(?x66, ?x2267), countries_within(?x2467, ?x1577), adjoins(?x1790, ?x4059), ?x6235 = 05b6rdt >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 8, 10, 19, 31, 39, 42, 43, 44, 45, 46, 53 EVAL 03_8r country 01n8qg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 034m8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 04sj3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 02k1b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 02k8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 0d05q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 04w58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 0ctw_b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 019rg5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 06mzp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03_8r country 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #13392-05y0cr PRED entity: 05y0cr PRED relation: film_crew_role PRED expected values: 09zzb8 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 31): 09zzb8 (0.64 #157, 0.63 #274, 0.62 #549), 0ch6mp2 (0.63 #752, 0.61 #557, 0.60 #401), 09vw2b7 (0.55 #1261, 0.53 #1144, 0.51 #556), 02r96rf (0.54 #277, 0.54 #1257, 0.52 #1140), 01pvkk (0.50 #54, 0.36 #210, 0.24 #1268), 0dxtw (0.30 #1266, 0.28 #717, 0.28 #2172), 01vx2h (0.26 #1267, 0.25 #53, 0.25 #2173), 02ynfr (0.22 #292, 0.18 #136, 0.18 #567), 04pyp5 (0.21 #176, 0.10 #98, 0.10 #293), 0215hd (0.20 #100, 0.14 #2041, 0.14 #375) >> Best rule #157 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 09rfpk; >> query: (?x9279, 09zzb8) <- genre(?x9279, ?x4088), genre(?x9279, ?x1403), titles(?x789, ?x9279), ?x4088 = 04xvh5, ?x1403 = 02l7c8, film_release_region(?x66, ?x789) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05y0cr film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.643 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13391-0dth6b PRED entity: 0dth6b PRED relation: award_winner PRED expected values: 0gl88b 0p50v => 30 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1947): 01wd9vs (0.50 #8770, 0.50 #5685, 0.37 #12332), 052hl (0.50 #7179, 0.40 #11807, 0.25 #8721), 05dppk (0.50 #4982, 0.25 #8067, 0.05 #23129), 02fn5r (0.40 #11170, 0.26 #21963, 0.25 #6542), 0dw4g (0.40 #11656, 0.25 #7028, 0.23 #17822), 0hl3d (0.40 #10823, 0.25 #6195, 0.15 #21616), 014z8v (0.40 #11417, 0.25 #6789, 0.14 #1541), 03cfjg (0.40 #11304, 0.25 #6676, 0.14 #17470), 0gbwp (0.40 #11400, 0.25 #6772, 0.11 #22193), 01mxt_ (0.40 #11664, 0.25 #7036, 0.09 #17830) >> Best rule #8770 for best value: >> intensional similarity = 19 >> extensional distance = 2 >> proper extension: 0bzk8w; 0bzkvd; >> query: (?x1793, 01wd9vs) <- award_winner(?x1793, ?x8473), award_winner(?x1793, ?x6518), award_winner(?x1793, ?x2800), ceremony(?x3066, ?x1793), ceremony(?x720, ?x1793), award_nominee(?x5720, ?x6518), award_nominee(?x647, ?x6518), award(?x6518, ?x1323), place_of_birth(?x8473, ?x739), people(?x1050, ?x8473), place_of_birth(?x6518, ?x362), award(?x647, ?x384), ?x5720 = 01l1rw, ?x1050 = 041rx, ?x720 = 018wng, ?x3066 = 0gqy2, award_winner(?x2112, ?x2800), profession(?x647, ?x987), award(?x8473, ?x458) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9539 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 3 *> proper extension: 02yv_b; 0fk0xk; 0c4hnm; *> query: (?x1793, 0gl88b) <- award_winner(?x1793, ?x8473), award_winner(?x1793, ?x6518), award_winner(?x1793, ?x2800), ceremony(?x6860, ?x1793), award_nominee(?x6519, ?x6518), award_nominee(?x647, ?x6518), award(?x6518, ?x1323), place_of_birth(?x8473, ?x739), people(?x1050, ?x8473), place_of_birth(?x6518, ?x362), award(?x647, ?x384), type_of_union(?x8473, ?x566), music(?x7750, ?x6519), people(?x4322, ?x6519), film(?x2800, ?x2112), ?x6860 = 018wdw, award_nominee(?x4323, ?x6519) *> conf = 0.20 ranks of expected_values: 148, 178 EVAL 0dth6b award_winner 0p50v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 30.000 24.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0dth6b award_winner 0gl88b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 30.000 24.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13390-07nt8p PRED entity: 07nt8p PRED relation: film! PRED expected values: 0zcbl => 91 concepts (64 used for prediction) PRED predicted values (max 10 best out of 949): 06449 (0.55 #97840, 0.45 #37475, 0.39 #20817), 0f0kz (0.18 #4680, 0.07 #19250, 0.06 #15088), 0p8r1 (0.15 #4750, 0.05 #19320, 0.04 #23484), 026gb3v (0.12 #93677), 03ym1 (0.12 #5178, 0.05 #19748, 0.04 #9342), 01vy_v8 (0.12 #4898, 0.04 #733, 0.03 #23632), 0f6_x (0.11 #626, 0.06 #4791, 0.02 #15199), 05k2s_ (0.10 #91595, 0.10 #89513, 0.10 #87431), 09l3p (0.09 #2832, 0.06 #4914, 0.04 #749), 015pkc (0.09 #4443, 0.04 #8607, 0.03 #14851) >> Best rule #97840 for best value: >> intensional similarity = 4 >> extensional distance = 571 >> proper extension: 01f3p_; 03g9xj; >> query: (?x2211, ?x2940) <- nominated_for(?x2940, ?x2211), titles(?x53, ?x2211), languages(?x2940, ?x254), profession(?x2940, ?x1183) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #13713 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 136 *> proper extension: 013q0p; 016z43; *> query: (?x2211, 0zcbl) <- genre(?x2211, ?x53), currency(?x2211, ?x170), award(?x2211, ?x3889), film_format(?x2211, ?x909) *> conf = 0.01 ranks of expected_values: 584 EVAL 07nt8p film! 0zcbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 91.000 64.000 0.553 http://example.org/film/actor/film./film/performance/film #13389-02cx90 PRED entity: 02cx90 PRED relation: award_winner! PRED expected values: 02ddq4 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 234): 03qbh5 (0.40 #5954, 0.39 #21687, 0.39 #22113), 054ks3 (0.40 #5954, 0.39 #21687, 0.39 #22113), 01c9f2 (0.40 #5954, 0.39 #21687, 0.39 #22113), 03qbnj (0.17 #1503, 0.15 #36576, 0.15 #35300), 01ckrr (0.15 #36576, 0.15 #35300, 0.14 #650), 0c4z8 (0.15 #36576, 0.15 #35300, 0.13 #1345), 01c92g (0.15 #36576, 0.15 #35300, 0.11 #2222), 0gqz2 (0.15 #36576, 0.15 #35300, 0.11 #1354), 025m98 (0.15 #36576, 0.15 #35300, 0.10 #231), 02581c (0.15 #36576, 0.15 #35300, 0.10 #120) >> Best rule #5954 for best value: >> intensional similarity = 3 >> extensional distance = 246 >> proper extension: 014v1q; >> query: (?x4343, ?x159) <- award(?x4343, ?x159), award_winner(?x139, ?x4343), instrumentalists(?x75, ?x4343) >> conf = 0.40 => this is the best rule for 3 predicted values *> Best rule #36576 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1531 *> proper extension: 0f6lx; *> query: (?x4343, ?x2420) <- award_winner(?x7258, ?x4343), award_winner(?x2420, ?x7258) *> conf = 0.15 ranks of expected_values: 23 EVAL 02cx90 award_winner! 02ddq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 123.000 123.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #13388-0j46b PRED entity: 0j46b PRED relation: team! PRED expected values: 071pf2 => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 132): 071pf2 (0.89 #12209, 0.88 #10897, 0.88 #12208), 04bsx1 (0.60 #1521, 0.33 #2950, 0.30 #3170), 0879xc (0.50 #481, 0.33 #926, 0.33 #370), 080dyk (0.35 #3101, 0.33 #229, 0.19 #2986), 0135nb (0.34 #3665, 0.20 #1468, 0.19 #2986), 05_6_y (0.33 #891, 0.33 #335, 0.33 #224), 0gtgp6 (0.33 #276, 0.31 #2598, 0.31 #1937), 054kmq (0.33 #2971, 0.31 #2530, 0.26 #3191), 09m465 (0.33 #400, 0.25 #1178, 0.21 #4477), 0c2rr7 (0.33 #164, 0.25 #721, 0.20 #1388) >> Best rule #12209 for best value: >> intensional similarity = 9 >> extensional distance = 176 >> proper extension: 02b15h; 01bdxz; 01kckd; 02b1mc; 04jbyg; 0266shh; 02q1hz; 05cws2; 07sqm1; 02b0y3; ... >> query: (?x10847, ?x7669) <- team(?x7669, ?x10847), team(?x60, ?x10847), ?x60 = 02nzb8, gender(?x7669, ?x231), team(?x11781, ?x10847), team(?x7669, ?x8678), teams(?x9969, ?x8678), position(?x10847, ?x203), team(?x3586, ?x8678) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j46b team! 071pf2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.889 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #13387-039cq4 PRED entity: 039cq4 PRED relation: titles PRED expected values: 039cq4 => 84 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1554): 041td_ (0.40 #4059, 0.08 #19683, 0.06 #32181), 0bz6sq (0.40 #4422, 0.05 #20046, 0.04 #32544), 0d87hc (0.40 #4541, 0.04 #20165, 0.03 #32663), 02ht1k (0.40 #3658, 0.04 #19282, 0.03 #31780), 04tz52 (0.40 #3519, 0.04 #19143, 0.03 #31641), 01sxly (0.40 #3193, 0.04 #18817, 0.03 #31315), 09cxm4 (0.40 #4345, 0.03 #19969, 0.02 #32467), 0g7pm1 (0.40 #4149, 0.03 #19773, 0.02 #32271), 02825cv (0.40 #4093, 0.03 #19717, 0.02 #32215), 0bt3j9 (0.40 #3888, 0.03 #19512, 0.02 #32010) >> Best rule #4059 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 04t36; 01z4y; 06qm3; >> query: (?x6884, 041td_) <- titles(?x6884, ?x1743), ?x1743 = 0c8tkt >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #32276 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 94 *> proper extension: 02bjrlw; 0jgd; 03rjj; 015fr; 06qd3; 07c52; 0653m; 012w70; 03spz; 07c9s; ... *> query: (?x6884, 039cq4) <- titles(?x6884, ?x1743), genre(?x1743, ?x225) *> conf = 0.01 ranks of expected_values: 1500 EVAL 039cq4 titles 039cq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 80.000 0.400 http://example.org/media_common/netflix_genre/titles #13386-07sbk PRED entity: 07sbk PRED relation: artists! PRED expected values: 03mb9 => 86 concepts (37 used for prediction) PRED predicted values (max 10 best out of 235): 064t9 (0.80 #1235, 0.62 #624, 0.50 #3679), 016clz (0.75 #616, 0.62 #311, 0.33 #921), 06by7 (0.75 #633, 0.59 #1549, 0.48 #2159), 0y3_8 (0.62 #354, 0.38 #659, 0.33 #964), 0ggx5q (0.55 #1300, 0.38 #384, 0.24 #1910), 02lnbg (0.55 #1280, 0.38 #669, 0.24 #1890), 025sc50 (0.50 #1273, 0.32 #1883, 0.25 #662), 012yc (0.50 #757, 0.25 #452, 0.15 #1368), 0xhtw (0.49 #1544, 0.25 #2154, 0.20 #17), 06j6l (0.40 #1271, 0.29 #5241, 0.27 #4936) >> Best rule #1235 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 05vzw3; >> query: (?x8332, 064t9) <- award(?x8332, ?x528), artists(?x474, ?x8332), award_nominee(?x8332, ?x8874), ?x528 = 02g3gj >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 01k3qj; *> query: (?x8332, 03mb9) <- artists(?x5909, ?x8332), artists(?x3915, ?x8332), ?x5909 = 041738, artists(?x3915, ?x475), ?x475 = 01pfr3 *> conf = 0.38 ranks of expected_values: 14 EVAL 07sbk artists! 03mb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 86.000 37.000 0.800 http://example.org/music/genre/artists #13385-0291ck PRED entity: 0291ck PRED relation: film! PRED expected values: 02mhfy => 56 concepts (45 used for prediction) PRED predicted values (max 10 best out of 676): 052hl (0.72 #70747, 0.72 #41620, 0.59 #76989), 02ch1w (0.72 #70747, 0.72 #41620, 0.59 #76989), 09dvgb8 (0.45 #70746, 0.44 #29137, 0.44 #41619), 04_by (0.09 #24975), 0c6qh (0.07 #14984, 0.02 #39952, 0.02 #27470), 0169dl (0.07 #14971, 0.02 #17053, 0.02 #29538), 015c4g (0.07 #4941, 0.05 #7022, 0.05 #11184), 0c0k1 (0.07 #5670, 0.04 #7751, 0.04 #3589), 0mdqp (0.07 #14689, 0.02 #27175, 0.02 #47981), 03n6r (0.06 #3030, 0.05 #11354, 0.04 #9273) >> Best rule #70747 for best value: >> intensional similarity = 3 >> extensional distance = 1229 >> proper extension: 01h72l; >> query: (?x9484, ?x2794) <- nominated_for(?x2794, ?x9484), film(?x2794, ?x1210), genre(?x9484, ?x258) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #4492 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 0jzw; 0bykpk; 0pd64; 0bmhn; *> query: (?x9484, 02mhfy) <- nominated_for(?x2794, ?x9484), list(?x9484, ?x3004), currency(?x9484, ?x170) *> conf = 0.03 ranks of expected_values: 79 EVAL 0291ck film! 02mhfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 56.000 45.000 0.722 http://example.org/film/actor/film./film/performance/film #13384-09b0xs PRED entity: 09b0xs PRED relation: location PRED expected values: 0h6l4 => 107 concepts (105 used for prediction) PRED predicted values (max 10 best out of 67): 02_286 (0.12 #3253, 0.12 #841, 0.11 #1645), 030qb3t (0.10 #29835, 0.10 #25010, 0.10 #22598), 0cr3d (0.06 #16227, 0.04 #75741, 0.04 #21052), 04jpl (0.05 #11275, 0.03 #35398, 0.03 #56307), 0cc56 (0.05 #2469, 0.04 #9707, 0.04 #3273), 01cx_ (0.03 #967, 0.03 #3379, 0.03 #1771), 027l4q (0.03 #1302, 0.03 #2106, 0.03 #2910), 0dclg (0.03 #1725, 0.02 #921, 0.02 #2529), 01531 (0.03 #17044, 0.03 #15436, 0.02 #17848), 059rby (0.03 #46657, 0.03 #52286, 0.02 #54698) >> Best rule #3253 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 0grwj; 01xdf5; 05ty4m; 0c4f4; 0bxtg; 02lf0c; 02773nt; 02q_cc; 02ndbd; 04wvhz; ... >> query: (?x3145, 02_286) <- award_nominee(?x129, ?x3145), program(?x3145, ?x3144), type_of_union(?x3145, ?x566) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09b0xs location 0h6l4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 105.000 0.123 http://example.org/people/person/places_lived./people/place_lived/location #13383-0bdw6t PRED entity: 0bdw6t PRED relation: nominated_for PRED expected values: 080dwhx 01b_lz 017f3m 0828jw => 50 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1441): 0kfv9 (0.86 #6295, 0.78 #3146, 0.78 #4979), 01fx1l (0.86 #6295, 0.78 #3146, 0.75 #4720), 05lfwd (0.78 #5612, 0.50 #2465, 0.31 #8762), 017f3m (0.78 #5476, 0.50 #2329, 0.31 #8626), 02rcwq0 (0.56 #5498, 0.38 #8648, 0.16 #10220), 0828jw (0.56 #5613, 0.25 #2466, 0.19 #8763), 05hjnw (0.50 #3906, 0.22 #7055, 0.20 #13350), 02c638 (0.50 #3451, 0.22 #6600, 0.17 #12895), 0h95927 (0.50 #4299, 0.22 #7448, 0.15 #13743), 0b6tzs (0.50 #3274, 0.16 #12718, 0.13 #20587) >> Best rule #6295 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0fbvqf; 0bdx29; 0gkts9; 0fbtbt; 0gkr9q; >> query: (?x2071, ?x782) <- award(?x10701, ?x2071), award(?x7116, ?x2071), award(?x782, ?x2071), ?x7116 = 015ppk, gender(?x10701, ?x231) >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #5476 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0fbvqf; 0bdx29; 0gkts9; 0fbtbt; 0gkr9q; *> query: (?x2071, 017f3m) <- award(?x10701, ?x2071), award(?x7116, ?x2071), ?x7116 = 015ppk, gender(?x10701, ?x231) *> conf = 0.78 ranks of expected_values: 4, 6, 36, 333 EVAL 0bdw6t nominated_for 0828jw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 50.000 20.000 0.864 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bdw6t nominated_for 017f3m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 50.000 20.000 0.864 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bdw6t nominated_for 01b_lz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 50.000 20.000 0.864 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bdw6t nominated_for 080dwhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 50.000 20.000 0.864 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13382-0byh8j PRED entity: 0byh8j PRED relation: location! PRED expected values: 0brddh => 140 concepts (12 used for prediction) PRED predicted values (max 10 best out of 586): 02fbpz (0.50 #4337, 0.33 #1822, 0.03 #26974), 048svj (0.33 #2515, 0.25 #5030, 0.02 #27667), 02hkvw (0.33 #2493, 0.25 #5008, 0.02 #27645), 0fy2s1 (0.33 #2212, 0.25 #4727, 0.02 #27364), 03fwln (0.12 #12221, 0.10 #17251, 0.09 #9705), 02xgdv (0.12 #13991, 0.11 #6445, 0.09 #8960), 084z0w (0.12 #13520, 0.11 #5974, 0.09 #8489), 02xfrd (0.12 #13437, 0.11 #5891, 0.09 #8406), 0dfjb8 (0.12 #13618, 0.11 #6072, 0.09 #8587), 0tj9 (0.12 #15016, 0.09 #9985, 0.06 #12501) >> Best rule #4337 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0fl2s; >> query: (?x7297, 02fbpz) <- location(?x13550, ?x7297), profession(?x13550, ?x319), location(?x13550, ?x13551), ?x13551 = 0fk98 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #10061 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01c1nm; *> query: (?x7297, ?x6249) <- contains(?x2146, ?x7297), ?x2146 = 03rk0, capital(?x7297, ?x6250), place_of_birth(?x6249, ?x6250) *> conf = 0.05 ranks of expected_values: 65 EVAL 0byh8j location! 0brddh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 140.000 12.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #13381-01shy7 PRED entity: 01shy7 PRED relation: film! PRED expected values: 070yzk => 64 concepts (30 used for prediction) PRED predicted values (max 10 best out of 969): 016ypb (0.29 #4598, 0.05 #6651, 0.04 #10758), 01l_yg (0.25 #1633), 0hz_1 (0.25 #1468), 01swck (0.20 #4892, 0.10 #2839, 0.02 #27481), 02rf1y (0.20 #2995), 057_yx (0.14 #5919, 0.02 #7972, 0.01 #12079), 01y665 (0.12 #512, 0.10 #2565, 0.02 #8724), 02kxwk (0.12 #750, 0.07 #39021, 0.06 #43129), 015t56 (0.12 #464, 0.06 #4570, 0.02 #18943), 05mlqj (0.12 #1580, 0.04 #5686, 0.02 #9792) >> Best rule #4598 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 016z9n; >> query: (?x2644, 016ypb) <- nominated_for(?x401, ?x2644), film(?x4277, ?x2644), film(?x4277, ?x3748), ?x3748 = 05zlld0 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #5566 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 016z9n; *> query: (?x2644, 070yzk) <- nominated_for(?x401, ?x2644), film(?x4277, ?x2644), film(?x4277, ?x3748), ?x3748 = 05zlld0 *> conf = 0.02 ranks of expected_values: 553 EVAL 01shy7 film! 070yzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 64.000 30.000 0.286 http://example.org/film/actor/film./film/performance/film #13380-02tc5y PRED entity: 02tc5y PRED relation: participant PRED expected values: 02vntj => 169 concepts (73 used for prediction) PRED predicted values (max 10 best out of 387): 0gy6z9 (0.15 #2829, 0.11 #3478, 0.03 #5425), 01fwk3 (0.15 #2783, 0.06 #3432, 0.03 #12518), 02g0mx (0.15 #2813, 0.06 #3462, 0.02 #11899), 0f4vbz (0.11 #2092, 0.08 #2741, 0.05 #7933), 0d_84 (0.11 #3256, 0.08 #2607, 0.03 #8448), 01cj6y (0.11 #2249), 0gyx4 (0.08 #2905, 0.07 #15886, 0.07 #8746), 0436kgz (0.08 #3040, 0.06 #3689, 0.03 #6285), 01vvb4m (0.08 #2809, 0.06 #3458, 0.02 #7352), 0c6qh (0.08 #2764, 0.06 #3413, 0.02 #7307) >> Best rule #2829 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0q5hw; 01w_10; 07jrjb; >> query: (?x10224, 0gy6z9) <- celebrity(?x10224, ?x1890), type_of_union(?x10224, ?x566), student(?x3995, ?x10224) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02tc5y participant 02vntj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 169.000 73.000 0.154 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #13379-04hw4b PRED entity: 04hw4b PRED relation: profession PRED expected values: 01d_h8 02jknp => 105 concepts (98 used for prediction) PRED predicted values (max 10 best out of 72): 02hrh1q (0.90 #13452, 0.77 #7609, 0.74 #3080), 01d_h8 (0.87 #5994, 0.85 #5702, 0.84 #6140), 02jknp (0.58 #299, 0.57 #2781, 0.56 #4534), 0kyk (0.48 #1050, 0.46 #2072, 0.43 #5870), 018gz8 (0.36 #453, 0.26 #1329, 0.25 #4980), 02krf9 (0.31 #1777, 0.31 #4990, 0.29 #2215), 02hv44_ (0.23 #202, 0.19 #786, 0.18 #2100), 0np9r (0.18 #457, 0.17 #19, 0.16 #6737), 09jwl (0.17 #11118, 0.17 #8489, 0.16 #13895), 0nbcg (0.11 #9378, 0.10 #12153, 0.10 #7918) >> Best rule #13452 for best value: >> intensional similarity = 3 >> extensional distance = 2780 >> proper extension: 06v8s0; 05d7rk; 04yywz; 06688p; 05bp8g; 05m63c; 01vw87c; 0c9d9; 0d_84; 0fp_v1x; ... >> query: (?x7106, 02hrh1q) <- profession(?x7106, ?x1041), profession(?x9359, ?x1041), ?x9359 = 016kft >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #5994 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 369 *> proper extension: 0gg9_5q; 0glyyw; 0gdhhy; 037q1z; 03g62; 03c9pqt; 01g04k; 0g_rs_; *> query: (?x7106, 01d_h8) <- profession(?x7106, ?x1041), produced_by(?x8068, ?x7106), profession(?x147, ?x1041), ?x147 = 012d40 *> conf = 0.87 ranks of expected_values: 2, 3 EVAL 04hw4b profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 98.000 0.898 http://example.org/people/person/profession EVAL 04hw4b profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 98.000 0.898 http://example.org/people/person/profession #13378-0jkvj PRED entity: 0jkvj PRED relation: medal PRED expected values: 02lpp7 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.90 #32, 0.88 #38, 0.86 #39) >> Best rule #32 for best value: >> intensional similarity = 9 >> extensional distance = 37 >> proper extension: 09n48; 0sx7r; 0swbd; 0sx8l; 0swff; 0kbvv; 0blfl; 0sx92; 019n8z; 0124ld; ... >> query: (?x7688, 02lpp7) <- olympics(?x766, ?x7688), sports(?x7688, ?x3127), country(?x3127, ?x87), medal(?x7688, ?x422), olympics(?x3127, ?x4255), olympics(?x3127, ?x2966), olympics(?x47, ?x2966), ?x4255 = 0lgxj, olympics(?x3728, ?x7688) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jkvj medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.897 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #13377-03d96s PRED entity: 03d96s PRED relation: artist PRED expected values: 047sxrj => 113 concepts (56 used for prediction) PRED predicted values (max 10 best out of 3172): 019g40 (0.50 #937, 0.40 #4285, 0.38 #5958), 01vw8mh (0.40 #4529, 0.25 #2018, 0.25 #1181), 01w60_p (0.40 #4300, 0.25 #1789, 0.25 #952), 01vxlbm (0.40 #4450, 0.25 #1939, 0.25 #1102), 0k6yt1 (0.40 #4951, 0.25 #2440, 0.25 #1603), 070b4 (0.40 #4837, 0.25 #2326, 0.25 #1489), 04_jsg (0.40 #4788, 0.25 #2277, 0.25 #1440), 016376 (0.38 #12465, 0.25 #6607, 0.25 #1586), 023p29 (0.38 #6583, 0.25 #1562, 0.20 #4910), 01wg25j (0.31 #12336, 0.16 #33249, 0.15 #40780) >> Best rule #937 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 073tm9; 01trtc; >> query: (?x7840, 019g40) <- artist(?x7840, ?x10427), artist(?x7840, ?x4836), ?x10427 = 04qzm, organization(?x4682, ?x7840), gender(?x4836, ?x231), award_winner(?x4836, ?x828) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5021 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0fb0v; *> query: (?x7840, ?x527) <- artist(?x7840, ?x10427), artist(?x7840, ?x1388), ?x10427 = 04qzm, category(?x7840, ?x134), award_nominee(?x1388, ?x527) *> conf = 0.11 ranks of expected_values: 575 EVAL 03d96s artist 047sxrj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 113.000 56.000 0.500 http://example.org/music/record_label/artist #13376-0j_c PRED entity: 0j_c PRED relation: religion PRED expected values: 0c8wxp => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 25): 03_gx (0.30 #59, 0.20 #1590, 0.18 #104), 0c8wxp (0.25 #51, 0.21 #366, 0.21 #2709), 01lp8 (0.25 #1, 0.05 #1171, 0.05 #316), 0kpl (0.18 #2038, 0.18 #2173, 0.16 #2353), 03j6c (0.09 #921, 0.06 #1416, 0.03 #1958), 0kq2 (0.06 #1458, 0.05 #2046, 0.05 #2181), 0n2g (0.06 #1453, 0.04 #2311, 0.04 #2581), 0631_ (0.05 #53, 0.03 #1945, 0.03 #458), 092bf5 (0.04 #241, 0.04 #286, 0.03 #1456), 06nzl (0.04 #1185, 0.03 #330, 0.02 #375) >> Best rule #59 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 030pr; 03bw6; >> query: (?x2465, 03_gx) <- film(?x2465, ?x1308), people(?x11563, ?x2465), people(?x5056, ?x2465) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #51 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 030pr; 03bw6; *> query: (?x2465, 0c8wxp) <- film(?x2465, ?x1308), people(?x11563, ?x2465), people(?x5056, ?x2465) *> conf = 0.25 ranks of expected_values: 2 EVAL 0j_c religion 0c8wxp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 128.000 0.300 http://example.org/people/person/religion #13375-0cw3yd PRED entity: 0cw3yd PRED relation: country PRED expected values: 0d060g => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 91): 0f8l9c (0.25 #434, 0.21 #672, 0.20 #136), 0345h (0.21 #798, 0.19 #680, 0.18 #1215), 0d060g (0.15 #185, 0.11 #67, 0.10 #662), 03_3d (0.10 #125, 0.08 #184, 0.05 #5861), 03rjj (0.10 #124, 0.07 #1018, 0.07 #660), 0chghy (0.08 #189, 0.07 #308, 0.07 #1083), 0ctw_b (0.08 #199, 0.05 #5861, 0.05 #975), 0154j (0.07 #659, 0.07 #242, 0.05 #5861), 0b90_r (0.07 #300, 0.06 #360, 0.05 #5861), 03h64 (0.07 #699, 0.05 #5861, 0.05 #520) >> Best rule #434 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0sxfd; 0ct5zc; 0571m; 0qm9n; 03vyw8; 03b1l8; 03b1sb; >> query: (?x2812, 0f8l9c) <- award(?x2812, ?x1716), film(?x5043, ?x2812), ?x1716 = 02y_rq5, genre(?x2812, ?x53) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #185 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0bmch_x; *> query: (?x2812, 0d060g) <- film_festivals(?x2812, ?x10083), featured_film_locations(?x2812, ?x10683), film_festivals(?x7700, ?x10083), ?x7700 = 0cp08zg *> conf = 0.15 ranks of expected_values: 3 EVAL 0cw3yd country 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 113.000 0.250 http://example.org/film/film/country #13374-09mfvx PRED entity: 09mfvx PRED relation: child! PRED expected values: 03xsby => 128 concepts (79 used for prediction) PRED predicted values (max 10 best out of 53): 0l8sx (0.25 #96, 0.17 #761, 0.15 #2681), 03rwz3 (0.25 #125, 0.12 #1209, 0.11 #2122), 03phgz (0.25 #123, 0.08 #290, 0.08 #373), 03mdt (0.25 #106, 0.08 #273, 0.02 #2691), 09b3v (0.24 #859, 0.23 #943, 0.21 #527), 018_q8 (0.15 #374, 0.08 #1208, 0.08 #1124), 02_l39 (0.11 #728, 0.11 #1562, 0.09 #2060), 049ql1 (0.11 #734, 0.09 #1068, 0.08 #319), 086k8 (0.09 #2082, 0.08 #1169, 0.08 #2417), 0sxdg (0.08 #1216, 0.07 #1382, 0.07 #548) >> Best rule #96 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03jvmp; >> query: (?x4708, 0l8sx) <- nominated_for(?x4708, ?x5706), production_companies(?x424, ?x4708), nominated_for(?x3139, ?x5706), nominated_for(?x899, ?x5706), ?x3139 = 0b_dy >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #513 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 03x_k5m; *> query: (?x4708, 03xsby) <- film(?x4708, ?x424), industry(?x4708, ?x373), ?x373 = 02vxn, state_province_region(?x4708, ?x335) *> conf = 0.07 ranks of expected_values: 14 EVAL 09mfvx child! 03xsby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 128.000 79.000 0.250 http://example.org/organization/organization/child./organization/organization_relationship/child #13373-06dv3 PRED entity: 06dv3 PRED relation: film PRED expected values: 02q0k7v => 92 concepts (76 used for prediction) PRED predicted values (max 10 best out of 637): 011yth (0.63 #12475, 0.63 #19605, 0.63 #16040), 01shy7 (0.05 #9327, 0.05 #2199, 0.04 #14674), 03bx2lk (0.04 #1964, 0.04 #3746, 0.02 #16222), 08r4x3 (0.04 #1934, 0.04 #3716, 0.02 #42923), 013q07 (0.04 #350, 0.03 #5696, 0.03 #12825), 06_wqk4 (0.04 #87336, 0.04 #17948, 0.03 #5472), 020bv3 (0.04 #87336, 0.03 #121209, 0.03 #64159), 011ywj (0.04 #87336, 0.03 #121209, 0.03 #64159), 0cc7hmk (0.04 #87336, 0.03 #121209, 0.03 #64159), 07024 (0.04 #87336, 0.03 #121209, 0.03 #64159) >> Best rule #12475 for best value: >> intensional similarity = 3 >> extensional distance = 319 >> proper extension: 012dr7; >> query: (?x262, ?x253) <- participant(?x262, ?x226), nominated_for(?x262, ?x253), type_of_union(?x262, ?x566) >> conf = 0.63 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06dv3 film 02q0k7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 76.000 0.633 http://example.org/film/actor/film./film/performance/film #13372-0hz6mv2 PRED entity: 0hz6mv2 PRED relation: film_release_region PRED expected values: 09c7w0 => 123 concepts (109 used for prediction) PRED predicted values (max 10 best out of 343): 09c7w0 (0.94 #15135, 0.94 #5610, 0.92 #14045), 03h64 (0.94 #5829, 0.93 #6919, 0.92 #6140), 03gj2 (0.90 #11726, 0.90 #11571, 0.90 #9060), 01znc_ (0.89 #7049, 0.89 #7674, 0.89 #6894), 0b90_r (0.88 #6078, 0.88 #5767, 0.88 #7637), 03rt9 (0.85 #6555, 0.82 #6087, 0.82 #7021), 02vzc (0.84 #6125, 0.84 #5814, 0.84 #10340), 05b4w (0.84 #4732, 0.82 #10352, 0.81 #9099), 05v8c (0.84 #4684, 0.75 #11562, 0.75 #10304), 04gzd (0.73 #7016, 0.73 #6861, 0.72 #11555) >> Best rule #15135 for best value: >> intensional similarity = 11 >> extensional distance = 378 >> proper extension: 02c6d; 01dyvs; 047n8xt; 07p62k; 0jdgr; 0418wg; 02qmsr; 0cw3yd; 03z20c; 04sntd; ... >> query: (?x9565, 09c7w0) <- executive_produced_by(?x9565, ?x12825), film_release_region(?x9565, ?x1353), film_release_region(?x9565, ?x1174), film_release_region(?x9565, ?x456), adjoins(?x1353, ?x1497), olympics(?x1353, ?x778), film_release_region(?x7832, ?x1174), ?x7832 = 0fphf3v, film_release_region(?x8657, ?x456), contains(?x6304, ?x1174), ?x8657 = 030z4z >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hz6mv2 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 109.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13371-01pl9g PRED entity: 01pl9g PRED relation: languages PRED expected values: 02h40lc => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 19): 02h40lc (0.36 #509, 0.35 #197, 0.35 #1913), 064_8sq (0.11 #600, 0.09 #717, 0.08 #1302), 02bjrlw (0.08 #2692, 0.08 #586, 0.07 #3356), 03_9r (0.08 #2692, 0.07 #3356, 0.02 #902), 0t_2 (0.08 #2692, 0.04 #5814, 0.02 #750), 06nm1 (0.07 #3356, 0.03 #552, 0.02 #786), 04306rv (0.07 #3356, 0.03 #588, 0.02 #1836), 06b_j (0.07 #3356, 0.03 #601, 0.02 #718), 03x42 (0.04 #5814), 03k50 (0.04 #979, 0.03 #3242, 0.03 #1525) >> Best rule #509 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 0chsq; 0320jz; 01wz3cx; 0169dl; 0bmh4; 01hb6v; 0315q3; 0bx_q; 0f7fy; >> query: (?x1568, 02h40lc) <- people(?x1816, ?x1568), people(?x1446, ?x1568), people(?x1446, ?x3934), ?x1816 = 09vc4s, music(?x9154, ?x3934) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pl9g languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 181.000 0.357 http://example.org/people/person/languages #13370-04rlf PRED entity: 04rlf PRED relation: genre! PRED expected values: 0h1cdwq 089j8p 03ydlnj => 104 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1831): 02q6gfp (0.71 #42552, 0.67 #40719, 0.25 #22388), 09fn1w (0.67 #41087, 0.57 #42920, 0.25 #22756), 02jkkv (0.57 #47413, 0.50 #18080, 0.33 #7080), 07l4zhn (0.57 #46815, 0.50 #17482, 0.33 #6482), 02xbyr (0.57 #52147, 0.50 #19148, 0.33 #6315), 0dr3sl (0.57 #51804, 0.50 #18805, 0.33 #5972), 02qydsh (0.57 #52854, 0.50 #19855, 0.33 #7022), 04f52jw (0.57 #51781, 0.50 #18782, 0.33 #5949), 06w839_ (0.57 #51849, 0.50 #18850, 0.33 #6017), 04hwbq (0.57 #51529, 0.50 #18530, 0.33 #5697) >> Best rule #42552 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 017fp; >> query: (?x8681, 02q6gfp) <- genre(?x9330, ?x8681), genre(?x5839, ?x8681), genre(?x3573, ?x8681), ?x3573 = 011yl_, film_distribution_medium(?x5839, ?x627), nominated_for(?x1053, ?x5839), film_crew_role(?x5839, ?x137), film(?x2275, ?x5839), award_winner(?x9330, ?x5940) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6915 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 05p553; *> query: (?x8681, 03ydlnj) <- genre(?x9330, ?x8681), genre(?x7819, ?x8681), genre(?x6832, ?x8681), genre(?x5538, ?x8681), genre(?x2287, ?x8681), ?x9330 = 0kbwb, ?x7819 = 025rxjq, ?x6832 = 03cyslc, film_release_region(?x5538, ?x94), nominated_for(?x4320, ?x2287), film(?x2125, ?x2287) *> conf = 0.33 ranks of expected_values: 368, 442, 1267 EVAL 04rlf genre! 03ydlnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 104.000 48.000 0.714 http://example.org/film/film/genre EVAL 04rlf genre! 089j8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 104.000 48.000 0.714 http://example.org/film/film/genre EVAL 04rlf genre! 0h1cdwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 104.000 48.000 0.714 http://example.org/film/film/genre #13369-07cyl PRED entity: 07cyl PRED relation: nominated_for! PRED expected values: 095zvfg => 82 concepts (38 used for prediction) PRED predicted values (max 10 best out of 386): 03mfqm (0.41 #11669, 0.40 #14003, 0.39 #4668), 04gc65 (0.34 #14004, 0.31 #72351, 0.31 #77020), 09nz_c (0.34 #14004, 0.31 #72351, 0.31 #77020), 03cglm (0.34 #14004, 0.31 #72351, 0.31 #77020), 092ys_y (0.25 #798, 0.05 #49013, 0.02 #17135), 04sry (0.25 #1575, 0.03 #8576, 0.02 #10910), 06cgy (0.25 #309, 0.02 #7310, 0.02 #53989), 0klh7 (0.25 #608, 0.02 #7609), 0bbxx9b (0.25 #825, 0.01 #5493, 0.01 #40503), 0b9l3x (0.25 #1130, 0.01 #8131) >> Best rule #11669 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 02d413; 0b2v79; 047q2k1; 090s_0; 011yrp; 095zlp; 0ds11z; 0g5qs2k; 04jwjq; 0fg04; ... >> query: (?x3471, ?x6327) <- genre(?x3471, ?x53), award_winner(?x3471, ?x450), costume_design_by(?x3471, ?x6327), nominated_for(?x198, ?x3471) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #4231 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 103 *> proper extension: 02wwmhc; *> query: (?x3471, 095zvfg) <- genre(?x3471, ?x53), award_winner(?x3471, ?x450), costume_design_by(?x3471, ?x6327), titles(?x3613, ?x3471) *> conf = 0.04 ranks of expected_values: 36 EVAL 07cyl nominated_for! 095zvfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 82.000 38.000 0.410 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #13368-0ft18 PRED entity: 0ft18 PRED relation: list PRED expected values: 05glt => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.40 #44, 0.40 #37, 0.40 #2) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 0gcrg; >> query: (?x8119, 05glt) <- film(?x11251, ?x8119), film_sets_designed(?x12378, ?x8119), nominated_for(?x591, ?x8119) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ft18 list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.400 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #13367-01vtg4q PRED entity: 01vtg4q PRED relation: artists! PRED expected values: 01730d => 109 concepts (69 used for prediction) PRED predicted values (max 10 best out of 274): 064t9 (0.61 #14923, 0.51 #18034, 0.44 #1254), 03_d0 (0.47 #3423, 0.40 #11, 0.33 #322), 01lyv (0.45 #965, 0.41 #1586, 0.38 #2206), 0gywn (0.40 #58, 0.35 #989, 0.28 #1299), 05w3f (0.35 #969, 0.33 #349, 0.22 #3140), 0xhtw (0.32 #8395, 0.25 #8706, 0.22 #18038), 05bt6j (0.32 #1285, 0.28 #14954, 0.28 #18065), 016clz (0.26 #8694, 0.25 #8383, 0.24 #10560), 02w4v (0.25 #356, 0.20 #976, 0.20 #45), 08jyyk (0.25 #379, 0.12 #1309, 0.10 #8446) >> Best rule #14923 for best value: >> intensional similarity = 5 >> extensional distance = 575 >> proper extension: 01l1b90; 01t_xp_; 01pfr3; 0147dk; 0c7ct; 0150jk; 01w61th; 07qnf; 02r3zy; 0lk90; ... >> query: (?x8305, 064t9) <- artists(?x378, ?x8305), artists(?x378, ?x6942), artists(?x378, ?x1398), ?x6942 = 04b7xr, ?x1398 = 01j4ls >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1551 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0kftt; *> query: (?x8305, 01730d) <- inductee(?x1091, ?x8305), profession(?x8305, ?x1032), ?x1032 = 02hrh1q, role(?x8305, ?x227) *> conf = 0.04 ranks of expected_values: 106 EVAL 01vtg4q artists! 01730d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 109.000 69.000 0.610 http://example.org/music/genre/artists #13366-0k_mf PRED entity: 0k_mf PRED relation: contains! PRED expected values: 030qb3t 0k_s5 => 83 concepts (35 used for prediction) PRED predicted values (max 10 best out of 127): 030qb3t (0.21 #1885, 0.20 #2779, 0.19 #4567), 0k_s5 (0.21 #2474, 0.16 #3368, 0.15 #5156), 07ssc (0.19 #22364, 0.13 #24153, 0.13 #9860), 02jx1 (0.16 #22418, 0.12 #24207, 0.11 #25101), 06pvr (0.16 #6420, 0.09 #7313, 0.08 #3738), 04_1l0v (0.13 #21888, 0.12 #23676, 0.09 #24570), 05k7sb (0.11 #7280, 0.05 #11746, 0.05 #21571), 0cb4j (0.08 #3608, 0.07 #6290, 0.06 #5396), 0d060g (0.06 #23240, 0.05 #19666, 0.04 #24134), 03_3d (0.06 #5373, 0.04 #3585, 0.01 #20558) >> Best rule #1885 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 027l4q; 07_fl; >> query: (?x11600, 030qb3t) <- contains(?x2949, ?x11600), contains(?x1227, ?x11600), ?x2949 = 0kpys, ?x1227 = 01n7q >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0k_mf contains! 0k_s5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 35.000 0.211 http://example.org/location/location/contains EVAL 0k_mf contains! 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 35.000 0.211 http://example.org/location/location/contains #13365-033gn8 PRED entity: 033gn8 PRED relation: student PRED expected values: 024jwt => 50 concepts (11 used for prediction) PRED predicted values (max 10 best out of 1323): 01n1gc (0.33 #604, 0.29 #6827, 0.25 #4753), 07f7jp (0.33 #1962, 0.25 #6111, 0.14 #8185), 07nx9j (0.33 #1303, 0.25 #5452, 0.14 #7526), 0432cd (0.33 #1309, 0.25 #5458, 0.14 #7532), 0d06m5 (0.33 #533, 0.25 #4682, 0.14 #6756), 05kfs (0.33 #96, 0.25 #4245, 0.08 #8394), 024jwt (0.33 #1794, 0.25 #5943, 0.07 #8017), 01x6v6 (0.33 #1163, 0.25 #5312, 0.07 #7386), 05bnp0 (0.33 #11, 0.25 #4160, 0.07 #6234), 01gq0b (0.33 #278, 0.25 #4427, 0.07 #6501) >> Best rule #604 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 08815; >> query: (?x10070, 01n1gc) <- student(?x10070, ?x9337), student(?x10070, ?x8151), student(?x10070, ?x5617), ?x9337 = 01x4r3, ?x8151 = 0d6d2, institution(?x3386, ?x10070), award_nominee(?x1116, ?x5617) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1794 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 08815; *> query: (?x10070, 024jwt) <- student(?x10070, ?x9337), student(?x10070, ?x8151), student(?x10070, ?x5617), ?x9337 = 01x4r3, ?x8151 = 0d6d2, institution(?x3386, ?x10070), award_nominee(?x1116, ?x5617) *> conf = 0.33 ranks of expected_values: 7 EVAL 033gn8 student 024jwt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 50.000 11.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #13364-01l50r PRED entity: 01l50r PRED relation: program PRED expected values: 05b6s5j => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 291): 02skyy (0.58 #3649, 0.32 #6568, 0.05 #2903), 098z9w (0.58 #3649, 0.32 #6568), 043p28m (0.58 #3649, 0.32 #6568), 04glx0 (0.33 #1075, 0.33 #832, 0.30 #1318), 097h2 (0.33 #1128, 0.33 #885, 0.30 #1371), 02sqkh (0.33 #797, 0.23 #1769, 0.22 #1040), 070ltt (0.29 #3648, 0.11 #2620, 0.11 #1162), 017dbx (0.22 #1199, 0.22 #956, 0.20 #1442), 05pbsry (0.22 #1208, 0.22 #965, 0.20 #1451), 0q9jk (0.22 #1106, 0.22 #863, 0.20 #1349) >> Best rule #3649 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 01nzs7; 0b275x; 01zcrv; 0187wh; 07y2b; 02kx91; 018kcp; 0hmxn; >> query: (?x12664, ?x11042) <- program(?x12664, ?x13964), actor(?x13964, ?x11630), program(?x11630, ?x10551), actor(?x11042, ?x11630) >> conf = 0.58 => this is the best rule for 3 predicted values *> Best rule #931 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0kcdl; *> query: (?x12664, 05b6s5j) <- citytown(?x12664, ?x739), program(?x12664, ?x6496), ?x739 = 02_286, child(?x7326, ?x12664), currency(?x7326, ?x170) *> conf = 0.11 ranks of expected_values: 141 EVAL 01l50r program 05b6s5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 115.000 115.000 0.582 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #13363-032t2z PRED entity: 032t2z PRED relation: artists! PRED expected values: 0xhtw => 104 concepts (45 used for prediction) PRED predicted values (max 10 best out of 263): 06by7 (0.62 #2504, 0.60 #2814, 0.54 #3745), 064t9 (0.47 #11528, 0.45 #2496, 0.43 #4048), 06j6l (0.45 #11563, 0.25 #3461, 0.24 #5325), 0xhtw (0.40 #17, 0.39 #1878, 0.36 #638), 03lty (0.32 #1889, 0.21 #5619, 0.20 #5932), 016clz (0.31 #5596, 0.30 #5909, 0.29 #6851), 02yv6b (0.27 #720, 0.20 #99, 0.18 #5690), 05bt6j (0.27 #11558, 0.26 #2836, 0.26 #2526), 0gywn (0.24 #11573, 0.19 #10952, 0.18 #3471), 01lyv (0.24 #3758, 0.18 #10928, 0.17 #7505) >> Best rule #2504 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 01l_vgt; >> query: (?x642, 06by7) <- nationality(?x642, ?x512), ?x512 = 07ssc, artist(?x2149, ?x642), artists(?x505, ?x642) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0c9d9; 0zjpz; 0144l1; *> query: (?x642, 0xhtw) <- instrumentalists(?x716, ?x642), instrumentalists(?x614, ?x642), profession(?x642, ?x1183), ?x716 = 018vs, artists(?x505, ?x642), ?x614 = 0mkg *> conf = 0.40 ranks of expected_values: 4 EVAL 032t2z artists! 0xhtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 104.000 45.000 0.617 http://example.org/music/genre/artists #13362-02bxd PRED entity: 02bxd PRED relation: instrumentalists PRED expected values: 01y_rz => 54 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1158): 0phx4 (0.67 #5204, 0.60 #3954, 0.50 #1457), 01vn35l (0.67 #5151, 0.60 #3901, 0.40 #2653), 01vtg4q (0.67 #5460, 0.60 #4210, 0.40 #2962), 0473q (0.67 #5399, 0.60 #4149, 0.40 #3525), 01sb5r (0.60 #2109, 0.50 #14646, 0.50 #5233), 09qr6 (0.60 #3807, 0.50 #5057, 0.39 #3739), 01x66d (0.60 #3787, 0.50 #5037, 0.33 #48), 018y81 (0.50 #14758, 0.50 #5345, 0.46 #13499), 0135xb (0.50 #5394, 0.50 #1647, 0.40 #4144), 01gg59 (0.50 #5214, 0.46 #13765, 0.46 #13368) >> Best rule #5204 for best value: >> intensional similarity = 32 >> extensional distance = 4 >> proper extension: 05148p4; >> query: (?x1662, 0phx4) <- role(?x4975, ?x1662), role(?x2957, ?x1662), role(?x2377, ?x1662), role(?x716, ?x1662), role(?x569, ?x1662), role(?x315, ?x1662), role(?x227, ?x1662), role(?x212, ?x1662), ?x2377 = 01bns_, ?x716 = 018vs, ?x315 = 0l14md, ?x227 = 0342h, role(?x2957, ?x7772), role(?x2957, ?x1466), role(?x2957, ?x3991), role(?x2957, ?x3161), role(?x2957, ?x1437), role(?x2957, ?x316), ?x3161 = 01v1d8, ?x1466 = 03bx0bm, role(?x1662, ?x1750), role(?x4052, ?x2957), ?x569 = 07c6l, role(?x885, ?x2957), ?x7772 = 0j862, ?x3991 = 05842k, performance_role(?x1495, ?x4975), ?x1437 = 01vdm0, ?x212 = 026t6, role(?x922, ?x4975), ?x1495 = 013y1f, role(?x115, ?x316) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #570 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 1 *> proper extension: 05r5c; *> query: (?x1662, 01y_rz) <- role(?x4311, ?x1662), role(?x2957, ?x1662), role(?x2377, ?x1662), role(?x1663, ?x1662), role(?x716, ?x1662), role(?x315, ?x1662), role(?x227, ?x1662), role(?x212, ?x1662), role(?x75, ?x1662), ?x2377 = 01bns_, ?x716 = 018vs, ?x315 = 0l14md, ?x227 = 0342h, ?x2957 = 01v8y9, ?x4311 = 01xqw, role(?x1662, ?x1750), role(?x960, ?x1663), role(?x1663, ?x6449), role(?x1663, ?x1212), ?x212 = 026t6, ?x960 = 04q7r, role(?x1662, ?x1166), role(?x1663, ?x1436), role(?x1663, ?x745), ?x75 = 07y_7, role(?x2158, ?x1663), ?x1436 = 0xzly, ?x6449 = 014zz1, ?x745 = 01vj9c, ?x1212 = 07xzm *> conf = 0.33 ranks of expected_values: 301 EVAL 02bxd instrumentalists 01y_rz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 43.000 0.667 http://example.org/music/instrument/instrumentalists #13361-02wk7b PRED entity: 02wk7b PRED relation: film! PRED expected values: 0clvcx => 73 concepts (32 used for prediction) PRED predicted values (max 10 best out of 803): 05kwx2 (0.50 #6258, 0.50 #6257, 0.49 #43801), 022_q8 (0.50 #6258, 0.50 #6257, 0.47 #56324), 03bxsw (0.50 #6258, 0.50 #6257, 0.47 #56324), 01nwwl (0.33 #504, 0.04 #10933, 0.03 #8848), 01tspc6 (0.33 #163, 0.03 #2249, 0.03 #4334), 0c_gcr (0.17 #1649, 0.02 #12078, 0.02 #30851), 071ynp (0.17 #551, 0.01 #10980), 0241jw (0.17 #296, 0.01 #27411), 0bw87 (0.07 #3255, 0.01 #50061, 0.01 #52149), 0lpjn (0.06 #4651, 0.04 #10909, 0.02 #31767) >> Best rule #6258 for best value: >> intensional similarity = 4 >> extensional distance = 100 >> proper extension: 0j_t1; 01gvts; >> query: (?x8247, ?x5591) <- nominated_for(?x5591, ?x8247), nominated_for(?x2880, ?x8247), award_winner(?x372, ?x5591), ?x2880 = 02ppm4q >> conf = 0.50 => this is the best rule for 3 predicted values *> Best rule #22945 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 521 *> proper extension: 07ykkx5; *> query: (?x8247, ?x241) <- nominated_for(?x1245, ?x8247), award(?x2028, ?x1245), award(?x241, ?x1245), ?x2028 = 028knk *> conf = 0.01 ranks of expected_values: 776 EVAL 02wk7b film! 0clvcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 32.000 0.498 http://example.org/film/actor/film./film/performance/film #13360-06q1r PRED entity: 06q1r PRED relation: contains PRED expected values: 030nwm 01zk9d 0p8bz => 258 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2863): 02m77 (0.83 #458600, 0.83 #385572, 0.81 #29207), 07ssc (0.71 #8761, 0.50 #458599, 0.50 #443994), 06q1r (0.71 #8761, 0.50 #458599, 0.50 #443994), 01zk9d (0.71 #8761, 0.50 #458599, 0.50 #443994), 0zc6f (0.56 #449835, 0.36 #140204, 0.33 #455677), 01zrs_ (0.56 #449835, 0.36 #140204, 0.33 #455677), 0h6rm (0.51 #350515, 0.50 #315459, 0.42 #330066), 0j7ng (0.45 #157729, 0.42 #303776, 0.42 #330067), 071vr (0.45 #157729, 0.42 #303776, 0.42 #330067), 0cbhh (0.45 #157729, 0.42 #303776, 0.42 #330067) >> Best rule #458600 for best value: >> intensional similarity = 2 >> extensional distance = 129 >> proper extension: 078lk; 06y9v; 02ly_; 0125q1; 0ck1d; 0nr2v; 01c6yz; 0n048; 04q42; 0gqm3; ... >> query: (?x6401, ?x6885) <- contains(?x6401, ?x4030), administrative_parent(?x6885, ?x6401) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #8761 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 02fvv; *> query: (?x6401, ?x512) <- contains(?x6401, ?x13707), contains(?x6401, ?x9124), contains(?x512, ?x9124), ?x13707 = 024cg8 *> conf = 0.71 ranks of expected_values: 4, 31, 223 EVAL 06q1r contains 0p8bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 258.000 162.000 0.834 http://example.org/location/location/contains EVAL 06q1r contains 01zk9d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 258.000 162.000 0.834 http://example.org/location/location/contains EVAL 06q1r contains 030nwm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 258.000 162.000 0.834 http://example.org/location/location/contains #13359-04_j5s PRED entity: 04_j5s PRED relation: category PRED expected values: 08mbj5d => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #18, 0.88 #17, 0.88 #25) >> Best rule #18 for best value: >> intensional similarity = 3 >> extensional distance = 418 >> proper extension: 02g839; 022xml; 031n8c; 0ymc8; 021l5s; 09krm_; 0269kx; 05cwl_; 01hnb; 02zc7f; ... >> query: (?x11711, 08mbj5d) <- organization(?x3484, ?x11711), contains(?x335, ?x11711), state_province_region(?x166, ?x335) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04_j5s category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.883 http://example.org/common/topic/webpage./common/webpage/category #13358-035_2h PRED entity: 035_2h PRED relation: film! PRED expected values: 018dnt 04yyhw => 99 concepts (56 used for prediction) PRED predicted values (max 10 best out of 861): 0bj9k (0.49 #51863, 0.41 #114105, 0.41 #112029), 03_fk9 (0.49 #51863, 0.41 #114105, 0.41 #112029), 05b49tt (0.41 #114105, 0.41 #112029, 0.41 #114104), 02pqgt8 (0.41 #114105, 0.41 #112029, 0.41 #114104), 039bp (0.14 #4329, 0.05 #10553, 0.03 #29222), 04__f (0.14 #5524, 0.04 #17972, 0.03 #15898), 0c0k1 (0.14 #5651, 0.04 #18099, 0.03 #32619), 04gc65 (0.14 #6117, 0.03 #16491, 0.03 #18565), 015c4g (0.14 #4922, 0.03 #15296, 0.03 #19444), 09fb5 (0.14 #4206, 0.03 #16654, 0.03 #24950) >> Best rule #51863 for best value: >> intensional similarity = 3 >> extensional distance = 495 >> proper extension: 01h1bf; 02kk_c; 01b7h8; >> query: (?x5294, ?x2035) <- honored_for(?x7105, ?x5294), nominated_for(?x2035, ?x5294), film(?x2035, ?x89) >> conf = 0.49 => this is the best rule for 2 predicted values *> Best rule #12446 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 01_1hw; *> query: (?x5294, 04yyhw) <- production_companies(?x5294, ?x5295), film(?x7980, ?x5294), ?x7980 = 020h2v *> conf = 0.05 ranks of expected_values: 56, 355 EVAL 035_2h film! 04yyhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 99.000 56.000 0.491 http://example.org/film/actor/film./film/performance/film EVAL 035_2h film! 018dnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 99.000 56.000 0.491 http://example.org/film/actor/film./film/performance/film #13357-0cbvg PRED entity: 0cbvg PRED relation: combatants PRED expected values: 0285m87 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 343): 0285m87 (0.67 #1539, 0.50 #1175, 0.50 #688), 09c7w0 (0.64 #1825, 0.63 #3288, 0.56 #5011), 0chghy (0.54 #2199, 0.50 #2563, 0.45 #1833), 0j5b8 (0.50 #605, 0.48 #1701, 0.47 #4020), 02c4s (0.50 #605, 0.47 #4020, 0.47 #4019), 0kn4c (0.50 #605, 0.47 #4020, 0.47 #4019), 014tss (0.50 #1157, 0.33 #1521, 0.33 #549), 0dv0z (0.50 #1172, 0.33 #1536, 0.33 #564), 040vgd (0.50 #1193, 0.33 #1557, 0.33 #585), 03gk2 (0.44 #1491, 0.39 #3933, 0.33 #1127) >> Best rule #1539 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 0ql7q; >> query: (?x10008, 0285m87) <- entity_involved(?x10008, ?x1328), combatants(?x10008, ?x4493), combatants(?x12673, ?x4493), combatants(?x10206, ?x4493), ?x10206 = 01_3rn, ?x12673 = 01hwkn, combatants(?x1612, ?x4493) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cbvg combatants 0285m87 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #13356-016fnb PRED entity: 016fnb PRED relation: artists! PRED expected values: 06cqb 016clz => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 221): 0glt670 (0.59 #40, 0.32 #6706, 0.31 #1555), 05bt6j (0.44 #2467, 0.37 #1861, 0.37 #2164), 0gywn (0.35 #2175, 0.31 #1569, 0.26 #1872), 016clz (0.34 #2429, 0.31 #1520, 0.28 #611), 0xhtw (0.31 #623, 0.23 #1229, 0.16 #16687), 017_qw (0.30 #7936, 0.30 #8239, 0.19 #10362), 0m0jc (0.28 #2433, 0.17 #2130, 0.16 #1827), 02k_kn (0.25 #2182, 0.14 #7030, 0.12 #1879), 08cyft (0.23 #2477, 0.18 #53, 0.14 #1871), 03_d0 (0.23 #2133, 0.19 #8497, 0.18 #10316) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 05mt_q; 047sxrj; >> query: (?x4628, 0glt670) <- award_nominee(?x527, ?x4628), ?x527 = 04lgymt, profession(?x4628, ?x131) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #2429 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 62 *> proper extension: 03xhj6; 02vgh; 02hzz; 04mx7s; 012vm6; 02twdq; *> query: (?x4628, 016clz) <- artists(?x3243, ?x4628), ?x3243 = 0y3_8 *> conf = 0.34 ranks of expected_values: 4, 85 EVAL 016fnb artists! 016clz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 135.000 135.000 0.588 http://example.org/music/genre/artists EVAL 016fnb artists! 06cqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 135.000 135.000 0.588 http://example.org/music/genre/artists #13355-01pl14 PRED entity: 01pl14 PRED relation: student PRED expected values: 02d4ct => 168 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1641): 01gbn6 (0.33 #1637, 0.11 #16271, 0.07 #24633), 020_95 (0.33 #946, 0.11 #34394, 0.08 #40665), 0335fp (0.33 #1379, 0.11 #34827, 0.08 #41098), 07s8hms (0.33 #623, 0.08 #17349, 0.07 #25711), 0146pg (0.33 #85, 0.08 #16811, 0.07 #25173), 0306ds (0.33 #408, 0.08 #19224, 0.05 #54760), 02cx72 (0.33 #602, 0.08 #19418, 0.05 #34050), 015wc0 (0.33 #1695, 0.05 #35143, 0.05 #37234), 01l1rw (0.33 #999, 0.05 #34447, 0.05 #36538), 03rs8y (0.33 #46, 0.05 #33494, 0.05 #35585) >> Best rule #1637 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 017z88; >> query: (?x466, 01gbn6) <- student(?x466, ?x3134), institution(?x865, ?x466), major_field_of_study(?x466, ?x947), ?x3134 = 01l9v7n >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8724 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 07wj1; *> query: (?x466, 02d4ct) <- company(?x8651, ?x466), company(?x3520, ?x466), ?x3520 = 03gkn5, place_of_death(?x8651, ?x739) *> conf = 0.25 ranks of expected_values: 76 EVAL 01pl14 student 02d4ct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 168.000 106.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #13354-0gy3w PRED entity: 0gy3w PRED relation: colors PRED expected values: 06fvc => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 21): 01l849 (0.42 #1, 0.39 #295, 0.33 #148), 083jv (0.36 #758, 0.35 #695, 0.35 #569), 01g5v (0.33 #4, 0.31 #193, 0.29 #781), 019sc (0.22 #365, 0.16 #491, 0.16 #743), 0jc_p (0.17 #257, 0.15 #341, 0.12 #152), 06fvc (0.17 #801, 0.17 #780, 0.17 #3), 038hg (0.17 #307, 0.14 #1872, 0.12 #160), 01jnf1 (0.17 #96, 0.15 #33, 0.10 #243), 03wkwg (0.17 #100, 0.14 #58, 0.12 #79), 03vtbc (0.16 #841, 0.09 #534, 0.08 #1640) >> Best rule #1 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 01bzw5; 02bjhv; 026ssfj; 0177sq; 013807; 037q2p; >> query: (?x7576, 01l849) <- school_type(?x7576, ?x1507), organization(?x346, ?x7576), major_field_of_study(?x7576, ?x7134), major_field_of_study(?x7576, ?x6756), ?x7134 = 02_7t, currency(?x7576, ?x170), ?x6756 = 0_jm >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #801 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 109 *> proper extension: 011xy1; *> query: (?x7576, 06fvc) <- major_field_of_study(?x7576, ?x7134), institution(?x620, ?x7576), major_field_of_study(?x5055, ?x7134), major_field_of_study(?x2895, ?x7134), ?x620 = 07s6fsf, ?x2895 = 0l2tk, citytown(?x5055, ?x2254) *> conf = 0.17 ranks of expected_values: 6 EVAL 0gy3w colors 06fvc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 164.000 164.000 0.417 http://example.org/education/educational_institution/colors #13353-015g28 PRED entity: 015g28 PRED relation: nominated_for! PRED expected values: 07kjk7c => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 199): 07z2lx (0.77 #20023, 0.69 #8444, 0.68 #7479), 0m7yy (0.69 #8444, 0.68 #7479, 0.68 #7237), 0bfvd4 (0.57 #2259, 0.53 #2501, 0.45 #2985), 07kjk7c (0.43 #2363, 0.40 #2605, 0.35 #2848), 0bdwft (0.43 #2226, 0.40 #2468, 0.35 #2952), 0gq9h (0.42 #19844, 0.38 #16947, 0.38 #17189), 0gs9p (0.38 #19846, 0.35 #16949, 0.35 #17191), 019f4v (0.38 #1742, 0.35 #19835, 0.35 #15731), 0gqyl (0.38 #1769, 0.23 #19862, 0.19 #13347), 0bfvw2 (0.36 #2183, 0.33 #2425, 0.30 #2909) >> Best rule #20023 for best value: >> intensional similarity = 3 >> extensional distance = 550 >> proper extension: 085bd1; >> query: (?x4037, ?x6024) <- award(?x4037, ?x6024), film(?x3575, ?x4037), ceremony(?x6024, ?x1265) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2363 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 090s_0; 02py4c8; 02bg8v; 02ppg1r; 03cv_gy; 0bbm7r; 02gd6x; 0b6m5fy; 08y2fn; 021gzd; ... *> query: (?x4037, 07kjk7c) <- actor(?x4037, ?x3295), genre(?x4037, ?x3515), award(?x4037, ?x3486), nominated_for(?x496, ?x4037) *> conf = 0.43 ranks of expected_values: 4 EVAL 015g28 nominated_for! 07kjk7c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 127.000 127.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13352-06jz0 PRED entity: 06jz0 PRED relation: award PRED expected values: 019f4v => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 246): 0gs9p (0.44 #2075, 0.39 #2475, 0.35 #1275), 019f4v (0.40 #2063, 0.35 #2463, 0.34 #1263), 09sb52 (0.35 #4040, 0.33 #7640, 0.32 #13240), 0ck27z (0.32 #5689, 0.15 #7689, 0.14 #13289), 05p1dby (0.32 #503, 0.14 #103, 0.14 #2903), 05b1610 (0.29 #37, 0.13 #437, 0.13 #1637), 0gr4k (0.25 #3632, 0.21 #1231, 0.21 #2031), 02pqp12 (0.25 #2067, 0.23 #1267, 0.22 #2467), 0gr51 (0.23 #3697, 0.22 #2496, 0.22 #2096), 04dn09n (0.22 #3643, 0.21 #1242, 0.20 #2042) >> Best rule #2075 for best value: >> intensional similarity = 3 >> extensional distance = 202 >> proper extension: 030pr; >> query: (?x10415, 0gs9p) <- film(?x10415, ?x430), type_of_union(?x10415, ?x566), award(?x10415, ?x198) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2063 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 202 *> proper extension: 030pr; *> query: (?x10415, 019f4v) <- film(?x10415, ?x430), type_of_union(?x10415, ?x566), award(?x10415, ?x198) *> conf = 0.40 ranks of expected_values: 2 EVAL 06jz0 award 019f4v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.436 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13351-020hyj PRED entity: 020hyj PRED relation: artist! PRED expected values: 02bh8z => 93 concepts (66 used for prediction) PRED predicted values (max 10 best out of 100): 015_1q (0.43 #160, 0.19 #2700, 0.19 #1712), 01w40h (0.29 #169, 0.07 #2709, 0.07 #1298), 011k11 (0.29 #176, 0.04 #2434, 0.04 #3565), 03rhqg (0.20 #15, 0.17 #297, 0.16 #438), 01clyr (0.20 #33, 0.14 #174, 0.08 #2432), 01xyqk (0.20 #81, 0.05 #1351, 0.03 #3470), 0181dw (0.20 #747, 0.14 #183, 0.12 #2300), 0mzkr (0.14 #166, 0.13 #307, 0.08 #589), 01f_3w (0.14 #175, 0.12 #739, 0.06 #598), 033hn8 (0.14 #154, 0.12 #577, 0.11 #2412) >> Best rule #160 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02twdq; >> query: (?x10180, 015_1q) <- artists(?x10319, ?x10180), artists(?x671, ?x10180), ?x671 = 064t9, ?x10319 = 01gjw >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #303 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 02fybl; *> query: (?x10180, 02bh8z) <- celebrity(?x2614, ?x10180), profession(?x10180, ?x2348), ?x2348 = 0nbcg *> conf = 0.07 ranks of expected_values: 32 EVAL 020hyj artist! 02bh8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 93.000 66.000 0.429 http://example.org/music/record_label/artist #13350-0641g8 PRED entity: 0641g8 PRED relation: artists! PRED expected values: 017_qw => 136 concepts (114 used for prediction) PRED predicted values (max 10 best out of 239): 017_qw (0.58 #1008, 0.28 #10116, 0.25 #66), 06by7 (0.51 #8189, 0.50 #7875, 0.49 #7561), 064t9 (0.45 #11320, 0.43 #12576, 0.42 #14146), 03_d0 (0.35 #5038, 0.35 #6294, 0.33 #5666), 016clz (0.29 #6601, 0.24 #9113, 0.23 #16966), 0xhtw (0.28 #7870, 0.26 #2845, 0.25 #9126), 06j6l (0.28 #1621, 0.28 #5391, 0.27 #2563), 0dl5d (0.25 #21, 0.22 #2848, 0.21 #3791), 059kh (0.25 #52, 0.22 #1622, 0.19 #4136), 0ggq0m (0.25 #955, 0.17 #10063, 0.11 #7237) >> Best rule #1008 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 014hr0; >> query: (?x4947, 017_qw) <- artists(?x7052, ?x4947), ?x7052 = 0l14gg >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0641g8 artists! 017_qw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 114.000 0.583 http://example.org/music/genre/artists #13349-023mdt PRED entity: 023mdt PRED relation: languages PRED expected values: 02h40lc => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 17): 02h40lc (0.43 #548, 0.40 #665, 0.37 #899), 03k50 (0.06 #511, 0.06 #199, 0.05 #316), 064_8sq (0.06 #912, 0.06 #1185, 0.05 #1224), 02bjrlw (0.04 #859, 0.03 #1015, 0.03 #1171), 07c9s (0.03 #676, 0.02 #949, 0.02 #988), 0999q (0.03 #686, 0.02 #959, 0.02 #998), 0t_2 (0.03 #87, 0.02 #633, 0.02 #165), 09s02 (0.02 #699, 0.02 #972, 0.02 #231), 06nm1 (0.02 #162, 0.02 #201, 0.02 #240), 0688f (0.02 #536, 0.02 #224, 0.02 #341) >> Best rule #548 for best value: >> intensional similarity = 2 >> extensional distance = 118 >> proper extension: 0f2c8g; >> query: (?x9207, 02h40lc) <- student(?x1368, ?x9207), film(?x9207, ?x603) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023mdt languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.433 http://example.org/people/person/languages #13348-0bpk2 PRED entity: 0bpk2 PRED relation: category PRED expected values: 08mbj5d => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.93 #14, 0.89 #38, 0.84 #40) >> Best rule #14 for best value: >> intensional similarity = 8 >> extensional distance = 56 >> proper extension: 01x15dc; >> query: (?x5751, 08mbj5d) <- award(?x5751, ?x4912), award(?x4157, ?x4912), award(?x3166, ?x4912), award(?x2170, ?x4912), ceremony(?x4912, ?x342), ?x4157 = 01kh2m1, artists(?x505, ?x2170), role(?x3166, ?x314) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bpk2 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.931 http://example.org/common/topic/webpage./common/webpage/category #13347-01rvgx PRED entity: 01rvgx PRED relation: contains! PRED expected values: 02jx1 => 74 concepts (36 used for prediction) PRED predicted values (max 10 best out of 172): 02jx1 (0.72 #7160, 0.65 #24265, 0.64 #4559), 09c7w0 (0.33 #7165, 0.30 #8061, 0.30 #8955), 02qkt (0.22 #20943, 0.14 #31694, 0.11 #19153), 02j9z (0.22 #18835, 0.09 #20625, 0.07 #29584), 0b_yz (0.20 #2416, 0.01 #10742, 0.01 #26866), 04jpl (0.18 #14350, 0.12 #24201, 0.08 #12558), 06q1r (0.18 #3033, 0.17 #3928, 0.11 #4824), 04_1l0v (0.17 #7612, 0.16 #11192, 0.16 #8508), 059rby (0.16 #5389, 0.09 #6285, 0.09 #7182), 07c5l (0.14 #7556, 0.08 #20991, 0.06 #30846) >> Best rule #7160 for best value: >> intensional similarity = 4 >> extensional distance = 170 >> proper extension: 0mgrh; >> query: (?x11408, ?x512) <- administrative_parent(?x11408, ?x10603), contains(?x10603, ?x5084), contains(?x512, ?x10603), country(?x124, ?x512) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rvgx contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 36.000 0.724 http://example.org/location/location/contains #13346-03cs_z7 PRED entity: 03cs_z7 PRED relation: profession PRED expected values: 0dxtg => 85 concepts (82 used for prediction) PRED predicted values (max 10 best out of 144): 0dxtg (0.86 #1206, 0.84 #1802, 0.84 #1653), 09jwl (0.82 #2553, 0.80 #3001, 0.78 #1956), 02hrh1q (0.79 #10451, 0.77 #10749, 0.68 #3445), 0nbcg (0.55 #3014, 0.54 #1969, 0.54 #2566), 01d_h8 (0.50 #7163, 0.49 #900, 0.48 #3138), 0dz3r (0.45 #2536, 0.44 #2984, 0.40 #1939), 016z4k (0.40 #2239, 0.34 #2986, 0.33 #2538), 039v1 (0.37 #2571, 0.36 #3019, 0.33 #1974), 02jknp (0.34 #7165, 0.25 #3140, 0.25 #306), 02krf9 (0.33 #325, 0.30 #2412, 0.30 #2711) >> Best rule #1206 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 0f1vrl; >> query: (?x1340, 0dxtg) <- program_creator(?x8775, ?x1340), profession(?x1340, ?x1041), ?x1041 = 03gjzk >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cs_z7 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 82.000 0.855 http://example.org/people/person/profession #13345-0bz6sq PRED entity: 0bz6sq PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.91 #16, 0.82 #36, 0.81 #41), 07c52 (0.21 #367, 0.04 #97, 0.04 #285), 07z4p (0.21 #367, 0.03 #25, 0.03 #79), 02nxhr (0.21 #367, 0.03 #136, 0.03 #121) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 04tz52; 02rn00y; 023p7l; 03q0r1; 05nlx4; 0b6l1st; 0ds5_72; 085wqm; 09v8clw; >> query: (?x9016, 029j_) <- film_release_region(?x9016, ?x94), ?x94 = 09c7w0, film(?x5636, ?x9016), ?x5636 = 054g1r >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bz6sq film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.914 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #13344-0btyf5z PRED entity: 0btyf5z PRED relation: genre PRED expected values: 03k9fj => 78 concepts (73 used for prediction) PRED predicted values (max 10 best out of 86): 05p553 (0.58 #4655, 0.39 #1313, 0.35 #1432), 03k9fj (0.50 #1201, 0.46 #130, 0.43 #1797), 02l7c8 (0.37 #4786, 0.32 #2158, 0.31 #3828), 01hmnh (0.29 #1206, 0.29 #4069, 0.27 #1802), 0lsxr (0.29 #4181, 0.22 #1556, 0.20 #1079), 060__y (0.26 #372, 0.20 #15, 0.19 #491), 04xvlr (0.25 #4054, 0.19 #4773, 0.19 #3456), 0hcr (0.23 #141, 0.14 #855, 0.13 #736), 03bxz7 (0.23 #411, 0.16 #530, 0.12 #2198), 02n4kr (0.20 #7, 0.17 #959, 0.16 #4180) >> Best rule #4655 for best value: >> intensional similarity = 5 >> extensional distance = 999 >> proper extension: 02pg45; >> query: (?x1932, 05p553) <- genre(?x1932, ?x1013), genre(?x6306, ?x1013), genre(?x1490, ?x1013), film_release_region(?x1490, ?x87), ?x6306 = 016dj8 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1201 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 110 *> proper extension: 04sh80; *> query: (?x1932, 03k9fj) <- genre(?x1932, ?x1013), ?x1013 = 06n90, music(?x1932, ?x4911) *> conf = 0.50 ranks of expected_values: 2 EVAL 0btyf5z genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 73.000 0.582 http://example.org/film/film/genre #13343-0kxbc PRED entity: 0kxbc PRED relation: artists! PRED expected values: 0133_p => 112 concepts (47 used for prediction) PRED predicted values (max 10 best out of 272): 064t9 (0.60 #12943, 0.58 #5861, 0.54 #8942), 05bt6j (0.52 #1580, 0.50 #41, 0.41 #348), 0pm85 (0.33 #154, 0.26 #1693, 0.07 #2462), 0155w (0.32 #1335, 0.27 #719, 0.25 #4415), 02yv6b (0.32 #2250, 0.31 #711, 0.29 #4407), 0glt670 (0.31 #5887, 0.19 #5580, 0.19 #4658), 025sc50 (0.31 #5895, 0.21 #12977, 0.20 #3743), 02lnbg (0.29 #5904, 0.17 #3752, 0.16 #4675), 0dl5d (0.29 #2173, 0.26 #3097, 0.23 #326), 01lyv (0.29 #1263, 0.27 #647, 0.25 #2801) >> Best rule #12943 for best value: >> intensional similarity = 4 >> extensional distance = 499 >> proper extension: 05mt_q; 01j4ls; 02zmh5; 0pyg6; 07ss8_; 047sxrj; 0cg9y; 02qlg7s; 01trhmt; 01wgxtl; ... >> query: (?x5635, 064t9) <- profession(?x5635, ?x1032), artists(?x2249, ?x5635), artists(?x2249, ?x7972), ?x7972 = 0326tc >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1691 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 0dvqq; 016fmf; 0mgcr; 018gm9; 07h76; 0838y; 017lb_; 03q_w5; 09jvl; 06lxn; *> query: (?x5635, 0133_p) <- artists(?x2995, ?x5635), artists(?x2249, ?x5635), ?x2995 = 01cbwl, parent_genre(?x2072, ?x2249) *> conf = 0.06 ranks of expected_values: 161 EVAL 0kxbc artists! 0133_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 112.000 47.000 0.597 http://example.org/music/genre/artists #13342-0g69lg PRED entity: 0g69lg PRED relation: award_winner! PRED expected values: 015ppk => 112 concepts (74 used for prediction) PRED predicted values (max 10 best out of 175): 02md2d (0.54 #7960, 0.54 #32976, 0.51 #11372), 039c26 (0.54 #7960, 0.54 #32976, 0.51 #11372), 015ppk (0.54 #7960, 0.51 #11372, 0.49 #6822), 02qkq0 (0.54 #7960, 0.51 #11372, 0.49 #6822), 01cvtf (0.34 #14784, 0.23 #30702, 0.15 #39802), 02sqkh (0.23 #30702), 017f3m (0.15 #39802, 0.11 #12510, 0.10 #39803), 03ln8b (0.14 #227, 0.03 #32065, 0.02 #35477), 02k_4g (0.14 #79, 0.02 #80736, 0.01 #35329), 03d17dg (0.07 #75050, 0.07 #81873, 0.04 #83010) >> Best rule #7960 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 0f721s; 09pl3s; 03m_k0; 07g7h2; 02661h; 01c1px; 036dyy; 07jrjb; 05hrq4; 017dpj; ... >> query: (?x6765, ?x4223) <- program(?x6765, ?x4223), award_winner(?x4921, ?x6765), award_winner(?x4223, ?x3446) >> conf = 0.54 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0g69lg award_winner! 015ppk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 74.000 0.542 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #13341-01b9w3 PRED entity: 01b9w3 PRED relation: genre PRED expected values: 0c4xc => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 77): 07s9rl0 (0.89 #3294, 0.67 #1, 0.64 #3957), 0c4xc (0.71 #206, 0.60 #370, 0.58 #452), 01t_vv (0.50 #33, 0.41 #115, 0.37 #525), 0hcr (0.40 #2322, 0.32 #2404, 0.25 #924), 06nbt (0.29 #512, 0.23 #595, 0.21 #430), 06n90 (0.28 #2399, 0.20 #3306, 0.18 #4299), 0vgkd (0.25 #502, 0.23 #420, 0.22 #256), 01htzx (0.21 #1499, 0.20 #3144, 0.19 #3557), 01hmnh (0.20 #2402, 0.15 #2320, 0.15 #922), 03k9fj (0.19 #3138, 0.17 #3551, 0.17 #4297) >> Best rule #3294 for best value: >> intensional similarity = 5 >> extensional distance = 146 >> proper extension: 070ltt; 07qht4; >> query: (?x4384, 07s9rl0) <- genre(?x4384, ?x8805), genre(?x12173, ?x8805), genre(?x5808, ?x8805), ?x12173 = 0pc_l, ?x5808 = 05lfwd >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #206 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 02r5qtm; 0431v3; *> query: (?x4384, 0c4xc) <- producer_type(?x4384, ?x632), nominated_for(?x2016, ?x4384), genre(?x4384, ?x258), ?x2016 = 0cjyzs *> conf = 0.71 ranks of expected_values: 2 EVAL 01b9w3 genre 0c4xc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 93.000 0.885 http://example.org/tv/tv_program/genre #13340-0hhggmy PRED entity: 0hhggmy PRED relation: film_release_region PRED expected values: 09c7w0 07ssc 015fr 015qh 0161c => 80 concepts (70 used for prediction) PRED predicted values (max 10 best out of 210): 09c7w0 (0.93 #8449, 0.92 #8585, 0.90 #2), 07ssc (0.87 #11, 0.83 #556, 0.82 #1100), 015fr (0.86 #421, 0.85 #557, 0.84 #965), 015qh (0.69 #577, 0.69 #32, 0.68 #985), 06c1y (0.69 #34, 0.56 #579, 0.54 #987), 03ryn (0.64 #68, 0.46 #613, 0.39 #1157), 077qn (0.64 #71, 0.40 #616, 0.33 #1296), 07t21 (0.62 #31, 0.40 #576, 0.39 #984), 01mjq (0.61 #988, 0.58 #580, 0.56 #35), 06qd3 (0.57 #1662, 0.57 #1526, 0.56 #846) >> Best rule #8449 for best value: >> intensional similarity = 6 >> extensional distance = 1313 >> proper extension: 0900j5; >> query: (?x8580, 09c7w0) <- film_release_region(?x8580, ?x2843), film_release_region(?x8580, ?x279), film_release_region(?x1919, ?x2843), ?x1919 = 0_7w6, form_of_government(?x2843, ?x48), country_of_origin(?x2447, ?x279) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 20 EVAL 0hhggmy film_release_region 0161c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 80.000 70.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hhggmy film_release_region 015qh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 70.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hhggmy film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 70.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hhggmy film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 70.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hhggmy film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 70.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13339-01vsnff PRED entity: 01vsnff PRED relation: profession PRED expected values: 09lbv 04f2zj => 121 concepts (93 used for prediction) PRED predicted values (max 10 best out of 74): 01d_h8 (0.38 #1734, 0.38 #148, 0.35 #1878), 0dxtg (0.36 #300, 0.35 #12, 0.34 #1886), 02jknp (0.36 #294, 0.31 #150, 0.22 #1736), 01c72t (0.35 #4350, 0.34 #3483, 0.34 #2905), 0kyk (0.29 #10399, 0.22 #27, 0.20 #1180), 0fnpj (0.29 #10399, 0.18 #3373, 0.16 #1497), 04f2zj (0.29 #10399, 0.09 #8087, 0.09 #957), 05z96 (0.29 #10399, 0.09 #8087, 0.07 #182), 064xm0 (0.29 #10399, 0.09 #8087, 0.04 #635), 0cbd2 (0.28 #1158, 0.26 #5, 0.24 #293) >> Best rule #1734 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 0f7fy; 051cc; 0c_md_; 0k_mt; 0d3k14; 06c0j; >> query: (?x2187, 01d_h8) <- person(?x1619, ?x2187), gender(?x2187, ?x231), award_winner(?x247, ?x2187) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #10399 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1437 *> proper extension: 035_2h; *> query: (?x2187, ?x131) <- award_winner(?x2187, ?x6383), award_winner(?x567, ?x6383), profession(?x6383, ?x131) *> conf = 0.29 ranks of expected_values: 7, 22 EVAL 01vsnff profession 04f2zj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 121.000 93.000 0.383 http://example.org/people/person/profession EVAL 01vsnff profession 09lbv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 121.000 93.000 0.383 http://example.org/people/person/profession #13338-0dn16 PRED entity: 0dn16 PRED relation: artists PRED expected values: 015srx => 59 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1222): 0gbwp (0.71 #5711, 0.67 #4638, 0.60 #6783), 01x1cn2 (0.71 #5557, 0.67 #4484, 0.60 #6629), 0136p1 (0.71 #5504, 0.67 #4431, 0.60 #6576), 09889g (0.71 #5811, 0.67 #4738, 0.60 #6883), 07ss8_ (0.71 #5527, 0.67 #4454, 0.60 #6599), 0dt1cm (0.71 #6114, 0.67 #5041, 0.60 #7186), 01trhmt (0.71 #5558, 0.67 #4485, 0.60 #6630), 018n6m (0.71 #5770, 0.67 #4697, 0.60 #6842), 086qd (0.71 #5518, 0.67 #4445, 0.60 #6590), 03t9sp (0.68 #11926, 0.67 #12997, 0.33 #122) >> Best rule #5711 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 02lnbg; >> query: (?x996, 0gbwp) <- artists(?x996, ?x11667), artists(?x996, ?x10502), artists(?x996, ?x8018), artists(?x996, ?x4842), ?x4842 = 0hvbj, ?x8018 = 09h4b5, category(?x11667, ?x134), ?x134 = 08mbj5d, artist(?x2193, ?x10502) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4813 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 064t9; *> query: (?x996, 015srx) <- artists(?x996, ?x8018), artists(?x996, ?x4842), ?x4842 = 0hvbj, celebrity(?x8018, ?x5058), people(?x1446, ?x8018), parent_genre(?x996, ?x671), place_of_birth(?x8018, ?x9260), participant(?x6613, ?x8018) *> conf = 0.50 ranks of expected_values: 70 EVAL 0dn16 artists 015srx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 59.000 25.000 0.714 http://example.org/music/genre/artists #13337-05q8pss PRED entity: 05q8pss PRED relation: nominated_for PRED expected values: 06wzvr 032016 07nnp_ => 51 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1564): 075wx7_ (0.80 #12874, 0.71 #9714, 0.50 #11295), 01mszz (0.71 #28462, 0.65 #36370, 0.64 #36369), 06zn1c (0.71 #28462, 0.65 #36370, 0.64 #36369), 0kvbl6 (0.71 #28462, 0.65 #36370, 0.64 #36369), 07nnp_ (0.71 #11041, 0.70 #14201, 0.38 #12622), 0gc_c_ (0.71 #10010, 0.70 #13170, 0.32 #14752), 0kv2hv (0.71 #9601, 0.60 #12761, 0.32 #14343), 0cn_b8 (0.71 #10035, 0.60 #13195, 0.32 #14777), 0gwgn1k (0.71 #10839, 0.60 #13999, 0.26 #15581), 0f2sx4 (0.71 #10689, 0.50 #13849, 0.32 #15431) >> Best rule #12874 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 05p1dby; >> query: (?x4317, 075wx7_) <- nominated_for(?x4317, ?x8443), award(?x3321, ?x4317), award_winner(?x3321, ?x1089), profession(?x3321, ?x131), ?x8443 = 02ywwy >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #11041 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 04ljl_l; 05f4m9q; 07bdd_; 07cbcy; 05p09zm; *> query: (?x4317, 07nnp_) <- nominated_for(?x4317, ?x188), award(?x11019, ?x4317), award(?x7088, ?x4317), ?x11019 = 0hqly, award_nominee(?x7088, ?x1974), artists(?x671, ?x7088) *> conf = 0.71 ranks of expected_values: 5, 47, 81 EVAL 05q8pss nominated_for 07nnp_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 51.000 24.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05q8pss nominated_for 032016 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 51.000 24.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05q8pss nominated_for 06wzvr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 51.000 24.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13336-0bscw PRED entity: 0bscw PRED relation: film! PRED expected values: 015grj 02661h => 76 concepts (33 used for prediction) PRED predicted values (max 10 best out of 858): 0kvrb (0.46 #58143, 0.45 #39449, 0.45 #66454), 03mfqm (0.46 #58143, 0.45 #39449, 0.45 #66454), 02mv9b (0.20 #2062, 0.04 #4138, 0.03 #14519), 0bmh4 (0.20 #419, 0.04 #2495, 0.03 #12876), 0gt3p (0.20 #1346, 0.04 #3422, 0.03 #5498), 0241jw (0.20 #296, 0.03 #12753, 0.02 #43897), 015wnl (0.20 #650, 0.02 #33869, 0.02 #44251), 01hkhq (0.20 #414, 0.01 #12871, 0.01 #44015), 0cgbf (0.20 #1209, 0.01 #13666), 0dvld (0.18 #3134, 0.17 #5210, 0.16 #9363) >> Best rule #58143 for best value: >> intensional similarity = 5 >> extensional distance = 338 >> proper extension: 0b60sq; 06zn1c; >> query: (?x1444, ?x6327) <- genre(?x1444, ?x1403), ?x1403 = 02l7c8, nominated_for(?x484, ?x1444), nominated_for(?x6327, ?x1444), profession(?x6327, ?x7630) >> conf = 0.46 => this is the best rule for 2 predicted values *> Best rule #22155 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 101 *> proper extension: 0pv2t; 09q5w2; 04jkpgv; 0p_th; 0k4kk; 09cr8; 02qmsr; 0fpmrm3; 02rcdc2; 01bb9r; ... *> query: (?x1444, 02661h) <- genre(?x1444, ?x1403), genre(?x2881, ?x1403), genre(?x2612, ?x1403), ?x2881 = 0bpx1k, ?x2612 = 083skw, honored_for(?x1444, ?x7432) *> conf = 0.02 ranks of expected_values: 441 EVAL 0bscw film! 02661h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 33.000 0.460 http://example.org/film/actor/film./film/performance/film EVAL 0bscw film! 015grj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 33.000 0.460 http://example.org/film/actor/film./film/performance/film #13335-030hbp PRED entity: 030hbp PRED relation: actor! PRED expected values: 02_1kl => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 165): 0g60z (0.47 #530, 0.20 #4737, 0.10 #23699), 02r1ysd (0.20 #124, 0.07 #650), 03ln8b (0.20 #30, 0.03 #6083, 0.03 #13984), 04p5cr (0.14 #380, 0.04 #906, 0.03 #1432), 027tbrc (0.14 #298, 0.04 #824, 0.02 #1876), 026bfsh (0.09 #4306, 0.08 #6149, 0.07 #1148), 0vjr (0.09 #883, 0.06 #1409, 0.05 #1935), 02zv4b (0.09 #813, 0.06 #1339, 0.05 #1865), 0828jw (0.09 #892, 0.06 #1418, 0.05 #1944), 0fhzwl (0.07 #700, 0.07 #3331, 0.04 #4647) >> Best rule #530 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 04bd8y; 0hvb2; 02bkdn; 011_3s; 02yj7w; 02mqc4; 04yqlk; 047c9l; 05l4yg; 05gnf9; ... >> query: (?x10491, 0g60z) <- award_nominee(?x336, ?x10491), ?x336 = 03x3qv, profession(?x10491, ?x1032) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #15137 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 492 *> proper extension: 06688p; 05bp8g; 049tjg; 01rrwf6; 018dnt; 01wjrn; 02wrhj; 045bs6; 02tqkf; 030x48; ... *> query: (?x10491, 02_1kl) <- actor(?x715, ?x10491), place_of_birth(?x10491, ?x4776), film(?x10491, ?x4643) *> conf = 0.02 ranks of expected_values: 131 EVAL 030hbp actor! 02_1kl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 132.000 132.000 0.467 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #13334-0dlv0 PRED entity: 0dlv0 PRED relation: administrative_parent PRED expected values: 09f07 => 166 concepts (141 used for prediction) PRED predicted values (max 10 best out of 45): 03rk0 (0.47 #2763, 0.42 #2765, 0.32 #686), 0bq0p9 (0.42 #2765, 0.26 #4691, 0.25 #685), 09c7w0 (0.14 #7181, 0.13 #8273, 0.12 #8136), 086g2 (0.13 #6632), 02j71 (0.10 #18683, 0.10 #19234, 0.10 #17858), 0jgd (0.07 #138, 0.05 #276, 0.05 #550), 0d05w3 (0.07 #183, 0.05 #458, 0.05 #595), 06f32 (0.07 #186, 0.05 #461, 0.04 #878), 017v_ (0.07 #172, 0.04 #1557, 0.03 #2939), 01mjq (0.05 #309, 0.05 #446, 0.04 #863) >> Best rule #2763 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 07p7g; >> query: (?x9466, ?x2146) <- capital(?x2146, ?x9466), capital(?x613, ?x9466), organization(?x613, ?x4230), administrative_parent(?x3411, ?x2146) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #19362 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 895 *> proper extension: 0j3b; 02qkt; 06mx8; 07c5l; 06srk; 04wsz; 02j7k; 06s_2; 0lm0n; 02v3m7; ... *> query: (?x9466, ?x2146) <- contains(?x9466, ?x13396), contains(?x2146, ?x13396) *> conf = 0.05 ranks of expected_values: 18 EVAL 0dlv0 administrative_parent 09f07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 166.000 141.000 0.472 http://example.org/base/aareas/schema/administrative_area/administrative_parent #13333-0162v PRED entity: 0162v PRED relation: country! PRED expected values: 06z6r => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 55): 06z6r (0.85 #2121, 0.84 #2726, 0.83 #2616), 071t0 (0.77 #2717, 0.75 #2112, 0.72 #957), 0486tv (0.71 #150, 0.45 #755, 0.45 #1580), 07gyv (0.64 #117, 0.62 #722, 0.61 #1547), 01lb14 (0.64 #125, 0.61 #1555, 0.60 #950), 06wrt (0.64 #126, 0.60 #951, 0.55 #731), 07jbh (0.64 #144, 0.57 #1574, 0.55 #749), 03hr1p (0.64 #133, 0.57 #1563, 0.55 #958), 064vjs (0.64 #142, 0.55 #1572, 0.49 #2067), 09w1n (0.64 #134, 0.45 #1564, 0.44 #189) >> Best rule #2121 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 0j11; >> query: (?x1957, 06z6r) <- participating_countries(?x1931, ?x1957), film_release_region(?x1956, ?x1957), official_language(?x1957, ?x254) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0162v country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.852 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #13332-012g92 PRED entity: 012g92 PRED relation: profession PRED expected values: 02hrh1q => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 50): 02hrh1q (0.89 #1966, 0.89 #3766, 0.88 #4817), 01d_h8 (0.45 #6, 0.36 #2107, 0.34 #1806), 0d1pc (0.29 #7803, 0.09 #52, 0.09 #352), 01c979 (0.29 #7803, 0.09 #384, 0.04 #534), 0dxtg (0.27 #3165, 0.27 #2865, 0.27 #6616), 03gjzk (0.27 #16, 0.24 #3167, 0.24 #6618), 02jknp (0.26 #308, 0.22 #8412, 0.21 #2859), 09jwl (0.18 #20, 0.18 #4972, 0.17 #1220), 0np9r (0.18 #22, 0.14 #11876, 0.14 #11726), 018gz8 (0.18 #18, 0.13 #7971, 0.13 #9472) >> Best rule #1966 for best value: >> intensional similarity = 3 >> extensional distance = 700 >> proper extension: 014x77; 0kr5_; 012c6x; 0htlr; 03gm48; 0134w7; 0f0p0; 0h1m9; 02lnhv; 0n6f8; ... >> query: (?x12218, 02hrh1q) <- film(?x12218, ?x1421), place_of_birth(?x12218, ?x4627), award_winner(?x1008, ?x12218) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012g92 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.889 http://example.org/people/person/profession #13331-01vsyjy PRED entity: 01vsyjy PRED relation: instrumentalists! PRED expected values: 07xzm 01xqw => 110 concepts (88 used for prediction) PRED predicted values (max 10 best out of 120): 018vs (0.56 #171, 0.54 #1297, 0.53 #571), 06w7v (0.48 #3051, 0.45 #3295, 0.43 #2492), 07y_7 (0.48 #3051, 0.45 #3295, 0.43 #2492), 01vj9c (0.44 #720, 0.43 #2492, 0.41 #3133), 0l14qv (0.44 #720, 0.39 #1528, 0.36 #725), 013y1f (0.44 #720, 0.39 #1528, 0.35 #961), 0gkd1 (0.44 #720, 0.39 #1528, 0.35 #961), 03qjg (0.36 #767, 0.33 #447, 0.27 #366), 02hnl (0.35 #1317, 0.32 #752, 0.29 #591), 026t6 (0.35 #482, 0.31 #1288, 0.29 #562) >> Best rule #171 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01w9wwg; 0191h5; >> query: (?x7272, 018vs) <- profession(?x7272, ?x2348), currency(?x7272, ?x1099), role(?x7272, ?x228), ?x228 = 0l14qv, ?x2348 = 0nbcg >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #142 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 0p76z; *> query: (?x7272, 01xqw) <- artists(?x7329, ?x7272), artists(?x2249, ?x7272), artists(?x1380, ?x7272), ?x2249 = 03lty, ?x7329 = 016jny, ?x1380 = 0dl5d *> conf = 0.14 ranks of expected_values: 20, 21 EVAL 01vsyjy instrumentalists! 01xqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 110.000 88.000 0.556 http://example.org/music/instrument/instrumentalists EVAL 01vsyjy instrumentalists! 07xzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 110.000 88.000 0.556 http://example.org/music/instrument/instrumentalists #13330-08cfr1 PRED entity: 08cfr1 PRED relation: award PRED expected values: 02wwsh8 => 115 concepts (86 used for prediction) PRED predicted values (max 10 best out of 216): 0gr42 (0.52 #6407, 0.26 #2429, 0.13 #1727), 02g3ft (0.42 #6386, 0.32 #2408, 0.26 #2642), 018wdw (0.26 #2513, 0.19 #6491, 0.15 #2981), 019f4v (0.25 #1926, 0.25 #288, 0.16 #11755), 04dn09n (0.25 #1907, 0.25 #269, 0.15 #6119), 0gs9p (0.25 #299, 0.20 #4277, 0.19 #1937), 0gq9h (0.25 #297, 0.19 #1935, 0.16 #5211), 0gr4k (0.25 #260, 0.14 #5174, 0.12 #1898), 0gqy2 (0.25 #356, 0.12 #5504, 0.11 #6206), 0gq_v (0.25 #1891, 0.11 #6337, 0.10 #10550) >> Best rule #6407 for best value: >> intensional similarity = 6 >> extensional distance = 60 >> proper extension: 06mmr; >> query: (?x6924, 0gr42) <- award(?x6924, ?x10747), award(?x2182, ?x10747), award(?x4680, ?x10747), award(?x1076, ?x10747), ?x1076 = 0k2sk, ?x4680 = 01f8hf >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #11894 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 331 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x6924, 02wwsh8) <- award(?x6924, ?x10747), disciplines_or_subjects(?x10747, ?x1013) *> conf = 0.02 ranks of expected_values: 153 EVAL 08cfr1 award 02wwsh8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 115.000 86.000 0.516 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13329-0b6mgp_ PRED entity: 0b6mgp_ PRED relation: profession PRED expected values: 02tx6q => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 49): 02hrh1q (0.73 #10969, 0.66 #12469, 0.66 #15619), 01d_h8 (0.43 #907, 0.39 #1207, 0.38 #1057), 02tx6q (0.38 #203, 0.25 #10504, 0.25 #353), 02jknp (0.34 #1059, 0.33 #1509, 0.27 #909), 0dxtg (0.33 #1065, 0.29 #1515, 0.28 #915), 0np9r (0.25 #10504, 0.08 #10976, 0.08 #16676), 03gjzk (0.24 #917, 0.24 #2117, 0.24 #6618), 09jwl (0.19 #2571, 0.19 #3771, 0.18 #3621), 0dz3r (0.13 #3753, 0.13 #2553, 0.13 #4804), 0nbcg (0.13 #3784, 0.13 #2584, 0.13 #4835) >> Best rule #10969 for best value: >> intensional similarity = 3 >> extensional distance = 1564 >> proper extension: 01wg982; 02lymt; 0d_skg; 0d608; 07pzc; 04qzm; 06cl2w; >> query: (?x4393, 02hrh1q) <- award(?x4393, ?x500), award_nominee(?x10262, ?x4393), film(?x10262, ?x2899) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #203 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 22 *> proper extension: 05f260; *> query: (?x4393, 02tx6q) <- award(?x4393, ?x500), ?x500 = 0p9sw *> conf = 0.38 ranks of expected_values: 3 EVAL 0b6mgp_ profession 02tx6q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 113.000 0.726 http://example.org/people/person/profession #13328-07rn0z PRED entity: 07rn0z PRED relation: profession PRED expected values: 018gz8 => 98 concepts (56 used for prediction) PRED predicted values (max 10 best out of 56): 09jwl (0.44 #2999, 0.42 #3297, 0.37 #5085), 01d_h8 (0.38 #1198, 0.35 #1049, 0.33 #155), 0nbcg (0.28 #3012, 0.27 #3310, 0.27 #5098), 0dxtg (0.27 #5826, 0.27 #1951, 0.27 #5677), 02jknp (0.27 #1200, 0.24 #1051, 0.21 #455), 0dz3r (0.24 #2982, 0.23 #3280, 0.22 #5068), 016z4k (0.23 #5070, 0.22 #2686, 0.22 #1792), 015cjr (0.21 #50, 0.21 #497, 0.14 #646), 03gjzk (0.20 #1356, 0.19 #2250, 0.18 #1654), 018gz8 (0.19 #1358, 0.16 #1656, 0.15 #5531) >> Best rule #2999 for best value: >> intensional similarity = 4 >> extensional distance = 761 >> proper extension: 09mq4m; 05qhnq; 02vwckw; 01wxdn3; 023slg; 051q39; >> query: (?x11568, 09jwl) <- gender(?x11568, ?x231), ?x231 = 05zppz, category(?x11568, ?x134), profession(?x11568, ?x1032) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1358 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 283 *> proper extension: 01vw917; *> query: (?x11568, 018gz8) <- people(?x5025, ?x11568), category(?x11568, ?x134), film(?x11568, ?x8657), ?x134 = 08mbj5d *> conf = 0.19 ranks of expected_values: 10 EVAL 07rn0z profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 98.000 56.000 0.438 http://example.org/people/person/profession #13327-0d5fb PRED entity: 0d5fb PRED relation: colors PRED expected values: 01g5v 038hg => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 20): 01g5v (0.48 #964, 0.29 #1584, 0.27 #123), 019sc (0.35 #907, 0.26 #1248, 0.19 #1588), 01l849 (0.35 #1242, 0.25 #1582, 0.20 #661), 06fvc (0.17 #902, 0.16 #963, 0.16 #1583), 0jc_p (0.16 #124, 0.12 #921, 0.12 #184), 04mkbj (0.15 #30, 0.12 #921, 0.09 #1642), 03wkwg (0.14 #215, 0.12 #921, 0.12 #75), 038hg (0.13 #692, 0.12 #921, 0.11 #132), 036k5h (0.12 #921, 0.10 #305, 0.09 #1586), 02rnmb (0.12 #921, 0.09 #53, 0.08 #33) >> Best rule #964 for best value: >> intensional similarity = 5 >> extensional distance = 271 >> proper extension: 01v3ht; 0k9wp; 01g6l8; 06b19; >> query: (?x13328, 01g5v) <- colors(?x13328, ?x663), colors(?x7485, ?x663), colors(?x4571, ?x663), ?x7485 = 019m60, draft(?x4571, ?x2569) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 0d5fb colors 038hg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 84.000 84.000 0.484 http://example.org/education/educational_institution/colors EVAL 0d5fb colors 01g5v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.484 http://example.org/education/educational_institution/colors #13326-01w56k PRED entity: 01w56k PRED relation: artist PRED expected values: 0m19t 07mvp 08w4pm => 59 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1157): 048xh (0.73 #11401, 0.33 #17256, 0.33 #16419), 0565cz (0.50 #6043, 0.50 #5205, 0.50 #1858), 01kph_c (0.50 #3681, 0.50 #2005, 0.33 #5352), 01q99h (0.50 #6292, 0.38 #7961, 0.36 #9631), 0qf3p (0.50 #3494, 0.38 #8507, 0.33 #983), 0kzy0 (0.50 #3375, 0.36 #10059, 0.33 #30), 01323p (0.50 #6410, 0.33 #5572, 0.33 #1390), 01w60_p (0.50 #3459, 0.33 #948, 0.33 #114), 06gcn (0.50 #3896, 0.33 #1385, 0.33 #551), 01wg25j (0.50 #3964, 0.33 #619, 0.31 #8977) >> Best rule #11401 for best value: >> intensional similarity = 13 >> extensional distance = 13 >> proper extension: 086k8; 01w40h; 01xjx6; 01txts; 03vtrv; 04t061; 0915l1; >> query: (?x13837, 048xh) <- artist(?x13837, ?x4840), artist(?x13837, ?x2005), artist(?x13837, ?x649), artists(?x3916, ?x2005), artists(?x474, ?x2005), award_winner(?x4840, ?x2806), award_nominee(?x4840, ?x4239), group(?x2297, ?x2005), ?x474 = 0m0jc, profession(?x649, ?x1032), artists(?x378, ?x4840), ?x2297 = 051hrr, parent_genre(?x2439, ?x3916) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #3927 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 01cl2y; *> query: (?x13837, 08w4pm) <- artist(?x13837, ?x4082), artist(?x13837, ?x2005), artist(?x13837, ?x1521), ?x2005 = 05k79, award(?x4082, ?x1008), type_of_union(?x4082, ?x566), place_of_birth(?x4082, ?x6764), instrumentalists(?x212, ?x1521), artists(?x378, ?x1521), location(?x4082, ?x4627), award_winner(?x219, ?x1521) *> conf = 0.25 ranks of expected_values: 219, 334, 438 EVAL 01w56k artist 08w4pm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 59.000 24.000 0.733 http://example.org/music/record_label/artist EVAL 01w56k artist 07mvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 59.000 24.000 0.733 http://example.org/music/record_label/artist EVAL 01w56k artist 0m19t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 24.000 0.733 http://example.org/music/record_label/artist #13325-0fhzwl PRED entity: 0fhzwl PRED relation: genre PRED expected values: 0lsxr 0vgkd 02fgmn => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 78): 05p553 (0.49 #1020, 0.48 #1254, 0.46 #2115), 0hcr (0.44 #327, 0.22 #2673, 0.19 #2595), 01z4y (0.36 #718, 0.35 #404, 0.35 #1030), 0c4xc (0.27 #1053, 0.25 #1287, 0.22 #663), 06n90 (0.24 #1495, 0.23 #322, 0.23 #1338), 01t_vv (0.22 #654, 0.21 #732, 0.21 #418), 01hmnh (0.21 #325, 0.17 #1498, 0.15 #2514), 0pr6f (0.21 #357, 0.10 #2312, 0.10 #2390), 01z77k (0.20 #492, 0.15 #413, 0.14 #1664), 0lsxr (0.18 #555, 0.16 #398, 0.14 #1336) >> Best rule #1020 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 0g60z; 080dwhx; 02_1rq; 072kp; 039fgy; 0kfpm; 0358x_; 019nnl; 0ddd0gc; 0124k9; ... >> query: (?x8870, 05p553) <- actor(?x8870, ?x879), nominated_for(?x435, ?x8870), producer_type(?x8870, ?x632) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #555 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 0jq2r; 06qxh; *> query: (?x8870, 0lsxr) <- genre(?x8870, ?x53), ?x53 = 07s9rl0, titles(?x2008, ?x8870) *> conf = 0.18 ranks of expected_values: 10, 18, 19 EVAL 0fhzwl genre 02fgmn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 79.000 79.000 0.491 http://example.org/tv/tv_program/genre EVAL 0fhzwl genre 0vgkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 79.000 79.000 0.491 http://example.org/tv/tv_program/genre EVAL 0fhzwl genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 79.000 79.000 0.491 http://example.org/tv/tv_program/genre #13324-07b3r9 PRED entity: 07b3r9 PRED relation: profession PRED expected values: 03gjzk => 104 concepts (102 used for prediction) PRED predicted values (max 10 best out of 62): 03gjzk (0.87 #2101, 0.86 #1058, 0.85 #1803), 02hrh1q (0.85 #13429, 0.75 #14025, 0.74 #3441), 0dxtg (0.67 #2099, 0.67 #1354, 0.67 #2844), 02jknp (0.53 #1646, 0.47 #3881, 0.46 #3583), 02krf9 (0.32 #2709, 0.31 #2262, 0.31 #772), 018gz8 (0.30 #166, 0.27 #315, 0.13 #5083), 0cbd2 (0.26 #155, 0.25 #9844, 0.22 #304), 09jwl (0.19 #6277, 0.18 #14626, 0.17 #7023), 0kyk (0.17 #9868, 0.13 #4947, 0.12 #5245), 0np9r (0.15 #170, 0.15 #319, 0.14 #766) >> Best rule #2101 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 02c0mv; >> query: (?x4383, 03gjzk) <- program(?x4383, ?x4384), award_winner(?x2179, ?x4383), profession(?x4383, ?x319) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07b3r9 profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 102.000 0.872 http://example.org/people/person/profession #13323-03kbr PRED entity: 03kbr PRED relation: religion! PRED expected values: 03czqs => 36 concepts (26 used for prediction) PRED predicted values (max 10 best out of 112): 03rk0 (0.60 #377, 0.44 #489, 0.20 #935), 01n4w (0.55 #834, 0.48 #1949, 0.48 #1503), 0vmt (0.55 #800, 0.45 #1915, 0.44 #1469), 05kkh (0.55 #786, 0.45 #1901, 0.44 #1455), 02xry (0.50 #828, 0.44 #716, 0.44 #1497), 01n7q (0.50 #806, 0.41 #1921, 0.40 #1475), 059_c (0.50 #805, 0.41 #1920, 0.40 #1474), 01x73 (0.50 #816, 0.41 #1931, 0.40 #1485), 0gyh (0.50 #832, 0.40 #1501, 0.39 #720), 03v1s (0.50 #792, 0.40 #1461, 0.39 #680) >> Best rule #377 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 06yyp; >> query: (?x11101, 03rk0) <- religion(?x10221, ?x11101), people(?x5025, ?x10221), ?x5025 = 0dryh9k, profession(?x10221, ?x1032), award(?x10221, ?x1937), type_of_union(?x10221, ?x566), location(?x10221, ?x14559) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #441 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 06yyp; *> query: (?x11101, 03czqs) <- religion(?x10221, ?x11101), people(?x5025, ?x10221), ?x5025 = 0dryh9k, profession(?x10221, ?x1032), award(?x10221, ?x1937), type_of_union(?x10221, ?x566), location(?x10221, ?x14559) *> conf = 0.40 ranks of expected_values: 45 EVAL 03kbr religion! 03czqs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 36.000 26.000 0.600 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #13322-0c5v2 PRED entity: 0c5v2 PRED relation: location! PRED expected values: 01s7z0 => 75 concepts (31 used for prediction) PRED predicted values (max 10 best out of 873): 0cqt90 (0.25 #752, 0.09 #3271, 0.02 #8309), 0168cl (0.25 #99, 0.09 #2618, 0.02 #7656), 017f4y (0.11 #7195, 0.04 #9714, 0.02 #17271), 012xdf (0.09 #4363, 0.05 #6882, 0.02 #14439), 02s2wq (0.09 #3818, 0.05 #6337, 0.02 #8856), 01_ztw (0.09 #3662, 0.05 #6181, 0.02 #8700), 06pj8 (0.09 #2903, 0.02 #7941, 0.02 #33131), 01w58n3 (0.05 #6950, 0.04 #9469, 0.01 #14507), 03ywyk (0.05 #6907, 0.04 #9426), 023kzp (0.05 #6255, 0.03 #16331, 0.03 #23888) >> Best rule #752 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0rjg8; 0rqf1; >> query: (?x13119, 0cqt90) <- contains(?x9290, ?x13119), contains(?x2623, ?x13119), ?x2623 = 02xry, ?x9290 = 0jrxx >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c5v2 location! 01s7z0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 31.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #13321-0lmm3 PRED entity: 0lmm3 PRED relation: colors PRED expected values: 06fvc => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 17): 083jv (0.88 #584, 0.85 #1341, 0.82 #772), 06fvc (0.65 #1133, 0.55 #1270, 0.54 #653), 038hg (0.57 #453, 0.25 #146, 0.21 #609), 01l849 (0.29 #788, 0.27 #531, 0.18 #719), 036k5h (0.20 #39, 0.18 #719, 0.16 #478), 02rnmb (0.19 #513, 0.18 #1129, 0.18 #1477), 06kqt3 (0.18 #1129, 0.18 #719, 0.16 #478), 0jc_p (0.18 #719, 0.16 #478, 0.14 #1651), 04d18d (0.18 #719, 0.16 #478, 0.14 #1651), 09ggk (0.18 #719, 0.16 #478, 0.13 #1582) >> Best rule #584 for best value: >> intensional similarity = 10 >> extensional distance = 73 >> proper extension: 01xn7x1; >> query: (?x13090, 083jv) <- position(?x13090, ?x60), team(?x11481, ?x13090), colors(?x13090, ?x3621), colors(?x7092, ?x3621), colors(?x14124, ?x3621), colors(?x5083, ?x3621), ?x7092 = 01g7_r, ?x14124 = 04l590, ?x5083 = 024d8w, team(?x11481, ?x1143) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1133 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 176 *> proper extension: 03lpp_; 06x68; 0512p; 01yhm; 01yjl; 05gg4; 07l2m; 0b6p3qf; 025v1sx; 02fp3; ... *> query: (?x13090, 06fvc) <- colors(?x13090, ?x4557), team(?x60, ?x13090), colors(?x6223, ?x4557), colors(?x4556, ?x4557), colors(?x8912, ?x4557), colors(?x8678, ?x4557), ?x8912 = 01lpx8, ?x6223 = 05d9y_, contains(?x1782, ?x4556), currency(?x4556, ?x170), ?x8678 = 0dwz3t *> conf = 0.65 ranks of expected_values: 2 EVAL 0lmm3 colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.880 http://example.org/sports/sports_team/colors #13320-04qdj PRED entity: 04qdj PRED relation: place_of_death! PRED expected values: 0jvtp => 139 concepts (100 used for prediction) PRED predicted values (max 10 best out of 560): 03_87 (0.33 #1055, 0.20 #2567, 0.20 #1811), 07dnx (0.33 #1192, 0.20 #2704, 0.20 #1948), 0bk5r (0.33 #985, 0.20 #2497, 0.20 #1741), 040db (0.20 #2347, 0.02 #15969, 0.02 #18237), 0hnjt (0.08 #3985, 0.06 #4742, 0.06 #6255), 0b_fw (0.08 #3861, 0.06 #4618, 0.06 #6131), 01h320 (0.08 #3912, 0.06 #4669, 0.06 #6182), 04n_g (0.08 #3934, 0.06 #4691, 0.06 #6204), 01zwy (0.08 #4206, 0.06 #4963, 0.06 #6476), 0484q (0.08 #4123, 0.06 #4880, 0.06 #6393) >> Best rule #1055 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 08966; >> query: (?x4893, 03_87) <- location_of_ceremony(?x566, ?x4893), country(?x4893, ?x774), administrative_division(?x4893, ?x11694), ?x566 = 04ztj, ?x774 = 06mzp >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04qdj place_of_death! 0jvtp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 139.000 100.000 0.333 http://example.org/people/deceased_person/place_of_death #13319-07p__7 PRED entity: 07p__7 PRED relation: legislative_sessions! PRED expected values: 02bqm0 => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 35): 02bqm0 (0.87 #299, 0.87 #298, 0.87 #151), 07p__7 (0.87 #633, 0.82 #783, 0.80 #488), 01gst_ (0.62 #600, 0.59 #707, 0.55 #563), 01gtbb (0.55 #562, 0.54 #599, 0.45 #526), 01gtc0 (0.55 #536, 0.50 #464, 0.46 #609), 01gtcc (0.54 #602, 0.47 #674, 0.45 #565), 01gsvp (0.47 #719, 0.46 #612, 0.46 #909), 01gtcq (0.46 #610, 0.45 #573, 0.45 #537), 01gsvb (0.46 #618, 0.45 #545, 0.44 #437), 01gtdd (0.46 #622, 0.45 #585, 0.41 #694) >> Best rule #299 for best value: >> intensional similarity = 35 >> extensional distance = 3 >> proper extension: 043djx; >> query: (?x845, ?x3766) <- legislative_sessions(?x845, ?x6728), legislative_sessions(?x845, ?x3766), district_represented(?x845, ?x7405), district_represented(?x845, ?x6521), district_represented(?x845, ?x3634), district_represented(?x845, ?x2623), district_represented(?x845, ?x2049), district_represented(?x845, ?x1767), district_represented(?x845, ?x1351), district_represented(?x845, ?x1227), district_represented(?x845, ?x335), district_represented(?x845, ?x177), legislative_sessions(?x652, ?x845), ?x335 = 059rby, ?x177 = 05kkh, contains(?x1351, ?x1350), religion(?x1351, ?x10681), jurisdiction_of_office(?x900, ?x1351), contains(?x94, ?x1351), ?x3634 = 07b_l, ?x10681 = 01s5nb, legislative_sessions(?x2860, ?x845), district_represented(?x6728, ?x1274), ?x2623 = 02xry, capital(?x2049, ?x12644), ?x7405 = 07_f2, location_of_ceremony(?x566, ?x1351), place_of_death(?x652, ?x5962), contains(?x2049, ?x5554), legislative_sessions(?x11440, ?x6728), state_province_region(?x99, ?x1227), location(?x9180, ?x6521), contains(?x1227, ?x191), ?x1767 = 04rrd, ?x9180 = 0f2zc >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07p__7 legislative_sessions! 02bqm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 32.000 0.870 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #13318-0147jt PRED entity: 0147jt PRED relation: gender PRED expected values: 05zppz => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #41, 0.84 #29, 0.82 #33), 02zsn (0.36 #8, 0.35 #18, 0.35 #66) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 183 >> proper extension: 09b0xs; 02nfjp; 05cgy8; >> query: (?x9103, 05zppz) <- profession(?x9103, ?x1614), award_winner(?x10556, ?x9103), ?x1614 = 01c72t >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0147jt gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.892 http://example.org/people/person/gender #13317-04xjp PRED entity: 04xjp PRED relation: type_of_union PRED expected values: 04ztj => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.83 #89, 0.82 #158, 0.81 #97), 01g63y (0.25 #531, 0.20 #30, 0.20 #26), 01bl8s (0.02 #128, 0.02 #136, 0.02 #132) >> Best rule #89 for best value: >> intensional similarity = 6 >> extensional distance = 28 >> proper extension: 03kxdw; >> query: (?x2162, 04ztj) <- profession(?x2162, ?x6421), influenced_by(?x118, ?x2162), languages(?x2162, ?x2502), profession(?x11899, ?x6421), ?x11899 = 03fnyk, gender(?x2162, ?x231) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04xjp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.833 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13316-07cn2c PRED entity: 07cn2c PRED relation: type_of_union PRED expected values: 04ztj => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.76 #9, 0.75 #21, 0.69 #213), 01g63y (0.20 #2, 0.14 #46, 0.13 #42) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 01bcq; 03k545; >> query: (?x4134, 04ztj) <- award(?x4134, ?x4135), nationality(?x4134, ?x774), actor(?x297, ?x4134) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07cn2c type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.760 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13315-0cy8v PRED entity: 0cy8v PRED relation: contains! PRED expected values: 04rrd => 76 concepts (41 used for prediction) PRED predicted values (max 10 best out of 139): 04rrd (0.77 #11630, 0.35 #26839, 0.26 #25050), 027rqbx (0.77 #11630, 0.35 #26839, 0.26 #25050), 07c5l (0.35 #26839, 0.26 #25050, 0.24 #24155), 05k7sb (0.34 #132, 0.32 #1026, 0.25 #1920), 01n7q (0.23 #2759, 0.22 #3653, 0.21 #14390), 07z1m (0.20 #985, 0.20 #91, 0.09 #9931), 059rby (0.18 #9859, 0.14 #11649, 0.13 #17911), 07ssc (0.17 #34932, 0.15 #35827, 0.03 #32244), 02jx1 (0.13 #35882, 0.09 #34987, 0.02 #32299), 01x73 (0.12 #1008, 0.10 #1902, 0.08 #114) >> Best rule #11630 for best value: >> intensional similarity = 4 >> extensional distance = 221 >> proper extension: 0f__1; 03l2n; 0c_m3; 0d7k1z; 0h3lt; 0n1rj; 013hxv; 0g_wn2; 0r2gj; 0_kq3; ... >> query: (?x10343, ?x1767) <- county(?x10343, ?x12680), time_zones(?x10343, ?x2674), second_level_divisions(?x94, ?x12680), contains(?x1767, ?x12680) >> conf = 0.77 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0cy8v contains! 04rrd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 41.000 0.767 http://example.org/location/location/contains #13314-03phtz PRED entity: 03phtz PRED relation: film! PRED expected values: 03p9hl => 91 concepts (58 used for prediction) PRED predicted values (max 10 best out of 916): 0gn30 (0.77 #948, 0.71 #3030, 0.68 #64558), 01ry0f (0.15 #850, 0.14 #2932, 0.10 #24986), 03xmy1 (0.15 #31235, 0.10 #24986, 0.10 #27069), 03_gd (0.15 #31235, 0.10 #27069, 0.08 #27070), 02f1c (0.15 #31235, 0.10 #27069, 0.08 #27070), 023361 (0.15 #31235, 0.10 #27069, 0.08 #27070), 032dg7 (0.15 #31235, 0.08 #27070, 0.07 #39566), 01fyzy (0.14 #5226, 0.14 #3144, 0.10 #24986), 04yywz (0.14 #2101, 0.10 #24986, 0.10 #27069), 05nn4k (0.12 #43733, 0.11 #74977, 0.11 #58311) >> Best rule #948 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01771z; 074rg9; >> query: (?x12648, 0gn30) <- honored_for(?x12648, ?x6206), honored_for(?x12648, ?x6205), ?x6205 = 01mszz, ?x6206 = 0cwfgz >> conf = 0.77 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03phtz film! 03p9hl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 58.000 0.769 http://example.org/film/actor/film./film/performance/film #13313-05tbn PRED entity: 05tbn PRED relation: contains! PRED expected values: 04_1l0v => 205 concepts (159 used for prediction) PRED predicted values (max 10 best out of 333): 04_1l0v (0.86 #11202, 0.83 #12097, 0.80 #26442), 02qkt (0.53 #34403, 0.30 #123177, 0.28 #130344), 0j0k (0.37 #34434, 0.15 #123208, 0.13 #130375), 05tbn (0.33 #2915, 0.33 #2017, 0.25 #3810), 07c5l (0.33 #394, 0.25 #3981, 0.15 #42519), 0f8l9c (0.29 #38537, 0.10 #94192, 0.07 #97778), 06mkj (0.29 #38537, 0.03 #12679, 0.02 #17166), 05r7t (0.29 #38537), 0mwh1 (0.25 #3758, 0.22 #89661, 0.17 #128205), 01n7q (0.23 #60140, 0.18 #113944, 0.17 #32342) >> Best rule #11202 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 011hq1; >> query: (?x3670, 04_1l0v) <- religion(?x3670, ?x109), jurisdiction_of_office(?x900, ?x3670), category(?x3670, ?x134) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05tbn contains! 04_1l0v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 205.000 159.000 0.857 http://example.org/location/location/contains #13312-023322 PRED entity: 023322 PRED relation: type_of_union PRED expected values: 04ztj => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.70 #109, 0.70 #53, 0.69 #97), 01g63y (0.27 #14, 0.25 #273, 0.19 #386), 0jgjn (0.19 #386), 01bl8s (0.19 #386) >> Best rule #109 for best value: >> intensional similarity = 5 >> extensional distance = 173 >> proper extension: 053y0s; 01cv3n; 03qd_; 01p45_v; 0gt_k; 021bk; 01tp5bj; 01m65sp; 02bh9; 04gycf; ... >> query: (?x10237, 04ztj) <- instrumentalists(?x315, ?x10237), group(?x10237, ?x4783), role(?x74, ?x315), performance_role(?x115, ?x315), role(?x569, ?x315) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023322 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.703 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13311-0fnpj PRED entity: 0fnpj PRED relation: profession! PRED expected values: 0137g1 03f6fl0 04vrxh 0ftqr => 69 concepts (31 used for prediction) PRED predicted values (max 10 best out of 4091): 02cx90 (0.71 #22205, 0.50 #18032, 0.50 #13859), 0407f (0.67 #17657, 0.57 #21830, 0.50 #13484), 01tp5bj (0.67 #17395, 0.50 #13222, 0.43 #21568), 02fybl (0.57 #23152, 0.50 #18979, 0.50 #14806), 014q2g (0.57 #21664, 0.50 #17491, 0.50 #13318), 03f7m4h (0.57 #23559, 0.50 #19386, 0.50 #15213), 01vw8mh (0.57 #22389, 0.50 #18216, 0.50 #14043), 01ydzx (0.57 #23021, 0.50 #18848, 0.50 #14675), 01k_n63 (0.57 #23216, 0.50 #19043, 0.50 #14870), 01w02sy (0.57 #21766, 0.50 #17593, 0.50 #13420) >> Best rule #22205 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 016z4k; 02hrh1q; 029bkp; >> query: (?x6565, 02cx90) <- profession(?x7088, ?x6565), profession(?x5170, ?x6565), profession(?x4237, ?x6565), profession(?x2765, ?x6565), ?x2765 = 01w724, award_winner(?x7088, ?x1974), award(?x5170, ?x1323), artist(?x4868, ?x4237) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #22440 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 016z4k; 02hrh1q; 029bkp; *> query: (?x6565, 03f6fl0) <- profession(?x7088, ?x6565), profession(?x5170, ?x6565), profession(?x4237, ?x6565), profession(?x2765, ?x6565), ?x2765 = 01w724, award_winner(?x7088, ?x1974), award(?x5170, ?x1323), artist(?x4868, ?x4237) *> conf = 0.57 ranks of expected_values: 14, 59, 347, 686 EVAL 0fnpj profession! 0ftqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 69.000 31.000 0.714 http://example.org/people/person/profession EVAL 0fnpj profession! 04vrxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 69.000 31.000 0.714 http://example.org/people/person/profession EVAL 0fnpj profession! 03f6fl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 69.000 31.000 0.714 http://example.org/people/person/profession EVAL 0fnpj profession! 0137g1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 31.000 0.714 http://example.org/people/person/profession #13310-05zjx PRED entity: 05zjx PRED relation: profession PRED expected values: 0np9r => 100 concepts (84 used for prediction) PRED predicted values (max 10 best out of 51): 02jknp (0.46 #1879, 0.23 #151, 0.23 #7), 09jwl (0.37 #1167, 0.37 #3903, 0.36 #879), 0cbd2 (0.28 #6913, 0.28 #7922, 0.26 #10516), 0kyk (0.28 #6913, 0.28 #7922, 0.13 #1465), 0fj9f (0.28 #6913, 0.28 #7922, 0.04 #3650), 0747nrk (0.28 #6913, 0.28 #7922, 0.02 #54), 08z956 (0.28 #6913, 0.28 #7922, 0.02 #2810), 016z4k (0.27 #1156, 0.25 #3028, 0.25 #2164), 0nbcg (0.27 #2187, 0.26 #891, 0.26 #2331), 0dz3r (0.25 #2162, 0.24 #1154, 0.24 #866) >> Best rule #1879 for best value: >> intensional similarity = 4 >> extensional distance = 529 >> proper extension: 02j8nx; 03flwk; 02778yp; 06j8wx; 012vct; 02hy9p; 0l9k1; 06w38l; 0p_r5; >> query: (?x7598, 02jknp) <- profession(?x7598, ?x1032), profession(?x7598, ?x987), ?x1032 = 02hrh1q, ?x987 = 0dxtg >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1313 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 509 *> proper extension: 01l3j; *> query: (?x7598, 0np9r) <- film(?x7598, ?x2512), category(?x7598, ?x134) *> conf = 0.17 ranks of expected_values: 11 EVAL 05zjx profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 100.000 84.000 0.463 http://example.org/people/person/profession #13309-07jqvw PRED entity: 07jqvw PRED relation: award_winner PRED expected values: 06hgj => 30 concepts (6 used for prediction) PRED predicted values (max 10 best out of 820): 0dvld (0.20 #1337, 0.06 #3813, 0.04 #6290), 0170s4 (0.20 #502, 0.04 #2978, 0.03 #14861), 01qq_lp (0.20 #856, 0.04 #3332, 0.02 #5809), 0187y5 (0.20 #122, 0.03 #14861, 0.03 #2598), 0hz_1 (0.20 #1847, 0.03 #14861, 0.03 #4323), 018ygt (0.20 #1410, 0.03 #14861, 0.03 #3886), 01h910 (0.20 #1380, 0.03 #14861, 0.03 #3856), 0jmj (0.20 #968, 0.03 #14861, 0.03 #3444), 01yk13 (0.20 #165, 0.03 #14861, 0.03 #2641), 0pnf3 (0.20 #2150, 0.03 #14861, 0.02 #4626) >> Best rule #1337 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 09qv3c; 024fz9; 09qrn4; >> query: (?x14766, 0dvld) <- award_winner(?x14766, ?x4432), ?x4432 = 02xwq9 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07jqvw award_winner 06hgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 6.000 0.200 http://example.org/award/award_category/winners./award/award_honor/award_winner #13308-0djd3 PRED entity: 0djd3 PRED relation: place_founded! PRED expected values: 04sv4 => 179 concepts (152 used for prediction) PRED predicted values (max 10 best out of 90): 02482c (0.08 #1447, 0.07 #3226, 0.04 #6232), 03xsby (0.05 #7, 0.04 #1231, 0.04 #2121), 016tw3 (0.05 #5, 0.04 #2119, 0.03 #338), 04htfd (0.05 #37, 0.03 #259, 0.03 #370), 02ktt7 (0.05 #105, 0.03 #327, 0.03 #438), 0206k5 (0.05 #64, 0.03 #286, 0.03 #397), 0181hw (0.05 #49, 0.03 #271, 0.03 #382), 0537b (0.05 #41, 0.03 #263, 0.03 #374), 025tlyv (0.05 #68, 0.03 #401, 0.03 #735), 0g768 (0.05 #39, 0.03 #372, 0.03 #706) >> Best rule #1447 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 013m43; >> query: (?x6683, ?x8937) <- location(?x8893, ?x6683), category(?x6683, ?x134), location_of_ceremony(?x8893, ?x151), citytown(?x8937, ?x6683) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0djd3 place_founded! 04sv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 179.000 152.000 0.084 http://example.org/organization/organization/place_founded #13307-0167bx PRED entity: 0167bx PRED relation: symptom_of! PRED expected values: 02tfl8 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 89): 01cdt5 (0.67 #105, 0.57 #759, 0.53 #977), 0j5fv (0.62 #744, 0.62 #225, 0.60 #963), 02tfl8 (0.62 #225, 0.58 #135, 0.57 #605), 097ns (0.53 #977, 0.50 #291, 0.49 #900), 098s1 (0.53 #387, 0.43 #17, 0.39 #1626), 09969 (0.52 #905, 0.49 #900, 0.45 #763), 01pf6 (0.50 #291, 0.50 #163, 0.49 #900), 0k95h (0.50 #291, 0.49 #900, 0.48 #454), 0hg45 (0.50 #163, 0.43 #17, 0.42 #1028), 02y0js (0.43 #1208, 0.42 #627, 0.36 #109) >> Best rule #105 for best value: >> intensional similarity = 37 >> extensional distance = 2 >> proper extension: 07jwr; >> query: (?x11739, ?x13487) <- symptom_of(?x13373, ?x11739), symptom_of(?x9438, ?x11739), symptom_of(?x9118, ?x11739), symptom_of(?x4905, ?x11739), ?x4905 = 01j6t0, ?x9118 = 0brgy, symptom_of(?x13373, ?x13485), symptom_of(?x13373, ?x12536), symptom_of(?x13373, ?x7006), symptom_of(?x13373, ?x3680), ?x7006 = 02psvcf, risk_factors(?x11739, ?x6781), ?x3680 = 025hl8, people(?x6781, ?x2145), symptom_of(?x9438, ?x9898), symptom_of(?x9438, ?x9119), symptom_of(?x9438, ?x8675), symptom_of(?x9438, ?x6655), symptom_of(?x9438, ?x4322), symptom_of(?x5802, ?x6781), ?x9119 = 011zdm, type_of_union(?x2145, ?x566), award(?x2145, ?x1937), ?x8675 = 01gkcc, profession(?x2145, ?x1032), ?x13485 = 07s4l, ?x1032 = 02hrh1q, symptom_of(?x3679, ?x12536), ?x566 = 04ztj, people(?x4322, ?x10516), risk_factors(?x4322, ?x231), ?x9898 = 09jg8, location(?x2145, ?x8297), ?x10516 = 0b22w, ?x3679 = 02tfl8, ?x6655 = 09d11, symptom_of(?x13487, ?x4322) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #225 for first EXPECTED value: *> intensional similarity = 40 *> extensional distance = 2 *> proper extension: 0gk4g; *> query: (?x11739, ?x3679) <- symptom_of(?x13373, ?x11739), symptom_of(?x10717, ?x11739), symptom_of(?x9510, ?x11739), symptom_of(?x9509, ?x11739), symptom_of(?x9438, ?x11739), symptom_of(?x9118, ?x11739), symptom_of(?x4905, ?x11739), ?x4905 = 01j6t0, symptom_of(?x13373, ?x13485), symptom_of(?x13373, ?x13131), symptom_of(?x13373, ?x10480), symptom_of(?x13373, ?x7006), symptom_of(?x13373, ?x4959), symptom_of(?x13373, ?x3680), symptom_of(?x9118, ?x14024), symptom_of(?x9118, ?x11064), symptom_of(?x9118, ?x9119), symptom_of(?x9118, ?x7586), symptom_of(?x9118, ?x5118), ?x9438 = 012qjw, people(?x5118, ?x5119), ?x13131 = 0d19y2, ?x3680 = 025hl8, ?x9119 = 011zdm, ?x9509 = 0gxb2, ?x14024 = 0h1wz, risk_factors(?x5118, ?x4195), ?x4959 = 01dcqj, symptom_of(?x3679, ?x5118), symptom_of(?x9510, ?x7007), ?x7006 = 02psvcf, ?x7007 = 097ns, ?x13485 = 07s4l, ?x10480 = 0h1n9, ?x11064 = 01n3bm, risk_factors(?x14001, ?x7586), ?x4195 = 02ctzb, people(?x7586, ?x11410), symptom_of(?x10717, ?x10613), ?x10613 = 014w_8 *> conf = 0.62 ranks of expected_values: 3 EVAL 0167bx symptom_of! 02tfl8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 63.000 63.000 0.667 http://example.org/medicine/symptom/symptom_of #13306-02js_6 PRED entity: 02js_6 PRED relation: award PRED expected values: 09qvc0 0bfvd4 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 307): 0cjyzs (0.50 #905, 0.14 #37300, 0.11 #2910), 05zr6wv (0.45 #1219, 0.20 #2422, 0.16 #2823), 09sb52 (0.36 #6054, 0.34 #22096, 0.32 #27310), 0cqhk0 (0.30 #14873, 0.25 #35, 0.18 #21289), 0ck27z (0.30 #21344, 0.22 #24554, 0.21 #25356), 05zvj3m (0.28 #2497, 0.19 #2898, 0.18 #1294), 01bgqh (0.25 #41, 0.19 #4051, 0.16 #4853), 0gkvb7 (0.25 #25, 0.19 #2431, 0.13 #5639), 03qbh5 (0.25 #202, 0.14 #4212, 0.14 #8223), 0c4z8 (0.25 #69, 0.11 #4079, 0.10 #22928) >> Best rule #905 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0gsg7; >> query: (?x12359, 0cjyzs) <- award_winner(?x12359, ?x5346), nominated_for(?x12359, ?x1542), ?x1542 = 0124k9 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1241 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 9 *> proper extension: 015lhm; *> query: (?x12359, 09qvc0) <- film(?x12359, ?x437), ?x437 = 034qrh *> conf = 0.18 ranks of expected_values: 36, 68 EVAL 02js_6 award 0bfvd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 126.000 126.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02js_6 award 09qvc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 126.000 126.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13305-03gvt PRED entity: 03gvt PRED relation: role! PRED expected values: 018x3 => 83 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1130): 082brv (0.71 #13745, 0.67 #12397, 0.61 #15544), 01vsnff (0.71 #5931, 0.50 #11775, 0.50 #10426), 02s6sh (0.70 #10311, 0.67 #5816, 0.62 #7162), 023l9y (0.67 #11891, 0.62 #6944, 0.62 #18186), 01wxdn3 (0.67 #8035, 0.60 #10285, 0.60 #9836), 01l4g5 (0.67 #5610, 0.44 #7855, 0.42 #11903), 01w9wwg (0.67 #5660, 0.43 #18248, 0.40 #10155), 023slg (0.67 #5831, 0.40 #10326, 0.40 #9877), 01vsl3_ (0.62 #6855, 0.60 #4163, 0.57 #5958), 0133x7 (0.62 #7035, 0.57 #6138, 0.50 #9735) >> Best rule #13745 for best value: >> intensional similarity = 18 >> extensional distance = 12 >> proper extension: 02k84w; >> query: (?x3716, 082brv) <- instrumentalists(?x3716, ?x130), role(?x3716, ?x2888), role(?x3716, ?x2310), role(?x3716, ?x716), role(?x3716, ?x314), role(?x1166, ?x3716), role(?x227, ?x3716), ?x2310 = 0gghm, instrumentalists(?x716, ?x9176), role(?x314, ?x645), ?x2888 = 02fsn, group(?x716, ?x10257), group(?x716, ?x3207), ?x10257 = 01v0sxx, ?x9176 = 01jgkj2, role(?x314, ?x74), ?x3207 = 01qqwp9, role(?x217, ?x716) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2043 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 2 *> proper extension: 05148p4; *> query: (?x3716, 018x3) <- instrumentalists(?x3716, ?x130), role(?x3716, ?x7033), role(?x3716, ?x3239), role(?x3716, ?x2310), role(?x3716, ?x2158), role(?x3716, ?x1433), role(?x3716, ?x716), role(?x3716, ?x432), role(?x1166, ?x3716), role(?x885, ?x3716), ?x2310 = 0gghm, ?x716 = 018vs, ?x885 = 0dwtp, role(?x3239, ?x214), ?x7033 = 0gkd1, ?x432 = 042v_gx, ?x2158 = 01dnws, role(?x211, ?x3716), role(?x547, ?x3239), ?x1433 = 0239kh *> conf = 0.50 ranks of expected_values: 66 EVAL 03gvt role! 018x3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 83.000 43.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #13304-02tzwd PRED entity: 02tzwd PRED relation: award! PRED expected values: 07w21 0lzb8 => 68 concepts (36 used for prediction) PRED predicted values (max 10 best out of 2191): 07w21 (0.71 #27138, 0.64 #20377, 0.60 #37286), 01pw9v (0.68 #74397, 0.64 #57481, 0.64 #111596), 014ps4 (0.64 #22559, 0.53 #29320, 0.40 #39468), 01dzz7 (0.60 #37643, 0.59 #27495, 0.55 #20734), 0c3kw (0.60 #24104, 0.53 #27485, 0.45 #20724), 01k56k (0.59 #30331, 0.55 #40479, 0.55 #23570), 048_p (0.59 #28673, 0.55 #38821, 0.52 #45586), 09dt7 (0.59 #27354, 0.55 #20593, 0.53 #23973), 02y49 (0.55 #22861, 0.53 #29622, 0.47 #26241), 01963w (0.55 #20617, 0.53 #27378, 0.36 #44291) >> Best rule #27138 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 040vk98; 02662b; 02664f; 0262yt; 05x2s; >> query: (?x11084, 07w21) <- disciplines_or_subjects(?x11084, ?x5864), award(?x3858, ?x11084), influenced_by(?x5345, ?x3858), award_winner(?x3337, ?x3858), influenced_by(?x3858, ?x8433), ?x8433 = 06bng >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 1084 EVAL 02tzwd award! 0lzb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 36.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02tzwd award! 07w21 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 36.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13303-02pyyld PRED entity: 02pyyld PRED relation: team! PRED expected values: 0b_6s7 0b_6mr => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 17): 0b_6_l (0.82 #652, 0.78 #487, 0.78 #231), 0b_6zk (0.78 #231, 0.75 #286, 0.75 #439), 0b_6xf (0.78 #231, 0.75 #286, 0.75 #454), 0bzrsh (0.78 #231, 0.75 #286, 0.74 #55), 0b_6rk (0.78 #231, 0.75 #286, 0.74 #55), 0b_6mr (0.78 #231, 0.75 #286, 0.74 #55), 0b_72t (0.78 #231, 0.75 #286, 0.74 #55), 0b_75k (0.78 #231, 0.75 #286, 0.74 #55), 0bzrxn (0.78 #231, 0.75 #286, 0.74 #55), 0b_71r (0.78 #231, 0.75 #286, 0.74 #55) >> Best rule #652 for best value: >> intensional similarity = 32 >> extensional distance = 9 >> proper extension: 02pjzvh; >> query: (?x11789, 0b_6_l) <- position(?x11789, ?x5755), position(?x11789, ?x1348), team(?x6848, ?x11789), position(?x11420, ?x5755), position(?x9909, ?x5755), position(?x7136, ?x5755), position(?x4571, ?x5755), position(?x2398, ?x5755), ?x4571 = 0jm6n, ?x7136 = 0jm74, team(?x9146, ?x11789), team(?x8824, ?x11789), ?x2398 = 0jmfb, team(?x1348, ?x6128), team(?x1348, ?x6003), ?x11420 = 0jmhr, ?x6128 = 0jm64, locations(?x9146, ?x4978), locations(?x9146, ?x659), colors(?x9909, ?x663), location(?x4977, ?x4978), team(?x8527, ?x9909), ?x4977 = 03f6fl0, contains(?x4978, ?x1506), category(?x4978, ?x134), sport(?x6003, ?x12913), place_of_birth(?x1775, ?x659), team(?x5755, ?x8228), time_zones(?x4978, ?x1638), ?x8527 = 0b_6v_, teams(?x659, ?x4487), ?x8824 = 05g_nr >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #231 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 3 *> proper extension: 026xxv_; *> query: (?x11789, ?x3797) <- team(?x10594, ?x11789), team(?x9974, ?x11789), team(?x8824, ?x11789), team(?x6802, ?x11789), ?x6802 = 0br1x_, ?x10594 = 0b_756, ?x9974 = 0b_6pv, colors(?x11789, ?x3189), colors(?x11789, ?x332), team(?x8824, ?x9833), team(?x8824, ?x6803), team(?x8824, ?x5032), team(?x8824, ?x4369), ?x6803 = 03by7wc, ?x5032 = 04088s0, ?x4369 = 02pqcfz, colors(?x12175, ?x332), colors(?x11349, ?x332), colors(?x8434, ?x332), colors(?x8427, ?x332), colors(?x5679, ?x332), ?x12175 = 036hnm, ?x8427 = 021996, colors(?x13166, ?x3189), colors(?x12526, ?x3189), colors(?x4804, ?x3189), colors(?x3188, ?x3189), colors(?x2971, ?x3189), colors(?x10071, ?x3189), colors(?x2948, ?x3189), team(?x3797, ?x4804), ?x2971 = 04112r, registering_agency(?x8434, ?x1982), ?x9833 = 03y9p40, student(?x2948, ?x129), ?x5679 = 022jr5, ?x10071 = 0gl6x, currency(?x11349, ?x170), ?x12526 = 0bg4f9, ?x3188 = 04k3r_, ?x13166 = 0j6tr *> conf = 0.78 ranks of expected_values: 6, 13 EVAL 02pyyld team! 0b_6mr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 58.000 58.000 0.818 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02pyyld team! 0b_6s7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 58.000 58.000 0.818 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #13302-0407yfx PRED entity: 0407yfx PRED relation: honored_for! PRED expected values: 0n8_m93 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 81): 04n2r9h (0.12 #2321, 0.06 #855, 0.05 #6715), 09bymc (0.12 #2321, 0.06 #855, 0.05 #6715), 0275n3y (0.06 #796, 0.03 #1652, 0.03 #1774), 0hr6lkl (0.06 #1234, 0.05 #378, 0.05 #1112), 0drtv8 (0.05 #787, 0.05 #6715, 0.04 #1643), 02q690_ (0.05 #786, 0.04 #1764, 0.04 #1886), 03gwpw2 (0.05 #1593, 0.05 #1715, 0.05 #2203), 05qb8vx (0.05 #6715, 0.03 #658, 0.02 #1636), 05zksls (0.05 #6715, 0.03 #1616, 0.03 #1738), 04110lv (0.05 #6715, 0.03 #1683, 0.03 #1805) >> Best rule #2321 for best value: >> intensional similarity = 4 >> extensional distance = 284 >> proper extension: 03czz87; >> query: (?x2155, ?x2988) <- nominated_for(?x3945, ?x2155), award_winner(?x1046, ?x3945), category(?x2155, ?x134), award_winner(?x2988, ?x3945) >> conf = 0.12 => this is the best rule for 2 predicted values *> Best rule #1203 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 152 *> proper extension: 0ds3t5x; 0h3xztt; 04hwbq; 04zyhx; 0btyf5z; 05qbckf; 0gydcp7; 02yvct; 0cc5mcj; 0bby9p5; ... *> query: (?x2155, 0n8_m93) <- film_release_region(?x2155, ?x344), film_release_region(?x2155, ?x172), ?x344 = 04gzd, country(?x150, ?x172) *> conf = 0.04 ranks of expected_values: 20 EVAL 0407yfx honored_for! 0n8_m93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 66.000 66.000 0.117 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #13301-03ft8 PRED entity: 03ft8 PRED relation: profession PRED expected values: 02hrh1q => 181 concepts (161 used for prediction) PRED predicted values (max 10 best out of 101): 02hrh1q (0.80 #16331, 0.80 #7070, 0.79 #16772), 01d_h8 (0.78 #12649, 0.77 #10738, 0.77 #12796), 02jknp (0.59 #7505, 0.58 #1771, 0.58 #3241), 02krf9 (0.50 #25, 0.42 #3259, 0.40 #5464), 0kyk (0.47 #4879, 0.42 #6791, 0.36 #6349), 018gz8 (0.37 #11482, 0.32 #2661, 0.29 #13834), 03sbb (0.33 #17496, 0.30 #18379, 0.06 #4496), 016z4k (0.28 #2503, 0.17 #7061, 0.11 #12206), 015cjr (0.27 #1665, 0.25 #2106, 0.16 #2694), 0np9r (0.27 #3253, 0.25 #19, 0.22 #2371) >> Best rule #16331 for best value: >> intensional similarity = 4 >> extensional distance = 320 >> proper extension: 044mz_; 04bdxl; 05cljf; 0c9d9; 023tp8; 01p7yb; 0prfz; 0159h6; 0h5g_; 0147dk; ... >> query: (?x1683, 02hrh1q) <- nationality(?x1683, ?x94), spouse(?x9957, ?x1683), type_of_union(?x1683, ?x566), profession(?x1683, ?x353) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03ft8 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 161.000 0.798 http://example.org/people/person/profession #13300-012mrr PRED entity: 012mrr PRED relation: nominated_for! PRED expected values: 027dtxw 02pqp12 0gs9p => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 178): 0gr0m (0.68 #4545, 0.67 #1817, 0.67 #4773), 018wdw (0.68 #4545, 0.67 #1817, 0.67 #4773), 02g3ft (0.68 #4545, 0.67 #1817, 0.67 #4773), 0gq9h (0.65 #738, 0.47 #511, 0.39 #1646), 0gs9p (0.60 #740, 0.40 #513, 0.35 #1648), 02pqp12 (0.42 #734, 0.20 #1642, 0.19 #1870), 0f4x7 (0.39 #704, 0.27 #477, 0.24 #1612), 0gr4k (0.38 #705, 0.27 #478, 0.21 #1613), 0gqy2 (0.37 #793, 0.25 #566, 0.25 #1701), 04kxsb (0.37 #767, 0.18 #1675, 0.17 #1903) >> Best rule #4545 for best value: >> intensional similarity = 3 >> extensional distance = 855 >> proper extension: 06w7mlh; >> query: (?x2914, ?x1243) <- award_winner(?x2914, ?x2135), award(?x2914, ?x1243), nominated_for(?x1243, ?x144) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #740 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 177 *> proper extension: 095zlp; 0gmcwlb; 0p7qm; 02tqm5; 0p_qr; 011yl_; 0kxf1; 04cj79; 03cw411; 0gw7p; ... *> query: (?x2914, 0gs9p) <- genre(?x2914, ?x53), nominated_for(?x746, ?x2914), ?x746 = 04dn09n *> conf = 0.60 ranks of expected_values: 5, 6, 15 EVAL 012mrr nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 44.000 44.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 012mrr nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 44.000 44.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 012mrr nominated_for! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 44.000 44.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13299-028rk PRED entity: 028rk PRED relation: inductee! PRED expected values: 04dm2n => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 8): 01nzmp (0.10 #97, 0.06 #178, 0.05 #486), 0g2c8 (0.08 #791, 0.07 #471, 0.06 #864), 06szd3 (0.06 #173, 0.06 #445, 0.05 #481), 04dm2n (0.06 #451, 0.05 #487, 0.05 #368), 0qjfl (0.05 #354, 0.03 #721, 0.03 #748), 04045y (0.05 #249, 0.04 #285, 0.03 #321), 0k89p (0.05 #364, 0.04 #447, 0.03 #483), 01b3l (0.03 #484, 0.02 #448, 0.02 #502) >> Best rule #97 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01xyt7; 0frmb1; >> query: (?x2663, 01nzmp) <- gender(?x2663, ?x231), company(?x2663, ?x94), athlete(?x1083, ?x2663) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #451 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 0cg39k; 0hcs3; *> query: (?x2663, 04dm2n) <- profession(?x2663, ?x5805), athlete(?x1083, ?x2663), nationality(?x2663, ?x94), ?x94 = 09c7w0 *> conf = 0.06 ranks of expected_values: 4 EVAL 028rk inductee! 04dm2n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 172.000 172.000 0.100 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #13298-0chgzm PRED entity: 0chgzm PRED relation: place_of_birth! PRED expected values: 0244r8 => 167 concepts (130 used for prediction) PRED predicted values (max 10 best out of 2164): 05qhnq (0.41 #190280, 0.36 #211136, 0.28 #231989), 0dzlk (0.36 #211136, 0.28 #231989, 0.28 #190279), 0170pk (0.34 #336269, 0.33 #237202, 0.33 #156393), 016ypb (0.34 #336269, 0.33 #237202, 0.33 #156393), 0184jc (0.34 #336269, 0.33 #237202, 0.33 #156393), 02j9lm (0.34 #336269, 0.33 #237202, 0.33 #156393), 02mq_y (0.28 #231989, 0.28 #190279, 0.26 #211135), 03h_9lg (0.25 #127, 0.12 #7949, 0.10 #13161), 013tcv (0.25 #1925, 0.12 #9747, 0.10 #14959), 02404v (0.25 #1597, 0.12 #9419, 0.10 #14631) >> Best rule #190280 for best value: >> intensional similarity = 2 >> extensional distance = 124 >> proper extension: 0jgx; 0r3tb; 02qjb7z; 0l3q2; 01zqy6t; 01zk9d; >> query: (?x8602, ?x7210) <- origin(?x7210, ?x8602), role(?x7210, ?x212) >> conf = 0.41 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0chgzm place_of_birth! 0244r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 130.000 0.406 http://example.org/people/person/place_of_birth #13297-085gk PRED entity: 085gk PRED relation: influenced_by PRED expected values: 0gzh => 124 concepts (57 used for prediction) PRED predicted values (max 10 best out of 366): 081k8 (0.30 #1886, 0.22 #4050, 0.20 #5783), 014z8v (0.27 #987, 0.12 #3147, 0.09 #9214), 03_87 (0.21 #2365, 0.21 #1933, 0.15 #10160), 028p0 (0.21 #1761, 0.12 #3925, 0.09 #21230), 0gz_ (0.20 #104, 0.14 #8328, 0.13 #7895), 02wh0 (0.20 #381, 0.13 #6008, 0.13 #2543), 07g2b (0.20 #13, 0.12 #1743, 0.09 #21230), 03f70xs (0.20 #70, 0.11 #2232, 0.09 #1800), 0465_ (0.20 #200, 0.09 #1930, 0.09 #21230), 07kb5 (0.20 #15, 0.05 #7806, 0.05 #8239) >> Best rule #1886 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 01g4bk; >> query: (?x12402, 081k8) <- profession(?x12402, ?x3746), profession(?x12402, ?x2225), ?x3746 = 05z96, ?x2225 = 0kyk >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #6490 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 109 *> proper extension: 03qcq; 084w8; 01xdf5; 02g8h; 01zkxv; 04411; 08433; 0c3kw; 016_mj; 073bb; ... *> query: (?x12402, 0gzh) <- nationality(?x12402, ?x94), location(?x12402, ?x335), influenced_by(?x12402, ?x4072), state_province_region(?x166, ?x335) *> conf = 0.02 ranks of expected_values: 251 EVAL 085gk influenced_by 0gzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 124.000 57.000 0.303 http://example.org/influence/influence_node/influenced_by #13296-0bjkpt PRED entity: 0bjkpt PRED relation: type_of_union PRED expected values: 04ztj => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.78 #21, 0.77 #45, 0.77 #29), 01g63y (0.12 #148, 0.12 #66, 0.12 #144) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 03h40_7; >> query: (?x5196, 04ztj) <- location(?x5196, ?x5143), award_nominee(?x5196, ?x2691), produced_by(?x463, ?x5196) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bjkpt type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.782 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13295-0m6x4 PRED entity: 0m6x4 PRED relation: film PRED expected values: 0gnkb => 148 concepts (69 used for prediction) PRED predicted values (max 10 best out of 865): 0kb07 (0.74 #8949, 0.73 #55478, 0.73 #28635), 0jsqk (0.74 #8949, 0.73 #55478, 0.73 #28635), 0bbgly (0.25 #3527, 0.17 #7106, 0.03 #93054), 0gt14 (0.25 #3555, 0.03 #93054, 0.03 #93053), 0gcpc (0.25 #2496, 0.03 #93054, 0.03 #93053), 03bdkd (0.25 #3458), 0jswp (0.25 #2335), 026y3cf (0.10 #16107, 0.09 #46529, 0.07 #66214), 0blpg (0.08 #7813, 0.04 #16761, 0.03 #27498), 02qr3k8 (0.08 #8447, 0.03 #44237, 0.03 #117612) >> Best rule #8949 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 03f2_rc; 015q43; >> query: (?x9356, ?x2612) <- nominated_for(?x9356, ?x2612), participant(?x9356, ?x1606), award_winner(?x1245, ?x9356), ?x1245 = 0gqwc >> conf = 0.74 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0m6x4 film 0gnkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 69.000 0.742 http://example.org/film/actor/film./film/performance/film #13294-01b66d PRED entity: 01b66d PRED relation: country_of_origin PRED expected values: 09c7w0 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 38): 09c7w0 (0.91 #144, 0.91 #78, 0.90 #133), 07ssc (0.13 #521, 0.11 #418, 0.11 #473), 0d060g (0.13 #521, 0.07 #37, 0.05 #103), 0chghy (0.13 #521, 0.02 #487), 02jx1 (0.13 #521, 0.01 #420, 0.01 #431), 04jpl (0.13 #521), 03_3d (0.09 #267, 0.08 #512, 0.07 #478), 03rjj (0.02 #487, 0.01 #134, 0.01 #145), 05v8c (0.02 #487, 0.01 #519), 07f1x (0.02 #487) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 02gl58; >> query: (?x3104, 09c7w0) <- award_winner(?x3104, ?x438), award(?x3104, ?x588), program(?x6337, ?x3104) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01b66d country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.908 http://example.org/tv/tv_program/country_of_origin #13293-095kp PRED entity: 095kp PRED relation: child! PRED expected values: 01w5m => 117 concepts (70 used for prediction) PRED predicted values (max 10 best out of 32): 0l8sx (0.07 #179, 0.04 #348, 0.04 #515), 023zl (0.06 #60, 0.03 #478, 0.02 #812), 09b3v (0.05 #194, 0.04 #363, 0.04 #530), 0fnmz (0.05 #941, 0.03 #1110, 0.03 #1446), 01dtcb (0.03 #630, 0.03 #379, 0.03 #546), 018_q8 (0.03 #627, 0.02 #2303, 0.02 #1212), 0bwfn (0.03 #43, 0.01 #461), 0sxdg (0.03 #1220, 0.03 #384, 0.03 #551), 03bnb (0.03 #396, 0.03 #479, 0.03 #563), 03f2fw (0.03 #142, 0.02 #1147, 0.02 #1483) >> Best rule #179 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0l8sx; 04htfd; 03lb_v; >> query: (?x6112, 0l8sx) <- state_province_region(?x6112, ?x335), company(?x3484, ?x6112), ?x335 = 059rby, organization(?x3484, ?x216) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 095kp child! 01w5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 70.000 0.070 http://example.org/organization/organization/child./organization/organization_relationship/child #13292-06n7h7 PRED entity: 06n7h7 PRED relation: profession PRED expected values: 02hrh1q => 99 concepts (98 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.89 #2536, 0.86 #1648, 0.86 #1796), 015cjr (0.44 #50, 0.29 #198, 0.28 #6965), 09jwl (0.37 #3872, 0.35 #1504, 0.20 #1208), 01d_h8 (0.34 #3415, 0.34 #1195, 0.32 #2675), 0dxtg (0.29 #2091, 0.28 #1943, 0.27 #3423), 0nbcg (0.28 #6965, 0.28 #6668, 0.27 #3885), 02jknp (0.28 #6965, 0.28 #6668, 0.25 #12146), 0kyk (0.28 #6965, 0.28 #6668, 0.25 #12146), 01d30f (0.28 #6965, 0.28 #6668, 0.25 #12146), 04gc2 (0.28 #6965, 0.28 #6668, 0.25 #12146) >> Best rule #2536 for best value: >> intensional similarity = 2 >> extensional distance = 890 >> proper extension: 06v8s0; 01vw87c; 02nb2s; 09byk; 03ds3; 04hpck; 01sxq9; 01yh3y; 01j4ls; 03fghg; ... >> query: (?x690, 02hrh1q) <- actor(?x3075, ?x690), profession(?x690, ?x1041) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06n7h7 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 98.000 0.887 http://example.org/people/person/profession #13291-074m2 PRED entity: 074m2 PRED relation: people PRED expected values: 0c73g => 77 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1124): 05np2 (0.67 #689, 0.67 #688, 0.56 #6202), 02dth1 (0.67 #689, 0.67 #688, 0.56 #6202), 0407f (0.67 #689, 0.67 #688, 0.56 #6202), 016dgz (0.67 #689, 0.67 #688, 0.56 #6202), 053yx (0.67 #689, 0.67 #688, 0.56 #6202), 02h48 (0.67 #689, 0.67 #688, 0.56 #6202), 012c6j (0.67 #689, 0.67 #688, 0.56 #6202), 0lrh (0.67 #689, 0.67 #688, 0.56 #6202), 07s3vqk (0.67 #689, 0.67 #688, 0.56 #6202), 015wfg (0.67 #689, 0.67 #688, 0.56 #6202) >> Best rule #689 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 02y0js; >> query: (?x7586, ?x510) <- symptom_of(?x10717, ?x7586), symptom_of(?x6780, ?x7586), risk_factors(?x14001, ?x7586), risk_factors(?x7586, ?x231), people(?x7586, ?x11497), people(?x7586, ?x11410), ?x6780 = 0j5fv, symptom_of(?x10717, ?x6656), symptom_of(?x10717, ?x6260), influenced_by(?x11497, ?x7386), ?x6656 = 03p41, religion(?x11497, ?x1985), people(?x9332, ?x11497), nationality(?x11410, ?x429), people(?x6260, ?x510) >> conf = 0.67 => this is the best rule for 406 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 75 EVAL 074m2 people 0c73g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 77.000 66.000 0.671 http://example.org/people/cause_of_death/people #13290-01n30p PRED entity: 01n30p PRED relation: film! PRED expected values: 02mhfy => 92 concepts (44 used for prediction) PRED predicted values (max 10 best out of 993): 0c3jz (0.47 #39541, 0.46 #87398, 0.45 #29131), 01vsn38 (0.38 #3933, 0.33 #10174, 0.03 #14334), 05txrz (0.25 #2845, 0.22 #9086, 0.12 #7006), 0bl2g (0.25 #2134, 0.22 #8375, 0.03 #10455), 0f4vbz (0.25 #2442, 0.22 #8683, 0.03 #14925), 012d40 (0.25 #2096, 0.22 #8337, 0.02 #74930), 01kwsg (0.25 #2918, 0.11 #9159, 0.02 #21642), 02js_6 (0.25 #4052, 0.02 #31103, 0.02 #24859), 0147dk (0.25 #2161, 0.02 #18805, 0.01 #14644), 07cjqy (0.22 #8923, 0.02 #75516, 0.02 #48468) >> Best rule #39541 for best value: >> intensional similarity = 4 >> extensional distance = 281 >> proper extension: 01vrwfv; >> query: (?x8158, ?x3705) <- category(?x8158, ?x134), ?x134 = 08mbj5d, nominated_for(?x3705, ?x8158), people(?x1050, ?x3705) >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01n30p film! 02mhfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 44.000 0.468 http://example.org/film/actor/film./film/performance/film #13289-01vvybv PRED entity: 01vvybv PRED relation: artists! PRED expected values: 07sbbz2 => 137 concepts (75 used for prediction) PRED predicted values (max 10 best out of 298): 064t9 (0.67 #14, 0.62 #950, 0.61 #3134), 016clz (0.56 #13120, 0.38 #3437, 0.31 #1253), 0gywn (0.52 #58, 0.41 #994, 0.33 #2242), 025sc50 (0.48 #50, 0.35 #986, 0.30 #3170), 06j6l (0.43 #48, 0.41 #984, 0.33 #2232), 0glt670 (0.43 #40, 0.38 #2536, 0.34 #3160), 05bt6j (0.31 #3475, 0.30 #5347, 0.28 #5659), 02yv6b (0.29 #7590, 0.25 #5404, 0.19 #13215), 02lnbg (0.29 #3179, 0.27 #4115, 0.23 #5675), 0ggx5q (0.29 #3199, 0.24 #6007, 0.24 #2575) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 012vd6; >> query: (?x10461, 064t9) <- artist(?x2190, ?x10461), ?x2190 = 01cszh, award_winner(?x2431, ?x10461) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1256 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 01w02sy; *> query: (?x10461, 07sbbz2) <- participant(?x10002, ?x10461), profession(?x10461, ?x131), artist(?x441, ?x10461), student(?x216, ?x10461) *> conf = 0.14 ranks of expected_values: 32 EVAL 01vvybv artists! 07sbbz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 137.000 75.000 0.667 http://example.org/music/genre/artists #13288-04wgh PRED entity: 04wgh PRED relation: member_states! PRED expected values: 085h1 => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 11): 085h1 (0.77 #23, 0.76 #213, 0.74 #156), 018cqq (0.38 #2, 0.34 #155, 0.32 #192), 02jxk (0.23 #267, 0.21 #154, 0.21 #191), 059dn (0.20 #157, 0.19 #194, 0.19 #270), 07t65 (0.11 #69, 0.06 #400, 0.06 #457), 02vk52z (0.11 #69, 0.06 #400, 0.06 #457), 0b6css (0.06 #474), 0gkjy (0.06 #474), 04k4l (0.06 #474), 0_2v (0.06 #474) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0jgd; 04v3q; 05cgv; 06c1y; 035dk; 03rj0; 088q4; 03__y; 06m_5; 04v09; >> query: (?x1273, 085h1) <- country(?x471, ?x1273), participating_countries(?x1931, ?x1273), featured_film_locations(?x549, ?x1273) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wgh member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 179.000 0.773 http://example.org/user/ktrueman/default_domain/international_organization/member_states #13287-03f1d47 PRED entity: 03f1d47 PRED relation: location PRED expected values: 0z1vw => 111 concepts (109 used for prediction) PRED predicted values (max 10 best out of 170): 02_286 (0.16 #841, 0.15 #17737, 0.15 #4864), 030qb3t (0.14 #4106, 0.12 #28241, 0.12 #22612), 0cc56 (0.14 #57, 0.05 #2471, 0.03 #4884), 0qkcb (0.09 #1996, 0.06 #1191, 0.03 #2801), 013yq (0.08 #5751, 0.06 #7359, 0.05 #8163), 05k7sb (0.08 #4132, 0.08 #2523, 0.06 #913), 01n7q (0.08 #4086, 0.03 #1672, 0.03 #2477), 059rby (0.07 #16, 0.06 #820, 0.06 #4039), 0ccvx (0.07 #222, 0.05 #5854, 0.04 #6658), 0n6dc (0.07 #602, 0.03 #5429, 0.01 #7038) >> Best rule #841 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 0bz5v2; 05f7snc; >> query: (?x4983, 02_286) <- student(?x735, ?x4983), award_nominee(?x3175, ?x4983), program(?x4983, ?x2583) >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03f1d47 location 0z1vw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 109.000 0.161 http://example.org/people/person/places_lived./people/place_lived/location #13286-0fzm0g PRED entity: 0fzm0g PRED relation: film! PRED expected values: 01wy5m 01gbn6 => 63 concepts (37 used for prediction) PRED predicted values (max 10 best out of 600): 02cllz (0.37 #6641, 0.02 #8718, 0.01 #14953), 0js9s (0.29 #5308, 0.20 #3231, 0.20 #1154), 0241jw (0.29 #4450, 0.20 #2373, 0.20 #296), 0294fd (0.29 #4872, 0.20 #2795, 0.20 #718), 01v9l67 (0.29 #4620, 0.20 #2543, 0.20 #466), 024n3z (0.29 #4619, 0.20 #2542, 0.20 #465), 02gvwz (0.29 #4342, 0.20 #2265, 0.20 #188), 01kwld (0.29 #4254, 0.20 #2177, 0.20 #100), 02ck7w (0.29 #5094, 0.20 #3017, 0.20 #940), 0svqs (0.29 #5030, 0.20 #2953, 0.20 #876) >> Best rule #6641 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 02t_h3; >> query: (?x12934, 02cllz) <- film(?x4999, ?x12934), award(?x4999, ?x704), location(?x4999, ?x2997), titles(?x162, ?x12934), ?x2997 = 06y9v >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #9168 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 52 *> proper extension: 0crh5_f; *> query: (?x12934, 01wy5m) <- genre(?x12934, ?x53), film_crew_role(?x12934, ?x3305), film_crew_role(?x12934, ?x1284), ?x1284 = 0ch6mp2, ?x3305 = 04pyp5 *> conf = 0.04 ranks of expected_values: 85, 131 EVAL 0fzm0g film! 01gbn6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 63.000 37.000 0.370 http://example.org/film/actor/film./film/performance/film EVAL 0fzm0g film! 01wy5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 63.000 37.000 0.370 http://example.org/film/actor/film./film/performance/film #13285-06y611 PRED entity: 06y611 PRED relation: genre PRED expected values: 07s9rl0 => 114 concepts (88 used for prediction) PRED predicted values (max 10 best out of 110): 07s9rl0 (0.86 #10116, 0.77 #6982, 0.67 #7710), 09b3v (0.72 #7102, 0.57 #4811, 0.51 #7709), 02kdv5l (0.59 #3611, 0.52 #9398, 0.51 #5176), 06cvj (0.58 #3132, 0.33 #845, 0.33 #966), 01jfsb (0.55 #9407, 0.43 #733, 0.43 #5790), 0hcr (0.53 #1106, 0.50 #1346, 0.41 #1466), 01hmnh (0.42 #1100, 0.39 #1340, 0.35 #5190), 04xvlr (0.32 #7346, 0.30 #7711, 0.27 #6983), 06n90 (0.31 #3621, 0.27 #5549, 0.27 #3381), 04t36 (0.30 #1089, 0.28 #1329, 0.23 #1449) >> Best rule #10116 for best value: >> intensional similarity = 8 >> extensional distance = 916 >> proper extension: 0170z3; 02d413; 0b76d_m; 0g22z; 0sxg4; 01br2w; 0140g4; 028_yv; 0c0yh4; 03g90h; ... >> query: (?x9804, 07s9rl0) <- film_release_distribution_medium(?x9804, ?x81), genre(?x9804, ?x1403), genre(?x7671, ?x1403), genre(?x6422, ?x1403), genre(?x1508, ?x1403), ?x7671 = 05dptj, ?x6422 = 02qk3fk, ?x1508 = 09z2b7 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06y611 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 88.000 0.858 http://example.org/film/film/genre #13284-0121h7 PRED entity: 0121h7 PRED relation: adjoins PRED expected values: 0hkq4 => 121 concepts (40 used for prediction) PRED predicted values (max 10 best out of 330): 0clzr (0.20 #364, 0.17 #1137, 0.15 #3456), 0clz7 (0.20 #161, 0.17 #934, 0.12 #1707), 01279v (0.18 #3073, 0.15 #3846, 0.12 #5395), 0jtf1 (0.17 #5006, 0.15 #3457, 0.12 #2684), 0m_cg (0.12 #2283, 0.05 #21723, 0.05 #24051), 01fj9_ (0.12 #2772, 0.10 #3545, 0.08 #5094), 0m_w6 (0.09 #10082, 0.08 #9300, 0.08 #5385), 0m__z (0.09 #10082, 0.08 #9300, 0.08 #10857), 01qs54 (0.09 #10082, 0.08 #9300, 0.08 #10857), 0121h7 (0.09 #10082, 0.08 #9300, 0.08 #10857) >> Best rule #364 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0n03f; 01qs54; 0m_w6; >> query: (?x9635, 0clzr) <- administrative_parent(?x9635, ?x429), ?x429 = 03rt9, contains(?x429, ?x9635), country(?x9635, ?x429) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #4740 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 22 *> proper extension: 0clz7; 0373qg; 011xy1; 01fj9_; 0m_zm; 0ng8v; *> query: (?x9635, 0hkq4) <- contains(?x429, ?x9635), ?x429 = 03rt9 *> conf = 0.08 ranks of expected_values: 14 EVAL 0121h7 adjoins 0hkq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 121.000 40.000 0.200 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #13283-0b13g7 PRED entity: 0b13g7 PRED relation: award PRED expected values: 0gq9h => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 306): 01l29r (0.71 #30320, 0.71 #30725, 0.70 #38004), 0gq9h (0.42 #9776, 0.41 #10989, 0.41 #11393), 09sb52 (0.39 #6910, 0.37 #4080, 0.37 #4484), 04kxsb (0.31 #2146, 0.30 #1742, 0.23 #2550), 019f4v (0.27 #9765, 0.26 #2490, 0.25 #11382), 0gs9p (0.26 #9778, 0.24 #10991, 0.24 #11395), 05pcn59 (0.26 #2101, 0.23 #1697, 0.18 #6951), 05zr6wv (0.24 #2036, 0.20 #1632, 0.20 #419), 0f4x7 (0.24 #2050, 0.20 #1646, 0.17 #1242), 07bdd_ (0.23 #5317, 0.21 #6530, 0.21 #6126) >> Best rule #30320 for best value: >> intensional similarity = 3 >> extensional distance = 1164 >> proper extension: 0g51l1; 0c_mvb; 01wz_ml; 016kkx; 06z4wj; 09h_q; 0gdqy; 05g7q; 0969fd; 0164y7; >> query: (?x3568, ?x198) <- profession(?x3568, ?x319), award_winner(?x198, ?x3568), award_winner(?x3568, ?x5973) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #9776 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 157 *> proper extension: 0jf1b; 0170vn; 081nh; 0184dt; 0p51w; 02l5rm; 0d9_96; 0jw67; 05jm7; 0171lb; ... *> query: (?x3568, 0gq9h) <- produced_by(?x2029, ?x3568), award_winner(?x198, ?x3568), award_winner(?x944, ?x3568) *> conf = 0.42 ranks of expected_values: 2 EVAL 0b13g7 award 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 139.000 139.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13282-02f5qb PRED entity: 02f5qb PRED relation: award_winner PRED expected values: 015f7 046p9 => 44 concepts (11 used for prediction) PRED predicted values (max 10 best out of 1146): 01vw20h (0.56 #10842, 0.50 #5920, 0.35 #19690), 0137g1 (0.50 #5512, 0.43 #9843, 0.43 #7973), 01v_pj6 (0.50 #5261, 0.43 #7722, 0.33 #339), 09hnb (0.50 #3033, 0.43 #7955, 0.32 #9844), 0dvqq (0.50 #5417, 0.33 #20189, 0.33 #495), 0838y (0.50 #6421, 0.33 #1499, 0.29 #19691), 0j1yf (0.42 #15157, 0.40 #14766, 0.40 #12696), 02qwg (0.42 #15502, 0.24 #17965, 0.11 #20429), 01vvydl (0.33 #12305, 0.33 #9858, 0.12 #17244), 02l840 (0.33 #9990, 0.32 #19692, 0.29 #19691) >> Best rule #10842 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 02f76h; 03t5b6; 02f75t; 023vrq; >> query: (?x2877, 01vw20h) <- award(?x6835, ?x2877), award(?x3893, ?x2877), award_nominee(?x6835, ?x140), ?x3893 = 01v40wd, ?x140 = 01vvydl, award_winner(?x154, ?x6835) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #19691 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 01l29r; 01cky2; *> query: (?x2877, ?x8166) <- award(?x8166, ?x2877), award(?x6835, ?x2877), award(?x4476, ?x2877), award_nominee(?x6835, ?x140), participant(?x2227, ?x6835), ?x4476 = 01vw20h *> conf = 0.29 ranks of expected_values: 56, 150 EVAL 02f5qb award_winner 046p9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 44.000 11.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02f5qb award_winner 015f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 44.000 11.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner #13281-042z_g PRED entity: 042z_g PRED relation: type_of_union PRED expected values: 04ztj => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.81 #9, 0.74 #17, 0.73 #21), 01g63y (0.14 #22, 0.13 #58, 0.13 #70) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 03q1vd; >> query: (?x5099, 04ztj) <- award_nominee(?x9655, ?x5099), film(?x5099, ?x522), ?x9655 = 02ct_k >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042z_g type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.812 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13280-059x66 PRED entity: 059x66 PRED relation: ceremony! PRED expected values: 0gr4k 0l8z1 0gr0m => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 341): 0gr0m (0.88 #772, 0.83 #4865, 0.82 #5587), 0gr4k (0.88 #1466, 0.86 #981, 0.84 #2429), 0l8z1 (0.86 #4133, 0.86 #2931, 0.86 #3893), 0gqzz (0.81 #1930, 0.79 #3613, 0.78 #3372), 02x201b (0.81 #1930, 0.79 #3613, 0.78 #3372), 0czp_ (0.81 #1930, 0.79 #3613, 0.78 #3372), 019f4v (0.35 #6261, 0.34 #4815, 0.34 #5056), 057xs89 (0.35 #6261, 0.34 #4815, 0.34 #5056), 02r22gf (0.35 #6261, 0.34 #4815, 0.34 #5056), 05ztjjw (0.35 #6261, 0.34 #4815, 0.34 #5056) >> Best rule #772 for best value: >> intensional similarity = 17 >> extensional distance = 15 >> proper extension: 02yvhx; >> query: (?x1449, 0gr0m) <- ceremony(?x4573, ?x1449), ceremony(?x4573, ?x7884), ceremony(?x4573, ?x7100), ceremony(?x4573, ?x3618), honored_for(?x1449, ?x7693), ?x7100 = 0bzmt8, award_winner(?x4573, ?x2426), award(?x5605, ?x4573), award(?x1933, ?x4573), production_companies(?x7693, ?x3920), nominated_for(?x637, ?x7693), ?x1933 = 0c94fn, ?x3618 = 0bzn6_, ?x7884 = 09306z, category(?x7693, ?x134), profession(?x5605, ?x319), award_winner(?x1449, ?x496) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 059x66 ceremony! 0gr0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.882 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 059x66 ceremony! 0l8z1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.882 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 059x66 ceremony! 0gr4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.882 http://example.org/award/award_category/winners./award/award_honor/ceremony #13279-01wjrn PRED entity: 01wjrn PRED relation: film PRED expected values: 063y9fp => 116 concepts (107 used for prediction) PRED predicted values (max 10 best out of 354): 01fs__ (0.49 #62528, 0.47 #25013, 0.41 #117901), 04g73n (0.25 #1405), 02pw_n (0.25 #1172), 01shy7 (0.06 #2209, 0.04 #3995, 0.03 #18289), 051zy_b (0.06 #2365, 0.04 #4151, 0.02 #5937), 09lxv9 (0.06 #3289, 0.04 #5075, 0.02 #8651), 034qzw (0.06 #2119, 0.04 #3905, 0.02 #36064), 01738w (0.06 #2915, 0.04 #4701, 0.02 #81519), 05sw5b (0.06 #2600, 0.04 #4386, 0.02 #29399), 07h9gp (0.06 #2051, 0.04 #3837, 0.01 #7413) >> Best rule #62528 for best value: >> intensional similarity = 3 >> extensional distance = 800 >> proper extension: 0b6yp2; 01l79yc; 03bw6; 01njxvw; 0bn3jg; >> query: (?x1447, ?x7365) <- gender(?x1447, ?x231), people(?x1446, ?x1447), nominated_for(?x1447, ?x7365) >> conf = 0.49 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wjrn film 063y9fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 107.000 0.494 http://example.org/film/actor/film./film/performance/film #13278-0l2nd PRED entity: 0l2nd PRED relation: currency PRED expected values: 09nqf => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.89 #23, 0.89 #22, 0.87 #21) >> Best rule #23 for best value: >> intensional similarity = 5 >> extensional distance = 245 >> proper extension: 0mw89; 0mk7z; 0m7d0; 0nht0; 0nm42; 0jch5; 0jrxx; 0mpbx; 0ff0x; 0n2kw; ... >> query: (?x13522, ?x170) <- adjoins(?x5892, ?x13522), second_level_divisions(?x94, ?x13522), contains(?x1227, ?x13522), adjoins(?x5892, ?x7520), currency(?x7520, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l2nd currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.887 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #13277-08sfxj PRED entity: 08sfxj PRED relation: film! PRED expected values: 0b_dy => 134 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1122): 02_n5d (0.63 #39568, 0.49 #39567, 0.48 #35401), 02lfwp (0.49 #39567, 0.48 #35401, 0.47 #79152), 04w1j9 (0.49 #39567, 0.48 #35401, 0.47 #79152), 0kvqv (0.49 #39567, 0.48 #35401, 0.47 #79152), 01hkhq (0.25 #413, 0.04 #168723, 0.02 #12909), 016ywr (0.25 #298, 0.04 #31534, 0.02 #33616), 01515w (0.25 #1086, 0.03 #3169, 0.03 #30240), 01xcfy (0.25 #493, 0.02 #4658, 0.02 #8823), 04jb97 (0.25 #1417, 0.02 #22243, 0.02 #26407), 0prfz (0.25 #56, 0.02 #62544, 0.02 #12552) >> Best rule #39568 for best value: >> intensional similarity = 5 >> extensional distance = 183 >> proper extension: 0b2v79; 02x0fs9; >> query: (?x5152, ?x3447) <- genre(?x5152, ?x53), film_release_region(?x5152, ?x94), award_winner(?x5152, ?x3447), titles(?x162, ?x5152), languages(?x3447, ?x254) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #65105 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 322 *> proper extension: 01gglm; *> query: (?x5152, 0b_dy) <- film(?x1739, ?x5152), award_winner(?x5152, ?x3447), titles(?x512, ?x5152), split_to(?x1310, ?x512) *> conf = 0.02 ranks of expected_values: 580 EVAL 08sfxj film! 0b_dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 134.000 83.000 0.629 http://example.org/film/actor/film./film/performance/film #13276-02bc74 PRED entity: 02bc74 PRED relation: role PRED expected values: 026t6 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 122): 0342h (0.40 #2998, 0.38 #3415, 0.37 #3309), 0l14j_ (0.25 #2580, 0.24 #4136, 0.24 #2475), 02hnl (0.25 #2580, 0.24 #4136, 0.24 #2475), 02sgy (0.24 #3000, 0.23 #3417, 0.23 #3311), 042v_gx (0.21 #3002, 0.21 #3313, 0.20 #3419), 05842k (0.17 #3382, 0.17 #3071, 0.16 #3488), 018vs (0.17 #3007, 0.17 #3424, 0.16 #3318), 026t6 (0.17 #2996, 0.16 #3413, 0.16 #3307), 0l14qv (0.16 #2999, 0.16 #3310, 0.16 #3416), 01vj9c (0.16 #3426, 0.16 #3320, 0.15 #3009) >> Best rule #2998 for best value: >> intensional similarity = 4 >> extensional distance = 420 >> proper extension: 01m7f5r; >> query: (?x12743, 0342h) <- profession(?x12743, ?x131), role(?x12743, ?x1166), role(?x248, ?x1166), instrumentalists(?x1166, ?x130) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2996 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 420 *> proper extension: 01m7f5r; *> query: (?x12743, 026t6) <- profession(?x12743, ?x131), role(?x12743, ?x1166), role(?x248, ?x1166), instrumentalists(?x1166, ?x130) *> conf = 0.17 ranks of expected_values: 8 EVAL 02bc74 role 026t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 114.000 114.000 0.398 http://example.org/music/artist/track_contributions./music/track_contribution/role #13275-03c_cxn PRED entity: 03c_cxn PRED relation: category PRED expected values: 08mbj5d => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.44 #3, 0.39 #7, 0.36 #4) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 05z_kps; 047n8xt; 02w9k1c; >> query: (?x5107, 08mbj5d) <- nominated_for(?x13107, ?x5107), ?x13107 = 0fq9zcx, award(?x5107, ?x1033), film_festivals(?x5107, ?x9189) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03c_cxn category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.444 http://example.org/common/topic/webpage./common/webpage/category #13274-01zwy PRED entity: 01zwy PRED relation: student! PRED expected values: 03bwzr4 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 14): 014mlp (0.59 #311, 0.58 #347, 0.57 #419), 019v9k (0.33 #63, 0.33 #45, 0.23 #81), 04zx3q1 (0.33 #38, 0.14 #164, 0.12 #182), 0bkj86 (0.17 #44, 0.17 #26, 0.14 #188), 01rr_d (0.17 #69, 0.04 #195, 0.03 #177), 028dcg (0.14 #358, 0.13 #430, 0.11 #322), 013zdg (0.10 #187, 0.08 #169, 0.03 #367), 03mkk4 (0.08 #318, 0.08 #84, 0.07 #714), 02mjs7 (0.08 #76, 0.02 #184, 0.02 #256), 03bwzr4 (0.08 #85, 0.01 #715, 0.01 #355) >> Best rule #311 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 058s57; >> query: (?x8508, 014mlp) <- award_nominee(?x2182, ?x8508), student(?x865, ?x8508), award_winner(?x3486, ?x8508) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 0yfp; *> query: (?x8508, 03bwzr4) <- award_nominee(?x2182, ?x8508), student(?x865, ?x8508), influenced_by(?x8508, ?x3542) *> conf = 0.08 ranks of expected_values: 10 EVAL 01zwy student! 03bwzr4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 140.000 140.000 0.592 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #13273-01445t PRED entity: 01445t PRED relation: profession! PRED expected values: 02d9k 01jqr_5 069d71 0443c => 41 concepts (23 used for prediction) PRED predicted values (max 10 best out of 4189): 03f3yfj (0.72 #8440, 0.50 #11024, 0.33 #2584), 01vx5w7 (0.72 #8440, 0.43 #13525, 0.33 #865), 0227vl (0.72 #8440, 0.33 #2896, 0.29 #15556), 01l47f5 (0.72 #8440, 0.33 #2105, 0.25 #10545), 015pxr (0.62 #17484, 0.53 #34363, 0.53 #30143), 0dpqk (0.62 #18492, 0.50 #52252, 0.50 #10051), 03lgg (0.62 #18472, 0.50 #10031, 0.50 #5811), 0fb1q (0.62 #17824, 0.50 #9383, 0.50 #5163), 0p_47 (0.62 #18082, 0.50 #9641, 0.50 #5421), 016gkf (0.62 #18623, 0.50 #10182, 0.50 #5962) >> Best rule #8440 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 0dxtg; >> query: (?x1581, ?x6467) <- profession(?x12052, ?x1581), profession(?x8996, ?x1581), profession(?x5582, ?x1581), profession(?x1213, ?x1581), ?x12052 = 02hg53, place_of_birth(?x1213, ?x1214), participant(?x1213, ?x6467), gender(?x8996, ?x231), nationality(?x1213, ?x94), team(?x8996, ?x660), student(?x5581, ?x5582) >> conf = 0.72 => this is the best rule for 4 predicted values *> Best rule #63308 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 22 *> proper extension: 04_tv; 0db79; *> query: (?x1581, ?x7622) <- specialization_of(?x7623, ?x1581), profession(?x9739, ?x7623), profession(?x7622, ?x7623), location(?x7622, ?x362), gender(?x9739, ?x231), nationality(?x9739, ?x142) *> conf = 0.39 ranks of expected_values: 896, 1797 EVAL 01445t profession! 0443c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 23.000 0.720 http://example.org/people/person/profession EVAL 01445t profession! 069d71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 23.000 0.720 http://example.org/people/person/profession EVAL 01445t profession! 01jqr_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 23.000 0.720 http://example.org/people/person/profession EVAL 01445t profession! 02d9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 23.000 0.720 http://example.org/people/person/profession #13272-0j1yf PRED entity: 0j1yf PRED relation: profession PRED expected values: 0dz3r => 141 concepts (140 used for prediction) PRED predicted values (max 10 best out of 78): 01d_h8 (0.54 #589, 0.51 #2342, 0.45 #735), 0cbd2 (0.51 #4534, 0.44 #6141, 0.42 #8041), 0nbcg (0.49 #7041, 0.48 #8357, 0.45 #9381), 01c72t (0.47 #2796, 0.46 #1627, 0.44 #1481), 03gjzk (0.46 #1035, 0.36 #159, 0.32 #451), 0dxtg (0.44 #1034, 0.43 #6147, 0.38 #12), 0dz3r (0.41 #7014, 0.40 #440, 0.39 #1608), 0kyk (0.37 #4555, 0.31 #6162, 0.28 #8062), 039v1 (0.29 #7046, 0.27 #8362, 0.26 #9386), 02jknp (0.28 #16219, 0.27 #153, 0.24 #12427) >> Best rule #589 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 02v3yy; 0k8y7; 0bqs56; 01ccr8; 01pllx; >> query: (?x1896, 01d_h8) <- participant(?x1896, ?x1503), award_winner(?x486, ?x1896), type_of_union(?x1896, ?x566) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #7014 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 389 *> proper extension: 0p5mw; *> query: (?x1896, 0dz3r) <- instrumentalists(?x227, ?x1896), role(?x1896, ?x1574) *> conf = 0.41 ranks of expected_values: 7 EVAL 0j1yf profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 141.000 140.000 0.536 http://example.org/people/person/profession #13271-0h0wc PRED entity: 0h0wc PRED relation: award_nominee PRED expected values: 01pgzn_ => 128 concepts (72 used for prediction) PRED predicted values (max 10 best out of 978): 01pgzn_ (0.81 #125210, 0.81 #141440, 0.81 #164626), 02qgqt (0.81 #125210, 0.81 #141440, 0.81 #164626), 01_p6t (0.81 #125210, 0.81 #141440, 0.81 #164626), 06pjs (0.76 #83479, 0.75 #92754, 0.75 #166946), 04zwtdy (0.22 #2196), 0c3p7 (0.17 #9273, 0.16 #11592, 0.16 #16229), 02bj6k (0.17 #9273, 0.16 #11592, 0.16 #16229), 01gw8b (0.17 #9273, 0.16 #11592, 0.16 #16229), 0154qm (0.15 #164627, 0.11 #727, 0.05 #10000), 051wwp (0.15 #164627, 0.11 #1155, 0.04 #86953) >> Best rule #125210 for best value: >> intensional similarity = 3 >> extensional distance = 1038 >> proper extension: 0162c8; 08m4c8; 06jvj7; 07qy0b; 05dxl5; 06jw0s; 01my_c; 06hzsx; 08qmfm; 02yygk; ... >> query: (?x2551, ?x92) <- place_of_birth(?x2551, ?x11893), award_nominee(?x92, ?x2551), nominated_for(?x2551, ?x414) >> conf = 0.81 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0h0wc award_nominee 01pgzn_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 72.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13270-06yykb PRED entity: 06yykb PRED relation: country PRED expected values: 0chghy => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 23): 07ssc (0.23 #558, 0.21 #678, 0.21 #3926), 0345h (0.21 #27, 0.16 #387, 0.15 #448), 0f8l9c (0.14 #379, 0.11 #440, 0.10 #2185), 0chghy (0.07 #72, 0.07 #252, 0.06 #735), 03_3d (0.07 #609, 0.04 #2534, 0.04 #2774), 0d060g (0.06 #308, 0.05 #1512, 0.05 #1152), 0d05w3 (0.05 #43, 0.03 #283, 0.03 #886), 0j1z8 (0.05 #11, 0.02 #71), 01mjq (0.05 #35, 0.02 #1059, 0.02 #335), 03rjj (0.05 #366, 0.04 #1090, 0.03 #2172) >> Best rule #558 for best value: >> intensional similarity = 3 >> extensional distance = 229 >> proper extension: 0180mw; >> query: (?x7975, 07ssc) <- nominated_for(?x10130, ?x7975), nominated_for(?x3056, ?x10130), nominated_for(?x102, ?x10130) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #72 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 060v34; 04fzfj; 0jjy0; 02c6d; 0gd0c7x; 02kfzz; 03tn80; 09fc83; 03t79f; 01kjr0; ... *> query: (?x7975, 0chghy) <- genre(?x7975, ?x571), film(?x1070, ?x7975), executive_produced_by(?x7975, ?x96), ?x571 = 03npn *> conf = 0.07 ranks of expected_values: 4 EVAL 06yykb country 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 72.000 0.225 http://example.org/film/film/country #13269-0r8c8 PRED entity: 0r8c8 PRED relation: place PRED expected values: 0r8c8 => 110 concepts (72 used for prediction) PRED predicted values (max 10 best out of 190): 0r8bh (0.53 #10320, 0.51 #8257, 0.37 #1032), 01cx_ (0.14 #64, 0.02 #3676, 0.01 #5225), 0hptm (0.14 #157, 0.01 #5835, 0.01 #6867), 0f2wj (0.12 #29429, 0.08 #18065), 0jbrr (0.05 #969, 0.04 #1485, 0.03 #2002), 0r3tq (0.05 #826, 0.03 #1859, 0.03 #2375), 0r2dp (0.05 #803, 0.03 #1836, 0.02 #3383), 0d7k1z (0.05 #659, 0.03 #1692, 0.02 #3239), 0r5wt (0.05 #622, 0.03 #1655, 0.02 #3202), 0r1jr (0.05 #568, 0.03 #1601, 0.02 #3148) >> Best rule #10320 for best value: >> intensional similarity = 5 >> extensional distance = 100 >> proper extension: 0ny57; >> query: (?x6367, ?x11966) <- contains(?x11967, ?x6367), contains(?x1227, ?x6367), place_of_birth(?x1796, ?x6367), county_seat(?x11967, ?x11966), location_of_ceremony(?x566, ?x1227) >> conf = 0.53 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r8c8 place 0r8c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 72.000 0.529 http://example.org/location/hud_county_place/place #13268-01_qc_ PRED entity: 01_qc_ PRED relation: symptom_of! PRED expected values: 0gxb2 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 68): 01j6t0 (0.86 #1026, 0.85 #954, 0.83 #904), 0cjf0 (0.75 #1587, 0.60 #743, 0.59 #1205), 0gxb2 (0.54 #595, 0.50 #309, 0.50 #168), 04kllm9 (0.54 #595, 0.33 #420, 0.29 #505), 01cdt5 (0.50 #919, 0.50 #567, 0.50 #397), 0j5fv (0.40 #288, 0.40 #248, 0.39 #1734), 0f3kl (0.40 #771, 0.38 #587, 0.33 #16), 02y0js (0.33 #1, 0.20 #1597, 0.17 #1361), 097ns (0.33 #420, 0.31 #180, 0.27 #992), 09969 (0.33 #420, 0.31 #180, 0.27 #992) >> Best rule #1026 for best value: >> intensional similarity = 11 >> extensional distance = 12 >> proper extension: 07s4l; >> query: (?x7260, 01j6t0) <- symptom_of(?x9438, ?x7260), people(?x7260, ?x9074), people(?x7260, ?x6934), people(?x7260, ?x1737), symptom_of(?x9438, ?x11739), symptom_of(?x9438, ?x4322), ?x11739 = 0167bx, influenced_by(?x1737, ?x920), ?x4322 = 0gk4g, artists(?x505, ?x9074), place_of_death(?x6934, ?x739) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #595 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 6 *> proper extension: 0dcp_; *> query: (?x7260, ?x9509) <- symptom_of(?x9510, ?x7260), symptom_of(?x9118, ?x7260), symptom_of(?x9118, ?x13131), symptom_of(?x9118, ?x5118), symptom_of(?x9118, ?x4906), symptom_of(?x9118, ?x4291), ?x13131 = 0d19y2, ?x5118 = 01bcp7, people(?x4291, ?x4055), symptom_of(?x9509, ?x9510), symptom_of(?x4905, ?x9510), ?x4905 = 01j6t0, risk_factors(?x4906, ?x514) *> conf = 0.54 ranks of expected_values: 3 EVAL 01_qc_ symptom_of! 0gxb2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 94.000 0.857 http://example.org/medicine/symptom/symptom_of #13267-04v3q PRED entity: 04v3q PRED relation: country! PRED expected values: 0bynt => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 57): 0bynt (0.89 #2690, 0.88 #5711, 0.86 #353), 071t0 (0.86 #310, 0.84 #196, 0.83 #4300), 01lb14 (0.82 #1100, 0.79 #644, 0.73 #2069), 03_8r (0.79 #195, 0.77 #309, 0.76 #1164), 06f41 (0.74 #187, 0.70 #2068, 0.68 #301), 01cgz (0.74 #186, 0.68 #2352, 0.67 #3492), 06wrt (0.73 #2070, 0.69 #132, 0.64 #360), 03hr1p (0.70 #2078, 0.68 #197, 0.67 #1109), 07jbh (0.70 #1119, 0.68 #207, 0.64 #2088), 064vjs (0.68 #205, 0.68 #319, 0.64 #775) >> Best rule #2690 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 049nq; >> query: (?x1061, 0bynt) <- currency(?x1061, ?x170), capital(?x1061, ?x13472), ?x170 = 09nqf, official_language(?x1061, ?x254) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04v3q country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #13266-032t2z PRED entity: 032t2z PRED relation: student! PRED expected values: 0fvd03 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 169): 02g839 (0.29 #2133, 0.10 #6349, 0.08 #3187), 017z88 (0.18 #2717, 0.09 #4825, 0.07 #11676), 01w5m (0.14 #1686, 0.09 #4848, 0.06 #27511), 01d34b (0.14 #1837, 0.08 #3418, 0.05 #4999), 01t0dy (0.14 #1798, 0.08 #3379, 0.03 #6541), 01g0p5 (0.14 #1788, 0.06 #4423, 0.02 #10220), 015wy_ (0.14 #2036, 0.03 #7306, 0.02 #14157), 04bfg (0.14 #2334, 0.01 #23416), 07tg4 (0.11 #5883, 0.10 #10099, 0.08 #20114), 02cw8s (0.09 #2705, 0.06 #4286, 0.03 #17463) >> Best rule #2133 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 06gd4; 01sb5r; 04kjrv; 01r0t_j; 01wmjkb; >> query: (?x642, 02g839) <- profession(?x642, ?x1183), role(?x642, ?x228), instrumentalists(?x7033, ?x642), artist(?x2149, ?x642), ?x7033 = 0gkd1 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #4189 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 01vrwfv; 02jqjm; 02vgh; 01w5n51; 015cqh; *> query: (?x642, 0fvd03) <- artists(?x1380, ?x642), artist(?x3265, ?x642), ?x1380 = 0dl5d, ?x3265 = 015_1q *> conf = 0.06 ranks of expected_values: 19 EVAL 032t2z student! 0fvd03 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 131.000 131.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #13265-0rydq PRED entity: 0rydq PRED relation: contains! PRED expected values: 09c7w0 => 73 concepts (26 used for prediction) PRED predicted values (max 10 best out of 130): 09c7w0 (0.70 #8951, 0.67 #8955, 0.65 #1793), 07ssc (0.23 #32, 0.16 #2717, 0.16 #17937), 01n7q (0.16 #9030, 0.13 #14401, 0.11 #4553), 04_1l0v (0.15 #4925, 0.15 #4030, 0.13 #10298), 02jx1 (0.15 #87, 0.12 #17992, 0.06 #7247), 013yq (0.13 #1936), 07h34 (0.10 #2915, 0.08 #230, 0.02 #3810), 059rby (0.08 #20, 0.06 #2705, 0.06 #4495), 07z1m (0.08 #92, 0.06 #2777, 0.03 #5462), 02_286 (0.08 #43, 0.03 #3623, 0.03 #2728) >> Best rule #8951 for best value: >> intensional similarity = 2 >> extensional distance = 224 >> proper extension: 0qm40; >> query: (?x14277, ?x94) <- state(?x14277, ?x3038), country(?x3038, ?x94) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rydq contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 26.000 0.704 http://example.org/location/location/contains #13264-01f7j9 PRED entity: 01f7j9 PRED relation: currency PRED expected values: 09nqf => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.42 #37, 0.41 #16, 0.40 #43), 01nv4h (0.07 #11, 0.05 #17, 0.02 #35) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 01yznp; 0282x; 03swmf; 05wm88; >> query: (?x2182, 09nqf) <- executive_produced_by(?x1477, ?x2182), religion(?x2182, ?x1985), award(?x2182, ?x68) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f7j9 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.415 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #13263-017t44 PRED entity: 017t44 PRED relation: contains PRED expected values: 018jcq => 52 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1930): 0g3cw (0.33 #2910, 0.08 #8803, 0.08 #5856), 059wk (0.30 #20622), 019q50 (0.27 #14733, 0.08 #4420, 0.07 #5892), 03_3d (0.24 #20623, 0.24 #50079, 0.22 #26515), 017t44 (0.24 #20623, 0.24 #50079, 0.22 #26515), 059f4 (0.10 #11854, 0.01 #23637, 0.01 #29526), 018qt8 (0.08 #5619, 0.07 #5892, 0.05 #14460), 018jn4 (0.08 #5566, 0.07 #5892, 0.05 #14407), 018qd6 (0.08 #5495, 0.07 #5892, 0.05 #14336), 018jkl (0.08 #4742, 0.07 #5892, 0.05 #13583) >> Best rule #2910 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 018jn4; >> query: (?x2652, 0g3cw) <- contains(?x2651, ?x2652), contains(?x252, ?x2652), ?x2651 = 0g3bw, contains(?x2652, ?x6395), ?x252 = 03_3d >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4220 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 049yf; 0g3bw; 09d4_; 024bqj; 07dfk; 0gqm3; 0fxrk; 01fv4z; 02lf_x; *> query: (?x2652, 018jcq) <- contains(?x252, ?x2652), ?x252 = 03_3d, contains(?x2652, ?x6395) *> conf = 0.08 ranks of expected_values: 13 EVAL 017t44 contains 018jcq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 52.000 17.000 0.333 http://example.org/location/location/contains #13262-033hn8 PRED entity: 033hn8 PRED relation: artist PRED expected values: 0cg9y 09r8l 01wn718 01mxt_ => 95 concepts (55 used for prediction) PRED predicted values (max 10 best out of 879): 01cwhp (0.64 #6022, 0.64 #4515, 0.54 #14313), 0153nq (0.60 #3761, 0.50 #2256, 0.08 #6020), 01vxlbm (0.50 #247, 0.20 #4009, 0.19 #8534), 01w524f (0.40 #3281, 0.25 #1776, 0.25 #1024), 0167_s (0.40 #3128, 0.25 #1623, 0.13 #6141), 01wbsdz (0.30 #4146, 0.25 #5653, 0.25 #384), 01vw917 (0.30 #4179, 0.25 #5686, 0.25 #417), 04qzm (0.30 #4426, 0.25 #5933, 0.25 #664), 03y82t6 (0.30 #4071, 0.25 #5578, 0.25 #309), 01vs4ff (0.30 #4213, 0.25 #5720, 0.17 #11001) >> Best rule #6022 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 06pwq; 017z88; >> query: (?x2299, ?x2461) <- organization(?x4682, ?x2299), company(?x2461, ?x2299), award(?x2461, ?x724), award_nominee(?x538, ?x2461), artists(?x671, ?x2461) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 03mp8k; 01trtc; *> query: (?x2299, 0cg9y) <- artist(?x2299, ?x6651), artist(?x2299, ?x3740), artist(?x2299, ?x2073), artist(?x2299, ?x1652), ?x3740 = 0fpj4lx, award_winner(?x342, ?x2073), artists(?x378, ?x6651), award_winner(?x369, ?x1652) *> conf = 0.25 ranks of expected_values: 32, 207, 742 EVAL 033hn8 artist 01mxt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 55.000 0.643 http://example.org/music/record_label/artist EVAL 033hn8 artist 01wn718 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 95.000 55.000 0.643 http://example.org/music/record_label/artist EVAL 033hn8 artist 09r8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 95.000 55.000 0.643 http://example.org/music/record_label/artist EVAL 033hn8 artist 0cg9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 95.000 55.000 0.643 http://example.org/music/record_label/artist #13261-03nsm5x PRED entity: 03nsm5x PRED relation: film_crew_role PRED expected values: 02r96rf => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.81 #219, 0.72 #435, 0.70 #940), 09vw2b7 (0.71 #43, 0.67 #1885, 0.66 #1415), 0dxtw (0.44 #948, 0.42 #191, 0.40 #1419), 01pvkk (0.30 #949, 0.28 #2256, 0.28 #2914), 02rh1dz (0.19 #947, 0.18 #82, 0.17 #154), 02ynfr (0.18 #1894, 0.18 #953, 0.17 #1424), 0215hd (0.17 #55, 0.14 #487, 0.13 #271), 0d2b38 (0.16 #98, 0.11 #963, 0.11 #278), 015h31 (0.12 #9, 0.11 #946, 0.10 #983), 01xy5l_ (0.12 #86, 0.12 #50, 0.10 #915) >> Best rule #219 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 0g56t9t; 0gtsx8c; 02vxq9m; 0gtv7pk; 0g5qs2k; 0dscrwf; 02x3lt7; 0c40vxk; 0gkz15s; 017gl1; ... >> query: (?x8025, 02r96rf) <- film(?x1559, ?x8025), film_crew_role(?x8025, ?x137), film_release_region(?x8025, ?x1536), ?x1536 = 06c1y >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03nsm5x film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.809 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13260-07m4c PRED entity: 07m4c PRED relation: group! PRED expected values: 01vj9c => 99 concepts (96 used for prediction) PRED predicted values (max 10 best out of 112): 0l14md (0.63 #2753, 0.61 #2010, 0.59 #3569), 03bx0bm (0.60 #2024, 0.58 #2767, 0.58 #3658), 03qjg (0.50 #112, 0.38 #630, 0.33 #38), 0l14qv (0.50 #79, 0.33 #5, 0.28 #2751), 028tv0 (0.46 #601, 0.38 #2012, 0.36 #3722), 01vj9c (0.38 #602, 0.28 #1791, 0.28 #3647), 042v_gx (0.33 #8, 0.25 #82, 0.12 #2754), 02k84w (0.33 #24, 0.25 #98, 0.08 #616), 02sgy (0.33 #6, 0.25 #80, 0.07 #2822), 01xqw (0.33 #57, 0.25 #131, 0.07 #2822) >> Best rule #2753 for best value: >> intensional similarity = 6 >> extensional distance = 128 >> proper extension: 01t_xp_; 01pfr3; 0m19t; 0150jk; 02r3zy; 067mj; 01vsxdm; 03t9sp; 01fl3; 05crg7; ... >> query: (?x7544, 0l14md) <- group(?x1166, ?x7544), group(?x615, ?x7544), ?x1166 = 05148p4, role(?x615, ?x315), role(?x615, ?x74), role(?x1321, ?x615) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #602 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 0ql36; *> query: (?x7544, 01vj9c) <- group(?x366, ?x7544), artist(?x3240, ?x7544), origin(?x7544, ?x1658), ?x3240 = 017l96, artists(?x1572, ?x7544) *> conf = 0.38 ranks of expected_values: 6 EVAL 07m4c group! 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 99.000 96.000 0.631 http://example.org/music/performance_role/regular_performances./music/group_membership/group #13259-059rby PRED entity: 059rby PRED relation: geographic_distribution! PRED expected values: 0g48m4 => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 39): 0d29z (0.39 #1181, 0.35 #1061, 0.35 #341), 0g48m4 (0.30 #401, 0.21 #201, 0.19 #1641), 071x0k (0.30 #1043, 0.30 #1163, 0.27 #1283), 04mvp8 (0.27 #194, 0.20 #354, 0.17 #714), 013b6_ (0.18 #187, 0.08 #547, 0.08 #627), 01rv7x (0.15 #342, 0.14 #262, 0.10 #702), 0g6ff (0.15 #330, 0.09 #850, 0.09 #170), 01xhh5 (0.11 #1180, 0.11 #1380, 0.11 #1260), 012f86 (0.10 #352, 0.07 #272, 0.06 #872), 06mvq (0.09 #898, 0.07 #1058, 0.07 #258) >> Best rule #1181 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 09pmkv; 06qd3; 05sb1; 05b4w; 06f32; 06vbd; 09lxtg; 01xbgx; 01crd5; >> query: (?x335, 0d29z) <- administrative_parent(?x334, ?x335), film_release_region(?x5317, ?x335), contains(?x335, ?x322) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #401 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 01vsb_; *> query: (?x335, 0g48m4) <- state_province_region(?x13185, ?x335), contact_category(?x13185, ?x897), category(?x13185, ?x134) *> conf = 0.30 ranks of expected_values: 2 EVAL 059rby geographic_distribution! 0g48m4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 186.000 186.000 0.386 http://example.org/people/ethnicity/geographic_distribution #13258-06bc59 PRED entity: 06bc59 PRED relation: edited_by PRED expected values: 0bn3jg => 93 concepts (50 used for prediction) PRED predicted values (max 10 best out of 18): 08h79x (0.14 #18, 0.04 #192, 0.02 #163), 02qggqc (0.14 #3, 0.03 #119, 0.02 #792), 03q8ch (0.05 #482, 0.05 #333, 0.04 #362), 04cy8rb (0.04 #204, 0.03 #88, 0.02 #175), 03cp7b3 (0.03 #111, 0.01 #286, 0.01 #315), 0gd9k (0.03 #108, 0.01 #283, 0.01 #312), 027pdrh (0.03 #330, 0.03 #359, 0.02 #419), 0dky9n (0.03 #122, 0.02 #151, 0.02 #180), 0bn3jg (0.03 #144, 0.01 #468), 02kxbx3 (0.03 #128, 0.01 #830) >> Best rule #18 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 09qljs; >> query: (?x9786, 08h79x) <- titles(?x812, ?x9786), film(?x902, ?x9786), ?x902 = 05qd_, genre(?x9786, ?x571), ?x571 = 03npn >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 0b6tzs; 0jjy0; 04mzf8; 05cj_j; 0cc7hmk; 0fy34l; 070fnm; 01j8wk; 05k2xy; 02kfzz; ... *> query: (?x9786, 0bn3jg) <- titles(?x4205, ?x9786), film(?x902, ?x9786), film_release_distribution_medium(?x9786, ?x81), country(?x9786, ?x205), ?x4205 = 0c3351 *> conf = 0.03 ranks of expected_values: 9 EVAL 06bc59 edited_by 0bn3jg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 93.000 50.000 0.143 http://example.org/film/film/edited_by #13257-07vj4v PRED entity: 07vj4v PRED relation: industry PRED expected values: 03qh03g => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 39): 02vxn (0.67 #628, 0.50 #484, 0.45 #1061), 01mw1 (0.41 #1876, 0.36 #1684, 0.33 #145), 020mfr (0.36 #1700, 0.35 #1892, 0.33 #1364), 03qh03g (0.33 #149, 0.25 #438, 0.25 #342), 04rlf (0.33 #158, 0.25 #447, 0.25 #351), 0h6dj (0.33 #34, 0.17 #612, 0.08 #1381), 06xw2 (0.33 #614, 0.09 #1287, 0.05 #2055), 02wbm (0.33 #61, 0.05 #3716, 0.04 #4443), 02jjt (0.20 #2027, 0.20 #1739, 0.15 #3278), 015p1m (0.20 #1807, 0.15 #2239, 0.14 #2578) >> Best rule #628 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 020h2v; >> query: (?x12640, 02vxn) <- child(?x8760, ?x12640), company(?x8314, ?x8760), category(?x8760, ?x134), place_founded(?x12640, ?x550), company(?x8314, ?x6092), ?x6092 = 0hm0k, citytown(?x8760, ?x12585) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 02b07b; *> query: (?x12640, 03qh03g) <- child(?x8760, ?x12640), company(?x8314, ?x8760), program(?x8760, ?x8759), program(?x8760, ?x8444), ?x8314 = 014l7h, citytown(?x12640, ?x12585), ?x8444 = 045qmr, genre(?x8759, ?x225) *> conf = 0.33 ranks of expected_values: 4 EVAL 07vj4v industry 03qh03g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 157.000 157.000 0.667 http://example.org/business/business_operation/industry #13256-0196pc PRED entity: 0196pc PRED relation: specialization_of! PRED expected values: 015h31 => 37 concepts (15 used for prediction) PRED predicted values (max 10 best out of 80): 0dxtg (0.33 #7, 0.25 #118, 0.11 #339), 05z96 (0.33 #23, 0.25 #134, 0.11 #355), 0d8qb (0.33 #49, 0.25 #160, 0.11 #381), 0kyk (0.33 #14, 0.25 #125, 0.11 #346), 02xlf (0.33 #37, 0.25 #148, 0.11 #369), 02hv44_ (0.33 #35, 0.25 #146, 0.11 #367), 01kyvx (0.14 #225, 0.08 #556, 0.06 #667), 0196pc (0.11 #378, 0.08 #599, 0.05 #111), 01nxfc (0.11 #415, 0.08 #636, 0.05 #111), 09jwl (0.11 #340, 0.08 #561, 0.05 #111) >> Best rule #7 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 0cbd2; >> query: (?x8310, 0dxtg) <- profession(?x13504, ?x8310), profession(?x11463, ?x8310), profession(?x7851, ?x8310), profession(?x4238, ?x8310), ?x4238 = 04snp2, ?x7851 = 079ws, ?x13504 = 02gn9g, ?x11463 = 0blgl >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #111 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 0cbd2; *> query: (?x8310, ?x987) <- profession(?x13504, ?x8310), profession(?x11463, ?x8310), profession(?x11326, ?x8310), profession(?x7851, ?x8310), profession(?x4238, ?x8310), ?x4238 = 04snp2, ?x7851 = 079ws, ?x13504 = 02gn9g, profession(?x11326, ?x987), ?x11463 = 0blgl *> conf = 0.05 ranks of expected_values: 42 EVAL 0196pc specialization_of! 015h31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 37.000 15.000 0.333 http://example.org/people/profession/specialization_of #13255-0d6lp PRED entity: 0d6lp PRED relation: month PRED expected values: 040fv => 234 concepts (234 used for prediction) PRED predicted values (max 10 best out of 1): 040fv (0.84 #29, 0.83 #44, 0.82 #43) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0l0mk; 08966; 05l64; >> query: (?x3125, 040fv) <- citytown(?x1168, ?x3125), month(?x3125, ?x1459), place_of_death(?x5790, ?x3125) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d6lp month 040fv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 234.000 234.000 0.844 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #13254-02825kb PRED entity: 02825kb PRED relation: film! PRED expected values: 086nl7 => 44 concepts (23 used for prediction) PRED predicted values (max 10 best out of 664): 06q5t7 (0.70 #16621, 0.67 #24939, 0.62 #20781), 04qt29 (0.67 #24939, 0.58 #47800, 0.41 #27018), 026c1 (0.33 #357, 0.11 #4511, 0.05 #6588), 02g0mx (0.33 #526, 0.11 #4680, 0.02 #6757), 0k6yt1 (0.33 #1837, 0.11 #5991, 0.02 #8068), 01n5309 (0.33 #104, 0.11 #4258, 0.02 #6335), 086nl7 (0.26 #7015, 0.25 #2861, 0.22 #4938), 05txrz (0.25 #2841, 0.22 #4918, 0.14 #6995), 03hh89 (0.25 #3039, 0.22 #5116, 0.07 #8310), 03qmj9 (0.25 #2328, 0.22 #4405, 0.05 #6482) >> Best rule #16621 for best value: >> intensional similarity = 3 >> extensional distance = 459 >> proper extension: 06dfz1; >> query: (?x6984, ?x6985) <- nominated_for(?x6985, ?x6984), participant(?x2221, ?x6985), gender(?x6985, ?x231) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #7015 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 0h1cdwq; 0bscw; 02f6g5; 034qzw; 0661m4p; 07x4qr; 05q4y12; 02ryz24; 02xtxw; 06q8qh; ... *> query: (?x6984, 086nl7) <- film(?x3927, ?x6984), award_nominee(?x4046, ?x3927), cast_members(?x905, ?x3927) *> conf = 0.26 ranks of expected_values: 7 EVAL 02825kb film! 086nl7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 44.000 23.000 0.696 http://example.org/film/actor/film./film/performance/film #13253-02r9p0c PRED entity: 02r9p0c PRED relation: produced_by PRED expected values: 03_2y => 102 concepts (71 used for prediction) PRED predicted values (max 10 best out of 147): 03ktjq (0.15 #11447, 0.12 #13386, 0.09 #14161), 0534v (0.08 #2514, 0.08 #2903, 0.07 #4067), 02q_cc (0.08 #3137, 0.07 #3911, 0.05 #5469), 01f7j9 (0.08 #3172, 0.07 #8603, 0.05 #5504), 06pj8 (0.08 #3171, 0.05 #5503, 0.04 #10538), 02xnjd (0.07 #9970, 0.07 #3764, 0.06 #10357), 05prs8 (0.07 #8202, 0.07 #7815, 0.05 #9363), 05ty4m (0.07 #8160, 0.05 #10871, 0.04 #10483), 0184dt (0.07 #3573, 0.05 #9779, 0.05 #6681), 030_3z (0.07 #4041, 0.05 #5986, 0.04 #7149) >> Best rule #11447 for best value: >> intensional similarity = 9 >> extensional distance = 64 >> proper extension: 05sxzwc; 0bxsk; 0b6l1st; 07f_t4; >> query: (?x6999, 03ktjq) <- film(?x382, ?x6999), language(?x6999, ?x254), ?x254 = 02h40lc, genre(?x6999, ?x2540), genre(?x6999, ?x225), ?x225 = 02kdv5l, ?x382 = 086k8, film(?x13156, ?x6999), genre(?x419, ?x2540) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02r9p0c produced_by 03_2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 71.000 0.152 http://example.org/film/film/produced_by #13252-026y23w PRED entity: 026y23w PRED relation: nationality PRED expected values: 02jx1 => 70 concepts (50 used for prediction) PRED predicted values (max 10 best out of 46): 09c7w0 (0.90 #3610, 0.80 #3710, 0.80 #3812), 02jx1 (0.84 #900, 0.83 #3202, 0.83 #2596), 0dyjz (0.78 #500, 0.69 #600, 0.67 #901), 021y1s (0.78 #500, 0.69 #600, 0.67 #901), 0k33p (0.50 #298, 0.20 #700), 01m4pc (0.50 #298, 0.20 #700), 034m8 (0.33 #290, 0.19 #4110, 0.16 #3609), 03rk0 (0.28 #2642, 0.06 #3554, 0.06 #3955), 06s_2 (0.25 #195, 0.19 #4110, 0.17 #294), 019rg5 (0.25 #122, 0.17 #221, 0.02 #4210) >> Best rule #3610 for best value: >> intensional similarity = 6 >> extensional distance = 3050 >> proper extension: 06v8s0; 01sl1q; 044mz_; 07nznf; 0q9kd; 04bdxl; 02s2ft; 079vf; 0grwj; 01pbxb; ... >> query: (?x5763, 09c7w0) <- nationality(?x5763, ?x512), nationality(?x8598, ?x512), region(?x54, ?x512), country(?x362, ?x512), ?x8598 = 07m69t, country(?x124, ?x512) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #900 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 74 *> proper extension: 01l_vgt; *> query: (?x5763, ?x1310) <- nationality(?x5763, ?x512), ?x512 = 07ssc, place_of_birth(?x5763, ?x9878), contains(?x1310, ?x9878), teams(?x9878, ?x6831), nationality(?x57, ?x1310) *> conf = 0.84 ranks of expected_values: 2 EVAL 026y23w nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 50.000 0.901 http://example.org/people/person/nationality #13251-019f2f PRED entity: 019f2f PRED relation: award PRED expected values: 09sb52 0gqyl => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 266): 09sb52 (0.67 #39, 0.52 #1619, 0.36 #9125), 0gqyl (0.50 #99, 0.46 #1679, 0.15 #23309), 02ppm4q (0.33 #150, 0.30 #1730, 0.15 #23309), 0bdw1g (0.33 #36, 0.15 #23309, 0.14 #26076), 01by1l (0.26 #501, 0.20 #4452, 0.20 #896), 03c7tr1 (0.24 #1636, 0.17 #56, 0.11 #20543), 01bgqh (0.22 #436, 0.18 #4387, 0.17 #831), 05pcn59 (0.22 #1656, 0.17 #76, 0.11 #9162), 099cng (0.22 #1661, 0.05 #2056, 0.04 #476), 09td7p (0.20 #1695, 0.05 #10781, 0.05 #9201) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 086sj; >> query: (?x2589, 09sb52) <- award_nominee(?x7512, ?x2589), nominated_for(?x2589, ?x1744), ?x7512 = 01q9b9 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 019f2f award 0gqyl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f2f award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13250-0c38gj PRED entity: 0c38gj PRED relation: language PRED expected values: 02h40lc 0x82 => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 59): 02h40lc (0.89 #120, 0.88 #1427, 0.88 #712), 064_8sq (0.18 #435, 0.17 #376, 0.16 #199), 04306rv (0.17 #418, 0.17 #300, 0.17 #359), 0jzc (0.15 #79, 0.12 #197, 0.11 #374), 06nm1 (0.12 #11, 0.11 #603, 0.11 #247), 032f6 (0.12 #56, 0.05 #174, 0.04 #473), 06b_j (0.09 #318, 0.09 #436, 0.09 #377), 03_9r (0.07 #305, 0.07 #364, 0.06 #187), 02bjrlw (0.07 #474, 0.07 #1007, 0.06 #1127), 0653m (0.06 #12, 0.06 #307, 0.05 #130) >> Best rule #120 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 02vxq9m; 01h7bb; 0fh694; 0dgst_d; 01719t; 0btyf5z; 0fy34l; 0260bz; 02c638; 02qmsr; ... >> query: (?x4633, 02h40lc) <- nominated_for(?x1703, ?x4633), nominated_for(?x451, ?x4633), genre(?x4633, ?x53), ?x451 = 099jhq, ceremony(?x1703, ?x78) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 34 EVAL 0c38gj language 0x82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 52.000 52.000 0.891 http://example.org/film/film/language EVAL 0c38gj language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.891 http://example.org/film/film/language #13249-057__d PRED entity: 057__d PRED relation: genre PRED expected values: 04xvlr => 102 concepts (88 used for prediction) PRED predicted values (max 10 best out of 146): 04xvlr (0.73 #716, 0.72 #3343, 0.71 #6573), 037hz (0.70 #596, 0.65 #1311, 0.64 #954), 03k9fj (0.59 #727, 0.47 #607, 0.41 #369), 03bxz7 (0.46 #292, 0.16 #1842, 0.16 #888), 01jfsb (0.37 #1204, 0.37 #1084, 0.34 #1323), 02kdv5l (0.34 #718, 0.32 #1672, 0.32 #1313), 04xvh5 (0.33 #32, 0.12 #2061, 0.11 #748), 0lsxr (0.26 #1200, 0.22 #3713, 0.20 #485), 06cvj (0.25 #2032, 0.25 #2749, 0.21 #4187), 02n4kr (0.22 #127, 0.19 #1199, 0.16 #3712) >> Best rule #716 for best value: >> intensional similarity = 5 >> extensional distance = 94 >> proper extension: 090s_0; 05jf85; 0b60sq; 02py4c8; 03rtz1; 02q52q; 01_1pv; 0kcn7; 0dyb1; 032016; ... >> query: (?x8633, ?x53) <- titles(?x1510, ?x8633), titles(?x53, ?x8633), nominated_for(?x5014, ?x8633), ?x1510 = 01hmnh, genre(?x54, ?x53) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 057__d genre 04xvlr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 88.000 0.727 http://example.org/film/film/genre #13248-02k8k PRED entity: 02k8k PRED relation: contains! PRED expected values: 07c5l => 111 concepts (108 used for prediction) PRED predicted values (max 10 best out of 174): 059g4 (0.72 #31350, 0.70 #27767, 0.69 #9850), 02j71 (0.67 #52847, 0.65 #39415, 0.64 #86014), 07c5l (0.65 #77051, 0.60 #92291, 0.39 #2186), 02qkt (0.63 #9301, 0.61 #29906, 0.59 #25425), 09c7w0 (0.56 #9853, 0.47 #67189, 0.47 #71668), 04_1l0v (0.34 #23736, 0.30 #8508, 0.27 #38072), 02j9z (0.33 #8982, 0.29 #31378, 0.28 #21522), 0dg3n1 (0.32 #20753, 0.31 #36881, 0.30 #24338), 0j0k (0.28 #12916, 0.28 #16498, 0.27 #29937), 07ssc (0.21 #61840, 0.17 #28695, 0.16 #70802) >> Best rule #31350 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 0160w; >> query: (?x6691, ?x8483) <- organization(?x6691, ?x127), countries_within(?x8483, ?x6691), form_of_government(?x6691, ?x6377) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #77051 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 307 *> proper extension: 0f4y_; 0ldff; 0nj1c; 0n5_g; 0nm8n; 0drr3; 01rxw2; 0n4z2; 01zlx; 0df4y; *> query: (?x6691, ?x7273) <- administrative_parent(?x6691, ?x551), adjoins(?x6691, ?x3720), contains(?x7273, ?x3720) *> conf = 0.65 ranks of expected_values: 3 EVAL 02k8k contains! 07c5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 108.000 0.718 http://example.org/location/location/contains #13247-019n8z PRED entity: 019n8z PRED relation: olympics! PRED expected values: 0d060g 01mk6 => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 327): 07ssc (0.81 #2671, 0.81 #2542, 0.81 #2806), 0chghy (0.81 #2536, 0.65 #2800, 0.63 #2665), 0d060g (0.79 #3053, 0.75 #2652, 0.74 #2796), 05qhw (0.75 #2145, 0.69 #2649, 0.69 #2541), 03gj2 (0.75 #2154, 0.62 #2550, 0.59 #3071), 0ctw_b (0.67 #2155, 0.62 #1625, 0.54 #2653), 0k6nt (0.67 #2153, 0.59 #2942, 0.58 #2414), 01mk6 (0.67 #1035, 0.58 #2230, 0.57 #1306), 015fr (0.67 #2148, 0.57 #1224, 0.54 #2653), 06c1y (0.67 #2169, 0.57 #1245, 0.50 #974) >> Best rule #2671 for best value: >> intensional similarity = 50 >> extensional distance = 25 >> proper extension: 0kbws; 016r9z; >> query: (?x6893, 07ssc) <- olympics(?x5177, ?x6893), olympics(?x5114, ?x6893), olympics(?x2513, ?x6893), olympics(?x789, ?x6893), jurisdiction_of_office(?x3341, ?x5114), olympics(?x5114, ?x4255), combatants(?x7430, ?x5114), combatants(?x583, ?x5114), combatants(?x456, ?x5114), jurisdiction_of_office(?x5161, ?x5114), ?x456 = 05qhw, sports(?x6893, ?x453), ?x2513 = 05b4w, capital(?x5114, ?x8745), combatants(?x5114, ?x2346), combatants(?x5114, ?x792), country(?x5177, ?x404), nationality(?x2693, ?x5114), country(?x359, ?x5114), ?x583 = 015fr, locations(?x5352, ?x5114), country(?x6941, ?x2346), country(?x6354, ?x2346), country(?x4673, ?x2346), exported_to(?x1780, ?x2346), nationality(?x10608, ?x2346), participating_countries(?x418, ?x2346), contains(?x2346, ?x1885), currency(?x2346, ?x170), ?x6941 = 02y74, country(?x4050, ?x2346), country(?x3863, ?x2346), olympics(?x2346, ?x3971), ?x4255 = 0lgxj, film_release_region(?x186, ?x2346), adjoins(?x2346, ?x2146), exported_to(?x2346, ?x291), ?x4050 = 0kv9d3, ?x4673 = 07jbh, ?x3863 = 0dx8gj, ?x10608 = 06kkgw, ?x7430 = 01mk6, locations(?x3654, ?x2346), combatants(?x1140, ?x2346), ?x6354 = 09_b4, jurisdiction_of_office(?x265, ?x2346), ?x3971 = 0jhn7, ?x792 = 0hzlz, film_release_region(?x66, ?x789), contains(?x5114, ?x13354) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #3053 for first EXPECTED value: *> intensional similarity = 58 *> extensional distance = 37 *> proper extension: 018wrk; *> query: (?x6893, 0d060g) <- olympics(?x5177, ?x6893), olympics(?x5114, ?x6893), olympics(?x2513, ?x6893), jurisdiction_of_office(?x3341, ?x5114), olympics(?x5114, ?x1741), combatants(?x7430, ?x5114), combatants(?x456, ?x5114), combatants(?x151, ?x5114), jurisdiction_of_office(?x5161, ?x5114), ?x456 = 05qhw, sports(?x6893, ?x453), ?x7430 = 01mk6, film_release_region(?x9902, ?x2513), film_release_region(?x9652, ?x2513), film_release_region(?x8891, ?x2513), film_release_region(?x6446, ?x2513), film_release_region(?x6422, ?x2513), film_release_region(?x6121, ?x2513), film_release_region(?x5315, ?x2513), film_release_region(?x3757, ?x2513), film_release_region(?x3377, ?x2513), film_release_region(?x3201, ?x2513), film_release_region(?x2896, ?x2513), film_release_region(?x2441, ?x2513), film_release_region(?x1421, ?x2513), film_release_region(?x1252, ?x2513), film_release_region(?x1173, ?x2513), film_release_region(?x972, ?x2513), country(?x3598, ?x2513), ?x5315 = 0glqh5_, ?x3598 = 03rbzn, ?x3377 = 0gj8nq2, ?x2896 = 0645k5, service_location(?x555, ?x2513), ?x6446 = 089j8p, combatants(?x326, ?x5114), ?x8891 = 0gwlfnb, ?x1421 = 07qg8v, olympics(?x2513, ?x418), country(?x11197, ?x2513), ?x151 = 0b90_r, ?x326 = 081pw, ?x9902 = 0j8f09z, participating_countries(?x1741, ?x2346), ?x972 = 017gl1, ?x3757 = 02vr3gz, ?x3201 = 01ffx4, language(?x1252, ?x254), film_crew_role(?x1173, ?x137), ?x2346 = 0d05w3, featured_film_locations(?x1173, ?x108), film(?x609, ?x1173), film_release_region(?x1173, ?x2188), ?x2441 = 0cc5mcj, ?x2188 = 0163v, film(?x2745, ?x6422), ?x6121 = 064lsn, ?x9652 = 0ddbjy4 *> conf = 0.79 ranks of expected_values: 3, 8 EVAL 019n8z olympics! 01mk6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 25.000 25.000 0.815 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 019n8z olympics! 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 25.000 25.000 0.815 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #13246-018qd6 PRED entity: 018qd6 PRED relation: category PRED expected values: 08mbj5d => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #5, 0.77 #3, 0.75 #2) >> Best rule #5 for best value: >> intensional similarity = 2 >> extensional distance = 15 >> proper extension: 018jmn; >> query: (?x13585, 08mbj5d) <- administrative_parent(?x13585, ?x252), ?x252 = 03_3d >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018qd6 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.824 http://example.org/common/topic/webpage./common/webpage/category #13245-04rtpt PRED entity: 04rtpt PRED relation: production_companies! PRED expected values: 0cz_ym 011yd2 065dc4 => 86 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1589): 0cz_ym (0.60 #5848, 0.50 #2459, 0.19 #10164), 011yd2 (0.50 #2495, 0.40 #5884, 0.27 #13555), 0830vk (0.40 #6039, 0.25 #2650, 0.25 #1521), 09g8vhw (0.40 #5863, 0.25 #2474, 0.13 #23939), 03b_fm5 (0.40 #6171, 0.25 #2782, 0.12 #15815), 07p62k (0.40 #5883, 0.25 #2494, 0.12 #15815), 09q23x (0.40 #6198, 0.25 #2809, 0.12 #15815), 0glqh5_ (0.40 #6236, 0.25 #2847, 0.12 #15815), 0b7l4x (0.40 #6307, 0.25 #2918, 0.12 #15815), 0fb7sd (0.40 #6193, 0.25 #2804, 0.12 #15815) >> Best rule #5848 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 054lpb6; >> query: (?x6560, 0cz_ym) <- production_companies(?x6298, ?x6560), production_companies(?x3222, ?x6560), production_companies(?x2488, ?x6560), production_companies(?x590, ?x6560), ?x2488 = 02qr69m, film_crew_role(?x3222, ?x137), nominated_for(?x5536, ?x6298), titles(?x2480, ?x590) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 284 EVAL 04rtpt production_companies! 065dc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 86.000 37.000 0.600 http://example.org/film/film/production_companies EVAL 04rtpt production_companies! 011yd2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 37.000 0.600 http://example.org/film/film/production_companies EVAL 04rtpt production_companies! 0cz_ym CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 37.000 0.600 http://example.org/film/film/production_companies #13244-033g54 PRED entity: 033g54 PRED relation: teams! PRED expected values: 0jgx => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 119): 0d0vqn (0.07 #8, 0.06 #278, 0.05 #548), 05bcl (0.07 #114, 0.06 #384, 0.05 #654), 0bjv6 (0.07 #96, 0.06 #366, 0.05 #636), 06mkj (0.07 #67, 0.06 #337, 0.05 #607), 01znc_ (0.07 #49, 0.06 #319, 0.05 #589), 04v3q (0.07 #31, 0.06 #301, 0.05 #571), 0k6nt (0.07 #27, 0.06 #297, 0.05 #567), 0j5g9 (0.07 #118, 0.06 #388, 0.05 #658), 0d0kn (0.07 #63, 0.05 #603, 0.05 #873), 0154j (0.07 #5, 0.05 #545, 0.05 #815) >> Best rule #8 for best value: >> intensional similarity = 12 >> extensional distance = 13 >> proper extension: 02_t6d; >> query: (?x12758, 0d0vqn) <- position(?x12758, ?x530), position(?x12758, ?x203), position(?x12758, ?x60), team(?x63, ?x12758), team(?x8594, ?x12758), ?x203 = 0dgrmp, ?x60 = 02nzb8, ?x530 = 02_j1w, ?x8594 = 07y9k, ?x63 = 02sdk9v, position(?x12758, ?x63), team(?x203, ?x12758) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 033g54 teams! 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 62.000 0.067 http://example.org/sports/sports_team_location/teams #13243-01c7qd PRED entity: 01c7qd PRED relation: award PRED expected values: 025m8l 02qvyrt => 120 concepts (97 used for prediction) PRED predicted values (max 10 best out of 344): 04njml (0.77 #16848, 0.70 #29689, 0.70 #31699), 01bgqh (0.49 #4455, 0.43 #4856, 0.40 #444), 01by1l (0.48 #4522, 0.42 #4923, 0.36 #511), 025m8l (0.33 #518, 0.21 #4930, 0.20 #14039), 01c92g (0.30 #4507, 0.28 #4908, 0.13 #496), 09sb52 (0.30 #25718, 0.26 #16889, 0.25 #23710), 02qvyrt (0.29 #9750, 0.24 #526, 0.24 #10954), 02h3d1 (0.29 #178, 0.15 #2985, 0.11 #579), 026mfs (0.29 #127, 0.09 #16573, 0.07 #18580), 0gr51 (0.28 #2103, 0.28 #3707, 0.27 #1301) >> Best rule #16848 for best value: >> intensional similarity = 2 >> extensional distance = 591 >> proper extension: 025vry; 01ky2h; 01lcxbb; 01wz_ml; 0lzkm; 01vsy3q; 0lsw9; 0f6lx; 013rds; 06lxn; >> query: (?x9834, ?x1869) <- award_winner(?x1869, ?x9834), artists(?x10332, ?x9834) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #518 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 0lbj1; 01vrz41; 012x4t; 02fgpf; 04xrx; 016h4r; 01svw8n; 02lfp4; 0dw4g; 01w9wwg; ... *> query: (?x9834, 025m8l) <- nominated_for(?x9834, ?x2901), award(?x9834, ?x1232), ?x1232 = 0c4z8 *> conf = 0.33 ranks of expected_values: 4, 7 EVAL 01c7qd award 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 120.000 97.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c7qd award 025m8l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 120.000 97.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13242-0bbgvp PRED entity: 0bbgvp PRED relation: nominated_for! PRED expected values: 0k611 => 51 concepts (46 used for prediction) PRED predicted values (max 10 best out of 200): 0gq9h (0.49 #1006, 0.25 #1242, 0.23 #298), 019f4v (0.42 #997, 0.22 #289, 0.21 #2413), 0gs96 (0.40 #1031, 0.18 #1267, 0.16 #1739), 0gs9p (0.39 #1008, 0.24 #300, 0.21 #2424), 0gr0m (0.36 #1003, 0.18 #1239, 0.17 #295), 0k611 (0.36 #1015, 0.18 #4083, 0.18 #2431), 0p9sw (0.33 #964, 0.16 #2616, 0.16 #4032), 0gqy2 (0.29 #1064, 0.15 #4132, 0.15 #2480), 040njc (0.29 #951, 0.17 #243, 0.16 #1187), 0gr4k (0.26 #970, 0.17 #1206, 0.16 #2386) >> Best rule #1006 for best value: >> intensional similarity = 5 >> extensional distance = 225 >> proper extension: 04z_x4v; >> query: (?x11998, 0gq9h) <- nominated_for(?x6164, ?x11998), nominated_for(?x1443, ?x11998), nominated_for(?x484, ?x11998), ?x484 = 0gq_v, award(?x84, ?x1443) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1015 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 225 *> proper extension: 04z_x4v; *> query: (?x11998, 0k611) <- nominated_for(?x6164, ?x11998), nominated_for(?x1443, ?x11998), nominated_for(?x484, ?x11998), ?x484 = 0gq_v, award(?x84, ?x1443) *> conf = 0.36 ranks of expected_values: 6 EVAL 0bbgvp nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 51.000 46.000 0.493 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13241-05slvm PRED entity: 05slvm PRED relation: gender PRED expected values: 02zsn => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.81 #4, 0.33 #2, 0.30 #8), 05zppz (0.72 #41, 0.72 #135, 0.71 #93) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 015nhn; 0161h5; 01dbgw; 06r3p2; >> query: (?x4125, 02zsn) <- award(?x4125, ?x686), film(?x4125, ?x167), ?x686 = 0bdw1g >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05slvm gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.811 http://example.org/people/person/gender #13240-0bwfwpj PRED entity: 0bwfwpj PRED relation: film_crew_role PRED expected values: 09vw2b7 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 27): 09vw2b7 (0.76 #1099, 0.74 #404, 0.70 #437), 015h31 (0.33 #8, 0.29 #41, 0.21 #472), 0d2b38 (0.33 #23, 0.21 #188, 0.20 #421), 02_n3z (0.33 #1, 0.14 #399, 0.14 #432), 01pvkk (0.33 #309, 0.30 #838, 0.29 #2008), 02rh1dz (0.29 #374, 0.28 #75, 0.27 #440), 01xy5l_ (0.24 #177, 0.17 #12, 0.16 #443), 089fss (0.24 #1098, 0.15 #137, 0.10 #635), 0215hd (0.21 #181, 0.19 #414, 0.17 #16), 089g0h (0.20 #415, 0.17 #17, 0.16 #182) >> Best rule #1099 for best value: >> intensional similarity = 4 >> extensional distance = 159 >> proper extension: 03xj05; >> query: (?x1012, 09vw2b7) <- film(?x609, ?x1012), film_crew_role(?x1012, ?x3197), genre(?x1012, ?x225), ?x3197 = 02ynfr >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bwfwpj film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.758 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13239-016y_f PRED entity: 016y_f PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #21, 0.82 #6, 0.82 #124), 07c52 (0.11 #3, 0.05 #13, 0.02 #332), 02nxhr (0.05 #22, 0.04 #110, 0.03 #68), 07z4p (0.02 #97, 0.02 #329, 0.02 #81) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 0267wwv; >> query: (?x4454, 029j_) <- production_companies(?x4454, ?x1104), genre(?x4454, ?x53), film(?x7310, ?x4454), ?x1104 = 016tw3 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016y_f film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.855 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #13238-0gkxgfq PRED entity: 0gkxgfq PRED relation: award_winner PRED expected values: 03b78r => 43 concepts (11 used for prediction) PRED predicted values (max 10 best out of 808): 01j7rd (0.62 #6431, 0.53 #7964, 0.40 #4898), 0p_2r (0.60 #4792, 0.31 #6325, 0.27 #7858), 03b78r (0.50 #2618, 0.33 #1086, 0.28 #15335), 02xs0q (0.46 #6677, 0.40 #8210, 0.40 #5144), 05bnq3j (0.40 #5321, 0.38 #6854, 0.33 #8387), 04ns3gy (0.40 #5921, 0.38 #7454, 0.33 #8987), 0265v21 (0.40 #12270, 0.38 #1532, 0.34 #1531), 08w6v_ (0.40 #12270, 0.38 #1532, 0.34 #1531), 0cp9f9 (0.40 #5782, 0.31 #7315, 0.27 #8848), 02773m2 (0.40 #4703, 0.23 #6236, 0.20 #7769) >> Best rule #6431 for best value: >> intensional similarity = 16 >> extensional distance = 11 >> proper extension: 03gwpw2; 05c1t6z; 07z31v; 0gvstc3; 0gx_st; 07y_p6; 0hn821n; >> query: (?x7721, 01j7rd) <- ceremony(?x588, ?x7721), honored_for(?x7721, ?x4891), honored_for(?x7721, ?x4114), honored_for(?x7721, ?x802), award_winner(?x7721, ?x4762), award_winner(?x7721, ?x2894), actor(?x802, ?x803), nominated_for(?x3675, ?x802), genre(?x4891, ?x14160), languages(?x802, ?x254), nominated_for(?x4260, ?x4891), program(?x3183, ?x4891), profession(?x2894, ?x1383), ?x1383 = 0np9r, award_winner(?x4762, ?x4433), country_of_origin(?x4114, ?x94) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2618 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 0lp_cd3; *> query: (?x7721, 03b78r) <- ceremony(?x2720, ?x7721), ceremony(?x588, ?x7721), honored_for(?x7721, ?x8976), honored_for(?x7721, ?x802), award_winner(?x7721, ?x3974), ?x802 = 0cwrr, category(?x8976, ?x134), nominated_for(?x588, ?x416), award(?x439, ?x2720), award_nominee(?x382, ?x3974), award_winner(?x439, ?x2476), nominated_for(?x7288, ?x8976), award_winner(?x5304, ?x3974), award_winner(?x1105, ?x3974), profession(?x439, ?x987), award_winner(?x2751, ?x439) *> conf = 0.50 ranks of expected_values: 3 EVAL 0gkxgfq award_winner 03b78r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 43.000 11.000 0.615 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13237-020923 PRED entity: 020923 PRED relation: category PRED expected values: 08mbj5d => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #29, 0.90 #22, 0.89 #7) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 284 >> proper extension: 0yl_j; >> query: (?x4227, 08mbj5d) <- currency(?x4227, ?x170), citytown(?x4227, ?x3976), currency(?x65, ?x170), currency(?x54, ?x170) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 020923 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.906 http://example.org/common/topic/webpage./common/webpage/category #13236-017drs PRED entity: 017drs PRED relation: team PRED expected values: 049n7 => 30 concepts (19 used for prediction) PRED predicted values (max 10 best out of 989): 01slc (0.80 #17439, 0.80 #16466, 0.75 #6129), 05g76 (0.80 #17439, 0.80 #16466, 0.73 #12589), 0x2p (0.80 #17439, 0.80 #16466, 0.73 #12589), 02__x (0.80 #17439, 0.80 #16466, 0.73 #12589), 04mjl (0.80 #17439, 0.80 #16466, 0.73 #12589), 03m1n (0.80 #17439, 0.80 #16466, 0.73 #12589), 06x68 (0.80 #17439, 0.80 #16466, 0.73 #12589), 0x0d (0.80 #17439, 0.80 #16466, 0.73 #12589), 07147 (0.80 #17439, 0.80 #16466, 0.73 #12589), 01ync (0.80 #17439, 0.80 #16466, 0.73 #12589) >> Best rule #17439 for best value: >> intensional similarity = 29 >> extensional distance = 16 >> proper extension: 047g8h; 02qpbqj; >> query: (?x10822, ?x580) <- team(?x10822, ?x12042), team(?x10822, ?x4243), team(?x10822, ?x260), draft(?x260, ?x8499), draft(?x260, ?x1633), school(?x260, ?x5844), school(?x260, ?x1428), ?x5844 = 0146hc, school(?x12042, ?x12736), school(?x12042, ?x4846), sport(?x12042, ?x5063), teams(?x2254, ?x12042), colors(?x260, ?x663), school(?x2820, ?x12736), currency(?x4846, ?x170), colors(?x4243, ?x3315), school(?x4243, ?x3021), team(?x11844, ?x12042), contains(?x94, ?x12736), school(?x1633, ?x3779), ?x3315 = 0jc_p, institution(?x865, ?x4846), draft(?x580, ?x1633), school_type(?x4846, ?x1044), category(?x4846, ?x134), school(?x8499, ?x8120), ?x2820 = 0jmj7, student(?x1428, ?x7961), ?x8120 = 01rc6f >> conf = 0.80 => this is the best rule for 26 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 16 EVAL 017drs team 049n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 30.000 19.000 0.800 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #13235-01p1v PRED entity: 01p1v PRED relation: country! PRED expected values: 07rlg => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 34): 064vjs (0.74 #389, 0.72 #219, 0.71 #83), 0194d (0.71 #96, 0.70 #674, 0.69 #232), 07rlg (0.67 #171, 0.58 #69, 0.55 #205), 0486tv (0.63 #294, 0.63 #192, 0.61 #328), 01z27 (0.60 #383, 0.59 #213, 0.58 #77), 0d1t3 (0.58 #86, 0.52 #188, 0.52 #222), 035d1m (0.56 #47, 0.52 #183, 0.50 #81), 07_53 (0.54 #91, 0.43 #397, 0.41 #227), 02vx4 (0.53 #243, 0.52 #175, 0.50 #73), 06z68 (0.50 #254, 0.50 #50, 0.48 #186) >> Best rule #389 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 07t21; 015qh; 077qn; >> query: (?x1917, 064vjs) <- film_release_region(?x8891, ?x1917), film_release_region(?x1916, ?x1917), ?x8891 = 0gwlfnb, nominated_for(?x902, ?x1916) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 03__y; 06vbd; 0165b; *> query: (?x1917, 07rlg) <- film_release_region(?x80, ?x1917), country(?x779, ?x1917), ?x779 = 096f8 *> conf = 0.67 ranks of expected_values: 3 EVAL 01p1v country! 07rlg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 157.000 157.000 0.743 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #13234-032_wv PRED entity: 032_wv PRED relation: genre PRED expected values: 0219x_ 0cshrf => 98 concepts (97 used for prediction) PRED predicted values (max 10 best out of 97): 01z4y (0.61 #8467, 0.50 #2742, 0.48 #10376), 02l7c8 (0.50 #15, 0.40 #8124, 0.38 #3471), 03k9fj (0.43 #6329, 0.35 #8000, 0.32 #8119), 02kdv5l (0.43 #7992, 0.34 #1192, 0.32 #716), 01t_vv (0.38 #291, 0.28 #410, 0.20 #648), 01jfsb (0.36 #3706, 0.35 #2513, 0.33 #4897), 0219x_ (0.34 #620, 0.33 #382, 0.30 #144), 04xvlr (0.25 #1, 0.23 #834, 0.21 #477), 06cvj (0.25 #3, 0.23 #241, 0.23 #3459), 017fp (0.25 #14, 0.22 #371, 0.20 #133) >> Best rule #8467 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 02xhwm; >> query: (?x1298, ?x2480) <- titles(?x2480, ?x1298), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #620 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 0cfhfz; *> query: (?x1298, 0219x_) <- nominated_for(?x2532, ?x1298), ?x2532 = 02x4wr9, film(?x902, ?x1298), film(?x722, ?x1298) *> conf = 0.34 ranks of expected_values: 7, 11 EVAL 032_wv genre 0cshrf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 98.000 97.000 0.612 http://example.org/film/film/genre EVAL 032_wv genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 98.000 97.000 0.612 http://example.org/film/film/genre #13233-01r2c7 PRED entity: 01r2c7 PRED relation: profession PRED expected values: 02jknp => 112 concepts (45 used for prediction) PRED predicted values (max 10 best out of 61): 02jknp (0.91 #896, 0.86 #1192, 0.85 #1340), 02hrh1q (0.82 #2530, 0.78 #1050, 0.77 #2975), 03gjzk (0.62 #903, 0.56 #2680, 0.53 #1643), 018gz8 (0.32 #2682, 0.19 #5941, 0.19 #4311), 0cbd2 (0.30 #3412, 0.29 #3116, 0.29 #4597), 09jwl (0.25 #5795, 0.24 #5202, 0.23 #5647), 02krf9 (0.23 #915, 0.21 #1063, 0.17 #1507), 0dz3r (0.21 #5779, 0.20 #5631, 0.19 #4742), 0np9r (0.20 #2686, 0.15 #3870, 0.13 #2982), 0nbcg (0.20 #5808, 0.19 #5215, 0.19 #5660) >> Best rule #896 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0162c8; >> query: (?x9354, 02jknp) <- profession(?x9354, ?x319), currency(?x9354, ?x170), film(?x9354, ?x5081), language(?x5081, ?x254) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r2c7 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 45.000 0.915 http://example.org/people/person/profession #13232-015grj PRED entity: 015grj PRED relation: film PRED expected values: 0bscw => 102 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1081): 0h03fhx (0.72 #12476, 0.67 #2561, 0.56 #17823), 01fx1l (0.72 #12476, 0.56 #17823, 0.49 #44563), 03mh94 (0.25 #64, 0.05 #5411, 0.04 #7193), 0jwmp (0.25 #551, 0.04 #9462, 0.03 #7680), 05z43v (0.25 #1351, 0.04 #10262, 0.01 #8480), 016z9n (0.25 #369, 0.04 #7498, 0.03 #9280), 019vhk (0.25 #462, 0.03 #9373, 0.03 #7591), 03hxsv (0.25 #1115, 0.03 #10026, 0.02 #17155), 02ywwy (0.25 #1442, 0.03 #8571, 0.02 #10353), 03cffvv (0.25 #1736, 0.02 #10647, 0.02 #5300) >> Best rule #12476 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 02zfg3; >> query: (?x968, ?x4607) <- award(?x968, ?x3066), nominated_for(?x968, ?x4607), ?x3066 = 0gqy2 >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #3781 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 01tsbmv; *> query: (?x968, 0bscw) <- award(?x968, ?x2252), award(?x968, ?x1921), ?x1921 = 0bs0bh, nominated_for(?x2252, ?x394) *> conf = 0.04 ranks of expected_values: 109 EVAL 015grj film 0bscw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 102.000 58.000 0.717 http://example.org/film/actor/film./film/performance/film #13231-01gb54 PRED entity: 01gb54 PRED relation: award_nominee! PRED expected values: 016tt2 => 125 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1173): 0f7hc (0.83 #111750, 0.82 #111748, 0.81 #27936), 016tw3 (0.82 #111748, 0.81 #111749, 0.81 #27936), 016tt2 (0.82 #111748, 0.81 #111749, 0.81 #27936), 0hpt3 (0.82 #111748, 0.81 #27936, 0.81 #186250), 0mdqp (0.43 #9456, 0.03 #93123, 0.02 #79157), 0dvmd (0.29 #10007, 0.14 #65883, 0.03 #93123), 0205dx (0.29 #10441), 0kx4m (0.25 #7152, 0.25 #4822, 0.20 #179264), 0jrqq (0.25 #7865, 0.25 #5535, 0.20 #179264), 024rgt (0.25 #7533, 0.25 #5203, 0.15 #40129) >> Best rule #111750 for best value: >> intensional similarity = 4 >> extensional distance = 100 >> proper extension: 02ndbd; 043q6n_; 0j_c; 07h07; 02kmx6; 04h6mm; 016bx2; 0dbpwb; 034hck; 027z0pl; ... >> query: (?x4564, ?x4657) <- award_nominee(?x4564, ?x7980), award_nominee(?x4564, ?x4657), profession(?x4657, ?x319), film(?x7980, ?x385) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #111748 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 100 *> proper extension: 02ndbd; 043q6n_; 0j_c; 07h07; 02kmx6; 04h6mm; 016bx2; 0dbpwb; 034hck; 027z0pl; ... *> query: (?x4564, ?x574) <- award_nominee(?x4564, ?x7980), award_nominee(?x4564, ?x4657), award_nominee(?x4564, ?x574), profession(?x4657, ?x319), film(?x7980, ?x385) *> conf = 0.82 ranks of expected_values: 3 EVAL 01gb54 award_nominee! 016tt2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 81.000 0.826 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13230-01r4bps PRED entity: 01r4bps PRED relation: gender PRED expected values: 02zsn => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.77 #170, 0.74 #59, 0.73 #33), 02zsn (0.46 #214, 0.45 #121, 0.45 #110) >> Best rule #170 for best value: >> intensional similarity = 5 >> extensional distance = 2156 >> proper extension: 01zkxv; 07kb5; 04zd4m; 028p0; 09dt7; 01963w; 0c3kw; 01dzz7; 0379s; 04snp2; ... >> query: (?x11090, 05zppz) <- profession(?x11090, ?x1383), profession(?x10152, ?x1383), profession(?x4109, ?x1383), award(?x4109, ?x2192), ?x10152 = 05vtbl >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #214 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4188 *> proper extension: 012ljv; 0f3zf_; 05pdbs; 05fg2; 0kn4c; 03cvfg; 09rp4r_; 04ktcgn; 05x2t7; 0dck27; ... *> query: (?x11090, ?x231) <- profession(?x11090, ?x1383), profession(?x4109, ?x1383), profession(?x3649, ?x1383), award(?x4109, ?x2192), gender(?x3649, ?x231) *> conf = 0.46 ranks of expected_values: 2 EVAL 01r4bps gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 106.000 0.766 http://example.org/people/person/gender #13229-03crmd PRED entity: 03crmd PRED relation: type_of_union PRED expected values: 04ztj => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.88 #21, 0.88 #81, 0.88 #77), 01g63y (0.37 #90, 0.33 #10, 0.33 #6) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 01p7yb; 0bxtg; 03f2_rc; 04bs3j; 01pcq3; 0htlr; 04shbh; 0sz28; 0prjs; 06pj8; ... >> query: (?x10520, 04ztj) <- student(?x5522, ?x10520), spouse(?x10050, ?x10520), film(?x10520, ?x11001), languages(?x10520, ?x90) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03crmd type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.878 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13228-08cn_n PRED entity: 08cn_n PRED relation: profession PRED expected values: 02krf9 => 98 concepts (77 used for prediction) PRED predicted values (max 10 best out of 58): 02hrh1q (0.87 #3985, 0.84 #5455, 0.69 #10162), 0dxtg (0.82 #1336, 0.79 #4572, 0.74 #306), 02krf9 (0.34 #25, 0.24 #319, 0.24 #1202), 0cbd2 (0.28 #1330, 0.19 #4566, 0.17 #1183), 09jwl (0.21 #3253, 0.17 #7520, 0.17 #2812), 0np9r (0.16 #5461, 0.13 #3991, 0.12 #19), 018gz8 (0.15 #162, 0.14 #4575, 0.14 #5457), 0nbcg (0.15 #3266, 0.12 #4443, 0.12 #6650), 0dz3r (0.14 #3238, 0.12 #4415, 0.12 #6622), 016z4k (0.13 #3240, 0.11 #4417, 0.10 #6771) >> Best rule #3985 for best value: >> intensional similarity = 3 >> extensional distance = 1139 >> proper extension: 01vw917; 02784z; 018fwv; >> query: (?x8118, 02hrh1q) <- nationality(?x8118, ?x94), place_of_birth(?x8118, ?x6930), film(?x8118, ?x5361) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #25 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 66 *> proper extension: 05ty4m; *> query: (?x8118, 02krf9) <- profession(?x8118, ?x319), executive_produced_by(?x903, ?x8118), film(?x8118, ?x5361) *> conf = 0.34 ranks of expected_values: 3 EVAL 08cn_n profession 02krf9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 77.000 0.871 http://example.org/people/person/profession #13227-01mxnvc PRED entity: 01mxnvc PRED relation: group PRED expected values: 02vgh => 83 concepts (34 used for prediction) PRED predicted values (max 10 best out of 59): 01v0sxx (0.25 #194, 0.08 #521, 0.07 #630), 01v0sx2 (0.17 #5, 0.15 #223, 0.05 #880), 02vgh (0.17 #51, 0.08 #160, 0.08 #269), 01qqwp9 (0.17 #130, 0.08 #239, 0.05 #457), 0dtd6 (0.17 #121, 0.05 #448, 0.05 #557), 07c0j (0.08 #113, 0.08 #222, 0.03 #440), 0123r4 (0.08 #153, 0.05 #1028, 0.03 #2666), 02r1tx7 (0.08 #234, 0.05 #1000, 0.03 #1438), 014kyy (0.06 #426, 0.02 #974, 0.02 #1191), 07mvp (0.03 #1468, 0.03 #1358, 0.03 #1030) >> Best rule #194 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 01kd57; >> query: (?x10802, 01v0sxx) <- artists(?x9063, ?x10802), artists(?x3061, ?x10802), ?x9063 = 0cx7f, ?x3061 = 05bt6j, nationality(?x10802, ?x1310) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #51 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 024zq; *> query: (?x10802, 02vgh) <- artists(?x9063, ?x10802), artist(?x10727, ?x10802), ?x10727 = 041p3y, artists(?x9063, ?x8539), ?x8539 = 01w9mnm *> conf = 0.17 ranks of expected_values: 3 EVAL 01mxnvc group 02vgh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 34.000 0.250 http://example.org/music/group_member/membership./music/group_membership/group #13226-04q00lw PRED entity: 04q00lw PRED relation: genre PRED expected values: 03q4nz => 100 concepts (87 used for prediction) PRED predicted values (max 10 best out of 116): 01chg (0.80 #4705, 0.72 #8207, 0.71 #9898), 05p553 (0.69 #6882, 0.46 #3262, 0.44 #3140), 03rk0 (0.61 #4704, 0.60 #8206, 0.59 #9897), 03k9fj (0.50 #373, 0.50 #4837, 0.35 #2302), 02n4lw (0.50 #212, 0.06 #10263, 0.06 #6154), 01jfsb (0.38 #736, 0.36 #9309, 0.36 #8826), 01hmnh (0.38 #5684, 0.26 #2308, 0.24 #741), 02kdv5l (0.37 #2292, 0.33 #2, 0.32 #725), 04xvlr (0.34 #5547, 0.27 #1085, 0.26 #4584), 04t36 (0.33 #6, 0.23 #4711, 0.19 #5311) >> Best rule #4705 for best value: >> intensional similarity = 7 >> extensional distance = 504 >> proper extension: 03kq98; >> query: (?x2381, ?x3741) <- titles(?x3741, ?x2381), genre(?x8074, ?x3741), genre(?x5247, ?x3741), genre(?x2617, ?x3741), ?x5247 = 0f42nz, ?x2617 = 01p3ty, ?x8074 = 052_mn >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #139 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 03rz2b; 07vfy4; *> query: (?x2381, 03q4nz) <- genre(?x2381, ?x53), film_crew_role(?x2381, ?x137), titles(?x2146, ?x2381), ?x2146 = 03rk0, film(?x3129, ?x2381) *> conf = 0.25 ranks of expected_values: 16 EVAL 04q00lw genre 03q4nz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 100.000 87.000 0.795 http://example.org/film/film/genre #13225-0421v9q PRED entity: 0421v9q PRED relation: genre PRED expected values: 02l7c8 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 170): 02l7c8 (0.87 #855, 0.86 #1215, 0.85 #1095), 03k9fj (0.51 #490, 0.50 #250, 0.41 #370), 02kdv5l (0.43 #482, 0.38 #242, 0.37 #362), 01hmnh (0.41 #257, 0.37 #737, 0.37 #377), 01jfsb (0.35 #731, 0.35 #371, 0.34 #251), 06n90 (0.31 #492, 0.22 #1452, 0.22 #1932), 01t_vv (0.28 #894, 0.24 #1614, 0.23 #1134), 0hcr (0.22 #503, 0.12 #1463, 0.12 #263), 060__y (0.21 #976, 0.16 #616, 0.13 #5538), 0lsxr (0.17 #3607, 0.17 #2767, 0.17 #3487) >> Best rule #855 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 0209xj; 0kv2hv; 0jyx6; 0c5dd; 03m4mj; 0sxfd; 01pgp6; 09tqkv2; 048qrd; 0p4v_; ... >> query: (?x6543, 02l7c8) <- genre(?x6543, ?x239), ?x239 = 06cvj, nominated_for(?x1336, ?x6543), award_winner(?x6543, ?x3308) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0421v9q genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.868 http://example.org/film/film/genre #13224-01cw7s PRED entity: 01cw7s PRED relation: award! PRED expected values: 01vwyqp 026spg 0277c3 02h9_l => 44 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2609): 086qd (0.79 #53837, 0.78 #53836, 0.78 #53835), 02l840 (0.67 #10274, 0.43 #13638, 0.25 #181), 01wcp_g (0.57 #7064, 0.50 #3699, 0.50 #335), 01wzlxj (0.57 #7786, 0.50 #4421, 0.29 #14514), 01vw20h (0.56 #11374, 0.36 #14738, 0.22 #60572), 01pq5j7 (0.56 #11617, 0.25 #1524, 0.21 #14981), 01w9wwg (0.56 #11886, 0.19 #6729, 0.15 #18615), 0bs1g5r (0.56 #12456, 0.15 #19185, 0.15 #29278), 01k_mc (0.50 #5095, 0.50 #1731, 0.43 #8460), 0flpy (0.50 #5197, 0.50 #1833, 0.43 #8562) >> Best rule #53837 for best value: >> intensional similarity = 5 >> extensional distance = 148 >> proper extension: 02r0d0; >> query: (?x6652, ?x2614) <- award_winner(?x6652, ?x2614), award_winner(?x6652, ?x1974), award(?x2614, ?x567), category_of(?x6652, ?x2421), nationality(?x1974, ?x94) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #5149 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 031b3h; 03r00m; *> query: (?x6652, 0277c3) <- award(?x3493, ?x6652), award(?x3065, ?x6652), ?x3493 = 044gyq, ceremony(?x6652, ?x486), ?x486 = 02rjjll, award_nominee(?x3065, ?x1378) *> conf = 0.50 ranks of expected_values: 12, 15, 110, 186 EVAL 01cw7s award! 02h9_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 44.000 23.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01cw7s award! 0277c3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 44.000 23.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01cw7s award! 026spg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 44.000 23.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01cw7s award! 01vwyqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 44.000 23.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13223-01wy6 PRED entity: 01wy6 PRED relation: role! PRED expected values: 042v_gx => 75 concepts (54 used for prediction) PRED predicted values (max 10 best out of 95): 02pprs (0.86 #356, 0.85 #2191, 0.83 #2472), 03qmg1 (0.86 #356, 0.83 #2936, 0.82 #4217), 01vj9c (0.79 #3592, 0.78 #1554, 0.77 #2107), 018j2 (0.79 #3615, 0.78 #1577, 0.75 #2756), 0bxl5 (0.78 #1600, 0.76 #2903, 0.76 #2938), 02w3w (0.78 #1618, 0.75 #1530, 0.62 #4202), 07brj (0.78 #1563, 0.75 #1475, 0.60 #471), 0dwsp (0.78 #1552, 0.75 #1464, 0.60 #460), 03_vpw (0.78 #1591, 0.75 #1503, 0.58 #1635), 042v_gx (0.77 #2103, 0.75 #2006, 0.75 #1371) >> Best rule #356 for best value: >> intensional similarity = 18 >> extensional distance = 2 >> proper extension: 05148p4; 03qjg; >> query: (?x2460, ?x74) <- role(?x2460, ?x2888), role(?x2460, ?x1332), role(?x2460, ?x74), role(?x2460, ?x3716), role(?x2460, ?x3215), role(?x2460, ?x1831), instrumentalists(?x2460, ?x6947), instrumentalists(?x2460, ?x3492), ?x6947 = 01vrnsk, ?x1332 = 03qlv7, role(?x120, ?x1831), ?x2888 = 02fsn, role(?x2944, ?x1831), role(?x3215, ?x432), performance_role(?x6208, ?x3215), ?x3716 = 03gvt, role(?x217, ?x3215), award_winner(?x5904, ?x3492) >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #2103 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 11 *> proper extension: 02fsn; *> query: (?x2460, 042v_gx) <- role(?x2460, ?x2888), role(?x2460, ?x1473), role(?x2460, ?x1332), role(?x2460, ?x1831), instrumentalists(?x2460, ?x6947), ?x6947 = 01vrnsk, performance_role(?x1332, ?x2944), role(?x2888, ?x432), role(?x1332, ?x1225), role(?x615, ?x2888), role(?x1332, ?x1147), ?x2944 = 0l14j_, performance_role(?x120, ?x1831), role(?x211, ?x1473) *> conf = 0.77 ranks of expected_values: 10 EVAL 01wy6 role! 042v_gx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 75.000 54.000 0.860 http://example.org/music/performance_role/regular_performances./music/group_membership/role #13222-04ch23 PRED entity: 04ch23 PRED relation: location PRED expected values: 049lr => 77 concepts (68 used for prediction) PRED predicted values (max 10 best out of 125): 030qb3t (0.33 #83, 0.09 #35482, 0.08 #33872), 01xd9 (0.22 #1693, 0.14 #2497, 0.05 #4105), 04vmp (0.20 #5983, 0.15 #7591, 0.14 #8396), 02_286 (0.14 #841, 0.14 #4861, 0.13 #4057), 0d6lp (0.14 #972, 0.07 #2580, 0.03 #4188), 06wxw (0.14 #1032, 0.07 #2640, 0.03 #4248), 01531 (0.12 #3374, 0.06 #6591, 0.04 #10614), 05l5n (0.12 #24131, 0.12 #25743, 0.12 #24937), 09b9m (0.11 #1886, 0.07 #2690, 0.03 #4298), 02h6_6p (0.11 #1739, 0.07 #2543, 0.03 #4151) >> Best rule #83 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03k545; >> query: (?x12071, 030qb3t) <- student(?x13856, ?x12071), ?x13856 = 0ym1n, gender(?x12071, ?x231), award(?x12071, ?x4687) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8494 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 145 *> proper extension: 0cfywh; *> query: (?x12071, 049lr) <- type_of_union(?x12071, ?x566), ?x566 = 04ztj, nationality(?x12071, ?x2146), ?x2146 = 03rk0 *> conf = 0.02 ranks of expected_values: 60 EVAL 04ch23 location 049lr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 77.000 68.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #13221-026v437 PRED entity: 026v437 PRED relation: profession PRED expected values: 0d1pc => 81 concepts (73 used for prediction) PRED predicted values (max 10 best out of 43): 01d_h8 (0.37 #1347, 0.31 #3881, 0.29 #1938), 03gjzk (0.35 #760, 0.29 #1356, 0.22 #3890), 0dxtg (0.30 #1355, 0.30 #759, 0.29 #1938), 02jknp (0.29 #1938, 0.24 #1349, 0.21 #3883), 09jwl (0.29 #1938, 0.19 #1658, 0.19 #1807), 02krf9 (0.29 #1938, 0.14 #1368, 0.14 #772), 0d1pc (0.29 #1938, 0.12 #2138, 0.12 #2287), 0np9r (0.20 #1511, 0.19 #1064, 0.12 #1213), 018gz8 (0.13 #464, 0.13 #1060, 0.13 #1209), 0dz3r (0.13 #2536, 0.12 #3430, 0.11 #4324) >> Best rule #1347 for best value: >> intensional similarity = 3 >> extensional distance = 604 >> proper extension: 0c_mvb; 0b_dh; >> query: (?x6359, 01d_h8) <- award_winner(?x3841, ?x6359), award_winner(?x3572, ?x3841), film(?x3572, ?x392) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #1938 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 817 *> proper extension: 07g2b; 0g51l1; 018swb; 0c01c; 01pcql; 02t_v1; 0h32q; 02wr2r; 0pmw9; 044k8; ... *> query: (?x6359, ?x1032) <- award_winner(?x3842, ?x6359), location(?x6359, ?x739), profession(?x3842, ?x1032) *> conf = 0.29 ranks of expected_values: 7 EVAL 026v437 profession 0d1pc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 81.000 73.000 0.366 http://example.org/people/person/profession #13220-04wx2v PRED entity: 04wx2v PRED relation: place_of_birth PRED expected values: 0cc56 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 73): 02_286 (0.33 #61997, 0.33 #2819, 0.33 #2132), 0cr3d (0.06 #94, 0.05 #43775, 0.05 #37435), 030qb3t (0.04 #13438, 0.04 #14846, 0.04 #65575), 01_d4 (0.04 #47974, 0.03 #68407, 0.03 #69817), 0cc56 (0.04 #2146, 0.03 #33, 0.02 #1441), 01531 (0.03 #2218, 0.03 #105, 0.02 #29691), 0fw2y (0.03 #92), 02dtg (0.03 #1418, 0.02 #8464, 0.01 #10), 04jpl (0.02 #27480, 0.02 #33118, 0.02 #13392), 05qtj (0.02 #1575, 0.01 #871, 0.01 #27639) >> Best rule #61997 for best value: >> intensional similarity = 2 >> extensional distance = 2264 >> proper extension: 07h1h5; 04m2zj; 04hqbbz; 0kbn5; >> query: (?x9437, ?x739) <- location(?x9437, ?x739), place_of_birth(?x65, ?x739) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2146 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 307 *> proper extension: 01ky2h; 014dq7; 0g51l1; 01m65sp; 01vsy3q; 012v1t; 05d1y; 02qnbs; 0232lm; 03f4k; ... *> query: (?x9437, 0cc56) <- nationality(?x9437, ?x94), location(?x9437, ?x739), ?x739 = 02_286 *> conf = 0.04 ranks of expected_values: 5 EVAL 04wx2v place_of_birth 0cc56 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 111.000 111.000 0.331 http://example.org/people/person/place_of_birth #13219-0dzlbx PRED entity: 0dzlbx PRED relation: award PRED expected values: 02g2wv => 60 concepts (52 used for prediction) PRED predicted values (max 10 best out of 236): 0m7yy (0.27 #1303, 0.09 #2239, 0.07 #3409), 0gr42 (0.24 #6792, 0.23 #6793, 0.22 #8904), 018wdw (0.24 #6792, 0.23 #6793, 0.22 #8904), 02hsq3m (0.24 #6792, 0.23 #6793, 0.22 #8904), 03m73lj (0.24 #6792, 0.23 #6793, 0.22 #8904), 05ztjjw (0.24 #6792, 0.22 #8904, 0.22 #8905), 0262s1 (0.20 #443, 0.05 #7499, 0.03 #2550), 027b9j5 (0.17 #5619, 0.08 #624, 0.04 #2262), 02g2wv (0.17 #5619, 0.07 #7029, 0.06 #11723), 04kxsb (0.17 #5619, 0.05 #3373, 0.05 #3607) >> Best rule #1303 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 0gpjbt; >> query: (?x4998, 0m7yy) <- honored_for(?x2988, ?x4998), award_winner(?x2988, ?x2143), ?x2143 = 015pxr >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #5619 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 775 *> proper extension: 07bz5; *> query: (?x4998, ?x2375) <- nominated_for(?x2373, ?x4998), award(?x4998, ?x1429), location(?x2373, ?x1131), award_winner(?x2375, ?x2373) *> conf = 0.17 ranks of expected_values: 9 EVAL 0dzlbx award 02g2wv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 60.000 52.000 0.268 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13218-02_3zj PRED entity: 02_3zj PRED relation: nominated_for PRED expected values: 01b66t => 47 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1365): 0kfv9 (0.86 #5024, 0.67 #3435, 0.60 #1848), 01g03q (0.79 #6134, 0.67 #4545, 0.40 #2958), 0g60z (0.71 #4805, 0.67 #3216, 0.60 #1629), 05lfwd (0.71 #5660, 0.67 #4071, 0.40 #2484), 017f3m (0.67 #3934, 0.64 #5523, 0.60 #2347), 0hz55 (0.67 #3938, 0.57 #5527, 0.40 #2351), 015ppk (0.67 #4264, 0.50 #5853, 0.40 #2677), 03d34x8 (0.67 #3461, 0.43 #5050, 0.40 #1874), 030k94 (0.67 #3641, 0.40 #2054, 0.36 #5230), 0180mw (0.64 #5780, 0.50 #4191, 0.40 #2604) >> Best rule #5024 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 0bp_b2; 0bdw1g; 0fbvqf; 0cqh6z; 0ck27z; 0gkts9; 02xcb6n; >> query: (?x7316, 0kfv9) <- nominated_for(?x7316, ?x493), award_winner(?x493, ?x3841), award_winner(?x493, ?x1169), ?x1169 = 02lfns, nominated_for(?x368, ?x493), nationality(?x3841, ?x94) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #8668 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 52 *> proper extension: 0bfvw2; 0gkvb7; 02p_7cr; 0cqhk0; 09qvc0; 09qj50; 09qv3c; 0cqh46; 047byns; 0bdwft; ... *> query: (?x7316, 01b66t) <- nominated_for(?x7316, ?x493), award_winner(?x493, ?x1169), award_winner(?x369, ?x1169), award_nominee(?x368, ?x1169), program(?x3381, ?x493) *> conf = 0.13 ranks of expected_values: 139 EVAL 02_3zj nominated_for 01b66t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 47.000 19.000 0.857 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13217-012hw PRED entity: 012hw PRED relation: people PRED expected values: 016hvl 0177g => 37 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1394): 09ld6g (0.50 #2716, 0.20 #3398, 0.12 #5450), 0jrny (0.40 #2839, 0.30 #6942, 0.25 #4891), 0gyy0 (0.40 #3113, 0.25 #5165, 0.25 #2431), 016gkf (0.40 #2946, 0.25 #4998, 0.25 #2264), 05v45k (0.40 #3347, 0.25 #5399, 0.25 #2665), 053yx (0.33 #5564, 0.33 #3513, 0.30 #6248), 07pzc (0.33 #1789, 0.33 #1105, 0.25 #12724), 01vz0g4 (0.33 #1723, 0.33 #1039, 0.19 #12658), 03d_zl4 (0.33 #1649, 0.33 #965, 0.17 #3698), 012z8_ (0.33 #1535, 0.33 #851, 0.17 #3584) >> Best rule #2716 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 0c58k; >> query: (?x12781, 09ld6g) <- people(?x12781, ?x11088), people(?x12781, ?x6138), religion(?x6138, ?x1985), celebrities_impersonated(?x3649, ?x11088), award_winner(?x14536, ?x11088), place_of_death(?x11088, ?x5719), location(?x6138, ?x335), company(?x11088, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1407 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 051_y; *> query: (?x12781, 016hvl) <- people(?x12781, ?x11088), people(?x12781, ?x9851), people(?x12781, ?x6138), religion(?x6138, ?x1985), place_of_death(?x6138, ?x1523), ?x9851 = 04jvt, profession(?x6138, ?x3342), influenced_by(?x11088, ?x7893), people(?x1446, ?x11088) *> conf = 0.33 ranks of expected_values: 81, 671 EVAL 012hw people 0177g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 28.000 0.500 http://example.org/people/cause_of_death/people EVAL 012hw people 016hvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 37.000 28.000 0.500 http://example.org/people/cause_of_death/people #13216-01dtcb PRED entity: 01dtcb PRED relation: artist PRED expected values: 016fmf 014_lq 01x0yrt 015bwt 014_xj => 158 concepts (101 used for prediction) PRED predicted values (max 10 best out of 817): 016376 (0.50 #10977, 0.25 #3083, 0.25 #2292), 01323p (0.50 #6854, 0.17 #10799, 0.14 #11589), 01pgzn_ (0.50 #1710, 0.17 #10395, 0.11 #18952), 0ycp3 (0.50 #6785, 0.14 #16259, 0.13 #18626), 01vwyqp (0.50 #6526, 0.11 #18952, 0.10 #11846), 0p76z (0.50 #7004, 0.11 #18952, 0.10 #11846), 01s7ns (0.43 #26059, 0.33 #11056, 0.24 #32376), 09889g (0.33 #10607, 0.29 #11397, 0.25 #1922), 01q99h (0.33 #6742, 0.29 #11477, 0.20 #18583), 01vxlbm (0.33 #6576, 0.29 #11311, 0.20 #18417) >> Best rule #10977 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 05s34b; >> query: (?x7793, 016376) <- child(?x1104, ?x7793), company(?x11026, ?x7793), award_winner(?x6487, ?x11026), origin(?x11026, ?x3501) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7087 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 01jv1z; 011k1h; 017l96; 0n85g; *> query: (?x7793, 014_xj) <- artist(?x7793, ?x11749), artist(?x7793, ?x1338), ?x1338 = 09qr6, award(?x11749, ?x1389), artists(?x302, ?x11749) *> conf = 0.33 ranks of expected_values: 21, 95, 502, 698, 700 EVAL 01dtcb artist 014_xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 158.000 101.000 0.500 http://example.org/music/record_label/artist EVAL 01dtcb artist 015bwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 158.000 101.000 0.500 http://example.org/music/record_label/artist EVAL 01dtcb artist 01x0yrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 158.000 101.000 0.500 http://example.org/music/record_label/artist EVAL 01dtcb artist 014_lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 158.000 101.000 0.500 http://example.org/music/record_label/artist EVAL 01dtcb artist 016fmf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 158.000 101.000 0.500 http://example.org/music/record_label/artist #13215-07jwr PRED entity: 07jwr PRED relation: risk_factors PRED expected values: 0c58k => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 83): 012jc (0.77 #1998, 0.57 #1208, 0.50 #1367), 0c58k (0.75 #2527, 0.57 #1201, 0.55 #1781), 01hbgs (0.60 #792, 0.55 #1835, 0.55 #1782), 05zppz (0.60 #767, 0.50 #1286, 0.50 #353), 0k95h (0.50 #465, 0.36 #1767, 0.33 #262), 0217g (0.46 #2017, 0.33 #1061, 0.25 #599), 0dcp_ (0.46 #2017, 0.33 #340, 0.25 #648), 025t3bg (0.46 #2017, 0.24 #1386, 0.22 #1388), 0jpmt (0.44 #2743, 0.40 #1621, 0.40 #790), 01psyx (0.42 #2236, 0.33 #336, 0.25 #644) >> Best rule #1998 for best value: >> intensional similarity = 9 >> extensional distance = 11 >> proper extension: 05mdx; >> query: (?x4291, 012jc) <- risk_factors(?x4291, ?x13131), risk_factors(?x4291, ?x13122), risk_factors(?x11659, ?x13131), risk_factors(?x6655, ?x13131), taxonomy(?x13122, ?x939), symptom_of(?x6780, ?x11659), ?x6780 = 0j5fv, ?x6655 = 09d11, ?x939 = 04n6k >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2527 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 18 *> proper extension: 01b_5g; *> query: (?x4291, 0c58k) <- risk_factors(?x4291, ?x13131), people(?x13131, ?x8835), people(?x13131, ?x8661), people(?x13131, ?x6585), type_of_union(?x6585, ?x566), place_of_birth(?x8661, ?x1705), music(?x1919, ?x8661), award(?x8661, ?x384), film(?x6585, ?x2423), profession(?x8835, ?x1032), gender(?x8835, ?x231) *> conf = 0.75 ranks of expected_values: 2 EVAL 07jwr risk_factors 0c58k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.769 http://example.org/medicine/disease/risk_factors #13214-06dkzt PRED entity: 06dkzt PRED relation: award PRED expected values: 02qyp19 => 108 concepts (70 used for prediction) PRED predicted values (max 10 best out of 316): 0gr51 (0.69 #1290, 0.48 #892, 0.38 #2086), 02qyp19 (0.56 #1195, 0.36 #797, 0.26 #1991), 07bdd_ (0.50 #4838, 0.19 #2848, 0.18 #8022), 040njc (0.48 #804, 0.43 #5182, 0.41 #2794), 0gq9h (0.41 #2859, 0.39 #8033, 0.39 #7237), 019f4v (0.40 #859, 0.33 #1257, 0.31 #5237), 02x1dht (0.39 #1245, 0.30 #1643, 0.28 #2041), 0gs9p (0.36 #871, 0.33 #5249, 0.31 #4055), 02pqp12 (0.33 #1260, 0.32 #862, 0.21 #4046), 0f_nbyh (0.32 #806, 0.25 #2796, 0.25 #1204) >> Best rule #1290 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 0js9s; 01vb6z; 06t8b; >> query: (?x8692, 0gr51) <- award_winner(?x5392, ?x8692), award(?x8692, ?x3435), ?x3435 = 03hl6lc >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1195 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 0js9s; 01vb6z; 06t8b; *> query: (?x8692, 02qyp19) <- award_winner(?x5392, ?x8692), award(?x8692, ?x3435), ?x3435 = 03hl6lc *> conf = 0.56 ranks of expected_values: 2 EVAL 06dkzt award 02qyp19 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 70.000 0.694 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13213-04z0g PRED entity: 04z0g PRED relation: influenced_by PRED expected values: 047g6 => 175 concepts (88 used for prediction) PRED predicted values (max 10 best out of 337): 05qmj (0.37 #5377, 0.22 #1057, 0.17 #6242), 0gz_ (0.33 #967, 0.30 #5287, 0.17 #6152), 03sbs (0.33 #5407, 0.21 #6272, 0.16 #16214), 026lj (0.33 #477, 0.15 #16424, 0.15 #5229), 015n8 (0.33 #1273, 0.15 #5593, 0.10 #6890), 039n1 (0.26 #5511, 0.12 #10265, 0.11 #1191), 042q3 (0.22 #1229, 0.19 #5549, 0.17 #797), 07kb5 (0.22 #879, 0.17 #447, 0.14 #14693), 02wh0 (0.22 #5567, 0.14 #6432, 0.14 #14693), 0j3v (0.22 #5245, 0.14 #6542, 0.13 #2653) >> Best rule #5377 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0j3v; 0dzkq; 02wh0; >> query: (?x5790, 05qmj) <- influenced_by(?x5790, ?x3711), company(?x5790, ?x3424), student(?x3424, ?x117), place_of_death(?x5790, ?x3125) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #16424 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 100 *> proper extension: 0459z; *> query: (?x5790, ?x1857) <- influenced_by(?x5790, ?x12441), place_of_birth(?x5790, ?x2850), gender(?x5790, ?x231), peers(?x12441, ?x1857) *> conf = 0.15 ranks of expected_values: 33 EVAL 04z0g influenced_by 047g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 175.000 88.000 0.370 http://example.org/influence/influence_node/influenced_by #13212-0bl06 PRED entity: 0bl06 PRED relation: featured_film_locations PRED expected values: 0rh6k => 79 concepts (43 used for prediction) PRED predicted values (max 10 best out of 57): 02_286 (0.24 #744, 0.18 #3631, 0.18 #3871), 030qb3t (0.07 #279, 0.06 #521, 0.06 #7977), 04jpl (0.06 #3620, 0.06 #1695, 0.06 #3860), 01_d4 (0.06 #287, 0.05 #529, 0.04 #47), 02nd_ (0.05 #1080, 0.04 #116, 0.03 #598), 0fsv2 (0.04 #226, 0.04 #466, 0.03 #708), 0rh6k (0.04 #1, 0.04 #965, 0.04 #3372), 05kj_ (0.04 #18, 0.02 #258, 0.02 #500), 0345h (0.04 #33, 0.02 #273, 0.02 #515), 01l3k6 (0.04 #190, 0.02 #430, 0.02 #672) >> Best rule #744 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 0k0rf; 048rn; 0fztbq; >> query: (?x5697, 02_286) <- film_release_distribution_medium(?x5697, ?x81), genre(?x5697, ?x53), film_sets_designed(?x2716, ?x5697) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 01gc7; 07xtqq; 04v8x9; 0209hj; 0_92w; 0qm98; 0bmpm; 0ywrc; 0hfzr; 0cq7kw; ... *> query: (?x5697, 0rh6k) <- award(?x5697, ?x1307), award(?x5697, ?x1107), ?x1307 = 0gq9h, nominated_for(?x2109, ?x5697), ?x1107 = 019f4v *> conf = 0.04 ranks of expected_values: 7 EVAL 0bl06 featured_film_locations 0rh6k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 79.000 43.000 0.244 http://example.org/film/film/featured_film_locations #13211-041738 PRED entity: 041738 PRED relation: parent_genre PRED expected values: 06by7 => 80 concepts (56 used for prediction) PRED predicted values (max 10 best out of 218): 06by7 (0.57 #679, 0.57 #2174, 0.55 #2504), 07gxw (0.33 #39, 0.25 #369, 0.25 #205), 011j5x (0.29 #685, 0.29 #520, 0.25 #1183), 064t9 (0.29 #674, 0.28 #6660, 0.25 #1172), 05bt6j (0.29 #693, 0.25 #1191, 0.22 #1359), 03mb9 (0.28 #6660, 0.16 #7334, 0.14 #8002), 05r6t (0.25 #3708, 0.25 #2047, 0.25 #1216), 0xhtw (0.25 #6999, 0.16 #3168, 0.16 #3666), 09jw2 (0.25 #1264, 0.22 #1432, 0.15 #1930), 02x8m (0.25 #1175, 0.22 #1343, 0.14 #3153) >> Best rule #679 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 0y3_8; 059kh; 05c6073; >> query: (?x5909, 06by7) <- artists(?x5909, ?x8332), artists(?x5909, ?x5916), artists(?x5909, ?x2005), ?x5916 = 02cpp, parent_genre(?x9881, ?x5909), artists(?x3916, ?x8332), ?x3916 = 08cyft, award(?x8332, ?x462), award_winner(?x8332, ?x8874), ?x2005 = 05k79 >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 041738 parent_genre 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 56.000 0.571 http://example.org/music/genre/parent_genre #13210-01rz1 PRED entity: 01rz1 PRED relation: organization! PRED expected values: 04w58 077qn => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 304): 04g61 (0.80 #830, 0.56 #3614, 0.56 #3613), 09c7w0 (0.60 #2231, 0.60 #1114, 0.55 #2507), 06qd3 (0.60 #1151, 0.50 #2268, 0.50 #1428), 03_3d (0.60 #1120, 0.50 #2237, 0.50 #1397), 0chghy (0.60 #1126, 0.50 #1403, 0.50 #291), 0b90_r (0.60 #1118, 0.50 #1395, 0.50 #283), 01p1v (0.60 #1165, 0.50 #1442, 0.50 #330), 03spz (0.60 #1239, 0.50 #1516, 0.50 #404), 0d05w3 (0.56 #3614, 0.56 #3613, 0.50 #343), 0d05q4 (0.56 #3614, 0.56 #3613, 0.50 #383) >> Best rule #830 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 07y2b; >> query: (?x1062, ?x172) <- organizations_founded(?x1229, ?x1062), organizations_founded(?x172, ?x1062), company(?x10118, ?x1229), basic_title(?x1328, ?x10118), jurisdiction_of_office(?x10118, ?x390) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3614 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 034h1h; *> query: (?x1062, ?x4059) <- organization(?x1497, ?x1062), organization(?x205, ?x1062), adjoins(?x4059, ?x1497), location(?x4587, ?x205) *> conf = 0.56 ranks of expected_values: 15, 33 EVAL 01rz1 organization! 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 22.000 22.000 0.800 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 01rz1 organization! 04w58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 22.000 22.000 0.800 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #13209-0fbdb PRED entity: 0fbdb PRED relation: nutrient PRED expected values: 02kcv4x 014yzm 07q0m => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 32): 02kcv4x (0.50 #251, 0.40 #239, 0.33 #226), 07q0m (0.50 #257, 0.40 #245, 0.33 #232), 014yzm (0.50 #252, 0.40 #240, 0.33 #227), 02kc_w5 (0.33 #219, 0.33 #207, 0.33 #195), 01w_3 (0.33 #221, 0.33 #144, 0.33 #131), 0f4k5 (0.33 #222, 0.33 #145, 0.33 #132), 075pwf (0.33 #216, 0.33 #139, 0.33 #126), 0f4l5 (0.33 #217, 0.33 #127, 0.33 #77), 02kd8zw (0.33 #123, 0.33 #73, 0.33 #49), 02y_3rt (0.33 #211, 0.33 #184, 0.33 #172) >> Best rule #251 for best value: >> intensional similarity = 82 >> extensional distance = 4 >> proper extension: 0dcfv; >> query: (?x7057, 02kcv4x) <- nutrient(?x7057, ?x12902), nutrient(?x7057, ?x12454), nutrient(?x7057, ?x11758), nutrient(?x7057, ?x11270), nutrient(?x7057, ?x10891), nutrient(?x7057, ?x10195), nutrient(?x7057, ?x9949), nutrient(?x7057, ?x9915), nutrient(?x7057, ?x9840), nutrient(?x7057, ?x9436), nutrient(?x7057, ?x8243), nutrient(?x7057, ?x7364), nutrient(?x7057, ?x6192), nutrient(?x7057, ?x5549), nutrient(?x7057, ?x5451), nutrient(?x7057, ?x5337), nutrient(?x7057, ?x3469), nutrient(?x7057, ?x2018), nutrient(?x9489, ?x10195), nutrient(?x8298, ?x10195), nutrient(?x6285, ?x10195), nutrient(?x6191, ?x10195), nutrient(?x5009, ?x10195), nutrient(?x4068, ?x10195), nutrient(?x3900, ?x10195), nutrient(?x3468, ?x10195), nutrient(?x2701, ?x10195), nutrient(?x1303, ?x10195), nutrient(?x1257, ?x10195), nutrient(?x10612, ?x9949), nutrient(?x9732, ?x9949), nutrient(?x9005, ?x9949), nutrient(?x6159, ?x9949), nutrient(?x6032, ?x9949), nutrient(?x1959, ?x9949), ?x12454 = 025rw19, ?x1959 = 0f25w9, ?x6285 = 01645p, ?x9915 = 025tkqy, nutrient(?x7719, ?x9436), nutrient(?x5373, ?x9436), ?x8243 = 014d7f, ?x4068 = 0fbw6, ?x8298 = 037ls6, ?x7719 = 0dj75, ?x1303 = 0fj52s, ?x2701 = 0hkxq, ?x9005 = 04zpv, ?x5337 = 06x4c, ?x9732 = 05z55, ?x9489 = 07j87, ?x2018 = 01sh2, ?x6032 = 01nkt, ?x10612 = 0frq6, ?x5549 = 025s7j4, ?x3900 = 061_f, ?x1257 = 09728, ?x5009 = 0fjfh, ?x6159 = 033cnk, ?x6191 = 014j1m, ?x3468 = 0cxn2, ?x6192 = 06jry, taxonomy(?x5451, ?x939), ?x939 = 04n6k, ?x5373 = 0971v, nutrient(?x9005, ?x12902), nutrient(?x7719, ?x11758), nutrient(?x1303, ?x9436), nutrient(?x5373, ?x11270), nutrient(?x4068, ?x3469), nutrient(?x6191, ?x11758), nutrient(?x1303, ?x10891), nutrient(?x3468, ?x10891), nutrient(?x9005, ?x3469), nutrient(?x9005, ?x7364), nutrient(?x6285, ?x11270), nutrient(?x6159, ?x10891), nutrient(?x3900, ?x7364), nutrient(?x3468, ?x3469), nutrient(?x2701, ?x12902), nutrient(?x1959, ?x9840), nutrient(?x4068, ?x9840) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0fbdb nutrient 07q0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.500 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fbdb nutrient 014yzm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.500 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fbdb nutrient 02kcv4x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.500 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #13208-0gn30 PRED entity: 0gn30 PRED relation: people! PRED expected values: 0xnvg => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 58): 06mvq (0.33 #34, 0.12 #111), 041rx (0.24 #620, 0.23 #2008, 0.22 #1082), 0x67 (0.19 #6095, 0.18 #3477, 0.16 #6866), 033tf_ (0.19 #161, 0.14 #931, 0.13 #2088), 0xnvg (0.12 #167, 0.12 #937, 0.11 #2479), 07hwkr (0.12 #166, 0.10 #936, 0.08 #1475), 02ctzb (0.12 #92, 0.09 #1401, 0.08 #862), 09vc4s (0.12 #163, 0.08 #1550, 0.07 #856), 063k3h (0.12 #108, 0.06 #185, 0.04 #1417), 022dp5 (0.12 #204, 0.05 #589, 0.03 #358) >> Best rule #34 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 02v406; >> query: (?x5338, 06mvq) <- spouse(?x1888, ?x5338), film(?x5338, ?x3217), ?x3217 = 0gffmn8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #167 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 03yf3z; *> query: (?x5338, 0xnvg) <- spouse(?x1888, ?x5338), award_nominee(?x5338, ?x703), student(?x8398, ?x5338) *> conf = 0.12 ranks of expected_values: 5 EVAL 0gn30 people! 0xnvg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 143.000 143.000 0.333 http://example.org/people/ethnicity/people #13207-0dgskx PRED entity: 0dgskx PRED relation: actor! PRED expected values: 03ffcz => 80 concepts (37 used for prediction) PRED predicted values (max 10 best out of 54): 0m313 (0.75 #1057, 0.27 #1588, 0.10 #3716), 03ctqqf (0.15 #766, 0.10 #2917, 0.04 #4779), 0524b41 (0.10 #130, 0.10 #2917, 0.08 #394), 06k176 (0.10 #234, 0.10 #2917, 0.08 #498), 0dl6fv (0.10 #170, 0.10 #2917, 0.08 #698), 02py4c8 (0.10 #2917, 0.08 #540, 0.01 #1069), 03ffcz (0.10 #2917, 0.08 #649), 0h3mh3q (0.10 #2917), 0828jw (0.04 #1426, 0.02 #1956, 0.02 #2755), 080dwhx (0.04 #1328, 0.02 #2657, 0.02 #1858) >> Best rule #1057 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 02w9895; 02pkpfs; 0clvcx; 0l6px; 07fpm3; 0bd2n4; 05y5kf; 017gxw; 08_83x; 01x4sb; ... >> query: (?x6612, ?x144) <- award_winner(?x6612, ?x988), award_winner(?x1434, ?x6612), award_winner(?x144, ?x6612), ?x1434 = 0ddd0gc >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2917 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 698 *> proper extension: 049gc; *> query: (?x6612, ?x715) <- award_winner(?x7077, ?x6612), profession(?x7077, ?x353), actor(?x715, ?x7077) *> conf = 0.10 ranks of expected_values: 7 EVAL 0dgskx actor! 03ffcz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 80.000 37.000 0.750 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #13206-07wm6 PRED entity: 07wm6 PRED relation: school_type PRED expected values: 05jxkf => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 21): 05jxkf (0.60 #52, 0.59 #28, 0.58 #268), 05pcjw (0.33 #73, 0.27 #577, 0.26 #889), 01rs41 (0.27 #1373, 0.26 #101, 0.25 #1829), 01_9fk (0.18 #194, 0.17 #122, 0.15 #146), 07tf8 (0.18 #465, 0.17 #297, 0.16 #561), 01_srz (0.10 #2715, 0.08 #75, 0.07 #1371), 02p0qmm (0.10 #2715, 0.07 #514, 0.07 #394), 04399 (0.10 #2715, 0.05 #86, 0.04 #446), 06cs1 (0.10 #2715, 0.05 #78, 0.03 #318), 04qbv (0.10 #2715, 0.04 #544, 0.04 #16) >> Best rule #52 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 01y9st; >> query: (?x12737, 05jxkf) <- colors(?x12737, ?x332), category(?x12737, ?x134), state_province_region(?x12737, ?x3824), currency(?x12737, ?x2244) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07wm6 school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.600 http://example.org/education/educational_institution/school_type #13205-06z4wj PRED entity: 06z4wj PRED relation: people! PRED expected values: 0dcsx => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 38): 0gk4g (0.20 #10, 0.16 #472, 0.13 #1992), 0qcr0 (0.16 #199, 0.10 #1, 0.07 #661), 0dq9p (0.16 #215, 0.08 #1205, 0.08 #1337), 02y0js (0.12 #68, 0.10 #2, 0.07 #266), 02knxx (0.11 #230, 0.07 #296, 0.03 #1352), 02k6hp (0.10 #37, 0.06 #103, 0.05 #499), 04p3w (0.10 #935, 0.07 #275, 0.05 #473), 01l2m3 (0.06 #82, 0.04 #280, 0.03 #874), 01ddth (0.06 #125, 0.04 #323, 0.02 #917), 01psyx (0.06 #111, 0.03 #903, 0.03 #2093) >> Best rule #10 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 06whf; 06c44; >> query: (?x6943, 0gk4g) <- gender(?x6943, ?x231), profession(?x6943, ?x11804), profession(?x6943, ?x2225), ?x11804 = 0q04f, ?x2225 = 0kyk >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #213 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 02vqpx8; *> query: (?x6943, 0dcsx) <- award_winner(?x12947, ?x6943), profession(?x6943, ?x1041), ?x1041 = 03gjzk, place_of_death(?x6943, ?x739) *> conf = 0.05 ranks of expected_values: 17 EVAL 06z4wj people! 0dcsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 106.000 106.000 0.200 http://example.org/people/cause_of_death/people #13204-06rjp PRED entity: 06rjp PRED relation: category PRED expected values: 08mbj5d => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.89 #42, 0.89 #77, 0.89 #63) >> Best rule #42 for best value: >> intensional similarity = 5 >> extensional distance = 130 >> proper extension: 04jr87; 01w_sh; 029qzx; >> query: (?x11555, 08mbj5d) <- institution(?x865, ?x11555), citytown(?x11555, ?x5168), student(?x11555, ?x6957), school_type(?x11555, ?x3092), ?x865 = 02h4rq6 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06rjp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.894 http://example.org/common/topic/webpage./common/webpage/category #13203-01540 PRED entity: 01540 PRED relation: major_field_of_study! PRED expected values: 01mkq => 54 concepts (42 used for prediction) PRED predicted values (max 10 best out of 130): 02h40lc (0.82 #2112, 0.82 #2108, 0.82 #2016), 01mkq (0.56 #1293, 0.50 #563, 0.33 #288), 04rjg (0.50 #660, 0.44 #1298, 0.40 #843), 02j62 (0.50 #1399, 0.40 #852, 0.40 #760), 0fdys (0.50 #584, 0.33 #1314, 0.33 #125), 03g3w (0.45 #1853, 0.38 #1671, 0.33 #115), 06ms6 (0.43 #930, 0.40 #840, 0.40 #748), 05qfh (0.40 #857, 0.40 #765, 0.33 #1679), 037mh8 (0.40 #883, 0.40 #791, 0.33 #1338), 05qjt (0.40 #831, 0.40 #739, 0.33 #372) >> Best rule #2112 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 0j0k; >> query: (?x6870, ?x4100) <- major_field_of_study(?x6870, ?x4100), taxonomy(?x6870, ?x939), ?x939 = 04n6k, major_field_of_study(?x4100, ?x2605), major_field_of_study(?x2605, ?x1327) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1293 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 7 *> proper extension: 06ms6; 05qfh; 03qsdpk; *> query: (?x6870, 01mkq) <- major_field_of_study(?x9658, ?x6870), major_field_of_study(?x6417, ?x6870), major_field_of_study(?x6315, ?x6870), major_field_of_study(?x2980, ?x6870), major_field_of_study(?x2760, ?x6870), organization(?x4095, ?x9658), ?x6417 = 01t0dy, major_field_of_study(?x734, ?x6870), school_type(?x2760, ?x3092), currency(?x2980, ?x170), institution(?x2636, ?x6315), major_field_of_study(?x6870, ?x254) *> conf = 0.56 ranks of expected_values: 2 EVAL 01540 major_field_of_study! 01mkq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 54.000 42.000 0.824 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #13202-033w9g PRED entity: 033w9g PRED relation: award_winner! PRED expected values: 09qvms => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 100): 09qvms (0.85 #13, 0.17 #5782, 0.10 #10438), 09bymc (0.17 #5782, 0.08 #121, 0.01 #262), 092c5f (0.10 #10438, 0.09 #7053, 0.09 #6347), 092t4b (0.06 #757, 0.04 #1462, 0.04 #193), 09g90vz (0.06 #829, 0.05 #970, 0.05 #1534), 0hr3c8y (0.06 #715, 0.04 #1420, 0.04 #1561), 03gyp30 (0.06 #822, 0.04 #1527, 0.04 #1668), 0418154 (0.06 #249, 0.02 #1941, 0.02 #813), 027hjff (0.05 #762, 0.04 #903, 0.04 #1326), 09gkdln (0.05 #968, 0.04 #827, 0.03 #1391) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01sl1q; 01kwld; 044mm6; 07f3xb; 031ydm; 02k4gv; 03x22w; 03_wvl; 03_wpf; 03_wtr; ... >> query: (?x4527, 09qvms) <- award_winner(?x10401, ?x4527), award_winner(?x4527, ?x628), ?x10401 = 044n3h >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033w9g award_winner! 09qvms CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.846 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13201-050_qx PRED entity: 050_qx PRED relation: profession PRED expected values: 02hrh1q => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 56): 02hrh1q (0.90 #1365, 0.88 #8867, 0.88 #1666), 01d_h8 (0.44 #156, 0.34 #9158, 0.33 #9308), 0dxtg (0.32 #4215, 0.30 #10516, 0.30 #314), 03gjzk (0.31 #1216, 0.27 #12455, 0.27 #616), 018gz8 (0.30 #318, 0.27 #12455, 0.23 #1218), 09jwl (0.28 #6022, 0.18 #470, 0.16 #10822), 02krf9 (0.27 #12455, 0.12 #928, 0.12 #1228), 0cbd2 (0.25 #1057, 0.18 #607, 0.16 #7659), 02jknp (0.24 #9160, 0.21 #11861, 0.20 #13513), 0np9r (0.20 #6624, 0.20 #6474, 0.20 #6324) >> Best rule #1365 for best value: >> intensional similarity = 3 >> extensional distance = 277 >> proper extension: 015njf; 01vxqyl; >> query: (?x8568, 02hrh1q) <- actor(?x7175, ?x8568), nominated_for(?x8568, ?x4329), film_crew_role(?x4329, ?x281) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050_qx profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.896 http://example.org/people/person/profession #13200-01rhrd PRED entity: 01rhrd PRED relation: olympics PRED expected values: 0l6mp => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 42): 06sks6 (0.90 #1631, 0.90 #1307, 0.89 #1671), 0kbws (0.76 #735, 0.67 #134, 0.62 #294), 018ctl (0.71 #288, 0.48 #128, 0.42 #729), 0kbvv (0.62 #146, 0.60 #306, 0.44 #346), 0kbvb (0.57 #127, 0.53 #287, 0.43 #728), 09n48 (0.53 #283, 0.52 #123, 0.40 #724), 0jdk_ (0.48 #147, 0.47 #307, 0.38 #67), 0swbd (0.42 #291, 0.38 #51, 0.35 #91), 0jhn7 (0.38 #68, 0.38 #148, 0.29 #108), 0l6m5 (0.38 #50, 0.29 #90, 0.28 #170) >> Best rule #1631 for best value: >> intensional similarity = 5 >> extensional distance = 188 >> proper extension: 0j11; >> query: (?x12163, 06sks6) <- contains(?x11687, ?x12163), olympics(?x12163, ?x4424), participating_countries(?x4424, ?x2267), film_release_region(?x5255, ?x2267), ?x5255 = 01sby_ >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0dv0z; 049nq; 088q1s; 0gtzp; *> query: (?x12163, 0l6mp) <- capital(?x12163, ?x11540), place_of_birth(?x8696, ?x11540), film_release_region(?x5089, ?x11540), jurisdiction_of_office(?x1195, ?x11540) *> conf = 0.31 ranks of expected_values: 12 EVAL 01rhrd olympics 0l6mp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 107.000 107.000 0.900 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #13199-09d28z PRED entity: 09d28z PRED relation: award! PRED expected values: 0sxlb => 30 concepts (29 used for prediction) PRED predicted values (max 10 best out of 847): 07xtqq (0.50 #2999, 0.33 #1021, 0.29 #2010), 0_9l_ (0.46 #4910, 0.33 #3921, 0.14 #2932), 0g9lm2 (0.46 #4366, 0.25 #3377, 0.13 #16823), 04lhc4 (0.38 #4631, 0.25 #3642, 0.14 #2653), 0bcp9b (0.38 #4695, 0.17 #3706, 0.13 #16823), 0qm9n (0.38 #4275, 0.17 #3286, 0.04 #5264), 01jc6q (0.33 #2979, 0.33 #1001, 0.31 #3968), 09gq0x5 (0.33 #3134, 0.33 #1156, 0.15 #4123), 0pd64 (0.33 #3717, 0.33 #1739, 0.10 #5695), 04q827 (0.33 #1923, 0.31 #4890, 0.17 #3901) >> Best rule #2999 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 027c924; 0f4x7; 094qd5; 0gqwc; 09cm54; 04kxsb; 0279c15; 02g2wv; 09cn0c; >> query: (?x8364, 07xtqq) <- award(?x8258, ?x8364), award(?x3471, ?x8364), award(?x945, ?x8364), award_winner(?x8364, ?x698), ?x3471 = 07cyl, nominated_for(?x198, ?x945), film_release_region(?x8258, ?x87) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2864 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02v1m7; *> query: (?x8364, 0sxlb) <- award_winner(?x8364, ?x10944), award_winner(?x8364, ?x8573), ?x10944 = 027zz, award_winner(?x8573, ?x8572) *> conf = 0.14 ranks of expected_values: 145 EVAL 09d28z award! 0sxlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 30.000 29.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #13198-025352 PRED entity: 025352 PRED relation: profession! PRED expected values: 01309x 01ttg5 02lfp4 0178rl 029h45 025cn2 0d0mbj 017g21 027hm_ 01rmnp 05q9g1 0cyhq => 48 concepts (18 used for prediction) PRED predicted values (max 10 best out of 4056): 02cx90 (0.78 #55365, 0.60 #30430, 0.60 #22117), 0161c2 (0.78 #54930, 0.60 #21682, 0.50 #59086), 02qwg (0.71 #38398, 0.67 #55022, 0.60 #30087), 01vw8mh (0.71 #38928, 0.67 #55552, 0.60 #22304), 01wgcvn (0.71 #38519, 0.60 #21895, 0.50 #59299), 03j24kf (0.67 #63818, 0.67 #55505, 0.60 #30570), 0473q (0.67 #56337, 0.60 #31402, 0.60 #23089), 01vsl3_ (0.67 #54832, 0.60 #21584, 0.58 #63145), 016jfw (0.67 #55988, 0.60 #22740, 0.50 #64301), 01j6mff (0.67 #57092, 0.60 #23844, 0.50 #15534) >> Best rule #55365 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 0n1h; >> query: (?x6476, 02cx90) <- profession(?x6947, ?x6476), profession(?x3118, ?x6476), profession(?x2483, ?x6476), profession(?x2170, ?x6476), profession(?x680, ?x6476), type_of_union(?x2170, ?x566), type_of_appearance(?x6947, ?x3429), award_nominee(?x2170, ?x3030), nationality(?x680, ?x94), award_winner(?x1323, ?x2483), ?x3118 = 01w02sy >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #20118 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 0nbcg; *> query: (?x6476, 0cyhq) <- profession(?x10412, ?x6476), profession(?x9179, ?x6476), profession(?x7571, ?x6476), profession(?x2170, ?x6476), profession(?x1645, ?x6476), ?x2170 = 0144l1, role(?x7571, ?x1473), ?x10412 = 016jll, religion(?x1645, ?x2694), artists(?x671, ?x9179) *> conf = 0.50 ranks of expected_values: 297, 314, 320, 325, 378, 563, 585, 1289, 1326, 1911, 2378, 3800 EVAL 025352 profession! 0cyhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 05q9g1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 01rmnp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 027hm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 017g21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 0d0mbj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 025cn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 029h45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 0178rl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 02lfp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 01ttg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 18.000 0.778 http://example.org/people/person/profession EVAL 025352 profession! 01309x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 18.000 0.778 http://example.org/people/person/profession #13197-061zc_ PRED entity: 061zc_ PRED relation: profession PRED expected values: 0fj9f => 122 concepts (59 used for prediction) PRED predicted values (max 10 best out of 90): 0fj9f (0.71 #1386, 0.71 #1830, 0.70 #350), 02jknp (0.57 #599, 0.48 #6227, 0.47 #4155), 0dxtg (0.50 #6529, 0.47 #6233, 0.41 #4161), 0cbd2 (0.33 #6, 0.23 #3266, 0.23 #3414), 03gjzk (0.32 #6530, 0.30 #6234, 0.22 #2679), 04gc2 (0.32 #2410, 0.31 #1817, 0.26 #1373), 0kyk (0.30 #325, 0.21 #1361, 0.17 #3289), 0np9r (0.27 #3131, 0.23 #2834, 0.21 #2685), 015cjr (0.26 #937, 0.20 #345, 0.19 #789), 02krf9 (0.25 #174, 0.14 #6246, 0.12 #6542) >> Best rule #1386 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 012v1t; >> query: (?x5568, 0fj9f) <- type_of_union(?x5568, ?x566), location(?x5568, ?x7771), people(?x5025, ?x5568), politician(?x13990, ?x5568) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 061zc_ profession 0fj9f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 59.000 0.714 http://example.org/people/person/profession #13196-02f75t PRED entity: 02f75t PRED relation: award_winner PRED expected values: 0147dk => 40 concepts (8 used for prediction) PRED predicted values (max 10 best out of 1283): 018n6m (0.33 #7413, 0.33 #1051, 0.31 #3521), 01vwbts (0.33 #7413, 0.31 #19766, 0.31 #14825), 01vvyc_ (0.33 #7413, 0.31 #19766, 0.31 #14825), 016ksk (0.33 #7413, 0.31 #19766, 0.31 #14825), 0126y2 (0.33 #7413, 0.31 #19766, 0.31 #14825), 01wgxtl (0.33 #7413, 0.31 #19766, 0.31 #14825), 01vvzb1 (0.33 #7413, 0.31 #19766, 0.31 #14825), 01wgcvn (0.33 #7413, 0.31 #19766, 0.31 #14825), 01vzx45 (0.33 #7413, 0.31 #19766, 0.31 #14825), 03f3yfj (0.33 #7413, 0.31 #19766, 0.31 #14825) >> Best rule #7413 for best value: >> intensional similarity = 6 >> extensional distance = 107 >> proper extension: 09ly2r6; >> query: (?x6287, ?x2732) <- award_winner(?x6287, ?x6231), award_winner(?x6287, ?x5536), award(?x2732, ?x6287), profession(?x5536, ?x131), category(?x6231, ?x134), music(?x6298, ?x5536) >> conf = 0.33 => this is the best rule for 14 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 02v1m7; 02f705; 02f5qb; 02f716; 02f76h; 02f6xy; 03t5b6; 099vwn; 01c9dd; 02f79n; *> query: (?x6287, 0147dk) <- award_winner(?x6287, ?x6231), award_winner(?x6287, ?x5536), award(?x2732, ?x6287), ?x5536 = 01vsgrn, artist(?x6230, ?x6231), location_of_ceremony(?x6231, ?x3415) *> conf = 0.17 ranks of expected_values: 26 EVAL 02f75t award_winner 0147dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 40.000 8.000 0.334 http://example.org/award/award_category/winners./award/award_honor/award_winner #13195-0168cl PRED entity: 0168cl PRED relation: artists! PRED expected values: 01lyv => 95 concepts (72 used for prediction) PRED predicted values (max 10 best out of 231): 06by7 (0.80 #332, 0.52 #13985, 0.49 #3746), 01lyv (0.57 #34, 0.36 #1276, 0.25 #2206), 0glt670 (0.48 #1593, 0.42 #3145, 0.34 #4696), 06j6l (0.40 #1601, 0.31 #3153, 0.30 #4704), 025sc50 (0.40 #361, 0.36 #3155, 0.35 #1603), 016clz (0.35 #3729, 0.24 #9933, 0.24 #9003), 0gywn (0.30 #369, 0.29 #4714, 0.21 #8127), 0ggx5q (0.30 #389, 0.28 #1631, 0.23 #3183), 02lnbg (0.28 #1612, 0.23 #3164, 0.20 #1922), 0xhtw (0.27 #3741, 0.20 #9945, 0.20 #327) >> Best rule #332 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01x1cn2; >> query: (?x672, 06by7) <- currency(?x672, ?x170), artist(?x2039, ?x672), student(?x2605, ?x672) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 01ww2fs; 016sqs; *> query: (?x672, 01lyv) <- award_nominee(?x7359, ?x672), artist(?x2039, ?x672), ?x7359 = 01k_n63 *> conf = 0.57 ranks of expected_values: 2 EVAL 0168cl artists! 01lyv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 72.000 0.800 http://example.org/music/genre/artists #13194-0f8l9c PRED entity: 0f8l9c PRED relation: origin! PRED expected values: 0qmpd => 278 concepts (278 used for prediction) PRED predicted values (max 10 best out of 431): 06nv27 (0.20 #3302, 0.20 #2788, 0.17 #6901), 03j0br4 (0.20 #3174, 0.20 #2660, 0.14 #4717), 015bwt (0.20 #3558, 0.20 #3044, 0.14 #5101), 01wv9p (0.20 #3252, 0.20 #2738, 0.14 #4795), 0d193h (0.20 #3256, 0.20 #2742, 0.14 #4799), 0837ql (0.20 #3286, 0.20 #2772, 0.14 #4829), 0136p1 (0.20 #3144, 0.20 #2630, 0.14 #4687), 0cbm64 (0.20 #3493, 0.20 #2979, 0.14 #5036), 0153nq (0.20 #3598, 0.20 #3084, 0.14 #5141), 01tpl1p (0.20 #3539, 0.20 #3025, 0.14 #5082) >> Best rule #3302 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 07tgn; >> query: (?x789, 06nv27) <- contains(?x789, ?x12257), currency(?x12257, ?x5696), company(?x346, ?x789) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f8l9c origin! 0qmpd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 278.000 278.000 0.200 http://example.org/music/artist/origin #13193-0chghy PRED entity: 0chghy PRED relation: service_location! PRED expected values: 06p8m => 218 concepts (199 used for prediction) PRED predicted values (max 10 best out of 129): 018mxj (0.40 #1857, 0.38 #2720, 0.32 #3835), 064f29 (0.36 #1284, 0.36 #1161, 0.28 #2643), 06_9lg (0.30 #13434, 0.14 #5397, 0.11 #6386), 0k9ts (0.30 #1932, 0.29 #1313, 0.29 #1190), 07zl6m (0.29 #1350, 0.29 #1227, 0.28 #1721), 04fv0k (0.29 #1307, 0.29 #1184, 0.27 #2173), 077w0b (0.29 #1167, 0.27 #2156, 0.27 #1415), 0dmtp (0.29 #1283, 0.22 #914, 0.21 #1160), 03s7h (0.29 #1331, 0.17 #1702, 0.16 #3928), 05w3y (0.22 #1657, 0.22 #917, 0.21 #1286) >> Best rule #1857 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0d060g; 082fr; 07twz; >> query: (?x390, 018mxj) <- film_release_region(?x1859, ?x390), contains(?x390, ?x901), ?x1859 = 0m491 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1698 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0ck1d; *> query: (?x390, 06p8m) <- entity_involved(?x3278, ?x390), contains(?x390, ?x901), administrative_parent(?x8506, ?x390) *> conf = 0.22 ranks of expected_values: 12 EVAL 0chghy service_location! 06p8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 218.000 199.000 0.400 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #13192-0335fp PRED entity: 0335fp PRED relation: award_winner! PRED expected values: 092t4b => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 103): 05c1t6z (0.22 #156, 0.17 #15, 0.03 #1848), 03nnm4t (0.17 #74, 0.11 #215, 0.03 #1061), 02q690_ (0.17 #65, 0.11 #206, 0.03 #1757), 07z31v (0.17 #31, 0.11 #172, 0.03 #1018), 02wzl1d (0.17 #11, 0.11 #152, 0.02 #575), 0418154 (0.17 #108, 0.11 #249, 0.02 #1095), 027n06w (0.17 #73, 0.11 #214, 0.02 #5431), 05zksls (0.17 #35, 0.11 #176, 0.02 #1163), 0drtv8 (0.17 #66, 0.11 #207, 0.01 #1053), 03gt46z (0.17 #63, 0.11 #204, 0.01 #5280) >> Best rule #156 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 03cs_z7; 01vz80y; 06dkzt; 03cs_xw; 02qzjj; >> query: (?x7977, 05c1t6z) <- student(?x1103, ?x7977), award_nominee(?x7977, ?x336), ?x1103 = 01k2wn >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #2308 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 763 *> proper extension: 06_bq1; 01d6jf; *> query: (?x7977, 092t4b) <- award_winner(?x820, ?x7977), film(?x7977, ?x69) *> conf = 0.04 ranks of expected_values: 24 EVAL 0335fp award_winner! 092t4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 100.000 100.000 0.222 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13191-01qqwp9 PRED entity: 01qqwp9 PRED relation: group! PRED expected values: 01w724 => 88 concepts (46 used for prediction) PRED predicted values (max 10 best out of 493): 01wg6y (0.33 #159, 0.12 #1509, 0.12 #1123), 0lzkm (0.25 #255, 0.20 #448, 0.17 #643), 03j0br4 (0.25 #1004, 0.14 #3508, 0.11 #1968), 09889g (0.25 #1050, 0.14 #3554, 0.09 #2593), 01zmpg (0.25 #997, 0.14 #3501, 0.09 #2540), 01vrx3g (0.25 #970, 0.11 #1934, 0.11 #1547), 01w724 (0.22 #1972, 0.18 #2551, 0.17 #2936), 01vs4ff (0.20 #501, 0.11 #2045, 0.10 #2430), 01vs4f3 (0.20 #538, 0.11 #2082, 0.10 #2467), 017g21 (0.20 #511, 0.11 #2055, 0.10 #2440) >> Best rule #159 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 05563d; >> query: (?x3207, 01wg6y) <- group(?x3321, ?x3207), group(?x1092, ?x3207), ?x1092 = 02whj, group(?x1750, ?x3207), people(?x5056, ?x3321), profession(?x3321, ?x131), artists(?x284, ?x3321), ?x1750 = 02hnl, instrumentalists(?x228, ?x3321) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1972 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 7 *> proper extension: 01wv9xn; 07mvp; 0134wr; 0cfgd; *> query: (?x3207, 01w724) <- group(?x4288, ?x3207), group(?x1092, ?x3207), profession(?x1092, ?x2348), ?x2348 = 0nbcg, group(?x227, ?x3207), artists(?x505, ?x1092), place_of_death(?x1092, ?x1523), award(?x4288, ?x2139), ?x227 = 0342h *> conf = 0.22 ranks of expected_values: 7 EVAL 01qqwp9 group! 01w724 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 88.000 46.000 0.333 http://example.org/music/group_member/membership./music/group_membership/group #13190-0g8st4 PRED entity: 0g8st4 PRED relation: award_nominee PRED expected values: 07lmxq => 79 concepts (38 used for prediction) PRED predicted values (max 10 best out of 669): 01gq0b (0.81 #48751, 0.81 #25535, 0.81 #48752), 0h1nt (0.81 #48751, 0.81 #25535, 0.81 #48752), 05dbf (0.81 #48751, 0.81 #25535, 0.81 #48752), 07lmxq (0.81 #48751, 0.81 #25535, 0.80 #76611), 0g8st4 (0.50 #1525, 0.24 #6965, 0.16 #3846), 014g22 (0.43 #3275, 0.25 #5596, 0.24 #6965), 034zc0 (0.41 #3673, 0.24 #5994, 0.24 #6965), 057_yx (0.38 #4519, 0.24 #6965, 0.20 #6840), 02ch1w (0.38 #3685, 0.24 #6965, 0.20 #6006), 02jsgf (0.38 #3259, 0.24 #6965, 0.20 #5580) >> Best rule #48751 for best value: >> intensional similarity = 4 >> extensional distance = 1479 >> proper extension: 0280mv7; >> query: (?x6708, ?x525) <- award_nominee(?x1890, ?x6708), award_nominee(?x525, ?x6708), type_of_union(?x6708, ?x1873), award(?x1890, ?x401) >> conf = 0.81 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 0g8st4 award_nominee 07lmxq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 79.000 38.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13189-0jqn5 PRED entity: 0jqn5 PRED relation: film_release_region PRED expected values: 0d0vqn 01znc_ 01mjq => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 126): 09c7w0 (0.94 #7674, 0.93 #2134, 0.93 #7532), 0d0vqn (0.92 #2422, 0.89 #1002, 0.89 #3700), 01znc_ (0.82 #2450, 0.76 #1172, 0.75 #1740), 0d060g (0.77 #2421, 0.76 #1143, 0.67 #1711), 04gzd (0.62 #2425, 0.55 #1005, 0.54 #1715), 01p1v (0.60 #2461, 0.57 #1041, 0.51 #1183), 01mjq (0.59 #1175, 0.56 #1743, 0.54 #2453), 09pmkv (0.57 #1017, 0.46 #1727, 0.43 #2437), 03rk0 (0.57 #2465, 0.49 #1755, 0.49 #1187), 015qh (0.57 #2449, 0.55 #1171, 0.54 #1739) >> Best rule #7674 for best value: >> intensional similarity = 3 >> extensional distance = 737 >> proper extension: 0170z3; 047q2k1; 04ddm4; 0fgpvf; 02c6d; 07qg8v; 026n4h6; 02r8hh_; 050xxm; 0fvr1; ... >> query: (?x1452, 09c7w0) <- nominated_for(?x143, ?x1452), currency(?x1452, ?x170), film_release_region(?x1452, ?x87) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #2422 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 134 *> proper extension: 0gx1bnj; 0gj8t_b; 07x4qr; 0gj8nq2; 0db94w; 0glqh5_; 0h95zbp; 0ggbfwf; 0bq6ntw; 07s3m4g; ... *> query: (?x1452, 0d0vqn) <- film_release_region(?x1452, ?x2843), ?x2843 = 016wzw, genre(?x1452, ?x53) *> conf = 0.92 ranks of expected_values: 2, 3, 7 EVAL 0jqn5 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 83.000 83.000 0.936 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jqn5 film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.936 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jqn5 film_release_region 0d0vqn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.936 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13188-09f07 PRED entity: 09f07 PRED relation: location! PRED expected values: 08d6bd => 223 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2244): 05yvfd (0.77 #196278, 0.76 #100655, 0.53 #183695), 01zt10 (0.33 #2457, 0.22 #7489, 0.17 #17555), 0pj8m (0.33 #1608, 0.08 #16706, 0.05 #153499), 03vrnh (0.22 #6540, 0.17 #14090, 0.13 #29187), 02xgdv (0.18 #8964, 0.14 #3931, 0.13 #24061), 01yzhn (0.18 #12197, 0.14 #19746, 0.10 #37359), 09yrh (0.18 #10979, 0.14 #36141, 0.09 #41174), 01_f_5 (0.18 #11339, 0.10 #36501, 0.09 #41534), 0jw67 (0.18 #10758, 0.10 #35920, 0.09 #40953), 02jt1k (0.18 #10364, 0.10 #35526, 0.09 #40559) >> Best rule #196278 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 0mnzd; 01llj3; >> query: (?x11801, ?x9465) <- contains(?x11801, ?x11800), place_of_birth(?x9465, ?x11801), location(?x9465, ?x7412), people(?x7838, ?x9465) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #6341 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0n84k; *> query: (?x11801, 08d6bd) <- country(?x11801, ?x2146), place_of_birth(?x9465, ?x11801), ?x2146 = 03rk0, people(?x7838, ?x9465) *> conf = 0.11 ranks of expected_values: 64 EVAL 09f07 location! 08d6bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 223.000 92.000 0.767 http://example.org/people/person/places_lived./people/place_lived/location #13187-06c1y PRED entity: 06c1y PRED relation: administrative_parent PRED expected values: 02j71 => 202 concepts (94 used for prediction) PRED predicted values (max 10 best out of 48): 02j71 (0.84 #9981, 0.83 #12327, 0.83 #10672), 09c7w0 (0.51 #6206, 0.38 #10387, 0.33 #11626), 09b69 (0.29 #3994, 0.25 #6343, 0.22 #9404), 02qkt (0.29 #3994, 0.25 #6343, 0.22 #9404), 02j9z (0.29 #3994, 0.25 #6343, 0.22 #9404), 03rjj (0.08 #11217, 0.04 #3861, 0.03 #10110), 06c1y (0.07 #582, 0.03 #2097, 0.03 #12591), 059rby (0.07 #9269, 0.07 #9552, 0.07 #9411), 07ssc (0.07 #1793, 0.05 #3731, 0.04 #3868), 0f8l9c (0.07 #3875) >> Best rule #9981 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 05v10; 03h2c; 05l8y; 05c74; >> query: (?x1536, 02j71) <- country(?x471, ?x1536), adjoins(?x1003, ?x1536), film_release_region(?x124, ?x1536), organization(?x1536, ?x127) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06c1y administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 94.000 0.835 http://example.org/base/aareas/schema/administrative_area/administrative_parent #13186-01pj3h PRED entity: 01pj3h PRED relation: gender PRED expected values: 05zppz => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #11, 0.86 #13, 0.84 #9), 02zsn (0.47 #20, 0.46 #18, 0.46 #185) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 0c11mj; 01qx13; 071pf2; 03lh3v; 0457w0; 02rnns; 0frmb1; 040j2_; 03xyp_; 07zr66; ... >> query: (?x11543, 05zppz) <- type_of_union(?x11543, ?x566), ?x566 = 04ztj, athlete(?x4833, ?x11543) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pj3h gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.901 http://example.org/people/person/gender #13185-05kr_ PRED entity: 05kr_ PRED relation: religion PRED expected values: 03j6c 06yyp => 234 concepts (234 used for prediction) PRED predicted values (max 10 best out of 28): 0631_ (0.83 #575, 0.83 #545, 0.77 #395), 051kv (0.83 #573, 0.81 #543, 0.80 #785), 019cr (0.83 #578, 0.81 #548, 0.76 #940), 04pk9 (0.79 #554, 0.77 #584, 0.72 #826), 05w5d (0.79 #558, 0.74 #588, 0.72 #830), 021_0p (0.60 #553, 0.57 #583, 0.56 #825), 01y0s9 (0.57 #576, 0.57 #788, 0.57 #396), 01s5nb (0.43 #590, 0.40 #560, 0.39 #199), 058x5 (0.37 #753, 0.36 #572, 0.34 #542), 02t7t (0.29 #407, 0.28 #557, 0.26 #196) >> Best rule #575 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 03gh4; >> query: (?x1905, 0631_) <- contains(?x1905, ?x1196), state_province_region(?x2327, ?x1905), district_represented(?x3473, ?x1905), currency(?x1905, ?x2244) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #2927 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 110 *> proper extension: 0hkt6; *> query: (?x1905, ?x1624) <- religion(?x1905, ?x1985), religion(?x3634, ?x1985), contains(?x8260, ?x3634), religion(?x3634, ?x1624) *> conf = 0.23 ranks of expected_values: 12, 22 EVAL 05kr_ religion 06yyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 234.000 234.000 0.830 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05kr_ religion 03j6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 234.000 234.000 0.830 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #13184-0yldt PRED entity: 0yldt PRED relation: student PRED expected values: 0tfc => 158 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1797): 041c4 (0.25 #7130, 0.05 #21745, 0.05 #25921), 0kvqv (0.17 #6990, 0.15 #19517, 0.09 #52927), 07g2b (0.17 #6338, 0.10 #20953, 0.10 #18865), 01tdnyh (0.17 #7152, 0.10 #21767, 0.06 #107361), 0cbgl (0.17 #8344, 0.10 #20871, 0.06 #50105), 0l6qt (0.17 #6279, 0.10 #20894, 0.06 #48040), 09gnn (0.17 #8033, 0.05 #22648, 0.05 #26824), 07s93v (0.15 #8600, 0.11 #2335, 0.11 #14861), 0ff3y (0.15 #22942, 0.12 #50088, 0.11 #4151), 0gd5z (0.15 #19172, 0.09 #52582, 0.08 #6645) >> Best rule #7130 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 01cyd5; 0dzbl; >> query: (?x13424, 041c4) <- student(?x13424, ?x2608), category(?x13424, ?x134), ?x134 = 08mbj5d, influenced_by(?x2608, ?x2161), politician(?x9679, ?x2608), influenced_by(?x7746, ?x2608) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #22882 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 18 *> proper extension: 07tgn; 0hd7j; 05zl0; *> query: (?x13424, 0tfc) <- institution(?x1368, ?x13424), institution(?x734, ?x13424), student(?x13424, ?x11018), currency(?x13424, ?x1099), ?x734 = 04zx3q1, ?x1368 = 014mlp, influenced_by(?x5345, ?x11018) *> conf = 0.10 ranks of expected_values: 86 EVAL 0yldt student 0tfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 158.000 101.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #13183-01l8t8 PRED entity: 01l8t8 PRED relation: institution! PRED expected values: 04zx3q1 02h4rq6 => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 18): 02h4rq6 (0.75 #105, 0.74 #65, 0.73 #185), 03bwzr4 (0.75 #11, 0.74 #73, 0.68 #113), 019v9k (0.64 #68, 0.64 #48, 0.63 #108), 027f2w (0.59 #69, 0.46 #109, 0.44 #7), 04zx3q1 (0.51 #64, 0.50 #2, 0.47 #104), 07s6fsf (0.51 #103, 0.50 #1, 0.46 #63), 013zdg (0.36 #67, 0.32 #107, 0.27 #26), 03mkk4 (0.32 #30, 0.31 #9, 0.28 #71), 071tyz (0.28 #636, 0.14 #50, 0.12 #90), 02m4yg (0.28 #636, 0.12 #95, 0.10 #75) >> Best rule #105 for best value: >> intensional similarity = 2 >> extensional distance = 55 >> proper extension: 03_c8p; >> query: (?x10659, 02h4rq6) <- organization(?x10659, ?x5487), organization(?x346, ?x10659) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 01l8t8 institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.754 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01l8t8 institution! 04zx3q1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 60.000 60.000 0.754 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13182-03y1mlp PRED entity: 03y1mlp PRED relation: costume_design_by! PRED expected values: 0bpm4yw => 94 concepts (37 used for prediction) PRED predicted values (max 10 best out of 190): 09d38d (0.08 #756, 0.07 #946, 0.05 #376), 015gm8 (0.05 #379, 0.05 #569, 0.04 #759), 033dbw (0.05 #378, 0.05 #568, 0.04 #758), 01c9d (0.05 #377, 0.05 #567, 0.04 #757), 04fjzv (0.05 #375, 0.05 #565, 0.04 #755), 01gvsn (0.05 #372, 0.05 #562, 0.04 #752), 02wwmhc (0.05 #371, 0.05 #561, 0.04 #751), 0k419 (0.05 #370, 0.05 #560, 0.04 #750), 01xlqd (0.05 #367, 0.05 #557, 0.04 #747), 0g_zyp (0.05 #365, 0.05 #555, 0.04 #745) >> Best rule #756 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 02w0dc0; 0ft7sr; 0gl88b; 05x2t7; 0c6g29; 0dck27; 0bytkq; 0bytfv; 02pqgt8; 04vzv4; ... >> query: (?x1500, 09d38d) <- award(?x1500, ?x507), nationality(?x1500, ?x512), costume_design_by(?x2006, ?x1500), award_winner(?x2006, ?x669) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03y1mlp costume_design_by! 0bpm4yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 37.000 0.083 http://example.org/film/film/costume_design_by #13181-06jnvs PRED entity: 06jnvs PRED relation: tv_program PRED expected values: 039cq4 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 80): 039cq4 (0.19 #708, 0.17 #1540, 0.15 #1457), 01b66d (0.13 #932, 0.09 #1431, 0.08 #1514), 0gj50 (0.11 #941, 0.07 #1523, 0.07 #1440), 01b66t (0.10 #947, 0.07 #614, 0.06 #1446), 0828jw (0.10 #620, 0.07 #703, 0.07 #953), 01j7mr (0.08 #605, 0.07 #938, 0.05 #1520), 0358x_ (0.08 #919, 0.05 #1418, 0.05 #1501), 0phrl (0.06 #937, 0.04 #1436, 0.04 #1519), 0d68qy (0.06 #15, 0.04 #596, 0.03 #264), 01ft14 (0.06 #66, 0.03 #315, 0.02 #564) >> Best rule #708 for best value: >> intensional similarity = 3 >> extensional distance = 92 >> proper extension: 04bs3j; 013cr; 02jm0n; 03d_zl4; 04pg29; 02dlfh; 0jvtp; 01z0lb; >> query: (?x3895, 039cq4) <- student(?x3439, ?x3895), award(?x3895, ?x2016), tv_program(?x3895, ?x1631) >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06jnvs tv_program 039cq4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.191 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #13180-05b1610 PRED entity: 05b1610 PRED relation: award_winner PRED expected values: 058frd => 57 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1619): 0127m7 (0.50 #7899, 0.38 #12828, 0.33 #10363), 01qg7c (0.33 #11897, 0.33 #9433, 0.33 #2041), 0f502 (0.33 #8356, 0.33 #964, 0.25 #13285), 0d608 (0.33 #4106, 0.29 #27113, 0.29 #41908), 01vvycq (0.33 #7513, 0.29 #27113, 0.29 #32043), 02r251z (0.33 #1563, 0.25 #6491, 0.17 #24649), 030_3z (0.33 #1014, 0.25 #5942, 0.17 #24649), 017s11 (0.33 #94, 0.25 #5022, 0.17 #24649), 05qd_ (0.33 #166, 0.25 #5094, 0.17 #24649), 0146mv (0.33 #2285, 0.25 #7213, 0.17 #24649) >> Best rule #7899 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 07cbcy; 05q8pss; >> query: (?x688, 0127m7) <- award(?x6334, ?x688), award(?x4032, ?x688), nominated_for(?x688, ?x103), ?x6334 = 0kvbl6, nominated_for(?x102, ?x4032), award(?x702, ?x688) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #24649 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 138 *> proper extension: 02581c; 026mml; *> query: (?x688, ?x1532) <- award(?x1387, ?x688), award_winner(?x688, ?x3692), story_by(?x626, ?x1387), award_winner(?x3692, ?x1532) *> conf = 0.17 ranks of expected_values: 105 EVAL 05b1610 award_winner 058frd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 57.000 23.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #13179-03_js PRED entity: 03_js PRED relation: taxonomy PRED expected values: 04n6k => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.50 #2, 0.40 #14, 0.36 #22) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 07cbs; >> query: (?x8991, 04n6k) <- profession(?x8991, ?x3342), peers(?x8991, ?x5254), basic_title(?x8991, ?x265), student(?x3439, ?x8991) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_js taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.500 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #13178-01tpvt PRED entity: 01tpvt PRED relation: organization! PRED expected values: 060c4 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 9): 060c4 (0.56 #431, 0.56 #405, 0.55 #444), 0dq_5 (0.37 #269, 0.33 #48, 0.30 #256), 07xl34 (0.37 #219, 0.37 #180, 0.36 #167), 0hm4q (0.15 #151, 0.15 #86, 0.15 #99), 05k17c (0.09 #306, 0.08 #410, 0.08 #475), 05c0jwl (0.05 #291, 0.04 #369, 0.03 #421), 0dq3c (0.03 #27, 0.02 #53), 08jcfy (0.02 #298, 0.02 #402, 0.02 #467), 04n1q6 (0.01 #162, 0.01 #175, 0.01 #279) >> Best rule #431 for best value: >> intensional similarity = 3 >> extensional distance = 469 >> proper extension: 05d9y_; 01v3k2; 02jztz; >> query: (?x6811, 060c4) <- contains(?x774, ?x6811), category(?x6811, ?x134), major_field_of_study(?x6811, ?x742) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tpvt organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.565 http://example.org/organization/role/leaders./organization/leadership/organization #13177-05mph PRED entity: 05mph PRED relation: religion PRED expected values: 02t7t => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 25): 03_gx (0.46 #2450, 0.42 #449, 0.41 #7), 0flw86 (0.46 #2450, 0.39 #1227, 0.37 #1540), 058x5 (0.46 #2450, 0.35 #158, 0.35 #132), 01s5nb (0.40 #249, 0.38 #41, 0.38 #171), 092bf5 (0.28 #450, 0.26 #528, 0.25 #919), 02t7t (0.25 #299, 0.25 #247, 0.23 #13), 072w0 (0.20 #484, 0.19 #302, 0.19 #250), 03j6c (0.09 #1550, 0.08 #1237, 0.08 #922), 04t_mf (0.04 #1556, 0.02 #459, 0.02 #537), 078tg (0.03 #1248, 0.03 #1561, 0.02 #1090) >> Best rule #2450 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 04tr1; >> query: (?x6521, ?x109) <- adjoins(?x3634, ?x6521), jurisdiction_of_office(?x900, ?x6521), religion(?x3634, ?x109) >> conf = 0.46 => this is the best rule for 3 predicted values *> Best rule #299 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 05j49; *> query: (?x6521, 02t7t) <- contains(?x6521, ?x859), country(?x6521, ?x94), district_represented(?x605, ?x6521) *> conf = 0.25 ranks of expected_values: 6 EVAL 05mph religion 02t7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 184.000 184.000 0.457 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #13176-015h31 PRED entity: 015h31 PRED relation: profession! PRED expected values: 01c58j 09b0xs 07d370 0c8hct 0250f 01vl17 => 54 concepts (24 used for prediction) PRED predicted values (max 10 best out of 4028): 0mdqp (0.71 #46517, 0.62 #59153, 0.57 #54941), 0bxtg (0.71 #46442, 0.62 #59078, 0.57 #54866), 0250f (0.71 #57243, 0.62 #61455, 0.50 #36185), 03b78r (0.71 #48731, 0.50 #61367, 0.43 #57155), 04cl1 (0.71 #47822, 0.50 #60458, 0.43 #56246), 0187y5 (0.71 #46498, 0.38 #59134, 0.33 #17018), 05wm88 (0.62 #62757, 0.57 #58545, 0.57 #50121), 02b29 (0.62 #61193, 0.57 #56981, 0.57 #48557), 015pxr (0.62 #59566, 0.57 #55354, 0.57 #46930), 09px1w (0.62 #61620, 0.57 #57408, 0.57 #48984) >> Best rule #46517 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 02hrh1q; 0np9r; >> query: (?x1966, 0mdqp) <- profession(?x11484, ?x1966), profession(?x11413, ?x1966), profession(?x5309, ?x1966), location(?x11413, ?x1659), story_by(?x3752, ?x11413), student(?x5981, ?x11484), location(?x11484, ?x739), ?x5309 = 03v40v >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #57243 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 5 *> proper extension: 03gjzk; *> query: (?x1966, 0250f) <- profession(?x13500, ?x1966), profession(?x11484, ?x1966), profession(?x11413, ?x1966), location(?x11413, ?x1659), profession(?x11413, ?x955), story_by(?x3752, ?x11413), ?x11484 = 0488g9, ?x955 = 0n1h, influenced_by(?x11413, ?x11104), nationality(?x13500, ?x94) *> conf = 0.71 ranks of expected_values: 3, 17, 42, 219, 224, 481 EVAL 015h31 profession! 01vl17 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 54.000 24.000 0.714 http://example.org/people/person/profession EVAL 015h31 profession! 0250f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 54.000 24.000 0.714 http://example.org/people/person/profession EVAL 015h31 profession! 0c8hct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 24.000 0.714 http://example.org/people/person/profession EVAL 015h31 profession! 07d370 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 54.000 24.000 0.714 http://example.org/people/person/profession EVAL 015h31 profession! 09b0xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 54.000 24.000 0.714 http://example.org/people/person/profession EVAL 015h31 profession! 01c58j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 54.000 24.000 0.714 http://example.org/people/person/profession #13175-02qhlwd PRED entity: 02qhlwd PRED relation: film_crew_role PRED expected values: 0dxtw => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 25): 0dxtw (0.42 #208, 0.39 #1580, 0.38 #1177), 01pvkk (0.31 #978, 0.31 #109, 0.30 #2051), 0215hd (0.19 #215, 0.14 #1519, 0.14 #681), 0d2b38 (0.14 #222, 0.12 #688, 0.12 #2310), 02rh1dz (0.14 #372, 0.14 #207, 0.13 #1176), 01xy5l_ (0.14 #211, 0.13 #12, 0.12 #677), 089fss (0.13 #5, 0.12 #2310, 0.11 #598), 089g0h (0.13 #216, 0.12 #2310, 0.12 #682), 02_n3z (0.13 #200, 0.12 #2310, 0.11 #598), 015h31 (0.12 #2310, 0.11 #598, 0.10 #1469) >> Best rule #208 for best value: >> intensional similarity = 4 >> extensional distance = 203 >> proper extension: 047svrl; >> query: (?x4188, 0dxtw) <- film_crew_role(?x4188, ?x137), featured_film_locations(?x4188, ?x1646), executive_produced_by(?x4188, ?x3042), film(?x1019, ?x4188) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qhlwd film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.420 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13174-02nfjp PRED entity: 02nfjp PRED relation: category PRED expected values: 08mbj5d => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.76 #10, 0.74 #12, 0.72 #14) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 259 >> proper extension: 01vsxdm; 05crg7; 028qdb; 03_0p; 02pt7h_; 0gr69; >> query: (?x5106, 08mbj5d) <- award(?x5106, ?x5923), role(?x5106, ?x227), award_winner(?x11702, ?x5106) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02nfjp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.759 http://example.org/common/topic/webpage./common/webpage/category #13173-0ywrc PRED entity: 0ywrc PRED relation: film_release_region PRED expected values: 02vzc 082fr => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 133): 0f8l9c (0.94 #1319, 0.90 #2299, 0.89 #3110), 06mkj (0.87 #1358, 0.85 #2825, 0.84 #3149), 02vzc (0.87 #1352, 0.84 #3143, 0.82 #3633), 0345h (0.77 #5407, 0.76 #2799, 0.76 #1332), 03h64 (0.74 #2837, 0.71 #5445, 0.70 #3651), 03gj2 (0.74 #1323, 0.72 #5398, 0.70 #2303), 0154j (0.70 #2769, 0.67 #1302, 0.66 #5377), 015fr (0.69 #5390, 0.67 #1315, 0.66 #2782), 035qy (0.69 #2801, 0.69 #5409, 0.63 #1334), 0d060g (0.68 #2771, 0.64 #5379, 0.58 #1304) >> Best rule #1319 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 02r8hh_; 026njb5; 02vr3gz; 07l50vn; 08j7lh; 065ym0c; 023vcd; 072hx4; >> query: (?x3157, 0f8l9c) <- titles(?x53, ?x3157), film_release_region(?x3157, ?x1229), award(?x3157, ?x198), ?x1229 = 059j2 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #1352 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 97 *> proper extension: 02r8hh_; 026njb5; 02vr3gz; 07l50vn; 08j7lh; 065ym0c; 023vcd; 072hx4; *> query: (?x3157, 02vzc) <- titles(?x53, ?x3157), film_release_region(?x3157, ?x1229), award(?x3157, ?x198), ?x1229 = 059j2 *> conf = 0.87 ranks of expected_values: 3, 40 EVAL 0ywrc film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 135.000 135.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ywrc film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 135.000 135.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13172-0dcsx PRED entity: 0dcsx PRED relation: people PRED expected values: 06z4wj 01dkpb => 67 concepts (35 used for prediction) PRED predicted values (max 10 best out of 813): 024qwq (0.40 #3803, 0.20 #13258, 0.17 #16636), 016z51 (0.33 #6286, 0.33 #5611, 0.29 #9664), 014z8v (0.33 #7562, 0.33 #5536, 0.22 #12291), 01d0b1 (0.33 #6464, 0.29 #9842, 0.29 #9167), 0jrny (0.33 #5500, 0.22 #12255, 0.20 #12930), 0cgbf (0.33 #5687, 0.20 #13793, 0.20 #3662), 0gyy0 (0.33 #5772, 0.20 #3747, 0.17 #15906), 016gkf (0.33 #5604, 0.20 #3579, 0.17 #15738), 05v45k (0.33 #6007, 0.20 #3982, 0.17 #16141), 0bxfmk (0.33 #809, 0.11 #11610, 0.11 #10934) >> Best rule #3803 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0hg11; >> query: (?x5801, 024qwq) <- risk_factors(?x5801, ?x8523), risk_factors(?x6483, ?x5801), ?x6483 = 02bft, risk_factors(?x8523, ?x8023) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #11970 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 012hw; *> query: (?x5801, 01dkpb) <- people(?x5801, ?x13488), people(?x5801, ?x1606), participant(?x1607, ?x1606), place_of_birth(?x1607, ?x3014), profession(?x13488, ?x319), nationality(?x1607, ?x94), people(?x1050, ?x1607), award_winner(?x2060, ?x1607) *> conf = 0.11 ranks of expected_values: 415 EVAL 0dcsx people 01dkpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 67.000 35.000 0.400 http://example.org/people/cause_of_death/people EVAL 0dcsx people 06z4wj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 35.000 0.400 http://example.org/people/cause_of_death/people #13171-04ynx7 PRED entity: 04ynx7 PRED relation: genre PRED expected values: 0c3351 => 84 concepts (70 used for prediction) PRED predicted values (max 10 best out of 95): 07s9rl0 (0.92 #3994, 0.60 #1878, 0.60 #1996), 05p553 (0.37 #4698, 0.37 #2940, 0.37 #4464), 01hmnh (0.33 #2480, 0.23 #834, 0.22 #132), 06n90 (0.32 #2124, 0.26 #830, 0.25 #2476), 02l7c8 (0.30 #4007, 0.29 #4944, 0.28 #3302), 03g3w (0.29 #22, 0.22 #139, 0.13 #1076), 0556j8 (0.25 #274, 0.05 #2153, 0.04 #976), 0lsxr (0.23 #1414, 0.22 #2121, 0.21 #1531), 06cvj (0.19 #353, 0.15 #470, 0.14 #587), 0hcr (0.18 #2486, 0.09 #4716, 0.07 #5419) >> Best rule #3994 for best value: >> intensional similarity = 4 >> extensional distance = 886 >> proper extension: 05jyb2; 0dmn0x; >> query: (?x9872, 07s9rl0) <- nominated_for(?x2214, ?x9872), genre(?x9872, ?x8280), titles(?x8280, ?x11544), ?x11544 = 08c4yn >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #971 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 89 *> proper extension: 02q7yfq; 043tvp3; 06r2h; *> query: (?x9872, 0c3351) <- film(?x338, ?x9872), film_crew_role(?x9872, ?x2178), ?x2178 = 01pvkk, crewmember(?x9872, ?x2887) *> conf = 0.05 ranks of expected_values: 37 EVAL 04ynx7 genre 0c3351 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 84.000 70.000 0.922 http://example.org/film/film/genre #13170-02k6hp PRED entity: 02k6hp PRED relation: symptom_of! PRED expected values: 0j5fv => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 59): 012qjw (0.54 #1253, 0.48 #1212, 0.46 #788), 0brgy (0.52 #1211, 0.40 #876, 0.36 #1190), 0gxb2 (0.46 #1254, 0.33 #180, 0.32 #1213), 0j5fv (0.42 #1250, 0.38 #934, 0.33 #1033), 02tfl8 (0.33 #22, 0.30 #479, 0.25 #930), 08g5q7 (0.33 #13, 0.30 #479, 0.25 #72), 0f3kl (0.33 #36, 0.30 #479, 0.21 #1051), 0dq9p (0.33 #25, 0.08 #711, 0.06 #40), 01l2m3 (0.33 #24, 0.08 #710, 0.06 #40), 0hgxh (0.30 #479, 0.25 #89, 0.25 #51) >> Best rule #1253 for best value: >> intensional similarity = 9 >> extensional distance = 24 >> proper extension: 024c2; >> query: (?x10199, 012qjw) <- symptom_of(?x13099, ?x10199), symptom_of(?x10717, ?x10199), symptom_of(?x10717, ?x8675), symptom_of(?x10717, ?x6260), ?x8675 = 01gkcc, risk_factors(?x6260, ?x7139), people(?x6260, ?x510), symptom_of(?x13099, ?x6781), ?x6781 = 035482 >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1250 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 24 *> proper extension: 024c2; *> query: (?x10199, 0j5fv) <- symptom_of(?x13099, ?x10199), symptom_of(?x10717, ?x10199), symptom_of(?x10717, ?x8675), symptom_of(?x10717, ?x6260), ?x8675 = 01gkcc, risk_factors(?x6260, ?x7139), people(?x6260, ?x510), symptom_of(?x13099, ?x6781), ?x6781 = 035482 *> conf = 0.42 ranks of expected_values: 4 EVAL 02k6hp symptom_of! 0j5fv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 84.000 84.000 0.538 http://example.org/medicine/symptom/symptom_of #13169-070m6c PRED entity: 070m6c PRED relation: legislative_sessions! PRED expected values: 0d3qd0 03txms => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 17): 0d3qd0 (0.75 #253, 0.71 #176, 0.71 #154), 03txms (0.60 #101, 0.50 #260, 0.50 #202), 02mjmr (0.50 #307, 0.50 #306, 0.46 #348), 06hx2 (0.50 #307, 0.50 #306, 0.46 #348), 0dq2k (0.25 #431, 0.20 #374, 0.18 #333), 01mvpv (0.23 #304, 0.18 #345, 0.17 #368), 0rlz (0.20 #376, 0.17 #433, 0.17 #358), 042fk (0.17 #444, 0.15 #387, 0.12 #462), 03_nq (0.12 #439, 0.12 #457, 0.12 #476), 0fd_1 (0.11 #361, 0.10 #379, 0.09 #417) >> Best rule #253 for best value: >> intensional similarity = 31 >> extensional distance = 10 >> proper extension: 02glc4; >> query: (?x653, 0d3qd0) <- legislative_sessions(?x6742, ?x653), legislative_sessions(?x5266, ?x653), district_represented(?x653, ?x7518), district_represented(?x653, ?x3038), district_represented(?x653, ?x2049), district_represented(?x653, ?x1274), district_represented(?x653, ?x1227), contains(?x2049, ?x5554), state_province_region(?x7633, ?x1227), partially_contains(?x3038, ?x10954), administrative_division(?x5893, ?x1227), state(?x8618, ?x1227), contains(?x1227, ?x191), legislative_sessions(?x355, ?x653), time_zones(?x3038, ?x2674), contains(?x3038, ?x2277), capital(?x1274, ?x7328), currency(?x3038, ?x170), first_level_division_of(?x7518, ?x94), company(?x265, ?x7633), category(?x8618, ?x134), ?x10954 = 0lm0n, contact_category(?x7633, ?x897), location(?x2499, ?x1227), location(?x5574, ?x3038), award_winner(?x286, ?x2499), ?x134 = 08mbj5d, award_winner(?x704, ?x2499), ?x6742 = 06bss, religion(?x2049, ?x1363), student(?x1368, ?x5266) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 070m6c legislative_sessions! 03txms CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.750 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 070m6c legislative_sessions! 0d3qd0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.750 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #13168-0g092b PRED entity: 0g092b PRED relation: genre! PRED expected values: 03t97y => 33 concepts (8 used for prediction) PRED predicted values (max 10 best out of 1839): 0jqd3 (0.67 #10520, 0.67 #8643, 0.65 #9360), 03kg2v (0.67 #9861, 0.67 #7984, 0.65 #9360), 01hw5kk (0.67 #11240, 0.67 #10069, 0.65 #9360), 01mgw (0.67 #10721, 0.65 #9360, 0.60 #6973), 049mql (0.67 #10076, 0.65 #9360, 0.60 #6328), 0168ls (0.67 #9618, 0.65 #9360, 0.60 #5870), 034qmv (0.67 #9379, 0.65 #9360, 0.60 #5631), 02wwmhc (0.67 #9242, 0.65 #9360, 0.60 #7371), 07nt8p (0.67 #7857, 0.65 #9360, 0.60 #5986), 029jt9 (0.67 #10928, 0.65 #9360, 0.60 #7180) >> Best rule #10520 for best value: >> intensional similarity = 19 >> extensional distance = 4 >> proper extension: 02kdv5l; >> query: (?x5199, 0jqd3) <- genre(?x7214, ?x5199), genre(?x5198, ?x5199), ?x7214 = 02dr9j, genre(?x5198, ?x1805), genre(?x5198, ?x811), produced_by(?x5198, ?x12439), film_art_direction_by(?x5198, ?x12512), ?x1805 = 01g6gs, genre(?x5128, ?x811), genre(?x4273, ?x811), genre(?x4087, ?x811), genre(?x1072, ?x811), genre(?x508, ?x811), genre(?x50, ?x811), ?x5128 = 08phg9, ?x1072 = 01_mdl, ?x4087 = 01hw5kk, ?x508 = 0ds33, ?x4273 = 062zjtt >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #9360 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 4 *> proper extension: 01jfsb; *> query: (?x5199, ?x66) <- genre(?x7214, ?x5199), genre(?x5198, ?x5199), ?x7214 = 02dr9j, genre(?x5198, ?x1805), genre(?x5198, ?x811), produced_by(?x5198, ?x12439), film_art_direction_by(?x5198, ?x12512), ?x1805 = 01g6gs, genre(?x9872, ?x811), genre(?x7989, ?x811), genre(?x5255, ?x811), genre(?x1450, ?x811), genre(?x66, ?x811), genre(?x50, ?x811), ?x5255 = 01sby_, ?x7989 = 015bpl, award_nominee(?x12512, ?x7876), ?x9872 = 04ynx7, ?x1450 = 0pb33 *> conf = 0.65 ranks of expected_values: 108 EVAL 0g092b genre! 03t97y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 33.000 8.000 0.667 http://example.org/film/film/genre #13167-01kqq7 PRED entity: 01kqq7 PRED relation: films! PRED expected values: 02jq1 => 68 concepts (36 used for prediction) PRED predicted values (max 10 best out of 50): 081pw (0.08 #1092, 0.07 #2033, 0.04 #3), 0fzyg (0.07 #53, 0.06 #208, 0.05 #1142), 06d4h (0.07 #42, 0.06 #2072, 0.06 #1131), 05489 (0.06 #1140, 0.04 #2081, 0.03 #362), 0fx2s (0.05 #1161, 0.05 #383, 0.05 #2102), 0kbq (0.04 #104, 0.03 #259, 0.03 #1193), 0bq3x (0.04 #2059, 0.04 #1118, 0.02 #653), 07s2s (0.04 #2128, 0.03 #1187, 0.03 #409), 03r8gp (0.04 #1178, 0.03 #2119, 0.02 #1804), 01vq3 (0.04 #2070, 0.03 #1129, 0.03 #351) >> Best rule #1092 for best value: >> intensional similarity = 4 >> extensional distance = 249 >> proper extension: 0d1qmz; 01gglm; >> query: (?x10173, 081pw) <- nominated_for(?x3879, ?x10173), film(?x4703, ?x10173), films(?x1804, ?x10173), award(?x3879, ?x500) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kqq7 films! 02jq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 36.000 0.076 http://example.org/film/film_subject/films #13166-011_6p PRED entity: 011_6p PRED relation: role! PRED expected values: 01wsl7c 01wxdn3 => 78 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1238): 050z2 (0.67 #15865, 0.64 #10630, 0.62 #6355), 05qhnq (0.60 #3162, 0.60 #2214, 0.50 #791), 023l9y (0.60 #2113, 0.54 #10180, 0.53 #14467), 0137g1 (0.60 #2017, 0.54 #10084, 0.50 #13419), 01wxdn3 (0.60 #2315, 0.50 #6582, 0.50 #892), 01kvqc (0.60 #2437, 0.50 #3384, 0.33 #68), 03ryks (0.57 #5050, 0.50 #8373, 0.50 #4101), 04bpm6 (0.53 #11938, 0.48 #15747, 0.47 #24322), 02qtywd (0.50 #6616, 0.50 #926, 0.43 #5670), 01vs4ff (0.50 #3629, 0.48 #16937, 0.43 #15984) >> Best rule #15865 for best value: >> intensional similarity = 18 >> extensional distance = 19 >> proper extension: 05842k; >> query: (?x2157, 050z2) <- role(?x2459, ?x2157), role(?x316, ?x2157), role(?x4583, ?x2157), role(?x1574, ?x2157), role(?x960, ?x2157), role(?x2157, ?x315), role(?x9321, ?x1574), performance_role(?x9246, ?x1574), role(?x1574, ?x4917), ?x9246 = 0pk41, ?x4917 = 06w7v, role(?x2459, ?x1473), ?x4583 = 0bmnm, instrumentalists(?x960, ?x248), award_winner(?x724, ?x9321), location(?x9321, ?x362), instrumentalists(?x316, ?x115), role(?x483, ?x316) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2315 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 3 *> proper extension: 01vdm0; *> query: (?x2157, 01wxdn3) <- role(?x5417, ?x2157), role(?x74, ?x2157), role(?x2764, ?x2157), role(?x1574, ?x2157), role(?x1212, ?x2157), role(?x960, ?x2157), role(?x2157, ?x315), ?x1574 = 0l15bq, ?x960 = 04q7r, role(?x5417, ?x569), instrumentalists(?x5417, ?x8246), instrumentalists(?x5417, ?x7937), ?x1212 = 07xzm, ?x2764 = 01s0ps, type_of_union(?x8246, ?x566), role(?x1694, ?x5417), profession(?x8246, ?x1032), group(?x5417, ?x2906), role(?x569, ?x3418), artist(?x2299, ?x8246), artists(?x1928, ?x7937), ?x3418 = 02w4b, role(?x433, ?x74) *> conf = 0.60 ranks of expected_values: 5, 45 EVAL 011_6p role! 01wxdn3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 011_6p role! 01wsl7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 78.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #13165-0gs9p PRED entity: 0gs9p PRED relation: award_winner PRED expected values: 06pj8 01j2xj 0dbbz => 59 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2005): 0151w_ (0.50 #9907, 0.36 #17200, 0.35 #19631), 01v5h (0.39 #29176, 0.31 #17017, 0.30 #29177), 0byfz (0.39 #29176, 0.31 #17017, 0.30 #29177), 0c12h (0.39 #29176, 0.31 #17017, 0.30 #29177), 0m9c1 (0.39 #29176, 0.31 #17017, 0.30 #29177), 09p06 (0.39 #29176, 0.31 #17017, 0.30 #29177), 042kbj (0.39 #29176, 0.31 #17017, 0.30 #29177), 01p1z_ (0.39 #29176, 0.31 #17017, 0.30 #29177), 09xvf7 (0.39 #29176, 0.30 #29177, 0.30 #17016), 0170pk (0.33 #10069, 0.27 #17362, 0.27 #19793) >> Best rule #9907 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 0f_nbyh; >> query: (?x1313, 0151w_) <- nominated_for(?x1313, ?x3133), nominated_for(?x1313, ?x2989), award_winner(?x1313, ?x276), ?x3133 = 07w8fz, award(?x197, ?x1313), ?x2989 = 02vqsll >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #17017 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 018wng; 0gq_d; 0gr07; *> query: (?x1313, ?x6643) <- award(?x6643, ?x1313), ceremony(?x1313, ?x7100), ?x7100 = 0bzmt8, gender(?x6643, ?x231), award_winner(?x1313, ?x276) *> conf = 0.31 ranks of expected_values: 19, 90, 554 EVAL 0gs9p award_winner 0dbbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 59.000 25.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0gs9p award_winner 01j2xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 25.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0gs9p award_winner 06pj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 59.000 25.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #13164-023zsh PRED entity: 023zsh PRED relation: award PRED expected values: 05p09zm => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 236): 09sb52 (0.33 #8140, 0.32 #8545, 0.32 #12190), 0gqy2 (0.25 #165, 0.11 #11910, 0.09 #975), 05ztrmj (0.18 #14986, 0.17 #185, 0.13 #25925), 09sdmz (0.17 #207, 0.13 #25925, 0.13 #27548), 027dtxw (0.17 #4, 0.10 #814, 0.09 #409), 099jhq (0.17 #18, 0.04 #11763, 0.04 #2448), 0ck27z (0.15 #13862, 0.15 #8597, 0.15 #8192), 05p09zm (0.13 #25925, 0.13 #27548, 0.13 #26737), 03c7tr1 (0.13 #25925, 0.13 #27548, 0.13 #26737), 05b4l5x (0.13 #25925, 0.13 #27548, 0.13 #26737) >> Best rule #8140 for best value: >> intensional similarity = 3 >> extensional distance = 1112 >> proper extension: 04lgymt; 05crg7; 01x15dc; 01dwrc; 03m9c8; 02k5sc; 01jkqfz; 015bwt; 01f2q5; >> query: (?x9780, 09sb52) <- award_nominee(?x5788, ?x9780), participant(?x5788, ?x1897), film(?x5788, ?x136) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #25925 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2206 *> proper extension: 04rcr; 02r3zy; 0dvqq; 014hr0; 0249kn; 09b3v; 0163m1; 02vyh; 028qdb; 0hvbj; ... *> query: (?x9780, ?x401) <- award_nominee(?x5788, ?x9780), award(?x5788, ?x401), nationality(?x5788, ?x94) *> conf = 0.13 ranks of expected_values: 8 EVAL 023zsh award 05p09zm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 89.000 89.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13163-0lzcs PRED entity: 0lzcs PRED relation: basic_title PRED expected values: 060bp => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 18): 060c4 (0.42 #165, 0.40 #201, 0.37 #255), 0fkvn (0.31 #328, 0.25 #4, 0.23 #418), 0789n (0.22 #262, 0.18 #424, 0.17 #334), 060bp (0.18 #73, 0.14 #109, 0.13 #127), 01gkgk (0.17 #168, 0.16 #204, 0.15 #456), 0dq3c (0.17 #326, 0.16 #218, 0.15 #254), 0p5vf (0.12 #84, 0.10 #120, 0.09 #318), 09d6p2 (0.09 #135, 0.08 #153, 0.08 #63), 01q24l (0.08 #337, 0.06 #409, 0.05 #427), 0pqc5 (0.08 #329, 0.04 #491, 0.04 #401) >> Best rule #165 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 083p7; 083q7; 083pr; 063vn; 0d06m5; 03f5vvx; 0d3qd0; 0dq2k; 06c97; 012gx2; ... >> query: (?x11411, 060c4) <- nationality(?x11411, ?x1310), profession(?x11411, ?x3342), politician(?x10498, ?x11411), ?x3342 = 04gc2, administrative_parent(?x3302, ?x1310) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #73 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 08849; *> query: (?x11411, 060bp) <- profession(?x11411, ?x3342), entity_involved(?x11431, ?x11411), student(?x13424, ?x11411), religion(?x11411, ?x14467), combatants(?x11431, ?x512) *> conf = 0.18 ranks of expected_values: 4 EVAL 0lzcs basic_title 060bp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 112.000 112.000 0.417 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #13162-043z0 PRED entity: 043z0 PRED relation: contains! PRED expected values: 04rrx => 138 concepts (43 used for prediction) PRED predicted values (max 10 best out of 94): 04rrx (0.76 #22479, 0.66 #35962, 0.66 #38660), 09c7w0 (0.50 #26970, 0.45 #9892, 0.44 #9893), 043z0 (0.44 #9893, 0.36 #37760, 0.34 #17083), 04_1l0v (0.33 #451, 0.27 #7645, 0.24 #27418), 02gt5s (0.31 #1597, 0.29 #2496, 0.25 #3394), 059rby (0.22 #14404, 0.17 #19796, 0.15 #35983), 059g4 (0.17 #463, 0.08 #1363, 0.06 #3160), 07c5l (0.17 #395, 0.08 #1295, 0.06 #3092), 0nj07 (0.15 #1304, 0.14 #2203, 0.12 #3101), 01n7q (0.15 #37838, 0.15 #27945, 0.13 #15361) >> Best rule #22479 for best value: >> intensional similarity = 6 >> extensional distance = 156 >> proper extension: 03s0w; 01mjq; 05rgl; 05k7sb; 050l8; 01n4w; 0jfvs; 05fjy; 050ks; 09krp; ... >> query: (?x10762, ?x1906) <- adjoins(?x10761, ?x10762), adjoins(?x9751, ?x10762), contains(?x1906, ?x9751), administrative_division(?x1106, ?x9751), contains(?x10761, ?x12488), contains(?x1106, ?x1681) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043z0 contains! 04rrx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 43.000 0.761 http://example.org/location/location/contains #13161-02x6dqb PRED entity: 02x6dqb PRED relation: film_release_region PRED expected values: 05r4w 03rjj => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 124): 03rjj (0.82 #327, 0.81 #1938, 0.80 #1294), 05r4w (0.82 #1291, 0.81 #1935, 0.81 #485), 05qhw (0.77 #338, 0.73 #1949, 0.69 #499), 035qy (0.76 #358, 0.75 #519, 0.72 #1969), 0154j (0.76 #326, 0.73 #487, 0.68 #1132), 03spz (0.76 #423, 0.65 #584, 0.61 #2034), 01znc_ (0.70 #1977, 0.69 #366, 0.67 #1333), 05b4w (0.69 #391, 0.68 #2002, 0.65 #1358), 0d060g (0.68 #329, 0.68 #1940, 0.65 #490), 06t2t (0.65 #388, 0.60 #1999, 0.56 #549) >> Best rule #327 for best value: >> intensional similarity = 6 >> extensional distance = 96 >> proper extension: 053tj7; 07s3m4g; >> query: (?x3268, 03rjj) <- film_release_region(?x3268, ?x1355), film_release_region(?x3268, ?x1229), film_release_region(?x3268, ?x512), ?x1355 = 0h7x, ?x1229 = 059j2, ?x512 = 07ssc >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02x6dqb film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.816 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02x6dqb film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.816 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13160-021yc7p PRED entity: 021yc7p PRED relation: crewmember! PRED expected values: 03s6l2 035yn8 => 95 concepts (44 used for prediction) PRED predicted values (max 10 best out of 316): 0hx4y (0.11 #97, 0.04 #407, 0.01 #717), 031t2d (0.09 #372, 0.04 #62, 0.01 #682), 01kff7 (0.09 #358, 0.04 #48, 0.01 #668), 07nxnw (0.09 #545, 0.02 #855), 02yvct (0.08 #932, 0.05 #931, 0.04 #78), 0gwjw0c (0.08 #932, 0.05 #931, 0.03 #1556), 037xlx (0.08 #932, 0.05 #931, 0.03 #1556), 024mpp (0.07 #129, 0.07 #439, 0.02 #749), 033dbw (0.07 #306, 0.07 #616), 04gknr (0.07 #33, 0.07 #343) >> Best rule #97 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 025_nbr; >> query: (?x1585, 0hx4y) <- crewmember(?x599, ?x1585), nationality(?x1585, ?x94), gender(?x1585, ?x231) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 021yc7p crewmember! 035yn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 44.000 0.107 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember EVAL 021yc7p crewmember! 03s6l2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 44.000 0.107 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #13159-0222qb PRED entity: 0222qb PRED relation: split_to! PRED expected values: 09y2k2 => 23 concepts (14 used for prediction) PRED predicted values (max 10 best out of 3): 03_gx (0.03 #265, 0.03 #371, 0.03 #474), 022dp5 (0.03 #278, 0.03 #487, 0.02 #1007), 012f86 (0.02 #598) >> Best rule #265 for best value: >> intensional similarity = 7 >> extensional distance = 35 >> proper extension: 02sch9; >> query: (?x10035, 03_gx) <- people(?x10035, ?x9363), people(?x10035, ?x6844), people(?x10035, ?x1134), nominated_for(?x9363, ?x89), person(?x6093, ?x1134), film(?x6844, ?x814), award(?x6844, ?x3019) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0222qb split_to! 09y2k2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 23.000 14.000 0.027 http://example.org/dataworld/gardening_hint/split_to #13158-0gtvrv3 PRED entity: 0gtvrv3 PRED relation: genre PRED expected values: 07s9rl0 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 117): 07s9rl0 (0.59 #124, 0.57 #616, 0.57 #3698), 02kdv5l (0.39 #495, 0.35 #372, 0.33 #1479), 05p553 (0.37 #743, 0.37 #989, 0.37 #1850), 01jfsb (0.35 #506, 0.32 #383, 0.32 #629), 03k9fj (0.35 #382, 0.33 #1489, 0.33 #1612), 02l7c8 (0.26 #5814, 0.26 #5937, 0.26 #1371), 01hmnh (0.26 #389, 0.25 #1619, 0.24 #1742), 06n90 (0.21 #1737, 0.21 #1614, 0.21 #1491), 0lsxr (0.19 #3337, 0.18 #3461, 0.18 #3584), 060__y (0.19 #1741, 0.18 #1495, 0.18 #1618) >> Best rule #124 for best value: >> intensional similarity = 4 >> extensional distance = 131 >> proper extension: 0209xj; 083skw; 015g28; 0bz3jx; 0symg; >> query: (?x1463, 07s9rl0) <- category(?x1463, ?x134), film_release_distribution_medium(?x1463, ?x81), film(?x5898, ?x1463), film(?x2473, ?x1463) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gtvrv3 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.586 http://example.org/film/film/genre #13157-04v09 PRED entity: 04v09 PRED relation: country! PRED expected values: 06z6r => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 53): 06z6r (0.85 #188, 0.80 #718, 0.80 #1036), 071t0 (0.83 #179, 0.60 #1027, 0.59 #709), 03hr1p (0.79 #180, 0.42 #1028, 0.40 #763), 06f41 (0.74 #171, 0.43 #1019, 0.39 #701), 01lb14 (0.72 #172, 0.48 #702, 0.48 #1020), 07jbh (0.70 #191, 0.39 #1039, 0.37 #774), 064vjs (0.68 #189, 0.36 #1037, 0.33 #772), 02y8z (0.68 #176, 0.31 #1024, 0.29 #759), 06wrt (0.66 #173, 0.36 #1021, 0.36 #756), 0w0d (0.60 #169, 0.40 #275, 0.38 #1017) >> Best rule #188 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 0163v; 0jhd; >> query: (?x9035, 06z6r) <- olympics(?x9035, ?x778), country(?x471, ?x9035), ?x778 = 0kbvb >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04v09 country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.851 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #13156-01k_yf PRED entity: 01k_yf PRED relation: group! PRED expected values: 07xzm => 86 concepts (56 used for prediction) PRED predicted values (max 10 best out of 116): 028tv0 (0.67 #335, 0.57 #416, 0.50 #741), 0l14qv (0.62 #653, 0.60 #817, 0.50 #572), 01vj9c (0.60 #174, 0.27 #2783, 0.27 #2457), 05r5c (0.43 #412, 0.38 #737, 0.38 #655), 03qjg (0.40 #286, 0.40 #205, 0.28 #1752), 0mkg (0.40 #171, 0.20 #252, 0.11 #1065), 07kc_ (0.33 #16, 0.20 #259, 0.12 #664), 026t6 (0.33 #3, 0.12 #651, 0.12 #570), 013y1f (0.20 #999, 0.20 #267, 0.20 #186), 07y_7 (0.20 #977, 0.20 #814, 0.20 #245) >> Best rule #335 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 03g5jw; 03d9d6; 01w5n51; 02k5sc; >> query: (?x5407, 028tv0) <- award(?x5407, ?x247), artists(?x5406, ?x5407), ?x5406 = 07yklv, group(?x1466, ?x5407), artist(?x2299, ?x5407), ?x1466 = 03bx0bm >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #730 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0qmpd; *> query: (?x5407, ?x228) <- artists(?x2996, ?x5407), artists(?x2808, ?x5407), ?x2808 = 0190_q, group(?x227, ?x5407), artists(?x2996, ?x6049), artists(?x2996, ?x2492), ?x2492 = 01tp5bj, role(?x6049, ?x228) *> conf = 0.09 ranks of expected_values: 75 EVAL 01k_yf group! 07xzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 86.000 56.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group #13155-07qy0b PRED entity: 07qy0b PRED relation: gender PRED expected values: 05zppz => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #35, 0.90 #33, 0.89 #49), 02zsn (0.25 #134, 0.25 #160, 0.25 #162) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 01vtmw6; >> query: (?x3371, 05zppz) <- music(?x3752, ?x3371), genre(?x3752, ?x258), type_of_union(?x3371, ?x566), film(?x3242, ?x3752) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07qy0b gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.903 http://example.org/people/person/gender #13154-01j5ql PRED entity: 01j5ql PRED relation: produced_by PRED expected values: 05nn4k => 81 concepts (64 used for prediction) PRED predicted values (max 10 best out of 131): 04g3p5 (0.55 #3109, 0.40 #8564, 0.36 #10906), 0gs1_ (0.33 #227, 0.09 #2557, 0.07 #3336), 02vyw (0.29 #1285, 0.07 #4008, 0.05 #5176), 04y8r (0.25 #845, 0.25 #458, 0.20 #3180), 04pqqb (0.25 #565, 0.09 #2508, 0.07 #3287), 02lf0c (0.14 #1185, 0.11 #1965, 0.09 #2743), 030_3z (0.14 #1325, 0.11 #2105, 0.04 #4436), 0c00lh (0.14 #1352, 0.11 #2132, 0.02 #4075), 04t38b (0.14 #1324, 0.02 #10679, 0.02 #13394), 08d9z7 (0.14 #1429, 0.02 #4930, 0.01 #8831) >> Best rule #3109 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 07nxvj; 0sxmx; 0c9t0y; 0yx1m; >> query: (?x6778, ?x4634) <- film(?x6777, ?x6778), genre(?x6778, ?x53), cinematography(?x6778, ?x185), film(?x6777, ?x7792), ?x7792 = 02rrh1w, film(?x4634, ?x6778) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #4834 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 57 *> proper extension: 03cvwkr; 0gjk1d; 0c_j9x; 059rc; 0946bb; 01jzyf; 016y_f; 02n72k; 011yhm; 05qm9f; ... *> query: (?x6778, 05nn4k) <- film(?x6777, ?x6778), genre(?x6778, ?x604), cinematography(?x6778, ?x185), film(?x6777, ?x7792), ?x604 = 0lsxr, titles(?x4205, ?x7792) *> conf = 0.02 ranks of expected_values: 90 EVAL 01j5ql produced_by 05nn4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 81.000 64.000 0.545 http://example.org/film/film/produced_by #13153-0b7gr2 PRED entity: 0b7gr2 PRED relation: profession PRED expected values: 03gjzk 02krf9 => 76 concepts (70 used for prediction) PRED predicted values (max 10 best out of 40): 03gjzk (0.81 #313, 0.67 #15, 0.60 #164), 02hrh1q (0.68 #2845, 0.66 #1951, 0.66 #1802), 01d_h8 (0.53 #155, 0.50 #6, 0.48 #900), 0np9r (0.50 #319, 0.25 #4918, 0.25 #5962), 02jknp (0.42 #902, 0.27 #604, 0.25 #4918), 0cbd2 (0.38 #305, 0.33 #7, 0.25 #4918), 015h31 (0.31 #326, 0.25 #4918, 0.25 #5962), 018gz8 (0.31 #315, 0.17 #911, 0.17 #7453), 02krf9 (0.25 #325, 0.20 #176, 0.18 #772), 0196pc (0.25 #4918, 0.25 #5962, 0.17 #7453) >> Best rule #313 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0gz5hs; 01_x6v; 03xpf_7; 07_s4b; 021yw7; 03xp8d5; 086nl7; 01_x6d; 01kws3; 03nb5v; ... >> query: (?x11764, 03gjzk) <- award(?x11764, ?x11272), award_nominee(?x11764, ?x2951), profession(?x11764, ?x987), ?x11272 = 0cjcbg >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 0b7gr2 profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 76.000 70.000 0.812 http://example.org/people/person/profession EVAL 0b7gr2 profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 70.000 0.812 http://example.org/people/person/profession #13152-0ph2w PRED entity: 0ph2w PRED relation: influenced_by! PRED expected values: 046lt => 166 concepts (100 used for prediction) PRED predicted values (max 10 best out of 473): 01xwv7 (0.40 #3956, 0.25 #415, 0.20 #14077), 049fgvm (0.40 #3801, 0.25 #260, 0.17 #1777), 01xwqn (0.33 #1951, 0.30 #3975, 0.25 #434), 046lt (0.33 #1623, 0.30 #3647, 0.25 #106), 01j7rd (0.30 #3611, 0.25 #70, 0.18 #13732), 016_mj (0.30 #3594, 0.25 #53, 0.18 #13715), 01x4r3 (0.30 #3914, 0.25 #373, 0.17 #4419), 0126rp (0.30 #3610, 0.25 #69, 0.17 #1586), 02633g (0.30 #3854, 0.25 #313, 0.17 #1830), 01hmk9 (0.30 #3820, 0.25 #279, 0.17 #1796) >> Best rule #3956 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01k9lpl; >> query: (?x4066, 01xwv7) <- influenced_by(?x4065, ?x4066), influenced_by(?x2817, ?x4066), ?x2817 = 0q5hw, currency(?x4065, ?x170) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1623 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 01nczg; 013tjc; *> query: (?x4066, 046lt) <- award(?x4066, ?x4386), influenced_by(?x1593, ?x4066), ?x1593 = 02p21g *> conf = 0.33 ranks of expected_values: 4 EVAL 0ph2w influenced_by! 046lt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 166.000 100.000 0.400 http://example.org/influence/influence_node/influenced_by #13151-0tr3p PRED entity: 0tr3p PRED relation: place PRED expected values: 0tr3p => 99 concepts (64 used for prediction) PRED predicted values (max 10 best out of 152): 0nm6k (0.33 #1546, 0.20 #2579, 0.08 #6707), 0c4kv (0.10 #888, 0.10 #373, 0.08 #27370), 0cf_n (0.10 #774, 0.10 #259, 0.04 #1289), 0tnkg (0.10 #948, 0.10 #433), 0rrwt (0.08 #27370, 0.07 #27889), 0tr3p (0.08 #27370, 0.07 #27889), 050ks (0.08 #27370, 0.07 #27889), 013h9 (0.04 #1341, 0.03 #1858, 0.02 #2374), 0tygl (0.04 #1188, 0.03 #1705, 0.02 #2221), 0mzvm (0.04 #1109, 0.03 #1626, 0.02 #2142) >> Best rule #1546 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0mn8t; 0cf_n; 0xhj2; >> query: (?x8907, ?x7059) <- category(?x8907, ?x134), county_seat(?x7059, ?x8907), currency(?x8907, ?x170), contains(?x7058, ?x7059) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #27370 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 688 *> proper extension: 05r4w; 02j9z; 0hzlz; 01xd9; 0dg3n1; 06pvr; 087vz; 01k6y1; 06jnv; 0h44w; ... *> query: (?x8907, ?x7058) <- location(?x2343, ?x8907), type_of_union(?x2343, ?x566), location(?x2343, ?x7058) *> conf = 0.08 ranks of expected_values: 6 EVAL 0tr3p place 0tr3p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 99.000 64.000 0.333 http://example.org/location/hud_county_place/place #13150-0ct2tf5 PRED entity: 0ct2tf5 PRED relation: featured_film_locations PRED expected values: 030qb3t => 70 concepts (43 used for prediction) PRED predicted values (max 10 best out of 51): 02_286 (0.15 #1944, 0.15 #1462, 0.15 #1222), 030qb3t (0.12 #279, 0.11 #1001, 0.10 #1241), 04jpl (0.10 #249, 0.08 #1451, 0.06 #971), 0rh6k (0.06 #1443, 0.05 #963, 0.04 #1203), 0b90_r (0.05 #4, 0.02 #244, 0.02 #966), 080h2 (0.04 #745, 0.04 #264, 0.04 #504), 035p3 (0.04 #954, 0.04 #713, 0.02 #473), 06y57 (0.04 #824, 0.02 #1545, 0.02 #2027), 01_d4 (0.04 #287, 0.04 #1249, 0.03 #1489), 0d6lp (0.04 #312, 0.02 #552, 0.02 #793) >> Best rule #1944 for best value: >> intensional similarity = 3 >> extensional distance = 363 >> proper extension: 091z_p; 064n1pz; 05dy7p; 04lqvlr; 04lqvly; 02phtzk; 0h95zbp; 03_wm6; 072r5v; 0gy0l_; ... >> query: (?x9421, 02_286) <- genre(?x9421, ?x225), film_crew_role(?x9421, ?x2154), ?x2154 = 01vx2h >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #279 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 46 *> proper extension: 09sh8k; 09m6kg; 0bth54; 03ckwzc; 0pb33; 09txzv; 04n52p6; 0btyf5z; 05qbckf; 09g8vhw; ... *> query: (?x9421, 030qb3t) <- film(?x56, ?x9421), film_crew_role(?x9421, ?x7591), film_crew_role(?x9421, ?x2095), ?x2095 = 0dxtw, ?x7591 = 0d2b38 *> conf = 0.12 ranks of expected_values: 2 EVAL 0ct2tf5 featured_film_locations 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 43.000 0.151 http://example.org/film/film/featured_film_locations #13149-040rmy PRED entity: 040rmy PRED relation: film_release_region PRED expected values: 07ylj 035qy 05qx1 04g61 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 106): 06mkj (0.87 #2028, 0.86 #1745, 0.85 #2310), 035qy (0.85 #1727, 0.83 #2010, 0.82 #2292), 03rt9 (0.71 #295, 0.70 #1992, 0.69 #1709), 05v8c (0.59 #1994, 0.57 #297, 0.55 #1711), 06t8v (0.57 #350, 0.49 #491, 0.46 #2047), 0ctw_b (0.57 #2001, 0.55 #1718, 0.54 #2283), 04gzd (0.56 #1987, 0.51 #1704, 0.49 #2269), 047yc (0.49 #2004, 0.46 #1721, 0.45 #2286), 016wzw (0.47 #2036, 0.45 #339, 0.42 #1753), 01ls2 (0.47 #1990, 0.41 #1707, 0.40 #2272) >> Best rule #2028 for best value: >> intensional similarity = 4 >> extensional distance = 205 >> proper extension: 0crfwmx; 01jrbb; 0gffmn8; 0dgpwnk; 0kxf1; 0gjcrrw; 0ggbfwf; 0cmdwwg; 0421v9q; 0gvvf4j; ... >> query: (?x2501, 06mkj) <- film_release_region(?x2501, ?x151), film_release_region(?x2501, ?x87), ?x87 = 05r4w, ?x151 = 0b90_r >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1727 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 188 *> proper extension: 0b76d_m; 0ds35l9; 0g56t9t; 0gtsx8c; 0c3ybss; 03g90h; 011yrp; 0gx1bnj; 0gtv7pk; 0h1cdwq; ... *> query: (?x2501, 035qy) <- film_release_region(?x2501, ?x1264), film_release_region(?x2501, ?x172), film_release_region(?x2501, ?x87), ?x87 = 05r4w, ?x172 = 0154j, ?x1264 = 0345h *> conf = 0.85 ranks of expected_values: 2, 12, 22, 65 EVAL 040rmy film_release_region 04g61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 77.000 77.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 040rmy film_release_region 05qx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 77.000 77.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 040rmy film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 77.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 040rmy film_release_region 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 77.000 77.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13148-038bh3 PRED entity: 038bh3 PRED relation: film! PRED expected values: 016tt2 => 77 concepts (67 used for prediction) PRED predicted values (max 10 best out of 49): 0c41qv (0.47 #2672, 0.46 #2821, 0.45 #1038), 016tt2 (0.29 #79, 0.20 #4, 0.19 #967), 086k8 (0.24 #595, 0.23 #669, 0.22 #743), 017s11 (0.21 #78, 0.19 #448, 0.13 #1930), 0g1rw (0.20 #7, 0.09 #1563, 0.09 #304), 024rgt (0.20 #19, 0.05 #1798, 0.05 #464), 031rx9 (0.20 #25, 0.02 #470, 0.01 #2323), 05qd_ (0.19 #675, 0.19 #601, 0.18 #527), 0jz9f (0.17 #150, 0.15 #224, 0.11 #298), 016tw3 (0.15 #1937, 0.15 #2756, 0.14 #2607) >> Best rule #2672 for best value: >> intensional similarity = 5 >> extensional distance = 753 >> proper extension: 0b60sq; 04cf_l; >> query: (?x4626, ?x7339) <- genre(?x4626, ?x812), language(?x4626, ?x90), currency(?x4626, ?x170), production_companies(?x4626, ?x7339), titles(?x812, ?x80) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #79 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 12 *> proper extension: 03p2xc; *> query: (?x4626, 016tt2) <- genre(?x4626, ?x53), film(?x9257, ?x4626), film(?x6777, ?x4626), film(?x548, ?x4626), location(?x6777, ?x1719), ?x9257 = 01gkmx, nominated_for(?x548, ?x278) *> conf = 0.29 ranks of expected_values: 2 EVAL 038bh3 film! 016tt2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 67.000 0.466 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #13147-0jvs0 PRED entity: 0jvs0 PRED relation: company! PRED expected values: 060c4 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 34): 060c4 (0.85 #3205, 0.79 #3500, 0.76 #3584), 0krdk (0.80 #1395, 0.79 #2029, 0.74 #1058), 05_wyz (0.55 #142, 0.53 #943, 0.49 #1659), 01yc02 (0.48 #1228, 0.46 #1397, 0.45 #1060), 09d6p2 (0.40 #1407, 0.38 #101, 0.34 #1660), 014l7h (0.35 #3964, 0.29 #4135, 0.21 #2024), 02k13d (0.35 #3964, 0.29 #4135, 0.21 #2024), 01kr6k (0.31 #867, 0.29 #825, 0.29 #4135), 04192r (0.21 #290, 0.21 #2024, 0.21 #1897), 09lq2c (0.21 #2024, 0.21 #1897, 0.20 #4007) >> Best rule #3205 for best value: >> intensional similarity = 5 >> extensional distance = 154 >> proper extension: 016tt2; 0f8l9c; 03rj0; 01_8w2; 02dq8f; 061v5m; 04hzj; 05c74; 01b39j; 0jpn8; ... >> query: (?x5789, 060c4) <- company(?x265, ?x5789), jurisdiction_of_office(?x265, ?x11052), jurisdiction_of_office(?x265, ?x4421), ?x11052 = 04ty8, ?x4421 = 0166v >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jvs0 company! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.846 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #13146-024bbl PRED entity: 024bbl PRED relation: diet PRED expected values: 07_hy => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 07_jd (0.07 #5, 0.05 #15, 0.04 #73), 07_hy (0.01 #66, 0.01 #62, 0.01 #56) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 03sww; >> query: (?x4681, 07_jd) <- award_nominee(?x71, ?x4681), actor(?x715, ?x4681), currency(?x4681, ?x170) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #66 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1828 *> proper extension: 079vf; 05d7rk; 04yywz; 06688p; 01l1b90; 05bp8g; 05m63c; 01vw87c; 02g8h; 0d_84; ... *> query: (?x4681, 07_hy) <- nationality(?x4681, ?x94), profession(?x4681, ?x1032), film(?x4681, ?x1496) *> conf = 0.01 ranks of expected_values: 2 EVAL 024bbl diet 07_hy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.074 http://example.org/base/eating/practicer_of_diet/diet #13145-0d3mlc PRED entity: 0d3mlc PRED relation: team PRED expected values: 01l0__ => 77 concepts (74 used for prediction) PRED predicted values (max 10 best out of 830): 01kj5h (0.33 #137, 0.29 #489, 0.12 #842), 01rl_3 (0.33 #104, 0.29 #456, 0.08 #809), 0182r9 (0.17 #31, 0.14 #383, 0.12 #736), 04ltf (0.17 #189, 0.14 #541, 0.10 #4065), 02b0_6 (0.17 #123, 0.14 #475, 0.10 #5408), 0cj_v7 (0.17 #179, 0.14 #531, 0.08 #884), 01kwhf (0.17 #64, 0.14 #416, 0.08 #769), 0k_l4 (0.17 #128, 0.14 #480, 0.08 #833), 02279c (0.17 #14, 0.14 #366, 0.08 #719), 02b0xq (0.17 #68, 0.14 #420, 0.08 #773) >> Best rule #137 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0bn9sc; 071pf2; 0879xc; 07nvmx; >> query: (?x12509, 01kj5h) <- team(?x12509, ?x11421), team(?x12509, ?x6871), teams(?x2863, ?x6871), team(?x60, ?x6871), nationality(?x12509, ?x390), sport(?x11421, ?x471), ?x390 = 0chghy >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #9516 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 96 *> proper extension: 0443c; *> query: (?x12509, ?x62) <- nationality(?x12509, ?x390), team(?x12509, ?x59), athlete(?x471, ?x12509), team(?x203, ?x59), team(?x203, ?x62) *> conf = 0.01 ranks of expected_values: 678 EVAL 0d3mlc team 01l0__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 77.000 74.000 0.333 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #13144-0bp_b2 PRED entity: 0bp_b2 PRED relation: nominated_for PRED expected values: 039fgy 02nf2c 0n2bh 01j67j 063ykwt 015ppk 06w7mlh => 44 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1437): 030k94 (0.83 #3148, 0.77 #7873, 0.77 #6297), 03cv_gy (0.33 #830, 0.20 #2403, 0.12 #3978), 0gmblvq (0.33 #605, 0.20 #2178, 0.11 #3753), 02py4c8 (0.33 #95, 0.20 #1668, 0.11 #3243), 06nr2h (0.33 #659, 0.20 #2232, 0.09 #12597), 05z43v (0.33 #1174, 0.20 #2747, 0.09 #4322), 0bbm7r (0.33 #923, 0.20 #2496, 0.09 #4071), 09fc83 (0.33 #794, 0.20 #2367, 0.09 #17327), 043mk4y (0.33 #1173, 0.20 #2746, 0.05 #4321), 025ts_z (0.33 #1302, 0.20 #2875, 0.02 #4450) >> Best rule #3148 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 09sb52; 0fbvqf; >> query: (?x435, ?x3169) <- award(?x3876, ?x435), ?x3876 = 02xv8m, nominated_for(?x435, ?x337), award(?x3169, ?x435) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #2651 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 09sb52; 0fbvqf; *> query: (?x435, 015ppk) <- award(?x3876, ?x435), ?x3876 = 02xv8m, nominated_for(?x435, ?x337), award(?x3169, ?x435) *> conf = 0.20 ranks of expected_values: 56, 83, 91, 163, 325 EVAL 0bp_b2 nominated_for 06w7mlh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 15.000 0.829 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bp_b2 nominated_for 015ppk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 44.000 15.000 0.829 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bp_b2 nominated_for 063ykwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 15.000 0.829 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bp_b2 nominated_for 01j67j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 44.000 15.000 0.829 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bp_b2 nominated_for 0n2bh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 15.000 0.829 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bp_b2 nominated_for 02nf2c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 15.000 0.829 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bp_b2 nominated_for 039fgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 44.000 15.000 0.829 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13143-07wm6 PRED entity: 07wm6 PRED relation: institution! PRED expected values: 016t_3 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 17): 03bwzr4 (0.74 #28, 0.61 #231, 0.51 #304), 016t_3 (0.67 #21, 0.57 #224, 0.50 #278), 07s6fsf (0.67 #19, 0.47 #222, 0.40 #331), 04zx3q1 (0.59 #20, 0.42 #223, 0.35 #277), 027f2w (0.37 #24, 0.33 #227, 0.29 #300), 0bjrnt (0.30 #23, 0.28 #1824, 0.24 #149), 03mkk4 (0.30 #26, 0.25 #229, 0.20 #157), 022h5x (0.28 #1824, 0.26 #33, 0.24 #149), 02m4yg (0.28 #1824, 0.24 #149, 0.08 #471), 01ysy9 (0.28 #1824, 0.24 #149, 0.07 #292) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 07szy; 05mv4; 01p79b; >> query: (?x12737, 03bwzr4) <- major_field_of_study(?x12737, ?x1695), institution(?x1526, ?x12737), ?x1526 = 0bkj86, ?x1695 = 06ms6 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 07szy; 05mv4; 01p79b; *> query: (?x12737, 016t_3) <- major_field_of_study(?x12737, ?x1695), institution(?x1526, ?x12737), ?x1526 = 0bkj86, ?x1695 = 06ms6 *> conf = 0.67 ranks of expected_values: 2 EVAL 07wm6 institution! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 160.000 0.741 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13142-01pfkw PRED entity: 01pfkw PRED relation: award_nominee! PRED expected values: 03bxwtd => 89 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1587): 01wd9lv (0.18 #1470, 0.08 #8453, 0.05 #43366), 070w7s (0.14 #63471, 0.11 #70453, 0.03 #96054), 026dg51 (0.13 #63026, 0.10 #70008, 0.03 #95609), 026n998 (0.12 #63470, 0.10 #70452, 0.03 #96053), 050023 (0.12 #62919, 0.09 #69901, 0.03 #95502), 0275_pj (0.12 #63376, 0.09 #70358, 0.03 #95959), 03clrng (0.12 #64240, 0.09 #71222, 0.02 #96823), 0b7xl8 (0.12 #11206, 0.04 #8879, 0.04 #34480), 04g3p5 (0.12 #10408, 0.04 #33682, 0.03 #42994), 0265v21 (0.11 #63025, 0.09 #70007, 0.03 #95608) >> Best rule #1470 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 012_53; 0d0l91; >> query: (?x4420, 01wd9lv) <- profession(?x4420, ?x967), ?x967 = 012t_z, actor(?x7433, ?x4420) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #47237 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 120 *> proper extension: 01wbz9; 017vkx; 01817f; 0d9xq; 02jq1; 0135xb; 03f7m4h; 01bmlb; 0dzlk; *> query: (?x4420, 03bxwtd) <- award(?x4420, ?x724), profession(?x4420, ?x131), ?x724 = 01bgqh *> conf = 0.02 ranks of expected_values: 724 EVAL 01pfkw award_nominee! 03bxwtd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 43.000 0.182 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13141-04cmrt PRED entity: 04cmrt PRED relation: languages PRED expected values: 0688f 09s02 => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 32): 064_8sq (0.44 #516, 0.11 #192, 0.10 #1993), 09s02 (0.22 #213, 0.19 #321, 0.17 #429), 0999q (0.18 #452, 0.17 #200, 0.17 #20), 09bnf (0.17 #216, 0.13 #432, 0.12 #468), 02bjrlw (0.16 #505, 0.05 #721, 0.05 #794), 06nm1 (0.13 #508, 0.04 #724, 0.04 #797), 055qm (0.12 #309, 0.11 #201, 0.11 #237), 01c7y (0.11 #208, 0.11 #244, 0.09 #100), 0688f (0.11 #206, 0.11 #242, 0.08 #314), 04306rv (0.11 #506, 0.03 #3063, 0.03 #3207) >> Best rule #516 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 01h4rj; 02tc5y; >> query: (?x11725, 064_8sq) <- place_of_birth(?x11725, ?x7412), languages(?x11725, ?x1882), language(?x257, ?x1882), titles(?x1882, ?x10774) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #213 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 04rs03; 0241wg; 08d6bd; 03vrnh; 046rfv; 09r_wb; 05nqq3; 0738y5; 02wyc0; 04j0s3; ... *> query: (?x11725, 09s02) <- place_of_birth(?x11725, ?x7412), languages(?x11725, ?x1882), nationality(?x11725, ?x2146), people(?x7838, ?x11725), ?x1882 = 03k50 *> conf = 0.22 ranks of expected_values: 2, 9 EVAL 04cmrt languages 09s02 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 189.000 189.000 0.435 http://example.org/people/person/languages EVAL 04cmrt languages 0688f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 189.000 189.000 0.435 http://example.org/people/person/languages #13140-03bdkd PRED entity: 03bdkd PRED relation: country PRED expected values: 09c7w0 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 91): 09c7w0 (0.91 #2, 0.84 #247, 0.84 #308), 07ssc (0.31 #384, 0.24 #201, 0.21 #1057), 0345h (0.13 #395, 0.12 #89, 0.09 #1316), 0f8l9c (0.10 #1308, 0.09 #387, 0.09 #3399), 0chghy (0.05 #135, 0.05 #380, 0.05 #747), 06mzp (0.05 #141, 0.05 #386, 0.01 #5220), 03rjj (0.05 #863, 0.04 #741, 0.04 #1295), 03rt9 (0.04 #382, 0.02 #137, 0.01 #5220), 0d060g (0.04 #4860, 0.04 #5044, 0.04 #5167), 03_3d (0.04 #130, 0.04 #5166, 0.03 #5043) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0147sh; 0kb57; 0fy66; 0jqd3; >> query: (?x10614, 09c7w0) <- film(?x9587, ?x10614), nominated_for(?x1703, ?x10614), film_sets_designed(?x200, ?x10614), ?x1703 = 0k611 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bdkd country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.913 http://example.org/film/film/country #13139-01c57n PRED entity: 01c57n PRED relation: state_province_region PRED expected values: 0g39h => 174 concepts (164 used for prediction) PRED predicted values (max 10 best out of 107): 0g39h (0.86 #8419, 0.85 #6557, 0.81 #2096), 059rby (0.71 #1975, 0.56 #4457, 0.45 #6933), 0chghy (0.68 #4578, 0.42 #2095, 0.40 #2841), 05nrg (0.42 #2095, 0.40 #2841, 0.34 #4579), 05fjf (0.29 #196, 0.04 #5394, 0.02 #6135), 05kr_ (0.28 #2372, 0.12 #3986, 0.12 #2870), 06q1r (0.25 #69, 0.10 #561, 0.07 #808), 0847q (0.25 #109, 0.07 #848, 0.06 #1464), 01w0v (0.23 #663, 0.11 #1402, 0.11 #5616), 01n7q (0.21 #4471, 0.19 #7443, 0.19 #6450) >> Best rule #8419 for best value: >> intensional similarity = 8 >> extensional distance = 156 >> proper extension: 017hnw; 0ym4t; >> query: (?x12489, ?x9725) <- school_type(?x12489, ?x3092), category(?x12489, ?x134), citytown(?x12489, ?x11731), location(?x6639, ?x11731), location(?x2865, ?x11731), state(?x11731, ?x9725), nationality(?x6639, ?x512), profession(?x2865, ?x220) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c57n state_province_region 0g39h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 164.000 0.861 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #13138-02bh9 PRED entity: 02bh9 PRED relation: place_of_birth PRED expected values: 030qb3t => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 122): 0dg3n1 (0.28 #45078, 0.28 #26060, 0.27 #54235), 0r00l (0.17 #487), 02_286 (0.14 #5652, 0.09 #6357, 0.09 #8469), 030qb3t (0.08 #758, 0.08 #3574, 0.07 #4279), 0cr3d (0.08 #798, 0.04 #1502, 0.04 #8544), 01_d4 (0.08 #770, 0.04 #1474, 0.04 #5699), 0d6lp (0.08 #818, 0.04 #1522, 0.04 #2226), 06_kh (0.08 #709, 0.04 #1413, 0.01 #8455), 0f8j6 (0.08 #1399, 0.04 #2807, 0.02 #4215), 04f_d (0.08 #777, 0.04 #2889, 0.03 #5706) >> Best rule #45078 for best value: >> intensional similarity = 3 >> extensional distance = 1657 >> proper extension: 01wbgdv; 0c3kw; 04r68; 048_p; 034rd; 01wgfp6; 041_y; 02n9k; 0l5yl; 024y6w; ... >> query: (?x3410, ?x2467) <- nationality(?x3410, ?x94), ?x94 = 09c7w0, location(?x3410, ?x2467) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #758 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 03f2_rc; 01lz4tf; *> query: (?x3410, 030qb3t) <- spouse(?x3410, ?x3361), music(?x751, ?x3410), genre(?x751, ?x53) *> conf = 0.08 ranks of expected_values: 4 EVAL 02bh9 place_of_birth 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 102.000 0.277 http://example.org/people/person/place_of_birth #13137-07s3m4g PRED entity: 07s3m4g PRED relation: film_release_region PRED expected values: 0154j 03rjj 03h64 => 94 concepts (89 used for prediction) PRED predicted values (max 10 best out of 206): 03h64 (0.89 #1168, 0.89 #1448, 0.87 #1587), 03rjj (0.87 #1539, 0.86 #1817, 0.85 #1400), 0154j (0.83 #2236, 0.81 #1538, 0.80 #1399), 03rj0 (0.80 #186, 0.68 #745, 0.67 #1163), 01p1v (0.80 #179, 0.64 #738, 0.61 #1156), 04gzd (0.75 #427, 0.68 #706, 0.67 #147), 047yc (0.71 #440, 0.69 #1417, 0.67 #1137), 03ryn (0.71 #489, 0.67 #209, 0.61 #768), 02k54 (0.67 #152, 0.54 #432, 0.54 #571), 015qh (0.66 #868, 0.65 #1566, 0.62 #450) >> Best rule #1168 for best value: >> intensional similarity = 13 >> extensional distance = 44 >> proper extension: 0gtsx8c; 02vxq9m; 0g9wdmc; 0ch26b_; 0661m4p; 0gvs1kt; 0dll_t2; 0hhggmy; >> query: (?x6587, 03h64) <- film_release_region(?x6587, ?x4743), film_release_region(?x6587, ?x2843), film_release_region(?x6587, ?x1892), film_release_region(?x6587, ?x1023), film_release_region(?x6587, ?x985), film_release_region(?x6587, ?x151), ?x151 = 0b90_r, ?x985 = 0k6nt, ?x1023 = 0ctw_b, ?x2843 = 016wzw, ?x4743 = 03spz, ?x1892 = 02vzc, film(?x574, ?x6587) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 07s3m4g film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 89.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07s3m4g film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 89.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07s3m4g film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 89.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13136-018y2s PRED entity: 018y2s PRED relation: award PRED expected values: 02wh75 => 129 concepts (102 used for prediction) PRED predicted values (max 10 best out of 275): 09sb52 (0.34 #27043, 0.24 #26237, 0.23 #30670), 01by1l (0.32 #6158, 0.30 #1322, 0.29 #13816), 0c4z8 (0.24 #4908, 0.23 #6117, 0.23 #878), 01bgqh (0.23 #13746, 0.23 #6894, 0.22 #6088), 054ks3 (0.20 #4978, 0.18 #6187, 0.18 #948), 01c92g (0.16 #904, 0.15 #1307, 0.14 #6143), 05pcn59 (0.16 #10158, 0.14 #15397, 0.13 #8142), 0gqz2 (0.16 #1290, 0.16 #4917, 0.16 #2096), 02wh75 (0.15 #815, 0.15 #3233, 0.14 #9), 025m8l (0.15 #926, 0.15 #1329, 0.13 #6165) >> Best rule #27043 for best value: >> intensional similarity = 3 >> extensional distance = 1166 >> proper extension: 0q9kd; 0184jc; 05vsxz; 07fq1y; 02qgqt; 0cnl80; 014zcr; 0h0jz; 03x3qv; 05cj4r; ... >> query: (?x1165, 09sb52) <- award_nominee(?x1165, ?x11186), award(?x1165, ?x2322), film(?x1165, ?x1066) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #815 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 128 *> proper extension: 0bg539; 03cs_z7; 0pgjm; 03m6_z; 03cs_xw; *> query: (?x1165, 02wh75) <- award_nominee(?x1165, ?x11186), award(?x1165, ?x2322), role(?x1165, ?x227) *> conf = 0.15 ranks of expected_values: 9 EVAL 018y2s award 02wh75 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 129.000 102.000 0.341 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13135-01k3s2 PRED entity: 01k3s2 PRED relation: citytown PRED expected values: 0nlh7 => 144 concepts (117 used for prediction) PRED predicted values (max 10 best out of 165): 0d060g (0.30 #14384, 0.28 #2949, 0.25 #4056), 0j95 (0.30 #14384, 0.28 #2949, 0.25 #4056), 02_286 (0.24 #3333, 0.21 #8502, 0.21 #5548), 0nlh7 (0.22 #17335, 0.02 #30981), 0h7h6 (0.15 #1135, 0.06 #9623, 0.05 #8147), 0978r (0.13 #6715, 0.10 #3024, 0.10 #7084), 04jpl (0.12 #6647, 0.10 #2956, 0.10 #375), 05l5n (0.10 #6677, 0.07 #10000, 0.05 #7046), 01qh7 (0.10 #430, 0.09 #798, 0.06 #1904), 0f2nf (0.10 #576, 0.09 #944, 0.05 #2788) >> Best rule #14384 for best value: >> intensional similarity = 4 >> extensional distance = 213 >> proper extension: 05p7tx; 027xq5; >> query: (?x4342, ?x279) <- major_field_of_study(?x4342, ?x2605), student(?x4342, ?x8256), contains(?x279, ?x4342), featured_film_locations(?x1064, ?x279) >> conf = 0.30 => this is the best rule for 2 predicted values *> Best rule #17335 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 269 *> proper extension: 086x3; *> query: (?x4342, ?x10718) <- state_province_region(?x4342, ?x10063), administrative_division(?x10718, ?x10063), country(?x10063, ?x279) *> conf = 0.22 ranks of expected_values: 4 EVAL 01k3s2 citytown 0nlh7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 144.000 117.000 0.301 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #13134-09kvv PRED entity: 09kvv PRED relation: major_field_of_study PRED expected values: 0h5k => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 98): 04rjg (0.62 #1488, 0.55 #1714, 0.52 #923), 05qfh (0.59 #824, 0.58 #372, 0.44 #1502), 05qjt (0.58 #347, 0.53 #799, 0.46 #1477), 0fdys (0.58 #375, 0.47 #827, 0.46 #1505), 04x_3 (0.50 #364, 0.47 #816, 0.43 #929), 01lj9 (0.50 #376, 0.44 #1506, 0.41 #828), 02ky346 (0.42 #354, 0.41 #806, 0.33 #1484), 02h40lc (0.42 #343, 0.41 #795, 0.30 #1021), 03nfmq (0.42 #374, 0.41 #826, 0.30 #1052), 01r4k (0.42 #415, 0.35 #867, 0.26 #1093) >> Best rule #1488 for best value: >> intensional similarity = 2 >> extensional distance = 37 >> proper extension: 0d06m5; 0d05fv; >> query: (?x1768, 04rjg) <- list(?x1768, ?x2197), organization(?x1768, ?x5487) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #361 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 10 *> proper extension: 06y3r; 023p29; 0n839; *> query: (?x1768, 0h5k) <- organizations_founded(?x1768, ?x5487), list(?x1768, ?x2197) *> conf = 0.33 ranks of expected_values: 17 EVAL 09kvv major_field_of_study 0h5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 103.000 103.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13133-043g7l PRED entity: 043g7l PRED relation: artist PRED expected values: 06449 01nn6c 01309x 01vvyfh 0hvbj 02bgmr 01wgjj5 0135xb => 179 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1365): 01vx5w7 (0.60 #2526, 0.50 #958, 0.43 #4095), 07h76 (0.50 #1189, 0.40 #2757, 0.40 #1973), 019g40 (0.50 #880, 0.40 #2448, 0.40 #1664), 020_4z (0.50 #1471, 0.40 #3039, 0.40 #2255), 01vrncs (0.50 #829, 0.40 #2397, 0.40 #1613), 09hnb (0.50 #935, 0.40 #2503, 0.40 #1719), 02vgh (0.50 #1246, 0.40 #2814, 0.40 #2030), 015cqh (0.50 #1358, 0.40 #2926, 0.40 #2142), 02lvtb (0.50 #1123, 0.40 #2691, 0.40 #1907), 01vtj38 (0.43 #5198, 0.33 #493, 0.25 #5982) >> Best rule #2526 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 06wcbk7; >> query: (?x5666, 01vx5w7) <- artist(?x5666, ?x10745), artist(?x5666, ?x5882), artist(?x5666, ?x2575), ?x5882 = 01wbsdz, role(?x2575, ?x614), award_nominee(?x2575, ?x2862), artists(?x302, ?x10745) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #252 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 015_1q; *> query: (?x5666, 01vvyfh) <- artist(?x5666, ?x3962), artist(?x5666, ?x3657), artist(?x5666, ?x2575), ?x2575 = 018pj3, ?x3962 = 01vrkdt, role(?x3657, ?x8014), ?x8014 = 0214km *> conf = 0.33 ranks of expected_values: 55, 96, 176, 228, 508, 531, 634, 778 EVAL 043g7l artist 0135xb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 179.000 117.000 0.600 http://example.org/music/record_label/artist EVAL 043g7l artist 01wgjj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 179.000 117.000 0.600 http://example.org/music/record_label/artist EVAL 043g7l artist 02bgmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 179.000 117.000 0.600 http://example.org/music/record_label/artist EVAL 043g7l artist 0hvbj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 179.000 117.000 0.600 http://example.org/music/record_label/artist EVAL 043g7l artist 01vvyfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 179.000 117.000 0.600 http://example.org/music/record_label/artist EVAL 043g7l artist 01309x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 179.000 117.000 0.600 http://example.org/music/record_label/artist EVAL 043g7l artist 01nn6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 179.000 117.000 0.600 http://example.org/music/record_label/artist EVAL 043g7l artist 06449 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 179.000 117.000 0.600 http://example.org/music/record_label/artist #13132-02n72k PRED entity: 02n72k PRED relation: currency PRED expected values: 09nqf => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.88 #331, 0.87 #197, 0.86 #457), 01nv4h (0.11 #353, 0.08 #297, 0.04 #584), 02l6h (0.02 #313, 0.01 #887), 088n7 (0.02 #217, 0.01 #603, 0.01 #645) >> Best rule #331 for best value: >> intensional similarity = 5 >> extensional distance = 117 >> proper extension: 065dc4; >> query: (?x6533, 09nqf) <- film_release_distribution_medium(?x6533, ?x81), language(?x6533, ?x254), ?x254 = 02h40lc, prequel(?x836, ?x6533), film(?x788, ?x6533) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02n72k currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.882 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #13131-05hj_k PRED entity: 05hj_k PRED relation: profession PRED expected values: 01d_h8 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 84): 02hrh1q (0.89 #6675, 0.87 #6231, 0.86 #12595), 01d_h8 (0.86 #1042, 0.85 #5927, 0.85 #5631), 0dxtg (0.67 #3861, 0.65 #3269, 0.58 #1345), 02jknp (0.53 #5485, 0.49 #1044, 0.48 #5929), 05sxg2 (0.34 #149, 0.04 #593, 0.04 #1481), 02krf9 (0.30 #3874, 0.26 #3282, 0.26 #6095), 0dz3r (0.29 #298, 0.23 #4739, 0.18 #2), 09jwl (0.25 #4755, 0.21 #5347, 0.21 #5199), 02hv44_ (0.24 #205, 0.04 #2721, 0.04 #11898), 0cbd2 (0.22 #3559, 0.20 #5040, 0.20 #4152) >> Best rule #6675 for best value: >> intensional similarity = 3 >> extensional distance = 727 >> proper extension: 02wr2r; 01d6jf; 05g7q; >> query: (?x4060, 02hrh1q) <- award_winner(?x4060, ?x4634), film(?x4060, ?x6205), profession(?x4060, ?x967) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1042 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 82 *> proper extension: 0pksh; *> query: (?x4060, 01d_h8) <- award_winner(?x4060, ?x4634), film(?x4060, ?x6205), produced_by(?x144, ?x4060) *> conf = 0.86 ranks of expected_values: 2 EVAL 05hj_k profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 117.000 0.890 http://example.org/people/person/profession #13130-027f7dj PRED entity: 027f7dj PRED relation: profession PRED expected values: 02hrh1q => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 56): 02hrh1q (0.88 #9848, 0.88 #10295, 0.87 #5229), 0dxtg (0.44 #162, 0.30 #3142, 0.30 #1652), 03gjzk (0.31 #313, 0.31 #462, 0.26 #3144), 09jwl (0.23 #317, 0.19 #2850, 0.19 #2105), 02jknp (0.22 #156, 0.22 #8500, 0.21 #5967), 0dz3r (0.21 #300, 0.14 #2833, 0.13 #4174), 018gz8 (0.20 #464, 0.14 #315, 0.13 #7616), 0nbcg (0.18 #330, 0.13 #2863, 0.13 #4204), 0cbd2 (0.16 #602, 0.15 #751, 0.15 #5668), 0np9r (0.15 #468, 0.15 #7620, 0.14 #10153) >> Best rule #9848 for best value: >> intensional similarity = 2 >> extensional distance = 1675 >> proper extension: 01n7qlf; >> query: (?x1559, 02hrh1q) <- film(?x1559, ?x9258), award_winner(?x9258, ?x976) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027f7dj profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.881 http://example.org/people/person/profession #13129-044bn PRED entity: 044bn PRED relation: type_of_union PRED expected values: 04ztj => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #77, 0.87 #154, 0.86 #130), 01g63y (0.24 #125, 0.14 #10, 0.14 #6), 01bl8s (0.24 #125, 0.08 #23, 0.03 #51), 0jgjn (0.02 #133, 0.01 #88) >> Best rule #77 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 09fb5; 033hqf; 0j582; 0157m; 016_mj; 0bymv; 0tc7; 0j_c; 014q2g; 0cj8x; ... >> query: (?x11177, 04ztj) <- profession(?x11177, ?x987), celebrities_impersonated(?x3649, ?x11177), film(?x11177, ?x11065) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044bn type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.880 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13128-05b6s5j PRED entity: 05b6s5j PRED relation: program! PRED expected values: 01l50r => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 54): 05gnf (0.88 #295, 0.25 #1766, 0.23 #1649), 03mdt (0.43 #120, 0.23 #344, 0.21 #513), 09d5h (0.33 #3, 0.13 #1755, 0.13 #2671), 0187wh (0.29 #138, 0.09 #1320, 0.08 #1147), 0b275x (0.29 #132, 0.07 #749, 0.06 #1598), 0gsg7 (0.24 #2446, 0.23 #2390, 0.21 #2670), 02hmvw (0.20 #211, 0.20 #98, 0.12 #267), 03jl0_ (0.20 #187, 0.20 #74, 0.12 #243), 026f5s (0.20 #198, 0.12 #254, 0.04 #647), 022tfp (0.20 #93, 0.10 #206, 0.06 #262) >> Best rule #295 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 02_1q9; 0fpxp; 045r_9; >> query: (?x11414, 05gnf) <- languages(?x11414, ?x254), program(?x11249, ?x11414), genre(?x11414, ?x225), program(?x11249, ?x4637), ?x4637 = 02sqkh >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #819 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 63 *> proper extension: 05h95s; 074j87; *> query: (?x11414, 01l50r) <- country_of_origin(?x11414, ?x94), languages(?x11414, ?x254), category(?x11414, ?x134), ?x94 = 09c7w0, language(?x54, ?x254) *> conf = 0.02 ranks of expected_values: 49 EVAL 05b6s5j program! 01l50r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 57.000 57.000 0.884 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #13127-06crk PRED entity: 06crk PRED relation: peers PRED expected values: 059y0 => 146 concepts (66 used for prediction) PRED predicted values (max 10 best out of 45): 06g4_ (0.20 #105, 0.08 #231, 0.08 #357), 02m7r (0.08 #148, 0.05 #652, 0.04 #904), 041mt (0.05 #523, 0.01 #2161, 0.01 #2288), 08433 (0.05 #510, 0.01 #2148, 0.01 #2275), 059y0 (0.04 #859, 0.04 #1867, 0.02 #1489), 06y7d (0.04 #872, 0.03 #1124, 0.02 #1502), 09gnn (0.04 #853, 0.03 #1105, 0.01 #2239), 029rk (0.04 #849, 0.02 #1479, 0.02 #1857), 0bk5r (0.04 #800, 0.02 #1556, 0.01 #2186), 0gs7x (0.04 #868, 0.02 #1876, 0.02 #2002) >> Best rule #105 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0b78hw; >> query: (?x6342, 06g4_) <- nationality(?x6342, ?x94), student(?x3437, ?x6342), people(?x11490, ?x6342), ?x11490 = 013b6_ >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #859 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0n839; *> query: (?x6342, 059y0) <- nationality(?x6342, ?x94), religion(?x6342, ?x2694), company(?x6342, ?x741), ?x2694 = 0kpl *> conf = 0.04 ranks of expected_values: 5 EVAL 06crk peers 059y0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 146.000 66.000 0.200 http://example.org/influence/influence_node/peers./influence/peer_relationship/peers #13126-034hzj PRED entity: 034hzj PRED relation: country PRED expected values: 0hzlz => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 148): 09c7w0 (0.86 #916, 0.85 #732, 0.83 #854), 02jx1 (0.42 #4081, 0.07 #4325, 0.06 #3960), 0345h (0.13 #208, 0.10 #270, 0.10 #2156), 0f8l9c (0.11 #445, 0.10 #1420, 0.10 #19), 03_3d (0.10 #189, 0.09 #2555, 0.08 #251), 03rjj (0.10 #188, 0.08 #372, 0.08 #250), 03mqtr (0.10 #607, 0.06 #3959, 0.06 #3958), 017fp (0.10 #607, 0.06 #3959, 0.06 #3958), 07s9rl0 (0.10 #607, 0.06 #3959, 0.06 #3958), 03rk0 (0.09 #2555, 0.07 #4325, 0.06 #3960) >> Best rule #916 for best value: >> intensional similarity = 4 >> extensional distance = 457 >> proper extension: 099bhp; >> query: (?x12430, 09c7w0) <- films(?x5069, ?x12430), country(?x12430, ?x512), film(?x2596, ?x12430), genre(?x12430, ?x53) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #4325 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1548 *> proper extension: 07hpv3; 02nf2c; 0gj9qxr; 09kn9; 01cjhz; 05sy2k_; 011yfd; 02648p; 01p4wv; 07l50vn; ... *> query: (?x12430, ?x94) <- titles(?x53, ?x12430), titles(?x53, ?x4396), country(?x4396, ?x94), nominated_for(?x1735, ?x4396) *> conf = 0.07 ranks of expected_values: 43 EVAL 034hzj country 0hzlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 85.000 85.000 0.858 http://example.org/film/film/country #13125-02x9g_ PRED entity: 02x9g_ PRED relation: institution! PRED expected values: 03bwzr4 => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 19): 014mlp (0.67 #348, 0.66 #1321, 0.64 #577), 03bwzr4 (0.56 #273, 0.53 #294, 0.53 #1028), 0bkj86 (0.47 #229, 0.47 #108, 0.45 #1024), 04zx3q1 (0.47 #103, 0.39 #346, 0.34 #244), 07s6fsf (0.42 #917, 0.36 #1078, 0.36 #1018), 027f2w (0.35 #109, 0.31 #352, 0.31 #230), 0bjrnt (0.35 #106, 0.29 #2993, 0.24 #127), 01gkg3 (0.29 #2993, 0.18 #3014, 0.17 #2516), 013zdg (0.29 #350, 0.28 #228, 0.25 #1023), 028dcg (0.29 #360, 0.18 #3014, 0.17 #2516) >> Best rule #348 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 033gn8; >> query: (?x8287, 014mlp) <- institution(?x3386, ?x8287), major_field_of_study(?x8287, ?x254), ?x3386 = 03mkk4 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #273 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 41 *> proper extension: 01j_cy; 019dwp; *> query: (?x8287, 03bwzr4) <- school_type(?x8287, ?x1507), institution(?x865, ?x8287), contains(?x94, ?x8287), ?x1507 = 01_9fk, major_field_of_study(?x8287, ?x254) *> conf = 0.56 ranks of expected_values: 2 EVAL 02x9g_ institution! 03bwzr4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 194.000 194.000 0.673 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13124-0j43swk PRED entity: 0j43swk PRED relation: genre PRED expected values: 02kdv5l 01jfsb => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 114): 02l7c8 (0.56 #383, 0.52 #627, 0.45 #139), 05p553 (0.41 #6715, 0.36 #736, 0.35 #980), 03k9fj (0.34 #256, 0.29 #500, 0.24 #866), 01jfsb (0.33 #7091, 0.33 #3063, 0.31 #4039), 04xvlr (0.32 #367, 0.28 #123, 0.27 #611), 02kdv5l (0.31 #7080, 0.28 #1710, 0.26 #3052), 060__y (0.31 #384, 0.30 #628, 0.28 #140), 0lsxr (0.24 #6110, 0.18 #7087, 0.17 #1961), 01hmnh (0.23 #263, 0.19 #507, 0.14 #873), 0219x_ (0.21 #150, 0.20 #394, 0.18 #638) >> Best rule #383 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 02q5g1z; 0g5879y; 05vxdh; 02d49z; 0j3d9tn; 0dr89x; 047myg9; 089j8p; 02yxbc; 0234j5; >> query: (?x3035, 02l7c8) <- nominated_for(?x618, ?x3035), ?x618 = 09qwmm, film(?x541, ?x3035), film(?x194, ?x3035) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #7091 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1459 *> proper extension: 06n90; 04svwx; *> query: (?x3035, 01jfsb) <- genre(?x3035, ?x53), genre(?x5275, ?x53), genre(?x273, ?x53), ?x5275 = 01q2nx *> conf = 0.33 ranks of expected_values: 4, 6 EVAL 0j43swk genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 73.000 73.000 0.559 http://example.org/film/film/genre EVAL 0j43swk genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 73.000 0.559 http://example.org/film/film/genre #13123-02bhj4 PRED entity: 02bhj4 PRED relation: school! PRED expected values: 0jmdb => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 92): 05m_8 (0.21 #371, 0.17 #831, 0.17 #463), 01slc (0.14 #426, 0.14 #886, 0.12 #794), 051vz (0.13 #391, 0.12 #483, 0.12 #851), 06x68 (0.13 #375, 0.11 #467, 0.11 #743), 01yhm (0.13 #388, 0.11 #848, 0.11 #756), 0713r (0.13 #404, 0.11 #864, 0.11 #772), 07l4z (0.13 #438, 0.11 #898, 0.09 #991), 061xq (0.13 #402, 0.09 #862, 0.08 #494), 01yjl (0.11 #398, 0.11 #766, 0.10 #490), 01d5z (0.11 #378, 0.11 #746, 0.10 #838) >> Best rule #371 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 02zkz7; >> query: (?x7202, 05m_8) <- colors(?x7202, ?x3364), organization(?x346, ?x7202), school(?x8586, ?x7202), currency(?x7202, ?x170) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #921 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 109 *> proper extension: 0frm7n; *> query: (?x7202, ?x660) <- school(?x2820, ?x7202), school(?x8586, ?x7202), draft(?x660, ?x8586) *> conf = 0.11 ranks of expected_values: 31 EVAL 02bhj4 school! 0jmdb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 104.000 104.000 0.214 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #13122-016l09 PRED entity: 016l09 PRED relation: award_winner! PRED expected values: 09n4nb => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 89): 056878 (0.36 #587, 0.20 #31, 0.14 #865), 0466p0j (0.29 #909, 0.20 #353, 0.20 #75), 01bx35 (0.21 #840, 0.18 #562, 0.14 #1118), 04n2r9h (0.20 #44, 0.11 #5423, 0.10 #322), 013b2h (0.18 #635, 0.14 #913, 0.14 #5083), 09n4nb (0.18 #603, 0.14 #881, 0.13 #1437), 01xqqp (0.18 #651, 0.14 #929, 0.10 #1485), 01c6qp (0.14 #852, 0.12 #4883, 0.12 #4605), 019bk0 (0.14 #849, 0.11 #2239, 0.11 #5423), 01mhwk (0.14 #874, 0.11 #5423, 0.10 #4905) >> Best rule #587 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0lzkm; >> query: (?x9791, 056878) <- artists(?x302, ?x9791), award_winner(?x486, ?x9791), award_winner(?x1565, ?x9791), ?x1565 = 01c4_6 >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #603 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0lzkm; *> query: (?x9791, 09n4nb) <- artists(?x302, ?x9791), award_winner(?x486, ?x9791), award_winner(?x1565, ?x9791), ?x1565 = 01c4_6 *> conf = 0.18 ranks of expected_values: 6 EVAL 016l09 award_winner! 09n4nb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 84.000 84.000 0.364 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13121-0vg8 PRED entity: 0vg8 PRED relation: industry! PRED expected values: 0p4wb => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 351): 0xwj (0.60 #794, 0.33 #1277, 0.33 #302), 01qxs3 (0.60 #872, 0.33 #1355, 0.33 #380), 045c7b (0.60 #1034, 0.21 #2491, 0.08 #3987), 04sv4 (0.50 #2549, 0.40 #1092, 0.40 #850), 03_05 (0.40 #934, 0.33 #1417, 0.24 #1459), 0dwl2 (0.40 #735, 0.33 #1218, 0.23 #2192), 07zl6m (0.40 #951, 0.33 #1434, 0.20 #975), 06p8m (0.40 #888, 0.33 #1371, 0.18 #2102), 01_4lx (0.40 #865, 0.33 #1348, 0.18 #2079), 01n073 (0.40 #769, 0.33 #1252, 0.18 #1983) >> Best rule #794 for best value: >> intensional similarity = 41 >> extensional distance = 3 >> proper extension: 020mfr; >> query: (?x1605, 0xwj) <- industry(?x9968, ?x1605), service_location(?x9968, ?x2152), service_location(?x9968, ?x2146), service_location(?x9968, ?x1355), nationality(?x111, ?x2146), film_release_region(?x5713, ?x2146), film_release_region(?x4607, ?x2146), film_release_region(?x3812, ?x2146), film_release_region(?x3088, ?x2146), film_release_region(?x2656, ?x2146), film_release_region(?x1999, ?x2146), film_release_region(?x1916, ?x2146), film_release_region(?x1701, ?x2146), film_release_region(?x1178, ?x2146), film_release_region(?x634, ?x2146), contains(?x2146, ?x1391), religion(?x2146, ?x109), ?x1916 = 0ch26b_, ?x3088 = 06w839_, organization(?x2146, ?x127), country(?x257, ?x2146), ?x2152 = 06mkj, ?x1178 = 053rxgm, ?x4607 = 0h03fhx, adjoins(?x2146, ?x2236), ?x1701 = 0bh8yn3, ?x1999 = 0gd0c7x, ?x3812 = 0c3xw46, country(?x11181, ?x2146), medal(?x2146, ?x1242), ?x634 = 0gx9rvq, member_states(?x2106, ?x1355), ?x5713 = 0cc97st, location_of_ceremony(?x4740, ?x2146), film_release_region(?x2094, ?x1355), vacationer(?x2146, ?x7046), ?x2656 = 03qnc6q, nationality(?x681, ?x1355), ?x2094 = 05z7c, adjoins(?x756, ?x1355), adjustment_currency(?x2146, ?x170) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #975 for first EXPECTED value: *> intensional similarity = 42 *> extensional distance = 3 *> proper extension: 020mfr; *> query: (?x1605, ?x1492) <- industry(?x9968, ?x1605), service_location(?x9968, ?x2152), service_location(?x9968, ?x2146), service_location(?x9968, ?x1355), nationality(?x111, ?x2146), film_release_region(?x5713, ?x2146), film_release_region(?x4607, ?x2146), film_release_region(?x3812, ?x2146), film_release_region(?x3088, ?x2146), film_release_region(?x2656, ?x2146), film_release_region(?x1999, ?x2146), film_release_region(?x1916, ?x2146), film_release_region(?x1701, ?x2146), film_release_region(?x1178, ?x2146), film_release_region(?x634, ?x2146), contains(?x2146, ?x1391), religion(?x2146, ?x109), ?x1916 = 0ch26b_, ?x3088 = 06w839_, organization(?x2146, ?x127), country(?x257, ?x2146), service_location(?x1492, ?x2146), ?x2152 = 06mkj, ?x1178 = 053rxgm, ?x4607 = 0h03fhx, adjoins(?x2146, ?x2236), ?x1701 = 0bh8yn3, ?x1999 = 0gd0c7x, ?x3812 = 0c3xw46, country(?x11181, ?x2146), medal(?x2146, ?x1242), ?x634 = 0gx9rvq, member_states(?x2106, ?x1355), ?x5713 = 0cc97st, location_of_ceremony(?x4740, ?x2146), film_release_region(?x2094, ?x1355), vacationer(?x2146, ?x7046), ?x2656 = 03qnc6q, nationality(?x681, ?x1355), ?x2094 = 05z7c, adjoins(?x756, ?x1355), adjustment_currency(?x2146, ?x170) *> conf = 0.20 ranks of expected_values: 107 EVAL 0vg8 industry! 0p4wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 18.000 18.000 0.600 http://example.org/business/business_operation/industry #13120-04cb4x PRED entity: 04cb4x PRED relation: genre! PRED expected values: 0y_9q => 32 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1907): 0571m (0.62 #6174, 0.53 #8049, 0.38 #4299), 0bw20 (0.55 #3169, 0.50 #1295, 0.46 #5043), 0cc5mcj (0.50 #407, 0.45 #2281, 0.38 #4155), 020y73 (0.50 #381, 0.45 #2255, 0.38 #4129), 04ynx7 (0.50 #1668, 0.36 #3542, 0.31 #5416), 07sc6nw (0.50 #187, 0.36 #2061, 0.31 #3935), 01kjr0 (0.46 #4876, 0.45 #3002, 0.42 #8626), 03cp4cn (0.46 #4892, 0.45 #3018, 0.38 #6767), 07z6xs (0.46 #4660, 0.45 #2786, 0.38 #6535), 02kfzz (0.46 #4453, 0.45 #2579, 0.38 #705) >> Best rule #6174 for best value: >> intensional similarity = 12 >> extensional distance = 14 >> proper extension: 0lsxr; 0hn10; 0219x_; 0glj9q; 073_6; >> query: (?x14039, 0571m) <- genre(?x3537, ?x14039), film(?x241, ?x3537), country(?x3537, ?x94), genre(?x3537, ?x3613), genre(?x3537, ?x812), film_crew_role(?x3537, ?x281), crewmember(?x3537, ?x2887), ?x812 = 01jfsb, language(?x3537, ?x254), ?x3613 = 09blyk, films(?x11988, ?x3537), nominated_for(?x241, ?x2231) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2822 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 9 *> proper extension: 07s9rl0; 02n4kr; 0c3351; *> query: (?x14039, 0y_9q) <- genre(?x3537, ?x14039), film(?x241, ?x3537), country(?x3537, ?x94), genre(?x3537, ?x3613), genre(?x3537, ?x812), film_crew_role(?x3537, ?x2178), crewmember(?x3537, ?x2887), ?x812 = 01jfsb, language(?x3537, ?x254), ?x3613 = 09blyk, featured_film_locations(?x3537, ?x726), ?x2178 = 01pvkk *> conf = 0.27 ranks of expected_values: 227 EVAL 04cb4x genre! 0y_9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 32.000 10.000 0.625 http://example.org/film/film/genre #13119-0dw4g PRED entity: 0dw4g PRED relation: group! PRED expected values: 02hnl => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 120): 02hnl (0.80 #724, 0.77 #1681, 0.76 #2639), 028tv0 (0.50 #621, 0.46 #969, 0.43 #1665), 05r5c (0.33 #355, 0.33 #616, 0.30 #703), 03qjg (0.32 #743, 0.30 #656, 0.30 #1787), 0l14qv (0.29 #1832, 0.27 #1658, 0.25 #962), 01vj9c (0.28 #361, 0.28 #2363, 0.28 #1840), 04rzd (0.20 #640, 0.14 #727, 0.13 #1771), 06ncr (0.17 #1865, 0.17 #995, 0.15 #1691), 013y1f (0.16 #722, 0.16 #1766, 0.14 #374), 042v_gx (0.15 #617, 0.14 #965, 0.13 #1835) >> Best rule #724 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 01vsxdm; 01wv9xn; 0frsw; 014_lq; 07mvp; 0178_w; 081wh1; 07r1_; 0b_xm; 01lf293; ... >> query: (?x5547, 02hnl) <- group(?x227, ?x5547), award_winner(?x247, ?x5547), group(?x1321, ?x5547) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dw4g group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.804 http://example.org/music/performance_role/regular_performances./music/group_membership/group #13118-014b6c PRED entity: 014b6c PRED relation: currency PRED expected values: 09nqf => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.87 #12, 0.85 #32, 0.85 #31) >> Best rule #12 for best value: >> intensional similarity = 5 >> extensional distance = 141 >> proper extension: 0n4m5; 0cb4j; 02cl1; 01531; 0n4mk; 0nrqh; 0nh1v; 0nv2x; 0mxsm; 0mmty; ... >> query: (?x13921, 09nqf) <- second_level_divisions(?x94, ?x13921), contains(?x1025, ?x13921), adjoins(?x1025, ?x1024), place_of_birth(?x2801, ?x1025), district_represented(?x176, ?x1025) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014b6c currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.874 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #13117-01crd5 PRED entity: 01crd5 PRED relation: countries_spoken_in! PRED expected values: 01r2l => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 55): 07zrf (0.73 #561, 0.69 #2075, 0.67 #1738), 02h40lc (0.36 #955, 0.35 #1179, 0.35 #1683), 064_8sq (0.33 #18, 0.20 #2093, 0.19 #1587), 02ztjwg (0.30 #85, 0.10 #758, 0.09 #926), 0jzc (0.19 #689, 0.17 #352, 0.15 #1810), 06nm1 (0.17 #2026, 0.16 #1858, 0.15 #120), 01lqm (0.17 #52, 0.04 #613, 0.04 #220), 02bjrlw (0.15 #169, 0.12 #225, 0.10 #505), 0880p (0.15 #153, 0.08 #602, 0.07 #377), 04306rv (0.12 #509, 0.12 #734, 0.11 #173) >> Best rule #561 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 05bcl; >> query: (?x8593, ?x393) <- country(?x10757, ?x8593), official_language(?x8593, ?x393), adjoins(?x2346, ?x8593) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #133 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 0jgd; 07f1x; *> query: (?x8593, 01r2l) <- film_release_region(?x124, ?x8593), geographic_distribution(?x9148, ?x8593), ?x124 = 0g56t9t *> conf = 0.11 ranks of expected_values: 14 EVAL 01crd5 countries_spoken_in! 01r2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 82.000 82.000 0.726 http://example.org/language/human_language/countries_spoken_in #13116-06nsb9 PRED entity: 06nsb9 PRED relation: people! PRED expected values: 0qcr0 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 11): 0gk4g (0.17 #10, 0.13 #76, 0.11 #142), 02k6hp (0.17 #37, 0.07 #103, 0.06 #169), 0qcr0 (0.07 #67, 0.06 #133, 0.02 #859), 01rt5h (0.07 #107, 0.06 #173), 0dq9p (0.02 #2591, 0.02 #1403, 0.02 #875), 04p3w (0.02 #209, 0.01 #1001, 0.01 #539), 02knxx (0.02 #230, 0.01 #626, 0.01 #2012), 02y0js (0.02 #1586, 0.02 #1256, 0.01 #1982), 07jwr (0.01 #867), 06z5s (0.01 #883) >> Best rule #10 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 019vgs; 03llf8; 03wy70; 02mv9b; >> query: (?x13108, 0gk4g) <- profession(?x13108, ?x5716), profession(?x13108, ?x1383), gender(?x13108, ?x231), ?x1383 = 0np9r, ?x5716 = 021wpb, ?x231 = 05zppz >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 045931; *> query: (?x13108, 0qcr0) <- profession(?x13108, ?x5716), ?x5716 = 021wpb, film(?x13108, ?x7524), film(?x2156, ?x7524), film_release_region(?x7524, ?x94) *> conf = 0.07 ranks of expected_values: 3 EVAL 06nsb9 people! 0qcr0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 84.000 0.167 http://example.org/people/cause_of_death/people #13115-0qlrh PRED entity: 0qlrh PRED relation: place PRED expected values: 0qlrh => 103 concepts (81 used for prediction) PRED predicted values (max 10 best out of 161): 0167q3 (0.08 #688, 0.08 #173, 0.06 #1718), 01m1zk (0.08 #607, 0.08 #92, 0.05 #3182), 02dtg (0.08 #524, 0.08 #9, 0.04 #4129), 0y1rf (0.08 #829, 0.08 #314, 0.04 #4434), 01tlmw (0.08 #525, 0.08 #10, 0.04 #4130), 0dq16 (0.08 #630, 0.08 #115, 0.04 #4235), 0pzmf (0.08 #679, 0.08 #164, 0.04 #4284), 0h6l4 (0.08 #891, 0.08 #376, 0.04 #6041), 0fvwg (0.08 #701, 0.08 #186, 0.04 #5851), 019fh (0.08 #78, 0.04 #4198, 0.04 #4713) >> Best rule #688 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 0mp3l; >> query: (?x13665, 0167q3) <- place_of_death(?x4447, ?x13665), source(?x13665, ?x958), category(?x13665, ?x134), county(?x13665, ?x8766), time_zones(?x13665, ?x2674), ?x2674 = 02hcv8, contains(?x6895, ?x8766) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qlrh place 0qlrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 81.000 0.083 http://example.org/location/hud_county_place/place #13114-04k4rt PRED entity: 04k4rt PRED relation: list! PRED expected values: 08z129 0k8z 01b39j 049ql1 => 7 concepts (7 used for prediction) PRED predicted values (max 10 best out of 503): 06py2 (0.85 #1528, 0.85 #1527, 0.85 #1526), 0206k5 (0.85 #1528, 0.85 #1527, 0.85 #1526), 01pf21 (0.85 #1528, 0.85 #1527, 0.85 #1526), 018_q8 (0.85 #1528, 0.85 #1527, 0.85 #1526), 02630g (0.85 #1528, 0.85 #1527, 0.85 #1526), 01yfp7 (0.85 #1528, 0.85 #1527, 0.85 #1526), 0k8z (0.85 #1528, 0.85 #1527, 0.85 #1526), 08z129 (0.85 #1528, 0.85 #1527, 0.85 #1526), 09d5h (0.85 #1528, 0.85 #1527, 0.85 #1526), 087c7 (0.85 #1528, 0.85 #1527, 0.85 #1526) >> Best rule #1528 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0dlj8q2; >> query: (?x5997, ?x2021) <- list(?x3230, ?x5997), list(?x266, ?x5997), list(?x2021, ?x7472), list(?x3230, ?x7472) >> conf = 0.85 => this is the best rule for 31 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7, 8 EVAL 04k4rt list! 049ql1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 7.000 7.000 0.854 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 04k4rt list! 01b39j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 7.000 7.000 0.854 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 04k4rt list! 0k8z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 7.000 7.000 0.854 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 04k4rt list! 08z129 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 7.000 7.000 0.854 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #13113-0grjmv PRED entity: 0grjmv PRED relation: artists PRED expected values: 01vsy7t 01vs4f3 => 57 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1052): 01vsl3_ (0.71 #2368, 0.50 #4520, 0.44 #5595), 095x_ (0.71 #2877, 0.40 #1803, 0.40 #729), 0137hn (0.57 #2742, 0.55 #8121, 0.50 #9195), 01kcms4 (0.57 #2795, 0.50 #4947, 0.44 #6022), 01vsy7t (0.57 #2550, 0.50 #4702, 0.44 #5777), 011z3g (0.57 #2748, 0.47 #10275, 0.45 #8127), 024qwq (0.57 #3012, 0.45 #8391, 0.42 #9465), 03j24kf (0.57 #2561, 0.40 #1487, 0.40 #413), 0fhxv (0.57 #2556, 0.40 #1482, 0.40 #408), 0178_w (0.57 #2757, 0.40 #1683, 0.40 #609) >> Best rule #2368 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 02k_kn; >> query: (?x9342, 01vsl3_) <- artists(?x9342, ?x9589), artists(?x9342, ?x4484), artists(?x9342, ?x1749), artists(?x3734, ?x1749), category(?x1749, ?x134), ?x3734 = 07v64s, group(?x227, ?x4484), artist(?x382, ?x4484), ?x134 = 08mbj5d, ?x9589 = 02cw1m >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2550 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 5 *> proper extension: 02k_kn; *> query: (?x9342, 01vsy7t) <- artists(?x9342, ?x9589), artists(?x9342, ?x4484), artists(?x9342, ?x1749), artists(?x3734, ?x1749), category(?x1749, ?x134), ?x3734 = 07v64s, group(?x227, ?x4484), artist(?x382, ?x4484), ?x134 = 08mbj5d, ?x9589 = 02cw1m *> conf = 0.57 ranks of expected_values: 5, 665 EVAL 0grjmv artists 01vs4f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 24.000 0.714 http://example.org/music/genre/artists EVAL 0grjmv artists 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 57.000 24.000 0.714 http://example.org/music/genre/artists #13112-0285m87 PRED entity: 0285m87 PRED relation: combatants! PRED expected values: 0cbvg => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 82): 03jqfx (0.65 #1745, 0.65 #1929, 0.65 #1927), 081pw (0.60 #1627, 0.51 #2411, 0.48 #2648), 0dr7s (0.54 #1130, 0.50 #104, 0.33 #470), 07j9n (0.50 #1052, 0.50 #814, 0.50 #633), 0cbvg (0.50 #514, 0.50 #86, 0.38 #1112), 03gqgt3 (0.50 #538, 0.45 #2042, 0.36 #1676), 0k4y6 (0.38 #1108, 0.33 #628, 0.26 #1025), 03jv8d (0.33 #533, 0.33 #45, 0.25 #166), 0cm2xh (0.33 #499, 0.27 #976, 0.23 #2003), 02tvsn (0.33 #51, 0.25 #234, 0.25 #111) >> Best rule #1745 for best value: >> intensional similarity = 7 >> extensional distance = 24 >> proper extension: 0jgd; 03pn9; 020d5; >> query: (?x9602, ?x6829) <- entity_involved(?x9939, ?x9602), entity_involved(?x6829, ?x9602), entity_involved(?x1777, ?x9602), jurisdiction_of_office(?x182, ?x9602), combatants(?x10176, ?x9602), combatants(?x1777, ?x1778), locations(?x9939, ?x456) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #514 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 05r4w; *> query: (?x9602, 0cbvg) <- combatants(?x10206, ?x9602), combatants(?x6829, ?x9602), ?x6829 = 0py8j, entity_involved(?x10206, ?x1590), combatants(?x10206, ?x512), ?x1590 = 02c4s, ?x512 = 07ssc *> conf = 0.50 ranks of expected_values: 5 EVAL 0285m87 combatants! 0cbvg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 174.000 174.000 0.652 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #13111-01vsykc PRED entity: 01vsykc PRED relation: gender PRED expected values: 05zppz => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.77 #27, 0.77 #59, 0.73 #19), 02zsn (0.47 #56, 0.46 #12, 0.45 #58) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 323 >> proper extension: 07_3qd; 04mx7s; >> query: (?x3290, 05zppz) <- artists(?x671, ?x3290), place_of_birth(?x3290, ?x12461), instrumentalists(?x227, ?x3290) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vsykc gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.766 http://example.org/people/person/gender #13110-0d075m PRED entity: 0d075m PRED relation: organizations_founded! PRED expected values: 0424m => 146 concepts (69 used for prediction) PRED predicted values (max 10 best out of 169): 06c0j (0.83 #335, 0.65 #3166, 0.56 #2941), 0gzh (0.83 #335, 0.65 #3166, 0.56 #2941), 034rd (0.83 #335, 0.65 #3166, 0.56 #2941), 09bg4l (0.29 #1512, 0.27 #1738, 0.21 #1360), 09gnn (0.25 #201, 0.20 #424, 0.07 #1676), 02vnp2 (0.25 #299, 0.07 #1663, 0.06 #2228), 05zl0 (0.25 #279, 0.07 #1643, 0.06 #2208), 01p5xy (0.25 #278, 0.07 #1642, 0.06 #2207), 02zd460 (0.25 #274, 0.07 #1638, 0.06 #2203), 07tds (0.25 #266, 0.07 #1630, 0.06 #2195) >> Best rule #335 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 034h1h; >> query: (?x8714, ?x5609) <- citytown(?x8714, ?x108), ?x108 = 0rh6k, organizations_founded(?x5254, ?x8714), religion(?x5254, ?x14017), organizations_founded(?x5254, ?x7127), organizations_founded(?x5609, ?x7127) >> conf = 0.83 => this is the best rule for 3 predicted values *> Best rule #3502 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 015zyd; 017l96; 01cl2y; 04gvyp; 09xwz; *> query: (?x8714, ?x2608) <- organizations_founded(?x5254, ?x8714), type_of_union(?x5254, ?x566), influenced_by(?x2608, ?x5254), profession(?x5254, ?x353) *> conf = 0.02 ranks of expected_values: 146 EVAL 0d075m organizations_founded! 0424m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 146.000 69.000 0.833 http://example.org/organization/organization_founder/organizations_founded #13109-01rlxt PRED entity: 01rlxt PRED relation: profession PRED expected values: 0dxtg => 107 concepts (84 used for prediction) PRED predicted values (max 10 best out of 63): 0dxtg (0.86 #2530, 0.83 #2234, 0.83 #605), 02hrh1q (0.76 #7860, 0.73 #7120, 0.70 #5492), 0cbd2 (0.56 #1634, 0.42 #1931, 0.41 #1782), 02jknp (0.52 #4893, 0.51 #4152, 0.48 #747), 02krf9 (0.32 #174, 0.31 #3283, 0.31 #3431), 0kyk (0.31 #1657, 0.25 #1805, 0.25 #1954), 018gz8 (0.28 #312, 0.18 #5790, 0.18 #2681), 0np9r (0.25 #316, 0.15 #908, 0.15 #1204), 09jwl (0.17 #7124, 0.16 #10676, 0.16 #9048), 02hv44_ (0.15 #1833, 0.14 #1093, 0.14 #1982) >> Best rule #2530 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 0bz5v2; 03mz9r; 0275_pj; 03bx_5q; 04gtdnh; 04crrxr; 0grmhb; 0564mx; 025vwmy; >> query: (?x5431, 0dxtg) <- award_nominee(?x4299, ?x5431), tv_program(?x5431, ?x13070), profession(?x5431, ?x319) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rlxt profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 84.000 0.859 http://example.org/people/person/profession #13108-0k0sb PRED entity: 0k0sb PRED relation: countries_spoken_in PRED expected values: 0bjv6 => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 283): 077qn (0.80 #4718, 0.80 #3442, 0.77 #3990), 012m_ (0.66 #3988, 0.64 #1619, 0.62 #1803), 07ytt (0.56 #1417, 0.42 #1964, 0.38 #1238), 0d060g (0.45 #5275, 0.45 #4915, 0.38 #4003), 06c1y (0.42 #4904, 0.40 #765, 0.38 #3443), 07t21 (0.40 #762, 0.33 #222, 0.31 #2030), 03pn9 (0.40 #786, 0.25 #606, 0.23 #2054), 0hzlz (0.38 #4019, 0.32 #3471, 0.30 #3107), 03gj2 (0.38 #2534, 0.38 #3443, 0.38 #1622), 0jdx (0.38 #2534, 0.38 #3443, 0.38 #1622) >> Best rule #4718 for best value: >> intensional similarity = 10 >> extensional distance = 28 >> proper extension: 012v8; >> query: (?x13473, ?x4059) <- official_language(?x9006, ?x13473), official_language(?x4059, ?x13473), country(?x5182, ?x4059), film_release_region(?x124, ?x4059), contains(?x455, ?x4059), nationality(?x4724, ?x9006), adjoins(?x1790, ?x4059), olympics(?x4059, ?x784), sports(?x391, ?x5182), countries_spoken_in(?x8650, ?x4059) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3443 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 19 *> proper extension: 02002f; *> query: (?x13473, ?x1790) <- official_language(?x9006, ?x13473), official_language(?x4059, ?x13473), country(?x5182, ?x4059), film_release_region(?x124, ?x4059), contains(?x455, ?x4059), nationality(?x4915, ?x9006), adjoins(?x1790, ?x4059), adjustment_currency(?x4059, ?x170), influenced_by(?x1236, ?x4915), sports(?x391, ?x5182) *> conf = 0.38 ranks of expected_values: 12 EVAL 0k0sb countries_spoken_in 0bjv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 38.000 38.000 0.797 http://example.org/language/human_language/countries_spoken_in #13107-06l3bl PRED entity: 06l3bl PRED relation: genre! PRED expected values: 01br2w 09q5w2 017jd9 0k2m6 04vq33 => 39 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1823): 0bx0l (0.72 #20107, 0.72 #7310, 0.71 #7309), 0pd4f (0.72 #20107, 0.72 #7310, 0.71 #7309), 04v8x9 (0.72 #20107, 0.72 #7310, 0.71 #7309), 0jvt9 (0.72 #20107, 0.72 #7310, 0.71 #7309), 04tng0 (0.72 #20107, 0.72 #7310, 0.71 #7309), 04kzqz (0.72 #20107, 0.72 #7310, 0.71 #7309), 034r25 (0.67 #11712, 0.50 #6230, 0.50 #2578), 0bw20 (0.67 #12219, 0.50 #6737, 0.50 #3085), 08fn5b (0.67 #11661, 0.50 #2527, 0.33 #8009), 0qm8b (0.67 #11211, 0.50 #2077, 0.25 #5729) >> Best rule #20107 for best value: >> intensional similarity = 7 >> extensional distance = 34 >> proper extension: 04t36; 03npn; 02n4kr; 0hn10; 03k9fj; 01jfsb; 0jtdp; 017fp; 01hmnh; 0219x_; ... >> query: (?x4757, ?x7073) <- titles(?x4757, ?x7073), genre(?x6023, ?x4757), genre(?x878, ?x4757), language(?x7073, ?x254), film(?x788, ?x878), nominated_for(?x880, ?x6023), genre(?x7073, ?x53) >> conf = 0.72 => this is the best rule for 6 predicted values *> Best rule #5501 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 07s9rl0; 04xvlr; *> query: (?x4757, 01br2w) <- titles(?x4757, ?x7672), titles(?x4757, ?x7073), titles(?x4757, ?x1048), ?x7073 = 016ywb, award(?x7672, ?x1429), genre(?x1048, ?x53), film(?x2451, ?x1048) *> conf = 0.50 ranks of expected_values: 67, 237, 245, 386, 1487 EVAL 06l3bl genre! 04vq33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 39.000 24.000 0.720 http://example.org/film/film/genre EVAL 06l3bl genre! 0k2m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.720 http://example.org/film/film/genre EVAL 06l3bl genre! 017jd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.720 http://example.org/film/film/genre EVAL 06l3bl genre! 09q5w2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.720 http://example.org/film/film/genre EVAL 06l3bl genre! 01br2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 39.000 24.000 0.720 http://example.org/film/film/genre #13106-01f2xy PRED entity: 01f2xy PRED relation: colors PRED expected values: 06fvc => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 20): 019sc (0.50 #107, 0.33 #267, 0.31 #367), 083jv (0.41 #262, 0.40 #1242, 0.39 #182), 088fh (0.33 #66, 0.25 #26, 0.20 #326), 06fvc (0.29 #403, 0.29 #383, 0.28 #363), 01l849 (0.27 #1261, 0.26 #1061, 0.26 #1221), 036k5h (0.25 #25, 0.17 #45, 0.15 #1025), 038hg (0.17 #72, 0.11 #432, 0.11 #2021), 09ggk (0.13 #336, 0.12 #416, 0.12 #396), 04mkbj (0.11 #2021, 0.11 #190, 0.11 #170), 03wkwg (0.11 #2021, 0.08 #975, 0.07 #1035) >> Best rule #107 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 01b_d4; >> query: (?x7355, 019sc) <- contains(?x512, ?x7355), currency(?x7355, ?x1099), student(?x7355, ?x164), colors(?x7355, ?x3189), ?x512 = 07ssc >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #403 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 0gjv_; 0gl6f; 0gl6x; *> query: (?x7355, 06fvc) <- currency(?x7355, ?x1099), colors(?x7355, ?x3189), ?x1099 = 01nv4h, contains(?x512, ?x7355), major_field_of_study(?x7355, ?x5179) *> conf = 0.29 ranks of expected_values: 4 EVAL 01f2xy colors 06fvc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 165.000 165.000 0.500 http://example.org/education/educational_institution/colors #13105-0jz9f PRED entity: 0jz9f PRED relation: award PRED expected values: 02x1z2s => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 280): 01lj_c (0.60 #1508, 0.11 #5135, 0.09 #8359), 01l78d (0.60 #1497, 0.11 #5124, 0.08 #4721), 01l29r (0.50 #1376, 0.27 #2585, 0.27 #2182), 02x1z2s (0.40 #602, 0.35 #8259, 0.33 #199), 09sdmz (0.40 #1012, 0.14 #41516, 0.13 #47163), 0gq9h (0.37 #4913, 0.35 #20231, 0.32 #21843), 09sb52 (0.36 #29868, 0.30 #847, 0.30 #38734), 0gq_d (0.33 #222, 0.29 #1834, 0.27 #2640), 0gr42 (0.33 #115, 0.21 #1727, 0.20 #4145), 018wng (0.33 #42, 0.21 #1654, 0.20 #4072) >> Best rule #1508 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 04cw0j; 03m9c8; 0dbpwb; 0b7xl8; >> query: (?x166, 01lj_c) <- award(?x166, ?x7285), award_winner(?x762, ?x166), ?x7285 = 01lk0l >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #602 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 016tw3; 09v3hq_; *> query: (?x166, 02x1z2s) <- film(?x166, ?x2529), film(?x166, ?x1224), ?x1224 = 020fcn, film(?x643, ?x2529) *> conf = 0.40 ranks of expected_values: 4 EVAL 0jz9f award 02x1z2s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 132.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13104-07_fj54 PRED entity: 07_fj54 PRED relation: currency PRED expected values: 09nqf => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.84 #15, 0.82 #29, 0.81 #50), 01nv4h (0.04 #79, 0.03 #44, 0.02 #184), 02l6h (0.02 #53, 0.02 #60, 0.02 #67), 02gsvk (0.01 #216) >> Best rule #15 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 09q5w2; 020fcn; 01719t; 072x7s; 01j8wk; 01shy7; 03z20c; 047p7fr; 0x25q; 05_5rjx; ... >> query: (?x4953, 09nqf) <- film(?x400, ?x4953), film_crew_role(?x4953, ?x3197), film_crew_role(?x4953, ?x1078), ?x3197 = 02ynfr, ?x1078 = 089fss >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07_fj54 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.837 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #13103-0b7l1f PRED entity: 0b7l1f PRED relation: athlete! PRED expected values: 02vx4 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 5): 02vx4 (0.90 #82, 0.89 #92, 0.88 #62), 0jm_ (0.20 #43, 0.17 #103, 0.16 #123), 018w8 (0.13 #146, 0.13 #126, 0.13 #136), 018jz (0.07 #147, 0.07 #137, 0.06 #127), 03tmr (0.02 #131, 0.02 #141, 0.01 #111) >> Best rule #82 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 0c11mj; 071pf2; 0fv6dr; 09lhln; 0f1pyf; 0bw7ly; 0457w0; 09r1j5; 0djvzd; 02rnns; ... >> query: (?x9672, 02vx4) <- team(?x9672, ?x983), team(?x530, ?x983), team(?x9672, ?x12271), ?x530 = 02_j1w, gender(?x9672, ?x231) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b7l1f athlete! 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.896 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #13102-02w9s PRED entity: 02w9s PRED relation: contains! PRED expected values: 02qkt 06mx8 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 38): 0dg3n1 (0.58 #6436, 0.33 #37036, 0.32 #20815), 02qkt (0.47 #22805, 0.45 #30920, 0.44 #11118), 07c5l (0.37 #4882, 0.36 #10269, 0.35 #5779), 0j0k (0.33 #3068, 0.26 #30951, 0.25 #27345), 02j9z (0.33 #28, 0.21 #26092, 0.18 #18889), 059g4 (0.29 #29670, 0.17 #1359, 0.14 #2256), 06mx8 (0.29 #29670, 0.09 #46788, 0.08 #36880), 04wsz (0.25 #3189, 0.12 #31072, 0.11 #27466), 05nrg (0.24 #7747, 0.22 #13132, 0.22 #12234), 04pnx (0.18 #29197, 0.14 #36408, 0.12 #51716) >> Best rule #6436 for best value: >> intensional similarity = 16 >> extensional distance = 22 >> proper extension: 06mzp; 02wzv; 01699; 05cc1; 01nyl; 04sj3; >> query: (?x9884, 0dg3n1) <- adjustment_currency(?x9884, ?x170), official_language(?x9884, ?x7791), language(?x11610, ?x7791), language(?x7819, ?x7791), languages_spoken(?x7790, ?x7791), music(?x7819, ?x3910), film_crew_role(?x7819, ?x4305), nominated_for(?x2963, ?x7819), film(?x3708, ?x7819), genre(?x7819, ?x53), ?x4305 = 0215hd, production_companies(?x7819, ?x6554), country(?x7819, ?x94), produced_by(?x7819, ?x3568), ?x11610 = 03cffvv, countries_spoken_in(?x7791, ?x12508) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #22805 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 45 *> proper extension: 047yc; *> query: (?x9884, 02qkt) <- adjustment_currency(?x9884, ?x170), form_of_government(?x9884, ?x1926), ?x170 = 09nqf, form_of_government(?x11553, ?x1926), form_of_government(?x7035, ?x1926), form_of_government(?x6923, ?x1926), form_of_government(?x2152, ?x1926), form_of_government(?x1122, ?x1926), form_of_government(?x421, ?x1926), form_of_government(?x279, ?x1926), ?x11553 = 0168t, country(?x2978, ?x7035), ?x279 = 0d060g, ?x421 = 03_r3, ?x6923 = 07fsv, ?x1122 = 09pmkv, ?x2978 = 03_8r, member_states(?x7695, ?x7035), official_language(?x7035, ?x254), adjoins(?x7035, ?x2804), countries_within(?x2467, ?x7035), ?x2152 = 06mkj *> conf = 0.47 ranks of expected_values: 2, 7 EVAL 02w9s contains! 06mx8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 70.000 70.000 0.583 http://example.org/location/location/contains EVAL 02w9s contains! 02qkt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.583 http://example.org/location/location/contains #13101-01p9hgt PRED entity: 01p9hgt PRED relation: nationality PRED expected values: 02jx1 => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 17): 09c7w0 (0.78 #101, 0.71 #1, 0.69 #4201), 03rk0 (0.50 #346, 0.05 #4846, 0.05 #5046), 02jx1 (0.18 #1233, 0.16 #1533, 0.16 #1633), 0d060g (0.11 #107, 0.08 #207, 0.05 #1307), 07ssc (0.09 #1515, 0.09 #1215, 0.09 #1615), 0jgd (0.04 #302, 0.03 #402), 03spz (0.04 #367, 0.02 #467), 03_3d (0.04 #306, 0.01 #2406, 0.01 #2006), 0162v (0.04 #345), 03gj2 (0.04 #326) >> Best rule #101 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 0p_47; 0pmw9; >> query: (?x1413, 09c7w0) <- award_winner(?x5656, ?x1413), nominated_for(?x2124, ?x1413) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1233 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 340 *> proper extension: 07_3qd; 04mx7s; 016lj_; *> query: (?x1413, 02jx1) <- artist(?x5744, ?x1413), role(?x1413, ?x227) *> conf = 0.18 ranks of expected_values: 3 EVAL 01p9hgt nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 53.000 53.000 0.778 http://example.org/people/person/nationality #13100-06fvc PRED entity: 06fvc PRED relation: colors! PRED expected values: 05g3b 0x2p 0ytc 04mnts 04wqsm 01cwm1 047g98 02b1hb 0kz4w 048ldh 02w59b 0lmm3 04l59s => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 866): 01ct6 (0.57 #2472, 0.36 #3462, 0.33 #3958), 0jm9w (0.50 #1633, 0.33 #2868, 0.33 #1387), 051vz (0.50 #1505, 0.33 #2740, 0.33 #1259), 04b5l3 (0.50 #1673, 0.33 #2908, 0.33 #1427), 04vn5 (0.50 #1571, 0.33 #2312, 0.33 #1325), 01y3v (0.50 #1510, 0.33 #1264, 0.24 #2961), 02d02 (0.50 #1605, 0.33 #1359, 0.24 #2961), 0jmcv (0.50 #1587, 0.33 #1341, 0.22 #2822), 07l8f (0.50 #1568, 0.33 #1322, 0.22 #2803), 01rlz4 (0.50 #1661, 0.33 #1415, 0.22 #2896) >> Best rule #2472 for best value: >> intensional similarity = 29 >> extensional distance = 5 >> proper extension: 09ggk; >> query: (?x1101, 01ct6) <- colors(?x11278, ?x1101), colors(?x10838, ?x1101), colors(?x6417, ?x1101), colors(?x2171, ?x1101), colors(?x7608, ?x1101), colors(?x6348, ?x1101), colors(?x2971, ?x1101), state_province_region(?x11278, ?x1906), category(?x11278, ?x134), major_field_of_study(?x11278, ?x1682), school_type(?x11278, ?x1044), currency(?x10838, ?x170), position(?x7608, ?x60), student(?x11278, ?x9597), major_field_of_study(?x6417, ?x5607), child(?x10513, ?x11278), teams(?x1523, ?x2971), institution(?x734, ?x2171), team(?x4244, ?x6348), contains(?x94, ?x10838), student(?x1682, ?x4026), student(?x5607, ?x4265), official_language(?x172, ?x5607), contains(?x335, ?x6417), current_club(?x9926, ?x2971), sport(?x7608, ?x471), citytown(?x2171, ?x8980), language(?x80, ?x5607), state_province_region(?x2171, ?x1767) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2881 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 7 *> proper extension: 06kqt3; *> query: (?x1101, 02b1hb) <- colors(?x12669, ?x1101), colors(?x12356, ?x1101), colors(?x11278, ?x1101), colors(?x10838, ?x1101), colors(?x9575, ?x1101), colors(?x6417, ?x1101), colors(?x2171, ?x1101), colors(?x14015, ?x1101), colors(?x11530, ?x1101), colors(?x10142, ?x1101), colors(?x9860, ?x1101), colors(?x8678, ?x1101), colors(?x7725, ?x1101), colors(?x7608, ?x1101), colors(?x5154, ?x1101), state_province_region(?x11278, ?x1906), category(?x11278, ?x134), major_field_of_study(?x11278, ?x1682), school_type(?x11278, ?x1044), currency(?x10838, ?x170), position(?x7608, ?x60), school_type(?x9575, ?x3092), student(?x11278, ?x9597), major_field_of_study(?x6417, ?x742), major_field_of_study(?x2171, ?x1668), teams(?x13724, ?x11530), team(?x1696, ?x8678), major_field_of_study(?x7278, ?x1682), institution(?x865, ?x12356), current_club(?x676, ?x9860), position(?x10142, ?x3724), citytown(?x6417, ?x12563), school(?x5154, ?x2399), student(?x6417, ?x2817), ?x865 = 02h4rq6, contains(?x279, ?x9575), season(?x7725, ?x2406), school(?x580, ?x2171), organization(?x12076, ?x12669), ?x7278 = 02sjgpq, team(?x2918, ?x14015) *> conf = 0.33 ranks of expected_values: 30, 34, 166, 172, 173, 192, 195, 196, 223, 224, 273, 328, 689 EVAL 06fvc colors! 04l59s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 0lmm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 02w59b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 048ldh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 0kz4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 02b1hb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 047g98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 01cwm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 04wqsm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 04mnts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 0ytc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 0x2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 22.000 0.571 http://example.org/sports/sports_team/colors EVAL 06fvc colors! 05g3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 22.000 22.000 0.571 http://example.org/sports/sports_team/colors #13099-015bwt PRED entity: 015bwt PRED relation: artist! PRED expected values: 04fcjt 01dtcb => 105 concepts (56 used for prediction) PRED predicted values (max 10 best out of 114): 01trtc (0.40 #1988, 0.17 #1440, 0.17 #1166), 03rhqg (0.30 #1248, 0.24 #563, 0.23 #4949), 015_1q (0.29 #5641, 0.28 #4953, 0.24 #1252), 01cl0d (0.28 #1971, 0.13 #190, 0.09 #53), 01dtcb (0.27 #45, 0.20 #182, 0.15 #730), 017l96 (0.27 #18, 0.20 #155, 0.15 #5640), 01clyr (0.27 #170, 0.18 #33, 0.10 #1540), 0g768 (0.20 #173, 0.18 #36, 0.15 #1543), 03y5g8 (0.20 #242, 0.04 #7407, 0.02 #3119), 033hn8 (0.19 #424, 0.18 #5635, 0.16 #1246) >> Best rule #1988 for best value: >> intensional similarity = 4 >> extensional distance = 142 >> proper extension: 0fp_v1x; 01q7cb_; 02whj; 07_3qd; 09prnq; 06k02; 0cg9y; 010hn; 02lbrd; 0fpj4lx; ... >> query: (?x11455, 01trtc) <- artist(?x7448, ?x11455), company(?x4960, ?x7448), artist(?x7448, ?x3756), ?x3756 = 01wgcvn >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #45 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01wp8w7; 01wj92r; 0pj9t; 0d9xq; 01bczm; 0277c3; 07mvp; 01k23t; 0dbb3; *> query: (?x11455, 01dtcb) <- artist(?x5891, ?x11455), origin(?x11455, ?x94), ?x5891 = 011k11, award(?x11455, ?x1389) *> conf = 0.27 ranks of expected_values: 5, 21 EVAL 015bwt artist! 01dtcb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 105.000 56.000 0.403 http://example.org/music/record_label/artist EVAL 015bwt artist! 04fcjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 105.000 56.000 0.403 http://example.org/music/record_label/artist #13098-01kcms4 PRED entity: 01kcms4 PRED relation: group! PRED expected values: 018vs => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 115): 018vs (0.77 #100, 0.71 #531, 0.62 #1649), 0l14md (0.64 #524, 0.62 #1642, 0.62 #93), 028tv0 (0.46 #1648, 0.40 #530, 0.36 #2853), 0l14qv (0.40 #522, 0.38 #91, 0.23 #2845), 01vj9c (0.27 #2855, 0.24 #532, 0.23 #1650), 05r5c (0.24 #1643, 0.23 #2848, 0.17 #525), 0l14j_ (0.21 #567, 0.11 #2890, 0.09 #50), 07y_7 (0.19 #519, 0.15 #88, 0.11 #2842), 013y1f (0.19 #545, 0.13 #2868, 0.11 #1663), 042v_gx (0.15 #95, 0.12 #1644, 0.12 #526) >> Best rule #100 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 07hgm; 01v0sxx; >> query: (?x7227, 018vs) <- artists(?x3061, ?x7227), artists(?x1380, ?x7227), group(?x227, ?x7227), ?x1380 = 0dl5d, ?x3061 = 05bt6j >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kcms4 group! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.769 http://example.org/music/performance_role/regular_performances./music/group_membership/group #13097-0blpg PRED entity: 0blpg PRED relation: nominated_for! PRED expected values: 02x73k6 => 54 concepts (50 used for prediction) PRED predicted values (max 10 best out of 194): 0gr51 (0.67 #321, 0.23 #5069, 0.22 #8207), 02qyp19 (0.50 #242, 0.23 #5069, 0.22 #8207), 02ppm4q (0.50 #360, 0.12 #1565, 0.12 #601), 03hl6lc (0.42 #374, 0.12 #133, 0.12 #856), 02z0dfh (0.42 #304, 0.11 #786, 0.09 #1027), 02x1dht (0.38 #45, 0.26 #2653, 0.26 #2654), 0gs9p (0.33 #307, 0.26 #3444, 0.26 #2653), 040njc (0.33 #248, 0.23 #5069, 0.22 #8207), 0gqyl (0.33 #323, 0.22 #8207, 0.21 #3862), 0gq_v (0.33 #261, 0.21 #3398, 0.19 #1707) >> Best rule #321 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 05jf85; 0jyx6; 06lpmt; 04smdd; 04t9c0; 0sxns; 07bxqz; >> query: (?x3988, 0gr51) <- nominated_for(?x6157, ?x3988), nominated_for(?x986, ?x3988), ?x986 = 081lh, participant(?x6157, ?x513) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2653 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 245 *> proper extension: 01p9hgt; 01kv4mb; 02fn5r; 0ggjt; 0bhvtc; 03cfjg; 0p_47; 0pmw9; *> query: (?x3988, ?x3066) <- nominated_for(?x13027, ?x3988), nominated_for(?x3066, ?x13027), award(?x92, ?x3066) *> conf = 0.26 ranks of expected_values: 17 EVAL 0blpg nominated_for! 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 54.000 50.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13096-024fz9 PRED entity: 024fz9 PRED relation: award_winner PRED expected values: 06m61 => 31 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1281): 0159h6 (0.40 #14782, 0.37 #14781, 0.34 #41891), 0bq2g (0.40 #14782, 0.37 #14781, 0.34 #41891), 01mqh5 (0.40 #14782, 0.37 #14781, 0.32 #27104), 0dqcm (0.37 #14781, 0.34 #41891, 0.32 #27104), 02661h (0.37 #14781, 0.32 #27104, 0.30 #39426), 01dhpj (0.33 #1762, 0.04 #9152, 0.04 #6688), 0149xx (0.33 #1156, 0.03 #27103, 0.03 #29568), 016k62 (0.33 #1166, 0.03 #27103, 0.03 #29568), 0127gn (0.33 #1159, 0.03 #8549, 0.03 #6085), 03_0p (0.33 #1165, 0.02 #15948, 0.01 #8555) >> Best rule #14782 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 0257yf; >> query: (?x4183, ?x11317) <- ceremony(?x4183, ?x1362), ?x1362 = 019bk0, award(?x11317, ?x4183), award_nominee(?x2681, ?x11317) >> conf = 0.40 => this is the best rule for 3 predicted values *> Best rule #36961 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 316 *> proper extension: 0dgr5xp; 05f3q; 02kgb7; *> query: (?x4183, ?x4608) <- award_winner(?x4183, ?x4609), award_winner(?x4609, ?x4608), profession(?x4609, ?x1032), type_of_union(?x4609, ?x566) *> conf = 0.16 ranks of expected_values: 16 EVAL 024fz9 award_winner 06m61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 31.000 20.000 0.401 http://example.org/award/award_category/winners./award/award_honor/award_winner #13095-0dprg PRED entity: 0dprg PRED relation: location! PRED expected values: 01rgr => 128 concepts (52 used for prediction) PRED predicted values (max 10 best out of 2080): 04glr5h (0.52 #40280, 0.50 #25175, 0.49 #50349), 0c9d9 (0.43 #7554, 0.38 #85592, 0.27 #108249), 09h_q (0.40 #21768, 0.40 #19251, 0.40 #16734), 014g9y (0.33 #2146, 0.25 #12218, 0.25 #9700), 019fz (0.33 #2373, 0.25 #12445, 0.25 #9927), 06wvj (0.33 #468, 0.25 #10540, 0.25 #8022), 01_f_5 (0.33 #1274, 0.25 #11346, 0.25 #8828), 01vsqvs (0.33 #1861, 0.25 #11933, 0.25 #9415), 01vh3r (0.33 #2339, 0.25 #12411, 0.25 #9893), 07ym0 (0.33 #1704, 0.25 #11776, 0.25 #9258) >> Best rule #40280 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0fn2g; >> query: (?x9499, ?x8933) <- place_of_birth(?x8933, ?x9499), location_of_ceremony(?x566, ?x9499), country(?x9499, ?x789), mode_of_transportation(?x9499, ?x4272) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1950 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 05qtj; *> query: (?x9499, 01rgr) <- contains(?x789, ?x9499), ?x789 = 0f8l9c, location(?x9170, ?x9499), award_winner(?x3618, ?x9170), award(?x9170, ?x1079) *> conf = 0.33 ranks of expected_values: 28 EVAL 0dprg location! 01rgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 128.000 52.000 0.523 http://example.org/people/person/places_lived./people/place_lived/location #13094-02q_cc PRED entity: 02q_cc PRED relation: executive_produced_by! PRED expected values: 02r8hh_ => 105 concepts (98 used for prediction) PRED predicted values (max 10 best out of 339): 02pxmgz (0.12 #1099, 0.05 #2139, 0.03 #2659), 0cc5qkt (0.10 #6769, 0.10 #5726, 0.07 #195), 03tn80 (0.10 #6769, 0.10 #5726, 0.07 #279), 0298n7 (0.10 #6769, 0.10 #5726, 0.04 #9894), 0jqn5 (0.10 #6769, 0.10 #5726, 0.04 #9894), 072x7s (0.10 #6769, 0.10 #5726, 0.04 #9894), 026p4q7 (0.10 #6769, 0.10 #5726, 0.04 #9894), 07j94 (0.10 #6769, 0.10 #5726, 0.04 #9894), 02rb84n (0.10 #6769, 0.10 #5726, 0.04 #9894), 08xvpn (0.10 #6769, 0.10 #5726, 0.04 #9894) >> Best rule #1099 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 03y2kr; 06y3r; 01s7z0; >> query: (?x846, 02pxmgz) <- profession(?x846, ?x319), executive_produced_by(?x1076, ?x846), organizations_founded(?x846, ?x1686) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02q_cc executive_produced_by! 02r8hh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 98.000 0.120 http://example.org/film/film/executive_produced_by #13093-032l1 PRED entity: 032l1 PRED relation: influenced_by! PRED expected values: 084w8 041mt 0lcx 06myp => 170 concepts (83 used for prediction) PRED predicted values (max 10 best out of 420): 084w8 (0.55 #5284, 0.50 #962, 0.33 #4804), 040db (0.50 #1031, 0.45 #5353, 0.40 #2953), 040_t (0.50 #1198, 0.40 #2639, 0.36 #5520), 0mb5x (0.50 #1275, 0.40 #2716, 0.36 #5597), 0j3v (0.50 #555, 0.38 #6799, 0.25 #1035), 047g6 (0.50 #928, 0.31 #7172, 0.17 #10055), 0d4jl (0.50 #1070, 0.27 #5392, 0.20 #2511), 014dq7 (0.40 #2460, 0.33 #59, 0.20 #2941), 06whf (0.40 #2556, 0.25 #1115, 0.25 #635), 014ps4 (0.40 #2691, 0.25 #1250, 0.22 #4611) >> Best rule #5284 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 04xjp; >> query: (?x3336, 084w8) <- influenced_by(?x14008, ?x3336), influenced_by(?x5262, ?x3336), influenced_by(?x3325, ?x3336), influenced_by(?x476, ?x5262), ?x3325 = 073v6, student(?x741, ?x14008) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11, 16, 97 EVAL 032l1 influenced_by! 06myp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 170.000 83.000 0.545 http://example.org/influence/influence_node/influenced_by EVAL 032l1 influenced_by! 0lcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 170.000 83.000 0.545 http://example.org/influence/influence_node/influenced_by EVAL 032l1 influenced_by! 041mt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 170.000 83.000 0.545 http://example.org/influence/influence_node/influenced_by EVAL 032l1 influenced_by! 084w8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 83.000 0.545 http://example.org/influence/influence_node/influenced_by #13092-017r13 PRED entity: 017r13 PRED relation: award PRED expected values: 057xs89 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 240): 02x73k6 (0.72 #27610, 0.70 #12801, 0.67 #24808), 05ztrmj (0.33 #180, 0.09 #2180, 0.09 #3380), 0gq9h (0.33 #1275, 0.30 #1675, 0.14 #16402), 040njc (0.26 #1207, 0.23 #1607, 0.09 #4807), 02x4w6g (0.22 #512, 0.17 #112, 0.12 #27209), 05zr6wv (0.22 #416, 0.17 #16, 0.12 #2016), 05pcn59 (0.18 #2079, 0.17 #79, 0.14 #3279), 0gs9p (0.17 #1277, 0.17 #1677, 0.14 #16402), 019f4v (0.17 #1264, 0.16 #1664, 0.14 #16402), 0ck27z (0.17 #90, 0.15 #12090, 0.15 #7690) >> Best rule #27610 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x6279, ?x1033) <- award_winner(?x1033, ?x6279), award(?x92, ?x1033) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #157 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0525b; *> query: (?x6279, 057xs89) <- award_nominee(?x6279, ?x91), film(?x6279, ?x5795), ?x5795 = 025rvx0 *> conf = 0.17 ranks of expected_values: 19 EVAL 017r13 award 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 83.000 83.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13091-0ct2tf5 PRED entity: 0ct2tf5 PRED relation: country PRED expected values: 0345h => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 99): 09c7w0 (0.81 #1714, 0.81 #430, 0.81 #2633), 07ssc (0.35 #200, 0.32 #384, 0.31 #139), 0345h (0.25 #150, 0.18 #211, 0.17 #700), 0f8l9c (0.12 #387, 0.10 #2222, 0.10 #631), 0d060g (0.10 #9, 0.09 #70, 0.07 #376), 0ctw_b (0.10 #24, 0.09 #85, 0.05 #452), 0chghy (0.09 #196, 0.07 #319, 0.07 #380), 03_3d (0.05 #2517, 0.04 #4599, 0.04 #924), 03h64 (0.04 #536, 0.03 #658, 0.03 #719), 03rjj (0.03 #1290, 0.03 #618, 0.03 #679) >> Best rule #1714 for best value: >> intensional similarity = 4 >> extensional distance = 833 >> proper extension: 0kbhf; 0m5s5; >> query: (?x9421, 09c7w0) <- nominated_for(?x298, ?x9421), film(?x2858, ?x9421), participant(?x2275, ?x2858), film_release_distribution_medium(?x9421, ?x81) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #150 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 05qbckf; 03cp4cn; 01gwk3; *> query: (?x9421, 0345h) <- film_crew_role(?x9421, ?x7591), film_format(?x9421, ?x909), ?x7591 = 0d2b38, crewmember(?x9421, ?x2887) *> conf = 0.25 ranks of expected_values: 3 EVAL 0ct2tf5 country 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.814 http://example.org/film/film/country #13090-03h64 PRED entity: 03h64 PRED relation: film_release_region! PRED expected values: 05p1tzf 0m_mm 053rxgm 07qg8v 02r1c18 0168ls 0j6b5 06ztvyx 0b_5d 0bmc4cm 047fjjr 0dr_9t7 06tpmy 03nm_fh 0gkz3nz 01rwpj 01sby_ 03yvf2 01d259 0h21v2 0gj96ln 07s3m4g 0bs8s1p 0ds1glg 032clf 02vz6dn 0h95927 0gvvf4j 05zvzf3 0m3gy 0gy7bj4 0ccck7 02wtp6 => 226 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1522): 0gy7bj4 (0.91 #24720, 0.80 #16439, 0.78 #11264), 03nm_fh (0.87 #29420, 0.87 #28385, 0.87 #16997), 05p1tzf (0.87 #16602, 0.87 #15567, 0.83 #23848), 06ztvyx (0.87 #29210, 0.83 #28175, 0.83 #44736), 0gkz3nz (0.87 #15964, 0.83 #25280, 0.83 #24245), 0ds1glg (0.87 #16230, 0.77 #23476, 0.75 #25546), 0gj96ln (0.83 #25456, 0.82 #23386, 0.81 #31669), 053rxgm (0.83 #23904, 0.80 #29081, 0.80 #16658), 07s3m4g (0.80 #17219, 0.80 #16184, 0.78 #24465), 0bs8s1p (0.80 #17256, 0.80 #16221, 0.78 #11046) >> Best rule #24720 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 06mzp; >> query: (?x2645, 0gy7bj4) <- film_release_region(?x8471, ?x2645), film_release_region(?x1386, ?x2645), ?x1386 = 0dtfn, ?x8471 = 0cp0t91 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 16, 26, 30, 34, 42, 43, 44, 50, 51, 53, 56, 59, 64, 68, 71, 76, 83, 91, 98, 100, 148 EVAL 03h64 film_release_region! 02wtp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0ccck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0gy7bj4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0m3gy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0gvvf4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0h95927 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 02vz6dn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 032clf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0ds1glg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0bs8s1p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 07s3m4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0gj96ln CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0h21v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 01d259 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 03yvf2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 01sby_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 01rwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0gkz3nz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 03nm_fh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 06tpmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0dr_9t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 047fjjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0bmc4cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0b_5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 06ztvyx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0j6b5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0168ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 02r1c18 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 07qg8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 053rxgm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 0m_mm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03h64 film_release_region! 05p1tzf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 104.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13089-02x8m PRED entity: 02x8m PRED relation: artists PRED expected values: 01vvycq 01wdqrx 0136p1 0161sp 01lvcs1 01vxlbm 01vvyfh 01pq5j7 015srx 021r7r 0dw3l => 68 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1024): 07s3vqk (0.75 #11066, 0.67 #8049, 0.60 #7044), 01wdqrx (0.67 #8120, 0.62 #11137, 0.60 #7115), 015xp4 (0.67 #8470, 0.62 #11487, 0.60 #7465), 01vvycq (0.67 #8083, 0.60 #7078, 0.60 #5067), 01pq5j7 (0.67 #8477, 0.60 #7472, 0.57 #9482), 01w60_p (0.67 #8187, 0.60 #7182, 0.57 #9192), 02jq1 (0.62 #11514, 0.50 #8497, 0.50 #3471), 0136p1 (0.60 #7168, 0.60 #6162, 0.60 #5157), 01x1cn2 (0.60 #7218, 0.60 #5207, 0.60 #4202), 015srx (0.60 #7524, 0.60 #4508, 0.50 #11546) >> Best rule #11066 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 07sbbz2; >> query: (?x1127, 07s3vqk) <- artists(?x1127, ?x8362), artists(?x1127, ?x5478), artists(?x1127, ?x1128), ?x8362 = 01wg25j, award_nominee(?x527, ?x5478), artists(?x5792, ?x1128), ?x5792 = 026z9, award(?x1128, ?x567) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #8120 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 0155w; *> query: (?x1127, 01wdqrx) <- artists(?x1127, ?x8362), artists(?x1127, ?x6289), artists(?x1127, ?x6129), ?x8362 = 01wg25j, parent_genre(?x1127, ?x3928), artists(?x2937, ?x6289), ?x2937 = 0glt670, award_winner(?x3647, ?x6129) *> conf = 0.67 ranks of expected_values: 2, 4, 5, 8, 10, 64, 85, 88, 99, 123, 496 EVAL 02x8m artists 0dw3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 21.000 0.750 http://example.org/music/genre/artists EVAL 02x8m artists 021r7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 68.000 21.000 0.750 http://example.org/music/genre/artists EVAL 02x8m artists 015srx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 68.000 21.000 0.750 http://example.org/music/genre/artists EVAL 02x8m artists 01pq5j7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 21.000 0.750 http://example.org/music/genre/artists EVAL 02x8m artists 01vvyfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 68.000 21.000 0.750 http://example.org/music/genre/artists EVAL 02x8m artists 01vxlbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 68.000 21.000 0.750 http://example.org/music/genre/artists EVAL 02x8m artists 01lvcs1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 68.000 21.000 0.750 http://example.org/music/genre/artists EVAL 02x8m artists 0161sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 68.000 21.000 0.750 http://example.org/music/genre/artists EVAL 02x8m artists 0136p1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 68.000 21.000 0.750 http://example.org/music/genre/artists EVAL 02x8m artists 01wdqrx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 21.000 0.750 http://example.org/music/genre/artists EVAL 02x8m artists 01vvycq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 21.000 0.750 http://example.org/music/genre/artists #13088-0c34mt PRED entity: 0c34mt PRED relation: nominated_for! PRED expected values: 02x2gy0 => 78 concepts (67 used for prediction) PRED predicted values (max 10 best out of 190): 02x4x18 (0.30 #102, 0.25 #582, 0.05 #14404), 0gq_v (0.28 #3380, 0.23 #1220, 0.20 #7460), 0gq9h (0.28 #7503, 0.26 #4143, 0.25 #3423), 0gr0m (0.28 #1260, 0.26 #3420, 0.23 #780), 0gs96 (0.27 #3451, 0.23 #811, 0.23 #1291), 099c8n (0.25 #777, 0.17 #1257, 0.16 #3417), 0gs9p (0.24 #4145, 0.24 #7505, 0.22 #3425), 019f4v (0.24 #4134, 0.24 #3414, 0.23 #7494), 0k611 (0.23 #4154, 0.21 #4394, 0.20 #7514), 0l8z1 (0.23 #1252, 0.21 #3412, 0.16 #772) >> Best rule #102 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 02d003; >> query: (?x3531, 02x4x18) <- film(?x4662, ?x3531), ?x4662 = 016vg8, titles(?x571, ?x3531), genre(?x124, ?x571), genre(?x3413, ?x571) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #823 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 072r5v; 0dmn0x; *> query: (?x3531, 02x2gy0) <- featured_film_locations(?x3531, ?x362), genre(?x3531, ?x162), ?x162 = 04xvlr, film_crew_role(?x3531, ?x137) *> conf = 0.18 ranks of expected_values: 27 EVAL 0c34mt nominated_for! 02x2gy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 78.000 67.000 0.300 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13087-01f9y_ PRED entity: 01f9y_ PRED relation: artists PRED expected values: 053yx => 50 concepts (16 used for prediction) PRED predicted values (max 10 best out of 999): 06mt91 (0.58 #2777, 0.33 #613, 0.30 #3859), 04mn81 (0.58 #2309, 0.33 #145, 0.25 #1227), 01k3qj (0.58 #2852, 0.33 #688, 0.25 #1770), 01vvycq (0.50 #2211, 0.40 #3293, 0.33 #47), 01dwrc (0.50 #2689, 0.35 #3771, 0.33 #525), 03f5spx (0.50 #2222, 0.35 #3304, 0.33 #58), 0127s7 (0.50 #2705, 0.35 #3787, 0.33 #541), 01vtj38 (0.50 #2828, 0.35 #3910, 0.33 #664), 0x3n (0.50 #2735, 0.35 #3817, 0.33 #571), 011z3g (0.50 #2769, 0.35 #3851, 0.27 #8180) >> Best rule #2777 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 0ggx5q; >> query: (?x12070, 06mt91) <- artists(?x12070, ?x5478), artists(?x12070, ?x3175), ?x5478 = 01yzl2, profession(?x3175, ?x1032), award(?x3175, ?x3365), award_winner(?x827, ?x3175), film(?x3175, ?x1877) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #11055 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 79 *> proper extension: 01skxk; *> query: (?x12070, 053yx) <- artists(?x12070, ?x8874), parent_genre(?x12070, ?x5630), religion(?x8874, ?x492), award_winner(?x8874, ?x8332), parent_genre(?x5630, ?x1127) *> conf = 0.09 ranks of expected_values: 756 EVAL 01f9y_ artists 053yx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 16.000 0.583 http://example.org/music/genre/artists #13086-01wgxtl PRED entity: 01wgxtl PRED relation: award_nominee PRED expected values: 03f19q4 => 124 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1177): 0677ng (0.81 #100263, 0.81 #144566, 0.81 #100264), 01q32bd (0.81 #100263, 0.81 #144566, 0.81 #100264), 01wgxtl (0.50 #2933, 0.29 #5264, 0.20 #7595), 01vsgrn (0.40 #3634, 0.29 #5965, 0.14 #139903), 06mt91 (0.36 #6214, 0.30 #8545, 0.30 #3883), 0288fyj (0.30 #7489, 0.29 #5158, 0.29 #496), 067nsm (0.30 #3831, 0.29 #6162, 0.15 #8493), 04lgymt (0.30 #7096, 0.29 #4765, 0.14 #103), 03f19q4 (0.30 #3556, 0.21 #5887, 0.14 #139903), 02wwwv5 (0.30 #4368, 0.14 #6699, 0.14 #139903) >> Best rule #100263 for best value: >> intensional similarity = 4 >> extensional distance = 354 >> proper extension: 012t1; 0k8y7; 02_0d2; 0184jw; 01wj5hp; 0hqly; >> query: (?x2732, ?x827) <- languages(?x2732, ?x254), award_nominee(?x4476, ?x2732), award_nominee(?x827, ?x2732), award(?x4476, ?x462) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #3556 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01wmxfs; 026yqrr; 02wwwv5; *> query: (?x2732, 03f19q4) <- award_nominee(?x2732, ?x4836), nationality(?x2732, ?x94), ?x4836 = 0837ql *> conf = 0.30 ranks of expected_values: 9 EVAL 01wgxtl award_nominee 03f19q4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 124.000 74.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13085-03s9b PRED entity: 03s9b PRED relation: award PRED expected values: 0gr51 => 134 concepts (122 used for prediction) PRED predicted values (max 10 best out of 310): 02wkmx (0.79 #5201, 0.72 #43170, 0.71 #33575), 09d28z (0.72 #43170, 0.71 #33575, 0.70 #21574), 027c924 (0.72 #43170, 0.71 #33575, 0.70 #21574), 02wwsh8 (0.70 #42769, 0.70 #40769, 0.69 #43169), 02pqp12 (0.65 #2068, 0.64 #1268, 0.22 #2469), 0gr51 (0.53 #2095, 0.50 #1295, 0.33 #2496), 02qyp19 (0.50 #1201, 0.47 #2001, 0.19 #7996), 09sb52 (0.42 #24414, 0.29 #31209, 0.26 #14023), 0gr4k (0.36 #1231, 0.35 #2031, 0.34 #4030), 03hl6lc (0.36 #1373, 0.35 #2173, 0.25 #4172) >> Best rule #5201 for best value: >> intensional similarity = 4 >> extensional distance = 159 >> proper extension: 012ljv; 02p65p; 0qf43; 042l3v; 0p_pd; 09fb5; 017149; 03f2_rc; 0c1pj; 0146pg; ... >> query: (?x6957, ?x372) <- award_winner(?x372, ?x6957), nominated_for(?x6957, ?x3252), award(?x810, ?x372), ?x810 = 0jzw >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #2095 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 0jf1b; 06b_0; 06t8b; *> query: (?x6957, 0gr51) <- award_winner(?x372, ?x6957), nominated_for(?x6957, ?x3252), ?x372 = 02wkmx, nationality(?x6957, ?x304) *> conf = 0.53 ranks of expected_values: 6 EVAL 03s9b award 0gr51 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 134.000 122.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13084-0p7vt PRED entity: 0p7vt PRED relation: time_zones PRED expected values: 02hcv8 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.69 #3, 0.65 #575, 0.44 #887), 02lcqs (0.30 #460, 0.24 #759, 0.22 #83), 02fqwt (0.23 #144, 0.23 #183, 0.21 #105), 02hczc (0.13 #1015, 0.07 #470, 0.07 #756), 02lcrv (0.13 #1015), 02llzg (0.08 #43, 0.07 #56, 0.07 #368), 03bdv (0.07 #162, 0.06 #214, 0.05 #448), 0gsrz4 (0.02 #34, 0.02 #60), 042g7t (0.02 #323, 0.01 #336, 0.01 #349), 03plfd (0.02 #140, 0.01 #283, 0.01 #192) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0n4m5; 0n4mk; 0n3ll; 0n474; 0n491; >> query: (?x10564, 02hcv8) <- source(?x10564, ?x958), contains(?x760, ?x10564), ?x760 = 05fkf >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p7vt time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.688 http://example.org/location/location/time_zones #13083-06bss PRED entity: 06bss PRED relation: category PRED expected values: 08mbj5d => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.64 #14, 0.60 #6, 0.57 #9) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 033wx9; >> query: (?x6742, 08mbj5d) <- religion(?x6742, ?x2769), student(?x3821, ?x6742), ?x2769 = 019cr, location(?x6742, ?x760) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06bss category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.636 http://example.org/common/topic/webpage./common/webpage/category #13082-02f72_ PRED entity: 02f72_ PRED relation: award! PRED expected values: 01vrt_c 05mt_q 0gcs9 01wyz92 01vvyfh 01vsgrn 0b_j2 0bk1p => 49 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2229): 0dw4g (0.77 #18214, 0.62 #21537, 0.59 #24859), 015mrk (0.76 #33219, 0.72 #59802, 0.63 #43188), 011z3g (0.76 #33219, 0.72 #59802, 0.63 #43188), 02qwg (0.65 #24166, 0.64 #27488, 0.62 #20844), 0gcs9 (0.65 #24053, 0.62 #20731, 0.62 #17408), 01vvycq (0.62 #16753, 0.59 #23398, 0.56 #20076), 01vsgrn (0.54 #18208, 0.50 #21531, 0.50 #11564), 01vsykc (0.54 #17494, 0.50 #20817, 0.47 #24139), 01vn35l (0.50 #10739, 0.47 #24028, 0.46 #17383), 0gdh5 (0.50 #10707, 0.38 #17351, 0.38 #20674) >> Best rule #18214 for best value: >> intensional similarity = 7 >> extensional distance = 11 >> proper extension: 05zkcn5; 01bgqh; 0c4z8; 025m8l; 054ks3; 02f72n; 02f79n; >> query: (?x4892, 0dw4g) <- award(?x10561, ?x4892), award(?x5550, ?x4892), award(?x5364, ?x4892), ?x5550 = 01bczm, group(?x227, ?x10561), participant(?x989, ?x5364), influenced_by(?x10561, ?x4942) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #24053 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 01c92g; *> query: (?x4892, 0gcs9) <- award(?x9638, ?x4892), award(?x5550, ?x4892), award(?x3175, ?x4892), ?x5550 = 01bczm, artists(?x2937, ?x3175), artist(?x2931, ?x9638) *> conf = 0.65 ranks of expected_values: 5, 7, 12, 15, 28, 54, 71, 292 EVAL 02f72_ award! 0bk1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 49.000 20.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72_ award! 0b_j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 49.000 20.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72_ award! 01vsgrn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 49.000 20.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72_ award! 01vvyfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 49.000 20.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72_ award! 01wyz92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 20.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72_ award! 0gcs9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 49.000 20.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72_ award! 05mt_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 49.000 20.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f72_ award! 01vrt_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 49.000 20.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13081-02p65p PRED entity: 02p65p PRED relation: film PRED expected values: 0hgnl3t 0ptdz => 89 concepts (62 used for prediction) PRED predicted values (max 10 best out of 643): 02b6n9 (0.70 #3345, 0.58 #49878, 0.41 #37406), 05sy_5 (0.58 #49878, 0.41 #37406, 0.41 #96202), 0418wg (0.58 #49878, 0.41 #37406, 0.41 #96202), 030p35 (0.58 #49878, 0.41 #37406, 0.39 #26718), 0180mw (0.58 #49878, 0.41 #37406, 0.39 #26718), 062zm5h (0.44 #850, 0.03 #106892, 0.03 #80167), 01hqhm (0.40 #2105, 0.05 #97984, 0.03 #106892), 0dzlbx (0.33 #844, 0.03 #106892), 062zjtt (0.22 #704, 0.04 #4266), 095zlp (0.20 #1839, 0.05 #97984, 0.03 #106892) >> Best rule #3345 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0bqdvt; >> query: (?x192, 02b6n9) <- award_nominee(?x192, ?x2692), type_of_union(?x192, ?x1873), ?x2692 = 0408np >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #5311 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 25 *> proper extension: 01s7z0; *> query: (?x192, 0ptdz) <- executive_produced_by(?x1218, ?x192), actor(?x4639, ?x192) *> conf = 0.04 ranks of expected_values: 280 EVAL 02p65p film 0ptdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 89.000 62.000 0.700 http://example.org/film/actor/film./film/performance/film EVAL 02p65p film 0hgnl3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 62.000 0.700 http://example.org/film/actor/film./film/performance/film #13080-0bq0p9 PRED entity: 0bq0p9 PRED relation: combatants! PRED expected values: 0bqtx => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 85): 048n7 (0.71 #235, 0.57 #289, 0.50 #1371), 081pw (0.65 #1083, 0.59 #2381, 0.50 #651), 0cm2xh (0.57 #225, 0.50 #117, 0.44 #334), 018w0j (0.57 #246, 0.33 #192, 0.29 #300), 01h6pn (0.50 #118, 0.50 #64, 0.35 #930), 0bqtx (0.50 #88, 0.44 #413, 0.40 #468), 0gfq9 (0.50 #112, 0.42 #654, 0.33 #329), 03gqgt3 (0.43 #1127, 0.39 #1505, 0.38 #1451), 07_nf (0.43 #230, 0.29 #1366, 0.29 #284), 01cpp0 (0.43 #264, 0.25 #102, 0.24 #3301) >> Best rule #235 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 09c7w0; >> query: (?x613, 048n7) <- organization(?x613, ?x4230), combatants(?x613, ?x94), form_of_government(?x613, ?x6065), combatants(?x1140, ?x613), ?x1140 = 01gjd0 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #88 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0d060g; 07ssc; *> query: (?x613, 0bqtx) <- official_language(?x613, ?x254), combatants(?x7241, ?x613), combatants(?x613, ?x94), ?x254 = 02h40lc, ?x7241 = 06k75 *> conf = 0.50 ranks of expected_values: 6 EVAL 0bq0p9 combatants! 0bqtx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 102.000 102.000 0.714 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #13079-0k2cb PRED entity: 0k2cb PRED relation: film_festivals PRED expected values: 05ys0xf => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 19): 059_y8d (0.06 #65, 0.03 #86, 0.02 #107), 05ys0ws (0.04 #125, 0.02 #314, 0.02 #377), 03nn7l2 (0.04 #122, 0.01 #563, 0.01 #311), 04_m9gk (0.03 #55, 0.02 #580, 0.02 #118), 09rwjly (0.03 #71, 0.02 #113, 0.02 #785), 03wf1p2 (0.03 #77, 0.01 #497), 0g57ws5 (0.03 #91, 0.03 #196, 0.02 #175), 0hrcs29 (0.03 #99, 0.02 #120, 0.02 #267), 04grdgy (0.02 #576, 0.02 #156, 0.01 #1248), 0bmj62v (0.02 #579, 0.02 #117, 0.01 #1188) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0hv81; >> query: (?x4751, 059_y8d) <- cinematography(?x4751, ?x4997), nominated_for(?x384, ?x4751), film_release_region(?x4751, ?x94), film_production_design_by(?x4751, ?x4449) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k2cb film_festivals 05ys0xf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 92.000 0.062 http://example.org/film/film/film_festivals #13078-049dk PRED entity: 049dk PRED relation: institution! PRED expected values: 014mlp => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 19): 014mlp (0.80 #84, 0.76 #64, 0.68 #728), 02_xgp2 (0.76 #71, 0.76 #91, 0.56 #131), 0bkj86 (0.71 #87, 0.68 #67, 0.47 #127), 07s6fsf (0.64 #42, 0.54 #82, 0.53 #62), 027f2w (0.54 #88, 0.53 #68, 0.26 #128), 013zdg (0.46 #86, 0.44 #66, 0.25 #387), 03mkk4 (0.35 #70, 0.34 #90, 0.18 #1599), 028dcg (0.27 #57, 0.24 #77, 0.22 #97), 022h5x (0.27 #58, 0.20 #459, 0.19 #279), 01rr_d (0.21 #519, 0.20 #95, 0.18 #1599) >> Best rule #84 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 06pwq; 01w3v; 0bx8pn; 01jq34; 0hd7j; 012mzw; 01qrb2; >> query: (?x1783, 014mlp) <- institution(?x1771, ?x1783), institution(?x734, ?x1783), school(?x387, ?x1783), ?x734 = 04zx3q1, student(?x1771, ?x744) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049dk institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.805 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #13077-033rq PRED entity: 033rq PRED relation: location PRED expected values: 015m08 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 89): 02_286 (0.21 #1645, 0.16 #4862, 0.13 #8883), 030qb3t (0.14 #16971, 0.13 #8929, 0.10 #20188), 06c62 (0.12 #45846, 0.01 #7574, 0.01 #5161), 01sn3 (0.07 #215, 0.03 #1019, 0.01 #3431), 0cr3d (0.06 #2557, 0.05 #30707, 0.05 #3361), 0cc56 (0.05 #4077, 0.04 #1665, 0.04 #4882), 04jpl (0.05 #16905, 0.04 #10471, 0.04 #23339), 01531 (0.04 #1766, 0.03 #10612, 0.02 #30720), 0r0m6 (0.04 #1826, 0.02 #9064, 0.02 #5043), 0dclg (0.04 #1725, 0.02 #4942, 0.01 #7355) >> Best rule #1645 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 030pr; >> query: (?x8573, 02_286) <- award_nominee(?x8572, ?x8573), film(?x8573, ?x5429), religion(?x8573, ?x1985) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 033rq location 015m08 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 99.000 0.213 http://example.org/people/person/places_lived./people/place_lived/location #13076-033g4d PRED entity: 033g4d PRED relation: nominated_for! PRED expected values: 057xs89 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 197): 0gq9h (0.37 #1736, 0.33 #2932, 0.32 #3171), 019f4v (0.34 #1727, 0.30 #1488, 0.27 #3162), 054krc (0.34 #1743, 0.29 #309, 0.28 #3178), 0gs9p (0.31 #1738, 0.26 #2934, 0.25 #3173), 09v51c2 (0.30 #924, 0.20 #18414, 0.19 #15064), 0l8z1 (0.30 #3160, 0.30 #1725, 0.26 #2921), 0gq_v (0.29 #1453, 0.29 #3127, 0.28 #2888), 02qvyrt (0.29 #1770, 0.21 #3205, 0.21 #2966), 0k611 (0.28 #1746, 0.28 #3181, 0.28 #2942), 09v92_x (0.25 #901, 0.05 #18654, 0.04 #1379) >> Best rule #1736 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 06mmr; >> query: (?x1185, 0gq9h) <- award_winner(?x1185, ?x9408), music(?x1842, ?x9408), award_winner(?x2238, ?x9408) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #18414 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1517 *> proper extension: 0clpml; *> query: (?x1185, ?x401) <- nominated_for(?x4767, ?x1185), award(?x4767, ?x401), nationality(?x4767, ?x94) *> conf = 0.20 ranks of expected_values: 37 EVAL 033g4d nominated_for! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 86.000 86.000 0.366 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13075-05b4w PRED entity: 05b4w PRED relation: film_release_region! PRED expected values: 0gtsx8c 0dtw1x 0bwfwpj 02d44q 01c22t 053rxgm 02r8hh_ 035yn8 047svrl 0kv238 0j43swk 0gh8zks 02fqrf 0bmhvpr 07s846j 0dlngsd 07k2mq 0gg5kmg 0jyb4 0gmd3k7 0dc_ms 0372j5 05ft32 09v3jyg 0bs8s1p 0cp08zg 0gtx63s 05zvzf3 => 179 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1215): 01c22t (0.85 #5420, 0.76 #13932, 0.75 #20316), 0bwfwpj (0.83 #14986, 0.80 #90, 0.77 #5410), 0dlngsd (0.83 #15341, 0.73 #445, 0.73 #5765), 07s846j (0.81 #5694, 0.80 #15270, 0.79 #14206), 087pfc (0.81 #6221, 0.80 #901, 0.76 #14733), 0kv238 (0.81 #5556, 0.80 #236, 0.76 #15132), 03z9585 (0.81 #6148, 0.80 #828, 0.75 #21044), 04pk1f (0.81 #5944, 0.74 #14456, 0.73 #624), 02r8hh_ (0.81 #5468, 0.74 #13980, 0.73 #148), 07jqjx (0.81 #6250, 0.73 #930, 0.71 #14762) >> Best rule #5420 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 06npd; 02vzc; 03rj0; 082fr; >> query: (?x2513, 01c22t) <- olympics(?x2513, ?x418), film_release_region(?x8258, ?x2513), ?x8258 = 05ldxl >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 9, 11, 13, 15, 16, 17, 18, 20, 22, 25, 28, 32, 34, 39, 45, 46, 56, 59, 60, 69, 86, 122, 165 EVAL 05b4w film_release_region! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0gtx63s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0cp08zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0bs8s1p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 09v3jyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 05ft32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0372j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0gmd3k7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0jyb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0gg5kmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 07k2mq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0dlngsd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 07s846j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0bmhvpr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 02fqrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0gh8zks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0j43swk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0kv238 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 047svrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 035yn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 02r8hh_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 053rxgm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 01c22t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 02d44q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0bwfwpj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0dtw1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05b4w film_release_region! 0gtsx8c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 179.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13074-0c33pl PRED entity: 0c33pl PRED relation: award_nominee PRED expected values: 04znsy => 89 concepts (40 used for prediction) PRED predicted values (max 10 best out of 883): 020ffd (0.23 #93665, 0.21 #25759, 0.08 #1437), 0c33pl (0.23 #93665, 0.21 #25759, 0.03 #4121), 04znsy (0.23 #93665, 0.21 #25759, 0.02 #1980), 04xrx (0.23 #93665, 0.21 #25759, 0.01 #16961), 03xsby (0.23 #93665, 0.21 #25759), 01kwld (0.10 #16506, 0.03 #49289, 0.02 #53974), 01yg9y (0.10 #1273, 0.06 #3614, 0.03 #12978), 047q2wc (0.08 #910, 0.05 #3251, 0.04 #12615), 02qssrm (0.08 #1436, 0.05 #3777, 0.03 #13141), 02pt6k_ (0.08 #1023, 0.05 #3364, 0.03 #12728) >> Best rule #93665 for best value: >> intensional similarity = 3 >> extensional distance = 1558 >> proper extension: 027_tg; 0kc9f; >> query: (?x7983, ?x1914) <- nominated_for(?x7983, ?x2742), award_winner(?x7644, ?x7983), nominated_for(?x1914, ?x2742) >> conf = 0.23 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0c33pl award_nominee 04znsy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 40.000 0.227 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #13073-0m4yg PRED entity: 0m4yg PRED relation: organization! PRED expected values: 08jcfy => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 9): 060c4 (0.66 #366, 0.66 #353, 0.63 #158), 07xl34 (0.57 #24, 0.38 #37, 0.37 #102), 05c0jwl (0.33 #31, 0.16 #122, 0.15 #135), 0dq_5 (0.25 #9, 0.24 #256, 0.23 #48), 05k17c (0.12 #59, 0.09 #163, 0.08 #293), 0hm4q (0.10 #99, 0.08 #151, 0.08 #216), 04n1q6 (0.07 #19, 0.03 #45, 0.03 #175), 08jcfy (0.04 #181, 0.04 #38, 0.04 #194), 09d6p2 (0.01 #192) >> Best rule #366 for best value: >> intensional similarity = 4 >> extensional distance = 229 >> proper extension: 01k2wn; 0ym8f; 07szy; 0lfgr; 01jsn5; 065r8g; 0j_sncb; 01c333; 0pspl; 05t7c1; ... >> query: (?x9844, 060c4) <- student(?x9844, ?x4019), category(?x9844, ?x134), currency(?x9844, ?x1099), profession(?x4019, ?x131) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #181 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 02583l; 07w5rq; 02qwgk; 04ld32; 0yl_w; 02bf58; 02vkzcx; *> query: (?x9844, 08jcfy) <- currency(?x9844, ?x1099), contains(?x362, ?x9844), institution(?x1368, ?x9844) *> conf = 0.04 ranks of expected_values: 8 EVAL 0m4yg organization! 08jcfy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 88.000 88.000 0.658 http://example.org/organization/role/leaders./organization/leadership/organization #13072-0b44shh PRED entity: 0b44shh PRED relation: film! PRED expected values: 024rbz => 108 concepts (82 used for prediction) PRED predicted values (max 10 best out of 56): 086k8 (0.20 #1101, 0.20 #367, 0.17 #1321), 016tw3 (0.17 #448, 0.16 #595, 0.15 #5463), 01gb54 (0.17 #1199, 0.14 #27, 0.11 #246), 017s11 (0.15 #368, 0.14 #1029, 0.13 #1396), 0jz9f (0.15 #293, 0.12 #1027, 0.08 #733), 025jfl (0.15 #297, 0.12 #737, 0.11 #78), 054g1r (0.14 #33, 0.11 #106, 0.08 #985), 020h2v (0.14 #43, 0.11 #116, 0.05 #775), 016tt2 (0.13 #223, 0.13 #1103, 0.13 #443), 0g1rw (0.12 #372, 0.07 #299, 0.07 #3834) >> Best rule #1101 for best value: >> intensional similarity = 3 >> extensional distance = 120 >> proper extension: 0522wp; >> query: (?x5109, 086k8) <- film_distribution_medium(?x5109, ?x2099), film(?x609, ?x5109), region(?x5109, ?x512) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #669 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 03kwtb; 05whq_9; 081l_; *> query: (?x5109, 024rbz) <- film_festivals(?x5109, ?x10083), category(?x5109, ?x134), ?x134 = 08mbj5d *> conf = 0.10 ranks of expected_values: 15 EVAL 0b44shh film! 024rbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 108.000 82.000 0.205 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #13071-05r7t PRED entity: 05r7t PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 22): 060c4 (0.85 #949, 0.71 #597, 0.69 #1169), 09n5b9 (0.82 #539, 0.55 #187, 0.51 #517), 0fkvn (0.75 #180, 0.74 #532, 0.49 #510), 060bp (0.62 #463, 0.62 #1651, 0.61 #595), 0pqc5 (0.58 #247, 0.58 #1501, 0.51 #2095), 0p5vf (0.33 #166, 0.26 #430, 0.24 #386), 04syw (0.27 #337, 0.22 #447, 0.21 #117), 01zq91 (0.27 #168, 0.26 #212, 0.20 #608), 0fj45 (0.21 #129, 0.19 #459, 0.18 #349), 0fkzq (0.21 #544, 0.16 #522, 0.15 #1160) >> Best rule #949 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 02wm6l; >> query: (?x6559, 060c4) <- form_of_government(?x6559, ?x48), ?x48 = 06cx9 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #180 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 01gh6z; *> query: (?x6559, 0fkvn) <- jurisdiction_of_office(?x3959, ?x6559), administrative_division(?x8428, ?x6559), origin(?x883, ?x8428) *> conf = 0.75 ranks of expected_values: 3 EVAL 05r7t jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 144.000 144.000 0.849 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #13070-0d7wh PRED entity: 0d7wh PRED relation: people PRED expected values: 0prjs 027pdrh 06mnbn 02z6l5f 0140t7 08z39v 02vkvcz => 33 concepts (28 used for prediction) PRED predicted values (max 10 best out of 2043): 01vwllw (0.43 #426, 0.17 #2119, 0.16 #3810), 01rrd4 (0.43 #889, 0.17 #2582, 0.14 #7659), 06qgvf (0.43 #7, 0.12 #5084, 0.11 #1700), 07r1h (0.43 #847, 0.12 #5924, 0.11 #4231), 06t61y (0.32 #3384, 0.13 #40631, 0.07 #27091), 03f4w4 (0.32 #3384), 0kr5_ (0.32 #3384), 0g824 (0.29 #875, 0.22 #2568, 0.21 #4259), 016z2j (0.29 #296, 0.22 #1989, 0.21 #3680), 046zh (0.29 #725, 0.22 #2418, 0.19 #5802) >> Best rule #426 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 09vc4s; >> query: (?x5042, 01vwllw) <- people(?x5042, ?x6122), people(?x5042, ?x3528), people(?x5042, ?x1567), spouse(?x6122, ?x2647), award_nominee(?x3528, ?x2499), produced_by(?x1916, ?x3528), ?x2499 = 0c6qh, nationality(?x1567, ?x512), participant(?x1567, ?x1568) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #10159 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 28 *> proper extension: 0dryh9k; *> query: (?x5042, ?x2647) <- people(?x5042, ?x9807), people(?x5042, ?x6122), people(?x5042, ?x3528), spouse(?x6122, ?x2647), award_nominee(?x3528, ?x2499), produced_by(?x1916, ?x3528), location(?x9807, ?x362), participant(?x2499, ?x1995), nationality(?x9807, ?x94) *> conf = 0.11 ranks of expected_values: 711, 949 EVAL 0d7wh people 02vkvcz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 28.000 0.429 http://example.org/people/ethnicity/people EVAL 0d7wh people 08z39v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 28.000 0.429 http://example.org/people/ethnicity/people EVAL 0d7wh people 0140t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 28.000 0.429 http://example.org/people/ethnicity/people EVAL 0d7wh people 02z6l5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 28.000 0.429 http://example.org/people/ethnicity/people EVAL 0d7wh people 06mnbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 28.000 0.429 http://example.org/people/ethnicity/people EVAL 0d7wh people 027pdrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 28.000 0.429 http://example.org/people/ethnicity/people EVAL 0d7wh people 0prjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 28.000 0.429 http://example.org/people/ethnicity/people #13069-065_cjc PRED entity: 065_cjc PRED relation: crewmember PRED expected values: 02xc1w4 => 99 concepts (68 used for prediction) PRED predicted values (max 10 best out of 35): 0c94fn (0.11 #58, 0.03 #390, 0.02 #1603), 04ktcgn (0.06 #153, 0.03 #489, 0.03 #391), 04wp63 (0.06 #183, 0.02 #713, 0.02 #911), 06rnl9 (0.06 #157, 0.02 #395, 0.02 #885), 094tsh6 (0.06 #180, 0.02 #566, 0.02 #710), 03m49ly (0.06 #273, 0.04 #414, 0.03 #1238), 021yc7p (0.04 #199, 0.03 #437, 0.02 #877), 02vxyl5 (0.04 #236, 0.02 #283), 0284n42 (0.04 #1207, 0.04 #1449, 0.03 #1838), 092ys_y (0.03 #399, 0.02 #1612, 0.02 #1319) >> Best rule #58 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02ht1k; 0cc97st; 0888c3; >> query: (?x6752, 0c94fn) <- genre(?x6752, ?x258), production_companies(?x6752, ?x1478), film(?x8896, ?x6752), ?x8896 = 07m77x, film_crew_role(?x6752, ?x137) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #1423 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 445 *> proper extension: 07xvf; 01xvjb; *> query: (?x6752, 02xc1w4) <- produced_by(?x6752, ?x3568), film(?x436, ?x6752), film(?x3462, ?x6752), film_crew_role(?x6752, ?x1284), ?x1284 = 0ch6mp2 *> conf = 0.02 ranks of expected_values: 16 EVAL 065_cjc crewmember 02xc1w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 99.000 68.000 0.111 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #13068-03crcpt PRED entity: 03crcpt PRED relation: edited_by! PRED expected values: 03c_cxn => 130 concepts (75 used for prediction) PRED predicted values (max 10 best out of 167): 0mbql (0.20 #284, 0.11 #1119, 0.10 #1286), 0f4yh (0.20 #231, 0.10 #1233, 0.08 #1567), 0dnqr (0.20 #222, 0.10 #1224, 0.08 #1558), 0dfw0 (0.20 #253, 0.10 #1255, 0.08 #1589), 0f3m1 (0.20 #312, 0.05 #1314, 0.04 #1648), 0hv8w (0.20 #262, 0.05 #1264, 0.04 #1598), 0ddjy (0.20 #211, 0.05 #1213, 0.04 #1547), 0k2m6 (0.20 #305, 0.04 #1975), 0hwpz (0.11 #1134, 0.10 #1301, 0.08 #1635), 02704ff (0.11 #1101, 0.10 #1268, 0.08 #1602) >> Best rule #284 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0kft; >> query: (?x5971, 0mbql) <- nationality(?x5971, ?x1310), people(?x5042, ?x5971), type_of_union(?x5971, ?x566), edited_by(?x835, ?x5971) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03crcpt edited_by! 03c_cxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 75.000 0.200 http://example.org/film/film/edited_by #13067-07s3vqk PRED entity: 07s3vqk PRED relation: artist! PRED expected values: 0g768 => 133 concepts (101 used for prediction) PRED predicted values (max 10 best out of 104): 01w40h (0.23 #163, 0.12 #1129, 0.11 #2234), 02p11jq (0.17 #2220, 0.16 #1115, 0.14 #149), 043g7l (0.16 #442, 0.10 #1546, 0.09 #580), 0mzkr (0.16 #436, 0.09 #574, 0.07 #1540), 017l96 (0.15 #2225, 0.14 #1120, 0.10 #844), 0181dw (0.14 #867, 0.14 #177, 0.13 #453), 03mp8k (0.13 #1582, 0.13 #64, 0.11 #478), 01clyr (0.13 #30, 0.10 #858, 0.10 #3208), 0n85g (0.13 #60, 0.09 #1578, 0.09 #612), 0g768 (0.13 #586, 0.11 #8050, 0.11 #9435) >> Best rule #163 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 01wkmgb; >> query: (?x215, 01w40h) <- artists(?x2664, ?x215), ?x2664 = 01lyv, languages(?x215, ?x254) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #586 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 03j0br4; *> query: (?x215, 0g768) <- award(?x215, ?x3835), artists(?x378, ?x215), ?x3835 = 01cky2 *> conf = 0.13 ranks of expected_values: 10 EVAL 07s3vqk artist! 0g768 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 133.000 101.000 0.227 http://example.org/music/record_label/artist #13066-02gs6r PRED entity: 02gs6r PRED relation: films! PRED expected values: 0fzyg => 112 concepts (40 used for prediction) PRED predicted values (max 10 best out of 65): 0fzyg (0.20 #371, 0.11 #1164, 0.06 #3547), 018h2 (0.12 #812, 0.03 #5585, 0.02 #4466), 0ddct (0.08 #2309, 0.04 #3581, 0.02 #5011), 0chghy (0.07 #3023, 0.04 #3339, 0.04 #3655), 0cm2xh (0.07 #3066, 0.04 #3382, 0.02 #3698), 07s2s (0.05 #6140, 0.02 #5022, 0.02 #5341), 01d5g (0.04 #3288, 0.02 #4076, 0.02 #4714), 07jq_ (0.04 #3260, 0.02 #4686, 0.01 #3892), 01vq3 (0.04 #4964, 0.04 #5283, 0.03 #5604), 0d1w9 (0.03 #4158, 0.03 #4640, 0.02 #4002) >> Best rule #371 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 02qm_f; >> query: (?x5286, 0fzyg) <- genre(?x5286, ?x811), actor(?x5286, ?x10919), profession(?x10919, ?x319), student(?x2605, ?x10919), award(?x10919, ?x1670) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02gs6r films! 0fzyg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 40.000 0.200 http://example.org/film/film_subject/films #13065-0n2bh PRED entity: 0n2bh PRED relation: program! PRED expected values: 0cjdk => 73 concepts (61 used for prediction) PRED predicted values (max 10 best out of 50): 0gsg7 (0.50 #2, 0.40 #59, 0.20 #234), 05gnf (0.30 #246, 0.30 #71, 0.30 #14), 09d5h (0.20 #60, 0.15 #697, 0.14 #522), 03mdt (0.14 #759, 0.12 #297, 0.11 #411), 0cjdk (0.12 #640, 0.12 #467, 0.11 #990), 07c52 (0.10 #1215, 0.04 #174, 0.01 #2656), 01fsyp (0.10 #107, 0.10 #50, 0.06 #165), 02hmvw (0.10 #43, 0.02 #1949, 0.02 #2236), 0kctd (0.07 #318, 0.05 #432, 0.05 #956), 0b275x (0.06 #366, 0.06 #423, 0.05 #481) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 02_1ky; >> query: (?x2137, 0gsg7) <- program_creator(?x2137, ?x2136), genre(?x2137, ?x8805), ?x8805 = 06q7n, actor(?x2137, ?x4407) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #640 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 118 *> proper extension: 017dcd; 072kp; 019nnl; 02hct1; 0d68qy; 03y3bp7; 015w8_; 07c72; 0cpz4k; 015g28; ... *> query: (?x2137, 0cjdk) <- program_creator(?x2137, ?x2136), genre(?x2137, ?x8805), genre(?x5808, ?x8805), award_winner(?x5808, ?x848) *> conf = 0.12 ranks of expected_values: 5 EVAL 0n2bh program! 0cjdk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 61.000 0.500 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #13064-0166v PRED entity: 0166v PRED relation: medal PRED expected values: 02lq5w => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 3): 02lq67 (0.76 #34, 0.76 #25, 0.73 #37), 02lq5w (0.76 #26, 0.71 #35, 0.69 #38), 02lpp7 (0.69 #27, 0.65 #36, 0.59 #39) >> Best rule #34 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 06c62; >> query: (?x4421, 02lq67) <- contains(?x2467, ?x4421), olympics(?x4421, ?x2966), taxonomy(?x4421, ?x939) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 03rj0; *> query: (?x4421, 02lq5w) <- currency(?x4421, ?x170), olympics(?x4421, ?x1931), ?x1931 = 0kbws *> conf = 0.76 ranks of expected_values: 2 EVAL 0166v medal 02lq5w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.765 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #13063-01v9724 PRED entity: 01v9724 PRED relation: people! PRED expected values: 02w7gg => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 48): 02w7gg (0.26 #4778, 0.26 #4701, 0.23 #6550), 0g6ff (0.25 #252, 0.22 #329, 0.18 #406), 041rx (0.23 #1082, 0.21 #2160, 0.21 #3700), 03ts0c (0.17 #103, 0.11 #5855, 0.11 #334), 09zyn5 (0.17 #227, 0.09 #535, 0.03 #997), 07mqps (0.17 #96, 0.08 #558, 0.02 #3176), 07hwkr (0.13 #628, 0.11 #705, 0.09 #1860), 033tf_ (0.12 #1239, 0.08 #8945, 0.08 #9179), 0x67 (0.12 #8871, 0.12 #2705, 0.12 #9492), 03lmx1 (0.11 #5855, 0.10 #4313, 0.02 #8413) >> Best rule #4778 for best value: >> intensional similarity = 4 >> extensional distance = 257 >> proper extension: 02k6rq; >> query: (?x5435, 02w7gg) <- nationality(?x5435, ?x1310), type_of_union(?x5435, ?x566), ?x1310 = 02jx1, ?x566 = 04ztj >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v9724 people! 02w7gg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.263 http://example.org/people/ethnicity/people #13062-01mt1fy PRED entity: 01mt1fy PRED relation: nationality PRED expected values: 09c7w0 => 90 concepts (87 used for prediction) PRED predicted values (max 10 best out of 53): 09c7w0 (0.86 #2813, 0.85 #3314, 0.85 #4525), 0488g (0.30 #5537, 0.30 #4828, 0.29 #5132), 0np52 (0.30 #5537, 0.30 #4828, 0.29 #5132), 0t6hk (0.25 #5741, 0.24 #5333), 0345h (0.17 #131, 0.04 #4420, 0.04 #4929), 02jx1 (0.15 #1538, 0.14 #234, 0.11 #2945), 07ssc (0.13 #1016, 0.10 #1520, 0.09 #316), 0d060g (0.10 #808, 0.09 #308, 0.08 #408), 03rt9 (0.09 #314, 0.04 #4420, 0.04 #4929), 0ctw_b (0.09 #328, 0.04 #4420, 0.04 #4929) >> Best rule #2813 for best value: >> intensional similarity = 5 >> extensional distance = 209 >> proper extension: 043q6n_; >> query: (?x4395, 09c7w0) <- student(?x6271, ?x4395), major_field_of_study(?x6271, ?x3489), organization(?x6271, ?x5487), ?x3489 = 0193x, currency(?x6271, ?x170) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mt1fy nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 87.000 0.858 http://example.org/people/person/nationality #13061-051kv PRED entity: 051kv PRED relation: religion! PRED expected values: 01x73 03s5t 0vbk => 36 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1247): 01x73 (0.79 #1001, 0.73 #1270, 0.69 #870), 03s5t (0.73 #1277, 0.71 #1008, 0.69 #877), 05kr_ (0.60 #214, 0.57 #411, 0.50 #344), 0156q (0.60 #212, 0.50 #342, 0.50 #82), 0vbk (0.54 #885, 0.50 #1016, 0.47 #1285), 03ryn (0.50 #354, 0.50 #94, 0.43 #487), 09c7w0 (0.50 #326, 0.47 #1185, 0.47 #1119), 0345h (0.50 #77, 0.43 #404, 0.40 #207), 0d060g (0.50 #68, 0.40 #198, 0.33 #328), 06y57 (0.50 #100, 0.40 #230, 0.33 #360) >> Best rule #1001 for best value: >> intensional similarity = 10 >> extensional distance = 12 >> proper extension: 058x5; 0631_; 01y0s9; 019cr; 021_0p; 04pk9; 05w5d; >> query: (?x1624, 01x73) <- religion(?x512, ?x1624), religion(?x177, ?x1624), ?x177 = 05kkh, film_release_region(?x4707, ?x512), film_release_region(?x4668, ?x512), contains(?x512, ?x362), award(?x4668, ?x13107), religion(?x652, ?x1624), service_location(?x555, ?x512), film(?x665, ?x4707) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5 EVAL 051kv religion! 0vbk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 36.000 32.000 0.786 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 051kv religion! 03s5t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 32.000 0.786 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 051kv religion! 01x73 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 32.000 0.786 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #13060-021yzs PRED entity: 021yzs PRED relation: type_of_union PRED expected values: 04ztj => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.79 #21, 0.79 #13, 0.78 #25), 01g63y (0.34 #297, 0.25 #2, 0.15 #54) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 304 >> proper extension: 07nznf; 0q9kd; 079vf; 016qtt; 012d40; 02p65p; 01xdf5; 04t2l2; 01l1b90; 014zcr; ... >> query: (?x4764, 04ztj) <- film(?x4764, ?x3619), profession(?x4764, ?x1041), ?x1041 = 03gjzk, film(?x902, ?x3619) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021yzs type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.791 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13059-0260bz PRED entity: 0260bz PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #36, 0.85 #41, 0.84 #51), 02nxhr (0.05 #47, 0.04 #12, 0.04 #42), 07c52 (0.03 #256, 0.03 #33, 0.03 #361), 07z4p (0.02 #388, 0.02 #363, 0.02 #65) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 185 >> proper extension: 0d1qmz; 025twgt; >> query: (?x2107, 029j_) <- currency(?x2107, ?x170), language(?x2107, ?x254), film(?x100, ?x2107), nominated_for(?x5927, ?x2107) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0260bz film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #13058-013ybx PRED entity: 013ybx PRED relation: special_performance_type PRED expected values: 09_gdc => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 4): 09_gdc (0.16 #10, 0.12 #15, 0.12 #31), 01kyvx (0.16 #30, 0.08 #14, 0.02 #108), 02t8yb (0.06 #11, 0.05 #32, 0.04 #16), 014kbl (0.02 #17, 0.01 #12, 0.01 #33) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 0jfx1; 0j_c; 019vgs; 0gn30; 043zg; 01vsgrn; 02hy9p; 020jqv; >> query: (?x11921, 09_gdc) <- profession(?x11921, ?x1032), award_nominee(?x5289, ?x11921), special_performance_type(?x11921, ?x4832) >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013ybx special_performance_type 09_gdc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.165 http://example.org/film/actor/film./film/performance/special_performance_type #13057-03f0vvr PRED entity: 03f0vvr PRED relation: award PRED expected values: 01ck6v => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 264): 0c4z8 (0.76 #72, 0.42 #878, 0.36 #8535), 01bgqh (0.57 #43, 0.47 #1655, 0.42 #849), 01by1l (0.52 #112, 0.47 #918, 0.46 #8575), 03qbh5 (0.43 #204, 0.36 #1010, 0.26 #2219), 02f6xy (0.38 #199, 0.22 #1005, 0.17 #8662), 03qbnj (0.33 #232, 0.31 #1038, 0.24 #2247), 02f73p (0.33 #187, 0.25 #3411, 0.24 #4217), 01c99j (0.33 #1031, 0.19 #3449, 0.18 #4255), 01ckcd (0.33 #1947, 0.27 #3559, 0.26 #4365), 054ks3 (0.29 #141, 0.23 #15052, 0.21 #8604) >> Best rule #72 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0lbj1; 02s2wq; >> query: (?x4798, 0c4z8) <- artists(?x2823, ?x4798), artist(?x6672, ?x4798), award(?x4798, ?x1801), ?x1801 = 01c92g >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #272 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 0lbj1; 02s2wq; *> query: (?x4798, 01ck6v) <- artists(?x2823, ?x4798), artist(?x6672, ?x4798), award(?x4798, ?x1801), ?x1801 = 01c92g *> conf = 0.24 ranks of expected_values: 15 EVAL 03f0vvr award 01ck6v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 102.000 102.000 0.762 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13056-0bdx29 PRED entity: 0bdx29 PRED relation: ceremony PRED expected values: 07z31v => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 130): 0gpjbt (0.48 #1849, 0.48 #1719, 0.38 #3411), 09n4nb (0.47 #1736, 0.46 #1866, 0.36 #3819), 0466p0j (0.46 #1761, 0.46 #1891, 0.36 #3453), 05pd94v (0.46 #1693, 0.44 #1823, 0.35 #3385), 02cg41 (0.46 #1938, 0.46 #1808, 0.36 #3891), 02rjjll (0.46 #1696, 0.45 #1826, 0.35 #3779), 056878 (0.46 #1722, 0.45 #1852, 0.35 #3805), 01c6qp (0.45 #1709, 0.44 #1839, 0.35 #3792), 07z31v (0.45 #421, 0.25 #681, 0.25 #160), 01mh_q (0.43 #1773, 0.43 #1903, 0.33 #3856) >> Best rule #1849 for best value: >> intensional similarity = 5 >> extensional distance = 172 >> proper extension: 02581q; 02wh75; 02g3gj; 01d38g; 02grdc; 018wng; 01bgqh; 0c4z8; 02g8mp; 01c9f2; ... >> query: (?x2041, 0gpjbt) <- ceremony(?x2041, ?x2213), award(?x931, ?x2041), honored_for(?x2213, ?x493), award_winner(?x2213, ?x4644), music(?x392, ?x4644) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 0gqmvn; *> query: (?x2041, 07z31v) <- ceremony(?x2041, ?x2213), award(?x1397, ?x2041), ?x2213 = 0gvstc3, participant(?x406, ?x1397), location(?x1397, ?x10662) *> conf = 0.45 ranks of expected_values: 9 EVAL 0bdx29 ceremony 07z31v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 55.000 55.000 0.483 http://example.org/award/award_category/winners./award/award_honor/ceremony #13055-023s8 PRED entity: 023s8 PRED relation: gender PRED expected values: 02zsn => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #221, 0.71 #231, 0.71 #229), 02zsn (0.49 #44, 0.48 #8, 0.48 #140) >> Best rule #221 for best value: >> intensional similarity = 2 >> extensional distance = 2862 >> proper extension: 019y64; 0c11mj; 071pf2; 0dj5q; 0bk4s; 0j5b8; 02rnns; 04mx7s; 015k7; 03d6q; ... >> query: (?x10473, 05zppz) <- type_of_union(?x10473, ?x566), ?x566 = 04ztj >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #44 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 206 *> proper extension: 02lnhv; 012_53; 02zrv7; 01vsqvs; 0dxmyh; *> query: (?x10473, 02zsn) <- participant(?x10473, ?x2373), participant(?x10473, ?x794) *> conf = 0.49 ranks of expected_values: 2 EVAL 023s8 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 117.000 0.716 http://example.org/people/person/gender #13054-0cqt90 PRED entity: 0cqt90 PRED relation: type_of_union PRED expected values: 04ztj => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.87 #70, 0.87 #21, 0.86 #33), 01g63y (0.25 #555, 0.18 #107, 0.18 #2), 01bl8s (0.25 #555), 0jgjn (0.01 #20) >> Best rule #70 for best value: >> intensional similarity = 3 >> extensional distance = 224 >> proper extension: 0cm03; >> query: (?x3884, 04ztj) <- location_of_ceremony(?x3884, ?x11345), nationality(?x3884, ?x94), country(?x54, ?x94) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cqt90 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.872 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13053-0ch6mp2 PRED entity: 0ch6mp2 PRED relation: film_crew_role! PRED expected values: 02v8kmz 0c0yh4 047gn4y 0bvn25 0czyxs 050r1z 04ddm4 0fgpvf 0209xj 0dsvzh 06_wqk4 0_b3d 0_92w 053rxgm 032_wv 02847m9 09gq0x5 050f0s 0kvgxk 0260bz 06v9_x 02rb607 08052t3 07x4qr 02q56mk 0gz6b6g 047svrl 04t6fk 04jwly 03177r 01bb9r 03hkch7 0gyfp9c 0571m 08gg47 02fqrf 0c34mt 0gc_c_ 03cw411 047tsx3 02d478 02kfzz 07jxpf 032zq6 06929s 02rmd_2 0hgnl3t 01gkp1 0bbw2z6 026qnh6 033f8n 03f7nt 035w2k 08phg9 012s1d 026lgs 0ddj0x 07_k0c0 02qsqmq 05pdd86 05b6rdt 03n3gl 011ykb 02bg55 0mbql 0bxxzb 017180 07nxnw 0b6l1st 0292qb 0233bn 06x43v 0cp08zg 026f__m 01cycq 072r5v 03cmsqb 0gtx63s 087vnr5 0ndsl1x 01s7w3 0hz6mv2 045r_9 0h1x5f 0dmn0x 024lt6 023vcd 085wqm 09fqgj 034b6k 07tj4c 0640m69 => 26 concepts (24 used for prediction) PRED predicted values (max 10 best out of 410): 08sk8l (0.78 #5963, 0.50 #4057, 0.50 #2530), 032zq6 (0.78 #5881, 0.50 #3975, 0.50 #2448), 08phg9 (0.69 #6296, 0.67 #4771, 0.60 #3627), 02fqrf (0.67 #5851, 0.67 #3945, 0.60 #2799), 0drnwh (0.67 #4832, 0.67 #4450, 0.40 #3688), 05pdd86 (0.67 #4808, 0.62 #6333, 0.60 #3664), 047svrl (0.67 #5432, 0.60 #3526, 0.60 #3145), 0ddt_ (0.67 #5826, 0.60 #2774, 0.50 #3920), 0bbw2z6 (0.67 #3998, 0.56 #5904, 0.50 #4379), 03tbg6 (0.67 #6075, 0.54 #6457, 0.50 #4169) >> Best rule #5963 for best value: >> intensional similarity = 17 >> extensional distance = 7 >> proper extension: 02rh1dz; >> query: (?x1284, 08sk8l) <- film_crew_role(?x9981, ?x1284), film_crew_role(?x7494, ?x1284), film_crew_role(?x6445, ?x1284), film_crew_role(?x4130, ?x1284), film_crew_role(?x2075, ?x1284), film_crew_role(?x1074, ?x1284), film_crew_role(?x708, ?x1284), film(?x398, ?x2075), film_release_region(?x7494, ?x789), language(?x6445, ?x254), ?x708 = 0fg04, nominated_for(?x986, ?x4130), genre(?x9981, ?x53), ?x789 = 0f8l9c, film_release_distribution_medium(?x6445, ?x81), award_winner(?x1074, ?x10780), featured_film_locations(?x7494, ?x1523) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #5881 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 7 *> proper extension: 02rh1dz; *> query: (?x1284, 032zq6) <- film_crew_role(?x9981, ?x1284), film_crew_role(?x7494, ?x1284), film_crew_role(?x6445, ?x1284), film_crew_role(?x4130, ?x1284), film_crew_role(?x2075, ?x1284), film_crew_role(?x1074, ?x1284), film_crew_role(?x708, ?x1284), film(?x398, ?x2075), film_release_region(?x7494, ?x789), language(?x6445, ?x254), ?x708 = 0fg04, nominated_for(?x986, ?x4130), genre(?x9981, ?x53), ?x789 = 0f8l9c, film_release_distribution_medium(?x6445, ?x81), award_winner(?x1074, ?x10780), featured_film_locations(?x7494, ?x1523) *> conf = 0.78 ranks of expected_values: 2, 3, 4, 6, 7, 9, 11, 12, 15, 16, 18, 19, 22, 24, 26, 32, 33, 34, 35, 37, 40, 41, 42, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 60, 64, 66, 69, 72, 73, 74, 77, 79, 84, 86, 89, 90, 105, 110, 113, 118, 120, 124, 125, 127, 135, 140, 146, 147, 151, 183, 187, 210, 213, 216, 217, 222, 225, 229, 234, 235, 249, 251, 253, 292, 300, 303, 305, 318, 323, 333, 352, 384, 386, 402 EVAL 0ch6mp2 film_crew_role! 0640m69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 07tj4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 034b6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 09fqgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 085wqm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 023vcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 024lt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0dmn0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0h1x5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 045r_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0hz6mv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 01s7w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0ndsl1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 087vnr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0gtx63s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 03cmsqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 072r5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 01cycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 026f__m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0cp08zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 06x43v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0233bn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0292qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0b6l1st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 07nxnw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 017180 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0bxxzb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0mbql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 02bg55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 011ykb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 03n3gl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 05b6rdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 05pdd86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 02qsqmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 07_k0c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0ddj0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 026lgs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 012s1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 08phg9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 035w2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 03f7nt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 033f8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 026qnh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0bbw2z6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 01gkp1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0hgnl3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 02rmd_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 06929s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 032zq6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 07jxpf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 02kfzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 02d478 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 047tsx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 03cw411 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0gc_c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0c34mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 02fqrf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 08gg47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0571m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0gyfp9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 03hkch7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 01bb9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 03177r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 04jwly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 04t6fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 047svrl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0gz6b6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 02q56mk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 07x4qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 08052t3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 02rb607 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 06v9_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0260bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0kvgxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 050f0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 09gq0x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 02847m9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 032_wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 053rxgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0_92w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0_b3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 06_wqk4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0dsvzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0209xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0fgpvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 04ddm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 050r1z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0czyxs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0bvn25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 047gn4y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 0c0yh4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ch6mp2 film_crew_role! 02v8kmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 26.000 24.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13052-07t21 PRED entity: 07t21 PRED relation: olympics PRED expected values: 0kbvb => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 38): 0kbvv (0.73 #58, 0.62 #22, 0.55 #383), 018ctl (0.73 #43, 0.62 #7, 0.50 #260), 0kbvb (0.60 #114, 0.57 #367, 0.50 #6), 0swff (0.40 #1339, 0.39 #1302, 0.38 #1193), 0jhn7 (0.40 #1339, 0.39 #1302, 0.38 #1193), 0l6vl (0.40 #110, 0.27 #2426, 0.25 #2), 0ldqf (0.33 #140, 0.18 #502, 0.18 #248), 0l6m5 (0.29 #81, 0.27 #117, 0.27 #2426), 0l6mp (0.27 #123, 0.27 #2426, 0.23 #376), 0l998 (0.27 #113, 0.27 #2426, 0.20 #366) >> Best rule #58 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0d0kn; 04w8f; 04g5k; 0jhd; >> query: (?x1471, 0kbvv) <- adjoins(?x1603, ?x1471), film_release_region(?x124, ?x1471), ?x1603 = 06bnz >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #114 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 02j71; *> query: (?x1471, 0kbvb) <- administrative_parent(?x6494, ?x1471), adjustment_currency(?x1471, ?x170), place_of_birth(?x558, ?x6494) *> conf = 0.60 ranks of expected_values: 3 EVAL 07t21 olympics 0kbvb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 123.000 0.727 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #13051-016vg8 PRED entity: 016vg8 PRED relation: award PRED expected values: 09sb52 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 253): 09sb52 (0.75 #444, 0.60 #41, 0.38 #8101), 0gqy2 (0.40 #165, 0.33 #568, 0.14 #32243), 02x73k6 (0.40 #61, 0.25 #464, 0.15 #39500), 07cbcy (0.40 #79, 0.17 #482, 0.15 #39500), 05pcn59 (0.30 #2903, 0.29 #2500, 0.28 #1694), 0ck27z (0.29 #12183, 0.27 #11377, 0.26 #15810), 05p09zm (0.27 #1737, 0.23 #2946, 0.23 #931), 09sdmz (0.25 #609, 0.20 #206, 0.14 #32243), 027dtxw (0.25 #407, 0.20 #4, 0.14 #32243), 0789_m (0.25 #423, 0.06 #11304, 0.05 #13722) >> Best rule #444 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 04t7ts; 02wgln; 0bqdvt; >> query: (?x4662, 09sb52) <- film(?x4662, ?x9533), ?x9533 = 02b6n9, award_nominee(?x157, ?x4662) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016vg8 award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #13050-03j0d PRED entity: 03j0d PRED relation: people! PRED expected values: 09vc4s => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 48): 041rx (0.35 #544, 0.33 #698, 0.22 #3163), 02g7sp (0.25 #95, 0.06 #404, 0.04 #789), 07hwkr (0.18 #321, 0.09 #3082, 0.08 #1014), 02w7gg (0.17 #388, 0.17 #156, 0.14 #234), 07bch9 (0.17 #177, 0.09 #3336, 0.07 #2950), 063k3h (0.17 #185, 0.04 #3344, 0.04 #802), 0x67 (0.16 #2244, 0.16 #1397, 0.15 #2321), 013xrm (0.16 #2023, 0.15 #560, 0.15 #714), 033tf_ (0.10 #5939, 0.09 #6710, 0.08 #4860), 07mqps (0.09 #328, 0.04 #790, 0.04 #636) >> Best rule #544 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 01h2_6; >> query: (?x10000, 041rx) <- influenced_by(?x10000, ?x11097), influenced_by(?x11214, ?x10000), ?x11097 = 02wh0, type_of_union(?x11214, ?x1873) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1935 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 85 *> proper extension: 028q6; 0h1_w; 01ty7ll; 057d89; 01g4zr; 083q7; 02sjf5; 01pl9g; 02lxj_; 083pr; ... *> query: (?x10000, 09vc4s) <- place_of_birth(?x10000, ?x8263), type_of_union(?x10000, ?x566), people(?x268, ?x10000), religion(?x10000, ?x2694) *> conf = 0.05 ranks of expected_values: 17 EVAL 03j0d people! 09vc4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 137.000 137.000 0.346 http://example.org/people/ethnicity/people #13049-07_dn PRED entity: 07_dn PRED relation: company! PRED expected values: 05_wyz => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 37): 05_wyz (0.67 #745, 0.53 #442, 0.51 #1511), 01yc02 (0.60 #7, 0.50 #348, 0.48 #2059), 0dq3c (0.57 #1156, 0.56 #2054, 0.55 #816), 02211by (0.45 #1538, 0.40 #3, 0.33 #302), 01kr6k (0.45 #1538, 0.31 #1649, 0.31 #965), 0142rn (0.45 #1538, 0.29 #151, 0.25 #322), 01rk91 (0.45 #1538, 0.21 #1327, 0.18 #2648), 021q0l (0.40 #8, 0.18 #2648, 0.17 #3534), 02y6fz (0.24 #662, 0.20 #919, 0.20 #447), 09lq2c (0.20 #28, 0.18 #799, 0.18 #2648) >> Best rule #745 for best value: >> intensional similarity = 7 >> extensional distance = 25 >> proper extension: 09j_g; 02p10m; >> query: (?x11051, 05_wyz) <- company(?x12865, ?x11051), company(?x5161, ?x11051), company(?x4682, ?x11051), ?x4682 = 0dq_5, ?x5161 = 09d6p2, company(?x12865, ?x3793), ?x3793 = 0k8z >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07_dn company! 05_wyz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.667 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #13048-057lbk PRED entity: 057lbk PRED relation: executive_produced_by PRED expected values: 01r2c7 => 124 concepts (70 used for prediction) PRED predicted values (max 10 best out of 129): 09pl3s (0.17 #313, 0.05 #816, 0.03 #1067), 0h5jg5 (0.17 #415, 0.05 #918, 0.03 #1169), 02z6l5f (0.16 #871, 0.07 #1877, 0.05 #2128), 046_v (0.14 #3524, 0.09 #8813, 0.09 #12094), 06q8hf (0.11 #4193, 0.10 #17550, 0.09 #10238), 02z2xdf (0.11 #911, 0.05 #2168, 0.05 #1917), 06pj8 (0.10 #4081, 0.08 #557, 0.08 #5339), 05hj_k (0.09 #17481, 0.09 #1857, 0.09 #10169), 02xnjd (0.08 #2941, 0.07 #1935, 0.05 #929), 0glyyw (0.08 #691, 0.07 #4215, 0.07 #1193) >> Best rule #313 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0g0x9c; >> query: (?x4378, 09pl3s) <- film_crew_role(?x4378, ?x5136), film_crew_role(?x4378, ?x632), ?x632 = 0ckd1, ?x5136 = 089g0h, music(?x4378, ?x3042), film(?x8716, ?x4378) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1210 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 085bd1; 017kz7; 02gqm3; *> query: (?x4378, 01r2c7) <- country(?x4378, ?x1264), story_by(?x4378, ?x96), ?x1264 = 0345h, film(?x8716, ?x4378), genre(?x4378, ?x225) *> conf = 0.03 ranks of expected_values: 34 EVAL 057lbk executive_produced_by 01r2c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 124.000 70.000 0.167 http://example.org/film/film/executive_produced_by #13047-04180vy PRED entity: 04180vy PRED relation: film_crew_role PRED expected values: 089fss 0263ycg => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 30): 0d2b38 (0.61 #432, 0.19 #1141, 0.17 #632), 01vx2h (0.52 #420, 0.43 #609, 0.41 #1118), 02_n3z (0.46 #412, 0.19 #1141, 0.17 #632), 01pvkk (0.37 #328, 0.35 #359, 0.33 #390), 015h31 (0.24 #418, 0.19 #1141, 0.17 #632), 02rh1dz (0.22 #419, 0.19 #1141, 0.17 #632), 033smt (0.22 #434, 0.19 #1141, 0.17 #632), 02ynfr (0.20 #1694, 0.20 #581, 0.20 #518), 089fss (0.19 #1141, 0.17 #632, 0.15 #68), 0263ycg (0.19 #1141, 0.17 #632, 0.15 #426) >> Best rule #432 for best value: >> intensional similarity = 7 >> extensional distance = 44 >> proper extension: 01gglm; >> query: (?x11686, 0d2b38) <- film_crew_role(?x11686, ?x4305), film_crew_role(?x11686, ?x2095), ?x4305 = 0215hd, film(?x71, ?x11686), ?x2095 = 0dxtw, country(?x11686, ?x94), language(?x11686, ?x90) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1141 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 160 *> proper extension: 07kb7vh; *> query: (?x11686, ?x2091) <- film_crew_role(?x11686, ?x137), film_distribution_medium(?x11686, ?x81), film_release_distribution_medium(?x7171, ?x81), film_release_distribution_medium(?x708, ?x81), film_crew_role(?x708, ?x2091), genre(?x7171, ?x307) *> conf = 0.19 ranks of expected_values: 9, 10 EVAL 04180vy film_crew_role 0263ycg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 89.000 89.000 0.609 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 04180vy film_crew_role 089fss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 89.000 89.000 0.609 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #13046-06f41 PRED entity: 06f41 PRED relation: sports! PRED expected values: 0l6vl => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 23): 0jdk_ (0.84 #329, 0.83 #470, 0.83 #415), 0l98s (0.84 #329, 0.83 #415, 0.81 #552), 0lk8j (0.84 #329, 0.83 #415, 0.80 #306), 06sks6 (0.78 #42, 0.78 #108, 0.78 #480), 0l6vl (0.78 #42, 0.76 #910, 0.75 #481), 0kbws (0.78 #42, 0.66 #653, 0.65 #631), 0c_tl (0.50 #253, 0.46 #196, 0.43 #294), 09n48 (0.46 #196, 0.46 #569, 0.45 #501), 0sx8l (0.46 #196, 0.46 #569, 0.45 #501), 0swbd (0.46 #196, 0.46 #569, 0.45 #501) >> Best rule #329 for best value: >> intensional similarity = 43 >> extensional distance = 5 >> proper extension: 019tzd; >> query: (?x2044, ?x584) <- sports(?x2553, ?x2044), country(?x2044, ?x3730), country(?x2044, ?x2000), country(?x2044, ?x1592), country(?x2044, ?x1558), country(?x2044, ?x1229), country(?x2044, ?x1023), country(?x2044, ?x789), country(?x2044, ?x456), ?x1229 = 059j2, ?x789 = 0f8l9c, teams(?x2000, ?x5641), sports(?x584, ?x2044), ?x1558 = 01mjq, country(?x1967, ?x2000), adjustment_currency(?x2000, ?x170), film_release_region(?x5644, ?x1592), film_release_region(?x3784, ?x1592), film_release_region(?x1999, ?x1592), film_release_region(?x1392, ?x1592), film_release_region(?x504, ?x1592), film_release_region(?x1956, ?x2000), ?x1956 = 05qbckf, ?x1392 = 017gm7, ?x504 = 0g5qs2k, organization(?x2000, ?x127), ?x5644 = 0dll_t2, ?x1967 = 01cgz, ?x3784 = 0bmhvpr, contains(?x2000, ?x13996), ?x456 = 05qhw, organization(?x1592, ?x4230), medal(?x2000, ?x422), participating_countries(?x2553, ?x774), official_language(?x1592, ?x2502), ?x1023 = 0ctw_b, ?x1999 = 0gd0c7x, contains(?x455, ?x2000), sports(?x584, ?x359), ?x170 = 09nqf, adjoins(?x3730, ?x1499), ?x359 = 02bkg, location(?x396, ?x2000) >> conf = 0.84 => this is the best rule for 3 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 59 *> extensional distance = 1 *> proper extension: 0bynt; *> query: (?x2044, ?x391) <- sports(?x7051, ?x2044), sports(?x6464, ?x2044), sports(?x4255, ?x2044), sports(?x2134, ?x2044), sports(?x2043, ?x2044), sports(?x1081, ?x2044), sports(?x867, ?x2044), sports(?x778, ?x2044), sports(?x358, ?x2044), country(?x2044, ?x8588), country(?x2044, ?x6827), country(?x2044, ?x2843), country(?x2044, ?x2513), country(?x2044, ?x2000), country(?x2044, ?x1592), country(?x2044, ?x1471), country(?x2044, ?x1453), country(?x2044, ?x1229), country(?x2044, ?x792), country(?x2044, ?x789), country(?x2044, ?x344), country(?x2044, ?x304), country(?x2044, ?x291), country(?x2044, ?x279), country(?x2044, ?x252), country(?x2044, ?x94), ?x1229 = 059j2, ?x789 = 0f8l9c, ?x2000 = 0d0kn, ?x291 = 0h3y, ?x6464 = 0lbd9, ?x1081 = 0l6m5, ?x792 = 0hzlz, ?x1471 = 07t21, ?x358 = 018wrk, ?x867 = 0l6ny, ?x94 = 09c7w0, ?x8588 = 0jhd, ?x344 = 04gzd, ?x304 = 0d0vqn, ?x7051 = 018ljb, ?x2513 = 05b4w, ?x2843 = 016wzw, ?x4255 = 0lgxj, olympics(?x2044, ?x391), ?x2043 = 0lv1x, ?x778 = 0kbvb, ?x2134 = 0blg2, ?x1592 = 05v10, ?x1453 = 06qd3, ?x279 = 0d060g, film_release_region(?x7502, ?x252), film_release_region(?x5499, ?x252), film_release_region(?x1919, ?x252), ?x6827 = 05cc1, country_of_origin(?x419, ?x252), ?x7502 = 0233bn, ?x5499 = 0gt1k, ?x1919 = 0_7w6 *> conf = 0.78 ranks of expected_values: 5 EVAL 06f41 sports! 0l6vl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 44.000 44.000 0.839 http://example.org/user/jg/default_domain/olympic_games/sports #13045-04fzfj PRED entity: 04fzfj PRED relation: genre PRED expected values: 01drsx => 103 concepts (54 used for prediction) PRED predicted values (max 10 best out of 109): 07s9rl0 (0.71 #2929, 0.65 #5042, 0.61 #1405), 024qqx (0.48 #3984, 0.06 #1639), 0jxy (0.45 #980, 0.04 #5320, 0.04 #2152), 0hcr (0.43 #958, 0.10 #2599, 0.08 #4476), 05p553 (0.41 #1408, 0.39 #472, 0.37 #1057), 03k9fj (0.39 #3877, 0.39 #5287, 0.38 #4818), 01hmnh (0.29 #952, 0.25 #367, 0.24 #484), 02l7c8 (0.28 #1418, 0.28 #833, 0.27 #14), 0lsxr (0.24 #4815, 0.23 #3874, 0.22 #5284), 02n4kr (0.24 #124, 0.22 #358, 0.17 #2935) >> Best rule #2929 for best value: >> intensional similarity = 4 >> extensional distance = 220 >> proper extension: 0fq27fp; >> query: (?x723, 07s9rl0) <- crewmember(?x723, ?x666), genre(?x723, ?x812), genre(?x11735, ?x812), ?x11735 = 02x2jl_ >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #276 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 02hct1; 07g9f; *> query: (?x723, 01drsx) <- nominated_for(?x154, ?x723), award_winner(?x723, ?x1689), award_winner(?x723, ?x1561), citytown(?x1561, ?x4801), story_by(?x1688, ?x1689) *> conf = 0.08 ranks of expected_values: 31 EVAL 04fzfj genre 01drsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 103.000 54.000 0.712 http://example.org/film/film/genre #13044-01t21q PRED entity: 01t21q PRED relation: location_of_ceremony! PRED expected values: 06b_0 => 111 concepts (23 used for prediction) PRED predicted values (max 10 best out of 153): 01nbq4 (0.09 #479, 0.09 #223, 0.08 #733), 01cwcr (0.09 #430, 0.08 #684, 0.06 #939), 0f8pz (0.09 #345, 0.08 #599, 0.06 #854), 02gvwz (0.09 #281, 0.08 #535, 0.06 #790), 02p5hf (0.08 #1755, 0.08 #2011, 0.07 #2267), 02m30v (0.08 #1783, 0.07 #2295, 0.04 #2039), 0dvld (0.08 #1677, 0.04 #1933, 0.03 #2189), 01rwcgb (0.04 #1757, 0.04 #2013, 0.03 #2269), 0h7pj (0.04 #1731, 0.04 #1987, 0.03 #2243), 03j24kf (0.04 #1641, 0.04 #1897, 0.03 #2153) >> Best rule #479 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0ncq_; 01dzq6; 0f485; >> query: (?x4984, 01nbq4) <- contains(?x1310, ?x4984), contains(?x362, ?x4984), location_of_ceremony(?x566, ?x4984), ?x362 = 04jpl, ?x1310 = 02jx1 >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01t21q location_of_ceremony! 06b_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 23.000 0.091 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #13043-0134wr PRED entity: 0134wr PRED relation: artist! PRED expected values: 03qy3l => 100 concepts (63 used for prediction) PRED predicted values (max 10 best out of 108): 015_1q (0.33 #17, 0.32 #702, 0.32 #565), 043g7l (0.33 #29, 0.14 #851, 0.14 #714), 027f3ys (0.33 #104, 0.05 #1474, 0.04 #2024), 033hn8 (0.23 #972, 0.20 #287, 0.16 #561), 017l96 (0.22 #427, 0.16 #975, 0.15 #1386), 01w40h (0.19 #1122, 0.14 #163, 0.12 #574), 02p11jq (0.17 #423, 0.14 #149, 0.14 #1108), 0181dw (0.16 #998, 0.14 #861, 0.14 #176), 011k1h (0.16 #968, 0.13 #1929, 0.12 #2203), 0mzkr (0.16 #571, 0.15 #297, 0.14 #708) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02jqjm; >> query: (?x8078, 015_1q) <- award(?x8078, ?x1827), artists(?x671, ?x8078), ?x1827 = 02nhxf, group(?x74, ?x8078) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1431 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 57 *> proper extension: 03f2_rc; 06cc_1; 07c0j; 01vrz41; 05crg7; 015_30; 015882; 0dtd6; 0pyg6; 010hn; ... *> query: (?x8078, 03qy3l) <- award(?x8078, ?x3647), award(?x8078, ?x1827), artists(?x671, ?x8078), ?x3647 = 01c9jp, ceremony(?x1827, ?x139) *> conf = 0.05 ranks of expected_values: 40 EVAL 0134wr artist! 03qy3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 100.000 63.000 0.333 http://example.org/music/record_label/artist #13042-07ylj PRED entity: 07ylj PRED relation: member_states! PRED expected values: 085h1 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 6): 085h1 (0.91 #15, 0.90 #19, 0.89 #3), 02jxk (0.46 #1, 0.39 #5, 0.37 #13), 059dn (0.46 #4, 0.36 #20, 0.34 #16), 018cqq (0.43 #2, 0.42 #6, 0.40 #14), 07t65 (0.09 #81, 0.05 #102, 0.05 #185), 02vk52z (0.09 #81, 0.05 #102, 0.05 #185) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 016wzw; >> query: (?x1203, 085h1) <- film_release_region(?x2189, ?x1203), film_release_region(?x607, ?x1203), ?x607 = 02x3lt7, ?x2189 = 02yvct >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ylj member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.914 http://example.org/user/ktrueman/default_domain/international_organization/member_states #13041-02y0js PRED entity: 02y0js PRED relation: people PRED expected values: 02w670 0p50v 015np0 05qzv 01jrs46 01p7b6b 02_01w => 75 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1582): 0gyy0 (0.40 #4212, 0.33 #6137, 0.30 #13846), 0jrny (0.40 #3953, 0.33 #5878, 0.29 #7162), 05v45k (0.40 #4432, 0.33 #6357, 0.29 #7641), 016gkf (0.40 #4055, 0.33 #5980, 0.29 #7264), 01938t (0.40 #4761, 0.33 #908, 0.29 #6685), 032l1 (0.40 #1285, 0.14 #1927, 0.13 #12201), 02dth1 (0.33 #5915, 0.29 #7840, 0.20 #14265), 0407f (0.33 #746, 0.25 #1388, 0.20 #14872), 0chsq (0.33 #656, 0.25 #1298, 0.20 #13501), 0b22w (0.33 #1104, 0.25 #1746, 0.20 #13949) >> Best rule #4212 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 0gk4g; 0m32h; 02k6hp; >> query: (?x1158, 0gyy0) <- people(?x1158, ?x10466), people(?x1158, ?x5573), people(?x1158, ?x5435), risk_factors(?x1158, ?x11678), risk_factors(?x1158, ?x231), ?x231 = 05zppz, ?x11678 = 0fltx, influenced_by(?x4072, ?x5435), people(?x1050, ?x5573), profession(?x10466, ?x987), award_winner(?x8132, ?x10466), profession(?x4072, ?x353) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1090 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 0dq9p; *> query: (?x1158, 01p7b6b) <- people(?x1158, ?x11265), people(?x1158, ?x10466), people(?x1158, ?x5797), risk_factors(?x1158, ?x231), symptom_of(?x1158, ?x10480), risk_factors(?x5784, ?x1158), nationality(?x10466, ?x94), people(?x5784, ?x3336), profession(?x11265, ?x1146), ?x1146 = 018gz8, influenced_by(?x1857, ?x5797) *> conf = 0.33 ranks of expected_values: 57, 590, 635, 797, 1478, 1519 EVAL 02y0js people 02_01w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 75.000 70.000 0.400 http://example.org/people/cause_of_death/people EVAL 02y0js people 01p7b6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 75.000 70.000 0.400 http://example.org/people/cause_of_death/people EVAL 02y0js people 01jrs46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 75.000 70.000 0.400 http://example.org/people/cause_of_death/people EVAL 02y0js people 05qzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 75.000 70.000 0.400 http://example.org/people/cause_of_death/people EVAL 02y0js people 015np0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 70.000 0.400 http://example.org/people/cause_of_death/people EVAL 02y0js people 0p50v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 75.000 70.000 0.400 http://example.org/people/cause_of_death/people EVAL 02y0js people 02w670 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 75.000 70.000 0.400 http://example.org/people/cause_of_death/people #13040-04sj3 PRED entity: 04sj3 PRED relation: country! PRED expected values: 03_8r => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 55): 03_8r (0.70 #463, 0.69 #408, 0.66 #1288), 01cgz (0.64 #454, 0.64 #399, 0.61 #344), 071t0 (0.60 #409, 0.60 #134, 0.59 #464), 07gyv (0.55 #447, 0.55 #392, 0.53 #337), 03fyrh (0.55 #140, 0.38 #250, 0.38 #305), 06f41 (0.55 #400, 0.54 #455, 0.49 #345), 07jbh (0.51 #420, 0.50 #475, 0.50 #145), 01lb14 (0.50 #511, 0.50 #126, 0.49 #401), 09w1n (0.50 #136, 0.42 #246, 0.29 #411), 03hr1p (0.49 #410, 0.48 #465, 0.45 #135) >> Best rule #463 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 05r4w; 09c7w0; 0b90_r; 03rjj; 03_3d; 0d060g; 0h3y; 0j1z8; 0chghy; 03rt9; ... >> query: (?x8781, 03_8r) <- organization(?x8781, ?x127), jurisdiction_of_office(?x182, ?x8781), exported_to(?x8781, ?x94) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sj3 country! 03_8r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.696 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #13039-02d003 PRED entity: 02d003 PRED relation: film! PRED expected values: 0170s4 => 74 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1057): 0h5g_ (0.33 #72, 0.03 #22907, 0.03 #8377), 0159h6 (0.33 #71, 0.02 #29134, 0.01 #6301), 0q9kd (0.19 #4158, 0.03 #26991, 0.02 #29067), 02cllz (0.17 #405, 0.06 #2481, 0.03 #6635), 02qgqt (0.17 #18, 0.06 #8323, 0.05 #10399), 073749 (0.17 #705, 0.04 #17314, 0.02 #29768), 03n08b (0.17 #232, 0.04 #16841, 0.02 #29295), 02fz3w (0.17 #1576, 0.03 #7806, 0.01 #18185), 01pcbg (0.17 #579, 0.03 #17188, 0.02 #29642), 05vsxz (0.17 #9, 0.03 #80954, 0.02 #4152) >> Best rule #72 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 020bv3; 0bmch_x; 01k0xy; 03ydlnj; >> query: (?x7072, 0h5g_) <- film(?x5216, ?x7072), film(?x287, ?x7072), ?x5216 = 03v1jf, participant(?x286, ?x287), genre(?x7072, ?x258), film_crew_role(?x7072, ?x137) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2470 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 01jw67; *> query: (?x7072, 0170s4) <- film(?x6221, ?x7072), film(?x5216, ?x7072), language(?x7072, ?x254), award_nominee(?x4508, ?x5216), ?x6221 = 015p3p, award_winner(?x3789, ?x4508) *> conf = 0.06 ranks of expected_values: 67 EVAL 02d003 film! 0170s4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 74.000 39.000 0.333 http://example.org/film/actor/film./film/performance/film #13038-015wfg PRED entity: 015wfg PRED relation: film PRED expected values: 01b195 0jyb4 => 143 concepts (39 used for prediction) PRED predicted values (max 10 best out of 781): 0cwrr (0.60 #32173, 0.34 #50051, 0.30 #21448), 04954r (0.09 #7767, 0.05 #34576, 0.05 #38150), 01jnc_ (0.09 #8717, 0.04 #14078, 0.01 #39100), 0jvt9 (0.08 #16625, 0.05 #23774, 0.04 #34499), 0cz_ym (0.08 #5658, 0.05 #9232), 0872p_c (0.08 #5539, 0.04 #9113, 0.01 #28772), 0gg5kmg (0.08 #6443, 0.04 #10017), 09txzv (0.08 #5617, 0.04 #9191), 0b3n61 (0.08 #6721, 0.03 #31742, 0.01 #49620), 06w839_ (0.08 #5873, 0.02 #30894, 0.02 #29106) >> Best rule #32173 for best value: >> intensional similarity = 4 >> extensional distance = 194 >> proper extension: 049_zz; 02v0ff; 01f9mq; >> query: (?x4370, ?x802) <- nominated_for(?x4370, ?x802), type_of_union(?x4370, ?x566), film(?x4370, ?x7246), category(?x4370, ?x134) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015wfg film 0jyb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 39.000 0.600 http://example.org/film/actor/film./film/performance/film EVAL 015wfg film 01b195 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 39.000 0.600 http://example.org/film/actor/film./film/performance/film #13037-09sh8k PRED entity: 09sh8k PRED relation: production_companies PRED expected values: 027jw0c => 110 concepts (88 used for prediction) PRED predicted values (max 10 best out of 55): 025jfl (0.36 #325, 0.32 #2933, 0.32 #4650), 06rq1k (0.17 #16, 0.15 #97, 0.02 #1397), 0kx4m (0.17 #7, 0.08 #88, 0.05 #250), 0hpt3 (0.17 #19, 0.02 #262, 0.01 #1563), 086k8 (0.15 #83, 0.12 #2034, 0.12 #4162), 01gb54 (0.15 #117, 0.08 #36, 0.08 #2068), 054lpb6 (0.13 #419, 0.10 #1394, 0.08 #13), 016tw3 (0.12 #904, 0.12 #579, 0.12 #498), 05qd_ (0.11 #3925, 0.11 #4168, 0.11 #170), 01795t (0.11 #182, 0.05 #1239, 0.04 #1727) >> Best rule #325 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 0140g4; >> query: (?x136, ?x574) <- prequel(?x136, ?x4378), featured_film_locations(?x136, ?x362), film(?x574, ?x136), place_of_death(?x587, ?x362) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #229 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 05_5_22; 05nlx4; 0cmf0m0; *> query: (?x136, 027jw0c) <- prequel(?x136, ?x4378), film_crew_role(?x136, ?x7591), film(?x965, ?x136), ?x7591 = 0d2b38 *> conf = 0.05 ranks of expected_values: 30 EVAL 09sh8k production_companies 027jw0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 110.000 88.000 0.363 http://example.org/film/film/production_companies #13036-01z9_x PRED entity: 01z9_x PRED relation: award_winner! PRED expected values: 01xqqp => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 112): 01s695 (0.24 #142, 0.15 #281, 0.14 #559), 013b2h (0.23 #357, 0.22 #218, 0.20 #774), 01c6qp (0.20 #157, 0.17 #296, 0.13 #435), 01xqqp (0.19 #372, 0.16 #233, 0.15 #789), 02cg41 (0.18 #263, 0.14 #1236, 0.13 #2765), 0466p0j (0.18 #214, 0.13 #353, 0.12 #3411), 0gpjbt (0.17 #862, 0.17 #1140, 0.11 #167), 02rjjll (0.16 #144, 0.15 #3341, 0.15 #2646), 0jzphpx (0.16 #594, 0.11 #316, 0.10 #455), 09n4nb (0.13 #186, 0.13 #325, 0.10 #5190) >> Best rule #142 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 09jm8; 016ppr; 012x03; >> query: (?x7882, 01s695) <- award_winner(?x725, ?x7882), artists(?x1572, ?x7882), ?x725 = 01bx35 >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #372 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 03f2_rc; 01wbgdv; 04bpm6; 0pyg6; 06449; 0m_v0; 02dbp7; 016k62; 031x_3; 016ggh; ... *> query: (?x7882, 01xqqp) <- performance_role(?x7882, ?x315), award_nominee(?x7882, ?x2169), award_winner(?x725, ?x7882) *> conf = 0.19 ranks of expected_values: 4 EVAL 01z9_x award_winner! 01xqqp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 111.000 0.244 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #13035-03mnk PRED entity: 03mnk PRED relation: list PRED expected values: 01pd60 => 216 concepts (216 used for prediction) PRED predicted values (max 10 best out of 4): 01pd60 (0.81 #891, 0.80 #813, 0.73 #702), 09g7thr (0.75 #256, 0.75 #196, 0.67 #201), 05glt (0.53 #809, 0.38 #887, 0.18 #437), 026cl_m (0.26 #528, 0.14 #438, 0.12 #810) >> Best rule #891 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 07bz5; >> query: (?x3230, ?x8915) <- list(?x3230, ?x7472), list(?x7471, ?x7472), list(?x7471, ?x8915) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mnk list 01pd60 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 216.000 216.000 0.814 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #13034-03n08b PRED entity: 03n08b PRED relation: film PRED expected values: 0prrm => 96 concepts (67 used for prediction) PRED predicted values (max 10 best out of 864): 03p2xc (0.17 #1240, 0.02 #4798, 0.01 #36820), 06fpsx (0.17 #1330, 0.02 #4888, 0.01 #17341), 02rmd_2 (0.17 #730, 0.02 #4288), 02_06s (0.17 #1241), 034qzw (0.08 #333, 0.07 #3891, 0.04 #5670), 08r4x3 (0.08 #153, 0.04 #3711, 0.03 #10827), 0bvn25 (0.08 #49, 0.04 #3607, 0.03 #5386), 059rc (0.08 #455, 0.04 #4013, 0.01 #20024), 02_1sj (0.08 #79, 0.04 #7195), 06ztvyx (0.08 #431, 0.03 #3989, 0.02 #11105) >> Best rule #1240 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 03_48k; >> query: (?x1461, 03p2xc) <- film(?x1461, ?x3084), ?x3084 = 03mh_tp >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #7974 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 130 *> proper extension: 0d0vj4; 028rk; 06hx2; 06bss; 0f7fy; 034ls; 051cc; 020hh3; 0163t3; 037s5h; ... *> query: (?x1461, 0prrm) <- location(?x1461, ?x1274), person(?x10840, ?x1461) *> conf = 0.04 ranks of expected_values: 114 EVAL 03n08b film 0prrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 96.000 67.000 0.167 http://example.org/film/actor/film./film/performance/film #13033-02rky4 PRED entity: 02rky4 PRED relation: state_province_region PRED expected values: 01n7q => 198 concepts (175 used for prediction) PRED predicted values (max 10 best out of 68): 01n7q (0.76 #1856, 0.76 #1750, 0.63 #15104), 0rh6k (0.38 #4947, 0.34 #4326, 0.31 #6927), 059rby (0.36 #376, 0.31 #11258, 0.30 #3958), 05tbn (0.36 #8657, 0.12 #1660, 0.11 #3266), 05kkh (0.36 #8657, 0.10 #126, 0.10 #2), 04rrx (0.36 #8657, 0.10 #30, 0.08 #649), 09c7w0 (0.29 #14980, 0.24 #12994, 0.24 #19200), 07b_l (0.14 #2030, 0.13 #917, 0.12 #3019), 05k7sb (0.14 #3492, 0.13 #3123, 0.08 #1269), 071vr (0.11 #2228, 0.05 #2844, 0.05 #2969) >> Best rule #1856 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 03fcbb; >> query: (?x10368, ?x1227) <- contains(?x1227, ?x10368), organization(?x346, ?x10368), ?x1227 = 01n7q, institution(?x865, ?x10368) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rky4 state_province_region 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 175.000 0.763 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #13032-043tvp3 PRED entity: 043tvp3 PRED relation: film_release_region PRED expected values: 02k54 06mzp 0k6nt 0h7x 06mkj 07f1x 0k3p => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 100): 06mkj (0.90 #1234, 0.88 #2076, 0.88 #514), 0k6nt (0.77 #2057, 0.75 #2177, 0.74 #1575), 06mzp (0.64 #732, 0.56 #492, 0.49 #1572), 07f1x (0.62 #566, 0.50 #806, 0.47 #1286), 02k54 (0.59 #730, 0.55 #970, 0.50 #490), 077qn (0.56 #539, 0.53 #1259, 0.48 #1019), 0h7x (0.55 #741, 0.45 #981, 0.44 #501), 05sb1 (0.45 #996, 0.44 #516, 0.42 #1236), 01crd5 (0.45 #1054, 0.38 #1294, 0.35 #1654), 0jgx (0.42 #1017, 0.35 #1257, 0.25 #537) >> Best rule #1234 for best value: >> intensional similarity = 6 >> extensional distance = 38 >> proper extension: 0gtvpkw; 0h63gl9; 02wtp6; >> query: (?x6882, 06mkj) <- film_release_region(?x6882, ?x2146), film_release_region(?x6882, ?x2000), organization(?x2146, ?x127), olympics(?x2146, ?x778), ?x2000 = 0d0kn, nationality(?x111, ?x2146) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 7, 53 EVAL 043tvp3 film_release_region 0k3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 78.000 78.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043tvp3 film_release_region 07f1x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043tvp3 film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043tvp3 film_release_region 0h7x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 78.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043tvp3 film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043tvp3 film_release_region 06mzp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 043tvp3 film_release_region 02k54 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13031-02qk3fk PRED entity: 02qk3fk PRED relation: film_release_region PRED expected values: 05r4w 0d0vqn 0f8l9c 035qy 06mkj => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 189): 0f8l9c (0.90 #1726, 0.89 #1442, 0.88 #2295), 0d0vqn (0.90 #1716, 0.89 #2285, 0.89 #862), 05r4w (0.89 #858, 0.86 #716, 0.86 #574), 06mkj (0.86 #762, 0.86 #1616, 0.86 #1474), 0d060g (0.86 #719, 0.86 #861, 0.82 #291), 035qy (0.86 #1597, 0.82 #315, 0.80 #1455), 03rk0 (0.86 #476, 0.82 #333, 0.80 #190), 04gzd (0.80 #152, 0.79 #438, 0.75 #865), 015qh (0.73 #320, 0.71 #606, 0.71 #463), 016wzw (0.73 #342, 0.70 #199, 0.70 #56) >> Best rule #1726 for best value: >> intensional similarity = 7 >> extensional distance = 132 >> proper extension: 0fq27fp; 035yn8; 0fq7dv_; 0crc2cp; 0gh65c5; 06zn2v2; 06tpmy; 0dt8xq; 02ylg6; 0gbfn9; ... >> query: (?x6422, 0f8l9c) <- film_release_region(?x6422, ?x5482), film_release_region(?x6422, ?x1453), film_release_region(?x6422, ?x172), ?x172 = 0154j, country(?x453, ?x5482), member_states(?x2106, ?x5482), ?x1453 = 06qd3 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6 EVAL 02qk3fk film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02qk3fk film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 69.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02qk3fk film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02qk3fk film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02qk3fk film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13030-03975z PRED entity: 03975z PRED relation: music! PRED expected values: 0fphgb => 124 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1010): 0209hj (0.74 #19120, 0.69 #5031, 0.07 #44278), 0k2cb (0.74 #19120, 0.69 #5031, 0.07 #44278), 0btpm6 (0.09 #740, 0.05 #3758, 0.04 #6777), 02fqrf (0.09 #337, 0.05 #3355, 0.04 #6374), 07bzz7 (0.08 #1532, 0.05 #15621, 0.02 #21658), 01pv91 (0.08 #1258, 0.05 #4276, 0.02 #5283), 0456zg (0.08 #1817, 0.02 #4835, 0.02 #5842), 01s7w3 (0.06 #12943, 0.06 #15961, 0.05 #16967), 0pdp8 (0.05 #8273, 0.05 #4247, 0.04 #5254), 09d38d (0.05 #9028, 0.02 #19091, 0.02 #14061) >> Best rule #19120 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 01nqfh_; 07qy0b; 01mkn_d; 01nc3rh; 06zd1c; 089kpp; >> query: (?x9396, ?x697) <- nominated_for(?x9396, ?x697), music(?x7656, ?x9396), language(?x7656, ?x90) >> conf = 0.74 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 03975z music! 0fphgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 84.000 0.738 http://example.org/film/film/music #13029-02tf1y PRED entity: 02tf1y PRED relation: type_of_union PRED expected values: 04ztj => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.78 #1, 0.74 #106, 0.70 #61), 01g63y (0.22 #2, 0.17 #38, 0.17 #66), 01bl8s (0.01 #27) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0159h6; 07vc_9; 01zmpg; 01xsbh; 0m31m; 0h32q; 01gw4f; 018ygt; 023nlj; 01pllx; ... >> query: (?x8897, 04ztj) <- profession(?x8897, ?x319), actor(?x6884, ?x8897), award(?x8897, ?x401), sibling(?x8897, ?x5064) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tf1y type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.778 http://example.org/people/person/spouse_s./people/marriage/type_of_union #13028-01_fjr PRED entity: 01_fjr PRED relation: jurisdiction_of_office PRED expected values: 0chghy => 17 concepts (16 used for prediction) PRED predicted values (max 10 best out of 633): 0f8l9c (0.78 #2344, 0.67 #1419, 0.64 #3267), 0hzlz (0.67 #1420, 0.60 #960, 0.56 #2345), 03rj0 (0.67 #1489, 0.60 #1029, 0.56 #2414), 03shp (0.67 #1552, 0.60 #1092, 0.50 #2013), 019rg5 (0.60 #963, 0.50 #1423, 0.50 #502), 09c7w0 (0.54 #4619, 0.47 #5079, 0.41 #1382), 03gj2 (0.50 #1428, 0.44 #2353, 0.40 #2814), 03spz (0.50 #1591, 0.40 #1131, 0.38 #2052), 014tss (0.50 #709, 0.40 #1170, 0.33 #1630), 015fr (0.41 #1382, 0.40 #951, 0.36 #3721) >> Best rule #2344 for best value: >> intensional similarity = 24 >> extensional distance = 7 >> proper extension: 01gkgk; 0789n; 0377k9; >> query: (?x12455, 0f8l9c) <- jurisdiction_of_office(?x12455, ?x1353), country(?x4355, ?x1353), country(?x3885, ?x1353), combatants(?x94, ?x1353), ?x3885 = 019w9j, film_release_region(?x9002, ?x1353), film_release_region(?x5992, ?x1353), film_release_region(?x5564, ?x1353), film_release_region(?x4430, ?x1353), film_release_region(?x1228, ?x1353), film_release_region(?x1080, ?x1353), film_release_region(?x186, ?x1353), combatants(?x326, ?x1353), organization(?x1353, ?x127), ?x1080 = 01c22t, form_of_government(?x1353, ?x4763), ?x4430 = 043sct5, ?x186 = 02vxq9m, ?x1228 = 05z_kps, countries_within(?x455, ?x1353), olympics(?x1353, ?x778), ?x5564 = 03yvf2, ?x9002 = 0ndsl1x, ?x5992 = 0g5q34q >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1382 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 3 *> proper extension: 060c4; *> query: (?x12455, ?x94) <- jurisdiction_of_office(?x12455, ?x1353), jurisdiction_of_office(?x12455, ?x1023), ?x1353 = 035qy, film_release_region(?x9941, ?x1023), film_release_region(?x7651, ?x1023), film_release_region(?x6931, ?x1023), film_release_region(?x6270, ?x1023), film_release_region(?x4707, ?x1023), film_release_region(?x1999, ?x1023), film_release_region(?x1370, ?x1023), film_release_region(?x607, ?x1023), jurisdiction_of_office(?x3444, ?x1023), ?x7651 = 0h95927, ?x4707 = 02xbyr, combatants(?x1023, ?x94), ?x607 = 02x3lt7, featured_film_locations(?x522, ?x1023), country(?x150, ?x1023), olympics(?x1023, ?x778), ?x9941 = 024lt6, film_crew_role(?x1370, ?x1284), olympics(?x1023, ?x452), award(?x1370, ?x68), ?x6931 = 09v3jyg, ?x6270 = 0g9zljd, ?x1999 = 0gd0c7x *> conf = 0.41 ranks of expected_values: 16 EVAL 01_fjr jurisdiction_of_office 0chghy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 17.000 16.000 0.778 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #13027-0lk90 PRED entity: 0lk90 PRED relation: vacationer! PRED expected values: 0b90_r 0ckhc => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 118): 05qtj (0.35 #1874, 0.31 #2840, 0.30 #3080), 02s838 (0.20 #106, 0.17 #226, 0.11 #346), 01_d4 (0.20 #37, 0.17 #157, 0.04 #1842), 06c62 (0.20 #83, 0.12 #1888, 0.11 #444), 01p8s (0.20 #98, 0.11 #459, 0.08 #579), 0cv3w (0.19 #2826, 0.19 #1860, 0.18 #3066), 04jpl (0.16 #850, 0.16 #1814, 0.14 #2780), 0b90_r (0.15 #484, 0.15 #4097, 0.13 #1808), 0160w (0.15 #483, 0.12 #723, 0.12 #843), 0chghy (0.15 #491, 0.08 #851, 0.07 #971) >> Best rule #1874 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 02lnhv; >> query: (?x1093, 05qtj) <- vacationer(?x3501, ?x1093), vacationer(?x1523, ?x1093), month(?x3501, ?x1459), film_release_region(?x204, ?x1523) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #484 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 04cr6qv; 02r3cn; *> query: (?x1093, 0b90_r) <- vacationer(?x1036, ?x1093), artists(?x671, ?x1093), participant(?x1093, ?x6035) *> conf = 0.15 ranks of expected_values: 8, 75 EVAL 0lk90 vacationer! 0ckhc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 177.000 177.000 0.349 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 0lk90 vacationer! 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 177.000 177.000 0.349 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #13026-05jcn8 PRED entity: 05jcn8 PRED relation: gender PRED expected values: 05zppz => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #7, 0.89 #13, 0.88 #11), 02zsn (0.28 #160, 0.27 #96, 0.27 #152) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 079vf; 049gc; 06z4wj; 01y8d4; 015zql; 011s9r; >> query: (?x3456, 05zppz) <- award_winner(?x1052, ?x3456), story_by(?x1080, ?x3456), nationality(?x3456, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05jcn8 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.890 http://example.org/people/person/gender #13025-014g_s PRED entity: 014g_s PRED relation: film PRED expected values: 087vnr5 => 139 concepts (83 used for prediction) PRED predicted values (max 10 best out of 727): 03t95n (0.70 #12511, 0.63 #78637, 0.61 #57189), 0ct2tf5 (0.50 #12279, 0.40 #6918), 024lff (0.40 #5970, 0.33 #11331), 09lxv9 (0.25 #1504, 0.14 #15802, 0.10 #17589), 01gglm (0.25 #1405, 0.14 #15703, 0.10 #17490), 01738w (0.25 #1129, 0.14 #15427, 0.10 #17214), 0963mq (0.25 #139, 0.14 #14437, 0.10 #16224), 08mg_b (0.25 #1122, 0.14 #15420, 0.10 #17207), 02q7yfq (0.20 #6566, 0.17 #11927, 0.02 #70905), 029zqn (0.20 #5628, 0.17 #10989, 0.01 #30647) >> Best rule #12511 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01sl1q; 01kgxf; >> query: (?x10780, ?x6615) <- film(?x10780, ?x6095), ?x6095 = 0bq6ntw, nominated_for(?x10780, ?x6615) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #49707 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 196 *> proper extension: 06w58f; *> query: (?x10780, 087vnr5) <- currency(?x10780, ?x170), nominated_for(?x10780, ?x6615), award_winner(?x1074, ?x10780) *> conf = 0.01 ranks of expected_values: 694 EVAL 014g_s film 087vnr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 139.000 83.000 0.700 http://example.org/film/actor/film./film/performance/film #13024-05qd_ PRED entity: 05qd_ PRED relation: production_companies! PRED expected values: 0f40w => 144 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1085): 0dr_4 (0.48 #14435, 0.47 #30922, 0.45 #38139), 0ch26b_ (0.48 #14435, 0.47 #30922, 0.45 #38139), 07nxnw (0.48 #14435, 0.47 #30922, 0.45 #38139), 035w2k (0.48 #14435, 0.47 #30922, 0.45 #38139), 01hvjx (0.48 #14435, 0.47 #30922, 0.45 #38139), 03qcfvw (0.48 #14435, 0.47 #30922, 0.45 #38139), 04mcw4 (0.48 #14435, 0.47 #30922, 0.45 #38139), 01633c (0.48 #14435, 0.47 #30922, 0.45 #38139), 04f52jw (0.48 #14435, 0.47 #30922, 0.45 #38139), 06t6dz (0.48 #14435, 0.47 #30922, 0.45 #38139) >> Best rule #14435 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0kx4m; 0hpt3; 046b0s; 0g5lhl7; 031rq5; 025hwq; >> query: (?x902, ?x103) <- nominated_for(?x902, ?x103), production_companies(?x66, ?x902), award_winner(?x574, ?x902) >> conf = 0.48 => this is the best rule for 15 predicted values No rule for expected values ranks of expected_values: EVAL 05qd_ production_companies! 0f40w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 132.000 0.482 http://example.org/film/film/production_companies #13023-0p8jf PRED entity: 0p8jf PRED relation: people! PRED expected values: 048z7l => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 47): 041rx (0.67 #1005, 0.63 #2699, 0.63 #3161), 02w7gg (0.33 #2, 0.08 #541, 0.08 #1157), 0x67 (0.28 #395, 0.21 #318, 0.14 #1088), 013xrm (0.15 #174, 0.14 #1021, 0.09 #1098), 013b6_ (0.14 #1054, 0.08 #207, 0.06 #2748), 019kn7 (0.12 #123), 033tf_ (0.12 #2009, 0.10 #6322, 0.09 #6861), 07hwkr (0.11 #320, 0.09 #1706, 0.08 #397), 0xnvg (0.11 #244, 0.08 #2631, 0.08 #552), 0g5y6 (0.10 #2193, 0.09 #2270, 0.09 #2732) >> Best rule #1005 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 058vp; >> query: (?x2993, 041rx) <- influenced_by(?x2993, ?x1947), religion(?x2993, ?x7131), ?x7131 = 03_gx >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #810 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 05ty4m; 08433; 0br1w; 0282x; 0hky; 056wb; 03dbds; 0hcvy; *> query: (?x2993, 048z7l) <- student(?x331, ?x2993), written_by(?x9524, ?x2993), influenced_by(?x2993, ?x1947), award(?x2993, ?x575) *> conf = 0.09 ranks of expected_values: 11 EVAL 0p8jf people! 048z7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 172.000 172.000 0.667 http://example.org/people/ethnicity/people #13022-03cvv4 PRED entity: 03cvv4 PRED relation: student! PRED expected values: 0cwx_ => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 61): 0bwfn (0.07 #275, 0.06 #31897, 0.06 #29262), 017j69 (0.07 #145, 0.03 #3834, 0.03 #9104), 04b_46 (0.07 #227, 0.03 #9713, 0.02 #6551), 01bm_ (0.07 #246, 0.02 #4462, 0.01 #9205), 02l9wl (0.07 #252, 0.01 #8157, 0.01 #1306), 01t0dy (0.07 #217, 0.01 #6014, 0.01 #1798), 021996 (0.07 #309, 0.01 #1890), 06pwq (0.07 #12, 0.01 #33215), 02hp70 (0.07 #431), 02gkxp (0.07 #387) >> Best rule #275 for best value: >> intensional similarity = 2 >> extensional distance = 13 >> proper extension: 015dqj; >> query: (?x9972, 0bwfn) <- film(?x9972, ?x5275), ?x5275 = 01q2nx >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #2349 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 167 *> proper extension: 01yh3y; 03fghg; 03wy70; 01wphh2; 01rmnp; 034rd9; 0392kz; 014y6; 01nsyf; 05z775; ... *> query: (?x9972, 0cwx_) <- actor(?x6706, ?x9972), film(?x9972, ?x924), category(?x9972, ?x134) *> conf = 0.02 ranks of expected_values: 29 EVAL 03cvv4 student! 0cwx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 124.000 124.000 0.067 http://example.org/education/educational_institution/students_graduates./education/education/student #13021-06j8q_ PRED entity: 06j8q_ PRED relation: place_of_birth PRED expected values: 04pry => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 28): 02_286 (0.33 #2823, 0.33 #2135, 0.33 #3530), 07bcn (0.33 #41615, 0.32 #31740, 0.32 #27509), 0cr3d (0.06 #13497, 0.05 #94, 0.05 #11380), 0cc56 (0.05 #1442, 0.04 #2149, 0.04 #2856), 030qb3t (0.05 #19101, 0.04 #50834, 0.04 #55770), 01531 (0.04 #1514, 0.04 #2221, 0.03 #2928), 01_d4 (0.04 #33922, 0.03 #55782, 0.03 #19113), 04jpl (0.03 #9881, 0.02 #18348, 0.02 #22582), 094jv (0.02 #61, 0.01 #18401, 0.01 #21223), 0f94t (0.02 #1437, 0.02 #2144, 0.01 #2851) >> Best rule #2823 for best value: >> intensional similarity = 4 >> extensional distance = 245 >> proper extension: 01bpc9; 01n4f8; 0lrh; 0cqt90; 03v1jf; 05bpg3; 0127s7; 013w7j; 026v437; 01ldw4; ... >> query: (?x10696, ?x739) <- nationality(?x10696, ?x94), award(?x10696, ?x783), location(?x10696, ?x739), ?x739 = 02_286 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06j8q_ place_of_birth 04pry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.332 http://example.org/people/person/place_of_birth #13020-0k0sv PRED entity: 0k0sv PRED relation: countries_spoken_in PRED expected values: 0h7x 01mjq 056vv => 33 concepts (19 used for prediction) PRED predicted values (max 10 best out of 238): 0166b (0.72 #1674, 0.72 #3527, 0.71 #2795), 07ytt (0.44 #715, 0.33 #1091, 0.33 #345), 0d060g (0.43 #376, 0.42 #1872, 0.40 #1685), 0hzlz (0.43 #392, 0.38 #767, 0.33 #26), 0697s (0.43 #441, 0.38 #816, 0.33 #75), 01mjq (0.35 #1537, 0.24 #3027, 0.22 #3209), 0162v (0.33 #609, 0.33 #239, 0.33 #55), 034m8 (0.33 #347, 0.33 #163, 0.22 #717), 05r7t (0.33 #308, 0.33 #124, 0.22 #678), 03h2c (0.33 #270, 0.33 #86, 0.22 #640) >> Best rule #1674 for best value: >> intensional similarity = 13 >> extensional distance = 18 >> proper extension: 0swlx; >> query: (?x5814, ?x6435) <- languages_spoken(?x9332, ?x5814), languages_spoken(?x3584, ?x5814), official_language(?x6435, ?x5814), people(?x9332, ?x11497), artists(?x597, ?x11497), gender(?x11497, ?x231), ?x231 = 05zppz, ?x3584 = 07hwkr, people(?x7586, ?x11497), adjoins(?x2979, ?x6435), profession(?x11497, ?x1614), olympics(?x6435, ?x2966), organization(?x6435, ?x127) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1537 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 18 *> proper extension: 0swlx; *> query: (?x5814, 01mjq) <- languages_spoken(?x9332, ?x5814), languages_spoken(?x3584, ?x5814), official_language(?x6435, ?x5814), people(?x9332, ?x11497), artists(?x597, ?x11497), gender(?x11497, ?x231), ?x231 = 05zppz, ?x3584 = 07hwkr, people(?x7586, ?x11497), adjoins(?x2979, ?x6435), profession(?x11497, ?x1614), olympics(?x6435, ?x2966), organization(?x6435, ?x127) *> conf = 0.35 ranks of expected_values: 6, 72, 81 EVAL 0k0sv countries_spoken_in 056vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 33.000 19.000 0.725 http://example.org/language/human_language/countries_spoken_in EVAL 0k0sv countries_spoken_in 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 33.000 19.000 0.725 http://example.org/language/human_language/countries_spoken_in EVAL 0k0sv countries_spoken_in 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 33.000 19.000 0.725 http://example.org/language/human_language/countries_spoken_in #13019-0hjy PRED entity: 0hjy PRED relation: administrative_parent! PRED expected values: 0l_v1 => 194 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1199): 0hjy (0.33 #38, 0.14 #52016, 0.12 #39003), 026mj (0.33 #249, 0.03 #10290, 0.03 #51422), 07_f2 (0.33 #242, 0.03 #10283, 0.03 #51422), 050ks (0.33 #232, 0.03 #10273, 0.03 #51422), 05fjf (0.33 #222, 0.03 #10263, 0.03 #51422), 05mph (0.33 #212, 0.03 #10253, 0.03 #51422), 05fjy (0.33 #189, 0.03 #10230, 0.03 #51422), 06yxd (0.33 #168, 0.03 #10209, 0.03 #51422), 0vbk (0.33 #167, 0.03 #10208, 0.03 #51422), 04tgp (0.33 #162, 0.03 #10203, 0.03 #51422) >> Best rule #38 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09c7w0; >> query: (?x953, 0hjy) <- contains(?x953, ?x13425), ?x13425 = 0l_n1, location(?x744, ?x953) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #51422 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 129 *> proper extension: 02j71; *> query: (?x953, ?x95) <- administrative_parent(?x14484, ?x953), contains(?x94, ?x14484), contains(?x94, ?x95) *> conf = 0.03 ranks of expected_values: 256 EVAL 0hjy administrative_parent! 0l_v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 194.000 126.000 0.333 http://example.org/base/aareas/schema/administrative_area/administrative_parent #13018-03h26tm PRED entity: 03h26tm PRED relation: award_winner! PRED expected values: 026lgs => 105 concepts (50 used for prediction) PRED predicted values (max 10 best out of 317): 0bt4g (0.52 #38501, 0.48 #29439, 0.44 #40769), 0m313 (0.23 #9068, 0.04 #8, 0.03 #3405), 0hx4y (0.15 #311, 0.14 #49824, 0.14 #1443), 07gp9 (0.15 #27, 0.14 #49824, 0.14 #1159), 017gl1 (0.15 #100, 0.14 #1232, 0.12 #3497), 0dr_4 (0.15 #169, 0.14 #1301, 0.12 #3566), 0f4yh (0.14 #55484, 0.14 #49824, 0.07 #36236), 0h21v2 (0.14 #55484, 0.14 #49824, 0.07 #36236), 06mmr (0.14 #55484, 0.14 #49824, 0.04 #1127), 0jqn5 (0.14 #49824, 0.08 #9211, 0.07 #1283) >> Best rule #38501 for best value: >> intensional similarity = 3 >> extensional distance = 1134 >> proper extension: 02js_6; >> query: (?x930, ?x10943) <- nominated_for(?x930, ?x10943), award_winner(?x930, ?x1643), award_winner(?x10943, ?x929) >> conf = 0.52 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03h26tm award_winner! 026lgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 50.000 0.516 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #13017-0gpx6 PRED entity: 0gpx6 PRED relation: featured_film_locations PRED expected values: 01f62 => 76 concepts (68 used for prediction) PRED predicted values (max 10 best out of 78): 04jpl (0.16 #2177, 0.16 #9, 0.10 #2902), 02_286 (0.15 #501, 0.15 #742, 0.14 #2913), 0rh6k (0.08 #723, 0.05 #1688, 0.04 #1447), 030qb3t (0.08 #1485, 0.07 #5105, 0.06 #39), 0156q (0.06 #41, 0.03 #763, 0.03 #281), 03rjj (0.05 #246, 0.03 #728, 0.02 #1211), 0345h (0.05 #755, 0.03 #33, 0.03 #2684), 0h7h6 (0.04 #524, 0.03 #765, 0.02 #3659), 0cr3d (0.04 #547, 0.02 #1512, 0.02 #9173), 02nd_ (0.03 #3009, 0.03 #838, 0.03 #3250) >> Best rule #2177 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 08cx5g; >> query: (?x7735, 04jpl) <- award_winner(?x7735, ?x5959), nominated_for(?x10949, ?x7735), titles(?x2152, ?x7735), film_release_region(?x66, ?x2152) >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gpx6 featured_film_locations 01f62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 68.000 0.161 http://example.org/film/film/featured_film_locations #13016-05qbckf PRED entity: 05qbckf PRED relation: nominated_for! PRED expected values: 02m501 => 76 concepts (29 used for prediction) PRED predicted values (max 10 best out of 727): 01twdk (0.52 #51403, 0.51 #56075, 0.45 #28033), 02x2t07 (0.37 #21023, 0.01 #13563), 02z3zp (0.31 #39719, 0.28 #49066, 0.26 #60751), 0f5xn (0.31 #39719, 0.28 #49066, 0.26 #60751), 0gd_b_ (0.31 #39719, 0.28 #49066, 0.26 #60751), 016z2j (0.31 #39719, 0.28 #49066, 0.26 #60751), 01chc7 (0.31 #39719, 0.28 #49066, 0.26 #60751), 0c9xjl (0.28 #49066, 0.26 #60751, 0.26 #39718), 079vf (0.25 #25696, 0.23 #23359, 0.13 #39721), 046_v (0.25 #25696, 0.23 #23359) >> Best rule #51403 for best value: >> intensional similarity = 3 >> extensional distance = 721 >> proper extension: 0d_wms; 058kh7; >> query: (?x1956, ?x4731) <- film(?x4731, ?x1956), film(?x902, ?x1956), genre(?x1956, ?x225) >> conf = 0.52 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05qbckf nominated_for! 02m501 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 29.000 0.518 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #13015-024_41 PRED entity: 024_41 PRED relation: ceremony PRED expected values: 01c6qp 01mh_q 0gx1673 => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 126): 01mh_q (0.93 #454, 0.92 #328, 0.91 #202), 01c6qp (0.90 #266, 0.89 #392, 0.89 #140), 0gx1673 (0.54 #862, 0.51 #736, 0.48 #988), 05c1t6z (0.19 #1019, 0.18 #1271, 0.12 #3042), 02q690_ (0.18 #1063, 0.16 #1315, 0.11 #2960), 03nnm4t (0.17 #1072, 0.15 #1324, 0.11 #2969), 0gx_st (0.17 #1038, 0.15 #1290, 0.10 #3061), 0gvstc3 (0.17 #1287, 0.16 #1035, 0.10 #3058), 0bzm81 (0.16 #1151, 0.14 #1025, 0.13 #1277), 0n8_m93 (0.16 #1238, 0.14 #1112, 0.13 #1364) >> Best rule #454 for best value: >> intensional similarity = 7 >> extensional distance = 54 >> proper extension: 02g8mp; 01c427; 026mff; 02gdjb; 03ncb2; 02gm9n; >> query: (?x8141, 01mh_q) <- ceremony(?x8141, ?x5766), ceremony(?x8141, ?x2704), ceremony(?x8141, ?x2431), award(?x1373, ?x8141), ?x2431 = 0jzphpx, ?x2704 = 01mhwk, award_winner(?x5766, ?x352) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 024_41 ceremony 0gx1673 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.929 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 024_41 ceremony 01mh_q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.929 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 024_41 ceremony 01c6qp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.929 http://example.org/award/award_category/winners./award/award_honor/ceremony #13014-01_bkd PRED entity: 01_bkd PRED relation: artists PRED expected values: 01gf5h 01wg982 01w8n89 01vng3b 01wt4wc 01516r 0b1hw => 55 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1002): 01w8n89 (0.57 #4528, 0.53 #10854, 0.50 #2419), 0gkg6 (0.57 #4461, 0.50 #1299, 0.33 #245), 01nrz4 (0.57 #5199, 0.50 #2037, 0.33 #983), 020hh3 (0.57 #4999, 0.50 #1837, 0.33 #783), 0560w (0.57 #5205, 0.50 #2043, 0.33 #989), 04rcr (0.57 #4255, 0.50 #1093, 0.33 #39), 01jcxwp (0.57 #4842, 0.41 #22153, 0.40 #3787), 016ntp (0.57 #5530, 0.25 #9748, 0.25 #2367), 0285c (0.50 #2245, 0.50 #1192, 0.43 #5408), 01vng3b (0.50 #2656, 0.50 #1603, 0.43 #5819) >> Best rule #4528 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 07bbw; 04f73rc; >> query: (?x3753, 01w8n89) <- artists(?x3753, ?x12246), artists(?x3753, ?x9706), group(?x2798, ?x9706), ?x12246 = 0bsj9, artist(?x2190, ?x9706), instrumentalists(?x2798, ?x211) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10, 11, 18, 19, 30, 34 EVAL 01_bkd artists 0b1hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 55.000 22.000 0.571 http://example.org/music/genre/artists EVAL 01_bkd artists 01516r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 55.000 22.000 0.571 http://example.org/music/genre/artists EVAL 01_bkd artists 01wt4wc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 55.000 22.000 0.571 http://example.org/music/genre/artists EVAL 01_bkd artists 01vng3b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 55.000 22.000 0.571 http://example.org/music/genre/artists EVAL 01_bkd artists 01w8n89 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 22.000 0.571 http://example.org/music/genre/artists EVAL 01_bkd artists 01wg982 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 55.000 22.000 0.571 http://example.org/music/genre/artists EVAL 01_bkd artists 01gf5h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 55.000 22.000 0.571 http://example.org/music/genre/artists #13013-01trhmt PRED entity: 01trhmt PRED relation: participant PRED expected values: 08swgx => 136 concepts (90 used for prediction) PRED predicted values (max 10 best out of 194): 08swgx (0.82 #7305, 0.81 #5682, 0.79 #1082), 01pcrw (0.14 #1352, 0.09 #11091, 0.09 #10011), 0456xp (0.08 #29, 0.02 #1381, 0.02 #5441), 0f4vbz (0.08 #61, 0.01 #3848, 0.01 #1413), 01z0rcq (0.08 #100, 0.01 #1452), 015f7 (0.08 #96), 0127s7 (0.07 #978, 0.04 #1248, 0.04 #2330), 0227vl (0.06 #1043, 0.05 #1583, 0.04 #7266), 01vs_v8 (0.06 #872, 0.04 #1412, 0.04 #7095), 01pgzn_ (0.05 #1416, 0.04 #876, 0.03 #7099) >> Best rule #7305 for best value: >> intensional similarity = 2 >> extensional distance = 265 >> proper extension: 03n93; >> query: (?x2562, ?x2844) <- profession(?x2562, ?x131), participant(?x2844, ?x2562) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01trhmt participant 08swgx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 90.000 0.816 http://example.org/base/popstra/celebrity/canoodled./base/popstra/canoodled/participant #13012-0mg1w PRED entity: 0mg1w PRED relation: major_field_of_study! PRED expected values: 065y4w7 0kz2w 01y9qr => 60 concepts (20 used for prediction) PRED predicted values (max 10 best out of 635): 01w5m (0.75 #5385, 0.75 #701, 0.70 #2456), 07tgn (0.75 #601, 0.50 #1771, 0.50 #1186), 07wjk (0.75 #650, 0.48 #2405, 0.45 #1235), 06pwq (0.70 #2351, 0.61 #7625, 0.58 #3523), 07tds (0.62 #750, 0.57 #2505, 0.50 #1335), 07szy (0.62 #626, 0.52 #2381, 0.50 #1796), 07wrz (0.62 #649, 0.52 #2404, 0.45 #1819), 07tg4 (0.62 #675, 0.40 #1260, 0.39 #2430), 02bqy (0.62 #785, 0.40 #1370, 0.36 #1955), 0dzst (0.62 #961, 0.39 #2716, 0.36 #2131) >> Best rule #5385 for best value: >> intensional similarity = 9 >> extensional distance = 46 >> proper extension: 05r79; 02xlf; >> query: (?x7070, 01w5m) <- major_field_of_study(?x1168, ?x7070), major_field_of_study(?x122, ?x7070), major_field_of_study(?x865, ?x7070), school_type(?x1168, ?x1044), major_field_of_study(?x122, ?x3490), ?x3490 = 05qfh, student(?x122, ?x7607), ?x7607 = 0432cd, state_province_region(?x1168, ?x1227) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #606 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 6 *> proper extension: 01mkq; *> query: (?x7070, 0kz2w) <- major_field_of_study(?x7546, ?x7070), major_field_of_study(?x1168, ?x7070), major_field_of_study(?x122, ?x7070), major_field_of_study(?x865, ?x7070), school_type(?x1168, ?x1044), ?x122 = 08815, currency(?x1168, ?x170), ?x7546 = 01_qgp, citytown(?x1168, ?x3125), place_of_birth(?x399, ?x3125) *> conf = 0.50 ranks of expected_values: 28, 91, 515 EVAL 0mg1w major_field_of_study! 01y9qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 60.000 20.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0mg1w major_field_of_study! 0kz2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 60.000 20.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0mg1w major_field_of_study! 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 60.000 20.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13011-04x4gw PRED entity: 04x4gw PRED relation: nominated_for! PRED expected values: 02r22gf => 71 concepts (52 used for prediction) PRED predicted values (max 10 best out of 204): 02hsq3m (0.60 #258, 0.50 #25, 0.46 #3288), 02r22gf (0.50 #24, 0.49 #3287, 0.44 #3753), 0p9sw (0.50 #251, 0.46 #2815, 0.43 #1417), 0gq9h (0.50 #58, 0.40 #1457, 0.38 #1223), 019f4v (0.50 #49, 0.38 #2846, 0.35 #1448), 0k611 (0.50 #69, 0.34 #2866, 0.32 #1468), 054krc (0.50 #65, 0.33 #2862, 0.23 #3794), 040njc (0.50 #5, 0.31 #1404, 0.29 #2569), 02pqp12 (0.50 #54, 0.30 #287, 0.26 #2618), 02qvyrt (0.50 #93, 0.26 #3356, 0.26 #3822) >> Best rule #258 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 01gc7; 011yxg; 0315w4; 035_2h; >> query: (?x11668, 02hsq3m) <- nominated_for(?x3458, ?x11668), nominated_for(?x507, ?x11668), country(?x11668, ?x512), ?x3458 = 0gqxm, ?x507 = 02g3v6 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 09cxm4; *> query: (?x11668, 02r22gf) <- nominated_for(?x507, ?x11668), nominated_for(?x384, ?x11668), ?x507 = 02g3v6, ?x384 = 03hkv_r, titles(?x53, ?x11668) *> conf = 0.50 ranks of expected_values: 2 EVAL 04x4gw nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 52.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #13010-040z9 PRED entity: 040z9 PRED relation: film PRED expected values: 09sr0 => 134 concepts (101 used for prediction) PRED predicted values (max 10 best out of 916): 02ntb8 (0.28 #31198, 0.08 #9766, 0.07 #13338), 03shpq (0.25 #1444, 0.08 #8588, 0.07 #12160), 01k0vq (0.25 #31675, 0.08 #10243, 0.07 #13815), 0gtsx8c (0.19 #30374, 0.04 #26802), 07pd_j (0.19 #31546, 0.01 #101207, 0.01 #99421), 0c0zq (0.19 #28349, 0.08 #10489, 0.07 #14061), 0jvt9 (0.17 #25542, 0.09 #29114, 0.08 #41620), 02gpkt (0.17 #31672, 0.08 #10240, 0.07 #13812), 03cffvv (0.15 #10670, 0.14 #14242, 0.06 #32102), 0bpbhm (0.15 #9608, 0.14 #13180, 0.06 #31040) >> Best rule #31198 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 03ds83; 063g7l; >> query: (?x7391, 02ntb8) <- film(?x7391, ?x4009), film(?x13442, ?x4009), ?x13442 = 04zn7g >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #5088 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 01vvy; 03txms; *> query: (?x7391, 09sr0) <- student(?x3439, ?x7391), people(?x4959, ?x7391), ?x4959 = 01dcqj *> conf = 0.12 ranks of expected_values: 17 EVAL 040z9 film 09sr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 134.000 101.000 0.278 http://example.org/film/actor/film./film/performance/film #13009-027yf83 PRED entity: 027yf83 PRED relation: team! PRED expected values: 0b_734 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 13): 0b_6x2 (0.75 #343, 0.73 #383, 0.61 #423), 0b_6lb (0.67 #387, 0.67 #347, 0.56 #427), 0b_6pv (0.67 #389, 0.62 #248, 0.61 #429), 0bzrsh (0.67 #207, 0.62 #247, 0.60 #388), 0b_6mr (0.67 #209, 0.62 #249, 0.60 #138), 0b_6q5 (0.62 #250, 0.60 #391, 0.60 #169), 0b_75k (0.60 #164, 0.60 #154, 0.60 #134), 0b_6jz (0.60 #152, 0.55 #252, 0.52 #201), 0b_734 (0.60 #160, 0.50 #251, 0.44 #282), 0b_6h7 (0.55 #252, 0.52 #201, 0.44 #425) >> Best rule #343 for best value: >> intensional similarity = 21 >> extensional distance = 10 >> proper extension: 02plv57; 03d555l; >> query: (?x4938, 0b_6x2) <- team(?x12451, ?x4938), team(?x8992, ?x4938), team(?x8824, ?x4938), team(?x2302, ?x4938), position(?x4938, ?x1579), team(?x8824, ?x9576), team(?x8824, ?x6847), team(?x8824, ?x6003), team(?x8824, ?x5032), team(?x8824, ?x3798), locations(?x8992, ?x8993), ?x5032 = 04088s0, ?x6003 = 02py8_w, team(?x8992, ?x8728), ?x6847 = 02r2qt7, ?x3798 = 02ptzz0, ?x2302 = 0b_77q, ?x8993 = 0fsb8, ?x8728 = 026xxv_, ?x12451 = 0b_6xf, ?x9576 = 02qk2d5 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #160 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 3 *> proper extension: 02pqcfz; 03by7wc; *> query: (?x4938, 0b_734) <- team(?x8824, ?x4938), team(?x7378, ?x4938), team(?x7042, ?x4938), team(?x5897, ?x4938), team(?x3797, ?x4938), ?x8824 = 05g_nr, team(?x5897, ?x12370), team(?x5897, ?x10846), team(?x5897, ?x10171), team(?x5897, ?x9909), team(?x5897, ?x9833), team(?x5897, ?x8728), ?x3797 = 0b_6zk, ?x7042 = 0b_72t, ?x9833 = 03y9p40, ?x8728 = 026xxv_, ?x7378 = 0bzrxn, ?x12370 = 026dqjm, ?x10171 = 026w398, ?x9909 = 026wlnm, ?x10846 = 02pzy52, locations(?x5897, ?x659), position(?x4938, ?x1579) *> conf = 0.60 ranks of expected_values: 9 EVAL 027yf83 team! 0b_734 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 95.000 95.000 0.750 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #13008-064n1pz PRED entity: 064n1pz PRED relation: currency PRED expected values: 09nqf => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.91 #92, 0.90 #99, 0.86 #113), 02gsvk (0.09 #153, 0.03 #328, 0.02 #349), 01nv4h (0.06 #387, 0.04 #65, 0.03 #72), 02l6h (0.05 #151, 0.04 #200, 0.03 #214), 088n7 (0.05 #154, 0.01 #329, 0.01 #350), 0kz1h (0.02 #152), 0ptk_ (0.01 #129) >> Best rule #92 for best value: >> intensional similarity = 8 >> extensional distance = 53 >> proper extension: 047p798; >> query: (?x2098, 09nqf) <- film_crew_role(?x2098, ?x7591), film_crew_role(?x2098, ?x468), ?x7591 = 0d2b38, film(?x609, ?x2098), titles(?x812, ?x2098), genre(?x2098, ?x600), film_release_region(?x2098, ?x94), ?x468 = 02r96rf >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 064n1pz currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.909 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #13007-01c333 PRED entity: 01c333 PRED relation: major_field_of_study PRED expected values: 05qjt 03g3w => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 119): 02j62 (0.39 #5863, 0.38 #4992, 0.35 #278), 03g3w (0.37 #3995, 0.36 #2878, 0.35 #522), 02lp1 (0.32 #5845, 0.31 #4974, 0.27 #508), 05qjt (0.29 #504, 0.29 #2860, 0.25 #3977), 01lj9 (0.27 #536, 0.22 #40, 0.20 #4009), 0g26h (0.27 #5005, 0.23 #5876, 0.23 #6126), 062z7 (0.27 #5860, 0.26 #7849, 0.26 #6234), 037mh8 (0.25 #565, 0.21 #4038, 0.21 #2921), 0193x (0.25 #531, 0.12 #4004, 0.12 #2887), 0fdys (0.22 #535, 0.20 #4008, 0.20 #1651) >> Best rule #5863 for best value: >> intensional similarity = 4 >> extensional distance = 244 >> proper extension: 08qnnv; >> query: (?x3044, 02j62) <- institution(?x1771, ?x3044), major_field_of_study(?x3044, ?x1668), category(?x3044, ?x134), ?x1771 = 019v9k >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #3995 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 147 *> proper extension: 0301dp; *> query: (?x3044, 03g3w) <- institution(?x1368, ?x3044), student(?x3044, ?x6320), influenced_by(?x2161, ?x6320), profession(?x6320, ?x353) *> conf = 0.37 ranks of expected_values: 2, 4 EVAL 01c333 major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 146.000 0.390 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01c333 major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 146.000 146.000 0.390 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #13006-03g62 PRED entity: 03g62 PRED relation: written_by! PRED expected values: 0pd64 => 115 concepts (91 used for prediction) PRED predicted values (max 10 best out of 95): 0kb1g (0.36 #9923, 0.35 #14555, 0.35 #18523), 058kh7 (0.11 #1917, 0.02 #5224, 0.01 #7208), 01fmys (0.11 #1448, 0.02 #4755, 0.01 #6739), 0m491 (0.11 #1435, 0.02 #4742, 0.01 #6726), 0cbn7c (0.11 #1839), 0hv27 (0.11 #1742), 03wy8t (0.05 #3242, 0.02 #9858, 0.01 #14490), 024mxd (0.05 #2884, 0.01 #6852), 01gvsn (0.05 #3276), 06x77g (0.05 #3212) >> Best rule #9923 for best value: >> intensional similarity = 3 >> extensional distance = 165 >> proper extension: 03f2_rc; 0c1pj; 02lf0c; 0kr5_; 081lh; 021bk; 0m32_; 02vyw; 09l3p; 0499lc; ... >> query: (?x11180, ?x9993) <- student(?x741, ?x11180), film(?x11180, ?x9993), profession(?x11180, ?x319) >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03g62 written_by! 0pd64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 91.000 0.363 http://example.org/film/film/written_by #13005-07_nf PRED entity: 07_nf PRED relation: films PRED expected values: 0bdjd 0333t => 100 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1606): 0fy66 (0.25 #695, 0.20 #5363, 0.20 #4845), 027pfg (0.25 #865, 0.20 #5533, 0.20 #5015), 03pc89 (0.25 #942, 0.20 #5610, 0.20 #5092), 0m_q0 (0.25 #736, 0.20 #5404, 0.20 #4886), 0m_h6 (0.25 #958, 0.20 #5626, 0.20 #5108), 01znj1 (0.25 #276, 0.20 #5463, 0.20 #3390), 0kb1g (0.25 #462, 0.20 #5649, 0.20 #3576), 04h4c9 (0.25 #430, 0.20 #5617, 0.20 #3544), 0hv27 (0.25 #304, 0.20 #5491, 0.20 #3418), 0cc5qkt (0.25 #172, 0.20 #5359, 0.20 #3286) >> Best rule #695 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 048n7; 01tffp; >> query: (?x7455, 0fy66) <- entity_involved(?x7455, ?x7747), entity_involved(?x7455, ?x5572), gender(?x5572, ?x231), adjoins(?x1122, ?x7747), film_release_region(?x80, ?x7747), combatants(?x7747, ?x550), organization(?x7747, ?x312), films(?x7455, ?x383) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3633 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 081pw; *> query: (?x7455, ?x186) <- films(?x7455, ?x6767), films(?x7455, ?x3857), combatants(?x7455, ?x2346), taxonomy(?x7455, ?x939), language(?x6767, ?x254), currency(?x3857, ?x170), contains(?x2346, ?x1885), film_release_region(?x186, ?x2346) *> conf = 0.01 ranks of expected_values: 387, 447 EVAL 07_nf films 0333t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 100.000 57.000 0.250 http://example.org/film/film_subject/films EVAL 07_nf films 0bdjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 57.000 0.250 http://example.org/film/film_subject/films #13004-0gy2y8r PRED entity: 0gy2y8r PRED relation: film_release_region PRED expected values: 0b90_r 0345h 0bjv6 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 189): 0345h (0.89 #900, 0.88 #1045, 0.88 #755), 0b90_r (0.86 #729, 0.86 #874, 0.85 #1019), 03_3d (0.82 #1021, 0.81 #2473, 0.81 #731), 04gzd (0.74 #734, 0.70 #1024, 0.70 #879), 03rk0 (0.74 #775, 0.70 #1065, 0.70 #920), 06qd3 (0.62 #34, 0.61 #1050, 0.60 #905), 06mzp (0.62 #18, 0.58 #744, 0.57 #889), 016wzw (0.61 #783, 0.59 #928, 0.58 #1073), 047yc (0.61 #750, 0.59 #2492, 0.58 #1040), 05qx1 (0.57 #908, 0.57 #1053, 0.53 #763) >> Best rule #900 for best value: >> intensional similarity = 5 >> extensional distance = 61 >> proper extension: 087wc7n; 01fmys; 0407yj_; 03mgx6z; 02qk3fk; 02825cv; 07s3m4g; 0fpgp26; >> query: (?x4041, 0345h) <- film_release_region(?x4041, ?x1790), film_release_region(?x4041, ?x550), ?x550 = 05v8c, ?x1790 = 01pj7, language(?x4041, ?x254) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 37 EVAL 0gy2y8r film_release_region 0bjv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 77.000 77.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gy2y8r film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gy2y8r film_release_region 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #13003-07f5x PRED entity: 07f5x PRED relation: country! PRED expected values: 07gyv => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 52): 071t0 (0.67 #230, 0.66 #334, 0.65 #1271), 01lb14 (0.57 #848, 0.51 #1056, 0.51 #1264), 03hr1p (0.51 #335, 0.50 #856, 0.49 #1064), 06f41 (0.51 #326, 0.50 #847, 0.49 #118), 06wrt (0.47 #328, 0.43 #120, 0.42 #1265), 07gyv (0.47 #840, 0.46 #475, 0.45 #736), 0w0d (0.46 #324, 0.45 #116, 0.44 #845), 02y8z (0.44 #573, 0.40 #227, 0.40 #331), 02vx4 (0.44 #573, 0.29 #317, 0.27 #265), 03fyrh (0.44 #860, 0.43 #131, 0.43 #339) >> Best rule #230 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 027rn; 05r4w; 09c7w0; 0160w; 0b90_r; 0154j; 03_3d; 0d0vqn; 0j1z8; 047lj; ... >> query: (?x8948, 071t0) <- countries_within(?x2467, ?x8948), organization(?x8948, ?x127), olympics(?x8948, ?x1931) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #840 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 02jx1; *> query: (?x8948, 07gyv) <- medal(?x8948, ?x422), adjoins(?x2051, ?x8948), country(?x1121, ?x8948) *> conf = 0.47 ranks of expected_values: 6 EVAL 07f5x country! 07gyv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 86.000 86.000 0.667 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #13002-0421ng PRED entity: 0421ng PRED relation: film! PRED expected values: 0mj1l 01gkmx => 113 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1123): 081lh (0.43 #74759, 0.40 #76836, 0.40 #26992), 0grrq8 (0.12 #93448, 0.11 #47761, 0.10 #78914), 0154qm (0.11 #8865, 0.05 #13018, 0.03 #40012), 02qgqt (0.09 #12475, 0.09 #8322, 0.06 #64374), 01kb2j (0.08 #13366, 0.07 #9213, 0.04 #64373), 06cgy (0.08 #33472, 0.05 #31396, 0.05 #12708), 0171cm (0.07 #8730, 0.05 #12883, 0.02 #35723), 02qgyv (0.07 #8689, 0.03 #12842, 0.03 #31530), 0lpjn (0.07 #8784, 0.03 #39931, 0.03 #480), 046zh (0.07 #19621, 0.02 #3011, 0.02 #5087) >> Best rule #74759 for best value: >> intensional similarity = 4 >> extensional distance = 420 >> proper extension: 09xbpt; 03s6l2; 04kkz8; 0m491; 01j8wk; 047svrl; 03l6q0; 0cp0ph6; 0blpg; 015g28; ... >> query: (?x5020, ?x986) <- featured_film_locations(?x5020, ?x739), country(?x5020, ?x94), titles(?x2480, ?x5020), nominated_for(?x986, ?x5020) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #92954 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 473 *> proper extension: 034r25; 01f39b; 03z9585; 07p12s; 076tw54; *> query: (?x5020, 01gkmx) <- film(?x3236, ?x5020), participant(?x3581, ?x3236), produced_by(?x5020, ?x4562) *> conf = 0.02 ranks of expected_values: 416, 815 EVAL 0421ng film! 01gkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 113.000 64.000 0.432 http://example.org/film/actor/film./film/performance/film EVAL 0421ng film! 0mj1l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 113.000 64.000 0.432 http://example.org/film/actor/film./film/performance/film #13001-01wmcbg PRED entity: 01wmcbg PRED relation: music! PRED expected values: 0j_t1 => 107 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1): 0pd57 (0.07 #12197, 0.05 #64036, 0.05 #25411) >> Best rule #12197 for best value: >> intensional similarity = 3 >> extensional distance = 623 >> proper extension: 03gm48; 0n6f8; 022_lg; 043js; 05728w1; 07z1_q; 02bfxb; 06nz46; 01pp3p; 015q43; ... >> query: (?x12809, ?x4179) <- award_winner(?x6595, ?x12809), type_of_union(?x12809, ?x566), award_winner(?x4179, ?x12809) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wmcbg music! 0j_t1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 66.000 0.065 http://example.org/film/film/music #13000-07yp0f PRED entity: 07yp0f PRED relation: film PRED expected values: 0992d9 => 99 concepts (47 used for prediction) PRED predicted values (max 10 best out of 490): 06z8s_ (0.20 #130, 0.03 #55438, 0.03 #44707), 01flv_ (0.20 #1065, 0.03 #55438, 0.03 #44707), 0418wg (0.20 #401, 0.03 #42917, 0.02 #41128), 09xbpt (0.20 #47, 0.03 #42917, 0.02 #41128), 0g9lm2 (0.12 #30397, 0.04 #2515, 0.02 #4303), 0h1x5f (0.12 #30397), 08nvyr (0.12 #30397), 072zl1 (0.11 #3067), 0ctb4g (0.11 #2343), 011yn5 (0.10 #925, 0.04 #4501, 0.03 #55438) >> Best rule #130 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 014zcr; 09fb5; 0hvb2; 0169dl; 0dvmd; 02yxwd; 042xrr; 018ygt; >> query: (?x3907, 06z8s_) <- award_winner(?x3293, ?x3907), award_winner(?x941, ?x3907), ?x3293 = 0gy6z9 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #6354 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 431 *> proper extension: 0bz5v2; 01wz01; *> query: (?x3907, 0992d9) <- award_winner(?x406, ?x3907), student(?x2775, ?x3907), film(?x3907, ?x1135) *> conf = 0.01 ranks of expected_values: 462 EVAL 07yp0f film 0992d9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 99.000 47.000 0.200 http://example.org/film/actor/film./film/performance/film #12999-0ddcbd5 PRED entity: 0ddcbd5 PRED relation: film! PRED expected values: 0b1q7c => 146 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1298): 0127m7 (0.18 #406, 0.04 #17048, 0.03 #8728), 01fh9 (0.15 #2397, 0.09 #6558, 0.04 #23199), 032xhg (0.14 #4225, 0.09 #10466, 0.07 #18787), 0147dk (0.13 #8404, 0.06 #35445, 0.04 #47925), 012d40 (0.13 #8338, 0.05 #24979, 0.03 #27059), 02q42j_ (0.13 #62406, 0.12 #141460, 0.11 #183068), 0b13g7 (0.13 #62406, 0.12 #141460, 0.11 #183068), 079vf (0.12 #27051, 0.05 #41611, 0.05 #45771), 02js_6 (0.10 #10294, 0.09 #8214, 0.03 #24855), 01q_ph (0.10 #8379, 0.06 #20860, 0.06 #16699) >> Best rule #406 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 09rvwmy; >> query: (?x4048, 0127m7) <- country(?x4048, ?x252), genre(?x4048, ?x8467), film(?x8566, ?x4048), film(?x820, ?x4048), ?x820 = 04bd8y, award_nominee(?x100, ?x8566), genre(?x1631, ?x8467) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ddcbd5 film! 0b1q7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 146.000 88.000 0.182 http://example.org/film/actor/film./film/performance/film #12998-06x58 PRED entity: 06x58 PRED relation: nationality PRED expected values: 0b90_r => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 24): 02jx1 (0.14 #529, 0.12 #330, 0.12 #3600), 07ssc (0.13 #908, 0.13 #312, 0.12 #511), 03rk0 (0.08 #3415, 0.08 #3812, 0.08 #4208), 0d060g (0.05 #1593, 0.05 #1791, 0.05 #204), 0chghy (0.03 #903, 0.03 #1596, 0.02 #3577), 0345h (0.03 #428, 0.02 #4193, 0.02 #6077), 0f8l9c (0.02 #3391, 0.02 #4184, 0.02 #3788), 06q1r (0.02 #573, 0.02 #374, 0.02 #970), 03rjj (0.02 #700, 0.02 #6051, 0.02 #1591), 0d05w3 (0.02 #347, 0.02 #2032, 0.01 #546) >> Best rule #529 for best value: >> intensional similarity = 2 >> extensional distance = 226 >> proper extension: 0cm03; 0457w0; >> query: (?x1880, 02jx1) <- type_of_union(?x1880, ?x566), location_of_ceremony(?x1880, ?x4627) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06x58 nationality 0b90_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 98.000 0.136 http://example.org/people/person/nationality #12997-02xs5v PRED entity: 02xs5v PRED relation: people! PRED expected values: 02g7sp => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 33): 02w7gg (0.33 #2, 0.10 #388, 0.08 #1861), 041rx (0.25 #158, 0.21 #467, 0.19 #312), 07bch9 (0.25 #100, 0.08 #177, 0.04 #798), 065b6q (0.25 #80, 0.08 #157, 0.02 #1477), 09kr66 (0.25 #120, 0.01 #429), 033tf_ (0.17 #161, 0.13 #315, 0.11 #1093), 0x67 (0.10 #2718, 0.09 #1407, 0.09 #2641), 03lmx1 (0.09 #245, 0.02 #1873, 0.01 #1488), 0xnvg (0.08 #167, 0.07 #321, 0.06 #476), 07hwkr (0.08 #166, 0.04 #475, 0.04 #553) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 02l4pj; >> query: (?x8064, 02w7gg) <- film(?x8064, ?x2339), nationality(?x8064, ?x6401), ?x2339 = 0d_2fb >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #404 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 276 *> proper extension: 06_bq1; *> query: (?x8064, 02g7sp) <- film(?x8064, ?x4668), award_winner(?x2596, ?x8064), film_festivals(?x4668, ?x7988) *> conf = 0.02 ranks of expected_values: 21 EVAL 02xs5v people! 02g7sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 104.000 104.000 0.333 http://example.org/people/ethnicity/people #12996-05h95s PRED entity: 05h95s PRED relation: nominated_for! PRED expected values: 0cjcbg => 106 concepts (98 used for prediction) PRED predicted values (max 10 best out of 213): 0m7yy (0.73 #2173, 0.73 #1931, 0.70 #2656), 027gs1_ (0.40 #5744, 0.33 #5261, 0.33 #1396), 0cjyzs (0.37 #5637, 0.30 #5154, 0.28 #1289), 0gq9h (0.35 #2962, 0.30 #20096, 0.30 #17681), 07bdd_ (0.33 #54, 0.29 #12551, 0.27 #22209), 05p1dby (0.33 #84, 0.29 #12551, 0.27 #22209), 05b1610 (0.33 #33, 0.28 #2931, 0.25 #22210), 0p9sw (0.33 #1710, 0.28 #2919, 0.21 #18604), 05f4m9q (0.33 #12, 0.25 #22210, 0.25 #22936), 05p09zm (0.33 #96, 0.25 #22936, 0.19 #2994) >> Best rule #2173 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 011yxg; 0b6tzs; 04vr_f; 026n4h6; 0dr_4; 0b1y_2; 0ccd3x; 047csmy; 0m63c; 04jplwp; ... >> query: (?x7566, ?x3486) <- award_winner(?x7566, ?x11453), category(?x7566, ?x134), ?x134 = 08mbj5d, award(?x7566, ?x3486), citytown(?x11453, ?x739) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #23663 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1003 *> proper extension: 076xkdz; *> query: (?x7566, ?x693) <- award(?x7566, ?x3486), award(?x6341, ?x3486), nominated_for(?x693, ?x6341), award_winner(?x3486, ?x105) *> conf = 0.11 ranks of expected_values: 96 EVAL 05h95s nominated_for! 0cjcbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 106.000 98.000 0.729 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12995-01w58n3 PRED entity: 01w58n3 PRED relation: languages PRED expected values: 06nm1 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 18): 03k50 (0.08 #2519, 0.06 #2075, 0.06 #1594), 02bjrlw (0.05 #1592, 0.05 #1703, 0.04 #2517), 06nm1 (0.04 #486, 0.04 #1337, 0.04 #42), 07c9s (0.04 #2528, 0.03 #2121, 0.02 #530), 06b_j (0.03 #495, 0.01 #1161), 04306rv (0.03 #2518, 0.03 #2111, 0.03 #1593), 03_9r (0.02 #485, 0.02 #2113, 0.01 #1595), 0999q (0.02 #2537, 0.01 #2130), 0t_2 (0.02 #267, 0.02 #1599, 0.02 #2080), 06mp7 (0.02 #269, 0.01 #380) >> Best rule #2519 for best value: >> intensional similarity = 2 >> extensional distance = 642 >> proper extension: 02r99xw; >> query: (?x9418, 03k50) <- languages(?x9418, ?x254), countries_spoken_in(?x254, ?x126) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #486 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 026ps1; 0kzy0; 0146pg; 0168cl; 0pcc0; 01vv7sc; 016kjs; 01kvqc; 015_30; 0136p1; ... *> query: (?x9418, 06nm1) <- artists(?x671, ?x9418), instrumentalists(?x227, ?x9418), languages(?x9418, ?x254) *> conf = 0.04 ranks of expected_values: 3 EVAL 01w58n3 languages 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 137.000 137.000 0.076 http://example.org/people/person/languages #12994-030znt PRED entity: 030znt PRED relation: award_winner! PRED expected values: 09qvms => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 128): 09qvms (0.77 #152, 0.10 #847, 0.10 #569), 092t4b (0.10 #52, 0.07 #886, 0.04 #1581), 05zksls (0.10 #35, 0.06 #313, 0.03 #591), 0clfdj (0.10 #4, 0.05 #838, 0.03 #1533), 09bymc (0.10 #119, 0.04 #536, 0.02 #4984), 02hn5v (0.10 #42, 0.02 #10149, 0.02 #8480), 05q7cj (0.10 #95, 0.02 #10149, 0.02 #8480), 09gkdln (0.08 #676, 0.08 #259, 0.05 #1093), 0275n3y (0.08 #214, 0.07 #631, 0.06 #909), 09pj68 (0.08 #243, 0.06 #382, 0.02 #660) >> Best rule #152 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 058ncz; 03zqc1; 035gjq; 06b0d2; 03lt8g; 05lb87; 0443y3; 05dxl5; 0308kx; 05lb30; ... >> query: (?x1343, 09qvms) <- award_winner(?x444, ?x1343), ?x444 = 01dw4q, award_nominee(?x1343, ?x1116) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030znt award_winner! 09qvms CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.769 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12993-027x7z5 PRED entity: 027x7z5 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 32): 09vw2b7 (0.77 #917, 0.74 #394, 0.72 #490), 02rh1dz (0.31 #204, 0.23 #236, 0.23 #73), 015h31 (0.29 #104, 0.23 #72, 0.17 #885), 089fss (0.26 #393, 0.21 #916, 0.20 #717), 0215hd (0.26 #499, 0.21 #339, 0.19 #242), 033smt (0.24 #120, 0.16 #3321, 0.14 #2961), 0d2b38 (0.23 #249, 0.22 #506, 0.18 #604), 01xy5l_ (0.23 #206, 0.20 #495, 0.19 #238), 094hwz (0.23 #76, 0.17 #12, 0.14 #2961), 089g0h (0.21 #340, 0.19 #243, 0.18 #500) >> Best rule #917 for best value: >> intensional similarity = 5 >> extensional distance = 127 >> proper extension: 0h95zbp; >> query: (?x8690, 09vw2b7) <- film_crew_role(?x8690, ?x3197), film_release_distribution_medium(?x8690, ?x81), ?x3197 = 02ynfr, language(?x8690, ?x254), ?x254 = 02h40lc >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027x7z5 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.767 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12992-024swd PRED entity: 024swd PRED relation: profession PRED expected values: 0dxtg => 141 concepts (116 used for prediction) PRED predicted values (max 10 best out of 106): 01d_h8 (0.97 #13476, 0.69 #1930, 0.64 #2374), 02hrh1q (0.85 #15706, 0.84 #15854, 0.75 #14966), 0dxtg (0.83 #2825, 0.79 #5341, 0.79 #5045), 09jwl (0.73 #13044, 0.55 #13784, 0.37 #5938), 0nbcg (0.50 #13057, 0.36 #327, 0.31 #2547), 02jknp (0.44 #13478, 0.38 #1044, 0.38 #1636), 0cbd2 (0.43 #13773, 0.27 #2819, 0.26 #6519), 02krf9 (0.42 #914, 0.40 #1358, 0.39 #4466), 0dz3r (0.36 #298, 0.31 #13028, 0.23 #2518), 018gz8 (0.29 #3124, 0.26 #2088, 0.25 #10674) >> Best rule #13476 for best value: >> intensional similarity = 5 >> extensional distance = 1006 >> proper extension: 05bnp0; 02p65p; 06151l; 01l1b90; 0byfz; 0qf43; 07f8wg; 02pp_q_; 02kxbwx; 03h_9lg; ... >> query: (?x4624, 01d_h8) <- profession(?x4624, ?x967), profession(?x10251, ?x967), profession(?x672, ?x967), ?x672 = 0168cl, ?x10251 = 07bty >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #2825 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 05cj4r; 0pz7h; *> query: (?x4624, 0dxtg) <- location(?x4624, ?x5893), program_creator(?x9076, ?x4624), languages(?x9076, ?x3592), nominated_for(?x2750, ?x9076) *> conf = 0.83 ranks of expected_values: 3 EVAL 024swd profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 141.000 116.000 0.967 http://example.org/people/person/profession #12991-0kvb6p PRED entity: 0kvb6p PRED relation: honored_for! PRED expected values: 0c53zb => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 101): 0fy6bh (0.25 #38, 0.16 #4881, 0.16 #6471), 0bz6l9 (0.16 #4881, 0.16 #6471, 0.16 #7448), 0fk0xk (0.16 #4881, 0.16 #6471, 0.16 #7448), 0c53zb (0.16 #4881, 0.16 #6471, 0.16 #7448), 0c6vcj (0.16 #4881, 0.16 #6471, 0.16 #7448), 0dth6b (0.16 #4881, 0.16 #6471, 0.16 #7448), 09gkdln (0.05 #472, 0.05 #350, 0.03 #716), 0dznvw (0.05 #484, 0.02 #1094, 0.02 #362), 02jp5r (0.04 #180, 0.02 #668, 0.01 #1278), 0fz0c2 (0.03 #457, 0.03 #335, 0.02 #213) >> Best rule #38 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0cwy47; 0bcndz; >> query: (?x8711, 0fy6bh) <- award_winner(?x8711, ?x12398), nominated_for(?x3237, ?x8711), ?x12398 = 0579tg2, production_companies(?x8711, ?x574) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4881 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 646 *> proper extension: 0b2v79; 028_yv; 03kq98; 0209xj; 0209hj; 0fg04; 01_mdl; 04hwbq; 04mzf8; 09p0ct; ... *> query: (?x8711, ?x3029) <- award_winner(?x8711, ?x12398), nominated_for(?x3237, ?x8711), award_winner(?x3029, ?x12398), titles(?x162, ?x8711) *> conf = 0.16 ranks of expected_values: 4 EVAL 0kvb6p honored_for! 0c53zb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 124.000 124.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12990-05nqz PRED entity: 05nqz PRED relation: combatants PRED expected values: 02psqkz => 69 concepts (39 used for prediction) PRED predicted values (max 10 best out of 425): 059z0 (0.68 #398, 0.61 #2389, 0.60 #4270), 07ssc (0.57 #279, 0.45 #3203, 0.44 #680), 03l5m1 (0.52 #3996, 0.51 #397, 0.44 #265), 07_m9_ (0.51 #397, 0.44 #265, 0.42 #799), 09c7w0 (0.42 #2256, 0.40 #4808, 0.40 #1721), 0bq0p9 (0.38 #416, 0.33 #550, 0.33 #17), 0c4b8 (0.38 #464, 0.33 #598, 0.31 #1920), 03gk2 (0.33 #2953, 0.33 #169, 0.26 #4170), 02psqkz (0.33 #53, 0.29 #133, 0.27 #3243), 0193qj (0.33 #70, 0.29 #133, 0.18 #4609) >> Best rule #398 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 048n7; >> query: (?x5352, ?x8687) <- locations(?x5352, ?x1471), entity_involved(?x5352, ?x8687), entity_involved(?x5352, ?x1892), contains(?x455, ?x1471), combatants(?x8687, ?x172), olympics(?x1892, ?x391), film_release_region(?x5400, ?x1892), film_release_region(?x3135, ?x1892), film_release_region(?x1293, ?x1892), ?x3135 = 0bmc4cm, ?x5400 = 0bhwhj, ?x1293 = 07g_0c >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #53 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 06k75; *> query: (?x5352, 02psqkz) <- locations(?x5352, ?x2517), locations(?x5352, ?x1471), entity_involved(?x5352, ?x13069), ?x1471 = 07t21, capital(?x2517, ?x8989), adjoins(?x2517, ?x7430), contains(?x455, ?x2517), taxonomy(?x2517, ?x939), ?x939 = 04n6k, combatants(?x3141, ?x13069) *> conf = 0.33 ranks of expected_values: 9 EVAL 05nqz combatants 02psqkz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 69.000 39.000 0.680 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #12989-0dznvw PRED entity: 0dznvw PRED relation: ceremony! PRED expected values: 018wng 0k611 => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 323): 018wng (0.91 #6204, 0.89 #6452, 0.88 #6700), 0k611 (0.91 #7475, 0.88 #6734, 0.88 #5248), 0gr4k (0.89 #4711, 0.85 #6196, 0.85 #4959), 0gr51 (0.84 #4758, 0.83 #5253, 0.80 #6739), 0gqng (0.79 #5189, 0.79 #4694, 0.76 #6179), 0gr07 (0.75 #8405, 0.75 #11868, 0.75 #11374), 0l8z1 (0.75 #8405, 0.75 #11868, 0.75 #11374), 0czp_ (0.75 #8405, 0.75 #11868, 0.75 #11374), 018wdw (0.75 #8405, 0.75 #11868, 0.75 #11374), 0gqxm (0.75 #8405, 0.75 #11868, 0.75 #11374) >> Best rule #6204 for best value: >> intensional similarity = 14 >> extensional distance = 32 >> proper extension: 050yyb; 073h9x; 0fz2y7; 02yvhx; 03tn9w; 0bvhz9; >> query: (?x11428, 018wng) <- honored_for(?x11428, ?x4841), award_winner(?x11428, ?x6971), award_winner(?x11428, ?x2110), award_winner(?x11428, ?x2109), ceremony(?x3617, ?x11428), ceremony(?x484, ?x11428), nominated_for(?x2109, ?x4404), award_winner(?x1443, ?x6971), film_sets_designed(?x4423, ?x4404), ?x484 = 0gq_v, profession(?x2110, ?x7630), award(?x4404, ?x1703), ?x3617 = 0gvx_, place_of_birth(?x2110, ?x479) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0dznvw ceremony! 0k611 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.912 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0dznvw ceremony! 018wng CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.912 http://example.org/award/award_category/winners./award/award_honor/ceremony #12988-011ykb PRED entity: 011ykb PRED relation: nominated_for! PRED expected values: 0f4x7 09td7p => 80 concepts (71 used for prediction) PRED predicted values (max 10 best out of 199): 0gqy2 (0.77 #4835, 0.68 #8752, 0.67 #8751), 027c95y (0.68 #8752, 0.67 #8751, 0.66 #4834), 040njc (0.40 #237, 0.29 #2997, 0.26 #697), 0gs9p (0.37 #3051, 0.32 #2361, 0.32 #4664), 02qyp19 (0.36 #231, 0.24 #3681, 0.22 #16122), 0gqyl (0.36 #305, 0.22 #3065, 0.20 #3912), 094qd5 (0.36 #265, 0.18 #725, 0.15 #3025), 019f4v (0.35 #3043, 0.31 #2353, 0.30 #3503), 03hkv_r (0.33 #14, 0.12 #3004, 0.12 #9907), 02pqp12 (0.32 #287, 0.23 #747, 0.21 #3047) >> Best rule #4835 for best value: >> intensional similarity = 2 >> extensional distance = 691 >> proper extension: 02nf2c; 03j63k; 0m123; 097h2; 02_1ky; 019g8j; 0147w8; 0300ml; 06mmr; 02rq7nd; >> query: (?x6472, ?x4091) <- award(?x6472, ?x4091), ceremony(?x4091, ?x873) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #484 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 01cgz; *> query: (?x6472, 0f4x7) <- films(?x1083, ?x6472), athlete(?x1083, ?x7749), type_of_union(?x7749, ?x566) *> conf = 0.31 ranks of expected_values: 11, 46 EVAL 011ykb nominated_for! 09td7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 80.000 71.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 011ykb nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 80.000 71.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12987-07f8wg PRED entity: 07f8wg PRED relation: location PRED expected values: 09bjv => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 90): 030qb3t (0.13 #12948, 0.12 #8123, 0.12 #8927), 02_286 (0.12 #841, 0.12 #7273, 0.12 #8077), 04jpl (0.05 #25733, 0.04 #30574, 0.04 #27358), 0cr3d (0.05 #5773, 0.04 #15424, 0.04 #55633), 0k049 (0.05 #2420, 0.04 #8, 0.03 #812), 0dclg (0.04 #1725, 0.03 #3333, 0.03 #4941), 0f__1 (0.04 #141, 0.02 #2553, 0.02 #4161), 059rby (0.04 #12881, 0.04 #14491, 0.04 #13685), 06_kh (0.04 #6443, 0.03 #7247, 0.03 #8051), 01531 (0.03 #3374, 0.03 #7394, 0.03 #5786) >> Best rule #12948 for best value: >> intensional similarity = 3 >> extensional distance = 195 >> proper extension: 02qggqc; >> query: (?x519, 030qb3t) <- award(?x519, ?x1105), executive_produced_by(?x324, ?x519), nominated_for(?x519, ?x5627) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07f8wg location 09bjv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 83.000 0.127 http://example.org/people/person/places_lived./people/place_lived/location #12986-0163r3 PRED entity: 0163r3 PRED relation: artists! PRED expected values: 06j6l => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 199): 017_qw (0.77 #376, 0.23 #1311, 0.15 #64), 064t9 (0.57 #1572, 0.56 #637, 0.48 #2816), 06by7 (0.45 #9051, 0.43 #5937, 0.43 #10921), 06j6l (0.31 #673, 0.31 #1608, 0.28 #7834), 016clz (0.31 #5, 0.24 #9034, 0.23 #5920), 03_d0 (0.30 #947, 0.22 #4370, 0.21 #1259), 025sc50 (0.29 #1610, 0.29 #675, 0.24 #7836), 0ggx5q (0.29 #701, 0.15 #1636, 0.14 #2880), 0glt670 (0.26 #665, 0.26 #1600, 0.25 #7826), 05bt6j (0.26 #1603, 0.25 #2847, 0.22 #9074) >> Best rule #376 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 0fpjyd; >> query: (?x6716, 017_qw) <- award(?x6716, ?x1443), ?x1443 = 054krc, artists(?x1127, ?x6716) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #673 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 044mfr; 01kymm; 02bwjv; 01wphh2; 02jyhv; 0gps0z; 0392kz; 01tpl1p; 0f8grf; *> query: (?x6716, 06j6l) <- profession(?x6716, ?x220), actor(?x9787, ?x6716), artists(?x1127, ?x6716) *> conf = 0.31 ranks of expected_values: 4 EVAL 0163r3 artists! 06j6l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 99.000 0.773 http://example.org/music/genre/artists #12985-0167xy PRED entity: 0167xy PRED relation: influenced_by! PRED expected values: 04rcr => 81 concepts (65 used for prediction) PRED predicted values (max 10 best out of 993): 07lp1 (0.40 #416, 0.19 #2988, 0.15 #5045), 09jm8 (0.27 #1455, 0.09 #33396, 0.08 #26209), 016m5c (0.26 #12853, 0.26 #10793, 0.26 #11822), 04k05 (0.22 #11823, 0.20 #14393, 0.17 #14392), 01vrncs (0.20 #30, 0.19 #4146, 0.14 #3086), 0lrh (0.20 #104, 0.16 #9869, 0.13 #4220), 02yl42 (0.20 #134, 0.13 #4250, 0.11 #5276), 014_lq (0.20 #2278, 0.12 #14909, 0.11 #14394), 086qd (0.20 #73, 0.10 #588, 0.09 #1104), 01w9ph_ (0.20 #318, 0.10 #4434, 0.10 #2890) >> Best rule #416 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 041mt; 013pp3; >> query: (?x10670, 07lp1) <- influenced_by(?x10670, ?x1089), influenced_by(?x1573, ?x10670), peers(?x12228, ?x10670), participant(?x1089, ?x1992) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #32368 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 354 *> proper extension: 01nczg; 04xjp; 011_3s; 0fx02; 02_p8v; 0bs8d; 05qmj; 014nvr; 08304; 0d0mbj; ... *> query: (?x10670, ?x248) <- influenced_by(?x5547, ?x10670), award(?x5547, ?x2238), award(?x248, ?x2238) *> conf = 0.01 ranks of expected_values: 971 EVAL 0167xy influenced_by! 04rcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 81.000 65.000 0.400 http://example.org/influence/influence_node/influenced_by #12984-09k2t1 PRED entity: 09k2t1 PRED relation: people! PRED expected values: 0x67 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 51): 0x67 (0.36 #934, 0.35 #1088, 0.27 #780), 02ctzb (0.17 #92, 0.12 #477, 0.09 #554), 041rx (0.17 #2932, 0.16 #1544, 0.16 #2238), 033tf_ (0.14 #854, 0.14 #2087, 0.11 #1547), 0xnvg (0.10 #167, 0.07 #2093, 0.06 #860), 07hwkr (0.09 #12, 0.07 #166, 0.07 #474), 02w7gg (0.07 #1234, 0.05 #3238, 0.05 #1542), 01qhm_ (0.06 #853, 0.05 #1238, 0.04 #314), 09vc4s (0.05 #163, 0.04 #1241, 0.04 #1549), 02g7sp (0.05 #172, 0.02 #249, 0.02 #788) >> Best rule #934 for best value: >> intensional similarity = 3 >> extensional distance = 150 >> proper extension: 012vm6; 01v27pl; >> query: (?x2226, 0x67) <- artists(?x3319, ?x2226), category(?x2226, ?x134), ?x3319 = 06j6l >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09k2t1 people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.355 http://example.org/people/ethnicity/people #12983-0kjgl PRED entity: 0kjgl PRED relation: award_winner! PRED expected values: 073hmq => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 125): 09q_6t (0.11 #8, 0.03 #288, 0.03 #1408), 02yv_b (0.11 #25, 0.02 #725, 0.02 #1425), 026kqs9 (0.11 #91, 0.01 #651, 0.01 #791), 09pnw5 (0.07 #663, 0.05 #803, 0.04 #1503), 09qvms (0.06 #3233, 0.06 #13, 0.05 #3513), 09p30_ (0.06 #85, 0.03 #505, 0.02 #925), 02ywhz (0.06 #79, 0.01 #2319, 0.01 #3719), 02hn5v (0.06 #42, 0.01 #602, 0.01 #742), 092t4b (0.05 #332, 0.04 #3272, 0.04 #3552), 0hndn2q (0.05 #460, 0.03 #1300, 0.03 #1440) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 06t8b; >> query: (?x7946, 09q_6t) <- nominated_for(?x7946, ?x1820), ?x1820 = 09cr8, award(?x7946, ?x401) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #2261 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 419 *> proper extension: 0fvppk; *> query: (?x7946, 073hmq) <- nominated_for(?x7946, ?x1820), cinematography(?x1820, ?x7903), featured_film_locations(?x1820, ?x108) *> conf = 0.02 ranks of expected_values: 85 EVAL 0kjgl award_winner! 073hmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 98.000 98.000 0.111 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12982-04znsy PRED entity: 04znsy PRED relation: nationality PRED expected values: 09c7w0 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 25): 09c7w0 (0.74 #2004, 0.73 #4609, 0.73 #5010), 07ssc (0.30 #8419, 0.09 #415, 0.09 #2419), 094jv (0.25 #7316, 0.25 #6714, 0.01 #4909), 04rrd (0.25 #7316, 0.25 #6714), 02jx1 (0.16 #33, 0.16 #133, 0.12 #1634), 03rk0 (0.06 #4554, 0.06 #5256, 0.06 #6458), 0d060g (0.05 #808, 0.05 #5317, 0.04 #8927), 03rjj (0.04 #205, 0.03 #906, 0.03 #705), 03_3d (0.03 #807, 0.03 #1307, 0.02 #4213), 0chghy (0.03 #2113, 0.02 #1511, 0.02 #10) >> Best rule #2004 for best value: >> intensional similarity = 3 >> extensional distance = 689 >> proper extension: 06jvj7; 027hnjh; 05f7snc; 04crrxr; 02f9wb; 02qssrm; 05yjhm; 055sjw; >> query: (?x9211, 09c7w0) <- student(?x9212, ?x9211), award_winner(?x2220, ?x9211), profession(?x9211, ?x1032) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04znsy nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.738 http://example.org/people/person/nationality #12981-085bd1 PRED entity: 085bd1 PRED relation: genre PRED expected values: 04xvh5 => 93 concepts (69 used for prediction) PRED predicted values (max 10 best out of 118): 07s9rl0 (0.85 #5247, 0.81 #5723, 0.80 #5366), 02kdv5l (0.73 #5011, 0.57 #7154, 0.48 #4414), 01jfsb (0.50 #3946, 0.39 #5020, 0.38 #4781), 04xvlr (0.40 #478, 0.33 #1311, 0.29 #1789), 04xvh5 (0.40 #509, 0.33 #33, 0.25 #152), 02l7c8 (0.39 #2280, 0.38 #2518, 0.37 #3233), 0hcr (0.38 #4076, 0.33 #23, 0.29 #4553), 060__y (0.36 #969, 0.33 #1088, 0.26 #7168), 06cvj (0.33 #4, 0.25 #3459, 0.25 #3221), 0vgkd (0.33 #11, 0.10 #3466, 0.09 #3228) >> Best rule #5247 for best value: >> intensional similarity = 7 >> extensional distance = 593 >> proper extension: 0cvkv5; >> query: (?x2795, 07s9rl0) <- award(?x2795, ?x7498), genre(?x2795, ?x1510), country(?x2795, ?x94), genre(?x8068, ?x1510), genre(?x3566, ?x1510), executive_produced_by(?x8068, ?x96), ?x3566 = 04jpk2 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #509 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0147sh; 02v_r7d; *> query: (?x2795, 04xvh5) <- film(?x5370, ?x2795), film(?x4969, ?x2795), genre(?x2795, ?x258), country(?x2795, ?x94), award_winner(?x594, ?x4969), ?x5370 = 016gkf *> conf = 0.40 ranks of expected_values: 5 EVAL 085bd1 genre 04xvh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 69.000 0.850 http://example.org/film/film/genre #12980-03h304l PRED entity: 03h304l PRED relation: produced_by! PRED expected values: 02_sr1 06x43v => 109 concepts (79 used for prediction) PRED predicted values (max 10 best out of 590): 0gxsh4 (0.39 #22408, 0.30 #14007, 0.30 #14942), 01gwk3 (0.08 #1547, 0.05 #3413, 0.05 #4348), 072x7s (0.08 #1078, 0.03 #2944, 0.03 #3879), 0408m53 (0.08 #1795, 0.03 #3661, 0.02 #6462), 0gwgn1k (0.08 #1746, 0.03 #3612, 0.02 #6413), 02hxhz (0.08 #1005, 0.03 #2871, 0.02 #5672), 087pfc (0.08 #1739, 0.03 #3605, 0.02 #7341), 02ph9tm (0.08 #1529, 0.03 #3395, 0.02 #7131), 0ch3qr1 (0.08 #1464, 0.03 #3330, 0.02 #7066), 07kb7vh (0.08 #1299, 0.03 #3165, 0.02 #6901) >> Best rule #22408 for best value: >> intensional similarity = 3 >> extensional distance = 274 >> proper extension: 07nznf; 0q9kd; 0grwj; 016qtt; 0fvf9q; 04t2l2; 06dv3; 014zcr; 05ty4m; 01qscs; ... >> query: (?x4946, ?x2207) <- award_nominee(?x1104, ?x4946), nominated_for(?x4946, ?x2207), produced_by(?x511, ?x4946) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #30808 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 864 *> proper extension: 09fqtq; 01k5t_3; 02vyh; 028qdb; 03xb2w; 022g44; 02j_j0; 078mgh; 0l5yl; 014g9y; *> query: (?x4946, ?x511) <- award_nominee(?x10430, ?x4946), award_nominee(?x1104, ?x4946), award_winner(?x1039, ?x1104), produced_by(?x511, ?x10430) *> conf = 0.05 ranks of expected_values: 96, 97 EVAL 03h304l produced_by! 06x43v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 109.000 79.000 0.393 http://example.org/film/film/produced_by EVAL 03h304l produced_by! 02_sr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 109.000 79.000 0.393 http://example.org/film/film/produced_by #12979-02d49z PRED entity: 02d49z PRED relation: film_crew_role PRED expected values: 01pvkk => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.72 #1940, 0.71 #1563, 0.70 #1828), 02r96rf (0.64 #115, 0.63 #1943, 0.63 #1831), 09vw2b7 (0.61 #1835, 0.60 #1947, 0.59 #1910), 01vx2h (0.40 #13, 0.30 #1840, 0.29 #1915), 0dxtw (0.35 #1951, 0.35 #1914, 0.34 #1839), 01pvkk (0.29 #1576, 0.27 #1991, 0.27 #1916), 02ynfr (0.16 #129, 0.15 #982, 0.15 #1019), 0215hd (0.13 #95, 0.12 #1583, 0.12 #651), 04pyp5 (0.12 #56, 0.06 #1581, 0.06 #130), 0d2b38 (0.11 #657, 0.09 #1854, 0.09 #1929) >> Best rule #1940 for best value: >> intensional similarity = 4 >> extensional distance = 1218 >> proper extension: 0fq27fp; >> query: (?x4596, 09zzb8) <- film_crew_role(?x4596, ?x6473), film_crew_role(?x835, ?x6473), ?x835 = 0164qt, genre(?x4596, ?x714) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1576 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 899 *> proper extension: 0dtw1x; 0cnztc4; 0gj9qxr; 043sct5; 03_wm6; 0bs8hvm; *> query: (?x4596, 01pvkk) <- language(?x4596, ?x254), genre(?x4596, ?x714), titles(?x53, ?x4596), film_crew_role(?x4596, ?x1284) *> conf = 0.29 ranks of expected_values: 6 EVAL 02d49z film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 81.000 0.720 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12978-03zqc1 PRED entity: 03zqc1 PRED relation: gender PRED expected values: 02zsn => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #155, 0.72 #199, 0.71 #175), 02zsn (0.50 #10, 0.43 #22, 0.43 #44) >> Best rule #155 for best value: >> intensional similarity = 2 >> extensional distance = 1760 >> proper extension: 0c9d9; 05g8ky; 02qjj7; 01nqfh_; 01vvy; 06y9c2; 0pcc0; 0d0vj4; 083p7; 04n7njg; ... >> query: (?x516, 05zppz) <- student(?x7545, ?x516), contains(?x205, ?x7545) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 01dw4q; 06b0d2; 03lt8g; 030znt; 0443y3; 04psyp; 09r9dp; 05dxl5; 0308kx; 08pth9; ... *> query: (?x516, 02zsn) <- award_nominee(?x516, ?x1342), award_winner(?x1112, ?x516), ?x1342 = 05lb87 *> conf = 0.50 ranks of expected_values: 2 EVAL 03zqc1 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.722 http://example.org/people/person/gender #12977-0432b PRED entity: 0432b PRED relation: religion PRED expected values: 0c8wxp => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 29): 0c8wxp (0.39 #681, 0.32 #96, 0.29 #186), 06nzl (0.17 #150, 0.02 #1276, 0.02 #1321), 03_gx (0.17 #239, 0.12 #1410, 0.09 #104), 0kpl (0.12 #235, 0.09 #1046, 0.08 #1813), 0kq2 (0.12 #243, 0.03 #693, 0.03 #828), 0631_ (0.09 #98, 0.08 #188, 0.07 #323), 019cr (0.09 #101, 0.07 #506, 0.04 #146), 0v53x (0.09 #119, 0.05 #524, 0.02 #344), 092bf5 (0.08 #241, 0.04 #916, 0.04 #962), 051kv (0.06 #50, 0.04 #365, 0.04 #455) >> Best rule #681 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 02cg2v; >> query: (?x6061, 0c8wxp) <- student(?x3424, ?x6061), people(?x1446, ?x6061), ?x1446 = 033tf_ >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0432b religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.387 http://example.org/people/person/religion #12976-021q0l PRED entity: 021q0l PRED relation: company PRED expected values: 07tgn => 38 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1021): 060ppp (0.92 #320, 0.78 #5375, 0.67 #3122), 02r5dz (0.92 #320, 0.78 #5208, 0.67 #2955), 07xyn1 (0.92 #320, 0.78 #5319, 0.67 #3066), 04sv4 (0.92 #320, 0.78 #5663, 0.46 #7919), 06pwq (0.92 #320, 0.73 #6765, 0.71 #4189), 019rl6 (0.92 #320, 0.67 #5294, 0.67 #3041), 0300cp (0.92 #320, 0.67 #5187, 0.67 #2934), 01npw8 (0.92 #320, 0.67 #5416, 0.67 #3163), 0cv9b (0.92 #320, 0.67 #2914, 0.56 #5167), 01_4lx (0.92 #320, 0.67 #3114, 0.56 #5367) >> Best rule #320 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 021q1c; >> query: (?x2998, ?x94) <- company(?x2998, ?x11768), company(?x2998, ?x8281), company(?x2998, ?x7392), company(?x2998, ?x3793), company(?x2998, ?x2999), currency(?x3793, ?x170), time_zones(?x7392, ?x5327), ?x11768 = 01hc1j, company(?x346, ?x7392), organizations_founded(?x9105, ?x3793), company(?x346, ?x94), category(?x2999, ?x134), institution(?x2636, ?x8281), school_type(?x8281, ?x3092), student(?x2999, ?x164) >> conf = 0.92 => this is the best rule for 127 predicted values *> Best rule #3207 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 4 *> proper extension: 0krdk; 09d6p2; *> query: (?x2998, ?x892) <- company(?x2998, ?x7392), company(?x2998, ?x3793), company(?x2998, ?x3439), company(?x2998, ?x893), currency(?x3793, ?x170), industry(?x3793, ?x5078), service_location(?x3793, ?x94), citytown(?x893, ?x1841), child(?x892, ?x893), state_province_region(?x7392, ?x2235), place_founded(?x3793, ?x3794), major_field_of_study(?x3439, ?x9111), ?x9111 = 04sh3, company(?x5796, ?x3439), student(?x3439, ?x562) *> conf = 0.24 ranks of expected_values: 241 EVAL 021q0l company 07tgn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 38.000 37.000 0.917 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #12975-01n8gr PRED entity: 01n8gr PRED relation: profession PRED expected values: 0lgw7 => 101 concepts (82 used for prediction) PRED predicted values (max 10 best out of 55): 0nbcg (0.59 #1329, 0.52 #3922, 0.50 #1037), 01d_h8 (0.45 #292, 0.39 #148, 0.30 #7078), 039v1 (0.40 #1334, 0.30 #897, 0.30 #3927), 02jknp (0.36 #294, 0.30 #150, 0.20 #11402), 0dxtg (0.34 #299, 0.30 #155, 0.28 #11407), 0fnpj (0.25 #488, 0.19 #1358, 0.16 #921), 03gjzk (0.22 #7950, 0.21 #7374, 0.21 #9534), 0cbd2 (0.17 #149, 0.12 #3036, 0.12 #9239), 01c8w0 (0.17 #1885, 0.08 #5203, 0.07 #4481), 02krf9 (0.16 #310, 0.13 #166, 0.09 #7816) >> Best rule #1329 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 0bkg4; 04cr6qv; 018y81; 01ydzx; >> query: (?x3358, 0nbcg) <- category(?x3358, ?x134), instrumentalists(?x227, ?x3358), group(?x3358, ?x1271) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #475 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 01sbf2; 03f0vvr; 01vtmw6; 0135xb; 02p68d; 03f7m4h; 01f9zw; 01bmlb; 063t3j; *> query: (?x3358, 0lgw7) <- award_winner(?x2420, ?x3358), award(?x3358, ?x1801), ?x1801 = 01c92g *> conf = 0.02 ranks of expected_values: 40 EVAL 01n8gr profession 0lgw7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 101.000 82.000 0.590 http://example.org/people/person/profession #12974-033071 PRED entity: 033071 PRED relation: award PRED expected values: 0bfvd4 => 86 concepts (83 used for prediction) PRED predicted values (max 10 best out of 231): 09sb52 (0.33 #41, 0.32 #2477, 0.30 #2071), 0bdwqv (0.33 #174, 0.25 #580, 0.19 #1798), 0bfvd4 (0.33 #116, 0.25 #522, 0.18 #1740), 0gqy2 (0.33 #166, 0.25 #572, 0.16 #1790), 04kxsb (0.33 #127, 0.25 #533, 0.10 #2563), 09qvc0 (0.33 #40, 0.13 #12587, 0.12 #446), 0f4x7 (0.33 #31, 0.13 #6527, 0.12 #437), 0789_m (0.33 #20, 0.12 #426, 0.12 #2456), 05ztrmj (0.33 #186, 0.12 #592, 0.12 #3028), 027dtxw (0.33 #4, 0.12 #410, 0.11 #1628) >> Best rule #41 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 03ym1; >> query: (?x11972, 09sb52) <- film(?x11972, ?x11610), film(?x11972, ?x4166), student(?x122, ?x11972), ?x4166 = 04y5j64, ?x11610 = 03cffvv >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #116 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 03ym1; *> query: (?x11972, 0bfvd4) <- film(?x11972, ?x11610), film(?x11972, ?x4166), student(?x122, ?x11972), ?x4166 = 04y5j64, ?x11610 = 03cffvv *> conf = 0.33 ranks of expected_values: 3 EVAL 033071 award 0bfvd4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 83.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12973-01wg6y PRED entity: 01wg6y PRED relation: student! PRED expected values: 013807 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 116): 02g839 (0.17 #552, 0.06 #3187, 0.06 #6876), 0hsb3 (0.17 #735, 0.02 #2843), 017z88 (0.11 #5352, 0.11 #1136, 0.09 #9041), 0bwfn (0.08 #1856, 0.06 #18720, 0.05 #29787), 01w5m (0.05 #2213, 0.03 #58604, 0.02 #54914), 04sylm (0.04 #5346, 0.04 #9035, 0.03 #7454), 09f2j (0.04 #4902, 0.03 #21766, 0.03 #5956), 08815 (0.03 #29514, 0.03 #18447, 0.03 #33203), 0217m9 (0.03 #2806, 0.01 #3860), 01pcj4 (0.03 #3531, 0.03 #4058, 0.03 #1950) >> Best rule #552 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03cd1q; >> query: (?x8978, 02g839) <- gender(?x8978, ?x231), award_winner(?x725, ?x8978), award_winner(?x9034, ?x8978), ?x9034 = 03nc9d >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wg6y student! 013807 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 146.000 146.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #12972-0p8jf PRED entity: 0p8jf PRED relation: influenced_by PRED expected values: 07w21 013pp3 => 136 concepts (38 used for prediction) PRED predicted values (max 10 best out of 410): 03_87 (0.67 #2307, 0.64 #3152, 0.43 #2729), 03f0324 (0.67 #2261, 0.50 #1839, 0.29 #3106), 02mpb (0.50 #1524, 0.50 #258, 0.08 #1689), 032l1 (0.50 #2198, 0.33 #1776, 0.29 #3043), 06kb_ (0.50 #1421, 0.25 #155, 0.12 #12668), 084w8 (0.50 #2114, 0.17 #1692, 0.12 #3381), 06whf (0.33 #2234, 0.29 #2656, 0.17 #1812), 081k8 (0.33 #2265, 0.29 #3110, 0.14 #2687), 02lt8 (0.33 #2229, 0.29 #2651, 0.12 #12668), 01v9724 (0.33 #2286, 0.25 #1019, 0.25 #597) >> Best rule #2307 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0683n; 01vdrw; >> query: (?x2993, 03_87) <- student(?x331, ?x2993), influenced_by(?x2993, ?x1947), award(?x2993, ?x10548), ?x10548 = 01ppdy >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1698 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 02yl42; 0821j; 01hc9_; *> query: (?x2993, 07w21) <- student(?x331, ?x2993), influenced_by(?x2993, ?x10313), award(?x2993, ?x575), ?x10313 = 07lp1 *> conf = 0.17 ranks of expected_values: 40, 41 EVAL 0p8jf influenced_by 013pp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 136.000 38.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 0p8jf influenced_by 07w21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 136.000 38.000 0.667 http://example.org/influence/influence_node/influenced_by #12971-06y9bd PRED entity: 06y9bd PRED relation: student! PRED expected values: 02bqy => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 82): 0bwfn (0.09 #1852, 0.09 #11321, 0.08 #15004), 03ksy (0.09 #1683, 0.06 #5365, 0.06 #3261), 017z88 (0.07 #607, 0.04 #14811, 0.03 #15864), 04b_46 (0.05 #1804, 0.04 #2330, 0.03 #3382), 09f2j (0.04 #158, 0.04 #18045, 0.04 #14888), 06kknt (0.04 #466), 01k2wn (0.04 #1075, 0.02 #1601, 0.02 #2653), 015nl4 (0.04 #14796, 0.04 #15849, 0.03 #6378), 01w5m (0.04 #17991, 0.03 #29037, 0.03 #5890), 08815 (0.03 #17889, 0.03 #15785, 0.03 #14732) >> Best rule #1852 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 0lzb8; 04n7njg; 01gbbz; 03m_k0; 029_3; 02y_2y; 09v6gc9; 014dm6; 04pg29; 0gd9k; ... >> query: (?x10160, 0bwfn) <- nominated_for(?x10160, ?x2084), producer_type(?x10160, ?x632), student(?x735, ?x10160) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06y9bd student! 02bqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.087 http://example.org/education/educational_institution/students_graduates./education/education/student #12970-0879bpq PRED entity: 0879bpq PRED relation: film_release_region PRED expected values: 0d0vqn 05qhw 05v8c 05qx1 => 84 concepts (71 used for prediction) PRED predicted values (max 10 best out of 103): 0d0vqn (0.91 #1355, 0.91 #1895, 0.91 #1085), 05qhw (0.91 #1359, 0.82 #1764, 0.80 #2034), 06c1y (0.79 #571, 0.42 #1382, 0.42 #1787), 09pmkv (0.79 #559, 0.42 #1370, 0.35 #1775), 05v8c (0.74 #550, 0.69 #1361, 0.61 #1766), 03rj0 (0.72 #1396, 0.68 #585, 0.64 #1801), 06mzp (0.71 #150, 0.67 #15, 0.56 #1501), 016wzw (0.67 #50, 0.63 #590, 0.61 #1401), 01mjq (0.67 #1383, 0.58 #572, 0.55 #1788), 07ylj (0.63 #561, 0.50 #21, 0.43 #156) >> Best rule #1355 for best value: >> intensional similarity = 5 >> extensional distance = 118 >> proper extension: 0gx1bnj; 03twd6; 03z9585; >> query: (?x2783, 0d0vqn) <- film_crew_role(?x2783, ?x468), film(?x1117, ?x2783), film_release_region(?x2783, ?x1497), ?x1497 = 015qh, award_nominee(?x444, ?x1117) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 13 EVAL 0879bpq film_release_region 05qx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 84.000 71.000 0.908 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0879bpq film_release_region 05v8c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 71.000 0.908 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0879bpq film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 71.000 0.908 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0879bpq film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 71.000 0.908 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12969-06w38l PRED entity: 06w38l PRED relation: profession PRED expected values: 01d_h8 => 89 concepts (57 used for prediction) PRED predicted values (max 10 best out of 76): 02jknp (0.74 #1185, 0.44 #2361, 0.40 #8), 01d_h8 (0.62 #1183, 0.52 #2800, 0.49 #2359), 0cbd2 (0.36 #448, 0.33 #742, 0.33 #301), 0kyk (0.33 #763, 0.27 #469, 0.24 #1646), 02krf9 (0.30 #2819, 0.26 #3702, 0.26 #3555), 0fj9f (0.29 #935, 0.22 #788, 0.22 #641), 018gz8 (0.28 #3545, 0.28 #3692, 0.27 #2809), 0np9r (0.22 #313, 0.20 #2813, 0.16 #3696), 0d8qb (0.22 #372, 0.13 #8385, 0.11 #1108), 06q2q (0.22 #778, 0.11 #631, 0.11 #925) >> Best rule #1185 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 05kfs; 081lh; 0151w_; 0170vn; 0136g9; 0bsb4j; 09ftwr; 02ld6x; 0693l; 0hw1j; ... >> query: (?x12891, 02jknp) <- profession(?x12891, ?x1032), award(?x12891, ?x1862), ?x1032 = 02hrh1q, ?x1862 = 0gr51 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1183 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 05kfs; 081lh; 0151w_; 0170vn; 0136g9; 0bsb4j; 09ftwr; 02ld6x; 0693l; 0hw1j; ... *> query: (?x12891, 01d_h8) <- profession(?x12891, ?x1032), award(?x12891, ?x1862), ?x1032 = 02hrh1q, ?x1862 = 0gr51 *> conf = 0.62 ranks of expected_values: 2 EVAL 06w38l profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 57.000 0.745 http://example.org/people/person/profession #12968-016lh0 PRED entity: 016lh0 PRED relation: religion PRED expected values: 05sfs => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 39): 0c8wxp (0.44 #2782, 0.41 #2430, 0.38 #1682), 0kpl (0.36 #1375, 0.30 #671, 0.29 #626), 05sfs (0.25 #3, 0.19 #311, 0.12 #267), 02rsw (0.25 #23, 0.17 #375, 0.14 #507), 0v53x (0.25 #28, 0.12 #336, 0.12 #292), 03_gx (0.24 #1378, 0.22 #674, 0.21 #1410), 0kq2 (0.21 #1410, 0.20 #105, 0.18 #149), 0n2g (0.20 #100, 0.18 #144, 0.13 #761), 051kv (0.17 #357, 0.14 #445, 0.12 #313), 0631_ (0.14 #801, 0.12 #316, 0.12 #536) >> Best rule #2782 for best value: >> intensional similarity = 4 >> extensional distance = 539 >> proper extension: 02lyx4; >> query: (?x5266, 0c8wxp) <- religion(?x5266, ?x2769), nationality(?x5266, ?x94), ?x94 = 09c7w0, religion(?x108, ?x2769) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0d1_f; *> query: (?x5266, 05sfs) <- currency(?x5266, ?x170), jurisdiction_of_office(?x5266, ?x94), religion(?x5266, ?x2769), country(?x108, ?x94) *> conf = 0.25 ranks of expected_values: 3 EVAL 016lh0 religion 05sfs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 138.000 138.000 0.438 http://example.org/people/person/religion #12967-014z8v PRED entity: 014z8v PRED relation: people! PRED expected values: 0gk4g => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 49): 0gk4g (0.27 #970, 0.23 #650, 0.21 #3466), 0qcr0 (0.17 #65, 0.10 #4801, 0.10 #3457), 02knxx (0.13 #991, 0.06 #671, 0.06 #543), 01tf_6 (0.12 #158, 0.11 #222, 0.06 #350), 0dq9p (0.12 #3600, 0.12 #3728, 0.11 #1488), 02y0js (0.12 #322, 0.11 #1474, 0.08 #4098), 012hw (0.12 #370, 0.04 #754, 0.03 #2546), 032s66 (0.12 #367, 0.04 #1391, 0.03 #559), 01_qc_ (0.11 #219, 0.06 #347, 0.05 #731), 0dcqh (0.11 #243, 0.01 #1587) >> Best rule #970 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 03bdm4; >> query: (?x4112, 0gk4g) <- film(?x4112, ?x994), profession(?x4112, ?x353), award_winner(?x2431, ?x4112), people(?x5855, ?x4112) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014z8v people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.270 http://example.org/people/cause_of_death/people #12966-0rng PRED entity: 0rng PRED relation: teams PRED expected values: 01jdxj => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 214): 037mjv (0.33 #108, 0.17 #1188, 0.11 #2988), 0j46b (0.25 #977, 0.25 #617, 0.12 #2777), 0j47s (0.25 #841, 0.25 #481, 0.12 #2641), 01h0b0 (0.25 #820, 0.25 #460, 0.12 #2620), 01s0t3 (0.25 #487, 0.14 #1927, 0.14 #1567), 017znw (0.25 #433, 0.14 #1873, 0.14 #1513), 01slcv (0.14 #2041, 0.12 #2761, 0.12 #2401), 01wx_y (0.14 #1982, 0.12 #2702, 0.12 #2342), 014nzp (0.14 #1739, 0.09 #3899, 0.05 #4259), 0dwz3t (0.14 #1620, 0.09 #3780, 0.05 #4140) >> Best rule #108 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 012d9h; >> query: (?x8653, 037mjv) <- contains(?x6401, ?x8653), place_of_birth(?x7794, ?x8653), category(?x8653, ?x134), ?x6401 = 06q1r >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0rng teams 01jdxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 183.000 183.000 0.333 http://example.org/sports/sports_team_location/teams #12965-0bx0l PRED entity: 0bx0l PRED relation: films! PRED expected values: 0cm2xh => 107 concepts (27 used for prediction) PRED predicted values (max 10 best out of 74): 081pw (0.08 #316, 0.07 #472, 0.05 #1259), 0bxg3 (0.08 #393, 0.07 #549, 0.01 #1179), 07_nf (0.08 #67, 0.04 #1007, 0.02 #1166), 0jm_ (0.08 #8, 0.04 #634, 0.02 #2518), 0mkz (0.08 #28, 0.02 #1127, 0.02 #1284), 07c1v (0.08 #145, 0.02 #929, 0.01 #1244), 02p0qmm (0.08 #53, 0.02 #837), 0l8bg (0.08 #117, 0.01 #1216, 0.01 #1373), 048n7 (0.08 #76, 0.01 #1175, 0.01 #1332), 05489 (0.07 #208, 0.06 #992, 0.05 #3827) >> Best rule #316 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 0mcl0; 0hfzr; 0sxgv; 01qz5; 0glbqt; 01gvsn; >> query: (?x2168, 081pw) <- nominated_for(?x1020, ?x2168), award(?x2168, ?x1079), ?x1079 = 0l8z1, nominated_for(?x601, ?x2168) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #203 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 028_yv; 02vp1f_; 0cwy47; 0m_mm; 0dtfn; 0jqn5; 0ct5zc; 0f4m2z; 02x6dqb; 0c8qq; ... *> query: (?x2168, 0cm2xh) <- film_release_region(?x2168, ?x2984), film_release_region(?x2168, ?x252), award(?x2168, ?x198), ?x252 = 03_3d, ?x2984 = 082fr *> conf = 0.03 ranks of expected_values: 19 EVAL 0bx0l films! 0cm2xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 107.000 27.000 0.077 http://example.org/film/film_subject/films #12964-01j_9c PRED entity: 01j_9c PRED relation: fraternities_and_sororities PRED expected values: 0325pb => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 2): 0325pb (0.64 #1, 0.39 #22, 0.34 #41), 04m8fy (0.09 #2, 0.05 #21, 0.05 #4) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 07w0v; 07szy; 0bx8pn; 02183k; 0kw4j; 07vyf; 0bqxw; 02zd460; 07ccs; >> query: (?x546, 0325pb) <- institution(?x9054, ?x546), contains(?x94, ?x546), company(?x346, ?x546), ?x9054 = 022h5x >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j_9c fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.636 http://example.org/education/university/fraternities_and_sororities #12963-040_lv PRED entity: 040_lv PRED relation: language PRED expected values: 064_8sq => 60 concepts (58 used for prediction) PRED predicted values (max 10 best out of 29): 064_8sq (0.22 #77, 0.20 #192, 0.17 #20), 0295r (0.17 #27), 06b_j (0.11 #135, 0.06 #940, 0.06 #482), 03_9r (0.11 #66, 0.05 #642, 0.05 #412), 0880p (0.11 #101, 0.03 #216, 0.01 #273), 04306rv (0.11 #923, 0.10 #465, 0.09 #637), 02bjrlw (0.08 #920, 0.06 #1264, 0.06 #748), 03k50 (0.04 #294, 0.04 #352, 0.02 #469), 0653m (0.04 #1504, 0.03 #529, 0.03 #1794), 0jzc (0.04 #937, 0.03 #1053, 0.03 #1570) >> Best rule #77 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 07yvsn; 0gkz3nz; 05t0_2v; 02825cv; 08s6mr; 05fm6m; 06t2t2; >> query: (?x6036, 064_8sq) <- film(?x4929, ?x6036), film(?x4294, ?x6036), ?x4929 = 02qfhb, award_nominee(?x4294, ?x2353), participant(?x4294, ?x1017) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 040_lv language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 58.000 0.222 http://example.org/film/film/language #12962-02lx0 PRED entity: 02lx0 PRED relation: form_of_government PRED expected values: 026wp => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 3): 01d9r3 (0.48 #14, 0.33 #38, 0.33 #20), 01q20 (0.31 #37, 0.31 #10, 0.27 #1), 026wp (0.06 #24, 0.06 #39, 0.05 #15) >> Best rule #14 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 02wm6l; >> query: (?x3656, 01d9r3) <- form_of_government(?x3656, ?x48), ?x48 = 06cx9 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 123 *> proper extension: 077qn; *> query: (?x3656, 026wp) <- olympics(?x3656, ?x2966), participating_countries(?x1931, ?x3656), currency(?x3656, ?x170) *> conf = 0.06 ranks of expected_values: 3 EVAL 02lx0 form_of_government 026wp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 46.000 46.000 0.479 http://example.org/location/country/form_of_government #12961-05pdh86 PRED entity: 05pdh86 PRED relation: film_crew_role PRED expected values: 01xy5l_ => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 27): 0215hd (0.63 #71, 0.27 #29, 0.15 #409), 01xy5l_ (0.46 #67, 0.27 #29, 0.12 #405), 0dxtw (0.40 #64, 0.38 #402, 0.35 #1134), 02_n3z (0.31 #58, 0.27 #29, 0.12 #1043), 01pvkk (0.28 #403, 0.28 #1332, 0.28 #1022), 02ynfr (0.27 #29, 0.19 #407, 0.17 #69), 089fss (0.13 #62, 0.12 #1043, 0.08 #34), 020xn5 (0.13 #63, 0.12 #1043, 0.02 #401), 094hwz (0.12 #1043, 0.08 #40, 0.04 #659), 0ckd1 (0.12 #1043, 0.07 #60, 0.03 #88) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 064n1pz; >> query: (?x4464, 0215hd) <- film_crew_role(?x4464, ?x7591), film_release_region(?x4464, ?x87), ?x7591 = 0d2b38, olympics(?x87, ?x778) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 97 *> proper extension: 064n1pz; *> query: (?x4464, 01xy5l_) <- film_crew_role(?x4464, ?x7591), film_release_region(?x4464, ?x87), ?x7591 = 0d2b38, olympics(?x87, ?x778) *> conf = 0.46 ranks of expected_values: 2 EVAL 05pdh86 film_crew_role 01xy5l_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.626 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12960-08xvpn PRED entity: 08xvpn PRED relation: edited_by PRED expected values: 03q8ch => 61 concepts (54 used for prediction) PRED predicted values (max 10 best out of 21): 08h79x (0.09 #18, 0.02 #109, 0.01 #169), 02lp3c (0.04 #17, 0.02 #76), 04cy8rb (0.04 #1, 0.01 #122, 0.01 #152), 03q8ch (0.03 #405, 0.03 #591, 0.03 #376), 03nqbvz (0.02 #44, 0.02 #14, 0.02 #73), 06pj8 (0.02 #301, 0.02 #300, 0.02 #9), 01wd9lv (0.02 #301, 0.02 #300, 0.02 #332), 02q_cc (0.02 #301, 0.02 #300, 0.02 #332), 02qggqc (0.02 #273, 0.02 #94, 0.02 #395), 03_gd (0.02 #4) >> Best rule #18 for best value: >> intensional similarity = 2 >> extensional distance = 53 >> proper extension: 0d1qmz; >> query: (?x9801, 08h79x) <- film_production_design_by(?x9801, ?x9062), films(?x3359, ?x9801) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #405 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 744 *> proper extension: 09rfpk; *> query: (?x9801, 03q8ch) <- country(?x9801, ?x94), music(?x9801, ?x6382), nominated_for(?x105, ?x9801) *> conf = 0.03 ranks of expected_values: 4 EVAL 08xvpn edited_by 03q8ch CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 61.000 54.000 0.091 http://example.org/film/film/edited_by #12959-082fr PRED entity: 082fr PRED relation: film_release_region! PRED expected values: 08720 0b_5d 0ywrc 04vvh9 02fttd 0h21v2 0hv81 077q8x 0pd64 0jdr0 027r7k => 173 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1277): 017jd9 (0.90 #21726, 0.86 #22971, 0.84 #60328), 0jjy0 (0.90 #21287, 0.86 #22532, 0.83 #12570), 02r8hh_ (0.90 #21359, 0.86 #22604, 0.83 #12642), 0bq6ntw (0.90 #21934, 0.86 #23179, 0.83 #13217), 0872p_c (0.90 #21293, 0.86 #22538, 0.83 #12576), 08hmch (0.87 #17542, 0.86 #59880, 0.86 #21278), 02vr3gz (0.87 #17875, 0.86 #21611, 0.83 #12894), 0gwjw0c (0.87 #18303, 0.86 #22039, 0.83 #13322), 05p1tzf (0.87 #17487, 0.86 #21223, 0.83 #12506), 06fcqw (0.87 #18219, 0.86 #21955, 0.82 #23200) >> Best rule #21726 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 05r4w; 035qy; 06bnz; 06f32; >> query: (?x2984, 017jd9) <- film_release_region(?x11809, ?x2984), film_release_region(?x6218, ?x2984), ?x11809 = 0b85mm, featured_film_locations(?x6218, ?x6226) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #11052 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 09c7w0; 01mjq; 03h64; *> query: (?x2984, 0jdr0) <- film_release_region(?x11809, ?x2984), film_release_region(?x5473, ?x2984), film_release_region(?x951, ?x2984), ?x11809 = 0b85mm, ?x5473 = 0hv8w, produced_by(?x951, ?x5438) *> conf = 0.82 ranks of expected_values: 86, 139, 194, 198, 253, 289, 312, 328, 329, 330, 368 EVAL 082fr film_release_region! 027r7k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 082fr film_release_region! 0jdr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 082fr film_release_region! 0pd64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 082fr film_release_region! 077q8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 082fr film_release_region! 0hv81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 082fr film_release_region! 0h21v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 082fr film_release_region! 02fttd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 082fr film_release_region! 04vvh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 082fr film_release_region! 0ywrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 082fr film_release_region! 0b_5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 082fr film_release_region! 08720 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 173.000 64.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12958-01cz7r PRED entity: 01cz7r PRED relation: category PRED expected values: 08mbj5d => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.50 #1, 0.32 #16, 0.29 #35) >> Best rule #1 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 017f3m; >> query: (?x7645, 08mbj5d) <- nominated_for(?x5246, ?x7645), nominated_for(?x1104, ?x7645), ?x5246 = 046zh, film(?x1104, ?x11395), film_release_region(?x11395, ?x87) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cz7r category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.500 http://example.org/common/topic/webpage./common/webpage/category #12957-02zfdp PRED entity: 02zfdp PRED relation: award_nominee PRED expected values: 0fthdk => 86 concepts (39 used for prediction) PRED predicted values (max 10 best out of 800): 03mcwq3 (0.81 #81245, 0.81 #34818, 0.80 #85891), 03zz8b (0.81 #81245, 0.81 #34818, 0.80 #85891), 0fthdk (0.81 #81245, 0.81 #34818, 0.80 #85891), 02zfdp (0.70 #6593, 0.62 #4272, 0.33 #1951), 01541z (0.48 #12041, 0.45 #7399, 0.23 #83568), 065ydwb (0.48 #12920, 0.45 #8278, 0.23 #83568), 06b0d2 (0.48 #11827, 0.45 #7185, 0.14 #88214), 0kryqm (0.43 #13156, 0.36 #8514, 0.23 #83568), 08s_lw (0.43 #12921, 0.36 #8279, 0.23 #83568), 04myfb7 (0.43 #12015, 0.36 #7373, 0.23 #83568) >> Best rule #81245 for best value: >> intensional similarity = 3 >> extensional distance = 1140 >> proper extension: 0284n42; >> query: (?x9152, ?x230) <- award_nominee(?x230, ?x9152), type_of_union(?x9152, ?x566), nominated_for(?x9152, ?x2973) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 02zfdp award_nominee 0fthdk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 39.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #12956-0bs5k8r PRED entity: 0bs5k8r PRED relation: genre PRED expected values: 03bxz7 => 87 concepts (55 used for prediction) PRED predicted values (max 10 best out of 167): 017fp (0.71 #5117, 0.71 #4068, 0.64 #3132), 05p553 (0.64 #1743, 0.61 #3135, 0.60 #3), 06cvj (0.47 #3134, 0.24 #930, 0.20 #2), 01hmnh (0.45 #247, 0.36 #131, 0.33 #4667), 0hn10 (0.40 #8, 0.39 #2790, 0.21 #1513), 0219x_ (0.40 #23, 0.32 #2108, 0.15 #602), 01t_vv (0.40 #50, 0.15 #1555, 0.14 #3363), 01jfsb (0.39 #4895, 0.35 #2908, 0.35 #1169), 02kdv5l (0.36 #2898, 0.33 #2665, 0.28 #4537), 03bxz7 (0.30 #1209, 0.17 #747, 0.17 #3767) >> Best rule #5117 for best value: >> intensional similarity = 6 >> extensional distance = 883 >> proper extension: 06cs95; 01q_y0; 039c26; >> query: (?x4276, ?x3506) <- nominated_for(?x13664, ?x4276), titles(?x3506, ?x4276), genre(?x4525, ?x3506), genre(?x4359, ?x3506), film_release_region(?x4359, ?x94), film_crew_role(?x4525, ?x137) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1209 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 44 *> proper extension: 02v8kmz; 0209hj; 0_b3d; 048scx; 09p0ct; 072x7s; 09cr8; 026gyn_; 011yth; 016z7s; ... *> query: (?x4276, 03bxz7) <- film_crew_role(?x4276, ?x137), titles(?x3506, ?x4276), genre(?x4276, ?x162), nominated_for(?x1197, ?x4276), ?x3506 = 03mqtr *> conf = 0.30 ranks of expected_values: 10 EVAL 0bs5k8r genre 03bxz7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 87.000 55.000 0.712 http://example.org/film/film/genre #12955-017lb_ PRED entity: 017lb_ PRED relation: artists! PRED expected values: 016ybr => 95 concepts (61 used for prediction) PRED predicted values (max 10 best out of 282): 016clz (0.83 #11714, 0.61 #4001, 0.55 #2158), 064t9 (0.81 #15421, 0.78 #14496, 0.59 #3396), 06by7 (0.80 #15739, 0.75 #16048, 0.72 #4327), 03lty (0.58 #8349, 0.58 #11124, 0.50 #644), 0xhtw (0.51 #11112, 0.50 #8337, 0.50 #632), 0dl5d (0.50 #942, 0.44 #1557, 0.43 #1249), 0cx7f (0.50 #1056, 0.44 #1671, 0.43 #1363), 012yc (0.45 #2299, 0.23 #3528, 0.11 #2913), 02yv6b (0.35 #3169, 0.31 #7797, 0.25 #709), 05w3f (0.35 #3113, 0.26 #5581, 0.25 #6198) >> Best rule #11714 for best value: >> intensional similarity = 6 >> extensional distance = 197 >> proper extension: 016qtt; 0137g1; 01271h; 03bxwtd; 01w02sy; 01w806h; 01w3lzq; 07r4c; 01t110; 05y7hc; ... >> query: (?x8226, 016clz) <- artists(?x5934, ?x8226), parent_genre(?x10969, ?x5934), artists(?x5934, ?x10091), category(?x8226, ?x134), ?x10091 = 048tgl, ?x10969 = 029fbr >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #4120 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 26 *> proper extension: 01vv126; *> query: (?x8226, 016ybr) <- artists(?x5934, ?x8226), artists(?x2995, ?x8226), parent_genre(?x2407, ?x5934), artists(?x5934, ?x9096), category(?x8226, ?x134), ?x2995 = 01cbwl, ?x9096 = 07rnh *> conf = 0.25 ranks of expected_values: 25 EVAL 017lb_ artists! 016ybr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 95.000 61.000 0.834 http://example.org/music/genre/artists #12954-03zj9 PRED entity: 03zj9 PRED relation: registering_agency PRED expected values: 03z19 => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.86 #39, 0.86 #33, 0.86 #38) >> Best rule #39 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 0473m9; >> query: (?x5733, 03z19) <- institution(?x865, ?x5733), currency(?x5733, ?x170), state_province_region(?x5733, ?x335), major_field_of_study(?x5733, ?x1695) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03zj9 registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.859 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #12953-06jzh PRED entity: 06jzh PRED relation: award_nominee! PRED expected values: 045c66 => 96 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1068): 0fthdk (0.81 #116060, 0.81 #116059, 0.81 #118381), 06jzh (0.67 #4745, 0.67 #104, 0.64 #2424), 045c66 (0.56 #4947, 0.50 #2626, 0.44 #306), 0fbx6 (0.27 #111417, 0.23 #46426, 0.20 #95168), 026dg51 (0.27 #111417, 0.23 #46426, 0.02 #88390), 050023 (0.27 #111417, 0.23 #46426, 0.02 #88283), 0265v21 (0.27 #111417, 0.23 #46426, 0.02 #88389), 025vw4t (0.27 #111417, 0.23 #46426, 0.01 #89623), 025vl4m (0.27 #111417, 0.23 #46426, 0.01 #89863), 026dd2b (0.27 #111417, 0.23 #46426) >> Best rule #116060 for best value: >> intensional similarity = 3 >> extensional distance = 1495 >> proper extension: 02v49c; 057xn_m; 01p0w_; >> query: (?x540, ?x4580) <- award_nominee(?x540, ?x4580), gender(?x540, ?x514), film(?x4580, ?x1965) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #4947 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 025t9b; *> query: (?x540, 045c66) <- award_nominee(?x4580, ?x540), award_nominee(?x3872, ?x540), people(?x743, ?x3872), ?x4580 = 026l37 *> conf = 0.56 ranks of expected_values: 3 EVAL 06jzh award_nominee! 045c66 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 55.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #12952-03l6q0 PRED entity: 03l6q0 PRED relation: genre PRED expected values: 05p553 => 93 concepts (64 used for prediction) PRED predicted values (max 10 best out of 101): 01jfsb (0.72 #716, 0.66 #4360, 0.65 #1421), 05p553 (0.68 #5176, 0.65 #591, 0.64 #1531), 07s9rl0 (0.65 #2233, 0.63 #2820, 0.62 #2467), 01z4y (0.62 #1056, 0.61 #6818, 0.61 #1291), 02kdv5l (0.57 #5410, 0.54 #823, 0.52 #4350), 03k9fj (0.45 #1068, 0.44 #832, 0.34 #2361), 06cvj (0.38 #473, 0.29 #590, 0.15 #5175), 02n4kr (0.33 #711, 0.28 #1181, 0.25 #946), 01hmnh (0.32 #1075, 0.30 #5190, 0.28 #839), 02l7c8 (0.31 #2249, 0.30 #2483, 0.30 #2836) >> Best rule #716 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 05_61y; >> query: (?x3317, 01jfsb) <- featured_film_locations(?x3317, ?x108), film_release_distribution_medium(?x3317, ?x81), genre(?x3317, ?x571), ?x108 = 0rh6k >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #5176 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 728 *> proper extension: 0j8f09z; *> query: (?x3317, 05p553) <- genre(?x3317, ?x571), film(?x166, ?x3317), genre(?x4489, ?x571), ?x4489 = 01qxc7 *> conf = 0.68 ranks of expected_values: 2 EVAL 03l6q0 genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 64.000 0.725 http://example.org/film/film/genre #12951-02hrlh PRED entity: 02hrlh PRED relation: role PRED expected values: 01vj9c => 40 concepts (31 used for prediction) PRED predicted values (max 10 best out of 127): 02w4b (0.85 #395, 0.63 #1593, 0.61 #1730), 0l14md (0.84 #3084, 0.82 #2283, 0.81 #1753), 03qjg (0.80 #1417, 0.76 #1948, 0.73 #1813), 013y1f (0.80 #1389, 0.69 #1920, 0.61 #2986), 0l14qv (0.76 #1886, 0.67 #1355, 0.62 #2008), 05148p4 (0.75 #2972, 0.74 #3235, 0.73 #3497), 0dwt5 (0.75 #637, 0.71 #503, 0.70 #1175), 028tv0 (0.73 #1363, 0.69 #1894, 0.62 #1759), 05r5c (0.73 #1358, 0.66 #2007, 0.66 #1889), 0mkg (0.73 #1757, 0.70 #1618, 0.68 #1481) >> Best rule #395 for best value: >> intensional similarity = 25 >> extensional distance = 4 >> proper extension: 013y1f; 02fsn; 01xqw; >> query: (?x11978, ?x3418) <- role(?x11978, ?x3418), role(?x11978, ?x1437), role(?x11978, ?x314), role(?x11978, ?x75), ?x1437 = 01vdm0, role(?x5417, ?x11978), role(?x1969, ?x11978), ?x1969 = 04rzd, role(?x1212, ?x3418), role(?x569, ?x3418), role(?x3418, ?x7033), role(?x3418, ?x1148), role(?x3418, ?x316), group(?x3418, ?x8429), ?x316 = 05r5c, role(?x925, ?x3418), ?x75 = 07y_7, ?x7033 = 0gkd1, role(?x3418, ?x614), ?x314 = 02sgy, ?x8429 = 01lf293, ?x1148 = 02qjv, ?x569 = 07c6l, ?x5417 = 02w3w, ?x1212 = 07xzm >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1366 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 13 *> proper extension: 02snj9; *> query: (?x11978, 01vj9c) <- group(?x11978, ?x1945), ?x1945 = 02_5x9 *> conf = 0.67 ranks of expected_values: 15 EVAL 02hrlh role 01vj9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 40.000 31.000 0.846 http://example.org/music/performance_role/regular_performances./music/group_membership/role #12950-01j1n2 PRED entity: 01j1n2 PRED relation: genre! PRED expected values: 02rv_dz 0m491 0cc5qkt 0fz3b1 027r9t 0yzbg 01n30p 03c7twt 03mr85 07ykkx5 => 61 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1832): 07nt8p (0.67 #11336, 0.50 #7677, 0.50 #4018), 027pfg (0.67 #12209, 0.50 #6721, 0.50 #4891), 0gd92 (0.67 #14119, 0.50 #15950, 0.50 #8631), 02x0fs9 (0.67 #14488, 0.50 #16319, 0.50 #9000), 0209xj (0.67 #12906, 0.50 #14737, 0.50 #7418), 03rz2b (0.67 #13278, 0.50 #15109, 0.50 #7790), 0sxns (0.67 #13898, 0.50 #15729, 0.50 #8410), 02rv_dz (0.67 #14881, 0.50 #13050, 0.50 #7562), 0ptdz (0.67 #14596, 0.50 #5449, 0.33 #16427), 09fqgj (0.67 #12666, 0.50 #5348, 0.33 #14495) >> Best rule #11336 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 03k9fj; >> query: (?x7223, 07nt8p) <- genre(?x7760, ?x7223), genre(?x6499, ?x7223), genre(?x5927, ?x7223), genre(?x3457, ?x7223), film_crew_role(?x5927, ?x1078), ?x7760 = 017kz7, nominated_for(?x298, ?x5927), titles(?x53, ?x6499), crewmember(?x3457, ?x3879), written_by(?x5927, ?x989) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #14881 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 02b5_l; *> query: (?x7223, 02rv_dz) <- genre(?x6499, ?x7223), genre(?x6451, ?x7223), genre(?x5927, ?x7223), genre(?x4756, ?x7223), film_crew_role(?x5927, ?x1078), film(?x1559, ?x6499), crewmember(?x6499, ?x9391), ?x6451 = 01l2b3, nominated_for(?x488, ?x4756) *> conf = 0.67 ranks of expected_values: 8, 11, 41, 382, 383, 655, 728, 821, 949, 998 EVAL 01j1n2 genre! 07ykkx5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 21.000 0.667 http://example.org/film/film/genre EVAL 01j1n2 genre! 03mr85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 21.000 0.667 http://example.org/film/film/genre EVAL 01j1n2 genre! 03c7twt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 61.000 21.000 0.667 http://example.org/film/film/genre EVAL 01j1n2 genre! 01n30p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 21.000 0.667 http://example.org/film/film/genre EVAL 01j1n2 genre! 0yzbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 21.000 0.667 http://example.org/film/film/genre EVAL 01j1n2 genre! 027r9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 61.000 21.000 0.667 http://example.org/film/film/genre EVAL 01j1n2 genre! 0fz3b1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 21.000 0.667 http://example.org/film/film/genre EVAL 01j1n2 genre! 0cc5qkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 21.000 0.667 http://example.org/film/film/genre EVAL 01j1n2 genre! 0m491 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 21.000 0.667 http://example.org/film/film/genre EVAL 01j1n2 genre! 02rv_dz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 61.000 21.000 0.667 http://example.org/film/film/genre #12949-02gjrc PRED entity: 02gjrc PRED relation: languages PRED expected values: 02h40lc => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 9): 02h40lc (0.93 #134, 0.93 #79, 0.89 #35), 06nm1 (0.59 #309, 0.04 #126, 0.03 #302), 064_8sq (0.59 #309, 0.03 #117, 0.02 #316), 02bjrlw (0.59 #309, 0.03 #111, 0.01 #310), 0jzc (0.59 #309), 0t_2 (0.05 #50, 0.03 #116, 0.03 #171), 03_9r (0.04 #500, 0.04 #511, 0.04 #522), 02bv9 (0.03 #119, 0.01 #318), 04306rv (0.03 #113, 0.01 #312) >> Best rule #134 for best value: >> intensional similarity = 6 >> extensional distance = 55 >> proper extension: 0300ml; >> query: (?x11482, 02h40lc) <- genre(?x11482, ?x5518), nominated_for(?x2192, ?x11482), award(?x8485, ?x2192), award(?x4587, ?x2192), ?x4587 = 015d3h, profession(?x8485, ?x319) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02gjrc languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.930 http://example.org/tv/tv_program/languages #12948-0kzy0 PRED entity: 0kzy0 PRED relation: place_of_birth PRED expected values: 0r0f7 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 144): 02_286 (0.17 #723, 0.11 #2132, 0.08 #11284), 0xl08 (0.17 #241, 0.05 #5170, 0.05 #5874), 02s838 (0.17 #405, 0.05 #5334, 0.05 #6038), 0rh6k (0.17 #706, 0.03 #16195, 0.02 #14787), 0cr3d (0.14 #1502, 0.10 #5023, 0.09 #5727), 0d9jr (0.14 #1602, 0.08 #3011, 0.08 #3715), 030qb3t (0.07 #4279, 0.05 #33144, 0.05 #4983), 013yq (0.07 #4304, 0.05 #5008, 0.05 #5712), 06c62 (0.07 #4482, 0.05 #5186, 0.05 #5890), 019k6n (0.07 #4332, 0.05 #5036, 0.05 #5740) >> Best rule #723 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 032t2z; 03j24kf; 0191h5; 0jsg0m; >> query: (?x654, 02_286) <- people(?x913, ?x654), artists(?x5379, ?x654), currency(?x654, ?x170), ?x5379 = 08jyyk >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #10872 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 01r4zfk; *> query: (?x654, 0r0f7) <- people(?x913, ?x654), category(?x654, ?x134), role(?x654, ?x745), profession(?x654, ?x131) *> conf = 0.01 ranks of expected_values: 98 EVAL 0kzy0 place_of_birth 0r0f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 114.000 114.000 0.167 http://example.org/people/person/place_of_birth #12947-0fjsl PRED entity: 0fjsl PRED relation: contains! PRED expected values: 03rjj => 83 concepts (55 used for prediction) PRED predicted values (max 10 best out of 381): 03rjj (0.89 #45014, 0.88 #47717, 0.87 #34209), 01ngn3 (0.81 #40507, 0.79 #42307, 0.78 #46812), 09c7w0 (0.69 #41413, 0.64 #45918, 0.62 #39613), 02j71 (0.49 #48618, 0.49 #45013, 0.47 #26097), 059rby (0.41 #14417, 0.33 #10820, 0.21 #25217), 01n7q (0.38 #18973, 0.35 #25275, 0.33 #27078), 0345h (0.29 #9982, 0.26 #11782, 0.20 #9082), 02qkt (0.27 #26098, 0.25 #43209, 0.24 #44111), 0bzty (0.25 #607, 0.20 #1506, 0.17 #4499), 07kg3 (0.25 #353, 0.20 #1252, 0.17 #4499) >> Best rule #45014 for best value: >> intensional similarity = 10 >> extensional distance = 70 >> proper extension: 0tbql; >> query: (?x13881, ?x205) <- administrative_division(?x13881, ?x12203), contains(?x205, ?x12203), partially_contains(?x205, ?x8154), currency(?x205, ?x170), taxonomy(?x205, ?x939), adjoins(?x774, ?x205), category(?x13881, ?x134), time_zones(?x13881, ?x2864), administrative_parent(?x205, ?x551), ?x134 = 08mbj5d >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fjsl contains! 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 55.000 0.889 http://example.org/location/location/contains #12946-0prhz PRED entity: 0prhz PRED relation: genre PRED expected values: 060__y => 60 concepts (59 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.70 #604, 0.68 #1085, 0.66 #1205), 081k8 (0.57 #241, 0.57 #603, 0.53 #3133), 05p553 (0.42 #1328, 0.42 #1208, 0.36 #1689), 03k9fj (0.33 #976, 0.30 #735, 0.23 #1697), 02kdv5l (0.30 #966, 0.27 #243, 0.27 #1687), 01jfsb (0.30 #616, 0.29 #1698, 0.29 #2179), 060__y (0.29 #137, 0.25 #499, 0.22 #740), 082gq (0.27 #272, 0.14 #151, 0.14 #393), 03g3w (0.27 #266, 0.09 #1109, 0.09 #507), 01hmnh (0.26 #982, 0.21 #741, 0.18 #1102) >> Best rule #604 for best value: >> intensional similarity = 2 >> extensional distance = 140 >> proper extension: 02vl9ln; >> query: (?x4678, 07s9rl0) <- country(?x4678, ?x789), ?x789 = 0f8l9c >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #137 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0cf8qb; *> query: (?x4678, 060__y) <- film(?x2531, ?x4678), film(?x2173, ?x4678), ?x2173 = 015gw6, titles(?x53, ?x4678), award_winner(?x2531, ?x72) *> conf = 0.29 ranks of expected_values: 7 EVAL 0prhz genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 60.000 59.000 0.697 http://example.org/film/film/genre #12945-0bxs_d PRED entity: 0bxs_d PRED relation: award_winner PRED expected values: 03pp73 01rcmg 05w88j => 36 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2131): 01j7rd (0.58 #6433, 0.57 #7966, 0.50 #4901), 0b7gxq (0.50 #4176, 0.33 #2642, 0.27 #1531), 02661h (0.50 #4227, 0.33 #2693, 0.21 #8826), 01rcmg (0.50 #4280, 0.33 #2746, 0.19 #7667), 02xs0q (0.43 #8214, 0.42 #6681, 0.42 #5149), 04ns3gy (0.42 #7459, 0.42 #5927, 0.36 #8992), 05bnq3j (0.33 #6860, 0.33 #5328, 0.29 #8393), 0cp9f9 (0.33 #7321, 0.29 #8854, 0.25 #5789), 0p_2r (0.33 #6328, 0.29 #7861, 0.25 #4796), 01_x6d (0.33 #6824, 0.29 #8357, 0.25 #5292) >> Best rule #6433 for best value: >> intensional similarity = 12 >> extensional distance = 10 >> proper extension: 0ds460j; >> query: (?x8238, 01j7rd) <- award_winner(?x8238, ?x12754), award_winner(?x8238, ?x8509), award_winner(?x8238, ?x7189), ceremony(?x870, ?x8238), award_nominee(?x7189, ?x2135), award_nominee(?x6045, ?x7189), producer_type(?x7189, ?x632), profession(?x8509, ?x524), award(?x7137, ?x870), program(?x8509, ?x337), ?x7137 = 01gbb4, award_winner(?x4535, ?x12754) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #4280 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 07y9ts; *> query: (?x8238, 01rcmg) <- honored_for(?x8238, ?x1395), honored_for(?x8238, ?x782), actor(?x1395, ?x4956), ceremony(?x435, ?x8238), award_winner(?x8238, ?x12754), award_winner(?x8238, ?x6190), genre(?x1395, ?x258), award(?x12754, ?x3906), award_nominee(?x4956, ?x3293), ?x6190 = 01h910, celebrity(?x7830, ?x4956), nominated_for(?x783, ?x782), languages(?x1395, ?x254) *> conf = 0.50 ranks of expected_values: 4, 269, 830 EVAL 0bxs_d award_winner 05w88j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 36.000 14.000 0.583 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bxs_d award_winner 01rcmg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 36.000 14.000 0.583 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bxs_d award_winner 03pp73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 14.000 0.583 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12944-03lt8g PRED entity: 03lt8g PRED relation: nationality PRED expected values: 09c7w0 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 22): 09c7w0 (0.81 #202, 0.81 #1206, 0.77 #1906), 02jx1 (0.13 #134, 0.11 #736, 0.11 #3944), 07ssc (0.09 #918, 0.09 #4528, 0.09 #4127), 03rk0 (0.08 #3757, 0.08 #4158, 0.08 #4358), 0d060g (0.07 #108, 0.05 #1412, 0.05 #1212), 0chghy (0.04 #311, 0.03 #111, 0.03 #1615), 0345h (0.03 #3440, 0.02 #4443, 0.02 #4544), 03rt9 (0.03 #1418, 0.02 #4325, 0.02 #2419), 03rjj (0.03 #3414, 0.02 #1210, 0.02 #3013), 0f8l9c (0.02 #3733, 0.02 #4535, 0.02 #4134) >> Best rule #202 for best value: >> intensional similarity = 2 >> extensional distance = 105 >> proper extension: 01p7yb; 02r_d4; 0168cl; 05ml_s; 01yk13; 0yfp; 049dyj; 01w8sf; 016h4r; 03gyh_z; ... >> query: (?x1117, 09c7w0) <- award_nominee(?x1117, ?x444), student(?x865, ?x1117) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03lt8g nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.813 http://example.org/people/person/nationality #12943-0dqcm PRED entity: 0dqcm PRED relation: nominated_for PRED expected values: 0dnw1 => 122 concepts (63 used for prediction) PRED predicted values (max 10 best out of 327): 0dnw1 (0.79 #98680, 0.79 #80887, 0.79 #46906), 04vr_f (0.10 #159, 0.04 #8245, 0.03 #9862), 07cw4 (0.10 #929, 0.02 #10632, 0.01 #13867), 033fqh (0.10 #774, 0.01 #13712, 0.01 #10477), 06cm5 (0.10 #973, 0.01 #10676), 011yn5 (0.10 #846, 0.01 #34810, 0.01 #33192), 0jsf6 (0.10 #987, 0.01 #44655, 0.01 #33333), 0419kt (0.10 #1554), 0by17xn (0.10 #1547), 04ltlj (0.10 #1545) >> Best rule #98680 for best value: >> intensional similarity = 3 >> extensional distance = 1233 >> proper extension: 025jfl; 0dky9n; 04f525m; 01795t; 0b79gfg; 0kk9v; 027_tg; 03q8ch; 024rdh; 056ws9; ... >> query: (?x9095, ?x6094) <- nominated_for(?x9095, ?x4504), award_winner(?x1245, ?x9095), award_winner(?x6094, ?x9095) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dqcm nominated_for 0dnw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 63.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12942-05v8c PRED entity: 05v8c PRED relation: geographic_distribution! PRED expected values: 071x0k => 176 concepts (164 used for prediction) PRED predicted values (max 10 best out of 38): 071x0k (0.46 #120, 0.42 #433, 0.36 #472), 01xhh5 (0.36 #176, 0.23 #137, 0.17 #450), 04mvp8 (0.27 #228, 0.23 #619, 0.22 #111), 0g48m4 (0.20 #1722, 0.14 #782, 0.13 #978), 013b6_ (0.15 #143, 0.13 #221, 0.11 #104), 0g6ff (0.15 #127, 0.12 #1692, 0.11 #88), 01rv7x (0.13 #216, 0.13 #607, 0.12 #724), 06mvq (0.13 #370, 0.12 #487, 0.08 #448), 04gfy7 (0.11 #110, 0.08 #149, 0.07 #188), 0ffjqy (0.11 #108, 0.08 #147, 0.07 #186) >> Best rule #120 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 02j9z; 0dg3n1; >> query: (?x550, 071x0k) <- service_location(?x1492, ?x550), ?x1492 = 0cv9b, contains(?x550, ?x4845) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v8c geographic_distribution! 071x0k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 164.000 0.462 http://example.org/people/ethnicity/geographic_distribution #12941-02mpyh PRED entity: 02mpyh PRED relation: film! PRED expected values: 0154qm => 107 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1268): 02hfp_ (0.54 #12478, 0.45 #99831, 0.45 #8319), 012ljv (0.54 #12478, 0.45 #99831, 0.45 #8319), 08mhyd (0.45 #8319, 0.45 #114390, 0.41 #20797), 0bytfv (0.45 #8319, 0.45 #114390, 0.41 #20797), 0jz9f (0.45 #8319, 0.45 #114390, 0.41 #20797), 0j_c (0.16 #408, 0.05 #42004, 0.04 #8727), 029m83 (0.11 #103991, 0.02 #11797, 0.01 #34674), 039bp (0.11 #2259, 0.04 #25136, 0.04 #180), 0bj9k (0.11 #2406, 0.04 #327, 0.03 #41923), 04__f (0.11 #3458, 0.03 #26335, 0.02 #51297) >> Best rule #12478 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 0g68zt; 0221zw; 02_kd; 0yx7h; 011yg9; 0sxns; 0y_pg; 0p9rz; 0sxlb; 0170xl; ... >> query: (?x8574, ?x84) <- titles(?x162, ?x8574), nominated_for(?x2880, ?x8574), award_winner(?x8574, ?x84), ?x2880 = 02ppm4q >> conf = 0.54 => this is the best rule for 2 predicted values *> Best rule #17197 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 100 *> proper extension: 0gy2y8r; *> query: (?x8574, 0154qm) <- costume_design_by(?x8574, ?x3685), nominated_for(?x84, ?x8574), film_crew_role(?x8574, ?x137) *> conf = 0.05 ranks of expected_values: 50 EVAL 02mpyh film! 0154qm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 107.000 60.000 0.543 http://example.org/film/actor/film./film/performance/film #12940-05vxdh PRED entity: 05vxdh PRED relation: film! PRED expected values: 071ynp => 82 concepts (35 used for prediction) PRED predicted values (max 10 best out of 626): 02q42j_ (0.42 #70806, 0.42 #70805, 0.41 #22906), 0b13g7 (0.42 #70806, 0.42 #70805, 0.41 #22906), 0136g9 (0.42 #70806, 0.42 #70805, 0.41 #22906), 0170qf (0.25 #367, 0.10 #2449, 0.06 #4531), 01f7dd (0.25 #1209, 0.10 #3291, 0.05 #58308), 044mz_ (0.25 #2, 0.10 #2084, 0.05 #58308), 06jzh (0.25 #88, 0.10 #2170, 0.03 #4252), 0lx2l (0.25 #420, 0.10 #2502, 0.02 #8748), 01f6zc (0.25 #944, 0.10 #3026, 0.02 #9272), 0219q (0.25 #726, 0.10 #2808) >> Best rule #70806 for best value: >> intensional similarity = 2 >> extensional distance = 1062 >> proper extension: 02rb607; 040rmy; 02n9bh; 04lqvly; 02hfk5; 0g9zljd; 072r5v; 02wk7b; 0cvkv5; 05zvzf3; ... >> query: (?x4592, ?x3568) <- currency(?x4592, ?x170), nominated_for(?x3568, ?x4592) >> conf = 0.42 => this is the best rule for 3 predicted values *> Best rule #6795 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 134 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x4592, 071ynp) <- titles(?x512, ?x4592), ?x512 = 07ssc *> conf = 0.01 ranks of expected_values: 249 EVAL 05vxdh film! 071ynp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 82.000 35.000 0.423 http://example.org/film/actor/film./film/performance/film #12939-0841v PRED entity: 0841v PRED relation: list PRED expected values: 01ptsx 01pd60 => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 5): 01ptsx (0.85 #83, 0.85 #179, 0.83 #197), 01pd60 (0.82 #923, 0.82 #922, 0.82 #909), 09g7thr (0.75 #104, 0.53 #657, 0.53 #651), 05glt (0.38 #904, 0.38 #917, 0.09 #736), 026cl_m (0.09 #905, 0.09 #918, 0.07 #737) >> Best rule #83 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 02bh8z; 0sxdg; >> query: (?x13100, 01ptsx) <- state_province_region(?x13100, ?x4758), company(?x1491, ?x13100), list(?x13100, ?x5997), currency(?x13100, ?x170), ?x1491 = 0krdk >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0841v list 01pd60 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 180.000 180.000 0.848 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 0841v list 01ptsx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 180.000 180.000 0.848 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #12938-0mtdx PRED entity: 0mtdx PRED relation: currency PRED expected values: 09nqf => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.82 #33, 0.82 #34, 0.82 #6) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 329 >> proper extension: 05ksh; 0h7h6; 0n5yh; 0fr61; 0mnm2; 0jgk3; 0mwxl; 0mrq3; 0fkhz; 0mkv3; ... >> query: (?x2003, 09nqf) <- second_level_divisions(?x94, ?x2003), contains(?x3778, ?x2003), district_represented(?x176, ?x3778) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mtdx currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.825 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #12937-0dscrwf PRED entity: 0dscrwf PRED relation: film! PRED expected values: 01ckhj => 75 concepts (39 used for prediction) PRED predicted values (max 10 best out of 688): 03h304l (0.12 #27071, 0.12 #27070, 0.12 #43738), 027z0pl (0.12 #27071, 0.12 #27070, 0.12 #43738), 03h40_7 (0.12 #27070, 0.12 #43738, 0.12 #43737), 03m6_z (0.07 #64567, 0.05 #54154, 0.05 #41654), 03zz8b (0.07 #64567, 0.05 #54154, 0.05 #41654), 02k4b2 (0.07 #64567, 0.05 #54154, 0.05 #41654), 01pgzn_ (0.07 #64567, 0.05 #54154, 0.05 #64568), 01q_ph (0.07 #64567, 0.05 #54154, 0.05 #64568), 02kxwk (0.07 #64567, 0.05 #54154, 0.05 #64568), 01cj6y (0.07 #64567, 0.05 #54154, 0.05 #64568) >> Best rule #27071 for best value: >> intensional similarity = 4 >> extensional distance = 591 >> proper extension: 01_1hw; 04sh80; >> query: (?x511, ?x10430) <- language(?x511, ?x254), produced_by(?x511, ?x10430), film(?x3560, ?x511), award_winner(?x10430, ?x3223) >> conf = 0.12 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0dscrwf film! 01ckhj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 39.000 0.124 http://example.org/film/actor/film./film/performance/film #12936-0kq39 PRED entity: 0kq39 PRED relation: currency PRED expected values: 09nqf => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.85 #52, 0.85 #51, 0.84 #55) >> Best rule #52 for best value: >> intensional similarity = 5 >> extensional distance = 273 >> proper extension: 0p07l; >> query: (?x6776, ?x170) <- contains(?x1227, ?x6776), adjoins(?x6776, ?x6858), adjoins(?x6776, ?x4181), currency(?x4181, ?x170), adjoins(?x6858, ?x12528) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kq39 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.847 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #12935-09qftb PRED entity: 09qftb PRED relation: award_winner PRED expected values: 032zg9 01w1kyf => 38 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1470): 052hl (0.60 #5583, 0.20 #7113, 0.14 #14748), 01vw20h (0.57 #14431, 0.20 #5266, 0.07 #17487), 02cx90 (0.43 #14400, 0.20 #5235, 0.07 #12869), 0gcs9 (0.43 #14176, 0.15 #20283, 0.10 #27913), 02l840 (0.43 #13843), 06fmdb (0.40 #5375, 0.29 #14540, 0.20 #6905), 0hl3d (0.40 #4611, 0.29 #13776, 0.20 #6141), 0x3b7 (0.40 #5214, 0.21 #14379, 0.20 #6744), 02qwg (0.40 #5081, 0.21 #14246, 0.20 #6611), 0ggjt (0.40 #5036, 0.21 #14201, 0.20 #6566) >> Best rule #5583 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 01c6qp; >> query: (?x8128, 052hl) <- award_winner(?x8128, ?x703), friend(?x9849, ?x703), award_nominee(?x703, ?x2353), award_nominee(?x703, ?x1958), gender(?x703, ?x231), award_nominee(?x1958, ?x488), film(?x1958, ?x224), profession(?x2353, ?x987), ?x9849 = 02nrdp, award_winner(?x3078, ?x2353), nominated_for(?x703, ?x770) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #9947 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 10 *> proper extension: 0c53zb; 0d__c3; *> query: (?x8128, 01w1kyf) <- honored_for(?x8128, ?x5386), award_winner(?x8128, ?x7138), award_winner(?x8128, ?x703), ceremony(?x746, ?x8128), celebrities_impersonated(?x3649, ?x7138), ?x3649 = 03m6t5, profession(?x703, ?x524), location(?x7138, ?x1426), award(?x7138, ?x458), nominated_for(?x678, ?x5386), participant(?x3870, ?x7138), participant(?x6331, ?x703) *> conf = 0.08 ranks of expected_values: 835 EVAL 09qftb award_winner 01w1kyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 38.000 20.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09qftb award_winner 032zg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 38.000 20.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12934-01v1d8 PRED entity: 01v1d8 PRED relation: instrumentalists PRED expected values: 02bgmr 0k60 => 90 concepts (45 used for prediction) PRED predicted values (max 10 best out of 973): 01gx5f (0.71 #614, 0.70 #2457, 0.66 #1841), 01271h (0.71 #614, 0.70 #2457, 0.65 #6763), 06k02 (0.71 #614, 0.70 #2457, 0.65 #6763), 02g40r (0.71 #614, 0.70 #2457, 0.65 #6763), 03h_fqv (0.71 #614, 0.70 #2457, 0.65 #6763), 0326tc (0.71 #614, 0.70 #2457, 0.65 #6763), 01w9mnm (0.71 #614, 0.70 #2457, 0.65 #6763), 04kjrv (0.71 #614, 0.70 #2457, 0.65 #6763), 01tv3x2 (0.71 #614, 0.70 #2457, 0.65 #6763), 02sjp (0.71 #614, 0.70 #2457, 0.65 #6763) >> Best rule #614 for best value: >> intensional similarity = 20 >> extensional distance = 1 >> proper extension: 0342h; >> query: (?x3161, ?x2306) <- instrumentalists(?x3161, ?x5478), role(?x1750, ?x3161), role(?x1662, ?x3161), role(?x3161, ?x4917), role(?x3161, ?x2957), role(?x3161, ?x1432), role(?x3161, ?x1166), role(?x3161, ?x716), ?x1432 = 0395lw, ?x1750 = 02hnl, group(?x3161, ?x3682), role(?x432, ?x3161), ?x4917 = 06w7v, role(?x2306, ?x3161), role(?x7772, ?x2957), instrumentalists(?x2957, ?x702), ?x1662 = 02bxd, ?x716 = 018vs, ?x5478 = 01yzl2, ?x1166 = 05148p4 >> conf = 0.71 => this is the best rule for 11 predicted values *> Best rule #612 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x3161, ?x702) <- instrumentalists(?x3161, ?x5478), role(?x1750, ?x3161), role(?x1662, ?x3161), role(?x3161, ?x4917), role(?x3161, ?x2957), role(?x3161, ?x1432), role(?x3161, ?x1166), role(?x3161, ?x716), ?x1432 = 0395lw, ?x1750 = 02hnl, group(?x3161, ?x3682), role(?x432, ?x3161), ?x4917 = 06w7v, role(?x2306, ?x3161), role(?x7772, ?x2957), instrumentalists(?x2957, ?x702), ?x1662 = 02bxd, ?x716 = 018vs, ?x5478 = 01yzl2, ?x1166 = 05148p4 *> conf = 0.55 ranks of expected_values: 203, 471 EVAL 01v1d8 instrumentalists 0k60 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 45.000 0.711 http://example.org/music/instrument/instrumentalists EVAL 01v1d8 instrumentalists 02bgmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 90.000 45.000 0.711 http://example.org/music/instrument/instrumentalists #12933-081yw PRED entity: 081yw PRED relation: district_represented! PRED expected values: 070m6c 02bn_p 024tcq 070mff => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 52): 070m6c (0.88 #629, 0.80 #317, 0.79 #213), 070mff (0.86 #659, 0.79 #243, 0.78 #347), 024tcq (0.80 #641, 0.78 #329, 0.75 #225), 02bn_p (0.67 #111, 0.65 #319, 0.65 #631), 03rl1g (0.60 #105, 0.55 #625, 0.54 #313), 043djx (0.54 #214, 0.53 #630, 0.52 #318), 02bqn1 (0.50 #217, 0.46 #321, 0.43 #633), 02cg7g (0.50 #231, 0.43 #335, 0.41 #647), 01h7xx (0.48 #352, 0.47 #664, 0.47 #144), 02gkzs (0.46 #228, 0.41 #332, 0.39 #644) >> Best rule #629 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 0g0syc; >> query: (?x4600, 070m6c) <- district_represented(?x3463, ?x4600), district_represented(?x605, ?x4600), ?x605 = 077g7n, legislative_sessions(?x652, ?x3463) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 081yw district_represented! 070mff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 081yw district_represented! 024tcq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 081yw district_represented! 02bn_p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 081yw district_represented! 070m6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #12932-056wb PRED entity: 056wb PRED relation: student! PRED expected values: 01mpwj 01wqg8 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 156): 078bz (0.25 #77, 0.07 #1129, 0.03 #4811), 021w0_ (0.13 #1375, 0.11 #2427, 0.06 #1901), 0bwfn (0.12 #3956, 0.11 #5534, 0.09 #5008), 017j69 (0.12 #1722, 0.04 #5404, 0.03 #4878), 01w5m (0.11 #5365, 0.07 #9047, 0.06 #21671), 065y4w7 (0.11 #2118, 0.09 #5274, 0.08 #540), 0fnmz (0.11 #2205, 0.07 #1153, 0.07 #5361), 04b_46 (0.08 #3908, 0.08 #752, 0.05 #14428), 02s62q (0.08 #578, 0.05 #3208, 0.05 #2682), 020ddc (0.08 #846, 0.05 #2950, 0.03 #4528) >> Best rule #77 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 03c6vl; >> query: (?x6045, 078bz) <- award_winner(?x6045, ?x7189), nominated_for(?x6045, ?x6482), ?x7189 = 03wbzp >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4314 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 01r4zfk; 03yf4d; *> query: (?x6045, 01mpwj) <- tv_program(?x6045, ?x6482), category(?x6045, ?x134), profession(?x6045, ?x319) *> conf = 0.03 ranks of expected_values: 49 EVAL 056wb student! 01wqg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 141.000 141.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 056wb student! 01mpwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 141.000 141.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #12931-083skw PRED entity: 083skw PRED relation: film_crew_role PRED expected values: 09zzb8 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 34): 0ch6mp2 (0.64 #1531, 0.60 #2201, 0.56 #1886), 09zzb8 (0.58 #976, 0.57 #2193, 0.57 #1523), 02r96rf (0.55 #1526, 0.51 #1253, 0.49 #2196), 09vw2b7 (0.55 #1530, 0.50 #2200, 0.48 #2239), 0dxtw (0.29 #2205, 0.28 #3034, 0.28 #2244), 01pvkk (0.28 #795, 0.22 #2207, 0.21 #3515), 01vx2h (0.28 #1536, 0.27 #2206, 0.24 #3712), 0215hd (0.18 #490, 0.12 #2214, 0.11 #2292), 02ynfr (0.17 #253, 0.15 #2211, 0.14 #1541), 04pyp5 (0.15 #20, 0.14 #59, 0.10 #176) >> Best rule #1531 for best value: >> intensional similarity = 4 >> extensional distance = 254 >> proper extension: 09dv8h; 032clf; 03hp2y1; >> query: (?x2612, 0ch6mp2) <- currency(?x2612, ?x170), nominated_for(?x788, ?x2612), genre(?x2612, ?x53), category(?x2612, ?x134) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #976 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 127 *> proper extension: 07sc6nw; 05p3738; 03sxd2; 035s95; 020y73; 0pvms; 0gyy53; 04grkmd; 09rsjpv; 04cv9m; ... *> query: (?x2612, 09zzb8) <- currency(?x2612, ?x170), film_release_distribution_medium(?x2612, ?x81), genre(?x2612, ?x162), ?x162 = 04xvlr *> conf = 0.58 ranks of expected_values: 2 EVAL 083skw film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.645 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12930-0c53zb PRED entity: 0c53zb PRED relation: honored_for PRED expected values: 0kvb6p => 39 concepts (27 used for prediction) PRED predicted values (max 10 best out of 743): 0bcndz (0.33 #692, 0.20 #2473, 0.20 #1879), 0k4kk (0.33 #693, 0.20 #2474, 0.20 #1880), 075cph (0.33 #737, 0.20 #2518, 0.20 #1924), 0m_mm (0.33 #650, 0.20 #2431, 0.20 #1837), 04954r (0.33 #815, 0.20 #2596, 0.20 #2002), 014kkm (0.33 #308, 0.20 #2091, 0.17 #8924), 0bl06 (0.33 #343, 0.20 #2126, 0.17 #3315), 0j80w (0.33 #295, 0.20 #2078, 0.17 #3267), 0m_h6 (0.33 #1698, 0.17 #3480, 0.16 #6538), 027rpym (0.33 #1482, 0.17 #3264, 0.07 #4455) >> Best rule #692 for best value: >> intensional similarity = 19 >> extensional distance = 1 >> proper extension: 0fy6bh; >> query: (?x4445, 0bcndz) <- ceremony(?x1972, ?x4445), ceremony(?x1862, ?x4445), ceremony(?x1323, ?x4445), ceremony(?x720, ?x4445), ?x1323 = 0gqz2, ?x1972 = 0gqyl, award_winner(?x4445, ?x4423), award_winner(?x4445, ?x3519), award_winner(?x4445, ?x902), award_winner(?x4445, ?x786), ?x1862 = 0gr51, ?x786 = 076lxv, honored_for(?x4445, ?x2112), ?x720 = 018wng, ?x3519 = 02sj1x, award_nominee(?x163, ?x902), ?x4423 = 076psv, nominated_for(?x902, ?x103), award(?x902, ?x1105) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8924 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 24 *> proper extension: 0bzk8w; *> query: (?x4445, ?x1973) <- ceremony(?x1972, ?x4445), ceremony(?x1862, ?x4445), ceremony(?x1323, ?x4445), ceremony(?x500, ?x4445), ?x1323 = 0gqz2, ?x1972 = 0gqyl, award_winner(?x4445, ?x786), ?x1862 = 0gr51, crewmember(?x785, ?x786), nominated_for(?x500, ?x5795), nominated_for(?x500, ?x1746), nominated_for(?x500, ?x908), ?x5795 = 025rvx0, award_winner(?x908, ?x629), ?x1746 = 0k4kk, award_winner(?x1973, ?x786), award(?x382, ?x500) *> conf = 0.17 ranks of expected_values: 19 EVAL 0c53zb honored_for 0kvb6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 39.000 27.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12929-01zg98 PRED entity: 01zg98 PRED relation: film PRED expected values: 01flv_ => 104 concepts (58 used for prediction) PRED predicted values (max 10 best out of 325): 0284b56 (0.22 #2775, 0.07 #985, 0.03 #53707), 06z8s_ (0.13 #130, 0.09 #1920, 0.03 #53707), 0dj0m5 (0.13 #97, 0.09 #1887, 0.03 #53707), 0418wg (0.13 #401, 0.04 #2191, 0.03 #53707), 092vkg (0.13 #1947, 0.07 #157, 0.03 #53707), 08phg9 (0.09 #16113, 0.07 #885, 0.06 #60870), 01cmp9 (0.09 #16113, 0.07 #1049, 0.06 #60870), 046488 (0.09 #16113, 0.07 #851, 0.06 #60870), 02d478 (0.09 #16113, 0.07 #674, 0.06 #60870), 016fyc (0.09 #16113, 0.07 #56, 0.06 #60870) >> Best rule #2775 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 01qscs; 0dlglj; 030h95; 04smkr; 028r4y; 0crvfq; >> query: (?x4248, 0284b56) <- award_winner(?x4248, ?x192), award_nominee(?x3139, ?x4248), ?x3139 = 0b_dy >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01zg98 film 01flv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 58.000 0.217 http://example.org/film/actor/film./film/performance/film #12928-0kr7k PRED entity: 0kr7k PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #45, 0.86 #27, 0.83 #35), 02zsn (0.46 #151, 0.46 #118, 0.45 #196) >> Best rule #45 for best value: >> intensional similarity = 8 >> extensional distance = 91 >> proper extension: 02dlfh; >> query: (?x10979, 05zppz) <- profession(?x10979, ?x8310), company(?x10979, ?x9309), profession(?x11484, ?x8310), profession(?x7587, ?x8310), profession(?x129, ?x8310), ?x11484 = 0488g9, ?x129 = 0dbpyd, ?x7587 = 01vz80y >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kr7k gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.871 http://example.org/people/person/gender #12927-0m2kd PRED entity: 0m2kd PRED relation: film_release_region PRED expected values: 03_3d 02vzc => 100 concepts (96 used for prediction) PRED predicted values (max 10 best out of 201): 02vzc (0.85 #714, 0.82 #3187, 0.81 #3351), 06mkj (0.83 #2699, 0.80 #3193, 0.80 #3357), 059j2 (0.79 #2670, 0.78 #3164, 0.77 #3328), 03_3d (0.78 #2641, 0.75 #3135, 0.74 #3299), 03h64 (0.73 #2710, 0.71 #3204, 0.70 #3368), 0345h (0.72 #2672, 0.71 #3166, 0.71 #3330), 01znc_ (0.70 #2682, 0.66 #3340, 0.65 #3176), 03gj2 (0.69 #2662, 0.69 #3156, 0.67 #3320), 035qy (0.69 #2674, 0.68 #3168, 0.66 #3332), 015fr (0.67 #2653, 0.65 #3147, 0.64 #3311) >> Best rule #714 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0j_tw; 03rg2b; >> query: (?x430, 02vzc) <- film(?x3366, ?x430), film_release_region(?x430, ?x2984), ?x2984 = 082fr, produced_by(?x430, ?x1712) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0m2kd film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 96.000 0.854 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0m2kd film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 100.000 96.000 0.854 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12926-021yzs PRED entity: 021yzs PRED relation: people! PRED expected values: 02w7gg => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 43): 02w7gg (0.40 #233, 0.33 #387, 0.30 #849), 033tf_ (0.30 #7, 0.25 #84, 0.19 #161), 041rx (0.30 #4, 0.25 #81, 0.15 #928), 01qhm_ (0.20 #6, 0.17 #83, 0.06 #160), 0x67 (0.19 #164, 0.10 #1242, 0.09 #4553), 022dp5 (0.12 #204, 0.10 #50, 0.08 #127), 048z7l (0.12 #194, 0.03 #1811, 0.03 #579), 0d7wh (0.11 #248, 0.10 #479, 0.09 #402), 07bch9 (0.10 #23, 0.08 #100, 0.06 #177), 065b6q (0.10 #3, 0.08 #80, 0.06 #157) >> Best rule #233 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 04jwp; >> query: (?x4764, 02w7gg) <- student(?x10940, ?x4764), location(?x4764, ?x362), ?x362 = 04jpl, contains(?x1310, ?x10940) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021yzs people! 02w7gg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.400 http://example.org/people/ethnicity/people #12925-0gffmn8 PRED entity: 0gffmn8 PRED relation: genre PRED expected values: 03k9fj => 89 concepts (88 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.69 #2664, 0.68 #3027, 0.64 #848), 05p553 (0.57 #2183, 0.55 #1456, 0.53 #1819), 03k9fj (0.45 #7637, 0.43 #1343, 0.43 #375), 01hmnh (0.33 #744, 0.28 #1349, 0.22 #7643), 02l7c8 (0.27 #1468, 0.26 #5826, 0.26 #8370), 0lsxr (0.27 #7513, 0.24 #1582, 0.23 #2309), 06n90 (0.27 #1344, 0.26 #1707, 0.26 #7638), 0556j8 (0.23 #285, 0.17 #164, 0.09 #2343), 0hcr (0.22 #1718, 0.20 #629, 0.19 #387), 06cvj (0.21 #1455, 0.19 #1818, 0.17 #2424) >> Best rule #2664 for best value: >> intensional similarity = 4 >> extensional distance = 133 >> proper extension: 0b76d_m; 0ds35l9; 028_yv; 0c3ybss; 011yrp; 0c40vxk; 01vksx; 03cvwkr; 0cwy47; 09gdm7q; ... >> query: (?x3217, 07s9rl0) <- film(?x2387, ?x3217), film_release_region(?x3217, ?x87), film_regional_debut_venue(?x3217, ?x362), film(?x1914, ?x3217) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #7637 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 815 *> proper extension: 06n90; *> query: (?x3217, 03k9fj) <- genre(?x3217, ?x812), genre(?x7989, ?x812), genre(?x7480, ?x812), ?x7989 = 015bpl, ?x7480 = 02vjp3 *> conf = 0.45 ranks of expected_values: 3 EVAL 0gffmn8 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 88.000 0.689 http://example.org/film/film/genre #12924-0dbbz PRED entity: 0dbbz PRED relation: award_winner! PRED expected values: 0gs9p => 103 concepts (87 used for prediction) PRED predicted values (max 10 best out of 238): 0gs9p (0.37 #25209, 0.37 #25208, 0.36 #23499), 040njc (0.37 #25209, 0.37 #25208, 0.36 #23499), 02pqp12 (0.37 #25209, 0.37 #25208, 0.36 #23499), 0gr4k (0.37 #25209, 0.37 #25208, 0.36 #23499), 09sb52 (0.23 #6878, 0.18 #8159, 0.16 #3886), 027c924 (0.12 #865, 0.12 #1720, 0.08 #11), 09d28z (0.12 #1154, 0.11 #2009, 0.07 #727), 0gq9h (0.11 #30765, 0.10 #76, 0.08 #503), 0l8z1 (0.11 #30765, 0.08 #28199, 0.07 #28200), 0gq_v (0.11 #30765, 0.08 #28199, 0.07 #28200) >> Best rule #25209 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 01czx; 016ppr; >> query: (?x9606, ?x746) <- award(?x9606, ?x746), award_winner(?x1601, ?x9606) >> conf = 0.37 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 0dbbz award_winner! 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 87.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner #12923-0dl5d PRED entity: 0dl5d PRED relation: parent_genre! PRED expected values: 0190xp 02w6s3 034gxk => 71 concepts (52 used for prediction) PRED predicted values (max 10 best out of 275): 0y3_8 (0.57 #1853, 0.50 #556, 0.40 #1334), 059kh (0.50 #558, 0.43 #1855, 0.33 #300), 02k_kn (0.50 #568, 0.33 #310, 0.29 #1865), 09jw2 (0.50 #647, 0.33 #389, 0.29 #1944), 01756d (0.50 #536, 0.33 #278, 0.29 #1833), 0pm85 (0.50 #643, 0.33 #385, 0.29 #1940), 0dn16 (0.43 #1825, 0.25 #528, 0.20 #1306), 03lty (0.36 #777, 0.33 #282, 0.25 #1059), 0dl5d (0.36 #777, 0.33 #274, 0.25 #1051), 01fh36 (0.36 #777, 0.33 #327, 0.25 #1104) >> Best rule #1853 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 064t9; >> query: (?x1380, 0y3_8) <- artists(?x1380, ?x6986), artists(?x1380, ?x3657), ?x6986 = 02vgh, artists(?x9248, ?x3657), role(?x3657, ?x212), artists(?x9248, ?x8873), ?x8873 = 0232lm >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2719 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 11 *> proper extension: 0ggq0m; *> query: (?x1380, 0190xp) <- artists(?x1380, ?x9999), artists(?x1380, ?x7506), artists(?x1380, ?x6986), artists(?x1380, ?x6406), artist(?x2299, ?x6986), role(?x7506, ?x212), ?x6406 = 01386_, group(?x228, ?x9999), ?x228 = 0l14qv *> conf = 0.08 ranks of expected_values: 171, 211, 235 EVAL 0dl5d parent_genre! 034gxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 52.000 0.571 http://example.org/music/genre/parent_genre EVAL 0dl5d parent_genre! 02w6s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 71.000 52.000 0.571 http://example.org/music/genre/parent_genre EVAL 0dl5d parent_genre! 0190xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 71.000 52.000 0.571 http://example.org/music/genre/parent_genre #12922-03xj05 PRED entity: 03xj05 PRED relation: films! PRED expected values: 081pw => 135 concepts (42 used for prediction) PRED predicted values (max 10 best out of 105): 081pw (0.20 #5214, 0.17 #1742, 0.17 #3), 03hzt (0.17 #134, 0.07 #765, 0.04 #1873), 0cm2xh (0.17 #46, 0.05 #5257, 0.03 #2257), 0htp (0.17 #120), 05f4p (0.17 #94), 0fx2s (0.14 #703, 0.14 #2757, 0.09 #1811), 018h2 (0.11 #1445, 0.08 #1919, 0.07 #810), 0fzyg (0.10 #209, 0.09 #1792, 0.08 #524), 0jm_ (0.10 #164, 0.08 #479, 0.03 #3007), 02z3r (0.10 #278, 0.08 #593, 0.01 #3279) >> Best rule #5214 for best value: >> intensional similarity = 5 >> extensional distance = 137 >> proper extension: 0fy66; 0kbhf; 0gndh; 0gy4k; >> query: (?x10619, 081pw) <- genre(?x10619, ?x3515), genre(?x10619, ?x1316), language(?x10619, ?x732), titles(?x1316, ?x89), ?x3515 = 082gq >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xj05 films! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 42.000 0.201 http://example.org/film/film_subject/films #12921-01rwyq PRED entity: 01rwyq PRED relation: film! PRED expected values: 0mj1l 043hg => 88 concepts (32 used for prediction) PRED predicted values (max 10 best out of 878): 01swck (0.50 #2872, 0.03 #11173, 0.03 #7022), 07myb2 (0.33 #1784, 0.25 #3859, 0.02 #12160), 0c6qh (0.33 #412, 0.03 #8713, 0.03 #6637), 03pmzt (0.33 #494, 0.02 #17097, 0.01 #15021), 0f4vbz (0.33 #360, 0.01 #10736), 067sqt (0.33 #1898), 0315q3 (0.33 #820), 034bgm (0.33 #448), 0f5xn (0.25 #3041, 0.05 #7191, 0.03 #9267), 04qsdh (0.25 #3474, 0.03 #66414) >> Best rule #2872 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0963mq; >> query: (?x3388, 01swck) <- titles(?x53, ?x3388), film(?x9404, ?x3388), ?x9404 = 01vh18t >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8607 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 69 *> proper extension: 05q_dw; 04ghz4m; 09qycb; *> query: (?x3388, 0mj1l) <- nominated_for(?x3209, ?x3388), film(?x447, ?x3388), ?x3209 = 02w9sd7, titles(?x53, ?x3388) *> conf = 0.04 ranks of expected_values: 81 EVAL 01rwyq film! 043hg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 32.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 01rwyq film! 0mj1l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 88.000 32.000 0.500 http://example.org/film/actor/film./film/performance/film #12920-05qfh PRED entity: 05qfh PRED relation: student PRED expected values: 01x66d => 84 concepts (47 used for prediction) PRED predicted values (max 10 best out of 321): 02z1yj (0.29 #2050, 0.09 #2753, 0.09 #2284), 0kn4c (0.25 #490, 0.17 #1653, 0.15 #2823), 09b6zr (0.25 #556, 0.14 #1953, 0.09 #2656), 08f3b1 (0.25 #477, 0.08 #5618, 0.08 #5850), 0d5_f (0.25 #562, 0.07 #3131, 0.04 #5237), 0tc7 (0.25 #503, 0.04 #5178, 0.04 #5644), 083q7 (0.20 #1182, 0.14 #1881, 0.09 #2584), 031v3p (0.20 #1390, 0.14 #2089, 0.09 #2323), 01dvtx (0.20 #1247, 0.14 #1946, 0.09 #2180), 099bk (0.20 #1246, 0.14 #1945, 0.09 #2179) >> Best rule #2050 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 03g3w; >> query: (?x3490, 02z1yj) <- major_field_of_study(?x7066, ?x3490), major_field_of_study(?x5288, ?x3490), major_field_of_study(?x3489, ?x3490), major_field_of_study(?x1668, ?x3490), major_field_of_study(?x734, ?x3490), taxonomy(?x3489, ?x939), student(?x5288, ?x3395), ?x3395 = 01_rh4, major_field_of_study(?x196, ?x1668), ?x7066 = 0885n >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3968 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 16 *> proper extension: 02rvwt; *> query: (?x3490, ?x123) <- major_field_of_study(?x1390, ?x3490), major_field_of_study(?x1368, ?x3490), ?x1390 = 0bjrnt, institution(?x1368, ?x9880), student(?x1368, ?x123), state_province_region(?x9880, ?x1905), colors(?x9880, ?x663) *> conf = 0.02 ranks of expected_values: 291 EVAL 05qfh student 01x66d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 47.000 0.286 http://example.org/education/field_of_study/students_majoring./education/education/student #12919-0ckhc PRED entity: 0ckhc PRED relation: vacationer PRED expected values: 0lk90 => 114 concepts (89 used for prediction) PRED predicted values (max 10 best out of 187): 05r5w (0.25 #75, 0.18 #609, 0.14 #1857), 0lk90 (0.25 #22, 0.18 #556, 0.10 #1268), 03_6y (0.25 #77, 0.06 #2217, 0.05 #1859), 0blt6 (0.25 #83, 0.06 #973, 0.03 #1865), 07swvb (0.25 #91, 0.06 #981, 0.03 #1873), 024dgj (0.25 #79, 0.05 #2040, 0.04 #2219), 02r3cn (0.25 #132, 0.05 #2093, 0.03 #4234), 01cwhp (0.25 #50, 0.03 #2011, 0.02 #3618), 049qx (0.25 #99, 0.03 #2060, 0.02 #4201), 04cr6qv (0.25 #118, 0.03 #2079, 0.02 #2258) >> Best rule #75 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0chghy; >> query: (?x12182, 05r5w) <- contains(?x94, ?x12182), vacationer(?x12182, ?x2275), adjoins(?x12182, ?x9605), ?x2275 = 05dbf >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #22 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0chghy; *> query: (?x12182, 0lk90) <- contains(?x94, ?x12182), vacationer(?x12182, ?x2275), adjoins(?x12182, ?x9605), ?x2275 = 05dbf *> conf = 0.25 ranks of expected_values: 2 EVAL 0ckhc vacationer 0lk90 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 89.000 0.250 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #12918-013w2r PRED entity: 013w2r PRED relation: artists! PRED expected values: 06by7 => 105 concepts (64 used for prediction) PRED predicted values (max 10 best out of 278): 06by7 (0.95 #5902, 0.88 #6211, 0.84 #18609), 016clz (0.60 #13026, 0.60 #9624, 0.58 #11170), 025sc50 (0.60 #1285, 0.43 #5001, 0.36 #12141), 03_d0 (0.55 #5584, 0.30 #15507, 0.23 #19220), 0155w (0.50 #724, 0.33 #415, 0.33 #106), 06j6l (0.44 #14923, 0.40 #4999, 0.40 #1283), 02lnbg (0.40 #1294, 0.36 #5010, 0.24 #12150), 0gywn (0.40 #5009, 0.31 #14933, 0.29 #10912), 0glt670 (0.40 #1276, 0.29 #17080, 0.24 #12132), 016jny (0.40 #1031, 0.23 #2888, 0.22 #1650) >> Best rule #5902 for best value: >> intensional similarity = 8 >> extensional distance = 98 >> proper extension: 02zmh5; 016jfw; >> query: (?x5858, 06by7) <- artists(?x3061, ?x5858), artists(?x1380, ?x5858), ?x3061 = 05bt6j, award(?x5858, ?x9828), artists(?x1380, ?x11425), artists(?x1380, ?x2901), ?x11425 = 02vnpv, ?x2901 = 01vrwfv >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013w2r artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 64.000 0.950 http://example.org/music/genre/artists #12917-01b4p4 PRED entity: 01b4p4 PRED relation: artists PRED expected values: 06gcn => 63 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1094): 0136p1 (0.71 #6609, 0.67 #7688, 0.50 #2298), 01vvycq (0.67 #3278, 0.67 #2201, 0.60 #4355), 01wj18h (0.67 #3499, 0.67 #2422, 0.60 #1345), 03f5spx (0.67 #3289, 0.67 #2212, 0.60 #1135), 016fnb (0.67 #2559, 0.60 #1482, 0.57 #6870), 049qx (0.67 #2537, 0.60 #1460, 0.57 #6848), 03t9sp (0.67 #3355, 0.60 #1201, 0.50 #6589), 03y82t6 (0.67 #3652, 0.60 #1498, 0.50 #2575), 0137hn (0.67 #3827, 0.60 #1673, 0.50 #2750), 03xhj6 (0.67 #3621, 0.60 #1467, 0.50 #2544) >> Best rule #6609 for best value: >> intensional similarity = 8 >> extensional distance = 12 >> proper extension: 06cqb; 059kh; 025sc50; 0gywn; 02lnbg; 026z9; 0ggx5q; 03mb9; >> query: (?x11737, 0136p1) <- artists(?x11737, ?x11906), artists(?x11737, ?x7407), artists(?x11737, ?x2538), award_nominee(?x7407, ?x5310), ?x2538 = 01x1cn2, award(?x7407, ?x567), group(?x227, ?x11906), award(?x11906, ?x4892) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4999 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 0fxgg9; *> query: (?x11737, 06gcn) <- artists(?x11737, ?x7407), artists(?x11737, ?x4620), artists(?x11737, ?x3773), award_winner(?x2855, ?x7407), award(?x7407, ?x567), ?x3773 = 01rm8b, artist(?x3265, ?x4620), origin(?x4620, ?x14311) *> conf = 0.20 ranks of expected_values: 599 EVAL 01b4p4 artists 06gcn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 63.000 31.000 0.714 http://example.org/music/genre/artists #12916-0ndsl1x PRED entity: 0ndsl1x PRED relation: genre PRED expected values: 07s9rl0 => 88 concepts (77 used for prediction) PRED predicted values (max 10 best out of 94): 07s9rl0 (0.85 #6866, 0.81 #5421, 0.66 #2407), 01z4y (0.61 #7346, 0.53 #5300, 0.52 #8308), 03k9fj (0.58 #132, 0.50 #12, 0.47 #252), 02kdv5l (0.51 #243, 0.47 #123, 0.35 #1085), 01jfsb (0.42 #253, 0.33 #1575, 0.32 #2782), 06n90 (0.33 #254, 0.26 #134, 0.21 #1096), 01hmnh (0.26 #1099, 0.22 #978, 0.21 #137), 06cvj (0.24 #2410, 0.12 #4, 0.12 #1807), 0lsxr (0.21 #2295, 0.19 #249, 0.18 #2175), 0hfjk (0.19 #63, 0.03 #1265, 0.03 #1987) >> Best rule #6866 for best value: >> intensional similarity = 3 >> extensional distance = 1188 >> proper extension: 0c0wvx; >> query: (?x9002, 07s9rl0) <- genre(?x9002, ?x1403), genre(?x5169, ?x1403), ?x5169 = 04jm_hq >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ndsl1x genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 77.000 0.845 http://example.org/film/film/genre #12915-01wcp_g PRED entity: 01wcp_g PRED relation: award_nominee PRED expected values: 01w9wwg => 122 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1103): 015mrk (0.83 #18712, 0.82 #37422, 0.82 #72501), 01w9wwg (0.83 #18712, 0.82 #37422, 0.82 #72501), 01w7nwm (0.83 #18712, 0.82 #37422, 0.82 #72501), 016732 (0.43 #1553, 0.01 #38975, 0.01 #45991), 01s1zk (0.29 #1709, 0.02 #39131, 0.02 #46147), 02pt7h_ (0.29 #1558, 0.01 #38980), 02qwg (0.14 #766, 0.06 #28833, 0.06 #33510), 01vvyvk (0.14 #1055, 0.03 #100568, 0.03 #114604), 01w272y (0.14 #762, 0.03 #100568, 0.03 #114604), 01wj18h (0.14 #719, 0.03 #3059, 0.02 #5398) >> Best rule #18712 for best value: >> intensional similarity = 3 >> extensional distance = 170 >> proper extension: 028q6; 0197tq; 0lbj1; 0hl3d; 032nwy; 026ps1; 03f2_rc; 0146pg; 0168cl; 025xt8y; ... >> query: (?x1378, ?x827) <- award_nominee(?x827, ?x1378), student(?x4672, ?x1378), artists(?x671, ?x1378) >> conf = 0.83 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01wcp_g award_nominee 01w9wwg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 52.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #12914-01k1k4 PRED entity: 01k1k4 PRED relation: executive_produced_by PRED expected values: 04q5zw => 92 concepts (48 used for prediction) PRED predicted values (max 10 best out of 47): 018grr (0.09 #8370, 0.02 #8369, 0.01 #9384), 0p__8 (0.08 #5331, 0.07 #1269, 0.06 #3809), 079vf (0.06 #2, 0.05 #511, 0.04 #256), 06q8hf (0.05 #167, 0.05 #4989, 0.04 #5498), 06pj8 (0.05 #55, 0.04 #564, 0.03 #309), 05hj_k (0.05 #859, 0.05 #98, 0.04 #5429), 02xnjd (0.04 #176, 0.02 #685, 0.02 #430), 0glyyw (0.03 #1458, 0.03 #443, 0.03 #5520), 03c9pqt (0.03 #6086, 0.03 #7095, 0.02 #6338), 016vg8 (0.03 #1522, 0.02 #11419, 0.02 #10147) >> Best rule #8370 for best value: >> intensional similarity = 3 >> extensional distance = 619 >> proper extension: 02gqm3; >> query: (?x408, ?x2101) <- genre(?x408, ?x225), film(?x2101, ?x408), executive_produced_by(?x750, ?x2101) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #1096 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 246 *> proper extension: 0g56t9t; 02y_lrp; 01br2w; 02v8kmz; 09m6kg; 03g90h; 011yxg; 0dnvn3; 0ds11z; 0ds33; ... *> query: (?x408, 04q5zw) <- music(?x408, ?x7856), written_by(?x408, ?x5940), film_crew_role(?x408, ?x137) *> conf = 0.02 ranks of expected_values: 32 EVAL 01k1k4 executive_produced_by 04q5zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 92.000 48.000 0.088 http://example.org/film/film/executive_produced_by #12913-0jgd PRED entity: 0jgd PRED relation: olympics PRED expected values: 0l98s 0lk8j 0l6mp 0jdk_ => 216 concepts (216 used for prediction) PRED predicted values (max 10 best out of 35): 0l6m5 (0.80 #287, 0.69 #504, 0.65 #380), 06sks6 (0.79 #80, 0.75 #763, 0.74 #390), 0jdk_ (0.72 #1045, 0.71 #206, 0.69 #516), 018ctl (0.71 #68, 0.60 #285, 0.54 #1741), 0l6ny (0.67 #224, 0.65 #503, 0.62 #752), 0l6mp (0.62 #758, 0.60 #292, 0.58 #509), 0jkvj (0.57 #89, 0.57 #399, 0.50 #772), 09n48 (0.57 #65, 0.54 #1741, 0.50 #282), 0swff (0.57 #78, 0.40 #295, 0.35 #1151), 0blfl (0.54 #1741, 0.48 #3266, 0.41 #4106) >> Best rule #287 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 05kr_; >> query: (?x142, 0l6m5) <- adjoins(?x583, ?x142), contains(?x142, ?x7661), film_release_region(?x886, ?x142) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1045 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 03rjj; 0d0vqn; 0j1z8; 04gzd; 047lj; 07ssc; 06npd; 03gj2; 035qy; 06c1y; ... *> query: (?x142, 0jdk_) <- country(?x471, ?x142), film_release_region(?x3784, ?x142), olympics(?x142, ?x775), ?x3784 = 0bmhvpr *> conf = 0.72 ranks of expected_values: 3, 6, 13, 15 EVAL 0jgd olympics 0jdk_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 216.000 216.000 0.800 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jgd olympics 0l6mp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 216.000 216.000 0.800 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jgd olympics 0lk8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 216.000 216.000 0.800 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jgd olympics 0l98s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 216.000 216.000 0.800 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #12912-03_qrp PRED entity: 03_qrp PRED relation: current_club PRED expected values: 02_lt 03yfh3 => 93 concepts (86 used for prediction) PRED predicted values (max 10 best out of 740): 0xbm (0.60 #308, 0.50 #163, 0.38 #888), 0y54 (0.50 #152, 0.40 #297, 0.36 #1167), 04ltf (0.50 #214, 0.40 #359, 0.33 #69), 0cttx (0.50 #272, 0.40 #417, 0.33 #127), 0266bd5 (0.33 #115, 0.29 #695, 0.25 #985), 045xx (0.33 #65, 0.29 #645, 0.25 #935), 01rly6 (0.33 #103, 0.25 #248, 0.20 #393), 01453 (0.33 #1, 0.25 #146, 0.20 #291), 06ls0l (0.33 #52, 0.25 #197, 0.20 #342), 0466hh (0.33 #124, 0.25 #269, 0.20 #414) >> Best rule #308 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 01l3vx; >> query: (?x8102, 0xbm) <- team(?x530, ?x8102), team(?x60, ?x8102), current_club(?x8102, ?x9860), current_club(?x8102, ?x1664), ?x60 = 02nzb8, team(?x1142, ?x8102), teams(?x2051, ?x8102), ?x530 = 02_j1w, category(?x1664, ?x134), colors(?x9860, ?x663), sport(?x9860, ?x471) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #627 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 03yl2t; *> query: (?x8102, 02_lt) <- team(?x530, ?x8102), team(?x63, ?x8102), team(?x60, ?x8102), current_club(?x8102, ?x1100), ?x60 = 02nzb8, team(?x1142, ?x8102), teams(?x2051, ?x8102), ?x530 = 02_j1w, participating_countries(?x784, ?x2051), ?x63 = 02sdk9v, adjoins(?x2051, ?x6431) *> conf = 0.14 ranks of expected_values: 67, 86 EVAL 03_qrp current_club 03yfh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 93.000 86.000 0.600 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club EVAL 03_qrp current_club 02_lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 93.000 86.000 0.600 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #12911-0rh6k PRED entity: 0rh6k PRED relation: place_founded! PRED expected values: 015_1q => 219 concepts (219 used for prediction) PRED predicted values (max 10 best out of 141): 01f9wm (0.17 #190, 0.11 #410, 0.10 #521), 04htfd (0.15 #809, 0.12 #257, 0.06 #3017), 06nfl (0.11 #440, 0.09 #771, 0.07 #992), 07rfp (0.11 #433, 0.09 #764, 0.07 #985), 0260p2 (0.11 #429, 0.09 #760, 0.07 #981), 06zl7g (0.11 #428, 0.09 #759, 0.07 #980), 05b0f7 (0.11 #416, 0.09 #747, 0.07 #968), 01bvx1 (0.11 #412, 0.09 #743, 0.07 #964), 01qckn (0.11 #390, 0.09 #721, 0.07 #942), 01dycg (0.11 #383, 0.09 #714, 0.07 #935) >> Best rule #190 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 070zc; >> query: (?x108, 01f9wm) <- locations(?x6583, ?x108), state_province_region(?x3228, ?x108), adjoins(?x1426, ?x108) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0rh6k place_founded! 015_1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 219.000 219.000 0.167 http://example.org/organization/organization/place_founded #12910-022q32 PRED entity: 022q32 PRED relation: student! PRED expected values: 07vyf => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 161): 05nrkb (0.14 #349, 0.11 #876, 0.06 #4039), 0bwfn (0.12 #2383, 0.07 #9762, 0.06 #19775), 04b_46 (0.11 #754, 0.03 #37645, 0.03 #35537), 065y4w7 (0.09 #1595, 0.08 #2650, 0.05 #37959), 09f2j (0.09 #1740, 0.06 #13335, 0.06 #5957), 0cwx_ (0.08 #2349, 0.03 #3404, 0.03 #9728), 015nl4 (0.08 #2703, 0.03 #3230, 0.03 #4284), 026gvfj (0.07 #20138, 0.05 #18557, 0.05 #8017), 03ksy (0.06 #1160, 0.04 #2214, 0.03 #3269), 0k__z (0.06 #1362, 0.04 #2944, 0.03 #3471) >> Best rule #349 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02jyhv; >> query: (?x10777, 05nrkb) <- participant(?x10777, ?x8793), ?x8793 = 0227vl, gender(?x10777, ?x514), participant(?x6328, ?x10777) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 022q32 student! 07vyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 144.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #12909-042xrr PRED entity: 042xrr PRED relation: award_nominee PRED expected values: 0163r3 => 107 concepts (62 used for prediction) PRED predicted values (max 10 best out of 951): 09fb5 (0.81 #49047, 0.81 #21020, 0.81 #49046), 01vw37m (0.81 #49047, 0.81 #21020, 0.81 #49046), 046m59 (0.50 #3617, 0.19 #65397, 0.19 #51384), 02sjf5 (0.42 #2582, 0.19 #65397, 0.19 #51384), 016kft (0.42 #4335, 0.19 #65397, 0.19 #51384), 01tfck (0.33 #2803, 0.19 #65397, 0.19 #51384), 02x7vq (0.25 #3633, 0.19 #65397, 0.19 #51384), 042xrr (0.19 #65397, 0.19 #51384, 0.18 #49048), 03q5dr (0.19 #65397, 0.19 #51384, 0.18 #49048), 04cf09 (0.19 #65397, 0.19 #51384, 0.17 #2583) >> Best rule #49047 for best value: >> intensional similarity = 3 >> extensional distance = 592 >> proper extension: 076df9; >> query: (?x4606, ?x2422) <- award_nominee(?x2422, ?x4606), actor(?x2042, ?x4606), award_nominee(?x192, ?x2422) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #49048 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 592 *> proper extension: 076df9; *> query: (?x4606, ?x192) <- award_nominee(?x2422, ?x4606), actor(?x2042, ?x4606), award_nominee(?x192, ?x2422) *> conf = 0.18 ranks of expected_values: 99 EVAL 042xrr award_nominee 0163r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 107.000 62.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #12908-0d234 PRED entity: 0d234 PRED relation: citytown! PRED expected values: 01hx2t => 141 concepts (29 used for prediction) PRED predicted values (max 10 best out of 527): 02sjgpq (0.08 #16998, 0.04 #15378, 0.02 #9255), 01hx2t (0.08 #16998, 0.04 #15378), 08qnnv (0.08 #16998, 0.04 #15378), 015zyd (0.08 #16998, 0.04 #15378), 02897w (0.08 #1003, 0.05 #1812, 0.05 #2621), 01nds (0.07 #10288, 0.07 #11097, 0.06 #12717), 03_c8p (0.05 #10287, 0.05 #11096, 0.05 #12716), 01gwck (0.05 #2308, 0.05 #3117, 0.04 #4735), 025rcc (0.05 #1882, 0.05 #2691, 0.04 #4309), 06bw5 (0.05 #1871, 0.05 #2680, 0.04 #4298) >> Best rule #16998 for best value: >> intensional similarity = 5 >> extensional distance = 82 >> proper extension: 09c7w0; 0d060g; 07ssc; 05v8c; 02jx1; 035qy; 0h7x; 0d0kn; 06mkj; 013yq; ... >> query: (?x2622, ?x99) <- locations(?x4803, ?x2622), contains(?x726, ?x2622), location(?x2295, ?x2622), nationality(?x2295, ?x94), student(?x99, ?x2295) >> conf = 0.08 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0d234 citytown! 01hx2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 29.000 0.081 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #12907-094tsh6 PRED entity: 094tsh6 PRED relation: crewmember! PRED expected values: 07y9w5 => 103 concepts (55 used for prediction) PRED predicted values (max 10 best out of 317): 0gfsq9 (0.39 #1865, 0.37 #1864, 0.14 #311), 051zy_b (0.39 #1865, 0.37 #1864, 0.14 #311), 031778 (0.39 #1865, 0.37 #1864, 0.14 #311), 027r9t (0.39 #1865, 0.37 #1864, 0.14 #311), 0sxfd (0.39 #1865, 0.37 #1864, 0.14 #311), 0yyg4 (0.39 #1865, 0.37 #1864, 0.14 #311), 047vnkj (0.37 #1864, 0.14 #311, 0.10 #312), 06gb1w (0.37 #1864, 0.14 #311, 0.10 #312), 02_nsc (0.37 #1864, 0.14 #311, 0.10 #312), 018js4 (0.37 #1864, 0.14 #311, 0.10 #312) >> Best rule #1865 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 076lxv; 027rwmr; 03h26tm; 021yc7p; 09rp4r_; 09pjnd; 0c94fn; 04ktcgn; 0gl88b; 07h1tr; ... >> query: (?x9391, ?x1402) <- nominated_for(?x9391, ?x1402), currency(?x1402, ?x170), award(?x1402, ?x289), crewmember(?x392, ?x9391) >> conf = 0.39 => this is the best rule for 6 predicted values *> Best rule #4356 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 440 *> proper extension: 0g5ff; *> query: (?x9391, ?x485) <- award_winner(?x637, ?x9391), award(?x3782, ?x637), crewmember(?x485, ?x3782) *> conf = 0.06 ranks of expected_values: 157 EVAL 094tsh6 crewmember! 07y9w5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 103.000 55.000 0.389 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #12906-08849 PRED entity: 08849 PRED relation: place_of_birth PRED expected values: 01w2v => 143 concepts (118 used for prediction) PRED predicted values (max 10 best out of 110): 013g3 (0.33 #1096, 0.03 #19421), 0f2nf (0.20 #2462, 0.09 #5280, 0.07 #8096), 0lphb (0.20 #2371, 0.09 #5189, 0.02 #21401), 05qtj (0.20 #3691, 0.09 #5100, 0.01 #46018), 06c62 (0.20 #3076, 0.05 #12235), 0dlv0 (0.17 #7399, 0.05 #19384, 0.02 #44092), 0cr3d (0.12 #28995, 0.12 #17714, 0.11 #31819), 04vmp (0.11 #19298, 0.08 #7313, 0.04 #44006), 02m77 (0.10 #12934, 0.07 #8001, 0.07 #9410), 02_286 (0.09 #4952, 0.09 #47278, 0.08 #53616) >> Best rule #1096 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 019_1h; >> query: (?x11617, 013g3) <- award_winner(?x10552, ?x11617), student(?x11229, ?x11617), ?x11229 = 02w6bq, religion(?x11617, ?x492), gender(?x11617, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08849 place_of_birth 01w2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 118.000 0.333 http://example.org/people/person/place_of_birth #12905-0pz7h PRED entity: 0pz7h PRED relation: actor! PRED expected values: 02q3fdr => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 5): 02q3fdr (0.04 #214, 0.04 #280, 0.02 #445), 0b60sq (0.03 #299), 016ztl (0.02 #479, 0.01 #2270), 02gs6r (0.02 #474, 0.01 #1037), 05pyrb (0.01 #1040) >> Best rule #214 for best value: >> intensional similarity = 2 >> extensional distance = 22 >> proper extension: 065y4w7; >> query: (?x906, 02q3fdr) <- award_winner(?x2016, ?x906), list(?x906, ?x5160) >> conf = 0.04 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pz7h actor! 02q3fdr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.042 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #12904-0fq9zdn PRED entity: 0fq9zdn PRED relation: nominated_for PRED expected values: 0g9wdmc 0djkrp 0crs0b8 => 46 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1448): 0m313 (0.73 #9464, 0.57 #6311, 0.50 #11038), 049xgc (0.68 #10320, 0.43 #7167, 0.38 #11894), 04qw17 (0.68 #31510, 0.65 #25200, 0.65 #25199), 05c46y6 (0.67 #5112, 0.62 #8264, 0.33 #1963), 0ds3t5x (0.67 #4771, 0.38 #7923, 0.33 #1622), 0bnzd (0.67 #5799, 0.38 #8951, 0.33 #2650), 09gq0x5 (0.64 #9703, 0.57 #6550, 0.50 #3400), 011yl_ (0.64 #9976, 0.38 #11550, 0.25 #3673), 011yph (0.64 #9533, 0.28 #11107, 0.25 #3230), 0gmcwlb (0.59 #9631, 0.47 #11205, 0.43 #6478) >> Best rule #9464 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 099c8n; >> query: (?x941, 0m313) <- nominated_for(?x941, ?x7982), nominated_for(?x941, ?x1228), film_release_region(?x1228, ?x87), ?x7982 = 016mhd >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #4968 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 09qwmm; 09sb52; 02z0dfh; *> query: (?x941, 0g9wdmc) <- award(?x8702, ?x941), nominated_for(?x941, ?x695), ?x8702 = 013zs9, nominated_for(?x695, ?x696) *> conf = 0.33 ranks of expected_values: 112, 354, 748 EVAL 0fq9zdn nominated_for 0crs0b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 24.000 0.727 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fq9zdn nominated_for 0djkrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 24.000 0.727 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fq9zdn nominated_for 0g9wdmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 46.000 24.000 0.727 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12903-0677ng PRED entity: 0677ng PRED relation: artists! PRED expected values: 0glt670 05bt6j => 130 concepts (127 used for prediction) PRED predicted values (max 10 best out of 207): 064t9 (0.74 #326, 0.57 #2817, 0.54 #637), 0glt670 (0.65 #355, 0.41 #2224, 0.36 #4402), 025sc50 (0.61 #365, 0.36 #2234, 0.30 #4412), 06by7 (0.49 #4070, 0.46 #2204, 0.45 #11236), 016clz (0.44 #2186, 0.27 #939, 0.25 #1562), 06j6l (0.35 #363, 0.30 #2854, 0.30 #674), 05bt6j (0.30 #46, 0.25 #2849, 0.24 #980), 0xhtw (0.27 #952, 0.25 #18, 0.23 #1887), 01lyv (0.27 #659, 0.24 #1282, 0.21 #8133), 05w3f (0.25 #40, 0.19 #974, 0.16 #1909) >> Best rule #326 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 05mxw33; >> query: (?x7259, 064t9) <- award_nominee(?x4476, ?x7259), profession(?x7259, ?x131), ?x4476 = 01vw20h >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #355 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 05mxw33; *> query: (?x7259, 0glt670) <- award_nominee(?x4476, ?x7259), profession(?x7259, ?x131), ?x4476 = 01vw20h *> conf = 0.65 ranks of expected_values: 2, 7 EVAL 0677ng artists! 05bt6j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 130.000 127.000 0.739 http://example.org/music/genre/artists EVAL 0677ng artists! 0glt670 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 127.000 0.739 http://example.org/music/genre/artists #12902-0584r4 PRED entity: 0584r4 PRED relation: languages PRED expected values: 02h40lc => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.91 #277, 0.91 #211, 0.91 #233), 0t_2 (0.13 #28, 0.12 #17, 0.11 #353), 03_9r (0.11 #353, 0.08 #114, 0.05 #390), 06nm1 (0.11 #353, 0.08 #115, 0.03 #104), 064_8sq (0.11 #353, 0.05 #117, 0.02 #194), 02bv9 (0.11 #353, 0.03 #119, 0.02 #108), 04306rv (0.11 #353, 0.03 #113, 0.02 #102), 02bjrlw (0.11 #353, 0.03 #111, 0.02 #100), 05zjd (0.11 #353, 0.02 #118), 07qv_ (0.11 #353) >> Best rule #277 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 05r1_t; 02wyzmv; 03y317; >> query: (?x1876, 02h40lc) <- genre(?x1876, ?x258), program(?x7169, ?x1876), titles(?x2008, ?x1876) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0584r4 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.910 http://example.org/tv/tv_program/languages #12901-016jfw PRED entity: 016jfw PRED relation: artist! PRED expected values: 01clyr => 86 concepts (54 used for prediction) PRED predicted values (max 10 best out of 96): 015_1q (0.19 #1582, 0.18 #162, 0.18 #3293), 03rhqg (0.14 #868, 0.14 #1294, 0.13 #1436), 0g768 (0.14 #464, 0.11 #1600, 0.10 #4882), 011k1h (0.12 #1572, 0.11 #1999, 0.10 #3283), 0181dw (0.12 #2032, 0.12 #1605, 0.11 #469), 033hn8 (0.11 #2003, 0.11 #1576, 0.10 #3287), 0fb0v (0.10 #149, 0.08 #1143, 0.08 #1285), 03mp8k (0.10 #1630, 0.10 #210, 0.09 #2057), 017l96 (0.10 #1581, 0.10 #161, 0.09 #871), 043g7l (0.10 #1594, 0.08 #2021, 0.08 #884) >> Best rule #1582 for best value: >> intensional similarity = 3 >> extensional distance = 294 >> proper extension: 01pfr3; 01v0sx2; 03t9sp; 01fl3; 03fbc; 016fmf; 0dm5l; 018ndc; 01rm8b; 0hvbj; ... >> query: (?x6129, 015_1q) <- award(?x6129, ?x1232), artists(?x671, ?x6129), ?x671 = 064t9 >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1170 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 238 *> proper extension: 01pbxb; 0f0y8; 028q6; 03c7ln; 0lbj1; 01vw87c; 0fp_v1x; 0m2l9; 01wl38s; 032t2z; ... *> query: (?x6129, 01clyr) <- nationality(?x6129, ?x512), artists(?x671, ?x6129), role(?x6129, ?x75) *> conf = 0.09 ranks of expected_values: 14 EVAL 016jfw artist! 01clyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 86.000 54.000 0.186 http://example.org/music/record_label/artist #12900-0qf3p PRED entity: 0qf3p PRED relation: type_of_union PRED expected values: 01g63y => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 3): 01g63y (0.29 #16, 0.28 #286, 0.27 #277), 0jgjn (0.07 #21, 0.03 #54, 0.02 #141), 01bl8s (0.01 #86) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 03m6pk; >> query: (?x2600, 01g63y) <- role(?x2600, ?x1166), award(?x2600, ?x4382), people(?x743, ?x2600), participant(?x2600, ?x3382) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qf3p type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.286 http://example.org/people/person/spouse_s./people/marriage/type_of_union #12899-073h5b PRED entity: 073h5b PRED relation: ceremony! PRED expected values: 0f4x7 0gr0m 0gqxm => 37 concepts (35 used for prediction) PRED predicted values (max 10 best out of 342): 0f4x7 (0.90 #3182, 0.88 #5119, 0.88 #2699), 0gr0m (0.86 #3214, 0.83 #4671, 0.83 #4186), 0gqxm (0.62 #3281, 0.53 #2311, 0.50 #2798), 04njml (0.37 #1949, 0.35 #1461, 0.28 #1217), 0c4z8 (0.37 #1949, 0.35 #1461, 0.28 #1217), 054krc (0.25 #4378, 0.24 #2925, 0.23 #4135), 02h3d1 (0.25 #4378, 0.24 #2925, 0.22 #2193), 025m8y (0.25 #4378, 0.24 #2925, 0.21 #4619), 054ks3 (0.25 #4378, 0.24 #2925, 0.21 #4619), 05q8pss (0.25 #4378, 0.24 #2925, 0.21 #4619) >> Best rule #3182 for best value: >> intensional similarity = 20 >> extensional distance = 27 >> proper extension: 02yw5r; 073h1t; 050yyb; 0bzkgg; 02ywhz; 073hd1; 09306z; 04110lv; >> query: (?x11087, 0f4x7) <- award_winner(?x11087, ?x1894), ceremony(?x4573, ?x11087), ceremony(?x3066, ?x11087), ceremony(?x1703, ?x11087), ceremony(?x1323, ?x11087), ?x1703 = 0k611, ?x3066 = 0gqy2, ?x4573 = 0gq_d, honored_for(?x11087, ?x7741), award_winner(?x1443, ?x1894), award(?x1894, ?x2379), award_winner(?x3732, ?x1894), ?x1323 = 0gqz2, nominated_for(?x2379, ?x4359), nominated_for(?x2379, ?x3573), nominated_for(?x2379, ?x3012), ?x3012 = 0ggbhy7, ?x4359 = 0g9lm2, ?x3573 = 011yl_, language(?x7741, ?x90) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 073h5b ceremony! 0gqxm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 35.000 0.897 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073h5b ceremony! 0gr0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 35.000 0.897 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073h5b ceremony! 0f4x7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 35.000 0.897 http://example.org/award/award_category/winners./award/award_honor/ceremony #12898-07gqbk PRED entity: 07gqbk PRED relation: artist PRED expected values: 01shhf => 152 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1093): 020_4z (0.50 #6578, 0.33 #15762, 0.33 #9085), 019g40 (0.50 #5944, 0.33 #15128, 0.31 #18467), 01vtj38 (0.50 #5533, 0.33 #6368, 0.29 #7204), 0knhk (0.50 #5574, 0.25 #26442, 0.23 #34785), 0178kd (0.50 #5456, 0.25 #26324, 0.22 #8798), 07hgm (0.50 #5699, 0.22 #9041, 0.17 #15718), 01ttg5 (0.50 #5277, 0.22 #8619, 0.17 #15296), 0167km (0.43 #7097, 0.33 #6261, 0.25 #26294), 0565cz (0.43 #6869, 0.31 #26066, 0.22 #29403), 01ww2fs (0.43 #6808, 0.12 #26005, 0.07 #49365) >> Best rule #6578 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 015_1q; 073tm9; >> query: (?x12752, 020_4z) <- citytown(?x12752, ?x5076), artist(?x12752, ?x646), industry(?x12752, ?x2271), award_winner(?x646, ?x9408), award(?x646, ?x2634), group(?x3024, ?x646), category(?x646, ?x134) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #30881 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 02y21l; *> query: (?x12752, ?x250) <- artist(?x12752, ?x12753), organization(?x4682, ?x12752), artists(?x3562, ?x12753), category(?x12752, ?x134), film(?x12753, ?x6839), artists(?x3562, ?x250) *> conf = 0.06 ranks of expected_values: 809 EVAL 07gqbk artist 01shhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 152.000 68.000 0.500 http://example.org/music/record_label/artist #12897-01wg3q PRED entity: 01wg3q PRED relation: gender PRED expected values: 05zppz => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.93 #9, 0.89 #1, 0.88 #43), 02zsn (0.46 #233, 0.46 #252, 0.28 #26) >> Best rule #9 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 012zng; 01vv6_6; 01w8n89; 0132k4; 02l_7y; 018d6l; 021r7r; 0326tc; 01k47c; 023322; ... >> query: (?x8754, 05zppz) <- category(?x8754, ?x134), artists(?x6210, ?x8754), role(?x8754, ?x227), ?x6210 = 01fh36, profession(?x8754, ?x131) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wg3q gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.931 http://example.org/people/person/gender #12896-0fbvqf PRED entity: 0fbvqf PRED relation: award! PRED expected values: 0f0p0 => 34 concepts (13 used for prediction) PRED predicted values (max 10 best out of 2731): 0jmj (0.70 #26633, 0.68 #36626, 0.68 #29963), 0hvb2 (0.50 #3796, 0.43 #7126, 0.33 #468), 01yk13 (0.50 #3518, 0.33 #190, 0.14 #6848), 021vwt (0.50 #3744, 0.33 #416, 0.13 #29964), 048lv (0.50 #3655, 0.33 #327, 0.12 #39956), 014gf8 (0.50 #4971, 0.33 #1643, 0.12 #39956), 015grj (0.50 #3543, 0.33 #215, 0.12 #43288), 0l786 (0.50 #5379, 0.33 #2051, 0.08 #12037), 02ldv0 (0.50 #5198, 0.33 #1870, 0.07 #43286), 0prfz (0.50 #3396, 0.33 #68, 0.07 #43286) >> Best rule #26633 for best value: >> intensional similarity = 4 >> extensional distance = 164 >> proper extension: 05qck; 02qkk9_; 02py7pj; 02kgb7; >> query: (?x783, ?x190) <- award_winner(?x783, ?x7048), award_winner(?x783, ?x190), award(?x7048, ?x704), diet(?x7048, ?x3130) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fbvqf award! 0f0p0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 13.000 0.696 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12895-09zzb8 PRED entity: 09zzb8 PRED relation: profession! PRED expected values: 0b79gfg => 54 concepts (30 used for prediction) PRED predicted values (max 10 best out of 4171): 05wm88 (0.50 #25044, 0.41 #71733, 0.32 #75977), 021yw7 (0.50 #22331, 0.41 #69020, 0.27 #73264), 01_x6v (0.50 #21902, 0.41 #68591, 0.27 #72835), 026dx (0.50 #22735, 0.41 #69424, 0.24 #73668), 02b29 (0.50 #23465, 0.41 #70154, 0.24 #74398), 015pxr (0.50 #21826, 0.41 #68515, 0.24 #72759), 0mdqp (0.50 #21411, 0.41 #68100, 0.22 #72344), 015njf (0.50 #22771, 0.41 #69460, 0.20 #90682), 0bxtg (0.50 #21336, 0.41 #68025, 0.19 #72269), 06pj8 (0.50 #21822, 0.38 #93378, 0.24 #68511) >> Best rule #25044 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 02hrh1q; >> query: (?x137, 05wm88) <- profession(?x2871, ?x137), ?x2871 = 03r1pr >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09zzb8 profession! 0b79gfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 30.000 0.500 http://example.org/people/person/profession #12894-05ftw3 PRED entity: 05ftw3 PRED relation: state_province_region PRED expected values: 065zr => 94 concepts (69 used for prediction) PRED predicted values (max 10 best out of 84): 065zr (0.33 #36, 0.28 #2593, 0.25 #5698), 05sb1 (0.30 #4705, 0.28 #2468, 0.28 #2593), 0xnt5 (0.28 #2468, 0.25 #2841, 0.25 #2842), 05kr_ (0.25 #153, 0.12 #522, 0.09 #768), 059rby (0.20 #3839, 0.14 #5452, 0.13 #6318), 01n7q (0.17 #3853, 0.14 #5466, 0.12 #6332), 01w0v (0.14 #909, 0.12 #1155, 0.11 #1032), 0j0k (0.12 #2843, 0.06 #8441), 02qkt (0.12 #2843, 0.06 #8441), 0jt5zcn (0.10 #1513, 0.08 #1266, 0.06 #1019) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03x83_; >> query: (?x9150, 065zr) <- institution(?x1368, ?x9150), ?x1368 = 014mlp, contains(?x7593, ?x9150), ?x7593 = 0xnt5 >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05ftw3 state_province_region 065zr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 69.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #12893-0fq27fp PRED entity: 0fq27fp PRED relation: film_regional_debut_venue PRED expected values: 018cvf => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 30): 0prpt (0.53 #288, 0.23 #1160, 0.22 #1421), 015hr (0.33 #274, 0.19 #1477, 0.19 #1407), 0gg7gsl (0.30 #765, 0.30 #764, 0.29 #1064), 0hr30wt (0.30 #765, 0.30 #764, 0.29 #1064), 018cvf (0.30 #1409, 0.29 #1546, 0.29 #1579), 09rwjly (0.12 #152, 0.08 #249, 0.03 #482), 0kfhjq0 (0.12 #746, 0.10 #979, 0.10 #1045), 0j63cyr (0.11 #778, 0.10 #607, 0.09 #1110), 02_286 (0.09 #768, 0.08 #1100, 0.07 #1396), 04_m9gk (0.08 #251, 0.07 #755, 0.06 #953) >> Best rule #288 for best value: >> intensional similarity = 8 >> extensional distance = 13 >> proper extension: 01kqq7; >> query: (?x622, 0prpt) <- film_regional_debut_venue(?x622, ?x3288), genre(?x622, ?x1403), film_regional_debut_venue(?x6492, ?x3288), film_regional_debut_venue(?x1283, ?x3288), ?x1403 = 02l7c8, ?x1283 = 0cnztc4, film_release_region(?x6492, ?x1003), ?x1003 = 03gj2 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1409 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 147 *> proper extension: 0bmc4cm; 0gffmn8; 03nqnnk; *> query: (?x622, 018cvf) <- film_regional_debut_venue(?x622, ?x3288), film_release_region(?x622, ?x985), film_release_region(?x622, ?x304), genre(?x622, ?x53), ?x304 = 0d0vqn, film_release_region(?x10095, ?x985), olympics(?x985, ?x391), ?x10095 = 0267wwv, contains(?x985, ?x8174) *> conf = 0.30 ranks of expected_values: 5 EVAL 0fq27fp film_regional_debut_venue 018cvf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 110.000 110.000 0.533 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #12892-01jw4r PRED entity: 01jw4r PRED relation: award PRED expected values: 03c7tr1 => 110 concepts (69 used for prediction) PRED predicted values (max 10 best out of 255): 027571b (0.72 #24791, 0.71 #23213, 0.71 #24790), 09cn0c (0.72 #24791, 0.71 #23213, 0.71 #24790), 02y_j8g (0.72 #24791, 0.71 #23213, 0.71 #24790), 02z1nbg (0.72 #24791, 0.71 #23213, 0.71 #24790), 094qd5 (0.49 #829, 0.20 #43, 0.13 #2401), 09qwmm (0.34 #820, 0.11 #2392, 0.10 #34), 0bfvw2 (0.26 #801, 0.13 #22818, 0.12 #408), 05pcn59 (0.25 #2827, 0.23 #5974, 0.23 #3613), 0ck27z (0.24 #1659, 0.21 #10701, 0.18 #480), 099cng (0.24 #867, 0.10 #81, 0.09 #2439) >> Best rule #24791 for best value: >> intensional similarity = 3 >> extensional distance = 1563 >> proper extension: 04cy8rb; >> query: (?x8612, ?x1245) <- award_nominee(?x949, ?x8612), award_winner(?x1245, ?x8612), award(?x241, ?x1245) >> conf = 0.72 => this is the best rule for 4 predicted values *> Best rule #2808 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 231 *> proper extension: 01sl1q; 04bdxl; 05bnp0; 06dv3; 014zcr; 0m2wm; 0prfz; 01q_ph; 09fb5; 01dw4q; ... *> query: (?x8612, 03c7tr1) <- award_nominee(?x949, ?x8612), participant(?x703, ?x8612), participant(?x9782, ?x8612) *> conf = 0.15 ranks of expected_values: 41 EVAL 01jw4r award 03c7tr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 110.000 69.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12891-0k1bs PRED entity: 0k1bs PRED relation: profession PRED expected values: 09jwl => 145 concepts (124 used for prediction) PRED predicted values (max 10 best out of 92): 09jwl (0.90 #1931, 0.84 #3696, 0.82 #2813), 02hrh1q (0.84 #5017, 0.83 #1190, 0.78 #6049), 016z4k (0.62 #592, 0.53 #2357, 0.51 #2798), 0dz3r (0.56 #10907, 0.55 #11054, 0.53 #1914), 0cbd2 (0.55 #6927, 0.46 #8847, 0.45 #8700), 0dxtg (0.55 #5016, 0.42 #3249, 0.40 #8853), 01c72t (0.47 #11223, 0.42 #1788, 0.36 #318), 02hv44_ (0.45 #2997, 0.15 #6976, 0.11 #3586), 01d_h8 (0.42 #5009, 0.35 #3242, 0.34 #1182), 0kyk (0.37 #6950, 0.35 #2971, 0.32 #3560) >> Best rule #1931 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 0pgjm; >> query: (?x6456, 09jwl) <- profession(?x6456, ?x2659), nationality(?x6456, ?x94), ?x2659 = 039v1, group(?x6456, ?x4642) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k1bs profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 124.000 0.897 http://example.org/people/person/profession #12890-02_l39 PRED entity: 02_l39 PRED relation: child PRED expected values: 024rbz => 211 concepts (211 used for prediction) PRED predicted values (max 10 best out of 309): 02_l39 (0.47 #18991, 0.44 #22600, 0.33 #101), 0dwcl (0.47 #18991, 0.44 #22600, 0.25 #461), 01jx9 (0.47 #18991, 0.44 #22600, 0.25 #371), 025txrl (0.47 #18991, 0.44 #22600, 0.25 #443), 09j_g (0.47 #18991, 0.44 #22600, 0.25 #374), 01dtcb (0.47 #18991, 0.44 #22600, 0.17 #721), 03d6fyn (0.47 #18991, 0.44 #22600, 0.08 #3640), 031rq5 (0.29 #3810, 0.29 #1195, 0.27 #5116), 07y2b (0.29 #1440, 0.25 #1603, 0.15 #3729), 017s11 (0.29 #1148, 0.20 #2454, 0.20 #493) >> Best rule #18991 for best value: >> intensional similarity = 5 >> extensional distance = 62 >> proper extension: 0136kr; 0f1r9; >> query: (?x10957, ?x4619) <- child(?x10957, ?x13197), child(?x10957, ?x1104), category(?x13197, ?x134), child(?x11468, ?x1104), child(?x11468, ?x4619) >> conf = 0.47 => this is the best rule for 7 predicted values *> Best rule #664 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 04fc6c; *> query: (?x10957, 024rbz) <- child(?x10957, ?x1104), citytown(?x10957, ?x739), child(?x10808, ?x10957), category(?x10957, ?x134), service_location(?x10808, ?x551) *> conf = 0.17 ranks of expected_values: 35 EVAL 02_l39 child 024rbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 211.000 211.000 0.474 http://example.org/organization/organization/child./organization/organization_relationship/child #12889-04qmr PRED entity: 04qmr PRED relation: group! PRED expected values: 02hnl => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 118): 02hnl (0.77 #446, 0.77 #1876, 0.76 #1960), 03qjg (0.54 #465, 0.46 #381, 0.42 #885), 013y1f (0.40 #864, 0.38 #444, 0.35 #696), 0l14qv (0.38 #425, 0.38 #845, 0.35 #677), 01vj9c (0.31 #431, 0.28 #1861, 0.27 #1945), 04rzd (0.24 #869, 0.24 #533, 0.23 #449), 07gql (0.20 #34, 0.17 #286, 0.15 #454), 042v_gx (0.20 #847, 0.15 #427, 0.13 #679), 06ncr (0.20 #876, 0.15 #1886, 0.15 #1970), 0mkg (0.16 #849, 0.10 #1859, 0.10 #1943) >> Best rule #446 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01czx; 0134tg; 016l09; 0134pk; >> query: (?x3682, 02hnl) <- group(?x316, ?x3682), ?x316 = 05r5c, award(?x3682, ?x1389), award_winner(?x3121, ?x3682) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04qmr group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.769 http://example.org/music/performance_role/regular_performances./music/group_membership/group #12888-03nc9d PRED entity: 03nc9d PRED relation: award_winner PRED expected values: 026spg => 44 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1804): 05pdbs (0.39 #39568, 0.37 #14838, 0.37 #17311), 026ps1 (0.39 #39568, 0.37 #14838, 0.37 #17311), 0d9xq (0.37 #14838, 0.37 #17311, 0.37 #12365), 0lbj1 (0.33 #36, 0.27 #2509, 0.11 #7455), 09889g (0.33 #1128, 0.16 #8547, 0.15 #6075), 0gcs9 (0.33 #646, 0.14 #3119, 0.12 #15484), 02z4b_8 (0.33 #1577, 0.11 #8996, 0.11 #4050), 03f2_rc (0.33 #93, 0.11 #7512, 0.11 #2566), 0dw4g (0.33 #1257, 0.11 #8676, 0.11 #11149), 02qwg (0.33 #737, 0.11 #3210, 0.11 #5684) >> Best rule #39568 for best value: >> intensional similarity = 5 >> extensional distance = 190 >> proper extension: 02w_6xj; 09d28z; 02wypbh; 0m57f; >> query: (?x9034, ?x1238) <- award_winner(?x9034, ?x5181), award(?x1238, ?x9034), award_winner(?x1362, ?x1238), gender(?x1238, ?x231), place_of_death(?x5181, ?x739) >> conf = 0.39 => this is the best rule for 2 predicted values *> Best rule #6011 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 02r9qt; *> query: (?x9034, 026spg) <- award_winner(?x9034, ?x5181), category_of(?x9034, ?x2421), notable_people_with_this_condition(?x12870, ?x5181), profession(?x5181, ?x1032) *> conf = 0.06 ranks of expected_values: 113 EVAL 03nc9d award_winner 026spg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 44.000 20.000 0.392 http://example.org/award/award_category/winners./award/award_honor/award_winner #12887-028k57 PRED entity: 028k57 PRED relation: type_of_union PRED expected values: 04ztj => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.84 #29, 0.83 #21, 0.83 #17), 01g63y (0.19 #14, 0.17 #42, 0.16 #78) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 01w_10; >> query: (?x4478, 04ztj) <- award_nominee(?x4478, ?x806), special_performance_type(?x4478, ?x3558), film(?x4478, ?x2153) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028k57 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.836 http://example.org/people/person/spouse_s./people/marriage/type_of_union #12886-03fts PRED entity: 03fts PRED relation: film! PRED expected values: 04wp3s => 70 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1162): 07qy0b (0.48 #29105, 0.47 #54062, 0.47 #24943), 02w0dc0 (0.47 #54062, 0.46 #29104, 0.45 #74847), 0dn3n (0.33 #522, 0.10 #10913), 01gkmx (0.33 #1584, 0.06 #11975, 0.04 #9897), 01l_yg (0.33 #1655, 0.04 #7890, 0.03 #22440), 02_p5w (0.33 #644, 0.03 #11035, 0.03 #15194), 044rvb (0.33 #101, 0.03 #10492, 0.02 #47925), 04fzk (0.33 #705, 0.03 #11096, 0.02 #48529), 04cl1 (0.33 #834, 0.03 #11225, 0.01 #25778), 0161h5 (0.33 #1823, 0.03 #12214, 0.01 #26767) >> Best rule #29105 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 047gn4y; 0ds33; 03s6l2; 0209xj; 02py4c8; 0b73_1d; 06_wqk4; 06z8s_; 0b6tzs; 0dgst_d; ... >> query: (?x1474, ?x3371) <- nominated_for(?x3371, ?x1474), genre(?x1474, ?x258), sibling(?x6783, ?x3371) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #15524 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 67 *> proper extension: 043sct5; *> query: (?x1474, 04wp3s) <- genre(?x1474, ?x811), genre(?x1474, ?x258), ?x258 = 05p553, titles(?x2480, ?x1474), ?x811 = 03k9fj *> conf = 0.03 ranks of expected_values: 512 EVAL 03fts film! 04wp3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 70.000 46.000 0.479 http://example.org/film/actor/film./film/performance/film #12885-019389 PRED entity: 019389 PRED relation: place_of_birth PRED expected values: 01_d4 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 129): 04jpl (0.17 #8, 0.04 #5640, 0.04 #2120), 02cft (0.17 #229, 0.03 #3749, 0.03 #5157), 0g284 (0.07 #2892, 0.03 #7820, 0.02 #12044), 0d9jr (0.06 #898, 0.06 #1602, 0.03 #4418), 0d6lp (0.06 #818, 0.06 #1522, 0.02 #12786), 0psxp (0.06 #915, 0.04 #2323, 0.03 #5139), 02_286 (0.06 #51411, 0.06 #52115, 0.06 #55635), 0r3tq (0.06 #1838, 0.02 #8174), 0gqkd (0.06 #1559), 030qb3t (0.05 #4982, 0.05 #11318, 0.04 #2166) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01vvycq; 01vwbts; 01vswwx; 0dw4g; >> query: (?x7874, 04jpl) <- artists(?x302, ?x7874), award(?x7874, ?x4317), ?x4317 = 05q8pss, ?x302 = 016clz >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #5698 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 0hnlx; 01p45_v; 013v5j; 0gkg6; 0p3r8; 04k15; 017yfz; 0hgqq; 01l87db; 06c44; ... *> query: (?x7874, 01_d4) <- artists(?x302, ?x7874), gender(?x7874, ?x231), profession(?x7874, ?x2225), ?x2225 = 0kyk *> conf = 0.04 ranks of expected_values: 11 EVAL 019389 place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 101.000 101.000 0.167 http://example.org/people/person/place_of_birth #12884-0n6kf PRED entity: 0n6kf PRED relation: influenced_by PRED expected values: 03f47xl 0gdqy => 142 concepts (50 used for prediction) PRED predicted values (max 10 best out of 333): 0zm1 (0.60 #123, 0.21 #1820, 0.21 #2668), 081k8 (0.35 #3121, 0.32 #2696, 0.29 #6093), 0j3v (0.35 #3029, 0.26 #6001, 0.25 #5152), 01tz6vs (0.33 #1444, 0.29 #1868, 0.25 #5264), 040_9 (0.33 #1368, 0.29 #1792, 0.21 #2640), 07dnx (0.33 #714, 0.25 #1138, 0.15 #2970), 0379s (0.29 #1774, 0.26 #2622, 0.25 #5170), 0lcx (0.29 #1809, 0.25 #960, 0.22 #1385), 02lt8 (0.25 #3087, 0.25 #965, 0.22 #1390), 040db (0.25 #902, 0.21 #1751, 0.19 #2175) >> Best rule #123 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 03_87; 0g72r; >> query: (?x4795, 0zm1) <- influenced_by(?x4795, ?x9508), influenced_by(?x4795, ?x3336), ?x3336 = 032l1, ?x9508 = 0c1jh, type_of_union(?x4795, ?x566) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1046 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 03vrp; 040rjq; *> query: (?x4795, 03f47xl) <- influenced_by(?x4795, ?x4915), influenced_by(?x4795, ?x4265), influenced_by(?x4795, ?x3336), ?x4915 = 03f0324, ?x4265 = 06whf, influenced_by(?x3336, ?x2162) *> conf = 0.25 ranks of expected_values: 13 EVAL 0n6kf influenced_by 0gdqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 50.000 0.600 http://example.org/influence/influence_node/influenced_by EVAL 0n6kf influenced_by 03f47xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 142.000 50.000 0.600 http://example.org/influence/influence_node/influenced_by #12883-085gk PRED entity: 085gk PRED relation: profession PRED expected values: 0cbd2 => 133 concepts (110 used for prediction) PRED predicted values (max 10 best out of 91): 02hrh1q (0.89 #14284, 0.74 #1633, 0.72 #12814), 0dxtg (0.76 #13989, 0.46 #1926, 0.44 #5310), 0cbd2 (0.71 #301, 0.66 #8834, 0.60 #7), 09jwl (0.43 #11937, 0.38 #1197, 0.32 #11349), 01d_h8 (0.40 #13981, 0.31 #8391, 0.29 #14275), 01c72t (0.38 #11354, 0.21 #1202, 0.17 #613), 02jknp (0.34 #13983, 0.26 #2795, 0.24 #5296), 02hv44_ (0.34 #8091, 0.31 #9122, 0.30 #7796), 03gjzk (0.30 #13991, 0.24 #1928, 0.21 #4723), 0nbcg (0.29 #11949, 0.26 #1209, 0.26 #2795) >> Best rule #14284 for best value: >> intensional similarity = 4 >> extensional distance = 1304 >> proper extension: 03xp8d5; >> query: (?x12402, 02hrh1q) <- people(?x5269, ?x12402), profession(?x12402, ?x9081), profession(?x2739, ?x9081), ?x2739 = 02dh86 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #301 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0379s; *> query: (?x12402, 0cbd2) <- influenced_by(?x9508, ?x12402), influenced_by(?x587, ?x12402), ?x9508 = 0c1jh, nationality(?x587, ?x94), influenced_by(?x12402, ?x4072) *> conf = 0.71 ranks of expected_values: 3 EVAL 085gk profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 110.000 0.887 http://example.org/people/person/profession #12882-05n6sq PRED entity: 05n6sq PRED relation: language PRED expected values: 02h40lc => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 55): 02h40lc (0.95 #1882, 0.95 #4774, 0.94 #3537), 064_8sq (0.20 #726, 0.17 #137, 0.17 #550), 04h9h (0.17 #42, 0.10 #158, 0.06 #453), 02bjrlw (0.17 #1, 0.09 #1409, 0.09 #706), 05zjd (0.17 #25, 0.04 #3772, 0.03 #83), 04306rv (0.16 #180, 0.14 #357, 0.13 #476), 06b_j (0.12 #197, 0.10 #433, 0.07 #257), 0jzc (0.11 #194, 0.06 #135, 0.05 #254), 03_9r (0.06 #598, 0.05 #892, 0.05 #1009), 012w70 (0.06 #70, 0.04 #3772, 0.04 #483) >> Best rule #1882 for best value: >> intensional similarity = 4 >> extensional distance = 375 >> proper extension: 0209xj; 01dyvs; 03tn80; 02x8fs; 02rlj20; >> query: (?x6343, 02h40lc) <- titles(?x53, ?x6343), language(?x6343, ?x2502), genre(?x6343, ?x6452), executive_produced_by(?x6343, ?x10944) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05n6sq language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.955 http://example.org/film/film/language #12881-0162v PRED entity: 0162v PRED relation: contains! PRED expected values: 07c5l => 121 concepts (49 used for prediction) PRED predicted values (max 10 best out of 189): 02j71 (0.65 #19671, 0.56 #14306), 09c7w0 (0.61 #38450, 0.42 #9837, 0.42 #17885), 02qkt (0.60 #39688, 0.58 #37900, 0.57 #41476), 0dg3n1 (0.50 #5519, 0.47 #8201, 0.19 #40390), 07c5l (0.44 #395, 0.34 #7547, 0.30 #11124), 07ssc (0.31 #13443, 0.26 #18808, 0.11 #28644), 0j0k (0.28 #39719, 0.27 #37931, 0.27 #41507), 02j9z (0.26 #42051, 0.25 #37581, 0.25 #39369), 04_1l0v (0.26 #4027, 0.20 #26380, 0.17 #13862), 02jx1 (0.22 #13498, 0.19 #18863, 0.09 #36746) >> Best rule #19671 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 01ly5m; 0f1_p; 03pbf; 0vbk; 020d8d; 07c98; 0dqyw; 03msf; 014kj2; 018qd6; ... >> query: (?x1957, ?x551) <- administrative_parent(?x1957, ?x551), origin(?x6835, ?x1957), contains(?x8882, ?x1957) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #395 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 7 *> proper extension: 04chyn; 0157g9; 01l3lx; *> query: (?x1957, 07c5l) <- contains(?x9729, ?x1957), ?x9729 = 0261m *> conf = 0.44 ranks of expected_values: 5 EVAL 0162v contains! 07c5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 121.000 49.000 0.651 http://example.org/location/location/contains #12880-02bjhv PRED entity: 02bjhv PRED relation: major_field_of_study PRED expected values: 01tbp => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 110): 02j62 (0.48 #8581, 0.40 #9804, 0.35 #6140), 01mkq (0.42 #1235, 0.39 #3681, 0.35 #2090), 062z7 (0.37 #1248, 0.32 #9312, 0.30 #9801), 03g3w (0.32 #9311, 0.26 #6136, 0.25 #9800), 04rjg (0.32 #9304, 0.29 #6129, 0.29 #3686), 05qfh (0.30 #1257, 0.24 #2112, 0.21 #3211), 01540 (0.30 #1281, 0.23 #3235, 0.23 #3481), 01tbp (0.28 #1280, 0.24 #3603, 0.23 #3234), 04x_3 (0.28 #1246, 0.23 #3200, 0.23 #3569), 06ms6 (0.26 #1237, 0.19 #2092, 0.18 #3191) >> Best rule #8581 for best value: >> intensional similarity = 4 >> extensional distance = 323 >> proper extension: 07x4c; >> query: (?x2388, 02j62) <- institution(?x620, ?x2388), major_field_of_study(?x2388, ?x4321), major_field_of_study(?x10945, ?x4321), ?x10945 = 01jsk6 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1280 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 01j_5k; 01nhgd; *> query: (?x2388, 01tbp) <- institution(?x620, ?x2388), school(?x2820, ?x2388), ?x2820 = 0jmj7, ?x620 = 07s6fsf *> conf = 0.28 ranks of expected_values: 8 EVAL 02bjhv major_field_of_study 01tbp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 148.000 148.000 0.483 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #12879-07s8z_l PRED entity: 07s8z_l PRED relation: honored_for! PRED expected values: 05c1t6z => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 87): 0lp_cd3 (0.60 #134, 0.57 #251, 0.18 #3410), 05c1t6z (0.42 #3404, 0.35 #596, 0.35 #1064), 0hndn2q (0.29 #264, 0.20 #147, 0.06 #1668), 0275n3y (0.23 #996, 0.16 #528, 0.15 #2283), 0hr3c8y (0.20 #123, 0.14 #240, 0.12 #1644), 0g5b0q5 (0.20 #131, 0.14 #248, 0.08 #2003), 07y_p6 (0.20 #196, 0.10 #3121, 0.09 #2536), 09p3h7 (0.15 #642, 0.15 #1461, 0.15 #1344), 0bxs_d (0.15 #681, 0.13 #1149, 0.12 #3138), 04n2r9h (0.14 #268, 0.09 #1672, 0.09 #2491) >> Best rule #134 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01j7mr; >> query: (?x10447, 0lp_cd3) <- honored_for(?x10010, ?x10447), program(?x1285, ?x10447), program(?x2062, ?x10447), ?x10010 = 0hn821n >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3404 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 06hwzy; 0gbtbm; 06mr2s; 04xbq3; 01b7h8; *> query: (?x10447, 05c1t6z) <- honored_for(?x2292, ?x10447), languages(?x10447, ?x254), award_winner(?x2292, ?x9503), ?x9503 = 04ns3gy *> conf = 0.42 ranks of expected_values: 2 EVAL 07s8z_l honored_for! 05c1t6z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 93.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12878-01vvb4m PRED entity: 01vvb4m PRED relation: award PRED expected values: 07bdd_ => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 279): 027986c (0.72 #17785, 0.71 #17388, 0.71 #17784), 027c95y (0.72 #17785, 0.71 #17388, 0.71 #17784), 09cm54 (0.72 #17785, 0.71 #17388, 0.71 #17784), 07bdd_ (0.67 #1248, 0.56 #2433, 0.53 #4408), 0gq9h (0.42 #865, 0.36 #5210, 0.33 #1260), 05pcn59 (0.37 #868, 0.30 #1658, 0.28 #2053), 040njc (0.37 #798, 0.26 #5143, 0.24 #8699), 05ztrmj (0.32 #965, 0.19 #1755, 0.17 #2150), 019f4v (0.32 #854, 0.17 #3619, 0.17 #8755), 02pqp12 (0.32 #858, 0.14 #3623, 0.12 #5203) >> Best rule #17785 for best value: >> intensional similarity = 3 >> extensional distance = 1268 >> proper extension: 0khth; 07mvp; 04k05; 014g91; 06lxn; >> query: (?x3056, ?x834) <- award_winner(?x4360, ?x3056), award_winner(?x834, ?x3056), award(?x2596, ?x834) >> conf = 0.72 => this is the best rule for 3 predicted values *> Best rule #1248 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 25 *> proper extension: 03qncl3; 030s5g; *> query: (?x3056, 07bdd_) <- award_nominee(?x382, ?x3056), ?x382 = 086k8 *> conf = 0.67 ranks of expected_values: 4 EVAL 01vvb4m award 07bdd_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12877-02jq1 PRED entity: 02jq1 PRED relation: currency PRED expected values: 09nqf => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.34 #58, 0.32 #46, 0.30 #52), 01nv4h (0.05 #8, 0.03 #2, 0.03 #47) >> Best rule #58 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 094xh; 01vw917; 031x_3; >> query: (?x5442, 09nqf) <- people(?x5741, ?x5442), origin(?x5442, ?x5381), artist(?x7089, ?x5442) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jq1 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.344 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #12876-0c1pj PRED entity: 0c1pj PRED relation: film PRED expected values: 01pv91 04jn6y7 => 139 concepts (78 used for prediction) PRED predicted values (max 10 best out of 910): 01br2w (0.59 #122922, 0.50 #10688, 0.37 #124705), 033g4d (0.59 #122922, 0.37 #124705, 0.33 #131831), 072r5v (0.50 #10688, 0.35 #16032, 0.32 #21377), 016017 (0.29 #3485, 0.20 #1704, 0.02 #8828), 01ry_x (0.29 #3479, 0.20 #1698), 0241y7 (0.29 #2845, 0.20 #1064), 0jnwx (0.29 #2075, 0.20 #294), 0_7w6 (0.29 #2081), 025rvx0 (0.20 #998, 0.14 #2779, 0.02 #8122), 0ndwt2w (0.20 #995, 0.14 #2776, 0.02 #17027) >> Best rule #122922 for best value: >> intensional similarity = 3 >> extensional distance = 1074 >> proper extension: 049tjg; 01wjrn; 01p47r; 0p_jc; 01f9mq; 035wq7; 033071; 01mylz; >> query: (?x556, ?x174) <- type_of_union(?x556, ?x566), film(?x556, ?x299), nominated_for(?x556, ?x174) >> conf = 0.59 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0c1pj film 04jn6y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 139.000 78.000 0.588 http://example.org/film/actor/film./film/performance/film EVAL 0c1pj film 01pv91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 139.000 78.000 0.588 http://example.org/film/actor/film./film/performance/film #12875-0bscw PRED entity: 0bscw PRED relation: genre PRED expected values: 06n90 => 70 concepts (67 used for prediction) PRED predicted values (max 10 best out of 94): 02n4kr (0.47 #480, 0.25 #244, 0.14 #3198), 02kdv5l (0.43 #356, 0.33 #120, 0.33 #1537), 05p553 (0.41 #5679, 0.41 #2603, 0.35 #3905), 0lsxr (0.40 #481, 0.29 #718, 0.28 #954), 060__y (0.33 #488, 0.32 #725, 0.28 #961), 03k9fj (0.33 #130, 0.28 #2848, 0.27 #5451), 017fp (0.33 #15, 0.25 #251, 0.10 #2377), 03bxz7 (0.33 #54, 0.25 #290, 0.10 #2416), 01hmnh (0.33 #135, 0.23 #608, 0.22 #2853), 06n90 (0.33 #131, 0.18 #1785, 0.18 #722) >> Best rule #480 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0413cff; >> query: (?x1444, 02n4kr) <- genre(?x1444, ?x11108), ?x11108 = 02xh1, currency(?x1444, ?x170) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #131 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 01738w; *> query: (?x1444, 06n90) <- film(?x4587, ?x1444), language(?x1444, ?x254), crewmember(?x1444, ?x6546), ?x4587 = 015d3h *> conf = 0.33 ranks of expected_values: 10 EVAL 0bscw genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 70.000 67.000 0.467 http://example.org/film/film/genre #12874-05l0j5 PRED entity: 05l0j5 PRED relation: award_nominee! PRED expected values: 0cnl1c => 62 concepts (29 used for prediction) PRED predicted values (max 10 best out of 562): 08hsww (0.81 #11615, 0.81 #51089, 0.81 #30188), 05l0j5 (0.67 #4032, 0.67 #1708, 0.65 #8677), 043js (0.67 #579, 0.65 #7548, 0.62 #5225), 04t2l2 (0.62 #4684, 0.60 #2362, 0.60 #38), 0cnl1c (0.62 #5653, 0.60 #3331, 0.60 #1007), 05xpms (0.60 #1980, 0.59 #8949, 0.56 #6626), 0cl0bk (0.59 #8475, 0.53 #1506, 0.50 #6152), 0cj2t3 (0.38 #6969, 0.37 #2324, 0.30 #11618), 06jnvs (0.38 #6969, 0.37 #2324, 0.30 #11618), 0bczgm (0.38 #6969, 0.37 #2324, 0.30 #11618) >> Best rule #11615 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 06151l; 06gp3f; 0fsm8c; 06mfvc; 02j9lm; 0cj2t3; 062ftr; 05txrz; 0b7gxq; 03qmfzx; ... >> query: (?x7752, ?x274) <- award_nominee(?x7752, ?x6263), award_nominee(?x7752, ?x274), award_winner(?x4332, ?x6263), ?x4332 = 0cnl1c >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #5653 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0bt4r4; *> query: (?x7752, 0cnl1c) <- award_nominee(?x7752, ?x6634), ?x6634 = 0cj36c, award_nominee(?x2912, ?x7752), award_winner(?x2912, ?x237) *> conf = 0.62 ranks of expected_values: 5 EVAL 05l0j5 award_nominee! 0cnl1c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 29.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #12873-013yq PRED entity: 013yq PRED relation: place_of_death! PRED expected values: 0407f => 190 concepts (173 used for prediction) PRED predicted values (max 10 best out of 715): 01k9lpl (0.07 #1972, 0.05 #2729, 0.04 #4242), 0ph2w (0.07 #1676, 0.05 #2433, 0.04 #3946), 0h326 (0.07 #2268, 0.05 #3025, 0.04 #4538), 05f0r8 (0.07 #2262, 0.05 #3019, 0.04 #4532), 01l3j (0.07 #2257, 0.05 #3014, 0.04 #4527), 067x44 (0.07 #2248, 0.05 #3005, 0.04 #4518), 058z1hb (0.07 #2244, 0.05 #3001, 0.04 #4514), 02rf51g (0.07 #2242, 0.05 #2999, 0.04 #4512), 02nygk (0.07 #2241, 0.05 #2998, 0.04 #4511), 01c5d5 (0.07 #2234, 0.05 #2991, 0.04 #4504) >> Best rule #1972 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 04vmp; 0y1rf; 0kstw; >> query: (?x2277, 01k9lpl) <- location(?x624, ?x2277), administrative_division(?x2277, ?x3038), place_founded(?x7169, ?x2277) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 013yq place_of_death! 0407f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 173.000 0.067 http://example.org/people/deceased_person/place_of_death #12872-05qtj PRED entity: 05qtj PRED relation: vacationer PRED expected values: 0jfx1 => 263 concepts (212 used for prediction) PRED predicted values (max 10 best out of 404): 01f492 (0.25 #730, 0.18 #2833, 0.16 #3435), 0j1yf (0.25 #178, 0.17 #1378, 0.12 #1978), 01pgzn_ (0.25 #189, 0.15 #3795, 0.12 #9222), 02d9k (0.25 #180, 0.12 #3335, 0.12 #3786), 01dw4q (0.25 #152, 0.11 #4212, 0.09 #5265), 046zh (0.25 #248, 0.08 #1448, 0.08 #7325), 0456xp (0.25 #169, 0.08 #1369, 0.07 #4832), 03_x5t (0.25 #292, 0.08 #1492, 0.06 #2092), 0dxmyh (0.25 #291, 0.08 #1491, 0.06 #2091), 0261x8t (0.16 #9305, 0.16 #7349, 0.15 #3878) >> Best rule #730 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 0gv10; >> query: (?x4627, 01f492) <- location_of_ceremony(?x1117, ?x4627), second_level_divisions(?x789, ?x4627) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3799 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 01bkb; *> query: (?x4627, 0jfx1) <- featured_film_locations(?x1685, ?x4627), vacationer(?x4627, ?x436), location_of_ceremony(?x1880, ?x4627) *> conf = 0.08 ranks of expected_values: 56 EVAL 05qtj vacationer 0jfx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 263.000 212.000 0.250 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #12871-03hpr PRED entity: 03hpr PRED relation: influenced_by PRED expected values: 02y49 => 136 concepts (45 used for prediction) PRED predicted values (max 10 best out of 300): 041h0 (0.44 #10, 0.07 #4769, 0.06 #3042), 0g5ff (0.22 #192, 0.16 #625, 0.12 #13006), 03f47xl (0.22 #202, 0.06 #3234, 0.06 #19080), 06bng (0.21 #712, 0.07 #4769, 0.03 #5482), 03_87 (0.18 #4099, 0.13 #7135, 0.13 #3232), 032l1 (0.17 #3987, 0.13 #7023, 0.12 #13006), 081k8 (0.14 #4052, 0.13 #7088, 0.12 #4922), 01v9724 (0.12 #4074, 0.12 #13006, 0.11 #175), 03sbs (0.12 #7155, 0.12 #7589, 0.11 #4989), 03hnd (0.12 #13006, 0.11 #98, 0.11 #531) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 07w21; 0jt90f5; 03vrp; 014ps4; 0c8br; 06d6y; 0dz46; >> query: (?x10275, 041h0) <- student(?x388, ?x10275), award_winner(?x4879, ?x10275), profession(?x10275, ?x353), ?x4879 = 047xyn >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #300 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 07w21; 0jt90f5; 03vrp; 014ps4; 0c8br; 06d6y; 0dz46; *> query: (?x10275, 02y49) <- student(?x388, ?x10275), award_winner(?x4879, ?x10275), profession(?x10275, ?x353), ?x4879 = 047xyn *> conf = 0.11 ranks of expected_values: 54 EVAL 03hpr influenced_by 02y49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 136.000 45.000 0.444 http://example.org/influence/influence_node/influenced_by #12870-0gpprt PRED entity: 0gpprt PRED relation: film PRED expected values: 033fqh => 85 concepts (74 used for prediction) PRED predicted values (max 10 best out of 266): 02rn00y (0.59 #49940, 0.57 #24971, 0.57 #19619), 04b_jc (0.59 #49940, 0.57 #24971, 0.57 #19619), 09xbpt (0.14 #47), 0418wg (0.09 #400, 0.01 #18235, 0.01 #32505), 0bc1yhb (0.09 #905, 0.01 #8040), 02x2jl_ (0.09 #1748), 0dnkmq (0.09 #1655), 0bmhvpr (0.09 #619), 05qbckf (0.09 #308), 06z8s_ (0.09 #130) >> Best rule #49940 for best value: >> intensional similarity = 3 >> extensional distance = 1273 >> proper extension: 025p38; 02lq10; 05hdf; 02zrv7; 0418ft; 0bkmf; 012x2b; 0q1lp; 03bdm4; 01gc7h; ... >> query: (?x8783, ?x3133) <- nominated_for(?x8783, ?x3133), nationality(?x8783, ?x94), film(?x8783, ?x964) >> conf = 0.59 => this is the best rule for 2 predicted values *> Best rule #2618 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 155 *> proper extension: 032md; *> query: (?x8783, 033fqh) <- place_of_birth(?x8783, ?x6895), film(?x8783, ?x10732), nominated_for(?x2374, ?x10732) *> conf = 0.01 ranks of expected_values: 147 EVAL 0gpprt film 033fqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 85.000 74.000 0.586 http://example.org/film/actor/film./film/performance/film #12869-027s39y PRED entity: 027s39y PRED relation: genre PRED expected values: 01hmnh 0hcr 01zhp => 93 concepts (71 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.75 #3989, 0.71 #2695, 0.70 #2461), 01jfsb (0.51 #5765, 0.45 #362, 0.36 #713), 09b3v (0.49 #3281, 0.48 #7633, 0.46 #4695), 02kdv5l (0.48 #5757, 0.47 #823, 0.47 #354), 0hcr (0.36 #489, 0.23 #138, 0.15 #841), 01hmnh (0.31 #835, 0.30 #483, 0.26 #132), 0lsxr (0.27 #8, 0.21 #5762, 0.18 #1531), 060__y (0.23 #14, 0.20 #365, 0.20 #1772), 04t36 (0.20 #473, 0.16 #122, 0.11 #1763), 04xvlr (0.19 #1760, 0.19 #2228, 0.18 #2696) >> Best rule #3989 for best value: >> intensional similarity = 4 >> extensional distance = 647 >> proper extension: 016ztl; >> query: (?x3946, 07s9rl0) <- film(?x1052, ?x3946), genre(?x3946, ?x239), genre(?x10105, ?x239), ?x10105 = 0bs5f0b >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #489 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 62 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x3946, 0hcr) <- titles(?x3920, ?x3946), production_companies(?x148, ?x3920) *> conf = 0.36 ranks of expected_values: 5, 6, 24 EVAL 027s39y genre 01zhp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 93.000 71.000 0.746 http://example.org/film/film/genre EVAL 027s39y genre 0hcr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 71.000 0.746 http://example.org/film/film/genre EVAL 027s39y genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 71.000 0.746 http://example.org/film/film/genre #12868-05kr_ PRED entity: 05kr_ PRED relation: contains PRED expected values: 0154fs 01t3h6 => 231 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2853): 01t3h6 (0.82 #148538, 0.82 #142712, 0.81 #131062), 01gbzb (0.82 #148538, 0.82 #142712, 0.81 #131062), 0kf14 (0.82 #148538, 0.82 #142712, 0.81 #131062), 01y9qr (0.77 #96115, 0.77 #81549, 0.76 #93202), 0jpkw (0.77 #96115, 0.77 #81549, 0.76 #93202), 07wjk (0.77 #96115, 0.76 #93202, 0.74 #154363), 02gt5s (0.63 #75722, 0.61 #195135, 0.14 #10750), 02dtg (0.63 #75722, 0.61 #195135, 0.14 #8785), 0d060g (0.60 #64074, 0.58 #104852, 0.58 #5824), 059g4 (0.60 #64074, 0.58 #104852, 0.58 #5824) >> Best rule #148538 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 0cv5l; >> query: (?x1905, ?x12755) <- contains(?x1905, ?x9699), state(?x12755, ?x1905), place_of_birth(?x1462, ?x9699) >> conf = 0.82 => this is the best rule for 3 predicted values ranks of expected_values: 1, 60 EVAL 05kr_ contains 01t3h6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 231.000 87.000 0.824 http://example.org/location/location/contains EVAL 05kr_ contains 0154fs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 231.000 87.000 0.824 http://example.org/location/location/contains #12867-0ddj0x PRED entity: 0ddj0x PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 25): 0ch6mp2 (0.77 #156, 0.75 #82, 0.74 #45), 09zzb8 (0.74 #931, 0.70 #1796, 0.68 #1611), 02r96rf (0.68 #263, 0.61 #1614, 0.59 #1799), 0dxtw (0.34 #160, 0.34 #1807, 0.33 #1622), 01vx2h (0.31 #272, 0.29 #1623, 0.28 #832), 0215hd (0.18 #57, 0.16 #94, 0.16 #168), 02ynfr (0.16 #350, 0.15 #276, 0.15 #1812), 02_n3z (0.13 #39, 0.12 #76, 0.12 #150), 015h31 (0.12 #455, 0.12 #380, 0.07 #1620), 089g0h (0.12 #58, 0.10 #840, 0.10 #1631) >> Best rule #156 for best value: >> intensional similarity = 5 >> extensional distance = 88 >> proper extension: 020fcn; 049xgc; >> query: (?x5578, 0ch6mp2) <- nominated_for(?x384, ?x5578), nominated_for(?x112, ?x5578), ?x112 = 027dtxw, film_crew_role(?x5578, ?x1171), award(?x164, ?x384) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ddj0x film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.767 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12866-0fdtd7 PRED entity: 0fdtd7 PRED relation: award_winner PRED expected values: 02lf0c => 57 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1520): 0hskw (0.40 #581, 0.07 #7992, 0.04 #15403), 06z4wj (0.40 #1531, 0.03 #8942, 0.02 #16353), 0q59y (0.40 #859, 0.02 #8270, 0.01 #15681), 012t1 (0.40 #199, 0.02 #7610, 0.01 #15021), 02lf0c (0.33 #29653, 0.32 #17293, 0.31 #27180), 09fb5 (0.29 #5005, 0.10 #12415, 0.08 #22299), 016yvw (0.29 #6158, 0.07 #13568, 0.06 #23452), 06cgy (0.29 #5251, 0.06 #12661, 0.05 #22545), 0bl2g (0.29 #5002, 0.05 #12412, 0.05 #14882), 015vq_ (0.29 #5845, 0.04 #13255, 0.04 #23139) >> Best rule #581 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01l29r; 0bm70b; >> query: (?x11230, 0hskw) <- award(?x595, ?x11230), award_winner(?x11230, ?x5898), ?x595 = 02lf0c, nominated_for(?x5898, ?x3306), award(?x5898, ?x1587) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #29653 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 156 *> proper extension: 0265wl; *> query: (?x11230, ?x595) <- award(?x595, ?x11230), award_winner(?x11230, ?x3828), produced_by(?x7844, ?x595), produced_by(?x278, ?x3828), film_crew_role(?x7844, ?x137) *> conf = 0.33 ranks of expected_values: 5 EVAL 0fdtd7 award_winner 02lf0c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 57.000 14.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #12865-05p606 PRED entity: 05p606 PRED relation: profession PRED expected values: 02hrh1q => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 37): 02hrh1q (0.91 #315, 0.90 #465, 0.89 #765), 0np9r (0.50 #22, 0.22 #772, 0.21 #172), 01d_h8 (0.36 #306, 0.31 #1658, 0.30 #1958), 0dxtg (0.27 #4367, 0.27 #4817, 0.26 #3917), 02jknp (0.21 #158, 0.21 #1660, 0.21 #1960), 03gjzk (0.19 #4369, 0.19 #3469, 0.18 #1668), 018gz8 (0.16 #918, 0.15 #2420, 0.15 #2571), 09jwl (0.16 #4373, 0.16 #6323, 0.16 #6173), 0cbd2 (0.12 #5410, 0.12 #5710, 0.12 #5860), 0nbcg (0.11 #4386, 0.10 #4986, 0.10 #5586) >> Best rule #315 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 01713c; 07s8r0; 043kzcr; 02mjf2; 021yzs; >> query: (?x11860, 02hrh1q) <- film(?x11860, ?x1370), film_release_region(?x1370, ?x2513), nominated_for(?x2183, ?x1370), ?x2183 = 02x4w6g, currency(?x2513, ?x170) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05p606 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.911 http://example.org/people/person/profession #12864-02r858_ PRED entity: 02r858_ PRED relation: nominated_for! PRED expected values: 02z0dfh => 109 concepts (101 used for prediction) PRED predicted values (max 10 best out of 193): 0gqng (0.59 #910, 0.17 #456, 0.10 #229), 099c8n (0.57 #1190, 0.56 #1417, 0.27 #1644), 0gq9h (0.51 #1196, 0.43 #1423, 0.36 #1877), 054knh (0.48 #1095, 0.30 #414, 0.08 #641), 019f4v (0.46 #1187, 0.35 #1868, 0.30 #1414), 02n9nmz (0.46 #1418, 0.43 #1191, 0.15 #737), 04dn09n (0.43 #1169, 0.32 #1396, 0.29 #1850), 0k611 (0.37 #1205, 0.33 #1432, 0.31 #1886), 03hkv_r (0.35 #1376, 0.31 #1149, 0.23 #695), 0gqyl (0.34 #1212, 0.27 #1439, 0.23 #758) >> Best rule #910 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 02gd6x; 0k20s; >> query: (?x8277, 0gqng) <- nominated_for(?x1657, ?x8277), nominated_for(?x8224, ?x8277), nominated_for(?x8224, ?x9805), ?x9805 = 07vfy4 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #17713 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1089 *> proper extension: 05h95s; *> query: (?x8277, ?x1254) <- award_winner(?x8277, ?x3096), award(?x3096, ?x1254), nominated_for(?x1254, ?x144) *> conf = 0.27 ranks of expected_values: 22 EVAL 02r858_ nominated_for! 02z0dfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 109.000 101.000 0.593 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12863-062zm5h PRED entity: 062zm5h PRED relation: film_release_region PRED expected values: 04gzd 05v8c 0k6nt 07ylj 06c1y => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 90): 0k6nt (0.79 #1566, 0.76 #1685, 0.29 #19), 05v8c (0.75 #129, 0.71 #10, 0.54 #1557), 04gzd (0.71 #5, 0.62 #124, 0.48 #1552), 03rt9 (0.66 #1555, 0.62 #1674, 0.43 #8), 06c1y (0.57 #31, 0.50 #150, 0.35 #1578), 06f32 (0.43 #45, 0.40 #1592, 0.39 #1711), 07ylj (0.43 #22, 0.38 #141, 0.24 #1569), 0163v (0.43 #39, 0.38 #158, 0.12 #5721), 02k8k (0.43 #78, 0.38 #197, 0.12 #5721), 0166b (0.43 #75, 0.38 #194, 0.12 #5721) >> Best rule #1566 for best value: >> intensional similarity = 6 >> extensional distance = 242 >> proper extension: 0b76d_m; 0ds35l9; 0gtsx8c; 02vxq9m; 028_yv; 02vp1f_; 01gc7; 011yrp; 0ddfwj1; 0ds3t5x; ... >> query: (?x5016, 0k6nt) <- film_release_region(?x5016, ?x9730), film_release_region(?x5016, ?x1003), film_release_region(?x5016, ?x87), ?x1003 = 03gj2, vacationer(?x9730, ?x2697), ?x87 = 05r4w >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 7 EVAL 062zm5h film_release_region 06c1y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.787 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 062zm5h film_release_region 07ylj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 70.000 70.000 0.787 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 062zm5h film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.787 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 062zm5h film_release_region 05v8c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.787 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 062zm5h film_release_region 04gzd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.787 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12862-071vr PRED entity: 071vr PRED relation: state PRED expected values: 01n7q => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 79): 01n7q (0.29 #2824, 0.27 #2909, 0.26 #2994), 03v0t (0.25 #42, 0.07 #6436, 0.03 #5154), 09c7w0 (0.19 #5883, 0.17 #5456, 0.15 #10838), 059_c (0.12 #183, 0.10 #353, 0.04 #2311), 059rby (0.12 #172, 0.08 #257, 0.06 #2897), 05k7sb (0.12 #194, 0.07 #6418, 0.05 #364), 07b_l (0.10 #2593, 0.09 #464, 0.09 #1315), 05kkh (0.08 #256, 0.02 #5113, 0.02 #2555), 05tbn (0.08 #1487, 0.04 #7033, 0.04 #2594), 01x73 (0.07 #1891, 0.07 #1976, 0.05 #3169) >> Best rule #2824 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0r1yc; 0qlrh; >> query: (?x6960, 01n7q) <- place_of_death(?x5806, ?x6960), time_zones(?x6960, ?x2950), county(?x6960, ?x9472) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 071vr state 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 171.000 0.294 http://example.org/base/biblioness/bibs_location/state #12861-07sqbl PRED entity: 07sqbl PRED relation: position PRED expected values: 02nzb8 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 45): 02nzb8 (0.83 #274, 0.83 #273, 0.83 #107), 02qvgy (0.50 #327, 0.37 #246, 0.04 #188), 03f0fp (0.50 #327, 0.35 #16, 0.31 #331), 02md_2 (0.31 #331, 0.30 #269, 0.28 #281), 02qvkj (0.04 #188, 0.04 #187), 02sddg (0.04 #188, 0.04 #187), 02dwpf (0.04 #188, 0.04 #187), 049k4w (0.04 #188, 0.04 #187), 0bgv8y (0.04 #188, 0.04 #187), 02sg4b (0.04 #188, 0.04 #187) >> Best rule #274 for best value: >> intensional similarity = 16 >> extensional distance = 414 >> proper extension: 05xzcz; >> query: (?x13480, ?x530) <- position(?x13480, ?x530), position(?x13480, ?x63), position(?x13480, ?x203), ?x63 = 02sdk9v, ?x203 = 0dgrmp, position(?x10342, ?x530), position(?x7699, ?x530), position(?x7523, ?x530), position(?x6670, ?x530), position(?x3587, ?x530), ?x10342 = 037ts6, ?x7523 = 070tng, position(?x852, ?x530), ?x3587 = 02s2lg, ?x7699 = 05gsd2, category(?x6670, ?x134) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07sqbl position 02nzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.835 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #12860-0152cw PRED entity: 0152cw PRED relation: artists! PRED expected values: 064t9 06by7 => 119 concepts (60 used for prediction) PRED predicted values (max 10 best out of 253): 064t9 (0.88 #12773, 0.76 #2816, 0.74 #13706), 06by7 (0.69 #13093, 0.53 #4691, 0.53 #4069), 03_d0 (0.49 #11837, 0.26 #14015, 0.19 #2814), 016clz (0.44 #317, 0.41 #1873, 0.37 #8407), 06j6l (0.39 #13740, 0.37 #14051, 0.33 #9383), 0gywn (0.36 #2860, 0.28 #13750, 0.27 #14061), 0xhtw (0.35 #4686, 0.24 #8420, 0.24 #13088), 0glt670 (0.32 #9377, 0.31 #6267, 0.31 #5334), 0ggx5q (0.31 #703, 0.30 #5371, 0.29 #1947), 02lnbg (0.31 #683, 0.29 #6284, 0.28 #5040) >> Best rule #12773 for best value: >> intensional similarity = 5 >> extensional distance = 396 >> proper extension: 0123r4; >> query: (?x872, 064t9) <- artists(?x3108, ?x872), artists(?x3108, ?x9179), artists(?x3108, ?x6819), ?x9179 = 01vsqvs, ?x6819 = 02pt7h_ >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0152cw artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 60.000 0.884 http://example.org/music/genre/artists EVAL 0152cw artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 60.000 0.884 http://example.org/music/genre/artists #12859-042ly5 PRED entity: 042ly5 PRED relation: award_nominee! PRED expected values: 0f502 => 79 concepts (46 used for prediction) PRED predicted values (max 10 best out of 716): 042ly5 (0.56 #3956, 0.14 #6970, 0.06 #106883), 0f502 (0.44 #3338, 0.14 #6970, 0.03 #12633), 04y79_n (0.41 #4936, 0.19 #102236, 0.01 #37472), 06lgq8 (0.36 #5079, 0.19 #102236, 0.01 #37615), 06b0d2 (0.32 #4865, 0.19 #102236, 0.14 #6970), 01541z (0.32 #5080, 0.19 #102236, 0.14 #6970), 05wqr1 (0.32 #6411, 0.19 #102236, 0.14 #6970), 065ydwb (0.32 #5967, 0.19 #102236, 0.14 #6970), 04myfb7 (0.32 #5053, 0.19 #102236, 0.14 #6970), 04mz10g (0.32 #4935, 0.19 #102236, 0.14 #6970) >> Best rule #3956 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02bkdn; 016fjj; 0f502; 06wm0z; 0gx_p; 07ddz9; 01jz6x; >> query: (?x7255, 042ly5) <- award_nominee(?x8285, ?x7255), ?x8285 = 03yrkt, film(?x7255, ?x97) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #3338 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 02bkdn; 016fjj; 0f502; 06wm0z; 0gx_p; 07ddz9; 01jz6x; *> query: (?x7255, 0f502) <- award_nominee(?x8285, ?x7255), ?x8285 = 03yrkt, film(?x7255, ?x97) *> conf = 0.44 ranks of expected_values: 2 EVAL 042ly5 award_nominee! 0f502 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 46.000 0.556 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #12858-02x17s4 PRED entity: 02x17s4 PRED relation: award! PRED expected values: 076_74 => 44 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2513): 02pv_d (0.68 #36809, 0.66 #36806, 0.62 #40153), 0154qm (0.50 #10928, 0.39 #24314, 0.33 #891), 0kszw (0.50 #10698, 0.38 #20737, 0.33 #661), 043kzcr (0.50 #10693, 0.33 #24079, 0.33 #656), 02f2dn (0.50 #10735, 0.33 #24121, 0.33 #698), 01jw4r (0.50 #12491, 0.33 #25877, 0.33 #2454), 02kxwk (0.50 #11258, 0.33 #24644, 0.33 #1221), 01hkhq (0.50 #10689, 0.33 #652, 0.31 #20728), 0dvld (0.50 #11765, 0.33 #1728, 0.28 #25151), 03mp9s (0.50 #12037, 0.33 #2000, 0.28 #25423) >> Best rule #36809 for best value: >> intensional similarity = 5 >> extensional distance = 154 >> proper extension: 05qck; 02qkk9_; 02py7pj; 05f3q; 01cd7p; >> query: (?x2341, ?x361) <- award_winner(?x2341, ?x8042), award_winner(?x2341, ?x361), award(?x361, ?x68), people(?x743, ?x8042), produced_by(?x3745, ?x8042) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #30114 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 027dtxw; 03hkv_r; 04dn09n; 02rdxsh; 019f4v; 0gq9h; 0gs9p; 02rdyk7; 04kxsb; 02x258x; ... *> query: (?x2341, ?x902) <- nominated_for(?x2341, ?x2920), nominated_for(?x2341, ?x2116), award_winner(?x2920, ?x902), ?x2116 = 02c638 *> conf = 0.15 ranks of expected_values: 368 EVAL 02x17s4 award! 076_74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 14.000 0.682 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12857-0gqzz PRED entity: 0gqzz PRED relation: ceremony PRED expected values: 04110lv => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 130): 0bzm81 (0.89 #412, 0.86 #282, 0.17 #932), 02yxh9 (0.89 #482, 0.86 #352, 0.16 #1002), 0bc773 (0.89 #440, 0.86 #310, 0.16 #960), 02yw5r (0.89 #402, 0.86 #272, 0.16 #922), 073h9x (0.89 #436, 0.79 #306, 0.15 #956), 04110lv (0.86 #361, 0.84 #491, 0.27 #5334), 05q7cj (0.86 #346, 0.84 #476, 0.27 #5334), 0bz6l9 (0.86 #307, 0.79 #437, 0.15 #957), 0fzrtf (0.86 #317, 0.74 #447, 0.14 #967), 0c6vcj (0.86 #353, 0.74 #483, 0.13 #1003) >> Best rule #412 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 018wng; >> query: (?x1053, 0bzm81) <- ceremony(?x1053, ?x6594), ceremony(?x1053, ?x2294), award_winner(?x1053, ?x1052), ?x2294 = 050yyb, ?x6594 = 02pgky2 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #361 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0gq_d; 0gr07; *> query: (?x1053, 04110lv) <- ceremony(?x1053, ?x2294), award_winner(?x1053, ?x3456), ?x2294 = 050yyb, film(?x3456, ?x1080) *> conf = 0.86 ranks of expected_values: 6 EVAL 0gqzz ceremony 04110lv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 49.000 49.000 0.895 http://example.org/award/award_category/winners./award/award_honor/ceremony #12856-0m9_5 PRED entity: 0m9_5 PRED relation: colors PRED expected values: 067z2v => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.35 #380, 0.34 #2, 0.34 #317), 01l849 (0.29 #442, 0.28 #64, 0.28 #1), 01g5v (0.21 #508, 0.21 #760, 0.21 #718), 019sc (0.21 #71, 0.20 #134, 0.20 #29), 03wkwg (0.17 #58, 0.10 #37, 0.09 #289), 06fvc (0.13 #444, 0.13 #1011, 0.13 #108), 0jc_p (0.11 #89, 0.11 #215, 0.10 #341), 038hg (0.11 #391, 0.10 #307, 0.09 #181), 04mkbj (0.10 #11, 0.09 #767, 0.08 #620), 036k5h (0.10 #27, 0.10 #48, 0.10 #510) >> Best rule #380 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 024y8p; 01w5m; 0g2jl; >> query: (?x4117, 083jv) <- major_field_of_study(?x4117, ?x4321), fraternities_and_sororities(?x4117, ?x3697), state_province_region(?x4117, ?x760), institution(?x865, ?x4117) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #94 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 01jssp; 05krk; 01j_9c; 06pwq; 02w2bc; 065y4w7; 07w0v; 01wdl3; 01j_cy; 07szy; ... *> query: (?x4117, 067z2v) <- major_field_of_study(?x4117, ?x4321), fraternities_and_sororities(?x4117, ?x3697), school(?x1883, ?x4117), institution(?x865, ?x4117) *> conf = 0.10 ranks of expected_values: 12 EVAL 0m9_5 colors 067z2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 133.000 133.000 0.351 http://example.org/education/educational_institution/colors #12855-0d060g PRED entity: 0d060g PRED relation: country_of_origin! PRED expected values: 06w7mlh => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 328): 06k176 (0.40 #1541, 0.27 #2065, 0.13 #2591), 04sskp (0.40 #1461, 0.18 #1985, 0.13 #2511), 05z43v (0.40 #1457, 0.18 #1981, 0.13 #2507), 0b005 (0.40 #1432, 0.18 #1956, 0.13 #2482), 01hn_t (0.40 #1379, 0.18 #1903, 0.13 #2429), 090s_0 (0.40 #1312, 0.18 #1836, 0.13 #2362), 02qfh (0.20 #1478, 0.18 #2002, 0.08 #2265), 0ctzf1 (0.20 #1445, 0.18 #1969, 0.07 #2495), 01_2n (0.20 #1509, 0.09 #2033, 0.08 #2296), 01cjhz (0.20 #1349, 0.09 #1873, 0.08 #2136) >> Best rule #1541 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 02j71; >> query: (?x279, 06k176) <- service_location(?x11188, ?x279), ?x11188 = 0z07 >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d060g country_of_origin! 06w7mlh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 201.000 201.000 0.400 http://example.org/tv/tv_program/country_of_origin #12854-015c2f PRED entity: 015c2f PRED relation: languages PRED expected values: 02h40lc => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.29 #275, 0.28 #665, 0.28 #743), 064_8sq (0.06 #15, 0.04 #639, 0.04 #171), 02bjrlw (0.03 #625, 0.02 #1, 0.02 #586), 03k50 (0.03 #1057, 0.03 #1135, 0.03 #1564), 06nm1 (0.02 #630, 0.02 #591, 0.01 #84), 04306rv (0.02 #3, 0.01 #198, 0.01 #393), 03115z (0.02 #28), 06mp7 (0.02 #11), 07c9s (0.01 #1612, 0.01 #1807, 0.01 #1690), 0t_2 (0.01 #1413) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 218 >> proper extension: 01csvq; 049g_xj; 0693l; 049qx; 0f7hc; 01wqmm8; >> query: (?x2813, 02h40lc) <- award_winner(?x1193, ?x2813), participant(?x2813, ?x4775), film(?x2813, ?x2231) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015c2f languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.291 http://example.org/people/person/languages #12853-089j8p PRED entity: 089j8p PRED relation: executive_produced_by PRED expected values: 02z6l5f => 100 concepts (89 used for prediction) PRED predicted values (max 10 best out of 172): 06q8hf (0.60 #166, 0.17 #1676, 0.13 #2429), 02z6l5f (0.23 #871, 0.17 #1123, 0.13 #2380), 02z2xdf (0.20 #157, 0.12 #911, 0.10 #1163), 05hj_k (0.20 #1608, 0.19 #2361, 0.15 #350), 04jspq (0.11 #1408, 0.10 #3166, 0.07 #2915), 01twdk (0.11 #1370, 0.05 #2877, 0.05 #2124), 06pj8 (0.08 #1313, 0.07 #2820, 0.06 #3824), 0glyyw (0.08 #1446, 0.07 #8232, 0.06 #8736), 0gg9_5q (0.08 #342, 0.05 #2604, 0.04 #8638), 0grrq8 (0.08 #360, 0.05 #1618, 0.03 #1869) >> Best rule #166 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0m313; 027m5wv; >> query: (?x6446, 06q8hf) <- film_release_region(?x6446, ?x87), executive_produced_by(?x6446, ?x4128), nominated_for(?x2805, ?x6446), ?x2805 = 0lpjn >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #871 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 05c5z8j; 02chhq; *> query: (?x6446, 02z6l5f) <- nominated_for(?x5886, ?x6446), ?x5886 = 0fq9zdv, film_crew_role(?x6446, ?x137), nominated_for(?x2805, ?x6446) *> conf = 0.23 ranks of expected_values: 2 EVAL 089j8p executive_produced_by 02z6l5f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 89.000 0.600 http://example.org/film/film/executive_produced_by #12852-01mz9lt PRED entity: 01mz9lt PRED relation: profession PRED expected values: 02hrh1q => 140 concepts (82 used for prediction) PRED predicted values (max 10 best out of 69): 02hrh1q (0.96 #8389, 0.90 #7044, 0.90 #9733), 01c72t (0.70 #2415, 0.68 #1366, 0.66 #2264), 09jwl (0.61 #6452, 0.60 #10335, 0.59 #10036), 0nbcg (0.59 #10498, 0.58 #6465, 0.46 #9601), 0dz3r (0.46 #1493, 0.38 #10317, 0.38 #10018), 01c8w0 (0.43 #1799, 0.37 #1650, 0.35 #903), 01d_h8 (0.40 #1945, 0.40 #155, 0.38 #4497), 016z4k (0.38 #6436, 0.37 #10469, 0.37 #9871), 025352 (0.33 #657, 0.25 #806, 0.20 #955), 0dxtg (0.30 #1057, 0.28 #8537, 0.27 #9433) >> Best rule #8389 for best value: >> intensional similarity = 5 >> extensional distance = 515 >> proper extension: 064nh4k; 0738b8; 01trhmt; 01y9xg; 02dth1; 01900g; 028r4y; 03_wvl; 01520h; 078mgh; ... >> query: (?x11462, 02hrh1q) <- profession(?x11462, ?x8353), place_of_birth(?x11462, ?x7412), actor(?x12165, ?x11462), profession(?x2264, ?x8353), ?x2264 = 025tdwc >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mz9lt profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 82.000 0.963 http://example.org/people/person/profession #12851-054rw PRED entity: 054rw PRED relation: contains! PRED expected values: 04wgh => 59 concepts (54 used for prediction) PRED predicted values (max 10 best out of 241): 09c7w0 (0.61 #30492, 0.60 #31388, 0.57 #38586), 06bnz (0.33 #105, 0.25 #1001, 0.17 #1897), 01mzwp (0.33 #815, 0.25 #1711, 0.17 #2607), 05vz3zq (0.33 #301, 0.25 #1197, 0.17 #2093), 03spz (0.25 #1182, 0.17 #2078, 0.08 #10149), 01n7q (0.18 #38661, 0.18 #39557, 0.16 #30567), 02xry (0.18 #7335, 0.18 #6438, 0.18 #5541), 02qkt (0.18 #46128, 0.17 #45229, 0.17 #44328), 02j9z (0.17 #1820, 0.08 #47613, 0.08 #45809), 01znc_ (0.17 #1892, 0.02 #27896, 0.01 #32385) >> Best rule #30492 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 0rh6k; 0k049; 02dtg; 013kcv; 0wh3; 05ksh; 094jv; 0f2w0; 013yq; 0pmp2; ... >> query: (?x12668, 09c7w0) <- location_of_ceremony(?x566, ?x12668), jurisdiction_of_office(?x1195, ?x12668), ?x1195 = 0pqc5, ?x566 = 04ztj >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #47581 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 459 *> proper extension: 0jcg8; *> query: (?x12668, ?x94) <- jurisdiction_of_office(?x1195, ?x12668), jurisdiction_of_office(?x1195, ?x12915), jurisdiction_of_office(?x1195, ?x8468), jurisdiction_of_office(?x1195, ?x859), place_of_birth(?x803, ?x859), contains(?x94, ?x12915), contains(?x8468, ?x3360) *> conf = 0.03 ranks of expected_values: 170 EVAL 054rw contains! 04wgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 59.000 54.000 0.605 http://example.org/location/location/contains #12850-053mhx PRED entity: 053mhx PRED relation: student PRED expected values: 010xjr 044zvm => 86 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1376): 0d3k14 (0.33 #8060, 0.08 #18428, 0.05 #26722), 0ff3y (0.33 #2050, 0.07 #12420, 0.06 #16567), 06hx2 (0.33 #7282, 0.06 #17650, 0.04 #32166), 0194xc (0.33 #7849, 0.06 #18217, 0.03 #32733), 030hcs (0.33 #4418, 0.06 #16860, 0.03 #10639), 03_nq (0.33 #7771, 0.05 #11918, 0.04 #16065), 01x6v6 (0.33 #1161, 0.05 #11531, 0.04 #15678), 0cp9f9 (0.33 #1414, 0.05 #11784, 0.04 #15931), 028r4y (0.33 #938, 0.05 #11308, 0.04 #15455), 0280mv7 (0.33 #854, 0.05 #11224, 0.04 #15371) >> Best rule #8060 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01mpwj; >> query: (?x8021, 0d3k14) <- student(?x8021, ?x4630), student(?x8021, ?x879), ?x879 = 01yk13, actor(?x6726, ?x4630), film(?x4630, ?x1451) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 053mhx student 044zvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 28.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 053mhx student 010xjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 28.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #12849-01mxnvc PRED entity: 01mxnvc PRED relation: location PRED expected values: 0k33p => 120 concepts (104 used for prediction) PRED predicted values (max 10 best out of 188): 0k33p (0.25 #1286, 0.06 #6110, 0.05 #6914), 059rby (0.25 #820, 0.04 #17707, 0.03 #13686), 0cc56 (0.25 #861, 0.03 #8901, 0.03 #12118), 04jpl (0.18 #32183, 0.15 #24945, 0.12 #11273), 02_286 (0.09 #72412, 0.09 #74021, 0.09 #75630), 030qb3t (0.09 #13753, 0.08 #17774, 0.08 #72458), 0cr3d (0.06 #5773, 0.05 #6577, 0.04 #22661), 0978r (0.06 #2587, 0.05 #4195, 0.02 #4999), 0ccvx (0.06 #2634, 0.03 #7458, 0.03 #9870), 0dj0x (0.06 #3187, 0.02 #5599, 0.02 #8815) >> Best rule #1286 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0g824; 0473q; >> query: (?x10802, 0k33p) <- instrumentalists(?x4311, ?x10802), instrumentalists(?x228, ?x10802), ?x228 = 0l14qv, ?x4311 = 01xqw, profession(?x10802, ?x2348), ?x2348 = 0nbcg >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mxnvc location 0k33p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 104.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #12848-03kg2v PRED entity: 03kg2v PRED relation: language PRED expected values: 02h40lc => 89 concepts (71 used for prediction) PRED predicted values (max 10 best out of 58): 02h40lc (0.92 #1079, 0.90 #1198, 0.89 #1558), 064_8sq (0.33 #199, 0.29 #140, 0.22 #378), 04h9h (0.33 #43, 0.09 #700, 0.08 #640), 083tk (0.33 #35, 0.03 #718, 0.03 #572), 04306rv (0.25 #182, 0.20 #64, 0.17 #361), 06nm1 (0.20 #70, 0.17 #1148, 0.14 #129), 0653m (0.12 #309, 0.06 #730, 0.05 #1149), 02bjrlw (0.10 #719, 0.07 #1138, 0.06 #658), 06b_j (0.09 #1160, 0.08 #1279, 0.08 #2907), 0jzc (0.08 #1097, 0.08 #797, 0.07 #977) >> Best rule #1079 for best value: >> intensional similarity = 8 >> extensional distance = 70 >> proper extension: 09sh8k; 06gb1w; 01f8hf; >> query: (?x2917, 02h40lc) <- country(?x2917, ?x512), ?x512 = 07ssc, film_crew_role(?x2917, ?x137), produced_by(?x2917, ?x1285), film(?x72, ?x2917), produced_by(?x9642, ?x1285), genre(?x2917, ?x53), edited_by(?x9642, ?x707) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03kg2v language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 71.000 0.917 http://example.org/film/film/language #12847-0462hhb PRED entity: 0462hhb PRED relation: award PRED expected values: 0fq9zdn => 133 concepts (104 used for prediction) PRED predicted values (max 10 best out of 204): 099jhq (0.38 #468, 0.38 #250, 0.28 #1410), 04dn09n (0.28 #1679, 0.25 #268, 0.17 #2151), 040njc (0.28 #1410, 0.28 #1878, 0.28 #2114), 0gqwc (0.28 #1410, 0.28 #1878, 0.28 #2114), 027dtxw (0.28 #1410, 0.28 #1878, 0.28 #2114), 02pqp12 (0.28 #1410, 0.28 #1878, 0.28 #2114), 0gq9h (0.28 #1410, 0.28 #1878, 0.28 #2114), 02n9nmz (0.28 #1410, 0.28 #1878, 0.28 #2114), 0gr4k (0.28 #1410, 0.28 #1878, 0.28 #2114), 0f_nbyh (0.28 #1410, 0.28 #1878, 0.28 #2114) >> Best rule #468 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0b6tzs; 02c638; 04cj79; 07s846j; 0194zl; 02q7fl9; >> query: (?x4756, ?x451) <- nominated_for(?x1180, ?x4756), nominated_for(?x451, ?x4756), titles(?x53, ?x4756), ?x1180 = 02n9nmz, country(?x4756, ?x94), ?x451 = 099jhq >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1410 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 19 *> proper extension: 01f8hf; 0gt1k; *> query: (?x4756, ?x112) <- nominated_for(?x1245, ?x4756), nominated_for(?x112, ?x4756), currency(?x4756, ?x170), film_release_distribution_medium(?x4756, ?x81), ?x1245 = 0gqwc, executive_produced_by(?x4756, ?x4857) *> conf = 0.28 ranks of expected_values: 17 EVAL 0462hhb award 0fq9zdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 133.000 104.000 0.375 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12846-02wr6r PRED entity: 02wr6r PRED relation: place_of_death PRED expected values: 02_286 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 30): 01j2_7 (0.33 #186), 030qb3t (0.18 #2364, 0.17 #1975, 0.16 #1194), 0f__1 (0.12 #1759, 0.09 #1758, 0.09 #2342), 02_286 (0.10 #2746, 0.09 #3332, 0.09 #2160), 0hptm (0.09 #1758, 0.09 #2342, 0.08 #2732), 0rh6k (0.09 #392, 0.08 #782, 0.01 #1565), 0ftkx (0.09 #558), 056_y (0.09 #455), 0f2wj (0.08 #792, 0.04 #1575, 0.04 #2745), 0k049 (0.08 #1956, 0.08 #2345, 0.07 #1175) >> Best rule #186 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0h1m9; >> query: (?x9775, 01j2_7) <- film(?x9775, ?x10831), location(?x9775, ?x2740), people(?x10199, ?x9775), ?x10831 = 0ckrnn >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2746 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 333 *> proper extension: 05dxl_; *> query: (?x9775, 02_286) <- gender(?x9775, ?x231), people(?x10199, ?x9775), place_of_birth(?x9775, ?x6253) *> conf = 0.10 ranks of expected_values: 4 EVAL 02wr6r place_of_death 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 113.000 113.000 0.333 http://example.org/people/deceased_person/place_of_death #12845-01rh0w PRED entity: 01rh0w PRED relation: notable_people_with_this_condition! PRED expected values: 0h99n => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 3): 0h99n (0.06 #93, 0.06 #114, 0.05 #72), 01g2q (0.01 #50), 0j8hd (0.01 #392) >> Best rule #93 for best value: >> intensional similarity = 2 >> extensional distance = 100 >> proper extension: 04d_mtq; >> query: (?x1424, 0h99n) <- friend(?x1424, ?x4005), people(?x1423, ?x1424) >> conf = 0.06 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rh0w notable_people_with_this_condition! 0h99n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.059 http://example.org/medicine/disease/notable_people_with_this_condition #12844-05lls PRED entity: 05lls PRED relation: major_field_of_study! PRED expected values: 035wtd => 53 concepts (28 used for prediction) PRED predicted values (max 10 best out of 640): 0bwfn (0.53 #4448, 0.38 #6224, 0.36 #5632), 06pwq (0.51 #5935, 0.43 #7119, 0.41 #5343), 01w5m (0.46 #6041, 0.43 #5449, 0.41 #7225), 09f2j (0.46 #6101, 0.41 #5509, 0.38 #7285), 03ksy (0.43 #6042, 0.43 #5450, 0.40 #4266), 02zd460 (0.43 #6117, 0.40 #4341, 0.39 #5525), 07szy (0.41 #5966, 0.33 #1228, 0.31 #7150), 08815 (0.40 #4148, 0.38 #5924, 0.36 #5332), 01w3v (0.34 #5938, 0.33 #1200, 0.33 #5346), 017j69 (0.34 #6085, 0.33 #1347, 0.31 #5493) >> Best rule #4448 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0l14jd; >> query: (?x888, 0bwfn) <- student(?x888, ?x5105), profession(?x5105, ?x1032), people(?x11053, ?x5105), artists(?x13359, ?x5105) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #3699 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 01z4y; 02rvwt; *> query: (?x888, 035wtd) <- artists(?x888, ?x6204), location(?x6204, ?x4627), major_field_of_study(?x1200, ?x888) *> conf = 0.17 ranks of expected_values: 148 EVAL 05lls major_field_of_study! 035wtd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 28.000 0.533 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #12843-01d1yr PRED entity: 01d1yr PRED relation: nationality PRED expected values: 0d05w3 => 100 concepts (96 used for prediction) PRED predicted values (max 10 best out of 41): 09c7w0 (0.85 #5858, 0.84 #5958, 0.79 #6255), 0d05w3 (0.35 #7647, 0.34 #7747, 0.02 #1930), 07ssc (0.25 #3569, 0.12 #509, 0.11 #1103), 04p3c (0.25 #3569), 02jx1 (0.13 #824, 0.13 #527, 0.13 #2012), 03rk0 (0.07 #7891, 0.07 #6798, 0.06 #8981), 0345h (0.06 #3072, 0.05 #3370, 0.05 #3867), 03_3d (0.06 #3072, 0.05 #3370, 0.05 #3867), 03gj2 (0.06 #3072, 0.05 #3370, 0.05 #3867), 02vzc (0.06 #3072, 0.05 #3370, 0.05 #3867) >> Best rule #5858 for best value: >> intensional similarity = 3 >> extensional distance = 1633 >> proper extension: 0143wl; 03m6pk; 01bbwp; >> query: (?x6374, 09c7w0) <- film(?x6374, ?x5230), nationality(?x6374, ?x279), first_level_division_of(?x1905, ?x279) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #7647 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2194 *> proper extension: 09jm8; *> query: (?x6374, ?x2346) <- award_nominee(?x10668, ?x6374), nationality(?x10668, ?x2346), gender(?x10668, ?x231) *> conf = 0.35 ranks of expected_values: 2 EVAL 01d1yr nationality 0d05w3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 96.000 0.854 http://example.org/people/person/nationality #12842-07cz2 PRED entity: 07cz2 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.69 #46, 0.68 #84, 0.59 #696), 09zzb8 (0.57 #689, 0.56 #383, 0.55 #497), 09vw2b7 (0.51 #695, 0.50 #350, 0.49 #83), 01vx2h (0.36 #356, 0.27 #89, 0.24 #165), 0dxtw (0.33 #355, 0.29 #700, 0.26 #164), 01pvkk (0.23 #90, 0.22 #1006, 0.22 #357), 0d2b38 (0.19 #104, 0.19 #66, 0.11 #371), 02ynfr (0.16 #94, 0.16 #56, 0.15 #361), 02rh1dz (0.14 #354, 0.09 #49, 0.08 #87), 0215hd (0.12 #97, 0.11 #403, 0.11 #479) >> Best rule #46 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 0gkz15s; 02qm_f; 0qm8b; 0gj9tn5; 0gfsq9; 0dr3sl; 01jrbb; 04tqtl; 051zy_b; 017jd9; ... >> query: (?x2770, 0ch6mp2) <- nominated_for(?x298, ?x2770), ?x298 = 05ztjjw, production_companies(?x2770, ?x2548) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #695 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 732 *> proper extension: 04grkmd; 058kh7; *> query: (?x2770, 09vw2b7) <- film(?x6917, ?x2770), produced_by(?x2770, ?x5781), participant(?x6917, ?x719) *> conf = 0.51 ranks of expected_values: 3 EVAL 07cz2 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 68.000 0.688 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12841-02d6n_ PRED entity: 02d6n_ PRED relation: type_of_union PRED expected values: 04ztj => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.78 #29, 0.76 #17, 0.74 #21), 01g63y (0.65 #45, 0.56 #167, 0.52 #66), 0jgjn (0.01 #36), 01bl8s (0.01 #23) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 0lh0c; >> query: (?x11220, 04ztj) <- profession(?x11220, ?x1032), special_performance_type(?x11220, ?x4832), ?x4832 = 01pb34 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d6n_ type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.778 http://example.org/people/person/spouse_s./people/marriage/type_of_union #12840-0cq7kw PRED entity: 0cq7kw PRED relation: honored_for! PRED expected values: 0c4hx0 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 117): 0c53zb (0.12 #2075, 0.04 #172, 0.02 #294), 0d__c3 (0.12 #2075, 0.02 #475, 0.02 #231), 0c4hx0 (0.12 #2075, 0.02 #234, 0.02 #3296), 0fz2y7 (0.12 #2075, 0.02 #3296), 0jzphpx (0.12 #2075), 0dznvw (0.09 #118, 0.05 #240, 0.02 #3296), 0bvhz9 (0.06 #602, 0.05 #114, 0.04 #480), 03gwpw2 (0.05 #1957, 0.05 #493, 0.04 #615), 0275n3y (0.05 #552, 0.05 #64, 0.03 #430), 02pgky2 (0.05 #564, 0.03 #442, 0.02 #2028) >> Best rule #2075 for best value: >> intensional similarity = 4 >> extensional distance = 286 >> proper extension: 03y3bp7; >> query: (?x4504, ?x4388) <- nominated_for(?x4505, ?x4504), award(?x4505, ?x601), category(?x4504, ?x134), award_winner(?x4388, ?x4505) >> conf = 0.12 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0cq7kw honored_for! 0c4hx0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 98.000 0.118 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12839-0gjv_ PRED entity: 0gjv_ PRED relation: institution! PRED expected values: 02h4rq6 01kxxq => 160 concepts (113 used for prediction) PRED predicted values (max 10 best out of 12): 02h4rq6 (0.83 #129, 0.75 #653, 0.75 #510), 013zdg (0.56 #34, 0.32 #1313, 0.32 #1135), 07s6fsf (0.53 #171, 0.49 #652, 0.44 #15), 02mjs7 (0.37 #159, 0.22 #32, 0.22 #1566), 0bjrnt (0.33 #18, 0.32 #1501, 0.32 #1313), 071tyz (0.32 #1501, 0.32 #1313, 0.32 #1135), 01ysy9 (0.32 #1501, 0.32 #1313, 0.32 #1135), 01gkg3 (0.32 #1501, 0.03 #404, 0.02 #475), 022h5x (0.32 #1313, 0.32 #1135, 0.22 #1566), 03mkk4 (0.29 #134, 0.26 #402, 0.24 #177) >> Best rule #129 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 071_8; 01xvlc; >> query: (?x6127, 02h4rq6) <- major_field_of_study(?x6127, ?x1682), institution(?x1368, ?x6127), ?x1368 = 014mlp, ?x1682 = 02ky346 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12 EVAL 0gjv_ institution! 01kxxq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 160.000 113.000 0.829 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0gjv_ institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 113.000 0.829 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #12838-0190vc PRED entity: 0190vc PRED relation: artist PRED expected values: 0411q => 33 concepts (9 used for prediction) PRED predicted values (max 10 best out of 831): 0qf3p (0.33 #153, 0.21 #1828, 0.17 #3502), 01vtj38 (0.33 #528, 0.18 #2203, 0.17 #3040), 0dtd6 (0.33 #112, 0.18 #1787, 0.15 #3461), 0134s5 (0.33 #236, 0.18 #1911, 0.14 #1073), 06gcn (0.33 #554, 0.18 #2229, 0.13 #3903), 0178kd (0.33 #452, 0.15 #2127, 0.14 #1289), 0144l1 (0.33 #119, 0.14 #956, 0.12 #1794), 07c0j (0.33 #55, 0.12 #1730, 0.11 #2567), 02qwg (0.33 #234, 0.10 #1071, 0.10 #5024), 0b_xm (0.33 #557, 0.10 #1394, 0.09 #2232) >> Best rule #153 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 03qy3l; >> query: (?x11666, 0qf3p) <- artist(?x11666, ?x8305), artist(?x11666, ?x3401), ?x3401 = 01wz_ml, artists(?x3319, ?x8305), instrumentalists(?x227, ?x8305), ?x227 = 0342h, ?x3319 = 06j6l, film(?x8305, ?x9507) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 03qy3l; *> query: (?x11666, 0411q) <- artist(?x11666, ?x8305), artist(?x11666, ?x3401), ?x3401 = 01wz_ml, artists(?x3319, ?x8305), instrumentalists(?x227, ?x8305), ?x227 = 0342h, ?x3319 = 06j6l, film(?x8305, ?x9507) *> conf = 0.33 ranks of expected_values: 12 EVAL 0190vc artist 0411q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 33.000 9.000 0.333 http://example.org/music/record_label/artist #12837-05k7sb PRED entity: 05k7sb PRED relation: state_province_region! PRED expected values: 01c6k4 0kz2w => 151 concepts (102 used for prediction) PRED predicted values (max 10 best out of 752): 017v71 (0.30 #46218, 0.30 #23102, 0.29 #47666), 02g839 (0.30 #46218, 0.30 #23102, 0.29 #47666), 01ky7c (0.30 #46218, 0.29 #47666, 0.29 #48389), 0lfgr (0.30 #46218, 0.29 #47666, 0.29 #48389), 03ksy (0.30 #46218, 0.29 #47666, 0.29 #48389), 0ty_b (0.23 #23824, 0.23 #16599, 0.23 #8663), 01m2n1 (0.23 #23824, 0.23 #16599, 0.23 #8663), 01m8dg (0.23 #23824, 0.23 #16599, 0.23 #8663), 01m7mv (0.23 #23824, 0.23 #16599, 0.23 #8663), 0t_48 (0.23 #23824, 0.23 #16599, 0.23 #8663) >> Best rule #46218 for best value: >> intensional similarity = 4 >> extensional distance = 126 >> proper extension: 012wyq; >> query: (?x2020, ?x1151) <- contains(?x2020, ?x12012), contains(?x2020, ?x1151), school_type(?x1151, ?x3205), place_of_birth(?x6320, ?x12012) >> conf = 0.30 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 05k7sb state_province_region! 0kz2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 151.000 102.000 0.298 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 05k7sb state_province_region! 01c6k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 151.000 102.000 0.298 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #12836-04n7njg PRED entity: 04n7njg PRED relation: award PRED expected values: 0h3vhfb => 138 concepts (133 used for prediction) PRED predicted values (max 10 best out of 383): 0cjyzs (0.50 #513, 0.36 #7415, 0.35 #6603), 0fbtbt (0.36 #10790, 0.31 #6730, 0.30 #9978), 0gqz2 (0.33 #1705, 0.25 #487, 0.17 #3329), 0cjcbg (0.33 #367, 0.22 #2397, 0.22 #1991), 05zr6wv (0.33 #829, 0.22 #2047, 0.21 #5295), 0drtkx (0.33 #300, 0.11 #2330, 0.11 #4766), 0cc8l6d (0.30 #3017, 0.29 #1393, 0.22 #2611), 0ck27z (0.27 #21612, 0.25 #22424, 0.25 #27297), 09sb52 (0.26 #4913, 0.25 #3289, 0.23 #30902), 025m8l (0.25 #526, 0.22 #1744, 0.17 #932) >> Best rule #513 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0bg539; 08n__5; >> query: (?x1182, 0cjyzs) <- profession(?x1182, ?x1183), program_creator(?x8554, ?x1182), tv_program(?x1182, ?x11454), ?x1183 = 09jwl, gender(?x1182, ?x231) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #30861 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 734 *> proper extension: 02xb2bt; *> query: (?x1182, ?x11338) <- nationality(?x1182, ?x94), nominated_for(?x1182, ?x8554), nominated_for(?x11338, ?x8554), actor(?x8554, ?x381) *> conf = 0.15 ranks of expected_values: 37 EVAL 04n7njg award 0h3vhfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 138.000 133.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12835-029_l PRED entity: 029_l PRED relation: type_of_union PRED expected values: 04ztj => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #53, 0.71 #29, 0.70 #69), 01g63y (0.20 #10, 0.17 #14, 0.16 #18) >> Best rule #53 for best value: >> intensional similarity = 2 >> extensional distance = 1163 >> proper extension: 02wrhj; 01ry0f; 02784z; 05p606; >> query: (?x5377, 04ztj) <- film(?x5377, ?x3455), honored_for(?x2220, ?x3455) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 029_l type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.721 http://example.org/people/person/spouse_s./people/marriage/type_of_union #12834-01gkgk PRED entity: 01gkgk PRED relation: jurisdiction_of_office PRED expected values: 05k7sb => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1618): 081yw (0.71 #4370, 0.67 #461, 0.60 #2050), 05kkh (0.71 #4175, 0.67 #461, 0.50 #5563), 07_f2 (0.67 #461, 0.60 #2131, 0.57 #4451), 07b_l (0.67 #461, 0.60 #2008, 0.43 #4328), 059rby (0.67 #461, 0.57 #4184, 0.40 #5572), 05kj_ (0.67 #461, 0.45 #6016, 0.43 #4197), 07srw (0.67 #461, 0.45 #6016, 0.43 #4272), 05fjy (0.67 #461, 0.45 #6016, 0.40 #2076), 059_c (0.67 #461, 0.45 #6016, 0.40 #1897), 01n4w (0.67 #461, 0.45 #6016, 0.40 #1985) >> Best rule #4370 for best value: >> intensional similarity = 15 >> extensional distance = 5 >> proper extension: 0fkzq; >> query: (?x2358, 081yw) <- jurisdiction_of_office(?x2358, ?x1227), basic_title(?x2357, ?x2358), location(?x397, ?x1227), contains(?x1227, ?x8811), contains(?x1227, ?x4555), profession(?x2357, ?x353), state_province_region(?x9205, ?x1227), jurisdiction_of_office(?x12525, ?x1227), source(?x8811, ?x958), school_type(?x4555, ?x1507), featured_film_locations(?x324, ?x8811), student(?x4555, ?x496), district_represented(?x176, ?x1227), company(?x1600, ?x9205), religion(?x1227, ?x109) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #461 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 0f6c3; *> query: (?x2358, ?x177) <- jurisdiction_of_office(?x2358, ?x448), basic_title(?x2357, ?x2358), person(?x6093, ?x2357), student(?x9691, ?x2357), legislative_sessions(?x2357, ?x1027), celebrities_impersonated(?x5915, ?x2357), legislative_sessions(?x2860, ?x1027), district_represented(?x1027, ?x177), legislative_sessions(?x6743, ?x1027), legislative_sessions(?x356, ?x1027), ?x6743 = 04h1rz, ?x448 = 03v1s, ?x356 = 05l2z4 *> conf = 0.67 ranks of expected_values: 11 EVAL 01gkgk jurisdiction_of_office 05k7sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 21.000 21.000 0.714 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #12833-0dhrqx PRED entity: 0dhrqx PRED relation: nationality PRED expected values: 03spz => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 59): 09c7w0 (0.84 #801, 0.80 #2604, 0.80 #3304), 07ssc (0.40 #15, 0.19 #315, 0.18 #715), 02jx1 (0.28 #1233, 0.28 #933, 0.28 #1433), 0chghy (0.20 #10, 0.11 #510, 0.10 #610), 05bcl (0.20 #60, 0.08 #760, 0.07 #4504), 0ctw_b (0.09 #527, 0.08 #627, 0.08 #1427), 0j5g9 (0.08 #962, 0.07 #4504, 0.07 #1162), 06qd3 (0.08 #936, 0.07 #4504, 0.07 #1136), 015fr (0.07 #4504, 0.07 #317, 0.06 #517), 06mkj (0.07 #4504, 0.07 #447, 0.06 #1047) >> Best rule #801 for best value: >> intensional similarity = 6 >> extensional distance = 48 >> proper extension: 01vsy9_; >> query: (?x8324, 09c7w0) <- athlete(?x471, ?x8324), people(?x1050, ?x8324), people(?x1050, ?x10259), people(?x1050, ?x8966), award_winner(?x91, ?x8966), award_nominee(?x513, ?x10259) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2770 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 247 *> proper extension: 022769; 0p51w; 0dzkq; 03_0p; 017khj; 03bw6; 02ln1; 07h1q; 02784z; 047g6; *> query: (?x8324, 03spz) <- gender(?x8324, ?x231), ?x231 = 05zppz, people(?x1050, ?x8324), ?x1050 = 041rx *> conf = 0.02 ranks of expected_values: 55 EVAL 0dhrqx nationality 03spz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 52.000 52.000 0.840 http://example.org/people/person/nationality #12832-05p09zm PRED entity: 05p09zm PRED relation: award_winner PRED expected values: 04zqmj => 49 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1918): 0jrqq (0.50 #3286, 0.22 #5740, 0.12 #13110), 0p__8 (0.44 #6227, 0.33 #36839, 0.33 #3773), 015f7 (0.36 #29471, 0.35 #51575, 0.33 #36839), 04xrx (0.36 #29471, 0.33 #36839, 0.33 #24556), 01pgzn_ (0.36 #29471, 0.33 #36839, 0.33 #24556), 0f7hc (0.36 #29471, 0.33 #36839, 0.33 #24556), 02z4b_8 (0.35 #11383, 0.25 #1562, 0.20 #8926), 019vgs (0.35 #51575, 0.33 #36839, 0.32 #19645), 0f502 (0.35 #51575, 0.33 #5867, 0.33 #24556), 0bksh (0.35 #51575, 0.32 #19645, 0.30 #19644) >> Best rule #3286 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 05f4m9q; 05q8pss; >> query: (?x2325, 0jrqq) <- award(?x1372, ?x2325), award_winner(?x2325, ?x521), award(?x286, ?x2325), ?x1372 = 01kff7 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #46663 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 177 *> proper extension: 09v7wsg; *> query: (?x2325, ?x541) <- award(?x770, ?x2325), award_winner(?x2325, ?x1335), location(?x1335, ?x2850), award_nominee(?x1335, ?x541) *> conf = 0.08 ranks of expected_values: 478 EVAL 05p09zm award_winner 04zqmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 22.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #12831-03h4mp PRED entity: 03h4mp PRED relation: category PRED expected values: 08mbj5d => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.79 #65, 0.79 #74, 0.77 #83) >> Best rule #65 for best value: >> intensional similarity = 3 >> extensional distance = 319 >> proper extension: 01qkqwg; 01vsy3q; 0191h5; 051m56; 0f6lx; 013rds; >> query: (?x3690, 08mbj5d) <- artists(?x4910, ?x3690), award_winner(?x1079, ?x3690), type_of_union(?x3690, ?x566) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03h4mp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.791 http://example.org/common/topic/webpage./common/webpage/category #12830-01x6jd PRED entity: 01x6jd PRED relation: gender PRED expected values: 02zsn => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #70, 0.72 #21, 0.72 #139), 02zsn (0.52 #41, 0.52 #102, 0.46 #161) >> Best rule #70 for best value: >> intensional similarity = 3 >> extensional distance = 1780 >> proper extension: 017r2; 026lj; 0mj0c; 01l4g5; 05gpy; 034ls; 0c8br; 01wxdn3; 03d8njj; 01g0jn; ... >> query: (?x12003, 05zppz) <- profession(?x12003, ?x1032), student(?x5149, ?x12003), institution(?x620, ?x5149) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #41 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1113 *> proper extension: 01p0w_; *> query: (?x12003, ?x514) <- award_nominee(?x12003, ?x3709), gender(?x3709, ?x514), student(?x5149, ?x12003) *> conf = 0.52 ranks of expected_values: 2 EVAL 01x6jd gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.726 http://example.org/people/person/gender #12829-088fh PRED entity: 088fh PRED relation: colors! PRED expected values: 01n6r0 0ylgz => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1513): 0bsnm (0.60 #3081, 0.36 #4950, 0.33 #2147), 016sd3 (0.50 #4577, 0.36 #5044, 0.36 #5511), 01s7pm (0.50 #4618, 0.33 #2749, 0.33 #877), 015zyd (0.50 #1873, 0.03 #7481), 021996 (0.43 #4023, 0.40 #3089, 0.38 #4491), 02607j (0.43 #3829, 0.40 #2895, 0.38 #4297), 07lx1s (0.43 #3769, 0.36 #4704, 0.33 #2368), 01hjy5 (0.43 #4021, 0.33 #2620, 0.33 #280), 0yls9 (0.43 #3947, 0.33 #2546, 0.33 #206), 02vnp2 (0.40 #3604, 0.40 #3137, 0.38 #4539) >> Best rule #3081 for best value: >> intensional similarity = 31 >> extensional distance = 3 >> proper extension: 04mkbj; >> query: (?x3621, 0bsnm) <- colors(?x12905, ?x3621), colors(?x6837, ?x3621), colors(?x4556, ?x3621), colors(?x3949, ?x3621), citytown(?x3949, ?x7548), contains(?x94, ?x3949), school(?x8786, ?x4556), student(?x3949, ?x6360), position(?x12905, ?x530), position(?x12905, ?x60), institution(?x865, ?x3949), teams(?x9124, ?x12905), major_field_of_study(?x4556, ?x888), currency(?x4556, ?x170), major_field_of_study(?x3949, ?x6870), ?x530 = 02_j1w, team(?x8576, ?x12905), school(?x3333, ?x4556), currency(?x6837, ?x1099), contains(?x3302, ?x6837), institution(?x1200, ?x6837), organization(?x3484, ?x3949), student(?x6837, ?x488), organization(?x11157, ?x6837), award_nominee(?x5831, ?x6360), award_winner(?x6360, ?x494), ?x60 = 02nzb8, award_winner(?x873, ?x6360), award_winner(?x1670, ?x6360), ?x5831 = 0dyztm, ?x3333 = 01yjl >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1076 for first EXPECTED value: *> intensional similarity = 33 *> extensional distance = 1 *> proper extension: 038hg; *> query: (?x3621, 01n6r0) <- colors(?x14124, ?x3621), colors(?x12905, ?x3621), colors(?x3620, ?x3621), colors(?x11516, ?x3621), colors(?x4556, ?x3621), colors(?x3949, ?x3621), citytown(?x3949, ?x7548), contains(?x94, ?x3949), school(?x8786, ?x4556), student(?x3949, ?x3402), position(?x12905, ?x63), institution(?x865, ?x3949), teams(?x9124, ?x12905), major_field_of_study(?x4556, ?x888), currency(?x4556, ?x170), major_field_of_study(?x3949, ?x10417), ?x11516 = 01xysf, school(?x12042, ?x4556), ?x63 = 02sdk9v, film(?x3402, ?x1477), major_field_of_study(?x4981, ?x10417), ?x4981 = 03bwzr4, nominated_for(?x3402, ?x2710), award(?x3402, ?x693), award_nominee(?x3402, ?x806), major_field_of_study(?x2396, ?x10417), ?x2396 = 07xpm, profession(?x3402, ?x1032), current_club(?x978, ?x3620), position(?x14124, ?x5234), ?x12042 = 05xvj, team(?x2918, ?x14124), gender(?x3402, ?x231) *> conf = 0.33 ranks of expected_values: 361, 364 EVAL 088fh colors! 0ylgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.600 http://example.org/education/educational_institution/colors EVAL 088fh colors! 01n6r0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.600 http://example.org/education/educational_institution/colors #12828-01shy7 PRED entity: 01shy7 PRED relation: film_crew_role PRED expected values: 09zzb8 02r96rf => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 22): 09zzb8 (0.74 #526, 0.71 #774, 0.69 #739), 02r96rf (0.70 #284, 0.69 #319, 0.68 #249), 09vw2b7 (0.67 #112, 0.64 #532, 0.58 #780), 01vx2h (0.41 #292, 0.37 #187, 0.32 #327), 0dxtw (0.35 #536, 0.32 #784, 0.32 #749), 02vs3x5 (0.33 #23, 0.22 #163, 0.08 #233), 02rh1dz (0.21 #255, 0.20 #185, 0.18 #290), 0d2b38 (0.20 #200, 0.19 #270, 0.18 #305), 0215hd (0.20 #53, 0.17 #88, 0.14 #368), 089g0h (0.20 #54, 0.17 #89, 0.14 #229) >> Best rule #526 for best value: >> intensional similarity = 3 >> extensional distance = 680 >> proper extension: 05dy7p; 02n9bh; 04lqvly; 027ct7c; >> query: (?x2644, 09zzb8) <- nominated_for(?x400, ?x2644), film_crew_role(?x2644, ?x1284), ?x1284 = 0ch6mp2 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01shy7 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.740 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01shy7 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.740 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12827-0391jz PRED entity: 0391jz PRED relation: award PRED expected values: 09sb52 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 252): 09sb52 (0.37 #1249, 0.34 #7697, 0.32 #2458), 0cqhk0 (0.18 #36, 0.09 #4469, 0.09 #4066), 03c7tr1 (0.18 #21764, 0.15 #31441, 0.14 #1267), 02x4w6g (0.18 #21764, 0.15 #31441, 0.13 #12897), 05zrvfd (0.18 #21764, 0.15 #31441, 0.13 #12897), 05p09zm (0.17 #1332, 0.14 #2541, 0.13 #12897), 04kxsb (0.17 #1334, 0.13 #12897, 0.12 #2543), 0f4x7 (0.16 #1239, 0.15 #31441, 0.13 #12897), 0gqwc (0.15 #2089, 0.15 #2492, 0.15 #74), 099t8j (0.15 #31441, 0.13 #12897, 0.11 #140) >> Best rule #1249 for best value: >> intensional similarity = 3 >> extensional distance = 178 >> proper extension: 0c01c; 0h32q; 0hwbd; 02t__3; 06_bq1; >> query: (?x3560, 09sb52) <- award_winner(?x1707, ?x3560), participant(?x3560, ?x6242), award_winner(?x5242, ?x3560) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0391jz award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.367 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12826-0ckd1 PRED entity: 0ckd1 PRED relation: producer_type! PRED expected values: 02ndbd 03cs_z7 0crx5w 07b3r9 06msq2 01_x6d 08q3s0 05gp3x 02b9g4 06q8hf 01qbjg 07jrjb 01ynzf 01j851 02wk_43 0fz27v 04j_gs 0f87jy 0133sq 02q4mt 066yfh 05bnx3j => 32 concepts (28 used for prediction) No prediction ranks of expected_values: EVAL 0ckd1 producer_type! 05bnx3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 066yfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 02q4mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 0133sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 0f87jy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 04j_gs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 0fz27v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 02wk_43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 01j851 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 01ynzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 07jrjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 01qbjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 06q8hf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 02b9g4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 05gp3x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 08q3s0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 01_x6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 06msq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 07b3r9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 0crx5w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 03cs_z7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 02ndbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 28.000 0.000 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #12825-03x33n PRED entity: 03x33n PRED relation: school_type PRED expected values: 01_srz => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 18): 01rs41 (0.34 #1017, 0.28 #1790, 0.28 #731), 05pcjw (0.31 #1015, 0.31 #287, 0.28 #463), 07tf8 (0.22 #227, 0.19 #73, 0.19 #117), 01_srz (0.14 #24, 0.11 #46, 0.09 #1016), 02p0qmm (0.07 #581, 0.05 #1066, 0.05 #714), 04399 (0.05 #188, 0.05 #364, 0.05 #342), 02dk5q (0.03 #291, 0.02 #644, 0.02 #1526), 01y64 (0.03 #318, 0.03 #98, 0.03 #1995), 01jlsn (0.03 #2000, 0.02 #2044, 0.02 #654), 0m4mb (0.03 #1994, 0.02 #2038, 0.02 #1530) >> Best rule #1017 for best value: >> intensional similarity = 4 >> extensional distance = 254 >> proper extension: 01rr31; 0172jm; 03205_; >> query: (?x4161, 01rs41) <- currency(?x4161, ?x170), major_field_of_study(?x4161, ?x2606), ?x170 = 09nqf, school_type(?x4161, ?x1507) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0fvvz; 0tln7; 013d7t; 0tk02; 0tn9j; 013gz; *> query: (?x4161, 01_srz) <- contains(?x3908, ?x4161), category(?x4161, ?x134), ?x3908 = 04ly1, ?x134 = 08mbj5d *> conf = 0.14 ranks of expected_values: 4 EVAL 03x33n school_type 01_srz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 163.000 163.000 0.344 http://example.org/education/educational_institution/school_type #12824-01tsbmv PRED entity: 01tsbmv PRED relation: award_winner! PRED expected values: 02z13jg => 91 concepts (59 used for prediction) PRED predicted values (max 10 best out of 254): 0bfvd4 (0.37 #18126, 0.37 #18125, 0.36 #15966), 04kxsb (0.37 #18126, 0.37 #18125, 0.36 #15966), 0bs0bh (0.37 #18126, 0.37 #18125, 0.36 #15966), 0ck27z (0.24 #8723, 0.19 #4406, 0.07 #20376), 09sb52 (0.23 #41, 0.14 #8240, 0.13 #7376), 0bdwqv (0.17 #171, 0.05 #12252, 0.05 #12684), 099tbz (0.13 #58, 0.11 #489, 0.10 #920), 027dtxw (0.13 #4, 0.05 #435, 0.04 #25028), 03nqnk3 (0.10 #135, 0.05 #4880, 0.05 #1428), 027c95y (0.08 #7924, 0.07 #158, 0.06 #7493) >> Best rule #18126 for best value: >> intensional similarity = 3 >> extensional distance = 1091 >> proper extension: 04cw0j; 0fvt2; >> query: (?x11684, ?x2192) <- student(?x2486, ?x11684), award_winner(?x6878, ?x11684), award(?x11684, ?x2192) >> conf = 0.37 => this is the best rule for 3 predicted values *> Best rule #7816 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 352 *> proper extension: 01vvycq; *> query: (?x11684, 02z13jg) <- people(?x6736, ?x11684), award(?x11684, ?x6878), award(?x9526, ?x6878), ?x9526 = 01gbn6 *> conf = 0.03 ranks of expected_values: 81 EVAL 01tsbmv award_winner! 02z13jg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 91.000 59.000 0.365 http://example.org/award/award_category/winners./award/award_honor/award_winner #12823-06q1r PRED entity: 06q1r PRED relation: nationality! PRED expected values: 02tr7d 01wk3c 05m7zg => 251 concepts (150 used for prediction) PRED predicted values (max 10 best out of 4046): 0d1_f (0.67 #64552, 0.55 #92797, 0.54 #108936), 0g_92 (0.56 #455910, 0.24 #250148, 0.07 #35105), 01_k0d (0.56 #455910, 0.24 #250148, 0.07 #34337), 02tr7d (0.56 #455910, 0.24 #250148), 026lj (0.52 #342946, 0.39 #302597, 0.34 #476084), 06mr6 (0.39 #302597, 0.34 #476084, 0.07 #34086), 0263tn1 (0.37 #306632, 0.14 #18729, 0.13 #34866), 015t7v (0.37 #306632, 0.14 #17668, 0.13 #33805), 05y5kf (0.37 #306632, 0.14 #17614, 0.13 #33751), 01846t (0.37 #306632, 0.14 #17033, 0.07 #33170) >> Best rule #64552 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 06frc; >> query: (?x6401, ?x3444) <- jurisdiction_of_office(?x3444, ?x6401), capital(?x6401, ?x6885), form_of_government(?x6401, ?x6065) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #455910 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 059g4; *> query: (?x6401, ?x12575) <- contains(?x6401, ?x14119), film_release_region(?x2155, ?x6401), place_of_birth(?x12575, ?x14119) *> conf = 0.56 ranks of expected_values: 4, 744 EVAL 06q1r nationality! 05m7zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 251.000 150.000 0.667 http://example.org/people/person/nationality EVAL 06q1r nationality! 01wk3c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 251.000 150.000 0.667 http://example.org/people/person/nationality EVAL 06q1r nationality! 02tr7d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 251.000 150.000 0.667 http://example.org/people/person/nationality #12822-04qr6d PRED entity: 04qr6d PRED relation: profession PRED expected values: 01c72t => 84 concepts (64 used for prediction) PRED predicted values (max 10 best out of 93): 02hrh1q (0.94 #2463, 0.92 #2751, 0.92 #2607), 01c72t (0.79 #886, 0.62 #2328, 0.61 #1895), 0cbd2 (0.71 #582, 0.40 #150, 0.39 #7078), 09jwl (0.59 #4779, 0.55 #3913, 0.55 #5069), 03gjzk (0.51 #7229, 0.36 #6074, 0.35 #7661), 0nbcg (0.45 #3926, 0.42 #4503, 0.42 #5082), 0dz3r (0.37 #4764, 0.36 #5054, 0.35 #3898), 02krf9 (0.37 #889, 0.20 #4065, 0.16 #7241), 016z4k (0.36 #3900, 0.35 #4477, 0.34 #4911), 01c8w0 (0.34 #1880, 0.33 #1736, 0.33 #1592) >> Best rule #2463 for best value: >> intensional similarity = 7 >> extensional distance = 167 >> proper extension: 05d7rk; 067jsf; 0292l3; 040wdl; 02vmzp; 01n8_g; 06pwf6; 0241wg; 0288crq; 02wxvtv; ... >> query: (?x8756, 02hrh1q) <- nationality(?x8756, ?x2146), profession(?x8756, ?x987), ?x2146 = 03rk0, profession(?x12775, ?x987), profession(?x9167, ?x987), ?x9167 = 07pzc, ?x12775 = 01s7z0 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #886 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 17 *> proper extension: 015grj; 0c8hct; *> query: (?x8756, 01c72t) <- profession(?x8756, ?x8353), profession(?x8756, ?x987), profession(?x8756, ?x524), ?x987 = 0dxtg, ?x524 = 02jknp, profession(?x11718, ?x8353), ?x11718 = 0561xh *> conf = 0.79 ranks of expected_values: 2 EVAL 04qr6d profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 64.000 0.941 http://example.org/people/person/profession #12821-028qyn PRED entity: 028qyn PRED relation: artists! PRED expected values: 064t9 => 134 concepts (64 used for prediction) PRED predicted values (max 10 best out of 222): 064t9 (0.69 #5300, 0.55 #1879, 0.51 #6233), 06by7 (0.62 #1265, 0.48 #5308, 0.47 #1887), 0gywn (0.53 #5345, 0.47 #1924, 0.33 #3479), 025sc50 (0.42 #5337, 0.37 #1916, 0.27 #6581), 0155w (0.33 #1352, 0.24 #3529, 0.22 #5395), 0glt670 (0.33 #1907, 0.30 #6572, 0.30 #5328), 016clz (0.29 #1871, 0.24 #10896, 0.21 #8405), 09nwwf (0.27 #2004, 0.05 #6669, 0.04 #11029), 05bt6j (0.26 #1288, 0.25 #5642, 0.23 #5331), 02lnbg (0.24 #5346, 0.20 #1925, 0.17 #6590) >> Best rule #5300 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 07mvp; 011z3g; 0178_w; 016376; 012x03; >> query: (?x10539, 064t9) <- award_winner(?x724, ?x10539), artists(?x3319, ?x10539), ?x3319 = 06j6l >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028qyn artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 64.000 0.693 http://example.org/music/genre/artists #12820-04110lv PRED entity: 04110lv PRED relation: honored_for PRED expected values: 03hmt9b 0btpm6 => 45 concepts (26 used for prediction) PRED predicted values (max 10 best out of 505): 0gjk1d (0.33 #658, 0.25 #1250, 0.20 #2436), 011yd2 (0.33 #730, 0.25 #1322, 0.20 #2508), 0f4vx (0.33 #760, 0.25 #1352, 0.20 #2538), 016fyc (0.33 #612, 0.25 #1204, 0.20 #2390), 01gc7 (0.33 #605, 0.25 #1197, 0.20 #2383), 04t9c0 (0.33 #917, 0.25 #1509, 0.20 #2695), 011yfd (0.33 #826, 0.25 #1418, 0.20 #2604), 023p7l (0.33 #811, 0.25 #1403, 0.20 #2589), 01cmp9 (0.33 #359, 0.08 #7474, 0.07 #8069), 064lsn (0.33 #369, 0.08 #7484, 0.07 #8079) >> Best rule #658 for best value: >> intensional similarity = 18 >> extensional distance = 1 >> proper extension: 05q7cj; >> query: (?x7936, 0gjk1d) <- ceremony(?x5409, ?x7936), ceremony(?x1972, ?x7936), ceremony(?x1862, ?x7936), ceremony(?x1243, ?x7936), honored_for(?x7936, ?x2490), ?x1972 = 0gqyl, award_winner(?x7936, ?x2443), ?x5409 = 0gr07, award(?x2443, ?x3019), award(?x2443, ?x2375), ?x3019 = 057xs89, participant(?x262, ?x2443), spouse(?x2443, ?x6977), ?x2375 = 04kxsb, award_winner(?x3078, ?x2443), type_of_union(?x2443, ?x1873), ?x1862 = 0gr51, ?x1243 = 0gr0m >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #11874 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 24 *> proper extension: 0bzm__; 073hd1; *> query: (?x7936, ?x4007) <- ceremony(?x6860, ?x7936), ceremony(?x1972, ?x7936), ceremony(?x1313, ?x7936), ceremony(?x484, ?x7936), honored_for(?x7936, ?x2490), award_winner(?x7936, ?x276), award(?x4367, ?x1972), ?x6860 = 018wdw, nominated_for(?x1972, ?x9533), nominated_for(?x1972, ?x8773), ?x484 = 0gq_v, ?x9533 = 02b6n9, ?x1313 = 0gs9p, award_winner(?x1972, ?x1559), ?x8773 = 0cq806, award_winner(?x762, ?x4367), film(?x276, ?x4007) *> conf = 0.13 ranks of expected_values: 93, 131 EVAL 04110lv honored_for 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 45.000 26.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 04110lv honored_for 03hmt9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 45.000 26.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12819-03nfnx PRED entity: 03nfnx PRED relation: film_distribution_medium PRED expected values: 0735l => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.83 #76, 0.80 #172, 0.78 #95), 029j_ (0.25 #20, 0.20 #91, 0.16 #104), 0dq6p (0.14 #93, 0.12 #28, 0.12 #22), 02nxhr (0.12 #27, 0.12 #92, 0.11 #33) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 52 >> proper extension: 0dq626; 07x4qr; 04z257; 0k54q; 0ndsl1x; 02bj22; 0df2zx; >> query: (?x8075, 0735l) <- production_companies(?x8075, ?x902), film(?x5222, ?x8075), currency(?x5222, ?x170), award_nominee(?x5222, ?x5413), region(?x8075, ?x512) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03nfnx film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.833 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #12818-0dc_ms PRED entity: 0dc_ms PRED relation: category PRED expected values: 08mbj5d => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.56 #5, 0.33 #9, 0.32 #29) >> Best rule #5 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 0340hj; 07tw_b; 05sns6; >> query: (?x6528, 08mbj5d) <- film_crew_role(?x6528, ?x5928), film(?x665, ?x6528), featured_film_locations(?x6528, ?x739), film(?x541, ?x6528), ?x5928 = 05smlt >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dc_ms category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.562 http://example.org/common/topic/webpage./common/webpage/category #12817-07ssc PRED entity: 07ssc PRED relation: contains PRED expected values: 018m5q 01t21q 09bkv 0133ch 026m3y 025r_t 014d4v 01v2xl 03lkp 01h8sf 013t85 0kqb0 0cdw6 01z2sn 01z645 => 208 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2674): 0glh3 (0.96 #32285, 0.84 #236809, 0.83 #188368), 0cxgc (0.96 #32285, 0.84 #236809, 0.83 #188368), 0dm0f (0.96 #32285, 0.84 #236809, 0.83 #188368), 025r_t (0.96 #32285, 0.84 #236809, 0.83 #188368), 02ly_ (0.96 #32285, 0.84 #236809, 0.83 #188368), 0hvlp (0.96 #32285, 0.75 #166838, 0.75 #269101), 029skd (0.96 #32285, 0.75 #166838, 0.75 #269101), 02hvkf (0.96 #32285, 0.75 #166838, 0.75 #269101), 029r_2 (0.96 #32285, 0.75 #166838, 0.75 #269101), 04jt2 (0.96 #32285, 0.75 #166838, 0.75 #269101) >> Best rule #32285 for best value: >> intensional similarity = 2 >> extensional distance = 9 >> proper extension: 06rny; >> query: (?x512, ?x892) <- split_to(?x1310, ?x512), contains(?x1310, ?x892) >> conf = 0.96 => this is the best rule for 78 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 14, 22, 32, 53, 56, 65, 70, 74, 2315, 2316 EVAL 07ssc contains 01z645 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 01z2sn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 0cdw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 0kqb0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 013t85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 01h8sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 03lkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 01v2xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 014d4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 025r_t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 026m3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 0133ch CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 09bkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 01t21q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 208.000 122.000 0.955 http://example.org/location/location/contains EVAL 07ssc contains 018m5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 208.000 122.000 0.955 http://example.org/location/location/contains #12816-09y6pb PRED entity: 09y6pb PRED relation: nominated_for! PRED expected values: 014kq6 => 81 concepts (37 used for prediction) PRED predicted values (max 10 best out of 121): 0kv2hv (0.07 #21, 0.04 #528, 0.02 #1288), 07gghl (0.07 #187, 0.04 #694, 0.02 #1454), 05pxnmb (0.07 #214, 0.04 #721, 0.02 #1481), 0bxxzb (0.07 #188, 0.04 #695, 0.02 #1455), 026f__m (0.05 #213, 0.03 #720, 0.02 #1480), 04tc1g (0.05 #22, 0.03 #529, 0.02 #1289), 0d1qmz (0.05 #1624, 0.05 #1877, 0.02 #610), 02qrv7 (0.05 #1554, 0.05 #1807, 0.02 #540), 0fztbq (0.05 #1767, 0.04 #2020, 0.02 #753), 01kf4tt (0.05 #1596, 0.04 #1849, 0.02 #582) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 09sh8k; 08lr6s; 0ds33; 0pc62; 01r97z; 0164qt; 0kv2hv; 04tc1g; 0bshwmp; 0872p_c; ... >> query: (?x9379, 0kv2hv) <- nominated_for(?x2200, ?x9379), nominated_for(?x2325, ?x9379), film(?x3533, ?x9379), ?x2325 = 05p09zm >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #1585 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 225 *> proper extension: 026p_bs; 05css_; 01jr4j; *> query: (?x9379, 014kq6) <- language(?x9379, ?x254), genre(?x9379, ?x53), nominated_for(?x9379, ?x6244) *> conf = 0.04 ranks of expected_values: 18 EVAL 09y6pb nominated_for! 014kq6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 81.000 37.000 0.071 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #12815-025y9fn PRED entity: 025y9fn PRED relation: nominated_for PRED expected values: 0kfv9 => 89 concepts (39 used for prediction) PRED predicted values (max 10 best out of 203): 03cv_gy (0.22 #848, 0.02 #18651, 0.01 #5703), 08bytj (0.22 #1205, 0.01 #19008, 0.01 #4441), 0gmblvq (0.22 #616), 027qgy (0.22 #26), 0kfv9 (0.11 #267, 0.04 #18070, 0.03 #19689), 080dwhx (0.11 #60, 0.04 #13009, 0.04 #17863), 0180mw (0.11 #1038, 0.04 #18841, 0.03 #22080), 04vr_f (0.11 #159, 0.02 #5014, 0.02 #8253), 08xvpn (0.11 #1437, 0.02 #6292, 0.01 #9531), 0bdjd (0.11 #1139, 0.02 #2757, 0.02 #4375) >> Best rule #848 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05m9f9; >> query: (?x10215, 03cv_gy) <- nominated_for(?x10215, ?x8668), award_nominee(?x3570, ?x10215), ?x8668 = 02qjv1p >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #267 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 05m9f9; *> query: (?x10215, 0kfv9) <- nominated_for(?x10215, ?x8668), award_nominee(?x3570, ?x10215), ?x8668 = 02qjv1p *> conf = 0.11 ranks of expected_values: 5 EVAL 025y9fn nominated_for 0kfv9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 89.000 39.000 0.222 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12814-099ck7 PRED entity: 099ck7 PRED relation: award! PRED expected values: 0p_pd 0147dk 05mkhs 015c4g => 55 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2470): 0170pk (0.79 #23448, 0.77 #67012, 0.76 #67013), 09fb5 (0.79 #23448, 0.77 #67012, 0.76 #67013), 016yvw (0.79 #23448, 0.77 #67012, 0.76 #67013), 048lv (0.60 #13721, 0.50 #7022, 0.46 #20422), 0p_pd (0.60 #13460, 0.50 #6761, 0.38 #20161), 01vvb4m (0.60 #14219, 0.50 #7520, 0.33 #17570), 01cj6y (0.60 #14608, 0.50 #7909, 0.33 #17959), 06cgy (0.58 #17122, 0.38 #20472, 0.28 #27175), 015grj (0.58 #16962, 0.25 #10261, 0.23 #20312), 07r1h (0.54 #21878, 0.50 #8478, 0.42 #18528) >> Best rule #23448 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 027dtxw; 0cqh46; 05pcn59; 0c422z4; 0gqy2; 02w9sd7; 05ztrmj; 09sdmz; >> query: (?x6729, ?x406) <- award(?x828, ?x6729), ?x828 = 01wmxfs, nominated_for(?x6729, ?x6281), nominated_for(?x193, ?x6281), award_winner(?x6729, ?x406) >> conf = 0.79 => this is the best rule for 3 predicted values *> Best rule #13460 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 02x4w6g; *> query: (?x6729, 0p_pd) <- award(?x8066, ?x6729), award(?x828, ?x6729), ?x828 = 01wmxfs, nominated_for(?x6729, ?x6281), nominated_for(?x193, ?x6281), ?x8066 = 031k24 *> conf = 0.60 ranks of expected_values: 5, 12, 41, 45 EVAL 099ck7 award! 015c4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 55.000 21.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099ck7 award! 05mkhs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 55.000 21.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099ck7 award! 0147dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 55.000 21.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099ck7 award! 0p_pd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 55.000 21.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12813-09v42sf PRED entity: 09v42sf PRED relation: film! PRED expected values: 05d6q1 => 99 concepts (89 used for prediction) PRED predicted values (max 10 best out of 90): 016tw3 (0.31 #461, 0.24 #536, 0.20 #311), 086k8 (0.29 #152, 0.27 #377, 0.25 #227), 032j_n (0.29 #208, 0.09 #433, 0.04 #1258), 03xq0f (0.25 #80, 0.20 #305, 0.19 #830), 016tt2 (0.23 #454, 0.20 #304, 0.14 #2481), 01gb54 (0.20 #329, 0.08 #479, 0.07 #1229), 017s11 (0.18 #528, 0.14 #3692, 0.13 #1728), 024rdh (0.17 #637, 0.16 #937, 0.12 #1012), 017jv5 (0.16 #765, 0.08 #1140, 0.07 #3097), 061dn_ (0.14 #174, 0.09 #399, 0.08 #624) >> Best rule #461 for best value: >> intensional similarity = 8 >> extensional distance = 11 >> proper extension: 0c34mt; 0315w4; 012kyx; >> query: (?x10535, 016tw3) <- genre(?x10535, ?x571), genre(?x10535, ?x258), country(?x10535, ?x94), film_crew_role(?x10535, ?x137), ?x571 = 03npn, films(?x271, ?x10535), genre(?x2500, ?x258), ?x2500 = 0418wg >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #1021 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 31 *> proper extension: 05z_kps; 04jkpgv; 09k56b7; 09v71cj; 0db94w; 07k2mq; 01sby_; 064lsn; 0cbn7c; *> query: (?x10535, 05d6q1) <- film_release_region(?x10535, ?x1892), film_release_region(?x10535, ?x1353), film_release_region(?x10535, ?x583), film_release_region(?x10535, ?x172), film_release_region(?x10535, ?x94), ?x1892 = 02vzc, ?x1353 = 035qy, titles(?x812, ?x10535), ?x172 = 0154j, ?x94 = 09c7w0, film_regional_debut_venue(?x10535, ?x1658), currency(?x583, ?x170) *> conf = 0.06 ranks of expected_values: 26 EVAL 09v42sf film! 05d6q1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 99.000 89.000 0.308 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #12812-0xpp5 PRED entity: 0xpp5 PRED relation: place PRED expected values: 0xpp5 => 133 concepts (125 used for prediction) PRED predicted values (max 10 best out of 207): 02_286 (0.33 #529, 0.14 #39198, 0.10 #48997), 0xpp5 (0.14 #39198, 0.10 #48997, 0.08 #52092), 0165b (0.14 #39198, 0.10 #48997, 0.08 #52092), 0n5df (0.07 #3093, 0.06 #15993, 0.05 #10317), 0hptm (0.06 #1187, 0.05 #1703, 0.03 #2219), 0fvxz (0.06 #1052, 0.05 #1568, 0.03 #2084), 0h6l4 (0.06 #1406, 0.05 #1922, 0.03 #2438), 0xn7q (0.06 #1376, 0.05 #1892, 0.03 #2408), 0xt3t (0.06 #1375, 0.05 #1891, 0.03 #2407), 0xn5b (0.06 #1164, 0.05 #1680, 0.03 #2196) >> Best rule #529 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02_286; >> query: (?x6142, 02_286) <- location(?x7946, ?x6142), source(?x6142, ?x958), ?x958 = 0jbk9, ?x7946 = 0kjgl >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #39198 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 325 *> proper extension: 0l2vz; 0t_3w; 0bxc4; *> query: (?x6142, ?x739) <- location(?x7946, ?x6142), source(?x6142, ?x958), ?x958 = 0jbk9, location(?x7946, ?x739) *> conf = 0.14 ranks of expected_values: 2 EVAL 0xpp5 place 0xpp5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 125.000 0.333 http://example.org/location/hud_county_place/place #12811-01k60v PRED entity: 01k60v PRED relation: film_release_region PRED expected values: 0k6nt => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 129): 059j2 (0.86 #542, 0.85 #4579, 0.84 #1047), 0k6nt (0.86 #533, 0.83 #4570, 0.81 #3391), 03_3d (0.82 #342, 0.81 #511, 0.80 #3706), 03rjj (0.80 #1014, 0.80 #4546, 0.79 #4378), 0345h (0.80 #1049, 0.78 #4581, 0.75 #5253), 01znc_ (0.76 #386, 0.69 #4592, 0.67 #3750), 03h64 (0.76 #583, 0.74 #4620, 0.72 #4956), 03gj2 (0.76 #4571, 0.72 #4907, 0.72 #5075), 015fr (0.74 #1029, 0.69 #4561, 0.68 #4393), 0d060g (0.74 #1017, 0.68 #4549, 0.65 #5221) >> Best rule #542 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0jjy0; >> query: (?x4448, 059j2) <- film_release_region(?x4448, ?x2152), titles(?x53, ?x4448), costume_design_by(?x4448, ?x3685), ?x2152 = 06mkj >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #533 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 0jjy0; *> query: (?x4448, 0k6nt) <- film_release_region(?x4448, ?x2152), titles(?x53, ?x4448), costume_design_by(?x4448, ?x3685), ?x2152 = 06mkj *> conf = 0.86 ranks of expected_values: 2 EVAL 01k60v film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12810-02xs6_ PRED entity: 02xs6_ PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.89 #56, 0.89 #46, 0.88 #81), 02nxhr (0.33 #2, 0.14 #12, 0.05 #190), 07c52 (0.10 #38, 0.06 #23, 0.05 #33), 07z4p (0.05 #80, 0.04 #116, 0.03 #100) >> Best rule #56 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 04hwbq; 0cz8mkh; 02n72k; 027x7z5; 02qydsh; >> query: (?x4991, 029j_) <- titles(?x600, ?x4991), film_release_region(?x4991, ?x94), prequel(?x4991, ?x3471), production_companies(?x4991, ?x788) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xs6_ film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12809-09p3h7 PRED entity: 09p3h7 PRED relation: honored_for PRED expected values: 0194zl => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 886): 017gl1 (0.50 #1230, 0.33 #640, 0.12 #3004), 0dr3sl (0.50 #1347, 0.33 #757, 0.06 #3712), 01rwpj (0.33 #889, 0.25 #1479, 0.03 #3844), 0fhzwl (0.33 #496, 0.17 #4631, 0.16 #5222), 01b_lz (0.33 #199, 0.17 #4334, 0.13 #4925), 05lfwd (0.33 #342, 0.16 #3886, 0.16 #5068), 04p5cr (0.33 #390, 0.14 #4525, 0.13 #5116), 03ln8b (0.33 #122, 0.14 #4257, 0.13 #4848), 0cs134 (0.33 #553, 0.14 #4688, 0.11 #6459), 030k94 (0.33 #187, 0.11 #4322, 0.09 #4913) >> Best rule #1230 for best value: >> intensional similarity = 16 >> extensional distance = 2 >> proper extension: 02yvhx; >> query: (?x5392, 017gl1) <- award_winner(?x5392, ?x4949), award_winner(?x5392, ?x3960), award_winner(?x5392, ?x2900), award_winner(?x5392, ?x2275), honored_for(?x5392, ?x308), award_nominee(?x3960, ?x4928), ?x4949 = 0fgg4, nominated_for(?x3960, ?x8551), genre(?x8551, ?x258), ceremony(?x746, ?x5392), ?x308 = 011yxg, film(?x2275, ?x414), nominated_for(?x746, ?x69), film(?x3960, ?x2816), film(?x4436, ?x8551), award(?x2900, ?x941) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1471 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 02yvhx; *> query: (?x5392, 0194zl) <- award_winner(?x5392, ?x4949), award_winner(?x5392, ?x3960), award_winner(?x5392, ?x2900), award_winner(?x5392, ?x2275), honored_for(?x5392, ?x308), award_nominee(?x3960, ?x4928), ?x4949 = 0fgg4, nominated_for(?x3960, ?x8551), genre(?x8551, ?x258), ceremony(?x746, ?x5392), ?x308 = 011yxg, film(?x2275, ?x414), nominated_for(?x746, ?x69), film(?x3960, ?x2816), film(?x4436, ?x8551), award(?x2900, ?x941) *> conf = 0.25 ranks of expected_values: 46 EVAL 09p3h7 honored_for 0194zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 32.000 32.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12808-0fq9zdn PRED entity: 0fq9zdn PRED relation: award! PRED expected values: 0159h6 06jzh 0h0wc 02f2dn => 41 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2395): 01tspc6 (0.69 #60518, 0.69 #50431, 0.68 #36979), 028knk (0.62 #10607, 0.25 #522, 0.17 #3883), 0154qm (0.56 #10981, 0.33 #4257, 0.29 #7619), 020_95 (0.56 #11676, 0.25 #1591, 0.17 #4952), 043kzcr (0.56 #10748, 0.25 #663, 0.17 #4024), 01kb2j (0.56 #11563, 0.07 #21647, 0.07 #25008), 01p7yb (0.56 #10155, 0.07 #20239, 0.07 #23600), 0h0wc (0.56 #10761, 0.07 #24206, 0.07 #27568), 07lt7b (0.50 #156, 0.44 #10241, 0.33 #3517), 0h32q (0.50 #11327, 0.33 #4603, 0.29 #7965) >> Best rule #60518 for best value: >> intensional similarity = 3 >> extensional distance = 277 >> proper extension: 05qck; 02qkk9_; 0d085; 02py7pj; 058vy5; 0bqsk5; >> query: (?x941, ?x4872) <- award_winner(?x941, ?x4872), award_winner(?x4872, ?x91), people(?x743, ?x4872) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #10761 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 03nqnk3; 0bb57s; *> query: (?x941, 0h0wc) <- award(?x11983, ?x941), award(?x4247, ?x941), award(?x2805, ?x941), ?x2805 = 0lpjn, award_winner(?x4756, ?x11983), award_nominee(?x123, ?x4247) *> conf = 0.56 ranks of expected_values: 8, 18, 19, 576 EVAL 0fq9zdn award! 02f2dn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 41.000 19.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fq9zdn award! 0h0wc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 41.000 19.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fq9zdn award! 06jzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 19.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fq9zdn award! 0159h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 41.000 19.000 0.688 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12807-0cks1m PRED entity: 0cks1m PRED relation: genre PRED expected values: 05p553 => 86 concepts (82 used for prediction) PRED predicted values (max 10 best out of 143): 07s9rl0 (0.92 #5212, 0.86 #7581, 0.86 #7699), 05p553 (0.89 #4147, 0.69 #7110, 0.69 #7228), 01jfsb (0.65 #6762, 0.63 #6998, 0.40 #3684), 03k9fj (0.64 #3683, 0.54 #1428, 0.50 #4037), 03q4nz (0.60 #490, 0.44 #2147, 0.40 #2503), 06n90 (0.52 #6290, 0.50 #2499, 0.50 #1666), 06cvj (0.49 #4146, 0.38 #1301, 0.28 #4383), 04t36 (0.40 #2017, 0.34 #5690, 0.25 #360), 0219x_ (0.35 #5945, 0.10 #8435, 0.09 #4168), 04pbhw (0.33 #3725, 0.25 #880, 0.20 #998) >> Best rule #5212 for best value: >> intensional similarity = 12 >> extensional distance = 316 >> proper extension: 02ppg1r; >> query: (?x5633, 07s9rl0) <- genre(?x5633, ?x1510), genre(?x5633, ?x1403), film(?x13175, ?x5633), ?x1403 = 02l7c8, genre(?x419, ?x1510), genre(?x6788, ?x1510), genre(?x6610, ?x1510), genre(?x1804, ?x1510), ?x6788 = 01f8f7, ?x1804 = 02q52q, titles(?x1510, ?x83), ?x6610 = 07ghv5 >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #4147 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 185 *> proper extension: 02v8kmz; 047q2k1; 0ddfwj1; 0209xj; 0416y94; 0sxfd; 0gxfz; 05q4y12; 03tps5; 0cbv4g; ... *> query: (?x5633, 05p553) <- genre(?x5633, ?x1510), genre(?x5633, ?x1403), film(?x13175, ?x5633), ?x1403 = 02l7c8, country(?x5633, ?x252), genre(?x9303, ?x1510), genre(?x6099, ?x1510), genre(?x3423, ?x1510), genre(?x908, ?x1510), ?x9303 = 05567m, film_release_region(?x908, ?x87), ?x3423 = 09g7vfw, ?x6099 = 0473rc *> conf = 0.89 ranks of expected_values: 2 EVAL 0cks1m genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 82.000 0.915 http://example.org/film/film/genre #12806-0f04v PRED entity: 0f04v PRED relation: adjoins! PRED expected values: 0r6cx => 222 concepts (200 used for prediction) PRED predicted values (max 10 best out of 528): 0r6cx (0.86 #3920, 0.85 #29778, 0.85 #14895), 0r6ff (0.86 #3920, 0.85 #29778, 0.85 #24298), 0f04v (0.40 #297, 0.10 #17543, 0.07 #23029), 05fjf (0.20 #1092, 0.12 #8145, 0.12 #7362), 0f04c (0.20 #146, 0.10 #17392, 0.06 #32914), 059rby (0.16 #20401, 0.08 #52517, 0.07 #60353), 0kpzy (0.15 #11759, 0.10 #1086, 0.06 #8139), 0l2vz (0.15 #11759, 0.02 #29223, 0.02 #44107), 06bnz (0.11 #58072, 0.08 #63559, 0.06 #87059), 01n7q (0.10 #29782, 0.08 #44666, 0.06 #61119) >> Best rule #3920 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0dc95; >> query: (?x6703, ?x3794) <- adjoins(?x6703, ?x3794), county_seat(?x7964, ?x6703), teams(?x6703, ?x7766) >> conf = 0.86 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0f04v adjoins! 0r6cx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 222.000 200.000 0.857 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #12805-01dyk8 PRED entity: 01dyk8 PRED relation: major_field_of_study PRED expected values: 04x_3 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 111): 02lp1 (0.46 #1720, 0.38 #378, 0.33 #134), 02j62 (0.46 #397, 0.44 #275, 0.37 #153), 01lj9 (0.41 #162, 0.33 #284, 0.29 #406), 062z7 (0.41 #394, 0.39 #150, 0.36 #272), 04rjg (0.41 #264, 0.39 #386, 0.35 #142), 03g3w (0.38 #393, 0.38 #271, 0.35 #149), 037mh8 (0.37 #190, 0.31 #312, 0.25 #434), 05qjt (0.36 #252, 0.35 #130, 0.33 #374), 01540 (0.31 #183, 0.27 #1769, 0.25 #305), 05qfh (0.31 #159, 0.27 #281, 0.23 #1745) >> Best rule #1720 for best value: >> intensional similarity = 3 >> extensional distance = 153 >> proper extension: 01w_sh; >> query: (?x9227, 02lp1) <- institution(?x4981, ?x9227), ?x4981 = 03bwzr4, school_type(?x9227, ?x3092) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1734 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 153 *> proper extension: 01w_sh; *> query: (?x9227, 04x_3) <- institution(?x4981, ?x9227), ?x4981 = 03bwzr4, school_type(?x9227, ?x3092) *> conf = 0.24 ranks of expected_values: 15 EVAL 01dyk8 major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 58.000 58.000 0.465 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #12804-01jr6 PRED entity: 01jr6 PRED relation: source PRED expected values: 0jbk9 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.86 #111, 0.83 #64, 0.83 #15) >> Best rule #111 for best value: >> intensional similarity = 4 >> extensional distance = 350 >> proper extension: 01y9pk; 0mk7z; 0p0cw; 0dlhg; 0mpfn; 0m28g; 0mpbj; 0f6_j; 0rjg8; 0f63n; ... >> query: (?x3976, 0jbk9) <- contains(?x1227, ?x3976), adjoins(?x2552, ?x3976), state(?x581, ?x1227), state_province_region(?x99, ?x1227) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jr6 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.858 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #12803-08hmch PRED entity: 08hmch PRED relation: film! PRED expected values: 07vc_9 => 98 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1273): 01w9wwg (0.71 #62434, 0.49 #104070, 0.49 #83253), 01nwwl (0.40 #501, 0.33 #4664, 0.03 #17149), 05y5fw (0.29 #18729, 0.19 #97825, 0.17 #74925), 016z2j (0.21 #6631, 0.06 #8711, 0.02 #46170), 02zyy4 (0.20 #269, 0.17 #4432, 0.11 #8593), 02ck7w (0.20 #939, 0.17 #5102, 0.08 #83255), 05dbf (0.20 #363, 0.17 #4526, 0.06 #12849), 0h5g_ (0.20 #73, 0.17 #4236, 0.05 #20882), 04yj5z (0.20 #120, 0.17 #4283, 0.04 #10526), 0k269 (0.20 #609, 0.17 #4772, 0.03 #17257) >> Best rule #62434 for best value: >> intensional similarity = 4 >> extensional distance = 246 >> proper extension: 0k2sk; 02pxmgz; 0g5pv3; 04mzf8; 021y7yw; 09p7fh; 0dnqr; 0d1qmz; 0mcl0; 015whm; ... >> query: (?x1035, ?x399) <- nominated_for(?x399, ?x1035), cinematography(?x1035, ?x7427), film(?x399, ?x675), film(?x10398, ?x1035) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #8524 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0170_p; *> query: (?x1035, 07vc_9) <- nominated_for(?x399, ?x1035), cinematography(?x1035, ?x7427), student(?x1368, ?x399), executive_produced_by(?x1035, ?x2464) *> conf = 0.06 ranks of expected_values: 228 EVAL 08hmch film! 07vc_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 98.000 63.000 0.712 http://example.org/film/actor/film./film/performance/film #12802-0727_ PRED entity: 0727_ PRED relation: place_of_birth! PRED expected values: 039n1 => 153 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1517): 04k15 (0.12 #11182, 0.12 #8570, 0.09 #19018), 01vsqvs (0.12 #9747, 0.09 #20195, 0.09 #17583), 07mz77 (0.12 #9525, 0.09 #19973, 0.09 #17361), 02h761 (0.12 #8611, 0.09 #19059, 0.09 #16447), 07m9cm (0.12 #8762, 0.09 #19210, 0.09 #16598), 01h2_6 (0.12 #12924, 0.09 #18148, 0.08 #23372), 0l9k1 (0.12 #12737, 0.09 #17961, 0.08 #23185), 0277c3 (0.12 #11705, 0.09 #16929, 0.08 #22153), 018dyl (0.12 #11300, 0.09 #16524, 0.08 #21748), 0hskw (0.12 #10970, 0.09 #16194, 0.08 #21418) >> Best rule #11182 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 04kf4; 02z0j; 0150n; 0cm5m; >> query: (?x7475, 04k15) <- contains(?x1264, ?x7475), ?x1264 = 0345h, location_of_ceremony(?x566, ?x7475), place_of_birth(?x1689, ?x7475) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #78365 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 40 *> proper extension: 01f62; 08966; 01gfhk; 0hv7l; *> query: (?x7475, ?x380) <- contains(?x1264, ?x7475), film_release_region(?x903, ?x7475), nationality(?x380, ?x1264), country(?x136, ?x1264), country(?x150, ?x1264) *> conf = 0.02 ranks of expected_values: 1381 EVAL 0727_ place_of_birth! 039n1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 153.000 42.000 0.125 http://example.org/people/person/place_of_birth #12801-01svw8n PRED entity: 01svw8n PRED relation: artists! PRED expected values: 06j6l => 121 concepts (50 used for prediction) PRED predicted values (max 10 best out of 246): 03lty (0.52 #2802, 0.49 #5266, 0.39 #1876), 05bt6j (0.44 #41, 0.44 #2198, 0.42 #2507), 0gywn (0.44 #55, 0.29 #4678, 0.26 #9609), 016clz (0.43 #313, 0.33 #5, 0.27 #2162), 06j6l (0.41 #4669, 0.37 #1586, 0.33 #662), 0ggx5q (0.33 #75, 0.33 #1615, 0.25 #4698), 017_qw (0.32 #8072, 0.12 #15162, 0.11 #10537), 02yv6b (0.30 #5335, 0.29 #2871, 0.27 #1945), 02k_kn (0.28 #2219, 0.27 #2528, 0.22 #62), 0dl5d (0.28 #5258, 0.23 #2794, 0.16 #1868) >> Best rule #2802 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 01vw87c; 01r9fv; 012zng; 0zjpz; 09prnq; 0gcs9; 0gkg6; 016ntp; 01nn6c; 07g2v; ... >> query: (?x3930, 03lty) <- artists(?x1000, ?x3930), profession(?x3930, ?x220), ?x1000 = 0xhtw >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #4669 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 148 *> proper extension: 07s3vqk; 0197tq; 0147dk; 06cc_1; 03f5spx; 01jrz5j; 01gf5h; 016kjs; 01wbgdv; 07c0j; ... *> query: (?x3930, 06j6l) <- artists(?x671, ?x3930), award_winner(?x748, ?x3930), ?x671 = 064t9 *> conf = 0.41 ranks of expected_values: 5 EVAL 01svw8n artists! 06j6l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 121.000 50.000 0.515 http://example.org/music/genre/artists #12800-02vwckw PRED entity: 02vwckw PRED relation: nationality PRED expected values: 09c7w0 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.77 #1909, 0.76 #3919, 0.74 #502), 07ssc (0.33 #15, 0.15 #115, 0.08 #617), 02xry (0.22 #3013, 0.22 #3315, 0.21 #3518), 02jx1 (0.12 #2744, 0.11 #2443, 0.11 #2844), 0d060g (0.11 #7720, 0.06 #1412, 0.05 #1814), 04wgh (0.08 #132), 03rk0 (0.06 #7766, 0.05 #8266, 0.05 #8066), 0f2v0 (0.05 #803, 0.04 #2310, 0.04 #904), 06q1r (0.04 #578, 0.04 #880, 0.04 #779), 0345h (0.03 #231, 0.02 #331, 0.02 #1436) >> Best rule #1909 for best value: >> intensional similarity = 3 >> extensional distance = 249 >> proper extension: 02vptk_; 02_nkp; >> query: (?x8185, 09c7w0) <- student(?x9745, ?x8185), currency(?x8185, ?x170), ?x170 = 09nqf >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vwckw nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.769 http://example.org/people/person/nationality #12799-06fc0b PRED entity: 06fc0b PRED relation: award PRED expected values: 0cqhk0 => 93 concepts (88 used for prediction) PRED predicted values (max 10 best out of 242): 09sb52 (0.38 #4101, 0.38 #447, 0.37 #3289), 0f4x7 (0.38 #437, 0.33 #31, 0.11 #11806), 04kxsb (0.38 #533, 0.17 #127, 0.10 #3375), 027dtxw (0.38 #410, 0.09 #3252, 0.09 #34112), 02w9sd7 (0.38 #578, 0.07 #5044, 0.07 #11947), 099ck7 (0.33 #269, 0.25 #675, 0.06 #2705), 05zr6wv (0.33 #17, 0.16 #2047, 0.16 #3265), 09qv_s (0.33 #153, 0.12 #559, 0.10 #3401), 057xs89 (0.33 #162, 0.12 #568, 0.09 #974), 0c422z4 (0.33 #144, 0.09 #956, 0.06 #2174) >> Best rule #4101 for best value: >> intensional similarity = 3 >> extensional distance = 150 >> proper extension: 01lbp; 03d_w3h; 01l9p; 016_mj; 0c6qh; 019pm_; 01xcfy; 05mkhs; 07swvb; 02mjf2; ... >> query: (?x7823, 09sb52) <- nominated_for(?x7823, ?x6070), participant(?x2352, ?x7823), award_nominee(?x1909, ?x7823) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #7751 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 594 *> proper extension: 044rvb; 016kjs; 02lg9w; 0806vbn; 04rsd2; 08cn4_; 07sgfsl; 050t68; 062hgx; 02xwq9; ... *> query: (?x7823, 0cqhk0) <- award_nominee(?x1909, ?x7823), actor(?x6070, ?x7823) *> conf = 0.18 ranks of expected_values: 23 EVAL 06fc0b award 0cqhk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 93.000 88.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12798-02stbw PRED entity: 02stbw PRED relation: film! PRED expected values: 02qx69 => 76 concepts (48 used for prediction) PRED predicted values (max 10 best out of 805): 01pk3z (0.29 #3056, 0.05 #9281, 0.01 #44557), 0f0kz (0.14 #4664, 0.14 #514, 0.05 #6739), 01wbg84 (0.14 #4197, 0.14 #47, 0.03 #6272), 02s2ft (0.14 #4157, 0.14 #7, 0.03 #6232), 034np8 (0.14 #4441, 0.14 #291, 0.03 #6516), 01j5ws (0.14 #4662, 0.14 #512, 0.03 #6737), 023mdt (0.14 #5721, 0.14 #1571, 0.03 #7796), 01d1st (0.14 #5357, 0.14 #1207, 0.03 #7432), 02gf_l (0.14 #5412, 0.14 #1262, 0.02 #11637), 01trf3 (0.14 #4877, 0.14 #727, 0.02 #9027) >> Best rule #3056 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 016z7s; 0296vv; >> query: (?x2384, 01pk3z) <- genre(?x2384, ?x11464), category(?x2384, ?x134), film(?x1345, ?x2384), ?x11464 = 03p5xs >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #4703 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02ht1k; *> query: (?x2384, 02qx69) <- production_companies(?x2384, ?x382), nominated_for(?x1033, ?x2384), film(?x2383, ?x2384), ?x2383 = 028d4v *> conf = 0.14 ranks of expected_values: 25 EVAL 02stbw film! 02qx69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 76.000 48.000 0.286 http://example.org/film/actor/film./film/performance/film #12797-05pt0l PRED entity: 05pt0l PRED relation: film! PRED expected values: 0170pk => 71 concepts (37 used for prediction) PRED predicted values (max 10 best out of 541): 0301bq (0.58 #60449, 0.47 #66703, 0.46 #62534), 0bq2g (0.18 #2691, 0.11 #607, 0.03 #11030), 0292l3 (0.18 #4400, 0.07 #8570, 0.05 #12739), 015wnl (0.11 #651, 0.09 #4819, 0.09 #2735), 01xcfy (0.11 #494, 0.09 #4662, 0.09 #2578), 03jj93 (0.11 #1899, 0.09 #6067, 0.09 #3983), 07vc_9 (0.11 #203, 0.09 #2287, 0.07 #8541), 03hzl42 (0.11 #790, 0.09 #2874, 0.07 #9128), 0klh7 (0.11 #490, 0.09 #2574, 0.04 #15081), 06cgy (0.11 #251, 0.09 #2335, 0.04 #14842) >> Best rule #60449 for best value: >> intensional similarity = 3 >> extensional distance = 774 >> proper extension: 01h1bf; 02kk_c; 0gxsh4; >> query: (?x7481, ?x10701) <- award_winner(?x7481, ?x10701), film(?x10701, ?x1941), type_of_union(?x10701, ?x566) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #8620 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 28 *> proper extension: 01f8gz; 04h4c9; *> query: (?x7481, 0170pk) <- genre(?x7481, ?x1626), genre(?x7481, ?x53), ?x53 = 07s9rl0, film(?x8626, ?x7481), ?x1626 = 03q4nz, award_winner(?x7481, ?x10701) *> conf = 0.03 ranks of expected_values: 144 EVAL 05pt0l film! 0170pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 71.000 37.000 0.580 http://example.org/film/actor/film./film/performance/film #12796-050llt PRED entity: 050llt PRED relation: place_of_birth PRED expected values: 01sv6k => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 59): 049lr (0.50 #1410, 0.29 #4932, 0.27 #77512), 01sv6k (0.33 #595, 0.04 #4118), 0c8tk (0.25 #859, 0.20 #1565, 0.15 #3678), 04vmp (0.25 #972, 0.20 #1678, 0.13 #8722), 0cvw9 (0.11 #2414, 0.07 #4526, 0.04 #8753), 0dlv0 (0.11 #2469, 0.06 #3173, 0.05 #8103), 013yq (0.11 #2194, 0.02 #4306, 0.02 #6419), 020skc (0.11 #2196, 0.02 #4308), 02_286 (0.08 #24679, 0.08 #28906, 0.08 #32430), 0fk98 (0.06 #3452, 0.04 #4156, 0.02 #9087) >> Best rule #1410 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01gg59; 0f5zj6; >> query: (?x12189, ?x9315) <- nominated_for(?x12189, ?x4444), nationality(?x12189, ?x2146), location(?x12189, ?x9315), ?x4444 = 09fn1w >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #595 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 040nwr; *> query: (?x12189, 01sv6k) <- languages(?x12189, ?x9113), film(?x12189, ?x3742), ?x9113 = 02hxcvy *> conf = 0.33 ranks of expected_values: 2 EVAL 050llt place_of_birth 01sv6k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 128.000 0.500 http://example.org/people/person/place_of_birth #12795-0gg5kmg PRED entity: 0gg5kmg PRED relation: genre PRED expected values: 0lsxr => 59 concepts (55 used for prediction) PRED predicted values (max 10 best out of 118): 03k9fj (0.41 #133, 0.35 #254, 0.30 #617), 05p553 (0.41 #2064, 0.40 #4, 0.40 #1943), 02kdv5l (0.40 #123, 0.37 #244, 0.34 #607), 02l7c8 (0.34 #3654, 0.33 #16, 0.31 #1834), 0lsxr (0.33 #9, 0.25 #1463, 0.18 #1827), 0vgkd (0.27 #11, 0.07 #495, 0.05 #1950), 04xvlr (0.22 #1455, 0.21 #1819, 0.19 #1698), 06n90 (0.20 #255, 0.20 #134, 0.19 #376), 060__y (0.20 #1471, 0.18 #1835, 0.16 #1714), 02n4kr (0.19 #1462, 0.13 #1705, 0.13 #3161) >> Best rule #133 for best value: >> intensional similarity = 6 >> extensional distance = 90 >> proper extension: 0ds35l9; 0g56t9t; 02vxq9m; 0ds3t5x; 0g5qs2k; 05p1tzf; 02x3lt7; 0c40vxk; 0gx9rvq; 0gkz15s; ... >> query: (?x6175, 03k9fj) <- film_release_region(?x6175, ?x1892), film_release_region(?x6175, ?x344), film_release_region(?x6175, ?x252), ?x344 = 04gzd, ?x1892 = 02vzc, ?x252 = 03_3d >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 02p76f9; *> query: (?x6175, 0lsxr) <- film(?x3462, ?x6175), film(?x2499, ?x6175), ?x2499 = 0c6qh, film_release_distribution_medium(?x6175, ?x81) *> conf = 0.33 ranks of expected_values: 5 EVAL 0gg5kmg genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 59.000 55.000 0.413 http://example.org/film/film/genre #12794-02vr7 PRED entity: 02vr7 PRED relation: profession PRED expected values: 016z4k => 129 concepts (107 used for prediction) PRED predicted values (max 10 best out of 84): 0dxtg (0.54 #748, 0.41 #1925, 0.39 #307), 0nbcg (0.53 #4737, 0.53 #2089, 0.52 #4002), 02jknp (0.50 #7, 0.27 #742, 0.27 #301), 016z4k (0.49 #3240, 0.47 #2210, 0.45 #5151), 01d_h8 (0.44 #740, 0.42 #299, 0.38 #1475), 0cbd2 (0.44 #1329, 0.42 #3684, 0.42 #3537), 018gz8 (0.41 #751, 0.23 #898, 0.21 #310), 0kyk (0.35 #1351, 0.32 #1204, 0.28 #3559), 01c72t (0.32 #4582, 0.31 #2965, 0.31 #4435), 03gjzk (0.31 #308, 0.31 #749, 0.24 #455) >> Best rule #748 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 04wqr; 01nrq5; 0djywgn; 01nfys; 022q4j; 0p_jc; 0c5vh; 0btj0; >> query: (?x8311, 0dxtg) <- gender(?x8311, ?x231), influenced_by(?x8864, ?x8311), film(?x8311, ?x4304) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #3240 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 352 *> proper extension: 0frsw; 01yzl2; 03d9d6; 09lwrt; 089pg7; 02ht0ln; *> query: (?x8311, 016z4k) <- instrumentalists(?x227, ?x8311), artist(?x382, ?x8311), award(?x8311, ?x1323) *> conf = 0.49 ranks of expected_values: 4 EVAL 02vr7 profession 016z4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 129.000 107.000 0.537 http://example.org/people/person/profession #12793-0frf6 PRED entity: 0frf6 PRED relation: currency PRED expected values: 09nqf => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.87 #26, 0.87 #25, 0.86 #34) >> Best rule #26 for best value: >> intensional similarity = 5 >> extensional distance = 216 >> proper extension: 0l30v; 0m24v; 0kwmc; 0l2nd; >> query: (?x10767, ?x170) <- time_zones(?x10767, ?x2674), adjoins(?x12846, ?x10767), second_level_divisions(?x94, ?x10767), currency(?x12846, ?x170), ?x94 = 09c7w0 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0frf6 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.872 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #12792-05b4rcb PRED entity: 05b4rcb PRED relation: gender PRED expected values: 05zppz => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #43, 0.74 #11, 0.73 #13), 02zsn (0.40 #2, 0.24 #64, 0.23 #107) >> Best rule #43 for best value: >> intensional similarity = 2 >> extensional distance = 1273 >> proper extension: 01l1b90; 02qjj7; 09byk; 042rnl; 03ds3; 0hnlx; 067jsf; 01pr_j6; 0177s6; 0126rp; ... >> query: (?x2230, 05zppz) <- profession(?x2230, ?x1078), film_crew_role(?x148, ?x1078) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05b4rcb gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.784 http://example.org/people/person/gender #12791-02m4yg PRED entity: 02m4yg PRED relation: institution PRED expected values: 02zd460 02jztz 01dq0z => 23 concepts (22 used for prediction) PRED predicted values (max 10 best out of 753): 01w5m (0.87 #9448, 0.82 #10071, 0.80 #11310), 07szy (0.80 #11240, 0.79 #4355, 0.79 #10622), 09f2j (0.80 #8889, 0.79 #4355, 0.78 #7646), 03ksy (0.79 #4355, 0.78 #7582, 0.75 #6960), 01bm_ (0.79 #4355, 0.78 #7745, 0.75 #7123), 065y4w7 (0.79 #4355, 0.75 #6858, 0.73 #9347), 07wlf (0.79 #4355, 0.75 #6924, 0.72 #2489), 0bwfn (0.79 #4355, 0.75 #7151, 0.72 #2489), 01ky7c (0.79 #4355, 0.75 #7100, 0.72 #2489), 0f1nl (0.79 #4355, 0.75 #6918, 0.72 #2489) >> Best rule #9448 for best value: >> intensional similarity = 27 >> extensional distance = 13 >> proper extension: 03mkk4; >> query: (?x6117, 01w5m) <- major_field_of_study(?x6117, ?x2601), institution(?x6117, ?x11963), institution(?x6117, ?x5085), institution(?x6117, ?x3354), institution(?x6117, ?x122), institution(?x7636, ?x5085), currency(?x5085, ?x2244), colors(?x3354, ?x3189), major_field_of_study(?x5085, ?x947), contains(?x279, ?x5085), ?x122 = 08815, major_field_of_study(?x8850, ?x2601), major_field_of_study(?x7660, ?x2601), major_field_of_study(?x6271, ?x2601), student(?x11963, ?x12147), organization(?x346, ?x5085), ?x3189 = 01g5v, ?x7636 = 01rr_d, school(?x1578, ?x6271), major_field_of_study(?x11963, ?x2014), institution(?x620, ?x8850), student(?x6271, ?x1129), ?x12147 = 06y7d, ?x7660 = 01qd_r, contains(?x2316, ?x3354), currency(?x8850, ?x170), colors(?x5085, ?x332) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #7660 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 7 *> proper extension: 0bjrnt; *> query: (?x6117, 02zd460) <- major_field_of_study(?x6117, ?x2601), major_field_of_study(?x6117, ?x742), institution(?x6117, ?x11963), institution(?x6117, ?x6056), institution(?x6117, ?x5085), institution(?x6117, ?x4672), institution(?x6117, ?x3354), institution(?x6117, ?x122), currency(?x5085, ?x2244), colors(?x3354, ?x3189), major_field_of_study(?x5085, ?x4100), contains(?x279, ?x5085), ?x122 = 08815, major_field_of_study(?x2830, ?x2601), student(?x11963, ?x361), organization(?x346, ?x5085), ?x2830 = 01wdj_, major_field_of_study(?x3354, ?x254), ?x346 = 060c4, ?x4100 = 01lj9, contains(?x512, ?x11963), ?x6056 = 05zl0, ?x4672 = 07tds, school_type(?x11963, ?x3092), student(?x2601, ?x2873), ?x742 = 05qjt *> conf = 0.78 ranks of expected_values: 42, 195, 198 EVAL 02m4yg institution 01dq0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 23.000 22.000 0.867 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02m4yg institution 02jztz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 23.000 22.000 0.867 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02m4yg institution 02zd460 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 23.000 22.000 0.867 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #12790-09743 PRED entity: 09743 PRED relation: people PRED expected values: 01zh29 => 31 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1789): 01k5zk (0.40 #7413, 0.07 #29903, 0.06 #38554), 05d7rk (0.38 #8658, 0.33 #9, 0.30 #10389), 01zp33 (0.38 #9691, 0.30 #11422, 0.22 #12111), 08d6bd (0.33 #910, 0.25 #9559, 0.25 #6099), 03vrnh (0.33 #1045, 0.25 #9694, 0.25 #6234), 047jhq (0.33 #1676, 0.25 #10325, 0.25 #6865), 021j72 (0.33 #1489, 0.25 #6678, 0.25 #4947), 0f5zj6 (0.33 #891, 0.25 #6080, 0.25 #4349), 03f02ct (0.33 #1471, 0.25 #6660, 0.25 #4929), 04cmrt (0.33 #1591, 0.25 #6780, 0.25 #5049) >> Best rule #7413 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 048z7l; 033qxt; >> query: (?x14168, 01k5zk) <- people(?x14168, ?x10452), languages_spoken(?x14168, ?x13310), languages_spoken(?x14168, ?x9113), ?x13310 = 032f6, language(?x4534, ?x9113), language(?x4444, ?x9113), language(?x2814, ?x9113), ?x2814 = 078sj4, titles(?x2146, ?x4444), award_winner(?x4444, ?x6308), nominated_for(?x4443, ?x4444), ?x4534 = 02phtzk >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #8649 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 048z7l; 033qxt; *> query: (?x14168, ?x6308) <- people(?x14168, ?x10452), languages_spoken(?x14168, ?x13310), languages_spoken(?x14168, ?x9113), ?x13310 = 032f6, language(?x4534, ?x9113), language(?x4444, ?x9113), language(?x2814, ?x9113), ?x2814 = 078sj4, titles(?x2146, ?x4444), award_winner(?x4444, ?x6308), nominated_for(?x4443, ?x4444), ?x4534 = 02phtzk *> conf = 0.04 ranks of expected_values: 1615 EVAL 09743 people 01zh29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 29.000 0.400 http://example.org/people/ethnicity/people #12789-03rjj PRED entity: 03rjj PRED relation: exported_to! PRED expected values: 0l3h => 240 concepts (229 used for prediction) PRED predicted values (max 10 best out of 199): 09c7w0 (0.42 #498, 0.41 #1168, 0.40 #553), 0ctw_b (0.25 #511, 0.18 #1181, 0.18 #1125), 0j4b (0.25 #541, 0.17 #875, 0.14 #1659), 05r4w (0.20 #1001, 0.19 #607, 0.18 #3402), 047t_ (0.18 #1204, 0.18 #1148, 0.17 #1259), 0d05w3 (0.18 #3432, 0.13 #582, 0.09 #1197), 0jdd (0.17 #864, 0.17 #530, 0.14 #1200), 04wlh (0.17 #882, 0.17 #548, 0.14 #1218), 0l3h (0.17 #539, 0.16 #930, 0.14 #1209), 07dzf (0.17 #535, 0.14 #1094, 0.14 #1205) >> Best rule #498 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 03h64; >> query: (?x205, 09c7w0) <- location(?x4587, ?x205), film_release_region(?x5826, ?x205), ?x5826 = 0gl02yg >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #539 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 03h64; *> query: (?x205, 0l3h) <- location(?x4587, ?x205), film_release_region(?x5826, ?x205), ?x5826 = 0gl02yg *> conf = 0.17 ranks of expected_values: 9 EVAL 03rjj exported_to! 0l3h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 240.000 229.000 0.417 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #12788-05mcjs PRED entity: 05mcjs PRED relation: student! PRED expected values: 07tg4 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 191): 015nl4 (0.16 #5853, 0.07 #13743, 0.05 #21633), 0bwfn (0.13 #2905, 0.12 #8165, 0.11 #7113), 07tg4 (0.12 #5872, 0.06 #13762, 0.05 #2716), 065y4w7 (0.12 #540, 0.09 #1592, 0.07 #4222), 03ksy (0.10 #4314, 0.07 #7470, 0.07 #8522), 02g839 (0.10 #25, 0.04 #551, 0.03 #1077), 078bz (0.10 #77, 0.04 #603, 0.03 #1655), 01qd_r (0.10 #281, 0.03 #1333, 0.03 #1859), 07tgn (0.09 #5803, 0.04 #13693, 0.04 #2121), 04b_46 (0.07 #2857, 0.04 #4435, 0.04 #3909) >> Best rule #5853 for best value: >> intensional similarity = 3 >> extensional distance = 174 >> proper extension: 03wjb7; 01qn8k; 03z0l6; >> query: (?x6673, 015nl4) <- student(?x10348, ?x6673), nationality(?x6673, ?x1310), ?x1310 = 02jx1 >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #5872 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 174 *> proper extension: 03wjb7; 01qn8k; 03z0l6; *> query: (?x6673, 07tg4) <- student(?x10348, ?x6673), nationality(?x6673, ?x1310), ?x1310 = 02jx1 *> conf = 0.12 ranks of expected_values: 3 EVAL 05mcjs student! 07tg4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.165 http://example.org/education/educational_institution/students_graduates./education/education/student #12787-01nr63 PRED entity: 01nr63 PRED relation: place_of_death PRED expected values: 030qb3t => 106 concepts (63 used for prediction) PRED predicted values (max 10 best out of 14): 030qb3t (0.15 #1188, 0.15 #606, 0.14 #800), 0k049 (0.13 #587, 0.10 #781, 0.08 #1169), 0fn2g (0.06 #91), 06_kh (0.05 #1171, 0.04 #783, 0.04 #589), 02_286 (0.03 #6430, 0.03 #791, 0.03 #9358), 0f2wj (0.03 #790, 0.02 #596, 0.02 #1178), 02dtg (0.03 #9343, 0.02 #11104, 0.02 #10907), 0mzww (0.02 #493), 0r3w7 (0.02 #1343, 0.01 #955), 04jpl (0.02 #2923, 0.01 #785, 0.01 #10525) >> Best rule #1188 for best value: >> intensional similarity = 2 >> extensional distance = 124 >> proper extension: 0d9kl; 057ph; 0dng4; >> query: (?x12412, 030qb3t) <- celebrities_impersonated(?x6707, ?x12412), location(?x6707, ?x1755) >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nr63 place_of_death 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 63.000 0.151 http://example.org/people/deceased_person/place_of_death #12786-016vj5 PRED entity: 016vj5 PRED relation: artists! PRED expected values: 011j5x 059kh => 116 concepts (45 used for prediction) PRED predicted values (max 10 best out of 268): 064t9 (0.75 #636, 0.69 #3128, 0.67 #5624), 0xhtw (0.50 #7502, 0.47 #3754, 0.46 #5314), 059kh (0.50 #672, 0.30 #983, 0.24 #12797), 025sc50 (0.41 #3165, 0.33 #9408, 0.28 #13470), 0dl5d (0.40 #331, 0.33 #4381, 0.31 #3757), 03lty (0.40 #339, 0.29 #7513, 0.27 #10635), 0cx7f (0.40 #451, 0.25 #140, 0.25 #4501), 06j6l (0.38 #3163, 0.35 #1605, 0.33 #2228), 0y3_8 (0.38 #670, 0.24 #12797, 0.23 #6237), 0ggx5q (0.36 #3194, 0.26 #1636, 0.25 #9437) >> Best rule #636 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 01wgjj5; >> query: (?x11906, 064t9) <- artist(?x1543, ?x11906), artists(?x11737, ?x11906), artists(?x3061, ?x11906), category(?x11906, ?x134), ?x11737 = 01b4p4, artists(?x3061, ?x11182), ?x11182 = 03x82v >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #672 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 01wgjj5; *> query: (?x11906, 059kh) <- artist(?x1543, ?x11906), artists(?x11737, ?x11906), artists(?x3061, ?x11906), category(?x11906, ?x134), ?x11737 = 01b4p4, artists(?x3061, ?x11182), ?x11182 = 03x82v *> conf = 0.50 ranks of expected_values: 3, 17 EVAL 016vj5 artists! 059kh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 45.000 0.750 http://example.org/music/genre/artists EVAL 016vj5 artists! 011j5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 116.000 45.000 0.750 http://example.org/music/genre/artists #12785-02pxst PRED entity: 02pxst PRED relation: genre PRED expected values: 05c3mp2 => 74 concepts (72 used for prediction) PRED predicted values (max 10 best out of 131): 04xvlr (0.73 #4143, 0.73 #2199, 0.64 #1463), 07ssc (0.64 #1463, 0.64 #1954, 0.62 #2198), 05p553 (0.44 #5858, 0.37 #2568, 0.35 #493), 02l7c8 (0.39 #5138, 0.39 #139, 0.33 #383), 02kdv5l (0.34 #4880, 0.29 #8169, 0.26 #2689), 01jfsb (0.33 #1845, 0.30 #2700, 0.29 #4157), 060__y (0.27 #384, 0.23 #1359, 0.23 #140), 0lsxr (0.25 #1841, 0.21 #984, 0.20 #253), 03k9fj (0.23 #622, 0.23 #8179, 0.22 #501), 04xvh5 (0.22 #401, 0.20 #157, 0.15 #279) >> Best rule #4143 for best value: >> intensional similarity = 4 >> extensional distance = 1013 >> proper extension: 06wzvr; 0436yk; 085bd1; 01f39b; 08sk8l; 08cfr1; 04jn6y7; >> query: (?x7170, ?x53) <- titles(?x53, ?x7170), genre(?x4810, ?x53), genre(?x273, ?x53), language(?x4810, ?x254) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1637 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 471 *> proper extension: 01cgz; *> query: (?x7170, 05c3mp2) <- films(?x326, ?x7170), films(?x326, ?x11125), film_release_region(?x11125, ?x87), produced_by(?x11125, ?x5438) *> conf = 0.01 ranks of expected_values: 129 EVAL 02pxst genre 05c3mp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 74.000 72.000 0.733 http://example.org/film/film/genre #12784-0gyx4 PRED entity: 0gyx4 PRED relation: award PRED expected values: 019f4v 02w9sd7 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 293): 019f4v (0.72 #4695, 0.70 #41468, 0.70 #37555), 02w_6xj (0.72 #4695, 0.70 #41468, 0.70 #37555), 027c924 (0.72 #4695, 0.70 #41468, 0.70 #37555), 09d28z (0.72 #4695, 0.70 #41468, 0.70 #37555), 027c95y (0.72 #4695, 0.70 #41468, 0.70 #37555), 040njc (0.50 #791, 0.33 #18396, 0.32 #18788), 03hkv_r (0.41 #5101, 0.25 #798, 0.22 #2753), 09sb52 (0.39 #5515, 0.38 #1603, 0.37 #10599), 05zr6wv (0.38 #2363, 0.29 #3145, 0.22 #7839), 02pqp12 (0.38 #849, 0.22 #2804, 0.21 #18846) >> Best rule #4695 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 03xgm3; 01g257; 058s57; 01vsnff; 0q5hw; 01w7nww; 062dn7; 01xzb6; 013w7j; 063_t; ... >> query: (?x4397, ?x289) <- award(?x4397, ?x350), celebrity(?x1126, ?x4397), award_winner(?x289, ?x4397) >> conf = 0.72 => this is the best rule for 5 predicted values ranks of expected_values: 1, 19 EVAL 0gyx4 award 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 137.000 137.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gyx4 award 019f4v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12783-05zjx PRED entity: 05zjx PRED relation: nominated_for PRED expected values: 02h2vv => 108 concepts (55 used for prediction) PRED predicted values (max 10 best out of 397): 0n_hp (0.28 #8112, 0.27 #86011, 0.27 #6489), 07x4qr (0.28 #8112, 0.27 #86011, 0.27 #6489), 0124k9 (0.08 #6710, 0.07 #3465, 0.06 #5087), 08jgk1 (0.07 #16455, 0.06 #1853, 0.06 #19699), 039cq4 (0.06 #15688, 0.06 #2708, 0.04 #1086), 0828jw (0.05 #17138, 0.05 #20382, 0.04 #23627), 07g9f (0.05 #17690, 0.04 #3088, 0.04 #20934), 04vr_f (0.05 #3403, 0.05 #8271, 0.05 #11516), 072kp (0.05 #11443, 0.04 #13065, 0.04 #86), 0cs134 (0.04 #14490, 0.04 #17735, 0.03 #6377) >> Best rule #8112 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 0gd9k; 01l1ls; >> query: (?x7598, ?x2512) <- producer_type(?x7598, ?x632), film(?x7598, ?x2512), student(?x99, ?x7598) >> conf = 0.28 => this is the best rule for 2 predicted values *> Best rule #12374 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 01xdf5; 02l840; 01t6b4; 06x58; 0gz5hs; 04y8r; 01dw9z; 0c9c0; 0gy6z9; 05r5w; ... *> query: (?x7598, 02h2vv) <- producer_type(?x7598, ?x632), people(?x1050, ?x7598), profession(?x7598, ?x319) *> conf = 0.02 ranks of expected_values: 60 EVAL 05zjx nominated_for 02h2vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 108.000 55.000 0.276 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12782-04twmk PRED entity: 04twmk PRED relation: award PRED expected values: 02h3d1 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 226): 08_vwq (0.43 #269, 0.13 #670, 0.05 #16444), 09sb52 (0.35 #3649, 0.33 #2847, 0.32 #9664), 0bdw6t (0.29 #108, 0.16 #15641, 0.14 #18450), 09qvc0 (0.29 #39, 0.13 #26071, 0.13 #10427), 0cqhk0 (0.24 #437, 0.20 #1640, 0.18 #1239), 0gqy2 (0.21 #564, 0.14 #163, 0.12 #2569), 0cjyzs (0.18 #505, 0.10 #3312, 0.06 #6520), 0bfvd4 (0.16 #514, 0.14 #113, 0.09 #915), 04ljl_l (0.16 #404, 0.13 #26071, 0.13 #10427), 0fbvqf (0.16 #15641, 0.14 #18450, 0.13 #26071) >> Best rule #269 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01nvmd_; 02sjf5; 01wb8bs; 01d0b1; 01gbn6; >> query: (?x9435, 08_vwq) <- award_nominee(?x3709, ?x9435), award(?x9435, ?x7788), ?x7788 = 09lvl1 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #26071 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2131 *> proper extension: 054lpb6; *> query: (?x9435, ?x704) <- award_nominee(?x9435, ?x2657), award(?x9435, ?x435), award(?x2657, ?x704) *> conf = 0.13 ranks of expected_values: 33 EVAL 04twmk award 02h3d1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 99.000 99.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12781-01x53m PRED entity: 01x53m PRED relation: diet PRED expected values: 07_jd => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 2): 07_jd (0.11 #51, 0.08 #17, 0.07 #131), 07_hy (0.02 #124, 0.02 #52, 0.02 #132) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 0p_pd; 0kzy0; 0j1yf; 05qw5; 01vsl3_; 0693l; 0fb1q; 02qwg; 016yzz; 0315q3; ... >> query: (?x9173, 07_jd) <- award(?x9173, ?x11020), nationality(?x9173, ?x390), influenced_by(?x9173, ?x118), languages(?x9173, ?x254) >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x53m diet 07_jd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.107 http://example.org/base/eating/practicer_of_diet/diet #12780-02zkz7 PRED entity: 02zkz7 PRED relation: colors PRED expected values: 01l849 => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.65 #1683, 0.46 #42, 0.40 #1583), 01l849 (0.56 #762, 0.38 #1, 0.34 #622), 019sc (0.39 #908, 0.27 #267, 0.24 #368), 09ggk (0.25 #16, 0.12 #637, 0.10 #577), 06fvc (0.22 #904, 0.18 #1724, 0.17 #1684), 0jc_p (0.20 #24, 0.15 #224, 0.13 #465), 036k5h (0.15 #726, 0.13 #25, 0.12 #966), 03vtbc (0.14 #361, 0.10 #68, 0.09 #108), 038hg (0.14 #413, 0.11 #92, 0.11 #192), 04mkbj (0.12 #10, 0.10 #471, 0.10 #1611) >> Best rule #1683 for best value: >> intensional similarity = 7 >> extensional distance = 199 >> proper extension: 07tlg; >> query: (?x6075, 083jv) <- category(?x6075, ?x134), organization(?x346, ?x6075), colors(?x6075, ?x3189), colors(?x7122, ?x3189), colors(?x5850, ?x3189), ?x7122 = 01zhs3, ?x5850 = 037mjv >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #762 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 103 *> proper extension: 0bsnm; *> query: (?x6075, 01l849) <- currency(?x6075, ?x170), school_type(?x6075, ?x3092), colors(?x6075, ?x3189), organization(?x346, ?x6075), colors(?x9025, ?x3189), ?x9025 = 01vg0s *> conf = 0.56 ranks of expected_values: 2 EVAL 02zkz7 colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 193.000 193.000 0.652 http://example.org/education/educational_institution/colors #12779-08b8vd PRED entity: 08b8vd PRED relation: gender PRED expected values: 02zsn => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #154, 0.83 #70, 0.83 #182), 02zsn (0.58 #164, 0.57 #167, 0.56 #12) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 532 >> proper extension: 03_0p; 0qkj7; >> query: (?x3628, 05zppz) <- type_of_union(?x3628, ?x566), ?x566 = 04ztj, people(?x9771, ?x3628) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #164 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 560 *> proper extension: 01hxs4; 012x4t; 086qd; 02bwc7; 0kxbc; *> query: (?x3628, ?x231) <- participant(?x3628, ?x6668), gender(?x6668, ?x231), award(?x6668, ?x2060) *> conf = 0.58 ranks of expected_values: 2 EVAL 08b8vd gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 122.000 0.837 http://example.org/people/person/gender #12778-02v8kmz PRED entity: 02v8kmz PRED relation: film_crew_role PRED expected values: 0ch6mp2 0215hd => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.73 #704, 0.71 #815, 0.71 #227), 02r96rf (0.62 #699, 0.62 #810, 0.60 #735), 09vw2b7 (0.61 #703, 0.59 #814, 0.58 #226), 0dxtw (0.34 #708, 0.34 #819, 0.34 #231), 01pvkk (0.30 #233, 0.28 #746, 0.27 #159), 01vx2h (0.29 #709, 0.29 #820, 0.28 #745), 089fss (0.20 #5, 0.14 #41, 0.08 #114), 02ynfr (0.15 #714, 0.15 #237, 0.15 #825), 0215hd (0.14 #129, 0.12 #166, 0.11 #828), 01xy5l_ (0.10 #124, 0.09 #161, 0.09 #712) >> Best rule #704 for best value: >> intensional similarity = 4 >> extensional distance = 1046 >> proper extension: 0gtsx8c; >> query: (?x240, 0ch6mp2) <- film(?x3876, ?x240), film_crew_role(?x240, ?x137), country(?x240, ?x94), award_nominee(?x3876, ?x710) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 02v8kmz film_crew_role 0215hd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 58.000 58.000 0.731 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02v8kmz film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.731 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12777-07nxnw PRED entity: 07nxnw PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #61, 0.82 #101, 0.81 #71), 07c52 (0.07 #13, 0.05 #23, 0.05 #18), 07z4p (0.07 #15, 0.05 #25, 0.05 #20), 02nxhr (0.05 #22, 0.05 #17, 0.04 #57) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 143 >> proper extension: 03g90h; 011yxg; 0ds3t5x; 0dnvn3; 07xtqq; 01k1k4; 0ds11z; 0ds33; 0bth54; 01cssf; ... >> query: (?x6881, 029j_) <- crewmember(?x6881, ?x929), music(?x6881, ?x7701), produced_by(?x6881, ?x2182), film(?x450, ?x6881) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07nxnw film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.834 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12776-0gkz15s PRED entity: 0gkz15s PRED relation: film_release_region PRED expected values: 05v8c 015fr 0hzlz 0k6nt 035qy 0h7x 07t21 05qx1 06mkj 04g5k 01xbgx => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 83): 035qy (0.94 #515, 0.85 #638, 0.82 #761), 06mkj (0.91 #653, 0.87 #776, 0.85 #530), 015fr (0.85 #625, 0.83 #748, 0.82 #502), 05v8c (0.81 #624, 0.74 #501, 0.69 #747), 0k6nt (0.79 #878, 0.75 #632, 0.73 #755), 047lj (0.74 #498, 0.55 #621, 0.47 #744), 03rj0 (0.65 #779, 0.61 #902, 0.58 #656), 05qx1 (0.58 #644, 0.53 #521, 0.45 #767), 06mzp (0.55 #628, 0.50 #505, 0.46 #874), 0hzlz (0.50 #506, 0.32 #629, 0.25 #752) >> Best rule #515 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 0h1cdwq; 0gx9rvq; 087wc7n; 0879bpq; 0gjcrrw; 047fjjr; 043tvp3; 03z9585; >> query: (?x781, 035qy) <- genre(?x781, ?x225), film_release_region(?x781, ?x7413), film_release_region(?x781, ?x1174), ?x1174 = 047yc, ?x7413 = 04hqz >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 8, 10, 11, 13, 17, 24 EVAL 0gkz15s film_release_region 01xbgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz15s film_release_region 04g5k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz15s film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz15s film_release_region 05qx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz15s film_release_region 07t21 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz15s film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz15s film_release_region 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz15s film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz15s film_release_region 0hzlz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz15s film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gkz15s film_release_region 05v8c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.941 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12775-0m2kd PRED entity: 0m2kd PRED relation: featured_film_locations PRED expected values: 05kj_ => 78 concepts (53 used for prediction) PRED predicted values (max 10 best out of 96): 05kj_ (0.33 #18, 0.04 #1704, 0.03 #2184), 02_286 (0.15 #2426, 0.15 #3871, 0.15 #1225), 04jpl (0.12 #731, 0.12 #250, 0.06 #5791), 030qb3t (0.12 #280, 0.10 #1244, 0.08 #761), 0cr3d (0.08 #307, 0.01 #2472), 01_d4 (0.06 #529, 0.05 #1733, 0.04 #2213), 0h7h6 (0.04 #284, 0.03 #1729, 0.02 #9396), 01cx_ (0.04 #312, 0.02 #1034, 0.02 #1276), 0fvzz (0.04 #450), 0r771 (0.04 #422) >> Best rule #18 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0mbql; >> query: (?x430, 05kj_) <- genre(?x430, ?x53), film(?x7836, ?x430), film_release_region(?x430, ?x87), ?x7836 = 0mdyn, film_crew_role(?x430, ?x137) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m2kd featured_film_locations 05kj_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 53.000 0.333 http://example.org/film/film/featured_film_locations #12774-0466p0j PRED entity: 0466p0j PRED relation: award_winner PRED expected values: 02jxmr 01vvyvk 026spg 01w9wwg 07n3s => 69 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1355): 0hl3d (0.50 #8955, 0.46 #13419, 0.42 #10444), 016szr (0.50 #3707, 0.36 #8168, 0.33 #11144), 03h_fk5 (0.50 #3373, 0.36 #7834, 0.33 #10810), 02r3zy (0.50 #3112, 0.36 #7573, 0.33 #10549), 05d8vw (0.50 #1762, 0.33 #274, 0.27 #7711), 01wmgrf (0.50 #3418, 0.33 #442, 0.27 #7879), 01dwrc (0.50 #2353, 0.33 #865, 0.25 #9789), 02qwg (0.46 #13878, 0.36 #7927, 0.33 #10903), 01htxr (0.45 #8352, 0.42 #11328, 0.42 #9839), 01vsy95 (0.45 #7922, 0.42 #10898, 0.33 #9409) >> Best rule #8955 for best value: >> intensional similarity = 9 >> extensional distance = 10 >> proper extension: 05pd94v; 01mh_q; >> query: (?x5656, 0hl3d) <- ceremony(?x8409, ?x5656), ceremony(?x5123, ?x5656), ceremony(?x1389, ?x5656), award_winner(?x5656, ?x1378), artists(?x671, ?x1378), ?x8409 = 03ncb2, award_winner(?x5123, ?x487), award_winner(?x2335, ?x1378), ?x1389 = 01c427 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #11081 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 10 *> proper extension: 01mhwk; *> query: (?x5656, 01vvyvk) <- ceremony(?x8409, ?x5656), ceremony(?x5123, ?x5656), award_winner(?x5656, ?x6783), award_winner(?x5656, ?x1378), artists(?x671, ?x1378), ?x8409 = 03ncb2, ?x5123 = 025m98, award_winner(?x186, ?x6783) *> conf = 0.42 ranks of expected_values: 13, 179, 230, 286 EVAL 0466p0j award_winner 07n3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 56.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0466p0j award_winner 01w9wwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 69.000 56.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0466p0j award_winner 026spg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 69.000 56.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0466p0j award_winner 01vvyvk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 69.000 56.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0466p0j award_winner 02jxmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 69.000 56.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12773-05mph PRED entity: 05mph PRED relation: district_represented! PRED expected values: 070m6c => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 49): 070m6c (0.83 #299, 0.80 #642, 0.79 #201), 02bqn1 (0.71 #204, 0.53 #302, 0.46 #155), 02cg7g (0.68 #217, 0.50 #315, 0.46 #168), 02gkzs (0.64 #214, 0.47 #312, 0.43 #1618), 03rl1g (0.54 #638, 0.54 #197, 0.54 #148), 043djx (0.52 #643, 0.50 #202, 0.50 #153), 01h7xx (0.50 #331, 0.50 #184, 0.48 #674), 01gt99 (0.43 #682, 0.43 #1618, 0.43 #192), 03rtmz (0.43 #1618, 0.43 #210, 0.30 #651), 01gtcc (0.43 #1618, 0.41 #653, 0.39 #212) >> Best rule #299 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0488g; >> query: (?x6521, 070m6c) <- district_represented(?x6933, ?x6521), ?x6933 = 024tkd, capital(?x6521, ?x2941) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05mph district_represented! 070m6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.833 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #12772-03q5dr PRED entity: 03q5dr PRED relation: gender PRED expected values: 02zsn => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.71 #123, 0.71 #129, 0.71 #137), 02zsn (0.56 #6, 0.52 #110, 0.51 #99) >> Best rule #123 for best value: >> intensional similarity = 2 >> extensional distance = 2742 >> proper extension: 079vf; 05d7rk; 084w8; 0f0y8; 04yywz; 01l1b90; 05m63c; 0c9d9; 02g8h; 05g8ky; ... >> query: (?x9817, 05zppz) <- type_of_union(?x9817, ?x566), nationality(?x9817, ?x94) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 01wk7b7; *> query: (?x9817, 02zsn) <- actor(?x6482, ?x9817), actor(?x3169, ?x9817), ?x6482 = 0180mw, genre(?x3169, ?x53) *> conf = 0.56 ranks of expected_values: 2 EVAL 03q5dr gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.711 http://example.org/people/person/gender #12771-02kth6 PRED entity: 02kth6 PRED relation: colors PRED expected values: 038hg => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 16): 083jv (0.41 #52, 0.40 #290, 0.38 #35), 01g5v (0.38 #37, 0.35 #190, 0.34 #54), 06fvc (0.26 #172, 0.26 #155, 0.23 #223), 04mkbj (0.13 #450, 0.12 #8, 0.11 #297), 038hg (0.12 #95, 0.12 #10, 0.10 #231), 036k5h (0.12 #379, 0.11 #447, 0.10 #158), 0jc_p (0.12 #4, 0.09 #55, 0.09 #89), 067z2v (0.10 #177, 0.10 #160, 0.06 #143), 09ggk (0.09 #65, 0.08 #48, 0.06 #303), 09q2t (0.08 #80, 0.07 #216, 0.06 #131) >> Best rule #52 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 06jk5_; 037njl; 027mdh; 01_s9q; 02km0m; 021q2j; 01yqqv; 02l424; 01tntf; 0l0wv; ... >> query: (?x1609, 083jv) <- currency(?x1609, ?x170), institution(?x865, ?x1609), ?x865 = 02h4rq6, country(?x1609, ?x94) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #95 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 05zjtn4; 065y4w7; 07szy; 0dplh; 078bz; 0j_sncb; 02183k; 03ksy; 025v3k; 0lyjf; ... *> query: (?x1609, 038hg) <- student(?x1609, ?x8328), contains(?x94, ?x1609), artists(?x671, ?x8328), role(?x8328, ?x227) *> conf = 0.12 ranks of expected_values: 5 EVAL 02kth6 colors 038hg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 135.000 135.000 0.409 http://example.org/education/educational_institution/colors #12770-0chghy PRED entity: 0chghy PRED relation: contains PRED expected values: 04rkkv 02bm8 01r9nk => 261 concepts (128 used for prediction) PRED predicted values (max 10 best out of 2889): 05fly (0.87 #119466, 0.85 #338011, 0.85 #253508), 0vh3 (0.87 #119466, 0.85 #338011, 0.85 #253508), 01nf9x (0.75 #346753, 0.74 #241852), 01_qgp (0.52 #14569, 0.09 #14568, 0.07 #5827), 02ngbs (0.33 #7246, 0.20 #18900, 0.20 #4331), 0ch280 (0.33 #8436, 0.20 #20090, 0.20 #5521), 07w0v (0.33 #5923, 0.20 #17577, 0.20 #3008), 01s7pm (0.33 #7806, 0.20 #19460, 0.20 #4891), 02fgdx (0.33 #6250, 0.20 #17904, 0.20 #3335), 03np_7 (0.33 #8081, 0.20 #19735, 0.20 #5166) >> Best rule #119466 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0fxrk; >> query: (?x390, ?x8506) <- contains(?x390, ?x8602), administrative_parent(?x8506, ?x390), film_release_region(?x1861, ?x8602) >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #81555 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 24 *> proper extension: 01pj48; *> query: (?x390, 01r9nk) <- contains(?x10150, ?x390), ?x10150 = 05nrg *> conf = 0.04 ranks of expected_values: 2132 EVAL 0chghy contains 01r9nk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 261.000 128.000 0.872 http://example.org/location/location/contains EVAL 0chghy contains 02bm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 261.000 128.000 0.872 http://example.org/location/location/contains EVAL 0chghy contains 04rkkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 261.000 128.000 0.872 http://example.org/location/location/contains #12769-03z0l6 PRED entity: 03z0l6 PRED relation: gender PRED expected values: 05zppz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #49, 0.88 #61, 0.87 #71), 02zsn (0.46 #212, 0.46 #211, 0.46 #217) >> Best rule #49 for best value: >> intensional similarity = 7 >> extensional distance = 261 >> proper extension: 02pp_q_; 0177s6; 01t07j; 0gv40; 015njf; 0c12h; 0js9s; 08hhm6; 0gv2r; 030tj5; ... >> query: (?x9991, 05zppz) <- profession(?x9991, ?x987), profession(?x9991, ?x524), profession(?x9991, ?x319), ?x987 = 0dxtg, ?x319 = 01d_h8, nationality(?x9991, ?x1310), ?x524 = 02jknp >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03z0l6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.890 http://example.org/people/person/gender #12768-0sw62 PRED entity: 0sw62 PRED relation: student! PRED expected values: 02fy0z => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 184): 078bz (0.25 #603, 0.25 #77, 0.04 #3233), 02zd460 (0.25 #169, 0.11 #2273, 0.04 #3325), 03ksy (0.17 #1158, 0.08 #45870, 0.04 #22724), 01t0dy (0.17 #1268, 0.03 #3898, 0.02 #4950), 0lfgr (0.17 #1095, 0.02 #6355, 0.01 #14245), 01vmv_ (0.17 #1485), 053mhx (0.14 #1872, 0.07 #2924, 0.03 #16600), 01mpwj (0.14 #1685, 0.02 #45871, 0.02 #15361), 025v3k (0.14 #1698, 0.01 #33259, 0.01 #39045), 0pspl (0.14 #1687, 0.01 #22727, 0.01 #25357) >> Best rule #603 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 065mm1; >> query: (?x10109, 078bz) <- film(?x10109, ?x11073), film(?x10109, ?x1965), ?x11073 = 01ry_x, category(?x10109, ?x134), film_crew_role(?x1965, ?x955), nominated_for(?x1723, ?x1965) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0sw62 student! 02fy0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 133.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #12767-0m_v0 PRED entity: 0m_v0 PRED relation: award_winner! PRED expected values: 0gpjbt => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 130): 01bx35 (0.60 #7, 0.12 #275, 0.12 #5367), 01mh_q (0.40 #84, 0.29 #218, 0.10 #3300), 01xqqp (0.40 #91, 0.14 #225, 0.12 #359), 0jzphpx (0.29 #172, 0.20 #38, 0.08 #6068), 01s695 (0.25 #539, 0.20 #3, 0.18 #405), 019bk0 (0.25 #552, 0.15 #686, 0.14 #1222), 02rjjll (0.23 #1211, 0.20 #5, 0.17 #2015), 01c6qp (0.22 #689, 0.20 #19, 0.17 #957), 0gpjbt (0.20 #565, 0.18 #431, 0.13 #1235), 09n4nb (0.20 #47, 0.15 #717, 0.14 #1253) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02qwg; 0dw4g; 01z9_x; >> query: (?x3442, 01bx35) <- origin(?x3442, ?x4356), award_nominee(?x2169, ?x3442), ?x2169 = 01w60_p >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #565 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 01cblr; *> query: (?x3442, 0gpjbt) <- origin(?x3442, ?x4356), award(?x3442, ?x4416), ?x4416 = 099vwn *> conf = 0.20 ranks of expected_values: 9 EVAL 0m_v0 award_winner! 0gpjbt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 134.000 134.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12766-038bht PRED entity: 038bht PRED relation: profession PRED expected values: 02hrh1q => 102 concepts (98 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.89 #9487, 0.88 #2974, 0.88 #2086), 0dxtg (0.71 #1197, 0.67 #1345, 0.64 #1493), 0gl2ny2 (0.36 #659, 0.34 #1103, 0.33 #955), 02krf9 (0.32 #1210, 0.31 #1358, 0.28 #1506), 018gz8 (0.29 #16, 0.13 #9489, 0.13 #2532), 02jknp (0.27 #1191, 0.26 #7, 0.24 #1339), 01445t (0.26 #170, 0.16 #762, 0.15 #1058), 09jwl (0.22 #3422, 0.19 #2830, 0.19 #4606), 0np9r (0.21 #2980, 0.21 #2536, 0.20 #20), 0cbd2 (0.19 #1486, 0.17 #1338, 0.15 #450) >> Best rule #9487 for best value: >> intensional similarity = 3 >> extensional distance = 1817 >> proper extension: 0f2c8g; 01d5vk; 085q5; 0fs9jn; 065mm1; 0652ty; 0cgfb; 03k1vm; 01kym3; 045gzq; >> query: (?x4001, 02hrh1q) <- gender(?x4001, ?x231), film(?x4001, ?x343), profession(?x4001, ?x319) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 038bht profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 98.000 0.889 http://example.org/people/person/profession #12765-08mbj5d PRED entity: 08mbj5d PRED relation: category! PRED expected values: 012ljv 08815 02y_lrp 05zjtn4 0c3ybss 0t015 0ddfwj1 023rwm 09bjv 087c7 02rgz4 03f2_rc 04bs3j 0151ns 0146pg 06y9c2 05b1610 0fhp9 05qd_ 04l3_z 01b1mj 01vrncs 0lk90 0wh3 059_c 02prw4h 03bx2lk 015rmq 01p9hgt 01j_06 0244r8 0wp9b 01r9fv 024y8p 01f62 01wv9xn 04bpm6 01qkqwg 01l9p 09kn9 02lz1s 01_d4 0j1yf 07wbk 04r1t 02hft3 01xr2s 05d8vw 018grr 05qw5 086qd 01y9pk 07srw 013yq 07wrz 0783m_ 01hw6wq 0ddjy 015f47 0f1nl 02lf1j 018pj3 06hwzy 02t_tp 0qf3p 0ftvz 0mn0v 0408np 06btq 02301 0f__1 07cz2 014q2g 0cchk3 0dm5l 0l2tk 0645k5 0161sp 04cbtrw 06449 046lt 03_8r 01vw20_ 043ljr 0x25q 05q5t0b 01t7jy 0jrny 0373qg 01vwyqp 0djlxb 02bh9 0p8r1 017xm3 01vw26l 0830vk 01n7qlf 03h4mp 03pvt 0fpj4lx 01wgcvn 01wbz9 0164nb 0xddr 0qr4n 01svw8n 03hmt9b 02mt51 016dsy 026lg0s 05fky 0_75d 020923 01w524f 0ccvx 016yr0 0c8tk 0bqxw 02jxmr 0db94w 03f4xvm 04mcw4 0432_5 0p7h7 01kph_c 0r04p 0f6_4 06m61 01rr31 03sww 02dq8f 0304nh 0svqs 05xq9 0d9xq 047c9l 02nfjp 03c_cxn 0l_q9 0f42nz 01cbt3 0cbv4g 012vd6 0282x 01yg9y 027dpx 02wvfxl 0kr_t 06xpp7 027ct7c 0cks1m 05rfst 02zd2b 0bpk2 05lfwd 02dw1_ 01t38b 04mkft 09j_g 07bcn 077rj 01k_mc 02cpp 0h3lt 04pk1f 01y6dz 0n1rj 02qdyj 04pmnt 01tx9m 02fs_d 01lw3kh 0r8c8 0bxbb 08966 02xpy5 01cpqk 0dc_ms 0gs6vr 034qbx 05lb30 0wsr 02_0d2 01skmp 02r1ysd 06bss 0130sy 05fjf 017v3q 0jbyg 0ys4f 02y9bj 07x4c 02ts3h 0280061 0m75g 03ftmg 01vsyjy 01cwcr 01qgr3 01q9mk 016ckq 0btpm6 01lv85 02xfj0 0gy3w 0xnt5 012mzw 01cz7r 01vrx35 0bt4g 085h1 0gpx6 01k23t 01fxck 03j1p2n 03g5_y 07kbp5 03fnjv 01qwb5 0b06q 014pg1 04bbpm 01wvxw1 0bsnm 02mpb 036dyy 04gp58p 0cvw9 0xrz2 017yxq 0kb3n 0sbbq 09bw4_ 03hfmm 0d075m 03kxdw 02x9cv 0173s9 01sg4_ 0_lr1 0fhzwl 0xqf3 0jpn8 0sxdg 02hrb2 0d58_ 0167v4 01d4cb 02pptm 0q9nj 01s7w3 015fsv 0581vn8 01x2tm8 04mhbh 068g3p 0qzhw 0k9p4 01qf54 09z1lg 01nm8w 06rf7 0f2s6 06br6t 016l09 04knvh 01xlqd 04gd8j 01tt27 03m6zs 033cw 0bxtyq 016wvy 01v0sxx 0283sdr 01qckn 0nmj 01n2m6 02rky4 0t_3w 026m3y 07_pf 03qx_f 02x8s9 016nvh 01r9c_ 04k05 04gdr 01s560x 07vn_9 0h7t36 0x335 0mzy7 06zpgb2 0dr31 0__wm 020yvh 01n4w_ 0q48z 0pkgt 0cjcbg 0r6ff 01p7x7 02hp70 01vv6xv 03bnd9 0t6hk 06rjp 028q7m 079yb 010h9y 01tsbmv 0gys2jp 0560w 04_j5s 0fn7r 02ndf1 02ps55 0fxrk 01mskc3 0s987 02_01w 05qgd9 019fbp 013crh 01qmy04 018n1k 050kh5 01l63 0gmf0nj 0jltp 0qkyj 0r0ss 02pbrn 013f1h 013fn 0jpy_ 010z5n 01nn3m 013hvr 02h758 01bqnc 05q_mg 0fw3f 018dh3 02c9dj 0135p7 01p726 0393g 01fv4z 0bsxd3 0zpfy 0fnb4 01r9md 0tnkg 0sl2w 01s9ftn 02wvf2s 01g_k3 0889d 0fw2f 0ftns 018qd6 01z_jj 0179q0 01m23s 027xq5 0_kfv 014m1m 07p7g 07rfp 086h6p 0nqph 0162kb 0kc9f 04qb6g 018ldw 02gjp 02rq7nd 01kyln 0rydq 01fsyp 02wbnv 0lw_s 09c7b 0c0cs 0345kr 01tlyq 0dlm_ 01zb_g 017ht 01pk8b 01y68z 0q96 024tv_ 0zq7r 0171c7 080v2 01x8f6 03f_jk 027752 => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 08mbj5d category! 027752 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03f_jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01x8f6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 080v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0171c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0zq7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 024tv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0q96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01y68z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01pk8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 017ht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01zb_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0dlm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01tlyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0345kr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0c0cs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 09c7b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0lw_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02wbnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01fsyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0rydq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01kyln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02rq7nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02gjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 018ldw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04qb6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0kc9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0162kb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0nqph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 086h6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07rfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07p7g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 014m1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0_kfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 027xq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01m23s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0179q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01z_jj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 018qd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0ftns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0fw2f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0889d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01g_k3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02wvf2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01s9ftn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0sl2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0tnkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01r9md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0fnb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0zpfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0bsxd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01fv4z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0393g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01p726 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0135p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02c9dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 018dh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0fw3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05q_mg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01bqnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02h758 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 013hvr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01nn3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 010z5n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0jpy_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 013fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 013f1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02pbrn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0r0ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0qkyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0jltp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0gmf0nj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01l63 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 050kh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 018n1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01qmy04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 013crh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 019fbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05qgd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02_01w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0s987 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01mskc3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0fxrk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02ps55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02ndf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0fn7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04_j5s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0560w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0gys2jp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01tsbmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 010h9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 079yb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 028q7m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06rjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0t6hk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03bnd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01vv6xv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02hp70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01p7x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0r6ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0cjcbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0pkgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0q48z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01n4w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 020yvh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0__wm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0dr31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06zpgb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0mzy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0x335 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0h7t36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07vn_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01s560x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04gdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04k05 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01r9c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 016nvh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02x8s9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03qx_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07_pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 026m3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0t_3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02rky4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01n2m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0nmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01qckn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0283sdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01v0sxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 016wvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0bxtyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 033cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03m6zs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01tt27 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04gd8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01xlqd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04knvh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 016l09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06br6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0f2s6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06rf7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01nm8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 09z1lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01qf54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0k9p4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0qzhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 068g3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04mhbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01x2tm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0581vn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 015fsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01s7w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0q9nj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02pptm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01d4cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0167v4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0d58_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02hrb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0sxdg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0jpn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0xqf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0fhzwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0_lr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01sg4_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0173s9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02x9cv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03kxdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0d075m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03hfmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 09bw4_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0sbbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0kb3n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 017yxq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0xrz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0cvw9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04gp58p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 036dyy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02mpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0bsnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01wvxw1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04bbpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 014pg1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0b06q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01qwb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03fnjv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07kbp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03g5_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03j1p2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01fxck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01k23t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0gpx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 085h1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0bt4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01vrx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01cz7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 012mzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0xnt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0gy3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02xfj0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01lv85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 016ckq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01q9mk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01qgr3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01cwcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01vsyjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03ftmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0m75g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0280061 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02ts3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07x4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02y9bj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0ys4f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0jbyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 017v3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05fjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0130sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06bss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02r1ysd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01skmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02_0d2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0wsr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05lb30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 034qbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0gs6vr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01cpqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02xpy5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 08966 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0bxbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0r8c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01lw3kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02fs_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01tx9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04pmnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02qdyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0n1rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01y6dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04pk1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0h3lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02cpp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01k_mc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 077rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07bcn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 09j_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04mkft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01t38b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02dw1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05lfwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0bpk2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02zd2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05rfst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0cks1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 027ct7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06xpp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0kr_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02wvfxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 027dpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01yg9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0282x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 012vd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0cbv4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01cbt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0f42nz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0l_q9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03c_cxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02nfjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 047c9l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0d9xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05xq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0svqs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0304nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02dq8f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03sww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01rr31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06m61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0f6_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0r04p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01kph_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0p7h7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0432_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04mcw4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03f4xvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0db94w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02jxmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0bqxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0c8tk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 016yr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0ccvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01w524f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 020923 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0_75d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05fky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 026lg0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 016dsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02mt51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03hmt9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01svw8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0qr4n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0xddr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0164nb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01wbz9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01wgcvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0fpj4lx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03pvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03h4mp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01n7qlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0830vk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01vw26l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 017xm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0p8r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02bh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0djlxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01vwyqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0373qg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0jrny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01t7jy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05q5t0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0x25q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 043ljr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01vw20_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03_8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 046lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06449 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04cbtrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0161sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0645k5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0l2tk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0dm5l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0cchk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 014q2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07cz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0f__1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02301 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06btq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0408np CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0mn0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0ftvz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0qf3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02t_tp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06hwzy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 018pj3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02lf1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0f1nl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 015f47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0ddjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01hw6wq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0783m_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07wrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 013yq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07srw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01y9pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05qw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 018grr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05d8vw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01xr2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02hft3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04r1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 07wbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0j1yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02lz1s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 09kn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01l9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01qkqwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04bpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01wv9xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01f62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 024y8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01r9fv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0wp9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0244r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01j_06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01p9hgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 015rmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03bx2lk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02prw4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 059_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0wh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0lk90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01vrncs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 01b1mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04l3_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05qd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0fhp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05b1610 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 06y9c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0146pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0151ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 04bs3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 03f2_rc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02rgz4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 087c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 09bjv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 023rwm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0ddfwj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0t015 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 0c3ybss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 05zjtn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 02y_lrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category EVAL 08mbj5d category! 012ljv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/common/topic/webpage./common/webpage/category #12764-01zfrt PRED entity: 01zfrt PRED relation: teams PRED expected values: 01rlz4 => 24 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2): 02029f (0.25 #176, 0.17 #536), 02b13y (0.17 #640) >> Best rule #176 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 018x0q; 0gyvgw; >> query: (?x12110, 02029f) <- place_of_birth(?x5205, ?x12110), contains(?x14351, ?x12110), contains(?x1758, ?x12110), ?x14351 = 0kqb0, location(?x2122, ?x1758) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01zfrt teams 01rlz4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 24.000 15.000 0.250 http://example.org/sports/sports_team_location/teams #12763-015btn PRED entity: 015btn PRED relation: profession! PRED expected values: 01pfkw => 67 concepts (12 used for prediction) PRED predicted values (max 10 best out of 3984): 03lgg (0.67 #18524, 0.60 #22758, 0.58 #26993), 016gkf (0.67 #18675, 0.60 #22909, 0.50 #27144), 02dh86 (0.67 #17741, 0.50 #21975, 0.42 #26210), 04g_wd (0.67 #20448, 0.40 #24682, 0.33 #28917), 029pnn (0.60 #23880, 0.58 #28115, 0.50 #19646), 02dlfh (0.60 #23846, 0.58 #28081, 0.50 #19612), 0bqs56 (0.60 #23129, 0.58 #27364, 0.50 #18895), 0dn44 (0.60 #24973, 0.50 #29208, 0.50 #20739), 0fb1q (0.60 #22113, 0.50 #26348, 0.50 #17879), 04t2l2 (0.58 #25453, 0.50 #21218, 0.35 #46580) >> Best rule #18524 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0d8qb; >> query: (?x11999, 03lgg) <- profession(?x2161, ?x11999), profession(?x2127, ?x11999), ?x2127 = 01j7rd, influenced_by(?x476, ?x2161), people(?x11064, ?x2161), profession(?x2161, ?x3746), ?x3746 = 05z96 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #18333 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 0d8qb; *> query: (?x11999, 01pfkw) <- profession(?x2161, ?x11999), profession(?x2127, ?x11999), ?x2127 = 01j7rd, influenced_by(?x476, ?x2161), people(?x11064, ?x2161), profession(?x2161, ?x3746), ?x3746 = 05z96 *> conf = 0.33 ranks of expected_values: 506 EVAL 015btn profession! 01pfkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 67.000 12.000 0.667 http://example.org/people/person/profession #12762-03vrp PRED entity: 03vrp PRED relation: influenced_by PRED expected values: 034ks => 155 concepts (75 used for prediction) PRED predicted values (max 10 best out of 355): 04xjp (0.43 #1345, 0.33 #56, 0.29 #914), 03_87 (0.33 #2345, 0.33 #198, 0.29 #1487), 081k8 (0.33 #153, 0.33 #12175, 0.21 #9598), 037jz (0.33 #205, 0.29 #1494, 0.17 #2352), 040_9 (0.33 #98, 0.29 #1387, 0.14 #956), 058vp (0.33 #2328, 0.29 #1470, 0.14 #1039), 03jxw (0.33 #334, 0.23 #6344, 0.14 #1623), 03j0d (0.33 #331, 0.23 #6341, 0.14 #1620), 084w8 (0.33 #3, 0.20 #6013, 0.16 #8161), 02wh0 (0.33 #377, 0.16 #12399, 0.14 #1666) >> Best rule #1345 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 06kb_; 03f47xl; 037jz; 0hcvy; >> query: (?x4914, 04xjp) <- influenced_by(?x4914, ?x6400), influenced_by(?x4914, ?x5336), award(?x4914, ?x9285), ?x6400 = 06lbp, location(?x5336, ?x1658) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03vrp influenced_by 034ks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 75.000 0.429 http://example.org/influence/influence_node/influenced_by #12761-0j6cj PRED entity: 0j6cj PRED relation: profession PRED expected values: 09jwl 039v1 => 183 concepts (122 used for prediction) PRED predicted values (max 10 best out of 96): 02hrh1q (0.87 #6890, 0.87 #12903, 0.86 #5717), 09jwl (0.86 #3967, 0.84 #15840, 0.84 #8068), 0dxtg (0.62 #6302, 0.62 #7477, 0.51 #10703), 0cbd2 (0.59 #2347, 0.52 #10550, 0.48 #13041), 016z4k (0.57 #12013, 0.51 #9370, 0.50 #5999), 039v1 (0.56 #1204, 0.45 #1350, 0.45 #6470), 01d_h8 (0.50 #7469, 0.48 #6294, 0.47 #8786), 018gz8 (0.46 #7481, 0.45 #6306, 0.25 #10707), 03gjzk (0.42 #6304, 0.42 #7479, 0.34 #10997), 0n1h (0.41 #1619, 0.35 #595, 0.30 #1911) >> Best rule #6890 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 0154qm; 0205dx; 0fn5bx; 020ffd; 01nbq4; 023s8; 067sqt; 01x6jd; >> query: (?x7987, 02hrh1q) <- location(?x7987, ?x3976), film(?x7987, ?x559), student(?x8681, ?x7987), profession(?x7987, ?x131) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #3967 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 03qd_; *> query: (?x7987, 09jwl) <- instrumentalists(?x227, ?x7987), currency(?x7987, ?x170), role(?x7987, ?x645) *> conf = 0.86 ranks of expected_values: 2, 6 EVAL 0j6cj profession 039v1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 183.000 122.000 0.872 http://example.org/people/person/profession EVAL 0j6cj profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 122.000 0.872 http://example.org/people/person/profession #12760-01vw_dv PRED entity: 01vw_dv PRED relation: instrumentalists! PRED expected values: 0cfdd => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 118): 0342h (0.67 #521, 0.67 #1467, 0.63 #4051), 05r5c (0.50 #2244, 0.48 #352, 0.48 #524), 05148p4 (0.46 #4067, 0.37 #1483, 0.35 #365), 018vs (0.40 #4059, 0.28 #6475, 0.26 #6563), 02hnl (0.22 #4080, 0.16 #550, 0.16 #6496), 03qjg (0.18 #4097, 0.16 #567, 0.15 #4185), 026t6 (0.17 #4049, 0.15 #519, 0.12 #6553), 0l14md (0.16 #4053, 0.13 #523, 0.12 #609), 018j2 (0.13 #4084, 0.09 #2274, 0.09 #812), 04rzd (0.11 #1499, 0.11 #4083, 0.10 #2532) >> Best rule #521 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 015076; >> query: (?x6659, 0342h) <- participant(?x5996, ?x6659), instrumentalists(?x228, ?x6659), award_winner(?x4837, ?x6659) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #160 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0126y2; 01vw26l; 016ksk; 01vw8mh; 03sww; 01vvzb1; 01vvyc_; 0bqvs2; 01vz0g4; 07pzc; *> query: (?x6659, 0cfdd) <- artists(?x11545, ?x6659), award(?x6659, ?x724), ?x11545 = 036jv *> conf = 0.06 ranks of expected_values: 21 EVAL 01vw_dv instrumentalists! 0cfdd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 164.000 164.000 0.672 http://example.org/music/instrument/instrumentalists #12759-0gmcwlb PRED entity: 0gmcwlb PRED relation: film! PRED expected values: 01c65z => 86 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1078): 024bbl (0.51 #35394, 0.51 #12491, 0.45 #16656), 061dn_ (0.51 #12491, 0.45 #16656, 0.44 #37476), 0c6qh (0.17 #12906, 0.06 #2496, 0.05 #33726), 01l2fn (0.14 #263, 0.03 #14837, 0.03 #2344), 03f1zdw (0.14 #194, 0.03 #2275, 0.03 #4358), 06mnps (0.14 #569, 0.01 #13060), 03bxsw (0.14 #575), 0f4vbz (0.12 #12854, 0.04 #33674, 0.03 #6608), 05hj_k (0.11 #56211, 0.10 #43721, 0.07 #29146), 06q8hf (0.11 #56211, 0.10 #43721, 0.07 #29146) >> Best rule #35394 for best value: >> intensional similarity = 4 >> extensional distance = 258 >> proper extension: 05gnf; >> query: (?x1370, ?x9415) <- nominated_for(?x9415, ?x1370), student(?x4268, ?x9415), type_of_union(?x9415, ?x566), profession(?x9415, ?x1032) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #8226 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 37 *> proper extension: 08hmch; 047vnkj; 02wtp6; *> query: (?x1370, 01c65z) <- nominated_for(?x3462, ?x1370), film_release_region(?x1370, ?x1203), film_release_region(?x1370, ?x550), ?x550 = 05v8c, ?x1203 = 07ylj *> conf = 0.03 ranks of expected_values: 445 EVAL 0gmcwlb film! 01c65z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 86.000 41.000 0.508 http://example.org/film/actor/film./film/performance/film #12758-0175yg PRED entity: 0175yg PRED relation: artists PRED expected values: 0411q 02qsjt 01yndb => 47 concepts (11 used for prediction) PRED predicted values (max 10 best out of 1039): 07mvp (0.60 #4898, 0.55 #5976, 0.50 #2742), 021r7r (0.60 #4982, 0.50 #2826, 0.45 #6060), 033s6 (0.60 #5173, 0.50 #3017, 0.36 #6251), 02qwg (0.60 #4608, 0.50 #2452, 0.36 #5686), 01vw20_ (0.60 #4559, 0.50 #2403, 0.36 #5637), 01pny5 (0.60 #5367, 0.50 #3211, 0.36 #6445), 01vsy3q (0.60 #4751, 0.50 #2595, 0.36 #5829), 04r1t (0.60 #4449, 0.50 #2293, 0.36 #5527), 044k8 (0.60 #4715, 0.50 #2559, 0.36 #5793), 0zjpz (0.60 #4455, 0.50 #2299, 0.36 #5533) >> Best rule #4898 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 0xhtw; 06by7; 0155w; >> query: (?x12114, 07mvp) <- artists(?x12114, ?x10907), artists(?x12114, ?x5385), artists(?x12114, ?x5141), role(?x5141, ?x1495), role(?x5141, ?x716), ?x10907 = 01tw31, artist(?x4081, ?x5141), ?x5385 = 0134tg, ?x1495 = 013y1f, ?x716 = 018vs >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2998 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 02yv6b; *> query: (?x12114, 01yndb) <- artists(?x12114, ?x10907), artists(?x12114, ?x5385), artists(?x12114, ?x5141), artists(?x12114, ?x4157), role(?x5141, ?x316), ?x10907 = 01tw31, artist(?x4081, ?x5141), award_winner(?x139, ?x5141), group(?x227, ?x5385), ?x4157 = 01kh2m1 *> conf = 0.50 ranks of expected_values: 37, 322, 346 EVAL 0175yg artists 01yndb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 47.000 11.000 0.600 http://example.org/music/genre/artists EVAL 0175yg artists 02qsjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 11.000 0.600 http://example.org/music/genre/artists EVAL 0175yg artists 0411q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 11.000 0.600 http://example.org/music/genre/artists #12757-03h2d4 PRED entity: 03h2d4 PRED relation: film PRED expected values: 09gkx35 09fqgj => 125 concepts (102 used for prediction) PRED predicted values (max 10 best out of 766): 0872p_c (0.06 #3731, 0.04 #1952, 0.01 #98022), 099bhp (0.06 #5166, 0.04 #3387), 05sw5b (0.06 #2587, 0.05 #4366), 03177r (0.05 #7576, 0.02 #20029, 0.02 #14692), 047csmy (0.05 #4464, 0.04 #2685), 0ndwt2w (0.05 #6330, 0.03 #8109, 0.02 #29457), 031hcx (0.05 #8382, 0.02 #20835, 0.02 #6603), 031786 (0.05 #8383, 0.02 #6604, 0.02 #1267), 02wgk1 (0.04 #2530, 0.04 #4309, 0.01 #98600), 0prrm (0.04 #2632, 0.03 #4411, 0.02 #9748) >> Best rule #3731 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 01yh3y; 04w391; 04fzk; 01v3vp; 02dth1; 06tp4h; 02gf_l; 04mlh8; 024my5; 08wjf4; ... >> query: (?x4286, 0872p_c) <- film(?x4286, ?x3012), language(?x4286, ?x254), country(?x3012, ?x512) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #8766 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 127 *> proper extension: 03z0l6; 01vzz1c; *> query: (?x4286, 09fqgj) <- location(?x4286, ?x2611), location(?x4286, ?x362), ?x362 = 04jpl, location_of_ceremony(?x566, ?x2611) *> conf = 0.03 ranks of expected_values: 27 EVAL 03h2d4 film 09fqgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 125.000 102.000 0.061 http://example.org/film/actor/film./film/performance/film EVAL 03h2d4 film 09gkx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 102.000 0.061 http://example.org/film/actor/film./film/performance/film #12756-0fczy PRED entity: 0fczy PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 115 concepts (56 used for prediction) PRED predicted values (max 10 best out of 6): 09c7w0 (0.89 #136, 0.89 #194, 0.88 #206), 059rby (0.15 #312, 0.14 #90, 0.12 #299), 0fczy (0.08 #624), 02jx1 (0.05 #528, 0.05 #673, 0.05 #606), 03rt9 (0.02 #291, 0.01 #587, 0.01 #653), 03rjj (0.01 #599, 0.01 #666, 0.01 #521) >> Best rule #136 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 0mn0v; 0nvd8; 0cc1v; 0nt4s; 0mlzk; 0mvxt; 0msck; >> query: (?x8447, 09c7w0) <- source(?x8447, ?x958), currency(?x8447, ?x170), county(?x8245, ?x8447), ?x958 = 0jbk9 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fczy second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 56.000 0.888 http://example.org/location/country/second_level_divisions #12755-03bdkd PRED entity: 03bdkd PRED relation: nominated_for! PRED expected values: 01b9ck => 101 concepts (30 used for prediction) PRED predicted values (max 10 best out of 738): 0cb77r (0.80 #37375, 0.77 #65411, 0.62 #2336), 039xcr (0.39 #30367, 0.27 #53729), 013sg6 (0.39 #30367, 0.27 #53729), 0kftt (0.25 #1778, 0.05 #8784, 0.04 #15790), 02cyfz (0.20 #2783, 0.09 #7453, 0.05 #12123), 01s7zw (0.20 #2870, 0.09 #7540, 0.05 #12210), 015t7v (0.20 #3454, 0.09 #8124, 0.05 #12794), 0f3zf_ (0.20 #2553, 0.09 #7223, 0.05 #11893), 0bxtg (0.20 #2420, 0.05 #11760, 0.05 #7090), 03h26tm (0.20 #2519, 0.05 #11859, 0.05 #7189) >> Best rule #37375 for best value: >> intensional similarity = 4 >> extensional distance = 229 >> proper extension: 03twd6; >> query: (?x10614, ?x200) <- genre(?x10614, ?x53), ?x53 = 07s9rl0, award_winner(?x10614, ?x200), films(?x1159, ?x10614) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #60997 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 403 *> proper extension: 07gp9; 08720; 01vksx; 0m_mm; 09p0ct; 04m1bm; 075wx7_; 0hmm7; 0ddjy; 09p7fh; ... *> query: (?x10614, 01b9ck) <- award(?x10614, ?x1703), award_winner(?x1703, ?x323), nominated_for(?x1703, ?x7554), nominated_for(?x1703, ?x1163), ?x7554 = 01mgw, film_release_region(?x1163, ?x87) *> conf = 0.01 ranks of expected_values: 459 EVAL 03bdkd nominated_for! 01b9ck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 101.000 30.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12754-01t7jy PRED entity: 01t7jy PRED relation: category PRED expected values: 08mbj5d => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #10, 0.80 #54, 0.79 #53) >> Best rule #10 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 04sylm; 017z88; 02607j; 03p7gb; 05njyy; 05nrkb; 06thjt; 01p7x7; 09k9d0; 01cf5; ... >> query: (?x3147, 08mbj5d) <- citytown(?x3147, ?x3148), country(?x3147, ?x94), ?x94 = 09c7w0, contains(?x335, ?x3148), ?x335 = 059rby >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01t7jy category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.850 http://example.org/common/topic/webpage./common/webpage/category #12753-01s0ps PRED entity: 01s0ps PRED relation: role! PRED expected values: 026t6 => 87 concepts (56 used for prediction) PRED predicted values (max 10 best out of 83): 04rzd (0.88 #3434, 0.88 #3376, 0.83 #2426), 02fsn (0.83 #1422, 0.74 #2758, 0.73 #1265), 01v8y9 (0.83 #1426, 0.66 #227, 0.63 #760), 026t6 (0.82 #3114, 0.82 #3039, 0.80 #3276), 0mkg (0.82 #1007, 0.82 #763, 0.71 #3037), 01679d (0.82 #1030, 0.82 #763, 0.71 #713), 07y_7 (0.82 #1229, 0.75 #764, 0.69 #379), 0680x0 (0.82 #763, 0.69 #1585, 0.67 #2325), 0l1589 (0.82 #763, 0.69 #379, 0.68 #228), 07_l6 (0.79 #997, 0.75 #1429, 0.70 #3902) >> Best rule #3434 for best value: >> intensional similarity = 18 >> extensional distance = 23 >> proper extension: 0dwvl; 01rhl; >> query: (?x2764, ?x1969) <- role(?x7033, ?x2764), role(?x3418, ?x2764), role(?x2309, ?x2764), role(?x1473, ?x2764), role(?x1432, ?x2764), ?x7033 = 0gkd1, role(?x3214, ?x2764), role(?x158, ?x2764), role(?x2764, ?x2048), ?x1432 = 0395lw, role(?x3214, ?x1969), instrumentalists(?x2309, ?x5745), role(?x214, ?x3418), role(?x211, ?x1473), group(?x2309, ?x1751), family(?x2309, ?x3156), ?x5745 = 01l87db, ?x1969 = 04rzd >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3114 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 20 *> proper extension: 02pprs; 03gvt; 0dwt5; 0gkd1; *> query: (?x2764, ?x212) <- role(?x7033, ?x2764), role(?x1437, ?x2764), role(?x228, ?x2764), role(?x212, ?x7033), ?x212 = 026t6, ?x1437 = 01vdm0, instrumentalists(?x7033, ?x9087), instrumentalists(?x7033, ?x5508), role(?x8362, ?x2764), ?x5508 = 0jn5l, ?x9087 = 0kj34, artists(?x378, ?x8362), ?x378 = 07sbbz2, location(?x8362, ?x13996), ?x228 = 0l14qv, group(?x2764, ?x3207) *> conf = 0.82 ranks of expected_values: 4 EVAL 01s0ps role! 026t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 56.000 0.880 http://example.org/music/performance_role/track_performances./music/track_contribution/role #12752-02jq1 PRED entity: 02jq1 PRED relation: films PRED expected values: 01kqq7 => 188 concepts (172 used for prediction) PRED predicted values (max 10 best out of 14): 0djlxb (0.14 #1756, 0.12 #3349, 0.12 #2818), 0b4lkx (0.07 #6787, 0.06 #7849, 0.03 #13162), 02vqsll (0.07 #6522, 0.06 #7584, 0.03 #12897), 03m5y9p (0.06 #7859, 0.06 #9453, 0.02 #17952), 09sr0 (0.06 #7886, 0.03 #13199, 0.02 #16917), 03xj05 (0.03 #13242), 02yvct (0.03 #12856), 047bynf (0.02 #16810, 0.02 #19465, 0.01 #21058), 042y1c (0.02 #17114, 0.02 #18707, 0.01 #27736), 0m313 (0.02 #18594) >> Best rule #1756 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01pfkw; >> query: (?x5442, 0djlxb) <- celebrities_impersonated(?x3649, ?x5442), languages(?x5442, ?x254), ?x254 = 02h40lc, artist(?x7089, ?x5442) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02jq1 films 01kqq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 188.000 172.000 0.143 http://example.org/film/film_subject/films #12751-01m24m PRED entity: 01m24m PRED relation: time_zones PRED expected values: 02hcv8 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.92 #29, 0.87 #42, 0.86 #68), 02lcqs (0.32 #96, 0.28 #239, 0.27 #344), 02fqwt (0.20 #353, 0.18 #744, 0.18 #679), 02llzg (0.08 #590, 0.08 #551, 0.07 #890), 02hczc (0.08 #171, 0.07 #641, 0.07 #654), 03bdv (0.05 #710, 0.04 #788, 0.04 #579), 042g7t (0.04 #63), 02lcrv (0.03 #20, 0.01 #293), 03plfd (0.03 #596, 0.02 #636, 0.02 #909) >> Best rule #29 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 01m1_t; 0rd5k; 01zmqw; 0rd6b; 0t_07; 0hz35; 01m20m; 01m1_d; 01lxw6; 0_j_z; ... >> query: (?x13619, 02hcv8) <- contains(?x12075, ?x13619), category(?x13619, ?x134), adjoins(?x3164, ?x12075), currency(?x13619, ?x170), second_level_divisions(?x94, ?x12075) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m24m time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.923 http://example.org/location/location/time_zones #12750-027b9k6 PRED entity: 027b9k6 PRED relation: award_winner PRED expected values: 01p7yb 01jmv8 => 47 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1836): 01hkhq (0.56 #7909, 0.50 #10373, 0.50 #2981), 0159h6 (0.50 #2542, 0.44 #7470, 0.40 #9934), 0h1mt (0.50 #2676, 0.33 #7604, 0.30 #10068), 03bxsw (0.50 #3177, 0.22 #8105, 0.20 #10569), 09l3p (0.44 #8335, 0.40 #10799, 0.25 #3407), 0mz73 (0.44 #9088, 0.40 #11552, 0.25 #4160), 01p7yb (0.44 #7450, 0.40 #9914, 0.11 #32051), 015q43 (0.44 #8530, 0.40 #10994, 0.05 #13459), 03ym1 (0.38 #13599, 0.05 #16066, 0.05 #20999), 01bj6y (0.33 #9674, 0.30 #12138, 0.25 #4746) >> Best rule #7909 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 027c924; 09qwmm; 0gqwc; 099cng; 02z1nbg; >> query: (?x4226, 01hkhq) <- award(?x6899, ?x4226), award_winner(?x4226, ?x988), ?x6899 = 04lhc4, actor(?x6597, ?x988) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #7450 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 027c924; 09qwmm; 0gqwc; 099cng; 02z1nbg; *> query: (?x4226, 01p7yb) <- award(?x6899, ?x4226), award_winner(?x4226, ?x988), ?x6899 = 04lhc4, actor(?x6597, ?x988) *> conf = 0.44 ranks of expected_values: 7, 187 EVAL 027b9k6 award_winner 01jmv8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 47.000 19.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027b9k6 award_winner 01p7yb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 47.000 19.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner #12749-07w21 PRED entity: 07w21 PRED relation: influenced_by! PRED expected values: 0p8jf 02yl42 => 139 concepts (44 used for prediction) PRED predicted values (max 10 best out of 424): 0j0pf (0.40 #205, 0.13 #17393, 0.13 #3785), 01hb6v (0.16 #2652, 0.13 #17393, 0.12 #4185), 0282x (0.13 #17393, 0.07 #10741, 0.05 #2780), 067xw (0.13 #17393, 0.07 #282, 0.05 #2840), 01zkxv (0.13 #17393, 0.06 #3085, 0.06 #4107), 05cv8 (0.13 #17393, 0.05 #13301, 0.03 #4005), 01g6bk (0.13 #17393, 0.03 #3543, 0.02 #4054), 02yl42 (0.13 #3714, 0.10 #7294, 0.10 #8316), 0683n (0.12 #2895, 0.11 #3406, 0.10 #4428), 034bs (0.10 #2711, 0.06 #4244, 0.05 #3222) >> Best rule #205 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 033cw; >> query: (?x476, 0j0pf) <- profession(?x476, ?x353), award_winner(?x1375, ?x476), award(?x476, ?x5050), ?x1375 = 0262zm >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3714 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 92 *> proper extension: 012cph; *> query: (?x476, 02yl42) <- profession(?x476, ?x353), award(?x476, ?x575), ?x353 = 0cbd2, influenced_by(?x2934, ?x476) *> conf = 0.13 ranks of expected_values: 8, 13 EVAL 07w21 influenced_by! 02yl42 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 139.000 44.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 07w21 influenced_by! 0p8jf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 139.000 44.000 0.400 http://example.org/influence/influence_node/influenced_by #12748-09zcbg PRED entity: 09zcbg PRED relation: artist PRED expected values: 01518s => 46 concepts (18 used for prediction) PRED predicted values (max 10 best out of 929): 02mslq (0.60 #2553, 0.25 #1711, 0.12 #11835), 0565cz (0.50 #4404, 0.40 #5251, 0.33 #1038), 01q99h (0.50 #4655, 0.40 #5502, 0.29 #7193), 01s560x (0.50 #4970, 0.30 #5817, 0.25 #2444), 0g824 (0.50 #4668, 0.30 #5515, 0.18 #9739), 03g5jw (0.50 #4295, 0.30 #5142, 0.14 #6833), 046p9 (0.50 #4812, 0.30 #5659, 0.14 #7350), 01wj18h (0.50 #4423, 0.30 #5270, 0.14 #6961), 016m5c (0.50 #5019, 0.30 #5866, 0.10 #11776), 018dyl (0.50 #4509, 0.30 #5356, 0.10 #6204) >> Best rule #2553 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 01cl2y; 01cf93; 01xyqk; >> query: (?x13965, 02mslq) <- category(?x13965, ?x134), ?x134 = 08mbj5d, artist(?x13965, ?x9463), artists(?x9248, ?x9463), artists(?x1572, ?x9463), artists(?x1380, ?x9463), artists(?x302, ?x9463), ?x1572 = 06by7, ?x302 = 016clz, group(?x315, ?x9463), ?x315 = 0l14md, ?x1380 = 0dl5d, artists(?x9248, ?x10565), ?x10565 = 0c9l1, parent_genre(?x9248, ?x7220) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #5055 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 4 *> proper extension: 033hn8; *> query: (?x13965, ?x8012) <- artist(?x13965, ?x9463), origin(?x9463, ?x10980), artists(?x13087, ?x9463), artists(?x10471, ?x9463), artists(?x8011, ?x9463), artists(?x5379, ?x9463), artists(?x1572, ?x9463), ?x8011 = 0172rj, artists(?x10471, ?x6406), artists(?x10471, ?x3399), ?x6406 = 01386_, ?x3399 = 01gx5f, artists(?x13087, ?x8012), artists(?x5379, ?x4182), ?x4182 = 07yg2, parent_genre(?x114, ?x1572) *> conf = 0.10 ranks of expected_values: 689 EVAL 09zcbg artist 01518s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 18.000 0.600 http://example.org/music/record_label/artist #12747-0b1y_2 PRED entity: 0b1y_2 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.87 #16, 0.85 #51, 0.84 #26), 02nxhr (0.04 #87, 0.04 #97, 0.04 #47), 07c52 (0.04 #8, 0.03 #13, 0.03 #352), 07z4p (0.03 #20, 0.03 #135, 0.02 #145) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 047vnkj; >> query: (?x2920, 029j_) <- film_crew_role(?x2920, ?x7591), nominated_for(?x828, ?x2920), honored_for(?x3624, ?x2920), ?x7591 = 0d2b38 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b1y_2 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12746-018wng PRED entity: 018wng PRED relation: ceremony PRED expected values: 0ftlkg 04110lv 0dznvw => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 79): 04110lv (0.91 #1391, 0.90 #1225, 0.89 #1142), 0fzrtf (0.88 #951, 0.86 #702, 0.81 #868), 0d__c3 (0.82 #985, 0.65 #1234, 0.64 #736), 0fz0c2 (0.71 #973, 0.67 #1139, 0.65 #1222), 0dznvw (0.71 #991, 0.64 #742, 0.62 #908), 0fy59t (0.65 #979, 0.60 #1228, 0.57 #1311), 0ftlkg (0.62 #848, 0.61 #1097, 0.59 #1346), 0gpjbt (0.51 #3759, 0.47 #3676, 0.34 #3925), 09n4nb (0.49 #3772, 0.45 #3689, 0.34 #3938), 0466p0j (0.49 #3787, 0.45 #3704, 0.33 #3953) >> Best rule #1391 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0gqxm; >> query: (?x720, 04110lv) <- award_winner(?x720, ?x382), ceremony(?x720, ?x7100), ceremony(?x720, ?x5761), ?x5761 = 02ywhz, honored_for(?x7100, ?x251) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 7 EVAL 018wng ceremony 0dznvw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 62.000 62.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 018wng ceremony 04110lv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 018wng ceremony 0ftlkg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 62.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony #12745-0pd57 PRED entity: 0pd57 PRED relation: nominated_for! PRED expected values: 0gr4k 054krc => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 182): 019f4v (0.65 #52, 0.52 #2140, 0.48 #1908), 02pqp12 (0.60 #57, 0.30 #1913, 0.30 #2145), 0gs9p (0.56 #2149, 0.49 #1917, 0.45 #61), 02r0csl (0.55 #5, 0.26 #2325, 0.24 #701), 04dn09n (0.50 #34, 0.41 #2122, 0.35 #1890), 02qyntr (0.50 #174, 0.34 #2262, 0.30 #2030), 099c8n (0.50 #55, 0.30 #2143, 0.25 #1911), 02qvyrt (0.50 #91, 0.29 #787, 0.25 #2411), 040njc (0.45 #7, 0.39 #2095, 0.37 #1863), 027dtxw (0.45 #4, 0.24 #700, 0.23 #1860) >> Best rule #52 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 0cbv4g; 0ckrnn; 0_9l_; >> query: (?x4179, 019f4v) <- nominated_for(?x3519, ?x4179), nominated_for(?x2880, ?x4179), nominated_for(?x2222, ?x4179), ?x2222 = 0gs96, ?x2880 = 02ppm4q >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #489 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 01wb95; 0gcrg; 02q_4ph; 0g5ptf; 03mr85; *> query: (?x4179, 0gr4k) <- film(?x4180, ?x4179), language(?x4179, ?x254), ?x254 = 02h40lc, film_art_direction_by(?x4179, ?x6766) *> conf = 0.42 ranks of expected_values: 11, 21 EVAL 0pd57 nominated_for! 054krc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 97.000 97.000 0.650 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0pd57 nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 97.000 97.000 0.650 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12744-070ltt PRED entity: 070ltt PRED relation: country_of_origin PRED expected values: 09c7w0 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 56): 09c7w0 (0.90 #349, 0.89 #474, 0.89 #325), 0d060g (0.53 #869, 0.46 #916, 0.46 #928), 07ssc (0.53 #869, 0.46 #916, 0.46 #928), 03_3d (0.46 #916, 0.46 #928, 0.46 #788), 03rjj (0.22 #940, 0.15 #12, 0.04 #707), 0d0vqn (0.22 #940, 0.15 #12, 0.04 #707), 02jx1 (0.15 #12, 0.03 #892, 0.02 #775), 03rt9 (0.15 #12, 0.03 #892, 0.01 #469), 04jpl (0.15 #12, 0.01 #467, 0.01 #490), 03h64 (0.04 #707, 0.03 #892) >> Best rule #349 for best value: >> intensional similarity = 7 >> extensional distance = 38 >> proper extension: 01hn_t; >> query: (?x10551, 09c7w0) <- tv_program(?x12775, ?x10551), nationality(?x12775, ?x94), program(?x6678, ?x10551), program(?x2554, ?x10551), award_winner(?x5592, ?x2554), program(?x6678, ?x3544), award(?x3544, ?x2720) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 070ltt country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.900 http://example.org/tv/tv_program/country_of_origin #12743-04rrd PRED entity: 04rrd PRED relation: district_represented! PRED expected values: 02gkzs 01gtc0 01gsvb => 238 concepts (238 used for prediction) PRED predicted values (max 10 best out of 20): 01gtc0 (0.83 #131, 0.55 #1261, 0.47 #431), 01gsvb (0.83 #136, 0.55 #1261, 0.47 #436), 02gkzs (0.56 #270, 0.55 #1261, 0.43 #130), 03rtmz (0.55 #1261, 0.38 #267, 0.32 #587), 02glc4 (0.55 #1261, 0.35 #272, 0.35 #132), 03tcbx (0.55 #1261, 0.35 #266, 0.30 #126), 03z5xd (0.55 #1261, 0.22 #125, 0.21 #265), 03ww_x (0.55 #1261, 0.21 #263, 0.20 #23), 032ft5 (0.55 #1261, 0.20 #24, 0.13 #124), 0495ys (0.55 #1261, 0.20 #21, 0.10 #721) >> Best rule #131 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 026mj; >> query: (?x1767, 01gtc0) <- state(?x1705, ?x1767), district_represented(?x2019, ?x1767), ?x2019 = 01gtbb >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 04rrd district_represented! 01gsvb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 238.000 238.000 0.826 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04rrd district_represented! 01gtc0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 238.000 238.000 0.826 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04rrd district_represented! 02gkzs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 238.000 238.000 0.826 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #12742-016khd PRED entity: 016khd PRED relation: award_nominee! PRED expected values: 03k7bd => 116 concepts (50 used for prediction) PRED predicted values (max 10 best out of 803): 015rkw (0.77 #51255, 0.77 #116486, 0.77 #60573), 01swck (0.77 #51255, 0.77 #116486, 0.77 #60573), 0f4vbz (0.77 #51255, 0.77 #116486, 0.77 #60573), 01pj5q (0.77 #51255, 0.77 #60573, 0.77 #86198), 02t__3 (0.77 #51255, 0.77 #60573, 0.77 #86198), 0169dl (0.77 #51255, 0.77 #102504, 0.76 #41936), 06cgy (0.77 #51255, 0.77 #102504, 0.76 #41936), 01wz01 (0.77 #51255, 0.77 #102504, 0.76 #41936), 0h10vt (0.77 #51255, 0.77 #102504, 0.76 #41936), 018ygt (0.77 #116486, 0.77 #60573, 0.77 #86198) >> Best rule #51255 for best value: >> intensional similarity = 3 >> extensional distance = 977 >> proper extension: 04cy8rb; 086k8; 017s11; 016tt2; 0g1rw; 0785v8; 0kx4m; 05qd_; 03h26tm; 016tw3; ... >> query: (?x851, ?x1554) <- award_nominee(?x851, ?x91), award_winner(?x4174, ?x851), award_winner(?x851, ?x1554) >> conf = 0.77 => this is the best rule for 9 predicted values No rule for expected values ranks of expected_values: EVAL 016khd award_nominee! 03k7bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 50.000 0.767 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #12741-0f276 PRED entity: 0f276 PRED relation: location PRED expected values: 080h2 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 130): 0ckhc (0.69 #48943, 0.67 #21665, 0.59 #9630), 02_286 (0.45 #38549, 0.45 #39351, 0.32 #46573), 030qb3t (0.35 #38595, 0.35 #39397, 0.33 #885), 06y57 (0.27 #1858, 0.11 #1056, 0.05 #30489), 018d5b (0.25 #738, 0.03 #5551, 0.01 #6353), 04jpl (0.22 #819, 0.18 #1621, 0.17 #38529), 0rh6k (0.22 #806, 0.18 #1608, 0.07 #2410), 0ccvx (0.16 #3428, 0.13 #2626, 0.02 #68418), 0cr3d (0.11 #946, 0.09 #1748, 0.07 #68342), 0430_ (0.11 #1503, 0.09 #2305, 0.07 #3107) >> Best rule #48943 for best value: >> intensional similarity = 3 >> extensional distance = 1146 >> proper extension: 01pbxb; 0197tq; 0fp_v1x; 07w21; 01zkxv; 07g2b; 025xt8y; 01w61th; 04411; 07q1v4; ... >> query: (?x9781, ?x12182) <- award(?x9781, ?x2325), place_of_birth(?x9781, ?x12182), location(?x9781, ?x1658) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #5669 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 81 *> proper extension: 0l56b; *> query: (?x9781, 080h2) <- award_nominee(?x9781, ?x3580), nationality(?x9781, ?x279), ?x279 = 0d060g *> conf = 0.07 ranks of expected_values: 15 EVAL 0f276 location 080h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 119.000 119.000 0.692 http://example.org/people/person/places_lived./people/place_lived/location #12740-01gsry PRED entity: 01gsry PRED relation: district_represented PRED expected values: 05k7sb 05tbn => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 284): 05k7sb (0.89 #1595, 0.89 #1542, 0.88 #1488), 05tbn (0.84 #767, 0.84 #1499, 0.83 #1392), 0g0syc (0.84 #767, 0.81 #1199, 0.80 #1087), 03v1s (0.82 #1204, 0.81 #1199, 0.81 #1092), 04tgp (0.82 #1204, 0.81 #1199, 0.81 #1092), 0gyh (0.82 #1204, 0.81 #1199, 0.81 #1092), 050ks (0.82 #1204, 0.81 #1199, 0.81 #1092), 03v0t (0.82 #1204, 0.81 #1199, 0.81 #1092), 04ly1 (0.82 #1204, 0.81 #1199, 0.81 #1092), 04ych (0.82 #1204, 0.81 #1199, 0.81 #1092) >> Best rule #1595 for best value: >> intensional similarity = 32 >> extensional distance = 52 >> proper extension: 0495ys; 05l2z4; 032ft5; 04gp1d; 06r713; 060ny2; 04h1rz; >> query: (?x9416, 05k7sb) <- district_represented(?x9416, ?x1767), district_represented(?x9416, ?x177), legislative_sessions(?x2712, ?x9416), location(?x2913, ?x1767), contains(?x177, ?x12260), contains(?x177, ?x11058), contains(?x177, ?x10235), district_represented(?x12714, ?x1767), district_represented(?x10638, ?x1767), adjoins(?x177, ?x1905), currency(?x177, ?x170), religion(?x177, ?x2591), religion(?x177, ?x1363), contains(?x1767, ?x1768), time_zones(?x1767, ?x2674), adjoins(?x10235, ?x9101), partially_contains(?x177, ?x12511), jurisdiction_of_office(?x10093, ?x1767), award_nominee(?x2913, ?x237), location(?x1324, ?x11058), jurisdiction_of_office(?x5402, ?x177), ?x2591 = 0631_, ?x12714 = 05rrw9, jurisdiction_of_office(?x1157, ?x177), religion(?x5593, ?x1363), location(?x932, ?x177), state_province_region(?x3379, ?x1767), registering_agency(?x12260, ?x1982), ?x10638 = 01grmk, ?x10093 = 09n5b9, organization(?x346, ?x1768), institution(?x734, ?x1768) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01gsry district_represented 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.889 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gsry district_represented 05k7sb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.889 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #12739-07tlfx PRED entity: 07tlfx PRED relation: nominated_for! PRED expected values: 0gr0m 0gqy2 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 179): 0gq9h (0.41 #1247, 0.41 #1484, 0.40 #773), 019f4v (0.37 #764, 0.36 #1238, 0.35 #1475), 0gs9p (0.37 #775, 0.36 #1249, 0.36 #1486), 0gqy2 (0.34 #3203, 0.29 #833, 0.27 #1307), 040njc (0.32 #718, 0.31 #1192, 0.29 #1429), 0gq_v (0.31 #730, 0.31 #1204, 0.31 #1441), 0k611 (0.31 #784, 0.31 #1495, 0.31 #1258), 04dn09n (0.29 #746, 0.26 #1220, 0.26 #3116), 0f4x7 (0.27 #736, 0.26 #1210, 0.25 #25), 0gr0m (0.27 #770, 0.25 #1481, 0.25 #1244) >> Best rule #1247 for best value: >> intensional similarity = 5 >> extensional distance = 437 >> proper extension: 076zy_g; 0bs4r; 02k1pr; 0h3k3f; 04wddl; 014knw; 0k419; >> query: (?x9978, 0gq9h) <- film(?x5888, ?x9978), film(?x2012, ?x9978), nominated_for(?x5888, ?x1877), honored_for(?x762, ?x9978), location(?x2012, ?x279) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #3203 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 548 *> proper extension: 053x8hr; *> query: (?x9978, 0gqy2) <- nominated_for(?x1033, ?x9978), award(?x2035, ?x1033), ?x2035 = 0bj9k *> conf = 0.34 ranks of expected_values: 4, 10 EVAL 07tlfx nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 77.000 0.415 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07tlfx nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 77.000 77.000 0.415 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12738-09hgk PRED entity: 09hgk PRED relation: student PRED expected values: 03f0324 => 205 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1387): 0jcx (0.40 #10473, 0.40 #8907, 0.40 #8378), 0bk5r (0.20 #9270, 0.20 #7175, 0.08 #11365), 0n00 (0.20 #8925, 0.17 #11020, 0.09 #31970), 03_nq (0.20 #7848, 0.17 #12038, 0.06 #16227), 02sdx (0.20 #8135, 0.08 #12325, 0.07 #26992), 08h79x (0.17 #11742, 0.06 #36882, 0.05 #47358), 012t1 (0.17 #10618, 0.06 #35758, 0.05 #46234), 02lk1s (0.17 #10588, 0.06 #35728, 0.05 #46204), 0cbgl (0.17 #12561, 0.06 #37701, 0.05 #48177), 0157m (0.14 #12817, 0.06 #29578, 0.05 #44244) >> Best rule #10473 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0194_r; >> query: (?x5281, ?x3335) <- company(?x3335, ?x5281), ?x3335 = 0jcx, contains(?x1558, ?x5281), contains(?x1458, ?x5281), contains(?x7430, ?x1458), adjoins(?x1558, ?x456) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #20948 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 02bd_f; 035yzw; 0342z_; *> query: (?x5281, ?x4915) <- organization(?x4095, ?x5281), contains(?x1458, ?x5281), contains(?x7430, ?x1458), place_of_birth(?x4915, ?x1458), ?x4095 = 0hm4q *> conf = 0.11 ranks of expected_values: 14 EVAL 09hgk student 03f0324 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 205.000 133.000 0.400 http://example.org/education/educational_institution/students_graduates./education/education/student #12737-014kq6 PRED entity: 014kq6 PRED relation: nominated_for PRED expected values: 09y6pb 025twgt => 70 concepts (30 used for prediction) PRED predicted values (max 10 best out of 153): 02sg5v (0.85 #1218, 0.84 #3414, 0.84 #3413), 02qrv7 (0.85 #1218, 0.84 #3414, 0.84 #3413), 0fztbq (0.85 #1218, 0.84 #3414, 0.84 #3413), 025twgt (0.53 #3415, 0.06 #238, 0.05 #2431), 014kq6 (0.53 #3415, 0.06 #61, 0.05 #2254), 09y6pb (0.53 #3415, 0.02 #7077, 0.02 #4885), 01s9vc (0.06 #2669, 0.06 #2425, 0.05 #3157), 06_wqk4 (0.06 #20, 0.05 #1483, 0.04 #994), 03176f (0.06 #120, 0.04 #851, 0.04 #1583), 031786 (0.06 #198, 0.03 #929, 0.03 #1416) >> Best rule #1218 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 0g60z; 0180mw; >> query: (?x2160, ?x835) <- nominated_for(?x835, ?x2160), award(?x2160, ?x154), nominated_for(?x2160, ?x1851) >> conf = 0.85 => this is the best rule for 3 predicted values *> Best rule #3415 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 231 *> proper extension: 02fn5r; *> query: (?x2160, ?x11362) <- nominated_for(?x2506, ?x2160), nominated_for(?x2160, ?x1851), nominated_for(?x2506, ?x11362) *> conf = 0.53 ranks of expected_values: 4, 6 EVAL 014kq6 nominated_for 025twgt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 70.000 30.000 0.847 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for EVAL 014kq6 nominated_for 09y6pb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 70.000 30.000 0.847 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #12736-019fm7 PRED entity: 019fm7 PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.42 #1017, 0.19 #463, 0.07 #1202), 060bp (0.37 #1015, 0.12 #461, 0.08 #1200), 0fkvn (0.30 #464, 0.26 #487, 0.25 #510), 0f6c3 (0.27 #468, 0.26 #491, 0.25 #514), 09n5b9 (0.24 #495, 0.24 #472, 0.23 #518), 0pqc5 (0.23 #718, 0.21 #1411, 0.20 #1457), 0dq3c (0.07 #1016, 0.05 #462, 0.01 #715), 0fkzq (0.07 #500, 0.07 #477, 0.07 #523), 0p5vf (0.07 #473, 0.04 #450, 0.04 #1027), 04syw (0.07 #1021, 0.02 #1206, 0.02 #720) >> Best rule #1017 for best value: >> intensional similarity = 4 >> extensional distance = 281 >> proper extension: 0154j; 0d0vqn; 0j1z8; 04gzd; 047lj; 01ls2; 03_r3; 05qhw; 02k54; 019rg5; ... >> query: (?x10242, 060c4) <- administrative_parent(?x10242, ?x2146), administrative_parent(?x8940, ?x2146), citytown(?x9810, ?x8940), currency(?x2146, ?x170) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #464 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 125 *> proper extension: 027rn; 05r4w; 0160w; 0b90_r; 03rjj; 03_3d; 0h3y; 0chghy; 05v8c; 015fr; ... *> query: (?x10242, 0fkvn) <- administrative_parent(?x10242, ?x2146), location(?x5043, ?x10242), service_location(?x1492, ?x2146) *> conf = 0.30 ranks of expected_values: 3 EVAL 019fm7 jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 120.000 0.420 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #12735-027ct7c PRED entity: 027ct7c PRED relation: category PRED expected values: 08mbj5d => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #5, 0.27 #2, 0.27 #34) >> Best rule #5 for best value: >> intensional similarity = 5 >> extensional distance = 86 >> proper extension: 02wwmhc; >> query: (?x5533, 08mbj5d) <- genre(?x5533, ?x1403), ?x1403 = 02l7c8, honored_for(?x5924, ?x5533), film_crew_role(?x5533, ?x1284), country(?x5533, ?x94) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027ct7c category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.273 http://example.org/common/topic/webpage./common/webpage/category #12734-05f7w84 PRED entity: 05f7w84 PRED relation: actor PRED expected values: 02gf_l 024my5 => 104 concepts (75 used for prediction) PRED predicted values (max 10 best out of 950): 0582cf (0.67 #12648, 0.40 #8048, 0.36 #14486), 0gz5hs (0.40 #8422, 0.33 #10261, 0.33 #2908), 02gf_l (0.33 #1485, 0.25 #13437, 0.25 #6999), 030x48 (0.33 #1167, 0.25 #13119, 0.25 #6681), 022s1m (0.33 #1805, 0.25 #7319, 0.20 #8238), 0f87jy (0.33 #3543, 0.25 #6300, 0.20 #9057), 01nd6v (0.33 #1837, 0.25 #7351, 0.19 #22064), 02wrhj (0.33 #1056, 0.25 #6570, 0.19 #22064), 01b3bp (0.33 #5512, 0.20 #8269, 0.19 #22064), 02hblj (0.33 #5447, 0.20 #8204, 0.19 #22064) >> Best rule #12648 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 05h95s; >> query: (?x5938, 0582cf) <- category(?x5938, ?x134), actor(?x5938, ?x478), genre(?x5938, ?x10159), genre(?x5938, ?x1510), genre(?x5938, ?x809), ?x1510 = 01hmnh, genre(?x8396, ?x809), genre(?x7317, ?x809), genre(?x1395, ?x809), ?x10159 = 025s89p, ?x1395 = 019nnl, ?x8396 = 03nymk, ?x7317 = 05p9_ql >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1485 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 01h72l; *> query: (?x5938, 02gf_l) <- category(?x5938, ?x134), program(?x3145, ?x5938), actor(?x5938, ?x12000), actor(?x5938, ?x478), ?x478 = 01rrwf6, genre(?x5938, ?x1510), gender(?x12000, ?x231), program(?x11954, ?x5938), genre(?x97, ?x1510) *> conf = 0.33 ranks of expected_values: 3, 14 EVAL 05f7w84 actor 024my5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 104.000 75.000 0.667 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 05f7w84 actor 02gf_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 75.000 0.667 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #12733-06w58f PRED entity: 06w58f PRED relation: gender PRED expected values: 05zppz => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #13, 0.80 #11, 0.80 #21), 02zsn (0.50 #127, 0.28 #62, 0.28 #34) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 216 >> proper extension: 0g51l1; 0c_mvb; >> query: (?x10575, 05zppz) <- profession(?x10575, ?x524), award_winner(?x10575, ?x201), ?x524 = 02jknp >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06w58f gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.812 http://example.org/people/person/gender #12732-03_nq PRED entity: 03_nq PRED relation: basic_title PRED expected values: 01gkgk => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 16): 01gkgk (0.47 #85, 0.43 #133, 0.37 #117), 0fkvn (0.37 #147, 0.36 #67, 0.33 #227), 0dq3c (0.26 #146, 0.22 #162, 0.19 #242), 01dz7z (0.12 #63, 0.02 #255), 060bp (0.12 #209, 0.12 #273, 0.10 #193), 02079p (0.12 #89, 0.09 #73, 0.05 #121), 0p5vf (0.10 #202, 0.08 #250, 0.08 #218), 0pqc5 (0.09 #68, 0.06 #196, 0.06 #212), 01q24l (0.09 #75, 0.06 #219, 0.06 #235), 0f6c3 (0.09 #71, 0.06 #87, 0.06 #103) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 012v1t; >> query: (?x9046, 01gkgk) <- politician(?x10558, ?x9046), legislative_sessions(?x9046, ?x4437), politician(?x10558, ?x13592), ?x13592 = 08959 >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_nq basic_title 01gkgk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.471 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #12731-06p8m PRED entity: 06p8m PRED relation: service_location PRED expected values: 0chghy => 196 concepts (194 used for prediction) PRED predicted values (max 10 best out of 290): 0d060g (0.60 #194, 0.57 #382, 0.30 #6894), 0345h (0.43 #400, 0.20 #212, 0.14 #6912), 0f8l9c (0.43 #395, 0.20 #207, 0.12 #6907), 0chghy (0.29 #386, 0.20 #198, 0.12 #6898), 02j71 (0.27 #3602, 0.26 #4260, 0.25 #7943), 03h64 (0.17 #1784, 0.15 #8210, 0.11 #5184), 01n7q (0.14 #658, 0.14 #587, 0.12 #845), 03rjj (0.14 #380, 0.07 #5755, 0.07 #6892), 0h7x (0.14 #402, 0.04 #11511, 0.04 #5777), 06bnz (0.14 #404, 0.03 #8806, 0.03 #8713) >> Best rule #194 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 05w3y; 04sv4; 049mr; >> query: (?x11427, 0d060g) <- service_location(?x11427, ?x1229), service_location(?x11427, ?x172), service_location(?x11427, ?x94), ?x1229 = 059j2, ?x94 = 09c7w0, film_release_region(?x2512, ?x172), ?x2512 = 07x4qr, contains(?x172, ?x4826) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #386 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 0p4wb; 018mxj; *> query: (?x11427, 0chghy) <- service_location(?x11427, ?x1229), service_location(?x11427, ?x94), ?x1229 = 059j2, location(?x1222, ?x94), place_of_birth(?x129, ?x94), nationality(?x51, ?x94), contains(?x94, ?x95) *> conf = 0.29 ranks of expected_values: 4 EVAL 06p8m service_location 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 196.000 194.000 0.600 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #12730-09swkk PRED entity: 09swkk PRED relation: role PRED expected values: 04q7r 03qjg 05842k => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 83): 0342h (0.56 #838, 0.37 #2623, 0.36 #1363), 05r5c (0.49 #528, 0.46 #320, 0.39 #2627), 018vs (0.34 #1464, 0.33 #940, 0.33 #848), 02sgy (0.33 #840, 0.25 #1365, 0.24 #526), 01vj9c (0.29 #850, 0.16 #2635, 0.14 #328), 01vdm0 (0.27 #867, 0.27 #2652, 0.25 #553), 042v_gx (0.23 #113, 0.23 #1368, 0.23 #529), 0l14qv (0.21 #839, 0.17 #317, 0.16 #525), 05842k (0.19 #914, 0.17 #2699, 0.16 #1439), 013y1f (0.17 #350, 0.16 #872, 0.16 #558) >> Best rule #838 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 032t2z; 023l9y; >> query: (?x4940, 0342h) <- instrumentalists(?x716, ?x4940), ?x716 = 018vs, role(?x4940, ?x212) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #914 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 133 *> proper extension: 032t2z; 023l9y; *> query: (?x4940, 05842k) <- instrumentalists(?x716, ?x4940), ?x716 = 018vs, role(?x4940, ?x212) *> conf = 0.19 ranks of expected_values: 9, 11 EVAL 09swkk role 05842k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 127.000 127.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 09swkk role 03qjg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 127.000 127.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 09swkk role 04q7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 127.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role #12729-0884hk PRED entity: 0884hk PRED relation: award_winner! PRED expected values: 06jrhz => 73 concepts (40 used for prediction) PRED predicted values (max 10 best out of 514): 01xndd (0.82 #38492, 0.82 #32078, 0.82 #36889), 0884hk (0.64 #2281, 0.60 #678, 0.31 #12831), 0h5jg5 (0.54 #41697, 0.53 #64153, 0.53 #56132), 08q3s0 (0.54 #41697, 0.53 #64153, 0.53 #56132), 047cqr (0.52 #51321, 0.52 #62549, 0.51 #59341), 06jrhz (0.45 #2592, 0.40 #989, 0.31 #12831), 01rzqj (0.31 #12831, 0.19 #11225, 0.04 #5361), 059j4x (0.31 #12831, 0.19 #11225, 0.04 #6380), 04wvhz (0.29 #57737, 0.19 #11225, 0.10 #48113), 03_wtr (0.21 #22457, 0.02 #41319, 0.01 #46129) >> Best rule #38492 for best value: >> intensional similarity = 3 >> extensional distance = 896 >> proper extension: 079vf; 015rmq; 0g51l1; 010hn; 0c_mvb; 01pcql; 0lzkm; 036px; 0k8y7; 02wr2r; ... >> query: (?x4022, ?x2650) <- award_winner(?x4022, ?x2650), place_of_birth(?x4022, ?x94), award_winner(?x8094, ?x4022) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #2592 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0brkwj; 0697kh; *> query: (?x4022, 06jrhz) <- award_nominee(?x4022, ?x4023), ?x4023 = 09hd16, award_winner(?x4022, ?x2802) *> conf = 0.45 ranks of expected_values: 6 EVAL 0884hk award_winner! 06jrhz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 73.000 40.000 0.824 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #12728-027dpx PRED entity: 027dpx PRED relation: instrumentalists! PRED expected values: 018vs => 123 concepts (81 used for prediction) PRED predicted values (max 10 best out of 125): 018vs (0.83 #366, 0.56 #543, 0.54 #1164), 0342h (0.83 #711, 0.76 #1066, 0.73 #1155), 05r5c (0.56 #1248, 0.56 #1070, 0.52 #1780), 05148p4 (0.50 #994, 0.48 #728, 0.44 #3044), 02hnl (0.44 #653, 0.42 #4889, 0.41 #3112), 09lbv (0.42 #4889), 03qjg (0.35 #759, 0.27 #1114, 0.26 #1557), 04rzd (0.33 #391, 0.25 #568, 0.25 #214), 05842k (0.31 #1594, 0.30 #3113, 0.30 #2668), 0l14md (0.28 #1424, 0.25 #625, 0.21 #980) >> Best rule #366 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 04m2zj; 0889x; >> query: (?x5437, 018vs) <- artists(?x5436, ?x5437), gender(?x5437, ?x231), ?x5436 = 0hdf8, instrumentalists(?x212, ?x5437), ?x231 = 05zppz >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027dpx instrumentalists! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 81.000 0.833 http://example.org/music/instrument/instrumentalists #12727-02pyyld PRED entity: 02pyyld PRED relation: teams! PRED expected values: 068p2 => 72 concepts (65 used for prediction) PRED predicted values (max 10 best out of 93): 030qb3t (0.33 #1402, 0.33 #50, 0.22 #5467), 0d9y6 (0.33 #1214, 0.25 #1754, 0.17 #4464), 0fvzg (0.33 #357, 0.25 #2249, 0.17 #4149), 0f__1 (0.33 #622, 0.25 #2513, 0.09 #7133), 0fpzwf (0.25 #3110, 0.17 #4200, 0.10 #6373), 0vzm (0.25 #1989, 0.17 #4429, 0.09 #7149), 0ftxw (0.25 #2247, 0.11 #5502, 0.11 #5230), 0t6hk (0.25 #2654, 0.11 #5910, 0.09 #6999), 0fvyg (0.25 #3738, 0.11 #5908), 071cn (0.25 #2811, 0.09 #7161, 0.08 #7432) >> Best rule #1402 for best value: >> intensional similarity = 29 >> extensional distance = 1 >> proper extension: 02pqcfz; >> query: (?x11789, 030qb3t) <- team(?x10736, ?x11789), team(?x9974, ?x11789), team(?x8824, ?x11789), team(?x4368, ?x11789), team(?x2302, ?x11789), position(?x11789, ?x1348), ?x8824 = 05g_nr, team(?x10736, ?x10171), team(?x10736, ?x9975), team(?x10736, ?x9909), team(?x10736, ?x8528), team(?x10736, ?x6847), team(?x10736, ?x6803), ?x6803 = 03by7wc, ?x9975 = 03d5m8w, team(?x2302, ?x9576), team(?x2302, ?x9147), ?x4368 = 0b_6x2, instance_of_recurring_event(?x10736, ?x10863), ?x1348 = 01pv51, ?x8528 = 091tgz, ?x9576 = 02qk2d5, ?x9147 = 0263cyj, ?x10171 = 026w398, ?x6847 = 02r2qt7, ?x9974 = 0b_6pv, team(?x6848, ?x11789), ?x10863 = 02jp2w, ?x9909 = 026wlnm >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #12333 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 36 *> proper extension: 0hn6d; 0j5m6; 04l5d0; 04l57x; *> query: (?x11789, 068p2) <- team(?x1348, ?x11789), colors(?x11789, ?x332), team(?x1348, ?x9931), team(?x1348, ?x9760), team(?x1348, ?x8079), team(?x1348, ?x5154), team(?x1348, ?x1578), company(?x4486, ?x1578), colors(?x9931, ?x4557), ?x332 = 01l849, teams(?x2850, ?x9931), sport(?x8079, ?x4833), ?x4557 = 019sc, colors(?x8079, ?x8271), category(?x5154, ?x134), team(?x4834, ?x9760), ?x8271 = 02rnmb *> conf = 0.08 ranks of expected_values: 24 EVAL 02pyyld teams! 068p2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 72.000 65.000 0.333 http://example.org/sports/sports_team_location/teams #12726-02x3y41 PRED entity: 02x3y41 PRED relation: country PRED expected values: 09c7w0 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.90 #1929, 0.90 #1110, 0.89 #1871), 0345h (0.33 #84, 0.23 #258, 0.21 #374), 0d060g (0.25 #9, 0.22 #67, 0.12 #357), 03rjj (0.15 #123, 0.04 #2111, 0.04 #2170), 03_3d (0.11 #66, 0.07 #2521, 0.05 #356), 03rt9 (0.11 #73, 0.06 #305, 0.03 #1826), 06mzp (0.11 #76, 0.03 #308, 0.02 #366), 0ctw_b (0.07 #370, 0.04 #428, 0.04 #777), 059j2 (0.05 #141, 0.03 #315, 0.01 #1836), 0d0vqn (0.05 #127, 0.03 #301, 0.01 #2115) >> Best rule #1929 for best value: >> intensional similarity = 4 >> extensional distance = 290 >> proper extension: 091rc5; 011x_4; 01dc0c; >> query: (?x7843, 09c7w0) <- country(?x7843, ?x512), genre(?x7843, ?x53), films(?x2391, ?x7843), production_companies(?x7843, ?x1104) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02x3y41 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.897 http://example.org/film/film/country #12725-044mrh PRED entity: 044mrh PRED relation: location PRED expected values: 0hv7l => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 65): 02_286 (0.33 #37, 0.15 #7273, 0.15 #12099), 030qb3t (0.18 #887, 0.17 #83, 0.17 #6515), 059rby (0.18 #1624, 0.04 #5644, 0.04 #6448), 0r0m6 (0.17 #218, 0.03 #5042, 0.02 #16302), 0chgzm (0.17 #411), 0chrx (0.17 #405), 0vbk (0.17 #246), 0d35y (0.17 #231), 07b_l (0.09 #1795, 0.09 #991, 0.08 #2599), 0cr3d (0.09 #1753, 0.08 #2557, 0.05 #4969) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 04bdzg; >> query: (?x4965, 02_286) <- film(?x4965, ?x3938), award_nominee(?x4965, ?x56), ?x3938 = 024mpp >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 044mrh location 0hv7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 100.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #12724-07k8rt4 PRED entity: 07k8rt4 PRED relation: film_crew_role PRED expected values: 02r96rf 02ynfr => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 21): 02r96rf (0.80 #184, 0.73 #489, 0.72 #671), 01pvkk (0.31 #191, 0.30 #313, 0.30 #10), 02ynfr (0.28 #43, 0.21 #194, 0.19 #103), 02_n3z (0.20 #1, 0.10 #182, 0.09 #213), 02rh1dz (0.19 #190, 0.16 #99, 0.16 #312), 015h31 (0.18 #189, 0.10 #8, 0.10 #311), 0ckd1 (0.11 #34, 0.04 #185, 0.02 #124), 089fss (0.10 #96, 0.10 #6, 0.09 #309), 033smt (0.10 #22, 0.10 #203, 0.05 #82), 020xn5 (0.10 #7, 0.05 #188, 0.04 #97) >> Best rule #184 for best value: >> intensional similarity = 4 >> extensional distance = 309 >> proper extension: 04lqvlr; 0bx_hnp; >> query: (?x4427, 02r96rf) <- film_crew_role(?x4427, ?x2154), film_release_distribution_medium(?x4427, ?x81), genre(?x4427, ?x258), ?x2154 = 01vx2h >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 07k8rt4 film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.801 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 07k8rt4 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.801 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12723-0g5838s PRED entity: 0g5838s PRED relation: film! PRED expected values: 086k8 => 71 concepts (55 used for prediction) PRED predicted values (max 10 best out of 46): 086k8 (0.16 #973, 0.16 #2770, 0.15 #2621), 03xq0f (0.15 #456, 0.14 #679, 0.13 #230), 01gb54 (0.14 #28, 0.07 #999, 0.05 #2722), 024rgt (0.14 #19, 0.04 #619, 0.04 #244), 025tlyv (0.14 #58, 0.04 #283, 0.03 #658), 09glbnt (0.14 #37, 0.03 #563, 0.02 #187), 04mkft (0.14 #35, 0.02 #1080, 0.02 #3256), 016tw3 (0.14 #2704, 0.14 #981, 0.14 #461), 017s11 (0.14 #228, 0.13 #677, 0.13 #2622), 05qd_ (0.13 #2628, 0.13 #2777, 0.12 #683) >> Best rule #973 for best value: >> intensional similarity = 3 >> extensional distance = 427 >> proper extension: 09p35z; 03ckwzc; 0963mq; 03t97y; 0d_2fb; 014nq4; 05_5rjx; 032zq6; 038bh3; 01q2nx; ... >> query: (?x3076, 086k8) <- film_crew_role(?x3076, ?x2095), genre(?x3076, ?x53), ?x2095 = 0dxtw >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g5838s film! 086k8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 55.000 0.159 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #12722-063_j5 PRED entity: 063_j5 PRED relation: produced_by PRED expected values: 03ktjq => 71 concepts (60 used for prediction) PRED predicted values (max 10 best out of 134): 030_3z (0.33 #550, 0.03 #2488, 0.03 #2877), 0fvf9q (0.12 #1556, 0.12 #1168, 0.07 #1943), 0j_c (0.12 #1630, 0.12 #1242, 0.07 #2017), 016z2j (0.12 #15115, 0.11 #17450, 0.10 #16673), 0d_84 (0.11 #15504, 0.11 #14726, 0.11 #12401), 03ktjq (0.07 #2138, 0.03 #4852, 0.02 #4078), 04dyqk (0.07 #2298, 0.01 #2686, 0.01 #3075), 026g4l_ (0.07 #2137, 0.01 #2525, 0.01 #2914), 01gp_x (0.07 #2024, 0.01 #2412, 0.01 #2801), 01t6b4 (0.07 #3145, 0.04 #3920, 0.03 #6245) >> Best rule #550 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0k2sk; >> query: (?x8859, 030_3z) <- genre(?x8859, ?x809), genre(?x8859, ?x600), award_winner(?x8859, ?x338), ?x600 = 02n4kr, ?x809 = 0vgkd >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2138 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0yyg4; 06rmdr; 0cbn7c; 0443v1; *> query: (?x8859, 03ktjq) <- genre(?x8859, ?x9897), ?x9897 = 0vjs6, film(?x338, ?x8859), titles(?x2480, ?x8859) *> conf = 0.07 ranks of expected_values: 6 EVAL 063_j5 produced_by 03ktjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 71.000 60.000 0.333 http://example.org/film/film/produced_by #12721-0lfyx PRED entity: 0lfyx PRED relation: county PRED expected values: 0kvt9 => 75 concepts (32 used for prediction) PRED predicted values (max 10 best out of 29): 01n7q (0.21 #197, 0.17 #786, 0.13 #3544), 09c7w0 (0.21 #197, 0.17 #786, 0.13 #3544), 0kpys (0.19 #13, 0.15 #210, 0.11 #406), 0cb4j (0.09 #2, 0.08 #199, 0.07 #591), 0l2xl (0.06 #263, 0.06 #66, 0.04 #655), 0l2rj (0.06 #282, 0.06 #85, 0.04 #674), 0kq1l (0.06 #59, 0.05 #256, 0.04 #452), 0l2q3 (0.06 #107, 0.03 #696, 0.02 #1089), 0kvt9 (0.05 #292, 0.04 #95, 0.03 #684), 0l2lk (0.04 #45, 0.02 #634, 0.02 #1027) >> Best rule #197 for best value: >> intensional similarity = 5 >> extensional distance = 52 >> proper extension: 07vht; 07vjm; 027ybp; 02gnmp; 0r785; 0r111; 02fzs; >> query: (?x13689, ?x94) <- contains(?x1227, ?x13689), contains(?x94, ?x13689), category(?x13689, ?x134), time_zones(?x13689, ?x2950), ?x1227 = 01n7q >> conf = 0.21 => this is the best rule for 2 predicted values *> Best rule #292 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 60 *> proper extension: 0cb4j; 01jr6; 0l1pj; 0l2q3; 0qjfl; *> query: (?x13689, 0kvt9) <- contains(?x1227, ?x13689), contains(?x94, ?x13689), ?x94 = 09c7w0, ?x1227 = 01n7q, time_zones(?x13689, ?x2950) *> conf = 0.05 ranks of expected_values: 9 EVAL 0lfyx county 0kvt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 75.000 32.000 0.214 http://example.org/location/hud_county_place/county #12720-05zpghd PRED entity: 05zpghd PRED relation: film! PRED expected values: 02r_d4 01jfrg 049fgvm 048wrb => 113 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1238): 05txrz (0.33 #2839, 0.31 #4916, 0.21 #17379), 04t2l2 (0.25 #2104, 0.23 #4181, 0.19 #16644), 086nl7 (0.25 #2859, 0.23 #4936, 0.08 #17399), 032w8h (0.23 #4432, 0.17 #2355, 0.12 #16895), 0cmt6q (0.23 #5294, 0.17 #3217, 0.10 #17757), 04zkj5 (0.23 #5485, 0.17 #3408, 0.08 #17948), 08vr94 (0.23 #4827, 0.17 #2750, 0.06 #17290), 0716t2 (0.23 #18522, 0.08 #3982, 0.08 #6059), 07m77x (0.20 #1539, 0.17 #18156, 0.15 #5693), 07y8l9 (0.20 #970, 0.15 #5124, 0.10 #17587) >> Best rule #2839 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0bvn25; 0c0nhgv; 07p62k; 047svrl; 0fz3b1; 04gv3db; 040_lv; 02825kb; 087pfc; >> query: (?x5534, 05txrz) <- currency(?x5534, ?x170), film(?x4325, ?x5534), ?x4325 = 0fby2t, ?x170 = 09nqf, film_crew_role(?x5534, ?x137) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #24928 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 56 *> proper extension: 0pc62; 092vkg; 033g4d; 02c6d; 05p3738; 075wx7_; 050xxm; 01pgp6; 0cz_ym; 020bv3; ... *> query: (?x5534, ?x237) <- currency(?x5534, ?x170), film(?x2143, ?x5534), film_release_region(?x5534, ?x279), film_crew_role(?x5534, ?x137), award_nominee(?x2143, ?x237), student(?x2142, ?x2143) *> conf = 0.05 ranks of expected_values: 189, 248, 386, 804 EVAL 05zpghd film! 048wrb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 113.000 86.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 05zpghd film! 049fgvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 113.000 86.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 05zpghd film! 01jfrg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 113.000 86.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 05zpghd film! 02r_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 113.000 86.000 0.333 http://example.org/film/actor/film./film/performance/film #12719-0499lc PRED entity: 0499lc PRED relation: award PRED expected values: 03hkv_r => 85 concepts (71 used for prediction) PRED predicted values (max 10 best out of 254): 0d085 (0.71 #8716, 0.70 #7526, 0.70 #9905), 03hkv_r (0.41 #15, 0.13 #19416, 0.13 #16246), 0gs9p (0.39 #868, 0.31 #76, 0.13 #3640), 040njc (0.34 #799, 0.27 #7, 0.15 #13868), 019f4v (0.34 #855, 0.26 #63, 0.12 #3627), 02n9nmz (0.34 #66, 0.09 #858, 0.09 #3630), 0gq9h (0.30 #74, 0.27 #866, 0.15 #13868), 09sb52 (0.24 #5979, 0.24 #4395, 0.24 #4791), 02pqp12 (0.23 #859, 0.21 #67, 0.09 #3631), 02rdyk7 (0.19 #88, 0.19 #880, 0.08 #14661) >> Best rule #8716 for best value: >> intensional similarity = 3 >> extensional distance = 1219 >> proper extension: 0khth; 014l4w; 07mvp; 07sbk; 04k05; 07k2d; >> query: (?x4666, ?x5863) <- award(?x4666, ?x68), award_winner(?x4666, ?x163), award_winner(?x5863, ?x4666) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 108 *> proper extension: 0l6qt; 0qf43; 014zcr; 0h5f5n; 0159h6; 04r7jc; 05kfs; 02kxbwx; 0yfp; 012cph; ... *> query: (?x4666, 03hkv_r) <- award(?x4666, ?x601), profession(?x4666, ?x353), ?x601 = 0gr4k *> conf = 0.41 ranks of expected_values: 2 EVAL 0499lc award 03hkv_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 71.000 0.715 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12718-0330r PRED entity: 0330r PRED relation: nominated_for! PRED expected values: 015grj 07s6tbm => 78 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1031): 0jt90f5 (0.52 #25446, 0.46 #46272, 0.45 #41644), 06msq2 (0.25 #20819, 0.17 #30073, 0.15 #92558), 0988cp (0.25 #20819, 0.17 #30073, 0.15 #92558), 051wwp (0.25 #20819, 0.17 #30073, 0.15 #92558), 02tr7d (0.25 #20819, 0.17 #30073, 0.15 #92558), 0l6px (0.25 #20819, 0.17 #30073, 0.15 #92558), 01pcq3 (0.25 #20819, 0.17 #30073, 0.15 #92558), 018yj6 (0.25 #20819, 0.17 #30073, 0.15 #92558), 0h0yt (0.25 #20819, 0.17 #30073, 0.15 #92558), 0755wz (0.25 #20819, 0.17 #30073, 0.15 #92558) >> Best rule #25446 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 0147w8; >> query: (?x9541, ?x2343) <- nominated_for(?x678, ?x9541), actor(?x9541, ?x2343), award(?x9541, ?x7510) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #30073 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 133 *> proper extension: 0gfzgl; 0d7vtk; 0h95b81; 0cskb; *> query: (?x9541, ?x968) <- nominated_for(?x3039, ?x9541), award_winner(?x3039, ?x968), program(?x6678, ?x9541) *> conf = 0.17 ranks of expected_values: 36, 337 EVAL 0330r nominated_for! 07s6tbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 78.000 47.000 0.523 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0330r nominated_for! 015grj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 78.000 47.000 0.523 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12717-0166v PRED entity: 0166v PRED relation: organization PRED expected values: 07t65 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 48): 07t65 (0.93 #324, 0.92 #419, 0.91 #153), 04k4l (0.34 #138, 0.33 #157, 0.33 #119), 0_2v (0.31 #1625, 0.31 #460, 0.30 #308), 01rz1 (0.31 #1625, 0.30 #306, 0.28 #154), 018cqq (0.31 #1625, 0.22 #313, 0.22 #161), 085h1 (0.31 #1625, 0.21 #477, 0.02 #333), 02jxk (0.31 #1625, 0.18 #155, 0.16 #307), 059dn (0.31 #1625, 0.05 #165, 0.05 #317), 034h1h (0.21 #1442, 0.18 #1613, 0.02 #694), 02_l9 (0.07 #1617, 0.05 #449, 0.02 #1716) >> Best rule #324 for best value: >> intensional similarity = 3 >> extensional distance = 122 >> proper extension: 04j53; 0hdx8; >> query: (?x4421, 07t65) <- currency(?x4421, ?x170), adjoins(?x792, ?x4421), organization(?x4421, ?x127) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0166v organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.927 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #12716-0cmdwwg PRED entity: 0cmdwwg PRED relation: films! PRED expected values: 0qcr0 => 74 concepts (29 used for prediction) PRED predicted values (max 10 best out of 35): 0d1w9 (0.20 #36, 0.11 #192, 0.03 #348), 0fx2s (0.20 #73, 0.11 #229, 0.03 #2915), 081pw (0.03 #315, 0.03 #2528, 0.03 #2845), 03hzt (0.03 #447, 0.02 #920, 0.01 #1392), 06d4h (0.03 #3200, 0.03 #3042, 0.02 #2091), 01vq3 (0.02 #511, 0.02 #3516, 0.02 #3198), 0fzyg (0.02 #3211, 0.02 #3053, 0.02 #4167), 05489 (0.02 #364, 0.02 #3847, 0.02 #4482), 07c52 (0.02 #332, 0.02 #3019, 0.01 #3495), 07s2s (0.02 #3574, 0.01 #3894, 0.01 #3256) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 043t8t; >> query: (?x6394, 0d1w9) <- nominated_for(?x3435, ?x6394), film(?x241, ?x6394), ?x241 = 01j5ts, film_crew_role(?x6394, ?x1284) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cmdwwg films! 0qcr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 29.000 0.200 http://example.org/film/film_subject/films #12715-0x3b7 PRED entity: 0x3b7 PRED relation: performance_role PRED expected values: 03bx0bm => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 17): 03bx0bm (0.14 #106, 0.09 #150, 0.08 #511), 0l14md (0.09 #138, 0.06 #229, 0.06 #275), 0d8lm (0.08 #42, 0.04 #177, 0.02 #174), 026t6 (0.05 #628, 0.04 #495, 0.04 #134), 02sgy (0.04 #178, 0.04 #177, 0.04 #538), 0342h (0.04 #178, 0.04 #538, 0.04 #671), 042v_gx (0.04 #178, 0.04 #538, 0.04 #671), 0dq630k (0.04 #178, 0.04 #538, 0.04 #671), 05r5c (0.04 #177, 0.03 #139, 0.02 #410), 03gvt (0.04 #177, 0.02 #126, 0.01 #664) >> Best rule #106 for best value: >> intensional similarity = 2 >> extensional distance = 62 >> proper extension: 04k25; >> query: (?x4239, 03bx0bm) <- award(?x4239, ?x4481), ?x4481 = 02x17c2 >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0x3b7 performance_role 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.141 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #12714-01jmv8 PRED entity: 01jmv8 PRED relation: award_winner! PRED expected values: 027b9k6 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 222): 09cn0c (0.57 #745, 0.14 #1603, 0.04 #1174), 02z1nbg (0.43 #621, 0.17 #1479, 0.12 #17167), 0gqwc (0.39 #503, 0.37 #9011, 0.37 #17597), 0bdwft (0.37 #9011, 0.37 #17597, 0.37 #12445), 094qd5 (0.37 #9011, 0.37 #17597, 0.37 #12445), 0gqyl (0.37 #9011, 0.37 #17597, 0.37 #12445), 02z0dfh (0.37 #9011, 0.37 #17597, 0.37 #12445), 0cqgl9 (0.37 #9011, 0.37 #17597, 0.37 #12445), 07h0cl (0.37 #9011, 0.37 #17597, 0.37 #12445), 027571b (0.30 #702, 0.11 #1560, 0.04 #1131) >> Best rule #745 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 01gvr1; 01skmp; >> query: (?x8674, 09cn0c) <- nominated_for(?x8674, ?x1330), award_winner(?x1716, ?x8674), ?x1716 = 02y_rq5 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #8152 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 884 *> proper extension: 02f9wb; 07zhd7; *> query: (?x8674, ?x375) <- award_winner(?x4581, ?x8674), award_winner(?x472, ?x8674), award(?x4581, ?x375) *> conf = 0.17 ranks of expected_values: 15 EVAL 01jmv8 award_winner! 027b9k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 91.000 91.000 0.565 http://example.org/award/award_category/winners./award/award_honor/award_winner #12713-0h3y PRED entity: 0h3y PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 21): 0f6c3 (0.72 #711, 0.45 #1349, 0.43 #1063), 0pqc5 (0.66 #818, 0.51 #2424, 0.43 #1236), 09n5b9 (0.66 #715, 0.40 #1353, 0.38 #1067), 060bp (0.65 #485, 0.64 #1365, 0.63 #1761), 0fkvn (0.62 #707, 0.38 #1059, 0.37 #1345), 04syw (0.36 #2707, 0.25 #160, 0.21 #138), 0377k9 (0.36 #2707, 0.15 #81, 0.13 #477), 01_fjr (0.36 #2707, 0.14 #193, 0.11 #479), 09d6p2 (0.36 #2707, 0.06 #514, 0.06 #184), 0p5vf (0.33 #34, 0.31 #188, 0.30 #78) >> Best rule #711 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 011hq1; >> query: (?x291, 0f6c3) <- religion(?x291, ?x13970), jurisdiction_of_office(?x346, ?x291), location(?x2162, ?x291) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #485 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 01z88t; 02wt0; 047t_; 0l3h; 06q1r; 07fsv; 0fv4v; 0jdx; 04hhv; 016zwt; ... *> query: (?x291, 060bp) <- country(?x471, ?x291), exported_to(?x291, ?x94), administrative_parent(?x291, ?x551) *> conf = 0.65 ranks of expected_values: 4 EVAL 0h3y jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 151.000 151.000 0.721 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #12712-01d5z PRED entity: 01d5z PRED relation: school PRED expected values: 01jzyx => 80 concepts (53 used for prediction) PRED predicted values (max 10 best out of 675): 065y4w7 (0.60 #2713, 0.55 #2893, 0.49 #5967), 01dzg0 (0.60 #1057, 0.50 #1960, 0.43 #2320), 06fq2 (0.57 #2110, 0.47 #3553, 0.38 #3913), 01vs5c (0.50 #1708, 0.50 #445, 0.40 #2608), 01pl14 (0.50 #1449, 0.50 #366, 0.33 #3251), 06pwq (0.50 #1088, 0.43 #4335, 0.43 #4155), 01lnyf (0.43 #2226, 0.40 #3489, 0.40 #3308), 02pptm (0.43 #2121, 0.33 #3564, 0.33 #1581), 012vwb (0.40 #2573, 0.40 #950, 0.33 #3295), 09f2j (0.40 #793, 0.37 #3679, 0.33 #1153) >> Best rule #2713 for best value: >> intensional similarity = 14 >> extensional distance = 8 >> proper extension: 0jm3v; 01y49; 0jmj7; 051q5; >> query: (?x1010, 065y4w7) <- school(?x1010, ?x8479), school(?x1010, ?x6814), school(?x1010, ?x1428), team(?x12323, ?x1010), school_type(?x1428, ?x4994), colors(?x8479, ?x3315), ?x6814 = 03tw2s, major_field_of_study(?x8479, ?x1154), team(?x4244, ?x1010), ?x4994 = 07tf8, institution(?x865, ?x8479), student(?x8479, ?x9385), contains(?x94, ?x1428), currency(?x8479, ?x170) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #261 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 01ypc; *> query: (?x1010, 01jzyx) <- season(?x1010, ?x12645), season(?x1010, ?x10017), season(?x1010, ?x8923), season(?x1010, ?x701), position(?x1010, ?x4244), ?x701 = 05kcgsf, school(?x1010, ?x6953), ?x4244 = 028c_8, ?x8923 = 03c74_8, organization(?x346, ?x6953), ?x12645 = 03c6s24, student(?x6953, ?x117), draft(?x1010, ?x1161), ?x10017 = 026fmqm, team(?x12323, ?x1010) *> conf = 0.33 ranks of expected_values: 23 EVAL 01d5z school 01jzyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 80.000 53.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #12711-071vr PRED entity: 071vr PRED relation: teams PRED expected values: 07147 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 220): 027yf83 (0.06 #92, 0.06 #451, 0.06 #810), 0jnlm (0.06 #351, 0.06 #710, 0.04 #1428), 0jm74 (0.06 #147, 0.06 #506, 0.04 #1224), 01slc (0.06 #143, 0.06 #502, 0.04 #1220), 01yjl (0.06 #57, 0.06 #416, 0.04 #1134), 01y3v (0.06 #48, 0.06 #407, 0.04 #1125), 0bwjj (0.06 #216, 0.06 #575, 0.04 #1293), 0j2zj (0.06 #210, 0.06 #569, 0.04 #1287), 02wvfxl (0.06 #101, 0.06 #460, 0.04 #1178), 01d5z (0.06 #18, 0.06 #377, 0.04 #1095) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0gv10; >> query: (?x6960, 027yf83) <- county_seat(?x9472, ?x6960), location_of_ceremony(?x6744, ?x6960), profession(?x6744, ?x1032) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 071vr teams 07147 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 165.000 0.062 http://example.org/sports/sports_team_location/teams #12710-053tj7 PRED entity: 053tj7 PRED relation: currency PRED expected values: 09nqf => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.87 #113, 0.85 #428, 0.85 #498), 01nv4h (0.27 #815, 0.27 #807, 0.12 #865), 02l6h (0.27 #815, 0.27 #807, 0.10 #207), 0kz1h (0.12 #865), 0ptk_ (0.12 #865), 02gsvk (0.03 #160, 0.02 #314, 0.01 #623), 088n7 (0.03 #210, 0.02 #301, 0.01 #357) >> Best rule #113 for best value: >> intensional similarity = 7 >> extensional distance = 21 >> proper extension: 0d4htf; 04ynx7; >> query: (?x1315, 09nqf) <- film(?x3381, ?x1315), film(?x1104, ?x1315), production_companies(?x1315, ?x6560), genre(?x1315, ?x1014), nominated_for(?x3381, ?x1542), ?x6560 = 04rtpt, production_companies(?x144, ?x1104) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 053tj7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.870 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #12709-02k21g PRED entity: 02k21g PRED relation: student! PRED expected values: 05wkw => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 9): 02822 (0.08 #93, 0.04 #1271, 0.03 #341), 03g3w (0.08 #83, 0.01 #1511, 0.01 #827), 01lhf (0.08 #118), 03qsdpk (0.02 #780, 0.02 #1276, 0.02 #1526), 0w7c (0.02 #290, 0.02 #1282, 0.01 #476), 01zc2w (0.01 #1413, 0.01 #1288, 0.01 #1538), 0fdys (0.01 #1394, 0.01 #1519, 0.01 #215), 02vxn (0.01 #624, 0.01 #810, 0.01 #1244), 02h40lc (0.01 #251) >> Best rule #93 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 086nl7; 04h07s; 05drr9; 04s430; 03q45x; 030wkp; >> query: (?x4490, 02822) <- profession(?x4490, ?x1032), cast_members(?x4490, ?x905), film(?x4490, ?x86) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02k21g student! 05wkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 104.000 0.077 http://example.org/education/field_of_study/students_majoring./education/education/student #12708-0f1nl PRED entity: 0f1nl PRED relation: school! PRED expected values: 01d6g => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 83): 05m_8 (0.18 #750, 0.17 #1414, 0.17 #1663), 051vz (0.16 #767, 0.12 #1431, 0.12 #518), 01slc (0.14 #1464, 0.14 #1713, 0.12 #1215), 07147 (0.14 #307, 0.14 #141, 0.12 #805), 01yhm (0.14 #100, 0.12 #266, 0.12 #764), 0289q (0.14 #121, 0.11 #38, 0.08 #785), 0jm6n (0.14 #120, 0.08 #286, 0.07 #1199), 07l4z (0.13 #808, 0.12 #1472, 0.11 #1721), 0jmm4 (0.12 #313, 0.08 #1226, 0.08 #1143), 04wmvz (0.12 #817, 0.10 #568, 0.10 #1481) >> Best rule #750 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 0fht9f; >> query: (?x2497, 05m_8) <- school(?x7725, ?x2497), school(?x7643, ?x2497), team(?x261, ?x7725), position(?x7643, ?x180) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #1474 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 02jztz; *> query: (?x2497, 01d6g) <- school(?x660, ?x2497), school(?x685, ?x2497), major_field_of_study(?x2497, ?x1154) *> conf = 0.10 ranks of expected_values: 26 EVAL 0f1nl school! 01d6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 101.000 101.000 0.184 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #12707-02bqm0 PRED entity: 02bqm0 PRED relation: district_represented PRED expected values: 0488g 081mh => 37 concepts (34 used for prediction) PRED predicted values (max 10 best out of 369): 081mh (0.84 #939, 0.82 #774, 0.82 #633), 03v0t (0.82 #774, 0.77 #982, 0.76 #999), 026mj (0.82 #774, 0.77 #763, 0.76 #999), 06yxd (0.82 #774, 0.77 #761, 0.76 #999), 06btq (0.82 #774, 0.76 #999, 0.73 #326), 04ly1 (0.82 #774, 0.76 #999, 0.73 #326), 0488g (0.82 #774, 0.76 #999, 0.73 #326), 0846v (0.82 #774, 0.76 #999, 0.73 #326), 0vbk (0.82 #774, 0.76 #999, 0.73 #326), 07h34 (0.82 #774, 0.76 #999, 0.73 #326) >> Best rule #939 for best value: >> intensional similarity = 28 >> extensional distance = 17 >> proper extension: 04gp1d; >> query: (?x4821, 081mh) <- legislative_sessions(?x4730, ?x4821), legislative_sessions(?x1137, ?x4821), district_represented(?x4821, ?x6521), district_represented(?x4821, ?x4622), location(?x5562, ?x4622), location(?x3397, ?x4622), award_winner(?x6694, ?x5562), ?x4730 = 02cg7g, contains(?x4622, ?x1505), adjoins(?x3778, ?x4622), award_winner(?x3263, ?x5562), legislative_sessions(?x652, ?x4821), award_nominee(?x5562, ?x803), ?x1137 = 02bqn1, religion(?x4622, ?x109), district_represented(?x3669, ?x4622), district_represented(?x2019, ?x4622), district_represented(?x605, ?x4622), ?x605 = 077g7n, actor(?x802, ?x5562), jurisdiction_of_office(?x3959, ?x6521), jurisdiction_of_office(?x12562, ?x4622), ?x2019 = 01gtbb, ?x3959 = 0f6c3, people(?x4195, ?x3397), type_of_union(?x5562, ?x566), ?x3669 = 01gtcc, artist(?x5666, ?x3397) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 02bqm0 district_represented 081mh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 34.000 0.842 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 02bqm0 district_represented 0488g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 37.000 34.000 0.842 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #12706-0mwl2 PRED entity: 0mwl2 PRED relation: contains PRED expected values: 013ksx => 191 concepts (68 used for prediction) PRED predicted values (max 10 best out of 2031): 013ksx (0.67 #2947, 0.60 #79557, 0.59 #35357), 01jssp (0.25 #29, 0.07 #8868, 0.07 #32441), 0cwx_ (0.25 #922, 0.07 #9761, 0.05 #12706), 01q2sk (0.25 #415, 0.07 #9254, 0.05 #26938), 02s8qk (0.25 #819, 0.07 #9658, 0.05 #12603), 0mw_q (0.25 #2571, 0.05 #14355, 0.05 #79556), 0mwq_ (0.25 #1631, 0.05 #13415, 0.05 #79556), 0mwxl (0.25 #1434, 0.05 #13218, 0.05 #79556), 0m7fm (0.25 #135, 0.05 #11919, 0.05 #79556), 0mw93 (0.25 #91, 0.05 #11875, 0.05 #79556) >> Best rule #2947 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 08xpv_; >> query: (?x855, ?x3068) <- contains(?x3670, ?x855), ?x3670 = 05tbn, contains(?x855, ?x854), adjoins(?x3068, ?x854), contains(?x94, ?x3068) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwl2 contains 013ksx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 68.000 0.667 http://example.org/location/location/contains #12705-09tqkv2 PRED entity: 09tqkv2 PRED relation: nominated_for! PRED expected values: 099jhq 099tbz 0gqwc => 90 concepts (77 used for prediction) PRED predicted values (max 10 best out of 196): 0gqwc (0.83 #498, 0.25 #3830, 0.25 #4052), 0gs9p (0.69 #1167, 0.66 #4055, 0.65 #3833), 099c8n (0.69 #272, 0.62 #494, 0.61 #716), 040njc (0.69 #449, 0.47 #1115, 0.45 #3781), 09cn0c (0.67 #8220, 0.66 #10887, 0.66 #11334), 019f4v (0.63 #1157, 0.56 #3823, 0.56 #4045), 099jhq (0.58 #681, 0.58 #903, 0.54 #237), 0k611 (0.58 #1175, 0.51 #3841, 0.51 #4063), 0f4x7 (0.53 #1133, 0.42 #3799, 0.41 #4021), 02pqp12 (0.52 #496, 0.46 #1162, 0.44 #2494) >> Best rule #498 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 01242_; 03cfkrw; >> query: (?x2052, 0gqwc) <- nominated_for(?x1307, ?x2052), nominated_for(?x618, ?x2052), titles(?x2480, ?x2052), award(?x4383, ?x1307), ?x4383 = 07b3r9, ?x618 = 09qwmm >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 18 EVAL 09tqkv2 nominated_for! 0gqwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 77.000 0.828 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09tqkv2 nominated_for! 099tbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 90.000 77.000 0.828 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09tqkv2 nominated_for! 099jhq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 77.000 0.828 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12704-0jjw PRED entity: 0jjw PRED relation: major_field_of_study! PRED expected values: 040p_q => 56 concepts (54 used for prediction) PRED predicted values (max 10 best out of 125): 02j62 (0.62 #640, 0.45 #551, 0.42 #1788), 06ms6 (0.50 #275, 0.36 #540, 0.32 #703), 0fdys (0.50 #292, 0.32 #703, 0.27 #734), 02h40lc (0.50 #263, 0.23 #881, 0.20 #1324), 03g3w (0.45 #548, 0.41 #725, 0.41 #1077), 064_8sq (0.45 #564, 0.31 #653, 0.18 #741), 02822 (0.40 #120, 0.38 #294, 0.36 #736), 037mh8 (0.38 #317, 0.32 #703, 0.32 #759), 02_7t (0.38 #668, 0.15 #350, 0.12 #314), 062z7 (0.32 #703, 0.26 #902, 0.25 #284) >> Best rule #640 for best value: >> intensional similarity = 10 >> extensional distance = 14 >> proper extension: 03xks; >> query: (?x3440, 02j62) <- major_field_of_study(?x546, ?x3440), major_field_of_study(?x3490, ?x3440), major_field_of_study(?x1668, ?x3490), major_field_of_study(?x8715, ?x3490), major_field_of_study(?x5750, ?x3490), ?x5750 = 01nnsv, major_field_of_study(?x1200, ?x3440), ?x1200 = 016t_3, category(?x8715, ?x134), films(?x3490, ?x1454) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #148 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 02vxn; 01zc2w; *> query: (?x3440, 040p_q) <- major_field_of_study(?x546, ?x3440), major_field_of_study(?x3440, ?x3490), student(?x3440, ?x12927), major_field_of_study(?x8398, ?x3440), place_of_birth(?x12927, ?x2446), location(?x12927, ?x7412), ?x8398 = 028dcg, religion(?x12927, ?x8967), spouse(?x12927, ?x14232) *> conf = 0.20 ranks of expected_values: 25 EVAL 0jjw major_field_of_study! 040p_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 56.000 54.000 0.625 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #12703-0d68qy PRED entity: 0d68qy PRED relation: nominated_for! PRED expected values: 09qrn4 09v7wsg => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 265): 09qvc0 (0.70 #2992, 0.70 #4374, 0.70 #6216), 09qrn4 (0.70 #2992, 0.70 #4374, 0.70 #6216), 0m7yy (0.70 #2992, 0.70 #4374, 0.70 #6216), 0gq9h (0.37 #8809, 0.37 #8578, 0.36 #7658), 0gs9p (0.34 #8811, 0.33 #8580, 0.33 #7660), 019f4v (0.34 #7649, 0.32 #8800, 0.32 #8569), 0fbtbt (0.31 #2915, 0.29 #3607, 0.26 #4297), 0k611 (0.30 #7669, 0.29 #762, 0.28 #8589), 040njc (0.29 #7604, 0.27 #8755, 0.27 #8524), 04dn09n (0.29 #725, 0.27 #7632, 0.23 #8552) >> Best rule #2992 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 097h2; 02gl58; >> query: (?x2528, ?x693) <- nominated_for(?x678, ?x2528), award(?x2528, ?x693), program(?x829, ?x2528) >> conf = 0.70 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 76 EVAL 0d68qy nominated_for! 09v7wsg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 68.000 68.000 0.704 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0d68qy nominated_for! 09qrn4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.704 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12702-01mpwj PRED entity: 01mpwj PRED relation: currency PRED expected values: 09nqf => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.82 #611, 0.79 #372, 0.74 #519), 02l6h (0.29 #547, 0.16 #123, 0.11 #74), 01nv4h (0.21 #107, 0.18 #408, 0.16 #366), 0ptk_ (0.07 #45, 0.06 #136, 0.06 #59), 0kz1h (0.06 #54, 0.05 #334, 0.05 #355), 088n7 (0.02 #224, 0.01 #525), 02gsvk (0.01 #595) >> Best rule #611 for best value: >> intensional similarity = 4 >> extensional distance = 255 >> proper extension: 06xpp7; 05cwl_; 02zc7f; 02gn8s; 02h7qr; 0sxgh; 02lwv5; >> query: (?x3485, 09nqf) <- student(?x3485, ?x11440), contains(?x94, ?x3485), ?x94 = 09c7w0, profession(?x11440, ?x353) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mpwj currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.817 http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency #12701-04sskp PRED entity: 04sskp PRED relation: nominated_for! PRED expected values: 0bfvw2 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 182): 02r22gf (0.50 #269, 0.29 #748, 0.10 #10552), 0fbtbt (0.38 #2315, 0.33 #162, 0.25 #3510), 0bdx29 (0.33 #85, 0.28 #2238, 0.20 #3433), 0fbvqf (0.33 #39, 0.27 #2192, 0.20 #3387), 0bdw1g (0.33 #32, 0.26 #2185, 0.17 #3859), 0gkr9q (0.33 #210, 0.25 #2363, 0.16 #3558), 0bp_b2 (0.33 #17, 0.24 #2170, 0.18 #3365), 0gkts9 (0.33 #125, 0.21 #2278, 0.17 #2756), 0cqh6z (0.33 #56, 0.18 #2209, 0.12 #2687), 02xcb6n (0.33 #202, 0.14 #2355, 0.10 #1638) >> Best rule #269 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0hx4y; 0_b9f; >> query: (?x8062, 02r22gf) <- film(?x12733, ?x8062), film(?x2531, ?x8062), ?x12733 = 01ckhj, spouse(?x2531, ?x4314), nominated_for(?x2531, ?x1508) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1928 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 34 *> proper extension: 0bx_hnp; *> query: (?x8062, 0bfvw2) <- languages(?x8062, ?x254), genre(?x8062, ?x4088) *> conf = 0.25 ranks of expected_values: 27 EVAL 04sskp nominated_for! 0bfvw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 70.000 70.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12700-01fwqn PRED entity: 01fwqn PRED relation: team! PRED expected values: 0355pl => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 8): 0355pl (0.40 #179, 0.33 #107, 0.30 #91), 07y9k (0.33 #12, 0.17 #28, 0.15 #388), 059yj (0.32 #149, 0.29 #133, 0.28 #141), 03zv9 (0.18 #98, 0.14 #258, 0.13 #378), 0h69c (0.14 #166, 0.13 #190, 0.12 #78), 01ddbl (0.12 #79, 0.12 #71, 0.07 #167), 0356lc (0.12 #650, 0.10 #89, 0.08 #385), 021q23 (0.07 #248, 0.05 #280, 0.05 #240) >> Best rule #179 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 086x3; >> query: (?x11948, 0355pl) <- teams(?x13406, ?x11948), country(?x13406, ?x512), ?x512 = 07ssc >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fwqn team! 0355pl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.400 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #12699-064q5v PRED entity: 064q5v PRED relation: films! PRED expected values: 075fzd => 84 concepts (28 used for prediction) PRED predicted values (max 10 best out of 70): 075fzd (0.27 #601, 0.17 #287, 0.08 #918), 081pw (0.23 #2687, 0.20 #4265, 0.06 #948), 07jq_ (0.17 #238, 0.09 #552, 0.04 #4344), 01w1sx (0.17 #247, 0.06 #2775, 0.04 #4353), 07_nf (0.09 #537, 0.09 #2751, 0.06 #4329), 0fx2s (0.09 #543, 0.06 #1018, 0.06 #2916), 0fzyg (0.09 #524, 0.04 #2738, 0.03 #2103), 0kbq (0.07 #2789, 0.04 #4367, 0.01 #2948), 0cm2xh (0.06 #2731, 0.05 #4309, 0.01 #2890), 06d4h (0.05 #3043, 0.05 #673, 0.04 #3200) >> Best rule #601 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 03hkch7; >> query: (?x6093, 075fzd) <- person(?x6093, ?x2357), profession(?x2357, ?x5805), ?x5805 = 0fj9f, award_winner(?x6093, ?x6092) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 064q5v films! 075fzd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 28.000 0.273 http://example.org/film/film_subject/films #12698-09bkv PRED entity: 09bkv PRED relation: place_of_death! PRED expected values: 0btyl => 92 concepts (44 used for prediction) PRED predicted values (max 10 best out of 688): 015np0 (0.25 #1191, 0.25 #436, 0.14 #1946), 0151xv (0.14 #2100, 0.08 #2855, 0.03 #7391), 015nvj (0.14 #2080, 0.08 #2835, 0.03 #7371), 01t_wfl (0.14 #2158, 0.08 #2913, 0.03 #7449), 03s9v (0.14 #1845, 0.08 #2600, 0.03 #7136), 01pq5j7 (0.14 #1742, 0.03 #7033), 0219q (0.09 #3778, 0.07 #3777, 0.05 #9826), 014x77 (0.09 #3778, 0.07 #3777, 0.05 #9826), 0p3r8 (0.07 #3777, 0.05 #9826, 0.04 #9827), 0cbxl0 (0.07 #3777, 0.05 #9826, 0.04 #6045) >> Best rule #1191 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0bvqq; >> query: (?x10042, 015np0) <- contains(?x13447, ?x10042), location_of_ceremony(?x566, ?x10042), ?x13447 = 0f485, ?x566 = 04ztj >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09bkv place_of_death! 0btyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 44.000 0.250 http://example.org/people/deceased_person/place_of_death #12697-04bdxl PRED entity: 04bdxl PRED relation: languages PRED expected values: 02h40lc => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.31 #41, 0.29 #236, 0.29 #80), 064_8sq (0.04 #249, 0.04 #444, 0.04 #132), 0t_2 (0.03 #9, 0.02 #48, 0.01 #87), 03k50 (0.02 #1057, 0.02 #979, 0.02 #1174), 02bjrlw (0.02 #118, 0.02 #430, 0.01 #1015), 04306rv (0.02 #81, 0.01 #120, 0.01 #159), 06nm1 (0.01 #357, 0.01 #318, 0.01 #201), 07c9s (0.01 #1066, 0.01 #988, 0.01 #832) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 01tnbn; 01933d; >> query: (?x91, 02h40lc) <- award_nominee(?x91, ?x92), participant(?x828, ?x91), spouse(?x91, ?x5880) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bdxl languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.314 http://example.org/people/person/languages #12696-01ft14 PRED entity: 01ft14 PRED relation: genre PRED expected values: 01z4y => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 84): 01z4y (0.76 #1014, 0.76 #1097, 0.73 #1180), 07s9rl0 (0.55 #4818, 0.55 #2409, 0.52 #3074), 06nbt (0.47 #768, 0.37 #1266, 0.27 #1515), 01t_vv (0.33 #698, 0.28 #2941, 0.22 #2608), 06n90 (0.30 #1507, 0.30 #1258, 0.21 #2753), 0hcr (0.25 #19, 0.21 #2094, 0.20 #2344), 0lsxr (0.23 #1586, 0.13 #3248, 0.12 #3332), 01htzx (0.22 #1262, 0.22 #1511, 0.19 #2508), 06q7n (0.22 #1953, 0.21 #2036, 0.19 #2119), 0vgkd (0.20 #757, 0.19 #1255, 0.15 #2667) >> Best rule #1014 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 02nf2c; >> query: (?x10249, 01z4y) <- nominated_for(?x5235, ?x10249), program(?x3381, ?x10249), ?x5235 = 09qrn4, award_winner(?x10249, ?x5642) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ft14 genre 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.762 http://example.org/tv/tv_program/genre #12695-0jqn5 PRED entity: 0jqn5 PRED relation: film! PRED expected values: 01k5zk => 121 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1318): 06pj8 (0.43 #25005, 0.43 #135461, 0.43 #114616), 02q_cc (0.43 #25005, 0.43 #135461, 0.43 #114616), 04wp63 (0.43 #25005, 0.43 #135461, 0.43 #114616), 0146pg (0.43 #25005, 0.43 #135461, 0.43 #114616), 06rnl9 (0.43 #135461, 0.43 #114616, 0.42 #81270), 01vttb9 (0.43 #135461, 0.43 #114616, 0.42 #81270), 02lfp4 (0.43 #135461, 0.43 #114616, 0.42 #81270), 05ccxr (0.43 #135461, 0.43 #114616, 0.42 #81270), 02ryx0 (0.43 #135461, 0.43 #114616, 0.42 #81270), 016szr (0.43 #135461, 0.43 #114616, 0.42 #81270) >> Best rule #25005 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 047svrl; >> query: (?x1452, ?x669) <- film(?x2135, ?x1452), film_release_region(?x1452, ?x87), nominated_for(?x669, ?x1452), film_festivals(?x1452, ?x9932) >> conf = 0.43 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 0jqn5 film! 01k5zk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 80.000 0.433 http://example.org/film/actor/film./film/performance/film #12694-0dszr0 PRED entity: 0dszr0 PRED relation: type_of_union PRED expected values: 04ztj => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.74 #17, 0.74 #9, 0.73 #117), 01g63y (0.25 #241, 0.19 #258, 0.14 #58), 01bl8s (0.25 #241, 0.19 #258), 0jgjn (0.19 #258) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 0gcdzz; 05183k; 06hhrs; 02_2v2; 0gyx4; 036dyy; 03vrv9; >> query: (?x13195, 04ztj) <- profession(?x13195, ?x353), student(?x4410, ?x13195), ?x4410 = 017j69 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dszr0 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.742 http://example.org/people/person/spouse_s./people/marriage/type_of_union #12693-01gglm PRED entity: 01gglm PRED relation: executive_produced_by PRED expected values: 021lby => 82 concepts (47 used for prediction) PRED predicted values (max 10 best out of 88): 05prs8 (0.20 #44, 0.03 #1301, 0.02 #3568), 06q8hf (0.12 #1423, 0.10 #4699, 0.09 #4950), 0glyyw (0.12 #691, 0.07 #3712, 0.05 #4972), 05hj_k (0.10 #1354, 0.09 #4630, 0.09 #4881), 05183k (0.10 #3019, 0.10 #3272, 0.08 #4027), 079vf (0.08 #1259, 0.07 #1761, 0.06 #3526), 06pj8 (0.05 #3578, 0.05 #4838, 0.04 #1562), 02z6l5f (0.05 #1374, 0.04 #4901, 0.03 #369), 03c9pqt (0.04 #3770, 0.04 #1503, 0.04 #4779), 0gg9_5q (0.04 #3613, 0.03 #341, 0.03 #4873) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01738w; >> query: (?x8089, 05prs8) <- film(?x10533, ?x8089), film_crew_role(?x8089, ?x137), ?x10533 = 02bf2s >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01gglm executive_produced_by 021lby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 47.000 0.200 http://example.org/film/film/executive_produced_by #12692-027hjff PRED entity: 027hjff PRED relation: ceremony! PRED expected values: 0cqhk0 => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 316): 018wdw (0.90 #2905, 0.84 #3403, 0.53 #5146), 0k611 (0.90 #2793, 0.81 #3291, 0.81 #5034), 018wng (0.90 #2757, 0.81 #3255, 0.77 #4998), 0gvx_ (0.90 #2856, 0.81 #5097, 0.77 #3354), 0gs96 (0.90 #2811, 0.79 #3309, 0.79 #5052), 0gr42 (0.89 #5050, 0.80 #2809, 0.70 #3307), 0gqyl (0.86 #5042, 0.85 #2801, 0.74 #3299), 0p9sw (0.86 #4985, 0.80 #2744, 0.72 #3242), 0gqy2 (0.85 #2841, 0.84 #5082, 0.79 #3339), 0gq9h (0.85 #2782, 0.77 #5023, 0.77 #3280) >> Best rule #2905 for best value: >> intensional similarity = 17 >> extensional distance = 18 >> proper extension: 059x66; 0dth6b; 0gmdkyy; 0bzn6_; 05q7cj; 0bzmt8; 02yxh9; 0bzjvm; >> query: (?x3624, 018wdw) <- honored_for(?x3624, ?x9599), award_winner(?x3624, ?x6634), award_winner(?x3624, ?x6263), award_winner(?x3624, ?x4333), gender(?x6634, ?x514), ceremony(?x3722, ?x3624), ceremony(?x2771, ?x3624), award(?x4333, ?x678), award_nominee(?x6634, ?x679), nominated_for(?x2771, ?x763), ?x763 = 061681, award(?x9599, ?x2915), award_nominee(?x4333, ?x6622), nominated_for(?x112, ?x9599), award(?x241, ?x3722), ?x2915 = 027c95y, student(?x11009, ?x6263) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1763 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 3 *> proper extension: 09g90vz; *> query: (?x3624, 0cqhk0) <- honored_for(?x3624, ?x1631), award_winner(?x3624, ?x6634), award_winner(?x3624, ?x6324), award_winner(?x3624, ?x6262), gender(?x6634, ?x514), ceremony(?x3722, ?x3624), ceremony(?x2771, ?x3624), ceremony(?x1670, ?x3624), ?x2771 = 03m73lj, participant(?x6262, ?x436), film(?x6262, ?x2788), award_winner(?x1670, ?x10161), award_winner(?x1670, ?x3842), award(?x190, ?x1670), ?x6324 = 018ygt, award(?x6262, ?x678), nominated_for(?x1670, ?x337), ?x3722 = 0cqgl9, award_nominee(?x3842, ?x156), religion(?x10161, ?x9362) *> conf = 0.60 ranks of expected_values: 24 EVAL 027hjff ceremony! 0cqhk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 31.000 31.000 0.900 http://example.org/award/award_category/winners./award/award_honor/ceremony #12691-0jgx PRED entity: 0jgx PRED relation: taxonomy PRED expected values: 04n6k => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.81 #39, 0.79 #13, 0.77 #17) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 0bzjf; >> query: (?x3855, 04n6k) <- contains(?x3855, ?x13391), administrative_parent(?x3855, ?x551), currency(?x3855, ?x170) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgx taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.805 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #12690-0sxdg PRED entity: 0sxdg PRED relation: industry PRED expected values: 02jjt => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 43): 02vxn (0.63 #3198, 0.50 #1533, 0.42 #1081), 01mw1 (0.56 #541, 0.39 #3827, 0.36 #901), 02jjt (0.51 #2438, 0.34 #1988, 0.31 #1313), 020mfr (0.44 #556, 0.42 #1081, 0.36 #916), 029g_vk (0.33 #10, 0.25 #55, 0.14 #1000), 0h6dj (0.33 #31, 0.25 #76, 0.14 #256), 01mf0 (0.33 #1110, 0.14 #254, 0.12 #1425), 01mfj (0.33 #1115, 0.14 #259, 0.12 #304), 04rlf (0.25 #463, 0.20 #2444, 0.19 #1319), 015p1m (0.25 #1378, 0.14 #252, 0.14 #207) >> Best rule #3198 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 013x0b; 05h4t7; 024rbz; 05d6kv; 01p5yn; 0fqy4p; 0278rq7; 04rtpt; 025hwq; 020h2v; ... >> query: (?x9077, 02vxn) <- industry(?x9077, ?x2271), industry(?x3265, ?x2271), major_field_of_study(?x3437, ?x2271), artist(?x3265, ?x215) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #2438 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 02rr_z4; *> query: (?x9077, 02jjt) <- industry(?x9077, ?x2271), industry(?x10997, ?x2271), ?x10997 = 01t9_0 *> conf = 0.51 ranks of expected_values: 3 EVAL 0sxdg industry 02jjt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 164.000 164.000 0.630 http://example.org/business/business_operation/industry #12689-01vvydl PRED entity: 01vvydl PRED relation: gender PRED expected values: 05zppz => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #1, 0.80 #17, 0.80 #53), 02zsn (0.51 #207, 0.37 #24, 0.36 #28) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 04lgymt; 01vsgrn; >> query: (?x140, 05zppz) <- award_winner(?x8705, ?x140), award_nominee(?x527, ?x140), ?x8705 = 01c9dd >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vvydl gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.800 http://example.org/people/person/gender #12688-071rlr PRED entity: 071rlr PRED relation: position PRED expected values: 02_j1w => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 3): 02_j1w (0.86 #167, 0.84 #140, 0.84 #136), 03f0fp (0.50 #210, 0.50 #209), 02md_2 (0.50 #210, 0.50 #209) >> Best rule #167 for best value: >> intensional similarity = 21 >> extensional distance = 674 >> proper extension: 02b1yn; >> query: (?x12325, 02_j1w) <- position(?x12325, ?x203), position(?x13706, ?x203), position(?x13033, ?x203), position(?x11532, ?x203), position(?x9971, ?x203), position(?x9663, ?x203), position(?x8836, ?x203), position(?x5433, ?x203), position(?x4281, ?x203), position(?x3049, ?x203), team(?x203, ?x7198), sport(?x5433, ?x471), ?x7198 = 01tqfs, ?x4281 = 035qlx, ?x9971 = 02s2ys, ?x11532 = 0329qp, ?x9663 = 05jx5r, ?x3049 = 041xyk, ?x8836 = 043y95, ?x13033 = 0882j7, ?x13706 = 04j1n8 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 071rlr position 02_j1w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.858 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #12687-0p_tz PRED entity: 0p_tz PRED relation: honored_for! PRED expected values: 026kq4q => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 119): 05c1t6z (0.15 #2817, 0.14 #2939, 0.14 #2207), 02q690_ (0.14 #2250, 0.14 #2860, 0.13 #2982), 0gvstc3 (0.13 #2955, 0.12 #3077, 0.12 #2223), 09p3h7 (0.12 #60, 0.09 #182, 0.08 #304), 02yvhx (0.12 #65, 0.03 #1407, 0.02 #3725), 0clfdj (0.12 #2, 0.03 #1344, 0.02 #1588), 073hmq (0.12 #15, 0.01 #1967, 0.01 #7201), 026kqs9 (0.12 #77, 0.01 #2151, 0.01 #7201), 0bz6sb (0.12 #53, 0.01 #2127, 0.01 #7201), 03nnm4t (0.11 #3113, 0.11 #2259, 0.10 #2869) >> Best rule #2817 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 0n2bh; 01h1bf; 03y3bp7; 02sqkh; 02kk_c; 028k2x; 06dfz1; 01b7h8; 06r1k; 025x1t; ... >> query: (?x6740, 05c1t6z) <- country_of_origin(?x6740, ?x94), nominated_for(?x10167, ?x6740), award(?x10167, ?x435) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #2233 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 152 *> proper extension: 0gfzgl; 01f3p_; 03g9xj; 03cf9ly; *> query: (?x6740, 026kq4q) <- country_of_origin(?x6740, ?x94), nominated_for(?x10167, ?x6740), film(?x10167, ?x2816) *> conf = 0.03 ranks of expected_values: 72 EVAL 0p_tz honored_for! 026kq4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 80.000 80.000 0.148 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12686-04hpck PRED entity: 04hpck PRED relation: film PRED expected values: 0d99k_ => 86 concepts (46 used for prediction) PRED predicted values (max 10 best out of 826): 03p2xc (0.33 #1242, 0.05 #3029, 0.04 #4816), 012mrr (0.33 #476, 0.04 #4050), 043mk4y (0.33 #1350, 0.01 #29944, 0.01 #6711), 06lpmt (0.33 #684, 0.01 #29278, 0.01 #14980), 011ywj (0.33 #1433, 0.01 #6794, 0.01 #35388), 05k4my (0.33 #1649, 0.01 #7010), 04gcyg (0.33 #1381, 0.01 #6742), 0bl1_ (0.33 #801, 0.01 #6162), 0888c3 (0.33 #1412), 01n30p (0.33 #1409) >> Best rule #1242 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 051wwp; >> query: (?x1031, 03p2xc) <- profession(?x1031, ?x1032), award(?x1031, ?x2192), film(?x1031, ?x6375), ?x6375 = 0b6m5fy >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04hpck film 0d99k_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 46.000 0.333 http://example.org/film/actor/film./film/performance/film #12685-04w58 PRED entity: 04w58 PRED relation: organization PRED expected values: 01rz1 => 144 concepts (142 used for prediction) PRED predicted values (max 10 best out of 19): 02vk52z (0.88 #1571, 0.88 #2032, 0.87 #1594), 0b6css (0.67 #34, 0.58 #2171, 0.55 #11), 01rz1 (0.64 #2, 0.58 #2171, 0.56 #324), 0_2v (0.64 #4, 0.58 #2171, 0.50 #234), 018cqq (0.58 #2171, 0.45 #12, 0.38 #380), 02jxk (0.58 #2171, 0.36 #3, 0.32 #2701), 0gkjy (0.42 #31, 0.32 #537, 0.32 #2701), 041288 (0.41 #224, 0.35 #1703, 0.33 #40), 04k4l (0.37 #143, 0.36 #5, 0.34 #120), 0j7v_ (0.37 #144, 0.34 #190, 0.34 #420) >> Best rule #1571 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 02khs; 03h2c; 0d05q4; 04wlh; 034m8; 05c17; >> query: (?x3912, 02vk52z) <- adjustment_currency(?x3912, ?x170), ?x170 = 09nqf, adjoins(?x3912, ?x789) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 052gtg; *> query: (?x3912, 01rz1) <- contains(?x455, ?x3912), adjoins(?x789, ?x3912), ?x789 = 0f8l9c *> conf = 0.64 ranks of expected_values: 3 EVAL 04w58 organization 01rz1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 144.000 142.000 0.885 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #12684-09c7w0 PRED entity: 09c7w0 PRED relation: form_of_government PRED expected values: 06cx9 => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 5): 01q20 (0.44 #64, 0.43 #99, 0.42 #79), 018wl5 (0.42 #77, 0.38 #177, 0.37 #197), 01fpfn (0.40 #248, 0.37 #353, 0.37 #218), 06cx9 (0.39 #666, 0.35 #676, 0.33 #616), 026wp (0.11 #65, 0.10 #200, 0.10 #100) >> Best rule #64 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 0v74; >> query: (?x94, 01q20) <- combatants(?x2391, ?x94), ?x2391 = 0d06vc >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #666 for first EXPECTED value: *> intensional similarity = 1 *> extensional distance = 183 *> proper extension: 02wm6l; *> query: (?x94, 06cx9) <- form_of_government(?x94, ?x6377) *> conf = 0.39 ranks of expected_values: 4 EVAL 09c7w0 form_of_government 06cx9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 182.000 182.000 0.444 http://example.org/location/country/form_of_government #12683-016m9h PRED entity: 016m9h PRED relation: profession! PRED expected values: 03_nq => 76 concepts (32 used for prediction) PRED predicted values (max 10 best out of 4154): 012x4t (0.67 #25897, 0.25 #81050, 0.24 #114984), 02hy5d (0.60 #15802, 0.50 #24287, 0.40 #11562), 0c_md_ (0.60 #15922, 0.50 #24407, 0.40 #11682), 042f1 (0.60 #11709, 0.40 #15949, 0.33 #24434), 03j24kf (0.56 #26956, 0.41 #73624, 0.38 #107560), 015pxr (0.53 #59996, 0.47 #55753, 0.42 #76962), 05wm88 (0.53 #63211, 0.47 #58968, 0.40 #46241), 02b29 (0.53 #61634, 0.47 #57391, 0.40 #44664), 01lct6 (0.53 #71593, 0.50 #46142, 0.47 #63112), 0gs5q (0.50 #45307, 0.40 #62277, 0.40 #58034) >> Best rule #25897 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 09lbv; >> query: (?x14074, 012x4t) <- profession(?x2669, ?x14074), friend(?x2669, ?x105), specialization_of(?x14074, ?x3342), participant(?x2669, ?x286), profession(?x2669, ?x5805), profession(?x7540, ?x5805), ?x7540 = 034ls, ?x105 = 0grwj >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7230 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 0fj9f; *> query: (?x14074, 03_nq) <- profession(?x9686, ?x14074), profession(?x6742, ?x14074), profession(?x2669, ?x14074), profession(?x652, ?x14074), ?x652 = 021sv1, ?x2669 = 02mjmr, ?x9686 = 02p8v8, legislative_sessions(?x6742, ?x5339), legislative_sessions(?x6742, ?x1028), legislative_sessions(?x6742, ?x1027), ?x1028 = 032ft5, ?x5339 = 02glc4, ?x1027 = 02bn_p *> conf = 0.33 ranks of expected_values: 402 EVAL 016m9h profession! 03_nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 32.000 0.667 http://example.org/people/person/profession #12682-03clwtw PRED entity: 03clwtw PRED relation: film! PRED expected values: 03rwz3 => 109 concepts (94 used for prediction) PRED predicted values (max 10 best out of 130): 03xsby (0.70 #2504, 0.66 #4869, 0.63 #6128), 054lpb6 (0.66 #4869, 0.63 #6128, 0.59 #3410), 017jv5 (0.63 #6128, 0.63 #5151, 0.59 #3410), 025hwq (0.63 #5151, 0.55 #2503, 0.51 #6127), 031rq5 (0.63 #5151, 0.55 #2503, 0.51 #6127), 016tw3 (0.48 #707, 0.24 #983, 0.22 #1192), 03xq0f (0.46 #3690, 0.41 #634, 0.18 #214), 01gb54 (0.35 #2457, 0.17 #24, 0.10 #2738), 086k8 (0.33 #72, 0.27 #908, 0.26 #977), 017s11 (0.33 #3, 0.25 #494, 0.25 #142) >> Best rule #2504 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 04jn6y7; >> query: (?x7145, ?x1914) <- genre(?x7145, ?x225), production_companies(?x7145, ?x1914), film(?x1914, ?x8471), ?x8471 = 0cp0t91 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #530 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 01sxly; 0hmr4; 03mh_tp; 02_kd; 0gy2y8r; 01hw5kk; 02qhlwd; 0n83s; 0dgq_kn; 02n72k; ... *> query: (?x7145, 03rwz3) <- genre(?x7145, ?x225), film(?x788, ?x7145), executive_produced_by(?x7145, ?x4060), ?x788 = 0g1rw, film(?x2125, ?x7145) *> conf = 0.08 ranks of expected_values: 24 EVAL 03clwtw film! 03rwz3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 109.000 94.000 0.701 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #12681-0f__1 PRED entity: 0f__1 PRED relation: state PRED expected values: 0498y => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 93): 0498y (0.53 #4350, 0.46 #8292, 0.27 #1874), 0f2pf9 (0.27 #1874, 0.19 #8979), 09c7w0 (0.23 #1107, 0.20 #7695, 0.19 #5462), 0nn83 (0.22 #4178, 0.20 #6233, 0.20 #3239), 01n7q (0.19 #3253, 0.18 #3423, 0.18 #3508), 0f__1 (0.14 #8805, 0.03 #12151, 0.02 #7609), 07b_l (0.12 #379, 0.11 #975, 0.11 #1741), 02xry (0.10 #27, 0.10 #282, 0.09 #1134), 059rby (0.10 #2, 0.08 #172, 0.07 #512), 05k7sb (0.10 #24, 0.08 #194, 0.05 #8487) >> Best rule #4350 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 01f1q8; >> query: (?x2740, ?x4061) <- citytown(?x12485, ?x2740), administrative_division(?x2740, ?x10845), state_province_region(?x12485, ?x4061) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f__1 state 0498y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.526 http://example.org/base/biblioness/bibs_location/state #12680-0jmcv PRED entity: 0jmcv PRED relation: draft PRED expected values: 038c0q => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 18): 025tn92 (0.86 #74, 0.81 #397, 0.79 #489), 038c0q (0.86 #74, 0.76 #794, 0.76 #793), 06439y (0.86 #74, 0.76 #794, 0.76 #793), 038981 (0.86 #74, 0.76 #794, 0.76 #793), 09l0x9 (0.41 #165, 0.36 #634, 0.34 #729), 02r6gw6 (0.41 #165, 0.36 #600, 0.35 #324), 02pq_rp (0.41 #165, 0.36 #595, 0.35 #319), 047dpm0 (0.41 #165, 0.34 #238, 0.33 #604), 02qw1zx (0.41 #165, 0.34 #238, 0.32 #496), 02pq_x5 (0.41 #326, 0.35 #363, 0.31 #733) >> Best rule #74 for best value: >> intensional similarity = 13 >> extensional distance = 3 >> proper extension: 05m_8; 02c_4; >> query: (?x8228, ?x2569) <- school(?x8228, ?x1675), team(?x5755, ?x8228), colors(?x8228, ?x663), team(?x5755, ?x5483), team(?x5755, ?x4571), team(?x5755, ?x660), team(?x8996, ?x660), school(?x660, ?x2497), ?x1675 = 01j_cy, school(?x5483, ?x581), sport(?x4571, ?x4833), school(?x4571, ?x546), draft(?x5483, ?x2569) >> conf = 0.86 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0jmcv draft 038c0q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 59.000 59.000 0.864 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #12679-06rgq PRED entity: 06rgq PRED relation: award_winner! PRED expected values: 01bx35 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 125): 026kqs9 (0.25 #87, 0.17 #498, 0.02 #16990), 02rjjll (0.17 #826, 0.17 #278, 0.13 #1100), 013b2h (0.17 #899, 0.13 #8160, 0.12 #1447), 0466p0j (0.17 #347, 0.12 #8156, 0.09 #9389), 05c1t6z (0.17 #288, 0.07 #2206, 0.05 #3439), 0bz6l9 (0.17 #596, 0.02 #1007, 0.01 #1418), 0gwdy4 (0.17 #406, 0.01 #2324), 05pd94v (0.16 #1646, 0.12 #8085, 0.11 #2468), 0gpjbt (0.15 #848, 0.10 #8109, 0.10 #2492), 056878 (0.15 #1125, 0.13 #851, 0.12 #1262) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 057hz; >> query: (?x8490, 026kqs9) <- participant(?x8490, ?x2987), organization(?x8490, ?x4542), award_winner(?x724, ?x8490) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2472 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 112 *> proper extension: 08wq0g; 03qd_; 0bg539; 021bk; 08n__5; 0ckcvk; *> query: (?x8490, 01bx35) <- award_nominee(?x8490, ?x248), student(?x9847, ?x8490), instrumentalists(?x227, ?x8490) *> conf = 0.12 ranks of expected_values: 12 EVAL 06rgq award_winner! 01bx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 133.000 133.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12678-01qklj PRED entity: 01qklj PRED relation: athlete! PRED expected values: 0ltv => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 4): 018jz (0.17 #7, 0.10 #27), 018w8 (0.17 #6, 0.10 #26), 0jm_ (0.02 #143, 0.02 #103, 0.01 #623), 02vx4 (0.02 #972, 0.02 #962, 0.02 #932) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0p_47; 02j4sk; 054c1; 019803; >> query: (?x9567, 018jz) <- profession(?x9567, ?x1383), film(?x9567, ?x2642), ?x2642 = 03h3x5, location(?x9567, ?x2879) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01qklj athlete! 0ltv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.167 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #12677-018ygt PRED entity: 018ygt PRED relation: award_winner! PRED expected values: 0fqpc7d 058m5m4 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 100): 019bk0 (0.33 #14, 0.06 #1965, 0.04 #2615), 02wzl1d (0.20 #1691, 0.16 #5983, 0.11 #400), 02q690_ (0.20 #1691, 0.16 #5983, 0.10 #1621), 058m5m4 (0.20 #1691, 0.16 #5983, 0.07 #5852), 0fqpc7d (0.20 #1691, 0.04 #293, 0.03 #813), 09v0p2c (0.16 #5983, 0.08 #986, 0.07 #5852), 013b2h (0.16 #5983, 0.07 #5852, 0.07 #853), 05pd94v (0.16 #5983, 0.07 #5852, 0.05 #782), 03gt46z (0.16 #5983, 0.07 #5852, 0.05 #1619), 092c5f (0.16 #5983, 0.07 #5852, 0.05 #4564) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01mh8zn; >> query: (?x6324, 019bk0) <- award_nominee(?x6324, ?x3917), ?x3917 = 0p_47, film(?x6324, ?x667) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1691 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 186 *> proper extension: 04rtpt; *> query: (?x6324, ?x873) <- program(?x6324, ?x2528), honored_for(?x873, ?x2528) *> conf = 0.20 ranks of expected_values: 4, 5 EVAL 018ygt award_winner! 058m5m4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 103.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 018ygt award_winner! 0fqpc7d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 103.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12676-085jw PRED entity: 085jw PRED relation: performance_role! PRED expected values: 0d8lm => 81 concepts (58 used for prediction) PRED predicted values (max 10 best out of 125): 0342h (0.71 #2857, 0.67 #2679, 0.59 #3203), 018vs (0.67 #1895, 0.50 #993, 0.50 #904), 0l14j_ (0.60 #582, 0.57 #1387, 0.56 #1932), 05r5c (0.60 #541, 0.57 #1076, 0.50 #1437), 04rzd (0.60 #566, 0.57 #1101, 0.50 #1462), 03bx0bm (0.60 #558, 0.50 #1006, 0.44 #2404), 0l14qv (0.57 #1344, 0.47 #3289, 0.45 #2408), 02sgy (0.50 #988, 0.50 #632, 0.50 #274), 02hnl (0.50 #1011, 0.50 #922, 0.45 #1160), 07y_7 (0.50 #2148, 0.50 #984, 0.45 #2498) >> Best rule #2857 for best value: >> intensional similarity = 20 >> extensional distance = 12 >> proper extension: 03qjg; >> query: (?x3156, 0342h) <- role(?x2620, ?x3156), role(?x3156, ?x5417), role(?x3156, ?x2048), role(?x3156, ?x315), group(?x3156, ?x9841), ?x315 = 0l14md, performance_role(?x3156, ?x212), ?x5417 = 02w3w, role(?x316, ?x2620), role(?x2945, ?x2620), role(?x2048, ?x3296), role(?x2253, ?x2048), ?x2253 = 01679d, group(?x2048, ?x4909), award_winner(?x5656, ?x9841), ?x4909 = 01cblr, instrumentalists(?x2048, ?x367), ?x3296 = 07_l6, role(?x2048, ?x780), artists(?x497, ?x9841) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1507 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 6 *> proper extension: 0859_; *> query: (?x3156, 0d8lm) <- role(?x2309, ?x3156), role(?x3156, ?x2048), family(?x6039, ?x3156), role(?x2798, ?x6039), role(?x6039, ?x2459), role(?x6039, ?x1466), group(?x3156, ?x2901), role(?x3215, ?x2798), role(?x1436, ?x2798), role(?x1433, ?x2798), ?x2309 = 06ncr, role(?x211, ?x2798), ?x1436 = 0xzly, ?x1466 = 03bx0bm, group(?x2798, ?x4715), ?x3215 = 0bxl5, ?x4715 = 0khth, ?x1433 = 0239kh, role(?x11689, ?x2459), ?x11689 = 06p03s, instrumentalists(?x2048, ?x367) *> conf = 0.38 ranks of expected_values: 31 EVAL 085jw performance_role! 0d8lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 81.000 58.000 0.714 http://example.org/music/performance_role/guest_performances./music/recording_contribution/performance_role #12675-0g4vmj8 PRED entity: 0g4vmj8 PRED relation: film! PRED expected values: 05fnl9 => 85 concepts (51 used for prediction) PRED predicted values (max 10 best out of 809): 03m6_z (0.71 #29143, 0.65 #62455, 0.64 #64541), 07lt7b (0.09 #114, 0.05 #2195, 0.03 #10520), 016z2j (0.09 #389, 0.03 #2470, 0.03 #12877), 01f6zc (0.09 #944, 0.03 #3025, 0.03 #5106), 0gnbw (0.09 #1271, 0.03 #3352, 0.03 #5433), 02xs5v (0.09 #1407, 0.03 #5569, 0.03 #13895), 0p8r1 (0.08 #4748, 0.05 #6830, 0.05 #10992), 0f5xn (0.07 #5132, 0.05 #970, 0.03 #17620), 01wy5m (0.07 #2940, 0.05 #859, 0.03 #13347), 06cgy (0.05 #12738, 0.05 #250, 0.03 #2331) >> Best rule #29143 for best value: >> intensional similarity = 4 >> extensional distance = 308 >> proper extension: 06dfz1; >> query: (?x7275, ?x7156) <- nominated_for(?x7156, ?x7275), titles(?x812, ?x7275), participant(?x7156, ?x3308), participant(?x5597, ?x7156) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #14838 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 166 *> proper extension: 0fq27fp; 0c40vxk; 0gx9rvq; *> query: (?x7275, 05fnl9) <- film_release_region(?x7275, ?x1603), film_release_region(?x7275, ?x1353), film_release_region(?x7275, ?x87), ?x1603 = 06bnz, ?x87 = 05r4w, ?x1353 = 035qy *> conf = 0.02 ranks of expected_values: 302 EVAL 0g4vmj8 film! 05fnl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 85.000 51.000 0.711 http://example.org/film/actor/film./film/performance/film #12674-01l2b3 PRED entity: 01l2b3 PRED relation: film_distribution_medium PRED expected values: 0735l => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 6): 0735l (0.88 #55, 0.81 #68, 0.79 #139), 029j_ (0.20 #31, 0.18 #51, 0.17 #37), 0dq6p (0.15 #33, 0.06 #53, 0.05 #137), 02nxhr (0.15 #52, 0.10 #32, 0.07 #93), 07z4p (0.05 #36, 0.04 #43, 0.03 #62), 07c52 (0.02 #763) >> Best rule #55 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 0dq626; 064n1pz; 0cc846d; 04z257; 0mbql; >> query: (?x6451, 0735l) <- films(?x471, ?x6451), film(?x609, ?x6451), film_crew_role(?x6451, ?x137), region(?x6451, ?x512), ?x512 = 07ssc >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l2b3 film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.882 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #12673-06gd4 PRED entity: 06gd4 PRED relation: role PRED expected values: 02sgy => 88 concepts (58 used for prediction) PRED predicted values (max 10 best out of 124): 0342h (0.60 #110, 0.58 #216, 0.56 #534), 01vdm0 (0.58 #245, 0.44 #563, 0.40 #34), 042v_gx (0.50 #115, 0.40 #433, 0.28 #539), 05r5c (0.50 #220, 0.35 #1068, 0.34 #2227), 05148p4 (0.48 #636, 0.40 #211, 0.36 #529), 026t6 (0.42 #214, 0.33 #532, 0.21 #1062), 0gkd1 (0.40 #211, 0.36 #529, 0.36 #317), 02sgy (0.40 #112, 0.33 #430, 0.33 #218), 05842k (0.36 #718, 0.33 #292, 0.28 #610), 013y1f (0.34 #1166, 0.25 #250, 0.22 #568) >> Best rule #110 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 0qdyf; 01mxt_; 02l_7y; 0j6cj; 01wg3q; 023322; 01304j; >> query: (?x3869, 0342h) <- instrumentalists(?x227, ?x3869), artists(?x6210, ?x3869), artists(?x6107, ?x3869), artists(?x497, ?x3869), parent_genre(?x497, ?x2808), ?x6107 = 0126t5, ?x6210 = 01fh36 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #112 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 0qdyf; 01mxt_; 02l_7y; 0j6cj; 01wg3q; 023322; 01304j; *> query: (?x3869, 02sgy) <- instrumentalists(?x227, ?x3869), artists(?x6210, ?x3869), artists(?x6107, ?x3869), artists(?x497, ?x3869), parent_genre(?x497, ?x2808), ?x6107 = 0126t5, ?x6210 = 01fh36 *> conf = 0.40 ranks of expected_values: 8 EVAL 06gd4 role 02sgy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 88.000 58.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role #12672-072hv PRED entity: 072hv PRED relation: symptom_of! PRED expected values: 0cjf0 => 66 concepts (49 used for prediction) PRED predicted values (max 10 best out of 82): 0cjf0 (0.78 #87, 0.78 #81, 0.71 #941), 01cdt5 (0.78 #87, 0.78 #81, 0.67 #52), 012qjw (0.78 #87, 0.78 #81, 0.67 #52), 0brgy (0.78 #87, 0.78 #81, 0.67 #819), 0hgxh (0.78 #87, 0.78 #81, 0.64 #506), 04kllm9 (0.78 #87, 0.78 #81, 0.64 #506), 098s1 (0.78 #87, 0.71 #941, 0.58 #475), 063yv (0.78 #87, 0.56 #241, 0.44 #181), 02tfl8 (0.64 #506, 0.58 #475, 0.56 #1223), 08g5q7 (0.63 #1412, 0.07 #1205, 0.06 #1272) >> Best rule #87 for best value: >> intensional similarity = 23 >> extensional distance = 1 >> proper extension: 0167bx; >> query: (?x11659, ?x13487) <- symptom_of(?x9509, ?x11659), symptom_of(?x4905, ?x11659), ?x4905 = 01j6t0, risk_factors(?x11659, ?x13738), risk_factors(?x11659, ?x13131), risk_factors(?x11659, ?x12536), risk_factors(?x11392, ?x13738), ?x9509 = 0gxb2, symptom_of(?x13373, ?x12536), symptom_of(?x6260, ?x12536), risk_factors(?x7007, ?x12536), people(?x6260, ?x510), risk_factors(?x6260, ?x7139), people(?x13131, ?x8661), people(?x13131, ?x6585), symptom_of(?x13487, ?x11392), symptom_of(?x13373, ?x14430), risk_factors(?x9119, ?x7007), symptom_of(?x9510, ?x7007), symptom_of(?x9118, ?x13131), profession(?x6585, ?x1032), ?x14430 = 024c2, award(?x8661, ?x384) >> conf = 0.78 => this is the best rule for 8 predicted values ranks of expected_values: 1 EVAL 072hv symptom_of! 0cjf0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 49.000 0.778 http://example.org/medicine/symptom/symptom_of #12671-06_wqk4 PRED entity: 06_wqk4 PRED relation: nominated_for PRED expected values: 07kdkfj => 99 concepts (55 used for prediction) PRED predicted values (max 10 best out of 185): 07kdkfj (0.81 #4706, 0.81 #4458, 0.81 #2971), 07_fj54 (0.81 #4706, 0.81 #4458, 0.81 #2971), 02q7yfq (0.53 #4954, 0.40 #2723, 0.34 #2722), 06_wqk4 (0.53 #4954, 0.25 #21, 0.05 #3240), 0bbw2z6 (0.53 #4954, 0.01 #3105, 0.01 #3353), 05q7874 (0.53 #4954), 05qbbfb (0.53 #4954), 01s9vc (0.08 #1967, 0.06 #2959, 0.05 #4694), 05css_ (0.08 #1888, 0.06 #2880, 0.04 #4615), 02r_pp (0.07 #1873, 0.05 #2865, 0.04 #4600) >> Best rule #4706 for best value: >> intensional similarity = 3 >> extensional distance = 238 >> proper extension: 0fztbq; >> query: (?x857, ?x1721) <- film(?x828, ?x857), nominated_for(?x1721, ?x857), genre(?x857, ?x239) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 06_wqk4 nominated_for 07kdkfj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 55.000 0.813 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #12670-03_nq PRED entity: 03_nq PRED relation: student! PRED expected values: 014mlp => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 18): 014mlp (0.22 #286, 0.19 #366, 0.18 #326), 019v9k (0.12 #70, 0.11 #290, 0.10 #330), 07s6fsf (0.12 #61, 0.06 #281, 0.05 #321), 02_xgp2 (0.12 #74, 0.05 #854, 0.04 #434), 016t_3 (0.12 #64, 0.04 #424, 0.03 #524), 01rr_d (0.11 #137, 0.10 #237, 0.10 #277), 02h4rq6 (0.09 #343, 0.06 #403, 0.06 #383), 013zdg (0.08 #288, 0.07 #348, 0.06 #568), 0bkj86 (0.06 #129, 0.04 #369, 0.04 #469), 027f2w (0.05 #171, 0.05 #211, 0.05 #191) >> Best rule #286 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 0dj5q; >> query: (?x9046, 014mlp) <- nationality(?x9046, ?x94), gender(?x9046, ?x231), basic_title(?x9046, ?x346), people(?x4195, ?x9046) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_nq student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.222 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #12669-0f2v0 PRED entity: 0f2v0 PRED relation: teams PRED expected values: 07kcvl => 235 concepts (235 used for prediction) PRED predicted values (max 10 best out of 294): 01_1kk (0.20 #698, 0.07 #1056, 0.07 #1414), 03dj48 (0.20 #603, 0.04 #4183, 0.04 #3825), 098knd (0.20 #669, 0.02 #21076, 0.01 #29310), 0cqt41 (0.08 #5041, 0.07 #745, 0.07 #1103), 0bwjj (0.07 #931, 0.07 #1289, 0.06 #1647), 0j2zj (0.07 #925, 0.07 #1283, 0.06 #1641), 02wvfxl (0.07 #816, 0.07 #1174, 0.06 #1532), 01d5z (0.07 #734, 0.07 #1092, 0.06 #1450), 0jmk7 (0.07 #1017, 0.07 #1375, 0.06 #1733), 0jnq8 (0.07 #943, 0.07 #1301, 0.06 #1659) >> Best rule #698 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0261m; >> query: (?x3501, 01_1kk) <- teams(?x3501, ?x1578), vacationer(?x3501, ?x8716), ?x8716 = 01yf85 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f2v0 teams 07kcvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 235.000 235.000 0.200 http://example.org/sports/sports_team_location/teams #12668-0typ5 PRED entity: 0typ5 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.27 #45, 0.25 #77, 0.25 #49) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 231 >> proper extension: 0mn0v; 0f04v; >> query: (?x10440, 04ztj) <- source(?x10440, ?x958), ?x958 = 0jbk9, time_zones(?x10440, ?x2674), county(?x10440, ?x10893) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0typ5 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.270 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #12667-0sw62 PRED entity: 0sw62 PRED relation: location PRED expected values: 029cr => 140 concepts (72 used for prediction) PRED predicted values (max 10 best out of 137): 0cr3d (0.33 #948, 0.25 #1752, 0.17 #2556), 0h7h6 (0.25 #1697, 0.17 #2501, 0.04 #4913), 02_286 (0.22 #37011, 0.20 #49877, 0.19 #9681), 02cft (0.14 #3522, 0.02 #8344, 0.02 #7540), 02jx1 (0.14 #3287, 0.02 #8109, 0.02 #26594), 0d060g (0.14 #3229, 0.01 #6443), 087vz (0.14 #3408), 0hzlz (0.14 #3257), 04jpl (0.09 #36991, 0.09 #10464, 0.07 #44227), 059rby (0.06 #11267, 0.05 #10463, 0.05 #18500) >> Best rule #948 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 08jtv5; >> query: (?x10109, 0cr3d) <- nationality(?x10109, ?x94), actor(?x11599, ?x10109), ?x11599 = 019g8j, type_of_union(?x10109, ?x1873), ?x94 = 09c7w0 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0sw62 location 029cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 72.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #12666-0ntwb PRED entity: 0ntwb PRED relation: currency PRED expected values: 09nqf => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.85 #30, 0.85 #29, 0.83 #28) >> Best rule #30 for best value: >> intensional similarity = 3 >> extensional distance = 292 >> proper extension: 0p07l; >> query: (?x9368, ?x170) <- adjoins(?x9368, ?x13667), contains(?x3818, ?x13667), currency(?x13667, ?x170) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ntwb currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.847 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #12665-03nqnnk PRED entity: 03nqnnk PRED relation: film! PRED expected values: 06x58 0btpx => 96 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1284): 015p3p (0.33 #5228, 0.12 #36303, 0.04 #86033), 025h4z (0.33 #4203, 0.08 #22848, 0.06 #26992), 0sz28 (0.33 #4332, 0.08 #35407, 0.04 #49916), 01nwwl (0.33 #6712, 0.07 #37788, 0.03 #68873), 03yj_0n (0.33 #4750, 0.06 #25466, 0.06 #31682), 0klh7 (0.33 #2555, 0.05 #48139, 0.03 #39845), 030hbp (0.33 #7979, 0.04 #51491, 0.01 #88784), 0p_pd (0.33 #52, 0.04 #51849, 0.03 #55995), 021b_ (0.33 #1776, 0.04 #53573, 0.03 #57719), 06jz0 (0.33 #7965, 0.03 #41112, 0.03 #43185) >> Best rule #5228 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0dgpwnk; >> query: (?x5929, 015p3p) <- film_regional_debut_venue(?x5929, ?x12806), film(?x6363, ?x5929), film(?x396, ?x5929), award_winner(?x6363, ?x635), ?x396 = 01p7yb, student(?x12936, ?x6363) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03nqnnk film! 0btpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 45.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 03nqnnk film! 06x58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 45.000 0.333 http://example.org/film/actor/film./film/performance/film #12664-0422v0 PRED entity: 0422v0 PRED relation: film! PRED expected values: 030znt => 102 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1005): 01515w (0.75 #6229, 0.67 #56077, 0.66 #43615), 09l3p (0.33 #745, 0.08 #19436, 0.03 #4897), 01rh0w (0.33 #229, 0.05 #23074, 0.04 #18920), 046zh (0.33 #931, 0.04 #23776, 0.03 #34158), 030hcs (0.33 #291, 0.03 #47770, 0.03 #45693), 081lh (0.33 #160, 0.03 #25082, 0.03 #6390), 026c1 (0.33 #357, 0.02 #10740, 0.02 #54357), 021yzs (0.33 #845, 0.02 #19536, 0.02 #31996), 0cwtm (0.33 #1666, 0.02 #20357, 0.01 #24511), 0jmj (0.33 #755, 0.02 #19446, 0.01 #23600) >> Best rule #6229 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 09k56b7; 03f7xg; >> query: (?x13027, ?x6157) <- award_winner(?x13027, ?x6157), participant(?x950, ?x6157), titles(?x812, ?x13027), ?x812 = 01jfsb >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #4365 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 09k56b7; 03f7xg; *> query: (?x13027, 030znt) <- award_winner(?x13027, ?x6157), participant(?x950, ?x6157), titles(?x812, ?x13027), ?x812 = 01jfsb *> conf = 0.03 ranks of expected_values: 419 EVAL 0422v0 film! 030znt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 102.000 68.000 0.750 http://example.org/film/actor/film./film/performance/film #12663-043s3 PRED entity: 043s3 PRED relation: people! PRED expected values: 02w7gg => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 44): 02w7gg (0.40 #310, 0.20 #233, 0.20 #2235), 041rx (0.33 #543, 0.29 #1775, 0.25 #466), 013xrm (0.29 #713, 0.26 #1098, 0.25 #97), 03lmx1 (0.25 #476, 0.12 #861, 0.10 #553), 02g7sp (0.20 #326, 0.08 #788, 0.07 #942), 013b6_ (0.10 #592, 0.08 #1824, 0.06 #1131), 033tf_ (0.09 #5245, 0.09 #5477, 0.08 #6249), 0d7wh (0.08 #787, 0.07 #941, 0.06 #2481), 03bkbh (0.08 #802, 0.07 #956, 0.04 #648), 0g6ff (0.08 #714, 0.05 #1176, 0.04 #637) >> Best rule #310 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0136g9; >> query: (?x4033, 02w7gg) <- student(?x6548, ?x4033), student(?x892, ?x4033), ?x6548 = 0yls9, ?x892 = 07tgn, profession(?x4033, ?x6630) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043s3 people! 02w7gg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.400 http://example.org/people/ethnicity/people #12662-058cm PRED entity: 058cm PRED relation: place PRED expected values: 058cm => 103 concepts (44 used for prediction) PRED predicted values (max 10 best out of 112): 0lphb (0.33 #175, 0.08 #690, 0.02 #1720), 0fttg (0.08 #893, 0.01 #3611), 0q6lr (0.08 #876, 0.01 #3611), 0q48z (0.08 #831, 0.01 #3611), 0q8jl (0.08 #807, 0.01 #3611), 0q8s4 (0.08 #625, 0.01 #3611), 0kwmc (0.05 #8769, 0.01 #3611), 0dzt9 (0.02 #1294, 0.02 #1809, 0.01 #2325), 013yq (0.02 #1075, 0.02 #1590, 0.01 #2106), 0q_0z (0.02 #1438, 0.02 #1953, 0.01 #2984) >> Best rule #175 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0lphb; >> query: (?x13429, 0lphb) <- contains(?x2831, ?x13429), origin(?x672, ?x13429), ?x2831 = 0gyh, featured_film_locations(?x2914, ?x13429) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3611 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 114 *> proper extension: 0s3y5; 02dtg; 0cb4j; 0f2r6; 02_286; 0_3cs; 013kcv; 0r62v; 0f94t; 080h2; ... *> query: (?x13429, ?x1201) <- contains(?x2831, ?x13429), origin(?x672, ?x13429), contains(?x2831, ?x1201), district_represented(?x176, ?x2831) *> conf = 0.01 ranks of expected_values: 93 EVAL 058cm place 058cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 103.000 44.000 0.333 http://example.org/location/hud_county_place/place #12661-03q58q PRED entity: 03q58q PRED relation: artist PRED expected values: 0ql36 => 56 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1656): 01wgxtl (0.50 #164, 0.43 #2669, 0.40 #999), 0677ng (0.43 #3029, 0.25 #4704, 0.25 #524), 03f7jfh (0.40 #1488, 0.33 #2322, 0.29 #3158), 032nl2 (0.40 #1421, 0.33 #2255, 0.29 #3091), 01vvlyt (0.40 #1220, 0.33 #2054, 0.29 #2890), 01vxlbm (0.33 #4446, 0.28 #10315, 0.28 #6963), 0m2l9 (0.33 #1690, 0.20 #856, 0.14 #2526), 033wx9 (0.33 #9209, 0.17 #1829, 0.14 #14240), 01_ztw (0.33 #9209, 0.14 #14240, 0.13 #3742), 01pgk0 (0.33 #9209, 0.14 #14240, 0.07 #4139) >> Best rule #164 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 06wcbk7; 05clg8; >> query: (?x12090, 01wgxtl) <- artist(?x12090, ?x12880), artist(?x12090, ?x2352), ?x2352 = 01pgzn_, category(?x12090, ?x134), artists(?x378, ?x12880), artists(?x378, ?x1282), ?x1282 = 01wdqrx >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #10869 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 30 *> proper extension: 0k_kr; *> query: (?x12090, 0ql36) <- artist(?x12090, ?x5297), artist(?x12090, ?x2352), participant(?x2352, ?x123), participant(?x2352, ?x400), category(?x2352, ?x134), vacationer(?x126, ?x2352), award_nominee(?x5297, ?x115), role(?x5297, ?x212) *> conf = 0.09 ranks of expected_values: 419 EVAL 03q58q artist 0ql36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 43.000 0.500 http://example.org/music/record_label/artist #12660-086sj PRED entity: 086sj PRED relation: profession PRED expected values: 01d_h8 => 120 concepts (119 used for prediction) PRED predicted values (max 10 best out of 71): 01d_h8 (0.80 #4930, 0.79 #4781, 0.79 #5526), 0dxtg (0.52 #3297, 0.46 #5087, 0.45 #4789), 03gjzk (0.48 #5535, 0.48 #4939, 0.47 #5088), 09jwl (0.43 #1214, 0.38 #19, 0.36 #2985), 02jknp (0.41 #3291, 0.37 #5081, 0.37 #4783), 0nbcg (0.38 #32, 0.36 #1227, 0.36 #2985), 018gz8 (0.36 #166, 0.15 #6729, 0.14 #465), 02krf9 (0.36 #2985, 0.33 #299, 0.32 #1494), 0d1pc (0.36 #2985, 0.33 #299, 0.31 #4328), 039v1 (0.36 #2985, 0.32 #1494, 0.31 #4328) >> Best rule #4930 for best value: >> intensional similarity = 3 >> extensional distance = 182 >> proper extension: 05zrx3v; >> query: (?x4126, 01d_h8) <- award_nominee(?x3078, ?x4126), executive_produced_by(?x6493, ?x4126), award_winner(?x264, ?x3078) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 086sj profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 119.000 0.799 http://example.org/people/person/profession #12659-02q_4ph PRED entity: 02q_4ph PRED relation: film! PRED expected values: 016tt2 => 180 concepts (131 used for prediction) PRED predicted values (max 10 best out of 75): 05qd_ (0.46 #749, 0.33 #1637, 0.33 #971), 086k8 (0.33 #2, 0.31 #816, 0.26 #1408), 016tw3 (0.25 #899, 0.18 #4826, 0.18 #7941), 017jv5 (0.25 #903, 0.14 #1051, 0.14 #385), 0g1rw (0.23 #822, 0.22 #1266, 0.19 #3786), 016tt2 (0.22 #1484, 0.22 #670, 0.21 #2298), 03xq0f (0.20 #2077, 0.20 #4005, 0.17 #4598), 017s11 (0.19 #1779, 0.15 #2445, 0.15 #3559), 01795t (0.16 #3128, 0.14 #2831, 0.14 #3054), 0jz9f (0.14 #297, 0.14 #5186, 0.12 #519) >> Best rule #749 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 0ddjy; 02q56mk; 0dp7wt; >> query: (?x4300, 05qd_) <- genre(?x4300, ?x307), award(?x4300, ?x500), nominated_for(?x4404, ?x4300), story_by(?x4300, ?x2483), cinematography(?x4300, ?x3237) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1484 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 0kb57; 0fy66; 048rn; 0gl3hr; *> query: (?x4300, 016tt2) <- genre(?x4300, ?x307), film_sets_designed(?x4423, ?x4300), film(?x2875, ?x4300), language(?x4300, ?x254), featured_film_locations(?x4300, ?x11382) *> conf = 0.22 ranks of expected_values: 6 EVAL 02q_4ph film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 180.000 131.000 0.462 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #12658-08hmch PRED entity: 08hmch PRED relation: featured_film_locations PRED expected values: 0dv9v => 58 concepts (41 used for prediction) PRED predicted values (max 10 best out of 50): 02_286 (0.27 #1908, 0.25 #3086, 0.17 #493), 030qb3t (0.19 #38, 0.15 #3104, 0.15 #1926), 0rh6k (0.14 #1889, 0.12 #3067, 0.10 #1), 04jpl (0.12 #1897, 0.11 #3075, 0.07 #955), 01_d4 (0.05 #46, 0.04 #1934, 0.04 #3112), 0156q (0.05 #40, 0.03 #3106, 0.03 #1928), 05qtj (0.05 #95, 0.03 #1041, 0.02 #1983), 0345h (0.05 #32, 0.02 #1920, 0.02 #3807), 09949m (0.05 #148, 0.01 #384), 0djd3 (0.05 #119, 0.01 #355) >> Best rule #1908 for best value: >> intensional similarity = 4 >> extensional distance = 174 >> proper extension: 015qsq; 02qzmz6; >> query: (?x1035, 02_286) <- genre(?x1035, ?x812), ?x812 = 01jfsb, nominated_for(?x399, ?x1035), featured_film_locations(?x1035, ?x1036) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08hmch featured_film_locations 0dv9v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 41.000 0.267 http://example.org/film/film/featured_film_locations #12657-01cpqk PRED entity: 01cpqk PRED relation: film PRED expected values: 04smdd 07bxqz => 126 concepts (93 used for prediction) PRED predicted values (max 10 best out of 899): 05jf85 (0.73 #80435, 0.72 #82224, 0.65 #80434), 0q9b0 (0.10 #1273, 0.04 #3060, 0.04 #4849), 0gj8t_b (0.10 #181, 0.04 #1968, 0.04 #3757), 02vyyl8 (0.10 #966, 0.04 #2753, 0.03 #8116), 01shy7 (0.07 #18295, 0.06 #16508, 0.05 #20082), 0f42nz (0.06 #15207, 0.05 #24142, 0.03 #43806), 02p76f9 (0.05 #6791, 0.05 #1428, 0.03 #19300), 03lrht (0.05 #5620, 0.05 #257, 0.02 #16342), 0fphf3v (0.05 #6725, 0.04 #19234, 0.03 #17447), 01vw8k (0.05 #6016, 0.03 #14951, 0.02 #16738) >> Best rule #80435 for best value: >> intensional similarity = 4 >> extensional distance = 1009 >> proper extension: 050_qx; >> query: (?x6525, ?x9772) <- nominated_for(?x6525, ?x9772), nationality(?x6525, ?x94), film(?x6525, ?x1330), film(?x6993, ?x9772) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #7095 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 01wz3cx; 01w7nwm; 013w7j; *> query: (?x6525, 07bxqz) <- celebrity(?x986, ?x6525), profession(?x6525, ?x1032), religion(?x6525, ?x1985) *> conf = 0.02 ranks of expected_values: 490, 616 EVAL 01cpqk film 07bxqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 126.000 93.000 0.733 http://example.org/film/actor/film./film/performance/film EVAL 01cpqk film 04smdd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 126.000 93.000 0.733 http://example.org/film/actor/film./film/performance/film #12656-01vksx PRED entity: 01vksx PRED relation: film! PRED expected values: 054g1r => 76 concepts (63 used for prediction) PRED predicted values (max 10 best out of 61): 054g1r (0.44 #538, 0.15 #33, 0.13 #177), 086k8 (0.23 #146, 0.23 #435, 0.20 #1591), 05qd_ (0.23 #8, 0.17 #1090, 0.17 #152), 016tt2 (0.21 #797, 0.16 #293, 0.15 #725), 017s11 (0.19 #364, 0.18 #580, 0.17 #75), 04mkft (0.16 #323, 0.09 #1333, 0.08 #1188), 016tw3 (0.15 #10, 0.14 #1889, 0.14 #947), 06jntd (0.13 #318, 0.08 #1328, 0.07 #1111), 025tlyv (0.13 #345, 0.07 #1355, 0.06 #273), 05s_k6 (0.13 #350, 0.07 #782, 0.05 #1143) >> Best rule #538 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 0cks1m; >> query: (?x908, 054g1r) <- film(?x629, ?x908), film(?x2156, ?x908), ?x2156 = 01795t, profession(?x629, ?x1032) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vksx film! 054g1r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 63.000 0.444 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #12655-04g9gd PRED entity: 04g9gd PRED relation: country PRED expected values: 0f8l9c => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 63): 09c7w0 (0.91 #968, 0.91 #3020, 0.90 #3322), 02jx1 (0.44 #4829, 0.42 #1087, 0.03 #5743), 0345h (0.27 #2863, 0.25 #147, 0.24 #3286), 0f8l9c (0.24 #2855, 0.21 #3278, 0.12 #3520), 0d060g (0.11 #189, 0.08 #129, 0.06 #369), 03_3d (0.10 #3267, 0.08 #128, 0.06 #2301), 0chghy (0.08 #133, 0.06 #193, 0.05 #919), 0ctw_b (0.08 #143, 0.06 #263, 0.04 #2897), 03rt9 (0.08 #135, 0.04 #375, 0.03 #435), 0b90_r (0.08 #125, 0.04 #2897, 0.03 #5743) >> Best rule #968 for best value: >> intensional similarity = 6 >> extensional distance = 172 >> proper extension: 04969y; >> query: (?x2418, 09c7w0) <- film_release_distribution_medium(?x2418, ?x81), genre(?x2418, ?x225), film(?x2549, ?x2418), executive_produced_by(?x2418, ?x12790), produced_by(?x2418, ?x9785), country(?x2418, ?x512) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #2855 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 585 *> proper extension: 02vl9ln; *> query: (?x2418, 0f8l9c) <- country(?x2418, ?x512), titles(?x512, ?x144), contains(?x512, ?x362), participating_countries(?x358, ?x512), combatants(?x512, ?x94) *> conf = 0.24 ranks of expected_values: 4 EVAL 04g9gd country 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.914 http://example.org/film/film/country #12654-0661ql3 PRED entity: 0661ql3 PRED relation: genre PRED expected values: 01jfsb 06n90 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 95): 07s9rl0 (0.75 #1445, 0.75 #843, 0.74 #2771), 09blyk (0.65 #3132, 0.61 #7598, 0.59 #3131), 024qqx (0.59 #3131, 0.54 #7597, 0.52 #6870), 01jfsb (0.53 #491, 0.36 #131, 0.36 #11), 05p553 (0.51 #6994, 0.37 #5427, 0.35 #5787), 03npn (0.45 #7, 0.14 #127, 0.10 #6997), 02l7c8 (0.33 #2545, 0.32 #2183, 0.31 #3267), 06n90 (0.33 #492, 0.32 #132, 0.21 #613), 04xvlr (0.25 #844, 0.25 #723, 0.24 #1085), 01hmnh (0.24 #377, 0.23 #257, 0.23 #497) >> Best rule #1445 for best value: >> intensional similarity = 3 >> extensional distance = 183 >> proper extension: 014kkm; 0286gm1; 01gvts; 0kt_4; 025scjj; 0k419; >> query: (?x2394, 07s9rl0) <- nominated_for(?x198, ?x2394), film(?x748, ?x2394), ?x198 = 040njc >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #491 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 01hr1; 0ds33; 04fzfj; 0p3_y; 0dnqr; 0x25q; 02_sr1; 01hqk; 06sfk6; 031hcx; ... *> query: (?x2394, 01jfsb) <- nominated_for(?x68, ?x2394), film(?x748, ?x2394), titles(?x8581, ?x2394), ?x8581 = 024qqx *> conf = 0.53 ranks of expected_values: 4, 8 EVAL 0661ql3 genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 90.000 90.000 0.746 http://example.org/film/film/genre EVAL 0661ql3 genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 90.000 0.746 http://example.org/film/film/genre #12653-03n6r PRED entity: 03n6r PRED relation: religion PRED expected values: 02rsw => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 30): 0c8wxp (0.38 #231, 0.32 #411, 0.31 #591), 0kpl (0.29 #775, 0.22 #1047, 0.21 #912), 03_gx (0.25 #14, 0.20 #1907, 0.19 #2360), 05sfs (0.18 #138, 0.06 #318, 0.04 #1355), 0kq2 (0.11 #783, 0.11 #648, 0.09 #920), 0631_ (0.10 #98, 0.06 #233, 0.06 #323), 07w8f (0.10 #125, 0.03 #800, 0.03 #665), 04pk9 (0.10 #110, 0.03 #785, 0.02 #1913), 02rxj (0.10 #97, 0.02 #999, 0.02 #1089), 092bf5 (0.09 #151, 0.08 #466, 0.06 #331) >> Best rule #231 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 01vvyc_; 01xyt7; 01kmd4; 01vhrz; >> query: (?x5348, 0c8wxp) <- company(?x5348, ?x13773), participant(?x5348, ?x1149), people(?x5269, ?x5348) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1781 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 119 *> proper extension: 02m7r; 0c2dl; 01bpn; 036jp8; 034ls; 0g7k2g; 054fvj; 032r1; *> query: (?x5348, 02rsw) <- company(?x5348, ?x13773), student(?x13219, ?x5348), location(?x5348, ?x739) *> conf = 0.02 ranks of expected_values: 26 EVAL 03n6r religion 02rsw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 173.000 173.000 0.375 http://example.org/people/person/religion #12652-01kwh5j PRED entity: 01kwh5j PRED relation: film PRED expected values: 05pyrb => 102 concepts (22 used for prediction) PRED predicted values (max 10 best out of 650): 02v5xg (0.50 #16138, 0.41 #34078, 0.25 #34079), 02z9hqn (0.33 #3718, 0.14 #7303, 0.12 #10889), 026q3s3 (0.29 #10964, 0.25 #9172, 0.21 #12756), 01lk02 (0.25 #34079, 0.25 #34077, 0.24 #16137), 03d3ht (0.25 #34079, 0.25 #34077, 0.24 #16137), 0ckr7s (0.25 #38, 0.20 #1833, 0.17 #3627), 0ckrgs (0.25 #519, 0.20 #2314, 0.10 #5901), 02vw1w2 (0.23 #7388, 0.21 #12766, 0.21 #10974), 0dh8v4 (0.21 #11703, 0.18 #13495, 0.17 #9911), 07ng9k (0.18 #7380, 0.17 #10966, 0.17 #9174) >> Best rule #16138 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 069z_5; >> query: (?x8988, ?x8717) <- actor(?x8717, ?x8988), profession(?x8988, ?x1383), ?x1383 = 0np9r, country_of_origin(?x8717, ?x252), genre(?x8717, ?x53) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #17931 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 44 *> proper extension: 079vf; 0bl2g; 0bxtg; 0mdqp; 01nm3s; 01z7s_; 03b78r; 029pnn; 01x2tm8; 01qklj; ... *> query: (?x8988, ?x297) <- profession(?x8988, ?x1383), ?x1383 = 0np9r, special_performance_type(?x8988, ?x296), nationality(?x8988, ?x252), film(?x296, ?x297) *> conf = 0.05 ranks of expected_values: 48 EVAL 01kwh5j film 05pyrb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 102.000 22.000 0.500 http://example.org/film/actor/film./film/performance/film #12651-019vgs PRED entity: 019vgs PRED relation: influenced_by PRED expected values: 014zfs => 88 concepts (41 used for prediction) PRED predicted values (max 10 best out of 624): 0l5yl (0.12 #707, 0.04 #2889, 0.04 #3763), 02jq1 (0.12 #616, 0.04 #3235, 0.03 #4108), 0lrh (0.12 #511, 0.03 #4440, 0.02 #7059), 03sbs (0.10 #5026, 0.07 #8952, 0.06 #7208), 032l1 (0.10 #4892, 0.08 #8818, 0.08 #9254), 014z8v (0.09 #5360, 0.07 #4050, 0.07 #2740), 081k8 (0.09 #4959, 0.08 #7141, 0.08 #9321), 01hmk9 (0.08 #5460, 0.07 #3714, 0.07 #2404), 02lt8 (0.08 #557, 0.06 #8849, 0.06 #9285), 081nh (0.08 #501, 0.06 #1373, 0.02 #3120) >> Best rule #707 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0bk1p; 07hgm; >> query: (?x3853, 0l5yl) <- influenced_by(?x3853, ?x11357), award_winner(?x5734, ?x3853), inductee(?x11145, ?x3853) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #3953 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 153 *> proper extension: 01w5n51; 016ppr; *> query: (?x3853, 014zfs) <- influenced_by(?x3853, ?x11357), award_nominee(?x496, ?x3853), award(?x3853, ?x401) *> conf = 0.08 ranks of expected_values: 20 EVAL 019vgs influenced_by 014zfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 88.000 41.000 0.125 http://example.org/influence/influence_node/influenced_by #12650-0140t7 PRED entity: 0140t7 PRED relation: people! PRED expected values: 0d7wh => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 49): 0x67 (0.31 #1319, 0.31 #1011, 0.27 #1473), 041rx (0.19 #312, 0.19 #466, 0.12 #4778), 033tf_ (0.14 #1085, 0.09 #2933, 0.08 #5089), 06v41q (0.10 #106, 0.06 #260, 0.03 #1107), 07hwkr (0.07 #2938, 0.06 #628, 0.06 #5094), 0xnvg (0.06 #1476, 0.06 #1091, 0.06 #860), 07bch9 (0.06 #2949, 0.04 #5105, 0.04 #4797), 02w7gg (0.06 #6855, 0.06 #5084, 0.06 #2928), 01qhm_ (0.06 #1084, 0.04 #2932, 0.03 #5088), 09vc4s (0.05 #86, 0.04 #856, 0.04 #1318) >> Best rule #1319 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 0lk90; 01x1cn2; 016ksk; 012xdf; >> query: (?x9321, 0x67) <- award(?x9321, ?x1232), artist(?x3887, ?x9321), film(?x9321, ?x5570) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 05myd2; *> query: (?x9321, 0d7wh) <- award(?x9321, ?x3045), profession(?x9321, ?x1032), ?x3045 = 02sp_v *> conf = 0.03 ranks of expected_values: 24 EVAL 0140t7 people! 0d7wh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 143.000 143.000 0.309 http://example.org/people/ethnicity/people #12649-01yzhn PRED entity: 01yzhn PRED relation: program PRED expected values: 06hwzy => 144 concepts (118 used for prediction) PRED predicted values (max 10 best out of 17): 06hwzy (0.45 #162, 0.35 #422, 0.35 #214), 01j7mr (0.40 #112, 0.09 #372, 0.09 #190), 0304nh (0.25 #88, 0.05 #374, 0.05 #166), 0cpz4k (0.12 #217, 0.09 #373, 0.08 #451), 026bfsh (0.10 #167, 0.05 #453, 0.04 #219), 02zv4b (0.10 #159, 0.04 #367, 0.03 #419), 01b7h8 (0.05 #383, 0.05 #175, 0.05 #461), 01h1bf (0.05 #423, 0.05 #449, 0.04 #371), 025ljp (0.05 #174, 0.04 #382, 0.03 #460), 02xhwm (0.05 #178, 0.03 #282, 0.02 #386) >> Best rule #162 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 012_53; 018z_c; 0mbw0; 03f1zhf; >> query: (?x10592, 06hwzy) <- location(?x10592, ?x335), person(?x3480, ?x10592), participant(?x10592, ?x2524), gender(?x10592, ?x231) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01yzhn program 06hwzy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 118.000 0.450 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #12648-0sxfd PRED entity: 0sxfd PRED relation: award PRED expected values: 0gr4k => 71 concepts (56 used for prediction) PRED predicted values (max 10 best out of 189): 0gs9p (0.27 #8719, 0.27 #8269, 0.27 #8718), 0p9sw (0.27 #8719, 0.27 #8269, 0.27 #8718), 0k611 (0.27 #8719, 0.27 #8269, 0.27 #8718), 019f4v (0.27 #8719, 0.27 #8269, 0.27 #8718), 0gq_v (0.27 #8719, 0.27 #8269, 0.27 #8718), 0l8z1 (0.27 #8719, 0.27 #8269, 0.27 #8718), 094qd5 (0.27 #8719, 0.27 #8269, 0.27 #8718), 09sdmz (0.22 #358, 0.05 #8943, 0.05 #9167), 054ky1 (0.21 #7599, 0.21 #8494, 0.20 #6257), 0gr4k (0.21 #7599, 0.21 #8494, 0.17 #248) >> Best rule #8719 for best value: >> intensional similarity = 3 >> extensional distance = 1000 >> proper extension: 02nf2c; 03j63k; 0m123; 097h2; 02gl58; 03xj05; 02_1ky; 019g8j; 0147w8; 0300ml; ... >> query: (?x1402, ?x746) <- nominated_for(?x746, ?x1402), award(?x1402, ?x289), award(?x276, ?x746) >> conf = 0.27 => this is the best rule for 7 predicted values *> Best rule #7599 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 900 *> proper extension: 07s8z_l; 03d17dg; *> query: (?x1402, ?x1245) <- award_winner(?x1402, ?x5094), award_winner(?x1402, ?x4385), award_winner(?x1245, ?x5094), nominated_for(?x4385, ?x758), award_nominee(?x4385, ?x635) *> conf = 0.21 ranks of expected_values: 10 EVAL 0sxfd award 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 71.000 56.000 0.271 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12647-02k_4g PRED entity: 02k_4g PRED relation: country_of_origin PRED expected values: 09c7w0 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.89 #159, 0.88 #23, 0.87 #68), 07ssc (0.11 #178, 0.10 #334, 0.10 #323), 03_3d (0.09 #351, 0.08 #306, 0.08 #172), 0d060g (0.04 #173, 0.03 #251, 0.03 #218), 07c52 (0.03 #259, 0.01 #123), 02jx1 (0.02 #180, 0.01 #325, 0.01 #336), 05v8c (0.01 #358, 0.01 #380), 03rt9 (0.01 #30, 0.01 #41), 03rjj (0.01 #24, 0.01 #35) >> Best rule #159 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 05h95s; 02_1ky; >> query: (?x782, 09c7w0) <- award_winner(?x782, ?x1343), actor(?x782, ?x1397), award(?x1343, ?x375) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02k_4g country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.892 http://example.org/tv/tv_program/country_of_origin #12646-0j_sncb PRED entity: 0j_sncb PRED relation: organization PRED expected values: 034h1h => 124 concepts (86 used for prediction) PRED predicted values (max 10 best out of 13): 034h1h (0.60 #59, 0.43 #155, 0.43 #204), 07t65 (0.08 #170, 0.04 #146, 0.04 #557), 02vk52z (0.06 #169, 0.04 #145, 0.03 #556), 0b6css (0.04 #156, 0.04 #180, 0.02 #807), 0_2v (0.04 #149, 0.02 #391, 0.02 #173), 018cqq (0.04 #181, 0.02 #808, 0.02 #568), 01rz1 (0.02 #147, 0.02 #171, 0.02 #798), 04k4l (0.02 #150, 0.02 #174, 0.01 #801), 059dn (0.02 #185, 0.01 #812), 041288 (0.02 #186) >> Best rule #59 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02zcz3; >> query: (?x2948, 034h1h) <- school(?x9049, ?x2948), ?x9049 = 0jmm4, institution(?x620, ?x2948) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j_sncb organization 034h1h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 86.000 0.600 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #12645-040fb PRED entity: 040fb PRED relation: month! PRED expected values: 0h7h6 013yq 06t2t 02h6_6p 0fn2g 06c62 0h3tv => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1129): 06c62 (0.90 #107, 0.89 #51, 0.88 #36), 013yq (0.90 #107, 0.89 #51, 0.88 #36), 0fn2g (0.90 #107, 0.89 #51, 0.88 #36), 0h3tv (0.90 #107, 0.89 #51, 0.88 #36), 06t2t (0.90 #107, 0.89 #51, 0.88 #36), 0h7h6 (0.90 #107, 0.89 #51, 0.88 #36), 0l0mk (0.90 #107, 0.89 #51, 0.88 #36), 02h6_6p (0.90 #107, 0.88 #36, 0.86 #10), 03czqs (0.90 #107, 0.88 #36, 0.86 #10), 0f67f (0.44 #96, 0.01 #34, 0.01 #106) >> Best rule #107 for best value: >> intensional similarity = 90 >> extensional distance = 2 >> proper extension: 04wzr; >> query: (?x2140, ?x1658) <- month(?x11237, ?x2140), month(?x9605, ?x2140), month(?x9559, ?x2140), month(?x8252, ?x2140), month(?x6494, ?x2140), month(?x6458, ?x2140), month(?x5267, ?x2140), month(?x5168, ?x2140), month(?x4698, ?x2140), month(?x3501, ?x2140), month(?x3373, ?x2140), month(?x3106, ?x2140), month(?x3052, ?x2140), month(?x3026, ?x2140), month(?x2474, ?x2140), month(?x1458, ?x2140), ?x1458 = 05ywg, ?x3501 = 0f2v0, seasonal_months(?x2140, ?x3107), seasonal_months(?x2140, ?x1650), ?x3026 = 0cv3w, seasonal_months(?x4827, ?x2140), seasonal_months(?x2255, ?x2140), ?x4698 = 056_y, ?x1650 = 06vkl, ?x6494 = 02sn34, ?x6458 = 08966, ?x5168 = 06mxs, locations(?x4368, ?x5267), place_of_birth(?x2281, ?x5267), place_of_birth(?x1001, ?x5267), ?x9559 = 07dfk, location(?x2841, ?x5267), ?x8252 = 0k3p, ?x2474 = 052p7, ?x3373 = 0ply0, contains(?x5267, ?x3543), contains(?x4600, ?x5267), time_zones(?x5267, ?x2950), artists(?x302, ?x1001), award_winner(?x2281, ?x192), religion(?x2281, ?x7300), award(?x1001, ?x247), ?x4827 = 03_ly, award_winner(?x1596, ?x2281), ?x3106 = 049d1, ?x2255 = 040fv, place_founded(?x3795, ?x5267), origin(?x3293, ?x3052), location(?x9289, ?x3052), location(?x8134, ?x3052), citytown(?x6717, ?x5267), contains(?x4600, ?x10213), contains(?x3052, ?x1520), mode_of_transportation(?x5267, ?x4272), adjoins(?x726, ?x4600), category(?x3052, ?x134), role(?x1001, ?x227), location_of_ceremony(?x566, ?x4600), dog_breed(?x3052, ?x1706), origin(?x1060, ?x5267), place_of_birth(?x9659, ?x3052), ?x10213 = 010tkc, state(?x3052, ?x2020), ?x9605 = 02frhbc, artist(?x2299, ?x1001), ?x11237 = 03khn, award_nominee(?x2307, ?x9289), nominated_for(?x9659, ?x6094), colors(?x1520, ?x663), school_type(?x1520, ?x1044), profession(?x1001, ?x220), award(?x8134, ?x154), geographic_distribution(?x1176, ?x4600), month(?x1658, ?x3107), participant(?x2281, ?x5881), team(?x4368, ?x3798), ?x3798 = 02ptzz0, spouse(?x9301, ?x2841), award_nominee(?x2841, ?x2842), partially_contains(?x4600, ?x6195), film(?x9289, ?x83), featured_film_locations(?x813, ?x3052), participant(?x2373, ?x8134), ?x2950 = 02lcqs, jurisdiction_of_office(?x1195, ?x5267), award(?x9289, ?x704), adjoins(?x3052, ?x3007), ?x566 = 04ztj, award_nominee(?x1001, ?x7027) >> conf = 0.90 => this is the best rule for 9 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 8 EVAL 040fb month! 0h3tv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.904 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fb month! 06c62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.904 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fb month! 0fn2g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.904 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fb month! 02h6_6p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 12.000 12.000 0.904 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fb month! 06t2t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.904 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fb month! 013yq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.904 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fb month! 0h7h6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.904 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #12644-01z215 PRED entity: 01z215 PRED relation: geographic_distribution! PRED expected values: 071x0k => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 38): 0d29z (0.39 #138, 0.36 #295, 0.36 #530), 071x0k (0.28 #828, 0.26 #629, 0.26 #120), 013b6_ (0.25 #27, 0.15 #66, 0.08 #340), 0g48m4 (0.16 #1060, 0.06 #3183, 0.06 #3222), 0g6ff (0.14 #1069, 0.14 #362, 0.11 #558), 01xhh5 (0.14 #215, 0.13 #137, 0.12 #255), 01rv7x (0.09 #925, 0.09 #648, 0.09 #139), 04gfy7 (0.08 #72, 0.04 #150, 0.03 #228), 0ffjqy (0.08 #70, 0.04 #148, 0.03 #226), 0cn68 (0.08 #68, 0.04 #146, 0.03 #224) >> Best rule #138 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 07ytt; >> query: (?x1781, 0d29z) <- entity_involved(?x12976, ?x1781), administrative_area_type(?x1781, ?x2792), contains(?x6304, ?x1781) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #828 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 0l3h; 0fv4v; *> query: (?x1781, 071x0k) <- organization(?x1781, ?x127), contains(?x6304, ?x1781), exported_to(?x1781, ?x94) *> conf = 0.28 ranks of expected_values: 2 EVAL 01z215 geographic_distribution! 071x0k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.391 http://example.org/people/ethnicity/geographic_distribution #12643-048ldh PRED entity: 048ldh PRED relation: colors PRED expected values: 083jv 06fvc => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.81 #1000, 0.81 #2164, 0.80 #1019), 019sc (0.63 #2059, 0.56 #506, 0.53 #814), 01l849 (0.63 #2059, 0.37 #383, 0.33 #1457), 06fvc (0.61 #772, 0.45 #309, 0.43 #214), 03vtbc (0.37 #383, 0.33 #1457, 0.30 #557), 038hg (0.37 #383, 0.30 #385, 0.21 #617), 088fh (0.37 #383, 0.30 #385, 0.21 #617), 0680m7 (0.33 #1457, 0.30 #557, 0.30 #385), 02rnmb (0.33 #13, 0.30 #385, 0.21 #617), 0jc_p (0.33 #44, 0.22 #272, 0.20 #139) >> Best rule #1000 for best value: >> intensional similarity = 8 >> extensional distance = 46 >> proper extension: 01rl_3; 0cj_v7; 0272vm; >> query: (?x11995, 083jv) <- teams(?x10683, ?x11995), team(?x13270, ?x11995), colors(?x11995, ?x3189), team(?x2918, ?x11995), colors(?x11474, ?x3189), colors(?x10178, ?x3189), ?x11474 = 03bnd9, ?x10178 = 01tntf >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 048ldh colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 122.000 122.000 0.812 http://example.org/sports/sports_team/colors EVAL 048ldh colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.812 http://example.org/sports/sports_team/colors #12642-033w9g PRED entity: 033w9g PRED relation: award PRED expected values: 02x4w6g => 89 concepts (85 used for prediction) PRED predicted values (max 10 best out of 250): 0f4x7 (0.58 #1240, 0.11 #1643, 0.10 #3658), 04kxsb (0.54 #1334, 0.13 #528, 0.12 #1737), 09sb52 (0.43 #1250, 0.34 #9713, 0.33 #3668), 0gqy2 (0.40 #1373, 0.11 #1776, 0.10 #2179), 09qv_s (0.33 #1360, 0.10 #1763, 0.07 #554), 027dtxw (0.32 #1213, 0.10 #4, 0.07 #3228), 0bdwqv (0.29 #1380, 0.10 #2186, 0.10 #171), 09sdmz (0.29 #1414, 0.10 #205, 0.09 #1817), 02x4w6g (0.28 #1322, 0.07 #516, 0.06 #1725), 099ck7 (0.28 #1475, 0.07 #669, 0.06 #1878) >> Best rule #1240 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 013cr; 015qt5; 046qq; 02y_2y; 016k6x; 012v9y; 0l786; 053xw6; 0gm34; 016ynj; ... >> query: (?x4527, 0f4x7) <- award(?x4527, ?x3209), film(?x4527, ?x8615), ?x3209 = 02w9sd7 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1322 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 013cr; 015qt5; 046qq; 02y_2y; 016k6x; 012v9y; 0l786; 053xw6; 0gm34; 016ynj; ... *> query: (?x4527, 02x4w6g) <- award(?x4527, ?x3209), film(?x4527, ?x8615), ?x3209 = 02w9sd7 *> conf = 0.28 ranks of expected_values: 9 EVAL 033w9g award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 89.000 85.000 0.583 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12641-025rcc PRED entity: 025rcc PRED relation: colors PRED expected values: 09ggk => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 20): 01g5v (0.34 #183, 0.33 #63, 0.32 #203), 01l849 (0.30 #61, 0.27 #521, 0.26 #221), 038hg (0.25 #32, 0.22 #52, 0.13 #432), 03wkwg (0.25 #15, 0.12 #35, 0.11 #55), 019sc (0.22 #167, 0.19 #227, 0.19 #1747), 06fvc (0.18 #1042, 0.16 #422, 0.16 #1742), 036k5h (0.15 #285, 0.14 #185, 0.13 #445), 0jc_p (0.12 #24, 0.11 #44, 0.10 #164), 04mkbj (0.10 #530, 0.10 #190, 0.10 #90), 01jnf1 (0.10 #71, 0.06 #91, 0.05 #711) >> Best rule #183 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 05zjtn4; >> query: (?x5887, 01g5v) <- institution(?x1200, ?x5887), contains(?x9605, ?x5887), currency(?x5887, ?x170), county_seat(?x11062, ?x9605), colors(?x5887, ?x663) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 049dk; *> query: (?x5887, 09ggk) <- institution(?x1200, ?x5887), contains(?x94, ?x5887), major_field_of_study(?x5887, ?x6870), ?x6870 = 01540, ?x1200 = 016t_3 *> conf = 0.07 ranks of expected_values: 13 EVAL 025rcc colors 09ggk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 170.000 170.000 0.343 http://example.org/education/educational_institution/colors #12640-05v10 PRED entity: 05v10 PRED relation: olympics PRED expected values: 06sks6 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 39): 06sks6 (0.89 #1544, 0.87 #1349, 0.87 #2756), 09n48 (0.59 #81, 0.39 #42, 0.35 #120), 0kbvv (0.53 #102, 0.42 #63, 0.40 #141), 0jdk_ (0.53 #103, 0.39 #64, 0.33 #142), 018ctl (0.47 #85, 0.42 #46, 0.37 #124), 0swbd (0.47 #88, 0.36 #49, 0.33 #127), 0swff (0.38 #99, 0.27 #60, 0.21 #138), 0l6m5 (0.35 #87, 0.21 #48, 0.21 #126), 0sxrz (0.30 #58, 0.26 #97, 0.16 #136), 0l6vl (0.29 #80, 0.18 #41, 0.14 #119) >> Best rule #1544 for best value: >> intensional similarity = 2 >> extensional distance = 129 >> proper extension: 02jxk; >> query: (?x1592, 06sks6) <- member_states(?x7695, ?x1592), jurisdiction_of_office(?x265, ?x1592) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v10 olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.885 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #12639-019bnn PRED entity: 019bnn PRED relation: ceremony PRED expected values: 09n4nb => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 132): 09n4nb (0.60 #176, 0.35 #44, 0.34 #1100), 01c6qp (0.57 #149, 0.33 #17, 0.33 #1469), 01mh_q (0.54 #215, 0.31 #1535, 0.31 #1139), 01bx35 (0.54 #137, 0.31 #1457, 0.30 #1061), 019bk0 (0.54 #146, 0.31 #1070, 0.31 #1466), 01s695 (0.54 #134, 0.31 #1454, 0.30 #1058), 013b2h (0.53 #207, 0.31 #1527, 0.30 #1131), 01mhwk (0.52 #169, 0.31 #1489, 0.30 #1093), 0jzphpx (0.45 #167, 0.28 #1585, 0.26 #1487), 05c1t6z (0.28 #1585, 0.16 #409, 0.13 #805) >> Best rule #176 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 07n52; 02xzd9; >> query: (?x6739, 09n4nb) <- category_of(?x6739, ?x2421), category_of(?x1088, ?x2421), disciplines_or_subjects(?x1088, ?x8681) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019bnn ceremony 09n4nb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.598 http://example.org/award/award_category/winners./award/award_honor/ceremony #12638-0dh73w PRED entity: 0dh73w PRED relation: influenced_by PRED expected values: 071jv5 => 100 concepts (59 used for prediction) PRED predicted values (max 10 best out of 51): 03sbs (0.05 #2849, 0.03 #5472, 0.03 #4160), 03_87 (0.05 #2829, 0.03 #4140, 0.02 #5452), 05qmj (0.04 #2819, 0.03 #5442, 0.03 #4130), 0gz_ (0.04 #2729, 0.03 #5352, 0.02 #4040), 02wh0 (0.04 #3010, 0.03 #4321, 0.02 #5633), 081k8 (0.03 #2782, 0.03 #4093, 0.03 #5405), 032l1 (0.03 #2715, 0.02 #5338, 0.02 #9715), 03hnd (0.03 #3162, 0.03 #2725, 0.02 #3599), 015n8 (0.03 #3038, 0.02 #5661, 0.02 #4349), 042q3 (0.03 #2992, 0.02 #5615, 0.02 #4303) >> Best rule #2849 for best value: >> intensional similarity = 2 >> extensional distance = 330 >> proper extension: 017r2; 026lj; 017yfz; 03_hd; 07c37; 0hky; 06gn7r; 03_js; 042f1; 042d1; ... >> query: (?x4168, 03sbs) <- place_of_death(?x4168, ?x6987), student(?x735, ?x4168) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dh73w influenced_by 071jv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 59.000 0.051 http://example.org/influence/influence_node/influenced_by #12637-018qpq PRED entity: 018qpq PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 20): 0fkvn (0.30 #27, 0.25 #50, 0.24 #142), 060c4 (0.26 #348, 0.07 #1061, 0.05 #1200), 0f6c3 (0.23 #100, 0.22 #146, 0.17 #169), 060bp (0.23 #346, 0.06 #1059, 0.04 #1198), 09n5b9 (0.19 #104, 0.18 #150, 0.16 #173), 0pqc5 (0.09 #488, 0.08 #465, 0.08 #534), 0fkzq (0.07 #155, 0.07 #109, 0.05 #178), 04syw (0.05 #352, 0.01 #1065, 0.01 #1204), 0dq3c (0.05 #347, 0.01 #1060), 0789n (0.04 #102, 0.03 #125, 0.03 #171) >> Best rule #27 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 018jk2; 0193fp; 018jcq; 018txg; 01f1ps; 018jkl; 018jn4; 018qt8; >> query: (?x6506, 0fkvn) <- contains(?x2651, ?x6506), contains(?x252, ?x6506), ?x252 = 03_3d, ?x2651 = 0g3bw, category(?x6506, ?x134) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018qpq jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.300 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #12636-0fpv_3_ PRED entity: 0fpv_3_ PRED relation: film_release_region PRED expected values: 03_3d 0chghy 0345h 0163v 077qn => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 173): 0chghy (0.93 #1166, 0.93 #586, 0.92 #354), 0345h (0.90 #834, 0.89 #370, 0.89 #254), 03_3d (0.87 #351, 0.87 #583, 0.85 #1163), 0k6nt (0.80 #829, 0.80 #597, 0.80 #1177), 06qd3 (0.73 #258, 0.66 #838, 0.64 #606), 07twz (0.71 #68, 0.62 #184, 0.42 #300), 06mzp (0.62 #245, 0.59 #593, 0.57 #361), 077qn (0.57 #61, 0.50 #177, 0.42 #1338), 0jgx (0.57 #59, 0.50 #175, 0.25 #871), 02k54 (0.56 #241, 0.48 #357, 0.46 #589) >> Best rule #1166 for best value: >> intensional similarity = 6 >> extensional distance = 102 >> proper extension: 0jjy0; 07g_0c; 07f_7h; 047svrl; 05q4y12; 02rmd_2; 02bg55; 049w1q; >> query: (?x2340, 0chghy) <- film_release_region(?x2340, ?x1475), film_release_region(?x2340, ?x550), film_release_region(?x2340, ?x47), ?x1475 = 05qx1, official_language(?x47, ?x2502), country(?x7195, ?x550) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 8, 14 EVAL 0fpv_3_ film_release_region 077qn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 101.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fpv_3_ film_release_region 0163v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 101.000 101.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fpv_3_ film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fpv_3_ film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fpv_3_ film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12635-03y9ccy PRED entity: 03y9ccy PRED relation: written_by! PRED expected values: 02vrgnr => 92 concepts (86 used for prediction) PRED predicted values (max 10 best out of 47): 025ts_z (0.17 #560, 0.12 #1222, 0.10 #1884), 01g3gq (0.17 #488, 0.12 #1150, 0.10 #1812), 07g9f (0.11 #662, 0.11 #14561, 0.09 #20521), 03d34x8 (0.11 #662, 0.11 #14561, 0.09 #20521), 06fqlk (0.03 #2430, 0.03 #3091, 0.02 #4414), 03n3gl (0.03 #2425, 0.02 #3086), 01hvjx (0.03 #2135, 0.02 #2796), 01h7bb (0.03 #2009, 0.02 #2670), 050f0s (0.03 #4089, 0.03 #5413, 0.02 #6736), 0cc5mcj (0.02 #2801, 0.02 #2140, 0.02 #4124) >> Best rule #560 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01gp_x; 0b05xm; 07lwsz; 02q5xsx; >> query: (?x3727, 025ts_z) <- award_nominee(?x4671, ?x3727), ?x4671 = 027hnjh, nominated_for(?x3727, ?x2009) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03y9ccy written_by! 02vrgnr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 86.000 0.167 http://example.org/film/film/written_by #12634-01svq8 PRED entity: 01svq8 PRED relation: tv_program PRED expected values: 039cq4 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 34): 07c72 (0.33 #21, 0.25 #108, 0.20 #195), 039cq4 (0.17 #744, 0.13 #570, 0.10 #309), 0cpz4k (0.10 #290, 0.01 #1508), 0d_rw (0.06 #522, 0.06 #435, 0.01 #1655), 04x4gj (0.06 #518, 0.06 #431, 0.01 #1651), 01j7mr (0.04 #548, 0.04 #1567, 0.04 #1157), 05_z42 (0.04 #563, 0.03 #650, 0.03 #737), 0124k9 (0.04 #1567, 0.01 #4449, 0.01 #3664), 0ph24 (0.03 #689, 0.03 #863, 0.02 #1211), 06qw_ (0.03 #694, 0.02 #2875, 0.01 #4011) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0bqs56; >> query: (?x13118, 07c72) <- influenced_by(?x13118, ?x2942), ?x2942 = 046lt, participant(?x13118, ?x496), category(?x13118, ?x134) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #744 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 030wkp; *> query: (?x13118, 039cq4) <- influenced_by(?x13118, ?x2942), location(?x2942, ?x1110), person(?x9723, ?x2942), influenced_by(?x2942, ?x1726) *> conf = 0.17 ranks of expected_values: 2 EVAL 01svq8 tv_program 039cq4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 153.000 153.000 0.333 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #12633-0yzbg PRED entity: 0yzbg PRED relation: genre PRED expected values: 01j1n2 => 70 concepts (69 used for prediction) PRED predicted values (max 10 best out of 93): 07qht4 (0.61 #4813, 0.54 #4812, 0.52 #5054), 02kdv5l (0.42 #241, 0.41 #361, 0.30 #3368), 05p553 (0.38 #3370, 0.37 #2286, 0.36 #4093), 03k9fj (0.33 #371, 0.31 #251, 0.23 #3378), 01jfsb (0.32 #2777, 0.32 #1215, 0.31 #2415), 02l7c8 (0.31 #2539, 0.31 #2059, 0.30 #1459), 06n90 (0.24 #373, 0.24 #253, 0.14 #3380), 0lsxr (0.24 #128, 0.24 #608, 0.22 #728), 0219x_ (0.22 #27, 0.11 #147, 0.11 #627), 0hcr (0.22 #384, 0.21 #264, 0.08 #3391) >> Best rule #4813 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 02xhwm; >> query: (?x7243, ?x53) <- titles(?x53, ?x7243), genre(?x273, ?x53) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #60 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 25 *> proper extension: 0m313; 011yph; 0209xj; 0dr_4; 0661ql3; 019vhk; 07024; 0j43swk; 07w8fz; 0ywrc; ... *> query: (?x7243, 01j1n2) <- nominated_for(?x6909, ?x7243), nominated_for(?x3435, ?x7243), nominated_for(?x198, ?x7243), ?x6909 = 02qyntr, ?x198 = 040njc, ?x3435 = 03hl6lc *> conf = 0.07 ranks of expected_values: 33 EVAL 0yzbg genre 01j1n2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 70.000 69.000 0.612 http://example.org/film/film/genre #12632-07_m9_ PRED entity: 07_m9_ PRED relation: jurisdiction_of_office PRED expected values: 059z0 => 198 concepts (162 used for prediction) PRED predicted values (max 10 best out of 53): 0345h (0.57 #1991, 0.57 #1374, 0.52 #4452), 09c7w0 (0.57 #1324, 0.52 #4402, 0.50 #1120), 059rby (0.20 #917, 0.14 #1329, 0.09 #3183), 0d060g (0.20 #916, 0.08 #2104, 0.08 #1945), 01n7q (0.20 #878, 0.05 #2886, 0.04 #3194), 05vz3zq (0.17 #2134, 0.13 #2290, 0.12 #2698), 07b_l (0.17 #1148, 0.11 #1711, 0.11 #1660), 07ssc (0.14 #3483, 0.13 #3440, 0.12 #3236), 059z0 (0.14 #3483, 0.03 #3126, 0.01 #5834), 0f8l9c (0.14 #3483, 0.01 #4870, 0.01 #6357) >> Best rule #1991 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 0zm1; >> query: (?x4736, ?x1264) <- nationality(?x4736, ?x1264), notable_people_with_this_condition(?x9933, ?x4736), profession(?x4736, ?x5805), profession(?x5266, ?x5805), profession(?x5171, ?x5805), ?x5266 = 016lh0, student(?x2605, ?x5171) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #3483 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 29 *> proper extension: 0kn4c; 02mjmr; 08_hns; 01jcjt; *> query: (?x4736, ?x3142) <- nationality(?x4736, ?x1264), profession(?x4736, ?x353), gender(?x4736, ?x231), entity_involved(?x12031, ?x4736), entity_involved(?x12031, ?x9178), nationality(?x9178, ?x3142) *> conf = 0.14 ranks of expected_values: 9 EVAL 07_m9_ jurisdiction_of_office 059z0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 198.000 162.000 0.571 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #12631-01p4wv PRED entity: 01p4wv PRED relation: genre PRED expected values: 05p553 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 79): 07s9rl0 (0.97 #1793, 0.96 #652, 0.96 #1141), 05p553 (0.92 #410, 0.91 #1471, 0.67 #1715), 0lsxr (0.33 #9, 0.25 #171, 0.19 #660), 02fgmn (0.33 #63, 0.25 #225, 0.14 #714), 0gs6m (0.33 #34, 0.25 #196, 0.11 #277), 0hcr (0.32 #1728, 0.23 #1484, 0.21 #2782), 01t_vv (0.31 #1499, 0.29 #438, 0.27 #683), 06n90 (0.27 #1724, 0.24 #2778, 0.24 #1643), 01hmnh (0.25 #177, 0.21 #2780, 0.19 #1726), 01z77k (0.25 #189, 0.16 #840, 0.16 #270) >> Best rule #1793 for best value: >> intensional similarity = 6 >> extensional distance = 114 >> proper extension: 045r_9; 04x4gj; 05397h; 02rq7nd; >> query: (?x5307, 07s9rl0) <- languages(?x5307, ?x254), genre(?x5307, ?x162), titles(?x162, ?x3388), titles(?x162, ?x167), ?x3388 = 01rwyq, ?x167 = 083shs >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #410 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 50 *> proper extension: 02nf2c; 02xhpl; 01cjhz; 0jq2r; 06f0k; *> query: (?x5307, 05p553) <- languages(?x5307, ?x254), genre(?x5307, ?x2480), genre(?x5307, ?x162), country_of_origin(?x5307, ?x94), ?x2480 = 01z4y, genre(?x144, ?x162) *> conf = 0.92 ranks of expected_values: 2 EVAL 01p4wv genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.974 http://example.org/tv/tv_program/genre #12630-0bbxx9b PRED entity: 0bbxx9b PRED relation: crewmember! PRED expected values: 087wc7n => 91 concepts (46 used for prediction) PRED predicted values (max 10 best out of 306): 0dtfn (0.21 #351, 0.17 #653, 0.17 #49), 024mpp (0.14 #429, 0.11 #731, 0.11 #1033), 0jqn5 (0.14 #356, 0.11 #658, 0.11 #960), 031t2d (0.14 #365, 0.11 #969, 0.11 #1271), 01kff7 (0.14 #350, 0.11 #954, 0.11 #1256), 0bwfwpj (0.12 #37, 0.11 #339, 0.09 #641), 033dbw (0.12 #298, 0.09 #902, 0.08 #1204), 0ddjy (0.11 #383, 0.09 #685, 0.08 #81), 0hx4y (0.11 #399, 0.09 #701, 0.08 #1003), 0gy0n (0.11 #601, 0.07 #1809, 0.07 #2111) >> Best rule #351 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 0b79gfg; 02h1rt; 02xc1w4; 051z6rz; >> query: (?x3879, 0dtfn) <- crewmember(?x2006, ?x3879), nominated_for(?x669, ?x2006), film_distribution_medium(?x2006, ?x81) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bbxx9b crewmember! 087wc7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 46.000 0.214 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #12629-03jldb PRED entity: 03jldb PRED relation: profession PRED expected values: 02hrh1q => 107 concepts (84 used for prediction) PRED predicted values (max 10 best out of 59): 02hrh1q (0.92 #8134, 0.90 #303, 0.89 #6104), 02jknp (0.55 #2617, 0.52 #2472, 0.51 #1167), 09jwl (0.39 #1321, 0.39 #5382, 0.36 #6397), 02krf9 (0.37 #603, 0.36 #748, 0.31 #893), 0dz3r (0.34 #1307, 0.24 #5368, 0.21 #6383), 0nbcg (0.29 #1333, 0.28 #5394, 0.27 #11748), 0cbd2 (0.28 #5366, 0.28 #2616, 0.27 #441), 015h31 (0.28 #5366, 0.22 #24, 0.06 #2054), 01c72t (0.28 #5366, 0.16 #5386, 0.13 #6401), 016z4k (0.27 #11748, 0.27 #1309, 0.27 #5370) >> Best rule #8134 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 04bs3j; 014x77; 0lzb8; 0kr5_; 012c6x; 03gm48; 0f0p0; 04hpck; 05sq84; 0j582; ... >> query: (?x1537, 02hrh1q) <- nominated_for(?x1537, ?x1395), profession(?x1537, ?x319), film(?x1537, ?x3133) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03jldb profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 84.000 0.916 http://example.org/people/person/profession #12628-03bnv PRED entity: 03bnv PRED relation: nationality PRED expected values: 02jx1 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 29): 09c7w0 (0.77 #801, 0.72 #5404, 0.72 #2502), 02jx1 (0.40 #33, 0.33 #8511, 0.29 #133), 07ssc (0.33 #8511, 0.27 #8409, 0.14 #1115), 013p59 (0.33 #8511, 0.27 #8409), 0dbdy (0.33 #8511, 0.27 #8409), 03rk0 (0.08 #4749, 0.06 #5751, 0.06 #6851), 03_3d (0.06 #206, 0.03 #3908, 0.02 #5009), 0345h (0.06 #1831, 0.03 #4333, 0.03 #1431), 0d060g (0.06 #1507, 0.06 #1107, 0.05 #1407), 0h7x (0.04 #1835, 0.02 #1435, 0.02 #4337) >> Best rule #801 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 0frmb1; >> query: (?x3321, 09c7w0) <- inductee(?x1091, ?x3321), type_of_union(?x3321, ?x566), gender(?x3321, ?x231) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #33 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 012x4t; 01vsl3_; 01vrnsk; *> query: (?x3321, 02jx1) <- award_nominee(?x4701, ?x3321), award_nominee(?x3321, ?x1930), ?x4701 = 03j24kf *> conf = 0.40 ranks of expected_values: 2 EVAL 03bnv nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 93.000 0.765 http://example.org/people/person/nationality #12627-01gc7 PRED entity: 01gc7 PRED relation: genre PRED expected values: 02kdv5l => 82 concepts (43 used for prediction) PRED predicted values (max 10 best out of 104): 04xvlr (0.63 #1291, 0.55 #3756, 0.54 #1290), 02l7c8 (0.46 #132, 0.33 #1187, 0.31 #2011), 05p553 (0.46 #4, 0.36 #4697, 0.36 #355), 01jfsb (0.42 #3649, 0.33 #12, 0.33 #2359), 02kdv5l (0.39 #2, 0.31 #353, 0.29 #3287), 01hmnh (0.37 #17, 0.33 #251, 0.31 #368), 0hcr (0.31 #726, 0.18 #23, 0.14 #140), 0lsxr (0.27 #3646, 0.23 #594, 0.18 #1420), 060__y (0.26 #133, 0.19 #2012, 0.18 #601), 04rlf (0.21 #765, 0.06 #1411, 0.06 #179) >> Best rule #1291 for best value: >> intensional similarity = 3 >> extensional distance = 332 >> proper extension: 0h95b81; 07s8z_l; 03czz87; >> query: (?x299, ?x53) <- honored_for(?x6861, ?x299), titles(?x53, ?x299), genre(?x273, ?x53) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 0c00zd0; 038bh3; 06y611; *> query: (?x299, 02kdv5l) <- genre(?x299, ?x53), production_companies(?x299, ?x574), music(?x299, ?x2214), region(?x299, ?x512) *> conf = 0.39 ranks of expected_values: 5 EVAL 01gc7 genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 82.000 43.000 0.630 http://example.org/film/film/genre #12626-0cj36c PRED entity: 0cj36c PRED relation: award_winner! PRED expected values: 03gyp30 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 86): 03gyp30 (0.85 #116, 0.71 #256, 0.62 #396), 027n06w (0.21 #212, 0.17 #3082, 0.15 #72), 09pj68 (0.17 #3082, 0.12 #2801, 0.08 #104), 03gt46z (0.17 #3082, 0.10 #8544, 0.10 #7703), 0gx_st (0.17 #3082, 0.10 #8544, 0.10 #7703), 02q690_ (0.10 #8544, 0.10 #7703, 0.07 #204), 0275n3y (0.10 #8544, 0.10 #7703, 0.07 #214), 07z31v (0.10 #8544, 0.10 #7703, 0.07 #171), 07y9ts (0.10 #8544, 0.10 #7703, 0.07 #207), 09qvms (0.08 #433, 0.06 #573, 0.06 #713) >> Best rule #116 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0bt4r4; >> query: (?x6634, 03gyp30) <- award_nominee(?x6634, ?x6633), award_winner(?x3924, ?x6634), ?x6633 = 0cl0bk >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cj36c award_winner! 03gyp30 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.846 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12625-016k62 PRED entity: 016k62 PRED relation: nationality PRED expected values: 03spz => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 26): 03spz (0.35 #5758, 0.35 #3773, 0.35 #3873), 0jgd (0.35 #5758, 0.35 #3773, 0.35 #3873), 05vz3zq (0.35 #5758, 0.35 #3773, 0.35 #3873), 02jx1 (0.21 #131, 0.16 #1524, 0.16 #1920), 07ssc (0.10 #113, 0.10 #3190, 0.10 #1209), 03rk0 (0.06 #8778, 0.05 #5406, 0.05 #9571), 0d060g (0.05 #105, 0.05 #1201, 0.05 #204), 017z88 (0.04 #994, 0.03 #1095, 0.03 #1195), 0345h (0.04 #1024, 0.03 #725, 0.03 #3206), 03rjj (0.03 #103, 0.02 #699, 0.02 #9133) >> Best rule #5758 for best value: >> intensional similarity = 4 >> extensional distance = 1475 >> proper extension: 079vf; 079ws; 01y8d4; 02c0mv; 023jq1; 08849; 011s9r; 08f3yq; 0cbxl0; >> query: (?x5151, ?x94) <- award_winner(?x5151, ?x8129), award_winner(?x5151, ?x5125), award_winner(?x8129, ?x10677), nationality(?x5125, ?x94) >> conf = 0.35 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 016k62 nationality 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.353 http://example.org/people/person/nationality #12624-09v92_x PRED entity: 09v92_x PRED relation: award! PRED expected values: 042rnl 0pkr1 => 61 concepts (8 used for prediction) PRED predicted values (max 10 best out of 1055): 01t2h2 (0.50 #476, 0.40 #3853, 0.11 #7230), 02kxbx3 (0.26 #14499, 0.09 #11121, 0.08 #17879), 05kfs (0.26 #13674, 0.06 #10296, 0.05 #17054), 0184jw (0.26 #15781, 0.03 #12403, 0.03 #19161), 054k_8 (0.25 #1626, 0.20 #5003, 0.11 #8380), 0451j (0.25 #2212, 0.20 #5589, 0.11 #8966), 02p59ry (0.25 #2039, 0.20 #5416, 0.11 #8793), 07ftc0 (0.25 #2382, 0.20 #5759, 0.06 #12515), 04jb97 (0.25 #2357, 0.20 #5734, 0.06 #12490), 02wk4d (0.25 #1748, 0.20 #5125, 0.06 #11881) >> Best rule #476 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 09v8db5; 09v51c2; >> query: (?x7215, 01t2h2) <- award_winner(?x7215, ?x2086), nominated_for(?x7215, ?x7554), nominated_for(?x7215, ?x3376), nominated_for(?x7215, ?x467), ?x7554 = 01mgw, ?x467 = 0dckvs, ?x3376 = 05g8pg >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #158 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 09v8db5; 09v51c2; *> query: (?x7215, 042rnl) <- award_winner(?x7215, ?x2086), nominated_for(?x7215, ?x7554), nominated_for(?x7215, ?x3376), nominated_for(?x7215, ?x467), ?x7554 = 01mgw, ?x467 = 0dckvs, ?x3376 = 05g8pg *> conf = 0.25 ranks of expected_values: 13, 119 EVAL 09v92_x award! 0pkr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 61.000 8.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09v92_x award! 042rnl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 61.000 8.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12623-05r3qc PRED entity: 05r3qc PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 6): 029j_ (0.86 #181, 0.85 #106, 0.85 #51), 02nxhr (0.15 #456, 0.11 #37, 0.11 #12), 07c52 (0.15 #456, 0.07 #38, 0.04 #68), 07z4p (0.15 #456, 0.02 #130, 0.02 #345), 0735l (0.15 #456), 0dq6p (0.15 #456) >> Best rule #181 for best value: >> intensional similarity = 5 >> extensional distance = 259 >> proper extension: 0gtvrv3; >> query: (?x6167, 029j_) <- currency(?x6167, ?x170), film_crew_role(?x6167, ?x2154), ?x170 = 09nqf, ?x2154 = 01vx2h, country(?x6167, ?x94) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05r3qc film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.858 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12622-026mmy PRED entity: 026mmy PRED relation: ceremony PRED expected values: 02rjjll => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 129): 02rjjll (0.81 #2196, 0.68 #1551, 0.62 #1809), 01c6qp (0.78 #2208, 0.69 #1563, 0.64 #1821), 01mh_q (0.75 #2272, 0.68 #1627, 0.61 #1885), 05c1t6z (0.71 #270, 0.22 #2592, 0.20 #1947), 0gvstc3 (0.71 #286, 0.18 #2608, 0.18 #1963), 01mhwk (0.71 #2228, 0.64 #1583, 0.59 #1841), 01xqqp (0.66 #2279, 0.59 #1634, 0.55 #860), 0jzphpx (0.64 #2226, 0.60 #807, 0.57 #1581), 02q690_ (0.57 #316, 0.19 #1219, 0.19 #2638), 0gx_st (0.57 #289, 0.18 #2611, 0.18 #1966) >> Best rule #2196 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 054knh; >> query: (?x10881, 02rjjll) <- ceremony(?x10881, ?x8500), locations(?x8500, ?x1523), place_of_birth(?x338, ?x1523) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026mmy ceremony 02rjjll CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.808 http://example.org/award/award_category/winners./award/award_honor/ceremony #12621-07dfk PRED entity: 07dfk PRED relation: film_release_region! PRED expected values: 02d44q 05dss7 => 263 concepts (199 used for prediction) PRED predicted values (max 10 best out of 1322): 017gm7 (0.92 #36981, 0.92 #34351, 0.79 #50131), 017jd9 (0.89 #50560, 0.85 #37410, 0.84 #34780), 0fpgp26 (0.89 #51109, 0.81 #37959, 0.80 #35329), 03nm_fh (0.88 #37423, 0.88 #34793, 0.79 #50573), 04hwbq (0.86 #50117, 0.73 #36967, 0.72 #34337), 0dzlbx (0.85 #37467, 0.84 #34837, 0.82 #50617), 05zlld0 (0.85 #37289, 0.84 #34659, 0.82 #50439), 0gd0c7x (0.85 #37059, 0.84 #34429, 0.82 #50209), 04f52jw (0.85 #37147, 0.84 #34517, 0.79 #50297), 0fpv_3_ (0.85 #37099, 0.84 #34469, 0.71 #50249) >> Best rule #36981 for best value: >> intensional similarity = 2 >> extensional distance = 24 >> proper extension: 05qx1; >> query: (?x9559, 017gm7) <- film_release_region(?x10860, ?x9559), ?x10860 = 049w1q >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #36944 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 24 *> proper extension: 05qx1; *> query: (?x9559, 02d44q) <- film_release_region(?x10860, ?x9559), ?x10860 = 049w1q *> conf = 0.73 ranks of expected_values: 111, 178 EVAL 07dfk film_release_region! 05dss7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 263.000 199.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07dfk film_release_region! 02d44q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 263.000 199.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12620-09r94m PRED entity: 09r94m PRED relation: films! PRED expected values: 07jq_ => 106 concepts (53 used for prediction) PRED predicted values (max 10 best out of 79): 081pw (0.22 #316, 0.19 #160, 0.12 #628), 01w1sx (0.09 #403, 0.05 #872, 0.04 #3073), 03r8gp (0.09 #559, 0.06 #89, 0.04 #3072), 06d4h (0.07 #1768, 0.06 #200, 0.06 #43), 05489 (0.07 #1777, 0.06 #522, 0.05 #2405), 0bq3x (0.06 #1755, 0.06 #2383, 0.06 #2543), 0fx2s (0.06 #385, 0.06 #72, 0.06 #542), 04gb7 (0.06 #358, 0.06 #202, 0.03 #827), 0jxxt (0.06 #451, 0.06 #295, 0.03 #920), 07_nf (0.06 #223, 0.04 #691, 0.04 #1005) >> Best rule #316 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 09rsjpv; 03z106; 0bw20; >> query: (?x5331, 081pw) <- titles(?x53, ?x5331), genre(?x5331, ?x2605), ?x2605 = 03g3w, film_crew_role(?x5331, ?x137), films(?x5954, ?x5331) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #4625 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 479 *> proper extension: 01cgz; *> query: (?x5331, 07jq_) <- films(?x5954, ?x5331), films(?x5954, ?x5648), award_winner(?x5648, ?x2332), film_crew_role(?x5648, ?x137) *> conf = 0.03 ranks of expected_values: 43 EVAL 09r94m films! 07jq_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 106.000 53.000 0.219 http://example.org/film/film_subject/films #12619-0mn0v PRED entity: 0mn0v PRED relation: contains! PRED expected values: 07z1m => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 264): 09c7w0 (0.76 #48404, 0.71 #8068, 0.71 #56469), 07ssc (0.36 #26921, 0.35 #25127, 0.30 #39468), 01n7q (0.33 #78, 0.30 #2766, 0.26 #48479), 02_286 (0.33 #3627, 0.20 #1835, 0.19 #14382), 0f8l9c (0.33 #943, 0.07 #7216, 0.06 #9008), 04_1l0v (0.26 #38095, 0.25 #53332, 0.22 #46163), 059rby (0.25 #3604, 0.20 #1812, 0.19 #8981), 07z1m (0.25 #3676, 0.20 #34151, 0.19 #14431), 05k7sb (0.24 #35088, 0.20 #1925, 0.17 #5510), 02xry (0.24 #10917, 0.21 #8228, 0.18 #11813) >> Best rule #48404 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 010bnr; >> query: (?x2673, 09c7w0) <- jurisdiction_of_office(?x1195, ?x2673), source(?x2673, ?x958), ?x958 = 0jbk9 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #3676 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0n5d1; *> query: (?x2673, 07z1m) <- second_level_divisions(?x94, ?x2673), origin(?x959, ?x2673), source(?x2673, ?x958) *> conf = 0.25 ranks of expected_values: 8 EVAL 0mn0v contains! 07z1m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 125.000 125.000 0.759 http://example.org/location/location/contains #12618-03d0d7 PRED entity: 03d0d7 PRED relation: current_club! PRED expected values: 03zrc_ 032jlh => 109 concepts (100 used for prediction) PRED predicted values (max 10 best out of 61): 02s9vc (0.57 #466, 0.33 #21, 0.32 #556), 03zrc_ (0.40 #338, 0.33 #15, 0.23 #353), 03y_f8 (0.29 #266, 0.23 #814, 0.21 #1125), 02pp1 (0.29 #289, 0.16 #560, 0.14 #470), 02ltg3 (0.27 #756, 0.26 #787, 0.25 #602), 03_44z (0.25 #174, 0.25 #145, 0.23 #353), 03xh50 (0.25 #157, 0.25 #98, 0.20 #215), 02rqxc (0.25 #95, 0.20 #574, 0.19 #666), 03z8bw (0.25 #107, 0.15 #616, 0.14 #678), 03ylxn (0.25 #111, 0.15 #590, 0.14 #682) >> Best rule #466 for best value: >> intensional similarity = 14 >> extensional distance = 12 >> proper extension: 04112r; 09cl0w; >> query: (?x13134, 02s9vc) <- team(?x530, ?x13134), team(?x203, ?x13134), team(?x63, ?x13134), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x203 = 0dgrmp, current_club(?x59, ?x13134), team(?x10179, ?x59), athlete(?x471, ?x10179), team(?x10179, ?x1833), current_club(?x59, ?x6477), gender(?x10179, ?x231), ?x6477 = 02_lt, team(?x3551, ?x59) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #338 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 8 *> proper extension: 0d2psv; 01xbp7; 01vcnl; *> query: (?x13134, 03zrc_) <- team(?x530, ?x13134), team(?x203, ?x13134), team(?x63, ?x13134), team(?x60, ?x13134), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x203 = 0dgrmp, current_club(?x59, ?x13134), current_club(?x59, ?x11139), current_club(?x59, ?x6477), ?x60 = 02nzb8, team(?x982, ?x11139), teams(?x390, ?x59), teams(?x12884, ?x11139), ?x6477 = 02_lt *> conf = 0.40 ranks of expected_values: 2, 12 EVAL 03d0d7 current_club! 032jlh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 109.000 100.000 0.571 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club EVAL 03d0d7 current_club! 03zrc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 100.000 0.571 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #12617-0b_6s7 PRED entity: 0b_6s7 PRED relation: locations PRED expected values: 013yq 0vm39 => 59 concepts (43 used for prediction) PRED predicted values (max 10 best out of 453): 013yq (0.67 #2507, 0.59 #2857, 0.48 #3386), 0d9y6 (0.50 #796, 0.50 #621, 0.45 #2024), 0ftxw (0.50 #413, 0.33 #1288, 0.31 #2692), 0vzm (0.43 #942, 0.33 #1292, 0.30 #1644), 0f2rq (0.42 #2554, 0.40 #1677, 0.40 #1500), 0kcw2 (0.36 #2098, 0.35 #2974, 0.33 #169), 04f_d (0.33 #1272, 0.33 #397, 0.30 #1624), 099ty (0.33 #1273, 0.33 #47, 0.30 #1625), 02cl1 (0.33 #1244, 0.33 #18, 0.30 #1596), 0djd3 (0.33 #810, 0.33 #635, 0.29 #985) >> Best rule #2507 for best value: >> intensional similarity = 12 >> extensional distance = 10 >> proper extension: 0b_75k; 0b_6lb; 0bzrsh; 0b_6pv; 0b_6mr; >> query: (?x8992, 013yq) <- team(?x8992, ?x9983), team(?x8992, ?x9909), team(?x8992, ?x4938), instance_of_recurring_event(?x8992, ?x10863), locations(?x8992, ?x674), ?x9983 = 02q4ntp, ?x9909 = 026wlnm, team(?x12451, ?x4938), team(?x10594, ?x4938), position(?x4938, ?x1579), ?x10594 = 0b_756, ?x12451 = 0b_6xf >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 48 EVAL 0b_6s7 locations 0vm39 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 59.000 43.000 0.667 http://example.org/time/event/locations EVAL 0b_6s7 locations 013yq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 43.000 0.667 http://example.org/time/event/locations #12616-016zdd PRED entity: 016zdd PRED relation: people! PRED expected values: 041rx => 144 concepts (142 used for prediction) PRED predicted values (max 10 best out of 49): 041rx (0.63 #679, 0.50 #379, 0.49 #454), 033tf_ (0.39 #307, 0.33 #82, 0.25 #7), 0x67 (0.27 #1511, 0.26 #2562, 0.20 #1060), 09vc4s (0.25 #9, 0.17 #309, 0.07 #909), 02ctzb (0.25 #15, 0.17 #90, 0.05 #6618), 03lmx1 (0.25 #14, 0.07 #239, 0.03 #2566), 03bkbh (0.25 #31, 0.04 #1682, 0.04 #331), 0bbz66j (0.25 #46, 0.01 #1171, 0.01 #796), 059_w (0.17 #104, 0.04 #329, 0.02 #1304), 0dbxy (0.17 #120, 0.03 #495, 0.02 #1696) >> Best rule #679 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 045m1_; >> query: (?x10834, 041rx) <- religion(?x10834, ?x7131), ?x7131 = 03_gx, gender(?x10834, ?x514) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016zdd people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 142.000 0.628 http://example.org/people/ethnicity/people #12615-01j53q PRED entity: 01j53q PRED relation: award_winner! PRED expected values: 0m7yy => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 175): 0m7yy (0.69 #8390, 0.69 #7958, 0.69 #8822), 07bdd_ (0.50 #1363, 0.43 #3956, 0.35 #9140), 05p1dby (0.50 #1405, 0.43 #3998, 0.33 #9182), 0gq9h (0.25 #1375, 0.20 #2672, 0.17 #3104), 02x1z2s (0.25 #1495, 0.20 #2792, 0.17 #3224), 01lk0l (0.25 #1575, 0.20 #2872, 0.17 #3304), 09sb52 (0.18 #14732, 0.13 #17757, 0.11 #22509), 01l29r (0.16 #6648, 0.12 #7512, 0.06 #6216), 0cjyzs (0.13 #12638, 0.04 #9181, 0.03 #24736), 07kjk7c (0.11 #4613) >> Best rule #8390 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 03mdt; 018_q8; 0146mv; >> query: (?x10166, 0m7yy) <- award_winner(?x10166, ?x10844), award_winner(?x10166, ?x2776), award_winner(?x10844, ?x11078), program(?x2776, ?x4063), program_creator(?x4063, ?x9793) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j53q award_winner! 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.694 http://example.org/award/award_category/winners./award/award_honor/award_winner #12614-05l5n PRED entity: 05l5n PRED relation: place_of_death! PRED expected values: 015vql => 199 concepts (154 used for prediction) PRED predicted values (max 10 best out of 735): 0hnjt (0.10 #205, 0.06 #3224, 0.06 #3979), 0b_fw (0.10 #81, 0.06 #3100, 0.06 #3855), 081t6 (0.10 #704, 0.06 #3723, 0.06 #4478), 02y49 (0.10 #444, 0.06 #3463, 0.06 #4218), 04z0g (0.10 #262, 0.06 #3281, 0.06 #4036), 02x2t07 (0.10 #463, 0.06 #3482, 0.06 #4237), 02vqpx8 (0.10 #326, 0.06 #3345, 0.06 #4100), 01h320 (0.10 #132, 0.06 #3151, 0.05 #4663), 034cj9 (0.10 #735, 0.06 #4509, 0.05 #5266), 01dbhb (0.10 #725, 0.06 #4499, 0.05 #5256) >> Best rule #205 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 07g0_; >> query: (?x1841, 0hnjt) <- country(?x1841, ?x512), state(?x1841, ?x2235), place_of_death(?x6796, ?x1841) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05l5n place_of_death! 015vql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 154.000 0.100 http://example.org/people/deceased_person/place_of_death #12613-05kjlr PRED entity: 05kjlr PRED relation: category_of PRED expected values: 05kjlr => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 14): 0c4ys (0.44 #363, 0.42 #405, 0.42 #469), 0g_w (0.20 #45, 0.13 #279, 0.10 #450), 07n52 (0.17 #100), 0gcf2r (0.09 #638, 0.08 #701, 0.08 #512), 01ppdy (0.02 #197, 0.02 #176, 0.02 #219), 02tzwd (0.02 #201, 0.02 #180, 0.02 #223), 01tgwv (0.02 #202, 0.02 #181, 0.02 #224), 01b8bn (0.02 #198, 0.02 #177, 0.02 #220), 058vy5 (0.02 #179, 0.02 #222, 0.02 #243), 02v1ws (0.02 #189, 0.02 #232, 0.02 #253) >> Best rule #363 for best value: >> intensional similarity = 7 >> extensional distance = 88 >> proper extension: 04ljl_l; 02wh75; 027c924; 026mg3; 05zkcn5; 0gkvb7; 01d38g; 05b1610; 01bgqh; 09qv3c; ... >> query: (?x13257, 0c4ys) <- award_winner(?x13257, ?x12622), profession(?x12622, ?x8368), location(?x12622, ?x938), profession(?x11077, ?x8368), type_of_union(?x12622, ?x566), ?x11077 = 0d__g, gender(?x12622, ?x231) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05kjlr category_of 05kjlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 50.000 0.444 http://example.org/award/award_category/category_of #12612-04f525m PRED entity: 04f525m PRED relation: state_province_region PRED expected values: 01n7q => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 48): 01n7q (0.71 #1609, 0.58 #2710, 0.57 #995), 015jr (0.51 #4531, 0.36 #11428, 0.35 #3795), 059rby (0.38 #7355, 0.34 #4780, 0.31 #9078), 04jpl (0.36 #11428, 0.16 #11057, 0.14 #1223), 036wy (0.36 #11428, 0.14 #1212, 0.04 #2927), 07ssc (0.23 #10808, 0.16 #11057, 0.14 #618), 07z1m (0.20 #265, 0.10 #7721, 0.05 #1612), 01llj3 (0.16 #11057), 02j9z (0.16 #11057), 09c7w0 (0.16 #11057) >> Best rule #1609 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 02vyh; >> query: (?x963, 01n7q) <- industry(?x963, ?x373), ?x373 = 02vxn, state_province_region(?x963, ?x1310) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04f525m state_province_region 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.714 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #12611-077qn PRED entity: 077qn PRED relation: country! PRED expected values: 01cgz 01lb14 03_8r 06z6r => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 41): 06z6r (0.86 #1211, 0.86 #2646, 0.85 #2441), 01lb14 (0.82 #832, 0.75 #996, 0.71 #299), 03_8r (0.80 #1206, 0.78 #1739, 0.73 #2436), 06wrt (0.79 #833, 0.70 #997, 0.67 #1202), 01cgz (0.75 #1200, 0.73 #1692, 0.72 #1118), 0w0d (0.72 #1117, 0.62 #994, 0.61 #1199), 0194d (0.70 #1019, 0.64 #855, 0.57 #1142), 0486tv (0.67 #1217, 0.57 #602, 0.53 #1135), 019tzd (0.64 #316, 0.61 #849, 0.60 #1013), 01gqfm (0.64 #857, 0.53 #365, 0.50 #1021) >> Best rule #1211 for best value: >> intensional similarity = 5 >> extensional distance = 49 >> proper extension: 05tr7; >> query: (?x4059, 06z6r) <- country(?x6150, ?x4059), country(?x3641, ?x4059), participating_countries(?x784, ?x4059), ?x3641 = 03fyrh, olympics(?x6150, ?x584) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5 EVAL 077qn country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.863 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 077qn country! 03_8r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.863 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 077qn country! 01lb14 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.863 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 077qn country! 01cgz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 186.000 186.000 0.863 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #12610-0c0nhgv PRED entity: 0c0nhgv PRED relation: film_release_region PRED expected values: 04gzd 01ls2 07ssc 059j2 02vzc 01p1v 03rj0 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 126): 07ssc (0.88 #568, 0.86 #707, 0.85 #846), 02vzc (0.88 #596, 0.84 #735, 0.78 #874), 059j2 (0.86 #997, 0.86 #858, 0.84 #719), 0chghy (0.82 #981, 0.81 #842, 0.77 #703), 04gzd (0.77 #284, 0.76 #562, 0.70 #701), 03ryn (0.77 #348, 0.63 #626, 0.58 #765), 01p1v (0.73 #319, 0.72 #736, 0.71 #597), 01pj7 (0.73 #315, 0.51 #593, 0.51 #732), 01ls2 (0.68 #287, 0.61 #565, 0.58 #704), 09pmkv (0.68 #298, 0.51 #576, 0.51 #715) >> Best rule #568 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 087wc7n; 03bx2lk; >> query: (?x1163, 07ssc) <- film_release_region(?x1163, ?x7413), film_release_region(?x1163, ?x172), ?x7413 = 04hqz, ?x172 = 0154j >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 7, 9, 11 EVAL 0c0nhgv film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 63.000 63.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c0nhgv film_release_region 01p1v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 63.000 63.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c0nhgv film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c0nhgv film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c0nhgv film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c0nhgv film_release_region 01ls2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 63.000 63.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c0nhgv film_release_region 04gzd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12609-0jfx1 PRED entity: 0jfx1 PRED relation: participant! PRED expected values: 01mwsnc => 122 concepts (99 used for prediction) PRED predicted values (max 10 best out of 401): 09wj5 (0.83 #4415, 0.81 #18918, 0.81 #22705), 04__f (0.83 #4415, 0.81 #18918, 0.81 #22705), 01bpnd (0.83 #4415, 0.81 #18918, 0.81 #22705), 0hwbd (0.83 #4415, 0.81 #18918, 0.81 #22705), 027jq2 (0.40 #6308, 0.38 #7569, 0.36 #5046), 01fx2g (0.40 #6308, 0.38 #7569, 0.36 #5046), 02z1yj (0.11 #1262, 0.09 #17656, 0.06 #27751), 086sj (0.07 #9461, 0.04 #18287), 01trhmt (0.07 #2067, 0.06 #2698, 0.04 #5221), 01rr9f (0.06 #5079, 0.06 #5710, 0.06 #6971) >> Best rule #4415 for best value: >> intensional similarity = 2 >> extensional distance = 89 >> proper extension: 04cr6qv; 026_dq6; >> query: (?x2444, ?x117) <- participant(?x2444, ?x117), participant(?x2444, ?x5665) >> conf = 0.83 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 0jfx1 participant! 01mwsnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 99.000 0.829 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #12608-024mpp PRED entity: 024mpp PRED relation: crewmember PRED expected values: 02xc1w4 => 125 concepts (76 used for prediction) PRED predicted values (max 10 best out of 40): 0284n42 (0.18 #1051, 0.15 #614, 0.13 #1403), 04ktcgn (0.18 #1057, 0.13 #620, 0.12 #53), 02xc1w4 (0.15 #23, 0.12 #66, 0.09 #284), 092ys_y (0.12 #235, 0.11 #192, 0.11 #627), 03r1pr (0.10 #318, 0.10 #361, 0.09 #406), 027y151 (0.10 #1435, 0.06 #2395, 0.06 #2481), 0b79gfg (0.10 #1062, 0.09 #625, 0.08 #2460), 051z6rz (0.10 #1072, 0.09 #635, 0.07 #1424), 095zvfg (0.10 #1036, 0.09 #1213, 0.08 #1302), 0cw67g (0.10 #387, 0.09 #301, 0.08 #1087) >> Best rule #1051 for best value: >> intensional similarity = 5 >> extensional distance = 89 >> proper extension: 0qm8b; 05r3qc; >> query: (?x3938, 0284n42) <- film_crew_role(?x3938, ?x137), currency(?x3938, ?x170), crewmember(?x3938, ?x929), genre(?x3938, ?x225), ?x225 = 02kdv5l >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #23 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0g5ptf; *> query: (?x3938, 02xc1w4) <- music(?x3938, ?x3410), prequel(?x10515, ?x3938), featured_film_locations(?x3938, ?x6226), geographic_distribution(?x7562, ?x6226) *> conf = 0.15 ranks of expected_values: 3 EVAL 024mpp crewmember 02xc1w4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 76.000 0.176 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #12607-0f1vrl PRED entity: 0f1vrl PRED relation: nationality PRED expected values: 09c7w0 03spz => 161 concepts (155 used for prediction) PRED predicted values (max 10 best out of 32): 09c7w0 (0.90 #903, 0.87 #6642, 0.85 #3920), 02k54 (0.88 #4827, 0.88 #10181, 0.88 #6743), 0f8l9c (0.40 #15052, 0.33 #22, 0.02 #10102), 07b_l (0.29 #13017, 0.28 #13833, 0.27 #14137), 0mr_8 (0.29 #13017, 0.28 #13833, 0.27 #14137), 0mq17 (0.29 #13017, 0.28 #13833, 0.27 #14137), 02jx1 (0.20 #433, 0.16 #533, 0.15 #333), 0f2s6 (0.15 #10080), 07ssc (0.13 #415, 0.11 #215, 0.11 #515), 0d060g (0.07 #11202, 0.07 #407, 0.06 #4935) >> Best rule #903 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 08qvhv; >> query: (?x1798, 09c7w0) <- profession(?x1798, ?x1041), place_of_birth(?x1798, ?x10321), type_of_union(?x1798, ?x566), program_creator(?x50, ?x1798), location(?x1798, ?x9713) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 25 EVAL 0f1vrl nationality 03spz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 161.000 155.000 0.903 http://example.org/people/person/nationality EVAL 0f1vrl nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 155.000 0.903 http://example.org/people/person/nationality #12606-0sxkh PRED entity: 0sxkh PRED relation: film_regional_debut_venue PRED expected values: 0prpt => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 16): 018cvf (0.13 #361, 0.10 #326, 0.06 #1120), 0prpt (0.09 #373, 0.07 #338, 0.04 #29), 015hr (0.07 #324, 0.06 #359, 0.04 #15), 0kfhjq0 (0.04 #360, 0.04 #325, 0.02 #671), 0j63cyr (0.04 #358, 0.02 #14, 0.02 #253), 07751 (0.03 #319, 0.03 #354, 0.02 #596), 0gg7gsl (0.03 #317, 0.02 #8, 0.02 #352), 07zmj (0.03 #341, 0.02 #411, 0.02 #376), 02_286 (0.02 #347, 0.01 #312, 0.01 #382), 04jpl (0.01 #310, 0.01 #70, 0.01 #104) >> Best rule #361 for best value: >> intensional similarity = 4 >> extensional distance = 265 >> proper extension: 0fq7dv_; 01fmys; 0407yj_; 0j43swk; 03mgx6z; 0gmgwnv; 0gwjw0c; 07ghq; 072hx4; >> query: (?x4315, 018cvf) <- language(?x4315, ?x254), film_release_region(?x4315, ?x142), country(?x4315, ?x583), ?x142 = 0jgd >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #373 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 265 *> proper extension: 0fq7dv_; 01fmys; 0407yj_; 0j43swk; 03mgx6z; 0gmgwnv; 0gwjw0c; 07ghq; 072hx4; *> query: (?x4315, 0prpt) <- language(?x4315, ?x254), film_release_region(?x4315, ?x142), country(?x4315, ?x583), ?x142 = 0jgd *> conf = 0.09 ranks of expected_values: 2 EVAL 0sxkh film_regional_debut_venue 0prpt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.127 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #12605-011j5x PRED entity: 011j5x PRED relation: artists PRED expected values: 01wn718 0l8g0 016vj5 => 68 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1058): 01kd57 (0.67 #6720, 0.60 #5682, 0.57 #7760), 06gd4 (0.67 #6559, 0.50 #11754, 0.50 #3444), 016t0h (0.60 #6169, 0.57 #8247, 0.50 #10325), 0qf3p (0.60 #5385, 0.57 #7463, 0.50 #9541), 016fmf (0.60 #5397, 0.57 #7475, 0.50 #9553), 01ydzx (0.60 #5779, 0.57 #7857, 0.40 #4740), 01vvycq (0.60 #5236, 0.57 #7314, 0.40 #4197), 0qf11 (0.60 #5565, 0.57 #7643, 0.40 #4526), 01kph_c (0.60 #4570, 0.57 #7687, 0.40 #5609), 01wbz9 (0.60 #5512, 0.57 #7590, 0.33 #321) >> Best rule #6720 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0fd3y; >> query: (?x2491, 01kd57) <- artists(?x2491, ?x4595), artists(?x2491, ?x248), ?x4595 = 023l9y, parent_genre(?x302, ?x2491), award_nominee(?x4343, ?x248), award(?x248, ?x247) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #10932 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 034487; *> query: (?x2491, 0l8g0) <- artists(?x2491, ?x9155), artists(?x2491, ?x5916), artists(?x2491, ?x4942), artists(?x2491, ?x4595), ?x4942 = 05xq9, role(?x4595, ?x212), award(?x9155, ?x2634), artist(?x2149, ?x5916) *> conf = 0.44 ranks of expected_values: 72, 88, 397 EVAL 011j5x artists 016vj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 68.000 40.000 0.667 http://example.org/music/genre/artists EVAL 011j5x artists 0l8g0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 68.000 40.000 0.667 http://example.org/music/genre/artists EVAL 011j5x artists 01wn718 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 68.000 40.000 0.667 http://example.org/music/genre/artists #12604-034r25 PRED entity: 034r25 PRED relation: film_crew_role PRED expected values: 09zzb8 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 33): 09zzb8 (0.81 #929, 0.80 #997, 0.79 #35), 01vx2h (0.50 #250, 0.50 #44, 0.45 #938), 02rh1dz (0.33 #145, 0.29 #180, 0.29 #43), 02ynfr (0.29 #48, 0.21 #391, 0.20 #185), 089fss (0.21 #40, 0.17 #108, 0.14 #74), 0d2b38 (0.21 #126, 0.20 #195, 0.18 #92), 0215hd (0.18 #188, 0.17 #257, 0.17 #119), 02_n3z (0.17 #104, 0.14 #70, 0.12 #755), 089g0h (0.16 #464, 0.14 #258, 0.14 #52), 01xy5l_ (0.15 #355, 0.14 #46, 0.14 #183) >> Best rule #929 for best value: >> intensional similarity = 4 >> extensional distance = 320 >> proper extension: 0c40vxk; 05sxzwc; 024l2y; 0bh8yn3; 01hqhm; 0kv238; 0htww; 02nczh; 0b6l1st; 01hv3t; ... >> query: (?x4452, 09zzb8) <- film(?x1634, ?x4452), film_crew_role(?x4452, ?x2095), currency(?x4452, ?x170), ?x2095 = 0dxtw >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034r25 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.814 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12603-01cj6y PRED entity: 01cj6y PRED relation: film PRED expected values: 011ysn 08s6mr 03wy8t => 85 concepts (47 used for prediction) PRED predicted values (max 10 best out of 523): 064lsn (0.47 #46378, 0.43 #78489, 0.41 #39241), 02q56mk (0.20 #416, 0.01 #3983, 0.01 #5766), 04n52p6 (0.10 #260, 0.05 #60646, 0.03 #41025), 016z9n (0.10 #368, 0.03 #3935, 0.02 #19983), 02fqrf (0.10 #565, 0.03 #4132), 02x2jl_ (0.10 #1748, 0.03 #41025, 0.02 #7098), 0dlngsd (0.10 #779, 0.03 #41025, 0.01 #4346), 011ywj (0.10 #1431, 0.02 #19263, 0.02 #15697), 0dnkmq (0.10 #1655, 0.02 #5222, 0.01 #7005), 0bc1yhb (0.10 #909, 0.02 #4476, 0.01 #11609) >> Best rule #46378 for best value: >> intensional similarity = 3 >> extensional distance = 1165 >> proper extension: 02rgz4; 02dh86; 02wb6yq; 0bxfmk; 02z6l5f; 0gv07g; 01m7f5r; 01507p; 03g62; 0bkq_8; ... >> query: (?x4337, ?x6121) <- nominated_for(?x4337, ?x6121), location(?x4337, ?x4253), nationality(?x4337, ?x94) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #41025 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1113 *> proper extension: 01my_c; 02yygk; *> query: (?x4337, ?x1077) <- award_nominee(?x4337, ?x4053), type_of_union(?x4337, ?x1873), film(?x4053, ?x1077) *> conf = 0.03 ranks of expected_values: 113, 164 EVAL 01cj6y film 03wy8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 47.000 0.471 http://example.org/film/actor/film./film/performance/film EVAL 01cj6y film 08s6mr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 85.000 47.000 0.471 http://example.org/film/actor/film./film/performance/film EVAL 01cj6y film 011ysn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 85.000 47.000 0.471 http://example.org/film/actor/film./film/performance/film #12602-07kcvl PRED entity: 07kcvl PRED relation: teams! PRED expected values: 0f2v0 => 52 concepts (39 used for prediction) PRED predicted values (max 10 best out of 60): 02cl1 (0.25 #832, 0.05 #3805, 0.04 #4076), 068p2 (0.14 #2558, 0.08 #3368, 0.06 #3638), 0f2tj (0.14 #2587, 0.08 #3397, 0.05 #3937), 0wh3 (0.14 #2467, 0.08 #3277, 0.05 #3817), 01_d4 (0.14 #2495, 0.06 #3575, 0.05 #3845), 0dc95 (0.08 #3323, 0.06 #3593, 0.05 #3863), 013yq (0.08 #3318, 0.05 #3858, 0.04 #4129), 0d6lp (0.08 #3340, 0.05 #3880, 0.04 #4151), 0ggh3 (0.08 #3413, 0.05 #3953, 0.04 #4224), 0rh7t (0.08 #3386, 0.05 #3926, 0.04 #4197) >> Best rule #832 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 0289q; >> query: (?x5603, 02cl1) <- position_s(?x5603, ?x10168), position_s(?x5603, ?x2573), position_s(?x5603, ?x2247), position_s(?x5603, ?x2147), position_s(?x5603, ?x1517), position_s(?x5603, ?x1240), ?x1517 = 02g_6j, ?x2147 = 04nfpk, ?x2247 = 01_9c1, ?x10168 = 0bgv4g, ?x1240 = 023wyl, position(?x684, ?x2573), team(?x2573, ?x387) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3618 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 026xxv_; *> query: (?x5603, 0f2v0) <- team(?x10780, ?x5603), film(?x10780, ?x8214), nationality(?x10780, ?x94), ?x94 = 09c7w0, people(?x2510, ?x10780), nominated_for(?x5235, ?x8214) *> conf = 0.06 ranks of expected_values: 16 EVAL 07kcvl teams! 0f2v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 52.000 39.000 0.250 http://example.org/sports/sports_team_location/teams #12601-0770cd PRED entity: 0770cd PRED relation: role PRED expected values: 0dwvl 05148p4 0l15bq => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 113): 05842k (0.50 #67, 0.38 #159, 0.24 #527), 07brj (0.50 #22, 0.38 #114, 0.05 #2514), 02sgy (0.48 #464, 0.27 #2496, 0.25 #96), 042v_gx (0.48 #466, 0.24 #2498, 0.22 #1110), 01vj9c (0.33 #12, 0.31 #472, 0.25 #104), 026t6 (0.33 #2, 0.25 #94, 0.24 #462), 0cfdd (0.33 #81, 0.25 #173, 0.10 #357), 0l14md (0.33 #5, 0.25 #97, 0.07 #465), 0mkg (0.33 #9, 0.25 #101, 0.04 #2955), 0gkd1 (0.30 #361, 0.04 #2577, 0.04 #1558) >> Best rule #67 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01l4g5; >> query: (?x1818, 05842k) <- role(?x1818, ?x894), role(?x1818, ?x316), ?x894 = 03m5k, ?x316 = 05r5c >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #203 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 01f2q5; *> query: (?x1818, 05148p4) <- artists(?x671, ?x1818), award_nominee(?x1818, ?x3175), ?x3175 = 01w7nwm *> conf = 0.25 ranks of expected_values: 11, 17, 47 EVAL 0770cd role 0l15bq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 103.000 103.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0770cd role 05148p4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 103.000 103.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0770cd role 0dwvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 103.000 103.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #12600-06r2h PRED entity: 06r2h PRED relation: story_by PRED expected values: 03ft8 => 79 concepts (69 used for prediction) PRED predicted values (max 10 best out of 50): 0n8bn (0.19 #429, 0.03 #428, 0.03 #859), 079vf (0.07 #431, 0.03 #1501, 0.03 #647), 0fx02 (0.05 #705, 0.05 #1559, 0.02 #5836), 0jbp0 (0.05 #645, 0.03 #428, 0.03 #859), 081k8 (0.04 #1585, 0.01 #4150, 0.01 #4577), 042xh (0.03 #857, 0.02 #1071), 0m32_ (0.03 #428, 0.03 #859, 0.02 #1713), 046_v (0.03 #599, 0.02 #1669, 0.02 #2097), 079ws (0.03 #557, 0.02 #773, 0.01 #1627), 041h0 (0.03 #434, 0.02 #864) >> Best rule #429 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 03d17dg; >> query: (?x9017, ?x6968) <- nominated_for(?x6968, ?x9017), category(?x6968, ?x134), story_by(?x66, ?x6968) >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06r2h story_by 03ft8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 69.000 0.188 http://example.org/film/film/story_by #12599-01m65sp PRED entity: 01m65sp PRED relation: student! PRED expected values: 01w5m => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 150): 01rtm4 (0.20 #1058, 0.05 #3693, 0.04 #5801), 01hb1t (0.20 #1145, 0.02 #8523, 0.01 #11685), 0212zp (0.20 #1226), 01qd_r (0.18 #2389), 0bwfn (0.12 #1856, 0.09 #15558, 0.09 #11869), 02183k (0.12 #1685, 0.08 #5901, 0.05 #3266), 04b_46 (0.12 #1808, 0.03 #11821, 0.02 #19726), 04gd8j (0.12 #1949, 0.03 #10381, 0.02 #17232), 0m4yg (0.12 #1946, 0.02 #17229, 0.01 #18810), 015nl4 (0.10 #3756, 0.07 #6918, 0.03 #38011) >> Best rule #1058 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01hc9_; >> query: (?x3206, 01rtm4) <- place_of_birth(?x3206, ?x739), gender(?x3206, ?x231), people(?x10798, ?x3206), ?x10798 = 019kn7 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #22766 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 321 *> proper extension: 019fnv; *> query: (?x3206, 01w5m) <- location(?x3206, ?x739), ?x739 = 02_286, gender(?x3206, ?x231) *> conf = 0.07 ranks of expected_values: 19 EVAL 01m65sp student! 01w5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 120.000 120.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #12598-06lbp PRED entity: 06lbp PRED relation: influenced_by! PRED expected values: 02kz_ => 146 concepts (54 used for prediction) PRED predicted values (max 10 best out of 379): 02kz_ (0.50 #221, 0.25 #731, 0.18 #1749), 0d4jl (0.50 #116, 0.25 #626, 0.14 #7139), 07g2b (0.50 #17, 0.25 #527, 0.13 #5606), 05qzv (0.45 #1928, 0.13 #5606, 0.13 #7140), 073v6 (0.36 #1645, 0.25 #627, 0.25 #117), 0683n (0.36 #1863, 0.25 #845, 0.13 #5606), 013pp3 (0.36 #1748, 0.25 #220, 0.12 #8888), 0zm1 (0.36 #1693, 0.12 #2038, 0.08 #10707), 014dq7 (0.36 #1592, 0.07 #27049, 0.07 #27050), 080r3 (0.36 #1741, 0.04 #7353, 0.04 #6329) >> Best rule #221 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 040_9; >> query: (?x6400, 02kz_) <- profession(?x6400, ?x353), influenced_by(?x11271, ?x6400), influenced_by(?x2161, ?x6400), influenced_by(?x6400, ?x4072), ?x11271 = 0hcvy, ?x2161 = 040db >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06lbp influenced_by! 02kz_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 54.000 0.500 http://example.org/influence/influence_node/influenced_by #12597-0bz6sq PRED entity: 0bz6sq PRED relation: nominated_for! PRED expected values: 04ljl_l => 78 concepts (68 used for prediction) PRED predicted values (max 10 best out of 187): 05f4m9q (0.55 #718, 0.12 #7060, 0.10 #1425), 04ljl_l (0.41 #709, 0.12 #7060, 0.09 #10353), 07cbcy (0.36 #768, 0.12 #7060, 0.10 #1710), 0gq_v (0.33 #1198, 0.21 #5669, 0.19 #6609), 0gqwc (0.33 #59, 0.15 #5708, 0.15 #1237), 05b4l5x (0.32 #712, 0.19 #947, 0.13 #1419), 019f4v (0.30 #1230, 0.24 #5701, 0.21 #6406), 0gq9h (0.29 #5710, 0.26 #6415, 0.26 #6650), 0gs9p (0.26 #5712, 0.23 #6417, 0.23 #6652), 0gs96 (0.22 #1267, 0.14 #5738, 0.13 #1502) >> Best rule #718 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0d6b7; >> query: (?x9016, 05f4m9q) <- nominated_for(?x9406, ?x9016), nominated_for(?x4317, ?x9016), ?x4317 = 05q8pss >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #709 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 0d6b7; *> query: (?x9016, 04ljl_l) <- nominated_for(?x9406, ?x9016), nominated_for(?x4317, ?x9016), ?x4317 = 05q8pss *> conf = 0.41 ranks of expected_values: 2 EVAL 0bz6sq nominated_for! 04ljl_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 68.000 0.545 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12596-0j582 PRED entity: 0j582 PRED relation: people! PRED expected values: 09kr66 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 50): 02w7gg (0.33 #2, 0.17 #310, 0.09 #695), 07bch9 (0.29 #408, 0.11 #793, 0.11 #639), 0xnvg (0.25 #90, 0.22 #475, 0.12 #706), 041rx (0.25 #312, 0.15 #1082, 0.15 #1775), 063k3h (0.18 #416, 0.06 #801, 0.05 #647), 03bkbh (0.17 #340, 0.07 #725, 0.06 #879), 033tf_ (0.16 #700, 0.16 #469, 0.13 #931), 048z7l (0.12 #502, 0.06 #964, 0.06 #425), 0d7wh (0.12 #402, 0.08 #325, 0.03 #479), 09kr66 (0.12 #428, 0.03 #890, 0.03 #967) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01vh3r; >> query: (?x1548, 02w7gg) <- languages(?x1548, ?x5607), profession(?x1548, ?x1032), executive_produced_by(?x1547, ?x1548), ?x5607 = 064_8sq >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #428 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 015_30; 03h_fk5; 02jq1; 01dkpb; 0pqzh; *> query: (?x1548, 09kr66) <- languages(?x1548, ?x732), award_winner(?x3846, ?x1548), celebrities_impersonated(?x3649, ?x1548), ?x3649 = 03m6t5 *> conf = 0.12 ranks of expected_values: 10 EVAL 0j582 people! 09kr66 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 120.000 120.000 0.333 http://example.org/people/ethnicity/people #12595-04gbl3 PRED entity: 04gbl3 PRED relation: industry PRED expected values: 01mw1 => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 04gbl3 industry 01mw1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/business/business_operation/industry #12594-01g4zr PRED entity: 01g4zr PRED relation: profession PRED expected values: 0196pc => 94 concepts (42 used for prediction) PRED predicted values (max 10 best out of 69): 03gjzk (0.45 #876, 0.45 #444, 0.44 #12), 02krf9 (0.44 #23, 0.36 #455, 0.33 #887), 0196pc (0.40 #357, 0.33 #645, 0.30 #213), 0cbd2 (0.28 #1302, 0.27 #438, 0.25 #1590), 01c72t (0.22 #20, 0.18 #452, 0.11 #2612), 018gz8 (0.21 #1598, 0.18 #5632, 0.16 #5776), 0kyk (0.20 #1321, 0.19 #1609, 0.16 #3193), 09jwl (0.18 #4625, 0.15 #5778, 0.15 #4193), 0nbcg (0.12 #4204, 0.11 #27, 0.10 #4636), 025352 (0.11 #55, 0.09 #487, 0.04 #1207) >> Best rule #876 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 04gcd1; 05jcn8; 07rd7; 01s7qqw; 0534v; 04jspq; 05vtbl; 04vlh5; 060pl5; 03yf4d; ... >> query: (?x1109, 03gjzk) <- profession(?x1109, ?x1966), profession(?x1109, ?x1032), ?x1966 = 015h31, profession(?x10212, ?x1032), ?x10212 = 02pzck >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #357 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01c59k; 01c1px; 03k1vm; 0488g9; 0jnb0; 05h7tk; *> query: (?x1109, 0196pc) <- profession(?x1109, ?x1966), ?x1966 = 015h31, place_of_death(?x1109, ?x11930), people(?x5855, ?x1109) *> conf = 0.40 ranks of expected_values: 3 EVAL 01g4zr profession 0196pc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 42.000 0.455 http://example.org/people/person/profession #12593-0564x PRED entity: 0564x PRED relation: film_release_region PRED expected values: 0b90_r => 114 concepts (99 used for prediction) PRED predicted values (max 10 best out of 197): 0k6nt (0.92 #6560, 0.91 #6207, 0.85 #6912), 03_3d (0.92 #8644, 0.83 #7937, 0.78 #7769), 06mkj (0.91 #6247, 0.91 #7480, 0.89 #8540), 0f8l9c (0.89 #7789, 0.88 #8496, 0.87 #6203), 03gj2 (0.88 #6561, 0.87 #6208, 0.85 #6913), 0d0vqn (0.87 #6185, 0.85 #7418, 0.85 #6890), 05r4w (0.85 #8469, 0.84 #6529, 0.83 #6176), 03rjj (0.85 #8474, 0.79 #7414, 0.76 #7767), 02vzc (0.85 #6946, 0.83 #6241, 0.79 #7474), 01znc_ (0.84 #6582, 0.83 #6229, 0.81 #6934) >> Best rule #6560 for best value: >> intensional similarity = 8 >> extensional distance = 23 >> proper extension: 0hgnl3t; >> query: (?x10826, 0k6nt) <- music(?x10826, ?x535), film_release_region(?x10826, ?x456), film_release_region(?x10826, ?x94), ?x456 = 05qhw, film(?x5287, ?x10826), ?x94 = 09c7w0, film(?x2156, ?x10826), production_companies(?x10826, ?x3920) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #8472 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 79 *> proper extension: 0gtsx8c; *> query: (?x10826, 0b90_r) <- film_release_region(?x10826, ?x512), film_release_region(?x10826, ?x456), film_release_region(?x10826, ?x94), ?x456 = 05qhw, country(?x10826, ?x252), language(?x10826, ?x2164), production_companies(?x10826, ?x3920), ?x512 = 07ssc, ?x94 = 09c7w0 *> conf = 0.73 ranks of expected_values: 20 EVAL 0564x film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 114.000 99.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12592-01wvxw1 PRED entity: 01wvxw1 PRED relation: artists! PRED expected values: 05jg58 => 201 concepts (116 used for prediction) PRED predicted values (max 10 best out of 233): 03_d0 (0.84 #17546, 0.33 #10, 0.31 #9385), 064t9 (0.56 #15735, 0.50 #6665, 0.48 #24207), 06j6l (0.54 #650, 0.50 #348, 0.43 #9420), 05w3f (0.50 #339, 0.33 #36, 0.18 #5178), 01lyv (0.46 #940, 0.32 #24831, 0.26 #19985), 0glt670 (0.45 #7297, 0.39 #9413, 0.36 #3063), 025sc50 (0.38 #652, 0.30 #7306, 0.30 #15770), 05bt6j (0.38 #949, 0.28 #4880, 0.27 #19994), 0mhfr (0.33 #22, 0.31 #930, 0.18 #7583), 02x8m (0.33 #320, 0.23 #622, 0.21 #17553) >> Best rule #17546 for best value: >> intensional similarity = 5 >> extensional distance = 163 >> proper extension: 067mj; 03t9sp; 017j6; 05563d; 02lbrd; 01rm8b; 0163m1; 016890; 011z3g; 014pg1; ... >> query: (?x8143, 03_d0) <- artists(?x283, ?x8143), artists(?x283, ?x6124), artists(?x283, ?x1613), ?x1613 = 01sbf2, ?x6124 = 0277c3 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2836 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0p_47; 015076; *> query: (?x8143, 05jg58) <- profession(?x8143, ?x220), participant(?x219, ?x8143), role(?x8143, ?x227), participant(?x8143, ?x5521) *> conf = 0.08 ranks of expected_values: 57 EVAL 01wvxw1 artists! 05jg58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 201.000 116.000 0.836 http://example.org/music/genre/artists #12591-034x61 PRED entity: 034x61 PRED relation: gender PRED expected values: 05zppz => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.76 #7, 0.72 #200, 0.71 #184), 02zsn (0.55 #10, 0.51 #147, 0.51 #16) >> Best rule #7 for best value: >> intensional similarity = 2 >> extensional distance = 27 >> proper extension: 04hpck; 06sn8m; 0301bq; >> query: (?x848, 05zppz) <- award(?x848, ?x8250), ?x8250 = 0cqhb3 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034x61 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.759 http://example.org/people/person/gender #12590-0k2m6 PRED entity: 0k2m6 PRED relation: honored_for! PRED expected values: 09306z => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 112): 0bvhz9 (0.20 #114, 0.08 #358, 0.07 #602), 0275n3y (0.20 #64, 0.08 #308, 0.05 #552), 03gwpw2 (0.20 #5, 0.05 #493, 0.05 #2445), 02wzl1d (0.20 #7, 0.03 #1959, 0.03 #1471), 02pgky2 (0.20 #76, 0.03 #2272, 0.03 #1540), 073hd1 (0.20 #85, 0.03 #1671, 0.02 #939), 0bvfqq (0.14 #148, 0.06 #392, 0.02 #514), 050yyb (0.08 #275, 0.06 #397, 0.04 #2227), 09p2r9 (0.08 #323, 0.06 #445, 0.03 #2397), 09bymc (0.08 #349, 0.06 #471, 0.02 #593) >> Best rule #114 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 05y0cr; >> query: (?x7978, 0bvhz9) <- award(?x7978, ?x143), titles(?x2164, ?x7978), ?x143 = 02r0csl, language(?x10590, ?x2164), film(?x4800, ?x10590) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3022 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 140 *> proper extension: 0fgpvf; 01hp5; 04n52p6; 01_1pv; 05dy7p; 0k5g9; 0k4f3; 07w8fz; 084302; 01wb95; ... *> query: (?x7978, 09306z) <- genre(?x7978, ?x53), titles(?x252, ?x7978), film_release_distribution_medium(?x7978, ?x81), nominated_for(?x484, ?x7978), ?x484 = 0gq_v *> conf = 0.01 ranks of expected_values: 91 EVAL 0k2m6 honored_for! 09306z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 127.000 127.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12589-0gwjw0c PRED entity: 0gwjw0c PRED relation: nominated_for! PRED expected values: 02qyntr => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 181): 02qyp19 (0.68 #2903, 0.66 #2902, 0.66 #3575), 0gs9p (0.26 #2509, 0.25 #3182, 0.25 #2733), 0f4x7 (0.22 #22, 0.19 #10500, 0.19 #9160), 0k611 (0.22 #2517, 0.21 #3190, 0.21 #2966), 0gq_v (0.22 #2470, 0.21 #3143, 0.19 #2919), 040njc (0.20 #2460, 0.20 #3133, 0.19 #2684), 04kxsb (0.19 #85, 0.19 #10500, 0.19 #9160), 02qyntr (0.19 #165, 0.17 #2619, 0.16 #2843), 0gs96 (0.19 #79, 0.14 #2533, 0.14 #3206), 0gr4k (0.19 #2477, 0.19 #3150, 0.18 #2926) >> Best rule #2903 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 06mmr; >> query: (?x6886, ?x3066) <- award(?x6886, ?x3066), award_winner(?x6886, ?x3101), award(?x92, ?x3066) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 03n0cd; *> query: (?x6886, 02qyntr) <- executive_produced_by(?x6886, ?x4060), film(?x828, ?x6886), film_production_design_by(?x6886, ?x9062) *> conf = 0.19 ranks of expected_values: 8 EVAL 0gwjw0c nominated_for! 02qyntr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 59.000 59.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12588-02q_ncg PRED entity: 02q_ncg PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #134, 0.83 #169, 0.83 #174), 07c52 (0.22 #3, 0.06 #8, 0.03 #55), 07z4p (0.11 #5, 0.05 #10, 0.03 #57), 02nxhr (0.11 #2, 0.04 #27, 0.03 #54) >> Best rule #134 for best value: >> intensional similarity = 4 >> extensional distance = 519 >> proper extension: 02_1sj; 02z3r8t; 03ckwzc; 02sg5v; 0jjy0; 07sc6nw; 07g_0c; 02847m9; 028cg00; 03sxd2; ... >> query: (?x11355, 029j_) <- genre(?x11355, ?x53), country(?x11355, ?x94), featured_film_locations(?x11355, ?x151), currency(?x11355, ?x170) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q_ncg film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.839 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12587-01jrbb PRED entity: 01jrbb PRED relation: film_release_region PRED expected values: 0154j 047lj 059j2 01p1v 06mkj => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 111): 06mkj (0.90 #579, 0.84 #713, 0.83 #1520), 059j2 (0.83 #2708, 0.80 #692, 0.79 #558), 0chghy (0.81 #2694, 0.78 #1485, 0.77 #1074), 0154j (0.79 #539, 0.75 #673, 0.73 #2689), 01ls2 (0.64 #10, 0.43 #2696, 0.42 #546), 03rk0 (0.56 #578, 0.48 #2728, 0.36 #42), 01p1v (0.55 #38, 0.48 #2724, 0.44 #574), 07t21 (0.55 #28, 0.29 #296, 0.29 #162), 047lj (0.45 #9, 0.39 #2695, 0.37 #545), 07ylj (0.45 #20, 0.38 #556, 0.29 #2284) >> Best rule #579 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 0ds35l9; 02vxq9m; 011yrp; 0ds3t5x; 05p1tzf; 01vksx; 017gl1; 0c0nhgv; 04hwbq; 0dgst_d; ... >> query: (?x2893, 06mkj) <- genre(?x2893, ?x258), film_release_region(?x2893, ?x1603), honored_for(?x5902, ?x2893), ?x1603 = 06bnz >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 7, 9 EVAL 01jrbb film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01jrbb film_release_region 01p1v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 74.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01jrbb film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01jrbb film_release_region 047lj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 74.000 74.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01jrbb film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12586-0cvkv5 PRED entity: 0cvkv5 PRED relation: genre PRED expected values: 05p553 => 89 concepts (52 used for prediction) PRED predicted values (max 10 best out of 102): 05p553 (0.86 #709, 0.84 #1181, 0.83 #591), 01jfsb (0.63 #1425, 0.52 #1779, 0.34 #2015), 06nm1 (0.59 #587, 0.57 #235, 0.52 #4714), 06mkj (0.59 #587, 0.57 #235, 0.52 #4714), 02l7c8 (0.53 #367, 0.46 #1075, 0.44 #720), 02kdv5l (0.34 #2006, 0.30 #2242, 0.30 #1770), 0219x_ (0.33 #25, 0.30 #612, 0.28 #494), 060__y (0.31 #368, 0.23 #133, 0.21 #1076), 04xvlr (0.31 #118, 0.27 #353, 0.26 #470), 06cvj (0.27 #708, 0.24 #1180, 0.20 #590) >> Best rule #709 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 0963mq; 0gbfn9; >> query: (?x8496, 05p553) <- titles(?x2152, ?x8496), genre(?x8496, ?x6674), ?x6674 = 01t_vv, film_crew_role(?x8496, ?x137) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cvkv5 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 52.000 0.857 http://example.org/film/film/genre #12585-06tpmy PRED entity: 06tpmy PRED relation: film_release_region PRED expected values: 03rjj 02k54 03rj0 03h64 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 111): 03h64 (0.85 #216, 0.81 #976, 0.80 #672), 05r4w (0.84 #610, 0.83 #762, 0.83 #914), 03rjj (0.83 #157, 0.82 #1373, 0.82 #917), 0jgd (0.82 #612, 0.81 #916, 0.80 #764), 0k6nt (0.78 #635, 0.78 #1395, 0.77 #939), 05qhw (0.77 #929, 0.77 #625, 0.77 #777), 03_3d (0.76 #615, 0.75 #919, 0.74 #159), 0d060g (0.76 #768, 0.75 #920, 0.74 #616), 03rt9 (0.66 #624, 0.65 #928, 0.64 #1384), 03rj0 (0.58 #970, 0.58 #210, 0.57 #666) >> Best rule #216 for best value: >> intensional similarity = 5 >> extensional distance = 130 >> proper extension: 014lc_; 0crfwmx; 0407yfx; 0k5g9; 0407yj_; 03mgx6z; 0gmgwnv; 0gwjw0c; 01cm8w; 0cp08zg; ... >> query: (?x4514, 03h64) <- film_release_region(?x4514, ?x583), film_release_region(?x4514, ?x512), ?x583 = 015fr, ?x512 = 07ssc, production_companies(?x4514, ?x382) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 10, 27 EVAL 06tpmy film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.848 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06tpmy film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 65.000 65.000 0.848 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06tpmy film_release_region 02k54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 65.000 65.000 0.848 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06tpmy film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 65.000 0.848 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12584-087vz PRED entity: 087vz PRED relation: taxonomy PRED expected values: 04n6k => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.84 #23, 0.76 #32, 0.76 #79) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 04w58; >> query: (?x3728, 04n6k) <- olympics(?x3728, ?x584), participating_countries(?x1608, ?x3728), ?x1608 = 09x3r >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087vz taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.844 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #12583-053xw6 PRED entity: 053xw6 PRED relation: student! PRED expected values: 0373qt => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 62): 02l9wl (0.33 #251, 0.02 #12353, 0.02 #16035), 01f6ss (0.25 #1042), 017z88 (0.15 #1133, 0.12 #1659, 0.05 #5870), 0bwfn (0.09 #5010, 0.08 #6063, 0.08 #1326), 09f2j (0.08 #1210, 0.05 #1736, 0.04 #6999), 08815 (0.08 #1054, 0.04 #6843, 0.04 #4738), 02htv6 (0.08 #1515, 0.03 #2041), 07w0v (0.08 #1072, 0.02 #4756, 0.01 #6861), 02kj7g (0.08 #1567), 0gl6f (0.08 #1340) >> Best rule #251 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02cbs0; >> query: (?x7147, 02l9wl) <- film(?x7147, ?x8030), film(?x7147, ?x6605), ?x6605 = 012kyx, nominated_for(?x375, ?x8030) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 053xw6 student! 0373qt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 96.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #12582-099jhq PRED entity: 099jhq PRED relation: award_winner PRED expected values: 017149 => 51 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1510): 09fb5 (0.44 #7459, 0.38 #51795, 0.35 #49329), 0170pk (0.44 #7749, 0.38 #351, 0.35 #49329), 0170s4 (0.38 #51795, 0.35 #49329, 0.35 #36995), 0151w_ (0.38 #51795, 0.35 #49329, 0.35 #36995), 01qscs (0.38 #55, 0.35 #49329, 0.35 #36995), 01kwsg (0.38 #1062, 0.35 #49329, 0.35 #36995), 0237fw (0.38 #501, 0.35 #49329, 0.35 #36995), 055c8 (0.38 #684, 0.34 #14794, 0.34 #24662), 0bj9k (0.38 #7809, 0.33 #5343, 0.11 #2877), 015c4g (0.38 #8381, 0.25 #983, 0.22 #3449) >> Best rule #7459 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 04ljl_l; 027dtxw; 0f4x7; 0cqh46; 02x73k6; 02x4w6g; 0bfvd4; 04kxsb; 09qv_s; 0gqy2; ... >> query: (?x451, 09fb5) <- award(?x394, ?x451), award(?x4969, ?x451), ?x4969 = 016k6x, nominated_for(?x451, ?x186) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #88 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 09sb52; 05ztrmj; *> query: (?x451, 017149) <- award(?x394, ?x451), award(?x4835, ?x451), award(?x1846, ?x451), ?x4835 = 01wy5m, award_nominee(?x1846, ?x230) *> conf = 0.25 ranks of expected_values: 62 EVAL 099jhq award_winner 017149 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 51.000 21.000 0.438 http://example.org/award/award_category/winners./award/award_honor/award_winner #12581-09p0ct PRED entity: 09p0ct PRED relation: nominated_for! PRED expected values: 040njc 03hkv_r 099c8n => 89 concepts (45 used for prediction) PRED predicted values (max 10 best out of 215): 02x258x (0.68 #3847, 0.68 #9974, 0.67 #9746), 09td7p (0.68 #3847, 0.68 #9974, 0.67 #9746), 0gq9h (0.68 #1639, 0.46 #2997, 0.42 #734), 0gs9p (0.66 #1641, 0.41 #2999, 0.37 #2773), 04dn09n (0.51 #1617, 0.30 #2975, 0.28 #2523), 040njc (0.48 #1590, 0.38 #2948, 0.35 #233), 0gq_v (0.42 #1603, 0.32 #1829, 0.31 #698), 0gqy2 (0.40 #1695, 0.27 #3279, 0.27 #3053), 0f4x7 (0.39 #1609, 0.27 #2967, 0.26 #3193), 0gr0m (0.37 #1636, 0.26 #2994, 0.26 #1862) >> Best rule #3847 for best value: >> intensional similarity = 4 >> extensional distance = 454 >> proper extension: 0kfpm; 0358x_; 0ddd0gc; 02hct1; 01b64v; 0phrl; 01j7mr; 0gj50; 01b65l; 030cx; ... >> query: (?x1415, ?x941) <- award(?x1415, ?x941), award_winner(?x1415, ?x548), nominated_for(?x601, ?x1415), honored_for(?x1112, ?x1415) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #1590 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 229 *> proper extension: 01f69m; *> query: (?x1415, 040njc) <- genre(?x1415, ?x53), nominated_for(?x2577, ?x1415), nominated_for(?x1107, ?x1415), ?x1107 = 019f4v, award_winner(?x2577, ?x396) *> conf = 0.48 ranks of expected_values: 6, 16, 18 EVAL 09p0ct nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 89.000 45.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09p0ct nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 89.000 45.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09p0ct nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 89.000 45.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12580-02q7fl9 PRED entity: 02q7fl9 PRED relation: country PRED expected values: 0345h => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 97): 09c7w0 (0.87 #2295, 0.85 #4346, 0.83 #1646), 0345h (0.13 #2020, 0.12 #965, 0.11 #4019), 03mqtr (0.12 #295, 0.09 #1466, 0.09 #2173), 0d060g (0.10 #244, 0.06 #304, 0.05 #2003), 03rk0 (0.09 #37, 0.03 #803, 0.03 #393), 0chghy (0.06 #1010, 0.05 #894, 0.04 #129), 03rjj (0.04 #124, 0.04 #655, 0.04 #714), 06mkj (0.04 #155, 0.03 #334, 0.02 #2033), 02jx1 (0.04 #143, 0.03 #322, 0.02 #498), 0ctw_b (0.04 #198, 0.03 #257, 0.03 #1019) >> Best rule #2295 for best value: >> intensional similarity = 3 >> extensional distance = 744 >> proper extension: 0hgnl3t; >> query: (?x5976, 09c7w0) <- nominated_for(?x488, ?x5976), music(?x5976, ?x9128), country(?x5976, ?x252) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #2020 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 661 *> proper extension: 0170z3; 0b76d_m; 014_x2; 0ds35l9; 0d90m; 03qcfvw; 0m313; 034qmv; 01br2w; 028_yv; ... *> query: (?x5976, 0345h) <- titles(?x3506, ?x5976), film_release_region(?x5976, ?x94), country(?x5976, ?x252), film_crew_role(?x5976, ?x137) *> conf = 0.13 ranks of expected_values: 2 EVAL 02q7fl9 country 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.873 http://example.org/film/film/country #12579-01g_bs PRED entity: 01g_bs PRED relation: parent_genre! PRED expected values: 01z9l_ => 80 concepts (29 used for prediction) PRED predicted values (max 10 best out of 274): 0193f (0.50 #1439, 0.33 #903, 0.25 #3851), 0283d (0.40 #623, 0.33 #1425, 0.33 #1159), 07lnk (0.40 #563, 0.33 #1099, 0.29 #1634), 01y3_q (0.38 #2498, 0.29 #1693, 0.27 #3566), 01ym9b (0.33 #1113, 0.29 #1648, 0.25 #2453), 0mmp3 (0.33 #1156, 0.29 #1691, 0.25 #2496), 0g_bh (0.33 #912, 0.25 #1984, 0.25 #378), 06cp5 (0.33 #3289, 0.20 #613, 0.17 #6788), 03xnwz (0.25 #1903, 0.25 #297, 0.22 #3241), 0bt7w (0.25 #359, 0.25 #90, 0.22 #3303) >> Best rule #1439 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 0193f; 0hh2s; >> query: (?x14374, 0193f) <- artists(?x14374, ?x8636), artists(?x14374, ?x8199), artists(?x14374, ?x1674), place_of_birth(?x8636, ?x10314), artists(?x14355, ?x8636), artists(?x497, ?x8636), ?x1674 = 01v_pj6, ?x497 = 0fd3y, ?x8199 = 016lmg, ?x14355 = 01hydr >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #763 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 0fd3y; 07gxw; 0m0fw; *> query: (?x14374, 01z9l_) <- artists(?x14374, ?x8636), artists(?x14374, ?x1674), ?x8636 = 0k60, parent_genre(?x12831, ?x14374), role(?x1674, ?x227), parent_genre(?x14374, ?x7267), profession(?x1674, ?x131), nationality(?x1674, ?x512), award(?x1674, ?x528) *> conf = 0.20 ranks of expected_values: 31 EVAL 01g_bs parent_genre! 01z9l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 80.000 29.000 0.500 http://example.org/music/genre/parent_genre #12578-03xf_m PRED entity: 03xf_m PRED relation: nominated_for! PRED expected values: 099c8n => 89 concepts (80 used for prediction) PRED predicted values (max 10 best out of 213): 0gr51 (0.81 #71, 0.73 #298, 0.33 #753), 099c8n (0.78 #734, 0.69 #52, 0.68 #279), 03hl6lc (0.69 #120, 0.68 #347, 0.22 #802), 0p9sw (0.68 #12728, 0.68 #12957, 0.67 #7952), 02w9sd7 (0.68 #12728, 0.68 #12957, 0.67 #7952), 027b9j5 (0.68 #12728, 0.68 #12957, 0.67 #7952), 027c95y (0.68 #12728, 0.68 #12957, 0.67 #7952), 02qyntr (0.62 #169, 0.59 #396, 0.47 #851), 019f4v (0.61 #2547, 0.60 #731, 0.54 #5273), 040njc (0.59 #233, 0.56 #6, 0.56 #688) >> Best rule #71 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 02r1c18; 07w8fz; >> query: (?x6281, 0gr51) <- nominated_for(?x1307, ?x6281), nominated_for(?x899, ?x6281), nominated_for(?x68, ?x6281), ?x1307 = 0gq9h, ?x899 = 02x1dht, ?x68 = 02qyp19 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #734 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 083shs; 011yrp; 0ds3t5x; 0b6tzs; 017gl1; 092vkg; 09q5w2; 04vr_f; 017gm7; 011yqc; ... *> query: (?x6281, 099c8n) <- nominated_for(?x1307, ?x6281), nominated_for(?x704, ?x6281), ?x1307 = 0gq9h, ?x704 = 09sb52 *> conf = 0.78 ranks of expected_values: 2 EVAL 03xf_m nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 80.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12577-049d1 PRED entity: 049d1 PRED relation: month PRED expected values: 05cw8 => 222 concepts (222 used for prediction) PRED predicted values (max 10 best out of 1): 05cw8 (0.90 #53, 0.90 #50, 0.90 #14) >> Best rule #53 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 03hrz; >> query: (?x3106, 05cw8) <- month(?x3106, ?x7298), month(?x3106, ?x2140), ?x7298 = 04wzr, contains(?x1122, ?x3106), ?x2140 = 040fb >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049d1 month 05cw8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 222.000 222.000 0.902 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #12576-01vsy7t PRED entity: 01vsy7t PRED relation: role PRED expected values: 02pprs 05r5c 01xqw => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 116): 05r5c (0.52 #1641, 0.41 #6160, 0.39 #7025), 03bx0bm (0.38 #3269, 0.04 #7693, 0.04 #7694), 03gvt (0.32 #1827, 0.25 #72, 0.23 #1538), 06ncr (0.32 #1827, 0.23 #1538, 0.22 #7596), 0gkd1 (0.29 #282, 0.20 #570, 0.10 #666), 0l14j_ (0.29 #254, 0.20 #542, 0.10 #638), 01vj9c (0.27 #3186, 0.21 #1648, 0.16 #7032), 02sgy (0.27 #1158, 0.25 #6158, 0.25 #5), 026t6 (0.25 #1637, 0.25 #3, 0.24 #3175), 018vs (0.25 #12, 0.25 #3184, 0.21 #1646) >> Best rule #1641 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0dm5l; 06br6t; >> query: (?x4620, 05r5c) <- role(?x4620, ?x1166), ?x1166 = 05148p4, artists(?x671, ?x4620) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1, 49, 67 EVAL 01vsy7t role 01xqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 151.000 151.000 0.519 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vsy7t role 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.519 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vsy7t role 02pprs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 151.000 151.000 0.519 http://example.org/music/artist/track_contributions./music/track_contribution/role #12575-0yls9 PRED entity: 0yls9 PRED relation: institution! PRED expected values: 0bkj86 => 198 concepts (127 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.76 #427, 0.72 #794, 0.70 #1125), 02_xgp2 (0.67 #70, 0.66 #455, 0.63 #334), 03bwzr4 (0.67 #72, 0.54 #477, 0.54 #457), 0bkj86 (0.64 #552, 0.63 #167, 0.57 #452), 013zdg (0.56 #66, 0.44 #612, 0.29 #430), 04zx3q1 (0.54 #447, 0.53 #326, 0.50 #547), 07s6fsf (0.33 #61, 0.33 #1405, 0.29 #1485), 0bjrnt (0.29 #429, 0.29 #2317, 0.29 #450), 071tyz (0.29 #2317, 0.21 #168, 0.20 #88), 03mkk4 (0.23 #333, 0.22 #69, 0.20 #474) >> Best rule #427 for best value: >> intensional similarity = 6 >> extensional distance = 32 >> proper extension: 01rr31; >> query: (?x6548, 02h4rq6) <- contains(?x512, ?x6548), major_field_of_study(?x6548, ?x2606), major_field_of_study(?x6548, ?x2605), ?x2605 = 03g3w, school_type(?x6548, ?x4994), ?x2606 = 062z7 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #552 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 42 *> proper extension: 07w0v; 07tl0; 01jq34; 07wjk; 01k7xz; 02301; 086xm; 025v3k; 0hd7j; 0g8rj; ... *> query: (?x6548, 0bkj86) <- contains(?x512, ?x6548), major_field_of_study(?x6548, ?x2605), ?x2605 = 03g3w, student(?x6548, ?x11104), influenced_by(?x2161, ?x11104) *> conf = 0.64 ranks of expected_values: 4 EVAL 0yls9 institution! 0bkj86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 198.000 127.000 0.765 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #12574-039wsf PRED entity: 039wsf PRED relation: film PRED expected values: 0571m => 129 concepts (61 used for prediction) PRED predicted values (max 10 best out of 825): 07h9gp (0.25 #266, 0.01 #16389, 0.01 #14597), 06rzwx (0.25 #1244, 0.01 #37078), 03kx49 (0.12 #1343, 0.07 #21050, 0.02 #40760), 0ckrnn (0.12 #1689, 0.05 #3480, 0.03 #8853), 01738w (0.12 #1131, 0.05 #2922, 0.01 #19046), 0jvt9 (0.12 #540, 0.05 #20247, 0.02 #9496), 03rg2b (0.12 #1095, 0.05 #10051, 0.03 #11843), 02z3r8t (0.12 #108, 0.03 #7272, 0.03 #5481), 03l6q0 (0.12 #544, 0.03 #7708, 0.03 #5917), 0jswp (0.12 #548, 0.02 #9504, 0.02 #11296) >> Best rule #266 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0fb7c; >> query: (?x12287, 07h9gp) <- actor(?x3725, ?x12287), profession(?x12287, ?x524), place_of_birth(?x12287, ?x739), ?x3725 = 05jyb2 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 039wsf film 0571m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 61.000 0.250 http://example.org/film/actor/film./film/performance/film #12573-02y8bn PRED entity: 02y8bn PRED relation: team PRED expected values: 06x6s => 130 concepts (110 used for prediction) PRED predicted values (max 10 best out of 391): 0fbtm7 (0.33 #1253, 0.17 #1964, 0.17 #1608), 0jnng (0.33 #615, 0.02 #3553, 0.02 #6751), 0jnrk (0.33 #528, 0.02 #3553, 0.02 #6751), 0cqt41 (0.19 #2872, 0.17 #1451, 0.14 #2162), 027ffq (0.17 #2076, 0.17 #1720, 0.14 #2431), 050fh (0.17 #1877, 0.17 #1521, 0.14 #2232), 011v3 (0.17 #1883, 0.17 #1527, 0.14 #2238), 06l22 (0.17 #1930, 0.17 #1574, 0.14 #2285), 01k2yr (0.17 #1811, 0.17 #1455, 0.14 #2166), 02pp1 (0.17 #2052, 0.17 #1696, 0.14 #2407) >> Best rule #1253 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 037gjc; >> query: (?x11825, 0fbtm7) <- location(?x11825, ?x5783), team(?x11825, ?x3298), contains(?x1227, ?x5783), ?x1227 = 01n7q, nationality(?x11825, ?x279), place_founded(?x4878, ?x5783) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3553 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 24 *> proper extension: 0234pg; *> query: (?x11825, ?x2919) <- location(?x11825, ?x5783), team(?x11825, ?x3298), place_of_birth(?x2101, ?x5783), citytown(?x4878, ?x5783), athlete(?x453, ?x11825), sport(?x2919, ?x453) *> conf = 0.02 ranks of expected_values: 232 EVAL 02y8bn team 06x6s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 130.000 110.000 0.333 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #12572-087lqx PRED entity: 087lqx PRED relation: genre! PRED expected values: 048rn => 56 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1984): 0gnjh (0.75 #16886, 0.75 #20641, 0.70 #15008), 034xyf (0.71 #7123, 0.67 #5248, 0.62 #10876), 0jqb8 (0.67 #5350, 0.62 #9102, 0.60 #3475), 0g7pm1 (0.67 #4993, 0.62 #10621, 0.60 #3118), 0k4f3 (0.67 #4213, 0.62 #7965, 0.60 #2338), 0gxfz (0.67 #4200, 0.62 #7952, 0.60 #2325), 03m4mj (0.67 #3959, 0.60 #2084, 0.57 #5834), 05fgt1 (0.67 #4160, 0.60 #2285, 0.57 #6035), 033fqh (0.67 #4616, 0.60 #2741, 0.57 #6491), 048qrd (0.60 #2213, 0.57 #5963, 0.50 #9716) >> Best rule #16886 for best value: >> intensional similarity = 12 >> extensional distance = 11 >> proper extension: 04pbhw; >> query: (?x13974, ?x6604) <- genre(?x3438, ?x13974), language(?x3438, ?x254), nominated_for(?x484, ?x3438), nominated_for(?x3811, ?x3438), nominated_for(?x6604, ?x3438), film_art_direction_by(?x3438, ?x8402), genre(?x3438, ?x258), genre(?x7629, ?x258), genre(?x1904, ?x258), genre(?x419, ?x258), ?x7629 = 02825nf, ?x1904 = 09146g >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #5622 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 06qm3; *> query: (?x13974, ?x80) <- genre(?x12829, ?x13974), genre(?x3438, ?x13974), ?x3438 = 0glnm, film_release_region(?x12829, ?x94), language(?x12829, ?x5607), ?x94 = 09c7w0, genre(?x12829, ?x225), nominated_for(?x601, ?x12829), ?x225 = 02kdv5l, countries_spoken_in(?x5607, ?x172), language(?x4626, ?x5607), language(?x80, ?x5607), ?x4626 = 038bh3 *> conf = 0.16 ranks of expected_values: 1707 EVAL 087lqx genre! 048rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 18.000 0.755 http://example.org/film/film/genre #12571-07y_p6 PRED entity: 07y_p6 PRED relation: award_winner PRED expected values: 06msq2 023kzp 03c6vl 05w88j => 35 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1859): 018ygt (0.50 #2498, 0.29 #4033, 0.24 #13253), 01z_g6 (0.50 #2328, 0.14 #3863, 0.09 #26106), 026rm_y (0.50 #2775, 0.13 #13530, 0.13 #5849), 04znsy (0.50 #2831, 0.10 #5905, 0.09 #7442), 0794g (0.50 #2034, 0.10 #3569, 0.09 #6645), 0382m4 (0.50 #2417, 0.10 #3952, 0.06 #5491), 026c1 (0.50 #1850, 0.09 #26106, 0.09 #9213), 02bkdn (0.33 #261, 0.24 #3331, 0.09 #18695), 03c6vl (0.33 #1297, 0.22 #4609, 0.14 #10751), 0bxtg (0.33 #59, 0.11 #23096, 0.10 #10814) >> Best rule #2498 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 05zksls; 09g90vz; >> query: (?x7085, 018ygt) <- honored_for(?x7085, ?x337), award_winner(?x7085, ?x9545), award_winner(?x7085, ?x4574), award_nominee(?x9830, ?x4574), location(?x4574, ?x8916), ceremony(?x435, ?x7085), award_winner(?x2139, ?x9830), participant(?x4782, ?x9545), ?x4782 = 0bksh, profession(?x9545, ?x1032), award_winner(?x4225, ?x9545), location(?x9830, ?x2474), award_winner(?x6384, ?x9830) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1297 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 0bxs_d; *> query: (?x7085, 03c6vl) <- honored_for(?x7085, ?x337), award_winner(?x7085, ?x8509), award_winner(?x7085, ?x6190), award_winner(?x7085, ?x3751), award_winner(?x7085, ?x1343), ?x8509 = 02rhfsc, award_winner(?x1343, ?x444), award(?x1343, ?x2041), award_nominee(?x2589, ?x1343), award_nominee(?x1343, ?x3687), ?x6190 = 01h910, ?x2041 = 0bdx29, ?x3751 = 01d8yn *> conf = 0.33 ranks of expected_values: 9, 84, 670, 910 EVAL 07y_p6 award_winner 05w88j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 07y_p6 award_winner 03c6vl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 35.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 07y_p6 award_winner 023kzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 07y_p6 award_winner 06msq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 35.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12570-01wj92r PRED entity: 01wj92r PRED relation: inductee! PRED expected values: 0g2c8 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 6): 0g2c8 (0.42 #73, 0.17 #10, 0.15 #19), 0qjfl (0.17 #12, 0.04 #93, 0.04 #30), 06szd3 (0.06 #101, 0.03 #137, 0.03 #155), 04045y (0.03 #51, 0.03 #69, 0.02 #114), 04dm2n (0.03 #53), 027jbr (0.01 #135) >> Best rule #73 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 01vrx3g; 089tm; 01j4ls; 01bpc9; 01vn35l; 0gcs9; 0407f; 01wz_ml; 0134s5; 050z2; ... >> query: (?x2806, 0g2c8) <- award_winner(?x594, ?x2806), artists(?x378, ?x2806), ?x378 = 07sbbz2 >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wj92r inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.421 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #12569-06j6l PRED entity: 06j6l PRED relation: artists PRED expected values: 016kjs 01wbgdv 04mn81 01w7nww 01vwyqp 01vxlbm 01svw8n 018dyl 0837ql 04bgy 0gs6vr 0163r3 02wwwv5 015bwt 0163kf => 56 concepts (30 used for prediction) PRED predicted values (max 10 best out of 907): 016jfw (0.71 #4076, 0.50 #6796, 0.50 #1356), 0137hn (0.71 #4111, 0.50 #6831, 0.50 #1391), 016h9b (0.71 #3812, 0.38 #6532, 0.36 #11973), 0b_j2 (0.67 #1389, 0.57 #4109, 0.50 #6829), 095x_ (0.67 #1506, 0.57 #4226, 0.50 #6946), 08w4pm (0.67 #1505, 0.56 #7851, 0.38 #6945), 01w61th (0.67 #1859, 0.53 #12740, 0.46 #10927), 01vsy7t (0.67 #1238, 0.50 #6678, 0.44 #8492), 01wbz9 (0.67 #1173, 0.50 #6613, 0.44 #8427), 033s6 (0.67 #1613, 0.44 #7959, 0.43 #4333) >> Best rule #4076 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 02qdgx; 02k_kn; >> query: (?x3319, 016jfw) <- artists(?x3319, ?x9848), artists(?x3319, ?x6854), artists(?x3319, ?x3503), artists(?x3319, ?x1001), ?x6854 = 0178_w, ?x9848 = 01wk7ql, award(?x3503, ?x247), award_winner(?x1001, ?x7027) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1382 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 0xhtw; 06by7; 05bt6j; *> query: (?x3319, 04bgy) <- artists(?x3319, ?x6854), artists(?x3319, ?x1001), artists(?x3319, ?x827), ?x6854 = 0178_w, ?x1001 = 01gf5h, parent_genre(?x671, ?x3319), award_nominee(?x527, ?x827) *> conf = 0.67 ranks of expected_values: 12, 26, 33, 134, 138, 139, 153, 187, 219, 255, 268, 300, 398, 403, 611 EVAL 06j6l artists 0163kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 015bwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 02wwwv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 0163r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 0gs6vr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 04bgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 0837ql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 018dyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 01svw8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 01vxlbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 01vwyqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 01w7nww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 04mn81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 01wbgdv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 56.000 30.000 0.714 http://example.org/music/genre/artists EVAL 06j6l artists 016kjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 56.000 30.000 0.714 http://example.org/music/genre/artists #12568-01fs_4 PRED entity: 01fs_4 PRED relation: inductee! PRED expected values: 06szd3 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 5): 0g2c8 (0.10 #92, 0.10 #65, 0.10 #776), 06szd3 (0.09 #11, 0.05 #183, 0.04 #309), 04dm2n (0.03 #81, 0.01 #315), 0qjfl (0.02 #103, 0.02 #57, 0.02 #76), 04045y (0.01 #51, 0.01 #223, 0.01 #79) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 04cr6qv; 01vw917; >> query: (?x3868, 0g2c8) <- people(?x1050, ?x3868), film(?x3868, ?x10492), artists(?x2480, ?x3868) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 9 *> proper extension: 04sd0; *> query: (?x3868, 06szd3) <- artists(?x2480, ?x3868), ?x2480 = 01z4y *> conf = 0.09 ranks of expected_values: 2 EVAL 01fs_4 inductee! 06szd3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 162.000 162.000 0.102 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #12567-01f9zw PRED entity: 01f9zw PRED relation: award PRED expected values: 0c4z8 01c427 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 299): 01by1l (0.61 #14217, 0.49 #515, 0.44 #1321), 01bgqh (0.52 #1252, 0.33 #14148, 0.31 #446), 0c4z8 (0.46 #14177, 0.46 #1281, 0.36 #475), 03qbh5 (0.37 #1413, 0.33 #1010, 0.31 #4637), 054ks3 (0.35 #947, 0.27 #1350, 0.26 #4574), 01d38g (0.33 #431, 0.24 #10103, 0.13 #1640), 02f6xy (0.31 #1408, 0.23 #1005, 0.19 #1811), 01ck6h (0.31 #1331, 0.17 #1734, 0.15 #928), 03t5kl (0.28 #629, 0.13 #14331, 0.12 #4256), 02f764 (0.26 #623, 0.09 #4250, 0.09 #10295) >> Best rule #14217 for best value: >> intensional similarity = 4 >> extensional distance = 218 >> proper extension: 0jdhp; 02v3yy; 01vvdm; 01pr6q7; 012wg; 02_jkc; 02zft0; 025cn2; 01wd9vs; 01vrlqd; ... >> query: (?x8856, 01by1l) <- nationality(?x8856, ?x94), award(?x8856, ?x3835), award(?x9623, ?x3835), ?x9623 = 02wwwv5 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #14177 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 218 *> proper extension: 0jdhp; 02v3yy; 01vvdm; 01pr6q7; 012wg; 02_jkc; 02zft0; 025cn2; 01wd9vs; 01vrlqd; ... *> query: (?x8856, 0c4z8) <- nationality(?x8856, ?x94), award(?x8856, ?x3835), award(?x9623, ?x3835), ?x9623 = 02wwwv5 *> conf = 0.46 ranks of expected_values: 3, 12 EVAL 01f9zw award 01c427 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 121.000 121.000 0.609 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01f9zw award 0c4z8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 121.000 121.000 0.609 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12566-017s11 PRED entity: 017s11 PRED relation: nominated_for PRED expected values: 0416y94 025n07 => 134 concepts (93 used for prediction) PRED predicted values (max 10 best out of 810): 02fqxm (0.79 #124411, 0.78 #143558, 0.78 #140365), 07p62k (0.40 #3515, 0.33 #323, 0.07 #9892), 0gm2_0 (0.40 #4600, 0.06 #12572, 0.04 #22143), 0c0nhgv (0.33 #1753, 0.15 #33495, 0.15 #17545), 011yn5 (0.33 #2433, 0.11 #12001, 0.11 #8811), 0cn_b8 (0.33 #558, 0.07 #10127, 0.06 #11722), 0315rp (0.33 #1266, 0.07 #10835, 0.06 #12430), 02vqsll (0.33 #447, 0.07 #10016, 0.06 #11611), 0c9t0y (0.33 #1099, 0.07 #10668, 0.06 #12263), 02z9rr (0.33 #1205, 0.07 #10774, 0.06 #12369) >> Best rule #124411 for best value: >> intensional similarity = 3 >> extensional distance = 976 >> proper extension: 0c01c; 06_bq1; >> query: (?x541, ?x12720) <- nominated_for(?x541, ?x770), award_winner(?x163, ?x541), award_winner(?x12720, ?x541) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #70199 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 99 *> proper extension: 01w92; 01vw20h; 05zh9c; 02_l96; 016bx2; 0gv2r; 03m9c8; 02mc79; 0b7xl8; 030g9z; ... *> query: (?x541, ?x66) <- award_winner(?x1105, ?x541), award_nominee(?x541, ?x902), production_companies(?x66, ?x902) *> conf = 0.01 ranks of expected_values: 757 EVAL 017s11 nominated_for 025n07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 134.000 93.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 017s11 nominated_for 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 93.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12565-031ldd PRED entity: 031ldd PRED relation: genre PRED expected values: 0lsxr => 74 concepts (60 used for prediction) PRED predicted values (max 10 best out of 115): 03h64 (0.55 #4705, 0.54 #3251, 0.53 #4464), 01jfsb (0.51 #494, 0.49 #976, 0.48 #1578), 0lsxr (0.50 #249, 0.40 #128, 0.40 #8), 03k9fj (0.47 #493, 0.46 #855, 0.41 #734), 05p553 (0.40 #244, 0.37 #2410, 0.36 #2650), 02l7c8 (0.38 #1342, 0.36 #618, 0.33 #1462), 06n90 (0.35 #495, 0.25 #1579, 0.24 #857), 01hmnh (0.31 #1223, 0.31 #982, 0.31 #862), 04t2t (0.30 #299, 0.20 #58, 0.08 #660), 0556j8 (0.30 #283, 0.06 #1247, 0.06 #1006) >> Best rule #4705 for best value: >> intensional similarity = 4 >> extensional distance = 1104 >> proper extension: 05jyb2; >> query: (?x6014, ?x2645) <- titles(?x2645, ?x6014), genre(?x6014, ?x1626), genre(?x5230, ?x1626), ?x5230 = 0mb8c >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #249 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 02rqwhl; *> query: (?x6014, 0lsxr) <- titles(?x2645, ?x6014), language(?x6014, ?x9980), ?x9980 = 0459q4, film(?x1864, ?x6014) *> conf = 0.50 ranks of expected_values: 3 EVAL 031ldd genre 0lsxr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 60.000 0.549 http://example.org/film/film/genre #12564-0sydc PRED entity: 0sydc PRED relation: industry! PRED expected values: 01ym8l => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 519): 04sv4 (0.67 #5864, 0.64 #7197, 0.59 #5376), 0300cp (0.67 #5864, 0.59 #5376, 0.51 #6840), 02b07b (0.67 #5864, 0.59 #5376, 0.51 #6840), 01t9_0 (0.67 #5864, 0.59 #5376, 0.51 #6840), 09b3v (0.67 #5864, 0.59 #5376, 0.51 #6840), 039cpd (0.67 #5864, 0.59 #5376, 0.51 #6840), 081g_l (0.67 #5864, 0.59 #5376, 0.51 #6840), 011k1h (0.67 #5864, 0.59 #5376, 0.51 #6840), 07gqbk (0.67 #5864, 0.59 #5376, 0.51 #6840), 01tlrp (0.67 #5864, 0.59 #5376, 0.51 #6840) >> Best rule #5864 for best value: >> intensional similarity = 14 >> extensional distance = 7 >> proper extension: 019mlh; >> query: (?x13321, ?x2270) <- industry(?x10699, ?x13321), industry(?x9923, ?x13321), organization(?x4682, ?x9923), child(?x9923, ?x2062), industry(?x9923, ?x12816), company(?x1491, ?x9923), industry(?x2270, ?x12816), currency(?x9923, ?x170), state_province_region(?x9923, ?x335), citytown(?x10699, ?x1860), contains(?x335, ?x322), adjoins(?x1755, ?x335), location(?x101, ?x335), partially_contains(?x335, ?x10954) >> conf = 0.67 => this is the best rule for 26 predicted values *> Best rule #2451 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 07c52; *> query: (?x13321, ?x2270) <- industry(?x9923, ?x13321), industry(?x2776, ?x13321), award_winner(?x10166, ?x2776), award_winner(?x5007, ?x2776), industry(?x9923, ?x12816), company(?x3796, ?x2776), child(?x9923, ?x2062), country(?x5007, ?x94), film(?x5007, ?x8733), student(?x2142, ?x3796), award_winner(?x902, ?x5007), award_winner(?x6597, ?x2776), gender(?x3796, ?x231), nominated_for(?x5007, ?x1434), citytown(?x10166, ?x10059), profession(?x3796, ?x353), industry(?x2270, ?x12816) *> conf = 0.34 ranks of expected_values: 58 EVAL 0sydc industry! 01ym8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 41.000 41.000 0.672 http://example.org/business/business_operation/industry #12563-03cv_gy PRED entity: 03cv_gy PRED relation: honored_for! PRED expected values: 058m5m4 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 93): 0gvstc3 (0.28 #741, 0.27 #1813, 0.26 #1456), 02q690_ (0.28 #767, 0.27 #2673, 0.25 #3030), 05c1t6z (0.28 #725, 0.25 #2988, 0.24 #2631), 09qvms (0.25 #953, 0.22 #4168, 0.17 #128), 0g5b0q5 (0.25 #953, 0.22 #4168, 0.17 #133), 09bymc (0.25 #953, 0.22 #4168, 0.17 #221), 0275n3y (0.25 #953, 0.22 #4168, 0.13 #776), 0bxs_d (0.25 #953, 0.22 #4168, 0.11 #2501), 0hn821n (0.25 #953, 0.22 #4168, 0.11 #2501), 07z31v (0.25 #953, 0.22 #4168, 0.11 #2501) >> Best rule #741 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 05fgr_; 07s8z_l; >> query: (?x5328, 0gvstc3) <- producer_type(?x5328, ?x632), award_winner(?x5328, ?x2887), honored_for(?x2988, ?x5328), titles(?x53, ?x5328) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #953 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 04bp0l; *> query: (?x5328, ?x873) <- nominated_for(?x6980, ?x5328), nominated_for(?x3381, ?x5328), award_winner(?x873, ?x6980), program(?x3381, ?x493) *> conf = 0.25 ranks of expected_values: 12 EVAL 03cv_gy honored_for! 058m5m4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 107.000 107.000 0.277 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12562-076df9 PRED entity: 076df9 PRED relation: nationality PRED expected values: 09c7w0 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.83 #4211, 0.83 #1804, 0.83 #201), 0d060g (0.33 #7, 0.08 #107, 0.08 #1310), 0154j (0.17 #4, 0.01 #1307), 02jx1 (0.16 #333, 0.11 #2537, 0.10 #4345), 07ssc (0.14 #515, 0.11 #315, 0.09 #1318), 03gyl (0.06 #266, 0.02 #867, 0.02 #1068), 0b90_r (0.05 #503, 0.01 #1406, 0.01 #1506), 03rk0 (0.05 #8380, 0.05 #8580, 0.04 #4358), 03rjj (0.03 #706, 0.03 #806, 0.03 #1107), 0ctw_b (0.03 #929, 0.03 #1229, 0.02 #1430) >> Best rule #4211 for best value: >> intensional similarity = 3 >> extensional distance = 724 >> proper extension: 01q415; 0bt4r4; 0cj2t3; 0347xl; 0fn5bx; 02vqpx8; >> query: (?x10230, 09c7w0) <- award_nominee(?x5202, ?x10230), location(?x10230, ?x2850), adjoins(?x4253, ?x2850) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 076df9 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.835 http://example.org/people/person/nationality #12561-05dmmc PRED entity: 05dmmc PRED relation: music PRED expected values: 03zrp => 103 concepts (58 used for prediction) PRED predicted values (max 10 best out of 120): 012201 (0.25 #151, 0.02 #780, 0.02 #4348), 0146pg (0.15 #1268, 0.08 #1057, 0.08 #2946), 01tc9r (0.11 #275, 0.07 #903, 0.04 #694), 01gz9n (0.11 #5670, 0.10 #210, 0.08 #1258), 09r9m7 (0.11 #5670, 0.10 #210, 0.08 #1258), 04vzv4 (0.11 #5670, 0.10 #210, 0.08 #1258), 072twv (0.11 #5670, 0.07 #2097, 0.07 #6931), 05x2t7 (0.11 #5670, 0.07 #2097, 0.07 #6931), 012wg (0.10 #210, 0.08 #1258, 0.07 #11992), 036jb (0.10 #210, 0.08 #1258, 0.07 #11992) >> Best rule #151 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03bxp5; >> query: (?x4513, 012201) <- nominated_for(?x198, ?x4513), nominated_for(?x786, ?x4513), ?x786 = 076lxv, film(?x4439, ?x4513) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1659 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 67 *> proper extension: 0b9rdk; *> query: (?x4513, 03zrp) <- nominated_for(?x198, ?x4513), nominated_for(?x786, ?x4513), film_sets_designed(?x786, ?x1804) *> conf = 0.03 ranks of expected_values: 48 EVAL 05dmmc music 03zrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 103.000 58.000 0.250 http://example.org/film/film/music #12560-0fvt2 PRED entity: 0fvt2 PRED relation: award PRED expected values: 01yz0x => 129 concepts (112 used for prediction) PRED predicted values (max 10 best out of 284): 01bb1c (0.76 #16407, 0.76 #15606, 0.74 #10003), 0265vt (0.64 #725, 0.59 #2726, 0.56 #4326), 01yz0x (0.50 #175, 0.47 #4177, 0.46 #4577), 0262x6 (0.47 #2717, 0.45 #4317, 0.44 #2317), 02662b (0.45 #477, 0.44 #2878, 0.41 #2478), 045xh (0.45 #775, 0.40 #374, 0.26 #2376), 02664f (0.41 #2219, 0.41 #2619, 0.38 #3019), 040_9s0 (0.39 #5118, 0.36 #717, 0.32 #4718), 0265wl (0.38 #2638, 0.36 #637, 0.33 #4238), 09sb52 (0.29 #21652, 0.25 #19249, 0.25 #17648) >> Best rule #16407 for best value: >> intensional similarity = 3 >> extensional distance = 487 >> proper extension: 05xbx; 026v1z; >> query: (?x11262, ?x14213) <- award_nominee(?x4895, ?x11262), award_winner(?x14213, ?x11262), category(?x11262, ?x134) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #175 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 04r68; 048_p; 042xh; *> query: (?x11262, 01yz0x) <- award(?x11262, ?x11579), ?x11579 = 058bzgm, location(?x11262, ?x9026), award_winner(?x14213, ?x11262) *> conf = 0.50 ranks of expected_values: 3 EVAL 0fvt2 award 01yz0x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 112.000 0.758 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12559-0dt645q PRED entity: 0dt645q PRED relation: actor! PRED expected values: 051kd => 80 concepts (43 used for prediction) PRED predicted values (max 10 best out of 144): 05hd32 (0.33 #57, 0.05 #322, 0.04 #588), 03d3ht (0.25 #719, 0.11 #1251, 0.05 #3364), 02rhwjr (0.17 #789, 0.07 #1321, 0.05 #523), 01lk02 (0.17 #695, 0.07 #1227, 0.04 #3340), 031kyy (0.17 #682, 0.07 #1214, 0.03 #3327), 017dtf (0.12 #731, 0.05 #1263, 0.05 #465), 045nc5 (0.12 #791, 0.05 #1323, 0.02 #3436), 04svwx (0.12 #768, 0.05 #1300, 0.02 #3413), 08cl7s (0.12 #685, 0.05 #1217, 0.02 #3330), 0gxr1c (0.12 #784, 0.05 #1316, 0.02 #4222) >> Best rule #57 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 042gr4; >> query: (?x10418, 05hd32) <- film(?x10418, ?x10642), film(?x10418, ?x6610), type_of_union(?x10418, ?x566), ?x10642 = 05vc35, genre(?x6610, ?x53) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #524 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 034np8; 04gcd1; 0311wg; 01vwllw; 01kp66; 0338g8; 0725ny; 085q5; 031c2r; 01wgx4; *> query: (?x10418, 051kd) <- film(?x10418, ?x10642), film(?x10418, ?x2508), type_of_union(?x10418, ?x566), genre(?x2508, ?x1013), actor(?x10642, ?x6414) *> conf = 0.05 ranks of expected_values: 35 EVAL 0dt645q actor! 051kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 80.000 43.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #12558-05z_kps PRED entity: 05z_kps PRED relation: film_release_region PRED expected values: 015fr 06mkj => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 240): 06mkj (0.90 #1139, 0.86 #829, 0.86 #1759), 059j2 (0.89 #805, 0.88 #1889, 0.87 #1115), 07ssc (0.87 #1097, 0.85 #168, 0.82 #1252), 0chghy (0.87 #1712, 0.86 #1866, 0.82 #782), 015fr (0.86 #635, 0.84 #1099, 0.79 #1873), 03h64 (0.80 #1770, 0.79 #1924, 0.77 #840), 01znc_ (0.77 #1744, 0.77 #1898, 0.65 #814), 06bnz (0.76 #1128, 0.72 #1748, 0.72 #664), 06t2t (0.69 #1765, 0.68 #1145, 0.65 #1919), 03rj0 (0.64 #1917, 0.63 #833, 0.62 #1763) >> Best rule #1139 for best value: >> intensional similarity = 5 >> extensional distance = 61 >> proper extension: 0c3ybss; 04969y; 01vksx; 09gdm7q; 0gmcwlb; 0dtfn; 0fpkhkz; 0by1wkq; 09k56b7; 0j6b5; ... >> query: (?x1228, 06mkj) <- film_release_region(?x1228, ?x4743), film_release_region(?x1228, ?x252), ?x252 = 03_3d, film_regional_debut_venue(?x1228, ?x6601), ?x4743 = 03spz >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 05z_kps film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05z_kps film_release_region 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 96.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12557-01n4w_ PRED entity: 01n4w_ PRED relation: school_type PRED expected values: 01rs41 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 18): 01rs41 (0.63 #142, 0.51 #50, 0.51 #303), 05jxkf (0.52 #2051, 0.51 #1614, 0.49 #1452), 07tf8 (0.18 #31, 0.17 #100, 0.14 #1135), 01_9fk (0.16 #1105, 0.15 #944, 0.14 #1243), 01_srz (0.13 #140, 0.12 #48, 0.11 #301), 04qbv (0.06 #153, 0.05 #130, 0.05 #199), 01y64 (0.05 #126, 0.05 #149, 0.05 #195), 06cs1 (0.04 #258, 0.04 #28, 0.04 #143), 04399 (0.04 #36, 0.04 #174, 0.03 #588), 02p0qmm (0.04 #814, 0.04 #1458, 0.04 #1274) >> Best rule #142 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 06mvyf; 016sd3; >> query: (?x11185, 01rs41) <- currency(?x11185, ?x170), school_type(?x11185, ?x1044), registering_agency(?x11185, ?x1982) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n4w_ school_type 01rs41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.627 http://example.org/education/educational_institution/school_type #12556-019n9w PRED entity: 019n9w PRED relation: campuses PRED expected values: 019n9w => 195 concepts (123 used for prediction) PRED predicted values (max 10 best out of 298): 019n9w (0.36 #28952, 0.07 #52441, 0.06 #61187), 07tgn (0.36 #28952, 0.03 #4382, 0.02 #6566), 04cnp4 (0.10 #54627, 0.08 #49709, 0.07 #52441), 01pq4w (0.07 #648, 0.05 #1194, 0.04 #2286), 07wrz (0.07 #602, 0.05 #1148, 0.04 #2240), 0bwfn (0.07 #809, 0.05 #1355, 0.03 #5178), 01bk1y (0.07 #815, 0.02 #5730, 0.02 #11738), 0cwx_ (0.07 #778, 0.02 #7878, 0.02 #11155), 01swxv (0.07 #620, 0.02 #7720, 0.02 #11543), 02fgdx (0.07 #638, 0.02 #7738, 0.02 #11561) >> Best rule #28952 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 01dnnt; >> query: (?x8525, ?x892) <- student(?x8525, ?x3495), student(?x892, ?x3495), instrumentalists(?x316, ?x3495), award(?x3495, ?x1232) >> conf = 0.36 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 019n9w campuses 019n9w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 123.000 0.364 http://example.org/education/educational_institution/campuses #12555-01_k0d PRED entity: 01_k0d PRED relation: profession PRED expected values: 02hv44_ => 122 concepts (86 used for prediction) PRED predicted values (max 10 best out of 98): 02hrh1q (0.91 #10606, 0.91 #12096, 0.82 #11351), 0cbd2 (0.80 #2691, 0.74 #5377, 0.73 #11790), 09jwl (0.69 #12399, 0.58 #765, 0.53 #12250), 01c72t (0.62 #6588, 0.47 #621, 0.37 #770), 0dxtg (0.59 #7024, 0.57 #2996, 0.50 #163), 0nbcg (0.58 #777, 0.50 #926, 0.41 #628), 01d_h8 (0.44 #7016, 0.41 #2237, 0.41 #4027), 0n1h (0.42 #1789, 0.41 #2237, 0.41 #4027), 0196pc (0.42 #1789, 0.41 #2237, 0.41 #4027), 0dz96 (0.42 #1789, 0.41 #2237, 0.41 #4027) >> Best rule #10606 for best value: >> intensional similarity = 5 >> extensional distance = 430 >> proper extension: 0436f4; 015882; 0127m7; 01vx5w7; 01w02sy; 01wmgrf; 07z1_q; 01wz_ml; 01s21dg; 01vsgrn; ... >> query: (?x6723, 02hrh1q) <- profession(?x6723, ?x2225), category(?x6723, ?x134), place_of_birth(?x6723, ?x4030), profession(?x5332, ?x2225), ?x5332 = 06ltr >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #5818 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 119 *> proper extension: 017yfz; 02dlfh; 01svq8; *> query: (?x6723, ?x353) <- profession(?x6723, ?x2225), influenced_by(?x6723, ?x8753), category(?x6723, ?x134), profession(?x8753, ?x353), influenced_by(?x8753, ?x1645) *> conf = 0.36 ranks of expected_values: 15 EVAL 01_k0d profession 02hv44_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 122.000 86.000 0.914 http://example.org/people/person/profession #12554-048xh PRED entity: 048xh PRED relation: artist! PRED expected values: 03rhqg 01cf93 => 86 concepts (80 used for prediction) PRED predicted values (max 10 best out of 100): 03rhqg (0.50 #274, 0.50 #144, 0.45 #534), 01t04r (0.33 #319, 0.27 #579, 0.18 #709), 033hn8 (0.32 #662, 0.18 #4043, 0.17 #272), 02p3cr5 (0.32 #675, 0.08 #3666, 0.08 #3276), 015_1q (0.30 #1447, 0.24 #797, 0.20 #4698), 0181dw (0.29 #427, 0.17 #297, 0.11 #7972), 0mzkr (0.27 #543, 0.17 #283, 0.14 #413), 01clyr (0.25 #30, 0.20 #810, 0.17 #3671), 041p3y (0.25 #69, 0.17 #199, 0.14 #459), 03qy3l (0.25 #58, 0.14 #708, 0.06 #3699) >> Best rule #274 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0qf11; >> query: (?x7476, 03rhqg) <- artists(?x1380, ?x7476), artist(?x13110, ?x7476), ?x13110 = 03vtrv, ?x1380 = 0dl5d >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 17 EVAL 048xh artist! 01cf93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 86.000 80.000 0.500 http://example.org/music/record_label/artist EVAL 048xh artist! 03rhqg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 80.000 0.500 http://example.org/music/record_label/artist #12553-02sj1x PRED entity: 02sj1x PRED relation: nominated_for PRED expected values: 0jvt9 02q_4ph => 160 concepts (107 used for prediction) PRED predicted values (max 10 best out of 573): 0bcndz (0.80 #124570, 0.79 #122952, 0.79 #126189), 0kbf1 (0.80 #124570, 0.79 #122952, 0.79 #126189), 034xyf (0.80 #124570, 0.79 #122952, 0.79 #126189), 0m_h6 (0.80 #124570, 0.79 #122952, 0.79 #101919), 0bykpk (0.42 #14559, 0.35 #14558, 0.33 #19413), 0gy4k (0.37 #27501, 0.35 #14558, 0.33 #19413), 0kvb6p (0.37 #27501, 0.35 #14558, 0.33 #19413), 0bkq7 (0.37 #27501, 0.35 #14558, 0.33 #19413), 01wb95 (0.35 #14558, 0.33 #19413, 0.32 #24265), 0cwy47 (0.30 #4983, 0.20 #3366, 0.04 #48666) >> Best rule #124570 for best value: >> intensional similarity = 3 >> extensional distance = 927 >> proper extension: 0d_84; 042l3v; 041h0; 01nqfh_; 0kr5_; 02w0dc0; 012c6x; 0dky9n; 03gm48; 0456xp; ... >> query: (?x3519, ?x1745) <- place_of_birth(?x3519, ?x9336), award_winner(?x1745, ?x3519), nominated_for(?x3519, ?x4179) >> conf = 0.80 => this is the best rule for 4 predicted values *> Best rule #10200 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 33 *> proper extension: 01v3bn; *> query: (?x3519, 0jvt9) <- award_nominee(?x3771, ?x3519), place_of_burial(?x3519, ?x3691) *> conf = 0.03 ranks of expected_values: 149 EVAL 02sj1x nominated_for 02q_4ph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 107.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 02sj1x nominated_for 0jvt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 160.000 107.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12552-03_fk9 PRED entity: 03_fk9 PRED relation: profession PRED expected values: 0kyk => 112 concepts (80 used for prediction) PRED predicted values (max 10 best out of 48): 02hrh1q (0.89 #11349, 0.88 #5236, 0.87 #8516), 01d_h8 (0.41 #2987, 0.40 #2391, 0.37 #3136), 02jknp (0.34 #2989, 0.33 #157, 0.31 #7309), 09jwl (0.33 #169, 0.31 #7309, 0.19 #5688), 0dz3r (0.33 #151, 0.31 #7309, 0.13 #5670), 0cbd2 (0.33 #156, 0.31 #7309, 0.13 #2690), 0dxtg (0.32 #2995, 0.31 #7309, 0.28 #4786), 03gjzk (0.31 #7309, 0.23 #4788, 0.22 #4490), 0nbcg (0.31 #7309, 0.17 #181, 0.13 #5700), 01c72t (0.31 #7309, 0.17 #174, 0.10 #9122) >> Best rule #11349 for best value: >> intensional similarity = 2 >> extensional distance = 2012 >> proper extension: 05m63c; 033hqf; 045bs6; 030x48; 07cjqy; 08b8vd; 0k8y7; 03xb2w; 022g44; 01h8f; ... >> query: (?x10650, 02hrh1q) <- profession(?x10650, ?x2265), film(?x10650, ?x810) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #7309 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1392 *> proper extension: 05vtbl; 0bkq_8; *> query: (?x10650, ?x131) <- award_winner(?x3157, ?x10650), award_winner(?x3157, ?x2306), titles(?x53, ?x3157), profession(?x2306, ?x131) *> conf = 0.31 ranks of expected_values: 15 EVAL 03_fk9 profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 112.000 80.000 0.892 http://example.org/people/person/profession #12551-01w272y PRED entity: 01w272y PRED relation: role PRED expected values: 01vdm0 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 92): 01vdm0 (0.86 #132, 0.30 #336, 0.28 #1874), 02sgy (0.53 #312, 0.29 #108, 0.25 #927), 05r5c (0.46 #621, 0.43 #110, 0.43 #314), 042v_gx (0.45 #315, 0.27 #930, 0.23 #1135), 013y1f (0.32 #137, 0.30 #341, 0.15 #956), 026t6 (0.32 #309, 0.14 #105, 0.13 #1642), 05842k (0.29 #179, 0.23 #383, 0.15 #1716), 0bxl5 (0.29 #171, 0.13 #375, 0.11 #2152), 0l14qv (0.25 #107, 0.21 #311, 0.17 #209), 03bx0bm (0.24 #613) >> Best rule #132 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 03k0yw; 02ryx0; >> query: (?x3384, 01vdm0) <- award_nominee(?x3384, ?x1660), role(?x3384, ?x1166), ?x1166 = 05148p4 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w272y role 01vdm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.857 http://example.org/music/artist/track_contributions./music/track_contribution/role #12550-0dl9_4 PRED entity: 0dl9_4 PRED relation: nominated_for! PRED expected values: 09qv_s 02qsfzv => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 220): 02qysm0 (0.71 #2327, 0.69 #3489, 0.69 #5351), 02qwzkm (0.71 #2327, 0.69 #3489, 0.69 #5351), 0gq9h (0.46 #6109, 0.44 #6341, 0.43 #760), 0gs9p (0.44 #6111, 0.39 #6343, 0.35 #762), 019f4v (0.41 #751, 0.38 #6332, 0.35 #6100), 0gr51 (0.40 #6124, 0.22 #6356, 0.20 #1240), 0gq_v (0.37 #718, 0.31 #6299, 0.28 #6532), 0k611 (0.33 #6351, 0.31 #6119, 0.28 #6816), 04dn09n (0.33 #6082, 0.28 #733, 0.28 #6314), 040njc (0.33 #6286, 0.32 #6054, 0.28 #705) >> Best rule #2327 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 011yxg; 01k1k4; 0ds11z; 060v34; 02x3lt7; 08720; 061681; 0dsvzh; 0b73_1d; 0b6tzs; ... >> query: (?x5185, ?x2599) <- film_crew_role(?x5185, ?x137), award(?x5185, ?x2599), category(?x5185, ?x134), country(?x5185, ?x94) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #8145 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 527 *> proper extension: 01h1bf; 02kk_c; 0c3xpwy; 03czz87; *> query: (?x5185, ?x1079) <- honored_for(?x762, ?x5185), nominated_for(?x3910, ?x5185), award(?x3910, ?x1079), award_winner(?x472, ?x3910) *> conf = 0.25 ranks of expected_values: 21, 87 EVAL 0dl9_4 nominated_for! 02qsfzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 92.000 92.000 0.705 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0dl9_4 nominated_for! 09qv_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 92.000 92.000 0.705 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12549-02v5_g PRED entity: 02v5_g PRED relation: executive_produced_by PRED expected values: 05hj_k => 87 concepts (53 used for prediction) PRED predicted values (max 10 best out of 65): 079vf (0.11 #1007, 0.06 #1513, 0.05 #1765), 05hj_k (0.11 #852, 0.08 #2364, 0.07 #1354), 0gg9_5q (0.11 #591, 0.02 #3619, 0.02 #1346), 012d40 (0.11 #505, 0.01 #2270), 01pcmd (0.11 #557), 01q_ph (0.11 #513), 02xnjd (0.08 #1180, 0.04 #1686, 0.02 #2946), 0glyyw (0.06 #942, 0.04 #2454, 0.04 #1444), 02q42j_ (0.06 #891, 0.03 #2403, 0.02 #1393), 0b13g7 (0.06 #840, 0.03 #2352) >> Best rule #1007 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 0c_j9x; 05zlld0; >> query: (?x4663, 079vf) <- film(?x399, ?x4663), film(?x10884, ?x4663), story_by(?x4663, ?x10407), prequel(?x1184, ?x4663) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #852 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 02zk08; *> query: (?x4663, 05hj_k) <- genre(?x4663, ?x6452), ?x6452 = 02b5_l, award_winner(?x4663, ?x413) *> conf = 0.11 ranks of expected_values: 2 EVAL 02v5_g executive_produced_by 05hj_k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 53.000 0.113 http://example.org/film/film/executive_produced_by #12548-01pkhw PRED entity: 01pkhw PRED relation: location PRED expected values: 04lh6 01_5bb => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 215): 02_286 (0.29 #3245, 0.26 #839, 0.25 #1641), 0fhp9 (0.14 #7263), 02m77 (0.12 #7550, 0.03 #5143), 0hyxv (0.11 #7430, 0.03 #14650, 0.02 #6628), 059rby (0.11 #818, 0.10 #1620, 0.09 #5632), 01_d4 (0.10 #1705, 0.08 #3309, 0.07 #4111), 0cr3d (0.10 #4957, 0.08 #45061, 0.06 #6562), 04jpl (0.08 #40924, 0.08 #44934, 0.08 #21676), 0cc56 (0.07 #11288, 0.07 #8080, 0.06 #13695), 0rh6k (0.07 #4014, 0.06 #14444, 0.05 #806) >> Best rule #3245 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0prjs; 01x1cn2; 02v406; 02z1yj; 02p5hf; 0tj9; >> query: (?x4053, 02_286) <- student(?x254, ?x4053), spouse(?x5485, ?x4053), film(?x4053, ?x1077) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #22094 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 283 *> proper extension: 022769; 03ym1; 026rm_y; *> query: (?x4053, 04lh6) <- award_nominee(?x400, ?x4053), film(?x4053, ?x1077), languages(?x4053, ?x254) *> conf = 0.01 ranks of expected_values: 214 EVAL 01pkhw location 01_5bb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 110.000 0.292 http://example.org/people/person/places_lived./people/place_lived/location EVAL 01pkhw location 04lh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 110.000 110.000 0.292 http://example.org/people/person/places_lived./people/place_lived/location #12547-0f502 PRED entity: 0f502 PRED relation: people! PRED expected values: 0xnvg => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 54): 0x67 (0.40 #2594, 0.30 #2061, 0.18 #5563), 041rx (0.24 #4111, 0.23 #5558, 0.18 #2056), 0xnvg (0.17 #88, 0.13 #772, 0.11 #1000), 063k3h (0.17 #106, 0.12 #30, 0.11 #410), 07bch9 (0.14 #402, 0.12 #22, 0.11 #98), 02ctzb (0.14 #394, 0.12 #622, 0.07 #926), 09vc4s (0.12 #8, 0.08 #768, 0.06 #996), 048z7l (0.12 #39, 0.06 #875, 0.06 #115), 0dbxy (0.12 #46, 0.06 #122, 0.04 #502), 06v41q (0.11 #104, 0.06 #28, 0.04 #484) >> Best rule #2594 for best value: >> intensional similarity = 2 >> extensional distance = 363 >> proper extension: 0f0y8; 01vvy; 0hnlx; 0pcc0; 01pr_j6; 01wj9y9; 06wvj; 0p3sf; 024zq; 01sxd1; ... >> query: (?x4360, 0x67) <- artists(?x671, ?x4360), people(?x1446, ?x4360) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #88 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 01vvpjj; *> query: (?x4360, 0xnvg) <- award(?x4360, ?x4416), ?x4416 = 099vwn, currency(?x4360, ?x170) *> conf = 0.17 ranks of expected_values: 3 EVAL 0f502 people! 0xnvg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 133.000 0.403 http://example.org/people/ethnicity/people #12546-02y0yt PRED entity: 02y0yt PRED relation: celebrities_impersonated! PRED expected values: 01n5309 => 80 concepts (44 used for prediction) PRED predicted values (max 10 best out of 5): 03m6t5 (0.06 #19, 0.06 #35, 0.06 #3), 03d_zl4 (0.05 #6, 0.02 #22, 0.02 #14), 0pz04 (0.04 #24, 0.03 #40, 0.03 #16), 04s430 (0.02 #21, 0.01 #37, 0.01 #45), 01n5309 (0.01 #1) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 030pr; >> query: (?x8513, 03m6t5) <- award(?x8513, ?x102), person(?x424, ?x8513), type_of_union(?x8513, ?x566) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 0522wp; *> query: (?x8513, 01n5309) <- award(?x8513, ?x102), gender(?x8513, ?x231), ?x102 = 04ljl_l *> conf = 0.01 ranks of expected_values: 5 EVAL 02y0yt celebrities_impersonated! 01n5309 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 80.000 44.000 0.064 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #12545-06f32 PRED entity: 06f32 PRED relation: country! PRED expected values: 02y8z => 185 concepts (185 used for prediction) PRED predicted values (max 10 best out of 41): 064vjs (0.82 #1007, 0.76 #1048, 0.75 #679), 01cgz (0.77 #503, 0.76 #2512, 0.75 #3251), 02y8z (0.74 #916, 0.73 #506, 0.71 #1121), 019tzd (0.71 #932, 0.68 #522, 0.67 #1014), 07rlg (0.71 #903, 0.64 #493, 0.62 #329), 01sgl (0.68 #526, 0.67 #362, 0.63 #1141), 03rbzn (0.68 #511, 0.67 #347, 0.63 #1126), 02vx4 (0.67 #333, 0.64 #497, 0.55 #907), 01z27 (0.65 #1037, 0.65 #914, 0.64 #996), 035d1m (0.65 #920, 0.57 #633, 0.55 #510) >> Best rule #1007 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 05r4w; 0jgd; 0d0vqn; 03rt9; 01p1v; 06mkj; 06t2t; 06t8v; 077qn; >> query: (?x2629, 064vjs) <- country(?x668, ?x2629), olympics(?x2629, ?x775), film_release_region(?x1602, ?x2629), ?x1602 = 0gxtknx >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #916 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 07fj_; *> query: (?x2629, 02y8z) <- country(?x2315, ?x2629), olympics(?x2629, ?x775), country(?x12372, ?x2629), ?x2315 = 06wrt *> conf = 0.74 ranks of expected_values: 3 EVAL 06f32 country! 02y8z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 185.000 185.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #12544-0kv9d3 PRED entity: 0kv9d3 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #107, 0.82 #127, 0.82 #51), 02nxhr (0.05 #73, 0.04 #128, 0.04 #113), 07c52 (0.04 #38, 0.04 #43, 0.03 #48), 07z4p (0.03 #35, 0.03 #188, 0.02 #235) >> Best rule #107 for best value: >> intensional similarity = 4 >> extensional distance = 228 >> proper extension: 04969y; 016kz1; 02q3fdr; >> query: (?x4050, 029j_) <- executive_produced_by(?x4050, ?x9439), produced_by(?x4050, ?x12159), award_nominee(?x12159, ?x192), language(?x4050, ?x254) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kv9d3 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.835 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12543-07x4qr PRED entity: 07x4qr PRED relation: film_release_region PRED expected values: 03rt9 0k6nt 0345h 015qh 0d05w3 03ryn => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 138): 0345h (0.85 #433, 0.85 #569, 0.84 #161), 0k6nt (0.79 #426, 0.78 #290, 0.78 #154), 03rk0 (0.78 #181, 0.65 #453, 0.59 #589), 03rt9 (0.75 #416, 0.69 #688, 0.68 #552), 015qh (0.66 #440, 0.66 #168, 0.59 #712), 0ctw_b (0.65 #427, 0.60 #155, 0.60 #699), 01mjq (0.59 #443, 0.59 #171, 0.56 #307), 06mzp (0.53 #150, 0.53 #422, 0.52 #286), 06c1y (0.53 #170, 0.51 #442, 0.46 #578), 06f32 (0.52 #459, 0.50 #595, 0.50 #731) >> Best rule #433 for best value: >> intensional similarity = 5 >> extensional distance = 105 >> proper extension: 0gtsx8c; 02vxq9m; 0gx1bnj; 0ds3t5x; 0g5qs2k; 0dscrwf; 02x3lt7; 0401sg; 0gkz15s; 08hmch; ... >> query: (?x2512, 0345h) <- film_release_region(?x2512, ?x2843), film_release_region(?x2512, ?x2316), ?x2843 = 016wzw, film(?x905, ?x2512), ?x2316 = 06t2t >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5, 14, 22 EVAL 07x4qr film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 62.000 62.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07x4qr film_release_region 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 62.000 62.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07x4qr film_release_region 015qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 62.000 62.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07x4qr film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07x4qr film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07x4qr film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 62.000 62.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12542-051x52f PRED entity: 051x52f PRED relation: film_sets_designed PRED expected values: 0k0rf 072192 => 97 concepts (59 used for prediction) PRED predicted values (max 10 best out of 102): 0jwvf (0.33 #53, 0.15 #369, 0.14 #329), 075cph (0.33 #14, 0.15 #369, 0.10 #1106), 014knw (0.33 #82, 0.14 #358, 0.13 #451), 04wddl (0.33 #80, 0.14 #356, 0.13 #449), 0dnw1 (0.33 #56, 0.14 #332, 0.13 #425), 0bl06 (0.33 #52, 0.14 #328, 0.13 #421), 029jt9 (0.33 #77, 0.14 #353, 0.13 #446), 0k4kk (0.33 #10, 0.14 #286, 0.13 #379), 0k5g9 (0.33 #17, 0.14 #293, 0.10 #1106), 0h3k3f (0.33 #74, 0.10 #1106, 0.07 #350) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 07h1tr; >> query: (?x7876, 0jwvf) <- award_nominee(?x7876, ?x786), film_sets_designed(?x7876, ?x5095), award_nominee(?x6921, ?x7876), ?x5095 = 02r_pp >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #369 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 076psv; 0584j4n; 04_1nk; 053j4w4; 057bc6m; 05b49tt; 058vfp4; 0c0tzp; 0579tg2; 051ysmf; ... *> query: (?x7876, ?x1708) <- award_nominee(?x7876, ?x786), film_sets_designed(?x7876, ?x5095), award_nominee(?x6921, ?x7876), nominated_for(?x5095, ?x1708) *> conf = 0.15 ranks of expected_values: 14 EVAL 051x52f film_sets_designed 072192 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 59.000 0.333 http://example.org/film/film_set_designer/film_sets_designed EVAL 051x52f film_sets_designed 0k0rf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 97.000 59.000 0.333 http://example.org/film/film_set_designer/film_sets_designed #12541-01j5ws PRED entity: 01j5ws PRED relation: type_of_union PRED expected values: 01g63y => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2): 01g63y (0.32 #19, 0.30 #31, 0.30 #64), 0jgjn (0.01 #24, 0.01 #27) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 01qscs; 01q_ph; 0159h6; 04wqr; 01rr9f; 01kwld; 09wj5; 01csvq; 0mdqp; 016khd; ... >> query: (?x3025, 01g63y) <- celebrity(?x3025, ?x513), location(?x3025, ?x4499), award_winner(?x4489, ?x3025) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j5ws type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.315 http://example.org/people/person/spouse_s./people/marriage/type_of_union #12540-0tt6k PRED entity: 0tt6k PRED relation: contains! PRED expected values: 04rrd => 69 concepts (35 used for prediction) PRED predicted values (max 10 best out of 132): 04rrd (0.70 #10734, 0.69 #7155, 0.69 #16102), 01n7q (0.20 #15284, 0.19 #7232, 0.19 #5442), 0mwq7 (0.19 #24154), 0mnlq (0.19 #24154), 0cc07 (0.19 #24154), 0mwq_ (0.19 #24154), 0mwx6 (0.19 #24154), 0mw89 (0.19 #24154), 02xry (0.18 #7317, 0.15 #11791, 0.06 #1056), 0d060g (0.14 #23271, 0.05 #10747, 0.05 #12537) >> Best rule #10734 for best value: >> intensional similarity = 4 >> extensional distance = 331 >> proper extension: 0f94t; 07wrz; 02301; 0cr3d; 01531; 02bb47; 0ccvx; 02nd_; 04b_46; 01m94f; ... >> query: (?x10096, ?x1767) <- contains(?x11541, ?x10096), adjoins(?x11541, ?x10528), contains(?x1767, ?x11541), source(?x11541, ?x958) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tt6k contains! 04rrd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 35.000 0.701 http://example.org/location/location/contains #12539-01nqj PRED entity: 01nqj PRED relation: country! PRED expected values: 0bynt => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 54): 0bynt (0.86 #395, 0.86 #615, 0.86 #340), 06z6r (0.78 #196, 0.74 #691, 0.74 #1351), 071t0 (0.63 #187, 0.54 #462, 0.54 #627), 0w0d (0.52 #177, 0.35 #727, 0.34 #672), 03hr1p (0.52 #188, 0.34 #573, 0.34 #628), 07jbh (0.47 #199, 0.37 #584, 0.36 #639), 01lb14 (0.45 #180, 0.42 #620, 0.42 #400), 06f41 (0.45 #179, 0.34 #1664, 0.33 #729), 064vjs (0.45 #197, 0.33 #747, 0.32 #637), 06wrt (0.45 #181, 0.33 #346, 0.33 #786) >> Best rule #395 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 0167v; >> query: (?x11289, 0bynt) <- countries_within(?x2467, ?x11289), organization(?x11289, ?x127), currency(?x11289, ?x170) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nqj country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.865 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #12538-0l8sx PRED entity: 0l8sx PRED relation: child PRED expected values: 04mkft => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 382): 0c41qv (0.44 #14054, 0.20 #3365, 0.20 #845), 03yxwq (0.44 #14054, 0.20 #840, 0.16 #2515), 04mkft (0.44 #14054, 0.20 #826, 0.16 #2515), 07y2b (0.44 #14054, 0.20 #3273, 0.14 #2169), 017s11 (0.44 #14054, 0.17 #3779, 0.17 #944), 025t8bv (0.44 #14054, 0.14 #2120, 0.14 #1805), 024c1b (0.44 #14054, 0.14 #2194, 0.14 #1879), 02975m (0.44 #14054, 0.14 #2171, 0.14 #1856), 01_8w2 (0.44 #14054, 0.14 #2070, 0.14 #1755), 09d5h (0.44 #14054, 0.14 #2060, 0.14 #1745) >> Best rule #14054 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 01swmr; 018zqj; >> query: (?x1908, ?x2062) <- child(?x1908, ?x8817), child(?x9923, ?x8817), child(?x9923, ?x2062) >> conf = 0.44 => this is the best rule for 12 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0l8sx child 04mkft CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 124.000 124.000 0.441 http://example.org/organization/organization/child./organization/organization_relationship/child #12537-0l8sx PRED entity: 0l8sx PRED relation: list PRED expected values: 01ptsx => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.81 #548, 0.81 #542, 0.81 #249), 09g7thr (0.62 #251, 0.49 #466, 0.44 #421), 05glt (0.38 #538, 0.38 #544), 026cl_m (0.09 #539, 0.09 #545) >> Best rule #548 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 07bz5; >> query: (?x1908, ?x7472) <- list(?x1908, ?x8915), list(?x12322, ?x8915), list(?x12322, ?x7472) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l8sx list 01ptsx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.814 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #12536-0t6hk PRED entity: 0t6hk PRED relation: category PRED expected values: 08mbj5d => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.81 #23, 0.81 #20, 0.80 #64) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 0l1pj; 0r785; 0r111; 03pcgf; >> query: (?x11511, 08mbj5d) <- place_of_death(?x1029, ?x11511), contains(?x94, ?x11511), ?x94 = 09c7w0, time_zones(?x11511, ?x1638) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0t6hk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.814 http://example.org/common/topic/webpage./common/webpage/category #12535-01fmys PRED entity: 01fmys PRED relation: film_distribution_medium PRED expected values: 0735l => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 3): 0735l (0.94 #26, 0.91 #23, 0.83 #14), 07z4p (0.02 #12, 0.02 #21, 0.01 #15), 07c52 (0.01 #16) >> Best rule #26 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 0522wp; >> query: (?x2050, 0735l) <- film(?x574, ?x2050), region(?x2050, ?x512), ?x512 = 07ssc, film_distribution_medium(?x2050, ?x81) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fmys film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.941 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #12534-0g55tzk PRED entity: 0g55tzk PRED relation: award_winner PRED expected values: 03f1zdw 07s8hms 0cjsxp 02l6dy 040981l 0404wqb => 27 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2471): 0cjsxp (0.63 #6093, 0.34 #12189, 0.33 #571), 07s8hms (0.63 #6093, 0.34 #12189, 0.33 #570), 0bx0lc (0.63 #6093, 0.34 #12189, 0.33 #894), 0f830f (0.63 #6093, 0.34 #12189, 0.33 #69), 02lfns (0.63 #6093, 0.34 #12189, 0.33 #152), 0fx0mw (0.63 #6093, 0.34 #12189, 0.33 #476), 02l6dy (0.63 #6093, 0.34 #12189, 0.33 #918), 027dtv3 (0.63 #6093, 0.34 #12189, 0.31 #12188), 01z7_f (0.63 #6093, 0.34 #12189, 0.31 #12188), 01dy7j (0.63 #6093, 0.34 #12189, 0.31 #12188) >> Best rule #6093 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 027hjff; >> query: (?x11738, ?x369) <- award_winner(?x11738, ?x3709), award_winner(?x11738, ?x368), award_nominee(?x368, ?x369), student(?x3564, ?x368), film(?x368, ?x6932), award(?x368, ?x9343), participant(?x8160, ?x3709), ?x6932 = 027pfg, award_nominee(?x2657, ?x3709), ?x2657 = 043js, award(?x3709, ?x678), award_winner(?x9343, ?x3961) >> conf = 0.63 => this is the best rule for 17 predicted values ranks of expected_values: 1, 2, 7, 18, 35, 54 EVAL 0g55tzk award_winner 0404wqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 27.000 19.000 0.632 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0g55tzk award_winner 040981l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 27.000 19.000 0.632 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0g55tzk award_winner 02l6dy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 27.000 19.000 0.632 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0g55tzk award_winner 0cjsxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 27.000 19.000 0.632 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0g55tzk award_winner 07s8hms CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 27.000 19.000 0.632 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0g55tzk award_winner 03f1zdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 27.000 19.000 0.632 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12533-049bp4 PRED entity: 049bp4 PRED relation: team! PRED expected values: 03zv9 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 8): 03zv9 (0.33 #42, 0.25 #18, 0.24 #737), 059yj (0.32 #213, 0.31 #173, 0.27 #253), 0355pl (0.26 #379, 0.25 #371, 0.25 #387), 0356lc (0.24 #737, 0.17 #41, 0.06 #441), 07y9k (0.15 #444, 0.12 #308, 0.11 #596), 0h69c (0.09 #582, 0.08 #254, 0.06 #174), 01ddbl (0.08 #399, 0.06 #167, 0.04 #809), 021q23 (0.05 #232, 0.03 #802, 0.02 #793) >> Best rule #42 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 0d8_wz; >> query: (?x8361, 03zv9) <- team(?x9106, ?x8361), team(?x8360, ?x8361), position(?x8361, ?x530), position(?x8361, ?x203), position(?x8361, ?x63), ?x203 = 0dgrmp, ?x8360 = 0c2rr7, ?x530 = 02_j1w, ?x63 = 02sdk9v, gender(?x9106, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049bp4 team! 03zv9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.333 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #12532-01nz1q6 PRED entity: 01nz1q6 PRED relation: artist! PRED expected values: 0fb0v 01clyr => 154 concepts (115 used for prediction) PRED predicted values (max 10 best out of 116): 0n85g (0.43 #343, 0.40 #63, 0.17 #763), 0mzkr (0.40 #25, 0.29 #305, 0.19 #865), 015_1q (0.28 #2540, 0.26 #1700, 0.25 #2400), 01clyr (0.25 #593, 0.20 #33, 0.19 #1153), 01w40h (0.25 #588, 0.20 #28, 0.11 #1708), 0g768 (0.25 #597, 0.14 #1717, 0.12 #11521), 017l96 (0.22 #719, 0.19 #859, 0.17 #159), 03rhqg (0.22 #716, 0.17 #576, 0.16 #4776), 04fc6c (0.20 #77, 0.11 #497, 0.10 #917), 01cl0d (0.20 #55, 0.10 #895, 0.07 #1315) >> Best rule #343 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 07sbk; 017_hq; >> query: (?x10924, 0n85g) <- artist(?x11292, ?x10924), award_winner(?x2139, ?x10924), ?x11292 = 01txts >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 02r1tx7; 02hzz; *> query: (?x10924, 01clyr) <- artist(?x3888, ?x10924), artists(?x474, ?x10924), ?x3888 = 01gfq4 *> conf = 0.25 ranks of expected_values: 4, 23 EVAL 01nz1q6 artist! 01clyr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 154.000 115.000 0.429 http://example.org/music/record_label/artist EVAL 01nz1q6 artist! 0fb0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 154.000 115.000 0.429 http://example.org/music/record_label/artist #12531-0gbtbm PRED entity: 0gbtbm PRED relation: genre PRED expected values: 01z77k => 136 concepts (132 used for prediction) PRED predicted values (max 10 best out of 144): 01z77k (0.67 #455, 0.62 #710, 0.56 #1304), 01f9r0 (0.56 #1361, 0.42 #1360, 0.26 #2209), 05p553 (0.54 #5590, 0.48 #3228, 0.46 #1620), 082gq (0.42 #1360, 0.26 #2209, 0.26 #2208), 01z4y (0.42 #1634, 0.39 #2822, 0.38 #3242), 0hcr (0.39 #2485, 0.33 #5268, 0.22 #3329), 01t_vv (0.35 #1481, 0.33 #1650, 0.26 #2073), 03k9fj (0.33 #437, 0.33 #267, 0.24 #5428), 01htzx (0.33 #273, 0.23 #4506, 0.21 #5434), 06n90 (0.28 #4502, 0.27 #2479, 0.25 #610) >> Best rule #455 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 09fc83; 06zsk51; 032xky; >> query: (?x4529, 01z77k) <- genre(?x4529, ?x10122), genre(?x6427, ?x10122), genre(?x2814, ?x10122), ?x6427 = 02nczh, featured_film_locations(?x4529, ?x1658), actor(?x4529, ?x2849), ?x2814 = 078sj4 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gbtbm genre 01z77k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 132.000 0.667 http://example.org/tv/tv_program/genre #12530-01wn718 PRED entity: 01wn718 PRED relation: award PRED expected values: 02f76h => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 272): 01by1l (0.55 #1316, 0.33 #4524, 0.32 #7732), 01cky2 (0.40 #597, 0.30 #1399, 0.11 #2201), 01bgqh (0.36 #1246, 0.31 #4454, 0.28 #7662), 02f5qb (0.33 #1360, 0.15 #12187, 0.15 #11786), 02f6xy (0.33 #1405, 0.15 #3811, 0.14 #4613), 0c4z8 (0.30 #1275, 0.21 #4483, 0.20 #8493), 023vrq (0.30 #1527, 0.20 #725, 0.13 #2329), 02f76h (0.27 #1382, 0.20 #580, 0.06 #2184), 05ztrmj (0.25 #186, 0.12 #5800, 0.10 #9008), 09sdmz (0.25 #207, 0.08 #5821, 0.07 #9029) >> Best rule #1316 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 02vwckw; 01f2q5; >> query: (?x3977, 01by1l) <- award_winner(?x12835, ?x3977), artist(?x2190, ?x3977), award(?x3977, ?x4837), ?x4837 = 03t5kl >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1382 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 02vwckw; 01f2q5; *> query: (?x3977, 02f76h) <- award_winner(?x12835, ?x3977), artist(?x2190, ?x3977), award(?x3977, ?x4837), ?x4837 = 03t5kl *> conf = 0.27 ranks of expected_values: 8 EVAL 01wn718 award 02f76h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 125.000 125.000 0.545 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12529-0sxdg PRED entity: 0sxdg PRED relation: child PRED expected values: 0f721s 06jplb => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 259): 0b275x (0.29 #577, 0.25 #743, 0.18 #2065), 031rq5 (0.24 #3353, 0.22 #3519, 0.22 #877), 01scmq (0.23 #2792, 0.14 #643, 0.12 #3122), 05gnf (0.22 #885, 0.18 #3361, 0.14 #4519), 011k1h (0.22 #1011, 0.17 #2167, 0.15 #2332), 016tw3 (0.20 #1828, 0.18 #3314, 0.17 #3480), 093h7p (0.20 #258, 0.11 #1416, 0.11 #1251), 0gsg7 (0.20 #179, 0.11 #1337, 0.11 #1172), 0c_j5d (0.20 #170, 0.11 #1328, 0.11 #1163), 0jz9f (0.20 #166, 0.11 #1324, 0.11 #1159) >> Best rule #577 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0hm0k; >> query: (?x9077, 0b275x) <- company(?x346, ?x9077), industry(?x9077, ?x13321), ?x13321 = 0sydc >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3470 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 026s90; *> query: (?x9077, ?x902) <- child(?x9077, ?x14089), child(?x9077, ?x574), award_winner(?x574, ?x902), citytown(?x14089, ?x739) *> conf = 0.07 ranks of expected_values: 107 EVAL 0sxdg child 06jplb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 143.000 0.286 http://example.org/organization/organization/child./organization/organization_relationship/child EVAL 0sxdg child 0f721s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 143.000 143.000 0.286 http://example.org/organization/organization/child./organization/organization_relationship/child #12528-03prz_ PRED entity: 03prz_ PRED relation: film_release_region PRED expected values: 09c7w0 => 75 concepts (74 used for prediction) PRED predicted values (max 10 best out of 218): 09c7w0 (0.72 #726, 0.71 #1629, 0.70 #185), 059j2 (0.49 #362, 0.47 #1446, 0.45 #543), 0f8l9c (0.49 #362, 0.47 #1446, 0.45 #543), 07ssc (0.49 #362, 0.47 #1446, 0.45 #543), 03rjj (0.49 #362, 0.47 #1446, 0.45 #543), 06bnz (0.49 #362, 0.47 #1446, 0.45 #543), 02jx1 (0.49 #362, 0.47 #1446, 0.45 #543), 0d0vqn (0.28 #735, 0.21 #10455, 0.21 #10275), 0k6nt (0.28 #759, 0.18 #11919, 0.18 #2927), 06mkj (0.27 #799, 0.19 #10698, 0.19 #620) >> Best rule #726 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 03_wm6; 0gh6j94; 0cp08zg; >> query: (?x5759, 09c7w0) <- language(?x5759, ?x254), country(?x5759, ?x789), genre(?x5759, ?x53), ?x789 = 0f8l9c >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03prz_ film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 74.000 0.724 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12527-03c602 PRED entity: 03c602 PRED relation: artist! PRED expected values: 02bh8z => 137 concepts (72 used for prediction) PRED predicted values (max 10 best out of 93): 015_1q (0.33 #304, 0.20 #20, 0.19 #588), 03mp8k (0.20 #68, 0.17 #352, 0.09 #636), 05byxm (0.20 #71, 0.17 #355, 0.04 #639), 05cl8y (0.20 #58, 0.17 #342, 0.04 #626), 01trtc (0.17 #358, 0.15 #642, 0.09 #3345), 0mzkr (0.17 #310, 0.11 #594, 0.08 #1021), 017l96 (0.17 #303, 0.09 #2580, 0.08 #2150), 04fc6c (0.17 #362, 0.04 #646, 0.02 #3349), 03rhqg (0.14 #2861, 0.14 #2719, 0.13 #1011), 033hn8 (0.13 #582, 0.12 #2859, 0.12 #3569) >> Best rule #304 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03y82t6; >> query: (?x10477, 015_1q) <- award_winner(?x3666, ?x10477), award_nominee(?x3200, ?x10477), ?x3200 = 01wj18h, award(?x10477, ?x884) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2867 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 420 *> proper extension: 0m19t; 07qnf; 02_5x9; 0167_s; 01qqwp9; 03xhj6; 0394y; 02t3ln; 01j59b0; 06nv27; ... *> query: (?x10477, 02bh8z) <- artists(?x12082, ?x10477), origin(?x10477, ?x4698), category(?x10477, ?x134) *> conf = 0.05 ranks of expected_values: 28 EVAL 03c602 artist! 02bh8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 137.000 72.000 0.333 http://example.org/music/record_label/artist #12526-0jgwf PRED entity: 0jgwf PRED relation: award PRED expected values: 02n9nmz 02pqp12 => 101 concepts (66 used for prediction) PRED predicted values (max 10 best out of 273): 027c924 (0.69 #11549, 0.68 #20313, 0.68 #20312), 09d28z (0.69 #11549, 0.68 #20313, 0.68 #20312), 05h5nb8 (0.69 #11549, 0.68 #20313, 0.68 #20312), 0gq9h (0.55 #2862, 0.37 #1269, 0.35 #871), 0gr51 (0.47 #892, 0.46 #494, 0.32 #1290), 02n9nmz (0.38 #465, 0.37 #863, 0.22 #4780), 03hl6lc (0.37 #970, 0.37 #572, 0.19 #1368), 02pqp12 (0.34 #1262, 0.28 #4447, 0.26 #3651), 02qyntr (0.34 #266, 0.22 #4780, 0.22 #1595), 02x17s4 (0.33 #519, 0.33 #917, 0.11 #1315) >> Best rule #11549 for best value: >> intensional similarity = 3 >> extensional distance = 852 >> proper extension: 04ns3gy; >> query: (?x8645, ?x198) <- type_of_union(?x8645, ?x566), award_winner(?x198, ?x8645), award_winner(?x4598, ?x8645) >> conf = 0.69 => this is the best rule for 3 predicted values *> Best rule #465 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 0h5f5n; 0159h6; 04r7jc; 05m883; 05183k; 01q415; 03m_k0; 05ldnp; 085pr; 098n5; ... *> query: (?x8645, 02n9nmz) <- award(?x8645, ?x746), profession(?x8645, ?x987), ?x746 = 04dn09n, ?x987 = 0dxtg *> conf = 0.38 ranks of expected_values: 6, 8 EVAL 0jgwf award 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 101.000 66.000 0.695 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0jgwf award 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 101.000 66.000 0.695 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12525-01jcxwp PRED entity: 01jcxwp PRED relation: group! PRED expected values: 02hnl => 83 concepts (48 used for prediction) PRED predicted values (max 10 best out of 123): 02hnl (0.82 #1510, 0.81 #813, 0.81 #1597), 05148p4 (0.75 #1500, 0.74 #1850, 0.73 #1587), 03qjg (0.33 #1529, 0.33 #1616, 0.29 #1180), 01vj9c (0.32 #1581, 0.30 #1494, 0.27 #2456), 05r5c (0.31 #1576, 0.30 #1489, 0.27 #1314), 0l14qv (0.27 #1837, 0.26 #1487, 0.24 #1574), 013y1f (0.19 #1508, 0.17 #1595, 0.17 #287), 04rzd (0.17 #292, 0.15 #1863, 0.14 #1164), 0mkg (0.17 #271, 0.13 #1929, 0.10 #1317), 042v_gx (0.17 #269, 0.11 #1577, 0.11 #1490) >> Best rule #1510 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 05crg7; 02_5x9; 01qqwp9; 0123r4; 0qmpd; >> query: (?x7125, 02hnl) <- group(?x227, ?x7125), artists(?x1127, ?x7125), group(?x10091, ?x7125), ?x227 = 0342h >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jcxwp group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 48.000 0.824 http://example.org/music/performance_role/regular_performances./music/group_membership/group #12524-02qyv3h PRED entity: 02qyv3h PRED relation: film_format PRED expected values: 0cj16 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 3): 0cj16 (0.38 #53, 0.36 #41, 0.35 #188), 017fx5 (0.30 #25, 0.27 #31, 0.21 #48), 07fb8_ (0.24 #176, 0.22 #34, 0.21 #171) >> Best rule #53 for best value: >> intensional similarity = 8 >> extensional distance = 32 >> proper extension: 03hj5lq; >> query: (?x5877, 0cj16) <- film_crew_role(?x5877, ?x137), film_festivals(?x5877, ?x11852), genre(?x5877, ?x225), country(?x5877, ?x390), film_release_distribution_medium(?x5877, ?x81), category(?x5877, ?x134), genre(?x8477, ?x225), film_release_region(?x8477, ?x94) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qyv3h film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.382 http://example.org/film/film/film_format #12523-01m3x5p PRED entity: 01m3x5p PRED relation: profession PRED expected values: 0nbcg => 125 concepts (74 used for prediction) PRED predicted values (max 10 best out of 81): 0dxtg (0.64 #1443, 0.48 #2589, 0.48 #8344), 0nbcg (0.57 #3463, 0.57 #3894, 0.52 #2318), 0kyk (0.50 #170, 0.34 #4470, 0.33 #2603), 0dz3r (0.49 #2147, 0.46 #3436, 0.46 #3867), 0cbd2 (0.48 #4449, 0.43 #6179, 0.42 #6608), 02jknp (0.45 #8338, 0.36 #1437, 0.27 #1294), 016z4k (0.43 #5022, 0.42 #6320, 0.41 #4590), 03gjzk (0.41 #1444, 0.31 #8345, 0.26 #2159), 039v1 (0.38 #3468, 0.37 #3899, 0.28 #5630), 01c8w0 (0.36 #723, 0.28 #437, 0.21 #1152) >> Best rule #1443 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 049gc; 079ws; >> query: (?x4184, 0dxtg) <- student(?x2909, ?x4184), award_winner(?x5172, ?x4184), influenced_by(?x4184, ?x3378) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #3463 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 186 *> proper extension: 0f0y8; 028q6; 03c7ln; 01vw87c; 0fp_v1x; 0m2l9; 01wl38s; 06cc_1; 0kzy0; 01cv3n; ... *> query: (?x4184, 0nbcg) <- artists(?x505, ?x4184), gender(?x4184, ?x231), category(?x4184, ?x134), role(?x4184, ?x2206) *> conf = 0.57 ranks of expected_values: 2 EVAL 01m3x5p profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 74.000 0.643 http://example.org/people/person/profession #12522-0b05xm PRED entity: 0b05xm PRED relation: nationality PRED expected values: 09c7w0 => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 17): 09c7w0 (0.85 #501, 0.81 #801, 0.81 #601), 07ssc (0.10 #715, 0.08 #3216, 0.08 #4216), 02jx1 (0.09 #4234, 0.09 #4334, 0.09 #4735), 03rk0 (0.07 #1846, 0.05 #4347, 0.05 #4247), 0d060g (0.06 #307, 0.05 #707, 0.04 #607), 0chghy (0.02 #1210, 0.02 #2010, 0.02 #1610), 02k54 (0.02 #418), 05v8c (0.02 #416), 0b90_r (0.02 #403), 0f8l9c (0.02 #4223, 0.02 #4323, 0.02 #4423) >> Best rule #501 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 05g8ky; 03m_k0; 014dm6; 02661h; 023jq1; 017dpj; 01s7z0; >> query: (?x3570, 09c7w0) <- program(?x3570, ?x10089), profession(?x3570, ?x319), ?x319 = 01d_h8 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b05xm nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.846 http://example.org/people/person/nationality #12521-07ssc PRED entity: 07ssc PRED relation: jurisdiction_of_office! PRED expected values: 060bp 04syw => 239 concepts (239 used for prediction) PRED predicted values (max 10 best out of 27): 060c4 (0.76 #2065, 0.74 #760, 0.73 #1855), 0f6c3 (0.74 #2175, 0.72 #2322, 0.72 #2385), 060bp (0.74 #758, 0.73 #2063, 0.73 #1874), 09n5b9 (0.66 #2178, 0.65 #2325, 0.64 #2388), 0fkvn (0.61 #2171, 0.58 #2318, 0.58 #2381), 0pqc5 (0.51 #4065, 0.49 #4170, 0.47 #3644), 04syw (0.50 #1122, 0.32 #1669, 0.25 #1080), 0fj45 (0.50 #1133, 0.30 #1680, 0.29 #459), 0dq3c (0.33 #506, 0.33 #2, 0.29 #633), 02079p (0.33 #10, 0.24 #746, 0.17 #430) >> Best rule #2065 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 020p1; >> query: (?x512, 060c4) <- country(?x4310, ?x512), participating_countries(?x358, ?x512), ?x4310 = 064vjs >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #758 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0j4b; *> query: (?x512, 060bp) <- country(?x7687, ?x512), adjoins(?x512, ?x429), ?x7687 = 03krj *> conf = 0.74 ranks of expected_values: 3, 7 EVAL 07ssc jurisdiction_of_office! 04syw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 239.000 239.000 0.764 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 07ssc jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 239.000 239.000 0.764 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #12520-046f3p PRED entity: 046f3p PRED relation: featured_film_locations PRED expected values: 027kp3 => 102 concepts (60 used for prediction) PRED predicted values (max 10 best out of 81): 02_286 (0.20 #743, 0.18 #1225, 0.16 #984), 04jpl (0.16 #250, 0.12 #9, 0.12 #491), 030qb3t (0.11 #280, 0.10 #1968, 0.10 #2930), 0156q (0.11 #282, 0.08 #523, 0.03 #1487), 0ctw_b (0.11 #264, 0.08 #505), 0dclg (0.06 #53, 0.03 #1258, 0.01 #2463), 017j7y (0.06 #223, 0.03 #1428), 0vzm (0.06 #74, 0.01 #2724, 0.01 #3934), 01r32 (0.06 #37, 0.01 #1242), 05fjf (0.06 #127) >> Best rule #743 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 080lkt7; >> query: (?x7664, 02_286) <- films(?x2286, ?x7664), genre(?x7664, ?x6887), genre(?x7664, ?x53), ?x6887 = 03bxz7, ?x53 = 07s9rl0 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1064 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 09gdm7q; 0415ggl; 03m5y9p; *> query: (?x7664, 027kp3) <- films(?x2286, ?x7664), genre(?x7664, ?x6887), ?x6887 = 03bxz7, nominated_for(?x617, ?x7664) *> conf = 0.02 ranks of expected_values: 38 EVAL 046f3p featured_film_locations 027kp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 102.000 60.000 0.195 http://example.org/film/film/featured_film_locations #12519-02hh8j PRED entity: 02hh8j PRED relation: languages PRED expected values: 064_8sq => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.26 #1952, 0.25 #2069, 0.24 #2108), 064_8sq (0.19 #15, 0.16 #93, 0.08 #132), 02bjrlw (0.12 #1, 0.05 #79, 0.05 #118), 04306rv (0.12 #3, 0.05 #81, 0.03 #315), 03k50 (0.02 #199, 0.02 #1252, 0.02 #394), 06b_j (0.01 #406), 06nm1 (0.01 #318, 0.01 #474, 0.01 #591), 07c9s (0.01 #3602) >> Best rule #1952 for best value: >> intensional similarity = 2 >> extensional distance = 886 >> proper extension: 02zq43; >> query: (?x9717, 02h40lc) <- nominated_for(?x9717, ?x9900), people(?x1050, ?x9717) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 045bg; 0840vq; 03d0ns; 0gdqy; 01q8fxx; *> query: (?x9717, 064_8sq) <- award(?x9717, ?x198), place_of_birth(?x9717, ?x4627), profession(?x9717, ?x319), ?x4627 = 05qtj *> conf = 0.19 ranks of expected_values: 2 EVAL 02hh8j languages 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 161.000 161.000 0.256 http://example.org/people/person/languages #12518-034cj9 PRED entity: 034cj9 PRED relation: profession PRED expected values: 03gjzk => 100 concepts (39 used for prediction) PRED predicted values (max 10 best out of 63): 01d_h8 (0.68 #4301, 0.68 #1782, 0.67 #4005), 0dxtg (0.62 #4308, 0.62 #5048, 0.60 #4012), 03gjzk (0.26 #4309, 0.25 #5049, 0.25 #4013), 0cbd2 (0.25 #3561, 0.21 #155, 0.19 #3413), 02krf9 (0.23 #1802, 0.22 #4025, 0.21 #4321), 0kyk (0.18 #177, 0.17 #29, 0.14 #3435), 01c72t (0.18 #171, 0.14 #1355, 0.13 #2984), 02hv44_ (0.15 #57, 0.13 #205, 0.07 #1685), 09jwl (0.14 #2831, 0.13 #18, 0.13 #166), 025352 (0.13 #59, 0.10 #207, 0.04 #3020) >> Best rule #4301 for best value: >> intensional similarity = 4 >> extensional distance = 555 >> proper extension: 07nznf; 05bnp0; 012d40; 0337vz; 02rchht; 083chw; 0qf43; 014zcr; 01wbg84; 09fb5; ... >> query: (?x13595, 01d_h8) <- gender(?x13595, ?x231), profession(?x13595, ?x524), ?x524 = 02jknp, ?x231 = 05zppz >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #4309 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 555 *> proper extension: 07nznf; 05bnp0; 012d40; 0337vz; 02rchht; 083chw; 0qf43; 014zcr; 01wbg84; 09fb5; ... *> query: (?x13595, 03gjzk) <- gender(?x13595, ?x231), profession(?x13595, ?x524), ?x524 = 02jknp, ?x231 = 05zppz *> conf = 0.26 ranks of expected_values: 3 EVAL 034cj9 profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 100.000 39.000 0.679 http://example.org/people/person/profession #12517-049dzz PRED entity: 049dzz PRED relation: sport PRED expected values: 02vx4 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.90 #345, 0.88 #426, 0.88 #219), 0jm_ (0.28 #481, 0.17 #491, 0.14 #591), 018jz (0.21 #204, 0.15 #483, 0.14 #593), 018w8 (0.17 #482, 0.14 #203, 0.13 #492), 03tmr (0.14 #200, 0.14 #690, 0.13 #489), 09xp_ (0.09 #160, 0.09 #151, 0.07 #205), 039yzs (0.04 #696, 0.04 #669, 0.04 #987), 0z74 (0.03 #596, 0.02 #771, 0.01 #533) >> Best rule #345 for best value: >> intensional similarity = 11 >> extensional distance = 27 >> proper extension: 0449sw; 0329t7; 0415zv; 046vvc; >> query: (?x10248, 02vx4) <- teams(?x1646, ?x10248), position(?x10248, ?x530), position(?x10248, ?x203), position(?x10248, ?x63), position(?x10248, ?x60), ?x60 = 02nzb8, ?x203 = 0dgrmp, ?x530 = 02_j1w, administrative_parent(?x1646, ?x1264), ?x63 = 02sdk9v, adjoins(?x6325, ?x1646) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049dzz sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.897 http://example.org/sports/sports_team/sport #12516-030b93 PRED entity: 030b93 PRED relation: student! PRED expected values: 07szy => 66 concepts (55 used for prediction) PRED predicted values (max 10 best out of 79): 01w5m (0.11 #105, 0.04 #2735, 0.03 #19568), 0gl5_ (0.11 #244, 0.02 #11290, 0.01 #4978), 0146hc (0.11 #191), 019dwp (0.11 #158), 0j_sncb (0.11 #83), 0bwfn (0.09 #2904, 0.07 #11320, 0.06 #19737), 09f2j (0.07 #685, 0.05 #4893, 0.04 #2789), 017z88 (0.06 #1134, 0.06 #1660, 0.05 #4816), 065y4w7 (0.06 #2644, 0.04 #11060, 0.04 #22107), 0fr9jp (0.05 #870, 0.03 #2974, 0.02 #5078) >> Best rule #105 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 03n69x; 0ckm4x; >> query: (?x7132, 01w5m) <- profession(?x7132, ?x524), student(?x6953, ?x7132), ?x6953 = 01jq0j >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 030b93 student! 07szy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 55.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #12515-0ptdz PRED entity: 0ptdz PRED relation: film_crew_role PRED expected values: 01pvkk => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 33): 09vw2b7 (0.73 #822, 0.72 #2149, 0.71 #1070), 0dxtw (0.44 #185, 0.42 #1145, 0.40 #1322), 01vx2h (0.43 #467, 0.41 #1901, 0.40 #11), 01pvkk (0.34 #2921, 0.34 #1710, 0.33 #187), 02rh1dz (0.34 #1710, 0.27 #79, 0.20 #9), 0d2b38 (0.34 #1710, 0.20 #25, 0.18 #95), 01xy5l_ (0.34 #1710, 0.20 #14, 0.17 #4418), 015h31 (0.34 #1710, 0.20 #8, 0.17 #4418), 089g0h (0.34 #1710, 0.17 #4418, 0.17 #194), 04pyp5 (0.34 #1710, 0.17 #4418, 0.16 #1999) >> Best rule #822 for best value: >> intensional similarity = 7 >> extensional distance = 159 >> proper extension: 0gh6j94; >> query: (?x11909, 09vw2b7) <- film_crew_role(?x11909, ?x468), film_crew_role(?x11909, ?x137), genre(?x11909, ?x258), language(?x11909, ?x90), ?x468 = 02r96rf, ?x258 = 05p553, ?x137 = 09zzb8 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2921 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 960 *> proper extension: 047svrl; *> query: (?x11909, 01pvkk) <- film_crew_role(?x11909, ?x1284), film(?x2726, ?x11909), film_crew_role(?x8471, ?x1284), film_crew_role(?x7729, ?x1284), film_crew_role(?x3979, ?x1284), ?x7729 = 05pxnmb, ?x3979 = 01vw8k, ?x8471 = 0cp0t91 *> conf = 0.34 ranks of expected_values: 4 EVAL 0ptdz film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 125.000 125.000 0.733 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12514-02k6rq PRED entity: 02k6rq PRED relation: film PRED expected values: 026hh0m => 105 concepts (79 used for prediction) PRED predicted values (max 10 best out of 592): 02ht1k (0.23 #7777, 0.06 #5990, 0.03 #123324), 04yg13l (0.18 #4440, 0.18 #2653, 0.12 #6227), 050xxm (0.18 #3849, 0.12 #5636, 0.06 #7423), 02_kd (0.18 #4159, 0.09 #2372, 0.06 #5946), 03wjm2 (0.18 #3544, 0.06 #7118, 0.03 #10692), 03177r (0.18 #5824, 0.09 #4037, 0.09 #2250), 031786 (0.18 #6636, 0.09 #4849, 0.09 #3062), 02stbw (0.17 #7530, 0.06 #5743, 0.03 #123324), 0prh7 (0.14 #835, 0.09 #4409, 0.09 #2622), 0888c3 (0.14 #8562, 0.06 #6775, 0.03 #46466) >> Best rule #7777 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0bzyh; >> query: (?x2045, 02ht1k) <- award_nominee(?x4928, ?x2045), award_nominee(?x2173, ?x2045), award_winner(?x926, ?x2173), ?x4928 = 051wwp >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02k6rq film 026hh0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 79.000 0.229 http://example.org/film/actor/film./film/performance/film #12513-026mfs PRED entity: 026mfs PRED relation: ceremony PRED expected values: 02rjjll 01bx35 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 122): 01bx35 (0.87 #509, 0.71 #761, 0.63 #887), 02rjjll (0.86 #507, 0.78 #759, 0.68 #885), 0gx1673 (0.48 #610, 0.44 #862, 0.40 #232), 04n2r9h (0.27 #3910, 0.26 #3152, 0.21 #4289), 02yvhx (0.21 #4289, 0.21 #3783, 0.16 #697), 0bz6sb (0.21 #4289, 0.21 #3783, 0.16 #685), 09pj68 (0.21 #4289, 0.21 #3783, 0.04 #973), 09p3h7 (0.21 #4289, 0.21 #3783, 0.04 #944), 0bzm81 (0.19 #648, 0.16 #900, 0.16 #1026), 0bzm__ (0.19 #706, 0.16 #958, 0.14 #1084) >> Best rule #509 for best value: >> intensional similarity = 5 >> extensional distance = 67 >> proper extension: 0257yf; >> query: (?x2420, 01bx35) <- award(?x133, ?x2420), ceremony(?x2420, ?x1480), ceremony(?x2420, ?x342), ?x1480 = 01c6qp, ?x342 = 01s695 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 026mfs ceremony 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.870 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 026mfs ceremony 02rjjll CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.870 http://example.org/award/award_category/winners./award/award_honor/ceremony #12512-03h4fq7 PRED entity: 03h4fq7 PRED relation: executive_produced_by PRED expected values: 027z0pl => 78 concepts (59 used for prediction) PRED predicted values (max 10 best out of 47): 06q8hf (0.10 #1169, 0.09 #918, 0.04 #3678), 05hj_k (0.09 #1101, 0.09 #850, 0.04 #3610), 06pj8 (0.05 #1059, 0.04 #808, 0.03 #305), 0glyyw (0.05 #940, 0.04 #1191, 0.03 #1441), 03c9pqt (0.04 #998, 0.03 #1249, 0.03 #495), 01zfmm (0.04 #501, 0.03 #4514, 0.02 #6017), 04t2l2 (0.04 #2508, 0.02 #1004, 0.02 #7526), 079vf (0.03 #1006, 0.03 #503, 0.02 #2009), 02z6l5f (0.03 #870, 0.03 #1121, 0.02 #367), 0343h (0.03 #42, 0.03 #1046, 0.02 #795) >> Best rule #1169 for best value: >> intensional similarity = 3 >> extensional distance = 470 >> proper extension: 0hz6mv2; 0hr41p6; >> query: (?x5113, 06q8hf) <- language(?x5113, ?x254), ?x254 = 02h40lc, executive_produced_by(?x5113, ?x2790) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #971 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 395 *> proper extension: 0b76kw1; *> query: (?x5113, 027z0pl) <- film(?x237, ?x5113), executive_produced_by(?x5113, ?x2790), titles(?x2480, ?x5113) *> conf = 0.02 ranks of expected_values: 29 EVAL 03h4fq7 executive_produced_by 027z0pl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 78.000 59.000 0.095 http://example.org/film/film/executive_produced_by #12511-0z4s PRED entity: 0z4s PRED relation: type_of_union PRED expected values: 04ztj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.87 #5, 0.78 #1, 0.75 #53), 01g63y (0.16 #42, 0.16 #10, 0.15 #78) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0bl2g; 09fb5; 03f2_rc; 039bp; 01b9ck; 0bwh6; 04nw9; 06cgy; 01mqz0; 0bj9k; ... >> query: (?x450, 04ztj) <- award_winner(?x2060, ?x450), award(?x450, ?x112), ?x2060 = 054ky1 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0z4s type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.870 http://example.org/people/person/spouse_s./people/marriage/type_of_union #12510-02y49 PRED entity: 02y49 PRED relation: nationality PRED expected values: 09c7w0 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 39): 09c7w0 (0.76 #601, 0.75 #1602, 0.74 #1903), 06pvr (0.33 #5211), 01w65s (0.33 #8422), 03v0t (0.33 #8422), 02jx1 (0.23 #934, 0.18 #733, 0.15 #333), 07ssc (0.18 #515, 0.18 #1316, 0.17 #916), 0345h (0.12 #1032, 0.10 #932, 0.10 #831), 03rt9 (0.10 #713, 0.10 #313, 0.03 #2617), 06m_5 (0.10 #183, 0.08 #283, 0.06 #9925), 0d060g (0.10 #307, 0.06 #9925, 0.04 #6020) >> Best rule #601 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0k57l; 0bqch; >> query: (?x8908, 09c7w0) <- influenced_by(?x2343, ?x8908), profession(?x8908, ?x1032), ?x1032 = 02hrh1q, place_of_death(?x8908, ?x3125) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y49 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.762 http://example.org/people/person/nationality #12509-067z2v PRED entity: 067z2v PRED relation: colors! PRED expected values: 0m9_5 => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1354): 0bsnm (0.99 #951, 0.60 #5057, 0.40 #5542), 07lx1s (0.99 #951, 0.50 #4341, 0.43 #6253), 0gl6x (0.99 #951, 0.50 #3696, 0.43 #6567), 01jq34 (0.99 #951, 0.50 #6750, 0.40 #5320), 02vnp2 (0.99 #951, 0.50 #7031, 0.40 #5601), 065r8g (0.99 #951, 0.50 #4378, 0.40 #5339), 01lnyf (0.99 #951, 0.50 #3478, 0.34 #954), 05zl0 (0.99 #951, 0.50 #2579, 0.34 #954), 03wv2g (0.99 #951, 0.50 #2867, 0.34 #954), 01hx2t (0.99 #951, 0.50 #2679, 0.33 #279) >> Best rule #951 for best value: >> intensional similarity = 53 >> extensional distance = 1 >> proper extension: 083jv; >> query: (?x5845, ?x1103) <- colors(?x12260, ?x5845), colors(?x5907, ?x5845), colors(?x3779, ?x5845), category(?x12260, ?x134), colors(?x13438, ?x5845), ?x13438 = 02wvfxz, school(?x6823, ?x3779), school(?x4208, ?x3779), school(?x2820, ?x3779), ?x2820 = 0jmj7, country(?x12260, ?x94), registering_agency(?x3779, ?x1982), institution(?x620, ?x3779), major_field_of_study(?x3779, ?x9111), colors(?x3779, ?x4557), draft(?x6823, ?x1161), ?x9111 = 04sh3, team(?x2010, ?x6823), colors(?x6823, ?x663), list(?x3779, ?x2197), currency(?x3779, ?x170), school_type(?x12260, ?x1044), school(?x4208, ?x10666), major_field_of_study(?x12260, ?x2606), colors(?x7918, ?x4557), colors(?x1103, ?x4557), colors(?x12043, ?x4557), colors(?x11368, ?x4557), colors(?x11153, ?x4557), colors(?x10085, ?x4557), colors(?x9543, ?x4557), colors(?x2919, ?x4557), colors(?x2677, ?x4557), colors(?x1115, ?x4557), colors(?x260, ?x4557), ?x10085 = 02fbb5, ?x170 = 09nqf, ?x11153 = 080_y, ?x11368 = 032yps, ?x12043 = 03jb2n, colors(?x4208, ?x332), student(?x5907, ?x3762), major_field_of_study(?x5907, ?x2172), ?x2919 = 0c41y70, ?x260 = 01ypc, ?x2677 = 0g701n, institution(?x865, ?x5907), ?x1115 = 01y3c, ?x10666 = 01dzg0, organization(?x5510, ?x3779), ?x7918 = 0gl6f, ?x9543 = 07s8qm7, ?x3762 = 04x4s2 >> conf = 0.99 => this is the best rule for 79 predicted values *> Best rule #954 for first EXPECTED value: *> intensional similarity = 53 *> extensional distance = 1 *> proper extension: 083jv; *> query: (?x5845, ?x2522) <- colors(?x12260, ?x5845), colors(?x5907, ?x5845), colors(?x3779, ?x5845), category(?x12260, ?x134), colors(?x13438, ?x5845), ?x13438 = 02wvfxz, school(?x6823, ?x3779), school(?x4208, ?x3779), school(?x2820, ?x3779), ?x2820 = 0jmj7, country(?x12260, ?x94), registering_agency(?x3779, ?x1982), institution(?x620, ?x3779), major_field_of_study(?x3779, ?x9111), colors(?x3779, ?x4557), draft(?x6823, ?x1161), ?x9111 = 04sh3, team(?x2010, ?x6823), colors(?x6823, ?x663), list(?x3779, ?x2197), currency(?x3779, ?x170), school_type(?x12260, ?x1044), school(?x4208, ?x10666), school(?x4208, ?x2522), major_field_of_study(?x12260, ?x2606), colors(?x7918, ?x4557), colors(?x12043, ?x4557), colors(?x11368, ?x4557), colors(?x11153, ?x4557), colors(?x10085, ?x4557), colors(?x9543, ?x4557), colors(?x2919, ?x4557), colors(?x2677, ?x4557), colors(?x1115, ?x4557), colors(?x260, ?x4557), ?x10085 = 02fbb5, ?x170 = 09nqf, ?x11153 = 080_y, ?x11368 = 032yps, ?x12043 = 03jb2n, colors(?x4208, ?x332), student(?x5907, ?x3762), major_field_of_study(?x5907, ?x2172), ?x2919 = 0c41y70, ?x260 = 01ypc, ?x2677 = 0g701n, institution(?x865, ?x5907), ?x1115 = 01y3c, ?x10666 = 01dzg0, organization(?x5510, ?x3779), ?x7918 = 0gl6f, ?x9543 = 07s8qm7, ?x3762 = 04x4s2 *> conf = 0.34 ranks of expected_values: 229 EVAL 067z2v colors! 0m9_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 21.000 21.000 0.989 http://example.org/education/educational_institution/colors #12508-0fbw6 PRED entity: 0fbw6 PRED relation: nutrient PRED expected values: 025s7x6 0h1sg 0h1tg 0h1tz => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 35): 0h1tz (0.50 #314, 0.40 #299, 0.33 #284), 0h1sg (0.50 #312, 0.40 #297, 0.33 #282), 025s7x6 (0.50 #311, 0.40 #296, 0.33 #281), 0h1tg (0.50 #313, 0.40 #298, 0.33 #283), 02kd0rh (0.33 #315, 0.33 #285, 0.33 #269), 08lb68 (0.33 #308, 0.33 #278, 0.33 #229), 02kc_w5 (0.33 #273, 0.33 #257, 0.33 #208), 01w_3 (0.33 #209, 0.33 #163, 0.33 #133), 0466p20 (0.33 #309, 0.33 #247, 0.33 #215), 0f4k5 (0.33 #210, 0.33 #164, 0.33 #134) >> Best rule #314 for best value: >> intensional similarity = 63 >> extensional distance = 4 >> proper extension: 0dcfv; >> query: (?x4068, 0h1tz) <- nutrient(?x4068, ?x12454), nutrient(?x4068, ?x9915), nutrient(?x4068, ?x9436), nutrient(?x4068, ?x8243), nutrient(?x4068, ?x6192), nutrient(?x4068, ?x5549), nutrient(?x4068, ?x5451), nutrient(?x4068, ?x5374), nutrient(?x4068, ?x5337), nutrient(?x4068, ?x2702), nutrient(?x4068, ?x2018), nutrient(?x9732, ?x5374), nutrient(?x9489, ?x5374), nutrient(?x9005, ?x5374), nutrient(?x8298, ?x5374), nutrient(?x7719, ?x5374), nutrient(?x7057, ?x5374), nutrient(?x6285, ?x5374), nutrient(?x6191, ?x5374), nutrient(?x6159, ?x5374), nutrient(?x6032, ?x5374), nutrient(?x5373, ?x5374), nutrient(?x5009, ?x5374), nutrient(?x3900, ?x5374), nutrient(?x2701, ?x5374), nutrient(?x1959, ?x5374), nutrient(?x1303, ?x5374), nutrient(?x1257, ?x5374), ?x9915 = 025tkqy, ?x5009 = 0fjfh, ?x8243 = 014d7f, ?x2018 = 01sh2, ?x6192 = 06jry, ?x9005 = 04zpv, ?x5373 = 0971v, ?x1959 = 0f25w9, ?x12454 = 025rw19, ?x9489 = 07j87, nutrient(?x10612, ?x9436), nutrient(?x3468, ?x9436), ?x1303 = 0fj52s, ?x1257 = 09728, ?x7057 = 0fbdb, ?x6191 = 014j1m, ?x10612 = 0frq6, ?x3900 = 061_f, ?x5549 = 025s7j4, ?x5337 = 06x4c, taxonomy(?x5451, ?x939), ?x3468 = 0cxn2, ?x6159 = 033cnk, ?x7719 = 0dj75, ?x939 = 04n6k, ?x2701 = 0hkxq, ?x6285 = 01645p, ?x9732 = 05z55, ?x8298 = 037ls6, ?x6032 = 01nkt, nutrient(?x5337, ?x2702), nutrient(?x1303, ?x9436), nutrient(?x9732, ?x9436), nutrient(?x1257, ?x5451), nutrient(?x6159, ?x9436) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0fbw6 nutrient 0h1tz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.500 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fbw6 nutrient 0h1tg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.500 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fbw6 nutrient 0h1sg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.500 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fbw6 nutrient 025s7x6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.500 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #12507-082xp PRED entity: 082xp PRED relation: nationality PRED expected values: 02jx1 => 95 concepts (88 used for prediction) PRED predicted values (max 10 best out of 52): 09c7w0 (0.83 #1393, 0.78 #8639, 0.78 #2685), 02jx1 (0.40 #3712, 0.39 #4009, 0.25 #5265), 01z3d2 (0.25 #5265, 0.25 #3680, 0.25 #6456), 04jpl (0.25 #5265, 0.25 #3680, 0.25 #6456), 0d060g (0.15 #205, 0.08 #900, 0.07 #3188), 06q1r (0.15 #274, 0.06 #474, 0.06 #3756), 014tss (0.15 #273, 0.05 #2087, 0.05 #1166), 0h7x (0.10 #133, 0.06 #432, 0.06 #531), 0gk7z (0.10 #1292, 0.03 #3380, 0.03 #3580), 0f8l9c (0.08 #914, 0.06 #518, 0.05 #2087) >> Best rule #1393 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 0xnc3; >> query: (?x11492, 09c7w0) <- student(?x9741, ?x11492), gender(?x11492, ?x231), celebrities_impersonated(?x3649, ?x11492), ?x231 = 05zppz >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #3712 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 231 *> proper extension: 02784z; *> query: (?x11492, 02jx1) <- gender(?x11492, ?x231), nationality(?x11492, ?x512), ?x231 = 05zppz, ?x512 = 07ssc *> conf = 0.40 ranks of expected_values: 2 EVAL 082xp nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 88.000 0.828 http://example.org/people/person/nationality #12506-01pcvn PRED entity: 01pcvn PRED relation: profession PRED expected values: 0d1pc => 175 concepts (136 used for prediction) PRED predicted values (max 10 best out of 80): 0d1pc (0.47 #349, 0.38 #945, 0.35 #1392), 01d_h8 (0.40 #5073, 0.39 #11777, 0.38 #16100), 09jwl (0.39 #11777, 0.38 #16100, 0.34 #1658), 0dxtg (0.39 #11777, 0.38 #16100, 0.33 #20277), 039v1 (0.39 #11777, 0.38 #16100, 0.33 #20277), 02jknp (0.33 #20277, 0.33 #20276, 0.33 #9389), 03gjzk (0.33 #20277, 0.33 #20276, 0.33 #18485), 0nbcg (0.33 #20277, 0.33 #20276, 0.33 #18485), 01c979 (0.33 #20277, 0.33 #20276, 0.33 #18485), 016z4k (0.33 #20277, 0.33 #20276, 0.33 #18485) >> Best rule #349 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0mz73; >> query: (?x5665, 0d1pc) <- participant(?x3101, ?x5665), ?x3101 = 0dvmd, nationality(?x5665, ?x1310) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pcvn profession 0d1pc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 136.000 0.467 http://example.org/people/person/profession #12505-0kz1h PRED entity: 0kz1h PRED relation: currency! PRED expected values: 0bs5k8r => 8 concepts (6 used for prediction) PRED predicted values (max 10 best out of 1881): 02vyyl8 (0.75 #4018, 0.70 #4014, 0.68 #4028), 05650n (0.75 #4018, 0.70 #4014, 0.68 #4028), 0221zw (0.75 #4018, 0.70 #4014, 0.68 #4028), 0hv8w (0.75 #4018, 0.70 #4014, 0.68 #4028), 0bcp9b (0.75 #4018, 0.70 #4014, 0.68 #4028), 0dr_4 (0.75 #4018, 0.70 #4014, 0.68 #4028), 0p9lw (0.75 #4018, 0.70 #4014, 0.68 #4028), 02yvct (0.75 #4018, 0.70 #4014, 0.68 #4028), 017kz7 (0.75 #4018, 0.70 #4014, 0.68 #4028), 02wgk1 (0.75 #4018, 0.70 #4014, 0.68 #4028) >> Best rule #4018 for best value: >> intensional similarity = 133 >> extensional distance = 1 >> proper extension: 09nqf; >> query: (?x7888, ?x97) <- currency(?x13150, ?x7888), currency(?x11640, ?x7888), currency(?x9181, ?x7888), currency(?x7887, ?x7888), currency(?x4545, ?x7888), category(?x7888, ?x134), film_release_region(?x7887, ?x2152), film_release_region(?x7887, ?x1558), film_release_region(?x7887, ?x1353), film_release_region(?x7887, ?x1003), film_release_region(?x7887, ?x789), film_release_region(?x7887, ?x774), film_release_region(?x7887, ?x550), film_release_region(?x7887, ?x512), film_release_region(?x7887, ?x279), film_release_region(?x7887, ?x151), genre(?x7887, ?x809), genre(?x7887, ?x258), ?x774 = 06mzp, film_release_distribution_medium(?x7887, ?x627), ?x1003 = 03gj2, film_regional_debut_venue(?x7887, ?x5416), currency(?x7950, ?x7888), ?x151 = 0b90_r, major_field_of_study(?x7950, ?x6364), major_field_of_study(?x7950, ?x2605), major_field_of_study(?x7950, ?x2601), language(?x7887, ?x254), institution(?x4981, ?x7950), institution(?x1305, ?x7950), citytown(?x13150, ?x8602), currency(?x11199, ?x7888), ?x2601 = 04x_3, ?x5416 = 015hr, major_field_of_study(?x11640, ?x10391), major_field_of_study(?x11640, ?x4321), major_field_of_study(?x11640, ?x3400), ?x4321 = 0g26h, ?x1353 = 035qy, ?x134 = 08mbj5d, film_crew_role(?x4545, ?x1171), ?x1558 = 01mjq, organization(?x5510, ?x11640), ?x550 = 05v8c, major_field_of_study(?x6364, ?x2981), institution(?x1305, ?x13618), institution(?x1305, ?x12475), institution(?x1305, ?x7716), institution(?x1305, ?x6908), institution(?x1305, ?x4955), institution(?x1305, ?x2948), institution(?x1305, ?x1681), ?x13618 = 01f6ss, ?x7716 = 01n_g9, ?x258 = 05p553, ?x512 = 07ssc, ?x4955 = 09f2j, ?x1681 = 07szy, interests(?x1236, ?x6364), major_field_of_study(?x4692, ?x6364), ?x6908 = 01dthg, school_type(?x13150, ?x3092), ?x789 = 0f8l9c, film_release_distribution_medium(?x10960, ?x627), film_release_distribution_medium(?x8379, ?x627), film_release_distribution_medium(?x5318, ?x627), film_release_distribution_medium(?x4041, ?x627), film_release_distribution_medium(?x3812, ?x627), film_release_distribution_medium(?x1642, ?x627), film_release_distribution_medium(?x1452, ?x627), film_distribution_medium(?x10590, ?x627), film_distribution_medium(?x9496, ?x627), film_distribution_medium(?x7760, ?x627), film_distribution_medium(?x7366, ?x627), film_distribution_medium(?x5513, ?x627), film_distribution_medium(?x4502, ?x627), film_distribution_medium(?x4375, ?x627), film_distribution_medium(?x2586, ?x627), film_distribution_medium(?x2470, ?x627), film_distribution_medium(?x2350, ?x627), film_distribution_medium(?x2189, ?x627), film_distribution_medium(?x2006, ?x627), film_distribution_medium(?x1456, ?x627), film_distribution_medium(?x97, ?x627), ?x254 = 02h40lc, ?x1452 = 0jqn5, ?x7760 = 017kz7, ?x2948 = 0j_sncb, ?x1456 = 0cz8mkh, film(?x157, ?x7887), ?x2152 = 06mkj, citytown(?x7950, ?x8963), ?x10960 = 02ndy4, film_release_region(?x4545, ?x410), ?x4041 = 0gy2y8r, ?x10590 = 080dfr7, ?x279 = 0d060g, ?x5513 = 0d4htf, ?x9496 = 03bzyn4, ?x12475 = 02_jjm, ?x3400 = 0pf2, student(?x1305, ?x3341), ?x5318 = 0353xq, ?x4502 = 02wgk1, ?x4692 = 0345gh, ?x10391 = 02jfc, state_province_region(?x7950, ?x12854), ?x4375 = 01rxyb, genre(?x5966, ?x809), genre(?x5649, ?x809), genre(?x3475, ?x809), genre(?x1331, ?x809), ?x5649 = 07l4zhn, ?x1331 = 01vfqh, ?x5966 = 04h41v, state_province_region(?x11640, ?x9494), ?x2470 = 01f7kl, ?x3812 = 0c3xw46, ?x4981 = 03bwzr4, ?x7366 = 01g3gq, ?x1171 = 09vw2b7, colors(?x9181, ?x3621), major_field_of_study(?x1305, ?x5179), ?x3475 = 02krdz, ?x2605 = 03g3w, genre(?x808, ?x809), ?x2350 = 0661m4p, ?x2586 = 05h43ls, language(?x4545, ?x5607), ?x8379 = 02g5q1, ?x1642 = 0bq8tmw, ?x2189 = 02yvct, ?x2006 = 031778 >> conf = 0.75 => this is the best rule for 92 predicted values *> Best rule #4014 for first EXPECTED value: *> intensional similarity = 133 *> extensional distance = 1 *> proper extension: 09nqf; *> query: (?x7888, ?x770) <- currency(?x13150, ?x7888), currency(?x11640, ?x7888), currency(?x9181, ?x7888), currency(?x7887, ?x7888), currency(?x4545, ?x7888), category(?x7888, ?x134), film_release_region(?x7887, ?x2152), film_release_region(?x7887, ?x1558), film_release_region(?x7887, ?x1353), film_release_region(?x7887, ?x1003), film_release_region(?x7887, ?x789), film_release_region(?x7887, ?x774), film_release_region(?x7887, ?x550), film_release_region(?x7887, ?x512), film_release_region(?x7887, ?x279), film_release_region(?x7887, ?x151), genre(?x7887, ?x809), genre(?x7887, ?x258), ?x774 = 06mzp, film_release_distribution_medium(?x7887, ?x627), ?x1003 = 03gj2, film_regional_debut_venue(?x7887, ?x5416), currency(?x7950, ?x7888), ?x151 = 0b90_r, major_field_of_study(?x7950, ?x6364), major_field_of_study(?x7950, ?x2605), major_field_of_study(?x7950, ?x2601), language(?x7887, ?x254), institution(?x4981, ?x7950), institution(?x1305, ?x7950), citytown(?x13150, ?x8602), currency(?x11199, ?x7888), ?x2601 = 04x_3, ?x5416 = 015hr, major_field_of_study(?x11640, ?x10391), major_field_of_study(?x11640, ?x4321), major_field_of_study(?x11640, ?x3400), ?x4321 = 0g26h, ?x1353 = 035qy, ?x134 = 08mbj5d, film_crew_role(?x4545, ?x1171), ?x1558 = 01mjq, organization(?x5510, ?x11640), ?x550 = 05v8c, major_field_of_study(?x6364, ?x2981), institution(?x1305, ?x13618), institution(?x1305, ?x12475), institution(?x1305, ?x7716), institution(?x1305, ?x6908), institution(?x1305, ?x4955), institution(?x1305, ?x2948), institution(?x1305, ?x1681), ?x13618 = 01f6ss, ?x7716 = 01n_g9, ?x258 = 05p553, ?x512 = 07ssc, ?x4955 = 09f2j, ?x1681 = 07szy, interests(?x1236, ?x6364), major_field_of_study(?x4692, ?x6364), ?x6908 = 01dthg, school_type(?x13150, ?x3092), ?x789 = 0f8l9c, film_release_distribution_medium(?x10960, ?x627), film_release_distribution_medium(?x8379, ?x627), film_release_distribution_medium(?x5318, ?x627), film_release_distribution_medium(?x4041, ?x627), film_release_distribution_medium(?x3812, ?x627), film_release_distribution_medium(?x1642, ?x627), film_release_distribution_medium(?x1452, ?x627), film_release_distribution_medium(?x770, ?x627), film_distribution_medium(?x10590, ?x627), film_distribution_medium(?x9496, ?x627), film_distribution_medium(?x7760, ?x627), film_distribution_medium(?x7366, ?x627), film_distribution_medium(?x5513, ?x627), film_distribution_medium(?x4502, ?x627), film_distribution_medium(?x4375, ?x627), film_distribution_medium(?x2586, ?x627), film_distribution_medium(?x2470, ?x627), film_distribution_medium(?x2350, ?x627), film_distribution_medium(?x2189, ?x627), film_distribution_medium(?x2006, ?x627), film_distribution_medium(?x1456, ?x627), ?x254 = 02h40lc, ?x1452 = 0jqn5, ?x7760 = 017kz7, ?x2948 = 0j_sncb, ?x1456 = 0cz8mkh, film(?x157, ?x7887), ?x2152 = 06mkj, citytown(?x7950, ?x8963), ?x10960 = 02ndy4, film_release_region(?x4545, ?x410), ?x4041 = 0gy2y8r, ?x10590 = 080dfr7, ?x279 = 0d060g, ?x5513 = 0d4htf, ?x9496 = 03bzyn4, ?x12475 = 02_jjm, ?x3400 = 0pf2, student(?x1305, ?x3341), ?x5318 = 0353xq, ?x4502 = 02wgk1, ?x4692 = 0345gh, ?x10391 = 02jfc, state_province_region(?x7950, ?x12854), ?x4375 = 01rxyb, genre(?x5966, ?x809), genre(?x5649, ?x809), genre(?x3475, ?x809), genre(?x1331, ?x809), ?x5649 = 07l4zhn, ?x1331 = 01vfqh, ?x5966 = 04h41v, state_province_region(?x11640, ?x9494), ?x2470 = 01f7kl, ?x3812 = 0c3xw46, ?x4981 = 03bwzr4, ?x7366 = 01g3gq, ?x1171 = 09vw2b7, colors(?x9181, ?x3621), major_field_of_study(?x1305, ?x5179), ?x3475 = 02krdz, ?x2605 = 03g3w, genre(?x808, ?x809), ?x2350 = 0661m4p, ?x2586 = 05h43ls, language(?x4545, ?x5607), ?x8379 = 02g5q1, ?x1642 = 0bq8tmw, ?x2189 = 02yvct, ?x2006 = 031778 *> conf = 0.70 ranks of expected_values: 99 EVAL 0kz1h currency! 0bs5k8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 8.000 6.000 0.750 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #12504-01twdk PRED entity: 01twdk PRED relation: profession PRED expected values: 02jknp 0np9r => 192 concepts (90 used for prediction) PRED predicted values (max 10 best out of 88): 02jknp (0.91 #4241, 0.88 #3511, 0.88 #7307), 0dxtg (0.73 #2057, 0.72 #889, 0.72 #3517), 03gjzk (0.61 #7022, 0.59 #4978, 0.58 #890), 09jwl (0.41 #3959, 0.41 #4105, 0.36 #3229), 0dz3r (0.34 #4090, 0.26 #3214, 0.25 #3944), 016z4k (0.31 #4092, 0.30 #3946, 0.25 #5260), 0nbcg (0.31 #4117, 0.29 #3241, 0.28 #3971), 0np9r (0.28 #4691, 0.26 #6005, 0.19 #19), 0cbd2 (0.23 #736, 0.18 #4240, 0.18 #1904), 0d1pc (0.23 #3260, 0.18 #6326, 0.17 #7787) >> Best rule #4241 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 042l3v; 042rnl; 01t07j; 04k25; 02645b; 01ycck; 015njf; 012vct; 0flddp; 02404v; ... >> query: (?x4731, 02jknp) <- profession(?x4731, ?x1032), film(?x4731, ?x1956), ?x1032 = 02hrh1q >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 01twdk profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 192.000 90.000 0.910 http://example.org/people/person/profession EVAL 01twdk profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 90.000 0.910 http://example.org/people/person/profession #12503-021y7yw PRED entity: 021y7yw PRED relation: film_crew_role PRED expected values: 01pvkk => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.79 #1136, 0.72 #758, 0.69 #207), 01vx2h (0.36 #765, 0.36 #1143, 0.31 #696), 02ynfr (0.30 #49, 0.22 #117, 0.19 #770), 01pvkk (0.29 #1178, 0.29 #904, 0.28 #491), 02vs3x5 (0.24 #124, 0.16 #1719, 0.11 #2406), 02rh1dz (0.20 #43, 0.16 #1719, 0.13 #764), 01xy5l_ (0.20 #47, 0.16 #1719, 0.13 #183), 0215hd (0.16 #187, 0.16 #1719, 0.15 #772), 089g0h (0.16 #1719, 0.13 #773, 0.12 #1151), 0d2b38 (0.16 #1719, 0.12 #779, 0.11 #1157) >> Best rule #1136 for best value: >> intensional similarity = 4 >> extensional distance = 805 >> proper extension: 03_wm6; >> query: (?x2458, 02r96rf) <- film_crew_role(?x2458, ?x1171), film_release_distribution_medium(?x2458, ?x81), film_crew_role(?x670, ?x1171), ?x670 = 0dqytn >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1178 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 809 *> proper extension: 0bs8hvm; *> query: (?x2458, 01pvkk) <- film_crew_role(?x2458, ?x137), language(?x2458, ?x254), genre(?x2458, ?x53), ?x137 = 09zzb8 *> conf = 0.29 ranks of expected_values: 4 EVAL 021y7yw film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.788 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12502-0l14qv PRED entity: 0l14qv PRED relation: role! PRED expected values: 03qlv7 01xqw => 93 concepts (79 used for prediction) PRED predicted values (max 10 best out of 56): 0214km (0.87 #508, 0.83 #1357, 0.83 #980), 013y1f (0.87 #508, 0.83 #1357, 0.83 #980), 05842k (0.87 #508, 0.83 #1357, 0.83 #980), 0l14j_ (0.87 #508, 0.83 #1357, 0.83 #980), 01xqw (0.87 #508, 0.83 #1357, 0.83 #980), 01v8y9 (0.87 #508, 0.83 #1357, 0.83 #980), 02fsn (0.87 #508, 0.83 #1357, 0.83 #980), 07brj (0.87 #508, 0.83 #1357, 0.83 #980), 0xzly (0.87 #508, 0.83 #1357, 0.83 #980), 02pprs (0.87 #508, 0.83 #1357, 0.83 #980) >> Best rule #508 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0214km; >> query: (?x228, ?x214) <- role(?x1660, ?x228), role(?x4616, ?x228), role(?x3418, ?x228), role(?x315, ?x228), ?x3418 = 02w4b, ?x4616 = 01rhl, group(?x315, ?x5512), role(?x228, ?x214), award_winner(?x1362, ?x1660), ?x5512 = 02jqjm, currency(?x1660, ?x170) >> conf = 0.87 => this is the best rule for 17 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 26 EVAL 0l14qv role! 01xqw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 79.000 0.873 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 0l14qv role! 03qlv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 93.000 79.000 0.873 http://example.org/music/performance_role/track_performances./music/track_contribution/role #12501-021vwt PRED entity: 021vwt PRED relation: film PRED expected values: 049xgc => 84 concepts (52 used for prediction) PRED predicted values (max 10 best out of 711): 08r4x3 (0.48 #7294, 0.38 #10866, 0.38 #9079), 02k_4g (0.48 #55353, 0.48 #60711, 0.48 #60712), 02md2d (0.48 #55353, 0.48 #60711, 0.48 #60712), 01q7h2 (0.25 #3358, 0.08 #5143, 0.06 #8714), 035_2h (0.25 #2700, 0.03 #8056, 0.03 #20557), 02v8kmz (0.25 #1813, 0.03 #7169, 0.03 #78569), 01cssf (0.25 #1873, 0.03 #7229, 0.03 #78569), 02704ff (0.25 #979, 0.02 #11692, 0.01 #20621), 0hvvf (0.25 #3134, 0.01 #20991), 020y73 (0.15 #3936, 0.06 #7507, 0.05 #11079) >> Best rule #7294 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 06151l; 0hvb2; 01pgzn_; 03q1vd; 015t56; 014488; 03_6y; 04w391; 016ks_; 016vg8; ... >> query: (?x1677, 08r4x3) <- award_nominee(?x450, ?x1677), ?x450 = 0z4s, film(?x1677, ?x306) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #15254 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 308 *> proper extension: 0343h; 01wyy_; 06mn7; 0kb3n; 05yzt_; 0gs5q; 0fvt2; *> query: (?x1677, 049xgc) <- award_nominee(?x450, ?x1677), film(?x450, ?x518), notable_people_with_this_condition(?x1502, ?x450) *> conf = 0.02 ranks of expected_values: 510 EVAL 021vwt film 049xgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 84.000 52.000 0.485 http://example.org/film/actor/film./film/performance/film #12500-03bx2lk PRED entity: 03bx2lk PRED relation: category PRED expected values: 08mbj5d => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.40 #3, 0.35 #19, 0.30 #11) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 07kb7vh; >> query: (?x1219, 08mbj5d) <- film(?x2156, ?x1219), film(?x3927, ?x1219), cast_members(?x905, ?x3927), award(?x3927, ?x68), languages(?x3927, ?x254) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bx2lk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.400 http://example.org/common/topic/webpage./common/webpage/category #12499-0jg77 PRED entity: 0jg77 PRED relation: group! PRED expected values: 0342h 05148p4 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 97): 0342h (0.91 #1467, 0.91 #1553, 0.91 #1295), 05148p4 (0.79 #449, 0.70 #1567, 0.69 #1654), 0l14md (0.60 #953, 0.58 #1555, 0.57 #1814), 028tv0 (0.37 #1734, 0.36 #1648, 0.36 #959), 01vj9c (0.32 #960, 0.29 #1476, 0.28 #1304), 03qjg (0.29 #1337, 0.29 #1509, 0.28 #993), 05r5c (0.29 #94, 0.26 #954, 0.25 #352), 026t6 (0.26 #1635, 0.12 #261, 0.04 #949), 02qjv (0.26 #1635, 0.11 #448, 0.03 #4220), 01vdm0 (0.26 #1635, 0.04 #2755, 0.03 #2754) >> Best rule #1467 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 01qqwp9; 02t3ln; >> query: (?x13142, 0342h) <- origin(?x13142, ?x4090), group(?x1750, ?x13142), artists(?x497, ?x13142), ?x1750 = 02hnl >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0jg77 group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.911 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0jg77 group! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.911 http://example.org/music/performance_role/regular_performances./music/group_membership/group #12498-0c_j9x PRED entity: 0c_j9x PRED relation: genre PRED expected values: 060__y => 78 concepts (65 used for prediction) PRED predicted values (max 10 best out of 99): 01jfsb (0.73 #4302, 0.62 #2514, 0.61 #4301), 04xvlr (0.73 #4302, 0.61 #4301, 0.54 #834), 07ssc (0.61 #4301, 0.54 #834, 0.53 #5979), 02kdv5l (0.60 #2, 0.53 #1432, 0.52 #2028), 02l7c8 (0.46 #134, 0.38 #968, 0.36 #2160), 03k9fj (0.44 #844, 0.43 #1202, 0.42 #2036), 05p553 (0.40 #4, 0.34 #5625, 0.33 #5744), 060__y (0.33 #135, 0.27 #373, 0.25 #3116), 01hmnh (0.31 #1209, 0.30 #1805, 0.29 #1447), 04xvh5 (0.25 #153, 0.19 #3134, 0.19 #987) >> Best rule #4302 for best value: >> intensional similarity = 4 >> extensional distance = 758 >> proper extension: 0cp08zg; 0267wwv; >> query: (?x2345, ?x812) <- film(?x902, ?x2345), titles(?x812, ?x2345), nominated_for(?x1779, ?x2345), genre(?x66, ?x812) >> conf = 0.73 => this is the best rule for 2 predicted values *> Best rule #135 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 0m313; 02vxq9m; 095zlp; 011yph; 0209hj; 0pv3x; 09gq0x5; 0b1y_2; 0g68zt; 011yl_; ... *> query: (?x2345, 060__y) <- nominated_for(?x2880, ?x2345), nominated_for(?x2379, ?x2345), ?x2379 = 02qvyrt, ?x2880 = 02ppm4q, genre(?x2345, ?x53) *> conf = 0.33 ranks of expected_values: 8 EVAL 0c_j9x genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 78.000 65.000 0.726 http://example.org/film/film/genre #12497-01585b PRED entity: 01585b PRED relation: genre! PRED expected values: 0c34mt 0900j5 0c9t0y => 39 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1851): 03cp4cn (0.71 #15980, 0.67 #19693, 0.67 #14124), 01kjr0 (0.71 #15964, 0.67 #14108, 0.67 #10396), 0c34mt (0.67 #19152, 0.67 #9871, 0.60 #6161), 09v42sf (0.67 #12851, 0.60 #9140, 0.50 #14707), 029k4p (0.60 #8279, 0.60 #6424, 0.50 #11990), 0dqytn (0.60 #7521, 0.50 #20513, 0.50 #11232), 04y9mm8 (0.60 #8641, 0.50 #12352, 0.50 #4931), 02pcq92 (0.60 #7365, 0.50 #11075, 0.50 #5510), 09g7vfw (0.60 #7992, 0.50 #11703, 0.50 #4282), 0g68zt (0.57 #15369, 0.56 #19082, 0.50 #13513) >> Best rule #15980 for best value: >> intensional similarity = 16 >> extensional distance = 5 >> proper extension: 09blyk; >> query: (?x6625, 03cp4cn) <- genre(?x8791, ?x6625), genre(?x8130, ?x6625), genre(?x6624, ?x6625), genre(?x1184, ?x6625), ?x8791 = 0cqr0q, currency(?x1184, ?x170), film(?x4638, ?x1184), ?x170 = 09nqf, genre(?x1184, ?x8467), film_crew_role(?x1184, ?x137), ?x6624 = 033qdy, genre(?x8457, ?x8467), ?x8457 = 034xyf, profession(?x4638, ?x1032), production_companies(?x8130, ?x10884), ?x137 = 09zzb8 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #19152 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 7 *> proper extension: 04xvlr; 060__y; *> query: (?x6625, 0c34mt) <- genre(?x8791, ?x6625), genre(?x6624, ?x6625), genre(?x1184, ?x6625), ?x8791 = 0cqr0q, currency(?x1184, ?x170), film(?x10212, ?x1184), film(?x2969, ?x1184), ?x170 = 09nqf, film_crew_role(?x1184, ?x137), music(?x6624, ?x3410), film(?x6157, ?x6624), film_crew_role(?x6624, ?x2091), nationality(?x10212, ?x94), ?x6157 = 01515w, profession(?x2969, ?x1032) *> conf = 0.67 ranks of expected_values: 3, 39, 64 EVAL 01585b genre! 0c9t0y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 39.000 16.000 0.714 http://example.org/film/film/genre EVAL 01585b genre! 0900j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 39.000 16.000 0.714 http://example.org/film/film/genre EVAL 01585b genre! 0c34mt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 39.000 16.000 0.714 http://example.org/film/film/genre #12496-0sx92 PRED entity: 0sx92 PRED relation: olympics! PRED expected values: 06mzp => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 344): 06mzp (0.82 #2763, 0.79 #3671, 0.78 #3802), 06mkj (0.81 #2741, 0.81 #2876, 0.78 #1700), 09c7w0 (0.81 #2876, 0.80 #3006, 0.79 #3399), 059j2 (0.78 #2236, 0.78 #2110, 0.77 #3940), 0d05w3 (0.78 #2264, 0.58 #639, 0.52 #647), 06qd3 (0.78 #2242, 0.52 #647, 0.51 #2332), 0k6nt (0.73 #2872, 0.73 #2766, 0.73 #2631), 03gj2 (0.73 #2767, 0.73 #2632, 0.64 #3421), 0chghy (0.73 #2753, 0.64 #3407, 0.62 #3019), 05qhw (0.73 #2758, 0.57 #3412, 0.56 #3926) >> Best rule #2763 for best value: >> intensional similarity = 44 >> extensional distance = 9 >> proper extension: 0lk8j; >> query: (?x5176, 06mzp) <- sports(?x5176, ?x2884), olympics(?x94, ?x5176), sports(?x418, ?x2884), olympics(?x11872, ?x5176), olympics(?x7430, ?x5176), olympics(?x1355, ?x5176), olympics(?x304, ?x5176), olympics(?x205, ?x5176), country(?x2884, ?x4059), country(?x2884, ?x1264), ?x4059 = 077qn, film_release_region(?x7379, ?x11872), film_release_region(?x6181, ?x11872), film_release_region(?x1547, ?x11872), film_release_region(?x1452, ?x11872), medal(?x11872, ?x422), olympics(?x1037, ?x418), ?x7430 = 01mk6, participating_countries(?x418, ?x2629), participating_countries(?x418, ?x1917), participating_countries(?x418, ?x985), participating_countries(?x418, ?x142), ?x2629 = 06f32, ?x1264 = 0345h, official_language(?x11872, ?x732), ?x1452 = 0jqn5, ?x7379 = 032clf, country(?x3757, ?x11872), ?x6181 = 0hv27, ?x94 = 09c7w0, ?x1917 = 01p1v, ?x985 = 0k6nt, organization(?x11872, ?x312), participating_countries(?x4255, ?x11872), ?x142 = 0jgd, ?x1355 = 0h7x, major_field_of_study(?x735, ?x732), languages(?x147, ?x732), ?x1547 = 0168ls, language(?x943, ?x732), ?x205 = 03rjj, ?x304 = 0d0vqn, service_language(?x555, ?x732), ?x943 = 0963mq >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sx92 olympics! 06mzp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 32.000 0.818 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #12495-016kb7 PRED entity: 016kb7 PRED relation: film PRED expected values: 0b4lkx => 102 concepts (47 used for prediction) PRED predicted values (max 10 best out of 309): 042y1c (0.59 #44681, 0.58 #60767, 0.37 #50044), 0qm98 (0.40 #222), 0y_yw (0.20 #1057, 0.06 #2844, 0.06 #4631), 011yth (0.20 #299, 0.06 #2086, 0.04 #5660), 01k60v (0.20 #742, 0.06 #2529, 0.04 #6103), 0jsf6 (0.20 #1086, 0.02 #10021), 0bxsk (0.20 #1206, 0.01 #10141, 0.01 #20867), 07g1sm (0.20 #1231, 0.01 #10166), 01gglm (0.20 #1402, 0.01 #10337), 0ds2n (0.20 #522, 0.01 #9457) >> Best rule #44681 for best value: >> intensional similarity = 3 >> extensional distance = 1270 >> proper extension: 01sl1q; 01j5ts; 0cnl80; 02zq43; 0p_pd; 07lmxq; 06jzh; 027dtv3; 01gvr1; 01csvq; ... >> query: (?x7866, ?x2376) <- film(?x7866, ?x89), gender(?x7866, ?x231), nominated_for(?x7866, ?x2376) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016kb7 film 0b4lkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 47.000 0.594 http://example.org/film/actor/film./film/performance/film #12494-04pbhw PRED entity: 04pbhw PRED relation: genre! PRED expected values: 07k8rt4 02fj8n 0f61tk 0dcz8_ => 54 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1908): 0cc5mcj (0.71 #14936, 0.50 #9483, 0.50 #7664), 02fj8n (0.67 #10388, 0.50 #4934, 0.43 #15841), 01vw8k (0.60 #18834, 0.50 #6110, 0.33 #657), 0p7qm (0.60 #18658, 0.33 #481, 0.29 #15024), 03t97y (0.57 #14704, 0.50 #9251, 0.50 #5614), 02g5q1 (0.57 #15986, 0.50 #10533, 0.50 #8714), 0bh8yn3 (0.57 #14802, 0.50 #9349, 0.50 #7530), 07nt8p (0.57 #14898, 0.50 #11262, 0.45 #23989), 03kg2v (0.57 #15022, 0.50 #5932, 0.40 #18656), 01hw5kk (0.57 #15224, 0.50 #6134, 0.40 #18858) >> Best rule #14936 for best value: >> intensional similarity = 16 >> extensional distance = 5 >> proper extension: 07s9rl0; 03k9fj; >> query: (?x6888, 0cc5mcj) <- genre(?x7975, ?x6888), genre(?x7425, ?x6888), genre(?x1956, ?x6888), genre(?x1511, ?x6888), ?x1511 = 0340hj, film_release_region(?x1956, ?x2316), film_release_region(?x1956, ?x1471), film_release_region(?x1956, ?x1453), film_release_region(?x1956, ?x205), ?x2316 = 06t2t, film(?x773, ?x7425), ?x205 = 03rjj, ?x1471 = 07t21, film_crew_role(?x1956, ?x137), ?x1453 = 06qd3, ?x7975 = 06yykb >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #10388 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 01jfsb; 01hmnh; *> query: (?x6888, 02fj8n) <- genre(?x7881, ?x6888), genre(?x7425, ?x6888), genre(?x2816, ?x6888), genre(?x1956, ?x6888), genre(?x1511, ?x6888), ?x1511 = 0340hj, ?x7881 = 01hq1, film(?x902, ?x1956), film_release_region(?x1956, ?x2843), titles(?x307, ?x2816), film_release_distribution_medium(?x2816, ?x81), film(?x773, ?x7425), language(?x7425, ?x254), ?x2843 = 016wzw *> conf = 0.67 ranks of expected_values: 2, 45, 166, 380 EVAL 04pbhw genre! 0dcz8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 54.000 25.000 0.714 http://example.org/film/film/genre EVAL 04pbhw genre! 0f61tk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 54.000 25.000 0.714 http://example.org/film/film/genre EVAL 04pbhw genre! 02fj8n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 54.000 25.000 0.714 http://example.org/film/film/genre EVAL 04pbhw genre! 07k8rt4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 25.000 0.714 http://example.org/film/film/genre #12493-0191h5 PRED entity: 0191h5 PRED relation: artists! PRED expected values: 03w94xt => 86 concepts (41 used for prediction) PRED predicted values (max 10 best out of 225): 0glt670 (0.50 #37, 0.21 #2710, 0.21 #2413), 064t9 (0.48 #308, 0.47 #2684, 0.43 #2387), 05bt6j (0.41 #336, 0.24 #930, 0.21 #9858), 059kh (0.28 #342, 0.07 #9864, 0.07 #5993), 06j6l (0.26 #2717, 0.25 #44, 0.23 #2420), 025sc50 (0.25 #46, 0.22 #2719, 0.22 #937), 016_nr (0.25 #67, 0.07 #661, 0.05 #1552), 036jv (0.25 #180, 0.06 #1071, 0.05 #774), 01flzq (0.25 #109, 0.05 #1594, 0.05 #703), 0y3_8 (0.24 #340, 0.08 #934, 0.07 #9862) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02_nkp; >> query: (?x7221, 0glt670) <- location(?x7221, ?x4253), currency(?x7221, ?x170), ?x4253 = 0ccvx >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #780 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 41 *> proper extension: 0854hr; *> query: (?x7221, 03w94xt) <- location(?x7221, ?x4253), ?x4253 = 0ccvx *> conf = 0.02 ranks of expected_values: 177 EVAL 0191h5 artists! 03w94xt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 86.000 41.000 0.500 http://example.org/music/genre/artists #12492-023slg PRED entity: 023slg PRED relation: artists! PRED expected values: 01fh36 => 145 concepts (57 used for prediction) PRED predicted values (max 10 best out of 281): 06by7 (0.80 #9673, 0.78 #5624, 0.67 #14961), 06j6l (0.56 #3468, 0.39 #8453, 0.38 #13744), 0xhtw (0.50 #1256, 0.50 #946, 0.46 #6864), 03lty (0.50 #1267, 0.33 #957, 0.33 #27), 016clz (0.48 #3114, 0.37 #9658, 0.36 #14946), 0cx7f (0.48 #3247, 0.33 #758, 0.31 #6986), 0gywn (0.46 #3478, 0.27 #13754, 0.25 #8463), 05w3f (0.41 #3146, 0.33 #657, 0.33 #37), 05bt6j (0.36 #1594, 0.36 #3463, 0.34 #13739), 025sc50 (0.34 #8455, 0.33 #13746, 0.28 #3470) >> Best rule #9673 for best value: >> intensional similarity = 5 >> extensional distance = 155 >> proper extension: 016qtt; 0197tq; 03f5spx; 01vv7sc; 01pr_j6; 01vrt_c; 01vs14j; 04dqdk; 01r9fv; 015_30; ... >> query: (?x11916, 06by7) <- origin(?x11916, ?x11086), artists(?x5436, ?x11916), artists(?x5436, ?x7121), profession(?x11916, ?x655), ?x7121 = 04kjrv >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #706 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 02ndj5; *> query: (?x11916, 01fh36) <- origin(?x11916, ?x11086), artists(?x5436, ?x11916), artists(?x671, ?x11916), ?x5436 = 0hdf8, ?x671 = 064t9, category(?x11916, ?x134) *> conf = 0.33 ranks of expected_values: 14 EVAL 023slg artists! 01fh36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 145.000 57.000 0.796 http://example.org/music/genre/artists #12491-0c9d9 PRED entity: 0c9d9 PRED relation: artists! PRED expected values: 0dl5d => 147 concepts (73 used for prediction) PRED predicted values (max 10 best out of 224): 064t9 (0.56 #2492, 0.52 #9923, 0.50 #3728), 06by7 (0.50 #1569, 0.48 #3737, 0.46 #10241), 0m0jc (0.50 #9, 0.17 #318, 0.15 #1247), 08cyft (0.50 #58, 0.12 #2537, 0.09 #3155), 016clz (0.42 #8056, 0.33 #314, 0.24 #10224), 05bt6j (0.36 #1591, 0.24 #10263, 0.23 #9954), 0glt670 (0.33 #350, 0.24 #9951, 0.23 #13983), 02x8m (0.33 #328, 0.17 #8070, 0.14 #1566), 03ckfl9 (0.33 #473, 0.15 #1402, 0.11 #2023), 0y3_8 (0.33 #357, 0.15 #1286, 0.10 #3763) >> Best rule #2492 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 01dw9z; 043zg; 0cgfb; >> query: (?x317, 064t9) <- category(?x317, ?x134), artists(?x497, ?x317), gender(?x317, ?x231), artist(?x2299, ?x317), spouse(?x5330, ?x317) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1258 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 0lbj1; 050z2; 01l4g5; *> query: (?x317, 0dl5d) <- category(?x317, ?x134), artists(?x12590, ?x317), profession(?x317, ?x131), ?x12590 = 02v2lh, ?x134 = 08mbj5d *> conf = 0.31 ranks of expected_values: 13 EVAL 0c9d9 artists! 0dl5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 147.000 73.000 0.558 http://example.org/music/genre/artists #12490-02vxn PRED entity: 02vxn PRED relation: disciplines_or_subjects! PRED expected values: 04dn09n 0fm3kw 0fm3nb 0776h1v => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 247): 03x3wf (0.33 #3, 0.25 #435, 0.23 #3390), 02x4wr9 (0.33 #300, 0.23 #3390, 0.21 #3028), 019f4v (0.33 #292, 0.23 #3390, 0.21 #3028), 0265wl (0.29 #1316, 0.25 #2686, 0.25 #1388), 02664f (0.29 #1314, 0.25 #2684, 0.25 #1386), 045xh (0.29 #1346, 0.25 #2716, 0.25 #1418), 01b8bn (0.29 #1335, 0.25 #2705, 0.25 #1407), 0262x6 (0.29 #1324, 0.25 #2694, 0.25 #1396), 0262yt (0.29 #1319, 0.25 #2689, 0.25 #1391), 02662b (0.29 #1302, 0.25 #2672, 0.25 #1374) >> Best rule #3 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 04rlf; >> query: (?x373, 03x3wf) <- major_field_of_study(?x1695, ?x373), industry(?x9001, ?x373), major_field_of_study(?x9823, ?x373), disciplines_or_subjects(?x277, ?x373), ?x9823 = 0gk7z, film(?x9001, ?x288) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3390 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 31 *> proper extension: 0jm_; *> query: (?x373, ?x749) <- disciplines_or_subjects(?x7521, ?x373), award_winner(?x7521, ?x8626), award_winner(?x7521, ?x3327), award_winner(?x749, ?x3327), gender(?x8626, ?x514) *> conf = 0.23 ranks of expected_values: 115, 172, 189, 190 EVAL 02vxn disciplines_or_subjects! 0776h1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 77.000 77.000 0.333 http://example.org/award/award_category/disciplines_or_subjects EVAL 02vxn disciplines_or_subjects! 0fm3nb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 77.000 77.000 0.333 http://example.org/award/award_category/disciplines_or_subjects EVAL 02vxn disciplines_or_subjects! 0fm3kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 77.000 77.000 0.333 http://example.org/award/award_category/disciplines_or_subjects EVAL 02vxn disciplines_or_subjects! 04dn09n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 77.000 77.000 0.333 http://example.org/award/award_category/disciplines_or_subjects #12489-0cqnss PRED entity: 0cqnss PRED relation: honored_for! PRED expected values: 0dthsy => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 115): 0275n3y (0.10 #64, 0.05 #430, 0.03 #1284), 0dznvw (0.08 #7446, 0.05 #240, 0.01 #362), 0d__c3 (0.08 #7446, 0.03 #231, 0.03 #963), 0bz6l9 (0.08 #7446, 0.03 #163, 0.02 #2807), 0ftlxj (0.08 #7446, 0.03 #303, 0.02 #2807), 0dthsy (0.08 #7446, 0.03 #300, 0.02 #2807), 0c4hx0 (0.08 #7446, 0.02 #2807, 0.02 #234), 0dth6b (0.08 #7446, 0.02 #2807, 0.02 #140), 0fzrhn (0.08 #7446, 0.01 #5492, 0.01 #7323), 0hhtgcw (0.05 #73, 0.03 #561, 0.03 #805) >> Best rule #64 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 044g_k; 024mxd; >> query: (?x4970, 0275n3y) <- film_release_region(?x4970, ?x94), film(?x382, ?x4970), ?x382 = 086k8, honored_for(?x2168, ?x4970) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #7446 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1276 *> proper extension: 04bp0l; *> query: (?x4970, ?x5369) <- nominated_for(?x8758, ?x4970), award_winner(?x1972, ?x8758), award_winner(?x5369, ?x8758) *> conf = 0.08 ranks of expected_values: 6 EVAL 0cqnss honored_for! 0dthsy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 77.000 0.100 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12488-049tjg PRED entity: 049tjg PRED relation: nationality PRED expected values: 09c7w0 => 150 concepts (130 used for prediction) PRED predicted values (max 10 best out of 39): 09c7w0 (0.90 #6524, 0.90 #1403, 0.90 #5314), 06btq (0.38 #1002, 0.33 #8737, 0.33 #9344), 0345h (0.25 #131, 0.05 #3539, 0.05 #3037), 02jx1 (0.15 #533, 0.15 #2436, 0.12 #4746), 03rjj (0.15 #505, 0.11 #806, 0.06 #706), 07ssc (0.14 #2418, 0.13 #2017, 0.12 #3323), 059j2 (0.11 #830, 0.08 #1031, 0.05 #9849), 0d060g (0.10 #407, 0.08 #507, 0.07 #608), 03gj2 (0.10 #426, 0.08 #526, 0.06 #727), 05kyr (0.10 #468, 0.08 #568, 0.06 #769) >> Best rule #6524 for best value: >> intensional similarity = 4 >> extensional distance = 428 >> proper extension: 021sv1; 01gf5h; 01sbf2; 01qkqwg; 04myfb7; 0c6g29; 01vvdm; 06m6z6; 03hbzj; 01ws9n6; ... >> query: (?x305, 09c7w0) <- people(?x1423, ?x305), place_of_birth(?x305, ?x12472), citytown(?x13514, ?x12472), source(?x12472, ?x958) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049tjg nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 130.000 0.900 http://example.org/people/person/nationality #12487-0gy4k PRED entity: 0gy4k PRED relation: edited_by PRED expected values: 027rfxc => 92 concepts (72 used for prediction) PRED predicted values (max 10 best out of 22): 027rfxc (0.17 #22, 0.02 #259), 0j_c (0.10 #60, 0.08 #509, 0.06 #1485), 04cy8rb (0.10 #30, 0.02 #268, 0.02 #297), 03_gd (0.10 #33, 0.01 #420, 0.01 #271), 0gv5c (0.09 #59, 0.07 #508, 0.05 #936), 02kxbwx (0.06 #65, 0.05 #483, 0.04 #123), 02kxbx3 (0.06 #72, 0.04 #490, 0.04 #130), 0343h (0.06 #154, 0.01 #607, 0.01 #787), 08ff1k (0.05 #511, 0.04 #1330, 0.02 #1423), 02qggqc (0.05 #448, 0.04 #270, 0.03 #419) >> Best rule #22 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0cwy47; 0k0rf; 0ktpx; 0gl3hr; >> query: (?x11125, 027rfxc) <- film_release_region(?x11125, ?x87), written_by(?x11125, ?x4477), ?x4477 = 0gv5c, genre(?x11125, ?x812), film(?x2465, ?x11125) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gy4k edited_by 027rfxc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 72.000 0.167 http://example.org/film/film/edited_by #12486-01wmcbg PRED entity: 01wmcbg PRED relation: profession PRED expected values: 02hrh1q => 111 concepts (103 used for prediction) PRED predicted values (max 10 best out of 58): 02hrh1q (0.88 #1198, 0.88 #1494, 0.87 #9488), 09jwl (0.61 #463, 0.60 #315, 0.18 #3867), 01d_h8 (0.49 #2670, 0.48 #746, 0.37 #1782), 03gjzk (0.48 #755, 0.37 #2679, 0.24 #1643), 02jknp (0.42 #2672, 0.38 #748, 0.28 #1784), 0dz3r (0.40 #446, 0.39 #298, 0.13 #4886), 016z4k (0.32 #300, 0.32 #448, 0.11 #4888), 039v1 (0.25 #332, 0.24 #480, 0.05 #3884), 099md (0.22 #72, 0.01 #4512, 0.01 #4660), 0cbd2 (0.22 #2671, 0.19 #747, 0.16 #4447) >> Best rule #1198 for best value: >> intensional similarity = 3 >> extensional distance = 733 >> proper extension: 023n39; >> query: (?x12809, 02hrh1q) <- film(?x12809, ?x1708), nominated_for(?x1708, ?x2094), titles(?x600, ?x1708) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wmcbg profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 103.000 0.883 http://example.org/people/person/profession #12485-07s3m4g PRED entity: 07s3m4g PRED relation: featured_film_locations PRED expected values: 071vr => 71 concepts (42 used for prediction) PRED predicted values (max 10 best out of 79): 02_286 (0.18 #1466, 0.16 #4850, 0.15 #6303), 030qb3t (0.16 #1243, 0.16 #1002, 0.14 #1726), 0rh6k (0.12 #1, 0.10 #1447, 0.07 #2413), 04jpl (0.11 #249, 0.10 #1455, 0.10 #490), 080h2 (0.07 #1470, 0.06 #24, 0.05 #2436), 0h7h6 (0.07 #283, 0.06 #1489, 0.06 #2215), 01_d4 (0.06 #47, 0.04 #1734, 0.04 #1010), 07b_l (0.06 #77, 0.04 #317, 0.03 #1523), 0d6lp (0.06 #72, 0.03 #1518, 0.02 #794), 02nd_ (0.06 #116, 0.03 #1562, 0.02 #838) >> Best rule #1466 for best value: >> intensional similarity = 7 >> extensional distance = 66 >> proper extension: 02pxmgz; 05sns6; 048yqf; >> query: (?x6587, 02_286) <- film(?x574, ?x6587), ?x574 = 016tt2, genre(?x6587, ?x600), genre(?x5361, ?x600), genre(?x1999, ?x600), ?x1999 = 0gd0c7x, ?x5361 = 04fv5b >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07s3m4g featured_film_locations 071vr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 42.000 0.176 http://example.org/film/film/featured_film_locations #12484-0dt8xq PRED entity: 0dt8xq PRED relation: titles! PRED expected values: 01z4y => 126 concepts (89 used for prediction) PRED predicted values (max 10 best out of 79): 07s9rl0 (0.47 #103, 0.42 #3074, 0.34 #2767), 04xvlr (0.27 #106, 0.26 #309, 0.25 #3077), 01z4y (0.27 #36, 0.25 #8886, 0.21 #1260), 024qqx (0.26 #692, 0.22 #590, 0.21 #996), 01jfsb (0.20 #20, 0.14 #1964, 0.13 #1244), 017fp (0.20 #126, 0.13 #329, 0.10 #4536), 01hmnh (0.20 #1251, 0.16 #1560, 0.14 #8877), 05p553 (0.18 #9055, 0.11 #8952), 07c52 (0.15 #1356, 0.14 #4133, 0.13 #5159), 03mqtr (0.13 #148, 0.10 #3119, 0.09 #1372) >> Best rule #103 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 034hzj; >> query: (?x5070, 07s9rl0) <- language(?x5070, ?x254), film(?x5643, ?x5070), films(?x5069, ?x5070), ?x5069 = 06d4h >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #36 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0ds33; 03hj5lq; 0gzlb9; *> query: (?x5070, 01z4y) <- film_crew_role(?x5070, ?x6473), award(?x5070, ?x834), ?x6473 = 02vs3x5, featured_film_locations(?x5070, ?x739) *> conf = 0.27 ranks of expected_values: 3 EVAL 0dt8xq titles! 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 89.000 0.467 http://example.org/media_common/netflix_genre/titles #12483-02w9k1c PRED entity: 02w9k1c PRED relation: honored_for! PRED expected values: 04n2r9h => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 105): 05c1t6z (0.07 #4851, 0.06 #5578, 0.05 #5094), 02q690_ (0.07 #4894, 0.06 #5621, 0.05 #5984), 03gwpw2 (0.06 #2062, 0.06 #2183, 0.06 #2304), 0gvstc3 (0.06 #4867, 0.05 #5594, 0.04 #5957), 09gkdln (0.06 #226, 0.05 #1799, 0.05 #2525), 0hr6lkl (0.06 #133, 0.04 #1706, 0.04 #2432), 02wzl1d (0.06 #128, 0.04 #249, 0.04 #2064), 03nnm4t (0.06 #4903, 0.05 #5630, 0.04 #5993), 05q7cj (0.06 #444, 0.04 #1291, 0.04 #1533), 04n2r9h (0.06 #399, 0.04 #5603, 0.04 #278) >> Best rule #4851 for best value: >> intensional similarity = 3 >> extensional distance = 413 >> proper extension: 0cwrr; 0gpjbt; 06hwzy; 06mr2s; 0c3xpwy; 01b7h8; 06mmr; >> query: (?x5819, 05c1t6z) <- honored_for(?x7936, ?x5819), ceremony(?x77, ?x7936), instance_of_recurring_event(?x7936, ?x3459) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #399 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 51 *> proper extension: 0k4kk; 05znbh7; 05z43v; 09yxcz; *> query: (?x5819, 04n2r9h) <- genre(?x5819, ?x2605), genre(?x5819, ?x53), nominated_for(?x143, ?x5819), film(?x1634, ?x5819), ?x53 = 07s9rl0, ?x2605 = 03g3w *> conf = 0.06 ranks of expected_values: 10 EVAL 02w9k1c honored_for! 04n2r9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 107.000 107.000 0.075 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12482-09m6kg PRED entity: 09m6kg PRED relation: honored_for! PRED expected values: 092t4b => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 100): 02glmx (0.25 #65, 0.04 #184, 0.02 #779), 09qvms (0.09 #127, 0.03 #246, 0.02 #1793), 073hgx (0.06 #317, 0.04 #198, 0.03 #793), 0bzjvm (0.06 #331, 0.01 #807, 0.01 #1283), 05c1t6z (0.06 #1795, 0.05 #3104, 0.04 #2390), 03gwpw2 (0.05 #1194, 0.04 #1789, 0.04 #123), 0hr6lkl (0.05 #1201, 0.03 #1558, 0.03 #3105), 04n2r9h (0.05 #749, 0.04 #1225, 0.04 #1820), 02q690_ (0.05 #3147, 0.05 #1838, 0.04 #2433), 09bymc (0.05 #1292, 0.03 #1887, 0.03 #3196) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 09qycb; >> query: (?x253, 02glmx) <- film(?x7929, ?x253), country(?x253, ?x94), nominated_for(?x198, ?x253), ?x7929 = 08jfkw >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #160 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 04hk0w; *> query: (?x253, 092t4b) <- film(?x262, ?x253), award_winner(?x253, ?x1039), award_nominee(?x9797, ?x262), ?x9797 = 010xjr *> conf = 0.04 ranks of expected_values: 31 EVAL 09m6kg honored_for! 092t4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 104.000 104.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12481-02185j PRED entity: 02185j PRED relation: major_field_of_study PRED expected values: 05qjt => 138 concepts (126 used for prediction) PRED predicted values (max 10 best out of 107): 01mkq (0.71 #262, 0.71 #16, 0.57 #1247), 03g3w (0.71 #28, 0.57 #274, 0.45 #4963), 037mh8 (0.64 #315, 0.57 #69, 0.38 #1300), 05qjt (0.64 #254, 0.57 #8, 0.37 #2842), 04rjg (0.64 #267, 0.43 #21, 0.42 #2855), 02lp1 (0.57 #1243, 0.50 #1613, 0.49 #997), 02j62 (0.57 #278, 0.57 #32, 0.47 #1263), 062z7 (0.57 #275, 0.57 #29, 0.38 #1260), 0fdys (0.57 #40, 0.50 #286, 0.32 #2874), 01lj9 (0.50 #287, 0.43 #41, 0.36 #2875) >> Best rule #262 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 04rwx; 01mpwj; 015cz0; 02zd460; 02bqy; 01wv24; 013719; >> query: (?x11488, 01mkq) <- organization(?x4095, ?x11488), major_field_of_study(?x11488, ?x9111), major_field_of_study(?x11488, ?x3400), ?x3400 = 0pf2, major_field_of_study(?x388, ?x9111), ?x388 = 05krk >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #254 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 04rwx; 01mpwj; 015cz0; 02zd460; 02bqy; 01wv24; 013719; *> query: (?x11488, 05qjt) <- organization(?x4095, ?x11488), major_field_of_study(?x11488, ?x9111), major_field_of_study(?x11488, ?x3400), ?x3400 = 0pf2, major_field_of_study(?x388, ?x9111), ?x388 = 05krk *> conf = 0.64 ranks of expected_values: 4 EVAL 02185j major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 138.000 126.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #12480-01ydzx PRED entity: 01ydzx PRED relation: artists! PRED expected values: 09qxq7 => 104 concepts (52 used for prediction) PRED predicted values (max 10 best out of 267): 06j6l (0.82 #10790, 0.75 #11404, 0.68 #7106), 064t9 (0.71 #627, 0.68 #1853, 0.66 #7074), 025sc50 (0.47 #7107, 0.41 #10791, 0.39 #15100), 01lyv (0.44 #953, 0.23 #3102, 0.22 #4331), 0glt670 (0.41 #15093, 0.32 #7100, 0.30 #10784), 02k_kn (0.39 #981, 0.33 #61, 0.20 #3130), 03lty (0.39 #1254, 0.25 #2483, 0.22 #7702), 0155w (0.37 #1942, 0.35 #4400, 0.33 #1022), 0ggx5q (0.35 #688, 0.22 #7135, 0.22 #381), 026z9 (0.35 #687, 0.16 #1913, 0.14 #7134) >> Best rule #10790 for best value: >> intensional similarity = 7 >> extensional distance = 218 >> proper extension: 01fmz6; 01yzl2; 07mvp; 0178_w; 01dq9q; 03c3yf; 0134wr; 012vm6; 01v27pl; 014kyy; >> query: (?x6774, 06j6l) <- artists(?x3928, ?x6774), artists(?x3928, ?x7902), artists(?x3928, ?x5637), artists(?x3928, ?x4474), ?x4474 = 01vvyvk, ?x7902 = 03f3_p3, ?x5637 = 016890 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #229 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 03k0yw; *> query: (?x6774, 09qxq7) <- role(?x6774, ?x3215), role(?x6774, ?x227), category(?x6774, ?x134), nationality(?x6774, ?x1310), ?x3215 = 0bxl5, ?x227 = 0342h *> conf = 0.17 ranks of expected_values: 38 EVAL 01ydzx artists! 09qxq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 104.000 52.000 0.818 http://example.org/music/genre/artists #12479-02nzb8 PRED entity: 02nzb8 PRED relation: team PRED expected values: 07sqbl 037css => 14 concepts (13 used for prediction) PRED predicted values (max 10 best out of 410): 01fwqn (0.84 #1181, 0.83 #1974, 0.82 #1578), 07s8qm7 (0.84 #1181, 0.83 #1974, 0.82 #1578), 049bp4 (0.84 #1181, 0.83 #1974, 0.82 #1578), 02mplj (0.84 #1181, 0.83 #1974, 0.82 #1578), 019mcm (0.84 #1181, 0.83 #1974, 0.82 #1578), 0346qt (0.84 #1181, 0.83 #1974, 0.82 #1578), 0h3c3g (0.84 #1181, 0.83 #1974, 0.82 #1578), 0175tv (0.84 #1181, 0.83 #1974, 0.82 #1578), 03qx63 (0.84 #1181, 0.83 #1974, 0.82 #1578), 0177gl (0.84 #1181, 0.83 #1974, 0.82 #1578) >> Best rule #1181 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 02sdk9v; >> query: (?x60, ?x1026) <- team(?x60, ?x13411), team(?x60, ?x12853), team(?x60, ?x9644), team(?x60, ?x8960), team(?x60, ?x2433), ?x13411 = 05btx9, position(?x13031, ?x60), position(?x3791, ?x60), position(?x1026, ?x60), ?x2433 = 044l47, ?x9644 = 02s6tk, ?x12853 = 09hldj, ?x8960 = 03zbg0, ?x13031 = 02qdzd, ?x3791 = 02mplj >> conf = 0.84 => this is the best rule for 124 predicted values *> Best rule #2366 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 5 *> proper extension: 02md_2; *> query: (?x60, ?x1026) <- team(?x60, ?x11153), team(?x60, ?x8537), team(?x60, ?x5433), team(?x60, ?x3363), position(?x1026, ?x60), team(?x63, ?x5433), position(?x11153, ?x12598), sport(?x3363, ?x471), colors(?x8537, ?x663), ?x63 = 02sdk9v *> conf = 0.81 ranks of expected_values: 126, 165 EVAL 02nzb8 team 037css CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 14.000 13.000 0.843 http://example.org/sports/sports_position/players./sports/sports_team_roster/team EVAL 02nzb8 team 07sqbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 14.000 13.000 0.843 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #12478-01nwwl PRED entity: 01nwwl PRED relation: film PRED expected values: 034qmv 04qw17 04qk12 => 103 concepts (86 used for prediction) PRED predicted values (max 10 best out of 870): 0184tc (0.07 #649, 0.02 #5956, 0.02 #9494), 01v1ln (0.07 #1211, 0.02 #36591, 0.02 #2980), 03shpq (0.07 #1426, 0.02 #36806, 0.01 #26192), 04ynx7 (0.07 #1581, 0.02 #3350), 0f61tk (0.07 #1450, 0.02 #3219), 05j82v (0.07 #231, 0.02 #2000), 01718w (0.07 #1379, 0.01 #81377), 0hgnl3t (0.07 #752, 0.01 #6059), 03bzjpm (0.07 #1296, 0.01 #18986, 0.01 #11910), 08phg9 (0.07 #870, 0.01 #96400, 0.01 #103476) >> Best rule #649 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0bn3jg; >> query: (?x2938, 0184tc) <- award(?x2938, ?x112), award_winner(?x5703, ?x2938), ?x5703 = 02yvhx >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #10629 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 323 *> proper extension: 03j90; *> query: (?x2938, 034qmv) <- languages(?x2938, ?x254), student(?x8021, ?x2938) *> conf = 0.02 ranks of expected_values: 278, 454 EVAL 01nwwl film 04qk12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 103.000 86.000 0.067 http://example.org/film/actor/film./film/performance/film EVAL 01nwwl film 04qw17 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 86.000 0.067 http://example.org/film/actor/film./film/performance/film EVAL 01nwwl film 034qmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 103.000 86.000 0.067 http://example.org/film/actor/film./film/performance/film #12477-08yx9q PRED entity: 08yx9q PRED relation: award_winner! PRED expected values: 0275n3y => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 96): 09gkdln (0.11 #121, 0.05 #681, 0.05 #1521), 092t4b (0.11 #52, 0.05 #612, 0.04 #2152), 092_25 (0.11 #71, 0.04 #631, 0.02 #2871), 09qvms (0.10 #7421, 0.08 #573, 0.06 #293), 0275n3y (0.10 #7421, 0.04 #1474, 0.04 #634), 02q690_ (0.10 #7421, 0.04 #1464, 0.03 #3564), 09pj68 (0.10 #7421, 0.03 #384, 0.02 #2484), 09g90vz (0.07 #683, 0.05 #1523, 0.05 #2223), 03gyp30 (0.07 #676, 0.04 #2216, 0.04 #396), 092c5f (0.06 #294, 0.05 #14, 0.05 #154) >> Best rule #121 for best value: >> intensional similarity = 2 >> extensional distance = 17 >> proper extension: 01mylz; >> query: (?x4391, 09gkdln) <- film(?x4391, ?x4392), ?x4392 = 06gb1w >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #7421 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1975 *> proper extension: 0l6qt; 07f8wg; 01wl38s; 06cc_1; 0168cl; 0265v21; 026dg51; 030pr; 02pkpfs; 02r5w9; ... *> query: (?x4391, ?x3624) <- award_nominee(?x4391, ?x5593), award_nominee(?x2061, ?x4391), award_winner(?x3624, ?x5593) *> conf = 0.10 ranks of expected_values: 5 EVAL 08yx9q award_winner! 0275n3y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 83.000 83.000 0.105 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12476-015rkw PRED entity: 015rkw PRED relation: award PRED expected values: 0789_m => 84 concepts (67 used for prediction) PRED predicted values (max 10 best out of 261): 05pcn59 (0.29 #482, 0.27 #884, 0.18 #80), 05zr6wv (0.29 #419, 0.27 #821, 0.15 #12869), 0gqy2 (0.27 #163, 0.19 #565, 0.18 #967), 04kxsb (0.27 #124, 0.18 #18502, 0.17 #7640), 0bs0bh (0.27 #102, 0.18 #18502, 0.17 #7640), 02w9sd7 (0.24 #571, 0.23 #973, 0.18 #169), 057xs89 (0.24 #561, 0.23 #963, 0.15 #12869), 02z0dfh (0.19 #476, 0.18 #878, 0.18 #18502), 0gqwc (0.19 #475, 0.18 #877, 0.18 #18502), 02x73k6 (0.18 #59, 0.18 #18502, 0.17 #7640) >> Best rule #482 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 012d40; 01j5ts; 07lt7b; 0mdqp; 01lbp; 039bp; 06cgy; 01y_px; 02ld6x; 0154qm; ... >> query: (?x1739, 05pcn59) <- award_nominee(?x400, ?x1739), ?x400 = 01q_ph, award(?x1739, ?x704) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #18502 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1519 *> proper extension: 018_q8; *> query: (?x1739, ?x375) <- award_winner(?x1739, ?x374), award_winner(?x375, ?x374) *> conf = 0.18 ranks of expected_values: 38 EVAL 015rkw award 0789_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 84.000 67.000 0.286 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12475-09889g PRED entity: 09889g PRED relation: profession PRED expected values: 016z4k => 120 concepts (119 used for prediction) PRED predicted values (max 10 best out of 95): 016z4k (0.65 #997, 0.62 #429, 0.61 #2417), 0n1h (0.64 #294, 0.38 #436, 0.35 #1004), 0dxtg (0.59 #3561, 0.53 #1857, 0.47 #6969), 039v1 (0.55 #315, 0.31 #1025, 0.31 #457), 09lbv (0.50 #16, 0.40 #868, 0.37 #2572), 0cbd2 (0.47 #1567, 0.47 #7389, 0.46 #6963), 03gjzk (0.45 #3562, 0.42 #1858, 0.42 #1290), 018gz8 (0.42 #1292, 0.40 #1860, 0.39 #3564), 0kyk (0.32 #6983, 0.31 #7409, 0.31 #2155), 02jknp (0.29 #4692, 0.27 #290, 0.27 #2846) >> Best rule #997 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 06y9c2; 02wb6yq; 04cr6qv; 04f7c55; 0gs6vr; 04d_mtq; >> query: (?x4960, 016z4k) <- instrumentalists(?x212, ?x4960), friend(?x1231, ?x4960), artists(?x671, ?x4960) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09889g profession 016z4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 119.000 0.654 http://example.org/people/person/profession #12474-0hwqg PRED entity: 0hwqg PRED relation: award PRED expected values: 0bdwqv => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 277): 09sb52 (0.34 #33251, 0.33 #20290, 0.33 #21505), 0gqy2 (0.24 #4620, 0.24 #2595, 0.14 #570), 01by1l (0.20 #12262, 0.20 #16312, 0.20 #11452), 054ky1 (0.19 #39692, 0.10 #109, 0.09 #1324), 05qck (0.19 #39692), 0gq9h (0.17 #2102, 0.10 #77, 0.10 #11012), 01bgqh (0.17 #16242, 0.16 #12192, 0.16 #17052), 0ck27z (0.16 #17507, 0.15 #21557, 0.15 #20342), 0c4z8 (0.15 #12221, 0.14 #11411, 0.13 #16271), 040njc (0.14 #413, 0.07 #29971, 0.07 #8) >> Best rule #33251 for best value: >> intensional similarity = 3 >> extensional distance = 1166 >> proper extension: 0byfz; 03x3qv; 01vrx3g; 0bg539; 05tk7y; 06lj1m; 07b2lv; 02d4ct; 05th8t; 01fwk3; ... >> query: (?x10795, 09sb52) <- award(?x10795, ?x591), award_nominee(?x10795, ?x6331), film(?x10795, ?x1547) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #4628 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 144 *> proper extension: 028lc8; 015rhv; 09qh1; 016ynj; 015nvj; 0p9qb; *> query: (?x10795, 0bdwqv) <- award(?x10795, ?x591), film(?x10795, ?x1547), place_of_death(?x10795, ?x12935) *> conf = 0.13 ranks of expected_values: 14 EVAL 0hwqg award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 148.000 148.000 0.341 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12473-040j2_ PRED entity: 040j2_ PRED relation: location PRED expected values: 013yq => 126 concepts (83 used for prediction) PRED predicted values (max 10 best out of 232): 0n1rj (0.77 #6435, 0.77 #32969, 0.76 #31359), 0dc95 (0.33 #130, 0.14 #2543, 0.05 #4152), 02_286 (0.32 #32202, 0.31 #30592, 0.25 #36227), 030qb3t (0.25 #887, 0.20 #1691, 0.17 #56392), 04jpl (0.20 #26546, 0.06 #47477, 0.06 #55522), 0jj6k (0.18 #28138, 0.17 #29749, 0.12 #30555), 0cr3d (0.16 #30699, 0.16 #32309, 0.13 #28282), 01n7q (0.16 #22569, 0.09 #26592, 0.04 #56372), 0f2v0 (0.15 #4826, 0.14 #2595, 0.05 #4204), 01531 (0.15 #4826, 0.11 #27489, 0.09 #29101) >> Best rule #6435 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 07h1h5; 07vfqj; 07m69t; >> query: (?x8110, ?x6084) <- team(?x8110, ?x1632), location(?x8110, ?x2623), sport(?x1632, ?x5063), place_of_birth(?x8110, ?x6084) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #4826 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 0cymln; *> query: (?x8110, ?x739) <- team(?x8110, ?x8111), team(?x8110, ?x1632), location(?x8110, ?x2623), school(?x8111, ?x1011), teams(?x739, ?x1632) *> conf = 0.15 ranks of expected_values: 11 EVAL 040j2_ location 013yq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 126.000 83.000 0.773 http://example.org/people/person/places_lived./people/place_lived/location #12472-07bcn PRED entity: 07bcn PRED relation: teams PRED expected values: 0jmmn => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 212): 04cxw5b (0.14 #166, 0.11 #886, 0.11 #526), 0jmk7 (0.14 #303, 0.11 #1023, 0.08 #1743), 0jnq8 (0.14 #229, 0.11 #949, 0.08 #1669), 0jmjr (0.14 #222, 0.11 #942, 0.08 #1662), 04mjl (0.14 #156, 0.11 #876, 0.08 #1596), 02pqcfz (0.14 #82, 0.11 #802, 0.08 #1522), 04112r (0.14 #51, 0.11 #771, 0.08 #1491), 07k53y (0.14 #12, 0.11 #732, 0.08 #1452), 0d3fdn (0.14 #335, 0.11 #1055, 0.07 #2135), 0jnpv (0.14 #277, 0.11 #997, 0.07 #2077) >> Best rule #166 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0f2v0; >> query: (?x5893, 04cxw5b) <- locations(?x12451, ?x5893), dog_breed(?x5893, ?x1706), citytown(?x8641, ?x5893), ?x12451 = 0b_6xf >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07bcn teams 0jmmn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 141.000 141.000 0.143 http://example.org/sports/sports_team_location/teams #12471-022_lg PRED entity: 022_lg PRED relation: gender PRED expected values: 05zppz => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #15, 0.90 #13, 0.89 #72), 02zsn (0.45 #55, 0.45 #79, 0.45 #67) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 05ty4m; 03dbds; >> query: (?x1431, 05zppz) <- film(?x1431, ?x1744), award(?x1431, ?x198), profession(?x1431, ?x319), influenced_by(?x1431, ?x8043) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022_lg gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.903 http://example.org/people/person/gender #12470-01vzz1c PRED entity: 01vzz1c PRED relation: nationality PRED expected values: 07ssc => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 67): 09c7w0 (0.84 #796, 0.84 #7890, 0.81 #11676), 02jx1 (0.59 #2327, 0.55 #1325, 0.43 #231), 01hpnh (0.47 #2096, 0.46 #7591, 0.42 #8089), 07ssc (0.36 #2309, 0.32 #1307, 0.29 #213), 0d060g (0.11 #4899, 0.07 #11078, 0.07 #7497), 0345h (0.11 #130, 0.07 #1323, 0.06 #627), 0f8l9c (0.09 #4914, 0.03 #11295, 0.02 #11093), 04jpl (0.06 #1794, 0.06 #2894, 0.06 #1894), 022tq4 (0.06 #8090, 0.04 #7590, 0.04 #2095), 01q_22 (0.06 #8090) >> Best rule #796 for best value: >> intensional similarity = 5 >> extensional distance = 43 >> proper extension: 03_gx; >> query: (?x11442, 09c7w0) <- artists(?x13359, ?x11442), artists(?x5717, ?x11442), artists(?x13359, ?x12743), ?x12743 = 02bc74, ?x5717 = 016cjb >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2309 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 120 *> proper extension: 0xnc3; 01bh6y; 06p0s1; *> query: (?x11442, 07ssc) <- location(?x11442, ?x10632), location(?x11442, ?x362), nationality(?x11442, ?x2146), ?x362 = 04jpl, state_province_region(?x1391, ?x10632) *> conf = 0.36 ranks of expected_values: 4 EVAL 01vzz1c nationality 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 157.000 157.000 0.844 http://example.org/people/person/nationality #12469-09146g PRED entity: 09146g PRED relation: film! PRED expected values: 011zd3 => 62 concepts (34 used for prediction) PRED predicted values (max 10 best out of 904): 0150t6 (0.45 #20757, 0.44 #26984, 0.42 #53982), 06fxnf (0.45 #20757, 0.44 #26984, 0.42 #53982), 056ws9 (0.44 #26984, 0.42 #53982, 0.41 #62285), 0mdqp (0.20 #4267, 0.15 #6342, 0.03 #47871), 0jfx1 (0.18 #2477, 0.06 #33616, 0.06 #10777), 01chc7 (0.18 #2631, 0.06 #10931, 0.02 #27540), 0z4s (0.18 #2141, 0.03 #37435, 0.03 #39512), 0143wl (0.18 #3141, 0.03 #11441, 0.01 #28050), 017r13 (0.18 #3183, 0.03 #9408, 0.02 #59241), 01r9c_ (0.18 #3864, 0.01 #12164) >> Best rule #20757 for best value: >> intensional similarity = 3 >> extensional distance = 177 >> proper extension: 07s8z_l; 06mmr; >> query: (?x1904, ?x4020) <- award_winner(?x1904, ?x4020), category(?x1904, ?x134), people(?x5042, ?x4020) >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #2446 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 05pbl56; 02qr69m; 06ztvyx; 049mql; 0bbw2z6; 0287477; 07nxnw; 01xbxn; 03z9585; *> query: (?x1904, 011zd3) <- genre(?x1904, ?x225), film(?x2258, ?x1904), film_release_region(?x1904, ?x94), ?x2258 = 0f4vbz *> conf = 0.09 ranks of expected_values: 58 EVAL 09146g film! 011zd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 62.000 34.000 0.446 http://example.org/film/actor/film./film/performance/film #12468-01rhl PRED entity: 01rhl PRED relation: role! PRED expected values: 03t852 => 75 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1118): 023l9y (0.71 #6296, 0.70 #9112, 0.60 #3956), 04bpm6 (0.70 #8970, 0.67 #4750, 0.62 #7565), 02s6sh (0.70 #9337, 0.60 #4181, 0.50 #3244), 05qhnq (0.62 #11090, 0.60 #4055, 0.57 #6395), 01wxdn3 (0.60 #9311, 0.60 #4155, 0.57 #6495), 082brv (0.60 #4013, 0.58 #10581, 0.57 #6826), 0lzkm (0.60 #3916, 0.57 #6256, 0.50 #9072), 045zr (0.60 #3853, 0.50 #9009, 0.50 #2916), 0326tc (0.60 #9248, 0.50 #3155, 0.50 #2688), 01wgjj5 (0.60 #4010, 0.50 #3073, 0.50 #1667) >> Best rule #6296 for best value: >> intensional similarity = 27 >> extensional distance = 5 >> proper extension: 05r5c; 018vs; >> query: (?x4616, 023l9y) <- role(?x227, ?x4616), role(?x4616, ?x3716), role(?x4616, ?x2957), role(?x4616, ?x2158), role(?x4616, ?x960), role(?x4616, ?x716), role(?x2048, ?x4616), ?x2048 = 018j2, role(?x960, ?x614), ?x2158 = 01dnws, role(?x1148, ?x716), role(?x75, ?x716), instrumentalists(?x716, ?x7221), instrumentalists(?x716, ?x5126), instrumentalists(?x716, ?x4140), instrumentalists(?x716, ?x2782), ?x4140 = 01sb5r, role(?x3442, ?x716), ?x3716 = 03gvt, group(?x716, ?x7810), ?x3442 = 0m_v0, ?x2782 = 014q2g, ?x2957 = 01v8y9, ?x7221 = 0191h5, ?x7810 = 0187x8, ?x1148 = 02qjv, ?x5126 = 03h502k >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6429 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 5 *> proper extension: 05r5c; 018vs; *> query: (?x4616, 03t852) <- role(?x227, ?x4616), role(?x4616, ?x3716), role(?x4616, ?x2957), role(?x4616, ?x2158), role(?x4616, ?x960), role(?x4616, ?x716), role(?x2048, ?x4616), ?x2048 = 018j2, role(?x960, ?x614), ?x2158 = 01dnws, role(?x1148, ?x716), role(?x75, ?x716), instrumentalists(?x716, ?x7221), instrumentalists(?x716, ?x5126), instrumentalists(?x716, ?x4140), instrumentalists(?x716, ?x2782), ?x4140 = 01sb5r, role(?x3442, ?x716), ?x3716 = 03gvt, group(?x716, ?x7810), ?x3442 = 0m_v0, ?x2782 = 014q2g, ?x2957 = 01v8y9, ?x7221 = 0191h5, ?x7810 = 0187x8, ?x1148 = 02qjv, ?x5126 = 03h502k *> conf = 0.29 ranks of expected_values: 289 EVAL 01rhl role! 03t852 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 75.000 45.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #12467-0ftxw PRED entity: 0ftxw PRED relation: dog_breed PRED expected values: 01_gx_ => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 1): 01_gx_ (0.82 #23, 0.82 #24, 0.59 #17) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0d6lp; 02j3w; 02hrh0_; 0chrx; >> query: (?x2879, 01_gx_) <- location(?x5346, ?x2879), dog_breed(?x2879, ?x1706), religion(?x5346, ?x2694) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ftxw dog_breed 01_gx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.822 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #12466-0424m PRED entity: 0424m PRED relation: organizations_founded PRED expected values: 0d075m => 164 concepts (132 used for prediction) PRED predicted values (max 10 best out of 43): 06dr9 (0.67 #290, 0.15 #3152, 0.15 #3050), 05f4p (0.14 #380, 0.08 #2936, 0.06 #1403), 015dvh (0.12 #605, 0.10 #707, 0.08 #2957), 07x4c (0.12 #555, 0.07 #1067, 0.07 #3315), 016hjr (0.12 #612, 0.07 #1124, 0.05 #1943), 07wbk (0.10 #3289, 0.10 #2065, 0.08 #2881), 07t65 (0.10 #615, 0.07 #1127, 0.06 #1332), 0g8fs (0.10 #673, 0.07 #1185, 0.06 #1287), 02vk52z (0.09 #716, 0.03 #3476, 0.03 #3888), 01prf3 (0.09 #781, 0.03 #3541, 0.03 #3953) >> Best rule #290 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 04jvt; >> query: (?x5978, 06dr9) <- religion(?x5978, ?x9362), entity_involved(?x8416, ?x5978), influenced_by(?x5978, ?x1857), profession(?x5978, ?x2225) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1384 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 041wm; *> query: (?x5978, 0d075m) <- jurisdiction_of_office(?x5978, ?x94), profession(?x5978, ?x2225), profession(?x11440, ?x2225), profession(?x2934, ?x2225), ?x11440 = 01lct6, ?x2934 = 04cbtrw *> conf = 0.06 ranks of expected_values: 17 EVAL 0424m organizations_founded 0d075m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 164.000 132.000 0.667 http://example.org/organization/organization_founder/organizations_founded #12465-01dnws PRED entity: 01dnws PRED relation: group PRED expected values: 02mq_y => 92 concepts (52 used for prediction) PRED predicted values (max 10 best out of 182): 07mvp (0.67 #5112, 0.67 #2123, 0.62 #3994), 01cblr (0.67 #2095, 0.60 #607, 0.57 #2280), 07m4c (0.67 #2137, 0.60 #649, 0.57 #2322), 013w2r (0.67 #2112, 0.57 #2297, 0.56 #5101), 02cw1m (0.67 #5166, 0.57 #2735, 0.50 #3859), 05563d (0.64 #5631, 0.47 #7890, 0.44 #7327), 014pg1 (0.62 #4021, 0.56 #5139, 0.50 #2150), 01k_yf (0.62 #3597, 0.50 #431, 0.44 #5093), 048xh (0.60 #1021, 0.50 #1390, 0.42 #5784), 0mjn2 (0.57 #3124, 0.57 #2748, 0.56 #5179) >> Best rule #5112 for best value: >> intensional similarity = 19 >> extensional distance = 7 >> proper extension: 0l14md; >> query: (?x2158, 07mvp) <- role(?x2158, ?x2923), role(?x1147, ?x2158), role(?x2158, ?x4917), role(?x2158, ?x212), group(?x2158, ?x5838), ?x1147 = 07kc_, artist(?x4483, ?x5838), ?x2923 = 02k856, ?x4917 = 06w7v, performance_role(?x1282, ?x212), role(?x2888, ?x212), family(?x3967, ?x212), ?x2888 = 02fsn, instrumentalists(?x212, ?x5285), instrumentalists(?x212, ?x3657), instrumentalists(?x212, ?x1294), ?x5285 = 01xzb6, ?x1294 = 0bg539, ?x3657 = 01w8n89 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4717 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 6 *> proper extension: 03q5t; *> query: (?x2158, 02mq_y) <- role(?x2158, ?x5417), role(?x2158, ?x1969), role(?x2158, ?x885), role(?x1147, ?x2158), role(?x2158, ?x4769), role(?x2158, ?x2460), group(?x2158, ?x4791), ?x4769 = 0dwt5, ?x1969 = 04rzd, role(?x1147, ?x75), role(?x1147, ?x316), ?x2460 = 01wy6, ?x885 = 0dwtp, role(?x367, ?x5417), ?x75 = 07y_7, instrumentalists(?x5417, ?x642), instrumentalists(?x1147, ?x5623), ?x367 = 01lmj3q *> conf = 0.50 ranks of expected_values: 18 EVAL 01dnws group 02mq_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 92.000 52.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group #12464-0c12h PRED entity: 0c12h PRED relation: religion PRED expected values: 0kpl => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 18): 0c8wxp (0.21 #141, 0.18 #321, 0.17 #366), 03_gx (0.10 #194, 0.09 #329, 0.09 #374), 0kpl (0.08 #190, 0.08 #730, 0.06 #460), 092bf5 (0.06 #16, 0.02 #61, 0.02 #196), 0kq2 (0.05 #288, 0.05 #378, 0.04 #333), 0n2g (0.04 #373, 0.03 #328, 0.03 #283), 019cr (0.03 #146, 0.03 #11, 0.02 #371), 03j6c (0.03 #786, 0.03 #21, 0.02 #66), 051kv (0.03 #5, 0.01 #905, 0.01 #185), 06nzl (0.02 #60, 0.02 #240, 0.01 #645) >> Best rule #141 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 02knnd; 01v3bn; 08b8vd; 0ly5n; 04bgy; 013qvn; 027r0_f; 03k1vm; 0btj0; 070px; >> query: (?x6239, 0c8wxp) <- film(?x6239, ?x3638), profession(?x6239, ?x319), place_of_burial(?x6239, ?x11261) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #190 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 85 *> proper extension: 01twdk; *> query: (?x6239, 0kpl) <- film(?x6239, ?x3638), award_winner(?x601, ?x6239), film(?x6239, ?x6181) *> conf = 0.08 ranks of expected_values: 3 EVAL 0c12h religion 0kpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 105.000 0.207 http://example.org/people/person/religion #12463-015ppk PRED entity: 015ppk PRED relation: award PRED expected values: 0m7yy => 99 concepts (97 used for prediction) PRED predicted values (max 10 best out of 166): 0m7yy (0.69 #582, 0.55 #355, 0.49 #2852), 0cqh6z (0.44 #228, 0.42 #5452, 0.42 #5451), 0bdw1g (0.38 #486, 0.27 #259, 0.17 #31), 02xcb6n (0.38 #643, 0.18 #416, 0.17 #188), 0bp_b2 (0.27 #244, 0.19 #471, 0.09 #7497), 0cjyzs (0.18 #2578, 0.18 #2351, 0.17 #2805), 027gs1_ (0.18 #2673, 0.17 #2900, 0.17 #2446), 09qj50 (0.17 #2308, 0.17 #37, 0.16 #2535), 0cqhk0 (0.17 #30, 0.11 #2301, 0.10 #2755), 09qvf4 (0.17 #141, 0.09 #7497, 0.09 #7496) >> Best rule #582 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0300ml; >> query: (?x7116, 0m7yy) <- award(?x7116, ?x4921), genre(?x7116, ?x53), ?x4921 = 0fbtbt >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015ppk award 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 97.000 0.688 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12462-07g9f PRED entity: 07g9f PRED relation: award_winner PRED expected values: 03d_w3h => 75 concepts (54 used for prediction) PRED predicted values (max 10 best out of 943): 07lwsz (0.54 #8197, 0.47 #8196, 0.47 #31151), 04h68j (0.47 #8196, 0.46 #11474, 0.46 #4917), 02k76g (0.47 #8196, 0.46 #11474, 0.45 #16392), 0b05xm (0.47 #31151, 0.46 #4917, 0.46 #3278), 025y9fn (0.47 #31151, 0.46 #4917, 0.46 #3278), 0f721s (0.47 #31151, 0.42 #6557, 0.42 #24591), 0bbxd3 (0.47 #31151, 0.42 #6557, 0.42 #24591), 02nwxc (0.46 #4917, 0.46 #73769, 0.46 #3278), 0k2mxq (0.46 #4917, 0.46 #73769, 0.46 #3278), 0443xn (0.46 #4917, 0.46 #73769, 0.46 #3278) >> Best rule #8197 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 02_1ky; >> query: (?x10089, ?x3571) <- nominated_for(?x435, ?x10089), tv_program(?x3571, ?x10089), award_winner(?x4921, ?x3571) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #55740 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 197 *> proper extension: 02zv4b; 070g7; 026bfsh; 01f39b; *> query: (?x10089, ?x525) <- actor(?x10089, ?x4137), nominated_for(?x4137, ?x2336), award_nominee(?x525, ?x4137) *> conf = 0.05 ranks of expected_values: 125 EVAL 07g9f award_winner 03d_w3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 75.000 54.000 0.544 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #12461-02l4rh PRED entity: 02l4rh PRED relation: award_winner! PRED expected values: 0clfdj => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 134): 09pnw5 (0.14 #102, 0.04 #242, 0.03 #662), 0bzlrh (0.14 #103, 0.03 #243, 0.02 #11341), 03tn9w (0.14 #93, 0.02 #11341, 0.02 #373), 09gkdln (0.06 #261, 0.04 #401, 0.04 #4321), 0clfdj (0.06 #144, 0.04 #284, 0.03 #564), 09qvms (0.06 #573, 0.05 #1973, 0.05 #2113), 0hr3c8y (0.05 #150, 0.04 #290, 0.04 #1970), 0hndn2q (0.05 #180, 0.04 #320, 0.02 #4240), 09bymc (0.05 #260, 0.04 #400, 0.02 #4320), 02q690_ (0.05 #204, 0.03 #344, 0.03 #4264) >> Best rule #102 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 046qq; >> query: (?x7045, 09pnw5) <- award(?x7045, ?x154), film(?x7045, ?x5008), ?x5008 = 035w2k >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 018z_c; 01gw8b; *> query: (?x7045, 0clfdj) <- award(?x7045, ?x1245), ?x1245 = 0gqwc, award_nominee(?x7045, ?x374) *> conf = 0.06 ranks of expected_values: 5 EVAL 02l4rh award_winner! 0clfdj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 93.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12460-0c6cwg PRED entity: 0c6cwg PRED relation: locations PRED expected values: 0jdd => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 119): 0d05q4 (0.40 #450, 0.36 #820, 0.36 #636), 0jdd (0.33 #70, 0.23 #2417, 0.15 #4105), 059g4 (0.17 #1243, 0.10 #1615, 0.08 #3672), 02j9z (0.16 #1682, 0.16 #2055, 0.16 #1868), 05rgl (0.15 #968, 0.11 #1154, 0.10 #412), 02k54 (0.15 #941, 0.11 #1314, 0.10 #1686), 04gqr (0.15 #1007, 0.11 #1380, 0.09 #637), 0j3b (0.11 #1323, 0.11 #1136, 0.10 #1695), 04wsz (0.11 #3304, 0.10 #509, 0.10 #3492), 03spz (0.10 #460, 0.09 #830, 0.09 #646) >> Best rule #450 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0cm2xh; 01y998; 048n7; 018w0j; 0cwt70; 01cpp0; >> query: (?x13362, 0d05q4) <- entity_involved(?x13362, ?x11288), profession(?x11288, ?x8290), combatants(?x13362, ?x14253), award_winner(?x5631, ?x11288), student(?x7127, ?x11288), type_of_union(?x11288, ?x566) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #70 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 03gqgt3; *> query: (?x13362, 0jdd) <- entity_involved(?x13362, ?x13363), entity_involved(?x13362, ?x11288), entity_involved(?x13362, ?x8868), ?x11288 = 08_hns, ?x13363 = 07jqh, combatants(?x13362, ?x14253), ?x14253 = 0v74, combatants(?x10849, ?x8868) *> conf = 0.33 ranks of expected_values: 2 EVAL 0c6cwg locations 0jdd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 23.000 23.000 0.400 http://example.org/time/event/locations #12459-09sh8k PRED entity: 09sh8k PRED relation: film_crew_role PRED expected values: 015h31 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 15): 089g0h (0.30 #35, 0.12 #155, 0.12 #83), 02ynfr (0.24 #176, 0.22 #395, 0.21 #543), 04pyp5 (0.20 #33, 0.09 #81, 0.08 #809), 015h31 (0.14 #77, 0.13 #125, 0.12 #101), 089fss (0.12 #3, 0.10 #27, 0.09 #390), 05smlt (0.10 #36, 0.08 #60, 0.07 #108), 0263ycg (0.10 #34, 0.04 #397, 0.03 #154), 026sdt1 (0.10 #40, 0.02 #88), 094hwz (0.09 #55, 0.08 #79, 0.06 #103), 02vs3x5 (0.06 #401, 0.06 #134, 0.06 #206) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0gwjw0c; >> query: (?x136, 089g0h) <- film(?x10643, ?x136), ?x10643 = 07myb2, language(?x136, ?x2164) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #77 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 040rmy; 02phtzk; *> query: (?x136, 015h31) <- film_crew_role(?x136, ?x2095), category(?x136, ?x134), music(?x136, ?x3042), ?x2095 = 0dxtw *> conf = 0.14 ranks of expected_values: 4 EVAL 09sh8k film_crew_role 015h31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 72.000 0.300 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12458-01kws3 PRED entity: 01kws3 PRED relation: location PRED expected values: 0cr3d => 114 concepts (110 used for prediction) PRED predicted values (max 10 best out of 202): 02_286 (0.23 #3251, 0.19 #5661, 0.17 #55474), 030qb3t (0.20 #3214, 0.19 #29726, 0.16 #5707), 0cr3d (0.08 #948, 0.07 #22639, 0.07 #25852), 0cc56 (0.08 #860, 0.06 #4075, 0.06 #4878), 05fkf (0.07 #2448, 0.02 #8072, 0.02 #8876), 01cx_ (0.06 #5787, 0.05 #8197, 0.05 #4181), 04jpl (0.05 #42603, 0.05 #53848, 0.05 #68310), 01531 (0.05 #9799, 0.04 #11406, 0.04 #14618), 0fhp9 (0.05 #3257, 0.04 #2453, 0.03 #28965), 0chrx (0.05 #3619, 0.02 #7635, 0.02 #2815) >> Best rule #3251 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 033hqf; 069z_5; >> query: (?x5393, 02_286) <- profession(?x5393, ?x353), place_of_death(?x5393, ?x1523), ?x1523 = 030qb3t, location(?x5393, ?x11299) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #948 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 084w8; 019z7q; 0yfp; 01tz6vs; 040_t; 05x8n; 017_pb; 0hcvy; 03cdg; 0py5b; ... *> query: (?x5393, 0cr3d) <- profession(?x5393, ?x353), award(?x5393, ?x2016), ?x353 = 0cbd2, people(?x268, ?x5393) *> conf = 0.08 ranks of expected_values: 3 EVAL 01kws3 location 0cr3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 110.000 0.231 http://example.org/people/person/places_lived./people/place_lived/location #12457-0gn30 PRED entity: 0gn30 PRED relation: profession PRED expected values: 0dxtg 02hrh1q => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 96): 02hrh1q (0.89 #8247, 0.89 #7365, 0.88 #12512), 0dxtg (0.85 #6335, 0.83 #5600, 0.82 #4718), 09jwl (0.41 #1046, 0.36 #7516, 0.36 #10750), 016z4k (0.36 #1033, 0.25 #2356, 0.24 #7503), 0nbcg (0.34 #1059, 0.26 #7529, 0.26 #10763), 0np9r (0.29 #4135, 0.29 #4577, 0.20 #166), 0cbd2 (0.29 #6623, 0.28 #6329, 0.28 #5741), 0dz3r (0.28 #1031, 0.24 #7501, 0.22 #6472), 0d1pc (0.28 #1078, 0.21 #2254, 0.17 #2401), 0n1h (0.25 #10, 0.16 #2362, 0.12 #1039) >> Best rule #8247 for best value: >> intensional similarity = 2 >> extensional distance = 537 >> proper extension: 01l1b90; 05m63c; 02g8h; 023tp8; 0m2wm; 0prfz; 06jzh; 01ty7ll; 033hqf; 04bs3j; ... >> query: (?x5338, 02hrh1q) <- participant(?x5338, ?x3083), film(?x5338, ?x188) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0gn30 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.892 http://example.org/people/person/profession EVAL 0gn30 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.892 http://example.org/people/person/profession #12456-02hnl PRED entity: 02hnl PRED relation: instrumentalists PRED expected values: 01nqfh_ 0zjpz 0137g1 01vn35l 01wzlxj 012z8_ 01vng3b 0fq117k 01s1zk 02mx98 01mr2g6 03f1zhf 017f4y 018gkb => 67 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1146): 0407f (0.67 #3857, 0.56 #4224, 0.52 #15332), 01nn6c (0.67 #3855, 0.56 #4224, 0.52 #15332), 018gkb (0.67 #4186, 0.50 #7888, 0.50 #7359), 032t2z (0.67 #3719, 0.50 #6892, 0.50 #4248), 01p0w_ (0.67 #4212, 0.50 #7385, 0.50 #4741), 09mq4m (0.67 #3776, 0.50 #6949, 0.50 #4305), 016ntp (0.67 #3849, 0.50 #2268, 0.50 #1740), 01w923 (0.67 #3770, 0.50 #2189, 0.50 #1661), 0137g1 (0.67 #3825, 0.50 #2244, 0.38 #6998), 01ww_vs (0.67 #4205, 0.50 #2624, 0.38 #7378) >> Best rule #3857 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0342h; >> query: (?x1750, 0407f) <- role(?x1750, ?x314), instrumentalists(?x1750, ?x300), role(?x314, ?x214), group(?x1750, ?x10561), role(?x314, ?x780), role(?x211, ?x1750), role(?x217, ?x314), ?x10561 = 09jm8 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4186 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 0342h; *> query: (?x1750, 018gkb) <- role(?x1750, ?x314), instrumentalists(?x1750, ?x300), role(?x314, ?x214), group(?x1750, ?x10561), role(?x314, ?x780), role(?x211, ?x1750), role(?x217, ?x314), ?x10561 = 09jm8 *> conf = 0.67 ranks of expected_values: 3, 9, 27, 41, 47, 65, 85, 90, 96, 98, 172, 253, 316, 398 EVAL 02hnl instrumentalists 018gkb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 017f4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 03f1zhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 01mr2g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 02mx98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 01s1zk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 0fq117k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 01vng3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 012z8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 01wzlxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 01vn35l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 0137g1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 0zjpz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 02hnl instrumentalists 01nqfh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 67.000 36.000 0.667 http://example.org/music/instrument/instrumentalists #12455-0mbql PRED entity: 0mbql PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 34): 09vw2b7 (0.89 #1242, 0.78 #1692, 0.74 #1208), 0ch6mp2 (0.84 #3955, 0.81 #4024, 0.80 #1693), 01vx2h (0.67 #1246, 0.53 #1696, 0.49 #1385), 02ynfr (0.27 #1250, 0.25 #388, 0.25 #320), 0215hd (0.26 #527, 0.22 #425, 0.20 #187), 01xy5l_ (0.22 #522, 0.20 #832, 0.19 #936), 015h31 (0.21 #621, 0.20 #179, 0.17 #519), 0d2b38 (0.20 #194, 0.18 #1399, 0.17 #1710), 089g0h (0.20 #188, 0.17 #1393, 0.16 #942), 094hwz (0.20 #183, 0.17 #421, 0.13 #285) >> Best rule #1242 for best value: >> intensional similarity = 6 >> extensional distance = 71 >> proper extension: 01kff7; 02nx2k; >> query: (?x6620, 09vw2b7) <- language(?x6620, ?x90), film_crew_role(?x6620, ?x2091), music(?x6620, ?x5949), film(?x2414, ?x6620), genre(?x6620, ?x258), ?x2091 = 02rh1dz >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #3955 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 539 *> proper extension: 027ct7c; *> query: (?x6620, 0ch6mp2) <- language(?x6620, ?x90), film_crew_role(?x6620, ?x468), music(?x6620, ?x5949), film_crew_role(?x2917, ?x468), film_crew_role(?x2331, ?x468), ?x2917 = 03kg2v, ?x2331 = 0g3zrd *> conf = 0.84 ranks of expected_values: 2 EVAL 0mbql film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 149.000 149.000 0.890 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12454-0kjgl PRED entity: 0kjgl PRED relation: student! PRED expected values: 01f1r4 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 122): 0bwfn (0.09 #5018, 0.08 #1856, 0.08 #4491), 065y4w7 (0.08 #1595, 0.07 #2649, 0.06 #14), 08815 (0.07 #1583, 0.07 #529, 0.02 #13704), 017hnw (0.07 #1036), 07x4c (0.07 #786), 04b_46 (0.06 #3389, 0.04 #3916, 0.04 #2862), 015nl4 (0.06 #67, 0.05 #13769, 0.03 #25366), 06pwq (0.06 #12, 0.04 #3701, 0.03 #5282), 05nrkb (0.06 #349, 0.03 #876, 0.02 #1403), 0g8rj (0.06 #176, 0.03 #703, 0.01 #1757) >> Best rule #5018 for best value: >> intensional similarity = 3 >> extensional distance = 194 >> proper extension: 076_74; 09pl3f; 0184jw; 081l_; 029ghl; 02qx1m2; 016z1c; 0pksh; >> query: (?x7946, 0bwfn) <- award(?x7946, ?x401), produced_by(?x407, ?x7946), award_winner(?x192, ?x7946) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0kjgl student! 01f1r4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 108.000 0.087 http://example.org/education/educational_institution/students_graduates./education/education/student #12453-06dv3 PRED entity: 06dv3 PRED relation: award_winner! PRED expected values: 027c95y => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 228): 05ztrmj (0.37 #16617, 0.37 #17471, 0.37 #17044), 02w9sd7 (0.37 #16617, 0.37 #17471, 0.37 #17044), 09sb52 (0.18 #40, 0.17 #6857, 0.16 #7709), 0ck27z (0.13 #8612, 0.11 #6908, 0.11 #9890), 05p09zm (0.13 #1401, 0.12 #975, 0.09 #1827), 0bdwqv (0.12 #167, 0.08 #20456, 0.03 #9540), 027b9k6 (0.12 #206, 0.04 #632, 0.02 #2762), 0gq9h (0.10 #3484, 0.04 #928, 0.04 #11580), 0cqhk0 (0.10 #8557, 0.09 #888, 0.08 #6853), 0gs9p (0.10 #3486, 0.04 #11582, 0.03 #14138) >> Best rule #16617 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 012ljv; 0411q; 015rmq; 0244r8; 030_1_; 010hn; 014hr0; 094wz7q; 076_74; 036px; ... >> query: (?x262, ?x591) <- award_winner(?x262, ?x844), award(?x262, ?x591) >> conf = 0.37 => this is the best rule for 2 predicted values *> Best rule #1006 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 01438g; *> query: (?x262, 027c95y) <- award_nominee(?x262, ?x71), award_winner(?x253, ?x262), friend(?x6187, ?x262) *> conf = 0.07 ranks of expected_values: 60 EVAL 06dv3 award_winner! 027c95y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 87.000 87.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #12452-0qkcb PRED entity: 0qkcb PRED relation: contains! PRED expected values: 09c7w0 05k7sb => 97 concepts (72 used for prediction) PRED predicted values (max 10 best out of 190): 09c7w0 (0.80 #9849, 0.80 #8956, 0.71 #45661), 03rjj (0.58 #16115, 0.57 #32232, 0.56 #59988), 04_1l0v (0.25 #451, 0.23 #3136, 0.13 #11195), 029jpy (0.25 #216, 0.04 #2901, 0.01 #22599), 01n7q (0.21 #5449, 0.20 #17983, 0.19 #9031), 07z1m (0.18 #3672, 0.10 #987, 0.09 #6358), 059rby (0.18 #1810, 0.14 #12554, 0.13 #29566), 01x73 (0.18 #1905, 0.12 #3695, 0.05 #12649), 07ssc (0.17 #44797, 0.16 #13461, 0.16 #14356), 02jx1 (0.17 #44852, 0.12 #63655, 0.12 #14411) >> Best rule #9849 for best value: >> intensional similarity = 3 >> extensional distance = 171 >> proper extension: 018mmj; 018mrd; 018mlg; >> query: (?x8171, ?x94) <- contains(?x9065, ?x8171), country(?x9065, ?x94), source(?x9065, ?x958) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11 EVAL 0qkcb contains! 05k7sb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 97.000 72.000 0.803 http://example.org/location/location/contains EVAL 0qkcb contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 72.000 0.803 http://example.org/location/location/contains #12451-02w5q6 PRED entity: 02w5q6 PRED relation: gender PRED expected values: 05zppz => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #277, 0.77 #55, 0.77 #63), 02zsn (0.62 #16, 0.61 #30, 0.54 #14) >> Best rule #277 for best value: >> intensional similarity = 3 >> extensional distance = 1900 >> proper extension: 02778qt; 027kmrb; >> query: (?x6817, 05zppz) <- profession(?x6817, ?x2225), profession(?x3858, ?x2225), ?x3858 = 05jm7 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02w5q6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.787 http://example.org/people/person/gender #12450-024rwx PRED entity: 024rwx PRED relation: actor PRED expected values: 031296 => 56 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1069): 0sw6y (0.36 #6343, 0.31 #8176, 0.20 #3593), 01tszq (0.36 #5710, 0.24 #8459, 0.19 #7543), 01r4bps (0.33 #1718, 0.27 #6300, 0.20 #3550), 0582cf (0.33 #1607, 0.27 #6189, 0.19 #40337), 02gf_l (0.33 #566, 0.25 #7897, 0.25 #2398), 01h910 (0.33 #4162, 0.25 #2330, 0.24 #4582), 01d_4t (0.33 #673, 0.25 #2505, 0.20 #3421), 02_p5w (0.33 #301, 0.25 #2133, 0.20 #3049), 06czxq (0.33 #888, 0.25 #2720, 0.20 #3636), 01rw116 (0.33 #798, 0.25 #2630, 0.20 #3546) >> Best rule #6343 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 07vqnc; >> query: (?x5852, 0sw6y) <- actor(?x5852, ?x8439), actor(?x5955, ?x8439), country_of_origin(?x5852, ?x94), genre(?x5852, ?x10159), location(?x8439, ?x682), ?x10159 = 025s89p >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #9459 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 20 *> proper extension: 0170k0; *> query: (?x5852, 031296) <- actor(?x5852, ?x8439), program_creator(?x5852, ?x5832), film(?x8439, ?x2893), language(?x8439, ?x254), profession(?x8439, ?x1032), type_of_union(?x8439, ?x566) *> conf = 0.05 ranks of expected_values: 253 EVAL 024rwx actor 031296 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 53.000 0.364 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #12449-0zz6w PRED entity: 0zz6w PRED relation: location_of_ceremony! PRED expected values: 04ztj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.25 #29, 0.25 #37, 0.24 #33) >> Best rule #29 for best value: >> intensional similarity = 5 >> extensional distance = 366 >> proper extension: 0_rwf; 0_wm_; 010bnr; 0104lr; >> query: (?x14706, 04ztj) <- category(?x14706, ?x134), source(?x14706, ?x958), ?x958 = 0jbk9, ?x134 = 08mbj5d, place(?x14706, ?x14706) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0zz6w location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.247 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #12448-02h3tp PRED entity: 02h3tp PRED relation: location PRED expected values: 02_286 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 171): 02_286 (0.30 #31327, 0.19 #54594, 0.17 #16885), 04jpl (0.10 #31307, 0.07 #54574, 0.06 #26493), 0cc56 (0.10 #2465, 0.09 #3267, 0.08 #4871), 0cr3d (0.08 #9772, 0.07 #54701, 0.07 #8970), 02jx1 (0.08 #2479, 0.07 #3281, 0.07 #4083), 059rby (0.06 #14457, 0.06 #31306, 0.04 #16864), 01n7q (0.06 #63, 0.04 #31353, 0.04 #24933), 04f_d (0.06 #107, 0.02 #7328, 0.01 #8933), 04lh6 (0.06 #434, 0.02 #10864, 0.01 #11666), 0978r (0.06 #174, 0.01 #6593) >> Best rule #31327 for best value: >> intensional similarity = 3 >> extensional distance = 720 >> proper extension: 02wrhj; >> query: (?x7921, 02_286) <- nominated_for(?x7921, ?x7379), location(?x7921, ?x1523), location_of_ceremony(?x147, ?x1523) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02h3tp location 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.302 http://example.org/people/person/places_lived./people/place_lived/location #12447-03d_w3h PRED entity: 03d_w3h PRED relation: actor! PRED expected values: 07g9f => 117 concepts (42 used for prediction) PRED predicted values (max 10 best out of 162): 0443v1 (0.37 #4494, 0.31 #7402, 0.14 #6344), 03np63f (0.31 #7402, 0.08 #10310, 0.01 #1851), 0gydcp7 (0.31 #7402, 0.08 #10310), 02kk_c (0.20 #84, 0.04 #1142, 0.04 #2992), 0828jw (0.20 #105, 0.03 #4334, 0.02 #898), 07g9f (0.20 #200, 0.02 #993, 0.02 #1258), 0h63q6t (0.20 #228, 0.02 #1021, 0.02 #1286), 03g9xj (0.20 #193, 0.02 #986, 0.02 #1515), 05sy2k_ (0.20 #61, 0.02 #854, 0.02 #1383), 026bfsh (0.08 #2476, 0.06 #1155, 0.06 #626) >> Best rule #4494 for best value: >> intensional similarity = 3 >> extensional distance = 342 >> proper extension: 01mvth; 03gm48; 05fnl9; 0gd_b_; 07z1_q; 08_83x; 02tkzn; 030b93; 0g476; 0bbvr84; ... >> query: (?x940, ?x11615) <- award_winner(?x11615, ?x940), award_winner(?x686, ?x940), actor(?x7424, ?x940) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #200 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 044mvs; 01vh3r; *> query: (?x940, 07g9f) <- location(?x940, ?x362), location(?x940, ?x335), ?x335 = 059rby, ?x362 = 04jpl *> conf = 0.20 ranks of expected_values: 6 EVAL 03d_w3h actor! 07g9f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 117.000 42.000 0.369 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #12446-0dt1cm PRED entity: 0dt1cm PRED relation: profession PRED expected values: 016z4k => 140 concepts (108 used for prediction) PRED predicted values (max 10 best out of 89): 02hrh1q (0.88 #5056, 0.88 #11587, 0.87 #7582), 016z4k (0.60 #3, 0.57 #891, 0.54 #2522), 0nbcg (0.55 #1957, 0.53 #2253, 0.51 #2698), 01d_h8 (0.47 #4601, 0.45 #1042, 0.44 #4008), 039v1 (0.40 #36, 0.25 #1962, 0.24 #2110), 0n1h (0.35 #2530, 0.33 #2678, 0.32 #455), 0dxtg (0.33 #4016, 0.32 #4609, 0.29 #1198), 03gjzk (0.33 #1052, 0.31 #4018, 0.30 #4611), 01c979 (0.29 #10092, 0.07 #526, 0.04 #822), 01c72t (0.28 #7443, 0.27 #13083, 0.25 #6997) >> Best rule #5056 for best value: >> intensional similarity = 3 >> extensional distance = 177 >> proper extension: 02hhtj; >> query: (?x8200, 02hrh1q) <- participant(?x8200, ?x7547), profession(?x8200, ?x131), languages(?x8200, ?x254) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 01wj18h; 0bqsy; *> query: (?x8200, 016z4k) <- artists(?x12611, ?x8200), artists(?x3319, ?x8200), ?x3319 = 06j6l, artist(?x12171, ?x8200), ?x12611 = 0233qs *> conf = 0.60 ranks of expected_values: 2 EVAL 0dt1cm profession 016z4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 140.000 108.000 0.883 http://example.org/people/person/profession #12445-03f1r6t PRED entity: 03f1r6t PRED relation: film PRED expected values: 0ch3qr1 => 153 concepts (97 used for prediction) PRED predicted values (max 10 best out of 892): 02ph9tm (0.21 #2890, 0.04 #13624, 0.04 #18991), 09dv8h (0.15 #6537, 0.01 #54840, 0.01 #53051), 02yvct (0.14 #2141, 0.02 #25398, 0.02 #14664), 0f4_l (0.14 #2139, 0.02 #12873, 0.02 #14662), 023vcd (0.14 #3426, 0.02 #14160, 0.02 #17738), 0dq626 (0.14 #1841, 0.02 #19731), 04qk12 (0.14 #3249), 04gv3db (0.10 #6120, 0.02 #11487, 0.02 #13276), 02bqvs (0.10 #6864), 02c7k4 (0.10 #6471) >> Best rule #2890 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02jm0n; 0693l; 03m6pk; >> query: (?x5222, 02ph9tm) <- award(?x5222, ?x4260), film(?x5222, ?x2907), ?x2907 = 03z20c >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2765 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 02jm0n; 0693l; 03m6pk; *> query: (?x5222, 0ch3qr1) <- award(?x5222, ?x4260), film(?x5222, ?x2907), ?x2907 = 03z20c *> conf = 0.07 ranks of expected_values: 39 EVAL 03f1r6t film 0ch3qr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 153.000 97.000 0.214 http://example.org/film/actor/film./film/performance/film #12444-063_j5 PRED entity: 063_j5 PRED relation: genre PRED expected values: 0gf28 02xh1 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 83): 07s9rl0 (0.67 #1, 0.66 #2061, 0.65 #1145), 01z4y (0.50 #5728, 0.50 #2865, 0.50 #2864), 03k9fj (0.38 #350, 0.33 #236, 0.26 #1609), 06n90 (0.33 #237, 0.31 #351, 0.19 #1725), 04xvlr (0.33 #2, 0.25 #116, 0.23 #345), 02l7c8 (0.28 #2760, 0.27 #2991, 0.27 #2876), 0219x_ (0.25 #136, 0.21 #479, 0.20 #593), 01hmnh (0.23 #356, 0.22 #242, 0.17 #1615), 09blyk (0.23 #713, 0.12 #1744, 0.06 #1171), 06nbt (0.21 #592, 0.15 #478, 0.12 #135) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0jzw; 01rnly; 08xvpn; >> query: (?x8859, 07s9rl0) <- genre(?x8859, ?x225), film(?x5626, ?x8859), ?x5626 = 014gf8, films(?x5011, ?x8859) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #630 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 0vgkd; *> query: (?x8859, 0gf28) <- genre(?x8859, ?x809), genre(?x8859, ?x258), ?x809 = 0vgkd, genre(?x419, ?x258) *> conf = 0.16 ranks of expected_values: 18, 29 EVAL 063_j5 genre 02xh1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 66.000 66.000 0.667 http://example.org/film/film/genre EVAL 063_j5 genre 0gf28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 66.000 66.000 0.667 http://example.org/film/film/genre #12443-0x2p PRED entity: 0x2p PRED relation: colors PRED expected values: 06fvc => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.95 #1994, 0.94 #1956, 0.94 #1937), 0jc_p (0.50 #101, 0.50 #82, 0.40 #120), 06fvc (0.46 #367, 0.45 #2078, 0.44 #251), 019sc (0.42 #547, 0.38 #761, 0.38 #39), 01l849 (0.40 #135, 0.38 #327, 0.38 #39), 01g5v (0.38 #39, 0.33 #23, 0.32 #1796), 036k5h (0.38 #39, 0.32 #1796, 0.27 #1097), 03vtbc (0.38 #39, 0.32 #1796, 0.27 #1097), 09ggk (0.38 #39, 0.32 #1796, 0.27 #1097), 04d18d (0.38 #39, 0.32 #1796, 0.27 #1097) >> Best rule #1994 for best value: >> intensional similarity = 13 >> extensional distance = 225 >> proper extension: 03qx63; 044crp; 027yf83; 02pjzvh; 0b256b; 0690dn; 025v1sx; 03x6w8; 0175tv; 04n7ps6; ... >> query: (?x2405, 083jv) <- colors(?x2405, ?x8271), colors(?x10941, ?x8271), colors(?x5154, ?x8271), ?x5154 = 0jm8l, colors(?x10899, ?x8271), colors(?x10368, ?x8271), colors(?x9691, ?x8271), ?x10899 = 01fsv9, team(?x261, ?x2405), institution(?x865, ?x9691), category(?x9691, ?x134), position(?x10941, ?x2918), currency(?x10368, ?x170) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #367 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 11 *> proper extension: 04b5l3; *> query: (?x2405, 06fvc) <- colors(?x2405, ?x8271), position(?x2405, ?x2010), team(?x2010, ?x11919), team(?x2010, ?x8521), team(?x2010, ?x3333), team(?x2010, ?x2011), team(?x2010, ?x1160), ?x3333 = 01yjl, ?x8271 = 02rnmb, ?x1160 = 049n7, ?x11919 = 04b5l3, ?x8521 = 01v3x8, ?x2011 = 04913k *> conf = 0.46 ranks of expected_values: 3 EVAL 0x2p colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 119.000 0.952 http://example.org/sports/sports_team/colors #12442-0cz8mkh PRED entity: 0cz8mkh PRED relation: genre PRED expected values: 03npn => 108 concepts (91 used for prediction) PRED predicted values (max 10 best out of 124): 07s9rl0 (0.97 #7454, 0.76 #6004, 0.73 #6366), 05p553 (0.95 #5888, 0.94 #4568, 0.52 #8301), 03k9fj (0.67 #853, 0.48 #1093, 0.47 #253), 02kdv5l (0.64 #123, 0.60 #1683, 0.60 #603), 01hmnh (0.40 #739, 0.38 #859, 0.35 #619), 06n90 (0.40 #254, 0.35 #614, 0.32 #974), 0hcr (0.33 #25, 0.25 #1345, 0.22 #1105), 04t36 (0.33 #7, 0.11 #1327, 0.10 #4570), 0lsxr (0.33 #2891, 0.27 #130, 0.26 #1690), 02l7c8 (0.33 #5900, 0.32 #7470, 0.31 #10611) >> Best rule #7454 for best value: >> intensional similarity = 7 >> extensional distance = 785 >> proper extension: 02ppg1r; >> query: (?x1456, 07s9rl0) <- film(?x8440, ?x1456), titles(?x812, ?x1456), genre(?x1456, ?x6452), genre(?x13178, ?x6452), genre(?x7243, ?x6452), ?x7243 = 0yzbg, ?x13178 = 07ykkx5 >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #248 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 13 *> proper extension: 018nnz; *> query: (?x1456, 03npn) <- film(?x382, ?x1456), production_companies(?x1456, ?x2549), prequel(?x1456, ?x7366), film_distribution_medium(?x1456, ?x81), ?x81 = 029j_, genre(?x1456, ?x812) *> conf = 0.20 ranks of expected_values: 14 EVAL 0cz8mkh genre 03npn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 108.000 91.000 0.970 http://example.org/film/film/genre #12441-02hrh0_ PRED entity: 02hrh0_ PRED relation: location! PRED expected values: 02mjmr => 160 concepts (124 used for prediction) PRED predicted values (max 10 best out of 2101): 02mjmr (0.74 #166053, 0.57 #77986, 0.57 #30186), 05dbf (0.74 #166053, 0.57 #77986, 0.57 #30186), 01pqy_ (0.74 #166053, 0.57 #77986, 0.57 #30186), 05ml_s (0.74 #166053, 0.57 #30186, 0.56 #75469), 03c6v3 (0.74 #166053, 0.57 #30186, 0.56 #75469), 021sv1 (0.57 #30187, 0.57 #30186, 0.56 #75469), 01qkqwg (0.57 #30186, 0.56 #75469, 0.54 #88057), 06gh0t (0.57 #30186, 0.56 #75469, 0.54 #88057), 0bs1g5r (0.57 #30186, 0.56 #75469, 0.54 #88057), 01dw9z (0.32 #45282, 0.31 #166055, 0.30 #123282) >> Best rule #166053 for best value: >> intensional similarity = 5 >> extensional distance = 150 >> proper extension: 036k0s; 0mn0v; 0gqkd; 056_y; 0xq63; 05r7t; 0fw4v; 0n95v; 019fbp; 0th3k; ... >> query: (?x5193, ?x2669) <- place_of_birth(?x2669, ?x5193), place_of_birth(?x652, ?x5193), type_of_union(?x652, ?x566), origin(?x2683, ?x5193), location(?x2669, ?x108) >> conf = 0.74 => this is the best rule for 5 predicted values ranks of expected_values: 1 EVAL 02hrh0_ location! 02mjmr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 124.000 0.744 http://example.org/people/person/places_lived./people/place_lived/location #12440-0_kfv PRED entity: 0_kfv PRED relation: category PRED expected values: 08mbj5d => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #40, 0.77 #5, 0.77 #12) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 470 >> proper extension: 0mn0v; 0_rwf; 0_wm_; 010bnr; 0104lr; >> query: (?x13788, 08mbj5d) <- source(?x13788, ?x958), ?x958 = 0jbk9, place(?x13788, ?x13788) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_kfv category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.778 http://example.org/common/topic/webpage./common/webpage/category #12439-05169r PRED entity: 05169r PRED relation: team! PRED expected values: 0fw2d3 => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 74): 0135nb (0.04 #474, 0.04 #926, 0.04 #1039), 0d3f83 (0.04 #178, 0.03 #630, 0.03 #404), 0bn9sc (0.03 #568, 0.03 #116, 0.03 #342), 0dhrqx (0.03 #730, 0.03 #165, 0.03 #278), 080dyk (0.03 #797, 0.03 #1249, 0.03 #232), 04bsx1 (0.03 #868, 0.03 #303, 0.03 #77), 0c2rr7 (0.03 #279, 0.03 #1070, 0.02 #731), 07h1h5 (0.03 #241, 0.03 #15, 0.02 #693), 07nv3_ (0.03 #353, 0.02 #579, 0.02 #805), 0dv1hh (0.03 #1084, 0.02 #1536, 0.02 #293) >> Best rule #474 for best value: >> intensional similarity = 11 >> extensional distance = 151 >> proper extension: 02279c; 0dy68h; 0y54; 0d2psv; 06xj93; 041xyk; 0212mp; 03xzxb; 048xg8; 051n13; ... >> query: (?x12376, 0135nb) <- team(?x530, ?x12376), team(?x203, ?x12376), team(?x60, ?x12376), ?x60 = 02nzb8, position(?x12376, ?x63), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x203 = 0dgrmp, position(?x12376, ?x60), position(?x12376, ?x60), position(?x12376, ?x203) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #158 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 139 *> proper extension: 02gys2; 0d_q40; 02q3n9c; 0cgwt8; 01kj5h; 0303jw; 01dtl; 03fmw_; 04v9wn; 04257b; ... *> query: (?x12376, 0fw2d3) <- team(?x530, ?x12376), team(?x203, ?x12376), team(?x60, ?x12376), ?x60 = 02nzb8, position(?x12376, ?x63), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x203 = 0dgrmp, position(?x12376, ?x530), position(?x12376, ?x60), position(?x12376, ?x60) *> conf = 0.02 ranks of expected_values: 25 EVAL 05169r team! 0fw2d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 35.000 35.000 0.039 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #12438-04k15 PRED entity: 04k15 PRED relation: languages PRED expected values: 04306rv => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 14): 02h40lc (0.41 #587, 0.40 #236, 0.34 #665), 064_8sq (0.10 #249, 0.09 #288, 0.08 #366), 02bjrlw (0.10 #235, 0.05 #469, 0.05 #547), 06nm1 (0.09 #279, 0.08 #357, 0.05 #591), 03_9r (0.09 #278, 0.01 #2111, 0.01 #2306), 06b_j (0.07 #445, 0.05 #523, 0.05 #1069), 04306rv (0.06 #822, 0.05 #939, 0.04 #1329), 01c7y (0.05 #577, 0.01 #1708), 0t_2 (0.03 #750, 0.01 #1647), 0349s (0.02 #1553, 0.01 #1709) >> Best rule #587 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0m2l9; 01vrncs; 01wcp_g; 01wp8w7; 05qw5; 086qd; 01vs_v8; 01vsl3_; 06449; 0gcs9; ... >> query: (?x3774, 02h40lc) <- influenced_by(?x3774, ?x5600), artists(?x597, ?x3774), origin(?x3774, ?x10334), profession(?x3774, ?x1183), ?x1183 = 09jwl >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #822 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 0bhtzw; *> query: (?x3774, 04306rv) <- nationality(?x3774, ?x1264), place_of_birth(?x3774, ?x10334), ?x1264 = 0345h, gender(?x3774, ?x231) *> conf = 0.06 ranks of expected_values: 7 EVAL 04k15 languages 04306rv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 124.000 124.000 0.409 http://example.org/people/person/languages #12437-01smm PRED entity: 01smm PRED relation: source PRED expected values: 0jbk9 => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #139, 0.89 #64, 0.88 #69) >> Best rule #139 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x6453, 0jbk9) <- category(?x6453, ?x134), ?x134 = 08mbj5d, place(?x6453, ?x6453) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01smm source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #12436-0f4_2k PRED entity: 0f4_2k PRED relation: film! PRED expected values: 0bxtg => 154 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1357): 0lpjn (0.40 #8812, 0.30 #17145, 0.11 #33816), 03kpvp (0.40 #8965, 0.30 #17298, 0.09 #50638), 03knl (0.40 #8490, 0.20 #16823, 0.08 #18906), 03ym1 (0.33 #5180, 0.20 #9346, 0.17 #11429), 02wgln (0.33 #4482, 0.17 #10731, 0.12 #12815), 01chc7 (0.33 #4727, 0.17 #10976, 0.12 #13060), 0bxtg (0.33 #4243, 0.17 #10492, 0.12 #12576), 01r7t9 (0.33 #1882, 0.17 #20630, 0.12 #16464), 0f7hc (0.33 #2915, 0.14 #21663, 0.05 #25831), 06gb2q (0.33 #3364, 0.14 #22112, 0.05 #26280) >> Best rule #8812 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 08gsvw; >> query: (?x5960, 0lpjn) <- film(?x541, ?x5960), film_crew_role(?x5960, ?x2154), language(?x5960, ?x732), prequel(?x5960, ?x3953), ?x732 = 04306rv, ?x2154 = 01vx2h >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #4243 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 065dc4; *> query: (?x5960, 0bxtg) <- film_format(?x5960, ?x6392), language(?x5960, ?x11038), produced_by(?x5960, ?x1039), ?x11038 = 04h9h, film_crew_role(?x5960, ?x2091), ?x2091 = 02rh1dz *> conf = 0.33 ranks of expected_values: 7 EVAL 0f4_2k film! 0bxtg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 154.000 101.000 0.400 http://example.org/film/actor/film./film/performance/film #12435-0291ck PRED entity: 0291ck PRED relation: nominated_for! PRED expected values: 0p9sw => 81 concepts (80 used for prediction) PRED predicted values (max 10 best out of 208): 02g3ft (0.69 #5525, 0.67 #9134, 0.67 #6247), 0262s1 (0.69 #5525, 0.67 #9134, 0.67 #6247), 0gq9h (0.65 #784, 0.63 #1264, 0.60 #1024), 0gs9p (0.54 #305, 0.52 #1266, 0.52 #1026), 019f4v (0.51 #1255, 0.48 #1015, 0.46 #775), 0k611 (0.47 #1035, 0.46 #1275, 0.41 #795), 040njc (0.47 #968, 0.43 #1208, 0.38 #728), 0gq_v (0.42 #260, 0.41 #741, 0.39 #1221), 027gs1_ (0.40 #189, 0.07 #5473, 0.04 #8600), 0cjyzs (0.40 #82, 0.06 #5366, 0.04 #8493) >> Best rule #5525 for best value: >> intensional similarity = 3 >> extensional distance = 340 >> proper extension: 02nf2c; 05h95s; 0m123; 02gl58; >> query: (?x9484, ?x1429) <- award(?x9484, ?x1429), award_winner(?x9484, ?x2794), category(?x2794, ?x134) >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #1222 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 03kx49; *> query: (?x9484, 0p9sw) <- nominated_for(?x2794, ?x9484), list(?x9484, ?x3004), genre(?x9484, ?x258) *> conf = 0.32 ranks of expected_values: 18 EVAL 0291ck nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 81.000 80.000 0.690 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12434-063fh9 PRED entity: 063fh9 PRED relation: film_crew_role PRED expected values: 09zzb8 01vx2h => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.91 #1645, 0.90 #161, 0.89 #129), 01vx2h (0.69 #362, 0.59 #9, 0.50 #73), 01pvkk (0.36 #10, 0.35 #363, 0.32 #2010), 02vs3x5 (0.30 #52, 0.21 #180, 0.20 #148), 015h31 (0.22 #361, 0.21 #458, 0.20 #554), 01xy5l_ (0.19 #365, 0.14 #979, 0.14 #12), 0d2b38 (0.18 #375, 0.17 #407, 0.16 #278), 0215hd (0.18 #982, 0.17 #1433, 0.16 #1530), 089g0h (0.14 #369, 0.14 #1434, 0.13 #1338), 094hwz (0.14 #13, 0.11 #2353, 0.10 #3097) >> Best rule #1645 for best value: >> intensional similarity = 4 >> extensional distance = 637 >> proper extension: 05dy7p; 03_wm6; >> query: (?x6642, 09zzb8) <- film_crew_role(?x6642, ?x3305), production_companies(?x6642, ?x3920), film_crew_role(?x7265, ?x3305), ?x7265 = 04tng0 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 063fh9 film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.914 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 063fh9 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.914 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12433-0bfvw2 PRED entity: 0bfvw2 PRED relation: award! PRED expected values: 01j5ts 01sxq9 0kszw 02g0mx 01wc7p 030b93 02l3_5 0kftt 04wx2v 030hbp => 48 concepts (12 used for prediction) PRED predicted values (max 10 best out of 2721): 031sg0 (0.83 #9952, 0.83 #9953, 0.81 #19909), 057hz (0.83 #9952, 0.83 #9953, 0.81 #19909), 028knk (0.73 #7149, 0.09 #29865, 0.08 #39822), 0lpjn (0.64 #7385, 0.19 #29867, 0.19 #29866), 0159h6 (0.64 #6732, 0.09 #29865, 0.08 #39822), 01kb2j (0.64 #8099, 0.09 #29865, 0.08 #39822), 0mz73 (0.64 #8871, 0.08 #39822, 0.06 #36503), 01jw4r (0.55 #9082, 0.33 #2447, 0.20 #5764), 01j5ts (0.55 #6678, 0.33 #43, 0.09 #29865), 02l4pj (0.55 #7568, 0.23 #26547, 0.19 #29867) >> Best rule #9952 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 09qwmm; 09sb52; 094qd5; 0gqwc; 02z0dfh; 02y_rq5; 02x4x18; 0cqgl9; >> query: (?x375, ?x374) <- award_winner(?x375, ?x4380), award_winner(?x375, ?x374), award(?x715, ?x375), film(?x4380, ?x2177), award(?x1244, ?x375), ?x1244 = 0h1nt >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #6678 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 09qwmm; 09sb52; 094qd5; 0gqwc; 02z0dfh; 02y_rq5; 02x4x18; 0cqgl9; *> query: (?x375, 01j5ts) <- award_winner(?x375, ?x4380), award(?x715, ?x375), film(?x4380, ?x2177), award(?x1244, ?x375), ?x1244 = 0h1nt *> conf = 0.55 ranks of expected_values: 9, 15, 39, 53, 141, 158, 171, 183, 655, 846 EVAL 0bfvw2 award! 030hbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 48.000 12.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvw2 award! 04wx2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 48.000 12.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvw2 award! 0kftt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 48.000 12.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvw2 award! 02l3_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 48.000 12.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvw2 award! 030b93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 48.000 12.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvw2 award! 01wc7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 48.000 12.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvw2 award! 02g0mx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 12.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvw2 award! 0kszw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 48.000 12.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvw2 award! 01sxq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 12.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvw2 award! 01j5ts CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 48.000 12.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12432-03h_9lg PRED entity: 03h_9lg PRED relation: inductee! PRED expected values: 0qjfl => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 3): 0g2c8 (0.03 #690, 0.03 #280, 0.03 #100), 06szd3 (0.02 #555, 0.02 #525, 0.02 #516), 0qjfl (0.02 #21, 0.01 #39, 0.01 #48) >> Best rule #690 for best value: >> intensional similarity = 3 >> extensional distance = 1810 >> proper extension: 0kc6x; 065y4w7; 01y67v; 0l2tk; 01gl9g; 02y9bj; 02p10m; 01ygv2; 0ljc_; 01fkr_; ... >> query: (?x844, 0g2c8) <- award_winner(?x537, ?x844), award_winner(?x537, ?x10905), influenced_by(?x1593, ?x10905) >> conf = 0.03 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 122 *> proper extension: 02wb6yq; 06_bq1; *> query: (?x844, 0qjfl) <- vacationer(?x608, ?x844), nominated_for(?x844, ?x97) *> conf = 0.02 ranks of expected_values: 3 EVAL 03h_9lg inductee! 0qjfl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.035 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #12431-018ctl PRED entity: 018ctl PRED relation: olympics! PRED expected values: 04wgh 0hg5 016wzw => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 414): 05b4w (0.70 #2336, 0.69 #977, 0.68 #1475), 03rjj (0.70 #2295, 0.69 #977, 0.68 #1475), 0d0vqn (0.69 #977, 0.68 #1475, 0.67 #1474), 03_3d (0.69 #977, 0.68 #1475, 0.67 #1474), 047lj (0.69 #977, 0.68 #1475, 0.67 #1474), 01mjq (0.69 #977, 0.68 #1475, 0.67 #1474), 015qh (0.64 #2485, 0.63 #1303, 0.60 #321), 05v8c (0.63 #1303, 0.60 #321, 0.57 #1306), 0154j (0.63 #1303, 0.60 #321, 0.57 #1306), 015fr (0.63 #1303, 0.60 #321, 0.57 #1306) >> Best rule #2336 for best value: >> intensional similarity = 52 >> extensional distance = 8 >> proper extension: 0124ld; >> query: (?x784, 05b4w) <- olympics(?x8588, ?x784), olympics(?x3357, ?x784), olympics(?x3227, ?x784), olympics(?x2346, ?x784), olympics(?x2236, ?x784), olympics(?x792, ?x784), country(?x1037, ?x3357), organization(?x3357, ?x127), olympics(?x3309, ?x784), olympics(?x3357, ?x3971), olympics(?x3357, ?x2966), olympics(?x3357, ?x2233), olympics(?x3357, ?x778), organization(?x8588, ?x1062), currency(?x3227, ?x170), ?x778 = 0kbvb, ?x2233 = 0l6mp, medal(?x3357, ?x422), contains(?x6304, ?x3357), country(?x3554, ?x3227), ?x2966 = 06sks6, country(?x11419, ?x8588), adjoins(?x3227, ?x1497), olympics(?x792, ?x1608), vacationer(?x792, ?x2596), ?x3309 = 09w1n, combatants(?x7430, ?x792), organization(?x7430, ?x4230), film_release_region(?x6886, ?x3227), film_release_region(?x5271, ?x3227), ?x6886 = 0gwjw0c, ?x170 = 09nqf, ?x3554 = 035d1m, ?x1037 = 09_bl, film(?x100, ?x5271), participating_countries(?x784, ?x421), nationality(?x477, ?x792), capital(?x3227, ?x13383), award_winner(?x458, ?x2596), contains(?x792, ?x841), film_release_region(?x10246, ?x792), film_release_region(?x5791, ?x792), film_release_region(?x2050, ?x792), ?x10246 = 023vcd, organization(?x421, ?x4403), ?x2050 = 01fmys, country(?x150, ?x2346), ?x3971 = 0jhn7, titles(?x2346, ?x2889), exported_to(?x1780, ?x2346), partially_contains(?x2236, ?x8666), written_by(?x5791, ?x4685) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #975 for first EXPECTED value: *> intensional similarity = 60 *> extensional distance = 1 *> proper extension: 0kbws; *> query: (?x784, ?x2146) <- olympics(?x8620, ?x784), olympics(?x7413, ?x784), olympics(?x3635, ?x784), olympics(?x3357, ?x784), olympics(?x3277, ?x784), olympics(?x2346, ?x784), olympics(?x2236, ?x784), olympics(?x1355, ?x784), olympics(?x1264, ?x784), olympics(?x512, ?x784), ?x3357 = 04w8f, olympics(?x5177, ?x784), olympics(?x3345, ?x784), medal(?x784, ?x422), film_release_region(?x428, ?x7413), participating_countries(?x4255, ?x7413), olympics(?x404, ?x784), olympics(?x205, ?x784), service_location(?x8082, ?x7413), jurisdiction_of_office(?x182, ?x7413), sports(?x1277, ?x3345), countries_spoken_in(?x5359, ?x7413), ?x1355 = 0h7x, capital(?x7413, ?x461), ?x404 = 047lj, participating_countries(?x784, ?x2645), participating_countries(?x784, ?x2267), ?x2267 = 03rj0, ?x1264 = 0345h, form_of_government(?x7413, ?x48), film_release_region(?x9002, ?x2236), film_release_region(?x6175, ?x2236), film_release_region(?x3217, ?x2236), film_release_region(?x2471, ?x2236), film_release_region(?x1259, ?x2236), ?x205 = 03rjj, official_language(?x2236, ?x9113), currency(?x7413, ?x170), ?x9002 = 0ndsl1x, ?x4255 = 0lgxj, ?x2346 = 0d05w3, organization(?x7413, ?x127), adjoins(?x8620, ?x2146), ?x428 = 0h1cdwq, ?x1259 = 04hwbq, ?x6175 = 0gg5kmg, ?x2471 = 08052t3, country(?x1121, ?x2236), ?x3217 = 0gffmn8, sports(?x452, ?x5177), ?x2645 = 03h64, contains(?x6304, ?x7413), olympics(?x3277, ?x3729), ?x3635 = 019pcs, organization(?x8620, ?x3750), countries_spoken_in(?x9113, ?x3016), ?x512 = 07ssc, film_release_region(?x86, ?x3277), contains(?x2236, ?x2364), contains(?x8620, ?x12483) *> conf = 0.40 ranks of expected_values: 77, 107, 108 EVAL 018ctl olympics! 016wzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 30.000 30.000 0.700 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 018ctl olympics! 0hg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 30.000 30.000 0.700 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 018ctl olympics! 04wgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 30.000 30.000 0.700 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #12430-02y9bj PRED entity: 02y9bj PRED relation: school! PRED expected values: 0jm74 => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 91): 05m_8 (0.50 #94, 0.28 #367, 0.18 #2008), 0cqt41 (0.50 #108, 0.22 #381, 0.08 #2022), 01yjl (0.28 #393, 0.25 #211, 0.25 #120), 07147 (0.28 #430, 0.14 #339, 0.11 #1615), 0512p (0.28 #378, 0.14 #287, 0.09 #2019), 01y3v (0.28 #391, 0.14 #300, 0.08 #1849), 01ypc (0.28 #365, 0.08 #2006, 0.08 #1823), 06rpd (0.25 #255, 0.25 #164, 0.11 #1914), 01yhm (0.25 #201, 0.22 #383, 0.14 #292), 051vz (0.25 #113, 0.22 #386, 0.12 #2027) >> Best rule #94 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 065y4w7; 01jq34; >> query: (?x7071, 05m_8) <- school(?x9995, ?x7071), colors(?x7071, ?x3189), award_winner(?x3486, ?x7071), colors(?x9995, ?x332) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1914 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 06mkj; 0d05w3; *> query: (?x7071, ?x387) <- school(?x6462, ?x7071), contains(?x94, ?x7071), draft(?x387, ?x6462) *> conf = 0.11 ranks of expected_values: 62 EVAL 02y9bj school! 0jm74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 152.000 152.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #12429-0v9qg PRED entity: 0v9qg PRED relation: location! PRED expected values: 0c_md_ => 146 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2003): 01vh3r (0.50 #4849, 0.04 #12388, 0.04 #17414), 044mvs (0.50 #4575, 0.04 #12114, 0.04 #17140), 0219q (0.50 #3335, 0.03 #8361, 0.03 #10874), 0151ns (0.33 #84, 0.25 #2597, 0.09 #7623), 07r4c (0.33 #1257, 0.25 #3770, 0.09 #8796), 02gyl0 (0.33 #945, 0.25 #3458, 0.09 #8484), 0dn3n (0.33 #587, 0.25 #3100, 0.06 #8126), 01kph_c (0.33 #973, 0.25 #3486, 0.06 #8512), 017c87 (0.33 #1755, 0.25 #4268, 0.06 #9294), 01cyjx (0.33 #1377, 0.25 #3890, 0.06 #8916) >> Best rule #4849 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 059rby; 04jpl; >> query: (?x4025, 01vh3r) <- location(?x10620, ?x4025), location(?x940, ?x4025), ?x940 = 03d_w3h, category(?x4025, ?x134), film(?x10620, ?x3839) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #14531 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 70 *> proper extension: 06wjf; 0p9nv; *> query: (?x4025, 0c_md_) <- location(?x3934, ?x4025), location(?x940, ?x4025), contains(?x94, ?x4025), music(?x9154, ?x3934), award(?x940, ?x686) *> conf = 0.01 ranks of expected_values: 1647 EVAL 0v9qg location! 0c_md_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 146.000 94.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #12428-0vjr PRED entity: 0vjr PRED relation: nominated_for! PRED expected values: 01ccr8 => 70 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1023): 01my4f (0.81 #13983, 0.78 #30299, 0.78 #60602), 0cjdk (0.81 #13983, 0.78 #30299, 0.78 #60602), 0f721s (0.67 #18645, 0.57 #41953, 0.50 #11652), 01vhb0 (0.51 #23308, 0.50 #6991, 0.48 #44283), 01vw87c (0.51 #23308, 0.50 #6991, 0.48 #44283), 02jyhv (0.51 #23308, 0.50 #6991, 0.48 #44283), 031k24 (0.33 #1712, 0.10 #95567), 0210hf (0.33 #1055, 0.10 #44284, 0.08 #62935), 01gq0b (0.33 #380, 0.01 #7371, 0.01 #9701), 0509bl (0.33 #404, 0.01 #79250) >> Best rule #13983 for best value: >> intensional similarity = 5 >> extensional distance = 77 >> proper extension: 0bx_hnp; >> query: (?x5386, ?x2373) <- program(?x2554, ?x5386), award_winner(?x5386, ?x2373), award_winner(?x5386, ?x2307), student(?x1681, ?x2307), film(?x2307, ?x1022) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #6432 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 07hpv3; 01p4wv; 099pks; 025ljp; 01_2n; 03r0rq; 02py9yf; *> query: (?x5386, 01ccr8) <- program(?x2554, ?x5386), genre(?x5386, ?x2480), actor(?x5386, ?x300), ?x2480 = 01z4y *> conf = 0.02 ranks of expected_values: 614 EVAL 0vjr nominated_for! 01ccr8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 70.000 41.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12427-054krc PRED entity: 054krc PRED relation: award! PRED expected values: 0m63c => 53 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1220): 0mcl0 (0.60 #7339, 0.27 #26901, 0.23 #29896), 0hfzr (0.57 #8968, 0.57 #8368, 0.50 #7370), 0c0zq (0.44 #6856, 0.30 #8851, 0.27 #26901), 01gvsn (0.40 #2936, 0.33 #5924, 0.33 #4928), 019kyn (0.40 #2441, 0.33 #5429, 0.33 #4433), 0209hj (0.40 #7033, 0.33 #6036, 0.30 #8031), 0glbqt (0.40 #7900, 0.27 #26901, 0.23 #29896), 07024 (0.40 #7253, 0.27 #26901, 0.23 #29896), 0bx0l (0.40 #7178, 0.27 #26901, 0.23 #29896), 05sbv3 (0.40 #7926, 0.22 #6929, 0.20 #2945) >> Best rule #7339 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0gq_v; 0p9sw; 02r22gf; 0gr0m; 0k611; 0gs96; >> query: (?x1443, 0mcl0) <- award(?x308, ?x1443), award(?x84, ?x1443), nominated_for(?x1443, ?x5795), ?x5795 = 025rvx0 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #26901 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 214 *> proper extension: 02p_7cr; 0cqhk0; 0bdw1g; 09qvc0; 047byns; 0cqh6z; 0ck27z; 0bdx29; 0bdw6t; 09qs08; ... *> query: (?x1443, ?x4216) <- award(?x308, ?x1443), award(?x84, ?x1443), nominated_for(?x1443, ?x4216), award(?x4216, ?x112) *> conf = 0.27 ranks of expected_values: 84 EVAL 054krc award! 0m63c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 53.000 36.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12426-01wf86y PRED entity: 01wf86y PRED relation: role PRED expected values: 01vdm0 => 114 concepts (77 used for prediction) PRED predicted values (max 10 best out of 115): 05r5c (0.41 #3067, 0.40 #851, 0.34 #535), 0342h (0.37 #3063, 0.33 #2534, 0.32 #3164), 0l14md (0.32 #3164, 0.27 #5171, 0.24 #2636), 02g9p4 (0.32 #3164, 0.27 #5171, 0.24 #2636), 05ljv7 (0.32 #3164, 0.27 #5171, 0.24 #2636), 07c6l (0.32 #3164, 0.24 #2636, 0.24 #4959), 01vdm0 (0.30 #33, 0.28 #3091, 0.27 #243), 02sgy (0.24 #3065, 0.22 #533, 0.20 #2536), 0l14qv (0.23 #111, 0.18 #848, 0.15 #3064), 042v_gx (0.21 #3068, 0.20 #220, 0.18 #852) >> Best rule #3067 for best value: >> intensional similarity = 3 >> extensional distance = 277 >> proper extension: 0p5mw; 0p_47; 01vd7hn; 03k0yw; 0pmw9; 0149xx; 03_0p; 02ryx0; 0kp2_; 015076; ... >> query: (?x7581, 05r5c) <- instrumentalists(?x227, ?x7581), award(?x7581, ?x528), role(?x7581, ?x1166) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #33 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0dm5l; 0840vq; 049qx; 01d_h; *> query: (?x7581, 01vdm0) <- origin(?x7581, ?x739), award(?x7581, ?x8331), ?x8331 = 056jm_ *> conf = 0.30 ranks of expected_values: 7 EVAL 01wf86y role 01vdm0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 114.000 77.000 0.405 http://example.org/music/artist/track_contributions./music/track_contribution/role #12425-058z2d PRED entity: 058z2d PRED relation: institution! PRED expected values: 014mlp => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 23): 014mlp (0.78 #153, 0.76 #128, 0.73 #552), 02h4rq6 (0.70 #175, 0.69 #224, 0.69 #249), 02_xgp2 (0.54 #160, 0.49 #135, 0.46 #383), 0bkj86 (0.50 #156, 0.46 #131, 0.38 #181), 016t_3 (0.47 #151, 0.44 #176, 0.43 #126), 03bwzr4 (0.45 #187, 0.44 #212, 0.42 #162), 04zx3q1 (0.33 #149, 0.29 #124, 0.22 #372), 07s6fsf (0.31 #173, 0.31 #247, 0.30 #198), 027f2w (0.28 #157, 0.26 #132, 0.19 #182), 02cq61 (0.25 #19, 0.18 #1242, 0.12 #141) >> Best rule #153 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0yl_3; >> query: (?x11800, 014mlp) <- category(?x11800, ?x134), ?x134 = 08mbj5d, major_field_of_study(?x11800, ?x2605), ?x2605 = 03g3w >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 058z2d institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.783 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #12424-01kcty PRED entity: 01kcty PRED relation: artists PRED expected values: 026ps1 01wy61y 01wd9lv => 82 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1109): 09hnb (0.73 #5641, 0.57 #4556, 0.56 #8900), 01nqfh_ (0.60 #2204, 0.50 #1118, 0.43 #4378), 01pbs9w (0.60 #2694, 0.50 #1608, 0.43 #4868), 012z8_ (0.60 #2569, 0.50 #1483, 0.40 #3655), 01wd9lv (0.60 #2748, 0.50 #1662, 0.40 #3834), 011z3g (0.55 #6034, 0.50 #7119, 0.50 #1689), 01vvycq (0.50 #1131, 0.45 #13074, 0.44 #6561), 012vd6 (0.50 #1565, 0.45 #5910, 0.40 #3737), 0163m1 (0.50 #1435, 0.45 #5780, 0.40 #3607), 03j0br4 (0.50 #1281, 0.45 #5626, 0.40 #3453) >> Best rule #5641 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 01fbr2; 01fh36; >> query: (?x12560, 09hnb) <- parent_genre(?x3232, ?x12560), artists(?x12560, ?x1940), artists(?x505, ?x1940), parent_genre(?x12560, ?x14532), award(?x1940, ?x1079), ?x505 = 03_d0, ?x1079 = 0l8z1, role(?x1940, ?x74) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2748 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 017_qw; *> query: (?x12560, 01wd9lv) <- artists(?x12560, ?x1940), artists(?x12560, ?x1732), ?x1940 = 04zwjd, category(?x1732, ?x134), artists(?x7267, ?x1732), artists(?x3243, ?x1732), ?x3243 = 0y3_8, parent_genre(?x9881, ?x7267) *> conf = 0.60 ranks of expected_values: 5, 181, 193 EVAL 01kcty artists 01wd9lv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 82.000 26.000 0.727 http://example.org/music/genre/artists EVAL 01kcty artists 01wy61y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 82.000 26.000 0.727 http://example.org/music/genre/artists EVAL 01kcty artists 026ps1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 82.000 26.000 0.727 http://example.org/music/genre/artists #12423-02896 PRED entity: 02896 PRED relation: colors PRED expected values: 02rnmb 06kqt3 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 17): 019sc (0.67 #23, 0.50 #369, 0.50 #144), 01l849 (0.50 #35, 0.45 #226, 0.44 #156), 06fvc (0.42 #1620, 0.42 #1585, 0.38 #123), 02rnmb (0.33 #976, 0.30 #924, 0.24 #1382), 036k5h (0.24 #1382, 0.24 #1383, 0.23 #1636), 0jc_p (0.24 #1382, 0.24 #1383, 0.20 #103), 067z2v (0.24 #1382, 0.24 #1383, 0.20 #103), 09ggk (0.24 #1382, 0.24 #1383, 0.20 #103), 038hg (0.24 #1382, 0.24 #1383, 0.20 #103), 04d18d (0.24 #1382, 0.24 #1383, 0.20 #103) >> Best rule #23 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 02fbb5; >> query: (?x387, 019sc) <- colors(?x387, ?x5325), colors(?x387, ?x663), team(?x11323, ?x387), sport(?x387, ?x1083), ?x663 = 083jv, ?x5325 = 03vtbc, athlete(?x1083, ?x445), teams(?x13949, ?x387), organization(?x4682, ?x11323) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #976 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 49 *> proper extension: 06wpc; *> query: (?x387, 02rnmb) <- draft(?x387, ?x465), team(?x180, ?x387), colors(?x387, ?x663), colors(?x5154, ?x663), colors(?x1520, ?x663), colors(?x1043, ?x663), ?x5154 = 0jm8l, student(?x1043, ?x2661), contains(?x94, ?x1520), ?x94 = 09c7w0 *> conf = 0.33 ranks of expected_values: 4, 15 EVAL 02896 colors 06kqt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 101.000 101.000 0.667 http://example.org/sports/sports_team/colors EVAL 02896 colors 02rnmb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 101.000 101.000 0.667 http://example.org/sports/sports_team/colors #12422-0cc846d PRED entity: 0cc846d PRED relation: film! PRED expected values: 016ypb => 92 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1060): 0bq2g (0.23 #605, 0.07 #8920, 0.03 #19312), 016z2j (0.23 #389, 0.06 #2468, 0.05 #8704), 01twdk (0.23 #843, 0.05 #9158, 0.02 #11236), 012c6x (0.18 #4272, 0.04 #20900, 0.02 #35447), 0jrny (0.18 #4702, 0.04 #21330, 0.02 #35877), 0738b8 (0.17 #2483, 0.06 #6640, 0.05 #8719), 042ly5 (0.15 #1263, 0.14 #5420, 0.04 #17891), 03h_9lg (0.15 #132, 0.09 #4289, 0.07 #12604), 0c9xjl (0.15 #970, 0.09 #9285, 0.06 #7206), 0jbp0 (0.15 #1755, 0.09 #5912, 0.05 #14227) >> Best rule #605 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 0d90m; 0340hj; 0dzlbx; 0bc1yhb; >> query: (?x2766, 0bq2g) <- film(?x4702, ?x2766), film(?x96, ?x2766), genre(?x2766, ?x225), language(?x2766, ?x7599), ?x96 = 079vf, award_nominee(?x4702, ?x521) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #8814 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 04969y; *> query: (?x2766, 016ypb) <- film(?x902, ?x2766), executive_produced_by(?x2766, ?x96), ?x902 = 05qd_, film_release_distribution_medium(?x2766, ?x81) *> conf = 0.07 ranks of expected_values: 112 EVAL 0cc846d film! 016ypb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 92.000 50.000 0.231 http://example.org/film/actor/film./film/performance/film #12421-02r251z PRED entity: 02r251z PRED relation: produced_by! PRED expected values: 02f6g5 0f2sx4 0640m69 => 97 concepts (63 used for prediction) PRED predicted values (max 10 best out of 504): 0b6f8pf (0.17 #3733, 0.13 #18665, 0.12 #19600), 08xvpn (0.08 #838, 0.05 #2704, 0.02 #1771), 0gm2_0 (0.07 #2700, 0.04 #4567, 0.03 #3633), 0g22z (0.06 #941, 0.05 #8, 0.05 #2807), 03bzyn4 (0.06 #1755, 0.05 #3621, 0.02 #9222), 05h43ls (0.06 #1157, 0.05 #3023, 0.02 #8624), 01s7w3 (0.05 #805, 0.05 #2671, 0.04 #1738), 072x7s (0.05 #143, 0.05 #2009, 0.03 #2942), 060__7 (0.05 #770, 0.04 #1703, 0.03 #2636), 02q3fdr (0.05 #557, 0.04 #1490, 0.03 #2423) >> Best rule #3733 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 0jz9f; 086k8; 017s11; 016tt2; 0g1rw; 05qd_; 04f525m; 030_1m; 017jv5; 03xsby; ... >> query: (?x7090, ?x821) <- award(?x7090, ?x2022), nominated_for(?x7090, ?x821), ?x2022 = 05p1dby >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #21467 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1022 *> proper extension: 0f721s; 0gsg7; 0cjdk; 01p5yn; 03yxwq; 0283xx2; 01zcrv; 04rqd; 03lpbx; 05s34b; ... *> query: (?x7090, ?x1066) <- award_winner(?x1105, ?x7090), award_winner(?x1335, ?x7090), award_winner(?x1066, ?x1335) *> conf = 0.02 ranks of expected_values: 346, 348, 478 EVAL 02r251z produced_by! 0640m69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 97.000 63.000 0.173 http://example.org/film/film/produced_by EVAL 02r251z produced_by! 0f2sx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 97.000 63.000 0.173 http://example.org/film/film/produced_by EVAL 02r251z produced_by! 02f6g5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 97.000 63.000 0.173 http://example.org/film/film/produced_by #12420-02mmwk PRED entity: 02mmwk PRED relation: genre PRED expected values: 06n90 => 79 concepts (66 used for prediction) PRED predicted values (max 10 best out of 90): 05p553 (0.69 #3988, 0.37 #1407, 0.37 #2695), 060__y (0.43 #3057, 0.20 #1067, 0.18 #2121), 01hmnh (0.34 #2473, 0.31 #717, 0.27 #132), 0219x_ (0.33 #24, 0.15 #7153, 0.11 #843), 02l7c8 (0.29 #3998, 0.27 #6110, 0.27 #5406), 06n90 (0.26 #127, 0.25 #2468, 0.23 #5050), 0lsxr (0.22 #358, 0.21 #241, 0.21 #5047), 0hcr (0.19 #2479, 0.16 #723, 0.10 #3064), 02n4kr (0.17 #6, 0.15 #7153, 0.14 #5046), 017fp (0.17 #12, 0.08 #5405, 0.08 #7047) >> Best rule #3988 for best value: >> intensional similarity = 4 >> extensional distance = 647 >> proper extension: 02n9bh; 02hfk5; 027ct7c; 0gpx6; 02wk7b; 04cf_l; 0hr41p6; >> query: (?x7225, 05p553) <- nominated_for(?x2870, ?x7225), genre(?x7225, ?x811), genre(?x10492, ?x811), ?x10492 = 0199wf >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #127 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 0b60sq; *> query: (?x7225, 06n90) <- nominated_for(?x2870, ?x7225), genre(?x7225, ?x225), category(?x7225, ?x134), ?x225 = 02kdv5l *> conf = 0.26 ranks of expected_values: 6 EVAL 02mmwk genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 66.000 0.695 http://example.org/film/film/genre #12419-07ss8_ PRED entity: 07ss8_ PRED relation: award PRED expected values: 01d38g 02f5qb 03t5kl => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 252): 03qbnj (0.42 #619, 0.18 #1801, 0.17 #2589), 01bgqh (0.39 #437, 0.34 #1619, 0.32 #2407), 01d38g (0.35 #1604, 0.35 #1210, 0.34 #2392), 01by1l (0.35 #1688, 0.34 #2476, 0.34 #1294), 09sb52 (0.33 #18954, 0.32 #7527, 0.31 #3193), 01c99j (0.33 #612, 0.21 #1400, 0.20 #1794), 02f6ym (0.33 #643, 0.18 #1431, 0.17 #1825), 02f5qb (0.30 #547, 0.12 #4487, 0.12 #1729), 05pcn59 (0.29 #3234, 0.28 #2052, 0.26 #4022), 02f73b (0.27 #672, 0.12 #4612, 0.10 #1854) >> Best rule #619 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0147dk; 02l840; 0lk90; 0l12d; 0j1yf; 01vs_v8; 01pgzn_; 01cwhp; 01trhmt; 04xrx; ... >> query: (?x2227, 03qbnj) <- award_nominee(?x1125, ?x2227), artists(?x671, ?x2227), vacationer(?x789, ?x2227) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1604 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 114 *> proper extension: 01rm8b; 015srx; 011z3g; 046p9; 016376; 012x03; *> query: (?x2227, 01d38g) <- artists(?x3928, ?x2227), ?x3928 = 0gywn, award(?x2227, ?x1389) *> conf = 0.35 ranks of expected_values: 3, 8, 16 EVAL 07ss8_ award 03t5kl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 105.000 105.000 0.424 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07ss8_ award 02f5qb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 105.000 105.000 0.424 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07ss8_ award 01d38g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 105.000 0.424 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12418-0bv7t PRED entity: 0bv7t PRED relation: award PRED expected values: 0c_dx => 157 concepts (147 used for prediction) PRED predicted values (max 10 best out of 341): 0ddd9 (0.70 #53233, 0.70 #25406, 0.69 #21375), 058vy5 (0.70 #53233, 0.70 #25406, 0.69 #21375), 05qck (0.70 #53233, 0.70 #25406, 0.69 #21375), 0265vt (0.33 #1535, 0.20 #1132, 0.14 #5162), 0gr51 (0.30 #15019, 0.27 #14614, 0.26 #16230), 01by1l (0.27 #6964, 0.17 #38020, 0.15 #25115), 05b1610 (0.25 #4472, 0.20 #9714, 0.18 #8503), 0c_dx (0.23 #5114, 0.13 #6323, 0.12 #6726), 0gr4k (0.22 #14546, 0.21 #16162, 0.21 #15356), 09sb52 (0.22 #2459, 0.20 #21012, 0.19 #37141) >> Best rule #53233 for best value: >> intensional similarity = 2 >> extensional distance = 2276 >> proper extension: 02x2097; 07k2d; >> query: (?x5261, ?x921) <- award(?x5261, ?x8842), award_winner(?x921, ?x5261) >> conf = 0.70 => this is the best rule for 3 predicted values *> Best rule #5114 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 03f47xl; 043hg; 07dnx; 0gthm; *> query: (?x5261, 0c_dx) <- profession(?x5261, ?x353), award(?x5261, ?x11471), ?x11471 = 0g9wd99 *> conf = 0.23 ranks of expected_values: 8 EVAL 0bv7t award 0c_dx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 157.000 147.000 0.701 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12417-026sdt1 PRED entity: 026sdt1 PRED relation: profession! PRED expected values: 02vkvcz => 56 concepts (14 used for prediction) PRED predicted values (max 10 best out of 3996): 05wm88 (0.70 #33287, 0.60 #16439, 0.50 #29075), 02b29 (0.70 #31712, 0.60 #14864, 0.50 #27500), 015pxr (0.70 #30082, 0.60 #13234, 0.50 #25870), 026dx (0.60 #30986, 0.60 #14138, 0.50 #26774), 09px1w (0.60 #32142, 0.60 #15294, 0.50 #27930), 021yw7 (0.60 #30586, 0.60 #13738, 0.50 #26374), 01_x6v (0.60 #30157, 0.60 #13309, 0.50 #25945), 015njf (0.60 #31021, 0.60 #14173, 0.50 #26809), 04g3p5 (0.60 #30959, 0.60 #14111, 0.50 #26747), 01xndd (0.60 #30726, 0.60 #13878, 0.50 #26514) >> Best rule #33287 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 0cbd2; 03gjzk; 02krf9; >> query: (?x7630, 05wm88) <- profession(?x4691, ?x7630), profession(?x2068, ?x7630), profession(?x1779, ?x7630), costume_design_by(?x1006, ?x2068), place_of_birth(?x2068, ?x3052), film_production_design_by(?x3111, ?x1779), crewmember(?x972, ?x4691) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026sdt1 profession! 02vkvcz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 14.000 0.700 http://example.org/people/person/profession #12416-07lp1 PRED entity: 07lp1 PRED relation: influenced_by! PRED expected values: 04cbtrw 05cv8 => 125 concepts (41 used for prediction) PRED predicted values (max 10 best out of 346): 041mt (0.29 #77, 0.07 #15324, 0.07 #10721), 0j0pf (0.28 #3779, 0.12 #1225, 0.12 #713), 0683n (0.27 #3400, 0.27 #2887, 0.25 #846), 0ff3y (0.27 #3566, 0.27 #3053, 0.12 #1012), 014ps4 (0.22 #1841, 0.14 #309, 0.07 #19421), 05jm7 (0.19 #4223, 0.18 #3202, 0.18 #2689), 013pp3 (0.18 #3283, 0.18 #2770, 0.12 #729), 019z7q (0.18 #3088, 0.18 #2575, 0.11 #1557), 0399p (0.18 #3390, 0.18 #2877, 0.07 #10721), 07lp1 (0.18 #2963, 0.14 #413, 0.10 #5006) >> Best rule #77 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01pwz; >> query: (?x10313, 041mt) <- location(?x10313, ?x3415), profession(?x10313, ?x14050), type_of_union(?x10313, ?x566), ?x14050 = 016wtf >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1129 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0gd5z; 01g6bk; *> query: (?x10313, 04cbtrw) <- influenced_by(?x10313, ?x4055), ?x4055 = 034bs, profession(?x10313, ?x353), location(?x10313, ?x3415) *> conf = 0.12 ranks of expected_values: 27, 42 EVAL 07lp1 influenced_by! 05cv8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 125.000 41.000 0.286 http://example.org/influence/influence_node/influenced_by EVAL 07lp1 influenced_by! 04cbtrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 125.000 41.000 0.286 http://example.org/influence/influence_node/influenced_by #12415-0mnm2 PRED entity: 0mnm2 PRED relation: county! PRED expected values: 0mnm2 => 86 concepts (69 used for prediction) PRED predicted values (max 10 best out of 56): 01ymvk (0.62 #5529, 0.57 #5528, 0.55 #6756), 01n4w_ (0.57 #5528, 0.55 #6756, 0.41 #6448), 0mp3l (0.05 #948, 0.05 #641, 0.03 #1254), 0dzt9 (0.05 #1093, 0.05 #786, 0.03 #1399), 0mm_4 (0.05 #1120, 0.03 #1426, 0.03 #1732), 0mnyn (0.05 #1192, 0.03 #1804, 0.03 #2725), 0fwc0 (0.05 #1106, 0.03 #2331, 0.03 #2639), 0dwh5 (0.05 #915, 0.03 #2447, 0.02 #3061), 0qxzd (0.05 #868, 0.03 #2400, 0.02 #3014), 0135g (0.05 #689, 0.03 #2221, 0.02 #2835) >> Best rule #5529 for best value: >> intensional similarity = 4 >> extensional distance = 119 >> proper extension: 0nh0f; 0nvd8; 0nh57; 0cc1v; 043z0; 0mlzk; 0mww2; 0msck; >> query: (?x7548, ?x3949) <- contains(?x7548, ?x3949), currency(?x7548, ?x170), category(?x3949, ?x134), second_level_divisions(?x94, ?x7548) >> conf = 0.62 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0mnm2 county! 0mnm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 69.000 0.618 http://example.org/location/hud_county_place/county #12414-0f5xn PRED entity: 0f5xn PRED relation: award_nominee PRED expected values: 0dvmd => 114 concepts (55 used for prediction) PRED predicted values (max 10 best out of 934): 0kjrx (0.27 #124148, 0.17 #1814), 0bgrsl (0.27 #124148, 0.01 #35647), 06y0xx (0.27 #124148), 0418ft (0.27 #124148), 0f5xn (0.27 #124148), 033tln (0.27 #124148), 0693l (0.27 #124148), 02t_tp (0.27 #124148), 054_mz (0.27 #124148), 02qgyv (0.17 #501, 0.03 #12211, 0.03 #68425) >> Best rule #124148 for best value: >> intensional similarity = 3 >> extensional distance = 1490 >> proper extension: 025vry; 06v_gh; 0dck27; 02_2v2; 0d1mp3; 0bczgm; 02cm2m; 05zh9c; 013pp3; 06czyr; ... >> query: (?x5462, ?x2389) <- profession(?x5462, ?x319), award_winner(?x6499, ?x5462), nominated_for(?x2389, ?x6499) >> conf = 0.27 => this is the best rule for 9 predicted values *> Best rule #103759 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1177 *> proper extension: 0bm9xk; *> query: (?x5462, 0dvmd) <- location(?x5462, ?x108), award_nominee(?x5462, ?x828), nationality(?x5462, ?x94) *> conf = 0.02 ranks of expected_values: 172 EVAL 0f5xn award_nominee 0dvmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 114.000 55.000 0.266 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #12413-02x1dht PRED entity: 02x1dht PRED relation: ceremony PRED expected values: 09gkdln => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 135): 0h_cssd (0.56 #297, 0.33 #432, 0.12 #702), 0gpjbt (0.36 #2324, 0.34 #3135, 0.34 #2460), 09n4nb (0.35 #2341, 0.33 #2477, 0.33 #3152), 0466p0j (0.35 #2368, 0.33 #2504, 0.33 #3179), 02cg41 (0.35 #2416, 0.32 #3227, 0.32 #2552), 02rjjll (0.34 #2301, 0.33 #2437, 0.33 #3112), 056878 (0.34 #2327, 0.32 #3138, 0.32 #2463), 01c6qp (0.33 #2314, 0.32 #2450, 0.32 #3125), 05pd94v (0.33 #2298, 0.32 #2434, 0.32 #3109), 01mh_q (0.32 #2380, 0.31 #2516, 0.31 #3191) >> Best rule #297 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02r22gf; 02hsq3m; 094qd5; 02qvyrt; 057xs89; 05ztrmj; 02qyntr; >> query: (?x899, 0h_cssd) <- nominated_for(?x899, ?x8496), nominated_for(?x899, ?x4375), ?x4375 = 01rxyb, award(?x286, ?x899), film_crew_role(?x8496, ?x137) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #811 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 110 *> proper extension: 06196; *> query: (?x899, ?x3624) <- award(?x4359, ?x899), ceremony(?x899, ?x762), award_winner(?x899, ?x286), honored_for(?x3624, ?x4359) *> conf = 0.30 ranks of expected_values: 24 EVAL 02x1dht ceremony 09gkdln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 49.000 49.000 0.556 http://example.org/award/award_category/winners./award/award_honor/ceremony #12412-0h32q PRED entity: 0h32q PRED relation: people! PRED expected values: 0g96wd => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 53): 02w7gg (0.26 #1927, 0.24 #387, 0.23 #3083), 0g96wd (0.25 #64, 0.17 #141, 0.01 #1989), 033tf_ (0.22 #700, 0.17 #623, 0.17 #777), 041rx (0.20 #1082, 0.18 #2160, 0.18 #1159), 0d7wh (0.18 #402, 0.14 #171, 0.08 #710), 048z7l (0.15 #271, 0.11 #502, 0.06 #2196), 06v41q (0.15 #260, 0.05 #568, 0.03 #1492), 065b6q (0.14 #157, 0.11 #465, 0.08 #696), 03lmx1 (0.14 #168, 0.11 #553, 0.08 #245), 0xnvg (0.14 #167, 0.10 #1091, 0.08 #1168) >> Best rule #1927 for best value: >> intensional similarity = 3 >> extensional distance = 181 >> proper extension: 07_3qd; 0784v1; 09lhln; 0135nb; 0bw7ly; 0d1swh; 0djvzd; 0b7l1f; >> query: (?x4398, 02w7gg) <- place_of_birth(?x4398, ?x10508), nationality(?x4398, ?x1310), ?x1310 = 02jx1 >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #64 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0lpjn; 015nhn; *> query: (?x4398, 0g96wd) <- award(?x4398, ?x13235), award(?x4398, ?x1132), ?x13235 = 07t_l23, ?x1132 = 0bdwft *> conf = 0.25 ranks of expected_values: 2 EVAL 0h32q people! 0g96wd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 122.000 0.257 http://example.org/people/ethnicity/people #12411-024tcq PRED entity: 024tcq PRED relation: legislative_sessions! PRED expected values: 0bymv 012v1t => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 58): 0bymv (0.85 #369, 0.71 #450, 0.71 #212), 012v1t (0.77 #375, 0.71 #456, 0.71 #218), 01lct6 (0.60 #148, 0.60 #449, 0.45 #723), 02mjmr (0.60 #449, 0.45 #723, 0.45 #722), 06hx2 (0.60 #449, 0.45 #723, 0.45 #722), 0dq2k (0.30 #314, 0.26 #512, 0.25 #530), 0rlz (0.22 #276, 0.14 #587, 0.14 #455), 01mvpv (0.21 #445, 0.17 #168, 0.11 #598), 042fk (0.20 #544, 0.20 #485, 0.16 #526), 0835q (0.13 #483, 0.10 #326, 0.07 #597) >> Best rule #369 for best value: >> intensional similarity = 36 >> extensional distance = 11 >> proper extension: 02bqmq; >> query: (?x3540, 0bymv) <- district_represented(?x3540, ?x5575), district_represented(?x3540, ?x3908), district_represented(?x3540, ?x3778), district_represented(?x3540, ?x3086), district_represented(?x3540, ?x2977), district_represented(?x3540, ?x1782), district_represented(?x3540, ?x961), contains(?x8260, ?x3086), legislative_sessions(?x6743, ?x3540), taxonomy(?x3086, ?x939), ?x6743 = 04h1rz, religion(?x3086, ?x2672), religion(?x3086, ?x2591), religion(?x3086, ?x109), ?x5575 = 05fjy, jurisdiction_of_office(?x900, ?x3086), legislative_sessions(?x3540, ?x952), location(?x396, ?x3778), ?x8260 = 04_1l0v, district_represented(?x6712, ?x3778), district_represented(?x6021, ?x3778), contains(?x3778, ?x1506), ?x6712 = 01gst9, ?x2591 = 0631_, ?x109 = 01lp8, ?x6021 = 01gsvp, ?x961 = 03s0w, contains(?x3908, ?x466), location(?x5507, ?x3086), category(?x1782, ?x134), administrative_parent(?x3087, ?x3086), religion(?x3778, ?x1985), ?x2977 = 081mh, state_province_region(?x3172, ?x3908), ?x2672 = 01y0s9, adjoins(?x3086, ?x1024) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 024tcq legislative_sessions! 012v1t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.846 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 024tcq legislative_sessions! 0bymv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.846 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #12410-01znc_ PRED entity: 01znc_ PRED relation: film_release_region! PRED expected values: 014lc_ 0g56t9t 0c40vxk 03hjv97 03bx2lk 0jqn5 03twd6 03qnvdl 05qbckf 0j_tw 06v9_x 0661m4p 07f_7h 04f52jw 05q4y12 0gh8zks 0gjc4d3 0dgpwnk 07cyl 0cp0ph6 02xbyr 04yg13l 01d259 067ghz 0g5q34q 0btpm6 01mgw 0gvvm6l 078mm1 0gwlfnb 0gvt53w 07jqjx 09tcg4 02wtp6 => 190 concepts (118 used for prediction) PRED predicted values (max 10 best out of 1062): 0661m4p (0.88 #11870, 0.85 #21411, 0.81 #22471), 04f52jw (0.88 #21443, 0.83 #22503, 0.83 #17203), 0gj8t_b (0.87 #17069, 0.85 #11768, 0.82 #21309), 027pfg (0.85 #12376, 0.80 #17677, 0.76 #21917), 05qbckf (0.83 #17136, 0.83 #26676, 0.81 #22436), 0btpm6 (0.83 #17719, 0.81 #12418, 0.79 #21959), 03qnvdl (0.82 #21336, 0.80 #17096, 0.77 #11795), 02xbyr (0.82 #21664, 0.78 #22724, 0.77 #12123), 0jqn5 (0.81 #11788, 0.71 #21329, 0.70 #17089), 01mgw (0.81 #12425, 0.71 #21966, 0.67 #17726) >> Best rule #11870 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0b90_r; 04gzd; 07ssc; 05v8c; 015fr; 0k6nt; 0ctw_b; 059j2; 03rj0; 016wzw; >> query: (?x1499, 0661m4p) <- film_release_region(?x7629, ?x1499), member_states(?x7416, ?x1499), ?x7629 = 02825nf >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 21, 22, 23, 26, 30, 32, 34, 38, 53, 54, 57, 58, 65, 67, 81, 86, 117, 131 EVAL 01znc_ film_release_region! 02wtp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 09tcg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 07jqjx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0gvt53w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0gwlfnb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 078mm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0gvvm6l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 01mgw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0btpm6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0g5q34q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 067ghz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 01d259 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 04yg13l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 02xbyr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0cp0ph6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 07cyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0dgpwnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0gjc4d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0gh8zks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 05q4y12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 04f52jw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 07f_7h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0661m4p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 06v9_x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0j_tw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 05qbckf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 03qnvdl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 03twd6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0jqn5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 03bx2lk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 03hjv97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0c40vxk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 0g56t9t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01znc_ film_release_region! 014lc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 190.000 118.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12409-015qh PRED entity: 015qh PRED relation: film_release_region! PRED expected values: 0bwfwpj 0dgst_d 0407yfx 07x4qr 0kv238 0gjcrrw 02rmd_2 0bh8tgs 0glqh5_ 0dll_t2 0cc97st 0cmdwwg 0bs8s1p 0g4vmj8 0hhggmy => 146 concepts (78 used for prediction) PRED predicted values (max 10 best out of 1685): 04f52jw (0.86 #15761, 0.86 #14570, 0.77 #3848), 0dzlbx (0.82 #4129, 0.80 #16042, 0.80 #14851), 02vr3gz (0.82 #3968, 0.73 #14690, 0.73 #15881), 0ch26b_ (0.78 #14487, 0.76 #15678, 0.73 #3765), 05pdh86 (0.77 #4053, 0.76 #15966, 0.76 #14775), 0h95zbp (0.77 #4219, 0.67 #14941, 0.65 #16132), 0bh8tgs (0.76 #16060, 0.76 #14869, 0.73 #4147), 0661m4p (0.73 #3809, 0.73 #15722, 0.71 #14531), 03nsm5x (0.73 #4497, 0.69 #15219, 0.69 #16410), 07s3m4g (0.73 #4348, 0.69 #16261, 0.67 #15070) >> Best rule #15761 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 09pmkv; 05qx1; >> query: (?x1497, 04f52jw) <- film_release_region(?x1496, ?x1497), film_release_region(?x972, ?x1497), ?x972 = 017gl1, featured_film_locations(?x1496, ?x1523) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #16060 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 09pmkv; 05qx1; *> query: (?x1497, 0bh8tgs) <- film_release_region(?x1496, ?x1497), film_release_region(?x972, ?x1497), ?x972 = 017gl1, featured_film_locations(?x1496, ?x1523) *> conf = 0.76 ranks of expected_values: 7, 16, 17, 19, 22, 28, 32, 35, 36, 41, 58, 61, 79, 84, 103 EVAL 015qh film_release_region! 0hhggmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0g4vmj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0bs8s1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0cmdwwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0dll_t2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0glqh5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0bh8tgs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 02rmd_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0gjcrrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0kv238 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 07x4qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0407yfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0dgst_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015qh film_release_region! 0bwfwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 146.000 78.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12408-02r_pp PRED entity: 02r_pp PRED relation: country PRED expected values: 09c7w0 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 33): 09c7w0 (0.87 #1166, 0.85 #737, 0.84 #982), 07ssc (0.43 #3443, 0.38 #629, 0.25 #1859), 0f8l9c (0.13 #2539, 0.13 #1307, 0.12 #1985), 0345h (0.12 #1131, 0.12 #457, 0.12 #2547), 0d0vqn (0.12 #440, 0.11 #562, 0.05 #929), 0c3351 (0.09 #1657, 0.07 #1534, 0.06 #5477), 01jfsb (0.09 #1657, 0.07 #1534, 0.06 #5477), 02n4kr (0.09 #1657, 0.07 #1534, 0.06 #5477), 03rjj (0.06 #436, 0.06 #558, 0.05 #1664), 0d060g (0.06 #438, 0.06 #560, 0.05 #1851) >> Best rule #1166 for best value: >> intensional similarity = 4 >> extensional distance = 100 >> proper extension: 04cbbz; 0ch3qr1; 02z9rr; >> query: (?x5095, 09c7w0) <- nominated_for(?x1708, ?x5095), production_companies(?x5095, ?x902), produced_by(?x5095, ?x2465), genre(?x5095, ?x258) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r_pp country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.873 http://example.org/film/film/country #12407-0n5by PRED entity: 0n5by PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 106 concepts (58 used for prediction) PRED predicted values (max 10 best out of 18): 09c7w0 (0.88 #108, 0.88 #121, 0.86 #81), 05fjf (0.10 #120, 0.09 #183, 0.07 #199), 04_1l0v (0.08 #467, 0.06 #492), 02jx1 (0.06 #301, 0.06 #314, 0.05 #327), 0xmp9 (0.02 #725), 0n5dt (0.02 #725), 0ljsz (0.02 #725), 0n58p (0.02 #725), 0n5d1 (0.02 #725), 0n5bk (0.02 #725) >> Best rule #108 for best value: >> intensional similarity = 4 >> extensional distance = 248 >> proper extension: 0f4y_; 0mlyw; 0nvd8; 0nh57; 0cc1v; 043z0; 0drr3; 09dfcj; 0mlzk; 0l2mg; ... >> query: (?x13375, 09c7w0) <- currency(?x13375, ?x170), adjoins(?x8766, ?x13375), contains(?x6895, ?x8766), source(?x8766, ?x958) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n5by second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 58.000 0.884 http://example.org/location/country/second_level_divisions #12406-0d0x8 PRED entity: 0d0x8 PRED relation: state_province_region! PRED expected values: 0k9ts => 226 concepts (94 used for prediction) PRED predicted values (max 10 best out of 751): 077w0b (0.25 #339, 0.07 #13626, 0.06 #18055), 0rydq (0.24 #26578, 0.23 #19933, 0.23 #18455), 0rt80 (0.24 #26578, 0.23 #19933, 0.23 #18455), 0rw2x (0.24 #26578, 0.23 #19933, 0.23 #18455), 0nzny (0.24 #26578, 0.23 #19933, 0.23 #18455), 0rwq6 (0.24 #26578, 0.23 #19933, 0.23 #18455), 0rwgm (0.24 #26578, 0.23 #19933, 0.23 #18455), 0nzw2 (0.24 #26578, 0.23 #19933, 0.23 #18455), 0rxyk (0.24 #26578, 0.23 #19933, 0.23 #18455), 0nzlp (0.24 #26578, 0.23 #19933, 0.23 #18455) >> Best rule #339 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0f8x_r; >> query: (?x3038, 077w0b) <- adjoins(?x3038, ?x3778), adjoins(?x760, ?x3038), ?x3778 = 07h34 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d0x8 state_province_region! 0k9ts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 226.000 94.000 0.250 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #12405-07ssc PRED entity: 07ssc PRED relation: service_location! PRED expected values: 0g5lhl7 0gvbw 0196bp 04sv4 => 218 concepts (217 used for prediction) PRED predicted values (max 10 best out of 483): 06_9lg (0.49 #8207, 0.36 #12897, 0.30 #14271), 04sv4 (0.45 #2357, 0.33 #2929, 0.27 #2242), 0cv9b (0.40 #1606, 0.36 #2179, 0.33 #2866), 01nn79 (0.40 #1662, 0.36 #2235, 0.22 #2121), 03_c8p (0.40 #1687, 0.27 #2260, 0.22 #2031), 0gvbw (0.27 #2302, 0.27 #2187, 0.22 #2073), 045c7b (0.27 #2210, 0.22 #2096, 0.20 #2897), 069vt (0.27 #2259, 0.22 #2145, 0.20 #1686), 049mr (0.27 #2368, 0.20 #3054, 0.20 #2940), 01n073 (0.22 #1960, 0.20 #1616, 0.20 #1501) >> Best rule #8207 for best value: >> intensional similarity = 2 >> extensional distance = 47 >> proper extension: 01d88c; 09c6w; 02_n7; 027wvb; 04vmp; 029kpy; 01j922; 01_yvy; 02cb1j; 01c1nm; ... >> query: (?x512, 06_9lg) <- service_location(?x555, ?x512), place_of_birth(?x5184, ?x512) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #2357 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 06m_5; *> query: (?x512, 04sv4) <- nationality(?x111, ?x512), region(?x54, ?x512), location(?x399, ?x512) *> conf = 0.45 ranks of expected_values: 2, 6, 210, 221 EVAL 07ssc service_location! 04sv4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 218.000 217.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 07ssc service_location! 0196bp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 218.000 217.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 07ssc service_location! 0gvbw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 218.000 217.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 07ssc service_location! 0g5lhl7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 218.000 217.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #12404-07s846j PRED entity: 07s846j PRED relation: honored_for! PRED expected values: 0fqpc7d => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 123): 0hr6lkl (0.27 #133, 0.17 #6659, 0.16 #6660), 0bxs_d (0.17 #6659, 0.16 #6660, 0.09 #11021), 0bx6zs (0.17 #6659, 0.16 #6660, 0.09 #11021), 07y_p6 (0.17 #6659, 0.16 #6660, 0.09 #11021), 02pgky2 (0.17 #6659, 0.16 #6660, 0.09 #11021), 0fqpc7d (0.17 #6659, 0.16 #6660, 0.09 #11021), 0ds460j (0.16 #6660, 0.09 #11021, 0.08 #10778), 0gx1673 (0.16 #6660, 0.09 #11021, 0.08 #10778), 0h_9252 (0.16 #6660, 0.09 #11021, 0.08 #10778), 0gmdkyy (0.14 #144, 0.07 #265, 0.04 #1112) >> Best rule #133 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0j8f09z; >> query: (?x4047, 0hr6lkl) <- nominated_for(?x1162, ?x4047), film_release_region(?x4047, ?x1174), ?x1162 = 099c8n, ?x1174 = 047yc >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #6659 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 833 *> proper extension: 03czz87; *> query: (?x4047, ?x2245) <- award_winner(?x4047, ?x3751), nominated_for(?x3751, ?x337), award_winner(?x2245, ?x3751), honored_for(?x2245, ?x1259) *> conf = 0.17 ranks of expected_values: 6 EVAL 07s846j honored_for! 0fqpc7d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 111.000 111.000 0.273 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12403-0dgq_kn PRED entity: 0dgq_kn PRED relation: language PRED expected values: 02h40lc => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.91 #415, 0.91 #1779, 0.91 #1838), 064_8sq (0.20 #317, 0.20 #22, 0.20 #435), 012w70 (0.20 #13, 0.03 #190, 0.03 #1315), 0121sr (0.20 #48), 0880p (0.20 #46), 032f6 (0.14 #115, 0.08 #174, 0.01 #1358), 0t_2 (0.14 #73, 0.05 #250, 0.01 #1197), 06nm1 (0.12 #306, 0.11 #424, 0.10 #720), 04306rv (0.12 #536, 0.12 #1485, 0.11 #2020), 06b_j (0.09 #377, 0.08 #554, 0.07 #259) >> Best rule #415 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 0k20s; >> query: (?x6007, 02h40lc) <- nominated_for(?x2379, ?x6007), genre(?x6007, ?x53), ?x2379 = 02qvyrt, nominated_for(?x163, ?x6007) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dgq_kn language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.911 http://example.org/film/film/language #12402-0d0kn PRED entity: 0d0kn PRED relation: film_release_region! PRED expected values: 024mpp 0gg5kmg 0g5qmbz => 112 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1295): 0fpgp26 (0.88 #3692, 0.80 #12722, 0.79 #10142), 03nm_fh (0.81 #9614, 0.78 #8324, 0.78 #3164), 0dzlbx (0.81 #9658, 0.78 #8368, 0.76 #3208), 017gl1 (0.81 #9139, 0.76 #7849, 0.71 #2689), 02vxq9m (0.81 #9048, 0.74 #7758, 0.68 #2598), 0gj8nq2 (0.80 #2983, 0.80 #8143, 0.77 #9433), 0661ql3 (0.80 #2864, 0.79 #9314, 0.74 #8024), 05zlld0 (0.80 #8196, 0.79 #9486, 0.73 #3036), 06ztvyx (0.79 #9342, 0.76 #2892, 0.72 #8052), 01fmys (0.79 #9268, 0.76 #2818, 0.72 #7978) >> Best rule #3692 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 05r4w; >> query: (?x2000, 0fpgp26) <- film_release_region(?x5016, ?x2000), film_release_region(?x4615, ?x2000), ?x5016 = 062zm5h, ?x4615 = 0dlngsd >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #8218 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d0vqn; 0j1z8; 04gzd; 0chghy; ... *> query: (?x2000, 024mpp) <- film_release_region(?x1173, ?x2000), countries_spoken_in(?x5671, ?x2000), ?x1173 = 0872p_c *> conf = 0.70 ranks of expected_values: 49, 96, 158 EVAL 0d0kn film_release_region! 0g5qmbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 112.000 69.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0kn film_release_region! 0gg5kmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 112.000 69.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d0kn film_release_region! 024mpp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 112.000 69.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12401-01n6r0 PRED entity: 01n6r0 PRED relation: citytown PRED expected values: 02mf7 => 118 concepts (96 used for prediction) PRED predicted values (max 10 best out of 125): 02frhbc (0.33 #1692, 0.29 #584, 0.11 #1323), 05kj_ (0.26 #4067, 0.23 #6282, 0.22 #3696), 09c7w0 (0.26 #4067, 0.23 #6282, 0.22 #3696), 02mf7 (0.26 #4067, 0.23 #6282, 0.22 #3696), 0dclg (0.17 #42, 0.12 #780, 0.11 #1150), 0snty (0.17 #319, 0.12 #1057, 0.11 #1427), 01jr6 (0.17 #86, 0.12 #824, 0.06 #22535), 0d23k (0.14 #528, 0.11 #1636, 0.04 #3115), 02_286 (0.12 #31773, 0.12 #32882, 0.11 #28815), 0k_mf (0.11 #1382, 0.02 #4343, 0.01 #6187) >> Best rule #1692 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01950l; >> query: (?x4980, 02frhbc) <- category(?x4980, ?x134), state_province_region(?x4980, ?x726), ?x726 = 05kj_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4067 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 46 *> proper extension: 05rznz; *> query: (?x4980, ?x94) <- contains(?x726, ?x4980), contains(?x94, ?x4980), organization(?x4980, ?x5487), category(?x4980, ?x134), adjoins(?x726, ?x1138) *> conf = 0.26 ranks of expected_values: 4 EVAL 01n6r0 citytown 02mf7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 118.000 96.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #12400-030dx5 PRED entity: 030dx5 PRED relation: gender PRED expected values: 05zppz => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #5, 0.90 #3, 0.86 #75), 02zsn (0.57 #77, 0.55 #150, 0.50 #141) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 0520r2x; 025vry; 05x2t7; 0fd6qb; >> query: (?x9015, 05zppz) <- nationality(?x9015, ?x94), place_of_death(?x9015, ?x682), ?x682 = 0f2wj, country(?x108, ?x94) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030dx5 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.912 http://example.org/people/person/gender #12399-01wk7ql PRED entity: 01wk7ql PRED relation: artists! PRED expected values: 03_d0 05bt6j => 95 concepts (54 used for prediction) PRED predicted values (max 10 best out of 218): 03_d0 (0.41 #927, 0.26 #1537, 0.25 #8858), 0glt670 (0.41 #1564, 0.29 #1259, 0.26 #954), 026z9 (0.31 #988, 0.11 #1598, 0.07 #8919), 02lnbg (0.27 #1275, 0.26 #970, 0.23 #2190), 0ggx5q (0.27 #1294, 0.24 #1599, 0.24 #2209), 016clz (0.26 #13431, 0.21 #615, 0.21 #16181), 05bt6j (0.25 #4313, 0.24 #2177, 0.24 #13468), 016cjb (0.22 #1596, 0.18 #986, 0.08 #4037), 07sbbz2 (0.21 #923, 0.09 #1533, 0.08 #8854), 01lyv (0.20 #4304, 0.20 #3999, 0.19 #6744) >> Best rule #927 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 0qmny; >> query: (?x9848, 03_d0) <- artists(?x3928, ?x9848), artists(?x1127, ?x9848), ?x3928 = 0gywn, ?x1127 = 02x8m >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 01wk7ql artists! 05bt6j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 95.000 54.000 0.410 http://example.org/music/genre/artists EVAL 01wk7ql artists! 03_d0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 54.000 0.410 http://example.org/music/genre/artists #12398-03x22w PRED entity: 03x22w PRED relation: film PRED expected values: 0g3zrd => 79 concepts (44 used for prediction) PRED predicted values (max 10 best out of 127): 0828jw (0.57 #21497, 0.46 #5374, 0.45 #44787), 0404j37 (0.07 #1139, 0.03 #48372, 0.03 #8957), 05sy_5 (0.07 #1056, 0.03 #8957, 0.03 #44786), 0b7l4x (0.07 #1040, 0.03 #8957, 0.03 #44786), 0ndwt2w (0.07 #1001, 0.03 #8957, 0.03 #44786), 0422v0 (0.07 #1785, 0.03 #8957, 0.03 #44786), 0n08r (0.07 #1706, 0.03 #8957, 0.03 #44786), 045r_9 (0.07 #1578, 0.03 #8957, 0.03 #44786), 0353tm (0.07 #1542, 0.03 #8957, 0.03 #44786), 0f61tk (0.07 #1472, 0.03 #8957, 0.03 #44786) >> Best rule #21497 for best value: >> intensional similarity = 2 >> extensional distance = 939 >> proper extension: 025p38; 05wjnt; 05hdf; 0glmv; 01nrq5; 0d05fv; 01twdk; 0n8bn; 01lqnff; 0bkmf; ... >> query: (?x5748, ?x5810) <- award_winner(?x5810, ?x5748), film(?x5748, ?x2847) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03x22w film 0g3zrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 44.000 0.574 http://example.org/film/actor/film./film/performance/film #12397-01nln PRED entity: 01nln PRED relation: country! PRED expected values: 06f41 07jbh 0486tv => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 53): 071t0 (0.74 #180, 0.73 #1028, 0.72 #1081), 01lb14 (0.62 #173, 0.57 #67, 0.56 #1021), 064vjs (0.61 #83, 0.56 #189, 0.47 #1090), 06f41 (0.59 #172, 0.59 #66, 0.53 #543), 0194d (0.59 #205, 0.49 #99, 0.44 #1106), 03hr1p (0.59 #75, 0.54 #181, 0.52 #1082), 07gyv (0.57 #165, 0.51 #59, 0.51 #271), 06wrt (0.57 #68, 0.52 #174, 0.49 #280), 03fyrh (0.57 #80, 0.47 #557, 0.45 #292), 0w0d (0.55 #64, 0.54 #170, 0.51 #276) >> Best rule #180 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 02wt0; 03__y; 04xn_; 01xbgx; 0jdx; 016zwt; 0162b; >> query: (?x6974, 071t0) <- member_states(?x7695, ?x6974), adjoins(?x1241, ?x6974), country(?x14027, ?x6974) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #172 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 02wt0; 03__y; 04xn_; 01xbgx; 0jdx; 016zwt; 0162b; *> query: (?x6974, 06f41) <- member_states(?x7695, ?x6974), adjoins(?x1241, ?x6974), country(?x14027, ?x6974) *> conf = 0.59 ranks of expected_values: 4, 11, 12 EVAL 01nln country! 0486tv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 117.000 117.000 0.738 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01nln country! 07jbh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 117.000 117.000 0.738 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01nln country! 06f41 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 117.000 0.738 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #12396-02t__l PRED entity: 02t__l PRED relation: film PRED expected values: 01m13b => 108 concepts (58 used for prediction) PRED predicted values (max 10 best out of 229): 01jc6q (0.44 #7170, 0.43 #50185, 0.42 #100373), 07tw_b (0.08 #2474), 01hvjx (0.08 #2167), 08720 (0.05 #90, 0.04 #1882), 017jd9 (0.05 #781, 0.02 #20497, 0.02 #24081), 017gl1 (0.05 #143, 0.02 #19859, 0.02 #23443), 03lvwp (0.05 #1045, 0.01 #91410), 05rfst (0.05 #978, 0.01 #91410), 026390q (0.05 #188, 0.01 #91410), 0prrm (0.05 #862, 0.01 #6239, 0.01 #8032) >> Best rule #7170 for best value: >> intensional similarity = 3 >> extensional distance = 253 >> proper extension: 01nqfh_; 0d02km; 01mkn_d; 0n8bn; >> query: (?x1034, ?x197) <- profession(?x1034, ?x1032), category(?x1034, ?x134), award_winner(?x197, ?x1034) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02t__l film 01m13b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 58.000 0.436 http://example.org/film/actor/film./film/performance/film #12395-05gnf PRED entity: 05gnf PRED relation: company! PRED expected values: 01rk91 => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 37): 060c4 (0.45 #3832, 0.43 #390, 0.42 #992), 0dq3c (0.33 #303, 0.33 #2, 0.27 #3099), 05_wyz (0.25 #661, 0.23 #3113, 0.17 #317), 01yc02 (0.24 #3105, 0.17 #3708, 0.17 #309), 09d6p2 (0.17 #3114, 0.17 #662, 0.17 #318), 01rk91 (0.17 #302, 0.08 #2108, 0.06 #904), 01kr6k (0.14 #3122, 0.09 #3725, 0.08 #3854), 021q1c (0.12 #2676, 0.10 #2891, 0.10 #2978), 02y6fz (0.08 #667, 0.08 #710, 0.07 #3119), 033smt (0.08 #672, 0.08 #715, 0.07 #887) >> Best rule #3832 for best value: >> intensional similarity = 2 >> extensional distance = 226 >> proper extension: 08815; 087c7; 0l8sx; 01y9pk; 04qhdf; 07wrz; 0f1nl; 02bb47; 0bqxw; 04hgpt; ... >> query: (?x6678, 060c4) <- state_province_region(?x6678, ?x335), company(?x1491, ?x6678) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #302 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0300cp; 0cv_2; *> query: (?x6678, 01rk91) <- category(?x6678, ?x134), company(?x3775, ?x6678), ?x3775 = 02k13d *> conf = 0.17 ranks of expected_values: 6 EVAL 05gnf company! 01rk91 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 188.000 188.000 0.447 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #12394-01pqx6 PRED entity: 01pqx6 PRED relation: category_of PRED expected values: 01pqx6 => 45 concepts (37 used for prediction) PRED predicted values (max 10 best out of 6): 0c4ys (0.26 #106, 0.25 #561, 0.24 #609), 0gcf2r (0.25 #44, 0.11 #65, 0.10 #424), 0g_w (0.14 #87, 0.09 #108, 0.09 #357), 058vy5 (0.02 #116, 0.01 #139, 0.01 #162), 01tgwv (0.01 #141, 0.01 #164), 01b8bn (0.01 #137, 0.01 #160) >> Best rule #106 for best value: >> intensional similarity = 5 >> extensional distance = 64 >> proper extension: 0dt39; 011w54; 06zrp44; 05fmy; 0m57f; >> query: (?x14761, 0c4ys) <- award_winner(?x14761, ?x14008), award_winner(?x14761, ?x6534), nationality(?x6534, ?x94), company(?x14008, ?x8525), people(?x6821, ?x14008) >> conf = 0.26 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pqx6 category_of 01pqx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 45.000 37.000 0.258 http://example.org/award/award_category/category_of #12393-012x4t PRED entity: 012x4t PRED relation: nationality PRED expected values: 09c7w0 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.81 #5003, 0.77 #2203, 0.77 #3903), 07ssc (0.40 #115, 0.18 #2317, 0.16 #715), 0nj3m (0.27 #11807), 02jx1 (0.26 #2835, 0.25 #2335, 0.19 #833), 0d060g (0.11 #207, 0.09 #407, 0.07 #1207), 03rk0 (0.06 #10250, 0.05 #11953, 0.05 #12053), 0345h (0.05 #1131, 0.03 #4533, 0.03 #1031), 04wgh (0.05 #232, 0.01 #3034), 02dtg (0.05 #1801), 06q1r (0.04 #2879, 0.03 #2179, 0.03 #1177) >> Best rule #5003 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 0kctd; >> query: (?x1660, 09c7w0) <- nominated_for(?x1660, ?x8536), nominated_for(?x7510, ?x8536), ?x7510 = 027gs1_ >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012x4t nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.815 http://example.org/people/person/nationality #12392-0f7h2g PRED entity: 0f7h2g PRED relation: profession PRED expected values: 02jknp => 109 concepts (84 used for prediction) PRED predicted values (max 10 best out of 46): 02hrh1q (0.76 #3418, 0.76 #2678, 0.75 #1198), 03gjzk (0.69 #459, 0.67 #311, 0.26 #2235), 02jknp (0.50 #304, 0.50 #452, 0.22 #5336), 01d_h8 (0.49 #302, 0.45 #450, 0.33 #2226), 089fss (0.44 #164, 0.04 #608, 0.04 #756), 09jwl (0.19 #1795, 0.18 #1055, 0.18 #3867), 018gz8 (0.18 #461, 0.15 #313, 0.10 #4309), 0cbd2 (0.15 #451, 0.15 #8889, 0.15 #4447), 0np9r (0.14 #465, 0.13 #317, 0.10 #9939), 0nbcg (0.13 #1807, 0.12 #2547, 0.12 #3139) >> Best rule #3418 for best value: >> intensional similarity = 3 >> extensional distance = 1243 >> proper extension: 01wp8w7; 02645b; 05bxwh; 011hdn; 01wn718; 02t_99; 01wgfp6; 01tnbn; 0flpy; 02ts3h; ... >> query: (?x9448, 02hrh1q) <- award_nominee(?x9448, ?x199), profession(?x9448, ?x987), location(?x9448, ?x5771) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #304 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 139 *> proper extension: 022_lg; *> query: (?x9448, 02jknp) <- award_winner(?x5183, ?x9448), profession(?x9448, ?x1943), ?x1943 = 02krf9 *> conf = 0.50 ranks of expected_values: 3 EVAL 0f7h2g profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 84.000 0.757 http://example.org/people/person/profession #12391-01wxdn3 PRED entity: 01wxdn3 PRED relation: artist! PRED expected values: 0181hw => 126 concepts (119 used for prediction) PRED predicted values (max 10 best out of 115): 011k1h (0.25 #151, 0.17 #715, 0.12 #7202), 03rhqg (0.20 #16, 0.18 #1849, 0.18 #1285), 0fb0v (0.20 #7, 0.15 #430, 0.15 #289), 02y21l (0.20 #97, 0.15 #379, 0.12 #661), 01cl2y (0.20 #31, 0.15 #313, 0.11 #877), 01clyr (0.20 #34, 0.15 #316, 0.10 #2290), 015_1q (0.19 #5943, 0.19 #1853, 0.18 #5238), 0n85g (0.17 #769, 0.17 #205, 0.12 #1333), 017l96 (0.17 #160, 0.12 #1147, 0.11 #2134), 01dtcb (0.17 #189, 0.11 #753, 0.09 #1035) >> Best rule #151 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 09qr6; 0137g1; 01w806h; 0m_v0; 01lvcs1; 0lzkm; 050z2; 023l9y; 01mxt_; 082brv; >> query: (?x9735, 011k1h) <- role(?x9735, ?x716), role(?x9735, ?x314), role(?x9735, ?x228), ?x228 = 0l14qv, ?x314 = 02sgy, profession(?x9735, ?x319), ?x716 = 018vs >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3437 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 174 *> proper extension: 02b25y; 01wj92r; 044gyq; 039bpc; 01vxlbm; 017yfz; 0kvnn; 044k8; 0641g8; 07j8kh; ... *> query: (?x9735, 0181hw) <- profession(?x9735, ?x319), gender(?x9735, ?x231), artist(?x14593, ?x9735), student(?x1103, ?x9735) *> conf = 0.02 ranks of expected_values: 92 EVAL 01wxdn3 artist! 0181hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 126.000 119.000 0.250 http://example.org/music/record_label/artist #12390-0hkq4 PRED entity: 0hkq4 PRED relation: adjoins PRED expected values: 0121h7 => 170 concepts (74 used for prediction) PRED predicted values (max 10 best out of 492): 0m_w6 (0.40 #3823, 0.25 #1512, 0.15 #26977), 0clzr (0.38 #4987, 0.27 #38546, 0.25 #17726), 0hkq4 (0.33 #99, 0.27 #38546, 0.25 #17726), 0clz7 (0.27 #38546, 0.25 #33919, 0.23 #21583), 0p54z (0.25 #1235, 0.20 #3546, 0.15 #26977), 05bcl (0.23 #56282), 07nf6 (0.15 #11308, 0.05 #13622, 0.03 #16707), 01mjq (0.14 #3936, 0.08 #10870, 0.03 #38632), 06k5_ (0.14 #4401, 0.05 #13649, 0.04 #11335), 06npd (0.14 #3887, 0.04 #10821, 0.03 #37040) >> Best rule #3823 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0clz7; >> query: (?x1788, 0m_w6) <- adjoins(?x1788, ?x7986), contains(?x429, ?x1788), ?x429 = 03rt9, ?x7986 = 0jtf1 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #4622 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 05r7t; *> query: (?x1788, ?x3198) <- administrative_division(?x1789, ?x1788), administrative_parent(?x1789, ?x429), contains(?x429, ?x3198), film_release_region(?x86, ?x429) *> conf = 0.02 ranks of expected_values: 290 EVAL 0hkq4 adjoins 0121h7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 170.000 74.000 0.400 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #12389-05g76 PRED entity: 05g76 PRED relation: company! PRED expected values: 060c4 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 50): 0krdk (0.76 #1878, 0.66 #3461, 0.63 #2993), 060c4 (0.74 #4021, 0.73 #4066, 0.71 #4112), 02md_2 (0.50 #1158, 0.40 #697, 0.33 #559), 0dq3c (0.47 #1873, 0.45 #3549, 0.44 #3596), 05_wyz (0.41 #3471, 0.39 #1888, 0.39 #3564), 06b1q (0.33 #458, 0.29 #322, 0.19 #1053), 033smt (0.33 #29, 0.21 #3594, 0.19 #3593), 09d6p2 (0.29 #3004, 0.26 #1889, 0.26 #3472), 02y6fz (0.29 #339, 0.23 #889, 0.22 #475), 01kr6k (0.26 #1897, 0.21 #3594, 0.19 #3620) >> Best rule #1878 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 087c7; 0l8sx; 0hpt3; 0300cp; 04qhdf; 02r5dz; 08z129; 0k8z; 0178g; 09b3v; ... >> query: (?x2067, 0krdk) <- company(?x4682, ?x2067), company(?x1907, ?x2067), ?x4682 = 0dq_5, ?x1907 = 01yc02 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #4021 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 176 *> proper extension: 09c7w0; 08815; 02vk52z; 017s11; 01j_9c; 016tt2; 06pwq; 025jfl; 0f8l9c; 0288zy; ... *> query: (?x2067, 060c4) <- company(?x4682, ?x2067), organization(?x4682, ?x12930), organization(?x4682, ?x12074), organization(?x4682, ?x8934), organization(?x4682, ?x8125), state_province_region(?x12074, ?x760), service_location(?x8934, ?x94), award_winner(?x3486, ?x12930), organization(?x12930, ?x14299), citytown(?x8125, ?x8951) *> conf = 0.74 ranks of expected_values: 2 EVAL 05g76 company! 060c4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 116.000 0.763 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #12388-03v6t PRED entity: 03v6t PRED relation: company! PRED expected values: 07t3gd => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 35): 060c4 (0.25 #1130, 0.25 #238, 0.25 #285), 021q1c (0.19 #246, 0.19 #293, 0.18 #58), 04n1q6 (0.14 #59, 0.08 #153, 0.08 #247), 0dq_5 (0.13 #2560, 0.13 #1100, 0.13 #1571), 0krdk (0.13 #2549, 0.12 #1089, 0.12 #2077), 07t3gd (0.10 #258, 0.09 #305, 0.09 #70), 05_wyz (0.09 #1572, 0.09 #1101, 0.09 #1854), 05k17c (0.09 #531, 0.09 #60, 0.09 #342), 0dq3c (0.09 #2544, 0.09 #1555, 0.08 #3819), 09d6p2 (0.07 #1102, 0.07 #2562, 0.06 #3837) >> Best rule #1130 for best value: >> intensional similarity = 4 >> extensional distance = 166 >> proper extension: 087c7; 016tt2; 05qd_; 03xsby; 024rgt; 09j_g; 077w0b; 03rwz3; 0z90c; 01dtcb; ... >> query: (?x1667, ?x346) <- citytown(?x1667, ?x10350), organization(?x346, ?x1667), source(?x10350, ?x958), country(?x10350, ?x94) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #258 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 045c7b; 03_c8p; 0cv_2; 02z_b; *> query: (?x1667, 07t3gd) <- citytown(?x1667, ?x10350), organization(?x1667, ?x5487), citytown(?x5487, ?x108) *> conf = 0.10 ranks of expected_values: 6 EVAL 03v6t company! 07t3gd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 146.000 146.000 0.250 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #12387-0ylvj PRED entity: 0ylvj PRED relation: student PRED expected values: 08304 => 135 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1707): 016xh5 (0.25 #1066, 0.09 #9426, 0.07 #11516), 01pk8v (0.25 #951, 0.09 #9311, 0.07 #11401), 04r7jc (0.25 #79, 0.09 #8439, 0.07 #10529), 04y9dk (0.25 #294, 0.09 #8654, 0.07 #10744), 01wd02c (0.17 #3267, 0.14 #5357, 0.13 #48073), 01tdnyh (0.17 #2978, 0.14 #5068, 0.09 #9248), 01wd3l (0.17 #3237, 0.14 #5327, 0.09 #9507), 02lq10 (0.17 #2423, 0.14 #4513, 0.09 #8693), 0157m (0.17 #2337, 0.14 #4427, 0.09 #8607), 03k545 (0.17 #3996, 0.14 #6086, 0.09 #10266) >> Best rule #1066 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0yl_3; 0ym20; >> query: (?x6034, 016xh5) <- currency(?x6034, ?x1099), institution(?x3437, ?x6034), citytown(?x6034, ?x1841), ?x1841 = 05l5n, ?x3437 = 02_xgp2, student(?x6034, ?x164) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #110790 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 119 *> proper extension: 02hp6p; *> query: (?x6034, ?x5334) <- student(?x6034, ?x12584), student(?x6034, ?x477), student(?x6034, ?x164), film(?x12584, ?x951), award_winner(?x164, ?x163), influenced_by(?x477, ?x5334), profession(?x477, ?x353) *> conf = 0.03 ranks of expected_values: 405 EVAL 0ylvj student 08304 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 135.000 91.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #12386-016wyn PRED entity: 016wyn PRED relation: school_type PRED expected values: 04qbv => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 19): 05pcjw (0.57 #392, 0.33 #231, 0.33 #185), 05jxkf (0.54 #280, 0.50 #1502, 0.50 #464), 01_9fk (0.21 #2, 0.20 #278, 0.14 #462), 07tf8 (0.21 #8, 0.17 #353, 0.16 #284), 01_srz (0.11 #3, 0.08 #394, 0.08 #693), 04qbv (0.06 #245, 0.05 #314, 0.03 #498), 02p0qmm (0.05 #9, 0.04 #262, 0.04 #377), 01jlsn (0.05 #269, 0.04 #177, 0.04 #223), 01y64 (0.05 #34, 0.04 #310, 0.04 #80), 0bwd5 (0.03 #248, 0.03 #317, 0.02 #409) >> Best rule #392 for best value: >> intensional similarity = 4 >> extensional distance = 199 >> proper extension: 09krm_; 04_j5s; 02vkzcx; >> query: (?x6787, 05pcjw) <- contains(?x94, ?x6787), school_type(?x6787, ?x3205), school_type(?x6912, ?x3205), ?x6912 = 0gl5_ >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #245 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 115 *> proper extension: 02bf58; *> query: (?x6787, 04qbv) <- contains(?x94, ?x6787), school_type(?x6787, ?x3205), ?x3205 = 01rs41, category(?x6787, ?x134) *> conf = 0.06 ranks of expected_values: 6 EVAL 016wyn school_type 04qbv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 117.000 117.000 0.567 http://example.org/education/educational_institution/school_type #12385-03hnd PRED entity: 03hnd PRED relation: story_by! PRED expected values: 09zf_q => 191 concepts (148 used for prediction) PRED predicted values (max 10 best out of 309): 02kfzz (0.28 #6841), 01srq2 (0.25 #244, 0.03 #5374, 0.03 #6058), 04x4gw (0.17 #1021, 0.17 #679, 0.12 #1363), 05sxr_ (0.17 #1005, 0.17 #663, 0.12 #1347), 03whyr (0.17 #983, 0.17 #641, 0.12 #1325), 04ltlj (0.17 #1016, 0.17 #674, 0.03 #4778), 0291ck (0.17 #981, 0.17 #639, 0.03 #4743), 0ccd3x (0.17 #844, 0.17 #502, 0.03 #4606), 0fzm0g (0.17 #1026, 0.17 #684, 0.03 #4788), 02rtqvb (0.17 #1025, 0.17 #683, 0.03 #4787) >> Best rule #6841 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 03kpvp; >> query: (?x3542, ?x4089) <- story_by(?x7822, ?x3542), nominated_for(?x4089, ?x7822), genre(?x7822, ?x225), film(?x6850, ?x7822) >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03hnd story_by! 09zf_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 191.000 148.000 0.283 http://example.org/film/film/story_by #12384-06kb_ PRED entity: 06kb_ PRED relation: religion PRED expected values: 0n2g => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 30): 03j6c (0.46 #606, 0.07 #1146, 0.06 #1371), 0kpl (0.33 #190, 0.33 #10, 0.25 #145), 0kq2 (0.33 #63, 0.25 #153, 0.25 #108), 0n2g (0.33 #13, 0.21 #328, 0.20 #373), 03_gx (0.19 #1409, 0.17 #1319, 0.17 #824), 0flw86 (0.18 #587, 0.02 #2838, 0.02 #1037), 0c8wxp (0.18 #411, 0.17 #1852, 0.17 #1356), 04pk9 (0.10 #290, 0.09 #1846, 0.05 #470), 05tgm (0.10 #297, 0.09 #1846, 0.05 #477), 0631_ (0.10 #458, 0.09 #1846, 0.03 #638) >> Best rule #606 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 064p92m; 015npr; 02tq2r; 0f5zj6; 02ctyy; 03m3nzf; 05nw9m; 05yvfd; 04y0yc; 02jxsq; ... >> query: (?x5040, 03j6c) <- profession(?x5040, ?x353), location(?x5040, ?x7412), ?x7412 = 04vmp >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 034bs; *> query: (?x5040, 0n2g) <- influenced_by(?x7828, ?x5040), ?x7828 = 014ps4, profession(?x5040, ?x353), languages(?x5040, ?x254) *> conf = 0.33 ranks of expected_values: 4 EVAL 06kb_ religion 0n2g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 102.000 0.464 http://example.org/people/person/religion #12383-0f04v PRED entity: 0f04v PRED relation: teams PRED expected values: 0jnrk => 227 concepts (227 used for prediction) PRED predicted values (max 10 best out of 310): 0d3fdn (0.08 #334, 0.07 #693, 0.06 #1770), 0jnpv (0.08 #276, 0.07 #635, 0.06 #1712), 02d02 (0.08 #186, 0.07 #545, 0.06 #1263), 02fp3 (0.08 #185, 0.07 #544, 0.06 #1262), 02c_4 (0.08 #161, 0.07 #520, 0.06 #1238), 0x0d (0.08 #259, 0.07 #618, 0.06 #1336), 05l71 (0.08 #74, 0.06 #1151, 0.03 #3664), 05m_8 (0.08 #7, 0.06 #1084, 0.03 #3597), 0fsb_6 (0.08 #215, 0.06 #1292, 0.03 #4523), 0bwjj (0.08 #216, 0.06 #2011, 0.04 #2729) >> Best rule #334 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0393g; >> query: (?x6703, 0d3fdn) <- location(?x5809, ?x6703), adjoins(?x3794, ?x6703), administrative_division(?x6703, ?x7964) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f04v teams 0jnrk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 227.000 227.000 0.083 http://example.org/sports/sports_team_location/teams #12382-0bq6ntw PRED entity: 0bq6ntw PRED relation: film! PRED expected values: 03_x5t => 67 concepts (37 used for prediction) PRED predicted values (max 10 best out of 821): 016z2j (0.08 #388, 0.07 #2467, 0.03 #4546), 02w29z (0.07 #5569, 0.07 #3490, 0.05 #1411), 02_0d2 (0.07 #3253, 0.05 #1174, 0.02 #5332), 02gf_l (0.07 #3346, 0.03 #5425, 0.03 #1267), 01kwld (0.06 #47822, 0.04 #2177, 0.04 #54061), 044mrh (0.06 #47822, 0.04 #54061, 0.04 #27030), 07f3xb (0.06 #47822, 0.04 #54061, 0.04 #27030), 03_wpf (0.06 #47822, 0.04 #54061, 0.04 #27030), 044n3h (0.06 #47822, 0.04 #54061, 0.04 #27030), 044mjy (0.06 #47822, 0.04 #54061, 0.04 #27030) >> Best rule #388 for best value: >> intensional similarity = 7 >> extensional distance = 36 >> proper extension: 05p1tzf; 0401sg; 087wc7n; 01vksx; 0crfwmx; 08hmch; 0jjy0; 053rxgm; 03twd6; 0fpkhkz; ... >> query: (?x6095, 016z2j) <- film_release_region(?x6095, ?x1558), film_release_region(?x6095, ?x1471), film_release_region(?x6095, ?x583), ?x1558 = 01mjq, genre(?x6095, ?x53), ?x1471 = 07t21, ?x583 = 015fr >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #7988 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 61 *> proper extension: 0963mq; *> query: (?x6095, 03_x5t) <- film_crew_role(?x6095, ?x1171), language(?x6095, ?x2502), ?x1171 = 09vw2b7, ?x2502 = 06nm1, genre(?x6095, ?x53) *> conf = 0.03 ranks of expected_values: 172 EVAL 0bq6ntw film! 03_x5t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 67.000 37.000 0.079 http://example.org/film/actor/film./film/performance/film #12381-03rwz3 PRED entity: 03rwz3 PRED relation: award PRED expected values: 01l29r => 138 concepts (126 used for prediction) PRED predicted values (max 10 best out of 225): 0gq9h (0.44 #27080, 0.33 #5316, 0.33 #2092), 05ztrmj (0.39 #26381, 0.13 #47562, 0.13 #50790), 057xs89 (0.35 #26357, 0.05 #27969, 0.04 #37642), 0gq_d (0.33 #2236, 0.31 #2639, 0.25 #1430), 0gr42 (0.31 #2533, 0.27 #2130, 0.25 #1324), 09sb52 (0.31 #34701, 0.30 #26238, 0.29 #35507), 018wng (0.27 #2057, 0.25 #2460, 0.24 #5281), 01l29r (0.25 #2988, 0.25 #2585, 0.23 #1779), 040njc (0.25 #27011, 0.14 #24996, 0.10 #27817), 05pcn59 (0.22 #26278, 0.13 #47562, 0.13 #50790) >> Best rule #27080 for best value: >> intensional similarity = 3 >> extensional distance = 340 >> proper extension: 07g2b; 0hnjt; 0gthm; 0hcvy; 05mc7y; >> query: (?x7526, 0gq9h) <- award(?x7526, ?x3911), award_winner(?x3911, ?x2156), production_companies(?x124, ?x2156) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2988 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0fvppk; 049ql1; *> query: (?x7526, 01l29r) <- organization(?x4682, ?x7526), film(?x7526, ?x6244), film_release_distribution_medium(?x6244, ?x81) *> conf = 0.25 ranks of expected_values: 8 EVAL 03rwz3 award 01l29r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 138.000 126.000 0.442 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12380-06mz5 PRED entity: 06mz5 PRED relation: district_represented! PRED expected values: 02bp37 => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 51): 02bp37 (0.76 #60, 0.59 #162, 0.59 #256), 02bqm0 (0.70 #76, 0.60 #332, 0.59 #256), 02bqmq (0.67 #66, 0.59 #256, 0.57 #322), 03rl1g (0.62 #154, 0.60 #205, 0.57 #359), 02bqn1 (0.61 #58, 0.59 #256, 0.47 #314), 043djx (0.60 #209, 0.59 #158, 0.57 #363), 02cg7g (0.59 #256, 0.58 #73, 0.45 #329), 02gkzs (0.59 #256, 0.58 #70, 0.45 #326), 03rtmz (0.59 #256, 0.43 #1787, 0.43 #1839), 03tcbx (0.59 #256, 0.43 #1787, 0.43 #1839) >> Best rule #60 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0g0syc; >> query: (?x1351, 02bp37) <- district_represented(?x1027, ?x1351), district_represented(?x952, ?x1351), ?x1027 = 02bn_p, ?x952 = 06f0dc >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mz5 district_represented! 02bp37 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.758 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #12379-0c1ps1 PRED entity: 0c1ps1 PRED relation: student! PRED expected values: 01jzyx => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 29): 09f2j (0.08 #159, 0.04 #3321, 0.03 #4902), 033x5p (0.08 #142), 0bwfn (0.06 #802, 0.05 #5545, 0.05 #2383), 04b_46 (0.06 #754, 0.03 #1281, 0.02 #2335), 08815 (0.06 #529, 0.02 #5799, 0.02 #5272), 06182p (0.06 #825, 0.01 #2933, 0.01 #9257), 0cwx_ (0.06 #768, 0.01 #1295), 027xq5 (0.06 #1048), 01cf5 (0.06 #1001), 01vg13 (0.06 #746) >> Best rule #159 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01gw4f; >> query: (?x10469, 09f2j) <- nationality(?x10469, ?x205), actor(?x1849, ?x10469), ?x205 = 03rjj >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c1ps1 student! 01jzyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 62.000 0.083 http://example.org/education/educational_institution/students_graduates./education/education/student #12378-03ckwzc PRED entity: 03ckwzc PRED relation: genre PRED expected values: 0lsxr => 108 concepts (106 used for prediction) PRED predicted values (max 10 best out of 118): 04xvlr (0.75 #6888, 0.74 #7252, 0.73 #6161), 02kdv5l (0.59 #844, 0.55 #965, 0.47 #1808), 01jfsb (0.53 #976, 0.51 #855, 0.39 #1819), 02l7c8 (0.43 #137, 0.40 #257, 0.38 #498), 05p553 (0.40 #7982, 0.37 #3989, 0.37 #5680), 03k9fj (0.38 #2303, 0.38 #1577, 0.38 #2060), 06n90 (0.28 #856, 0.23 #977, 0.20 #1579), 01hmnh (0.26 #2309, 0.25 #2066, 0.25 #1824), 04t36 (0.25 #6, 0.11 #1571, 0.10 #1451), 0hcr (0.25 #24, 0.10 #2315, 0.08 #1589) >> Best rule #6888 for best value: >> intensional similarity = 4 >> extensional distance = 562 >> proper extension: 04gcyg; 03b1sb; 04sh80; >> query: (?x805, ?x53) <- titles(?x53, ?x805), music(?x805, ?x7240), genre(?x54, ?x53), genre(?x273, ?x53) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #731 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 03kg2v; *> query: (?x805, 0lsxr) <- country(?x805, ?x94), film(?x7980, ?x805), ?x7980 = 020h2v, film_crew_role(?x805, ?x1284), ?x1284 = 0ch6mp2 *> conf = 0.24 ranks of expected_values: 13 EVAL 03ckwzc genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 108.000 106.000 0.753 http://example.org/film/film/genre #12377-03gn1x PRED entity: 03gn1x PRED relation: institution! PRED expected values: 02h4rq6 014mlp => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.74 #103, 0.67 #279, 0.64 #482), 014mlp (0.71 #182, 0.69 #132, 0.69 #485), 019v9k (0.61 #136, 0.59 #489, 0.59 #286), 02_xgp2 (0.52 #114, 0.50 #14, 0.48 #290), 016t_3 (0.52 #104, 0.50 #4, 0.43 #130), 03bwzr4 (0.52 #116, 0.46 #292, 0.39 #495), 0bkj86 (0.50 #9, 0.45 #135, 0.41 #109), 07s6fsf (0.50 #1, 0.32 #101, 0.32 #277), 013zdg (0.50 #8, 0.19 #234, 0.19 #184), 028dcg (0.50 #21, 0.13 #172, 0.13 #197) >> Best rule #103 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 0jpkw; >> query: (?x8592, 02h4rq6) <- major_field_of_study(?x8592, ?x742), currency(?x8592, ?x170), organization(?x346, ?x8592), ?x742 = 05qjt >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03gn1x institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.739 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03gn1x institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.739 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #12376-06j0md PRED entity: 06j0md PRED relation: award_winner! PRED expected values: 0330r => 94 concepts (54 used for prediction) PRED predicted values (max 10 best out of 185): 0330r (0.46 #61233, 0.45 #44217, 0.45 #58963), 030cx (0.46 #61233, 0.45 #44217, 0.45 #58963), 01lv85 (0.46 #61233, 0.45 #58963, 0.45 #58962), 05zr0xl (0.14 #2048, 0.03 #7715, 0.02 #9981), 072kp (0.12 #17000, 0.07 #47620, 0.06 #2329), 07c72 (0.12 #17000, 0.05 #1479, 0.02 #2614), 0h03fhx (0.10 #505, 0.05 #1639, 0.01 #25442), 01rf57 (0.10 #445, 0.05 #1579), 0d68qy (0.10 #273, 0.04 #9340, 0.03 #7074), 04vr_f (0.10 #117, 0.02 #15983, 0.02 #6918) >> Best rule #61233 for best value: >> intensional similarity = 3 >> extensional distance = 1615 >> proper extension: 07nznf; 0q9kd; 06qgvf; 0grwj; 05bnp0; 016qtt; 0jz9f; 04qvl7; 01k7d9; 02bfmn; ... >> query: (?x201, ?x2293) <- award_nominee(?x201, ?x2285), nominated_for(?x201, ?x2293), award_winner(?x2293, ?x4411) >> conf = 0.46 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 06j0md award_winner! 0330r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 54.000 0.460 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #12375-03x23q PRED entity: 03x23q PRED relation: campuses PRED expected values: 03x23q => 127 concepts (101 used for prediction) PRED predicted values (max 10 best out of 217): 03x33n (0.07 #117, 0.06 #663, 0.02 #1211), 01pl14 (0.07 #8, 0.06 #554, 0.02 #1102), 019tfm (0.07 #546, 0.06 #1092, 0.01 #44818), 02vkzcx (0.07 #543, 0.06 #1089, 0.01 #44818), 035ktt (0.07 #175, 0.06 #721), 02fs_d (0.06 #749, 0.02 #2389, 0.02 #3481), 0gy3w (0.06 #813), 09f2j (0.02 #1245, 0.02 #1791, 0.02 #2337), 02klny (0.02 #1474, 0.02 #2020, 0.02 #2566), 025v3k (0.02 #1200, 0.02 #1746, 0.01 #4476) >> Best rule #117 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0fvvz; 0tln7; 013d7t; 0tk02; 0tn9j; 013gz; >> query: (?x12732, 03x33n) <- category(?x12732, ?x134), contains(?x3908, ?x12732), ?x3908 = 04ly1, ?x134 = 08mbj5d >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #44818 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 599 *> proper extension: 01nn79; *> query: (?x12732, ?x466) <- category(?x12732, ?x134), state_province_region(?x12732, ?x3908), ?x134 = 08mbj5d, state_province_region(?x466, ?x3908) *> conf = 0.01 ranks of expected_values: 128 EVAL 03x23q campuses 03x23q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 127.000 101.000 0.071 http://example.org/education/educational_institution/campuses #12374-0mnzd PRED entity: 0mnzd PRED relation: location! PRED expected values: 09btt1 => 164 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1774): 06cddt (0.57 #25198, 0.56 #100785, 0.55 #12600), 01w20rx (0.57 #25198, 0.47 #103306, 0.46 #100784), 03fwln (0.25 #2161, 0.11 #12240, 0.07 #34918), 05bpg3 (0.25 #1104, 0.11 #11183, 0.05 #33861), 060j8b (0.25 #1271, 0.11 #11350, 0.05 #34028), 01wk51 (0.25 #1536, 0.11 #11615, 0.05 #34293), 01fwpt (0.25 #669, 0.11 #10748, 0.05 #33426), 03q45x (0.25 #1558, 0.08 #12601, 0.05 #11637), 0285c (0.25 #349, 0.05 #10428, 0.04 #15469), 04t2l2 (0.25 #25, 0.05 #10104, 0.03 #25223) >> Best rule #25198 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 05ksh; 0dbdy; 0jcg8; 036k0s; 02m77; 0mpbx; 0cxgc; 03lrc; 02fvv; 09ctj; >> query: (?x1427, ?x9711) <- contains(?x1426, ?x1427), contains(?x1427, ?x9443), place_of_birth(?x9711, ?x1427), second_level_divisions(?x94, ?x1427) >> conf = 0.57 => this is the best rule for 2 predicted values *> Best rule #46264 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 0xqf3; *> query: (?x1427, 09btt1) <- time_zones(?x1427, ?x2674), place_of_birth(?x10628, ?x1427), artists(?x302, ?x10628), county(?x1427, ?x12702) *> conf = 0.01 ranks of expected_values: 1621 EVAL 0mnzd location! 09btt1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 164.000 46.000 0.565 http://example.org/people/person/places_lived./people/place_lived/location #12373-0c4b8 PRED entity: 0c4b8 PRED relation: capital PRED expected values: 01_vrh => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 122): 0156q (0.25 #840, 0.17 #130, 0.15 #1666), 05ksh (0.25 #7, 0.17 #243, 0.14 #479), 0dp90 (0.25 #80, 0.17 #316, 0.14 #434), 04jpl (0.22 #1304, 0.17 #122, 0.14 #476), 0dlv0 (0.17 #279, 0.17 #161, 0.14 #515), 02p3my (0.17 #335, 0.17 #217, 0.14 #571), 0cvw9 (0.17 #271, 0.17 #153, 0.14 #507), 05qtj (0.17 #256, 0.14 #492, 0.11 #966), 0n2z (0.17 #177, 0.09 #1595, 0.08 #13269), 081m_ (0.15 #1695, 0.14 #1813, 0.11 #3111) >> Best rule #840 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0h7x; >> query: (?x5738, 0156q) <- organization(?x5738, ?x4230), capital(?x5738, ?x8751), country(?x8751, ?x792), adjoins(?x1144, ?x8751), location_of_ceremony(?x566, ?x8751), contains(?x8751, ?x9861) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c4b8 capital 01_vrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 127.000 0.250 http://example.org/location/country/capital #12372-07gql PRED entity: 07gql PRED relation: instrumentalists PRED expected values: 06w2sn5 => 70 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1288): 0fpjd_g (0.71 #7341, 0.33 #685, 0.29 #6128), 01sb5r (0.70 #11753, 0.67 #9926, 0.62 #8101), 01vw20_ (0.70 #11680, 0.67 #9853, 0.62 #8028), 01vvycq (0.62 #7902, 0.60 #11554, 0.60 #10942), 018y81 (0.62 #8212, 0.60 #11864, 0.60 #11252), 0bkg4 (0.60 #11733, 0.56 #9906, 0.50 #8081), 032t2z (0.57 #6077, 0.50 #9113, 0.50 #4261), 07zft (0.57 #7139, 0.50 #2901, 0.38 #8350), 0407f (0.57 #6840, 0.50 #2602, 0.33 #15355), 01vrnsk (0.50 #4609, 0.50 #2795, 0.50 #2190) >> Best rule #7341 for best value: >> intensional similarity = 16 >> extensional distance = 5 >> proper extension: 06ch55; >> query: (?x2206, 0fpjd_g) <- instrumentalists(?x2206, ?x7240), instrumentalists(?x2206, ?x6469), instrumentalists(?x2206, ?x1970), award_winner(?x5656, ?x7240), award_winner(?x139, ?x7240), type_of_union(?x7240, ?x566), student(?x5807, ?x7240), artists(?x671, ?x6469), award_winner(?x2212, ?x7240), ?x671 = 064t9, ?x5656 = 0466p0j, ?x139 = 05pd94v, artist(?x4868, ?x1970), nationality(?x1970, ?x94), award_winner(?x12304, ?x7240), participant(?x1970, ?x3034) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1282 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 1 *> proper extension: 05r5c; *> query: (?x2206, 06w2sn5) <- role(?x7869, ?x2206), role(?x4583, ?x2206), role(?x2944, ?x2206), role(?x2205, ?x2206), role(?x2048, ?x2206), role(?x716, ?x2206), role(?x227, ?x2206), ?x716 = 018vs, ?x2205 = 0dq630k, instrumentalists(?x2206, ?x1826), role(?x2206, ?x214), group(?x2206, ?x1751), role(?x75, ?x2206), ?x2048 = 018j2, ?x7869 = 0l14v3, award_winner(?x4416, ?x1826), ?x4583 = 0bmnm, artists(?x671, ?x1826), ?x2944 = 0l14j_, ?x4416 = 099vwn, ?x227 = 0342h, award_winner(?x2862, ?x1826) *> conf = 0.33 ranks of expected_values: 123 EVAL 07gql instrumentalists 06w2sn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 70.000 51.000 0.714 http://example.org/music/instrument/instrumentalists #12371-07z31v PRED entity: 07z31v PRED relation: ceremony! PRED expected values: 0bp_b2 0bdx29 0gkr9q => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 302): 0bdx29 (0.83 #3807, 0.80 #3309, 0.75 #2812), 0gkr9q (0.70 #3448, 0.67 #3946, 0.62 #2951), 0bp_b2 (0.67 #3742, 0.62 #2747, 0.61 #2985), 02xcb6n (0.62 #2942, 0.61 #2985, 0.57 #746), 0bdwft (0.62 #2783, 0.61 #2985, 0.57 #746), 0bdwqv (0.62 #2857, 0.61 #2985, 0.57 #746), 09qj50 (0.62 #2769, 0.42 #3764, 0.42 #3515), 09qs08 (0.61 #2985, 0.57 #746, 0.56 #1989), 09qvc0 (0.61 #2985, 0.57 #746, 0.56 #1989), 09v7wsg (0.61 #2985, 0.57 #746, 0.56 #1989) >> Best rule #3807 for best value: >> intensional similarity = 15 >> extensional distance = 10 >> proper extension: 07y9ts; >> query: (?x2126, 0bdx29) <- honored_for(?x2126, ?x337), award_winner(?x2126, ?x5595), award_winner(?x2126, ?x1039), ceremony(?x870, ?x2126), award_winner(?x870, ?x4266), award(?x7138, ?x870), award(?x1676, ?x870), ?x4266 = 015qt5, ?x7138 = 0l786, award_winner(?x1039, ?x496), award(?x5595, ?x458), produced_by(?x542, ?x1039), award_nominee(?x968, ?x1676), award_winner(?x704, ?x1676), award(?x631, ?x870) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 07z31v ceremony! 0gkr9q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 07z31v ceremony! 0bdx29 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 07z31v ceremony! 0bp_b2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony #12370-09bnf PRED entity: 09bnf PRED relation: languages! PRED expected values: 0dfjb8 => 23 concepts (9 used for prediction) PRED predicted values (max 10 best out of 3547): 03x31g (0.71 #2529, 0.57 #1883, 0.50 #3175), 040nwr (0.64 #3859, 0.57 #2567, 0.57 #1921), 06kl0k (0.62 #3124, 0.57 #2478, 0.57 #1832), 05vzql (0.57 #1857, 0.50 #3149, 0.50 #1211), 0dfjb8 (0.57 #2235, 0.50 #943, 0.43 #1589), 04cmrt (0.50 #1253, 0.43 #2545, 0.43 #1899), 0jrqq (0.50 #864, 0.43 #2156, 0.43 #1510), 084z0w (0.50 #911, 0.33 #265, 0.29 #2203), 0kst7v (0.43 #2475, 0.43 #1829, 0.38 #3121), 03wpmd (0.43 #1412, 0.38 #2704, 0.29 #2058) >> Best rule #2529 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 03k50; 0999q; 09s02; >> query: (?x14455, 03x31g) <- languages(?x13716, ?x14455), languages(?x8622, ?x14455), languages(?x8380, ?x14455), languages(?x8097, ?x14455), ?x8380 = 09r_wb, people(?x8523, ?x13716), ?x8097 = 046rfv, location(?x8622, ?x9315), profession(?x13716, ?x1146), nationality(?x8622, ?x2146), ?x2146 = 03rk0 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2235 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 03k50; 0999q; 09s02; *> query: (?x14455, 0dfjb8) <- languages(?x13716, ?x14455), languages(?x8622, ?x14455), languages(?x8380, ?x14455), languages(?x8097, ?x14455), ?x8380 = 09r_wb, people(?x8523, ?x13716), ?x8097 = 046rfv, location(?x8622, ?x9315), profession(?x13716, ?x1146), nationality(?x8622, ?x2146), ?x2146 = 03rk0 *> conf = 0.57 ranks of expected_values: 5 EVAL 09bnf languages! 0dfjb8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 23.000 9.000 0.714 http://example.org/people/person/languages #12369-0cbgl PRED entity: 0cbgl PRED relation: place_of_birth PRED expected values: 03v_5 => 132 concepts (124 used for prediction) PRED predicted values (max 10 best out of 132): 03v0t (0.40 #706, 0.33 #62708, 0.28 #66935), 0rh6k (0.20 #2, 0.17 #708, 0.12 #1413), 01cx_ (0.20 #109, 0.12 #1520, 0.08 #2929), 02_286 (0.17 #725, 0.12 #1430, 0.10 #11997), 02cl1 (0.17 #722, 0.12 #1427, 0.02 #14108), 0s5cg (0.09 #2297, 0.08 #3001, 0.06 #7226), 0cc56 (0.09 #2149, 0.08 #3558, 0.07 #6374), 04jpl (0.09 #2124, 0.08 #3533, 0.06 #7053), 06wxw (0.09 #2273, 0.08 #3682, 0.06 #7202), 0chrx (0.08 #3125, 0.07 #4534, 0.07 #5238) >> Best rule #706 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 07y8l9; 059_gf; 01ldw4; >> query: (?x14008, ?x3818) <- nationality(?x14008, ?x94), student(?x6732, ?x14008), ?x6732 = 0gdm1, location(?x14008, ?x3818) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cbgl place_of_birth 03v_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 124.000 0.400 http://example.org/people/person/place_of_birth #12368-02js9p PRED entity: 02js9p PRED relation: award PRED expected values: 0gkts9 => 89 concepts (87 used for prediction) PRED predicted values (max 10 best out of 218): 09sb52 (0.40 #849, 0.38 #445, 0.32 #12163), 0ck27z (0.34 #1304, 0.33 #1708, 0.26 #3728), 0cqhk0 (0.20 #1249, 0.15 #1653, 0.15 #3673), 0bdw6t (0.17 #109, 0.15 #11313, 0.13 #29499), 0cqhb3 (0.17 #305, 0.15 #11313, 0.13 #29499), 05pcn59 (0.16 #889, 0.15 #11313, 0.14 #15355), 04kxsb (0.16 #933, 0.15 #11313, 0.13 #29499), 0f4x7 (0.15 #11313, 0.14 #15355, 0.13 #29499), 0gqy2 (0.15 #11313, 0.14 #15355, 0.13 #29499), 05b4l5x (0.15 #11313, 0.14 #15355, 0.13 #29499) >> Best rule #849 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0m2wm; 02zq43; 01p7yb; 0p_pd; 0159h6; 0h5g_; 0bxtg; 01wmxfs; 03f1zdw; 01yhvv; ... >> query: (?x7014, 09sb52) <- award_nominee(?x7014, ?x1867), film(?x1867, ?x8631), ?x8631 = 01_1hw >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #5824 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 874 *> proper extension: 0jgd; 058j2; 02sch9; 027rfxc; 02bh_v; 012x1l; 04gtq43; 015c1b; 01nd9f; 0513yzt; *> query: (?x7014, 0gkts9) <- gender(?x7014, ?x514), ?x514 = 02zsn *> conf = 0.06 ranks of expected_values: 66 EVAL 02js9p award 0gkts9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 89.000 87.000 0.395 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12367-099t8j PRED entity: 099t8j PRED relation: nominated_for PRED expected values: 0fh694 0gmcwlb 049xgc 01chpn => 54 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1416): 0dr_4 (0.71 #18812, 0.60 #6410, 0.50 #3314), 05hjnw (0.68 #34103, 0.67 #31001, 0.67 #34102), 09m6kg (0.68 #34103, 0.67 #31001, 0.67 #34102), 02q7fl9 (0.67 #31001, 0.67 #34102, 0.65 #29451), 01633c (0.67 #31001, 0.67 #34102, 0.65 #29451), 0gmcwlb (0.67 #18772, 0.60 #6370, 0.50 #3274), 0m313 (0.67 #18606, 0.50 #3108, 0.47 #15505), 049xgc (0.62 #19443, 0.60 #7041, 0.50 #3945), 0pv3x (0.62 #18752, 0.50 #3254, 0.40 #6350), 0h1x5f (0.60 #7552, 0.54 #15305, 0.50 #4456) >> Best rule #18812 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 02r0csl; 02r22gf; 02hsq3m; 0gr0m; 0k611; >> query: (?x2577, 0dr_4) <- nominated_for(?x2577, ?x1988), award(?x9211, ?x2577), award(?x495, ?x2577), type_of_union(?x9211, ?x566), award_nominee(?x495, ?x221), ?x1988 = 09k56b7 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #18772 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 19 *> proper extension: 02r0csl; 02r22gf; 02hsq3m; 0gr0m; 0k611; *> query: (?x2577, 0gmcwlb) <- nominated_for(?x2577, ?x1988), award(?x9211, ?x2577), award(?x495, ?x2577), type_of_union(?x9211, ?x566), award_nominee(?x495, ?x221), ?x1988 = 09k56b7 *> conf = 0.67 ranks of expected_values: 6, 8, 34, 79 EVAL 099t8j nominated_for 01chpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 54.000 24.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099t8j nominated_for 049xgc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 54.000 24.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099t8j nominated_for 0gmcwlb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 54.000 24.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099t8j nominated_for 0fh694 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 54.000 24.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12366-04x56 PRED entity: 04x56 PRED relation: influenced_by! PRED expected values: 0yxl => 106 concepts (31 used for prediction) PRED predicted values (max 10 best out of 411): 0j0pf (0.50 #205, 0.12 #4837, 0.11 #5866), 0683n (0.25 #2397, 0.10 #11668, 0.10 #13220), 014ps4 (0.25 #2369, 0.09 #11847, 0.08 #11848), 02yl42 (0.25 #2193, 0.09 #6824, 0.08 #5795), 01_k0d (0.17 #11845, 0.17 #783, 0.11 #1298), 07rd7 (0.17 #683, 0.11 #1198, 0.09 #1712), 07w21 (0.17 #2070, 0.09 #11847, 0.08 #11848), 01hb6v (0.17 #2153, 0.08 #15039, 0.08 #4210), 01zkxv (0.17 #16, 0.08 #4132, 0.07 #15978), 013pp3 (0.17 #2280, 0.08 #11551, 0.07 #13103) >> Best rule #205 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01dhmw; 0klw; 018fq; 01g6bk; >> query: (?x10232, 0j0pf) <- profession(?x10232, ?x2225), ?x2225 = 0kyk, award_winner(?x14213, ?x10232), influenced_by(?x10232, ?x1029), ?x14213 = 01bb1c >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #11847 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 193 *> proper extension: 04r1t; *> query: (?x10232, ?x477) <- influenced_by(?x3858, ?x10232), influenced_by(?x3858, ?x477), influenced_by(?x477, ?x5334), peers(?x6723, ?x3858) *> conf = 0.09 ranks of expected_values: 30 EVAL 04x56 influenced_by! 0yxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 106.000 31.000 0.500 http://example.org/influence/influence_node/influenced_by #12365-01645p PRED entity: 01645p PRED relation: nutrient PRED expected values: 01sh2 0dcfv 06x4c 06jry 025rsfk 09gwd 025sqz8 01n78x 02y_3rt => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 37): 06jry (0.80 #409, 0.80 #385, 0.71 #376), 025sqz8 (0.60 #410, 0.60 #389, 0.57 #372), 09gwd (0.60 #408, 0.60 #388, 0.57 #370), 06x4c (0.60 #399, 0.60 #384, 0.57 #381), 01sh2 (0.60 #406, 0.60 #382, 0.57 #378), 025rsfk (0.57 #379, 0.57 #358, 0.50 #404), 01n78x (0.57 #380, 0.57 #363, 0.50 #403), 0dcfv (0.43 #354, 0.33 #300, 0.33 #282), 02kc_w5 (0.33 #293, 0.33 #235, 0.33 #216), 061xhr (0.33 #324, 0.33 #307, 0.33 #289) >> Best rule #409 for best value: >> intensional similarity = 78 >> extensional distance = 8 >> proper extension: 0dcfv; >> query: (?x6285, ?x6192) <- nutrient(?x6285, ?x12454), nutrient(?x6285, ?x9915), nutrient(?x6285, ?x8243), nutrient(?x6285, ?x7652), nutrient(?x6285, ?x6026), nutrient(?x6285, ?x5549), nutrient(?x6285, ?x5451), nutrient(?x10612, ?x7652), nutrient(?x9732, ?x7652), nutrient(?x9489, ?x7652), nutrient(?x9005, ?x7652), nutrient(?x8298, ?x7652), nutrient(?x7719, ?x7652), nutrient(?x7057, ?x7652), nutrient(?x6191, ?x7652), nutrient(?x6159, ?x7652), nutrient(?x6032, ?x7652), nutrient(?x5373, ?x7652), nutrient(?x5009, ?x7652), nutrient(?x4068, ?x7652), nutrient(?x3900, ?x7652), nutrient(?x3468, ?x7652), nutrient(?x2701, ?x7652), nutrient(?x1959, ?x7652), nutrient(?x1303, ?x7652), nutrient(?x1257, ?x7652), ?x10612 = 0frq6, ?x12454 = 025rw19, ?x6159 = 033cnk, ?x9732 = 05z55, ?x1959 = 0f25w9, ?x8243 = 014d7f, ?x1257 = 09728, ?x7719 = 0dj75, ?x6191 = 014j1m, ?x9005 = 04zpv, ?x8298 = 037ls6, ?x5549 = 025s7j4, nutrient(?x5337, ?x6026), ?x5009 = 0fjfh, nutrient(?x3468, ?x14210), nutrient(?x3468, ?x13545), nutrient(?x3468, ?x13126), nutrient(?x3468, ?x12336), nutrient(?x3468, ?x12083), nutrient(?x3468, ?x10453), nutrient(?x3468, ?x9436), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7135), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x2018), ?x2701 = 0hkxq, ?x9436 = 025sqz8, ?x6192 = 06jry, ?x9489 = 07j87, ?x5373 = 0971v, ?x7431 = 09gwd, taxonomy(?x5451, ?x939), ?x5337 = 06x4c, ?x4068 = 0fbw6, ?x3900 = 061_f, ?x1303 = 0fj52s, ?x9915 = 025tkqy, ?x939 = 04n6k, ?x6032 = 01nkt, ?x14210 = 0f4k5, ?x2018 = 01sh2, ?x13545 = 01w_3, ?x7135 = 025rsfk, ?x12083 = 01n78x, ?x12336 = 0f4l5, ?x7057 = 0fbdb, ?x10453 = 075pwf, ?x13126 = 02kc_w5, nutrient(?x8298, ?x5451), nutrient(?x1257, ?x5451), nutrient(?x3468, ?x5337), nutrient(?x5009, ?x5451) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 16 EVAL 01645p nutrient 02y_3rt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01645p nutrient 01n78x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01645p nutrient 025sqz8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01645p nutrient 09gwd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01645p nutrient 025rsfk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01645p nutrient 06jry CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01645p nutrient 06x4c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01645p nutrient 0dcfv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01645p nutrient 01sh2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.800 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #12364-0dclg PRED entity: 0dclg PRED relation: location! PRED expected values: 02pzck => 195 concepts (139 used for prediction) PRED predicted values (max 10 best out of 2304): 019f9z (0.51 #301152, 0.48 #321066, 0.47 #116964), 0k6yt1 (0.49 #301151, 0.48 #321066, 0.47 #116964), 02rk45 (0.49 #301151, 0.48 #321066, 0.47 #116964), 07k2p6 (0.49 #301151, 0.48 #321066, 0.47 #116964), 04fcx7 (0.49 #301151, 0.48 #321066, 0.47 #116964), 04954 (0.49 #301151, 0.48 #321066, 0.47 #116964), 044gyq (0.48 #321066, 0.47 #116964, 0.47 #44790), 02pbrn (0.48 #321066, 0.47 #116964, 0.47 #44790), 01fh0q (0.48 #321066, 0.47 #116964, 0.47 #44790), 032nwy (0.48 #321066, 0.47 #116964, 0.47 #44790) >> Best rule #301152 for best value: >> intensional similarity = 4 >> extensional distance = 189 >> proper extension: 016v46; 0r8c8; 0xynl; >> query: (?x2254, ?x6651) <- place_of_birth(?x9864, ?x2254), place_of_birth(?x6651, ?x2254), participant(?x6651, ?x2562), film(?x9864, ?x97) >> conf = 0.51 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dclg location! 02pzck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 195.000 139.000 0.511 http://example.org/people/person/places_lived./people/place_lived/location #12363-0b3wk PRED entity: 0b3wk PRED relation: taxonomy PRED expected values: 04n6k => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.33 #1, 0.14 #5, 0.14 #4) >> Best rule #1 for best value: >> intensional similarity = 76 >> extensional distance = 1 >> proper extension: 07t58; >> query: (?x2860, 04n6k) <- legislative_sessions(?x2860, ?x11142), legislative_sessions(?x2860, ?x10803), legislative_sessions(?x2860, ?x10291), legislative_sessions(?x2860, ?x9702), legislative_sessions(?x2860, ?x7973), legislative_sessions(?x2860, ?x7715), legislative_sessions(?x2860, ?x7714), legislative_sessions(?x2860, ?x6743), legislative_sessions(?x2860, ?x6728), legislative_sessions(?x2860, ?x6021), legislative_sessions(?x2860, ?x5977), legislative_sessions(?x2860, ?x5252), legislative_sessions(?x2860, ?x5006), legislative_sessions(?x2860, ?x4821), legislative_sessions(?x2860, ?x4812), legislative_sessions(?x2860, ?x4787), legislative_sessions(?x2860, ?x4730), legislative_sessions(?x2860, ?x4437), legislative_sessions(?x2860, ?x3766), legislative_sessions(?x2860, ?x2976), legislative_sessions(?x2860, ?x2712), legislative_sessions(?x2860, ?x2019), legislative_sessions(?x2860, ?x1830), legislative_sessions(?x2860, ?x1829), legislative_sessions(?x2860, ?x1754), legislative_sessions(?x2860, ?x1028), legislative_sessions(?x2860, ?x845), legislative_sessions(?x2860, ?x606), legislative_sessions(?x2860, ?x605), legislative_sessions(?x2860, ?x355), legislative_sessions(?x2860, ?x176), ?x176 = 03rl1g, ?x7714 = 01grr2, ?x10291 = 01gtdd, ?x6743 = 04h1rz, ?x5977 = 06r713, ?x9702 = 01gssz, ?x10803 = 01gt99, ?x4437 = 01gsrl, ?x606 = 03ww_x, ?x5006 = 01gtc0, category(?x2860, ?x134), ?x1830 = 03z5xd, ?x1028 = 032ft5, ?x1754 = 01grnp, ?x4821 = 02bqm0, ?x845 = 07p__7, ?x1829 = 02bp37, ?x605 = 077g7n, district_represented(?x355, ?x4754), district_represented(?x355, ?x2977), district_represented(?x355, ?x335), ?x2977 = 081mh, ?x2712 = 01gst_, ?x3766 = 02gkzs, ?x6728 = 070mff, legislative_sessions(?x9569, ?x355), legislative_sessions(?x652, ?x355), legislative_sessions(?x3463, ?x355), ?x2019 = 01gtbb, ?x335 = 059rby, ?x4730 = 02cg7g, ?x7715 = 01grp0, ?x6021 = 01gsvp, ?x4787 = 01grpq, ?x134 = 08mbj5d, ?x7973 = 01gsvb, ?x5252 = 01gtcq, district_represented(?x11142, ?x2713), ?x652 = 021sv1, legislative_sessions(?x7914, ?x11142), ?x4754 = 0g0syc, ?x9569 = 0194xc, ?x2713 = 06btq, ?x4812 = 01grpc, ?x2976 = 03rtmz >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b3wk taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 5.000 5.000 0.333 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #12362-02g3gj PRED entity: 02g3gj PRED relation: ceremony PRED expected values: 01s695 013b2h => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 126): 01mh_q (0.56 #595, 0.56 #466, 0.54 #1499), 01mhwk (0.56 #421, 0.55 #550, 0.52 #1454), 01s695 (0.54 #1422, 0.54 #518, 0.50 #131), 013b2h (0.53 #587, 0.53 #1491, 0.50 #329), 01xqqp (0.53 #602, 0.49 #1506, 0.46 #1764), 0jzphpx (0.46 #548, 0.45 #1452, 0.45 #2066), 0bc773 (0.45 #2066, 0.36 #2841, 0.27 #5551), 0bzm__ (0.45 #2066, 0.36 #2841, 0.27 #5551), 0bzlrh (0.45 #2066, 0.36 #2841, 0.27 #5551), 073hmq (0.45 #2066, 0.34 #904, 0.27 #5551) >> Best rule #595 for best value: >> intensional similarity = 6 >> extensional distance = 83 >> proper extension: 02grdc; 09sb52; 09qvf4; 056jm_; 03q27t; >> query: (?x528, 01mh_q) <- award(?x6162, ?x528), award(?x959, ?x528), artists(?x3061, ?x959), ?x3061 = 05bt6j, instrumentalists(?x716, ?x6162), ceremony(?x528, ?x139) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1422 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 125 *> proper extension: 07n52; 02xzd9; *> query: (?x528, 01s695) <- category_of(?x528, ?x2421), category_of(?x1088, ?x2421), disciplines_or_subjects(?x1088, ?x8681) *> conf = 0.54 ranks of expected_values: 3, 4 EVAL 02g3gj ceremony 013b2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 54.000 54.000 0.565 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02g3gj ceremony 01s695 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 54.000 54.000 0.565 http://example.org/award/award_category/winners./award/award_honor/ceremony #12361-01twdk PRED entity: 01twdk PRED relation: film PRED expected values: 07kb7vh 05dss7 => 123 concepts (59 used for prediction) PRED predicted values (max 10 best out of 599): 05qbckf (0.15 #3570, 0.14 #28549, 0.14 #28550), 0cc5mcj (0.15 #3570, 0.14 #28549, 0.14 #28550), 062zm5h (0.12 #16060, 0.10 #1785, 0.10 #14275), 017jd9 (0.07 #25759, 0.02 #104259, 0.02 #70362), 017gl1 (0.06 #25123, 0.02 #46534, 0.01 #62590), 017gm7 (0.06 #25190, 0.01 #103690, 0.01 #46601), 01shy7 (0.05 #11128, 0.05 #45029, 0.04 #7560), 04gv3db (0.04 #9674, 0.03 #20380, 0.02 #36438), 0ds3t5x (0.04 #25034), 0fphf3v (0.04 #31692, 0.03 #8496, 0.03 #1358) >> Best rule #3570 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 02rchht; 06cv1; 019z7q; 0162c8; 07s93v; 01gzm2; 052gzr; 0h1p; 01f7j9; 02fcs2; ... >> query: (?x4731, ?x1956) <- nationality(?x4731, ?x94), student(?x4824, ?x4731), ?x94 = 09c7w0, film(?x4731, ?x1956) >> conf = 0.15 => this is the best rule for 2 predicted values *> Best rule #18529 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 193 *> proper extension: 04qmr; *> query: (?x4731, 07kb7vh) <- category(?x4731, ?x134), participant(?x4631, ?x4731), award_nominee(?x4631, ?x91) *> conf = 0.02 ranks of expected_values: 200 EVAL 01twdk film 05dss7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 59.000 0.148 http://example.org/film/actor/film./film/performance/film EVAL 01twdk film 07kb7vh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 123.000 59.000 0.148 http://example.org/film/actor/film./film/performance/film #12360-02tz9z PRED entity: 02tz9z PRED relation: major_field_of_study PRED expected values: 02cm61 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 117): 01mkq (0.45 #870, 0.41 #1480, 0.39 #1725), 02lp1 (0.42 #866, 0.39 #1476, 0.38 #133), 02j62 (0.36 #1496, 0.35 #886, 0.35 #1741), 062z7 (0.34 #883, 0.30 #1493, 0.28 #1738), 02_7t (0.33 #1529, 0.32 #1774, 0.31 #552), 04rjg (0.33 #875, 0.32 #142, 0.30 #1485), 04x_3 (0.27 #881, 0.22 #1491, 0.21 #1736), 0_jm (0.27 #3477, 0.26 #2745, 0.26 #1400), 01tbp (0.26 #181, 0.25 #914, 0.21 #547), 03g3w (0.26 #882, 0.24 #4425, 0.23 #1492) >> Best rule #870 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 02zd460; 02bqy; 0ks67; 08qnnv; >> query: (?x12127, 01mkq) <- institution(?x620, ?x12127), major_field_of_study(?x12127, ?x254), fraternities_and_sororities(?x12127, ?x3697) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #958 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 113 *> proper extension: 02zd460; 02bqy; 0ks67; 08qnnv; *> query: (?x12127, 02cm61) <- institution(?x620, ?x12127), major_field_of_study(?x12127, ?x254), fraternities_and_sororities(?x12127, ?x3697) *> conf = 0.05 ranks of expected_values: 46 EVAL 02tz9z major_field_of_study 02cm61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 97.000 97.000 0.452 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #12359-09g7vfw PRED entity: 09g7vfw PRED relation: film! PRED expected values: 07rd7 => 69 concepts (52 used for prediction) PRED predicted values (max 10 best out of 78): 07rd7 (0.33 #379, 0.31 #654, 0.21 #929), 03v1w7 (0.21 #10195, 0.21 #10196), 02pq9yv (0.21 #10195, 0.21 #10196), 04sry (0.17 #168, 0.05 #1269, 0.02 #2369), 0js9s (0.17 #155, 0.04 #2356, 0.03 #2630), 034bgm (0.17 #68, 0.04 #2269, 0.03 #2543), 027zz (0.11 #529, 0.08 #804, 0.05 #1079), 081lh (0.08 #576, 0.05 #851, 0.02 #1677), 0184dt (0.08 #2265, 0.06 #2539, 0.05 #2813), 02qggqc (0.07 #1376, 0.05 #8537, 0.05 #8536) >> Best rule #379 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 031hcx; >> query: (?x3423, 07rd7) <- film_release_distribution_medium(?x3423, ?x81), film(?x2531, ?x3423), ?x2531 = 0kszw, genre(?x3423, ?x812), genre(?x1035, ?x812), ?x1035 = 08hmch >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09g7vfw film! 07rd7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 52.000 0.333 http://example.org/film/director/film #12358-01qvcr PRED entity: 01qvcr PRED relation: industry PRED expected values: 020mfr => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 37): 01mf0 (0.75 #1268, 0.54 #1271, 0.54 #1269), 020mfr (0.73 #1184, 0.68 #3088, 0.67 #3907), 019z7b (0.68 #1270, 0.54 #1271, 0.54 #1269), 02jjt (0.60 #4804, 0.32 #4997, 0.30 #4034), 07c1v (0.54 #1271, 0.54 #1269, 0.48 #1116), 0hz28 (0.54 #1271, 0.54 #1269, 0.48 #1116), 01mfj (0.54 #1271, 0.54 #1269, 0.48 #1116), 06xw2 (0.54 #1271, 0.54 #1269, 0.48 #1116), 02vxn (0.46 #1417, 0.45 #1117, 0.45 #2305), 029g_vk (0.40 #4947, 0.39 #4899, 0.34 #5045) >> Best rule #1268 for best value: >> intensional similarity = 13 >> extensional distance = 9 >> proper extension: 01swdw; >> query: (?x12217, ?x12987) <- child(?x9469, ?x12217), industry(?x12217, ?x245), ?x245 = 01mw1, industry(?x9469, ?x12987), industry(?x9469, ?x5078), industry(?x13349, ?x12987), industry(?x10665, ?x5078), industry(?x5077, ?x5078), ?x10665 = 01qxs3, ?x5077 = 0xwj, organization(?x4682, ?x9469), ?x13349 = 05b5c, ?x4682 = 0dq_5 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1184 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 9 *> proper extension: 01rt2z; 027lf1; *> query: (?x12217, 020mfr) <- child(?x9469, ?x12217), category(?x12217, ?x134), industry(?x9469, ?x12987), industry(?x9469, ?x12816), industry(?x9469, ?x5078), industry(?x9469, ?x245), industry(?x11939, ?x12816), ?x245 = 01mw1, industry(?x10665, ?x5078), ?x11939 = 01tlrp, ?x10665 = 01qxs3, industry(?x13349, ?x12987), ?x13349 = 05b5c *> conf = 0.73 ranks of expected_values: 2 EVAL 01qvcr industry 020mfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.750 http://example.org/business/business_operation/industry #12357-0dgrwqr PRED entity: 0dgrwqr PRED relation: film! PRED expected values: 02lf1j => 108 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1172): 0f0kz (0.33 #516, 0.25 #15095, 0.12 #38006), 016k6x (0.33 #891, 0.17 #2973, 0.14 #7138), 03k545 (0.33 #1883, 0.09 #14378, 0.07 #18545), 02yxwd (0.33 #744, 0.08 #15323, 0.06 #42400), 05sq84 (0.33 #236, 0.06 #27312, 0.06 #21064), 0c9c0 (0.33 #474, 0.06 #21302, 0.03 #29633), 07s8hms (0.33 #659, 0.06 #21487, 0.03 #40232), 04sry (0.33 #1278, 0.06 #22106, 0.03 #40851), 0gd9k (0.20 #112466, 0.20 #120799, 0.20 #124965), 035rnz (0.18 #13189, 0.17 #2776, 0.14 #6941) >> Best rule #516 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0ch26b_; >> query: (?x7494, 0f0kz) <- edited_by(?x7494, ?x7984), film(?x1914, ?x7494), film_crew_role(?x7494, ?x468), film_release_region(?x7494, ?x1061), ?x1061 = 04v3q >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #25424 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 24 *> proper extension: 074rg9; *> query: (?x7494, 02lf1j) <- film(?x2545, ?x7494), film_release_distribution_medium(?x7494, ?x627), ?x627 = 02nxhr, country(?x7494, ?x94), currency(?x7494, ?x170) *> conf = 0.04 ranks of expected_values: 357 EVAL 0dgrwqr film! 02lf1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 108.000 82.000 0.333 http://example.org/film/actor/film./film/performance/film #12356-04w4s PRED entity: 04w4s PRED relation: organization PRED expected values: 02vk52z => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 50): 02vk52z (0.89 #727, 0.88 #683, 0.87 #705), 0_2v (0.50 #113, 0.40 #267, 0.39 #157), 0b6css (0.46 #120, 0.40 #296, 0.38 #428), 018cqq (0.46 #121, 0.32 #1876, 0.30 #55), 04k4l (0.37 #136, 0.35 #114, 0.32 #158), 041288 (0.34 #852, 0.33 #434, 0.32 #962), 0gkjy (0.32 #1876, 0.31 #733, 0.31 #711), 02jxk (0.32 #1876, 0.31 #112, 0.30 #46), 0j7v_ (0.32 #1876, 0.26 #1238, 0.25 #1282), 059dn (0.32 #1876, 0.15 #59, 0.12 #125) >> Best rule #727 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 05rznz; >> query: (?x3041, 02vk52z) <- countries_within(?x455, ?x3041), adjoins(?x3041, ?x1471), organization(?x3041, ?x312) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04w4s organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.892 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #12355-02f764 PRED entity: 02f764 PRED relation: award! PRED expected values: 012x4t 086qd => 36 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2213): 0g824 (0.81 #16814, 0.65 #33629, 0.64 #26901), 013w7j (0.81 #16814, 0.65 #33629, 0.64 #26901), 01vvlyt (0.71 #11666, 0.60 #8303, 0.57 #15030), 012x4t (0.71 #13870, 0.40 #17233, 0.40 #7143), 01k_mc (0.60 #8453, 0.57 #15180, 0.43 #11816), 0flpy (0.60 #8555, 0.43 #11918, 0.33 #1831), 03j24kf (0.57 #14808, 0.40 #18171, 0.20 #8081), 01wbgdv (0.50 #3631, 0.43 #10356, 0.40 #6993), 012vd6 (0.50 #4905, 0.43 #11630, 0.40 #8267), 030155 (0.50 #4269, 0.43 #14358, 0.29 #10994) >> Best rule #16814 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 01bgqh; 0c4z8; 01cky2; 03qbh5; >> query: (?x4532, ?x6151) <- award(?x7162, ?x4532), award(?x3493, ?x4532), award(?x2614, ?x4532), award_nominee(?x3493, ?x3492), ?x2614 = 04xrx, ?x7162 = 0ffgh, award_winner(?x4532, ?x6151) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #13870 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 01bgqh; 0c4z8; 01cky2; 03qbh5; *> query: (?x4532, 012x4t) <- award(?x7162, ?x4532), award(?x3493, ?x4532), award(?x2614, ?x4532), award_nominee(?x3493, ?x3492), ?x2614 = 04xrx, ?x7162 = 0ffgh, award_winner(?x4532, ?x6151) *> conf = 0.71 ranks of expected_values: 4, 14 EVAL 02f764 award! 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 36.000 14.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f764 award! 012x4t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 36.000 14.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12354-020yvh PRED entity: 020yvh PRED relation: category PRED expected values: 08mbj5d => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.89 #14, 0.88 #11, 0.87 #45) >> Best rule #14 for best value: >> intensional similarity = 8 >> extensional distance = 157 >> proper extension: 06jk5_; 031n8c; 04344j; 04d5v9; 02_2kg; 029d_; 05njyy; 0172jm; 02l1fn; 04p_hy; ... >> query: (?x11112, 08mbj5d) <- school_type(?x11112, ?x1962), school_type(?x12127, ?x1962), school_type(?x7596, ?x1962), school_type(?x347, ?x1962), ?x7596 = 012mzw, major_field_of_study(?x12127, ?x254), ?x347 = 04wlz2, country(?x12127, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 020yvh category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.887 http://example.org/common/topic/webpage./common/webpage/category #12353-09lxtg PRED entity: 09lxtg PRED relation: country! PRED expected values: 07jjt => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 53): 02y8z (0.81 #71, 0.76 #124, 0.72 #177), 01lb14 (0.81 #68, 0.76 #121, 0.72 #174), 03hr1p (0.81 #75, 0.76 #128, 0.72 #181), 03_8r (0.75 #74, 0.71 #233, 0.71 #127), 06f41 (0.75 #67, 0.71 #120, 0.67 #173), 07jbh (0.75 #85, 0.71 #138, 0.67 #191), 019tzd (0.75 #92, 0.71 #145, 0.67 #198), 0w0d (0.69 #65, 0.65 #118, 0.61 #171), 07gyv (0.69 #60, 0.65 #113, 0.61 #166), 064vjs (0.69 #83, 0.65 #136, 0.61 #189) >> Best rule #71 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 09c7w0; 03rjj; 0d060g; 05qhw; 07ssc; 06mzp; 0f8l9c; 059j2; 0345h; 035qy; ... >> query: (?x4569, 02y8z) <- first_level_division_of(?x11662, ?x4569), organization(?x4569, ?x312), olympics(?x4569, ?x778) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #73 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 09c7w0; 03rjj; 0d060g; 05qhw; 07ssc; 06mzp; 0f8l9c; 059j2; 0345h; 035qy; ... *> query: (?x4569, 07jjt) <- first_level_division_of(?x11662, ?x4569), organization(?x4569, ?x312), olympics(?x4569, ?x778) *> conf = 0.62 ranks of expected_values: 18 EVAL 09lxtg country! 07jjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 77.000 77.000 0.812 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #12352-03t852 PRED entity: 03t852 PRED relation: role PRED expected values: 01rhl => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 121): 0342h (0.49 #318, 0.40 #214, 0.37 #2300), 05148p4 (0.38 #233, 0.24 #3239, 0.24 #1042), 02sgy (0.31 #320, 0.23 #2302, 0.23 #7), 0l14qv (0.31 #215, 0.19 #6, 0.16 #2301), 042v_gx (0.29 #322, 0.23 #9, 0.21 #1155), 05842k (0.27 #288, 0.17 #2374, 0.17 #2584), 01vj9c (0.21 #225, 0.16 #2311, 0.15 #2521), 013y1f (0.21 #246, 0.14 #1183, 0.14 #350), 018vs (0.19 #223, 0.17 #14, 0.16 #2309), 026t6 (0.19 #212, 0.16 #2298, 0.16 #2508) >> Best rule #318 for best value: >> intensional similarity = 3 >> extensional distance = 158 >> proper extension: 02fybl; 01h5f8; >> query: (?x7924, 0342h) <- profession(?x7924, ?x220), role(?x7924, ?x316), ?x220 = 016z4k >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #2401 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 438 *> proper extension: 01k5t_3; 015rmq; 05crg7; 02qlg7s; 06x4l_; 014hr0; 01l9v7n; 017vkx; 016k62; 016jfw; ... *> query: (?x7924, ?x74) <- artists(?x671, ?x7924), role(?x7924, ?x1437), role(?x74, ?x1437) *> conf = 0.04 ranks of expected_values: 42 EVAL 03t852 role 01rhl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 89.000 89.000 0.494 http://example.org/music/artist/track_contributions./music/track_contribution/role #12351-07r1h PRED entity: 07r1h PRED relation: executive_produced_by! PRED expected values: 02tjl3 => 148 concepts (143 used for prediction) PRED predicted values (max 10 best out of 220): 0bt4g (0.07 #954, 0.03 #3080, 0.02 #3612), 0mbql (0.07 #910, 0.03 #3036, 0.02 #3568), 01f7kl (0.07 #666, 0.03 #2792, 0.02 #3324), 01f7jt (0.07 #1045, 0.03 #3171, 0.02 #3703), 01bn3l (0.07 #961, 0.03 #3087, 0.02 #3619), 016y_f (0.07 #781, 0.03 #2907, 0.02 #3439), 0k2sk (0.07 #580, 0.03 #2706, 0.02 #3238), 01q2nx (0.07 #832, 0.03 #2958, 0.02 #3490), 09gdh6k (0.07 #942, 0.03 #3068, 0.02 #3600), 034b6k (0.07 #1041, 0.03 #3167, 0.02 #3699) >> Best rule #954 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 02fb1n; 02lq10; 0gnbw; 01w_10; >> query: (?x6187, 0bt4g) <- location(?x6187, ?x191), film(?x6187, ?x2218), ?x2218 = 013q07 >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07r1h executive_produced_by! 02tjl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 143.000 0.067 http://example.org/film/film/executive_produced_by #12350-013807 PRED entity: 013807 PRED relation: major_field_of_study PRED expected values: 01mkq 05qfh => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 101): 02j62 (0.41 #2653, 0.39 #1626, 0.39 #9043), 01mkq (0.40 #2638, 0.34 #9028, 0.32 #1611), 03g3w (0.40 #1622, 0.35 #2649, 0.30 #3903), 02lp1 (0.34 #3547, 0.33 #4003, 0.30 #2635), 062z7 (0.32 #1623, 0.31 #2650, 0.29 #3904), 01lj9 (0.28 #1635, 0.21 #2662, 0.21 #381), 05qjt (0.28 #350, 0.27 #2631, 0.23 #3201), 05qfh (0.24 #3912, 0.24 #2658, 0.21 #1631), 0fdys (0.24 #2661, 0.23 #1634, 0.19 #3915), 06ms6 (0.21 #359, 0.15 #2640, 0.14 #3552) >> Best rule #2653 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 06jk5_; 01bvw5; 01y17m; 01y888; 0143hl; 0bqxw; 0gy3w; 01wv24; 02482c; 015fsv; ... >> query: (?x10910, 02j62) <- contains(?x94, ?x10910), institution(?x1526, ?x10910), major_field_of_study(?x10910, ?x1527), ?x1526 = 0bkj86 >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #2638 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 129 *> proper extension: 06jk5_; 01bvw5; 01y17m; 01y888; 0143hl; 0bqxw; 0gy3w; 01wv24; 02482c; 015fsv; ... *> query: (?x10910, 01mkq) <- contains(?x94, ?x10910), institution(?x1526, ?x10910), major_field_of_study(?x10910, ?x1527), ?x1526 = 0bkj86 *> conf = 0.40 ranks of expected_values: 2, 8 EVAL 013807 major_field_of_study 05qfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 153.000 153.000 0.412 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 013807 major_field_of_study 01mkq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 153.000 153.000 0.412 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #12349-01309x PRED entity: 01309x PRED relation: nationality PRED expected values: 09c7w0 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 29): 09c7w0 (0.77 #1101, 0.74 #6912, 0.72 #6610), 05fkf (0.27 #10723), 02jx1 (0.23 #633, 0.20 #933, 0.19 #233), 07ssc (0.12 #215, 0.11 #615, 0.10 #315), 0f8l9c (0.10 #22, 0.08 #122, 0.03 #5428), 0d04z6 (0.10 #71, 0.08 #171, 0.02 #471), 0d060g (0.10 #7, 0.08 #907, 0.08 #607), 03rk0 (0.08 #5051, 0.07 #4950, 0.06 #2148), 0chghy (0.04 #210, 0.02 #4313, 0.02 #3112), 0hzlz (0.04 #223, 0.01 #623, 0.01 #923) >> Best rule #1101 for best value: >> intensional similarity = 2 >> extensional distance = 123 >> proper extension: 02n9k; >> query: (?x3632, 09c7w0) <- inductee(?x1091, ?x3632), profession(?x3632, ?x220) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01309x nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.768 http://example.org/people/person/nationality #12348-02glc4 PRED entity: 02glc4 PRED relation: district_represented PRED expected values: 03s0w 050l8 => 43 concepts (36 used for prediction) PRED predicted values (max 10 best out of 158): 04ych (0.88 #1263, 0.87 #1151, 0.87 #1097), 04rrd (0.83 #1646, 0.83 #1593, 0.82 #114), 0gyh (0.82 #114, 0.80 #1164, 0.80 #1110), 050l8 (0.82 #114, 0.80 #925, 0.80 #886), 05fky (0.82 #114, 0.80 #920, 0.80 #901), 0vmt (0.82 #114, 0.80 #873, 0.77 #932), 03s0w (0.82 #114, 0.80 #875, 0.75 #1307), 059f4 (0.82 #114, 0.75 #1634, 0.75 #488), 059_c (0.82 #114, 0.75 #488, 0.71 #1683), 05mph (0.82 #114, 0.75 #488, 0.71 #1683) >> Best rule #1263 for best value: >> intensional similarity = 28 >> extensional distance = 14 >> proper extension: 01gsvb; >> query: (?x5339, 04ych) <- legislative_sessions(?x2860, ?x5339), legislative_sessions(?x4567, ?x5339), legislative_sessions(?x2357, ?x5339), legislative_sessions(?x3766, ?x5339), district_represented(?x5339, ?x1906), district_represented(?x3766, ?x2831), district_represented(?x3766, ?x961), religion(?x1906, ?x109), contains(?x1906, ?x12411), contains(?x1906, ?x9751), contains(?x1906, ?x8969), state_province_region(?x266, ?x1906), country(?x1906, ?x94), legislative_sessions(?x5932, ?x3766), source(?x9751, ?x958), adjoins(?x1906, ?x279), people(?x5741, ?x2357), profession(?x2357, ?x353), legislative_sessions(?x952, ?x3766), politician(?x1912, ?x2357), contains(?x12411, ?x12873), ?x2831 = 0gyh, adjoins(?x177, ?x1906), type_of_union(?x4567, ?x566), notable_people_with_this_condition(?x6720, ?x4567), place_of_birth(?x7224, ?x8969), ?x961 = 03s0w, ?x5741 = 07bch9 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #114 for first EXPECTED value: *> intensional similarity = 45 *> extensional distance = 1 *> proper extension: 07p__7; *> query: (?x5339, ?x177) <- legislative_sessions(?x2860, ?x5339), legislative_sessions(?x4567, ?x5339), legislative_sessions(?x2357, ?x5339), legislative_sessions(?x6743, ?x5339), legislative_sessions(?x6728, ?x5339), legislative_sessions(?x3766, ?x5339), legislative_sessions(?x3463, ?x5339), legislative_sessions(?x1027, ?x5339), legislative_sessions(?x845, ?x5339), legislative_sessions(?x605, ?x5339), legislative_sessions(?x356, ?x5339), district_represented(?x5339, ?x448), district_represented(?x5339, ?x335), ?x4567 = 0d3qd0, ?x448 = 03v1s, ?x1027 = 02bn_p, legislative_sessions(?x5339, ?x4821), legislative_sessions(?x5339, ?x1829), legislative_sessions(?x5339, ?x952), ?x952 = 06f0dc, ?x4821 = 02bqm0, district_represented(?x845, ?x4600), district_represented(?x845, ?x3908), district_represented(?x845, ?x2768), district_represented(?x845, ?x2713), district_represented(?x845, ?x1351), district_represented(?x845, ?x938), district_represented(?x845, ?x177), ?x3463 = 02bqmq, ?x938 = 0vmt, ?x2768 = 03s5t, ?x3908 = 04ly1, ?x2713 = 06btq, ?x335 = 059rby, ?x1829 = 02bp37, ?x3766 = 02gkzs, ?x605 = 077g7n, ?x6743 = 04h1rz, legislative_sessions(?x3445, ?x845), ?x1351 = 06mz5, ?x3445 = 0d06m5, ?x2357 = 0bymv, ?x356 = 05l2z4, ?x6728 = 070mff, ?x4600 = 081yw *> conf = 0.82 ranks of expected_values: 4, 7 EVAL 02glc4 district_represented 050l8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 43.000 36.000 0.875 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 02glc4 district_represented 03s0w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 43.000 36.000 0.875 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #12347-05drq5 PRED entity: 05drq5 PRED relation: award PRED expected values: 04dn09n => 104 concepts (89 used for prediction) PRED predicted values (max 10 best out of 263): 04dn09n (0.78 #27633, 0.76 #25227, 0.75 #29236), 03hl6lc (0.78 #27633, 0.76 #25227, 0.75 #29236), 02w_6xj (0.75 #29236, 0.72 #16011, 0.72 #13607), 09d28z (0.75 #29236, 0.72 #16011, 0.72 #13607), 02qt02v (0.72 #13607, 0.71 #22419, 0.71 #16010), 03hkv_r (0.55 #416, 0.26 #1217, 0.22 #817), 02n9nmz (0.42 #468, 0.21 #1269, 0.20 #869), 02x17s4 (0.42 #520, 0.19 #1321, 0.16 #921), 0gq9h (0.34 #475, 0.29 #876, 0.28 #1676), 040njc (0.34 #1609, 0.32 #3209, 0.32 #3609) >> Best rule #27633 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 01wv9xn; 0hwd8; 0frsw; 01vrwfv; 02jqjm; 0g5ff; 0178_w; 07r1_; 0b_xm; 046p9; ... >> query: (?x1314, ?x601) <- award_winner(?x601, ?x1314), award(?x164, ?x601), ceremony(?x601, ?x78) >> conf = 0.78 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 05drq5 award 04dn09n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 89.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12346-0d4fqn PRED entity: 0d4fqn PRED relation: place_of_birth PRED expected values: 0cr3d => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 71): 01_d4 (0.11 #66, 0.05 #2178, 0.05 #4994), 01m23s (0.11 #652, 0.02 #1356, 0.01 #2060), 02_286 (0.08 #723, 0.08 #4947, 0.07 #5651), 0cr3d (0.08 #798, 0.06 #1502, 0.04 #9246), 0rh6k (0.04 #2114, 0.03 #3522, 0.02 #1410), 030qb3t (0.03 #27511, 0.03 #12726, 0.03 #8502), 09c7w0 (0.03 #2113, 0.03 #7041, 0.03 #5633), 094jv (0.02 #1469, 0.02 #2173, 0.02 #7101), 01sn3 (0.02 #2965, 0.02 #853, 0.02 #2261), 01531 (0.02 #809, 0.02 #13481, 0.01 #12073) >> Best rule #66 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 06j0md; 04cl1; 03wh8kl; 03wh8pq; 03cws8h; >> query: (?x636, 01_d4) <- award_nominee(?x636, ?x4806), award_winner(?x2016, ?x636), ?x4806 = 02bvt >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #798 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 09ftwr; 07jrjb; 05hrq4; 017dpj; 0b1s_q; *> query: (?x636, 0cr3d) <- award_winner(?x2016, ?x636), ?x2016 = 0cjyzs, profession(?x636, ?x1032) *> conf = 0.08 ranks of expected_values: 4 EVAL 0d4fqn place_of_birth 0cr3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 66.000 66.000 0.111 http://example.org/people/person/place_of_birth #12345-01bh6y PRED entity: 01bh6y PRED relation: award PRED expected values: 0gqyl => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 266): 02z1nbg (0.71 #26941, 0.70 #26538, 0.70 #36995), 0bdwft (0.33 #67, 0.13 #25733, 0.13 #36190), 05pcn59 (0.23 #5708, 0.20 #9326, 0.20 #8924), 054ky1 (0.19 #24124, 0.17 #107, 0.13 #36190), 02py7pj (0.19 #24124), 05qck (0.19 #24124), 0m7yy (0.19 #24124), 0ck27z (0.19 #492, 0.15 #17780, 0.15 #24616), 05ztrmj (0.19 #584, 0.15 #986, 0.11 #5811), 0bb57s (0.17 #242, 0.13 #25733, 0.13 #36190) >> Best rule #26941 for best value: >> intensional similarity = 3 >> extensional distance = 1536 >> proper extension: 012ljv; 0411q; 012t1; 015rmq; 0244r8; 01sbf2; 030_1_; 01dzz7; 01dw9z; 094wz7q; ... >> query: (?x9604, ?x3902) <- award(?x9604, ?x375), award_nominee(?x2589, ?x9604), award_winner(?x3902, ?x9604) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #25733 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1462 *> proper extension: 01m3b1t; 07sbk; *> query: (?x9604, ?x375) <- award_nominee(?x9604, ?x1343), award_winner(?x9604, ?x9716), award(?x1343, ?x375) *> conf = 0.13 ranks of expected_values: 19 EVAL 01bh6y award 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 110.000 110.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12344-025_nbr PRED entity: 025_nbr PRED relation: costume_design_by! PRED expected values: 0gndh => 81 concepts (73 used for prediction) PRED predicted values (max 10 best out of 20): 09d38d (0.20 #199, 0.10 #404, 0.08 #609), 0h0wd9 (0.20 #191, 0.05 #396, 0.04 #601), 0286gm1 (0.20 #137, 0.05 #342, 0.04 #547), 0gl3hr (0.20 #135, 0.05 #340, 0.04 #545), 0bl5c (0.20 #121, 0.05 #326, 0.04 #531), 0ccd3x (0.20 #93, 0.05 #298, 0.04 #503), 0bcndz (0.20 #32, 0.05 #237, 0.04 #442), 06krf3 (0.20 #18, 0.05 #223, 0.04 #428), 0c5qvw (0.05 #408, 0.04 #613, 0.03 #1023), 034xyf (0.05 #375, 0.04 #580, 0.03 #990) >> Best rule #199 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0gl88b; >> query: (?x12096, 09d38d) <- nationality(?x12096, ?x94), crewmember(?x10829, ?x12096), people(?x4195, ?x12096), profession(?x12096, ?x7630) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 025_nbr costume_design_by! 0gndh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 73.000 0.200 http://example.org/film/film/costume_design_by #12343-054g1r PRED entity: 054g1r PRED relation: film PRED expected values: 01vksx 01c22t 0fgrm 049xgc 04g73n => 100 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1514): 0j6b5 (0.74 #36154, 0.71 #36153, 0.64 #27113), 0cc97st (0.74 #36154, 0.71 #36153, 0.64 #27113), 032zq6 (0.60 #3598, 0.15 #24686, 0.09 #36740), 03mh_tp (0.40 #3439, 0.29 #4945, 0.22 #6451), 05b6rdt (0.40 #3944, 0.20 #25032, 0.16 #23526), 047gpsd (0.40 #4025, 0.15 #16074, 0.15 #25113), 0jqj5 (0.40 #3766, 0.15 #15815, 0.15 #24854), 045j3w (0.40 #3430, 0.15 #15479, 0.15 #24518), 03s9kp (0.40 #4498, 0.15 #25586, 0.14 #6004), 01kqq7 (0.40 #4390, 0.15 #25478, 0.12 #48079) >> Best rule #36154 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0cjdk; >> query: (?x5636, ?x2163) <- citytown(?x5636, ?x11930), nominated_for(?x5636, ?x2163), film_release_region(?x2163, ?x87) >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #6845 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0kx4m; *> query: (?x5636, 049xgc) <- organizations_founded(?x2426, ?x5636), state_province_region(?x5636, ?x1227), child(?x5636, ?x10258) *> conf = 0.22 ranks of expected_values: 67, 430, 659, 661, 664 EVAL 054g1r film 04g73n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 81.000 0.739 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 054g1r film 049xgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 100.000 81.000 0.739 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 054g1r film 0fgrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 81.000 0.739 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 054g1r film 01c22t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 81.000 0.739 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 054g1r film 01vksx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 81.000 0.739 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #12342-0l15n PRED entity: 0l15n PRED relation: profession PRED expected values: 01d_h8 => 129 concepts (107 used for prediction) PRED predicted values (max 10 best out of 56): 01d_h8 (0.83 #302, 0.81 #1042, 0.81 #1190), 0dxtg (0.73 #1345, 0.72 #309, 0.72 #1049), 02hrh1q (0.71 #7712, 0.70 #5786, 0.69 #458), 03gjzk (0.42 #755, 0.41 #1347, 0.41 #1051), 02krf9 (0.33 #27, 0.28 #1211, 0.26 #1063), 09jwl (0.20 #4607, 0.20 #4755, 0.19 #9642), 0nbcg (0.17 #179, 0.13 #9654, 0.13 #9327), 0kyk (0.17 #178, 0.13 #9327, 0.13 #15843), 0dz3r (0.17 #150, 0.13 #9327, 0.12 #9625), 01c72t (0.17 #172, 0.13 #9327, 0.11 #4612) >> Best rule #302 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 05drq5; >> query: (?x11297, 01d_h8) <- award_winner(?x8478, ?x11297), award(?x11297, ?x1107), profession(?x11297, ?x524), ?x1107 = 019f4v >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l15n profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 107.000 0.828 http://example.org/people/person/profession #12341-0241jw PRED entity: 0241jw PRED relation: gender PRED expected values: 05zppz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #11, 0.72 #17, 0.72 #175), 02zsn (0.30 #6, 0.30 #48, 0.30 #46) >> Best rule #11 for best value: >> intensional similarity = 2 >> extensional distance = 467 >> proper extension: 02vptk_; >> query: (?x1846, 05zppz) <- nationality(?x1846, ?x512), currency(?x1846, ?x170) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0241jw gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.723 http://example.org/people/person/gender #12340-0cmf0m0 PRED entity: 0cmf0m0 PRED relation: film_crew_role PRED expected values: 0dxtw => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.76 #1022, 0.71 #80, 0.70 #369), 09zzb8 (0.73 #1015, 0.71 #73, 0.69 #1307), 09vw2b7 (0.64 #1021, 0.60 #43, 0.57 #115), 0dxtw (0.40 #48, 0.35 #1026, 0.33 #1318), 01pvkk (0.40 #49, 0.29 #121, 0.27 #1027), 02ynfr (0.29 #197, 0.18 #1031, 0.18 #269), 02vs3x5 (0.29 #133, 0.05 #277, 0.05 #1039), 02rh1dz (0.20 #47, 0.20 #299, 0.18 #191), 014kbl (0.20 #69, 0.02 #682, 0.01 #357), 015h31 (0.18 #334, 0.16 #659, 0.13 #842) >> Best rule #1022 for best value: >> intensional similarity = 5 >> extensional distance = 581 >> proper extension: 01gglm; >> query: (?x8292, 0ch6mp2) <- film(?x1979, ?x8292), film_crew_role(?x8292, ?x468), award_winner(?x898, ?x1979), spouse(?x2258, ?x1979), award(?x1979, ?x384) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 01kff7; 09cr8; *> query: (?x8292, 0dxtw) <- film(?x1880, ?x8292), nominated_for(?x1053, ?x8292), film_crew_role(?x8292, ?x468), ?x1880 = 06x58 *> conf = 0.40 ranks of expected_values: 4 EVAL 0cmf0m0 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 76.000 0.762 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12339-019v9k PRED entity: 019v9k PRED relation: split_to! PRED expected values: 014mlp => 25 concepts (24 used for prediction) PRED predicted values (max 10 best out of 22): 07s6fsf (0.33 #325, 0.10 #2087, 0.07 #2319), 01ysy9 (0.06 #424), 01kxxq (0.06 #424), 022h5x (0.06 #424), 028dcg (0.06 #424), 02cq61 (0.06 #424), 01rr_d (0.06 #424), 02m4yg (0.06 #424), 01gkg3 (0.06 #424), 03bwzr4 (0.06 #424) >> Best rule #325 for best value: >> intensional similarity = 25 >> extensional distance = 1 >> proper extension: 07s6fsf; >> query: (?x1771, 07s6fsf) <- institution(?x1771, ?x12761), institution(?x1771, ?x11714), institution(?x1771, ?x6455), institution(?x1771, ?x6132), institution(?x1771, ?x5638), institution(?x1771, ?x4889), institution(?x1771, ?x4410), institution(?x1771, ?x4293), organizations_founded(?x11554, ?x6132), major_field_of_study(?x1771, ?x12158), major_field_of_study(?x1771, ?x742), ?x6455 = 026vcc, student(?x1771, ?x4480), major_field_of_study(?x581, ?x12158), ?x12761 = 0225v9, major_field_of_study(?x3437, ?x12158), school_type(?x11714, ?x3205), place_of_birth(?x4480, ?x108), ?x5638 = 02bqy, major_field_of_study(?x5031, ?x742), currency(?x4480, ?x170), ?x4410 = 017j69, ?x3437 = 02_xgp2, citytown(?x4293, ?x1494), ?x4889 = 02dq8f >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #424 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 1 *> proper extension: 07s6fsf; *> query: (?x1771, ?x865) <- institution(?x1771, ?x12761), institution(?x1771, ?x11714), institution(?x1771, ?x6455), institution(?x1771, ?x6132), institution(?x1771, ?x5638), institution(?x1771, ?x4889), institution(?x1771, ?x4410), institution(?x1771, ?x4293), organizations_founded(?x11554, ?x6132), major_field_of_study(?x1771, ?x12158), major_field_of_study(?x1771, ?x742), ?x6455 = 026vcc, student(?x1771, ?x4480), major_field_of_study(?x581, ?x12158), ?x12761 = 0225v9, major_field_of_study(?x3437, ?x12158), major_field_of_study(?x865, ?x12158), school_type(?x11714, ?x3205), place_of_birth(?x4480, ?x108), ?x5638 = 02bqy, major_field_of_study(?x5031, ?x742), currency(?x4480, ?x170), ?x4410 = 017j69, ?x3437 = 02_xgp2, citytown(?x4293, ?x1494), ?x4889 = 02dq8f *> conf = 0.06 ranks of expected_values: 18 EVAL 019v9k split_to! 014mlp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 25.000 24.000 0.333 http://example.org/dataworld/gardening_hint/split_to #12338-04q01mn PRED entity: 04q01mn PRED relation: film_crew_role PRED expected values: 09vw2b7 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 32): 0ch6mp2 (0.80 #621, 0.78 #331, 0.78 #2188), 09vw2b7 (0.69 #620, 0.67 #2187, 0.66 #1272), 0dxtw (0.46 #625, 0.42 #335, 0.40 #2192), 01vx2h (0.35 #1278, 0.35 #806, 0.33 #2193), 02ynfr (0.21 #630, 0.20 #738, 0.18 #1282), 0215hd (0.20 #127, 0.16 #1285, 0.14 #163), 02_n3z (0.14 #37, 0.12 #109, 0.10 #3532), 089g0h (0.13 #1286, 0.13 #634, 0.11 #2201), 0d2b38 (0.12 #1292, 0.12 #640, 0.11 #929), 01xy5l_ (0.12 #1280, 0.11 #122, 0.10 #338) >> Best rule #621 for best value: >> intensional similarity = 5 >> extensional distance = 195 >> proper extension: 0ds11z; 02_1sj; 03ckwzc; 0963mq; 048scx; 0jyx6; 0416y94; 0340hj; 029zqn; 0fdv3; ... >> query: (?x13884, 0ch6mp2) <- genre(?x13884, ?x53), film_release_distribution_medium(?x13884, ?x81), film_crew_role(?x13884, ?x137), films(?x8435, ?x13884), ?x137 = 09zzb8 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #620 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 195 *> proper extension: 0ds11z; 02_1sj; 03ckwzc; 0963mq; 048scx; 0jyx6; 0416y94; 0340hj; 029zqn; 0fdv3; ... *> query: (?x13884, 09vw2b7) <- genre(?x13884, ?x53), film_release_distribution_medium(?x13884, ?x81), film_crew_role(?x13884, ?x137), films(?x8435, ?x13884), ?x137 = 09zzb8 *> conf = 0.69 ranks of expected_values: 2 EVAL 04q01mn film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.802 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12337-01n8gr PRED entity: 01n8gr PRED relation: role PRED expected values: 018vs => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 42): 0342h (0.52 #1029, 0.49 #773, 0.47 #1093), 01vj9c (0.29 #16, 0.10 #1040, 0.09 #144), 018vs (0.28 #1217, 0.17 #1552, 0.17 #1617), 03qjg (0.28 #1217, 0.16 #3477, 0.15 #2763), 05148p4 (0.27 #1107, 0.27 #1043, 0.27 #1171), 05r5c (0.20 #905, 0.20 #137, 0.19 #1161), 028tv0 (0.18 #1038, 0.14 #1551, 0.14 #1616), 02hnl (0.17 #1054, 0.17 #1632, 0.17 #926), 0l14md (0.12 #1032, 0.12 #1675, 0.12 #1610), 0l14qv (0.08 #1030, 0.08 #1158, 0.07 #1543) >> Best rule #1029 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 09g0h; >> query: (?x3358, 0342h) <- gender(?x3358, ?x231), role(?x3358, ?x1466), group(?x3358, ?x1271) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1217 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 154 *> proper extension: 023slg; *> query: (?x3358, ?x227) <- award(?x3358, ?x724), role(?x3358, ?x1466), profession(?x3358, ?x131), instrumentalists(?x227, ?x3358) *> conf = 0.28 ranks of expected_values: 3 EVAL 01n8gr role 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 130.000 0.517 http://example.org/music/group_member/membership./music/group_membership/role #12336-089kpp PRED entity: 089kpp PRED relation: nationality PRED expected values: 0ctw_b => 119 concepts (113 used for prediction) PRED predicted values (max 10 best out of 28): 0ctw_b (0.80 #9623, 0.56 #10625, 0.34 #7014), 09c7w0 (0.76 #5912, 0.76 #5011, 0.76 #5211), 02jx1 (0.31 #1335, 0.30 #1736, 0.18 #2136), 07ssc (0.28 #1718, 0.28 #1317, 0.14 #1015), 04lc0h (0.25 #5611, 0.25 #8817, 0.24 #8115), 012ts (0.25 #5611, 0.25 #8817, 0.24 #8115), 05nrg (0.25 #5611, 0.25 #8817, 0.24 #8115), 03rk0 (0.13 #746, 0.05 #4655, 0.05 #2851), 0d060g (0.09 #1309, 0.07 #1710, 0.05 #807), 03rjj (0.07 #105, 0.04 #705, 0.04 #1507) >> Best rule #9623 for best value: >> intensional similarity = 3 >> extensional distance = 1869 >> proper extension: 0dj5q; 0ct_yc; >> query: (?x12768, ?x1023) <- place_of_birth(?x12768, ?x13174), contains(?x1023, ?x13174), combatants(?x94, ?x1023) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 089kpp nationality 0ctw_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 113.000 0.803 http://example.org/people/person/nationality #12335-0pgm3 PRED entity: 0pgm3 PRED relation: profession PRED expected values: 02jknp => 71 concepts (64 used for prediction) PRED predicted values (max 10 best out of 139): 03gjzk (0.48 #157, 0.47 #3785, 0.43 #447), 02jknp (0.38 #2907, 0.34 #3779, 0.25 #3052), 0np9r (0.30 #452, 0.29 #742, 0.28 #1032), 09jwl (0.20 #7545, 0.17 #4513, 0.16 #2626), 02krf9 (0.20 #7545, 0.15 #3796, 0.14 #2924), 0kyk (0.15 #606, 0.13 #461, 0.12 #751), 01c72t (0.12 #310, 0.10 #1905, 0.09 #1760), 0nbcg (0.11 #1768, 0.11 #8445, 0.11 #5106), 015cjr (0.10 #481, 0.09 #191, 0.09 #1061), 016z4k (0.10 #5082, 0.10 #6097, 0.09 #8421) >> Best rule #157 for best value: >> intensional similarity = 5 >> extensional distance = 156 >> proper extension: 02jm0n; 0hskw; 0fby2t; 0863x_; 08hsww; 0h27vc; 03y_46; 0cmt6q; 06q5t7; 0d608; ... >> query: (?x12710, 03gjzk) <- profession(?x12710, ?x1146), profession(?x12710, ?x1032), ?x1146 = 018gz8, ?x1032 = 02hrh1q, nominated_for(?x12710, ?x1811) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2907 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1101 *> proper extension: 0dbpyd; 06j0md; 02rchht; 05g8ky; 0h5f5n; 03ckxdg; 050023; 026dcvf; 017149; 04r7jc; ... *> query: (?x12710, 02jknp) <- profession(?x12710, ?x1146), profession(?x7318, ?x1146), profession(?x4629, ?x1146), ?x4629 = 05bnq3j, ?x7318 = 06gb2q *> conf = 0.38 ranks of expected_values: 2 EVAL 0pgm3 profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 64.000 0.481 http://example.org/people/person/profession #12334-013xrm PRED entity: 013xrm PRED relation: languages_spoken PRED expected values: 04306rv => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 56): 06mp7 (0.40 #182, 0.20 #238, 0.10 #744), 0t_2 (0.38 #1414, 0.36 #1470, 0.34 #1358), 0880p (0.33 #44, 0.20 #212, 0.09 #1502), 03hkp (0.33 #13, 0.20 #181, 0.09 #1471), 02h40lc (0.26 #1628, 0.26 #1572, 0.26 #1684), 064_8sq (0.22 #1421, 0.20 #187, 0.15 #1645), 06nm1 (0.21 #1355, 0.20 #177, 0.16 #1411), 02ztjwg (0.20 #199, 0.20 #143, 0.12 #536), 05f_3 (0.20 #248, 0.20 #192, 0.10 #754), 0295r (0.20 #250, 0.20 #194, 0.10 #756) >> Best rule #182 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 07hwkr; 03bkbh; 06mvq; >> query: (?x5540, 06mp7) <- people(?x5540, ?x7250), people(?x5540, ?x4992), people(?x5540, ?x2595), people(?x5540, ?x2507), people(?x5540, ?x380), profession(?x2595, ?x319), award(?x2595, ?x3617), nominated_for(?x2507, ?x2111), ?x4992 = 0lkr7, nationality(?x380, ?x1264), influenced_by(?x587, ?x7250) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #173 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 07hwkr; 03bkbh; 06mvq; *> query: (?x5540, 04306rv) <- people(?x5540, ?x7250), people(?x5540, ?x4992), people(?x5540, ?x2595), people(?x5540, ?x2507), people(?x5540, ?x380), profession(?x2595, ?x319), award(?x2595, ?x3617), nominated_for(?x2507, ?x2111), ?x4992 = 0lkr7, nationality(?x380, ?x1264), influenced_by(?x587, ?x7250) *> conf = 0.20 ranks of expected_values: 15 EVAL 013xrm languages_spoken 04306rv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 37.000 37.000 0.400 http://example.org/people/ethnicity/languages_spoken #12333-01gq0b PRED entity: 01gq0b PRED relation: film PRED expected values: 01gwk3 => 107 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1083): 0gvsh7l (0.58 #42822, 0.56 #44607, 0.55 #46392), 06z8s_ (0.09 #1913, 0.06 #3697, 0.06 #5481), 08r4x3 (0.09 #1937, 0.06 #3721, 0.06 #5505), 0418wg (0.09 #2183, 0.06 #3967, 0.06 #5751), 027r9t (0.09 #4812, 0.08 #6596, 0.06 #3028), 07tlfx (0.06 #5174, 0.06 #1605, 0.06 #6958), 03bzjpm (0.06 #4880, 0.06 #6664, 0.06 #3096), 01l_pn (0.06 #4533, 0.06 #6317, 0.06 #2749), 0blpg (0.06 #4222, 0.06 #6006, 0.05 #11358), 01shy7 (0.06 #25399, 0.05 #20046, 0.04 #64654) >> Best rule #42822 for best value: >> intensional similarity = 4 >> extensional distance = 234 >> proper extension: 01pnn3; 01vtqml; 0c2ry; 0cgbf; >> query: (?x1890, ?x414) <- spouse(?x10224, ?x1890), nominated_for(?x1890, ?x638), nominated_for(?x1890, ?x414), film(?x804, ?x638) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01gq0b film 01gwk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 79.000 0.578 http://example.org/film/actor/film./film/performance/film #12332-0dgst_d PRED entity: 0dgst_d PRED relation: film! PRED expected values: 016gr2 => 61 concepts (30 used for prediction) PRED predicted values (max 10 best out of 921): 05hj_k (0.50 #12457, 0.43 #60204, 0.37 #2077), 016yvw (0.29 #947, 0.04 #37369, 0.03 #18684), 02qgyv (0.29 #381, 0.03 #8686, 0.02 #2460), 0171cm (0.14 #422, 0.11 #17030, 0.08 #2078), 01tspc6 (0.14 #162, 0.08 #2078, 0.04 #37369), 03y_46 (0.14 #1014, 0.08 #2078, 0.04 #37369), 0bq2g (0.14 #601, 0.08 #2078, 0.04 #37369), 03t0k1 (0.14 #449, 0.08 #2078, 0.04 #37369), 0151w_ (0.14 #163, 0.08 #2078, 0.04 #37369), 0m31m (0.14 #450, 0.08 #2078, 0.04 #37369) >> Best rule #12457 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 014lc_; 0b76d_m; 0ds35l9; 0g56t9t; 02vxq9m; 0c3ybss; 011yrp; 0ds3t5x; 0g5qs2k; 0dscrwf; ... >> query: (?x1263, ?x4060) <- film(?x981, ?x1263), film_release_region(?x1263, ?x1603), nominated_for(?x4060, ?x1263), ?x1603 = 06bnz >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #16802 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 166 *> proper extension: 0g5pv3; 03ffcz; 03p2xc; 021gzd; 058kh7; *> query: (?x1263, 016gr2) <- film(?x6612, ?x1263), award_nominee(?x6612, ?x2372), ?x2372 = 0l6px, award_winner(?x931, ?x6612) *> conf = 0.06 ranks of expected_values: 76 EVAL 0dgst_d film! 016gr2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 61.000 30.000 0.498 http://example.org/film/actor/film./film/performance/film #12331-0c1pj PRED entity: 0c1pj PRED relation: profession PRED expected values: 01d_h8 => 115 concepts (96 used for prediction) PRED predicted values (max 10 best out of 83): 01d_h8 (0.89 #882, 0.85 #5409, 0.84 #5701), 0nbcg (0.44 #29, 0.21 #3680, 0.15 #175), 018gz8 (0.34 #1036, 0.29 #2058, 0.20 #1182), 0d1pc (0.33 #48, 0.20 #3699, 0.17 #1800), 02krf9 (0.33 #900, 0.30 #1338, 0.26 #2798), 0fj9f (0.29 #782, 0.19 #1658, 0.10 #1950), 0cbd2 (0.29 #5264, 0.25 #2051, 0.19 #1905), 09jwl (0.28 #3667, 0.22 #16, 0.21 #5565), 012t_z (0.22 #12, 0.16 #1618, 0.12 #742), 016z4k (0.21 #3655, 0.15 #5553, 0.13 #5845) >> Best rule #882 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0hskw; >> query: (?x556, 01d_h8) <- film(?x556, ?x174), nominated_for(?x556, ?x1185), participant(?x262, ?x556) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c1pj profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 96.000 0.891 http://example.org/people/person/profession #12330-02114t PRED entity: 02114t PRED relation: languages PRED expected values: 02h40lc => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 11): 02h40lc (0.40 #2, 0.35 #665, 0.33 #314), 064_8sq (0.06 #2146, 0.06 #678, 0.05 #405), 04306rv (0.06 #2146, 0.02 #198, 0.02 #237), 06b_j (0.06 #2146), 03_9r (0.06 #2146), 02bjrlw (0.04 #196, 0.04 #235, 0.03 #352), 0t_2 (0.02 #165, 0.02 #126, 0.01 #243), 03k50 (0.02 #3203, 0.02 #3320, 0.02 #1408), 06nm1 (0.01 #396, 0.01 #864, 0.01 #981), 07c9s (0.01 #3212, 0.01 #3329, 0.01 #1417) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 024rbz; >> query: (?x3705, 02h40lc) <- award_winner(?x696, ?x3705), ?x696 = 0209xj, nominated_for(?x3705, ?x1956) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02114t languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.400 http://example.org/people/person/languages #12329-02h400t PRED entity: 02h400t PRED relation: industry! PRED expected values: 01dfb6 => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 399): 01dfb6 (0.65 #1963, 0.37 #4168, 0.33 #127), 04sv4 (0.43 #3551, 0.41 #4530, 0.41 #4285), 019rl6 (0.27 #2051, 0.15 #3033, 0.15 #2788), 02b07b (0.27 #4139, 0.19 #5367, 0.17 #5857), 0dwl2 (0.25 #1229, 0.23 #2947, 0.21 #3436), 0k8z (0.25 #2495, 0.21 #3477, 0.18 #1515), 05b5c (0.25 #2654, 0.21 #3636, 0.18 #4860), 0xwj (0.25 #1289, 0.21 #3496, 0.18 #4230), 01qxs3 (0.25 #1366, 0.21 #3573, 0.18 #4307), 02r5dz (0.25 #1021, 0.18 #2002, 0.18 #1757) >> Best rule #1963 for best value: >> intensional similarity = 17 >> extensional distance = 9 >> proper extension: 0vg8; >> query: (?x12014, ?x9873) <- industry(?x5789, ?x12014), industry(?x2607, ?x12014), company(?x4792, ?x2607), company(?x4682, ?x2607), category(?x2607, ?x134), service_location(?x2607, ?x789), service_location(?x2607, ?x94), currency(?x2607, ?x170), citytown(?x2607, ?x739), ?x4792 = 05_wyz, ?x94 = 09c7w0, ?x4682 = 0dq_5, industry(?x2607, ?x12352), list(?x2607, ?x5997), industry(?x9873, ?x12352), service_language(?x5789, ?x254), organization(?x789, ?x127) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02h400t industry! 01dfb6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.652 http://example.org/business/business_operation/industry #12328-04k3r_ PRED entity: 04k3r_ PRED relation: teams! PRED expected values: 05v8c => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 141): 09wwlj (0.17 #270, 0.14 #540, 0.12 #810), 077qn (0.17 #113, 0.14 #383, 0.12 #653), 06t8v (0.17 #98, 0.14 #368, 0.12 #638), 06mkj (0.17 #67, 0.14 #337, 0.12 #607), 0pfd9 (0.14 #519, 0.10 #1059, 0.06 #1329), 030qb3t (0.12 #590, 0.10 #860, 0.06 #1130), 01ly5m (0.06 #1166, 0.06 #1436, 0.05 #1707), 0154j (0.06 #1355, 0.03 #2170, 0.02 #2710), 04swd (0.05 #1798, 0.04 #2069, 0.03 #5854), 05qtj (0.05 #1748, 0.04 #2019, 0.01 #5264) >> Best rule #270 for best value: >> intensional similarity = 15 >> extensional distance = 4 >> proper extension: 03y_f8; 03ys48; 019mdt; 02w64f; >> query: (?x3188, 09wwlj) <- position(?x3188, ?x530), position(?x3188, ?x63), position(?x3188, ?x60), ?x60 = 02nzb8, colors(?x3188, ?x3189), colors(?x3188, ?x1101), colors(?x3188, ?x663), ?x1101 = 06fvc, ?x3189 = 01g5v, ?x663 = 083jv, ?x530 = 02_j1w, sport(?x3188, ?x471), ?x471 = 02vx4, ?x63 = 02sdk9v, team(?x203, ?x3188) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04k3r_ teams! 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 86.000 0.167 http://example.org/sports/sports_team_location/teams #12327-01vng3b PRED entity: 01vng3b PRED relation: instrumentalists! PRED expected values: 02hnl => 149 concepts (145 used for prediction) PRED predicted values (max 10 best out of 126): 0342h (0.81 #2038, 0.78 #764, 0.78 #2209), 03qjg (0.55 #133, 0.30 #979, 0.29 #217), 0l14md (0.46 #1610, 0.46 #1355, 0.45 #3383), 01vj9c (0.46 #1610, 0.46 #1355, 0.45 #3383), 03bx0bm (0.46 #1610, 0.46 #1355, 0.45 #3383), 02hnl (0.32 #791, 0.29 #1557, 0.28 #1302), 026t6 (0.30 #6269, 0.30 #5338, 0.30 #5508), 06w7v (0.24 #237, 0.17 #999, 0.17 #660), 02sgy (0.20 #7, 0.18 #91, 0.11 #4572), 042v_gx (0.20 #9, 0.11 #4572, 0.11 #3723) >> Best rule #2038 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 03wjb7; >> query: (?x6225, 0342h) <- artists(?x302, ?x6225), ?x302 = 016clz, instrumentalists(?x716, ?x6225), role(?x74, ?x716) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #791 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 02bgmr; *> query: (?x6225, 02hnl) <- artists(?x302, ?x6225), ?x302 = 016clz, instrumentalists(?x716, ?x6225), ?x716 = 018vs *> conf = 0.32 ranks of expected_values: 6 EVAL 01vng3b instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 149.000 145.000 0.811 http://example.org/music/instrument/instrumentalists #12326-03cp4cn PRED entity: 03cp4cn PRED relation: film! PRED expected values: 0k525 03k545 => 94 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1226): 095zvfg (0.50 #16628, 0.46 #20786, 0.46 #93553), 06r_by (0.50 #16628, 0.46 #20786, 0.46 #93553), 0bytkq (0.50 #16628, 0.46 #20786, 0.46 #93553), 04sry (0.33 #1273, 0.14 #14549, 0.14 #18707), 02qgyv (0.33 #385, 0.06 #91474, 0.04 #97712), 09fb5 (0.17 #58, 0.06 #91474, 0.06 #97713), 01r93l (0.17 #747, 0.06 #91474, 0.05 #2826), 07vc_9 (0.17 #203, 0.06 #91474, 0.05 #2282), 0154qm (0.17 #561, 0.06 #91474, 0.04 #97712), 0bksh (0.17 #853, 0.06 #91474, 0.04 #97712) >> Best rule #16628 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 05c26ss; 032clf; 02wtp6; 04hk0w; >> query: (?x6267, ?x3080) <- nominated_for(?x3080, ?x6267), genre(?x6267, ?x53), category(?x6267, ?x134), film_format(?x6267, ?x6392) >> conf = 0.50 => this is the best rule for 3 predicted values *> Best rule #22627 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 106 *> proper extension: 0gxtknx; 0g57wgv; *> query: (?x6267, 0k525) <- nominated_for(?x3080, ?x6267), nominated_for(?x2393, ?x6267), film(?x2374, ?x6267), film_regional_debut_venue(?x6267, ?x9080) *> conf = 0.02 ranks of expected_values: 542, 554 EVAL 03cp4cn film! 03k545 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 94.000 67.000 0.495 http://example.org/film/actor/film./film/performance/film EVAL 03cp4cn film! 0k525 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 94.000 67.000 0.495 http://example.org/film/actor/film./film/performance/film #12325-01gkp1 PRED entity: 01gkp1 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.72 #286, 0.71 #955, 0.70 #743), 0dxtw (0.36 #290, 0.34 #959, 0.34 #677), 01vx2h (0.32 #46, 0.31 #10, 0.30 #81), 01pvkk (0.28 #785, 0.28 #679, 0.27 #292), 02ynfr (0.15 #296, 0.14 #683, 0.14 #965), 0263ycg (0.12 #17, 0.03 #193, 0.03 #158), 0215hd (0.12 #686, 0.12 #792, 0.12 #968), 015h31 (0.11 #43, 0.10 #78, 0.08 #675), 0d2b38 (0.10 #693, 0.10 #799, 0.09 #96), 089g0h (0.10 #687, 0.10 #793, 0.09 #969) >> Best rule #286 for best value: >> intensional similarity = 4 >> extensional distance = 498 >> proper extension: 02v8kmz; >> query: (?x4768, 0ch6mp2) <- nominated_for(?x2705, ?x4768), award(?x2705, ?x68), film_crew_role(?x4768, ?x137), produced_by(?x4651, ?x2705) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gkp1 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.720 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12324-02g3gj PRED entity: 02g3gj PRED relation: award! PRED expected values: 015f7 01yzl2 02cpp 03j1p2n => 40 concepts (25 used for prediction) PRED predicted values (max 10 best out of 4138): 0478__m (0.84 #20164, 0.83 #16803, 0.82 #13442), 06mt91 (0.84 #20164, 0.83 #16803, 0.82 #13442), 015f7 (0.84 #20164, 0.83 #16803, 0.82 #13442), 0j1yf (0.84 #20164, 0.83 #16803, 0.82 #13442), 0415mzy (0.84 #20164, 0.83 #16803, 0.82 #13442), 0412f5y (0.84 #20164, 0.83 #16803, 0.82 #13442), 01xzb6 (0.62 #14978, 0.60 #11617, 0.50 #18339), 046p9 (0.62 #15799, 0.60 #12438, 0.50 #19160), 02z4b_8 (0.62 #15503, 0.54 #25586, 0.50 #22225), 03y82t6 (0.62 #14814, 0.50 #8093, 0.42 #21536) >> Best rule #20164 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 02f6yz; >> query: (?x528, ?x1896) <- award(?x2723, ?x528), award(?x2237, ?x528), ?x2723 = 016fmf, award(?x2237, ?x4481), ?x4481 = 02x17c2, award_winner(?x528, ?x1896) >> conf = 0.84 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 145, 222, 369 EVAL 02g3gj award! 03j1p2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 40.000 25.000 0.836 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02g3gj award! 02cpp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 40.000 25.000 0.836 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02g3gj award! 01yzl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 40.000 25.000 0.836 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02g3gj award! 015f7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 40.000 25.000 0.836 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12323-0g56t9t PRED entity: 0g56t9t PRED relation: nominated_for! PRED expected values: 0drtkx => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 196): 0drtkx (0.25 #2090, 0.18 #431, 0.17 #3512), 0gq9h (0.22 #8123, 0.22 #8835, 0.22 #10969), 02hsq3m (0.20 #1452, 0.19 #3111, 0.18 #267), 0gr0m (0.20 #60, 0.15 #8832, 0.15 #8120), 05f4m9q (0.20 #12, 0.13 #1197, 0.11 #723), 07bdd_ (0.20 #53, 0.13 #764, 0.11 #1238), 03hl6lc (0.20 #130, 0.12 #4396, 0.10 #4870), 05p1dby (0.20 #82, 0.10 #556, 0.08 #1267), 07cbcy (0.20 #64, 0.09 #1249, 0.08 #8361), 099tbz (0.20 #47, 0.09 #2654, 0.09 #2891) >> Best rule #2090 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 02_fm2; 01_1pv; 02lk60; 02c7k4; 016017; >> query: (?x124, 0drtkx) <- genre(?x124, ?x2540), music(?x124, ?x3410), ?x2540 = 0hcr, film(?x2383, ?x124) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g56t9t nominated_for! 0drtkx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.250 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12322-0165b PRED entity: 0165b PRED relation: jurisdiction_of_office! PRED expected values: 0fkx3 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.72 #839, 0.71 #1304, 0.70 #795), 060bp (0.70 #419, 0.67 #199, 0.66 #375), 0f6c3 (0.51 #645, 0.50 #579, 0.47 #733), 0pqc5 (0.48 #1083, 0.36 #1613, 0.36 #1680), 09n5b9 (0.45 #583, 0.43 #649, 0.40 #737), 04syw (0.40 #380, 0.33 #50, 0.29 #116), 0dq3c (0.27 #90, 0.23 #178, 0.23 #244), 0fj45 (0.24 #173, 0.23 #261, 0.22 #63), 01zq91 (0.23 #212, 0.17 #322, 0.17 #344), 0p5vf (0.21 #342, 0.21 #122, 0.20 #210) >> Best rule #839 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 06tw8; >> query: (?x7479, 060c4) <- contains(?x1144, ?x7479), olympics(?x7479, ?x7688), country(?x150, ?x7479), jurisdiction_of_office(?x900, ?x7479) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1632 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 440 *> proper extension: 014tss; 0m__z; 0fwdr; 0nbzp; 022b_; *> query: (?x7479, ?x346) <- contains(?x7273, ?x7479), jurisdiction_of_office(?x900, ?x7479), contains(?x7273, ?x9816), jurisdiction_of_office(?x346, ?x9816) *> conf = 0.19 ranks of expected_values: 15 EVAL 0165b jurisdiction_of_office! 0fkx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 122.000 122.000 0.725 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #12321-0g_bh PRED entity: 0g_bh PRED relation: artists PRED expected values: 0lzkm => 63 concepts (30 used for prediction) PRED predicted values (max 10 best out of 933): 067mj (0.60 #10942, 0.54 #13109, 0.53 #3253), 03t9sp (0.58 #27246, 0.54 #29413, 0.53 #3253), 06br6t (0.55 #12824, 0.53 #3253, 0.32 #21505), 0p76z (0.53 #3253, 0.50 #11762, 0.46 #13929), 01l_w0 (0.53 #3253, 0.50 #11634, 0.46 #13801), 012zng (0.53 #3253, 0.50 #10976, 0.38 #13143), 06gd4 (0.53 #3253, 0.50 #11181, 0.38 #13348), 01pfr3 (0.53 #3253, 0.50 #8701, 0.33 #2194), 011z3g (0.53 #3253, 0.50 #9282, 0.33 #2775), 01w8n89 (0.53 #3253, 0.46 #13328, 0.45 #12246) >> Best rule #10942 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 064t9; 016jhr; 0xhtw; 0dl5d; 05w3f; 08jyyk; 05r6t; 01fh36; >> query: (?x8747, 067mj) <- parent_genre(?x8747, ?x5379), artists(?x8747, ?x9841), artists(?x5379, ?x9463), artists(?x5379, ?x5391), ?x9841 = 02ndj5, ?x5391 = 03h_fqv, parent_genre(?x13652, ?x8747), ?x9463 = 01shhf >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3253 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 06by7; *> query: (?x8747, ?x483) <- parent_genre(?x8747, ?x5379), artists(?x8747, ?x9841), artists(?x5379, ?x5391), artists(?x5379, ?x483), ?x9841 = 02ndj5, ?x5391 = 03h_fqv, parent_genre(?x13652, ?x8747), ?x13652 = 0ccxx6 *> conf = 0.53 ranks of expected_values: 508 EVAL 0g_bh artists 0lzkm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 63.000 30.000 0.600 http://example.org/music/genre/artists #12320-0411q PRED entity: 0411q PRED relation: artists! PRED expected values: 0175yg => 120 concepts (118 used for prediction) PRED predicted values (max 10 best out of 213): 06by7 (0.73 #335, 0.72 #959, 0.69 #1583), 064t9 (0.55 #5007, 0.55 #4695, 0.52 #950), 02k_kn (0.45 #381, 0.31 #1005, 0.31 #1629), 05bt6j (0.36 #358, 0.36 #1606, 0.35 #2230), 0xhtw (0.36 #330, 0.30 #4075, 0.28 #1578), 05w3f (0.36 #352, 0.25 #2536, 0.25 #2848), 06j6l (0.35 #1299, 0.35 #2235, 0.33 #675), 0glt670 (0.33 #4724, 0.32 #5036, 0.26 #10345), 025sc50 (0.31 #5046, 0.30 #4734, 0.25 #10355), 02yv6b (0.30 #103, 0.27 #415, 0.23 #4160) >> Best rule #335 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 07yg2; 01v0sxx; >> query: (?x219, 06by7) <- artist(?x5744, ?x219), inductee(?x1091, ?x219), ?x5744 = 01clyr >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #212 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01x15dc; *> query: (?x219, 0175yg) <- award_winner(?x2704, ?x219), award_winner(?x6623, ?x219), ?x6623 = 0248jb *> conf = 0.20 ranks of expected_values: 22 EVAL 0411q artists! 0175yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 120.000 118.000 0.727 http://example.org/music/genre/artists #12319-0k419 PRED entity: 0k419 PRED relation: nominated_for! PRED expected values: 05683cn => 84 concepts (29 used for prediction) PRED predicted values (max 10 best out of 585): 05683cn (0.86 #14004, 0.61 #16339, 0.58 #23341), 02cqbx (0.78 #65355, 0.77 #44350, 0.72 #23342), 05qd_ (0.22 #25677, 0.14 #51353, 0.10 #7176), 0g1rw (0.17 #2473, 0.16 #7141, 0.12 #16478), 07h1tr (0.17 #569, 0.13 #2903, 0.13 #7571), 01b9ck (0.17 #261, 0.13 #2595, 0.11 #14266), 016tt2 (0.17 #109, 0.10 #18782, 0.10 #16448), 0fmqp6 (0.17 #1493, 0.06 #15498, 0.05 #20166), 017s11 (0.17 #100, 0.06 #9437, 0.05 #35115), 053vcrp (0.17 #2110, 0.06 #18449, 0.05 #25452) >> Best rule #14004 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 03hjv97; 04mzf8; 0dtfn; 0bcndz; 0k4kk; 02q52q; 070fnm; 083skw; 0kcn7; 0k4f3; ... >> query: (?x10435, ?x4896) <- film_art_direction_by(?x10435, ?x4896), nominated_for(?x484, ?x10435), award_winner(?x10435, ?x5611), ?x484 = 0gq_v >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k419 nominated_for! 05683cn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 29.000 0.857 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12318-0tnkg PRED entity: 0tnkg PRED relation: category PRED expected values: 08mbj5d => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #21, 0.79 #15, 0.78 #1) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 164 >> proper extension: 0wh3; 0yc84; 0fvxz; 0r1yc; 0r7fy; 0r540; 0d234; 0r1jr; 0fw2y; 0n6bs; ... >> query: (?x13204, 08mbj5d) <- time_zones(?x13204, ?x2674), source(?x13204, ?x958), state(?x13204, ?x7058), contains(?x94, ?x13204) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tnkg category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.801 http://example.org/common/topic/webpage./common/webpage/category #12317-04f52jw PRED entity: 04f52jw PRED relation: currency PRED expected values: 09nqf => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.80 #57, 0.77 #71, 0.77 #64), 01nv4h (0.04 #114, 0.02 #44, 0.02 #226), 02l6h (0.02 #130, 0.02 #109, 0.02 #116), 0kz1h (0.02 #26, 0.01 #54) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 02bj22; >> query: (?x2746, 09nqf) <- genre(?x2746, ?x1510), film(?x2473, ?x2746), ?x1510 = 01hmnh, award_winner(?x2746, ?x3879) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04f52jw currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.800 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #12316-04qb6g PRED entity: 04qb6g PRED relation: category PRED expected values: 08mbj5d => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.69 #7, 0.67 #5, 0.64 #11) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01nzs7; >> query: (?x13986, 08mbj5d) <- program(?x13986, ?x2447), nominated_for(?x1630, ?x2447), nominated_for(?x2448, ?x2447), award_winner(?x6093, ?x13986) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04qb6g category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.688 http://example.org/common/topic/webpage./common/webpage/category #12315-0c3kw PRED entity: 0c3kw PRED relation: gender PRED expected values: 05zppz => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #21, 0.88 #15, 0.87 #48), 02zsn (0.50 #191, 0.27 #82, 0.26 #195) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 156 >> proper extension: 0j3v; 02ln1; 047g6; 01h2_6; >> query: (?x1727, 05zppz) <- influenced_by(?x1727, ?x8433), student(?x4257, ?x1727), company(?x7749, ?x4257), major_field_of_study(?x4257, ?x373) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c3kw gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.892 http://example.org/people/person/gender #12314-0gpjbt PRED entity: 0gpjbt PRED relation: ceremony! PRED expected values: 02gx2k 02h3d1 031b3h 01ck6v 0257__ => 60 concepts (58 used for prediction) PRED predicted values (max 10 best out of 220): 031b3h (0.83 #2581, 0.77 #7273, 0.75 #1874), 02h3d1 (0.80 #2395, 0.78 #2043, 0.77 #7273), 0257__ (0.77 #7273, 0.75 #2643, 0.62 #1936), 02gx2k (0.77 #7273, 0.70 #2349, 0.67 #2527), 02flpq (0.77 #7273, 0.60 #1026, 0.56 #2472), 01ck6h (0.77 #7273, 0.56 #2472, 0.56 #2649), 01ck6v (0.77 #7273, 0.56 #2472, 0.56 #2649), 02tj96 (0.77 #7273, 0.56 #2472, 0.56 #2649), 03x3wf (0.77 #7273, 0.56 #2472, 0.56 #2649), 031b91 (0.77 #7273, 0.50 #2457, 0.50 #869) >> Best rule #2581 for best value: >> intensional similarity = 14 >> extensional distance = 10 >> proper extension: 01mhwk; 09n4nb; 01mh_q; >> query: (?x2054, 031b3h) <- ceremony(?x8705, ?x2054), ceremony(?x8409, ?x2054), award_winner(?x2054, ?x8045), award_winner(?x2054, ?x4850), award_winner(?x2054, ?x954), ?x8409 = 03ncb2, ?x8705 = 01c9dd, gender(?x8045, ?x514), profession(?x954, ?x131), profession(?x8045, ?x987), artist(?x11031, ?x954), type_of_union(?x4850, ?x566), award_nominee(?x8045, ?x101), award_winner(?x375, ?x8045) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 7 EVAL 0gpjbt ceremony! 0257__ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 58.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gpjbt ceremony! 01ck6v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 58.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gpjbt ceremony! 031b3h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 58.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gpjbt ceremony! 02h3d1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 58.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gpjbt ceremony! 02gx2k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 58.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony #12313-01s0ps PRED entity: 01s0ps PRED relation: group PRED expected values: 0163m1 => 91 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1043): 07mvp (0.75 #3450, 0.70 #4766, 0.64 #5142), 05563d (0.71 #2463, 0.67 #2089, 0.56 #5853), 0134tg (0.67 #2111, 0.50 #427, 0.44 #3616), 01czx (0.67 #2076, 0.50 #392, 0.40 #4148), 02dw1_ (0.62 #5884, 0.62 #3438, 0.55 #7202), 0fb2l (0.62 #2757, 0.50 #4642, 0.50 #3325), 0123r4 (0.62 #3068, 0.50 #1379, 0.43 #2499), 014pg1 (0.60 #5733, 0.57 #2533, 0.56 #3852), 0163m1 (0.60 #5667, 0.57 #2467, 0.56 #3786), 027kwc (0.60 #4302, 0.50 #4490, 0.50 #2981) >> Best rule #3450 for best value: >> intensional similarity = 14 >> extensional distance = 6 >> proper extension: 0mkg; >> query: (?x2764, 07mvp) <- instrumentalists(?x2764, ?x8305), instrumentalists(?x2764, ?x3492), role(?x2764, ?x315), ?x315 = 0l14md, role(?x2764, ?x2048), role(?x4917, ?x2764), artists(?x505, ?x3492), ?x4917 = 06w7v, role(?x3316, ?x2764), ?x2048 = 018j2, inductee(?x1091, ?x3316), instrumentalists(?x212, ?x3316), people(?x4322, ?x8305), artists(?x378, ?x3316) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #5667 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 13 *> proper extension: 0l14md; 07gql; *> query: (?x2764, 0163m1) <- instrumentalists(?x2764, ?x2765), performance_role(?x2698, ?x2764), role(?x2764, ?x3418), role(?x2764, ?x2460), role(?x2764, ?x1495), ?x3418 = 02w4b, role(?x2460, ?x5990), role(?x2460, ?x2675), role(?x716, ?x2764), ?x5990 = 0192l, ?x2675 = 020w2, instrumentalists(?x2460, ?x680), role(?x1495, ?x645), role(?x211, ?x1495), role(?x158, ?x2764), group(?x1495, ?x997), role(?x130, ?x1495) *> conf = 0.60 ranks of expected_values: 9 EVAL 01s0ps group 0163m1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 91.000 48.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group #12312-01vvybv PRED entity: 01vvybv PRED relation: film PRED expected values: 06cgf => 135 concepts (94 used for prediction) PRED predicted values (max 10 best out of 575): 01jnc_ (0.10 #17660, 0.08 #23024, 0.07 #31964), 027fwmt (0.10 #1591, 0.05 #5167, 0.03 #21260), 0ds5_72 (0.10 #1457, 0.05 #5033, 0.03 #22914), 0h3k3f (0.10 #1489, 0.05 #5065, 0.02 #6853), 01shy7 (0.07 #7576, 0.06 #34397, 0.05 #29033), 0c8tkt (0.06 #2056, 0.03 #16361, 0.03 #12784), 027pfg (0.06 #3012, 0.03 #13740, 0.02 #6588), 03bx2lk (0.06 #1973, 0.02 #5549, 0.02 #123558), 0bvn25 (0.06 #1838, 0.02 #5414, 0.02 #8990), 0b7l4x (0.06 #2828, 0.02 #20709, 0.02 #8192) >> Best rule #17660 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 03ds3; 015076; >> query: (?x10461, 01jnc_) <- profession(?x10461, ?x131), instrumentalists(?x227, ?x10461), film(?x10461, ?x5564), award_winner(?x7005, ?x10461) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01vvybv film 06cgf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 94.000 0.102 http://example.org/film/actor/film./film/performance/film #12311-03m6zs PRED entity: 03m6zs PRED relation: colors PRED expected values: 083jv => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 17): 083jv (0.83 #935, 0.82 #916, 0.63 #1126), 06fvc (0.42 #592, 0.40 #1222, 0.40 #1261), 019sc (0.28 #539, 0.28 #1169, 0.28 #425), 02rnmb (0.25 #32, 0.15 #686, 0.15 #685), 01l849 (0.21 #628, 0.18 #877, 0.17 #763), 038hg (0.20 #50, 0.15 #686, 0.15 #685), 06kqt3 (0.17 #187, 0.15 #686, 0.15 #685), 03vtbc (0.17 #179, 0.12 #1105, 0.09 #941), 088fh (0.15 #686, 0.15 #685, 0.15 #405), 0jc_p (0.15 #686, 0.15 #685, 0.12 #1105) >> Best rule #935 for best value: >> intensional similarity = 5 >> extensional distance = 118 >> proper extension: 04088s0; 026xxv_; >> query: (?x10026, 083jv) <- team(?x6390, ?x10026), colors(?x10026, ?x3189), athlete(?x471, ?x6390), colors(?x387, ?x3189), ?x387 = 02896 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03m6zs colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.833 http://example.org/sports/sports_team/colors #12310-01y20v PRED entity: 01y20v PRED relation: colors PRED expected values: 0jc_p => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.43 #122, 0.43 #182, 0.41 #1002), 01l849 (0.28 #401, 0.28 #1021, 0.26 #1001), 019sc (0.19 #1047, 0.18 #1747, 0.18 #1967), 06fvc (0.18 #183, 0.18 #123, 0.18 #1043), 036k5h (0.15 #5, 0.11 #265, 0.11 #385), 04mkbj (0.13 #130, 0.11 #270, 0.11 #190), 038hg (0.11 #412, 0.10 #832, 0.10 #1012), 09ggk (0.09 #76, 0.08 #36, 0.08 #16), 0jc_p (0.09 #124, 0.08 #404, 0.08 #1064), 04d18d (0.08 #299, 0.08 #19, 0.07 #439) >> Best rule #122 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 02d9nr; >> query: (?x6846, 083jv) <- colors(?x6846, ?x3189), state_province_region(?x6846, ?x3818), currency(?x6846, ?x170), contains(?x94, ?x6846) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 02d9nr; *> query: (?x6846, 0jc_p) <- colors(?x6846, ?x3189), state_province_region(?x6846, ?x3818), currency(?x6846, ?x170), contains(?x94, ?x6846) *> conf = 0.09 ranks of expected_values: 9 EVAL 01y20v colors 0jc_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 165.000 165.000 0.429 http://example.org/education/educational_institution/colors #12309-05c46y6 PRED entity: 05c46y6 PRED relation: film_festivals PRED expected values: 04_m9gk => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 19): 04_m9gk (0.22 #193, 0.21 #213, 0.20 #133), 0gg7gsl (0.21 #221, 0.20 #121, 0.17 #181), 0bmj62v (0.17 #212, 0.14 #232, 0.12 #672), 03wf1p2 (0.14 #674, 0.10 #714, 0.02 #634), 059_y8d (0.13 #122, 0.12 #22, 0.09 #182), 0kfhjq0 (0.12 #645, 0.11 #665, 0.09 #45), 05f5rsr (0.12 #31, 0.07 #131, 0.06 #711), 04grdgy (0.11 #649, 0.10 #629, 0.08 #709), 09rwjly (0.09 #628, 0.09 #668, 0.09 #648), 0g57ws5 (0.09 #667, 0.09 #47, 0.09 #187) >> Best rule #193 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0ggbfwf; >> query: (?x2742, 04_m9gk) <- film_release_region(?x2742, ?x94), film_festivals(?x2742, ?x13076), genre(?x2742, ?x53), nominated_for(?x1914, ?x2742) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05c46y6 film_festivals 04_m9gk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.217 http://example.org/film/film/film_festivals #12308-09q2t PRED entity: 09q2t PRED relation: colors! PRED expected values: 03y5ky => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1281): 0bsnm (0.99 #1906, 0.67 #3840, 0.61 #5291), 025v3k (0.99 #1906, 0.67 #3840, 0.61 #5291), 01jq34 (0.99 #1906, 0.67 #3840, 0.61 #5291), 02vnp2 (0.99 #1906, 0.67 #3840, 0.61 #5291), 02607j (0.99 #1906, 0.67 #3840, 0.61 #5291), 021996 (0.99 #1906, 0.67 #3840, 0.61 #5291), 07wlt (0.99 #1906, 0.67 #3840, 0.61 #5291), 06b19 (0.99 #1906, 0.67 #3840, 0.61 #5291), 07vk2 (0.99 #1906, 0.67 #3840, 0.61 #5291), 01dthg (0.99 #1906, 0.67 #3840, 0.61 #5291) >> Best rule #1906 for best value: >> intensional similarity = 45 >> extensional distance = 2 >> proper extension: 019sc; >> query: (?x8632, ?x331) <- colors(?x12737, ?x8632), colors(?x12669, ?x8632), colors(?x11215, ?x8632), major_field_of_study(?x11215, ?x9093), major_field_of_study(?x11215, ?x1527), institution(?x9054, ?x11215), institution(?x865, ?x11215), ?x1527 = 04_tv, currency(?x11215, ?x170), major_field_of_study(?x10910, ?x9093), major_field_of_study(?x3424, ?x9093), major_field_of_study(?x735, ?x9093), major_field_of_study(?x581, ?x9093), major_field_of_study(?x388, ?x9093), student(?x9093, ?x1188), colors(?x11215, ?x332), fraternities_and_sororities(?x11215, ?x4348), ?x735 = 065y4w7, ?x388 = 05krk, major_field_of_study(?x1368, ?x9093), organization(?x12076, ?x12669), organization(?x346, ?x12737), ?x581 = 06pwq, contains(?x94, ?x11215), student(?x12737, ?x8844), student(?x12669, ?x7587), ?x3424 = 01w5m, institution(?x9054, ?x13680), institution(?x9054, ?x6545), institution(?x9054, ?x5621), institution(?x9054, ?x3416), major_field_of_study(?x9054, ?x4321), state_province_region(?x12737, ?x3824), colors(?x580, ?x332), colors(?x331, ?x332), ?x6545 = 01ky7c, ?x5621 = 01vs5c, ?x13680 = 01nhgd, currency(?x12737, ?x2244), ?x3416 = 02183k, ?x4321 = 0g26h, ?x10910 = 013807, award_winner(?x3306, ?x8844), citytown(?x12669, ?x13212), student(?x865, ?x1117) >> conf = 0.99 => this is the best rule for 113 predicted values *> Best rule #478 for first EXPECTED value: *> intensional similarity = 43 *> extensional distance = 1 *> proper extension: 01g5v; *> query: (?x8632, ?x388) <- colors(?x12669, ?x8632), colors(?x11502, ?x8632), colors(?x11215, ?x8632), colors(?x6548, ?x8632), colors(?x1772, ?x8632), major_field_of_study(?x11215, ?x7070), major_field_of_study(?x11215, ?x2606), major_field_of_study(?x11215, ?x2605), institution(?x9054, ?x11215), institution(?x865, ?x11215), ?x12669 = 0dbns, ?x2606 = 062z7, currency(?x11502, ?x170), contains(?x94, ?x11215), student(?x11215, ?x5904), student(?x11215, ?x3853), category(?x11502, ?x134), organization(?x346, ?x11215), school_type(?x11215, ?x3092), institution(?x8398, ?x11502), institution(?x1200, ?x11502), artists(?x2937, ?x5904), school_type(?x11502, ?x1044), award(?x5904, ?x567), award_winner(?x5904, ?x3492), ?x1200 = 016t_3, ?x7070 = 0mg1w, ?x865 = 02h4rq6, ?x94 = 09c7w0, ?x170 = 09nqf, ?x6548 = 0yls9, student(?x1772, ?x9998), award_nominee(?x5904, ?x2614), ?x2605 = 03g3w, institution(?x9054, ?x1681), institution(?x9054, ?x388), institution(?x8398, ?x4904), gender(?x3853, ?x231), profession(?x3853, ?x296), state_province_region(?x1772, ?x3670), student(?x8398, ?x516), ?x4904 = 0lyjf, ?x1681 = 07szy *> conf = 0.19 ranks of expected_values: 593 EVAL 09q2t colors! 03y5ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 20.000 20.000 0.990 http://example.org/education/educational_institution/colors #12307-01yk13 PRED entity: 01yk13 PRED relation: profession PRED expected values: 0cbd2 09jwl => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 65): 03gjzk (0.38 #3586, 0.35 #4622, 0.30 #311), 01d_h8 (0.38 #600, 0.36 #5649, 0.35 #5501), 0dxtg (0.32 #4029, 0.32 #4621, 0.31 #3585), 02jknp (0.26 #156, 0.25 #5651, 0.24 #1048), 0cbd2 (0.20 #2387, 0.19 #2834, 0.17 #2983), 0np9r (0.20 #4776, 0.20 #4924, 0.17 #317), 02krf9 (0.18 #3598, 0.17 #27, 0.15 #4634), 09jwl (0.18 #8477, 0.17 #8921, 0.17 #11141), 018gz8 (0.17 #165, 0.16 #1354, 0.14 #313), 0kyk (0.16 #2410, 0.16 #3006, 0.16 #1218) >> Best rule #3586 for best value: >> intensional similarity = 3 >> extensional distance = 405 >> proper extension: 0f721s; 0gsg7; 0cjdk; 027_tg; 06jntd; 05gnf; 0283xx2; 01zcrv; 0kctd; 03lpbx; >> query: (?x879, 03gjzk) <- award_winner(?x7511, ?x879), titles(?x2008, ?x7511), country_of_origin(?x7511, ?x94) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2387 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 121 *> proper extension: 01dvtx; 02wxvtv; 04m_kpx; 0c73z; *> query: (?x879, 0cbd2) <- student(?x2605, ?x879), people(?x5741, ?x879), profession(?x879, ?x1032) *> conf = 0.20 ranks of expected_values: 5, 8 EVAL 01yk13 profession 09jwl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 113.000 113.000 0.378 http://example.org/people/person/profession EVAL 01yk13 profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 113.000 113.000 0.378 http://example.org/people/person/profession #12306-02xwq9 PRED entity: 02xwq9 PRED relation: nationality PRED expected values: 09c7w0 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 75): 09c7w0 (0.89 #5934, 0.85 #3619, 0.81 #5126), 07ssc (0.40 #11060, 0.14 #315, 0.08 #1921), 02jx1 (0.40 #11060, 0.10 #1739, 0.10 #1939), 0ndh6 (0.33 #11162, 0.25 #7944), 04ych (0.33 #11162, 0.25 #7944), 0d060g (0.10 #207, 0.08 #2719, 0.07 #7), 03rk0 (0.06 #10701, 0.05 #11208, 0.05 #6479), 0chghy (0.03 #210, 0.03 #8547, 0.03 #6635), 05v8c (0.03 #316, 0.03 #8547, 0.03 #6635), 06q1r (0.03 #277, 0.02 #5001, 0.01 #2890) >> Best rule #5934 for best value: >> intensional similarity = 2 >> extensional distance = 1420 >> proper extension: 07m69t; >> query: (?x4432, 09c7w0) <- place_of_birth(?x4432, ?x4356), source(?x4356, ?x958) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xwq9 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.891 http://example.org/people/person/nationality #12305-07zhjj PRED entity: 07zhjj PRED relation: program! PRED expected values: 0f721s => 82 concepts (71 used for prediction) PRED predicted values (max 10 best out of 264): 03cs_xw (0.69 #3356, 0.69 #3615, 0.40 #1289), 09_99w (0.33 #205, 0.04 #1753, 0.04 #1236), 0bbxd3 (0.33 #224, 0.03 #1772, 0.03 #1255), 01vz80y (0.33 #179, 0.02 #1727, 0.01 #5605), 02f9wb (0.33 #146, 0.01 #1694), 0c01c (0.24 #2582, 0.23 #4651, 0.22 #4652), 0347db (0.24 #2582, 0.23 #4651, 0.22 #4652), 03y82t6 (0.23 #4651, 0.22 #6716, 0.22 #4652), 015f7 (0.23 #4651, 0.22 #6716, 0.22 #4652), 09d5h (0.22 #4652, 0.21 #2581, 0.21 #4650) >> Best rule #3356 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 0dk0dj; 054gwt; >> query: (?x8775, ?x1340) <- genre(?x8775, ?x258), country_of_origin(?x8775, ?x94), program_creator(?x8775, ?x1340) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1312 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 02k_4g; 0c3xpwy; *> query: (?x8775, 0f721s) <- nominated_for(?x2560, ?x8775), program(?x1340, ?x8775), award_winner(?x6684, ?x2560), honored_for(?x1265, ?x8775) *> conf = 0.15 ranks of expected_values: 11 EVAL 07zhjj program! 0f721s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 82.000 71.000 0.694 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/program #12304-0br1x_ PRED entity: 0br1x_ PRED relation: instance_of_recurring_event PRED expected values: 02jp2w => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1): 02jp2w (0.75 #223, 0.75 #215, 0.71 #207) >> Best rule #223 for best value: >> intensional similarity = 82 >> extensional distance = 6 >> proper extension: 0b_770; >> query: (?x6802, 02jp2w) <- team(?x6802, ?x11789), team(?x6802, ?x10846), team(?x6802, ?x9975), team(?x6802, ?x9909), team(?x6802, ?x9833), team(?x6802, ?x8728), team(?x6802, ?x8528), team(?x6802, ?x6803), team(?x6802, ?x4804), team(?x6802, ?x4369), team(?x6802, ?x2303), ?x4369 = 02pqcfz, ?x9833 = 03y9p40, ?x8528 = 091tgz, team(?x12451, ?x4804), team(?x10736, ?x4804), team(?x2302, ?x4804), position(?x11789, ?x1348), team(?x13209, ?x11789), team(?x10594, ?x11789), team(?x9974, ?x11789), team(?x9146, ?x11789), ?x10594 = 0b_756, team(?x6848, ?x9975), ?x6803 = 03by7wc, ?x10736 = 0f9rw9, ?x13209 = 0b_734, teams(?x6088, ?x4804), colors(?x4804, ?x3189), ?x8728 = 026xxv_, position(?x9975, ?x1579), team(?x1348, ?x12124), team(?x1348, ?x9049), team(?x1348, ?x7158), team(?x1348, ?x5419), team(?x1348, ?x1578), team(?x1348, ?x1347), team(?x1348, ?x799), team(?x1348, ?x660), position(?x8228, ?x1348), position(?x2820, ?x1348), ?x5419 = 0jmmn, ?x2820 = 0jmj7, ?x9049 = 0jmm4, ?x12124 = 0jmgb, ?x660 = 0jmdb, ?x2302 = 0b_77q, colors(?x9975, ?x9778), ?x7158 = 0jm4v, ?x9909 = 026wlnm, locations(?x9146, ?x5259), ?x9974 = 0b_6pv, ?x5259 = 0d9y6, ?x1579 = 0ctt4z, team(?x9956, ?x2303), ?x12451 = 0b_6xf, ?x3189 = 01g5v, ?x799 = 0jm3v, sport(?x10846, ?x12913), ?x9956 = 0bzrsh, ?x8228 = 0jmcv, ?x6848 = 02_ssl, colors(?x2303, ?x1101), ?x1347 = 0jmfv, ?x1578 = 0jm2v, teams(?x2740, ?x2303), colors(?x13580, ?x1101), colors(?x7286, ?x1101), colors(?x7122, ?x1101), colors(?x11452, ?x1101), colors(?x7816, ?x1101), colors(?x3949, ?x1101), colors(?x1440, ?x1101), colors(?x481, ?x1101), ?x7286 = 01z1r, ?x13580 = 01_1kk, ?x3949 = 01ymvk, ?x7122 = 01zhs3, ?x481 = 052nd, ?x11452 = 03k7dn, ?x7816 = 015y3j, ?x1440 = 017zq0 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0br1x_ instance_of_recurring_event 02jp2w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.750 http://example.org/time/event/instance_of_recurring_event #12303-0cbvg PRED entity: 0cbvg PRED relation: films PRED expected values: 0b2qtl => 77 concepts (37 used for prediction) PRED predicted values (max 10 best out of 600): 01ry_x (0.33 #504, 0.03 #15859, 0.02 #16919), 0_7w6 (0.33 #87, 0.03 #15442, 0.02 #16502), 02xbyr (0.33 #235, 0.03 #15590, 0.02 #16650), 0k4d7 (0.33 #119, 0.03 #15474, 0.02 #16534), 0jnwx (0.33 #84, 0.03 #15439, 0.02 #16499), 08hmch (0.18 #3749, 0.13 #4807, 0.11 #6927), 02q0k7v (0.14 #2511, 0.12 #3040, 0.09 #4098), 02x3y41 (0.14 #2519, 0.12 #3048, 0.09 #4106), 0404j37 (0.14 #2445, 0.12 #2974, 0.09 #4032), 0fjyzt (0.13 #5035, 0.11 #8213, 0.10 #8744) >> Best rule #504 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 0bxg3; >> query: (?x10008, 01ry_x) <- films(?x10008, ?x3612), films(?x10008, ?x1224), ?x3612 = 04z257, nominated_for(?x500, ?x1224), language(?x1224, ?x254), film(?x166, ?x1224), ?x500 = 0p9sw, film_crew_role(?x1224, ?x468), ?x468 = 02r96rf >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cbvg films 0b2qtl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 37.000 0.333 http://example.org/film/film_subject/films #12302-0661m4p PRED entity: 0661m4p PRED relation: film! PRED expected values: 06w2sn5 07rd7 01kwsg => 97 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1292): 0q9kd (0.38 #35210, 0.12 #8288, 0.11 #14501), 0738b8 (0.38 #35210, 0.08 #2472, 0.06 #16969), 0gn30 (0.38 #35210, 0.08 #3014, 0.05 #29938), 02661h (0.38 #35210, 0.04 #24173, 0.03 #17960), 02_hj4 (0.38 #35210, 0.04 #23047, 0.02 #47903), 01k8rb (0.38 #35210, 0.04 #23002, 0.02 #47858), 01kwsg (0.38 #35210, 0.04 #80782, 0.03 #78709), 02tqkf (0.38 #35210, 0.03 #27428, 0.03 #31570), 06pj8 (0.38 #35210, 0.02 #23125, 0.02 #56265), 02j4sk (0.38 #35210, 0.02 #24512, 0.02 #28655) >> Best rule #35210 for best value: >> intensional similarity = 5 >> extensional distance = 70 >> proper extension: 02z3r8t; 0g5pv3; 02mc5v; 0g5ptf; >> query: (?x2350, ?x1397) <- prequel(?x2350, ?x1673), featured_film_locations(?x2350, ?x739), film(?x488, ?x2350), film(?x1397, ?x1673), film(?x541, ?x2350) >> conf = 0.38 => this is the best rule for 17 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7 EVAL 0661m4p film! 01kwsg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 71.000 0.383 http://example.org/film/actor/film./film/performance/film EVAL 0661m4p film! 07rd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 71.000 0.383 http://example.org/film/actor/film./film/performance/film EVAL 0661m4p film! 06w2sn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 71.000 0.383 http://example.org/film/actor/film./film/performance/film #12301-0bt3j9 PRED entity: 0bt3j9 PRED relation: film_release_region PRED expected values: 0jgd 03_3d 02k54 03gj2 06qd3 03spz => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 114): 03_3d (0.90 #438, 0.87 #726, 0.85 #149), 03gj2 (0.87 #309, 0.87 #1174, 0.87 #597), 0jgd (0.86 #1157, 0.80 #436, 0.80 #724), 03spz (0.85 #374, 0.85 #85, 0.83 #662), 06mzp (0.80 #160, 0.67 #737, 0.66 #449), 06qd3 (0.70 #753, 0.70 #176, 0.68 #465), 01p1v (0.70 #331, 0.67 #619, 0.66 #1196), 04gzd (0.68 #297, 0.67 #585, 0.60 #1162), 016wzw (0.66 #344, 0.63 #1209, 0.62 #632), 0h7x (0.65 #173, 0.61 #750, 0.60 #462) >> Best rule #438 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 0ds3t5x; 0g5qs2k; 0gkz15s; 017gl1; 01c22t; 053rxgm; 017gm7; 0d6b7; 0ch26b_; 05qbckf; ... >> query: (?x5142, 03_3d) <- nominated_for(?x143, ?x5142), film_release_region(?x5142, ?x756), ?x756 = 06npd, film_crew_role(?x5142, ?x468) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 17 EVAL 0bt3j9 film_release_region 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bt3j9 film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bt3j9 film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bt3j9 film_release_region 02k54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 83.000 83.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bt3j9 film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bt3j9 film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12300-01jt2w PRED entity: 01jt2w PRED relation: major_field_of_study PRED expected values: 02_7t => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 124): 01mkq (0.63 #1474, 0.61 #1110, 0.54 #866), 0g26h (0.58 #525, 0.52 #648, 0.50 #1013), 02lp1 (0.58 #1106, 0.53 #1470, 0.51 #495), 02_7t (0.51 #547, 0.40 #1400, 0.39 #792), 04rjg (0.50 #1479, 0.47 #2209, 0.45 #1115), 062z7 (0.44 #1122, 0.43 #1486, 0.39 #1364), 01tbp (0.37 #542, 0.37 #1517, 0.32 #1153), 01lj9 (0.37 #1497, 0.35 #889, 0.34 #1133), 05qjt (0.37 #1102, 0.35 #858, 0.34 #2196), 04x_3 (0.35 #510, 0.34 #1485, 0.33 #755) >> Best rule #1474 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 065y4w7; 0f102; 04hgpt; 017cy9; 08qnnv; 01n_g9; 01hx2t; 01qrb2; >> query: (?x7707, 01mkq) <- student(?x7707, ?x1145), institution(?x3437, ?x7707), ?x3437 = 02_xgp2, major_field_of_study(?x7707, ?x373), school(?x799, ?x7707) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #547 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 41 *> proper extension: 04bfg; *> query: (?x7707, 02_7t) <- currency(?x7707, ?x170), institution(?x1200, ?x7707), institution(?x620, ?x7707), ?x620 = 07s6fsf, ?x1200 = 016t_3 *> conf = 0.51 ranks of expected_values: 4 EVAL 01jt2w major_field_of_study 02_7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 138.000 138.000 0.629 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #12299-05kms PRED entity: 05kms PRED relation: instrumentalists PRED expected values: 04mky3 => 74 concepts (47 used for prediction) PRED predicted values (max 10 best out of 987): 018x3 (0.69 #4947, 0.57 #11125, 0.56 #618), 045zr (0.69 #4947, 0.57 #11125, 0.50 #620), 016ntp (0.60 #3273, 0.56 #618, 0.50 #7598), 0473q (0.60 #3498, 0.50 #5967, 0.50 #2883), 018gkb (0.60 #3666, 0.50 #6135, 0.50 #3051), 01nqfh_ (0.60 #3117, 0.50 #5586, 0.50 #1881), 09prnq (0.60 #4449, 0.50 #2597, 0.50 #620), 01lvcs1 (0.56 #618, 0.56 #10712, 0.50 #2683), 01vrncs (0.56 #618, 0.50 #7468, 0.50 #2528), 01vn35l (0.56 #618, 0.50 #7580, 0.50 #2640) >> Best rule #4947 for best value: >> intensional similarity = 21 >> extensional distance = 3 >> proper extension: 01wy6; 0l14j_; >> query: (?x6039, ?x2662) <- role(?x5417, ?x6039), role(?x2956, ?x6039), role(?x645, ?x6039), role(?x74, ?x6039), role(?x6039, ?x315), ?x645 = 028tv0, role(?x6039, ?x6938), role(?x6039, ?x2798), ?x6938 = 023r2x, role(?x2785, ?x2956), instrumentalists(?x6039, ?x3030), ?x74 = 03q5t, role(?x2888, ?x2956), role(?x1225, ?x2956), instrumentalists(?x5417, ?x367), ?x2785 = 0jtg0, ?x2888 = 02fsn, role(?x2662, ?x6039), group(?x5417, ?x2906), ?x1225 = 01qbl, ?x2798 = 03qjg >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #618 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x6039, ?x4019) <- role(?x5417, ?x6039), role(?x2956, ?x6039), role(?x2460, ?x6039), role(?x2309, ?x6039), role(?x2157, ?x6039), role(?x6039, ?x1166), ?x2956 = 0myk8, role(?x2798, ?x6039), ?x5417 = 02w3w, ?x2460 = 01wy6, ?x2309 = 06ncr, ?x2798 = 03qjg, ?x2157 = 011_6p, instrumentalists(?x6039, ?x3030), instrumentalists(?x1166, ?x4620), instrumentalists(?x1166, ?x3316), ?x4620 = 01vsy7t, role(?x1166, ?x3214), role(?x925, ?x1166), artists(?x378, ?x3316), group(?x1166, ?x442), role(?x248, ?x1166), performance_role(?x4019, ?x3214) *> conf = 0.56 ranks of expected_values: 47 EVAL 05kms instrumentalists 04mky3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 74.000 47.000 0.690 http://example.org/music/instrument/instrumentalists #12298-0992d9 PRED entity: 0992d9 PRED relation: film_release_region PRED expected values: 09c7w0 02vzc => 90 concepts (88 used for prediction) PRED predicted values (max 10 best out of 311): 0d0vqn (0.95 #851, 0.91 #1187, 0.89 #1692), 09c7w0 (0.93 #3034, 0.92 #11789, 0.92 #11621), 059j2 (0.89 #1217, 0.89 #2228, 0.88 #1722), 05r4w (0.89 #1516, 0.88 #675, 0.88 #1684), 05qhw (0.88 #693, 0.88 #861, 0.85 #1197), 03gj2 (0.88 #706, 0.87 #874, 0.87 #1210), 035qy (0.86 #884, 0.86 #1220, 0.84 #2231), 06bnz (0.86 #898, 0.81 #730, 0.80 #1234), 0chghy (0.85 #856, 0.83 #1697, 0.83 #1529), 07ssc (0.84 #863, 0.82 #2210, 0.81 #1199) >> Best rule #851 for best value: >> intensional similarity = 5 >> extensional distance = 112 >> proper extension: 0bmc4cm; >> query: (?x5730, 0d0vqn) <- genre(?x5730, ?x225), film_release_region(?x5730, ?x4743), ?x4743 = 03spz, genre(?x3055, ?x225), ?x3055 = 0x25q >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #3034 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 268 *> proper extension: 0bhwhj; 012jfb; *> query: (?x5730, 09c7w0) <- genre(?x5730, ?x3613), films(?x2008, ?x5730), film_release_region(?x5730, ?x172), film_release_distribution_medium(?x5730, ?x81), titles(?x3613, ?x253) *> conf = 0.93 ranks of expected_values: 2, 11 EVAL 0992d9 film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 90.000 88.000 0.947 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0992d9 film_release_region 09c7w0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 88.000 0.947 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12297-04h68j PRED entity: 04h68j PRED relation: award PRED expected values: 0gkr9q => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 248): 0gs9p (0.41 #2915, 0.41 #5751, 0.40 #7371), 019f4v (0.39 #7358, 0.38 #2902, 0.38 #5738), 040njc (0.37 #5679, 0.37 #4869, 0.36 #2843), 0gq9h (0.34 #2913, 0.30 #5749, 0.29 #4939), 0gr4k (0.30 #8540, 0.28 #5299, 0.28 #7730), 0gr51 (0.27 #8608, 0.26 #6177, 0.26 #4151), 04dn09n (0.27 #8551, 0.25 #5310, 0.24 #7741), 09sb52 (0.25 #15839, 0.24 #16244, 0.23 #17054), 0ck27z (0.25 #1308, 0.24 #13055, 0.23 #13461), 0cjyzs (0.25 #1727, 0.21 #3752, 0.19 #3347) >> Best rule #2915 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 042l3v; 0h1p; 02645b; 0kvqv; 07g7h2; 01v5h; 03_2y; 06y0xx; >> query: (?x10216, 0gs9p) <- film(?x10216, ?x7366), type_of_union(?x10216, ?x566), award_nominee(?x10216, ?x2643) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #7697 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 236 *> proper extension: 0m32_; *> query: (?x10216, ?x435) <- film(?x10216, ?x7366), nominated_for(?x10216, ?x10089), nominated_for(?x435, ?x10089) *> conf = 0.21 ranks of expected_values: 18 EVAL 04h68j award 0gkr9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 79.000 79.000 0.414 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12296-0jnwx PRED entity: 0jnwx PRED relation: film! PRED expected values: 06b4wb => 64 concepts (21 used for prediction) PRED predicted values (max 10 best out of 564): 02fgpf (0.35 #27074, 0.28 #35408, 0.28 #31240), 0dzf_ (0.35 #27074, 0.28 #35408, 0.28 #22907), 0mfj2 (0.20 #1535, 0.07 #3617, 0.07 #5700), 01f5q5 (0.20 #1913, 0.07 #3995, 0.07 #6078), 01pj5q (0.20 #1345, 0.07 #3427, 0.07 #5510), 078g3l (0.20 #1116, 0.07 #3198, 0.07 #5281), 01b9z4 (0.20 #1647, 0.07 #5812, 0.06 #7895), 0mbs8 (0.20 #1951, 0.07 #6116, 0.06 #8199), 0mdyn (0.20 #1367, 0.07 #5532, 0.06 #7615), 03n_7k (0.20 #398, 0.07 #4563, 0.06 #6646) >> Best rule #27074 for best value: >> intensional similarity = 4 >> extensional distance = 306 >> proper extension: 06mmr; >> query: (?x1893, ?x1894) <- award(?x1893, ?x1443), award_winner(?x1893, ?x1894), nominated_for(?x1443, ?x1452), ?x1452 = 0jqn5 >> conf = 0.35 => this is the best rule for 2 predicted values *> Best rule #16513 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 104 *> proper extension: 05jf85; 011yxg; 07gp9; 0bth54; 08720; 0dqytn; 0fg04; 01hp5; 061681; 08gsvw; ... *> query: (?x1893, 06b4wb) <- nominated_for(?x640, ?x1893), genre(?x1893, ?x258), ?x640 = 02hsq3m, film(?x3758, ?x1893) *> conf = 0.02 ranks of expected_values: 324 EVAL 0jnwx film! 06b4wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 21.000 0.349 http://example.org/film/actor/film./film/performance/film #12295-0gx1bnj PRED entity: 0gx1bnj PRED relation: country PRED expected values: 03ryn => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 199): 07ssc (0.26 #2165, 0.25 #2755, 0.24 #3640), 0f8l9c (0.19 #2444, 0.17 #4217, 0.17 #4577), 0chghy (0.19 #2444, 0.17 #4217, 0.17 #4577), 059j2 (0.19 #2444, 0.17 #4217, 0.17 #4577), 05r4w (0.19 #2444, 0.17 #4217, 0.14 #4156), 03rjj (0.19 #2444, 0.17 #4577, 0.11 #2563), 015fr (0.19 #2444, 0.17 #4577, 0.11 #2563), 05qhw (0.19 #2444, 0.17 #4577, 0.11 #4278), 03h64 (0.19 #2444, 0.14 #4156, 0.11 #2563), 0d0vqn (0.19 #2444, 0.11 #2563, 0.11 #4278) >> Best rule #2165 for best value: >> intensional similarity = 5 >> extensional distance = 588 >> proper extension: 0qm8b; 09tqkv2; 0g3zrd; 07w8fz; 0194zl; 0gs973; 0y_9q; 0415ggl; 04h41v; 0dgq_kn; ... >> query: (?x343, 07ssc) <- film(?x237, ?x343), genre(?x343, ?x53), film_crew_role(?x343, ?x137), country(?x343, ?x94), ?x53 = 07s9rl0 >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #1618 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 299 *> proper extension: 09p35z; 05c5z8j; 06nr2h; 03bzyn4; *> query: (?x343, ?x94) <- language(?x343, ?x254), genre(?x343, ?x258), production_companies(?x343, ?x1414), ?x258 = 05p553, film(?x1414, ?x5856), titles(?x812, ?x5856), film_release_region(?x5856, ?x94) *> conf = 0.02 ranks of expected_values: 89 EVAL 0gx1bnj country 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 90.000 90.000 0.264 http://example.org/film/film/country #12294-032_jg PRED entity: 032_jg PRED relation: nominated_for PRED expected values: 0418wg => 129 concepts (56 used for prediction) PRED predicted values (max 10 best out of 665): 0418wg (0.33 #1622, 0.30 #56729, 0.25 #43757), 011ypx (0.33 #1622, 0.30 #56729, 0.25 #43757), 09xbpt (0.33 #1622, 0.30 #56729, 0.25 #43757), 0gd92 (0.33 #1622, 0.30 #56729, 0.25 #43757), 051ys82 (0.33 #1622, 0.30 #56729, 0.25 #43757), 07pd_j (0.33 #1622, 0.30 #56729, 0.25 #43757), 0gtsxr4 (0.33 #1622, 0.30 #56729, 0.25 #43757), 0dsvzh (0.33 #1622, 0.30 #56729, 0.25 #43757), 04vr_f (0.32 #158, 0.05 #8262, 0.03 #1780), 0180mw (0.23 #4282, 0.05 #1039, 0.02 #31828) >> Best rule #1622 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 02p65p; 09fb5; 0bxtg; 0151w_; 01fwj8; 0hvb2; 0dvmd; 0gy6z9; 07yp0f; 02yxwd; ... >> query: (?x875, ?x349) <- award_nominee(?x875, ?x2422), film(?x875, ?x349), ?x2422 = 0169dl >> conf = 0.33 => this is the best rule for 8 predicted values ranks of expected_values: 1 EVAL 032_jg nominated_for 0418wg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 56.000 0.330 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12293-04hvw PRED entity: 04hvw PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 21): 060bp (0.76 #111, 0.73 #45, 0.72 #23), 060c4 (0.72 #487, 0.72 #421, 0.71 #311), 0pqc5 (0.36 #1655, 0.13 #1391, 0.08 #1457), 0fj45 (0.34 #41, 0.29 #63, 0.26 #129), 0f6c3 (0.31 #821, 0.28 #601, 0.28 #1085), 0fkvn (0.28 #818, 0.27 #1082, 0.25 #598), 09n5b9 (0.26 #825, 0.24 #605, 0.23 #1089), 0dq3c (0.19 #2, 0.16 #442, 0.16 #134), 0p5vf (0.19 #12, 0.11 #188, 0.11 #56), 01zq91 (0.10 #432, 0.10 #322, 0.09 #498) >> Best rule #111 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 0168t; >> query: (?x11774, 060bp) <- country(?x668, ?x11774), form_of_government(?x11774, ?x1926), ?x1926 = 018wl5 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04hvw jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.760 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #12292-014g91 PRED entity: 014g91 PRED relation: people! PRED expected values: 097ns => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 42): 0gk4g (0.24 #725, 0.23 #2610, 0.22 #270), 0qcr0 (0.12 #716, 0.12 #1171, 0.12 #2601), 04p3w (0.12 #206, 0.08 #1181, 0.08 #1701), 02k6hp (0.09 #296, 0.08 #231, 0.07 #1921), 01l2m3 (0.09 #276, 0.06 #16, 0.06 #211), 02knxx (0.09 #291, 0.05 #2761, 0.05 #2631), 02y0js (0.08 #2537, 0.08 #1887, 0.07 #3772), 01_qc_ (0.08 #222, 0.04 #2562, 0.04 #1652), 09jg8 (0.06 #33, 0.02 #228, 0.01 #1268), 04psf (0.06 #202, 0.03 #2542, 0.02 #2737) >> Best rule #725 for best value: >> intensional similarity = 3 >> extensional distance = 239 >> proper extension: 014kg4; 018qql; >> query: (?x10879, 0gk4g) <- award(?x10879, ?x2561), place_of_birth(?x10879, ?x12314), people(?x6260, ?x10879) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #221 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 042d1; *> query: (?x10879, 097ns) <- people(?x6260, ?x10879), place_of_death(?x10879, ?x739), ?x739 = 02_286 *> conf = 0.02 ranks of expected_values: 28 EVAL 014g91 people! 097ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 129.000 129.000 0.237 http://example.org/people/cause_of_death/people #12291-05sb1 PRED entity: 05sb1 PRED relation: jurisdiction_of_office! PRED expected values: 060c4 => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.85 #883, 0.73 #1060, 0.72 #2275), 0pqc5 (0.77 #2078, 0.38 #2100, 0.36 #3135), 0f6c3 (0.49 #734, 0.46 #954, 0.45 #932), 09n5b9 (0.49 #738, 0.45 #936, 0.41 #958), 0fkvn (0.42 #730, 0.40 #1568, 0.38 #928), 0p5vf (0.26 #78, 0.24 #56, 0.23 #321), 04syw (0.25 #689, 0.22 #491, 0.21 #1438), 0dq3c (0.22 #67, 0.21 #266, 0.18 #332), 01zq91 (0.18 #235, 0.17 #80, 0.16 #301), 0fj45 (0.18 #63, 0.17 #702, 0.14 #504) >> Best rule #883 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 02wm6l; >> query: (?x2236, 060c4) <- form_of_government(?x2236, ?x48), ?x48 = 06cx9 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05sb1 jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.849 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #12290-0d6b7 PRED entity: 0d6b7 PRED relation: film_release_region PRED expected values: 03_3d 07ssc => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 134): 059j2 (0.86 #1960, 0.85 #2762, 0.84 #354), 03gj2 (0.84 #25, 0.78 #1792, 0.77 #1952), 015fr (0.80 #1944, 0.78 #17, 0.77 #2746), 03_3d (0.79 #1773, 0.79 #6, 0.76 #2735), 07ssc (0.79 #1942, 0.78 #336, 0.78 #15), 0154j (0.78 #1931, 0.76 #1771, 0.76 #2733), 01znc_ (0.76 #1971, 0.73 #2773, 0.73 #1811), 03h64 (0.75 #1996, 0.75 #1836, 0.75 #69), 084n_ (0.74 #2088, 0.73 #2890, 0.60 #8993), 05b4w (0.73 #67, 0.71 #1834, 0.70 #1994) >> Best rule #1960 for best value: >> intensional similarity = 5 >> extensional distance = 207 >> proper extension: 0ds3t5x; 07g_0c; 0407yfx; 047svrl; 05q4y12; 02vr3gz; 02prwdh; 07l50vn; 0cc97st; 01mgw; ... >> query: (?x1546, 059j2) <- country(?x1546, ?x1264), film_release_region(?x1546, ?x1353), film_release_region(?x1546, ?x390), ?x1353 = 035qy, ?x390 = 0chghy >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1773 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 173 *> proper extension: 02d44q; 0gh8zks; 07k2mq; 0372j5; *> query: (?x1546, 03_3d) <- language(?x1546, ?x12328), nominated_for(?x7088, ?x1546), film_release_region(?x1546, ?x1353), ?x1353 = 035qy *> conf = 0.79 ranks of expected_values: 4, 5 EVAL 0d6b7 film_release_region 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 88.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d6b7 film_release_region 03_3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 88.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12289-042rnl PRED entity: 042rnl PRED relation: type_of_union PRED expected values: 04ztj => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.82 #25, 0.80 #29, 0.76 #57), 01g63y (0.20 #34, 0.17 #10, 0.12 #74) >> Best rule #25 for best value: >> intensional similarity = 6 >> extensional distance = 77 >> proper extension: 0k_mt; 072vj; >> query: (?x754, 04ztj) <- profession(?x754, ?x1032), profession(?x754, ?x987), ?x987 = 0dxtg, ?x1032 = 02hrh1q, award_winner(?x5923, ?x754), film(?x754, ?x755) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042rnl type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.823 http://example.org/people/person/spouse_s./people/marriage/type_of_union #12288-060bp PRED entity: 060bp PRED relation: jurisdiction_of_office PRED expected values: 0h3y 0chghy 03_r3 07ssc 06qd3 0169t 04j53 0bjv6 087vz 06v36 05cc1 06dfg 06m_5 04vjh 04hvw 03f2w => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 619): 09c7w0 (0.60 #684, 0.58 #2743, 0.54 #3432), 07ssc (0.60 #684, 0.53 #3430, 0.52 #3428), 0d05w3 (0.60 #684, 0.50 #754, 0.34 #5143), 0j4b (0.60 #684, 0.50 #857, 0.34 #5143), 06tw8 (0.60 #684, 0.50 #837, 0.34 #5143), 047t_ (0.60 #684, 0.50 #813, 0.33 #470), 0345h (0.60 #684, 0.34 #5143, 0.33 #379), 0chghy (0.60 #684, 0.34 #5143, 0.33 #10), 0b90_r (0.60 #684, 0.34 #5143, 0.33 #346), 0jdd (0.60 #684, 0.34 #5143, 0.33 #446) >> Best rule #684 for best value: >> intensional similarity = 25 >> extensional distance = 1 >> proper extension: 060c4; >> query: (?x182, ?x1264) <- basic_title(?x1328, ?x182), jurisdiction_of_office(?x182, ?x10457), jurisdiction_of_office(?x182, ?x8588), jurisdiction_of_office(?x182, ?x8558), jurisdiction_of_office(?x182, ?x8033), jurisdiction_of_office(?x182, ?x6923), jurisdiction_of_office(?x182, ?x2979), jurisdiction_of_office(?x182, ?x2152), jurisdiction_of_office(?x182, ?x1174), jurisdiction_of_office(?x182, ?x1061), jurisdiction_of_office(?x182, ?x1003), jurisdiction_of_office(?x182, ?x789), jurisdiction_of_office(?x182, ?x87), ?x2979 = 056vv, exported_to(?x6923, ?x1264), ?x10457 = 0162b, ?x87 = 05r4w, ?x1061 = 04v3q, ?x789 = 0f8l9c, ?x8588 = 0jhd, ?x8558 = 027jk, ?x1003 = 03gj2, ?x2152 = 06mkj, organization(?x1174, ?x127), ?x8033 = 04hhv >> conf = 0.60 => this is the best rule for 15 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 8, 11, 25, 28, 40, 42, 45, 49, 52, 60, 90, 123, 210, 211, 251 EVAL 060bp jurisdiction_of_office 03f2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 04hvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 04vjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 06m_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 06dfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 05cc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 06v36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 087vz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 0bjv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 04j53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 0169t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 06qd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 03_r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 060bp jurisdiction_of_office 0h3y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 18.000 18.000 0.603 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #12287-0mmp3 PRED entity: 0mmp3 PRED relation: parent_genre! PRED expected values: 0y3_8 0163zw => 70 concepts (28 used for prediction) PRED predicted values (max 10 best out of 261): 0283d (0.48 #3408, 0.15 #4437, 0.14 #6163), 0g_bh (0.44 #2405, 0.39 #2661, 0.33 #617), 0bt7w (0.40 #1109, 0.33 #1364, 0.33 #598), 03xnwz (0.40 #1049, 0.33 #1304, 0.33 #538), 05jt_ (0.38 #1633, 0.33 #357, 0.30 #2143), 04f73rc (0.38 #1751, 0.30 #2261, 0.30 #2006), 0y3_8 (0.33 #1317, 0.33 #297, 0.25 #1573), 01gbcf (0.33 #515, 0.33 #261, 0.25 #2303), 0xjl2 (0.33 #549, 0.33 #295, 0.25 #804), 01243b (0.33 #547, 0.33 #293, 0.25 #802) >> Best rule #3408 for best value: >> intensional similarity = 6 >> extensional distance = 21 >> proper extension: 06__c; >> query: (?x7220, 0283d) <- parent_genre(?x4711, ?x7220), artists(?x4711, ?x8636), artists(?x4711, ?x4712), ?x8636 = 0k60, role(?x4712, ?x227), profession(?x4712, ?x220) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1317 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 0y3_8; *> query: (?x7220, 0y3_8) <- artists(?x7220, ?x9706), artists(?x7220, ?x8199), ?x9706 = 01fchy, award(?x8199, ?x1565), parent_genre(?x2439, ?x7220), category(?x8199, ?x134), ?x1565 = 01c4_6 *> conf = 0.33 ranks of expected_values: 7, 84 EVAL 0mmp3 parent_genre! 0163zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 70.000 28.000 0.478 http://example.org/music/genre/parent_genre EVAL 0mmp3 parent_genre! 0y3_8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 70.000 28.000 0.478 http://example.org/music/genre/parent_genre #12286-06y9bd PRED entity: 06y9bd PRED relation: gender PRED expected values: 02zsn => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #7, 0.84 #11, 0.84 #23), 02zsn (0.29 #44, 0.28 #38, 0.28 #52) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 05g8ky; 02ndbd; 014zfs; 04n7njg; 03ft8; 01pcmd; 02_2v2; 0q5hw; 01jbx1; 01wyy_; ... >> query: (?x10160, 05zppz) <- student(?x735, ?x10160), place_of_birth(?x10160, ?x1860), program_creator(?x5808, ?x10160) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #44 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 566 *> proper extension: 0kzy0; 02whj; 01ky2h; 01vvpjj; 0137g1; 057hz; 014z8v; 015qt5; 015xp4; 01qgry; ... *> query: (?x10160, 02zsn) <- award_winner(?x4781, ?x10160), people(?x2510, ?x10160), profession(?x10160, ?x524) *> conf = 0.29 ranks of expected_values: 2 EVAL 06y9bd gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 105.000 0.860 http://example.org/people/person/gender #12285-01dbgw PRED entity: 01dbgw PRED relation: profession PRED expected values: 02hrh1q => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 57): 02hrh1q (0.92 #916, 0.89 #1516, 0.88 #3016), 03gjzk (0.34 #3617, 0.33 #2417, 0.23 #5267), 01d_h8 (0.32 #1057, 0.32 #1357, 0.32 #2557), 0dxtg (0.32 #1365, 0.31 #1215, 0.29 #3615), 0dz3r (0.31 #152, 0.09 #7654, 0.09 #7954), 016z4k (0.28 #154, 0.12 #2105, 0.11 #1805), 09jwl (0.25 #170, 0.16 #4821, 0.16 #4221), 02jknp (0.23 #1059, 0.23 #608, 0.22 #1209), 0np9r (0.20 #3473, 0.20 #322, 0.20 #3773), 0nbcg (0.19 #183, 0.10 #9335, 0.10 #5734) >> Best rule #916 for best value: >> intensional similarity = 2 >> extensional distance = 115 >> proper extension: 02rmxx; 02l0sf; 01kgg9; >> query: (?x12183, 02hrh1q) <- award(?x12183, ?x1972), ?x1972 = 0gqyl >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dbgw profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.923 http://example.org/people/person/profession #12284-013zs9 PRED entity: 013zs9 PRED relation: film PRED expected values: 050xxm => 69 concepts (33 used for prediction) PRED predicted values (max 10 best out of 443): 011yxg (0.05 #51918, 0.04 #37591, 0.04 #39382), 0b44shh (0.05 #51918, 0.04 #37591, 0.04 #39382), 0qf2t (0.05 #51918, 0.04 #37591, 0.04 #39382), 05mrf_p (0.05 #51918, 0.04 #37591, 0.04 #39382), 07tj4c (0.05 #51918, 0.04 #37591, 0.04 #39382), 083shs (0.05 #51918, 0.03 #51919, 0.03 #55501), 0fjyzt (0.05 #51918, 0.03 #51919, 0.03 #55501), 0p7pw (0.05 #51918, 0.03 #51919, 0.03 #55501), 084302 (0.05 #51918, 0.03 #51919, 0.03 #55501), 0sxns (0.05 #51918, 0.03 #51919, 0.03 #55501) >> Best rule #51918 for best value: >> intensional similarity = 5 >> extensional distance = 1704 >> proper extension: 03jvmp; 0g5lhl7; 01w92; 05xbx; 04glx0; >> query: (?x8702, ?x5074) <- award_nominee(?x3580, ?x8702), award_nominee(?x2938, ?x8702), award_winner(?x944, ?x2938), nominated_for(?x3580, ?x308), award_winner(?x5074, ?x3580) >> conf = 0.05 => this is the best rule for 12 predicted values *> Best rule #18175 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 395 *> proper extension: 0785v8; 04sx9_; 03f1zdw; 01v42g; 030znt; 01hkhq; 03q1vd; 01438g; 050t68; 01z7_f; ... *> query: (?x8702, 050xxm) <- award_nominee(?x3580, ?x8702), award(?x8702, ?x704), student(?x9844, ?x3580), ?x704 = 09sb52 *> conf = 0.02 ranks of expected_values: 192 EVAL 013zs9 film 050xxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 69.000 33.000 0.046 http://example.org/film/actor/film./film/performance/film #12283-02py_sj PRED entity: 02py_sj PRED relation: award! PRED expected values: 02_1q9 => 33 concepts (17 used for prediction) PRED predicted values (max 10 best out of 753): 01b64v (0.78 #1298, 0.68 #2041, 0.53 #1020), 01b65l (0.78 #1430, 0.33 #409, 0.15 #3469), 02_1rq (0.68 #2041, 0.67 #1070, 0.53 #1020), 02_1kl (0.68 #2041, 0.53 #1020, 0.44 #1742), 0147w8 (0.68 #2041, 0.53 #1020, 0.39 #4080), 01q_y0 (0.50 #2267, 0.21 #3286, 0.19 #4306), 0d68qy (0.50 #2287, 0.18 #3306, 0.16 #4326), 0l76z (0.44 #2500, 0.18 #3519, 0.16 #4539), 0330r (0.44 #2949, 0.18 #3968, 0.16 #4988), 030cx (0.44 #2491, 0.18 #3510, 0.16 #4530) >> Best rule #1298 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 02q1tc5; 02pzz3p; 02pz3j5; 02pzxlw; 027qq9b; 02p_04b; >> query: (?x9869, 01b64v) <- award(?x9029, ?x9869), program_creator(?x9029, ?x2176), nominated_for(?x588, ?x9029), nominated_for(?x9869, ?x589), ceremony(?x9869, ?x2751), program(?x2062, ?x9029), ?x588 = 02p_7cr >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1056 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 02q1tc5; 02pzz3p; 02pz3j5; 02pzxlw; 027qq9b; 02p_04b; *> query: (?x9869, 02_1q9) <- award(?x9029, ?x9869), program_creator(?x9029, ?x2176), nominated_for(?x588, ?x9029), nominated_for(?x9869, ?x589), ceremony(?x9869, ?x2751), program(?x2062, ?x9029), ?x588 = 02p_7cr *> conf = 0.33 ranks of expected_values: 18 EVAL 02py_sj award! 02_1q9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 33.000 17.000 0.778 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12282-0f8l9c PRED entity: 0f8l9c PRED relation: combatants! PRED expected values: 081pw => 292 concepts (292 used for prediction) PRED predicted values (max 10 best out of 59): 081pw (0.75 #898, 0.61 #1913, 0.58 #2249), 03jqfx (0.64 #2474, 0.58 #7927, 0.56 #5619), 06k75 (0.33 #910, 0.33 #406, 0.25 #1248), 0d06vc (0.33 #397, 0.32 #1689, 0.25 #1239), 01y998 (0.33 #467, 0.22 #747, 0.17 #915), 07_nf (0.30 #1927, 0.25 #912, 0.24 #2996), 0gfq9 (0.29 #567, 0.17 #903, 0.17 #455), 0bqtx (0.25 #1609, 0.25 #933, 0.22 #1948), 01cpp0 (0.25 #947, 0.24 #1679, 0.22 #1962), 075k5 (0.25 #920, 0.23 #1033, 0.18 #864) >> Best rule #898 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 09c7w0; 0b90_r; 0154j; 0d060g; 0chghy; 07ssc; 035qy; 087vz; 05vz3zq; 01mk6; >> query: (?x789, 081pw) <- film_release_region(?x66, ?x789), combatants(?x789, ?x2513), ?x2513 = 05b4w >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f8l9c combatants! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 292.000 292.000 0.750 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #12281-06gb1w PRED entity: 06gb1w PRED relation: film! PRED expected values: 01lqnff => 129 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1333): 094tsh6 (0.45 #82692, 0.45 #86829, 0.44 #121977), 02t_y3 (0.33 #1676, 0.20 #3743, 0.05 #18212), 012c6x (0.21 #10450, 0.20 #14584, 0.13 #20786), 04mlmx (0.21 #11765, 0.20 #15899, 0.13 #22101), 0n8bn (0.20 #3277, 0.03 #36354, 0.02 #63227), 0lzb8 (0.20 #2164, 0.02 #57980, 0.02 #62114), 04fzk (0.15 #17238, 0.14 #29643, 0.05 #44115), 044rvb (0.15 #16637, 0.11 #29042, 0.06 #33178), 01f7dd (0.15 #17737, 0.11 #30142, 0.05 #34278), 0f5xn (0.15 #17499, 0.08 #36107, 0.08 #29904) >> Best rule #82692 for best value: >> intensional similarity = 5 >> extensional distance = 178 >> proper extension: 01sxly; 032016; 09fc83; 01bl7g; 02pw_n; 01k0vq; 01gglm; >> query: (?x4392, ?x9391) <- executive_produced_by(?x4392, ?x96), language(?x4392, ?x254), profession(?x96, ?x319), nominated_for(?x9391, ?x4392), film(?x96, ?x97) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #38574 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 76 *> proper extension: 02sg5v; 04cbbz; 0g5pvv; 042fgh; 09wnnb; 0g5ptf; *> query: (?x4392, 01lqnff) <- genre(?x4392, ?x225), language(?x4392, ?x254), film(?x96, ?x4392), prequel(?x4392, ?x936), featured_film_locations(?x4392, ?x739) *> conf = 0.01 ranks of expected_values: 1138 EVAL 06gb1w film! 01lqnff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 129.000 59.000 0.453 http://example.org/film/actor/film./film/performance/film #12280-02ptzz0 PRED entity: 02ptzz0 PRED relation: team! PRED expected values: 0f9rw9 0b_734 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 16): 0bqthy (0.83 #351, 0.78 #239, 0.69 #367), 0b_72t (0.78 #247, 0.78 #231, 0.75 #343), 0br1x_ (0.75 #214, 0.75 #198, 0.75 #182), 0b_6pv (0.75 #346, 0.67 #250, 0.67 #234), 0b_71r (0.75 #203, 0.67 #251, 0.67 #235), 0b_75k (0.67 #245, 0.67 #229, 0.67 #149), 0bzrsh (0.67 #345, 0.67 #233, 0.67 #137), 0b_6mr (0.67 #348, 0.67 #236, 0.66 #369), 0f9rw9 (0.66 #369, 0.64 #317, 0.56 #253), 0b_6rk (0.66 #369, 0.62 #196, 0.62 #356) >> Best rule #351 for best value: >> intensional similarity = 13 >> extensional distance = 10 >> proper extension: 02pyyld; >> query: (?x3798, 0bqthy) <- colors(?x3798, ?x4557), team(?x12798, ?x3798), team(?x8992, ?x3798), team(?x2302, ?x3798), team(?x8992, ?x5551), ?x5551 = 02pjzvh, locations(?x8992, ?x3983), team(?x1579, ?x3798), instance_of_recurring_event(?x12798, ?x10863), place_of_birth(?x1817, ?x3983), ?x2302 = 0b_77q, country(?x3983, ?x94), dog_breed(?x3983, ?x1706) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #369 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 11 *> proper extension: 0263cyj; *> query: (?x3798, ?x5897) <- colors(?x3798, ?x4557), team(?x8992, ?x3798), team(?x7378, ?x3798), ?x8992 = 0b_6s7, locations(?x7378, ?x2017), team(?x7378, ?x10846), team(?x7378, ?x9983), team(?x7378, ?x5032), ?x9983 = 02q4ntp, ?x2017 = 04f_d, colors(?x5032, ?x663), ?x10846 = 02pzy52, team(?x5897, ?x5032) *> conf = 0.66 ranks of expected_values: 9, 13 EVAL 02ptzz0 team! 0b_734 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 87.000 87.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02ptzz0 team! 0f9rw9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 87.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #12279-04qk12 PRED entity: 04qk12 PRED relation: film! PRED expected values: 01nwwl => 108 concepts (47 used for prediction) PRED predicted values (max 10 best out of 866): 05dbf (0.29 #364, 0.05 #2442, 0.03 #44014), 05mcjs (0.18 #74833, 0.17 #64438, 0.11 #8314), 01kb2j (0.14 #908, 0.08 #2986, 0.05 #9223), 03v3xp (0.14 #617, 0.05 #45730, 0.05 #56122), 02l4rh (0.14 #1232, 0.05 #45730, 0.05 #56122), 02k6rq (0.14 #328, 0.05 #45730, 0.05 #56122), 01sp81 (0.14 #148, 0.05 #45730, 0.05 #56122), 051wwp (0.14 #874, 0.05 #45730, 0.05 #56122), 0h0yt (0.14 #1343, 0.05 #45730, 0.05 #56122), 01hkhq (0.14 #412, 0.05 #45730, 0.05 #56122) >> Best rule #364 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0sxg4; 095zlp; 05q96q6; 02cbhg; 011ywj; >> query: (?x8555, 05dbf) <- film_crew_role(?x8555, ?x137), film(?x374, ?x8555), ?x374 = 05cj4r, film(?x1414, ?x8555), titles(?x53, ?x8555) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #12973 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 137 *> proper extension: 09r94m; *> query: (?x8555, 01nwwl) <- film_crew_role(?x8555, ?x137), film(?x374, ?x8555), film_format(?x8555, ?x909), award_nominee(?x374, ?x473), award(?x8555, ?x2489) *> conf = 0.04 ranks of expected_values: 101 EVAL 04qk12 film! 01nwwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 108.000 47.000 0.286 http://example.org/film/actor/film./film/performance/film #12278-026kq4q PRED entity: 026kq4q PRED relation: ceremony! PRED expected values: 04dn09n 054ky1 054knh => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 322): 0gqy2 (0.70 #4774, 0.67 #5292, 0.64 #6071), 0gq_d (0.67 #5330, 0.65 #4812, 0.64 #6109), 0gqwc (0.65 #4712, 0.65 #5230, 0.64 #6009), 0gr0m (0.65 #4711, 0.63 #3156, 0.62 #5229), 0gs96 (0.65 #4742, 0.62 #5260, 0.61 #5001), 018wng (0.65 #4687, 0.62 #5984, 0.61 #4946), 0gq9h (0.65 #5433, 0.65 #5231, 0.63 #4915), 0gr51 (0.65 #5247, 0.64 #4988, 0.63 #3174), 0gs9p (0.63 #3159, 0.62 #5232, 0.60 #4714), 0gvx_ (0.63 #4789, 0.62 #5307, 0.62 #6086) >> Best rule #4774 for best value: >> intensional similarity = 13 >> extensional distance = 41 >> proper extension: 02yv_b; 0ftlkg; 0fz2y7; 0c4hnm; >> query: (?x3001, 0gqy2) <- honored_for(?x3001, ?x3943), award_winner(?x3001, ?x4385), award_winner(?x3001, ?x1314), nominated_for(?x1314, ?x1072), produced_by(?x1402, ?x4385), profession(?x4385, ?x319), award(?x4385, ?x1307), ?x1307 = 0gq9h, cinematography(?x3943, ?x8248), award(?x3943, ?x2915), genre(?x3943, ?x53), film(?x382, ?x3943), music(?x3943, ?x4013) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #3877 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 39 *> proper extension: 0466p0j; *> query: (?x3001, ?x2016) <- ceremony(?x1107, ?x3001), award_winner(?x3001, ?x5677), award_winner(?x3001, ?x3002), people(?x1446, ?x3002), award(?x3002, ?x3247), place_of_death(?x3002, ?x1523), award_winner(?x3651, ?x3002), award_winner(?x2016, ?x5677), award_winner(?x3247, ?x450), category_of(?x3247, ?x2758) *> conf = 0.40 ranks of expected_values: 34, 36, 75 EVAL 026kq4q ceremony! 054knh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 40.000 40.000 0.698 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 026kq4q ceremony! 054ky1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 40.000 40.000 0.698 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 026kq4q ceremony! 04dn09n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 40.000 40.000 0.698 http://example.org/award/award_category/winners./award/award_honor/ceremony #12277-047p7fr PRED entity: 047p7fr PRED relation: genre PRED expected values: 05p553 03k9fj => 115 concepts (48 used for prediction) PRED predicted values (max 10 best out of 112): 05p553 (0.98 #5136, 0.55 #118, 0.47 #2799), 01z4y (0.56 #5017, 0.53 #2212, 0.48 #582), 01jfsb (0.48 #4323, 0.39 #1872, 0.38 #941), 01hmnh (0.45 #130, 0.27 #1527, 0.24 #1062), 03k9fj (0.41 #4322, 0.40 #1521, 0.38 #1056), 082gq (0.41 #2824, 0.23 #726, 0.21 #4808), 06n90 (0.38 #10, 0.27 #4324, 0.25 #1407), 0d63kt (0.29 #547, 0.06 #583, 0.06 #2680), 04xvlr (0.26 #3384, 0.25 #466, 0.25 #4899), 060__y (0.25 #1061, 0.22 #1526, 0.19 #4911) >> Best rule #5136 for best value: >> intensional similarity = 6 >> extensional distance = 360 >> proper extension: 07bxqz; >> query: (?x2961, 05p553) <- film(?x3713, ?x2961), film(?x1550, ?x2961), genre(?x2961, ?x239), nominated_for(?x384, ?x2961), genre(?x3567, ?x239), ?x3567 = 02_kd >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 047p7fr genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 48.000 0.975 http://example.org/film/film/genre EVAL 047p7fr genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 48.000 0.975 http://example.org/film/film/genre #12276-0g5lhl7 PRED entity: 0g5lhl7 PRED relation: award_winner! PRED expected values: 03mdt 0kc8y => 188 concepts (76 used for prediction) PRED predicted values (max 10 best out of 172): 03mdt (0.86 #67453, 0.86 #67452, 0.84 #57812), 05qd_ (0.33 #29026, 0.30 #41875, 0.28 #56329), 09d5h (0.33 #24398, 0.25 #37247, 0.25 #35640), 0hm0k (0.33 #4238, 0.25 #17081, 0.22 #67454), 0g5lhl7 (0.33 #3659, 0.25 #16502, 0.22 #67454), 01w92 (0.33 #3785, 0.25 #16628, 0.22 #67454), 01jq34 (0.33 #3549, 0.25 #16392, 0.22 #67454), 0gsg7 (0.33 #24350, 0.25 #37199, 0.20 #40412), 03m9c8 (0.33 #28417, 0.09 #46086, 0.07 #49296), 04cw0j (0.22 #67454, 0.18 #64240, 0.17 #29425) >> Best rule #67453 for best value: >> intensional similarity = 2 >> extensional distance = 22 >> proper extension: 030_1_; 01_8w2; 02j_j0; 03yxwq; 0gsgr; 05s34b; >> query: (?x2776, ?x10166) <- award_winner(?x2776, ?x10166), company(?x3796, ?x2776) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 13 EVAL 0g5lhl7 award_winner! 0kc8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 188.000 76.000 0.856 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner EVAL 0g5lhl7 award_winner! 03mdt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 76.000 0.856 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #12275-01vt5c_ PRED entity: 01vt5c_ PRED relation: profession PRED expected values: 016z4k => 144 concepts (117 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.81 #2366, 0.76 #10314, 0.75 #6926), 09jwl (0.77 #3694, 0.70 #1048, 0.69 #4870), 0nbcg (0.55 #3706, 0.52 #7531, 0.51 #5176), 016z4k (0.52 #1473, 0.49 #1032, 0.47 #4413), 01d_h8 (0.49 #593, 0.46 #887, 0.40 #5), 039v1 (0.35 #1065, 0.33 #3711, 0.28 #4887), 02jknp (0.32 #595, 0.26 #889, 0.23 #2947), 0dxtg (0.29 #15176, 0.29 #11343, 0.29 #12676), 0n1h (0.27 #746, 0.25 #1481, 0.22 #1187), 01c8w0 (0.27 #13840, 0.18 #2066, 0.08 #3830) >> Best rule #2366 for best value: >> intensional similarity = 3 >> extensional distance = 224 >> proper extension: 015c1b; >> query: (?x7951, 02hrh1q) <- religion(?x7951, ?x8967), gender(?x7951, ?x514), ?x514 = 02zsn >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1473 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 03cd1q; *> query: (?x7951, 016z4k) <- type_of_union(?x7951, ?x566), award_winner(?x3365, ?x7951), award(?x7951, ?x724), ?x724 = 01bgqh *> conf = 0.52 ranks of expected_values: 4 EVAL 01vt5c_ profession 016z4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 144.000 117.000 0.810 http://example.org/people/person/profession #12274-0gmdkyy PRED entity: 0gmdkyy PRED relation: ceremony! PRED expected values: 0gqy2 => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 324): 0gqy2 (0.94 #4152, 0.93 #4867, 0.88 #3197), 02x201b (0.78 #5480, 0.75 #6912, 0.72 #5241), 0czp_ (0.78 #5480, 0.75 #6912, 0.72 #5241), 02qyp19 (0.35 #5003, 0.33 #238, 0.29 #2858), 027dtxw (0.35 #5003, 0.33 #239, 0.29 #2858), 094qd5 (0.35 #5003, 0.33 #264, 0.29 #2858), 02r0csl (0.35 #5003, 0.33 #240, 0.29 #2858), 09qwmm (0.35 #5003, 0.29 #2858, 0.27 #2144), 099cng (0.35 #5003, 0.29 #2858, 0.27 #2144), 02y_rq5 (0.35 #5003, 0.29 #2858, 0.27 #2144) >> Best rule #4152 for best value: >> intensional similarity = 15 >> extensional distance = 48 >> proper extension: 0bzknt; >> query: (?x2082, 0gqy2) <- award_winner(?x2082, ?x986), honored_for(?x2082, ?x1803), language(?x1803, ?x254), ceremony(?x6860, ?x2082), ceremony(?x5409, ?x2082), production_companies(?x1803, ?x9041), ceremony(?x6860, ?x2707), award(?x186, ?x6860), ?x2707 = 02hn5v, nominated_for(?x6860, ?x2340), nominated_for(?x6860, ?x908), ?x908 = 01vksx, film_release_region(?x2340, ?x47), ?x5409 = 0gr07, music(?x2340, ?x10700) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gmdkyy ceremony! 0gqy2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.940 http://example.org/award/award_category/winners./award/award_honor/ceremony #12273-059gkk PRED entity: 059gkk PRED relation: award_winner PRED expected values: 02lfcm => 95 concepts (40 used for prediction) PRED predicted values (max 10 best out of 381): 02lfcm (0.82 #35364, 0.82 #64308, 0.82 #28931), 01r42_g (0.82 #35364, 0.82 #64308, 0.82 #28931), 02lfl4 (0.58 #48228, 0.53 #64309, 0.50 #40189), 02lfns (0.58 #48228, 0.53 #64309, 0.50 #40189), 0c1ps1 (0.53 #64309, 0.46 #48227, 0.45 #51447), 0bl60p (0.53 #64309, 0.46 #48227, 0.45 #51447), 050_qx (0.19 #49839, 0.18 #48229, 0.18 #1325), 0755wz (0.19 #49839, 0.18 #48229, 0.16 #33756), 09xrxq (0.19 #49839, 0.18 #48229, 0.16 #33756), 01nfys (0.19 #49839, 0.18 #48229, 0.16 #33756) >> Best rule #35364 for best value: >> intensional similarity = 3 >> extensional distance = 1007 >> proper extension: 0cjdk; 04qb6g; >> query: (?x3284, ?x369) <- award_winner(?x369, ?x3284), award_winner(?x1849, ?x3284), award_winner(?x3284, ?x1651) >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 059gkk award_winner 02lfcm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 40.000 0.816 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #12272-015cz0 PRED entity: 015cz0 PRED relation: student PRED expected values: 012bk => 124 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1438): 08k1lz (0.15 #3831, 0.05 #5924, 0.04 #8017), 04hw4b (0.14 #1234, 0.04 #9606, 0.02 #32631), 025j1t (0.14 #1063, 0.02 #32460, 0.02 #11528), 02l5rm (0.14 #476, 0.02 #31873, 0.02 #10941), 01wz01 (0.14 #694, 0.02 #32091, 0.02 #11159), 02lgj6 (0.14 #226, 0.02 #31623, 0.02 #10691), 02d4ct (0.14 #362, 0.02 #31759, 0.02 #10827), 072vj (0.14 #2051, 0.02 #12516, 0.02 #10423), 0814k3 (0.14 #1997, 0.02 #12462, 0.02 #10369), 0cv9fc (0.14 #1921, 0.02 #12386, 0.02 #10293) >> Best rule #3831 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 02s8qk; 01yqqv; >> query: (?x5167, 08k1lz) <- major_field_of_study(?x5167, ?x6756), major_field_of_study(?x5167, ?x742), contains(?x4743, ?x5167), ?x6756 = 0_jm, ?x742 = 05qjt >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015cz0 student 012bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 93.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #12271-0gs9p PRED entity: 0gs9p PRED relation: category_of PRED expected values: 0g_w => 63 concepts (39 used for prediction) PRED predicted values (max 10 best out of 4): 0g_w (0.82 #235, 0.60 #45, 0.50 #150), 0c4ys (0.36 #773, 0.36 #796, 0.35 #819), 0gcf2r (0.24 #382, 0.24 #361, 0.24 #340), 04jhhng (0.01 #334) >> Best rule #235 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x1313, 0g_w) <- award(?x777, ?x1313), nominated_for(?x777, ?x776), ceremony(?x1313, ?x1998), ?x1998 = 073h1t, religion(?x777, ?x7422) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gs9p category_of 0g_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 39.000 0.824 http://example.org/award/award_category/category_of #12270-012vd6 PRED entity: 012vd6 PRED relation: influenced_by PRED expected values: 0d9xq 03h_yfh => 116 concepts (77 used for prediction) PRED predicted values (max 10 best out of 322): 012vd6 (0.15 #1476, 0.09 #17453, 0.08 #11338), 05qmj (0.14 #8039, 0.13 #11094, 0.09 #17209), 081k8 (0.13 #8002, 0.11 #15864, 0.11 #11933), 032l1 (0.12 #11866, 0.10 #15797, 0.10 #8373), 03_87 (0.12 #11980, 0.12 #8487, 0.10 #8049), 014z8v (0.11 #4481, 0.09 #8405, 0.08 #11898), 0gz_ (0.11 #11004, 0.09 #7949, 0.09 #17119), 03sbs (0.11 #11124, 0.09 #15931, 0.09 #17239), 02lt8 (0.10 #11897, 0.10 #7966, 0.09 #14083), 014zfs (0.10 #4384, 0.07 #9180, 0.06 #5256) >> Best rule #1476 for best value: >> intensional similarity = 2 >> extensional distance = 52 >> proper extension: 016ppr; >> query: (?x5310, 012vd6) <- award(?x5310, ?x567), ?x567 = 01d38g >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 012vd6 influenced_by 03h_yfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 77.000 0.148 http://example.org/influence/influence_node/influenced_by EVAL 012vd6 influenced_by 0d9xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 77.000 0.148 http://example.org/influence/influence_node/influenced_by #12269-03mb9 PRED entity: 03mb9 PRED relation: artists PRED expected values: 06k02 07sbk 01lqf49 016nvh => 65 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1050): 06p03s (0.75 #10591, 0.50 #12724, 0.50 #6325), 049qx (0.67 #5701, 0.62 #9967, 0.43 #7833), 0m19t (0.67 #4289, 0.50 #2158, 0.29 #12821), 01dw_f (0.62 #9197, 0.56 #11331, 0.50 #3866), 02ndj5 (0.62 #9411, 0.56 #11545, 0.36 #12611), 03f5spx (0.62 #9651, 0.50 #3253, 0.50 #10660), 0191h5 (0.57 #12363, 0.50 #10230, 0.47 #13429), 01v_pj6 (0.50 #9711, 0.50 #5445, 0.50 #117), 06mt91 (0.50 #10192, 0.50 #5926, 0.50 #3794), 019x62 (0.50 #10216, 0.50 #5950, 0.43 #8082) >> Best rule #10591 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 016clz; 0m0jc; 0y3_8; >> query: (?x7267, 06p03s) <- artists(?x7267, ?x11709), artists(?x7267, ?x2635), parent_genre(?x8847, ?x7267), ?x2635 = 03fbc, company(?x11709, ?x14145), artists(?x8847, ?x6162), role(?x6162, ?x212) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #10661 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 016clz; 0m0jc; 0y3_8; *> query: (?x7267, ?x959) <- artists(?x7267, ?x11709), artists(?x7267, ?x2635), parent_genre(?x8847, ?x7267), ?x2635 = 03fbc, company(?x11709, ?x14145), artists(?x8847, ?x6162), artists(?x8847, ?x959), role(?x6162, ?x212) *> conf = 0.49 ranks of expected_values: 106, 107, 109, 114 EVAL 03mb9 artists 016nvh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 65.000 32.000 0.750 http://example.org/music/genre/artists EVAL 03mb9 artists 01lqf49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 65.000 32.000 0.750 http://example.org/music/genre/artists EVAL 03mb9 artists 07sbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 65.000 32.000 0.750 http://example.org/music/genre/artists EVAL 03mb9 artists 06k02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 65.000 32.000 0.750 http://example.org/music/genre/artists #12268-01xk7r PRED entity: 01xk7r PRED relation: student PRED expected values: 03_1pg => 196 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1797): 0306ds (0.17 #2500, 0.11 #4592, 0.11 #21328), 0405l (0.14 #1853, 0.11 #6037, 0.07 #22773), 03c6v3 (0.14 #1828, 0.09 #10196, 0.08 #16472), 0crqcc (0.14 #1221, 0.08 #15865, 0.06 #5405), 01n1gc (0.14 #611, 0.06 #67556, 0.06 #4795), 01z5tr (0.14 #1376, 0.06 #36940, 0.06 #5560), 05kfs (0.14 #98, 0.06 #4282, 0.06 #2190), 0432cd (0.14 #1319, 0.06 #5503, 0.04 #68264), 07nx9j (0.14 #1313, 0.04 #9681, 0.04 #68258), 037lyl (0.11 #4845, 0.11 #2753, 0.09 #9029) >> Best rule #2500 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 03bmmc; 03_fmr; >> query: (?x6936, 0306ds) <- institution(?x3386, ?x6936), student(?x6936, ?x6456), school_type(?x6936, ?x1044), registering_agency(?x6936, ?x1982), artists(?x505, ?x6456), artist(?x2190, ?x6456) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01xk7r student 03_1pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 84.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #12267-01w1kyf PRED entity: 01w1kyf PRED relation: award PRED expected values: 02y_rq5 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 268): 09cn0c (0.70 #21912, 0.70 #11150, 0.69 #20316), 027571b (0.70 #21912, 0.70 #11150, 0.69 #20316), 02z1nbg (0.70 #21912, 0.70 #11150, 0.69 #20316), 0gqyl (0.41 #1695, 0.16 #1297, 0.16 #1195), 0gq9h (0.33 #74, 0.23 #472, 0.17 #5251), 02y_rq5 (0.33 #1685, 0.16 #15133, 0.16 #15134), 05b4l5x (0.32 #1201, 0.12 #17924, 0.12 #19916), 05p09zm (0.32 #1315, 0.07 #2907, 0.06 #4501), 0bfvw2 (0.30 #1608, 0.08 #7580, 0.07 #1210), 09sb52 (0.29 #11988, 0.28 #1634, 0.27 #6810) >> Best rule #21912 for best value: >> intensional similarity = 3 >> extensional distance = 1907 >> proper extension: 06lxn; >> query: (?x5094, ?x2060) <- award_winner(?x2060, ?x5094), award_winner(?x1245, ?x5094), ceremony(?x1245, ?x78) >> conf = 0.70 => this is the best rule for 3 predicted values *> Best rule #1685 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 02jt1k; 01wk51; *> query: (?x5094, 02y_rq5) <- award(?x5094, ?x1132), film(?x5094, ?x857), ?x1132 = 0bdwft *> conf = 0.33 ranks of expected_values: 6 EVAL 01w1kyf award 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 88.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12266-09gkdln PRED entity: 09gkdln PRED relation: award_winner PRED expected values: 04qvl7 026c1 05xbx => 22 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1916): 0794g (0.60 #1982, 0.07 #4991, 0.07 #7999), 0pz7h (0.50 #3125, 0.20 #1620, 0.15 #10649), 0cp9f9 (0.50 #4166, 0.20 #2661, 0.13 #11690), 018ygt (0.40 #2439, 0.33 #3944, 0.19 #5448), 026c1 (0.40 #1807, 0.25 #12034, 0.21 #4510), 0382m4 (0.40 #2359, 0.25 #12034, 0.16 #9022), 01z_g6 (0.40 #2274, 0.08 #11303, 0.07 #5283), 0flw6 (0.40 #2139, 0.07 #8156, 0.07 #6653), 02zft0 (0.35 #18051, 0.33 #899, 0.25 #12034), 02kxbwx (0.35 #18051, 0.31 #18052, 0.29 #15042) >> Best rule #1982 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 05zksls; 05qb8vx; 09g90vz; >> query: (?x8964, 0794g) <- honored_for(?x8964, ?x1364), award_winner(?x8964, ?x9211), award_winner(?x8964, ?x4005), award_winner(?x8964, ?x843), ?x9211 = 04znsy, nominated_for(?x843, ?x97), type_of_union(?x843, ?x566), award(?x843, ?x375), award_nominee(?x396, ?x4005), profession(?x4005, ?x1032), ceremony(?x2393, ?x8964), participant(?x4005, ?x400), participant(?x1424, ?x4005), award_nominee(?x843, ?x157) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1807 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 3 *> proper extension: 05zksls; 05qb8vx; 09g90vz; *> query: (?x8964, 026c1) <- honored_for(?x8964, ?x1364), award_winner(?x8964, ?x9211), award_winner(?x8964, ?x4005), award_winner(?x8964, ?x843), ?x9211 = 04znsy, nominated_for(?x843, ?x97), type_of_union(?x843, ?x566), award(?x843, ?x375), award_nominee(?x396, ?x4005), profession(?x4005, ?x1032), ceremony(?x2393, ?x8964), participant(?x4005, ?x400), participant(?x1424, ?x4005), award_nominee(?x843, ?x157) *> conf = 0.40 ranks of expected_values: 5, 37, 386 EVAL 09gkdln award_winner 05xbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 12.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09gkdln award_winner 026c1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 22.000 12.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09gkdln award_winner 04qvl7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 22.000 12.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12265-025twgf PRED entity: 025twgf PRED relation: film_production_design_by PRED expected values: 04kj2v => 115 concepts (86 used for prediction) PRED predicted values (max 10 best out of 18): 04_1nk (0.16 #107, 0.10 #295, 0.09 #419), 04kj2v (0.15 #34, 0.09 #252, 0.07 #65), 0d5wn3 (0.15 #72, 0.05 #165, 0.04 #322), 0dh73w (0.14 #8, 0.02 #194, 0.02 #289), 0cdf37 (0.08 #171, 0.08 #47, 0.06 #233), 02x2t07 (0.04 #523, 0.03 #429, 0.03 #148), 03mdw3c (0.03 #116, 0.03 #428, 0.01 #1768), 05b5_tj (0.03 #123, 0.02 #247, 0.02 #902), 0bytkq (0.03 #130, 0.03 #161, 0.02 #192), 05km8z (0.03 #142, 0.03 #486, 0.03 #828) >> Best rule #107 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 04gcyg; >> query: (?x8737, 04_1nk) <- film(?x1850, ?x8737), currency(?x8737, ?x170), story_by(?x8737, ?x3686), people(?x4322, ?x3686), written_by(?x1261, ?x3686) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 0p4v_; *> query: (?x8737, 04kj2v) <- film(?x1850, ?x8737), nominated_for(?x650, ?x8737), language(?x8737, ?x254), film_release_distribution_medium(?x8737, ?x81), ?x1850 = 017jv5 *> conf = 0.15 ranks of expected_values: 2 EVAL 025twgf film_production_design_by 04kj2v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 86.000 0.156 http://example.org/film/film/film_production_design_by #12264-0fq27fp PRED entity: 0fq27fp PRED relation: language PRED expected values: 02h40lc => 98 concepts (92 used for prediction) PRED predicted values (max 10 best out of 57): 02h40lc (0.91 #1276, 0.88 #913, 0.88 #1638), 064_8sq (0.29 #204, 0.26 #755, 0.25 #453), 02bjrlw (0.25 #61, 0.23 #1032, 0.20 #122), 0t_2 (0.23 #1032, 0.20 #135, 0.14 #3757), 04306rv (0.15 #916, 0.14 #3757, 0.12 #976), 0jzc (0.14 #202, 0.11 #263, 0.10 #326), 06nm1 (0.14 #3757, 0.12 #862, 0.11 #1285), 03_9r (0.14 #3757, 0.11 #253, 0.10 #316), 05zjd (0.14 #3757, 0.09 #3151, 0.08 #937), 02bv9 (0.14 #3757, 0.09 #3151, 0.04 #939) >> Best rule #1276 for best value: >> intensional similarity = 11 >> extensional distance = 45 >> proper extension: 05p1tzf; 02x3lt7; 0872p_c; 053rxgm; 0gj8t_b; 03twd6; 04n52p6; 0gj9tn5; 0_7w6; 08052t3; ... >> query: (?x622, 02h40lc) <- film_release_region(?x622, ?x1497), film_release_region(?x622, ?x456), film_release_region(?x622, ?x429), film_release_region(?x622, ?x94), ?x94 = 09c7w0, ?x456 = 05qhw, ?x429 = 03rt9, genre(?x622, ?x53), currency(?x622, ?x1099), film_crew_role(?x622, ?x137), ?x1497 = 015qh >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fq27fp language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 92.000 0.915 http://example.org/film/film/language #12263-02_06s PRED entity: 02_06s PRED relation: award PRED expected values: 09cn0c => 66 concepts (52 used for prediction) PRED predicted values (max 10 best out of 171): 02w9sd7 (0.35 #465, 0.35 #355, 0.26 #464), 09cm54 (0.32 #308, 0.05 #6014, 0.05 #773), 0f4x7 (0.28 #257, 0.09 #722, 0.08 #2340), 04dn09n (0.26 #464, 0.21 #8793, 0.21 #10181), 0gr51 (0.26 #464, 0.21 #8793, 0.21 #10181), 02x4w6g (0.26 #464, 0.21 #8793, 0.21 #10181), 0gqwc (0.26 #464, 0.21 #8793, 0.21 #10181), 09qwmm (0.26 #464, 0.21 #8793, 0.21 #10181), 02y_rq5 (0.26 #464, 0.21 #8793, 0.21 #10181), 099c8n (0.26 #464, 0.21 #10181, 0.21 #6246) >> Best rule #465 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 064lsn; >> query: (?x7129, ?x3209) <- language(?x7129, ?x254), nominated_for(?x3209, ?x7129), ?x3209 = 02w9sd7 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #6014 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x7129, ?x68) <- award(?x7129, ?x3435), award(?x6079, ?x3435), award(?x6079, ?x68) *> conf = 0.05 ranks of expected_values: 78 EVAL 02_06s award 09cn0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 66.000 52.000 0.347 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12262-0bt7ws PRED entity: 0bt7ws PRED relation: languages PRED expected values: 02h40lc => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 14): 02h40lc (0.91 #154, 0.91 #230, 0.91 #192), 064_8sq (0.09 #167, 0.09 #281, 0.09 #510), 03k50 (0.07 #499, 0.02 #2096, 0.02 #804), 02bjrlw (0.04 #496, 0.04 #229, 0.04 #267), 07c9s (0.04 #508, 0.01 #2105, 0.01 #813), 06nm1 (0.03 #501, 0.03 #196, 0.03 #234), 04306rv (0.03 #193, 0.03 #269, 0.03 #498), 0999q (0.02 #518), 0t_2 (0.02 #199, 0.02 #237, 0.02 #275), 09s02 (0.02 #530) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 141 >> proper extension: 018swb; 0dqcm; 0gdqy; >> query: (?x3852, 02h40lc) <- award_winner(?x6262, ?x3852), languages(?x3852, ?x12394), participant(?x6262, ?x436) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bt7ws languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.909 http://example.org/people/person/languages #12261-029cr PRED entity: 029cr PRED relation: location! PRED expected values: 02bkdn 0sw62 => 169 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2037): 0hvb2 (0.48 #176021, 0.47 #62861, 0.47 #75440), 039crh (0.33 #885, 0.14 #3399, 0.10 #8427), 04s430 (0.33 #1213, 0.14 #3727, 0.10 #8755), 0j1yf (0.33 #332, 0.14 #2846, 0.10 #7874), 0cymln (0.33 #2043, 0.14 #4557, 0.10 #9585), 09r9dp (0.33 #730, 0.14 #3244, 0.10 #8272), 01q_ph (0.33 #50, 0.10 #5078, 0.08 #113159), 07r4c (0.33 #1259, 0.10 #6287, 0.08 #113159), 0mz73 (0.33 #1568, 0.10 #9110, 0.08 #113159), 01m4yn (0.33 #1376, 0.10 #6404, 0.08 #113159) >> Best rule #176021 for best value: >> intensional similarity = 4 >> extensional distance = 201 >> proper extension: 020skc; 03hrz; 01531; 0y2dl; 09b8m; 0978r; 0psxp; 0bxbr; 0h3lt; 0bdg5; ... >> query: (?x2504, ?x1870) <- location(?x4554, ?x2504), citytown(?x2388, ?x2504), place_of_birth(?x1870, ?x2504), contains(?x94, ?x2504) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2045 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 030qb3t; *> query: (?x2504, 0sw62) <- locations(?x4803, ?x2504), location(?x10161, ?x2504), ?x10161 = 01ggc9, contains(?x2504, ?x2388) *> conf = 0.33 ranks of expected_values: 203, 681 EVAL 029cr location! 0sw62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 169.000 96.000 0.480 http://example.org/people/person/places_lived./people/place_lived/location EVAL 029cr location! 02bkdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 169.000 96.000 0.480 http://example.org/people/person/places_lived./people/place_lived/location #12260-03hkch7 PRED entity: 03hkch7 PRED relation: currency PRED expected values: 09nqf => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.79 #85, 0.77 #99, 0.76 #239), 01nv4h (0.57 #533, 0.10 #2, 0.05 #65), 02l6h (0.03 #95, 0.03 #39, 0.02 #256), 02gsvk (0.02 #48, 0.02 #55), 0kz1h (0.02 #68) >> Best rule #85 for best value: >> intensional similarity = 3 >> extensional distance = 167 >> proper extension: 0gh6j94; >> query: (?x3124, 09nqf) <- film_crew_role(?x3124, ?x468), films(?x8435, ?x3124), featured_film_locations(?x3124, ?x739) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hkch7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.793 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #12259-02bkdn PRED entity: 02bkdn PRED relation: award PRED expected values: 03qgjwc => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 253): 03c7tr1 (0.33 #456, 0.15 #16362, 0.13 #32726), 02x73k6 (0.27 #59, 0.17 #458, 0.15 #16362), 0gqy2 (0.27 #161, 0.17 #560, 0.15 #16362), 05pcn59 (0.25 #478, 0.15 #16362, 0.13 #32726), 0bdwft (0.25 #465, 0.15 #16362, 0.13 #32726), 0cqhb3 (0.22 #1098, 0.15 #16362, 0.14 #27536), 09sdmz (0.18 #203, 0.17 #602, 0.15 #16362), 057xs89 (0.18 #157, 0.15 #16362, 0.13 #12769), 0cqhk0 (0.18 #37, 0.15 #16362, 0.13 #32726), 0bdwqv (0.18 #169, 0.15 #16362, 0.13 #32726) >> Best rule #456 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0dn3n; 01j7z7; 08wjf4; 01z5tr; 03_x5t; >> query: (?x1871, 03c7tr1) <- award_nominee(?x2028, ?x1871), award(?x1871, ?x686), ?x2028 = 028knk >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #16362 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1181 *> proper extension: 02z6l5f; *> query: (?x1871, ?x678) <- award_winner(?x873, ?x1871), award_nominee(?x10491, ?x1871), award(?x10491, ?x678) *> conf = 0.15 ranks of expected_values: 59 EVAL 02bkdn award 03qgjwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 98.000 98.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12258-0nm9h PRED entity: 0nm9h PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 179 concepts (66 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.91 #300, 0.89 #337, 0.89 #324), 04_1l0v (0.36 #720), 029jpy (0.36 #720), 050ks (0.36 #746, 0.28 #272, 0.27 #335), 0nm9h (0.28 #272, 0.27 #335, 0.27 #223), 02jx1 (0.06 #822, 0.05 #530, 0.04 #570), 0d060g (0.02 #423, 0.02 #471), 03rt9 (0.02 #616, 0.02 #682), 0kcw2 (0.02 #471), 01lxw6 (0.02 #471) >> Best rule #300 for best value: >> intensional similarity = 5 >> extensional distance = 114 >> proper extension: 0nm87; >> query: (?x12290, 09c7w0) <- county(?x12289, ?x12290), contains(?x7058, ?x12290), source(?x12289, ?x958), category(?x12289, ?x134), time_zones(?x12289, ?x2674) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nm9h second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 66.000 0.905 http://example.org/location/country/second_level_divisions #12257-02mt51 PRED entity: 02mt51 PRED relation: film! PRED expected values: 0738b8 0171cm => 104 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1111): 02mt4k (0.50 #16650, 0.48 #18731, 0.48 #6247), 092kgw (0.50 #16650, 0.48 #18731, 0.48 #6247), 0lpjn (0.18 #2561, 0.06 #478, 0.03 #77477), 0bl2g (0.13 #2138, 0.11 #4166, 0.04 #66595), 0jfx1 (0.11 #4166, 0.09 #4165, 0.09 #2490), 015q43 (0.11 #4166, 0.05 #2984, 0.04 #66595), 09y20 (0.11 #4166, 0.05 #18980, 0.04 #66595), 0z05l (0.11 #4166, 0.04 #58269, 0.04 #58270), 06fmdb (0.11 #4166, 0.04 #58269, 0.04 #58270), 01m3x5p (0.11 #4166, 0.04 #58269, 0.04 #58270) >> Best rule #16650 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 014lc_; 0h3xztt; 0fq7dv_; 01fmys; 0407yj_; 01sby_; 0gt1k; 02825cv; >> query: (?x4040, ?x2534) <- nominated_for(?x2534, ?x4040), film_release_region(?x4040, ?x1603), ?x1603 = 06bnz, film(?x2762, ?x4040) >> conf = 0.50 => this is the best rule for 2 predicted values *> Best rule #2508 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 080dwhx; 01b_lz; *> query: (?x4040, 0171cm) <- nominated_for(?x5951, ?x4040), titles(?x1510, ?x4040), award_nominee(?x2444, ?x5951), ?x2444 = 0jfx1 *> conf = 0.05 ranks of expected_values: 54, 388 EVAL 02mt51 film! 0171cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 104.000 59.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 02mt51 film! 0738b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 104.000 59.000 0.500 http://example.org/film/actor/film./film/performance/film #12256-03f0324 PRED entity: 03f0324 PRED relation: influenced_by! PRED expected values: 06whf 03f47xl 0cbgl => 166 concepts (67 used for prediction) PRED predicted values (max 10 best out of 399): 07lp1 (0.40 #2346, 0.33 #884, 0.20 #2834), 05jm7 (0.35 #10890, 0.26 #8927, 0.23 #13829), 084w8 (0.33 #979, 0.33 #2, 0.32 #7820), 01v_0b (0.33 #3388, 0.33 #950, 0.20 #8279), 0399p (0.33 #3241, 0.33 #803, 0.19 #5681), 07h07 (0.33 #3072, 0.33 #634, 0.12 #24443), 019z7q (0.33 #2952, 0.33 #25, 0.12 #9313), 07dnx (0.33 #1320, 0.26 #7669, 0.17 #4248), 032l1 (0.33 #1091, 0.20 #7932, 0.19 #8419), 08433 (0.33 #518, 0.19 #17607, 0.17 #2956) >> Best rule #2346 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 073v6; 013pp3; 040_t; >> query: (?x4915, 07lp1) <- influenced_by(?x13298, ?x4915), influenced_by(?x2609, ?x4915), award(?x13298, ?x11471), ?x2609 = 01w8sf, influenced_by(?x4915, ?x2240), people(?x1050, ?x4915) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1227 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 02lt8; *> query: (?x4915, 03f47xl) <- influenced_by(?x13298, ?x4915), influenced_by(?x10275, ?x4915), influenced_by(?x2609, ?x4915), influenced_by(?x2161, ?x4915), award(?x13298, ?x11471), ?x2161 = 040db, ?x10275 = 03hpr, student(?x2196, ?x2609) *> conf = 0.33 ranks of expected_values: 22, 41, 61 EVAL 03f0324 influenced_by! 0cbgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 166.000 67.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 03f0324 influenced_by! 03f47xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 166.000 67.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 03f0324 influenced_by! 06whf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 166.000 67.000 0.400 http://example.org/influence/influence_node/influenced_by #12255-03mz5b PRED entity: 03mz5b PRED relation: nominated_for! PRED expected values: 03hl6lc => 65 concepts (52 used for prediction) PRED predicted values (max 10 best out of 217): 0gqwc (0.45 #780, 0.26 #2931, 0.20 #1736), 094qd5 (0.36 #755, 0.22 #2906, 0.16 #4102), 0gq9h (0.31 #2933, 0.27 #6997, 0.27 #3411), 019f4v (0.27 #773, 0.26 #2924, 0.22 #5554), 0gqy2 (0.27 #842, 0.24 #7173, 0.22 #11241), 0gqyl (0.27 #800, 0.23 #2951, 0.19 #1756), 02n9nmz (0.27 #777, 0.13 #2928, 0.13 #3167), 0gs9p (0.27 #2935, 0.24 #6999, 0.24 #6521), 0gq_v (0.25 #2889, 0.20 #1694, 0.20 #2172), 0f4x7 (0.24 #7173, 0.22 #11241, 0.20 #11481) >> Best rule #780 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0c9k8; >> query: (?x5051, 0gqwc) <- genre(?x5051, ?x1403), genre(?x5051, ?x1316), ?x1403 = 02l7c8, produced_by(?x5051, ?x2596), ?x1316 = 017fp >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #11721 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1455 *> proper extension: 0fkwzs; 04mx8h4; *> query: (?x5051, ?x77) <- nominated_for(?x617, ?x5051), nominated_for(?x2183, ?x5051), nominated_for(?x617, ?x9805), nominated_for(?x77, ?x9805) *> conf = 0.12 ranks of expected_values: 63 EVAL 03mz5b nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 65.000 52.000 0.455 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12254-050gkf PRED entity: 050gkf PRED relation: nominated_for! PRED expected values: 0k611 => 114 concepts (106 used for prediction) PRED predicted values (max 10 best out of 206): 099jhq (0.68 #14689, 0.67 #7225, 0.67 #14688), 0gq9h (0.57 #1227, 0.50 #2392, 0.45 #8219), 027dtxw (0.55 #1168, 0.30 #2333, 0.20 #2100), 0gs9p (0.50 #1229, 0.41 #2394, 0.38 #8221), 019f4v (0.46 #54, 0.45 #1219, 0.41 #2384), 04dn09n (0.45 #1200, 0.41 #2365, 0.27 #8192), 0k611 (0.43 #1238, 0.43 #2403, 0.31 #73), 09sb52 (0.43 #1199, 0.34 #2364, 0.21 #15156), 02pqp12 (0.41 #1223, 0.36 #2388, 0.25 #990), 0gqyl (0.39 #1245, 0.26 #8237, 0.25 #2410) >> Best rule #14689 for best value: >> intensional similarity = 4 >> extensional distance = 847 >> proper extension: 06mmr; >> query: (?x1968, ?x451) <- award_winner(?x1968, ?x6980), award(?x1968, ?x451), nominated_for(?x451, ?x186), award(?x123, ?x451) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1238 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 42 *> proper extension: 083shs; 09gq0x5; 0gvs1kt; 093dqjy; 04b2qn; *> query: (?x1968, 0k611) <- film(?x166, ?x1968), nominated_for(?x3066, ?x1968), nominated_for(?x1162, ?x1968), ?x1162 = 099c8n, ?x3066 = 0gqy2 *> conf = 0.43 ranks of expected_values: 7 EVAL 050gkf nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 114.000 106.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12253-04mp75 PRED entity: 04mp75 PRED relation: colors PRED expected values: 01g5v => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 19): 06fvc (0.65 #1101, 0.57 #437, 0.50 #128), 01g5v (0.51 #1193, 0.34 #1266, 0.33 #21), 01l849 (0.31 #768, 0.23 #528, 0.20 #529), 02rnmb (0.26 #417, 0.25 #217, 0.23 #528), 038hg (0.25 #137, 0.23 #528, 0.20 #529), 088fh (0.23 #528, 0.20 #529, 0.18 #657), 036k5h (0.23 #528, 0.20 #529, 0.18 #657), 0jc_p (0.23 #528, 0.20 #529, 0.18 #657), 04mkbj (0.23 #528, 0.20 #529, 0.18 #657), 04d18d (0.23 #528, 0.20 #529, 0.18 #657) >> Best rule #1101 for best value: >> intensional similarity = 17 >> extensional distance = 181 >> proper extension: 0223bl; 0xbm; 01fjz9; 01x4wq; 0k_l4; 03c0t9; 0487_; 01xn6mc; 04ltf; 049n2l; ... >> query: (?x8051, 06fvc) <- colors(?x8051, ?x4557), colors(?x6645, ?x4557), colors(?x2114, ?x4557), ?x6645 = 0wsr, colors(?x12761, ?x4557), colors(?x12732, ?x4557), colors(?x12356, ?x4557), colors(?x10217, ?x4557), position_s(?x2114, ?x1717), position_s(?x2114, ?x1517), currency(?x12732, ?x170), student(?x12732, ?x3176), ?x1517 = 02g_6j, ?x1717 = 02g_6x, ?x12356 = 07wkd, ?x12761 = 0225v9, ?x10217 = 03818y >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #1193 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 205 *> proper extension: 05jx2d; 05g76; 0hvgt; 0gxkm; 02pqcfz; 0frm7n; 03d555l; 0289q; 03j7cf; 0bl8l; ... *> query: (?x8051, 01g5v) <- colors(?x8051, ?x4557), colors(?x6645, ?x4557), sport(?x6645, ?x1083), colors(?x12732, ?x4557), colors(?x12699, ?x4557), colors(?x6814, ?x4557), colors(?x546, ?x4557), ?x12699 = 03b8c4, student(?x546, ?x547), major_field_of_study(?x546, ?x742), currency(?x12732, ?x170), school(?x580, ?x6814), team(?x180, ?x6645) *> conf = 0.51 ranks of expected_values: 2 EVAL 04mp75 colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 92.000 0.650 http://example.org/sports/sports_team/colors #12252-01gct2 PRED entity: 01gct2 PRED relation: currency PRED expected values: 09nqf => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.60 #13, 0.58 #10, 0.57 #4), 02l6h (0.02 #36, 0.01 #45), 01nv4h (0.02 #35, 0.01 #44) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 03lh3v; 01sg7_; 012xdf; 03l26m; 02_nkp; 02lm0t; 02cg2v; >> query: (?x9266, 09nqf) <- team(?x9266, ?x10409), position(?x10409, ?x1348), school(?x10409, ?x4296), gender(?x9266, ?x231) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gct2 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.600 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #12251-08815 PRED entity: 08815 PRED relation: category PRED expected values: 08mbj5d => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #66, 0.90 #64, 0.90 #82) >> Best rule #66 for best value: >> intensional similarity = 2 >> extensional distance = 189 >> proper extension: 0fht9f; 0frm7n; >> query: (?x122, 08mbj5d) <- school(?x7725, ?x122), team(?x261, ?x7725) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08815 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.906 http://example.org/common/topic/webpage./common/webpage/category #12250-0h63gl9 PRED entity: 0h63gl9 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #126, 0.83 #91, 0.83 #41), 02nxhr (0.45 #331, 0.09 #72, 0.08 #82), 07z4p (0.45 #331, 0.08 #10, 0.07 #125), 07c52 (0.45 #331, 0.08 #138, 0.07 #123) >> Best rule #126 for best value: >> intensional similarity = 4 >> extensional distance = 310 >> proper extension: 02y_lrp; 09tqkv2; 02ht1k; 01qvz8; 04h41v; 05fm6m; 04g73n; 0888c3; >> query: (?x6621, 029j_) <- film_crew_role(?x6621, ?x468), nominated_for(?x4400, ?x6621), genre(?x6621, ?x258), ?x258 = 05p553 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h63gl9 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.843 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12249-07ytt PRED entity: 07ytt PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.70 #256, 0.69 #463, 0.69 #693), 060bp (0.70 #254, 0.62 #231, 0.62 #507), 0f6c3 (0.69 #307, 0.43 #376, 0.41 #399), 09n5b9 (0.56 #311, 0.40 #380, 0.34 #449), 0fkvn (0.53 #303, 0.38 #372, 0.35 #395), 0p5vf (0.27 #174, 0.25 #197, 0.22 #289), 0pqc5 (0.23 #879, 0.22 #833, 0.16 #994), 02079p (0.21 #149, 0.20 #80, 0.13 #172), 04syw (0.21 #122, 0.17 #191, 0.16 #283), 0789n (0.20 #171, 0.20 #79, 0.17 #309) >> Best rule #256 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 03rj0; 04g5k; 0jhd; >> query: (?x9376, 060c4) <- contains(?x455, ?x9376), ?x455 = 02j9z, administrative_parent(?x9376, ?x551) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #303 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 05kr_; *> query: (?x9376, 0fkvn) <- location_of_ceremony(?x566, ?x9376), taxonomy(?x9376, ?x939), religion(?x9376, ?x1985) *> conf = 0.53 ranks of expected_values: 5 EVAL 07ytt jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 62.000 0.700 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #12248-01d8l PRED entity: 01d8l PRED relation: medal PRED expected values: 02lq67 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2): 02lq67 (0.83 #78, 0.82 #72, 0.82 #42), 02lpp7 (0.82 #73, 0.82 #43, 0.82 #101) >> Best rule #78 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 047yc; >> query: (?x10801, 02lq67) <- combatants(?x8949, ?x10801), contains(?x10801, ?x1458), combatants(?x1679, ?x8949), olympics(?x10801, ?x2553), locations(?x2553, ?x4627) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d8l medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.833 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #12247-04mcw4 PRED entity: 04mcw4 PRED relation: award PRED expected values: 05p1dby => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 173): 02hsq3m (0.27 #10068, 0.27 #10067, 0.25 #11008), 03m73lj (0.27 #10068, 0.27 #10067, 0.25 #11008), 05p1dby (0.27 #10068, 0.27 #10067, 0.25 #11008), 0m7yy (0.16 #1302, 0.13 #15932, 0.12 #16402), 019f4v (0.14 #521, 0.13 #756, 0.09 #1458), 07bdd_ (0.14 #1223, 0.13 #15932, 0.12 #16402), 0p9sw (0.13 #15932, 0.12 #16402, 0.11 #1425), 05b1610 (0.13 #15932, 0.12 #16402, 0.11 #499), 02g3ft (0.13 #15932, 0.12 #16402, 0.06 #1706), 02x1z2s (0.13 #15932, 0.12 #16402, 0.05 #10303) >> Best rule #10068 for best value: >> intensional similarity = 2 >> extensional distance = 1002 >> proper extension: 02rq7nd; >> query: (?x4551, ?x2771) <- nominated_for(?x2771, ?x4551), award(?x4551, ?x507) >> conf = 0.27 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 04mcw4 award 05p1dby CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.269 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12246-0139q5 PRED entity: 0139q5 PRED relation: film PRED expected values: 027m67 => 128 concepts (45 used for prediction) PRED predicted values (max 10 best out of 739): 031hcx (0.11 #4852, 0.11 #6641, 0.08 #13797), 03176f (0.09 #4286, 0.09 #6075, 0.04 #13231), 03hxsv (0.09 #4697, 0.09 #6486, 0.03 #13642), 03177r (0.07 #4043, 0.07 #5832, 0.06 #12988), 031778 (0.07 #3895, 0.07 #5684, 0.05 #12840), 027r9t (0.06 #8403, 0.06 #1246, 0.05 #3035), 04jpg2p (0.06 #5041, 0.05 #6830, 0.04 #13986), 031786 (0.06 #4853, 0.05 #6642, 0.03 #13798), 011yg9 (0.06 #4608, 0.05 #6397, 0.03 #11764), 0dl6fv (0.06 #5066, 0.05 #6855, 0.03 #14011) >> Best rule #4852 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 01l2fn; 01bpnd; 040dv; 07rzf; 01vh3r; 015010; >> query: (?x9809, 031hcx) <- nationality(?x9809, ?x1310), languages(?x9809, ?x254), ?x1310 = 02jx1, location(?x9809, ?x206) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #24529 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 272 *> proper extension: 054g1r; 032j_n; *> query: (?x9809, 027m67) <- nominated_for(?x9809, ?x6376), film_release_region(?x6376, ?x2629), ?x2629 = 06f32 *> conf = 0.01 ranks of expected_values: 702 EVAL 0139q5 film 027m67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 45.000 0.111 http://example.org/film/actor/film./film/performance/film #12245-0168nq PRED entity: 0168nq PRED relation: contact_category PRED expected values: 03w5xm => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 3): 03w5xm (0.83 #83, 0.80 #119, 0.79 #144), 02zdwq (0.42 #216, 0.34 #49, 0.33 #52), 014dgf (0.42 #216, 0.33 #55, 0.26 #169) >> Best rule #83 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 03sc8; 07l1c; 03phgz; 04f0xq; 0z90c; 0py9b; 01b39j; 01dfb6; 05njw; 01_lh1; ... >> query: (?x4793, 03w5xm) <- company(?x1491, ?x4793), service_location(?x4793, ?x94), ?x1491 = 0krdk, currency(?x4793, ?x170), ?x170 = 09nqf >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0168nq contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.829 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #12244-0j3b PRED entity: 0j3b PRED relation: contains PRED expected values: 036b_ => 161 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1725): 0j3b (0.55 #35328, 0.50 #32385, 0.49 #52991), 02j7k (0.55 #35328), 059qw (0.55 #35328), 06n3y (0.50 #32385, 0.49 #52991, 0.49 #52990), 0261m (0.50 #32385, 0.49 #52991, 0.49 #52990), 02613 (0.50 #32385, 0.49 #52991, 0.49 #52990), 07c5l (0.50 #32385, 0.49 #52991, 0.49 #52990), 014tss (0.44 #11780, 0.03 #41214), 03gh4 (0.33 #815, 0.29 #21427, 0.29 #18486), 05c17 (0.33 #2424, 0.20 #11260, 0.20 #5369) >> Best rule #35328 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 03v9w; >> query: (?x1144, ?x9857) <- taxonomy(?x1144, ?x939), contains(?x1144, ?x11138), locations(?x326, ?x1144), adjoins(?x11138, ?x9857), partially_contains(?x1144, ?x87) >> conf = 0.55 => this is the best rule for 3 predicted values *> Best rule #39008 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 04pnx; *> query: (?x1144, 036b_) <- taxonomy(?x1144, ?x939), contains(?x1144, ?x11138), contains(?x1144, ?x7479), contains(?x11138, ?x13274), featured_film_locations(?x6636, ?x13274), country(?x150, ?x7479) *> conf = 0.08 ranks of expected_values: 1361 EVAL 0j3b contains 036b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 161.000 18.000 0.552 http://example.org/location/location/contains #12243-02508x PRED entity: 02508x PRED relation: nationality PRED expected values: 02jx1 => 119 concepts (91 used for prediction) PRED predicted values (max 10 best out of 41): 09c7w0 (0.84 #1296, 0.80 #6285, 0.79 #1895), 02jx1 (0.66 #3520, 0.40 #2524, 0.39 #2225), 0dyjz (0.53 #8101, 0.50 #1894, 0.36 #8102), 03rk0 (0.28 #3434, 0.14 #243, 0.06 #8548), 021y1s (0.28 #796, 0.27 #6894, 0.27 #6892), 02qkt (0.21 #8100), 0hzlz (0.12 #8503, 0.09 #7499, 0.09 #7297), 0f8l9c (0.09 #3410, 0.03 #4604, 0.02 #1615), 0d060g (0.08 #804, 0.07 #2896, 0.07 #1202), 0345h (0.08 #724, 0.08 #3419, 0.06 #427) >> Best rule #1296 for best value: >> intensional similarity = 5 >> extensional distance = 94 >> proper extension: 01d494; 01hkhq; >> query: (?x5423, 09c7w0) <- type_of_union(?x5423, ?x566), place_of_birth(?x5423, ?x11840), ?x566 = 04ztj, company(?x5423, ?x2776), nationality(?x5423, ?x512) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #3520 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 597 *> proper extension: 0784v1; *> query: (?x5423, 02jx1) <- nationality(?x5423, ?x512), contains(?x512, ?x9026), ?x9026 = 04lh6, country(?x124, ?x512) *> conf = 0.66 ranks of expected_values: 2 EVAL 02508x nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 91.000 0.844 http://example.org/people/person/nationality #12242-0282x PRED entity: 0282x PRED relation: category PRED expected values: 08mbj5d => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #84, 0.77 #87, 0.74 #95) >> Best rule #84 for best value: >> intensional similarity = 3 >> extensional distance = 383 >> proper extension: 01yzl2; >> query: (?x5345, 08mbj5d) <- nationality(?x5345, ?x512), award(?x5345, ?x575), instrumentalists(?x227, ?x5345) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0282x category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.779 http://example.org/common/topic/webpage./common/webpage/category #12241-02tb17 PRED entity: 02tb17 PRED relation: contains! PRED expected values: 0jgd => 138 concepts (59 used for prediction) PRED predicted values (max 10 best out of 199): 0jgd (0.74 #42984, 0.74 #27763, 0.70 #43880), 09c7w0 (0.69 #18812, 0.62 #30454, 0.61 #39405), 06mkj (0.69 #12539, 0.68 #29555, 0.68 #18809), 0j3b (0.57 #1863), 07c5l (0.43 #2186, 0.21 #48358, 0.09 #32636), 07ssc (0.42 #6301, 0.41 #31378, 0.33 #3614), 02jx1 (0.33 #6356, 0.29 #7251, 0.25 #31433), 0kpys (0.33 #1077, 0.25 #2867, 0.12 #17198), 01ly5m (0.33 #179, 0.14 #1970), 02qkt (0.24 #51394, 0.14 #32588, 0.13 #34378) >> Best rule #42984 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 01rwbd; >> query: (?x12243, ?x142) <- contains(?x11247, ?x12243), teams(?x12243, ?x13795), contains(?x142, ?x11247), olympics(?x142, ?x775) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tb17 contains! 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 59.000 0.742 http://example.org/location/location/contains #12240-0h10vt PRED entity: 0h10vt PRED relation: award_winner! PRED expected values: 01pj5q => 104 concepts (47 used for prediction) PRED predicted values (max 10 best out of 684): 06dv3 (0.82 #36803, 0.82 #33604, 0.81 #62393), 0kszw (0.82 #33604, 0.81 #62393, 0.81 #19202), 0169dl (0.82 #33604, 0.81 #62393, 0.81 #19202), 015rkw (0.82 #33604, 0.81 #62393, 0.81 #19202), 0h10vt (0.55 #3013, 0.33 #1413, 0.29 #62395), 01pj5q (0.55 #2823, 0.33 #1223, 0.29 #62395), 016gr2 (0.33 #173, 0.29 #62395, 0.28 #22406), 02tr7d (0.33 #248, 0.29 #62395, 0.27 #49602), 0h0yt (0.33 #1224, 0.29 #62395, 0.27 #49602), 03v3xp (0.33 #588, 0.29 #62395, 0.27 #49602) >> Best rule #36803 for best value: >> intensional similarity = 3 >> extensional distance = 722 >> proper extension: 01wz_ml; 01vqrm; >> query: (?x9561, ?x2258) <- award_winner(?x9561, ?x2258), location(?x9561, ?x362), place_of_birth(?x2258, ?x1523) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #2823 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 016khd; 06cgy; 030hcs; 0169dl; 0c6qh; 01wz01; 01swck; 018ygt; *> query: (?x9561, 01pj5q) <- award_winner(?x9561, ?x2258), ?x2258 = 0f4vbz, profession(?x9561, ?x4773) *> conf = 0.55 ranks of expected_values: 6 EVAL 0h10vt award_winner! 01pj5q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 104.000 47.000 0.818 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #12239-01mpwj PRED entity: 01mpwj PRED relation: student PRED expected values: 0bwx3 056wb => 99 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1097): 06l6nj (0.33 #5994, 0.06 #14315, 0.03 #12234), 0gs7x (0.33 #6091, 0.06 #10251, 0.03 #12331), 0gd5z (0.33 #4541, 0.06 #8701, 0.03 #10781), 0pk41 (0.33 #5745, 0.06 #9905, 0.03 #11985), 02mqc4 (0.33 #4851, 0.06 #9011, 0.03 #11091), 03_js (0.33 #5707, 0.03 #11947, 0.03 #14028), 01jrvr6 (0.33 #4994, 0.03 #11234, 0.03 #13315), 0d3qd0 (0.25 #2862, 0.09 #7022, 0.06 #9102), 083pr (0.25 #2367, 0.09 #6527, 0.03 #14848), 0dq2k (0.25 #3010, 0.09 #7170) >> Best rule #5994 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 04rwx; 01k7xz; 03ksy; 014zws; >> query: (?x3485, 06l6nj) <- contains(?x3007, ?x3485), student(?x3485, ?x879), major_field_of_study(?x3485, ?x254), ?x3007 = 01qh7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5172 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 04rwx; 01k7xz; 03ksy; 014zws; *> query: (?x3485, 0bwx3) <- contains(?x3007, ?x3485), student(?x3485, ?x879), major_field_of_study(?x3485, ?x254), ?x3007 = 01qh7 *> conf = 0.17 ranks of expected_values: 46, 62 EVAL 01mpwj student 056wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 99.000 63.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01mpwj student 0bwx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 99.000 63.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #12238-016yvw PRED entity: 016yvw PRED relation: nationality PRED expected values: 02jx1 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 32): 09c7w0 (0.82 #1079, 0.82 #2745, 0.81 #2941), 02jx1 (0.50 #325, 0.47 #423, 0.39 #2089), 06q1r (0.10 #75, 0.07 #369, 0.06 #467), 0j5g9 (0.10 #60, 0.07 #354, 0.06 #452), 06mkj (0.08 #143, 0.06 #535, 0.03 #8240), 03rk0 (0.06 #1318, 0.06 #10346, 0.06 #7891), 0d060g (0.05 #1869, 0.05 #1477, 0.05 #2359), 0chghy (0.03 #8240, 0.03 #990, 0.02 #1480), 0f8l9c (0.03 #8240, 0.03 #4333, 0.02 #2274), 0345h (0.03 #8240, 0.03 #1107, 0.02 #1499) >> Best rule #1079 for best value: >> intensional similarity = 3 >> extensional distance = 182 >> proper extension: 02r3cn; >> query: (?x5363, 09c7w0) <- participant(?x5363, ?x5246), spouse(?x5363, ?x11354), nationality(?x5363, ?x429) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #325 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 01w3lzq; 01bpnd; *> query: (?x5363, 02jx1) <- people(?x5056, ?x5363), ?x5056 = 02g7sp, award(?x5363, ?x591) *> conf = 0.50 ranks of expected_values: 2 EVAL 016yvw nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 113.000 0.821 http://example.org/people/person/nationality #12237-0gj9qxr PRED entity: 0gj9qxr PRED relation: film_release_region PRED expected values: 0154j 0d060g 0ctw_b 035qy => 148 concepts (133 used for prediction) PRED predicted values (max 10 best out of 258): 035qy (0.90 #1410, 0.89 #4016, 0.89 #3098), 05qhw (0.90 #3540, 0.89 #3081, 0.85 #3999), 0154j (0.89 #3836, 0.88 #3530, 0.84 #3989), 03h64 (0.87 #2057, 0.87 #2363, 0.84 #3131), 0b90_r (0.85 #1382, 0.83 #2302, 0.81 #3835), 05v8c (0.85 #1395, 0.83 #2009, 0.82 #3083), 05b4w (0.83 #2054, 0.82 #3434, 0.78 #6497), 06bnz (0.82 #3569, 0.80 #4028, 0.80 #3110), 0d060g (0.78 #3532, 0.76 #3073, 0.75 #1385), 06qd3 (0.76 #3561, 0.74 #2028, 0.70 #1414) >> Best rule #1410 for best value: >> intensional similarity = 11 >> extensional distance = 18 >> proper extension: 01fmys; >> query: (?x1552, 035qy) <- currency(?x1552, ?x1099), film_release_region(?x1552, ?x3749), film_release_region(?x1552, ?x1892), film_release_region(?x1552, ?x1122), film_release_region(?x1552, ?x774), film_release_region(?x1552, ?x512), ?x3749 = 03ryn, ?x774 = 06mzp, ?x1892 = 02vzc, ?x512 = 07ssc, ?x1122 = 09pmkv >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 9, 14 EVAL 0gj9qxr film_release_region 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 133.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gj9qxr film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 148.000 133.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gj9qxr film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 148.000 133.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gj9qxr film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 148.000 133.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12236-03s2y9 PRED entity: 03s2y9 PRED relation: nationality PRED expected values: 09c7w0 => 124 concepts (120 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.88 #201, 0.87 #1404, 0.87 #1002), 07ssc (0.20 #15, 0.19 #415, 0.19 #1618), 02jx1 (0.20 #33, 0.14 #433, 0.12 #534), 0d060g (0.07 #808, 0.06 #1008, 0.05 #1108), 03rk0 (0.06 #11174, 0.06 #11074, 0.05 #11574), 03rt9 (0.06 #413, 0.05 #1616, 0.05 #514), 0345h (0.05 #632, 0.05 #732, 0.04 #832), 03rjj (0.05 #606, 0.04 #906, 0.03 #1207), 0d05w3 (0.04 #2455, 0.03 #2355, 0.03 #2956), 0h7x (0.03 #335, 0.02 #736, 0.02 #3442) >> Best rule #201 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 05zbm4; 0lrh; 085pr; 0crqcc; 04xn2m; >> query: (?x11625, 09c7w0) <- profession(?x11625, ?x987), student(?x3424, ?x11625), ?x3424 = 01w5m, ?x987 = 0dxtg >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s2y9 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 120.000 0.880 http://example.org/people/person/nationality #12235-03ftmg PRED entity: 03ftmg PRED relation: category PRED expected values: 08mbj5d => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.56 #80, 0.51 #41, 0.51 #40) >> Best rule #80 for best value: >> intensional similarity = 5 >> extensional distance = 899 >> proper extension: 07q1v4; 01p9hgt; 0244r8; 09mq4m; 09hnb; 01k98nm; 01309x; 01wy61y; 01l4g5; 07j8kh; ... >> query: (?x7264, 08mbj5d) <- profession(?x7264, ?x2225), profession(?x12194, ?x2225), profession(?x8753, ?x2225), ?x12194 = 01mbwlb, ?x8753 = 0yxl >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03ftmg category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.558 http://example.org/common/topic/webpage./common/webpage/category #12234-0c3zjn7 PRED entity: 0c3zjn7 PRED relation: country PRED expected values: 03rk0 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 24): 0chghy (0.51 #2712, 0.44 #1565, 0.42 #3554), 0d060g (0.51 #2712, 0.07 #3855, 0.07 #68), 07ssc (0.24 #196, 0.22 #376, 0.22 #1340), 0345h (0.17 #207, 0.16 #387, 0.14 #267), 0f8l9c (0.11 #199, 0.10 #1584, 0.09 #2368), 03_3d (0.10 #127, 0.07 #3855, 0.06 #548), 0j1z8 (0.07 #3855, 0.07 #11), 03rjj (0.07 #3855, 0.04 #186, 0.04 #667), 03h64 (0.07 #3855, 0.03 #226, 0.03 #406), 0d05w3 (0.07 #3855, 0.03 #584, 0.02 #463) >> Best rule #2712 for best value: >> intensional similarity = 4 >> extensional distance = 1230 >> proper extension: 0kbhf; >> query: (?x5553, ?x390) <- film(?x844, ?x5553), genre(?x5553, ?x225), participant(?x226, ?x844), nationality(?x844, ?x390) >> conf = 0.51 => this is the best rule for 2 predicted values *> Best rule #2266 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1143 *> proper extension: 01h72l; *> query: (?x5553, 03rk0) <- language(?x5553, ?x254), nominated_for(?x844, ?x5553), film(?x844, ?x97) *> conf = 0.01 ranks of expected_values: 24 EVAL 0c3zjn7 country 03rk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 73.000 73.000 0.509 http://example.org/film/film/country #12233-01j_x PRED entity: 01j_x PRED relation: company! PRED expected values: 0d4jl => 95 concepts (90 used for prediction) PRED predicted values (max 10 best out of 678): 034ls (0.40 #2327, 0.29 #4266, 0.25 #874), 03gkn5 (0.40 #2240, 0.29 #4179, 0.25 #1029), 0d05fv (0.25 #10753, 0.25 #1056, 0.25 #814), 0203v (0.25 #750, 0.20 #2203, 0.20 #1961), 0157m (0.25 #751, 0.20 #2204, 0.20 #1962), 028rk (0.25 #773, 0.20 #2226, 0.20 #1984), 0d06m5 (0.25 #786, 0.20 #2239, 0.20 #1997), 042kg (0.25 #943, 0.20 #2396, 0.20 #2154), 042fk (0.25 #965, 0.20 #2418, 0.20 #2176), 06c0j (0.25 #961, 0.20 #2414, 0.20 #2172) >> Best rule #2327 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0d6qjf; >> query: (?x13471, 034ls) <- company(?x11217, ?x13471), company(?x4988, ?x13471), entity_involved(?x10351, ?x13471), location(?x4988, ?x957) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #9995 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 017d77; 03p7gb; *> query: (?x13471, 0d4jl) <- company(?x4988, ?x13471), student(?x2999, ?x4988), influenced_by(?x5940, ?x4988), profession(?x4988, ?x319), award(?x4988, ?x68) *> conf = 0.10 ranks of expected_values: 72 EVAL 01j_x company! 0d4jl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 95.000 90.000 0.400 http://example.org/people/person/employment_history./business/employment_tenure/company #12232-019389 PRED entity: 019389 PRED relation: nationality PRED expected values: 09c7w0 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.76 #1701, 0.72 #1301, 0.70 #6502), 02jx1 (0.26 #833, 0.24 #1433, 0.24 #1833), 07ssc (0.13 #815, 0.13 #1415, 0.13 #1815), 035qy (0.12 #34, 0.06 #334, 0.03 #534), 0chghy (0.08 #210, 0.02 #3110, 0.02 #7413), 03rt9 (0.08 #213, 0.02 #1813, 0.02 #1413), 03_3d (0.08 #206, 0.02 #2506, 0.02 #7109), 0jgx (0.06 #358, 0.05 #458, 0.03 #558), 03rk0 (0.06 #9450, 0.06 #9950, 0.06 #9550), 0d060g (0.05 #1307, 0.05 #2007, 0.05 #3907) >> Best rule #1701 for best value: >> intensional similarity = 4 >> extensional distance = 171 >> proper extension: 01wyq0w; >> query: (?x7874, 09c7w0) <- gender(?x7874, ?x231), award_nominee(?x3118, ?x7874), artist(?x1693, ?x3118), celebrity(?x7375, ?x3118) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019389 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.757 http://example.org/people/person/nationality #12231-0l6vl PRED entity: 0l6vl PRED relation: locations PRED expected values: 04swd => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 36): 06y57 (0.33 #680, 0.25 #1859, 0.25 #1664), 052p7 (0.33 #447, 0.25 #1426, 0.20 #2209), 02h6_6p (0.33 #256, 0.25 #1038, 0.09 #3191), 03khn (0.33 #162, 0.17 #2506, 0.14 #2705), 04sqj (0.33 #910, 0.17 #2472, 0.14 #2671), 04jpl (0.25 #1381, 0.25 #989, 0.14 #2754), 01f62 (0.25 #1606, 0.14 #2780, 0.07 #3560), 013yq (0.25 #1817, 0.14 #2796, 0.07 #3576), 07dfk (0.25 #2105, 0.07 #3670, 0.06 #3868), 030qb3t (0.20 #2187, 0.18 #3165, 0.14 #2777) >> Best rule #680 for best value: >> intensional similarity = 55 >> extensional distance = 1 >> proper extension: 0jdk_; >> query: (?x391, 06y57) <- olympics(?x7413, ?x391), olympics(?x3728, ?x391), olympics(?x3357, ?x391), olympics(?x1892, ?x391), olympics(?x1203, ?x391), olympics(?x1003, ?x391), olympics(?x421, ?x391), olympics(?x304, ?x391), ?x1892 = 02vzc, sports(?x391, ?x5396), sports(?x391, ?x3127), sports(?x391, ?x1967), sports(?x391, ?x171), ?x5396 = 0486tv, ?x3127 = 03hr1p, olympics(?x279, ?x391), olympics(?x151, ?x391), film_release_region(?x7832, ?x7413), film_release_region(?x3076, ?x7413), film_release_region(?x2746, ?x7413), film_release_region(?x2656, ?x7413), film_release_region(?x1956, ?x7413), ?x304 = 0d0vqn, ?x7832 = 0fphf3v, ?x421 = 03_r3, ?x3076 = 0g5838s, ?x1003 = 03gj2, medal(?x3357, ?x422), locations(?x7241, ?x3357), ?x2656 = 03qnc6q, participating_countries(?x418, ?x3357), film_release_region(?x4707, ?x1203), film_release_region(?x2889, ?x1203), film_release_region(?x2050, ?x1203), film_release_region(?x1988, ?x1203), contains(?x7273, ?x1203), ?x4707 = 02xbyr, ?x1956 = 05qbckf, ?x422 = 02lq67, ?x2746 = 04f52jw, ?x171 = 0d1tm, participating_countries(?x4255, ?x1203), official_language(?x1203, ?x2502), contains(?x7413, ?x461), combatants(?x3728, ?x792), ?x1967 = 01cgz, location(?x5283, ?x1203), administrative_area_type(?x1203, ?x2792), location(?x2012, ?x3728), ?x279 = 0d060g, ?x2050 = 01fmys, ?x151 = 0b90_r, ?x2889 = 040b5k, ?x1988 = 09k56b7, ?x7273 = 07c5l >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0l6vl locations 04swd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.333 http://example.org/time/event/locations #12230-047xyn PRED entity: 047xyn PRED relation: award_winner PRED expected values: 02y49 => 42 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1133): 0210f1 (0.50 #6488, 0.44 #8954, 0.40 #16343), 02y49 (0.50 #6836, 0.33 #1907, 0.31 #9302), 03772 (0.50 #6071, 0.33 #1142, 0.31 #8537), 01dzz7 (0.50 #5289, 0.33 #360, 0.20 #12681), 04mhl (0.50 #5913, 0.33 #984, 0.20 #15768), 01dhmw (0.38 #8117, 0.35 #13043, 0.33 #10580), 05x8n (0.33 #1479, 0.25 #13800, 0.25 #8874), 03rx9 (0.33 #2055, 0.25 #6984, 0.20 #16839), 018fq (0.33 #1151, 0.25 #6080, 0.12 #8546), 0fpzt5 (0.31 #9293, 0.28 #16682, 0.28 #11756) >> Best rule #6488 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 0262zm; >> query: (?x4879, 0210f1) <- award_winner(?x4879, ?x10275), award_winner(?x4879, ?x7828), award_winner(?x4879, ?x6055), award_winner(?x4879, ?x2343), ?x10275 = 03hpr, award(?x7828, ?x575), influenced_by(?x1287, ?x6055), student(?x5288, ?x6055), influenced_by(?x2343, ?x118) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6836 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 0262zm; *> query: (?x4879, 02y49) <- award_winner(?x4879, ?x10275), award_winner(?x4879, ?x7828), award_winner(?x4879, ?x6055), award_winner(?x4879, ?x2343), ?x10275 = 03hpr, award(?x7828, ?x575), influenced_by(?x1287, ?x6055), student(?x5288, ?x6055), influenced_by(?x2343, ?x118) *> conf = 0.50 ranks of expected_values: 2 EVAL 047xyn award_winner 02y49 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 42.000 16.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #12229-022yb4 PRED entity: 022yb4 PRED relation: award PRED expected values: 0bp_b2 0bdw6t 02x4w6g => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 229): 09sb52 (0.36 #9311, 0.34 #6489, 0.34 #15356), 05pcn59 (0.26 #887, 0.18 #1290, 0.17 #2096), 0bfvd4 (0.25 #114, 0.12 #24183, 0.09 #6562), 0bs0bh (0.25 #102, 0.12 #24183, 0.09 #505), 0bp_b2 (0.22 #421, 0.12 #18, 0.12 #9270), 05p09zm (0.21 #929, 0.17 #1332, 0.15 #2138), 0fbtbt (0.17 #635, 0.12 #232, 0.12 #9270), 0bdx29 (0.17 #510, 0.12 #9270, 0.12 #24183), 05b4l5x (0.17 #812, 0.14 #1215, 0.14 #2021), 0gqy2 (0.16 #6612, 0.15 #9837, 0.13 #7418) >> Best rule #9311 for best value: >> intensional similarity = 3 >> extensional distance = 743 >> proper extension: 08_83x; 02fgm7; >> query: (?x8431, 09sb52) <- award_nominee(?x8431, ?x7367), award_winner(?x6706, ?x8431), actor(?x8870, ?x7367) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 21 *> proper extension: 05gnf; *> query: (?x8431, 0bp_b2) <- nominated_for(?x8431, ?x337), ?x337 = 0g60z *> conf = 0.22 ranks of expected_values: 5, 15, 35 EVAL 022yb4 award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 101.000 101.000 0.360 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 022yb4 award 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 101.000 101.000 0.360 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 022yb4 award 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 101.000 0.360 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12228-0241jw PRED entity: 0241jw PRED relation: film PRED expected values: 0gs973 02dr9j => 79 concepts (43 used for prediction) PRED predicted values (max 10 best out of 261): 0btyf5z (0.33 #8916, 0.30 #39229), 049xgc (0.08 #969, 0.06 #51711, 0.03 #58848), 0djlxb (0.08 #532, 0.06 #51711, 0.03 #58848), 02r858_ (0.08 #1420, 0.06 #51711, 0.03 #58848), 08nvyr (0.08 #764, 0.06 #51711, 0.03 #58848), 01vksx (0.08 #135, 0.06 #51711, 0.03 #58848), 027m5wv (0.08 #1052, 0.06 #51711, 0.02 #21398), 01qz5 (0.08 #1411, 0.06 #51711, 0.02 #21398), 02qzh2 (0.08 #690, 0.03 #58848, 0.02 #21398), 07f_t4 (0.08 #1327, 0.03 #58848, 0.02 #21398) >> Best rule #8916 for best value: >> intensional similarity = 2 >> extensional distance = 432 >> proper extension: 024rbz; 01nzs7; 0p5mw; 0ccd3x; 09mfvx; >> query: (?x1846, ?x1392) <- category(?x1846, ?x134), nominated_for(?x1846, ?x1392) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0241jw film 02dr9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 43.000 0.329 http://example.org/film/actor/film./film/performance/film EVAL 0241jw film 0gs973 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 43.000 0.329 http://example.org/film/actor/film./film/performance/film #12227-03h_yfh PRED entity: 03h_yfh PRED relation: nationality PRED expected values: 09c7w0 => 116 concepts (111 used for prediction) PRED predicted values (max 10 best out of 66): 09c7w0 (0.89 #6131, 0.89 #6031, 0.88 #6838), 0gyh (0.45 #3816, 0.33 #8756, 0.31 #3817), 020d5 (0.37 #1304), 01n7q (0.32 #6636, 0.02 #9559), 02jx1 (0.19 #233, 0.18 #2841, 0.16 #1638), 07ssc (0.19 #1218, 0.14 #15, 0.13 #515), 0f8l9c (0.16 #1024, 0.12 #1225, 0.05 #322), 0h7x (0.14 #35, 0.07 #535, 0.06 #1037), 03gj2 (0.14 #26, 0.03 #526, 0.02 #927), 0d060g (0.11 #107, 0.06 #1009, 0.06 #2915) >> Best rule #6131 for best value: >> intensional similarity = 4 >> extensional distance = 592 >> proper extension: 05jcn8; 03_wvl; 07h5d; >> query: (?x7803, ?x94) <- award(?x7803, ?x2139), place_of_birth(?x7803, ?x12384), country(?x12384, ?x94), source(?x12384, ?x958) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03h_yfh nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 111.000 0.891 http://example.org/people/person/nationality #12226-0h63q6t PRED entity: 0h63q6t PRED relation: genre PRED expected values: 01htzx => 117 concepts (64 used for prediction) PRED predicted values (max 10 best out of 197): 03k9fj (0.75 #247, 0.55 #1163, 0.52 #1329), 02kdv5l (0.75 #247, 0.20 #824, 0.16 #906), 01htzx (0.53 #1002, 0.52 #1167, 0.51 #1250), 0hcr (0.50 #839, 0.33 #181, 0.32 #921), 05p553 (0.49 #1406, 0.48 #4145, 0.46 #1906), 0jxy (0.40 #852, 0.33 #194, 0.26 #934), 01z77k (0.40 #438, 0.15 #2261, 0.12 #4419), 03npn (0.38 #501, 0.36 #747, 0.30 #665), 0fdjb (0.38 #525, 0.30 #689, 0.27 #771), 01z4y (0.33 #1918, 0.32 #1418, 0.31 #4157) >> Best rule #247 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 07ng9k; >> query: (?x11549, ?x225) <- genre(?x11549, ?x1510), genre(?x11549, ?x1013), genre(?x11549, ?x53), ?x53 = 07s9rl0, ?x1013 = 06n90, ?x1510 = 01hmnh, film(?x1677, ?x11549), genre(?x11549, ?x225) >> conf = 0.75 => this is the best rule for 2 predicted values *> Best rule #1002 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 32 *> proper extension: 06cs95; 027tbrc; 0431v3; 0dr1c2; 04f6hhm; 0fhzwl; 02q_x_l; 053x8hr; 02py9yf; 0123qq; ... *> query: (?x11549, 01htzx) <- genre(?x11549, ?x1013), genre(?x11549, ?x53), ?x53 = 07s9rl0, genre(?x8971, ?x1013), ?x8971 = 012gk9, program(?x6678, ?x11549) *> conf = 0.53 ranks of expected_values: 3 EVAL 0h63q6t genre 01htzx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 64.000 0.750 http://example.org/tv/tv_program/genre #12225-0dv9v PRED entity: 0dv9v PRED relation: featured_film_locations! PRED expected values: 08hmch => 107 concepts (45 used for prediction) PRED predicted values (max 10 best out of 514): 08hmch (0.33 #67, 0.12 #804, 0.11 #1541), 0x25q (0.33 #221, 0.12 #958, 0.11 #1695), 05pt0l (0.33 #546, 0.12 #1283, 0.11 #2020), 02b61v (0.33 #433, 0.12 #1170, 0.11 #1907), 0dfw0 (0.33 #362, 0.12 #1099, 0.11 #1836), 026qnh6 (0.33 #354, 0.12 #1091, 0.11 #1828), 0ddt_ (0.33 #214, 0.12 #951, 0.11 #1688), 07cz2 (0.33 #202, 0.12 #939, 0.11 #1676), 0fdv3 (0.33 #124, 0.12 #861, 0.11 #1598), 044g_k (0.33 #97, 0.12 #834, 0.11 #1571) >> Best rule #67 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 06y57; >> query: (?x13836, 08hmch) <- state(?x13836, ?x8506), ?x8506 = 05fly, location(?x4153, ?x13836), country(?x13836, ?x390) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dv9v featured_film_locations! 08hmch CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 45.000 0.333 http://example.org/film/film/featured_film_locations #12224-08cn_n PRED entity: 08cn_n PRED relation: type_of_appearance PRED expected values: 01jdpf => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1): 01jdpf (0.04 #11, 0.04 #8, 0.04 #5) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 232 >> proper extension: 0p8jf; 0gv5c; 043hg; 06z4wj; 0c4y8; 0ff2k; 02xyl; >> query: (?x8118, 01jdpf) <- award_winner(?x10550, ?x8118), award(?x8118, ?x350), written_by(?x5361, ?x8118) >> conf = 0.04 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08cn_n type_of_appearance 01jdpf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.038 http://example.org/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance #12223-03rg2b PRED entity: 03rg2b PRED relation: film_release_region PRED expected values: 0154j 0h7x => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 114): 09c7w0 (0.94 #5455, 0.93 #4945, 0.93 #4774), 03h64 (0.84 #1784, 0.75 #251, 0.71 #1444), 05r4w (0.81 #1365, 0.80 #1705, 0.79 #1875), 03rjj (0.80 #1711, 0.79 #178, 0.78 #1371), 0chghy (0.78 #1719, 0.76 #1379, 0.76 #1889), 03_3d (0.77 #180, 0.74 #1713, 0.72 #1373), 0jgd (0.76 #1368, 0.74 #1708, 0.73 #1878), 0345h (0.75 #212, 0.75 #1745, 0.74 #1405), 03gj2 (0.75 #203, 0.73 #1396, 0.71 #1736), 015fr (0.70 #1727, 0.67 #1387, 0.64 #1897) >> Best rule #5455 for best value: >> intensional similarity = 4 >> extensional distance = 644 >> proper extension: 014lc_; 0g22z; 018js4; 01jc6q; 027qgy; 08lr6s; 016fyc; 034qrh; 016z5x; 026p_bs; ... >> query: (?x6218, 09c7w0) <- film(?x1548, ?x6218), film_release_region(?x6218, ?x5036), produced_by(?x6218, ?x3637), place_of_death(?x13091, ?x5036) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #1710 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 350 *> proper extension: 0fq27fp; *> query: (?x6218, 0154j) <- film_release_region(?x6218, ?x5036), month(?x5036, ?x1459), mode_of_transportation(?x5036, ?x6665) *> conf = 0.68 ranks of expected_values: 13, 26 EVAL 03rg2b film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 81.000 81.000 0.940 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rg2b film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 81.000 81.000 0.940 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12222-0h14ln PRED entity: 0h14ln PRED relation: film_crew_role PRED expected values: 09vw2b7 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 22): 09vw2b7 (0.64 #1038, 0.62 #1494, 0.62 #1137), 01pvkk (0.35 #75, 0.29 #944, 0.29 #1271), 02rh1dz (0.17 #42, 0.14 #587, 0.11 #1041), 02vs3x5 (0.17 #53, 0.10 #2633, 0.06 #405), 0215hd (0.15 #80, 0.13 #400, 0.12 #982), 089g0h (0.15 #81, 0.11 #177, 0.11 #209), 015h31 (0.13 #586, 0.10 #2633, 0.09 #1040), 02_n3z (0.11 #194, 0.11 #98, 0.10 #2633), 0d2b38 (0.11 #215, 0.11 #183, 0.11 #989), 01xy5l_ (0.11 #205, 0.11 #173, 0.10 #237) >> Best rule #1038 for best value: >> intensional similarity = 5 >> extensional distance = 766 >> proper extension: 03mh94; 0c40vxk; 0gkz15s; 0bq8tmw; 031t2d; 024l2y; 0bh8yn3; 0j_tw; 02qr69m; 07x4qr; ... >> query: (?x9292, 09vw2b7) <- film(?x398, ?x9292), film_crew_role(?x9292, ?x137), award_winner(?x1588, ?x398), production_companies(?x9292, ?x4564), genre(?x9292, ?x53) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h14ln film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.641 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12221-04c9bn PRED entity: 04c9bn PRED relation: position PRED expected values: 02wszf => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 49): 02wszf (0.87 #83, 0.86 #330, 0.85 #179), 02lyr4 (0.87 #83, 0.85 #84, 0.85 #72), 049k4w (0.35 #190, 0.30 #99, 0.28 #100), 02dwn9 (0.35 #190, 0.30 #99, 0.28 #100), 02s7tr (0.35 #190, 0.30 #99, 0.28 #100), 017drs (0.35 #190, 0.30 #99, 0.28 #100), 02rsl1 (0.35 #190, 0.30 #99, 0.28 #100), 01sdzg (0.35 #190, 0.30 #99, 0.28 #100), 02sg4b (0.35 #190, 0.30 #99, 0.28 #100), 01yvvn (0.35 #190, 0.30 #99, 0.28 #100) >> Best rule #83 for best value: >> intensional similarity = 26 >> extensional distance = 11 >> proper extension: 05g76; >> query: (?x13733, ?x5727) <- team(?x5727, ?x13733), team(?x2010, ?x13733), ?x2010 = 02lyr4, colors(?x13733, ?x1101), colors(?x9835, ?x1101), colors(?x7725, ?x1101), colors(?x2096, ?x1101), colors(?x978, ?x1101), colors(?x12356, ?x1101), colors(?x10178, ?x1101), colors(?x4692, ?x1101), colors(?x4220, ?x1101), colors(?x3671, ?x1101), ?x10178 = 01tntf, sport(?x13733, ?x5063), school_type(?x3671, ?x5931), position(?x2096, ?x60), major_field_of_study(?x4692, ?x5179), ?x4220 = 01v3ht, student(?x12356, ?x7155), state_province_region(?x12356, ?x11542), ?x978 = 03y_f8, ?x5179 = 04gb7, ?x7725 = 07l8x, current_club(?x10501, ?x2096), team(?x13270, ?x9835) >> conf = 0.87 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 04c9bn position 02wszf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.873 http://example.org/sports/sports_team/roster./baseball/baseball_roster_position/position #12220-0125xq PRED entity: 0125xq PRED relation: film_crew_role PRED expected values: 09zzb8 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 30): 02r96rf (0.80 #115, 0.73 #300, 0.72 #448), 09zzb8 (0.77 #928, 0.75 #1188, 0.75 #1114), 0ch6mp2 (0.77 #1195, 0.77 #935, 0.77 #119), 01vx2h (0.50 #12, 0.47 #641, 0.45 #49), 0dxtw (0.50 #11, 0.42 #307, 0.40 #529), 01pvkk (0.36 #13, 0.30 #198, 0.30 #1200), 02ynfr (0.21 #17, 0.20 #350, 0.20 #1204), 094hwz (0.21 #16, 0.13 #53, 0.12 #3247), 0215hd (0.18 #316, 0.16 #464, 0.15 #353), 0d2b38 (0.17 #323, 0.14 #360, 0.13 #471) >> Best rule #115 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 02vxq9m; 011yrp; 0gx9rvq; 01vksx; 0g9wdmc; 03qnc6q; 0879bpq; 0g5838s; 0dgpwnk; 0gh65c5; ... >> query: (?x4441, 02r96rf) <- film_release_region(?x4441, ?x4059), film(?x541, ?x4441), film_crew_role(?x4441, ?x1171), ?x4059 = 077qn >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #928 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 325 *> proper extension: 0gs973; *> query: (?x4441, 09zzb8) <- featured_film_locations(?x4441, ?x739), film_crew_role(?x4441, ?x1171), country(?x4441, ?x94), production_companies(?x4441, ?x541) *> conf = 0.77 ranks of expected_values: 2 EVAL 0125xq film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 106.000 0.797 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12219-02zd2b PRED entity: 02zd2b PRED relation: student PRED expected values: 02s529 => 75 concepts (49 used for prediction) PRED predicted values (max 10 best out of 123): 017r13 (0.06 #1094), 02jsgf (0.06 #680), 0378zn (0.03 #2083), 014v1q (0.03 #2001), 04bdpf (0.03 #1954), 01rs5p (0.03 #1807), 03k48_ (0.03 #1798), 0347xz (0.03 #1711), 033jj1 (0.03 #1682), 07m77x (0.03 #1541) >> Best rule #1094 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0s3y5; 0s69k; 0sf9_; 0s5cg; 0sbbq; 0sjqm; 0sgtz; 0s9z_; 0s987; 0s2z0; ... >> query: (?x5737, 017r13) <- contains(?x3818, ?x5737), contains(?x94, ?x5737), ?x94 = 09c7w0, ?x3818 = 03v0t >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02zd2b student 02s529 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 49.000 0.061 http://example.org/education/educational_institution/students_graduates./education/education/student #12218-0jm64 PRED entity: 0jm64 PRED relation: teams! PRED expected values: 013yq => 66 concepts (65 used for prediction) PRED predicted values (max 10 best out of 77): 030qb3t (0.33 #50, 0.20 #862, 0.17 #1674), 0dyl9 (0.25 #683, 0.20 #1225, 0.17 #1497), 04f_d (0.20 #1146, 0.17 #1418, 0.16 #4057), 0f2w0 (0.20 #869, 0.17 #1681, 0.14 #10833), 0f2tj (0.20 #964, 0.17 #1776, 0.14 #2858), 071vr (0.17 #1510, 0.14 #2591, 0.12 #3132), 01sn3 (0.17 #1739, 0.14 #2821, 0.12 #3361), 02_286 (0.16 #4057, 0.14 #2186, 0.12 #2998), 06wxw (0.16 #4057, 0.14 #2556, 0.12 #3097), 0cr3d (0.14 #10833, 0.14 #2247, 0.14 #1977) >> Best rule #50 for best value: >> intensional similarity = 20 >> extensional distance = 1 >> proper extension: 0jmk7; >> query: (?x6128, 030qb3t) <- position(?x6128, ?x4747), position(?x6128, ?x1579), position(?x6128, ?x1348), ?x4747 = 02sf_r, sport(?x6128, ?x4833), ?x4833 = 018w8, draft(?x6128, ?x8586), draft(?x6128, ?x2569), ?x8586 = 038981, ?x2569 = 038c0q, ?x1348 = 01pv51, school(?x6128, ?x9200), school(?x6128, ?x2948), school(?x6128, ?x581), ?x1579 = 0ctt4z, student(?x9200, ?x4137), ?x581 = 06pwq, institution(?x2636, ?x2948), major_field_of_study(?x2948, ?x1154), ?x2636 = 027f2w >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10633 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 34 *> proper extension: 01lpx8; *> query: (?x6128, 013yq) <- team(?x13926, ?x6128), team(?x13926, ?x9995), team(?x13926, ?x9931), location(?x13926, ?x13535), gender(?x13926, ?x231), colors(?x9995, ?x332), athlete(?x4833, ?x13926), ?x231 = 05zppz, team(?x1348, ?x9931), type_of_union(?x13926, ?x566), teams(?x2879, ?x9995), people(?x2510, ?x13926), ?x2510 = 0x67, contains(?x3634, ?x13535) *> conf = 0.06 ranks of expected_values: 35 EVAL 0jm64 teams! 013yq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 66.000 65.000 0.333 http://example.org/sports/sports_team_location/teams #12217-01pl14 PRED entity: 01pl14 PRED relation: contains! PRED expected values: 0fvvz => 196 concepts (131 used for prediction) PRED predicted values (max 10 best out of 326): 0fvvz (0.69 #23258, 0.68 #101992, 0.67 #101095), 059rby (0.32 #103802, 0.19 #49223, 0.19 #22382), 07b_l (0.25 #221, 0.20 #63741, 0.16 #67318), 01n7q (0.25 #4548, 0.17 #2760, 0.15 #109227), 03s0w (0.25 #57, 0.07 #63577, 0.06 #67154), 0_ytw (0.25 #103, 0.07 #8151, 0.05 #13520), 0nmj (0.25 #571, 0.03 #20249, 0.02 #29198), 0f2tj (0.21 #7519, 0.17 #12886, 0.07 #9307), 039b7_ (0.20 #2639, 0.06 #13371, 0.01 #93937), 03rjj (0.17 #2692, 0.10 #5375, 0.07 #6269) >> Best rule #23258 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 031n8c; 032d52; >> query: (?x466, ?x1248) <- registering_agency(?x466, ?x1982), school_type(?x466, ?x1507), category(?x466, ?x134), citytown(?x466, ?x1248) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pl14 contains! 0fvvz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 131.000 0.691 http://example.org/location/location/contains #12216-03lt8g PRED entity: 03lt8g PRED relation: languages PRED expected values: 06nm1 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 11): 064_8sq (0.08 #242, 0.08 #1306, 0.05 #204), 03k50 (0.07 #1295, 0.03 #1599, 0.02 #421), 0t_2 (0.04 #236, 0.02 #350, 0.02 #388), 02bjrlw (0.04 #1293, 0.03 #229, 0.02 #1255), 07c9s (0.04 #1304, 0.02 #430, 0.01 #1608), 06nm1 (0.03 #1297, 0.03 #233, 0.01 #955), 04306rv (0.03 #230, 0.03 #1294, 0.01 #534), 0999q (0.02 #440, 0.02 #1314), 09s02 (0.02 #453, 0.02 #1327), 03_9r (0.01 #1296) >> Best rule #242 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 04bs3j; 04shbh; 0n6f8; 031zkw; 0285c; 03rl84; 046lt; 02wb6yq; 01jbx1; 05r5w; ... >> query: (?x1117, 064_8sq) <- participant(?x9781, ?x1117), participant(?x444, ?x1117), languages(?x1117, ?x254) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #1297 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 639 *> proper extension: 03gkn5; 043hg; 067xw; 01x53m; 063tn; 0kr7k; 03z_g7; *> query: (?x1117, 06nm1) <- languages(?x1117, ?x254), profession(?x1117, ?x319) *> conf = 0.03 ranks of expected_values: 6 EVAL 03lt8g languages 06nm1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 106.000 106.000 0.085 http://example.org/people/person/languages #12215-02rsw PRED entity: 02rsw PRED relation: religion! PRED expected values: 0bymv 03n6r 08959 => 38 concepts (17 used for prediction) PRED predicted values (max 10 best out of 3214): 0d06m5 (0.50 #1324, 0.43 #6615, 0.43 #5556), 0948xk (0.50 #805, 0.27 #13503, 0.18 #12443), 0xnc3 (0.50 #691, 0.27 #13389, 0.09 #12329), 02c4s (0.50 #103, 0.18 #12801, 0.09 #11741), 0157m (0.38 #7515, 0.33 #4340, 0.30 #9629), 0bymv (0.38 #7574, 0.33 #4399, 0.30 #9688), 0315q3 (0.33 #4618, 0.33 #3561, 0.29 #6735), 0cqt90 (0.33 #4541, 0.33 #3484, 0.29 #6658), 043gj (0.33 #4620, 0.33 #3563, 0.29 #6737), 0mb5x (0.33 #9162, 0.33 #2813, 0.25 #16568) >> Best rule #1324 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 05sfs; 051kv; >> query: (?x9362, 0d06m5) <- religion(?x11956, ?x9362), religion(?x10161, ?x9362), religion(?x1600, ?x9362), company(?x1600, ?x94), location(?x1600, ?x3014), ?x94 = 09c7w0, politician(?x8714, ?x1600), ?x8714 = 0d075m, profession(?x1600, ?x5805), languages(?x10161, ?x254), entity_involved(?x8416, ?x11956), student(?x620, ?x1600), award_nominee(?x10161, ?x336) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7574 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 6 *> proper extension: 0v53x; *> query: (?x9362, 0bymv) <- religion(?x10161, ?x9362), religion(?x4196, ?x9362), religion(?x1600, ?x9362), company(?x1600, ?x94), location(?x1600, ?x3014), ?x94 = 09c7w0, politician(?x8714, ?x1600), ?x8714 = 0d075m, profession(?x1600, ?x5805), languages(?x10161, ?x254), celebrities_impersonated(?x2101, ?x4196), gender(?x1600, ?x231), student(?x122, ?x4196) *> conf = 0.38 ranks of expected_values: 6, 993, 1199 EVAL 02rsw religion! 08959 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 38.000 17.000 0.500 http://example.org/people/person/religion EVAL 02rsw religion! 03n6r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 38.000 17.000 0.500 http://example.org/people/person/religion EVAL 02rsw religion! 0bymv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 38.000 17.000 0.500 http://example.org/people/person/religion #12214-0184tc PRED entity: 0184tc PRED relation: film_release_region PRED expected values: 0d060g 0k6nt => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 143): 06mkj (0.87 #1038, 0.87 #3474, 0.86 #2663), 0k6nt (0.85 #1978, 0.84 #1816, 0.84 #2140), 0345h (0.85 #2636, 0.84 #849, 0.82 #3285), 07ssc (0.85 #992, 0.84 #3428, 0.83 #1806), 035qy (0.82 #1827, 0.82 #2800, 0.82 #3449), 0154j (0.79 #2768, 0.78 #2606, 0.77 #3255), 05b4w (0.78 #2834, 0.78 #1861, 0.76 #3483), 01znc_ (0.77 #2647, 0.77 #3458, 0.75 #1022), 03spz (0.76 #2704, 0.75 #1079, 0.72 #2866), 0d060g (0.76 #3419, 0.76 #2770, 0.74 #1797) >> Best rule #1038 for best value: >> intensional similarity = 6 >> extensional distance = 51 >> proper extension: 0g56t9t; 0gkz15s; 02prwdh; 0bdjd; 01mgw; >> query: (?x3998, 06mkj) <- film_release_region(?x3998, ?x1558), film_release_region(?x3998, ?x1229), written_by(?x3998, ?x11797), ?x1229 = 059j2, genre(?x3998, ?x307), ?x1558 = 01mjq >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1978 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 106 *> proper extension: 07k2mq; *> query: (?x3998, 0k6nt) <- film_release_region(?x3998, ?x1229), written_by(?x3998, ?x11797), ?x1229 = 059j2, film(?x3396, ?x3998) *> conf = 0.85 ranks of expected_values: 2, 10 EVAL 0184tc film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0184tc film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 100.000 100.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12213-05wqr1 PRED entity: 05wqr1 PRED relation: award PRED expected values: 0ck27z => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 243): 0ck27z (0.64 #499, 0.32 #4153, 0.31 #6183), 0cqhk0 (0.52 #849, 0.18 #6127, 0.18 #4097), 09sb52 (0.35 #7349, 0.34 #8567, 0.32 #4507), 0cqh6z (0.25 #68, 0.03 #6158, 0.03 #3722), 0bdwqv (0.16 #1798, 0.14 #30454, 0.12 #16647), 0bfvd4 (0.15 #1740, 0.10 #2958, 0.08 #11078), 0gqyl (0.15 #26393, 0.14 #30454, 0.12 #106), 02z0dfh (0.15 #26393, 0.12 #76, 0.12 #16647), 0cqgl9 (0.15 #26393, 0.12 #194, 0.12 #16647), 0bfvw2 (0.15 #26393, 0.12 #15, 0.12 #16647) >> Best rule #499 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 06b0d2; >> query: (?x7992, 0ck27z) <- award_nominee(?x7992, ?x7473), award_nominee(?x7992, ?x2545), ?x7473 = 06w2yp9, nationality(?x2545, ?x94) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05wqr1 award 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.636 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12212-0d5wn3 PRED entity: 0d5wn3 PRED relation: film_production_design_by! PRED expected values: 057__d => 87 concepts (27 used for prediction) PRED predicted values (max 10 best out of 160): 03wy8t (0.08 #455, 0.08 #612, 0.07 #769), 0286hyp (0.04 #472, 0.04 #629, 0.04 #786), 02bqxb (0.04 #471, 0.04 #628, 0.04 #785), 07bxqz (0.04 #470, 0.04 #627, 0.04 #784), 01c9d (0.04 #469, 0.04 #626, 0.04 #783), 0by17xn (0.04 #468, 0.04 #625, 0.04 #782), 0g5ptf (0.04 #465, 0.04 #622, 0.04 #779), 0glbqt (0.04 #464, 0.04 #621, 0.04 #778), 01s9vc (0.04 #463, 0.04 #620, 0.04 #777), 029v40 (0.04 #460, 0.04 #617, 0.04 #774) >> Best rule #455 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 0ft7sr; 04kj2v; 04gmp_z; 0bytkq; 03gyh_z; 0638kv; 07hhnl; 04_1nk; 03wd5tk; 0cdf37; ... >> query: (?x4449, 03wy8t) <- film_production_design_by(?x12430, ?x4449), film_production_design_by(?x2869, ?x4449), award(?x4449, ?x484), film(?x382, ?x2869), titles(?x53, ?x12430) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d5wn3 film_production_design_by! 057__d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 27.000 0.080 http://example.org/film/film/film_production_design_by #12211-05qbbfb PRED entity: 05qbbfb PRED relation: genre PRED expected values: 02kdv5l 01hmnh => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 93): 02kdv5l (0.75 #122, 0.43 #3, 0.39 #1431), 07s9rl0 (0.62 #4881, 0.62 #4643, 0.62 #3572), 01jfsb (0.46 #132, 0.43 #13, 0.40 #1679), 03q4nz (0.38 #138, 0.06 #3709, 0.06 #3590), 05p553 (0.36 #2623, 0.36 #1909, 0.35 #1195), 02l7c8 (0.30 #4896, 0.30 #4658, 0.29 #2872), 0lsxr (0.29 #129, 0.24 #248, 0.23 #1438), 060__y (0.29 #17, 0.17 #731, 0.16 #1683), 04xvlr (0.25 #121, 0.18 #1311, 0.17 #716), 01hmnh (0.21 #137, 0.20 #970, 0.18 #613) >> Best rule #122 for best value: >> intensional similarity = 2 >> extensional distance = 22 >> proper extension: 0436yk; >> query: (?x6053, 02kdv5l) <- genre(?x6053, ?x7160), ?x7160 = 04t2t >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 05qbbfb genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 93.000 93.000 0.750 http://example.org/film/film/genre EVAL 05qbbfb genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.750 http://example.org/film/film/genre #12210-0hsb3 PRED entity: 0hsb3 PRED relation: student PRED expected values: 0d3k14 => 94 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1667): 06hx2 (0.14 #3157, 0.11 #1067, 0.07 #13604), 0194xc (0.14 #3729, 0.11 #1639, 0.07 #14176), 02lt8 (0.14 #2764, 0.11 #674, 0.06 #15301), 047g6 (0.14 #25077, 0.10 #31346, 0.08 #60597), 073v6 (0.11 #525, 0.09 #4705, 0.08 #8884), 01d494 (0.11 #263, 0.07 #2353, 0.04 #4443), 049gc (0.11 #924, 0.07 #3014, 0.04 #5104), 04411 (0.11 #124, 0.07 #2214, 0.04 #4304), 013pp3 (0.11 #921, 0.07 #3011, 0.04 #5101), 0hskw (0.11 #433, 0.07 #2523, 0.04 #4613) >> Best rule #3157 for best value: >> intensional similarity = 2 >> extensional distance = 12 >> proper extension: 04gdr; >> query: (?x6132, 06hx2) <- contains(?x362, ?x6132), organizations_founded(?x11554, ?x6132) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #6032 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 05krk; 06pwq; 04rwx; 07wrz; 0cchk3; 01y8zd; 02607j; 03ksy; 01pq4w; 025v3k; ... *> query: (?x6132, 0d3k14) <- institution(?x734, ?x6132), student(?x6132, ?x1291), service_language(?x6132, ?x254) *> conf = 0.09 ranks of expected_values: 51 EVAL 0hsb3 student 0d3k14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 94.000 91.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #12209-0244r8 PRED entity: 0244r8 PRED relation: role PRED expected values: 0cfdd => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 104): 05r5c (0.30 #640, 0.28 #1486, 0.27 #2013), 0342h (0.25 #2325, 0.24 #2433, 0.24 #2960), 0mkg (0.24 #3062, 0.24 #1054, 0.23 #3061), 02sgy (0.19 #1484, 0.17 #2327, 0.16 #2116), 01vdm0 (0.18 #2143, 0.18 #2462, 0.17 #2354), 042v_gx (0.14 #957, 0.14 #2119, 0.14 #2330), 05842k (0.13 #1558, 0.11 #3036, 0.10 #2509), 018vs (0.13 #1492, 0.11 #2970, 0.10 #2335), 0l14qv (0.12 #321, 0.12 #1483, 0.10 #2961), 026t6 (0.10 #2323, 0.10 #2958, 0.09 #3067) >> Best rule #640 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0jn5l; >> query: (?x1489, 05r5c) <- music(?x1077, ?x1489), artists(?x497, ?x1489), award_nominee(?x3069, ?x1489) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #409 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 0fp_v1x; 07q1v4; 015_30; 09hnb; 037lyl; 050z2; 0fhxv; 01m3b1t; 01vttb9; 04f9r2; *> query: (?x1489, 0cfdd) <- award(?x1489, ?x1443), artists(?x497, ?x1489), ?x1443 = 054krc *> conf = 0.03 ranks of expected_values: 35 EVAL 0244r8 role 0cfdd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 98.000 98.000 0.300 http://example.org/music/artist/track_contributions./music/track_contribution/role #12208-0h7pj PRED entity: 0h7pj PRED relation: location PRED expected values: 0r0m6 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 237): 05fjf (0.44 #4809, 0.40 #12821, 0.30 #41675), 01qh7 (0.18 #154, 0.15 #955, 0.03 #4963), 0cr3d (0.15 #943, 0.09 #142, 0.08 #1744), 027l4q (0.09 #495, 0.08 #1296, 0.06 #2900), 0nbrp (0.09 #656, 0.08 #1457, 0.06 #3061), 0rk71 (0.09 #500, 0.08 #1301, 0.06 #2905), 0qpqn (0.09 #450, 0.08 #1251, 0.06 #2855), 01snm (0.09 #317, 0.08 #1118, 0.06 #2722), 0pzmf (0.09 #312, 0.08 #1113, 0.06 #2717), 06q1r (0.09 #307, 0.08 #1108, 0.06 #2712) >> Best rule #4809 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 03rl84; 01dw9z; 049qx; 01wqflx; >> query: (?x8898, ?x6895) <- nominated_for(?x8898, ?x508), film(?x8898, ?x814), origin(?x8898, ?x6895) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2620 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 03ft8; *> query: (?x8898, 0r0m6) <- written_by(?x8578, ?x8898), religion(?x8898, ?x8613), spouse(?x2763, ?x8898) *> conf = 0.06 ranks of expected_values: 31 EVAL 0h7pj location 0r0m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 125.000 125.000 0.439 http://example.org/people/person/places_lived./people/place_lived/location #12207-02l6h PRED entity: 02l6h PRED relation: currency! PRED expected values: 0pz6q => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 845): 01g6l8 (0.80 #1370, 0.79 #2278, 0.79 #1827), 01lhdt (0.80 #1370, 0.75 #1824, 0.75 #2281), 011xy1 (0.80 #1370, 0.75 #1824, 0.74 #1823), 05t7c1 (0.79 #2278, 0.79 #1827, 0.75 #1824), 057bxr (0.79 #2278, 0.79 #1827, 0.75 #1824), 01fy2s (0.79 #2278, 0.79 #1827, 0.75 #2281), 014b4h (0.79 #2278, 0.79 #1827, 0.75 #2281), 09b_0m (0.79 #2278, 0.79 #1827, 0.75 #917), 02bbyw (0.75 #1824, 0.75 #2281, 0.74 #1823), 07w6r (0.75 #1824, 0.75 #2281, 0.74 #1823) >> Best rule #1370 for best value: >> intensional similarity = 64 >> extensional distance = 1 >> proper extension: 09nqf; >> query: (?x5696, ?x9344) <- currency(?x10706, ?x5696), currency(?x3939, ?x5696), currency(?x2985, ?x5696), currency(?x9688, ?x5696), currency(?x9344, ?x5696), currency(?x7154, ?x5696), major_field_of_study(?x7154, ?x742), currency(?x13491, ?x5696), currency(?x2351, ?x5696), institution(?x3437, ?x7154), institution(?x1771, ?x7154), contains(?x1229, ?x2351), contains(?x2984, ?x2985), colors(?x9344, ?x663), currency(?x12877, ?x5696), category(?x7154, ?x134), major_field_of_study(?x9344, ?x1668), student(?x7154, ?x2239), organization(?x4095, ?x7154), teams(?x2985, ?x3791), state_province_region(?x2351, ?x9186), contains(?x10706, ?x1356), film_release_region(?x951, ?x2984), organization(?x346, ?x2351), adjoins(?x2985, ?x3623), institution(?x7817, ?x12877), currency(?x2685, ?x5696), film(?x5699, ?x2685), nominated_for(?x1245, ?x2685), nominated_for(?x2887, ?x2685), olympics(?x2984, ?x867), participating_countries(?x1741, ?x2984), organization(?x11157, ?x13491), contains(?x512, ?x13491), olympics(?x2984, ?x584), contains(?x455, ?x9688), capital(?x3939, ?x13192), ?x1771 = 019v9k, administrative_parent(?x9792, ?x10706), ?x1741 = 0sx8l, peers(?x4808, ?x2239), month(?x2985, ?x2140), film_release_region(?x2685, ?x2645), film_release_region(?x2685, ?x2316), film_release_region(?x2685, ?x205), film_release_region(?x2685, ?x142), currency(?x13491, ?x1099), country(?x6345, ?x2984), ?x7817 = 02cq61, ?x142 = 0jgd, ?x951 = 0cwy47, student(?x12877, ?x3994), state_province_region(?x7154, ?x1679), countries_within(?x455, ?x404), ?x1245 = 0gqwc, film(?x1914, ?x2685), ?x3437 = 02_xgp2, currency(?x2440, ?x5696), ?x2645 = 03h64, ?x2316 = 06t2t, adjoins(?x455, ?x1144), ?x2140 = 040fb, ?x205 = 03rjj, nationality(?x12564, ?x2984) >> conf = 0.80 => this is the best rule for 3 predicted values *> Best rule #454 for first EXPECTED value: *> intensional similarity = 69 *> extensional distance = 1 *> proper extension: 0ptk_; *> query: (?x5696, ?x1220) <- currency(?x12203, ?x5696), currency(?x11886, ?x5696), currency(?x10706, ?x5696), currency(?x2985, ?x5696), currency(?x9688, ?x5696), currency(?x9344, ?x5696), currency(?x9018, ?x5696), currency(?x7154, ?x5696), major_field_of_study(?x7154, ?x742), currency(?x13491, ?x5696), currency(?x2351, ?x5696), institution(?x1771, ?x7154), contains(?x1229, ?x2351), contains(?x2984, ?x2985), colors(?x9344, ?x663), currency(?x12877, ?x5696), currency(?x196, ?x5696), category(?x7154, ?x134), major_field_of_study(?x9344, ?x1668), student(?x7154, ?x1645), organization(?x4095, ?x7154), teams(?x2985, ?x3791), state_province_region(?x2351, ?x9186), contains(?x10706, ?x1356), film_release_region(?x9900, ?x2984), film_release_region(?x5509, ?x2984), organization(?x346, ?x2351), adjoins(?x2985, ?x3623), ?x9900 = 0qmfk, student(?x9344, ?x5597), student(?x12877, ?x3994), school_type(?x9344, ?x3092), organization(?x4095, ?x1220), ?x3092 = 05jxkf, participating_countries(?x1741, ?x2984), major_field_of_study(?x13491, ?x1327), major_field_of_study(?x13424, ?x1327), major_field_of_study(?x122, ?x1327), capital(?x11886, ?x6959), country(?x4045, ?x2984), country(?x1121, ?x2984), student(?x9688, ?x4292), ?x1121 = 0bynt, ?x134 = 08mbj5d, ?x4045 = 06z6r, state_province_region(?x12877, ?x10524), contains(?x456, ?x9018), contains(?x455, ?x9688), contains(?x205, ?x10706), ?x13424 = 0yldt, adjoins(?x3623, ?x7049), currency(?x9657, ?x5696), state(?x11769, ?x3623), ?x122 = 08815, major_field_of_study(?x196, ?x2014), administrative_division(?x3622, ?x3623), genre(?x9657, ?x53), ?x5509 = 0cy__l, film_release_region(?x9657, ?x87), olympics(?x2984, ?x8189), ?x8189 = 015l4k, ?x742 = 05qjt, institution(?x1771, ?x4363), major_field_of_study(?x1771, ?x4268), ?x4363 = 033x5p, ?x4268 = 02822, film_release_region(?x66, ?x456), administrative_division(?x13881, ?x12203), country(?x150, ?x456) *> conf = 0.04 ranks of expected_values: 778 EVAL 02l6h currency! 0pz6q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.801 http://example.org/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency #12206-0jjy0 PRED entity: 0jjy0 PRED relation: film_release_region PRED expected values: 07ylj 0h7x => 118 concepts (117 used for prediction) PRED predicted values (max 10 best out of 193): 05v8c (0.84 #949, 0.83 #1217, 0.82 #681), 03rk0 (0.83 #844, 0.71 #710, 0.69 #576), 04gzd (0.78 #810, 0.75 #1212, 0.74 #944), 047lj (0.65 #678, 0.62 #544, 0.57 #1348), 09pmkv (0.65 #1224, 0.63 #1358, 0.59 #1627), 06t8v (0.62 #593, 0.59 #727, 0.52 #1666), 03ryn (0.57 #1405, 0.56 #1271, 0.53 #869), 01pj7 (0.56 #570, 0.53 #704, 0.50 #838), 06c1y (0.56 #833, 0.53 #967, 0.52 #1235), 0h7x (0.51 #2438, 0.39 #5121, 0.38 #5658) >> Best rule #949 for best value: >> intensional similarity = 7 >> extensional distance = 36 >> proper extension: 05p1tzf; 0bq8tmw; 0661m4p; 05c26ss; 0gj96ln; >> query: (?x1108, 05v8c) <- film_release_region(?x1108, ?x1471), film_release_region(?x1108, ?x151), ?x151 = 0b90_r, ?x1471 = 07t21, genre(?x1108, ?x53), genre(?x7141, ?x53), ?x7141 = 027r9t >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2438 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 115 *> proper extension: 014lc_; 0ds35l9; 0h1cdwq; 0gkz15s; 017gl1; 02d44q; 01c22t; 0_92w; 0c0nhgv; 05z_kps; ... *> query: (?x1108, 0h7x) <- film_release_region(?x1108, ?x1471), film_release_region(?x1108, ?x1453), film_release_region(?x1108, ?x151), ?x151 = 0b90_r, contains(?x1471, ?x6223), ?x1453 = 06qd3, country(?x150, ?x1471) *> conf = 0.51 ranks of expected_values: 10, 13 EVAL 0jjy0 film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 118.000 117.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jjy0 film_release_region 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 118.000 117.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12205-0432cd PRED entity: 0432cd PRED relation: film PRED expected values: 03x7hd => 118 concepts (87 used for prediction) PRED predicted values (max 10 best out of 849): 0jzw (0.22 #118, 0.03 #1908, 0.02 #39498), 06gb1w (0.22 #733, 0.03 #2523, 0.02 #31163), 0j_t1 (0.22 #433, 0.01 #30863, 0.01 #39813), 03z20c (0.11 #475, 0.07 #11215, 0.05 #23745), 09g8vhw (0.11 #324, 0.06 #12854, 0.04 #16434), 04tc1g (0.11 #132, 0.05 #3712, 0.02 #7292), 07cw4 (0.11 #1024, 0.04 #13554, 0.04 #17134), 0gkz3nz (0.11 #799, 0.04 #9749, 0.03 #2589), 0f4_l (0.11 #348, 0.04 #16458, 0.03 #12878), 01xdxy (0.11 #1567, 0.03 #14097, 0.03 #3357) >> Best rule #118 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05xd_v; 04gc65; >> query: (?x7607, 0jzw) <- type_of_union(?x7607, ?x566), company(?x7607, ?x13490), ?x13490 = 01skqzw >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #4140 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 0h0jz; 01wbg84; 0z4s; 0343h; 03k7bd; 01j7rd; 06pj8; 0161c2; 0gbwp; 01gy7r; ... *> query: (?x7607, 03x7hd) <- languages(?x7607, ?x254), profession(?x7607, ?x524), film(?x7607, ?x638), company(?x7607, ?x13490) *> conf = 0.03 ranks of expected_values: 213 EVAL 0432cd film 03x7hd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 118.000 87.000 0.222 http://example.org/film/actor/film./film/performance/film #12204-014_xj PRED entity: 014_xj PRED relation: artists! PRED expected values: 03lty 01dqhq => 118 concepts (86 used for prediction) PRED predicted values (max 10 best out of 280): 06by7 (0.77 #23309, 0.64 #7893, 0.64 #9466), 03lty (0.71 #2860, 0.50 #9473, 0.50 #17030), 064t9 (0.61 #4103, 0.59 #3789, 0.52 #25187), 016clz (0.60 #4725, 0.59 #3465, 0.57 #2521), 05bt6j (0.60 #20819, 0.40 #6657, 0.40 #988), 0cx7f (0.50 #2343, 0.43 #2658, 0.35 #3602), 03_d0 (0.46 #20471, 0.20 #7253, 0.18 #26128), 0dl5d (0.43 #2536, 0.42 #2221, 0.35 #3480), 0155w (0.40 #1053, 0.25 #1683, 0.20 #23397), 0126t5 (0.40 #1031, 0.25 #1661, 0.12 #3233) >> Best rule #23309 for best value: >> intensional similarity = 5 >> extensional distance = 504 >> proper extension: 01pr_j6; 01vsqvs; 0djc3s; >> query: (?x12449, 06by7) <- artists(?x1000, ?x12449), artists(?x1000, ?x6626), artists(?x1000, ?x483), ?x6626 = 0b_j2, ?x483 = 0m2l9 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2860 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 011hdn; *> query: (?x12449, 03lty) <- award(?x12449, ?x11068), category(?x12449, ?x134), artists(?x1000, ?x12449), ?x134 = 08mbj5d, ?x11068 = 02x4wb *> conf = 0.71 ranks of expected_values: 2, 116 EVAL 014_xj artists! 01dqhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 118.000 86.000 0.769 http://example.org/music/genre/artists EVAL 014_xj artists! 03lty CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 86.000 0.769 http://example.org/music/genre/artists #12203-03yxwq PRED entity: 03yxwq PRED relation: award_winner! PRED expected values: 0vhm => 128 concepts (113 used for prediction) PRED predicted values (max 10 best out of 321): 0cn_b8 (0.40 #3820, 0.33 #4957, 0.18 #18596), 0vhm (0.33 #1728, 0.17 #92077, 0.17 #6275), 047csmy (0.27 #18783, 0.20 #4007, 0.17 #6282), 0n83s (0.22 #11956, 0.18 #19911, 0.13 #30144), 06bd5j (0.20 #4038, 0.17 #7451, 0.17 #5175), 01r97z (0.20 #3484, 0.17 #6897, 0.17 #4621), 01qvz8 (0.20 #3933, 0.17 #7346, 0.17 #5070), 01l_pn (0.20 #4037, 0.17 #7450, 0.17 #5174), 07s846j (0.20 #3848, 0.17 #7261, 0.17 #4985), 0c5dd (0.20 #3524, 0.17 #6937, 0.17 #4661) >> Best rule #3820 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 016tw3; 01gb54; >> query: (?x6948, 0cn_b8) <- award_winner(?x1686, ?x6948), place_founded(?x6948, ?x682), child(?x382, ?x6948), production_companies(?x549, ?x1686) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1728 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 03lpbx; *> query: (?x6948, 0vhm) <- award_winner(?x1686, ?x6948), ?x1686 = 030_1_, award_winner(?x3486, ?x6948), ?x3486 = 0m7yy *> conf = 0.33 ranks of expected_values: 2 EVAL 03yxwq award_winner! 0vhm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 113.000 0.400 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #12202-05kj_ PRED entity: 05kj_ PRED relation: district_represented! PRED expected values: 024tcq => 195 concepts (195 used for prediction) PRED predicted values (max 10 best out of 50): 024tcq (0.85 #566, 0.84 #716, 0.81 #966), 03rl1g (0.70 #51, 0.58 #951, 0.56 #551), 02bqmq (0.70 #64, 0.56 #564, 0.56 #1001), 043djx (0.60 #55, 0.58 #955, 0.56 #555), 02bqn1 (0.56 #1001, 0.49 #557, 0.47 #707), 02cg7g (0.56 #1001, 0.46 #572, 0.44 #722), 02gkzs (0.56 #1001, 0.44 #569, 0.42 #719), 03rtmz (0.56 #1001, 0.32 #563, 0.30 #63), 02glc4 (0.56 #1001, 0.30 #79, 0.29 #579), 03tcbx (0.56 #1001, 0.30 #62, 0.29 #562) >> Best rule #566 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 059rby; 03v1s; 05fkf; 0vmt; 03s0w; 059_c; 04ykg; 06mz5; 01x73; 0488g; ... >> query: (?x726, 024tcq) <- religion(?x726, ?x962), district_represented(?x6728, ?x726), ?x6728 = 070mff >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05kj_ district_represented! 024tcq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 195.000 0.854 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #12201-015c2f PRED entity: 015c2f PRED relation: location PRED expected values: 02_286 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 76): 02_286 (0.33 #841, 0.28 #37, 0.20 #1645), 030qb3t (0.22 #887, 0.20 #7320, 0.19 #6516), 0ftyc (0.11 #259, 0.07 #1867), 0cc56 (0.11 #57, 0.06 #4077, 0.06 #861), 01531 (0.07 #1766, 0.06 #962, 0.03 #5786), 059rby (0.06 #68355, 0.06 #820, 0.05 #65137), 09c7w0 (0.06 #68355, 0.02 #35381), 0cr3d (0.06 #949, 0.06 #145, 0.05 #9794), 0ccvx (0.06 #222, 0.03 #1830, 0.03 #17108), 0vp5f (0.06 #687, 0.03 #2295) >> Best rule #841 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 02t_99; 0bx_q; >> query: (?x2813, 02_286) <- type_of_union(?x2813, ?x566), award_winner(?x1007, ?x2813), ?x1007 = 03c7tr1 >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015c2f location 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #12200-05mkhs PRED entity: 05mkhs PRED relation: location PRED expected values: 0f25y => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 88): 02_286 (0.20 #14492, 0.18 #7264, 0.17 #13689), 02xry (0.17 #132, 0.15 #935, 0.03 #2541), 0k_p5 (0.17 #290, 0.08 #1093, 0.04 #1896), 01sn3 (0.17 #214, 0.03 #26505, 0.02 #36945), 01cx_ (0.11 #1768, 0.03 #2571, 0.02 #3374), 01n7q (0.08 #866, 0.07 #1669, 0.05 #5684), 059rby (0.08 #819, 0.07 #1622, 0.05 #3228), 0k049 (0.08 #811, 0.04 #1614, 0.03 #4023), 06yxd (0.08 #1049, 0.04 #1852, 0.03 #26505), 0f2rq (0.08 #1083, 0.04 #1886, 0.01 #4295) >> Best rule #14492 for best value: >> intensional similarity = 3 >> extensional distance = 483 >> proper extension: 05m63c; 02g8h; 02qjj7; 01ty7ll; 033hqf; 0htlr; 03_vx9; 0prjs; 0f2df; 01pl9g; ... >> query: (?x3816, 02_286) <- participant(?x3816, ?x1410), profession(?x3816, ?x1032), location(?x3816, ?x1523) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05mkhs location 0f25y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 80.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location #12199-02p11jq PRED entity: 02p11jq PRED relation: artist PRED expected values: 03cfjg 07g2v => 27 concepts (19 used for prediction) PRED predicted values (max 10 best out of 813): 03xhj6 (0.67 #3440, 0.57 #4227, 0.25 #1076), 015xp4 (0.67 #3497, 0.57 #4284, 0.10 #5857), 01kph_c (0.60 #1893, 0.50 #2683, 0.50 #1106), 0kzy0 (0.60 #1604, 0.50 #2394, 0.50 #817), 047cx (0.50 #3469, 0.43 #4256, 0.25 #1105), 0qf3p (0.50 #930, 0.40 #1717, 0.33 #3294), 06gcn (0.50 #1305, 0.40 #2092, 0.33 #3669), 016szr (0.50 #1112, 0.40 #1899, 0.33 #2689), 0x3b7 (0.50 #1064, 0.40 #1851, 0.33 #2641), 017_hq (0.50 #1530, 0.40 #2317, 0.33 #3107) >> Best rule #3440 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0mzkr; 0g768; 01cf93; 0n85g; >> query: (?x2241, 03xhj6) <- artist(?x2241, ?x12825), artist(?x2241, ?x10181), artist(?x2241, ?x6027), artist(?x2241, ?x4873), instrumentalists(?x227, ?x6027), gender(?x6027, ?x231), ?x12825 = 013rds, award_nominee(?x10181, ?x702), location(?x4873, ?x362), award(?x10181, ?x567), artists(?x671, ?x10181) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3371 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 0mzkr; 0g768; 01cf93; 0n85g; *> query: (?x2241, 07g2v) <- artist(?x2241, ?x12825), artist(?x2241, ?x10181), artist(?x2241, ?x6027), artist(?x2241, ?x4873), instrumentalists(?x227, ?x6027), gender(?x6027, ?x231), ?x12825 = 013rds, award_nominee(?x10181, ?x702), location(?x4873, ?x362), award(?x10181, ?x567), artists(?x671, ?x10181) *> conf = 0.17 ranks of expected_values: 303, 331 EVAL 02p11jq artist 07g2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 19.000 0.667 http://example.org/music/record_label/artist EVAL 02p11jq artist 03cfjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 19.000 0.667 http://example.org/music/record_label/artist #12198-030xr_ PRED entity: 030xr_ PRED relation: people! PRED expected values: 033tf_ => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 28): 033tf_ (0.25 #84, 0.08 #238, 0.08 #546), 0x67 (0.17 #87, 0.10 #549, 0.10 #703), 041rx (0.15 #158, 0.12 #1699, 0.12 #1082), 01qhm_ (0.11 #6, 0.03 #391, 0.03 #545), 02vsw1 (0.11 #51), 01336l (0.11 #41), 01xhh5 (0.11 #36), 02w7gg (0.08 #79, 0.07 #1542, 0.07 #387), 07hwkr (0.08 #89, 0.04 #1707, 0.04 #1090), 07bch9 (0.08 #100, 0.03 #947, 0.03 #254) >> Best rule #84 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 039bp; 02qgyv; 01438g; 0686zv; 013knm; 01wgcvn; 0dzf_; 02x7vq; 048s0r; 01nxzv; >> query: (?x9289, 033tf_) <- award_winner(?x7268, ?x9289), ?x7268 = 02__7n, film(?x9289, ?x83) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030xr_ people! 033tf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.250 http://example.org/people/ethnicity/people #12197-01fx1l PRED entity: 01fx1l PRED relation: nominated_for! PRED expected values: 0cqhb3 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 184): 0bdw6t (0.68 #1874, 0.68 #11950, 0.67 #11478), 0bdw1g (0.68 #1874, 0.68 #11950, 0.67 #11478), 0bdx29 (0.60 #315, 0.26 #783, 0.23 #1251), 0gq9h (0.34 #8958, 0.33 #9192, 0.30 #9426), 02x8n1n (0.33 #90, 0.21 #9600, 0.19 #15702), 02x4w6g (0.33 #85, 0.05 #9919, 0.04 #9685), 0gs9p (0.31 #8960, 0.29 #9194, 0.26 #9428), 019f4v (0.30 #8950, 0.28 #9184, 0.25 #9418), 027gs1_ (0.29 #1354, 0.25 #2761, 0.25 #1823), 0cjyzs (0.27 #1249, 0.26 #1953, 0.24 #1405) >> Best rule #1874 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 05h95s; 019g8j; >> query: (?x5594, ?x686) <- actor(?x5594, ?x1129), award(?x5594, ?x686), people(?x1050, ?x1129) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #898 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 02k_4g; 019nnl; 01h72l; 030k94; 07c72; 030p35; 0hz55; 05f4vxd; 0vjr; 04p5cr; ... *> query: (?x5594, 0cqhb3) <- nominated_for(?x914, ?x5594), program(?x1394, ?x5594), ?x1394 = 0f721s, genre(?x5594, ?x53) *> conf = 0.26 ranks of expected_values: 11 EVAL 01fx1l nominated_for! 0cqhb3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 73.000 73.000 0.683 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12196-0hz6mv2 PRED entity: 0hz6mv2 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.99 #312, 0.99 #299, 0.96 #118), 02nxhr (0.21 #361, 0.17 #2, 0.14 #10), 07z4p (0.21 #361, 0.08 #234, 0.07 #282), 0735l (0.02 #104) >> Best rule #312 for best value: >> intensional similarity = 7 >> extensional distance = 832 >> proper extension: 07kb7vh; >> query: (?x9565, 029j_) <- film(?x1104, ?x9565), country(?x9565, ?x94), film_release_distribution_medium(?x9565, ?x2008), nominated_for(?x1104, ?x365), award_winner(?x1104, ?x7855), award(?x7855, ?x350), production_companies(?x253, ?x1104) >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hz6mv2 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.986 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12195-080dfr7 PRED entity: 080dfr7 PRED relation: currency PRED expected values: 09nqf => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.90 #92, 0.89 #71, 0.88 #134), 01nv4h (0.25 #617, 0.25 #632, 0.09 #23), 02l6h (0.03 #123, 0.01 #459, 0.01 #466), 02gsvk (0.01 #258, 0.01 #195, 0.01 #272) >> Best rule #92 for best value: >> intensional similarity = 5 >> extensional distance = 77 >> proper extension: 05_5_22; >> query: (?x10590, 09nqf) <- film_crew_role(?x10590, ?x4305), language(?x10590, ?x254), ?x254 = 02h40lc, produced_by(?x10590, ?x1714), ?x4305 = 0215hd >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 080dfr7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.899 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #12194-0b1xl PRED entity: 0b1xl PRED relation: major_field_of_study PRED expected values: 0fdys 02_7t 09s1f => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 117): 02lp1 (0.69 #251, 0.60 #371, 0.50 #611), 01mkq (0.62 #255, 0.60 #375, 0.54 #1697), 02j62 (0.55 #6281, 0.52 #8934, 0.52 #869), 04rjg (0.54 #259, 0.43 #379, 0.39 #499), 041y2 (0.54 #316, 0.24 #676, 0.21 #556), 03g3w (0.47 #386, 0.46 #266, 0.42 #6278), 04x_3 (0.47 #385, 0.38 #265, 0.28 #985), 01lj9 (0.46 #279, 0.40 #399, 0.31 #999), 05qfh (0.46 #275, 0.39 #755, 0.37 #395), 0fdys (0.46 #278, 0.37 #398, 0.27 #518) >> Best rule #251 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 0j_sncb; >> query: (?x5145, 02lp1) <- major_field_of_study(?x5145, ?x5614), major_field_of_study(?x5145, ?x2606), ?x5614 = 03qsdpk, institution(?x4981, ?x5145), ?x2606 = 062z7, ?x4981 = 03bwzr4 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #278 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 0j_sncb; *> query: (?x5145, 0fdys) <- major_field_of_study(?x5145, ?x5614), major_field_of_study(?x5145, ?x2606), ?x5614 = 03qsdpk, institution(?x4981, ?x5145), ?x2606 = 062z7, ?x4981 = 03bwzr4 *> conf = 0.46 ranks of expected_values: 10, 11, 13 EVAL 0b1xl major_field_of_study 09s1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 140.000 140.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0b1xl major_field_of_study 02_7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 140.000 140.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0b1xl major_field_of_study 0fdys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 140.000 140.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #12193-0jwmp PRED entity: 0jwmp PRED relation: genre PRED expected values: 07s9rl0 => 94 concepts (92 used for prediction) PRED predicted values (max 10 best out of 94): 07s9rl0 (0.81 #4002, 0.70 #485, 0.70 #1092), 02kdv5l (0.61 #972, 0.35 #1580, 0.34 #1337), 05p553 (0.52 #731, 0.37 #126, 0.37 #1825), 07ssc (0.52 #4731, 0.48 #8007, 0.48 #9705), 01jfsb (0.45 #983, 0.36 #2682, 0.33 #1591), 03k9fj (0.43 #982, 0.26 #3409, 0.25 #3166), 02l7c8 (0.35 #380, 0.33 #1108, 0.33 #17), 04xvlr (0.27 #1093, 0.20 #365, 0.16 #5096), 0lsxr (0.23 #1344, 0.23 #494, 0.21 #1587), 060__y (0.21 #381, 0.19 #1109, 0.16 #866) >> Best rule #4002 for best value: >> intensional similarity = 4 >> extensional distance = 755 >> proper extension: 01h72l; >> query: (?x3392, 07s9rl0) <- award_winner(?x3392, ?x7352), genre(?x3392, ?x1013), genre(?x1366, ?x1013), ?x1366 = 07ng9k >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jwmp genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 92.000 0.807 http://example.org/film/film/genre #12192-02bn75 PRED entity: 02bn75 PRED relation: award PRED expected values: 02x201b => 139 concepts (120 used for prediction) PRED predicted values (max 10 best out of 283): 054ks3 (0.62 #3366, 0.52 #6993, 0.38 #543), 02qvyrt (0.44 #6172, 0.44 #1334, 0.38 #2947), 025m8l (0.39 #3343, 0.35 #6970, 0.21 #1326), 0c4z8 (0.39 #3297, 0.33 #6924, 0.31 #474), 025m8y (0.36 #1306, 0.33 #6144, 0.30 #4129), 01bgqh (0.32 #3269, 0.17 #6896, 0.17 #4478), 02gdjb (0.28 #1428, 0.17 #6266, 0.16 #3445), 02x17c2 (0.27 #3444, 0.20 #7071, 0.15 #2233), 0fhpv4 (0.26 #6241, 0.26 #1403, 0.25 #4226), 04njml (0.25 #502, 0.20 #6952, 0.19 #3728) >> Best rule #3366 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 09mq4m; >> query: (?x7857, 054ks3) <- award(?x7857, ?x1323), ?x1323 = 0gqz2, category(?x7857, ?x134) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1483 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0b82vw; 04zwjd; 01l9v7n; 0b6yp2; 04ls53; 09bx1k; 09r9m7; 02z81h; 025cn2; 01l3mk3; *> query: (?x7857, 02x201b) <- award(?x7857, ?x1079), student(?x2909, ?x7857), ?x1079 = 0l8z1 *> conf = 0.23 ranks of expected_values: 13 EVAL 02bn75 award 02x201b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 139.000 120.000 0.625 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12191-0gjv_ PRED entity: 0gjv_ PRED relation: major_field_of_study PRED expected values: 062z7 04gb7 => 173 concepts (123 used for prediction) PRED predicted values (max 10 best out of 116): 04rjg (0.69 #707, 0.60 #17, 0.52 #2203), 02lp1 (0.65 #1391, 0.57 #585, 0.52 #2196), 01mkq (0.64 #2199, 0.62 #1394, 0.60 #473), 062z7 (0.60 #24, 0.52 #2210, 0.44 #714), 04gb7 (0.57 #4297, 0.29 #2801, 0.29 #615), 05qfh (0.50 #606, 0.44 #721, 0.35 #1412), 04x_3 (0.50 #598, 0.41 #828, 0.32 #1404), 02h40lc (0.44 #694, 0.43 #234, 0.30 #464), 0fdys (0.43 #609, 0.40 #34, 0.38 #2220), 0g26h (0.40 #498, 0.40 #38, 0.38 #2224) >> Best rule #707 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 0kz2w; >> query: (?x6127, 04rjg) <- major_field_of_study(?x6127, ?x9111), major_field_of_study(?x6127, ?x1327), institution(?x734, ?x6127), interests(?x7341, ?x9111), ?x1327 = 01lhy >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0lk0l; *> query: (?x6127, 062z7) <- major_field_of_study(?x6127, ?x9111), institution(?x7817, ?x6127), ?x9111 = 04sh3, ?x7817 = 02cq61, student(?x6127, ?x1515) *> conf = 0.60 ranks of expected_values: 4, 5 EVAL 0gjv_ major_field_of_study 04gb7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 173.000 123.000 0.688 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0gjv_ major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 173.000 123.000 0.688 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #12190-01vs14j PRED entity: 01vs14j PRED relation: profession PRED expected values: 09lbv => 110 concepts (83 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.91 #11721, 0.89 #12165, 0.87 #10537), 0dz3r (0.61 #1186, 0.60 #1038, 0.58 #2962), 016z4k (0.49 #2964, 0.47 #1040, 0.45 #3705), 01c72t (0.48 #3280, 0.43 #1208, 0.42 #1060), 01d_h8 (0.45 #5039, 0.44 #2226, 0.41 #2374), 039v1 (0.41 #2848, 0.41 #4181, 0.40 #4773), 0dxtg (0.35 #3566, 0.35 #2234, 0.33 #3122), 0fnpj (0.33 #1244, 0.22 #652, 0.21 #504), 03gjzk (0.30 #5049, 0.27 #6827, 0.27 #5789), 0n1h (0.29 #752, 0.27 #456, 0.22 #1640) >> Best rule #11721 for best value: >> intensional similarity = 3 >> extensional distance = 1652 >> proper extension: 027l0b; 05slvm; 02vntj; 051wwp; 01520h; 02hy9p; 0cw67g; 01hkck; 045931; >> query: (?x1321, 02hrh1q) <- profession(?x1321, ?x1183), film(?x1321, ?x10796), award(?x1321, ?x1323) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #612 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 05qhnq; *> query: (?x1321, 09lbv) <- gender(?x1321, ?x231), ?x231 = 05zppz, group(?x1321, ?x5547), performance_role(?x1321, ?x212) *> conf = 0.24 ranks of expected_values: 16 EVAL 01vs14j profession 09lbv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 110.000 83.000 0.908 http://example.org/people/person/profession #12189-01l2b3 PRED entity: 01l2b3 PRED relation: genre PRED expected values: 02l7c8 01t_vv => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 102): 01z4y (0.61 #5947, 0.61 #3680, 0.54 #3679), 02vx4 (0.54 #3679, 0.54 #5946, 0.53 #3322), 01hmnh (0.50 #18, 0.25 #136, 0.21 #373), 03bxz7 (0.46 #290, 0.17 #172, 0.17 #54), 01jfsb (0.43 #603, 0.35 #485, 0.32 #1788), 02l7c8 (0.42 #134, 0.29 #3576, 0.29 #4055), 02kdv5l (0.37 #357, 0.37 #593, 0.33 #6542), 03k9fj (0.37 #366, 0.33 #11, 0.25 #7261), 060__y (0.25 #135, 0.17 #17, 0.17 #1080), 04xvlr (0.23 #237, 0.22 #1064, 0.21 #1419) >> Best rule #5947 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 07s8z_l; 02xhwm; >> query: (?x6451, ?x2480) <- titles(?x2480, ?x6451), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #134 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 01vksx; *> query: (?x6451, 02l7c8) <- film(?x609, ?x6451), film(?x1634, ?x6451), ?x1634 = 01l2fn, language(?x6451, ?x254) *> conf = 0.42 ranks of expected_values: 6, 20 EVAL 01l2b3 genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 76.000 76.000 0.612 http://example.org/film/film/genre EVAL 01l2b3 genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 76.000 76.000 0.612 http://example.org/film/film/genre #12188-0dhdp PRED entity: 0dhdp PRED relation: place_of_birth! PRED expected values: 0fv6dr => 126 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1529): 045bg (0.06 #204, 0.05 #2813, 0.05 #5422), 031v3p (0.06 #2456, 0.05 #5065, 0.05 #7674), 012g92 (0.06 #2426, 0.05 #5035, 0.05 #7644), 011s9r (0.06 #2378, 0.05 #4987, 0.05 #7596), 0d_w7 (0.06 #2331, 0.05 #4940, 0.05 #7549), 01q8fxx (0.06 #2293, 0.05 #4902, 0.05 #7511), 0csdzz (0.06 #2196, 0.05 #4805, 0.05 #7414), 0gdqy (0.06 #2140, 0.05 #4749, 0.05 #7358), 02184q (0.06 #2042, 0.05 #4651, 0.05 #7260), 02hh8j (0.06 #2019, 0.05 #4628, 0.05 #7237) >> Best rule #204 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0mpbx; >> query: (?x1156, 045bg) <- place_of_birth(?x8345, ?x1156), second_level_divisions(?x1310, ?x1156), profession(?x8345, ?x319), produced_by(?x1685, ?x8345) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dhdp place_of_birth! 0fv6dr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 12.000 0.059 http://example.org/people/person/place_of_birth #12187-06bnz PRED entity: 06bnz PRED relation: nationality! PRED expected values: 040696 => 205 concepts (82 used for prediction) PRED predicted values (max 10 best out of 4066): 02ck1 (0.80 #178255, 0.49 #170151, 0.48 #255227), 0h25 (0.80 #178255, 0.48 #255227, 0.31 #48614), 0cj2k3 (0.80 #178255, 0.12 #6849, 0.09 #10901), 0151ns (0.80 #178255, 0.12 #4180, 0.09 #8232), 0127gn (0.80 #178255), 01hkhq (0.49 #170151, 0.35 #157996, 0.06 #20939), 0lrh (0.49 #170151, 0.31 #48614, 0.12 #4858), 03f0324 (0.49 #170151, 0.31 #48614, 0.09 #9607), 048cl (0.49 #170151, 0.24 #133688, 0.10 #26626), 0hskw (0.49 #170151, 0.15 #12919, 0.12 #4816) >> Best rule #178255 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 049nq; >> query: (?x1603, ?x558) <- nationality(?x889, ?x1603), contains(?x1603, ?x6494), place_of_birth(?x558, ?x6494) >> conf = 0.80 => this is the best rule for 5 predicted values *> Best rule #6295 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 02j7k; *> query: (?x1603, 040696) <- contains(?x1603, ?x992), adjoins(?x344, ?x1603), place_of_burial(?x10328, ?x1603) *> conf = 0.12 ranks of expected_values: 1387 EVAL 06bnz nationality! 040696 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 205.000 82.000 0.798 http://example.org/people/person/nationality #12186-047g6 PRED entity: 047g6 PRED relation: influenced_by! PRED expected values: 04z0g => 183 concepts (111 used for prediction) PRED predicted values (max 10 best out of 397): 0969fd (0.50 #436, 0.25 #953, 0.18 #3017), 047g6 (0.33 #1515, 0.27 #4094, 0.27 #3062), 01d494 (0.27 #2632, 0.20 #7788, 0.17 #1085), 01hb6v (0.25 #611, 0.25 #94, 0.17 #1128), 01wp_jm (0.25 #926, 0.25 #409, 0.17 #1443), 018x3 (0.25 #749, 0.25 #232, 0.17 #1266), 0399p (0.25 #8067, 0.18 #2911, 0.17 #1364), 043tg (0.25 #329, 0.12 #8066, 0.06 #41288), 01h2_6 (0.25 #495, 0.09 #3076, 0.07 #8232), 041jlr (0.25 #362, 0.09 #3975, 0.06 #41288) >> Best rule #436 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 07h1q; >> query: (?x12216, 0969fd) <- influenced_by(?x12216, ?x7296), people(?x1050, ?x12216), ?x7296 = 04hcw, religion(?x12216, ?x7131) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3853 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 09p06; *> query: (?x12216, 04z0g) <- nationality(?x12216, ?x1355), ?x1355 = 0h7x, gender(?x12216, ?x231), student(?x2637, ?x12216) *> conf = 0.18 ranks of expected_values: 12 EVAL 047g6 influenced_by! 04z0g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 183.000 111.000 0.500 http://example.org/influence/influence_node/influenced_by #12185-09ly2r6 PRED entity: 09ly2r6 PRED relation: nominated_for PRED expected values: 09gq0x5 => 58 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1581): 09gq0x5 (0.69 #9752, 0.43 #3419, 0.28 #14508), 026p4q7 (0.66 #9854, 0.43 #3521, 0.28 #14610), 05mrf_p (0.64 #20591, 0.64 #15836, 0.10 #12665), 0gmgwnv (0.63 #10456, 0.43 #4123, 0.27 #15212), 049xgc (0.63 #10368, 0.24 #15124, 0.21 #19879), 07w8fz (0.63 #9955, 0.20 #14711, 0.18 #19466), 0m313 (0.57 #9509, 0.27 #14265, 0.24 #19020), 011yqc (0.54 #9705, 0.43 #3372, 0.22 #14461), 0dr_4 (0.54 #9720, 0.29 #3387, 0.25 #14476), 03hkch7 (0.54 #9954, 0.21 #14710, 0.19 #19465) >> Best rule #9752 for best value: >> intensional similarity = 6 >> extensional distance = 33 >> proper extension: 099c8n; >> query: (?x6165, 09gq0x5) <- nominated_for(?x6165, ?x9893), nominated_for(?x6165, ?x3965), nominated_for(?x6165, ?x1370), country(?x3965, ?x94), ?x1370 = 0gmcwlb, genre(?x9893, ?x53) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09ly2r6 nominated_for 09gq0x5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 17.000 0.686 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12184-05zy3sc PRED entity: 05zy3sc PRED relation: films! PRED expected values: 0jm_ => 86 concepts (30 used for prediction) PRED predicted values (max 10 best out of 61): 06c97 (0.15 #204), 0fx2s (0.10 #1175, 0.06 #1801, 0.06 #2114), 081pw (0.08 #159, 0.07 #1105, 0.06 #318), 02_h0 (0.08 #256, 0.02 #4034, 0.02 #3719), 04gb7 (0.08 #201, 0.02 #1930, 0.02 #673), 018h2 (0.08 #178, 0.02 #650, 0.02 #493), 07jq_ (0.08 #238, 0.02 #3071, 0.02 #2283), 07wh1 (0.08 #278, 0.01 #1224), 0mzj_ (0.08 #271, 0.01 #1217), 0htp (0.08 #277) >> Best rule #204 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0gbtbm; 043mk4y; 0b4lkx; >> query: (?x6438, 06c97) <- honored_for(?x2220, ?x6438), genre(?x6438, ?x10122), ?x10122 = 01f9r0 >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #636 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 0413cff; *> query: (?x6438, 0jm_) <- genre(?x6438, ?x1316), film_release_region(?x6438, ?x94), ?x1316 = 017fp, currency(?x6438, ?x170) *> conf = 0.04 ranks of expected_values: 19 EVAL 05zy3sc films! 0jm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 86.000 30.000 0.154 http://example.org/film/film_subject/films #12183-013l6l PRED entity: 013l6l PRED relation: place PRED expected values: 013l6l => 130 concepts (83 used for prediction) PRED predicted values (max 10 best out of 120): 030qb3t (0.16 #21136, 0.15 #27842, 0.13 #27843), 02_286 (0.16 #21136, 0.15 #27842, 0.13 #27843), 043yj (0.04 #932, 0.02 #1447, 0.02 #1962), 0ftvg (0.04 #803, 0.02 #1318, 0.02 #1833), 0dyl9 (0.04 #669, 0.02 #1184, 0.02 #1699), 0f2rq (0.04 #654, 0.02 #1169, 0.02 #1684), 0c_m3 (0.04 #647, 0.02 #1162, 0.02 #1677), 06wxw (0.04 #615, 0.02 #1130, 0.02 #1645), 0vzm (0.04 #586, 0.02 #1101, 0.02 #1616), 0fw2y (0.04 #569, 0.02 #1084, 0.02 #1599) >> Best rule #21136 for best value: >> intensional similarity = 4 >> extensional distance = 321 >> proper extension: 03pbf; 0zygc; 027wvb; 0p9z5; 06k5_; 07gdw; 0d7_n; 06n8j; 02lbc; 05mwx; ... >> query: (?x11163, ?x739) <- place_of_birth(?x5216, ?x11163), contains(?x1351, ?x11163), category(?x11163, ?x134), location(?x5216, ?x739) >> conf = 0.16 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 013l6l place 013l6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 83.000 0.155 http://example.org/location/hud_county_place/place #12182-04gb7 PRED entity: 04gb7 PRED relation: student PRED expected values: 02dh86 => 81 concepts (33 used for prediction) PRED predicted values (max 10 best out of 578): 04z0g (0.33 #353, 0.33 #123, 0.25 #2663), 0kn4c (0.33 #26, 0.25 #2566, 0.17 #2336), 06c0j (0.33 #456, 0.25 #1609, 0.13 #3918), 01k165 (0.33 #286, 0.25 #1439, 0.12 #2596), 071xj (0.33 #419, 0.25 #1572, 0.12 #2729), 01zh29 (0.33 #382, 0.25 #1535, 0.12 #2692), 036jb (0.33 #330, 0.25 #1483, 0.12 #2640), 083q7 (0.33 #2329, 0.17 #2097, 0.12 #2559), 03j2gxx (0.33 #210, 0.17 #2057, 0.12 #2750), 0dx97 (0.33 #113, 0.17 #1960, 0.12 #2653) >> Best rule #353 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 02j62; >> query: (?x5179, 04z0g) <- major_field_of_study(?x9200, ?x5179), major_field_of_study(?x7918, ?x5179), major_field_of_study(?x5167, ?x5179), major_field_of_study(?x5163, ?x5179), major_field_of_study(?x2142, ?x5179), ?x9200 = 0dzst, ?x5167 = 015cz0, ?x2142 = 0dplh, major_field_of_study(?x5179, ?x2606), ?x7918 = 0gl6f, contains(?x94, ?x5163), organization(?x3484, ?x5163) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3511 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 02k54; 034cm; 03_gx; 0jdx; 02vxy_; *> query: (?x5179, 02dh86) <- taxonomy(?x5179, ?x939), split_to(?x5179, ?x3342), ?x939 = 04n6k *> conf = 0.08 ranks of expected_values: 99 EVAL 04gb7 student 02dh86 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 81.000 33.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #12181-0184jw PRED entity: 0184jw PRED relation: film PRED expected values: 01chpn => 97 concepts (44 used for prediction) PRED predicted values (max 10 best out of 141): 011yqc (0.49 #35857, 0.49 #46614, 0.48 #39442), 02q56mk (0.49 #35857, 0.49 #46614, 0.48 #39442), 017d93 (0.49 #35857, 0.49 #46614, 0.48 #39442), 09sr0 (0.03 #60959, 0.03 #60958, 0.01 #26621), 0kvbl6 (0.03 #60959, 0.03 #60958), 0127ps (0.03 #60959, 0.03 #60958), 011yrp (0.03 #60959, 0.03 #60958), 01lbcqx (0.03 #1452, 0.01 #5037), 016dj8 (0.03 #2908, 0.02 #6494, 0.02 #8287), 0prrm (0.02 #4447, 0.02 #15207, 0.01 #18791) >> Best rule #35857 for best value: >> intensional similarity = 3 >> extensional distance = 949 >> proper extension: 01j5x6; >> query: (?x7815, ?x1496) <- location(?x7815, ?x1138), nominated_for(?x7815, ?x1496), award_nominee(?x4854, ?x7815) >> conf = 0.49 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0184jw film 01chpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 44.000 0.488 http://example.org/film/actor/film./film/performance/film #12180-02jjdr PRED entity: 02jjdr PRED relation: artist PRED expected values: 01m3b1t => 48 concepts (25 used for prediction) PRED predicted values (max 10 best out of 980): 01817f (0.60 #2805, 0.38 #6142, 0.25 #6668), 0k1bs (0.60 #2954, 0.38 #6291, 0.25 #6668), 0394y (0.60 #2821, 0.38 #6158, 0.25 #6668), 03xhj6 (0.50 #3637, 0.43 #4470, 0.38 #6140), 0565cz (0.50 #3521, 0.43 #4354, 0.38 #5188), 012vd6 (0.50 #3708, 0.43 #4541, 0.38 #5375), 0pkyh (0.50 #1850, 0.38 #5183, 0.33 #184), 024zq (0.50 #2074, 0.33 #3740, 0.33 #1241), 01wg25j (0.50 #2284, 0.33 #618, 0.23 #8121), 0knhk (0.50 #2233, 0.33 #567, 0.20 #3065) >> Best rule #2805 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 03rhqg; 0229rs; >> query: (?x2193, 01817f) <- artist(?x2193, ?x5059), artist(?x2193, ?x1556), category(?x1556, ?x134), artists(?x1572, ?x1556), ?x1572 = 06by7, artist(?x9286, ?x1556), artist(?x2190, ?x1556), ?x9286 = 01t04r, ?x5059 = 01vt9p3, company(?x3126, ?x2190) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1353 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 015_1q; *> query: (?x2193, 01m3b1t) <- artist(?x2193, ?x9210), artist(?x2193, ?x2194), artist(?x2193, ?x1556), ?x1556 = 03qmj9, award(?x2194, ?x724), artists(?x671, ?x2194), ?x671 = 064t9, award_nominee(?x3522, ?x2194), film(?x2194, ?x607), ?x9210 = 03d2k, artist(?x6672, ?x2194) *> conf = 0.33 ranks of expected_values: 179 EVAL 02jjdr artist 01m3b1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 48.000 25.000 0.600 http://example.org/music/record_label/artist #12179-0jgx PRED entity: 0jgx PRED relation: country! PRED expected values: 07gyv 06f41 071t0 03fyrh 06z6r => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 50): 071t0 (0.90 #320, 0.86 #520, 0.85 #220), 06z6r (0.83 #1478, 0.83 #628, 0.82 #1629), 06f41 (0.83 #313, 0.72 #513, 0.72 #613), 03hr1p (0.79 #321, 0.72 #621, 0.65 #521), 01lb14 (0.79 #314, 0.70 #614, 0.65 #514), 0w0d (0.79 #311, 0.65 #211, 0.65 #611), 06wrt (0.76 #315, 0.70 #615, 0.63 #515), 07jbh (0.76 #330, 0.67 #630, 0.65 #530), 02y8z (0.72 #317, 0.59 #617, 0.56 #517), 0194d (0.69 #343, 0.65 #643, 0.61 #893) >> Best rule #320 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 05r4w; 0f8l9c; >> query: (?x3855, 071t0) <- film_release_region(?x1490, ?x3855), film_release_region(?x633, ?x3855), ?x633 = 0c40vxk, ?x1490 = 0fpkhkz >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 11, 15 EVAL 0jgx country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0jgx country! 03fyrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 111.000 111.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0jgx country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0jgx country! 06f41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0jgx country! 07gyv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 111.000 111.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #12178-07xvf PRED entity: 07xvf PRED relation: currency PRED expected values: 09nqf => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.84 #190, 0.84 #57, 0.83 #43), 01nv4h (0.27 #16, 0.06 #79, 0.04 #100), 02l6h (0.06 #81, 0.02 #158, 0.01 #403) >> Best rule #190 for best value: >> intensional similarity = 4 >> extensional distance = 381 >> proper extension: 02qzmz6; 0413cff; >> query: (?x7373, 09nqf) <- featured_film_locations(?x7373, ?x1061), genre(?x7373, ?x811), genre(?x721, ?x811), ?x721 = 0fr63l >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07xvf currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.836 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #12177-0f61tk PRED entity: 0f61tk PRED relation: genre PRED expected values: 03k9fj 04pbhw => 73 concepts (44 used for prediction) PRED predicted values (max 10 best out of 90): 07s9rl0 (0.79 #591, 0.68 #473, 0.66 #2245), 03k9fj (0.61 #365, 0.56 #11, 0.56 #129), 01jfsb (0.56 #957, 0.51 #1665, 0.51 #1311), 05p553 (0.54 #121, 0.53 #3, 0.52 #4850), 0hcr (0.46 #258, 0.46 #140, 0.42 #22), 02l7c8 (0.42 #487, 0.40 #4862, 0.40 #605), 04pbhw (0.32 #409, 0.14 #1708, 0.14 #1000), 060__y (0.28 #606, 0.20 #488, 0.18 #2364), 0lsxr (0.23 #3911, 0.23 #953, 0.22 #1307), 03bxz7 (0.21 #644, 0.18 #2364, 0.10 #526) >> Best rule #591 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 011yfd; >> query: (?x8615, 07s9rl0) <- genre(?x8615, ?x162), language(?x8615, ?x254), award(?x8615, ?x507), ?x162 = 04xvlr >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #365 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 74 *> proper extension: 04svwx; *> query: (?x8615, 03k9fj) <- genre(?x8615, ?x1510), genre(?x8615, ?x225), country(?x8615, ?x94), ?x1510 = 01hmnh, ?x225 = 02kdv5l *> conf = 0.61 ranks of expected_values: 2, 7 EVAL 0f61tk genre 04pbhw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 73.000 44.000 0.790 http://example.org/film/film/genre EVAL 0f61tk genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 44.000 0.790 http://example.org/film/film/genre #12176-04zwjd PRED entity: 04zwjd PRED relation: instrumentalists! PRED expected values: 06ch55 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 65): 0342h (0.63 #1334, 0.62 #2844, 0.60 #3823), 05148p4 (0.38 #1350, 0.32 #3839, 0.32 #2860), 018vs (0.37 #1342, 0.32 #2407, 0.30 #3473), 03q5t (0.28 #1419, 0.26 #973, 0.26 #3551), 02hnl (0.23 #1364, 0.17 #2429, 0.16 #3495), 03qjg (0.18 #1381, 0.15 #2891, 0.15 #2356), 0l14md (0.15 #8, 0.13 #1337, 0.13 #450), 026t6 (0.15 #3, 0.13 #1332, 0.11 #3463), 07c6l (0.15 #10, 0.05 #186, 0.03 #363), 07gql (0.12 #131, 0.10 #219, 0.06 #307) >> Best rule #1334 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 0130sy; >> query: (?x1940, 0342h) <- artists(?x1572, ?x1940), role(?x1940, ?x74), ?x1572 = 06by7 >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #83 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 01wd9vs; *> query: (?x1940, 06ch55) <- award_winner(?x1854, ?x1940), award(?x1940, ?x7099), nationality(?x1940, ?x142), ?x7099 = 02x201b *> conf = 0.08 ranks of expected_values: 18 EVAL 04zwjd instrumentalists! 06ch55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 140.000 140.000 0.635 http://example.org/music/instrument/instrumentalists #12175-06439y PRED entity: 06439y PRED relation: draft! PRED expected values: 0jmbv 0jm9w => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 168): 0jmbv (0.77 #144, 0.62 #146, 0.62 #145), 0jm5b (0.77 #144, 0.62 #146, 0.62 #145), 0jm74 (0.77 #144, 0.62 #146, 0.62 #145), 0jmcv (0.77 #144, 0.62 #146, 0.62 #145), 0jm4v (0.77 #144, 0.62 #146, 0.62 #145), 0jm7n (0.62 #146, 0.62 #145, 0.59 #1424), 0jm9w (0.62 #146, 0.62 #145, 0.59 #1424), 04cxw5b (0.62 #146, 0.62 #145, 0.59 #1424), 02yjk8 (0.62 #146, 0.62 #145, 0.59 #1424), 01jvgt (0.62 #146, 0.62 #145, 0.59 #1424) >> Best rule #144 for best value: >> intensional similarity = 54 >> extensional distance = 1 >> proper extension: 02pq_rp; >> query: (?x12852, ?x7136) <- draft(?x9760, ?x12852), draft(?x5483, ?x12852), draft(?x799, ?x12852), school(?x12852, ?x12485), school(?x12852, ?x9847), school(?x12852, ?x4296), ?x9847 = 0187nd, team(?x13105, ?x9760), teams(?x739, ?x799), team(?x1348, ?x9760), school(?x9760, ?x8937), school(?x9760, ?x6763), draft(?x5483, ?x8542), draft(?x5483, ?x4979), school(?x799, ?x7707), colors(?x12485, ?x4557), ?x7707 = 01jt2w, school(?x8542, ?x6856), currency(?x12485, ?x170), school(?x2067, ?x4296), team(?x5755, ?x799), athlete(?x4833, ?x13105), ?x6856 = 0jkhr, school(?x4979, ?x2171), school(?x4979, ?x1884), ?x2171 = 01jq34, major_field_of_study(?x4296, ?x5614), major_field_of_study(?x4296, ?x2314), ?x4557 = 019sc, profession(?x13105, ?x1581), ?x1884 = 0bx8pn, draft(?x7136, ?x4979), ?x2314 = 0h5k, company(?x4486, ?x4296), institution(?x1368, ?x8937), ?x1581 = 01445t, team(?x11620, ?x799), student(?x5614, ?x396), student(?x4296, ?x8163), major_field_of_study(?x254, ?x5614), ?x1368 = 014mlp, ?x170 = 09nqf, program(?x8163, ?x1542), major_field_of_study(?x7418, ?x5614), major_field_of_study(?x6912, ?x5614), organization(?x346, ?x8937), citytown(?x8937, ?x6683), award_nominee(?x364, ?x8163), ?x7418 = 03cz83, colors(?x4296, ?x8271), ?x6912 = 0gl5_, people(?x2510, ?x13105), team(?x261, ?x2067), contains(?x94, ?x6763) >> conf = 0.77 => this is the best rule for 5 predicted values ranks of expected_values: 1, 7 EVAL 06439y draft! 0jm9w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 20.000 20.000 0.769 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 06439y draft! 0jmbv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 20.000 20.000 0.769 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #12174-025n07 PRED entity: 025n07 PRED relation: music PRED expected values: 05_pkf => 73 concepts (59 used for prediction) PRED predicted values (max 10 best out of 68): 0146pg (0.25 #10, 0.17 #858, 0.15 #1280), 02qggqc (0.17 #211, 0.05 #846, 0.04 #1481), 06cv1 (0.17 #4, 0.04 #216, 0.02 #639), 027t8fw (0.15 #212), 02g1jh (0.08 #128, 0.03 #2031, 0.02 #2452), 0fp_v1x (0.08 #3, 0.01 #426), 03h610 (0.08 #925, 0.08 #712, 0.07 #1136), 02bh9 (0.07 #899, 0.07 #474, 0.06 #686), 025n3p (0.07 #8231, 0.07 #8444, 0.06 #4225), 04pf4r (0.06 #491, 0.04 #703, 0.03 #1338) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0c40vxk; >> query: (?x2968, 0146pg) <- edited_by(?x2968, ?x707), executive_produced_by(?x3423, ?x707), cinematography(?x2968, ?x7249) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1754 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 268 *> proper extension: 0g5qs2k; 0gkz15s; 09q5w2; 0qm8b; 0bq8tmw; 0bh8yn3; 03kg2v; 0ds2n; 0299hs; 03wbqc4; ... *> query: (?x2968, 05_pkf) <- film_release_distribution_medium(?x2968, ?x81), film_crew_role(?x2968, ?x137), genre(?x2968, ?x225), ?x225 = 02kdv5l *> conf = 0.01 ranks of expected_values: 46 EVAL 025n07 music 05_pkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 73.000 59.000 0.250 http://example.org/film/film/music #12173-047d21r PRED entity: 047d21r PRED relation: titles! PRED expected values: 017fp => 123 concepts (104 used for prediction) PRED predicted values (max 10 best out of 70): 07c52 (0.45 #2241, 0.21 #1338, 0.15 #4358), 01z4y (0.41 #434, 0.25 #34, 0.23 #3054), 01hmnh (0.40 #2239, 0.25 #26, 0.11 #10133), 04xvlr (0.39 #3224, 0.31 #2821, 0.26 #1815), 017fp (0.31 #123, 0.21 #223, 0.17 #3244), 01j1n2 (0.25 #73), 03bxz7 (0.22 #9803, 0.18 #8279, 0.18 #8684), 01jfsb (0.15 #3039, 0.14 #10126, 0.14 #5156), 024qqx (0.13 #379, 0.11 #1791, 0.09 #782), 07ssc (0.12 #509, 0.12 #2827, 0.11 #7782) >> Best rule #2241 for best value: >> intensional similarity = 3 >> extensional distance = 237 >> proper extension: 0n2bh; 0gfzgl; 03y3bp7; 01f3p_; 08cx5g; 02sqkh; 06dfz1; 07wqr6; 03g9xj; 0cskb; ... >> query: (?x3743, 07c52) <- titles(?x1967, ?x3743), nominated_for(?x3961, ?x3743), films(?x1967, ?x188) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 11 *> proper extension: 01cgz; *> query: (?x3743, 017fp) <- films(?x1967, ?x3743), ?x1967 = 01cgz *> conf = 0.31 ranks of expected_values: 5 EVAL 047d21r titles! 017fp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 123.000 104.000 0.452 http://example.org/media_common/netflix_genre/titles #12172-01nqj PRED entity: 01nqj PRED relation: organization PRED expected values: 0b6css => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 48): 0b6css (0.67 #9, 0.56 #49, 0.34 #29), 04k4l (0.34 #64, 0.26 #741, 0.25 #584), 0j7v_ (0.31 #5, 0.27 #325, 0.26 #505), 01rz1 (0.30 #181, 0.26 #61, 0.26 #741), 0_2v (0.29 #603, 0.29 #443, 0.29 #583), 018cqq (0.26 #741, 0.20 #70, 0.17 #190), 02jxk (0.26 #741, 0.14 #182, 0.13 #62), 085h1 (0.26 #741, 0.05 #31, 0.04 #111), 034h1h (0.18 #951, 0.02 #1116), 02_l9 (0.07 #956) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 07p7g; >> query: (?x11289, 0b6css) <- administrative_parent(?x11289, ?x551), contains(?x2467, ?x11289), ?x2467 = 0dg3n1 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nqj organization 0b6css CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #12171-04ktcgn PRED entity: 04ktcgn PRED relation: nationality PRED expected values: 09c7w0 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 82): 09c7w0 (0.70 #4703, 0.70 #4303, 0.70 #2702), 07ssc (0.18 #215, 0.09 #815, 0.09 #3216), 0345h (0.12 #131, 0.04 #3302, 0.03 #831), 02jx1 (0.10 #3234, 0.10 #4235, 0.09 #6535), 0ctw_b (0.07 #527, 0.07 #727, 0.07 #627), 01p1v (0.06 #244, 0.04 #3302, 0.02 #7505), 0d060g (0.05 #807, 0.04 #307, 0.04 #3302), 03rk0 (0.05 #7048, 0.05 #7350, 0.05 #7249), 0chghy (0.04 #810, 0.04 #3302, 0.03 #410), 0f8l9c (0.04 #3302, 0.02 #522, 0.02 #722) >> Best rule #4703 for best value: >> intensional similarity = 3 >> extensional distance = 1992 >> proper extension: 01zmpg; 05bxwh; 011hdn; 0flpy; 02q6cv4; 057xn_m; >> query: (?x1983, 09c7w0) <- profession(?x1983, ?x5654), award_nominee(?x1983, ?x4393), award(?x1983, ?x500) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ktcgn nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.697 http://example.org/people/person/nationality #12170-01c8v0 PRED entity: 01c8v0 PRED relation: award PRED expected values: 0c4z8 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 266): 01by1l (0.41 #1724, 0.36 #3739, 0.30 #6963), 01bgqh (0.32 #1654, 0.31 #3669, 0.24 #8505), 01ckcd (0.27 #1141, 0.16 #3962, 0.16 #5574), 0c4z8 (0.26 #1683, 0.24 #1280, 0.22 #2489), 054ks3 (0.25 #1752, 0.24 #140, 0.20 #2961), 0gqz2 (0.24 #80, 0.14 #1289, 0.13 #8543), 09sb52 (0.23 #14145, 0.21 #9309, 0.21 #17369), 03qbh5 (0.23 #1816, 0.20 #204, 0.18 #7055), 01c92g (0.21 #1306, 0.17 #1709, 0.15 #2515), 054krc (0.20 #87, 0.10 #8550, 0.10 #490) >> Best rule #1724 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 07c0j; 018ndc; 017j6; 0khth; 0kr_t; 0dw4g; 07mvp; 0187x8; 0134wr; 04k05; ... >> query: (?x4029, 01by1l) <- artists(?x1572, ?x4029), ?x1572 = 06by7, award_winner(?x4584, ?x4029) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1683 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 126 *> proper extension: 07c0j; 018ndc; 017j6; 0khth; 0kr_t; 0dw4g; 07mvp; 0187x8; 0134wr; 04k05; ... *> query: (?x4029, 0c4z8) <- artists(?x1572, ?x4029), ?x1572 = 06by7, award_winner(?x4584, ?x4029) *> conf = 0.26 ranks of expected_values: 4 EVAL 01c8v0 award 0c4z8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 106.000 106.000 0.414 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12169-065_cjc PRED entity: 065_cjc PRED relation: currency PRED expected values: 09nqf => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.89 #43, 0.84 #71, 0.83 #78), 01nv4h (0.12 #568, 0.07 #86, 0.04 #72), 02l6h (0.12 #568, 0.02 #95, 0.01 #424), 02gsvk (0.12 #568, 0.01 #132, 0.01 #146), 088n7 (0.12 #568, 0.01 #196), 0kz1h (0.12 #568), 0ptk_ (0.12 #568) >> Best rule #43 for best value: >> intensional similarity = 5 >> extensional distance = 73 >> proper extension: 026n4h6; 02krdz; 02825cv; 02t_h3; >> query: (?x6752, 09nqf) <- titles(?x512, ?x6752), film(?x4371, ?x6752), production_companies(?x6752, ?x1478), category(?x6752, ?x134), influenced_by(?x4371, ?x364) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065_cjc currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.893 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #12168-0ck91 PRED entity: 0ck91 PRED relation: film PRED expected values: 04yc76 => 119 concepts (61 used for prediction) PRED predicted values (max 10 best out of 572): 09lxv9 (0.23 #6880, 0.12 #15835, 0.12 #17626), 02ryz24 (0.15 #5842, 0.11 #2260, 0.08 #14797), 01f69m (0.15 #7111, 0.11 #3529, 0.08 #16066), 07bx6 (0.15 #6676, 0.08 #15631, 0.08 #17422), 0sxfd (0.15 #5585, 0.08 #14540, 0.08 #16331), 07xtqq (0.15 #5430, 0.08 #14385, 0.08 #16176), 0cfhfz (0.14 #13030, 0.12 #18403, 0.06 #7657), 014kq6 (0.11 #80602, 0.11 #2137, 0.01 #25421), 025twgt (0.11 #80602), 0fztbq (0.11 #80602) >> Best rule #6880 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0q9kd; 09fb5; 01wjrn; 01vvb4m; 015g_7; >> query: (?x11601, 09lxv9) <- location(?x11601, ?x4151), type_of_union(?x11601, ?x566), people(?x1050, ?x11601), ?x4151 = 0r0m6 >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #14771 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0p_pd; 01vrncs; 05bpg3; 04d2yp; *> query: (?x11601, 04yc76) <- location(?x11601, ?x4151), award(?x11601, ?x375), ?x4151 = 0r0m6, film(?x11601, ?x6077) *> conf = 0.04 ranks of expected_values: 325 EVAL 0ck91 film 04yc76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 119.000 61.000 0.231 http://example.org/film/actor/film./film/performance/film #12167-0dl567 PRED entity: 0dl567 PRED relation: award PRED expected values: 02f6ym => 114 concepts (107 used for prediction) PRED predicted values (max 10 best out of 298): 09sb52 (0.36 #21263, 0.35 #14582, 0.30 #24802), 0gqz2 (0.33 #5581, 0.14 #7546, 0.13 #11083), 03qbnj (0.31 #3760, 0.21 #1009, 0.19 #616), 05p09zm (0.26 #2083, 0.23 #3262, 0.19 #4048), 01c92g (0.24 #486, 0.23 #3630, 0.18 #5595), 03c7tr1 (0.23 #2023, 0.17 #3202, 0.15 #1630), 01d38g (0.22 #3565, 0.10 #12997, 0.10 #421), 02wh75 (0.21 #795, 0.15 #5904, 0.15 #6297), 02f5qb (0.20 #3684, 0.17 #1719, 0.16 #2505), 02f73p (0.19 #3716, 0.13 #1751, 0.13 #2537) >> Best rule #21263 for best value: >> intensional similarity = 3 >> extensional distance = 1045 >> proper extension: 050_qx; >> query: (?x4080, 09sb52) <- award_nominee(?x2083, ?x4080), nominated_for(?x4080, ?x857), film(?x4080, ?x607) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #3785 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 92 *> proper extension: 03fbc; 0hvbj; *> query: (?x4080, 02f6ym) <- award(?x4080, ?x4018), artists(?x302, ?x4080), ?x4018 = 03qbh5 *> conf = 0.15 ranks of expected_values: 26 EVAL 0dl567 award 02f6ym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 114.000 107.000 0.361 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12166-03vfr_ PRED entity: 03vfr_ PRED relation: genre PRED expected values: 03k9fj => 72 concepts (58 used for prediction) PRED predicted values (max 10 best out of 114): 07s9rl0 (0.69 #5781, 0.58 #5540, 0.57 #4819), 03k9fj (0.56 #132, 0.40 #493, 0.37 #734), 02kdv5l (0.49 #484, 0.48 #604, 0.46 #725), 01hmnh (0.49 #139, 0.28 #741, 0.27 #500), 01jfsb (0.43 #614, 0.39 #494, 0.38 #735), 02l7c8 (0.38 #17, 0.31 #1100, 0.28 #5797), 06n90 (0.27 #615, 0.26 #495, 0.26 #736), 06cvj (0.25 #4, 0.19 #1087, 0.10 #847), 01zhp (0.24 #197, 0.09 #318, 0.07 #558), 0jxy (0.22 #165, 0.06 #45, 0.05 #6982) >> Best rule #5781 for best value: >> intensional similarity = 3 >> extensional distance = 1449 >> proper extension: 0vgkd; >> query: (?x10327, 07s9rl0) <- genre(?x10327, ?x2540), genre(?x7887, ?x2540), ?x7887 = 04z_3pm >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #132 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 123 *> proper extension: 01h72l; *> query: (?x10327, 03k9fj) <- genre(?x10327, ?x2540), ?x2540 = 0hcr *> conf = 0.56 ranks of expected_values: 2 EVAL 03vfr_ genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 58.000 0.693 http://example.org/film/film/genre #12165-026c1 PRED entity: 026c1 PRED relation: film PRED expected values: 03s6l2 => 143 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1140): 0q9b0 (0.25 #1265, 0.17 #3043, 0.03 #10155), 0f4_l (0.17 #2125, 0.05 #9237, 0.04 #19905), 02yvct (0.17 #2126, 0.05 #9238, 0.04 #11016), 084qpk (0.17 #1898, 0.05 #9010, 0.04 #10788), 03z20c (0.17 #2251, 0.05 #9363, 0.03 #170695), 02pg45 (0.17 #2705, 0.03 #15151, 0.03 #31153), 0ds5_72 (0.17 #3225, 0.03 #6781, 0.02 #38786), 03bx2lk (0.14 #3740, 0.09 #10852, 0.09 #5518), 01shy7 (0.14 #3976, 0.09 #5754, 0.09 #12866), 011ysn (0.14 #4118, 0.08 #9452, 0.06 #7674) >> Best rule #1265 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01w02sy; 01vswwx; >> query: (?x2221, 0q9b0) <- participant(?x7375, ?x2221), ?x7375 = 0484q, vacationer(?x126, ?x2221) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3638 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 017yxq; *> query: (?x2221, 03s6l2) <- award_winner(?x401, ?x2221), film(?x2221, ?x10475), participant(?x2221, ?x3536) *> conf = 0.14 ranks of expected_values: 13 EVAL 026c1 film 03s6l2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 143.000 111.000 0.250 http://example.org/film/actor/film./film/performance/film #12164-0ldqf PRED entity: 0ldqf PRED relation: olympics! PRED expected values: 0chghy 06qd3 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 219): 0b90_r (0.84 #334, 0.78 #1004, 0.78 #333), 06mzp (0.81 #1797, 0.79 #2129, 0.79 #4696), 0chghy (0.79 #2234, 0.77 #1568, 0.75 #1790), 0h7x (0.78 #4483, 0.76 #4706, 0.69 #3362), 059j2 (0.76 #4478, 0.75 #1802, 0.75 #1357), 06qd3 (0.75 #1808, 0.75 #1363, 0.67 #473), 0d04z6 (0.67 #1410, 0.67 #520, 0.56 #1855), 02k54 (0.67 #1461, 0.60 #235, 0.57 #570), 06mkj (0.67 #1376, 0.60 #261, 0.57 #596), 019rg5 (0.67 #463, 0.58 #1353, 0.50 #1798) >> Best rule #334 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0kbvb; >> query: (?x7441, ?x7430) <- olympics(?x471, ?x7441), medal(?x7441, ?x422), olympics(?x7430, ?x7441), olympics(?x792, ?x7441), sports(?x7441, ?x171), olympics(?x456, ?x7441), ?x456 = 05qhw, ?x471 = 02vx4, adjoins(?x7430, ?x2517), combatants(?x7430, ?x390), ?x792 = 0hzlz >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2234 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 17 *> proper extension: 0l98s; 0l6m5; 0lk8j; 0blg2; 018ljb; *> query: (?x7441, 0chghy) <- olympics(?x359, ?x7441), medal(?x7441, ?x422), locations(?x7441, ?x8602), sports(?x7441, ?x3659), ?x3659 = 0dwxr, olympics(?x1023, ?x7441), film_release_region(?x1999, ?x1023), combatants(?x326, ?x1023), ?x1999 = 0gd0c7x, country(?x150, ?x1023) *> conf = 0.79 ranks of expected_values: 3, 6 EVAL 0ldqf olympics! 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 62.000 0.839 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0ldqf olympics! 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 62.000 0.839 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #12163-02kc_w5 PRED entity: 02kc_w5 PRED relation: nutrient! PRED expected values: 05z55 => 46 concepts (45 used for prediction) PRED predicted values (max 10 best out of 14): 0fjfh (0.95 #847, 0.93 #904, 0.93 #892), 0fj52s (0.93 #857, 0.93 #843, 0.92 #766), 05z55 (0.92 #502, 0.92 #496, 0.92 #484), 061_f (0.91 #710, 0.91 #693, 0.90 #927), 0fbdb (0.91 #895, 0.90 #658, 0.90 #651), 0fbw6 (0.89 #976, 0.89 #959, 0.88 #846), 09728 (0.88 #149, 0.87 #170, 0.87 #45), 01645p (0.88 #149, 0.87 #170, 0.87 #45), 07j87 (0.88 #149, 0.87 #170, 0.87 #45), 06x4c (0.88 #149, 0.87 #170, 0.87 #45) >> Best rule #847 for best value: >> intensional similarity = 125 >> extensional distance = 38 >> proper extension: 0hkwr; >> query: (?x13126, 0fjfh) <- nutrient(?x8298, ?x13126), nutrient(?x7719, ?x13126), nutrient(?x6191, ?x13126), nutrient(?x6159, ?x13126), nutrient(?x2701, ?x13126), nutrient(?x1959, ?x13126), nutrient(?x6159, ?x13498), nutrient(?x6159, ?x12902), nutrient(?x6159, ?x12868), nutrient(?x6159, ?x12454), nutrient(?x6159, ?x12083), nutrient(?x6159, ?x11758), nutrient(?x6159, ?x11592), nutrient(?x6159, ?x11409), nutrient(?x6159, ?x10891), nutrient(?x6159, ?x10098), nutrient(?x6159, ?x9949), nutrient(?x6159, ?x9915), nutrient(?x6159, ?x9855), nutrient(?x6159, ?x9840), nutrient(?x6159, ?x9795), nutrient(?x6159, ?x9733), nutrient(?x6159, ?x9490), nutrient(?x6159, ?x9436), nutrient(?x6159, ?x9426), nutrient(?x6159, ?x9365), nutrient(?x6159, ?x8487), nutrient(?x6159, ?x8442), nutrient(?x6159, ?x8413), nutrient(?x6159, ?x7720), nutrient(?x6159, ?x7652), nutrient(?x6159, ?x7431), nutrient(?x6159, ?x7364), nutrient(?x6159, ?x7362), nutrient(?x6159, ?x7219), nutrient(?x6159, ?x7135), nutrient(?x6159, ?x6586), nutrient(?x6159, ?x6517), nutrient(?x6159, ?x6192), nutrient(?x6159, ?x6160), nutrient(?x6159, ?x6033), nutrient(?x6159, ?x6026), nutrient(?x6159, ?x5549), nutrient(?x6159, ?x5526), nutrient(?x6159, ?x5451), nutrient(?x6159, ?x5374), nutrient(?x6159, ?x5337), nutrient(?x6159, ?x5010), nutrient(?x6159, ?x4069), nutrient(?x6159, ?x3469), nutrient(?x6159, ?x3264), nutrient(?x6159, ?x2702), nutrient(?x6159, ?x2018), nutrient(?x6159, ?x1960), ?x9365 = 04k8n, ?x9733 = 0h1tz, ?x6026 = 025sf8g, ?x9436 = 025sqz8, ?x8413 = 02kc4sf, ?x6033 = 04zjxcz, ?x12902 = 0fzjh, ?x10098 = 0h1_c, ?x5451 = 05wvs, ?x8442 = 02kcv4x, ?x2701 = 0hkxq, ?x2018 = 01sh2, ?x10891 = 0g5gq, ?x12868 = 03d49, ?x8298 = 037ls6, ?x8487 = 014yzm, ?x6192 = 06jry, ?x9949 = 02kd0rh, ?x6586 = 05gh50, ?x4069 = 0hqw8p_, ?x13498 = 07q0m, ?x9915 = 025tkqy, ?x7135 = 025rsfk, ?x11592 = 025sf0_, ?x11409 = 0h1yf, ?x9490 = 0h1sg, ?x6191 = 014j1m, nutrient(?x1959, ?x11784), nutrient(?x1959, ?x11270), nutrient(?x1959, ?x7894), nutrient(?x1959, ?x1258), ?x7894 = 0f4hc, ?x1258 = 0h1wg, ?x7364 = 09gvd, ?x9855 = 0d9t0, ?x7720 = 025s7x6, ?x12083 = 01n78x, nutrient(?x7719, ?x13944), nutrient(?x7719, ?x9708), nutrient(?x7719, ?x8243), nutrient(?x7719, ?x6286), ?x11270 = 02kc008, ?x7431 = 09gwd, ?x9708 = 061xhr, ?x11784 = 07zqy, ?x13944 = 0f4kp, nutrient(?x9732, ?x6517), ?x7219 = 0h1vg, ?x11758 = 0q01m, ?x5337 = 06x4c, ?x12454 = 025rw19, ?x6160 = 041r51, ?x7362 = 02kc5rj, ?x7652 = 025s0s0, ?x6286 = 02y_3rf, nutrient(?x1303, ?x1960), nutrient(?x1257, ?x1960), ?x3264 = 0dcfv, ?x9795 = 05v_8y, ?x1257 = 09728, ?x8243 = 014d7f, ?x5549 = 025s7j4, ?x5010 = 0h1vz, ?x5374 = 025s0zp, ?x5526 = 09pbb, ?x2702 = 0838f, ?x9426 = 0h1yy, ?x1303 = 0fj52s, ?x9732 = 05z55, ?x3469 = 0h1zw, ?x9840 = 02p0tjr >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #502 for first EXPECTED value: *> intensional similarity = 127 *> extensional distance = 22 *> proper extension: 0g5gq; *> query: (?x13126, ?x9732) <- nutrient(?x10612, ?x13126), nutrient(?x9005, ?x13126), nutrient(?x8298, ?x13126), nutrient(?x7719, ?x13126), nutrient(?x6191, ?x13126), nutrient(?x6159, ?x13126), nutrient(?x6032, ?x13126), nutrient(?x5373, ?x13126), nutrient(?x3468, ?x13126), nutrient(?x2701, ?x13126), nutrient(?x1959, ?x13126), ?x6159 = 033cnk, ?x6191 = 014j1m, ?x2701 = 0hkxq, ?x9005 = 04zpv, ?x3468 = 0cxn2, ?x10612 = 0frq6, nutrient(?x7719, ?x13944), nutrient(?x7719, ?x13498), nutrient(?x7719, ?x12902), nutrient(?x7719, ?x12868), nutrient(?x7719, ?x12454), nutrient(?x7719, ?x11758), nutrient(?x7719, ?x11592), nutrient(?x7719, ?x11270), nutrient(?x7719, ?x10709), nutrient(?x7719, ?x9840), nutrient(?x7719, ?x9795), nutrient(?x7719, ?x9733), nutrient(?x7719, ?x9708), nutrient(?x7719, ?x9619), nutrient(?x7719, ?x9490), nutrient(?x7719, ?x9426), nutrient(?x7719, ?x8487), nutrient(?x7719, ?x8442), nutrient(?x7719, ?x8413), nutrient(?x7719, ?x7720), nutrient(?x7719, ?x7652), nutrient(?x7719, ?x7431), nutrient(?x7719, ?x7364), nutrient(?x7719, ?x7362), nutrient(?x7719, ?x7219), nutrient(?x7719, ?x7135), nutrient(?x7719, ?x6586), nutrient(?x7719, ?x6192), nutrient(?x7719, ?x6026), nutrient(?x7719, ?x5374), nutrient(?x7719, ?x5337), nutrient(?x7719, ?x5010), nutrient(?x7719, ?x4069), nutrient(?x7719, ?x3264), nutrient(?x7719, ?x3203), nutrient(?x7719, ?x2702), nutrient(?x7719, ?x2018), nutrient(?x7719, ?x1960), nutrient(?x7719, ?x1258), ?x2702 = 0838f, ?x10709 = 0h1sz, ?x1960 = 07hnp, ?x8442 = 02kcv4x, ?x1258 = 0h1wg, ?x11592 = 025sf0_, nutrient(?x1959, ?x13545), nutrient(?x1959, ?x12336), nutrient(?x1959, ?x12083), nutrient(?x1959, ?x11409), nutrient(?x1959, ?x10098), nutrient(?x1959, ?x9949), nutrient(?x1959, ?x6517), nutrient(?x1959, ?x6033), nutrient(?x1959, ?x5549), nutrient(?x1959, ?x5451), nutrient(?x1959, ?x1304), ?x9619 = 0h1tg, ?x6026 = 025sf8g, ?x9949 = 02kd0rh, ?x5010 = 0h1vz, ?x7652 = 025s0s0, ?x5374 = 025s0zp, ?x11409 = 0h1yf, ?x9490 = 0h1sg, ?x7219 = 0h1vg, ?x4069 = 0hqw8p_, ?x5549 = 025s7j4, ?x12083 = 01n78x, ?x9795 = 05v_8y, ?x8413 = 02kc4sf, ?x13944 = 0f4kp, ?x3264 = 0dcfv, ?x13498 = 07q0m, ?x7720 = 025s7x6, ?x8487 = 014yzm, ?x11758 = 0q01m, ?x3203 = 04kl74p, ?x7364 = 09gvd, ?x5451 = 05wvs, ?x5337 = 06x4c, ?x9733 = 0h1tz, ?x11270 = 02kc008, ?x6032 = 01nkt, nutrient(?x9732, ?x9708), nutrient(?x7057, ?x9708), ?x12454 = 025rw19, ?x8298 = 037ls6, ?x12336 = 0f4l5, ?x9840 = 02p0tjr, ?x6586 = 05gh50, nutrient(?x5009, ?x7431), nutrient(?x1257, ?x7431), ?x6033 = 04zjxcz, ?x9426 = 0h1yy, ?x7362 = 02kc5rj, ?x10098 = 0h1_c, ?x12902 = 0fzjh, ?x1304 = 08lb68, ?x12868 = 03d49, ?x7057 = 0fbdb, ?x6192 = 06jry, ?x2018 = 01sh2, ?x5009 = 0fjfh, ?x6517 = 02kd8zw, ?x7135 = 025rsfk, ?x13545 = 01w_3, nutrient(?x5373, ?x14210), ?x14210 = 0f4k5, ?x9732 = 05z55, ?x1257 = 09728 *> conf = 0.92 ranks of expected_values: 3 EVAL 02kc_w5 nutrient! 05z55 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 46.000 45.000 0.950 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #12162-01skxk PRED entity: 01skxk PRED relation: parent_genre PRED expected values: 06by7 => 62 concepts (46 used for prediction) PRED predicted values (max 10 best out of 252): 06by7 (0.95 #1296, 0.78 #1617, 0.44 #975), 05r6t (0.53 #852, 0.50 #532, 0.28 #1654), 064t9 (0.29 #970, 0.16 #810, 0.11 #1763), 05bt6j (0.29 #987, 0.11 #1308, 0.11 #827), 03lty (0.26 #2106, 0.25 #178, 0.18 #5966), 011j5x (0.25 #341, 0.25 #181, 0.20 #501), 018ysx (0.25 #453, 0.10 #1764, 0.10 #613), 06j6l (0.24 #991, 0.18 #671, 0.10 #1151), 0y3_8 (0.21 #990, 0.08 #5786, 0.08 #6109), 0xhtw (0.20 #492, 0.12 #972, 0.12 #2100) >> Best rule #1296 for best value: >> intensional similarity = 8 >> extensional distance = 60 >> proper extension: 01gbcf; 01h0kx; 018ysx; 028cl7; 017ht; >> query: (?x8385, 06by7) <- parent_genre(?x8385, ?x8386), artists(?x8386, ?x10502), artists(?x8386, ?x7764), artists(?x8386, ?x680), ?x7764 = 01wphh2, group(?x228, ?x10502), ?x680 = 01cv3n, artist(?x2193, ?x10502) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01skxk parent_genre 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 46.000 0.952 http://example.org/music/genre/parent_genre #12161-0884hk PRED entity: 0884hk PRED relation: award_winner PRED expected values: 06jrhz => 96 concepts (52 used for prediction) PRED predicted values (max 10 best out of 653): 0brkwj (0.82 #14484, 0.82 #83693, 0.82 #33796), 0884hk (0.58 #2286, 0.33 #677, 0.32 #35409), 0h5jg5 (0.54 #67591, 0.54 #69203, 0.54 #75642), 08q3s0 (0.54 #67591, 0.54 #69203, 0.54 #75642), 047cqr (0.54 #69203, 0.54 #75642, 0.48 #65982), 06jrhz (0.44 #990, 0.42 #2599, 0.32 #35409), 059j4x (0.32 #35409, 0.20 #78864, 0.16 #54719), 01rzqj (0.32 #35409, 0.20 #78864, 0.16 #54719), 04snp2 (0.28 #40238), 04wvhz (0.23 #27359, 0.20 #78864, 0.16 #54719) >> Best rule #14484 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 079vf; 079ws; 011s9r; >> query: (?x4022, ?x2650) <- award_winner(?x2650, ?x4022), award_winner(?x4022, ?x4035), story_by(?x2649, ?x4022) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #990 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 047cqr; *> query: (?x4022, 06jrhz) <- place_of_birth(?x4022, ?x94), award_nominee(?x4022, ?x2650), ?x2650 = 0d7hg4 *> conf = 0.44 ranks of expected_values: 6 EVAL 0884hk award_winner 06jrhz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 52.000 0.821 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #12160-0b0pf PRED entity: 0b0pf PRED relation: religion PRED expected values: 03_gx => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 20): 03_gx (0.29 #149, 0.22 #59, 0.18 #194), 0kpl (0.18 #910, 0.18 #640, 0.18 #325), 0c8wxp (0.16 #321, 0.13 #636, 0.13 #771), 0kq2 (0.09 #198, 0.06 #918, 0.06 #828), 0g5llry (0.06 #73, 0.05 #118, 0.05 #163), 058x5 (0.06 #49, 0.05 #94, 0.05 #139), 092bf5 (0.06 #286, 0.05 #511, 0.05 #556), 04pk9 (0.03 #335, 0.02 #830, 0.02 #1010), 0n2g (0.03 #1183, 0.03 #1273, 0.03 #1138), 01lp8 (0.03 #901, 0.02 #811, 0.02 #991) >> Best rule #149 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 05jm7; 0klw; 02ghq; 01g6bk; >> query: (?x5333, 03_gx) <- award(?x5333, ?x8880), ?x8880 = 0262x6, nationality(?x5333, ?x94), gender(?x5333, ?x231) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b0pf religion 03_gx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.286 http://example.org/people/person/religion #12159-03hkv_r PRED entity: 03hkv_r PRED relation: nominated_for PRED expected values: 083shs 0c0nhgv 0pv3x 0416y94 09p0ct 09z2b7 02rx2m5 0fpv_3_ 02vqsll 01jrbv 0cbv4g 02jxbw 071nw5 0_9l_ => 44 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1830): 0c0nhgv (0.71 #1643, 0.50 #3138, 0.33 #148), 07g1sm (0.71 #2510, 0.38 #4005, 0.33 #1015), 0pd64 (0.71 #2595, 0.33 #1100, 0.25 #4090), 0b4lkx (0.71 #2640, 0.33 #1145, 0.25 #4135), 01jc6q (0.71 #1517, 0.33 #22, 0.25 #3012), 042y1c (0.71 #1815, 0.33 #320, 0.12 #3310), 03s9kp (0.62 #4456, 0.43 #2961, 0.33 #1466), 071nw5 (0.62 #3894, 0.33 #904, 0.14 #2399), 047d21r (0.62 #3513, 0.29 #2018, 0.24 #16463), 03hkch7 (0.62 #3419, 0.29 #1924, 0.24 #16463) >> Best rule #1643 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0p9sw; 04dn09n; 0gq9h; 0k611; >> query: (?x384, 0c0nhgv) <- nominated_for(?x384, ?x9838), nominated_for(?x384, ?x6782), nominated_for(?x384, ?x3882), ?x6782 = 07jnt, ?x3882 = 0mcl0, genre(?x9838, ?x53) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8, 17, 39, 82, 110, 144, 156, 176, 195, 264, 337, 560, 584 EVAL 03hkv_r nominated_for 0_9l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 071nw5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 02jxbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 0cbv4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 01jrbv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 02vqsll CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 0fpv_3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 02rx2m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 09z2b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 09p0ct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 0pv3x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 0c0nhgv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkv_r nominated_for 083shs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 44.000 14.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12158-017gm7 PRED entity: 017gm7 PRED relation: award PRED expected values: 02g3v6 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 202): 0gs9p (0.30 #2827, 0.23 #3290, 0.21 #3756), 0gq9h (0.29 #2825, 0.28 #2996, 0.26 #3459), 019f4v (0.28 #2996, 0.26 #3459, 0.25 #461), 0k611 (0.28 #2996, 0.26 #3459, 0.25 #461), 0p9sw (0.28 #2996, 0.26 #3459, 0.25 #461), 0gq_v (0.28 #2996, 0.26 #3459, 0.25 #461), 02pqp12 (0.28 #2996, 0.26 #3459, 0.25 #461), 02x17s4 (0.28 #2996, 0.26 #3459, 0.25 #461), 02r0csl (0.28 #2996, 0.26 #3459, 0.25 #461), 0fhpv4 (0.28 #2996, 0.26 #3459, 0.25 #461) >> Best rule #2827 for best value: >> intensional similarity = 5 >> extensional distance = 118 >> proper extension: 0yyg4; 0gzy02; 04v8x9; 0n0bp; 020fcn; 0sxfd; 09cr8; 026p4q7; 019vhk; 012mrr; ... >> query: (?x1392, 0gs9p) <- nominated_for(?x1703, ?x1392), nominated_for(?x1107, ?x1392), nominated_for(?x230, ?x1392), ?x1107 = 019f4v, ?x1703 = 0k611 >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #2996 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 118 *> proper extension: 0yyg4; 0gzy02; 04v8x9; 0n0bp; 020fcn; 0sxfd; 09cr8; 026p4q7; 019vhk; 012mrr; ... *> query: (?x1392, ?x143) <- nominated_for(?x1703, ?x1392), nominated_for(?x1107, ?x1392), nominated_for(?x143, ?x1392), nominated_for(?x230, ?x1392), ?x1107 = 019f4v, ?x1703 = 0k611 *> conf = 0.28 ranks of expected_values: 16 EVAL 017gm7 award 02g3v6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 80.000 80.000 0.300 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12157-0qm98 PRED entity: 0qm98 PRED relation: award PRED expected values: 0gqy2 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 148): 094qd5 (0.27 #6192, 0.27 #6191, 0.26 #3900), 0gqwc (0.27 #6192, 0.27 #6191, 0.26 #3900), 04dn09n (0.27 #6192, 0.27 #6191, 0.26 #3900), 02y_rq5 (0.27 #6192, 0.27 #6191, 0.26 #3900), 02x73k6 (0.27 #6192, 0.27 #6191, 0.26 #3900), 027571b (0.16 #400, 0.07 #631, 0.03 #3153), 02z1nbg (0.15 #364, 0.08 #595, 0.04 #135), 0gr51 (0.14 #534, 0.13 #303, 0.07 #994), 040njc (0.14 #236, 0.13 #467, 0.06 #3218), 0k611 (0.13 #529, 0.10 #69, 0.10 #989) >> Best rule #6192 for best value: >> intensional similarity = 2 >> extensional distance = 1002 >> proper extension: 044g_k; 023p33; 0299hs; 02pg45; 059lwy; 05_61y; 076xkdz; 097h2; 0j8f09z; 02gl58; ... >> query: (?x1454, ?x749) <- award(?x1454, ?x289), nominated_for(?x749, ?x1454) >> conf = 0.27 => this is the best rule for 5 predicted values *> Best rule #9175 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1257 *> proper extension: 0h95b81; 04bp0l; *> query: (?x1454, ?x458) <- nominated_for(?x5979, ?x1454), nominated_for(?x434, ?x1454), award_winner(?x5979, ?x851), award_winner(?x458, ?x434) *> conf = 0.12 ranks of expected_values: 15 EVAL 0qm98 award 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 67.000 67.000 0.268 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12156-0175zz PRED entity: 0175zz PRED relation: parent_genre PRED expected values: 01243b => 49 concepts (36 used for prediction) PRED predicted values (max 10 best out of 287): 06by7 (0.86 #2288, 0.72 #2611, 0.71 #2772), 05r6t (0.80 #1350, 0.72 #1675, 0.59 #2164), 03lty (0.65 #1478, 0.43 #3100, 0.25 #2936), 01243b (0.56 #513, 0.44 #351, 0.39 #1650), 05w3f (0.43 #835, 0.19 #809, 0.19 #671), 03_d0 (0.38 #1792, 0.12 #1140, 0.11 #654), 0y3_8 (0.34 #1002, 0.16 #1815, 0.09 #3606), 011j5x (0.33 #344, 0.33 #21, 0.22 #182), 059kh (0.33 #33, 0.24 #1004, 0.22 #356), 064t9 (0.33 #10, 0.11 #333, 0.11 #820) >> Best rule #2288 for best value: >> intensional similarity = 9 >> extensional distance = 67 >> proper extension: 05hs4r; 0m0jc; 015pdg; 064t9; 016jhr; 0xhtw; 061fhg; 01756d; 0mhfr; 05bt6j; ... >> query: (?x11242, 06by7) <- parent_genre(?x11242, ?x302), artists(?x302, ?x8560), artists(?x302, ?x3894), artists(?x302, ?x1412), artists(?x302, ?x717), ?x3894 = 01vxlbm, role(?x8560, ?x212), ?x717 = 0150jk, ?x1412 = 067mj >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #513 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 14 *> proper extension: 01_bkd; 034487; 01skxk; 016y3j; 029fbr; 01rthc; *> query: (?x11242, 01243b) <- parent_genre(?x11242, ?x3061), parent_genre(?x11242, ?x302), ?x302 = 016clz, artists(?x3061, ?x6854), artists(?x3061, ?x6639), group(?x75, ?x6854), artist(?x2190, ?x6854), people(?x4959, ?x6639), profession(?x6639, ?x1032) *> conf = 0.56 ranks of expected_values: 4 EVAL 0175zz parent_genre 01243b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 49.000 36.000 0.855 http://example.org/music/genre/parent_genre #12155-07m4c PRED entity: 07m4c PRED relation: artists! PRED expected values: 016jny => 80 concepts (77 used for prediction) PRED predicted values (max 10 best out of 273): 017_qw (0.68 #3767, 0.63 #8087, 0.61 #6852), 0xhtw (0.58 #1869, 0.50 #942, 0.45 #9275), 016jny (0.50 #1646, 0.47 #2574, 0.43 #2264), 07sbbz2 (0.50 #624, 0.33 #2788, 0.27 #1241), 016clz (0.48 #11423, 0.39 #9262, 0.39 #10497), 02w4v (0.45 #1277, 0.40 #2824, 0.33 #44), 064t9 (0.43 #15755, 0.43 #21005, 0.42 #21933), 05bt6j (0.43 #2202, 0.42 #1584, 0.40 #2512), 05hs4r (0.38 #617, 0.27 #2781, 0.24 #9875), 016cjb (0.36 #1309, 0.33 #384, 0.24 #9875) >> Best rule #3767 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 01jpmpv; >> query: (?x7544, 017_qw) <- music(?x1069, ?x7544), featured_film_locations(?x1069, ?x3832), film_release_region(?x1069, ?x304), ?x304 = 0d0vqn, produced_by(?x1069, ?x1070) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 07qnf; 0249kn; *> query: (?x7544, 016jny) <- artists(?x1928, ?x7544), group(?x227, ?x7544), ?x1928 = 0mhfr, ?x227 = 0342h *> conf = 0.50 ranks of expected_values: 3 EVAL 07m4c artists! 016jny CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 80.000 77.000 0.680 http://example.org/music/genre/artists #12154-013w7j PRED entity: 013w7j PRED relation: award PRED expected values: 04g2jz2 02f75t 023vrq => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 283): 023vrq (0.77 #20606, 0.74 #21819, 0.70 #33135), 02f764 (0.77 #20606, 0.74 #21819, 0.70 #33135), 09sb52 (0.40 #21051, 0.40 #11354, 0.36 #6910), 01bgqh (0.39 #1255, 0.32 #6104, 0.31 #1659), 01by1l (0.33 #1728, 0.31 #1324, 0.30 #6173), 02f5qb (0.31 #1368, 0.20 #1772, 0.16 #5408), 03qbh5 (0.29 #1822, 0.28 #1418, 0.27 #6267), 02f6ym (0.29 #1875, 0.28 #1471, 0.23 #2279), 02f71y (0.28 #1395, 0.24 #1799, 0.19 #2203), 02f73b (0.28 #1500, 0.22 #1904, 0.15 #5540) >> Best rule #20606 for best value: >> intensional similarity = 2 >> extensional distance = 591 >> proper extension: 07q1v4; 0244r8; 015cxv; 023361; >> query: (?x6151, ?x4532) <- artists(?x671, ?x6151), award_winner(?x4532, ?x6151) >> conf = 0.77 => this is the best rule for 2 predicted values ranks of expected_values: 1, 62, 235 EVAL 013w7j award 023vrq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 013w7j award 02f75t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 123.000 123.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 013w7j award 04g2jz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 123.000 123.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12153-02jyhv PRED entity: 02jyhv PRED relation: nationality PRED expected values: 09c7w0 => 143 concepts (138 used for prediction) PRED predicted values (max 10 best out of 77): 09c7w0 (0.91 #2301, 0.91 #2001, 0.91 #3602), 02jx1 (0.25 #7539, 0.17 #1733, 0.16 #933), 07ssc (0.16 #7521, 0.15 #1715, 0.08 #8322), 03_3d (0.12 #806, 0.12 #1906, 0.07 #4508), 0345h (0.11 #131, 0.09 #12515, 0.06 #7537), 0chghy (0.11 #110, 0.06 #410, 0.03 #1210), 03rk0 (0.11 #9553, 0.09 #8153, 0.08 #3546), 03rjj (0.09 #3606, 0.09 #12515, 0.08 #7511), 0k6nt (0.09 #12515, 0.03 #10711, 0.03 #5404), 03rt9 (0.08 #913, 0.04 #2413, 0.04 #7519) >> Best rule #2301 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 018z_c; >> query: (?x8020, 09c7w0) <- profession(?x8020, ?x1032), people(?x3591, ?x8020), actor(?x5386, ?x8020), participant(?x8365, ?x8020) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jyhv nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 138.000 0.911 http://example.org/people/person/nationality #12152-0mbhr PRED entity: 0mbhr PRED relation: award PRED expected values: 0bsjcw => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 292): 09sb52 (0.67 #853, 0.42 #8163, 0.32 #21969), 0gqy2 (0.44 #1385, 0.40 #166, 0.21 #6257), 027dtxw (0.40 #4, 0.33 #1223, 0.22 #816), 057xs89 (0.33 #974, 0.26 #2193, 0.18 #1787), 05zr6wv (0.30 #2048, 0.27 #1642, 0.19 #2454), 07cbcy (0.27 #1704, 0.19 #2110, 0.17 #485), 05zvj3m (0.27 #1719, 0.19 #2125, 0.14 #20710), 0f4x7 (0.26 #2062, 0.22 #843, 0.22 #2468), 04kxsb (0.26 #2158, 0.22 #1346, 0.20 #127), 05pcn59 (0.26 #2113, 0.17 #488, 0.14 #20710) >> Best rule #853 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0f0kz; >> query: (?x11081, 09sb52) <- film(?x11081, ?x2795), film(?x11081, ?x1797), award(?x2795, ?x7498), ?x1797 = 050xxm, film(?x5332, ?x2795), profession(?x5332, ?x1032) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #22946 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 780 *> proper extension: 02vkvcz; *> query: (?x11081, 0bsjcw) <- gender(?x11081, ?x514), nationality(?x11081, ?x613), capital(?x613, ?x8297), ?x514 = 02zsn *> conf = 0.04 ranks of expected_values: 136 EVAL 0mbhr award 0bsjcw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 115.000 115.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12151-03w4sh PRED entity: 03w4sh PRED relation: award_nominee! PRED expected values: 03zqc1 05dxl5 => 82 concepts (34 used for prediction) PRED predicted values (max 10 best out of 539): 06hgym (0.81 #51111, 0.81 #67376, 0.81 #72023), 04vmqg (0.80 #4403, 0.63 #11372, 0.63 #9049), 05dxl5 (0.75 #5547, 0.73 #3224, 0.73 #12516), 0gd_b_ (0.73 #3002, 0.59 #12294, 0.53 #9971), 06b0d2 (0.69 #4866, 0.68 #9512, 0.64 #11835), 03zqc1 (0.68 #9387, 0.62 #4741, 0.60 #2418), 07z1_q (0.67 #3054, 0.59 #12346, 0.58 #7700), 0308kx (0.63 #10250, 0.45 #12573, 0.38 #14896), 02s_qz (0.53 #4143, 0.47 #8789, 0.44 #6466), 05lb87 (0.53 #9565, 0.45 #11888, 0.38 #4919) >> Best rule #51111 for best value: >> intensional similarity = 3 >> extensional distance = 1120 >> proper extension: 04nw9; 01t2h2; 01vb403; 038rzr; 06449; 011hdn; 01k5zk; 0hwqz; 06chvn; 02ts3h; ... >> query: (?x6538, ?x444) <- award_nominee(?x6538, ?x444), gender(?x6538, ?x231), film(?x6538, ?x1562) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #5547 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0308kx; *> query: (?x6538, 05dxl5) <- award_nominee(?x6851, ?x6538), film(?x6538, ?x1562), ?x6851 = 05lb65 *> conf = 0.75 ranks of expected_values: 3, 6 EVAL 03w4sh award_nominee! 05dxl5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 34.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 03w4sh award_nominee! 03zqc1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 82.000 34.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #12150-05yzt_ PRED entity: 05yzt_ PRED relation: place_of_birth PRED expected values: 02_286 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 81): 02_286 (0.41 #4931, 0.27 #12677, 0.27 #38027), 0cc56 (0.20 #33, 0.11 #1441, 0.02 #4964), 06wxw (0.20 #157), 0cvw9 (0.15 #3116, 0.07 #2412), 0c8tk (0.12 #859, 0.07 #2972, 0.07 #2268), 01ly5m (0.12 #801, 0.07 #2210, 0.04 #2914), 0f2s6 (0.12 #1071, 0.07 #2480, 0.04 #3184), 095w_ (0.12 #752, 0.07 #2161, 0.04 #2865), 02dtg (0.11 #1418, 0.02 #3532, 0.01 #4236), 03dm7 (0.11 #1867) >> Best rule #4931 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 07s3vqk; 0fp_v1x; 06cv1; 01vvycq; 03ds3; 07q1v4; 01vrncs; 01kx_81; 01kv4mb; 0lccn; ... >> query: (?x8532, ?x739) <- music(?x810, ?x8532), award(?x8532, ?x1079), location(?x8532, ?x739) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05yzt_ place_of_birth 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.408 http://example.org/people/person/place_of_birth #12149-01wdl3 PRED entity: 01wdl3 PRED relation: fraternities_and_sororities PRED expected values: 0325pb => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 2): 0325pb (0.48 #3, 0.38 #13, 0.38 #7), 04m8fy (0.04 #6, 0.03 #24, 0.03 #28) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 09f2j; >> query: (?x1201, 0325pb) <- school(?x2820, ?x1201), major_field_of_study(?x1201, ?x4321), school(?x4979, ?x1201), ?x4979 = 0f4vx0 >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wdl3 fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.481 http://example.org/education/university/fraternities_and_sororities #12148-0dc_ms PRED entity: 0dc_ms PRED relation: film! PRED expected values: 03hhd3 => 72 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1019): 079vf (0.26 #12490, 0.20 #8330, 0.19 #6250), 0jfx1 (0.22 #406, 0.05 #35772, 0.03 #46174), 03knl (0.16 #8479, 0.15 #12639, 0.14 #6399), 042ly5 (0.16 #9589, 0.15 #13749, 0.14 #7509), 03h_9lg (0.16 #8454, 0.15 #12614, 0.12 #16774), 01k53x (0.16 #9960, 0.15 #14120, 0.10 #7880), 0jbp0 (0.16 #10081, 0.11 #14241, 0.10 #18401), 0p8r1 (0.14 #4748, 0.12 #10988, 0.10 #15148), 01yf85 (0.14 #7753, 0.08 #9833, 0.07 #13993), 01kwld (0.13 #2182, 0.10 #4262, 0.08 #10502) >> Best rule #12490 for best value: >> intensional similarity = 7 >> extensional distance = 25 >> proper extension: 01hr1; >> query: (?x6528, 079vf) <- genre(?x6528, ?x6888), ?x6888 = 04pbhw, language(?x6528, ?x254), nominated_for(?x507, ?x6528), film(?x9655, ?x6528), nominated_for(?x9655, ?x2336), category(?x9655, ?x134) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #34776 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 147 *> proper extension: 0277j40; 04gcyg; *> query: (?x6528, 03hhd3) <- genre(?x6528, ?x6888), genre(?x11333, ?x6888), genre(?x8886, ?x6888), ?x8886 = 076xkps, film(?x8273, ?x11333), ?x8273 = 0725ny, story_by(?x6528, ?x12856) *> conf = 0.03 ranks of expected_values: 425 EVAL 0dc_ms film! 03hhd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 43.000 0.259 http://example.org/film/actor/film./film/performance/film #12147-0hx4y PRED entity: 0hx4y PRED relation: genre PRED expected values: 06n90 => 145 concepts (142 used for prediction) PRED predicted values (max 10 best out of 117): 07s9rl0 (0.78 #14014, 0.74 #1764, 0.72 #4471), 05p553 (0.67 #4, 0.62 #4005, 0.52 #4828), 029tx (0.63 #236, 0.60 #3531, 0.59 #590), 01hmnh (0.43 #1427, 0.36 #5074, 0.33 #3899), 02l7c8 (0.42 #14262, 0.36 #3780, 0.35 #4484), 04xvlr (0.40 #8003, 0.29 #120, 0.28 #474), 06n90 (0.32 #1657, 0.30 #1423, 0.25 #6953), 0lsxr (0.25 #1537, 0.21 #126, 0.20 #3421), 0hcr (0.22 #21, 0.21 #5080, 0.19 #3905), 0vgkd (0.22 #10, 0.11 #128, 0.10 #482) >> Best rule #14014 for best value: >> intensional similarity = 4 >> extensional distance = 1038 >> proper extension: 05jyb2; >> query: (?x2878, 07s9rl0) <- titles(?x811, ?x2878), genre(?x2878, ?x571), genre(?x2547, ?x571), ?x2547 = 06g77c >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1657 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 92 *> proper extension: 0m5s5; *> query: (?x2878, 06n90) <- genre(?x2878, ?x811), ?x811 = 03k9fj, featured_film_locations(?x2878, ?x6226), nominated_for(?x298, ?x2878) *> conf = 0.32 ranks of expected_values: 7 EVAL 0hx4y genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 145.000 142.000 0.783 http://example.org/film/film/genre #12146-02l48d PRED entity: 02l48d PRED relation: list PRED expected values: 01ptsx => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.86 #104, 0.85 #339, 0.85 #69), 09g7thr (0.53 #622, 0.49 #672, 0.44 #587), 05glt (0.38 #810, 0.38 #816), 026cl_m (0.09 #811, 0.09 #817) >> Best rule #104 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 01zpmq; >> query: (?x12013, 01ptsx) <- state_province_region(?x12013, ?x335), contact_category(?x12013, ?x897), citytown(?x12013, ?x739), list(?x12013, ?x5997), adjoins(?x739, ?x3670) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02l48d list 01ptsx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 201.000 201.000 0.857 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #12145-04kkz8 PRED entity: 04kkz8 PRED relation: film_crew_role PRED expected values: 02r96rf => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 33): 02r96rf (0.71 #318, 0.70 #213, 0.70 #388), 01vx2h (0.45 #290, 0.45 #220, 0.44 #150), 01pvkk (0.34 #186, 0.33 #221, 0.33 #151), 02rh1dz (0.21 #149, 0.20 #219, 0.19 #289), 02ynfr (0.18 #225, 0.17 #295, 0.17 #330), 0215hd (0.14 #123, 0.14 #263, 0.14 #333), 0d2b38 (0.14 #235, 0.13 #305, 0.12 #200), 015h31 (0.13 #148, 0.12 #288, 0.11 #218), 089g0h (0.12 #264, 0.12 #334, 0.12 #404), 02_n3z (0.11 #106, 0.09 #3014, 0.08 #211) >> Best rule #318 for best value: >> intensional similarity = 5 >> extensional distance = 297 >> proper extension: 0872p_c; 0gj8t_b; 07x4qr; 05zlld0; 0btbyn; 05_5_22; 095z4q; 0gfh84d; 07kdkfj; 0cmf0m0; ... >> query: (?x974, 02r96rf) <- film(?x11606, ?x974), film(?x8835, ?x974), language(?x11606, ?x254), film_crew_role(?x974, ?x137), people(?x3584, ?x8835) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04kkz8 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.709 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12144-0gm34 PRED entity: 0gm34 PRED relation: profession PRED expected values: 02hrh1q => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 84): 02hrh1q (0.91 #9316, 0.90 #1815, 0.90 #8116), 01d_h8 (0.46 #1356, 0.44 #2556, 0.41 #2106), 0dxtg (0.43 #314, 0.36 #764, 0.31 #2114), 02jknp (0.33 #608, 0.31 #2108, 0.30 #2558), 0np9r (0.29 #322, 0.27 #472, 0.23 #7522), 03gjzk (0.29 #316, 0.25 #4366, 0.25 #3316), 02krf9 (0.21 #328, 0.17 #1378, 0.15 #2128), 018gz8 (0.21 #318, 0.15 #1668, 0.15 #2118), 09jwl (0.21 #5570, 0.17 #14573, 0.17 #7821), 0d1pc (0.19 #3652, 0.17 #4552, 0.15 #5152) >> Best rule #9316 for best value: >> intensional similarity = 3 >> extensional distance = 412 >> proper extension: 01sl1q; 04bdxl; 01j5ts; 06dv3; 014zcr; 02g8h; 0d_84; 023tp8; 0m2wm; 01qscs; ... >> query: (?x7458, 02hrh1q) <- participant(?x7458, ?x2524), nominated_for(?x7458, ?x5667), film(?x7458, ?x675) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gm34 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.908 http://example.org/people/person/profession #12143-05fkf PRED entity: 05fkf PRED relation: district_represented! PRED expected values: 03rl1g 01grqd 01gssz => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 29): 024tcq (0.87 #304, 0.81 #507, 0.75 #420), 024tkd (0.74 #314, 0.70 #517, 0.63 #430), 02bn_p (0.74 #295, 0.67 #498, 0.62 #411), 03rl1g (0.64 #175, 0.57 #291, 0.56 #494), 02bp37 (0.63 #298, 0.61 #501, 0.58 #414), 02bqm0 (0.61 #308, 0.57 #511, 0.56 #424), 02bqmq (0.57 #302, 0.55 #842, 0.54 #418), 02bqn1 (0.55 #842, 0.46 #297, 0.44 #500), 01gssz (0.55 #842, 0.46 #1075, 0.38 #200), 01grqd (0.55 #842, 0.46 #1075, 0.31 #193) >> Best rule #304 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0hjy; 0gj4fx; >> query: (?x760, 024tcq) <- district_represented(?x845, ?x760), ?x845 = 07p__7, contains(?x760, ?x552) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #175 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 01vsb_; *> query: (?x760, 03rl1g) <- state_province_region(?x12667, ?x760), state_province_region(?x4117, ?x760), category(?x4117, ?x134), currency(?x12667, ?x170) *> conf = 0.64 ranks of expected_values: 4, 9, 10 EVAL 05fkf district_represented! 01gssz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 182.000 182.000 0.870 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05fkf district_represented! 01grqd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 182.000 182.000 0.870 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05fkf district_represented! 03rl1g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 182.000 182.000 0.870 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #12142-02fwfb PRED entity: 02fwfb PRED relation: genre PRED expected values: 01q03 => 73 concepts (28 used for prediction) PRED predicted values (max 10 best out of 88): 01z4y (0.59 #1802, 0.44 #1915), 02l7c8 (0.46 #688, 0.44 #1026, 0.43 #1138), 02kdv5l (0.41 #564, 0.29 #901, 0.27 #227), 0lsxr (0.33 #569, 0.29 #120, 0.27 #232), 01jfsb (0.32 #460, 0.29 #1359, 0.29 #1472), 04xvlr (0.29 #114, 0.26 #788, 0.25 #1689), 06cvj (0.28 #677, 0.28 #1015, 0.27 #1127), 01hmnh (0.28 #353, 0.17 #803, 0.16 #915), 03k9fj (0.25 #908, 0.25 #9, 0.19 #796), 082gq (0.25 #27, 0.21 #3049, 0.19 #2961) >> Best rule #1802 for best value: >> intensional similarity = 4 >> extensional distance = 343 >> proper extension: 01jc6q; 0c0yh4; 0yyg4; 011yrp; 07xtqq; 0n0bp; 0dj0m5; 01vksx; 0_b3d; 048scx; ... >> query: (?x7292, ?x714) <- films(?x390, ?x7292), film(?x72, ?x7292), nominated_for(?x68, ?x7292), titles(?x714, ?x7292) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #566 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 033g4d; 01kff7; 032016; *> query: (?x7292, 01q03) <- genre(?x7292, ?x5231), ?x5231 = 0556j8, film(?x72, ?x7292), currency(?x7292, ?x170) *> conf = 0.10 ranks of expected_values: 27 EVAL 02fwfb genre 01q03 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 73.000 28.000 0.586 http://example.org/film/film/genre #12141-01jvgt PRED entity: 01jvgt PRED relation: colors PRED expected values: 01l849 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 18): 01g5v (0.45 #878, 0.41 #947, 0.39 #1212), 019sc (0.43 #157, 0.43 #144, 0.40 #300), 01l849 (0.38 #193, 0.34 #210, 0.33 #735), 03vtbc (0.34 #210, 0.32 #156, 0.26 #299), 02rnmb (0.34 #210, 0.31 #364, 0.28 #381), 09ggk (0.32 #716, 0.22 #717, 0.22 #875), 036k5h (0.24 #405, 0.22 #787, 0.18 #945), 0jc_p (0.22 #717, 0.15 #876, 0.14 #107), 088fh (0.14 #1158, 0.14 #1157, 0.13 #1139), 067z2v (0.14 #1158, 0.14 #1157, 0.13 #1139) >> Best rule #878 for best value: >> intensional similarity = 18 >> extensional distance = 69 >> proper extension: 0182r9; 01j95f; 02mplj; 01xn7x1; 0j5m6; 037mjv; 016gp5; 03by7wc; 019m60; 02029f; ... >> query: (?x13914, 01g5v) <- colors(?x13914, ?x1101), colors(?x13914, ?x663), ?x663 = 083jv, teams(?x5267, ?x13914), colors(?x7912, ?x1101), colors(?x3208, ?x1101), ?x7912 = 06b19, colors(?x13580, ?x1101), colors(?x12042, ?x1101), colors(?x4546, ?x1101), colors(?x1438, ?x1101), ?x4546 = 05gg4, ?x13580 = 01_1kk, school(?x1438, ?x466), ?x12042 = 05xvj, institution(?x865, ?x3208), category(?x3208, ?x134), contains(?x94, ?x3208) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #193 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 6 *> proper extension: 0jmj7; *> query: (?x13914, 01l849) <- school(?x13914, ?x6953), position(?x13914, ?x6848), colors(?x13914, ?x1101), colors(?x11632, ?x1101), colors(?x10838, ?x1101), colors(?x6908, ?x1101), colors(?x5306, ?x1101), colors(?x10990, ?x1101), colors(?x10066, ?x1101), colors(?x9048, ?x1101), colors(?x1823, ?x1101), colors(?x1639, ?x1101), ?x11632 = 0mbwf, position(?x10066, ?x60), currency(?x10838, ?x170), ?x6908 = 01dthg, position_s(?x1639, ?x180), major_field_of_study(?x5306, ?x866), school(?x1823, ?x546), season(?x1823, ?x701), sport(?x9048, ?x471), ?x10990 = 0329gm *> conf = 0.38 ranks of expected_values: 3 EVAL 01jvgt colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 79.000 0.451 http://example.org/sports/sports_team/colors #12140-05qck PRED entity: 05qck PRED relation: award_winner PRED expected values: 0ph2w 034ls 051cc 0d3k14 01m4kpp => 37 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1657): 0z4s (0.50 #9838, 0.33 #71, 0.25 #17164), 09fb5 (0.50 #9830, 0.33 #63, 0.25 #17156), 0bj9k (0.50 #10179, 0.33 #412, 0.25 #17505), 039bp (0.50 #9977, 0.33 #210, 0.25 #17303), 0bl2g (0.50 #9827, 0.33 #60, 0.25 #17153), 0d6d2 (0.50 #11522, 0.33 #1755, 0.25 #18848), 040z9 (0.50 #11382, 0.33 #1615, 0.25 #18708), 06cgy (0.50 #10070, 0.33 #303, 0.25 #17396), 0kjgl (0.50 #11473, 0.33 #1706, 0.25 #18799), 0cj8x (0.50 #10410, 0.33 #643, 0.25 #17736) >> Best rule #9838 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0f4x7; >> query: (?x3846, 0z4s) <- award_winner(?x3846, ?x9000), award_winner(?x3846, ?x1399), award_winner(?x3846, ?x1089), ?x9000 = 0k9j_, award_winner(?x1089, ?x1930), location(?x1089, ?x739), award_nominee(?x1399, ?x158) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6696 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 05f3q; *> query: (?x3846, 051cc) <- award_winner(?x3846, ?x10552), award_winner(?x3846, ?x6331), award_winner(?x3846, ?x2426), ?x10552 = 05g7q, influenced_by(?x1855, ?x2426), nationality(?x6331, ?x1264), profession(?x2426, ?x319) *> conf = 0.33 ranks of expected_values: 71, 211 EVAL 05qck award_winner 01m4kpp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 24.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 05qck award_winner 0d3k14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 24.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 05qck award_winner 051cc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 37.000 24.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 05qck award_winner 034ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 24.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 05qck award_winner 0ph2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 37.000 24.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #12139-0dr_4 PRED entity: 0dr_4 PRED relation: nominated_for! PRED expected values: 099c8n 0gqwc 02qvyrt => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 171): 099c8n (0.50 #44, 0.39 #460, 0.28 #1916), 0gr4k (0.47 #1893, 0.45 #2309, 0.45 #2101), 0gqy2 (0.39 #2176, 0.39 #2384, 0.37 #1968), 0f4x7 (0.39 #2100, 0.39 #2308, 0.37 #1892), 02qvyrt (0.35 #490, 0.23 #1946, 0.21 #2570), 02n9nmz (0.33 #45, 0.28 #461, 0.21 #1917), 02g3v6 (0.33 #16, 0.28 #432, 0.12 #1472), 0fhpv4 (0.33 #112, 0.24 #528, 0.23 #2913), 03hkv_r (0.33 #12, 0.20 #1884, 0.17 #2092), 02rdyk7 (0.33 #52, 0.16 #2132, 0.16 #1924) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 017gl1; 017jd9; 02dr9j; >> query: (?x1597, 099c8n) <- nominated_for(?x143, ?x1597), award_winner(?x1597, ?x1983), ?x1983 = 04ktcgn >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 14 EVAL 0dr_4 nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 80.000 80.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0dr_4 nominated_for! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 80.000 80.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0dr_4 nominated_for! 099c8n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12138-04511f PRED entity: 04511f PRED relation: student! PRED expected values: 04bfg => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 194): 0bx8pn (0.25 #45, 0.17 #571, 0.04 #2149), 0bwfn (0.17 #5534, 0.11 #16580, 0.09 #10268), 02zd460 (0.17 #695, 0.02 #4377, 0.02 #20157), 08qnnv (0.17 #739, 0.02 #42611, 0.01 #5999), 07tgn (0.16 #3699, 0.11 #4751, 0.08 #11063), 03ksy (0.16 #9574, 0.14 #6944, 0.12 #10626), 065y4w7 (0.14 #2644, 0.12 #3696, 0.09 #4222), 01w5m (0.12 #3787, 0.09 #4839, 0.09 #12203), 05zl0 (0.11 #1253, 0.03 #9669, 0.03 #5987), 015zyd (0.08 #2631, 0.07 #4209, 0.04 #8943) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01rlxt; 08nz99; >> query: (?x4299, 0bx8pn) <- award_nominee(?x4299, ?x5432), ?x5432 = 046mxj, story_by(?x5871, ?x4299) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04511f student! 04bfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 123.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #12137-08gsvw PRED entity: 08gsvw PRED relation: language PRED expected values: 02bjrlw => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 34): 02bjrlw (0.12 #1, 0.12 #278, 0.11 #112), 06b_j (0.12 #130, 0.11 #186, 0.08 #685), 0jzc (0.08 #128, 0.05 #17, 0.05 #683), 012w70 (0.07 #399, 0.07 #455, 0.07 #121), 03_9r (0.07 #619, 0.06 #785, 0.05 #1171), 0653m (0.05 #176, 0.05 #454, 0.05 #398), 04h9h (0.05 #650, 0.04 #372, 0.04 #428), 0349s (0.04 #41, 0.03 #318, 0.03 #374), 03k50 (0.03 #618, 0.03 #63, 0.02 #839), 05zjd (0.03 #78, 0.03 #909, 0.02 #964) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 0140g4; 0g5pv3; 03l6q0; 0bmssv; 013q0p; 0277j40; 02gpkt; 02p76f9; 0g5ptf; 02fqxm; >> query: (?x787, 02bjrlw) <- prequel(?x186, ?x787), nominated_for(?x2805, ?x787), featured_film_locations(?x787, ?x205), award_nominee(?x2805, ?x748) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08gsvw language 02bjrlw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.125 http://example.org/film/film/language #12136-09hd16 PRED entity: 09hd16 PRED relation: award_winner! PRED expected values: 07y9ts 09pj68 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 97): 07y9ts (0.57 #68, 0.33 #208, 0.13 #3922), 09pj68 (0.57 #105, 0.33 #245, 0.13 #3922), 02q690_ (0.10 #485, 0.08 #625, 0.07 #765), 05c1t6z (0.08 #855, 0.08 #575, 0.07 #715), 027n06w (0.08 #493, 0.08 #353, 0.07 #913), 0gvstc3 (0.08 #314, 0.07 #874, 0.06 #594), 03nnm4t (0.07 #634, 0.07 #494, 0.07 #774), 09v0p2c (0.07 #363, 0.06 #503, 0.05 #923), 0gx_st (0.07 #457, 0.06 #597, 0.06 #737), 03gt46z (0.06 #343, 0.05 #903, 0.04 #483) >> Best rule #68 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0h53p1; 01xndd; 08q3s0; 0h5jg5; 0697kh; >> query: (?x4023, 07y9ts) <- award_nominee(?x4023, ?x10340), ?x10340 = 09hd6f, award_winner(?x4034, ?x4023) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 09hd16 award_winner! 09pj68 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09hd16 award_winner! 07y9ts CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12135-016tw3 PRED entity: 016tw3 PRED relation: company! PRED expected values: 060c4 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 31): 0dq_5 (0.40 #2185, 0.39 #2215, 0.34 #1809), 0krdk (0.40 #2174, 0.33 #7, 0.30 #2504), 014l7h (0.33 #217, 0.30 #452, 0.15 #1348), 060c4 (0.33 #2170, 0.31 #757, 0.27 #521), 0dq3c (0.26 #2169, 0.21 #1793, 0.19 #2358), 02k13d (0.22 #202, 0.15 #437, 0.11 #956), 01yc02 (0.22 #2176, 0.18 #527, 0.17 #2129), 05_wyz (0.21 #1810, 0.20 #1149, 0.19 #2186), 09d6p2 (0.19 #774, 0.16 #2187, 0.15 #1150), 01kr6k (0.12 #2195, 0.10 #1819, 0.09 #2525) >> Best rule #2185 for best value: >> intensional similarity = 2 >> extensional distance = 111 >> proper extension: 01m_zd; 01xl5; 07gyp7; >> query: (?x1104, 0dq_5) <- organization(?x4682, ?x1104), industry(?x1104, ?x373) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2170 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 111 *> proper extension: 01m_zd; 01xl5; 07gyp7; *> query: (?x1104, 060c4) <- organization(?x4682, ?x1104), industry(?x1104, ?x373) *> conf = 0.33 ranks of expected_values: 4 EVAL 016tw3 company! 060c4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 141.000 141.000 0.398 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #12134-0jm_ PRED entity: 0jm_ PRED relation: films PRED expected values: 05zy3sc 042zrm => 66 concepts (43 used for prediction) PRED predicted values (max 10 best out of 982): 0170z3 (0.33 #1, 0.25 #3131, 0.25 #1566), 04xx9s (0.33 #332, 0.25 #3462, 0.25 #1897), 01l2b3 (0.33 #853, 0.25 #2417, 0.17 #5025), 0fy66 (0.33 #1224, 0.15 #7480, 0.10 #6959), 0m_q0 (0.33 #1267, 0.10 #7002, 0.08 #7523), 027pfg (0.33 #1397, 0.10 #7132, 0.08 #7653), 0m_h6 (0.33 #1486, 0.10 #7221, 0.08 #7742), 0qm98 (0.30 #6843, 0.07 #16750, 0.04 #21977), 0c0nhgv (0.25 #1617, 0.17 #4746, 0.17 #4225), 02ndy4 (0.25 #2059, 0.17 #5188, 0.17 #4667) >> Best rule #1 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 018w8; >> query: (?x1083, 0170z3) <- athlete(?x1083, ?x7732), athlete(?x1083, ?x7064), athlete(?x1083, ?x703), sport(?x4856, ?x1083), profession(?x7064, ?x1032), award_nominee(?x157, ?x703), film(?x7732, ?x6206), team(?x706, ?x4856), school(?x4856, ?x3314) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #17208 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 42 *> proper extension: 0bqtx; *> query: (?x1083, ?x161) <- films(?x1083, ?x8551), nominated_for(?x3960, ?x8551), nominated_for(?x1307, ?x8551), nominated_for(?x601, ?x8551), titles(?x3155, ?x8551), film(?x4436, ?x8551), ?x601 = 0gr4k, award(?x2332, ?x1307), nominated_for(?x1307, ?x161), ?x2332 = 04y8r *> conf = 0.01 ranks of expected_values: 933 EVAL 0jm_ films 042zrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 43.000 0.333 http://example.org/film/film_subject/films EVAL 0jm_ films 05zy3sc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 43.000 0.333 http://example.org/film/film_subject/films #12133-07l450 PRED entity: 07l450 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #217, 0.83 #197, 0.83 #207), 07c52 (0.07 #28, 0.06 #23, 0.03 #103), 02nxhr (0.06 #22, 0.05 #32, 0.04 #82), 07z4p (0.05 #105, 0.04 #75, 0.04 #100) >> Best rule #217 for best value: >> intensional similarity = 5 >> extensional distance = 577 >> proper extension: 0c40vxk; >> query: (?x9599, 029j_) <- film_crew_role(?x9599, ?x1171), genre(?x9599, ?x53), ?x1171 = 09vw2b7, film(?x940, ?x9599), currency(?x9599, ?x170) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07l450 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.834 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12132-078mgh PRED entity: 078mgh PRED relation: film PRED expected values: 023g6w => 77 concepts (66 used for prediction) PRED predicted values (max 10 best out of 663): 03s9kp (0.33 #1763, 0.03 #98472, 0.03 #73409), 0gzlb9 (0.17 #1463, 0.03 #98472, 0.03 #53712), 01y9r2 (0.17 #1347, 0.03 #98472, 0.03 #73409), 02ndy4 (0.17 #1699, 0.03 #98472, 0.03 #73409), 01xdxy (0.17 #1567, 0.03 #98472, 0.03 #73409), 0bdjd (0.17 #1282, 0.03 #98472, 0.03 #73409), 09q23x (0.17 #853, 0.03 #98472, 0.03 #73409), 011yd2 (0.17 #356, 0.03 #98472, 0.03 #73409), 0c00zd0 (0.17 #260, 0.03 #98472, 0.03 #73409), 02d413 (0.17 #3, 0.03 #98472, 0.03 #73409) >> Best rule #1763 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 04wg38; >> query: (?x8135, 03s9kp) <- award_nominee(?x8135, ?x6068), award_nominee(?x8135, ?x2422), ?x2422 = 0169dl, ?x6068 = 025j1t >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3272 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 72 *> proper extension: 02jyhv; 0g476; *> query: (?x8135, 023g6w) <- participant(?x2221, ?x8135), actor(?x5810, ?x8135) *> conf = 0.01 ranks of expected_values: 326 EVAL 078mgh film 023g6w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 66.000 0.333 http://example.org/film/actor/film./film/performance/film #12131-02lv2v PRED entity: 02lv2v PRED relation: colors PRED expected values: 01g5v => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.46 #61, 0.44 #1101, 0.43 #501), 01g5v (0.33 #603, 0.31 #543, 0.29 #1503), 036k5h (0.20 #85, 0.17 #405, 0.13 #245), 06fvc (0.18 #1102, 0.18 #862, 0.18 #102), 019sc (0.18 #107, 0.17 #207, 0.17 #1507), 04mkbj (0.18 #110, 0.17 #50, 0.13 #610), 09ggk (0.15 #76, 0.14 #36, 0.13 #96), 07plts (0.14 #38, 0.14 #18, 0.10 #3082), 02rnmb (0.14 #33, 0.14 #13, 0.08 #53), 0jc_p (0.14 #4, 0.10 #3082, 0.10 #604) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0c0sl; >> query: (?x8434, 083jv) <- currency(?x8434, ?x170), service_language(?x8434, ?x254), registering_agency(?x8434, ?x1982), citytown(?x8434, ?x739) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #603 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 037s9x; 02d9nr; 01qwb5; *> query: (?x8434, 01g5v) <- currency(?x8434, ?x170), colors(?x8434, ?x332), state_province_region(?x8434, ?x335), student(?x8434, ?x446) *> conf = 0.33 ranks of expected_values: 2 EVAL 02lv2v colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 183.000 0.462 http://example.org/education/educational_institution/colors #12130-0lphb PRED entity: 0lphb PRED relation: source PRED expected values: 0jbk9 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.79 #57, 0.79 #39, 0.79 #38) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 0jq27; >> query: (?x6952, 0jbk9) <- administrative_division(?x6952, ?x8350), contains(?x94, ?x8350), time_zones(?x8350, ?x1638) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lphb source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.794 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #12129-02mzg9 PRED entity: 02mzg9 PRED relation: institution! PRED expected values: 014mlp 019v9k => 128 concepts (65 used for prediction) PRED predicted values (max 10 best out of 16): 014mlp (0.88 #2, 0.85 #19, 0.77 #191), 019v9k (0.76 #73, 0.73 #4, 0.68 #21), 027f2w (0.39 #5, 0.35 #22, 0.32 #159), 022h5x (0.29 #569, 0.24 #31, 0.21 #14), 01ysy9 (0.29 #569, 0.19 #1117, 0.09 #136), 071tyz (0.29 #569, 0.19 #1117, 0.07 #126), 01kxxq (0.29 #569, 0.19 #1117, 0.04 #67), 01gkg3 (0.29 #569, 0.02 #112, 0.02 #146), 03mkk4 (0.23 #76, 0.20 #7, 0.19 #1117), 0bjrnt (0.21 #3, 0.19 #20, 0.19 #1117) >> Best rule #2 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 07tgn; 07tg4; 015cz0; 02zd460; 02bqy; 0ks67; 01hc1j; 050xpd; >> query: (?x10861, 014mlp) <- institution(?x1526, ?x10861), institution(?x620, ?x10861), ?x1526 = 0bkj86, ?x620 = 07s6fsf, student(?x10861, ?x4946) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02mzg9 institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 65.000 0.875 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02mzg9 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 65.000 0.875 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #12128-01xdf5 PRED entity: 01xdf5 PRED relation: student! PRED expected values: 017j69 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 161): 0bwfn (0.17 #802, 0.06 #1329, 0.06 #5545), 01w5m (0.17 #105, 0.09 #632, 0.05 #11172), 017v3q (0.17 #245, 0.03 #1299, 0.02 #2353), 02bqy (0.17 #182, 0.02 #5452, 0.01 #47439), 013nky (0.17 #382, 0.01 #47439, 0.01 #51656), 03ksy (0.11 #9065, 0.10 #1160, 0.09 #9592), 065y4w7 (0.09 #541, 0.09 #3703, 0.08 #5811), 01jq34 (0.09 #584, 0.04 #4800, 0.03 #5854), 026036 (0.06 #1447, 0.01 #4082, 0.01 #4609), 0gl5_ (0.05 #2352, 0.05 #3406, 0.04 #2879) >> Best rule #802 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 04h07s; >> query: (?x236, 0bwfn) <- nationality(?x236, ?x94), tv_program(?x236, ?x3626), influenced_by(?x236, ?x1145) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #4888 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 77 *> proper extension: 02q6cv4; *> query: (?x236, 017j69) <- award(?x236, ?x537), award_nominee(?x236, ?x1040), program_creator(?x5698, ?x236) *> conf = 0.03 ranks of expected_values: 64 EVAL 01xdf5 student! 017j69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 134.000 134.000 0.174 http://example.org/education/educational_institution/students_graduates./education/education/student #12127-026spg PRED entity: 026spg PRED relation: award_winner! PRED expected values: 01cw7s 03nc9d => 112 concepts (96 used for prediction) PRED predicted values (max 10 best out of 269): 01c427 (0.39 #31258, 0.39 #430, 0.39 #429), 01c99j (0.39 #31258, 0.39 #430, 0.39 #429), 01d38g (0.39 #31258, 0.39 #430, 0.39 #429), 0gkvb7 (0.39 #31258, 0.39 #430, 0.39 #429), 01cky2 (0.17 #190, 0.12 #4481, 0.10 #4909), 02nhxf (0.17 #98, 0.10 #39826, 0.07 #1815), 09sb52 (0.16 #18456, 0.13 #15888, 0.12 #17172), 03qbnj (0.15 #29544, 0.09 #658, 0.08 #228), 02g8mp (0.15 #29544, 0.08 #72, 0.08 #931), 02681xs (0.15 #29544, 0.03 #34260, 0.03 #41111) >> Best rule #31258 for best value: >> intensional similarity = 3 >> extensional distance = 1368 >> proper extension: 0hl3d; 019_1h; 030pr; 015rmq; 030_1_; 01r216; 0j_c; 03mdt; 061dn_; 0b13g7; ... >> query: (?x4675, ?x4382) <- award(?x4675, ?x4382), award_winner(?x4675, ?x4574), ceremony(?x4382, ?x139) >> conf = 0.39 => this is the best rule for 4 predicted values *> Best rule #260 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 04dqdk; 086qd; 01cwhp; 01wwvc5; 028qdb; 03k0yw; 01wwvd2; 01ldw4; 01mxqyk; *> query: (?x4675, 01cw7s) <- award(?x4675, ?x4382), award_winner(?x4675, ?x4574), ?x4574 = 02dbp7, award_winner(?x4382, ?x1238) *> conf = 0.08 ranks of expected_values: 29, 112 EVAL 026spg award_winner! 03nc9d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 112.000 96.000 0.394 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 026spg award_winner! 01cw7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 112.000 96.000 0.394 http://example.org/award/award_category/winners./award/award_honor/award_winner #12126-0flddp PRED entity: 0flddp PRED relation: people! PRED expected values: 0gk4g => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 26): 02k6hp (0.17 #103, 0.03 #169, 0.02 #433), 0gk4g (0.09 #142, 0.08 #736, 0.07 #406), 0dq9p (0.04 #413, 0.03 #149, 0.03 #743), 04p3w (0.04 #143, 0.03 #407, 0.02 #1067), 0qcr0 (0.03 #331, 0.03 #397, 0.03 #133), 02knxx (0.03 #164, 0.03 #428, 0.02 #230), 01l2m3 (0.03 #148, 0.02 #412), 01psyx (0.02 #243, 0.01 #837, 0.01 #309), 01mtqf (0.02 #202, 0.01 #334, 0.01 #400), 02y0js (0.02 #134, 0.02 #728, 0.01 #5217) >> Best rule #103 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02sj1x; 051z6mv; >> query: (?x7542, 02k6hp) <- award_winner(?x5220, ?x7542), gender(?x7542, ?x231), nominated_for(?x7542, ?x9059), ?x5220 = 0kbf1 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #142 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 91 *> proper extension: 021yc7p; 04gmp_z; 03q8ch; 07fzq3; 0fqjks; 01bh6y; 05683cn; 071jv5; *> query: (?x7542, 0gk4g) <- award_winner(?x5220, ?x7542), gender(?x7542, ?x231), nominated_for(?x7542, ?x9059), film_art_direction_by(?x5220, ?x8800) *> conf = 0.09 ranks of expected_values: 2 EVAL 0flddp people! 0gk4g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.167 http://example.org/people/cause_of_death/people #12125-0f61tk PRED entity: 0f61tk PRED relation: film! PRED expected values: 05xpms => 64 concepts (31 used for prediction) PRED predicted values (max 10 best out of 840): 06w33f8 (0.40 #8308, 0.40 #22843, 0.38 #45685), 0f5xn (0.22 #968, 0.07 #9276, 0.07 #7198), 0bq2g (0.17 #605, 0.15 #2682, 0.07 #8913), 07_m9_ (0.15 #12462, 0.12 #10385, 0.11 #8307), 08d9z7 (0.11 #53991, 0.11 #64377, 0.10 #26997), 03fbb6 (0.11 #977, 0.07 #9285, 0.07 #7207), 0h5g_ (0.11 #4228, 0.06 #74, 0.04 #12536), 0q9kd (0.11 #4, 0.05 #8312, 0.05 #6234), 01chc7 (0.11 #559, 0.05 #8867, 0.05 #6789), 0c9xjl (0.11 #970, 0.05 #9278, 0.05 #7200) >> Best rule #8308 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 0bx_hnp; >> query: (?x8615, ?x1760) <- country(?x8615, ?x94), person(?x8615, ?x4736), nominated_for(?x1760, ?x8615), genre(?x8615, ?x162) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f61tk film! 05xpms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 31.000 0.401 http://example.org/film/actor/film./film/performance/film #12124-059j2 PRED entity: 059j2 PRED relation: film_release_region! PRED expected values: 04ddm4 08hmch 0c0nhgv 03bx2lk 04hwbq 0cz8mkh 04n52p6 0gd0c7x 0k5g9 01jrbb 0crc2cp 01ffx4 06r2_ 0c3xw46 07s846j 043sct5 0h03fhx 0dlngsd 0gg5qcw 0bc1yhb 03wh49y 01d259 067ghz 031ldd 0jyb4 0ds6bmk 0k7tq 0372j5 0gh6j94 0m63c 0fphf3v 0cmf0m0 0g57wgv 049w1q 0n08r => 300 concepts (220 used for prediction) PRED predicted values (max 10 best out of 988): 08hmch (0.89 #78730, 0.89 #42354, 0.88 #50219), 0gd0c7x (0.84 #38499, 0.81 #97485, 0.81 #78807), 04hwbq (0.82 #50238, 0.80 #38441, 0.79 #78749), 07s846j (0.80 #38681, 0.79 #78989, 0.76 #78006), 04n52p6 (0.80 #38474, 0.79 #78782, 0.74 #77799), 0cmf0m0 (0.80 #39106, 0.71 #22392, 0.68 #79414), 0gg5qcw (0.80 #38813, 0.70 #42745, 0.70 #44712), 067ghz (0.76 #38883, 0.74 #35934, 0.74 #34951), 01jrbb (0.76 #38580, 0.72 #78888, 0.72 #77905), 0h03fhx (0.76 #38748, 0.70 #42680, 0.65 #50545) >> Best rule #78730 for best value: >> intensional similarity = 2 >> extensional distance = 45 >> proper extension: 07dfk; >> query: (?x1229, 08hmch) <- film_release_region(?x2471, ?x1229), ?x2471 = 08052t3 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 22, 26, 34, 35, 37, 38, 39, 44, 52, 55, 60, 71, 73, 76, 121, 123 EVAL 059j2 film_release_region! 0n08r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 049w1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0g57wgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0cmf0m0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0fphf3v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0m63c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0gh6j94 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0372j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0k7tq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0ds6bmk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0jyb4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 031ldd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 067ghz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 01d259 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 03wh49y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0bc1yhb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0gg5qcw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0dlngsd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0h03fhx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 043sct5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 07s846j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0c3xw46 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 06r2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 01ffx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0crc2cp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 01jrbb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0k5g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0gd0c7x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 04n52p6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0cz8mkh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 04hwbq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 03bx2lk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 0c0nhgv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 08hmch CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 059j2 film_release_region! 04ddm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 300.000 220.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12123-02rrfzf PRED entity: 02rrfzf PRED relation: film! PRED expected values: 01dw4q 01cwcr 01xllf => 86 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1059): 01mkn_d (0.43 #12468, 0.40 #72729, 0.39 #18702), 06cv1 (0.35 #2079, 0.34 #2078, 0.19 #10390), 04pf4r (0.34 #2078, 0.04 #60259, 0.04 #60258), 08c9b0 (0.34 #2078, 0.04 #60259, 0.04 #60258), 02bh9 (0.34 #2078, 0.04 #60259, 0.04 #60258), 015cxv (0.34 #2078, 0.04 #60258, 0.03 #78963), 0jfx1 (0.15 #2484, 0.04 #41961, 0.03 #10795), 021bk (0.15 #377, 0.02 #39855, 0.02 #27388), 0pgjm (0.15 #214, 0.01 #39692, 0.01 #27225), 079vf (0.12 #4165, 0.04 #18710, 0.03 #20787) >> Best rule #12468 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 04m1bm; 02n9bh; 02hfk5; >> query: (?x3344, ?x6664) <- language(?x3344, ?x254), film(?x166, ?x3344), ?x166 = 0jz9f, nominated_for(?x6664, ?x3344) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1271 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 02ht1k; 0888c3; 0m3gy; *> query: (?x3344, 01cwcr) <- music(?x3344, ?x523), profession(?x523, ?x319), film(?x523, ?x814), genre(?x3344, ?x258) *> conf = 0.05 ranks of expected_values: 93, 105 EVAL 02rrfzf film! 01xllf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 86.000 39.000 0.430 http://example.org/film/actor/film./film/performance/film EVAL 02rrfzf film! 01cwcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 86.000 39.000 0.430 http://example.org/film/actor/film./film/performance/film EVAL 02rrfzf film! 01dw4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 39.000 0.430 http://example.org/film/actor/film./film/performance/film #12122-023zl PRED entity: 023zl PRED relation: school_type PRED expected values: 05jxkf => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.50 #1804, 0.45 #2288, 0.44 #1972), 01rs41 (0.48 #749, 0.47 #989, 0.43 #461), 05pcjw (0.40 #985, 0.39 #481, 0.35 #457), 07tf8 (0.20 #1233, 0.20 #57, 0.19 #1809), 01_9fk (0.15 #1442, 0.14 #1802, 0.14 #1682), 02p0qmm (0.09 #2235, 0.09 #1090, 0.06 #1234), 01_srz (0.09 #2235, 0.08 #1443, 0.05 #1803), 0bwd5 (0.09 #2235, 0.05 #1459, 0.04 #499), 04399 (0.09 #2235, 0.05 #1454, 0.03 #2006), 047951 (0.09 #2235, 0.05 #392, 0.04 #440) >> Best rule #1804 for best value: >> intensional similarity = 5 >> extensional distance = 185 >> proper extension: 014b4h; 03fcbb; >> query: (?x10759, 05jxkf) <- institution(?x4981, ?x10759), institution(?x1200, ?x10759), ?x4981 = 03bwzr4, institution(?x1200, ?x735), ?x735 = 065y4w7 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023zl school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.503 http://example.org/education/educational_institution/school_type #12121-068p_ PRED entity: 068p_ PRED relation: notable_people_with_this_condition PRED expected values: 016k62 => 13 concepts (7 used for prediction) PRED predicted values (max 10 best out of 484): 0484q (0.40 #70, 0.33 #298, 0.29 #411), 034rd (0.38 #626, 0.11 #738), 01pw2f1 (0.33 #242, 0.29 #355, 0.25 #467), 0n839 (0.33 #336, 0.29 #449, 0.25 #561), 06x58 (0.33 #246, 0.29 #359, 0.25 #471), 02_fj (0.20 #144, 0.20 #112, 0.20 #30), 0c1pj (0.20 #120, 0.20 #6, 0.17 #234), 01vv6xv (0.20 #220, 0.20 #106, 0.17 #334), 0hcvy (0.20 #218, 0.20 #104, 0.17 #332), 01lc5 (0.20 #217, 0.20 #103, 0.17 #331) >> Best rule #70 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 02k6hp; >> query: (?x13845, 0484q) <- notable_people_with_this_condition(?x13845, ?x12571), notable_people_with_this_condition(?x13845, ?x11400), notable_people_with_this_condition(?x13845, ?x11284), profession(?x11284, ?x1943), gender(?x11284, ?x514), risk_factors(?x3680, ?x514), nationality(?x11400, ?x94), profession(?x11400, ?x1032), spouse(?x7893, ?x12571), student(?x3439, ?x12571), profession(?x10299, ?x1943), profession(?x2143, ?x1943), profession(?x1266, ?x1943), ?x1266 = 0277470, ?x10299 = 0pnf3, ?x2143 = 015pxr >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 068p_ notable_people_with_this_condition 016k62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 13.000 7.000 0.400 http://example.org/medicine/disease/notable_people_with_this_condition #12120-0f6lx PRED entity: 0f6lx PRED relation: people! PRED expected values: 0x67 => 174 concepts (160 used for prediction) PRED predicted values (max 10 best out of 51): 0x67 (0.53 #241, 0.35 #549, 0.29 #164), 041rx (0.21 #1390, 0.19 #851, 0.17 #2160), 07bch9 (0.15 #408, 0.11 #100, 0.08 #793), 02g7sp (0.14 #172, 0.09 #480, 0.09 #557), 02w7gg (0.12 #2, 0.09 #1542, 0.07 #849), 06gbnc (0.12 #27, 0.03 #1028), 0xnvg (0.11 #90, 0.10 #937, 0.10 #398), 0d7wh (0.11 #94, 0.07 #171, 0.04 #556), 07hwkr (0.11 #89, 0.07 #166, 0.04 #782), 019lrz (0.11 #115, 0.05 #423, 0.04 #808) >> Best rule #241 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0dbb3; >> query: (?x9021, 0x67) <- profession(?x9021, ?x1183), artists(?x505, ?x9021), influenced_by(?x2208, ?x9021), ?x505 = 03_d0 >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f6lx people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 160.000 0.533 http://example.org/people/ethnicity/people #12119-0k9ts PRED entity: 0k9ts PRED relation: state_province_region PRED expected values: 0d0x8 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 115): 0d0x8 (0.85 #3332, 0.72 #14224, 0.71 #539), 059rby (0.35 #4330, 0.34 #2842, 0.33 #3212), 01n7q (0.31 #1500, 0.30 #2117, 0.29 #1007), 0nzny (0.29 #10639, 0.26 #16462, 0.25 #16463), 09c7w0 (0.26 #16462, 0.25 #16463, 0.25 #15095), 05fly (0.19 #9033, 0.03 #3046, 0.03 #1073), 013yq (0.16 #15591, 0.12 #15964, 0.06 #2962), 07b_l (0.11 #1902, 0.10 #668, 0.09 #2272), 03v0t (0.10 #671, 0.09 #1782, 0.07 #5244), 081yw (0.10 #679, 0.07 #926, 0.07 #1790) >> Best rule #3332 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 05qd_; >> query: (?x9968, ?x3038) <- currency(?x9968, ?x170), company(?x1491, ?x9968), citytown(?x9968, ?x2277), state(?x2277, ?x3038) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k9ts state_province_region 0d0x8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.851 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #12118-0486tv PRED entity: 0486tv PRED relation: olympics PRED expected values: 0kbws => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 36): 0kbws (0.84 #1133, 0.81 #1062, 0.81 #1098), 0jdk_ (0.69 #38, 0.63 #953, 0.62 #1179), 0l6m5 (0.69 #38, 0.50 #665, 0.50 #589), 0l6vl (0.69 #38, 0.50 #658, 0.50 #582), 0ldqf (0.69 #38, 0.50 #303, 0.48 #776), 0lbbj (0.69 #38, 0.50 #288, 0.48 #776), 0l98s (0.69 #38, 0.50 #164, 0.48 #776), 0l6ny (0.69 #38, 0.48 #776, 0.48 #37), 0lgxj (0.69 #38, 0.48 #776, 0.48 #37), 0lv1x (0.69 #38, 0.48 #776, 0.48 #37) >> Best rule #1133 for best value: >> intensional similarity = 47 >> extensional distance = 29 >> proper extension: 01hp22; 018w8; >> query: (?x5396, 0kbws) <- country(?x5396, ?x8593), country(?x5396, ?x4743), country(?x5396, ?x2513), country(?x5396, ?x2000), country(?x5396, ?x789), country(?x5396, ?x583), countries_within(?x455, ?x2513), film_release_region(?x8682, ?x2513), film_release_region(?x8580, ?x2513), film_release_region(?x5827, ?x2513), film_release_region(?x4514, ?x2513), film_release_region(?x3603, ?x2513), film_release_region(?x2441, ?x2513), film_release_region(?x1915, ?x2513), film_release_region(?x1263, ?x2513), ?x1263 = 0dgst_d, member_states(?x7416, ?x2513), country(?x3345, ?x2513), country(?x3309, ?x2513), country(?x1037, ?x2513), country(?x520, ?x2513), ?x8682 = 0bmfnjs, service_location(?x555, ?x2513), ?x583 = 015fr, ?x1037 = 09_bl, ?x8580 = 0hhggmy, combatants(?x2513, ?x172), participating_countries(?x784, ?x2000), official_language(?x8593, ?x393), adjoins(?x8593, ?x7748), currency(?x2513, ?x170), olympics(?x5396, ?x358), ?x3309 = 09w1n, nationality(?x2724, ?x4743), ?x789 = 0f8l9c, ?x4514 = 06tpmy, ?x3345 = 09qgm, ?x5827 = 0ggbfwf, ?x3603 = 09gkx35, ?x520 = 01dys, nationality(?x2610, ?x2513), olympics(?x2513, ?x418), olympics(?x2513, ?x584), film_release_region(?x80, ?x4743), ?x1915 = 0fq7dv_, ?x2441 = 0cc5mcj, combatants(?x7419, ?x4743) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0486tv olympics 0kbws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.839 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #12117-01q_y0 PRED entity: 01q_y0 PRED relation: honored_for! PRED expected values: 0bx6zs => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 79): 0gvstc3 (0.26 #615, 0.19 #969, 0.19 #261), 0bx6zs (0.25 #1653, 0.07 #461, 0.07 #107), 02q690_ (0.25 #994, 0.23 #640, 0.23 #404), 05c1t6z (0.24 #599, 0.24 #953, 0.18 #9), 03nnm4t (0.23 #649, 0.20 #1003, 0.15 #1475), 0lp_cd3 (0.17 #605, 0.14 #15, 0.13 #959), 0bxs_d (0.11 #96, 0.10 #1040, 0.07 #450), 07y_p6 (0.11 #79, 0.08 #1023, 0.07 #669), 026kq4q (0.11 #34, 0.04 #624, 0.03 #4840), 0gkxgfq (0.10 #678, 0.06 #1032, 0.06 #206) >> Best rule #615 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 06hwzy; >> query: (?x2293, 0gvstc3) <- producer_type(?x2293, ?x632), honored_for(?x2292, ?x2293), ceremony(?x375, ?x2292) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #1653 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 121 *> proper extension: 03j63k; 02gjrc; *> query: (?x2293, ?x1193) <- nominated_for(?x678, ?x2293), genre(?x2293, ?x258), actor(?x2293, ?x4411), award_winner(?x1193, ?x4411) *> conf = 0.25 ranks of expected_values: 2 EVAL 01q_y0 honored_for! 0bx6zs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.256 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12116-01w2v PRED entity: 01w2v PRED relation: place_of_birth! PRED expected values: 08849 => 158 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1710): 0fp_v1x (0.55 #65287, 0.36 #5223, 0.36 #49616), 019_1h (0.36 #5223, 0.36 #49616, 0.36 #229825), 0bs8d (0.11 #3708, 0.07 #8932, 0.06 #11543), 017g2y (0.11 #4269, 0.07 #9493, 0.06 #12104), 01vsps (0.11 #3489, 0.07 #8713, 0.06 #11324), 01w5gg6 (0.11 #4532, 0.07 #9756, 0.06 #12367), 0f1vrl (0.11 #2922, 0.06 #10757, 0.04 #23815), 01my_c (0.11 #4042, 0.04 #24935, 0.03 #35379), 0ngg (0.08 #7814, 0.05 #15648, 0.04 #18260), 041wm (0.08 #7489, 0.05 #15323, 0.04 #17935) >> Best rule #65287 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 06pvr; >> query: (?x6366, ?x460) <- location(?x460, ?x6366), place_of_birth(?x460, ?x11046), music(?x153, ?x460), type_of_union(?x460, ?x566) >> conf = 0.55 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w2v place_of_birth! 08849 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 91.000 0.554 http://example.org/people/person/place_of_birth #12115-041bnw PRED entity: 041bnw PRED relation: child! PRED expected values: 02bh8z => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 58): 02bh8z (0.50 #276, 0.40 #192, 0.25 #109), 09b3v (0.38 #445, 0.22 #362, 0.05 #2634), 01dtcb (0.14 #1050, 0.14 #1135, 0.14 #965), 0l8sx (0.12 #430, 0.11 #347, 0.10 #513), 086k8 (0.12 #419, 0.03 #502, 0.03 #2608), 03mp8k (0.06 #552, 0.04 #719, 0.04 #803), 054g1r (0.06 #449, 0.02 #2522), 043g7l (0.04 #617, 0.04 #700, 0.04 #784), 049ql1 (0.03 #569, 0.03 #2675, 0.03 #2507), 016tw3 (0.03 #511, 0.02 #595, 0.02 #678) >> Best rule #276 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 03rhqg; 0229rs; 01cf93; >> query: (?x9671, 02bh8z) <- artist(?x9671, ?x9116), artist(?x9671, ?x4715), artist(?x9671, ?x4239), ?x4239 = 0x3b7, artists(?x302, ?x9116), award(?x4715, ?x1565), group(?x74, ?x4715) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 041bnw child! 02bh8z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.500 http://example.org/organization/organization/child./organization/organization_relationship/child #12114-05qsxy PRED entity: 05qsxy PRED relation: profession PRED expected values: 0dxtg => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 59): 0dxtg (0.85 #465, 0.85 #164, 0.85 #765), 03gjzk (0.74 #467, 0.69 #767, 0.69 #917), 02hrh1q (0.68 #5716, 0.68 #3766, 0.67 #2266), 01d_h8 (0.34 #6607, 0.34 #2257, 0.34 #457), 02jknp (0.29 #301, 0.24 #2259, 0.24 #6609), 0cbd2 (0.25 #908, 0.25 #458, 0.23 #758), 02krf9 (0.24 #479, 0.22 #779, 0.21 #929), 018gz8 (0.20 #469, 0.19 #769, 0.19 #919), 09jwl (0.18 #3621, 0.18 #3171, 0.17 #4521), 0np9r (0.17 #473, 0.15 #773, 0.15 #923) >> Best rule #465 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 05g8ky; 04bs3j; 04n7njg; 013cr; 03ft8; 02jm0n; 01wyzyl; 0jt90f5; 03m_k0; 0glmv; ... >> query: (?x2543, 0dxtg) <- place_of_birth(?x2543, ?x760), student(?x1884, ?x2543), tv_program(?x2543, ?x4011) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qsxy profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.850 http://example.org/people/person/profession #12113-019n8z PRED entity: 019n8z PRED relation: medal PRED expected values: 02lq67 => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.88 #39, 0.88 #38, 0.88 #37) >> Best rule #39 for best value: >> intensional similarity = 54 >> extensional distance = 41 >> proper extension: 018wrk; >> query: (?x6893, 02lq67) <- olympics(?x1229, ?x6893), olympics(?x304, ?x6893), olympics(?x252, ?x6893), film_release_region(?x7114, ?x304), film_release_region(?x6751, ?x304), film_release_region(?x6603, ?x304), film_release_region(?x6556, ?x304), film_release_region(?x6492, ?x304), film_release_region(?x5980, ?x304), film_release_region(?x5644, ?x304), film_release_region(?x5578, ?x304), film_release_region(?x5142, ?x304), film_release_region(?x5089, ?x304), film_release_region(?x4448, ?x304), film_release_region(?x4352, ?x304), film_release_region(?x3423, ?x304), film_release_region(?x3137, ?x304), film_release_region(?x3000, ?x304), film_release_region(?x1370, ?x304), film_release_region(?x1108, ?x304), film_release_region(?x781, ?x304), ?x3000 = 045j3w, ?x1370 = 0gmcwlb, ?x4448 = 01k60v, ?x5142 = 0bt3j9, film_release_region(?x2676, ?x252), ?x6492 = 0ds6bmk, sports(?x6893, ?x3309), country(?x2978, ?x252), country(?x2266, ?x252), ?x7114 = 06rzwx, nationality(?x256, ?x252), ?x6603 = 094g2z, ?x2676 = 0f4m2z, ?x2266 = 01lb14, country(?x596, ?x252), ?x5644 = 0dll_t2, ?x5578 = 0ddj0x, ?x3423 = 09g7vfw, ?x781 = 0gkz15s, country(?x3309, ?x1790), nominated_for(?x102, ?x5089), ?x1790 = 01pj7, medal(?x6893, ?x1242), country_of_origin(?x419, ?x252), ?x2978 = 03_8r, ?x6556 = 05dss7, ?x6751 = 0372j5, organization(?x304, ?x127), ?x4352 = 09v71cj, ?x3137 = 0htww, ?x5980 = 0hv81, ?x1108 = 0jjy0, combatants(?x613, ?x1229) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019n8z medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.884 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #12112-0f3kl PRED entity: 0f3kl PRED relation: symptom_of PRED expected values: 04psf 09jg8 => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 84): 09jg8 (0.78 #522, 0.67 #491, 0.67 #467), 07jwr (0.67 #232, 0.50 #238, 0.50 #189), 09d11 (0.67 #232, 0.50 #197, 0.48 #443), 01gkcc (0.67 #232, 0.50 #206, 0.48 #443), 072hv (0.67 #232, 0.48 #443, 0.43 #644), 02bft (0.67 #232, 0.48 #443, 0.40 #595), 0h1wz (0.67 #232, 0.44 #484, 0.43 #644), 097ns (0.67 #232, 0.39 #490, 0.36 #281), 074m2 (0.60 #302, 0.50 #253, 0.50 #204), 03p41 (0.60 #296, 0.50 #198, 0.43 #644) >> Best rule #522 for best value: >> intensional similarity = 21 >> extensional distance = 7 >> proper extension: 097ns; >> query: (?x13373, 09jg8) <- symptom_of(?x13373, ?x13131), people(?x13131, ?x10724), people(?x13131, ?x8661), award(?x8661, ?x384), award_winner(?x3254, ?x8661), music(?x1919, ?x8661), symptom_of(?x10717, ?x13131), symptom_of(?x9438, ?x13131), symptom_of(?x6780, ?x13131), symptom_of(?x4905, ?x13131), people(?x1050, ?x8661), award_nominee(?x1894, ?x8661), ?x4905 = 01j6t0, place_of_birth(?x8661, ?x1705), ?x6780 = 0j5fv, ?x10717 = 0cjf0, ?x9438 = 012qjw, place_of_death(?x8661, ?x739), profession(?x8661, ?x1183), award_winner(?x1323, ?x8661), location(?x10724, ?x3052) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14 EVAL 0f3kl symptom_of 09jg8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 21.000 21.000 0.778 http://example.org/medicine/symptom/symptom_of EVAL 0f3kl symptom_of 04psf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 21.000 21.000 0.778 http://example.org/medicine/symptom/symptom_of #12111-01hl_w PRED entity: 01hl_w PRED relation: institution! PRED expected values: 014mlp => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 16): 014mlp (0.90 #191, 0.81 #516, 0.79 #245), 03bwzr4 (0.71 #198, 0.69 #252, 0.67 #215), 07s6fsf (0.60 #18, 0.59 #243, 0.56 #189), 04zx3q1 (0.51 #207, 0.50 #19, 0.48 #244), 027f2w (0.50 #23, 0.45 #248, 0.42 #108), 013zdg (0.40 #22, 0.38 #247, 0.37 #210), 0bjrnt (0.40 #21, 0.29 #1327, 0.23 #209), 028dcg (0.40 #30, 0.20 #201, 0.19 #1039), 02cq61 (0.31 #114, 0.20 #29, 0.20 #235), 022h5x (0.30 #31, 0.29 #1327, 0.19 #1039) >> Best rule #191 for best value: >> intensional similarity = 7 >> extensional distance = 39 >> proper extension: 017j69; 01jt2w; >> query: (?x10889, 014mlp) <- institution(?x1771, ?x10889), institution(?x1526, ?x10889), institution(?x1200, ?x10889), ?x1526 = 0bkj86, ?x1771 = 019v9k, organization(?x5510, ?x10889), ?x1200 = 016t_3 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hl_w institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.902 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #12110-03d_w3h PRED entity: 03d_w3h PRED relation: nominated_for PRED expected values: 07g9f => 126 concepts (77 used for prediction) PRED predicted values (max 10 best out of 569): 07l450 (0.28 #59880, 0.25 #8091, 0.24 #116535), 0ddcbd5 (0.28 #59880, 0.25 #8091, 0.24 #116535), 0bpm4yw (0.20 #660, 0.06 #7132, 0.05 #10369), 07c72 (0.10 #477, 0.06 #6949, 0.03 #10186), 06_wqk4 (0.10 #119, 0.06 #6591, 0.03 #9828), 04gp58p (0.10 #1270, 0.06 #7742, 0.03 #10979), 05hjnw (0.10 #777, 0.06 #7249, 0.03 #10486), 012s1d (0.10 #843, 0.05 #10552, 0.05 #12171), 0jym0 (0.10 #301, 0.03 #8392, 0.02 #22959), 05q_dw (0.10 #820, 0.03 #18623, 0.03 #10529) >> Best rule #59880 for best value: >> intensional similarity = 3 >> extensional distance = 488 >> proper extension: 01pnn3; 08b8vd; 03ds83; 04cr6qv; 02hhtj; 04mlmx; 028pzq; 01syr4; 0202p_; >> query: (?x940, ?x4048) <- nationality(?x940, ?x94), participant(?x3583, ?x940), film(?x940, ?x4048) >> conf = 0.28 => this is the best rule for 2 predicted values *> Best rule #7935 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 02jyhv; *> query: (?x940, 07g9f) <- diet(?x940, ?x11141), participant(?x3583, ?x940), film(?x940, ?x4048) *> conf = 0.06 ranks of expected_values: 75 EVAL 03d_w3h nominated_for 07g9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 126.000 77.000 0.283 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12109-0ctw_b PRED entity: 0ctw_b PRED relation: organization PRED expected values: 02vk52z 0j7v_ => 209 concepts (209 used for prediction) PRED predicted values (max 10 best out of 16): 02vk52z (0.85 #301, 0.85 #622, 0.85 #2348), 01rz1 (0.64 #182, 0.63 #302, 0.62 #362), 0b6css (0.56 #309, 0.52 #149, 0.52 #229), 0j7v_ (0.52 #384, 0.29 #24, 0.27 #284), 02jxk (0.50 #183, 0.45 #363, 0.37 #303), 041288 (0.46 #294, 0.42 #394, 0.36 #2341), 059dn (0.29 #33, 0.23 #193, 0.22 #133), 0gkjy (0.27 #2353, 0.26 #2373, 0.25 #2454), 034h1h (0.18 #3432, 0.02 #750, 0.02 #3189), 02_l9 (0.07 #3436, 0.05 #2460, 0.02 #3773) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 03rj0; >> query: (?x1023, 02vk52z) <- film_release_region(?x6932, ?x1023), film_release_region(?x66, ?x1023), ?x6932 = 027pfg, ?x66 = 014lc_ >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0ctw_b organization 0j7v_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 209.000 209.000 0.852 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0ctw_b organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 209.000 209.000 0.852 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #12108-07vfy4 PRED entity: 07vfy4 PRED relation: film_festivals PRED expected values: 0bmj62v => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 14): 0g57ws5 (0.09 #28, 0.05 #49, 0.02 #217), 0gg7gsl (0.09 #22, 0.02 #43, 0.02 #946), 0bmj62v (0.09 #33, 0.02 #54, 0.02 #957), 04_m9gk (0.05 #55, 0.03 #349, 0.02 #1105), 09rwjly (0.02 #155, 0.02 #50, 0.02 #218), 04grdgy (0.02 #51, 0.02 #282, 0.02 #219), 059_y8d (0.02 #44, 0.02 #107, 0.01 #569), 0fpkxfd (0.02 #48, 0.01 #1119), 05ys0ws (0.02 #62), 03nn7l2 (0.02 #59) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 05dl1s; >> query: (?x9805, 0g57ws5) <- award_winner(?x9805, ?x10700), nominated_for(?x8843, ?x9805), genre(?x9805, ?x53), ?x8843 = 02qrwjt >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #33 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 05dl1s; *> query: (?x9805, 0bmj62v) <- award_winner(?x9805, ?x10700), nominated_for(?x8843, ?x9805), genre(?x9805, ?x53), ?x8843 = 02qrwjt *> conf = 0.09 ranks of expected_values: 3 EVAL 07vfy4 film_festivals 0bmj62v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 94.000 0.091 http://example.org/film/film/film_festivals #12107-04kllm9 PRED entity: 04kllm9 PRED relation: symptom_of PRED expected values: 035482 => 16 concepts (16 used for prediction) PRED predicted values (max 10 best out of 69): 0h1n9 (0.75 #457, 0.70 #348, 0.60 #298), 0d19y2 (0.70 #358, 0.60 #308, 0.58 #467), 01n3bm (0.67 #140, 0.50 #300, 0.45 #402), 09jg8 (0.60 #345, 0.60 #295, 0.55 #397), 02k6hp (0.56 #242, 0.50 #192, 0.50 #87), 0167bx (0.50 #465, 0.50 #356, 0.50 #306), 07jwr (0.50 #170, 0.50 #65, 0.40 #325), 0dq9p (0.50 #177, 0.50 #72, 0.36 #483), 0lcdk (0.50 #198, 0.50 #93, 0.36 #483), 074m2 (0.50 #186, 0.40 #341, 0.40 #291) >> Best rule #457 for best value: >> intensional similarity = 15 >> extensional distance = 10 >> proper extension: 02y0js; >> query: (?x13605, 0h1n9) <- symptom_of(?x13605, ?x12870), symptom_of(?x13605, ?x7007), risk_factors(?x12870, ?x4195), risk_factors(?x7007, ?x11160), symptom_of(?x4905, ?x7007), symptom_of(?x13487, ?x12870), symptom_of(?x13487, ?x11126), symptom_of(?x13487, ?x6260), symptom_of(?x13487, ?x3680), ?x6260 = 0dq9p, ?x4905 = 01j6t0, ?x3680 = 025hl8, ?x4195 = 02ctzb, risk_factors(?x7006, ?x7007), ?x11126 = 0hg45 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #703 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 14 *> proper extension: 01l2m3; 0dq9p; *> query: (?x13605, ?x3799) <- symptom_of(?x13605, ?x12870), symptom_of(?x13605, ?x9510), symptom_of(?x13487, ?x12870), symptom_of(?x9509, ?x9510), symptom_of(?x3679, ?x9510), symptom_of(?x9509, ?x14096), symptom_of(?x9509, ?x11064), symptom_of(?x9509, ?x10480), symptom_of(?x9509, ?x3799), ?x10480 = 0h1n9, ?x11064 = 01n3bm, symptom_of(?x13487, ?x4959), symptom_of(?x13487, ?x3680), ?x4959 = 01dcqj, ?x3680 = 025hl8, ?x14096 = 0h3bn, ?x3679 = 02tfl8 *> conf = 0.34 ranks of expected_values: 30 EVAL 04kllm9 symptom_of 035482 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 16.000 16.000 0.750 http://example.org/medicine/symptom/symptom_of #12106-06jrhz PRED entity: 06jrhz PRED relation: award_winner PRED expected values: 0884hk 0brkwj => 86 concepts (41 used for prediction) PRED predicted values (max 10 best out of 812): 09_99w (0.82 #16087, 0.82 #43448, 0.82 #57939), 09hd6f (0.82 #16087, 0.82 #43448, 0.82 #57939), 05vtbl (0.82 #16087, 0.82 #43448, 0.82 #51501), 09gb9xh (0.82 #16087, 0.82 #43448, 0.82 #51501), 0884hk (0.62 #2282, 0.43 #674, 0.34 #11261), 08q3s0 (0.50 #2519, 0.43 #911, 0.34 #11261), 06jrhz (0.50 #2595, 0.34 #11261, 0.34 #27355), 0brkwj (0.50 #2886, 0.34 #11261, 0.34 #27355), 0h5jg5 (0.38 #2794, 0.34 #11261, 0.34 #27355), 05m9f9 (0.34 #11261, 0.34 #27355, 0.28 #43449) >> Best rule #16087 for best value: >> intensional similarity = 4 >> extensional distance = 175 >> proper extension: 01jrz5j; 0lccn; 05pq9; 01wj92r; 014hr0; 0686zv; 0pj9t; 01r6jt2; 0p7h7; 06m61; ... >> query: (?x5832, ?x129) <- award_winner(?x4035, ?x5832), award_winner(?x129, ?x5832), award_nominee(?x4035, ?x1039), story_by(?x1012, ?x4035) >> conf = 0.82 => this is the best rule for 4 predicted values *> Best rule #2282 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0h53p1; 0h584v; 01xndd; 0brkwj; 0697kh; 09hd6f; *> query: (?x5832, 0884hk) <- award_winner(?x4023, ?x5832), tv_program(?x5832, ?x3144), ?x4023 = 09hd16 *> conf = 0.62 ranks of expected_values: 5, 8 EVAL 06jrhz award_winner 0brkwj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 86.000 41.000 0.819 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner EVAL 06jrhz award_winner 0884hk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 41.000 0.819 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #12105-02y8z PRED entity: 02y8z PRED relation: country PRED expected values: 0h3y 01ls2 07ssc 06f32 03ryn 05cc1 04hqz => 44 concepts (41 used for prediction) PRED predicted values (max 10 best out of 616): 07ssc (0.89 #5456, 0.88 #4302, 0.88 #5290), 0chghy (0.86 #5784, 0.84 #4791, 0.83 #4627), 06f32 (0.82 #3505, 0.73 #3670, 0.73 #2473), 07f1x (0.82 #3748, 0.73 #3583, 0.73 #2473), 04w4s (0.82 #3678, 0.73 #3513, 0.71 #2521), 01mjq (0.80 #3321, 0.78 #2825, 0.77 #1153), 02vzc (0.78 #2831, 0.77 #1153, 0.73 #3494), 05vz3zq (0.77 #1153, 0.73 #2473, 0.70 #2967), 087vz (0.77 #1153, 0.73 #2473, 0.70 #2967), 019pcs (0.77 #1153, 0.73 #2473, 0.70 #2302) >> Best rule #5456 for best value: >> intensional similarity = 46 >> extensional distance = 25 >> proper extension: 07bs0; >> query: (?x2867, 07ssc) <- country(?x2867, ?x8197), country(?x2867, ?x2645), country(?x2867, ?x583), country(?x2867, ?x151), olympics(?x2867, ?x778), ?x583 = 015fr, sports(?x7051, ?x2867), sports(?x775, ?x2867), sports(?x391, ?x2867), film_release_region(?x9832, ?x2645), film_release_region(?x9002, ?x2645), film_release_region(?x8025, ?x2645), film_release_region(?x7493, ?x2645), film_release_region(?x4607, ?x2645), film_release_region(?x4430, ?x2645), film_release_region(?x4040, ?x2645), film_release_region(?x2512, ?x2645), film_release_region(?x1724, ?x2645), film_release_region(?x385, ?x2645), ?x385 = 0ds3t5x, ?x1724 = 02r8hh_, country(?x1135, ?x2645), ?x9832 = 01xlqd, ?x9002 = 0ndsl1x, ?x8025 = 03nsm5x, olympics(?x512, ?x7051), location_of_ceremony(?x566, ?x2645), organization(?x8197, ?x127), ?x4430 = 043sct5, medal(?x2645, ?x422), sports(?x391, ?x766), adjoins(?x8197, ?x5700), location(?x4245, ?x2645), combatants(?x5114, ?x151), film_release_region(?x6931, ?x151), service_location(?x1540, ?x151), ?x6931 = 09v3jyg, medal(?x151, ?x1242), ?x5114 = 05vz3zq, currency(?x2645, ?x170), ?x4607 = 0h03fhx, locations(?x775, ?x8181), ?x4040 = 02mt51, ?x2512 = 07x4qr, ?x766 = 01hp22, ?x7493 = 0btpm6 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 15, 23, 33, 37, 54 EVAL 02y8z country 04hqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 44.000 41.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02y8z country 05cc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 44.000 41.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02y8z country 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 44.000 41.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02y8z country 06f32 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 41.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02y8z country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 41.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02y8z country 01ls2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 44.000 41.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02y8z country 0h3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 44.000 41.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #12104-0s9b_ PRED entity: 0s9b_ PRED relation: location! PRED expected values: 06688p 0993r => 84 concepts (72 used for prediction) PRED predicted values (max 10 best out of 735): 06688p (0.33 #19, 0.04 #5053, 0.04 #7570), 0br1w (0.06 #10802, 0.04 #13319, 0.03 #18353), 02sjf5 (0.05 #17821, 0.05 #12787, 0.04 #27889), 0sx5w (0.05 #12209, 0.03 #14726, 0.02 #19760), 0f_y9 (0.05 #11546, 0.02 #16580, 0.02 #19097), 0lrh (0.05 #10616, 0.02 #15650, 0.02 #18167), 017f4y (0.05 #12223, 0.02 #19774, 0.02 #34876), 045c66 (0.04 #5290, 0.04 #7807, 0.02 #10324), 01v3vp (0.04 #5838, 0.04 #8355, 0.02 #25974), 054c1 (0.04 #7364, 0.04 #9881) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0s69k; >> query: (?x13208, 06688p) <- county(?x13208, ?x11658), category(?x13208, ?x134), ?x11658 = 0l3kx, location(?x6328, ?x13208) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0s9b_ location! 0993r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 72.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0s9b_ location! 06688p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 72.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #12103-0f2sx4 PRED entity: 0f2sx4 PRED relation: nominated_for! PRED expected values: 05b1610 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 208): 0gq9h (0.47 #9190, 0.29 #763, 0.25 #5445), 0gs9p (0.36 #9191, 0.24 #764, 0.20 #14339), 019f4v (0.31 #9181, 0.26 #754, 0.23 #1456), 040njc (0.31 #9135, 0.15 #14049, 0.15 #8433), 0k611 (0.29 #9199, 0.19 #772, 0.17 #14347), 02x1z2s (0.27 #139, 0.11 #19897, 0.11 #5524), 09tqxt (0.27 #71, 0.08 #5456, 0.06 #5690), 0gqzz (0.27 #48, 0.05 #5433, 0.05 #517), 05p1dby (0.25 #4448, 0.25 #4449, 0.20 #78), 05b1610 (0.25 #4448, 0.25 #4449, 0.17 #5415) >> Best rule #9190 for best value: >> intensional similarity = 3 >> extensional distance = 614 >> proper extension: 02nf2c; 03j63k; 019g8j; >> query: (?x7967, 0gq9h) <- nominated_for(?x1105, ?x7967), award(?x1039, ?x1105), ?x1039 = 04wvhz >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #4448 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 226 *> proper extension: 02fn5r; *> query: (?x7967, ?x2022) <- nominated_for(?x7967, ?x2907), nominated_for(?x4054, ?x7967), nominated_for(?x2022, ?x4054), award(?x166, ?x2022) *> conf = 0.25 ranks of expected_values: 10 EVAL 0f2sx4 nominated_for! 05b1610 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 93.000 93.000 0.466 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12102-04_1nk PRED entity: 04_1nk PRED relation: film_production_design_by! PRED expected values: 0164qt => 105 concepts (87 used for prediction) PRED predicted values (max 10 best out of 162): 0g5ptf (0.51 #1612, 0.14 #1450, 0.04 #1119), 027ct7c (0.51 #1612, 0.14 #1450, 0.03 #1538), 02vnmc9 (0.10 #450, 0.07 #611, 0.03 #1579), 0hv81 (0.10 #419, 0.07 #580, 0.03 #1548), 015qqg (0.10 #400, 0.07 #561, 0.03 #1529), 03wy8t (0.09 #1110, 0.06 #1594, 0.06 #787), 06pyc2 (0.06 #797, 0.04 #1442, 0.04 #1120), 06y611 (0.06 #791, 0.04 #1436, 0.04 #1114), 0gy0l_ (0.06 #784, 0.04 #1429, 0.04 #1107), 04x4nv (0.06 #783, 0.04 #1428, 0.04 #1106) >> Best rule #1612 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 06cv1; 0ft7sr; 04kj2v; 05728w1; 04gmp_z; 0bytkq; 03gyh_z; 0dh73w; 05v1sb; 0d5wn3; ... >> query: (?x5532, ?x5533) <- film_production_design_by(?x4680, ?x5532), nominated_for(?x5532, ?x5533), film(?x2557, ?x4680) >> conf = 0.51 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 04_1nk film_production_design_by! 0164qt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 87.000 0.513 http://example.org/film/film/film_production_design_by #12101-03nt7j PRED entity: 03nt7j PRED relation: draft! PRED expected values: 0289q 0ws7 => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 393): 0487_ (0.60 #472, 0.57 #543, 0.53 #357), 07l2m (0.60 #458, 0.57 #529, 0.53 #357), 02896 (0.60 #431, 0.57 #502, 0.53 #357), 01ct6 (0.60 #435, 0.57 #506, 0.53 #357), 03wnh (0.60 #463, 0.57 #534, 0.53 #357), 0ws7 (0.60 #469, 0.57 #540, 0.53 #357), 06rny (0.53 #357, 0.43 #533, 0.40 #462), 0289q (0.53 #357, 0.43 #527, 0.40 #456), 02wvfxz (0.53 #357, 0.39 #1164, 0.39 #943), 0fw9n7 (0.53 #357, 0.39 #1164, 0.39 #943) >> Best rule #472 for best value: >> intensional similarity = 50 >> extensional distance = 3 >> proper extension: 09l0x9; >> query: (?x3089, 0487_) <- school(?x3089, ?x1681), major_field_of_study(?x1681, ?x6870), major_field_of_study(?x1681, ?x2981), student(?x1681, ?x12116), student(?x1681, ?x2237), draft(?x6976, ?x3089), draft(?x3658, ?x3089), draft(?x2574, ?x3089), contains(?x94, ?x1681), ?x2981 = 02j62, team(?x2573, ?x2574), team(?x1717, ?x2574), ?x1717 = 02g_6x, ?x3658 = 03b3j, ?x6976 = 04vn5, ?x2573 = 05b3ts, colors(?x2574, ?x663), list(?x1681, ?x2197), institution(?x620, ?x1681), award(?x2237, ?x154), school(?x260, ?x1681), nominated_for(?x2237, ?x408), profession(?x2237, ?x319), celebrity(?x2237, ?x5565), major_field_of_study(?x11853, ?x6870), major_field_of_study(?x5651, ?x6870), major_field_of_study(?x2760, ?x6870), award_nominee(?x2237, ?x1896), ?x11853 = 04jhp, participant(?x1208, ?x2237), category(?x1681, ?x134), ?x5651 = 027mdh, film(?x2237, ?x4249), school(?x2574, ?x2948), institution(?x620, ?x10303), institution(?x620, ?x3821), institution(?x620, ?x2830), institution(?x620, ?x2775), location(?x2237, ?x479), ?x2775 = 078bz, participant(?x2237, ?x1909), participant(?x12116, ?x1897), religion(?x12116, ?x1985), currency(?x12116, ?x170), award_nominee(?x959, ?x2237), ?x3821 = 0kw4j, ?x2830 = 01wdj_, ?x2760 = 07wlf, ?x10303 = 03hpkp, award_winner(?x10746, ?x12116) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #469 for first EXPECTED value: *> intensional similarity = 50 *> extensional distance = 3 *> proper extension: 09l0x9; *> query: (?x3089, 0ws7) <- school(?x3089, ?x1681), major_field_of_study(?x1681, ?x6870), major_field_of_study(?x1681, ?x2981), student(?x1681, ?x12116), student(?x1681, ?x2237), draft(?x6976, ?x3089), draft(?x3658, ?x3089), draft(?x2574, ?x3089), contains(?x94, ?x1681), ?x2981 = 02j62, team(?x2573, ?x2574), team(?x1717, ?x2574), ?x1717 = 02g_6x, ?x3658 = 03b3j, ?x6976 = 04vn5, ?x2573 = 05b3ts, colors(?x2574, ?x663), list(?x1681, ?x2197), institution(?x620, ?x1681), award(?x2237, ?x154), school(?x260, ?x1681), nominated_for(?x2237, ?x408), profession(?x2237, ?x319), celebrity(?x2237, ?x5565), major_field_of_study(?x11853, ?x6870), major_field_of_study(?x5651, ?x6870), major_field_of_study(?x2760, ?x6870), award_nominee(?x2237, ?x1896), ?x11853 = 04jhp, participant(?x1208, ?x2237), category(?x1681, ?x134), ?x5651 = 027mdh, film(?x2237, ?x4249), school(?x2574, ?x2948), institution(?x620, ?x10303), institution(?x620, ?x3821), institution(?x620, ?x2830), institution(?x620, ?x2775), location(?x2237, ?x479), ?x2775 = 078bz, participant(?x2237, ?x1909), participant(?x12116, ?x1897), religion(?x12116, ?x1985), currency(?x12116, ?x170), award_nominee(?x959, ?x2237), ?x3821 = 0kw4j, ?x2830 = 01wdj_, ?x2760 = 07wlf, ?x10303 = 03hpkp, award_winner(?x10746, ?x12116) *> conf = 0.60 ranks of expected_values: 6, 8 EVAL 03nt7j draft! 0ws7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 17.000 17.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 03nt7j draft! 0289q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 17.000 17.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #12100-0mcl0 PRED entity: 0mcl0 PRED relation: titles! PRED expected values: 07s9rl0 => 100 concepts (61 used for prediction) PRED predicted values (max 10 best out of 60): 07s9rl0 (0.65 #204, 0.42 #1016, 0.42 #408), 02l7c8 (0.35 #4691, 0.32 #305, 0.32 #304), 03bxz7 (0.32 #305, 0.32 #304, 0.30 #2854), 060__y (0.32 #305, 0.32 #304, 0.23 #3566), 01z4y (0.29 #4008, 0.23 #1455, 0.22 #1352), 03mqtr (0.23 #247, 0.10 #44, 0.08 #756), 01hmnh (0.19 #25, 0.10 #432, 0.09 #636), 07ssc (0.16 #110, 0.14 #9, 0.14 #1024), 01jfsb (0.15 #426, 0.13 #1034, 0.13 #2056), 07c52 (0.15 #1964, 0.11 #2983, 0.11 #3594) >> Best rule #204 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0bx_hnp; >> query: (?x3882, 07s9rl0) <- genre(?x3882, ?x6887), film(?x8041, ?x3882), ?x6887 = 03bxz7 >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mcl0 titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 61.000 0.654 http://example.org/media_common/netflix_genre/titles #12099-01p0w_ PRED entity: 01p0w_ PRED relation: award PRED expected values: 02wh75 => 161 concepts (117 used for prediction) PRED predicted values (max 10 best out of 306): 02f72n (0.50 #1761, 0.47 #2165, 0.44 #2569), 02f716 (0.50 #1792, 0.47 #2196, 0.38 #2600), 02f72_ (0.43 #1845, 0.40 #2249, 0.33 #633), 01bgqh (0.38 #3679, 0.33 #4891, 0.31 #3275), 01by1l (0.36 #4959, 0.35 #14251, 0.34 #8999), 02f73p (0.36 #1803, 0.33 #2207, 0.20 #3823), 03qbh5 (0.33 #3437, 0.30 #3841, 0.22 #6265), 0c4z8 (0.32 #8960, 0.30 #3708, 0.29 #6132), 01c92g (0.29 #4944, 0.20 #6156, 0.17 #3732), 02f5qb (0.29 #1771, 0.28 #3791, 0.27 #2175) >> Best rule #1761 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 089tm; 01pfr3; 04qmr; 0kr_t; 0187x8; 016lmg; 0jg77; >> query: (?x12422, 02f72n) <- artists(?x12498, ?x12422), ?x12498 = 05c6073, award_winner(?x1584, ?x12422) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4453 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 02qfhb; *> query: (?x12422, 02wh75) <- participant(?x7053, ?x12422), role(?x12422, ?x227), profession(?x12422, ?x955) *> conf = 0.24 ranks of expected_values: 34 EVAL 01p0w_ award 02wh75 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 161.000 117.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12098-05szq8z PRED entity: 05szq8z PRED relation: genre PRED expected values: 03k9fj => 97 concepts (69 used for prediction) PRED predicted values (max 10 best out of 112): 05p553 (0.54 #2986, 0.38 #360, 0.38 #1910), 01jfsb (0.53 #1204, 0.48 #1561, 0.42 #12), 03k9fj (0.49 #606, 0.49 #845, 0.41 #1560), 060__y (0.33 #17, 0.19 #136, 0.18 #255), 02n4kr (0.33 #7, 0.13 #2033, 0.13 #1318), 02l7c8 (0.32 #969, 0.31 #2520, 0.31 #3719), 06n90 (0.27 #1562, 0.26 #1205, 0.23 #1085), 0lsxr (0.27 #2991, 0.25 #8, 0.24 #1200), 0hcr (0.22 #857, 0.19 #618, 0.10 #1095), 04xvlr (0.20 #954, 0.19 #2505, 0.18 #3704) >> Best rule #2986 for best value: >> intensional similarity = 4 >> extensional distance = 796 >> proper extension: 09rfh9; >> query: (?x5458, 05p553) <- nominated_for(?x2022, ?x5458), genre(?x5458, ?x225), genre(?x2218, ?x225), ?x2218 = 013q07 >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #606 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 152 *> proper extension: 0b60sq; 02q3fdr; 06zn1c; *> query: (?x5458, 03k9fj) <- nominated_for(?x382, ?x5458), nominated_for(?x2022, ?x5458), genre(?x5458, ?x1510), ?x1510 = 01hmnh *> conf = 0.49 ranks of expected_values: 3 EVAL 05szq8z genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 69.000 0.536 http://example.org/film/film/genre #12097-01kt_j PRED entity: 01kt_j PRED relation: honored_for! PRED expected values: 0gx_st => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 85): 05c1t6z (0.42 #132, 0.22 #11, 0.21 #495), 03nnm4t (0.39 #183, 0.21 #667, 0.20 #425), 0gvstc3 (0.29 #148, 0.24 #390, 0.19 #1116), 0gx_st (0.23 #151, 0.13 #514, 0.12 #1240), 0275n3y (0.22 #63, 0.16 #184, 0.13 #547), 07y9ts (0.22 #56, 0.10 #177, 0.10 #540), 0lp_cd3 (0.16 #138, 0.12 #985, 0.12 #501), 027hjff (0.13 #168, 0.11 #47, 0.08 #531), 0bxs_d (0.12 #341, 0.11 #99, 0.10 #1067), 0bx6zs (0.11 #110, 0.10 #1078, 0.10 #352) >> Best rule #132 for best value: >> intensional similarity = 2 >> extensional distance = 29 >> proper extension: 0gpjbt; >> query: (?x10595, 05c1t6z) <- honored_for(?x4760, ?x10595), ?x4760 = 02q690_ >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #151 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 29 *> proper extension: 0gpjbt; *> query: (?x10595, 0gx_st) <- honored_for(?x4760, ?x10595), ?x4760 = 02q690_ *> conf = 0.23 ranks of expected_values: 4 EVAL 01kt_j honored_for! 0gx_st CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 110.000 0.419 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #12096-01wgcvn PRED entity: 01wgcvn PRED relation: profession PRED expected values: 018gz8 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 54): 0nbcg (0.49 #4156, 0.49 #595, 0.48 #1448), 01c72t (0.44 #445, 0.30 #871, 0.28 #2150), 039v1 (0.33 #174, 0.27 #2306, 0.24 #4161), 0n1h (0.29 #719, 0.28 #577, 0.27 #1003), 0dxtg (0.29 #5563, 0.29 #8263, 0.28 #8547), 02jknp (0.22 #8257, 0.22 #8541, 0.22 #6125), 01c8w0 (0.18 #432, 0.07 #2565, 0.06 #2708), 02dsz (0.18 #51, 0.04 #1614, 0.04 #1756), 05vyk (0.14 #514, 0.08 #2219, 0.08 #372), 0np9r (0.13 #158, 0.13 #11108, 0.13 #11392) >> Best rule #4156 for best value: >> intensional similarity = 3 >> extensional distance = 629 >> proper extension: 0f0y8; 03c7ln; 0c9d9; 0c7ct; 06y9c2; 01q7cb_; 01w923; 012zng; 0zjpz; 09prnq; ... >> query: (?x3756, 0nbcg) <- artist(?x382, ?x3756), artists(?x505, ?x3756), profession(?x3756, ?x131) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #3286 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 467 *> proper extension: 02vptk_; *> query: (?x3756, 018gz8) <- nationality(?x3756, ?x94), currency(?x3756, ?x170) *> conf = 0.13 ranks of expected_values: 13 EVAL 01wgcvn profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 103.000 103.000 0.490 http://example.org/people/person/profession #12095-0g83dv PRED entity: 0g83dv PRED relation: genre PRED expected values: 02l7c8 => 97 concepts (93 used for prediction) PRED predicted values (max 10 best out of 94): 02kdv5l (0.59 #4126, 0.31 #851, 0.29 #5709), 05p553 (0.54 #5711, 0.37 #2672, 0.36 #2793), 02l7c8 (0.50 #258, 0.43 #622, 0.32 #501), 04xvlr (0.31 #607, 0.25 #243, 0.22 #486), 03k9fj (0.29 #4136, 0.24 #1712, 0.24 #2680), 06n90 (0.27 #4137, 0.19 #862, 0.14 #3772), 060__y (0.25 #259, 0.25 #623, 0.18 #1838), 082gq (0.25 #273, 0.12 #9588, 0.12 #1852), 0lsxr (0.25 #4133, 0.22 #858, 0.21 #3404), 03npn (0.22 #492, 0.12 #4131, 0.09 #5714) >> Best rule #4126 for best value: >> intensional similarity = 3 >> extensional distance = 773 >> proper extension: 06n90; >> query: (?x4158, 02kdv5l) <- genre(?x4158, ?x812), genre(?x3218, ?x812), ?x3218 = 0ds2n >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #258 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0pv3x; *> query: (?x4158, 02l7c8) <- nominated_for(?x4254, ?x4158), film_crew_role(?x4158, ?x1284), ?x4254 = 0fbx6, ?x1284 = 0ch6mp2 *> conf = 0.50 ranks of expected_values: 3 EVAL 0g83dv genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 93.000 0.592 http://example.org/film/film/genre #12094-09hy79 PRED entity: 09hy79 PRED relation: genre PRED expected values: 03q4nz => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 105): 07s9rl0 (0.82 #121, 0.78 #361, 0.76 #481), 02kdv5l (0.59 #4716, 0.37 #2179, 0.36 #2663), 03k9fj (0.50 #613, 0.40 #13, 0.29 #4726), 05p553 (0.42 #605, 0.38 #3630, 0.37 #2302), 02l7c8 (0.30 #3522, 0.28 #6899, 0.28 #5935), 06n90 (0.27 #4727, 0.17 #614, 0.15 #1706), 04xvlr (0.26 #242, 0.23 #723, 0.23 #1087), 0lsxr (0.25 #4723, 0.23 #130, 0.21 #973), 03bxz7 (0.23 #295, 0.21 #175, 0.20 #415), 082gq (0.21 #1237, 0.20 #871, 0.19 #2812) >> Best rule #121 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 09tkzy; >> query: (?x7012, 07s9rl0) <- nominated_for(?x112, ?x7012), ?x112 = 027dtxw, written_by(?x7012, ?x3434), produced_by(?x7012, ?x6589) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #8817 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1550 *> proper extension: 07hpv3; 09kn9; 0n2bh; 0gfzgl; 01h72l; 03y3bp7; 01f3p_; 05sy2k_; 02648p; 02sqkh; ... *> query: (?x7012, ?x258) <- titles(?x1510, ?x7012), titles(?x1510, ?x1259), genre(?x1259, ?x258) *> conf = 0.06 ranks of expected_values: 29 EVAL 09hy79 genre 03q4nz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 82.000 82.000 0.821 http://example.org/film/film/genre #12093-09snz PRED entity: 09snz PRED relation: place! PRED expected values: 09snz => 174 concepts (126 used for prediction) PRED predicted values (max 10 best out of 301): 030qb3t (0.33 #30, 0.21 #42289, 0.21 #41773), 010v8k (0.21 #42289, 0.21 #41773, 0.20 #48485), 09snz (0.21 #42289, 0.21 #41773, 0.20 #48485), 04gxf (0.21 #42289, 0.21 #41773, 0.20 #48485), 0d9jr (0.12 #44870, 0.06 #1160, 0.05 #57260), 0fw1y (0.12 #44870, 0.06 #1524, 0.05 #57260), 02_286 (0.10 #529, 0.03 #2589, 0.01 #9292), 01sn3 (0.10 #608, 0.02 #7308, 0.01 #11435), 071cn (0.10 #595, 0.01 #11938, 0.01 #12969), 0k049 (0.10 #518, 0.01 #11861, 0.01 #14953) >> Best rule #30 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 030qb3t; >> query: (?x9141, 030qb3t) <- source(?x9141, ?x958), location(?x7831, ?x9141), ?x7831 = 0mz73, county(?x9141, ?x10733), ?x958 = 0jbk9 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #42289 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 189 *> proper extension: 0sqgt; *> query: (?x9141, ?x1523) <- source(?x9141, ?x958), location(?x7831, ?x9141), category(?x9141, ?x134), location(?x7831, ?x1523), film(?x7831, ?x5724) *> conf = 0.21 ranks of expected_values: 3 EVAL 09snz place! 09snz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 174.000 126.000 0.333 http://example.org/location/hud_county_place/place #12092-04swd PRED entity: 04swd PRED relation: locations! PRED expected values: 0l6vl => 259 concepts (215 used for prediction) PRED predicted values (max 10 best out of 130): 0bzrsh (0.25 #731, 0.15 #2679, 0.15 #2550), 0b_6qj (0.21 #2277, 0.13 #978, 0.12 #1496), 0bzrxn (0.20 #3429, 0.19 #1483, 0.18 #3687), 0b_6pv (0.20 #991, 0.16 #3455, 0.16 #2290), 0jzphpx (0.20 #150, 0.12 #408, 0.08 #669), 0b_6q5 (0.19 #1524, 0.17 #747, 0.13 #1006), 0b_6lb (0.19 #1506, 0.14 #7876, 0.13 #988), 0b_6xf (0.17 #758, 0.13 #1146, 0.13 #1017), 0b_6jz (0.17 #685, 0.13 #944, 0.12 #3408), 0b_6mr (0.17 #740, 0.13 #999, 0.10 #2688) >> Best rule #731 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0k049; >> query: (?x8745, 0bzrsh) <- place_of_death(?x10218, ?x8745), politician(?x14092, ?x10218), nationality(?x10218, ?x1603), jurisdiction_of_office(?x900, ?x8745) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #8835 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 0gclb; *> query: (?x8745, ?x391) <- capital(?x5114, ?x8745), olympics(?x5114, ?x391), official_language(?x5114, ?x5671) *> conf = 0.03 ranks of expected_values: 79 EVAL 04swd locations! 0l6vl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 259.000 215.000 0.250 http://example.org/time/event/locations #12091-021pqy PRED entity: 021pqy PRED relation: film_production_design_by PRED expected values: 07s9tsr => 82 concepts (49 used for prediction) PRED predicted values (max 10 best out of 18): 03cp7b3 (0.13 #57, 0.02 #88, 0.01 #370), 0dh73w (0.04 #8, 0.02 #195, 0.02 #70), 03qhyn8 (0.03 #60), 02x2t07 (0.02 #462, 0.01 #306, 0.01 #1187), 0d5wn3 (0.02 #824, 0.02 #323, 0.01 #700), 0bytkq (0.02 #257, 0.02 #68, 0.01 #413), 05b2gsm (0.02 #79, 0.01 #738, 0.01 #1149), 015npr (0.01 #973, 0.01 #1259, 0.01 #879), 04_1nk (0.01 #828), 03wd5tk (0.01 #642, 0.01 #108) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 0bz3jx; >> query: (?x4579, 03cp7b3) <- genre(?x4579, ?x53), titles(?x1882, ?x4579), countries_spoken_in(?x1882, ?x792), film(?x2065, ?x4579) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 021pqy film_production_design_by 07s9tsr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 49.000 0.128 http://example.org/film/film/film_production_design_by #12090-02bv9 PRED entity: 02bv9 PRED relation: service_language! PRED expected values: 07zl6m => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 155): 01c6k4 (0.83 #1408, 0.78 #707, 0.73 #1127), 064f29 (0.67 #764, 0.60 #344, 0.56 #624), 069b85 (0.60 #413, 0.45 #1113, 0.44 #833), 05b5c (0.60 #412, 0.33 #832, 0.33 #692), 0gvbw (0.60 #305, 0.33 #725, 0.33 #585), 018mxj (0.44 #710, 0.44 #570, 0.40 #290), 04fv0k (0.40 #368, 0.36 #1068, 0.33 #1489), 07zl6m (0.40 #417, 0.33 #837, 0.33 #697), 06q07 (0.40 #361, 0.33 #781, 0.33 #641), 02vk52z (0.40 #282, 0.33 #702, 0.33 #1) >> Best rule #1408 for best value: >> intensional similarity = 8 >> extensional distance = 10 >> proper extension: 02bjrlw; 06nm1; 06mp7; 06b_j; 01r2l; 05zjd; 02hwhyv; >> query: (?x7658, 01c6k4) <- languages(?x5597, ?x7658), service_language(?x9469, ?x7658), official_language(?x10190, ?x7658), countries_spoken_in(?x7658, ?x792), languages_spoken(?x3584, ?x7658), company(?x233, ?x9469), participating_countries(?x1931, ?x10190), industry(?x9469, ?x245) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #417 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 04306rv; 064_8sq; *> query: (?x7658, 07zl6m) <- languages(?x11601, ?x7658), languages(?x5597, ?x7658), service_language(?x9469, ?x7658), official_language(?x172, ?x7658), countries_spoken_in(?x7658, ?x792), ?x9469 = 04sv4, profession(?x11601, ?x319), language(?x1470, ?x7658), ?x5597 = 02pk6x *> conf = 0.40 ranks of expected_values: 8 EVAL 02bv9 service_language! 07zl6m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 40.000 40.000 0.833 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #12089-02_ssl PRED entity: 02_ssl PRED relation: position! PRED expected values: 02plv57 => 24 concepts (14 used for prediction) PRED predicted values (max 10 best out of 238): 0jmj7 (0.84 #82, 0.84 #50, 0.82 #100), 091tgz (0.84 #82, 0.84 #50, 0.82 #100), 02yjk8 (0.84 #82, 0.84 #50, 0.82 #100), 02pyyld (0.84 #82, 0.84 #50, 0.82 #100), 0jmdb (0.84 #82, 0.84 #50, 0.82 #100), 0jm4v (0.84 #82, 0.84 #50, 0.82 #100), 0jml5 (0.84 #82, 0.84 #50, 0.82 #100), 0jmhr (0.84 #82, 0.84 #50, 0.82 #100), 02pjzvh (0.50 #59, 0.33 #23, 0.25 #92), 02pzy52 (0.33 #46, 0.33 #9, 0.25 #96) >> Best rule #82 for best value: >> intensional similarity = 29 >> extensional distance = 3 >> proper extension: 01pv51; >> query: (?x6848, ?x799) <- team(?x6848, ?x6128), team(?x6848, ?x5419), team(?x6848, ?x2820), team(?x6848, ?x2398), team(?x6848, ?x799), team(?x6848, ?x660), ?x2398 = 0jmfb, school(?x2820, ?x10104), school(?x2820, ?x7596), school(?x2820, ?x5581), school(?x2820, ?x4846), school(?x2820, ?x4296), school(?x2820, ?x3513), school(?x2820, ?x347), ?x6128 = 0jm64, draft(?x2820, ?x2569), ?x660 = 0jmdb, ?x4296 = 07vyf, currency(?x10104, ?x170), ?x3513 = 0pspl, ?x5419 = 0jmmn, major_field_of_study(?x5581, ?x1154), ?x1154 = 02lp1, country(?x4846, ?x94), list(?x7596, ?x2197), major_field_of_study(?x7596, ?x7134), ?x7134 = 02_7t, student(?x347, ?x4558), institution(?x734, ?x7596) >> conf = 0.84 => this is the best rule for 8 predicted values *> Best rule #71 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 3 *> proper extension: 01pv51; *> query: (?x6848, 02plv57) <- team(?x6848, ?x6128), team(?x6848, ?x5419), team(?x6848, ?x2820), team(?x6848, ?x2398), team(?x6848, ?x660), ?x2398 = 0jmfb, school(?x2820, ?x10104), school(?x2820, ?x7596), school(?x2820, ?x5581), school(?x2820, ?x4846), school(?x2820, ?x4296), school(?x2820, ?x3513), school(?x2820, ?x347), ?x6128 = 0jm64, draft(?x2820, ?x2569), ?x660 = 0jmdb, ?x4296 = 07vyf, currency(?x10104, ?x170), ?x3513 = 0pspl, ?x5419 = 0jmmn, major_field_of_study(?x5581, ?x1154), ?x1154 = 02lp1, country(?x4846, ?x94), list(?x7596, ?x2197), major_field_of_study(?x7596, ?x7134), ?x7134 = 02_7t, student(?x347, ?x4558), institution(?x734, ?x7596) *> conf = 0.20 ranks of expected_values: 75 EVAL 02_ssl position! 02plv57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 24.000 14.000 0.840 http://example.org/sports/sports_team/roster./basketball/basketball_roster_position/position #12088-07b_l PRED entity: 07b_l PRED relation: religion PRED expected values: 04pk9 => 221 concepts (221 used for prediction) PRED predicted values (max 10 best out of 25): 04pk9 (0.79 #687, 0.74 #843, 0.74 #426), 058x5 (0.49 #1172, 0.46 #3004, 0.40 #678), 072w0 (0.49 #1172, 0.46 #3004, 0.26 #432), 03_gx (0.45 #500, 0.43 #1230, 0.42 #1256), 01s5nb (0.44 #431, 0.43 #692, 0.42 #509), 092bf5 (0.33 #163, 0.33 #7, 0.31 #267), 03j6c (0.33 #11, 0.25 #115, 0.25 #89), 0kpl (0.33 #3, 0.25 #107, 0.25 #81), 07w8f (0.33 #20, 0.25 #124, 0.25 #98), 02t7t (0.31 #273, 0.26 #1080, 0.26 #846) >> Best rule #687 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 015qh; 0162b; >> query: (?x3634, 04pk9) <- contains(?x3634, ?x216), religion(?x3634, ?x109), capital(?x3634, ?x3269) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07b_l religion 04pk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 221.000 221.000 0.786 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #12087-0ycp3 PRED entity: 0ycp3 PRED relation: artist! PRED expected values: 02bh8z => 78 concepts (56 used for prediction) PRED predicted values (max 10 best out of 133): 015_1q (0.46 #3470, 0.32 #4162, 0.27 #1259), 02p3cr5 (0.33 #163, 0.25 #25, 0.20 #301), 033hn8 (0.33 #151, 0.20 #565, 0.17 #1117), 01trtc (0.32 #484, 0.26 #898, 0.20 #346), 0mzkr (0.25 #23, 0.22 #161, 0.20 #299), 01dtcb (0.25 #45, 0.22 #735, 0.20 #597), 0181dw (0.23 #3493, 0.16 #4185, 0.13 #1834), 0g768 (0.23 #1139, 0.22 #1001, 0.20 #1553), 0k_kr (0.22 #180, 0.20 #318, 0.12 #42), 01t04r (0.22 #752, 0.16 #1304, 0.14 #1028) >> Best rule #3470 for best value: >> intensional similarity = 6 >> extensional distance = 267 >> proper extension: 01pfkw; >> query: (?x6876, 015_1q) <- artist(?x9224, ?x6876), award(?x6876, ?x247), artist(?x9224, ?x8149), artist(?x9224, ?x4620), ?x4620 = 01vsy7t, people(?x1816, ?x8149) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #571 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 18 *> proper extension: 05563d; 018gm9; 01v0sxx; 0ycfj; *> query: (?x6876, 02bh8z) <- artists(?x1572, ?x6876), artists(?x1000, ?x6876), ?x1572 = 06by7, ?x1000 = 0xhtw, group(?x645, ?x6876), artist(?x2149, ?x6876), ?x645 = 028tv0 *> conf = 0.20 ranks of expected_values: 12 EVAL 0ycp3 artist! 02bh8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 78.000 56.000 0.465 http://example.org/music/record_label/artist #12086-07y_7 PRED entity: 07y_7 PRED relation: role! PRED expected values: 02_jkc => 103 concepts (55 used for prediction) PRED predicted values (max 10 best out of 731): 050z2 (0.73 #10376, 0.67 #7132, 0.67 #4815), 082brv (0.71 #6744, 0.67 #4892, 0.64 #10453), 04bpm6 (0.67 #7018, 0.67 #4237, 0.64 #10262), 01wxdn3 (0.67 #4572, 0.57 #6888, 0.56 #7353), 0326tc (0.67 #4508, 0.57 #6824, 0.56 #7289), 04mx7s (0.60 #3137, 0.50 #4987, 0.33 #5450), 02s6sh (0.57 #6914, 0.50 #5525, 0.50 #5062), 01vs4ff (0.57 #6317, 0.50 #4465, 0.44 #7246), 01vsy7t (0.57 #6693, 0.50 #4841, 0.40 #2991), 01vrncs (0.57 #6050, 0.33 #5125, 0.33 #4198) >> Best rule #10376 for best value: >> intensional similarity = 11 >> extensional distance = 9 >> proper extension: 02sgy; 018j2; 07gql; >> query: (?x75, 050z2) <- role(?x75, ?x8957), role(?x75, ?x1147), role(?x75, ?x922), ?x1147 = 07kc_, role(?x922, ?x212), role(?x75, ?x2206), role(?x1887, ?x75), ?x8957 = 03f5mt, instrumentalists(?x75, ?x535), group(?x75, ?x1751), instrumentalists(?x2206, ?x669) >> conf = 0.73 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07y_7 role! 02_jkc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 55.000 0.727 http://example.org/music/artist/track_contributions./music/track_contribution/role #12085-01hb1t PRED entity: 01hb1t PRED relation: campuses! PRED expected values: 01hb1t => 138 concepts (81 used for prediction) PRED predicted values (max 10 best out of 313): 017z88 (0.17 #36078, 0.04 #38267, 0.04 #73), 01hb1t (0.17 #36078, 0.04 #38267, 0.03 #43191), 065y4w7 (0.17 #36078, 0.02 #1650), 02lv2v (0.04 #38267, 0.04 #845, 0.04 #299), 01p7x7 (0.04 #38267, 0.04 #969, 0.04 #423), 04ftdq (0.04 #38267, 0.04 #855, 0.04 #309), 02lwv5 (0.04 #38267, 0.04 #957, 0.04 #411), 01jzyx (0.04 #38267, 0.04 #716, 0.04 #170), 03zj9 (0.04 #38267, 0.04 #726, 0.04 #180), 02qw_v (0.04 #38267, 0.04 #937, 0.04 #391) >> Best rule #36078 for best value: >> intensional similarity = 3 >> extensional distance = 452 >> proper extension: 0ylzs; >> query: (?x3123, ?x735) <- student(?x3123, ?x65), institution(?x1200, ?x3123), student(?x735, ?x65) >> conf = 0.17 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01hb1t campuses! 01hb1t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 81.000 0.173 http://example.org/education/educational_institution/campuses #12084-06qd3 PRED entity: 06qd3 PRED relation: nationality! PRED expected values: 02_4fn => 160 concepts (67 used for prediction) PRED predicted values (max 10 best out of 4120): 02l4rh (0.22 #26554, 0.22 #22491, 0.14 #30616), 082mw (0.22 #26673, 0.22 #22610, 0.14 #30735), 01l_vgt (0.22 #25335, 0.22 #21272, 0.14 #29397), 02h761 (0.22 #25528, 0.22 #21465, 0.14 #29590), 04kj2v (0.22 #25059, 0.22 #20996, 0.14 #29121), 01kwld (0.22 #24513, 0.22 #20450, 0.14 #28575), 08c7cz (0.22 #26723, 0.22 #22660, 0.14 #30785), 048cl (0.22 #26696, 0.22 #22633, 0.14 #30758), 0jcx (0.22 #25321, 0.21 #29383, 0.17 #17196), 059xvg (0.22 #25425, 0.17 #17300, 0.14 #29487) >> Best rule #26554 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02j71; >> query: (?x1453, 02l4rh) <- currency(?x1453, ?x170), administrative_parent(?x9310, ?x1453), service_location(?x555, ?x1453), ?x555 = 01c6k4 >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06qd3 nationality! 02_4fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 67.000 0.222 http://example.org/people/person/nationality #12083-03lgtv PRED entity: 03lgtv PRED relation: profession! PRED expected values: 01pbs9w 01ycfv => 51 concepts (20 used for prediction) PRED predicted values (max 10 best out of 4219): 02qwg (0.75 #5254, 0.46 #8477, 0.35 #17975), 01vsl3_ (0.75 #5061, 0.42 #17782, 0.41 #22019), 01vw8mh (0.75 #5795, 0.35 #18516, 0.33 #22753), 03j24kf (0.62 #5747, 0.58 #18468, 0.56 #22705), 02fybl (0.62 #6573, 0.46 #19294, 0.44 #23531), 014q2g (0.62 #5053, 0.35 #17774, 0.33 #22011), 0161sp (0.62 #5096, 0.35 #17817, 0.33 #22054), 01k_n63 (0.62 #6641, 0.34 #4238, 0.33 #2403), 02cx90 (0.62 #5605, 0.34 #4238, 0.33 #1367), 01wp8w7 (0.62 #4642, 0.33 #404, 0.31 #17363) >> Best rule #5254 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 016z4k; 01d_h8; 0n1h; 02hrh1q; 09jwl; >> query: (?x13040, 02qwg) <- profession(?x1930, ?x13040), profession(?x1270, ?x13040), ?x1270 = 0137n0, award_winner(?x1930, ?x7995), ?x7995 = 0pj8m >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1885 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 0dz3r; *> query: (?x13040, 01pbs9w) <- profession(?x2124, ?x13040), profession(?x1930, ?x13040), profession(?x1270, ?x13040), ?x1270 = 0137n0, ?x1930 = 0gt_k, award_nominee(?x2124, ?x1089), award_winner(?x341, ?x2124) *> conf = 0.33 ranks of expected_values: 469, 1350 EVAL 03lgtv profession! 01ycfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 20.000 0.750 http://example.org/people/person/profession EVAL 03lgtv profession! 01pbs9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 20.000 0.750 http://example.org/people/person/profession #12082-0gs9p PRED entity: 0gs9p PRED relation: award! PRED expected values: 014zcr 06pj8 01_vfy 01j2xj 06n9lt 04ld94 02pv_d 03g62 => 71 concepts (29 used for prediction) PRED predicted values (max 10 best out of 2880): 0qf43 (0.82 #29448, 0.82 #13088, 0.78 #81812), 0c921 (0.82 #29448, 0.82 #13088, 0.78 #81812), 0bwh6 (0.82 #29448, 0.82 #13088, 0.78 #81812), 022_lg (0.82 #29448, 0.82 #13088, 0.78 #81812), 0p51w (0.82 #29448, 0.82 #13088, 0.78 #81812), 027vps (0.82 #29448, 0.82 #13088, 0.78 #81812), 01v80y (0.82 #29448, 0.82 #13088, 0.78 #81812), 0hskw (0.82 #29448, 0.82 #13088, 0.78 #81812), 06t8b (0.82 #29448, 0.82 #13088, 0.78 #81812), 015nvj (0.82 #29448, 0.82 #13088, 0.78 #81812) >> Best rule #29448 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 019f4v; 0k611; >> query: (?x1313, ?x276) <- nominated_for(?x1313, ?x2151), nominated_for(?x1313, ?x2112), ceremony(?x1313, ?x78), ?x2112 = 0bm2g, award_winner(?x1313, ?x276), film_release_region(?x2151, ?x87) >> conf = 0.82 => this is the best rule for 10 predicted values *> Best rule #19682 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 04dn09n; 02pqp12; 02ppm4q; *> query: (?x1313, 014zcr) <- nominated_for(?x1313, ?x414), nominated_for(?x1313, ?x161), ceremony(?x1313, ?x78), award(?x269, ?x1313), ?x161 = 0sxg4, ?x414 = 095zlp *> conf = 0.67 ranks of expected_values: 11, 12, 13, 19, 79, 399, 1195, 1200 EVAL 0gs9p award! 03g62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 71.000 29.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gs9p award! 02pv_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 71.000 29.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gs9p award! 04ld94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 71.000 29.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gs9p award! 06n9lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 71.000 29.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gs9p award! 01j2xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 71.000 29.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gs9p award! 01_vfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 71.000 29.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gs9p award! 06pj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 71.000 29.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gs9p award! 014zcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 71.000 29.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12081-07rd7 PRED entity: 07rd7 PRED relation: profession PRED expected values: 0dxtg => 138 concepts (137 used for prediction) PRED predicted values (max 10 best out of 81): 02hrh1q (0.88 #5524, 0.88 #6684, 0.86 #8714), 0dxtg (0.84 #301, 0.83 #156, 0.82 #4508), 0cbd2 (0.69 #1891, 0.52 #4648, 0.51 #3341), 0kyk (0.45 #1911, 0.35 #4668, 0.33 #3361), 018gz8 (0.39 #884, 0.37 #1754, 0.37 #2334), 09jwl (0.36 #596, 0.31 #9283, 0.30 #9720), 0nbcg (0.33 #608, 0.14 #2928, 0.13 #9893), 02krf9 (0.31 #314, 0.31 #169, 0.29 #749), 0np9r (0.31 #9283, 0.30 #9720, 0.23 #888), 0196pc (0.31 #9283, 0.30 #9720, 0.11 #650) >> Best rule #5524 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 049_zz; 02qfhb; 06s6hs; 0d02km; 0fqjhm; >> query: (?x4314, 02hrh1q) <- award_winner(?x1797, ?x4314), participant(?x2444, ?x4314), award_nominee(?x2691, ?x4314) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #301 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 0dr5y; *> query: (?x4314, 0dxtg) <- influenced_by(?x4314, ?x3028), film(?x4314, ?x8906), film(?x71, ?x8906) *> conf = 0.84 ranks of expected_values: 2 EVAL 07rd7 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 137.000 0.879 http://example.org/people/person/profession #12080-018ygt PRED entity: 018ygt PRED relation: film PRED expected values: 0gj8t_b 01qxc7 0yyn5 => 104 concepts (75 used for prediction) PRED predicted values (max 10 best out of 847): 049xgc (0.46 #4520, 0.34 #99532, 0.33 #72870), 04vr_f (0.38 #65759, 0.34 #99532, 0.33 #72870), 0d68qy (0.38 #65759, 0.34 #99532, 0.33 #72870), 01q_y0 (0.34 #99532, 0.33 #72870, 0.25 #49759), 02b6n9 (0.32 #10448, 0.08 #5117, 0.04 #8671), 07024 (0.25 #7587, 0.04 #9364, 0.03 #111982), 0418wg (0.21 #7509, 0.11 #2178, 0.04 #9286), 09xbpt (0.21 #7155, 0.07 #8932, 0.03 #111982), 01flv_ (0.20 #1060, 0.14 #9945, 0.08 #8168), 04cj79 (0.20 #592, 0.11 #2369, 0.08 #7700) >> Best rule #4520 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 07vc_9; 0pz91; 02qgyv; 0154qm; 0jmj; 016fnb; 016zp5; 01kgv4; >> query: (?x6324, 049xgc) <- award_nominee(?x5485, ?x6324), award_nominee(?x3557, ?x6324), ?x5485 = 01pk8v, award_nominee(?x906, ?x3557) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #7288 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 02p65p; 0bxtg; 032_jg; 01fwj8; 025n3p; 03jqw5; 0gy6z9; 013knm; 07yp0f; 0dzf_; ... *> query: (?x6324, 0gj8t_b) <- award_nominee(?x2422, ?x6324), ?x2422 = 0169dl, film(?x6324, ?x667) *> conf = 0.04 ranks of expected_values: 360, 573 EVAL 018ygt film 0yyn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 104.000 75.000 0.462 http://example.org/film/actor/film./film/performance/film EVAL 018ygt film 01qxc7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 75.000 0.462 http://example.org/film/actor/film./film/performance/film EVAL 018ygt film 0gj8t_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 104.000 75.000 0.462 http://example.org/film/actor/film./film/performance/film #12079-021y7yw PRED entity: 021y7yw PRED relation: nominated_for! PRED expected values: 0gr4k 04dn09n 0gqwc 0gqyl 0fq9zdv => 93 concepts (74 used for prediction) PRED predicted values (max 10 best out of 207): 0gq9h (0.49 #290, 0.45 #520, 0.39 #1671), 0k611 (0.46 #530, 0.43 #300, 0.28 #1681), 054krc (0.46 #526, 0.35 #296, 0.16 #2367), 019f4v (0.44 #513, 0.40 #283, 0.32 #1664), 04dn09n (0.43 #267, 0.34 #497, 0.26 #1648), 040njc (0.42 #469, 0.35 #239, 0.28 #1620), 02qvyrt (0.42 #552, 0.29 #322, 0.15 #1703), 0gq_v (0.40 #482, 0.25 #1172, 0.24 #252), 0gs9p (0.39 #292, 0.38 #522, 0.32 #2363), 02qyntr (0.38 #635, 0.37 #405, 0.21 #1786) >> Best rule #290 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0bth54; 04vr_f; 0c0nhgv; 0ch26b_; 0fpv_3_; 02qr69m; 01bb9r; 02vqsll; 0j43swk; 0gvs1kt; ... >> query: (?x2458, 0gq9h) <- nominated_for(?x1162, ?x2458), music(?x2458, ?x2940), language(?x2458, ?x254), ?x1162 = 099c8n >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #267 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 0bth54; 04vr_f; 0c0nhgv; 0ch26b_; 0fpv_3_; 02qr69m; 01bb9r; 02vqsll; 0j43swk; 0gvs1kt; ... *> query: (?x2458, 04dn09n) <- nominated_for(?x1162, ?x2458), music(?x2458, ?x2940), language(?x2458, ?x254), ?x1162 = 099c8n *> conf = 0.43 ranks of expected_values: 5, 13, 16, 18, 62 EVAL 021y7yw nominated_for! 0fq9zdv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 93.000 74.000 0.488 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 021y7yw nominated_for! 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 93.000 74.000 0.488 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 021y7yw nominated_for! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 93.000 74.000 0.488 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 021y7yw nominated_for! 04dn09n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 74.000 0.488 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 021y7yw nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 93.000 74.000 0.488 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12078-02x2gy0 PRED entity: 02x2gy0 PRED relation: award! PRED expected values: 02jxrw => 44 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1525): 017jd9 (0.60 #3515, 0.38 #9623, 0.38 #6566), 026p4q7 (0.50 #6346, 0.40 #3295, 0.36 #8384), 0pv3x (0.50 #2143, 0.40 #3162, 0.33 #107), 01xq8v (0.50 #1794, 0.33 #4850, 0.33 #776), 03hmt9b (0.46 #9555, 0.19 #13623, 0.18 #12605), 049xgc (0.40 #3620, 0.38 #6671, 0.33 #565), 0209hj (0.40 #3116, 0.33 #61, 0.31 #9224), 01cmp9 (0.40 #3666, 0.33 #611, 0.31 #9774), 05sbv3 (0.40 #4029, 0.33 #974, 0.31 #10137), 0m313 (0.40 #3061, 0.33 #6, 0.25 #7123) >> Best rule #3515 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0gq_v; 02hsq3m; >> query: (?x2489, 017jd9) <- nominated_for(?x2489, ?x7554), award(?x4190, ?x2489), costume_design_by(?x240, ?x4190), ceremony(?x2489, ?x762), ?x7554 = 01mgw >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7031 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 02r0csl; 0gqxm; 09v82c0; *> query: (?x2489, 02jxrw) <- nominated_for(?x2489, ?x7554), award(?x4190, ?x2489), costume_design_by(?x240, ?x4190), ceremony(?x2489, ?x762), award(?x7554, ?x77) *> conf = 0.25 ranks of expected_values: 103 EVAL 02x2gy0 award! 02jxrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 44.000 26.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12077-01mbwlb PRED entity: 01mbwlb PRED relation: program PRED expected values: 06hwzy => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 17): 06hwzy (0.36 #188, 0.34 #214, 0.33 #32), 01b7h8 (0.12 #45, 0.07 #71, 0.05 #201), 0cpz4k (0.11 #61, 0.08 #191, 0.07 #217), 01j7mr (0.10 #164, 0.07 #60, 0.06 #268), 026bfsh (0.08 #37, 0.07 #63, 0.04 #167), 0304nh (0.07 #62, 0.05 #218, 0.03 #270), 02zv4b (0.07 #55, 0.03 #211, 0.02 #263), 01h1bf (0.07 #215, 0.06 #267, 0.04 #59), 025ljp (0.04 #44, 0.03 #200, 0.02 #148), 070ltt (0.04 #72, 0.02 #176, 0.02 #228) >> Best rule #188 for best value: >> intensional similarity = 2 >> extensional distance = 57 >> proper extension: 01xdf5; 04t2l2; 03ds3; 014zfs; 01wdqrx; 01kvqc; 0mj1l; 01wj9y9; 06mmb; 0q5hw; ... >> query: (?x12194, 06hwzy) <- people(?x1816, ?x12194), person(?x3480, ?x12194) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mbwlb program 06hwzy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.356 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #12076-0c5vh PRED entity: 0c5vh PRED relation: people! PRED expected values: 02k6hp => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 46): 0gk4g (0.25 #74, 0.23 #334, 0.21 #1894), 0dq9p (0.23 #341, 0.18 #731, 0.18 #406), 02k6hp (0.23 #361, 0.12 #1011, 0.10 #491), 04p3w (0.18 #400, 0.15 #985, 0.11 #920), 02knxx (0.13 #746, 0.12 #1006, 0.12 #421), 0qcr0 (0.12 #976, 0.11 #911, 0.11 #2666), 01l2m3 (0.10 #145, 0.09 #210, 0.08 #340), 01_qc_ (0.10 #157, 0.09 #222, 0.04 #1262), 0dcqh (0.10 #182, 0.09 #247, 0.02 #1092), 01tf_6 (0.09 #225, 0.04 #1070, 0.03 #1785) >> Best rule #74 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 029cpw; 0223g8; >> query: (?x12065, 0gk4g) <- gender(?x12065, ?x231), film(?x12065, ?x5946), ?x5946 = 063zky >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #361 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 030pr; *> query: (?x12065, 02k6hp) <- type_of_union(?x12065, ?x566), people(?x1158, ?x12065), people(?x1050, ?x12065), person(?x7800, ?x12065) *> conf = 0.23 ranks of expected_values: 3 EVAL 0c5vh people! 02k6hp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 125.000 0.250 http://example.org/people/cause_of_death/people #12075-0bs8s1p PRED entity: 0bs8s1p PRED relation: film_release_region PRED expected values: 015qh 05b4w 03h64 012wgb => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 148): 03h64 (0.90 #203, 0.83 #638, 0.83 #1073), 03rt9 (0.89 #301, 0.84 #1026, 0.77 #591), 06t2t (0.85 #343, 0.79 #1068, 0.75 #923), 05b4w (0.82 #925, 0.82 #1070, 0.79 #635), 05v8c (0.79 #593, 0.76 #883, 0.76 #158), 04gzd (0.72 #1021, 0.63 #296, 0.57 #151), 047yc (0.69 #603, 0.67 #313, 0.64 #1038), 03rk0 (0.67 #1063, 0.63 #338, 0.56 #628), 01p1v (0.67 #1059, 0.52 #189, 0.52 #334), 01ls2 (0.67 #299, 0.63 #1024, 0.54 #589) >> Best rule #203 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0_92w; 0jqn5; 011yqc; 09k56b7; 0661ql3; 064lsn; 0g4vmj8; 0gvvm6l; >> query: (?x7009, 03h64) <- nominated_for(?x1198, ?x7009), film_release_region(?x7009, ?x172), ?x172 = 0154j, ?x1198 = 02pqp12 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 11, 41 EVAL 0bs8s1p film_release_region 012wgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 86.000 86.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bs8s1p film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bs8s1p film_release_region 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bs8s1p film_release_region 015qh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 86.000 86.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12074-06z8s_ PRED entity: 06z8s_ PRED relation: film_crew_role PRED expected values: 01pvkk => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 23): 0dxtw (0.37 #115, 0.37 #9, 0.34 #536), 01pvkk (0.30 #538, 0.29 #46, 0.28 #117), 01vx2h (0.28 #824, 0.27 #681, 0.27 #537), 02ynfr (0.21 #15, 0.21 #50, 0.16 #86), 01xy5l_ (0.21 #13, 0.21 #48, 0.09 #540), 0215hd (0.12 #545, 0.11 #124, 0.09 #689), 02rh1dz (0.10 #535, 0.10 #822, 0.09 #679), 089g0h (0.10 #546, 0.08 #690, 0.08 #1047), 0d2b38 (0.09 #131, 0.09 #552, 0.08 #696), 02_n3z (0.08 #36, 0.07 #528, 0.07 #672) >> Best rule #115 for best value: >> intensional similarity = 4 >> extensional distance = 159 >> proper extension: 0cnztc4; 064n1pz; 0crh5_f; 026njb5; 07l50vn; 08j7lh; >> query: (?x876, 0dxtw) <- genre(?x876, ?x604), ?x604 = 0lsxr, film_release_region(?x876, ?x94), film_crew_role(?x876, ?x137) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #538 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 528 *> proper extension: 0g5qmbz; 0bx_hnp; *> query: (?x876, 01pvkk) <- genre(?x876, ?x225), film(?x7903, ?x876), film_crew_role(?x876, ?x137) *> conf = 0.30 ranks of expected_values: 2 EVAL 06z8s_ film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 58.000 0.373 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12073-030p35 PRED entity: 030p35 PRED relation: nominated_for! PRED expected values: 01gvyp => 83 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1129): 07s8r0 (0.79 #51280, 0.78 #34961, 0.78 #23308), 050t68 (0.79 #51280, 0.78 #34961, 0.78 #23308), 0f721s (0.63 #37295, 0.59 #46619), 09d5h (0.28 #27969, 0.28 #23307, 0.26 #41956), 078jt5 (0.28 #13983, 0.05 #2969, 0.04 #5300), 019pkm (0.28 #13983, 0.04 #4255, 0.03 #41957), 0hvb2 (0.13 #37294, 0.10 #60604, 0.10 #370), 05yh_t (0.13 #37294, 0.10 #60604, 0.09 #67595), 01kb2j (0.13 #37294, 0.10 #60604, 0.09 #67595), 0c3p7 (0.13 #37294, 0.10 #60604, 0.09 #67595) >> Best rule #51280 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 0bx_hnp; >> query: (?x4639, ?x1641) <- nominated_for(?x190, ?x4639), award_winner(?x4639, ?x1641), languages(?x4639, ?x254) >> conf = 0.79 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 030p35 nominated_for! 01gvyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 46.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #12072-01nqfh_ PRED entity: 01nqfh_ PRED relation: artists! PRED expected values: 01z7dr 02v2lh => 111 concepts (56 used for prediction) PRED predicted values (max 10 best out of 219): 064t9 (0.49 #11205, 0.49 #10584, 0.49 #7161), 05bt6j (0.31 #10615, 0.29 #11236, 0.25 #7192), 016clz (0.30 #13061, 0.29 #5, 0.25 #7154), 05lls (0.26 #3431, 0.12 #5922, 0.11 #325), 06j6l (0.26 #7197, 0.25 #670, 0.25 #11241), 0xhtw (0.22 #638, 0.22 #13072, 0.22 #2500), 05r6t (0.21 #82, 0.12 #13138, 0.11 #704), 0155w (0.21 #11299, 0.20 #10678, 0.20 #13162), 06q6jz (0.21 #3604, 0.08 #498, 0.08 #1120), 02yv6b (0.19 #720, 0.16 #2582, 0.16 #7247) >> Best rule #11205 for best value: >> intensional similarity = 3 >> extensional distance = 275 >> proper extension: 01dw9z; 01svw8n; 019f9z; 04bbv7; >> query: (?x562, 064t9) <- profession(?x562, ?x563), artists(?x1572, ?x562), ?x1572 = 06by7 >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #6752 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 126 *> proper extension: 01rw116; *> query: (?x562, 02v2lh) <- nationality(?x562, ?x94), profession(?x562, ?x1614), profession(?x562, ?x1183), ?x1614 = 01c72t, ?x1183 = 09jwl *> conf = 0.05 ranks of expected_values: 69, 103 EVAL 01nqfh_ artists! 02v2lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 111.000 56.000 0.495 http://example.org/music/genre/artists EVAL 01nqfh_ artists! 01z7dr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 111.000 56.000 0.495 http://example.org/music/genre/artists #12071-09q_6t PRED entity: 09q_6t PRED relation: ceremony! PRED expected values: 054ks3 => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 333): 0gqy2 (0.56 #7045, 0.55 #8587, 0.54 #8074), 0gq_d (0.56 #7083, 0.53 #6826, 0.53 #8625), 0gqwc (0.56 #6983, 0.53 #8525, 0.52 #8012), 0k611 (0.55 #6995, 0.53 #8537, 0.52 #8024), 0gvx_ (0.53 #7060, 0.52 #8089, 0.52 #8602), 0f4x7 (0.53 #6949, 0.52 #7978, 0.51 #8491), 018wng (0.52 #7988, 0.52 #6959, 0.51 #8501), 0gqyl (0.52 #7003, 0.51 #8545, 0.50 #8032), 0p9sw (0.52 #6944, 0.51 #8486, 0.50 #7973), 0gs9p (0.52 #6985, 0.50 #8527, 0.49 #8014) >> Best rule #7045 for best value: >> intensional similarity = 12 >> extensional distance = 86 >> proper extension: 0fzrtf; >> query: (?x747, 0gqy2) <- award_winner(?x747, ?x2940), award_winner(?x747, ?x2258), honored_for(?x747, ?x7432), honored_for(?x747, ?x2928), nominated_for(?x143, ?x2928), award_winner(?x2928, ?x1933), profession(?x2258, ?x319), award_nominee(?x2258, ?x2499), award_winner(?x1972, ?x2258), award(?x2258, ?x375), film(?x1324, ?x7432), artists(?x497, ?x2940) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #511 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 02rjjll; 056878; *> query: (?x747, ?x2016) <- award_winner(?x747, ?x11884), award_winner(?x747, ?x7837), award_winner(?x747, ?x6913), ceremony(?x746, ?x747), student(?x4794, ?x11884), award_nominee(?x7837, ?x3751), type_of_union(?x11884, ?x566), gender(?x11884, ?x231), nominated_for(?x7837, ?x2215), profession(?x6913, ?x3342), ?x3342 = 04gc2, award_winner(?x2016, ?x6913), award(?x3751, ?x384), award_winner(?x7837, ?x3406) *> conf = 0.50 ranks of expected_values: 77 EVAL 09q_6t ceremony! 054ks3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 36.000 36.000 0.557 http://example.org/award/award_category/winners./award/award_honor/ceremony #12070-0s5cg PRED entity: 0s5cg PRED relation: location! PRED expected values: 01vw37m 02mpb => 103 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1678): 0glmv (0.68 #98050, 0.49 #77937, 0.48 #105590), 02kz_ (0.49 #85481, 0.49 #77937, 0.48 #105590), 0l15n (0.49 #77937, 0.48 #105590, 0.47 #35199), 05h72z (0.49 #77937, 0.48 #105590, 0.47 #35199), 023kzp (0.33 #1215, 0.09 #3729, 0.04 #31384), 01kph_c (0.33 #973, 0.09 #3487, 0.04 #31142), 033jkj (0.33 #885, 0.09 #3399, 0.03 #31054), 0c0k1 (0.33 #1767, 0.09 #4281, 0.02 #31936), 016tbr (0.33 #2075, 0.09 #4589, 0.02 #32244), 067sqt (0.33 #2305, 0.09 #4819, 0.02 #32474) >> Best rule #98050 for best value: >> intensional similarity = 4 >> extensional distance = 324 >> proper extension: 0sg6b; 0zygc; 0t_gg; 04kf4; 027wvb; 0hpyv; 034lk7; 0p9z5; 0_g_6; 01423b; ... >> query: (?x5037, ?x11437) <- contains(?x94, ?x5037), place_of_birth(?x11437, ?x5037), student(?x6611, ?x11437), location(?x11437, ?x1523) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #3784 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01_d4; 0sf9_; 0psxp; 0sbbq; 0s9z_; 0s987; 0s2z0; 0sbv7; 0s3pw; *> query: (?x5037, 01vw37m) <- location(?x1593, ?x5037), place_of_birth(?x3043, ?x5037), contains(?x3818, ?x5037), ?x3818 = 03v0t *> conf = 0.09 ranks of expected_values: 320 EVAL 0s5cg location! 02mpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 60.000 0.680 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0s5cg location! 01vw37m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 103.000 60.000 0.680 http://example.org/people/person/places_lived./people/place_lived/location #12069-04ydr95 PRED entity: 04ydr95 PRED relation: genre PRED expected values: 01jfsb => 87 concepts (46 used for prediction) PRED predicted values (max 10 best out of 92): 01jfsb (0.66 #2589, 0.62 #3880, 0.60 #4467), 05p553 (0.62 #822, 0.49 #1642, 0.39 #1407), 01hmnh (0.61 #4120, 0.36 #3651, 0.35 #1186), 02n4kr (0.50 #592, 0.25 #1177, 0.22 #2585), 03npn (0.40 #357, 0.25 #708, 0.18 #3054), 02l7c8 (0.40 #1535, 0.38 #833, 0.32 #2006), 06cvj (0.38 #821, 0.11 #1523, 0.11 #2111), 01drsx (0.33 #510, 0.25 #744, 0.03 #1917), 0lsxr (0.28 #2821, 0.27 #2586, 0.25 #125), 03g3w (0.25 #140, 0.20 #257, 0.17 #608) >> Best rule #2589 for best value: >> intensional similarity = 7 >> extensional distance = 134 >> proper extension: 011ydl; 0q9sg; 01sby_; 0y_yw; 0hv4t; 0hvvf; >> query: (?x3532, 01jfsb) <- genre(?x3532, ?x12924), genre(?x3532, ?x53), genre(?x7849, ?x12924), ?x7849 = 02z9rr, produced_by(?x3532, ?x2596), production_companies(?x3532, ?x1186), ?x53 = 07s9rl0 >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ydr95 genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 46.000 0.662 http://example.org/film/film/genre #12068-059fjj PRED entity: 059fjj PRED relation: profession PRED expected values: 02hrh1q => 126 concepts (125 used for prediction) PRED predicted values (max 10 best out of 65): 02hrh1q (0.90 #10078, 0.90 #8746, 0.89 #2086), 0dxtg (0.34 #161, 0.29 #8301, 0.29 #5637), 0cbd2 (0.27 #154, 0.16 #6518, 0.15 #5926), 0np9r (0.26 #14951, 0.21 #5496, 0.18 #3276), 02jknp (0.22 #155, 0.21 #303, 0.21 #12737), 09jwl (0.20 #3422, 0.20 #3570, 0.19 #8010), 0kyk (0.20 #177, 0.13 #6541, 0.13 #5949), 0d1pc (0.16 #494, 0.13 #1678, 0.13 #1974), 018gz8 (0.15 #1348, 0.15 #312, 0.14 #2680), 0dz3r (0.14 #6662, 0.13 #3554, 0.13 #8142) >> Best rule #10078 for best value: >> intensional similarity = 3 >> extensional distance = 1341 >> proper extension: 0ccqd7; 04j5fx; 02d6n_; >> query: (?x8113, 02hrh1q) <- location(?x8113, ?x739), profession(?x8113, ?x319), film(?x8113, ?x9060) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059fjj profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 125.000 0.903 http://example.org/people/person/profession #12067-030_1m PRED entity: 030_1m PRED relation: film PRED expected values: 04cbbz => 108 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1589): 02__34 (0.67 #25066, 0.65 #9398, 0.63 #21933), 04cbbz (0.67 #25066, 0.65 #9398, 0.63 #21933), 016z5x (0.67 #25066, 0.65 #9398, 0.63 #21933), 016z43 (0.67 #25066, 0.65 #9398, 0.63 #21933), 03459x (0.67 #25066, 0.65 #9398, 0.63 #21933), 03nsm5x (0.50 #2785, 0.33 #1219, 0.25 #4352), 02rrh1w (0.50 #2756, 0.33 #1190, 0.25 #4323), 02vrgnr (0.50 #2256, 0.33 #690, 0.25 #3823), 02wgk1 (0.50 #2235, 0.33 #669, 0.25 #3802), 065dc4 (0.50 #2140, 0.33 #574, 0.25 #3707) >> Best rule #25066 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0kx4m; 0hpt3; 046b0s; 0g5lhl7; 0kk9v; 031rq5; 025hwq; >> query: (?x1561, ?x69) <- nominated_for(?x1561, ?x723), award_nominee(?x1561, ?x1689), production_companies(?x69, ?x1561) >> conf = 0.67 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 030_1m film 04cbbz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 75.000 0.665 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #12066-07s9tsr PRED entity: 07s9tsr PRED relation: film_production_design_by! PRED expected values: 021pqy => 55 concepts (22 used for prediction) PRED predicted values (max 10 best out of 35): 0286hyp (0.10 #171, 0.07 #343, 0.03 #516), 029v40 (0.10 #158, 0.07 #330, 0.03 #503), 0p_rk (0.10 #139, 0.07 #311, 0.03 #484), 0hwpz (0.10 #134, 0.07 #306, 0.03 #479), 02n72k (0.10 #119, 0.07 #291, 0.03 #464), 0127ps (0.10 #104, 0.07 #276, 0.03 #449), 01npcx (0.10 #96, 0.07 #268, 0.03 #441), 01f8hf (0.10 #82, 0.07 #254, 0.03 #427), 02qzmz6 (0.10 #64, 0.07 #236, 0.03 #409), 03r0g9 (0.10 #62, 0.07 #234, 0.03 #407) >> Best rule #171 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 027rwmr; >> query: (?x12444, 0286hyp) <- profession(?x12444, ?x3197), film_crew_role(?x12403, ?x3197), film_crew_role(?x6752, ?x3197), ?x6752 = 065_cjc, ?x12403 = 03ntbmw >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07s9tsr film_production_design_by! 021pqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 22.000 0.100 http://example.org/film/film/film_production_design_by #12065-0dz46 PRED entity: 0dz46 PRED relation: influenced_by! PRED expected values: 042xh => 123 concepts (43 used for prediction) PRED predicted values (max 10 best out of 333): 05jm7 (0.11 #14407, 0.11 #3602, 0.09 #5287), 0p8jf (0.11 #14407, 0.11 #3602, 0.06 #2685), 067xw (0.11 #14407, 0.11 #3602, 0.03 #1314), 01hb6v (0.09 #3181, 0.08 #4726, 0.06 #13986), 040db (0.09 #10883, 0.09 #10369, 0.08 #11911), 0683n (0.08 #9602, 0.07 #10632, 0.07 #12174), 02yl42 (0.08 #2708, 0.08 #3737, 0.07 #4767), 0dz46 (0.07 #14408, 0.07 #9778, 0.07 #16985), 041h0 (0.07 #14408, 0.07 #9778, 0.07 #16985), 06hmd (0.07 #14408, 0.07 #9778, 0.07 #16985) >> Best rule #14407 for best value: >> intensional similarity = 2 >> extensional distance = 353 >> proper extension: 07scx; >> query: (?x8997, ?x3858) <- influenced_by(?x9794, ?x8997), influenced_by(?x3858, ?x9794) >> conf = 0.11 => this is the best rule for 3 predicted values *> Best rule #16983 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 436 *> proper extension: 0d6b7; *> query: (?x8997, ?x576) <- influenced_by(?x5034, ?x8997), influenced_by(?x5034, ?x8433), influenced_by(?x576, ?x8433) *> conf = 0.05 ranks of expected_values: 79 EVAL 0dz46 influenced_by! 042xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 123.000 43.000 0.112 http://example.org/influence/influence_node/influenced_by #12064-02x2khw PRED entity: 02x2khw PRED relation: school PRED expected values: 03hpkp => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 946): 065y4w7 (0.70 #1514, 0.62 #1507, 0.62 #1406), 07w0v (0.67 #1392, 0.67 #1298, 0.62 #1500), 0jkhr (0.57 #1179, 0.55 #1616, 0.50 #1034), 0lyjf (0.57 #1179, 0.50 #1556, 0.50 #1448), 0g8rj (0.57 #1179, 0.50 #1182, 0.50 #1131), 01lnyf (0.57 #1179, 0.46 #1611, 0.38 #1400), 02pptm (0.57 #1179, 0.46 #1611, 0.38 #1400), 02rv1w (0.57 #1179, 0.46 #1611, 0.35 #1395), 01hx2t (0.57 #1179, 0.46 #1611, 0.33 #840), 021w0_ (0.57 #1179, 0.46 #1611, 0.33 #842) >> Best rule #1514 for best value: >> intensional similarity = 57 >> extensional distance = 8 >> proper extension: 02qw1zx; >> query: (?x1161, 065y4w7) <- school(?x1161, ?x5907), school(?x1161, ?x3360), draft(?x8894, ?x1161), draft(?x4487, ?x1161), draft(?x3333, ?x1161), team(?x2010, ?x8894), school(?x8894, ?x5486), school(?x8894, ?x4556), school(?x8894, ?x735), major_field_of_study(?x3360, ?x7134), major_field_of_study(?x3360, ?x6756), ?x7134 = 02_7t, ?x6756 = 0_jm, colors(?x3360, ?x663), student(?x3360, ?x3917), draft(?x8894, ?x8786), profession(?x3917, ?x319), student(?x5907, ?x10097), student(?x5907, ?x3762), list(?x5907, ?x2197), colors(?x4556, ?x3621), award_winner(?x2124, ?x3917), school(?x3333, ?x1011), school(?x8786, ?x6856), participant(?x3917, ?x496), ?x1011 = 07w0v, school(?x4571, ?x5907), gender(?x3917, ?x231), sport(?x3333, ?x5063), colors(?x3333, ?x3189), category(?x3360, ?x134), organization(?x346, ?x4556), influenced_by(?x3917, ?x5208), ?x5486 = 0g8rj, major_field_of_study(?x5907, ?x2172), type_of_union(?x3917, ?x566), nationality(?x3917, ?x94), student(?x735, ?x65), school(?x3089, ?x735), currency(?x4556, ?x170), award(?x3917, ?x384), major_field_of_study(?x735, ?x373), ?x3089 = 03nt7j, fraternities_and_sororities(?x735, ?x3697), award_nominee(?x6324, ?x3917), state_province_region(?x5907, ?x760), film(?x3917, ?x2642), colors(?x8894, ?x8271), participant(?x3917, ?x1817), teams(?x659, ?x4487), award_nominee(?x722, ?x3762), profession(?x10097, ?x1581), institution(?x4981, ?x735), state_province_region(?x4556, ?x1782), ?x6856 = 0jkhr, ?x4981 = 03bwzr4, colors(?x817, ?x8271) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #211 for first EXPECTED value: *> intensional similarity = 75 *> extensional distance = 1 *> proper extension: 092j54; *> query: (?x1161, ?x2948) <- school(?x1161, ?x10572), school(?x1161, ?x5907), school(?x1161, ?x4161), school(?x1161, ?x3360), draft(?x8894, ?x1161), draft(?x4487, ?x1161), team(?x2010, ?x8894), school(?x8894, ?x5486), school(?x8894, ?x4556), school(?x8894, ?x1884), major_field_of_study(?x3360, ?x7134), major_field_of_study(?x3360, ?x6756), major_field_of_study(?x3360, ?x3489), ?x7134 = 02_7t, ?x6756 = 0_jm, colors(?x3360, ?x663), student(?x3360, ?x800), ?x5907 = 01jq4b, school_type(?x10572, ?x1044), contains(?x94, ?x3360), contains(?x3908, ?x10572), currency(?x10572, ?x170), ?x94 = 09c7w0, ?x170 = 09nqf, institution(?x620, ?x3360), colors(?x11415, ?x663), colors(?x10178, ?x663), colors(?x10038, ?x663), colors(?x9988, ?x663), colors(?x9108, ?x663), colors(?x6548, ?x663), colors(?x5941, ?x663), institution(?x734, ?x10572), colors(?x13580, ?x663), colors(?x13166, ?x663), colors(?x12526, ?x663), colors(?x12050, ?x663), colors(?x11312, ?x663), colors(?x10636, ?x663), colors(?x10248, ?x663), colors(?x8537, ?x663), colors(?x4511, ?x663), colors(?x2971, ?x663), colors(?x1115, ?x663), ?x9988 = 0pz6q, ?x5941 = 017v71, state_province_region(?x4556, ?x1782), ?x8537 = 02029f, ?x11312 = 03w7kx, school(?x2820, ?x10572), ?x1884 = 0bx8pn, category(?x4161, ?x134), student(?x4161, ?x1583), major_field_of_study(?x10572, ?x10417), ?x12526 = 0bg4f9, ?x6548 = 0yls9, school_type(?x4161, ?x1507), ?x12050 = 02k9k9, ?x10038 = 06rkfs, ?x10417 = 01r4k, ?x10636 = 04h54p, ?x13166 = 0j6tr, ?x1115 = 01y3c, ?x4511 = 01xn7x1, ?x13580 = 01_1kk, ?x3489 = 0193x, school(?x4487, ?x2948), major_field_of_study(?x5486, ?x4268), ?x4268 = 02822, student(?x5486, ?x118), ?x10178 = 01tntf, ?x11415 = 02j416, ?x10248 = 049dzz, ?x2971 = 04112r, ?x9108 = 01v3k2 *> conf = 0.25 ranks of expected_values: 90 EVAL 02x2khw school 03hpkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 17.000 17.000 0.700 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #12063-0f8l9c PRED entity: 0f8l9c PRED relation: film_release_region! PRED expected values: 0g56t9t 011yrp 04ddm4 03bx2lk 03twd6 0gj9tn5 06ztvyx 0k5g9 0g5838s 0gyfp9c 0gvs1kt 0c8qq 0gmgwnv 02qk3fk 0gfh84d 09v3jyg 04tng0 027m67 0btpm6 01mgw 02825nf 0gwlfnb 0gvt53w 01kqq7 047p798 => 276 concepts (109 used for prediction) PRED predicted values (max 10 best out of 999): 0gg8z1f (0.82 #24884, 0.78 #38911, 0.75 #23014), 0g5838s (0.82 #24557, 0.70 #61025, 0.67 #72246), 0gj9tn5 (0.81 #22573, 0.78 #38470, 0.76 #24443), 0btpm6 (0.78 #61449, 0.71 #72670, 0.71 #24981), 06ztvyx (0.76 #24514, 0.75 #22644, 0.74 #38541), 09v3jyg (0.76 #24939, 0.75 #23069, 0.62 #61407), 0gvs1kt (0.76 #24573, 0.73 #21767, 0.70 #38600), 0125xq (0.76 #24681, 0.72 #61149, 0.69 #22811), 011yrp (0.76 #24337, 0.63 #44910, 0.62 #60805), 03twd6 (0.75 #22552, 0.72 #60890, 0.71 #24422) >> Best rule #24884 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 05r4w; 0b90_r; 0154j; 03rjj; 03_3d; 0d0vqn; 015fr; 02_286; 0k6nt; 059j2; ... >> query: (?x789, 0gg8z1f) <- film_release_region(?x11218, ?x789), adjoins(?x789, ?x172), ?x11218 = 0ccck7 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #24557 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 05r4w; 0b90_r; 0154j; 03rjj; 03_3d; 0d0vqn; 015fr; 02_286; 0k6nt; 059j2; ... *> query: (?x789, 0g5838s) <- film_release_region(?x11218, ?x789), adjoins(?x789, ?x172), ?x11218 = 0ccck7 *> conf = 0.82 ranks of expected_values: 2, 3, 4, 5, 6, 7, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 23, 92, 99, 101, 149, 193, 196 EVAL 0f8l9c film_release_region! 047p798 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 01kqq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0gvt53w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0gwlfnb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 02825nf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 01mgw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0btpm6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 027m67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 04tng0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 09v3jyg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0gfh84d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 02qk3fk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0gmgwnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0c8qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0gvs1kt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0gyfp9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0g5838s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0k5g9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 06ztvyx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0gj9tn5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 03twd6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 03bx2lk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 04ddm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 011yrp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0f8l9c film_release_region! 0g56t9t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 276.000 109.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12062-0132k4 PRED entity: 0132k4 PRED relation: instrumentalists! PRED expected values: 05r5c 06ncr => 173 concepts (120 used for prediction) PRED predicted values (max 10 best out of 125): 0342h (0.69 #869, 0.69 #5540, 0.68 #4239), 05r5c (0.67 #355, 0.65 #441, 0.60 #1045), 026t6 (0.53 #6843, 0.36 #778, 0.35 #691), 04rzd (0.53 #6843, 0.36 #778, 0.35 #691), 05148p4 (0.48 #1832, 0.44 #367, 0.43 #4255), 01vj9c (0.42 #4322, 0.42 #1725, 0.41 #5190), 026g73 (0.36 #778, 0.35 #691, 0.33 #6933), 0239kh (0.36 #778, 0.35 #691, 0.33 #6933), 03gvt (0.28 #410, 0.15 #496, 0.15 #1100), 03qjg (0.26 #915, 0.21 #2383, 0.21 #1862) >> Best rule #869 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 020jqv; >> query: (?x6996, 0342h) <- artist(?x2299, ?x6996), profession(?x6996, ?x7998), profession(?x1376, ?x7998), ?x1376 = 01963w, instrumentalists(?x716, ?x6996) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #355 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 01w9ph_; *> query: (?x6996, 05r5c) <- artist(?x2299, ?x6996), profession(?x6996, ?x131), artists(?x505, ?x6996), people(?x12333, ?x6996), group(?x6996, ?x8429) *> conf = 0.67 ranks of expected_values: 2, 21 EVAL 0132k4 instrumentalists! 06ncr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 173.000 120.000 0.690 http://example.org/music/instrument/instrumentalists EVAL 0132k4 instrumentalists! 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 173.000 120.000 0.690 http://example.org/music/instrument/instrumentalists #12061-06mt91 PRED entity: 06mt91 PRED relation: award_winner! PRED expected values: 0hhtgcw => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 104): 013b2h (0.23 #768, 0.12 #906, 0.11 #630), 0gpjbt (0.22 #441, 0.17 #9662, 0.10 #717), 056878 (0.17 #9662, 0.17 #168, 0.17 #30), 02cg41 (0.17 #9662, 0.17 #675, 0.16 #951), 09n4nb (0.17 #9662, 0.17 #46, 0.12 #874), 0466p0j (0.17 #9662, 0.17 #74, 0.12 #902), 0hhtgcw (0.17 #9662, 0.17 #84, 0.11 #498), 01c6qp (0.17 #9662, 0.13 #707, 0.09 #5262), 019bk0 (0.17 #152, 0.17 #14, 0.11 #428), 01s695 (0.17 #554, 0.17 #140, 0.11 #416) >> Best rule #768 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 02wb6yq; >> query: (?x6835, 013b2h) <- artists(?x3996, ?x6835), award_winner(?x139, ?x6835), ?x3996 = 02lnbg >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #9662 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1352 *> proper extension: 01j53q; *> query: (?x6835, ?x139) <- award_winner(?x11402, ?x6835), award_winner(?x827, ?x6835), award_winner(?x139, ?x11402), award_nominee(?x827, ?x828) *> conf = 0.17 ranks of expected_values: 7 EVAL 06mt91 award_winner! 0hhtgcw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 117.000 117.000 0.231 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #12060-02knnd PRED entity: 02knnd PRED relation: location PRED expected values: 0fvzg => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 131): 02_286 (0.27 #37, 0.25 #841, 0.25 #17700), 030qb3t (0.25 #17745, 0.22 #2492, 0.22 #1689), 0r3tq (0.10 #15257, 0.03 #8575, 0.01 #5364), 01cx_ (0.09 #162, 0.08 #966, 0.03 #17825), 04jpl (0.08 #17680, 0.06 #6442, 0.06 #3230), 0f2r6 (0.07 #1640, 0.04 #2443, 0.03 #3246), 0cc56 (0.06 #17720, 0.04 #18522, 0.04 #8087), 0cr3d (0.06 #18609, 0.06 #8174, 0.06 #42675), 059rby (0.06 #17679, 0.05 #16, 0.04 #820), 04f_d (0.05 #4123, 0.03 #3320, 0.03 #6532) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 01l3j; >> query: (?x1357, 02_286) <- place_of_burial(?x1357, ?x3691), celebrities_impersonated(?x3649, ?x1357), nationality(?x1357, ?x94) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02knnd location 0fvzg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 81.000 0.273 http://example.org/people/person/places_lived./people/place_lived/location #12059-01ljpm PRED entity: 01ljpm PRED relation: campuses PRED expected values: 01ljpm => 150 concepts (112 used for prediction) PRED predicted values (max 10 best out of 501): 0bwfn (0.18 #60658, 0.06 #809, 0.03 #2993), 06182p (0.18 #60658, 0.02 #5200, 0.02 #9568), 08815 (0.18 #60658, 0.02 #8192, 0.02 #8738), 01ljpm (0.18 #60658, 0.02 #10922, 0.01 #43703), 02bqy (0.18 #60658, 0.01 #12192, 0.01 #13285), 033gn8 (0.18 #60658), 07wrz (0.12 #56, 0.04 #2240, 0.03 #3332), 07ccs (0.12 #208, 0.04 #2392, 0.03 #3484), 02w2bc (0.12 #10, 0.04 #2194, 0.03 #3286), 0ylsr (0.12 #258, 0.03 #3534, 0.02 #4626) >> Best rule #60658 for best value: >> intensional similarity = 4 >> extensional distance = 446 >> proper extension: 0yjf0; 023p18; 0ylzs; 0ym1n; 0301dp; >> query: (?x6501, ?x7545) <- institution(?x1368, ?x6501), student(?x6501, ?x4247), profession(?x4247, ?x955), student(?x7545, ?x4247) >> conf = 0.18 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 01ljpm campuses 01ljpm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 150.000 112.000 0.176 http://example.org/education/educational_institution/campuses #12058-06v41q PRED entity: 06v41q PRED relation: people PRED expected values: 023tp8 0320jz 029ghl => 16 concepts (7 used for prediction) PRED predicted values (max 10 best out of 1741): 0315q3 (0.33 #645, 0.20 #4051, 0.08 #7455), 01tpl1p (0.33 #1452, 0.10 #4858, 0.05 #6560), 073v6 (0.33 #442, 0.10 #3848, 0.04 #7252), 0p_2r (0.33 #180, 0.10 #3586, 0.03 #3406), 03dn9v (0.33 #1507, 0.10 #4913, 0.02 #6615), 01twmp (0.33 #1347, 0.10 #4753, 0.02 #6455), 02_t2t (0.33 #1158, 0.10 #4564, 0.02 #6266), 04mlmx (0.33 #1143, 0.10 #4549, 0.02 #6251), 0fn8jc (0.33 #998, 0.10 #4404, 0.02 #6106), 01trf3 (0.33 #579, 0.10 #3985, 0.02 #5687) >> Best rule #645 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0g8_vp; >> query: (?x7063, 0315q3) <- people(?x7063, ?x8544), people(?x7063, ?x6324), ?x8544 = 070yzk, religion(?x6324, ?x1985), student(?x5750, ?x6324), award(?x6324, ?x102), award_nominee(?x406, ?x6324) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7043 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 48 *> proper extension: 02w7gg; 065b6q; 041rx; 01qhm_; 033tf_; 09vc4s; 0x67; 07hwkr; 0xnvg; 03lmx1; ... *> query: (?x7063, 0320jz) <- people(?x7063, ?x8544), people(?x7063, ?x4964), gender(?x8544, ?x231), spouse(?x4536, ?x8544), film(?x4964, ?x857), award(?x4964, ?x102) *> conf = 0.04 ranks of expected_values: 408, 1121, 1603 EVAL 06v41q people 029ghl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 16.000 7.000 0.333 http://example.org/people/ethnicity/people EVAL 06v41q people 0320jz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 16.000 7.000 0.333 http://example.org/people/ethnicity/people EVAL 06v41q people 023tp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 16.000 7.000 0.333 http://example.org/people/ethnicity/people #12057-0ckd1 PRED entity: 0ckd1 PRED relation: film_crew_role! PRED expected values: 047vnkj 0dkv90 => 27 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1409): 0bth54 (0.82 #10139, 0.73 #15183, 0.73 #11399), 0270k40 (0.82 #11315, 0.73 #12575, 0.71 #8794), 07yk1xz (0.82 #10352, 0.67 #6571, 0.64 #11612), 05pbl56 (0.73 #10268, 0.70 #9007, 0.67 #15312), 05qbckf (0.73 #10319, 0.67 #15363, 0.67 #6538), 05m_jsg (0.73 #10561, 0.67 #6780, 0.60 #9300), 047vnkj (0.73 #10750, 0.67 #6969, 0.60 #9489), 076xkps (0.73 #12428, 0.64 #11168, 0.60 #16212), 05p1qyh (0.73 #11631, 0.64 #10371, 0.60 #15415), 08052t3 (0.71 #7865, 0.70 #9125, 0.67 #6605) >> Best rule #10139 for best value: >> intensional similarity = 17 >> extensional distance = 9 >> proper extension: 01xy5l_; 0d2b38; >> query: (?x632, 0bth54) <- film_crew_role(?x7844, ?x632), film_crew_role(?x7012, ?x632), film_crew_role(?x5128, ?x632), film_crew_role(?x4378, ?x632), film_crew_role(?x3507, ?x632), ?x5128 = 08phg9, produced_by(?x7012, ?x3434), nominated_for(?x1336, ?x7844), ?x4378 = 057lbk, film(?x2156, ?x7844), nominated_for(?x350, ?x3507), genre(?x7012, ?x812), titles(?x53, ?x7012), genre(?x3507, ?x225), nominated_for(?x112, ?x7012), film(?x400, ?x3507), language(?x7012, ?x254) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #10750 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 9 *> proper extension: 01xy5l_; 0d2b38; *> query: (?x632, 047vnkj) <- film_crew_role(?x7844, ?x632), film_crew_role(?x7012, ?x632), film_crew_role(?x5128, ?x632), film_crew_role(?x4378, ?x632), film_crew_role(?x3507, ?x632), ?x5128 = 08phg9, produced_by(?x7012, ?x3434), nominated_for(?x1336, ?x7844), ?x4378 = 057lbk, film(?x2156, ?x7844), nominated_for(?x350, ?x3507), genre(?x7012, ?x812), titles(?x53, ?x7012), genre(?x3507, ?x225), nominated_for(?x112, ?x7012), film(?x400, ?x3507), language(?x7012, ?x254) *> conf = 0.73 ranks of expected_values: 7, 739 EVAL 0ckd1 film_crew_role! 0dkv90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 19.000 0.818 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0ckd1 film_crew_role! 047vnkj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 27.000 19.000 0.818 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12056-0f67f PRED entity: 0f67f PRED relation: time_zones PRED expected values: 02hcv8 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.81 #55, 0.73 #29, 0.67 #16), 02fqwt (0.48 #1329, 0.22 #950, 0.22 #1159), 02lcqs (0.25 #239, 0.24 #265, 0.22 #174), 02hczc (0.22 #950, 0.22 #1159, 0.16 #1462), 02lcrv (0.22 #950, 0.22 #1159, 0.16 #1462), 042g7t (0.16 #1462, 0.16 #1516, 0.15 #1530), 02llzg (0.08 #940, 0.07 #1045, 0.07 #446), 03bdv (0.05 #500, 0.05 #422, 0.04 #396), 03plfd (0.02 #946, 0.02 #1155, 0.02 #1116), 05jphn (0.01 #429) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 0njvn; 0njdm; 0njpq; >> query: (?x7098, 02hcv8) <- source(?x7098, ?x958), contains(?x1906, ?x7098), ?x1906 = 04rrx >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f67f time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.815 http://example.org/location/location/time_zones #12055-01wttr1 PRED entity: 01wttr1 PRED relation: nationality PRED expected values: 09c7w0 => 94 concepts (86 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.78 #2580, 0.77 #1885, 0.77 #2085), 055vr (0.32 #3971, 0.25 #3972, 0.25 #3771), 0j0k (0.32 #3971, 0.25 #3972, 0.25 #3771), 07ssc (0.11 #1602, 0.11 #2495, 0.10 #2396), 02jx1 (0.11 #3407, 0.11 #1421, 0.11 #2216), 0d060g (0.05 #2091, 0.05 #4573, 0.04 #5367), 03rjj (0.03 #1294, 0.03 #1493, 0.03 #3379), 03rt9 (0.02 #1004, 0.02 #1104, 0.02 #1203), 05sb1 (0.02 #642, 0.02 #741, 0.01 #3970), 0chghy (0.02 #1200, 0.02 #1498, 0.02 #1795) >> Best rule #2580 for best value: >> intensional similarity = 4 >> extensional distance = 587 >> proper extension: 016_mj; >> query: (?x14044, 09c7w0) <- award_winner(?x10156, ?x14044), student(?x11607, ?x14044), state_province_region(?x11607, ?x12040), location(?x14044, ?x7412) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wttr1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 86.000 0.784 http://example.org/people/person/nationality #12054-01m7mv PRED entity: 01m7mv PRED relation: location! PRED expected values: 03x16f => 123 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1521): 070w7s (0.56 #7556, 0.54 #17629, 0.49 #115858), 03n6r (0.20 #50373, 0.14 #35258, 0.13 #50374), 09b6zr (0.20 #50373, 0.14 #35258, 0.13 #50374), 03x16f (0.20 #50373, 0.14 #35258, 0.13 #50374), 040z9 (0.20 #50373, 0.14 #35258, 0.13 #50374), 043js (0.20 #50373, 0.14 #35258, 0.13 #50374), 034ls (0.20 #50373, 0.14 #35258, 0.13 #50374), 0438pz (0.14 #35258, 0.13 #50374, 0.13 #65485), 02mpb (0.14 #35258, 0.13 #50374, 0.13 #65485), 02lt8 (0.12 #5833, 0.07 #15906, 0.05 #20943) >> Best rule #7556 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 01p726; >> query: (?x13533, ?x2811) <- contains(?x2020, ?x13533), place_of_birth(?x2811, ?x13533), citytown(?x13219, ?x13533), currency(?x13533, ?x170) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #50373 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 167 *> proper extension: 0mnsf; 01zk9d; *> query: (?x13533, ?x4196) <- citytown(?x13219, ?x13533), category(?x13219, ?x134), student(?x13219, ?x4196), location(?x4196, ?x108) *> conf = 0.20 ranks of expected_values: 4 EVAL 01m7mv location! 03x16f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 123.000 69.000 0.559 http://example.org/people/person/places_lived./people/place_lived/location #12053-019l3m PRED entity: 019l3m PRED relation: people! PRED expected values: 08g5q7 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 12): 0gk4g (0.05 #142, 0.04 #4234, 0.04 #406), 02k6hp (0.03 #169, 0.03 #367, 0.02 #499), 0dq9p (0.03 #545, 0.02 #2657, 0.02 #2195), 0j8hd (0.03 #113, 0.02 #47), 0qcr0 (0.02 #2377, 0.02 #4225, 0.02 #1915), 025hl8 (0.02 #6), 04p3w (0.02 #77, 0.02 #539, 0.01 #2189), 02knxx (0.02 #362, 0.01 #560, 0.01 #2012), 02y0js (0.01 #200, 0.01 #860, 0.01 #4226), 0m32h (0.01 #551) >> Best rule #142 for best value: >> intensional similarity = 3 >> extensional distance = 201 >> proper extension: 01ky2h; 0g51l1; 01vsy3q; 04z0g; 0c8br; 05d1y; 0f1jhc; 07hyk; 01g0jn; 0443c; >> query: (?x8946, 0gk4g) <- award_winner(?x1972, ?x8946), location(?x8946, ?x739), ?x739 = 02_286 >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 019l3m people! 08g5q7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 82.000 0.049 http://example.org/people/cause_of_death/people #12052-09gmmt6 PRED entity: 09gmmt6 PRED relation: film! PRED expected values: 073w14 => 140 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1387): 02f2dn (0.33 #4618, 0.04 #44215, 0.02 #27543), 0h5g_ (0.22 #4242, 0.04 #43839, 0.03 #89693), 02bfmn (0.20 #2110, 0.08 #10446, 0.07 #6278), 0f0kz (0.20 #2601, 0.07 #56789, 0.07 #8853), 0jfx1 (0.20 #2491, 0.05 #56679, 0.05 #60847), 0525b (0.20 #4000, 0.04 #12336, 0.04 #24841), 01xllf (0.20 #3809, 0.04 #12145, 0.04 #30902), 05sq84 (0.20 #2320, 0.04 #10656, 0.03 #56508), 015rkw (0.20 #2367, 0.04 #10703, 0.03 #75312), 016fjj (0.20 #2720, 0.04 #11056, 0.03 #19393) >> Best rule #4618 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 03j63k; >> query: (?x6536, 02f2dn) <- titles(?x7173, ?x6536), titles(?x512, ?x6536), nominated_for(?x11466, ?x6536), titles(?x7173, ?x7741), films(?x7173, ?x4464), ?x512 = 07ssc, film_release_distribution_medium(?x7741, ?x81) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #11180 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 22 *> proper extension: 0b2v79; 0191n; 01q7h2; 06bc59; 0ckrnn; 0422v0; *> query: (?x6536, 073w14) <- titles(?x571, ?x6536), film_release_region(?x6536, ?x87), film(?x902, ?x6536), ?x902 = 05qd_, genre(?x9786, ?x571), ?x9786 = 06bc59 *> conf = 0.08 ranks of expected_values: 53 EVAL 09gmmt6 film! 073w14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 140.000 89.000 0.333 http://example.org/film/actor/film./film/performance/film #12051-03h4fq7 PRED entity: 03h4fq7 PRED relation: film_crew_role PRED expected values: 09zzb8 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 24): 09zzb8 (0.81 #225, 0.77 #866, 0.75 #546), 02r96rf (0.77 #228, 0.72 #869, 0.71 #36), 01vx2h (0.50 #43, 0.38 #235, 0.35 #876), 0dxtw (0.42 #234, 0.40 #875, 0.40 #555), 02_n3z (0.36 #34, 0.10 #226, 0.10 #98), 01pvkk (0.28 #1231, 0.28 #461, 0.28 #1874), 02ynfr (0.23 #239, 0.18 #880, 0.18 #47), 015h31 (0.19 #40, 0.12 #8, 0.09 #681), 02rh1dz (0.16 #233, 0.13 #874, 0.13 #105), 033smt (0.15 #56, 0.07 #248, 0.05 #505) >> Best rule #225 for best value: >> intensional similarity = 3 >> extensional distance = 344 >> proper extension: 0gh6j94; >> query: (?x5113, 09zzb8) <- film_crew_role(?x5113, ?x1171), ?x1171 = 09vw2b7, featured_film_locations(?x5113, ?x1523) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03h4fq7 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.812 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12050-093l8p PRED entity: 093l8p PRED relation: nominated_for! PRED expected values: 099cng => 88 concepts (85 used for prediction) PRED predicted values (max 10 best out of 227): 0fbtbt (0.57 #625, 0.07 #2263, 0.04 #5540), 0ck27z (0.43 #539, 0.08 #17793, 0.06 #2177), 0bdw1g (0.43 #499, 0.07 #2137, 0.03 #5414), 0bdx29 (0.43 #551, 0.06 #2189, 0.04 #5466), 0bp_b2 (0.43 #485, 0.05 #2123, 0.05 #18733), 0fbvqf (0.43 #506, 0.05 #2144, 0.05 #18733), 0bdw6t (0.43 #552, 0.05 #2190, 0.03 #5467), 0gkts9 (0.43 #588, 0.05 #2226, 0.03 #5503), 0gq9h (0.43 #2636, 0.38 #5445, 0.37 #5679), 019f4v (0.40 #2628, 0.33 #5437, 0.33 #5671) >> Best rule #625 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02k_4g; 02rzdcp; 02pqs8l; 02qkq0; >> query: (?x7584, 0fbtbt) <- award_winner(?x7584, ?x516), nominated_for(?x618, ?x7584), award_nominee(?x3272, ?x516), ?x3272 = 07z1_q >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #5852 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 488 *> proper extension: 0c3xpwy; *> query: (?x7584, ?x618) <- award_winner(?x7584, ?x516), award(?x516, ?x618), honored_for(?x7573, ?x7584) *> conf = 0.29 ranks of expected_values: 34 EVAL 093l8p nominated_for! 099cng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 88.000 85.000 0.571 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12049-014zwb PRED entity: 014zwb PRED relation: country PRED expected values: 09c7w0 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 39): 09c7w0 (0.84 #185, 0.82 #796, 0.82 #983), 07ssc (0.22 #4194, 0.21 #444, 0.21 #2662), 0chghy (0.20 #196, 0.10 #440, 0.07 #562), 0345h (0.12 #2978, 0.12 #2673, 0.11 #2489), 0f8l9c (0.10 #325, 0.10 #2114, 0.10 #2665), 0d0vqn (0.06 #133, 0.06 #5526), 0k6nt (0.06 #144), 01z4y (0.06 #4912, 0.06 #3198, 0.06 #3135), 0d060g (0.06 #5526, 0.06 #5219, 0.06 #1794), 06mkj (0.06 #5526, 0.06 #5219, 0.03 #773) >> Best rule #185 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 02ht1k; 0dln8jk; 027gy0k; 0cbn7c; 0gzlb9; 05nyqk; >> query: (?x3071, 09c7w0) <- film_crew_role(?x3071, ?x137), ?x137 = 09zzb8, nominated_for(?x382, ?x3071), ?x382 = 086k8 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014zwb country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.840 http://example.org/film/film/country #12048-01ngz1 PRED entity: 01ngz1 PRED relation: institution! PRED expected values: 01gkg3 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.83 #50, 0.82 #26, 0.70 #193), 03bwzr4 (0.67 #61, 0.58 #37, 0.46 #85), 02_xgp2 (0.66 #35, 0.65 #59, 0.53 #83), 0bkj86 (0.53 #79, 0.52 #55, 0.43 #174), 016t_3 (0.50 #51, 0.45 #170, 0.44 #27), 07s6fsf (0.46 #48, 0.40 #24, 0.34 #167), 013zdg (0.46 #54, 0.40 #30, 0.17 #592), 04zx3q1 (0.44 #49, 0.32 #25, 0.30 #1102), 027f2w (0.30 #32, 0.29 #56, 0.24 #80), 01gkg3 (0.30 #1102, 0.29 #1695, 0.28 #1746) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 017j69; >> query: (?x1350, 02h4rq6) <- currency(?x1350, ?x170), ?x170 = 09nqf, major_field_of_study(?x1350, ?x4100), ?x4100 = 01lj9 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1102 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 371 *> proper extension: 02kth6; 07vk2; 01bvw5; 07xpm; 027xx3; 02183k; 018m5q; 01b1pf; 025v3k; 0pmcz; ... *> query: (?x1350, ?x734) <- colors(?x1350, ?x663), category(?x1350, ?x134), major_field_of_study(?x1350, ?x4100), major_field_of_study(?x734, ?x4100) *> conf = 0.30 ranks of expected_values: 10 EVAL 01ngz1 institution! 01gkg3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 133.000 133.000 0.827 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #12047-0d0x8 PRED entity: 0d0x8 PRED relation: location! PRED expected values: 023kzp => 221 concepts (176 used for prediction) PRED predicted values (max 10 best out of 2077): 0465_ (0.33 #3810, 0.11 #28964, 0.11 #26448), 03nb5v (0.27 #8867, 0.14 #11383, 0.11 #28991), 0x3b7 (0.25 #830, 0.10 #5860, 0.10 #36046), 02x_h0 (0.25 #1123, 0.05 #143371, 0.03 #36339), 0f0y8 (0.25 #10, 0.04 #57865, 0.03 #35226), 016srn (0.25 #602, 0.03 #35818, 0.03 #38333), 073x6y (0.25 #1365, 0.03 #36581, 0.03 #39096), 0382m4 (0.25 #1163, 0.03 #36379, 0.03 #38894), 03p01x (0.25 #2093, 0.03 #37309, 0.03 #39824), 03yk8z (0.25 #2008, 0.03 #37224, 0.03 #39739) >> Best rule #3810 for best value: >> intensional similarity = 2 >> extensional distance = 4 >> proper extension: 0j11; >> query: (?x3038, 0465_) <- first_level_division_of(?x3038, ?x94), olympics(?x3038, ?x1931) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #59070 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 0drs7; 013d_f; *> query: (?x3038, 023kzp) <- contains(?x3038, ?x10170), adjoins(?x3038, ?x2623), country(?x10170, ?x94) *> conf = 0.07 ranks of expected_values: 533 EVAL 0d0x8 location! 023kzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 221.000 176.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #12046-09hy79 PRED entity: 09hy79 PRED relation: film_crew_role PRED expected values: 09zzb8 089g0h => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.80 #720, 0.79 #312, 0.75 #1291), 01vx2h (0.53 #39, 0.45 #8, 0.42 #727), 01pvkk (0.35 #226, 0.31 #633, 0.30 #383), 094hwz (0.27 #12, 0.13 #43, 0.11 #1571), 015h31 (0.27 #37, 0.11 #725, 0.11 #1571), 02ynfr (0.21 #324, 0.18 #606, 0.17 #732), 01xy5l_ (0.20 #42, 0.12 #699, 0.12 #322), 02rh1dz (0.18 #7, 0.17 #726, 0.12 #318), 0d2b38 (0.18 #22, 0.13 #53, 0.12 #364), 089g0h (0.13 #47, 0.13 #327, 0.12 #358) >> Best rule #720 for best value: >> intensional similarity = 4 >> extensional distance = 373 >> proper extension: 0c40vxk; 0bh8yn3; 06v9_x; 0ckrgs; 0ct2tf5; >> query: (?x7012, 09zzb8) <- film(?x447, ?x7012), film_crew_role(?x7012, ?x2095), film(?x902, ?x7012), ?x2095 = 0dxtw >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 09hy79 film_crew_role 089g0h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 88.000 88.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09hy79 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12045-0jm3v PRED entity: 0jm3v PRED relation: sport PRED expected values: 018w8 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 8): 018w8 (0.85 #273, 0.84 #255, 0.83 #190), 039yzs (0.65 #424, 0.56 #128, 0.55 #138), 02vx4 (0.59 #334, 0.58 #343, 0.57 #445), 0jm_ (0.47 #371, 0.37 #381, 0.34 #362), 018jz (0.34 #364, 0.33 #133, 0.33 #86), 03tmr (0.22 #129, 0.16 #288, 0.16 #279), 0z74 (0.03 #286, 0.01 #395, 0.01 #489), 09xp_ (0.02 #293, 0.02 #497, 0.02 #329) >> Best rule #273 for best value: >> intensional similarity = 16 >> extensional distance = 24 >> proper extension: 0jmcb; 0jmmn; 0jmhr; >> query: (?x799, 018w8) <- team(?x5755, ?x799), team(?x4570, ?x799), ?x4570 = 03558l, school(?x799, ?x2948), position(?x11789, ?x5755), position(?x11168, ?x5755), position(?x2820, ?x5755), ?x11168 = 01k8vh, colors(?x11789, ?x332), team(?x13002, ?x11789), ?x2820 = 0jmj7, major_field_of_study(?x2948, ?x1154), team(?x2302, ?x11789), institution(?x1771, ?x2948), student(?x2948, ?x129), ?x1771 = 019v9k >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jm3v sport 018w8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.846 http://example.org/sports/sports_team/sport #12044-01lnyf PRED entity: 01lnyf PRED relation: school! PRED expected values: 04mjl => 209 concepts (209 used for prediction) PRED predicted values (max 10 best out of 87): 0jm2v (0.40 #15, 0.25 #102, 0.18 #189), 05m_8 (0.28 #612, 0.23 #699, 0.20 #351), 051vz (0.21 #455, 0.20 #368, 0.20 #281), 01slc (0.21 #663, 0.20 #315, 0.18 #576), 07l8x (0.20 #584, 0.12 #758, 0.11 #1045), 01d5z (0.20 #357, 0.20 #270, 0.19 #618), 01k8vh (0.20 #340, 0.16 #427, 0.14 #514), 0bwjj (0.20 #70, 0.12 #157, 0.10 #1027), 02896 (0.20 #2, 0.12 #89, 0.10 #263), 0jm74 (0.20 #56, 0.12 #143, 0.10 #317) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 077w0b; >> query: (?x4556, 0jm2v) <- organization(?x346, ?x4556), citytown(?x4556, ?x3521), state_province_region(?x4556, ?x1782), ?x1782 = 0488g >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #407 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 01tx9m; 012mzw; 015fsv; *> query: (?x4556, 04mjl) <- institution(?x9054, ?x4556), school_type(?x4556, ?x3092), school(?x700, ?x4556), contains(?x1782, ?x4556), ?x9054 = 022h5x *> conf = 0.12 ranks of expected_values: 29 EVAL 01lnyf school! 04mjl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 209.000 209.000 0.400 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #12043-04ld94 PRED entity: 04ld94 PRED relation: award PRED expected values: 019f4v 0gs9p => 96 concepts (77 used for prediction) PRED predicted values (max 10 best out of 269): 040njc (0.78 #4450, 0.78 #4052, 0.72 #31156), 027b9ly (0.72 #31156, 0.71 #28727, 0.70 #20231), 027c924 (0.72 #31156, 0.71 #28727, 0.70 #20231), 020qjg (0.70 #20231, 0.70 #20230, 0.70 #10116), 0gq9h (0.64 #4121, 0.28 #5746, 0.17 #6556), 019f4v (0.44 #4111, 0.36 #875, 0.35 #5736), 0gs9p (0.39 #4123, 0.36 #887, 0.34 #5748), 02rdyk7 (0.36 #1304, 0.36 #899, 0.33 #2921), 0f_nbyh (0.31 #4054, 0.12 #5679, 0.06 #6489), 0gr51 (0.28 #4144, 0.26 #5769, 0.15 #6983) >> Best rule #4450 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 07y_r; >> query: (?x5817, ?x198) <- profession(?x5817, ?x319), award_winner(?x198, ?x5817), ?x198 = 040njc >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4111 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 07y_r; *> query: (?x5817, 019f4v) <- profession(?x5817, ?x319), award_winner(?x198, ?x5817), ?x198 = 040njc *> conf = 0.44 ranks of expected_values: 6, 7 EVAL 04ld94 award 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 77.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04ld94 award 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 77.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12042-02rv_dz PRED entity: 02rv_dz PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.86 #118, 0.86 #153, 0.85 #92), 07z4p (0.17 #5, 0.04 #96, 0.03 #194), 0735l (0.16 #76), 07c52 (0.04 #192, 0.03 #84, 0.03 #187), 02nxhr (0.04 #72, 0.04 #323, 0.03 #380) >> Best rule #118 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 02q8ms8; 02825cv; >> query: (?x1531, 029j_) <- titles(?x2480, ?x1531), ?x2480 = 01z4y, nominated_for(?x617, ?x1531), language(?x1531, ?x254) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rv_dz film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12041-02p21g PRED entity: 02p21g PRED relation: film PRED expected values: 0f4_l 02pg45 => 168 concepts (104 used for prediction) PRED predicted values (max 10 best out of 954): 0cmf0m0 (0.41 #171751, 0.02 #12164, 0.01 #78359), 02_1sj (0.18 #1869, 0.12 #80, 0.06 #3658), 03q0r1 (0.18 #2426, 0.06 #4215, 0.05 #31050), 0q9b0 (0.12 #1274, 0.09 #3063, 0.03 #17375), 01shy7 (0.12 #423, 0.08 #16524, 0.06 #4001), 05fm6m (0.12 #1321, 0.04 #21000, 0.03 #17422), 01jrbb (0.12 #471, 0.03 #64877, 0.02 #98868), 04k9y6 (0.12 #1043, 0.03 #31456, 0.02 #47557), 05qbckf (0.12 #308, 0.02 #7464, 0.02 #9253), 0g9z_32 (0.12 #1278, 0.02 #12012, 0.02 #17379) >> Best rule #171751 for best value: >> intensional similarity = 3 >> extensional distance = 684 >> proper extension: 05ml_s; 0m31m; 0m32_; 02dbn2; 02clgg; 09nz_c; 07ddz9; 04f62k; 0pgm3; 045gzq; >> query: (?x1593, ?x8292) <- film(?x1593, ?x8794), profession(?x1593, ?x987), prequel(?x8292, ?x8794) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #148490 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 512 *> proper extension: 08b8vd; *> query: (?x1593, ?x814) <- participant(?x3117, ?x1593), film(?x1593, ?x1692), film(?x3117, ?x814) *> conf = 0.03 ranks of expected_values: 178, 181 EVAL 02p21g film 02pg45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 168.000 104.000 0.405 http://example.org/film/actor/film./film/performance/film EVAL 02p21g film 0f4_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 168.000 104.000 0.405 http://example.org/film/actor/film./film/performance/film #12040-0167_s PRED entity: 0167_s PRED relation: group! PRED expected values: 0342h 0l14md 05148p4 => 82 concepts (69 used for prediction) PRED predicted values (max 10 best out of 121): 0342h (0.93 #2302, 0.93 #2833, 0.90 #2479), 05148p4 (0.82 #1344, 0.80 #636, 0.80 #460), 0l14md (0.72 #1154, 0.70 #2305, 0.70 #889), 0l14qv (0.45 #887, 0.44 #1152, 0.39 #1683), 03qjg (0.35 #929, 0.29 #1813, 0.29 #1725), 01vj9c (0.35 #895, 0.29 #278, 0.28 #3373), 05r5c (0.29 #273, 0.25 #1333, 0.25 #713), 0l14j_ (0.29 #316, 0.25 #933, 0.20 #1198), 013y1f (0.29 #292, 0.25 #909, 0.20 #1174), 07y_7 (0.22 #354, 0.20 #90, 0.17 #1679) >> Best rule #2302 for best value: >> intensional similarity = 10 >> extensional distance = 89 >> proper extension: 04r1t; 08w4pm; 016l09; 0c9l1; 04k05; 0560w; 0jltp; >> query: (?x2250, 0342h) <- group(?x645, ?x2250), artists(?x1380, ?x2250), artists(?x1380, ?x9463), artists(?x1380, ?x7620), artists(?x1380, ?x6067), artists(?x1380, ?x1684), ?x9463 = 01shhf, category(?x7620, ?x134), ?x1684 = 01wv9xn, ?x6067 = 018y81 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0167_s group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 69.000 0.934 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0167_s group! 0l14md CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 69.000 0.934 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0167_s group! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 69.000 0.934 http://example.org/music/performance_role/regular_performances./music/group_membership/group #12039-07f5x PRED entity: 07f5x PRED relation: taxonomy PRED expected values: 04n6k => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.72 #3, 0.71 #38, 0.70 #27) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 0rh6k; 02_286; 04ykg; 01ly5m; 03hrz; >> query: (?x8948, 04n6k) <- administrative_parent(?x8948, ?x551), jurisdiction_of_office(?x182, ?x8948), teams(?x8948, ?x5433) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07f5x taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.725 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #12038-0308kx PRED entity: 0308kx PRED relation: student! PRED expected values: 017v3q => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 50): 0bwfn (0.20 #798, 0.08 #7098, 0.08 #10249), 04b_46 (0.13 #751, 0.06 #1276, 0.03 #10202), 017z88 (0.07 #81, 0.07 #606, 0.06 #1131), 01qgr3 (0.07 #265, 0.06 #1315), 053mhx (0.07 #293, 0.02 #2918, 0.02 #9218), 078bz (0.07 #76, 0.01 #26863, 0.01 #9001), 017hnw (0.07 #507), 02vnp2 (0.07 #356), 0trv (0.07 #842, 0.06 #1367), 07vjm (0.07 #752, 0.06 #1277) >> Best rule #798 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 01dw4q; 058ncz; 03zqc1; 06b0d2; 03lt8g; 05lb87; 030znt; 0443y3; 038g2x; 04psyp; ... >> query: (?x4149, 0bwfn) <- award_winner(?x4149, ?x1094), award_winner(?x515, ?x4149), ?x1094 = 035gjq >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0308kx student! 017v3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 82.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #12037-04sylm PRED entity: 04sylm PRED relation: campuses PRED expected values: 04sylm => 154 concepts (120 used for prediction) PRED predicted values (max 10 best out of 353): 017z88 (0.05 #73, 0.04 #619, 0.03 #1165), 02lv2v (0.05 #299, 0.04 #845, 0.03 #1391), 06182p (0.05 #286, 0.04 #832, 0.03 #1378), 03bmmc (0.05 #191, 0.04 #737, 0.03 #1283), 01p7x7 (0.05 #423, 0.04 #969, 0.03 #1515), 05njyy (0.05 #161, 0.04 #707, 0.03 #1253), 04ftdq (0.05 #309, 0.04 #855, 0.03 #1401), 021q2j (0.05 #313, 0.04 #859, 0.03 #1405), 04b_46 (0.05 #219, 0.04 #765, 0.03 #1311), 09k9d0 (0.05 #458, 0.04 #1004, 0.03 #1550) >> Best rule #73 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 0f94t; 0ccvx; >> query: (?x2767, 017z88) <- contains(?x739, ?x2767), contains(?x335, ?x2767), contains(?x94, ?x2767), ?x335 = 059rby, ?x739 = 02_286, ?x94 = 09c7w0 >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #59561 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 659 *> proper extension: 01swdw; 07vj4v; *> query: (?x2767, ?x1005) <- citytown(?x2767, ?x739), contains(?x739, ?x1005) *> conf = 0.03 ranks of expected_values: 22 EVAL 04sylm campuses 04sylm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 154.000 120.000 0.050 http://example.org/education/educational_institution/campuses #12036-0ywrc PRED entity: 0ywrc PRED relation: award PRED expected values: 0gr4k => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 196): 09d28z (0.35 #405, 0.13 #4426, 0.13 #3309), 0gr0m (0.27 #447, 0.26 #3352, 0.26 #3351), 02qvyrt (0.27 #447, 0.26 #3352, 0.26 #3351), 02qyntr (0.27 #447, 0.26 #3352, 0.26 #3351), 02pqp12 (0.27 #447, 0.26 #3352, 0.26 #3351), 027dtxw (0.27 #447, 0.26 #3352, 0.26 #3351), 03hl6lc (0.27 #447, 0.26 #3352, 0.26 #3351), 02r22gf (0.27 #447, 0.26 #3352, 0.26 #3351), 02r0csl (0.27 #447, 0.26 #3352, 0.26 #3351), 02hsq3m (0.27 #447, 0.26 #3352, 0.26 #3351) >> Best rule #405 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 0209xj; 0p_th; 09cr8; 012mrr; 0_816; 0hfzr; 0jqj5; 0k4p0; 0sxns; 02gd6x; ... >> query: (?x3157, 09d28z) <- nominated_for(?x1107, ?x3157), nominated_for(?x1063, ?x3157), ?x1107 = 019f4v, ?x1063 = 02rdxsh, award(?x3157, ?x2222) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #4267 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 213 *> proper extension: 0sxg4; 0yyg4; 0gzy02; 0ds11z; 04v8x9; 0n0bp; 0170_p; 020fcn; 0sxfd; 0cz_ym; ... *> query: (?x3157, 0gr4k) <- nominated_for(?x1107, ?x3157), nominated_for(?x1063, ?x3157), ?x1107 = 019f4v, nominated_for(?x1063, ?x1064), ?x1064 = 092vkg *> conf = 0.17 ranks of expected_values: 20 EVAL 0ywrc award 0gr4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 109.000 109.000 0.350 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #12035-03wj4r8 PRED entity: 03wj4r8 PRED relation: genre PRED expected values: 03bxz7 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 141): 05p553 (0.48 #3, 0.45 #121, 0.38 #475), 01jfsb (0.37 #1191, 0.33 #1547, 0.31 #1784), 02kdv5l (0.36 #1181, 0.33 #1537, 0.30 #1), 03k9fj (0.27 #482, 0.25 #1546, 0.24 #600), 06n90 (0.24 #12, 0.15 #1192, 0.15 #484), 0lsxr (0.20 #7, 0.17 #1662, 0.17 #2608), 01hmnh (0.19 #134, 0.18 #1552, 0.18 #488), 0hcr (0.17 #140, 0.09 #494, 0.08 #730), 0jtdp (0.17 #13, 0.03 #485, 0.03 #603), 060__y (0.16 #2616, 0.14 #1432, 0.14 #2380) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0f42nz; >> query: (?x8501, 05p553) <- nominated_for(?x3054, ?x8501), genre(?x8501, ?x53), person(?x8501, ?x5442) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2654 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1005 *> proper extension: 0413cff; 015qy1; *> query: (?x8501, 03bxz7) <- genre(?x8501, ?x53), ?x53 = 07s9rl0 *> conf = 0.09 ranks of expected_values: 20 EVAL 03wj4r8 genre 03bxz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 72.000 72.000 0.478 http://example.org/film/film/genre #12034-02cttt PRED entity: 02cttt PRED relation: institution! PRED expected values: 04zx3q1 014mlp => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.79 #183, 0.75 #23, 0.67 #443), 016t_3 (0.71 #24, 0.58 #84, 0.57 #64), 014mlp (0.69 #26, 0.68 #446, 0.67 #610), 0bkj86 (0.67 #29, 0.54 #69, 0.54 #89), 04zx3q1 (0.52 #22, 0.43 #62, 0.42 #82), 07s6fsf (0.48 #21, 0.43 #61, 0.41 #181), 027f2w (0.46 #30, 0.39 #70, 0.38 #90), 013zdg (0.29 #28, 0.28 #88, 0.26 #68), 0bjrnt (0.21 #27, 0.19 #67, 0.18 #87), 028dcg (0.19 #17, 0.16 #137, 0.15 #57) >> Best rule #183 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 01wqg8; >> query: (?x918, 02h4rq6) <- student(?x918, ?x919), institution(?x4981, ?x918), ?x4981 = 03bwzr4 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 05rznz; *> query: (?x918, 014mlp) <- contains(?x94, ?x918), category(?x918, ?x134), organization(?x918, ?x5487) *> conf = 0.69 ranks of expected_values: 3, 5 EVAL 02cttt institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 98.000 0.786 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02cttt institution! 04zx3q1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.786 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #12033-06vnh2 PRED entity: 06vnh2 PRED relation: place_of_birth PRED expected values: 018f94 => 51 concepts (32 used for prediction) PRED predicted values (max 10 best out of 55): 0156q (0.10 #2170, 0.06 #2874, 0.03 #761), 05qtj (0.07 #3690, 0.05 #6513, 0.04 #5807), 02_286 (0.07 #11311, 0.07 #10605, 0.06 #12017), 04jpl (0.06 #9180, 0.06 #4943, 0.05 #5648), 02h6_6p (0.06 #87, 0.05 #791, 0.04 #1495), 0h7h6 (0.06 #4287, 0.02 #9230, 0.01 #9937), 01v8c (0.05 #1394, 0.04 #2098, 0.04 #2803), 02z0j (0.04 #1739, 0.04 #331, 0.03 #1035), 06mxs (0.04 #3005, 0.01 #6534, 0.01 #7240), 0cm5m (0.04 #452, 0.03 #1156, 0.03 #1860) >> Best rule #2170 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 0lzb8; 01kwld; 04kj2v; 0hskw; 01dvtx; 0l9k1; 01h2_6; >> query: (?x10448, 0156q) <- nationality(?x10448, ?x1264), ?x1264 = 0345h >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #667 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 06y9c2; 0k4gf; 032v0v; 0j3v; 0136pk; 05whq_9; 0150t6; 0jcx; 04k15; 099bk; ... *> query: (?x10448, 018f94) <- gender(?x10448, ?x231), nationality(?x10448, ?x1264), ?x1264 = 0345h, ?x231 = 05zppz *> conf = 0.02 ranks of expected_values: 29 EVAL 06vnh2 place_of_birth 018f94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 51.000 32.000 0.096 http://example.org/people/person/place_of_birth #12032-0cwy47 PRED entity: 0cwy47 PRED relation: country PRED expected values: 09c7w0 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 101): 09c7w0 (0.86 #1314, 0.82 #477, 0.81 #1136), 0345h (0.39 #60, 0.34 #714, 0.17 #86), 0jgd (0.39 #60, 0.34 #714, 0.14 #297), 0d0vqn (0.39 #60, 0.34 #714, 0.14 #297), 02jx1 (0.38 #3577, 0.02 #620, 0.01 #2533), 03rjj (0.22 #67, 0.14 #297, 0.14 #3815), 0f8l9c (0.14 #297, 0.14 #3815, 0.13 #4470), 03_3d (0.14 #297, 0.14 #3815, 0.13 #4470), 0d05w3 (0.14 #297, 0.14 #3815, 0.13 #4470), 0chghy (0.14 #297, 0.14 #3815, 0.13 #4470) >> Best rule #1314 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 02y_lrp; 0g22z; 09m6kg; 011yxg; 016fyc; 01k1k4; 034qrh; 0ds33; 0bth54; 02x3lt7; ... >> query: (?x951, 09c7w0) <- award_winner(?x951, ?x3237), produced_by(?x951, ?x5438), music(?x951, ?x7857), featured_film_locations(?x951, ?x362) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cwy47 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.857 http://example.org/film/film/country #12031-06wrt PRED entity: 06wrt PRED relation: sports! PRED expected values: 0ldqf => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 21): 0lbd9 (0.82 #1030, 0.82 #1032, 0.81 #1031), 0kbws (0.81 #160, 0.79 #92, 0.78 #182), 0ldqf (0.80 #451, 0.78 #406, 0.75 #359), 0c_tl (0.72 #70, 0.70 #69, 0.60 #170), 09n48 (0.72 #70, 0.70 #69, 0.53 #93), 0swff (0.72 #70, 0.70 #69, 0.53 #93), 0swbd (0.72 #70, 0.70 #69, 0.53 #93), 0sx8l (0.72 #70, 0.70 #69, 0.53 #93), 0sx7r (0.72 #70, 0.70 #69, 0.53 #93), 018wrk (0.72 #70, 0.70 #69, 0.53 #93) >> Best rule #1030 for best value: >> intensional similarity = 39 >> extensional distance = 50 >> proper extension: 04lgq; >> query: (?x2315, ?x391) <- sports(?x6464, ?x2315), sports(?x2369, ?x2315), sports(?x391, ?x2315), olympics(?x7430, ?x2369), olympics(?x2236, ?x6464), olympics(?x583, ?x6464), film_release_region(?x8176, ?x583), film_release_region(?x3986, ?x583), film_release_region(?x3606, ?x583), film_release_region(?x2655, ?x583), film_release_region(?x2189, ?x583), film_release_region(?x1868, ?x583), film_release_region(?x343, ?x583), adjoins(?x410, ?x583), ?x1868 = 0cc7hmk, sports(?x6464, ?x766), medal(?x6464, ?x422), ?x2655 = 0fpmrm3, currency(?x583, ?x170), ?x343 = 0gx1bnj, olympics(?x7430, ?x1741), ?x3986 = 0jymd, olympics(?x7430, ?x1617), ?x3606 = 0gh65c5, titles(?x583, ?x7081), combatants(?x7430, ?x1353), ?x1617 = 01f1jy, combatants(?x326, ?x7430), adjoins(?x2236, ?x2146), film_release_region(?x1259, ?x7430), official_language(?x2236, ?x254), ?x2189 = 02yvct, ?x8176 = 0gvvm6l, film_release_region(?x124, ?x2236), country(?x453, ?x7430), jurisdiction_of_office(?x182, ?x583), country(?x150, ?x583), combatants(?x12844, ?x2236), contains(?x583, ?x1167) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #451 for first EXPECTED value: *> intensional similarity = 46 *> extensional distance = 8 *> proper extension: 07jjt; *> query: (?x2315, 0ldqf) <- olympics(?x2315, ?x778), country(?x2315, ?x1790), country(?x2315, ?x583), country(?x2315, ?x429), country(?x2315, ?x94), sports(?x2369, ?x2315), sports(?x1081, ?x2315), sports(?x391, ?x2315), ?x391 = 0l6vl, film_release_region(?x5644, ?x1790), film_release_region(?x5564, ?x1790), film_release_region(?x2954, ?x1790), film_release_region(?x2163, ?x1790), official_language(?x1790, ?x5814), entity_involved(?x14661, ?x1790), ?x1081 = 0l6m5, ?x2954 = 0crh5_f, ?x2369 = 0lbbj, ?x2163 = 0j6b5, film_release_region(?x7700, ?x583), film_release_region(?x6492, ?x583), film_release_region(?x3377, ?x583), film_release_region(?x2094, ?x583), film_release_region(?x1283, ?x583), film_release_region(?x1080, ?x583), film_release_region(?x634, ?x583), film_release_region(?x86, ?x583), ?x1080 = 01c22t, adjustment_currency(?x583, ?x170), ?x5564 = 03yvf2, country(?x3507, ?x583), jurisdiction_of_office(?x182, ?x583), teams(?x583, ?x12089), ?x7700 = 0cp08zg, ?x1283 = 0cnztc4, ?x429 = 03rt9, ?x86 = 0ds35l9, ?x3377 = 0gj8nq2, ?x634 = 0gx9rvq, adjoins(?x1790, ?x2979), ?x94 = 09c7w0, ?x5644 = 0dll_t2, combatants(?x1790, ?x756), ?x6492 = 0ds6bmk, combatants(?x326, ?x1790), ?x2094 = 05z7c *> conf = 0.80 ranks of expected_values: 3 EVAL 06wrt sports! 0ldqf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 47.000 47.000 0.817 http://example.org/olympics/olympic_games/sports #12030-01w724 PRED entity: 01w724 PRED relation: languages PRED expected values: 02h40lc => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 4): 02h40lc (0.19 #158, 0.18 #80, 0.18 #3083), 064_8sq (0.02 #3096, 0.02 #1965, 0.02 #561), 03k50 (0.02 #3085, 0.01 #1642, 0.01 #1525), 02bjrlw (0.01 #781, 0.01 #547) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 04k05; 06lxn; >> query: (?x2765, 02h40lc) <- artist(?x7448, ?x2765), award_winner(?x2765, ?x1089), people(?x11490, ?x1089) >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w724 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.188 http://example.org/people/person/languages #12029-0cw4l PRED entity: 0cw4l PRED relation: location! PRED expected values: 0cfz_z => 213 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1316): 02xgdv (0.33 #6453, 0.20 #3934, 0.10 #31643), 02qvhbb (0.33 #2440, 0.12 #9997, 0.10 #32668), 03fwln (0.20 #12237, 0.18 #17275, 0.17 #7199), 040wdl (0.20 #2862, 0.17 #5381, 0.08 #20495), 05_zc7 (0.20 #4582, 0.17 #7101, 0.08 #22215), 0265z9l (0.20 #3949, 0.17 #6468, 0.08 #21582), 027lfrs (0.20 #5031, 0.10 #12588, 0.09 #15107), 047s_cr (0.18 #17536, 0.12 #9979, 0.09 #15017), 0tj9 (0.17 #7480, 0.10 #12518, 0.10 #32670), 01vzz1c (0.17 #7300, 0.10 #12338, 0.09 #17376) >> Best rule #6453 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0f1_p; 075_t2; 01hpnh; 055vr; >> query: (?x12193, 02xgdv) <- contains(?x12193, ?x10315), adjoins(?x12193, ?x3411), country(?x12193, ?x2146), ?x2146 = 03rk0 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cw4l location! 0cfz_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 213.000 86.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #12028-028qdb PRED entity: 028qdb PRED relation: gender PRED expected values: 05zppz => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #25, 0.85 #3, 0.83 #33), 02zsn (0.51 #155, 0.25 #98, 0.25 #2) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 188 >> proper extension: 01r4zfk; >> query: (?x4206, 05zppz) <- type_of_union(?x4206, ?x566), ?x566 = 04ztj, role(?x4206, ?x316) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028qdb gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.853 http://example.org/people/person/gender #12027-0jvt9 PRED entity: 0jvt9 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #74, 0.81 #262, 0.80 #27), 02nxhr (0.21 #341, 0.04 #140, 0.03 #223), 07z4p (0.21 #341, 0.04 #15, 0.03 #226), 07c52 (0.21 #341, 0.03 #53, 0.03 #224) >> Best rule #74 for best value: >> intensional similarity = 3 >> extensional distance = 334 >> proper extension: 015g28; 08cfr1; >> query: (?x3294, 029j_) <- film(?x2416, ?x3294), award(?x3294, ?x500), featured_film_locations(?x3294, ?x3983) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jvt9 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #12026-02p7_k PRED entity: 02p7_k PRED relation: location PRED expected values: 07b_l => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 38): 030qb3t (0.27 #83, 0.13 #8929, 0.13 #8125), 02_286 (0.18 #37, 0.15 #7274, 0.13 #10491), 0d6lp (0.09 #168, 0.07 #972, 0.05 #1776), 059rby (0.09 #16, 0.03 #4840, 0.03 #4036), 01n7q (0.09 #63, 0.02 #10517, 0.02 #4083), 07_fl (0.09 #567), 0b2lw (0.09 #350), 0t0n5 (0.09 #294), 0fpzwf (0.09 #282), 05tbn (0.09 #188) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 02m501; >> query: (?x3660, 030qb3t) <- award(?x3660, ?x704), film(?x3660, ?x3093), ?x3093 = 04tqtl >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02p7_k location 07b_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 64.000 0.273 http://example.org/people/person/places_lived./people/place_lived/location #12025-0ds33 PRED entity: 0ds33 PRED relation: film! PRED expected values: 025t9b => 100 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1130): 0151w_ (0.70 #45706, 0.66 #51941, 0.65 #87272), 0h7pj (0.66 #51941, 0.65 #87272, 0.63 #89351), 01xndd (0.43 #114278, 0.40 #97659, 0.40 #45705), 02zj61 (0.43 #114278, 0.40 #97659, 0.40 #45705), 02qzjj (0.43 #114278, 0.40 #97659, 0.40 #45705), 020h2v (0.43 #114278, 0.40 #97659, 0.40 #45705), 01t6b4 (0.38 #35313, 0.29 #4154, 0.28 #16615), 09zw90 (0.25 #4153, 0.23 #35312, 0.20 #16614), 09wj5 (0.22 #4254, 0.20 #10482, 0.12 #6330), 0jfx1 (0.22 #4557, 0.20 #10785, 0.12 #6633) >> Best rule #45706 for best value: >> intensional similarity = 4 >> extensional distance = 279 >> proper extension: 0clpml; >> query: (?x508, ?x989) <- nominated_for(?x8898, ?x508), nominated_for(?x989, ?x508), spouse(?x2763, ?x8898), participant(?x287, ?x989) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #6894 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 018js4; 03kg2v; 023gxx; 033srr; 05nlx4; 07bx6; *> query: (?x508, 025t9b) <- produced_by(?x508, ?x1285), ?x1285 = 01t6b4, production_companies(?x508, ?x7980), film(?x368, ?x508) *> conf = 0.06 ranks of expected_values: 111 EVAL 0ds33 film! 025t9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 100.000 71.000 0.703 http://example.org/film/actor/film./film/performance/film #12024-0gthm PRED entity: 0gthm PRED relation: people! PRED expected values: 0dq9p => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 35): 0gk4g (0.20 #10, 0.13 #1066, 0.12 #406), 06z5s (0.12 #421, 0.04 #2005, 0.03 #1807), 0qcr0 (0.07 #529, 0.05 #463, 0.04 #1123), 07jwr (0.06 #405, 0.05 #1065, 0.03 #2385), 01tf_6 (0.06 #427, 0.02 #2209, 0.02 #2671), 01n3bm (0.06 #439, 0.01 #2419, 0.01 #835), 09d11 (0.06 #416, 0.01 #812), 01k9gb (0.06 #459), 01l2m3 (0.06 #808, 0.04 #544, 0.02 #874), 0dq9p (0.06 #1007, 0.05 #479, 0.05 #3714) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01mvpv; >> query: (?x9854, 0gk4g) <- student(?x2064, ?x9854), profession(?x9854, ?x353), ?x2064 = 01cyd5 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1007 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 07s3vqk; 04wqr; 02lk1s; 0pz91; 01nczg; 0gt_k; 01vb403; 01w60_p; 0f0kz; 01w7nww; ... *> query: (?x9854, 0dq9p) <- film(?x9854, ?x1444), influenced_by(?x1725, ?x9854), profession(?x9854, ?x353) *> conf = 0.06 ranks of expected_values: 10 EVAL 0gthm people! 0dq9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 118.000 118.000 0.200 http://example.org/people/cause_of_death/people #12023-049k07 PRED entity: 049k07 PRED relation: award_winner! PRED expected values: 06mmb => 87 concepts (42 used for prediction) PRED predicted values (max 10 best out of 499): 04bdxl (0.81 #67296, 0.81 #64088, 0.81 #36848), 049k07 (0.28 #48065, 0.26 #28837, 0.14 #22429), 048lv (0.28 #48065, 0.26 #28837, 0.14 #22429), 0c3jz (0.28 #48065, 0.26 #28837, 0.14 #22429), 02s2ft (0.28 #48065, 0.26 #28837, 0.11 #67295), 09r9dp (0.28 #48065, 0.26 #28837, 0.03 #8633), 02bkdn (0.28 #48065, 0.26 #28837, 0.02 #9898), 0f6_dy (0.28 #48065, 0.26 #28837, 0.01 #8334), 06_bq1 (0.28 #48065, 0.26 #28837, 0.01 #4353), 018swb (0.28 #48065, 0.26 #28837, 0.01 #8333) >> Best rule #67296 for best value: >> intensional similarity = 2 >> extensional distance = 1477 >> proper extension: 04nw9; 01t2h2; 01vb403; 09d5h; 0h1p; 03wpmd; 017vkx; 07g7h2; 0gv2r; 01m3b1t; ... >> query: (?x1773, ?x1460) <- award_nominee(?x1773, ?x91), award_winner(?x1773, ?x1460) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #48065 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1145 *> proper extension: 0gsg7; 0cjdk; 01zcrv; *> query: (?x1773, ?x2559) <- nominated_for(?x1773, ?x1861), award_winner(?x8691, ?x1773), award_winner(?x2559, ?x8691) *> conf = 0.28 ranks of expected_values: 12 EVAL 049k07 award_winner! 06mmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 87.000 42.000 0.813 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #12022-0d61px PRED entity: 0d61px PRED relation: film_crew_role PRED expected values: 09vw2b7 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.75 #43, 0.72 #296, 0.70 #805), 09vw2b7 (0.75 #42, 0.62 #78, 0.61 #295), 01vx2h (0.30 #300, 0.29 #809, 0.29 #1319), 01pvkk (0.27 #1320, 0.27 #810, 0.27 #301), 02ynfr (0.25 #52, 0.23 #88, 0.17 #124), 089fss (0.17 #41, 0.15 #77, 0.10 #113), 0d2b38 (0.17 #62, 0.13 #134, 0.10 #315), 089g0h (0.17 #56, 0.12 #128, 0.10 #309), 0215hd (0.13 #127, 0.13 #308, 0.12 #817), 02rh1dz (0.12 #118, 0.09 #808, 0.09 #299) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 03cp4cn; >> query: (?x4175, 0ch6mp2) <- film(?x3101, ?x4175), ?x3101 = 0dvmd, film_crew_role(?x4175, ?x468), ?x468 = 02r96rf >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 03cp4cn; *> query: (?x4175, 09vw2b7) <- film(?x3101, ?x4175), ?x3101 = 0dvmd, film_crew_role(?x4175, ?x468), ?x468 = 02r96rf *> conf = 0.75 ranks of expected_values: 2 EVAL 0d61px film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12021-03_3d PRED entity: 03_3d PRED relation: film_release_region! PRED expected values: 0m2kd 0dscrwf 0401sg 0jqp3 07g_0c 0d6b7 0168ls 04n52p6 035yn8 0cc7hmk 05qbckf 0yzvw 0fpv_3_ 01p3ty 0hx4y 0crc2cp 0gffmn8 0gj8nq2 0gh65c5 0jymd 06zn2v2 0gkz3nz 04nm0n0 0dt8xq 0bt3j9 0j3d9tn 02ylg6 0gbfn9 0dll_t2 067ghz 0g9zljd 04cppj 0dc_ms 0ds1glg 0pd64 0gtx63s 0ds2l81 05zvzf3 0gwlfnb 0gy4k 072hx4 => 163 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1042): 0gj8nq2 (0.90 #28385, 0.86 #10323, 0.81 #16344), 06wbm8q (0.88 #28315, 0.82 #10253, 0.81 #16274), 0fpv_3_ (0.86 #28291, 0.81 #16250, 0.78 #19259), 0gtsx8c (0.83 #28102, 0.82 #10040, 0.81 #19070), 05qbckf (0.83 #28259, 0.78 #19227, 0.77 #10197), 0gffmn8 (0.82 #10307, 0.81 #28369, 0.78 #16328), 0dll_t2 (0.82 #10569, 0.79 #28631, 0.75 #16590), 0glqh5_ (0.82 #10546, 0.78 #19576, 0.77 #11550), 0g9zljd (0.82 #11659, 0.78 #16676, 0.77 #10655), 0h63gl9 (0.82 #10693, 0.74 #28755, 0.72 #16714) >> Best rule #28385 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 0d060g; 0d0vqn; 04gzd; 0chghy; ... >> query: (?x252, 0gj8nq2) <- film_release_region(?x1202, ?x252), ?x1202 = 0gj8t_b, olympics(?x252, ?x418) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 5, 6, 7, 9, 13, 14, 15, 16, 17, 18, 21, 22, 24, 27, 28, 36, 38, 41, 47, 48, 49, 50, 52, 53, 56, 60, 61, 64, 66, 73, 75, 76, 83, 84, 86, 89, 151, 156, 205 EVAL 03_3d film_release_region! 072hx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0gy4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0gwlfnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0ds2l81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0gtx63s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0pd64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0ds1glg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 04cppj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0g9zljd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 067ghz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0dll_t2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0gbfn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 02ylg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0j3d9tn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0bt3j9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0dt8xq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 04nm0n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0gkz3nz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 06zn2v2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0jymd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0gh65c5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0gj8nq2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0gffmn8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0crc2cp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0hx4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 01p3ty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0fpv_3_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0yzvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 05qbckf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0cc7hmk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 035yn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 04n52p6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0168ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0d6b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 07g_0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0jqp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0401sg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0dscrwf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03_3d film_release_region! 0m2kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 163.000 153.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12020-0gmgwnv PRED entity: 0gmgwnv PRED relation: film_release_region PRED expected values: 0f8l9c 03rj0 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 110): 09c7w0 (0.94 #5639, 0.94 #4189, 0.93 #4028), 0f8l9c (0.89 #1956, 0.87 #2923, 0.86 #2600), 02vzc (0.82 #1987, 0.76 #2954, 0.76 #2631), 03_3d (0.78 #1938, 0.74 #2582, 0.74 #2905), 035qy (0.78 #2612, 0.77 #2935, 0.76 #1968), 03gj2 (0.78 #2603, 0.76 #2926, 0.76 #1959), 05qhw (0.74 #2592, 0.74 #2915, 0.74 #1948), 0d060g (0.72 #2906, 0.72 #2583, 0.71 #1939), 0b90_r (0.71 #2580, 0.70 #2903, 0.67 #1936), 06bnz (0.68 #2947, 0.68 #2624, 0.63 #1980) >> Best rule #5639 for best value: >> intensional similarity = 4 >> extensional distance = 496 >> proper extension: 07jqjx; >> query: (?x6176, 09c7w0) <- nominated_for(?x846, ?x6176), award_winner(?x1452, ?x846), produced_by(?x153, ?x846), film_release_region(?x6176, ?x87) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #1956 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 177 *> proper extension: 0ddfwj1; 0gtv7pk; 03qnvdl; 0gd0c7x; 0879bpq; 045j3w; 06w839_; 0gtsxr4; 0gh65c5; 0gjcrrw; ... *> query: (?x6176, 0f8l9c) <- film_release_region(?x6176, ?x2513), nominated_for(?x112, ?x6176), ?x2513 = 05b4w *> conf = 0.89 ranks of expected_values: 2, 14 EVAL 0gmgwnv film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 101.000 101.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gmgwnv film_release_region 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #12019-01vsy9_ PRED entity: 01vsy9_ PRED relation: award_winner! PRED expected values: 03x3wf => 123 concepts (110 used for prediction) PRED predicted values (max 10 best out of 299): 01by1l (0.29 #1823, 0.26 #2251, 0.13 #17662), 05qck (0.25 #1473, 0.17 #617, 0.15 #4898), 02py7pj (0.25 #1588, 0.16 #5441, 0.13 #5013), 03x3wf (0.19 #1776, 0.17 #2204, 0.17 #2632), 0l8z1 (0.19 #8625, 0.13 #11622, 0.13 #11194), 025m8y (0.18 #8661, 0.13 #11658, 0.13 #11230), 054krc (0.18 #8649, 0.12 #11646, 0.11 #11218), 0gkvb7 (0.17 #456, 0.17 #28, 0.06 #3881), 0gq_v (0.17 #879, 0.12 #10725, 0.07 #11131), 054ks3 (0.17 #140, 0.11 #3993, 0.11 #11699) >> Best rule #1823 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 014q2g; 013qvn; 03h_yfh; >> query: (?x8803, 01by1l) <- artist(?x5634, ?x8803), artists(?x505, ?x8803), celebrities_impersonated(?x3649, ?x8803) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1776 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 014q2g; 013qvn; 03h_yfh; *> query: (?x8803, 03x3wf) <- artist(?x5634, ?x8803), artists(?x505, ?x8803), celebrities_impersonated(?x3649, ?x8803) *> conf = 0.19 ranks of expected_values: 4 EVAL 01vsy9_ award_winner! 03x3wf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 123.000 110.000 0.286 http://example.org/award/award_category/winners./award/award_honor/award_winner #12018-0gr4k PRED entity: 0gr4k PRED relation: award! PRED expected values: 02fcs2 027l0b 01_6dw 027vps 033rq 02rk45 06w38l 07db6x => 54 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2751): 01d8yn (0.78 #29505, 0.78 #32784, 0.65 #68862), 01ts_3 (0.54 #15103, 0.33 #5269, 0.20 #1992), 0136g9 (0.50 #3594, 0.31 #13428, 0.20 #317), 02f93t (0.46 #15728, 0.33 #5894, 0.22 #9171), 0693l (0.46 #13940, 0.17 #4106, 0.11 #17218), 01ycck (0.38 #14213, 0.33 #4379, 0.20 #1102), 06pj8 (0.38 #13650, 0.33 #3816, 0.14 #55734), 081lh (0.38 #13335, 0.22 #6778, 0.17 #3501), 0bzyh (0.38 #14179, 0.22 #7622, 0.17 #4345), 0151w_ (0.38 #13337, 0.17 #3503, 0.15 #19892) >> Best rule #29505 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 09v7wsg; >> query: (?x601, ?x488) <- award(?x167, ?x601), nominated_for(?x601, ?x89), ceremony(?x601, ?x78), award_winner(?x601, ?x488) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #5118 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 03nqnk3; 0bm7fy; *> query: (?x601, 01_6dw) <- ceremony(?x601, ?x78), award(?x8683, ?x601), award(?x8382, ?x601), ?x8382 = 0mb5x, award_winner(?x5863, ?x8683) *> conf = 0.33 ranks of expected_values: 15, 16, 58, 59, 92, 797, 1235, 1529 EVAL 0gr4k award! 07db6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr4k award! 06w38l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr4k award! 02rk45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 54.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr4k award! 033rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 54.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr4k award! 027vps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 54.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr4k award! 01_6dw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 54.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr4k award! 027l0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr4k award! 02fcs2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 54.000 24.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12017-0jjy0 PRED entity: 0jjy0 PRED relation: film! PRED expected values: 093h7p => 123 concepts (113 used for prediction) PRED predicted values (max 10 best out of 76): 05qd_ (0.50 #155, 0.31 #739, 0.29 #520), 01gb54 (0.50 #175, 0.29 #467, 0.23 #832), 086k8 (0.43 #440, 0.31 #805, 0.30 #951), 01f_mw (0.33 #47, 0.17 #266, 0.07 #923), 03xq0f (0.25 #151, 0.23 #1246, 0.22 #2416), 016tt2 (0.25 #77, 0.20 #880, 0.18 #2634), 025jfl (0.25 #79, 0.17 #298, 0.14 #517), 0fvppk (0.25 #128, 0.14 #566, 0.11 #639), 016tw3 (0.21 #1033, 0.20 #668, 0.20 #2349), 017s11 (0.17 #368, 0.17 #295, 0.16 #1244) >> Best rule #155 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0ds11z; >> query: (?x1108, 05qd_) <- genre(?x1108, ?x571), genre(?x1108, ?x53), produced_by(?x1108, ?x3873), costume_design_by(?x1108, ?x3685), ?x571 = 03npn, ?x53 = 07s9rl0, gender(?x3685, ?x514) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5049 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 563 *> proper extension: 015g28; 021gzd; *> query: (?x1108, ?x902) <- genre(?x1108, ?x53), produced_by(?x1108, ?x3873), genre(?x9496, ?x53), genre(?x6267, ?x53), genre(?x5304, ?x53), ?x5304 = 0y_9q, film_distribution_medium(?x9496, ?x81), film(?x902, ?x6267), produced_by(?x6267, ?x595) *> conf = 0.02 ranks of expected_values: 46 EVAL 0jjy0 film! 093h7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 123.000 113.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #12016-0c0k1 PRED entity: 0c0k1 PRED relation: film PRED expected values: 0ddjy 0dnqr 02tktw => 113 concepts (88 used for prediction) PRED predicted values (max 10 best out of 906): 0llcx (0.56 #12440, 0.50 #55090, 0.49 #63976), 0kvgtf (0.14 #617, 0.04 #2394, 0.03 #4171), 0f61tk (0.14 #1460, 0.03 #5014, 0.02 #17454), 0qm9n (0.14 #551, 0.03 #129744), 09146g (0.14 #297, 0.02 #21622, 0.02 #19845), 03nqnnk (0.14 #1016, 0.02 #40111, 0.02 #38334), 0fgrm (0.14 #783, 0.01 #9668, 0.01 #20331), 07k8rt4 (0.14 #736, 0.01 #9621), 0k2sk (0.14 #162, 0.01 #9047), 01rxyb (0.14 #728, 0.01 #34492, 0.01 #38046) >> Best rule #12440 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 0d_84; 01wjrn; 0c01c; 01fwpt; 02yplc; 039crh; 033jkj; 045cq; 0432b; 0cf2h; ... >> query: (?x8704, ?x2441) <- people(?x1446, ?x8704), ?x1446 = 033tf_, nominated_for(?x8704, ?x2441) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #27031 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 266 *> proper extension: 02zq43; 08w7vj; 01j5x6; 01v3s2_; 08m4c8; 07ymr5; 02k6rq; 02lgfh; 01qrbf; *> query: (?x8704, 0ddjy) <- people(?x1050, ?x8704), award_nominee(?x8704, ?x3999), actor(?x10492, ?x8704) *> conf = 0.01 ranks of expected_values: 861 EVAL 0c0k1 film 02tktw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 88.000 0.564 http://example.org/film/actor/film./film/performance/film EVAL 0c0k1 film 0dnqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 88.000 0.564 http://example.org/film/actor/film./film/performance/film EVAL 0c0k1 film 0ddjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 113.000 88.000 0.564 http://example.org/film/actor/film./film/performance/film #12015-0b6f8pf PRED entity: 0b6f8pf PRED relation: genre PRED expected values: 0vgkd => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.70 #1591, 0.63 #1835, 0.62 #1467), 01z4y (0.52 #2693, 0.50 #2079, 0.50 #2570), 02kdv5l (0.51 #492, 0.44 #736, 0.43 #858), 01jfsb (0.36 #502, 0.35 #746, 0.33 #1234), 03k9fj (0.33 #501, 0.29 #867, 0.28 #745), 01hmnh (0.33 #19, 0.21 #264, 0.19 #630), 02l7c8 (0.33 #1729, 0.33 #628, 0.31 #1607), 06n90 (0.31 #503, 0.22 #137, 0.19 #625), 06cvj (0.21 #1716, 0.19 #249, 0.14 #127), 060__y (0.21 #1117, 0.20 #1608, 0.14 #3815) >> Best rule #1591 for best value: >> intensional similarity = 3 >> extensional distance = 483 >> proper extension: 064n1pz; 02n9bh; 0gcrg; 011yfd; 02phtzk; 02h22; 064lsn; 03q8xj; 0k2m6; 0581vn8; ... >> query: (?x9920, 07s9rl0) <- nominated_for(?x688, ?x9920), award(?x7762, ?x688), ?x7762 = 09v6tz >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 02z3r8t; 09v9mks; 01738w; 033pf1; *> query: (?x9920, 0vgkd) <- film(?x5240, ?x9920), ?x5240 = 01fx2g, genre(?x9920, ?x258), titles(?x2480, ?x9920) *> conf = 0.17 ranks of expected_values: 13 EVAL 0b6f8pf genre 0vgkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 62.000 62.000 0.701 http://example.org/film/film/genre #12014-02bfmn PRED entity: 02bfmn PRED relation: film PRED expected values: 04ghz4m => 73 concepts (24 used for prediction) PRED predicted values (max 10 best out of 430): 039c26 (0.50 #19582, 0.45 #32044, 0.40 #33825), 0pdp8 (0.25 #364, 0.02 #9264), 01c22t (0.25 #163, 0.02 #9063), 03q0r1 (0.25 #633, 0.01 #36239), 0prh7 (0.25 #831, 0.01 #15072), 0ds5_72 (0.25 #1452), 01zfzb (0.25 #920), 06lpmt (0.25 #681), 03s5lz (0.25 #195), 076tw54 (0.17 #8897) >> Best rule #19582 for best value: >> intensional similarity = 3 >> extensional distance = 996 >> proper extension: 02z6l5f; >> query: (?x230, ?x407) <- award_nominee(?x1424, ?x230), nominated_for(?x230, ?x407), participant(?x1424, ?x2782) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02bfmn film 04ghz4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 24.000 0.495 http://example.org/film/actor/film./film/performance/film #12013-0353xq PRED entity: 0353xq PRED relation: titles! PRED expected values: 01qzt1 => 118 concepts (93 used for prediction) PRED predicted values (max 10 best out of 163): 07ssc (0.38 #4488, 0.28 #4175, 0.28 #4081), 0lsxr (0.34 #3566, 0.33 #6207, 0.31 #3667), 04rlf (0.34 #3566, 0.33 #6207, 0.31 #3667), 01z4y (0.33 #135, 0.29 #439, 0.24 #3803), 01jfsb (0.30 #624, 0.29 #322, 0.28 #930), 017fp (0.25 #123, 0.21 #1652, 0.19 #1035), 01hmnh (0.24 #3895, 0.14 #6029, 0.14 #8781), 024qqx (0.18 #383, 0.12 #991, 0.10 #3848), 01qzt1 (0.17 #159, 0.09 #463, 0.02 #1071), 0c3351 (0.15 #354, 0.09 #7068, 0.09 #962) >> Best rule #4488 for best value: >> intensional similarity = 7 >> extensional distance = 349 >> proper extension: 01cjhz; 08cx5g; 03j63k; 0jq2r; 02qr46y; 06f0k; >> query: (?x5318, 07ssc) <- titles(?x162, ?x5318), titles(?x162, ?x5519), titles(?x162, ?x3430), titles(?x162, ?x2128), ?x5519 = 09p3_s, ?x3430 = 0ctb4g, film(?x489, ?x2128) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #159 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 0kbwb; *> query: (?x5318, 01qzt1) <- film_release_distribution_medium(?x5318, ?x81), genre(?x5318, ?x8681), titles(?x162, ?x5318), country(?x5318, ?x512), featured_film_locations(?x5318, ?x10165), ?x8681 = 04rlf *> conf = 0.17 ranks of expected_values: 9 EVAL 0353xq titles! 01qzt1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 118.000 93.000 0.385 http://example.org/media_common/netflix_genre/titles #12012-02qd04y PRED entity: 02qd04y PRED relation: nominated_for! PRED expected values: 09v4bym => 79 concepts (59 used for prediction) PRED predicted values (max 10 best out of 191): 09v4bym (0.68 #7838, 0.68 #8316, 0.68 #8077), 09v8db5 (0.56 #410, 0.53 #647, 0.50 #173), 07kfzsg (0.47 #701, 0.44 #464, 0.44 #227), 0gs9p (0.32 #3384, 0.28 #4338, 0.27 #5289), 0gq9h (0.32 #4336, 0.31 #3382, 0.31 #5287), 09v478h (0.31 #455, 0.31 #218, 0.29 #692), 019f4v (0.28 #3373, 0.27 #4327, 0.25 #5040), 0gr0m (0.25 #1483, 0.20 #3379, 0.18 #1246), 0k611 (0.25 #1497, 0.24 #3393, 0.23 #4347), 0p9sw (0.24 #1443, 0.21 #2391, 0.21 #2865) >> Best rule #7838 for best value: >> intensional similarity = 4 >> extensional distance = 985 >> proper extension: 06w7mlh; 06mmr; >> query: (?x9175, ?x9217) <- award(?x9175, ?x9217), award(?x1864, ?x9217), profession(?x1864, ?x319), nominated_for(?x9217, ?x467) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qd04y nominated_for! 09v4bym CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 59.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #12011-0btpm6 PRED entity: 0btpm6 PRED relation: language PRED expected values: 0653m => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 47): 064_8sq (0.25 #314, 0.19 #197, 0.18 #848), 03_9r (0.21 #953, 0.17 #894, 0.06 #537), 06nm1 (0.15 #69, 0.13 #1305, 0.12 #538), 04306rv (0.15 #1006, 0.12 #1241, 0.11 #1656), 032f6 (0.15 #114, 0.05 #290, 0.04 #348), 02bjrlw (0.14 #177, 0.12 #1003, 0.10 #529), 06b_j (0.10 #550, 0.08 #729, 0.08 #374), 04h9h (0.10 #218, 0.08 #42, 0.05 #810), 02hxcvy (0.09 #268, 0.03 #918, 0.02 #444), 0t_2 (0.08 #13, 0.05 #1073, 0.05 #189) >> Best rule #314 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0m_mm; 0_92w; 0c0nhgv; 0gmcwlb; 0jqn5; 0ch26b_; 09k56b7; 02yvct; 0fpv_3_; 0htww; ... >> query: (?x7493, 064_8sq) <- nominated_for(?x1703, ?x7493), ?x1703 = 0k611, film_release_region(?x7493, ?x151), ?x151 = 0b90_r >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #70 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 020fcn; 011ykb; *> query: (?x7493, 0653m) <- nominated_for(?x1703, ?x7493), nominated_for(?x451, ?x7493), ?x1703 = 0k611, language(?x7493, ?x254), ?x451 = 099jhq *> conf = 0.08 ranks of expected_values: 12 EVAL 0btpm6 language 0653m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 106.000 106.000 0.250 http://example.org/film/film/language #12010-03y9p40 PRED entity: 03y9p40 PRED relation: teams! PRED expected values: 0z4_0 => 58 concepts (57 used for prediction) PRED predicted values (max 10 best out of 96): 0d9y6 (0.33 #1361, 0.33 #1221, 0.25 #2043), 0snty (0.33 #510, 0.20 #3790, 0.20 #3517), 071cn (0.33 #928, 0.20 #3388, 0.11 #5027), 0dyl9 (0.33 #143, 0.17 #4245, 0.06 #7244), 030qb3t (0.25 #2234, 0.25 #1689, 0.20 #2782), 0t6hk (0.25 #2682, 0.17 #4050, 0.11 #5140), 0vzm (0.25 #2557, 0.14 #4742, 0.08 #6108), 0ftxw (0.20 #2817, 0.09 #5826, 0.09 #1364), 0fvzg (0.20 #2819, 0.08 #6646, 0.08 #6375), 0fvyg (0.20 #3499, 0.04 #10353, 0.04 #10354) >> Best rule #1361 for best value: >> intensional similarity = 37 >> extensional distance = 1 >> proper extension: 03by7wc; >> query: (?x9833, ?x5259) <- team(?x13209, ?x9833), team(?x12798, ?x9833), team(?x12162, ?x9833), team(?x11210, ?x9833), team(?x10673, ?x9833), team(?x9146, ?x9833), team(?x8824, ?x9833), team(?x7042, ?x9833), team(?x6802, ?x9833), team(?x4803, ?x9833), team(?x2302, ?x9833), ?x4803 = 0b_6jz, ?x6802 = 0br1x_, ?x8824 = 05g_nr, team(?x1348, ?x9833), ?x11210 = 0b_6q5, ?x2302 = 0b_77q, ?x7042 = 0b_72t, ?x13209 = 0b_734, team(?x9146, ?x9576), locations(?x9146, ?x13387), locations(?x9146, ?x5771), locations(?x9146, ?x5267), locations(?x9146, ?x5259), locations(?x9146, ?x2277), locations(?x9146, ?x2087), ?x5771 = 0fpzwf, ?x5259 = 0d9y6, ?x9576 = 02qk2d5, administrative_division(?x13387, ?x12068), category(?x2087, ?x134), ?x12162 = 0b_6_l, ?x2277 = 013yq, contains(?x94, ?x2087), ?x10673 = 0b_6mr, origin(?x1001, ?x5267), ?x12798 = 0b_770 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03y9p40 teams! 0z4_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 57.000 0.333 http://example.org/sports/sports_team_location/teams #12009-067ghz PRED entity: 067ghz PRED relation: film_crew_role PRED expected values: 01pvkk => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 30): 02r96rf (0.76 #346, 0.75 #270, 0.74 #498), 09vw2b7 (0.56 #350, 0.53 #502, 0.50 #1191), 09zzb8 (0.56 #1184, 0.55 #686, 0.54 #495), 01vx2h (0.41 #279, 0.41 #355, 0.38 #507), 0dxtw (0.32 #430, 0.30 #278, 0.29 #1195), 01pvkk (0.23 #699, 0.22 #508, 0.22 #280), 02rh1dz (0.19 #353, 0.18 #277, 0.17 #505), 02ynfr (0.15 #360, 0.15 #284, 0.14 #512), 089fss (0.12 #7, 0.08 #501, 0.08 #273), 0215hd (0.12 #401, 0.10 #1204, 0.09 #1509) >> Best rule #346 for best value: >> intensional similarity = 5 >> extensional distance = 104 >> proper extension: 0gtsx8c; 0gtvrv3; >> query: (?x5825, 02r96rf) <- film(?x3780, ?x5825), film_release_region(?x5825, ?x2146), film_release_region(?x5825, ?x279), ?x2146 = 03rk0, ?x279 = 0d060g >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #699 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 276 *> proper extension: 0dckvs; 0djb3vw; 0fq27fp; 04969y; 0d6b7; 0bmc4cm; 0192hw; 026njb5; 043sct5; 0g5q34q; ... *> query: (?x5825, 01pvkk) <- film_release_region(?x5825, ?x1917), film_release_region(?x5825, ?x279), ?x279 = 0d060g, country(?x668, ?x1917), ?x668 = 07gyv *> conf = 0.23 ranks of expected_values: 6 EVAL 067ghz film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 72.000 72.000 0.764 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #12008-0jnr_ PRED entity: 0jnr_ PRED relation: position PRED expected values: 02qvzf => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 31): 02qvzf (0.80 #65, 0.79 #88, 0.76 #157), 02qvgy (0.77 #56, 0.75 #153, 0.75 #50), 02qvl7 (0.75 #153, 0.75 #50, 0.75 #171), 02qvkj (0.53 #12, 0.50 #148, 0.05 #119), 02_j1w (0.12 #37, 0.07 #141), 0dgrmp (0.12 #37, 0.07 #141), 02sdk9v (0.12 #37, 0.07 #141), 02nzb8 (0.12 #37), 02sddg (0.07 #141), 01z9v6 (0.07 #141) >> Best rule #65 for best value: >> intensional similarity = 15 >> extensional distance = 8 >> proper extension: 0j2zj; >> query: (?x10950, 02qvzf) <- team(?x5234, ?x10950), team(?x2918, ?x10950), ?x5234 = 02qvdc, teams(?x4978, ?x10950), contains(?x3778, ?x4978), location(?x8693, ?x4978), location(?x4480, ?x4978), friend(?x8693, ?x1093), team(?x2918, ?x14124), participant(?x4480, ?x2669), award(?x8693, ?x462), ?x14124 = 04l590, film(?x4480, ?x7768), position(?x7174, ?x2918), participant(?x8693, ?x4964) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jnr_ position 02qvzf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.800 http://example.org/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position #12007-07ccs PRED entity: 07ccs PRED relation: colors PRED expected values: 083jv => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.42 #262, 0.41 #242, 0.36 #2162), 01g5v (0.29 #1304, 0.27 #2164, 0.24 #2204), 01l849 (0.29 #41, 0.29 #21, 0.28 #1301), 019sc (0.23 #568, 0.18 #868, 0.18 #628), 03wkwg (0.21 #35, 0.14 #55, 0.13 #335), 06fvc (0.17 #1103, 0.17 #1303, 0.14 #1623), 09ggk (0.14 #76, 0.14 #56, 0.12 #156), 067z2v (0.14 #70, 0.14 #50, 0.11 #90), 02rnmb (0.14 #33, 0.10 #253, 0.07 #273), 04d18d (0.14 #59, 0.07 #39, 0.05 #459) >> Best rule #262 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 08qnnv; >> query: (?x6333, 083jv) <- institution(?x865, ?x6333), school(?x700, ?x6333), company(?x3131, ?x6333), major_field_of_study(?x6333, ?x742) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ccs colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 170.000 0.419 http://example.org/education/educational_institution/colors #12006-07ssc PRED entity: 07ssc PRED relation: combatants! PRED expected values: 0154j 0345h 05vz3zq => 214 concepts (183 used for prediction) PRED predicted values (max 10 best out of 326): 09c7w0 (0.85 #2631, 0.84 #1752, 0.83 #6332), 0154j (0.85 #2631, 0.84 #1752, 0.83 #6332), 0ctw_b (0.85 #2631, 0.84 #1752, 0.83 #6332), 01mk6 (0.85 #2631, 0.84 #1752, 0.83 #6332), 05qhw (0.85 #2631, 0.84 #1752, 0.83 #6332), 0345h (0.85 #2631, 0.84 #1752, 0.83 #6332), 015fr (0.85 #2631, 0.84 #1752, 0.83 #6332), 059z0 (0.85 #2631, 0.84 #1752, 0.83 #6332), 02psqkz (0.85 #2631, 0.84 #1752, 0.83 #6332), 06bnz (0.85 #2631, 0.84 #1752, 0.83 #6332) >> Best rule #2631 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 01mzwp; >> query: (?x512, ?x94) <- contains(?x512, ?x12461), combatants(?x512, ?x94), location(?x488, ?x12461) >> conf = 0.85 => this is the best rule for 13 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 6, 15 EVAL 07ssc combatants! 05vz3zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 214.000 183.000 0.850 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants EVAL 07ssc combatants! 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 214.000 183.000 0.850 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants EVAL 07ssc combatants! 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 214.000 183.000 0.850 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #12005-01nwwl PRED entity: 01nwwl PRED relation: gender PRED expected values: 05zppz => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #5, 0.72 #39, 0.72 #29), 02zsn (0.42 #4, 0.38 #26, 0.37 #10) >> Best rule #5 for best value: >> intensional similarity = 2 >> extensional distance = 162 >> proper extension: 01tp5bj; 01l87db; 0399p; 0ct9_; 09k0f; 03j0d; 04xzm; 03_lf; 0k1wz; 0177g; ... >> query: (?x2938, 05zppz) <- religion(?x2938, ?x2694), ?x2694 = 0kpl >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nwwl gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.780 http://example.org/people/person/gender #12004-0bxy67 PRED entity: 0bxy67 PRED relation: gender PRED expected values: 05zppz => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #9, 0.85 #5, 0.83 #7), 02zsn (0.30 #40, 0.30 #32, 0.30 #34) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0f5zj6; 08hhm6; 02xgdv; 0239zv; 021j72; >> query: (?x10487, 05zppz) <- profession(?x10487, ?x1032), award_winner(?x4687, ?x10487), award(?x10221, ?x4687), ?x10221 = 07t3x8 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bxy67 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.879 http://example.org/people/person/gender #12003-01r47h PRED entity: 01r47h PRED relation: contains! PRED expected values: 09c7w0 => 178 concepts (101 used for prediction) PRED predicted values (max 10 best out of 307): 09c7w0 (0.93 #44722, 0.87 #59030, 0.85 #50979), 059rby (0.28 #19691, 0.26 #24162, 0.16 #45633), 01n7q (0.26 #75214, 0.21 #24220, 0.20 #19749), 0345h (0.20 #82, 0.15 #75218, 0.04 #3658), 0rh6k (0.20 #4, 0.05 #6262, 0.04 #3580), 059j2 (0.20 #79, 0.04 #3655, 0.04 #9915), 0cm5m (0.20 #651, 0.02 #10487, 0.02 #9592), 07b_l (0.18 #4692, 0.11 #10058, 0.08 #75358), 04_1l0v (0.17 #8047), 0d060g (0.15 #75149, 0.14 #6271, 0.12 #8060) >> Best rule #44722 for best value: >> intensional similarity = 5 >> extensional distance = 255 >> proper extension: 015zyd; 01jtp7; 02bqy; 03gn1x; >> query: (?x11480, 09c7w0) <- contains(?x6895, ?x11480), category(?x11480, ?x134), currency(?x11480, ?x170), ?x170 = 09nqf, jurisdiction_of_office(?x1159, ?x6895) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r47h contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 101.000 0.934 http://example.org/location/location/contains #12002-0d608 PRED entity: 0d608 PRED relation: award PRED expected values: 05f4m9q => 105 concepts (104 used for prediction) PRED predicted values (max 10 best out of 317): 05zr6wv (0.43 #418, 0.38 #1222, 0.33 #2026), 0gkvb7 (0.43 #428, 0.33 #2036, 0.13 #32572), 0gq9h (0.35 #17364, 0.34 #14951, 0.32 #18168), 09sb52 (0.35 #24162, 0.32 #24967, 0.29 #4863), 02x17c2 (0.33 #2629, 0.33 #217, 0.15 #4639), 01ck6h (0.33 #121, 0.22 #2533, 0.15 #4543), 054ks3 (0.33 #2552, 0.19 #4562, 0.11 #11799), 01ck6v (0.33 #271, 0.11 #4693, 0.11 #2683), 099vwn (0.33 #2626, 0.11 #4636, 0.04 #21120), 05pcn59 (0.32 #4904, 0.29 #884, 0.29 #482) >> Best rule #418 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01n5309; 018grr; 0f7hc; 03d_zl4; 0pz04; >> query: (?x7522, 05zr6wv) <- film(?x7522, ?x518), award(?x7522, ?x102), celebrities_impersonated(?x7522, ?x5572) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4032 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 48 *> proper extension: 01vvycq; 022_lg; *> query: (?x7522, 05f4m9q) <- award(?x7522, ?x688), ?x688 = 05b1610 *> conf = 0.28 ranks of expected_values: 21 EVAL 0d608 award 05f4m9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 105.000 104.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12001-01whg97 PRED entity: 01whg97 PRED relation: award PRED expected values: 05q8pss => 122 concepts (116 used for prediction) PRED predicted values (max 10 best out of 372): 01by1l (0.31 #4973, 0.30 #2138, 0.29 #5378), 01bgqh (0.30 #2068, 0.25 #4903, 0.25 #5308), 01ckcd (0.27 #338, 0.25 #1958, 0.22 #743), 09sb52 (0.25 #34466, 0.24 #28796, 0.23 #23126), 03qbh5 (0.22 #3852, 0.22 #2232, 0.22 #1827), 01c427 (0.22 #3325, 0.18 #2110, 0.18 #4945), 054ks3 (0.20 #1763, 0.17 #5003, 0.17 #5408), 01c9jp (0.20 #191, 0.17 #596, 0.12 #1811), 0c4z8 (0.19 #9387, 0.18 #2097, 0.18 #4932), 01c99j (0.19 #3468, 0.19 #1038, 0.15 #2253) >> Best rule #4973 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 01fwj8; 016z2j; 0fb1q; 022g44; 0dpqk; 06g2d1; 01tt43d; 01j7z7; 0mbw0; 010xjr; ... >> query: (?x8149, 01by1l) <- profession(?x8149, ?x220), people(?x1816, ?x8149), ?x220 = 016z4k, award(?x8149, ?x10169) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #215 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 01vsxdm; 0g_g2; 01q99h; 01shhf; 0134pk; *> query: (?x8149, 05q8pss) <- artists(?x10306, ?x8149), artists(?x2249, ?x8149), ?x10306 = 09jw2, ?x2249 = 03lty *> conf = 0.07 ranks of expected_values: 92 EVAL 01whg97 award 05q8pss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 122.000 116.000 0.310 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12000-0c0nhgv PRED entity: 0c0nhgv PRED relation: film! PRED expected values: 02qgqt => 94 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1165): 07s93v (0.48 #66573, 0.45 #118598, 0.44 #79059), 01d8yn (0.48 #66573, 0.45 #118598, 0.44 #79059), 04q5zw (0.45 #118598, 0.44 #79059, 0.44 #93629), 03rwz3 (0.45 #118598, 0.44 #79059, 0.44 #93629), 05txrz (0.29 #2844, 0.03 #31967, 0.03 #27807), 04t2l2 (0.29 #2108, 0.02 #45794, 0.02 #58277), 086nl7 (0.29 #2864, 0.02 #73601, 0.02 #69439), 04zkj5 (0.29 #3414, 0.02 #34617, 0.01 #20056), 04bdxl (0.20 #6, 0.14 #2086, 0.09 #8327), 014zcr (0.20 #37, 0.06 #120681, 0.06 #114437) >> Best rule #66573 for best value: >> intensional similarity = 4 >> extensional distance = 245 >> proper extension: 01gglm; >> query: (?x1163, ?x1616) <- film(?x976, ?x1163), award_winner(?x1163, ?x1616), executive_produced_by(?x1163, ?x163), award_winner(?x1674, ?x976) >> conf = 0.48 => this is the best rule for 2 predicted values *> Best rule #8339 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 0209xj; 0p4v_; 07w8fz; *> query: (?x1163, 02qgqt) <- film(?x540, ?x1163), nominated_for(?x2656, ?x1163), nominated_for(?x591, ?x1163), ?x591 = 0f4x7 *> conf = 0.04 ranks of expected_values: 228 EVAL 0c0nhgv film! 02qgqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 94.000 61.000 0.478 http://example.org/film/actor/film./film/performance/film #11999-0c0zq PRED entity: 0c0zq PRED relation: genre PRED expected values: 05p553 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 78): 05p553 (0.50 #4, 0.36 #1057, 0.35 #2814), 02kdv5l (0.33 #1172, 0.27 #2812, 0.26 #3280), 01jfsb (0.33 #1181, 0.31 #1884, 0.30 #2119), 04xvlr (0.29 #118, 0.19 #235, 0.18 #469), 060__y (0.25 #15, 0.19 #132, 0.16 #249), 0219x_ (0.25 #24, 0.10 #1077, 0.10 #1312), 01t_vv (0.25 #52, 0.08 #1457, 0.08 #1574), 03k9fj (0.24 #1063, 0.22 #3288, 0.21 #2820), 082gq (0.21 #145, 0.13 #262, 0.12 #1433), 0lsxr (0.21 #1179, 0.19 #477, 0.18 #1999) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 05q4y12; >> query: (?x9452, 05p553) <- film(?x1871, ?x9452), ?x1871 = 02bkdn, production_companies(?x9452, ?x4564) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c0zq genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.500 http://example.org/film/film/genre #11998-015f7 PRED entity: 015f7 PRED relation: currency PRED expected values: 09nqf => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.67 #10, 0.58 #25, 0.55 #22), 01nv4h (0.07 #20, 0.07 #17, 0.04 #35) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 01pfkw; >> query: (?x3397, 09nqf) <- award(?x3397, ?x154), participant(?x556, ?x3397), program(?x3397, ?x5529) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015f7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.667 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #11997-033jj1 PRED entity: 033jj1 PRED relation: film PRED expected values: 03tbg6 => 108 concepts (82 used for prediction) PRED predicted values (max 10 best out of 715): 01q_y0 (0.55 #26871, 0.37 #141521, 0.36 #94946), 0h1fktn (0.08 #970, 0.03 #24257, 0.02 #31424), 04gv3db (0.06 #753, 0.03 #6128, 0.02 #11502), 02ppg1r (0.05 #96738), 02825cv (0.04 #1143, 0.03 #6518, 0.02 #4726), 0bvn25 (0.04 #50, 0.03 #1841, 0.02 #5425), 02c7k4 (0.04 #1104, 0.02 #27975, 0.01 #8270), 01l_pn (0.04 #967, 0.02 #8133, 0.02 #18881), 05q54f5 (0.04 #471, 0.02 #13011, 0.02 #4054), 01y9jr (0.04 #1162, 0.02 #36991, 0.01 #19076) >> Best rule #26871 for best value: >> intensional similarity = 2 >> extensional distance = 355 >> proper extension: 04shbh; >> query: (?x9815, ?x2293) <- nominated_for(?x9815, ?x2293), languages(?x9815, ?x254) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #3448 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 182 *> proper extension: 02_j7t; 01jb26; 0dxmyh; 01gc7h; 07bsj; *> query: (?x9815, 03tbg6) <- actor(?x2293, ?x9815), participant(?x9232, ?x9815) *> conf = 0.02 ranks of expected_values: 231 EVAL 033jj1 film 03tbg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 108.000 82.000 0.555 http://example.org/film/actor/film./film/performance/film #11996-01pq4w PRED entity: 01pq4w PRED relation: school_type PRED expected values: 05pcjw => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.57 #1324, 0.54 #1612, 0.54 #1420), 01rs41 (0.52 #1253, 0.49 #1277, 0.48 #965), 05pcjw (0.46 #1273, 0.44 #1249, 0.44 #961), 07tf8 (0.39 #345, 0.35 #225, 0.35 #273), 01_9fk (0.33 #338, 0.29 #1322, 0.29 #1418), 01_srz (0.18 #75, 0.15 #579, 0.12 #291), 06cs1 (0.08 #102, 0.06 #174, 0.05 #846), 04399 (0.08 #302, 0.04 #1742, 0.04 #1046), 02p0qmm (0.06 #178, 0.06 #370, 0.05 #802), 01y64 (0.06 #180, 0.05 #780, 0.05 #804) >> Best rule #1324 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 01jssp; 02w2bc; 01j_06; 01ptt7; 01jsn5; 0f1nl; 01jswq; 01wdj_; 0j_sncb; 01swxv; ... >> query: (?x3779, 05jxkf) <- contains(?x94, ?x3779), institution(?x620, ?x3779), school(?x1633, ?x3779) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1273 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 0kz2w; 01xk7r; *> query: (?x3779, 05pcjw) <- currency(?x3779, ?x170), institution(?x1526, ?x3779), major_field_of_study(?x1526, ?x254) *> conf = 0.46 ranks of expected_values: 3 EVAL 01pq4w school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 159.000 159.000 0.566 http://example.org/education/educational_institution/school_type #11995-01swck PRED entity: 01swck PRED relation: film PRED expected values: 047csmy => 104 concepts (74 used for prediction) PRED predicted values (max 10 best out of 905): 0266s9 (0.60 #58582, 0.37 #97644, 0.36 #81662), 04vr_f (0.15 #1944, 0.05 #169, 0.02 #26795), 0320fn (0.11 #655, 0.04 #126053, 0.01 #23731), 09qycb (0.11 #1631, 0.02 #5181, 0.02 #6956), 03p2xc (0.11 #1232, 0.02 #34959, 0.01 #47384), 0g22z (0.11 #16, 0.01 #8891), 0bbgvp (0.11 #1749), 0d_wms (0.11 #629), 02c638 (0.11 #334), 0gzy02 (0.11 #44) >> Best rule #58582 for best value: >> intensional similarity = 3 >> extensional distance = 931 >> proper extension: 01nrq5; 0bkmf; >> query: (?x4520, ?x5013) <- award_winner(?x693, ?x4520), nominated_for(?x4520, ?x5013), film(?x4520, ?x805) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01swck film 047csmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 74.000 0.596 http://example.org/film/actor/film./film/performance/film #11994-0dgst_d PRED entity: 0dgst_d PRED relation: film_release_region PRED expected values: 0ctw_b 015qh => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 96): 07ssc (0.85 #148, 0.80 #838, 0.79 #562), 03rt9 (0.77 #560, 0.73 #146, 0.73 #836), 0ctw_b (0.68 #571, 0.63 #157, 0.61 #847), 015qh (0.65 #171, 0.59 #585, 0.49 #861), 06qd3 (0.63 #168, 0.56 #582, 0.52 #858), 047yc (0.62 #159, 0.58 #573, 0.51 #849), 01mjq (0.60 #173, 0.59 #863, 0.59 #587), 06mzp (0.59 #567, 0.56 #843, 0.56 #153), 09pmkv (0.58 #158, 0.44 #572, 0.34 #848), 06f32 (0.58 #604, 0.50 #880, 0.46 #190) >> Best rule #148 for best value: >> intensional similarity = 5 >> extensional distance = 50 >> proper extension: 0ds35l9; 0c3ybss; 05p1tzf; 017gl1; 0bwfwpj; 08hmch; 0c0nhgv; 0872p_c; 053rxgm; 04hwbq; ... >> query: (?x1263, 07ssc) <- film_release_region(?x1263, ?x985), film_release_region(?x1263, ?x404), ?x985 = 0k6nt, nominated_for(?x1371, ?x1263), ?x404 = 047lj >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #571 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 109 *> proper extension: 0c40vxk; *> query: (?x1263, 0ctw_b) <- film_release_region(?x1263, ?x4743), film_release_region(?x1263, ?x985), film_release_region(?x1263, ?x550), ?x985 = 0k6nt, ?x4743 = 03spz, ?x550 = 05v8c *> conf = 0.68 ranks of expected_values: 3, 4 EVAL 0dgst_d film_release_region 015qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 58.000 58.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dgst_d film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 58.000 58.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11993-01n6c PRED entity: 01n6c PRED relation: official_language PRED expected values: 0jzc => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 42): 02h40lc (0.51 #693, 0.38 #2, 0.37 #1382), 071fb (0.42 #389, 0.39 #648, 0.37 #1380), 0jzc (0.19 #14, 0.15 #100, 0.14 #57), 06nm1 (0.16 #440, 0.15 #699, 0.14 #569), 05zjd (0.07 #148, 0.06 #451, 0.06 #710), 04306rv (0.07 #696, 0.06 #91, 0.05 #177), 0349s (0.04 #76, 0.04 #119, 0.03 #248), 02hwyss (0.04 #73, 0.04 #116, 0.03 #245), 06mp7 (0.04 #54, 0.04 #97, 0.03 #226), 0k0sv (0.04 #60, 0.04 #103, 0.03 #232) >> Best rule #693 for best value: >> intensional similarity = 2 >> extensional distance = 134 >> proper extension: 0bq0p9; 03b79; 02psqkz; 01k6y1; 06jnv; 0c4b8; 03f2w; >> query: (?x2468, 02h40lc) <- official_language(?x2468, ?x5607), languages(?x419, ?x5607) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 0gclb; 05d49; *> query: (?x2468, 0jzc) <- time_zones(?x2468, ?x6582), ?x6582 = 0gsrz4 *> conf = 0.19 ranks of expected_values: 3 EVAL 01n6c official_language 0jzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 55.000 55.000 0.515 http://example.org/location/country/official_language #11992-01w7nww PRED entity: 01w7nww PRED relation: artist! PRED expected values: 073tm9 => 100 concepts (68 used for prediction) PRED predicted values (max 10 best out of 98): 015_1q (0.22 #3103, 0.21 #2963, 0.21 #160), 033hn8 (0.21 #154, 0.11 #5344, 0.10 #3659), 03mp8k (0.21 #206, 0.08 #3711, 0.08 #3149), 0g768 (0.18 #177, 0.13 #1017, 0.12 #5367), 043g7l (0.18 #171, 0.09 #3114, 0.08 #2974), 01cszh (0.16 #151, 0.08 #291, 0.07 #991), 01trtc (0.16 #1052, 0.11 #352, 0.09 #3155), 03rhqg (0.15 #156, 0.15 #5346, 0.14 #2959), 0181dw (0.15 #182, 0.11 #322, 0.11 #3827), 0n85g (0.14 #342, 0.08 #5392, 0.08 #2303) >> Best rule #3103 for best value: >> intensional similarity = 3 >> extensional distance = 408 >> proper extension: 01vw917; >> query: (?x3176, 015_1q) <- award_nominee(?x1338, ?x3176), artist(?x5021, ?x3176), artists(?x505, ?x3176) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1016 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 120 *> proper extension: 01gx5f; *> query: (?x3176, 073tm9) <- profession(?x3176, ?x131), artists(?x2937, ?x3176), ?x2937 = 0glt670 *> conf = 0.14 ranks of expected_values: 11 EVAL 01w7nww artist! 073tm9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 100.000 68.000 0.217 http://example.org/music/record_label/artist #11991-0l2tk PRED entity: 0l2tk PRED relation: major_field_of_study PRED expected values: 03g3w => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 115): 02lp1 (0.67 #356, 0.55 #3117, 0.50 #816), 01mkq (0.64 #820, 0.60 #130, 0.50 #360), 03g3w (0.57 #830, 0.44 #485, 0.35 #2556), 04rjg (0.57 #824, 0.40 #134, 0.35 #2550), 05qfh (0.56 #609, 0.50 #839, 0.44 #494), 0fdys (0.56 #497, 0.50 #727, 0.44 #612), 062z7 (0.50 #831, 0.36 #3017, 0.35 #3132), 01lj9 (0.43 #843, 0.33 #383, 0.29 #3029), 0g4gr (0.40 #144, 0.19 #3250, 0.19 #4055), 037mh8 (0.36 #867, 0.33 #752, 0.33 #637) >> Best rule #356 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 065y4w7; >> query: (?x2895, 02lp1) <- school(?x1823, ?x2895), major_field_of_study(?x2895, ?x9079), major_field_of_study(?x2895, ?x4321), ?x9079 = 0l5mz, ?x4321 = 0g26h >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #830 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 07tds; *> query: (?x2895, 03g3w) <- school(?x1823, ?x2895), major_field_of_study(?x2895, ?x9079), ?x9079 = 0l5mz *> conf = 0.57 ranks of expected_values: 3 EVAL 0l2tk major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 169.000 169.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11990-0260bz PRED entity: 0260bz PRED relation: titles! PRED expected values: 07s9rl0 => 119 concepts (68 used for prediction) PRED predicted values (max 10 best out of 65): 07s9rl0 (0.76 #4544, 0.67 #4852, 0.49 #518), 01jfsb (0.48 #1669, 0.19 #1051, 0.14 #1360), 02n4kr (0.33 #620, 0.33 #6506, 0.29 #1753), 02p0szs (0.33 #620, 0.25 #1752, 0.21 #6921), 03g3w (0.33 #620, 0.25 #1752, 0.21 #6921), 07c52 (0.33 #854, 0.17 #133, 0.14 #752), 01z4y (0.31 #655, 0.23 #758, 0.21 #2306), 07ssc (0.22 #4860, 0.12 #4552, 0.11 #526), 09blyk (0.20 #1696, 0.06 #5101, 0.06 #1078), 017fp (0.18 #540, 0.16 #4874, 0.14 #4566) >> Best rule #4544 for best value: >> intensional similarity = 3 >> extensional distance = 524 >> proper extension: 01qn7n; 03y317; >> query: (?x2107, 07s9rl0) <- titles(?x162, ?x2107), titles(?x162, ?x6704), ?x6704 = 02wyzmv >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0260bz titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 68.000 0.764 http://example.org/media_common/netflix_genre/titles #11989-0yls9 PRED entity: 0yls9 PRED relation: student PRED expected values: 0cm03 => 191 concepts (141 used for prediction) PRED predicted values (max 10 best out of 1533): 01tdnyh (0.25 #888, 0.22 #9249, 0.14 #15520), 0kvqv (0.25 #726, 0.22 #9087, 0.14 #15358), 0dx97 (0.25 #905, 0.22 #9266, 0.14 #15537), 0b_dh (0.25 #1865, 0.22 #10226, 0.14 #16497), 016xh5 (0.25 #1066, 0.22 #9427, 0.12 #21968), 01pk8v (0.25 #951, 0.22 #9312, 0.12 #21853), 01wd3l (0.25 #1147, 0.22 #9508, 0.08 #13688), 05np2 (0.25 #1206, 0.22 #9567, 0.08 #13747), 0cj2w (0.25 #1883, 0.22 #10244, 0.08 #14424), 01wd02c (0.25 #1177, 0.22 #9538, 0.08 #13718) >> Best rule #888 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0b6k40; >> query: (?x6548, 01tdnyh) <- student(?x6548, ?x8938), student(?x6548, ?x1367), ?x8938 = 082_p, award(?x1367, ?x68), award_nominee(?x1367, ?x3568) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #19622 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 01xcgf; *> query: (?x6548, 0cm03) <- state_province_region(?x6548, ?x2235), school_type(?x6548, ?x5931), ?x5931 = 02p0qmm *> conf = 0.06 ranks of expected_values: 377 EVAL 0yls9 student 0cm03 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 191.000 141.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #11988-03bnv PRED entity: 03bnv PRED relation: profession PRED expected values: 02hrh1q => 141 concepts (116 used for prediction) PRED predicted values (max 10 best out of 69): 02hrh1q (0.91 #12952, 0.88 #12382, 0.87 #7548), 0dxtg (0.48 #12097, 0.29 #1575, 0.28 #6264), 02jknp (0.45 #12091, 0.24 #4410, 0.23 #1569), 01c72t (0.34 #3716, 0.33 #3148, 0.31 #3290), 0n1h (0.33 #151, 0.33 #577, 0.23 #9), 03gjzk (0.32 #1292, 0.31 #12099, 0.29 #2287), 0cbd2 (0.19 #4125, 0.17 #10242, 0.16 #9958), 018gz8 (0.17 #2289, 0.14 #12385, 0.13 #7551), 0np9r (0.15 #870, 0.15 #1155, 0.11 #2292), 09lbv (0.14 #2149, 0.13 #159, 0.11 #2718) >> Best rule #12952 for best value: >> intensional similarity = 3 >> extensional distance = 1117 >> proper extension: 021yzs; 03fwln; 045931; 0h1q6; >> query: (?x3321, 02hrh1q) <- award_winner(?x2139, ?x3321), profession(?x3321, ?x131), film(?x3321, ?x5139) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bnv profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 116.000 0.906 http://example.org/people/person/profession #11987-0gt3p PRED entity: 0gt3p PRED relation: place_of_death PRED expected values: 0l35f => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 31): 030qb3t (0.19 #22, 0.14 #2545, 0.14 #1380), 02_286 (0.10 #2148, 0.10 #1953, 0.09 #2536), 0k049 (0.10 #3, 0.08 #2138, 0.08 #2526), 06_kh (0.10 #5, 0.05 #2334, 0.04 #1363), 0d6lp (0.09 #2135, 0.08 #2912, 0.07 #3495), 0k_p5 (0.05 #88, 0.02 #2805, 0.02 #2417), 0r04p (0.05 #67, 0.01 #1425, 0.01 #261), 0f2rq (0.05 #86, 0.01 #2026, 0.01 #280), 0qpqn (0.05 #130, 0.01 #324), 0r3tb (0.05 #116, 0.01 #310) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 01pl9g; 0cj8x; 014z8v; 015qt5; 04bcb1; 01pp3p; 045cq; 0432b; 0cf2h; 0484q; ... >> query: (?x7759, 030qb3t) <- gender(?x7759, ?x231), people(?x1446, ?x7759), ?x1446 = 033tf_, people(?x268, ?x7759) >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gt3p place_of_death 0l35f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 144.000 0.190 http://example.org/people/deceased_person/place_of_death #11986-08wq0g PRED entity: 08wq0g PRED relation: gender PRED expected values: 05zppz => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #55, 0.85 #41, 0.85 #27), 02zsn (0.38 #2, 0.33 #4, 0.31 #6) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 223 >> proper extension: 03c7ln; 032t2z; 07_3qd; 019g40; 0fpj4lx; 027dpx; 04cr6qv; 044mfr; 04f7c55; 0132k4; ... >> query: (?x679, 05zppz) <- instrumentalists(?x227, ?x679), role(?x679, ?x645), nationality(?x679, ?x94) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08wq0g gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.853 http://example.org/people/person/gender #11985-03bpn6 PRED entity: 03bpn6 PRED relation: award_winner! PRED expected values: 0c53vt => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 122): 0c4hgj (0.11 #92, 0.04 #15089, 0.02 #18475), 0bz6l9 (0.11 #51, 0.04 #15089, 0.02 #18475), 05hmp6 (0.07 #1270, 0.04 #15089, 0.04 #5164), 0dthsy (0.07 #1270, 0.04 #15089, 0.02 #5144), 0c53zb (0.07 #1270, 0.04 #5138, 0.03 #202), 0bzkvd (0.04 #15089, 0.04 #1101, 0.03 #2230), 0fz0c2 (0.04 #15089, 0.03 #529, 0.03 #670), 0c4hnm (0.04 #15089, 0.03 #552, 0.03 #1681), 0d__c3 (0.04 #15089, 0.03 #5202, 0.02 #1395), 0bzm81 (0.04 #15089, 0.03 #1856, 0.02 #18475) >> Best rule #92 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0202p_; 0cg9f; >> query: (?x4597, 0c4hgj) <- people(?x5801, ?x4597), type_of_union(?x4597, ?x566), film(?x4597, ?x8217), ?x8217 = 04v89z >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #15089 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1897 *> proper extension: 0kk9v; 06lxn; *> query: (?x4597, ?x78) <- award_winner(?x3066, ?x4597), ceremony(?x3066, ?x78), award(?x92, ?x3066) *> conf = 0.04 ranks of expected_values: 17 EVAL 03bpn6 award_winner! 0c53vt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 140.000 140.000 0.111 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11984-09g7thr PRED entity: 09g7thr PRED relation: list! PRED expected values: 01w3v 07wjk 025v3k 0h6rm 01bcwk 05zl0 01_qgp 01dbns 0gk7z => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 223): 02ktt7 (0.60 #445, 0.50 #222, 0.43 #891), 07gyp7 (0.60 #444, 0.50 #221, 0.43 #890), 0dq23 (0.60 #436, 0.50 #213, 0.43 #882), 0hkqn (0.60 #431, 0.50 #208, 0.43 #877), 0lwkh (0.60 #429, 0.50 #206, 0.43 #875), 03s7h (0.60 #418, 0.50 #195, 0.43 #864), 0k9ts (0.60 #395, 0.50 #172, 0.43 #841), 01dfb6 (0.60 #394, 0.50 #171, 0.43 #840), 035nm (0.60 #390, 0.50 #167, 0.43 #836), 04sv4 (0.60 #389, 0.50 #166, 0.43 #835) >> Best rule #445 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01pd60; >> query: (?x2197, 02ktt7) <- list(?x4599, ?x2197), list(?x1681, ?x2197), company(?x2998, ?x4599), category(?x1681, ?x134) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09g7thr list! 0gk7z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 09g7thr list! 01dbns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 09g7thr list! 01_qgp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 09g7thr list! 05zl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 09g7thr list! 01bcwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 09g7thr list! 0h6rm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 09g7thr list! 025v3k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 09g7thr list! 07wjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 09g7thr list! 01w3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #11983-023g6w PRED entity: 023g6w PRED relation: film_crew_role PRED expected values: 09vw2b7 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 28): 0ch6mp2 (0.78 #1054, 0.75 #1271, 0.74 #1415), 09vw2b7 (0.67 #1053, 0.64 #1270, 0.63 #1414), 0dxtw (0.42 #155, 0.40 #227, 0.37 #1058), 01vx2h (0.33 #1059, 0.32 #1276, 0.31 #1420), 089fss (0.24 #1626, 0.11 #41, 0.11 #185), 02ynfr (0.18 #1063, 0.17 #1280, 0.17 #774), 0215hd (0.17 #307, 0.16 #344, 0.14 #1066), 089g0h (0.14 #164, 0.13 #308, 0.13 #1067), 01xy5l_ (0.12 #1061, 0.11 #1278, 0.11 #1422), 0d2b38 (0.12 #170, 0.11 #1073, 0.11 #1290) >> Best rule #1054 for best value: >> intensional similarity = 4 >> extensional distance = 729 >> proper extension: 03mh94; 0c40vxk; 01vksx; 02v63m; 0kv238; 0b1y_2; 057__d; 07vn_9; >> query: (?x8679, 0ch6mp2) <- film(?x2275, ?x8679), currency(?x8679, ?x170), film_crew_role(?x8679, ?x137), participant(?x262, ?x2275) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1053 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 729 *> proper extension: 03mh94; 0c40vxk; 01vksx; 02v63m; 0kv238; 0b1y_2; 057__d; 07vn_9; *> query: (?x8679, 09vw2b7) <- film(?x2275, ?x8679), currency(?x8679, ?x170), film_crew_role(?x8679, ?x137), participant(?x262, ?x2275) *> conf = 0.67 ranks of expected_values: 2 EVAL 023g6w film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11982-02g5h5 PRED entity: 02g5h5 PRED relation: type_of_union PRED expected values: 04ztj => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.75 #21, 0.72 #33, 0.72 #65), 01g63y (0.14 #166, 0.14 #86, 0.14 #182) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 392 >> proper extension: 06688p; 049tjg; 018dnt; 09byk; 01wjrn; 045bs6; 02tqkf; 01_rh4; 01n7qlf; 01lly5; ... >> query: (?x3815, 04ztj) <- actor(?x4932, ?x3815), film(?x3815, ?x5045), cinematography(?x5045, ?x5046) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02g5h5 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.749 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11981-04mky3 PRED entity: 04mky3 PRED relation: instrumentalists! PRED expected values: 05kms => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 120): 05148p4 (0.40 #902, 0.33 #18, 0.33 #3125), 0l14md (0.39 #890, 0.33 #6, 0.16 #562), 0l15bq (0.34 #636, 0.26 #717, 0.25 #800), 0395lw (0.34 #636, 0.26 #717, 0.25 #800), 03qjg (0.33 #44, 0.29 #362, 0.22 #600), 048j4l (0.33 #73, 0.04 #709, 0.03 #875), 03gvt (0.13 #376, 0.11 #694, 0.10 #860), 06ch55 (0.11 #630, 0.10 #793, 0.09 #710), 06ncr (0.10 #922, 0.08 #2030, 0.08 #2191), 04rzd (0.10 #2425, 0.08 #835, 0.08 #193) >> Best rule #902 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 01yzl2; 09lwrt; >> query: (?x11947, 05148p4) <- instrumentalists(?x212, ?x11947), ?x212 = 026t6, artists(?x302, ?x11947) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3425 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 614 *> proper extension: 01l03w2; 0cm03; 03_0p; *> query: (?x11947, ?x75) <- instrumentalists(?x212, ?x11947), role(?x75, ?x212) *> conf = 0.03 ranks of expected_values: 101 EVAL 04mky3 instrumentalists! 05kms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 105.000 105.000 0.403 http://example.org/music/instrument/instrumentalists #11980-04n32 PRED entity: 04n32 PRED relation: artists! PRED expected values: 015y_n => 120 concepts (59 used for prediction) PRED predicted values (max 10 best out of 218): 06by7 (0.63 #3456, 0.47 #10957, 0.46 #9704), 064t9 (0.52 #949, 0.47 #6259, 0.46 #7821), 016clz (0.47 #12506, 0.25 #6564, 0.23 #9687), 06j6l (0.37 #4733, 0.31 #5045, 0.30 #986), 015y_n (0.33 #848, 0.09 #1160, 0.07 #4595), 0xhtw (0.31 #10952, 0.30 #3451, 0.19 #12831), 017_qw (0.30 #5373, 0.18 #4436, 0.16 #1937), 0gywn (0.29 #4743, 0.24 #5055, 0.21 #3494), 03lty (0.27 #10964, 0.12 #6588, 0.12 #10023), 0glt670 (0.26 #6288, 0.22 #8162, 0.22 #13480) >> Best rule #3456 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 01wv9xn; 01czx; 0134s5; 0134tg; 015srx; 07h76; 07mvp; 0bk1p; 033s6; 07hgm; ... >> query: (?x9367, 06by7) <- award(?x9367, ?x724), artists(?x505, ?x9367), inductee(?x1091, ?x9367) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #848 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 01fl3; *> query: (?x9367, 015y_n) <- award(?x9367, ?x724), artists(?x9427, ?x9367), ?x9427 = 0m40d *> conf = 0.33 ranks of expected_values: 5 EVAL 04n32 artists! 015y_n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 120.000 59.000 0.630 http://example.org/music/genre/artists #11979-01c22t PRED entity: 01c22t PRED relation: film! PRED expected values: 054g1r => 89 concepts (59 used for prediction) PRED predicted values (max 10 best out of 72): 054g1r (0.60 #34, 0.49 #404, 0.33 #108), 0kk9v (0.34 #1114, 0.13 #2902, 0.13 #593), 03xq0f (0.30 #301, 0.23 #523, 0.18 #1193), 086k8 (0.22 #150, 0.20 #818, 0.19 #224), 05qd_ (0.21 #453, 0.21 #527, 0.18 #825), 016tt2 (0.17 #448, 0.17 #1340, 0.15 #1711), 07k2x (0.15 #189, 0.13 #263, 0.04 #559), 017s11 (0.15 #1265, 0.14 #1042, 0.14 #1561), 016tw3 (0.15 #976, 0.13 #1495, 0.13 #1050), 016szr (0.13 #2902, 0.13 #593, 0.06 #2903) >> Best rule #34 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01xdxy; >> query: (?x1080, 054g1r) <- music(?x1080, ?x4850), nominated_for(?x3456, ?x1080), genre(?x1080, ?x258), ?x3456 = 05jcn8 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c22t film! 054g1r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 59.000 0.600 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #11978-03f7xg PRED entity: 03f7xg PRED relation: country PRED expected values: 09c7w0 0d060g => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.79 #3141, 0.78 #371, 0.78 #554), 07ssc (0.50 #17, 0.41 #4364, 0.24 #1242), 0d060g (0.41 #4364, 0.14 #316, 0.14 #253), 03_3d (0.17 #8, 0.07 #130, 0.07 #377), 0345h (0.16 #824, 0.15 #947, 0.14 #885), 0f8l9c (0.12 #572, 0.11 #81, 0.11 #389), 06mkj (0.10 #163, 0.04 #471, 0.03 #224), 09blyk (0.07 #1595, 0.07 #3873, 0.07 #3872), 0djd22 (0.07 #1595, 0.07 #3873, 0.07 #3872), 01jfsb (0.07 #1595, 0.07 #3873, 0.07 #3872) >> Best rule #3141 for best value: >> intensional similarity = 2 >> extensional distance = 908 >> proper extension: 05f67hw; >> query: (?x3306, 09c7w0) <- produced_by(?x3306, ?x5898), nominated_for(?x5898, ?x4111) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 03f7xg country 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.788 http://example.org/film/film/country EVAL 03f7xg country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.788 http://example.org/film/film/country #11977-04mhbh PRED entity: 04mhbh PRED relation: profession PRED expected values: 018gz8 => 121 concepts (50 used for prediction) PRED predicted values (max 10 best out of 95): 01d_h8 (0.93 #3196, 0.54 #1021, 0.50 #2181), 0cbd2 (0.83 #4213, 0.72 #2327, 0.69 #1747), 09jwl (0.71 #2483, 0.26 #5820, 0.23 #4079), 018gz8 (0.60 #1031, 0.50 #16, 0.46 #2191), 03gjzk (0.58 #1029, 0.45 #449, 0.44 #304), 0nbcg (0.39 #2494, 0.16 #754, 0.13 #4090), 01c72t (0.39 #5824, 0.12 #2487, 0.11 #4083), 016z4k (0.32 #2469, 0.14 #149, 0.14 #729), 02jknp (0.31 #2038, 0.31 #3198, 0.28 #3053), 0dz3r (0.31 #2467, 0.13 #5804, 0.09 #4934) >> Best rule #3196 for best value: >> intensional similarity = 4 >> extensional distance = 191 >> proper extension: 01b9ck; 03n93; 0gv40; 01j2xj; 01ts_3; 01vhrz; 06s26c; 02vtnf; >> query: (?x9288, 01d_h8) <- profession(?x9288, ?x4725), participant(?x9288, ?x8445), profession(?x5438, ?x4725), ?x5438 = 08ff1k >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1031 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 0167xy; *> query: (?x9288, 018gz8) <- influenced_by(?x9288, ?x4066), influenced_by(?x10101, ?x4066), ?x10101 = 01wp_jm, influenced_by(?x4066, ?x2283) *> conf = 0.60 ranks of expected_values: 4 EVAL 04mhbh profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 121.000 50.000 0.927 http://example.org/people/person/profession #11976-0jyb4 PRED entity: 0jyb4 PRED relation: film_release_region PRED expected values: 059j2 05b4w 01mk6 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 123): 059j2 (0.87 #822, 0.81 #1613, 0.81 #1455), 0345h (0.82 #824, 0.76 #1615, 0.76 #1457), 035qy (0.80 #826, 0.69 #1617, 0.69 #1459), 0154j (0.78 #794, 0.66 #1585, 0.66 #1427), 05b4w (0.75 #857, 0.68 #1490, 0.66 #1648), 06bnz (0.72 #838, 0.60 #1471, 0.59 #1629), 0b90_r (0.72 #793, 0.62 #1584, 0.62 #1426), 0d060g (0.71 #796, 0.64 #1587, 0.63 #1429), 03rt9 (0.70 #804, 0.58 #1595, 0.58 #1437), 06t2t (0.69 #854, 0.56 #1487, 0.56 #1645) >> Best rule #822 for best value: >> intensional similarity = 4 >> extensional distance = 170 >> proper extension: 0gtsx8c; >> query: (?x6215, 059j2) <- film_release_region(?x6215, ?x2267), film_release_region(?x6215, ?x390), ?x390 = 0chghy, ?x2267 = 03rj0 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 56 EVAL 0jyb4 film_release_region 01mk6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 68.000 68.000 0.866 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jyb4 film_release_region 05b4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 68.000 0.866 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jyb4 film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.866 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11975-01y9st PRED entity: 01y9st PRED relation: student PRED expected values: 02184q => 115 concepts (80 used for prediction) PRED predicted values (max 10 best out of 300): 012j5h (0.29 #5246, 0.22 #9434, 0.18 #13622), 0391jz (0.17 #568, 0.14 #4756, 0.14 #2662), 02v49c (0.17 #1499, 0.14 #3593, 0.12 #7781), 02wd48 (0.17 #1481, 0.14 #3575, 0.12 #7763), 028k57 (0.17 #763, 0.14 #2857, 0.12 #7045), 01ycbq (0.17 #305, 0.14 #2399, 0.12 #6587), 02lf0c (0.17 #77, 0.14 #2171, 0.12 #6359), 031v3p (0.14 #6195, 0.11 #10383, 0.09 #14571), 01m5m5b (0.14 #6002, 0.11 #10190, 0.09 #14378), 0ywqc (0.14 #5987, 0.11 #10175, 0.09 #14363) >> Best rule #5246 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01w3vc; >> query: (?x5221, 012j5h) <- citytown(?x5221, ?x1658), ?x1658 = 0h7h6, state_province_region(?x5221, ?x1905), school_type(?x5221, ?x3092) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01y9st student 02184q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 80.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #11974-03xl77 PRED entity: 03xl77 PRED relation: location_of_ceremony PRED expected values: 0r62v => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 36): 03gh4 (0.08 #301, 0.02 #1376, 0.02 #5079), 0cv3w (0.07 #870, 0.06 #1467, 0.04 #1586), 04jpl (0.07 #485, 0.06 #605, 0.03 #844), 012wgb (0.07 #518, 0.06 #638, 0.02 #1236), 04w58 (0.07 #521, 0.03 #999, 0.02 #1239), 06y57 (0.07 #533, 0.01 #1727, 0.01 #2087), 027rn (0.07 #477, 0.01 #1671, 0.01 #2031), 06c62 (0.07 #544, 0.01 #2098, 0.01 #2337), 0r4h3 (0.06 #596, 0.03 #1790, 0.02 #2509), 030qb3t (0.06 #615, 0.03 #854, 0.03 #973) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0c3jz; >> query: (?x2946, 03gh4) <- profession(?x2946, ?x131), participant(?x8793, ?x2946), category(?x2946, ?x134), diet(?x2946, ?x3130) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #1568 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 01q_ph; 01wmxfs; 0h7pj; *> query: (?x2946, 0r62v) <- film(?x2946, ?x6684), artist(?x1954, ?x2946), participant(?x8793, ?x2946) *> conf = 0.01 ranks of expected_values: 33 EVAL 03xl77 location_of_ceremony 0r62v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 159.000 159.000 0.077 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #11973-07w0v PRED entity: 07w0v PRED relation: institution! PRED expected values: 014mlp 02_xgp2 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 15): 02_xgp2 (0.79 #202, 0.75 #71, 0.70 #218), 014mlp (0.75 #458, 0.75 #426, 0.73 #394), 027f2w (0.50 #68, 0.43 #199, 0.41 #151), 03mkk4 (0.29 #70, 0.27 #201, 0.27 #153), 0bjrnt (0.29 #67, 0.24 #150, 0.21 #198), 01rr_d (0.25 #75, 0.21 #403, 0.20 #158), 028dcg (0.24 #142, 0.24 #93, 0.21 #45), 02cq61 (0.17 #12, 0.11 #76, 0.10 #159), 02mjs7 (0.14 #81, 0.12 #196, 0.11 #212), 01ysy9 (0.12 #127, 0.06 #568, 0.05 #144) >> Best rule #202 for best value: >> intensional similarity = 2 >> extensional distance = 65 >> proper extension: 0mj0c; 014635; 07hyk; >> query: (?x1011, 02_xgp2) <- organization(?x1011, ?x5487), currency(?x5487, ?x170) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07w0v institution! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.791 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07w0v institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.791 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #11972-05c3mp2 PRED entity: 05c3mp2 PRED relation: genre! PRED expected values: 02pxst => 39 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1896): 03kg2v (0.67 #2368, 0.45 #9857, 0.44 #15483), 02q8ms8 (0.67 #3007, 0.45 #10496, 0.43 #6752), 03twd6 (0.67 #2108, 0.38 #15223, 0.36 #9597), 0dlngsd (0.67 #2680, 0.38 #15795, 0.29 #3745), 033srr (0.67 #2555, 0.38 #15670, 0.29 #3745), 0btpm6 (0.67 #3218, 0.31 #16333, 0.29 #3745), 05znxx (0.67 #2779, 0.31 #15894, 0.29 #4653), 02fqrf (0.67 #2461, 0.29 #3745, 0.29 #4335), 0g5q34q (0.57 #6689, 0.57 #4818, 0.50 #1071), 0c0zq (0.57 #7240, 0.57 #5369, 0.50 #1622) >> Best rule #2368 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 0lsxr; 03k9fj; >> query: (?x6508, 03kg2v) <- genre(?x6206, ?x6508), genre(?x2961, ?x6508), film_crew_role(?x2961, ?x7591), film(?x1550, ?x2961), film_release_region(?x2961, ?x94), ?x7591 = 0d2b38, film_release_region(?x2961, ?x205), ?x6206 = 0cwfgz, film(?x3713, ?x2961), currency(?x205, ?x170), contains(?x205, ?x1356), country(?x150, ?x205), film_release_region(?x5067, ?x205), film_release_region(?x1707, ?x205), ?x5067 = 01rwpj, ?x1707 = 04n52p6, olympics(?x205, ?x391) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3745 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 4 *> proper extension: 0lsxr; 03k9fj; *> query: (?x6508, ?x66) <- genre(?x6206, ?x6508), genre(?x2961, ?x6508), film_crew_role(?x2961, ?x7591), film(?x1550, ?x2961), film_release_region(?x2961, ?x94), ?x7591 = 0d2b38, film_release_region(?x2961, ?x205), ?x6206 = 0cwfgz, film(?x3713, ?x2961), currency(?x205, ?x170), contains(?x205, ?x1356), country(?x150, ?x205), film_release_region(?x5067, ?x205), film_release_region(?x1707, ?x205), film_release_region(?x66, ?x205), ?x5067 = 01rwpj, ?x1707 = 04n52p6, olympics(?x205, ?x391) *> conf = 0.29 ranks of expected_values: 944 EVAL 05c3mp2 genre! 02pxst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 19.000 0.667 http://example.org/film/film/genre #11971-01yg9y PRED entity: 01yg9y PRED relation: student! PRED expected values: 029qzx => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 112): 0bwfn (0.08 #802, 0.05 #14504, 0.05 #6072), 03ksy (0.06 #633, 0.04 #5903, 0.02 #11700), 04b_46 (0.06 #754, 0.02 #227, 0.02 #6024), 02_gzx (0.05 #384), 0288zy (0.05 #16), 07vhb (0.04 #696, 0.02 #169, 0.02 #1223), 09f2j (0.04 #3321, 0.04 #2794, 0.02 #10699), 026gvfj (0.04 #3273, 0.04 #2746, 0.01 #3800), 065y4w7 (0.03 #1068, 0.03 #5811, 0.03 #20043), 0pspl (0.03 #1163, 0.02 #636, 0.01 #1690) >> Best rule #802 for best value: >> intensional similarity = 2 >> extensional distance = 49 >> proper extension: 04qw17; >> query: (?x5413, 0bwfn) <- nominated_for(?x5413, ?x6678), award_winner(?x6678, ?x1686) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01yg9y student! 029qzx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 87.000 0.078 http://example.org/education/educational_institution/students_graduates./education/education/student #11970-03qjwc PRED entity: 03qjwc PRED relation: artist PRED expected values: 01vsy7t => 48 concepts (24 used for prediction) PRED predicted values (max 10 best out of 906): 033s6 (0.47 #4890, 0.32 #8250, 0.30 #9090), 01q99h (0.41 #5484, 0.32 #7164, 0.29 #3804), 01323p (0.41 #5602, 0.28 #9804, 0.27 #8124), 01k23t (0.41 #7287, 0.38 #1406, 0.33 #2245), 0g824 (0.38 #2975, 0.38 #1296, 0.33 #2135), 01wg25j (0.38 #1463, 0.33 #4826, 0.33 #2302), 05_swj (0.38 #1330, 0.33 #2169, 0.33 #491), 01vwbts (0.38 #1171, 0.33 #2010, 0.31 #2850), 09889g (0.36 #3719, 0.23 #3357, 0.23 #7079), 02qsjt (0.33 #493, 0.27 #6720, 0.25 #1332) >> Best rule #4890 for best value: >> intensional similarity = 12 >> extensional distance = 13 >> proper extension: 033hn8; 03rhqg; 015_1q; 025t8bv; 01t04r; 02y21l; >> query: (?x14556, 033s6) <- artist(?x14556, ?x8029), artists(?x3061, ?x8029), artists(?x1000, ?x8029), artists(?x671, ?x8029), ?x671 = 064t9, artist(?x5634, ?x8029), inductee(?x1091, ?x8029), ?x1000 = 0xhtw, group(?x1750, ?x8029), ?x5634 = 01cl2y, ?x1750 = 02hnl, ?x3061 = 05bt6j >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #6720 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 19 *> proper extension: 06x2ww; *> query: (?x14556, ?x250) <- artist(?x14556, ?x8029), artists(?x1928, ?x8029), artists(?x671, ?x8029), ?x671 = 064t9, artist(?x7089, ?x8029), artist(?x5666, ?x8029), ?x1928 = 0mhfr, artist(?x5666, ?x317), child(?x9492, ?x7089), artist(?x7089, ?x250), category(?x5666, ?x134), ?x317 = 0c9d9 *> conf = 0.27 ranks of expected_values: 75 EVAL 03qjwc artist 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 48.000 24.000 0.467 http://example.org/music/record_label/artist #11969-0bx0l PRED entity: 0bx0l PRED relation: nominated_for! PRED expected values: 0f4x7 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 211): 0gs9p (0.68 #14340, 0.67 #14107, 0.67 #7168), 02pqp12 (0.54 #516, 0.32 #3754, 0.27 #978), 02qyntr (0.51 #635, 0.30 #3873, 0.26 #1097), 04dn09n (0.47 #494, 0.41 #3732, 0.34 #956), 0gr0m (0.46 #517, 0.37 #3755, 0.35 #979), 04kxsb (0.43 #551, 0.27 #3789, 0.24 #1013), 0f4x7 (0.41 #485, 0.40 #947, 0.39 #3723), 02qvyrt (0.37 #552, 0.20 #3790, 0.19 #783), 099c8n (0.34 #514, 0.25 #3752, 0.20 #1901), 0gqyl (0.32 #997, 0.30 #3773, 0.28 #535) >> Best rule #14340 for best value: >> intensional similarity = 3 >> extensional distance = 986 >> proper extension: 02_1q9; 027tbrc; 0524b41; 02_1kl; 03j63k; 06qwh; 0fpxp; 097h2; 04xbq3; 023ny6; ... >> query: (?x2168, ?x1079) <- award(?x2168, ?x1079), nominated_for(?x1079, ?x167), award(?x669, ?x1079) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #485 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 03pc89; 0dmn0x; *> query: (?x2168, 0f4x7) <- music(?x2168, ?x9170), nominated_for(?x198, ?x2168), film_release_region(?x2168, ?x87), ?x198 = 040njc *> conf = 0.41 ranks of expected_values: 7 EVAL 0bx0l nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 91.000 91.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11968-01tl50z PRED entity: 01tl50z PRED relation: artist! PRED expected values: 03rhqg => 112 concepts (98 used for prediction) PRED predicted values (max 10 best out of 93): 03rhqg (0.15 #4917, 0.14 #6180, 0.13 #5619), 0n85g (0.14 #482, 0.11 #1042, 0.09 #2583), 0181dw (0.14 #1581, 0.12 #2001, 0.10 #2562), 033hn8 (0.13 #1554, 0.12 #154, 0.11 #1974), 011k1h (0.13 #1550, 0.11 #1970, 0.11 #150), 0g768 (0.12 #6200, 0.12 #456, 0.12 #4937), 03mp8k (0.10 #1606, 0.09 #2026, 0.09 #346), 043g7l (0.10 #1570, 0.09 #170, 0.08 #1990), 01trtc (0.10 #352, 0.08 #1612, 0.08 #3573), 0mzkr (0.09 #444, 0.09 #164, 0.07 #1564) >> Best rule #4917 for best value: >> intensional similarity = 2 >> extensional distance = 643 >> proper extension: 089tm; 01t_xp_; 01pfr3; 04rcr; 0150jk; 02r3zy; 07c0j; 01v0sx2; 067mj; 01vsxdm; ... >> query: (?x8758, 03rhqg) <- award(?x8758, ?x375), artist(?x3240, ?x8758) >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tl50z artist! 03rhqg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 98.000 0.146 http://example.org/music/record_label/artist #11967-02f6g5 PRED entity: 02f6g5 PRED relation: film! PRED expected values: 03xq0f => 61 concepts (55 used for prediction) PRED predicted values (max 10 best out of 47): 03xq0f (0.88 #78, 0.50 #4, 0.12 #599), 06rq1k (0.45 #2235), 086k8 (0.18 #76, 0.15 #970, 0.15 #1343), 05qd_ (0.16 #82, 0.12 #2168, 0.12 #1647), 016tw3 (0.15 #2170, 0.15 #1351, 0.14 #1276), 016tt2 (0.14 #77, 0.13 #747, 0.12 #3), 01gb54 (0.09 #102, 0.07 #252, 0.07 #327), 054g1r (0.08 #34, 0.06 #2194, 0.06 #108), 020h2v (0.08 #44, 0.05 #1385, 0.04 #1160), 0jz9f (0.07 #522, 0.07 #820, 0.07 #374) >> Best rule #78 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 0522wp; >> query: (?x1810, 03xq0f) <- region(?x1810, ?x512), film(?x541, ?x1810), ?x512 = 07ssc >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02f6g5 film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 55.000 0.881 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #11966-0jkhr PRED entity: 0jkhr PRED relation: school! PRED expected values: 09l0x9 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 15): 0f4vx0 (0.44 #85, 0.29 #145, 0.28 #160), 02qw1zx (0.37 #80, 0.22 #260, 0.21 #155), 092j54 (0.29 #83, 0.19 #437, 0.19 #436), 09l0x9 (0.22 #86, 0.19 #437, 0.19 #436), 05vsb7 (0.21 #121, 0.21 #76, 0.20 #151), 0g3zpp (0.19 #437, 0.19 #436, 0.16 #77), 038c0q (0.19 #437, 0.19 #436, 0.15 #156), 06439y (0.19 #437, 0.19 #436, 0.14 #135), 02x2khw (0.19 #437, 0.19 #436, 0.14 #123), 02z6872 (0.19 #437, 0.19 #436, 0.13 #129) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 06mkj; 0d05w3; >> query: (?x6856, 0f4vx0) <- school(?x8133, ?x6856), school(?x8133, ?x2948), ?x2948 = 0j_sncb, draft(?x1347, ?x8133) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #86 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 06mkj; 0d05w3; *> query: (?x6856, 09l0x9) <- school(?x8133, ?x6856), school(?x8133, ?x2948), ?x2948 = 0j_sncb, draft(?x1347, ?x8133) *> conf = 0.22 ranks of expected_values: 4 EVAL 0jkhr school! 09l0x9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 146.000 146.000 0.444 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #11965-0345h PRED entity: 0345h PRED relation: country! PRED expected values: 01hp22 0w0d 018w8 09f6b => 209 concepts (209 used for prediction) PRED predicted values (max 10 best out of 11): 0w0d (0.73 #244, 0.70 #101, 0.69 #288), 01hp22 (0.68 #199, 0.67 #78, 0.62 #45), 06z68 (0.62 #47, 0.55 #102, 0.52 #201), 018w8 (0.62 #48, 0.40 #103, 0.36 #202), 09_b4 (0.50 #51, 0.44 #216, 0.42 #95), 0crlz (0.50 #50, 0.35 #105, 0.33 #61), 09f6b (0.47 #97, 0.43 #174, 0.42 #273), 018jz (0.30 #672, 0.14 #137, 0.13 #247), 06br8 (0.30 #672, 0.11 #63, 0.10 #74), 037hz (0.30 #672, 0.11 #66, 0.10 #77) >> Best rule #244 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 070zc; >> query: (?x1264, 0w0d) <- contains(?x1264, ?x7154), combatants(?x13022, ?x1264), major_field_of_study(?x7154, ?x742) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 7 EVAL 0345h country! 09f6b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 209.000 209.000 0.733 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0345h country! 018w8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 209.000 209.000 0.733 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0345h country! 0w0d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 209.000 209.000 0.733 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0345h country! 01hp22 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 209.000 209.000 0.733 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #11964-07s8r0 PRED entity: 07s8r0 PRED relation: gender PRED expected values: 02zsn => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.86 #4, 0.53 #81, 0.32 #18), 05zppz (0.72 #132, 0.71 #100, 0.71 #144) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 01yd8v; 03k545; >> query: (?x1641, 02zsn) <- award(?x1641, ?x3499), film(?x1641, ?x167), ?x3499 = 03qgjwc >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07s8r0 gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.857 http://example.org/people/person/gender #11963-02_nkp PRED entity: 02_nkp PRED relation: team PRED expected values: 0jmjr => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 365): 0jm3v (0.33 #14, 0.13 #2865, 0.09 #6425), 0jm3b (0.33 #238, 0.07 #3089, 0.06 #9854), 0jml5 (0.33 #97, 0.07 #2948, 0.05 #4016), 0jm2v (0.25 #741, 0.22 #3592, 0.18 #3236), 0bwjj (0.25 #943, 0.18 #3438, 0.17 #3794), 0jm4b (0.25 #815, 0.08 #2242, 0.07 #2598), 026dqjm (0.25 #1026, 0.08 #2453, 0.07 #2809), 0jmbv (0.25 #824, 0.07 #2963, 0.06 #8303), 02pqcfz (0.25 #790, 0.07 #2929, 0.05 #3997), 01lpx8 (0.19 #5549, 0.18 #6617, 0.16 #4125) >> Best rule #14 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02cg2v; >> query: (?x12607, 0jm3v) <- student(?x4341, ?x12607), people(?x2510, ?x12607), athlete(?x4833, ?x12607), team(?x12607, ?x8228), ?x8228 = 0jmcv >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4158 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 014g_s; *> query: (?x12607, 0jmjr) <- student(?x4341, ?x12607), people(?x2510, ?x12607), athlete(?x4833, ?x12607), team(?x12607, ?x8228), colors(?x8228, ?x663) *> conf = 0.05 ranks of expected_values: 103 EVAL 02_nkp team 0jmjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 181.000 181.000 0.333 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #11962-09v8db5 PRED entity: 09v8db5 PRED relation: award! PRED expected values: 065d1h => 62 concepts (31 used for prediction) PRED predicted values (max 10 best out of 2306): 01f873 (0.65 #97958, 0.64 #47283, 0.62 #84444), 012d40 (0.50 #16899, 0.50 #6771, 0.43 #13523), 069_0y (0.33 #8995, 0.33 #2243, 0.30 #19123), 03_2y (0.33 #9622, 0.33 #2870, 0.29 #16374), 01f7v_ (0.33 #7927, 0.33 #1175, 0.29 #14679), 0342vg (0.33 #9152, 0.33 #2400, 0.29 #15904), 012ykt (0.33 #5190, 0.20 #18694, 0.17 #11942), 0451j (0.33 #2210, 0.20 #19090, 0.17 #12338), 02p59ry (0.33 #2037, 0.17 #12165, 0.17 #8789), 0jlv5 (0.33 #12083, 0.14 #15459, 0.10 #18835) >> Best rule #97958 for best value: >> intensional similarity = 6 >> extensional distance = 175 >> proper extension: 0m7yy; 02wwsh8; 03ybrwc; 02vl9ln; 0468g4r; >> query: (?x5923, ?x1864) <- award(?x3376, ?x5923), award_winner(?x5923, ?x1864), award_winner(?x5923, ?x754), profession(?x754, ?x319), ?x319 = 01d_h8, gender(?x754, ?x231) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09v8db5 award! 065d1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 31.000 0.655 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11961-03fyrh PRED entity: 03fyrh PRED relation: sports! PRED expected values: 0jkvj => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 22): 09x3r (0.87 #533, 0.86 #417, 0.83 #579), 0ldqf (0.87 #533, 0.86 #417, 0.83 #579), 0kbws (0.79 #556, 0.75 #416, 0.74 #487), 0jkvj (0.78 #482, 0.77 #620, 0.75 #807), 018wrk (0.60 #511, 0.60 #255, 0.55 #557), 016r9z (0.60 #543, 0.60 #173, 0.54 #115), 09n48 (0.58 #163, 0.54 #115, 0.53 #322), 018ctl (0.58 #163, 0.54 #115, 0.53 #322), 0kbvv (0.58 #163, 0.53 #322, 0.52 #208), 01f1jy (0.58 #163, 0.53 #322, 0.52 #208) >> Best rule #533 for best value: >> intensional similarity = 44 >> extensional distance = 8 >> proper extension: 02vx4; >> query: (?x3641, ?x1608) <- sports(?x5395, ?x3641), sports(?x2369, ?x3641), ?x5395 = 018qb4, country(?x3641, ?x9455), country(?x3641, ?x4521), country(?x3641, ?x985), country(?x3641, ?x390), medal(?x2369, ?x422), teams(?x4521, ?x3436), sports(?x2369, ?x171), film_release_region(?x10829, ?x985), film_release_region(?x9832, ?x985), film_release_region(?x8770, ?x985), film_release_region(?x7379, ?x985), film_release_region(?x6175, ?x985), film_release_region(?x4464, ?x985), film_release_region(?x3757, ?x985), film_release_region(?x2958, ?x985), film_release_region(?x2104, ?x985), film_release_region(?x1642, ?x985), film_release_region(?x1546, ?x985), film_release_region(?x1470, ?x985), film_release_region(?x1315, ?x985), olympics(?x2051, ?x2369), adjustment_currency(?x4521, ?x170), jurisdiction_of_office(?x182, ?x985), ?x4464 = 05pdh86, ?x9832 = 01xlqd, sports(?x1608, ?x3641), ?x10829 = 0jz71, ?x1642 = 0bq8tmw, ?x1470 = 03twd6, ?x2051 = 035dk, ?x3757 = 02vr3gz, ?x1315 = 053tj7, ?x6175 = 0gg5kmg, organization(?x985, ?x127), music(?x1546, ?x7088), ?x8770 = 025ts_z, ?x2958 = 0b_5d, ?x2104 = 0j_tw, administrative_area_type(?x9455, ?x2792), ?x7379 = 032clf, ?x390 = 0chghy >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #482 for first EXPECTED value: *> intensional similarity = 45 *> extensional distance = 7 *> proper extension: 096f8; *> query: (?x3641, 0jkvj) <- sports(?x5395, ?x3641), sports(?x2748, ?x3641), sports(?x2496, ?x3641), sports(?x2131, ?x3641), country(?x3641, ?x4743), country(?x3641, ?x1264), country(?x3641, ?x774), ?x774 = 06mzp, olympics(?x512, ?x5395), olympics(?x3641, ?x1931), ?x2131 = 0lk8j, olympics(?x172, ?x5395), ?x1264 = 0345h, contains(?x6956, ?x4743), film_release_region(?x6216, ?x4743), film_release_region(?x6168, ?x4743), film_release_region(?x2889, ?x4743), film_release_region(?x1988, ?x4743), film_release_region(?x1701, ?x4743), film_release_region(?x903, ?x4743), film_release_region(?x622, ?x4743), film_release_region(?x504, ?x4743), film_release_region(?x141, ?x4743), film_release_region(?x124, ?x4743), ?x1701 = 0bh8yn3, medal(?x5395, ?x422), official_language(?x4743, ?x3966), country(?x3885, ?x4743), ?x6168 = 0gj96ln, organization(?x4743, ?x127), ?x2889 = 040b5k, ?x124 = 0g56t9t, ?x6216 = 06fcqw, ?x903 = 04969y, ?x1988 = 09k56b7, geographic_distribution(?x11490, ?x4743), olympics(?x3127, ?x2748), sports(?x5395, ?x359), ?x2496 = 0sxrz, ?x3885 = 019w9j, ?x622 = 0fq27fp, adjoins(?x3951, ?x4743), ?x504 = 0g5qs2k, language(?x174, ?x3966), ?x141 = 0gtsx8c *> conf = 0.78 ranks of expected_values: 4 EVAL 03fyrh sports! 0jkvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 48.000 48.000 0.871 http://example.org/olympics/olympic_games/sports #11960-0f_y9 PRED entity: 0f_y9 PRED relation: profession PRED expected values: 0cbd2 01c72t => 129 concepts (95 used for prediction) PRED predicted values (max 10 best out of 56): 0dz3r (0.49 #1576, 0.46 #1289, 0.46 #1719), 0dxtg (0.48 #156, 0.28 #9903, 0.28 #6750), 01c72t (0.33 #1166, 0.28 #1452, 0.28 #880), 0kyk (0.32 #171, 0.13 #3178, 0.13 #5332), 01d_h8 (0.31 #6313, 0.30 #4592, 0.30 #6742), 0n1h (0.29 #583, 0.24 #1871, 0.24 #3880), 0cbd2 (0.24 #149, 0.19 #2583, 0.18 #4163), 02hv44_ (0.24 #196, 0.04 #6790, 0.04 #6361), 02jknp (0.21 #11615, 0.21 #4594, 0.20 #6315), 03gjzk (0.20 #12051, 0.20 #9904, 0.19 #12909) >> Best rule #1576 for best value: >> intensional similarity = 5 >> extensional distance = 138 >> proper extension: 01tp5bj; 01s7qqw; 03wjb7; 04d_mtq; >> query: (?x7345, 0dz3r) <- profession(?x7345, ?x2659), profession(?x7345, ?x1183), ?x2659 = 039v1, ?x1183 = 09jwl, artists(?x671, ?x7345) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1166 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 131 *> proper extension: 0cm03; *> query: (?x7345, 01c72t) <- gender(?x7345, ?x231), instrumentalists(?x75, ?x7345), type_of_union(?x7345, ?x566), student(?x7716, ?x7345) *> conf = 0.33 ranks of expected_values: 3, 7 EVAL 0f_y9 profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 95.000 0.486 http://example.org/people/person/profession EVAL 0f_y9 profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 129.000 95.000 0.486 http://example.org/people/person/profession #11959-031b3h PRED entity: 031b3h PRED relation: award_winner PRED expected values: 016732 => 40 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1471): 02s2wq (0.50 #6348, 0.20 #13755, 0.17 #16223), 012x4t (0.39 #32079, 0.36 #34546, 0.33 #15147), 01364q (0.39 #32079, 0.36 #34546, 0.33 #4935), 012vd6 (0.39 #32079, 0.36 #34546, 0.33 #29611), 01vvycq (0.39 #32079, 0.36 #34546, 0.33 #29611), 0163m1 (0.39 #32079, 0.36 #34546, 0.32 #29610), 016376 (0.39 #32079, 0.36 #34546, 0.32 #29610), 086qd (0.39 #32079, 0.33 #5371, 0.33 #29611), 01wwvc5 (0.39 #32079, 0.33 #29611, 0.32 #29610), 03f3yfj (0.39 #32079, 0.33 #29611, 0.32 #29610) >> Best rule #6348 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01bgqh; 0c4z8; 01cw7s; >> query: (?x3937, 02s2wq) <- award(?x10712, ?x3937), award(?x3320, ?x3937), artists(?x378, ?x10712), ?x3320 = 030155, ceremony(?x3937, ?x139) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13840 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01c427; 01by1l; *> query: (?x3937, 016732) <- award(?x10712, ?x3937), award(?x5405, ?x3937), artists(?x378, ?x10712), ?x5405 = 01vvlyt *> conf = 0.10 ranks of expected_values: 226 EVAL 031b3h award_winner 016732 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 40.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #11958-0n2z PRED entity: 0n2z PRED relation: locations! PRED expected values: 0kbvb => 229 concepts (220 used for prediction) PRED predicted values (max 10 best out of 130): 0b_6pv (0.22 #2779, 0.20 #4449, 0.15 #7916), 0b_6rk (0.19 #2744, 0.18 #8139, 0.18 #6083), 03jqfx (0.18 #3033, 0.18 #4190, 0.17 #7271), 0b_6mr (0.18 #8182, 0.18 #6126, 0.17 #9078), 0b_6q5 (0.18 #8189, 0.16 #9982, 0.16 #6646), 0b_6qj (0.18 #6105, 0.17 #4436, 0.16 #8161), 0bzrxn (0.18 #6092, 0.16 #6605, 0.15 #9044), 0b_6zk (0.17 #4398, 0.16 #8123, 0.16 #6067), 06k75 (0.17 #7246, 0.09 #13150, 0.09 #13793), 0b_6x2 (0.17 #9022, 0.16 #9919, 0.16 #8126) >> Best rule #2779 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 07bcn; >> query: (?x11096, 0b_6pv) <- location(?x4930, ?x11096), country(?x11096, ?x1353), locations(?x2748, ?x11096), spouse(?x4930, ?x6324) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #3727 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 07_kq; *> query: (?x11096, ?x418) <- location_of_ceremony(?x566, ?x11096), capital(?x11095, ?x11096), country(?x11096, ?x1353), participating_countries(?x418, ?x1353) *> conf = 0.02 ranks of expected_values: 120 EVAL 0n2z locations! 0kbvb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 229.000 220.000 0.219 http://example.org/time/event/locations #11957-03hj5vf PRED entity: 03hj5vf PRED relation: award! PRED expected values: 06fpsx => 46 concepts (9 used for prediction) PRED predicted values (max 10 best out of 1561): 02_06s (0.50 #5840, 0.40 #4814, 0.25 #2767), 0209xj (0.50 #5182, 0.40 #4156, 0.10 #6207), 011yhm (0.40 #4770, 0.33 #5796, 0.25 #2723), 04b2qn (0.40 #4886, 0.33 #5912, 0.17 #6937), 0f4_l (0.40 #4312, 0.33 #5338, 0.17 #6363), 01qncf (0.40 #4319, 0.33 #5345, 0.04 #7394), 02rqwhl (0.40 #4226, 0.33 #5252, 0.03 #6277), 047msdk (0.33 #6145, 0.33 #5244, 0.23 #5118), 05p1tzf (0.33 #1068, 0.25 #2048, 0.23 #5118), 0m313 (0.33 #5, 0.25 #2053, 0.23 #6151) >> Best rule #5840 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 02x1dht; >> query: (?x3190, 02_06s) <- nominated_for(?x3190, ?x1642), nominated_for(?x3190, ?x1364), nominated_for(?x3190, ?x365), film(?x4832, ?x365), ?x1364 = 047msdk, award(?x364, ?x3190), film_release_distribution_medium(?x1642, ?x81) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2824 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 05zvj3m; 03hl6lc; *> query: (?x3190, 06fpsx) <- nominated_for(?x3190, ?x10769), nominated_for(?x3190, ?x365), ?x365 = 0bvn25, genre(?x10769, ?x53), nominated_for(?x385, ?x10769), film(?x237, ?x10769) *> conf = 0.25 ranks of expected_values: 45 EVAL 03hj5vf award! 06fpsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 46.000 9.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #11956-0184dt PRED entity: 0184dt PRED relation: award PRED expected values: 02pqp12 0gq9h => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 288): 02g3ft (0.73 #43381, 0.72 #44565, 0.71 #41408), 02qt02v (0.71 #29967, 0.71 #43380, 0.70 #29572), 0gs9p (0.45 #5196, 0.44 #73, 0.43 #5985), 02pqp12 (0.44 #64, 0.32 #2035, 0.31 #2429), 0gr4k (0.44 #29, 0.30 #10672, 0.29 #1212), 0gq9h (0.42 #10319, 0.38 #7559, 0.36 #8348), 09sb52 (0.33 #17774, 0.30 #16986, 0.26 #19744), 02rdyk7 (0.31 #85, 0.22 #5208, 0.22 #5997), 02x17s4 (0.31 #118, 0.17 #6818, 0.15 #1301), 02n9nmz (0.25 #63, 0.17 #6763, 0.17 #10706) >> Best rule #43381 for best value: >> intensional similarity = 3 >> extensional distance = 2245 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01vsxdm; 01wv9xn; 03j43; 02xb2bt; 0frsw; 046b0s; 03fbc; ... >> query: (?x2533, ?x1429) <- award_winner(?x1429, ?x2533), award(?x2533, ?x68), award(?x276, ?x1429) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #64 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 05drq5; *> query: (?x2533, 02pqp12) <- award_winner(?x3435, ?x2533), profession(?x2533, ?x319), ?x3435 = 03hl6lc *> conf = 0.44 ranks of expected_values: 4, 6 EVAL 0184dt award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 132.000 132.000 0.728 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0184dt award 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 132.000 0.728 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11955-04r7p PRED entity: 04r7p PRED relation: religion PRED expected values: 01lp8 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 18): 0c8wxp (0.22 #141, 0.19 #277, 0.18 #774), 03_gx (0.18 #421, 0.09 #962, 0.09 #512), 0kpl (0.11 #417, 0.11 #958, 0.11 #508), 0kq2 (0.04 #425, 0.04 #966, 0.03 #1057), 0n2g (0.04 #238, 0.03 #329, 0.02 #420), 03j6c (0.04 #156, 0.03 #2917, 0.03 #879), 01lp8 (0.04 #136, 0.03 #408, 0.02 #769), 092bf5 (0.04 #151, 0.03 #469, 0.02 #829), 0flw86 (0.04 #137, 0.02 #1311, 0.02 #3890), 06nzl (0.03 #286, 0.02 #558, 0.02 #648) >> Best rule #141 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 01_rh4; 043tg; 01k_0fp; 06g4_; >> query: (?x6958, 0c8wxp) <- profession(?x6958, ?x353), place_of_birth(?x6958, ?x9559), type_of_union(?x6958, ?x566), type_of_appearance(?x6958, ?x3429) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #136 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 01_rh4; 043tg; 01k_0fp; 06g4_; *> query: (?x6958, 01lp8) <- profession(?x6958, ?x353), place_of_birth(?x6958, ?x9559), type_of_union(?x6958, ?x566), type_of_appearance(?x6958, ?x3429) *> conf = 0.04 ranks of expected_values: 7 EVAL 04r7p religion 01lp8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 123.000 123.000 0.222 http://example.org/people/person/religion #11954-02q7fl9 PRED entity: 02q7fl9 PRED relation: film! PRED expected values: 02661h => 88 concepts (66 used for prediction) PRED predicted values (max 10 best out of 962): 0159h6 (0.60 #128708, 0.58 #116253, 0.45 #99649), 02rk45 (0.47 #14535, 0.45 #64357, 0.45 #99649), 017r13 (0.20 #1107, 0.06 #5260, 0.06 #7337), 0f5xn (0.17 #3041, 0.11 #7195, 0.07 #9272), 0bq2g (0.17 #2680, 0.06 #4757, 0.06 #6834), 01chc7 (0.17 #2634, 0.06 #6788, 0.04 #8865), 0c9xjl (0.17 #3043, 0.06 #7197, 0.04 #9274), 0f6_x (0.15 #10383, 0.13 #4153, 0.12 #6230), 01n4f8 (0.15 #10383, 0.13 #4153, 0.12 #6230), 0hskw (0.15 #58130, 0.14 #70588, 0.12 #6229) >> Best rule #128708 for best value: >> intensional similarity = 3 >> extensional distance = 1199 >> proper extension: 03g9xj; >> query: (?x5976, ?x1979) <- nominated_for(?x1979, ?x5976), film(?x1979, ?x508), location(?x1979, ?x1227) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #28383 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 207 *> proper extension: 02d413; 014_x2; 0g22z; 01jc6q; 09m6kg; 05jf85; 011yxg; 0gzy02; 07xtqq; 016z5x; ... *> query: (?x5976, 02661h) <- cinematography(?x5976, ?x5014), currency(?x5976, ?x170), nominated_for(?x384, ?x5976) *> conf = 0.02 ranks of expected_values: 360 EVAL 02q7fl9 film! 02661h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 88.000 66.000 0.597 http://example.org/film/actor/film./film/performance/film #11953-05vz3zq PRED entity: 05vz3zq PRED relation: film_release_region! PRED expected values: 067ghz => 205 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1823): 01fmys (0.91 #33346, 0.86 #18781, 0.81 #32022), 02xbyr (0.86 #19149, 0.82 #33714, 0.67 #41658), 08hmch (0.86 #18653, 0.79 #17329, 0.76 #31894), 07s3m4g (0.86 #19424, 0.77 #33989, 0.75 #8832), 05pdh86 (0.86 #19106, 0.76 #32347, 0.73 #33671), 05qbckf (0.86 #18773, 0.75 #8181, 0.73 #33338), 02vr3gz (0.86 #19011, 0.75 #8419, 0.71 #32252), 03nsm5x (0.86 #19579, 0.75 #8987, 0.68 #34144), 0gd0c7x (0.86 #18776, 0.68 #33341, 0.65 #67769), 043tvp3 (0.81 #32699, 0.79 #19458, 0.76 #52559) >> Best rule #33346 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 07f1x; >> query: (?x5114, 01fmys) <- combatants(?x390, ?x5114), country(?x7687, ?x5114), sports(?x391, ?x7687), ?x390 = 0chghy >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #33868 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 07f1x; *> query: (?x5114, 067ghz) <- combatants(?x390, ?x5114), country(?x7687, ?x5114), sports(?x391, ?x7687), ?x390 = 0chghy *> conf = 0.68 ranks of expected_values: 139 EVAL 05vz3zq film_release_region! 067ghz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 205.000 77.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11952-02p59ry PRED entity: 02p59ry PRED relation: nationality PRED expected values: 0d05w3 => 68 concepts (57 used for prediction) PRED predicted values (max 10 best out of 172): 09c7w0 (0.74 #1614, 0.74 #2522, 0.74 #1716), 0d05w3 (0.62 #152, 0.62 #657, 0.60 #50), 03h64 (0.52 #1008, 0.33 #660, 0.25 #155), 0j0k (0.33 #5750, 0.33 #5749, 0.32 #3935), 02qkt (0.33 #5750, 0.33 #5749, 0.32 #3935), 03rk0 (0.24 #753, 0.17 #853, 0.16 #553), 07ssc (0.18 #1411, 0.16 #1225, 0.13 #1426), 06t2t (0.18 #1411, 0.12 #151, 0.05 #656), 06f32 (0.18 #1411, 0.10 #659, 0.03 #4236), 0f8l9c (0.18 #1411, 0.08 #325, 0.06 #427) >> Best rule #1614 for best value: >> intensional similarity = 5 >> extensional distance = 204 >> proper extension: 0jf1b; 01wg982; 04g865; 01vqrm; 030vmc; 05bnx3j; >> query: (?x7004, 09c7w0) <- award(?x7004, ?x9217), produced_by(?x5187, ?x7004), place_of_birth(?x7004, ?x2645), titles(?x2480, ?x5187), film(?x147, ?x5187) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #152 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 012d40; 01t2h2; 02mz_6; 0451j; 03_2y; 0pksh; *> query: (?x7004, 0d05w3) <- award(?x7004, ?x9217), film(?x7004, ?x5826), profession(?x7004, ?x319), ?x319 = 01d_h8, ?x9217 = 09v51c2 *> conf = 0.62 ranks of expected_values: 2 EVAL 02p59ry nationality 0d05w3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 57.000 0.743 http://example.org/people/person/nationality #11951-0xkq4 PRED entity: 0xkq4 PRED relation: source PRED expected values: 0jbk9 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #23, 0.92 #24, 0.91 #73) >> Best rule #23 for best value: >> intensional similarity = 2 >> extensional distance = 192 >> proper extension: 0s3y5; 0s69k; 0rp46; 0rj0z; 0lhql; 0l0mk; 01j8yr; 0dq16; 0qy5v; 0rsjf; ... >> query: (?x1189, 0jbk9) <- location(?x1188, ?x1189), county(?x1189, ?x321) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xkq4 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.928 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #11950-01m5m5b PRED entity: 01m5m5b PRED relation: music! PRED expected values: 092vkg 03mh_tp => 139 concepts (103 used for prediction) PRED predicted values (max 10 best out of 494): 0dgq_kn (0.33 #599, 0.03 #8607, 0.02 #5604), 07s846j (0.33 #391, 0.01 #8399), 04ydr95 (0.33 #338, 0.01 #8346), 03_gz8 (0.09 #2648, 0.04 #4650, 0.04 #5651), 01s7w3 (0.06 #5867, 0.04 #2864, 0.04 #8870), 02rrfzf (0.04 #2321, 0.03 #30349, 0.03 #25344), 0btpm6 (0.04 #2736, 0.03 #8742, 0.02 #3737), 02fqrf (0.04 #2334, 0.03 #8340, 0.02 #3335), 034r25 (0.04 #2435, 0.03 #8441, 0.02 #3436), 09q5w2 (0.04 #2102, 0.03 #8108, 0.02 #3103) >> Best rule #599 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 02g40r; >> query: (?x10700, 0dgq_kn) <- role(?x10700, ?x1437), music(?x755, ?x10700), ?x755 = 02z3r8t >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01m5m5b music! 03mh_tp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 139.000 103.000 0.333 http://example.org/film/film/music EVAL 01m5m5b music! 092vkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 139.000 103.000 0.333 http://example.org/film/film/music #11949-01qqtr PRED entity: 01qqtr PRED relation: award PRED expected values: 02z0dfh => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 243): 0gqwc (0.75 #75, 0.48 #476, 0.48 #877), 09cn0c (0.72 #16449, 0.71 #25277, 0.71 #15644), 09qwmm (0.60 #34, 0.46 #435, 0.45 #836), 094qd5 (0.55 #45, 0.39 #446, 0.38 #847), 09sb52 (0.50 #442, 0.42 #843, 0.40 #41), 0cqgl9 (0.45 #190, 0.33 #591, 0.28 #992), 0bdwft (0.40 #69, 0.25 #871, 0.24 #470), 0gqyl (0.35 #506, 0.34 #907, 0.30 #105), 02z0dfh (0.35 #76, 0.31 #477, 0.28 #878), 0bfvw2 (0.30 #15, 0.26 #416, 0.23 #817) >> Best rule #75 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0n6f8; 01skmp; 02jr26; 0btxr; 01bj6y; >> query: (?x8966, 0gqwc) <- award(?x8966, ?x2478), award(?x8966, ?x1716), ?x2478 = 02x4x18, ?x1716 = 02y_rq5 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 0n6f8; 01skmp; 02jr26; 0btxr; 01bj6y; *> query: (?x8966, 02z0dfh) <- award(?x8966, ?x2478), award(?x8966, ?x1716), ?x2478 = 02x4x18, ?x1716 = 02y_rq5 *> conf = 0.35 ranks of expected_values: 9 EVAL 01qqtr award 02z0dfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 99.000 99.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11948-06f32 PRED entity: 06f32 PRED relation: administrative_parent PRED expected values: 02j71 => 239 concepts (99 used for prediction) PRED predicted values (max 10 best out of 28): 02j71 (0.80 #13123, 0.80 #12156, 0.79 #12709), 0d05w3 (0.31 #1420, 0.25 #47, 0.08 #1006), 0j0k (0.30 #9379, 0.23 #7305, 0.10 #1785), 02qkt (0.30 #9379, 0.23 #7305, 0.10 #1785), 09c7w0 (0.26 #7997, 0.23 #9241, 0.22 #12561), 0345h (0.17 #3612, 0.11 #7191, 0.09 #9681), 03rjj (0.16 #9521, 0.11 #1789, 0.05 #9244), 07ssc (0.12 #3322, 0.12 #4292, 0.09 #6079), 059j2 (0.08 #7190, 0.04 #4856, 0.02 #9127), 0f8l9c (0.07 #1113, 0.07 #9258, 0.06 #1665) >> Best rule #13123 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 01f08r; >> query: (?x2629, 02j71) <- exported_to(?x6428, ?x2629), jurisdiction_of_office(?x346, ?x2629), country(?x1121, ?x6428) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06f32 administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 239.000 99.000 0.797 http://example.org/base/aareas/schema/administrative_area/administrative_parent #11947-0j_c PRED entity: 0j_c PRED relation: nationality PRED expected values: 02jx1 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 62): 09c7w0 (0.75 #1502, 0.74 #101, 0.74 #1102), 0d0vqn (0.25 #9, 0.04 #3602, 0.03 #209), 02jx1 (0.24 #5006, 0.18 #1034, 0.16 #934), 07ssc (0.24 #5006, 0.15 #1416, 0.15 #415), 0345h (0.11 #531, 0.10 #1732, 0.08 #3733), 03rk0 (0.08 #5652, 0.07 #6052, 0.07 #6352), 0d060g (0.07 #307, 0.04 #3602, 0.04 #1408), 0f8l9c (0.07 #1723, 0.05 #2423, 0.05 #522), 03rt9 (0.05 #313, 0.03 #2114, 0.03 #213), 0h7x (0.05 #535, 0.04 #3602, 0.04 #2436) >> Best rule #1502 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 01438g; 02qw2xb; >> query: (?x2465, 09c7w0) <- film(?x2465, ?x4136), genre(?x4136, ?x53), friend(?x2465, ?x3628) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #5006 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 387 *> proper extension: 012vm6; 01fsz; *> query: (?x2465, ?x94) <- influenced_by(?x2135, ?x2465), nationality(?x2135, ?x94) *> conf = 0.24 ranks of expected_values: 3 EVAL 0j_c nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 99.000 0.746 http://example.org/people/person/nationality #11946-0454s1 PRED entity: 0454s1 PRED relation: nationality PRED expected values: 09c7w0 => 90 concepts (82 used for prediction) PRED predicted values (max 10 best out of 94): 09c7w0 (0.83 #3111, 0.80 #1608, 0.80 #503), 01n7q (0.32 #5027, 0.31 #3311, 0.31 #5231), 0l2q3 (0.32 #5027, 0.31 #3311, 0.31 #5231), 02jx1 (0.19 #5129, 0.16 #735, 0.15 #635), 07ssc (0.11 #316, 0.10 #3829, 0.09 #3225), 06bnz (0.11 #342, 0.06 #241, 0.02 #2649), 0h7x (0.08 #537, 0.08 #838, 0.07 #939), 03rk0 (0.07 #3357, 0.07 #5780, 0.07 #2654), 0chghy (0.06 #210, 0.05 #311, 0.02 #1517), 03rt9 (0.06 #213, 0.05 #314, 0.02 #5935) >> Best rule #3111 for best value: >> intensional similarity = 4 >> extensional distance = 308 >> proper extension: 015n8; >> query: (?x7962, 09c7w0) <- gender(?x7962, ?x231), place_of_death(?x7962, ?x11000), ?x231 = 05zppz, state(?x11000, ?x1227) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0454s1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 82.000 0.826 http://example.org/people/person/nationality #11945-0h1nt PRED entity: 0h1nt PRED relation: artist! PRED expected values: 0g768 => 85 concepts (41 used for prediction) PRED predicted values (max 10 best out of 37): 03rhqg (0.17 #300, 0.04 #1871, 0.03 #4882), 015_1q (0.11 #304, 0.06 #1875, 0.05 #732), 01w40h (0.11 #313, 0.02 #741, 0.02 #1170), 01cf93 (0.11 #343, 0.01 #4925, 0.01 #1772), 03qx_f (0.11 #359), 06x2ww (0.11 #334), 011k11 (0.11 #320), 0g768 (0.06 #322, 0.03 #4904, 0.02 #1893), 0181dw (0.06 #327, 0.03 #4909, 0.02 #3764), 033hn8 (0.06 #298, 0.02 #3162, 0.02 #726) >> Best rule #300 for best value: >> intensional similarity = 2 >> extensional distance = 16 >> proper extension: 0152cw; 01x0yrt; 01fkxr; >> query: (?x1244, 03rhqg) <- award(?x1244, ?x7691), ?x7691 = 026m9w >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #322 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 16 *> proper extension: 0152cw; 01x0yrt; 01fkxr; *> query: (?x1244, 0g768) <- award(?x1244, ?x7691), ?x7691 = 026m9w *> conf = 0.06 ranks of expected_values: 8 EVAL 0h1nt artist! 0g768 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 85.000 41.000 0.167 http://example.org/music/record_label/artist #11944-0tfc PRED entity: 0tfc PRED relation: profession PRED expected values: 0kyk => 141 concepts (89 used for prediction) PRED predicted values (max 10 best out of 100): 0cbd2 (0.89 #11706, 0.57 #3413, 0.54 #5485), 0fj9f (0.86 #3906, 0.29 #500, 0.26 #6812), 02hrh1q (0.70 #12898, 0.67 #12454, 0.66 #12158), 05z96 (0.50 #340, 0.26 #3450, 0.23 #1969), 0dxtg (0.41 #11713, 0.40 #10972, 0.40 #11416), 0kyk (0.40 #5361, 0.40 #5509, 0.36 #3437), 03jgz (0.39 #2518, 0.32 #11551, 0.26 #6812), 01l5t6 (0.39 #2518, 0.32 #11551, 0.14 #555), 06q2q (0.32 #11551, 0.26 #6812, 0.25 #10366), 05t4q (0.32 #11551, 0.19 #1247, 0.17 #359) >> Best rule #11706 for best value: >> intensional similarity = 4 >> extensional distance = 477 >> proper extension: 05fnl9; 01dw9z; 05mxw33; >> query: (?x12441, 0cbd2) <- profession(?x12441, ?x10210), gender(?x12441, ?x231), profession(?x5790, ?x10210), ?x5790 = 04z0g >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #5361 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 154 *> proper extension: 099bk; *> query: (?x12441, 0kyk) <- influenced_by(?x11837, ?x12441), student(?x892, ?x12441), influenced_by(?x12441, ?x1857), place_of_birth(?x11837, ?x863) *> conf = 0.40 ranks of expected_values: 6 EVAL 0tfc profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 141.000 89.000 0.894 http://example.org/people/person/profession #11943-022p06 PRED entity: 022p06 PRED relation: religion PRED expected values: 03_gx => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 24): 0c8wxp (0.29 #6, 0.20 #276, 0.17 #231), 03_gx (0.27 #104, 0.18 #509, 0.18 #194), 03j6c (0.15 #66, 0.03 #2498, 0.03 #336), 0kpl (0.14 #10, 0.13 #280, 0.13 #235), 0631_ (0.10 #278, 0.10 #233, 0.06 #368), 01lp8 (0.07 #271, 0.07 #226, 0.05 #451), 02rsw (0.07 #249, 0.03 #654, 0.02 #790), 0kq2 (0.05 #513, 0.05 #965, 0.05 #1055), 092bf5 (0.05 #466, 0.05 #691, 0.04 #601), 019cr (0.05 #641, 0.04 #146, 0.04 #191) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0hnp7; 0bkmf; >> query: (?x4943, 0c8wxp) <- place_of_burial(?x4943, ?x3691), place_of_burial(?x4943, ?x1227), ?x3691 = 018mmj, ?x1227 = 01n7q >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #104 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 06hzsx; 03bdm4; *> query: (?x4943, 03_gx) <- place_of_burial(?x4943, ?x3691), ?x3691 = 018mmj, nominated_for(?x4943, ?x3009) *> conf = 0.27 ranks of expected_values: 2 EVAL 022p06 religion 03_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 117.000 0.286 http://example.org/people/person/religion #11942-0l14qv PRED entity: 0l14qv PRED relation: role PRED expected values: 03qlv7 020w2 => 97 concepts (78 used for prediction) PRED predicted values (max 10 best out of 55): 05r5c (0.87 #3008, 0.84 #275, 0.84 #2331), 0395lw (0.86 #1997, 0.86 #1614, 0.84 #275), 04rzd (0.85 #2390, 0.84 #275, 0.84 #2295), 026t6 (0.84 #275, 0.83 #2805, 0.83 #936), 018vs (0.84 #275, 0.83 #936, 0.82 #602), 03t22m (0.84 #275, 0.83 #936, 0.82 #602), 011k_j (0.84 #275, 0.83 #936, 0.82 #602), 01wy6 (0.84 #275, 0.83 #936, 0.82 #602), 01679d (0.84 #275, 0.83 #936, 0.82 #602), 020w2 (0.84 #275, 0.83 #936, 0.82 #602) >> Best rule #3008 for best value: >> intensional similarity = 9 >> extensional distance = 36 >> proper extension: 02qjv; 0239kh; 021bmf; 016622; 0bmnm; >> query: (?x228, 05r5c) <- role(?x3171, ?x228), group(?x228, ?x9999), role(?x228, ?x1267), performance_role(?x228, ?x212), artists(?x302, ?x9999), role(?x74, ?x228), ?x1267 = 07brj, artists(?x5355, ?x3171), role(?x228, ?x645) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #275 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 026t6; *> query: (?x228, ?x74) <- role(?x130, ?x228), group(?x228, ?x1573), role(?x228, ?x4913), instrumentalists(?x228, ?x140), role(?x1831, ?x228), role(?x868, ?x228), role(?x74, ?x228), role(?x1291, ?x228), ?x868 = 0dwvl, ?x1831 = 03t22m, ?x4913 = 03ndd *> conf = 0.84 ranks of expected_values: 10, 33 EVAL 0l14qv role 020w2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 97.000 78.000 0.868 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 0l14qv role 03qlv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 97.000 78.000 0.868 http://example.org/music/performance_role/track_performances./music/track_contribution/role #11941-0cp08zg PRED entity: 0cp08zg PRED relation: film_release_region PRED expected values: 0jgd 0b90_r 05b4w => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 201): 02vzc (0.91 #706, 0.88 #869, 0.86 #1357), 0d0vqn (0.91 #1958, 0.91 #3748, 0.90 #6030), 03gj2 (0.90 #2954, 0.90 #1327, 0.88 #839), 0chghy (0.89 #5219, 0.88 #4730, 0.87 #2940), 0k6nt (0.88 #838, 0.87 #675, 0.86 #3441), 035qy (0.85 #2964, 0.83 #5243, 0.82 #6383), 01znc_ (0.82 #533, 0.79 #3461, 0.77 #2973), 0jgd (0.81 #6350, 0.81 #979, 0.81 #1792), 0154j (0.79 #3421, 0.77 #493, 0.77 #981), 05v8c (0.77 #505, 0.76 #1318, 0.73 #993) >> Best rule #706 for best value: >> intensional similarity = 7 >> extensional distance = 21 >> proper extension: 01d259; >> query: (?x7700, 02vzc) <- film_release_region(?x7700, ?x4743), film_release_region(?x7700, ?x774), titles(?x1014, ?x7700), ?x774 = 06mzp, written_by(?x7700, ?x2595), language(?x7700, ?x254), ?x4743 = 03spz >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #6350 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 111 *> proper extension: 02vxq9m; 0fq27fp; 0401sg; 0c0nhgv; 02fqrf; 0g9zljd; 0h63gl9; 0hhggmy; 0ddbjy4; *> query: (?x7700, 0jgd) <- film_release_region(?x7700, ?x1264), film_release_region(?x7700, ?x789), film_release_region(?x7700, ?x774), film_release_region(?x7700, ?x512), ?x789 = 0f8l9c, genre(?x7700, ?x1014), ?x774 = 06mzp, ?x1264 = 0345h, titles(?x512, ?x144) *> conf = 0.81 ranks of expected_values: 8, 11, 12 EVAL 0cp08zg film_release_region 05b4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 119.000 119.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cp08zg film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 119.000 119.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cp08zg film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 119.000 119.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11940-01dbns PRED entity: 01dbns PRED relation: institution! PRED expected values: 02h4rq6 => 175 concepts (121 used for prediction) PRED predicted values (max 10 best out of 17): 02h4rq6 (0.85 #605, 0.83 #568, 0.83 #1162), 019v9k (0.82 #328, 0.76 #610, 0.75 #348), 0bkj86 (0.79 #609, 0.75 #646, 0.73 #572), 016t_3 (0.73 #569, 0.73 #495, 0.73 #324), 013zdg (0.70 #571, 0.52 #608, 0.50 #645), 027f2w (0.64 #611, 0.60 #140, 0.58 #648), 0bjrnt (0.62 #112, 0.60 #136, 0.55 #1180), 071tyz (0.62 #112, 0.55 #1180, 0.50 #910), 01gkg3 (0.62 #112, 0.39 #2207, 0.36 #1964), 07s6fsf (0.60 #567, 0.55 #604, 0.54 #493) >> Best rule #605 for best value: >> intensional similarity = 12 >> extensional distance = 31 >> proper extension: 03ksy; 0gjv_; 09vzz; >> query: (?x7950, 02h4rq6) <- major_field_of_study(?x7950, ?x2605), institution(?x1305, ?x7950), institution(?x734, ?x7950), citytown(?x7950, ?x8963), ?x2605 = 03g3w, institution(?x1305, ?x10572), institution(?x1305, ?x6953), institution(?x1305, ?x5306), ?x734 = 04zx3q1, ?x10572 = 0160nk, ?x5306 = 0217m9, ?x6953 = 01jq0j >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dbns institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 121.000 0.848 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #11939-02_1sj PRED entity: 02_1sj PRED relation: film! PRED expected values: 01pnn3 01gbbz => 93 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1287): 017149 (0.38 #27026, 0.14 #4227, 0.12 #8371), 016ks_ (0.33 #2851, 0.20 #15285, 0.12 #6995), 015wnl (0.33 #2717, 0.20 #15151, 0.12 #6861), 02xv8m (0.33 #665, 0.17 #2737, 0.14 #17243), 05vsxz (0.33 #9, 0.12 #6225, 0.10 #24879), 01gkmx (0.33 #1579, 0.12 #7795, 0.10 #14012), 0gpprt (0.33 #1517, 0.07 #18095, 0.07 #26387), 0fs9jn (0.33 #1717, 0.07 #18295, 0.04 #22440), 02qx5h (0.33 #2001, 0.07 #18579, 0.04 #22724), 0205dx (0.33 #845, 0.07 #17423, 0.04 #21568) >> Best rule #27026 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 02z0f6l; >> query: (?x590, 017149) <- film(?x2927, ?x590), award_nominee(?x6242, ?x2927), ?x6242 = 04bdzg, award_winner(?x2858, ?x2927) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #41903 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 98 *> proper extension: 047msdk; 02r1c18; 0gc_c_; 03cyslc; *> query: (?x590, 01pnn3) <- film(?x806, ?x590), music(?x590, ?x3805), titles(?x2480, ?x590), film_crew_role(?x590, ?x137), ?x2480 = 01z4y *> conf = 0.02 ranks of expected_values: 787 EVAL 02_1sj film! 01gbbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 43.000 0.382 http://example.org/film/actor/film./film/performance/film EVAL 02_1sj film! 01pnn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 93.000 43.000 0.382 http://example.org/film/actor/film./film/performance/film #11938-0jvtp PRED entity: 0jvtp PRED relation: profession PRED expected values: 01d_h8 02hrh1q => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.93 #165, 0.91 #2115, 0.90 #765), 0dxtg (0.85 #614, 0.84 #1964, 0.84 #914), 03gjzk (0.73 #466, 0.73 #916, 0.67 #2566), 01d_h8 (0.45 #4206, 0.40 #1506, 0.39 #1206), 02jknp (0.34 #4208, 0.33 #1358, 0.32 #1208), 0cbd2 (0.27 #907, 0.23 #2557, 0.20 #2707), 016wtf (0.25 #130, 0.02 #430), 01p5_g (0.25 #92, 0.02 #3092, 0.02 #1742), 018gz8 (0.21 #468, 0.21 #918, 0.19 #2568), 02krf9 (0.21 #478, 0.20 #928, 0.17 #628) >> Best rule #165 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0hwd8; 01t94_1; 0121rx; >> query: (?x8254, 02hrh1q) <- award(?x8254, ?x3066), award_winner(?x2915, ?x8254), ?x3066 = 0gqy2, people(?x4322, ?x8254) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0jvtp profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.930 http://example.org/people/person/profession EVAL 0jvtp profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 127.000 127.000 0.930 http://example.org/people/person/profession #11937-0k3p PRED entity: 0k3p PRED relation: featured_film_locations! PRED expected values: 01k7b0 => 293 concepts (204 used for prediction) PRED predicted values (max 10 best out of 698): 02yvct (0.25 #7526, 0.22 #9737, 0.20 #11212), 06fqlk (0.25 #7856, 0.22 #10067, 0.20 #11542), 0473rc (0.25 #454, 0.18 #13725, 0.14 #5614), 02fqxm (0.25 #734, 0.18 #14005, 0.14 #5894), 04j14qc (0.25 #601, 0.14 #5761, 0.14 #38934), 03hkch7 (0.25 #226, 0.14 #5386, 0.12 #9071), 02sg5v (0.25 #54, 0.14 #5214, 0.12 #8899), 07bx6 (0.25 #547, 0.14 #5707, 0.12 #9392), 06rhz7 (0.25 #470, 0.14 #5630, 0.12 #9315), 01s9vc (0.25 #684, 0.14 #5844, 0.12 #9529) >> Best rule #7526 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 07mgr; >> query: (?x8252, 02yvct) <- time_zones(?x8252, ?x2864), capital(?x1229, ?x8252), ?x2864 = 02llzg, citytown(?x6945, ?x8252) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k3p featured_film_locations! 01k7b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 293.000 204.000 0.250 http://example.org/film/film/featured_film_locations #11936-01vn0t_ PRED entity: 01vn0t_ PRED relation: place_of_death PRED expected values: 0f8j6 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 39): 04jpl (0.17 #395, 0.12 #201, 0.07 #2530), 030qb3t (0.14 #9727, 0.14 #1187, 0.13 #2545), 02_286 (0.13 #596, 0.09 #1760, 0.09 #1566), 0k049 (0.10 #586, 0.08 #1168, 0.08 #1750), 05qtj (0.09 #452, 0.04 #6856, 0.03 #647), 05jbn (0.04 #459, 0.04 #2594, 0.03 #654), 0d9jr (0.04 #468, 0.03 #857, 0.03 #1245), 0978r (0.04 #436, 0.03 #825, 0.03 #1213), 0qpqn (0.04 #518, 0.01 #2459, 0.01 #2653), 01tlmw (0.04 #398, 0.01 #2727, 0.01 #2533) >> Best rule #395 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 028q6; 01pbs9w; >> query: (?x8708, 04jpl) <- profession(?x8708, ?x220), role(?x8708, ?x316), people(?x6260, ?x8708), nationality(?x8708, ?x512) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01vn0t_ place_of_death 0f8j6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 110.000 0.174 http://example.org/people/deceased_person/place_of_death #11935-07ftc0 PRED entity: 07ftc0 PRED relation: place_of_birth PRED expected values: 01jr6 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 133): 03h64 (0.33 #52154, 0.33 #36651, 0.32 #37356), 0ccvx (0.14 #1561, 0.01 #17773, 0.01 #8611), 0r22d (0.14 #1748, 0.01 #4565), 06wjf (0.14 #1562), 06_kh (0.12 #2118, 0.02 #9169, 0.01 #12693), 03l2n (0.12 #2282, 0.01 #7920, 0.01 #30476), 0zdkh (0.12 #2613), 0rgxp (0.12 #2496), 02_286 (0.12 #19048, 0.11 #27507, 0.08 #9183), 0c_m3 (0.10 #3014, 0.02 #3718, 0.01 #5833) >> Best rule #52154 for best value: >> intensional similarity = 3 >> extensional distance = 2166 >> proper extension: 07h1h5; >> query: (?x8180, ?x2645) <- location(?x8180, ?x2645), profession(?x8180, ?x524), place_of_birth(?x3382, ?x2645) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6484 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 95 *> proper extension: 01xyt7; *> query: (?x8180, 01jr6) <- type_of_union(?x8180, ?x566), ?x566 = 04ztj, location_of_ceremony(?x8180, ?x792), student(?x4980, ?x8180) *> conf = 0.02 ranks of expected_values: 46 EVAL 07ftc0 place_of_birth 01jr6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 104.000 104.000 0.328 http://example.org/people/person/place_of_birth #11934-03gt7s PRED entity: 03gt7s PRED relation: parent_genre PRED expected values: 04f73rc => 51 concepts (43 used for prediction) PRED predicted values (max 10 best out of 198): 03lty (0.78 #1663, 0.50 #183, 0.43 #1005), 06by7 (0.58 #5994, 0.50 #3151, 0.50 #2156), 01243b (0.50 #522, 0.43 #1181, 0.43 #686), 07bbw (0.33 #81, 0.25 #410, 0.25 #245), 04_sqm (0.33 #126, 0.25 #455, 0.25 #290), 0y3_8 (0.30 #1844, 0.24 #2673, 0.07 #2506), 059kh (0.30 #1846, 0.17 #2675, 0.07 #2840), 016clz (0.30 #1979, 0.21 #2973, 0.17 #1815), 011j5x (0.29 #1174, 0.20 #1997, 0.18 #3157), 0mmp3 (0.29 #2707, 0.13 #1878, 0.07 #1150) >> Best rule #1663 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 016clz; 01_bkd; 0hdf8; 01jwt; 01dqhq; 0296y; 0172rj; 0190xp; 04_sqm; 0b_6yv; >> query: (?x14303, 03lty) <- parent_genre(?x12808, ?x14303), artists(?x12808, ?x764), parent_genre(?x14303, ?x7808), artists(?x7808, ?x10106), parent_genre(?x13095, ?x12808), ?x10106 = 016lj_ >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4135 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 74 *> proper extension: 06__c; *> query: (?x14303, ?x7808) <- parent_genre(?x12808, ?x14303), artists(?x12808, ?x764), parent_genre(?x12808, ?x7808), parent_genre(?x12808, ?x1572), parent_genre(?x13095, ?x12808), artists(?x7808, ?x4877), artists(?x1572, ?x9407), ?x9407 = 024qwq, ?x4877 = 03sww *> conf = 0.25 ranks of expected_values: 13 EVAL 03gt7s parent_genre 04f73rc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 51.000 43.000 0.778 http://example.org/music/genre/parent_genre #11933-09r9m7 PRED entity: 09r9m7 PRED relation: award_winner! PRED expected values: 026gyn_ => 137 concepts (82 used for prediction) PRED predicted values (max 10 best out of 471): 01fwzk (0.16 #21532, 0.15 #22667, 0.15 #23801), 0mcl0 (0.15 #91804, 0.07 #89536, 0.02 #17422), 026gyn_ (0.15 #91804, 0.07 #89536, 0.02 #4739), 0y_9q (0.15 #91804, 0.07 #89536, 0.02 #5134), 0dtfn (0.15 #91804, 0.04 #4673, 0.03 #82734), 06mmr (0.15 #91804, 0.04 #5661, 0.03 #3395), 0jqn5 (0.10 #2418, 0.08 #3551, 0.08 #4684), 0jsf6 (0.08 #13168, 0.06 #18835, 0.04 #5237), 03hmt9b (0.08 #1567, 0.03 #82734, 0.03 #2700), 02czd5 (0.08 #2043, 0.02 #8841, 0.01 #9974) >> Best rule #21532 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 03c_8t; >> query: (?x5772, ?x8827) <- place_of_birth(?x5772, ?x739), music(?x8827, ?x5772), nominated_for(?x112, ?x8827) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #91804 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1244 *> proper extension: 01j53q; *> query: (?x5772, ?x1386) <- award_winner(?x5251, ?x5772), award_winner(?x1386, ?x5251), award_nominee(?x1974, ?x5251) *> conf = 0.15 ranks of expected_values: 3 EVAL 09r9m7 award_winner! 026gyn_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 137.000 82.000 0.158 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #11932-0bvqq PRED entity: 0bvqq PRED relation: featured_film_locations! PRED expected values: 02z0f6l => 80 concepts (47 used for prediction) PRED predicted values (max 10 best out of 542): 017z49 (0.33 #246, 0.25 #983, 0.20 #3194), 065zlr (0.33 #177, 0.25 #914, 0.20 #3125), 047csmy (0.29 #5555, 0.09 #7029, 0.09 #18084), 033srr (0.29 #5439, 0.09 #6913, 0.04 #17968), 0dpl44 (0.25 #1260, 0.20 #3471, 0.20 #2734), 0298n7 (0.25 #1311, 0.20 #3522, 0.20 #2785), 0315w4 (0.25 #1090, 0.20 #3301, 0.20 #2564), 0c57yj (0.25 #1010, 0.20 #3221, 0.20 #2484), 01rxyb (0.20 #2525, 0.11 #6210, 0.08 #7684), 042zrm (0.20 #2806, 0.11 #6491, 0.08 #7965) >> Best rule #246 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0k_q_; >> query: (?x4435, 017z49) <- contains(?x13447, ?x4435), place_of_burial(?x9728, ?x4435), place_of_burial(?x7251, ?x4435), location_of_ceremony(?x3444, ?x4435), company(?x7251, ?x2999), nationality(?x9728, ?x512) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #17462 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 0pswc; *> query: (?x4435, 02z0f6l) <- location_of_ceremony(?x566, ?x4435), ?x566 = 04ztj, location_of_ceremony(?x3444, ?x4435), currency(?x3444, ?x170) *> conf = 0.03 ranks of expected_values: 312 EVAL 0bvqq featured_film_locations! 02z0f6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 80.000 47.000 0.333 http://example.org/film/film/featured_film_locations #11931-0349s PRED entity: 0349s PRED relation: countries_spoken_in PRED expected values: 0hzlz => 42 concepts (39 used for prediction) PRED predicted values (max 10 best out of 310): 01ppq (0.79 #3411, 0.76 #1793, 0.76 #4846), 06frc (0.63 #1431, 0.63 #1073, 0.62 #1792), 07ytt (0.50 #695, 0.36 #874, 0.33 #1233), 0hzlz (0.36 #741, 0.33 #3437, 0.33 #1100), 03rk0 (0.33 #3468, 0.33 #1131, 0.33 #235), 0697s (0.33 #252, 0.27 #789, 0.27 #1148), 0162v (0.33 #232, 0.27 #769, 0.27 #1128), 05bmq (0.33 #338, 0.25 #696, 0.25 #516), 09pmkv (0.33 #210, 0.25 #568, 0.25 #388), 07ssc (0.33 #198, 0.25 #556, 0.25 #376) >> Best rule #3411 for best value: >> intensional similarity = 10 >> extensional distance = 22 >> proper extension: 01r2l; >> query: (?x11590, ?x8958) <- official_language(?x8958, ?x11590), language(?x8787, ?x11590), award_winner(?x8787, ?x989), music(?x8787, ?x12768), film_release_region(?x8787, ?x94), member_states(?x2106, ?x8958), countries_spoken_in(?x254, ?x8958), countries_spoken_in(?x11590, ?x279), jurisdiction_of_office(?x346, ?x8958), country(?x1121, ?x8958) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #741 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 9 *> proper extension: 03k50; 0688f; *> query: (?x11590, 0hzlz) <- language(?x7207, ?x11590), countries_spoken_in(?x11590, ?x279), film_release_region(?x7207, ?x512), film(?x1634, ?x7207), film(?x609, ?x7207), nominated_for(?x640, ?x7207), nominated_for(?x640, ?x308), ?x308 = 011yxg, ?x1634 = 01l2fn, nationality(?x111, ?x512) *> conf = 0.36 ranks of expected_values: 4 EVAL 0349s countries_spoken_in 0hzlz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 42.000 39.000 0.792 http://example.org/language/human_language/countries_spoken_in #11930-03f4n1 PRED entity: 03f4n1 PRED relation: capital PRED expected values: 0h5m7 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 249): 01ly5m (0.46 #8377, 0.33 #358, 0.33 #8378), 0156q (0.44 #2878, 0.33 #4194, 0.33 #4073), 04jpl (0.40 #719, 0.33 #4, 0.25 #4186), 07dfk (0.40 #997, 0.20 #3149, 0.14 #4707), 056_y (0.33 #1808, 0.33 #8378, 0.29 #1929), 04llb (0.33 #176, 0.25 #534, 0.21 #5740), 05qtj (0.33 #1569, 0.22 #2885, 0.17 #4201), 0fw4v (0.33 #273, 0.02 #11642, 0.02 #13320), 09bkv (0.25 #645, 0.25 #407, 0.21 #5740), 0k3p (0.25 #391, 0.21 #4696, 0.17 #1464) >> Best rule #8377 for best value: >> intensional similarity = 7 >> extensional distance = 27 >> proper extension: 019rg5; >> query: (?x13063, ?x2911) <- capital(?x13063, ?x1649), location(?x2161, ?x1649), location_of_ceremony(?x566, ?x1649), ?x566 = 04ztj, influenced_by(?x2161, ?x118), influenced_by(?x476, ?x2161), place_of_birth(?x2161, ?x2911) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #12806 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 51 *> proper extension: 07twz; *> query: (?x13063, ?x1356) <- capital(?x13063, ?x12198), contains(?x205, ?x12198), location_of_ceremony(?x566, ?x12198), country(?x150, ?x205), contains(?x205, ?x1356), film_release_region(?x4998, ?x205), ?x4998 = 0dzlbx, olympics(?x205, ?x358) *> conf = 0.02 ranks of expected_values: 144 EVAL 03f4n1 capital 0h5m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 144.000 144.000 0.463 http://example.org/location/country/capital #11929-05rgl PRED entity: 05rgl PRED relation: contains PRED expected values: 034tl => 195 concepts (107 used for prediction) PRED predicted values (max 10 best out of 2939): 02hrh0_ (0.81 #97047, 0.69 #314718, 0.69 #294123), 09c7w0 (0.69 #26464, 0.19 #111751, 0.17 #20585), 0f8l9c (0.69 #26464, 0.19 #111751, 0.17 #20585), 0chghy (0.65 #123514, 0.63 #297063, 0.59 #297064), 07z5n (0.65 #123514, 0.63 #297063, 0.59 #297064), 02j71 (0.59 #152925, 0.54 #191153, 0.54 #205854), 03ryn (0.54 #173505, 0.49 #135278, 0.39 #255863), 0ctw_b (0.54 #173505, 0.49 #135278, 0.39 #255863), 02wt0 (0.54 #173505, 0.49 #135278, 0.39 #255863), 02lx0 (0.54 #173505, 0.49 #135278, 0.39 #255863) >> Best rule #97047 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 03gh4; >> query: (?x1879, ?x5193) <- contains(?x1879, ?x6226), featured_film_locations(?x11998, ?x1879), administrative_division(?x5193, ?x6226) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #135278 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 05j49; *> query: (?x1879, ?x3704) <- contains(?x1879, ?x6226), contains(?x1879, ?x4164), partially_contains(?x1879, ?x94), contains(?x6226, ?x3704), organization(?x4164, ?x127) *> conf = 0.49 ranks of expected_values: 32 EVAL 05rgl contains 034tl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 195.000 107.000 0.806 http://example.org/location/location/contains #11928-07p12s PRED entity: 07p12s PRED relation: film_crew_role PRED expected values: 02r96rf => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 24): 02r96rf (0.79 #424, 0.74 #212, 0.72 #852), 0d2b38 (0.41 #230, 0.40 #110, 0.40 #260), 015h31 (0.32 #97, 0.18 #217, 0.16 #247), 01pvkk (0.30 #431, 0.29 #616, 0.28 #1166), 02ynfr (0.25 #72, 0.21 #434, 0.20 #102), 089fss (0.25 #5, 0.13 #215, 0.12 #65), 033smt (0.25 #232, 0.22 #262, 0.20 #112), 02rh1dz (0.24 #98, 0.20 #430, 0.13 #615), 0ckd1 (0.16 #213, 0.12 #243, 0.12 #93), 020xn5 (0.16 #96, 0.14 #36, 0.05 #216) >> Best rule #424 for best value: >> intensional similarity = 5 >> extensional distance = 318 >> proper extension: 02qm_f; 0125xq; 0333t; >> query: (?x10722, 02r96rf) <- film_crew_role(?x10722, ?x281), genre(?x10722, ?x225), film_crew_role(?x4086, ?x281), ?x4086 = 06_x996, ?x225 = 02kdv5l >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07p12s film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.791 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11927-02t_99 PRED entity: 02t_99 PRED relation: place_of_birth PRED expected values: 01_d4 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 96): 030qb3t (0.33 #57764, 0.32 #38741, 0.32 #42265), 01_d4 (0.33 #57764, 0.32 #38741, 0.32 #42265), 04jpl (0.14 #8, 0.04 #1417, 0.03 #6347), 013yq (0.14 #79, 0.02 #23325, 0.01 #16986), 0r0f7 (0.14 #311), 02_286 (0.11 #3542, 0.11 #37348, 0.10 #42989), 01smm (0.08 #1643, 0.01 #12914, 0.01 #15730), 0cr3d (0.07 #6433, 0.05 #7842, 0.05 #31788), 0cc56 (0.07 #738, 0.03 #5668, 0.03 #2852), 0t0n5 (0.07 #922, 0.02 #5148, 0.02 #5852) >> Best rule #57764 for best value: >> intensional similarity = 2 >> extensional distance = 2264 >> proper extension: 019_1h; 03q1vd; 01qx13; 0dzkq; 015wfg; 03lh3v; 094xh; 0854hr; 05dtsb; 027n4zv; ... >> query: (?x4638, ?x1860) <- location(?x4638, ?x1860), place_of_birth(?x193, ?x1860) >> conf = 0.33 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02t_99 place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 104.000 0.331 http://example.org/people/person/place_of_birth #11926-0294mx PRED entity: 0294mx PRED relation: genre PRED expected values: 07s9rl0 0219x_ => 83 concepts (75 used for prediction) PRED predicted values (max 10 best out of 94): 07s9rl0 (0.93 #6292, 0.83 #355, 0.82 #119), 04jjy (0.55 #7120, 0.51 #8189, 0.50 #1658), 01jfsb (0.48 #1431, 0.41 #11, 0.35 #7132), 05p553 (0.43 #1424, 0.36 #1543, 0.32 #7005), 02l7c8 (0.33 #3219, 0.33 #3456, 0.33 #1196), 02kdv5l (0.33 #7597, 0.31 #5701, 0.28 #4040), 03k9fj (0.25 #4286, 0.22 #4641, 0.22 #6774), 082gq (0.23 #856, 0.22 #3589, 0.19 #1806), 02n4kr (0.22 #8, 0.18 #5108, 0.17 #1428), 060__y (0.20 #842, 0.19 #15, 0.18 #5108) >> Best rule #6292 for best value: >> intensional similarity = 4 >> extensional distance = 980 >> proper extension: 0jyx6; 0c5dd; 0h1v19; 0gt1k; 0gnjh; 0291ck; >> query: (?x7283, 07s9rl0) <- country(?x7283, ?x94), genre(?x7283, ?x6887), genre(?x6137, ?x6887), ?x6137 = 06cm5 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 16 EVAL 0294mx genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 83.000 75.000 0.930 http://example.org/film/film/genre EVAL 0294mx genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 75.000 0.930 http://example.org/film/film/genre #11925-0f_nbyh PRED entity: 0f_nbyh PRED relation: nominated_for PRED expected values: 0ds3t5x 05jzt3 04hwbq 07yk1xz 09gb_4p 011yn5 => 52 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1424): 02c638 (0.71 #3388, 0.70 #8030, 0.64 #11123), 095zlp (0.71 #3144, 0.65 #7786, 0.54 #9332), 04b2qn (0.71 #4252, 0.55 #8894, 0.50 #11987), 017gl1 (0.70 #7862, 0.68 #10955, 0.65 #9408), 011yqc (0.70 #7935, 0.64 #11028, 0.62 #9481), 04q827 (0.65 #9183, 0.50 #12276, 0.46 #10729), 017jd9 (0.57 #3770, 0.55 #8412, 0.50 #11505), 0bdjd (0.57 #4177, 0.55 #5724, 0.54 #10365), 0mcl0 (0.57 #3646, 0.55 #5193, 0.50 #6741), 011ycb (0.57 #3837, 0.50 #8479, 0.50 #2290) >> Best rule #3388 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0gr4k; 099tbz; 02n9nmz; >> query: (?x277, 02c638) <- nominated_for(?x277, ?x5092), nominated_for(?x277, ?x945), ?x945 = 0b6tzs, award(?x163, ?x277), ?x5092 = 0gg5qcw >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 040njc; 0gq9h; *> query: (?x277, 0ds3t5x) <- nominated_for(?x277, ?x5092), nominated_for(?x277, ?x945), ?x945 = 0b6tzs, award(?x6944, ?x277), film_release_region(?x5092, ?x87), ?x6944 = 02z2xdf *> conf = 0.50 ranks of expected_values: 79, 169, 215, 251, 283, 363 EVAL 0f_nbyh nominated_for 011yn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 52.000 19.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f_nbyh nominated_for 09gb_4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 19.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f_nbyh nominated_for 07yk1xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 19.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f_nbyh nominated_for 04hwbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 52.000 19.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f_nbyh nominated_for 05jzt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 52.000 19.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f_nbyh nominated_for 0ds3t5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 52.000 19.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11924-02rcdc2 PRED entity: 02rcdc2 PRED relation: nominated_for! PRED expected values: 02rdxsh => 128 concepts (104 used for prediction) PRED predicted values (max 10 best out of 204): 019f4v (0.75 #228, 0.69 #10004, 0.69 #10233), 02wypbh (0.75 #228, 0.69 #10004, 0.69 #10233), 0gq9h (0.62 #57, 0.46 #4372, 0.45 #7329), 040njc (0.47 #7, 0.34 #689, 0.32 #4549), 099tbz (0.47 #43, 0.20 #725, 0.14 #3223), 02qyntr (0.44 #170, 0.38 #852, 0.27 #3350), 02qvyrt (0.44 #89, 0.27 #771, 0.25 #3269), 0p9sw (0.41 #19, 0.30 #3199, 0.29 #4334), 027dtxw (0.41 #4, 0.26 #686, 0.22 #3184), 02x17s4 (0.38 #87, 0.33 #769, 0.31 #6360) >> Best rule #228 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0j43swk; 0gmgwnv; 0gwjw0c; >> query: (?x2903, ?x1107) <- nominated_for(?x277, ?x2903), award(?x2903, ?x1107), production_companies(?x2903, ?x3331), ?x277 = 0f_nbyh >> conf = 0.75 => this is the best rule for 2 predicted values *> Best rule #3228 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 159 *> proper extension: 02phtzk; *> query: (?x2903, 02rdxsh) <- film_crew_role(?x2903, ?x137), award_winner(?x2903, ?x9754), production_companies(?x2903, ?x3331), honored_for(?x762, ?x2903) *> conf = 0.14 ranks of expected_values: 76 EVAL 02rcdc2 nominated_for! 02rdxsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 128.000 104.000 0.755 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11923-0jsf6 PRED entity: 0jsf6 PRED relation: list PRED expected values: 05glt => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.25 #2, 0.21 #23, 0.19 #128) >> Best rule #2 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 011yqc; 0dr_4; 0jyb4; >> query: (?x6213, 05glt) <- nominated_for(?x6909, ?x6213), nominated_for(?x1079, ?x6213), ?x6909 = 02qyntr, ?x1079 = 0l8z1, honored_for(?x6213, ?x2047) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jsf6 list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.250 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #11922-0gk4g PRED entity: 0gk4g PRED relation: people PRED expected values: 02lkcc 01lcxbb 014z8v 029h45 01hmk9 0flddp 01zlh5 016ynj 0knjh 01h4rj 015wc0 026m0 0cyhq 0pqzh 03s2y9 01hdht 01pny5 => 66 concepts (48 used for prediction) PRED predicted values (max 10 best out of 2861): 02dth1 (0.33 #127, 0.22 #3966, 0.21 #2194), 01938t (0.33 #234, 0.22 #4073, 0.20 #2428), 0136p1 (0.33 #52, 0.22 #3891, 0.20 #2246), 08bqy9 (0.33 #213, 0.22 #4052, 0.20 #2407), 06y7d (0.33 #490, 0.22 #4329, 0.20 #2684), 02cvp8 (0.33 #441, 0.22 #4280, 0.20 #2635), 01kws3 (0.33 #182, 0.22 #4021, 0.20 #2376), 015wfg (0.33 #135, 0.21 #2194, 0.20 #2878), 02nrdp (0.33 #373, 0.20 #2567, 0.18 #5858), 0byfz (0.33 #6, 0.20 #2200, 0.18 #5491) >> Best rule #127 for best value: >> intensional similarity = 14 >> extensional distance = 1 >> proper extension: 0qcr0; >> query: (?x4322, 02dth1) <- people(?x4322, ?x11260), people(?x4322, ?x11054), people(?x4322, ?x10654), people(?x4322, ?x9367), people(?x4322, ?x8460), people(?x4322, ?x6061), film(?x9367, ?x10362), nationality(?x10654, ?x1778), celebrities_impersonated(?x3649, ?x6061), participant(?x9604, ?x11054), sibling(?x11260, ?x12098), origin(?x9367, ?x6769), location_of_ceremony(?x8460, ?x11794), people(?x1050, ?x8460) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1841 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 05mdx; *> query: (?x4322, 029h45) <- risk_factors(?x4322, ?x11678), risk_factors(?x4322, ?x8524), ?x8524 = 01hbgs, risk_factors(?x13744, ?x11678), risk_factors(?x4906, ?x11678), risk_factors(?x6483, ?x4322), ?x6483 = 02bft, ?x4906 = 0hg11, symptom_of(?x9509, ?x13744), people(?x13744, ?x2871) *> conf = 0.25 ranks of expected_values: 86, 144, 462, 916, 981, 985, 1088, 1117, 1319, 1458, 2606 EVAL 0gk4g people 01pny5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 01hdht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 03s2y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 0pqzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 0cyhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 026m0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 015wc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 01h4rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 0knjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 016ynj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 01zlh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 0flddp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 01hmk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 029h45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 014z8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 01lcxbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 48.000 0.333 http://example.org/people/cause_of_death/people EVAL 0gk4g people 02lkcc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 48.000 0.333 http://example.org/people/cause_of_death/people #11921-047fjjr PRED entity: 047fjjr PRED relation: film_release_region PRED expected values: 03gj2 06t2t 03h64 => 103 concepts (91 used for prediction) PRED predicted values (max 10 best out of 210): 09c7w0 (0.92 #12918, 0.92 #12765, 0.87 #2154), 0f8l9c (0.92 #2943, 0.92 #2328, 0.89 #3096), 05qhw (0.90 #2936, 0.89 #1248, 0.86 #3242), 03h64 (0.90 #2986, 0.88 #3292, 0.86 #1298), 03gj2 (0.89 #1259, 0.89 #2947, 0.88 #951), 03rjj (0.89 #1239, 0.88 #3233, 0.88 #2927), 06bnz (0.89 #1278, 0.88 #1125, 0.82 #2198), 0154j (0.88 #930, 0.88 #622, 0.88 #468), 02vzc (0.87 #2665, 0.85 #1131, 0.85 #976), 03rt9 (0.86 #1247, 0.82 #2628, 0.82 #2167) >> Best rule #12918 for best value: >> intensional similarity = 7 >> extensional distance = 1247 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; 018js4; ... >> query: (?x3850, 09c7w0) <- film_release_region(?x3850, ?x2629), film_release_region(?x3850, ?x304), film(?x4859, ?x3850), currency(?x304, ?x170), film_release_region(?x6621, ?x304), medal(?x2629, ?x422), ?x6621 = 0h63gl9 >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #2986 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 86 *> proper extension: 0gtsx8c; 0hgnl3t; *> query: (?x3850, 03h64) <- film_release_region(?x3850, ?x2629), film_release_region(?x3850, ?x1499), film_release_region(?x3850, ?x390), film(?x10388, ?x3850), ?x2629 = 06f32, ?x390 = 0chghy, ?x1499 = 01znc_, profession(?x10388, ?x987) *> conf = 0.90 ranks of expected_values: 4, 5, 12 EVAL 047fjjr film_release_region 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 91.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047fjjr film_release_region 06t2t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 103.000 91.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047fjjr film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 91.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11920-01wzlxj PRED entity: 01wzlxj PRED relation: artists! PRED expected values: 064t9 => 111 concepts (48 used for prediction) PRED predicted values (max 10 best out of 262): 064t9 (0.63 #12037, 0.57 #3706, 0.55 #4321), 06by7 (0.50 #2483, 0.49 #5257, 0.48 #6182), 016clz (0.50 #5, 0.38 #1233, 0.31 #6165), 0m0jc (0.50 #9, 0.17 #2161, 0.15 #2470), 012yc (0.50 #146, 0.16 #4453, 0.14 #3838), 08cyft (0.50 #56, 0.11 #3133, 0.10 #2517), 05bt6j (0.33 #1272, 0.31 #6204, 0.30 #6511), 03_d0 (0.29 #1240, 0.25 #12035, 0.20 #7712), 0xhtw (0.26 #6178, 0.24 #5253, 0.21 #2479), 0ggx5q (0.25 #3153, 0.25 #76, 0.24 #4383) >> Best rule #12037 for best value: >> intensional similarity = 3 >> extensional distance = 558 >> proper extension: 01nqfh_; 01jrz5j; 01pr_j6; 01p45_v; 02fgpf; 04gycf; 05_pkf; 037lyl; 0dzc16; 0f502; ... >> query: (?x3834, 064t9) <- artists(?x2937, ?x3834), parent_genre(?x3232, ?x2937), ?x3232 = 01ym9b >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wzlxj artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 48.000 0.629 http://example.org/music/genre/artists #11919-05dbf PRED entity: 05dbf PRED relation: film PRED expected values: 02_nsc => 124 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1113): 01hr1 (0.63 #51509, 0.35 #83486, 0.32 #97698), 04jplwp (0.63 #51509, 0.03 #95920, 0.02 #3139), 02mpyh (0.21 #1454, 0.02 #15664, 0.02 #12112), 0g9lm2 (0.16 #10658, 0.16 #21315, 0.14 #8881), 04cj79 (0.14 #591, 0.05 #2367, 0.04 #4143), 03bx2lk (0.14 #182, 0.03 #16168, 0.03 #14392), 011xg5 (0.14 #1423, 0.03 #95920, 0.02 #3199), 04cv9m (0.14 #698, 0.02 #2474, 0.01 #14908), 07nxnw (0.14 #1203, 0.02 #2979, 0.01 #8307), 0_9wr (0.14 #1225, 0.01 #8329) >> Best rule #51509 for best value: >> intensional similarity = 2 >> extensional distance = 420 >> proper extension: 012x2b; >> query: (?x2275, ?x308) <- nominated_for(?x2275, ?x308), participant(?x2275, ?x286) >> conf = 0.63 => this is the best rule for 2 predicted values *> Best rule #6894 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 82 *> proper extension: 036px; *> query: (?x2275, 02_nsc) <- award(?x2275, ?x2585), award_winner(?x2275, ?x748), ?x2585 = 054ks3 *> conf = 0.01 ranks of expected_values: 786 EVAL 05dbf film 02_nsc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 58.000 0.627 http://example.org/film/actor/film./film/performance/film #11918-03hkch7 PRED entity: 03hkch7 PRED relation: nominated_for! PRED expected values: 09sb52 02rdyk7 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 196): 09cm54 (0.70 #2557, 0.68 #14725, 0.68 #4051), 027986c (0.70 #2557, 0.68 #14725, 0.68 #4051), 09d28z (0.70 #2557, 0.68 #14725, 0.68 #4051), 09sb52 (0.62 #881, 0.52 #1094, 0.39 #3225), 02ppm4q (0.54 #3294, 0.35 #950, 0.28 #1589), 04dn09n (0.51 #882, 0.44 #2373, 0.42 #1095), 03hkv_r (0.49 #863, 0.38 #1076, 0.28 #1502), 019f4v (0.46 #897, 0.44 #2388, 0.37 #2815), 0gqyl (0.46 #918, 0.38 #3262, 0.35 #1131), 027dtxw (0.44 #1494, 0.38 #855, 0.33 #1068) >> Best rule #2557 for best value: >> intensional similarity = 4 >> extensional distance = 115 >> proper extension: 0j8f09z; >> query: (?x3124, ?x591) <- film_crew_role(?x3124, ?x468), award(?x3124, ?x591), nominated_for(?x1162, ?x3124), ?x1162 = 099c8n >> conf = 0.70 => this is the best rule for 3 predicted values *> Best rule #881 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 0gmgwnv; *> query: (?x3124, 09sb52) <- film(?x123, ?x3124), nominated_for(?x995, ?x3124), ?x995 = 099tbz, honored_for(?x3609, ?x3124) *> conf = 0.62 ranks of expected_values: 4, 33 EVAL 03hkch7 nominated_for! 02rdyk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 104.000 104.000 0.701 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hkch7 nominated_for! 09sb52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 104.000 104.000 0.701 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11917-0czyxs PRED entity: 0czyxs PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.83 #557, 0.83 #688, 0.81 #327), 01pvkk (0.36 #170, 0.35 #138, 0.33 #9), 02ynfr (0.27 #270, 0.23 #1320, 0.23 #399), 01xy5l_ (0.23 #140, 0.13 #397, 0.13 #2700), 0215hd (0.20 #48, 0.15 #112, 0.14 #2284), 089fss (0.20 #37, 0.13 #2700, 0.11 #1770), 015h31 (0.20 #955, 0.19 #137, 0.16 #265), 0d2b38 (0.20 #118, 0.15 #969, 0.13 #311), 089g0h (0.16 #146, 0.13 #2700, 0.13 #568), 02_n3z (0.13 #2700, 0.12 #97, 0.12 #130) >> Best rule #557 for best value: >> intensional similarity = 6 >> extensional distance = 169 >> proper extension: 01br2w; 091z_p; 05dy7p; 02h22; 064lsn; 0581vn8; 03xj05; >> query: (?x383, 0ch6mp2) <- genre(?x383, ?x225), films(?x7455, ?x383), film(?x382, ?x383), film_crew_role(?x383, ?x1171), ?x1171 = 09vw2b7, currency(?x383, ?x170) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0czyxs film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.830 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11916-02zfdp PRED entity: 02zfdp PRED relation: award PRED expected values: 0ck27z => 103 concepts (99 used for prediction) PRED predicted values (max 10 best out of 269): 09sb52 (0.45 #2061, 0.34 #12565, 0.32 #17413), 0ck27z (0.40 #901, 0.32 #497, 0.26 #7769), 01by1l (0.29 #5768, 0.29 #5364, 0.29 #6172), 01bgqh (0.24 #1255, 0.23 #5699, 0.22 #6103), 0bfvd4 (0.23 #923, 0.14 #519, 0.13 #33132), 054ks3 (0.22 #4586, 0.17 #4182, 0.17 #3778), 0bdwqv (0.22 #1789, 0.20 #981, 0.20 #173), 0gqy2 (0.22 #1781, 0.13 #2993, 0.13 #33132), 027dtxw (0.22 #1620, 0.13 #33132, 0.13 #35558), 0bp_b2 (0.20 #826, 0.06 #4866, 0.06 #7694) >> Best rule #2061 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 0dbc1s; 0gls4q_; 0fxky3; >> query: (?x9152, 09sb52) <- award_nominee(?x9152, ?x4580), film(?x4580, ?x2933), ?x2933 = 0407yj_ >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #901 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 03gm48; 02tkzn; *> query: (?x9152, 0ck27z) <- award(?x9152, ?x2071), actor(?x3303, ?x9152), ?x2071 = 0bdw6t *> conf = 0.40 ranks of expected_values: 2 EVAL 02zfdp award 0ck27z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 99.000 0.455 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11915-05szp PRED entity: 05szp PRED relation: artists! PRED expected values: 064t9 => 107 concepts (104 used for prediction) PRED predicted values (max 10 best out of 225): 064t9 (0.63 #2494, 0.56 #3114, 0.50 #634), 06by7 (0.61 #1263, 0.51 #16146, 0.45 #643), 0glt670 (0.42 #662, 0.36 #2522, 0.36 #4382), 016clz (0.38 #625, 0.23 #3105, 0.22 #16128), 017_qw (0.29 #3472, 0.12 #16495, 0.12 #7812), 03_d0 (0.29 #1252, 0.16 #9312, 0.16 #11483), 0155w (0.29 #1346, 0.16 #2896, 0.13 #2586), 05bt6j (0.25 #665, 0.25 #16168, 0.24 #3145), 016jny (0.25 #104, 0.17 #1344, 0.10 #16227), 02w4v (0.25 #46, 0.14 #1286, 0.12 #666) >> Best rule #2494 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 01wvxw1; >> query: (?x6666, 064t9) <- award_winner(?x2180, ?x6666), artists(?x1952, ?x6666), participant(?x4420, ?x6666) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05szp artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 104.000 0.630 http://example.org/music/genre/artists #11914-024d8w PRED entity: 024d8w PRED relation: colors PRED expected values: 01g5v => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 17): 019sc (0.88 #56, 0.55 #703, 0.33 #363), 038hg (0.87 #27, 0.20 #239, 0.18 #44), 01g5v (0.52 #785, 0.51 #479, 0.45 #428), 02rnmb (0.20 #239, 0.16 #888, 0.16 #887), 01l849 (0.20 #239, 0.16 #888, 0.16 #887), 09ggk (0.20 #239, 0.16 #888, 0.16 #887), 0jc_p (0.20 #239, 0.16 #888, 0.16 #887), 06kqt3 (0.20 #239, 0.16 #888, 0.16 #887), 036k5h (0.20 #239, 0.16 #888, 0.16 #887), 04mkbj (0.20 #239, 0.16 #888, 0.16 #887) >> Best rule #56 for best value: >> intensional similarity = 12 >> extensional distance = 24 >> proper extension: 01lpx8; >> query: (?x5083, 019sc) <- colors(?x5083, ?x3621), colors(?x5083, ?x1101), colors(?x5083, ?x663), ?x1101 = 06fvc, ?x663 = 083jv, colors(?x14124, ?x3621), colors(?x13090, ?x3621), colors(?x3702, ?x3621), ?x13090 = 0lmm3, position(?x3702, ?x60), colors(?x817, ?x3621), ?x14124 = 04l590 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #785 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 200 *> proper extension: 02pqcfz; 0frm7n; 03d555l; 0fsb_6; 02pyyld; *> query: (?x5083, 01g5v) <- colors(?x5083, ?x1101), colors(?x13154, ?x1101), colors(?x12202, ?x1101), position(?x12202, ?x60), colors(?x10994, ?x1101), colors(?x5920, ?x1101), colors(?x4672, ?x1101), ?x13154 = 02w64f, ?x4672 = 07tds, currency(?x10994, ?x1099), category(?x5920, ?x134) *> conf = 0.52 ranks of expected_values: 3 EVAL 024d8w colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.885 http://example.org/sports/sports_team/colors #11913-04rcl7 PRED entity: 04rcl7 PRED relation: company! PRED expected values: 060c4 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 40): 0dq_5 (0.33 #18, 0.30 #2230, 0.28 #817), 0krdk (0.33 #7, 0.28 #2219, 0.24 #2360), 060c4 (0.33 #3, 0.25 #567, 0.25 #50), 01yc02 (0.33 #9, 0.16 #2221, 0.16 #1467), 09d6p2 (0.33 #20, 0.12 #1243, 0.10 #2373), 01kr6k (0.33 #28, 0.09 #827, 0.09 #2240), 0dq3c (0.25 #96, 0.19 #2214, 0.15 #2355), 014l7h (0.25 #452, 0.16 #640, 0.15 #1299), 05_wyz (0.16 #2231, 0.12 #2372, 0.08 #1477), 02k13d (0.15 #437, 0.12 #108, 0.12 #61) >> Best rule #18 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09b3v; >> query: (?x10685, 0dq_5) <- award(?x10685, ?x3911), production_companies(?x2425, ?x10685), ?x2425 = 0k4d7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 09b3v; *> query: (?x10685, 060c4) <- award(?x10685, ?x3911), production_companies(?x2425, ?x10685), ?x2425 = 0k4d7 *> conf = 0.33 ranks of expected_values: 3 EVAL 04rcl7 company! 060c4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 111.000 0.333 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #11912-03bdkd PRED entity: 03bdkd PRED relation: genre PRED expected values: 03mqtr => 79 concepts (62 used for prediction) PRED predicted values (max 10 best out of 102): 017fp (0.42 #976, 0.35 #256, 0.15 #616), 02l7c8 (0.41 #17, 0.32 #1339, 0.29 #1097), 02kdv5l (0.37 #362, 0.32 #482, 0.29 #3615), 04xvlr (0.35 #241, 0.34 #961, 0.25 #121), 05p553 (0.35 #4, 0.33 #5538, 0.32 #6139), 03g3w (0.30 #986, 0.12 #266, 0.11 #146), 01jfsb (0.30 #3626, 0.29 #6148, 0.28 #4947), 03k9fj (0.28 #372, 0.27 #492, 0.24 #3625), 03q4nz (0.23 #980, 0.06 #3974, 0.06 #260), 082gq (0.21 #1231, 0.21 #511, 0.21 #991) >> Best rule #976 for best value: >> intensional similarity = 4 >> extensional distance = 245 >> proper extension: 0cp08zg; 09rfpk; >> query: (?x10614, 017fp) <- nominated_for(?x574, ?x10614), genre(?x10614, ?x3312), genre(?x6273, ?x3312), ?x6273 = 048xyn >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #270 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 100 *> proper extension: 03s6l2; 04kkz8; 0gyy53; 08gg47; 05n6sq; 03hp2y1; *> query: (?x10614, 03mqtr) <- nominated_for(?x574, ?x10614), genre(?x10614, ?x6887), film(?x9587, ?x10614), ?x6887 = 03bxz7 *> conf = 0.13 ranks of expected_values: 20 EVAL 03bdkd genre 03mqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 79.000 62.000 0.421 http://example.org/film/film/genre #11911-0k2sk PRED entity: 0k2sk PRED relation: film! PRED expected values: 02_p5w 01l_yg => 109 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1147): 01f7j9 (0.41 #62294, 0.41 #56063, 0.39 #78910), 03h26tm (0.41 #62294, 0.41 #56063, 0.39 #78910), 0f3zf_ (0.41 #62294, 0.39 #78910, 0.39 #33221), 028d4v (0.20 #4543, 0.20 #2467, 0.03 #12848), 0q9kd (0.20 #2080, 0.13 #24918, 0.04 #6232), 015pvh (0.20 #5253, 0.09 #7329, 0.02 #13558), 041c4 (0.20 #5046, 0.05 #7122, 0.04 #13351), 01j5ws (0.20 #4665, 0.05 #6741, 0.03 #12970), 01wbg84 (0.20 #2123, 0.04 #22884, 0.04 #6275), 0bl2g (0.20 #4207, 0.04 #10435, 0.04 #6283) >> Best rule #62294 for best value: >> intensional similarity = 4 >> extensional distance = 323 >> proper extension: 015qsq; 0d90m; 03qcfvw; 02y_lrp; 028_yv; 047q2k1; 01k1k4; 0ds33; 016z5x; 01h7bb; ... >> query: (?x1076, ?x930) <- film(?x1802, ?x1076), award(?x1076, ?x1429), featured_film_locations(?x1076, ?x1523), nominated_for(?x930, ?x1076) >> conf = 0.41 => this is the best rule for 3 predicted values *> Best rule #13102 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 015qy1; *> query: (?x1076, 02_p5w) <- genre(?x1076, ?x2540), ?x2540 = 0hcr, film_release_distribution_medium(?x1076, ?x81) *> conf = 0.09 ranks of expected_values: 41, 238 EVAL 0k2sk film! 01l_yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 109.000 53.000 0.415 http://example.org/film/actor/film./film/performance/film EVAL 0k2sk film! 02_p5w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 109.000 53.000 0.415 http://example.org/film/actor/film./film/performance/film #11910-01jr6 PRED entity: 01jr6 PRED relation: place! PRED expected values: 01jr6 => 133 concepts (126 used for prediction) PRED predicted values (max 10 best out of 340): 01jr6 (0.16 #36589, 0.05 #40198, 0.04 #60820), 0kpzy (0.16 #36589, 0.04 #60820, 0.01 #39682), 01n7q (0.16 #36589, 0.04 #60820), 09c7w0 (0.16 #36589, 0.04 #60820), 06pvr (0.16 #36589), 030qb3t (0.05 #40198, 0.02 #30, 0.02 #545), 0dc95 (0.05 #40198, 0.02 #49, 0.02 #1079), 0r62v (0.05 #40198, 0.02 #17, 0.02 #1564), 0k_q_ (0.05 #40198, 0.02 #47, 0.02 #1594), 0r7fy (0.05 #40198, 0.02 #541, 0.02 #1573) >> Best rule #36589 for best value: >> intensional similarity = 2 >> extensional distance = 266 >> proper extension: 0_wm_; 0fngy; 07sb1; >> query: (?x3976, ?x94) <- citytown(?x4227, ?x3976), contains(?x94, ?x4227) >> conf = 0.16 => this is the best rule for 5 predicted values ranks of expected_values: 1 EVAL 01jr6 place! 01jr6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 126.000 0.157 http://example.org/location/hud_county_place/place #11909-042kg PRED entity: 042kg PRED relation: profession PRED expected values: 0cbd2 => 98 concepts (65 used for prediction) PRED predicted values (max 10 best out of 105): 02hrh1q (0.81 #4397, 0.76 #3959, 0.75 #4543), 0cbd2 (0.68 #6141, 0.52 #4973, 0.50 #2051), 04gc2 (0.47 #1648, 0.46 #2378, 0.45 #2524), 09jwl (0.44 #5278, 0.43 #5424, 0.40 #20), 0dxtg (0.42 #6148, 0.41 #4980, 0.35 #5126), 0nbcg (0.40 #32, 0.30 #5436, 0.29 #5290), 01d_h8 (0.38 #1320, 0.38 #3656, 0.38 #4534), 0dz3r (0.33 #5406, 0.32 #5260, 0.20 #2), 099md (0.32 #802, 0.32 #656, 0.18 #510), 016z4k (0.31 #5262, 0.30 #5408, 0.20 #4) >> Best rule #4397 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 030pr; >> query: (?x11290, 02hrh1q) <- award_winner(?x594, ?x11290), person(?x3124, ?x11290), nationality(?x11290, ?x94), award(?x11290, ?x14536) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #6141 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 698 *> proper extension: 079vf; 01yznp; 04rs03; 0168cl; 06y9c2; 07kb5; 09byk; 02lk1s; 04l3_z; 08433; ... *> query: (?x11290, 0cbd2) <- profession(?x11290, ?x2225), profession(?x4795, ?x2225), ?x4795 = 0n6kf *> conf = 0.68 ranks of expected_values: 2 EVAL 042kg profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 65.000 0.806 http://example.org/people/person/profession #11908-0214m4 PRED entity: 0214m4 PRED relation: time_zones PRED expected values: 03bdv => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.37 #55, 0.32 #81, 0.30 #94), 03bdv (0.27 #6, 0.22 #71, 0.21 #32), 02fqwt (0.18 #79, 0.14 #118, 0.14 #274), 02llzg (0.14 #43, 0.11 #368, 0.08 #212), 02lcqs (0.13 #226, 0.11 #122, 0.11 #148), 02hczc (0.06 #288, 0.05 #275, 0.05 #158), 03plfd (0.03 #218, 0.01 #101, 0.01 #413), 042g7t (0.02 #154, 0.01 #193, 0.01 #258), 0gsrz4 (0.01 #47, 0.01 #60), 052vwh (0.01 #532) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 05d49; >> query: (?x8251, 02hcv8) <- place_of_birth(?x1674, ?x8251), student(?x8925, ?x1674), nationality(?x1674, ?x512), major_field_of_study(?x122, ?x8925) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 0fm2_; 01n4nd; 03msf; 01m3b7; 0j7ng; 014kj2; *> query: (?x8251, 03bdv) <- contains(?x1310, ?x8251), contains(?x512, ?x8251), ?x1310 = 02jx1, ?x512 = 07ssc, origin(?x12497, ?x8251) *> conf = 0.27 ranks of expected_values: 2 EVAL 0214m4 time_zones 03bdv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 75.000 0.368 http://example.org/location/location/time_zones #11907-0d02km PRED entity: 0d02km PRED relation: profession PRED expected values: 02hrh1q => 102 concepts (101 used for prediction) PRED predicted values (max 10 best out of 83): 02hrh1q (0.89 #4785, 0.89 #2102, 0.88 #4636), 0dxtg (0.60 #610, 0.50 #1654, 0.48 #6423), 02jknp (0.50 #604, 0.48 #1648, 0.45 #6417), 03gjzk (0.44 #612, 0.39 #1954, 0.37 #1656), 09jwl (0.37 #7323, 0.30 #3728, 0.29 #1213), 0np9r (0.30 #3728, 0.25 #13266, 0.20 #5090), 02krf9 (0.30 #3728, 0.25 #13266, 0.16 #624), 0d1pc (0.30 #3728, 0.25 #13266, 0.12 #4077), 01xr66 (0.30 #3728, 0.02 #214, 0.02 #1557), 0nbcg (0.27 #7336, 0.25 #13266, 0.21 #1226) >> Best rule #4785 for best value: >> intensional similarity = 3 >> extensional distance = 727 >> proper extension: 01ycbq; 01vsnff; 0gdh5; 0205dx; 01nrgq; 03h_0_z; 02js_6; >> query: (?x5999, 02hrh1q) <- award_winner(?x5999, ?x1384), profession(?x5999, ?x319), film(?x5999, ?x1868) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d02km profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 101.000 0.890 http://example.org/people/person/profession #11906-016kft PRED entity: 016kft PRED relation: award_nominee PRED expected values: 02p8v8 => 100 concepts (34 used for prediction) PRED predicted values (max 10 best out of 963): 04cf09 (0.81 #18727, 0.81 #30431, 0.81 #28089), 03mp9s (0.33 #3927, 0.18 #72561, 0.10 #1587), 02d42t (0.33 #3496, 0.18 #72561, 0.10 #1156), 02qgqt (0.33 #2360, 0.18 #72561, 0.08 #4701), 02mt4k (0.33 #3495, 0.18 #72561, 0.08 #5836), 03zg2x (0.33 #3190, 0.18 #72561, 0.08 #5531), 039bp (0.33 #2569, 0.18 #72561, 0.02 #44473), 016kft (0.30 #30430, 0.30 #2004, 0.28 #65538), 02p8v8 (0.30 #30430, 0.28 #65538, 0.25 #65539), 042xrr (0.30 #30430, 0.28 #65538, 0.03 #8112) >> Best rule #18727 for best value: >> intensional similarity = 3 >> extensional distance = 512 >> proper extension: 01sl1q; 044mz_; 0q9kd; 02s2ft; 06qgvf; 0grwj; 01vvydl; 01k7d9; 02p65p; 01xdf5; ... >> query: (?x9359, ?x1204) <- actor(?x4898, ?x9359), award_nominee(?x1204, ?x9359), film(?x9359, ?x4231) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #30430 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 594 *> proper extension: 02yygk; *> query: (?x9359, ?x2726) <- actor(?x4898, ?x9359), award_nominee(?x1204, ?x9359), actor(?x4898, ?x2726) *> conf = 0.30 ranks of expected_values: 9 EVAL 016kft award_nominee 02p8v8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 100.000 34.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11905-03jxw PRED entity: 03jxw PRED relation: influenced_by PRED expected values: 02lt8 => 155 concepts (48 used for prediction) PRED predicted values (max 10 best out of 386): 03sbs (0.60 #9937, 0.43 #652, 0.38 #1517), 05qmj (0.60 #9937, 0.29 #623, 0.25 #1488), 039n1 (0.60 #9937, 0.29 #755, 0.25 #1620), 03s9v (0.60 #9937, 0.20 #221, 0.07 #2814), 0j3v (0.57 #492, 0.38 #1357, 0.17 #3084), 028p0 (0.54 #1761, 0.14 #2193, 0.13 #3055), 032l1 (0.50 #1386, 0.21 #2251, 0.20 #89), 07ym0 (0.50 #1572, 0.14 #707, 0.07 #19884), 042q3 (0.43 #794, 0.38 #1659, 0.17 #3386), 01lwx (0.43 #837, 0.25 #1702, 0.12 #1270) >> Best rule #9937 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 03d9d6; >> query: (?x10090, ?x5811) <- peers(?x6320, ?x10090), peers(?x6320, ?x11499), influenced_by(?x11499, ?x5811) >> conf = 0.60 => this is the best rule for 4 predicted values *> Best rule #2282 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0hcvy; *> query: (?x10090, 02lt8) <- profession(?x10090, ?x353), influenced_by(?x10090, ?x2162), ?x2162 = 04xjp, ?x353 = 0cbd2 *> conf = 0.29 ranks of expected_values: 16 EVAL 03jxw influenced_by 02lt8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 155.000 48.000 0.600 http://example.org/influence/influence_node/influenced_by #11904-03z0l6 PRED entity: 03z0l6 PRED relation: type_of_union PRED expected values: 04ztj => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.81 #5, 0.78 #93, 0.76 #249), 01g63y (0.43 #477, 0.25 #534, 0.21 #18), 0jgjn (0.25 #534, 0.19 #551, 0.01 #52), 01bl8s (0.19 #551, 0.01 #67) >> Best rule #5 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 02mslq; >> query: (?x9991, 04ztj) <- category(?x9991, ?x134), student(?x6675, ?x9991), nationality(?x9991, ?x1310), colors(?x6675, ?x9778), ?x9778 = 09ggk >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03z0l6 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.810 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11903-04fgkf_ PRED entity: 04fgkf_ PRED relation: award! PRED expected values: 020ffd => 60 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2562): 01zlh5 (0.33 #2381, 0.25 #5754, 0.11 #9128), 0fz27v (0.33 #2901, 0.25 #6274, 0.11 #9648), 03f2_rc (0.33 #6863, 0.25 #3489, 0.11 #27107), 0794g (0.33 #7656, 0.25 #4282, 0.10 #14402), 019pm_ (0.33 #7496, 0.25 #4122, 0.10 #14242), 01gbn6 (0.33 #9469, 0.25 #6095, 0.08 #22965), 026c1 (0.33 #7326, 0.25 #3952, 0.08 #20822), 043zg (0.33 #8320, 0.25 #4946, 0.08 #45436), 01p85y (0.33 #9263, 0.25 #5889, 0.06 #74236), 0159h6 (0.29 #13591, 0.26 #16966, 0.11 #27089) >> Best rule #2381 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0h53c_5; >> query: (?x7644, 01zlh5) <- award_winner(?x7644, ?x10754), award_winner(?x7644, ?x2894), ?x10754 = 0c1j_, award(?x12194, ?x7644), ?x2894 = 01gbbz, ?x12194 = 01mbwlb >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #50612 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 211 *> proper extension: 02rdxsh; 02qysm0; 02qwzkm; *> query: (?x7644, ?x4259) <- nominated_for(?x7644, ?x4891), honored_for(?x7721, ?x4891), nominated_for(?x4259, ?x4891), location(?x4259, ?x3052) *> conf = 0.12 ranks of expected_values: 196 EVAL 04fgkf_ award! 020ffd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 60.000 22.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11902-0135xb PRED entity: 0135xb PRED relation: artist! PRED expected values: 01gfq4 043g7l => 164 concepts (115 used for prediction) PRED predicted values (max 10 best out of 123): 04fc6c (0.64 #1048, 0.02 #15997, 0.02 #2438), 043g7l (0.50 #449, 0.33 #32, 0.18 #1005), 0mzkr (0.45 #999, 0.14 #860, 0.12 #1416), 011k1h (0.43 #844, 0.17 #705, 0.15 #8074), 0g768 (0.40 #594, 0.33 #316, 0.18 #1011), 033hn8 (0.33 #14, 0.27 #987, 0.25 #431), 016ckq (0.33 #44, 0.25 #461, 0.18 #1017), 03mp8k (0.33 #65, 0.25 #482, 0.11 #1733), 02bh8z (0.33 #300, 0.20 #578, 0.14 #856), 037h1k (0.33 #194, 0.17 #750, 0.03 #2140) >> Best rule #1048 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 016376; 01f2q5; >> query: (?x7211, 04fc6c) <- artist(?x8125, ?x7211), artists(?x505, ?x7211), award(?x7211, ?x724), contact_category(?x8125, ?x897), service_location(?x8125, ?x94) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #449 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 01dpts; *> query: (?x7211, 043g7l) <- artist(?x8125, ?x7211), artists(?x505, ?x7211), ?x8125 = 06q07, artists(?x505, ?x669), award_winner(?x1386, ?x669) *> conf = 0.50 ranks of expected_values: 2, 37 EVAL 0135xb artist! 043g7l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 164.000 115.000 0.636 http://example.org/music/record_label/artist EVAL 0135xb artist! 01gfq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 164.000 115.000 0.636 http://example.org/music/record_label/artist #11901-04vq3h PRED entity: 04vq3h PRED relation: location PRED expected values: 01cx_ => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 39): 030qb3t (0.38 #887, 0.33 #1691, 0.29 #83), 02_286 (0.16 #4058, 0.14 #4862, 0.14 #37), 0n2z (0.14 #552, 0.12 #1356, 0.11 #2160), 0chgzm (0.14 #411, 0.12 #1215, 0.11 #2019), 0g34_ (0.14 #397, 0.12 #1201, 0.11 #2005), 06y57 (0.14 #256, 0.12 #1060, 0.11 #1864), 0k049 (0.12 #812, 0.11 #1616, 0.02 #3224), 0r0m6 (0.12 #1022, 0.11 #1826, 0.02 #17114), 0d6lp (0.11 #1776, 0.02 #17868, 0.02 #17064), 0rh6k (0.05 #36200, 0.02 #4025, 0.02 #2416) >> Best rule #887 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0q9kd; >> query: (?x9998, 030qb3t) <- film(?x9998, ?x1496), ?x1496 = 011yqc, profession(?x9998, ?x1032) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #3379 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 352 *> proper extension: 0277990; 08n__5; 030b93; 07q0g5; 02yygk; 03rgvr; 0f87jy; 01gw8b; 031v3p; *> query: (?x9998, 01cx_) <- student(?x1772, ?x9998), actor(?x10595, ?x9998), award_nominee(?x9998, ?x2296) *> conf = 0.03 ranks of expected_values: 24 EVAL 04vq3h location 01cx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 95.000 95.000 0.375 http://example.org/people/person/places_lived./people/place_lived/location #11900-0r111 PRED entity: 0r111 PRED relation: source PRED expected values: 0jbk9 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #42, 0.92 #40, 0.92 #9) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 150 >> proper extension: 0rj0z; >> query: (?x12299, 0jbk9) <- location(?x5604, ?x12299), category(?x12299, ?x134), county(?x12299, ?x2949), profession(?x5604, ?x319) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r111 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.928 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #11899-0h1tg PRED entity: 0h1tg PRED relation: nutrient! PRED expected values: 0hkxq 0fbw6 05z55 => 56 concepts (54 used for prediction) PRED predicted values (max 10 best out of 13): 0hkxq (0.91 #420, 0.91 #409, 0.90 #380), 05z55 (0.90 #377, 0.89 #325, 0.89 #318), 0fbw6 (0.89 #145, 0.89 #103, 0.88 #178), 06x4c (0.89 #145, 0.89 #103, 0.88 #178), 0dcfv (0.89 #145, 0.89 #103, 0.88 #178), 01sh2 (0.03 #573, 0.02 #17, 0.02 #556), 04k8n (0.03 #573, 0.02 #17), 05wvs (0.03 #573, 0.02 #17), 025rw19 (0.02 #556), 025tkqy (0.02 #556) >> Best rule #420 for best value: >> intensional similarity = 117 >> extensional distance = 20 >> proper extension: 0hkwr; >> query: (?x9619, ?x2701) <- nutrient(?x10612, ?x9619), nutrient(?x9489, ?x9619), nutrient(?x8298, ?x9619), nutrient(?x7719, ?x9619), nutrient(?x7057, ?x9619), nutrient(?x6285, ?x9619), nutrient(?x6191, ?x9619), nutrient(?x6159, ?x9619), nutrient(?x5373, ?x9619), nutrient(?x5009, ?x9619), nutrient(?x3900, ?x9619), nutrient(?x3468, ?x9619), nutrient(?x1303, ?x9619), nutrient(?x1257, ?x9619), ?x3900 = 061_f, ?x3468 = 0cxn2, ?x6285 = 01645p, ?x1257 = 09728, ?x9489 = 07j87, ?x7057 = 0fbdb, ?x5009 = 0fjfh, ?x6191 = 014j1m, ?x1303 = 0fj52s, nutrient(?x10612, ?x13944), nutrient(?x10612, ?x13498), nutrient(?x10612, ?x12902), nutrient(?x10612, ?x12454), nutrient(?x10612, ?x11592), nutrient(?x10612, ?x11270), nutrient(?x10612, ?x10098), nutrient(?x10612, ?x9949), nutrient(?x10612, ?x9915), nutrient(?x10612, ?x9840), nutrient(?x10612, ?x9795), nutrient(?x10612, ?x9733), nutrient(?x10612, ?x9490), nutrient(?x10612, ?x9436), nutrient(?x10612, ?x9426), nutrient(?x10612, ?x8487), nutrient(?x10612, ?x8442), nutrient(?x10612, ?x8413), nutrient(?x10612, ?x7720), nutrient(?x10612, ?x7652), nutrient(?x10612, ?x7364), nutrient(?x10612, ?x7135), nutrient(?x10612, ?x6586), nutrient(?x10612, ?x6192), nutrient(?x10612, ?x6160), nutrient(?x10612, ?x6026), nutrient(?x10612, ?x5549), nutrient(?x10612, ?x5526), nutrient(?x10612, ?x5451), nutrient(?x10612, ?x5010), nutrient(?x10612, ?x3901), nutrient(?x10612, ?x3469), nutrient(?x10612, ?x3203), nutrient(?x10612, ?x1960), nutrient(?x10612, ?x1258), ?x7720 = 025s7x6, ?x13944 = 0f4kp, ?x5526 = 09pbb, ?x7364 = 09gvd, ?x5549 = 025s7j4, ?x5451 = 05wvs, ?x13498 = 07q0m, ?x3901 = 0466p20, ?x6026 = 025sf8g, ?x8298 = 037ls6, ?x8442 = 02kcv4x, ?x9795 = 05v_8y, ?x7652 = 025s0s0, nutrient(?x5373, ?x8243), nutrient(?x5373, ?x6033), ?x12902 = 0fzjh, nutrient(?x6159, ?x9855), nutrient(?x6159, ?x5337), nutrient(?x6159, ?x4069), nutrient(?x6159, ?x3264), nutrient(?x6159, ?x2018), ?x3203 = 04kl74p, ?x9436 = 025sqz8, ?x8243 = 014d7f, ?x9840 = 02p0tjr, ?x11592 = 025sf0_, ?x9490 = 0h1sg, ?x3264 = 0dcfv, ?x1258 = 0h1wg, nutrient(?x9732, ?x7135), nutrient(?x2701, ?x7135), ?x3469 = 0h1zw, ?x11270 = 02kc008, ?x6192 = 06jry, ?x4069 = 0hqw8p_, ?x9855 = 0d9t0, ?x9733 = 0h1tz, ?x2701 = 0hkxq, ?x9426 = 0h1yy, nutrient(?x7719, ?x11784), nutrient(?x7719, ?x9708), nutrient(?x7719, ?x6286), ?x6586 = 05gh50, ?x6160 = 041r51, ?x6033 = 04zjxcz, ?x11784 = 07zqy, ?x5337 = 06x4c, ?x5010 = 0h1vz, ?x2018 = 01sh2, ?x10098 = 0h1_c, ?x8413 = 02kc4sf, ?x9708 = 061xhr, ?x6286 = 02y_3rf, ?x1960 = 07hnp, ?x8487 = 014yzm, ?x9915 = 025tkqy, ?x12454 = 025rw19, ?x9732 = 05z55, ?x9949 = 02kd0rh >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0h1tg nutrient! 05z55 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 54.000 0.909 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0h1tg nutrient! 0fbw6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 54.000 0.909 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0h1tg nutrient! 0hkxq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 54.000 0.909 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #11898-030_3z PRED entity: 030_3z PRED relation: executive_produced_by! PRED expected values: 016y_f 025rvx0 01f7jt => 112 concepts (96 used for prediction) PRED predicted values (max 10 best out of 332): 025ts_z (0.13 #1506, 0.03 #9350, 0.03 #10395), 04mcw4 (0.11 #253, 0.10 #12547, 0.10 #10979), 01bn3l (0.10 #1467, 0.05 #423, 0.05 #20912), 05qbbfb (0.10 #12547, 0.10 #10979, 0.05 #342), 0298n7 (0.10 #12547, 0.10 #10979, 0.05 #420), 026p4q7 (0.10 #12547, 0.10 #10979, 0.04 #8888), 061681 (0.10 #12547, 0.10 #10979, 0.04 #8888), 07j94 (0.10 #12547, 0.10 #10979, 0.02 #26139), 08xvpn (0.10 #12547, 0.10 #10979, 0.02 #26138), 0k2sk (0.05 #49, 0.05 #1093, 0.05 #20912) >> Best rule #1506 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 0grwj; 05ty4m; 0bxtg; 02lf0c; 0415svh; 02q_cc; 04wvhz; 06pj8; 01pcmd; 0bgrsl; ... >> query: (?x4552, 025ts_z) <- award_nominee(?x4552, ?x361), executive_produced_by(?x1470, ?x4552), program(?x4552, ?x4588) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #506 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 017s11; 016tt2; 04cw0j; 0hw1j; 01gb54; 0p__8; 0dbpwb; 028qyn; 0146mv; *> query: (?x4552, 01f7jt) <- award_nominee(?x4552, ?x6382), award_nominee(?x4552, ?x902), ?x902 = 05qd_, type_of_union(?x6382, ?x566) *> conf = 0.05 ranks of expected_values: 11, 12, 314 EVAL 030_3z executive_produced_by! 01f7jt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 112.000 96.000 0.128 http://example.org/film/film/executive_produced_by EVAL 030_3z executive_produced_by! 025rvx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 112.000 96.000 0.128 http://example.org/film/film/executive_produced_by EVAL 030_3z executive_produced_by! 016y_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 112.000 96.000 0.128 http://example.org/film/film/executive_produced_by #11897-01hx2t PRED entity: 01hx2t PRED relation: citytown PRED expected values: 0d234 => 178 concepts (119 used for prediction) PRED predicted values (max 10 best out of 202): 02frhbc (0.33 #215, 0.15 #583, 0.12 #9952), 05kj_ (0.23 #1474, 0.23 #7371, 0.23 #8846), 0d23k (0.15 #27660, 0.11 #159, 0.08 #527), 02_286 (0.13 #36895, 0.12 #38004, 0.12 #38745), 030qb3t (0.07 #15137, 0.05 #764, 0.05 #27317), 02dtg (0.06 #1850, 0.03 #4800, 0.03 #5905), 0ftvz (0.05 #788, 0.04 #1157, 0.03 #5213), 071cn (0.05 #818, 0.04 #1187, 0.02 #2660), 0mzww (0.05 #894, 0.04 #1263, 0.02 #2736), 0rn8q (0.05 #873, 0.04 #1242, 0.02 #4190) >> Best rule #215 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01950l; >> query: (?x8479, 02frhbc) <- category(?x8479, ?x134), state_province_region(?x8479, ?x726), ?x134 = 08mbj5d, ?x726 = 05kj_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #22492 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 349 *> proper extension: 01rgdw; 0325dj; 0p7tb; *> query: (?x8479, ?x2622) <- major_field_of_study(?x8479, ?x1154), institution(?x865, ?x8479), state_province_region(?x8479, ?x726), state(?x2622, ?x726) *> conf = 0.04 ranks of expected_values: 19 EVAL 01hx2t citytown 0d234 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 178.000 119.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #11896-02hnl PRED entity: 02hnl PRED relation: role! PRED expected values: 023slg => 73 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1264): 050z2 (0.69 #11009, 0.60 #12893, 0.60 #3953), 0137g1 (0.69 #10938, 0.50 #11406, 0.45 #9526), 01wxdn3 (0.64 #11702, 0.62 #6058, 0.60 #4178), 023l9y (0.62 #11034, 0.57 #11502, 0.50 #1158), 082brv (0.60 #12975, 0.60 #4035, 0.50 #2628), 04bpm6 (0.60 #3836, 0.56 #6186, 0.50 #5246), 0326tc (0.60 #4114, 0.54 #11170, 0.50 #2707), 02qtywd (0.60 #4212, 0.50 #6092, 0.50 #2805), 01w272y (0.60 #3921, 0.50 #2514, 0.50 #2044), 05qhnq (0.56 #7842, 0.56 #6428, 0.54 #11134) >> Best rule #11009 for best value: >> intensional similarity = 9 >> extensional distance = 11 >> proper extension: 0214km; >> query: (?x1750, 050z2) <- role(?x1750, ?x4471), role(?x1750, ?x1332), role(?x1750, ?x1212), ?x4471 = 026g73, role(?x615, ?x1750), performance_role(?x214, ?x1332), instrumentalists(?x1332, ?x120), ?x1212 = 07xzm, role(?x885, ?x1332) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1871 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 0l14qv; *> query: (?x1750, 023slg) <- group(?x1750, ?x2901), group(?x1750, ?x1684), group(?x1750, ?x646), role(?x1750, ?x3703), role(?x211, ?x1750), ?x2901 = 01vrwfv, ?x1684 = 01wv9xn, role(?x1750, ?x432), award(?x646, ?x2634), ?x3703 = 02dlh2 *> conf = 0.50 ranks of expected_values: 53 EVAL 02hnl role! 023slg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 73.000 53.000 0.692 http://example.org/music/artist/track_contributions./music/track_contribution/role #11895-0vjr PRED entity: 0vjr PRED relation: award PRED expected values: 0m7yy => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 177): 0cjyzs (0.45 #235, 0.45 #81, 0.45 #1868), 0m7yy (0.45 #599, 0.43 #1765, 0.42 #2232), 027gs1_ (0.45 #1868, 0.42 #3035, 0.41 #1168), 09qj50 (0.45 #1868, 0.42 #3035, 0.41 #1168), 09qv3c (0.45 #1868, 0.42 #3035, 0.41 #1168), 09qs08 (0.45 #1868, 0.42 #3035, 0.41 #1168), 09qvc0 (0.45 #1868, 0.42 #3035, 0.41 #1168), 0cqhmg (0.45 #1868, 0.42 #3035, 0.41 #1168), 03ccq3s (0.45 #1868, 0.42 #3035, 0.41 #1168), 09qrn4 (0.27 #157, 0.10 #1325, 0.09 #625) >> Best rule #235 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 02nf2c; >> query: (?x5386, ?x2016) <- award_winner(?x5386, ?x2307), nominated_for(?x2016, ?x5386), ?x2016 = 0cjyzs >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #599 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 04kzqz; 06w7mlh; *> query: (?x5386, 0m7yy) <- award_winner(?x5386, ?x2307), program(?x1394, ?x5386), titles(?x2008, ?x5386) *> conf = 0.45 ranks of expected_values: 2 EVAL 0vjr award 0m7yy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.455 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #11894-05fjf PRED entity: 05fjf PRED relation: contains PRED expected values: 0n5j_ 0xrz2 0xr0t 0qlrh => 163 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2750): 0r2l7 (0.83 #75365, 0.33 #189, 0.07 #11783), 0xkq4 (0.83 #75365), 05zl0 (0.72 #121755, 0.50 #110154, 0.49 #127553), 01r3w7 (0.72 #121755, 0.50 #110154, 0.49 #127553), 0n5j_ (0.60 #171033, 0.49 #217418, 0.35 #252204), 0y62n (0.60 #171033, 0.33 #1325, 0.07 #12919), 0mwht (0.60 #171033, 0.05 #10492, 0.03 #22084), 0mwxz (0.60 #171033, 0.05 #9721, 0.03 #21313), 0fkh6 (0.60 #171033, 0.04 #13262, 0.03 #16160), 0mws3 (0.60 #171033) >> Best rule #75365 for best value: >> intensional similarity = 2 >> extensional distance = 72 >> proper extension: 09hzw; >> query: (?x6895, ?x1189) <- state(?x1189, ?x6895), country(?x6895, ?x94) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #171033 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 126 *> proper extension: 065ky; *> query: (?x6895, ?x7500) <- contains(?x6895, ?x12221), contains(?x6895, ?x11893), time_zones(?x11893, ?x2674), adjoins(?x7500, ?x12221) *> conf = 0.60 ranks of expected_values: 5, 795, 852 EVAL 05fjf contains 0qlrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 163.000 116.000 0.830 http://example.org/location/location/contains EVAL 05fjf contains 0xr0t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 163.000 116.000 0.830 http://example.org/location/location/contains EVAL 05fjf contains 0xrz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 163.000 116.000 0.830 http://example.org/location/location/contains EVAL 05fjf contains 0n5j_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 163.000 116.000 0.830 http://example.org/location/location/contains #11893-08j7lh PRED entity: 08j7lh PRED relation: prequel! PRED expected values: 0dckvs => 99 concepts (30 used for prediction) PRED predicted values (max 10 best out of 15): 01f85k (0.22 #291, 0.14 #110, 0.02 #651), 01f8f7 (0.11 #297, 0.02 #657, 0.01 #1017), 09wnnb (0.02 #523, 0.01 #1243), 0gyv0b4 (0.02 #525), 08nvyr (0.02 #441), 0bpm4yw (0.02 #615, 0.01 #795, 0.01 #975), 031ldd (0.02 #1547, 0.01 #2992), 02xs6_ (0.01 #809, 0.01 #3157), 03k8th (0.01 #896), 017jd9 (0.01 #804) >> Best rule #291 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 05znbh7; >> query: (?x9216, 01f85k) <- genre(?x9216, ?x53), ?x53 = 07s9rl0, language(?x9216, ?x3271), nominated_for(?x9217, ?x9216), ?x3271 = 012w70, ?x9217 = 09v51c2 >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08j7lh prequel! 0dckvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 30.000 0.222 http://example.org/film/film/prequel #11892-02xnjd PRED entity: 02xnjd PRED relation: produced_by! PRED expected values: 0340hj => 111 concepts (46 used for prediction) PRED predicted values (max 10 best out of 668): 03cvwkr (0.20 #1948, 0.09 #7556, 0.07 #8490), 06rzwx (0.20 #2527, 0.06 #8135, 0.05 #9069), 059lwy (0.20 #2507, 0.06 #8115, 0.05 #9049), 024mxd (0.20 #2193, 0.06 #7801, 0.05 #8735), 02tqm5 (0.20 #2157, 0.06 #7765, 0.05 #8699), 0dqcs3 (0.20 #2310, 0.02 #13522, 0.02 #23808), 0m313 (0.12 #2807, 0.05 #3741, 0.05 #9349), 0kvbl6 (0.12 #3408, 0.04 #10884, 0.04 #13686), 0g22z (0.11 #4678, 0.11 #3743, 0.09 #7483), 02ywwy (0.11 #1700, 0.10 #2634, 0.04 #5437) >> Best rule #1948 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01dhpj; 012bk; 03f1zhf; >> query: (?x7976, 03cvwkr) <- gender(?x7976, ?x231), nationality(?x7976, ?x4743), ?x231 = 05zppz, ?x4743 = 03spz >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02xnjd produced_by! 0340hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 46.000 0.200 http://example.org/film/film/produced_by #11891-048kw PRED entity: 048kw PRED relation: contains PRED expected values: 0214m4 => 108 concepts (45 used for prediction) PRED predicted values (max 10 best out of 2426): 0nlg4 (0.62 #32362, 0.60 #85336, 0.59 #76507), 01_c4 (0.62 #32362, 0.60 #85336, 0.59 #76507), 0nlc7 (0.62 #32362, 0.60 #85336, 0.59 #76507), 0f485 (0.62 #32362, 0.59 #76507, 0.58 #70620), 02gw_w (0.50 #5491, 0.33 #2550, 0.17 #8433), 0n9dn (0.50 #3617, 0.33 #676, 0.08 #6559), 0f8j6 (0.50 #5791, 0.33 #2850, 0.08 #8733), 049kw (0.50 #4666, 0.33 #1725, 0.08 #7608), 0nbfm (0.50 #4646, 0.33 #1705, 0.06 #34069), 0m4yg (0.50 #4418, 0.33 #1477, 0.06 #33841) >> Best rule #32362 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 05l5n; 0jcg8; 0843m; 068p2; 034cm; 0l2lk; 0ck1d; 0clzr; 0fb18; 0m_z3; ... >> query: (?x11933, ?x7104) <- contains(?x11933, ?x11049), contains(?x1310, ?x11933), adjoins(?x11049, ?x7104), country(?x1552, ?x1310) >> conf = 0.62 => this is the best rule for 4 predicted values *> Best rule #1165 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 07ssc; *> query: (?x11933, 0214m4) <- contains(?x11933, ?x14570), contains(?x512, ?x11933), state_province_region(?x9989, ?x11933), ?x14570 = 019rvp *> conf = 0.33 ranks of expected_values: 161 EVAL 048kw contains 0214m4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 108.000 45.000 0.625 http://example.org/location/location/contains #11890-09k56b7 PRED entity: 09k56b7 PRED relation: film_release_region PRED expected values: 015fr 01p1v => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 123): 015fr (0.85 #1184, 0.84 #401, 0.81 #793), 047yc (0.74 #803, 0.71 #1324, 0.65 #1194), 0ctw_b (0.70 #1192, 0.60 #801, 0.56 #1322), 01p1v (0.67 #820, 0.60 #1211, 0.59 #1341), 0h7x (0.64 #416, 0.60 #285, 0.53 #547), 06mzp (0.58 #405, 0.53 #274, 0.49 #536), 01ls2 (0.56 #790, 0.51 #1181, 0.51 #398), 06f32 (0.55 #1220, 0.45 #1350, 0.44 #437), 03ryn (0.49 #847, 0.43 #1368, 0.42 #324), 01pj7 (0.49 #1208, 0.45 #817, 0.42 #294) >> Best rule #1184 for best value: >> intensional similarity = 6 >> extensional distance = 82 >> proper extension: 0bh8yn3; >> query: (?x1988, 015fr) <- film_release_region(?x1988, ?x3277), film_release_region(?x1988, ?x2316), film_release_region(?x1988, ?x279), ?x279 = 0d060g, ?x3277 = 06t8v, ?x2316 = 06t2t >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 09k56b7 film_release_region 01p1v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 71.000 0.845 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09k56b7 film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.845 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11889-012yc PRED entity: 012yc PRED relation: parent_genre PRED expected values: 016_nr => 71 concepts (59 used for prediction) PRED predicted values (max 10 best out of 217): 06by7 (0.67 #669, 0.50 #1156, 0.47 #2456), 06j6l (0.50 #2311, 0.44 #1660, 0.39 #4104), 05r6t (0.46 #3637, 0.38 #1194, 0.33 #216), 0gywn (0.44 #1667, 0.37 #3948, 0.29 #4766), 01243b (0.42 #3611, 0.38 #1168, 0.33 #28), 016_nr (0.38 #1512, 0.38 #1349, 0.33 #209), 02x8m (0.38 #1479, 0.37 #2944, 0.36 #3271), 064t9 (0.33 #11, 0.28 #3756, 0.28 #4409), 05bt6j (0.33 #29, 0.25 #1169, 0.22 #4593), 03lty (0.33 #181, 0.24 #7697, 0.23 #8028) >> Best rule #669 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 016clz; 05bt6j; 05r6t; 0pm85; >> query: (?x9630, 06by7) <- parent_genre(?x9630, ?x2937), artists(?x9630, ?x6035), artists(?x2937, ?x7614), artists(?x2937, ?x5536), artists(?x2937, ?x3384), ?x6035 = 02r3cn, ?x3384 = 01w272y, artist(?x8738, ?x5536), artist(?x3265, ?x7614) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1512 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 025tjk_; *> query: (?x9630, 016_nr) <- parent_genre(?x9630, ?x12988), parent_genre(?x9630, ?x2937), ?x12988 = 016_rm, ?x2937 = 0glt670 *> conf = 0.38 ranks of expected_values: 6 EVAL 012yc parent_genre 016_nr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 71.000 59.000 0.667 http://example.org/music/genre/parent_genre #11888-0fphgb PRED entity: 0fphgb PRED relation: film_release_region PRED expected values: 09pmkv => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 211): 09c7w0 (0.94 #3213, 0.94 #964, 0.93 #10749), 0d0vqn (0.93 #2092, 0.91 #2735, 0.91 #5138), 035qy (0.90 #675, 0.90 #515, 0.87 #355), 05r4w (0.84 #5132, 0.83 #2729, 0.81 #5932), 0k6nt (0.84 #2752, 0.81 #665, 0.81 #6435), 01mjq (0.80 #527, 0.76 #687, 0.73 #367), 0154j (0.79 #2732, 0.74 #5935, 0.73 #6255), 05b4w (0.76 #2794, 0.75 #5197, 0.73 #387), 0d060g (0.74 #2734, 0.72 #5137, 0.71 #6257), 01znc_ (0.74 #2771, 0.72 #5174, 0.69 #6294) >> Best rule #3213 for best value: >> intensional similarity = 4 >> extensional distance = 141 >> proper extension: 0dln8jk; >> query: (?x3619, 09c7w0) <- nominated_for(?x3619, ?x4176), film_release_distribution_medium(?x3619, ?x81), ?x81 = 029j_, film_release_region(?x3619, ?x142) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #668 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 03z9585; *> query: (?x3619, 09pmkv) <- costume_design_by(?x3619, ?x771), film_release_region(?x3619, ?x456), ?x456 = 05qhw, film(?x3593, ?x3619), award_winner(?x372, ?x3593) *> conf = 0.48 ranks of expected_values: 25 EVAL 0fphgb film_release_region 09pmkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 110.000 110.000 0.944 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11887-03phtz PRED entity: 03phtz PRED relation: currency PRED expected values: 09nqf => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.88 #57, 0.82 #176, 0.81 #85), 01nv4h (0.03 #44, 0.03 #170, 0.03 #198), 02l6h (0.02 #60, 0.01 #172, 0.01 #368), 02gsvk (0.01 #258) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 05r3qc; 027gy0k; >> query: (?x12648, 09nqf) <- film_crew_role(?x12648, ?x2091), genre(?x12648, ?x225), ?x225 = 02kdv5l, ?x2091 = 02rh1dz >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03phtz currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.877 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11886-0w0d PRED entity: 0w0d PRED relation: sports! PRED expected values: 0l6mp 0jhn7 => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 34): 0kbvb (0.89 #1021, 0.88 #1168, 0.85 #489), 0jhn7 (0.88 #1183, 0.87 #921, 0.86 #844), 0l98s (0.87 #904, 0.85 #489, 0.85 #1199), 018wrk (0.85 #489, 0.85 #1199, 0.85 #938), 0l6vl (0.85 #489, 0.85 #1199, 0.85 #938), 0l6mp (0.85 #489, 0.85 #1199, 0.85 #938), 0nbjq (0.77 #762, 0.75 #725, 0.70 #186), 0lgxj (0.73 #922, 0.71 #845, 0.70 #186), 0l998 (0.73 #905, 0.71 #828, 0.70 #186), 0lk8j (0.71 #836, 0.70 #186, 0.67 #913) >> Best rule #1021 for best value: >> intensional similarity = 55 >> extensional distance = 17 >> proper extension: 07jjt; 019w9j; 06z68; 0crlz; >> query: (?x1352, 0kbvb) <- country(?x1352, ?x2346), country(?x1352, ?x1892), country(?x1352, ?x304), sports(?x1081, ?x1352), ?x1081 = 0l6m5, olympics(?x1892, ?x418), film_release_region(?x11351, ?x1892), film_release_region(?x9652, ?x1892), film_release_region(?x9616, ?x1892), film_release_region(?x7897, ?x1892), film_release_region(?x7493, ?x1892), film_release_region(?x5980, ?x1892), film_release_region(?x5877, ?x1892), film_release_region(?x5644, ?x1892), film_release_region(?x5318, ?x1892), film_release_region(?x5317, ?x1892), film_release_region(?x5013, ?x1892), film_release_region(?x3854, ?x1892), film_release_region(?x3603, ?x1892), film_release_region(?x2350, ?x1892), film_release_region(?x2104, ?x1892), film_release_region(?x972, ?x1892), film_release_region(?x641, ?x1892), film_release_region(?x186, ?x1892), film_release_region(?x66, ?x1892), ?x5317 = 04zl8, ?x641 = 08720, ?x972 = 017gl1, ?x11351 = 02wtp6, ?x5877 = 02qyv3h, country(?x6733, ?x1892), ?x7897 = 03np63f, nationality(?x2083, ?x304), contains(?x455, ?x304), ?x5318 = 0353xq, ?x2104 = 0j_tw, ?x6733 = 01sgl, ?x2346 = 0d05w3, ?x5644 = 0dll_t2, ?x3603 = 09gkx35, ?x9616 = 045r_9, contains(?x6820, ?x1892), ?x9652 = 0ddbjy4, combatants(?x326, ?x1892), film_release_region(?x80, ?x304), contains(?x1892, ?x7061), ?x3854 = 03q0r1, ?x5013 = 011ycb, ?x186 = 02vxq9m, ?x2350 = 0661m4p, ?x7493 = 0btpm6, ?x66 = 014lc_, time_zones(?x304, ?x2864), ?x5980 = 0hv81, countries_spoken_in(?x4442, ?x1892) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1183 for first EXPECTED value: *> intensional similarity = 55 *> extensional distance = 24 *> proper extension: 018jz; *> query: (?x1352, 0jhn7) <- country(?x1352, ?x2346), country(?x1352, ?x1892), country(?x1352, ?x304), sports(?x1081, ?x1352), ?x1081 = 0l6m5, olympics(?x1892, ?x418), film_release_region(?x11351, ?x1892), film_release_region(?x10029, ?x1892), film_release_region(?x7897, ?x1892), film_release_region(?x7170, ?x1892), film_release_region(?x6175, ?x1892), film_release_region(?x5877, ?x1892), film_release_region(?x5318, ?x1892), film_release_region(?x5317, ?x1892), film_release_region(?x5255, ?x1892), film_release_region(?x5016, ?x1892), film_release_region(?x4441, ?x1892), film_release_region(?x4315, ?x1892), film_release_region(?x4290, ?x1892), film_release_region(?x3745, ?x1892), film_release_region(?x3453, ?x1892), film_release_region(?x2104, ?x1892), film_release_region(?x1069, ?x1892), film_release_region(?x972, ?x1892), film_release_region(?x641, ?x1892), ?x5317 = 04zl8, ?x641 = 08720, ?x972 = 017gl1, ?x11351 = 02wtp6, ?x5877 = 02qyv3h, country(?x6733, ?x1892), ?x7897 = 03np63f, nationality(?x2083, ?x304), contains(?x455, ?x304), ?x5318 = 0353xq, ?x2104 = 0j_tw, ?x6733 = 01sgl, ?x5255 = 01sby_, ?x4290 = 0gtxj2q, ?x3745 = 03cw411, administrative_parent(?x206, ?x2346), administrative_parent(?x304, ?x551), ?x4315 = 0sxkh, participating_countries(?x1741, ?x2346), ?x5016 = 062zm5h, ?x3453 = 0dgpwnk, ?x1069 = 0jqp3, combatants(?x1892, ?x3141), olympics(?x304, ?x452), organization(?x304, ?x127), ?x10029 = 02vzpb, ?x6175 = 0gg5kmg, nationality(?x754, ?x2346), ?x4441 = 0125xq, ?x7170 = 02pxst *> conf = 0.88 ranks of expected_values: 2, 6 EVAL 0w0d sports! 0jhn7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 41.000 0.895 http://example.org/olympics/olympic_games/sports EVAL 0w0d sports! 0l6mp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 41.000 41.000 0.895 http://example.org/olympics/olympic_games/sports #11885-0k4p0 PRED entity: 0k4p0 PRED relation: nominated_for! PRED expected values: 02r0csl 02x73k6 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 183): 02z0dfh (0.66 #7742, 0.66 #7741, 0.23 #495), 02qyntr (0.58 #163, 0.35 #605, 0.28 #1047), 0gqy2 (0.42 #548, 0.36 #1432, 0.33 #106), 027dtxw (0.42 #4, 0.29 #446, 0.25 #888), 0gr4k (0.42 #1350, 0.38 #466, 0.32 #908), 09qv_s (0.33 #99, 0.27 #541, 0.27 #983), 0gq_v (0.30 #1124, 0.30 #1345, 0.29 #2671), 099c8n (0.29 #51, 0.27 #493, 0.24 #935), 0l8z1 (0.29 #489, 0.25 #47, 0.23 #2699), 03hkv_r (0.29 #456, 0.23 #898, 0.17 #14) >> Best rule #7742 for best value: >> intensional similarity = 1 >> extensional distance = 1025 >> proper extension: 0lcdk; 0542n; 087z2; >> query: (?x5712, ?x1254) <- award(?x5712, ?x1254) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #487 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 46 *> proper extension: 0yyg4; 0209xj; 0209hj; 0_b3d; 092vkg; 0_92w; 0c0nhgv; 0gmcwlb; 011yth; 02c638; ... *> query: (?x5712, 02x73k6) <- nominated_for(?x3209, ?x5712), nominated_for(?x1307, ?x5712), ?x3209 = 02w9sd7, film(?x397, ?x5712), ?x1307 = 0gq9h *> conf = 0.27 ranks of expected_values: 14, 53 EVAL 0k4p0 nominated_for! 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 59.000 59.000 0.657 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0k4p0 nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 59.000 59.000 0.657 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11884-03hj5vf PRED entity: 03hj5vf PRED relation: nominated_for PRED expected values: 0cbv4g 02825kb 06fpsx 0bs5vty => 46 concepts (4 used for prediction) PRED predicted values (max 10 best out of 2044): 02rv_dz (0.72 #3140, 0.25 #1781, 0.12 #3352), 0dt8xq (0.72 #3140, 0.04 #6284, 0.04 #3920), 0m313 (0.50 #1580, 0.33 #4725, 0.23 #3151), 09gq0x5 (0.50 #1820, 0.33 #4965, 0.20 #3391), 0gmgwnv (0.50 #2518, 0.32 #5663, 0.23 #4089), 049xgc (0.50 #2433, 0.31 #5578, 0.18 #4004), 095zlp (0.50 #1620, 0.28 #4765, 0.23 #3191), 011yl_ (0.50 #2091, 0.26 #5236, 0.20 #3662), 02c638 (0.50 #1873, 0.24 #5018, 0.23 #3444), 0c0zq (0.50 #2930, 0.24 #6075, 0.16 #4501) >> Best rule #3140 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 027dtxw; 04dn09n; >> query: (?x3190, ?x1531) <- nominated_for(?x3190, ?x6798), nominated_for(?x3190, ?x2846), ?x2846 = 076tq0z, film(?x123, ?x6798), award(?x1531, ?x3190), country(?x6798, ?x94) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #2992 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 027dtxw; 04dn09n; *> query: (?x3190, 0bs5vty) <- nominated_for(?x3190, ?x6798), nominated_for(?x3190, ?x2846), ?x2846 = 076tq0z, film(?x123, ?x6798), award(?x1531, ?x3190), country(?x6798, ?x94) *> conf = 0.25 ranks of expected_values: 163, 343, 1179, 1186 EVAL 03hj5vf nominated_for 0bs5vty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 4.000 0.724 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hj5vf nominated_for 06fpsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 4.000 0.724 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hj5vf nominated_for 02825kb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 4.000 0.724 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03hj5vf nominated_for 0cbv4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 4.000 0.724 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11883-02bvt PRED entity: 02bvt PRED relation: profession PRED expected values: 03gjzk => 85 concepts (66 used for prediction) PRED predicted values (max 10 best out of 65): 03gjzk (0.85 #611, 0.85 #1804, 0.84 #462), 02hrh1q (0.77 #2697, 0.73 #4187, 0.73 #8212), 01d_h8 (0.55 #3136, 0.49 #453, 0.48 #4328), 02jknp (0.43 #3138, 0.42 #4330, 0.38 #3287), 02krf9 (0.30 #1369, 0.30 #7452, 0.30 #1518), 018gz8 (0.29 #1342, 0.20 #3147, 0.18 #3296), 0np9r (0.29 #1342, 0.14 #3151, 0.11 #1810), 0cbd2 (0.27 #603, 0.25 #454, 0.25 #3137), 01c72t (0.20 #173, 0.14 #769, 0.12 #1216), 09jwl (0.19 #4192, 0.19 #5086, 0.18 #6576) >> Best rule #611 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 02k76g; >> query: (?x4806, 03gjzk) <- profession(?x4806, ?x987), program(?x4806, ?x9541), student(?x5324, ?x4806) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bvt profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 66.000 0.847 http://example.org/people/person/profession #11882-0dt8xq PRED entity: 0dt8xq PRED relation: film_release_region PRED expected values: 03_3d 0d060g 04g5k => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 192): 06bnz (0.87 #1897, 0.86 #3756, 0.85 #4616), 03_3d (0.81 #5158, 0.80 #433, 0.80 #3152), 0d060g (0.80 #4585, 0.79 #1866, 0.79 #3725), 03rj0 (0.73 #3770, 0.71 #4630, 0.70 #1911), 047yc (0.68 #1883, 0.64 #3742, 0.63 #4602), 01mjq (0.68 #3754, 0.65 #4614, 0.58 #1895), 016wzw (0.64 #484, 0.62 #1916, 0.58 #4635), 01p1v (0.62 #3763, 0.60 #4623, 0.60 #472), 06mzp (0.60 #445, 0.57 #1877, 0.53 #3736), 07f1x (0.52 #535, 0.47 #1967, 0.42 #4686) >> Best rule #1897 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 0407yj_; 0j43swk; 0gwjw0c; >> query: (?x5070, 06bnz) <- film_release_region(?x5070, ?x1497), ?x1497 = 015qh, production_companies(?x5070, ?x574), nominated_for(?x298, ?x5070) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #5158 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 164 *> proper extension: 0ds35l9; 028_yv; 0h1cdwq; 0djb3vw; 0gkz15s; 01vksx; 017gl1; 0bwfwpj; 08hmch; 0jqp3; ... *> query: (?x5070, 03_3d) <- film_release_region(?x5070, ?x1453), film_crew_role(?x5070, ?x137), ?x1453 = 06qd3 *> conf = 0.81 ranks of expected_values: 2, 3, 53 EVAL 0dt8xq film_release_region 04g5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 135.000 135.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dt8xq film_release_region 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 135.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dt8xq film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 135.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11881-07g9f PRED entity: 07g9f PRED relation: actor PRED expected values: 03d_w3h => 96 concepts (70 used for prediction) PRED predicted values (max 10 best out of 839): 02dth1 (0.42 #9263, 0.41 #12040, 0.41 #11114), 03y9ccy (0.41 #12040, 0.41 #11114, 0.40 #12966), 01gp_x (0.41 #12040, 0.41 #11114, 0.40 #12966), 02q5xsx (0.41 #12040, 0.41 #11114, 0.40 #12966), 027hnjh (0.41 #12040, 0.41 #11114, 0.40 #12966), 0cjdk (0.41 #12040, 0.41 #11114, 0.40 #12966), 025y9fn (0.39 #13893, 0.37 #12967, 0.36 #26855), 07lwsz (0.39 #13893, 0.37 #12967, 0.36 #26855), 04h68j (0.39 #13893, 0.37 #12967, 0.36 #26855), 0b05xm (0.39 #13893, 0.37 #12967, 0.36 #26855) >> Best rule #9263 for best value: >> intensional similarity = 3 >> extensional distance = 92 >> proper extension: 01fszq; >> query: (?x10089, ?x4204) <- program(?x1394, ?x10089), nominated_for(?x4204, ?x10089), religion(?x4204, ?x1985) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #2856 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 0bbm7r; 04glx0; 0dl6fv; *> query: (?x10089, 03d_w3h) <- award(?x10089, ?x3486), nominated_for(?x2554, ?x10089), ?x3486 = 0m7yy *> conf = 0.02 ranks of expected_values: 455 EVAL 07g9f actor 03d_w3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 96.000 70.000 0.416 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11880-03p01x PRED entity: 03p01x PRED relation: profession PRED expected values: 02hrh1q => 124 concepts (67 used for prediction) PRED predicted values (max 10 best out of 140): 02hrh1q (0.78 #7265, 0.69 #8136, 0.67 #7701), 02krf9 (0.50 #748, 0.46 #2488, 0.38 #1908), 0np9r (0.50 #452, 0.40 #1757, 0.37 #1322), 018gz8 (0.45 #1753, 0.42 #1318, 0.30 #448), 09jwl (0.33 #15, 0.25 #160, 0.24 #8865), 0kyk (0.32 #9311, 0.31 #9602, 0.27 #2346), 015h31 (0.30 #459, 0.25 #749, 0.19 #1909), 01c72t (0.25 #745, 0.21 #890, 0.20 #455), 015cjr (0.25 #1786, 0.16 #1351, 0.10 #2801), 0196pc (0.20 #360, 0.17 #650, 0.14 #2535) >> Best rule #7265 for best value: >> intensional similarity = 5 >> extensional distance = 225 >> proper extension: 020jqv; 05mxw33; >> query: (?x10407, 02hrh1q) <- profession(?x10407, ?x353), profession(?x10407, ?x319), ?x353 = 0cbd2, profession(?x1291, ?x319), ?x1291 = 01kx_81 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03p01x profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 67.000 0.775 http://example.org/people/person/profession #11879-01q2nx PRED entity: 01q2nx PRED relation: film_production_design_by PRED expected values: 05b2f_k => 135 concepts (86 used for prediction) PRED predicted values (max 10 best out of 16): 04_1nk (0.14 #45, 0.05 #107, 0.04 #138), 02x2t07 (0.07 #210, 0.06 #458, 0.05 #521), 05b2gsm (0.04 #172, 0.03 #389, 0.01 #1832), 02vxyl5 (0.04 #1874, 0.02 #2541, 0.02 #2608), 0bytkq (0.03 #973, 0.03 #1004, 0.03 #566), 03wd5tk (0.03 #232, 0.03 #325, 0.01 #606), 03mdw3c (0.03 #364, 0.02 #1523, 0.02 #426), 04kj2v (0.02 #1282, 0.02 #468, 0.02 #500), 0d5wn3 (0.02 #1574, 0.02 #444, 0.02 #1918), 06cv1 (0.02 #466, 0.02 #498) >> Best rule #45 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0dr_4; >> query: (?x5275, 04_1nk) <- film_format(?x5275, ?x909), film(?x10004, ?x5275), film_release_distribution_medium(?x5275, ?x81), genre(?x5275, ?x5276), award_nominee(?x444, ?x10004), ?x5276 = 01drsx >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01q2nx film_production_design_by 05b2f_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 86.000 0.143 http://example.org/film/film/film_production_design_by #11878-05pcr PRED entity: 05pcr PRED relation: teams! PRED expected values: 05ksh => 33 concepts (29 used for prediction) PRED predicted values (max 10 best out of 46): 03pzf (0.33 #209, 0.25 #749, 0.25 #479), 019fh (0.20 #916, 0.04 #1996, 0.04 #2266), 0dclg (0.06 #1152, 0.05 #1422, 0.05 #1692), 02cl1 (0.06 #1100, 0.05 #1370, 0.05 #1640), 068p2 (0.06 #1203, 0.05 #1473, 0.05 #1743), 02dtg (0.06 #1096, 0.05 #1366, 0.05 #1636), 0n1rj (0.06 #1222, 0.05 #1492, 0.05 #1762), 0hptm (0.06 #1224, 0.05 #1494, 0.05 #1764), 080h2 (0.06 #1110, 0.05 #1380, 0.05 #1650), 0qplq (0.06 #1310, 0.05 #1580, 0.05 #1850) >> Best rule #209 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 048ldh; >> query: (?x7174, 03pzf) <- position(?x7174, ?x5234), position(?x7174, ?x3724), position(?x7174, ?x2918), sport(?x7174, ?x453), ?x5234 = 02qvdc, ?x3724 = 02qvzf, team(?x3299, ?x7174), ?x3299 = 02qvgy, ?x453 = 03tmr, ?x2918 = 02qvl7, team(?x5234, ?x7174) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05pcr teams! 05ksh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 29.000 0.333 http://example.org/sports/sports_team_location/teams #11877-0h1wz PRED entity: 0h1wz PRED relation: symptom_of! PRED expected values: 02tfl8 0gxb2 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 74): 01j6t0 (0.91 #2482, 0.82 #2161, 0.82 #1504), 0gxb2 (0.78 #345, 0.69 #2070, 0.67 #121), 02tfl8 (0.78 #345, 0.67 #701, 0.67 #121), 0j5fv (0.78 #345, 0.62 #462, 0.61 #523), 0f3kl (0.78 #345, 0.62 #462, 0.61 #523), 02y0js (0.78 #345, 0.62 #462, 0.61 #523), 0hgxh (0.67 #121, 0.54 #475, 0.54 #474), 04kllm9 (0.67 #121, 0.54 #475, 0.54 #474), 097ns (0.61 #523, 0.47 #1563, 0.39 #1726), 08g5q7 (0.61 #523, 0.36 #532, 0.23 #1250) >> Best rule #2482 for best value: >> intensional similarity = 29 >> extensional distance = 33 >> proper extension: 01g2q; >> query: (?x14024, 01j6t0) <- symptom_of(?x13487, ?x14024), symptom_of(?x10717, ?x14024), symptom_of(?x10717, ?x13744), symptom_of(?x10717, ?x13560), symptom_of(?x10717, ?x13485), symptom_of(?x10717, ?x11739), symptom_of(?x10717, ?x9898), symptom_of(?x10717, ?x8675), symptom_of(?x10717, ?x7586), symptom_of(?x10717, ?x4291), symptom_of(?x10717, ?x3799), ?x13560 = 04nz3, ?x4291 = 07jwr, ?x9898 = 09jg8, symptom_of(?x13487, ?x11392), symptom_of(?x13487, ?x5855), symptom_of(?x13487, ?x4959), symptom_of(?x13487, ?x4906), ?x13485 = 07s4l, ?x4959 = 01dcqj, ?x4906 = 0hg11, ?x11739 = 0167bx, ?x11392 = 0lcdk, ?x3799 = 04psf, people(?x5855, ?x158), symptom_of(?x5855, ?x12536), ?x7586 = 074m2, ?x8675 = 01gkcc, people(?x13744, ?x2871) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #345 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 0d19y2; *> query: (?x14024, ?x1158) <- risk_factors(?x9510, ?x14024), risk_factors(?x9119, ?x14024), symptom_of(?x9509, ?x9510), symptom_of(?x4905, ?x9510), symptom_of(?x10717, ?x14024), ?x4905 = 01j6t0, risk_factors(?x9119, ?x10480), risk_factors(?x9119, ?x7007), risk_factors(?x9510, ?x14228), symptom_of(?x13373, ?x10480), symptom_of(?x6780, ?x10480), symptom_of(?x1158, ?x10480), people(?x14228, ?x5912), people(?x7007, ?x2208), ?x13373 = 0f3kl, risk_factors(?x7007, ?x514), notable_people_with_this_condition(?x14228, ?x5609), ?x6780 = 0j5fv, ?x9509 = 0gxb2, ?x10717 = 0cjf0, ?x514 = 02zsn *> conf = 0.78 ranks of expected_values: 2, 3 EVAL 0h1wz symptom_of! 0gxb2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.914 http://example.org/medicine/symptom/symptom_of EVAL 0h1wz symptom_of! 02tfl8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.914 http://example.org/medicine/symptom/symptom_of #11876-0by17xn PRED entity: 0by17xn PRED relation: film_release_region PRED expected values: 0jgd 0k6nt 06t2t => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 172): 0chghy (0.83 #286, 0.80 #842, 0.74 #981), 06t2t (0.80 #329, 0.72 #885, 0.62 #51), 0jgd (0.80 #837, 0.78 #281, 0.75 #3), 0k6nt (0.79 #298, 0.77 #854, 0.68 #993), 03rt9 (0.74 #288, 0.70 #844, 0.69 #10), 03rj0 (0.64 #883, 0.59 #327, 0.56 #49), 06c1y (0.62 #37, 0.50 #315, 0.36 #871), 05qx1 (0.61 #313, 0.56 #35, 0.38 #869), 0ctw_b (0.60 #299, 0.57 #855, 0.50 #21), 06qd3 (0.59 #310, 0.52 #866, 0.44 #32) >> Best rule #286 for best value: >> intensional similarity = 5 >> extensional distance = 80 >> proper extension: 0c40vxk; 0bh8yn3; 047svrl; 02vr3gz; 0cmc26r; 0ndsl1x; >> query: (?x11313, 0chghy) <- film_release_region(?x11313, ?x456), film_release_region(?x11313, ?x404), ?x404 = 047lj, film(?x1554, ?x11313), ?x456 = 05qhw >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #329 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 80 *> proper extension: 0c40vxk; 0bh8yn3; 047svrl; 02vr3gz; 0cmc26r; 0ndsl1x; *> query: (?x11313, 06t2t) <- film_release_region(?x11313, ?x456), film_release_region(?x11313, ?x404), ?x404 = 047lj, film(?x1554, ?x11313), ?x456 = 05qhw *> conf = 0.80 ranks of expected_values: 2, 3, 4 EVAL 0by17xn film_release_region 06t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0by17xn film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0by17xn film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11875-08nvyr PRED entity: 08nvyr PRED relation: nominated_for! PRED expected values: 06pj8 => 103 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1086): 092kgw (0.79 #11666, 0.03 #19887, 0.02 #26888), 01gb54 (0.22 #1014, 0.04 #24347, 0.03 #12681), 05qd_ (0.14 #25668, 0.14 #23507, 0.13 #37338), 0fqy4p (0.13 #37338, 0.12 #25667, 0.02 #10332), 04ktcgn (0.11 #399, 0.05 #7398, 0.03 #5065), 024rbz (0.11 #281, 0.05 #11948, 0.02 #44620), 094tsh6 (0.11 #1936, 0.04 #4269, 0.03 #6602), 02qgqt (0.11 #17, 0.04 #2350, 0.03 #4683), 0sz28 (0.11 #243, 0.04 #2576, 0.03 #4909), 04bdxl (0.11 #7, 0.03 #18674, 0.02 #25675) >> Best rule #11666 for best value: >> intensional similarity = 5 >> extensional distance = 62 >> proper extension: 0gxsh4; >> query: (?x4541, ?x3281) <- award_winner(?x4541, ?x4940), award_winner(?x4541, ?x3281), nominated_for(?x6944, ?x4541), instrumentalists(?x227, ?x4940), executive_produced_by(?x1228, ?x6944) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #14435 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 109 *> proper extension: 023p7l; 02fqxm; *> query: (?x4541, 06pj8) <- nominated_for(?x1443, ?x4541), nominated_for(?x746, ?x4541), ?x1443 = 054krc, music(?x4541, ?x4940), ceremony(?x746, ?x747) *> conf = 0.06 ranks of expected_values: 52 EVAL 08nvyr nominated_for! 06pj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 103.000 26.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #11874-053rxgm PRED entity: 053rxgm PRED relation: film_crew_role PRED expected values: 0ch6mp2 089g0h => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.84 #566, 0.81 #1097, 0.80 #1724), 0dxtw (0.64 #382, 0.42 #662, 0.41 #1100), 01pvkk (0.50 #10, 0.37 #383, 0.29 #570), 089fss (0.25 #5, 0.22 #565, 0.14 #67), 0d2b38 (0.25 #21, 0.18 #394, 0.17 #145), 015h31 (0.19 #381, 0.15 #661, 0.13 #350), 089g0h (0.14 #575, 0.14 #388, 0.12 #15), 020xn5 (0.12 #7, 0.09 #380, 0.04 #349), 094hwz (0.11 #354, 0.09 #198, 0.08 #136), 02_n3z (0.10 #250, 0.10 #530, 0.10 #654) >> Best rule #566 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 0416y94; >> query: (?x1178, 0ch6mp2) <- currency(?x1178, ?x170), film_crew_role(?x1178, ?x3197), nominated_for(?x562, ?x1178), ?x3197 = 02ynfr >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 053rxgm film_crew_role 089g0h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 88.000 0.839 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 053rxgm film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.839 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11873-04xm_ PRED entity: 04xm_ PRED relation: company PRED expected values: 0dy04 => 131 concepts (88 used for prediction) PRED predicted values (max 10 best out of 113): 07wrz (0.25 #416, 0.14 #4028, 0.11 #796), 01w5m (0.16 #4041, 0.12 #429, 0.08 #1189), 09c7w0 (0.13 #5137, 0.12 #4756, 0.08 #6280), 01kvrz (0.12 #711, 0.12 #521, 0.06 #1851), 05zl0 (0.12 #471, 0.11 #851, 0.10 #4656), 01k2wn (0.12 #399, 0.11 #779, 0.10 #969), 017j69 (0.12 #447, 0.11 #827, 0.10 #1017), 08815 (0.12 #382, 0.11 #762, 0.10 #952), 03hdz8 (0.12 #487, 0.08 #1247, 0.05 #2197), 0277jc (0.12 #400, 0.08 #1540, 0.05 #2300) >> Best rule #416 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 073bb; 0nk72; >> query: (?x10111, 07wrz) <- nationality(?x10111, ?x1264), ?x1264 = 0345h, influenced_by(?x10111, ?x2240), company(?x10111, ?x4096), place_of_death(?x10111, ?x2611) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4606 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 66 *> proper extension: 0ct9_; *> query: (?x10111, 0dy04) <- gender(?x10111, ?x231), company(?x10111, ?x4096), influenced_by(?x10111, ?x2240) *> conf = 0.01 ranks of expected_values: 98 EVAL 04xm_ company 0dy04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 131.000 88.000 0.250 http://example.org/people/person/employment_history./business/employment_tenure/company #11872-01j_06 PRED entity: 01j_06 PRED relation: school! PRED expected values: 047dpm0 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 19): 02pq_x5 (0.31 #17, 0.20 #171, 0.19 #21), 0f4vx0 (0.29 #146, 0.26 #203, 0.24 #165), 02qw1zx (0.25 #159, 0.23 #5, 0.23 #140), 092j54 (0.23 #9, 0.20 #163, 0.19 #144), 02z6872 (0.23 #10, 0.19 #21, 0.15 #164), 02pq_rp (0.23 #8, 0.19 #21, 0.15 #289), 09l0x9 (0.23 #12, 0.17 #166, 0.16 #204), 05vsb7 (0.23 #155, 0.17 #193, 0.17 #212), 02x2khw (0.19 #21, 0.16 #157, 0.16 #138), 047dpm0 (0.19 #21, 0.15 #20, 0.15 #18) >> Best rule #17 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 02zc7f; >> query: (?x1428, 02pq_x5) <- school(?x1823, ?x1428), school(?x1160, ?x1428), ?x1160 = 049n7, draft(?x1823, ?x11905), ?x11905 = 047dpm0 >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 02zc7f; *> query: (?x1428, ?x1161) <- school(?x1823, ?x1428), school(?x1160, ?x1428), ?x1160 = 049n7, draft(?x1823, ?x11905), draft(?x1823, ?x1161), ?x11905 = 047dpm0 *> conf = 0.19 ranks of expected_values: 10 EVAL 01j_06 school! 047dpm0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 121.000 121.000 0.308 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #11871-04gxp2 PRED entity: 04gxp2 PRED relation: campuses! PRED expected values: 04gxp2 => 86 concepts (53 used for prediction) PRED predicted values (max 10 best out of 135): 02bq1j (0.25 #163, 0.23 #5464, 0.19 #12018), 04gxp2 (0.23 #5464, 0.19 #12018), 07szy (0.23 #5464, 0.19 #12018), 02rff2 (0.05 #633, 0.02 #2274, 0.02 #3366), 0g8fs (0.05 #900, 0.02 #2541, 0.02 #4179), 03ksy (0.05 #640, 0.02 #2281, 0.02 #3919), 02ccqg (0.05 #629, 0.02 #2270, 0.02 #3908), 01n951 (0.05 #820, 0.02 #3553, 0.02 #4099), 02pdhz (0.05 #1038, 0.02 #4317, 0.01 #5409), 07tds (0.05 #686, 0.02 #3965, 0.01 #5057) >> Best rule #163 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 07szy; 02bq1j; >> query: (?x13215, 02bq1j) <- student(?x13215, ?x9684), category(?x13215, ?x134), contains(?x94, ?x13215), ?x9684 = 0c_md_ >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #5464 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 01dnnt; *> query: (?x13215, ?x1681) <- student(?x13215, ?x9684), basic_title(?x9684, ?x265), type_of_union(?x9684, ?x566), student(?x1681, ?x9684) *> conf = 0.23 ranks of expected_values: 2 EVAL 04gxp2 campuses! 04gxp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 53.000 0.250 http://example.org/education/educational_institution/campuses #11870-01wb95 PRED entity: 01wb95 PRED relation: film! PRED expected values: 03bggl 0131kb 01wgx4 => 62 concepts (22 used for prediction) PRED predicted values (max 10 best out of 718): 0520r2x (0.39 #24900, 0.39 #29049, 0.38 #41497), 0gm34 (0.25 #1296, 0.20 #3370, 0.02 #9594), 0cf2h (0.25 #1098, 0.20 #3172, 0.02 #9396), 0bw87 (0.25 #1165, 0.20 #3239, 0.01 #5313), 0gm8_p (0.25 #1360, 0.20 #3434, 0.01 #7583), 039wsf (0.25 #1959, 0.20 #4033), 012vct (0.20 #16600, 0.19 #20750, 0.14 #41498), 01vsn38 (0.20 #3922, 0.04 #22598, 0.04 #26748), 0h7pj (0.20 #3615, 0.04 #22291, 0.04 #26441), 044rvb (0.20 #2175, 0.01 #31225, 0.01 #22925) >> Best rule #24900 for best value: >> intensional similarity = 4 >> extensional distance = 505 >> proper extension: 0267wwv; >> query: (?x3783, ?x199) <- titles(?x162, ?x3783), film(?x7232, ?x3783), nominated_for(?x199, ?x3783), country(?x3783, ?x94) >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wb95 film! 01wgx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 22.000 0.393 http://example.org/film/actor/film./film/performance/film EVAL 01wb95 film! 0131kb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 22.000 0.393 http://example.org/film/actor/film./film/performance/film EVAL 01wb95 film! 03bggl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 22.000 0.393 http://example.org/film/actor/film./film/performance/film #11869-05cl8y PRED entity: 05cl8y PRED relation: state_province_region PRED expected values: 059rby => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 123): 059rby (0.75 #2577, 0.75 #6014, 0.54 #6505), 01n7q (0.43 #2836, 0.41 #3572, 0.38 #10605), 09c7w0 (0.30 #8227, 0.25 #17289, 0.23 #17661), 0d060g (0.30 #8227, 0.25 #17289, 0.23 #17661), 05kr_ (0.30 #8227, 0.25 #17289, 0.23 #17661), 081yw (0.25 #60, 0.14 #426, 0.05 #4724), 0mtl5 (0.25 #17289, 0.23 #17661, 0.23 #18528), 03v0t (0.18 #6800, 0.07 #10639, 0.06 #11260), 07z1m (0.17 #6155, 0.14 #6770, 0.06 #10609), 04rrd (0.13 #6160, 0.11 #6775, 0.04 #10614) >> Best rule #2577 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 03mdt; 0bwfn; 023zl; >> query: (?x8559, 059rby) <- child(?x8559, ?x10882), citytown(?x8559, ?x1658), citytown(?x8559, ?x739), ?x739 = 02_286, contains(?x1658, ?x1306), location_of_ceremony(?x566, ?x1658) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05cl8y state_province_region 059rby CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.750 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #11868-02dpl9 PRED entity: 02dpl9 PRED relation: film_release_region PRED expected values: 05qhw 07ssc 06mzp => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 186): 06mkj (0.88 #1205, 0.86 #2508, 0.85 #3319), 07ssc (0.83 #1162, 0.83 #2465, 0.80 #1814), 05qhw (0.82 #2463, 0.74 #3274, 0.74 #3762), 0jgd (0.81 #3749, 0.81 #3261, 0.79 #2450), 0154j (0.80 #2452, 0.72 #1801, 0.72 #1149), 015fr (0.79 #2467, 0.74 #2629, 0.72 #3278), 0d060g (0.77 #2454, 0.77 #1151, 0.69 #3265), 0b90_r (0.77 #2451, 0.68 #1800, 0.65 #3262), 03spz (0.74 #2549, 0.64 #3360, 0.62 #3848), 06bnz (0.73 #2496, 0.65 #2658, 0.64 #1845) >> Best rule #1205 for best value: >> intensional similarity = 5 >> extensional distance = 79 >> proper extension: 0ddfwj1; 026njb5; 08tq4x; 0db94w; 080lkt7; 0g5q34q; 05dss7; 0j8f09z; >> query: (?x3897, 06mkj) <- film_release_region(?x3897, ?x1892), film_release_region(?x3897, ?x1003), ?x1003 = 03gj2, ?x1892 = 02vzc, film_regional_debut_venue(?x3897, ?x12806) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1162 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 79 *> proper extension: 0ddfwj1; 026njb5; 08tq4x; 0db94w; 080lkt7; 0g5q34q; 05dss7; 0j8f09z; *> query: (?x3897, 07ssc) <- film_release_region(?x3897, ?x1892), film_release_region(?x3897, ?x1003), ?x1003 = 03gj2, ?x1892 = 02vzc, film_regional_debut_venue(?x3897, ?x12806) *> conf = 0.83 ranks of expected_values: 2, 3, 18 EVAL 02dpl9 film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 83.000 83.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02dpl9 film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02dpl9 film_release_region 05qhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11867-0c1sgd3 PRED entity: 0c1sgd3 PRED relation: honored_for! PRED expected values: 0hhtgcw => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 87): 0bvfqq (0.25 #26, 0.06 #270, 0.04 #5735), 09pj68 (0.25 #90, 0.06 #334, 0.04 #5735), 09qvms (0.25 #9, 0.06 #253, 0.04 #5735), 03nnm4t (0.12 #307, 0.03 #2747, 0.02 #3357), 04n2r9h (0.06 #280, 0.04 #646, 0.03 #4062), 05c1t6z (0.06 #255, 0.04 #2695, 0.03 #4037), 02q690_ (0.06 #298, 0.03 #2738, 0.02 #6765), 073hkh (0.06 #245, 0.02 #489), 04110lv (0.04 #461, 0.02 #1071, 0.02 #1559), 09gkdln (0.04 #5735, 0.02 #594, 0.02 #2790) >> Best rule #26 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05jzt3; 032_wv; >> query: (?x4729, 0bvfqq) <- genre(?x4729, ?x53), film_crew_role(?x4729, ?x137), nominated_for(?x1299, ?x4729), ?x1299 = 0n6f8 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1049 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 173 *> proper extension: 02725hs; 03mh_tp; 02w86hz; 0gtt5fb; 03ydlnj; 03z9585; 0ds2l81; 04180vy; *> query: (?x4729, 0hhtgcw) <- genre(?x4729, ?x1403), film_crew_role(?x4729, ?x468), ?x1403 = 02l7c8, ?x468 = 02r96rf *> conf = 0.02 ranks of expected_values: 42 EVAL 0c1sgd3 honored_for! 0hhtgcw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 99.000 99.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #11866-0jltp PRED entity: 0jltp PRED relation: group! PRED expected values: 018vs => 103 concepts (86 used for prediction) PRED predicted values (max 10 best out of 87): 05148p4 (0.76 #911, 0.75 #1268, 0.75 #2427), 0l14md (0.75 #1611, 0.74 #1254, 0.71 #897), 03bx0bm (0.68 #1899, 0.65 #1631, 0.64 #1274), 018vs (0.66 #1261, 0.65 #2242, 0.64 #1886), 028tv0 (0.46 #1617, 0.45 #1885, 0.42 #2241), 03qjg (0.40 #672, 0.38 #138, 0.29 #2367), 05r5c (0.29 #7, 0.27 #630, 0.25 #2593), 01vj9c (0.27 #3045, 0.27 #1887, 0.27 #3937), 0l14qv (0.26 #2233, 0.25 #1877, 0.25 #1609), 04rzd (0.25 #122, 0.17 #656, 0.14 #33) >> Best rule #911 for best value: >> intensional similarity = 6 >> extensional distance = 47 >> proper extension: 089tm; 01t_xp_; 01pfr3; 0150jk; 067mj; 01vsxdm; 04r1t; 0dtd6; 01vrwfv; 05563d; ... >> query: (?x12211, 05148p4) <- group(?x75, ?x12211), artists(?x1572, ?x12211), artists(?x1000, ?x12211), artist(?x2299, ?x12211), ?x1000 = 0xhtw, ?x1572 = 06by7 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1261 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 51 *> proper extension: 06nv27; 02jqjm; 013w2r; 016vn3; *> query: (?x12211, 018vs) <- group(?x75, ?x12211), artists(?x1572, ?x12211), artists(?x1000, ?x12211), artist(?x2299, ?x12211), ?x1000 = 0xhtw, artists(?x1572, ?x8344), ?x8344 = 01jfnvd *> conf = 0.66 ranks of expected_values: 4 EVAL 0jltp group! 018vs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 86.000 0.755 http://example.org/music/performance_role/regular_performances./music/group_membership/group #11865-0gkts9 PRED entity: 0gkts9 PRED relation: nominated_for PRED expected values: 01j67j 02pqs8l => 51 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1326): 0g60z (0.78 #6352, 0.75 #7930, 0.50 #1619), 06w7mlh (0.67 #26837, 0.67 #31573, 0.65 #25258), 03d34x8 (0.67 #6596, 0.50 #8174, 0.50 #1863), 039c26 (0.56 #6798, 0.50 #2065, 0.42 #8376), 030k94 (0.56 #6777, 0.42 #8355, 0.25 #2044), 0ddd0gc (0.50 #8083, 0.33 #6505, 0.33 #195), 05hjnw (0.50 #3916, 0.20 #16544, 0.20 #18123), 05c46y6 (0.50 #3548, 0.15 #16176, 0.14 #17755), 0170xl (0.50 #4660, 0.07 #17288, 0.07 #26764), 02qpt1w (0.50 #4040, 0.05 #16668, 0.05 #26144) >> Best rule #6352 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0bp_b2; 0fbvqf; 0fbtbt; 0cqhb3; 0gkr9q; >> query: (?x3184, 0g60z) <- nominated_for(?x3184, ?x4932), nominated_for(?x3184, ?x1849), award(?x241, ?x3184), ?x1849 = 0kfv9, ?x4932 = 0hz55 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2147 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0ck27z; 0bdx29; *> query: (?x3184, 02pqs8l) <- nominated_for(?x3184, ?x1849), award(?x5586, ?x3184), ?x1849 = 0kfv9, ?x5586 = 03rwng *> conf = 0.25 ranks of expected_values: 158, 159 EVAL 0gkts9 nominated_for 02pqs8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 20.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gkts9 nominated_for 01j67j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 20.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11864-03tcnt PRED entity: 03tcnt PRED relation: award_winner PRED expected values: 018x3 023p29 => 37 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1534): 0gdh5 (0.50 #3059, 0.38 #32053, 0.36 #36986), 0d193h (0.50 #8333, 0.36 #10800, 0.36 #4928), 01vrt_c (0.50 #2697, 0.33 #233, 0.09 #10095), 0pk41 (0.40 #29586, 0.40 #17261, 0.38 #32053), 01j4ls (0.40 #29586, 0.40 #17261, 0.38 #32053), 016ntp (0.40 #29586, 0.40 #17261, 0.38 #32053), 02r4qs (0.40 #29586, 0.38 #32053, 0.36 #36986), 0lgsq (0.40 #29586, 0.38 #32053, 0.36 #36986), 02qwg (0.40 #17261, 0.38 #32053, 0.37 #22192), 0qdyf (0.40 #17261, 0.38 #32053, 0.37 #22192) >> Best rule #3059 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01ckbq; >> query: (?x3103, 0gdh5) <- award(?x3168, ?x3103), award(?x2395, ?x3103), ?x3168 = 016ntp, award_winner(?x3365, ?x2395), award(?x379, ?x3365) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #14797 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 55 *> proper extension: 05zkcn5; 05p09zm; 02f705; 02f76h; 02f71y; 02f73p; 02f6xy; 05q8pss; 02f764; 02f72_; ... *> query: (?x3103, ?x4476) <- award(?x9116, ?x3103), award(?x3682, ?x3103), award(?x3168, ?x3103), award_nominee(?x4476, ?x3682), role(?x3168, ?x227), award_nominee(?x9116, ?x3160), group(?x315, ?x3682) *> conf = 0.13 ranks of expected_values: 92, 182 EVAL 03tcnt award_winner 023p29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 37.000 20.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 03tcnt award_winner 018x3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 37.000 20.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #11863-05d1y PRED entity: 05d1y PRED relation: nationality PRED expected values: 012m_ => 95 concepts (88 used for prediction) PRED predicted values (max 10 best out of 61): 03gj2 (0.76 #4863, 0.71 #4056, 0.29 #5364), 0345h (0.33 #324, 0.25 #226, 0.23 #822), 02_286 (0.29 #5364, 0.28 #4560, 0.28 #5867), 01mk6 (0.29 #5364, 0.28 #4560, 0.28 #5867), 01d8l (0.29 #5364, 0.28 #4560, 0.28 #5867), 01mjq (0.29 #5364, 0.28 #4560, 0.28 #5867), 059rby (0.29 #5364, 0.28 #4560, 0.28 #5867), 0p07l (0.28 #5867, 0.27 #6671, 0.26 #6567), 02qkt (0.28 #5867, 0.27 #6671, 0.26 #6567), 01n4w (0.28 #5867, 0.27 #6671, 0.26 #6567) >> Best rule #4863 for best value: >> intensional similarity = 3 >> extensional distance = 1383 >> proper extension: 01ry0f; >> query: (?x8299, ?x1003) <- location(?x8299, ?x1374), location_of_ceremony(?x566, ?x1374), country(?x1374, ?x1003) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #692 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 0prfz; 01pcq3; 0134w7; 04jzj; 0136p1; 02lf70; 03rl84; 03_wj_; 02m7r; 09pl3s; ... *> query: (?x8299, ?x9006) <- location(?x8299, ?x9006), profession(?x8299, ?x3802), gender(?x8299, ?x231), combatants(?x612, ?x9006) *> conf = 0.25 ranks of expected_values: 16 EVAL 05d1y nationality 012m_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 95.000 88.000 0.760 http://example.org/people/person/nationality #11862-0bxbr PRED entity: 0bxbr PRED relation: citytown! PRED expected values: 0hkqn => 88 concepts (67 used for prediction) PRED predicted values (max 10 best out of 576): 087c7 (0.13 #2437, 0.06 #3244, 0.05 #4051), 0g8fs (0.10 #482, 0.06 #1289, 0.06 #2096), 01j53q (0.10 #508, 0.06 #1315, 0.06 #2122), 072w0 (0.10 #593, 0.06 #1400, 0.06 #2207), 02h7qr (0.10 #364, 0.06 #1171, 0.06 #1978), 01jq34 (0.10 #86, 0.06 #893, 0.06 #1700), 059wk (0.09 #3686, 0.03 #19833, 0.03 #16603), 064f29 (0.06 #3542, 0.05 #4349, 0.05 #5156), 01nds (0.05 #7840, 0.04 #7032, 0.04 #3804), 0gsg7 (0.04 #2483, 0.03 #5711, 0.02 #7326) >> Best rule #2437 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0qlrh; >> query: (?x5962, 087c7) <- time_zones(?x5962, ?x2674), place_of_death(?x652, ?x5962), source(?x5962, ?x958), ?x2674 = 02hcv8 >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #48455 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 846 *> proper extension: 0nc1c; 01dvzy; *> query: (?x5962, ?x3379) <- time_zones(?x5962, ?x2674), contains(?x1767, ?x5962), state_province_region(?x3379, ?x1767) *> conf = 0.01 ranks of expected_values: 573 EVAL 0bxbr citytown! 0hkqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 88.000 67.000 0.130 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #11861-02x4x18 PRED entity: 02x4x18 PRED relation: award! PRED expected values: 01dy7j 01fx2g => 54 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2639): 02f8lw (0.52 #36559, 0.48 #36558, 0.47 #29910), 0htlr (0.52 #36559, 0.48 #36558, 0.45 #29909), 0154qm (0.45 #17496, 0.33 #10848, 0.27 #14172), 043kzcr (0.45 #17265, 0.33 #10617, 0.27 #13941), 0184dt (0.45 #13947, 0.33 #10623, 0.25 #7300), 0lpjn (0.45 #17361, 0.25 #7390, 0.18 #14037), 0bwgc_ (0.45 #19760, 0.10 #23084, 0.09 #16436), 02kxbx3 (0.36 #14255, 0.33 #10931, 0.25 #7608), 05kfs (0.36 #13449, 0.33 #10125, 0.25 #6802), 022wxh (0.36 #14487, 0.33 #11163, 0.25 #7840) >> Best rule #36559 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 02r0d0; >> query: (?x2478, ?x241) <- award_winner(?x2478, ?x241), disciplines_or_subjects(?x2478, ?x6760), award(?x241, ?x995) >> conf = 0.52 => this is the best rule for 2 predicted values *> Best rule #4122 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 09qj50; *> query: (?x2478, 01dy7j) <- award(?x2012, ?x2478), nominated_for(?x2478, ?x915), ?x2012 = 03rl84 *> conf = 0.25 ranks of expected_values: 246, 483 EVAL 02x4x18 award! 01fx2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 21.000 0.523 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4x18 award! 01dy7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 54.000 21.000 0.523 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11860-0kftt PRED entity: 0kftt PRED relation: film PRED expected values: 0d4htf 02qydsh => 114 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1026): 0kcn7 (0.62 #21446, 0.60 #28596, 0.59 #103667), 033pf1 (0.17 #1410, 0.03 #31793, 0.02 #28218), 07bx6 (0.17 #1302, 0.03 #31685, 0.02 #40620), 0888c3 (0.17 #1414, 0.03 #31797, 0.02 #40732), 01mszz (0.17 #1086, 0.02 #22532, 0.02 #40404), 01hvjx (0.17 #375, 0.02 #23608, 0.02 #25396), 015x74 (0.17 #287, 0.02 #30670, 0.01 #39605), 03r0g9 (0.17 #608, 0.02 #30991, 0.01 #39926), 035bcl (0.17 #1010, 0.01 #18881, 0.01 #20668), 02rq8k8 (0.17 #645, 0.01 #52472) >> Best rule #21446 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 05zbm4; 03_vx9; 0456xp; 01l2fn; 02wb6yq; 09qh1; 01g23m; 01hrqc; 01yf85; 016tbr; >> query: (?x8423, ?x2640) <- nationality(?x8423, ?x1310), friend(?x8423, ?x9095), award_winner(?x2640, ?x8423) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #6311 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 0h1m9; 0gmtm; 0cgbf; 0m6x4; *> query: (?x8423, 0d4htf) <- nationality(?x8423, ?x1310), award_winner(?x1245, ?x8423), ?x1245 = 0gqwc *> conf = 0.02 ranks of expected_values: 392, 442 EVAL 0kftt film 02qydsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 114.000 87.000 0.620 http://example.org/film/actor/film./film/performance/film EVAL 0kftt film 0d4htf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 114.000 87.000 0.620 http://example.org/film/actor/film./film/performance/film #11859-02rsz0 PRED entity: 02rsz0 PRED relation: religion PRED expected values: 0kpl => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 24): 0kpl (0.38 #101, 0.36 #56, 0.29 #191), 03_gx (0.35 #14, 0.26 #376, 0.24 #737), 0kq2 (0.16 #64, 0.14 #154, 0.13 #199), 0c8wxp (0.14 #1630, 0.14 #639, 0.13 #6), 092bf5 (0.06 #287, 0.05 #424, 0.04 #785), 0n2g (0.04 #239, 0.04 #284, 0.04 #962), 019cr (0.04 #11, 0.03 #373, 0.03 #147), 0v53x (0.04 #29, 0.03 #391, 0.03 #165), 05sfs (0.04 #3, 0.03 #365, 0.03 #139), 05w5d (0.04 #25, 0.03 #161, 0.03 #206) >> Best rule #101 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 01nbq4; >> query: (?x12474, 0kpl) <- profession(?x12474, ?x2225), profession(?x12474, ?x353), ?x2225 = 0kyk, ?x353 = 0cbd2, nationality(?x12474, ?x512), ?x512 = 07ssc >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rsz0 religion 0kpl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.379 http://example.org/people/person/religion #11858-02m0b0 PRED entity: 02m0b0 PRED relation: organization! PRED expected values: 060c4 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 18): 060c4 (0.84 #132, 0.84 #158, 0.82 #80), 0dq_5 (0.34 #503, 0.34 #425, 0.31 #412), 07xl34 (0.23 #271, 0.21 #635, 0.21 #297), 05k17c (0.10 #657, 0.09 #748, 0.09 #969), 05c0jwl (0.08 #187, 0.06 #317, 0.04 #889), 0hm4q (0.08 #489, 0.07 #294, 0.05 #1127), 01t7n9 (0.07 #1015, 0.03 #1537, 0.03 #1406), 02079p (0.07 #1015, 0.03 #1537, 0.03 #1406), 0789n (0.07 #1015, 0.03 #1537, 0.03 #1406), 0f6c3 (0.07 #1015, 0.03 #1537, 0.03 #1406) >> Best rule #132 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 027mdh; >> query: (?x10497, 060c4) <- institution(?x1368, ?x10497), school_type(?x10497, ?x3205), registering_agency(?x10497, ?x1982), currency(?x10497, ?x170) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02m0b0 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.844 http://example.org/organization/role/leaders./organization/leadership/organization #11857-01d2v1 PRED entity: 01d2v1 PRED relation: music PRED expected values: 01l1rw => 85 concepts (66 used for prediction) PRED predicted values (max 10 best out of 105): 01pr6q7 (0.17 #484, 0.06 #1535, 0.04 #1745), 0drc1 (0.17 #571, 0.04 #1832, 0.03 #1202), 0146pg (0.14 #3167, 0.08 #4224, 0.07 #3799), 01hw6wq (0.10 #38, 0.06 #881, 0.04 #4252), 01m7f5r (0.10 #160, 0.03 #2053, 0.01 #4585), 0csdzz (0.10 #187, 0.02 #7563, 0.02 #5458), 02ryx0 (0.10 #110), 015wc0 (0.10 #1439, 0.09 #1649, 0.09 #1859), 0150t6 (0.09 #257, 0.08 #678, 0.07 #2361), 06fxnf (0.09 #280, 0.08 #701, 0.06 #2384) >> Best rule #484 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 016kz1; >> query: (?x11174, 01pr6q7) <- film_sets_designed(?x2230, ?x11174), genre(?x11174, ?x6277), executive_produced_by(?x11174, ?x10360), genre(?x3413, ?x6277) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1996 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 0fvr1; 02754c9; *> query: (?x11174, 01l1rw) <- film(?x7522, ?x11174), genre(?x11174, ?x225), film(?x7522, ?x1810), ?x1810 = 02f6g5 *> conf = 0.02 ranks of expected_values: 74 EVAL 01d2v1 music 01l1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 85.000 66.000 0.167 http://example.org/film/film/music #11856-08swgx PRED entity: 08swgx PRED relation: film PRED expected values: 02wgk1 => 97 concepts (65 used for prediction) PRED predicted values (max 10 best out of 873): 017jd9 (0.39 #2569, 0.04 #116294, 0.03 #780), 017gl1 (0.29 #1932, 0.04 #116294, 0.03 #143), 017gm7 (0.26 #1999, 0.04 #116294, 0.03 #100190), 0ndwt2w (0.13 #2789, 0.03 #1000, 0.03 #6367), 0jzw (0.10 #1908, 0.04 #67986, 0.04 #66196), 020bv3 (0.07 #3896, 0.04 #9263, 0.02 #21786), 0bwhdbl (0.07 #1408, 0.03 #8564, 0.03 #6775), 04z4j2 (0.07 #1627, 0.03 #3416, 0.03 #6994), 06gb1w (0.07 #734, 0.03 #4312, 0.03 #6101), 01qb5d (0.07 #138, 0.03 #3716, 0.03 #5505) >> Best rule #2569 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 06151l; 02bfmn; 02gvwz; 0241jw; 0hvb2; 024n3z; 0f0kz; 01846t; 014488; 0294fd; ... >> query: (?x2844, 017jd9) <- award_nominee(?x2844, ?x2762), profession(?x2844, ?x1032), ?x2762 = 015t56 >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #6125 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 0q9kd; 01vvydl; 0bl2g; 01gvr1; 014zfs; 058s57; 086qd; 01w7nww; 03bnv; 025ldg; ... *> query: (?x2844, 02wgk1) <- award_nominee(?x2844, ?x450), profession(?x2844, ?x1032), diet(?x2844, ?x3130) *> conf = 0.03 ranks of expected_values: 336 EVAL 08swgx film 02wgk1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 97.000 65.000 0.387 http://example.org/film/actor/film./film/performance/film #11855-0tlq9 PRED entity: 0tlq9 PRED relation: contains! PRED expected values: 04ly1 => 92 concepts (29 used for prediction) PRED predicted values (max 10 best out of 86): 04ly1 (0.60 #21507, 0.58 #5374, 0.44 #24196), 0tlq9 (0.40 #22404, 0.33 #14334, 0.29 #25093), 04_1l0v (0.33 #450, 0.31 #3138, 0.25 #1345), 07c5l (0.25 #1289, 0.17 #2185, 0.02 #3082), 01n7q (0.14 #22481, 0.11 #21584, 0.09 #20687), 07ssc (0.10 #19744, 0.09 #24227, 0.09 #17054), 059rby (0.07 #21526, 0.07 #3602, 0.07 #2707), 02jx1 (0.07 #24282, 0.06 #19799, 0.06 #17109), 05k7sb (0.06 #22536, 0.04 #20742, 0.04 #2820), 07b_l (0.06 #3804, 0.05 #21728, 0.04 #20831) >> Best rule #21507 for best value: >> intensional similarity = 5 >> extensional distance = 312 >> proper extension: 01t12z; >> query: (?x14130, ?x3908) <- contains(?x14130, ?x6186), contains(?x3908, ?x6186), contains(?x94, ?x6186), ?x94 = 09c7w0, district_represented(?x176, ?x3908) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tlq9 contains! 04ly1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 29.000 0.595 http://example.org/location/location/contains #11854-0b73_1d PRED entity: 0b73_1d PRED relation: film_crew_role PRED expected values: 02r96rf => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.80 #888, 0.74 #71, 0.72 #207), 0dxtw (0.48 #895, 0.45 #78, 0.43 #588), 02rh1dz (0.32 #77, 0.26 #3249, 0.26 #145), 01pvkk (0.32 #896, 0.29 #589, 0.29 #1102), 02vs3x5 (0.29 #23, 0.26 #3249, 0.18 #57), 0215hd (0.26 #3249, 0.24 #256, 0.21 #222), 01xy5l_ (0.26 #3249, 0.22 #251, 0.21 #217), 02ynfr (0.26 #3249, 0.21 #900, 0.19 #593), 089g0h (0.26 #3249, 0.16 #87, 0.16 #155), 020xn5 (0.26 #3249, 0.04 #211, 0.04 #892) >> Best rule #888 for best value: >> intensional similarity = 3 >> extensional distance = 363 >> proper extension: 0gx1bnj; 0h1cdwq; 03t97y; 07sc6nw; 0cz8mkh; 05p3738; 028cg00; 02qhqz4; 0d_2fb; 08052t3; ... >> query: (?x825, 02r96rf) <- film_crew_role(?x825, ?x2154), ?x2154 = 01vx2h, genre(?x825, ?x53) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b73_1d film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.797 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11853-06v99d PRED entity: 06v99d PRED relation: company! PRED expected values: 014l7h => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 24): 014l7h (0.44 #265, 0.33 #642, 0.33 #76), 0dq_5 (0.34 #2047, 0.33 #112, 0.26 #961), 0krdk (0.33 #431, 0.33 #101, 0.32 #2036), 09d6p2 (0.33 #161, 0.33 #114, 0.25 #539), 05_wyz (0.33 #113, 0.23 #1415, 0.21 #2266), 060c4 (0.26 #663, 0.25 #2032, 0.25 #522), 02k13d (0.23 #1415, 0.21 #2266, 0.17 #1179), 01kr6k (0.23 #1415, 0.21 #2266, 0.17 #1179), 028fjr (0.23 #1415, 0.17 #1179, 0.15 #2362), 01yc02 (0.21 #433, 0.21 #2266, 0.19 #292) >> Best rule #265 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 01y67v; >> query: (?x13340, 014l7h) <- program(?x13340, ?x14278), award_winner(?x3486, ?x13340), genre(?x14278, ?x258), category(?x13340, ?x134), languages(?x14278, ?x254), ?x254 = 02h40lc >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06v99d company! 014l7h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.438 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #11852-01vn0t_ PRED entity: 01vn0t_ PRED relation: artists! PRED expected values: 05bt6j => 124 concepts (67 used for prediction) PRED predicted values (max 10 best out of 251): 05bt6j (0.41 #1582, 0.27 #1274, 0.25 #4664), 0ggx5q (0.40 #77, 0.23 #5933, 0.17 #2542), 0155w (0.33 #1339, 0.24 #4729, 0.22 #20373), 017_qw (0.31 #1910, 0.29 #10545, 0.25 #7151), 06j6l (0.30 #3127, 0.30 #2511, 0.30 #10222), 03_d0 (0.30 #3093, 0.30 #3401, 0.29 #4019), 026z9 (0.30 #76, 0.17 #19756, 0.14 #3157), 02yv6b (0.30 #4721, 0.22 #20373, 0.20 #1331), 0gywn (0.29 #981, 0.28 #3137, 0.23 #3445), 016clz (0.28 #9256, 0.27 #7712, 0.27 #7403) >> Best rule #1582 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 03xhj6; 06gcn; 01shhf; >> query: (?x8708, 05bt6j) <- artists(?x10306, ?x8708), ?x10306 = 09jw2, artist(?x2149, ?x8708) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vn0t_ artists! 05bt6j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 67.000 0.409 http://example.org/music/genre/artists #11851-0dkv90 PRED entity: 0dkv90 PRED relation: nominated_for! PRED expected values: 09v92_x 09v1lrz => 70 concepts (66 used for prediction) PRED predicted values (max 10 best out of 196): 0gq_v (0.63 #952, 0.52 #1185, 0.50 #486), 02hsq3m (0.50 #495, 0.44 #961, 0.40 #1660), 02r0csl (0.46 #937, 0.34 #1170, 0.29 #471), 0gs96 (0.43 #557, 0.33 #91, 0.29 #1023), 0p9sw (0.42 #1186, 0.38 #4914, 0.38 #1885), 0k611 (0.42 #307, 0.33 #74, 0.30 #1938), 0gq9h (0.38 #6587, 0.38 #7752, 0.38 #1927), 02r22gf (0.36 #494, 0.34 #960, 0.34 #1193), 0gr0m (0.36 #526, 0.33 #293, 0.33 #60), 099c8n (0.36 #523, 0.33 #57, 0.27 #989) >> Best rule #952 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 0f4yh; 0p_rk; 02p76f9; 0f3m1; 04x4gw; 016z43; >> query: (?x7789, 0gq_v) <- nominated_for(?x2489, ?x7789), nominated_for(?x507, ?x7789), ?x507 = 02g3v6, nominated_for(?x2489, ?x5819), ?x5819 = 02w9k1c >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #181 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 01mgw; *> query: (?x7789, 09v92_x) <- nominated_for(?x5923, ?x7789), nominated_for(?x2489, ?x7789), nominated_for(?x507, ?x7789), ?x507 = 02g3v6, ?x2489 = 02x2gy0, ?x5923 = 09v8db5 *> conf = 0.33 ranks of expected_values: 19, 117 EVAL 0dkv90 nominated_for! 09v1lrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 70.000 66.000 0.634 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0dkv90 nominated_for! 09v92_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 70.000 66.000 0.634 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11850-02_5x9 PRED entity: 02_5x9 PRED relation: artists! PRED expected values: 06by7 => 91 concepts (61 used for prediction) PRED predicted values (max 10 best out of 281): 016clz (0.95 #13916, 0.93 #17693, 0.60 #11701), 0m0jc (0.90 #9490, 0.63 #7593, 0.49 #6314), 05w3f (0.87 #11420, 0.49 #6314, 0.49 #6313), 08cyft (0.81 #7644, 0.43 #9541, 0.33 #60), 0fd3y (0.75 #6643, 0.33 #11, 0.25 #1278), 06by7 (0.67 #10140, 0.67 #6020, 0.66 #9823), 05r6t (0.66 #11782, 0.49 #6314, 0.49 #6313), 0xhtw (0.65 #16452, 0.55 #16748, 0.52 #10769), 03lty (0.55 #13308, 0.42 #16464, 0.42 #10431), 0dl5d (0.52 #14880, 0.42 #4438, 0.39 #6969) >> Best rule #13916 for best value: >> intensional similarity = 10 >> extensional distance = 172 >> proper extension: 016qtt; 0197tq; 01cv3n; 01vvycq; 03f5spx; 018y2s; 0l12d; 01v_pj6; 04mn81; 01wsl7c; ... >> query: (?x1945, 016clz) <- category(?x1945, ?x134), ?x134 = 08mbj5d, artists(?x9831, ?x1945), parent_genre(?x9831, ?x1000), artists(?x9831, ?x10091), artists(?x9831, ?x8199), ?x10091 = 048tgl, artists(?x1000, ?x9589), ?x9589 = 02cw1m, ?x8199 = 016lmg >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #10140 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 77 *> proper extension: 07bzp; *> query: (?x1945, 06by7) <- category(?x1945, ?x134), ?x134 = 08mbj5d, group(?x2945, ?x1945), group(?x4311, ?x1945), artists(?x302, ?x2945), role(?x1399, ?x4311), role(?x74, ?x4311), role(?x4311, ?x1969), ?x1969 = 04rzd *> conf = 0.67 ranks of expected_values: 6 EVAL 02_5x9 artists! 06by7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 91.000 61.000 0.954 http://example.org/music/genre/artists #11849-0fx2s PRED entity: 0fx2s PRED relation: films PRED expected values: 02d413 0bv8h2 0c38gj 01jw67 => 60 concepts (26 used for prediction) PRED predicted values (max 10 best out of 888): 08xvpn (0.30 #1969, 0.21 #3489, 0.21 #2983), 0qm98 (0.30 #1579, 0.08 #8177, 0.07 #10711), 03s9kp (0.20 #2016, 0.08 #8614, 0.08 #2523), 040rmy (0.15 #2142, 0.14 #3155, 0.14 #2649), 09p4w8 (0.15 #2254, 0.14 #3267, 0.13 #4281), 0260bz (0.15 #2121, 0.14 #3134, 0.13 #4148), 01719t (0.15 #2089, 0.14 #2596, 0.13 #4116), 0170_p (0.15 #2054, 0.14 #2561, 0.13 #4081), 064lsn (0.15 #2320, 0.14 #3333, 0.10 #1813), 011yrp (0.15 #2040, 0.14 #3053, 0.10 #1533) >> Best rule #1969 for best value: >> intensional similarity = 12 >> extensional distance = 8 >> proper extension: 05qfh; >> query: (?x8435, 08xvpn) <- films(?x8435, ?x10024), films(?x8435, ?x9261), films(?x8435, ?x4216), films(?x8435, ?x1753), nominated_for(?x749, ?x1753), film(?x426, ?x10024), genre(?x1753, ?x1509), ?x1509 = 060__y, nominated_for(?x786, ?x9261), award_winner(?x1753, ?x5951), film(?x1104, ?x4216), ?x749 = 094qd5 >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #2028 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 11 *> proper extension: 02z3r; *> query: (?x8435, 02d413) <- films(?x8435, ?x10024), films(?x8435, ?x1753), nominated_for(?x2341, ?x1753), film(?x426, ?x10024), produced_by(?x1753, ?x163), ?x2341 = 02x17s4, nominated_for(?x185, ?x1753), titles(?x53, ?x10024), executive_produced_by(?x1753, ?x4857), film_crew_role(?x10024, ?x137) *> conf = 0.08 ranks of expected_values: 147, 759, 802 EVAL 0fx2s films 01jw67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 26.000 0.300 http://example.org/film/film_subject/films EVAL 0fx2s films 0c38gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 26.000 0.300 http://example.org/film/film_subject/films EVAL 0fx2s films 0bv8h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 26.000 0.300 http://example.org/film/film_subject/films EVAL 0fx2s films 02d413 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 60.000 26.000 0.300 http://example.org/film/film_subject/films #11848-0151w_ PRED entity: 0151w_ PRED relation: vacationer! PRED expected values: 03gh4 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 107): 03gh4 (0.23 #1179, 0.17 #935, 0.12 #1301), 05qtj (0.16 #1170, 0.16 #926, 0.11 #1048), 0160w (0.08 #1103, 0.08 #859, 0.08 #2), 04jpl (0.08 #1109, 0.08 #865, 0.08 #8), 0f2v0 (0.07 #1161, 0.07 #917, 0.06 #1405), 0261m (0.07 #1200, 0.05 #956, 0.04 #466), 06c62 (0.06 #1185, 0.06 #941, 0.03 #2653), 07fr_ (0.06 #1194, 0.03 #1683, 0.03 #3395), 0jbs5 (0.06 #954, 0.04 #1198, 0.03 #97), 035qy (0.05 #32, 0.04 #399, 0.03 #889) >> Best rule #1179 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 01kwld; 01tj34; 01d1st; 05vk_d; >> query: (?x989, 03gh4) <- award_nominee(?x286, ?x989), participant(?x989, ?x287), vacationer(?x151, ?x989) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0151w_ vacationer! 03gh4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.235 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #11847-0d3mlc PRED entity: 0d3mlc PRED relation: type_of_union PRED expected values: 04ztj => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.75 #5, 0.69 #121, 0.69 #165), 01g63y (0.25 #237, 0.19 #250, 0.14 #2) >> Best rule #5 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 01xyt7; >> query: (?x12509, 04ztj) <- gender(?x12509, ?x231), team(?x12509, ?x6871), company(?x4486, ?x6871), team(?x60, ?x6871), team(?x4486, ?x5292) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d3mlc type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.750 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11846-01sv6k PRED entity: 01sv6k PRED relation: place_of_birth! PRED expected values: 050llt => 121 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1634): 04qp06 (0.25 #5185, 0.25 #2572, 0.09 #10409), 08s0m7 (0.25 #5091, 0.25 #2478, 0.09 #10315), 04cmrt (0.25 #4966, 0.25 #2353, 0.09 #10190), 02wyc0 (0.25 #4718, 0.25 #2105, 0.09 #9942), 06kb_ (0.25 #3647, 0.25 #1034, 0.09 #8871), 0241wg (0.25 #3214, 0.25 #601, 0.09 #8438), 04b19t (0.25 #3108, 0.25 #495, 0.09 #8332), 03wpmd (0.25 #3049, 0.25 #436, 0.09 #8273), 040wdl (0.25 #2954, 0.25 #341, 0.09 #8178), 04rs03 (0.25 #2678, 0.25 #65, 0.09 #7902) >> Best rule #5185 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0c8tk; >> query: (?x13121, 04qp06) <- place_of_birth(?x12675, ?x13121), languages(?x12675, ?x1882), ?x1882 = 03k50, category(?x12675, ?x134), contains(?x2146, ?x13121) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #13062 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 02cbvn; 019jlq; *> query: (?x13121, ?x8622) <- contains(?x9315, ?x13121), contains(?x2146, ?x13121), ?x2146 = 03rk0, location(?x8622, ?x9315), administrative_division(?x12210, ?x9315) *> conf = 0.07 ranks of expected_values: 80 EVAL 01sv6k place_of_birth! 050llt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 121.000 23.000 0.250 http://example.org/people/person/place_of_birth #11845-09p35z PRED entity: 09p35z PRED relation: film! PRED expected values: 05l0j5 => 124 concepts (82 used for prediction) PRED predicted values (max 10 best out of 871): 0bxtg (0.31 #18743, 0.27 #12497, 0.23 #2083), 01j2xj (0.21 #18742, 0.20 #2082, 0.18 #12495), 0415svh (0.21 #18742, 0.20 #2082, 0.18 #12495), 014zcr (0.18 #37, 0.08 #2122, 0.03 #10450), 02qgqt (0.18 #18, 0.04 #6266, 0.03 #10431), 05fnl9 (0.18 #270, 0.01 #77307, 0.01 #66894), 0lpjn (0.15 #2565, 0.07 #4647, 0.03 #44198), 0170qf (0.15 #2453, 0.05 #4535, 0.03 #12865), 02114t (0.15 #2720, 0.04 #8967, 0.03 #13132), 02xs5v (0.15 #3490, 0.02 #30553, 0.02 #34716) >> Best rule #18743 for best value: >> intensional similarity = 5 >> extensional distance = 197 >> proper extension: 03b1sb; >> query: (?x797, ?x496) <- language(?x797, ?x254), produced_by(?x797, ?x496), genre(?x797, ?x53), ?x53 = 07s9rl0, film(?x496, ?x69) >> conf = 0.31 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09p35z film! 05l0j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 82.000 0.306 http://example.org/film/actor/film./film/performance/film #11844-03hnd PRED entity: 03hnd PRED relation: influenced_by PRED expected values: 014635 => 191 concepts (96 used for prediction) PRED predicted values (max 10 best out of 381): 01vh096 (0.60 #1164, 0.14 #1599, 0.09 #37334), 04xjp (0.60 #927, 0.09 #3528, 0.09 #3963), 03hnd (0.40 #533, 0.20 #970, 0.14 #1405), 06kb_ (0.40 #592, 0.20 #1029, 0.09 #27773), 081k8 (0.40 #1027, 0.18 #15345, 0.16 #1895), 02lt8 (0.40 #991, 0.16 #3159, 0.10 #27023), 03cdg (0.20 #14754, 0.18 #7378, 0.16 #11717), 03sbs (0.20 #1093, 0.20 #15411, 0.15 #14540), 01v9724 (0.20 #1049, 0.17 #4085, 0.16 #1917), 037jz (0.20 #1081, 0.17 #4117, 0.16 #11718) >> Best rule #1164 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0379s; >> query: (?x3542, 01vh096) <- nationality(?x3542, ?x512), influenced_by(?x9982, ?x3542), people(?x11064, ?x3542), ?x9982 = 05qzv >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #544 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0g5ff; *> query: (?x3542, 014635) <- nationality(?x3542, ?x512), influenced_by(?x13125, ?x3542), ?x13125 = 02xyl *> conf = 0.20 ranks of expected_values: 20 EVAL 03hnd influenced_by 014635 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 191.000 96.000 0.600 http://example.org/influence/influence_node/influenced_by #11843-05typm PRED entity: 05typm PRED relation: film PRED expected values: 0k2cb => 114 concepts (54 used for prediction) PRED predicted values (max 10 best out of 761): 0180mw (0.60 #62643, 0.60 #69801, 0.57 #66222), 0cqr0q (0.39 #14316), 0cfhfz (0.13 #2282, 0.06 #492, 0.05 #5860), 040_lv (0.11 #1048, 0.04 #4627, 0.03 #2838), 02r1ysd (0.08 #25056, 0.07 #17896, 0.07 #19686), 03nqnnk (0.07 #2814, 0.06 #6392, 0.04 #4603), 0ds3t5x (0.07 #1844, 0.06 #54, 0.04 #3633), 02q7fl9 (0.07 #2823, 0.06 #1033, 0.04 #4612), 02rv_dz (0.07 #2029, 0.06 #239, 0.02 #3818), 06_wqk4 (0.07 #1917, 0.02 #12653, 0.02 #3706) >> Best rule #62643 for best value: >> intensional similarity = 4 >> extensional distance = 849 >> proper extension: 0f830f; 025p38; 08w7vj; 04smkr; 038g2x; 01nrq5; 03kpvp; 0dh73w; 03ds83; 06vsbt; ... >> query: (?x4630, ?x6482) <- film(?x4630, ?x1451), gender(?x4630, ?x514), nominated_for(?x4630, ?x6482), award_winner(?x3989, ?x4630) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #813 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 05cj4r; 01nczg; 02bkdn; 01vhb0; 0bmh4; 0lpjn; 011_3s; 02mqc4; 0h32q; 020_95; ... *> query: (?x4630, 0k2cb) <- film(?x4630, ?x1451), award(?x4630, ?x5455), ?x5455 = 0bb57s, actor(?x6726, ?x4630) *> conf = 0.06 ranks of expected_values: 45 EVAL 05typm film 0k2cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 114.000 54.000 0.602 http://example.org/film/actor/film./film/performance/film #11842-04w4s PRED entity: 04w4s PRED relation: country! PRED expected values: 01lb14 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 42): 03fyrh (0.74 #146, 0.68 #188, 0.65 #566), 01lb14 (0.74 #138, 0.65 #222, 0.57 #600), 03rbzn (0.68 #145, 0.61 #229, 0.51 #565), 07gyv (0.65 #216, 0.63 #132, 0.56 #300), 07bs0 (0.65 #221, 0.58 #137, 0.57 #11), 02y8z (0.63 #140, 0.52 #224, 0.51 #560), 019tzd (0.63 #155, 0.48 #239, 0.44 #323), 01hp22 (0.58 #133, 0.57 #217, 0.50 #175), 035d1m (0.53 #144, 0.48 #228, 0.42 #1178), 0194d (0.53 #162, 0.43 #246, 0.42 #1178) >> Best rule #146 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 09c7w0; 0jgd; 0d060g; 06npd; 06c1y; 077qn; >> query: (?x3041, 03fyrh) <- partially_contains(?x3041, ?x10517), olympics(?x3041, ?x1931), ?x1931 = 0kbws >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #138 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 09c7w0; 0jgd; 0d060g; 06npd; 06c1y; 077qn; *> query: (?x3041, 01lb14) <- partially_contains(?x3041, ?x10517), olympics(?x3041, ?x1931), ?x1931 = 0kbws *> conf = 0.74 ranks of expected_values: 2 EVAL 04w4s country! 01lb14 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.737 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #11841-05zjtn4 PRED entity: 05zjtn4 PRED relation: category PRED expected values: 08mbj5d => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #29, 0.91 #37, 0.90 #40) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 251 >> proper extension: 02g839; 0269kx; 02zkz7; 02zc7f; 02h7qr; 057wlm; 06l32y; 016sd3; 03wv2g; 019tfm; >> query: (?x216, 08mbj5d) <- colors(?x216, ?x663), currency(?x216, ?x170), ?x170 = 09nqf, organization(?x3484, ?x216) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05zjtn4 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.913 http://example.org/common/topic/webpage./common/webpage/category #11840-0cw10 PRED entity: 0cw10 PRED relation: jurisdiction_of_office PRED expected values: 014tss => 109 concepts (101 used for prediction) PRED predicted values (max 10 best out of 42): 07ssc (0.60 #305, 0.60 #263, 0.43 #408), 09c7w0 (0.25 #1074, 0.21 #1174, 0.21 #1328), 07b_l (0.14 #335, 0.12 #438, 0.10 #489), 0cdbq (0.10 #306, 0.04 #409, 0.04 #1224), 05kyr (0.10 #306, 0.04 #409, 0.04 #1224), 01fmy9 (0.10 #306, 0.04 #409, 0.04 #1224), 01tdpv (0.10 #306, 0.04 #409, 0.04 #1224), 05vz3zq (0.07 #1208, 0.06 #748, 0.06 #799), 05fjf (0.07 #705, 0.06 #755, 0.04 #1115), 0dj0x (0.06 #357, 0.05 #460) >> Best rule #305 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 02c4s; 03f77; 0948xk; >> query: (?x11911, ?x512) <- nationality(?x11911, ?x512), ?x512 = 07ssc, entity_involved(?x4814, ?x11911), combatants(?x4814, ?x4492), type_of_union(?x11911, ?x566) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cw10 jurisdiction_of_office 014tss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 101.000 0.600 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #11839-04kjrv PRED entity: 04kjrv PRED relation: artists! PRED expected values: 0190_q 0cx7f => 163 concepts (93 used for prediction) PRED predicted values (max 10 best out of 248): 064t9 (0.62 #10271, 0.62 #27993, 0.61 #9961), 0cx7f (0.54 #1690, 0.50 #1380, 0.20 #759), 06j6l (0.46 #20882, 0.30 #9996, 0.30 #10306), 05bt6j (0.40 #1284, 0.37 #26162, 0.34 #18077), 0glt670 (0.33 #9988, 0.33 #10298, 0.22 #6260), 025sc50 (0.33 #10308, 0.32 #9998, 0.22 #20884), 0xhtw (0.32 #7793, 0.31 #3434, 0.30 #4370), 05w3f (0.31 #1588, 0.21 #7189, 0.21 #20871), 02lnbg (0.31 #10317, 0.29 #10007, 0.25 #3166), 0ggx5q (0.30 #3185, 0.25 #10026, 0.25 #10336) >> Best rule #10271 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 01l1b90; 0147dk; 01vrt_c; 07ss8_; 01pgzn_; 01trhmt; 04xrx; 033wx9; 01wgxtl; 0gy6z9; ... >> query: (?x7121, 064t9) <- artists(?x302, ?x7121), category(?x7121, ?x134), profession(?x7121, ?x220), participant(?x7121, ?x3503) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1690 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 012vm6; 0pqp3; *> query: (?x7121, 0cx7f) <- artists(?x11106, ?x7121), category(?x7121, ?x134), ?x11106 = 0781g *> conf = 0.54 ranks of expected_values: 2, 84 EVAL 04kjrv artists! 0cx7f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 163.000 93.000 0.618 http://example.org/music/genre/artists EVAL 04kjrv artists! 0190_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 163.000 93.000 0.618 http://example.org/music/genre/artists #11838-01p6xx PRED entity: 01p6xx PRED relation: film PRED expected values: 09p4w8 => 96 concepts (52 used for prediction) PRED predicted values (max 10 best out of 365): 027fwmt (0.38 #4963), 02z3r8t (0.04 #1699, 0.03 #872, 0.01 #5836), 03wy8t (0.03 #1582, 0.03 #2409, 0.01 #3236), 02yvct (0.03 #1008, 0.01 #1835, 0.01 #2662), 0bz3jx (0.02 #3042, 0.02 #6352, 0.02 #4696), 04sh80 (0.01 #1641, 0.01 #2468, 0.01 #6605), 0322yj (0.01 #1650, 0.01 #2477), 02bqxb (0.01 #1646, 0.01 #2473), 0ptdz (0.01 #1644, 0.01 #2471), 01c9d (0.01 #1638, 0.01 #2465) >> Best rule #4963 for best value: >> intensional similarity = 3 >> extensional distance = 225 >> proper extension: 01wg982; 05jm7; 02_l96; 030tjk; 0cj2k3; 030g9z; 01kt17; 0dbb3; 0cj2w; 06pcz0; ... >> query: (?x8928, ?x9800) <- award_nominee(?x3849, ?x8928), award(?x8928, ?x350), written_by(?x9800, ?x8928) >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01p6xx film 09p4w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 52.000 0.379 http://example.org/film/director/film #11837-03qy3l PRED entity: 03qy3l PRED relation: artist PRED expected values: 0pj9t 0134wr 0jn38 01v0sxx => 65 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1208): 047cx (0.60 #3632, 0.57 #7755, 0.50 #2806), 06449 (0.50 #6781, 0.43 #8431, 0.33 #9257), 01v0sxx (0.50 #3192, 0.40 #4018, 0.35 #12377), 01wp8w7 (0.50 #2552, 0.40 #3378, 0.33 #5851), 07zft (0.50 #3116, 0.40 #3942, 0.33 #6415), 0kj34 (0.50 #6431, 0.40 #3958, 0.33 #1481), 0c9d9 (0.50 #2487, 0.40 #3313, 0.33 #836), 0134wr (0.50 #3055, 0.40 #3881, 0.33 #1404), 016t0h (0.50 #3255, 0.40 #4081, 0.33 #1604), 01t8399 (0.50 #3218, 0.40 #4044, 0.33 #1567) >> Best rule #3632 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 0mzkr; >> query: (?x9243, 047cx) <- artist(?x9243, ?x11633), artist(?x9243, ?x7620), artist(?x9243, ?x3401), artist(?x9243, ?x219), ?x7620 = 06gcn, people(?x2510, ?x3401), award_winner(?x1088, ?x3401), place_of_birth(?x3401, ?x4356), role(?x219, ?x780), ?x780 = 01qzyz, award_winner(?x4018, ?x219), category(?x9243, ?x134), award(?x215, ?x4018), instrumentalists(?x315, ?x11633) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3192 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 03rhqg; *> query: (?x9243, 01v0sxx) <- artist(?x9243, ?x7620), artist(?x9243, ?x3420), artist(?x9243, ?x3401), artist(?x9243, ?x219), ?x7620 = 06gcn, people(?x2510, ?x3401), award_winner(?x1088, ?x3401), place_of_birth(?x3401, ?x4356), role(?x219, ?x780), ?x780 = 01qzyz, award_winner(?x4018, ?x219), award_winner(?x1521, ?x219), award_winner(?x219, ?x9246), artists(?x378, ?x3420) *> conf = 0.50 ranks of expected_values: 3, 8, 76, 115 EVAL 03qy3l artist 01v0sxx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 25.000 0.600 http://example.org/music/record_label/artist EVAL 03qy3l artist 0jn38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 65.000 25.000 0.600 http://example.org/music/record_label/artist EVAL 03qy3l artist 0134wr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 65.000 25.000 0.600 http://example.org/music/record_label/artist EVAL 03qy3l artist 0pj9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 65.000 25.000 0.600 http://example.org/music/record_label/artist #11836-047svrl PRED entity: 047svrl PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 22): 0ch6mp2 (0.85 #38, 0.80 #6, 0.79 #1130), 09vw2b7 (0.76 #5, 0.76 #37, 0.68 #1129), 0d2b38 (0.50 #54, 0.48 #22, 0.12 #855), 0dxtw (0.42 #1134, 0.39 #586, 0.38 #1102), 01vx2h (0.41 #43, 0.38 #587, 0.36 #235), 089fss (0.24 #4, 0.09 #36, 0.09 #420), 02ynfr (0.20 #14, 0.19 #1138, 0.18 #1106), 033smt (0.19 #56, 0.05 #1116, 0.05 #1308), 0263ycg (0.17 #48, 0.12 #16, 0.04 #432), 015h31 (0.17 #40, 0.09 #1196, 0.09 #1292) >> Best rule #38 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 07yk1xz; 05h43ls; 05c5z8j; 0415ggl; 04y9mm8; 047rkcm; 02_06s; 04jplwp; 01gglm; >> query: (?x2695, 0ch6mp2) <- titles(?x2480, ?x2695), executive_produced_by(?x2695, ?x4371), film_crew_role(?x2695, ?x4305), ?x4305 = 0215hd >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047svrl film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.852 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11835-052bw PRED entity: 052bw PRED relation: origin! PRED expected values: 01rm8b => 223 concepts (68 used for prediction) PRED predicted values (max 10 best out of 482): 01w524f (0.41 #3589, 0.29 #7177, 0.11 #1026), 04k05 (0.20 #457, 0.08 #970, 0.08 #1483), 06nv27 (0.20 #219, 0.07 #13547, 0.06 #3808), 024dgj (0.20 #146, 0.07 #7836, 0.07 #8349), 0892sx (0.20 #97, 0.07 #1635, 0.07 #3172), 02lfp4 (0.20 #209, 0.05 #13537, 0.04 #7899), 0dm5l (0.20 #108, 0.05 #13436, 0.04 #7798), 03fbc (0.20 #92, 0.04 #7782, 0.03 #8808), 079kr (0.20 #497, 0.04 #8187, 0.03 #8700), 01_wfj (0.20 #432, 0.04 #8122, 0.03 #8635) >> Best rule #3589 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 010rvx; >> query: (?x8771, ?x4237) <- place_of_birth(?x4237, ?x8771), contains(?x512, ?x8771), diet(?x4237, ?x3130), role(?x4237, ?x316) >> conf = 0.41 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 052bw origin! 01rm8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 223.000 68.000 0.412 http://example.org/music/artist/origin #11834-0jgd PRED entity: 0jgd PRED relation: film_release_region! PRED expected values: 0c3ybss 0gx1bnj 0djb3vw 05p1tzf 035yn8 0by1wkq 05qbckf 0cc5mcj 08052t3 07j8r 0645k5 0gtsxr4 06r2_ 0gjcrrw 03q0r1 02mt51 0gtxj2q 0bpm4yw 05pdh86 0k4fz 01rwpj 0bt3j9 02gs6r 02prwdh 0hv8w 0hv81 0bq6ntw 01f85k 02825cv 0dc_ms 09v3jyg 027pfg 035zr0 0m63c 0cp08zg 042zrm 0ndsl1x 0g57wgv 0gy4k 04fjzv 0by17xn 02wtp6 => 207 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1072): 0bpm4yw (0.88 #25723, 0.88 #39911, 0.88 #24709), 02mt51 (0.86 #8461, 0.84 #7447, 0.78 #6434), 05p1tzf (0.85 #25380, 0.83 #6125, 0.76 #8152), 0bq6ntw (0.85 #25935, 0.81 #8707, 0.80 #40123), 05pdh86 (0.84 #24724, 0.84 #7496, 0.82 #25738), 07l50vn (0.84 #24850, 0.84 #7622, 0.81 #8636), 05qbckf (0.84 #24484, 0.83 #6243, 0.83 #39686), 0dc_ms (0.84 #7737, 0.76 #25979, 0.72 #31045), 0645k5 (0.83 #6329, 0.82 #19502, 0.81 #8356), 027pfg (0.83 #6762, 0.81 #25003, 0.79 #26017) >> Best rule #25723 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 04gzd; 01ls2; 05v8c; 07ylj; 05qx1; >> query: (?x142, 0bpm4yw) <- film_release_region(?x5070, ?x142), film_release_region(?x2746, ?x142), ?x2746 = 04f52jw, ?x5070 = 0dt8xq, country(?x471, ?x142) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 21, 23, 25, 26, 27, 28, 29, 31, 33, 34, 38, 40, 42, 57, 59, 66, 67, 68, 76, 77, 83, 93, 96, 100, 126, 154 EVAL 0jgd film_release_region! 02wtp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0by17xn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 04fjzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0gy4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0g57wgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0ndsl1x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 042zrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0cp08zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0m63c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 035zr0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 027pfg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 09v3jyg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 02825cv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 01f85k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0bq6ntw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0hv81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0hv8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 02prwdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 02gs6r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0bt3j9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 01rwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0k4fz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 05pdh86 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0bpm4yw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0gtxj2q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 02mt51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 03q0r1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0gjcrrw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 06r2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0gtsxr4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0645k5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 07j8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 08052t3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0cc5mcj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 05qbckf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0by1wkq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 035yn8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 05p1tzf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0djb3vw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0gx1bnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgd film_release_region! 0c3ybss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 207.000 119.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11833-02cw1m PRED entity: 02cw1m PRED relation: artist! PRED expected values: 011k1h 01clyr 0k_kr => 89 concepts (83 used for prediction) PRED predicted values (max 10 best out of 132): 015_1q (0.48 #6557, 0.35 #2383, 0.31 #3637), 03rhqg (0.44 #710, 0.40 #849, 0.36 #1127), 043g7l (0.43 #3649, 0.40 #1560, 0.34 #4761), 011k1h (0.41 #1956, 0.31 #2374, 0.19 #2791), 01cszh (0.38 #1401, 0.30 #1540, 0.17 #1262), 01clyr (0.33 #1284, 0.33 #450, 0.30 #867), 0g768 (0.33 #454, 0.20 #871, 0.19 #1705), 011k11 (0.33 #452, 0.20 #869, 0.18 #1147), 03mp8k (0.26 #3684, 0.25 #1595, 0.25 #622), 0k_kr (0.25 #1294, 0.25 #599, 0.14 #1989) >> Best rule #6557 for best value: >> intensional similarity = 10 >> extensional distance = 311 >> proper extension: 01q_ph; >> query: (?x9589, 015_1q) <- artist(?x2299, ?x9589), artist(?x2299, ?x7987), artist(?x2299, ?x6418), artist(?x2299, ?x6368), artist(?x2299, ?x3929), ?x3929 = 01vvyfh, people(?x3591, ?x7987), group(?x7987, ?x11551), award_winner(?x724, ?x6418), artists(?x302, ?x6368) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1956 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 35 *> proper extension: 01vrz41; 04dqdk; 0qf3p; 01vwyqp; 01wbz9; 03j24kf; 03y82t6; 094xh; 01_ztw; 04b7xr; ... *> query: (?x9589, 011k1h) <- artist(?x7089, ?x9589), artists(?x3061, ?x9589), artists(?x1572, ?x9589), ?x3061 = 05bt6j, ?x1572 = 06by7, artist(?x7089, ?x3324), ?x3324 = 014488, child(?x9492, ?x7089) *> conf = 0.41 ranks of expected_values: 4, 6, 10 EVAL 02cw1m artist! 0k_kr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 89.000 83.000 0.476 http://example.org/music/record_label/artist EVAL 02cw1m artist! 01clyr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 89.000 83.000 0.476 http://example.org/music/record_label/artist EVAL 02cw1m artist! 011k1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 89.000 83.000 0.476 http://example.org/music/record_label/artist #11832-01bgqh PRED entity: 01bgqh PRED relation: award_winner PRED expected values: 09mq4m 01kd57 01vttb9 => 44 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1757): 01vvyvk (0.60 #8293, 0.38 #29239, 0.38 #29238), 0gcs9 (0.50 #10384, 0.50 #5510, 0.38 #29239), 09889g (0.50 #5981, 0.38 #29239, 0.38 #29238), 0b_j2 (0.50 #6310, 0.38 #29239, 0.38 #29238), 01xzb6 (0.50 #10927, 0.38 #29239, 0.38 #29238), 01vs_v8 (0.47 #22389, 0.38 #29239, 0.38 #29238), 0fhxv (0.41 #22966, 0.38 #29239, 0.38 #29238), 0g824 (0.40 #8704, 0.38 #29239, 0.38 #29238), 01wbgdv (0.40 #7525, 0.33 #214, 0.11 #53599), 01364q (0.40 #7764, 0.17 #12639, 0.12 #17511) >> Best rule #8293 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01d38g; 03qbh5; >> query: (?x724, 01vvyvk) <- award(?x12102, ?x724), award_winner(?x724, ?x1238), ceremony(?x724, ?x139), ?x12102 = 0163kf >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #29239 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 02581q; 02wh75; 026mg3; 02g3gj; 03x3wf; 02g8mp; 01ckbq; 01c4_6; 02gx2k; 01c92g; ... *> query: (?x724, ?x5536) <- award(?x5536, ?x724), award_winner(?x724, ?x1238), ceremony(?x724, ?x725), ?x725 = 01bx35 *> conf = 0.38 ranks of expected_values: 14, 119, 120 EVAL 01bgqh award_winner 01vttb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 44.000 27.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01bgqh award_winner 01kd57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 44.000 27.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01bgqh award_winner 09mq4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 44.000 27.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #11831-016ky6 PRED entity: 016ky6 PRED relation: film! PRED expected values: 078g3l => 137 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1216): 0bxtg (0.69 #6250, 0.69 #4243, 0.43 #89571), 0b455l (0.47 #79156, 0.43 #89571, 0.43 #97904), 01tc9r (0.47 #79156, 0.43 #97904, 0.43 #120819), 01yznp (0.25 #60, 0.05 #10475, 0.05 #14640), 03q5dr (0.25 #1682, 0.05 #12097, 0.05 #16262), 015g_7 (0.25 #1503, 0.05 #11918, 0.05 #16083), 0htlr (0.25 #147, 0.05 #10562, 0.05 #14727), 0q9kd (0.25 #4, 0.03 #20832, 0.03 #22914), 01j5ts (0.25 #29, 0.03 #22939, 0.02 #27104), 01mt1fy (0.25 #773, 0.03 #23683, 0.02 #29932) >> Best rule #6250 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 015g28; 01y9r2; 01xdxy; >> query: (?x5812, ?x496) <- titles(?x1510, ?x5812), nominated_for(?x496, ?x5812), film(?x609, ?x5812), ?x496 = 0bxtg >> conf = 0.69 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016ky6 film! 078g3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 61.000 0.692 http://example.org/film/actor/film./film/performance/film #11830-04cf09 PRED entity: 04cf09 PRED relation: award_nominee! PRED expected values: 02__7n => 108 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1043): 016kft (0.81 #118878, 0.81 #135194, 0.81 #107225), 02__7n (0.81 #118878, 0.81 #135194, 0.81 #107225), 046m59 (0.81 #118878, 0.81 #135194, 0.81 #107225), 04cf09 (0.50 #2572, 0.33 #11654, 0.32 #104892), 0350l7 (0.50 #8500, 0.15 #142187, 0.14 #130531), 0210hf (0.50 #8121, 0.14 #130531, 0.12 #46622), 04qz6n (0.43 #8621, 0.15 #142187, 0.14 #130531), 03rs8y (0.43 #7065, 0.15 #142187, 0.14 #130531), 0mbs8 (0.37 #69933, 0.33 #11654, 0.30 #81586), 0m66w (0.37 #69933, 0.33 #2331, 0.15 #55948) >> Best rule #118878 for best value: >> intensional similarity = 3 >> extensional distance = 1177 >> proper extension: 03qcq; 0197tq; 05cljf; 0m2l9; 026ps1; 06cc_1; 01zkxv; 0168cl; 01vvycq; 02l840; ... >> query: (?x1205, ?x1204) <- location(?x1205, ?x739), gender(?x1205, ?x231), award_nominee(?x1205, ?x1204) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 04cf09 award_nominee! 02__7n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 61.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11829-016s_5 PRED entity: 016s_5 PRED relation: artists! PRED expected values: 0155w 016zgj => 109 concepts (50 used for prediction) PRED predicted values (max 10 best out of 212): 064t9 (0.70 #10678, 0.54 #12813, 0.49 #3970), 0xhtw (0.47 #928, 0.38 #319, 0.30 #13427), 06j6l (0.44 #654, 0.27 #10712, 0.26 #4004), 0155w (0.40 #1016, 0.28 #712, 0.27 #1320), 05bt6j (0.33 #13452, 0.30 #8574, 0.28 #12842), 016clz (0.31 #6400, 0.29 #5180, 0.29 #1221), 016jny (0.27 #1014, 0.25 #405, 0.20 #2840), 02w4v (0.25 #2172, 0.25 #1563, 0.20 #1867), 025sc50 (0.25 #47, 0.24 #4006, 0.24 #10714), 05w3f (0.24 #947, 0.19 #1251, 0.18 #4601) >> Best rule #10678 for best value: >> intensional similarity = 4 >> extensional distance = 426 >> proper extension: 02fgpf; 06x4l_; 0412f5y; 0415mzy; 01kymm; 01hrqc; 01vs73g; 01wwnh2; 01vs8ng; >> query: (?x5452, 064t9) <- profession(?x5452, ?x131), artists(?x2664, ?x5452), artists(?x2664, ?x2906), ?x2906 = 0249kn >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1016 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 089tm; 01vrncs; 0lgsq; 018y2s; 01kx_81; 067mj; 01czx; 01kv4mb; 01w60_p; 01vsnff; ... *> query: (?x5452, 0155w) <- artist(?x1954, ?x5452), artists(?x7083, ?x5452), award(?x5452, ?x2322), ?x7083 = 02yv6b *> conf = 0.40 ranks of expected_values: 4, 18 EVAL 016s_5 artists! 016zgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 109.000 50.000 0.699 http://example.org/music/genre/artists EVAL 016s_5 artists! 0155w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 50.000 0.699 http://example.org/music/genre/artists #11828-0f87jy PRED entity: 0f87jy PRED relation: type_of_union PRED expected values: 04ztj => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.75 #73, 0.74 #65, 0.73 #29), 01g63y (0.12 #70, 0.12 #174, 0.11 #134) >> Best rule #73 for best value: >> intensional similarity = 3 >> extensional distance = 355 >> proper extension: 0glyyw; 03g62; 024c1b; >> query: (?x10593, 04ztj) <- produced_by(?x10942, ?x10593), film(?x574, ?x10942), film(?x1522, ?x10942) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f87jy type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.745 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11827-01rr_d PRED entity: 01rr_d PRED relation: institution PRED expected values: 02583l 0820xz 01qd_r 01n4w_ 01v2xl 01pxcf => 25 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1031): 02zd460 (0.78 #10006, 0.78 #9428, 0.74 #11750), 05krk (0.78 #9833, 0.78 #9255, 0.67 #6943), 025v3k (0.78 #9373, 0.67 #9951, 0.67 #7061), 01wdl3 (0.78 #9273, 0.67 #9851, 0.67 #6961), 07tgn (0.71 #7529, 0.71 #10419, 0.67 #10998), 07wlf (0.71 #7590, 0.67 #9902, 0.67 #9324), 0j_sncb (0.71 #8754, 0.67 #9910, 0.58 #11654), 0bwfn (0.67 #11273, 0.67 #10116, 0.67 #9538), 0g8rj (0.67 #10012, 0.67 #9434, 0.67 #7122), 0gl5_ (0.67 #10087, 0.67 #9509, 0.67 #7197) >> Best rule #10006 for best value: >> intensional similarity = 28 >> extensional distance = 7 >> proper extension: 02mjs7; >> query: (?x7636, 02zd460) <- institution(?x7636, ?x11963), institution(?x7636, ?x11711), institution(?x7636, ?x7308), institution(?x7636, ?x6132), institution(?x7636, ?x4209), institution(?x7636, ?x3439), student(?x7636, ?x1984), ?x4209 = 02gr81, registering_agency(?x7308, ?x1982), major_field_of_study(?x11963, ?x4100), major_field_of_study(?x11963, ?x2981), major_field_of_study(?x11963, ?x2014), student(?x3439, ?x6045), student(?x3439, ?x4330), currency(?x7308, ?x170), contains(?x512, ?x11963), major_field_of_study(?x3439, ?x8221), ?x8221 = 037mh8, ?x2014 = 04rjg, produced_by(?x5152, ?x4330), organizations_founded(?x11554, ?x6132), institution(?x2636, ?x3439), program_creator(?x6482, ?x6045), citytown(?x11711, ?x739), ?x2981 = 02j62, ?x2636 = 027f2w, category(?x3439, ?x134), ?x4100 = 01lj9 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6812 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 4 *> proper extension: 027f2w; *> query: (?x7636, 01n4w_) <- institution(?x7636, ?x9181), institution(?x7636, ?x6505), institution(?x7636, ?x5178), institution(?x7636, ?x4209), student(?x7636, ?x11605), school(?x260, ?x4209), contains(?x390, ?x9181), currency(?x9181, ?x7888), legislative_sessions(?x11605, ?x605), type_of_union(?x11605, ?x566), organization(?x346, ?x9181), major_field_of_study(?x7636, ?x2981), school_type(?x6505, ?x4994), major_field_of_study(?x6505, ?x1154), colors(?x9181, ?x3621), state_province_region(?x5178, ?x1755), category(?x6505, ?x134), student(?x5178, ?x1620), contains(?x512, ?x6505), citytown(?x4209, ?x4733), ?x512 = 07ssc *> conf = 0.50 ranks of expected_values: 148, 186, 381, 490, 515, 597 EVAL 01rr_d institution 01pxcf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01rr_d institution 01v2xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01rr_d institution 01n4w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 25.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01rr_d institution 01qd_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01rr_d institution 0820xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 25.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01rr_d institution 02583l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #11826-0mwxl PRED entity: 0mwxl PRED relation: source PRED expected values: 0jbk9 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #6, 0.93 #11, 0.93 #8) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 0mn0v; 0njcw; >> query: (?x9627, 0jbk9) <- time_zones(?x9627, ?x2674), ?x2674 = 02hcv8, currency(?x9627, ?x170), second_level_divisions(?x94, ?x9627) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwxl source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.940 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #11825-078bz PRED entity: 078bz PRED relation: major_field_of_study PRED expected values: 0_jm 01bt59 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 112): 02j62 (0.60 #476, 0.58 #925, 0.54 #588), 05qjt (0.51 #232, 0.47 #905, 0.46 #456), 03g3w (0.49 #249, 0.46 #922, 0.42 #137), 05qfh (0.44 #930, 0.42 #817, 0.42 #481), 04x_3 (0.44 #472, 0.42 #921, 0.40 #808), 0g26h (0.42 #1945, 0.39 #39, 0.39 #2057), 037mh8 (0.38 #284, 0.37 #957, 0.35 #844), 0fdys (0.36 #933, 0.33 #820, 0.33 #260), 06ms6 (0.36 #240, 0.33 #800, 0.32 #913), 01540 (0.36 #278, 0.32 #951, 0.31 #614) >> Best rule #476 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05rznz; >> query: (?x2775, 02j62) <- organization(?x2775, ?x5487), contains(?x94, ?x2775), category(?x2775, ?x134) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2071 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 01b1mj; 01t8sr; 02t4yc; *> query: (?x2775, 0_jm) <- student(?x2775, ?x1447), school(?x4979, ?x2775), institution(?x620, ?x2775) *> conf = 0.33 ranks of expected_values: 13, 30 EVAL 078bz major_field_of_study 01bt59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 114.000 114.000 0.604 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 078bz major_field_of_study 0_jm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 114.000 114.000 0.604 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11824-0bq4j6 PRED entity: 0bq4j6 PRED relation: gender PRED expected values: 05zppz => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #21, 0.85 #25, 0.84 #23), 02zsn (0.32 #6, 0.27 #8, 0.26 #50) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 272 >> proper extension: 02pp_q_; 03ft8; 0b05xm; 0b478; 0cm89v; 06n9lt; 013zyw; 03ys2f; 03ysmg; 032md; ... >> query: (?x10236, 05zppz) <- profession(?x10236, ?x987), ?x987 = 0dxtg, written_by(?x4312, ?x10236) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bq4j6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.869 http://example.org/people/person/gender #11823-02h0f3 PRED entity: 02h0f3 PRED relation: profession PRED expected values: 02hrh1q => 105 concepts (104 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.92 #5379, 0.90 #1207, 0.90 #5528), 01d_h8 (0.33 #8499, 0.31 #6413, 0.29 #8648), 0dxtg (0.31 #8507, 0.30 #6421, 0.28 #3888), 03gjzk (0.31 #3890, 0.24 #8658, 0.23 #8509), 02jknp (0.23 #2243, 0.23 #8501, 0.21 #2839), 0np9r (0.21 #5535, 0.21 #22, 0.20 #5684), 0cbd2 (0.21 #1795, 0.21 #901, 0.20 #1050), 018gz8 (0.19 #18, 0.14 #5233, 0.14 #5978), 09jwl (0.17 #10003, 0.16 #13582, 0.16 #10897), 0kyk (0.15 #924, 0.14 #1073, 0.13 #1818) >> Best rule #5379 for best value: >> intensional similarity = 3 >> extensional distance = 687 >> proper extension: 01gvr1; 01n5309; 066m4g; 0gcdzz; 05tk7y; 0m32_; 01vx5w7; 055c8; 01jbx1; 01v3vp; ... >> query: (?x7550, 02hrh1q) <- award(?x7550, ?x783), actor(?x3725, ?x7550), profession(?x7550, ?x1943) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02h0f3 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 104.000 0.922 http://example.org/people/person/profession #11822-02rq8k8 PRED entity: 02rq8k8 PRED relation: currency PRED expected values: 09nqf => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.77 #29, 0.76 #64, 0.76 #50), 01nv4h (0.25 #288, 0.06 #9, 0.02 #16), 02l6h (0.25 #288, 0.02 #25) >> Best rule #29 for best value: >> intensional similarity = 2 >> extensional distance = 245 >> proper extension: 02fn5r; >> query: (?x3904, 09nqf) <- nominated_for(?x12720, ?x3904), nominated_for(?x154, ?x12720) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rq8k8 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.765 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11821-0cj2k3 PRED entity: 0cj2k3 PRED relation: gender PRED expected values: 05zppz => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.82 #13, 0.81 #15, 0.81 #37), 02zsn (0.46 #155, 0.24 #52, 0.24 #22) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 391 >> proper extension: 015zql; >> query: (?x8872, 05zppz) <- profession(?x8872, ?x319), award_winner(?x2143, ?x8872), ?x319 = 01d_h8 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cj2k3 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.817 http://example.org/people/person/gender #11820-04mhl PRED entity: 04mhl PRED relation: influenced_by! PRED expected values: 0j0pf => 91 concepts (37 used for prediction) PRED predicted values (max 10 best out of 248): 0j0pf (0.38 #723, 0.35 #1756, 0.33 #1240), 05jm7 (0.25 #3240, 0.18 #2724, 0.17 #141), 01zkxv (0.18 #2599, 0.17 #16, 0.13 #1049), 01g6bk (0.17 #478, 0.13 #1511, 0.12 #2027), 0821j (0.17 #358, 0.12 #874, 0.07 #2941), 04cbtrw (0.17 #108, 0.12 #624, 0.07 #2691), 03v36 (0.17 #495, 0.12 #1011, 0.07 #1528), 0534v (0.17 #218, 0.12 #734, 0.07 #1251), 0c3kw (0.17 #50, 0.07 #2633, 0.07 #1083), 049gc (0.17 #226, 0.07 #1259, 0.06 #1775) >> Best rule #723 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0c3kw; >> query: (?x4417, 0j0pf) <- award_winner(?x9285, ?x4417), award_winner(?x6687, ?x4417), award_winner(?x1375, ?x4417), ?x9285 = 0265vt, ?x1375 = 0262zm, award(?x476, ?x6687) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mhl influenced_by! 0j0pf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 37.000 0.375 http://example.org/influence/influence_node/influenced_by #11819-05vsb7 PRED entity: 05vsb7 PRED relation: school PRED expected values: 01jq34 0325dj => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 647): 06pwq (0.53 #1665, 0.38 #1367, 0.33 #101), 05krk (0.50 #1172, 0.50 #582, 0.40 #1070), 012vwb (0.50 #1298, 0.50 #612, 0.39 #483), 07vyf (0.50 #718, 0.40 #1701, 0.39 #483), 01qgr3 (0.50 #1334, 0.40 #1136, 0.39 #483), 0j_sncb (0.50 #702, 0.40 #1092, 0.39 #483), 015q1n (0.50 #737, 0.39 #483, 0.33 #446), 01j_9c (0.50 #778, 0.39 #483, 0.33 #390), 0frm7n (0.50 #623, 0.39 #483, 0.33 #43), 09f2j (0.50 #724, 0.39 #483, 0.33 #433) >> Best rule #1665 for best value: >> intensional similarity = 46 >> extensional distance = 13 >> proper extension: 02x2khw; 038981; >> query: (?x465, 06pwq) <- school(?x465, ?x6814), school(?x465, ?x735), draft(?x9172, ?x465), draft(?x4222, ?x465), contains(?x94, ?x6814), school(?x9172, ?x2171), major_field_of_study(?x735, ?x1682), institution(?x1771, ?x735), institution(?x1526, ?x735), ?x1526 = 0bkj86, ?x1682 = 02ky346, institution(?x1771, ?x12863), institution(?x1771, ?x12728), institution(?x1771, ?x9822), institution(?x1771, ?x5068), institution(?x1771, ?x3696), institution(?x1771, ?x3021), institution(?x1771, ?x2175), institution(?x1771, ?x1768), institution(?x1771, ?x1665), institution(?x1771, ?x621), institution(?x1771, ?x579), student(?x1771, ?x744), ?x12863 = 02c9dj, student(?x735, ?x8374), student(?x735, ?x1182), ?x2175 = 01ptt7, ?x579 = 01fpvz, school_type(?x6814, ?x3092), ?x1768 = 09kvv, team(?x935, ?x4222), ?x12728 = 01gwck, sport(?x4222, ?x1083), ?x1665 = 04rwx, ?x3696 = 02t4yc, ?x5068 = 01h8rk, ?x621 = 02w2bc, award(?x8374, ?x1079), major_field_of_study(?x1771, ?x1527), ?x9822 = 019q50, award_winner(?x8554, ?x1182), school(?x580, ?x735), ?x3021 = 027xx3, ?x1527 = 04_tv, ?x580 = 05m_8, currency(?x735, ?x170) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #483 for first EXPECTED value: *> intensional similarity = 34 *> extensional distance = 1 *> proper extension: 0f4vx0; *> query: (?x465, ?x2821) <- school(?x465, ?x6973), school(?x465, ?x6953), school(?x465, ?x5621), school(?x465, ?x2760), school(?x465, ?x735), draft(?x9172, ?x465), draft(?x6976, ?x465), draft(?x6645, ?x465), draft(?x1239, ?x465), draft(?x387, ?x465), ?x735 = 065y4w7, team(?x1114, ?x387), ?x6953 = 01jq0j, list(?x2760, ?x2197), school(?x9172, ?x4296), school(?x387, ?x388), split_to(?x2760, ?x9165), colors(?x1239, ?x663), team(?x10287, ?x1239), ?x4296 = 07vyf, currency(?x5621, ?x170), institution(?x865, ?x5621), ?x6973 = 05x_5, major_field_of_study(?x2760, ?x8278), major_field_of_study(?x2760, ?x254), school(?x6645, ?x2821), ?x170 = 09nqf, sport(?x6976, ?x1083), teams(?x2277, ?x6645), films(?x8278, ?x1295), position(?x1792, ?x1114), teams(?x4090, ?x1239), position(?x1114, ?x8329), ?x254 = 02h40lc *> conf = 0.39 ranks of expected_values: 21, 50 EVAL 05vsb7 school 0325dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 19.000 19.000 0.533 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 05vsb7 school 01jq34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 19.000 19.000 0.533 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #11818-0smfm PRED entity: 0smfm PRED relation: contains! PRED expected values: 09c7w0 => 155 concepts (52 used for prediction) PRED predicted values (max 10 best out of 402): 09c7w0 (0.99 #40346, 0.77 #23308, 0.76 #34962), 03rk0 (0.52 #8195, 0.49 #11786, 0.31 #14479), 02qkt (0.47 #11097, 0.41 #12894, 0.40 #9301), 04_1l0v (0.32 #25992, 0.28 #31376, 0.08 #4925), 0ntpv (0.26 #11647, 0.25 #219, 0.16 #45726), 0nt6b (0.26 #11647, 0.20 #1525, 0.16 #45726), 0nt4s (0.26 #11647, 0.11 #4221, 0.08 #5116), 059rby (0.25 #30499, 0.22 #34084, 0.20 #37672), 02j9z (0.25 #10778, 0.25 #9880, 0.20 #8982), 01n7q (0.19 #32348, 0.16 #35036, 0.15 #21591) >> Best rule #40346 for best value: >> intensional similarity = 6 >> extensional distance = 681 >> proper extension: 015zyd; 01rtm4; 03s0w; 07lx1s; 04rwx; 05k7sb; 050l8; 07w3r; 02zccd; 02rff2; ... >> query: (?x11574, 09c7w0) <- contains(?x448, ?x11574), category(?x11574, ?x134), ?x134 = 08mbj5d, contains(?x448, ?x449), ?x449 = 0plyy, adjoins(?x448, ?x177) >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0smfm contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 52.000 0.994 http://example.org/location/location/contains #11817-07vf5c PRED entity: 07vf5c PRED relation: nominated_for! PRED expected values: 02x2gy0 => 110 concepts (96 used for prediction) PRED predicted values (max 10 best out of 216): 0gq9h (0.71 #2346, 0.58 #514, 0.56 #743), 02x2gy0 (0.67 #4584, 0.67 #11003, 0.66 #13753), 0gs9p (0.63 #2348, 0.53 #745, 0.53 #516), 02pqp12 (0.58 #511, 0.45 #740, 0.43 #282), 040njc (0.56 #464, 0.50 #235, 0.48 #693), 02hsq3m (0.50 #255, 0.40 #942, 0.29 #484), 0l8z1 (0.45 #278, 0.37 #507, 0.35 #965), 04dn09n (0.45 #2322, 0.39 #490, 0.36 #719), 0gs96 (0.43 #312, 0.42 #999, 0.35 #541), 054krc (0.41 #292, 0.37 #750, 0.34 #521) >> Best rule #2346 for best value: >> intensional similarity = 4 >> extensional distance = 191 >> proper extension: 01jc6q; 0yyg4; 0gzy02; 04v8x9; 0n0bp; 0p_sc; 0m_mm; 018f8; 026390q; 0hv1t; ... >> query: (?x4249, 0gq9h) <- award_winner(?x4249, ?x2237), nominated_for(?x1703, ?x4249), film(?x6658, ?x4249), ?x1703 = 0k611 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4584 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 439 *> proper extension: 019kyn; *> query: (?x4249, ?x2489) <- award_winner(?x4249, ?x2237), award(?x4249, ?x2489), production_companies(?x4249, ?x8796), award_winner(?x154, ?x2237) *> conf = 0.67 ranks of expected_values: 2 EVAL 07vf5c nominated_for! 02x2gy0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 96.000 0.710 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11816-04tqtl PRED entity: 04tqtl PRED relation: film_crew_role PRED expected values: 01vx2h 02ynfr => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 25): 0dxtw (0.42 #1102, 0.39 #325, 0.37 #749), 01vx2h (0.36 #115, 0.35 #502, 0.34 #1103), 01pvkk (0.32 #11, 0.28 #751, 0.28 #645), 02ynfr (0.19 #1108, 0.18 #755, 0.18 #472), 0215hd (0.18 #18, 0.15 #1111, 0.15 #510), 02vs3x5 (0.15 #23, 0.06 #657, 0.06 #58), 0d2b38 (0.14 #130, 0.13 #517, 0.12 #552), 02rh1dz (0.14 #1101, 0.13 #113, 0.12 #288), 01xy5l_ (0.13 #540, 0.13 #505, 0.12 #48), 089g0h (0.12 #124, 0.12 #511, 0.12 #1112) >> Best rule #1102 for best value: >> intensional similarity = 4 >> extensional distance = 652 >> proper extension: 0gx9rvq; 0cz8mkh; 0gh65c5; 03q5db; 0gtt5fb; 0bq6ntw; 0ds2l81; 09p5mwg; 0fh2v5; 037cr1; ... >> query: (?x3093, 0dxtw) <- language(?x3093, ?x254), film_crew_role(?x3093, ?x1171), film(?x395, ?x3093), ?x1171 = 09vw2b7 >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #115 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 151 *> proper extension: 07l50vn; *> query: (?x3093, 01vx2h) <- featured_film_locations(?x3093, ?x3269), film(?x10884, ?x3093), film_format(?x3093, ?x6392), genre(?x3093, ?x53) *> conf = 0.36 ranks of expected_values: 2, 4 EVAL 04tqtl film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 84.000 0.417 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 04tqtl film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.417 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11815-085jw PRED entity: 085jw PRED relation: performance_role PRED expected values: 0d8lm => 71 concepts (56 used for prediction) PRED predicted values (max 10 best out of 126): 0l14md (0.83 #3590, 0.79 #1052, 0.78 #2245), 0l14qv (0.70 #1566, 0.69 #2171, 0.60 #527), 0l14j_ (0.62 #1099, 0.60 #481, 0.50 #1524), 03gvt (0.60 #1619, 0.47 #2142, 0.36 #3142), 018vs (0.58 #263, 0.50 #188, 0.40 #625), 03qjg (0.58 #263, 0.50 #923, 0.35 #962), 05148p4 (0.58 #263, 0.44 #1390, 0.44 #1325), 02hnl (0.58 #263, 0.33 #1422, 0.33 #174), 0342h (0.56 #1312, 0.50 #881, 0.50 #342), 05r5c (0.50 #801, 0.50 #255, 0.50 #184) >> Best rule #3590 for best value: >> intensional similarity = 21 >> extensional distance = 23 >> proper extension: 0g2dz; 03gvt; 0gkd1; >> query: (?x3156, ?x10843) <- role(?x2048, ?x3156), role(?x1166, ?x3156), role(?x315, ?x3156), ?x1166 = 05148p4, role(?x3156, ?x1332), group(?x3156, ?x3516), group(?x4769, ?x3516), role(?x4913, ?x2048), role(?x3161, ?x2048), role(?x894, ?x2048), role(?x74, ?x2048), ?x4769 = 0dwt5, role(?x8599, ?x2048), ?x315 = 0l14md, ?x894 = 03m5k, role(?x2575, ?x2048), ?x8599 = 01nkxvx, ?x74 = 03q5t, ?x3161 = 01v1d8, ?x4913 = 03ndd, performance_role(?x10843, ?x3156) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1126 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 6 *> proper extension: 0859_; *> query: (?x3156, 0d8lm) <- role(?x2620, ?x3156), role(?x75, ?x3156), family(?x1473, ?x3156), group(?x3156, ?x2901), role(?x7033, ?x75), role(?x1495, ?x75), role(?x716, ?x75), instrumentalists(?x75, ?x4020), instrumentalists(?x75, ?x3667), role(?x75, ?x3215), music(?x463, ?x4020), ?x716 = 018vs, ?x1495 = 013y1f, role(?x214, ?x75), gender(?x4020, ?x231), ?x2620 = 01kcd, ?x3215 = 0bxl5, profession(?x3667, ?x131), ?x7033 = 0gkd1 *> conf = 0.38 ranks of expected_values: 29 EVAL 085jw performance_role 0d8lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 71.000 56.000 0.830 http://example.org/music/performance_role/guest_performances./music/recording_contribution/performance_role #11814-0b68vs PRED entity: 0b68vs PRED relation: artist! PRED expected values: 017l96 => 118 concepts (73 used for prediction) PRED predicted values (max 10 best out of 103): 015_1q (0.25 #442, 0.25 #724, 0.20 #1288), 0g768 (0.15 #319, 0.12 #178, 0.12 #4556), 03rhqg (0.14 #720, 0.14 #1002, 0.14 #1284), 0n85g (0.13 #345, 0.10 #204, 0.07 #4582), 017l96 (0.13 #723, 0.13 #441, 0.09 #159), 01clyr (0.13 #174, 0.10 #315, 0.08 #4552), 0181dw (0.12 #2016, 0.12 #3147, 0.11 #324), 033hn8 (0.11 #3118, 0.11 #4532, 0.10 #154), 043g7l (0.11 #736, 0.10 #454, 0.08 #3136), 01cszh (0.09 #151, 0.07 #3115, 0.07 #292) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 016qtt; 028q6; 07s3vqk; 0197tq; 0lbj1; 0m2l9; 03f2_rc; 01w61th; 02r3zy; 07c0j; ... >> query: (?x1181, 015_1q) <- award_nominee(?x217, ?x1181), award(?x1181, ?x724), ?x724 = 01bgqh >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #723 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 139 *> proper extension: 0frsw; 01vrwfv; 02jqjm; 015cxv; 011z3g; 0178_w; 07r1_; 01lf293; 017959; 0mjn2; *> query: (?x1181, 017l96) <- award(?x1181, ?x724), artists(?x671, ?x1181), ?x724 = 01bgqh *> conf = 0.13 ranks of expected_values: 5 EVAL 0b68vs artist! 017l96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 118.000 73.000 0.252 http://example.org/music/record_label/artist #11813-0chw_ PRED entity: 0chw_ PRED relation: award PRED expected values: 02y_rq5 => 117 concepts (109 used for prediction) PRED predicted values (max 10 best out of 311): 02g2yr (0.71 #38333, 0.70 #17965, 0.70 #35937), 09cn0c (0.71 #38333, 0.70 #17965, 0.70 #35937), 09sb52 (0.64 #439, 0.53 #1237, 0.52 #1636), 09td7p (0.46 #516, 0.35 #1314, 0.33 #1713), 099t8j (0.38 #535, 0.31 #1333, 0.29 #1732), 0cqgl9 (0.36 #586, 0.24 #1384, 0.23 #1783), 03qgjwc (0.33 #577, 0.32 #1375, 0.30 #1774), 02y_rq5 (0.33 #491, 0.26 #1289, 0.24 #1688), 099cng (0.33 #482, 0.23 #1280, 0.21 #1679), 0bfvw2 (0.31 #414, 0.27 #1212, 0.26 #1611) >> Best rule #38333 for best value: >> intensional similarity = 3 >> extensional distance = 2105 >> proper extension: 01lcxbb; 0lzkm; 01t265; 08xz51; 051m56; 0f6lx; >> query: (?x9033, ?x1254) <- award_winner(?x1254, ?x9033), profession(?x9033, ?x319), award(?x91, ?x1254) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #491 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 01phtd; 022411; *> query: (?x9033, 02y_rq5) <- award(?x9033, ?x1254), ?x1254 = 02z0dfh, award_winner(?x78, ?x9033) *> conf = 0.33 ranks of expected_values: 8 EVAL 0chw_ award 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 117.000 109.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11812-0k4kk PRED entity: 0k4kk PRED relation: genre PRED expected values: 03k9fj => 83 concepts (81 used for prediction) PRED predicted values (max 10 best out of 90): 060__y (0.84 #1080, 0.32 #3446, 0.27 #726), 02l7c8 (0.69 #3445, 0.45 #371, 0.44 #135), 082gq (0.43 #267, 0.25 #739, 0.19 #2276), 01g6gs (0.41 #140, 0.31 #376, 0.25 #22), 04xvlr (0.41 #237, 0.36 #709, 0.31 #1063), 05p553 (0.39 #1540, 0.39 #6028, 0.37 #476), 01jfsb (0.33 #603, 0.30 #4149, 0.30 #4621), 02kdv5l (0.32 #592, 0.29 #4138, 0.29 #3666), 03k9fj (0.27 #248, 0.26 #720, 0.25 #2732), 0lsxr (0.25 #599, 0.23 #835, 0.20 #1307) >> Best rule #1080 for best value: >> intensional similarity = 5 >> extensional distance = 247 >> proper extension: 0cnztc4; >> query: (?x1746, 060__y) <- genre(?x1746, ?x4757), genre(?x9261, ?x4757), genre(?x6890, ?x4757), ?x9261 = 0p9rz, ?x6890 = 0gnkb >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #248 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 05dy7p; 0bmc4cm; 027ct7c; 0dkv90; 072r5v; *> query: (?x1746, 03k9fj) <- genre(?x1746, ?x4757), ?x4757 = 06l3bl, nominated_for(?x484, ?x1746) *> conf = 0.27 ranks of expected_values: 9 EVAL 0k4kk genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 83.000 81.000 0.843 http://example.org/film/film/genre #11811-0hx4y PRED entity: 0hx4y PRED relation: film_release_region PRED expected values: 03_3d 0chghy 0ctw_b => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 146): 0chghy (0.89 #1572, 0.86 #2352, 0.85 #2196), 03rjj (0.89 #2188, 0.87 #1564, 0.84 #2656), 03_3d (0.86 #2346, 0.85 #1566, 0.81 #3751), 035qy (0.84 #2217, 0.81 #1593, 0.80 #2685), 06bnz (0.83 #2228, 0.73 #2696, 0.67 #1604), 0154j (0.82 #2187, 0.75 #1563, 0.75 #2655), 06t2t (0.80 #2245, 0.72 #2713, 0.67 #997), 0b90_r (0.78 #2186, 0.75 #2654, 0.72 #1562), 0d060g (0.78 #2191, 0.71 #1567, 0.70 #2659), 03spz (0.77 #2280, 0.74 #1656, 0.73 #2748) >> Best rule #1572 for best value: >> intensional similarity = 5 >> extensional distance = 83 >> proper extension: 014lc_; 0djb3vw; 0bwfwpj; 08hmch; 01c22t; 0bh8yn3; 04n52p6; 0fq7dv_; 05qbckf; 01fmys; ... >> query: (?x2878, 0chghy) <- film_release_region(?x2878, ?x1453), film_release_region(?x2878, ?x583), nominated_for(?x929, ?x2878), ?x1453 = 06qd3, ?x583 = 015fr >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 13 EVAL 0hx4y film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 125.000 125.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hx4y film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hx4y film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.894 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11810-0lyjf PRED entity: 0lyjf PRED relation: student PRED expected values: 03f6fl0 0pkgt => 195 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1758): 06pj8 (0.12 #322, 0.06 #135100, 0.03 #14870), 01zfmm (0.12 #439, 0.03 #12909, 0.03 #14987), 03r1pr (0.12 #460, 0.03 #15008, 0.02 #27481), 01vwbts (0.09 #2886, 0.04 #31985, 0.04 #34063), 0ff3y (0.07 #4133, 0.06 #2055, 0.05 #31154), 02779r4 (0.06 #135100, 0.06 #1157, 0.04 #5313), 01gp_x (0.06 #135100, 0.06 #411, 0.03 #14959), 028r4y (0.06 #135100, 0.06 #943, 0.03 #19647), 023361 (0.06 #135100, 0.06 #1447, 0.03 #24310), 0crvfq (0.06 #135100, 0.06 #1361, 0.02 #43648) >> Best rule #322 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 02zkz7; >> query: (?x4904, 06pj8) <- school(?x8786, ?x4904), ?x8786 = 02pq_x5, colors(?x4904, ?x663), school(?x729, ?x4904) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #8313 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 03rj0; 04hzj; *> query: (?x4904, ?x5833) <- contains(?x6050, ?x4904), company(?x346, ?x4904), ?x346 = 060c4, location(?x5833, ?x6050) *> conf = 0.02 ranks of expected_values: 1214 EVAL 0lyjf student 0pkgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 195.000 105.000 0.118 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0lyjf student 03f6fl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 195.000 105.000 0.118 http://example.org/education/educational_institution/students_graduates./education/education/student #11809-0gr0m PRED entity: 0gr0m PRED relation: ceremony PRED expected values: 059x66 0bz6l9 0bzn6_ 0dthsy 0c4hgj 03tn9w 0c6vcj 0bzjvm 073h5b 0fzrhn => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 87): 059x66 (0.81 #711, 0.50 #276, 0.33 #363), 0bzjvm (0.76 #764, 0.67 #329, 0.50 #242), 073h5b (0.76 #777, 0.67 #342, 0.50 #255), 0bz6l9 (0.76 #730, 0.67 #295, 0.50 #208), 0c6vcj (0.76 #759, 0.50 #324, 0.50 #237), 03tn9w (0.71 #756, 0.67 #321, 0.50 #234), 0bzn6_ (0.67 #733, 0.67 #298, 0.50 #211), 0dthsy (0.67 #305, 0.57 #740, 0.33 #131), 0c53zb (0.67 #736, 0.50 #214, 0.33 #562), 0c4hgj (0.67 #754, 0.33 #319, 0.33 #145) >> Best rule #711 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x1243, 059x66) <- ceremony(?x1243, ?x5902), award_winner(?x1243, ?x2466), award(?x185, ?x1243), ?x5902 = 02glmx >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 10, 11 EVAL 0gr0m ceremony 0fzrhn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 54.000 54.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr0m ceremony 073h5b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr0m ceremony 0bzjvm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr0m ceremony 0c6vcj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr0m ceremony 03tn9w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr0m ceremony 0c4hgj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 54.000 54.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr0m ceremony 0dthsy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr0m ceremony 0bzn6_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr0m ceremony 0bz6l9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr0m ceremony 059x66 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony #11808-0bx9y PRED entity: 0bx9y PRED relation: county! PRED expected values: 0bxbr => 150 concepts (92 used for prediction) PRED predicted values (max 10 best out of 178): 0s4sj (0.12 #606, 0.08 #1516, 0.05 #1819), 0dzt9 (0.12 #777, 0.08 #1383, 0.05 #1686), 0mp3l (0.12 #634, 0.08 #1240, 0.05 #1543), 013h9 (0.12 #810, 0.08 #1416, 0.03 #2628), 0t_48 (0.12 #881, 0.08 #1487, 0.03 #2699), 0t_4_ (0.12 #807, 0.08 #1413, 0.03 #2625), 0t_3w (0.12 #791, 0.08 #1397, 0.03 #2609), 0qkcb (0.12 #739, 0.08 #1345, 0.03 #2557), 0t_71 (0.12 #684, 0.08 #1290, 0.03 #2502), 01qh7 (0.12 #640, 0.08 #1246, 0.03 #2458) >> Best rule #606 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02xry; 01cx_; >> query: (?x9889, 0s4sj) <- currency(?x9889, ?x170), adjoins(?x9889, ?x12267), contains(?x9889, ?x5466), country(?x9889, ?x94) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bx9y county! 0bxbr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 92.000 0.125 http://example.org/location/hud_county_place/county #11807-04zx08r PRED entity: 04zx08r PRED relation: award! PRED expected values: 01g4bk => 64 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1586): 0f7hc (0.43 #14893, 0.40 #18276, 0.11 #92726), 0hqly (0.43 #16588, 0.40 #19971, 0.05 #94421), 0h1p (0.43 #7311, 0.13 #24229, 0.11 #34382), 02_l96 (0.36 #15025, 0.33 #18408, 0.05 #92858), 030g9z (0.36 #16177, 0.33 #19560, 0.05 #94010), 0534v (0.33 #1551, 0.14 #4934, 0.12 #11702), 01g4bk (0.33 #2778, 0.06 #23079, 0.05 #81216), 01tt43d (0.29 #15416, 0.27 #18799, 0.10 #96633), 0f502 (0.29 #14778, 0.27 #18161, 0.09 #92611), 0jrqq (0.29 #14617, 0.27 #18000, 0.09 #85681) >> Best rule #14893 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0gqng; 04ljl_l; 05b4l5x; 05f4m9q; 03c7tr1; 07bdd_; 07cbcy; 05p1dby; 05p09zm; 05q8pss; ... >> query: (?x5497, 0f7hc) <- award(?x5322, ?x5497), nominated_for(?x5497, ?x5255), category(?x5497, ?x134), film_release_distribution_medium(?x5255, ?x81), film_release_region(?x5255, ?x94) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2778 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 0776drd; *> query: (?x5497, 01g4bk) <- award(?x5322, ?x5497), disciplines_or_subjects(?x5497, ?x373), nominated_for(?x5497, ?x3135), ?x5322 = 02bxjp, ?x373 = 02vxn *> conf = 0.33 ranks of expected_values: 7 EVAL 04zx08r award! 01g4bk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 64.000 29.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11806-03mnk PRED entity: 03mnk PRED relation: service_location PRED expected values: 09c7w0 => 234 concepts (209 used for prediction) PRED predicted values (max 10 best out of 99): 09c7w0 (0.90 #1581, 0.86 #4649, 0.84 #3657), 02j71 (0.54 #2091, 0.48 #2487, 0.47 #1200), 0d060g (0.38 #7230, 0.36 #3862, 0.33 #6140), 07ssc (0.30 #1694, 0.23 #5455, 0.22 #508), 0345h (0.22 #420, 0.13 #7250, 0.13 #5467), 01n7q (0.19 #3063, 0.18 #4052, 0.16 #5142), 06pvr (0.19 #3063, 0.18 #4052, 0.16 #5142), 0chghy (0.17 #6144, 0.17 #110, 0.15 #7234), 0b90_r (0.17 #102, 0.15 #891, 0.12 #298), 0f8l9c (0.17 #120, 0.12 #4175, 0.11 #7244) >> Best rule #1581 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 01bvx1; 069b85; >> query: (?x3230, 09c7w0) <- citytown(?x3230, ?x2935), organization(?x4682, ?x3230), contact_category(?x3230, ?x897), industry(?x3230, ?x12987), taxonomy(?x12987, ?x939) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mnk service_location 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 234.000 209.000 0.900 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #11805-06t8v PRED entity: 06t8v PRED relation: film_release_region! PRED expected values: 02vxq9m 017gl1 017gm7 0661m4p 0cc5mcj 07f_7h 0g5838s 09g7vfw 0c3xw46 064lsn 0dc_ms 09v3jyg 01mgw => 139 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1376): 017gm7 (0.84 #3726, 0.82 #4922, 0.73 #7314), 06ztvyx (0.84 #3861, 0.73 #5057, 0.62 #7449), 0661ql3 (0.82 #5033, 0.79 #3837, 0.73 #7425), 017gl1 (0.81 #3684, 0.71 #4880, 0.71 #7272), 087wc7n (0.80 #4863, 0.79 #3667, 0.71 #7255), 02vxq9m (0.80 #4798, 0.77 #7190, 0.74 #3602), 04f52jw (0.79 #7456, 0.79 #3868, 0.78 #5064), 0cc5mcj (0.79 #3842, 0.73 #5038, 0.62 #7430), 0bwfwpj (0.79 #3691, 0.71 #4887, 0.65 #7279), 0cz8mkh (0.79 #3735, 0.67 #4931, 0.54 #7323) >> Best rule #3726 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 012wgb; >> query: (?x3277, 017gm7) <- film_release_region(?x5016, ?x3277), film_release_region(?x1701, ?x3277), ?x1701 = 0bh8yn3, ?x5016 = 062zm5h >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 6, 8, 13, 14, 28, 32, 38, 61, 63, 80, 102 EVAL 06t8v film_release_region! 01mgw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 09v3jyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 0c3xw46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 09g7vfw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 0g5838s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 07f_7h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 0cc5mcj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 0661m4p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 017gm7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 017gl1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t8v film_release_region! 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 139.000 75.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11804-031786 PRED entity: 031786 PRED relation: film_crew_role PRED expected values: 02r96rf 09vw2b7 01vx2h => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 29): 09vw2b7 (0.74 #404, 0.70 #79, 0.70 #840), 02r96rf (0.71 #400, 0.71 #762, 0.70 #436), 01vx2h (0.50 #84, 0.42 #120, 0.41 #445), 0dxtw (0.50 #263, 0.41 #844, 0.39 #516), 01pvkk (0.31 #846, 0.31 #518, 0.29 #410), 02rh1dz (0.30 #82, 0.19 #118, 0.15 #697), 0215hd (0.23 #127, 0.15 #452, 0.14 #925), 02ynfr (0.22 #53, 0.22 #414, 0.20 #850), 0d2b38 (0.20 #98, 0.15 #423, 0.13 #386), 015h31 (0.19 #117, 0.14 #369, 0.13 #442) >> Best rule #404 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 0872p_c; 05pbl56; 01hqhm; 0284b56; 02nt3d; 09v8clw; >> query: (?x7305, 09vw2b7) <- film_crew_role(?x7305, ?x1284), film(?x981, ?x7305), ?x1284 = 0ch6mp2, nominated_for(?x2006, ?x7305) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 031786 film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.740 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 031786 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.740 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 031786 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.740 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11803-0106dv PRED entity: 0106dv PRED relation: place PRED expected values: 0106dv => 173 concepts (83 used for prediction) PRED predicted values (max 10 best out of 300): 030qb3t (0.33 #30, 0.25 #545, 0.15 #13407), 0106dv (0.18 #27854, 0.15 #13407, 0.14 #18570), 07b_l (0.18 #27854, 0.15 #13407, 0.14 #18570), 09c7w0 (0.18 #27854, 0.14 #18570, 0.14 #18569), 02_286 (0.15 #13407, 0.13 #11343, 0.04 #42304), 0mqs0 (0.08 #19086, 0.07 #17021, 0.07 #8764), 0f2w0 (0.07 #1067, 0.06 #1582, 0.06 #2614), 0f2s6 (0.07 #1291, 0.06 #1806, 0.06 #2322), 0f2rq (0.07 #1169, 0.06 #1684, 0.06 #2716), 013m_x (0.07 #1168, 0.06 #1683, 0.06 #2715) >> Best rule #30 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 030qb3t; >> query: (?x10364, 030qb3t) <- location(?x11992, ?x10364), location(?x2559, ?x10364), ?x11992 = 01pgk0, ?x2559 = 06mmb, citytown(?x6177, ?x10364) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #27854 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 171 *> proper extension: 03khn; *> query: (?x10364, ?x94) <- category(?x10364, ?x134), citytown(?x6177, ?x10364), contains(?x94, ?x6177) *> conf = 0.18 ranks of expected_values: 2 EVAL 0106dv place 0106dv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 173.000 83.000 0.333 http://example.org/location/hud_county_place/place #11802-018d6l PRED entity: 018d6l PRED relation: nationality PRED expected values: 07ssc => 136 concepts (113 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.75 #5803, 0.72 #10606, 0.71 #10206), 02jx1 (0.36 #233, 0.30 #1033, 0.29 #1934), 07ssc (0.33 #115, 0.20 #615, 0.17 #1515), 06q1r (0.33 #177, 0.04 #1377, 0.03 #1777), 0chghy (0.17 #10, 0.05 #410, 0.03 #3312), 0b90_r (0.07 #203, 0.06 #303, 0.02 #803), 04jpl (0.07 #1801), 03rk0 (0.07 #10551, 0.06 #5448, 0.05 #5348), 05bcl (0.06 #360, 0.02 #860, 0.02 #960), 0d060g (0.05 #3309, 0.05 #5409, 0.05 #3909) >> Best rule #5803 for best value: >> intensional similarity = 4 >> extensional distance = 509 >> proper extension: 0hskw; 0347xl; 028r4y; 03_wvl; 01xv77; 01520h; 0ccqd7; 02d6n_; 016z68; 04dyqk; ... >> query: (?x7193, 09c7w0) <- student(?x13639, ?x7193), profession(?x7193, ?x1032), ?x1032 = 02hrh1q, company(?x346, ?x13639) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #115 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0clvcx; *> query: (?x7193, 07ssc) <- type_of_union(?x7193, ?x566), ?x566 = 04ztj, student(?x13639, ?x7193), ?x13639 = 031ns1 *> conf = 0.33 ranks of expected_values: 3 EVAL 018d6l nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 136.000 113.000 0.748 http://example.org/people/person/nationality #11801-0bx8pn PRED entity: 0bx8pn PRED relation: school_type PRED expected values: 05jxkf => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 18): 05jxkf (0.87 #1149, 0.56 #378, 0.52 #312), 05pcjw (0.40 #67, 0.39 #331, 0.36 #155), 01_9fk (0.32 #46, 0.30 #376, 0.27 #200), 01rs41 (0.26 #908, 0.24 #1305, 0.24 #643), 02p0qmm (0.20 #9, 0.16 #31, 0.04 #251), 01_srz (0.13 #91, 0.12 #223, 0.07 #641), 0bwd5 (0.06 #237, 0.01 #259, 0.01 #567), 04399 (0.05 #233, 0.04 #475, 0.04 #453), 06cs1 (0.04 #94, 0.03 #226, 0.03 #72), 0bpgx (0.04 #151, 0.02 #922, 0.02 #1319) >> Best rule #1149 for best value: >> intensional similarity = 3 >> extensional distance = 318 >> proper extension: 021l5s; >> query: (?x1884, 05jxkf) <- school_type(?x1884, ?x4994), school_type(?x11480, ?x4994), ?x11480 = 01r47h >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bx8pn school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.869 http://example.org/education/educational_institution/school_type #11800-01qd_r PRED entity: 01qd_r PRED relation: school_type PRED expected values: 01_9fk 05jxkf => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.53 #100, 0.52 #28, 0.47 #52), 05pcjw (0.36 #1, 0.27 #457, 0.24 #626), 07tf8 (0.30 #9, 0.26 #57, 0.22 #33), 01rs41 (0.30 #461, 0.24 #630, 0.23 #558), 01_9fk (0.26 #98, 0.25 #2, 0.22 #26), 01_srz (0.06 #291, 0.06 #556, 0.06 #267), 047951 (0.04 #56, 0.03 #32, 0.02 #128), 04399 (0.04 #254, 0.04 #278, 0.04 #302), 02p0qmm (0.04 #418, 0.04 #322, 0.03 #346), 01y64 (0.03 #372, 0.02 #781, 0.02 #1169) >> Best rule #100 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 05kj_; >> query: (?x7660, 05jxkf) <- contains(?x94, ?x7660), ?x94 = 09c7w0, school(?x8586, ?x7660) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 01qd_r school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.529 http://example.org/education/educational_institution/school_type EVAL 01qd_r school_type 01_9fk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 97.000 0.529 http://example.org/education/educational_institution/school_type #11799-0m7yh PRED entity: 0m7yh PRED relation: contains! PRED expected values: 0345h => 191 concepts (68 used for prediction) PRED predicted values (max 10 best out of 257): 0345h (0.91 #8961, 0.91 #8147, 0.90 #8964), 09ksp (0.90 #8964, 0.84 #6271, 0.83 #37659), 09c7w0 (0.76 #56490, 0.74 #30486, 0.73 #41247), 02jx1 (0.65 #58370, 0.34 #15331, 0.26 #10844), 07ssc (0.55 #58315, 0.21 #15276, 0.12 #1823), 04jpl (0.50 #15266, 0.33 #10779, 0.28 #6294), 082fr (0.35 #7168, 0.35 #16140, 0.35 #10757), 02j9z (0.33 #28, 0.13 #4508, 0.07 #9888), 06bnz (0.33 #105, 0.13 #4585, 0.05 #6377), 04swd (0.33 #473, 0.13 #4953, 0.03 #6745) >> Best rule #8961 for best value: >> intensional similarity = 6 >> extensional distance = 43 >> proper extension: 02h6_6p; 04kf4; 02z0j; 0d58_; 0dr31; 019fv4; 09f8q; >> query: (?x7508, ?x1264) <- contains(?x10334, ?x7508), contains(?x7934, ?x10334), contains(?x1264, ?x10334), ?x1264 = 0345h, adjoins(?x3623, ?x7934), administrative_parent(?x11274, ?x7934) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m7yh contains! 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 68.000 0.911 http://example.org/location/location/contains #11798-0b1q7c PRED entity: 0b1q7c PRED relation: film PRED expected values: 0ddcbd5 => 110 concepts (43 used for prediction) PRED predicted values (max 10 best out of 670): 0ddcbd5 (0.33 #672, 0.04 #4253, 0.02 #20369), 07l450 (0.33 #1575, 0.04 #5156), 03np63f (0.33 #1377, 0.04 #4958), 031hcx (0.29 #3066, 0.07 #20972, 0.04 #12020), 03177r (0.29 #2256, 0.05 #20162, 0.04 #11210), 03176f (0.29 #2499, 0.04 #11453, 0.04 #20405), 031778 (0.14 #2107, 0.05 #11061, 0.05 #20013), 0dr_4 (0.14 #2038, 0.04 #3828, 0.02 #19944), 0ch26b_ (0.14 #2093, 0.03 #19999, 0.02 #23579), 03wjm2 (0.14 #3551, 0.02 #7131, 0.02 #21457) >> Best rule #672 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03d_w3h; >> query: (?x11302, 0ddcbd5) <- actor(?x7424, ?x11302), type_of_union(?x11302, ?x566), gender(?x11302, ?x231), ?x7424 = 08y2fn >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b1q7c film 0ddcbd5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 43.000 0.333 http://example.org/film/actor/film./film/performance/film #11797-030g9z PRED entity: 030g9z PRED relation: story_by! PRED expected values: 03l6q0 => 93 concepts (30 used for prediction) PRED predicted values (max 10 best out of 34): 03l6q0 (0.17 #108, 0.06 #450, 0.03 #793), 01pj_5 (0.10 #9929, 0.10 #8900, 0.04 #685), 02v8kmz (0.06 #352, 0.03 #695, 0.03 #1037), 03lvwp (0.03 #2607, 0.02 #2949, 0.02 #3291), 0jsqk (0.03 #2559, 0.02 #2901, 0.02 #3243), 09z2b7 (0.03 #2442, 0.02 #2784, 0.02 #3126), 0dyb1 (0.02 #8659), 0btpm6 (0.01 #7092), 0bpm4yw (0.01 #6992), 0419kt (0.01 #5807) >> Best rule #108 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 017s11; 030_1m; >> query: (?x9195, 03l6q0) <- award_nominee(?x7935, ?x9195), ?x7935 = 025hwq, award(?x9195, ?x688) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030g9z story_by! 03l6q0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 30.000 0.167 http://example.org/film/film/story_by #11796-0n1s0 PRED entity: 0n1s0 PRED relation: film_release_region PRED expected values: 09c7w0 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 142): 09c7w0 (0.70 #5385, 0.70 #3235, 0.70 #4490), 03rjj (0.37 #4487, 0.23 #7356, 0.23 #2340), 0f8l9c (0.30 #32, 0.28 #2364, 0.27 #211), 02vzc (0.30 #70, 0.27 #249, 0.25 #2402), 059j2 (0.30 #45, 0.27 #224, 0.25 #2377), 06mkj (0.30 #76, 0.27 #255, 0.25 #5458), 0k6nt (0.30 #36, 0.27 #215, 0.24 #2368), 07ssc (0.30 #24, 0.27 #203, 0.24 #5406), 0chghy (0.30 #17, 0.27 #196, 0.24 #2349), 0jgd (0.30 #5, 0.27 #184, 0.22 #2337) >> Best rule #5385 for best value: >> intensional similarity = 3 >> extensional distance = 1170 >> proper extension: 0dtw1x; 0192hw; 043sct5; 0g5q34q; 03_wm6; 0bs8hvm; 0bx_hnp; 0267wwv; 09rfpk; >> query: (?x5984, 09c7w0) <- language(?x5984, ?x254), film_crew_role(?x5984, ?x1171), genre(?x5984, ?x53) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n1s0 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.704 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11795-0c0nhgv PRED entity: 0c0nhgv PRED relation: film_crew_role PRED expected values: 09zzb8 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 25): 09zzb8 (0.72 #1438, 0.70 #1508, 0.69 #1368), 09vw2b7 (0.65 #1444, 0.62 #252, 0.60 #602), 01vx2h (0.35 #256, 0.32 #1448, 0.31 #606), 01pvkk (0.26 #1449, 0.26 #1834, 0.26 #712), 0215hd (0.19 #263, 0.15 #123, 0.15 #228), 01xy5l_ (0.14 #259, 0.12 #609, 0.11 #1451), 02vs3x5 (0.14 #58, 0.07 #128, 0.06 #93), 0d2b38 (0.13 #270, 0.11 #690, 0.11 #620), 02_n3z (0.13 #247, 0.09 #597, 0.09 #457), 089g0h (0.12 #229, 0.12 #614, 0.12 #264) >> Best rule #1438 for best value: >> intensional similarity = 3 >> extensional distance = 645 >> proper extension: 02_1sj; 09p35z; 03t97y; 05p3738; 047qxs; 035s95; 014nq4; 0c57yj; 05_5rjx; 038bh3; ... >> query: (?x1163, 09zzb8) <- production_companies(?x1163, ?x541), currency(?x1163, ?x170), film_crew_role(?x1163, ?x468) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c0nhgv film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.719 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11794-0jbp0 PRED entity: 0jbp0 PRED relation: location PRED expected values: 0134bf => 123 concepts (121 used for prediction) PRED predicted values (max 10 best out of 180): 030qb3t (0.40 #83, 0.24 #13752, 0.22 #7320), 02_286 (0.21 #1645, 0.20 #2449, 0.20 #37), 050ks (0.20 #339, 0.01 #12400), 013ksx (0.20 #164), 04jpl (0.18 #27357, 0.17 #18511, 0.07 #1625), 0f2wj (0.17 #838, 0.02 #2446, 0.02 #36221), 03zv2t (0.17 #1338), 01vsl (0.17 #1175), 0n6bs (0.17 #970), 0cr3d (0.08 #3361, 0.07 #2557, 0.07 #10598) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 04511f; 046mxj; >> query: (?x10398, 030qb3t) <- nominated_for(?x10398, ?x9017), award(?x10398, ?x458), ?x9017 = 06r2h >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jbp0 location 0134bf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 121.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #11793-04d_mtq PRED entity: 04d_mtq PRED relation: artist! PRED expected values: 015_1q 02swsm => 115 concepts (105 used for prediction) PRED predicted values (max 10 best out of 123): 02swsm (0.62 #96, 0.50 #806, 0.43 #664), 015_1q (0.36 #588, 0.22 #1014, 0.20 #5274), 081g_l (0.25 #24, 0.14 #592, 0.07 #734), 0181dw (0.21 #1321, 0.20 #2031, 0.18 #2315), 03mp8k (0.20 #210, 0.15 #494, 0.12 #1346), 043g7l (0.20 #174, 0.15 #458, 0.12 #1310), 017l96 (0.20 #1581, 0.18 #303, 0.15 #445), 03rhqg (0.17 #1720, 0.17 #3850, 0.16 #3282), 0g768 (0.17 #1742, 0.15 #1458, 0.14 #4724), 033hn8 (0.15 #440, 0.15 #1434, 0.12 #1718) >> Best rule #96 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0gs6vr; >> query: (?x10353, 02swsm) <- profession(?x10353, ?x1032), artists(?x8878, ?x10353), people(?x3591, ?x10353), ?x8878 = 02ny8t, instrumentalists(?x227, ?x10353) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04d_mtq artist! 02swsm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 105.000 0.625 http://example.org/music/record_label/artist EVAL 04d_mtq artist! 015_1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 105.000 0.625 http://example.org/music/record_label/artist #11792-06cgy PRED entity: 06cgy PRED relation: nominated_for PRED expected values: 0bpx1k => 91 concepts (38 used for prediction) PRED predicted values (max 10 best out of 308): 07nxvj (0.78 #53095, 0.77 #40223, 0.49 #6432), 0h6r5 (0.78 #53095, 0.77 #40223, 0.29 #17701), 020bv3 (0.50 #293, 0.29 #1900, 0.08 #9651), 05vxdh (0.50 #707, 0.29 #2314, 0.08 #9651), 016y_f (0.46 #14481, 0.41 #11261, 0.39 #4823), 04j14qc (0.46 #14481, 0.41 #11261, 0.39 #4823), 03tps5 (0.46 #14481, 0.41 #11261, 0.39 #4823), 035xwd (0.29 #17701, 0.26 #57923, 0.25 #33792), 09fqgj (0.29 #17701, 0.26 #57923, 0.25 #33792), 0234j5 (0.29 #17701, 0.26 #57923, 0.25 #33792) >> Best rule #53095 for best value: >> intensional similarity = 2 >> extensional distance = 1247 >> proper extension: 0gv2r; 04mx__; >> query: (?x1554, ?x1295) <- award_nominee(?x1554, ?x400), award_winner(?x1295, ?x1554) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #426 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0136g9; 02q42j_; *> query: (?x1554, 0bpx1k) <- award_nominee(?x1554, ?x3568), ?x3568 = 0b13g7, people(?x743, ?x1554) *> conf = 0.25 ranks of expected_values: 28 EVAL 06cgy nominated_for 0bpx1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 91.000 38.000 0.776 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #11791-06wbm8q PRED entity: 06wbm8q PRED relation: film_release_region PRED expected values: 05r4w 03ryn 03spz => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 91): 05r4w (0.95 #264, 0.86 #395, 0.86 #657), 03spz (0.86 #467, 0.86 #729, 0.68 #1123), 03_3d (0.86 #398, 0.85 #660, 0.79 #267), 06qd3 (0.61 #682, 0.54 #420, 0.46 #1076), 05qx1 (0.60 #422, 0.52 #684, 0.36 #291), 03ryn (0.58 #457, 0.51 #719, 0.29 #326), 07twz (0.56 #466, 0.45 #728, 0.29 #335), 0h7x (0.52 #679, 0.46 #417, 0.39 #1073), 02k54 (0.50 #405, 0.49 #667, 0.23 #1061), 01pj7 (0.48 #428, 0.41 #690, 0.40 #297) >> Best rule #264 for best value: >> intensional similarity = 6 >> extensional distance = 40 >> proper extension: 0gtsx8c; 02d44q; 0hgnl3t; >> query: (?x2628, 05r4w) <- film_release_region(?x2628, ?x4059), film_release_region(?x2628, ?x1558), film_release_region(?x2628, ?x172), ?x1558 = 01mjq, ?x4059 = 077qn, ?x172 = 0154j >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6 EVAL 06wbm8q film_release_region 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.952 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06wbm8q film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 65.000 65.000 0.952 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06wbm8q film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.952 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11790-0f2rq PRED entity: 0f2rq PRED relation: teams PRED expected values: 0jmcv => 250 concepts (250 used for prediction) PRED predicted values (max 10 best out of 303): 0jmh7 (0.11 #242, 0.06 #2390, 0.05 #3464), 02d02 (0.08 #545, 0.07 #903, 0.06 #1619), 02fp3 (0.08 #544, 0.07 #902, 0.06 #1618), 02c_4 (0.08 #519, 0.07 #877, 0.06 #1593), 0x0d (0.08 #618, 0.07 #976, 0.06 #1692), 0bwjj (0.08 #575, 0.06 #1291, 0.06 #1649), 0j2zj (0.08 #569, 0.06 #1285, 0.06 #1643), 02wvfxl (0.08 #459, 0.06 #1175, 0.06 #1533), 01d5z (0.08 #376, 0.06 #1092, 0.06 #1450), 01k2xy (0.08 #522, 0.06 #1238, 0.06 #1954) >> Best rule #242 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0mrhq; 0mskq; 0ms1n; >> query: (?x5719, 0jmh7) <- location_of_ceremony(?x566, ?x5719), contains(?x3634, ?x5719), ?x3634 = 07b_l >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f2rq teams 0jmcv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 250.000 250.000 0.111 http://example.org/sports/sports_team_location/teams #11789-0j582 PRED entity: 0j582 PRED relation: award_winner! PRED expected values: 02py7pj => 112 concepts (103 used for prediction) PRED predicted values (max 10 best out of 278): 0f4x7 (0.37 #25456, 0.37 #25455, 0.34 #4746), 0bdwqv (0.37 #25456, 0.37 #25455, 0.34 #4746), 0bdw6t (0.37 #25456, 0.37 #25455, 0.34 #4746), 0bp_b2 (0.37 #25456, 0.37 #25455, 0.34 #4746), 07cbcy (0.37 #25456, 0.37 #25455, 0.34 #4746), 027c95y (0.27 #6199, 0.26 #6632, 0.22 #2313), 054ky1 (0.25 #110, 0.22 #972, 0.17 #2265), 09sb52 (0.19 #2196, 0.15 #4787, 0.14 #14283), 027986c (0.18 #6090, 0.17 #6523, 0.17 #2204), 04kxsb (0.17 #2282, 0.15 #6168, 0.15 #6601) >> Best rule #25456 for best value: >> intensional similarity = 3 >> extensional distance = 1091 >> proper extension: 031x_3; 09jd9; >> query: (?x1548, ?x591) <- student(?x8056, ?x1548), award(?x1548, ?x591), award_winner(?x3846, ?x1548) >> conf = 0.37 => this is the best rule for 5 predicted values *> Best rule #5486 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 94 *> proper extension: 0d9kl; 057ph; 0dng4; *> query: (?x1548, 02py7pj) <- celebrities_impersonated(?x3649, ?x1548), ?x3649 = 03m6t5 *> conf = 0.11 ranks of expected_values: 36 EVAL 0j582 award_winner! 02py7pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 112.000 103.000 0.366 http://example.org/award/award_category/winners./award/award_honor/award_winner #11788-06mkj PRED entity: 06mkj PRED relation: partially_contains! PRED expected values: 03v9w => 222 concepts (174 used for prediction) PRED predicted values (max 10 best out of 110): 02j9z (0.33 #196, 0.29 #757, 0.29 #383), 05rgl (0.29 #408, 0.29 #314, 0.21 #782), 059g4 (0.29 #448, 0.29 #354, 0.17 #261), 0f8l9c (0.28 #3369, 0.12 #1510, 0.09 #3290), 0d060g (0.28 #3369, 0.08 #1781, 0.06 #3280), 09c7w0 (0.28 #3369, 0.08 #1777, 0.06 #3276), 059f4 (0.28 #3369, 0.04 #1505, 0.03 #3285), 01tjt2 (0.28 #3369), 01ly8d (0.28 #3369), 017jq (0.28 #3369) >> Best rule #196 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01smm; >> query: (?x2152, 02j9z) <- location(?x4055, ?x2152), partially_contains(?x1144, ?x2152), teams(?x2152, ?x13154) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #273 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 01smm; *> query: (?x2152, 03v9w) <- location(?x4055, ?x2152), partially_contains(?x1144, ?x2152), teams(?x2152, ?x13154) *> conf = 0.17 ranks of expected_values: 23 EVAL 06mkj partially_contains! 03v9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 222.000 174.000 0.333 http://example.org/location/location/partially_contains #11787-02nzb8 PRED entity: 02nzb8 PRED relation: position! PRED expected values: 0329r5 07sqbl 037css => 11 concepts (7 used for prediction) PRED predicted values (max 10 best out of 153): 017znw (0.87 #926, 0.84 #304, 0.82 #300), 019m60 (0.87 #926, 0.84 #304, 0.82 #300), 085v7 (0.87 #926, 0.84 #304, 0.82 #300), 06l22 (0.87 #926, 0.84 #304, 0.82 #300), 045xx (0.87 #926, 0.84 #304, 0.82 #300), 0cnk2q (0.87 #926, 0.84 #304, 0.82 #300), 026n13j (0.87 #926, 0.84 #304, 0.82 #300), 0425gc (0.87 #926, 0.84 #304, 0.82 #300), 029q3k (0.87 #926, 0.84 #304, 0.82 #300), 06jd89 (0.87 #926, 0.84 #304, 0.82 #300) >> Best rule #926 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 02md_2; >> query: (?x60, ?x7423) <- position(?x14018, ?x60), position(?x13708, ?x60), position(?x12463, ?x60), position(?x7423, ?x60), team(?x60, ?x202), position(?x7423, ?x63), team(?x530, ?x14018), ?x63 = 02sdk9v, sport(?x13708, ?x471), ?x471 = 02vx4, colors(?x12463, ?x663) >> conf = 0.87 => this is the best rule for 87 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 39, 79, 134 EVAL 02nzb8 position! 037css CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 11.000 7.000 0.873 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position EVAL 02nzb8 position! 07sqbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 11.000 7.000 0.873 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position EVAL 02nzb8 position! 0329r5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 11.000 7.000 0.873 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #11786-06vnh2 PRED entity: 06vnh2 PRED relation: profession PRED expected values: 09j9h => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 57): 02hrh1q (0.57 #5717, 0.57 #7369, 0.57 #5567), 0dxtg (0.26 #1514, 0.26 #1664, 0.26 #2114), 01d_h8 (0.26 #1506, 0.26 #1656, 0.26 #2106), 0cbd2 (0.21 #7, 0.21 #157, 0.19 #307), 03gjzk (0.19 #1516, 0.19 #1666, 0.18 #1816), 02jknp (0.18 #1358, 0.18 #4659, 0.18 #4209), 09jwl (0.17 #1070, 0.17 #3771, 0.16 #1670), 0kyk (0.17 #31, 0.15 #181, 0.11 #331), 01c72t (0.15 #25, 0.12 #175, 0.12 #325), 0nbcg (0.11 #1083, 0.11 #1683, 0.11 #1533) >> Best rule #5717 for best value: >> intensional similarity = 11 >> extensional distance = 2988 >> proper extension: 01pbxb; 01vvydl; 06151l; 05m63c; 04cy8rb; 023tp8; 03zqc1; 033hqf; 04bs3j; 01nqfh_; ... >> query: (?x10448, 02hrh1q) <- nationality(?x10448, ?x1264), film_release_region(?x8137, ?x1264), film_release_region(?x5315, ?x1264), film_release_region(?x4430, ?x1264), film_release_region(?x504, ?x1264), ?x5315 = 0glqh5_, ?x4430 = 043sct5, ?x504 = 0g5qs2k, ?x8137 = 0gtx63s, country(?x136, ?x1264), contains(?x1264, ?x196) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06vnh2 profession 09j9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 54.000 0.571 http://example.org/people/person/profession #11785-0c5lg PRED entity: 0c5lg PRED relation: person PRED expected values: 07rn0z => 83 concepts (41 used for prediction) PRED predicted values (max 10 best out of 918): 02b9g4 (0.50 #286, 0.40 #674, 0.33 #50), 01jbx1 (0.50 #256, 0.40 #644, 0.33 #20), 01yznp (0.50 #240, 0.40 #628, 0.33 #4), 025ldg (0.40 #655, 0.40 #498, 0.33 #31), 01wf86y (0.33 #54, 0.25 #290, 0.25 #212), 01s7z0 (0.33 #76, 0.25 #312, 0.25 #234), 03f1zhf (0.33 #63, 0.25 #299, 0.25 #221), 0mj1l (0.33 #11, 0.25 #247, 0.25 #169), 01mbwlb (0.33 #73, 0.25 #309, 0.25 #231), 02ktrs (0.33 #71, 0.25 #307, 0.25 #229) >> Best rule #286 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 026h21_; >> query: (?x9277, 02b9g4) <- person(?x9277, ?x8342), person(?x9277, ?x932), participant(?x932, ?x702), award_nominee(?x932, ?x400), profession(?x8342, ?x220), nominated_for(?x932, ?x1728), location(?x8342, ?x3125), nationality(?x932, ?x94), gender(?x932, ?x514) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c5lg person 07rn0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 41.000 0.500 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #11784-039_ym PRED entity: 039_ym PRED relation: teams! PRED expected values: 03spz => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 140): 0m75g (0.20 #429, 0.20 #159, 0.09 #969), 05bcl (0.12 #654, 0.08 #1194, 0.05 #1734), 06mkj (0.12 #607, 0.08 #1147, 0.05 #1957), 0j5g9 (0.12 #658, 0.08 #1198, 0.05 #2008), 059j2 (0.12 #578, 0.08 #1118, 0.05 #1928), 0k6nt (0.12 #567, 0.08 #1107, 0.05 #1917), 0f8l9c (0.12 #564, 0.08 #1104, 0.05 #1914), 0126hc (0.09 #1044, 0.08 #7298, 0.05 #1854), 0619_ (0.09 #1057, 0.08 #7298, 0.04 #2677), 01fbb3 (0.09 #1024, 0.05 #1834, 0.04 #2644) >> Best rule #429 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 03x73c; >> query: (?x11926, 0m75g) <- position(?x11926, ?x530), position(?x11926, ?x63), position(?x11926, ?x60), ?x63 = 02sdk9v, position(?x11926, ?x203), ?x530 = 02_j1w, ?x60 = 02nzb8, team(?x8324, ?x11926), ?x8324 = 0dhrqx, ?x203 = 0dgrmp >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 039_ym teams! 03spz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 70.000 0.200 http://example.org/sports/sports_team_location/teams #11783-02mg5r PRED entity: 02mg5r PRED relation: educational_institution PRED expected values: 02mg5r => 204 concepts (127 used for prediction) PRED predicted values (max 10 best out of 274): 0gl6x (0.10 #38315, 0.10 #1449, 0.10 #910), 015wy_ (0.10 #38315, 0.10 #1533, 0.10 #455), 0345gh (0.10 #38315, 0.10 #1212, 0.10 #673), 0hsb3 (0.10 #38315, 0.10 #1272, 0.10 #733), 026m3y (0.10 #38315, 0.10 #1468, 0.10 #47493), 01314k (0.10 #38315, 0.10 #204, 0.10 #47493), 02kzfw (0.10 #38315, 0.10 #196, 0.10 #47493), 0dplh (0.10 #38315, 0.10 #50, 0.10 #47493), 014xf6 (0.10 #38315, 0.10 #47493, 0.09 #1903), 015ln1 (0.10 #38315, 0.10 #47493, 0.09 #1801) >> Best rule #38315 for best value: >> intensional similarity = 4 >> extensional distance = 222 >> proper extension: 01b1pf; 01wv24; 012gyf; 01bdhf; >> query: (?x12605, ?x639) <- citytown(?x12605, ?x362), colors(?x12605, ?x663), organization(?x5510, ?x12605), contains(?x362, ?x639) >> conf = 0.10 => this is the best rule for 52 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 13 EVAL 02mg5r educational_institution 02mg5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 204.000 127.000 0.102 http://example.org/education/educational_institution_campus/educational_institution #11782-0kv238 PRED entity: 0kv238 PRED relation: featured_film_locations PRED expected values: 04jpl => 93 concepts (71 used for prediction) PRED predicted values (max 10 best out of 69): 02_286 (0.18 #4358, 0.17 #5081, 0.16 #3393), 030qb3t (0.15 #519, 0.13 #760, 0.13 #1002), 01_d4 (0.15 #527, 0.09 #768, 0.04 #3178), 02dtg (0.15 #492, 0.09 #733, 0.03 #975), 0345h (0.15 #513, 0.09 #754, 0.02 #1719), 07b_l (0.12 #317, 0.02 #2246, 0.02 #2728), 0b90_r (0.12 #244, 0.02 #2173, 0.02 #2655), 02jx1 (0.12 #275, 0.01 #1481, 0.01 #1963), 0rh6k (0.08 #481, 0.06 #1447, 0.05 #1929), 05kj_ (0.08 #498, 0.04 #739, 0.03 #1464) >> Best rule #4358 for best value: >> intensional similarity = 4 >> extensional distance = 284 >> proper extension: 011yxg; 0ds11z; 01ln5z; 0170_p; 09p35z; 03ckwzc; 0dsvzh; 0b73_1d; 07y9w5; 0340hj; ... >> query: (?x2714, 02_286) <- film_crew_role(?x2714, ?x468), language(?x2714, ?x254), category(?x2714, ?x134), ?x254 = 02h40lc >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #3140 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 135 *> proper extension: 0pv2t; 02_kd; 02rq8k8; 027rpym; 03bxp5; 052_mn; 04x4nv; 02q_x_l; 01c9d; *> query: (?x2714, 04jpl) <- film(?x9244, ?x2714), titles(?x571, ?x2714), place_of_birth(?x9244, ?x242), costume_design_by(?x2714, ?x4190) *> conf = 0.07 ranks of expected_values: 15 EVAL 0kv238 featured_film_locations 04jpl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 93.000 71.000 0.178 http://example.org/film/film/featured_film_locations #11781-0gy1_ PRED entity: 0gy1_ PRED relation: contact_category PRED expected values: 03w5xm => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 2): 03w5xm (0.88 #67, 0.88 #140, 0.88 #87), 02zdwq (0.56 #26, 0.45 #30, 0.43 #16) >> Best rule #67 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 01s73z; 07xyn1; >> query: (?x13900, 03w5xm) <- state_province_region(?x13900, ?x1755), contact_category(?x13900, ?x3231), citytown(?x13900, ?x4074), company(?x346, ?x13900), ?x346 = 060c4 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gy1_ contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.885 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #11780-02wk_43 PRED entity: 02wk_43 PRED relation: producer_type PRED expected values: 0ckd1 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.56 #1, 0.42 #3, 0.42 #4) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 06j0md; 02bvt; 03wh8pq; >> query: (?x10011, 0ckd1) <- award_nominee(?x10011, ?x7095), award_winner(?x2016, ?x10011), ?x7095 = 03wh8kl >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wk_43 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.556 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #11779-06x43v PRED entity: 06x43v PRED relation: film_crew_role PRED expected values: 0ch6mp2 02ynfr => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.78 #210, 0.74 #1172, 0.74 #244), 01pvkk (0.37 #214, 0.33 #248, 0.30 #78), 02ynfr (0.29 #14, 0.26 #218, 0.24 #48), 02rh1dz (0.25 #145, 0.22 #213, 0.22 #77), 04pyp5 (0.19 #49, 0.09 #459, 0.08 #390), 01xy5l_ (0.15 #216, 0.15 #560, 0.14 #148), 0d2b38 (0.15 #262, 0.13 #296, 0.13 #330), 0215hd (0.14 #17, 0.13 #1183, 0.12 #1873), 015h31 (0.14 #8, 0.13 #452, 0.11 #246), 089g0h (0.14 #18, 0.10 #256, 0.10 #1770) >> Best rule #210 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 048yqf; >> query: (?x7514, 0ch6mp2) <- prequel(?x7514, ?x4038), executive_produced_by(?x7514, ?x12790), language(?x7514, ?x254), film_crew_role(?x7514, ?x137) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 06x43v film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 93.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 06x43v film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11778-02f4s3 PRED entity: 02f4s3 PRED relation: colors PRED expected values: 083jv => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 20): 01l849 (0.40 #61, 0.35 #41, 0.27 #161), 083jv (0.38 #162, 0.38 #862, 0.37 #422), 01g5v (0.32 #164, 0.28 #64, 0.27 #1204), 019sc (0.19 #348, 0.18 #1088, 0.18 #208), 06fvc (0.19 #163, 0.17 #143, 0.17 #343), 036k5h (0.15 #106, 0.14 #6, 0.09 #346), 04d18d (0.14 #19, 0.05 #39, 0.04 #59), 038hg (0.11 #352, 0.10 #32, 0.09 #872), 0jc_p (0.10 #265, 0.10 #205, 0.10 #285), 09ggk (0.10 #36, 0.09 #136, 0.07 #96) >> Best rule #61 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 04bfg; >> query: (?x9676, 01l849) <- institution(?x9054, ?x9676), institution(?x620, ?x9676), colors(?x9676, ?x7179), ?x620 = 07s6fsf, ?x9054 = 022h5x >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #162 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 95 *> proper extension: 02yr3z; 01fsv9; 02hp6p; *> query: (?x9676, 083jv) <- institution(?x620, ?x9676), currency(?x9676, ?x170), ?x620 = 07s6fsf, colors(?x9676, ?x7179) *> conf = 0.38 ranks of expected_values: 2 EVAL 02f4s3 colors 083jv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 128.000 0.400 http://example.org/education/educational_institution/colors #11777-01jswq PRED entity: 01jswq PRED relation: school! PRED expected values: 0wsr 04wmvz => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 90): 0jmj7 (0.76 #1919, 0.74 #2459, 0.69 #5159), 02d02 (0.33 #157, 0.25 #517, 0.18 #337), 04wmvz (0.33 #166, 0.25 #526, 0.17 #976), 05tfm (0.33 #105, 0.18 #285, 0.17 #465), 01y49 (0.33 #111, 0.17 #471, 0.10 #741), 05m_8 (0.26 #2254, 0.21 #2974, 0.20 #2704), 051vz (0.23 #2273, 0.19 #2723, 0.18 #2453), 0cqt41 (0.18 #287, 0.17 #107, 0.15 #2268), 01yjl (0.18 #300, 0.17 #120, 0.13 #2281), 05g49 (0.18 #314, 0.17 #134, 0.11 #2295) >> Best rule #1919 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 05zl0; 01nhgd; >> query: (?x2711, 0jmj7) <- institution(?x865, ?x2711), major_field_of_study(?x2711, ?x6859), school(?x700, ?x2711), ?x6859 = 01tbp >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 02l424; *> query: (?x2711, 04wmvz) <- school_type(?x2711, ?x3092), time_zones(?x2711, ?x2674), institution(?x865, ?x2711), currency(?x2711, ?x170), school(?x1883, ?x2711) *> conf = 0.33 ranks of expected_values: 3, 33 EVAL 01jswq school! 04wmvz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 178.000 178.000 0.763 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01jswq school! 0wsr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 178.000 178.000 0.763 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #11776-011yhm PRED entity: 011yhm PRED relation: nominated_for! PRED expected values: 02qyp19 0gqy2 02qyntr => 82 concepts (68 used for prediction) PRED predicted values (max 10 best out of 254): 02qyp19 (0.73 #223, 0.71 #1, 0.57 #445), 02x4wr9 (0.68 #11079, 0.68 #11304, 0.68 #10633), 02x4x18 (0.68 #11079, 0.68 #11304, 0.68 #10633), 027b9ly (0.68 #11079, 0.68 #11304, 0.68 #10633), 09d28z (0.68 #11079, 0.68 #11304, 0.68 #10633), 02z1nbg (0.68 #11079, 0.68 #11304, 0.68 #10633), 027c924 (0.68 #11079, 0.68 #11304, 0.68 #10633), 027b9k6 (0.68 #11079, 0.68 #11304, 0.68 #10633), 02wypbh (0.68 #11079, 0.68 #11304, 0.68 #10633), 02qyntr (0.52 #163, 0.50 #385, 0.43 #607) >> Best rule #223 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 011ywj; >> query: (?x6553, 02qyp19) <- award(?x6553, ?x3435), ?x3435 = 03hl6lc, nominated_for(?x198, ?x6553) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10, 15 EVAL 011yhm nominated_for! 02qyntr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 82.000 68.000 0.727 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 011yhm nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 82.000 68.000 0.727 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 011yhm nominated_for! 02qyp19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 68.000 0.727 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11775-01f2q5 PRED entity: 01f2q5 PRED relation: award PRED expected values: 01d38g => 73 concepts (70 used for prediction) PRED predicted values (max 10 best out of 238): 01by1l (0.35 #2118, 0.31 #4925, 0.30 #915), 01d38g (0.35 #2033, 0.19 #17245, 0.18 #19251), 01ckcd (0.34 #3141, 0.24 #3943, 0.20 #334), 01bgqh (0.34 #2048, 0.24 #845, 0.23 #8464), 01c9jp (0.28 #2998, 0.20 #3800, 0.13 #2196), 03qbh5 (0.28 #2211, 0.20 #206, 0.19 #1008), 026mfs (0.27 #932, 0.20 #130, 0.20 #1333), 09sb52 (0.25 #20496, 0.24 #21298, 0.20 #18489), 01c427 (0.24 #2090, 0.19 #2892, 0.13 #23263), 01cky2 (0.24 #2201, 0.19 #17245, 0.18 #19251) >> Best rule #2118 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 01f9zw; >> query: (?x11897, 01by1l) <- award(?x11897, ?x3937), artists(?x3928, ?x11897), ?x3928 = 0gywn >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #2033 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 114 *> proper extension: 01f9zw; *> query: (?x11897, 01d38g) <- award(?x11897, ?x3937), artists(?x3928, ?x11897), ?x3928 = 0gywn *> conf = 0.35 ranks of expected_values: 2 EVAL 01f2q5 award 01d38g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 70.000 0.353 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11774-0kx4m PRED entity: 0kx4m PRED relation: production_companies! PRED expected values: 0dtfn => 129 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1519): 0drnwh (0.33 #1887, 0.16 #15544, 0.14 #18958), 04t6fk (0.33 #1431, 0.14 #18502, 0.14 #8259), 07y9w5 (0.33 #1294, 0.14 #8122, 0.12 #11536), 09g8vhw (0.23 #5910, 0.21 #8186, 0.18 #2496), 047d21r (0.23 #6097, 0.21 #8373, 0.18 #2683), 0cc5qkt (0.23 #40974, 0.20 #48942, 0.17 #1532), 0jqn5 (0.23 #40974, 0.20 #48942, 0.17 #1289), 0dtfn (0.23 #40974, 0.20 #48942, 0.09 #27316), 01cssf (0.21 #8029, 0.19 #11443, 0.18 #2339), 08984j (0.21 #8746, 0.19 #12160, 0.18 #3056) >> Best rule #1887 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01j53q; >> query: (?x847, 0drnwh) <- award_winner(?x847, ?x846), country(?x847, ?x94), state_province_region(?x847, ?x1227) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #40974 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 54 *> proper extension: 02zc7f; *> query: (?x847, ?x1386) <- company(?x1387, ?x847), award_winner(?x1386, ?x1387) *> conf = 0.23 ranks of expected_values: 8 EVAL 0kx4m production_companies! 0dtfn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 129.000 91.000 0.333 http://example.org/film/film/production_companies #11773-04vr_f PRED entity: 04vr_f PRED relation: nominated_for! PRED expected values: 099jhq 02n9nmz 04kxsb => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 195): 027cyf7 (0.69 #1528, 0.68 #1309, 0.67 #4146), 0p9sw (0.45 #17, 0.25 #6985, 0.24 #3508), 0f4x7 (0.36 #22, 0.28 #9390, 0.26 #3513), 04kxsb (0.36 #81, 0.28 #9390, 0.25 #8951), 0gs96 (0.36 #76, 0.22 #3567, 0.19 #4003), 02r22gf (0.36 #24, 0.19 #1333, 0.17 #3515), 02hsq3m (0.36 #25, 0.17 #1334, 0.16 #1553), 0gq_v (0.31 #3507, 0.27 #16, 0.27 #3943), 02n9nmz (0.28 #9390, 0.25 #8951, 0.25 #6985), 09qrn4 (0.28 #9390, 0.25 #8951, 0.25 #6985) >> Best rule #1528 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 026njb5; 04lqvlr; 07l50vn; >> query: (?x1135, ?x384) <- film_release_distribution_medium(?x1135, ?x81), film_format(?x1135, ?x909), award(?x1135, ?x384) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #81 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0ch26b_; 07cyl; 0dl9_4; 049xgc; 01srq2; *> query: (?x1135, 04kxsb) <- award_winner(?x1135, ?x382), nominated_for(?x3910, ?x1135), ?x3910 = 01tc9r *> conf = 0.36 ranks of expected_values: 4, 9, 53 EVAL 04vr_f nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 80.000 80.000 0.689 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04vr_f nominated_for! 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 80.000 80.000 0.689 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04vr_f nominated_for! 099jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 80.000 80.000 0.689 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11772-0crd8q6 PRED entity: 0crd8q6 PRED relation: film! PRED expected values: 023zsh => 94 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1046): 09yrh (0.73 #6234, 0.65 #70644, 0.65 #83116), 06ltr (0.25 #943, 0.07 #7178, 0.05 #3020), 0l6px (0.25 #385, 0.06 #2462, 0.06 #6620), 0134w7 (0.25 #159, 0.06 #6394, 0.05 #2236), 065jlv (0.25 #311, 0.06 #6546, 0.05 #2388), 09y20 (0.20 #246, 0.07 #6481, 0.05 #2323), 013_vh (0.20 #658, 0.06 #6893, 0.03 #2735), 05sq84 (0.15 #233, 0.06 #6468, 0.03 #2310), 03y_46 (0.15 #1014, 0.04 #7249, 0.03 #9326), 0162c8 (0.11 #62334) >> Best rule #6234 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 0g60z; 0180mw; >> query: (?x10191, ?x2108) <- nominated_for(?x3019, ?x10191), nominated_for(?x2108, ?x10191), nominated_for(?x1066, ?x10191), participant(?x540, ?x2108) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #14131 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 105 *> proper extension: 0ds35l9; 03qcfvw; 0gtsx8c; 01gc7; 0dq626; 0czyxs; 01k1k4; 0gtv7pk; 0ds11z; 0cpllql; ... *> query: (?x10191, 023zsh) <- film(?x382, ?x10191), film_crew_role(?x10191, ?x137), production_companies(?x10191, ?x2549), film_distribution_medium(?x10191, ?x2099) *> conf = 0.02 ranks of expected_values: 440 EVAL 0crd8q6 film! 023zsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 94.000 43.000 0.730 http://example.org/film/actor/film./film/performance/film #11771-03177r PRED entity: 03177r PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.80 #176, 0.77 #830, 0.75 #108), 01pvkk (0.33 #214, 0.31 #283, 0.29 #2238), 02rh1dz (0.29 #77, 0.25 #9, 0.17 #1072), 04pyp5 (0.29 #83, 0.17 #219, 0.17 #49), 015h31 (0.25 #110, 0.25 #8, 0.22 #144), 0d2b38 (0.25 #126, 0.25 #24, 0.20 #194), 0215hd (0.25 #119, 0.25 #17, 0.20 #187), 02zdwq (0.25 #123, 0.25 #21, 0.20 #191), 02_n3z (0.25 #1, 0.17 #35, 0.14 #69), 0263ycg (0.25 #16, 0.17 #50, 0.14 #84) >> Best rule #176 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0g57wgv; >> query: (?x2869, 0ch6mp2) <- genre(?x2869, ?x600), film(?x1549, ?x2869), film(?x981, ?x2869), award_winner(?x112, ?x1549), ?x981 = 0134w7 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03177r film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11770-03ywyk PRED entity: 03ywyk PRED relation: place_of_birth PRED expected values: 0rnmy => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 110): 0rnmy (0.33 #74687, 0.29 #74686, 0.28 #16915), 0ftvz (0.33 #74687, 0.29 #74686, 0.28 #16915), 02_286 (0.11 #43699, 0.10 #21864, 0.10 #725), 0r0ss (0.08 #518, 0.02 #3341, 0.02 #5454), 030qb3t (0.08 #3582, 0.07 #7106, 0.07 #2877), 0cr3d (0.07 #1505, 0.05 #19122, 0.05 #800), 0281s1 (0.04 #288, 0.02 #994, 0.02 #1699), 071vr (0.04 #258, 0.02 #964, 0.02 #1669), 013n0n (0.04 #513, 0.02 #1219, 0.02 #1924), 0t0n5 (0.04 #217, 0.02 #923, 0.02 #1628) >> Best rule #74687 for best value: >> intensional similarity = 2 >> extensional distance = 2264 >> proper extension: 07m69t; >> query: (?x9232, ?x2624) <- location(?x9232, ?x2624), place_of_birth(?x3594, ?x2624) >> conf = 0.33 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 03ywyk place_of_birth 0rnmy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.331 http://example.org/people/person/place_of_birth #11769-02pb53 PRED entity: 02pb53 PRED relation: gender PRED expected values: 05zppz => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #49, 0.89 #55, 0.88 #53), 02zsn (0.36 #6, 0.30 #116, 0.30 #112) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 174 >> proper extension: 099bk; 04107; 07c37; 03j90; >> query: (?x1726, 05zppz) <- influenced_by(?x1725, ?x1726), influenced_by(?x1726, ?x4554), student(?x122, ?x1726) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pb53 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.909 http://example.org/people/person/gender #11768-0h2zvzr PRED entity: 0h2zvzr PRED relation: film! PRED expected values: 06j8wx => 66 concepts (36 used for prediction) PRED predicted values (max 10 best out of 849): 04cbtrw (0.49 #8328, 0.44 #60373, 0.43 #52044), 016z2j (0.11 #2471, 0.10 #4553, 0.06 #14962), 0d0xs5 (0.11 #56208, 0.10 #16656, 0.09 #2082), 0f0kz (0.10 #516, 0.10 #4680, 0.09 #2598), 03ym1 (0.10 #1014, 0.05 #7260, 0.05 #9342), 079vf (0.09 #2090, 0.08 #4172, 0.07 #14581), 0f5xn (0.08 #971, 0.07 #3053, 0.06 #5135), 0c9xjl (0.08 #973, 0.07 #3055, 0.06 #5137), 01chc7 (0.08 #560, 0.07 #2642, 0.06 #4724), 02w29z (0.08 #1415, 0.06 #9743, 0.05 #11824) >> Best rule #8328 for best value: >> intensional similarity = 5 >> extensional distance = 78 >> proper extension: 0h3xztt; 01fmys; 030z4z; >> query: (?x8381, ?x2934) <- film_release_region(?x8381, ?x2146), ?x2146 = 03rk0, nominated_for(?x2934, ?x8381), language(?x8381, ?x254), film(?x1445, ?x8381) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #46762 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 331 *> proper extension: 0cq8nx; *> query: (?x8381, 06j8wx) <- country(?x8381, ?x512), language(?x8381, ?x254), genre(?x8381, ?x53), ?x512 = 07ssc *> conf = 0.02 ranks of expected_values: 615 EVAL 0h2zvzr film! 06j8wx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 66.000 36.000 0.486 http://example.org/film/actor/film./film/performance/film #11767-048lv PRED entity: 048lv PRED relation: award_winner! PRED expected values: 057xs89 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 211): 04kxsb (0.36 #31969, 0.34 #18752, 0.34 #17473), 09qv_s (0.36 #31969, 0.34 #18752, 0.34 #17473), 02x73k6 (0.36 #31969, 0.34 #18752, 0.34 #17473), 09sdmz (0.36 #31969, 0.34 #18752, 0.34 #17473), 0cqh46 (0.36 #31969, 0.34 #18752, 0.34 #17473), 0bdwqv (0.36 #31969, 0.34 #18752, 0.34 #17473), 02x4w6g (0.36 #31969, 0.34 #18752, 0.34 #17473), 0789_m (0.36 #31969, 0.34 #18752, 0.34 #17473), 04g2jz2 (0.36 #31969, 0.34 #18752, 0.34 #17473), 0gqyl (0.25 #102, 0.09 #31116, 0.06 #19605) >> Best rule #31969 for best value: >> intensional similarity = 2 >> extensional distance = 2276 >> proper extension: 089tm; 01pfr3; 02mslq; 01v0sx2; 01vsxdm; 01wv9xn; 0cg9y; 0hwd8; 0frsw; 016fmf; ... >> query: (?x1384, ?x458) <- award(?x1384, ?x458), award_winner(?x451, ?x1384) >> conf = 0.36 => this is the best rule for 9 predicted values *> Best rule #21310 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1577 *> proper extension: 06jntd; *> query: (?x1384, ?x68) <- award_winner(?x9452, ?x1384), nominated_for(?x68, ?x9452) *> conf = 0.07 ranks of expected_values: 50 EVAL 048lv award_winner! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 91.000 91.000 0.364 http://example.org/award/award_category/winners./award/award_honor/award_winner #11766-0f4vx0 PRED entity: 0f4vx0 PRED relation: school PRED expected values: 07t90 01jszm 0dzst 01jpqb => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1446): 06pwq (0.67 #1165, 0.56 #973, 0.54 #1443), 01jpyb (0.56 #973, 0.50 #937, 0.35 #1069), 019dwp (0.56 #973, 0.40 #1014, 0.35 #1069), 01ptt7 (0.56 #973, 0.40 #986, 0.35 #1069), 049dk (0.56 #973, 0.40 #984, 0.33 #362), 02y9bj (0.56 #973, 0.33 #677, 0.33 #585), 0jkhr (0.56 #973, 0.33 #675, 0.33 #408), 02482c (0.56 #973, 0.33 #423, 0.31 #263), 08qnnv (0.56 #973, 0.33 #404, 0.31 #263), 02t4yc (0.56 #973, 0.33 #376, 0.31 #263) >> Best rule #1165 for best value: >> intensional similarity = 52 >> extensional distance = 10 >> proper extension: 02x2khw; 02pq_rp; 02z6872; 02pq_x5; >> query: (?x4979, 06pwq) <- draft(?x12141, ?x4979), draft(?x8228, ?x4979), draft(?x2820, ?x4979), draft(?x2398, ?x4979), draft(?x1347, ?x4979), school(?x4979, ?x6953), school(?x4979, ?x6271), school(?x4979, ?x5357), school(?x4979, ?x3513), school(?x4979, ?x546), colors(?x2398, ?x663), school(?x10600, ?x6953), school(?x12141, ?x3948), team(?x1348, ?x12141), teams(?x5381, ?x1347), major_field_of_study(?x6953, ?x3213), major_field_of_study(?x6271, ?x947), team(?x8996, ?x1347), team(?x4747, ?x2398), student(?x3513, ?x12436), student(?x3513, ?x7824), ?x3948 = 025v3k, institution(?x865, ?x5357), state_province_region(?x5357, ?x2982), film(?x12436, ?x650), school(?x2174, ?x6953), ?x2174 = 051vz, ?x10600 = 04f4z1k, organization(?x5510, ?x6271), colors(?x8228, ?x5325), type_of_union(?x12436, ?x566), school(?x2820, ?x13736), school(?x2820, ?x13680), school(?x2820, ?x3021), school(?x2820, ?x2760), award_winner(?x691, ?x7824), ?x5325 = 03vtbc, company(?x6010, ?x2820), organization(?x6271, ?x5487), school_type(?x3021, ?x3205), fraternities_and_sororities(?x13736, ?x3697), award_nominee(?x690, ?x7824), currency(?x3021, ?x170), contains(?x94, ?x13736), category(?x6953, ?x134), major_field_of_study(?x546, ?x10046), student(?x5357, ?x7732), state_province_region(?x13680, ?x177), institution(?x734, ?x2760), student(?x6953, ?x117), ?x734 = 04zx3q1, ?x10046 = 041y2 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #262 for first EXPECTED value: *> intensional similarity = 53 *> extensional distance = 1 *> proper extension: 02qw1zx; *> query: (?x4979, ?x7707) <- draft(?x12141, ?x4979), draft(?x9931, ?x4979), draft(?x4571, ?x4979), draft(?x2398, ?x4979), draft(?x1347, ?x4979), school(?x4979, ?x6973), school(?x4979, ?x6953), school(?x4979, ?x6333), school(?x4979, ?x6271), school(?x4979, ?x2171), school(?x4979, ?x2152), school(?x4979, ?x1884), colors(?x2398, ?x663), school(?x12141, ?x7707), team(?x1348, ?x12141), teams(?x5381, ?x1347), major_field_of_study(?x6953, ?x3213), ?x6271 = 015q1n, institution(?x1771, ?x6953), institution(?x1305, ?x6953), ?x6973 = 05x_5, ?x1771 = 019v9k, teams(?x6769, ?x4571), teams(?x2850, ?x9931), contact_category(?x6333, ?x897), category(?x6953, ?x134), student(?x6333, ?x5350), major_field_of_study(?x6333, ?x742), ?x1884 = 0bx8pn, team(?x13926, ?x9931), fraternities_and_sororities(?x6953, ?x3697), teams(?x4733, ?x2398), school(?x13914, ?x6953), school(?x1010, ?x6953), student(?x7707, ?x1145), ?x3697 = 0325pb, institution(?x1519, ?x7707), contains(?x2624, ?x6953), ?x6769 = 0f2tj, team(?x9070, ?x12141), ?x1519 = 013zdg, location(?x4836, ?x2624), county_seat(?x11670, ?x5381), student(?x6953, ?x117), place_of_birth(?x1896, ?x5381), currency(?x6333, ?x170), origin(?x2807, ?x5381), state_province_region(?x2171, ?x1767), dog_breed(?x5381, ?x1706), ?x1305 = 02mjs7, sport(?x13914, ?x4833), season(?x1010, ?x701), time_zones(?x2152, ?x2864) *> conf = 0.39 ranks of expected_values: 20, 35, 39, 41 EVAL 0f4vx0 school 01jpqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 17.000 17.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 0f4vx0 school 0dzst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 17.000 17.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 0f4vx0 school 01jszm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 17.000 17.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 0f4vx0 school 07t90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 17.000 17.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #11765-033_1p PRED entity: 033_1p PRED relation: type_of_union PRED expected values: 04ztj => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.95 #255, 0.94 #316, 0.94 #310) >> Best rule #255 for best value: >> intensional similarity = 3 >> extensional distance = 1860 >> proper extension: 067xw; 0k57l; 0835q; 06yj20; >> query: (?x10002, 04ztj) <- type_of_union(?x10002, ?x1873), nationality(?x10002, ?x94), ?x94 = 09c7w0 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033_1p type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.951 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11764-0jbk9 PRED entity: 0jbk9 PRED relation: company! PRED expected values: 0dq_5 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 35): 0dq_5 (0.92 #2115, 0.90 #1841, 0.81 #2295), 0krdk (0.84 #2605, 0.74 #2284, 0.72 #2650), 060c4 (0.66 #2829, 0.66 #2923, 0.66 #2876), 0dq3c (0.58 #1962, 0.57 #2279, 0.56 #2325), 09d6p2 (0.57 #2006, 0.57 #2525, 0.49 #2116), 01yc02 (0.57 #2006, 0.50 #101, 0.45 #1832), 01kr6k (0.57 #2006, 0.27 #2323, 0.25 #2369), 04192r (0.27 #2323, 0.25 #2369, 0.25 #132), 02211by (0.27 #2323, 0.25 #2369, 0.24 #2552), 02y6fz (0.27 #2323, 0.25 #2369, 0.24 #2552) >> Best rule #2115 for best value: >> intensional similarity = 13 >> extensional distance = 35 >> proper extension: 087c7; 02bh8z; 02630g; 018_q8; 0z90c; 03y7ml; 01_4lx; 01npw8; >> query: (?x958, 0dq_5) <- company(?x6403, ?x958), company(?x4792, ?x958), ?x4792 = 05_wyz, company(?x6403, ?x12122), company(?x6403, ?x10652), company(?x6403, ?x9968), company(?x6403, ?x5789), ?x5789 = 0jvs0, citytown(?x10652, ?x674), ?x674 = 0f2r6, ?x9968 = 0k9ts, company(?x966, ?x12122), organizations_founded(?x3563, ?x12122) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jbk9 company! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.919 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #11763-09dv8h PRED entity: 09dv8h PRED relation: film! PRED expected values: 093h7p => 135 concepts (80 used for prediction) PRED predicted values (max 10 best out of 127): 03xq0f (0.61 #2152, 0.58 #3193, 0.57 #2227), 01795t (0.60 #92, 0.45 #684, 0.29 #314), 017s11 (0.40 #2745, 0.24 #595, 0.20 #151), 04mkft (0.40 #554, 0.33 #480, 0.33 #36), 054g1r (0.33 #479, 0.30 #553, 0.29 #257), 086k8 (0.33 #2, 0.22 #446, 0.20 #520), 016tt2 (0.25 #2746, 0.15 #670, 0.14 #300), 05qd_ (0.25 #3272, 0.24 #2751, 0.18 #3717), 016tw3 (0.23 #825, 0.18 #1417, 0.17 #1565), 06jntd (0.20 #179, 0.14 #327, 0.14 #253) >> Best rule #2152 for best value: >> intensional similarity = 5 >> extensional distance = 69 >> proper extension: 07kb7vh; >> query: (?x6614, 03xq0f) <- film_distribution_medium(?x6614, ?x81), film(?x5058, ?x6614), participant(?x5058, ?x6666), currency(?x5058, ?x170), award_nominee(?x1871, ?x5058) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #130 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 0b60sq; *> query: (?x6614, 093h7p) <- film(?x13952, ?x6614), genre(?x6614, ?x6459), genre(?x6614, ?x239), film_release_region(?x6614, ?x94), genre(?x1810, ?x239), ?x6459 = 0bj8m2, ?x1810 = 02f6g5 *> conf = 0.20 ranks of expected_values: 12 EVAL 09dv8h film! 093h7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 135.000 80.000 0.606 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #11762-03gdf1 PRED entity: 03gdf1 PRED relation: contains! PRED expected values: 03rk0 => 176 concepts (95 used for prediction) PRED predicted values (max 10 best out of 197): 09c7w0 (0.79 #54651, 0.75 #68988, 0.74 #48374), 086g2 (0.78 #67189, 0.78 #63605, 0.76 #49267), 03rk0 (0.76 #12539, 0.67 #3720, 0.62 #68086), 02jx1 (0.59 #67276, 0.38 #9042, 0.29 #5461), 04jpl (0.57 #8977, 0.21 #34059, 0.15 #60045), 01n7q (0.42 #73541, 0.16 #11720, 0.14 #4556), 07ssc (0.41 #67221, 0.24 #8987, 0.18 #7197), 059rby (0.36 #60043, 0.31 #73483, 0.16 #10767), 0bq0p9 (0.35 #9851), 06q1r (0.29 #5726, 0.25 #1248, 0.20 #3039) >> Best rule #54651 for best value: >> intensional similarity = 5 >> extensional distance = 326 >> proper extension: 06xpp7; 05p7tx; 02sdwt; 05bjp6; >> query: (?x11798, 09c7w0) <- student(?x11798, ?x6741), contains(?x8297, ?x11798), place_of_birth(?x2385, ?x8297), place_of_death(?x8296, ?x8297), location_of_ceremony(?x566, ?x8297) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #12539 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 103 *> proper extension: 04s934; 05nrkb; 01w3vc; *> query: (?x11798, ?x2146) <- student(?x11798, ?x6741), citytown(?x11798, ?x8297), state_province_region(?x11798, ?x12420), location_of_ceremony(?x566, ?x8297), administrative_parent(?x12420, ?x2146) *> conf = 0.76 ranks of expected_values: 3 EVAL 03gdf1 contains! 03rk0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 176.000 95.000 0.793 http://example.org/location/location/contains #11761-02bft PRED entity: 02bft PRED relation: notable_people_with_this_condition PRED expected values: 0rlz => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 89): 04n65n (0.33 #200, 0.09 #2215, 0.05 #3344), 0205dx (0.33 #182, 0.09 #2197, 0.05 #3326), 05wh0sh (0.33 #165, 0.09 #2180, 0.05 #3309), 032l1 (0.33 #164, 0.09 #2179, 0.05 #3308), 01vvycq (0.33 #136, 0.09 #2151, 0.05 #3280), 0m2l9 (0.33 #130, 0.09 #2145, 0.05 #3274), 034rd (0.17 #1323, 0.11 #1959, 0.09 #2205), 0zm1 (0.17 #1308, 0.11 #1944, 0.09 #2190), 037s5h (0.14 #1482, 0.12 #1736, 0.12 #1614), 017yxq (0.09 #2224) >> Best rule #200 for best value: >> intensional similarity = 27 >> extensional distance = 1 >> proper extension: 02vrr; >> query: (?x6483, 04n65n) <- risk_factors(?x6483, ?x4322), people(?x4322, ?x12010), people(?x4322, ?x9964), people(?x4322, ?x7863), people(?x4322, ?x6768), people(?x4322, ?x6073), people(?x4322, ?x5132), people(?x4322, ?x3690), people(?x4322, ?x767), place_of_death(?x3690, ?x1523), risk_factors(?x4322, ?x231), profession(?x767, ?x524), award_winner(?x1821, ?x12010), type_of_union(?x7863, ?x566), basic_title(?x6768, ?x265), music(?x6722, ?x9964), award_winner(?x6869, ?x5132), location(?x7863, ?x1131), award_winner(?x198, ?x767), instrumentalists(?x316, ?x5132), award(?x12010, ?x484), award_winner(?x1822, ?x12010), location(?x6073, ?x512), participant(?x9356, ?x6073), award(?x7863, ?x1232), category(?x767, ?x134), nominated_for(?x767, ?x197) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02bft notable_people_with_this_condition 0rlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 43.000 0.333 http://example.org/medicine/disease/notable_people_with_this_condition #11760-02_nkp PRED entity: 02_nkp PRED relation: type_of_union PRED expected values: 04ztj => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.95 #34, 0.95 #130, 0.95 #356) >> Best rule #34 for best value: >> intensional similarity = 5 >> extensional distance = 36 >> proper extension: 0tc7; >> query: (?x12607, 04ztj) <- student(?x4341, ?x12607), gender(?x12607, ?x231), athlete(?x4833, ?x12607), ?x231 = 05zppz, type_of_union(?x12607, ?x1873) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_nkp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.947 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11759-012vd6 PRED entity: 012vd6 PRED relation: award PRED expected values: 099vwn => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 279): 03x3wf (0.72 #24351, 0.70 #17162, 0.70 #16762), 01bgqh (0.50 #441, 0.23 #2037, 0.22 #12813), 09sb52 (0.32 #13210, 0.23 #16003, 0.21 #18001), 01by1l (0.28 #12883, 0.25 #511, 0.25 #4502), 03qbh5 (0.25 #603, 0.22 #2199, 0.20 #1002), 02f716 (0.25 #575, 0.18 #20755, 0.15 #21554), 05q8pss (0.25 #611, 0.18 #20755, 0.15 #21554), 01ckcd (0.25 #2327, 0.17 #3525, 0.15 #1928), 02f72n (0.25 #544, 0.17 #2140, 0.11 #2938), 02f5qb (0.25 #554, 0.15 #2150, 0.12 #1751) >> Best rule #24351 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 099ks0; >> query: (?x5310, ?x567) <- award_winner(?x567, ?x5310), award(?x568, ?x567) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #3193 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 0hnlx; 07yg2; 06c44; 01w9ph_; 0qmny; 070b4; 06lxn; *> query: (?x5310, ?x401) <- influenced_by(?x2138, ?x5310), artists(?x505, ?x5310), award(?x2138, ?x401) *> conf = 0.11 ranks of expected_values: 58 EVAL 012vd6 award 099vwn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 74.000 74.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11758-0d35y PRED entity: 0d35y PRED relation: dog_breed PRED expected values: 01t032 => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 1): 01t032 (0.88 #10, 0.84 #22, 0.83 #32) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 019fh; >> query: (?x4419, 01t032) <- place_of_death(?x12622, ?x4419), dog_breed(?x4419, ?x1706), contains(?x94, ?x4419) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d35y dog_breed 01t032 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.880 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #11757-045931 PRED entity: 045931 PRED relation: film PRED expected values: 09p4w8 => 62 concepts (9 used for prediction) PRED predicted values (max 10 best out of 485): 0c_j9x (0.38 #1784, 0.13 #12484, 0.01 #11070), 0p9lw (0.38 #1784, 0.13 #12484), 01q7h2 (0.25 #3354, 0.20 #1570, 0.02 #6920), 0hvvf (0.25 #3130, 0.20 #1346), 016z9n (0.25 #2150, 0.07 #3933, 0.02 #5716), 02v8kmz (0.25 #1811, 0.02 #8944, 0.01 #14295), 035_2h (0.25 #2697, 0.01 #13397), 051zy_b (0.20 #575, 0.12 #2359, 0.07 #4142), 0296rz (0.20 #1637, 0.12 #3421, 0.07 #5204), 0m9p3 (0.20 #384, 0.12 #2168, 0.03 #5734) >> Best rule #1784 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 09fb5; 01csvq; 039bp; >> query: (?x11741, ?x2345) <- film(?x11741, ?x2943), film(?x11741, ?x89), ?x2943 = 0c9k8, production_companies(?x89, ?x902), nominated_for(?x2345, ?x89) >> conf = 0.38 => this is the best rule for 2 predicted values *> Best rule #6175 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 244 *> proper extension: 02mjmr; 01wj92r; 0c2dl; 06m61; 0bvzp; 014dm6; 01q9b9; 051cc; 042kg; 0cj2w; ... *> query: (?x11741, 09p4w8) <- award_winner(?x2183, ?x11741), award(?x4969, ?x2183), ?x4969 = 016k6x *> conf = 0.01 ranks of expected_values: 363 EVAL 045931 film 09p4w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 9.000 0.375 http://example.org/film/actor/film./film/performance/film #11756-04ydr95 PRED entity: 04ydr95 PRED relation: film! PRED expected values: 017s11 => 79 concepts (38 used for prediction) PRED predicted values (max 10 best out of 109): 03xq0f (0.83 #871, 0.49 #655, 0.22 #583), 017s11 (0.56 #437, 0.43 #74, 0.19 #364), 05h4t7 (0.53 #2026), 016tt2 (0.29 #148, 0.29 #75, 0.22 #1087), 05qd_ (0.24 #731, 0.20 #1092, 0.19 #659), 017jv5 (0.20 #303, 0.14 #520, 0.12 #375), 01795t (0.20 #16, 0.11 #739, 0.09 #233), 04mkft (0.18 #251, 0.10 #613, 0.08 #685), 016tw3 (0.17 #1962, 0.15 #1093, 0.14 #2251), 0g1rw (0.14 #79, 0.09 #224, 0.06 #1960) >> Best rule #871 for best value: >> intensional similarity = 7 >> extensional distance = 162 >> proper extension: 0522wp; >> query: (?x3532, 03xq0f) <- film(?x963, ?x3532), film(?x963, ?x6394), film(?x963, ?x4352), film_festivals(?x6394, ?x2686), film_release_region(?x6394, ?x87), ?x2686 = 0gg7gsl, ?x4352 = 09v71cj >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #437 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 14 *> proper extension: 05pbl56; 04cj79; 05q7874; 02vzpb; *> query: (?x3532, 017s11) <- film_crew_role(?x3532, ?x2154), ?x2154 = 01vx2h, film(?x5391, ?x3532), film(?x7526, ?x3532), currency(?x3532, ?x170), ?x7526 = 03rwz3, award(?x5391, ?x7005) *> conf = 0.56 ranks of expected_values: 2 EVAL 04ydr95 film! 017s11 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 38.000 0.829 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #11755-01520h PRED entity: 01520h PRED relation: nationality PRED expected values: 09c7w0 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 45): 09c7w0 (0.84 #101, 0.75 #804, 0.75 #703), 0chghy (0.27 #2611, 0.04 #2915, 0.04 #1607), 03h64 (0.27 #2611, 0.04 #2915, 0.04 #1607), 0d05w3 (0.27 #2611, 0.01 #2258, 0.01 #2864), 06mzp (0.27 #2611), 0d060g (0.24 #5132, 0.12 #7, 0.11 #7448), 01xbgx (0.24 #5132), 0jgx (0.24 #5132), 02jx1 (0.14 #435, 0.14 #234, 0.12 #33), 07ssc (0.14 #216, 0.12 #417, 0.11 #7448) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 01vv6_6; 023n39; 0m76b; >> query: (?x6755, 09c7w0) <- student(?x9318, ?x6755), ?x9318 = 0fr9jp, location(?x6755, ?x1227) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01520h nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.842 http://example.org/people/person/nationality #11754-06hgym PRED entity: 06hgym PRED relation: location PRED expected values: 030qb3t => 102 concepts (97 used for prediction) PRED predicted values (max 10 best out of 166): 0n1rj (0.70 #39373, 0.69 #36157, 0.66 #3213), 030qb3t (0.32 #1689, 0.25 #2492, 0.24 #13741), 02_286 (0.22 #1643, 0.19 #35390, 0.18 #2446), 059rby (0.14 #18493, 0.06 #819, 0.05 #14477), 01n7q (0.12 #18540, 0.07 #1669, 0.04 #7295), 0vzm (0.11 #172, 0.04 #1778, 0.04 #2581), 04jpl (0.10 #1623, 0.08 #14478, 0.07 #13675), 0cr3d (0.07 #35497, 0.07 #39517, 0.06 #38713), 0cc56 (0.07 #1663, 0.05 #2466, 0.05 #4074), 05jbn (0.06 #1055, 0.06 #252, 0.02 #9091) >> Best rule #39373 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 02c4s; 07vfqj; 07m69t; 01qklj; 02d6n_; 04dyqk; >> query: (?x8376, ?x6084) <- place_of_birth(?x8376, ?x6084), location(?x8376, ?x2623) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1689 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 05m63c; *> query: (?x8376, 030qb3t) <- languages(?x8376, ?x254), location(?x8376, ?x2623), participant(?x8376, ?x12047) *> conf = 0.32 ranks of expected_values: 2 EVAL 06hgym location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 97.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #11753-0l98s PRED entity: 0l98s PRED relation: olympics! PRED expected values: 0jgd 03_r3 0ctw_b 05cgv 035dk => 61 concepts (45 used for prediction) PRED predicted values (max 10 best out of 215): 03rjj (0.83 #1754, 0.82 #3751, 0.82 #1650), 0d0vqn (0.82 #821, 0.80 #3329, 0.80 #718), 03_r3 (0.82 #825, 0.71 #417, 0.57 #316), 09c7w0 (0.78 #813, 0.78 #102, 0.75 #3748), 01mk6 (0.78 #813, 0.78 #102, 0.75 #586), 03spz (0.78 #813, 0.78 #102, 0.64 #2370), 0ctw_b (0.73 #1662, 0.73 #831, 0.70 #728), 0hzlz (0.64 #1661, 0.61 #1765, 0.60 #1140), 05r4w (0.64 #815, 0.57 #407, 0.38 #3848), 0d05w3 (0.64 #856, 0.57 #448, 0.38 #3848) >> Best rule #1754 for best value: >> intensional similarity = 14 >> extensional distance = 21 >> proper extension: 0blg2; >> query: (?x584, 03rjj) <- sports(?x584, ?x3015), sports(?x584, ?x2867), olympics(?x3015, ?x1931), country(?x3015, ?x4120), country(?x3015, ?x1497), country(?x3015, ?x291), country(?x3015, ?x172), sports(?x7688, ?x3015), ?x291 = 0h3y, ?x172 = 0154j, ?x4120 = 04gqr, ?x2867 = 02y8z, ?x1497 = 015qh, ?x7688 = 0jkvj >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #825 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 9 *> proper extension: 0l6ny; 0l6m5; 0kbws; *> query: (?x584, 03_r3) <- sports(?x584, ?x6150), sports(?x584, ?x3015), sports(?x584, ?x2978), ?x3015 = 071t0, olympics(?x5114, ?x584), olympics(?x985, ?x584), country(?x2978, ?x7032), country(?x2978, ?x6428), ?x7032 = 01c4pv, ?x985 = 0k6nt, ?x6428 = 0j4b, combatants(?x326, ?x5114), ?x6150 = 07_53 *> conf = 0.82 ranks of expected_values: 3, 7, 24, 27, 53 EVAL 0l98s olympics! 035dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 61.000 45.000 0.826 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l98s olympics! 05cgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 61.000 45.000 0.826 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l98s olympics! 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 61.000 45.000 0.826 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l98s olympics! 03_r3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 61.000 45.000 0.826 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l98s olympics! 0jgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 61.000 45.000 0.826 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #11752-0qf3p PRED entity: 0qf3p PRED relation: role PRED expected values: 02sgy => 155 concepts (110 used for prediction) PRED predicted values (max 10 best out of 122): 05r5c (0.47 #3170, 0.43 #1483, 0.42 #5487), 0342h (0.44 #1373, 0.43 #530, 0.39 #5483), 05148p4 (0.31 #3267, 0.30 #5585, 0.28 #7283), 03qjg (0.31 #3267, 0.30 #5585, 0.26 #843), 02sgy (0.27 #5485, 0.25 #6020, 0.23 #2431), 013y1f (0.24 #1512, 0.15 #775, 0.15 #3199), 05842k (0.23 #1028, 0.21 #605, 0.20 #3978), 018vs (0.22 #1383, 0.18 #540, 0.17 #3176), 01vj9c (0.22 #1385, 0.16 #6030, 0.16 #1491), 042v_gx (0.22 #5488, 0.22 #6023, 0.21 #535) >> Best rule #3170 for best value: >> intensional similarity = 4 >> extensional distance = 141 >> proper extension: 02pzc4; 05y7hc; 01l3mk3; 04n32; 03f4k; >> query: (?x2600, 05r5c) <- artists(?x302, ?x2600), instrumentalists(?x227, ?x2600), people(?x743, ?x2600), role(?x2600, ?x1437) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #5485 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 253 *> proper extension: 02rgz4; 01vvy; 0146pg; 0274ck; 0pcc0; 01vs14j; 0244r8; 02r4qs; 01kvqc; 01qkqwg; ... *> query: (?x2600, 02sgy) <- artists(?x302, ?x2600), type_of_union(?x2600, ?x566), instrumentalists(?x227, ?x2600), role(?x2600, ?x1437) *> conf = 0.27 ranks of expected_values: 5 EVAL 0qf3p role 02sgy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 155.000 110.000 0.469 http://example.org/music/artist/track_contributions./music/track_contribution/role #11751-03h_fqv PRED entity: 03h_fqv PRED relation: film PRED expected values: 016z43 => 152 concepts (117 used for prediction) PRED predicted values (max 10 best out of 976): 02b6n9 (0.15 #42650, 0.08 #3355, 0.03 #26574), 07bwr (0.15 #41947, 0.04 #18726, 0.03 #27657), 0bpbhm (0.15 #2463, 0.10 #41758, 0.02 #31041), 03cffvv (0.15 #3526, 0.05 #16028, 0.03 #39249), 03p2xc (0.15 #3029, 0.03 #42324, 0.01 #40538), 06929s (0.15 #28578, 0.08 #19647, 0.03 #73232), 095zlp (0.14 #41141, 0.08 #1846), 02pg45 (0.13 #6287, 0.12 #8073, 0.10 #17003), 03nfnx (0.13 #4972, 0.11 #13902, 0.07 #21047), 0888c3 (0.13 #28203, 0.02 #72857, 0.02 #37134) >> Best rule #42650 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 04t7ts; 01fwj8; 012q4n; 02pby8; 032wdd; 01vh3r; >> query: (?x5391, 02b6n9) <- film(?x5391, ?x4009), nominated_for(?x5097, ?x4009), ?x5097 = 01kb2j >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #28557 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 07y8l9; *> query: (?x5391, 016z43) <- film(?x5391, ?x1481), music(?x4312, ?x5391), profession(?x5391, ?x220) *> conf = 0.03 ranks of expected_values: 395 EVAL 03h_fqv film 016z43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 152.000 117.000 0.155 http://example.org/film/actor/film./film/performance/film #11750-02qydsh PRED entity: 02qydsh PRED relation: film! PRED expected values: 0bksh 0p__8 0kftt => 55 concepts (33 used for prediction) PRED predicted values (max 10 best out of 902): 06x58 (0.29 #10677, 0.25 #4451, 0.14 #2376), 0bksh (0.27 #9155, 0.25 #854, 0.18 #7080), 0p8r1 (0.26 #15112, 0.17 #19263, 0.07 #21338), 0p__8 (0.25 #1055, 0.18 #9356, 0.18 #7281), 0kftt (0.25 #1462, 0.18 #9763, 0.18 #7688), 0dpqk (0.25 #893, 0.18 #7119, 0.12 #5043), 0170pk (0.25 #281, 0.14 #2356, 0.12 #4431), 05txrz (0.25 #766, 0.12 #4916, 0.09 #9067), 02zfdp (0.25 #1562, 0.12 #5712, 0.09 #9863), 060j8b (0.25 #1102, 0.12 #5252, 0.09 #9403) >> Best rule #10677 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 02d413; 0946bb; 02rrfzf; 02pg45; 02p76f9; 025s1wg; >> query: (?x8794, 06x58) <- film(?x7040, ?x8794), film(?x6658, ?x8794), ?x6658 = 0436kgz, film(?x902, ?x8794), gender(?x7040, ?x231), location(?x7040, ?x1523) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #9155 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 031778; 03176f; 01l_pn; 09v3jyg; *> query: (?x8794, 0bksh) <- film(?x6658, ?x8794), film(?x4988, ?x8794), film(?x6658, ?x10060), ?x4988 = 041c4, film_crew_role(?x8794, ?x1966), award(?x10060, ?x143) *> conf = 0.27 ranks of expected_values: 2, 4, 5 EVAL 02qydsh film! 0kftt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 55.000 33.000 0.286 http://example.org/film/actor/film./film/performance/film EVAL 02qydsh film! 0p__8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 55.000 33.000 0.286 http://example.org/film/actor/film./film/performance/film EVAL 02qydsh film! 0bksh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 55.000 33.000 0.286 http://example.org/film/actor/film./film/performance/film #11749-0d6d2 PRED entity: 0d6d2 PRED relation: award_winner! PRED expected values: 0bc773 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 120): 05c1t6z (0.06 #14, 0.05 #152, 0.05 #428), 01s695 (0.05 #141, 0.05 #3, 0.04 #5661), 0gpjbt (0.05 #166, 0.04 #5686, 0.03 #28), 013b2h (0.05 #5736, 0.04 #4770, 0.04 #216), 0275n3y (0.05 #2833, 0.03 #7111, 0.03 #3661), 09gkdln (0.04 #2879, 0.03 #533, 0.03 #7157), 0466p0j (0.04 #5732, 0.04 #7112, 0.04 #4766), 09g90vz (0.04 #3571, 0.04 #2881, 0.04 #2743), 05pd94v (0.04 #5660, 0.04 #7040, 0.04 #4694), 02cg41 (0.04 #5781, 0.04 #7161, 0.03 #5367) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 02jyhv; >> query: (?x8151, 05c1t6z) <- gender(?x8151, ?x231), diet(?x8151, ?x3130), film(?x8151, ?x650) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #605 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 164 *> proper extension: 01w3v; 0mcf4; *> query: (?x8151, 0bc773) <- religion(?x8151, ?x7131), ?x7131 = 03_gx *> conf = 0.01 ranks of expected_values: 103 EVAL 0d6d2 award_winner! 0bc773 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 97.000 97.000 0.057 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11748-03y0pn PRED entity: 03y0pn PRED relation: film_release_region PRED expected values: 0chghy => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 130): 06mkj (0.35 #605, 0.29 #960, 0.27 #1670), 03_3d (0.33 #540, 0.28 #895, 0.26 #1605), 0jgd (0.33 #535, 0.27 #890, 0.25 #1423), 0f8l9c (0.33 #916, 0.32 #561, 0.29 #6767), 0chghy (0.31 #10995, 0.27 #547, 0.27 #902), 02jx1 (0.31 #10995), 0d0vqn (0.31 #542, 0.29 #6748, 0.29 #5682), 059j2 (0.31 #574, 0.28 #1639, 0.28 #1462), 05r4w (0.31 #533, 0.25 #888, 0.25 #5496), 05v8c (0.31 #554, 0.22 #1442, 0.22 #909) >> Best rule #605 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 01jrbb; 0dgq_kn; 05r3qc; 0bdjd; 07xvf; >> query: (?x7207, 06mkj) <- film(?x629, ?x7207), nominated_for(?x6860, ?x7207), ?x6860 = 018wdw >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #10995 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1780 *> proper extension: 0dr1c2; *> query: (?x7207, ?x390) <- film(?x1738, ?x7207), nationality(?x1738, ?x390) *> conf = 0.31 ranks of expected_values: 5 EVAL 03y0pn film_release_region 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 64.000 0.347 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11747-02js_6 PRED entity: 02js_6 PRED relation: languages PRED expected values: 02h40lc => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 12): 02h40lc (0.50 #80, 0.31 #470, 0.31 #626), 064_8sq (0.06 #444, 0.05 #483, 0.05 #93), 02bjrlw (0.03 #743, 0.03 #469, 0.03 #586), 03k50 (0.02 #3246, 0.02 #3480, 0.02 #3597), 0x82 (0.02 #310), 02bv9 (0.02 #293), 04306rv (0.02 #745, 0.01 #471, 0.01 #666), 0t_2 (0.01 #633, 0.01 #477, 0.01 #672), 06nm1 (0.01 #1099, 0.01 #318, 0.01 #1372), 0999q (0.01 #335) >> Best rule #80 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0d07j8; 033jkj; 071jv5; >> query: (?x12359, 02h40lc) <- award(?x12359, ?x102), location(?x12359, ?x682), ?x682 = 0f2wj >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02js_6 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.500 http://example.org/people/person/languages #11746-0chw_ PRED entity: 0chw_ PRED relation: award_winner! PRED expected values: 02z1nbg => 132 concepts (97 used for prediction) PRED predicted values (max 10 best out of 249): 0gqyl (0.37 #23383, 0.37 #30188, 0.34 #31890), 094qd5 (0.37 #23383, 0.37 #30188, 0.34 #31890), 02y_rq5 (0.32 #93, 0.07 #27211, 0.07 #31464), 099cng (0.26 #84, 0.03 #1359, 0.03 #934), 02z1nbg (0.21 #190, 0.06 #1465, 0.05 #15494), 027571b (0.21 #270, 0.03 #1545, 0.03 #15574), 0gs9p (0.18 #2627, 0.16 #1777, 0.07 #3477), 0ck27z (0.17 #9865, 0.10 #17946, 0.10 #15819), 019f4v (0.15 #1766, 0.15 #2616, 0.07 #27211), 09sb52 (0.14 #15769, 0.13 #8965, 0.13 #11941) >> Best rule #23383 for best value: >> intensional similarity = 3 >> extensional distance = 1255 >> proper extension: 0cnl80; 0770cd; 02778qt; 01w7nwm; 0884hk; 0h584v; 0h005; 07hhnl; 087v17; 05_swj; ... >> query: (?x9033, ?x618) <- gender(?x9033, ?x514), award_winner(?x78, ?x9033), award(?x9033, ?x618) >> conf = 0.37 => this is the best rule for 2 predicted values *> Best rule #190 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 01j5ts; 049g_xj; 01jmv8; 02x0dzw; *> query: (?x9033, 02z1nbg) <- gender(?x9033, ?x514), award_winner(?x2478, ?x9033), ?x2478 = 02x4x18 *> conf = 0.21 ranks of expected_values: 5 EVAL 0chw_ award_winner! 02z1nbg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 132.000 97.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner #11745-07jxpf PRED entity: 07jxpf PRED relation: film_crew_role PRED expected values: 0ch6mp2 01pvkk => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 22): 0ch6mp2 (0.84 #186, 0.79 #126, 0.74 #66), 01vx2h (0.41 #129, 0.38 #189, 0.30 #310), 01pvkk (0.35 #10, 0.30 #130, 0.29 #190), 02ynfr (0.22 #193, 0.18 #73, 0.17 #133), 0215hd (0.16 #195, 0.14 #15, 0.12 #135), 02_n3z (0.14 #1, 0.10 #181, 0.07 #1031), 015h31 (0.10 #128, 0.09 #98, 0.09 #309), 089fss (0.09 #125, 0.08 #185, 0.06 #761), 033smt (0.07 #22, 0.05 #202, 0.05 #82), 094hwz (0.06 #102, 0.03 #72, 0.03 #313) >> Best rule #186 for best value: >> intensional similarity = 5 >> extensional distance = 461 >> proper extension: 0fq27fp; >> query: (?x4118, 0ch6mp2) <- film_crew_role(?x4118, ?x1171), film_crew_role(?x4118, ?x137), ?x1171 = 09vw2b7, currency(?x4118, ?x170), ?x137 = 09zzb8 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 07jxpf film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.844 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 07jxpf film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.844 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11744-0r00l PRED entity: 0r00l PRED relation: citytown! PRED expected values: 03yxwq 0kc9f => 125 concepts (110 used for prediction) PRED predicted values (max 10 best out of 639): 020h2v (0.45 #3971, 0.41 #10331, 0.39 #8740), 01nds (0.25 #1360, 0.22 #2154, 0.13 #2948), 03_c8p (0.25 #1359, 0.11 #2153, 0.09 #2947), 01dtcb (0.19 #1172, 0.17 #1966, 0.13 #2760), 05cl8y (0.19 #1202, 0.11 #1996, 0.08 #5968), 0146mv (0.12 #1369, 0.11 #2163, 0.09 #2957), 06182p (0.12 #1182, 0.11 #1976, 0.09 #2770), 04f525m (0.12 #826, 0.11 #1620, 0.09 #2414), 0338lq (0.12 #819, 0.11 #1613, 0.09 #2407), 09glbnt (0.12 #1068, 0.11 #1862, 0.06 #5834) >> Best rule #3971 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 0r679; 06kx2; >> query: (?x11930, ?x2156) <- place_founded(?x2156, ?x11930), jurisdiction_of_office(?x1195, ?x11930), citytown(?x382, ?x11930) >> conf = 0.45 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r00l citytown! 0kc9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 110.000 0.448 http://example.org/organization/organization/headquarters./location/mailing_address/citytown EVAL 0r00l citytown! 03yxwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 110.000 0.448 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #11743-04dn09n PRED entity: 04dn09n PRED relation: nominated_for PRED expected values: 0661ql3 021y7yw 0320fn 09q23x 071nw5 0bj25 016yxn => 62 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1412): 04j13sx (0.79 #9956, 0.78 #25617, 0.78 #7110), 0y_9q (0.79 #9956, 0.78 #25617, 0.78 #7110), 0bj25 (0.79 #9956, 0.78 #25617, 0.78 #7110), 0f4_l (0.79 #9956, 0.78 #25617, 0.78 #7110), 011yg9 (0.79 #9956, 0.78 #25617, 0.78 #7110), 03hj3b3 (0.79 #9956, 0.78 #25617, 0.78 #7110), 011ypx (0.79 #9956, 0.78 #25617, 0.78 #7110), 05hjnw (0.79 #9956, 0.78 #25617, 0.78 #7110), 07s846j (0.79 #9956, 0.78 #25617, 0.78 #7110), 0hfzr (0.79 #9956, 0.78 #25617, 0.78 #7110) >> Best rule #9956 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 02y_rq5; 02qvyrt; 02ppm4q; 03hl6lc; >> query: (?x746, ?x144) <- award(?x144, ?x746), nominated_for(?x746, ?x5584), nominated_for(?x746, ?x2903), award(?x276, ?x746), ?x5584 = 0yyn5, film(?x166, ?x2903) >> conf = 0.79 => this is the best rule for 14 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 86, 135, 200, 312, 337, 465 EVAL 04dn09n nominated_for 016yxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 29.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04dn09n nominated_for 0bj25 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 29.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04dn09n nominated_for 071nw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 62.000 29.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04dn09n nominated_for 09q23x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 29.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04dn09n nominated_for 0320fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 29.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04dn09n nominated_for 021y7yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 29.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04dn09n nominated_for 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 62.000 29.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11742-0mmpm PRED entity: 0mmpm PRED relation: time_zones PRED expected values: 02lcqs => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 10): 02lcqs (0.83 #983, 0.82 #70, 0.82 #105), 02hcv8 (0.56 #1326, 0.55 #1339, 0.55 #1287), 02fqwt (0.39 #132, 0.27 #145, 0.26 #106), 02hczc (0.25 #133, 0.17 #146, 0.14 #251), 02llzg (0.13 #109, 0.07 #1144, 0.07 #1157), 03bdv (0.04 #1447, 0.04 #320, 0.04 #1724), 03plfd (0.03 #1150, 0.03 #1163, 0.03 #1281), 042g7t (0.02 #325, 0.02 #351, 0.02 #142), 05jphn (0.02 #144, 0.02 #262, 0.01 #575), 0gsrz4 (0.02 #1660, 0.01 #1527, 0.01 #1621) >> Best rule #983 for best value: >> intensional similarity = 3 >> extensional distance = 297 >> proper extension: 0133h8; 0qjd; 0bwtj; >> query: (?x12408, ?x2950) <- adjoins(?x12408, ?x11569), second_level_divisions(?x94, ?x11569), time_zones(?x11569, ?x2950) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mmpm time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.830 http://example.org/location/location/time_zones #11741-09hy79 PRED entity: 09hy79 PRED relation: film! PRED expected values: 01vwllw => 117 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1117): 0js9s (0.57 #2081, 0.57 #1155, 0.31 #49941), 03ym1 (0.57 #1012, 0.09 #9333, 0.04 #34305), 0241jw (0.57 #294, 0.04 #8615, 0.03 #21098), 0f0kz (0.43 #515, 0.12 #8836, 0.06 #33808), 015t56 (0.43 #469, 0.05 #8790, 0.04 #25437), 0154qm (0.43 #561, 0.04 #6802, 0.04 #8882), 01v9l67 (0.43 #464, 0.04 #12947, 0.03 #8785), 02ck7w (0.43 #939, 0.03 #13422, 0.03 #9260), 02gvwz (0.43 #186, 0.03 #12669, 0.03 #8507), 0svqs (0.43 #874, 0.03 #13357, 0.03 #9195) >> Best rule #2081 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 017gl1; 017gm7; 017jd9; 02dr9j; >> query: (?x7012, ?x6589) <- film(?x6589, ?x7012), film_crew_role(?x7012, ?x281), ?x6589 = 0js9s, nominated_for(?x112, ?x7012) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #52569 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 256 *> proper extension: 01cgz; *> query: (?x7012, 01vwllw) <- films(?x3530, ?x7012), films(?x3530, ?x2094), film_release_region(?x2094, ?x94), film_release_region(?x2094, ?x87) *> conf = 0.02 ranks of expected_values: 669 EVAL 09hy79 film! 01vwllw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 117.000 77.000 0.571 http://example.org/film/actor/film./film/performance/film #11740-01s7z0 PRED entity: 01s7z0 PRED relation: location PRED expected values: 0c5v2 => 148 concepts (137 used for prediction) PRED predicted values (max 10 best out of 306): 02_286 (0.31 #5653, 0.31 #17689, 0.29 #1641), 027l4q (0.29 #2101, 0.25 #3705, 0.25 #2903), 0cr3d (0.25 #946, 0.14 #1748, 0.12 #5760), 01snm (0.25 #319, 0.14 #1923, 0.12 #3527), 0qpqn (0.25 #452, 0.14 #2056, 0.12 #3660), 0rk71 (0.25 #502, 0.14 #2106, 0.12 #3710), 0pzmf (0.25 #314, 0.14 #1918, 0.12 #3522), 0100mt (0.25 #383, 0.14 #1987, 0.12 #3591), 030qb3t (0.23 #9709, 0.21 #69908, 0.20 #45025), 0rh6k (0.18 #10433, 0.12 #3212, 0.12 #2410) >> Best rule #5653 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 0807ml; >> query: (?x12775, 02_286) <- gender(?x12775, ?x231), ?x231 = 05zppz, actor(?x8818, ?x12775), location(?x12775, ?x1131), ?x1131 = 0cc56 >> conf = 0.31 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01s7z0 location 0c5v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 137.000 0.312 http://example.org/people/person/places_lived./people/place_lived/location #11739-05ywg PRED entity: 05ywg PRED relation: contains! PRED expected values: 012m_ => 179 concepts (131 used for prediction) PRED predicted values (max 10 best out of 312): 09c7w0 (0.69 #106382, 0.63 #104596, 0.63 #105489), 012m_ (0.57 #104593, 0.57 #115321, 0.54 #109061), 07ssc (0.57 #104593, 0.54 #109061, 0.54 #109062), 04_1l0v (0.55 #38868, 0.53 #51383, 0.51 #52278), 0345h (0.38 #81418, 0.26 #72393, 0.25 #6335), 01nhhz (0.38 #112639, 0.36 #52724, 0.31 #56301), 02qkt (0.34 #117109, 0.30 #103699, 0.30 #103698), 02j9z (0.34 #117109, 0.30 #103699, 0.30 #103698), 0j0k (0.29 #4844, 0.20 #2164, 0.15 #11990), 05ywg (0.27 #94755, 0.23 #53619, 0.03 #111744) >> Best rule #106382 for best value: >> intensional similarity = 5 >> extensional distance = 435 >> proper extension: 0cb4j; 0f2wj; 0f94t; 05ksh; 022_6; 0284jb; 0wp9b; 0pmpl; 0121c1; 09nyf; ... >> query: (?x1458, 09c7w0) <- contains(?x10801, ?x1458), contains(?x7430, ?x1458), entity_involved(?x6982, ?x10801), location(?x4915, ?x1458), combatants(?x94, ?x7430) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #104593 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 391 *> proper extension: 018f94; *> query: (?x1458, ?x512) <- place_of_birth(?x10454, ?x1458), category(?x1458, ?x134), ?x134 = 08mbj5d, nationality(?x10454, ?x512) *> conf = 0.57 ranks of expected_values: 2 EVAL 05ywg contains! 012m_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 179.000 131.000 0.693 http://example.org/location/location/contains #11738-07twz PRED entity: 07twz PRED relation: teams PRED expected values: 0329r5 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 162): 023zd7 (0.04 #162, 0.04 #1242, 0.03 #522), 02bh_v (0.04 #215, 0.03 #575, 0.02 #935), 03zb6t (0.04 #338, 0.03 #698, 0.02 #1418), 03dj48 (0.04 #247, 0.03 #607, 0.02 #1327), 03d8m4 (0.04 #93, 0.03 #453, 0.02 #1173), 033nzk (0.04 #10, 0.02 #730, 0.02 #1090), 03rrdb (0.04 #207, 0.02 #927, 0.02 #1287), 03zkr8 (0.04 #304, 0.02 #1024, 0.02 #1384), 01352_ (0.04 #300, 0.02 #1020, 0.02 #1380), 032c08 (0.04 #334, 0.02 #1414, 0.01 #5374) >> Best rule #162 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 04vg8; >> query: (?x4737, 023zd7) <- contains(?x12315, ?x4737), contains(?x7273, ?x4737), ?x7273 = 07c5l, geographic_distribution(?x13662, ?x12315) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07twz teams 0329r5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.042 http://example.org/sports/sports_team_location/teams #11737-0693l PRED entity: 0693l PRED relation: location PRED expected values: 0_vn7 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 229): 0_vn7 (0.59 #18472, 0.54 #21685, 0.53 #48998), 030qb3t (0.22 #44261, 0.21 #39442, 0.20 #52296), 0x335 (0.20 #533, 0.06 #1336, 0.05 #2942), 07tcs (0.20 #470, 0.05 #2879, 0.05 #3682), 0ftlx (0.20 #267, 0.01 #13115), 0cr3d (0.18 #948, 0.16 #1751, 0.14 #6569), 02_286 (0.17 #8870, 0.17 #4855, 0.17 #48231), 0vzm (0.09 #3385, 0.06 #4188, 0.05 #2582), 04jpl (0.06 #44195, 0.06 #56246, 0.06 #52230), 01n7q (0.06 #4078, 0.06 #5684, 0.05 #7290) >> Best rule #18472 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 04wqr; 0k4gf; 026lj; 01hb6v; 03f70xs; 032l1; 052h3; 04k15; 0372p; 02lt8; ... >> query: (?x3117, ?x4350) <- influenced_by(?x12392, ?x3117), people(?x1446, ?x3117), place_of_birth(?x3117, ?x4350) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0693l location 0_vn7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.593 http://example.org/people/person/places_lived./people/place_lived/location #11736-0n5gq PRED entity: 0n5gq PRED relation: time_zones PRED expected values: 02hcv8 => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.95 #133, 0.92 #81, 0.86 #565), 02lcqs (0.25 #267, 0.25 #44, 0.22 #228), 02fqwt (0.25 #66, 0.25 #27, 0.24 #92), 02hczc (0.25 #28, 0.23 #158, 0.16 #172), 02lcrv (0.25 #33, 0.16 #1926, 0.08 #72), 042g7t (0.25 #37, 0.08 #76, 0.08 #102), 03bdv (0.11 #110, 0.07 #123, 0.05 #952), 02llzg (0.11 #201, 0.11 #160, 0.09 #753), 052vwh (0.04 #77, 0.04 #103, 0.04 #116), 05jphn (0.04 #78, 0.04 #104, 0.04 #117) >> Best rule #133 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0fvxz; 0xmlp; 0xpp5; 0xq63; 0pzmf; 0xqf3; 010cw1; 0xkyn; 0xt3t; 0h6l4; >> query: (?x6252, 02hcv8) <- source(?x6252, ?x958), contains(?x6895, ?x6252), ?x6895 = 05fjf, ?x958 = 0jbk9 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n5gq time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.946 http://example.org/location/location/time_zones #11735-023p33 PRED entity: 023p33 PRED relation: produced_by PRED expected values: 081nh => 84 concepts (57 used for prediction) PRED predicted values (max 10 best out of 105): 02bfxb (0.09 #889, 0.09 #1276, 0.03 #2825), 03h304l (0.06 #186, 0.02 #3284, 0.02 #7933), 027z0pl (0.06 #343, 0.02 #8090, 0.02 #3441), 0bzyh (0.06 #136), 016qtt (0.06 #5), 07rd7 (0.06 #537, 0.03 #1698, 0.02 #2473), 0js9s (0.06 #1003, 0.06 #1390, 0.02 #2939), 03v1w7 (0.06 #999, 0.03 #1386, 0.02 #2935), 01t6b4 (0.06 #1205, 0.03 #818, 0.03 #1592), 02xnjd (0.05 #1822, 0.04 #5695, 0.03 #5307) >> Best rule #889 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 03wh49y; >> query: (?x2097, 02bfxb) <- film_release_region(?x2097, ?x512), ?x512 = 07ssc, film_release_distribution_medium(?x2097, ?x81), genre(?x2097, ?x1510), ?x1510 = 01hmnh >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #2012 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 96 *> proper extension: 0cks1m; 02r9p0c; 03gyvwg; *> query: (?x2097, 081nh) <- film(?x12005, ?x2097), film(?x5537, ?x2097), genre(?x2097, ?x2540), ?x2540 = 0hcr *> conf = 0.02 ranks of expected_values: 53 EVAL 023p33 produced_by 081nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 84.000 57.000 0.088 http://example.org/film/film/produced_by #11734-0d060g PRED entity: 0d060g PRED relation: film_release_region! PRED expected values: 05j82v 0bs5k8r 01z452 => 212 concepts (164 used for prediction) PRED predicted values (max 10 best out of 452): 0299hs (0.33 #43, 0.27 #1075, 0.18 #1854), 0ktpx (0.33 #466, 0.25 #595, 0.01 #11857), 043h78 (0.33 #114, 0.14 #1795, 0.14 #2055), 02bg8v (0.33 #18, 0.14 #1699, 0.13 #1050), 02psgq (0.33 #74, 0.14 #2015, 0.13 #1106), 03nqnnk (0.33 #83, 0.13 #1115, 0.12 #857), 025ts_z (0.33 #112, 0.13 #1144, 0.11 #1405), 05fcbk7 (0.33 #35, 0.13 #1067, 0.11 #1328), 0kv2hv (0.33 #8, 0.13 #1040, 0.10 #1689), 05z7c (0.33 #22, 0.13 #1054, 0.10 #1703) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09c7w0; >> query: (?x279, 0299hs) <- nationality(?x11298, ?x279), ?x11298 = 0k29f, combatants(?x94, ?x279) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 09c7w0; *> query: (?x279, 0bs5k8r) <- nationality(?x11298, ?x279), ?x11298 = 0k29f, combatants(?x94, ?x279) *> conf = 0.33 ranks of expected_values: 58 EVAL 0d060g film_release_region! 01z452 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 212.000 164.000 0.333 http://example.org/film/film/runtime./film/film_cut/film_release_region EVAL 0d060g film_release_region! 0bs5k8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 212.000 164.000 0.333 http://example.org/film/film/runtime./film/film_cut/film_release_region EVAL 0d060g film_release_region! 05j82v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 212.000 164.000 0.333 http://example.org/film/film/runtime./film/film_cut/film_release_region #11733-0ft18 PRED entity: 0ft18 PRED relation: genre PRED expected values: 01g6gs => 157 concepts (136 used for prediction) PRED predicted values (max 10 best out of 101): 05p553 (0.61 #14065, 0.42 #7331, 0.40 #2408), 01jfsb (0.40 #2657, 0.37 #4578, 0.36 #3377), 02kdv5l (0.37 #242, 0.36 #2646, 0.34 #122), 04xvlr (0.33 #481, 0.26 #1323, 0.23 #241), 060__y (0.32 #1701, 0.26 #497, 0.26 #257), 0lsxr (0.28 #729, 0.22 #1572, 0.20 #4574), 03k9fj (0.28 #2176, 0.28 #2056, 0.27 #9389), 03bxz7 (0.25 #415, 0.13 #1859, 0.13 #2579), 01g6gs (0.24 #621, 0.23 #861, 0.21 #981), 06cvj (0.24 #7330, 0.19 #1325, 0.13 #14064) >> Best rule #14065 for best value: >> intensional similarity = 3 >> extensional distance = 957 >> proper extension: 0c0wvx; >> query: (?x8119, 05p553) <- genre(?x8119, ?x1403), genre(?x7336, ?x1403), ?x7336 = 0bdjd >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #621 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 0kb57; 02r_pp; 048rn; 0jqd3; 063hp4; 0k4bc; *> query: (?x8119, 01g6gs) <- production_companies(?x8119, ?x382), film(?x11251, ?x8119), film_sets_designed(?x12378, ?x8119), language(?x8119, ?x90) *> conf = 0.24 ranks of expected_values: 9 EVAL 0ft18 genre 01g6gs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 157.000 136.000 0.608 http://example.org/film/film/genre #11732-02t__3 PRED entity: 02t__3 PRED relation: film PRED expected values: 046f3p => 138 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1260): 07nxvj (0.64 #69816, 0.64 #41173, 0.64 #62654), 0gd92 (0.38 #3096, 0.30 #4886, 0.03 #23271), 09xbpt (0.25 #1837, 0.20 #3627, 0.06 #8997), 02qzh2 (0.25 #2483, 0.20 #4273, 0.06 #9643), 011ypx (0.25 #2812, 0.20 #4602, 0.06 #121735), 0418wg (0.25 #2191, 0.20 #3981, 0.04 #9351), 0prrm (0.25 #2651, 0.20 #4441, 0.03 #23271), 0p9lw (0.25 #1936, 0.20 #3726, 0.03 #23271), 06cm5 (0.25 #1070, 0.06 #121735, 0.03 #128897), 01xbxn (0.25 #1394, 0.03 #23271, 0.03 #13924) >> Best rule #69816 for best value: >> intensional similarity = 3 >> extensional distance = 288 >> proper extension: 0d_84; 0l8v5; 05zbm4; 03_vx9; 01fwj8; 0pyg6; 0tc7; 02bh9; 0blt6; 029_3; ... >> query: (?x5979, ?x4174) <- participant(?x3585, ?x5979), award_winner(?x4174, ?x5979), film(?x5979, ?x1454) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #19229 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 04bs3j; 01mqz0; 01w02sy; 01tnbn; *> query: (?x5979, 046f3p) <- spouse(?x3585, ?x5979), student(?x1103, ?x5979), participant(?x5979, ?x2258) *> conf = 0.03 ranks of expected_values: 307 EVAL 02t__3 film 046f3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 138.000 85.000 0.644 http://example.org/film/actor/film./film/performance/film #11731-0kw4j PRED entity: 0kw4j PRED relation: institution! PRED expected values: 016t_3 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 14): 02_xgp2 (0.67 #135, 0.58 #589, 0.56 #557), 016t_3 (0.59 #130, 0.56 #584, 0.48 #712), 04zx3q1 (0.53 #129, 0.35 #583, 0.35 #551), 027f2w (0.41 #133, 0.27 #587, 0.27 #555), 0bjrnt (0.33 #132, 0.30 #1347, 0.29 #20), 071tyz (0.30 #1347, 0.10 #134, 0.06 #572), 02m4yg (0.30 #1347, 0.08 #138, 0.06 #576), 01ysy9 (0.30 #1347, 0.08 #725, 0.06 #401), 01gkg3 (0.30 #1347, 0.05 #2532, 0.02 #815), 028dcg (0.28 #125, 0.19 #482, 0.17 #189) >> Best rule #135 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 01c57n; >> query: (?x3821, 02_xgp2) <- institution(?x1526, ?x3821), company(?x346, ?x3821), ?x1526 = 0bkj86 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #130 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 01c57n; *> query: (?x3821, 016t_3) <- institution(?x1526, ?x3821), company(?x346, ?x3821), ?x1526 = 0bkj86 *> conf = 0.59 ranks of expected_values: 2 EVAL 0kw4j institution! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 160.000 0.673 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #11730-092t4b PRED entity: 092t4b PRED relation: award_winner PRED expected values: 04bd8y 01pcdn 06j8wx 0335fp => 28 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2180): 018ygt (0.56 #13069, 0.50 #14585, 0.38 #11555), 070j61 (0.50 #10191, 0.44 #1515, 0.28 #12127), 0170pk (0.50 #3262, 0.36 #7579, 0.36 #7578), 06j8wx (0.44 #1515, 0.36 #7579, 0.36 #7578), 07ym6ss (0.44 #1515, 0.28 #12127, 0.28 #12125), 04bd8y (0.44 #1515, 0.28 #12127, 0.28 #12125), 01z5tr (0.44 #1515, 0.28 #12127, 0.28 #12125), 03fg0r (0.44 #1515, 0.28 #12127, 0.28 #12125), 024rgt (0.44 #1515, 0.28 #12127, 0.28 #12125), 09wj5 (0.44 #1515, 0.28 #12125, 0.26 #15157) >> Best rule #13069 for best value: >> intensional similarity = 16 >> extensional distance = 7 >> proper extension: 09g90vz; 0g55tzk; >> query: (?x3460, 018ygt) <- ceremony(?x2257, ?x3460), ceremony(?x1670, ?x3460), ceremony(?x704, ?x3460), award_winner(?x3460, ?x4969), award_winner(?x3460, ?x1871), honored_for(?x3460, ?x337), ?x2257 = 09td7p, award(?x4346, ?x1670), award(?x57, ?x1670), ?x704 = 09sb52, ?x57 = 044mz_, film(?x4969, ?x363), award_winner(?x1670, ?x368), award_nominee(?x525, ?x1871), award(?x4969, ?x102), ?x4346 = 0jmj >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1515 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 0clfdj; *> query: (?x3460, ?x3406) <- ceremony(?x2257, ?x3460), award_winner(?x3460, ?x1951), award_winner(?x3460, ?x1223), honored_for(?x3460, ?x4588), ?x1223 = 016gr2, nominated_for(?x2257, ?x6048), nominated_for(?x2257, ?x4541), nominated_for(?x2257, ?x1199), nominated_for(?x2257, ?x825), ?x1951 = 065jlv, award(?x6977, ?x2257), ?x4541 = 08nvyr, ?x6048 = 01cmp9, award_winner(?x4588, ?x3406), nominated_for(?x678, ?x4588), nominated_for(?x57, ?x1199), ?x825 = 0b73_1d, ?x6977 = 03mp9s *> conf = 0.44 ranks of expected_values: 4, 6, 54, 70 EVAL 092t4b award_winner 0335fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 28.000 17.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 092t4b award_winner 06j8wx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 28.000 17.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 092t4b award_winner 01pcdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 28.000 17.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 092t4b award_winner 04bd8y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 28.000 17.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11729-0f4_2k PRED entity: 0f4_2k PRED relation: country PRED expected values: 03rjj => 148 concepts (132 used for prediction) PRED predicted values (max 10 best out of 95): 06q1r (0.36 #5375), 07ssc (0.33 #1170, 0.30 #2943, 0.30 #3797), 0f8l9c (0.33 #19, 0.25 #685, 0.19 #2821), 03rjj (0.33 #6, 0.11 #370, 0.08 #1221), 0345h (0.29 #1545, 0.29 #1242, 0.24 #633), 09nm_ (0.27 #605, 0.26 #1033, 0.26 #911), 0d060g (0.20 #68, 0.16 #552, 0.13 #858), 06mkj (0.12 #768, 0.07 #6839, 0.06 #829), 0chghy (0.11 #436, 0.10 #1349, 0.09 #678), 01mjq (0.10 #216, 0.05 #459, 0.05 #519) >> Best rule #5375 for best value: >> intensional similarity = 4 >> extensional distance = 339 >> proper extension: 03pc89; >> query: (?x5960, ?x6401) <- film_release_region(?x5960, ?x94), film(?x3580, ?x5960), films(?x9376, ?x5960), nationality(?x3580, ?x6401) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 0gh6j94; *> query: (?x5960, 03rjj) <- film_crew_role(?x5960, ?x1284), film_format(?x5960, ?x6392), films(?x9376, ?x5960), language(?x5960, ?x10486), ?x1284 = 0ch6mp2, ?x10486 = 05qqm *> conf = 0.33 ranks of expected_values: 4 EVAL 0f4_2k country 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 148.000 132.000 0.361 http://example.org/film/film/country #11728-035s37 PRED entity: 035s37 PRED relation: team! PRED expected values: 07y9k => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 8): 07y9k (0.50 #28, 0.38 #76, 0.38 #68), 0355pl (0.19 #19, 0.14 #115, 0.14 #35), 0356lc (0.17 #65, 0.16 #49, 0.13 #73), 059yj (0.16 #101, 0.07 #382, 0.07 #390), 0h69c (0.13 #102, 0.06 #350, 0.06 #358), 03zv9 (0.11 #361, 0.11 #442, 0.09 #130), 021q23 (0.07 #8, 0.02 #369, 0.01 #450), 01ddbl (0.02 #432, 0.02 #440, 0.02 #449) >> Best rule #28 for best value: >> intensional similarity = 9 >> extensional distance = 26 >> proper extension: 03yvln; >> query: (?x12091, 07y9k) <- position(?x12091, ?x203), position(?x12091, ?x60), teams(?x1892, ?x12091), ?x203 = 0dgrmp, ?x60 = 02nzb8, film_release_region(?x1392, ?x1892), country(?x453, ?x1892), olympics(?x1892, ?x391), ?x1392 = 017gm7 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035s37 team! 07y9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.500 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #11727-0fh2v5 PRED entity: 0fh2v5 PRED relation: genre PRED expected values: 03k9fj => 74 concepts (47 used for prediction) PRED predicted values (max 10 best out of 99): 05p553 (0.37 #1049, 0.37 #3728, 0.36 #2332), 0lsxr (0.37 #587, 0.25 #239, 0.24 #123), 03k9fj (0.35 #126, 0.34 #474, 0.27 #358), 02l7c8 (0.33 #361, 0.29 #3389, 0.27 #1407), 082gq (0.24 #142, 0.14 #374, 0.13 #258), 04xvh5 (0.24 #146, 0.11 #378, 0.08 #30), 09blyk (0.21 #259, 0.18 #607, 0.17 #27), 03npn (0.19 #238, 0.18 #586, 0.17 #6), 04xvlr (0.17 #349, 0.17 #3377, 0.15 #1395), 01hmnh (0.17 #478, 0.16 #3157, 0.16 #1176) >> Best rule #1049 for best value: >> intensional similarity = 4 >> extensional distance = 242 >> proper extension: 02d44q; 0gtvrv3; >> query: (?x9901, 05p553) <- film_crew_role(?x9901, ?x137), film_release_region(?x9901, ?x94), film(?x100, ?x9901), category(?x9901, ?x134) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #126 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 065dc4; *> query: (?x9901, 03k9fj) <- film_format(?x9901, ?x909), film(?x100, ?x9901), film_crew_role(?x9901, ?x3305), ?x3305 = 04pyp5 *> conf = 0.35 ranks of expected_values: 3 EVAL 0fh2v5 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 47.000 0.373 http://example.org/film/film/genre #11726-085bd1 PRED entity: 085bd1 PRED relation: film! PRED expected values: 0fb1q 0525b => 60 concepts (29 used for prediction) PRED predicted values (max 10 best out of 985): 0l6px (0.60 #6612, 0.50 #4537, 0.43 #8687), 0134w7 (0.60 #6386, 0.50 #4311, 0.43 #8461), 065jlv (0.60 #6538, 0.50 #4463, 0.43 #8613), 013_vh (0.50 #6885, 0.50 #4810, 0.40 #660), 09y20 (0.50 #6473, 0.50 #4398, 0.36 #8548), 05sq84 (0.50 #4385, 0.40 #6460, 0.29 #8535), 03y_46 (0.40 #7237, 0.38 #5162, 0.29 #9312), 016nff (0.38 #5370, 0.30 #7445, 0.29 #9520), 015vq_ (0.34 #13162, 0.07 #9012, 0.02 #17312), 0gnbw (0.32 #13715, 0.07 #9565, 0.03 #38620) >> Best rule #6612 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 031786; >> query: (?x2795, 0l6px) <- country(?x2795, ?x512), country(?x2795, ?x94), genre(?x2795, ?x258), film(?x5332, ?x2795), ?x512 = 07ssc, ?x5332 = 06ltr, ?x94 = 09c7w0 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #8132 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 031786; *> query: (?x2795, 0525b) <- country(?x2795, ?x512), country(?x2795, ?x94), genre(?x2795, ?x258), film(?x5332, ?x2795), ?x512 = 07ssc, ?x5332 = 06ltr, ?x94 = 09c7w0 *> conf = 0.10 ranks of expected_values: 43, 804 EVAL 085bd1 film! 0525b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 60.000 29.000 0.600 http://example.org/film/actor/film./film/performance/film EVAL 085bd1 film! 0fb1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 29.000 0.600 http://example.org/film/actor/film./film/performance/film #11725-01jsk6 PRED entity: 01jsk6 PRED relation: major_field_of_study PRED expected values: 036hv => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 113): 01mkq (0.50 #251, 0.42 #960, 0.37 #3560), 04rjg (0.43 #137, 0.37 #964, 0.29 #3564), 03g3w (0.43 #144, 0.35 #971, 0.28 #3571), 041y2 (0.43 #192, 0.30 #310, 0.21 #1019), 02lp1 (0.40 #1429, 0.40 #1075, 0.37 #1665), 02_7t (0.40 #296, 0.37 #1123, 0.32 #2303), 01tbp (0.40 #292, 0.27 #1709, 0.26 #1827), 062z7 (0.39 #972, 0.34 #2507, 0.32 #2034), 05qfh (0.32 #979, 0.26 #2632, 0.22 #2987), 04x_3 (0.29 #143, 0.28 #1324, 0.27 #1442) >> Best rule #251 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 022lly; 012vwb; 01pq4w; 02gr81; 09f2j; 0trv; 02482c; 01dzg0; >> query: (?x10945, 01mkq) <- currency(?x10945, ?x170), school(?x4208, ?x10945), institution(?x620, ?x10945), ?x4208 = 061xq >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #247 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 022lly; 012vwb; 01pq4w; 02gr81; 09f2j; 0trv; 02482c; 01dzg0; *> query: (?x10945, 036hv) <- currency(?x10945, ?x170), school(?x4208, ?x10945), institution(?x620, ?x10945), ?x4208 = 061xq *> conf = 0.20 ranks of expected_values: 18 EVAL 01jsk6 major_field_of_study 036hv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 134.000 134.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11724-0c4kv PRED entity: 0c4kv PRED relation: location! PRED expected values: 022769 => 120 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1931): 022769 (0.71 #186221, 0.70 #206358, 0.51 #27677), 03359d (0.25 #3534, 0.06 #31211, 0.05 #38759), 01pcq3 (0.25 #2642, 0.06 #30319, 0.05 #37867), 0k8y7 (0.25 #3357, 0.04 #31034, 0.04 #53686), 086sj (0.25 #3321, 0.04 #30998, 0.04 #46098), 03rl84 (0.25 #2878, 0.04 #30555, 0.03 #38103), 0j5q3 (0.25 #3937, 0.04 #31614, 0.03 #39162), 02pjvc (0.25 #3694, 0.04 #31371, 0.03 #38919), 01x4sb (0.25 #3783, 0.04 #31460, 0.03 #39008), 02m7r (0.25 #2951, 0.04 #30628, 0.03 #38176) >> Best rule #186221 for best value: >> intensional similarity = 4 >> extensional distance = 432 >> proper extension: 0sg6b; 04kf4; 0281rb; 01vqq1; 0fdpd; 01423b; 0sq2v; 0r4h3; 0fttg; 01gln9; ... >> query: (?x12289, ?x2100) <- place_of_birth(?x2100, ?x12289), contains(?x12290, ?x12289), time_zones(?x12290, ?x2674), location(?x2100, ?x1131) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c4kv location! 022769 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 88.000 0.706 http://example.org/people/person/places_lived./people/place_lived/location #11723-0sx7r PRED entity: 0sx7r PRED relation: olympics! PRED expected values: 04g61 => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 330): 09c7w0 (0.88 #3323, 0.86 #3834, 0.82 #137), 07ssc (0.83 #2429, 0.83 #2303, 0.83 #2701), 05b4w (0.83 #2429, 0.83 #2303, 0.81 #1424), 0chghy (0.82 #137, 0.76 #2570, 0.75 #139), 0154j (0.82 #137, 0.75 #139, 0.66 #1170), 04j53 (0.81 #1423, 0.80 #663, 0.76 #1046), 04g5k (0.81 #1423, 0.80 #663, 0.76 #1046), 0k6nt (0.80 #1323, 0.75 #139, 0.71 #2583), 05qhw (0.75 #1062, 0.75 #139, 0.71 #2575), 015fr (0.75 #1065, 0.75 #139, 0.67 #933) >> Best rule #3323 for best value: >> intensional similarity = 60 >> extensional distance = 30 >> proper extension: 018wrk; >> query: (?x452, 09c7w0) <- sports(?x452, ?x453), olympics(?x1453, ?x452), olympics(?x789, ?x452), contains(?x6956, ?x1453), film_release_region(?x6882, ?x1453), film_release_region(?x3498, ?x1453), film_release_region(?x3471, ?x1453), film_release_region(?x2868, ?x1453), film_release_region(?x2094, ?x1453), film_release_region(?x1625, ?x1453), film_release_region(?x1035, ?x1453), film_release_region(?x1002, ?x1453), country(?x5177, ?x1453), ?x3471 = 07cyl, administrative_parent(?x9310, ?x1453), form_of_government(?x1453, ?x48), ?x2868 = 0dr3sl, participating_countries(?x452, ?x94), sports(?x452, ?x6354), ?x1002 = 0_b3d, ?x2094 = 05z7c, nominated_for(?x154, ?x3498), ?x1035 = 08hmch, nominated_for(?x1500, ?x3498), film_crew_role(?x3498, ?x137), film_release_region(?x3498, ?x2316), film_release_region(?x3498, ?x1174), film_release_region(?x3498, ?x583), film_release_region(?x3498, ?x390), ?x583 = 015fr, genre(?x3498, ?x53), ?x390 = 0chghy, country(?x2685, ?x789), ?x2685 = 0g5879y, geographic_distribution(?x8088, ?x1453), nationality(?x4389, ?x1453), film_release_region(?x7170, ?x789), film_release_region(?x5517, ?x789), film_release_region(?x3276, ?x789), film_release_region(?x3268, ?x789), olympics(?x789, ?x784), ?x1625 = 01f8gz, ?x6882 = 043tvp3, ?x5517 = 03wh49y, combatants(?x1140, ?x789), ?x3276 = 0gjc4d3, administrative_area_type(?x1453, ?x2792), adjoins(?x789, ?x172), service_location(?x555, ?x789), olympics(?x5177, ?x418), ?x8088 = 01xhh5, ?x1174 = 047yc, ?x7170 = 02pxst, ?x2792 = 0hzc9wc, jurisdiction_of_office(?x182, ?x789), ?x3268 = 02x6dqb, film_distribution_medium(?x3498, ?x81), nationality(?x317, ?x789), ?x2316 = 06t2t, contains(?x789, ?x790) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #145 for first EXPECTED value: *> intensional similarity = 74 *> extensional distance = 1 *> proper extension: 0kbvb; *> query: (?x452, ?x2984) <- sports(?x452, ?x3309), sports(?x452, ?x2884), sports(?x452, ?x453), olympics(?x7287, ?x452), olympics(?x2346, ?x452), olympics(?x1558, ?x452), olympics(?x1453, ?x452), olympics(?x1355, ?x452), olympics(?x1264, ?x452), olympics(?x304, ?x452), olympics(?x205, ?x452), ?x1453 = 06qd3, medal(?x452, ?x422), olympics(?x5177, ?x452), ?x1264 = 0345h, ?x1355 = 0h7x, ?x7287 = 05b7q, ?x422 = 02lq67, ?x205 = 03rjj, ?x304 = 0d0vqn, sports(?x3110, ?x5177), sports(?x1741, ?x5177), country(?x2884, ?x1790), country(?x2884, ?x1536), country(?x2884, ?x756), country(?x2884, ?x421), country(?x2884, ?x390), country(?x2884, ?x94), olympics(?x2513, ?x3110), olympics(?x1497, ?x3110), olympics(?x583, ?x3110), olympics(?x404, ?x3110), olympics(?x344, ?x3110), ?x2346 = 0d05w3, ?x1790 = 01pj7, ?x404 = 047lj, ?x756 = 06npd, sports(?x452, ?x6354), ?x2513 = 05b4w, country(?x3309, ?x8197), country(?x3309, ?x7413), country(?x3309, ?x3227), country(?x3309, ?x2979), country(?x3309, ?x1917), country(?x3309, ?x1273), country(?x3309, ?x512), country(?x3309, ?x410), country(?x3309, ?x151), ?x421 = 03_r3, ?x410 = 01ls2, ?x3227 = 0bjv6, olympics(?x2984, ?x1741), ?x94 = 09c7w0, ?x7413 = 04hqz, ?x1558 = 01mjq, ?x1536 = 06c1y, ?x1273 = 04wgh, ?x344 = 04gzd, ?x1497 = 015qh, ?x512 = 07ssc, ?x8197 = 06srk, ?x2979 = 056vv, ?x583 = 015fr, ?x151 = 0b90_r, ?x1917 = 01p1v, medal(?x3110, ?x1242), sport(?x2919, ?x453), locations(?x3110, ?x10537), film_release_region(?x6283, ?x390), film_release_region(?x1744, ?x390), nationality(?x72, ?x390), ?x6283 = 0gmd3k7, ?x1744 = 035yn8, country(?x308, ?x390) *> conf = 0.55 ranks of expected_values: 61 EVAL 0sx7r olympics! 04g61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 32.000 32.000 0.875 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #11722-053y0s PRED entity: 053y0s PRED relation: role PRED expected values: 05r5c => 133 concepts (89 used for prediction) PRED predicted values (max 10 best out of 124): 05r5c (0.56 #314, 0.48 #409, 0.47 #4016), 0342h (0.50 #1959, 0.49 #2061, 0.47 #4013), 05148p4 (0.48 #409, 0.36 #4111, 0.35 #1125), 03gvt (0.48 #409, 0.36 #4111, 0.35 #1125), 01s0ps (0.38 #264, 0.17 #571, 0.12 #775), 02sgy (0.33 #312, 0.31 #2062, 0.27 #4014), 05842k (0.33 #383, 0.26 #1099, 0.25 #894), 018vs (0.33 #319, 0.24 #830, 0.22 #1967), 0l14md (0.33 #313, 0.13 #1029, 0.10 #1447), 01vj9c (0.28 #1037, 0.25 #832, 0.24 #1245) >> Best rule #314 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0kp2_; >> query: (?x130, 05r5c) <- instrumentalists(?x316, ?x130), role(?x130, ?x1437), role(?x130, ?x614), ?x614 = 0mkg, ?x1437 = 01vdm0, profession(?x130, ?x131) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 053y0s role 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 89.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role #11721-01p8s PRED entity: 01p8s PRED relation: form_of_government PRED expected values: 01fpfn 01d9r3 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 5): 01d9r3 (0.50 #24, 0.48 #134, 0.36 #19), 01fpfn (0.45 #77, 0.43 #262, 0.42 #297), 01q20 (0.41 #68, 0.38 #53, 0.36 #18), 018wl5 (0.41 #51, 0.37 #66, 0.34 #286), 026wp (0.14 #40, 0.12 #25, 0.10 #55) >> Best rule #24 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 07ylj; 05v10; 016wzw; 03h2c; 02k8k; 05c74; 0165v; >> query: (?x9730, 01d9r3) <- contains(?x7708, ?x9730), country(?x668, ?x9730), ?x7708 = 04pnx >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01p8s form_of_government 01d9r3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.500 http://example.org/location/country/form_of_government EVAL 01p8s form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.500 http://example.org/location/country/form_of_government #11720-0168dy PRED entity: 0168dy PRED relation: film PRED expected values: 04tqtl => 144 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1137): 0fpgp26 (0.33 #3323, 0.12 #15839, 0.11 #17627), 0ndwt2w (0.20 #8153, 0.02 #88616, 0.02 #111861), 011ydl (0.20 #7677), 0147sh (0.17 #3707, 0.15 #10859, 0.11 #18012), 0m_mm (0.17 #3721, 0.09 #21602, 0.08 #10873), 01l_pn (0.17 #2755, 0.08 #13483, 0.06 #49244), 0gwgn1k (0.17 #3335, 0.08 #14063, 0.06 #15851), 0cwy47 (0.17 #3717, 0.08 #10869, 0.05 #18022), 0286gm1 (0.17 #4681, 0.08 #11833, 0.05 #18986), 01bn3l (0.17 #4932, 0.08 #12084, 0.05 #19237) >> Best rule #3323 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 047sxrj; >> query: (?x10770, 0fpgp26) <- award_nominee(?x5536, ?x10770), ?x5536 = 01vsgrn, film(?x10770, ?x2081) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7662 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 02hblj; *> query: (?x10770, 04tqtl) <- film(?x10770, ?x6298), film(?x10770, ?x5839), award_winner(?x6298, ?x5536), ?x5839 = 05650n *> conf = 0.10 ranks of expected_values: 20 EVAL 0168dy film 04tqtl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 144.000 114.000 0.333 http://example.org/film/actor/film./film/performance/film #11719-017vkx PRED entity: 017vkx PRED relation: language PRED expected values: 02h40lc => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 1): 02h40lc (0.83 #1, 0.82 #10, 0.03 #226) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 05dxl5; >> query: (?x3856, 02h40lc) <- profession(?x3856, ?x131), gender(?x3856, ?x231), award_winner(?x3856, ?x3290), actor(?x10873, ?x3856) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017vkx language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.833 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #11718-01vmv_ PRED entity: 01vmv_ PRED relation: student PRED expected values: 016xk5 => 170 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1817): 05np2 (0.25 #3298, 0.17 #5390, 0.10 #7481), 03_dj (0.25 #4088, 0.17 #6180, 0.10 #8271), 0686zv (0.25 #2584, 0.17 #4676, 0.10 #6767), 019gz (0.25 #3988, 0.17 #6080, 0.10 #8171), 06whf (0.25 #2799, 0.17 #4891, 0.10 #6982), 01w02sy (0.25 #2588, 0.17 #4680, 0.10 #6771), 02pkpfs (0.25 #2257, 0.17 #4349, 0.10 #6440), 06ltr (0.10 #7196, 0.06 #17651, 0.04 #40653), 0kh6b (0.10 #6889, 0.06 #38255, 0.06 #42437), 0tfc (0.10 #8282, 0.05 #45921, 0.04 #33374) >> Best rule #3298 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 05bcl; >> query: (?x11459, 05np2) <- contains(?x12190, ?x11459), contains(?x3699, ?x11459), contains(?x512, ?x12190), ?x3699 = 012wgb, place_of_birth(?x1371, ?x12190) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #184042 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 300 *> proper extension: 02w6bq; *> query: (?x11459, ?x57) <- student(?x11459, ?x489), institution(?x734, ?x11459), major_field_of_study(?x11459, ?x5179), award_nominee(?x489, ?x1222), award_nominee(?x1222, ?x57) *> conf = 0.03 ranks of expected_values: 661 EVAL 01vmv_ student 016xk5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 170.000 95.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #11717-0401sg PRED entity: 0401sg PRED relation: prequel! PRED expected values: 06_sc3 03cwwl => 103 concepts (35 used for prediction) PRED predicted values (max 10 best out of 45): 0gffmn8 (0.33 #59, 0.20 #239, 0.14 #602), 0gtv7pk (0.14 #372, 0.08 #1456, 0.06 #1817), 03177r (0.14 #415, 0.06 #1860, 0.05 #2222), 017jd9 (0.11 #987, 0.09 #1348, 0.06 #2070), 05qbckf (0.11 #947, 0.06 #2030, 0.03 #2938), 0hhggmy (0.11 #1048, 0.06 #2131, 0.02 #4126), 02xs6_ (0.08 #1533, 0.06 #1894, 0.05 #2256), 07cyl (0.08 #1509, 0.06 #1870, 0.05 #2232), 080dfr7 (0.08 #1611, 0.05 #2334, 0.05 #2515), 06_sc3 (0.08 #1586, 0.05 #2309, 0.05 #2490) >> Best rule #59 for best value: >> intensional similarity = 14 >> extensional distance = 1 >> proper extension: 053rxgm; >> query: (?x664, 0gffmn8) <- film_release_region(?x664, ?x4737), film_release_region(?x664, ?x2267), film_release_region(?x664, ?x1355), film_release_region(?x664, ?x1264), film_release_region(?x664, ?x1122), film(?x4800, ?x664), ?x1122 = 09pmkv, music(?x664, ?x1467), ?x1264 = 0345h, ?x2267 = 03rj0, ?x1355 = 0h7x, currency(?x664, ?x170), ?x4737 = 07twz, place_founded(?x4800, ?x4801) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1586 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 11 *> proper extension: 06_sc3; 03cwwl; *> query: (?x664, 06_sc3) <- country(?x664, ?x1264), genre(?x664, ?x812), genre(?x664, ?x811), ?x1264 = 0345h, film_distribution_medium(?x664, ?x81), genre(?x6615, ?x811), genre(?x2968, ?x811), ?x6615 = 03t95n, ?x2968 = 025n07, ?x812 = 01jfsb, genre(?x50, ?x811) *> conf = 0.08 ranks of expected_values: 10 EVAL 0401sg prequel! 03cwwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 35.000 0.333 http://example.org/film/film/prequel EVAL 0401sg prequel! 06_sc3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 103.000 35.000 0.333 http://example.org/film/film/prequel #11716-01n6r0 PRED entity: 01n6r0 PRED relation: school! PRED expected values: 07147 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 144): 05m_8 (0.21 #1249, 0.18 #1427, 0.17 #1784), 06x68 (0.20 #7, 0.11 #1253, 0.11 #1610), 01ync (0.20 #37, 0.06 #1283, 0.05 #1818), 07l4z (0.17 #1314, 0.13 #1492, 0.11 #1849), 051vz (0.16 #1446, 0.13 #1268, 0.12 #645), 01yhm (0.15 #642, 0.12 #731, 0.12 #1443), 07l8x (0.14 #1488, 0.13 #1310, 0.10 #1845), 01slc (0.14 #1837, 0.13 #1302, 0.12 #1480), 01yjl (0.13 #1274, 0.11 #384, 0.11 #1631), 01d6g (0.13 #1316, 0.09 #1494, 0.09 #1851) >> Best rule #1249 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 02jyr8; 02zkz7; 016sd3; >> query: (?x4980, 05m_8) <- currency(?x4980, ?x170), state_province_region(?x4980, ?x726), school(?x1883, ?x4980) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #332 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 014b4h; 02zd460; 0fvd03; *> query: (?x4980, 07147) <- currency(?x4980, ?x170), featured_film_locations(?x2104, ?x4980), organization(?x346, ?x4980) *> conf = 0.12 ranks of expected_values: 13 EVAL 01n6r0 school! 07147 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 110.000 110.000 0.211 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #11715-01by1l PRED entity: 01by1l PRED relation: award_winner PRED expected values: 02r4qs 0pkyh 0k7pf 01kd57 0g10g => 41 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1265): 03cd1q (0.57 #9606, 0.57 #9426, 0.43 #9601), 01s21dg (0.43 #9601, 0.43 #5839, 0.42 #9604), 0lbj1 (0.43 #9601, 0.43 #4837, 0.42 #9604), 01vs_v8 (0.43 #9601, 0.42 #9604, 0.42 #12453), 01wd9lv (0.43 #9601, 0.42 #9604, 0.41 #7202), 023p29 (0.43 #9601, 0.42 #9604, 0.41 #7202), 02_jkc (0.43 #9601, 0.42 #9604, 0.41 #7202), 026ps1 (0.43 #9601, 0.42 #9604, 0.40 #21606), 0197tq (0.43 #9601, 0.42 #9604, 0.40 #21606), 0478__m (0.43 #9601, 0.42 #9604, 0.40 #21606) >> Best rule #9606 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 02sp_v; 02h3d1; 02gm9n; >> query: (?x2139, ?x11469) <- award(?x11469, ?x2139), award(?x4101, ?x2139), award(?x2862, ?x2139), award(?x1613, ?x2139), ?x11469 = 03cd1q, award_winner(?x4101, ?x1399), gender(?x1613, ?x231), student(?x3439, ?x2862) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #618 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 01bgqh; *> query: (?x2139, 0pkyh) <- award(?x4701, ?x2139), award(?x4101, ?x2139), award(?x3632, ?x2139), award(?x2138, ?x2139), ?x3632 = 01309x, ?x2138 = 086qd, ?x4701 = 03j24kf, award_nominee(?x1399, ?x4101) *> conf = 0.33 ranks of expected_values: 132, 138, 190, 249, 1032 EVAL 01by1l award_winner 0g10g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 21.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01by1l award_winner 01kd57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 41.000 21.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01by1l award_winner 0k7pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 41.000 21.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01by1l award_winner 0pkyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 41.000 21.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01by1l award_winner 02r4qs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 41.000 21.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner #11714-02qm5j PRED entity: 02qm5j PRED relation: artists PRED expected values: 03xhj6 01whg97 => 56 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1046): 01dw_f (0.71 #4963, 0.56 #7105, 0.45 #9247), 01kcms4 (0.60 #3859, 0.50 #2788, 0.45 #9215), 02ndj5 (0.60 #4102, 0.50 #3031, 0.44 #11601), 06gd4 (0.60 #3549, 0.50 #2478, 0.44 #5691), 01w5n51 (0.60 #3899, 0.50 #2828, 0.44 #6041), 0p76z (0.60 #4119, 0.50 #3048, 0.44 #7333), 01vs4ff (0.60 #3842, 0.50 #2771, 0.33 #7056), 0b_xm (0.60 #3902, 0.50 #2831, 0.33 #7116), 01386_ (0.60 #3790, 0.50 #2719, 0.33 #7004), 032t2z (0.60 #3250, 0.50 #2179, 0.33 #5392) >> Best rule #4963 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 064t9; 0xhtw; 06by7; 016ybr; >> query: (?x9853, 01dw_f) <- artists(?x9853, ?x10144), artists(?x9853, ?x8035), artists(?x9853, ?x7620), artists(?x9853, ?x6469), group(?x75, ?x7620), artists(?x2809, ?x7620), ?x6469 = 04bgy, parent_genre(?x9853, ?x1572), ?x8035 = 095x_, ?x2809 = 05w3f, gender(?x10144, ?x231) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4674 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 064t9; 0xhtw; 06by7; 016ybr; *> query: (?x9853, 03xhj6) <- artists(?x9853, ?x10144), artists(?x9853, ?x8035), artists(?x9853, ?x7620), artists(?x9853, ?x6469), group(?x75, ?x7620), artists(?x2809, ?x7620), ?x6469 = 04bgy, parent_genre(?x9853, ?x1572), ?x8035 = 095x_, ?x2809 = 05w3f, gender(?x10144, ?x231) *> conf = 0.43 ranks of expected_values: 62, 160 EVAL 02qm5j artists 01whg97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 56.000 24.000 0.714 http://example.org/music/genre/artists EVAL 02qm5j artists 03xhj6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 56.000 24.000 0.714 http://example.org/music/genre/artists #11713-01l_yg PRED entity: 01l_yg PRED relation: location PRED expected values: 02_286 => 99 concepts (96 used for prediction) PRED predicted values (max 10 best out of 111): 030qb3t (0.29 #83, 0.23 #8917, 0.22 #7310), 0k049 (0.29 #8, 0.04 #4023, 0.04 #3220), 0r0m6 (0.29 #217, 0.03 #4232, 0.03 #3429), 02_286 (0.20 #2446, 0.17 #41005, 0.17 #16101), 0cc56 (0.14 #57, 0.06 #2466, 0.04 #5678), 01n7q (0.14 #63, 0.03 #4078, 0.03 #11307), 01n4w (0.14 #153, 0.02 #1759, 0.02 #2562), 01vsl (0.14 #370), 0lphb (0.14 #334), 0n6bs (0.14 #166) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01rw116; >> query: (?x9700, 030qb3t) <- type_of_union(?x9700, ?x566), film(?x9700, ?x407), ?x407 = 07xtqq >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2446 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 63 *> proper extension: 01h4rj; *> query: (?x9700, 02_286) <- award(?x9700, ?x2071), ?x2071 = 0bdw6t *> conf = 0.20 ranks of expected_values: 4 EVAL 01l_yg location 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 96.000 0.286 http://example.org/people/person/places_lived./people/place_lived/location #11712-06bw5 PRED entity: 06bw5 PRED relation: educational_institution! PRED expected values: 06bw5 => 194 concepts (125 used for prediction) PRED predicted values (max 10 best out of 260): 01n6r0 (0.20 #145, 0.14 #1224, 0.04 #2841), 012mzw (0.17 #799, 0.07 #1877, 0.06 #2416), 01g7_r (0.17 #775, 0.06 #2392, 0.03 #4010), 02h659 (0.17 #895, 0.06 #2512, 0.02 #7904), 01gwck (0.14 #1566, 0.13 #30200, 0.10 #63128), 025rcc (0.13 #30200, 0.10 #63128, 0.07 #43689), 06bw5 (0.13 #30200, 0.10 #63128, 0.07 #43689), 01_r9k (0.07 #1991, 0.06 #2530, 0.03 #4148), 07wrz (0.07 #1675, 0.06 #2214, 0.03 #3832), 05mv4 (0.07 #1732, 0.06 #2271, 0.01 #11437) >> Best rule #145 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0d23k; 02frhbc; >> query: (?x5777, 01n6r0) <- contains(?x726, ?x5777), ?x726 = 05kj_, featured_film_locations(?x1866, ?x5777) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #30200 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 126 *> proper extension: 05m_8; 0g5llry; 01w1w9; 04knq3; *> query: (?x5777, ?x5887) <- citytown(?x5777, ?x9605), contains(?x9605, ?x5887), location(?x476, ?x9605), county_seat(?x11062, ?x9605) *> conf = 0.13 ranks of expected_values: 7 EVAL 06bw5 educational_institution! 06bw5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 194.000 125.000 0.200 http://example.org/education/educational_institution_campus/educational_institution #11711-01p45_v PRED entity: 01p45_v PRED relation: artists! PRED expected values: 01lyv 05w3f => 117 concepts (52 used for prediction) PRED predicted values (max 10 best out of 249): 016clz (0.38 #4675, 0.32 #8103, 0.31 #3741), 06j6l (0.37 #8770, 0.31 #4406, 0.30 #7522), 0xhtw (0.37 #1261, 0.33 #15297, 0.28 #4687), 01lyv (0.34 #1903, 0.24 #14377, 0.22 #7198), 016jny (0.34 #1974, 0.15 #4775, 0.13 #7269), 0155w (0.32 #1976, 0.27 #1351, 0.22 #3531), 02k_kn (0.28 #4424, 0.21 #7540, 0.18 #8788), 05w3f (0.27 #1282, 0.21 #4708, 0.19 #5645), 07sbbz2 (0.27 #1252, 0.21 #3432, 0.18 #1565), 03_d0 (0.26 #15604, 0.20 #1881, 0.18 #8735) >> Best rule #4675 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 07_3qd; 04mx7s; >> query: (?x1534, 016clz) <- role(?x1534, ?x227), instrumentalists(?x1166, ?x1534), artists(?x671, ?x1534), ?x227 = 0342h >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1903 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 02pt7h_; *> query: (?x1534, 01lyv) <- gender(?x1534, ?x231), ?x231 = 05zppz, artists(?x3108, ?x1534), ?x3108 = 02w4v *> conf = 0.34 ranks of expected_values: 4, 8 EVAL 01p45_v artists! 05w3f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 117.000 52.000 0.378 http://example.org/music/genre/artists EVAL 01p45_v artists! 01lyv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 52.000 0.378 http://example.org/music/genre/artists #11710-01w923 PRED entity: 01w923 PRED relation: gender PRED expected values: 05zppz => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #39, 0.85 #59, 0.85 #61), 02zsn (0.46 #206, 0.46 #168, 0.46 #147) >> Best rule #39 for best value: >> intensional similarity = 6 >> extensional distance = 90 >> proper extension: 015grj; >> query: (?x1694, 05zppz) <- profession(?x1694, ?x1614), profession(?x1694, ?x1183), ?x1183 = 09jwl, type_of_union(?x1694, ?x566), ?x1614 = 01c72t, ?x566 = 04ztj >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w923 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.870 http://example.org/people/person/gender #11709-02ryx0 PRED entity: 02ryx0 PRED relation: award PRED expected values: 054krc => 122 concepts (118 used for prediction) PRED predicted values (max 10 best out of 280): 054krc (0.67 #1274, 0.57 #482, 0.51 #6027), 0c4z8 (0.47 #863, 0.39 #1655, 0.24 #467), 054ks3 (0.43 #535, 0.41 #931, 0.39 #1723), 01by1l (0.38 #902, 0.32 #9219, 0.30 #10407), 0fhpv4 (0.36 #1381, 0.23 #2173, 0.21 #3361), 025m8l (0.25 #909, 0.24 #1305, 0.24 #513), 09sb52 (0.24 #20636, 0.24 #20240, 0.24 #25389), 01bgqh (0.24 #439, 0.22 #835, 0.22 #9548), 02x17c2 (0.23 #2591, 0.20 #5364, 0.19 #5760), 02f73p (0.22 #977, 0.14 #1769, 0.08 #6522) >> Best rule #1274 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0fp_v1x; 0bs1yy; 0134s5; 05_pkf; 04ls53; 08c9b0; 01l79yc; 0fpjyd; 023361; 07v4dm; >> query: (?x5949, 054krc) <- music(?x6620, ?x5949), award(?x5949, ?x1079), region(?x6620, ?x512) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ryx0 award 054krc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 118.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11708-0778p PRED entity: 0778p PRED relation: institution! PRED expected values: 014mlp => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.68 #203, 0.66 #314, 0.66 #627), 02_xgp2 (0.51 #188, 0.43 #1367, 0.41 #589), 07s6fsf (0.50 #45, 0.32 #178, 0.31 #200), 03bwzr4 (0.48 #190, 0.42 #591, 0.42 #212), 0bkj86 (0.34 #963, 0.32 #184, 0.32 #117), 013zdg (0.30 #50, 0.22 #139, 0.21 #116), 04zx3q1 (0.27 #179, 0.20 #46, 0.20 #958), 022h5x (0.20 #63, 0.17 #1851, 0.14 #218), 028dcg (0.20 #62, 0.17 #1851, 0.12 #151), 02mjs7 (0.20 #47, 0.17 #1851, 0.09 #959) >> Best rule #203 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 017j69; >> query: (?x3543, 014mlp) <- institution(?x1771, ?x3543), ?x1771 = 019v9k, currency(?x3543, ?x170) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0778p institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.680 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #11707-01s9ftn PRED entity: 01s9ftn PRED relation: category PRED expected values: 08mbj5d => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.50 #3, 0.38 #12, 0.35 #8) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0320jz; 01wxyx1; 02cllz; 03l3jy; 01pk8v; >> query: (?x13276, 08mbj5d) <- nationality(?x13276, ?x94), gender(?x13276, ?x231), film(?x13276, ?x2037), ?x2037 = 0gvrws1 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01s9ftn category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.500 http://example.org/common/topic/webpage./common/webpage/category #11706-0k3kg PRED entity: 0k3kg PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 108 concepts (56 used for prediction) PRED predicted values (max 10 best out of 8): 09c7w0 (0.88 #224, 0.88 #290, 0.88 #187), 05k7sb (0.25 #223, 0.12 #610, 0.12 #277), 0k3kg (0.12 #277, 0.09 #57, 0.08 #370), 06btq (0.08 #565, 0.05 #674, 0.05 #708), 03rt9 (0.02 #506, 0.02 #522, 0.01 #583), 0d060g (0.01 #238), 0f8l9c (0.01 #120), 03rjj (0.01 #342) >> Best rule #224 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 0nv2x; 0nv5y; >> query: (?x4990, 09c7w0) <- source(?x4990, ?x958), county(?x4989, ?x4990), adjoins(?x5874, ?x4990), ?x958 = 0jbk9 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k3kg second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 56.000 0.880 http://example.org/location/country/second_level_divisions #11705-02m30v PRED entity: 02m30v PRED relation: type_of_union PRED expected values: 04ztj => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.90 #124, 0.88 #178, 0.87 #165), 01g63y (0.81 #182, 0.80 #169, 0.76 #78), 0jgjn (0.81 #182, 0.80 #169, 0.76 #78), 01bl8s (0.03 #40, 0.01 #138, 0.01 #64) >> Best rule #124 for best value: >> intensional similarity = 5 >> extensional distance = 138 >> proper extension: 0d3qd0; >> query: (?x14459, 04ztj) <- location_of_ceremony(?x14459, ?x12931), location_of_ceremony(?x14459, ?x362), location_of_ceremony(?x10591, ?x362), category(?x12931, ?x134), profession(?x10591, ?x1032) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02m30v type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.900 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11704-02pw_n PRED entity: 02pw_n PRED relation: nominated_for! PRED expected values: 03hl6lc => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 238): 0gq9h (0.26 #6521, 0.25 #10584, 0.24 #6999), 0gs9p (0.24 #6523, 0.21 #7001, 0.21 #10347), 0gqwc (0.23 #12912, 0.22 #14347, 0.22 #14587), 094qd5 (0.23 #12912, 0.22 #14347, 0.22 #14587), 09qwmm (0.23 #12912, 0.22 #14347, 0.22 #14587), 026mmy (0.23 #12912, 0.22 #14347, 0.22 #14587), 099cng (0.23 #12912, 0.22 #14347, 0.22 #14587), 019f4v (0.23 #6512, 0.22 #6990, 0.21 #8902), 099c8n (0.23 #58, 0.21 #1015, 0.19 #776), 09td7p (0.23 #94, 0.12 #1051, 0.11 #812) >> Best rule #6521 for best value: >> intensional similarity = 4 >> extensional distance = 640 >> proper extension: 02zk08; >> query: (?x6619, 0gq9h) <- genre(?x6619, ?x53), nominated_for(?x1336, ?x6619), film_release_distribution_medium(?x6619, ?x81), ?x53 = 07s9rl0 >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #2392 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 288 *> proper extension: 03y3bp7; *> query: (?x6619, ?x154) <- titles(?x2480, ?x6619), category(?x6619, ?x134), nominated_for(?x4295, ?x6619), award(?x4295, ?x154) *> conf = 0.20 ranks of expected_values: 18 EVAL 02pw_n nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 74.000 74.000 0.263 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11703-0blbxk PRED entity: 0blbxk PRED relation: nominated_for PRED expected values: 030k94 => 89 concepts (44 used for prediction) PRED predicted values (max 10 best out of 410): 06t6dz (0.37 #6482, 0.28 #19448, 0.26 #51871), 0c00zd0 (0.37 #6482, 0.28 #19448, 0.26 #51871), 0gwf191 (0.37 #6482, 0.28 #19448, 0.26 #51871), 0b7l4x (0.37 #6482, 0.28 #19448, 0.26 #51871), 02b6n9 (0.20 #3032, 0.09 #55116, 0.02 #17616), 05sy_5 (0.18 #2580, 0.09 #55116, 0.02 #5821), 09cr8 (0.16 #1882, 0.09 #55116, 0.02 #16466), 06z8s_ (0.12 #1741, 0.09 #55116, 0.02 #4982), 0418wg (0.10 #1987, 0.09 #55116, 0.01 #16571), 030p35 (0.09 #55116, 0.08 #2338, 0.01 #7200) >> Best rule #6482 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 0fb7c; 01xg_w; >> query: (?x1290, ?x1702) <- award(?x1290, ?x1336), film(?x1290, ?x1702), ?x1336 = 05pcn59 >> conf = 0.37 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 0blbxk nominated_for 030k94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 44.000 0.370 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #11702-02d02 PRED entity: 02d02 PRED relation: draft PRED expected values: 02pq_rp => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 13): 02pq_rp (0.79 #220, 0.78 #179, 0.76 #151), 05vsb7 (0.51 #201, 0.45 #389, 0.42 #432), 0f4vx0 (0.51 #201, 0.45 #389, 0.42 #432), 09l0x9 (0.42 #432, 0.41 #187, 0.38 #173), 02qw1zx (0.42 #432, 0.41 #187, 0.38 #173), 0g3zpp (0.41 #187, 0.38 #173, 0.38 #67), 03nt7j (0.41 #187, 0.38 #173, 0.38 #67), 025tn92 (0.41 #187, 0.38 #173, 0.38 #67), 09th87 (0.41 #187, 0.38 #173, 0.38 #67), 092j54 (0.38 #475, 0.36 #439, 0.36 #533) >> Best rule #220 for best value: >> intensional similarity = 12 >> extensional distance = 17 >> proper extension: 02__x; 05xvj; >> query: (?x8894, 02pq_rp) <- school(?x8894, ?x9131), school(?x8894, ?x1884), school(?x4243, ?x9131), state_province_region(?x9131, ?x1227), major_field_of_study(?x9131, ?x2601), school(?x465, ?x9131), currency(?x9131, ?x170), ?x4243 = 0713r, citytown(?x9131, ?x10904), position(?x8894, ?x4244), major_field_of_study(?x1884, ?x1668), student(?x1884, ?x1815) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d02 draft 02pq_rp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.789 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #11701-0btpm6 PRED entity: 0btpm6 PRED relation: nominated_for! PRED expected values: 02r0csl 02g3v6 => 77 concepts (69 used for prediction) PRED predicted values (max 10 best out of 233): 0gqy2 (0.75 #541, 0.73 #758, 0.67 #6512), 019f4v (0.71 #478, 0.61 #695, 0.44 #912), 0gq9h (0.67 #1136, 0.67 #919, 0.54 #485), 02qt02v (0.67 #6512, 0.67 #6511, 0.66 #10422), 040njc (0.56 #874, 0.51 #1091, 0.42 #440), 09sb52 (0.56 #894, 0.47 #1111, 0.33 #460), 0gs9p (0.55 #1138, 0.53 #921, 0.50 #487), 04dn09n (0.53 #895, 0.49 #1112, 0.42 #461), 02pqp12 (0.50 #916, 0.50 #482, 0.45 #699), 04kxsb (0.47 #951, 0.38 #517, 0.31 #1168) >> Best rule #541 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 09cr8; 0hfzr; 0jqj5; 035_2h; 0h1x5f; >> query: (?x7493, 0gqy2) <- nominated_for(?x1033, ?x7493), nominated_for(?x112, ?x7493), ?x112 = 027dtxw, ?x1033 = 02x73k6, honored_for(?x2988, ?x7493) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1534 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 0d1qmz; *> query: (?x7493, 02g3v6) <- genre(?x7493, ?x812), ?x812 = 01jfsb, nominated_for(?x1500, ?x7493), prequel(?x4336, ?x7493) *> conf = 0.23 ranks of expected_values: 31, 48 EVAL 0btpm6 nominated_for! 02g3v6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 77.000 69.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0btpm6 nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 77.000 69.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11700-0cmt6q PRED entity: 0cmt6q PRED relation: award_nominee! PRED expected values: 05xpms => 78 concepts (33 used for prediction) PRED predicted values (max 10 best out of 805): 0cnl09 (0.87 #3331, 0.81 #67360, 0.81 #48776), 043js (0.81 #67361, 0.81 #67360, 0.81 #48776), 0h3mrc (0.81 #67361, 0.81 #67360, 0.81 #48776), 0cms7f (0.81 #67361, 0.81 #67360, 0.81 #48776), 0cnl80 (0.81 #67361, 0.81 #67360, 0.81 #48776), 0cmt6q (0.60 #3806, 0.57 #1483, 0.53 #8452), 05xpms (0.58 #8950, 0.53 #4304, 0.43 #1981), 0cl0bk (0.53 #3831, 0.53 #8477, 0.43 #1508), 0cj2t3 (0.31 #37161, 0.28 #44130, 0.25 #39485), 06jnvs (0.31 #37161, 0.28 #44130, 0.25 #39485) >> Best rule #3331 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0bt4r4; 0cnl1c; 0cj36c; >> query: (?x6532, 0cnl09) <- award_nominee(?x6532, ?x6263), award_nominee(?x6532, ?x6262), ?x6262 = 060j8b, ?x6263 = 0cms7f >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #8950 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 08wq0g; 0bt7ws; *> query: (?x6532, 05xpms) <- award_nominee(?x6532, ?x6263), award_nominee(?x6532, ?x6262), ?x6262 = 060j8b, film(?x6263, ?x6543) *> conf = 0.58 ranks of expected_values: 7 EVAL 0cmt6q award_nominee! 05xpms CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 78.000 33.000 0.867 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11699-035dk PRED entity: 035dk PRED relation: form_of_government PRED expected values: 01d9r3 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 5): 06cx9 (0.55 #301, 0.53 #56, 0.46 #111), 01d9r3 (0.44 #304, 0.36 #299, 0.34 #114), 018wl5 (0.43 #97, 0.32 #122, 0.30 #82), 01q20 (0.35 #28, 0.33 #98, 0.32 #123), 026wp (0.15 #20, 0.11 #170, 0.10 #145) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 129 >> proper extension: 02wm6l; >> query: (?x2051, 06cx9) <- form_of_government(?x2051, ?x4763), form_of_government(?x7037, ?x4763), ?x7037 = 04hzj >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #304 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 129 *> proper extension: 02wm6l; *> query: (?x2051, 01d9r3) <- form_of_government(?x2051, ?x4763), form_of_government(?x7037, ?x4763), ?x7037 = 04hzj *> conf = 0.44 ranks of expected_values: 2 EVAL 035dk form_of_government 01d9r3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 146.000 0.550 http://example.org/location/country/form_of_government #11698-0lmgy PRED entity: 0lmgy PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 122 concepts (57 used for prediction) PRED predicted values (max 10 best out of 10): 09c7w0 (0.89 #608, 0.81 #697, 0.70 #648), 03gh4 (0.20 #710, 0.20 #709, 0.19 #711), 014wxc (0.20 #710, 0.20 #709, 0.19 #711), 0lmgy (0.20 #710, 0.19 #711, 0.11 #201), 05rgl (0.20 #709, 0.03 #399, 0.03 #605), 02jx1 (0.10 #497, 0.10 #522, 0.08 #547), 0d060g (0.09 #281, 0.02 #479, 0.02 #491), 07ssc (0.03 #665, 0.02 #558, 0.01 #640), 03rjj (0.02 #309, 0.02 #621, 0.02 #322), 0f8l9c (0.02 #314, 0.02 #327, 0.02 #341) >> Best rule #608 for best value: >> intensional similarity = 4 >> extensional distance = 130 >> proper extension: 0fr61; 0l30v; 0mwxl; 0mrq3; 0mlxt; 0mw_q; >> query: (?x9190, 09c7w0) <- currency(?x9190, ?x170), ?x170 = 09nqf, contains(?x6226, ?x9190), featured_film_locations(?x723, ?x6226) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lmgy second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 57.000 0.886 http://example.org/location/country/second_level_divisions #11697-08tq4x PRED entity: 08tq4x PRED relation: film_release_region PRED expected values: 0d060g 06mzp => 86 concepts (74 used for prediction) PRED predicted values (max 10 best out of 319): 0d060g (0.87 #338, 0.83 #1319, 0.81 #992), 03rjj (0.86 #1317, 0.80 #663, 0.80 #1154), 03h64 (0.83 #1383, 0.79 #1220, 0.76 #1545), 05b4w (0.83 #1380, 0.73 #563, 0.70 #5463), 0154j (0.82 #1316, 0.82 #1153, 0.81 #335), 0b90_r (0.82 #1315, 0.75 #1152, 0.74 #334), 06t2t (0.81 #1377, 0.72 #1214, 0.65 #2522), 05qhw (0.79 #1328, 0.79 #1165, 0.77 #347), 015fr (0.79 #1331, 0.78 #1168, 0.77 #350), 03spz (0.76 #1414, 0.75 #1251, 0.63 #2559) >> Best rule #338 for best value: >> intensional similarity = 12 >> extensional distance = 45 >> proper extension: 0ddfwj1; 047svrl; 0cmc26r; 07l50vn; >> query: (?x4355, 0d060g) <- film_regional_debut_venue(?x4355, ?x6601), film_release_region(?x4355, ?x2152), film_release_region(?x4355, ?x1353), film_release_region(?x4355, ?x1003), film_release_region(?x4355, ?x789), film_release_region(?x4355, ?x304), country(?x4355, ?x205), ?x304 = 0d0vqn, ?x1353 = 035qy, ?x789 = 0f8l9c, ?x2152 = 06mkj, ?x1003 = 03gj2 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 24 EVAL 08tq4x film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 86.000 74.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08tq4x film_release_region 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 74.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11696-0c38gj PRED entity: 0c38gj PRED relation: genre PRED expected values: 0lsxr => 83 concepts (37 used for prediction) PRED predicted values (max 10 best out of 86): 05p553 (0.71 #4269, 0.37 #1663, 0.35 #2728), 04xvlr (0.63 #1306, 0.25 #119, 0.24 #237), 03mqtr (0.53 #3081, 0.53 #3675, 0.10 #146), 0lsxr (0.37 #363, 0.34 #1076, 0.29 #719), 02n4kr (0.34 #362, 0.23 #7, 0.23 #1075), 02l7c8 (0.31 #4280, 0.29 #4043, 0.28 #2266), 06n90 (0.31 #841, 0.28 #1789, 0.27 #1553), 060__y (0.22 #1320, 0.19 #370, 0.17 #133), 01hmnh (0.20 #846, 0.18 #1794, 0.16 #2504), 017fp (0.19 #1318, 0.13 #249, 0.13 #131) >> Best rule #4269 for best value: >> intensional similarity = 5 >> extensional distance = 815 >> proper extension: 0vgkd; >> query: (?x4633, 05p553) <- genre(?x4633, ?x3515), genre(?x7114, ?x3515), genre(?x3549, ?x3515), ?x3549 = 017kct, film(?x2587, ?x7114) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #363 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 132 *> proper extension: 0b2v79; 026p_bs; 0p_sc; 04mzf8; 075cph; 0jdgr; 06g77c; 0g68zt; 051zy_b; 0yx7h; ... *> query: (?x4633, 0lsxr) <- genre(?x4633, ?x812), genre(?x4633, ?x53), music(?x4633, ?x4428), ?x53 = 07s9rl0, ?x812 = 01jfsb *> conf = 0.37 ranks of expected_values: 4 EVAL 0c38gj genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 37.000 0.714 http://example.org/film/film/genre #11695-0f6_4 PRED entity: 0f6_4 PRED relation: category PRED expected values: 08mbj5d => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.73 #143, 0.69 #121, 0.67 #118) >> Best rule #143 for best value: >> intensional similarity = 4 >> extensional distance = 955 >> proper extension: 01f38z; >> query: (?x4823, 08mbj5d) <- contains(?x335, ?x4823), location(?x101, ?x335), contains(?x335, ?x8797), ?x8797 = 04ftdq >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f6_4 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 179.000 0.727 http://example.org/common/topic/webpage./common/webpage/category #11694-09wnnb PRED entity: 09wnnb PRED relation: produced_by PRED expected values: 01t6b4 => 83 concepts (50 used for prediction) PRED predicted values (max 10 best out of 104): 01vvb4m (0.11 #7775, 0.10 #17102, 0.10 #12439), 046qq (0.11 #7775, 0.10 #12439, 0.10 #1166), 01t6b4 (0.05 #43, 0.03 #4706, 0.03 #432), 02xnjd (0.04 #662, 0.04 #1439, 0.04 #1829), 054_mz (0.04 #16, 0.01 #4679, 0.01 #7014), 06pj8 (0.04 #4342, 0.03 #3954, 0.03 #5509), 0j_c (0.03 #858, 0.03 #3578, 0.03 #2802), 02r251z (0.03 #1020, 0.03 #3352, 0.02 #2576), 03ktjq (0.03 #979, 0.03 #590, 0.02 #2535), 0pz91 (0.03 #824, 0.02 #2380, 0.02 #3544) >> Best rule #7775 for best value: >> intensional similarity = 3 >> extensional distance = 447 >> proper extension: 016ks5; 03b1sb; >> query: (?x10130, ?x4277) <- nominated_for(?x4277, ?x10130), music(?x10130, ?x2363), religion(?x4277, ?x1985) >> conf = 0.11 => this is the best rule for 2 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 0gbfn9; 06fqlk; 0mbql; 07ghq; 037cr1; 09qljs; 04180vy; *> query: (?x10130, 01t6b4) <- film(?x3056, ?x10130), film(?x3056, ?x814), ?x814 = 084qpk *> conf = 0.05 ranks of expected_values: 3 EVAL 09wnnb produced_by 01t6b4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 50.000 0.110 http://example.org/film/film/produced_by #11693-019pcs PRED entity: 019pcs PRED relation: medal PRED expected values: 02lq67 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.77 #19, 0.75 #27, 0.74 #55) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 0160w; 05v8c; >> query: (?x3635, 02lq67) <- countries_within(?x2467, ?x3635), olympics(?x3635, ?x391), taxonomy(?x3635, ?x939) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019pcs medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.772 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #11692-01dfb6 PRED entity: 01dfb6 PRED relation: industry PRED expected values: 02h400t => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 45): 01mw1 (0.67 #330, 0.43 #424, 0.40 #142), 03qh03g (0.51 #1884, 0.50 #616, 0.43 #381), 04rlf (0.51 #1884, 0.40 #108, 0.35 #1427), 020mfr (0.50 #346, 0.40 #158, 0.33 #299), 0vg8 (0.50 #50, 0.07 #5318, 0.07 #5412), 02jjt (0.40 #102, 0.31 #1044, 0.29 #384), 029g_vk (0.33 #575, 0.20 #622, 0.20 #105), 02vxn (0.29 #1838, 0.27 #2638, 0.22 #6870), 0hz28 (0.29 #405, 0.25 #546, 0.20 #971), 0sydc (0.29 #408, 0.25 #549, 0.20 #643) >> Best rule #330 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01dycg; >> query: (?x9873, 01mw1) <- child(?x9873, ?x2149), service_language(?x9873, ?x254), place_founded(?x9873, ?x739), service_location(?x9873, ?x94), industry(?x9873, ?x12352) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #732 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01_4mn; *> query: (?x9873, 02h400t) <- place_founded(?x9873, ?x739), state_province_region(?x9873, ?x335), ?x335 = 059rby, industry(?x9873, ?x12352) *> conf = 0.27 ranks of expected_values: 12 EVAL 01dfb6 industry 02h400t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 193.000 193.000 0.667 http://example.org/business/business_operation/industry #11691-015w8_ PRED entity: 015w8_ PRED relation: actor PRED expected values: 02wrhj => 88 concepts (63 used for prediction) PRED predicted values (max 10 best out of 835): 0sw62 (0.50 #760, 0.25 #1685, 0.04 #4461), 09b0xs (0.39 #11114, 0.37 #20374, 0.37 #26857), 06jrhz (0.39 #11114, 0.37 #20374, 0.37 #26857), 06pj8 (0.37 #26857, 0.36 #17596, 0.35 #24076), 02wrhj (0.25 #1062, 0.25 #137, 0.06 #3838), 0725ny (0.25 #1566, 0.25 #641, 0.04 #5269), 01tszq (0.25 #1137, 0.25 #212, 0.02 #3913), 083wr9 (0.25 #1838, 0.25 #913, 0.02 #4614), 01ccr8 (0.25 #1573, 0.25 #648, 0.02 #4349), 04mlh8 (0.25 #1496, 0.25 #571, 0.01 #32058) >> Best rule #760 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05f7w84; >> query: (?x3144, 0sw62) <- actor(?x3144, ?x12244), ?x12244 = 031c2r, program(?x2062, ?x3144), program(?x2135, ?x3144) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1062 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09g_31; *> query: (?x3144, 02wrhj) <- actor(?x3144, ?x12244), ?x12244 = 031c2r, country_of_origin(?x3144, ?x94), titles(?x7712, ?x3144) *> conf = 0.25 ranks of expected_values: 5 EVAL 015w8_ actor 02wrhj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 88.000 63.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11690-0135xb PRED entity: 0135xb PRED relation: instrumentalists! PRED expected values: 0l14qv => 125 concepts (52 used for prediction) PRED predicted values (max 10 best out of 117): 0342h (0.66 #2664, 0.61 #2274, 0.60 #2823), 018vs (0.50 #246, 0.50 #11, 0.48 #326), 02hnl (0.47 #186, 0.35 #265, 0.31 #345), 04rzd (0.33 #189, 0.25 #33, 0.24 #348), 0l14qv (0.31 #240, 0.28 #320, 0.27 #161), 018j2 (0.31 #269, 0.28 #349, 0.27 #190), 03gvt (0.27 #294, 0.27 #215, 0.24 #374), 0dwtp (0.25 #15, 0.13 #171, 0.08 #235), 0dwvl (0.25 #14, 0.08 #235, 0.08 #315), 07brj (0.25 #21, 0.08 #235, 0.08 #315) >> Best rule #2664 for best value: >> intensional similarity = 4 >> extensional distance = 553 >> proper extension: 01pbxb; 0f0y8; 053y0s; 016qtt; 01vvydl; 028q6; 07s3vqk; 0197tq; 0411q; 05cljf; ... >> query: (?x7211, 0342h) <- instrumentalists(?x1495, ?x7211), role(?x1495, ?x74), role(?x1437, ?x1495), ?x1437 = 01vdm0 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #240 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 24 *> proper extension: 03c7ln; 0m2l9; 032t2z; 0fpjd_g; 01vsnff; 09prnq; 02jg92; 01w724; 01vn35l; 01vw20_; ... *> query: (?x7211, 0l14qv) <- instrumentalists(?x1495, ?x7211), instrumentalists(?x1473, ?x7211), ?x1495 = 013y1f, role(?x1655, ?x1473), ?x1655 = 01hww_ *> conf = 0.31 ranks of expected_values: 5 EVAL 0135xb instrumentalists! 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 125.000 52.000 0.656 http://example.org/music/instrument/instrumentalists #11689-013q0p PRED entity: 013q0p PRED relation: film! PRED expected values: 020trj 01p8r8 => 82 concepts (34 used for prediction) PRED predicted values (max 10 best out of 963): 0154d7 (0.40 #5647), 0f502 (0.33 #2834, 0.12 #759, 0.09 #6983), 0c0k1 (0.30 #5652, 0.03 #10373, 0.02 #11875), 0f6_x (0.30 #4773, 0.02 #13070, 0.01 #31741), 0bksh (0.27 #7076, 0.25 #852, 0.10 #5002), 019pm_ (0.27 #2075, 0.13 #68467, 0.12 #467), 026c1 (0.25 #355, 0.18 #6579, 0.11 #2430), 023n39 (0.25 #1196, 0.18 #7420, 0.10 #5346), 011zd3 (0.25 #371, 0.18 #6595, 0.10 #4521), 01rrd4 (0.25 #1137, 0.18 #7361, 0.10 #5287) >> Best rule #5647 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0c8tkt; 0prrm; 0199wf; >> query: (?x4717, 0154d7) <- film(?x6059, ?x4717), country(?x4717, ?x1264), ?x6059 = 01tnbn, combatants(?x151, ?x1264) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #24536 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 159 *> proper extension: 04cf_l; 09rfh9; *> query: (?x4717, 01p8r8) <- prequel(?x408, ?x4717), genre(?x4717, ?x225), country(?x4717, ?x94) *> conf = 0.01 ranks of expected_values: 894 EVAL 013q0p film! 01p8r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 82.000 34.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 013q0p film! 020trj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 34.000 0.400 http://example.org/film/actor/film./film/performance/film #11688-01c8v0 PRED entity: 01c8v0 PRED relation: artists! PRED expected values: 01lyv 03jsvl => 120 concepts (69 used for prediction) PRED predicted values (max 10 best out of 222): 064t9 (0.67 #634, 0.55 #2495, 0.54 #2805), 0xhtw (0.50 #18, 0.41 #12732, 0.36 #1878), 06j6l (0.40 #3460, 0.31 #2530, 0.30 #8422), 0gywn (0.40 #3470, 0.29 #8432, 0.23 #9982), 016clz (0.39 #1865, 0.29 #15820, 0.27 #5277), 05bt6j (0.38 #664, 0.29 #3145, 0.28 #2835), 0glt670 (0.34 #8414, 0.30 #9034, 0.25 #661), 03_d0 (0.33 #12726, 0.33 #3423, 0.25 #5284), 0ggx5q (0.29 #700, 0.24 #2561, 0.24 #3181), 02lnbg (0.29 #680, 0.21 #2541, 0.21 #2851) >> Best rule #634 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0lk90; 01vrt_c; 01l1sq; 01vsl3_; 03h_fk5; 01vsykc; 0gy6z9; 017xm3; 01wv9p; 01vw20h; ... >> query: (?x4029, 064t9) <- location_of_ceremony(?x4029, ?x1957), award_winner(?x4584, ?x4029), artist(?x3887, ?x4029) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 017959; 0167xy; *> query: (?x4029, 03jsvl) <- artists(?x12178, ?x4029), ?x12178 = 04qftx, origin(?x4029, ?x4030) *> conf = 0.25 ranks of expected_values: 16, 18 EVAL 01c8v0 artists! 03jsvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 120.000 69.000 0.667 http://example.org/music/genre/artists EVAL 01c8v0 artists! 01lyv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 120.000 69.000 0.667 http://example.org/music/genre/artists #11687-07w8f PRED entity: 07w8f PRED relation: religion! PRED expected values: 0jrny 011vx3 0pqzh => 42 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2106): 0mb5x (0.33 #13494, 0.33 #7094, 0.25 #12424), 0jcx (0.33 #13049, 0.33 #6649, 0.25 #11979), 07cbs (0.33 #438, 0.25 #2569, 0.25 #1503), 03_nq (0.33 #760, 0.25 #2891, 0.25 #1825), 0dq2k (0.33 #452, 0.25 #2583, 0.25 #1517), 06myp (0.33 #7295, 0.25 #11558, 0.22 #14763), 03rx9 (0.33 #7209, 0.25 #11472, 0.22 #14677), 01pw9v (0.33 #7189, 0.25 #11452, 0.22 #14657), 0kp2_ (0.33 #6967, 0.25 #11230, 0.22 #14435), 01dvtx (0.33 #6716, 0.25 #10979, 0.22 #14184) >> Best rule #13494 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 0n2g; 0kq2; >> query: (?x13061, 0mb5x) <- religion(?x12402, ?x13061), religion(?x1913, ?x13061), people(?x10199, ?x1913), people(?x5741, ?x1913), people(?x5741, ?x10511), ?x10511 = 042d1, type_of_union(?x1913, ?x566), place_of_burial(?x1913, ?x7496), profession(?x1913, ?x3342), location(?x12402, ?x335), influenced_by(?x587, ?x12402) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #11724 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0flw86; *> query: (?x13061, ?x973) <- religion(?x1913, ?x13061), people(?x10199, ?x1913), people(?x5741, ?x1913), religion(?x94, ?x13061), risk_factors(?x10199, ?x231), people(?x10199, ?x973), symptom_of(?x4905, ?x10199), ?x94 = 09c7w0 *> conf = 0.04 ranks of expected_values: 1094, 1216 EVAL 07w8f religion! 0pqzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 15.000 0.333 http://example.org/people/person/religion EVAL 07w8f religion! 011vx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 15.000 0.333 http://example.org/people/person/religion EVAL 07w8f religion! 0jrny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 15.000 0.333 http://example.org/people/person/religion #11686-01n7q PRED entity: 01n7q PRED relation: adjoins! PRED expected values: 0vmt => 193 concepts (166 used for prediction) PRED predicted values (max 10 best out of 1418): 05rgl (0.83 #53209, 0.82 #39109, 0.81 #68092), 0d060g (0.25 #3139, 0.25 #2356, 0.20 #68091), 07h34 (0.25 #968, 0.17 #10351, 0.15 #37729), 0vbk (0.25 #1015, 0.17 #10398, 0.12 #23692), 0498y (0.25 #981, 0.17 #10364, 0.11 #8799), 05tbn (0.25 #10344, 0.13 #21294, 0.13 #31464), 05mph (0.25 #1071, 0.12 #23748, 0.11 #37832), 03s0w (0.25 #826, 0.11 #8644, 0.11 #31329), 03v0t (0.25 #970, 0.11 #8788, 0.09 #9571), 05fhy (0.25 #832, 0.11 #8650, 0.09 #9433) >> Best rule #53209 for best value: >> intensional similarity = 2 >> extensional distance = 67 >> proper extension: 035p3; >> query: (?x1227, ?x726) <- featured_film_locations(?x3859, ?x1227), adjoins(?x1227, ?x726) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #23501 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 0g0syc; *> query: (?x1227, 0vmt) <- district_represented(?x6728, ?x1227), district_represented(?x1027, ?x1227), ?x1027 = 02bn_p, ?x6728 = 070mff *> conf = 0.09 ranks of expected_values: 77 EVAL 01n7q adjoins! 0vmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 193.000 166.000 0.832 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #11685-0js9s PRED entity: 0js9s PRED relation: award_winner! PRED expected values: 027b9ly => 106 concepts (104 used for prediction) PRED predicted values (max 10 best out of 232): 02rdyk7 (0.54 #1265, 0.53 #1264, 0.37 #17276), 0gr51 (0.54 #1265, 0.53 #1264, 0.37 #17276), 03hl6lc (0.54 #1265, 0.53 #1264, 0.37 #17276), 02x17s4 (0.54 #1265, 0.53 #1264, 0.37 #17276), 0f_nbyh (0.54 #1265, 0.53 #1264, 0.37 #17276), 0p9sw (0.29 #865, 0.14 #444, 0.07 #23594), 027c924 (0.25 #1698, 0.22 #1275, 0.18 #2120), 027b9ly (0.24 #1500, 0.14 #1923, 0.13 #2345), 02r22gf (0.22 #875, 0.11 #454, 0.07 #23594), 02wkmx (0.22 #1279, 0.10 #1702, 0.08 #2546) >> Best rule #1265 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 094wz7q; >> query: (?x6589, ?x8059) <- award(?x6589, ?x8059), crewmember(?x5418, ?x6589), award_winner(?x8059, ?x846) >> conf = 0.54 => this is the best rule for 5 predicted values *> Best rule #1500 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 01t07j; 01_vfy; 04k25; 01f7v_; 022_q8; 05cgy8; 020x5r; 02f93t; 026670; 0gdqy; ... *> query: (?x6589, 027b9ly) <- nationality(?x6589, ?x1023), award(?x6589, ?x1587), ?x1587 = 02rdyk7 *> conf = 0.24 ranks of expected_values: 8 EVAL 0js9s award_winner! 027b9ly CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 106.000 104.000 0.537 http://example.org/award/award_category/winners./award/award_honor/award_winner #11684-0c9c0 PRED entity: 0c9c0 PRED relation: location PRED expected values: 01b8w_ => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 174): 02_286 (0.31 #35331, 0.25 #37, 0.24 #6453), 01cx_ (0.25 #162, 0.06 #2568, 0.05 #7380), 04rrd (0.25 #97, 0.02 #5711, 0.01 #13732), 0dqyw (0.25 #542), 0rh6k (0.23 #806, 0.18 #3212, 0.17 #4014), 01_d4 (0.15 #903, 0.14 #1705, 0.11 #2507), 01n7q (0.15 #865, 0.09 #3271, 0.09 #4073), 05qtj (0.11 #10427, 0.03 #9864, 0.03 #65212), 04jpl (0.10 #35311, 0.09 #64989, 0.06 #9641), 0r0m6 (0.08 #1019, 0.07 #1821, 0.06 #5831) >> Best rule #35331 for best value: >> intensional similarity = 3 >> extensional distance = 633 >> proper extension: 0136p1; 01wzlxj; 0884fm; 03hzl42; 01l03w2; 0fn5bx; 01_p6t; 0gs5q; 02qjpv5; >> query: (?x2790, 02_286) <- location(?x2790, ?x1523), award_nominee(?x2790, ?x2101), film_release_region(?x204, ?x1523) >> conf = 0.31 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c9c0 location 01b8w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 117.000 0.309 http://example.org/people/person/places_lived./people/place_lived/location #11683-02q87z6 PRED entity: 02q87z6 PRED relation: produced_by PRED expected values: 0fvf9q => 95 concepts (54 used for prediction) PRED predicted values (max 10 best out of 176): 09d5d5 (0.25 #296, 0.02 #2230, 0.02 #3002), 040rjq (0.25 #372), 02kxbwx (0.14 #1190, 0.02 #3123, 0.02 #5446), 02bfxb (0.12 #500, 0.04 #1662, 0.03 #2434), 06t8b (0.12 #654, 0.04 #1041), 014zcr (0.12 #395, 0.04 #782), 0js9s (0.12 #613, 0.03 #1775, 0.02 #2547), 06pjs (0.12 #690), 0c1pj (0.12 #408), 02lfcm (0.12 #400) >> Best rule #296 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0gxtknx; 02wtp6; >> query: (?x5964, 09d5d5) <- film(?x494, ?x5964), country(?x5964, ?x94), film_crew_role(?x5964, ?x137), ?x494 = 03w1v2 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2326 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 98 *> proper extension: 05dy7p; *> query: (?x5964, 0fvf9q) <- currency(?x5964, ?x170), genre(?x5964, ?x53), film(?x3873, ?x5964), costume_design_by(?x5964, ?x3685) *> conf = 0.05 ranks of expected_values: 24 EVAL 02q87z6 produced_by 0fvf9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 95.000 54.000 0.250 http://example.org/film/film/produced_by #11682-01vw37m PRED entity: 01vw37m PRED relation: award PRED expected values: 01bgqh 023vrq => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 269): 099tbz (0.77 #13074, 0.76 #6737, 0.73 #13471), 01by1l (0.49 #509, 0.38 #3283, 0.37 #2490), 01bgqh (0.37 #2024, 0.36 #2420, 0.31 #439), 023vrq (0.36 #716, 0.21 #4282, 0.19 #3490), 03qbh5 (0.34 #2580, 0.33 #2184, 0.22 #3373), 054krc (0.31 #880, 0.30 #4843, 0.25 #5635), 02f6xy (0.31 #595, 0.18 #3369, 0.15 #32497), 02f76h (0.31 #573, 0.16 #4139, 0.15 #32497), 02f5qb (0.31 #551, 0.16 #2136, 0.16 #2532), 054ks3 (0.29 #934, 0.25 #4897, 0.25 #2519) >> Best rule #13074 for best value: >> intensional similarity = 2 >> extensional distance = 591 >> proper extension: 01lcxbb; 013rds; >> query: (?x6264, ?x704) <- artists(?x2937, ?x6264), award_winner(?x704, ?x6264) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2024 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 91 *> proper extension: 012d40; 0892sx; 03y82t6; 01vzxld; 02ktrs; *> query: (?x6264, 01bgqh) <- artists(?x2937, ?x6264), award_winner(?x192, ?x6264), film(?x6264, ?x3700) *> conf = 0.37 ranks of expected_values: 3, 4 EVAL 01vw37m award 023vrq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 102.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vw37m award 01bgqh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 102.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11681-04pk9 PRED entity: 04pk9 PRED relation: religion! PRED expected values: 04fhn_ 04fzk => 38 concepts (18 used for prediction) PRED predicted values (max 10 best out of 3965): 04v7k2 (0.50 #1025, 0.29 #5202, 0.29 #4158), 049m19 (0.50 #942, 0.29 #5119, 0.29 #4075), 03xnq9_ (0.50 #466, 0.29 #4643, 0.29 #3599), 07_m9_ (0.50 #388, 0.29 #4565, 0.29 #3521), 0dj5q (0.50 #555, 0.29 #4732, 0.29 #3688), 04rfq (0.40 #2063, 0.29 #5196, 0.29 #4152), 0mb5x (0.33 #7998, 0.27 #11134, 0.27 #10090), 08f3b1 (0.33 #5265, 0.18 #12580, 0.14 #2133), 043gj (0.29 #4555, 0.29 #3511, 0.25 #378), 0cqt90 (0.29 #4480, 0.29 #3436, 0.25 #303) >> Best rule #1025 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 01lp8; 0c8wxp; >> query: (?x8613, 04v7k2) <- religion(?x11446, ?x8613), religion(?x10186, ?x8613), religion(?x9836, ?x8613), religion(?x3778, ?x8613), film(?x10186, ?x5230), location(?x9836, ?x1264), profession(?x10186, ?x524), ?x524 = 02jknp, inductee(?x1091, ?x11446), ?x3778 = 07h34, languages(?x11446, ?x254) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #18812 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 15 *> proper extension: 05tgm; *> query: (?x8613, ?x51) <- religion(?x10186, ?x8613), religion(?x8898, ?x8613), profession(?x10186, ?x1032), award(?x8898, ?x401), award_nominee(?x8898, ?x396), artist(?x7448, ?x8898), origin(?x8898, ?x6895), profession(?x51, ?x1032), location(?x8898, ?x739) *> conf = 0.02 ranks of expected_values: 3089, 3130 EVAL 04pk9 religion! 04fzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 38.000 18.000 0.500 http://example.org/people/person/religion EVAL 04pk9 religion! 04fhn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 38.000 18.000 0.500 http://example.org/people/person/religion #11680-02dw1_ PRED entity: 02dw1_ PRED relation: category PRED expected values: 08mbj5d => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.89 #25, 0.88 #38, 0.88 #30) >> Best rule #25 for best value: >> intensional similarity = 6 >> extensional distance = 26 >> proper extension: 0168cl; 01vrncs; 02x_h0; >> query: (?x5838, 08mbj5d) <- artists(?x284, ?x5838), origin(?x5838, ?x6885), location(?x576, ?x6885), artist(?x4483, ?x5838), contains(?x512, ?x6885), ?x4483 = 0mzkr >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02dw1_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.893 http://example.org/common/topic/webpage./common/webpage/category #11679-06mzp PRED entity: 06mzp PRED relation: exported_to! PRED expected values: 09c7w0 => 210 concepts (143 used for prediction) PRED predicted values (max 10 best out of 78): 09c7w0 (0.48 #479, 0.40 #239, 0.35 #777), 04sj3 (0.29 #233, 0.27 #293, 0.23 #592), 0l3h (0.21 #221, 0.20 #281, 0.15 #760), 0h3y (0.21 #184, 0.19 #723, 0.19 #484), 0ctw_b (0.20 #253, 0.19 #493, 0.19 #312), 0jdd (0.20 #272, 0.14 #452, 0.14 #571), 05r4w (0.20 #2577, 0.14 #478, 0.10 #3961), 047t_ (0.19 #516, 0.19 #1171, 0.18 #1710), 0d05w3 (0.18 #2608, 0.10 #509, 0.08 #4657), 03_3d (0.14 #482, 0.13 #242, 0.12 #301) >> Best rule #479 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01f08r; >> query: (?x774, 09c7w0) <- currency(?x774, ?x170), location(?x2580, ?x774), exported_to(?x6401, ?x774) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mzp exported_to! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 210.000 143.000 0.476 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #11678-030k94 PRED entity: 030k94 PRED relation: honored_for! PRED expected values: 09p2r9 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 85): 05c1t6z (0.28 #247, 0.24 #1545, 0.22 #2715), 0gvstc3 (0.27 #263, 0.22 #1561, 0.20 #381), 0bx6zs (0.25 #107, 0.22 #2715, 0.10 #5904), 09q_6t (0.25 #4, 0.22 #2715, 0.10 #5904), 07z31v (0.25 #25, 0.22 #2715, 0.10 #261), 0bxs_d (0.25 #96, 0.10 #332, 0.09 #1630), 07y_p6 (0.25 #79, 0.10 #315, 0.08 #787), 0hn821n (0.22 #2715, 0.10 #5904, 0.10 #347), 09p2r9 (0.22 #2715, 0.10 #5904, 0.09 #8502), 09qftb (0.22 #2715, 0.10 #5904, 0.09 #8502) >> Best rule #247 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 07s8z_l; >> query: (?x3169, 05c1t6z) <- program(?x6913, ?x3169), honored_for(?x2292, ?x3169), award(?x6913, ?x2016), program_creator(?x782, ?x6913) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #2715 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 145 *> proper extension: 04bp0l; *> query: (?x3169, ?x10337) <- nominated_for(?x9435, ?x3169), nominated_for(?x7489, ?x3169), genre(?x3169, ?x53), award_winner(?x782, ?x7489), award_winner(?x10337, ?x9435) *> conf = 0.22 ranks of expected_values: 9 EVAL 030k94 honored_for! 09p2r9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 91.000 91.000 0.283 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #11677-0f1vrl PRED entity: 0f1vrl PRED relation: profession PRED expected values: 0dxtg => 193 concepts (99 used for prediction) PRED predicted values (max 10 best out of 80): 0dxtg (0.86 #4633, 0.85 #3441, 0.85 #2994), 02hrh1q (0.83 #13875, 0.76 #12086, 0.73 #8061), 01d_h8 (0.76 #12077, 0.74 #1198, 0.64 #5370), 09jwl (0.50 #317, 0.40 #10450, 0.38 #10301), 0dz3r (0.50 #300, 0.31 #10135, 0.31 #5515), 016z4k (0.50 #302, 0.25 #153, 0.24 #13566), 02jknp (0.39 #11483, 0.37 #3137, 0.35 #753), 0n1h (0.38 #310, 0.25 #161, 0.15 #8803), 018gz8 (0.37 #5679, 0.37 #6424, 0.35 #5381), 02krf9 (0.33 #4795, 0.33 #9862, 0.32 #9265) >> Best rule #4633 for best value: >> intensional similarity = 6 >> extensional distance = 83 >> proper extension: 03cs_z7; >> query: (?x1798, 0dxtg) <- program_creator(?x12886, ?x1798), program_creator(?x50, ?x1798), languages(?x12886, ?x254), country_of_origin(?x12886, ?x94), ?x254 = 02h40lc, genre(?x50, ?x811) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f1vrl profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 99.000 0.859 http://example.org/people/person/profession #11676-03rjj PRED entity: 03rjj PRED relation: countries_within! PRED expected values: 02j9z => 250 concepts (222 used for prediction) PRED predicted values (max 10 best out of 5): 02j9z (0.57 #41, 0.56 #101, 0.54 #164), 0j0k (0.34 #375, 0.31 #320, 0.25 #311), 02qkt (0.33 #394, 0.28 #657, 0.27 #662), 059g4 (0.28 #52, 0.20 #151, 0.14 #237), 0dg3n1 (0.22 #664, 0.22 #754, 0.22 #391) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 09c7w0; 0jgd; 0d0vqn; 07ssc; 0f8l9c; 0k6nt; 059j2; 0345h; 02vzc; 06mkj; ... >> query: (?x205, 02j9z) <- film_release_region(?x66, ?x205), country(?x1009, ?x205), ?x1009 = 01m13b >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rjj countries_within! 02j9z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 250.000 222.000 0.571 http://example.org/base/locations/continents/countries_within #11675-0lk8j PRED entity: 0lk8j PRED relation: olympics! PRED expected values: 0jgd 0b90_r 05v8c 0345h => 54 concepts (47 used for prediction) PRED predicted values (max 10 best out of 221): 0ctw_b (0.75 #1080, 0.71 #370, 0.57 #253), 06c1y (0.71 #384, 0.71 #267, 0.70 #1094), 0b90_r (0.71 #357, 0.71 #240, 0.65 #1067), 03shp (0.71 #418, 0.70 #1128, 0.67 #183), 01znc_ (0.71 #266, 0.67 #148, 0.65 #1093), 015qh (0.71 #382, 0.60 #1092, 0.50 #147), 06qd3 (0.71 #379, 0.57 #262, 0.55 #1089), 0154j (0.70 #1068, 0.64 #1664, 0.60 #6), 015fr (0.65 #1076, 0.57 #366, 0.57 #249), 05b4w (0.62 #3489, 0.60 #1707, 0.59 #3254) >> Best rule #1080 for best value: >> intensional similarity = 9 >> extensional distance = 18 >> proper extension: 0kbws; >> query: (?x2131, 0ctw_b) <- olympics(?x94, ?x2131), olympics(?x456, ?x2131), sports(?x2131, ?x5396), country(?x5396, ?x608), country(?x5396, ?x291), ?x291 = 0h3y, ?x608 = 02k54, olympics(?x5396, ?x358), ?x456 = 05qhw >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #357 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 5 *> proper extension: 06sks6; *> query: (?x2131, 0b90_r) <- olympics(?x94, ?x2131), olympics(?x429, ?x2131), sports(?x2131, ?x5396), sports(?x2131, ?x2315), sports(?x2131, ?x766), ?x5396 = 0486tv, ?x429 = 03rt9, ?x766 = 01hp22, ?x2315 = 06wrt, medal(?x2131, ?x422) *> conf = 0.71 ranks of expected_values: 3, 11, 17, 59 EVAL 0lk8j olympics! 0345h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 54.000 47.000 0.750 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0lk8j olympics! 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 54.000 47.000 0.750 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0lk8j olympics! 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 54.000 47.000 0.750 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0lk8j olympics! 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 54.000 47.000 0.750 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #11674-02bjrlw PRED entity: 02bjrlw PRED relation: languages_spoken! PRED expected values: 0222qb => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 68): 059_w (0.50 #433, 0.33 #705, 0.33 #93), 0c41n (0.50 #476, 0.33 #68, 0.25 #544), 0fk3s (0.50 #469, 0.33 #61, 0.25 #537), 03x1x (0.50 #456, 0.33 #48, 0.25 #524), 0g8_vp (0.50 #425, 0.33 #17, 0.25 #493), 013b6_ (0.40 #656, 0.33 #44, 0.25 #452), 071x0k (0.33 #76, 0.33 #8, 0.30 #960), 0x67 (0.33 #78, 0.33 #10, 0.30 #894), 0fk1z (0.33 #132, 0.30 #948, 0.17 #744), 03w9bjf (0.33 #45, 0.29 #1473, 0.25 #453) >> Best rule #433 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 064_8sq; >> query: (?x90, 059_w) <- languages(?x8600, ?x90), ?x8600 = 0g7k2g, language(?x4464, ?x90), nominated_for(?x3860, ?x4464), major_field_of_study(?x2142, ?x90) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #856 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 03_9r; 0jzc; 04h9h; *> query: (?x90, 0222qb) <- languages(?x914, ?x90), language(?x11296, ?x90), language(?x6922, ?x90), language(?x3681, ?x90), film(?x3078, ?x6922), nominated_for(?x382, ?x3681), film_crew_role(?x11296, ?x137), major_field_of_study(?x3995, ?x90) *> conf = 0.10 ranks of expected_values: 55 EVAL 02bjrlw languages_spoken! 0222qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 80.000 80.000 0.500 http://example.org/people/ethnicity/languages_spoken #11673-03ksy PRED entity: 03ksy PRED relation: company! PRED expected values: 021q1c 09d6p2 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 44): 0krdk (0.65 #446, 0.57 #1640, 0.57 #800), 0dq_5 (0.60 #1650, 0.59 #2006, 0.56 #2402), 01yc02 (0.46 #404, 0.35 #448, 0.33 #272), 0dq3c (0.42 #1636, 0.38 #398, 0.36 #2168), 09d6p2 (0.38 #545, 0.30 #811, 0.27 #2183), 021q1c (0.33 #361, 0.32 #980, 0.26 #493), 01dz7z (0.33 #87, 0.25 #219, 0.25 #175), 0789n (0.33 #62, 0.25 #194, 0.25 #150), 04n1q6 (0.33 #362, 0.16 #626, 0.15 #406), 04192r (0.33 #303, 0.12 #611, 0.11 #1585) >> Best rule #446 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 03mdt; 01_4lx; >> query: (?x3439, 0krdk) <- company(?x346, ?x3439), child(?x3439, ?x4278), ?x346 = 060c4 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #545 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 027lf1; 01bfjy; *> query: (?x3439, 09d6p2) <- company(?x346, ?x3439), child(?x3439, ?x4278), jurisdiction_of_office(?x346, ?x47) *> conf = 0.38 ranks of expected_values: 5, 6 EVAL 03ksy company! 09d6p2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 128.000 128.000 0.647 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 03ksy company! 021q1c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 128.000 128.000 0.647 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #11672-01yx7f PRED entity: 01yx7f PRED relation: contact_category PRED expected values: 03w5xm => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 2): 03w5xm (0.90 #60, 0.89 #120, 0.88 #167), 014dgf (0.52 #19, 0.38 #15, 0.28 #74) >> Best rule #60 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 018mxj; 064f29; 069b85; >> query: (?x10637, 03w5xm) <- organization(?x4682, ?x10637), contact_category(?x10637, ?x6046), service_location(?x10637, ?x335), category(?x10637, ?x134), industry(?x10637, ?x12014) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01yx7f contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.900 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #11671-049wm PRED entity: 049wm PRED relation: contains! PRED expected values: 049yf => 33 concepts (22 used for prediction) PRED predicted values (max 10 best out of 160): 049yf (0.71 #4473, 0.69 #5370, 0.69 #5369), 09c7w0 (0.62 #8058, 0.61 #2687, 0.53 #8952), 02j71 (0.46 #17897, 0.24 #8055), 07ssc (0.43 #6296, 0.21 #7191, 0.18 #8086), 02qkt (0.37 #895, 0.05 #10189, 0.04 #19139), 0g3bw (0.31 #167, 0.02 #6432, 0.01 #7327), 02jx1 (0.29 #7246, 0.24 #6351, 0.12 #9929), 0345h (0.12 #6346, 0.09 #7241, 0.04 #9030), 03rk0 (0.11 #6401, 0.08 #7296, 0.08 #1031), 03rjj (0.11 #6275, 0.08 #7170, 0.05 #1799) >> Best rule #4473 for best value: >> intensional similarity = 3 >> extensional distance = 251 >> proper extension: 0t015; 0s3y5; 02dtg; 0ydpd; 0f2r6; 0_3cs; 01mc11; 0wh3; 0_7z2; 013jz2; ... >> query: (?x13343, ?x1054) <- administrative_division(?x13343, ?x12984), contains(?x252, ?x13343), contains(?x1054, ?x12984) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049wm contains! 049yf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 22.000 0.712 http://example.org/location/location/contains #11670-0bmpm PRED entity: 0bmpm PRED relation: language PRED expected values: 02h40lc => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 34): 02h40lc (0.96 #769, 0.96 #710, 0.95 #1064), 064_8sq (0.33 #22, 0.21 #376, 0.18 #317), 04306rv (0.22 #5, 0.14 #949, 0.14 #477), 02bjrlw (0.11 #60, 0.09 #827, 0.08 #119), 06nm1 (0.11 #1606, 0.10 #188, 0.10 #1963), 0c_v2 (0.10 #194, 0.05 #253, 0.03 #489), 0jzc (0.08 #138, 0.05 #197, 0.05 #256), 04h9h (0.08 #1164, 0.05 #869, 0.05 #338), 06b_j (0.07 #1797, 0.07 #1499, 0.07 #1737), 03_9r (0.05 #187, 0.05 #4576, 0.05 #1486) >> Best rule #769 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 0cq8nx; >> query: (?x3003, 02h40lc) <- film_art_direction_by(?x3003, ?x12186), award_winner(?x3003, ?x902), nominated_for(?x591, ?x3003), film_release_distribution_medium(?x3003, ?x81) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bmpm language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.962 http://example.org/film/film/language #11669-0162kb PRED entity: 0162kb PRED relation: category PRED expected values: 08mbj5d => 4 concepts (4 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.71 #4, 0.71 #3, 0.67 #2) >> Best rule #4 for best value: >> intensional similarity = 86 >> extensional distance = 5 >> proper extension: 0b3wk; 07t58; >> query: (?x13950, ?x134) <- legislative_sessions(?x13950, ?x11189), legislative_sessions(?x10543, ?x11189), legislative_sessions(?x8777, ?x11189), legislative_sessions(?x8776, ?x11189), district_represented(?x11189, ?x14386), district_represented(?x11189, ?x14129), district_represented(?x11189, ?x12125), district_represented(?x11189, ?x11542), district_represented(?x11189, ?x10544), district_represented(?x11189, ?x10063), district_represented(?x11189, ?x9370), district_represented(?x11189, ?x7468), district_represented(?x11189, ?x3824), district_represented(?x11189, ?x3474), state_province_region(?x4342, ?x10063), country(?x14386, ?x279), state(?x1411, ?x10063), legislative_sessions(?x3099, ?x11189), legislative_sessions(?x11189, ?x3473), geographic_distribution(?x14365, ?x9370), state_province_region(?x5085, ?x9370), featured_film_locations(?x5313, ?x10063), jurisdiction_of_office(?x14293, ?x14386), administrative_division(?x2541, ?x9370), adjoins(?x6842, ?x9370), taxonomy(?x9370, ?x939), category(?x8776, ?x134), state_province_region(?x5679, ?x3474), partially_contains(?x9370, ?x10954), ?x10954 = 0lm0n, ?x134 = 08mbj5d, ?x939 = 04n6k, student(?x2981, ?x3099), profession(?x3099, ?x5805), ?x5805 = 0fj9f, location(?x3099, ?x1658), contains(?x10063, ?x14421), state(?x8823, ?x12125), contains(?x12971, ?x3474), politician(?x3098, ?x3099), location(?x927, ?x12125), adjoins(?x12125, ?x12854), jurisdiction_of_office(?x900, ?x12125), basic_title(?x3099, ?x182), contains(?x12125, ?x10889), contains(?x390, ?x12125), gender(?x3099, ?x231), adjoins(?x7058, ?x11542), contains(?x11542, ?x12135), student(?x2327, ?x3099), contains(?x3824, ?x1275), state(?x6224, ?x3474), adjoins(?x4198, ?x3824), ?x7058 = 050ks, adjoins(?x3824, ?x1274), location(?x10626, ?x9370), type_of_union(?x3099, ?x566), contains(?x3474, ?x5678), state_province_region(?x1914, ?x7468), capital(?x7468, ?x8916), location(?x8720, ?x7468), contains(?x7468, ?x1036), religion(?x3099, ?x9091), ?x1914 = 03xsby, ?x900 = 0fkvn, ?x4198 = 05fky, state_province_region(?x12356, ?x11542), time_zones(?x7468, ?x2950), state_province_region(?x12737, ?x3824), adjoins(?x953, ?x7468), ?x1274 = 04ykg, ?x566 = 04ztj, featured_film_locations(?x1721, ?x7468), contains(?x9370, ?x11016), partially_contains(?x7468, ?x6195), ?x6195 = 0k3nk, vacationer(?x7468, ?x4884), ?x2950 = 02lcqs, legislative_sessions(?x8776, ?x10543), district_represented(?x3473, ?x14129), country(?x10544, ?x279), district_represented(?x8777, ?x3474), first_level_division_of(?x10544, ?x279), adjoins(?x7468, ?x14129), adjoins(?x10063, ?x10544), district_represented(?x10543, ?x3824) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0162kb category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 4.000 4.000 0.714 http://example.org/common/topic/webpage./common/webpage/category #11668-01zc2w PRED entity: 01zc2w PRED relation: major_field_of_study! PRED expected values: 014mlp => 65 concepts (36 used for prediction) PRED predicted values (max 10 best out of 18): 014mlp (0.88 #509, 0.88 #351, 0.88 #450), 04zx3q1 (0.79 #130, 0.73 #210, 0.73 #93), 03bwzr4 (0.77 #199, 0.77 #219, 0.73 #102), 0bkj86 (0.73 #195, 0.70 #215, 0.70 #80), 03mkk4 (0.70 #179, 0.55 #148, 0.41 #73), 0bjrnt (0.67 #41, 0.60 #78, 0.60 #23), 071tyz (0.55 #148, 0.54 #288, 0.41 #73), 013zdg (0.52 #208, 0.51 #228, 0.49 #169), 027f2w (0.52 #208, 0.51 #228, 0.49 #169), 02cq61 (0.52 #208, 0.51 #228, 0.49 #169) >> Best rule #509 for best value: >> intensional similarity = 11 >> extensional distance = 49 >> proper extension: 0hcr; >> query: (?x8925, 014mlp) <- student(?x8925, ?x123), major_field_of_study(?x9947, ?x8925), major_field_of_study(?x9879, ?x8925), major_field_of_study(?x7596, ?x8925), major_field_of_study(?x9947, ?x2605), currency(?x9947, ?x170), ?x2605 = 03g3w, state_province_region(?x9947, ?x2020), contains(?x94, ?x7596), colors(?x9947, ?x332), category(?x9879, ?x134) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01zc2w major_field_of_study! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 36.000 0.882 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #11667-0hc8h PRED entity: 0hc8h PRED relation: administrative_parent PRED expected values: 0cv5l => 65 concepts (36 used for prediction) PRED predicted values (max 10 best out of 44): 0cv5l (0.43 #972, 0.29 #692, 0.27 #831), 0ht8h (0.25 #220, 0.20 #498, 0.09 #776), 02jx1 (0.20 #3346, 0.17 #3062, 0.16 #2642), 0h924 (0.20 #277, 0.05 #932, 0.01 #1628), 0b_yz (0.20 #277), 02_286 (0.20 #277), 09ctj (0.14 #686, 0.09 #825, 0.05 #964), 02ly_ (0.14 #623, 0.05 #901, 0.03 #4181), 07ssc (0.13 #3064, 0.13 #3205, 0.12 #2783), 03lrc (0.10 #942, 0.04 #1638, 0.02 #2894) >> Best rule #972 for best value: >> intensional similarity = 8 >> extensional distance = 19 >> proper extension: 0dhdp; 05l5n; 09tlh; 0978r; 01d8wq; 0g251; 0grd7; 01jxlz; 0b_yz; 088cp; ... >> query: (?x12767, ?x13888) <- country(?x12767, ?x1310), state(?x12767, ?x13888), nationality(?x10716, ?x1310), nationality(?x1674, ?x1310), ?x1674 = 01v_pj6, contains(?x1310, ?x8549), ?x10716 = 04_by, ?x8549 = 018h8j >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hc8h administrative_parent 0cv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 36.000 0.429 http://example.org/base/aareas/schema/administrative_area/administrative_parent #11666-0jg77 PRED entity: 0jg77 PRED relation: artist! PRED expected values: 0mzkr => 75 concepts (36 used for prediction) PRED predicted values (max 10 best out of 113): 033hn8 (0.57 #428, 0.35 #980, 0.33 #1118), 03rhqg (0.40 #1258, 0.25 #844, 0.25 #706), 0fb0v (0.33 #7, 0.25 #145, 0.20 #283), 073tm9 (0.33 #36, 0.25 #174, 0.20 #312), 03d96s (0.33 #47, 0.25 #185, 0.20 #323), 01t04r (0.29 #1030, 0.28 #1168, 0.20 #340), 015_1q (0.29 #434, 0.25 #710, 0.24 #1539), 03vtrv (0.29 #514, 0.12 #1066, 0.11 #1204), 01dtcb (0.25 #874, 0.12 #736, 0.12 #1565), 023rwm (0.20 #278, 0.18 #968, 0.17 #1106) >> Best rule #428 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 01vtqml; >> query: (?x13142, 033hn8) <- award(?x13142, ?x11068), award(?x13142, ?x4912), ?x11068 = 02x4wb, artist(?x3888, ?x13142), award_winner(?x4912, ?x248), award_winner(?x2704, ?x13142) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2512 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 192 *> proper extension: 0gkg6; 0p3r8; 01tv3x2; 01wt4wc; 0232lm; 015196; 01w03jv; *> query: (?x13142, 0mzkr) <- artists(?x12498, ?x13142), artists(?x12498, ?x8131), artists(?x12498, ?x5916), artists(?x12149, ?x5916), ?x12149 = 01_qp_, ?x8131 = 02hzz *> conf = 0.09 ranks of expected_values: 46 EVAL 0jg77 artist! 0mzkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 75.000 36.000 0.571 http://example.org/music/record_label/artist #11665-0ckm4x PRED entity: 0ckm4x PRED relation: place_of_birth PRED expected values: 013d7t => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 133): 03l2n (0.50 #2281, 0.33 #873, 0.25 #3689), 07b_l (0.29 #40148, 0.28 #85952, 0.27 #93704), 0wq36 (0.25 #2042, 0.11 #7677, 0.07 #10496), 01cx_ (0.25 #2925, 0.08 #9267, 0.05 #11379), 07dfk (0.25 #3879, 0.04 #13038, 0.03 #15150), 04pry (0.17 #5465, 0.12 #6875, 0.12 #6170), 0f__1 (0.17 #5022, 0.12 #6432, 0.12 #5727), 02dtg (0.13 #9872, 0.10 #14801, 0.10 #16210), 013yq (0.12 #5713, 0.01 #28251, 0.01 #38112), 0_g_6 (0.11 #7428, 0.10 #8133, 0.09 #8837) >> Best rule #2281 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0cpjgj; >> query: (?x12353, 03l2n) <- actor(?x5430, ?x12353), actor(?x1334, ?x12353), actor(?x869, ?x12353), ?x5430 = 0dh8v4, ?x869 = 02z9hqn, film(?x296, ?x1334) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ckm4x place_of_birth 013d7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 158.000 0.500 http://example.org/people/person/place_of_birth #11664-0h7pj PRED entity: 0h7pj PRED relation: film PRED expected values: 053rxgm 0gffmn8 01l_pn => 141 concepts (129 used for prediction) PRED predicted values (max 10 best out of 1196): 0ds33 (0.67 #47883, 0.67 #95759, 0.62 #35470), 03rtz1 (0.67 #47883, 0.67 #95759, 0.62 #39017), 07nnp_ (0.67 #47883, 0.67 #95759, 0.62 #39017), 030cx (0.62 #35470, 0.62 #72708, 0.57 #60296), 0ptxj (0.25 #892, 0.13 #140096, 0.01 #29269), 0283_zv (0.25 #285, 0.13 #140096, 0.01 #37528), 04tng0 (0.25 #1254, 0.13 #140096, 0.01 #38497), 03q0r1 (0.25 #631, 0.04 #14823, 0.02 #55606), 0fpkhkz (0.25 #231, 0.03 #10874, 0.02 #12649), 01y9r2 (0.25 #1332, 0.03 #59854, 0.02 #56307) >> Best rule #47883 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 03_vx9; 0456xp; 0n6f8; 01wk7b7; 0jrny; 09qh1; 01pqy_; 01wc7p; 01wrcxr; 0cf2h; ... >> query: (?x8898, ?x508) <- film(?x8898, ?x814), friend(?x917, ?x8898), nominated_for(?x8898, ?x508) >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #101079 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 404 *> proper extension: 014zfs; 025ldg; 01xcr4; 0dt1cm; *> query: (?x8898, ?x351) <- participant(?x2763, ?x8898), award_nominee(?x396, ?x8898), film(?x2763, ?x351) *> conf = 0.04 ranks of expected_values: 135, 468, 469 EVAL 0h7pj film 01l_pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 141.000 129.000 0.674 http://example.org/film/actor/film./film/performance/film EVAL 0h7pj film 0gffmn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 141.000 129.000 0.674 http://example.org/film/actor/film./film/performance/film EVAL 0h7pj film 053rxgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 141.000 129.000 0.674 http://example.org/film/actor/film./film/performance/film #11663-014knw PRED entity: 014knw PRED relation: language PRED expected values: 06b_j => 81 concepts (80 used for prediction) PRED predicted values (max 10 best out of 39): 064_8sq (0.35 #20, 0.18 #479, 0.18 #421), 06nm1 (0.19 #9, 0.14 #410, 0.13 #468), 02bjrlw (0.18 #1, 0.09 #460, 0.09 #287), 06b_j (0.15 #21, 0.07 #364, 0.07 #480), 0jzc (0.07 #18, 0.06 #304, 0.05 #247), 012w70 (0.07 #11, 0.03 #1105, 0.03 #68), 04h9h (0.06 #98, 0.04 #41, 0.04 #500), 03_9r (0.06 #639, 0.06 #8, 0.05 #1102), 0653m (0.06 #239, 0.05 #67, 0.04 #10), 03hkp (0.04 #13, 0.02 #299, 0.02 #530) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 03_wm6; >> query: (?x9345, 064_8sq) <- production_companies(?x9345, ?x902), language(?x9345, ?x732), ?x732 = 04306rv, genre(?x9345, ?x53) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 03_wm6; *> query: (?x9345, 06b_j) <- production_companies(?x9345, ?x902), language(?x9345, ?x732), ?x732 = 04306rv, genre(?x9345, ?x53) *> conf = 0.15 ranks of expected_values: 4 EVAL 014knw language 06b_j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 80.000 0.353 http://example.org/film/film/language #11662-02wgbb PRED entity: 02wgbb PRED relation: currency PRED expected values: 09nqf => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.77 #57, 0.76 #36, 0.76 #43), 01nv4h (0.03 #16, 0.02 #65, 0.02 #163), 02gsvk (0.03 #27, 0.01 #34), 02l6h (0.01 #67, 0.01 #60) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 295 >> proper extension: 0872p_c; 03fts; 0b1y_2; 05zlld0; 01q2nx; 016ks5; 03cp4cn; 034qbx; 02mmwk; 01xbxn; ... >> query: (?x7800, 09nqf) <- film(?x382, ?x7800), genre(?x7800, ?x258), film(?x3557, ?x7800), artist(?x382, ?x547) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wgbb currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.771 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11661-01vvyvk PRED entity: 01vvyvk PRED relation: award_winner! PRED expected values: 0466p0j => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 120): 013b2h (0.21 #76, 0.15 #3068, 0.15 #1028), 056878 (0.21 #29, 0.12 #5197, 0.11 #4381), 01s695 (0.18 #11153, 0.17 #11698, 0.14 #3), 0466p0j (0.18 #11153, 0.17 #11698, 0.14 #72), 09n4nb (0.18 #11153, 0.17 #11698, 0.14 #44), 0jzphpx (0.18 #11153, 0.17 #11698, 0.14 #36), 01bx35 (0.18 #11153, 0.17 #11698, 0.10 #278), 01xqqp (0.18 #11153, 0.17 #11698, 0.09 #5260), 09p3h7 (0.18 #11153, 0.17 #11698, 0.02 #7955), 05pd94v (0.13 #5170, 0.11 #4354, 0.10 #274) >> Best rule #76 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0136p1; 01vt9p3; >> query: (?x4474, 013b2h) <- award_winner(?x248, ?x4474), award(?x4474, ?x6652), ?x6652 = 01cw7s >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #11153 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1293 *> proper extension: 0l56b; 03jvmp; 05hjmd; *> query: (?x4474, ?x486) <- award_winner(?x5310, ?x4474), award_nominee(?x4474, ?x828), award_winner(?x486, ?x5310) *> conf = 0.18 ranks of expected_values: 4 EVAL 01vvyvk award_winner! 0466p0j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 122.000 122.000 0.214 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11660-01vng3b PRED entity: 01vng3b PRED relation: profession PRED expected values: 0nbcg => 143 concepts (87 used for prediction) PRED predicted values (max 10 best out of 62): 02hrh1q (0.94 #12103, 0.86 #3790, 0.85 #12539), 016z4k (0.69 #1745, 0.62 #2908, 0.60 #2327), 0nbcg (0.64 #1916, 0.62 #5112, 0.60 #7152), 04f2zj (0.44 #3776, 0.33 #93, 0.31 #5374), 0n1h (0.42 #1753, 0.40 #2335, 0.39 #1608), 0gbbt (0.42 #589, 0.19 #3059, 0.18 #2623), 0fnpj (0.35 #3252, 0.33 #57, 0.27 #2526), 01d_h8 (0.35 #1602, 0.32 #6250, 0.32 #5233), 025352 (0.33 #56, 0.16 #4267, 0.15 #1798), 03gjzk (0.24 #7723, 0.23 #12540, 0.23 #3791) >> Best rule #12103 for best value: >> intensional similarity = 6 >> extensional distance = 569 >> proper extension: 01sl1q; 07nznf; 0184jc; 04bdxl; 0grwj; 05bnp0; 0337vz; 01xdf5; 04t2l2; 06dv3; ... >> query: (?x6225, 02hrh1q) <- participant(?x1955, ?x6225), profession(?x6225, ?x2659), profession(?x10738, ?x2659), profession(?x2784, ?x2659), ?x2784 = 0137g1, ?x10738 = 017f4y >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #1916 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 0lbj1; 01vrncs; 018y2s; 0137n0; 01kx_81; 0l12d; 0285c; 0zjpz; 01vs_v8; 014q2g; ... *> query: (?x6225, 0nbcg) <- participant(?x1955, ?x6225), role(?x6225, ?x212), artist(?x441, ?x6225), role(?x6225, ?x315) *> conf = 0.64 ranks of expected_values: 3 EVAL 01vng3b profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 143.000 87.000 0.942 http://example.org/people/person/profession #11659-01x4r3 PRED entity: 01x4r3 PRED relation: profession PRED expected values: 0dxtg => 155 concepts (88 used for prediction) PRED predicted values (max 10 best out of 85): 03gjzk (0.84 #5555, 0.81 #5270, 0.80 #1575), 0dxtg (0.76 #438, 0.75 #8680, 0.71 #154), 02jknp (0.45 #9100, 0.40 #6542, 0.36 #8674), 0nbcg (0.37 #1305, 0.27 #10968, 0.27 #8126), 0np9r (0.36 #159, 0.31 #3978, 0.29 #443), 01c72t (0.35 #1298, 0.24 #1724, 0.21 #2434), 016z4k (0.32 #1282, 0.28 #8103, 0.27 #2418), 015cjr (0.31 #3978, 0.29 #3125, 0.29 #7673), 0n1h (0.31 #3978, 0.29 #3125, 0.29 #7673), 0d8qb (0.31 #3978, 0.29 #3125, 0.29 #7673) >> Best rule #5555 for best value: >> intensional similarity = 3 >> extensional distance = 221 >> proper extension: 02778qt; 03wh8pq; >> query: (?x9337, 03gjzk) <- profession(?x9337, ?x319), gender(?x9337, ?x231), producer_type(?x9337, ?x632) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #438 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 018009; 02xfj0; 02633g; 02lj6p; 01xwv7; *> query: (?x9337, 0dxtg) <- film(?x9337, ?x6174), profession(?x9337, ?x319), influenced_by(?x9337, ?x7183), ?x7183 = 01hmk9 *> conf = 0.76 ranks of expected_values: 2 EVAL 01x4r3 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 155.000 88.000 0.839 http://example.org/people/person/profession #11658-01g257 PRED entity: 01g257 PRED relation: gender PRED expected values: 02zsn => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #1, 0.76 #29, 0.72 #35), 02zsn (0.53 #8, 0.51 #20, 0.49 #225) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01ry0f; >> query: (?x1564, 05zppz) <- film(?x1564, ?x8358), film(?x1564, ?x4749), ?x4749 = 07sgdw, genre(?x8358, ?x53) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 38 *> proper extension: 02pbp9; *> query: (?x1564, 02zsn) <- program(?x1564, ?x631), award_winner(?x11087, ?x1564) *> conf = 0.53 ranks of expected_values: 2 EVAL 01g257 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.800 http://example.org/people/person/gender #11657-01pvxl PRED entity: 01pvxl PRED relation: film! PRED expected values: 0sw6g => 77 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1060): 016tt2 (0.48 #58120, 0.43 #68499, 0.42 #62272), 07qy0b (0.48 #58120, 0.43 #68499, 0.42 #62272), 01nr36 (0.33 #3552, 0.02 #16004, 0.02 #18079), 02gf_l (0.30 #5415, 0.04 #9565, 0.04 #34470), 015pkc (0.29 #6503, 0.04 #8578, 0.03 #24904), 0716t2 (0.29 #8128, 0.04 #10203, 0.02 #60023), 02clgg (0.25 #1474, 0.10 #5624, 0.02 #34679), 0bxtg (0.25 #77, 0.07 #56043, 0.05 #10452), 0p8r1 (0.25 #585, 0.06 #33790, 0.02 #8885), 06cgy (0.25 #250, 0.03 #39683, 0.02 #58370) >> Best rule #58120 for best value: >> intensional similarity = 4 >> extensional distance = 701 >> proper extension: 0gy2y8r; >> query: (?x5243, ?x574) <- film(?x8412, ?x5243), nominated_for(?x8412, ?x6694), category(?x8412, ?x134), nominated_for(?x574, ?x5243) >> conf = 0.48 => this is the best rule for 2 predicted values *> Best rule #13852 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 91 *> proper extension: 08hmch; 09gdm7q; 0bh8yn3; 0661m4p; 0cc846d; 02vrgnr; 080lkt7; 035w2k; 027j9wd; 05n6sq; ... *> query: (?x5243, 0sw6g) <- film_distribution_medium(?x5243, ?x2099), music(?x5243, ?x3371), award_nominee(?x3371, ?x1314), genre(?x5243, ?x53) *> conf = 0.02 ranks of expected_values: 399 EVAL 01pvxl film! 0sw6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 48.000 0.478 http://example.org/film/actor/film./film/performance/film #11656-011yph PRED entity: 011yph PRED relation: film_crew_role PRED expected values: 0dxtw 01pvkk => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 26): 09zzb8 (0.71 #1578, 0.70 #1463, 0.70 #1078), 0ch6mp2 (0.71 #1585, 0.71 #1663, 0.70 #1470), 02r96rf (0.62 #1658, 0.61 #119, 0.59 #1080), 09vw2b7 (0.59 #1662, 0.58 #1584, 0.56 #1469), 0dxtw (0.35 #1667, 0.32 #1589, 0.32 #1474), 01vx2h (0.32 #168, 0.29 #1668, 0.27 #1090), 01pvkk (0.29 #1591, 0.27 #1669, 0.27 #1091), 02ynfr (0.15 #1673, 0.14 #1595, 0.14 #134), 0215hd (0.13 #137, 0.12 #1598, 0.12 #1098), 01xy5l_ (0.11 #132, 0.10 #209, 0.10 #1593) >> Best rule #1578 for best value: >> intensional similarity = 3 >> extensional distance = 975 >> proper extension: 0bs8hvm; >> query: (?x616, 09zzb8) <- genre(?x616, ?x258), titles(?x512, ?x616), film_crew_role(?x616, ?x281) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1667 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1010 *> proper extension: 0gtsx8c; *> query: (?x616, 0dxtw) <- country(?x616, ?x512), film(?x574, ?x616), film_crew_role(?x616, ?x281) *> conf = 0.35 ranks of expected_values: 5, 7 EVAL 011yph film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 79.000 0.713 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 011yph film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 79.000 0.713 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11655-01hv3t PRED entity: 01hv3t PRED relation: film! PRED expected values: 03qd_ => 94 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1324): 0c3ns (0.42 #47797, 0.41 #29088, 0.40 #54031), 0fvf9q (0.42 #47797, 0.41 #29088, 0.40 #54031), 051z6rz (0.42 #47797, 0.41 #29088, 0.40 #54031), 0jfx1 (0.12 #14948, 0.04 #21181, 0.04 #2482), 015p3p (0.10 #15634, 0.03 #5245, 0.02 #7323), 01vs_v8 (0.09 #4514, 0.05 #14903, 0.02 #23214), 01ps2h8 (0.09 #5092, 0.04 #3015, 0.04 #938), 0h96g (0.08 #9160, 0.08 #19547, 0.07 #7081), 016zp5 (0.07 #3051, 0.07 #974, 0.07 #7206), 03q1vd (0.07 #2538, 0.07 #461, 0.06 #58188) >> Best rule #47797 for best value: >> intensional similarity = 4 >> extensional distance = 538 >> proper extension: 03s6l2; 04kkz8; 0gyy53; 08gg47; 02dpl9; 0gy2y8r; 0ggbfwf; 05n6sq; 03cyslc; 032clf; ... >> query: (?x7432, ?x163) <- genre(?x7432, ?x53), nominated_for(?x163, ?x7432), ?x53 = 07s9rl0, film_crew_role(?x7432, ?x137) >> conf = 0.42 => this is the best rule for 3 predicted values *> Best rule #14665 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 07k2mq; *> query: (?x7432, 03qd_) <- nominated_for(?x112, ?x7432), film(?x2179, ?x7432), film(?x2940, ?x7432), role(?x2940, ?x316) *> conf = 0.03 ranks of expected_values: 489 EVAL 01hv3t film! 03qd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 94.000 44.000 0.424 http://example.org/film/actor/film./film/performance/film #11654-07p62k PRED entity: 07p62k PRED relation: music PRED expected values: 01mkn_d => 99 concepts (65 used for prediction) PRED predicted values (max 10 best out of 63): 01tc9r (0.11 #65, 0.03 #699, 0.03 #5778), 0jn5l (0.11 #96, 0.01 #941, 0.01 #2424), 0150t6 (0.08 #680, 0.04 #7026, 0.04 #3012), 0146pg (0.07 #1067, 0.05 #855, 0.05 #644), 03h304l (0.06 #10145, 0.06 #12047, 0.06 #10357), 027z0pl (0.06 #10145, 0.06 #12047, 0.06 #10357), 016tw3 (0.06 #10145, 0.06 #12047, 0.06 #10357), 02bh9 (0.06 #262, 0.06 #3017, 0.05 #473), 02g1jh (0.06 #762, 0.02 #6685, 0.02 #8586), 01x6v6 (0.04 #968, 0.04 #545, 0.03 #1393) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 07h9gp; 0dzz6g; 02bj22; >> query: (?x2207, 01tc9r) <- film(?x4263, ?x2207), genre(?x2207, ?x258), ?x4263 = 02yplc, language(?x2207, ?x254) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #8579 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 564 *> proper extension: 0cnztc4; 0gh6j94; *> query: (?x2207, 01mkn_d) <- film_crew_role(?x2207, ?x1171), film_crew_role(?x2207, ?x137), genre(?x2207, ?x258), ?x1171 = 09vw2b7, ?x137 = 09zzb8 *> conf = 0.01 ranks of expected_values: 45 EVAL 07p62k music 01mkn_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 99.000 65.000 0.111 http://example.org/film/film/music #11653-09zzb8 PRED entity: 09zzb8 PRED relation: film_crew_role! PRED expected values: 0170z3 0d90m 03qcfvw 034qmv 0209xj 0gkz15s 03t97y 0c0nhgv 03s5lz 0gmcwlb 0gj9qxr 0gxtknx 0by1wkq 0gydcp7 0j_tw 0pdp8 0g3zrd 03m8y5 083skw 02q56mk 01shy7 0kv238 0b_5d 0g5838s 03hkch7 0crc2cp 04cj79 0fphgb 024lff 0c3xw46 01w8g3 049mql 05c9zr 0125xq 034r25 01qxc7 04x4vj 01qvz8 0prh7 0dfw0 0fb7sd 03c_cxn 0b44shh 03h4fq7 08phg9 0bt3j9 0j3d9tn 0h21v2 0660b9b 0640y35 02h22 011ypx 0127ps 0n1s0 051ys82 0bq6ntw 077q8x 017d93 02nczh 01y9jr 012kyx 063fh9 01svry 047rkcm 07jnt 0g7pm1 027pfg 09hy79 01srq2 05pt0l 01633c 07f_t4 02q0k7v 05pxnmb 02cbhg 0ds2l81 09bw4_ 02qcr 0gy0l_ 01s7w3 087pfc 02bj22 05y0cr 0jdr0 056xkh 0g57wgv 04ynx7 0d6_s 049w1q 0270k40 02x2jl_ => 46 concepts (34 used for prediction) PRED predicted values (max 10 best out of 395): 0g7pm1 (0.71 #3834, 0.70 #5812, 0.60 #2649), 02q0k7v (0.71 #3870, 0.60 #5848, 0.60 #2685), 0270k40 (0.70 #5924, 0.62 #4737, 0.60 #2366), 07s846j (0.70 #5696, 0.60 #2138, 0.57 #3718), 063fh9 (0.67 #5012, 0.67 #3431, 0.62 #4617), 049mql (0.67 #3325, 0.57 #4115, 0.56 #4906), 0g5838s (0.67 #3280, 0.57 #4070, 0.56 #4861), 07f_t4 (0.62 #4658, 0.60 #5845, 0.60 #2287), 01738w (0.62 #4606, 0.60 #3025, 0.50 #3420), 05c9zr (0.62 #4512, 0.57 #3721, 0.56 #4907) >> Best rule #3834 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0d2b38; >> query: (?x137, 0g7pm1) <- film_crew_role(?x4991, ?x137), film_crew_role(?x2403, ?x137), film_crew_role(?x1932, ?x137), country(?x4991, ?x94), ?x1932 = 0btyf5z, genre(?x2403, ?x53) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 17, 19, 22, 24, 25, 27, 28, 34, 39, 40, 42, 45, 51, 52, 53, 55, 56, 57, 58, 60, 63, 65, 66, 68, 69, 70, 71, 73, 76, 78, 79, 80, 82, 86, 87, 95, 96, 102, 105, 107, 113, 118, 122, 125, 127, 128, 144, 146, 149, 151, 154, 161, 167, 168, 180, 182, 194, 198, 214, 223, 230, 231, 234, 244, 284, 287, 295, 300, 301, 314, 319, 327, 328, 329, 355 EVAL 09zzb8 film_crew_role! 02x2jl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0270k40 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 049w1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0d6_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 04ynx7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0g57wgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 056xkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0jdr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 05y0cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 02bj22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 087pfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 01s7w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0gy0l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 02qcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 09bw4_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0ds2l81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 02cbhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 05pxnmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 02q0k7v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 07f_t4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 01633c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 05pt0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 01srq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 09hy79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0g7pm1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 07jnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 047rkcm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 01svry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 063fh9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 012kyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 01y9jr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 02nczh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 017d93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 077q8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0bq6ntw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 051ys82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0n1s0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0127ps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 011ypx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 02h22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0640y35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0660b9b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0h21v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0j3d9tn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0bt3j9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 08phg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 03h4fq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0b44shh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 03c_cxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0fb7sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0dfw0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0prh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 01qvz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 04x4vj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 01qxc7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 034r25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0125xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 05c9zr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 049mql CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 01w8g3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0c3xw46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 024lff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0fphgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 04cj79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0crc2cp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 03hkch7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0g5838s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0b_5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0kv238 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 01shy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 02q56mk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 083skw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 03m8y5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0g3zrd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0pdp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0j_tw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0gydcp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0by1wkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0gxtknx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0gj9qxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0gmcwlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 03s5lz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0c0nhgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 03t97y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0209xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 034qmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 03qcfvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0d90m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09zzb8 film_crew_role! 0170z3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 34.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11652-0g5qs2k PRED entity: 0g5qs2k PRED relation: film_release_region PRED expected values: 03rjj 02k54 0ctw_b 06qd3 0d0x8 012wgb 01xbgx => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 81): 03rjj (0.84 #644, 0.83 #1028, 0.82 #1156), 0chghy (0.83 #648, 0.82 #1160, 0.81 #1032), 0k6nt (0.81 #659, 0.80 #1171, 0.79 #1043), 015fr (0.75 #1037, 0.75 #653, 0.75 #1165), 05b4w (0.75 #1071, 0.74 #687, 0.73 #1199), 01znc_ (0.73 #1056, 0.72 #1184, 0.71 #672), 0b90_r (0.72 #1027, 0.71 #643, 0.70 #1155), 03rt9 (0.70 #1035, 0.67 #651, 0.67 #1163), 015qh (0.51 #1055, 0.49 #1183, 0.48 #671), 0ctw_b (0.50 #1044, 0.48 #1172, 0.47 #660) >> Best rule #644 for best value: >> intensional similarity = 6 >> extensional distance = 137 >> proper extension: 04969y; 0d6b7; 0gj9qxr; 02h22; 0g5q34q; 064lsn; 0g9zljd; 0g5qmbz; 0hz6mv2; 0j8f09z; >> query: (?x504, 03rjj) <- film_release_region(?x504, ?x2152), film_release_region(?x504, ?x1558), film_release_region(?x504, ?x789), ?x789 = 0f8l9c, ?x1558 = 01mjq, ?x2152 = 06mkj >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10, 11, 15, 23, 30, 56 EVAL 0g5qs2k film_release_region 01xbgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 59.000 59.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5qs2k film_release_region 012wgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 59.000 59.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5qs2k film_release_region 0d0x8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 59.000 59.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5qs2k film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 59.000 59.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5qs2k film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 59.000 59.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5qs2k film_release_region 02k54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 59.000 59.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5qs2k film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11651-016dsy PRED entity: 016dsy PRED relation: split_to! PRED expected values: 016dsy => 144 concepts (91 used for prediction) PRED predicted values (max 10 best out of 4): 016zp5 (0.04 #936, 0.02 #1132, 0.02 #1231), 02g1jh (0.03 #1046, 0.02 #1438, 0.01 #1833), 01vsxdm (0.01 #1886, 0.01 #2082, 0.01 #1985), 0gr69 (0.01 #1933, 0.01 #2129, 0.01 #2032) >> Best rule #936 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 0d5_f; 0b478; 082mw; 07ym0; 01rgr; >> query: (?x4082, 016zp5) <- location(?x4082, ?x11072), location(?x4082, ?x4627), ?x4627 = 05qtj, people(?x743, ?x4082), location_of_ceremony(?x5951, ?x11072) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016dsy split_to! 016dsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 91.000 0.040 http://example.org/dataworld/gardening_hint/split_to #11650-0bj25 PRED entity: 0bj25 PRED relation: genre PRED expected values: 07s9rl0 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.86 #7618, 0.85 #1072, 0.83 #358), 05p553 (0.81 #1076, 0.41 #600, 0.35 #1433), 02l7c8 (0.45 #254, 0.42 #1087, 0.41 #611), 02xh1 (0.33 #86, 0.18 #205, 0.15 #324), 01jfsb (0.31 #2749, 0.31 #4058, 0.31 #4415), 02kdv5l (0.28 #4049, 0.27 #6430, 0.27 #6311), 0219x_ (0.27 #1097, 0.12 #2525, 0.10 #3120), 04xvlr (0.27 #478, 0.26 #2144, 0.18 #2858), 0lsxr (0.26 #1438, 0.24 #1795, 0.23 #1914), 06cvj (0.25 #1075, 0.15 #599, 0.14 #718) >> Best rule #7618 for best value: >> intensional similarity = 3 >> extensional distance = 1167 >> proper extension: 0fq27fp; >> query: (?x8769, 07s9rl0) <- genre(?x8769, ?x1805), genre(?x8617, ?x1805), ?x8617 = 0bkq7 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bj25 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.861 http://example.org/film/film/genre #11649-087qxp PRED entity: 087qxp PRED relation: award_winner! PRED expected values: 05c1t6z => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 119): 05c1t6z (0.71 #15, 0.62 #295, 0.57 #155), 0lp_cd3 (0.71 #23, 0.62 #303, 0.57 #163), 02q690_ (0.38 #344, 0.33 #484, 0.29 #204), 03nnm4t (0.33 #493, 0.29 #213, 0.29 #73), 09v0p2c (0.33 #782, 0.14 #1062, 0.14 #1482), 0gx_st (0.29 #36, 0.26 #3643, 0.25 #316), 07z31v (0.29 #31, 0.26 #3643, 0.25 #311), 07y9ts (0.29 #67, 0.26 #3643, 0.25 #347), 0418154 (0.28 #807, 0.10 #10650, 0.10 #11211), 07y_p6 (0.26 #3643, 0.24 #3784, 0.20 #3082) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0bz5v2; 01j7rd; 02xs0q; 05pzdk; 04crrxr; >> query: (?x7583, 05c1t6z) <- award_winner(?x7583, ?x4629), student(?x5638, ?x7583), ?x4629 = 05bnq3j, award_nominee(?x7583, ?x236) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087qxp award_winner! 05c1t6z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11648-0cskb PRED entity: 0cskb PRED relation: tv_program! PRED expected values: 0brkwj => 94 concepts (60 used for prediction) PRED predicted values (max 10 best out of 225): 02f9wb (0.47 #1342, 0.45 #1150, 0.43 #574), 0bbxd3 (0.47 #1342, 0.45 #1150, 0.43 #574), 01vz80y (0.47 #1342, 0.45 #1150, 0.43 #574), 0c01c (0.18 #191, 0.13 #4406, 0.12 #4597), 01f9mq (0.18 #191, 0.13 #4406, 0.12 #4597), 09hd6f (0.17 #576, 0.10 #168, 0.04 #551), 06jrhz (0.17 #576, 0.08 #486, 0.08 #679), 0h53p1 (0.17 #576, 0.05 #46, 0.04 #429), 01xndd (0.17 #576, 0.05 #74, 0.04 #457), 0h584v (0.17 #576, 0.05 #73, 0.04 #456) >> Best rule #1342 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 01b7h8; 0ph24; 01j95; >> query: (?x9843, ?x5958) <- tv_program(?x8785, ?x9843), languages(?x9843, ?x254), program(?x5958, ?x9843), genre(?x9843, ?x53) >> conf = 0.47 => this is the best rule for 3 predicted values *> Best rule #576 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 080dwhx; 02_1rq; 0358x_; 019nnl; 0ddd0gc; 08jgk1; 0464pz; 0kfv9; 03ln8b; 02hct1; ... *> query: (?x9843, ?x4948) <- tv_program(?x8785, ?x9843), languages(?x9843, ?x254), program(?x5958, ?x9843), award_winner(?x5958, ?x4948) *> conf = 0.17 ranks of expected_values: 11 EVAL 0cskb tv_program! 0brkwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 94.000 60.000 0.466 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #11647-041xyk PRED entity: 041xyk PRED relation: teams! PRED expected values: 0f2r6 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 7): 0947l (0.02 #182, 0.02 #452, 0.02 #722), 0d0vqn (0.02 #8, 0.02 #278, 0.02 #1088), 04jpl (0.02 #279, 0.02 #549, 0.01 #4599), 02fvv (0.02 #1062), 04llb (0.01 #4805), 030qb3t (0.01 #8156, 0.01 #8428, 0.01 #8700), 04swd (0.01 #5577, 0.01 #5847) >> Best rule #182 for best value: >> intensional similarity = 11 >> extensional distance = 100 >> proper extension: 0223bl; 04b4yg; 03_9hm; 01n_2f; 02b2np; 0ytc; 035qlx; 02rqxc; 01vqc7; 03xh50; ... >> query: (?x3049, 0947l) <- sport(?x3049, ?x471), position(?x3049, ?x530), position(?x3049, ?x203), position(?x3049, ?x63), position(?x3049, ?x60), ?x203 = 0dgrmp, ?x471 = 02vx4, ?x60 = 02nzb8, ?x530 = 02_j1w, ?x63 = 02sdk9v, position(?x3049, ?x60) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 041xyk teams! 0f2r6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.020 http://example.org/sports/sports_team_location/teams #11646-03t95n PRED entity: 03t95n PRED relation: music PRED expected values: 01mkn_d => 79 concepts (61 used for prediction) PRED predicted values (max 10 best out of 65): 01mkn_d (0.13 #4635, 0.11 #5686, 0.06 #7163), 02bh9 (0.11 #261, 0.09 #683, 0.08 #893), 03h610 (0.11 #77, 0.06 #287, 0.03 #2607), 0csdzz (0.11 #187, 0.03 #397, 0.03 #608), 014g_s (0.07 #8428, 0.06 #12869, 0.06 #11387), 016szr (0.06 #291, 0.02 #1133, 0.01 #4505), 0146pg (0.05 #1907, 0.05 #1483, 0.05 #1062), 01tc9r (0.05 #486, 0.04 #907, 0.03 #1117), 01nqfh_ (0.05 #429, 0.03 #850, 0.01 #4011), 0150t6 (0.05 #1098, 0.04 #4049, 0.03 #1519) >> Best rule #4635 for best value: >> intensional similarity = 3 >> extensional distance = 360 >> proper extension: 02xhpl; >> query: (?x6615, ?x6664) <- award_winner(?x6615, ?x6664), category(?x6664, ?x134), profession(?x6664, ?x563) >> conf = 0.13 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t95n music 01mkn_d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.126 http://example.org/film/film/music #11645-0653m PRED entity: 0653m PRED relation: language! PRED expected values: 02_sr1 0bs5k8r 0btpm6 01mgw 01718w => 56 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1830): 0198b6 (0.77 #15212, 0.76 #13521, 0.65 #16903), 0dx8gj (0.77 #15212, 0.76 #13521, 0.65 #16903), 02_sr1 (0.66 #20283, 0.64 #23667, 0.33 #4007), 034qmv (0.66 #20283, 0.64 #23667, 0.33 #3391), 02gpkt (0.66 #20283, 0.64 #23667, 0.33 #4604), 06ztvyx (0.66 #20283, 0.64 #23667, 0.33 #3787), 09146g (0.66 #20283, 0.64 #23667, 0.33 #3662), 02sfnv (0.66 #20283, 0.64 #23667, 0.33 #4222), 01mszz (0.66 #20283, 0.64 #23667, 0.33 #4391), 0g5qmbz (0.50 #13294, 0.46 #14985, 0.43 #16676) >> Best rule #15212 for best value: >> intensional similarity = 9 >> extensional distance = 11 >> proper extension: 07c9s; >> query: (?x2890, ?x467) <- titles(?x2890, ?x467), language(?x10446, ?x2890), language(?x5608, ?x2890), film_release_region(?x10446, ?x94), award_winner(?x5608, ?x4784), film(?x1206, ?x5608), written_by(?x10446, ?x3117), film_crew_role(?x5608, ?x137), produced_by(?x4860, ?x4784) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #20283 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 18 *> proper extension: 0999q; *> query: (?x2890, ?x148) <- countries_spoken_in(?x2890, ?x1122), languages_spoken(?x7562, ?x2890), languages(?x147, ?x2890), film(?x147, ?x148), film_release_region(?x86, ?x1122), award_winner(?x401, ?x147) *> conf = 0.66 ranks of expected_values: 3, 69, 571, 1577, 1627 EVAL 0653m language! 01718w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 56.000 23.000 0.767 http://example.org/film/film/language EVAL 0653m language! 01mgw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 23.000 0.767 http://example.org/film/film/language EVAL 0653m language! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 23.000 0.767 http://example.org/film/film/language EVAL 0653m language! 0bs5k8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 23.000 0.767 http://example.org/film/film/language EVAL 0653m language! 02_sr1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 56.000 23.000 0.767 http://example.org/film/film/language #11644-02wb6d PRED entity: 02wb6d PRED relation: people! PRED expected values: 0qcr0 => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 39): 0gk4g (0.19 #1132, 0.14 #2848, 0.14 #208), 0dq9p (0.12 #479, 0.11 #83, 0.09 #1139), 04p3w (0.10 #143, 0.09 #473, 0.09 #275), 0qcr0 (0.10 #2839, 0.10 #199, 0.09 #1123), 02y0js (0.09 #464, 0.06 #992, 0.06 #926), 01l2m3 (0.08 #16, 0.06 #874, 0.06 #82), 06z5s (0.08 #25, 0.06 #1015, 0.04 #421), 02knxx (0.08 #32, 0.04 #2870, 0.04 #1154), 01tf_6 (0.08 #31, 0.02 #1021, 0.02 #955), 02k6hp (0.08 #961, 0.05 #235, 0.04 #3337) >> Best rule #1132 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 0520r2x; 041h0; 0chsq; 012t1; 02lkcc; 01gzm2; 0c6g29; 018swb; 01pcmd; 0b_fw; ... >> query: (?x6971, 0gk4g) <- gender(?x6971, ?x231), place_of_death(?x6971, ?x1523), people(?x1050, ?x6971), nominated_for(?x6971, ?x4841) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #2839 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 186 *> proper extension: 0cb77r; 02rybfn; 08nz99; *> query: (?x6971, 0qcr0) <- gender(?x6971, ?x231), place_of_death(?x6971, ?x1523), type_of_union(?x6971, ?x566), nominated_for(?x6971, ?x4841) *> conf = 0.10 ranks of expected_values: 4 EVAL 02wb6d people! 0qcr0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 170.000 170.000 0.195 http://example.org/people/cause_of_death/people #11643-031k24 PRED entity: 031k24 PRED relation: profession PRED expected values: 02hrh1q => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 51): 02hrh1q (0.88 #5866, 0.88 #3015, 0.88 #5716), 03gjzk (0.46 #16, 0.30 #1666, 0.30 #166), 01d_h8 (0.38 #6, 0.38 #306, 0.36 #1656), 0dxtg (0.32 #1814, 0.31 #1664, 0.31 #14), 02jknp (0.31 #8, 0.26 #4351, 0.26 #8704), 0np9r (0.26 #4351, 0.26 #8704, 0.25 #8253), 09jwl (0.26 #4351, 0.26 #8704, 0.25 #8253), 02krf9 (0.26 #4351, 0.26 #8704, 0.25 #8253), 0cbd2 (0.26 #4351, 0.26 #8704, 0.25 #8253), 018gz8 (0.26 #4351, 0.26 #8704, 0.25 #8253) >> Best rule #5866 for best value: >> intensional similarity = 3 >> extensional distance = 1629 >> proper extension: 05d7rk; 04yywz; 01vw87c; 02g8h; 02nb2s; 04bs3j; 0151ns; 0lzb8; 03ds3; 0152cw; ... >> query: (?x8066, 02hrh1q) <- award(?x8066, ?x2375), film(?x8066, ?x763), nominated_for(?x2375, ?x89) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031k24 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.882 http://example.org/people/person/profession #11642-03j1p2n PRED entity: 03j1p2n PRED relation: award PRED expected values: 02g3gj 054ks3 => 104 concepts (90 used for prediction) PRED predicted values (max 10 best out of 289): 0gqz2 (0.49 #2493, 0.25 #3297, 0.25 #81), 09sb52 (0.47 #12905, 0.43 #16523, 0.42 #8483), 054ks3 (0.44 #2554, 0.28 #4564, 0.26 #3760), 01bgqh (0.44 #1249, 0.35 #2053, 0.34 #2857), 02x17c2 (0.38 #2631, 0.21 #1425, 0.20 #3435), 0c4z8 (0.37 #2484, 0.25 #72, 0.24 #5298), 01by1l (0.35 #1319, 0.35 #2525, 0.32 #5339), 02f6ym (0.29 #659, 0.17 #18092, 0.16 #16885), 01ckbq (0.29 #491, 0.17 #18092, 0.16 #16885), 01c427 (0.29 #487, 0.13 #889, 0.12 #10939) >> Best rule #2493 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 05ty4m; >> query: (?x7859, 0gqz2) <- award_nominee(?x7859, ?x1206), award(?x7859, ?x2238), ?x2238 = 025m8l >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #2554 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 05ty4m; *> query: (?x7859, 054ks3) <- award_nominee(?x7859, ?x1206), award(?x7859, ?x2238), ?x2238 = 025m8l *> conf = 0.44 ranks of expected_values: 3, 40 EVAL 03j1p2n award 054ks3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 90.000 0.492 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03j1p2n award 02g3gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 104.000 90.000 0.492 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11641-047vp1n PRED entity: 047vp1n PRED relation: currency PRED expected values: 09nqf => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.92 #78, 0.88 #71, 0.84 #100), 01nv4h (0.09 #23, 0.05 #199, 0.05 #30), 0kz1h (0.09 #26), 02l6h (0.03 #46, 0.03 #67, 0.03 #53), 02gsvk (0.02 #231, 0.02 #119, 0.02 #238) >> Best rule #78 for best value: >> intensional similarity = 5 >> extensional distance = 96 >> proper extension: 02vqhv0; 0b1y_2; 093dqjy; 05hjnw; 035bcl; 05nlx4; 0g0x9c; 0ct2tf5; 047p798; 028kj0; ... >> query: (?x7314, 09nqf) <- film_crew_role(?x7314, ?x5136), film_crew_role(?x7314, ?x1284), ?x5136 = 089g0h, film(?x368, ?x7314), ?x1284 = 0ch6mp2 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047vp1n currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.918 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11640-0chgzm PRED entity: 0chgzm PRED relation: citytown! PRED expected values: 046b0s => 216 concepts (186 used for prediction) PRED predicted values (max 10 best out of 687): 06q07 (0.34 #32261, 0.31 #32262, 0.29 #16936), 03mp8k (0.34 #32261, 0.31 #32262, 0.29 #16936), 07gqbk (0.34 #32261, 0.31 #32262, 0.29 #16936), 02975m (0.25 #3140, 0.08 #5561, 0.08 #7173), 01l50r (0.25 #3100, 0.08 #5521, 0.08 #7133), 032j_n (0.25 #2966, 0.08 #5387, 0.08 #6999), 07l1c (0.25 #2744, 0.08 #5165, 0.08 #6777), 01nds (0.23 #7026, 0.17 #12672, 0.17 #9446), 05cl8y (0.23 #6865, 0.12 #2832, 0.11 #10897), 049ql1 (0.17 #5425, 0.12 #3004, 0.11 #10263) >> Best rule #32261 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0gp5l6; >> query: (?x8602, ?x9492) <- citytown(?x8336, ?x8602), child(?x9492, ?x8336), citytown(?x9492, ?x739) >> conf = 0.34 => this is the best rule for 3 predicted values *> Best rule #7356 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 02cl1; 0fhp9; 02h6_6p; 0947l; *> query: (?x8602, 046b0s) <- place_of_birth(?x649, ?x8602), administrative_division(?x8602, ?x9494), mode_of_transportation(?x8602, ?x4272) *> conf = 0.06 ranks of expected_values: 319 EVAL 0chgzm citytown! 046b0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 216.000 186.000 0.336 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #11639-04fv0k PRED entity: 04fv0k PRED relation: service_language PRED expected values: 02h40lc => 204 concepts (204 used for prediction) PRED predicted values (max 10 best out of 17): 02h40lc (0.97 #1772, 0.97 #1755, 0.97 #1738), 06nm1 (0.38 #566, 0.34 #685, 0.33 #5), 03_9r (0.15 #565, 0.12 #684, 0.07 #1279), 02bjrlw (0.12 #562, 0.09 #681, 0.06 #1498), 02bv9 (0.08 #572, 0.06 #691, 0.06 #300), 06b_j (0.08 #570, 0.06 #689, 0.06 #1498), 02hwhyv (0.08 #573, 0.06 #692, 0.06 #1498), 01jb8r (0.08 #578, 0.06 #697, 0.06 #1498), 097kp (0.06 #1498, 0.06 #373, 0.05 #458), 0459q4 (0.06 #1498, 0.06 #371, 0.05 #456) >> Best rule #1772 for best value: >> intensional similarity = 5 >> extensional distance = 98 >> proper extension: 06_9lg; >> query: (?x9517, 02h40lc) <- service_location(?x9517, ?x583), service_language(?x9517, ?x732), category(?x9517, ?x134), contains(?x583, ?x1167), location(?x11208, ?x583) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04fv0k service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 204.000 204.000 0.970 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #11638-01h2_6 PRED entity: 01h2_6 PRED relation: profession PRED expected values: 0cbd2 => 256 concepts (256 used for prediction) PRED predicted values (max 10 best out of 111): 02hrh1q (0.72 #24332, 0.71 #13675, 0.71 #19379), 0cbd2 (0.59 #5413, 0.53 #2407, 0.51 #13367), 0dxtg (0.46 #7972, 0.46 #8722, 0.45 #13224), 0kyk (0.43 #5437, 0.40 #1831, 0.40 #8739), 05z96 (0.39 #7358, 0.38 #5406, 0.38 #5405), 025352 (0.39 #7358, 0.38 #5406, 0.38 #5405), 02hv44_ (0.39 #7358, 0.38 #5406, 0.38 #5405), 0q04f (0.39 #7358, 0.38 #5406, 0.38 #5405), 0frz0 (0.38 #1288, 0.33 #2038, 0.33 #1738), 01d_h8 (0.38 #756, 0.34 #13516, 0.33 #11866) >> Best rule #24332 for best value: >> intensional similarity = 4 >> extensional distance = 534 >> proper extension: 0136p1; 01wzlxj; >> query: (?x12592, 02hrh1q) <- people(?x1050, ?x12592), location(?x12592, ?x1646), nationality(?x12592, ?x1264), film_release_region(?x2350, ?x1646) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #5413 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 017r2; 0bt23; *> query: (?x12592, 0cbd2) <- student(?x7154, ?x12592), location(?x12592, ?x1646), influenced_by(?x12592, ?x4915), people(?x6821, ?x12592) *> conf = 0.59 ranks of expected_values: 2 EVAL 01h2_6 profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 256.000 256.000 0.720 http://example.org/people/person/profession #11637-09f07 PRED entity: 09f07 PRED relation: state_province_region! PRED expected values: 088gzp => 262 concepts (84 used for prediction) PRED predicted values (max 10 best out of 764): 050xpd (0.34 #48119, 0.23 #45113, 0.21 #60898), 058z2d (0.34 #48119, 0.20 #8272, 0.15 #8271), 02z6fs (0.25 #452, 0.17 #2708, 0.08 #7971), 05njw (0.22 #3582, 0.17 #2078, 0.10 #17116), 06182p (0.22 #3395, 0.17 #1891, 0.10 #16929), 0d2fd7 (0.17 #1950, 0.11 #3454, 0.09 #17740), 087c7 (0.17 #1517, 0.11 #3021, 0.07 #9037), 0sxdg (0.17 #1939, 0.11 #3443, 0.07 #9459), 03mbdx_ (0.17 #2243, 0.11 #3747, 0.07 #9763), 01fsyp (0.17 #2241, 0.11 #3745, 0.07 #9761) >> Best rule #48119 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 059f4; >> query: (?x11801, ?x11800) <- country(?x11801, ?x2146), contains(?x11801, ?x11800), major_field_of_study(?x11800, ?x2605), featured_film_locations(?x257, ?x11801) >> conf = 0.34 => this is the best rule for 2 predicted values *> Best rule #3760 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 059j2; *> query: (?x11801, ?x1391) <- administrative_parent(?x11801, ?x2146), service_location(?x10867, ?x11801), contains(?x2146, ?x1391), nationality(?x111, ?x2146) *> conf = 0.04 ranks of expected_values: 334 EVAL 09f07 state_province_region! 088gzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 262.000 84.000 0.341 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #11636-0pgjm PRED entity: 0pgjm PRED relation: film PRED expected values: 07tw_b => 110 concepts (89 used for prediction) PRED predicted values (max 10 best out of 456): 02qr3k8 (0.11 #1285, 0.02 #31630, 0.02 #104832), 01cmp9 (0.11 #1045, 0.02 #26035, 0.02 #2830), 0ndsl1x (0.11 #1510, 0.02 #3295), 09jcj6 (0.11 #796, 0.02 #2581), 03rtz1 (0.11 #168, 0.02 #7308, 0.01 #3738), 016z9n (0.11 #369, 0.02 #57497, 0.01 #75350), 03cffvv (0.11 #1739, 0.01 #5309, 0.01 #26729), 025s1wg (0.11 #1701, 0.01 #5271), 02_fz3 (0.11 #1379, 0.01 #4949), 02dr9j (0.11 #1256, 0.01 #4826) >> Best rule #1285 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 014y6; 0652ty; >> query: (?x1345, 02qr3k8) <- film(?x1345, ?x4811), profession(?x1345, ?x1146), ?x4811 = 0f4k49 >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pgjm film 07tw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 89.000 0.111 http://example.org/film/actor/film./film/performance/film #11635-0227vl PRED entity: 0227vl PRED relation: profession PRED expected values: 0kyk => 157 concepts (125 used for prediction) PRED predicted values (max 10 best out of 92): 09jwl (0.61 #4951, 0.56 #4806, 0.55 #10900), 01d_h8 (0.60 #6, 0.60 #2327, 0.45 #1311), 0nbcg (0.50 #4964, 0.48 #4819, 0.46 #320), 016z4k (0.49 #4938, 0.44 #4793, 0.41 #9726), 01c72t (0.47 #4230, 0.45 #2488, 0.38 #3214), 0dz3r (0.46 #292, 0.45 #4936, 0.42 #4791), 039v1 (0.32 #12626, 0.30 #7400, 0.29 #9722), 0kyk (0.32 #12626, 0.30 #7400, 0.29 #9722), 0n1h (0.32 #12626, 0.30 #4946, 0.26 #4801), 064xm0 (0.32 #12626, 0.29 #9722, 0.08 #350) >> Best rule #4951 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 05cljf; 01vw87c; 018y2s; 01j4ls; 058s57; 019g40; 0zjpz; 01wz3cx; 01wgxtl; 014q2g; ... >> query: (?x8793, 09jwl) <- participant(?x8793, ?x1410), nationality(?x8793, ?x94), artists(?x671, ?x8793) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #12626 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 585 *> proper extension: 07c0j; 0cbm64; *> query: (?x8793, ?x967) <- participant(?x8793, ?x7025), profession(?x7025, ?x967), participant(?x3020, ?x8793) *> conf = 0.32 ranks of expected_values: 8 EVAL 0227vl profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 157.000 125.000 0.606 http://example.org/people/person/profession #11634-06dkzt PRED entity: 06dkzt PRED relation: written_by! PRED expected values: 02qkwl => 125 concepts (54 used for prediction) PRED predicted values (max 10 best out of 393): 02vrgnr (0.46 #5877, 0.41 #1306, 0.22 #1960), 0b6l1st (0.46 #5877, 0.41 #1306, 0.22 #1960), 02ntb8 (0.46 #5877, 0.41 #1306, 0.22 #1960), 05sns6 (0.46 #5877, 0.41 #1306, 0.22 #1960), 05q54f5 (0.46 #5877, 0.41 #1306, 0.22 #1960), 05sxzwc (0.46 #5877, 0.41 #1306, 0.22 #1960), 04hk0w (0.46 #5877, 0.41 #1306, 0.22 #1960), 08phg9 (0.19 #3265, 0.19 #1305, 0.08 #1959), 0ds2n (0.04 #201, 0.01 #4120, 0.01 #853), 04fzfj (0.04 #35, 0.01 #3954, 0.01 #687) >> Best rule #5877 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 05jm7; 081l_; >> query: (?x8692, ?x1487) <- profession(?x8692, ?x319), produced_by(?x1487, ?x8692), written_by(?x253, ?x8692) >> conf = 0.46 => this is the best rule for 7 predicted values No rule for expected values ranks of expected_values: EVAL 06dkzt written_by! 02qkwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 54.000 0.463 http://example.org/film/film/written_by #11633-078sj4 PRED entity: 078sj4 PRED relation: honored_for! PRED expected values: 0bvfqq => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 101): 0hndn2q (0.16 #4601, 0.03 #2090, 0.02 #3905), 0hr6lkl (0.16 #4601, 0.03 #2312, 0.03 #3885), 0bvfqq (0.16 #4601, 0.02 #1842, 0.02 #1963), 0n8_m93 (0.16 #4601, 0.02 #2160, 0.02 #2402), 05c1t6z (0.08 #2069, 0.05 #4005, 0.05 #3884), 02q690_ (0.08 #2112, 0.06 #54, 0.05 #4048), 0275n3y (0.07 #2122, 0.04 #4058, 0.04 #669), 0gvstc3 (0.07 #2085, 0.04 #4021, 0.04 #3900), 03nnm4t (0.06 #2121, 0.04 #4057, 0.04 #3936), 04n2r9h (0.06 #36, 0.05 #2094, 0.04 #3909) >> Best rule #4601 for best value: >> intensional similarity = 4 >> extensional distance = 661 >> proper extension: 03czz87; >> query: (?x2814, ?x1442) <- award_winner(?x2814, ?x286), titles(?x3506, ?x2814), award_winner(?x1442, ?x286), nominated_for(?x286, ?x349) >> conf = 0.16 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 078sj4 honored_for! 0bvfqq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.160 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #11632-0154qm PRED entity: 0154qm PRED relation: film PRED expected values: 02mpyh => 96 concepts (69 used for prediction) PRED predicted values (max 10 best out of 985): 03_gz8 (0.60 #76296, 0.60 #83396, 0.58 #86947), 017gm7 (0.60 #76296, 0.60 #83396, 0.58 #86947), 05hjnw (0.50 #2606, 0.06 #79846, 0.05 #62099), 08r4x3 (0.44 #5474, 0.03 #53226, 0.02 #42731), 016z7s (0.29 #335, 0.05 #62099), 0bpm4yw (0.17 #2492, 0.05 #62099, 0.03 #53226), 01hqhm (0.14 #328, 0.13 #39031, 0.05 #62099), 0m313 (0.14 #13, 0.13 #39031, 0.03 #7109), 0bpbhm (0.14 #675, 0.13 #39031, 0.03 #5997), 0cp0790 (0.14 #1214, 0.08 #2988, 0.03 #53226) >> Best rule #76296 for best value: >> intensional similarity = 3 >> extensional distance = 1253 >> proper extension: 0q1lp; >> query: (?x3281, ?x1392) <- film(?x3281, ?x2458), nominated_for(?x3281, ?x1392), film_crew_role(?x2458, ?x137) >> conf = 0.60 => this is the best rule for 2 predicted values *> Best rule #62099 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1164 *> proper extension: 02t_99; 03ywyk; *> query: (?x3281, ?x2326) <- film(?x3281, ?x972), award_nominee(?x3281, ?x4294), nominated_for(?x4294, ?x2326) *> conf = 0.05 ranks of expected_values: 270 EVAL 0154qm film 02mpyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 96.000 69.000 0.601 http://example.org/film/actor/film./film/performance/film #11631-014yzm PRED entity: 014yzm PRED relation: nutrient! PRED expected values: 0cxn2 0fbdb => 56 concepts (55 used for prediction) PRED predicted values (max 10 best out of 14): 05z55 (0.91 #505, 0.89 #395, 0.89 #389), 0fbdb (0.90 #601, 0.89 #731, 0.89 #727), 0cxn2 (0.89 #677, 0.89 #30, 0.89 #213), 07j87 (0.89 #30, 0.89 #213, 0.88 #253), 06x4c (0.89 #30, 0.89 #213, 0.88 #253), 0dcfv (0.89 #30, 0.89 #213, 0.88 #253), 01sh2 (0.04 #733, 0.02 #160, 0.02 #579), 04k8n (0.04 #733), 05wvs (0.04 #733), 025rw19 (0.02 #160, 0.02 #579, 0.01 #92) >> Best rule #505 for best value: >> intensional similarity = 127 >> extensional distance = 20 >> proper extension: 02kd0rh; >> query: (?x8487, 05z55) <- nutrient(?x10612, ?x8487), nutrient(?x9005, ?x8487), nutrient(?x7719, ?x8487), nutrient(?x6285, ?x8487), nutrient(?x6191, ?x8487), nutrient(?x6159, ?x8487), nutrient(?x6032, ?x8487), nutrient(?x5373, ?x8487), nutrient(?x5009, ?x8487), nutrient(?x3900, ?x8487), nutrient(?x2701, ?x8487), nutrient(?x1959, ?x8487), nutrient(?x1303, ?x8487), nutrient(?x1257, ?x8487), ?x6285 = 01645p, ?x1303 = 0fj52s, ?x6191 = 014j1m, ?x3900 = 061_f, nutrient(?x5373, ?x14210), nutrient(?x5373, ?x13545), nutrient(?x5373, ?x13126), nutrient(?x5373, ?x12902), nutrient(?x5373, ?x12454), nutrient(?x5373, ?x11758), nutrient(?x5373, ?x11592), nutrient(?x5373, ?x11409), nutrient(?x5373, ?x11270), nutrient(?x5373, ?x10453), nutrient(?x5373, ?x10098), nutrient(?x5373, ?x9915), nutrient(?x5373, ?x9733), nutrient(?x5373, ?x9490), nutrient(?x5373, ?x9436), nutrient(?x5373, ?x9426), nutrient(?x5373, ?x9365), nutrient(?x5373, ?x8442), nutrient(?x5373, ?x8413), nutrient(?x5373, ?x8243), nutrient(?x5373, ?x7652), nutrient(?x5373, ?x7431), nutrient(?x5373, ?x7364), nutrient(?x5373, ?x7362), nutrient(?x5373, ?x7219), nutrient(?x5373, ?x7135), nutrient(?x5373, ?x6192), nutrient(?x5373, ?x6160), nutrient(?x5373, ?x6033), nutrient(?x5373, ?x6026), nutrient(?x5373, ?x5549), nutrient(?x5373, ?x5526), nutrient(?x5373, ?x5451), nutrient(?x5373, ?x5374), nutrient(?x5373, ?x5010), nutrient(?x5373, ?x3469), nutrient(?x5373, ?x1960), nutrient(?x5373, ?x1304), nutrient(?x5373, ?x1258), ?x1258 = 0h1wg, ?x7652 = 025s0s0, ?x13126 = 02kc_w5, ?x6192 = 06jry, ?x9915 = 025tkqy, ?x11270 = 02kc008, ?x6026 = 025sf8g, ?x9733 = 0h1tz, ?x7219 = 0h1vg, ?x1304 = 08lb68, ?x6032 = 01nkt, ?x12454 = 025rw19, ?x7362 = 02kc5rj, ?x5451 = 05wvs, ?x9426 = 0h1yy, nutrient(?x7719, ?x12868), nutrient(?x7719, ?x11784), nutrient(?x7719, ?x9855), nutrient(?x7719, ?x9840), nutrient(?x7719, ?x9708), nutrient(?x7719, ?x6286), nutrient(?x7719, ?x4069), nutrient(?x7719, ?x3203), nutrient(?x7719, ?x2702), nutrient(?x7719, ?x2018), ?x9840 = 02p0tjr, ?x11592 = 025sf0_, ?x6159 = 033cnk, ?x11409 = 0h1yf, ?x1959 = 0f25w9, ?x9490 = 0h1sg, ?x4069 = 0hqw8p_, ?x11784 = 07zqy, ?x8442 = 02kcv4x, ?x14210 = 0f4k5, ?x5009 = 0fjfh, ?x5526 = 09pbb, ?x5010 = 0h1vz, ?x9436 = 025sqz8, ?x10612 = 0frq6, ?x6160 = 041r51, ?x7364 = 09gvd, ?x10453 = 075pwf, ?x2701 = 0hkxq, ?x3203 = 04kl74p, nutrient(?x1257, ?x10891), nutrient(?x1257, ?x10195), ?x9708 = 061xhr, ?x5549 = 025s7j4, ?x12902 = 0fzjh, ?x6286 = 02y_3rf, ?x7431 = 09gwd, ?x8413 = 02kc4sf, ?x8243 = 014d7f, ?x9365 = 04k8n, ?x9005 = 04zpv, ?x10195 = 0hkwr, ?x9855 = 0d9t0, ?x13545 = 01w_3, ?x10891 = 0g5gq, ?x2702 = 0838f, ?x5374 = 025s0zp, ?x1960 = 07hnp, ?x10098 = 0h1_c, ?x3469 = 0h1zw, ?x11758 = 0q01m, ?x12868 = 03d49, ?x7135 = 025rsfk, ?x2018 = 01sh2, ?x6033 = 04zjxcz >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #601 for first EXPECTED value: *> intensional similarity = 120 *> extensional distance = 29 *> proper extension: 061xhr; *> query: (?x8487, 0fbdb) <- nutrient(?x10612, ?x8487), nutrient(?x9005, ?x8487), nutrient(?x8298, ?x8487), nutrient(?x7719, ?x8487), nutrient(?x6285, ?x8487), nutrient(?x6191, ?x8487), nutrient(?x6032, ?x8487), nutrient(?x3900, ?x8487), nutrient(?x1257, ?x8487), nutrient(?x6285, ?x13944), nutrient(?x6285, ?x13498), nutrient(?x6285, ?x12902), nutrient(?x6285, ?x12454), nutrient(?x6285, ?x11758), nutrient(?x6285, ?x11592), nutrient(?x6285, ?x11409), nutrient(?x6285, ?x11270), nutrient(?x6285, ?x10891), nutrient(?x6285, ?x10709), nutrient(?x6285, ?x10195), nutrient(?x6285, ?x10098), nutrient(?x6285, ?x9949), nutrient(?x6285, ?x9915), nutrient(?x6285, ?x9840), nutrient(?x6285, ?x9795), nutrient(?x6285, ?x9733), nutrient(?x6285, ?x9619), nutrient(?x6285, ?x9490), nutrient(?x6285, ?x9426), nutrient(?x6285, ?x9365), nutrient(?x6285, ?x8442), nutrient(?x6285, ?x8243), nutrient(?x6285, ?x7894), nutrient(?x6285, ?x7720), nutrient(?x6285, ?x7364), nutrient(?x6285, ?x7362), nutrient(?x6285, ?x7219), nutrient(?x6285, ?x6586), nutrient(?x6285, ?x6286), nutrient(?x6285, ?x6160), nutrient(?x6285, ?x6026), nutrient(?x6285, ?x5526), nutrient(?x6285, ?x5451), nutrient(?x6285, ?x5010), nutrient(?x6285, ?x4069), nutrient(?x6285, ?x3901), nutrient(?x6285, ?x3203), nutrient(?x6285, ?x1960), nutrient(?x6285, ?x1304), nutrient(?x6285, ?x1258), ?x7362 = 02kc5rj, ?x6586 = 05gh50, ?x6191 = 014j1m, ?x9490 = 0h1sg, ?x9733 = 0h1tz, ?x13498 = 07q0m, ?x9426 = 0h1yy, ?x6026 = 025sf8g, ?x11270 = 02kc008, ?x9949 = 02kd0rh, ?x5451 = 05wvs, ?x10195 = 0hkwr, ?x12454 = 025rw19, ?x12902 = 0fzjh, ?x5010 = 0h1vz, ?x9619 = 0h1tg, nutrient(?x9005, ?x14210), nutrient(?x9005, ?x13126), nutrient(?x9005, ?x12083), nutrient(?x9005, ?x10453), nutrient(?x9005, ?x9436), nutrient(?x9005, ?x7431), nutrient(?x9005, ?x7135), nutrient(?x9005, ?x5337), nutrient(?x9005, ?x2018), ?x7135 = 025rsfk, ?x1960 = 07hnp, ?x8442 = 02kcv4x, ?x1258 = 0h1wg, nutrient(?x1257, ?x14698), ?x12083 = 01n78x, ?x7894 = 0f4hc, ?x10453 = 075pwf, ?x7219 = 0h1vg, ?x14698 = 02kb_jm, ?x3901 = 0466p20, ?x13126 = 02kc_w5, ?x2018 = 01sh2, ?x10098 = 0h1_c, ?x5337 = 06x4c, ?x7720 = 025s7x6, ?x6286 = 02y_3rf, ?x13944 = 0f4kp, ?x10709 = 0h1sz, ?x1304 = 08lb68, ?x8243 = 014d7f, ?x5526 = 09pbb, nutrient(?x3468, ?x14210), ?x9915 = 025tkqy, ?x4069 = 0hqw8p_, ?x6160 = 041r51, ?x9365 = 04k8n, ?x11758 = 0q01m, nutrient(?x6032, ?x3264), ?x9436 = 025sqz8, ?x7364 = 09gvd, ?x11592 = 025sf0_, ?x10891 = 0g5gq, ?x9795 = 05v_8y, ?x3468 = 0cxn2, ?x11409 = 0h1yf, ?x3203 = 04kl74p, ?x7431 = 09gwd, ?x9840 = 02p0tjr, nutrient(?x10612, ?x12336), ?x3264 = 0dcfv, ?x12336 = 0f4l5, ?x7719 = 0dj75, ?x3900 = 061_f, ?x8298 = 037ls6 *> conf = 0.90 ranks of expected_values: 2, 3 EVAL 014yzm nutrient! 0fbdb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 55.000 0.909 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 014yzm nutrient! 0cxn2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 55.000 0.909 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #11630-02qt02v PRED entity: 02qt02v PRED relation: award! PRED expected values: 0dr_4 0sxmx => 60 concepts (28 used for prediction) PRED predicted values (max 10 best out of 987): 017jd9 (0.62 #2452, 0.33 #5460, 0.27 #6462), 03hmt9b (0.58 #3390, 0.50 #2386, 0.50 #383), 0gmcwlb (0.50 #120, 0.38 #2123, 0.33 #3127), 0404j37 (0.50 #2657, 0.33 #5665, 0.25 #654), 0b6tzs (0.50 #2088, 0.25 #5096, 0.25 #85), 05sy_5 (0.50 #2609, 0.17 #5617, 0.09 #13620), 0bmhvpr (0.50 #1366, 0.17 #3371, 0.09 #11011), 06_x996 (0.50 #1398, 0.17 #3403, 0.09 #11011), 0c0zq (0.38 #2886, 0.33 #5894, 0.17 #6896), 0h03fhx (0.38 #2451, 0.29 #5459, 0.27 #26031) >> Best rule #2452 for best value: >> intensional similarity = 9 >> extensional distance = 6 >> proper extension: 099c8n; >> query: (?x3233, 017jd9) <- nominated_for(?x3233, ?x6007), nominated_for(?x3233, ?x4336), nominated_for(?x3233, ?x1988), nominated_for(?x3233, ?x303), music(?x6007, ?x2945), ?x1988 = 09k56b7, ?x303 = 011yrp, film_crew_role(?x4336, ?x137), film_release_region(?x4336, ?x87) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3153 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0p9sw; 0gr0m; 0gqwc; 02qvyrt; 0fhpv4; 018wdw; *> query: (?x3233, 0dr_4) <- nominated_for(?x3233, ?x6007), ?x6007 = 0dgq_kn, award(?x9056, ?x3233), production_companies(?x9056, ?x382), genre(?x9056, ?x53) *> conf = 0.25 ranks of expected_values: 34, 51 EVAL 02qt02v award! 0sxmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 60.000 28.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02qt02v award! 0dr_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 60.000 28.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #11629-0f14q PRED entity: 0f14q PRED relation: location_of_ceremony PRED expected values: 03_3d => 155 concepts (88 used for prediction) PRED predicted values (max 10 best out of 25): 030qb3t (0.25 #19, 0.01 #1453, 0.01 #2409), 0cv3w (0.02 #1469, 0.02 #2425, 0.02 #4219), 0k049 (0.02 #1438, 0.02 #2394, 0.02 #2634), 03gh4 (0.02 #780, 0.01 #2093), 02_286 (0.02 #849, 0.01 #1447, 0.01 #1566), 059rby (0.02 #2038, 0.01 #2157, 0.01 #725), 0f25y (0.01 #678, 0.01 #1633, 0.01 #2710), 0rqf1 (0.01 #697), 03rk0 (0.01 #1460, 0.01 #2416), 0b90_r (0.01 #1437, 0.01 #839) >> Best rule #19 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 016sp_; 01sb5r; >> query: (?x9957, 030qb3t) <- student(?x4904, ?x9957), location(?x9957, ?x6453), ?x6453 = 01smm, category(?x9957, ?x134), profession(?x9957, ?x319) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f14q location_of_ceremony 03_3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 88.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #11628-050l8 PRED entity: 050l8 PRED relation: religion PRED expected values: 072w0 => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 22): 03_gx (0.42 #1637, 0.42 #78, 0.40 #318), 0flw86 (0.42 #1637, 0.39 #652, 0.39 #988), 02t7t (0.42 #1637, 0.37 #1998, 0.28 #108), 092bf5 (0.42 #1637, 0.37 #1998, 0.28 #79), 072w0 (0.42 #1637, 0.37 #1998, 0.25 #86), 03j6c (0.09 #1261, 0.09 #1117, 0.09 #226), 0kpl (0.04 #339, 0.03 #894, 0.02 #1110), 04t_mf (0.04 #1266, 0.03 #594, 0.03 #666), 0n2g (0.04 #991, 0.03 #655, 0.03 #1255), 078tg (0.03 #1151, 0.03 #671, 0.03 #1271) >> Best rule #1637 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 02wt0; >> query: (?x2049, ?x962) <- adjoins(?x2049, ?x2768), contains(?x2049, ?x5554), religion(?x2768, ?x962) >> conf = 0.42 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5 EVAL 050l8 religion 072w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 175.000 175.000 0.425 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #11627-019fv4 PRED entity: 019fv4 PRED relation: religion PRED expected values: 03_gx => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 28): 0flw86 (0.71 #457, 0.16 #562, 0.12 #107), 01lp8 (0.59 #456, 0.32 #561, 0.24 #631), 0c8wxp (0.45 #461, 0.33 #566, 0.26 #636), 092bf5 (0.45 #470, 0.13 #575, 0.10 #645), 051kv (0.33 #460, 0.33 #565, 0.25 #635), 05sfs (0.33 #458, 0.30 #563, 0.23 #633), 0631_ (0.31 #568, 0.29 #463, 0.24 #638), 04pk9 (0.31 #579, 0.29 #474, 0.24 #649), 019cr (0.30 #571, 0.28 #466, 0.24 #396), 03_gx (0.29 #469, 0.20 #574, 0.17 #399) >> Best rule #457 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 05kyr; >> query: (?x12642, 0flw86) <- religion(?x12642, ?x12643), religion(?x7747, ?x12643), ?x7747 = 07f1x >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #469 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 05kyr; *> query: (?x12642, 03_gx) <- religion(?x12642, ?x12643), religion(?x7747, ?x12643), ?x7747 = 07f1x *> conf = 0.29 ranks of expected_values: 10 EVAL 019fv4 religion 03_gx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 112.000 112.000 0.707 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #11626-02rx2m5 PRED entity: 02rx2m5 PRED relation: film! PRED expected values: 0fqy4p => 79 concepts (62 used for prediction) PRED predicted values (max 10 best out of 56): 016tt2 (0.20 #4, 0.16 #230, 0.13 #381), 01795t (0.20 #18, 0.11 #928, 0.11 #244), 054g1r (0.20 #35, 0.08 #945, 0.07 #261), 086k8 (0.20 #153, 0.17 #228, 0.17 #532), 03xq0f (0.18 #156, 0.13 #231, 0.12 #764), 05qd_ (0.17 #235, 0.15 #919, 0.13 #386), 016tw3 (0.15 #86, 0.15 #388, 0.14 #996), 017s11 (0.14 #988, 0.14 #838, 0.13 #1518), 0jz9f (0.12 #76, 0.09 #302, 0.08 #378), 025jfl (0.08 #81, 0.07 #459, 0.06 #611) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 016017; >> query: (?x1866, 016tt2) <- country(?x1866, ?x94), film(?x1865, ?x1866), currency(?x1866, ?x170), ?x1865 = 03k7bd >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #633 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 233 *> proper extension: 0cp08zg; *> query: (?x1866, 0fqy4p) <- country(?x1866, ?x94), category(?x1866, ?x134), nominated_for(?x91, ?x1866), titles(?x53, ?x1866) *> conf = 0.02 ranks of expected_values: 39 EVAL 02rx2m5 film! 0fqy4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 79.000 62.000 0.200 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #11625-025vw4t PRED entity: 025vw4t PRED relation: profession PRED expected values: 02krf9 => 82 concepts (78 used for prediction) PRED predicted values (max 10 best out of 41): 02hrh1q (0.76 #10672, 0.73 #10968, 0.70 #2234), 01d_h8 (0.49 #2522, 0.48 #2374, 0.45 #7704), 02jknp (0.42 #2524, 0.31 #7706, 0.27 #2376), 02krf9 (0.33 #1654, 0.30 #1802, 0.29 #1950), 0kyk (0.28 #5034, 0.12 #2545, 0.11 #7727), 02hv44_ (0.28 #5034, 0.07 #2573, 0.04 #7755), 0cbd2 (0.23 #1191, 0.22 #7705, 0.22 #747), 018gz8 (0.20 #2384, 0.18 #1200, 0.17 #1052), 09jwl (0.18 #10972, 0.18 #5348, 0.17 #4163), 0np9r (0.17 #7718, 0.14 #2388, 0.13 #1056) >> Best rule #10672 for best value: >> intensional similarity = 3 >> extensional distance = 3290 >> proper extension: 05m63c; 033hqf; 045bs6; 08b8vd; 01ydzx; 05nzw6; 03wjb7; 017b2p; 030dx5; 01qn8k; ... >> query: (?x6145, 02hrh1q) <- profession(?x6145, ?x987), profession(?x11705, ?x987), ?x11705 = 06s1qy >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1654 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 212 *> proper extension: 0f1vrl; 0bbxd3; 09zw90; *> query: (?x6145, 02krf9) <- profession(?x6145, ?x1041), program(?x6145, ?x1280), ?x1041 = 03gjzk *> conf = 0.33 ranks of expected_values: 4 EVAL 025vw4t profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 78.000 0.759 http://example.org/people/person/profession #11624-0408np PRED entity: 0408np PRED relation: category PRED expected values: 08mbj5d => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.40 #1, 0.34 #4, 0.32 #22) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 06dv3; 014zcr; 01qscs; 09fb5; 01vvycq; 02l840; 05zbm4; 06pk8; 0151w_; 05k2s_; ... >> query: (?x2692, 08mbj5d) <- award_nominee(?x2692, ?x157), profession(?x2692, ?x319), ?x319 = 01d_h8, participant(?x2692, ?x1207) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0408np category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.400 http://example.org/common/topic/webpage./common/webpage/category #11623-0fk1z PRED entity: 0fk1z PRED relation: people PRED expected values: 051q39 => 49 concepts (47 used for prediction) PRED predicted values (max 10 best out of 3752): 03knl (0.50 #5314, 0.20 #8772, 0.17 #10502), 02ts3h (0.40 #9642, 0.33 #11372, 0.25 #6184), 0x3n (0.40 #9535, 0.33 #11265, 0.25 #6077), 09h4b5 (0.40 #9762, 0.33 #11492, 0.09 #47829), 04n2vgk (0.33 #1312, 0.25 #8229, 0.25 #6500), 01wbgdv (0.33 #146, 0.25 #7063, 0.25 #5334), 02vntj (0.33 #593, 0.25 #7510, 0.25 #5781), 08swgx (0.33 #3844, 0.25 #5573, 0.20 #9031), 07ldhs (0.33 #707, 0.25 #7624, 0.17 #12812), 01svw8n (0.33 #545, 0.25 #7462, 0.17 #12650) >> Best rule #5314 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 02ctzb; >> query: (?x13892, 03knl) <- people(?x13892, ?x5364), risk_factors(?x8523, ?x13892), award_winner(?x1007, ?x5364), award(?x5364, ?x154), artists(?x671, ?x5364), award_winner(?x4799, ?x5364) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fk1z people 051q39 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 47.000 0.500 http://example.org/people/ethnicity/people #11622-01h4rj PRED entity: 01h4rj PRED relation: people! PRED expected values: 0gk4g => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 44): 0dq9p (0.25 #281, 0.18 #479, 0.18 #677), 0gk4g (0.22 #1132, 0.22 #1066, 0.21 #670), 02k6hp (0.18 #367, 0.14 #499, 0.10 #565), 02knxx (0.17 #32, 0.06 #692, 0.06 #362), 0dcsx (0.12 #279, 0.12 #147, 0.12 #81), 0148xv (0.12 #198, 0.12 #132, 0.03 #594), 0qcr0 (0.12 #133, 0.11 #727, 0.10 #529), 04p3w (0.12 #77, 0.09 #671, 0.08 #1992), 019dmc (0.12 #182, 0.06 #710, 0.05 #512), 01psyx (0.12 #177, 0.05 #2290, 0.05 #2092) >> Best rule #281 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0pqzh; >> query: (?x9709, 0dq9p) <- student(?x735, ?x9709), celebrities_impersonated(?x3649, ?x9709), languages(?x9709, ?x254), ?x254 = 02h40lc >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1132 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 06c97; *> query: (?x9709, 0gk4g) <- gender(?x9709, ?x231), place_of_death(?x9709, ?x191), celebrities_impersonated(?x3649, ?x9709) *> conf = 0.22 ranks of expected_values: 2 EVAL 01h4rj people! 0gk4g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 152.000 152.000 0.250 http://example.org/people/cause_of_death/people #11621-03f5vvx PRED entity: 03f5vvx PRED relation: religion PRED expected values: 051kv => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 40): 0kpl (0.57 #319, 0.33 #451, 0.33 #186), 0kq2 (0.43 #327, 0.17 #1339, 0.14 #1736), 0c8wxp (0.41 #2782, 0.39 #2208, 0.35 #4373), 05sfs (0.33 #91, 0.10 #884, 0.08 #972), 03_gx (0.29 #278, 0.25 #1335, 0.21 #2924), 04pk9 (0.22 #417, 0.17 #152, 0.13 #769), 019cr (0.21 #672, 0.21 #980, 0.20 #584), 01lp8 (0.20 #530, 0.20 #486, 0.17 #618), 0631_ (0.18 #1153, 0.17 #96, 0.16 #1637), 0n2g (0.17 #189, 0.17 #145, 0.17 #101) >> Best rule #319 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 06crk; >> query: (?x3864, 0kpl) <- gender(?x3864, ?x514), profession(?x3864, ?x3802), student(?x865, ?x3864), student(?x892, ?x3864), ?x3802 = 06q2q >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #225 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 05kh_; *> query: (?x3864, 051kv) <- gender(?x3864, ?x514), influenced_by(?x3864, ?x10500), films(?x3864, ?x1803), religion(?x3864, ?x13975), award_winner(?x3846, ?x3864) *> conf = 0.17 ranks of expected_values: 11 EVAL 03f5vvx religion 051kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 147.000 147.000 0.571 http://example.org/people/person/religion #11620-09lxtg PRED entity: 09lxtg PRED relation: member_states! PRED expected values: 085h1 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 13): 085h1 (0.83 #7, 0.79 #20, 0.78 #12), 018cqq (0.45 #6, 0.44 #11, 0.44 #19), 059dn (0.31 #8, 0.28 #21, 0.28 #13), 02jxk (0.28 #5, 0.27 #42, 0.27 #14), 041288 (0.14 #9, 0.09 #134, 0.09 #140), 0j7v_ (0.14 #9, 0.09 #134, 0.09 #140), 07t65 (0.14 #9, 0.09 #134, 0.09 #140), 0b6css (0.07 #330), 0gkjy (0.07 #330), 04k4l (0.07 #330) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 087vz; 01mk6; >> query: (?x4569, 085h1) <- olympics(?x4569, ?x2134), organization(?x4569, ?x312), ?x2134 = 0blg2 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09lxtg member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.828 http://example.org/user/ktrueman/default_domain/international_organization/member_states #11619-01795t PRED entity: 01795t PRED relation: nominated_for PRED expected values: 0pc62 => 132 concepts (93 used for prediction) PRED predicted values (max 10 best out of 638): 0m63c (0.80 #146721, 0.80 #140268, 0.80 #130590), 02rn00y (0.50 #6451, 0.50 #6450, 0.33 #5353), 01jrbb (0.50 #6451, 0.50 #6450, 0.33 #5270), 0407yfx (0.50 #6451, 0.50 #6450, 0.33 #5156), 02qm_f (0.50 #6451, 0.50 #6450, 0.33 #4983), 05nlx4 (0.50 #6451, 0.50 #6450, 0.15 #33850), 0kcn7 (0.50 #6451, 0.50 #6450, 0.15 #33850), 04hwbq (0.50 #6451, 0.50 #6450, 0.15 #33850), 03x7hd (0.50 #6451, 0.50 #6450, 0.15 #33850), 0_7w6 (0.50 #6451, 0.50 #6450, 0.15 #33850) >> Best rule #146721 for best value: >> intensional similarity = 3 >> extensional distance = 1436 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 012ljv; 04bdxl; 02s2ft; 05vsxz; 0grwj; ... >> query: (?x2156, ?x3845) <- award_winner(?x3845, ?x2156), award(?x2156, ?x1105), nominated_for(?x2156, ?x1080) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #87055 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 342 *> proper extension: 02cm2m; *> query: (?x2156, ?x667) <- award_winner(?x3845, ?x2156), award_nominee(?x11876, ?x2156), film(?x11876, ?x667) *> conf = 0.10 ranks of expected_values: 131 EVAL 01795t nominated_for 0pc62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 132.000 93.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #11618-01gf5h PRED entity: 01gf5h PRED relation: artists! PRED expected values: 01_bkd => 121 concepts (65 used for prediction) PRED predicted values (max 10 best out of 259): 0gywn (0.47 #5176, 0.44 #955, 0.36 #3669), 02yv6b (0.35 #997, 0.25 #2807, 0.17 #4313), 0glt670 (0.34 #4861, 0.31 #7880, 0.30 #6672), 03_d0 (0.31 #914, 0.28 #1516, 0.26 #2422), 07sbbz2 (0.27 #910, 0.16 #5131, 0.14 #1814), 02x8m (0.26 #5141, 0.17 #920, 0.15 #619), 0ggx5q (0.24 #10928, 0.22 #5197, 0.18 #3389), 02lnbg (0.24 #5177, 0.23 #10908, 0.21 #3369), 05w3f (0.23 #1842, 0.22 #2144, 0.20 #4254), 02k_kn (0.22 #3376, 0.17 #10915, 0.14 #5788) >> Best rule #5176 for best value: >> intensional similarity = 2 >> extensional distance = 179 >> proper extension: 01v27pl; >> query: (?x1001, 0gywn) <- artists(?x3319, ?x1001), ?x3319 = 06j6l >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #6079 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 208 *> proper extension: 02mq_y; 06br6t; *> query: (?x1001, 01_bkd) <- artists(?x302, ?x1001), ?x302 = 016clz *> conf = 0.10 ranks of expected_values: 32 EVAL 01gf5h artists! 01_bkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 121.000 65.000 0.470 http://example.org/music/genre/artists #11617-01k0xy PRED entity: 01k0xy PRED relation: genre PRED expected values: 05p553 => 86 concepts (58 used for prediction) PRED predicted values (max 10 best out of 91): 05p553 (0.99 #3108, 0.53 #5, 0.43 #124), 01jfsb (0.82 #2759, 0.48 #966, 0.48 #728), 07s9rl0 (0.68 #5389, 0.64 #5627, 0.60 #4668), 01z4y (0.61 #6465, 0.54 #2269, 0.52 #6226), 02kdv5l (0.55 #836, 0.54 #1313, 0.54 #955), 03k9fj (0.45 #846, 0.43 #1084, 0.41 #370), 01hmnh (0.31 #852, 0.30 #1090, 0.28 #495), 01j1n2 (0.29 #180, 0.24 #299, 0.07 #61), 0lsxr (0.28 #2755, 0.24 #1920, 0.23 #605), 03npn (0.27 #246, 0.20 #2753, 0.14 #127) >> Best rule #3108 for best value: >> intensional similarity = 5 >> extensional distance = 585 >> proper extension: 04svwx; 0hr41p6; >> query: (?x7348, 05p553) <- genre(?x7348, ?x12008), genre(?x8358, ?x12008), genre(?x4326, ?x12008), ?x4326 = 0fz3b1, ?x8358 = 0456zg >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k0xy genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 58.000 0.993 http://example.org/film/film/genre #11616-01ls2 PRED entity: 01ls2 PRED relation: film_release_region! PRED expected values: 02vxq9m 0c0nhgv 0gj8t_b 0gmcwlb 02yvct 0661ql3 0dyb1 03q0r1 09v3jyg 027pfg => 130 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1575): 047vnkj (0.89 #4200, 0.81 #11394, 0.78 #6598), 02vxq9m (0.88 #6010, 0.76 #3612, 0.75 #10806), 0by1wkq (0.80 #6187, 0.74 #3789, 0.71 #10983), 0661ql3 (0.79 #11034, 0.78 #6238, 0.72 #5039), 09k56b7 (0.79 #10989, 0.75 #6193, 0.74 #3795), 06fcqw (0.79 #4329, 0.78 #6727, 0.73 #11523), 01jrbb (0.76 #3892, 0.72 #6290, 0.67 #11086), 0ndsl1x (0.76 #4607, 0.71 #11801, 0.70 #7005), 02xbyr (0.75 #11312, 0.75 #6516, 0.69 #5317), 05pdh86 (0.75 #6478, 0.74 #4080, 0.73 #11274) >> Best rule #4200 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 04v3q; 05v10; >> query: (?x410, 047vnkj) <- jurisdiction_of_office(?x265, ?x410), film_release_region(?x7693, ?x410), ?x7693 = 0m63c >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #6010 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 02k8k; 01xbgx; *> query: (?x410, 02vxq9m) <- jurisdiction_of_office(?x265, ?x410), film_release_region(?x9652, ?x410), ?x9652 = 0ddbjy4 *> conf = 0.88 ranks of expected_values: 2, 4, 11, 12, 17, 20, 22, 25, 78, 247 EVAL 01ls2 film_release_region! 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 130.000 83.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ls2 film_release_region! 09v3jyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 130.000 83.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ls2 film_release_region! 03q0r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 130.000 83.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ls2 film_release_region! 0dyb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 130.000 83.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ls2 film_release_region! 0661ql3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 83.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ls2 film_release_region! 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 130.000 83.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ls2 film_release_region! 0gmcwlb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 130.000 83.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ls2 film_release_region! 0gj8t_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 130.000 83.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ls2 film_release_region! 0c0nhgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 130.000 83.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ls2 film_release_region! 02vxq9m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 83.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11615-04jkpgv PRED entity: 04jkpgv PRED relation: film_release_region PRED expected values: 03rt9 015fr 0h7x 06c1y 06f32 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 142): 015fr (0.84 #2401, 0.75 #3893, 0.73 #4489), 05qhw (0.83 #2398, 0.80 #3890, 0.70 #4486), 06t2t (0.76 #2444, 0.68 #3936, 0.58 #4532), 03spz (0.76 #2477, 0.68 #3969, 0.56 #4565), 03rt9 (0.73 #2397, 0.69 #3889, 0.63 #4485), 05v8c (0.67 #2400, 0.57 #3892, 0.50 #4488), 04gzd (0.62 #2393, 0.50 #3885, 0.43 #4481), 0ctw_b (0.58 #2410, 0.53 #3902, 0.47 #4498), 015qh (0.56 #2425, 0.48 #3917, 0.38 #4513), 06mzp (0.55 #2406, 0.50 #3898, 0.40 #4494) >> Best rule #2401 for best value: >> intensional similarity = 5 >> extensional distance = 137 >> proper extension: 014lc_; 0ds35l9; 0g56t9t; 02vxq9m; 0gx1bnj; 0ds3t5x; 05p1tzf; 02x3lt7; 0gx9rvq; 0401sg; ... >> query: (?x1498, 015fr) <- film_release_region(?x1498, ?x2267), film_release_region(?x1498, ?x151), genre(?x1498, ?x53), ?x2267 = 03rj0, ?x151 = 0b90_r >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 12, 17, 18 EVAL 04jkpgv film_release_region 06f32 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 99.000 99.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04jkpgv film_release_region 06c1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 99.000 99.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04jkpgv film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 99.000 99.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04jkpgv film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04jkpgv film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 99.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11614-0yt73 PRED entity: 0yt73 PRED relation: place! PRED expected values: 0yt73 => 103 concepts (46 used for prediction) PRED predicted values (max 10 best out of 54): 0n2sh (0.06 #3096, 0.05 #6708, 0.05 #10323), 0z1l8 (0.03 #514, 0.02 #4642), 0yw93 (0.03 #485, 0.02 #4642), 0yvjx (0.03 #466, 0.02 #4642), 0z18v (0.03 #455, 0.02 #4642), 0yzyn (0.03 #340, 0.02 #4642), 0z1vw (0.03 #331, 0.02 #4642), 07l5z (0.03 #313, 0.02 #4642), 0z2gq (0.03 #244, 0.02 #4642), 0z20d (0.03 #203, 0.02 #4642) >> Best rule #3096 for best value: >> intensional similarity = 4 >> extensional distance = 137 >> proper extension: 0_3cs; 01mc11; 0xkq4; 013yq; 01cx_; 01m1_t; 0mn8t; 0mb2b; 0f2nf; 0_565; ... >> query: (?x11029, ?x11028) <- county(?x11029, ?x11028), time_zones(?x11029, ?x2674), contains(?x94, ?x11029), ?x2674 = 02hcv8 >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #4642 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 153 *> proper extension: 02qjb7z; *> query: (?x11029, ?x388) <- contains(?x177, ?x11029), administrative_division(?x11029, ?x11028), contains(?x177, ?x388), district_represented(?x176, ?x177) *> conf = 0.02 ranks of expected_values: 28 EVAL 0yt73 place! 0yt73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 103.000 46.000 0.058 http://example.org/location/hud_county_place/place #11613-070m6c PRED entity: 070m6c PRED relation: district_represented PRED expected values: 0824r 081yw 04tgp 05mph 07_f2 => 43 concepts (40 used for prediction) PRED predicted values (max 10 best out of 97): 04tgp (0.91 #118, 0.88 #603, 0.87 #390), 07_f2 (0.91 #118, 0.87 #390, 0.86 #544), 0824r (0.91 #118, 0.87 #390, 0.81 #596), 05mph (0.91 #118, 0.87 #390, 0.81 #596), 081yw (0.91 #118, 0.87 #390, 0.81 #596), 04kbn (0.64 #414, 0.63 #330, 0.59 #621), 0d060g (0.64 #414, 0.63 #330, 0.59 #621), 0183z2 (0.64 #414, 0.63 #330, 0.59 #621), 015jr (0.64 #414, 0.63 #330, 0.56 #230), 06nrt (0.64 #414, 0.63 #330, 0.56 #230) >> Best rule #118 for best value: >> intensional similarity = 40 >> extensional distance = 1 >> proper extension: 077g7n; >> query: (?x653, ?x335) <- legislative_sessions(?x2860, ?x653), legislative_sessions(?x653, ?x6933), legislative_sessions(?x653, ?x4821), legislative_sessions(?x653, ?x3766), legislative_sessions(?x653, ?x2976), legislative_sessions(?x11440, ?x653), legislative_sessions(?x9334, ?x653), legislative_sessions(?x8607, ?x653), legislative_sessions(?x6742, ?x653), legislative_sessions(?x3445, ?x653), ?x3766 = 02gkzs, ?x2976 = 03rtmz, district_represented(?x4821, ?x5575), district_represented(?x4821, ?x4198), district_represented(?x4821, ?x2049), district_represented(?x4821, ?x2020), district_represented(?x4821, ?x1138), district_represented(?x4821, ?x448), district_represented(?x4821, ?x335), ?x11440 = 01lct6, district_represented(?x653, ?x953), district_represented(?x653, ?x760), ?x953 = 0hjy, ?x2049 = 050l8, ?x760 = 05fkf, ?x9334 = 02hy5d, legislative_sessions(?x1137, ?x653), ?x6933 = 024tkd, ?x8607 = 0226cw, ?x2020 = 05k7sb, basic_title(?x3445, ?x5402), ?x448 = 03v1s, legislative_sessions(?x4567, ?x4821), ?x1138 = 059_c, jurisdiction_of_office(?x3445, ?x94), religion(?x6742, ?x2769), student(?x3821, ?x6742), ?x5575 = 05fjy, ?x4198 = 05fky, profession(?x6742, ?x3342) >> conf = 0.91 => this is the best rule for 5 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 070m6c district_represented 07_f2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 40.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 070m6c district_represented 05mph CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 40.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 070m6c district_represented 04tgp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 40.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 070m6c district_represented 081yw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 40.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 070m6c district_represented 0824r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 40.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #11612-03q4nz PRED entity: 03q4nz PRED relation: genre! PRED expected values: 07qg8v 04q00lw 03rz2b 08gg47 0432_5 02v_r7d 09hy79 => 43 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1953): 0296rz (0.71 #28428, 0.33 #8775, 0.25 #15922), 072x7s (0.71 #27055, 0.33 #7402, 0.25 #14549), 04wddl (0.67 #24740, 0.57 #30102, 0.50 #26526), 02lxrv (0.67 #24233, 0.57 #29595, 0.50 #9941), 03m8y5 (0.67 #23631, 0.57 #28993, 0.50 #9339), 0f42nz (0.60 #18761, 0.50 #25907, 0.50 #15190), 05r3qc (0.60 #20715, 0.50 #24288, 0.50 #9996), 014nq4 (0.60 #20173, 0.50 #14815, 0.33 #25532), 0645k5 (0.60 #20118, 0.43 #27266, 0.35 #30842), 0cc5mcj (0.60 #20040, 0.40 #21826, 0.33 #7535) >> Best rule #28428 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 02qfv5d; >> query: (?x1626, 0296rz) <- genre(?x11114, ?x1626), genre(?x9893, ?x1626), genre(?x5429, ?x1626), genre(?x2617, ?x1626), costume_design_by(?x11114, ?x12771), film(?x656, ?x11114), nominated_for(?x2222, ?x5429), nominated_for(?x1937, ?x11114), ?x9893 = 0dmn0x, nominated_for(?x2222, ?x5080), ceremony(?x2222, ?x7884), ?x7884 = 09306z, ?x5080 = 014kkm, language(?x2617, ?x254) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #25473 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 01t_vv; *> query: (?x1626, 03rz2b) <- genre(?x11114, ?x1626), genre(?x7307, ?x1626), genre(?x5782, ?x1626), genre(?x4444, ?x1626), genre(?x3881, ?x1626), genre(?x1283, ?x1626), ?x11114 = 02tcgh, nominated_for(?x3890, ?x4444), film_festivals(?x1283, ?x6828), film_release_region(?x1283, ?x94), film_crew_role(?x3881, ?x137), category(?x1283, ?x134), award(?x5782, ?x507), award_winner(?x7307, ?x2530) *> conf = 0.50 ranks of expected_values: 91, 331, 525, 925, 1398, 1678, 1736 EVAL 03q4nz genre! 09hy79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 20.000 0.714 http://example.org/film/film/genre EVAL 03q4nz genre! 02v_r7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 20.000 0.714 http://example.org/film/film/genre EVAL 03q4nz genre! 0432_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 20.000 0.714 http://example.org/film/film/genre EVAL 03q4nz genre! 08gg47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 20.000 0.714 http://example.org/film/film/genre EVAL 03q4nz genre! 03rz2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 43.000 20.000 0.714 http://example.org/film/film/genre EVAL 03q4nz genre! 04q00lw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 20.000 0.714 http://example.org/film/film/genre EVAL 03q4nz genre! 07qg8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 20.000 0.714 http://example.org/film/film/genre #11611-03fwln PRED entity: 03fwln PRED relation: gender PRED expected values: 02zsn => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #35, 0.71 #115, 0.71 #117), 02zsn (0.65 #28, 0.65 #14, 0.65 #32) >> Best rule #35 for best value: >> intensional similarity = 2 >> extensional distance = 205 >> proper extension: 015k7; 0dhqyw; 0frpd5; 0cfywh; 02qfk4j; >> query: (?x10783, 05zppz) <- nationality(?x10783, ?x2146), ?x2146 = 03rk0 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #28 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 145 *> proper extension: 046rfv; *> query: (?x10783, 02zsn) <- profession(?x10783, ?x4773), profession(?x10783, ?x1032), ?x4773 = 0d1pc, profession(?x7395, ?x1032), ?x7395 = 03b78r *> conf = 0.65 ranks of expected_values: 2 EVAL 03fwln gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.739 http://example.org/people/person/gender #11610-05tbn PRED entity: 05tbn PRED relation: district_represented! PRED expected values: 01grnp 01gstn 01gst9 024tkd 01grrf 01gsry => 202 concepts (202 used for prediction) PRED predicted values (max 10 best out of 20): 024tkd (0.68 #494, 0.67 #414, 0.65 #394), 01gstn (0.47 #289, 0.44 #389, 0.42 #249), 01gst9 (0.42 #252, 0.40 #292, 0.38 #392), 01gsry (0.33 #257, 0.33 #297, 0.32 #137), 01grnp (0.32 #125, 0.30 #245, 0.30 #285), 01grrf (0.30 #255, 0.30 #295, 0.29 #395), 03ww_x (0.23 #123, 0.18 #403, 0.17 #443), 03z5xd (0.23 #126, 0.17 #446, 0.17 #486), 01gvxh (0.18 #796, 0.18 #56, 0.16 #616), 04lgybj (0.18 #787, 0.18 #47, 0.16 #607) >> Best rule #494 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 0h5qxv; >> query: (?x3670, 024tkd) <- jurisdiction_of_office(?x900, ?x3670), district_represented(?x176, ?x3670), contains(?x94, ?x3670) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6 EVAL 05tbn district_represented! 01gsry CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.679 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05tbn district_represented! 01grrf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.679 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05tbn district_represented! 024tkd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.679 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05tbn district_represented! 01gst9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.679 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05tbn district_represented! 01gstn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.679 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05tbn district_represented! 01grnp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.679 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #11609-014tss PRED entity: 014tss PRED relation: jurisdiction_of_office! PRED expected values: 0cmpn 0cw10 => 212 concepts (212 used for prediction) PRED predicted values (max 10 best out of 507): 0d1_f (0.64 #5615, 0.53 #4702, 0.50 #2808), 04xzm (0.25 #878, 0.14 #2084, 0.12 #2539), 0dj5q (0.20 #1239, 0.11 #2672, 0.09 #3130), 083pr (0.14 #5230, 0.14 #3636, 0.14 #3558), 0fd_1 (0.14 #2072, 0.14 #1997, 0.14 #1921), 02mjmr (0.14 #2048, 0.14 #1973, 0.14 #1897), 0gzh (0.14 #2108, 0.14 #2033, 0.14 #1957), 081t6 (0.14 #2105, 0.14 #2030, 0.14 #1954), 02yy8 (0.14 #2103, 0.14 #2028, 0.14 #1952), 06c0j (0.14 #2102, 0.14 #2027, 0.14 #1951) >> Best rule #5615 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 01n8qg; 020p1; >> query: (?x6371, 0d1_f) <- contains(?x11138, ?x6371), jurisdiction_of_office(?x182, ?x6371), form_of_government(?x6371, ?x1926), jurisdiction_of_office(?x4689, ?x6371), taxonomy(?x11138, ?x939) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #2184 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 013xrm; 0g5y6; *> query: (?x6371, ?x111) <- split_to(?x6371, ?x512), film_release_region(?x634, ?x512), nationality(?x111, ?x512), country(?x136, ?x512), ?x634 = 0gx9rvq *> conf = 0.01 ranks of expected_values: 255, 296 EVAL 014tss jurisdiction_of_office! 0cw10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 212.000 212.000 0.636 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office EVAL 014tss jurisdiction_of_office! 0cmpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 212.000 212.000 0.636 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #11608-0fj52s PRED entity: 0fj52s PRED relation: nutrient PRED expected values: 0h1zw 041r51 09gwd 014d7f => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 34): 09gwd (0.73 #292, 0.67 #317, 0.67 #302), 014d7f (0.73 #292, 0.60 #174, 0.56 #312), 041r51 (0.73 #292, 0.56 #314, 0.56 #300), 0h1zw (0.73 #292, 0.56 #313, 0.56 #298), 02kc_w5 (0.73 #292, 0.33 #259, 0.33 #215), 061xhr (0.73 #292, 0.33 #321, 0.33 #304), 0466p20 (0.73 #292, 0.33 #299, 0.33 #264), 027g6p7 (0.73 #292, 0.33 #258, 0.33 #214), 01w_3 (0.33 #202, 0.33 #187, 0.33 #171), 0f4k5 (0.33 #203, 0.33 #188, 0.33 #172) >> Best rule #292 for best value: >> intensional similarity = 79 >> extensional distance = 6 >> proper extension: 06x4c; >> query: (?x1303, ?x13126) <- nutrient(?x1303, ?x13498), nutrient(?x1303, ?x12902), nutrient(?x1303, ?x12454), nutrient(?x1303, ?x9915), nutrient(?x1303, ?x9365), nutrient(?x1303, ?x7652), nutrient(?x1303, ?x6192), nutrient(?x1303, ?x6026), nutrient(?x1303, ?x5549), nutrient(?x1303, ?x5451), nutrient(?x1303, ?x4069), nutrient(?x1303, ?x2702), nutrient(?x1303, ?x2018), ?x5549 = 025s7j4, nutrient(?x9005, ?x4069), nutrient(?x8298, ?x4069), nutrient(?x7719, ?x4069), nutrient(?x7057, ?x4069), nutrient(?x6285, ?x4069), nutrient(?x6191, ?x4069), nutrient(?x6159, ?x4069), nutrient(?x6032, ?x4069), nutrient(?x5009, ?x4069), nutrient(?x4068, ?x4069), nutrient(?x3900, ?x4069), nutrient(?x3468, ?x4069), nutrient(?x2701, ?x4069), nutrient(?x1959, ?x4069), nutrient(?x1257, ?x4069), ?x7719 = 0dj75, nutrient(?x10612, ?x13498), nutrient(?x9732, ?x13498), nutrient(?x9489, ?x13498), nutrient(?x5373, ?x13498), ?x9915 = 025tkqy, ?x9489 = 07j87, ?x6192 = 06jry, ?x1257 = 09728, ?x1959 = 0f25w9, ?x4068 = 0fbw6, ?x2701 = 0hkxq, ?x3468 = 0cxn2, ?x6159 = 033cnk, ?x9732 = 05z55, ?x6026 = 025sf8g, ?x9005 = 04zpv, ?x3900 = 061_f, ?x6032 = 01nkt, ?x12454 = 025rw19, ?x5373 = 0971v, ?x8298 = 037ls6, ?x7057 = 0fbdb, ?x2702 = 0838f, ?x6285 = 01645p, ?x5009 = 0fjfh, ?x10612 = 0frq6, ?x2018 = 01sh2, taxonomy(?x5451, ?x939), nutrient(?x6191, ?x13126), nutrient(?x6191, ?x12481), nutrient(?x6191, ?x9708), nutrient(?x6191, ?x8243), nutrient(?x6191, ?x6160), nutrient(?x6191, ?x3469), ?x9708 = 061xhr, ?x939 = 04n6k, ?x12481 = 027g6p7, ?x6160 = 041r51, ?x8243 = 014d7f, ?x3469 = 0h1zw, nutrient(?x6191, ?x6192), nutrient(?x9489, ?x7652), nutrient(?x3468, ?x7652), nutrient(?x9005, ?x9365), nutrient(?x1959, ?x7652), nutrient(?x5373, ?x12902), nutrient(?x10612, ?x9365), nutrient(?x9005, ?x12902), nutrient(?x3900, ?x9365) >> conf = 0.73 => this is the best rule for 8 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0fj52s nutrient 014d7f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 23.000 0.733 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fj52s nutrient 09gwd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 23.000 0.733 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fj52s nutrient 041r51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 23.000 0.733 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fj52s nutrient 0h1zw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 23.000 0.733 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #11607-0c38gj PRED entity: 0c38gj PRED relation: film_crew_role PRED expected values: 01vx2h => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 31): 01vx2h (0.53 #274, 0.38 #608, 0.36 #707), 02rh1dz (0.28 #273, 0.17 #607, 0.15 #206), 02ynfr (0.25 #13, 0.21 #46, 0.21 #711), 089fss (0.25 #5, 0.14 #38, 0.10 #104), 0215hd (0.20 #115, 0.19 #82, 0.18 #615), 089g0h (0.20 #116, 0.15 #616, 0.14 #50), 01xy5l_ (0.18 #110, 0.14 #709, 0.14 #610), 0d2b38 (0.17 #288, 0.16 #122, 0.14 #56), 02_n3z (0.15 #67, 0.10 #600, 0.10 #100), 015h31 (0.13 #272, 0.10 #1139, 0.10 #205) >> Best rule #274 for best value: >> intensional similarity = 4 >> extensional distance = 158 >> proper extension: 02vxq9m; 04q24zv; 0h63q6t; >> query: (?x4633, 01vx2h) <- film_crew_role(?x4633, ?x137), ?x137 = 09zzb8, genre(?x4633, ?x811), ?x811 = 03k9fj >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c38gj film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.525 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11606-081lh PRED entity: 081lh PRED relation: award_winner! PRED expected values: 0hr6lkl => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 124): 03gwpw2 (0.15 #694, 0.08 #968, 0.03 #8640), 0hhtgcw (0.14 #357, 0.09 #494, 0.03 #1590), 0clfdj (0.14 #278, 0.04 #8224, 0.04 #7676), 0h_9252 (0.14 #329, 0.02 #7727), 02wzl1d (0.12 #970, 0.10 #696, 0.08 #1381), 0hr6lkl (0.11 #565, 0.10 #1661, 0.04 #3168), 02glmx (0.11 #626, 0.05 #1448, 0.05 #1722), 09p2r9 (0.11 #638, 0.05 #775, 0.05 #1734), 02yxh9 (0.10 #782, 0.08 #1056, 0.08 #1467), 03gt46z (0.10 #745, 0.08 #1019, 0.03 #3622) >> Best rule #694 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0c31_; >> query: (?x986, 03gwpw2) <- nationality(?x986, ?x94), award_winner(?x5516, ?x986), ?x5516 = 027b9ly >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #565 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 06b_0; *> query: (?x986, 0hr6lkl) <- written_by(?x306, ?x986), religion(?x986, ?x2694), participant(?x986, ?x6525) *> conf = 0.11 ranks of expected_values: 6 EVAL 081lh award_winner! 0hr6lkl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 124.000 124.000 0.150 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11605-01k6zy PRED entity: 01k6zy PRED relation: colors PRED expected values: 038hg => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.83 #1105, 0.80 #515, 0.62 #1563), 01g5v (0.75 #1526, 0.31 #1565, 0.31 #1679), 019sc (0.74 #1435, 0.41 #293, 0.41 #350), 038hg (0.44 #812, 0.35 #317, 0.19 #1390), 06fvc (0.40 #516, 0.40 #2, 0.36 #1928), 03vtbc (0.27 #161, 0.27 #256, 0.25 #180), 09ggk (0.24 #320, 0.20 #149, 0.20 #130), 036k5h (0.23 #2024, 0.19 #686, 0.17 #1849), 02rnmb (0.22 #394, 0.19 #686, 0.19 #1098), 06kqt3 (0.20 #16, 0.12 #1734, 0.11 #1984) >> Best rule #1105 for best value: >> intensional similarity = 8 >> extensional distance = 100 >> proper extension: 01zhs3; 049dzz; 01rly6; 04ck0_; 0mmd6; >> query: (?x14005, 083jv) <- teams(?x10718, ?x14005), colors(?x14005, ?x332), place_of_birth(?x8256, ?x10718), colors(?x10285, ?x332), colors(?x5679, ?x332), ?x5679 = 022jr5, award_nominee(?x8256, ?x516), institution(?x1771, ?x10285) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #812 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 69 *> proper extension: 0b256b; *> query: (?x14005, 038hg) <- colors(?x14005, ?x332), colors(?x10910, ?x332), colors(?x9768, ?x332), currency(?x10910, ?x170), colors(?x9172, ?x332), ?x9768 = 027ybp, citytown(?x10910, ?x3125), draft(?x9172, ?x465) *> conf = 0.44 ranks of expected_values: 4 EVAL 01k6zy colors 038hg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 109.000 0.833 http://example.org/sports/sports_team/colors #11604-075pwf PRED entity: 075pwf PRED relation: nutrient! PRED expected values: 0frq6 => 27 concepts (25 used for prediction) PRED predicted values (max 10 best out of 24): 0fjfh (0.91 #538, 0.91 #517, 0.90 #659), 0fbdb (0.91 #521, 0.90 #683, 0.90 #663), 0hkxq (0.91 #759, 0.90 #432, 0.90 #413), 0frq6 (0.89 #359, 0.89 #295, 0.89 #292), 014j1m (0.89 #573, 0.89 #554, 0.89 #785), 0fj52s (0.89 #783, 0.89 #757, 0.89 #539), 061_f (0.89 #499, 0.89 #481, 0.88 #680), 0f25w9 (0.89 #233, 0.89 #216, 0.88 #173), 0fbw6 (0.86 #669, 0.86 #658, 0.84 #529), 09728 (0.83 #334, 0.83 #310, 0.83 #198) >> Best rule #538 for best value: >> intensional similarity = 135 >> extensional distance = 42 >> proper extension: 0hkwr; 07zqy; >> query: (?x10453, ?x5009) <- nutrient(?x9005, ?x10453), nutrient(?x8298, ?x10453), nutrient(?x6159, ?x10453), nutrient(?x5373, ?x10453), nutrient(?x3468, ?x10453), nutrient(?x5373, ?x14210), nutrient(?x5373, ?x13944), nutrient(?x5373, ?x13545), nutrient(?x5373, ?x13126), nutrient(?x5373, ?x12902), nutrient(?x5373, ?x12083), nutrient(?x5373, ?x11758), nutrient(?x5373, ?x11409), nutrient(?x5373, ?x11270), nutrient(?x5373, ?x10709), nutrient(?x5373, ?x10098), nutrient(?x5373, ?x9915), nutrient(?x5373, ?x9733), nutrient(?x5373, ?x9619), nutrient(?x5373, ?x9490), nutrient(?x5373, ?x9436), nutrient(?x5373, ?x9426), nutrient(?x5373, ?x9365), nutrient(?x5373, ?x8487), nutrient(?x5373, ?x8442), nutrient(?x5373, ?x8413), nutrient(?x5373, ?x7894), nutrient(?x5373, ?x7720), nutrient(?x5373, ?x7652), nutrient(?x5373, ?x7431), nutrient(?x5373, ?x7364), nutrient(?x5373, ?x7362), nutrient(?x5373, ?x7219), nutrient(?x5373, ?x7135), nutrient(?x5373, ?x6192), nutrient(?x5373, ?x6160), nutrient(?x5373, ?x6033), nutrient(?x5373, ?x6026), nutrient(?x5373, ?x5549), nutrient(?x5373, ?x5526), nutrient(?x5373, ?x5451), nutrient(?x5373, ?x5374), nutrient(?x5373, ?x5010), nutrient(?x5373, ?x3469), nutrient(?x5373, ?x1960), nutrient(?x5373, ?x1304), nutrient(?x5373, ?x1258), ?x8442 = 02kcv4x, ?x9365 = 04k8n, ?x11270 = 02kc008, ?x13944 = 0f4kp, ?x6192 = 06jry, ?x7652 = 025s0s0, ?x5549 = 025s7j4, ?x12083 = 01n78x, ?x7362 = 02kc5rj, ?x7431 = 09gwd, ?x9490 = 0h1sg, ?x6026 = 025sf8g, ?x7894 = 0f4hc, ?x13545 = 01w_3, ?x11409 = 0h1yf, ?x10098 = 0h1_c, ?x12902 = 0fzjh, ?x1304 = 08lb68, ?x10709 = 0h1sz, ?x8298 = 037ls6, ?x9915 = 025tkqy, nutrient(?x10612, ?x8487), nutrient(?x7719, ?x8487), nutrient(?x6285, ?x8487), nutrient(?x6191, ?x8487), nutrient(?x5009, ?x8487), nutrient(?x4068, ?x8487), nutrient(?x3900, ?x8487), nutrient(?x2701, ?x8487), nutrient(?x1959, ?x8487), nutrient(?x1303, ?x8487), nutrient(?x1257, ?x8487), ?x7720 = 025s7x6, nutrient(?x9005, ?x10891), nutrient(?x9005, ?x9949), nutrient(?x9005, ?x9840), nutrient(?x9005, ?x6586), nutrient(?x9005, ?x6286), nutrient(?x9005, ?x4069), nutrient(?x9005, ?x3203), nutrient(?x9005, ?x2018), nutrient(?x6159, ?x9855), nutrient(?x6159, ?x3264), ?x3264 = 0dcfv, ?x14210 = 0f4k5, ?x1258 = 0h1wg, ?x1303 = 0fj52s, ?x6033 = 04zjxcz, ?x9436 = 025sqz8, ?x9840 = 02p0tjr, ?x5009 = 0fjfh, ?x4069 = 0hqw8p_, ?x9619 = 0h1tg, ?x5010 = 0h1vz, ?x13126 = 02kc_w5, ?x9855 = 0d9t0, nutrient(?x9732, ?x5374), nutrient(?x9489, ?x5374), ?x6191 = 014j1m, ?x3900 = 061_f, ?x1257 = 09728, ?x2018 = 01sh2, ?x9733 = 0h1tz, ?x7219 = 0h1vg, ?x7719 = 0dj75, ?x2701 = 0hkxq, ?x10891 = 0g5gq, ?x6285 = 01645p, ?x9426 = 0h1yy, ?x10612 = 0frq6, ?x5526 = 09pbb, ?x1959 = 0f25w9, ?x9732 = 05z55, ?x11758 = 0q01m, ?x1960 = 07hnp, ?x3468 = 0cxn2, ?x6160 = 041r51, ?x4068 = 0fbw6, ?x3469 = 0h1zw, ?x6586 = 05gh50, ?x9949 = 02kd0rh, ?x6286 = 02y_3rf, ?x8413 = 02kc4sf, ?x5451 = 05wvs, ?x7364 = 09gvd, ?x7135 = 025rsfk, ?x9489 = 07j87, ?x3203 = 04kl74p >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #359 for first EXPECTED value: *> intensional similarity = 135 *> extensional distance = 35 *> proper extension: 07q0m; *> query: (?x10453, 0frq6) <- nutrient(?x9005, ?x10453), nutrient(?x8298, ?x10453), nutrient(?x6159, ?x10453), nutrient(?x6032, ?x10453), nutrient(?x5373, ?x10453), nutrient(?x3468, ?x10453), ?x5373 = 0971v, ?x6159 = 033cnk, nutrient(?x3468, ?x14210), nutrient(?x3468, ?x13944), nutrient(?x3468, ?x13545), nutrient(?x3468, ?x13126), nutrient(?x3468, ?x12902), nutrient(?x3468, ?x12336), nutrient(?x3468, ?x12083), nutrient(?x3468, ?x11758), nutrient(?x3468, ?x11409), nutrient(?x3468, ?x11270), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10098), nutrient(?x3468, ?x9949), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x9840), nutrient(?x3468, ?x9733), nutrient(?x3468, ?x9619), nutrient(?x3468, ?x9490), nutrient(?x3468, ?x9436), nutrient(?x3468, ?x9426), nutrient(?x3468, ?x9365), nutrient(?x3468, ?x8442), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x7894), nutrient(?x3468, ?x7720), nutrient(?x3468, ?x7652), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7364), nutrient(?x3468, ?x7362), nutrient(?x3468, ?x7219), nutrient(?x3468, ?x7135), nutrient(?x3468, ?x6586), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x6026), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5526), nutrient(?x3468, ?x5451), nutrient(?x3468, ?x5010), nutrient(?x3468, ?x4069), nutrient(?x3468, ?x3469), nutrient(?x3468, ?x3203), nutrient(?x3468, ?x2702), nutrient(?x3468, ?x2018), nutrient(?x3468, ?x1960), nutrient(?x3468, ?x1304), nutrient(?x3468, ?x1258), ?x7894 = 0f4hc, ?x9949 = 02kd0rh, ?x13126 = 02kc_w5, ?x14210 = 0f4k5, ?x9915 = 025tkqy, ?x11758 = 0q01m, ?x1304 = 08lb68, ?x8413 = 02kc4sf, ?x1960 = 07hnp, ?x7219 = 0h1vg, ?x7364 = 09gvd, ?x9426 = 0h1yy, ?x6586 = 05gh50, ?x7135 = 025rsfk, ?x1258 = 0h1wg, nutrient(?x8298, ?x12454), nutrient(?x8298, ?x11592), nutrient(?x8298, ?x9708), nutrient(?x8298, ?x8487), nutrient(?x8298, ?x5374), nutrient(?x7719, ?x4069), nutrient(?x7057, ?x4069), nutrient(?x6285, ?x4069), nutrient(?x6191, ?x4069), nutrient(?x5009, ?x4069), nutrient(?x4068, ?x4069), nutrient(?x2701, ?x4069), nutrient(?x1959, ?x4069), nutrient(?x1303, ?x4069), nutrient(?x1257, ?x4069), ?x9490 = 0h1sg, ?x1303 = 0fj52s, ?x9619 = 0h1tg, ?x6191 = 014j1m, ?x7057 = 0fbdb, ?x5451 = 05wvs, nutrient(?x9732, ?x11409), nutrient(?x9489, ?x11409), ?x12902 = 0fzjh, ?x1959 = 0f25w9, ?x9489 = 07j87, ?x13545 = 01w_3, ?x6285 = 01645p, ?x12083 = 01n78x, ?x9840 = 02p0tjr, ?x3203 = 04kl74p, ?x9365 = 04k8n, ?x6192 = 06jry, ?x2701 = 0hkxq, ?x8442 = 02kcv4x, ?x9708 = 061xhr, ?x10891 = 0g5gq, ?x5526 = 09pbb, ?x11592 = 025sf0_, ?x7431 = 09gwd, ?x6032 = 01nkt, ?x8487 = 014yzm, ?x9005 = 04zpv, ?x9733 = 0h1tz, ?x12336 = 0f4l5, ?x3469 = 0h1zw, ?x9436 = 025sqz8, ?x7719 = 0dj75, ?x4068 = 0fbw6, ?x5549 = 025s7j4, ?x7362 = 02kc5rj, ?x5009 = 0fjfh, ?x5010 = 0h1vz, ?x7652 = 025s0s0, ?x10098 = 0h1_c, ?x9732 = 05z55, ?x5374 = 025s0zp, ?x2702 = 0838f, ?x12454 = 025rw19, ?x13944 = 0f4kp, ?x6026 = 025sf8g, ?x11270 = 02kc008, ?x1257 = 09728, ?x7720 = 025s7x6, taxonomy(?x2018, ?x939), ?x939 = 04n6k *> conf = 0.89 ranks of expected_values: 4 EVAL 075pwf nutrient! 0frq6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 27.000 25.000 0.909 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #11603-05hrq4 PRED entity: 05hrq4 PRED relation: location PRED expected values: 02_286 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 157): 02_286 (0.57 #11273, 0.57 #1610, 0.57 #842), 0k049 (0.20 #2416, 0.14 #24146, 0.14 #29778), 05fjf (0.20 #332, 0.14 #1137, 0.04 #4359), 0cc56 (0.14 #862, 0.09 #5694, 0.09 #6499), 059rby (0.14 #821, 0.09 #5653, 0.06 #6458), 0r0m6 (0.14 #1023, 0.06 #7465, 0.04 #17124), 05k7sb (0.14 #914, 0.03 #5746, 0.03 #7356), 0vmt (0.14 #850, 0.03 #5682, 0.03 #6487), 0rh6k (0.12 #5641, 0.12 #6446, 0.08 #8056), 0cr3d (0.12 #4172, 0.10 #4977, 0.10 #3367) >> Best rule #11273 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 0d9xq; >> query: (?x9097, ?x739) <- place_of_birth(?x9097, ?x739), inductee(?x9953, ?x9097), location(?x163, ?x739), award_winner(?x2016, ?x9097) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05hrq4 location 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.571 http://example.org/people/person/places_lived./people/place_lived/location #11602-01qmy04 PRED entity: 01qmy04 PRED relation: place_of_birth PRED expected values: 04n3l => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 39): 0xn7q (0.38 #2820), 030qb3t (0.28 #34541, 0.27 #39478, 0.27 #26788), 0z2gq (0.20 #342), 02_286 (0.09 #28922, 0.08 #19053, 0.08 #33853), 01ly5m (0.06 #801, 0.05 #1505), 0n90z (0.06 #1389), 0n95v (0.06 #1174), 0fr0t (0.06 #848), 0cr3d (0.05 #1502, 0.04 #19128, 0.04 #33928), 01_d4 (0.05 #1474, 0.04 #35312, 0.03 #27561) >> Best rule #2820 for best value: >> intensional similarity = 3 >> extensional distance = 193 >> proper extension: 03n0q5; 01r6jt2; 012wg; 02w670; 08n__5; 025cn2; 03zz8b; 016jll; >> query: (?x12121, ?x11903) <- nationality(?x12121, ?x94), award_winner(?x11439, ?x12121), origin(?x12121, ?x11903) >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01qmy04 place_of_birth 04n3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 81.000 0.378 http://example.org/people/person/place_of_birth #11601-0cv9b PRED entity: 0cv9b PRED relation: service_location PRED expected values: 0d05w3 04wsz => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 219): 0d060g (0.70 #1564, 0.38 #1306, 0.33 #1047), 07ssc (0.42 #1570, 0.21 #4092, 0.19 #5223), 02j71 (0.31 #185, 0.28 #2613, 0.27 #1227), 0345h (0.25 #1580, 0.18 #1063, 0.14 #1236), 0f8l9c (0.23 #1575, 0.13 #535, 0.12 #1058), 015fr (0.18 #100, 0.11 #445, 0.09 #532), 07b_l (0.15 #3641, 0.11 #7810, 0.07 #9999), 059j2 (0.15 #1579, 0.06 #4883, 0.06 #5145), 06mkj (0.12 #1587, 0.09 #547, 0.08 #1243), 03rjj (0.10 #1563, 0.09 #1046, 0.09 #523) >> Best rule #1564 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 049mr; 02brqp; 07zl6m; 06rfy5; >> query: (?x1492, 0d060g) <- service_language(?x1492, ?x254), service_location(?x1492, ?x1453), category(?x1492, ?x134), film_release_region(?x4607, ?x1453), ?x4607 = 0h03fhx >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #117 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 01_4lx; *> query: (?x1492, 0d05w3) <- company(?x5161, ?x1492), company(?x4792, ?x1492), currency(?x1492, ?x170), industry(?x1492, ?x8126), ?x4792 = 05_wyz, ?x5161 = 09d6p2 *> conf = 0.09 ranks of expected_values: 11, 32 EVAL 0cv9b service_location 04wsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 144.000 144.000 0.700 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 0cv9b service_location 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 144.000 144.000 0.700 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #11600-0dx8gj PRED entity: 0dx8gj PRED relation: film! PRED expected values: 0292l3 => 87 concepts (57 used for prediction) PRED predicted values (max 10 best out of 715): 0h1p (0.46 #18755, 0.45 #54184, 0.44 #91697), 024rbz (0.46 #18755, 0.45 #54184, 0.44 #91697), 076_74 (0.44 #91697, 0.43 #25009, 0.43 #93781), 0csdzz (0.44 #91697, 0.43 #25009, 0.43 #93781), 079vf (0.13 #50017, 0.06 #6258, 0.03 #8343), 0171cm (0.13 #50017, 0.05 #6676, 0.03 #12928), 02bkdn (0.13 #50017, 0.04 #301, 0.01 #14887), 016xh5 (0.13 #50017, 0.03 #11503, 0.01 #15670), 0jlv5 (0.13 #50017, 0.03 #3266, 0.03 #5349), 0dvld (0.13 #50017, 0.03 #15649, 0.02 #26072) >> Best rule #18755 for best value: >> intensional similarity = 4 >> extensional distance = 244 >> proper extension: 0bth54; 02qm_f; 09q5w2; 0jyx6; 0c0nhgv; 0dgst_d; 02q5g1z; 0fdv3; 0j_t1; 0cw3yd; ... >> query: (?x3863, ?x1414) <- country(?x3863, ?x94), film(?x9813, ?x3863), award_winner(?x3863, ?x1414), executive_produced_by(?x3863, ?x3862) >> conf = 0.46 => this is the best rule for 2 predicted values *> Best rule #4398 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 01f8gz; *> query: (?x3863, 0292l3) <- genre(?x3863, ?x1626), nominated_for(?x1414, ?x3863), film_crew_role(?x3863, ?x468), ?x1626 = 03q4nz *> conf = 0.03 ranks of expected_values: 253 EVAL 0dx8gj film! 0292l3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 87.000 57.000 0.461 http://example.org/film/actor/film./film/performance/film #11599-0h3xztt PRED entity: 0h3xztt PRED relation: nominated_for! PRED expected values: 099tbz => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 193): 0bdwft (0.33 #55, 0.20 #14164, 0.20 #15109), 0bfvw2 (0.33 #14, 0.20 #14164, 0.20 #15109), 0bdx29 (0.33 #83, 0.20 #14164, 0.20 #15109), 0cqgl9 (0.33 #138, 0.20 #15109, 0.19 #4957), 0cqh6z (0.33 #54, 0.20 #15109, 0.19 #4957), 0fbtbt (0.33 #160, 0.06 #2520, 0.03 #8422), 0fbvqf (0.33 #38, 0.05 #15582, 0.04 #2398), 07kjk7c (0.33 #190, 0.05 #15582, 0.02 #2550), 09v82c0 (0.33 #185, 0.05 #15582, 0.02 #2545), 07z2lx (0.33 #171, 0.05 #15582) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0ddd0gc; >> query: (?x1150, 0bdwft) <- nominated_for(?x5144, ?x1150), ?x5144 = 017gxw, nominated_for(?x704, ?x1150) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #14164 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1388 *> proper extension: 04f6hhm; *> query: (?x1150, ?x375) <- nominated_for(?x2372, ?x1150), nominated_for(?x704, ?x1150), award_winner(?x375, ?x2372) *> conf = 0.20 ranks of expected_values: 35 EVAL 0h3xztt nominated_for! 099tbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 75.000 75.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11598-06mj4 PRED entity: 06mj4 PRED relation: group! PRED expected values: 05148p4 => 87 concepts (48 used for prediction) PRED predicted values (max 10 best out of 114): 05148p4 (0.75 #1039, 0.74 #698, 0.71 #953), 028tv0 (0.50 #691, 0.50 #436, 0.44 #351), 03qjg (0.35 #810, 0.32 #300, 0.32 #980), 01vj9c (0.32 #267, 0.32 #777, 0.31 #947), 0l14qv (0.29 #1026, 0.25 #940, 0.24 #770), 04rzd (0.20 #710, 0.18 #285, 0.13 #795), 013y1f (0.20 #791, 0.18 #281, 0.17 #961), 06ncr (0.17 #1057, 0.17 #801, 0.16 #971), 042v_gx (0.15 #687, 0.13 #1028, 0.12 #347), 0l14j_ (0.15 #1070, 0.11 #1495, 0.11 #814) >> Best rule #1039 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 0pqp3; >> query: (?x8060, 05148p4) <- artists(?x283, ?x8060), group(?x315, ?x8060), ?x315 = 0l14md >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mj4 group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 48.000 0.752 http://example.org/music/performance_role/regular_performances./music/group_membership/group #11597-01w02sy PRED entity: 01w02sy PRED relation: location PRED expected values: 02mf7 => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 457): 02_286 (0.60 #25547, 0.32 #41490, 0.30 #84531), 09c7w0 (0.16 #11963, 0.02 #90874, 0.02 #85293), 0kpys (0.14 #146, 0.06 #5730, 0.02 #90874), 059rby (0.14 #25526, 0.07 #41469, 0.07 #70162), 04rrd (0.12 #891, 0.10 #2486, 0.03 #5678), 01n7q (0.12 #25572, 0.10 #1656, 0.08 #3250), 0cv3w (0.11 #3988, 0.07 #35875, 0.03 #4941), 04jpl (0.11 #84511, 0.09 #70163, 0.08 #41470), 0cr3d (0.10 #4129, 0.09 #16884, 0.08 #9710), 07ssc (0.10 #11985, 0.02 #90874, 0.02 #8000) >> Best rule #25547 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 033hqf; 06hx2; >> query: (?x3118, 02_286) <- location(?x3118, ?x252), participant(?x970, ?x3118), administrative_parent(?x536, ?x252) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w02sy location 02mf7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 154.000 0.598 http://example.org/people/person/places_lived./people/place_lived/location #11596-01q415 PRED entity: 01q415 PRED relation: award_winner! PRED expected values: 0bvfqq => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 134): 09qvms (0.25 #13, 0.05 #4183, 0.04 #4323), 0hndn2q (0.21 #179, 0.09 #596, 0.07 #874), 02jp5r (0.14 #208, 0.04 #625, 0.03 #347), 02yxh9 (0.14 #240, 0.04 #935, 0.03 #1352), 09p2r9 (0.14 #232, 0.03 #927, 0.03 #649), 050yyb (0.14 #177, 0.03 #594, 0.03 #316), 0gmdkyy (0.14 #169, 0.02 #586, 0.02 #12653), 02q690_ (0.08 #65, 0.04 #4235, 0.04 #760), 09bymc (0.08 #120, 0.03 #676, 0.02 #3317), 05c1t6z (0.07 #710, 0.05 #1405, 0.04 #4185) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 06mmb; >> query: (?x2248, 09qvms) <- award_nominee(?x2793, ?x2248), location(?x2248, ?x3634), ?x3634 = 07b_l >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #12653 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2930 *> proper extension: 067mj; 054lpb6; 05k79; 0dtd6; 01rm8b; 0fcsd; 047cx; 01k_yf; 015srx; 013w2r; ... *> query: (?x2248, ?x78) <- award(?x2248, ?x601), ceremony(?x601, ?x78), award_winner(?x601, ?x488) *> conf = 0.02 ranks of expected_values: 90 EVAL 01q415 award_winner! 0bvfqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 102.000 102.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11595-07gknc PRED entity: 07gknc PRED relation: language PRED expected values: 02h40lc => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 3): 02h40lc (0.64 #1, 0.27 #34, 0.26 #4), 03_9r (0.02 #36, 0.01 #45, 0.01 #48), 064_8sq (0.01 #97) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01r4bps; >> query: (?x12000, 02h40lc) <- nationality(?x12000, ?x279), actor(?x5938, ?x12000), ?x5938 = 05f7w84, profession(?x12000, ?x1383) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07gknc language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.643 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #11594-0ym1n PRED entity: 0ym1n PRED relation: student PRED expected values: 02g3w => 169 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1758): 01h2_6 (0.33 #4118, 0.08 #16670, 0.02 #100364), 03j2gxx (0.25 #1859, 0.09 #89969, 0.09 #108806), 043s3 (0.25 #666, 0.09 #89969, 0.09 #108806), 082_p (0.25 #1548, 0.09 #89969, 0.09 #108806), 0136g9 (0.25 #202, 0.09 #89969, 0.09 #108806), 041xl (0.20 #7544, 0.09 #36834, 0.04 #57755), 04c636 (0.20 #7443, 0.08 #13719, 0.03 #70206), 0n00 (0.20 #6823, 0.06 #36113, 0.04 #57034), 04cbtrw (0.20 #6746, 0.06 #36036, 0.04 #56957), 02sdx (0.17 #3944, 0.10 #10220, 0.06 #22772) >> Best rule #4118 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0pz6q; >> query: (?x13856, 01h2_6) <- citytown(?x13856, ?x1841), student(?x13856, ?x11470), organization(?x4095, ?x13856), ?x4095 = 0hm4q, colors(?x13856, ?x3189), type_of_union(?x11470, ?x566) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #89969 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 93 *> proper extension: 03zw80; *> query: (?x13856, ?x11797) <- citytown(?x13856, ?x1841), colors(?x13856, ?x3189), contains(?x1841, ?x7971), location(?x11104, ?x1841), story_by(?x2795, ?x11104), student(?x7971, ?x11797) *> conf = 0.09 ranks of expected_values: 133 EVAL 0ym1n student 02g3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 169.000 62.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #11593-01dvbd PRED entity: 01dvbd PRED relation: currency PRED expected values: 09nqf => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.85 #50, 0.79 #134, 0.78 #120), 01nv4h (0.11 #44, 0.06 #30, 0.06 #23), 02l6h (0.02 #158, 0.01 #81, 0.01 #109), 02gsvk (0.01 #160, 0.01 #90, 0.01 #104) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 165 >> proper extension: 059lwy; 065ym0c; >> query: (?x3048, 09nqf) <- genre(?x3048, ?x5231), award(?x3048, ?x13311), genre(?x1311, ?x5231), ?x1311 = 069q4f >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dvbd currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.850 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11592-0gz5hs PRED entity: 0gz5hs PRED relation: film PRED expected values: 07bxqz => 144 concepts (128 used for prediction) PRED predicted values (max 10 best out of 875): 0584r4 (0.55 #71375, 0.51 #101709, 0.51 #105279), 06y_n (0.55 #71375, 0.51 #101709, 0.51 #105279), 02v5_g (0.25 #790, 0.03 #18634, 0.03 #65028), 01738w (0.25 #1127, 0.03 #13617, 0.02 #65365), 091xrc (0.25 #1762, 0.02 #10683, 0.01 #37450), 0n6ds (0.25 #1625, 0.01 #108688, 0.01 #15899), 043h78 (0.25 #1516, 0.01 #15790), 0hwpz (0.25 #1293, 0.01 #15567), 045j3w (0.25 #493, 0.01 #14767), 0ds5_72 (0.14 #5023, 0.03 #15729, 0.02 #37143) >> Best rule #71375 for best value: >> intensional similarity = 3 >> extensional distance = 347 >> proper extension: 04b19t; >> query: (?x1986, ?x1876) <- profession(?x1986, ?x1032), languages(?x1986, ?x254), nominated_for(?x1986, ?x1876) >> conf = 0.55 => this is the best rule for 2 predicted values *> Best rule #5298 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 023jq1; *> query: (?x1986, 07bxqz) <- religion(?x1986, ?x2694), producer_type(?x1986, ?x632), ?x2694 = 0kpl *> conf = 0.07 ranks of expected_values: 61 EVAL 0gz5hs film 07bxqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 144.000 128.000 0.552 http://example.org/film/actor/film./film/performance/film #11591-01pbxb PRED entity: 01pbxb PRED relation: profession PRED expected values: 05vyk => 126 concepts (112 used for prediction) PRED predicted values (max 10 best out of 83): 02hrh1q (0.77 #11070, 0.76 #9156, 0.76 #11364), 0nbcg (0.68 #766, 0.61 #3416, 0.53 #1354), 0dz3r (0.49 #1031, 0.46 #3387, 0.46 #2502), 0dxtg (0.47 #2072, 0.47 #1925, 0.44 #3103), 0cbd2 (0.47 #3834, 0.41 #4275, 0.41 #5305), 01d_h8 (0.46 #2064, 0.46 #1917, 0.36 #1181), 01c72t (0.41 #1641, 0.34 #905, 0.31 #1052), 0fnpj (0.33 #59, 0.32 #794, 0.29 #5741), 0kyk (0.33 #3857, 0.29 #5328, 0.26 #3119), 03gjzk (0.32 #1927, 0.32 #2074, 0.28 #1191) >> Best rule #11070 for best value: >> intensional similarity = 4 >> extensional distance = 1142 >> proper extension: 01sl1q; 0q9kd; 0184jc; 06qgvf; 03qcq; 07fq1y; 02qgqt; 0fvf9q; 0l6qt; 02bfmn; ... >> query: (?x115, 02hrh1q) <- award_nominee(?x5297, ?x115), location(?x115, ?x479), profession(?x115, ?x220), place_of_birth(?x478, ?x479) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #828 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 014zfs; 01364q; 0pj9t; 029b9k; *> query: (?x115, 05vyk) <- award_nominee(?x5297, ?x115), location(?x115, ?x479), inductee(?x1091, ?x115), artists(?x114, ?x115) *> conf = 0.18 ranks of expected_values: 17 EVAL 01pbxb profession 05vyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 126.000 112.000 0.768 http://example.org/people/person/profession #11590-0kh6b PRED entity: 0kh6b PRED relation: nationality PRED expected values: 07ssc => 132 concepts (123 used for prediction) PRED predicted values (max 10 best out of 45): 09c7w0 (0.88 #7696, 0.82 #2700, 0.80 #3807), 07ssc (0.56 #7212, 0.43 #1107, 0.39 #2513), 04jpl (0.27 #11094, 0.25 #10191, 0.22 #1996), 0978r (0.25 #10191), 02j9z (0.25 #10191), 06c1y (0.25 #137, 0.01 #2234, 0.01 #2536), 059j2 (0.25 #427), 06hhp (0.17 #398, 0.08 #2196, 0.07 #1394), 0g5lhl7 (0.17 #398, 0.08 #2196, 0.07 #1394), 06bnz (0.17 #636, 0.01 #7138, 0.01 #6641) >> Best rule #7696 for best value: >> intensional similarity = 4 >> extensional distance = 672 >> proper extension: 01l3j; >> query: (?x3796, 09c7w0) <- religion(?x3796, ?x8140), nationality(?x3796, ?x1310), nationality(?x9323, ?x1310), ?x9323 = 01qn8k >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #7212 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 597 *> proper extension: 02xb2bt; *> query: (?x3796, 07ssc) <- nationality(?x3796, ?x1310), contains(?x1310, ?x11888), ?x11888 = 0nlg4 *> conf = 0.56 ranks of expected_values: 2 EVAL 0kh6b nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 123.000 0.875 http://example.org/people/person/nationality #11589-02m3sd PRED entity: 02m3sd PRED relation: location_of_ceremony PRED expected values: 0dclg => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 29): 030qb3t (0.17 #138, 0.02 #1570, 0.02 #1810), 07fr_ (0.07 #431, 0.07 #311, 0.03 #551), 01cx_ (0.07 #274, 0.03 #514, 0.03 #871), 03rk0 (0.07 #264, 0.03 #742, 0.01 #1220), 035hm (0.07 #556, 0.03 #913, 0.02 #1153), 0cv3w (0.05 #870, 0.03 #1826, 0.03 #632), 0k049 (0.03 #482, 0.03 #720, 0.02 #959), 04w58 (0.03 #523, 0.03 #761, 0.01 #1239), 04lh6 (0.03 #555, 0.03 #912, 0.02 #1152), 0r62v (0.03 #495, 0.01 #1211, 0.01 #1330) >> Best rule #138 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 028lc8; >> query: (?x10841, 030qb3t) <- profession(?x10841, ?x13719), nominated_for(?x10841, ?x8608), ?x13719 = 01tkqy >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02m3sd location_of_ceremony 0dclg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 103.000 0.167 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #11588-0g9z_32 PRED entity: 0g9z_32 PRED relation: language PRED expected values: 0jzc => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 37): 064_8sq (0.33 #20, 0.25 #77, 0.15 #1115), 02bjrlw (0.33 #1, 0.25 #58, 0.12 #116), 04306rv (0.33 #4, 0.25 #61, 0.10 #522), 06nm1 (0.17 #182, 0.11 #528, 0.10 #1105), 05qqm (0.12 #154, 0.02 #557, 0.01 #845), 02hxc3j (0.12 #121), 06b_j (0.11 #193, 0.06 #366, 0.06 #3215), 03_9r (0.07 #239, 0.06 #3215, 0.05 #873), 0jzc (0.06 #3215, 0.06 #190, 0.04 #363), 071fb (0.06 #3215, 0.06 #188, 0.02 #361) >> Best rule #20 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02yvct; >> query: (?x7311, 064_8sq) <- film(?x4719, ?x7311), ?x4719 = 08hsww, featured_film_locations(?x7311, ?x739), film_crew_role(?x7311, ?x468), citytown(?x166, ?x739) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3215 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1594 *> proper extension: 0522wp; *> query: (?x7311, ?x254) <- film(?x902, ?x7311), film(?x902, ?x11672), film(?x902, ?x6798), language(?x11672, ?x254), film_crew_role(?x6798, ?x1078) *> conf = 0.06 ranks of expected_values: 9 EVAL 0g9z_32 language 0jzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 70.000 70.000 0.333 http://example.org/film/film/language #11587-03tn80 PRED entity: 03tn80 PRED relation: featured_film_locations PRED expected values: 0q_xk => 71 concepts (51 used for prediction) PRED predicted values (max 10 best out of 62): 02_286 (0.16 #4844, 0.15 #4603, 0.15 #3155), 030qb3t (0.09 #2934, 0.07 #4381, 0.07 #3416), 04jpl (0.07 #2663, 0.06 #4351, 0.06 #4592), 0rh6k (0.07 #2896, 0.03 #483, 0.03 #242), 06y57 (0.03 #344, 0.03 #827, 0.03 #103), 080h2 (0.03 #2919, 0.02 #2678, 0.02 #1230), 01_d4 (0.03 #1253, 0.02 #3666, 0.02 #3424), 0dclg (0.03 #53, 0.02 #1018, 0.02 #1500), 03gh4 (0.03 #115, 0.02 #356, 0.02 #1321), 07b_l (0.02 #318, 0.02 #559, 0.02 #801) >> Best rule #4844 for best value: >> intensional similarity = 4 >> extensional distance = 684 >> proper extension: 05f67hw; >> query: (?x5002, 02_286) <- country(?x5002, ?x94), language(?x5002, ?x254), ?x94 = 09c7w0, produced_by(?x5002, ?x846) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 77 *> proper extension: 01qvz8; *> query: (?x5002, 0q_xk) <- country(?x5002, ?x94), nominated_for(?x1105, ?x5002), film(?x1515, ?x5002), ?x1105 = 07bdd_ *> conf = 0.01 ranks of expected_values: 24 EVAL 03tn80 featured_film_locations 0q_xk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 71.000 51.000 0.156 http://example.org/film/film/featured_film_locations #11586-0cn_b8 PRED entity: 0cn_b8 PRED relation: genre PRED expected values: 03k9fj => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 154): 07s9rl0 (0.62 #6015, 0.61 #6735, 0.61 #3732), 02kdv5l (0.51 #603, 0.47 #123, 0.45 #723), 03k9fj (0.44 #371, 0.37 #1331, 0.35 #1211), 01jfsb (0.40 #132, 0.40 #2535, 0.39 #2656), 06n90 (0.31 #373, 0.25 #13, 0.20 #2296), 02l7c8 (0.28 #3747, 0.28 #5670, 0.28 #4229), 0219x_ (0.25 #26, 0.19 #7455, 0.12 #386), 017fp (0.25 #15, 0.19 #7455, 0.11 #255), 0jtdp (0.25 #14, 0.19 #7455, 0.07 #134), 06cvj (0.22 #244, 0.19 #7455, 0.11 #2407) >> Best rule #6015 for best value: >> intensional similarity = 3 >> extensional distance = 868 >> proper extension: 09g8vhw; 0g5qmbz; 072hx4; >> query: (?x3752, 07s9rl0) <- award_winner(?x3752, ?x1104), nominated_for(?x102, ?x3752), language(?x3752, ?x254) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #371 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 0d8w2n; *> query: (?x3752, 03k9fj) <- film_format(?x3752, ?x909), production_companies(?x3752, ?x1104), region(?x3752, ?x512) *> conf = 0.44 ranks of expected_values: 3 EVAL 0cn_b8 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 94.000 0.622 http://example.org/film/film/genre #11585-0n1s0 PRED entity: 0n1s0 PRED relation: genre PRED expected values: 0gf28 => 65 concepts (64 used for prediction) PRED predicted values (max 10 best out of 86): 01z4y (0.61 #4027, 0.61 #3314, 0.54 #3313), 02l7c8 (0.51 #1314, 0.38 #724, 0.35 #134), 060__y (0.43 #725, 0.35 #135, 0.31 #253), 01jfsb (0.41 #484, 0.35 #4629, 0.34 #602), 02kdv5l (0.30 #2719, 0.29 #1774, 0.29 #1893), 04xvlr (0.30 #2719, 0.27 #237, 0.26 #355), 0lsxr (0.30 #2719, 0.27 #8, 0.21 #834), 03bxz7 (0.30 #2719, 0.26 #645, 0.23 #291), 02n4kr (0.30 #2719, 0.24 #479, 0.13 #4624), 09blyk (0.30 #2719, 0.24 #503, 0.06 #857) >> Best rule #4027 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 07s8z_l; 02xhwm; >> query: (?x5984, ?x2480) <- titles(?x2480, ?x5984), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1007 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 215 *> proper extension: 0dnvn3; 03s6l2; 026mfbr; 02z3r8t; 04kkz8; 0gj8t_b; 03s5lz; 0436yk; 0c00zd0; 05cj_j; ... *> query: (?x5984, 0gf28) <- genre(?x5984, ?x258), written_by(?x5984, ?x6914), ?x258 = 05p553, film(?x318, ?x5984) *> conf = 0.14 ranks of expected_values: 23 EVAL 0n1s0 genre 0gf28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 65.000 64.000 0.612 http://example.org/film/film/genre #11584-026390q PRED entity: 026390q PRED relation: award PRED expected values: 02z1nbg => 108 concepts (96 used for prediction) PRED predicted values (max 10 best out of 204): 09sb52 (0.29 #725, 0.25 #495, 0.06 #2339), 0bdwqv (0.29 #126, 0.01 #21913), 0gr0m (0.28 #2536, 0.27 #6457, 0.27 #6458), 0gs9p (0.28 #2536, 0.27 #6457, 0.27 #6458), 0gqwc (0.28 #2536, 0.27 #6457, 0.27 #6458), 094qd5 (0.28 #2536, 0.27 #6457, 0.27 #6458), 02qyp19 (0.28 #2536, 0.27 #6457, 0.27 #6458), 02qyntr (0.28 #2536, 0.27 #6457, 0.27 #6458), 02pqp12 (0.28 #2536, 0.27 #6457, 0.27 #6458), 02y_rq5 (0.28 #2536, 0.27 #6457, 0.27 #6458) >> Best rule #725 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 011ywj; >> query: (?x1230, 09sb52) <- currency(?x1230, ?x170), award(?x1230, ?x3435), ?x3435 = 03hl6lc, titles(?x53, ?x1230) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #16835 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 995 *> proper extension: 02nf2c; 0m123; *> query: (?x1230, ?x1972) <- nominated_for(?x68, ?x1230), award_winner(?x1230, ?x2715), award_winner(?x1972, ?x2715) *> conf = 0.22 ranks of expected_values: 17 EVAL 026390q award 02z1nbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 108.000 96.000 0.294 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #11583-06_sc3 PRED entity: 06_sc3 PRED relation: film_distribution_medium PRED expected values: 0735l => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.95 #119, 0.95 #103, 0.83 #115), 0dq6p (0.33 #41, 0.27 #45, 0.22 #121), 07c52 (0.04 #42, 0.03 #46, 0.03 #94), 07z4p (0.04 #44, 0.03 #48, 0.03 #124) >> Best rule #119 for best value: >> intensional similarity = 6 >> extensional distance = 102 >> proper extension: 01vksx; >> query: (?x8234, 0735l) <- genre(?x8234, ?x225), language(?x8234, ?x254), film(?x609, ?x8234), ?x609 = 03xq0f, film_distribution_medium(?x8234, ?x81), film(?x558, ?x8234) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06_sc3 film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.952 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #11582-0f4zv PRED entity: 0f4zv PRED relation: currency PRED expected values: 09nqf => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.87 #67, 0.87 #66, 0.86 #16) >> Best rule #67 for best value: >> intensional similarity = 5 >> extensional distance = 228 >> proper extension: 0f4y_; 0mx4_; 0mw93; 0m7fm; 0n5fl; 0fr59; 0mx6c; 0mk7z; 0mlyw; 0dlhg; ... >> query: (?x11833, ?x170) <- source(?x11833, ?x958), second_level_divisions(?x94, ?x11833), ?x94 = 09c7w0, adjoins(?x13754, ?x11833), currency(?x13754, ?x170) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f4zv currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.870 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #11581-04cj79 PRED entity: 04cj79 PRED relation: film_crew_role PRED expected values: 09zzb8 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.82 #581, 0.80 #889, 0.74 #684), 01pvkk (0.32 #316, 0.32 #180, 0.31 #112), 0215hd (0.26 #85, 0.24 #51, 0.24 #187), 089g0h (0.21 #86, 0.18 #188, 0.15 #52), 02ynfr (0.20 #902, 0.17 #697, 0.17 #1690), 01xy5l_ (0.16 #80, 0.14 #182, 0.14 #250), 0d2b38 (0.14 #262, 0.13 #92, 0.13 #707), 02_n3z (0.14 #240, 0.12 #582, 0.10 #2330), 089fss (0.12 #40, 0.11 #74, 0.10 #2330), 04pyp5 (0.11 #117, 0.10 #2330, 0.09 #151) >> Best rule #581 for best value: >> intensional similarity = 4 >> extensional distance = 166 >> proper extension: 0cnztc4; >> query: (?x3605, 09zzb8) <- titles(?x53, ?x3605), film_crew_role(?x3605, ?x1171), ?x1171 = 09vw2b7, ?x53 = 07s9rl0 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cj79 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.821 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11580-02w59b PRED entity: 02w59b PRED relation: colors PRED expected values: 06fvc 01g5v => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 19): 06fvc (0.82 #267, 0.73 #496, 0.50 #78), 019sc (0.82 #267, 0.73 #496, 0.43 #64), 01g5v (0.43 #346, 0.42 #766, 0.42 #384), 038hg (0.25 #50, 0.21 #336, 0.18 #554), 01l849 (0.18 #554, 0.18 #1412, 0.18 #993), 06kqt3 (0.18 #554, 0.18 #1412, 0.18 #993), 04mkbj (0.18 #554, 0.18 #1412, 0.18 #993), 02rnmb (0.18 #554, 0.18 #1412, 0.18 #993), 088fh (0.18 #554, 0.18 #1412, 0.18 #993), 0jc_p (0.18 #554, 0.18 #1412, 0.18 #993) >> Best rule #267 for best value: >> intensional similarity = 14 >> extensional distance = 24 >> proper extension: 01j95f; 02mplj; 037mjv; 03c0vy; 0k_l4; 0199gx; 01zhs3; 02029f; 01rly6; >> query: (?x12463, ?x1101) <- position(?x12463, ?x530), position(?x12463, ?x63), position(?x12463, ?x60), ?x60 = 02nzb8, ?x530 = 02_j1w, team(?x203, ?x12463), teams(?x3285, ?x12463), ?x203 = 0dgrmp, ?x63 = 02sdk9v, teams(?x3285, ?x12706), place_of_birth(?x12856, ?x3285), colors(?x12463, ?x663), colors(?x12706, ?x1101), contains(?x151, ?x3285) >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1, 3 EVAL 02w59b colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.821 http://example.org/sports/sports_team/colors EVAL 02w59b colors 06fvc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.821 http://example.org/sports/sports_team/colors #11579-06w2sn5 PRED entity: 06w2sn5 PRED relation: film PRED expected values: 0661m4p => 133 concepts (91 used for prediction) PRED predicted values (max 10 best out of 997): 05sy_5 (0.35 #2849, 0.02 #29729, 0.02 #67363), 02y_lrp (0.22 #14, 0.04 #8974, 0.04 #17934), 03xf_m (0.15 #2902, 0.01 #47704, 0.01 #49496), 01shy7 (0.11 #424, 0.08 #16552, 0.08 #5800), 013q07 (0.11 #357, 0.08 #9317, 0.06 #18277), 048qrd (0.11 #328, 0.05 #3912, 0.04 #7496), 03nfnx (0.11 #1405, 0.05 #19325, 0.05 #35453), 08r4x3 (0.11 #154, 0.04 #39579, 0.04 #41372), 02pg45 (0.11 #933, 0.04 #13477, 0.03 #6309), 02q7yfq (0.11 #1206, 0.03 #28086, 0.03 #17334) >> Best rule #2849 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 04lgymt; >> query: (?x1462, 05sy_5) <- award_nominee(?x1462, ?x6264), award_winner(?x2855, ?x1462), award(?x1462, ?x2597), ?x6264 = 01vw37m >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #5752 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 0knjh; *> query: (?x1462, 0661m4p) <- award_nominee(?x1462, ?x6264), place_of_birth(?x1462, ?x9699), artist(?x4483, ?x1462), participant(?x6577, ?x1462) *> conf = 0.03 ranks of expected_values: 191 EVAL 06w2sn5 film 0661m4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 133.000 91.000 0.350 http://example.org/film/actor/film./film/performance/film #11578-01zwy PRED entity: 01zwy PRED relation: profession PRED expected values: 0cbd2 => 161 concepts (109 used for prediction) PRED predicted values (max 10 best out of 106): 02hrh1q (0.82 #11919, 0.81 #3064, 0.79 #10178), 0dxtg (0.73 #3646, 0.63 #884, 0.62 #3936), 018gz8 (0.67 #18, 0.31 #5682, 0.31 #4522), 0cbd2 (0.60 #1750, 0.59 #2766, 0.57 #4220), 01d_h8 (0.53 #876, 0.53 #2620, 0.52 #4510), 02jknp (0.50 #8, 0.44 #3640, 0.43 #878), 04s2z (0.47 #497, 0.42 #352, 0.14 #207), 03gjzk (0.43 #886, 0.39 #4520, 0.36 #9019), 015cjr (0.33 #48, 0.11 #1354, 0.11 #4406), 01pxg (0.29 #275, 0.21 #565, 0.08 #420) >> Best rule #11919 for best value: >> intensional similarity = 3 >> extensional distance = 853 >> proper extension: 0m2wm; 02zq43; 04wqr; 01j5x6; 05wjnt; 05hdf; 06mmb; 01pnn3; 02wb6yq; 07xr3w; ... >> query: (?x8508, 02hrh1q) <- people(?x1050, ?x8508), nominated_for(?x8508, ?x5418), profession(?x8508, ?x2225) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1750 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 0c73z; *> query: (?x8508, 0cbd2) <- student(?x742, ?x8508), profession(?x8508, ?x2225), influenced_by(?x8508, ?x3542) *> conf = 0.60 ranks of expected_values: 4 EVAL 01zwy profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 161.000 109.000 0.815 http://example.org/people/person/profession #11577-0c8wxp PRED entity: 0c8wxp PRED relation: religion! PRED expected values: 0h0jz 02g8h 09fb5 041h0 01kwld 05sq84 02p21g 039g82 0b_fw 01vvpjj 02cllz 0j_c 01tp5bj 012_53 0gr36 01j5ws 0dn3n 01wv9p 0f502 01swck 02t_99 015njf 01vswwx 0432b 02zjd 01pj5q 03g5_y 0l6wj 0226cw 05vk_d 04093 01r4zfk 01nfys 0194xc 025b3k 03_x5t 02sdx 0127xk 02qhm3 04n7gc6 02v2jy => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 2704): 020trj (0.50 #1421, 0.33 #322, 0.17 #3167), 02tk74 (0.50 #1421, 0.33 #528, 0.17 #3373), 081lh (0.50 #1421, 0.17 #2882, 0.14 #7144), 019pm_ (0.50 #1421, 0.15 #3555, 0.06 #16344), 04205z (0.50 #1421, 0.15 #3555, 0.06 #16344), 02z1yj (0.50 #1421, 0.15 #3555, 0.03 #9953), 0btyl (0.50 #1421, 0.09 #4468, 0.08 #5888), 06c0j (0.50 #1421, 0.08 #6362, 0.06 #8493), 0sz28 (0.50 #1421, 0.06 #16344, 0.04 #9241), 0kjgl (0.50 #1421, 0.06 #16344, 0.04 #9241) >> Best rule #1421 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 06nzl; >> query: (?x1985, ?x12525) <- religion(?x8103, ?x1985), religion(?x3593, ?x1985), religion(?x2194, ?x1985), religion(?x395, ?x1985), award_nominee(?x7946, ?x395), participant(?x395, ?x4229), type_of_union(?x3593, ?x566), ?x7946 = 0kjgl, spouse(?x8103, ?x12525), award(?x2194, ?x724) >> conf = 0.50 => this is the best rule for 45 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 11, 35, 92, 615, 665, 721, 734, 791, 813, 994, 995, 1014, 1107, 1138, 1466, 1725, 1732, 1933, 2149, 2165, 2185, 2223, 2324, 2413, 2445, 2452, 2627, 2629, 2642, 2662, 2693, 2700 EVAL 0c8wxp religion! 02v2jy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 04n7gc6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 02qhm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0127xk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 02sdx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 03_x5t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 025b3k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0194xc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 01nfys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 01r4zfk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 04093 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 05vk_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0226cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0l6wj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 03g5_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 01pj5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 02zjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0432b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 01vswwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 015njf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 02t_99 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 01swck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0f502 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 01wv9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0dn3n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 01j5ws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0gr36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 012_53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 01tp5bj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0j_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 02cllz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 01vvpjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0b_fw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 039g82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 02p21g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 05sq84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 01kwld CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 041h0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 09fb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 02g8h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion EVAL 0c8wxp religion! 0h0jz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.500 http://example.org/people/person/religion #11576-02psvcf PRED entity: 02psvcf PRED relation: symptom_of! PRED expected values: 0gxb2 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 77): 012qjw (0.71 #713, 0.67 #576, 0.60 #417), 0cjf0 (0.62 #1073, 0.58 #1327, 0.57 #691), 02tfl8 (0.60 #490, 0.60 #364, 0.57 #616), 0gxb2 (0.50 #548, 0.50 #385, 0.50 #384), 0j5fv (0.50 #287, 0.50 #232, 0.50 #208), 04kllm9 (0.50 #385, 0.50 #384, 0.46 #482), 01pf6 (0.50 #232, 0.50 #208, 0.41 #704), 0k95h (0.50 #232, 0.50 #208, 0.34 #949), 01cdt5 (0.50 #150, 0.45 #910, 0.44 #459), 0dq9p (0.50 #208, 0.34 #949, 0.32 #560) >> Best rule #713 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 0gk4g; >> query: (?x7006, 012qjw) <- symptom_of(?x9118, ?x7006), risk_factors(?x7006, ?x11739), risk_factors(?x11739, ?x6781), symptom_of(?x9118, ?x14024), symptom_of(?x9118, ?x11064), ?x14024 = 0h1wz, people(?x7006, ?x1946), symptom_of(?x9438, ?x11739), ?x11064 = 01n3bm >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #548 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 0hg11; *> query: (?x7006, 0gxb2) <- symptom_of(?x13373, ?x7006), symptom_of(?x9118, ?x7006), risk_factors(?x7006, ?x11739), risk_factors(?x11739, ?x6781), ?x9118 = 0brgy, symptom_of(?x13373, ?x13485), symptom_of(?x13373, ?x12536), symptom_of(?x9438, ?x11739), ?x13485 = 07s4l, risk_factors(?x11659, ?x12536) *> conf = 0.50 ranks of expected_values: 4 EVAL 02psvcf symptom_of! 0gxb2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 78.000 78.000 0.714 http://example.org/medicine/symptom/symptom_of #11575-046_v PRED entity: 046_v PRED relation: company PRED expected values: 058j2 => 152 concepts (117 used for prediction) PRED predicted values (max 10 best out of 67): 058j2 (0.33 #106, 0.10 #878, 0.04 #2230), 07wh1 (0.25 #374, 0.14 #567, 0.08 #1533), 03zj9 (0.25 #281, 0.14 #474, 0.08 #1440), 0ky6d (0.14 #572, 0.08 #1538, 0.02 #3469), 07wj1 (0.11 #725, 0.03 #3043, 0.02 #4395), 02hvd (0.10 #1036, 0.10 #843, 0.09 #2195), 0gsg7 (0.10 #1185, 0.04 #2343, 0.03 #2730), 059wk (0.08 #1682, 0.04 #2647), 06rq1k (0.07 #1767, 0.02 #4858, 0.02 #3312), 03mp8k (0.07 #1876) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 079vf; >> query: (?x10439, 058j2) <- story_by(?x1956, ?x10439), type_of_union(?x10439, ?x566), ?x1956 = 05qbckf, profession(?x10439, ?x353) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 046_v company 058j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 117.000 0.333 http://example.org/people/person/employment_history./business/employment_tenure/company #11574-033qxt PRED entity: 033qxt PRED relation: people PRED expected values: 05szp => 18 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1811): 01k5zk (0.50 #493, 0.40 #2223, 0.33 #5681), 046zh (0.40 #7666, 0.14 #9398, 0.07 #11130), 01vrt_c (0.33 #5342, 0.25 #154, 0.20 #7072), 01twdk (0.33 #5863, 0.25 #675, 0.20 #2405), 052hl (0.33 #6127, 0.25 #939, 0.20 #2669), 03rx9 (0.33 #6564, 0.25 #1376, 0.20 #3106), 05xpv (0.33 #6432, 0.25 #1244, 0.20 #2974), 02z1yj (0.33 #6591, 0.25 #1403, 0.20 #3133), 0427y (0.33 #6547, 0.25 #1359, 0.20 #3089), 04z0g (0.33 #6015, 0.25 #827, 0.20 #2557) >> Best rule #493 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 07hwkr; 048z7l; >> query: (?x12078, 01k5zk) <- languages_spoken(?x12078, ?x13310), languages_spoken(?x12078, ?x8531), languages_spoken(?x12078, ?x3966), ?x3966 = 03hkp, ?x13310 = 032f6, languages(?x3129, ?x8531), people(?x12078, ?x2817) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 033qxt people 05szp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 18.000 17.000 0.500 http://example.org/people/ethnicity/people #11573-02x2khw PRED entity: 02x2khw PRED relation: draft! PRED expected values: 06x68 0713r 02__x 04mjl => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 191): 0jmj7 (0.93 #676, 0.93 #74, 0.92 #372), 0jmhr (0.93 #676, 0.93 #74, 0.92 #372), 0jmk7 (0.93 #676, 0.93 #74, 0.92 #372), 0jm3b (0.93 #676, 0.93 #74, 0.92 #372), 0jml5 (0.93 #676, 0.93 #74, 0.92 #372), 0jm64 (0.93 #676, 0.93 #74, 0.92 #372), 0jmm4 (0.93 #676, 0.93 #74, 0.92 #372), 0jmfv (0.93 #676, 0.93 #74, 0.92 #372), 0jm6n (0.93 #676, 0.93 #74, 0.92 #372), 0jm5b (0.93 #676, 0.93 #74, 0.92 #372) >> Best rule #676 for best value: >> intensional similarity = 59 >> extensional distance = 1 >> proper extension: 0f4vx0; >> query: (?x1161, ?x799) <- school(?x1161, ?x10838), school(?x1161, ?x8202), school(?x1161, ?x5907), school(?x1161, ?x4209), school(?x1161, ?x3360), draft(?x10939, ?x1161), draft(?x1160, ?x1161), school(?x3334, ?x5907), school(?x2569, ?x5907), list(?x5907, ?x2197), student(?x3360, ?x2643), school(?x4571, ?x5907), contains(?x94, ?x3360), institution(?x1519, ?x5907), institution(?x865, ?x5907), ?x3334 = 02pq_rp, ?x1519 = 013zdg, student(?x5907, ?x12037), school(?x1160, ?x6333), school(?x1160, ?x1428), student(?x4209, ?x123), major_field_of_study(?x4209, ?x6870), major_field_of_study(?x4209, ?x4268), currency(?x10838, ?x170), ?x8202 = 06fq2, organization(?x346, ?x10838), company(?x7512, ?x5907), colors(?x5907, ?x4557), team(?x2010, ?x1160), company(?x4264, ?x4209), school(?x2569, ?x2497), celebrities_impersonated(?x3649, ?x12037), ?x2497 = 0f1nl, award(?x12037, ?x2071), ?x4268 = 02822, school_type(?x1428, ?x4994), draft(?x2820, ?x2569), draft(?x799, ?x2569), institution(?x1200, ?x4209), citytown(?x5907, ?x5837), origin(?x4977, ?x5837), ?x865 = 02h4rq6, place_of_birth(?x9924, ?x5837), ?x6333 = 07ccs, place_of_death(?x4473, ?x5837), producer_type(?x2643, ?x632), nominated_for(?x12037, ?x12533), ?x6870 = 01540, ?x2197 = 09g7thr, film(?x12037, ?x6218), sport(?x10939, ?x5063), student(?x1428, ?x7961), major_field_of_study(?x3360, ?x254), school_type(?x10838, ?x3205), ?x2820 = 0jmj7, award_nominee(?x2643, ?x3727), ?x3727 = 03y9ccy, source(?x5837, ?x958), ?x4994 = 07tf8 >> conf = 0.93 => this is the best rule for 26 predicted values *> Best rule #372 for first EXPECTED value: *> intensional similarity = 50 *> extensional distance = 1 *> proper extension: 038c0q; *> query: (?x1161, ?x799) <- school(?x1161, ?x10666), school(?x1161, ?x5907), school(?x1161, ?x4296), school(?x1161, ?x466), ?x5907 = 01jq4b, draft(?x8894, ?x1161), draft(?x2067, ?x1161), team(?x261, ?x2067), ?x10666 = 01dzg0, school(?x8542, ?x466), category(?x466, ?x134), country(?x466, ?x94), contains(?x3908, ?x466), citytown(?x466, ?x1248), major_field_of_study(?x466, ?x2601), school(?x2067, ?x5750), school(?x2067, ?x4955), school(?x2067, ?x1506), school(?x387, ?x466), student(?x4296, ?x3927), colors(?x466, ?x332), ?x5750 = 01nnsv, ?x134 = 08mbj5d, major_field_of_study(?x4296, ?x12158), major_field_of_study(?x4296, ?x3490), major_field_of_study(?x6912, ?x2601), major_field_of_study(?x2895, ?x2601), major_field_of_study(?x2399, ?x2601), ?x12158 = 09s1f, organization(?x346, ?x4296), location(?x1299, ?x3908), ?x3490 = 05qfh, colors(?x4296, ?x8271), school(?x8894, ?x6455), registering_agency(?x466, ?x1982), school_type(?x466, ?x1507), ?x6455 = 026vcc, currency(?x1506, ?x170), major_field_of_study(?x865, ?x2601), ?x6912 = 0gl5_, institution(?x2636, ?x4296), ?x2895 = 0l2tk, student(?x2601, ?x2873), state_province_region(?x1506, ?x3778), ?x4955 = 09f2j, colors(?x2067, ?x3189), ?x2399 = 01jsn5, draft(?x799, ?x8542), religion(?x3908, ?x109), state(?x6769, ?x3908) *> conf = 0.92 ranks of expected_values: 31, 34, 47, 71 EVAL 02x2khw draft! 04mjl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 18.000 18.000 0.926 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02x2khw draft! 02__x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 18.000 18.000 0.926 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02x2khw draft! 0713r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 18.000 18.000 0.926 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02x2khw draft! 06x68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 18.000 18.000 0.926 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #11572-011yl_ PRED entity: 011yl_ PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #16, 0.84 #221, 0.84 #141), 07c52 (0.05 #23, 0.05 #103, 0.04 #73), 07z4p (0.05 #75, 0.04 #105, 0.03 #35), 02nxhr (0.04 #304, 0.03 #72, 0.03 #257) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0dgpwnk; >> query: (?x3573, 029j_) <- award_winner(?x3573, ?x1738), featured_film_locations(?x3573, ?x362), nominated_for(?x3533, ?x3573), film_festivals(?x3573, ?x7988) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011yl_ film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #11571-01d38g PRED entity: 01d38g PRED relation: award_winner PRED expected values: 015bwt => 45 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1431): 01vs_v8 (0.56 #10304, 0.17 #7842, 0.11 #20144), 0gcs9 (0.50 #8026, 0.20 #5564, 0.11 #10488), 09mq4m (0.50 #7758, 0.20 #5296, 0.04 #15141), 02cx72 (0.50 #8188, 0.13 #2462, 0.06 #46756), 0m_v0 (0.50 #8124, 0.09 #15507, 0.08 #20426), 02fgpf (0.50 #7772, 0.05 #20074, 0.05 #15155), 0fhxv (0.44 #10894, 0.06 #20734, 0.06 #23196), 09889g (0.42 #2461, 0.41 #2460, 0.40 #6043), 01vvycq (0.42 #2461, 0.41 #2460, 0.40 #2582), 012x4t (0.42 #2461, 0.41 #2460, 0.40 #2796) >> Best rule #10304 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01c427; 02f72n; 02f705; 02sp_v; 02f716; 02f71y; 02f73b; >> query: (?x567, 01vs_v8) <- award_winner(?x567, ?x1378), award(?x7407, ?x567), ?x7407 = 01dq9q, award_winner(?x1378, ?x2335), place_of_birth(?x1378, ?x12558) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #4746 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 01cw51; 031b3h; *> query: (?x567, 015bwt) <- award_winner(?x567, ?x1378), award(?x8200, ?x567), ?x1378 = 01wcp_g, ceremony(?x567, ?x139), origin(?x8200, ?x14195) *> conf = 0.20 ranks of expected_values: 116 EVAL 01d38g award_winner 015bwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 45.000 21.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner #11570-02_1q9 PRED entity: 02_1q9 PRED relation: country_of_origin PRED expected values: 09c7w0 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 10): 09c7w0 (0.87 #178, 0.87 #167, 0.87 #145), 0h7x (0.44 #445, 0.44 #411, 0.42 #332), 07ssc (0.13 #75, 0.11 #108, 0.11 #241), 03_3d (0.08 #448, 0.07 #436, 0.05 #425), 02jx1 (0.07 #77, 0.01 #387, 0.01 #398), 0d060g (0.04 #59, 0.03 #70, 0.03 #336), 03rt9 (0.04 #63), 0chghy (0.03 #73), 04jpl (0.03 #72), 05v8c (0.01 #455) >> Best rule #178 for best value: >> intensional similarity = 4 >> extensional distance = 141 >> proper extension: 01h72l; 01hvv0; 07wqr6; >> query: (?x416, 09c7w0) <- actor(?x416, ?x1918), program(?x6678, ?x416), nominated_for(?x415, ?x416), type_of_union(?x1918, ?x566) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_1q9 country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.874 http://example.org/tv/tv_program/country_of_origin #11569-0p5mw PRED entity: 0p5mw PRED relation: type_of_union PRED expected values: 04ztj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.71 #221, 0.69 #1, 0.68 #37), 01g63y (0.12 #222, 0.11 #254, 0.11 #258) >> Best rule #221 for best value: >> intensional similarity = 3 >> extensional distance = 902 >> proper extension: 0p_th; >> query: (?x1887, 04ztj) <- award(?x1887, ?x1443), nominated_for(?x1443, ?x2989), ?x2989 = 02vqsll >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p5mw type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.712 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11568-01y49 PRED entity: 01y49 PRED relation: draft PRED expected values: 05vsb7 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 15): 05vsb7 (0.80 #46, 0.77 #257, 0.74 #349), 047dpm0 (0.43 #513, 0.37 #303, 0.37 #364), 04f4z1k (0.43 #512, 0.33 #43, 0.30 #300), 02pq_rp (0.40 #504, 0.37 #303, 0.37 #364), 02z6872 (0.37 #505, 0.37 #303, 0.33 #36), 02x2khw (0.37 #501, 0.23 #411, 0.23 #803), 025tn92 (0.37 #303, 0.37 #364, 0.35 #272), 02pq_x5 (0.37 #303, 0.37 #364, 0.35 #272), 0f4vx0 (0.37 #303, 0.37 #364, 0.35 #272), 02rl201 (0.37 #303, 0.34 #502, 0.24 #804) >> Best rule #46 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 05tfm; 0ws7; >> query: (?x2114, 05vsb7) <- colors(?x2114, ?x4557), position(?x2114, ?x935), position(?x2114, ?x180), position_s(?x2114, ?x1114), team(?x180, ?x7078), position(?x4723, ?x180), ?x4723 = 043tz8m, ?x4557 = 019sc, ?x935 = 06b1q, ?x7078 = 0ws7 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y49 draft 05vsb7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.800 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #11567-0jmbv PRED entity: 0jmbv PRED relation: draft PRED expected values: 06439y => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 17): 038981 (0.79 #82, 0.75 #133, 0.71 #201), 06439y (0.76 #290, 0.74 #273, 0.71 #376), 09th87 (0.76 #285, 0.74 #268, 0.71 #371), 092j54 (0.43 #470, 0.42 #487, 0.33 #52), 05vsb7 (0.43 #463, 0.42 #480, 0.33 #52), 0g3zpp (0.41 #464, 0.40 #481, 0.33 #52), 09l0x9 (0.41 #472, 0.40 #489, 0.33 #52), 03nt7j (0.33 #468, 0.33 #52, 0.32 #485), 02qw1zx (0.33 #52, 0.29 #467, 0.28 #484), 02pq_rp (0.33 #52, 0.28 #591, 0.28 #608) >> Best rule #82 for best value: >> intensional similarity = 9 >> extensional distance = 12 >> proper extension: 02q4ntp; >> query: (?x6089, 038981) <- position(?x6089, ?x6848), position(?x6089, ?x4747), sport(?x6089, ?x4833), ?x4747 = 02sf_r, team(?x4570, ?x6089), team(?x12339, ?x6089), teams(?x6088, ?x6089), ?x6848 = 02_ssl, position(?x660, ?x4570) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #290 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 27 *> proper extension: 0jmcb; 01k8vh; *> query: (?x6089, 06439y) <- draft(?x6089, ?x2569), position(?x6089, ?x1348), team(?x1348, ?x11420), team(?x1348, ?x9833), team(?x1348, ?x6003), team(?x1348, ?x4369), ?x6003 = 02py8_w, ?x9833 = 03y9p40, school(?x6089, ?x1011), team(?x3797, ?x4369), ?x11420 = 0jmhr *> conf = 0.76 ranks of expected_values: 2 EVAL 0jmbv draft 06439y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 51.000 51.000 0.786 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #11566-0674cw PRED entity: 0674cw PRED relation: award_winner! PRED expected values: 024030 => 167 concepts (100 used for prediction) PRED predicted values (max 10 best out of 285): 0b6k___ (0.64 #1297, 0.41 #7347, 0.37 #21182), 03rbj2 (0.44 #1086, 0.20 #7136, 0.12 #1519), 03r8tl (0.44 #969, 0.13 #7019, 0.12 #1402), 0j6j8 (0.17 #762, 0.05 #2059), 05b4l5x (0.17 #438, 0.03 #18152, 0.03 #3463), 01kpt (0.17 #677, 0.03 #18152, 0.01 #4998), 015cl6 (0.17 #855, 0.03 #18152, 0.01 #5176), 079sf (0.17 #847, 0.03 #18152), 068gn (0.17 #841, 0.03 #18152), 03x3wf (0.12 #4818, 0.05 #13031, 0.03 #20814) >> Best rule #1297 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02jxsq; >> query: (?x4434, ?x4443) <- place_of_death(?x4434, ?x7412), award(?x4434, ?x4443), profession(?x4434, ?x319), ?x7412 = 04vmp, people(?x11563, ?x4434) >> conf = 0.64 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0674cw award_winner! 024030 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 100.000 0.636 http://example.org/award/award_category/winners./award/award_honor/award_winner #11565-08jcfy PRED entity: 08jcfy PRED relation: company PRED expected values: 01v2xl => 32 concepts (14 used for prediction) PRED predicted values (max 10 best out of 791): 0gsg7 (0.57 #2119, 0.33 #36, 0.29 #2467), 018sg9 (0.50 #2019, 0.43 #3423, 0.40 #692), 0300cp (0.43 #2830, 0.43 #2133, 0.40 #4581), 060ppp (0.43 #3031, 0.40 #4782, 0.33 #4080), 019rl6 (0.43 #2943, 0.40 #4694, 0.33 #3992), 0sxdg (0.43 #2986, 0.40 #4737, 0.33 #206), 07xyn1 (0.43 #2968, 0.40 #4719, 0.33 #188), 01yfp7 (0.43 #2907, 0.40 #4658, 0.33 #127), 09b3v (0.43 #2870, 0.40 #4621, 0.33 #90), 02r5dz (0.43 #2852, 0.40 #4603, 0.33 #72) >> Best rule #2119 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 02k13d; 014l7h; >> query: (?x11157, 0gsg7) <- company(?x11157, ?x8223), list(?x8223, ?x2197), company(?x3484, ?x8223), child(?x6127, ?x8223), organization(?x5510, ?x6127), organization(?x3484, ?x216), company(?x3484, ?x1665), company(?x3484, ?x122), ?x122 = 08815, ?x1665 = 04rwx, citytown(?x8223, ?x362), category(?x8223, ?x134), ?x134 = 08mbj5d, location(?x361, ?x362) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08jcfy company 01v2xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 14.000 0.571 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #11564-0d05w3 PRED entity: 0d05w3 PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 208 concepts (208 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.90 #19, 0.90 #54, 0.88 #41) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 02k54; 01z215; 016zwt; >> query: (?x2346, 0hzc9wc) <- nationality(?x754, ?x2346), combatants(?x5114, ?x2346), exported_to(?x2346, ?x291) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d05w3 administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 208.000 208.000 0.905 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #11563-0cqhk0 PRED entity: 0cqhk0 PRED relation: award_winner PRED expected values: 07sgfsl 0404wqb => 33 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1539): 02__7n (0.33 #1552, 0.29 #26400, 0.29 #28804), 015grj (0.33 #174, 0.13 #2401, 0.08 #16798), 07r_dg (0.33 #2047, 0.13 #2401, 0.08 #16798), 02t_st (0.33 #1570, 0.13 #2401, 0.08 #16798), 0686zv (0.33 #644, 0.13 #2401, 0.08 #16798), 01438g (0.33 #643, 0.13 #2401, 0.08 #16798), 02qgyv (0.33 #464, 0.13 #2401, 0.08 #16798), 02bkdn (0.33 #368, 0.13 #2401, 0.05 #2769), 013knm (0.33 #774, 0.13 #2401, 0.05 #26401), 02x7vq (0.33 #1196, 0.13 #2401, 0.05 #26401) >> Best rule #1552 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 09sb52; >> query: (?x678, 02__7n) <- nominated_for(?x678, ?x1631), award(?x9655, ?x678), award(?x1116, ?x678), ?x9655 = 02ct_k, award_nominee(?x516, ?x1116) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #26400 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 216 *> proper extension: 0bm7fy; *> query: (?x678, ?x10259) <- award(?x10259, ?x678), award(?x3602, ?x678), award_nominee(?x516, ?x3602), actor(?x2078, ?x3602), award_nominee(?x10259, ?x4536) *> conf = 0.29 ranks of expected_values: 185, 280 EVAL 0cqhk0 award_winner 0404wqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 33.000 15.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0cqhk0 award_winner 07sgfsl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 15.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #11562-01dw_f PRED entity: 01dw_f PRED relation: instrumentalists! PRED expected values: 07brj => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 114): 0342h (0.74 #523, 0.70 #263, 0.67 #1643), 05r5c (0.50 #353, 0.49 #526, 0.47 #1560), 018vs (0.47 #530, 0.43 #357, 0.41 #1218), 03bx0bm (0.42 #1293, 0.41 #345, 0.36 #432), 05148p4 (0.41 #365, 0.40 #538, 0.40 #1226), 03qjg (0.29 #568, 0.26 #395, 0.25 #308), 02hnl (0.28 #292, 0.25 #552, 0.25 #206), 026t6 (0.28 #261, 0.21 #175, 0.18 #1209), 0l14qv (0.16 #351, 0.16 #6, 0.11 #1040), 03gvt (0.14 #322, 0.10 #1270, 0.08 #1098) >> Best rule #523 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 08wq0g; >> query: (?x7570, 0342h) <- profession(?x7570, ?x220), instrumentalists(?x315, ?x7570), role(?x7570, ?x1466), ?x1466 = 03bx0bm >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #4405 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 967 *> proper extension: 02_5x9; 014hr0; 01qqwp9; 02t3ln; 02mq_y; 015cxv; 03_gx; 03d6q; 0qmpd; 0h08p; *> query: (?x7570, ?x227) <- artists(?x5792, ?x7570), artists(?x5792, ?x4995), group(?x227, ?x4995) *> conf = 0.03 ranks of expected_values: 95 EVAL 01dw_f instrumentalists! 07brj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 116.000 116.000 0.737 http://example.org/music/instrument/instrumentalists #11561-0cgfb PRED entity: 0cgfb PRED relation: nationality PRED expected values: 02jx1 => 145 concepts (58 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.82 #301, 0.77 #4008, 0.76 #3405), 02jx1 (0.61 #3706, 0.61 #5712, 0.60 #5815), 07ssc (0.61 #3706, 0.61 #5712, 0.60 #5815), 02ly_ (0.29 #5311, 0.29 #5310), 0f8l9c (0.25 #222, 0.05 #622, 0.04 #3225), 03rk0 (0.11 #2148, 0.09 #1448, 0.06 #1748), 06q1r (0.09 #177, 0.03 #677, 0.02 #777), 0b90_r (0.08 #203, 0.01 #3206), 0d060g (0.08 #3210, 0.06 #507, 0.06 #1809), 0345h (0.06 #1333, 0.04 #2334, 0.04 #2434) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 02qjj7; >> query: (?x11098, 09c7w0) <- participant(?x11098, ?x1898), profession(?x11098, ?x967), participant(?x1898, ?x6187), ?x6187 = 07r1h >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #3706 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 203 *> proper extension: 015njf; 03d1y3; *> query: (?x11098, ?x512) <- spouse(?x1898, ?x11098), nationality(?x1898, ?x512), location(?x1898, ?x1523), citytown(?x234, ?x1523) *> conf = 0.61 ranks of expected_values: 2 EVAL 0cgfb nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 58.000 0.818 http://example.org/people/person/nationality #11560-07vfy4 PRED entity: 07vfy4 PRED relation: music PRED expected values: 01gg59 => 80 concepts (58 used for prediction) PRED predicted values (max 10 best out of 65): 025jfl (0.11 #4825, 0.08 #211, 0.08 #210), 0146pg (0.10 #431, 0.08 #850, 0.06 #2740), 0150t6 (0.07 #467, 0.05 #4871, 0.05 #5290), 02fgpf (0.06 #241, 0.01 #1291, 0.01 #3388), 02bh9 (0.05 #891, 0.05 #472, 0.04 #1731), 01x1fq (0.05 #596, 0.02 #175, 0.01 #4789), 02rgz4 (0.05 #5), 03h610 (0.04 #3435, 0.03 #3226, 0.03 #1547), 02jxmr (0.04 #495, 0.03 #3014, 0.03 #3432), 016szr (0.04 #502, 0.02 #4695, 0.02 #921) >> Best rule #4825 for best value: >> intensional similarity = 3 >> extensional distance = 616 >> proper extension: 01hvv0; 03g9xj; 0h95b81; 0cskb; >> query: (?x9805, ?x617) <- nominated_for(?x617, ?x9805), titles(?x1882, ?x9805), category(?x617, ?x134) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #64 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 047q2k1; 04jwjq; 021pqy; 05znbh7; 052_mn; 06zn1c; 02tcgh; *> query: (?x9805, 01gg59) <- nominated_for(?x617, ?x9805), genre(?x9805, ?x1626), ?x1626 = 03q4nz *> conf = 0.03 ranks of expected_values: 14 EVAL 07vfy4 music 01gg59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 80.000 58.000 0.110 http://example.org/film/film/music #11559-024lff PRED entity: 024lff PRED relation: genre PRED expected values: 01jfsb => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 79): 07s9rl0 (0.62 #848, 0.62 #485, 0.61 #364), 05p553 (0.41 #246, 0.35 #5102, 0.35 #3888), 02l7c8 (0.40 #500, 0.38 #379, 0.34 #258), 03k9fj (0.38 #132, 0.25 #1466, 0.25 #1222), 01jfsb (0.35 #1345, 0.33 #1467, 0.33 #1832), 0219x_ (0.33 #27, 0.15 #148, 0.09 #995), 04rlf (0.33 #66, 0.08 #187, 0.02 #4920), 026v1nw (0.33 #119), 01hmnh (0.31 #139, 0.18 #1229, 0.18 #1473), 06n90 (0.23 #134, 0.15 #1468, 0.15 #1833) >> Best rule #848 for best value: >> intensional similarity = 3 >> extensional distance = 502 >> proper extension: 016kz1; >> query: (?x3700, 07s9rl0) <- produced_by(?x3700, ?x1417), award_winner(?x3700, ?x7569), nationality(?x7569, ?x94) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1345 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 598 *> proper extension: 02z3r8t; 03ckwzc; 03t97y; 05p3738; 047qxs; 03m8y5; 0c57yj; 0gs973; 02z2mr7; 03_wm6; ... *> query: (?x3700, 01jfsb) <- film_crew_role(?x3700, ?x1284), film_crew_role(?x3700, ?x468), ?x468 = 02r96rf, ?x1284 = 0ch6mp2 *> conf = 0.35 ranks of expected_values: 5 EVAL 024lff genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 66.000 66.000 0.619 http://example.org/film/film/genre #11558-0k8z PRED entity: 0k8z PRED relation: company! PRED expected values: 02211by => 282 concepts (282 used for prediction) PRED predicted values (max 10 best out of 42): 0krdk (0.80 #1570, 0.80 #4443, 0.79 #2626), 060c4 (0.76 #4227, 0.74 #2538, 0.71 #2622), 0dq3c (0.60 #1565, 0.57 #468, 0.57 #426), 05_wyz (0.54 #2635, 0.47 #2085, 0.47 #1579), 09d6p2 (0.50 #736, 0.44 #904, 0.41 #1521), 02211by (0.41 #1521, 0.33 #807, 0.25 #3299), 01kr6k (0.41 #1521, 0.31 #3320, 0.31 #2815), 0142rn (0.41 #1521, 0.30 #995, 0.22 #6811), 02y6fz (0.33 #319, 0.29 #488, 0.29 #446), 021q1c (0.33 #8, 0.24 #3558, 0.23 #4105) >> Best rule #1570 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0537b; >> query: (?x3793, 0krdk) <- currency(?x3793, ?x170), place_founded(?x3793, ?x3794), state_province_region(?x3793, ?x1227), adjoins(?x3794, ?x6703) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1521 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 0c0cs; *> query: (?x3793, ?x265) <- company(?x9105, ?x3793), award_winner(?x9105, ?x6682), company(?x9105, ?x3230), religion(?x9105, ?x2694), company(?x265, ?x3230) *> conf = 0.41 ranks of expected_values: 6 EVAL 0k8z company! 02211by CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 282.000 282.000 0.800 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #11557-0kjgl PRED entity: 0kjgl PRED relation: profession PRED expected values: 01d_h8 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 66): 01d_h8 (0.86 #1496, 0.85 #3582, 0.85 #1794), 0dxtg (0.53 #4335, 0.53 #4037, 0.52 #3590), 02jknp (0.50 #3584, 0.49 #4329, 0.49 #4031), 03gjzk (0.48 #1058, 0.47 #1207, 0.47 #760), 09jwl (0.21 #5532, 0.21 #6128, 0.20 #913), 0cbd2 (0.16 #4775, 0.13 #13417, 0.12 #1944), 02krf9 (0.16 #623, 0.16 #1964, 0.15 #1219), 0nbcg (0.16 #926, 0.13 #5545, 0.13 #6141), 0d1pc (0.15 #2286, 0.14 #945, 0.14 #3478), 016z4k (0.15 #898, 0.14 #6113, 0.12 #5368) >> Best rule #1496 for best value: >> intensional similarity = 3 >> extensional distance = 193 >> proper extension: 029ghl; 016z1c; 014hdb; 0hsmh; >> query: (?x7946, 01d_h8) <- award(?x7946, ?x401), produced_by(?x407, ?x7946), award_winner(?x7946, ?x192) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kjgl profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.856 http://example.org/people/person/profession #11556-0146pg PRED entity: 0146pg PRED relation: profession PRED expected values: 01c72t => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 62): 02hrh1q (0.83 #4755, 0.83 #5497, 0.79 #7126), 01c72t (0.74 #320, 0.67 #468, 0.61 #1652), 09jwl (0.69 #4464, 0.66 #1795, 0.64 #4167), 01d_h8 (0.50 #6, 0.43 #154, 0.37 #5933), 0nbcg (0.47 #4477, 0.46 #2401, 0.45 #5218), 0dxtg (0.43 #161, 0.33 #5348, 0.31 #6680), 016z4k (0.42 #2373, 0.40 #4152, 0.38 #4449), 0dz3r (0.41 #4447, 0.40 #1926, 0.38 #1778), 039v1 (0.29 #4482, 0.22 #4185, 0.21 #5223), 0n1h (0.26 #2380, 0.20 #4159, 0.19 #4308) >> Best rule #4755 for best value: >> intensional similarity = 2 >> extensional distance = 422 >> proper extension: 02r99xw; >> query: (?x669, 02hrh1q) <- languages(?x669, ?x254), people(?x3584, ?x669) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #320 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 21 *> proper extension: 025jj7; *> query: (?x669, 01c72t) <- award_winner(?x5123, ?x669), ?x5123 = 025m98 *> conf = 0.74 ranks of expected_values: 2 EVAL 0146pg profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 128.000 0.830 http://example.org/people/person/profession #11555-020hyj PRED entity: 020hyj PRED relation: gender PRED expected values: 05zppz => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #95, 0.77 #107, 0.76 #119), 02zsn (0.50 #10, 0.49 #22, 0.45 #72) >> Best rule #95 for best value: >> intensional similarity = 3 >> extensional distance = 544 >> proper extension: 0cm03; 0459z; >> query: (?x10180, 05zppz) <- nationality(?x10180, ?x151), instrumentalists(?x316, ?x10180), contains(?x151, ?x3285) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 020hyj gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.777 http://example.org/people/person/gender #11554-023907r PRED entity: 023907r PRED relation: major_field_of_study! PRED expected values: 03bwzr4 => 44 concepts (36 used for prediction) PRED predicted values (max 10 best out of 23): 014mlp (0.86 #264, 0.83 #241, 0.80 #310), 016t_3 (0.76 #562, 0.76 #541, 0.73 #308), 019v9k (0.76 #547, 0.71 #268, 0.71 #574), 0bkj86 (0.75 #244, 0.73 #313, 0.71 #267), 02h4rq6 (0.71 #261, 0.70 #540, 0.67 #307), 02_xgp2 (0.64 #272, 0.63 #606, 0.63 #578), 03bwzr4 (0.56 #552, 0.53 #319, 0.51 #579), 04zx3q1 (0.50 #260, 0.50 #237, 0.47 #306), 01ysy9 (0.46 #647, 0.44 #756, 0.43 #729), 02m4yg (0.46 #647, 0.44 #756, 0.43 #729) >> Best rule #264 for best value: >> intensional similarity = 11 >> extensional distance = 12 >> proper extension: 01tbp; >> query: (?x11733, 014mlp) <- major_field_of_study(?x4780, ?x11733), major_field_of_study(?x3424, ?x11733), ?x4780 = 017cy9, major_field_of_study(?x3424, ?x12363), major_field_of_study(?x3424, ?x5900), ?x5900 = 0db86, student(?x3424, ?x710), state_province_region(?x3424, ?x335), gender(?x710, ?x514), award_nominee(?x710, ?x91), ?x12363 = 02cm61 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #552 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 52 *> proper extension: 04_tv; 02ky346; 06ms6; 04rjg; 0h5k; 04x_3; 03g3w; 0pf2; 05qfh; 0fdys; ... *> query: (?x11733, 03bwzr4) <- major_field_of_study(?x4780, ?x11733), major_field_of_study(?x4780, ?x1154), institution(?x8398, ?x4780), institution(?x1200, ?x4780), ?x1200 = 016t_3, ?x1154 = 02lp1, colors(?x4780, ?x332), ?x8398 = 028dcg, contains(?x279, ?x4780), organization(?x346, ?x4780) *> conf = 0.56 ranks of expected_values: 7 EVAL 023907r major_field_of_study! 03bwzr4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 44.000 36.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #11553-09ly2r6 PRED entity: 09ly2r6 PRED relation: award! PRED expected values: 0gdqy => 56 concepts (12 used for prediction) PRED predicted values (max 10 best out of 2418): 01njxvw (0.80 #20302, 0.80 #20301, 0.79 #37224), 0dvld (0.43 #11903, 0.11 #22058, 0.09 #38980), 02f2dn (0.43 #10857, 0.11 #21012, 0.07 #37934), 07lt7b (0.43 #10303, 0.07 #20458, 0.07 #37380), 01tspc6 (0.43 #10380, 0.06 #20535, 0.04 #37457), 05ldnp (0.33 #4280, 0.29 #11045, 0.18 #21200), 0fhxv (0.33 #18270, 0.21 #28422, 0.20 #25037), 0kft (0.33 #2629, 0.17 #9393, 0.14 #12776), 01t07j (0.33 #481, 0.17 #7245, 0.12 #14013), 01tt43d (0.33 #1882, 0.17 #8646, 0.12 #15414) >> Best rule #20302 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 02g3gj; 01bgqh; 0c4z8; 01c427; 02nhxf; 04njml; 01by1l; 02f705; 02f5qb; 02f716; ... >> query: (?x6165, ?x10949) <- award_winner(?x6165, ?x10949), award_winner(?x4534, ?x10949), award(?x84, ?x6165), music(?x1415, ?x10949), category(?x10949, ?x134) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #26592 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 69 *> proper extension: 02681vq; 054ky1; 02nbqh; 01l29r; 01c9jp; 03qpp9; 026rsl9; *> query: (?x6165, 0gdqy) <- award_winner(?x6165, ?x10949), award_winner(?x4534, ?x10949), award(?x84, ?x6165), music(?x1415, ?x10949), award(?x10949, ?x1079) *> conf = 0.01 ranks of expected_values: 1804 EVAL 09ly2r6 award! 0gdqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 12.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11552-031296 PRED entity: 031296 PRED relation: award_nominee PRED expected values: 096lf_ => 97 concepts (55 used for prediction) PRED predicted values (max 10 best out of 895): 096lf_ (0.81 #88763, 0.81 #81753, 0.81 #126136), 049dyj (0.81 #81753, 0.81 #126136, 0.81 #84091), 031296 (0.28 #72409, 0.20 #74745, 0.16 #128473), 03q5dr (0.28 #72409, 0.20 #74745), 05fnl9 (0.20 #74745, 0.01 #37728, 0.01 #5025), 02p65p (0.16 #128473, 0.13 #35037, 0.05 #37400), 044lyq (0.16 #128473, 0.13 #35037, 0.04 #6312), 0154qm (0.16 #128473, 0.13 #35037, 0.04 #35776), 0bt4r4 (0.16 #128473, 0.13 #35037, 0.03 #21671), 072bb1 (0.16 #128473, 0.13 #35037, 0.03 #21585) >> Best rule #88763 for best value: >> intensional similarity = 3 >> extensional distance = 1119 >> proper extension: 01lvzbl; 04n65n; 03f7jfh; 09jm8; >> query: (?x3709, ?x2657) <- award_nominee(?x2657, ?x3709), award_winner(?x873, ?x3709), award_winner(?x2126, ?x2657) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031296 award_nominee 096lf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 55.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11551-0g56t9t PRED entity: 0g56t9t PRED relation: film! PRED expected values: 01gb54 => 90 concepts (86 used for prediction) PRED predicted values (max 10 best out of 69): 01795t (0.41 #2180, 0.11 #319, 0.11 #919), 05qd_ (0.33 #234, 0.30 #159, 0.20 #385), 086k8 (0.33 #227, 0.18 #678, 0.17 #378), 020h2v (0.33 #45, 0.08 #270, 0.05 #3653), 016tw3 (0.30 #161, 0.16 #1289, 0.16 #1214), 017s11 (0.25 #604, 0.25 #78, 0.18 #904), 03xq0f (0.23 #531, 0.17 #831, 0.17 #230), 016tt2 (0.19 #981, 0.16 #755, 0.13 #680), 024rgt (0.12 #95, 0.10 #546, 0.07 #621), 054g1r (0.12 #411, 0.08 #260, 0.08 #1012) >> Best rule #2180 for best value: >> intensional similarity = 5 >> extensional distance = 302 >> proper extension: 0d8w2n; >> query: (?x124, ?x2156) <- genre(?x124, ?x258), ?x258 = 05p553, production_companies(?x124, ?x2156), film_release_distribution_medium(?x124, ?x81), country(?x124, ?x94) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #705 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 0k2m6; *> query: (?x124, 01gb54) <- film_release_region(?x124, ?x94), film_crew_role(?x124, ?x468), edited_by(?x124, ?x707), ?x94 = 09c7w0 *> conf = 0.10 ranks of expected_values: 17 EVAL 0g56t9t film! 01gb54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 90.000 86.000 0.415 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #11550-07bx6 PRED entity: 07bx6 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #41, 0.86 #71, 0.85 #61), 02nxhr (0.33 #2, 0.20 #7, 0.05 #37), 07c52 (0.03 #43, 0.03 #248, 0.03 #238), 07z4p (0.02 #250, 0.02 #70, 0.02 #240) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 0crh5_f; >> query: (?x7482, 029j_) <- film(?x5636, ?x7482), genre(?x7482, ?x225), ?x5636 = 054g1r >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07bx6 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.865 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #11549-037css PRED entity: 037css PRED relation: position PRED expected values: 02nzb8 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 5): 0dgrmp (0.92 #264, 0.86 #67, 0.86 #64), 02nzb8 (0.85 #47, 0.85 #37, 0.84 #73), 03f0fp (0.80 #52, 0.45 #223, 0.43 #317), 02md_2 (0.45 #223, 0.43 #317, 0.37 #230), 02qvgy (0.45 #223, 0.43 #317) >> Best rule #264 for best value: >> intensional similarity = 15 >> extensional distance = 445 >> proper extension: 09c8bc; >> query: (?x14056, ?x203) <- position(?x14056, ?x530), position(?x14056, ?x63), ?x530 = 02_j1w, ?x63 = 02sdk9v, team(?x203, ?x14056), position(?x13503, ?x203), position(?x8454, ?x203), position(?x5982, ?x203), position(?x2074, ?x203), position(?x983, ?x203), ?x2074 = 0j2pg, ?x13503 = 04gj8r, ?x8454 = 03zrc_, ?x5982 = 03c0vy, ?x983 = 01bdxz >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 104 *> proper extension: 01453; 0223bl; 056xx8; 049bmk; 025txtg; 07r78j; 01kckd; 03_9hm; 0ytc; 0425c5; ... *> query: (?x14056, 02nzb8) <- position(?x14056, ?x530), position(?x14056, ?x203), position(?x14056, ?x63), ?x530 = 02_j1w, team(?x9411, ?x14056), team(?x3031, ?x14056), ?x203 = 0dgrmp, ?x63 = 02sdk9v, team(?x3031, ?x3363), nationality(?x9411, ?x1310), position(?x3363, ?x60) *> conf = 0.85 ranks of expected_values: 2 EVAL 037css position 02nzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 62.000 62.000 0.917 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #11548-017s11 PRED entity: 017s11 PRED relation: film PRED expected values: 0dscrwf 0416y94 09gq0x5 025n07 04ydr95 04cj79 07kb7vh 0gtt5fb 0gj96ln 016dj8 => 128 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1475): 03459x (0.44 #4252, 0.42 #48190, 0.42 #48189), 087pfc (0.44 #4252, 0.42 #48190, 0.42 #48189), 02tgz4 (0.44 #4252, 0.42 #48190, 0.42 #48189), 07phbc (0.44 #4252, 0.42 #48190, 0.42 #48189), 032016 (0.44 #4252, 0.42 #48190, 0.42 #48189), 01cssf (0.44 #4252, 0.42 #48190, 0.42 #48189), 0qmhk (0.44 #4252, 0.42 #48190, 0.42 #48189), 04t6fk (0.44 #4252, 0.42 #48190, 0.42 #48189), 01d2v1 (0.44 #4252, 0.42 #48190, 0.42 #48189), 01gvsn (0.44 #4252, 0.42 #48190, 0.42 #48189) >> Best rule #4252 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 030_1_; >> query: (?x541, ?x80) <- production_companies(?x6679, ?x541), production_companies(?x80, ?x541), ?x6679 = 0drnwh, award_winner(?x163, ?x541) >> conf = 0.44 => this is the best rule for 19 predicted values *> Best rule #225 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 03rwz3; *> query: (?x541, 09gq0x5) <- film(?x541, ?x5317), film(?x541, ?x2133), ?x2133 = 02__34, ?x5317 = 04zl8 *> conf = 0.33 ranks of expected_values: 22, 26, 41, 578, 829, 1284, 1345, 1351 EVAL 017s11 film 016dj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 102.000 0.439 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017s11 film 0gj96ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 102.000 0.439 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017s11 film 0gtt5fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 102.000 0.439 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017s11 film 07kb7vh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 128.000 102.000 0.439 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017s11 film 04cj79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 128.000 102.000 0.439 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017s11 film 04ydr95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 128.000 102.000 0.439 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017s11 film 025n07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 102.000 0.439 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017s11 film 09gq0x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 128.000 102.000 0.439 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017s11 film 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 102.000 0.439 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 017s11 film 0dscrwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 102.000 0.439 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #11547-01wwnh2 PRED entity: 01wwnh2 PRED relation: student! PRED expected values: 04sylm => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 52): 01d34b (0.11 #256, 0.02 #1837, 0.01 #3945), 0bwfn (0.07 #802, 0.05 #18193, 0.04 #20301), 01qd_r (0.06 #1335), 02g839 (0.04 #2660, 0.04 #7403, 0.04 #552), 017z88 (0.04 #609, 0.03 #11149, 0.03 #4825), 02183k (0.04 #631, 0.03 #1158, 0.01 #6428), 029qzx (0.04 #932), 01qwb5 (0.04 #818), 01ljpm (0.04 #749), 0hd7j (0.04 #675) >> Best rule #256 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 047sxrj; 0412f5y; 01vw20h; 05vzw3; 03y82t6; 06mt91; 02wwwv5; >> query: (?x10326, 01d34b) <- award_nominee(?x6573, ?x10326), artists(?x671, ?x10326), profession(?x10326, ?x131), ?x6573 = 067nsm >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #3765 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 210 *> proper extension: 011zf2; 05crg7; 014hr0; 01vd7hn; 028qdb; 03_0p; 02pt7h_; *> query: (?x10326, 04sylm) <- award_nominee(?x3607, ?x10326), role(?x10326, ?x212), artists(?x671, ?x3607), award_winner(?x527, ?x3607) *> conf = 0.01 ranks of expected_values: 40 EVAL 01wwnh2 student! 04sylm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 67.000 67.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #11546-05dbf PRED entity: 05dbf PRED relation: participant! PRED expected values: 02r6c_ => 150 concepts (112 used for prediction) PRED predicted values (max 10 best out of 413): 0lx2l (0.80 #21494, 0.80 #20229, 0.80 #24023), 0gx_p (0.80 #21494, 0.80 #20229, 0.80 #24023), 05ty4m (0.25 #652, 0.02 #13293, 0.01 #20881), 018grr (0.12 #1265, 0.12 #770, 0.07 #6952), 09yrh (0.12 #948, 0.03 #4740, 0.03 #7269), 01vs_v8 (0.12 #780, 0.03 #6468, 0.03 #3308), 0pz91 (0.12 #715, 0.02 #3874, 0.02 #20944), 02ld6x (0.12 #819, 0.02 #5874, 0.01 #3978), 060j8b (0.12 #1045, 0.02 #6733, 0.02 #7366), 05r5w (0.12 #872, 0.02 #2768, 0.01 #3400) >> Best rule #21494 for best value: >> intensional similarity = 3 >> extensional distance = 410 >> proper extension: 01q32bd; 091yn0; 019f9z; 0fq117k; 029q_y; 09px1w; 02_j8x; 020l9r; 06rq2l; 04vmqg; >> query: (?x2275, ?x286) <- award_nominee(?x748, ?x2275), participant(?x2275, ?x286), award(?x2275, ?x154) >> conf = 0.80 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 05dbf participant! 02r6c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 112.000 0.802 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #11545-036hf4 PRED entity: 036hf4 PRED relation: nationality PRED expected values: 0d060g => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 83): 09c7w0 (0.77 #2813, 0.76 #4718, 0.76 #1710), 0d060g (0.13 #207, 0.10 #107, 0.07 #1315), 02jx1 (0.11 #1039, 0.11 #3246, 0.10 #8364), 07ssc (0.09 #6139, 0.09 #8346, 0.09 #7143), 03rk0 (0.08 #1052, 0.08 #6170, 0.05 #11287), 04hqz (0.07 #279, 0.04 #8432, 0.03 #8131), 03rjj (0.05 #3419, 0.05 #6023, 0.04 #8432), 0f8l9c (0.05 #6023, 0.04 #8432, 0.03 #3436), 0345h (0.05 #6023, 0.04 #8432, 0.03 #8131), 0chghy (0.05 #6023, 0.04 #8432, 0.03 #8131) >> Best rule #2813 for best value: >> intensional similarity = 3 >> extensional distance = 312 >> proper extension: 0fpj4lx; 01386_; 01vzz1c; 07f7jp; 01jz6d; >> query: (?x9084, 09c7w0) <- currency(?x9084, ?x170), ?x170 = 09nqf, place_of_birth(?x9084, ?x1036) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #207 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 016tt2; *> query: (?x9084, 0d060g) <- award_nominee(?x3308, ?x9084), ?x3308 = 0794g, award_winner(?x1701, ?x9084) *> conf = 0.13 ranks of expected_values: 2 EVAL 036hf4 nationality 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.771 http://example.org/people/person/nationality #11544-09d38d PRED entity: 09d38d PRED relation: written_by PRED expected values: 06n9lt => 86 concepts (50 used for prediction) PRED predicted values (max 10 best out of 89): 03bw6 (0.38 #2359, 0.36 #2697, 0.35 #9780), 0g10g (0.11 #14845, 0.10 #9443, 0.10 #14844), 0jvtp (0.11 #7418, 0.10 #9443, 0.10 #14844), 0gl88b (0.11 #7418, 0.10 #7756, 0.10 #13497), 0c0tzp (0.07 #4386, 0.07 #10117, 0.06 #4723), 03zrp (0.07 #4386, 0.07 #10117, 0.06 #4723), 01_k71 (0.07 #4386, 0.07 #10117, 0.06 #4723), 03thw4 (0.04 #141, 0.04 #479, 0.04 #3176), 02bfxb (0.04 #434, 0.03 #1781, 0.03 #96), 0js9s (0.04 #534, 0.03 #196, 0.02 #1881) >> Best rule #2359 for best value: >> intensional similarity = 3 >> extensional distance = 139 >> proper extension: 08j7lh; >> query: (?x11356, ?x7257) <- film_format(?x11356, ?x14581), film_release_distribution_medium(?x11356, ?x81), film(?x7257, ?x11356) >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09d38d written_by 06n9lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 50.000 0.375 http://example.org/film/film/written_by #11543-0889x PRED entity: 0889x PRED relation: artist! PRED expected values: 026s90 => 118 concepts (78 used for prediction) PRED predicted values (max 10 best out of 118): 033hn8 (0.42 #431, 0.36 #292, 0.16 #570), 01w40h (0.33 #29, 0.12 #585, 0.10 #2531), 08pn_9 (0.29 #269, 0.27 #408, 0.25 #547), 043g7l (0.29 #171, 0.18 #310, 0.17 #449), 011k1h (0.29 #149, 0.15 #983, 0.14 #1539), 015_1q (0.21 #576, 0.21 #1271, 0.20 #2522), 03rhqg (0.20 #3630, 0.17 #1545, 0.17 #5159), 01cl2y (0.17 #31, 0.14 #170, 0.12 #1143), 0fb0v (0.17 #7, 0.14 #146, 0.12 #702), 01jv1z (0.17 #5, 0.14 #144, 0.07 #561) >> Best rule #431 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 033s6; >> query: (?x12266, 033hn8) <- artists(?x6107, ?x12266), ?x6107 = 0126t5, award(?x12266, ?x4912), ceremony(?x4912, ?x342) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #319 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0134tg; *> query: (?x12266, 026s90) <- artists(?x6107, ?x12266), ?x6107 = 0126t5, award(?x12266, ?x4912), ?x4912 = 01ckrr *> conf = 0.09 ranks of expected_values: 26 EVAL 0889x artist! 026s90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 118.000 78.000 0.417 http://example.org/music/record_label/artist #11542-04n52p6 PRED entity: 04n52p6 PRED relation: genre PRED expected values: 03k9fj => 103 concepts (102 used for prediction) PRED predicted values (max 10 best out of 97): 07s9rl0 (0.77 #3826, 0.67 #1198, 0.61 #2153), 04xvlr (0.71 #2273, 0.61 #2272, 0.59 #3946), 024qqx (0.61 #2272, 0.57 #3945, 0.53 #4305), 03k9fj (0.50 #248, 0.47 #848, 0.45 #728), 05p553 (0.50 #123, 0.42 #601, 0.37 #2277), 02l7c8 (0.50 #133, 0.42 #611, 0.31 #3839), 06cvj (0.33 #122, 0.27 #600, 0.09 #2395), 01hmnh (0.32 #493, 0.30 #254, 0.27 #1093), 060__y (0.27 #612, 0.22 #3840, 0.21 #1212), 06n90 (0.23 #729, 0.21 #369, 0.21 #1805) >> Best rule #3826 for best value: >> intensional similarity = 3 >> extensional distance = 582 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x1707, 07s9rl0) <- titles(?x162, ?x1707), titles(?x162, ?x8555), ?x8555 = 04qk12 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #248 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 03mgx6z; *> query: (?x1707, 03k9fj) <- film_release_region(?x1707, ?x1790), titles(?x162, ?x1707), prequel(?x4615, ?x1707), ?x1790 = 01pj7 *> conf = 0.50 ranks of expected_values: 4 EVAL 04n52p6 genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 102.000 0.769 http://example.org/film/film/genre #11541-06b_j PRED entity: 06b_j PRED relation: official_language! PRED expected values: 06bnz 0cdbq => 87 concepts (80 used for prediction) PRED predicted values (max 10 best out of 348): 0d060g (0.53 #1099, 0.52 #3860, 0.51 #3307), 06bnz (0.53 #1099, 0.52 #3860, 0.51 #3307), 0d0kn (0.53 #1099, 0.52 #3860, 0.51 #3307), 04gzd (0.53 #1099, 0.52 #3860, 0.51 #3307), 01mjq (0.53 #1099, 0.52 #3860, 0.51 #3307), 04w4s (0.53 #1099, 0.52 #3860, 0.51 #3307), 06dfg (0.33 #120, 0.29 #1035, 0.25 #486), 01nln (0.33 #119, 0.29 #1034, 0.25 #485), 07z5n (0.33 #52, 0.29 #967, 0.25 #418), 0366c (0.33 #175, 0.29 #1090, 0.25 #541) >> Best rule #1099 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 02bv9; >> query: (?x5671, ?x279) <- language(?x5185, ?x5671), language(?x4551, ?x5671), language(?x3672, ?x5671), language(?x1640, ?x5671), countries_spoken_in(?x5671, ?x279), film_crew_role(?x5185, ?x137), nominated_for(?x1072, ?x3672), award_winner(?x4551, ?x846), film(?x844, ?x1640), service_language(?x610, ?x5671), ?x610 = 0p4wb >> conf = 0.53 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 81 EVAL 06b_j official_language! 0cdbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 87.000 80.000 0.528 http://example.org/location/country/official_language EVAL 06b_j official_language! 06bnz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 80.000 0.528 http://example.org/location/country/official_language #11540-04mzf8 PRED entity: 04mzf8 PRED relation: nominated_for! PRED expected values: 0gqyl => 78 concepts (72 used for prediction) PRED predicted values (max 10 best out of 209): 019f4v (0.69 #518, 0.66 #751, 0.64 #1683), 0gr0m (0.66 #6761, 0.66 #8862, 0.65 #7462), 04dn09n (0.50 #1664, 0.50 #499, 0.46 #732), 0gqyl (0.50 #541, 0.48 #774, 0.47 #1007), 040njc (0.49 #1638, 0.46 #473, 0.44 #3037), 094qd5 (0.48 #500, 0.48 #1665, 0.46 #733), 02pqp12 (0.40 #1688, 0.35 #523, 0.34 #3087), 0gqy2 (0.38 #816, 0.38 #1748, 0.38 #1049), 02qyntr (0.38 #641, 0.36 #1806, 0.34 #3205), 0gr51 (0.35 #539, 0.33 #1704, 0.31 #1005) >> Best rule #518 for best value: >> intensional similarity = 5 >> extensional distance = 46 >> proper extension: 02wk7b; >> query: (?x1308, 019f4v) <- nominated_for(?x1313, ?x1308), nominated_for(?x1245, ?x1308), ?x1313 = 0gs9p, ?x1245 = 0gqwc, titles(?x600, ?x1308) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #541 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 46 *> proper extension: 02wk7b; *> query: (?x1308, 0gqyl) <- nominated_for(?x1313, ?x1308), nominated_for(?x1245, ?x1308), ?x1313 = 0gs9p, ?x1245 = 0gqwc, titles(?x600, ?x1308) *> conf = 0.50 ranks of expected_values: 4 EVAL 04mzf8 nominated_for! 0gqyl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 78.000 72.000 0.688 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11539-05cqhl PRED entity: 05cqhl PRED relation: student! PRED expected values: 08815 => 116 concepts (77 used for prediction) PRED predicted values (max 10 best out of 94): 065y4w7 (0.13 #11041, 0.08 #4214, 0.07 #3689), 03ksy (0.13 #3256, 0.11 #1680, 0.11 #4306), 09f2j (0.11 #11186, 0.04 #12761, 0.04 #16962), 0bwfn (0.09 #12352, 0.09 #3950, 0.09 #12877), 04b_46 (0.06 #3902, 0.05 #4427, 0.04 #1801), 01jq34 (0.06 #581, 0.04 #1631, 0.04 #2156), 017j69 (0.06 #11172, 0.03 #4345, 0.03 #9071), 017z88 (0.05 #12159, 0.04 #9008, 0.04 #16885), 01w5m (0.04 #25837, 0.04 #10606, 0.03 #35819), 078bz (0.04 #11104, 0.03 #3227, 0.02 #3752) >> Best rule #11041 for best value: >> intensional similarity = 3 >> extensional distance = 541 >> proper extension: 0q1lp; 0ccqd7; 0cymln; 02d6n_; 0dszr0; >> query: (?x9571, 065y4w7) <- student(?x546, ?x9571), profession(?x9571, ?x987), school(?x4171, ?x546) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #14705 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 814 *> proper extension: 037hgm; 024y6w; *> query: (?x9571, 08815) <- student(?x546, ?x9571), type_of_union(?x9571, ?x566), award_nominee(?x9571, ?x6765) *> conf = 0.03 ranks of expected_values: 17 EVAL 05cqhl student! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 116.000 77.000 0.129 http://example.org/education/educational_institution/students_graduates./education/education/student #11538-07c72 PRED entity: 07c72 PRED relation: nominated_for! PRED expected values: 0b7gr2 => 79 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1072): 0glmv (0.80 #18618, 0.80 #13962, 0.79 #53536), 0f721s (0.80 #18618, 0.80 #13962, 0.79 #53536), 0cjdk (0.80 #18618, 0.80 #13962, 0.79 #53536), 04s04 (0.68 #34915, 0.68 #39570, 0.67 #46551), 0721cy (0.68 #34915, 0.68 #39570, 0.67 #46551), 07fvf1 (0.68 #34915, 0.68 #39570, 0.67 #46551), 03fykz (0.64 #27929, 0.63 #13961, 0.61 #18616), 06jnvs (0.64 #27929, 0.63 #13961, 0.61 #18616), 01h910 (0.64 #32587, 0.58 #32586, 0.56 #60520), 0h5g_ (0.64 #32587, 0.58 #32586, 0.56 #60520) >> Best rule #18618 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 0gxsh4; >> query: (?x3180, ?x1394) <- tv_program(?x3242, ?x3180), award_winner(?x3180, ?x1394), profession(?x3242, ?x987) >> conf = 0.80 => this is the best rule for 3 predicted values *> Best rule #44224 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 110 *> proper extension: 01h1bf; 02r5qtm; 02rcwq0; 01vnbh; 0431v3; 02_1kl; 01dvry; 06zsk51; *> query: (?x3180, ?x635) <- nominated_for(?x4385, ?x3180), award_nominee(?x635, ?x4385), producer_type(?x3180, ?x632) *> conf = 0.16 ranks of expected_values: 82 EVAL 07c72 nominated_for! 0b7gr2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 79.000 54.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #11537-06mkj PRED entity: 06mkj PRED relation: nationality! PRED expected values: 0436kgz 01dhpj 01s7ns 051q39 => 229 concepts (139 used for prediction) PRED predicted values (max 10 best out of 4141): 034bs (0.38 #101434, 0.30 #101433, 0.29 #32458), 01syr4 (0.34 #117669, 0.20 #11164, 0.15 #63908), 0c1pj (0.34 #117669, 0.20 #44757, 0.14 #65042), 01900g (0.34 #117669, 0.14 #25685, 0.14 #17571), 09r9dp (0.34 #117669, 0.14 #25437, 0.14 #17323), 0bj9k (0.34 #117669, 0.14 #24878, 0.14 #16764), 04zn7g (0.34 #117669, 0.14 #28300, 0.14 #20186), 02byfd (0.34 #117669, 0.14 #27149, 0.14 #19035), 0725ny (0.34 #117669, 0.14 #26930, 0.14 #18816), 016kb7 (0.34 #117669, 0.14 #26787, 0.14 #18673) >> Best rule #101434 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 06jnv; >> query: (?x2152, ?x4055) <- location(?x4055, ?x2152), influenced_by(?x2485, ?x4055), participating_countries(?x418, ?x2152) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #27791 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 0cgm9; *> query: (?x2152, 01s7ns) <- entity_involved(?x9939, ?x2152), partially_contains(?x1144, ?x2152) *> conf = 0.14 ranks of expected_values: 932, 2879 EVAL 06mkj nationality! 051q39 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 229.000 139.000 0.381 http://example.org/people/person/nationality EVAL 06mkj nationality! 01s7ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 229.000 139.000 0.381 http://example.org/people/person/nationality EVAL 06mkj nationality! 01dhpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 229.000 139.000 0.381 http://example.org/people/person/nationality EVAL 06mkj nationality! 0436kgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 229.000 139.000 0.381 http://example.org/people/person/nationality #11536-01bpnd PRED entity: 01bpnd PRED relation: role PRED expected values: 02hnl => 184 concepts (182 used for prediction) PRED predicted values (max 10 best out of 116): 0342h (0.53 #3566, 0.48 #486, 0.47 #3202), 05r5c (0.22 #791, 0.22 #1092, 0.20 #3570), 02hnl (0.19 #3587, 0.18 #1109, 0.17 #808), 01vj9c (0.17 #1868, 0.12 #193, 0.11 #5132), 0l14md (0.14 #3569, 0.12 #188, 0.12 #1814), 03qjg (0.12 #399, 0.12 #821, 0.11 #1122), 0l14qv (0.09 #126, 0.08 #3567, 0.07 #4111), 02sgy (0.06 #1150, 0.05 #1813, 0.05 #1875), 03_vpw (0.05 #281, 0.05 #341, 0.04 #221), 02fsn (0.05 #1544, 0.05 #1665, 0.04 #220) >> Best rule #3566 for best value: >> intensional similarity = 4 >> extensional distance = 241 >> proper extension: 01nhkxp; >> query: (?x5872, 0342h) <- profession(?x5872, ?x1183), role(?x5872, ?x716), instrumentalists(?x716, ?x211), group(?x716, ?x379) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #3587 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 241 *> proper extension: 01nhkxp; *> query: (?x5872, 02hnl) <- profession(?x5872, ?x1183), role(?x5872, ?x716), instrumentalists(?x716, ?x211), group(?x716, ?x379) *> conf = 0.19 ranks of expected_values: 3 EVAL 01bpnd role 02hnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 184.000 182.000 0.531 http://example.org/music/group_member/membership./music/group_membership/role #11535-0kw4j PRED entity: 0kw4j PRED relation: fraternities_and_sororities PRED expected values: 035tlh => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 3): 035tlh (0.36 #26, 0.30 #38, 0.24 #53), 0325pb (0.36 #25, 0.23 #37, 0.20 #161), 04m8fy (0.10 #9, 0.07 #15, 0.05 #27) >> Best rule #26 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 08qnnv; >> query: (?x3821, 035tlh) <- institution(?x3386, ?x3821), institution(?x1526, ?x3821), student(?x3821, ?x6742), ?x1526 = 0bkj86, ?x3386 = 03mkk4 >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kw4j fraternities_and_sororities 035tlh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.364 http://example.org/education/university/fraternities_and_sororities #11534-05wjnt PRED entity: 05wjnt PRED relation: religion PRED expected values: 0kq2 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 21): 0c8wxp (0.44 #842, 0.37 #1811, 0.36 #1723), 0kpl (0.36 #142, 0.24 #890, 0.23 #318), 092bf5 (0.09 #59, 0.04 #1820, 0.04 #323), 03j6c (0.09 #944, 0.08 #1825, 0.08 #1032), 0kq2 (0.08 #149, 0.08 #897, 0.07 #545), 01lp8 (0.06 #133, 0.05 #309, 0.05 #837), 0flw86 (0.05 #838, 0.05 #1454, 0.05 #2159), 0n2g (0.05 #145, 0.04 #893, 0.04 #1730), 04pk9 (0.03 #151, 0.03 #855, 0.03 #327), 06nzl (0.03 #850, 0.03 #322, 0.03 #366) >> Best rule #842 for best value: >> intensional similarity = 4 >> extensional distance = 330 >> proper extension: 04bdxl; 079vf; 06151l; 04t2l2; 01wbg84; 0bl2g; 01q_ph; 0l8v5; 01tvz5j; 0159h6; ... >> query: (?x2473, 0c8wxp) <- profession(?x2473, ?x353), religion(?x2473, ?x7131), place_of_birth(?x2473, ?x1196), film(?x2473, ?x365) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 84 *> proper extension: 04rs03; 07g2b; 012t1; 045bg; 016hvl; 028p0; 017r2; 03ft8; 0l56b; 0lccn; ... *> query: (?x2473, 0kq2) <- profession(?x2473, ?x353), religion(?x2473, ?x7131), place_of_birth(?x2473, ?x1196), ?x353 = 0cbd2 *> conf = 0.08 ranks of expected_values: 5 EVAL 05wjnt religion 0kq2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 119.000 119.000 0.437 http://example.org/people/person/religion #11533-0pb33 PRED entity: 0pb33 PRED relation: nominated_for! PRED expected values: 02hsq3m 02qyntr => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 203): 0p9sw (0.68 #3543, 0.68 #11342, 0.68 #3542), 02r22gf (0.68 #3543, 0.68 #11342, 0.68 #3542), 018wdw (0.68 #3543, 0.68 #11342, 0.68 #3542), 0641kkh (0.68 #3543, 0.68 #11342, 0.68 #3542), 0gq9h (0.37 #11166, 0.35 #5969, 0.35 #10458), 0gs9p (0.34 #11168, 0.33 #64, 0.33 #5261), 019f4v (0.33 #5723, 0.33 #5960, 0.33 #3596), 04dn09n (0.33 #34, 0.25 #5704, 0.25 #4994), 02x73k6 (0.33 #48, 0.24 #12760, 0.22 #25047), 02x4w6g (0.33 #85, 0.24 #12760, 0.22 #25047) >> Best rule #3543 for best value: >> intensional similarity = 4 >> extensional distance = 213 >> proper extension: 02hxhz; 091z_p; 047n8xt; 0j6b5; 07yvsn; 0198b6; 032zq6; 0125xq; 02ylg6; 04fv5b; ... >> query: (?x1450, ?x3019) <- written_by(?x1450, ?x4036), award(?x1450, ?x3019), film_crew_role(?x1450, ?x137), award(?x71, ?x3019) >> conf = 0.68 => this is the best rule for 4 predicted values *> Best rule #972 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 0d90m; 01vksx; 02rv_dz; 0dr3sl; 012s1d; 03hxsv; 07bx6; 04jpg2p; *> query: (?x1450, 02hsq3m) <- nominated_for(?x3308, ?x1450), nominated_for(?x298, ?x1450), film_crew_role(?x1450, ?x137), ?x298 = 05ztjjw *> conf = 0.28 ranks of expected_values: 18, 41 EVAL 0pb33 nominated_for! 02qyntr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 114.000 114.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0pb33 nominated_for! 02hsq3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 114.000 114.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11532-03f1zhf PRED entity: 03f1zhf PRED relation: instrumentalists! PRED expected values: 02hnl => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 125): 05148p4 (0.63 #1124, 0.50 #1209, 0.44 #2485), 02sgy (0.30 #6136, 0.30 #2553, 0.30 #6135), 02hnl (0.28 #2499, 0.27 #3010, 0.26 #3095), 03qjg (0.25 #1240, 0.24 #1580, 0.23 #1666), 06w7v (0.24 #1346, 0.17 #1431, 0.16 #1090), 03gvt (0.16 #1083, 0.14 #1339, 0.11 #488), 0l14md (0.15 #3069, 0.15 #1878, 0.14 #2473), 0l14qv (0.15 #1621, 0.14 #1535, 0.14 #2727), 018j2 (0.13 #3014, 0.12 #1397, 0.12 #2503), 04rzd (0.12 #375, 0.12 #2502, 0.11 #460) >> Best rule #1124 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 03j0br4; 01w02sy; >> query: (?x9762, 05148p4) <- artists(?x378, ?x9762), group(?x9762, ?x12880), people(?x1050, ?x9762), languages(?x9762, ?x254), profession(?x9762, ?x131) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #2499 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 116 *> proper extension: 09g0h; *> query: (?x9762, 02hnl) <- group(?x9762, ?x12880), profession(?x9762, ?x131), role(?x9762, ?x716), role(?x9762, ?x227) *> conf = 0.28 ranks of expected_values: 3 EVAL 03f1zhf instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 132.000 132.000 0.632 http://example.org/music/instrument/instrumentalists #11531-027qq9b PRED entity: 027qq9b PRED relation: ceremony PRED expected values: 0jt3qpk => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 139): 0jt3qpk (0.75 #321, 0.75 #3757, 0.71 #182), 05c1t6z (0.59 #432, 0.52 #571, 0.52 #849), 02q690_ (0.51 #482, 0.48 #621, 0.45 #899), 0gvstc3 (0.51 #451, 0.44 #590, 0.43 #868), 03nnm4t (0.49 #491, 0.46 #630, 0.42 #908), 0gx_st (0.45 #454, 0.41 #593, 0.38 #871), 0gpjbt (0.36 #3229, 0.34 #3368, 0.32 #3646), 09n4nb (0.36 #3248, 0.34 #3387, 0.32 #3665), 0466p0j (0.35 #3276, 0.33 #3415, 0.31 #3693), 02rjjll (0.35 #3205, 0.33 #3344, 0.31 #3622) >> Best rule #321 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02p_04b; >> query: (?x4115, 0jt3qpk) <- ceremony(?x4115, ?x7721), nominated_for(?x4115, ?x4011), ?x4011 = 0gj50 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027qq9b ceremony 0jt3qpk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony #11530-02lz1s PRED entity: 02lz1s PRED relation: category PRED expected values: 08mbj5d => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.72 #6, 0.72 #8, 0.70 #7) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 316 >> proper extension: 01l4g5; 01wg3q; >> query: (?x1852, 08mbj5d) <- profession(?x1852, ?x2348), ?x2348 = 0nbcg, award(?x1852, ?x1079) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lz1s category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.723 http://example.org/common/topic/webpage./common/webpage/category #11529-0jm5b PRED entity: 0jm5b PRED relation: school PRED expected values: 065y4w7 0bx8pn => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 200): 06fq2 (0.50 #510, 0.33 #322, 0.33 #134), 015q1n (0.35 #2927, 0.33 #2173, 0.33 #291), 0bx8pn (0.33 #2094, 0.33 #212, 0.33 #24), 065y4w7 (0.33 #197, 0.33 #9, 0.27 #6229), 01pl14 (0.33 #193, 0.33 #5, 0.25 #757), 07szy (0.33 #208, 0.33 #20, 0.25 #772), 01lnyf (0.33 #255, 0.33 #67, 0.25 #819), 026vcc (0.33 #294, 0.33 #106, 0.25 #858), 01stj9 (0.33 #368, 0.33 #180, 0.25 #932), 027xx3 (0.33 #230, 0.33 #42, 0.25 #794) >> Best rule #510 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 0jmhr; >> query: (?x11805, 06fq2) <- position(?x11805, ?x6848), position(?x11805, ?x5755), position(?x11805, ?x1348), ?x1348 = 01pv51, draft(?x11805, ?x8133), ?x8133 = 025tn92, school(?x11805, ?x6644), ?x5755 = 0355dz, ?x6644 = 01jpyb, team(?x6848, ?x660) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2094 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 16 *> proper extension: 0jm64; *> query: (?x11805, 0bx8pn) <- position(?x11805, ?x1348), ?x1348 = 01pv51, draft(?x11805, ?x8586), draft(?x11805, ?x8133), school(?x8133, ?x4955), ?x4955 = 09f2j, ?x8586 = 038981, school(?x11805, ?x3416) *> conf = 0.33 ranks of expected_values: 3, 4 EVAL 0jm5b school 0bx8pn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 48.000 48.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jm5b school 065y4w7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 48.000 48.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #11528-0bzkgg PRED entity: 0bzkgg PRED relation: ceremony! PRED expected values: 0gs96 => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 370): 0gs96 (0.90 #3934, 0.89 #3210, 0.88 #2728), 0gr42 (0.80 #6584, 0.80 #554, 0.78 #70), 018wdw (0.80 #651, 0.75 #3305, 0.73 #4029), 0gqxm (0.60 #600, 0.43 #1806, 0.42 #2772), 03nqnk3 (0.59 #242, 0.45 #243, 0.32 #7483), 040njc (0.59 #242, 0.32 #7483, 0.21 #8936), 0czp_ (0.56 #192, 0.32 #2123, 0.23 #3571), 025mb9 (0.45 #2061, 0.22 #7371, 0.18 #7856), 02nbqh (0.45 #2002, 0.22 #7312, 0.18 #7797), 02v1m7 (0.45 #1999, 0.22 #7309, 0.18 #7794) >> Best rule #3934 for best value: >> intensional similarity = 20 >> extensional distance = 28 >> proper extension: 0bzkvd; >> query: (?x2822, 0gs96) <- award_winner(?x2822, ?x767), ceremony(?x3617, ?x2822), ceremony(?x1703, ?x2822), ceremony(?x1307, ?x2822), ceremony(?x720, ?x2822), ceremony(?x484, ?x2822), ceremony(?x77, ?x2822), instance_of_recurring_event(?x2822, ?x3459), ?x1307 = 0gq9h, ?x3617 = 0gvx_, ?x1703 = 0k611, ?x720 = 018wng, ?x77 = 0gqng, nominated_for(?x484, ?x6345), nominated_for(?x484, ?x5137), nominated_for(?x484, ?x4559), ?x5137 = 0kb07, ?x4559 = 0ccd3x, award(?x199, ?x484), nominated_for(?x2530, ?x6345) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bzkgg ceremony! 0gs96 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.900 http://example.org/award/award_category/winners./award/award_honor/ceremony #11527-0p4wb PRED entity: 0p4wb PRED relation: service_location PRED expected values: 09c7w0 0j1z8 0d05w3 => 109 concepts (97 used for prediction) PRED predicted values (max 10 best out of 291): 09c7w0 (0.85 #4210, 0.84 #4484, 0.84 #2920), 02j71 (0.43 #466, 0.33 #196, 0.33 #15), 059g4 (0.33 #237, 0.33 #56, 0.20 #417), 03rjj (0.33 #6, 0.26 #547, 0.17 #187), 06mkj (0.33 #30, 0.21 #571, 0.20 #391), 06t2t (0.33 #31, 0.20 #392, 0.17 #212), 02vzc (0.33 #27, 0.20 #388, 0.17 #208), 02j9z (0.33 #12, 0.17 #193, 0.11 #1192), 0k6nt (0.33 #19, 0.17 #200, 0.11 #560), 06qd3 (0.33 #25, 0.17 #206, 0.10 #386) >> Best rule #4210 for best value: >> intensional similarity = 6 >> extensional distance = 126 >> proper extension: 05krk; 016tt2; 04rwx; 011k1h; 01xdn1; 0cchk3; 02607j; 03ksy; 0178g; 0221g_; ... >> query: (?x610, 09c7w0) <- service_location(?x610, ?x279), nationality(?x199, ?x279), medal(?x279, ?x422), country(?x1036, ?x279), country(?x136, ?x279), country(?x150, ?x279) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 21, 180 EVAL 0p4wb service_location 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 109.000 97.000 0.852 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 0p4wb service_location 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 109.000 97.000 0.852 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 0p4wb service_location 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 97.000 0.852 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #11526-0nvrd PRED entity: 0nvrd PRED relation: source PRED expected values: 0jbk9 => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #50, 0.92 #24, 0.92 #55) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 168 >> proper extension: 0k696; >> query: (?x1963, 0jbk9) <- currency(?x1963, ?x170), adjoins(?x1963, ?x6410), contains(?x3818, ?x1963), county(?x1860, ?x6410) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nvrd source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.941 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #11525-046zh PRED entity: 046zh PRED relation: location PRED expected values: 0r0m6 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 105): 030qb3t (0.25 #5696, 0.25 #6499, 0.23 #27355), 0r0m6 (0.20 #216, 0.07 #1820, 0.06 #2622), 01n7q (0.20 #62, 0.06 #6479, 0.06 #14501), 01qh7 (0.20 #155, 0.04 #1759, 0.03 #2561), 04ly1 (0.20 #201, 0.02 #9828, 0.02 #5815), 02xry (0.20 #131, 0.02 #5745, 0.02 #16174), 013gwb (0.20 #455), 0f2tj (0.20 #327), 0d9y6 (0.20 #266), 059rby (0.11 #1620, 0.07 #2422, 0.05 #3224) >> Best rule #5696 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 0152cw; 0134w7; 0456xp; 0n6f8; 01pw2f1; 01wxyx1; 047hpm; 05r5w; 07g2v; 01z0rcq; ... >> query: (?x5246, 030qb3t) <- participant(?x286, ?x5246), film(?x5246, ?x603), vacationer(?x957, ?x5246) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #216 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 014zcr; 0169dl; 0c6qh; *> query: (?x5246, 0r0m6) <- participant(?x286, ?x5246), award_nominee(?x5246, ?x875), ?x875 = 032_jg *> conf = 0.20 ranks of expected_values: 2 EVAL 046zh location 0r0m6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 129.000 0.254 http://example.org/people/person/places_lived./people/place_lived/location #11524-01dw9z PRED entity: 01dw9z PRED relation: people! PRED expected values: 041rx => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 40): 0x67 (0.43 #694, 0.23 #1530, 0.22 #86), 041rx (0.21 #2665, 0.21 #2741, 0.20 #4489), 033tf_ (0.16 #83, 0.11 #2744, 0.11 #2668), 09vc4s (0.14 #9, 0.05 #161, 0.04 #237), 03lmx1 (0.14 #14, 0.02 #1230, 0.02 #3891), 02w7gg (0.11 #230, 0.11 #2663, 0.09 #2739), 07bch9 (0.09 #99, 0.05 #3900, 0.05 #175), 0xnvg (0.07 #2750, 0.06 #2674, 0.06 #697), 07hwkr (0.06 #240, 0.06 #88, 0.06 #164), 065b6q (0.05 #459, 0.04 #231, 0.02 #2740) >> Best rule #694 for best value: >> intensional similarity = 3 >> extensional distance = 261 >> proper extension: 01vw917; >> query: (?x2683, 0x67) <- category(?x2683, ?x134), artists(?x671, ?x2683), people(?x9428, ?x2683) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2665 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 870 *> proper extension: 0m2wm; 02zq43; 03m8lq; 01j5x6; 0162c8; 025t9b; 02wycg2; 02pk6x; 0175wg; 06fc0b; ... *> query: (?x2683, 041rx) <- profession(?x2683, ?x220), award_nominee(?x11469, ?x2683), people(?x9428, ?x2683) *> conf = 0.21 ranks of expected_values: 2 EVAL 01dw9z people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.433 http://example.org/people/ethnicity/people #11523-0dgrwqr PRED entity: 0dgrwqr PRED relation: titles! PRED expected values: 09blyk => 118 concepts (86 used for prediction) PRED predicted values (max 10 best out of 157): 07ssc (0.74 #4146, 0.10 #828, 0.10 #8181), 07s9rl0 (0.40 #7550, 0.31 #5896, 0.28 #8172), 01jfsb (0.35 #6412, 0.32 #8586, 0.29 #7446), 024qqx (0.32 #1523, 0.29 #1002, 0.28 #1106), 04xvlr (0.30 #411, 0.28 #4140, 0.27 #2378), 01z4y (0.24 #647, 0.23 #5930, 0.21 #8621), 02kdv5l (0.22 #8171, 0.20 #8794, 0.20 #7964), 01hmnh (0.17 #3851, 0.14 #331, 0.13 #7161), 09blyk (0.17 #46, 0.15 #1281, 0.10 #2522), 03k9fj (0.15 #221, 0.12 #1668, 0.12 #5499) >> Best rule #4146 for best value: >> intensional similarity = 7 >> extensional distance = 181 >> proper extension: 01cjhz; 08cx5g; 03j63k; 0jq2r; 06f0k; >> query: (?x7494, 07ssc) <- titles(?x571, ?x7494), titles(?x571, ?x7366), titles(?x571, ?x6536), genre(?x7366, ?x271), film(?x1461, ?x7366), film(?x10216, ?x7366), ?x6536 = 09gmmt6 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 02pxmgz; *> query: (?x7494, 09blyk) <- edited_by(?x7494, ?x7984), film(?x1914, ?x7494), film_crew_role(?x7494, ?x1171), genre(?x7494, ?x571), ?x1171 = 09vw2b7, ?x571 = 03npn *> conf = 0.17 ranks of expected_values: 9 EVAL 0dgrwqr titles! 09blyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 118.000 86.000 0.738 http://example.org/media_common/netflix_genre/titles #11522-0bmfnjs PRED entity: 0bmfnjs PRED relation: country PRED expected values: 07ssc => 122 concepts (113 used for prediction) PRED predicted values (max 10 best out of 134): 03rk0 (0.42 #1512, 0.13 #2504, 0.09 #6572), 07ssc (0.41 #2332, 0.35 #898, 0.33 #16), 0345h (0.36 #2441, 0.17 #6508, 0.15 #1289), 04xvlr (0.24 #2378, 0.16 #61, 0.12 #2377), 0f8l9c (0.21 #19, 0.18 #6443, 0.17 #6508), 03rjj (0.18 #6443, 0.17 #6508, 0.13 #2504), 059j2 (0.18 #6443, 0.17 #6508, 0.13 #2504), 06qd3 (0.18 #6443, 0.17 #6508, 0.09 #6572), 0k6nt (0.18 #6443, 0.17 #6508, 0.09 #6572), 03gj2 (0.18 #6443, 0.17 #6508, 0.09 #6572) >> Best rule #1512 for best value: >> intensional similarity = 8 >> extensional distance = 180 >> proper extension: 0c3ybss; 03g90h; 0fq27fp; 0jjy0; 07g_0c; 0cz8mkh; 03twd6; 06v9_x; 07f_7h; 047svrl; ... >> query: (?x8682, ?x2146) <- film_release_region(?x8682, ?x2146), film_release_region(?x8682, ?x1264), film_release_region(?x8682, ?x172), ?x172 = 0154j, ?x1264 = 0345h, film_crew_role(?x8682, ?x468), nationality(?x3873, ?x2146), ?x3873 = 0jrqq >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #2332 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 259 *> proper extension: 02qrv7; 01kf3_9; 03ffcz; 0g5ptf; *> query: (?x8682, 07ssc) <- film_release_distribution_medium(?x8682, ?x81), titles(?x162, ?x8682), language(?x8682, ?x254), titles(?x162, ?x6653), titles(?x162, ?x6445), award_winner(?x6653, ?x406), ?x6445 = 05v38p *> conf = 0.41 ranks of expected_values: 2 EVAL 0bmfnjs country 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 113.000 0.422 http://example.org/film/film/country #11521-01q_ph PRED entity: 01q_ph PRED relation: profession PRED expected values: 01d_h8 0np9r => 108 concepts (107 used for prediction) PRED predicted values (max 10 best out of 80): 09jwl (0.64 #7567, 0.63 #7271, 0.58 #1646), 0nbcg (0.50 #1659, 0.49 #7580, 0.47 #7284), 01d_h8 (0.49 #7999, 0.47 #2226, 0.39 #4002), 0dz3r (0.47 #1630, 0.45 #1778, 0.40 #7551), 016z4k (0.47 #1632, 0.42 #1780, 0.42 #7553), 0cbd2 (0.45 #5927, 0.45 #2967, 0.45 #6371), 02jknp (0.42 #8001, 0.35 #7105, 0.27 #2228), 0n1h (0.36 #1640, 0.24 #900, 0.19 #7561), 03gjzk (0.36 #8007, 0.35 #7105, 0.35 #2234), 018gz8 (0.35 #7105, 0.32 #2236, 0.19 #2976) >> Best rule #7567 for best value: >> intensional similarity = 2 >> extensional distance = 640 >> proper extension: 03c7ln; 032t2z; 0p3sf; 01sxd1; 02l_7y; 05n19y; 0lsw9; 01w9mnm; 02pt27; 023322; ... >> query: (?x400, 09jwl) <- profession(?x400, ?x987), artist(?x9224, ?x400) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #7999 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 955 *> proper extension: 01pr_j6; 08n9ng; 04qr6d; 02p7xc; 0113sg; 0jnb0; 0894_x; 01qnfc; 06101p; *> query: (?x400, 01d_h8) <- profession(?x400, ?x987), ?x987 = 0dxtg *> conf = 0.49 ranks of expected_values: 3, 13 EVAL 01q_ph profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 108.000 107.000 0.636 http://example.org/people/person/profession EVAL 01q_ph profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 107.000 0.636 http://example.org/people/person/profession #11520-01_x6d PRED entity: 01_x6d PRED relation: producer_type PRED expected values: 0ckd1 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.77 #3, 0.77 #11, 0.77 #5) >> Best rule #3 for best value: >> intensional similarity = 2 >> extensional distance = 29 >> proper extension: 03p01x; >> query: (?x4466, 0ckd1) <- program_creator(?x9787, ?x4466), written_by(?x2349, ?x4466) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_x6d producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.774 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #11519-06mx8 PRED entity: 06mx8 PRED relation: contains PRED expected values: 02w9s => 111 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2028): 05b4w (0.55 #41220, 0.53 #52999, 0.38 #26496), 0d0vqn (0.55 #41220, 0.53 #52999, 0.38 #26496), 06bnz (0.55 #41220, 0.53 #52999, 0.38 #26496), 059g4 (0.55 #53001, 0.55 #53000, 0.55 #20605), 07c5l (0.55 #53001, 0.55 #53000, 0.55 #20605), 06mx8 (0.55 #53001, 0.55 #53000, 0.55 #20605), 02qkt (0.55 #53001, 0.55 #53000, 0.52 #61838), 02j9z (0.55 #53001, 0.55 #53000, 0.52 #61838), 01p8s (0.53 #52999, 0.50 #7344, 0.46 #58889), 0345_ (0.53 #52999, 0.50 #6520, 0.46 #58889) >> Best rule #41220 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02vzc; >> query: (?x6820, ?x304) <- region(?x1498, ?x6820), contains(?x6820, ?x1892), taxonomy(?x6820, ?x939), ?x939 = 04n6k, adjoins(?x1892, ?x304) >> conf = 0.55 => this is the best rule for 3 predicted values *> Best rule #47109 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 0157g9; *> query: (?x6820, ?x87) <- contains(?x6820, ?x12508), country(?x12508, ?x985), form_of_government(?x12508, ?x1926), taxonomy(?x6820, ?x939), form_of_government(?x87, ?x1926), ?x939 = 04n6k *> conf = 0.11 ranks of expected_values: 1594 EVAL 06mx8 contains 02w9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 111.000 27.000 0.551 http://example.org/location/location/contains #11518-01kx_81 PRED entity: 01kx_81 PRED relation: role PRED expected values: 03m5k => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 122): 05r5c (0.58 #2158, 0.56 #831, 0.44 #934), 02sgy (0.55 #521, 0.44 #932, 0.44 #926), 042v_gx (0.45 #524, 0.44 #832, 0.44 #926), 018vs (0.44 #926, 0.36 #529, 0.34 #4812), 0l14qv (0.44 #926, 0.34 #4812, 0.33 #4916), 0l14md (0.44 #926, 0.34 #4812, 0.33 #4916), 06w7v (0.44 #926, 0.34 #4812, 0.33 #4916), 03bx0bm (0.44 #926, 0.34 #4812, 0.33 #4916), 01vdm0 (0.42 #2183, 0.33 #856, 0.30 #4742), 026t6 (0.34 #2153, 0.29 #208, 0.25 #1439) >> Best rule #2158 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 016lj_; >> query: (?x1291, 05r5c) <- role(?x1291, ?x3991), ?x3991 = 05842k, artist(?x7793, ?x1291) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #843 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 01vs4ff; 04mx7s; 01wmjkb; 01wqflx; 01j590z; 01wxdn3; 01p95y0; *> query: (?x1291, 03m5k) <- role(?x1291, ?x2798), role(?x1291, ?x228), ?x2798 = 03qjg *> conf = 0.06 ranks of expected_values: 54 EVAL 01kx_81 role 03m5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 145.000 145.000 0.581 http://example.org/music/artist/track_contributions./music/track_contribution/role #11517-06dfg PRED entity: 06dfg PRED relation: form_of_government PRED expected values: 06cx9 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 3): 06cx9 (0.72 #16, 0.70 #4, 0.70 #1), 01q20 (0.68 #47, 0.49 #125, 0.36 #62), 026wp (0.10 #6, 0.10 #3, 0.09 #30) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0h3y; >> query: (?x7035, 06cx9) <- organization(?x7035, ?x4753), adjustment_currency(?x7035, ?x170), form_of_government(?x7035, ?x1926), ?x4753 = 0gkjy >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06dfg form_of_government 06cx9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.724 http://example.org/location/country/form_of_government #11516-02bg55 PRED entity: 02bg55 PRED relation: currency PRED expected values: 09nqf => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.85 #113, 0.84 #274, 0.84 #162), 01nv4h (0.25 #680, 0.07 #72, 0.04 #86), 02l6h (0.25 #680, 0.05 #39, 0.04 #193), 0kz1h (0.02 #40, 0.01 #180, 0.01 #145), 088n7 (0.02 #56, 0.02 #84, 0.01 #238), 02gsvk (0.01 #293, 0.01 #230, 0.01 #153) >> Best rule #113 for best value: >> intensional similarity = 6 >> extensional distance = 82 >> proper extension: 05c5z8j; >> query: (?x6520, 09nqf) <- titles(?x812, ?x6520), category(?x6520, ?x134), film(?x4438, ?x6520), nationality(?x4438, ?x94), executive_produced_by(?x6520, ?x12790), participant(?x495, ?x4438) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bg55 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.845 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11515-0bgrsl PRED entity: 0bgrsl PRED relation: location PRED expected values: 05tbn => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 60): 0dclg (0.47 #34597, 0.43 #22529, 0.01 #3334), 01531 (0.25 #158, 0.04 #962, 0.03 #2570), 02_286 (0.14 #2449, 0.12 #37, 0.12 #21760), 030qb3t (0.12 #83, 0.10 #15370, 0.09 #11346), 0d6lp (0.12 #168, 0.08 #972, 0.02 #1776), 0cc56 (0.12 #57, 0.03 #21780, 0.03 #9711), 027l4q (0.12 #498, 0.02 #2910, 0.02 #3715), 05tbn (0.12 #188, 0.01 #4209), 0_24q (0.12 #466), 0281rp (0.12 #413) >> Best rule #34597 for best value: >> intensional similarity = 2 >> extensional distance = 2312 >> proper extension: 05fh2; >> query: (?x2389, ?x2254) <- place_of_birth(?x2389, ?x2254), location(?x120, ?x2254) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #188 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 6 *> proper extension: 02p8v8; *> query: (?x2389, 05tbn) <- award_nominee(?x5460, ?x2389), ?x5460 = 046m59 *> conf = 0.12 ranks of expected_values: 8 EVAL 0bgrsl location 05tbn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 60.000 60.000 0.468 http://example.org/people/person/places_lived./people/place_lived/location #11514-01f7jt PRED entity: 01f7jt PRED relation: genre PRED expected values: 06n90 => 109 concepts (72 used for prediction) PRED predicted values (max 10 best out of 96): 06n90 (0.94 #1784, 0.57 #13, 0.45 #131), 01hmnh (0.89 #1671, 0.81 #962, 0.52 #3091), 07s9rl0 (0.68 #2599, 0.66 #4376, 0.62 #4850), 02kdv5l (0.60 #1774, 0.59 #357, 0.55 #1065), 01jfsb (0.54 #5098, 0.50 #1074, 0.46 #1783), 02l7c8 (0.41 #8413, 0.27 #6286, 0.26 #4865), 03bxz7 (0.33 #3128, 0.10 #2062, 0.09 #7506), 0lsxr (0.30 #6161, 0.27 #127, 0.26 #6751), 060__y (0.29 #17, 0.20 #7806, 0.18 #3445), 04xvlr (0.20 #4377, 0.19 #2600, 0.18 #2246) >> Best rule #1784 for best value: >> intensional similarity = 4 >> extensional distance = 190 >> proper extension: 076xkdz; >> query: (?x10943, 06n90) <- genre(?x10943, ?x6647), film(?x1104, ?x10943), disciplines_or_subjects(?x12769, ?x6647), ?x12769 = 05x2s >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f7jt genre 06n90 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 72.000 0.943 http://example.org/film/film/genre #11513-0gy0n PRED entity: 0gy0n PRED relation: film_crew_role PRED expected values: 01pvkk => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 27): 01vx2h (0.35 #111, 0.32 #316, 0.31 #1379), 01pvkk (0.29 #112, 0.29 #1175, 0.28 #248), 02ynfr (0.20 #116, 0.20 #48, 0.19 #252), 02rh1dz (0.17 #315, 0.14 #246, 0.11 #1035), 0d2b38 (0.12 #262, 0.12 #126, 0.11 #331), 0215hd (0.12 #1387, 0.12 #737, 0.12 #1182), 015h31 (0.12 #314, 0.11 #109, 0.08 #1034), 089fss (0.11 #73, 0.09 #243, 0.07 #39), 089g0h (0.11 #1388, 0.10 #1183, 0.10 #738), 01xy5l_ (0.10 #1177, 0.09 #1382, 0.09 #1893) >> Best rule #111 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 09rfh9; >> query: (?x11534, 01vx2h) <- country(?x11534, ?x94), nominated_for(?x3019, ?x11534), ?x3019 = 057xs89 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #112 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 09rfh9; *> query: (?x11534, 01pvkk) <- country(?x11534, ?x94), nominated_for(?x3019, ?x11534), ?x3019 = 057xs89 *> conf = 0.29 ranks of expected_values: 2 EVAL 0gy0n film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 79.000 0.354 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11512-01rgdw PRED entity: 01rgdw PRED relation: major_field_of_study PRED expected values: 0_jm => 141 concepts (133 used for prediction) PRED predicted values (max 10 best out of 117): 01mkq (0.45 #1016, 0.43 #391, 0.36 #641), 02j62 (0.45 #5536, 0.39 #1032, 0.38 #282), 02lp1 (0.44 #1012, 0.41 #387, 0.41 #262), 04rjg (0.43 #271, 0.36 #1021, 0.35 #5776), 062z7 (0.36 #1029, 0.33 #5533, 0.32 #279), 02_7t (0.33 #942, 0.32 #1317, 0.24 #3317), 01tbp (0.32 #312, 0.30 #437, 0.29 #687), 01lj9 (0.32 #292, 0.30 #417, 0.28 #1042), 03g3w (0.32 #278, 0.25 #5783, 0.25 #5532), 05qjt (0.30 #258, 0.29 #5763, 0.26 #1008) >> Best rule #1016 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 02zd460; 08qnnv; >> query: (?x5158, 01mkq) <- currency(?x5158, ?x170), fraternities_and_sororities(?x5158, ?x3697), institution(?x1771, ?x5158), category(?x5158, ?x134) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2935 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 162 *> proper extension: 01j_06; 027xx3; 0pspl; *> query: (?x5158, 0_jm) <- school(?x2820, ?x5158), contains(?x94, ?x5158), colors(?x5158, ?x332) *> conf = 0.29 ranks of expected_values: 11 EVAL 01rgdw major_field_of_study 0_jm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 141.000 133.000 0.449 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11511-012x2b PRED entity: 012x2b PRED relation: participant PRED expected values: 0bq2g => 90 concepts (54 used for prediction) PRED predicted values (max 10 best out of 180): 07r1h (0.20 #414, 0.04 #8767, 0.04 #18401), 01pk8v (0.20 #371, 0.02 #18358, 0.01 #19001), 04fzk (0.07 #2854, 0.02 #5424, 0.02 #16342), 033wx9 (0.06 #1470, 0.06 #2114, 0.01 #4042), 02jyhv (0.06 #1795, 0.03 #3082, 0.02 #5652), 0151w_ (0.06 #1349, 0.02 #18050, 0.02 #8416), 09wj5 (0.06 #1327, 0.01 #8394, 0.01 #14175), 03h_9lg (0.06 #1338, 0.01 #14186, 0.01 #18039), 084m3 (0.06 #1766), 01jfrg (0.06 #1695) >> Best rule #414 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02_j7t; 02l4pj; 02_j8x; >> query: (?x9601, 07r1h) <- film(?x9601, ?x814), film(?x523, ?x814), student(?x9823, ?x9601), ?x9823 = 0gk7z >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 012x2b participant 0bq2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 54.000 0.200 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #11510-045w_4 PRED entity: 045w_4 PRED relation: profession PRED expected values: 0dxtg => 95 concepts (90 used for prediction) PRED predicted values (max 10 best out of 43): 0dxtg (0.90 #163, 0.88 #461, 0.86 #312), 03gjzk (0.68 #909, 0.67 #1356, 0.67 #760), 01d_h8 (0.30 #6265, 0.30 #8351, 0.29 #4476), 02jknp (0.30 #5812, 0.26 #9240, 0.22 #6267), 0cbd2 (0.22 #1199, 0.21 #1348, 0.21 #901), 018gz8 (0.20 #1209, 0.18 #1805, 0.17 #1358), 0np9r (0.20 #2107, 0.20 #4044, 0.20 #1809), 02krf9 (0.18 #623, 0.18 #176, 0.17 #1219), 09jwl (0.17 #5085, 0.17 #5681, 0.17 #5980), 0nbcg (0.12 #5098, 0.12 #5694, 0.12 #5993) >> Best rule #163 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 013cr; 0jt90f5; 03m_k0; 02dlfh; 0pqzh; 0b1s_q; >> query: (?x4636, 0dxtg) <- tv_program(?x4636, ?x4011), award_winner(?x2720, ?x4636), profession(?x4636, ?x1032) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045w_4 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 90.000 0.900 http://example.org/people/person/profession #11509-099ty PRED entity: 099ty PRED relation: locations! PRED expected values: 0b_75k => 165 concepts (137 used for prediction) PRED predicted values (max 10 best out of 111): 0b_6mr (0.40 #86, 0.25 #1086, 0.25 #461), 0bzrsh (0.40 #77, 0.22 #2327, 0.22 #2202), 0b_6s7 (0.40 #64, 0.18 #1189, 0.18 #8277), 0b_6rk (0.31 #421, 0.24 #1171, 0.22 #796), 0b_6zk (0.31 #406, 0.24 #1156, 0.22 #781), 0b_75k (0.25 #424, 0.20 #2299, 0.20 #2174), 0b_6lb (0.22 #2325, 0.22 #2200, 0.20 #75), 0b_6q5 (0.21 #1218, 0.21 #1593, 0.20 #2343), 0b_6xf (0.20 #2354, 0.20 #2229, 0.20 #229), 0b_6pv (0.20 #78, 0.18 #1578, 0.18 #1203) >> Best rule #86 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 029cr; 071cn; >> query: (?x2087, 0b_6mr) <- administrative_division(?x2087, ?x2768), locations(?x4803, ?x2087), adjoins(?x2768, ?x726), ?x4803 = 0b_6jz >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #424 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 02cl1; 07bcn; *> query: (?x2087, 0b_75k) <- administrative_division(?x2087, ?x2768), locations(?x4803, ?x2087), adjoins(?x2768, ?x726), district_represented(?x605, ?x2768) *> conf = 0.25 ranks of expected_values: 6 EVAL 099ty locations! 0b_75k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 165.000 137.000 0.400 http://example.org/time/event/locations #11508-0381pn PRED entity: 0381pn PRED relation: award_winner PRED expected values: 01w92 => 134 concepts (32 used for prediction) PRED predicted values (max 10 best out of 187): 0146mv (0.33 #1567, 0.29 #4799, 0.18 #6416), 05mvd62 (0.29 #2781, 0.18 #6014, 0.17 #9246), 0hpt3 (0.29 #1930, 0.18 #5163, 0.17 #8395), 05xbx (0.29 #4103, 0.17 #871, 0.14 #2487), 03lpbx (0.18 #6454, 0.17 #9686, 0.14 #3221), 0f721s (0.18 #6676, 0.15 #9909, 0.12 #14760), 0gsg7 (0.18 #6737, 0.15 #9970, 0.12 #11586), 09d5h (0.18 #6784, 0.15 #10017, 0.12 #11633), 05gnf (0.18 #7571, 0.15 #10804, 0.12 #12420), 01gb54 (0.17 #788, 0.14 #4020, 0.14 #2404) >> Best rule #1567 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 05qd_; 04rqd; >> query: (?x14079, 0146mv) <- citytown(?x14079, ?x739), award_winner(?x3486, ?x14079), production_companies(?x7700, ?x14079), ?x3486 = 0m7yy, film_release_region(?x7700, ?x87) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #574 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 05qd_; 04rqd; *> query: (?x14079, 01w92) <- citytown(?x14079, ?x739), award_winner(?x3486, ?x14079), production_companies(?x7700, ?x14079), ?x3486 = 0m7yy, film_release_region(?x7700, ?x87) *> conf = 0.17 ranks of expected_values: 25 EVAL 0381pn award_winner 01w92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 134.000 32.000 0.333 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #11507-05cgv PRED entity: 05cgv PRED relation: film_release_region! PRED expected values: 02vxq9m => 138 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1862): 017gm7 (0.71 #8127, 0.65 #9454, 0.60 #13436), 017jd9 (0.71 #8561, 0.64 #20507, 0.61 #29796), 08hmch (0.71 #8084, 0.63 #43916, 0.62 #20030), 04f52jw (0.71 #8298, 0.62 #20244, 0.62 #9625), 03q0r1 (0.71 #8456, 0.55 #9783, 0.53 #13765), 0bpm4yw (0.68 #8514, 0.65 #9841, 0.64 #20460), 047vnkj (0.68 #8668, 0.62 #20614, 0.62 #9995), 0661ql3 (0.68 #8260, 0.57 #9587, 0.55 #20206), 05p1tzf (0.68 #8024, 0.57 #9351, 0.51 #13333), 0by1wkq (0.68 #8200, 0.55 #9527, 0.54 #20146) >> Best rule #8127 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 01k6y1; >> query: (?x1241, 017gm7) <- nationality(?x1935, ?x1241), place_of_birth(?x1935, ?x11783), location(?x1222, ?x1241) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #19928 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 077qn; 05r7t; *> query: (?x1241, 02vxq9m) <- olympics(?x1241, ?x778), official_language(?x1241, ?x254), nationality(?x1935, ?x1241) *> conf = 0.66 ranks of expected_values: 11 EVAL 05cgv film_release_region! 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 138.000 70.000 0.706 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11506-0k__z PRED entity: 0k__z PRED relation: institution! PRED expected values: 02h4rq6 => 161 concepts (111 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.84 #774, 0.84 #84, 0.83 #248), 02_xgp2 (0.60 #358, 0.60 #132, 0.57 #842), 07s6fsf (0.56 #82, 0.55 #184, 0.52 #123), 04zx3q1 (0.39 #62, 0.38 #350, 0.34 #834), 013zdg (0.38 #128, 0.33 #189, 0.30 #354), 022h5x (0.32 #1496, 0.29 #1824, 0.28 #2268), 028dcg (0.32 #1496, 0.29 #1824, 0.28 #2268), 0bjrnt (0.32 #1496, 0.29 #1824, 0.28 #2268), 02m4yg (0.32 #1496, 0.29 #1824, 0.28 #2268), 071tyz (0.32 #1496, 0.29 #1824, 0.28 #2268) >> Best rule #774 for best value: >> intensional similarity = 4 >> extensional distance = 131 >> proper extension: 01wdl3; 01j_06; 07wlf; 0cchk3; 01swxv; 0hd7j; 09s5q8; 05x_5; 02bhj4; 02mp0g; ... >> query: (?x8363, 02h4rq6) <- student(?x8363, ?x2046), school(?x700, ?x8363), colors(?x8363, ?x663), major_field_of_study(?x8363, ?x947) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k__z institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 111.000 0.842 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #11505-02hy5d PRED entity: 02hy5d PRED relation: legislative_sessions PRED expected values: 03z5xd 02glc4 060ny2 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 28): 02glc4 (0.50 #98, 0.50 #70, 0.50 #14), 03z5xd (0.50 #60, 0.33 #88, 0.33 #4), 060ny2 (0.50 #73, 0.33 #101, 0.22 #269), 06r713 (0.38 #71, 0.25 #99, 0.17 #15), 01gtc0 (0.18 #151, 0.12 #291, 0.10 #235), 01gsvp (0.12 #156, 0.10 #240, 0.09 #268), 01h7xx (0.12 #161, 0.10 #245, 0.08 #301), 043djx (0.12 #142, 0.10 #226, 0.08 #282), 01gtcc (0.12 #147, 0.10 #231, 0.08 #287), 01gtbb (0.12 #145, 0.10 #229, 0.08 #285) >> Best rule #98 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0d06m5; 0d3qd0; 03txms; >> query: (?x9334, 02glc4) <- legislative_sessions(?x9334, ?x6933), ?x6933 = 024tkd, nationality(?x9334, ?x94), profession(?x9334, ?x1032) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 02hy5d legislative_sessions 060ny2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.500 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 02hy5d legislative_sessions 02glc4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.500 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 02hy5d legislative_sessions 03z5xd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.500 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #11504-04k9y6 PRED entity: 04k9y6 PRED relation: film! PRED expected values: 0bl2g 01r93l => 117 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1267): 03mfqm (0.50 #126654, 0.47 #74744, 0.47 #141189), 01x6v6 (0.46 #47755, 0.45 #76820, 0.45 #72668), 0f0kz (0.22 #23352, 0.13 #21277, 0.13 #2591), 01vy_v8 (0.20 #2808, 0.18 #4885, 0.16 #17342), 09l3p (0.20 #2824, 0.18 #4901, 0.16 #9054), 0c0k1 (0.20 #18116, 0.16 #9812, 0.16 #7736), 016k6x (0.18 #5043, 0.08 #27880, 0.07 #29957), 0f5xn (0.16 #9274, 0.16 #7198, 0.15 #13426), 02yxwd (0.15 #11125, 0.14 #742, 0.12 #17353), 05dbf (0.14 #366, 0.13 #2443, 0.12 #4520) >> Best rule #126654 for best value: >> intensional similarity = 5 >> extensional distance = 462 >> proper extension: 01br2w; >> query: (?x6018, ?x6327) <- film_crew_role(?x6018, ?x137), nominated_for(?x6327, ?x6018), nominated_for(?x2534, ?x6018), currency(?x6018, ?x170), participant(?x709, ?x2534) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #746 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 048htn; *> query: (?x6018, 01r93l) <- edited_by(?x6018, ?x4215), film_distribution_medium(?x6018, ?x81), film_format(?x6018, ?x909), nominated_for(?x2534, ?x6018) *> conf = 0.14 ranks of expected_values: 12, 241 EVAL 04k9y6 film! 01r93l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 117.000 83.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 04k9y6 film! 0bl2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 117.000 83.000 0.500 http://example.org/film/actor/film./film/performance/film #11503-01vsy7t PRED entity: 01vsy7t PRED relation: artist! PRED expected values: 02p3cr5 03qjwc => 138 concepts (118 used for prediction) PRED predicted values (max 10 best out of 108): 01xyqk (0.25 #745, 0.05 #5842, 0.04 #6781), 0mcf4 (0.25 #724, 0.04 #6760, 0.03 #7699), 03rhqg (0.20 #284, 0.19 #5649, 0.17 #4441), 02p11jq (0.20 #281, 0.14 #549, 0.12 #817), 015mlw (0.20 #349, 0.14 #617, 0.12 #885), 0fb0v (0.16 #1615, 0.10 #2688, 0.10 #4164), 033hn8 (0.15 #4439, 0.14 #4573, 0.13 #1622), 0g768 (0.15 #4460, 0.13 #4594, 0.12 #5668), 01trtc (0.14 #603, 0.12 #871, 0.11 #1139), 04t53l (0.14 #544, 0.12 #812, 0.11 #1080) >> Best rule #745 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 032nwy; 01l4zqz; 01kvqc; 01ky2h; 01qdjm; 01l7cxq; >> query: (?x4620, 01xyqk) <- role(?x4620, ?x1332), profession(?x4620, ?x131), ?x1332 = 03qlv7 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4451 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 155 *> proper extension: 01t_xp_; 0150jk; 067mj; 04r1t; 0dtd6; 01czx; 0167_s; 05563d; 0mgcr; 018gm9; ... *> query: (?x4620, 02p3cr5) <- artists(?x1000, ?x4620), ?x1000 = 0xhtw, artist(?x3265, ?x4620) *> conf = 0.04 ranks of expected_values: 32 EVAL 01vsy7t artist! 03qjwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 118.000 0.250 http://example.org/music/record_label/artist EVAL 01vsy7t artist! 02p3cr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 138.000 118.000 0.250 http://example.org/music/record_label/artist #11502-02j04_ PRED entity: 02j04_ PRED relation: institution! PRED expected values: 019v9k => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 21): 019v9k (0.65 #125, 0.59 #494, 0.57 #378), 02_xgp2 (0.55 #175, 0.49 #129, 0.42 #267), 0bkj86 (0.54 #124, 0.52 #170, 0.39 #262), 03bwzr4 (0.49 #131, 0.42 #177, 0.41 #500), 016t_3 (0.49 #120, 0.42 #166, 0.38 #489), 04zx3q1 (0.43 #119, 0.31 #165, 0.28 #1116), 07s6fsf (0.38 #118, 0.33 #1, 0.31 #164), 03mkk4 (0.33 #11, 0.29 #35, 0.28 #1116), 028dcg (0.33 #19, 0.29 #43, 0.28 #1116), 022h5x (0.33 #20, 0.29 #44, 0.28 #1116) >> Best rule #125 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 02301; 04b_46; 017v3q; 03bnd9; >> query: (?x7271, 019v9k) <- student(?x7271, ?x4264), award_winner(?x163, ?x4264), currency(?x7271, ?x170), influenced_by(?x4264, ?x4265) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02j04_ institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.649 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #11501-01p8r8 PRED entity: 01p8r8 PRED relation: film PRED expected values: 013q0p => 74 concepts (33 used for prediction) PRED predicted values (max 10 best out of 410): 03q0r1 (0.09 #2425, 0.03 #636, 0.03 #7795), 0407yj_ (0.09 #2272, 0.03 #7642, 0.03 #14801), 099pks (0.08 #23266, 0.07 #30425, 0.06 #14318), 07tw_b (0.07 #680, 0.06 #2469, 0.01 #7839), 0prrm (0.07 #860, 0.04 #4438, 0.03 #2649), 0b3n61 (0.07 #1359, 0.03 #3148, 0.02 #15677), 0g9z_32 (0.07 #1277), 04hwbq (0.06 #1981, 0.04 #7351, 0.02 #14510), 0m63c (0.06 #3125, 0.03 #1336, 0.01 #15654), 02ctc6 (0.06 #2311, 0.03 #522, 0.01 #7681) >> Best rule #2425 for best value: >> intensional similarity = 6 >> extensional distance = 30 >> proper extension: 03mv0b; >> query: (?x10124, 03q0r1) <- profession(?x10124, ?x1383), profession(?x10124, ?x1032), profession(?x10124, ?x524), ?x1032 = 02hrh1q, ?x1383 = 0np9r, ?x524 = 02jknp >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #13334 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 332 *> proper extension: 02wrhj; 01hkhq; 015wfg; 01ry0f; *> query: (?x10124, 013q0p) <- actor(?x5583, ?x10124), location(?x10124, ?x3300), time_zones(?x3300, ?x1638), type_of_union(?x10124, ?x566) *> conf = 0.01 ranks of expected_values: 315 EVAL 01p8r8 film 013q0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 74.000 33.000 0.094 http://example.org/film/actor/film./film/performance/film #11500-09rvwmy PRED entity: 09rvwmy PRED relation: executive_produced_by PRED expected values: 0dqmt0 0g_rs_ => 79 concepts (57 used for prediction) PRED predicted values (max 10 best out of 76): 0glyyw (0.17 #189, 0.07 #1201, 0.05 #1962), 02pq9yv (0.12 #338, 0.03 #1604, 0.01 #3885), 02z6l5f (0.12 #1385, 0.04 #3665, 0.03 #3413), 02q42j_ (0.11 #643, 0.04 #1404, 0.01 #4443), 0b13g7 (0.11 #592, 0.04 #1353, 0.01 #5403), 0g_rs_ (0.11 #758, 0.04 #1519), 0dqmt0 (0.11 #669, 0.04 #1430), 0kjgl (0.10 #4812, 0.04 #2785, 0.02 #7340), 06pj8 (0.08 #2334, 0.08 #2081, 0.07 #1828), 02z2xdf (0.08 #1425, 0.04 #3705, 0.04 #3453) >> Best rule #189 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03bxp5; >> query: (?x10918, 0glyyw) <- country(?x10918, ?x94), film(?x3210, ?x10918), ?x3210 = 01vwllw, featured_film_locations(?x10918, ?x4794) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #758 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 047p798; *> query: (?x10918, 0g_rs_) <- film_crew_role(?x10918, ?x137), film_festivals(?x10918, ?x11147), ?x11147 = 04_m9gk, produced_by(?x10918, ?x7903) *> conf = 0.11 ranks of expected_values: 6, 7 EVAL 09rvwmy executive_produced_by 0g_rs_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 57.000 0.167 http://example.org/film/film/executive_produced_by EVAL 09rvwmy executive_produced_by 0dqmt0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 57.000 0.167 http://example.org/film/film/executive_produced_by #11499-01k_mc PRED entity: 01k_mc PRED relation: category PRED expected values: 08mbj5d => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #3, 0.86 #21, 0.84 #17) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 0152cw; 0cg9y; 03j0br4; 0161c2; 012gq6; 01817f; 01wf86y; 01x0yrt; 016s0m; 01fkxr; ... >> query: (?x5904, 08mbj5d) <- award(?x5904, ?x1389), gender(?x5904, ?x231), ?x1389 = 01c427 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k_mc category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.912 http://example.org/common/topic/webpage./common/webpage/category #11498-026wlnm PRED entity: 026wlnm PRED relation: teams! PRED expected values: 013hxv => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 92): 0t6hk (0.33 #222, 0.25 #3769, 0.25 #3498), 0vzm (0.33 #1189, 0.25 #2283, 0.20 #4188), 0fpzwf (0.33 #1504, 0.17 #6951, 0.10 #6408), 0fvyg (0.33 #1861, 0.05 #2731, 0.04 #9255), 0fr0t (0.25 #3658, 0.25 #2026, 0.20 #4202), 0d9y6 (0.25 #2318, 0.14 #5859, 0.08 #7215), 0f__1 (0.25 #2537, 0.10 #5179, 0.05 #2731), 0ygbf (0.25 #2054, 0.05 #7770, 0.05 #5999), 0snty (0.20 #4329, 0.05 #2731, 0.04 #9257), 0f2tj (0.17 #5060, 0.14 #5607, 0.14 #5333) >> Best rule #222 for best value: >> intensional similarity = 36 >> extensional distance = 1 >> proper extension: 091tgz; >> query: (?x9909, 0t6hk) <- team(?x6848, ?x9909), ?x6848 = 02_ssl, team(?x13209, ?x9909), team(?x13045, ?x9909), team(?x12798, ?x9909), team(?x12451, ?x9909), team(?x10736, ?x9909), team(?x10673, ?x9909), team(?x10594, ?x9909), team(?x10441, ?x9909), team(?x9956, ?x9909), team(?x4368, ?x9909), team(?x2302, ?x9909), ?x10441 = 0b_71r, ?x13209 = 0b_734, ?x13045 = 0bqthy, ?x10736 = 0f9rw9, ?x12798 = 0b_770, ?x9956 = 0bzrsh, ?x2302 = 0b_77q, colors(?x9909, ?x663), ?x10594 = 0b_756, ?x12451 = 0b_6xf, ?x4368 = 0b_6x2, ?x10673 = 0b_6mr, colors(?x2096, ?x663), colors(?x1599, ?x663), colors(?x700, ?x663), colors(?x14319, ?x663), colors(?x10666, ?x663), ?x1599 = 025txtg, draft(?x700, ?x3334), currency(?x14319, ?x170), position(?x2096, ?x60), school(?x580, ?x10666), ?x3334 = 02pq_rp >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026wlnm teams! 013hxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 52.000 0.333 http://example.org/sports/sports_team_location/teams #11497-02g87m PRED entity: 02g87m PRED relation: award_winner! PRED expected values: 06mmb => 123 concepts (69 used for prediction) PRED predicted values (max 10 best out of 529): 026dg51 (0.17 #41741, 0.04 #40259, 0.02 #82007), 03ckxdg (0.17 #41741, 0.03 #40179, 0.02 #81927), 03cl8lb (0.17 #41741, 0.03 #41222, 0.01 #82970), 070w7s (0.17 #41741, 0.03 #40595, 0.01 #82343), 026dcvf (0.17 #41741, 0.03 #40184, 0.01 #86750), 026n998 (0.17 #41741, 0.02 #40594, 0.01 #82342), 02_2v2 (0.17 #41741, 0.02 #40471, 0.01 #82219), 0646qh (0.17 #41741, 0.02 #41256, 0.01 #83004), 02760sl (0.17 #41741, 0.02 #41622), 063lqs (0.17 #41741, 0.02 #40762) >> Best rule #41741 for best value: >> intensional similarity = 2 >> extensional distance = 539 >> proper extension: 01vq3nl; >> query: (?x1460, ?x415) <- nominated_for(?x1460, ?x4721), tv_program(?x415, ?x4721) >> conf = 0.17 => this is the best rule for 11 predicted values No rule for expected values ranks of expected_values: EVAL 02g87m award_winner! 06mmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 69.000 0.165 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #11496-02pzc4 PRED entity: 02pzc4 PRED relation: nationality PRED expected values: 09c7w0 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.88 #7423, 0.88 #7323, 0.83 #902), 03kxzm (0.39 #9834, 0.39 #9531, 0.38 #7424), 0k3l5 (0.39 #9834, 0.39 #9531, 0.38 #7424), 05k7sb (0.39 #9834, 0.39 #9531, 0.38 #7424), 02jx1 (0.20 #133, 0.19 #1734, 0.17 #2938), 07ssc (0.10 #716, 0.10 #1716, 0.10 #1216), 0d060g (0.06 #307, 0.06 #2310, 0.06 #1008), 03rk0 (0.06 #8671, 0.05 #5361, 0.05 #5160), 06q1r (0.04 #477, 0.02 #778, 0.02 #878), 0d0vqn (0.04 #409) >> Best rule #7423 for best value: >> intensional similarity = 3 >> extensional distance = 1350 >> proper extension: 07m69t; >> query: (?x3375, ?x94) <- place_of_birth(?x3375, ?x14449), contains(?x94, ?x14449), ?x94 = 09c7w0 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pzc4 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.883 http://example.org/people/person/nationality #11495-01ps2h8 PRED entity: 01ps2h8 PRED relation: profession PRED expected values: 0kyk => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 45): 01d_h8 (0.36 #152, 0.36 #298, 0.32 #590), 02jknp (0.29 #154, 0.22 #5265, 0.21 #3220), 0dxtg (0.28 #2788, 0.27 #8339, 0.27 #2934), 0d1pc (0.28 #5988, 0.20 #340, 0.18 #632), 0np9r (0.28 #5988, 0.14 #7030, 0.14 #7469), 0kyk (0.28 #5988, 0.10 #5724, 0.10 #3387), 0q04f (0.28 #5988, 0.01 #5354, 0.01 #827), 03gjzk (0.25 #2789, 0.23 #2935, 0.23 #2497), 018gz8 (0.13 #893, 0.12 #7027, 0.12 #7466), 0cbd2 (0.13 #5702, 0.12 #7602, 0.12 #3365) >> Best rule #152 for best value: >> intensional similarity = 2 >> extensional distance = 141 >> proper extension: 01200d; 034cj9; >> query: (?x5283, 01d_h8) <- award(?x5283, ?x591), ?x591 = 0f4x7 >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #5988 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1524 *> proper extension: 01wz_ml; *> query: (?x5283, ?x1032) <- award_winner(?x5283, ?x1846), profession(?x1846, ?x1032) *> conf = 0.28 ranks of expected_values: 6 EVAL 01ps2h8 profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 76.000 76.000 0.364 http://example.org/people/person/profession #11494-025s7x6 PRED entity: 025s7x6 PRED relation: nutrient! PRED expected values: 0fbw6 => 59 concepts (56 used for prediction) PRED predicted values (max 10 best out of 11): 0fbw6 (0.89 #171, 0.89 #37, 0.89 #30), 06x4c (0.89 #171, 0.89 #37, 0.89 #30), 0dcfv (0.89 #171, 0.89 #37, 0.89 #30), 01sh2 (0.03 #384, 0.02 #173, 0.01 #437), 04k8n (0.03 #384, 0.01 #404), 05wvs (0.03 #384, 0.01 #404), 025rw19 (0.02 #173, 0.01 #437), 025tkqy (0.02 #173, 0.01 #437), 014d7f (0.02 #173, 0.01 #437), 06jry (0.02 #173, 0.01 #437) >> Best rule #171 for best value: >> intensional similarity = 122 >> extensional distance = 13 >> proper extension: 0838f; >> query: (?x7720, ?x4068) <- nutrient(?x10612, ?x7720), nutrient(?x9732, ?x7720), nutrient(?x9489, ?x7720), nutrient(?x9005, ?x7720), nutrient(?x8298, ?x7720), nutrient(?x7719, ?x7720), nutrient(?x7057, ?x7720), nutrient(?x6285, ?x7720), nutrient(?x6191, ?x7720), nutrient(?x6159, ?x7720), nutrient(?x6032, ?x7720), nutrient(?x5373, ?x7720), nutrient(?x5009, ?x7720), nutrient(?x3900, ?x7720), nutrient(?x1959, ?x7720), nutrient(?x1303, ?x7720), nutrient(?x1257, ?x7720), ?x9005 = 04zpv, ?x1257 = 09728, ?x6032 = 01nkt, ?x5009 = 0fjfh, ?x10612 = 0frq6, ?x1303 = 0fj52s, ?x6191 = 014j1m, ?x9489 = 07j87, ?x6159 = 033cnk, nutrient(?x5373, ?x14210), nutrient(?x5373, ?x13944), nutrient(?x5373, ?x13545), nutrient(?x5373, ?x13498), nutrient(?x5373, ?x13126), nutrient(?x5373, ?x12902), nutrient(?x5373, ?x12083), nutrient(?x5373, ?x11758), nutrient(?x5373, ?x11592), nutrient(?x5373, ?x11409), nutrient(?x5373, ?x11270), nutrient(?x5373, ?x10709), nutrient(?x5373, ?x10453), nutrient(?x5373, ?x10098), nutrient(?x5373, ?x9915), nutrient(?x5373, ?x9733), nutrient(?x5373, ?x9619), nutrient(?x5373, ?x9436), nutrient(?x5373, ?x9365), nutrient(?x5373, ?x8487), nutrient(?x5373, ?x8442), nutrient(?x5373, ?x8413), nutrient(?x5373, ?x8243), nutrient(?x5373, ?x7894), nutrient(?x5373, ?x7652), nutrient(?x5373, ?x7431), nutrient(?x5373, ?x7364), nutrient(?x5373, ?x7362), nutrient(?x5373, ?x7219), nutrient(?x5373, ?x6192), nutrient(?x5373, ?x6160), nutrient(?x5373, ?x6033), nutrient(?x5373, ?x6026), nutrient(?x5373, ?x5526), nutrient(?x5373, ?x5451), nutrient(?x5373, ?x5374), nutrient(?x5373, ?x5010), nutrient(?x5373, ?x1960), nutrient(?x5373, ?x1258), ?x7652 = 025s0s0, ?x5451 = 05wvs, ?x5010 = 0h1vz, ?x6285 = 01645p, ?x11270 = 02kc008, ?x6160 = 041r51, ?x11758 = 0q01m, ?x10098 = 0h1_c, ?x10453 = 075pwf, ?x6026 = 025sf8g, ?x8413 = 02kc4sf, ?x1258 = 0h1wg, ?x13126 = 02kc_w5, ?x13944 = 0f4kp, ?x11592 = 025sf0_, ?x8298 = 037ls6, ?x9365 = 04k8n, ?x9619 = 0h1tg, ?x7219 = 0h1vg, ?x9732 = 05z55, ?x9733 = 0h1tz, ?x7894 = 0f4hc, ?x8487 = 014yzm, ?x11409 = 0h1yf, ?x5374 = 025s0zp, ?x12083 = 01n78x, ?x1959 = 0f25w9, ?x9915 = 025tkqy, ?x7364 = 09gvd, ?x10709 = 0h1sz, ?x12902 = 0fzjh, ?x8442 = 02kcv4x, ?x7719 = 0dj75, ?x7057 = 0fbdb, ?x7431 = 09gwd, ?x13498 = 07q0m, nutrient(?x4068, ?x8243), nutrient(?x3264, ?x8243), ?x5526 = 09pbb, ?x9436 = 025sqz8, ?x6033 = 04zjxcz, ?x13545 = 01w_3, ?x1960 = 07hnp, ?x14210 = 0f4k5, ?x7362 = 02kc5rj, ?x6192 = 06jry, nutrient(?x3900, ?x10891), nutrient(?x3900, ?x9855), nutrient(?x3900, ?x9840), nutrient(?x3900, ?x3901), nutrient(?x3900, ?x2018), ?x9855 = 0d9t0, ?x2018 = 01sh2, ?x3901 = 0466p20, ?x3264 = 0dcfv, ?x10891 = 0g5gq, ?x9840 = 02p0tjr >> conf = 0.89 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 025s7x6 nutrient! 0fbw6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 56.000 0.889 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #11493-09pjnd PRED entity: 09pjnd PRED relation: nominated_for PRED expected values: 0p3_y => 105 concepts (68 used for prediction) PRED predicted values (max 10 best out of 424): 0ddjy (0.80 #58245, 0.79 #80895, 0.78 #92222), 06mmr (0.80 #58245, 0.79 #80895, 0.78 #92222), 09q23x (0.28 #9712, 0.24 #8092, 0.24 #3236), 0bq8tmw (0.28 #9712, 0.24 #8092, 0.24 #3236), 01jmyj (0.28 #9712, 0.24 #3236, 0.24 #4854), 034qbx (0.24 #8092, 0.24 #3236, 0.24 #4854), 0473rc (0.24 #8092, 0.24 #3236, 0.24 #4854), 07gp9 (0.12 #39, 0.12 #4893, 0.11 #8131), 0pv3x (0.12 #167, 0.08 #3403, 0.07 #5021), 017jd9 (0.12 #2330, 0.11 #3948, 0.10 #5566) >> Best rule #58245 for best value: >> intensional similarity = 3 >> extensional distance = 1191 >> proper extension: 07s6tbm; 0b478; 02qfhb; 01d1yr; 03ys2f; 01mkn_d; 0d500h; 044zvm; >> query: (?x1643, ?x2366) <- nominated_for(?x1643, ?x1386), award_nominee(?x930, ?x1643), award_winner(?x2366, ?x1643) >> conf = 0.80 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 09pjnd nominated_for 0p3_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 68.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #11492-0dcz8_ PRED entity: 0dcz8_ PRED relation: film! PRED expected values: 01mqh5 => 85 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1017): 0h96g (0.10 #7088, 0.08 #2930, 0.07 #5009), 01q_ph (0.09 #10452, 0.07 #12531, 0.05 #31245), 079vf (0.07 #8324, 0.06 #8, 0.06 #2087), 0p8r1 (0.07 #10981, 0.06 #35933, 0.06 #21377), 041c4 (0.07 #11289, 0.04 #32082, 0.03 #34161), 016ypb (0.06 #6736, 0.06 #2578, 0.05 #12973), 0f13b (0.06 #7715, 0.04 #3557, 0.03 #5636), 0227tr (0.06 #434, 0.04 #12908, 0.02 #42020), 0170s4 (0.06 #398, 0.03 #6635, 0.02 #17031), 0k525 (0.06 #1843, 0.03 #8080, 0.02 #3922) >> Best rule #7088 for best value: >> intensional similarity = 6 >> extensional distance = 61 >> proper extension: 08fbnx; 0k54q; >> query: (?x9715, 0h96g) <- genre(?x9715, ?x1013), genre(?x9715, ?x811), ?x811 = 03k9fj, film_release_region(?x9715, ?x94), film(?x665, ?x9715), ?x1013 = 06n90 >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #10178 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 67 *> proper extension: 070g7; 07ghq; *> query: (?x9715, 01mqh5) <- genre(?x9715, ?x1013), film(?x12856, ?x9715), ?x1013 = 06n90, film(?x1104, ?x9715), film(?x665, ?x9715) *> conf = 0.01 ranks of expected_values: 787 EVAL 0dcz8_ film! 01mqh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 85.000 48.000 0.095 http://example.org/film/actor/film./film/performance/film #11491-01ckcd PRED entity: 01ckcd PRED relation: ceremony PRED expected values: 0jzphpx 01mh_q => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 126): 01mh_q (0.80 #1840, 0.67 #202, 0.60 #580), 0jzphpx (0.67 #1795, 0.60 #535, 0.50 #157), 0gx1673 (0.51 #1870, 0.50 #232, 0.34 #2374), 03nnm4t (0.25 #3529, 0.16 #2710, 0.15 #2836), 073h1t (0.25 #3529, 0.13 #2289, 0.12 #2667), 0bzn6_ (0.25 #3529, 0.13 #2314, 0.11 #2692), 0bz6sb (0.25 #3529, 0.12 #2322, 0.11 #2700), 02yv_b (0.25 #3529, 0.12 #2287, 0.11 #2665), 0drtv8 (0.25 #3529, 0.06 #2072, 0.04 #2576), 09p30_ (0.25 #3529, 0.06 #2089, 0.04 #2593) >> Best rule #1840 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 024fz9; >> query: (?x9828, 01mh_q) <- award(?x9868, ?x9828), award_winner(?x2877, ?x9868), ceremony(?x9828, ?x2054), ?x2054 = 0gpjbt >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01ckcd ceremony 01mh_q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01ckcd ceremony 0jzphpx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony #11490-017r13 PRED entity: 017r13 PRED relation: profession PRED expected values: 02jknp => 97 concepts (95 used for prediction) PRED predicted values (max 10 best out of 62): 0dxtg (0.60 #605, 0.52 #1049, 0.50 #901), 02jknp (0.50 #599, 0.48 #1043, 0.48 #895), 0np9r (0.47 #316, 0.44 #464, 0.14 #8909), 03gjzk (0.44 #606, 0.41 #1050, 0.37 #902), 09jwl (0.26 #9926, 0.18 #6980, 0.16 #13053), 02krf9 (0.26 #9926, 0.16 #618, 0.14 #1062), 0cbd2 (0.18 #1635, 0.16 #4303, 0.16 #3859), 0kyk (0.16 #1658, 0.13 #4326, 0.12 #3882), 018gz8 (0.13 #312, 0.13 #8310, 0.13 #9497), 0d1pc (0.13 #1976, 0.13 #2273, 0.13 #1234) >> Best rule #605 for best value: >> intensional similarity = 2 >> extensional distance = 230 >> proper extension: 03c9pqt; >> query: (?x6279, 0dxtg) <- student(?x1440, ?x6279), produced_by(?x1531, ?x6279) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #599 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 230 *> proper extension: 03c9pqt; *> query: (?x6279, 02jknp) <- student(?x1440, ?x6279), produced_by(?x1531, ?x6279) *> conf = 0.50 ranks of expected_values: 2 EVAL 017r13 profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 95.000 0.599 http://example.org/people/person/profession #11489-0csdzz PRED entity: 0csdzz PRED relation: artists! PRED expected values: 017_qw => 151 concepts (137 used for prediction) PRED predicted values (max 10 best out of 182): 017_qw (0.65 #1011, 0.62 #2271, 0.62 #696), 064t9 (0.32 #11995, 0.30 #13255, 0.25 #13570), 06by7 (0.26 #13264, 0.20 #16100, 0.20 #23351), 0ggq0m (0.22 #958, 0.20 #4108, 0.20 #3478), 016clz (0.19 #635, 0.16 #2210, 0.13 #13246), 0y3_8 (0.19 #680, 0.16 #2255, 0.07 #3200), 06j6l (0.18 #12032, 0.17 #13292, 0.16 #13607), 05lls (0.18 #3481, 0.16 #3796, 0.15 #4111), 0gywn (0.18 #12042, 0.13 #13302, 0.13 #13617), 0glt670 (0.16 #12024, 0.14 #13599, 0.13 #13284) >> Best rule #1011 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 04k15; 02wb6d; 03_f0; 015wc0; 0c73z; >> query: (?x10634, 017_qw) <- music(?x2628, ?x10634), people(?x6734, ?x10634), film_release_region(?x2628, ?x583), ?x583 = 015fr >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0csdzz artists! 017_qw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 137.000 0.652 http://example.org/music/genre/artists #11488-01kgv4 PRED entity: 01kgv4 PRED relation: people! PRED expected values: 06mvq => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 51): 033tf_ (0.30 #1528, 0.22 #7, 0.20 #1299), 041rx (0.27 #156, 0.26 #3273, 0.24 #4034), 0x67 (0.20 #4194, 0.20 #2367, 0.18 #2443), 0xnvg (0.16 #393, 0.16 #1534, 0.13 #1382), 01qhm_ (0.11 #386, 0.07 #1451, 0.07 #994), 02w7gg (0.11 #3271, 0.11 #4339, 0.11 #2), 07bch9 (0.11 #23, 0.10 #327, 0.08 #251), 09vc4s (0.11 #9, 0.09 #161, 0.08 #237), 06v41q (0.11 #29, 0.08 #257, 0.04 #485), 01336l (0.11 #40, 0.08 #268, 0.02 #344) >> Best rule #1528 for best value: >> intensional similarity = 3 >> extensional distance = 206 >> proper extension: 040_9; 0dj5q; 0j5b8; 04pwg; 0c73z; >> query: (?x6730, 033tf_) <- religion(?x6730, ?x1985), people(?x7322, ?x6730), ?x1985 = 0c8wxp >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kgv4 people! 06mvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 123.000 0.298 http://example.org/people/ethnicity/people #11487-018vs PRED entity: 018vs PRED relation: role! PRED expected values: 01n8gr => 74 concepts (38 used for prediction) PRED predicted values (max 10 best out of 891): 05qhnq (0.71 #1879, 0.60 #1391, 0.44 #3348), 01m65sp (0.60 #1295, 0.40 #1049, 0.38 #2274), 01p95y0 (0.56 #3152, 0.40 #3893, 0.40 #1441), 04kjrv (0.50 #2609, 0.40 #1386, 0.33 #3343), 0137hn (0.50 #643, 0.20 #1378, 0.20 #1132), 03xl77 (0.43 #1778, 0.40 #1290, 0.33 #3247), 06p03s (0.43 #1950, 0.40 #1462, 0.28 #6119), 02jg92 (0.43 #1756, 0.36 #4457, 0.33 #43), 014q2g (0.43 #1771, 0.33 #58, 0.27 #4472), 03c7ln (0.42 #4907, 0.40 #1229, 0.38 #2452) >> Best rule #1879 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 0l14md; >> query: (?x716, 05qhnq) <- performance_role(?x212, ?x716), role(?x716, ?x228), role(?x211, ?x716), role(?x8114, ?x716), instrumentalists(?x716, ?x226), role(?x433, ?x716), ?x228 = 0l14qv, ?x8114 = 02mx98 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #74 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 03bx0bm; *> query: (?x716, 01n8gr) <- performance_role(?x212, ?x716), role(?x716, ?x74), role(?x8012, ?x716), role(?x806, ?x716), ?x806 = 03qd_, role(?x716, ?x433), group(?x716, ?x379), gender(?x8012, ?x231) *> conf = 0.33 ranks of expected_values: 129 EVAL 018vs role! 01n8gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 74.000 38.000 0.714 http://example.org/music/group_member/membership./music/group_membership/role #11486-02465 PRED entity: 02465 PRED relation: profession PRED expected values: 02jknp => 134 concepts (82 used for prediction) PRED predicted values (max 10 best out of 93): 0cbd2 (0.71 #870, 0.68 #1591, 0.62 #2168), 03gjzk (0.46 #4912, 0.46 #3327, 0.45 #5200), 02jknp (0.44 #5050, 0.41 #10527, 0.40 #3465), 09jwl (0.42 #11258, 0.38 #9528, 0.38 #10105), 018gz8 (0.38 #15, 0.38 #1456, 0.33 #5058), 0np9r (0.37 #5783, 0.14 #4630, 0.14 #5062), 0n1h (0.35 #4467, 0.32 #9367, 0.31 #1441), 05z96 (0.35 #4467, 0.32 #9367, 0.31 #1441), 01c979 (0.35 #4467, 0.32 #9367, 0.31 #1441), 0196pc (0.30 #2018, 0.25 #8502, 0.08 #10088) >> Best rule #870 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 09jd9; >> query: (?x11214, 0cbd2) <- award(?x11214, ?x8909), place_of_birth(?x11214, ?x9026), ?x8909 = 040_9s0 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5050 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 244 *> proper extension: 01pr_j6; 06j8wx; 03y_46; 01tt43d; 06gb2q; 02bj6k; 01v5h; 07y_r; 01m4kpp; *> query: (?x11214, 02jknp) <- profession(?x11214, ?x1032), profession(?x11214, ?x987), ?x1032 = 02hrh1q, people(?x743, ?x11214), ?x987 = 0dxtg *> conf = 0.44 ranks of expected_values: 3 EVAL 02465 profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 82.000 0.714 http://example.org/people/person/profession #11485-0524b41 PRED entity: 0524b41 PRED relation: actor PRED expected values: 0g8st4 => 83 concepts (70 used for prediction) PRED predicted values (max 10 best out of 961): 02624g (0.50 #25840, 0.39 #12921, 0.38 #9230), 05m9f9 (0.39 #12921, 0.38 #9230, 0.36 #4614), 03mdt (0.39 #12921, 0.38 #9230, 0.36 #4614), 03772 (0.39 #12921, 0.38 #9230, 0.36 #4614), 01x4sb (0.15 #19380, 0.14 #20303, 0.10 #31380), 05y5kf (0.15 #19380, 0.14 #20303, 0.10 #31380), 0bd2n4 (0.15 #19380, 0.14 #20303, 0.10 #31380), 027ht3n (0.15 #19380, 0.14 #20303, 0.10 #31380), 0263tn1 (0.15 #19380, 0.14 #20303, 0.10 #31380), 0dgskx (0.15 #19380, 0.14 #20303, 0.10 #31380) >> Best rule #25840 for best value: >> intensional similarity = 3 >> extensional distance = 152 >> proper extension: 03cf9ly; >> query: (?x7119, ?x7047) <- nominated_for(?x7047, ?x7119), country_of_origin(?x7119, ?x94), film(?x7047, ?x148) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5141 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 07s8z_l; *> query: (?x7119, 0g8st4) <- honored_for(?x873, ?x7119), program(?x5033, ?x7119), titles(?x2008, ?x7119), genre(?x7119, ?x53) *> conf = 0.02 ranks of expected_values: 636 EVAL 0524b41 actor 0g8st4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 83.000 70.000 0.501 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11484-0ktx_ PRED entity: 0ktx_ PRED relation: award PRED expected values: 0gr4k => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 169): 0gs9p (0.26 #996, 0.24 #1462, 0.24 #1229), 0gq9h (0.23 #4430, 0.23 #4196, 0.22 #15164), 0gr4k (0.23 #4430, 0.23 #4196, 0.22 #15164), 0czp_ (0.23 #4430, 0.23 #4196, 0.22 #15164), 0k611 (0.23 #1471, 0.22 #1005, 0.21 #1238), 0gq_v (0.16 #951, 0.15 #718, 0.12 #1417), 0gr0m (0.15 #59, 0.14 #1457, 0.13 #991), 02r22gf (0.15 #28, 0.11 #261, 0.10 #494), 019f4v (0.15 #1451, 0.13 #985, 0.13 #1218), 0f4x7 (0.15 #1423, 0.12 #1190, 0.12 #957) >> Best rule #996 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 0c5qvw; >> query: (?x11682, 0gs9p) <- language(?x11682, ?x254), nominated_for(?x574, ?x11682), music(?x11682, ?x13232), list(?x11682, ?x3004) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #4430 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 330 *> proper extension: 0g60z; 080dwhx; 06cs95; 072kp; 0ddd0gc; 0124k9; 0464pz; 0kfv9; 0d68qy; 02r5qtm; ... *> query: (?x11682, ?x601) <- nominated_for(?x574, ?x11682), nominated_for(?x601, ?x11682), film(?x574, ?x97) *> conf = 0.23 ranks of expected_values: 3 EVAL 0ktx_ award 0gr4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 75.000 75.000 0.265 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #11483-03bpn6 PRED entity: 03bpn6 PRED relation: nationality PRED expected values: 09c7w0 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 88): 09c7w0 (0.90 #701, 0.87 #2713, 0.86 #2011), 059rby (0.33 #11553, 0.32 #9043), 07ssc (0.27 #515, 0.20 #415, 0.14 #815), 02jx1 (0.20 #1337, 0.15 #3345, 0.13 #1842), 0j5g9 (0.10 #462, 0.09 #562, 0.03 #862), 03rk0 (0.09 #6270, 0.08 #6875, 0.08 #6370), 0f8l9c (0.06 #4534, 0.04 #622, 0.04 #3634), 0d060g (0.05 #4722, 0.05 #5526, 0.05 #5425), 0h7x (0.05 #1339, 0.04 #2546, 0.04 #635), 03rjj (0.05 #4417, 0.04 #3617, 0.04 #3117) >> Best rule #701 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 025cn2; >> query: (?x4597, 09c7w0) <- award_winner(?x3066, ?x4597), place_of_death(?x4597, ?x8618), place_of_birth(?x4597, ?x739), ?x739 = 02_286 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bpn6 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.903 http://example.org/people/person/nationality #11482-030qb3t PRED entity: 030qb3t PRED relation: contains PRED expected values: 0k049 0281rb 0k_p5 0k_mf => 185 concepts (155 used for prediction) PRED predicted values (max 10 best out of 2605): 06182p (0.67 #392264, 0.67 #403975, 0.66 #362989), 09f2j (0.67 #392264, 0.67 #403975, 0.66 #362989), 06xpp7 (0.66 #362989, 0.47 #377626, 0.46 #403974), 0146mv (0.47 #377626, 0.47 #436181, 0.46 #403974), 01nds (0.47 #377626, 0.47 #436181, 0.46 #403974), 01dtcb (0.47 #377626, 0.47 #436181, 0.46 #403974), 01w5gp (0.47 #377626, 0.47 #436181, 0.46 #403974), 0cjdk (0.47 #377626, 0.47 #436181, 0.46 #403974), 024rgt (0.47 #377626, 0.47 #436181, 0.46 #403974), 016tt2 (0.47 #377626, 0.47 #436181, 0.46 #403974) >> Best rule #392264 for best value: >> intensional similarity = 2 >> extensional distance = 248 >> proper extension: 02hyt; 0d8s8; 0jpkg; 0d99m; >> query: (?x1523, ?x4955) <- citytown(?x4955, ?x1523), major_field_of_study(?x4955, ?x373) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #5867 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 13 *> proper extension: 06cmp; 07ytt; 022b_; *> query: (?x1523, 0k049) <- films(?x1523, ?x6103), contains(?x94, ?x1523) *> conf = 0.07 ranks of expected_values: 179, 185, 215, 265 EVAL 030qb3t contains 0k_mf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 185.000 155.000 0.673 http://example.org/location/location/contains EVAL 030qb3t contains 0k_p5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 185.000 155.000 0.673 http://example.org/location/location/contains EVAL 030qb3t contains 0281rb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 185.000 155.000 0.673 http://example.org/location/location/contains EVAL 030qb3t contains 0k049 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 185.000 155.000 0.673 http://example.org/location/location/contains #11481-02hn5v PRED entity: 02hn5v PRED relation: ceremony! PRED expected values: 0gvx_ => 42 concepts (40 used for prediction) PRED predicted values (max 10 best out of 371): 0gvx_ (0.94 #3454, 0.94 #3215, 0.93 #2976), 0czp_ (0.77 #8340, 0.15 #4958, 0.15 #4246), 02x201b (0.77 #8340, 0.12 #4464, 0.12 #3507), 09td7p (0.35 #714, 0.33 #4296, 0.32 #3818), 099t8j (0.35 #714, 0.33 #4296, 0.32 #3818), 02pqp12 (0.35 #714, 0.33 #4296, 0.32 #3818), 094qd5 (0.35 #714, 0.33 #4296, 0.32 #3818), 09qwmm (0.35 #714, 0.33 #4296, 0.32 #3818), 03hkv_r (0.35 #714, 0.33 #4296, 0.32 #3818), 02r22gf (0.35 #714, 0.33 #4296, 0.32 #3818) >> Best rule #3454 for best value: >> intensional similarity = 18 >> extensional distance = 32 >> proper extension: 0c6vcj; >> query: (?x2707, 0gvx_) <- award_winner(?x2707, ?x92), ceremony(?x1972, ?x2707), ceremony(?x1323, ?x2707), ceremony(?x1307, ?x2707), honored_for(?x2707, ?x7738), honored_for(?x2707, ?x6048), ?x1307 = 0gq9h, ?x1972 = 0gqyl, ?x1323 = 0gqz2, nominated_for(?x749, ?x6048), film_crew_role(?x6048, ?x137), nominated_for(?x2307, ?x6048), country(?x6048, ?x94), film(?x4564, ?x7738), award(?x197, ?x749), award_winner(?x749, ?x488), award(?x396, ?x749), film(?x2353, ?x6048) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hn5v ceremony! 0gvx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 40.000 0.941 http://example.org/award/award_category/winners./award/award_honor/ceremony #11480-01x73 PRED entity: 01x73 PRED relation: contains PRED expected values: 08815 0m2gk => 182 concepts (106 used for prediction) PRED predicted values (max 10 best out of 2738): 0f1sm (0.84 #297677, 0.83 #105052, 0.82 #37935), 02h30z (0.72 #189698, 0.66 #32099, 0.56 #195536), 08815 (0.72 #189698, 0.66 #32099, 0.56 #195536), 02hp6p (0.66 #32099, 0.48 #67117, 0.48 #131318), 0m2gk (0.61 #204289, 0.60 #233471, 0.54 #145910), 0f4y3 (0.61 #204289, 0.60 #233471, 0.33 #4638), 0dc3_ (0.61 #204289, 0.60 #233471, 0.33 #3708), 0f6_j (0.61 #204289, 0.60 #233471, 0.33 #3707), 0cymp (0.61 #204289, 0.60 #233471, 0.33 #3522), 0k3jc (0.61 #204289, 0.60 #233471, 0.11 #169264) >> Best rule #297677 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 0mwh1; 01qtj9; 0n5gq; 07kg3; 017wh; 0d8rs; 0clzr; 0l3n4; 068cn; 015m08; ... >> query: (?x1755, ?x9445) <- adjoins(?x1755, ?x335), contains(?x1755, ?x503), administrative_division(?x9445, ?x1755) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #189698 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 0yl27; 01kk32; 0l1k8; 0n048; 048kw; 0ftkx; 036wy; 0jhwd; 0127c4; 02yc5b; *> query: (?x1755, ?x122) <- state_province_region(?x122, ?x1755), contains(?x1755, ?x503), major_field_of_study(?x122, ?x254) *> conf = 0.72 ranks of expected_values: 3, 5 EVAL 01x73 contains 0m2gk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 182.000 106.000 0.840 http://example.org/location/location/contains EVAL 01x73 contains 08815 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 182.000 106.000 0.840 http://example.org/location/location/contains #11479-03ryn PRED entity: 03ryn PRED relation: administrative_parent PRED expected values: 02j71 => 168 concepts (77 used for prediction) PRED predicted values (max 10 best out of 36): 02j71 (0.82 #9672, 0.81 #7055, 0.80 #7193), 09c7w0 (0.64 #4420, 0.43 #8285, 0.39 #9111), 05nrg (0.22 #8421, 0.20 #9247, 0.16 #6073), 0j0k (0.22 #8421, 0.20 #9247, 0.16 #6073), 02qkt (0.22 #8421, 0.20 #9247, 0.16 #6073), 073q1 (0.22 #8421, 0.20 #9247, 0.16 #6073), 07ssc (0.19 #702, 0.10 #1257, 0.06 #4703), 049nq (0.06 #648, 0.06 #1064, 0.05 #1202), 03ryn (0.05 #968, 0.03 #4830, 0.03 #5243), 0j1_3 (0.05 #968, 0.03 #4830, 0.03 #5243) >> Best rule #9672 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 035hm; 03t1s; >> query: (?x3749, 02j71) <- contains(?x6956, ?x3749), jurisdiction_of_office(?x265, ?x3749), contains(?x6956, ?x7747), ?x7747 = 07f1x >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03ryn administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 77.000 0.818 http://example.org/base/aareas/schema/administrative_area/administrative_parent #11478-0b_6rk PRED entity: 0b_6rk PRED relation: team PRED expected values: 02pqcfz 091tgz => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 25): 091tgz (0.89 #145, 0.86 #174, 0.82 #82), 03by7wc (0.86 #173, 0.83 #144, 0.82 #81), 02pqcfz (0.71 #51, 0.69 #114, 0.67 #30), 03d555l (0.67 #73, 0.62 #66, 0.57 #59), 02ptzz0 (0.67 #71, 0.50 #170, 0.50 #99), 0263cyj (0.42 #111, 0.42 #104, 0.41 #175), 02pyyld (0.42 #105, 0.38 #70, 0.36 #176), 02wvfxz (0.08 #302, 0.08 #327, 0.06 #303), 0wsr (0.08 #302, 0.08 #327, 0.06 #303), 0x2p (0.08 #302, 0.08 #327, 0.06 #303) >> Best rule #145 for best value: >> intensional similarity = 10 >> extensional distance = 16 >> proper extension: 0b_770; 0bqthy; >> query: (?x5897, 091tgz) <- team(?x5897, ?x9983), team(?x5897, ?x9909), team(?x5897, ?x2303), team(?x10441, ?x2303), team(?x7042, ?x2303), ?x7042 = 0b_72t, ?x9909 = 026wlnm, instance_of_recurring_event(?x5897, ?x10863), ?x10441 = 0b_71r, ?x9983 = 02q4ntp >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0b_6rk team 091tgz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.889 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_6rk team 02pqcfz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.889 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #11477-04mvp8 PRED entity: 04mvp8 PRED relation: geographic_distribution PRED expected values: 0d060g 047yc 0166v => 38 concepts (34 used for prediction) PRED predicted values (max 10 best out of 214): 06bnz (0.20 #211, 0.16 #461, 0.14 #1228), 015fr (0.20 #72, 0.09 #325, 0.08 #387), 05sb1 (0.18 #343, 0.17 #405, 0.14 #502), 06qd3 (0.16 #458, 0.10 #1225, 0.09 #1417), 03_3d (0.16 #443, 0.10 #1210, 0.09 #1402), 0d060g (0.14 #1211, 0.14 #502, 0.12 #1403), 0f8l9c (0.14 #502, 0.13 #1011, 0.10 #1206), 047yc (0.14 #502, 0.13 #1011, 0.10 #1206), 03__y (0.14 #502, 0.10 #1206, 0.10 #1398), 05rgl (0.14 #502, 0.10 #1206, 0.10 #1398) >> Best rule #211 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 041rx; 063k3h; 048z7l; >> query: (?x13008, 06bnz) <- people(?x13008, ?x9506), people(?x13008, ?x8097), people(?x13008, ?x7637), people(?x4322, ?x7637), place_of_birth(?x9506, ?x13551), student(?x7636, ?x7637), languages_spoken(?x13008, ?x8098), profession(?x7637, ?x524), nationality(?x7637, ?x2146), type_of_union(?x8097, ?x566) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1211 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 27 *> proper extension: 04l_pt; *> query: (?x13008, 0d060g) <- geographic_distribution(?x13008, ?x94), film_release_region(?x2370, ?x94), award(?x2370, ?x384), country(?x108, ?x94), olympics(?x94, ?x358), country(?x150, ?x94), combatants(?x94, ?x151) *> conf = 0.14 ranks of expected_values: 6, 8, 21 EVAL 04mvp8 geographic_distribution 0166v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 38.000 34.000 0.200 http://example.org/people/ethnicity/geographic_distribution EVAL 04mvp8 geographic_distribution 047yc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 38.000 34.000 0.200 http://example.org/people/ethnicity/geographic_distribution EVAL 04mvp8 geographic_distribution 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 38.000 34.000 0.200 http://example.org/people/ethnicity/geographic_distribution #11476-0hndn2q PRED entity: 0hndn2q PRED relation: honored_for PRED expected values: 0g9zljd 0h3mh3q => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 672): 06w99h3 (0.57 #1181, 0.14 #1767, 0.05 #10547), 05f4vxd (0.33 #298, 0.29 #2056, 0.14 #1470), 080dwhx (0.33 #22, 0.15 #26933, 0.14 #1780), 0b76kw1 (0.33 #110, 0.15 #26933, 0.14 #1868), 017jd9 (0.33 #856, 0.15 #26933, 0.10 #22834), 047d21r (0.33 #211, 0.14 #1969, 0.05 #10749), 09gq0x5 (0.33 #97, 0.14 #1855, 0.05 #10635), 04hwbq (0.33 #67, 0.14 #1825, 0.05 #10605), 09k56b7 (0.33 #109, 0.14 #1867, 0.05 #2453), 02r5qtm (0.33 #242, 0.14 #2000, 0.05 #2586) >> Best rule #1181 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0hr3c8y; 0hr6lkl; 0h_cssd; 0gmdkyy; 0hhtgcw; >> query: (?x2515, 06w99h3) <- award_winner(?x2515, ?x6589), award_winner(?x2515, ?x2135), honored_for(?x2515, ?x385), ?x385 = 0ds3t5x, film(?x6589, ?x972), award_winner(?x198, ?x6589), spouse(?x2135, ?x1802) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1548 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 0hr3c8y; 0hr6lkl; 0h_cssd; 0gmdkyy; 0hhtgcw; *> query: (?x2515, 0g9zljd) <- award_winner(?x2515, ?x6589), award_winner(?x2515, ?x2135), honored_for(?x2515, ?x385), ?x385 = 0ds3t5x, film(?x6589, ?x972), award_winner(?x198, ?x6589), spouse(?x2135, ?x1802) *> conf = 0.29 ranks of expected_values: 24, 164 EVAL 0hndn2q honored_for 0h3mh3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 47.000 47.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0hndn2q honored_for 0g9zljd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 47.000 47.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #11475-0l14j_ PRED entity: 0l14j_ PRED relation: role PRED expected values: 01v1d8 => 87 concepts (69 used for prediction) PRED predicted values (max 10 best out of 80): 02hnl (0.90 #151, 0.84 #460, 0.83 #459), 018j2 (0.90 #151, 0.84 #460, 0.83 #459), 03qlv7 (0.90 #151, 0.84 #460, 0.83 #459), 0gkd1 (0.90 #151, 0.84 #460, 0.83 #459), 07c6l (0.90 #151, 0.84 #460, 0.83 #459), 01dnws (0.90 #151, 0.83 #459, 0.83 #613), 02dlh2 (0.90 #151, 0.83 #459, 0.83 #613), 011_6p (0.90 #151, 0.83 #459, 0.83 #613), 023r2x (0.90 #151, 0.83 #459, 0.83 #613), 0859_ (0.90 #151, 0.83 #459, 0.83 #613) >> Best rule #151 for best value: >> intensional similarity = 13 >> extensional distance = 1 >> proper extension: 0dwt5; >> query: (?x2944, ?x74) <- role(?x7449, ?x2944), group(?x2944, ?x7476), group(?x2944, ?x6854), group(?x2944, ?x4783), role(?x2764, ?x2944), role(?x74, ?x2944), artists(?x114, ?x6854), role(?x2944, ?x1574), ?x4783 = 047cx, ?x7449 = 01vnt4, instrumentalists(?x2944, ?x120), ?x2764 = 01s0ps, ?x7476 = 048xh >> conf = 0.90 => this is the best rule for 11 predicted values *> Best rule #692 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 05148p4; *> query: (?x2944, ?x5417) <- role(?x4311, ?x2944), role(?x2206, ?x2944), role(?x1332, ?x2944), group(?x2944, ?x8497), group(?x2944, ?x6854), role(?x74, ?x2944), ?x6854 = 0178_w, role(?x2944, ?x1663), ?x8497 = 01l_w0, role(?x1524, ?x2944), performance_role(?x5417, ?x4311), role(?x2206, ?x1147), ?x1332 = 03qlv7 *> conf = 0.59 ranks of expected_values: 47 EVAL 0l14j_ role 01v1d8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 87.000 69.000 0.900 http://example.org/music/performance_role/regular_performances./music/group_membership/role #11474-08664q PRED entity: 08664q PRED relation: people! PRED expected values: 033tf_ => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 29): 041rx (0.14 #314, 0.14 #158, 0.14 #932), 033tf_ (0.13 #395, 0.11 #84, 0.11 #239), 0x67 (0.10 #1786, 0.10 #553, 0.10 #1401), 07bch9 (0.09 #23, 0.05 #100, 0.04 #489), 065b6q (0.09 #3, 0.04 #80, 0.02 #469), 038723 (0.09 #69), 02w7gg (0.07 #2009, 0.07 #545, 0.07 #1008), 0xnvg (0.06 #245, 0.06 #401, 0.06 #90), 01qhm_ (0.06 #83, 0.04 #394, 0.03 #316), 07hwkr (0.05 #89, 0.03 #478, 0.03 #2327) >> Best rule #314 for best value: >> intensional similarity = 3 >> extensional distance = 440 >> proper extension: 01wbgdv; 01qdjm; 09qc1; 0drc1; 01wj5hp; >> query: (?x2216, 041rx) <- award_nominee(?x1065, ?x2216), religion(?x2216, ?x1985), profession(?x2216, ?x1032) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #395 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 510 *> proper extension: 0276g40; *> query: (?x2216, 033tf_) <- film(?x2216, ?x5966), religion(?x2216, ?x1985), film(?x5959, ?x5966) *> conf = 0.13 ranks of expected_values: 2 EVAL 08664q people! 033tf_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.145 http://example.org/people/ethnicity/people #11473-03zrc_ PRED entity: 03zrc_ PRED relation: current_club PRED expected values: 03d0d7 => 100 concepts (81 used for prediction) PRED predicted values (max 10 best out of 738): 049f05 (0.43 #542, 0.33 #107, 0.29 #688), 085v7 (0.43 #620, 0.14 #3393, 0.11 #912), 04ltf (0.40 #1817, 0.38 #1672, 0.33 #2107), 03yfh3 (0.33 #143, 0.29 #578, 0.22 #1016), 03j6_5 (0.33 #94, 0.29 #675, 0.14 #529), 01rlz4 (0.33 #105, 0.22 #978, 0.14 #686), 02gys2 (0.33 #5, 0.15 #1608, 0.14 #586), 07245g (0.33 #57, 0.14 #638, 0.14 #492), 03m10r (0.33 #20, 0.14 #601, 0.14 #455), 0498yf (0.33 #120, 0.14 #701, 0.14 #555) >> Best rule #542 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 01l3wr; >> query: (?x8454, 049f05) <- current_club(?x8454, ?x9434), position(?x8454, ?x530), position(?x8454, ?x203), position(?x8454, ?x63), ?x203 = 0dgrmp, ?x530 = 02_j1w, team(?x8324, ?x9434), ?x8324 = 0dhrqx, position(?x11379, ?x63), ?x11379 = 046vvc >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3501 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 20 *> proper extension: 0329r5; *> query: (?x8454, ?x983) <- current_club(?x8454, ?x9434), position(?x8454, ?x530), position(?x8454, ?x203), ?x203 = 0dgrmp, ?x530 = 02_j1w, team(?x982, ?x9434), location(?x982, ?x362), team(?x982, ?x983) *> conf = 0.11 ranks of expected_values: 107 EVAL 03zrc_ current_club 03d0d7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 100.000 81.000 0.429 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #11472-0260bz PRED entity: 0260bz PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.82 #142, 0.79 #245, 0.78 #381), 01vx2h (0.52 #78, 0.47 #146, 0.38 #385), 02ynfr (0.29 #14, 0.24 #82, 0.20 #287), 015h31 (0.29 #8, 0.11 #281, 0.11 #247), 02rh1dz (0.26 #145, 0.22 #384, 0.21 #248), 089g0h (0.21 #86, 0.16 #154, 0.12 #223), 0d2b38 (0.20 #160, 0.17 #92, 0.12 #229), 04pyp5 (0.19 #49, 0.14 #15, 0.10 #220), 0215hd (0.17 #85, 0.16 #153, 0.14 #426), 01xy5l_ (0.16 #148, 0.12 #421, 0.11 #1070) >> Best rule #142 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 0jjy0; 03t79f; 05pdd86; 01gwk3; >> query: (?x2107, 0ch6mp2) <- produced_by(?x2107, ?x2135), crewmember(?x2107, ?x6546), executive_produced_by(?x2107, ?x1533) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0260bz film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.824 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11471-0432_5 PRED entity: 0432_5 PRED relation: genre PRED expected values: 07s9rl0 03q4nz => 88 concepts (87 used for prediction) PRED predicted values (max 10 best out of 170): 07s9rl0 (0.86 #6701, 0.76 #4375, 0.74 #7429), 01jfsb (0.60 #5121, 0.48 #1347, 0.46 #984), 03h64 (0.53 #6092, 0.53 #7306, 0.52 #5476), 03k9fj (0.46 #983, 0.45 #375, 0.43 #739), 01hmnh (0.34 #990, 0.31 #746, 0.29 #1232), 05p553 (0.33 #4, 0.33 #610, 0.33 #1097), 02l7c8 (0.33 #3537, 0.31 #1593, 0.30 #5370), 06n90 (0.26 #135, 0.25 #1348, 0.24 #1227), 04xvlr (0.20 #1579, 0.19 #2, 0.19 #3523), 03q4nz (0.19 #19, 0.15 #1596, 0.11 #4515) >> Best rule #6701 for best value: >> intensional similarity = 7 >> extensional distance = 1170 >> proper extension: 04svwx; >> query: (?x4604, 07s9rl0) <- genre(?x4604, ?x604), genre(?x11110, ?x604), genre(?x6209, ?x604), genre(?x2116, ?x604), ?x2116 = 02c638, ?x6209 = 01kjr0, ?x11110 = 0k20s >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 0432_5 genre 03q4nz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 88.000 87.000 0.858 http://example.org/film/film/genre EVAL 0432_5 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 87.000 0.858 http://example.org/film/film/genre #11470-07yvsn PRED entity: 07yvsn PRED relation: nominated_for! PRED expected values: 02x2gy0 => 64 concepts (54 used for prediction) PRED predicted values (max 10 best out of 227): 0gq9h (0.42 #3170, 0.35 #3888, 0.33 #3409), 0gs9p (0.38 #3172, 0.32 #3890, 0.30 #3411), 019f4v (0.34 #3161, 0.28 #3400, 0.28 #3879), 02hsq3m (0.33 #507, 0.33 #268, 0.33 #29), 02g3v6 (0.33 #260, 0.33 #21, 0.19 #12435), 0gq_v (0.33 #258, 0.30 #3126, 0.25 #3844), 0k611 (0.33 #552, 0.30 #3181, 0.29 #791), 0gr0m (0.33 #538, 0.29 #777, 0.26 #3167), 02x2gy0 (0.33 #580, 0.29 #819, 0.19 #12435), 0f4x7 (0.31 #1698, 0.29 #1220, 0.25 #3132) >> Best rule #3170 for best value: >> intensional similarity = 3 >> extensional distance = 577 >> proper extension: 05_61y; >> query: (?x3441, 0gq9h) <- award(?x3441, ?x2222), genre(?x3441, ?x53), ceremony(?x2222, ?x78) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #580 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 05q96q6; 049xgc; 0fh2v5; 04jn6y7; *> query: (?x3441, 02x2gy0) <- film(?x1286, ?x3441), production_companies(?x3441, ?x10503), ?x1286 = 07vc_9, titles(?x53, ?x3441) *> conf = 0.33 ranks of expected_values: 9 EVAL 07yvsn nominated_for! 02x2gy0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 64.000 54.000 0.416 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11469-0mgkg PRED entity: 0mgkg PRED relation: service_language PRED expected values: 02h40lc => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 20): 02h40lc (0.99 #1943, 0.96 #1382, 0.96 #1202), 064_8sq (0.25 #29, 0.22 #89, 0.18 #1009), 04306rv (0.25 #23, 0.22 #83, 0.17 #63), 05zjd (0.25 #32, 0.17 #72, 0.11 #112), 02bjrlw (0.25 #21, 0.17 #61, 0.11 #101), 03_9r (0.14 #185, 0.11 #85, 0.09 #325), 01r2l (0.11 #91, 0.08 #2162, 0.07 #591), 02bv9 (0.11 #94, 0.08 #2162, 0.07 #594), 06b_j (0.11 #90, 0.08 #2162, 0.06 #490), 02hwhyv (0.11 #95, 0.08 #2162, 0.06 #495) >> Best rule #1943 for best value: >> intensional similarity = 5 >> extensional distance = 139 >> proper extension: 059yj; >> query: (?x9198, 02h40lc) <- service_language(?x9198, ?x2502), language(?x8791, ?x2502), language(?x6362, ?x2502), ?x6362 = 03_gz8, ?x8791 = 0cqr0q >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mgkg service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.986 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #11468-0bmfnjs PRED entity: 0bmfnjs PRED relation: film_release_region PRED expected values: 04gzd 035qy 06t2t 0jgx => 107 concepts (93 used for prediction) PRED predicted values (max 10 best out of 287): 0chghy (0.94 #606, 0.88 #309, 0.86 #1940), 09c7w0 (0.93 #12660, 0.92 #12808, 0.92 #11315), 0d0vqn (0.93 #2231, 0.92 #897, 0.92 #749), 035qy (0.90 #773, 0.87 #921, 0.83 #1663), 0b90_r (0.85 #747, 0.82 #895, 0.78 #2229), 02vzc (0.84 #642, 0.81 #345, 0.81 #1976), 05v8c (0.76 #758, 0.75 #906, 0.69 #1203), 06t2t (0.76 #1689, 0.75 #947, 0.74 #651), 04gzd (0.71 #752, 0.70 #900, 0.62 #1642), 0ctw_b (0.67 #1655, 0.63 #2099, 0.61 #617) >> Best rule #606 for best value: >> intensional similarity = 6 >> extensional distance = 29 >> proper extension: 0gyfp9c; 0cp0t91; 04fjzv; >> query: (?x8682, 0chghy) <- film_release_region(?x8682, ?x2645), film_release_region(?x8682, ?x2629), film_crew_role(?x8682, ?x2178), ?x2629 = 06f32, ?x2645 = 03h64, ?x2178 = 01pvkk >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #773 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 57 *> proper extension: 0b76d_m; 0ds35l9; 0h1cdwq; 05p1tzf; 017gl1; 0bwfwpj; 08hmch; 0c0nhgv; 0872p_c; 04hwbq; ... *> query: (?x8682, 035qy) <- film_release_region(?x8682, ?x404), film_release_region(?x8682, ?x252), film_crew_role(?x8682, ?x468), film_release_distribution_medium(?x8682, ?x81), ?x81 = 029j_, ?x404 = 047lj, ?x252 = 03_3d *> conf = 0.90 ranks of expected_values: 4, 8, 9, 30 EVAL 0bmfnjs film_release_region 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 107.000 93.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmfnjs film_release_region 06t2t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 107.000 93.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmfnjs film_release_region 035qy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 93.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bmfnjs film_release_region 04gzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 107.000 93.000 0.935 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11467-04w391 PRED entity: 04w391 PRED relation: film PRED expected values: 04hk0w => 59 concepts (56 used for prediction) PRED predicted values (max 10 best out of 540): 047csmy (0.46 #19604, 0.42 #32082, 0.39 #32081), 017jd9 (0.41 #2557, 0.13 #4340, 0.04 #3565), 017gl1 (0.31 #1923, 0.10 #3706, 0.04 #3565), 017gm7 (0.28 #1989, 0.10 #3772, 0.04 #3565), 0ndwt2w (0.16 #2776, 0.07 #4559, 0.01 #20598), 0jzw (0.12 #117, 0.09 #1899, 0.04 #3565), 02b6n9 (0.12 #1565, 0.06 #3347, 0.04 #3565), 09cr8 (0.11 #3846, 0.06 #281, 0.04 #3565), 01dyvs (0.06 #2058, 0.06 #276, 0.04 #3565), 07cz2 (0.06 #2225, 0.06 #443, 0.04 #3565) >> Best rule #19604 for best value: >> intensional similarity = 2 >> extensional distance = 639 >> proper extension: 0p51w; 0b6yp2; 01l79yc; 03bw6; 01njxvw; 0bn3jg; >> query: (?x3999, ?x3748) <- people(?x1050, ?x3999), award_winner(?x3748, ?x3999) >> conf = 0.46 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04w391 film 04hk0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 56.000 0.456 http://example.org/film/actor/film./film/performance/film #11466-0c0nhgv PRED entity: 0c0nhgv PRED relation: nominated_for! PRED expected values: 03hkv_r 099ck7 => 93 concepts (92 used for prediction) PRED predicted values (max 10 best out of 219): 09cm54 (0.67 #8965, 0.66 #11655, 0.66 #13673), 0gs9p (0.65 #3194, 0.40 #9470, 0.37 #9022), 019f4v (0.56 #3186, 0.35 #9014, 0.35 #9462), 040njc (0.50 #3143, 0.32 #903, 0.30 #9419), 02pqp12 (0.44 #3189, 0.30 #949, 0.24 #2069), 02qyntr (0.39 #3303, 0.30 #1063, 0.24 #9579), 0gr0m (0.39 #3190, 0.30 #950, 0.26 #5827), 03hkv_r (0.37 #2030, 0.27 #11656, 0.25 #3150), 0gq_v (0.32 #3155, 0.27 #7862, 0.27 #8758), 0l8z1 (0.32 #944, 0.31 #3184, 0.23 #1840) >> Best rule #8965 for best value: >> intensional similarity = 3 >> extensional distance = 461 >> proper extension: 0cwrr; 04glx0; 05fgr_; 07bz5; 06mmr; >> query: (?x1163, ?x1770) <- award(?x1163, ?x1770), honored_for(?x1442, ?x1163), award_winner(?x1163, ?x1616) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2030 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 0170xl; *> query: (?x1163, 03hkv_r) <- nominated_for(?x3066, ?x1163), nominated_for(?x2341, ?x1163), ?x2341 = 02x17s4, award(?x92, ?x3066) *> conf = 0.37 ranks of expected_values: 8, 22 EVAL 0c0nhgv nominated_for! 099ck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 93.000 92.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0c0nhgv nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 93.000 92.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11465-03m49ly PRED entity: 03m49ly PRED relation: crewmember! PRED expected values: 04hk0w => 101 concepts (41 used for prediction) PRED predicted values (max 10 best out of 302): 0b6tzs (0.36 #2405, 0.35 #1202, 0.35 #2104), 0jqn5 (0.20 #50, 0.12 #951, 0.12 #1552), 0dfw0 (0.20 #151, 0.07 #452, 0.06 #752), 03nsm5x (0.20 #251, 0.07 #552, 0.06 #852), 0642xf3 (0.20 #159, 0.06 #1060, 0.06 #1661), 060__7 (0.20 #264, 0.06 #3309, 0.04 #565), 042zrm (0.20 #255, 0.06 #3309, 0.04 #556), 07xvf (0.20 #238, 0.06 #3309, 0.04 #539), 027gy0k (0.20 #210, 0.06 #3309, 0.04 #511), 04xx9s (0.20 #209, 0.06 #3309, 0.04 #510) >> Best rule #2405 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 026dx; 0b80__; >> query: (?x7675, ?x945) <- crewmember(?x1372, ?x7675), nominated_for(?x7675, ?x945), film(?x1207, ?x1372), award(?x1372, ?x350) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1201 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 06cv1; 05x2t7; 05b49tt; *> query: (?x7675, 04hk0w) <- crewmember(?x6009, ?x7675), nominated_for(?x7675, ?x945), produced_by(?x6009, ?x96), award_nominee(?x7675, ?x6232) *> conf = 0.03 ranks of expected_values: 248 EVAL 03m49ly crewmember! 04hk0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 101.000 41.000 0.355 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #11464-053y0s PRED entity: 053y0s PRED relation: place_of_birth PRED expected values: 0106dv => 132 concepts (129 used for prediction) PRED predicted values (max 10 best out of 140): 0106dv (0.33 #71837, 0.32 #50703, 0.28 #72543), 01jr6 (0.20 #143, 0.08 #1551, 0.02 #6479), 0r1jr (0.12 #793, 0.06 #2201, 0.04 #3609), 01531 (0.12 #809, 0.06 #2217, 0.04 #3625), 0dhdp (0.12 #738, 0.03 #4962, 0.02 #7778), 02_286 (0.08 #71151, 0.07 #72562, 0.07 #78204), 01_d4 (0.08 #5698, 0.05 #7810, 0.05 #6402), 0cb4j (0.08 #1423, 0.05 #2831, 0.03 #5647), 094jv (0.08 #1469, 0.02 #7101, 0.01 #8509), 0r00l (0.08 #1895, 0.02 #7527, 0.01 #10344) >> Best rule #71837 for best value: >> intensional similarity = 4 >> extensional distance = 1522 >> proper extension: 0bl60p; 07m69t; 0443c; >> query: (?x130, ?x10364) <- location(?x130, ?x10364), nationality(?x130, ?x94), ?x94 = 09c7w0, place_of_birth(?x2697, ?x10364) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 053y0s place_of_birth 0106dv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 129.000 0.327 http://example.org/people/person/place_of_birth #11463-01p9hgt PRED entity: 01p9hgt PRED relation: category PRED expected values: 08mbj5d => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #9, 0.81 #18, 0.78 #1) >> Best rule #9 for best value: >> intensional similarity = 2 >> extensional distance = 406 >> proper extension: 04rcr; 02r3zy; 07c0j; 011zf2; 03g5jw; 0dvqq; 03fbc; 03yf3z; 0249kn; 018ndc; ... >> query: (?x1413, 08mbj5d) <- artist(?x5744, ?x1413), award_nominee(?x1413, ?x1795) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p9hgt category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.831 http://example.org/common/topic/webpage./common/webpage/category #11462-01ycfv PRED entity: 01ycfv PRED relation: profession PRED expected values: 03lgtv => 123 concepts (122 used for prediction) PRED predicted values (max 10 best out of 74): 09jwl (0.71 #5313, 0.68 #6344, 0.67 #4430), 02hrh1q (0.69 #7957, 0.69 #8987, 0.68 #8398), 0dz3r (0.49 #2943, 0.47 #737, 0.43 #5296), 016z4k (0.44 #4856, 0.42 #5004, 0.42 #4268), 039v1 (0.38 #329, 0.29 #5329, 0.26 #6360), 01d_h8 (0.34 #4711, 0.33 #9861, 0.32 #10008), 0dxtg (0.31 #7956, 0.30 #8986, 0.29 #8397), 05vyk (0.29 #240, 0.15 #1416, 0.15 #1269), 0fnpj (0.25 #3000, 0.21 #59, 0.18 #647), 03gjzk (0.25 #7958, 0.25 #12062, 0.25 #8988) >> Best rule #5313 for best value: >> intensional similarity = 2 >> extensional distance = 339 >> proper extension: 016lj_; >> query: (?x9408, 09jwl) <- category(?x9408, ?x134), role(?x9408, ?x316) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1141 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 0bvzp; *> query: (?x9408, 03lgtv) <- award_nominee(?x248, ?x9408), role(?x9408, ?x316), music(?x1842, ?x9408) *> conf = 0.04 ranks of expected_values: 36 EVAL 01ycfv profession 03lgtv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 123.000 122.000 0.710 http://example.org/people/person/profession #11461-0j1z8 PRED entity: 0j1z8 PRED relation: medal PRED expected values: 02lpp7 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 3): 02lpp7 (0.87 #15, 0.83 #42, 0.78 #30), 02lq67 (0.83 #13, 0.83 #10, 0.81 #64), 02lq5w (0.79 #41, 0.77 #11, 0.76 #32) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 03_3d; 05v8c; 06qd3; 06f32; 03h64; >> query: (?x311, 02lpp7) <- film_release_region(?x4040, ?x311), film_release_region(?x1219, ?x311), ?x1219 = 03bx2lk, ?x4040 = 02mt51 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j1z8 medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #11460-087wc7n PRED entity: 087wc7n PRED relation: film! PRED expected values: 016tw3 => 47 concepts (29 used for prediction) PRED predicted values (max 10 best out of 48): 01795t (0.35 #168, 0.32 #393, 0.29 #693), 03xq0f (0.34 #230, 0.29 #80, 0.27 #5), 05qd_ (0.23 #84, 0.18 #9, 0.17 #159), 086k8 (0.22 #227, 0.16 #902, 0.16 #1277), 054g1r (0.21 #185, 0.20 #410, 0.17 #635), 016tt2 (0.18 #4, 0.15 #154, 0.14 #454), 017s11 (0.14 #528, 0.12 #453, 0.11 #1353), 016tw3 (0.14 #1211, 0.13 #1586, 0.13 #1061), 024rgt (0.11 #245, 0.09 #95, 0.08 #470), 01gb54 (0.09 #29, 0.08 #404, 0.08 #254) >> Best rule #168 for best value: >> intensional similarity = 6 >> extensional distance = 50 >> proper extension: 04svwx; >> query: (?x791, 01795t) <- genre(?x791, ?x2540), genre(?x791, ?x307), genre(?x791, ?x258), ?x2540 = 0hcr, ?x258 = 05p553, titles(?x307, ?x270) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1211 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 482 *> proper extension: 03f7xg; *> query: (?x791, 016tw3) <- genre(?x791, ?x258), film(?x9140, ?x791), language(?x791, ?x254), award_winner(?x594, ?x9140), award(?x11322, ?x594), ?x11322 = 0cj2w *> conf = 0.14 ranks of expected_values: 8 EVAL 087wc7n film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 47.000 29.000 0.346 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #11459-03mp9s PRED entity: 03mp9s PRED relation: award_nominee PRED expected values: 02624g => 94 concepts (33 used for prediction) PRED predicted values (max 10 best out of 738): 02l4pj (0.81 #58289, 0.80 #39631, 0.80 #65285), 05k2s_ (0.81 #58289, 0.80 #39631, 0.80 #65285), 02x7vq (0.81 #58289, 0.80 #39631, 0.80 #65285), 01pk3z (0.81 #58289, 0.80 #39631, 0.80 #65285), 03mp9s (0.29 #1580, 0.18 #76948, 0.16 #51292), 065jlv (0.18 #76948, 0.16 #51292, 0.02 #23729), 01yfm8 (0.18 #76948, 0.14 #69949, 0.14 #1664), 02p65p (0.18 #76948, 0.14 #69949, 0.07 #25667), 0dlglj (0.18 #76948, 0.14 #69949, 0.05 #2667), 0b_dy (0.18 #76948, 0.14 #69949, 0.04 #3030) >> Best rule #58289 for best value: >> intensional similarity = 3 >> extensional distance = 1163 >> proper extension: 03x3qv; 044rvb; 05gml8; 03qd_; 01yb09; 01v42g; 0bg539; 02wcx8c; 05tk7y; 02pb53; ... >> query: (?x6977, ?x91) <- award(?x6977, ?x618), film(?x6977, ?x1263), award_nominee(?x91, ?x6977) >> conf = 0.81 => this is the best rule for 4 predicted values *> Best rule #69949 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1229 *> proper extension: 01vw917; *> query: (?x6977, ?x5541) <- award_nominee(?x6977, ?x6650), film(?x6977, ?x1263), award_nominee(?x6650, ?x5541) *> conf = 0.14 ranks of expected_values: 133 EVAL 03mp9s award_nominee 02624g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 94.000 33.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11458-08ct6 PRED entity: 08ct6 PRED relation: film! PRED expected values: 01wz01 => 91 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1142): 06qn87 (0.43 #110506, 0.43 #104253, 0.42 #100083), 06mn7 (0.42 #100083, 0.40 #31276, 0.40 #29189), 0c0k1 (0.18 #3597, 0.07 #5684, 0.06 #7768), 06cgy (0.15 #251, 0.04 #23185, 0.04 #8592), 0ywqc (0.15 #1792, 0.02 #20557, 0.02 #10133), 03ym1 (0.13 #5187, 0.12 #7271, 0.05 #11440), 0f0kz (0.13 #4690, 0.12 #6774, 0.04 #31793), 0js9s (0.13 #5330, 0.12 #7414, 0.02 #11583), 015c4g (0.12 #2868, 0.08 #782, 0.03 #13293), 085q5 (0.12 #3809, 0.02 #28826) >> Best rule #110506 for best value: >> intensional similarity = 4 >> extensional distance = 798 >> proper extension: 0bh8yn3; 07x4qr; 0bby9p5; 025n07; 0bxsk; 0b6l1st; 09v8clw; >> query: (?x4699, ?x4895) <- genre(?x4699, ?x600), film_release_distribution_medium(?x4699, ?x81), nominated_for(?x4895, ?x4699), film_release_region(?x4699, ?x94) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08ct6 film! 01wz01 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 58.000 0.432 http://example.org/film/actor/film./film/performance/film #11457-02mpyh PRED entity: 02mpyh PRED relation: film_crew_role PRED expected values: 02_n3z => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 21): 0dxtw (0.49 #379, 0.36 #610, 0.35 #1090), 01pvkk (0.32 #380, 0.29 #9, 0.27 #1091), 02rh1dz (0.21 #378, 0.13 #1881, 0.10 #609), 015h31 (0.20 #377, 0.13 #1881, 0.09 #608), 01xy5l_ (0.16 #382, 0.13 #1881, 0.12 #182), 02_n3z (0.13 #1881, 0.11 #172, 0.10 #372), 033smt (0.13 #1881, 0.10 #391, 0.04 #622), 04pyp5 (0.13 #1881, 0.08 #184, 0.08 #269), 094hwz (0.13 #1881, 0.07 #383, 0.04 #212), 02vs3x5 (0.13 #1881, 0.07 #188, 0.06 #159) >> Best rule #379 for best value: >> intensional similarity = 4 >> extensional distance = 325 >> proper extension: 0d90m; 03qcfvw; 09sh8k; 02y_lrp; 034qmv; 06w99h3; 047gn4y; 0czyxs; 0ds3t5x; 0gtv7pk; ... >> query: (?x8574, 0dxtw) <- genre(?x8574, ?x53), film(?x166, ?x8574), film_crew_role(?x8574, ?x2154), ?x2154 = 01vx2h >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1881 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1594 *> proper extension: 0522wp; *> query: (?x8574, ?x137) <- film(?x166, ?x8574), film(?x166, ?x1968), film(?x166, ?x1184), nominated_for(?x68, ?x1968), film_crew_role(?x1184, ?x137) *> conf = 0.13 ranks of expected_values: 6 EVAL 02mpyh film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 70.000 70.000 0.489 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11456-07rzf PRED entity: 07rzf PRED relation: religion PRED expected values: 051kv => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 22): 0n2g (0.25 #13, 0.11 #103, 0.08 #148), 0kpl (0.19 #280, 0.10 #955, 0.10 #1045), 03_gx (0.17 #149, 0.11 #104, 0.09 #329), 0c8wxp (0.16 #996, 0.15 #231, 0.14 #1446), 0kq2 (0.08 #153, 0.06 #918, 0.05 #963), 01lp8 (0.06 #316, 0.03 #271, 0.03 #361), 03j6c (0.04 #1731, 0.04 #1596, 0.04 #246), 06nzl (0.04 #240, 0.03 #330, 0.03 #420), 05sfs (0.03 #273, 0.03 #318, 0.03 #408), 092bf5 (0.03 #286, 0.03 #1456, 0.02 #1501) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 040dv; >> query: (?x11465, 0n2g) <- location(?x11465, ?x9502), ?x9502 = 0bdg5, nationality(?x11465, ?x512), profession(?x11465, ?x1383) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #905 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 159 *> proper extension: 01p7yb; 06n7h7; 05ml_s; 01yk13; 0d0vj4; 049dyj; 083q7; 02r34n; 04jzj; 0prjs; ... *> query: (?x11465, 051kv) <- location(?x11465, ?x1296), student(?x254, ?x11465), gender(?x11465, ?x231), major_field_of_study(?x1695, ?x254) *> conf = 0.01 ranks of expected_values: 21 EVAL 07rzf religion 051kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 107.000 107.000 0.250 http://example.org/people/person/religion #11455-05vsxz PRED entity: 05vsxz PRED relation: award_nominee! PRED expected values: 03f1zdw 06mmb => 69 concepts (28 used for prediction) PRED predicted values (max 10 best out of 646): 06l9n8 (0.81 #25358, 0.81 #62253, 0.81 #9219), 07hbxm (0.81 #25358, 0.81 #62253, 0.81 #9219), 0785v8 (0.81 #25358, 0.81 #62253, 0.81 #9219), 03n_7k (0.81 #25358, 0.81 #62253, 0.81 #9219), 04qsdh (0.81 #25358, 0.81 #62253, 0.81 #9219), 0205dx (0.81 #25358, 0.81 #62253, 0.81 #9219), 023kzp (0.44 #1368, 0.44 #3672, 0.19 #64560), 01d1st (0.44 #1551, 0.44 #3855, 0.13 #4609), 019pm_ (0.42 #595, 0.41 #2899, 0.13 #4609), 06151l (0.42 #31, 0.41 #2335, 0.13 #4609) >> Best rule #25358 for best value: >> intensional similarity = 3 >> extensional distance = 1120 >> proper extension: 011hdn; >> query: (?x100, ?x101) <- award_nominee(?x100, ?x101), gender(?x100, ?x231), film(?x100, ?x943) >> conf = 0.81 => this is the best rule for 6 predicted values *> Best rule #4609 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 03_6y; 01z7s_; 05l4yg; *> query: (?x100, ?x1222) <- award_nominee(?x1870, ?x100), award_nominee(?x488, ?x100), ?x1870 = 0hvb2, award_nominee(?x488, ?x1222) *> conf = 0.13 ranks of expected_values: 131, 150 EVAL 05vsxz award_nominee! 06mmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 69.000 28.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 05vsxz award_nominee! 03f1zdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 69.000 28.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11454-01ypsj PRED entity: 01ypsj PRED relation: profession PRED expected values: 02jknp 02hrh1q => 112 concepts (65 used for prediction) PRED predicted values (max 10 best out of 54): 02hrh1q (0.92 #605, 0.91 #4158, 0.91 #4898), 02jknp (0.46 #2524, 0.46 #1043, 0.46 #1932), 03gjzk (0.39 #2235, 0.37 #1050, 0.37 #3863), 0cbd2 (0.25 #154, 0.24 #1042, 0.22 #3855), 09jwl (0.25 #166, 0.19 #2683, 0.19 #4015), 0dz3r (0.25 #150, 0.12 #2667, 0.11 #3555), 01c72t (0.25 #171, 0.09 #1503, 0.08 #2984), 05vyk (0.25 #242, 0.02 #4091, 0.02 #9571), 018gz8 (0.20 #1052, 0.18 #1941, 0.17 #2237), 0np9r (0.19 #1649, 0.16 #760, 0.16 #1797) >> Best rule #605 for best value: >> intensional similarity = 4 >> extensional distance = 349 >> proper extension: 01r42_g; 01hxs4; 01zmpg; 01_j71; 03zyvw; 0bt7ws; 018z_c; 018n6m; 06czyr; 02rmxx; ... >> query: (?x9813, 02hrh1q) <- people(?x7562, ?x9813), nationality(?x9813, ?x94), actor(?x9633, ?x9813), profession(?x9813, ?x319) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01ypsj profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 65.000 0.920 http://example.org/people/person/profession EVAL 01ypsj profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 65.000 0.920 http://example.org/people/person/profession #11453-07wbk PRED entity: 07wbk PRED relation: politician PRED expected values: 0chsq 0tc7 0cqt90 0gd9k 0cl_m 081t6 => 162 concepts (96 used for prediction) PRED predicted values (max 10 best out of 80): 03_nq (0.50 #644, 0.33 #1161, 0.25 #1532), 0rlz (0.40 #399, 0.33 #623, 0.25 #249), 0dq2k (0.40 #396, 0.33 #620, 0.25 #246), 06c0j (0.40 #449, 0.25 #294, 0.25 #218), 082xp (0.40 #366, 0.22 #1254, 0.17 #1625), 07cbs (0.40 #449, 0.20 #395, 0.17 #619), 042fk (0.33 #671, 0.29 #893, 0.25 #1115), 08959 (0.33 #672, 0.25 #298, 0.25 #222), 0fd_1 (0.33 #635, 0.22 #1152, 0.20 #411), 01k165 (0.33 #1124, 0.20 #383, 0.15 #2166) >> Best rule #644 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0d075m; 07wf9; 07wdw; 07wgm; >> query: (?x1912, 03_nq) <- politician(?x1912, ?x5266), politician(?x1912, ?x3520), people(?x4195, ?x5266), legislative_sessions(?x5266, ?x606), type_of_union(?x5266, ?x566), profession(?x3520, ?x5805), nationality(?x5266, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07wbk politician 081t6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 162.000 96.000 0.500 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician EVAL 07wbk politician 0cl_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 162.000 96.000 0.500 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician EVAL 07wbk politician 0gd9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 162.000 96.000 0.500 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician EVAL 07wbk politician 0cqt90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 162.000 96.000 0.500 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician EVAL 07wbk politician 0tc7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 162.000 96.000 0.500 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician EVAL 07wbk politician 0chsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 162.000 96.000 0.500 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #11452-05zy2cy PRED entity: 05zy2cy PRED relation: film_crew_role PRED expected values: 02r96rf 0dxtw => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 18): 02r96rf (0.83 #59, 0.81 #31, 0.81 #87), 0dxtw (0.50 #8, 0.48 #92, 0.44 #64), 02rh1dz (0.43 #91, 0.42 #7, 0.38 #35), 015h31 (0.33 #62, 0.33 #6, 0.31 #34), 033smt (0.31 #48, 0.28 #76, 0.25 #20), 01pvkk (0.30 #1985, 0.29 #177, 0.29 #93), 02ynfr (0.28 #68, 0.25 #40, 0.25 #12), 02_n3z (0.28 #169, 0.22 #57, 0.19 #85), 089fss (0.25 #33, 0.22 #61, 0.19 #89), 0263ycg (0.12 #42, 0.11 #70, 0.11 #182) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0g3zrd; >> query: (?x2649, 02r96rf) <- film_crew_role(?x2649, ?x1776), ?x1776 = 020xn5, nominated_for(?x2456, ?x2649), film(?x4053, ?x2649) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05zy2cy film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05zy2cy film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11451-02gr81 PRED entity: 02gr81 PRED relation: major_field_of_study PRED expected values: 0_jm 01bt59 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 120): 01mkq (0.59 #1087, 0.57 #1802, 0.53 #1563), 0g26h (0.58 #518, 0.57 #399, 0.52 #875), 02j62 (0.49 #1818, 0.49 #5034, 0.47 #1579), 01lj9 (0.47 #5520, 0.34 #1587, 0.33 #873), 062z7 (0.46 #147, 0.45 #1100, 0.43 #1815), 04rjg (0.43 #1092, 0.41 #1568, 0.41 #2997), 01tbp (0.41 #1131, 0.39 #1846, 0.35 #536), 04x_3 (0.39 #1098, 0.34 #1813, 0.29 #5029), 03g3w (0.39 #861, 0.39 #1575, 0.38 #385), 05qfh (0.37 #1108, 0.36 #1584, 0.33 #1823) >> Best rule #1087 for best value: >> intensional similarity = 5 >> extensional distance = 49 >> proper extension: 049dk; >> query: (?x4209, 01mkq) <- institution(?x1200, ?x4209), ?x1200 = 016t_3, school(?x2820, ?x4209), major_field_of_study(?x4209, ?x1154), ?x2820 = 0jmj7 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #534 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 41 *> proper extension: 03np_7; *> query: (?x4209, 0_jm) <- institution(?x620, ?x4209), student(?x4209, ?x123), major_field_of_study(?x4209, ?x7134), ?x7134 = 02_7t, state_province_region(?x4209, ?x3634) *> conf = 0.35 ranks of expected_values: 11, 26 EVAL 02gr81 major_field_of_study 01bt59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 158.000 158.000 0.588 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02gr81 major_field_of_study 0_jm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 158.000 158.000 0.588 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11450-0177z PRED entity: 0177z PRED relation: month PRED expected values: 04wzr => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 1): 04wzr (0.93 #21, 0.92 #19, 0.91 #31) >> Best rule #21 for best value: >> intensional similarity = 6 >> extensional distance = 38 >> proper extension: 06wjf; 0h3tv; >> query: (?x4826, 04wzr) <- month(?x4826, ?x6303), month(?x4826, ?x4925), month(?x4826, ?x2255), ?x6303 = 0lkm, ?x2255 = 040fv, ?x4925 = 0ll3 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0177z month 04wzr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.925 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #11449-01p79b PRED entity: 01p79b PRED relation: fraternities_and_sororities PRED expected values: 035tlh => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 3): 0325pb (0.31 #13, 0.30 #28, 0.29 #19), 035tlh (0.27 #2, 0.25 #14, 0.25 #20), 04m8fy (0.03 #21, 0.03 #30, 0.02 #33) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 02jztz; >> query: (?x7920, 0325pb) <- contains(?x94, ?x7920), major_field_of_study(?x7920, ?x1695), currency(?x7920, ?x170), school(?x2820, ?x7920) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 03gn1x; *> query: (?x7920, 035tlh) <- contains(?x177, ?x7920), major_field_of_study(?x7920, ?x1695), student(?x7920, ?x5558), ?x177 = 05kkh *> conf = 0.27 ranks of expected_values: 2 EVAL 01p79b fraternities_and_sororities 035tlh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.308 http://example.org/education/university/fraternities_and_sororities #11448-0627sn PRED entity: 0627sn PRED relation: nationality PRED expected values: 07ssc => 132 concepts (119 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.81 #2306, 0.77 #1906, 0.76 #2407), 07ssc (0.81 #10644, 0.40 #115, 0.39 #10645), 02jx1 (0.39 #10645, 0.33 #9840, 0.33 #11349), 04jpl (0.39 #10645, 0.33 #9840, 0.33 #11349), 0d05w3 (0.17 #250, 0.12 #10846, 0.10 #851), 03rt9 (0.14 #413, 0.11 #513, 0.01 #5937), 0chghy (0.12 #10846, 0.10 #811, 0.09 #1011), 03rjj (0.12 #10846, 0.08 #906, 0.06 #1006), 0f8l9c (0.12 #10846, 0.07 #1627, 0.04 #923), 0d0vqn (0.12 #10846, 0.06 #609, 0.04 #910) >> Best rule #2306 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 0c_mvb; 03g5_y; 032nl2; 0bbxd3; 05wm88; 02t901; 06z9yh; >> query: (?x5528, 09c7w0) <- place_of_birth(?x5528, ?x13032), profession(?x5528, ?x1943), gender(?x5528, ?x231), ?x1943 = 02krf9 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #10644 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2027 *> proper extension: 0784v1; *> query: (?x5528, ?x512) <- place_of_birth(?x5528, ?x13032), contains(?x512, ?x13032), participating_countries(?x358, ?x512) *> conf = 0.81 ranks of expected_values: 2 EVAL 0627sn nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 119.000 0.813 http://example.org/people/person/nationality #11447-026q3s3 PRED entity: 026q3s3 PRED relation: film! PRED expected values: 01kym3 => 80 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1248): 0dt645q (0.33 #24638, 0.33 #1764, 0.27 #18400), 01kym3 (0.33 #4130, 0.25 #6210, 0.12 #33244), 01wphh2 (0.33 #9667, 0.12 #20066, 0.11 #24225), 02h8hr (0.25 #11278, 0.23 #13358, 0.15 #34153), 02t1dv (0.25 #12431, 0.19 #26989, 0.18 #20750), 042gr4 (0.25 #6202, 0.17 #24918, 0.14 #26998), 04j5fx (0.23 #14319, 0.17 #12239, 0.17 #10159), 0sw6g (0.20 #7644, 0.08 #15963, 0.06 #22201), 01f6zc (0.20 #7182, 0.07 #42532, 0.05 #27978), 0479b (0.20 #7449, 0.06 #22006, 0.05 #28245) >> Best rule #24638 for best value: >> intensional similarity = 7 >> extensional distance = 16 >> proper extension: 02pb2bp; 08fbnx; 05pyrb; 0bh72t; 05vc35; >> query: (?x1334, 0dt645q) <- actor(?x1334, ?x5779), film(?x13180, ?x1334), country(?x1334, ?x252), language(?x1334, ?x90), genre(?x1334, ?x2540), ?x2540 = 0hcr, actor(?x12518, ?x13180) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4130 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 0dh8v4; *> query: (?x1334, 01kym3) <- actor(?x1334, ?x12353), film(?x13457, ?x1334), film(?x13180, ?x1334), country(?x1334, ?x252), ?x13180 = 03q64h, ?x12353 = 0ckm4x, ?x13457 = 01vs8ng, film(?x7030, ?x1334), ?x252 = 03_3d *> conf = 0.33 ranks of expected_values: 2 EVAL 026q3s3 film! 01kym3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 61.000 0.333 http://example.org/film/actor/film./film/performance/film #11446-041b4j PRED entity: 041b4j PRED relation: people! PRED expected values: 01k9gb => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 14): 0gk4g (0.05 #274, 0.04 #604, 0.04 #6478), 0qcr0 (0.03 #265, 0.03 #133, 0.03 #463), 025hl8 (0.03 #138), 0dq9p (0.03 #611, 0.03 #281, 0.03 #479), 0j8hd (0.03 #245), 02k6hp (0.02 #301, 0.02 #631, 0.02 #499), 02y0js (0.02 #266, 0.02 #464, 0.02 #596), 04p3w (0.02 #3773, 0.01 #3179, 0.01 #803), 01mtqf (0.01 #136, 0.01 #466), 04psf (0.01 #139) >> Best rule #274 for best value: >> intensional similarity = 4 >> extensional distance = 220 >> proper extension: 07h1q; 047g6; >> query: (?x9615, 0gk4g) <- gender(?x9615, ?x514), people(?x1050, ?x9615), place_of_birth(?x9615, ?x3014), ?x1050 = 041rx >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 041b4j people! 01k9gb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.054 http://example.org/people/cause_of_death/people #11445-02vntj PRED entity: 02vntj PRED relation: participant PRED expected values: 02tc5y => 119 concepts (83 used for prediction) PRED predicted values (max 10 best out of 338): 04bdzg (0.23 #5195, 0.02 #4965), 01pgzn_ (0.08 #155, 0.03 #4699, 0.02 #9246), 01dw4q (0.08 #19, 0.02 #3913, 0.02 #4563), 0456xp (0.08 #18188, 0.07 #9091, 0.07 #9741), 04205z (0.07 #9091, 0.07 #9741, 0.05 #22734), 046zh (0.05 #1010, 0.03 #19198, 0.02 #8802), 01vvb4m (0.05 #863, 0.02 #3459, 0.02 #2161), 0bx_q (0.05 #1033, 0.02 #4928, 0.02 #10774), 01gkmx (0.05 #1212, 0.02 #19400, 0.01 #26545), 03f2_rc (0.05 #682, 0.02 #7175) >> Best rule #5195 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 01n4f8; >> query: (?x4247, ?x6242) <- student(?x6501, ?x4247), award_nominee(?x123, ?x4247), participant(?x6242, ?x4247) >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02vntj participant 02tc5y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 83.000 0.230 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #11444-02gt5s PRED entity: 02gt5s PRED relation: contains PRED expected values: 0vm39 0vg8x => 112 concepts (53 used for prediction) PRED predicted values (max 10 best out of 2617): 02dtg (0.84 #26493, 0.66 #29435, 0.61 #35320), 0vm39 (0.84 #26493, 0.43 #85347, 0.30 #155964), 0nj0m (0.66 #29435, 0.61 #35320, 0.60 #52972), 05kr_ (0.66 #29435, 0.61 #35320, 0.60 #52972), 0d060g (0.60 #61800, 0.60 #94173, 0.25 #2942), 04kbn (0.60 #61800, 0.60 #94173, 0.25 #2941), 0njpq (0.60 #61800, 0.60 #94173, 0.20 #5351), 0fc2c (0.60 #61800, 0.60 #94173, 0.04 #64742), 02gt5s (0.52 #44146, 0.49 #88290, 0.48 #91231), 04rrx (0.52 #44146, 0.49 #88290, 0.48 #91231) >> Best rule #26493 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 03gh4; >> query: (?x11993, ?x8969) <- contains(?x11993, ?x8968), country(?x11993, ?x94), administrative_division(?x8969, ?x8968) >> conf = 0.84 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 31 EVAL 02gt5s contains 0vg8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 112.000 53.000 0.840 http://example.org/location/location/contains EVAL 02gt5s contains 0vm39 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 53.000 0.840 http://example.org/location/location/contains #11443-02hy9p PRED entity: 02hy9p PRED relation: gender PRED expected values: 05zppz => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #37, 0.87 #47, 0.85 #57), 02zsn (0.54 #40, 0.50 #28, 0.50 #14) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 165 >> proper extension: 03hbzj; 016lv3; 0g_rs_; >> query: (?x8159, 05zppz) <- profession(?x8159, ?x987), executive_produced_by(?x6213, ?x8159), place_of_birth(?x8159, ?x739) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hy9p gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.874 http://example.org/people/person/gender #11442-0t_07 PRED entity: 0t_07 PRED relation: contains! PRED expected values: 09c7w0 => 144 concepts (82 used for prediction) PRED predicted values (max 10 best out of 308): 09c7w0 (0.77 #48343, 0.77 #11634, 0.77 #72534), 029jpy (0.77 #48343, 0.25 #215, 0.14 #2003), 059g4 (0.77 #48343, 0.22 #26849, 0.20 #46549), 01qh7 (0.77 #48343, 0.22 #26849, 0.20 #46549), 0f8l9c (0.65 #47447, 0.60 #68952, 0.42 #40278), 01n7q (0.43 #13499, 0.43 #9021, 0.41 #8125), 0kpys (0.32 #9123, 0.28 #13601, 0.19 #8227), 0k3l5 (0.25 #398, 0.07 #14716, 0.06 #20984), 050ks (0.22 #26849, 0.20 #46549, 0.17 #52826), 01x73 (0.22 #14433, 0.21 #10849, 0.18 #4586) >> Best rule #48343 for best value: >> intensional similarity = 5 >> extensional distance = 257 >> proper extension: 0t015; 01mc11; 0_7z2; 010dft; 0rvty; 01vsl; 0y3k9; 0lpk3; 0q8sw; 0zrlp; ... >> query: (?x11331, ?x94) <- contains(?x9065, ?x11331), adjoins(?x9065, ?x4990), contains(?x9065, ?x11730), county(?x10431, ?x9065), contains(?x94, ?x11730) >> conf = 0.77 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 0t_07 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 82.000 0.767 http://example.org/location/location/contains #11441-0b44shh PRED entity: 0b44shh PRED relation: film_crew_role PRED expected values: 09zzb8 => 102 concepts (98 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.73 #411, 0.71 #1934, 0.70 #1896), 02r96rf (0.66 #562, 0.66 #488, 0.66 #263), 09vw2b7 (0.63 #1941, 0.59 #418, 0.59 #2277), 01vx2h (0.42 #50, 0.38 #310, 0.36 #235), 0dxtw (0.40 #422, 0.36 #1945, 0.36 #309), 01pvkk (0.28 #51, 0.28 #498, 0.28 #2209), 01xy5l_ (0.23 #53, 0.17 #3624, 0.13 #426), 015h31 (0.19 #47, 0.17 #3624, 0.12 #307), 089g0h (0.19 #59, 0.17 #3624, 0.10 #1955), 02ynfr (0.17 #3624, 0.16 #1951, 0.15 #240) >> Best rule #411 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 047qxs; 05_5rjx; 038bh3; 03_wm6; 06y611; >> query: (?x5109, 09zzb8) <- film(?x609, ?x5109), film_crew_role(?x5109, ?x1284), language(?x5109, ?x5607), ?x5607 = 064_8sq >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b44shh film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 98.000 0.726 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11440-03ftmg PRED entity: 03ftmg PRED relation: influenced_by PRED expected values: 03j0d => 110 concepts (28 used for prediction) PRED predicted values (max 10 best out of 317): 0p_47 (0.43 #974, 0.15 #4882, 0.07 #9226), 014z8v (0.22 #988, 0.16 #4896, 0.12 #3159), 03f0324 (0.20 #151, 0.13 #3624, 0.08 #4493), 02lt8 (0.20 #119, 0.11 #3592, 0.07 #9239), 03f70xs (0.20 #69, 0.08 #3542, 0.03 #9189), 03f47xl (0.20 #203, 0.04 #3676, 0.03 #7151), 0448r (0.20 #261, 0.03 #3734, 0.03 #5906), 01wd02c (0.20 #208, 0.03 #3681, 0.02 #3247), 01hmk9 (0.19 #1087, 0.17 #4995, 0.14 #3258), 081lh (0.16 #888, 0.13 #4796, 0.08 #3059) >> Best rule #974 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 01d5g; >> query: (?x7264, 0p_47) <- influenced_by(?x7264, ?x2465), produced_by(?x1708, ?x2465), award_winner(?x2465, ?x8286) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3809 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 93 *> proper extension: 01zkxv; 045bg; 09dt7; 01n4f8; 0c3kw; 073bb; 015pxr; 0lrh; 0d4jl; 073v6; ... *> query: (?x7264, 03j0d) <- profession(?x7264, ?x2225), influenced_by(?x7264, ?x2465), award(?x7264, ?x9629), ?x2225 = 0kyk *> conf = 0.06 ranks of expected_values: 37 EVAL 03ftmg influenced_by 03j0d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 110.000 28.000 0.432 http://example.org/influence/influence_node/influenced_by #11439-0161c PRED entity: 0161c PRED relation: administrative_parent PRED expected values: 02j71 => 174 concepts (99 used for prediction) PRED predicted values (max 10 best out of 37): 02j71 (0.84 #9871, 0.84 #8501, 0.84 #12906), 09c7w0 (0.33 #6022, 0.27 #10273, 0.25 #11102), 0j0k (0.17 #11514, 0.11 #5882, 0.11 #5881), 02qkt (0.17 #11514, 0.11 #5882, 0.11 #5881), 049nq (0.06 #916, 0.06 #233, 0.04 #505), 03rjj (0.06 #11658, 0.05 #10414, 0.03 #4106), 05r7t (0.06 #209), 03rk0 (0.05 #2091, 0.04 #588, 0.01 #11557), 07ssc (0.04 #420, 0.03 #1239, 0.02 #2471), 013p59 (0.04 #671, 0.03 #1765, 0.02 #3678) >> Best rule #9871 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 07bxhl; 01c4pv; >> query: (?x3683, 02j71) <- currency(?x3683, ?x170), olympics(?x3683, ?x1931), organization(?x3683, ?x312), ?x312 = 07t65 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0161c administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 99.000 0.841 http://example.org/base/aareas/schema/administrative_area/administrative_parent #11438-06x76 PRED entity: 06x76 PRED relation: sport PRED expected values: 0jm_ => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 9): 0jm_ (0.91 #265, 0.90 #310, 0.89 #220), 02vx4 (0.48 #554, 0.45 #621, 0.45 #601), 018jz (0.41 #258, 0.37 #240, 0.36 #339), 018w8 (0.21 #338, 0.20 #584, 0.16 #613), 03tmr (0.18 #118, 0.17 #371, 0.16 #434), 09xp_ (0.09 #259, 0.09 #123, 0.05 #232), 039yzs (0.09 #440, 0.07 #296, 0.06 #616), 06f3l (0.02 #370), 0z74 (0.02 #570, 0.02 #579) >> Best rule #265 for best value: >> intensional similarity = 20 >> extensional distance = 21 >> proper extension: 05g49; >> query: (?x11061, 0jm_) <- position(?x11061, ?x1240), position(?x11061, ?x180), position_s(?x11061, ?x706), ?x1240 = 023wyl, position_s(?x13083, ?x180), position_s(?x8902, ?x180), position_s(?x7019, ?x180), position_s(?x1516, ?x180), position_s(?x705, ?x180), ?x7019 = 026ldz7, ?x8902 = 01c_d, team(?x180, ?x7892), team(?x180, ?x5472), colors(?x11061, ?x332), ?x705 = 07k53y, ?x7892 = 07kbp5, ?x5472 = 02wvfxl, ?x13083 = 0fw9n7, ?x1516 = 0ft5vs, draft(?x11061, ?x465) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06x76 sport 0jm_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.913 http://example.org/sports/sports_team/sport #11437-03q1vd PRED entity: 03q1vd PRED relation: award PRED expected values: 0789_m => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 261): 0gqyl (0.43 #105, 0.16 #23491, 0.15 #27138), 02z0dfh (0.29 #75, 0.15 #27138, 0.13 #30785), 0bb57s (0.29 #245, 0.15 #27138, 0.13 #30785), 0bdwft (0.29 #68, 0.13 #30785, 0.12 #25112), 0cqgl9 (0.29 #193, 0.13 #30785, 0.12 #25112), 0ck27z (0.28 #5357, 0.26 #902, 0.26 #4547), 0f4x7 (0.23 #436, 0.17 #2056, 0.16 #23491), 0bfvd4 (0.23 #520, 0.13 #925, 0.13 #30785), 0gqy2 (0.22 #975, 0.15 #570, 0.13 #1785), 05zr6wv (0.20 #2042, 0.13 #1232, 0.11 #2852) >> Best rule #105 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 021vwt; 02jsgf; 014g22; 02ch1w; 057_yx; >> query: (?x2726, 0gqyl) <- award_nominee(?x5806, ?x2726), award_nominee(?x3660, ?x2726), ?x3660 = 02p7_k, ?x5806 = 034zc0 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #425 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 050t68; *> query: (?x2726, 0789_m) <- award_nominee(?x5840, ?x2726), gender(?x2726, ?x231), ?x5840 = 02ch1w *> conf = 0.15 ranks of expected_values: 28 EVAL 03q1vd award 0789_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 91.000 91.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11436-0xn7b PRED entity: 0xn7b PRED relation: place_of_birth! PRED expected values: 01b9z4 => 133 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1455): 06n7h7 (0.42 #182985, 0.42 #164677, 0.37 #33982), 02_fj (0.37 #33982, 0.33 #13070, 0.31 #120242), 0c8br (0.08 #1666, 0.07 #67964, 0.06 #73191), 0d__g (0.08 #2260, 0.03 #10102, 0.03 #7489), 047cqr (0.08 #2208, 0.03 #10050, 0.03 #7437), 021b_ (0.08 #2187, 0.03 #10029, 0.03 #7416), 03hpr (0.08 #2132, 0.03 #9974, 0.03 #7361), 04n2vgk (0.08 #1929, 0.03 #9771, 0.03 #7158), 016s0m (0.08 #1853, 0.03 #9695, 0.03 #7082), 01y8d4 (0.08 #1712, 0.03 #9554, 0.03 #6941) >> Best rule #182985 for best value: >> intensional similarity = 4 >> extensional distance = 366 >> proper extension: 087vz; >> query: (?x12263, ?x690) <- location(?x690, ?x12263), nominated_for(?x690, ?x3075), nationality(?x690, ?x2629), place_of_birth(?x690, ?x12372) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0xn7b place_of_birth! 01b9z4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 81.000 0.421 http://example.org/people/person/place_of_birth #11435-02__7n PRED entity: 02__7n PRED relation: award_nominee PRED expected values: 04cf09 => 82 concepts (48 used for prediction) PRED predicted values (max 10 best out of 767): 030xr_ (0.85 #4662, 0.81 #111900, 0.81 #79256), 01438g (0.85 #4662, 0.81 #111900, 0.81 #79256), 02qgyv (0.85 #4662, 0.81 #111900, 0.81 #79256), 04cf09 (0.85 #4662, 0.81 #79256, 0.81 #111899), 02__7n (0.50 #3975, 0.44 #6306, 0.29 #86253), 03yrkt (0.38 #6492, 0.18 #104904, 0.17 #4161), 06wm0z (0.38 #5862, 0.18 #104904, 0.17 #3531), 02bkdn (0.33 #2732, 0.18 #104904, 0.12 #5063), 01cwkq (0.31 #6876, 0.18 #104904, 0.17 #4545), 01jz6x (0.31 #6807, 0.18 #104904, 0.17 #4476) >> Best rule #4662 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 011zd3; 02qgyv; 01438g; 01wgcvn; >> query: (?x7268, ?x1205) <- nominated_for(?x7268, ?x3404), award_nominee(?x3079, ?x7268), award_nominee(?x1205, ?x7268), ?x3079 = 0686zv >> conf = 0.85 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 02__7n award_nominee 04cf09 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 48.000 0.845 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11434-04bbpm PRED entity: 04bbpm PRED relation: category PRED expected values: 08mbj5d => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #7, 0.90 #31, 0.90 #40) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 037s9x; >> query: (?x8069, 08mbj5d) <- contains(?x94, ?x8069), currency(?x8069, ?x170), school_type(?x8069, ?x3092) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bbpm category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.906 http://example.org/common/topic/webpage./common/webpage/category #11433-02f6ym PRED entity: 02f6ym PRED relation: award_winner PRED expected values: 0dl567 => 44 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1671): 01vwyqp (0.60 #9870, 0.60 #8102, 0.50 #3167), 086qd (0.60 #7837, 0.50 #2902, 0.40 #5370), 06rgq (0.60 #6757, 0.50 #4289, 0.40 #9224), 01dw9z (0.60 #5502, 0.50 #3034, 0.40 #7969), 02z4b_8 (0.50 #16376, 0.40 #8972, 0.40 #6505), 0197tq (0.50 #2494, 0.40 #7429, 0.40 #4962), 0dzlk (0.50 #4757, 0.40 #9692, 0.40 #7225), 01vw20h (0.50 #13333, 0.29 #18269, 0.17 #10867), 01vw37m (0.50 #13726, 0.12 #18662, 0.10 #16195), 01pq5j7 (0.40 #17273, 0.40 #15987, 0.40 #7402) >> Best rule #9870 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 01by1l; >> query: (?x6220, ?x3256) <- award(?x5225, ?x6220), award(?x3256, ?x6220), award(?x2194, ?x6220), ?x5225 = 01pq5j7, ?x3256 = 01vwyqp, artist(?x2193, ?x2194), award_winner(?x762, ?x2194) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #8293 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 01by1l; *> query: (?x6220, 0dl567) <- award(?x5225, ?x6220), award(?x3256, ?x6220), award(?x2194, ?x6220), ?x5225 = 01pq5j7, ?x3256 = 01vwyqp, artist(?x2193, ?x2194), award_winner(?x762, ?x2194) *> conf = 0.20 ranks of expected_values: 121 EVAL 02f6ym award_winner 0dl567 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 44.000 18.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #11432-06b19 PRED entity: 06b19 PRED relation: school_type PRED expected values: 05jxkf => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 21): 05jxkf (0.82 #76, 0.67 #4, 0.61 #196), 05pcjw (0.32 #337, 0.26 #553, 0.24 #1105), 01rs41 (0.29 #1085, 0.27 #1685, 0.26 #1013), 07tf8 (0.20 #345, 0.20 #561, 0.20 #105), 01_9fk (0.18 #266, 0.14 #1082, 0.13 #602), 01_srz (0.08 #2164, 0.07 #747, 0.07 #1011), 04qbv (0.08 #2164, 0.05 #64, 0.03 #976), 06cs1 (0.08 #2164, 0.05 #54, 0.02 #750), 0bwd5 (0.08 #2164, 0.04 #763, 0.03 #571), 04399 (0.08 #2164, 0.03 #710, 0.02 #1166) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 071_8; 0mbwf; >> query: (?x7912, 05jxkf) <- institution(?x620, ?x7912), currency(?x7912, ?x2244), organization(?x5510, ?x7912), ?x2244 = 0ptk_ >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06b19 school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.818 http://example.org/education/educational_institution/school_type #11431-0fp_xp PRED entity: 0fp_xp PRED relation: type_of_union PRED expected values: 04ztj => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.68 #191, 0.67 #235, 0.66 #126), 01g63y (0.25 #255, 0.24 #178, 0.19 #22), 0jgjn (0.02 #48, 0.01 #125) >> Best rule #191 for best value: >> intensional similarity = 7 >> extensional distance = 2564 >> proper extension: 016qtt; 05ty4m; 01vrx3g; 05cj4r; 02zq43; 0436f4; 01rr9f; 03f2_rc; 01ty7ll; 01gvr1; ... >> query: (?x8712, 04ztj) <- profession(?x8712, ?x7623), profession(?x11481, ?x7623), profession(?x8324, ?x7623), team(?x8324, ?x1599), athlete(?x471, ?x8324), team(?x11481, ?x1143), location(?x11481, ?x14378) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fp_xp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.684 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11430-070ltt PRED entity: 070ltt PRED relation: actor PRED expected values: 02t_99 => 96 concepts (30 used for prediction) PRED predicted values (max 10 best out of 374): 01rzxl (0.78 #9338, 0.60 #14010, 0.59 #8402), 01s7z0 (0.59 #8402, 0.57 #24289, 0.57 #12141), 01j7rd (0.50 #3898, 0.33 #10439, 0.22 #18845), 0163t3 (0.33 #9092, 0.33 #8157, 0.25 #13764), 03q45x (0.33 #1539, 0.22 #18351, 0.20 #6207), 0pyww (0.33 #1328, 0.20 #5996, 0.14 #25618), 030wkp (0.33 #1672, 0.20 #6340, 0.12 #13814), 02tf1y (0.33 #1620, 0.20 #6288, 0.12 #13762), 02dlfh (0.33 #1570, 0.20 #6238, 0.12 #13712), 04s430 (0.33 #1424, 0.20 #6092, 0.12 #13566) >> Best rule #9338 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 05fgr_; >> query: (?x10551, ?x11630) <- program(?x11630, ?x10551), nationality(?x11630, ?x94), genre(?x10551, ?x53), category(?x11630, ?x134), location(?x11630, ?x2623), profession(?x11630, ?x319), type_of_union(?x11630, ?x566), actor(?x11042, ?x11630) >> conf = 0.78 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 070ltt actor 02t_99 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 30.000 0.778 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11429-02kxbwx PRED entity: 02kxbwx PRED relation: award PRED expected values: 02n9nmz 02rdyk7 03hj5vf => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 273): 04dn09n (0.78 #22697, 0.77 #7043, 0.76 #32879), 02x4sn8 (0.72 #37578, 0.70 #28960, 0.70 #32878), 02wkmx (0.72 #37578, 0.70 #28960, 0.70 #32878), 027b9ly (0.72 #37578, 0.70 #28960, 0.70 #32878), 09d28z (0.72 #37578, 0.70 #28960, 0.70 #32878), 02n9nmz (0.50 #846, 0.22 #6713, 0.20 #7889), 0k611 (0.40 #477, 0.14 #7826, 0.14 #30525), 09sb52 (0.33 #36, 0.26 #1993, 0.26 #23908), 09qvc0 (0.33 #35, 0.07 #1209, 0.05 #2383), 02rdyk7 (0.26 #1256, 0.25 #3212, 0.20 #4385) >> Best rule #22697 for best value: >> intensional similarity = 3 >> extensional distance = 1311 >> proper extension: 01dw4q; 09fqtq; 07w21; 02pp_q_; 025vry; 01vvycq; 04sx9_; 02r3zy; 02r4qs; 04nw9; ... >> query: (?x826, ?x1862) <- award_winner(?x1862, ?x826), award(?x144, ?x1862), ceremony(?x1862, ?x78) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #846 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 01r216; *> query: (?x826, 02n9nmz) <- award_winner(?x826, ?x163), award(?x826, ?x2341), ?x2341 = 02x17s4 *> conf = 0.50 ranks of expected_values: 6, 10, 63 EVAL 02kxbwx award 03hj5vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 117.000 117.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02kxbwx award 02rdyk7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 117.000 117.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02kxbwx award 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 117.000 117.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11428-01d_h PRED entity: 01d_h PRED relation: artists! PRED expected values: 016clz 06by7 => 126 concepts (50 used for prediction) PRED predicted values (max 10 best out of 301): 016clz (0.62 #11816, 0.43 #911, 0.39 #7572), 064t9 (0.56 #3035, 0.53 #2733, 0.41 #7579), 0ggq0m (0.55 #5760, 0.40 #313, 0.25 #11822), 06by7 (0.50 #12135, 0.49 #3651, 0.44 #4862), 0y3_8 (0.35 #3070, 0.27 #2768, 0.24 #3328), 0glt670 (0.33 #1854, 0.31 #2156, 0.28 #7607), 03_d0 (0.33 #1522, 0.30 #5759, 0.24 #1219), 05bt6j (0.31 #3673, 0.26 #12157, 0.21 #4884), 0xhtw (0.30 #12130, 0.29 #1528, 0.27 #4554), 08jyyk (0.28 #4604, 0.13 #11877, 0.11 #5209) >> Best rule #11816 for best value: >> intensional similarity = 4 >> extensional distance = 154 >> proper extension: 06br6t; >> query: (?x8806, 016clz) <- role(?x8806, ?x227), artists(?x4910, ?x8806), artists(?x4910, ?x6910), ?x6910 = 05y7hc >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 01d_h artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 50.000 0.615 http://example.org/music/genre/artists EVAL 01d_h artists! 016clz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 50.000 0.615 http://example.org/music/genre/artists #11427-02bb47 PRED entity: 02bb47 PRED relation: citytown PRED expected values: 01zqy6t => 106 concepts (72 used for prediction) PRED predicted values (max 10 best out of 179): 01zqy6t (0.25 #343, 0.21 #4052, 0.21 #3683), 02_286 (0.21 #4421, 0.16 #11434, 0.15 #19177), 09c7w0 (0.21 #4052, 0.21 #3683, 0.20 #3314), 01n7q (0.21 #4052, 0.21 #3683, 0.20 #3314), 030qb3t (0.20 #3711, 0.17 #3342, 0.16 #9605), 0rh6k (0.12 #738, 0.12 #2210, 0.11 #1106), 0mp3l (0.12 #781, 0.11 #1149, 0.08 #2253), 04jpl (0.10 #2584, 0.07 #375, 0.05 #1480), 0r00l (0.10 #4333, 0.09 #9858, 0.07 #5439), 0r02m (0.10 #3278, 0.09 #4016, 0.09 #3647) >> Best rule #343 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 06pwq; >> query: (?x3212, 01zqy6t) <- student(?x3212, ?x11088), currency(?x3212, ?x170), ?x11088 = 0d3k14 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bb47 citytown 01zqy6t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 72.000 0.250 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #11426-02_qt PRED entity: 02_qt PRED relation: currency PRED expected values: 09nqf => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.78 #127, 0.76 #57, 0.76 #78), 088n7 (0.10 #49, 0.06 #56), 01nv4h (0.02 #121, 0.02 #65, 0.02 #149), 02l6h (0.01 #151, 0.01 #137) >> Best rule #127 for best value: >> intensional similarity = 6 >> extensional distance = 944 >> proper extension: 07gp9; 0gkz15s; 0jzw; 0872p_c; 0gj8t_b; 0jqn5; 03fts; 05sxzwc; 05pbl56; 0bq8tmw; ... >> query: (?x3844, 09nqf) <- film(?x9604, ?x3844), film(?x2910, ?x3844), profession(?x2910, ?x1146), film_release_distribution_medium(?x3844, ?x81), award_nominee(?x2589, ?x9604), participant(?x11054, ?x9604) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_qt currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.776 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11425-02llzg PRED entity: 02llzg PRED relation: time_zones! PRED expected values: 0154j 0h3y 0k6nt 0h7x 01lfvj 01mjq 08966 0glb5 05p7tx 06n8j 061k5 0gw2w 0c7f7 03qhnx => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1491): 0h7x (0.81 #5638, 0.77 #3387, 0.76 #9019), 04q_g (0.81 #5638, 0.77 #3387, 0.76 #9019), 07kg3 (0.81 #5638, 0.77 #3387, 0.76 #9019), 0ky0b (0.81 #5638, 0.77 #3387, 0.76 #9019), 0bzty (0.81 #5638, 0.77 #3387, 0.76 #9019), 052fbt (0.81 #5638, 0.77 #3387, 0.76 #9019), 0fqyc (0.81 #5638, 0.77 #3387, 0.76 #9019), 0c7hq (0.81 #5638, 0.77 #3387, 0.76 #9019), 052gtg (0.81 #5638, 0.77 #3387, 0.76 #9019), 068cn (0.81 #5638, 0.77 #3387, 0.76 #9019) >> Best rule #5638 for best value: >> intensional similarity = 55 >> extensional distance = 2 >> proper extension: 052vwh; >> query: (?x2864, ?x8990) <- time_zones(?x10691, ?x2864), time_zones(?x9660, ?x2864), time_zones(?x9485, ?x2864), time_zones(?x9283, ?x2864), time_zones(?x8989, ?x2864), time_zones(?x8174, ?x2864), time_zones(?x7101, ?x2864), time_zones(?x5274, ?x2864), time_zones(?x4826, ?x2864), time_zones(?x3277, ?x2864), time_zones(?x2979, ?x2864), time_zones(?x2756, ?x2864), time_zones(?x1679, ?x2864), time_zones(?x1374, ?x2864), category(?x10691, ?x134), combatants(?x6371, ?x1679), contains(?x1679, ?x7154), location(?x1279, ?x9660), film_release_region(?x11809, ?x1374), adjoins(?x1679, ?x8264), featured_film_locations(?x9715, ?x1374), featured_film_locations(?x9279, ?x1374), month(?x8174, ?x9905), month(?x8174, ?x2140), place_of_birth(?x11251, ?x1374), participating_countries(?x784, ?x5274), country(?x9283, ?x512), contains(?x455, ?x9283), teams(?x9660, ?x202), countries_spoken_in(?x5607, ?x5274), administrative_parent(?x2979, ?x551), jurisdiction_of_office(?x182, ?x9485), form_of_government(?x5274, ?x1926), honored_for(?x7105, ?x9279), countries_spoken_in(?x12283, ?x2979), combatants(?x9203, ?x5274), country(?x1121, ?x2979), ?x2140 = 040fb, citytown(?x2106, ?x4826), administrative_division(?x8989, ?x8990), organization(?x2756, ?x312), currency(?x2979, ?x170), ?x9905 = 028kb, location(?x2671, ?x8174), profession(?x11251, ?x524), film_release_region(?x249, ?x5274), administrative_parent(?x10479, ?x7101), mode_of_transportation(?x8174, ?x4272), film(?x665, ?x9715), place_of_death(?x10870, ?x8174), film_release_region(?x80, ?x3277), participating_countries(?x418, ?x2756), nationality(?x1328, ?x6371), ?x524 = 02jknp, country(?x6642, ?x3277) >> conf = 0.81 => this is the best rule for 21 predicted values ranks of expected_values: 1, 22, 26, 27, 28, 92, 117, 162, 169, 171, 173, 178, 1077 EVAL 02llzg time_zones! 03qhnx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 0c7f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 0gw2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 061k5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 06n8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 05p7tx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 0glb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 08966 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 01lfvj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 0h7x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 0k6nt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 0h3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 12.000 12.000 0.814 http://example.org/location/location/time_zones EVAL 02llzg time_zones! 0154j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 12.000 12.000 0.814 http://example.org/location/location/time_zones #11424-025t3bg PRED entity: 025t3bg PRED relation: mode_of_transportation! PRED expected values: 0rh6k 05ywg 0dclg 0d6lp 0ply0 071vr 0947l 02frhbc => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 65): 0rh6k (0.50 #49, 0.40 #129, 0.40 #113), 0947l (0.50 #58, 0.40 #138, 0.40 #122), 071vr (0.50 #57, 0.40 #137, 0.40 #121), 0d6lp (0.50 #53, 0.40 #133, 0.40 #117), 04jpl (0.50 #50, 0.40 #130, 0.40 #114), 0dclg (0.33 #20, 0.25 #52, 0.20 #132), 018lc_ (0.33 #32, 0.25 #64, 0.20 #144), 01gbzb (0.33 #31, 0.25 #63, 0.20 #143), 0mpbx (0.33 #30, 0.25 #62, 0.20 #142), 03pzf (0.33 #29, 0.25 #61, 0.20 #141) >> Best rule #49 for best value: >> intensional similarity = 22 >> extensional distance = 2 >> proper extension: 07jdr; >> query: (?x6665, 0rh6k) <- mode_of_transportation(?x11197, ?x6665), mode_of_transportation(?x10981, ?x6665), mode_of_transportation(?x9559, ?x6665), mode_of_transportation(?x8977, ?x6665), mode_of_transportation(?x8007, ?x6665), mode_of_transportation(?x5267, ?x6665), mode_of_transportation(?x4698, ?x6665), mode_of_transportation(?x3052, ?x6665), mode_of_transportation(?x2316, ?x6665), mode_of_transportation(?x739, ?x6665), mode_of_transportation(?x206, ?x6665), ?x5267 = 0d9jr, ?x4698 = 056_y, ?x2316 = 06t2t, ?x3052 = 01cx_, ?x8977 = 02z0j, contains(?x7405, ?x8007), month(?x11197, ?x1459), ?x739 = 02_286, contains(?x10981, ?x12944), ?x9559 = 07dfk, ?x206 = 01914 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 11, 14, 16 EVAL 025t3bg mode_of_transportation! 02frhbc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 107.000 107.000 0.500 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 025t3bg mode_of_transportation! 0947l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.500 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 025t3bg mode_of_transportation! 071vr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.500 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 025t3bg mode_of_transportation! 0ply0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 107.000 107.000 0.500 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 025t3bg mode_of_transportation! 0d6lp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.500 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 025t3bg mode_of_transportation! 0dclg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.500 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 025t3bg mode_of_transportation! 05ywg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 107.000 107.000 0.500 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 025t3bg mode_of_transportation! 0rh6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.500 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #11423-024c2 PRED entity: 024c2 PRED relation: symptom_of! PRED expected values: 0gxb2 0cjf0 => 20 concepts (16 used for prediction) PRED predicted values (max 10 best out of 52): 01j6t0 (0.89 #423, 0.89 #395, 0.87 #309), 0brgy (0.87 #137, 0.80 #171, 0.80 #158), 0gxb2 (0.80 #138, 0.71 #174, 0.59 #50), 0cjf0 (0.78 #405, 0.76 #486, 0.76 #476), 0hgxh (0.77 #76, 0.69 #416, 0.64 #308), 02tfl8 (0.76 #380, 0.69 #416, 0.67 #82), 0j5fv (0.69 #43, 0.59 #508, 0.58 #206), 01cdt5 (0.67 #105, 0.59 #50, 0.52 #228), 0dq9p (0.58 #206, 0.53 #272, 0.51 #392), 01l2m3 (0.58 #206, 0.53 #272, 0.51 #392) >> Best rule #423 for best value: >> intensional similarity = 64 >> extensional distance = 16 >> proper extension: 04psf; 0dq9p; 02k6hp; 04nz3; >> query: (?x14430, ?x4905) <- symptom_of(?x13373, ?x14430), symptom_of(?x9438, ?x14430), symptom_of(?x9438, ?x14024), symptom_of(?x9438, ?x13131), symptom_of(?x9438, ?x11739), symptom_of(?x9438, ?x11307), symptom_of(?x9438, ?x11064), symptom_of(?x9438, ?x10480), symptom_of(?x9438, ?x9898), symptom_of(?x9438, ?x9119), symptom_of(?x9438, ?x8675), symptom_of(?x9438, ?x7260), symptom_of(?x9438, ?x6781), symptom_of(?x9438, ?x6655), symptom_of(?x9438, ?x4322), ?x13131 = 0d19y2, symptom_of(?x13373, ?x13485), symptom_of(?x13373, ?x12536), symptom_of(?x13373, ?x7006), symptom_of(?x13373, ?x4959), symptom_of(?x13373, ?x3680), ?x4959 = 01dcqj, people(?x6781, ?x2145), ?x9119 = 011zdm, risk_factors(?x11064, ?x7007), ?x7007 = 097ns, ?x14024 = 0h1wz, notable_people_with_this_condition(?x7260, ?x7921), people(?x7260, ?x5440), symptom_of(?x13099, ?x6781), ?x10480 = 0h1n9, ?x9898 = 09jg8, ?x13485 = 07s4l, ?x7006 = 02psvcf, ?x8675 = 01gkcc, ?x13099 = 01pf6, risk_factors(?x7260, ?x8524), people(?x4322, ?x12507), people(?x4322, ?x8473), people(?x4322, ?x5370), people(?x4322, ?x3194), ?x12507 = 05v45k, risk_factors(?x11659, ?x12536), ?x3680 = 025hl8, ?x6655 = 09d11, symptom_of(?x13487, ?x4322), symptom_of(?x4905, ?x4322), ?x8473 = 0gyy0, ?x5440 = 016z51, ?x11659 = 072hv, ?x11739 = 0167bx, ?x5370 = 016gkf, symptom_of(?x11307, ?x7586), risk_factors(?x11307, ?x4195), people(?x11307, ?x6745), ?x13487 = 01cdt5, risk_factors(?x4322, ?x11678), risk_factors(?x6483, ?x4322), ?x6745 = 01938t, ?x4905 = 01j6t0, ?x3194 = 0jrny, ?x11678 = 0fltx, ?x8524 = 01hbgs, symptom_of(?x13373, ?x11739) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #138 for first EXPECTED value: *> intensional similarity = 56 *> extensional distance = 7 *> proper extension: 07s4l; *> query: (?x14430, ?x9509) <- symptom_of(?x13373, ?x14430), symptom_of(?x9438, ?x14430), symptom_of(?x9438, ?x14024), symptom_of(?x9438, ?x13131), symptom_of(?x9438, ?x11739), symptom_of(?x9438, ?x11307), symptom_of(?x9438, ?x11064), symptom_of(?x9438, ?x10480), symptom_of(?x9438, ?x9898), symptom_of(?x9438, ?x9119), symptom_of(?x9438, ?x8675), symptom_of(?x9438, ?x7260), symptom_of(?x9438, ?x6781), symptom_of(?x9438, ?x6655), symptom_of(?x9438, ?x4322), ?x13373 = 0f3kl, ?x4322 = 0gk4g, people(?x7260, ?x5440), ?x5440 = 016z51, symptom_of(?x9510, ?x7260), ?x11739 = 0167bx, ?x6655 = 09d11, ?x8675 = 01gkcc, ?x13131 = 0d19y2, risk_factors(?x7260, ?x8524), risk_factors(?x7260, ?x8523), risk_factors(?x7260, ?x8023), risk_factors(?x7260, ?x231), risk_factors(?x14024, ?x11160), ?x231 = 05zppz, people(?x11307, ?x6745), ?x11064 = 01n3bm, symptom_of(?x6780, ?x11307), ?x8524 = 01hbgs, risk_factors(?x11307, ?x4195), ?x8023 = 0jpmt, symptom_of(?x13487, ?x14024), symptom_of(?x10717, ?x14024), ?x10717 = 0cjf0, ?x11160 = 012jc, ?x9898 = 09jg8, ?x10480 = 0h1n9, ?x13487 = 01cdt5, ?x6780 = 0j5fv, people(?x6781, ?x2145), symptom_of(?x13099, ?x6781), symptom_of(?x9509, ?x6781), ?x13099 = 01pf6, ?x8523 = 0c58k, risk_factors(?x9119, ?x7007), ?x7007 = 097ns, risk_factors(?x9510, ?x7260), symptom_of(?x9509, ?x9119), risk_factors(?x9510, ?x14024), risk_factors(?x14024, ?x8023), risk_factors(?x6781, ?x8523) *> conf = 0.80 ranks of expected_values: 3, 4 EVAL 024c2 symptom_of! 0cjf0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 20.000 16.000 0.889 http://example.org/medicine/symptom/symptom_of EVAL 024c2 symptom_of! 0gxb2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 20.000 16.000 0.889 http://example.org/medicine/symptom/symptom_of #11422-0gs5q PRED entity: 0gs5q PRED relation: profession PRED expected values: 01d_h8 => 105 concepts (100 used for prediction) PRED predicted values (max 10 best out of 76): 01d_h8 (0.85 #3031, 0.83 #1878, 0.82 #294), 02jknp (0.63 #1591, 0.60 #295, 0.54 #2744), 0fj9f (0.50 #194, 0.05 #2499, 0.04 #3507), 09jwl (0.38 #4049, 0.36 #5345, 0.36 #6497), 018gz8 (0.32 #1454, 0.27 #1742, 0.24 #590), 0nbcg (0.28 #4061, 0.26 #6509, 0.26 #5069), 02krf9 (0.28 #312, 0.21 #1608, 0.20 #2041), 016z4k (0.27 #4037, 0.24 #5045, 0.24 #5333), 0dz3r (0.25 #4035, 0.23 #5043, 0.22 #5331), 012t_z (0.25 #156, 0.08 #1884, 0.07 #3037) >> Best rule #3031 for best value: >> intensional similarity = 3 >> extensional distance = 343 >> proper extension: 0ksf29; 0gg9_5q; 0gv40; 0d_skg; 02p59ry; 0522wp; 0342vg; 029ghl; 02hh8j; 02qx1m2; ... >> query: (?x8744, 01d_h8) <- gender(?x8744, ?x231), profession(?x8744, ?x353), produced_by(?x4853, ?x8744) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gs5q profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 100.000 0.852 http://example.org/people/person/profession #11421-01gvyp PRED entity: 01gvyp PRED relation: nationality PRED expected values: 09c7w0 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.86 #3715, 0.85 #2608, 0.82 #4317), 0kcrd (0.33 #9333, 0.33 #2606, 0.33 #9837), 0gyh (0.33 #9333, 0.33 #2606, 0.33 #9837), 0jfqp (0.25 #6825, 0.25 #7626), 05fkf (0.25 #6825, 0.25 #7626), 0d060g (0.17 #207, 0.05 #1611, 0.05 #5729), 02jx1 (0.12 #834, 0.12 #634, 0.10 #6156), 07ssc (0.11 #816, 0.09 #616, 0.08 #3427), 03rk0 (0.08 #2551, 0.07 #8274, 0.06 #9177), 0f8l9c (0.06 #623, 0.05 #823, 0.03 #723) >> Best rule #3715 for best value: >> intensional similarity = 3 >> extensional distance = 998 >> proper extension: 028q6; 02bfmn; 05m63c; 01tvz5j; 02qjj7; 02ndbd; 014zfs; 02sjf5; 01963w; 01j4ls; ... >> query: (?x6951, 09c7w0) <- student(?x1884, ?x6951), school(?x580, ?x1884), institution(?x620, ?x1884) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gvyp nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.860 http://example.org/people/person/nationality #11420-03fts PRED entity: 03fts PRED relation: film! PRED expected values: 016tw3 => 71 concepts (65 used for prediction) PRED predicted values (max 10 best out of 46): 0kc9f (0.33 #73), 017s11 (0.25 #77, 0.15 #299, 0.14 #595), 01795t (0.25 #92, 0.06 #684, 0.06 #1130), 086k8 (0.17 #372, 0.16 #817, 0.15 #1709), 016tw3 (0.17 #307, 0.16 #1495, 0.16 #1421), 05qd_ (0.17 #83, 0.16 #231, 0.15 #157), 016tt2 (0.17 #78, 0.12 #374, 0.11 #2383), 0jz9f (0.17 #75, 0.07 #149, 0.06 #223), 032j_n (0.17 #131, 0.02 #353, 0.02 #723), 0g1rw (0.10 #156, 0.10 #230, 0.06 #1715) >> Best rule #73 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 09dv8h; >> query: (?x1474, 0kc9f) <- film(?x9395, ?x1474), film(?x8022, ?x1474), ?x9395 = 09nhvw, location(?x8022, ?x4105) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #307 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 249 *> proper extension: 047svrl; 01gglm; *> query: (?x1474, 016tw3) <- film(?x9395, ?x1474), production_companies(?x1474, ?x4564), artists(?x671, ?x9395) *> conf = 0.17 ranks of expected_values: 5 EVAL 03fts film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 71.000 65.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #11419-01f8hf PRED entity: 01f8hf PRED relation: film! PRED expected values: 02d6n_ => 95 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1320): 03r1pr (0.44 #43713, 0.44 #35387, 0.43 #52039), 02cyfz (0.44 #43713, 0.44 #35387, 0.39 #116581), 04_1nk (0.44 #43713, 0.44 #35387, 0.39 #116581), 03_gd (0.43 #52039, 0.41 #110335, 0.36 #114499), 01y_px (0.25 #2447, 0.05 #10773, 0.04 #6610), 01fwk3 (0.25 #2543, 0.05 #10869, 0.04 #6706), 0j_c (0.17 #4575, 0.09 #46205, 0.06 #12901), 016zp5 (0.17 #977, 0.04 #69669, 0.04 #7222), 03vgp7 (0.17 #551, 0.04 #6796, 0.03 #8877), 01ggc9 (0.17 #1729, 0.04 #7974, 0.03 #10055) >> Best rule #43713 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 05c26ss; 0ggbfwf; 0k7tq; 03cyslc; 023g6w; 032sl_; 0bs5f0b; 01qdmh; >> query: (?x4680, ?x2214) <- genre(?x4680, ?x571), film_release_region(?x4680, ?x94), nominated_for(?x2214, ?x4680) >> conf = 0.44 => this is the best rule for 3 predicted values *> Best rule #10179 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 0jjy0; 03l6q0; 048rn; 02mc5v; 09qljs; *> query: (?x4680, 02d6n_) <- genre(?x4680, ?x571), currency(?x4680, ?x170), ?x571 = 03npn, executive_produced_by(?x4680, ?x11580) *> conf = 0.03 ranks of expected_values: 378 EVAL 01f8hf film! 02d6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 95.000 62.000 0.443 http://example.org/film/actor/film./film/performance/film #11418-03hxsv PRED entity: 03hxsv PRED relation: film_crew_role PRED expected values: 01vx2h => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 25): 089g0h (0.57 #75, 0.55 #339, 0.16 #104), 02_n3z (0.50 #1, 0.44 #324, 0.40 #60), 01vx2h (0.49 #97, 0.45 #332, 0.43 #361), 0dxtw (0.43 #860, 0.42 #1681, 0.41 #125), 01pvkk (0.33 #98, 0.33 #245, 0.29 #1450), 0263ycg (0.25 #15, 0.19 #74, 0.12 #338), 01d_h8 (0.25 #2, 0.03 #31, 0.02 #206), 02rh1dz (0.23 #36, 0.19 #95, 0.17 #211), 02ynfr (0.20 #865, 0.19 #72, 0.19 #1453), 089fss (0.14 #64, 0.11 #328, 0.10 #563) >> Best rule #75 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 02z2mr7; 047rkcm; 047vp1n; 04180vy; >> query: (?x6332, 089g0h) <- film_crew_role(?x6332, ?x4305), genre(?x6332, ?x1403), ?x1403 = 02l7c8, ?x4305 = 0215hd >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 0522wp; *> query: (?x6332, 01vx2h) <- category(?x6332, ?x134), region(?x6332, ?x512), film_distribution_medium(?x6332, ?x81) *> conf = 0.49 ranks of expected_values: 3 EVAL 03hxsv film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.571 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11417-0306ds PRED entity: 0306ds PRED relation: award_nominee! PRED expected values: 03zz8b 0fthdk => 78 concepts (41 used for prediction) PRED predicted values (max 10 best out of 725): 03zz8b (0.82 #6949, 0.82 #2316, 0.81 #90317), 0fthdk (0.82 #6949, 0.82 #2316, 0.81 #90317), 025t9b (0.82 #6949, 0.82 #2316, 0.81 #90317), 06jzh (0.82 #6949, 0.82 #2316, 0.81 #90317), 0306ds (0.43 #565, 0.15 #90316, 0.10 #57897), 02vntj (0.19 #973, 0.02 #3290, 0.01 #47289), 06dv3 (0.15 #90316, 0.14 #41, 0.03 #46357), 0kszw (0.15 #90316, 0.14 #539, 0.02 #2856), 0h10vt (0.15 #90316, 0.14 #2020, 0.01 #4337), 027f7dj (0.15 #90316, 0.10 #57897, 0.05 #321) >> Best rule #6949 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 011zf2; 03yf3z; 03h610; 01m5m5b; >> query: (?x2615, ?x539) <- award_nominee(?x2615, ?x539), student(?x2909, ?x2615), category(?x2615, ?x134) >> conf = 0.82 => this is the best rule for 4 predicted values ranks of expected_values: 1, 2 EVAL 0306ds award_nominee! 0fthdk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 41.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 0306ds award_nominee! 03zz8b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 41.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11416-02z13jg PRED entity: 02z13jg PRED relation: award_winner PRED expected values: 01qscs 02tv80 03jj93 01f873 01tsbmv => 58 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1198): 016yvw (0.53 #6112, 0.33 #3660, 0.08 #73612), 0170pk (0.53 #5254, 0.22 #2802, 0.08 #42062), 06cgy (0.47 #5207, 0.44 #2755, 0.14 #71158), 02qgqt (0.47 #4921, 0.33 #2469, 0.33 #17), 0z4s (0.47 #4974, 0.33 #2522, 0.08 #73612), 039bp (0.44 #2660, 0.40 #5112, 0.07 #41920), 0bj9k (0.44 #2864, 0.40 #5316, 0.06 #44577), 01vvb4m (0.44 #3109, 0.40 #5561, 0.06 #42369), 0l786 (0.44 #4025, 0.20 #6477, 0.04 #45738), 0bl2g (0.40 #4964, 0.33 #2512, 0.05 #44225) >> Best rule #6112 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 054ky1; >> query: (?x850, 016yvw) <- award_winner(?x850, ?x4470), award_winner(?x850, ?x1019), award_winner(?x850, ?x406), ?x406 = 09fb5, award_nominee(?x1019, ?x294), actor(?x2042, ?x4470) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #4749 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 0f4x7; 0cqh46; 09cm54; 0bfvd4; 027c95y; 02w9sd7; *> query: (?x850, 03jj93) <- nominated_for(?x850, ?x2189), award_winner(?x850, ?x10724), award_winner(?x850, ?x8151), gender(?x10724, ?x231), ?x8151 = 0d6d2, nationality(?x10724, ?x94) *> conf = 0.22 ranks of expected_values: 57, 75, 95, 133 EVAL 02z13jg award_winner 01tsbmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 34.000 0.533 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02z13jg award_winner 01f873 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 58.000 34.000 0.533 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02z13jg award_winner 03jj93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 58.000 34.000 0.533 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02z13jg award_winner 02tv80 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 58.000 34.000 0.533 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02z13jg award_winner 01qscs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 58.000 34.000 0.533 http://example.org/award/award_category/winners./award/award_honor/award_winner #11415-016_mj PRED entity: 016_mj PRED relation: film PRED expected values: 05c26ss => 117 concepts (104 used for prediction) PRED predicted values (max 10 best out of 981): 063fh9 (0.18 #2959, 0.15 #4742, 0.12 #6525), 03mh94 (0.18 #64, 0.10 #7196, 0.03 #16111), 0407yj_ (0.15 #4048, 0.12 #5831, 0.09 #2265), 013q07 (0.15 #7487, 0.08 #21751, 0.07 #19968), 04gv3db (0.10 #7884, 0.09 #752, 0.04 #47110), 02y_lrp (0.10 #7146, 0.08 #3580, 0.06 #5363), 03lrht (0.10 #7388, 0.07 #10954, 0.06 #9171), 03nfnx (0.10 #8531, 0.07 #22795, 0.06 #13880), 01jft4 (0.10 #8395, 0.03 #20876, 0.03 #22659), 02r79_h (0.10 #7359, 0.03 #19840, 0.02 #46585) >> Best rule #2959 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 05ty4m; 04bs3j; 0pz7h; 02_j7t; 0126rp; 01j7rd; 04h07s; 01s7qqw; 04l19_; >> query: (?x1835, 063fh9) <- influenced_by(?x1835, ?x3917), ?x3917 = 0p_47, student(?x10386, ?x1835) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #16677 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 01tvz5j; 03f2_rc; 0187y5; 05zbm4; 012_53; 01438g; 0jrny; 027r8p; 02dth1; 02k21g; ... *> query: (?x1835, 05c26ss) <- friend(?x2669, ?x1835), film(?x1835, ?x994), student(?x10386, ?x1835) *> conf = 0.05 ranks of expected_values: 85 EVAL 016_mj film 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 117.000 104.000 0.182 http://example.org/film/actor/film./film/performance/film #11414-064t9 PRED entity: 064t9 PRED relation: artists PRED expected values: 0lbj1 0152cw 07qnf 06w2sn5 04bpm6 0lccn 02b25y 0k7pf 01k98nm 01vwyqp 0phx4 01wzlxj 01cblr 01n44c 01wd9lv 0gs6vr 06tp4h 05szp 01k3qj 017l4 09h4b5 02wwwv5 01v0sxx 020jqv 028qyn 024yxd 01v27pl 0djc3s 01qmy04 => 71 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1014): 01vwyqp (0.62 #8990, 0.50 #9725, 0.50 #4580), 01vw20_ (0.60 #5291, 0.40 #10436, 0.40 #7495), 01k3qj (0.60 #7792, 0.40 #15879, 0.38 #9998), 01kph_c (0.60 #5412, 0.40 #10557, 0.33 #1740), 0pkyh (0.60 #5285, 0.40 #10430, 0.33 #1613), 03h_fk5 (0.60 #5277, 0.40 #10422, 0.27 #11893), 01yndb (0.60 #7170, 0.33 #14520, 0.33 #1294), 020_4z (0.50 #14582, 0.45 #12378, 0.40 #7966), 0gs6vr (0.50 #9198, 0.42 #12873, 0.41 #8079), 01w7nww (0.50 #9719, 0.42 #14129, 0.33 #2371) >> Best rule #8990 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 06by7; 0y3_8; 02lnbg; >> query: (?x671, 01vwyqp) <- artists(?x671, ?x8693), artists(?x671, ?x3547), artists(?x671, ?x827), ?x8693 = 0bdxs5, award_nominee(?x2335, ?x827), profession(?x3547, ?x2348), ?x2335 = 0288fyj >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 9, 14, 20, 21, 22, 39, 47, 67, 177, 178, 191, 195, 199, 210, 215, 223, 237, 243, 266, 272, 332, 453, 502, 523, 598, 666, 799 EVAL 064t9 artists 01qmy04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 0djc3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 01v27pl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 024yxd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 028qyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 020jqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 01v0sxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 02wwwv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 09h4b5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 017l4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 01k3qj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 05szp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 06tp4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 0gs6vr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 01wd9lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 01n44c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 01cblr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 01wzlxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 0phx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 01vwyqp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 01k98nm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 0k7pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 02b25y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 0lccn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 04bpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 06w2sn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 07qnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 0152cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 71.000 44.000 0.625 http://example.org/music/genre/artists EVAL 064t9 artists 0lbj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 71.000 44.000 0.625 http://example.org/music/genre/artists #11413-02rxd26 PRED entity: 02rxd26 PRED relation: instance_of_recurring_event PRED expected values: 07hn5 => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 02rxd26 instance_of_recurring_event 07hn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/time/event/instance_of_recurring_event #11412-018mxj PRED entity: 018mxj PRED relation: service_location PRED expected values: 035qy 01pj7 => 138 concepts (74 used for prediction) PRED predicted values (max 10 best out of 304): 09c7w0 (0.96 #5188, 0.94 #5274, 0.90 #5709), 02jx1 (0.72 #3716, 0.50 #348, 0.42 #6055), 0d060g (0.68 #2340, 0.68 #2252, 0.60 #873), 0chghy (0.33 #530, 0.27 #2344, 0.27 #2256), 03rk0 (0.20 #112, 0.17 #286, 0.13 #893), 03rt9 (0.20 #97, 0.17 #271, 0.13 #878), 07tp2 (0.20 #144, 0.17 #318, 0.07 #925), 0154j (0.20 #90, 0.10 #5966, 0.08 #524), 06bnz (0.17 #541, 0.10 #5966, 0.10 #1232), 02vzc (0.17 #544, 0.07 #891, 0.05 #1235) >> Best rule #5188 for best value: >> intensional similarity = 9 >> extensional distance = 91 >> proper extension: 02vk52z; 0kc6x; 02zs4; 05krk; 01c6k4; 06pwq; 0p4wb; 0cv9b; 07y2s; 0gztl; ... >> query: (?x896, 09c7w0) <- category(?x896, ?x134), service_location(?x896, ?x4743), film_release_region(?x9961, ?x4743), film_release_region(?x2889, ?x4743), film_release_region(?x1518, ?x4743), ?x2889 = 040b5k, ?x1518 = 04w7rn, contains(?x4743, ?x5167), ?x9961 = 0bx_hnp >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #2334 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 39 *> proper extension: 017vb_; *> query: (?x896, ?x94) <- category(?x896, ?x134), service_location(?x896, ?x4743), service_location(?x896, ?x985), film_release_region(?x3748, ?x4743), film_release_region(?x2889, ?x4743), ?x3748 = 05zlld0, contains(?x985, ?x8174), film_release_region(?x2889, ?x94), contains(?x455, ?x985) *> conf = 0.02 ranks of expected_values: 176, 185 EVAL 018mxj service_location 01pj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 138.000 74.000 0.957 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 018mxj service_location 035qy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 138.000 74.000 0.957 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #11411-07tg4 PRED entity: 07tg4 PRED relation: major_field_of_study PRED expected values: 04_tv 01540 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 96): 02lp1 (0.45 #2363, 0.39 #2812, 0.33 #3599), 0g26h (0.39 #2836, 0.27 #2387, 0.26 #931), 062z7 (0.39 #2375, 0.38 #919, 0.32 #1703), 02_7t (0.33 #2857, 0.24 #2408, 0.17 #3644), 02ky346 (0.32 #238, 0.20 #1134, 0.17 #2366), 037mh8 (0.31 #955, 0.29 #2411, 0.28 #1739), 05qfh (0.29 #2382, 0.28 #926, 0.24 #1710), 01540 (0.29 #1172, 0.28 #2404, 0.26 #948), 03nfmq (0.26 #256, 0.18 #928, 0.14 #1712), 04x_3 (0.26 #918, 0.25 #2374, 0.22 #2823) >> Best rule #2363 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 0mbwf; >> query: (?x2999, 02lp1) <- student(?x2999, ?x164), major_field_of_study(?x2999, ?x1668), ?x1668 = 01mkq >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1172 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 53 *> proper extension: 05g3b; 0kk9v; 03sc8; 01gb54; 09xwz; 0135cw; 07y0n; 0841v; 0f1r9; *> query: (?x2999, 01540) <- company(?x5131, ?x2999), category(?x2999, ?x134), company(?x2998, ?x2999) *> conf = 0.29 ranks of expected_values: 8, 27 EVAL 07tg4 major_field_of_study 01540 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 81.000 81.000 0.455 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07tg4 major_field_of_study 04_tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 81.000 81.000 0.455 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11410-06yj20 PRED entity: 06yj20 PRED relation: location PRED expected values: 02_286 => 82 concepts (51 used for prediction) PRED predicted values (max 10 best out of 121): 06wxw (0.33 #228, 0.25 #1033, 0.20 #2643), 01snm (0.33 #320, 0.25 #1125, 0.20 #2735), 01n7q (0.20 #2478, 0.05 #7307, 0.04 #8916), 0r7fy (0.20 #2492, 0.03 #11344, 0.01 #17784), 0jfqp (0.17 #3621, 0.11 #37841, 0.11 #4426), 059rby (0.17 #3236, 0.11 #4041, 0.09 #4846), 02xry (0.17 #3353, 0.11 #4158, 0.09 #4963), 0rh6k (0.17 #3224, 0.11 #4029, 0.09 #4834), 0f2rq (0.17 #3501, 0.11 #4306, 0.09 #5111), 0cr3d (0.15 #7389, 0.12 #8998, 0.11 #4170) >> Best rule #228 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 04pxcx; >> query: (?x12478, 06wxw) <- profession(?x12478, ?x7623), nationality(?x12478, ?x94), athlete(?x471, ?x12478), ?x7623 = 0gl2ny2, student(?x1884, ?x12478), ?x471 = 02vx4 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #29820 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 838 *> proper extension: 03zqc1; 01d494; 0f6_dy; 03yf3z; 06rnl9; 01z7_f; 015wfg; 03q95r; 01ry0f; 04107; ... *> query: (?x12478, 02_286) <- nationality(?x12478, ?x94), type_of_union(?x12478, ?x566), ?x566 = 04ztj, ?x94 = 09c7w0, student(?x1884, ?x12478), state_province_region(?x1884, ?x760) *> conf = 0.15 ranks of expected_values: 11 EVAL 06yj20 location 02_286 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 82.000 51.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #11409-026mj PRED entity: 026mj PRED relation: district_represented! PRED expected values: 01gtcc 01grp0 => 195 concepts (195 used for prediction) PRED predicted values (max 10 best out of 26): 01gtcc (0.76 #273, 0.75 #221, 0.57 #521), 01grp0 (0.75 #230, 0.67 #282, 0.57 #521), 02bn_p (0.70 #368, 0.70 #342, 0.69 #681), 02bp37 (0.62 #345, 0.61 #970, 0.61 #449), 02bqm0 (0.57 #980, 0.57 #521, 0.57 #277), 02bqmq (0.57 #521, 0.54 #375, 0.54 #349), 02bqn1 (0.57 #521, 0.48 #266, 0.45 #1770), 02cg7g (0.57 #521, 0.45 #1770, 0.43 #380), 02gkzs (0.57 #521, 0.45 #1770, 0.43 #379), 02glc4 (0.57 #521, 0.45 #1770, 0.33 #278) >> Best rule #273 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 04ych; >> query: (?x7518, 01gtcc) <- country(?x7518, ?x94), contains(?x7518, ?x2832), district_represented(?x2712, ?x7518), ?x2712 = 01gst_ >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 026mj district_represented! 01grp0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 195.000 0.762 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 026mj district_represented! 01gtcc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 195.000 0.762 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #11408-06ryl PRED entity: 06ryl PRED relation: vacationer PRED expected values: 03h_9lg => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 96): 01cwhp (0.09 #51, 0.02 #947, 0.02 #3990), 01l9p (0.07 #389, 0.04 #1464, 0.04 #31), 03lt8g (0.07 #381, 0.04 #1456, 0.04 #1635), 033wx9 (0.07 #418, 0.04 #1493, 0.04 #1672), 01yf85 (0.07 #517, 0.04 #1592, 0.04 #1771), 015f7 (0.07 #433, 0.04 #1508, 0.04 #1687), 06mt91 (0.05 #1217, 0.04 #1575, 0.04 #1754), 0320jz (0.05 #1109, 0.04 #34, 0.04 #1825), 0134w7 (0.05 #1094, 0.04 #1810, 0.03 #1989), 06_bq1 (0.05 #1042, 0.03 #683, 0.02 #2474) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 02jx1; >> query: (?x4402, 01cwhp) <- currency(?x4402, ?x170), jurisdiction_of_office(?x3444, ?x4402), form_of_government(?x4402, ?x1926), country(?x1121, ?x4402) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #2341 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 79 *> proper extension: 0160w; 0j1z8; 05qhw; 02k54; 09pmkv; 06c1y; 06bnz; 05b4w; 0hg5; 056vv; ... *> query: (?x4402, 03h_9lg) <- jurisdiction_of_office(?x182, ?x4402), ?x182 = 060bp, administrative_area_type(?x4402, ?x2792), form_of_government(?x4402, ?x1926) *> conf = 0.01 ranks of expected_values: 96 EVAL 06ryl vacationer 03h_9lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 85.000 85.000 0.087 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #11407-03rwz3 PRED entity: 03rwz3 PRED relation: production_companies! PRED expected values: 01l_pn => 106 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1247): 06bd5j (0.53 #2303, 0.40 #11507, 0.38 #21860), 016dj8 (0.53 #2303, 0.40 #11507, 0.38 #21860), 07phbc (0.53 #2303, 0.38 #21860, 0.33 #1060), 0c0nhgv (0.53 #2303, 0.38 #21860, 0.33 #124), 011yn5 (0.53 #2303, 0.38 #21860, 0.21 #44881), 01xq8v (0.37 #27612, 0.36 #10356, 0.36 #31065), 0dgq_kn (0.37 #27612, 0.36 #10356, 0.36 #31065), 04zl8 (0.37 #27612, 0.36 #10356, 0.36 #31065), 05g8pg (0.37 #27612, 0.36 #10356, 0.36 #31065), 0cmc26r (0.37 #27612, 0.36 #10356, 0.36 #31065) >> Best rule #2303 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0g5lhl7; >> query: (?x7526, ?x1163) <- award_nominee(?x519, ?x7526), service_language(?x7526, ?x254), nominated_for(?x7526, ?x1163) >> conf = 0.53 => this is the best rule for 5 predicted values *> Best rule #25311 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 01j53q; 05s34b; *> query: (?x7526, ?x351) <- state_province_region(?x7526, ?x1227), award_winner(?x8208, ?x7526), nominated_for(?x8208, ?x351) *> conf = 0.09 ranks of expected_values: 345 EVAL 03rwz3 production_companies! 01l_pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 106.000 90.000 0.529 http://example.org/film/film/production_companies #11406-047csmy PRED entity: 047csmy PRED relation: prequel! PRED expected values: 0872p_c => 98 concepts (52 used for prediction) PRED predicted values (max 10 best out of 28): 0dfw0 (0.02 #267, 0.02 #627, 0.02 #807), 0fdv3 (0.02 #218, 0.02 #578, 0.02 #758), 0prrm (0.02 #270, 0.02 #630, 0.01 #990), 09v8clw (0.02 #540, 0.02 #720), 0fpgp26 (0.02 #513, 0.02 #693), 0315rp (0.02 #503, 0.02 #683), 047csmy (0.02 #455, 0.02 #635), 091rc5 (0.02 #449, 0.02 #629), 013q07 (0.02 #407, 0.02 #587), 05qbckf (0.02 #403, 0.02 #583) >> Best rule #267 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 06wzvr; >> query: (?x5277, 0dfw0) <- nominated_for(?x154, ?x5277), ?x154 = 05b4l5x, genre(?x5277, ?x225), film_crew_role(?x5277, ?x137) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 047csmy prequel! 0872p_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 52.000 0.023 http://example.org/film/film/prequel #11405-03np_7 PRED entity: 03np_7 PRED relation: organization! PRED expected values: 060c4 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 15): 060c4 (0.74 #392, 0.73 #509, 0.73 #171), 07xl34 (0.29 #11, 0.26 #583, 0.24 #206), 0dq_5 (0.24 #74, 0.19 #347, 0.19 #425), 05k17c (0.14 #7, 0.13 #280, 0.13 #163), 0hm4q (0.07 #580, 0.05 #697, 0.05 #1009), 05c0jwl (0.04 #655, 0.04 #486, 0.04 #460), 08jcfy (0.03 #116, 0.02 #142, 0.02 #649), 01t7n9 (0.03 #1355), 02079p (0.03 #1355), 0789n (0.03 #1355) >> Best rule #392 for best value: >> intensional similarity = 4 >> extensional distance = 265 >> proper extension: 037s9x; 038czx; 01qwb5; 04gd8j; 015wy_; 01x5fb; >> query: (?x12795, 060c4) <- currency(?x12795, ?x170), colors(?x12795, ?x3315), major_field_of_study(?x12795, ?x1668), colors(?x733, ?x3315) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03np_7 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.742 http://example.org/organization/role/leaders./organization/leadership/organization #11404-02hfp_ PRED entity: 02hfp_ PRED relation: award PRED expected values: 03hkv_r => 135 concepts (112 used for prediction) PRED predicted values (max 10 best out of 300): 0gr51 (0.82 #91, 0.50 #4436, 0.48 #7991), 03hl6lc (0.68 #169, 0.45 #4514, 0.31 #8069), 02x1dht (0.47 #50, 0.23 #4395, 0.19 #7950), 03hkv_r (0.35 #13, 0.30 #3173, 0.27 #4358), 02rdyk7 (0.35 #2847, 0.33 #3637, 0.32 #4427), 09sb52 (0.31 #26507, 0.29 #23347, 0.26 #24927), 0gqyl (0.23 #9482, 0.22 #17385, 0.22 #9086), 02ppm4q (0.23 #9482, 0.22 #17385, 0.22 #9086), 027dtxw (0.23 #9482, 0.22 #17385, 0.22 #9086), 09td7p (0.23 #9482, 0.22 #17385, 0.22 #9086) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 0l6qt; 014zcr; 0h5f5n; 02kxbwx; 081lh; 05_k56; 0136g9; 05183k; 07s93v; 01gzm2; ... >> query: (?x8042, 0gr51) <- written_by(?x755, ?x8042), award(?x8042, ?x68), award_nominee(?x163, ?x8042), ?x68 = 02qyp19 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 0l6qt; 014zcr; 0h5f5n; 02kxbwx; 081lh; 05_k56; 0136g9; 05183k; 07s93v; 01gzm2; ... *> query: (?x8042, 03hkv_r) <- written_by(?x755, ?x8042), award(?x8042, ?x68), award_nominee(?x163, ?x8042), ?x68 = 02qyp19 *> conf = 0.35 ranks of expected_values: 4 EVAL 02hfp_ award 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 135.000 112.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11403-029qzx PRED entity: 029qzx PRED relation: school_type PRED expected values: 07tf8 => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.57 #371, 0.51 #2533, 0.49 #2395), 07tf8 (0.41 #399, 0.22 #100, 0.21 #8), 01rs41 (0.34 #625, 0.29 #1200, 0.29 #832), 01_9fk (0.26 #369, 0.21 #576, 0.21 #783), 02p0qmm (0.08 #55, 0.06 #722, 0.06 #170), 01_srz (0.07 #1635, 0.07 #623, 0.07 #830), 04399 (0.05 #381, 0.05 #289, 0.04 #588), 01y64 (0.05 #448, 0.03 #885, 0.03 #632), 0bwd5 (0.04 #179, 0.03 #248, 0.03 #64), 02dk5q (0.03 #443, 0.02 #880, 0.02 #627) >> Best rule #371 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 0m9_5; 027mdh; 04bfg; 02l1fn; 01q8hj; 020ddc; >> query: (?x10824, 05jxkf) <- state_province_region(?x10824, ?x6521), category(?x10824, ?x134), school_type(?x10824, ?x1044), school(?x7399, ?x10824) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #399 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 122 *> proper extension: 06klyh; *> query: (?x10824, 07tf8) <- citytown(?x10824, ?x859), school_type(?x10824, ?x1044), school_type(?x3439, ?x1044), ?x3439 = 03ksy *> conf = 0.41 ranks of expected_values: 2 EVAL 029qzx school_type 07tf8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 170.000 170.000 0.566 http://example.org/education/educational_institution/school_type #11402-0dnqr PRED entity: 0dnqr PRED relation: language PRED expected values: 0349s => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 45): 06nm1 (0.23 #123, 0.19 #408, 0.19 #180), 064_8sq (0.19 #305, 0.18 #134, 0.18 #534), 06b_j (0.15 #649, 0.14 #249, 0.10 #1106), 02bjrlw (0.14 #229, 0.12 #1315, 0.12 #172), 012w70 (0.13 #468, 0.08 #410, 0.07 #1497), 03_9r (0.11 #407, 0.08 #465, 0.07 #3270), 02002f (0.10 #29, 0.06 #86, 0.04 #200), 0653m (0.08 #467, 0.06 #1612, 0.06 #67), 03k50 (0.08 #978, 0.05 #464, 0.05 #121), 04h9h (0.08 #212, 0.06 #1069, 0.06 #440) >> Best rule #123 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 07cz2; >> query: (?x2947, 06nm1) <- nominated_for(?x6860, ?x2947), nominated_for(?x637, ?x2947), titles(?x811, ?x2947), ?x6860 = 018wdw, ?x637 = 02r22gf >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #100 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 01vksx; 04w7rn; 0p4v_; 04954r; 03q5db; 02ll45; 0y_9q; 03y0pn; 07f_t4; *> query: (?x2947, 0349s) <- nominated_for(?x112, ?x2947), titles(?x811, ?x2947), honored_for(?x7038, ?x2947), ?x811 = 03k9fj *> conf = 0.06 ranks of expected_values: 18 EVAL 0dnqr language 0349s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 134.000 134.000 0.227 http://example.org/film/film/language #11401-063y9fp PRED entity: 063y9fp PRED relation: film! PRED expected values: 01wjrn 06sn8m => 68 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1039): 0n8bn (0.50 #7446, 0.02 #32361, 0.01 #48970), 0725ny (0.33 #5598, 0.25 #9750, 0.09 #13902), 0159h6 (0.33 #73, 0.17 #4226, 0.12 #10454), 01rs5p (0.33 #1790, 0.17 #5943, 0.12 #10095), 02ct_k (0.33 #1647, 0.17 #5800, 0.12 #9952), 029cpw (0.33 #3303, 0.17 #5379, 0.12 #9531), 01trf3 (0.33 #730, 0.17 #4883, 0.12 #9035), 022qw7 (0.33 #1564, 0.17 #5717, 0.12 #9869), 05zbm4 (0.33 #151, 0.17 #4304, 0.12 #8456), 08141d (0.33 #2007, 0.17 #6160, 0.12 #10312) >> Best rule #7446 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01719t; 014nq4; >> query: (?x9169, 0n8bn) <- genre(?x9169, ?x225), film(?x1382, ?x9169), film(?x593, ?x9169), profession(?x1382, ?x1032), ?x593 = 0lzb8 >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 063y9fp film! 06sn8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 54.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 063y9fp film! 01wjrn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 54.000 0.500 http://example.org/film/actor/film./film/performance/film #11400-01tmng PRED entity: 01tmng PRED relation: list PRED expected values: 01ptsx => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.86 #154, 0.81 #650, 0.81 #64), 09g7thr (0.56 #407, 0.55 #326, 0.54 #311), 05glt (0.53 #575, 0.38 #646, 0.09 #493), 026cl_m (0.26 #399, 0.12 #576, 0.09 #647) >> Best rule #154 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 03d6fyn; >> query: (?x12219, 01ptsx) <- place_founded(?x12219, ?x1589), list(?x12219, ?x8915), list(?x502, ?x8915), ?x502 = 087c7 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tmng list 01ptsx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.857 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #11399-09nqf PRED entity: 09nqf PRED relation: currency! PRED expected values: 04_j5s => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 2287): 02p8454 (0.42 #301, 0.39 #119, 0.37 #120), 04hgpt (0.42 #301, 0.39 #119, 0.37 #120), 025v3k (0.42 #301, 0.39 #119, 0.37 #303), 02vkzcx (0.42 #301, 0.39 #119, 0.37 #303), 02bf58 (0.42 #301, 0.39 #119, 0.37 #303), 04ld32 (0.42 #301, 0.39 #119, 0.37 #303), 01csqg (0.42 #301, 0.10 #59, 0.04 #239), 015cz0 (0.42 #301, 0.10 #59, 0.04 #239), 05ftw3 (0.42 #301, 0.04 #121), 07w5rq (0.42 #301, 0.04 #121) >> Best rule #301 for best value: >> intensional similarity = 28 >> extensional distance = 2 >> proper extension: 02gsvk; >> query: (?x170, ?x1961) <- currency(?x12401, ?x170), currency(?x9715, ?x170), currency(?x9100, ?x170), currency(?x7554, ?x170), currency(?x6489, ?x170), currency(?x5724, ?x170), currency(?x5646, ?x170), currency(?x5157, ?x170), currency(?x4021, ?x170), currency(?x1915, ?x170), currency(?x485, ?x170), currency(?x1961, ?x170), currency(?x11318, ?x170), currency(?x2775, ?x170), film_release_region(?x1915, ?x87), category(?x4021, ?x134), award_winner(?x9100, ?x5611), nominated_for(?x591, ?x9100), school_type(?x11318, ?x1507), film(?x10061, ?x5724), costume_design_by(?x485, ?x6327), language(?x6489, ?x254), story_by(?x9715, ?x12856), nominated_for(?x1244, ?x5646), institution(?x620, ?x2775), genre(?x5157, ?x53), award(?x7554, ?x77), film(?x806, ?x12401) >> conf = 0.42 => this is the best rule for 10 predicted values *> Best rule #59 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 1 *> proper extension: 02l6h; *> query: (?x170, ?x191) <- currency(?x1227, ?x170), currency(?x760, ?x170), currency(?x311, ?x170), currency(?x9200, ?x170), currency(?x9452, ?x170), currency(?x5070, ?x170), currency(?x3743, ?x170), currency(?x1488, ?x170), currency(?x626, ?x170), currency(?x308, ?x170), currency(?x263, ?x170), currency(?x2566, ?x170), country(?x626, ?x94), contains(?x1227, ?x191), currency(?x902, ?x170), role(?x2566, ?x3703), currency(?x216, ?x170), location(?x120, ?x760), nominated_for(?x68, ?x308), nominated_for(?x3961, ?x3743), film_release_region(?x504, ?x311), currency(?x126, ?x170), religion(?x760, ?x109), films(?x1967, ?x1488), genre(?x9452, ?x809), organization(?x346, ?x9200), award_winner(?x9452, ?x1871), film_crew_role(?x5070, ?x137) *> conf = 0.10 ranks of expected_values: 673 EVAL 09nqf currency! 04_j5s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.420 http://example.org/education/university/local_tuition./measurement_unit/dated_money_value/currency #11398-015grj PRED entity: 015grj PRED relation: award_winner! PRED expected values: 0bvhz9 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 104): 09gkdln (0.12 #4201, 0.10 #7282, 0.05 #401), 03gyp30 (0.12 #4201, 0.10 #7282, 0.03 #1376), 05c1t6z (0.12 #4201, 0.10 #7282, 0.03 #1275), 09qftb (0.12 #4201, 0.10 #7282, 0.03 #112), 03nnm4t (0.12 #4201, 0.10 #7282, 0.03 #1333), 0gvstc3 (0.12 #4201, 0.10 #7282, 0.03 #1014), 0fqpc7d (0.12 #4201, 0.10 #7282, 0.03 #316), 09pj68 (0.12 #4201, 0.10 #7282, 0.02 #244), 0n8_m93 (0.12 #4201, 0.10 #7282, 0.02 #397), 05zksls (0.12 #4201, 0.10 #7282, 0.02 #1015) >> Best rule #4201 for best value: >> intensional similarity = 3 >> extensional distance = 1426 >> proper extension: 01w92; >> query: (?x968, ?x1265) <- award_nominee(?x968, ?x5758), award_winner(?x1265, ?x5758), award_winner(?x704, ?x968) >> conf = 0.12 => this is the best rule for 11 predicted values No rule for expected values ranks of expected_values: EVAL 015grj award_winner! 0bvhz9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 81.000 0.116 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11397-03rg2b PRED entity: 03rg2b PRED relation: film_format PRED expected values: 0cj16 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.33 #1, 0.16 #53, 0.15 #48), 0cj16 (0.17 #116, 0.16 #60, 0.11 #259), 017fx5 (0.04 #67, 0.04 #29, 0.03 #61) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02q7fl9; >> query: (?x6218, 07fb8_) <- genre(?x6218, ?x53), film_release_region(?x6218, ?x304), film(?x4553, ?x6218), ?x4553 = 01vyv9 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #116 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 497 *> proper extension: 0fq27fp; *> query: (?x6218, 0cj16) <- genre(?x6218, ?x53), film_release_region(?x6218, ?x2984), film_release_region(?x4841, ?x2984), ?x4841 = 0k4fz *> conf = 0.17 ranks of expected_values: 2 EVAL 03rg2b film_format 0cj16 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 73.000 0.333 http://example.org/film/film/film_format #11396-0pvms PRED entity: 0pvms PRED relation: genre PRED expected values: 07s9rl0 05p553 => 72 concepts (70 used for prediction) PRED predicted values (max 10 best out of 138): 07s9rl0 (0.94 #3096, 0.82 #4168, 0.81 #4766), 05p553 (0.92 #3931, 0.89 #5126, 0.89 #5604), 02l7c8 (0.86 #4900, 0.86 #3705, 0.85 #5019), 01z4y (0.62 #6679, 0.62 #6800, 0.61 #4885), 017fp (0.60 #847, 0.29 #1204, 0.22 #1442), 01jfsb (0.34 #4657, 0.32 #5971, 0.31 #5493), 03q4nz (0.33 #137, 0.33 #18, 0.16 #3113), 03bxz7 (0.33 #1363, 0.29 #1720, 0.29 #1244), 04rlf (0.33 #540, 0.29 #1135, 0.28 #1373), 060__y (0.33 #135, 0.25 #4303, 0.25 #4183) >> Best rule #3096 for best value: >> intensional similarity = 8 >> extensional distance = 66 >> proper extension: 01jc6q; 02q52q; 09dv8h; 03kx49; 09yxcz; >> query: (?x2565, 07s9rl0) <- genre(?x2565, ?x307), genre(?x2565, ?x162), ?x307 = 04t36, film(?x496, ?x2565), titles(?x162, ?x9129), titles(?x162, ?x2734), ?x2734 = 05cvgl, ?x9129 = 0n_hp >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0pvms genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 70.000 0.941 http://example.org/film/film/genre EVAL 0pvms genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 70.000 0.941 http://example.org/film/film/genre #11395-0hnp7 PRED entity: 0hnp7 PRED relation: nationality PRED expected values: 09c7w0 => 194 concepts (143 used for prediction) PRED predicted values (max 10 best out of 186): 09c7w0 (0.94 #1708, 0.90 #404, 0.89 #305), 07ssc (0.54 #8691, 0.40 #615, 0.38 #304), 06q1r (0.44 #677, 0.05 #7171, 0.02 #11768), 02jx1 (0.33 #133, 0.26 #302, 0.26 #804), 015jr (0.31 #10788), 07cfx (0.29 #303, 0.26 #302, 0.25 #201), 06y57 (0.26 #302, 0.25 #201, 0.13 #701), 03rk0 (0.21 #4942, 0.18 #7140, 0.09 #11037), 06m_5 (0.17 #183, 0.12 #284, 0.04 #683), 0f8l9c (0.14 #826, 0.07 #4918, 0.07 #7116) >> Best rule #1708 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 02x7vq; 0q9t7; >> query: (?x6073, 09c7w0) <- profession(?x6073, ?x1032), people(?x5741, ?x6073), ?x5741 = 07bch9, nationality(?x6073, ?x390) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hnp7 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 143.000 0.941 http://example.org/people/person/nationality #11394-02hhtj PRED entity: 02hhtj PRED relation: actor! PRED expected values: 01p4wv => 114 concepts (94 used for prediction) PRED predicted values (max 10 best out of 127): 01p4wv (0.08 #12212, 0.08 #11945, 0.02 #3276), 026bfsh (0.07 #1687, 0.03 #10184, 0.03 #7791), 02zv4b (0.06 #1085, 0.04 #3473, 0.04 #1350), 039cq4 (0.05 #1719, 0.02 #5965, 0.02 #659), 019nnl (0.03 #1609, 0.02 #5855, 0.02 #1875), 07c72 (0.03 #1638, 0.01 #7742, 0.01 #8273), 028k2x (0.03 #1736, 0.01 #8371, 0.01 #9169), 0n2bh (0.03 #1888, 0.02 #562, 0.02 #5603), 08jgk1 (0.03 #1878, 0.02 #6389, 0.01 #6655), 024rwx (0.03 #1962, 0.01 #6473, 0.01 #8864) >> Best rule #12212 for best value: >> intensional similarity = 4 >> extensional distance = 258 >> proper extension: 02tf1y; >> query: (?x5881, ?x5307) <- participant(?x5881, ?x5467), film(?x5467, ?x857), actor(?x5307, ?x5467), profession(?x5467, ?x1032) >> conf = 0.08 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hhtj actor! 01p4wv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 94.000 0.085 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11393-06br6t PRED entity: 06br6t PRED relation: artists! PRED expected values: 0k345 => 103 concepts (44 used for prediction) PRED predicted values (max 10 best out of 248): 06by7 (0.93 #11096, 0.69 #11995, 0.65 #11696), 016jny (0.79 #8782, 0.67 #3683, 0.67 #3384), 0xhtw (0.58 #10791, 0.55 #8098, 0.50 #12291), 0dl5d (0.56 #9897, 0.50 #1212, 0.45 #8101), 064t9 (0.50 #6895, 0.50 #1802, 0.45 #11389), 05bt6j (0.50 #5721, 0.40 #2427, 0.36 #7223), 03lty (0.50 #10803, 0.29 #298, 0.27 #12303), 01fh36 (0.44 #6363, 0.43 #5161, 0.33 #3665), 02yv6b (0.43 #4873, 0.33 #6375, 0.33 #3975), 05r6t (0.40 #2166, 0.33 #4258, 0.33 #76) >> Best rule #11096 for best value: >> intensional similarity = 7 >> extensional distance = 92 >> proper extension: 0lbj1; 01kx_81; 01p9hgt; 01wp8w7; 03qmj9; 04bpm6; 015_30; 0zjpz; 02zmh5; 01w60_p; ... >> query: (?x9757, 06by7) <- artists(?x2996, ?x9757), role(?x9757, ?x227), ?x227 = 0342h, artists(?x2996, ?x3206), ?x3206 = 01m65sp, parent_genre(?x10366, ?x2996), ?x10366 = 0621cs >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #3740 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 01vsqvs; *> query: (?x9757, 0k345) <- artists(?x11746, ?x9757), artists(?x10721, ?x9757), artists(?x2542, ?x9757), artists(?x474, ?x9757), ?x11746 = 03w94xt, artists(?x10721, ?x10094), artists(?x2542, ?x8599), ?x10094 = 01wdcxk, ?x8599 = 01nkxvx, artists(?x474, ?x2945), music(?x6007, ?x2945) *> conf = 0.33 ranks of expected_values: 15 EVAL 06br6t artists! 0k345 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 103.000 44.000 0.926 http://example.org/music/genre/artists #11392-016h9b PRED entity: 016h9b PRED relation: role PRED expected values: 013y1f => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 113): 03bx0bm (0.36 #1372, 0.33 #16, 0.30 #611), 042v_gx (0.20 #217, 0.16 #2014, 0.16 #1466), 028tv0 (0.14 #63, 0.14 #604, 0.13 #1365), 06ncr (0.14 #82, 0.07 #623, 0.05 #785), 01vj9c (0.14 #64, 0.07 #1366, 0.06 #172), 01xqw (0.14 #98, 0.04 #639, 0.03 #1411), 01vdm0 (0.10 #2181, 0.10 #2180, 0.10 #1030), 07y_7 (0.09 #218, 0.07 #109, 0.06 #163), 0cfdd (0.07 #158, 0.06 #212, 0.04 #267), 018j2 (0.07 #78, 0.06 #186, 0.04 #673) >> Best rule #1372 for best value: >> intensional similarity = 2 >> extensional distance = 289 >> proper extension: 01pbxb; 0f0y8; 03c7ln; 01vw87c; 01vrx3g; 0m2l9; 032t2z; 0kzy0; 06y9c2; 025xt8y; ... >> query: (?x2865, 03bx0bm) <- role(?x2865, ?x1166), role(?x75, ?x1166) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #74 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0fp_v1x; 01cv3n; 03xl77; 01m65sp; 037hgm; 01vs4ff; 05qhnq; 0473q; 04mx7s; 01mxnvc; ... *> query: (?x2865, 013y1f) <- role(?x2865, ?x1750), role(?x2865, ?x1166), ?x1166 = 05148p4, ?x1750 = 02hnl *> conf = 0.07 ranks of expected_values: 11 EVAL 016h9b role 013y1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 107.000 107.000 0.357 http://example.org/music/group_member/membership./music/group_membership/role #11391-011lvx PRED entity: 011lvx PRED relation: role PRED expected values: 02hrlh => 153 concepts (93 used for prediction) PRED predicted values (max 10 best out of 122): 0342h (0.80 #1606, 0.60 #806, 0.59 #2213), 02sgy (0.55 #1608, 0.41 #2215, 0.40 #808), 05r5c (0.52 #6434, 0.50 #2921, 0.48 #2114), 0l14qv (0.40 #907, 0.29 #1307, 0.25 #2918), 018vs (0.38 #414, 0.30 #816, 0.29 #314), 026t6 (0.33 #2915, 0.28 #2108, 0.17 #6428), 042v_gx (0.31 #2922, 0.31 #1211, 0.30 #4626), 0l14md (0.30 #909, 0.29 #2920, 0.25 #2106), 01vj9c (0.29 #2929, 0.29 #316, 0.25 #416), 02w3w (0.29 #387, 0.25 #487, 0.07 #3014) >> Best rule #1606 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 0jfx1; >> query: (?x7410, 0342h) <- role(?x7410, ?x7869), spouse(?x4712, ?x7410), role(?x7869, ?x74), ?x74 = 03q5t, role(?x716, ?x7869) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3014 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 50 *> proper extension: 01wdqrx; 06x4l_; 01vswwx; 06h2w; 01kd57; 01ldw4; 095x_; 04m2zj; 048tgl; *> query: (?x7410, ?x74) <- role(?x7410, ?x3991), role(?x7410, ?x75), category(?x7410, ?x134), gender(?x7410, ?x514), role(?x75, ?x74), ?x3991 = 05842k *> conf = 0.07 ranks of expected_values: 99 EVAL 011lvx role 02hrlh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 153.000 93.000 0.800 http://example.org/music/artist/track_contributions./music/track_contribution/role #11390-05h43ls PRED entity: 05h43ls PRED relation: film_crew_role PRED expected values: 02r96rf => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 23): 02r96rf (0.80 #405, 0.65 #1273, 0.64 #372), 09zzb8 (0.73 #402, 0.71 #1270, 0.70 #1136), 0dxtw (0.49 #412, 0.36 #1280, 0.35 #1446), 01pvkk (0.31 #413, 0.27 #1513, 0.27 #1745), 02ynfr (0.22 #82, 0.20 #417, 0.17 #384), 0d2b38 (0.19 #424, 0.13 #89, 0.12 #490), 015h31 (0.18 #410, 0.11 #176, 0.09 #75), 02rh1dz (0.17 #411, 0.17 #76, 0.13 #110), 01xy5l_ (0.14 #415, 0.13 #382, 0.12 #481), 094hwz (0.11 #148, 0.10 #182, 0.09 #115) >> Best rule #405 for best value: >> intensional similarity = 4 >> extensional distance = 248 >> proper extension: 0k4d7; >> query: (?x2586, 02r96rf) <- nominated_for(?x2548, ?x2586), film(?x1205, ?x2586), film_crew_role(?x2586, ?x2154), ?x2154 = 01vx2h >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05h43ls film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.796 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11389-07p62k PRED entity: 07p62k PRED relation: production_companies PRED expected values: 017s11 => 97 concepts (87 used for prediction) PRED predicted values (max 10 best out of 60): 031rq5 (0.21 #122, 0.05 #282, 0.02 #603), 05qd_ (0.17 #10, 0.14 #972, 0.12 #1936), 024rgt (0.17 #23, 0.07 #905, 0.07 #103), 030_1_ (0.17 #15, 0.05 #1380, 0.05 #495), 02slt7 (0.17 #28, 0.04 #990, 0.02 #1473), 086k8 (0.14 #82, 0.13 #242, 0.13 #964), 016tt2 (0.11 #806, 0.11 #1046, 0.10 #244), 01gb54 (0.11 #838, 0.08 #998, 0.08 #1320), 017s11 (0.11 #163, 0.08 #1929, 0.07 #4829), 03h304l (0.09 #1926, 0.08 #2170, 0.04 #561) >> Best rule #122 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0b6m5fy; >> query: (?x2207, 031rq5) <- film(?x5488, ?x2207), film(?x3186, ?x2207), award_winner(?x2107, ?x3186), award(?x3186, ?x451), ?x5488 = 07y8l9 >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #163 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 027gy0k; 0gvvf4j; *> query: (?x2207, 017s11) <- film_crew_role(?x2207, ?x137), genre(?x2207, ?x258), nominated_for(?x2022, ?x2207), ?x2022 = 05p1dby *> conf = 0.11 ranks of expected_values: 9 EVAL 07p62k production_companies 017s11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 97.000 87.000 0.214 http://example.org/film/film/production_companies #11388-0gq_v PRED entity: 0gq_v PRED relation: award! PRED expected values: 0b_c7 05v1sb 0584j4n => 44 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2566): 07fzq3 (0.64 #26656, 0.62 #29992, 0.62 #26655), 05v1sb (0.64 #26656, 0.62 #29992, 0.62 #26655), 051x52f (0.64 #26656, 0.62 #29992, 0.62 #26655), 0dh73w (0.64 #26656, 0.62 #29992, 0.62 #26655), 0ft7sr (0.62 #29992, 0.62 #26655, 0.04 #3784), 02kxbwx (0.47 #177, 0.29 #3509, 0.24 #33326), 02kxbx3 (0.41 #977, 0.29 #4309, 0.24 #33326), 0151w_ (0.41 #232, 0.29 #3564, 0.19 #6896), 03hy3g (0.41 #1841, 0.25 #5173, 0.14 #8505), 03_gd (0.41 #167, 0.24 #33326, 0.21 #3499) >> Best rule #26656 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 02vl9ln; >> query: (?x484, ?x12378) <- award(?x1386, ?x484), award_winner(?x484, ?x12378), award_nominee(?x12378, ?x2068), currency(?x1386, ?x170) >> conf = 0.64 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 100, 573 EVAL 0gq_v award! 0584j4n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.637 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq_v award! 05v1sb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 16.000 0.637 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq_v award! 0b_c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 44.000 16.000 0.637 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11387-09rsjpv PRED entity: 09rsjpv PRED relation: genre PRED expected values: 03k9fj 082gq => 110 concepts (82 used for prediction) PRED predicted values (max 10 best out of 102): 02l7c8 (0.68 #6187, 0.48 #133, 0.39 #8449), 01jfsb (0.54 #1552, 0.41 #4762, 0.41 #365), 05p553 (0.50 #1898, 0.39 #6648, 0.36 #6174), 06l3bl (0.44 #272, 0.33 #36, 0.09 #1222), 03k9fj (0.41 #1551, 0.29 #602, 0.29 #4761), 082gq (0.40 #264, 0.33 #28, 0.30 #146), 060__y (0.39 #134, 0.30 #965, 0.29 #1675), 03bxz7 (0.36 #289, 0.20 #53, 0.17 #1712), 04xvh5 (0.35 #150, 0.28 #268, 0.27 #32), 06n90 (0.30 #1553, 0.22 #843, 0.20 #4763) >> Best rule #6187 for best value: >> intensional similarity = 5 >> extensional distance = 592 >> proper extension: 0g9yrw; 0bbgvp; >> query: (?x3517, 02l7c8) <- film(?x1676, ?x3517), genre(?x3517, ?x162), language(?x3517, ?x90), titles(?x162, ?x308), ?x308 = 011yxg >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1551 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 208 *> proper extension: 076xkdz; *> query: (?x3517, 03k9fj) <- film_release_region(?x3517, ?x94), ?x94 = 09c7w0, production_companies(?x3517, ?x847), genre(?x3517, ?x225), ?x225 = 02kdv5l *> conf = 0.41 ranks of expected_values: 5, 6 EVAL 09rsjpv genre 082gq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 110.000 82.000 0.680 http://example.org/film/film/genre EVAL 09rsjpv genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 110.000 82.000 0.680 http://example.org/film/film/genre #11386-097kp PRED entity: 097kp PRED relation: languages_spoken! PRED expected values: 02gx2x => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 72): 07hwkr (0.58 #1165, 0.56 #589, 0.56 #517), 02vsw1 (0.56 #407, 0.44 #551, 0.43 #335), 059_w (0.50 #244, 0.50 #172, 0.50 #100), 0c41n (0.40 #72, 0.33 #289, 0.33 #217), 0fk3s (0.40 #65, 0.33 #282, 0.33 #210), 03x1x (0.40 #52, 0.33 #269, 0.33 #197), 0g8_vp (0.40 #19, 0.33 #236, 0.33 #164), 04czx7 (0.36 #1078, 0.25 #1294, 0.23 #1366), 071x0k (0.33 #1089, 0.33 #225, 0.33 #153), 0x67 (0.33 #227, 0.33 #155, 0.33 #83) >> Best rule #1165 for best value: >> intensional similarity = 13 >> extensional distance = 10 >> proper extension: 06b_j; >> query: (?x12394, 07hwkr) <- official_language(?x3749, ?x12394), service_language(?x1492, ?x12394), countries_spoken_in(?x254, ?x3749), capital(?x3749, ?x11611), film_release_region(?x2037, ?x3749), film_release_region(?x1259, ?x3749), film(?x1676, ?x2037), olympics(?x3749, ?x778), organization(?x3749, ?x127), countries_spoken_in(?x12394, ?x3656), film(?x541, ?x2037), honored_for(?x1553, ?x1259), nominated_for(?x601, ?x1259) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #283 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 4 *> proper extension: 02h40lc; 06nm1; 064_8sq; 02bv9; *> query: (?x12394, 02gx2x) <- official_language(?x3749, ?x12394), service_language(?x1492, ?x12394), countries_spoken_in(?x254, ?x3749), capital(?x3749, ?x11611), film_release_region(?x8193, ?x3749), film_release_region(?x5644, ?x3749), film_release_region(?x2783, ?x3749), film_release_region(?x2037, ?x3749), film_release_region(?x511, ?x3749), ?x2037 = 0gvrws1, contains(?x3749, ?x11382), ?x5644 = 0dll_t2, ?x8193 = 03z9585, nationality(?x6461, ?x3749), ?x511 = 0dscrwf, ?x2783 = 0879bpq *> conf = 0.17 ranks of expected_values: 41 EVAL 097kp languages_spoken! 02gx2x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 50.000 50.000 0.583 http://example.org/people/ethnicity/languages_spoken #11385-07kbp5 PRED entity: 07kbp5 PRED relation: sport PRED expected values: 0jm_ => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 8): 0jm_ (0.76 #21, 0.65 #334, 0.63 #12), 02vx4 (0.58 #418, 0.53 #428, 0.47 #436), 018jz (0.26 #366, 0.20 #329, 0.19 #230), 018w8 (0.24 #256, 0.23 #238, 0.21 #328), 03tmr (0.21 #190, 0.20 #262, 0.17 #353), 039yzs (0.07 #331, 0.06 #414, 0.05 #386), 09xp_ (0.03 #349, 0.03 #340, 0.03 #404), 0z74 (0.02 #197, 0.02 #360, 0.01 #406) >> Best rule #21 for best value: >> intensional similarity = 14 >> extensional distance = 19 >> proper extension: 0fbq2n; >> query: (?x7892, 0jm_) <- teams(?x3037, ?x7892), team(?x7079, ?x7892), team(?x3346, ?x7892), team(?x1240, ?x7892), team(?x180, ?x7892), ?x1240 = 023wyl, ?x180 = 01r3hr, ?x3346 = 02g_7z, position(?x4193, ?x7079), position(?x1718, ?x7079), ?x1718 = 0fgg8c, ?x4193 = 026bt_h, position(?x1639, ?x7079), ?x1639 = 07l24 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07kbp5 sport 0jm_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.762 http://example.org/sports/sports_team/sport #11384-0fsw_7 PRED entity: 0fsw_7 PRED relation: prequel PRED expected values: 01kf5lf => 98 concepts (52 used for prediction) PRED predicted values (max 10 best out of 49): 014kq6 (0.10 #728, 0.09 #364, 0.06 #1273), 01kf4tt (0.10 #728, 0.09 #364, 0.06 #1273), 01kf3_9 (0.10 #728, 0.09 #364, 0.06 #1273), 0fztbq (0.10 #728, 0.09 #364, 0.06 #1273), 0g5pv3 (0.10 #728, 0.09 #364, 0.06 #1273), 02sg5v (0.10 #728, 0.09 #364, 0.06 #1273), 0d1qmz (0.10 #728, 0.09 #364, 0.06 #1273), 025twgt (0.10 #728, 0.09 #364, 0.06 #1273), 02n72k (0.10 #728, 0.09 #364, 0.06 #1273), 0g5pvv (0.10 #728, 0.09 #364, 0.06 #1273) >> Best rule #728 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 05zlld0; >> query: (?x5399, ?x836) <- language(?x5399, ?x254), nominated_for(?x836, ?x5399), genre(?x5399, ?x225), story_by(?x5399, ?x3686) >> conf = 0.10 => this is the best rule for 11 predicted values No rule for expected values ranks of expected_values: EVAL 0fsw_7 prequel 01kf5lf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 52.000 0.104 http://example.org/film/film/prequel #11383-0ctw_b PRED entity: 0ctw_b PRED relation: form_of_government PRED expected values: 01fpfn => 255 concepts (255 used for prediction) PRED predicted values (max 10 best out of 4): 06cx9 (0.47 #681, 0.42 #697, 0.34 #685), 01fpfn (0.44 #42, 0.43 #134, 0.41 #698), 01d9r3 (0.37 #683, 0.36 #699, 0.33 #319), 026wp (0.18 #32, 0.17 #8, 0.15 #56) >> Best rule #681 for best value: >> intensional similarity = 3 >> extensional distance = 152 >> proper extension: 02wm6l; >> query: (?x1023, 06cx9) <- form_of_government(?x1023, ?x1926), form_of_government(?x792, ?x1926), ?x792 = 0hzlz >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 035v3; *> query: (?x1023, 01fpfn) <- form_of_government(?x1023, ?x1926), jurisdiction_of_office(?x182, ?x1023), featured_film_locations(?x522, ?x1023) *> conf = 0.44 ranks of expected_values: 2 EVAL 0ctw_b form_of_government 01fpfn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 255.000 255.000 0.468 http://example.org/location/country/form_of_government #11382-0b2v79 PRED entity: 0b2v79 PRED relation: nominated_for! PRED expected values: 0gq_v => 94 concepts (88 used for prediction) PRED predicted values (max 10 best out of 202): 0gqy2 (0.77 #6796, 0.67 #3279, 0.67 #7267), 027b9j5 (0.67 #3279, 0.67 #7267, 0.67 #3982), 04kxsb (0.57 #92, 0.23 #328, 0.21 #1966), 0f4x7 (0.52 #24, 0.33 #260, 0.28 #1898), 0gq_v (0.51 #488, 0.34 #1892, 0.29 #1424), 0gq9h (0.48 #60, 0.44 #1934, 0.41 #1466), 02w9sd7 (0.48 #121, 0.12 #11722, 0.12 #1527), 019f4v (0.39 #1925, 0.37 #1457, 0.33 #287), 0gs9p (0.39 #1468, 0.38 #1936, 0.33 #62), 04dn09n (0.38 #34, 0.28 #1908, 0.27 #1440) >> Best rule #6796 for best value: >> intensional similarity = 3 >> extensional distance = 682 >> proper extension: 06mmr; >> query: (?x195, ?x3066) <- award(?x195, ?x3066), award(?x92, ?x3066), ceremony(?x3066, ?x78) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #488 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 79 *> proper extension: 05dy7p; *> query: (?x195, 0gq_v) <- nominated_for(?x1760, ?x195), titles(?x162, ?x195), genre(?x195, ?x53), costume_design_by(?x1859, ?x1760) *> conf = 0.51 ranks of expected_values: 5 EVAL 0b2v79 nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 88.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11381-07tds PRED entity: 07tds PRED relation: company! PRED expected values: 060c4 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 45): 0dq_5 (0.64 #1475, 0.43 #1334, 0.41 #2886), 0krdk (0.61 #1464, 0.42 #1981, 0.41 #2875), 060c4 (0.51 #1460, 0.42 #238, 0.39 #1319), 0dq3c (0.42 #1459, 0.33 #2, 0.29 #1976), 021q1c (0.35 #481, 0.33 #246, 0.29 #528), 01yc02 (0.33 #9, 0.31 #1466, 0.19 #3394), 04n1q6 (0.33 #247, 0.29 #153, 0.24 #482), 01kr6k (0.33 #28, 0.17 #1485, 0.12 #2002), 02zdwq (0.33 #26, 0.12 #5460, 0.03 #919), 05_wyz (0.32 #1476, 0.26 #1993, 0.26 #1335) >> Best rule #1475 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 01n073; 03sc8; 04htfd; 04f0xq; 0py9b; 04sv4; 0lwkh; 0hkqn; 0841v; 07gyp7; ... >> query: (?x4672, 0dq_5) <- service_location(?x4672, ?x94), ?x94 = 09c7w0, list(?x4672, ?x2197) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #1460 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 01n073; 03sc8; 04htfd; 04f0xq; 0py9b; 04sv4; 0lwkh; 0hkqn; 0841v; 07gyp7; ... *> query: (?x4672, 060c4) <- service_location(?x4672, ?x94), ?x94 = 09c7w0, list(?x4672, ?x2197) *> conf = 0.51 ranks of expected_values: 3 EVAL 07tds company! 060c4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 146.000 146.000 0.644 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #11380-01bm_ PRED entity: 01bm_ PRED relation: contains! PRED expected values: 09c7w0 => 109 concepts (85 used for prediction) PRED predicted values (max 10 best out of 197): 09c7w0 (0.86 #17889, 0.83 #19677, 0.82 #16101), 02jx1 (0.44 #51872, 0.44 #47400, 0.17 #10819), 0mw5x (0.38 #561, 0.35 #74234, 0.30 #1455), 02qkt (0.34 #27175, 0.25 #39697), 059rby (0.21 #69780, 0.16 #1808, 0.15 #3596), 0dg3n1 (0.21 #26984, 0.15 #39506, 0.01 #8203), 0j0k (0.18 #27206, 0.13 #39728, 0.02 #12898), 05k7sb (0.16 #1921, 0.11 #69893, 0.08 #6393), 01qh7 (0.16 #1976, 0.06 #2870, 0.05 #6448), 01n7q (0.14 #2760, 0.12 #16176, 0.12 #17964) >> Best rule #17889 for best value: >> intensional similarity = 3 >> extensional distance = 167 >> proper extension: 016sd3; 03wv2g; >> query: (?x6925, 09c7w0) <- contains(?x8263, ?x6925), school(?x12042, ?x6925), place_of_birth(?x329, ?x8263) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bm_ contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 85.000 0.864 http://example.org/location/location/contains #11379-0j_t1 PRED entity: 0j_t1 PRED relation: film_crew_role PRED expected values: 01pvkk => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.72 #925, 0.70 #1307, 0.61 #465), 02r96rf (0.62 #920, 0.61 #1302, 0.55 #460), 09vw2b7 (0.60 #924, 0.58 #1306, 0.53 #464), 0dxtw (0.35 #929, 0.34 #1311, 0.29 #469), 01vx2h (0.31 #930, 0.29 #1312, 0.28 #470), 01pvkk (0.27 #1313, 0.27 #931, 0.21 #739), 0d2b38 (0.17 #28, 0.10 #945, 0.10 #485), 02ynfr (0.15 #935, 0.14 #1317, 0.14 #743), 0215hd (0.12 #938, 0.11 #478, 0.11 #593), 089g0h (0.11 #939, 0.10 #479, 0.09 #594) >> Best rule #925 for best value: >> intensional similarity = 3 >> extensional distance = 984 >> proper extension: 0gtsx8c; 0gtvrv3; 01gglm; >> query: (?x2719, 0ch6mp2) <- film(?x2263, ?x2719), film_crew_role(?x2719, ?x137), film_release_distribution_medium(?x2719, ?x81) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1313 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1267 *> proper extension: 0dtw1x; 0fq27fp; 0cnztc4; 0gj9qxr; 0crh5_f; 0192hw; 043sct5; 0h95zbp; 0g5q34q; 03_wm6; ... *> query: (?x2719, 01pvkk) <- genre(?x2719, ?x307), film_crew_role(?x2719, ?x137) *> conf = 0.27 ranks of expected_values: 6 EVAL 0j_t1 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 57.000 57.000 0.720 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11378-01p_2p PRED entity: 01p_2p PRED relation: artists PRED expected values: 01vsy95 => 51 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1190): 01dhjz (0.67 #3011, 0.46 #4095, 0.33 #844), 053yx (0.62 #3477, 0.44 #2393, 0.33 #4561), 09hnb (0.56 #2378, 0.54 #3462, 0.50 #1294), 03t9sp (0.53 #10970, 0.27 #7712, 0.23 #3373), 024zq (0.50 #1602, 0.38 #3770, 0.33 #2686), 0f0y8 (0.50 #1086, 0.38 #3254, 0.33 #2170), 01lcxbb (0.50 #1370, 0.33 #287, 0.31 #3538), 02pbrn (0.50 #2123, 0.33 #1040, 0.23 #4291), 01v0sx2 (0.39 #22777, 0.04 #5502, 0.02 #7674), 02r1tx7 (0.39 #22777, 0.04 #5615, 0.02 #7787) >> Best rule #3011 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 01lxd4; 03_d0; 0ggq0m; 07ym47; 01fh36; 037n97; >> query: (?x14611, 01dhjz) <- artists(?x14611, ?x1563), artists(?x14611, ?x1524), ?x1563 = 0fpjd_g, role(?x1524, ?x2460), award_winner(?x158, ?x1524), location(?x1524, ?x10708), role(?x74, ?x2460) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2452 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 01lxd4; 03_d0; 0ggq0m; 07ym47; 01fh36; 037n97; *> query: (?x14611, 01vsy95) <- artists(?x14611, ?x1563), artists(?x14611, ?x1524), ?x1563 = 0fpjd_g, role(?x1524, ?x2460), award_winner(?x158, ?x1524), location(?x1524, ?x10708), role(?x74, ?x2460) *> conf = 0.33 ranks of expected_values: 11 EVAL 01p_2p artists 01vsy95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 51.000 35.000 0.667 http://example.org/music/genre/artists #11377-015f7 PRED entity: 015f7 PRED relation: award PRED expected values: 02g3gj 02f5qb => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 309): 02g3gj (0.78 #16772, 0.72 #10531, 0.70 #36275), 01by1l (0.61 #890, 0.37 #2840, 0.33 #13371), 02f5qb (0.56 #933, 0.48 #2883, 0.19 #1713), 02f72n (0.50 #923, 0.37 #2873, 0.16 #17553), 01bgqh (0.50 #822, 0.36 #2772, 0.27 #3942), 03qbh5 (0.50 #980, 0.32 #1370, 0.27 #2930), 02v1m7 (0.44 #891, 0.30 #2841, 0.16 #1281), 02f705 (0.39 #930, 0.25 #1710, 0.21 #2880), 03t5kl (0.39 #1000, 0.18 #6850, 0.16 #1780), 09sb52 (0.35 #7840, 0.33 #17593, 0.32 #5500) >> Best rule #16772 for best value: >> intensional similarity = 2 >> extensional distance = 268 >> proper extension: 01vsxdm; >> query: (?x3397, ?x154) <- role(?x3397, ?x316), award_winner(?x154, ?x3397) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 015f7 award 02f5qb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 145.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 015f7 award 02g3gj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11376-0gtxj2q PRED entity: 0gtxj2q PRED relation: film_release_region PRED expected values: 0jgd 06bnz 01crd5 => 92 concepts (89 used for prediction) PRED predicted values (max 10 best out of 154): 0d060g (0.86 #732, 0.84 #1167, 0.83 #2620), 0b90_r (0.86 #1019, 0.85 #149, 0.84 #2472), 0jgd (0.84 #2616, 0.83 #2471, 0.83 #3051), 06bnz (0.84 #767, 0.84 #1057, 0.83 #2073), 01znc_ (0.84 #1053, 0.81 #1778, 0.81 #2651), 03rt9 (0.80 #1174, 0.79 #2482, 0.78 #2627), 03_3d (0.80 #151, 0.78 #1021, 0.75 #1311), 047yc (0.76 #460, 0.69 #1040, 0.68 #750), 01p1v (0.75 #49, 0.70 #194, 0.65 #2662), 06c1y (0.75 #40, 0.70 #185, 0.58 #1200) >> Best rule #732 for best value: >> intensional similarity = 12 >> extensional distance = 42 >> proper extension: 0gwjw0c; >> query: (?x4290, 0d060g) <- film_release_region(?x4290, ?x2513), film_release_region(?x4290, ?x2267), film_release_region(?x4290, ?x1023), film_release_region(?x4290, ?x390), film_release_region(?x4290, ?x344), film_release_region(?x4290, ?x205), ?x1023 = 0ctw_b, ?x344 = 04gzd, ?x390 = 0chghy, ?x2267 = 03rj0, ?x2513 = 05b4w, ?x205 = 03rjj >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2616 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 99 *> proper extension: 02vxq9m; 02x3lt7; 02yvct; 0fpv_3_; 0661m4p; 01jrbb; 0g5838s; 0gtvpkw; 0cmc26r; 02xbyr; ... *> query: (?x4290, 0jgd) <- film_release_region(?x4290, ?x1023), film_release_region(?x4290, ?x390), film_release_region(?x4290, ?x344), ?x1023 = 0ctw_b, ?x344 = 04gzd, film_release_region(?x7629, ?x390), film_release_region(?x5162, ?x390), film_release_region(?x4041, ?x390), ?x5162 = 0j3d9tn, ?x4041 = 0gy2y8r, ?x7629 = 02825nf, country(?x150, ?x390) *> conf = 0.84 ranks of expected_values: 3, 4, 26 EVAL 0gtxj2q film_release_region 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 92.000 89.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gtxj2q film_release_region 06bnz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 89.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gtxj2q film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 89.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11375-07ghq PRED entity: 07ghq PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 6): 029j_ (0.83 #71, 0.83 #81, 0.82 #168), 0735l (0.22 #301), 0dq6p (0.22 #301), 07c52 (0.14 #33, 0.10 #58, 0.10 #63), 07z4p (0.14 #35, 0.08 #70, 0.08 #147), 02nxhr (0.08 #67, 0.08 #32, 0.07 #102) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 149 >> proper extension: 03t97y; 01k0vq; 025twgt; >> query: (?x8370, 029j_) <- film(?x2387, ?x8370), prequel(?x324, ?x8370), currency(?x8370, ?x170), genre(?x8370, ?x225) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ghq film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.834 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #11374-03q3sy PRED entity: 03q3sy PRED relation: film PRED expected values: 03cyslc => 87 concepts (57 used for prediction) PRED predicted values (max 10 best out of 697): 0fz3b1 (0.58 #71545, 0.58 #71544, 0.45 #39343), 03cyslc (0.58 #71545, 0.58 #71544, 0.45 #39343), 02hct1 (0.58 #71545, 0.58 #71544, 0.43 #46499), 06ztvyx (0.20 #430, 0.03 #93016, 0.02 #4006), 025ts_z (0.20 #1491, 0.02 #14008, 0.02 #12220), 0bpm4yw (0.11 #2511, 0.02 #9664), 0b76kw1 (0.11 #2100, 0.02 #62602, 0.01 #62603), 0fzm0g (0.10 #1780, 0.05 #3568, 0.01 #17874), 016dj8 (0.10 #1113, 0.04 #4689, 0.03 #93016), 065_cjc (0.10 #1195, 0.04 #4771, 0.03 #93016) >> Best rule #71545 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 025p38; 05wjnt; 05hdf; 01pnn3; 02zrv7; 0n8bn; 01lqnff; 0418ft; 0bkmf; 012x2b; ... >> query: (?x5944, ?x2436) <- nominated_for(?x5944, ?x2436), film(?x5944, ?x603) >> conf = 0.58 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 03q3sy film 03cyslc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 57.000 0.585 http://example.org/film/actor/film./film/performance/film #11373-0k39j PRED entity: 0k39j PRED relation: time_zones PRED expected values: 02hczc => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.50 #55, 0.46 #42, 0.44 #68), 02lcqs (0.45 #187, 0.35 #226, 0.31 #278), 02fqwt (0.33 #14, 0.25 #79, 0.21 #131), 02hczc (0.33 #2, 0.13 #184, 0.09 #1119), 042g7t (0.09 #1119, 0.03 #89, 0.01 #791), 02lcrv (0.09 #1119, 0.02 #98, 0.02 #228), 02llzg (0.07 #277, 0.07 #316, 0.07 #290), 03bdv (0.04 #318, 0.03 #1085, 0.03 #1098), 03plfd (0.03 #88, 0.02 #647, 0.02 #868), 05jphn (0.03 #91) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 013gxt; 0ftxc; 0mnz0; 02zp1t; >> query: (?x9229, 02hcv8) <- location(?x966, ?x9229), source(?x9229, ?x958), basic_title(?x966, ?x265), nationality(?x966, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0fw3f; *> query: (?x9229, 02hczc) <- source(?x9229, ?x958), state(?x9229, ?x3086), ?x3086 = 0846v, contains(?x94, ?x9229) *> conf = 0.33 ranks of expected_values: 4 EVAL 0k39j time_zones 02hczc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 124.000 124.000 0.500 http://example.org/location/location/time_zones #11372-01c7j1 PRED entity: 01c7j1 PRED relation: company! PRED expected values: 02211by => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 34): 060c4 (0.87 #3954, 0.80 #2816, 0.80 #3496), 0krdk (0.80 #3046, 0.74 #1462, 0.74 #600), 0dq_5 (0.79 #1473, 0.79 #611, 0.78 #3102), 0dq3c (0.74 #595, 0.67 #366, 0.56 #1457), 09d6p2 (0.52 #1246, 0.47 #383, 0.45 #1925), 01yc02 (0.47 #419, 0.45 #783, 0.41 #1419), 02y6fz (0.33 #342, 0.17 #4999, 0.17 #934), 01kr6k (0.30 #1752, 0.30 #891, 0.26 #2249), 02211by (0.25 #687, 0.21 #505, 0.21 #914), 0142rn (0.21 #527, 0.20 #436, 0.17 #4999) >> Best rule #3954 for best value: >> intensional similarity = 7 >> extensional distance = 150 >> proper extension: 09c7w0; 02vk52z; 025jfl; 0f8l9c; 03rj0; 01jsn5; 0j_sncb; 01_8w2; 02hcxm; 0hm0k; ... >> query: (?x10133, 060c4) <- company(?x4792, ?x10133), company(?x4792, ?x10926), company(?x4792, ?x7218), company(?x4792, ?x3439), ?x10926 = 060ppp, ?x3439 = 03ksy, ?x7218 = 019rl6 >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #687 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 01rs59; 043ttv; *> query: (?x10133, 02211by) <- company(?x4792, ?x10133), state_province_region(?x10133, ?x1227), citytown(?x10133, ?x3125), ?x1227 = 01n7q, adjoins(?x3125, ?x3677) *> conf = 0.25 ranks of expected_values: 9 EVAL 01c7j1 company! 02211by CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 182.000 182.000 0.868 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #11371-014zfs PRED entity: 014zfs PRED relation: award PRED expected values: 0bdw6t 09qrn4 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 309): 05qck (0.70 #32728, 0.70 #29134, 0.69 #34325), 0gq9h (0.50 #76, 0.21 #9654, 0.15 #874), 040njc (0.50 #8, 0.13 #9586, 0.11 #25949), 0f_nbyh (0.50 #10, 0.11 #1606, 0.08 #808), 05p1dby (0.49 #9682, 0.25 #104, 0.04 #12875), 09sb52 (0.34 #16003, 0.25 #25183, 0.24 #22789), 0ck27z (0.32 #22839, 0.23 #1287, 0.20 #25632), 01bgqh (0.31 #5629, 0.30 #8024, 0.23 #21594), 04ljl_l (0.31 #801, 0.23 #1200, 0.14 #402), 0c4z8 (0.31 #868, 0.20 #18829, 0.19 #21622) >> Best rule #32728 for best value: >> intensional similarity = 2 >> extensional distance = 1585 >> proper extension: 04cy8rb; 09d5h; 03jvmp; 0g5lhl7; 01w92; 0kk9v; 05xbx; 026v1z; >> query: (?x1145, ?x688) <- award_nominee(?x1145, ?x2400), award_winner(?x688, ?x1145) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #635 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 052hl; *> query: (?x1145, 09qrn4) <- influenced_by(?x236, ?x1145), influenced_by(?x1145, ?x8286), ?x8286 = 0l5yl *> conf = 0.14 ranks of expected_values: 58, 138 EVAL 014zfs award 09qrn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 111.000 111.000 0.703 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 014zfs award 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 111.000 111.000 0.703 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11370-05h7tk PRED entity: 05h7tk PRED relation: profession PRED expected values: 01d_h8 => 63 concepts (49 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.86 #6630, 0.85 #6777, 0.83 #6924), 01d_h8 (0.77 #4564, 0.75 #1476, 0.74 #2064), 0np9r (0.62 #755, 0.61 #902, 0.58 #1196), 03gjzk (0.48 #1190, 0.47 #1043, 0.39 #896), 0196pc (0.44 #366, 0.42 #660, 0.40 #513), 02krf9 (0.33 #1055, 0.33 #761, 0.32 #1202), 0cbd2 (0.33 #742, 0.30 #889, 0.25 #5300), 015cjr (0.33 #48, 0.11 #342, 0.10 #489), 03sbb (0.33 #86, 0.11 #380, 0.10 #527), 09jwl (0.16 #7076, 0.16 #5164, 0.16 #4870) >> Best rule #6630 for best value: >> intensional similarity = 5 >> extensional distance = 2900 >> proper extension: 0157m; 01zmpg; 01_k0d; 01kp_1t; >> query: (?x13500, 02hrh1q) <- profession(?x13500, ?x524), profession(?x12677, ?x524), profession(?x856, ?x524), ?x12677 = 01385g, film(?x856, ?x857) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #4564 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1149 *> proper extension: 07nznf; 0q9kd; 079vf; 05bnp0; 0dbpyd; 012d40; 0fvf9q; 04qvl7; 02p65p; 0520r2x; ... *> query: (?x13500, 01d_h8) <- profession(?x13500, ?x1966), gender(?x13500, ?x231), film_crew_role(?x83, ?x1966) *> conf = 0.77 ranks of expected_values: 2 EVAL 05h7tk profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 49.000 0.861 http://example.org/people/person/profession #11369-02ctc6 PRED entity: 02ctc6 PRED relation: currency PRED expected values: 09nqf => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.83 #1, 0.77 #99, 0.76 #43), 01nv4h (0.03 #65, 0.03 #37, 0.03 #51), 02l6h (0.02 #4, 0.02 #32, 0.01 #130), 088n7 (0.02 #28) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 03ct7jd; >> query: (?x3211, 09nqf) <- film(?x338, ?x3211), production_companies(?x3211, ?x4564), ?x4564 = 01gb54, country(?x3211, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ctc6 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.833 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11368-02_n7 PRED entity: 02_n7 PRED relation: place_of_birth! PRED expected values: 0c2ry => 91 concepts (84 used for prediction) PRED predicted values (max 10 best out of 3165): 02x8s9 (0.12 #2193, 0.11 #4805, 0.04 #15253), 026dd2b (0.12 #1906, 0.11 #4518, 0.04 #14966), 029_3 (0.12 #804, 0.11 #3416, 0.04 #13864), 01lcxbb (0.12 #649, 0.11 #3261, 0.04 #13709), 0227tr (0.12 #485, 0.11 #3097, 0.04 #13545), 01wwvc5 (0.12 #521, 0.11 #3133, 0.04 #13581), 0582cf (0.12 #1921, 0.11 #4533, 0.04 #14981), 01j590z (0.12 #1836, 0.11 #4448, 0.04 #14896), 07r_dg (0.12 #2101, 0.06 #120159, 0.02 #22997), 0fsm8c (0.12 #292, 0.06 #120159, 0.02 #21188) >> Best rule #2193 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02_286; 01n7q; >> query: (?x6316, 02x8s9) <- contains(?x94, ?x6316), service_location(?x6315, ?x6316), ?x94 = 09c7w0 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #26121 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 059j2; *> query: (?x6316, ?x51) <- contains(?x94, ?x6316), service_location(?x6315, ?x6316), nationality(?x51, ?x94) *> conf = 0.01 ranks of expected_values: 2587 EVAL 02_n7 place_of_birth! 0c2ry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 84.000 0.125 http://example.org/people/person/place_of_birth #11367-0cnl1c PRED entity: 0cnl1c PRED relation: award_winner! PRED expected values: 027hjff => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 90): 027hjff (0.65 #57, 0.17 #3081, 0.10 #5602), 027n06w (0.17 #3081, 0.15 #73, 0.10 #5602), 0gx_st (0.17 #3081, 0.10 #5602, 0.05 #37), 03gt46z (0.17 #3081, 0.10 #5602, 0.05 #63), 09pj68 (0.17 #3081, 0.05 #105, 0.02 #245), 02q690_ (0.10 #5602, 0.05 #65, 0.05 #205), 0275n3y (0.10 #5602, 0.05 #75, 0.04 #1335), 07y9ts (0.10 #5602, 0.05 #68, 0.03 #208), 07z31v (0.10 #5602, 0.05 #31, 0.02 #171), 05c1t6z (0.07 #155, 0.04 #435, 0.03 #1275) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0cnl80; 0bt7ws; 0cnl09; 05l0j5; >> query: (?x4332, 027hjff) <- award_nominee(?x4332, ?x237), award_nominee(?x7663, ?x4332), ?x7663 = 04zkj5 >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cnl1c award_winner! 027hjff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.650 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11366-0gx_st PRED entity: 0gx_st PRED relation: award_winner PRED expected values: 04cl1 01bcq 09_99w => 32 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2221): 0cp9f9 (0.50 #8793, 0.50 #5746, 0.43 #13366), 05bnq3j (0.50 #8332, 0.50 #6807, 0.40 #9854), 01t6b4 (0.50 #7791, 0.45 #6095, 0.40 #9313), 0p_2r (0.50 #7811, 0.43 #13909, 0.40 #9333), 018ygt (0.50 #8570, 0.40 #10092, 0.38 #16189), 04glr5h (0.50 #8876, 0.40 #10398, 0.33 #4306), 01_x6d (0.50 #8296, 0.40 #9818, 0.33 #3726), 01_x6v (0.50 #7947, 0.40 #9469, 0.33 #3377), 02778qt (0.50 #8070, 0.40 #9592, 0.33 #3500), 02773m2 (0.50 #7725, 0.40 #9247, 0.33 #3155) >> Best rule #8793 for best value: >> intensional similarity = 20 >> extensional distance = 2 >> proper extension: 05c1t6z; >> query: (?x2292, 0cp9f9) <- ceremony(?x7850, ?x2292), ceremony(?x4225, ?x2292), ceremony(?x3247, ?x2292), ceremony(?x2041, ?x2292), ceremony(?x2016, ?x2292), ceremony(?x870, ?x2292), ceremony(?x686, ?x2292), ?x870 = 09qv3c, honored_for(?x2292, ?x6614), honored_for(?x2292, ?x3169), award_winner(?x2292, ?x2493), ?x686 = 0bdw1g, ?x2016 = 0cjyzs, ?x7850 = 07kjk7c, ?x4225 = 09qvf4, category(?x6614, ?x134), program_creator(?x3169, ?x6913), ?x2041 = 0bdx29, ?x3247 = 0bdwqv, award_nominee(?x473, ?x2493) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1528 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 03gt46z; *> query: (?x2292, ?x1285) <- ceremony(?x870, ?x2292), award_winner(?x2292, ?x2819), award_winner(?x2292, ?x1630), award_winner(?x870, ?x4266), award(?x1676, ?x870), honored_for(?x2292, ?x10447), honored_for(?x2292, ?x6614), nominated_for(?x870, ?x758), award_nominee(?x1630, ?x237), award_winner(?x1676, ?x968), film_release_region(?x6614, ?x94), film(?x4266, ?x3059), ?x2819 = 0bczgm, award_winner(?x10447, ?x1285) *> conf = 0.27 ranks of expected_values: 123, 206, 402 EVAL 0gx_st award_winner 09_99w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 32.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gx_st award_winner 01bcq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 32.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gx_st award_winner 04cl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 32.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11365-0p4v_ PRED entity: 0p4v_ PRED relation: genre PRED expected values: 02l7c8 => 76 concepts (74 used for prediction) PRED predicted values (max 10 best out of 103): 02l7c8 (0.83 #134, 0.82 #15, 0.34 #1566), 07s9rl0 (0.83 #834, 0.82 #715, 0.82 #596), 07ssc (0.54 #5850, 0.54 #1432, 0.53 #4537), 01hmnh (0.34 #3479, 0.15 #8617, 0.15 #2166), 02kdv5l (0.33 #956, 0.33 #479, 0.29 #360), 01jfsb (0.33 #487, 0.32 #964, 0.29 #249), 04xvlr (0.25 #1314, 0.24 #1074, 0.20 #597), 060__y (0.23 #611, 0.22 #849, 0.22 #730), 01t_vv (0.23 #172, 0.23 #53, 0.16 #2202), 0lsxr (0.21 #484, 0.20 #961, 0.20 #722) >> Best rule #134 for best value: >> intensional similarity = 4 >> extensional distance = 130 >> proper extension: 03mh_tp; 0gbfn9; 058kh7; 04180vy; 02rtqvb; >> query: (?x2898, 02l7c8) <- genre(?x2898, ?x239), film(?x3533, ?x2898), ?x239 = 06cvj, award(?x3533, ?x704) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p4v_ genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 74.000 0.833 http://example.org/film/film/genre #11364-0b80__ PRED entity: 0b80__ PRED relation: gender PRED expected values: 05zppz => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.82 #31, 0.76 #179, 0.75 #21), 02zsn (0.48 #12, 0.46 #34, 0.46 #217) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 01xyt7; >> query: (?x4774, 05zppz) <- company(?x4774, ?x541), place_of_birth(?x4774, ?x4627), type_of_union(?x4774, ?x566) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b80__ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.818 http://example.org/people/person/gender #11363-0301bq PRED entity: 0301bq PRED relation: film PRED expected values: 05fcbk7 => 97 concepts (48 used for prediction) PRED predicted values (max 10 best out of 599): 05pt0l (0.61 #23267, 0.60 #50123, 0.58 #19686), 0330r (0.60 #50123, 0.58 #19686, 0.58 #21476), 0ndwt2w (0.20 #1001, 0.02 #2790, 0.02 #15317), 011ydl (0.20 #525), 05fcbk7 (0.10 #461, 0.09 #2250), 017jd9 (0.10 #780, 0.07 #2569, 0.03 #15096), 011yxg (0.10 #42, 0.07 #1831, 0.01 #14358), 017gl1 (0.10 #143, 0.04 #1932, 0.03 #14459), 017gm7 (0.10 #211, 0.04 #2000, 0.03 #14527), 026qnh6 (0.10 #823, 0.04 #2612) >> Best rule #23267 for best value: >> intensional similarity = 3 >> extensional distance = 783 >> proper extension: 0b478; 0gs5q; 03h40_7; >> query: (?x10701, ?x7481) <- nominated_for(?x10701, ?x7481), film_crew_role(?x7481, ?x1284), location(?x10701, ?x739) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #461 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 02hblj; *> query: (?x10701, 05fcbk7) <- film(?x10701, ?x5839), film(?x10701, ?x1941), film_release_region(?x1941, ?x94), ?x5839 = 05650n *> conf = 0.10 ranks of expected_values: 5 EVAL 0301bq film 05fcbk7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 48.000 0.605 http://example.org/film/actor/film./film/performance/film #11362-040fv PRED entity: 040fv PRED relation: seasonal_months! PRED expected values: 028kb => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 3): 040fb (0.80 #38, 0.73 #13, 0.71 #26), 028kb (0.80 #38, 0.71 #26, 0.71 #17), 040fv (0.80 #38, 0.71 #26, 0.71 #17) >> Best rule #38 for best value: >> intensional similarity = 90 >> extensional distance = 2 >> proper extension: 0ll3; >> query: (?x2255, ?x2140) <- month(?x9559, ?x2255), month(?x8977, ?x2255), month(?x8956, ?x2255), month(?x8602, ?x2255), month(?x6960, ?x2255), month(?x6959, ?x2255), month(?x6357, ?x2255), month(?x5719, ?x2255), month(?x5168, ?x2255), month(?x5036, ?x2255), month(?x2611, ?x2255), month(?x2254, ?x2255), month(?x1860, ?x2255), month(?x1658, ?x2255), month(?x1458, ?x2255), month(?x1036, ?x2255), month(?x863, ?x2255), month(?x206, ?x2255), seasonal_months(?x1459, ?x2255), ?x8602 = 0chgzm, ?x206 = 01914, ?x1458 = 05ywg, ?x2254 = 0dclg, ?x5036 = 06y57, ?x8956 = 0947l, ?x6959 = 06c62, contains(?x6357, ?x8694), origin(?x10670, ?x6357), origin(?x5547, ?x6357), origin(?x1321, ?x6357), location(?x11396, ?x6357), location(?x3118, ?x6357), location(?x489, ?x6357), month(?x6357, ?x2140), influenced_by(?x1573, ?x10670), artists(?x1000, ?x10670), artist(?x2931, ?x10670), film(?x11396, ?x2896), ?x3118 = 01w02sy, ?x6960 = 071vr, people(?x7322, ?x11396), ?x9559 = 07dfk, ?x8977 = 02z0j, participant(?x489, ?x538), award_winner(?x834, ?x489), film(?x489, ?x3498), award(?x11396, ?x458), ?x5168 = 06mxs, group(?x227, ?x5547), vacationer(?x1036, ?x3583), vacationer(?x1036, ?x1093), award_nominee(?x5547, ?x1060), award(?x5547, ?x2180), award_nominee(?x489, ?x100), featured_film_locations(?x8072, ?x1036), location(?x5922, ?x1036), award_nominee(?x1410, ?x489), producer_type(?x3583, ?x632), ?x1093 = 0lk90, place_founded(?x1914, ?x1036), ?x2611 = 02h6_6p, institution(?x620, ?x8694), location(?x3583, ?x1131), student(?x8694, ?x1191), award_nominee(?x3583, ?x940), award_winner(?x725, ?x5547), ?x1860 = 01_d4, profession(?x489, ?x1032), participant(?x3583, ?x4126), executive_produced_by(?x8072, ?x4060), genre(?x8072, ?x258), ?x2180 = 02v1m7, organization(?x3464, ?x8694), ?x3498 = 02fqrf, vacationer(?x6357, ?x1656), award(?x3583, ?x693), influenced_by(?x10670, ?x115), category(?x8694, ?x134), profession(?x3583, ?x987), instrumentalists(?x212, ?x1321), ?x1658 = 0h7h6, country(?x1036, ?x279), ?x863 = 0fhp9, artists(?x2491, ?x1321), teams(?x1036, ?x934), major_field_of_study(?x8694, ?x90), student(?x11459, ?x11396), ?x5719 = 0f2rq, place_of_birth(?x585, ?x6357), profession(?x5922, ?x524) >> conf = 0.80 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 040fv seasonal_months! 028kb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 12.000 12.000 0.800 http://example.org/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months #11361-01wp8w7 PRED entity: 01wp8w7 PRED relation: religion PRED expected values: 0n2g => 105 concepts (65 used for prediction) PRED predicted values (max 10 best out of 16): 0kpl (0.19 #1228, 0.18 #1633, 0.18 #1363), 03_gx (0.14 #1412, 0.13 #1637, 0.13 #1367), 0c8wxp (0.13 #908, 0.13 #1089, 0.12 #953), 01lp8 (0.12 #226, 0.11 #271, 0.11 #46), 092bf5 (0.11 #106, 0.11 #61, 0.08 #196), 03j6c (0.10 #516, 0.08 #201, 0.07 #471), 0kq2 (0.06 #1236, 0.05 #1641, 0.05 #1371), 0n2g (0.05 #1231, 0.04 #1591, 0.04 #1501), 021_0p (0.04 #154, 0.01 #649), 0flw86 (0.04 #227, 0.03 #362, 0.02 #2031) >> Best rule #1228 for best value: >> intensional similarity = 3 >> extensional distance = 254 >> proper extension: 0fpzzp; >> query: (?x1521, 0kpl) <- influenced_by(?x2963, ?x1521), influenced_by(?x1521, ?x215), influenced_by(?x1573, ?x2963) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1231 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 254 *> proper extension: 0fpzzp; *> query: (?x1521, 0n2g) <- influenced_by(?x2963, ?x1521), influenced_by(?x1521, ?x215), influenced_by(?x1573, ?x2963) *> conf = 0.05 ranks of expected_values: 8 EVAL 01wp8w7 religion 0n2g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 105.000 65.000 0.191 http://example.org/people/person/religion #11360-02qkwl PRED entity: 02qkwl PRED relation: currency PRED expected values: 09nqf => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.86 #15, 0.85 #162, 0.85 #50), 02l6h (0.12 #575, 0.04 #46, 0.04 #60), 01nv4h (0.12 #575, 0.03 #72, 0.02 #219), 088n7 (0.12 #575), 02gsvk (0.12 #575), 0kz1h (0.12 #575), 0ptk_ (0.12 #575) >> Best rule #15 for best value: >> intensional similarity = 7 >> extensional distance = 42 >> proper extension: 024l2y; 025n07; >> query: (?x8001, 09nqf) <- genre(?x8001, ?x812), genre(?x8001, ?x225), film(?x521, ?x8001), ?x812 = 01jfsb, film_crew_role(?x8001, ?x3197), ?x3197 = 02ynfr, ?x225 = 02kdv5l >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qkwl currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.864 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11359-07lnk PRED entity: 07lnk PRED relation: artists PRED expected values: 0326tc => 62 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1075): 01wj18h (0.67 #5650, 0.50 #2418, 0.38 #12117), 07sbk (0.60 #5076, 0.52 #4308, 0.50 #3998), 03f5spx (0.59 #12986, 0.56 #11909, 0.50 #5442), 01vxlbm (0.56 #12190, 0.53 #13267, 0.50 #5723), 011z3g (0.56 #12452, 0.53 #13529, 0.50 #2753), 06mt91 (0.56 #12460, 0.53 #13537, 0.50 #2761), 0x3n (0.53 #13495, 0.50 #12418, 0.50 #2719), 07g2v (0.52 #4308, 0.40 #4604, 0.33 #296), 01vtj38 (0.50 #12511, 0.50 #2812, 0.47 #13588), 0127s7 (0.50 #12388, 0.50 #2689, 0.47 #13465) >> Best rule #5650 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0827d; >> query: (?x2439, 01wj18h) <- parent_genre(?x12831, ?x2439), artists(?x2439, ?x4052), artists(?x2439, ?x1674), award_winner(?x3488, ?x1674), nationality(?x1674, ?x512), award_nominee(?x1674, ?x976), ?x4052 = 050z2, ?x3488 = 02f71y >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #9340 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 10 *> proper extension: 0ggx5q; 05rwpb; *> query: (?x2439, 0326tc) <- artists(?x2439, ?x5760), artists(?x2439, ?x1674), award_winner(?x2180, ?x5760), ?x1674 = 01v_pj6, award(?x5760, ?x528), ?x528 = 02g3gj, ?x2180 = 02v1m7 *> conf = 0.25 ranks of expected_values: 327 EVAL 07lnk artists 0326tc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 29.000 0.667 http://example.org/music/genre/artists #11358-039n1 PRED entity: 039n1 PRED relation: people! PRED expected values: 024c2 => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 50): 06z5s (0.33 #290, 0.33 #158, 0.25 #356), 032s66 (0.33 #314, 0.07 #1304, 0.06 #1568), 01l2m3 (0.25 #347, 0.11 #677, 0.11 #2063), 0gk4g (0.21 #1199, 0.15 #2849, 0.13 #7074), 02k6hp (0.20 #434, 0.14 #1160, 0.12 #1952), 07jwr (0.17 #538, 0.11 #670, 0.10 #802), 09d11 (0.17 #549, 0.02 #2463, 0.02 #2661), 02y0js (0.14 #597, 0.10 #795, 0.08 #993), 02vrr (0.14 #609, 0.10 #807, 0.08 #1005), 0qcr0 (0.13 #1256, 0.07 #6471, 0.07 #5085) >> Best rule #290 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 07_m9_; >> query: (?x9600, 06z5s) <- place_of_death(?x9600, ?x1646), ?x1646 = 0156q, profession(?x9600, ?x353), gender(?x9600, ?x231), nationality(?x9600, ?x1264) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 039n1 people! 024c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 164.000 164.000 0.333 http://example.org/people/cause_of_death/people #11357-05148p4 PRED entity: 05148p4 PRED relation: group PRED expected values: 07qnf 01wv9xn 01czx 0167_s 0249kn 02jqjm 015srx 02cpp 06mj4 02hzz 0qmny 07rnh 02cw1m 04qzm 0cfgd 07n3s 0fsyx 0jg77 07n68 02ht0ln => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 497): 02cw1m (0.71 #689, 0.56 #929, 0.50 #1170), 01czx (0.57 #660, 0.50 #1141, 0.50 #481), 02jqjm (0.57 #672, 0.50 #1153, 0.50 #373), 06mj4 (0.57 #681, 0.50 #382, 0.44 #921), 0mgcr (0.57 #666, 0.50 #367, 0.44 #906), 017j6 (0.57 #664, 0.50 #365, 0.42 #2151), 0p8h0 (0.57 #708, 0.50 #409, 0.40 #589), 011xhx (0.57 #705, 0.50 #406, 0.40 #1186), 0bsj9 (0.57 #704, 0.50 #405, 0.40 #1185), 0knhk (0.57 #680, 0.50 #381, 0.40 #1161) >> Best rule #689 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 03qjg; >> query: (?x1166, 02cw1m) <- group(?x1166, ?x6102), instrumentalists(?x1166, ?x4207), instrumentalists(?x1166, ?x2731), role(?x1166, ?x75), award_nominee(?x2731, ?x1125), role(?x4207, ?x227), ?x6102 = 07h76, role(?x1166, ?x74) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 12, 13, 15, 17, 18, 24, 29, 30, 36, 37, 40, 42, 49, 50, 51, 56 EVAL 05148p4 group 02ht0ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 07n68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 0jg77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 0fsyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 07n3s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 0cfgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 04qzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 02cw1m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 07rnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 0qmny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 02hzz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 06mj4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 02cpp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 015srx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 02jqjm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 0249kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 0167_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 01czx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 01wv9xn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 05148p4 group 07qnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 65.000 65.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #11356-010xjr PRED entity: 010xjr PRED relation: student! PRED expected values: 053mhx => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 80): 015nl4 (0.09 #2702, 0.06 #1648, 0.06 #3229), 0bwfn (0.08 #275, 0.06 #1856, 0.05 #11870), 01w5m (0.08 #105, 0.05 #2213, 0.04 #632), 02g839 (0.08 #25, 0.04 #1079, 0.04 #552), 01cf5 (0.08 #474, 0.04 #1528, 0.04 #1001), 017v3q (0.08 #245, 0.04 #1299, 0.04 #772), 0g8rj (0.08 #176, 0.04 #1230, 0.04 #703), 025v3k (0.08 #120, 0.04 #1174, 0.04 #647), 0778p (0.08 #110, 0.04 #1164, 0.04 #637), 0677j (0.08 #328, 0.04 #1382, 0.04 #855) >> Best rule #2702 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 02p65p; 0byfz; 0h0jz; 0p_pd; 0bl2g; 0h1_w; 017149; 016khd; 015grj; 039bp; ... >> query: (?x9797, 015nl4) <- award(?x9797, ?x594), award(?x9797, ?x591), ?x591 = 0f4x7, award_winner(?x594, ?x3808), ?x3808 = 03xkps >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #8727 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 598 *> proper extension: 013rds; *> query: (?x9797, 053mhx) <- film(?x9797, ?x4235), film_distribution_medium(?x4235, ?x627), nominated_for(?x112, ?x4235) *> conf = 0.02 ranks of expected_values: 45 EVAL 010xjr student! 053mhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 87.000 87.000 0.091 http://example.org/education/educational_institution/students_graduates./education/education/student #11355-01wgcvn PRED entity: 01wgcvn PRED relation: category PRED expected values: 08mbj5d => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #8, 0.83 #11, 0.83 #7) >> Best rule #8 for best value: >> intensional similarity = 2 >> extensional distance = 280 >> proper extension: 0f6lx; >> query: (?x3756, 08mbj5d) <- artist(?x382, ?x3756), award_winner(?x2307, ?x3756) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wgcvn category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.833 http://example.org/common/topic/webpage./common/webpage/category #11354-099c8n PRED entity: 099c8n PRED relation: nominated_for PRED expected values: 0b73_1d 09p0ct 02r1c18 0dr_4 01hqhm 0661ql3 03qnc6q 05c46y6 011ypx 03hj5lq 064lsn 03xf_m 01chpn 0bnzd => 47 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1341): 0661ql3 (0.71 #26396, 0.56 #27846, 0.50 #13349), 0404j37 (0.67 #19756, 0.66 #49299, 0.65 #43492), 0_92w (0.67 #18982, 0.50 #13182, 0.50 #11732), 09tqkv2 (0.67 #11851, 0.50 #13301, 0.50 #6055), 0dr_4 (0.67 #19042, 0.50 #8894, 0.45 #23390), 0421ng (0.67 #12283, 0.50 #6487, 0.44 #18083), 0ywrc (0.67 #19253, 0.50 #9105, 0.43 #30851), 0_b9f (0.67 #19492, 0.50 #9344, 0.36 #26739), 0yxf4 (0.67 #19774, 0.50 #13974, 0.36 #27021), 026390q (0.67 #18994, 0.50 #13194, 0.33 #11744) >> Best rule #26396 for best value: >> intensional similarity = 8 >> extensional distance = 12 >> proper extension: 02x1dht; 0gr51; 0fhpv4; 02x1z2s; 018wdw; >> query: (?x1162, 0661ql3) <- nominated_for(?x1162, ?x3455), nominated_for(?x1162, ?x2903), nominated_for(?x1162, ?x1968), ?x3455 = 02rn00y, film_format(?x1968, ?x909), film(?x166, ?x2903), written_by(?x2903, ?x10381), featured_film_locations(?x1968, ?x739) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 11, 28, 29, 31, 37, 45, 53, 57, 114, 192, 224, 411 EVAL 099c8n nominated_for 0bnzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 01chpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 03xf_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 03hj5lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 011ypx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 05c46y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 03qnc6q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 0661ql3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 01hqhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 0dr_4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 02r1c18 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 09p0ct CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099c8n nominated_for 0b73_1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 47.000 36.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11353-03x7hd PRED entity: 03x7hd PRED relation: film! PRED expected values: 03q43g 0432cd => 93 concepts (32 used for prediction) PRED predicted values (max 10 best out of 994): 04jspq (0.46 #49895, 0.44 #43655, 0.42 #33260), 05_k56 (0.46 #49895, 0.44 #43655, 0.42 #33260), 04gcd1 (0.46 #49895, 0.44 #43655, 0.42 #33260), 05bm4sm (0.46 #49895, 0.44 #43655, 0.42 #33260), 04ls53 (0.46 #49895, 0.44 #43655, 0.42 #33260), 0bbxx9b (0.46 #49895, 0.44 #43655, 0.42 #33260), 02_p5w (0.13 #4802, 0.09 #13116, 0.08 #11038), 02gf_l (0.10 #5424, 0.09 #13738, 0.03 #11660), 085q5 (0.10 #5875, 0.06 #14189, 0.03 #12111), 0170pk (0.10 #281, 0.06 #6516, 0.04 #8595) >> Best rule #49895 for best value: >> intensional similarity = 4 >> extensional distance = 401 >> proper extension: 02hfk5; 0gpx6; 04cf_l; 0hr41p6; >> query: (?x3457, ?x1052) <- nominated_for(?x500, ?x3457), genre(?x3457, ?x258), ?x258 = 05p553, nominated_for(?x1052, ?x3457) >> conf = 0.46 => this is the best rule for 6 predicted values *> Best rule #13621 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x3457, 03q43g) <- titles(?x3920, ?x3457), production_companies(?x7199, ?x3920), nominated_for(?x102, ?x7199) *> conf = 0.02 ranks of expected_values: 756 EVAL 03x7hd film! 0432cd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 32.000 0.458 http://example.org/film/actor/film./film/performance/film EVAL 03x7hd film! 03q43g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 93.000 32.000 0.458 http://example.org/film/actor/film./film/performance/film #11352-0807ml PRED entity: 0807ml PRED relation: actor! PRED expected values: 024hbv => 102 concepts (73 used for prediction) PRED predicted values (max 10 best out of 84): 026bfsh (0.05 #624, 0.05 #888, 0.05 #1152), 0kfv9 (0.04 #1083, 0.03 #1875, 0.03 #555), 02_1q9 (0.03 #797, 0.03 #533, 0.03 #2645), 0180mw (0.03 #647, 0.02 #1175, 0.02 #1967), 0ddd0gc (0.03 #1868, 0.02 #1604, 0.02 #2660), 05f4vxd (0.03 #1936, 0.02 #2728, 0.02 #1144), 080dwhx (0.03 #1062, 0.02 #1854, 0.02 #798), 0828jw (0.03 #632, 0.03 #1952, 0.02 #1160), 08jgk1 (0.03 #1870, 0.02 #1606, 0.02 #1078), 0fhzwl (0.02 #1231, 0.02 #967, 0.02 #2023) >> Best rule #624 for best value: >> intensional similarity = 3 >> extensional distance = 333 >> proper extension: 02rmxx; 02_wxh; 0sx5w; 01m4kpp; >> query: (?x6361, 026bfsh) <- people(?x2510, ?x6361), award(?x6361, ?x783), actor(?x2009, ?x6361) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0807ml actor! 024hbv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 73.000 0.054 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11351-07cz2 PRED entity: 07cz2 PRED relation: films! PRED expected values: 0mkz => 104 concepts (45 used for prediction) PRED predicted values (max 10 best out of 50): 0fx2s (0.10 #230, 0.06 #1011, 0.05 #386), 0fzyg (0.08 #367, 0.06 #992, 0.05 #523), 05489 (0.08 #990, 0.05 #2559, 0.04 #3029), 07s2s (0.07 #1507, 0.06 #1664, 0.05 #1980), 081pw (0.06 #941, 0.04 #3764, 0.04 #3922), 07_nf (0.06 #1005, 0.01 #2574, 0.01 #5728), 02m4t (0.05 #1723, 0.01 #1565), 0ddct (0.05 #245, 0.03 #1811, 0.03 #401), 018h2 (0.05 #179, 0.03 #335, 0.03 #491), 07yjb (0.05 #222, 0.03 #378, 0.03 #534) >> Best rule #230 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0140g4; 05p1tzf; 0164qt; 0fdv3; 031778; 03177r; 0ddt_; 0dyb1; 02d478; 03176f; ... >> query: (?x2770, 0fx2s) <- prequel(?x3055, ?x2770), film(?x2922, ?x2770), nominated_for(?x1807, ?x3055), film_crew_role(?x3055, ?x137) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07cz2 films! 0mkz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 45.000 0.095 http://example.org/film/film_subject/films #11350-074tb5 PRED entity: 074tb5 PRED relation: role PRED expected values: 01xqw => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 52): 0342h (0.20 #3291, 0.20 #2231, 0.15 #2337), 05r5c (0.18 #3295, 0.17 #2235, 0.13 #2341), 02sgy (0.13 #2233, 0.13 #3293, 0.08 #2339), 01vdm0 (0.13 #3320, 0.12 #2260, 0.09 #2366), 042v_gx (0.12 #2236, 0.10 #3296, 0.08 #2342), 018vs (0.09 #3301, 0.09 #2241, 0.06 #2347), 026t6 (0.09 #3289, 0.08 #2229, 0.07 #2335), 05842k (0.09 #3367, 0.07 #2307, 0.07 #2413), 01vj9c (0.09 #2243, 0.08 #3303, 0.07 #2349), 0l14qv (0.08 #3292, 0.08 #2232, 0.04 #2338) >> Best rule #3291 for best value: >> intensional similarity = 2 >> extensional distance = 621 >> proper extension: 07q1v4; 0244r8; 01wy61y; 023l9y; 01l4g5; 07j8kh; 0f6lx; 06p03s; 023slg; >> query: (?x5880, 0342h) <- profession(?x5880, ?x1183), ?x1183 = 09jwl >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 074tb5 role 01xqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 107.000 0.202 http://example.org/music/artist/track_contributions./music/track_contribution/role #11349-0gtsx8c PRED entity: 0gtsx8c PRED relation: genre PRED expected values: 05p553 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 89): 02kdv5l (0.57 #249, 0.55 #865, 0.54 #1850), 03k9fj (0.57 #259, 0.44 #505, 0.43 #382), 07s9rl0 (0.55 #3452, 0.53 #7771, 0.53 #4688), 01jfsb (0.48 #1861, 0.46 #876, 0.46 #1738), 05p553 (0.43 #2715, 0.42 #620, 0.40 #1483), 01hmnh (0.38 #266, 0.33 #882, 0.29 #1006), 02l7c8 (0.33 #141, 0.26 #4954, 0.26 #5325), 02b5_l (0.33 #174, 0.06 #2021, 0.05 #1037), 06n90 (0.31 #261, 0.26 #507, 0.25 #1616), 03npn (0.25 #8, 0.12 #1117, 0.12 #500) >> Best rule #249 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 018nnz; 01f7kl; 0dnqr; 02mc5v; >> query: (?x141, 02kdv5l) <- film(?x1460, ?x141), film_distribution_medium(?x141, ?x81), prequel(?x141, ?x7348), film(?x1104, ?x141) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2715 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 247 *> proper extension: 0k4d7; *> query: (?x141, 05p553) <- film(?x6066, ?x141), film_crew_role(?x141, ?x468), film_release_distribution_medium(?x141, ?x81), language(?x6066, ?x254) *> conf = 0.43 ranks of expected_values: 5 EVAL 0gtsx8c genre 05p553 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 65.000 65.000 0.571 http://example.org/film/film/genre #11348-0827d PRED entity: 0827d PRED relation: artists PRED expected values: 01sbf2 01vvpjj 0lsw9 0dhqyw 01yndb 01p95y0 01rwcgb => 73 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1174): 0140t7 (0.67 #6144, 0.50 #4023, 0.48 #3178), 02l_7y (0.67 #5924, 0.50 #3803, 0.48 #3178), 01w8n89 (0.62 #7731, 0.58 #6669, 0.50 #5608), 011z3g (0.58 #6948, 0.54 #8010, 0.50 #5887), 03j0br4 (0.50 #5495, 0.50 #3374, 0.50 #2314), 0197tq (0.50 #5311, 0.50 #3190, 0.48 #3178), 02z4b_8 (0.50 #3798, 0.50 #2738, 0.48 #3178), 015882 (0.50 #3307, 0.50 #2247, 0.48 #3178), 02mslq (0.50 #5332, 0.50 #3211, 0.48 #3178), 07s3vqk (0.50 #3189, 0.50 #2129, 0.48 #3178) >> Best rule #6144 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0dl5d; 01fh36; >> query: (?x284, 0140t7) <- artists(?x284, ?x3374), artists(?x284, ?x883), parent_genre(?x283, ?x284), people(?x3077, ?x883), award(?x883, ?x8458), ?x3374 = 01vsy95, award_winner(?x8458, ?x702) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3291 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 03_d0; *> query: (?x284, 01sbf2) <- artists(?x284, ?x3374), artists(?x284, ?x883), parent_genre(?x283, ?x284), people(?x3077, ?x883), award(?x883, ?x8458), ?x3374 = 01vsy95, ?x8458 = 02f777 *> conf = 0.50 ranks of expected_values: 32, 386, 406, 419, 537, 1123 EVAL 0827d artists 01rwcgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 30.000 0.667 http://example.org/music/genre/artists EVAL 0827d artists 01p95y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 73.000 30.000 0.667 http://example.org/music/genre/artists EVAL 0827d artists 01yndb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 30.000 0.667 http://example.org/music/genre/artists EVAL 0827d artists 0dhqyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 30.000 0.667 http://example.org/music/genre/artists EVAL 0827d artists 0lsw9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 30.000 0.667 http://example.org/music/genre/artists EVAL 0827d artists 01vvpjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 30.000 0.667 http://example.org/music/genre/artists EVAL 0827d artists 01sbf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 73.000 30.000 0.667 http://example.org/music/genre/artists #11347-04q01mn PRED entity: 04q01mn PRED relation: films! PRED expected values: 06d4h => 149 concepts (59 used for prediction) PRED predicted values (max 10 best out of 70): 05489 (0.12 #52, 0.07 #2538, 0.07 #3158), 07c52 (0.12 #20, 0.07 #1107, 0.06 #641), 01d5g (0.12 #420, 0.08 #264, 0.08 #730), 081pw (0.09 #3, 0.09 #3109, 0.08 #8866), 0d1w9 (0.09 #36, 0.06 #657, 0.05 #1123), 06d4h (0.07 #6417, 0.07 #8128, 0.06 #7038), 07jq_ (0.06 #81, 0.06 #2101, 0.04 #6611), 07_nf (0.06 #67, 0.06 #688, 0.04 #3173), 01w1sx (0.06 #90, 0.04 #3196, 0.04 #711), 01s5q (0.06 #733, 0.04 #4618, 0.04 #2598) >> Best rule #52 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 0gmgwnv; >> query: (?x13884, 05489) <- film(?x382, ?x13884), country(?x13884, ?x94), nominated_for(?x3209, ?x13884), films(?x8435, ?x13884), ?x3209 = 02w9sd7 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #6417 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 300 *> proper extension: 0b2v79; 011yrp; 08720; 0dj0m5; 0fh694; 0m_mm; 0_b3d; 020fcn; 07qg8v; 01719t; ... *> query: (?x13884, 06d4h) <- film(?x382, ?x13884), country(?x13884, ?x94), nominated_for(?x3209, ?x13884), films(?x8435, ?x13884), titles(?x53, ?x13884) *> conf = 0.07 ranks of expected_values: 6 EVAL 04q01mn films! 06d4h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 149.000 59.000 0.121 http://example.org/film/film_subject/films #11346-0fs9vc PRED entity: 0fs9vc PRED relation: executive_produced_by PRED expected values: 04jspq => 71 concepts (51 used for prediction) PRED predicted values (max 10 best out of 60): 04jspq (0.20 #1162, 0.19 #910, 0.12 #404), 06pj8 (0.20 #55, 0.08 #561, 0.06 #1319), 05_k56 (0.12 #286, 0.08 #539, 0.07 #792), 06q8hf (0.12 #420, 0.08 #673, 0.04 #1935), 05hj_k (0.12 #351, 0.08 #604, 0.03 #3639), 0glyyw (0.12 #442, 0.08 #695, 0.03 #4739), 0gg9_5q (0.12 #343, 0.08 #596, 0.03 #2112), 0415svh (0.12 #280), 0bxtg (0.12 #270), 0grwj (0.08 #3540, 0.02 #3539) >> Best rule #1162 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 04lqvly; >> query: (?x7171, 04jspq) <- nominated_for(?x3911, ?x7171), ?x3911 = 02x1z2s, currency(?x7171, ?x170), nominated_for(?x541, ?x7171) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fs9vc executive_produced_by 04jspq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 51.000 0.200 http://example.org/film/film/executive_produced_by #11345-035hm PRED entity: 035hm PRED relation: currency PRED expected values: 09nqf => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.80 #246, 0.80 #237, 0.80 #233), 01nv4h (0.04 #51, 0.02 #116, 0.02 #136), 02l6h (0.04 #61, 0.03 #409, 0.02 #275) >> Best rule #246 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 06qd3; 03gyl; 0167v; 01nqj; >> query: (?x9283, 09nqf) <- contains(?x455, ?x9283), form_of_government(?x9283, ?x1926), countries_spoken_in(?x254, ?x9283) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035hm currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.803 http://example.org/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency #11344-0282x PRED entity: 0282x PRED relation: location PRED expected values: 04jpl => 157 concepts (144 used for prediction) PRED predicted values (max 10 best out of 332): 02_286 (0.42 #7269, 0.27 #4858, 0.25 #16106), 030qb3t (0.35 #25795, 0.31 #9725, 0.30 #16152), 0100mt (0.33 #383, 0.08 #10828, 0.06 #14846), 04jpl (0.28 #47430, 0.17 #49038, 0.17 #60296), 0cr3d (0.25 #6573, 0.23 #8180, 0.18 #4163), 0r62v (0.21 #45803, 0.19 #63495, 0.19 #14463), 0c4kv (0.20 #2253, 0.09 #5465, 0.08 #9482), 050ks (0.20 #1947, 0.09 #5159, 0.08 #9176), 0rrwt (0.20 #2076, 0.09 #5288, 0.08 #9305), 0tr3p (0.20 #2032, 0.09 #5244, 0.08 #9261) >> Best rule #7269 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 023n39; >> query: (?x5345, 02_286) <- friend(?x5345, ?x11007), student(?x2999, ?x5345), profession(?x5345, ?x2225), ?x2225 = 0kyk >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #47430 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 157 *> proper extension: 07m69t; *> query: (?x5345, 04jpl) <- nationality(?x5345, ?x512), location(?x5345, ?x3301), ?x512 = 07ssc *> conf = 0.28 ranks of expected_values: 4 EVAL 0282x location 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 157.000 144.000 0.417 http://example.org/people/person/places_lived./people/place_lived/location #11343-0gq_v PRED entity: 0gq_v PRED relation: award! PRED expected values: 0jsf6 => 49 concepts (36 used for prediction) PRED predicted values (max 10 best out of 953): 0y_9q (0.56 #1487, 0.33 #4420, 0.27 #32261), 0bmhn (0.56 #1871, 0.27 #32261, 0.27 #4804), 07s846j (0.47 #4289, 0.37 #5268, 0.22 #1356), 03hmt9b (0.44 #1349, 0.41 #5261, 0.40 #4282), 0dr_4 (0.44 #1119, 0.33 #142, 0.30 #5031), 0jqj5 (0.44 #1470, 0.33 #4403, 0.22 #5382), 0bs4r (0.44 #1561, 0.33 #4494, 0.19 #5473), 0b_5d (0.44 #1255, 0.27 #32261, 0.25 #29326), 0404j37 (0.44 #1610, 0.27 #4543, 0.26 #5522), 0_92w (0.40 #4006, 0.33 #1073, 0.27 #32261) >> Best rule #1487 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0p9sw; 0l8z1; 0gr0m; 0gq9h; 0gs9p; 0k611; 0gr51; >> query: (?x484, 0y_9q) <- nominated_for(?x484, ?x9185), ?x9185 = 01lsl, award(?x199, ?x484), ceremony(?x484, ?x78) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1581 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0p9sw; 0l8z1; 0gr0m; 0gq9h; 0gs9p; 0k611; 0gr51; *> query: (?x484, 0jsf6) <- nominated_for(?x484, ?x9185), ?x9185 = 01lsl, award(?x199, ?x484), ceremony(?x484, ?x78) *> conf = 0.22 ranks of expected_values: 217 EVAL 0gq_v award! 0jsf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 36.000 0.556 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #11342-02_qt PRED entity: 02_qt PRED relation: film! PRED expected values: 033m23 => 70 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1061): 0byfz (0.40 #2111), 02t_vx (0.33 #1376, 0.09 #9685, 0.06 #58174), 02rf1y (0.33 #960, 0.09 #9269, 0.02 #27967), 01nr36 (0.33 #1479, 0.09 #9788, 0.01 #22253), 04vq3h (0.33 #1700, 0.09 #10009), 0q9kd (0.33 #4158, 0.03 #16622, 0.03 #14544), 0f4vbz (0.25 #6593, 0.04 #19058, 0.03 #29447), 03hzl42 (0.25 #7018, 0.02 #27794, 0.01 #31950), 031y07 (0.20 #3092, 0.07 #11401), 0f0kz (0.20 #2592, 0.05 #31678, 0.05 #27522) >> Best rule #2111 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0kt_4; >> query: (?x3844, 0byfz) <- film(?x9604, ?x3844), genre(?x3844, ?x225), country(?x3844, ?x94), ?x9604 = 01bh6y >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #13826 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 61 *> proper extension: 015qy1; *> query: (?x3844, 033m23) <- language(?x3844, ?x254), genre(?x3844, ?x2540), ?x2540 = 0hcr, film_release_region(?x3844, ?x94), country(?x3844, ?x252) *> conf = 0.02 ranks of expected_values: 642 EVAL 02_qt film! 033m23 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 70.000 41.000 0.400 http://example.org/film/actor/film./film/performance/film #11341-04bpm6 PRED entity: 04bpm6 PRED relation: music! PRED expected values: 087vnr5 => 112 concepts (84 used for prediction) PRED predicted values (max 10 best out of 641): 07bzz7 (0.18 #1535, 0.08 #2544, 0.08 #3553), 01s7w3 (0.12 #4904, 0.12 #2886, 0.12 #3895), 0pdp8 (0.12 #2241, 0.12 #3250, 0.09 #4259), 01hp5 (0.09 #1068, 0.08 #2077, 0.08 #3086), 0dgq_kn (0.09 #1613, 0.04 #2622, 0.04 #3631), 035zr0 (0.09 #1750, 0.04 #2759, 0.04 #3768), 0dgpwnk (0.09 #1342, 0.04 #2351, 0.04 #3360), 0n83s (0.09 #1542, 0.04 #2551, 0.04 #3560), 0bpbhm (0.09 #1410, 0.04 #2419, 0.04 #3428), 034qmv (0.09 #1017, 0.04 #2026, 0.04 #3035) >> Best rule #1535 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01vvycq; 03kwtb; 0l12d; 01vsl3_; 01271h; 03bnv; 01c8v0; 03h502k; 01lz4tf; >> query: (?x1715, 07bzz7) <- role(?x1715, ?x227), role(?x1715, ?x314), ?x314 = 02sgy, music(?x750, ?x1715) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04bpm6 music! 087vnr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 84.000 0.182 http://example.org/film/film/music #11340-048xh PRED entity: 048xh PRED relation: artists! PRED expected values: 07gxw 08cyft 01pfpt => 73 concepts (56 used for prediction) PRED predicted values (max 10 best out of 258): 06by7 (0.75 #15457, 0.71 #15765, 0.61 #8357), 064t9 (0.68 #1866, 0.52 #16065, 0.49 #16373), 05r6t (0.61 #8110, 0.19 #5246, 0.19 #3167), 016clz (0.58 #14205, 0.50 #3090, 0.50 #5), 0cx7f (0.54 #447, 0.46 #1682, 0.45 #3223), 0ggx5q (0.52 #1931, 0.20 #5557, 0.20 #5556), 011j5x (0.50 #32, 0.19 #5246, 0.18 #6174), 05c6073 (0.50 #218, 0.19 #5246, 0.18 #6174), 0xhtw (0.49 #6808, 0.46 #7425, 0.43 #14526), 08cyft (0.48 #1910, 0.25 #57, 0.09 #14257) >> Best rule #15457 for best value: >> intensional similarity = 4 >> extensional distance = 460 >> proper extension: 07s3vqk; 01vrx3g; 06cc_1; 0kzy0; 01q7cb_; 016kjs; 01wbgdv; 0lgsq; 01kx_81; 01j4ls; ... >> query: (?x7476, 06by7) <- artists(?x1380, ?x7476), artist(?x382, ?x7476), artists(?x1380, ?x4461), ?x4461 = 0fcsd >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1910 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 29 *> proper extension: 04lgymt; *> query: (?x7476, 08cyft) <- award(?x7476, ?x8331), award(?x4594, ?x8331), award(?x4394, ?x8331), ?x4394 = 049qx, ?x4594 = 05vzw3 *> conf = 0.48 ranks of expected_values: 10, 44, 159 EVAL 048xh artists! 01pfpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 73.000 56.000 0.751 http://example.org/music/genre/artists EVAL 048xh artists! 08cyft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 73.000 56.000 0.751 http://example.org/music/genre/artists EVAL 048xh artists! 07gxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 73.000 56.000 0.751 http://example.org/music/genre/artists #11339-0sxfd PRED entity: 0sxfd PRED relation: film! PRED expected values: 0372kf => 82 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1117): 03xp8d5 (0.43 #43678, 0.42 #2080, 0.40 #47840), 094tsh6 (0.43 #43678, 0.42 #2080, 0.40 #47840), 0284n42 (0.42 #2080, 0.40 #47840, 0.40 #64480), 0f502 (0.25 #6998, 0.25 #2839, 0.14 #9077), 016ks_ (0.25 #7021, 0.14 #9100, 0.12 #2862), 07r1h (0.25 #1086, 0.12 #3166, 0.08 #7325), 011zd3 (0.25 #371, 0.03 #10768, 0.02 #12848), 01gkmx (0.25 #1584, 0.03 #18220, 0.02 #14061), 015pvh (0.25 #1099, 0.02 #13576, 0.02 #15655), 014v6f (0.25 #965, 0.02 #13442, 0.02 #36319) >> Best rule #43678 for best value: >> intensional similarity = 4 >> extensional distance = 380 >> proper extension: 02d413; 0140g4; 09q5w2; 0gjk1d; 09tqkv2; 0bm2g; 07w8fz; 0ds2n; 0p_qr; 0cmc26r; ... >> query: (?x1402, ?x4385) <- nominated_for(?x484, ?x1402), films(?x8435, ?x1402), nominated_for(?x4385, ?x1402), award_nominee(?x4385, ?x635) >> conf = 0.43 => this is the best rule for 2 predicted values *> Best rule #38352 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 345 *> proper extension: 02_fm2; 047bynf; 03h0byn; 04q01mn; *> query: (?x1402, 0372kf) <- nominated_for(?x484, ?x1402), films(?x8435, ?x1402), film_release_distribution_medium(?x1402, ?x81) *> conf = 0.01 ranks of expected_values: 889 EVAL 0sxfd film! 0372kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 82.000 37.000 0.426 http://example.org/film/actor/film./film/performance/film #11338-0198b6 PRED entity: 0198b6 PRED relation: nominated_for! PRED expected values: 02404v => 79 concepts (26 used for prediction) PRED predicted values (max 10 best out of 685): 01f873 (0.27 #58419, 0.27 #56082, 0.22 #16354), 04wp63 (0.19 #4398, 0.09 #6734, 0.06 #25424), 0146pg (0.14 #2457, 0.14 #4793, 0.03 #23483), 06rnl9 (0.14 #2949, 0.09 #5285, 0.03 #23975), 0bytkq (0.10 #657, 0.08 #17011, 0.07 #14674), 01tc9r (0.10 #832, 0.06 #14849, 0.06 #17186), 08h79x (0.10 #1581, 0.05 #6253, 0.05 #3917), 03mfqm (0.10 #1387, 0.05 #6059, 0.05 #3723), 0jfx1 (0.10 #504, 0.05 #12185, 0.05 #2840), 05qd_ (0.10 #174, 0.05 #2510, 0.04 #11855) >> Best rule #58419 for best value: >> intensional similarity = 4 >> extensional distance = 471 >> proper extension: 05jf85; 0gzy02; 01hr1; 01sxly; 050r1z; 0dj0m5; 0209xj; 0pv2t; 0kv2hv; 0g5pv3; ... >> query: (?x3886, ?x11657) <- language(?x3886, ?x2502), written_by(?x3886, ?x4169), film(?x11657, ?x3886), nominated_for(?x6374, ?x3886) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #10997 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 0dh8v4; *> query: (?x3886, 02404v) <- language(?x3886, ?x3271), language(?x9175, ?x3271), ?x9175 = 02qd04y, countries_spoken_in(?x3271, ?x1122) *> conf = 0.05 ranks of expected_values: 39 EVAL 0198b6 nominated_for! 02404v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 79.000 26.000 0.273 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #11337-0cr3d PRED entity: 0cr3d PRED relation: place_of_birth! PRED expected values: 0d4fqn 01kws3 0bz60q => 92 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2009): 01vrlqd (0.34 #25107, 0.34 #123024, 0.33 #67791), 016_mj (0.34 #25107, 0.34 #123024, 0.33 #67791), 0237fw (0.34 #25107, 0.34 #123024, 0.33 #67791), 0zcbl (0.34 #25107, 0.34 #123024, 0.33 #67791), 0cl0bk (0.34 #25107, 0.34 #123024, 0.33 #67791), 04n2vgk (0.34 #25107, 0.34 #123024, 0.33 #67791), 01wyz92 (0.34 #25107, 0.34 #123024, 0.33 #67791), 02lgfh (0.34 #25107, 0.34 #123024, 0.33 #67791), 01vhrz (0.34 #25107, 0.34 #123024, 0.33 #67791), 0161sp (0.34 #25107, 0.34 #123024, 0.33 #67791) >> Best rule #25107 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 05ywg; 03hrz; 01k4f; 0k3p; 0g251; 0150n; 03902; 0rxyk; 0ftxc; 0d33k; ... >> query: (?x2850, ?x916) <- place_of_birth(?x6771, ?x2850), location(?x916, ?x2850), influenced_by(?x1145, ?x6771) >> conf = 0.34 => this is the best rule for 58 predicted values *> Best rule #30130 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 119 *> proper extension: 0tcj6; *> query: (?x2850, ?x369) <- place_of_birth(?x12052, ?x2850), place_of_birth(?x839, ?x2850), place_of_death(?x12052, ?x1523), award_winner(?x369, ?x839) *> conf = 0.02 ranks of expected_values: 766, 1214 EVAL 0cr3d place_of_birth! 0bz60q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 89.000 0.337 http://example.org/people/person/place_of_birth EVAL 0cr3d place_of_birth! 01kws3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 92.000 89.000 0.337 http://example.org/people/person/place_of_birth EVAL 0cr3d place_of_birth! 0d4fqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 92.000 89.000 0.337 http://example.org/people/person/place_of_birth #11336-03gt0c5 PRED entity: 03gt0c5 PRED relation: gender PRED expected values: 02zsn => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #89, 0.85 #81, 0.83 #61), 02zsn (0.53 #12, 0.50 #10, 0.48 #16) >> Best rule #89 for best value: >> intensional similarity = 2 >> extensional distance = 747 >> proper extension: 075wq; 07c37; >> query: (?x13091, 05zppz) <- place_of_death(?x13091, ?x5036), contains(?x390, ?x5036) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 06w33f8; *> query: (?x13091, 02zsn) <- place_of_birth(?x13091, ?x1296), costume_design_by(?x3904, ?x13091), film(?x489, ?x3904) *> conf = 0.53 ranks of expected_values: 2 EVAL 03gt0c5 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.848 http://example.org/people/person/gender #11335-025h4z PRED entity: 025h4z PRED relation: people! PRED expected values: 041rx => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 28): 033tf_ (0.14 #7, 0.08 #1701, 0.08 #161), 07bch9 (0.14 #23, 0.04 #100, 0.03 #3873), 02rbdlq (0.14 #1), 041rx (0.14 #774, 0.13 #81, 0.12 #2006), 0x67 (0.10 #1088, 0.10 #1473, 0.09 #1627), 02w7gg (0.08 #79, 0.07 #1234, 0.06 #1388), 0xnvg (0.06 #167, 0.05 #244, 0.05 #1707), 07hwkr (0.04 #2014, 0.03 #782, 0.03 #1706), 048z7l (0.03 #117, 0.02 #1734, 0.02 #425), 09vc4s (0.03 #163, 0.02 #1703, 0.02 #1472) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 045931; >> query: (?x434, 033tf_) <- award(?x434, ?x435), film(?x434, ?x1454), ?x1454 = 0qm98 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #774 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1012 *> proper extension: 02mslq; 04107; 094xh; 0g5ff; 09jd9; *> query: (?x434, 041rx) <- award(?x434, ?x435), student(?x2730, ?x434), place_of_birth(?x434, ?x3014) *> conf = 0.14 ranks of expected_values: 4 EVAL 025h4z people! 041rx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 91.000 91.000 0.143 http://example.org/people/ethnicity/people #11334-02phtzk PRED entity: 02phtzk PRED relation: currency PRED expected values: 09nqf => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.83 #15, 0.83 #29, 0.83 #22), 01nv4h (0.04 #9, 0.03 #366, 0.03 #156), 02gsvk (0.02 #125, 0.01 #167, 0.01 #202), 02l6h (0.02 #270, 0.01 #571, 0.01 #312) >> Best rule #15 for best value: >> intensional similarity = 5 >> extensional distance = 58 >> proper extension: 0y_9q; 0bdjd; 01mgw; >> query: (?x4534, 09nqf) <- nominated_for(?x1443, ?x4534), ?x1443 = 054krc, film_crew_role(?x4534, ?x137), genre(?x4534, ?x53), honored_for(?x762, ?x4534) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02phtzk currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.833 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11333-047bynf PRED entity: 047bynf PRED relation: film_crew_role PRED expected values: 01vx2h 0215hd => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 20): 0dxtw (0.40 #649, 0.35 #489, 0.35 #1035), 01pvkk (0.33 #41, 0.33 #9, 0.29 #875), 02ynfr (0.33 #45, 0.33 #13, 0.25 #77), 01vx2h (0.33 #136, 0.32 #490, 0.31 #650), 0215hd (0.22 #208, 0.17 #176, 0.14 #240), 015h31 (0.17 #133, 0.17 #37, 0.17 #5), 089g0h (0.16 #209, 0.11 #851, 0.11 #499), 01xy5l_ (0.15 #171, 0.12 #203, 0.12 #493), 02rh1dz (0.11 #488, 0.11 #648, 0.09 #1163), 02vs3x5 (0.10 #213, 0.06 #342, 0.05 #278) >> Best rule #649 for best value: >> intensional similarity = 3 >> extensional distance = 658 >> proper extension: 0k4d7; >> query: (?x6636, 0dxtw) <- film(?x1890, ?x6636), film_crew_role(?x6636, ?x137), produced_by(?x6636, ?x7621) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #136 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0g5qs2k; 04n52p6; 07nt8p; 0645k5; 0cc5qkt; 02d478; 0dlngsd; 03c_cxn; 04pmnt; 0ds6bmk; *> query: (?x6636, 01vx2h) <- film(?x9643, ?x6636), nominated_for(?x112, ?x6636), ?x9643 = 0c_gcr *> conf = 0.33 ranks of expected_values: 4, 5 EVAL 047bynf film_crew_role 0215hd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 72.000 0.395 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 047bynf film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 72.000 0.395 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11332-016kv6 PRED entity: 016kv6 PRED relation: film! PRED expected values: 01wyzyl 05xd_v => 88 concepts (24 used for prediction) PRED predicted values (max 10 best out of 969): 016ks_ (0.66 #2086, 0.43 #31280, 0.39 #47962), 05kfs (0.43 #31280, 0.39 #47962, 0.39 #2085), 0z4s (0.15 #68, 0.07 #2088, 0.04 #2156), 017149 (0.15 #83, 0.07 #2088, 0.02 #12593), 015vq_ (0.12 #715, 0.07 #2088, 0.03 #9056), 01swck (0.08 #4973, 0.04 #11227, 0.02 #32081), 01vvb4m (0.08 #4695, 0.03 #10949, 0.02 #13033), 034zc0 (0.07 #2088, 0.05 #1030, 0.02 #3118), 02ch1w (0.07 #2088, 0.05 #1039), 014g22 (0.07 #2088, 0.02 #2087, 0.02 #719) >> Best rule #2086 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 02md2d; >> query: (?x3523, ?x4463) <- nominated_for(?x4463, ?x3523), nominated_for(?x591, ?x3523), award_nominee(?x4463, ?x4154), ?x4154 = 014g22 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #5991 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 0djb3vw; 0963mq; 01w8g3; 040_lv; 0286vp; 05ch98; *> query: (?x3523, 05xd_v) <- country(?x3523, ?x94), genre(?x3523, ?x2753), music(?x3523, ?x3690), ?x2753 = 0219x_ *> conf = 0.02 ranks of expected_values: 505 EVAL 016kv6 film! 05xd_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 88.000 24.000 0.655 http://example.org/film/actor/film./film/performance/film EVAL 016kv6 film! 01wyzyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 24.000 0.655 http://example.org/film/actor/film./film/performance/film #11331-01wj5hp PRED entity: 01wj5hp PRED relation: gender PRED expected values: 05zppz => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.82 #1, 0.74 #21, 0.73 #15), 02zsn (0.46 #195, 0.36 #44, 0.35 #58) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 04k15; 081k8; 0h0p_; >> query: (?x8829, 05zppz) <- profession(?x8829, ?x6476), profession(?x8829, ?x2225), ?x2225 = 0kyk, ?x6476 = 025352 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wj5hp gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.818 http://example.org/people/person/gender #11330-02624g PRED entity: 02624g PRED relation: award_nominee! PRED expected values: 03mp9s => 79 concepts (37 used for prediction) PRED predicted values (max 10 best out of 900): 06gh0t (0.82 #2327, 0.81 #48856, 0.81 #74450), 026g801 (0.82 #2327, 0.81 #48856, 0.81 #74450), 02624g (0.67 #8577, 0.50 #3923, 0.20 #1596), 07s8hms (0.40 #868, 0.02 #14828, 0.02 #33438), 0dgskx (0.22 #44203, 0.17 #3835, 0.11 #8489), 017gxw (0.22 #44203, 0.17 #3538, 0.11 #8192), 027ht3n (0.22 #44203, 0.17 #4492, 0.11 #9146), 0263tn1 (0.22 #44203, 0.17 #4160, 0.11 #8814), 01x4sb (0.22 #44203, 0.17 #3771, 0.11 #8425), 08_83x (0.22 #44203, 0.17 #3552, 0.11 #8206) >> Best rule #2327 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 06dn58; >> query: (?x7048, ?x2374) <- film(?x7048, ?x10732), award_nominee(?x7048, ?x2374), ?x10732 = 04b_jc >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #1579 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 06dn58; *> query: (?x7048, 03mp9s) <- film(?x7048, ?x10732), award_nominee(?x7048, ?x2374), ?x10732 = 04b_jc *> conf = 0.20 ranks of expected_values: 23 EVAL 02624g award_nominee! 03mp9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 79.000 37.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11329-033rq PRED entity: 033rq PRED relation: award PRED expected values: 0gr4k => 125 concepts (119 used for prediction) PRED predicted values (max 10 best out of 311): 0gqng (0.77 #28019, 0.77 #38438, 0.76 #38841), 09d28z (0.72 #38437, 0.71 #25214, 0.71 #31622), 019f4v (0.56 #2065, 0.43 #8070, 0.42 #7668), 0gq9h (0.55 #2076, 0.34 #3679, 0.32 #3278), 02pqp12 (0.45 #469, 0.36 #2069, 0.30 #69), 02rdyk7 (0.36 #489, 0.30 #89, 0.23 #2089), 02x4wr9 (0.36 #532, 0.20 #132, 0.13 #3334), 09sb52 (0.30 #39, 0.30 #16449, 0.25 #13649), 0f4x7 (0.30 #29, 0.18 #829, 0.18 #1629), 05f4m9q (0.27 #412, 0.19 #3214, 0.17 #5215) >> Best rule #28019 for best value: >> intensional similarity = 3 >> extensional distance = 1371 >> proper extension: 0kk9v; >> query: (?x8573, ?x77) <- award_nominee(?x8573, ?x8572), award_winner(?x77, ?x8573), ceremony(?x77, ?x78) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #3233 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 130 *> proper extension: 07nznf; 058kqy; 05drq5; 0162c8; 032v0v; 0b_c7; 01gzm2; 01f7j9; 03wpmd; 02ld6x; ... *> query: (?x8573, 0gr4k) <- award_nominee(?x8573, ?x8572), film(?x8573, ?x5429), award(?x5429, ?x372) *> conf = 0.26 ranks of expected_values: 11 EVAL 033rq award 0gr4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 125.000 119.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11328-01j5ts PRED entity: 01j5ts PRED relation: location PRED expected values: 07_fl => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 67): 030qb3t (0.27 #4098, 0.25 #2492, 0.23 #4901), 02_286 (0.20 #4052, 0.20 #36, 0.18 #5658), 0d6lp (0.20 #167, 0.02 #54622, 0.02 #17836), 04rrd (0.20 #97, 0.01 #9736), 0d0x8 (0.20 #160), 04jpl (0.13 #819, 0.07 #4835, 0.07 #2426), 059rby (0.06 #2425, 0.05 #3228, 0.05 #4834), 0cr3d (0.05 #4160, 0.05 #25846, 0.05 #31468), 0cc56 (0.05 #859, 0.05 #4072, 0.05 #5678), 01_d4 (0.04 #2511, 0.04 #3314, 0.03 #4920) >> Best rule #4098 for best value: >> intensional similarity = 3 >> extensional distance = 331 >> proper extension: 019n7x; >> query: (?x241, 030qb3t) <- award_nominee(?x397, ?x241), location(?x241, ?x242), participant(?x241, ?x406) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1369 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 99 *> proper extension: 0n6f8; 01zh29; *> query: (?x241, 07_fl) <- nominated_for(?x241, ?x9222), languages(?x241, ?x254), story_by(?x9222, ?x2248) *> conf = 0.02 ranks of expected_values: 38 EVAL 01j5ts location 07_fl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 106.000 106.000 0.273 http://example.org/people/person/places_lived./people/place_lived/location #11327-05q96q6 PRED entity: 05q96q6 PRED relation: film_crew_role PRED expected values: 02r96rf 09vw2b7 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 24): 02r96rf (0.71 #422, 0.71 #1031, 0.71 #1384), 09vw2b7 (0.70 #1388, 0.67 #1195, 0.66 #1035), 02ynfr (0.27 #77, 0.27 #45, 0.20 #1394), 01xy5l_ (0.18 #43, 0.12 #107, 0.12 #1039), 089fss (0.18 #70, 0.10 #134, 0.09 #457), 02rh1dz (0.18 #364, 0.17 #428, 0.17 #332), 0215hd (0.16 #1043, 0.16 #402, 0.16 #338), 033smt (0.14 #185, 0.14 #152, 0.14 #282), 0d2b38 (0.14 #313, 0.12 #1050, 0.12 #441), 089g0h (0.13 #1044, 0.12 #307, 0.11 #1204) >> Best rule #422 for best value: >> intensional similarity = 4 >> extensional distance = 211 >> proper extension: 09rfpk; >> query: (?x1038, 02r96rf) <- story_by(?x1038, ?x7761), film_crew_role(?x1038, ?x3305), film_crew_role(?x4315, ?x3305), ?x4315 = 0sxkh >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05q96q6 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05q96q6 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11326-02kmx6 PRED entity: 02kmx6 PRED relation: people! PRED expected values: 0j8hd => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 31): 0gk4g (0.13 #2914, 0.13 #2980, 0.12 #3310), 0dq9p (0.12 #83, 0.10 #1403, 0.08 #2987), 0qcr0 (0.12 #67, 0.07 #2245, 0.07 #1387), 02y0js (0.12 #68, 0.06 #2246, 0.06 #2312), 02knxx (0.12 #98, 0.04 #1418, 0.03 #2408), 02k6hp (0.12 #103, 0.04 #499, 0.03 #2941), 01_qc_ (0.12 #94, 0.02 #2932, 0.02 #3328), 04p3w (0.06 #473, 0.05 #2981, 0.04 #2915), 01dcqj (0.05 #1398, 0.03 #2190, 0.03 #2322), 04psf (0.04 #469, 0.03 #337, 0.02 #1393) >> Best rule #2914 for best value: >> intensional similarity = 3 >> extensional distance = 386 >> proper extension: 0h1_w; 041h0; 021sv1; 025vry; 0dky9n; 04411; 08433; 02whj; 01g4zr; 0h1m9; ... >> query: (?x4720, 0gk4g) <- place_of_birth(?x4720, ?x13065), place_of_death(?x4720, ?x11930), nationality(?x4720, ?x94) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #509 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 012gbb; *> query: (?x4720, 0j8hd) <- gender(?x4720, ?x514), ?x514 = 02zsn, place_of_death(?x4720, ?x11930) *> conf = 0.02 ranks of expected_values: 22 EVAL 02kmx6 people! 0j8hd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 110.000 110.000 0.129 http://example.org/people/cause_of_death/people #11325-0l8g0 PRED entity: 0l8g0 PRED relation: group! PRED expected values: 0l14md 05r5c => 115 concepts (76 used for prediction) PRED predicted values (max 10 best out of 121): 0l14md (0.67 #944, 0.63 #2049, 0.62 #1879), 03bx0bm (0.64 #2406, 0.64 #960, 0.62 #2150), 0l14qv (0.36 #942, 0.33 #600, 0.27 #2047), 03qjg (0.29 #215, 0.27 #2088, 0.26 #1918), 01vj9c (0.28 #2566, 0.28 #2652, 0.27 #1885), 05r5c (0.25 #7, 0.24 #1540, 0.24 #1880), 013y1f (0.25 #25, 0.23 #450, 0.19 #621), 02snj9 (0.25 #54, 0.08 #1927, 0.08 #479), 0l14j_ (0.22 #987, 0.15 #1922, 0.14 #645), 06ncr (0.21 #1909, 0.16 #2590, 0.16 #2676) >> Best rule #944 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 03k3b; 0qmpd; >> query: (?x6234, 0l14md) <- artists(?x1380, ?x6234), group(?x212, ?x6234), category(?x6234, ?x134), ?x1380 = 0dl5d, ?x134 = 08mbj5d >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 0l8g0 group! 05r5c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 115.000 76.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0l8g0 group! 0l14md CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 76.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group #11324-034vds PRED entity: 034vds PRED relation: actor PRED expected values: 01r7pq => 72 concepts (19 used for prediction) PRED predicted values (max 10 best out of 774): 0443y3 (0.40 #166, 0.20 #1093, 0.15 #2946), 0436f4 (0.20 #960, 0.20 #33, 0.17 #9276), 02lf70 (0.20 #1079, 0.20 #152, 0.17 #9276), 02lfns (0.20 #1020, 0.20 #93, 0.17 #9276), 021_rm (0.20 #1013, 0.20 #86, 0.17 #9276), 02lfcm (0.20 #961, 0.20 #34, 0.17 #9276), 01l1sq (0.20 #1055, 0.20 #128, 0.17 #9276), 01r42_g (0.20 #951, 0.20 #24, 0.17 #9276), 01dy7j (0.20 #1164, 0.20 #237, 0.17 #9276), 01z7_f (0.20 #1269, 0.20 #342, 0.17 #9276) >> Best rule #166 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 02_1q9; 0kfv9; 0147w8; >> query: (?x12712, 0443y3) <- actor(?x12712, ?x10127), actor(?x12712, ?x3688), ?x3688 = 03zyvw, film(?x10127, ?x7415), place_of_birth(?x10127, ?x2850), award(?x10127, ?x1132), award_nominee(?x436, ?x10127) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #4292 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 0kfpm; 01b64v; 01b66d; 063ykwt; 030p35; 01b66t; 01vnbh; 034fl9; 0199wf; *> query: (?x12712, 01r7pq) <- actor(?x12712, ?x3688), award_winner(?x369, ?x3688), program(?x2062, ?x12712), award_winner(?x1670, ?x3688), award_winner(?x5459, ?x3688), ?x2062 = 09d5h *> conf = 0.08 ranks of expected_values: 158 EVAL 034vds actor 01r7pq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 72.000 19.000 0.400 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11323-02zd2b PRED entity: 02zd2b PRED relation: category PRED expected values: 08mbj5d => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #3, 0.90 #9, 0.90 #20) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 80 >> proper extension: 0l2tk; 01bcwk; 015cz0; 02zd460; 02bqy; 0ks67; 050xpd; >> query: (?x5737, 08mbj5d) <- major_field_of_study(?x5737, ?x4321), institution(?x1771, ?x5737), institution(?x620, ?x5737), ?x620 = 07s6fsf, ?x1771 = 019v9k >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zd2b category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.902 http://example.org/common/topic/webpage./common/webpage/category #11322-0gdqy PRED entity: 0gdqy PRED relation: influenced_by PRED expected values: 017r2 => 140 concepts (46 used for prediction) PRED predicted values (max 10 best out of 255): 0lrh (0.33 #2260, 0.06 #6198, 0.06 #7513), 032md (0.25 #14448, 0.17 #16201, 0.14 #8753), 0448r (0.20 #701, 0.11 #2888, 0.09 #6825), 02xyl (0.20 #865, 0.11 #3052, 0.07 #3927), 02vyw (0.20 #538, 0.11 #2725, 0.07 #3600), 0f0kz (0.20 #517, 0.11 #2704, 0.07 #3579), 02jq1 (0.17 #2365, 0.06 #6303, 0.03 #5866), 04xfb (0.17 #2462, 0.04 #8592, 0.03 #5963), 01vvyfh (0.17 #2294, 0.03 #6670, 0.02 #7547), 081nh (0.17 #2250, 0.03 #6626, 0.02 #7503) >> Best rule #2260 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01vsl3_; 03j24kf; 09889g; 01nz1q6; >> query: (?x10354, 0lrh) <- award_winner(?x1365, ?x10354), location_of_ceremony(?x10354, ?x4627), peers(?x10354, ?x8043), type_of_union(?x10354, ?x1873) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8354 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 07h1q; *> query: (?x10354, 017r2) <- gender(?x10354, ?x231), people(?x6734, ?x10354), peers(?x10354, ?x8043) *> conf = 0.04 ranks of expected_values: 87 EVAL 0gdqy influenced_by 017r2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 140.000 46.000 0.333 http://example.org/influence/influence_node/influenced_by #11321-046lt PRED entity: 046lt PRED relation: notable_people_with_this_condition! PRED expected values: 029sk => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 10): 029sk (0.10 #155, 0.05 #133, 0.05 #89), 0g02vk (0.08 #34, 0.06 #56, 0.05 #78), 0m32h (0.05 #95, 0.04 #293, 0.03 #315), 068p_ (0.05 #108, 0.03 #328, 0.02 #614), 0h99n (0.05 #230, 0.04 #428, 0.04 #494), 01_qc_ (0.05 #228), 01g2q (0.02 #361, 0.02 #603, 0.02 #1153), 0dcsx (0.02 #378), 0brgy (0.01 #451), 02k6hp (0.01 #541) >> Best rule #155 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 025p38; 02v0ff; 01pfkw; 084z0w; 05j12n; 01zh29; 02wyc0; 01mbwlb; 0bkq_8; 047jhq; ... >> query: (?x2942, 029sk) <- profession(?x2942, ?x4725), languages(?x2942, ?x254), ?x4725 = 015cjr >> conf = 0.10 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 046lt notable_people_with_this_condition! 029sk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.095 http://example.org/medicine/disease/notable_people_with_this_condition #11320-03x6w8 PRED entity: 03x6w8 PRED relation: sport PRED expected values: 02vx4 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.88 #643, 0.87 #661, 0.87 #616), 03tmr (0.21 #109, 0.13 #192, 0.11 #813), 0jm_ (0.14 #194, 0.11 #813, 0.09 #671), 018jz (0.11 #813, 0.10 #673, 0.10 #709), 018w8 (0.11 #813, 0.08 #112, 0.06 #195), 0z74 (0.11 #813, 0.04 #107, 0.02 #199), 039yzs (0.11 #813, 0.04 #810, 0.04 #801), 09xp_ (0.11 #813, 0.02 #197, 0.01 #764) >> Best rule #643 for best value: >> intensional similarity = 16 >> extensional distance = 192 >> proper extension: 0371rb; 0gxkm; 01kwhf; 01vqc7; 051n13; 011v3; 0690dn; 02_lt; 0k_l4; 02nt75; ... >> query: (?x8826, 02vx4) <- position(?x8826, ?x530), colors(?x8826, ?x663), team(?x530, ?x11623), team(?x530, ?x11518), team(?x530, ?x11438), team(?x530, ?x10896), team(?x530, ?x9824), team(?x530, ?x7396), team(?x530, ?x5341), ?x11438 = 0cq4k_, ?x10896 = 03lygq, ?x9824 = 04knvh, ?x7396 = 046f25, ?x5341 = 05hywl, ?x11518 = 02b185, ?x11623 = 0f1kwr >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x6w8 sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.876 http://example.org/sports/sports_team/sport #11319-0n6f8 PRED entity: 0n6f8 PRED relation: nationality PRED expected values: 09c7w0 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 71): 09c7w0 (0.84 #801, 0.82 #1101, 0.82 #1902), 0345h (0.39 #8018, 0.08 #331, 0.04 #5911), 02jx1 (0.19 #933, 0.15 #733, 0.14 #5442), 0d060g (0.17 #507, 0.17 #7, 0.12 #107), 03rk0 (0.17 #346, 0.14 #4654, 0.13 #4254), 07ssc (0.17 #15, 0.12 #115, 0.11 #12334), 03_3d (0.13 #3812, 0.12 #4614, 0.12 #4214), 03spz (0.11 #12334, 0.04 #567, 0.04 #5911), 0chghy (0.11 #12334, 0.04 #5911, 0.04 #610), 0d0vqn (0.11 #12334, 0.04 #5911, 0.04 #709) >> Best rule #801 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 03f77; >> query: (?x1299, 09c7w0) <- student(?x1771, ?x1299), student(?x3995, ?x1299), participant(?x4536, ?x1299) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n6f8 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.839 http://example.org/people/person/nationality #11318-01zc2w PRED entity: 01zc2w PRED relation: major_field_of_study! PRED expected values: 01rtm4 0bwfn 01v2xl => 55 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1226): 01w3v (0.67 #3981, 0.67 #3414, 0.60 #1713), 01mpwj (0.67 #2943, 0.58 #3510, 0.58 #5211), 0bwfn (0.67 #3118, 0.56 #7653, 0.55 #5953), 02zd460 (0.61 #6418, 0.58 #6985, 0.58 #3583), 07tds (0.61 #6394, 0.58 #3559, 0.56 #2992), 0dzst (0.60 #4328, 0.60 #2060, 0.58 #3761), 05zl0 (0.60 #4187, 0.58 #3620, 0.56 #3053), 05krk (0.60 #1706, 0.50 #1140, 0.47 #3974), 07t90 (0.60 #1856, 0.50 #1290, 0.44 #4691), 07vhb (0.60 #1880, 0.50 #1314, 0.37 #5282) >> Best rule #3981 for best value: >> intensional similarity = 10 >> extensional distance = 13 >> proper extension: 04sh3; >> query: (?x8925, 01w3v) <- student(?x8925, ?x8535), major_field_of_study(?x8925, ?x2314), major_field_of_study(?x9108, ?x8925), major_field_of_study(?x7596, ?x8925), major_field_of_study(?x6925, ?x8925), ?x6925 = 01bm_, currency(?x9108, ?x1099), institution(?x620, ?x7596), gender(?x8535, ?x231), colors(?x7596, ?x332) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3118 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 7 *> proper extension: 04rjg; 02j62; 037mh8; *> query: (?x8925, 0bwfn) <- student(?x8925, ?x709), major_field_of_study(?x8925, ?x2314), major_field_of_study(?x9108, ?x8925), major_field_of_study(?x7596, ?x8925), major_field_of_study(?x6925, ?x8925), major_field_of_study(?x1103, ?x8925), ?x6925 = 01bm_, currency(?x9108, ?x1099), institution(?x620, ?x7596), people(?x3584, ?x709), ?x620 = 07s6fsf, ?x1103 = 01k2wn *> conf = 0.67 ranks of expected_values: 3, 314, 363 EVAL 01zc2w major_field_of_study! 01v2xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 33.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01zc2w major_field_of_study! 0bwfn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 55.000 33.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01zc2w major_field_of_study! 01rtm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 33.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11317-02w4v PRED entity: 02w4v PRED relation: artists PRED expected values: 015_30 01kv4mb 0136pk 02w4v 044k8 01kph_c 01_ztw 01d4cb 020jqv => 57 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1210): 01vvycq (0.70 #10183, 0.60 #2070, 0.50 #13226), 03f5spx (0.70 #10193, 0.40 #2080, 0.33 #7151), 0gbwp (0.70 #10456, 0.33 #6398, 0.25 #4370), 0j1yf (0.70 #10263, 0.28 #7097, 0.25 #1137), 020_4z (0.67 #8986, 0.64 #12030, 0.62 #4928), 01vwyqp (0.62 #4301, 0.56 #8359, 0.55 #11403), 01s21dg (0.62 #4432, 0.56 #8490, 0.45 #11534), 011z3g (0.60 #10694, 0.56 #6636, 0.50 #13737), 01kcms4 (0.60 #3639, 0.50 #4652, 0.45 #11754), 0qf11 (0.60 #3382, 0.50 #4395, 0.44 #8453) >> Best rule #10183 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 025sc50; 02lnbg; 0ggx5q; >> query: (?x3108, 01vvycq) <- artists(?x3108, ?x8490), artists(?x3108, ?x4062), artists(?x3108, ?x1292), award_nominee(?x248, ?x8490), friend(?x2669, ?x8490), group(?x1292, ?x7407), people(?x743, ?x1292), ?x4062 = 0bqsy >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #7479 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0mhfr; 03jsvl; *> query: (?x3108, 01kph_c) <- artists(?x3108, ?x8490), artists(?x3108, ?x3632), ?x8490 = 06rgq, inductee(?x1091, ?x3632), profession(?x3632, ?x220) *> conf = 0.56 ranks of expected_values: 39, 57, 124, 135, 228, 261, 622, 1004 EVAL 02w4v artists 020jqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 32.000 0.700 http://example.org/music/genre/artists EVAL 02w4v artists 01d4cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 32.000 0.700 http://example.org/music/genre/artists EVAL 02w4v artists 01_ztw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 57.000 32.000 0.700 http://example.org/music/genre/artists EVAL 02w4v artists 01kph_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 57.000 32.000 0.700 http://example.org/music/genre/artists EVAL 02w4v artists 044k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 57.000 32.000 0.700 http://example.org/music/genre/artists EVAL 02w4v artists 02w4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 32.000 0.700 http://example.org/music/genre/artists EVAL 02w4v artists 0136pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 32.000 0.700 http://example.org/music/genre/artists EVAL 02w4v artists 01kv4mb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 57.000 32.000 0.700 http://example.org/music/genre/artists EVAL 02w4v artists 015_30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 32.000 0.700 http://example.org/music/genre/artists #11316-01516r PRED entity: 01516r PRED relation: artists! PRED expected values: 018lg0 0dls3 01_bkd 03ckfl9 03339m 029fbr => 58 concepts (43 used for prediction) PRED predicted values (max 10 best out of 293): 05r6t (0.86 #1317, 0.63 #2249, 0.61 #8450), 0xhtw (0.76 #4662, 0.75 #6211, 0.72 #4042), 064t9 (0.65 #11792, 0.41 #11171, 0.41 #12724), 0cx7f (0.64 #5401, 0.56 #1682, 0.54 #1063), 0dl5d (0.64 #5904, 0.62 #946, 0.61 #6525), 05w3f (0.50 #37, 0.46 #963, 0.45 #2824), 011j5x (0.43 #1266, 0.32 #2198, 0.28 #4985), 09nwwf (0.42 #2302, 0.25 #135, 0.21 #1370), 01243b (0.40 #351, 0.34 #7165, 0.34 #7788), 0b_6yv (0.40 #560, 0.31 #1177, 0.29 #868) >> Best rule #1317 for best value: >> intensional similarity = 11 >> extensional distance = 12 >> proper extension: 01tp5bj; 01gx5f; 01wy61y; 01whg97; 01y_rz; 01ww_vs; >> query: (?x8165, 05r6t) <- artists(?x7808, ?x8165), artists(?x5379, ?x8165), ?x5379 = 08jyyk, artists(?x7808, ?x12619), artists(?x7808, ?x3390), artists(?x7808, ?x2392), ?x3390 = 017j6, ?x12619 = 01w03jv, parent_genre(?x3642, ?x7808), parent_genre(?x7808, ?x5934), ?x2392 = 01wwvt2 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #4080 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 45 *> proper extension: 04rcr; 0knhk; 016lj_; 01dpts; 0560w; 016m5c; 0bsj9; *> query: (?x8165, 01_bkd) <- artists(?x5379, ?x8165), artists(?x2249, ?x8165), artists(?x5379, ?x5494), artists(?x5379, ?x3657), artists(?x5379, ?x3399), ?x3657 = 01w8n89, award_winner(?x5493, ?x5494), ?x2249 = 03lty, group(?x227, ?x8165), ?x3399 = 01gx5f *> conf = 0.28 ranks of expected_values: 16, 18, 26, 49, 51, 79 EVAL 01516r artists! 029fbr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 58.000 43.000 0.857 http://example.org/music/genre/artists EVAL 01516r artists! 03339m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 58.000 43.000 0.857 http://example.org/music/genre/artists EVAL 01516r artists! 03ckfl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 58.000 43.000 0.857 http://example.org/music/genre/artists EVAL 01516r artists! 01_bkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 58.000 43.000 0.857 http://example.org/music/genre/artists EVAL 01516r artists! 0dls3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 58.000 43.000 0.857 http://example.org/music/genre/artists EVAL 01516r artists! 018lg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 58.000 43.000 0.857 http://example.org/music/genre/artists #11315-06hgj PRED entity: 06hgj PRED relation: award_winner! PRED expected values: 06196 07jqvw => 104 concepts (96 used for prediction) PRED predicted values (max 10 best out of 248): 0bqsk5 (0.33 #822, 0.11 #2551, 0.04 #6439), 0d085 (0.17 #4139, 0.16 #3707, 0.12 #4571), 01l78d (0.16 #5040, 0.13 #4176, 0.12 #3744), 0ddd9 (0.12 #3513, 0.12 #3081, 0.10 #3945), 0c_dx (0.12 #3300, 0.06 #6324, 0.05 #2868), 05qck (0.11 #2354, 0.04 #5378, 0.04 #3218), 02rdyk7 (0.11 #2253, 0.04 #3549, 0.03 #3981), 011w54 (0.11 #2566, 0.04 #3430, 0.03 #6022), 02wkmx (0.11 #2176, 0.03 #22468, 0.03 #5200), 02xj3rw (0.11 #2485, 0.03 #22468) >> Best rule #822 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 01d494; >> query: (?x8452, 0bqsk5) <- influenced_by(?x8452, ?x11097), influenced_by(?x8452, ?x7296), ?x7296 = 04hcw, ?x11097 = 02wh0, nationality(?x8452, ?x94), place_of_death(?x8452, ?x4298) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7689 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 144 *> proper extension: 0g5ff; *> query: (?x8452, 06196) <- influenced_by(?x8753, ?x8452), gender(?x8452, ?x231), story_by(?x383, ?x8753), nationality(?x8452, ?x94) *> conf = 0.05 ranks of expected_values: 32 EVAL 06hgj award_winner! 07jqvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 96.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 06hgj award_winner! 06196 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 104.000 96.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #11314-03m8lq PRED entity: 03m8lq PRED relation: location PRED expected values: 0k049 0cc56 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 235): 02_286 (0.61 #45064, 0.54 #29779, 0.53 #28973), 030qb3t (0.33 #4910, 0.32 #3301, 0.29 #21813), 04jpl (0.25 #17, 0.09 #821, 0.09 #27381), 0rh6k (0.18 #808, 0.03 #4027, 0.03 #28172), 01n7q (0.09 #867, 0.08 #8109, 0.07 #1671), 02xry (0.09 #937, 0.05 #2546, 0.05 #6570), 05fjf (0.09 #1136, 0.04 #1940, 0.03 #4355), 04ykg (0.09 #872, 0.04 #1676, 0.02 #3286), 0843m (0.09 #1005, 0.03 #58740, 0.02 #73223), 0ftyc (0.09 #1063, 0.03 #58740) >> Best rule #45064 for best value: >> intensional similarity = 2 >> extensional distance = 384 >> proper extension: 04rs03; 012t1; 073bb; 040wdl; 05qw5; 041mt; 01wyzyl; 01n8_g; 05wjnt; 03j0br4; ... >> query: (?x710, ?x739) <- languages(?x710, ?x254), place_of_birth(?x710, ?x739) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #4080 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 0b7t3p; *> query: (?x710, 0cc56) <- award_winner(?x91, ?x710), celebrity(?x794, ?x710), nominated_for(?x794, ?x437) *> conf = 0.08 ranks of expected_values: 14, 22 EVAL 03m8lq location 0cc56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 130.000 130.000 0.606 http://example.org/people/person/places_lived./people/place_lived/location EVAL 03m8lq location 0k049 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 130.000 130.000 0.606 http://example.org/people/person/places_lived./people/place_lived/location #11313-061y4q PRED entity: 061y4q PRED relation: location PRED expected values: 018jcq => 88 concepts (63 used for prediction) PRED predicted values (max 10 best out of 111): 07dfk (0.40 #2882, 0.38 #2077, 0.27 #3687), 09btk (0.20 #789, 0.10 #3204, 0.05 #4009), 0g3bc (0.17 #1607, 0.12 #2412, 0.09 #4827), 018jkl (0.17 #1361, 0.02 #6996), 02_286 (0.12 #1647, 0.12 #9695, 0.11 #10499), 04jpl (0.12 #1627, 0.10 #2432, 0.05 #7262), 030qb3t (0.10 #28241, 0.10 #7328, 0.09 #30654), 018qt8 (0.10 #3184, 0.05 #3989, 0.04 #4794), 09d4_ (0.10 #2724, 0.05 #3529, 0.04 #4334), 02lf_x (0.07 #7169, 0.05 #3949, 0.04 #4754) >> Best rule #2882 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 02bxjp; >> query: (?x14003, 07dfk) <- nationality(?x14003, ?x252), ?x252 = 03_3d, profession(?x14003, ?x319), award(?x14003, ?x198) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 061y4q location 018jcq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 63.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #11312-0ftqr PRED entity: 0ftqr PRED relation: artists! PRED expected values: 0ggq0m => 110 concepts (39 used for prediction) PRED predicted values (max 10 best out of 278): 0dl5d (0.78 #5906, 0.64 #9943, 0.47 #330), 064t9 (0.53 #11181, 0.49 #1563, 0.48 #3419), 03_d0 (0.42 #2180, 0.32 #941, 0.30 #7139), 0xhtw (0.39 #5903, 0.33 #18, 0.30 #9940), 03lty (0.33 #29, 0.22 #5914, 0.21 #10232), 01fh36 (0.33 #90, 0.21 #5975, 0.18 #1019), 0155w (0.33 #110, 0.20 #1968, 0.20 #11277), 0126t5 (0.33 #88, 0.08 #5973, 0.07 #1017), 017_qw (0.32 #9987, 0.28 #11851, 0.23 #9057), 05bt6j (0.31 #1594, 0.30 #974, 0.28 #11212) >> Best rule #5906 for best value: >> intensional similarity = 4 >> extensional distance = 112 >> proper extension: 01wv9xn; 01czx; 0167_s; 02jqjm; 0l8g0; 06gcn; 03k3b; 0jn38; 0qmny; 01_wfj; ... >> query: (?x10039, 0dl5d) <- artists(?x12590, ?x10039), artists(?x12590, ?x535), artist(?x7793, ?x10039), ?x535 = 02rgz4 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6517 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 116 *> proper extension: 02ryx0; 0cj2w; *> query: (?x10039, 0ggq0m) <- profession(?x10039, ?x1614), ?x1614 = 01c72t, instrumentalists(?x316, ?x10039), instrumentalists(?x316, ?x158), ?x158 = 028q6 *> conf = 0.21 ranks of expected_values: 17 EVAL 0ftqr artists! 0ggq0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 110.000 39.000 0.781 http://example.org/music/genre/artists #11311-01vs_v8 PRED entity: 01vs_v8 PRED relation: profession PRED expected values: 0dz3r => 132 concepts (131 used for prediction) PRED predicted values (max 10 best out of 86): 016z4k (0.49 #3981, 0.48 #3839, 0.45 #714), 01c72t (0.47 #446, 0.34 #4707, 0.30 #7551), 018gz8 (0.47 #2145, 0.24 #4133, 0.23 #2713), 0dz3r (0.45 #1281, 0.44 #3979, 0.42 #3837), 0cbd2 (0.42 #6399, 0.42 #8107, 0.41 #4409), 03gjzk (0.42 #2143, 0.30 #5837, 0.27 #154), 0n1h (0.31 #3845, 0.29 #862, 0.28 #3987), 039v1 (0.29 #883, 0.27 #5998, 0.27 #7562), 0d1pc (0.27 #1466, 0.22 #3170, 0.21 #3596), 012t_z (0.25 #16773, 0.23 #1574, 0.16 #2568) >> Best rule #3981 for best value: >> intensional similarity = 2 >> extensional distance = 152 >> proper extension: 04f7c55; 01wbsdz; 01vng3b; 032nl2; 020hh3; 09nhvw; 017f4y; >> query: (?x2237, 016z4k) <- artists(?x474, ?x2237), participant(?x2237, ?x1909) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1281 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 65 *> proper extension: 01l1b90; 06y9c2; 01vvycq; 02l840; 01q7cb_; 01vrncs; 0lk90; 01vrt_c; 09qr6; 0j1yf; ... *> query: (?x2237, 0dz3r) <- artists(?x474, ?x2237), participant(?x702, ?x2237) *> conf = 0.45 ranks of expected_values: 4 EVAL 01vs_v8 profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 131.000 0.487 http://example.org/people/person/profession #11310-04g73n PRED entity: 04g73n PRED relation: language PRED expected values: 02h40lc => 83 concepts (61 used for prediction) PRED predicted values (max 10 best out of 29): 02h40lc (0.94 #120, 0.90 #715, 0.89 #833), 02bjrlw (0.17 #60, 0.06 #416, 0.06 #119), 04306rv (0.16 #123, 0.11 #300, 0.08 #64), 064_8sq (0.14 #437, 0.14 #378, 0.14 #317), 06nm1 (0.11 #306, 0.10 #783, 0.10 #367), 0y1mh (0.08 #80, 0.03 #139), 03_9r (0.08 #187, 0.06 #128, 0.06 #305), 06b_j (0.06 #318, 0.06 #558, 0.06 #676), 071fb (0.05 #195, 0.02 #313, 0.01 #671), 0653m (0.05 #307, 0.04 #1379, 0.03 #1558) >> Best rule #120 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0gfzfj; >> query: (?x8112, 02h40lc) <- film(?x10398, ?x8112), film(?x10398, ?x1785), ?x1785 = 0gj9tn5, type_of_union(?x10398, ?x566) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04g73n language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 61.000 0.938 http://example.org/film/film/language #11309-03_gx PRED entity: 03_gx PRED relation: religion! PRED expected values: 03v0t 019fv4 => 70 concepts (51 used for prediction) PRED predicted values (max 10 best out of 254): 05kkh (0.54 #1457, 0.48 #1698, 0.47 #1941), 0d0x8 (0.50 #1485, 0.44 #1726, 0.43 #1888), 05kj_ (0.46 #1463, 0.44 #1704, 0.40 #1947), 050l8 (0.46 #1477, 0.41 #1718, 0.40 #1961), 03v0t (0.46 #1492, 0.41 #1733, 0.40 #1976), 07srw (0.46 #1479, 0.41 #1720, 0.39 #1882), 03s5t (0.42 #1483, 0.41 #1724, 0.40 #1967), 07h34 (0.42 #1491, 0.41 #1732, 0.37 #1975), 03s0w (0.42 #1466, 0.37 #1707, 0.37 #1950), 0824r (0.42 #1496, 0.37 #1737, 0.37 #1980) >> Best rule #1457 for best value: >> intensional similarity = 6 >> extensional distance = 22 >> proper extension: 04t_mf; >> query: (?x7131, 05kkh) <- religion(?x8974, ?x7131), religion(?x8257, ?x7131), profession(?x8257, ?x319), award(?x8974, ?x1862), religion(?x1227, ?x7131), contains(?x1227, ?x191) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1492 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 22 *> proper extension: 04t_mf; *> query: (?x7131, 03v0t) <- religion(?x8974, ?x7131), religion(?x8257, ?x7131), profession(?x8257, ?x319), award(?x8974, ?x1862), religion(?x1227, ?x7131), contains(?x1227, ?x191) *> conf = 0.46 ranks of expected_values: 5, 252 EVAL 03_gx religion! 019fv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 70.000 51.000 0.542 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 03_gx religion! 03v0t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 70.000 51.000 0.542 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #11308-0cv3w PRED entity: 0cv3w PRED relation: location_of_ceremony! PRED expected values: 03lt8g 0cg9f => 230 concepts (201 used for prediction) PRED predicted values (max 10 best out of 269): 02m30v (0.29 #1147, 0.18 #1605, 0.17 #2064), 06x58 (0.25 #36, 0.08 #5517, 0.05 #2330), 0gdqy (0.25 #201, 0.05 #2495, 0.04 #2724), 0c9c0 (0.25 #58, 0.05 #2352, 0.04 #2581), 06wvj (0.25 #52, 0.05 #2346, 0.04 #2575), 03lt8g (0.25 #20, 0.05 #2314, 0.04 #2543), 01g23m (0.25 #82, 0.04 #3527, 0.03 #4677), 01cwcr (0.20 #385, 0.08 #1990, 0.04 #2678), 01933d (0.20 #397, 0.08 #2920, 0.08 #3612), 018yj6 (0.20 #410, 0.04 #2703, 0.04 #3394) >> Best rule #1147 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02dtg; 0b1t1; >> query: (?x3026, 02m30v) <- featured_film_locations(?x1015, ?x3026), contains(?x3026, ?x9745), ?x1015 = 04dsnp >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0d1qn; *> query: (?x3026, 03lt8g) <- featured_film_locations(?x349, ?x3026), vacationer(?x3026, ?x2614), ?x2614 = 04xrx *> conf = 0.25 ranks of expected_values: 6 EVAL 0cv3w location_of_ceremony! 0cg9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 230.000 201.000 0.286 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony EVAL 0cv3w location_of_ceremony! 03lt8g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 230.000 201.000 0.286 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #11307-02lp1 PRED entity: 02lp1 PRED relation: major_field_of_study! PRED expected values: 019v9k => 49 concepts (37 used for prediction) PRED predicted values (max 10 best out of 15): 019v9k (0.77 #257, 0.76 #197, 0.75 #137), 0bkj86 (0.70 #165, 0.64 #180, 0.62 #136), 0bjrnt (0.62 #134, 0.50 #75, 0.43 #146), 022h5x (0.43 #146, 0.42 #15, 0.39 #283), 07s6fsf (0.43 #146, 0.42 #15, 0.39 #283), 01rr_d (0.43 #146, 0.42 #15, 0.39 #283), 013zdg (0.43 #146, 0.42 #15, 0.39 #283), 027f2w (0.43 #146, 0.42 #15, 0.39 #283), 028dcg (0.43 #146, 0.42 #15, 0.39 #283), 02cq61 (0.43 #146, 0.42 #15, 0.39 #283) >> Best rule #257 for best value: >> intensional similarity = 11 >> extensional distance = 59 >> proper extension: 03ytc; >> query: (?x1154, 019v9k) <- major_field_of_study(?x7545, ?x1154), major_field_of_study(?x1665, ?x1154), major_field_of_study(?x7545, ?x12158), major_field_of_study(?x7545, ?x2014), citytown(?x7545, ?x739), student(?x7545, ?x4247), ?x12158 = 09s1f, ?x2014 = 04rjg, institution(?x620, ?x7545), currency(?x1665, ?x170), award_winner(?x2245, ?x4247) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lp1 major_field_of_study! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 37.000 0.770 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #11306-01fkr_ PRED entity: 01fkr_ PRED relation: country PRED expected values: 09c7w0 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.31 #145, 0.30 #124, 0.30 #148), 07ssc (0.08 #11, 0.07 #7, 0.04 #45), 059rby (0.06 #223, 0.05 #284, 0.02 #324), 0yb_4 (0.02 #324), 02zp1t (0.02 #324), 0y617 (0.02 #324), 01nl79 (0.02 #324), 0d_kd (0.02 #324), 0drs7 (0.02 #324), 0dq16 (0.02 #324) >> Best rule #145 for best value: >> intensional similarity = 5 >> extensional distance = 160 >> proper extension: 01jssp; 0bthb; 02hft3; 037s9x; 01jtp7; 02rff2; 02qvvv; 01q2sk; 03fmfs; 012fvq; ... >> query: (?x12871, 09c7w0) <- state_province_region(?x12871, ?x335), state_province_region(?x7545, ?x335), student(?x7545, ?x157), place_of_death(?x1233, ?x335), religion(?x335, ?x492) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fkr_ country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.315 http://example.org/organization/organization/headquarters./location/mailing_address/country #11305-02xyl PRED entity: 02xyl PRED relation: people! PRED expected values: 0qcr0 01_qc_ => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 49): 0gk4g (0.29 #530, 0.26 #725, 0.25 #595), 02k6hp (0.25 #37, 0.20 #167, 0.17 #232), 06z5s (0.25 #25, 0.09 #2040, 0.09 #2105), 034qg (0.25 #33, 0.05 #358, 0.03 #2048), 0qcr0 (0.17 #586, 0.17 #716, 0.13 #521), 0dq9p (0.13 #3657, 0.13 #732, 0.13 #537), 02y0js (0.11 #1822, 0.10 #2212, 0.09 #1302), 04psf (0.10 #527, 0.07 #722, 0.05 #592), 01psyx (0.10 #370, 0.08 #1280, 0.05 #630), 04p3w (0.08 #3651, 0.07 #4237, 0.07 #3586) >> Best rule #530 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0jf1b; 012vct; 0cj2w; >> query: (?x13125, 0gk4g) <- written_by(?x7928, ?x13125), place_of_birth(?x13125, ?x9973), profession(?x13125, ?x353), people(?x13552, ?x13125) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #586 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 0488g9; *> query: (?x13125, 0qcr0) <- written_by(?x7928, ?x13125), award(?x13125, ?x575), people(?x13552, ?x13125), nationality(?x13125, ?x94) *> conf = 0.17 ranks of expected_values: 5, 21 EVAL 02xyl people! 01_qc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 155.000 155.000 0.290 http://example.org/people/cause_of_death/people EVAL 02xyl people! 0qcr0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 155.000 155.000 0.290 http://example.org/people/cause_of_death/people #11304-0326tc PRED entity: 0326tc PRED relation: role PRED expected values: 02sgy 01hww_ 05842k => 140 concepts (76 used for prediction) PRED predicted values (max 10 best out of 111): 05842k (0.67 #243, 0.27 #959, 0.25 #600), 02sgy (0.56 #183, 0.36 #809, 0.30 #540), 018j2 (0.44 #446, 0.44 #215, 0.33 #1879), 03ndd (0.44 #446, 0.32 #4766, 0.32 #4952), 028tv0 (0.44 #446, 0.28 #1878, 0.27 #2059), 0mkg (0.44 #187, 0.07 #625, 0.06 #903), 026t6 (0.33 #181, 0.31 #807, 0.27 #270), 01vj9c (0.33 #190, 0.25 #368, 0.24 #2075), 0bxl5 (0.33 #237, 0.20 #594, 0.09 #2964), 0l14md (0.33 #184, 0.10 #541, 0.09 #2964) >> Best rule #243 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 05qhnq; >> query: (?x7972, 05842k) <- role(?x7972, ?x885), ?x885 = 0dwtp, profession(?x7972, ?x131), artists(?x302, ?x7972) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 69 EVAL 0326tc role 05842k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 76.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0326tc role 01hww_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 140.000 76.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0326tc role 02sgy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 76.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #11303-01vs5c PRED entity: 01vs5c PRED relation: school! PRED expected values: 0jmj7 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 79): 0jmj7 (0.68 #3679, 0.67 #1530, 0.67 #3758), 01yhm (0.40 #174, 0.13 #1523, 0.13 #1760), 061xq (0.30 #185, 0.20 #343, 0.14 #1851), 05m_8 (0.22 #1746, 0.21 #2145, 0.21 #1350), 0713r (0.22 #107, 0.20 #344, 0.14 #265), 06rpd (0.22 #141, 0.14 #299, 0.12 #1824), 0jmm4 (0.22 #140, 0.14 #298, 0.11 #1409), 06wpc (0.20 #213, 0.20 #55, 0.12 #1824), 0cqt41 (0.20 #173, 0.15 #331, 0.14 #1122), 04wmvz (0.20 #225, 0.15 #383, 0.13 #620) >> Best rule #3679 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 01hhvg; 03v6t; 01nkcn; 02bjhv; 037njl; 01rgdw; 02s8qk; 02txdf; 015fsv; 01yqqv; ... >> query: (?x5621, 0jmj7) <- school(?x260, ?x5621), currency(?x5621, ?x170), major_field_of_study(?x5621, ?x254), major_field_of_study(?x1695, ?x254) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vs5c school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.683 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #11302-022769 PRED entity: 022769 PRED relation: currency PRED expected values: 09nqf => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.28 #19, 0.28 #49, 0.26 #40), 01nv4h (0.01 #2, 0.01 #11) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 240 >> proper extension: 01vvycq; >> query: (?x2100, 09nqf) <- award_winner(?x2100, ?x3329), participant(?x2100, ?x4407), profession(?x3329, ?x353) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022769 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.281 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #11301-07sgfsl PRED entity: 07sgfsl PRED relation: award PRED expected values: 0cqhk0 => 75 concepts (56 used for prediction) PRED predicted values (max 10 best out of 247): 0cqhk0 (0.80 #443, 0.73 #37, 0.68 #1255), 0ck27z (0.32 #5779, 0.27 #6185, 0.21 #7403), 09sb52 (0.32 #10599, 0.31 #11005, 0.31 #11411), 01c99j (0.19 #2259, 0.11 #5508, 0.08 #3071), 0gqwc (0.19 #5355, 0.14 #2106, 0.12 #7791), 0gqyl (0.18 #5386, 0.14 #2137, 0.12 #7822), 02x4x18 (0.16 #2165, 0.11 #5414, 0.09 #1758), 03c7tr1 (0.16 #2090, 0.08 #5339, 0.08 #8587), 01by1l (0.16 #2550, 0.15 #2956, 0.15 #3768), 094qd5 (0.15 #5325, 0.09 #8573, 0.09 #9385) >> Best rule #443 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 07sgfvl; 05ztm4r; 032q8q; 0fn8jc; >> query: (?x2780, 0cqhk0) <- award_nominee(?x1825, ?x2780), award_nominee(?x1824, ?x2780), ?x1825 = 0806vbn, profession(?x1824, ?x1032), award_winner(?x1824, ?x1796) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07sgfsl award 0cqhk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 56.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11300-048hf PRED entity: 048hf PRED relation: student! PRED expected values: 028dcg => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 12): 014mlp (0.10 #106, 0.07 #426, 0.07 #746), 028dcg (0.07 #38, 0.04 #78, 0.04 #138), 02h4rq6 (0.06 #43, 0.04 #63, 0.03 #83), 019v9k (0.02 #150, 0.02 #430, 0.02 #290), 0bkj86 (0.02 #109, 0.01 #429, 0.01 #349), 02_xgp2 (0.01 #1114, 0.01 #1741, 0.01 #1734), 016t_3 (0.01 #1741, 0.01 #424, 0.01 #104), 01ysy9 (0.01 #1741), 03bwzr4 (0.01 #1741), 027f2w (0.01 #1741) >> Best rule #106 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 02_j7t; 01vv6_6; 015wfg; 081jbk; 03wy70; 0g476; 0sx5w; 0d0l91; 01rzxl; >> query: (?x7842, 014mlp) <- category(?x7842, ?x134), actor(?x2078, ?x7842), student(?x4599, ?x7842) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 05lb87; 030znt; 04psyp; 05lb30; 05lb65; 04vmqg; 01rs5p; *> query: (?x7842, 028dcg) <- award_nominee(?x7842, ?x2578), award_nominee(?x7842, ?x1116), ?x1116 = 06b0d2, ?x2578 = 038g2x *> conf = 0.07 ranks of expected_values: 2 EVAL 048hf student! 028dcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.101 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #11299-05vyk PRED entity: 05vyk PRED relation: profession! PRED expected values: 01pbxb 0k4gf 018gqj 03f4k 0ckcvk 0hqgp 0459z => 37 concepts (14 used for prediction) PRED predicted values (max 10 best out of 4157): 02fybl (0.60 #6492, 0.33 #2304, 0.27 #23253), 01vsy7t (0.60 #5642, 0.33 #1454, 0.26 #9831), 0473q (0.60 #6517, 0.33 #2329, 0.26 #10706), 01271h (0.60 #5043, 0.33 #855, 0.26 #9232), 04s5_s (0.60 #8129, 0.33 #3941, 0.26 #12318), 02whj (0.60 #4465, 0.33 #277, 0.25 #8377), 014q2g (0.60 #4988, 0.33 #800, 0.21 #9177), 02cx90 (0.60 #5535, 0.33 #1347, 0.21 #9724), 0ddkf (0.60 #6386, 0.33 #2198, 0.21 #10575), 017b2p (0.60 #7101, 0.33 #2913, 0.21 #11290) >> Best rule #6492 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0dz3r; 0nbcg; >> query: (?x11127, 02fybl) <- profession(?x13248, ?x11127), profession(?x1894, ?x11127), profession(?x669, ?x11127), ?x1894 = 02fgpf, influenced_by(?x9297, ?x13248), music(?x670, ?x669), award_winner(?x725, ?x669) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7313 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 0dz3r; 0nbcg; *> query: (?x11127, 03f4k) <- profession(?x13248, ?x11127), profession(?x1894, ?x11127), profession(?x669, ?x11127), ?x1894 = 02fgpf, influenced_by(?x9297, ?x13248), music(?x670, ?x669), award_winner(?x725, ?x669) *> conf = 0.40 ranks of expected_values: 76, 79, 262, 435, 1080, 2052, 2302 EVAL 05vyk profession! 0459z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 14.000 0.600 http://example.org/people/person/profession EVAL 05vyk profession! 0hqgp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 14.000 0.600 http://example.org/people/person/profession EVAL 05vyk profession! 0ckcvk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 14.000 0.600 http://example.org/people/person/profession EVAL 05vyk profession! 03f4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 37.000 14.000 0.600 http://example.org/people/person/profession EVAL 05vyk profession! 018gqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 37.000 14.000 0.600 http://example.org/people/person/profession EVAL 05vyk profession! 0k4gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 14.000 0.600 http://example.org/people/person/profession EVAL 05vyk profession! 01pbxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 14.000 0.600 http://example.org/people/person/profession #11298-02vkvcz PRED entity: 02vkvcz PRED relation: profession PRED expected values: 026sdt1 => 92 concepts (91 used for prediction) PRED predicted values (max 10 best out of 89): 026sdt1 (0.89 #521, 0.85 #370, 0.50 #220), 02hrh1q (0.83 #2866, 0.82 #916, 0.81 #4219), 02jknp (0.43 #1059, 0.37 #3754, 0.37 #3302), 01d_h8 (0.37 #3754, 0.37 #3302, 0.36 #1057), 03gjzk (0.37 #3754, 0.37 #3302, 0.36 #3920), 0dxtg (0.37 #3754, 0.37 #3302, 0.34 #3918), 02krf9 (0.37 #3754, 0.37 #3302, 0.16 #3932), 0kyk (0.37 #3754, 0.37 #3302, 0.14 #1232), 0lgw7 (0.37 #3754, 0.37 #3302, 0.08 #11261), 02pjxr (0.25 #185, 0.15 #335, 0.14 #486) >> Best rule #521 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 02w0dc0; 03y1mlp; 0gl88b; 05x2t7; 0c6g29; 0dck27; 03wpmd; 0bytkq; 0bytfv; 02pqgt8; ... >> query: (?x12364, 026sdt1) <- costume_design_by(?x2943, ?x12364), nationality(?x12364, ?x1310), nominated_for(?x406, ?x2943), genre(?x2943, ?x53) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vkvcz profession 026sdt1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 91.000 0.893 http://example.org/people/person/profession #11297-062zjtt PRED entity: 062zjtt PRED relation: featured_film_locations PRED expected values: 030qb3t => 60 concepts (26 used for prediction) PRED predicted values (max 10 best out of 46): 02_286 (0.20 #20, 0.17 #1944, 0.17 #260), 04jpl (0.20 #9, 0.17 #249, 0.14 #971), 03rjj (0.20 #6, 0.17 #246, 0.12 #1448), 03gh4 (0.20 #115, 0.17 #355, 0.09 #595), 030qb3t (0.15 #761, 0.12 #1241, 0.12 #3645), 06y57 (0.15 #825, 0.12 #1305, 0.12 #1786), 05qtj (0.08 #818, 0.06 #1298, 0.06 #1538), 0156q (0.08 #763, 0.06 #1243, 0.06 #1483), 0345h (0.08 #755, 0.06 #1235, 0.06 #1475), 080h2 (0.08 #3630, 0.06 #4357, 0.06 #3873) >> Best rule #20 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0cc846d; 0hx4y; 0dzlbx; >> query: (?x4273, 02_286) <- film(?x5462, ?x4273), story_by(?x4273, ?x96), ?x5462 = 0f5xn, region(?x4273, ?x512), film_crew_role(?x4273, ?x137) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #761 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 02qm_f; 0fdv3; 0f4_l; 02yvct; 0ddt_; 025n07; *> query: (?x4273, 030qb3t) <- film(?x5462, ?x4273), ?x5462 = 0f5xn, executive_produced_by(?x4273, ?x96), film_crew_role(?x4273, ?x137), genre(?x4273, ?x225) *> conf = 0.15 ranks of expected_values: 5 EVAL 062zjtt featured_film_locations 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 60.000 26.000 0.200 http://example.org/film/film/featured_film_locations #11296-019sc PRED entity: 019sc PRED relation: split_to! PRED expected values: 06fvc => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 18): 06fvc (0.33 #744, 0.25 #1070, 0.25 #962), 019sc (0.09 #1054, 0.06 #1156, 0.06 #1047), 067z2v (0.09 #1054, 0.06 #1156, 0.06 #1047), 03vtbc (0.06 #1156, 0.06 #1047, 0.05 #835), 083jv (0.06 #1156, 0.06 #1047, 0.05 #835), 01l849 (0.06 #1156, 0.06 #1047, 0.05 #835), 01g5v (0.06 #1156, 0.06 #1047, 0.05 #1271), 09ggk (0.06 #1156, 0.06 #1047, 0.05 #1045), 0jc_p (0.06 #1156, 0.05 #835, 0.05 #1271), 03wkwg (0.06 #1047, 0.05 #835, 0.05 #1271) >> Best rule #744 for best value: >> intensional similarity = 41 >> extensional distance = 1 >> proper extension: 083jv; >> query: (?x4557, 06fvc) <- colors(?x14015, ?x4557), colors(?x13947, ?x4557), colors(?x13326, ?x4557), colors(?x11368, ?x4557), colors(?x8912, ?x4557), colors(?x8361, ?x4557), colors(?x5914, ?x4557), colors(?x5428, ?x4557), colors(?x2919, ?x4557), colors(?x2198, ?x4557), colors(?x1115, ?x4557), colors(?x684, ?x4557), ?x1115 = 01y3c, colors(?x9344, ?x4557), colors(?x5907, ?x4557), colors(?x5158, ?x4557), colors(?x3044, ?x4557), colors(?x2351, ?x4557), colors(?x546, ?x4557), ?x11368 = 032yps, team(?x12447, ?x13947), ?x8361 = 049bp4, ?x5428 = 019lxm, ?x2198 = 05g3v, school(?x4571, ?x5907), contains(?x94, ?x5158), ?x2919 = 0c41y70, major_field_of_study(?x5907, ?x2172), ?x8912 = 01lpx8, ?x3044 = 01c333, ?x684 = 01ct6, state_province_region(?x546, ?x961), ?x13326 = 0hm2b, currency(?x5158, ?x170), student(?x546, ?x8257), ?x9344 = 02nq10, ?x2351 = 0q19t, ?x14015 = 0jnlm, position(?x13947, ?x530), location(?x8257, ?x191), team(?x5471, ?x5914) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019sc split_to! 06fvc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 21.000 21.000 0.333 http://example.org/dataworld/gardening_hint/split_to #11295-01hng3 PRED entity: 01hng3 PRED relation: religion! PRED expected values: 0dzkq => 27 concepts (20 used for prediction) PRED predicted values (max 10 best out of 3721): 0jcx (0.50 #254, 0.43 #3450, 0.38 #4515), 01x4r3 (0.50 #778, 0.29 #3974, 0.25 #5039), 049gc (0.50 #447, 0.29 #3643, 0.25 #4708), 01tdnyh (0.50 #425, 0.29 #3621, 0.25 #4686), 0c6qh (0.50 #177, 0.29 #3373, 0.25 #4438), 03ft8 (0.50 #110, 0.29 #3306, 0.25 #4371), 03_gd (0.50 #47, 0.29 #3243, 0.25 #4308), 01zwy (0.50 #710, 0.29 #3906, 0.25 #4971), 0c5tl (0.50 #419, 0.29 #3615, 0.25 #4680), 0klw (0.50 #404, 0.29 #3600, 0.25 #4665) >> Best rule #254 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 0kpl; 0kq2; >> query: (?x14146, 0jcx) <- religion(?x8061, ?x14146), religion(?x4466, ?x14146), award_winner(?x8061, ?x1119), person(?x424, ?x8061), film(?x8061, ?x437), nominated_for(?x8061, ?x3180), ?x4466 = 01_x6d, award(?x8061, ?x693) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1325 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 0c8wxp; 03_gx; 06nzl; *> query: (?x14146, 0dzkq) <- religion(?x8061, ?x14146), religion(?x6666, ?x14146), award_winner(?x8061, ?x1119), person(?x424, ?x8061), film(?x8061, ?x437), spouse(?x8061, ?x495), friend(?x8061, ?x917), award_winner(?x704, ?x8061), award_nominee(?x1367, ?x6666) *> conf = 0.20 ranks of expected_values: 675 EVAL 01hng3 religion! 0dzkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 20.000 0.500 http://example.org/people/person/religion #11294-0bwhdbl PRED entity: 0bwhdbl PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.91 #76, 0.91 #71, 0.91 #41), 02nxhr (0.25 #401, 0.06 #12, 0.04 #177), 07c52 (0.04 #108, 0.03 #189, 0.03 #178), 07z4p (0.04 #110, 0.03 #180, 0.03 #135) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 127 >> proper extension: 04hwbq; 080dfr7; >> query: (?x8130, 029j_) <- film(?x8020, ?x8130), prequel(?x8130, ?x1184), participant(?x1093, ?x8020), country(?x8130, ?x94), film(?x399, ?x1184) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bwhdbl film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.915 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #11293-04ghz4m PRED entity: 04ghz4m PRED relation: film! PRED expected values: 02bfmn => 79 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1111): 017r13 (0.25 #1108, 0.09 #3186, 0.07 #5263), 03n_7k (0.25 #397, 0.09 #2475, 0.04 #8705), 08qxx9 (0.25 #1515, 0.09 #3593, 0.02 #26437), 045c66 (0.25 #237, 0.09 #2315, 0.02 #10623), 03q43g (0.25 #1147, 0.09 #3225, 0.01 #23992), 05vk_d (0.25 #1494, 0.09 #3572), 0dvld (0.25 #1057, 0.02 #7288, 0.02 #55051), 07nx9j (0.25 #1313, 0.01 #47002, 0.01 #55307), 0bl60p (0.25 #1336), 0336mc (0.14 #5669, 0.02 #11900, 0.01 #13976) >> Best rule #1108 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 05sxzwc; 02q5g1z; >> query: (?x7107, 017r13) <- film(?x3842, ?x7107), film(?x3594, ?x7107), ?x3842 = 0cjsxp, executive_produced_by(?x7107, ?x7146), award_nominee(?x3594, ?x368) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #62328 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 673 *> proper extension: 053tj7; *> query: (?x7107, 02bfmn) <- genre(?x7107, ?x53), film_release_region(?x7107, ?x94), produced_by(?x7107, ?x3056) *> conf = 0.01 ranks of expected_values: 814 EVAL 04ghz4m film! 02bfmn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 79.000 52.000 0.250 http://example.org/film/actor/film./film/performance/film #11292-03m6t5 PRED entity: 03m6t5 PRED relation: celebrities_impersonated PRED expected values: 028rk 0gn30 02f6s3 0gnbw 0gt3p => 124 concepts (60 used for prediction) PRED predicted values (max 10 best out of 54): 0f502 (0.50 #46, 0.29 #199, 0.25 #78), 01vb6z (0.33 #23, 0.25 #86, 0.20 #116), 01vsgrn (0.33 #22, 0.25 #85, 0.20 #115), 01pfkw (0.33 #16, 0.25 #79, 0.20 #109), 0bymv (0.33 #9, 0.25 #72, 0.20 #102), 0bxtg (0.33 #1, 0.25 #64, 0.20 #94), 0dzf_ (0.25 #81, 0.25 #49, 0.20 #111), 01svq8 (0.25 #93, 0.25 #61, 0.20 #123), 0m0nq (0.25 #90, 0.25 #58, 0.20 #120), 01t94_1 (0.25 #89, 0.25 #57, 0.20 #119) >> Best rule #46 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01n5309; >> query: (?x3649, 0f502) <- celebrities_impersonated(?x3649, ?x2465), influenced_by(?x2135, ?x2465), film(?x2465, ?x1308), participant(?x2465, ?x3628), profession(?x2465, ?x319) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #239 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 0d608; *> query: (?x3649, 0gnbw) <- celebrities_impersonated(?x3649, ?x4196), celebrities_impersonated(?x3649, ?x2465), celebrities_impersonated(?x3649, ?x1567), jurisdiction_of_office(?x4196, ?x94), award(?x2465, ?x198), politician(?x1912, ?x4196), profession(?x1567, ?x1032) *> conf = 0.14 ranks of expected_values: 29 EVAL 03m6t5 celebrities_impersonated 0gt3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 60.000 0.500 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated EVAL 03m6t5 celebrities_impersonated 0gnbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 124.000 60.000 0.500 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated EVAL 03m6t5 celebrities_impersonated 02f6s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 60.000 0.500 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated EVAL 03m6t5 celebrities_impersonated 0gn30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 60.000 0.500 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated EVAL 03m6t5 celebrities_impersonated 028rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 60.000 0.500 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #11291-07nx9j PRED entity: 07nx9j PRED relation: type_of_union PRED expected values: 04ztj => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.77 #29, 0.73 #49, 0.73 #69), 01g63y (0.23 #6, 0.19 #345, 0.19 #14), 0jgjn (0.19 #345), 01bl8s (0.19 #345) >> Best rule #29 for best value: >> intensional similarity = 5 >> extensional distance = 348 >> proper extension: 02t__l; 0hwd8; 0pj9t; 01vsps; 012dr7; 01t94_1; 01lc5; 0cj2w; 07y_r; 012c6j; ... >> query: (?x7585, 04ztj) <- award(?x7585, ?x1921), award(?x8473, ?x1921), award(?x1870, ?x1921), ?x8473 = 0gyy0, award_nominee(?x100, ?x1870) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07nx9j type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.766 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11290-01vvycq PRED entity: 01vvycq PRED relation: award_winner! PRED expected values: 02x201b => 147 concepts (144 used for prediction) PRED predicted values (max 10 best out of 320): 025m8l (0.41 #39263, 0.40 #45594, 0.39 #24910), 07cbcy (0.41 #39263, 0.40 #45594, 0.39 #24910), 05b1610 (0.41 #39263, 0.40 #45594, 0.39 #24910), 01c92g (0.41 #39263, 0.40 #45594, 0.39 #24910), 0c4z8 (0.41 #39263, 0.40 #45594, 0.39 #24910), 01by1l (0.41 #39263, 0.40 #45594, 0.39 #24910), 01d38g (0.41 #39263, 0.40 #45594, 0.39 #24910), 031b3h (0.41 #39263, 0.40 #45594, 0.39 #24910), 02nhxf (0.41 #39263, 0.40 #45594, 0.39 #24910), 01ck6v (0.41 #39263, 0.40 #45594, 0.39 #24910) >> Best rule #39263 for best value: >> intensional similarity = 4 >> extensional distance = 842 >> proper extension: 044mz_; 0184jc; 02s2ft; 02qgqt; 0fvf9q; 02p65p; 06151l; 06gp3f; 01xdf5; 04t2l2; ... >> query: (?x702, ?x567) <- award_winner(?x350, ?x702), award_winner(?x3962, ?x702), award_winner(?x486, ?x702), award(?x702, ?x567) >> conf = 0.41 => this is the best rule for 16 predicted values *> Best rule #56155 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2476 *> proper extension: 02m7r; 0c8br; 05d1y; *> query: (?x702, ?x462) <- award_winner(?x2563, ?x702), award_winner(?x2563, ?x4960), award_winner(?x462, ?x4960) *> conf = 0.05 ranks of expected_values: 140 EVAL 01vvycq award_winner! 02x201b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 147.000 144.000 0.409 http://example.org/award/award_category/winners./award/award_honor/award_winner #11289-0f6rc PRED entity: 0f6rc PRED relation: films PRED expected values: 0p7qm => 109 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1297): 0fjyzt (0.33 #275, 0.20 #5583, 0.20 #4523), 0czyxs (0.33 #20, 0.20 #5328, 0.20 #4268), 08hmch (0.29 #1107, 0.22 #3232, 0.20 #5354), 011yxg (0.23 #8508, 0.09 #26553, 0.09 #27616), 0hfzr (0.21 #10294, 0.18 #14012, 0.18 #12950), 08xvpn (0.19 #12682, 0.14 #17992, 0.06 #25417), 025rvx0 (0.19 #12495, 0.12 #2408, 0.12 #1875), 02dwj (0.17 #269, 0.15 #8762, 0.10 #5577), 02p86pb (0.17 #451, 0.14 #1512, 0.11 #3637), 0jqj5 (0.17 #261, 0.14 #1322, 0.11 #3447) >> Best rule #275 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 02jx1; 01w1sx; >> query: (?x9351, 0fjyzt) <- films(?x9351, ?x2402), locations(?x9351, ?x2346), film_crew_role(?x2402, ?x468), film_format(?x2402, ?x909), country(?x2402, ?x94), genre(?x2402, ?x53), nationality(?x754, ?x2346) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13274 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 15 *> proper extension: 0jxxt; *> query: (?x9351, ?x144) <- films(?x9351, ?x2402), film_crew_role(?x2402, ?x468), music(?x2402, ?x2940), nominated_for(?x484, ?x2402), genre(?x2402, ?x2605), ?x2605 = 03g3w, nominated_for(?x484, ?x144) *> conf = 0.01 ranks of expected_values: 602 EVAL 0f6rc films 0p7qm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 109.000 58.000 0.333 http://example.org/film/film_subject/films #11288-05szp PRED entity: 05szp PRED relation: people! PRED expected values: 041rx 033qxt => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 47): 0x67 (0.37 #934, 0.36 #857, 0.30 #1011), 041rx (0.17 #3010, 0.16 #158, 0.16 #81), 033tf_ (0.12 #3013, 0.11 #7, 0.11 #546), 02ctzb (0.11 #15, 0.11 #92, 0.08 #169), 0xnvg (0.09 #783, 0.07 #1554, 0.07 #1631), 07hwkr (0.06 #551, 0.06 #705, 0.06 #166), 048z7l (0.06 #194, 0.06 #40, 0.05 #117), 01qhm_ (0.06 #6, 0.04 #3012, 0.04 #1161), 02w7gg (0.05 #2005, 0.05 #1774, 0.05 #4474), 07bch9 (0.05 #100, 0.05 #1564, 0.05 #2412) >> Best rule #934 for best value: >> intensional similarity = 3 >> extensional distance = 159 >> proper extension: 07s3vqk; 01l1b90; 01vrx3g; 032nwy; 02mslq; 06cc_1; 01vvycq; 02l840; 03f5spx; 01gf5h; ... >> query: (?x6666, 0x67) <- artists(?x3319, ?x6666), award(?x6666, ?x2180), ?x3319 = 06j6l >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #3010 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 589 *> proper extension: 0cl_m; *> query: (?x6666, 041rx) <- nationality(?x6666, ?x94), ?x94 = 09c7w0, religion(?x6666, ?x14146) *> conf = 0.17 ranks of expected_values: 2 EVAL 05szp people! 033qxt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 103.000 0.366 http://example.org/people/ethnicity/people EVAL 05szp people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.366 http://example.org/people/ethnicity/people #11287-0mb8c PRED entity: 0mb8c PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 029j_ (0.99 #106, 0.99 #180, 0.99 #195), 02nxhr (0.21 #190, 0.07 #17, 0.07 #42) >> Best rule #106 for best value: >> intensional similarity = 5 >> extensional distance = 717 >> proper extension: 05dy7p; 027ct7c; >> query: (?x5230, 029j_) <- genre(?x5230, ?x53), award_winner(?x5230, ?x10388), language(?x5230, ?x254), film_release_distribution_medium(?x5230, ?x2008), award(?x10388, ?x5923) >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mb8c film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.987 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #11286-01qd_r PRED entity: 01qd_r PRED relation: school! PRED expected values: 0jm9w => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 90): 05g76 (0.20 #111, 0.08 #381, 0.08 #1191), 05m_8 (0.18 #1173, 0.17 #273, 0.16 #543), 051vz (0.15 #563, 0.15 #293, 0.12 #1193), 07l8x (0.15 #605, 0.13 #155, 0.12 #1235), 01slc (0.14 #1227, 0.13 #147, 0.10 #1677), 07l4z (0.13 #159, 0.13 #609, 0.13 #1239), 01yhm (0.13 #110, 0.12 #380, 0.12 #290), 0jmk7 (0.13 #177, 0.10 #357, 0.09 #627), 04wmvz (0.13 #167, 0.10 #1247, 0.07 #617), 051wf (0.13 #178, 0.07 #628, 0.07 #1258) >> Best rule #111 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 06pwq; 01w3v; 07szy; 0dplh; 07wrz; 01w5m; 03ksy; 07vyf; 0bqxw; 09f2j; ... >> query: (?x7660, 05g76) <- major_field_of_study(?x7660, ?x2314), major_field_of_study(?x7660, ?x1668), ?x1668 = 01mkq, ?x2314 = 0h5k >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 06pwq; 01w3v; 07szy; 0dplh; 07wrz; 01w5m; 03ksy; 07vyf; 0bqxw; 09f2j; ... *> query: (?x7660, 0jm9w) <- major_field_of_study(?x7660, ?x2314), major_field_of_study(?x7660, ?x1668), ?x1668 = 01mkq, ?x2314 = 0h5k *> conf = 0.07 ranks of expected_values: 53 EVAL 01qd_r school! 0jm9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 74.000 74.000 0.200 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #11285-02pq9yv PRED entity: 02pq9yv PRED relation: award_nominee! PRED expected values: 092kgw => 116 concepts (37 used for prediction) PRED predicted values (max 10 best out of 823): 04sry (0.80 #39548, 0.79 #60484, 0.79 #27917), 027z0pl (0.80 #39548, 0.79 #60484, 0.79 #27917), 092kgw (0.80 #39548, 0.79 #60484, 0.79 #27917), 02pq9yv (0.21 #81423, 0.20 #790, 0.09 #3116), 0154qm (0.21 #81423, 0.04 #14694, 0.03 #79830), 04t2l2 (0.21 #81423, 0.04 #7017, 0.03 #58195), 06r_by (0.21 #81423, 0.03 #3729, 0.01 #20013), 01_p6t (0.21 #81423, 0.02 #61832, 0.01 #80442), 015grj (0.21 #81423, 0.01 #58353, 0.01 #69986), 07r_dg (0.21 #81423) >> Best rule #39548 for best value: >> intensional similarity = 3 >> extensional distance = 279 >> proper extension: 058kqy; 05cv94; 052gzr; 021lby; 05jm7; 02dbp7; 05zh9c; 020trj; 01vz80y; 01qbjg; ... >> query: (?x3528, ?x5527) <- award_nominee(?x2135, ?x3528), produced_by(?x1916, ?x3528), award_nominee(?x3528, ?x5527) >> conf = 0.80 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 02pq9yv award_nominee! 092kgw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 37.000 0.796 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11284-07ss8_ PRED entity: 07ss8_ PRED relation: artist! PRED expected values: 01f_3w => 103 concepts (75 used for prediction) PRED predicted values (max 10 best out of 103): 01f_3w (0.25 #176, 0.20 #35, 0.16 #458), 073tm9 (0.25 #178, 0.20 #37, 0.10 #319), 015_1q (0.20 #20, 0.19 #6092, 0.19 #3830), 03mp8k (0.20 #67, 0.16 #1477, 0.14 #2889), 0n85g (0.19 #486, 0.13 #1473, 0.10 #2885), 03rhqg (0.16 #1285, 0.16 #2838, 0.15 #862), 01trtc (0.16 #1483, 0.12 #637, 0.09 #496), 043g7l (0.16 #455, 0.15 #1442, 0.12 #596), 0g768 (0.15 #1307, 0.15 #884, 0.14 #2860), 033hn8 (0.14 #2836, 0.13 #1424, 0.12 #437) >> Best rule #176 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 043zg; 03h_0_z; >> query: (?x2227, 01f_3w) <- participant(?x2227, ?x6835), award(?x2227, ?x3488), ?x3488 = 02f71y >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ss8_ artist! 01f_3w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 75.000 0.250 http://example.org/music/record_label/artist #11283-03gqgt3 PRED entity: 03gqgt3 PRED relation: combatants PRED expected values: 09c7w0 0d060g 06npd 0k6nt 01znc_ 05b4w 03m6j => 50 concepts (39 used for prediction) PRED predicted values (max 10 best out of 480): 09c7w0 (0.67 #1356, 0.59 #1817, 0.57 #329), 0chghy (0.57 #335, 0.50 #225, 0.44 #1823), 05vz3zq (0.50 #265, 0.33 #47, 0.29 #375), 05v8c (0.43 #337, 0.25 #227, 0.14 #3834), 08_hns (0.37 #3268, 0.34 #1355, 0.31 #1816), 059z0 (0.33 #65, 0.25 #283, 0.19 #2778), 0193qj (0.33 #55, 0.25 #273, 0.18 #610), 0d060g (0.33 #5, 0.25 #223, 0.15 #1821), 06f32 (0.33 #32, 0.25 #250, 0.14 #360), 07f1x (0.33 #59, 0.25 #277, 0.14 #387) >> Best rule #1356 for best value: >> intensional similarity = 12 >> extensional distance = 28 >> proper extension: 01gjd0; 0d06vc; 0gfq9; 03c3jzx; 031x2; 01h6pn; 06k75; 07_nf; 022840; 01y998; ... >> query: (?x13022, 09c7w0) <- combatants(?x13022, ?x1353), combatants(?x13022, ?x205), film_release_region(?x5576, ?x1353), film_release_region(?x4950, ?x1353), film_release_region(?x634, ?x1353), ?x4950 = 07k2mq, ?x634 = 0gx9rvq, administrative_parent(?x2856, ?x205), nationality(?x101, ?x205), country(?x150, ?x205), participating_countries(?x418, ?x205), ?x5576 = 0gbfn9 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8, 14, 17, 33, 49, 179 EVAL 03gqgt3 combatants 03m6j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 50.000 39.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 03gqgt3 combatants 05b4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 50.000 39.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 03gqgt3 combatants 01znc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 50.000 39.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 03gqgt3 combatants 0k6nt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 50.000 39.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 03gqgt3 combatants 06npd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 50.000 39.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 03gqgt3 combatants 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 50.000 39.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 03gqgt3 combatants 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 39.000 0.667 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #11282-034bs PRED entity: 034bs PRED relation: influenced_by PRED expected values: 0c1fs => 195 concepts (83 used for prediction) PRED predicted values (max 10 best out of 359): 081k8 (0.53 #18424, 0.40 #7802, 0.33 #1004), 03f0324 (0.40 #7798, 0.17 #6522, 0.15 #7372), 02lt8 (0.38 #18389, 0.32 #6915, 0.27 #4794), 04xjp (0.33 #908, 0.33 #481, 0.25 #1758), 0448r (0.33 #1106, 0.33 #679, 0.25 #1956), 05gpy (0.33 #617, 0.22 #6566, 0.17 #1044), 06jkm (0.33 #811, 0.17 #1238, 0.12 #2088), 0bwx3 (0.33 #607, 0.17 #1034, 0.12 #1884), 034bs (0.33 #115, 0.15 #7764, 0.11 #2666), 080r3 (0.33 #164, 0.14 #1441, 0.11 #6537) >> Best rule #18424 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 03vrp; 07dnx; 02y49; >> query: (?x4055, 081k8) <- influenced_by(?x2485, ?x4055), influenced_by(?x4055, ?x5435), influenced_by(?x7512, ?x5435), ?x7512 = 01q9b9 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #7924 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 073bb; 01vdrw; 01v_0b; *> query: (?x4055, 0c1fs) <- influenced_by(?x4055, ?x11092), influenced_by(?x4055, ?x6457), award_winner(?x10270, ?x4055), ?x6457 = 03_87, gender(?x11092, ?x231) *> conf = 0.10 ranks of expected_values: 106 EVAL 034bs influenced_by 0c1fs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 195.000 83.000 0.533 http://example.org/influence/influence_node/influenced_by #11281-03kbb8 PRED entity: 03kbb8 PRED relation: award PRED expected values: 0c422z4 => 105 concepts (83 used for prediction) PRED predicted values (max 10 best out of 281): 09sb52 (0.71 #19805, 0.70 #16571, 0.69 #11720), 0f4x7 (0.50 #435, 0.14 #3669, 0.14 #8517), 04kxsb (0.50 #531, 0.11 #8613, 0.11 #8209), 02w9sd7 (0.38 #575, 0.09 #8657, 0.08 #3809), 0gq9h (0.35 #9373, 0.33 #8969, 0.28 #6140), 040njc (0.26 #9303, 0.26 #8899, 0.22 #6070), 0gqy2 (0.25 #1377, 0.25 #569, 0.21 #1782), 02x4w6g (0.25 #1327, 0.21 #1732, 0.12 #519), 02x73k6 (0.25 #1273, 0.21 #1678, 0.07 #2890), 099ck7 (0.25 #671, 0.05 #8753, 0.05 #8349) >> Best rule #19805 for best value: >> intensional similarity = 3 >> extensional distance = 1196 >> proper extension: 04glx0; 0c9l1; >> query: (?x7093, ?x704) <- award_winner(?x7093, ?x624), award_nominee(?x1244, ?x7093), award_winner(?x704, ?x7093) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3378 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 80 *> proper extension: 0f5zj6; 017c87; 0bkq_8; *> query: (?x7093, 0c422z4) <- award_winner(?x385, ?x7093), student(?x8398, ?x7093) *> conf = 0.06 ranks of expected_values: 109 EVAL 03kbb8 award 0c422z4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 105.000 83.000 0.710 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11280-0gydcp7 PRED entity: 0gydcp7 PRED relation: film_crew_role PRED expected values: 09zzb8 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 34): 09zzb8 (0.77 #1410, 0.71 #1988, 0.71 #1663), 0dxtw (0.44 #838, 0.41 #1419, 0.36 #1997), 01vx2h (0.36 #839, 0.34 #1420, 0.33 #191), 01pvkk (0.31 #624, 0.28 #480, 0.28 #2216), 0215hd (0.23 #595, 0.22 #127, 0.21 #523), 02rh1dz (0.20 #189, 0.18 #837, 0.17 #693), 089g0h (0.19 #128, 0.17 #596, 0.17 #524), 02ynfr (0.18 #1425, 0.17 #124, 0.17 #844), 0d2b38 (0.15 #350, 0.14 #386, 0.14 #602), 01xy5l_ (0.15 #518, 0.14 #590, 0.14 #230) >> Best rule #1410 for best value: >> intensional similarity = 4 >> extensional distance = 474 >> proper extension: 072r5v; >> query: (?x2093, 09zzb8) <- production_companies(?x2093, ?x9518), film_crew_role(?x2093, ?x1171), country(?x2093, ?x94), ?x1171 = 09vw2b7 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gydcp7 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.773 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11279-021npv PRED entity: 021npv PRED relation: award PRED expected values: 04ljl_l => 95 concepts (71 used for prediction) PRED predicted values (max 10 best out of 239): 09sb52 (0.38 #443, 0.33 #1249, 0.33 #846), 0ck27z (0.29 #4928, 0.22 #6540, 0.21 #5331), 057xs89 (0.25 #563, 0.22 #1369, 0.22 #966), 02x4w6g (0.25 #517, 0.22 #920, 0.13 #23783), 04ljl_l (0.25 #406, 0.22 #809, 0.13 #23783), 0f4x7 (0.25 #434, 0.22 #837, 0.12 #1643), 04kxsb (0.25 #528, 0.22 #931, 0.11 #1334), 09qv_s (0.25 #554, 0.22 #957, 0.11 #1360), 099ck7 (0.25 #669, 0.22 #1072, 0.03 #7117), 019f4v (0.25 #66, 0.13 #23783, 0.12 #25800) >> Best rule #443 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 05lb87; 0jfx1; 03pmzt; 02y_2y; 01f7dd; 01pllx; >> query: (?x12123, 09sb52) <- film(?x12123, ?x4734), ?x4734 = 0sxmx, award(?x12123, ?x693) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 05lb87; 0jfx1; 03pmzt; 02y_2y; 01f7dd; 01pllx; *> query: (?x12123, 04ljl_l) <- film(?x12123, ?x4734), ?x4734 = 0sxmx, award(?x12123, ?x693) *> conf = 0.25 ranks of expected_values: 5 EVAL 021npv award 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 95.000 71.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11278-02zjd PRED entity: 02zjd PRED relation: influenced_by! PRED expected values: 0399p => 121 concepts (49 used for prediction) PRED predicted values (max 10 best out of 469): 0lrh (0.48 #1624, 0.17 #2134, 0.13 #3148), 013pp3 (0.33 #724, 0.33 #217, 0.30 #1739), 040db (0.33 #74, 0.30 #1596, 0.26 #2106), 02kz_ (0.33 #218, 0.27 #1522, 0.26 #1740), 01v_0b (0.33 #477, 0.26 #1999, 0.22 #984), 073v6 (0.33 #114, 0.26 #1636, 0.11 #16381), 084w8 (0.33 #1, 0.22 #2033, 0.22 #1523), 0d4jl (0.33 #113, 0.17 #1635, 0.14 #7615), 08433 (0.33 #27, 0.13 #2030, 0.13 #1549), 04x56 (0.33 #405, 0.11 #912, 0.09 #1927) >> Best rule #1624 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 015k7; >> query: (?x6163, 0lrh) <- influenced_by(?x117, ?x6163), participant(?x2444, ?x117), influenced_by(?x117, ?x1029), ?x1029 = 08433 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #830 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 084w8; 08433; 0lrh; 014635; 02kz_; 0h25; 018zvb; *> query: (?x6163, 0399p) <- influenced_by(?x117, ?x6163), ?x117 = 03qcq, nationality(?x6163, ?x94), location(?x6163, ?x1274) *> conf = 0.11 ranks of expected_values: 72 EVAL 02zjd influenced_by! 0399p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 121.000 49.000 0.478 http://example.org/influence/influence_node/influenced_by #11277-01sn3 PRED entity: 01sn3 PRED relation: place_of_birth! PRED expected values: 03cs_xw => 152 concepts (118 used for prediction) PRED predicted values (max 10 best out of 2194): 01sb5r (0.37 #207511, 0.33 #150450, 0.29 #204916), 0d6d2 (0.35 #101168, 0.35 #285326, 0.34 #241234), 03f0r5w (0.35 #285326, 0.34 #241234, 0.33 #114138), 01pcbg (0.35 #285326, 0.34 #241234, 0.33 #114138), 02yplc (0.35 #285326, 0.34 #241234, 0.33 #114138), 06g2d1 (0.35 #285326, 0.34 #241234, 0.33 #114138), 01k5zk (0.35 #285326, 0.34 #241234, 0.33 #114138), 01jz6d (0.35 #285326, 0.34 #241234, 0.33 #114138), 0jg77 (0.29 #204916, 0.28 #202322, 0.27 #191947), 01w7nwm (0.07 #3198, 0.06 #5793, 0.04 #8387) >> Best rule #207511 for best value: >> intensional similarity = 3 >> extensional distance = 187 >> proper extension: 0s3y5; 0162v; 0xmlp; 0pzmf; 01n4nd; 0kygv; 0r3tb; 0_z91; 02qjb7z; 04523f; ... >> query: (?x4090, ?x9262) <- origin(?x9262, ?x4090), contains(?x177, ?x4090), profession(?x9262, ?x131) >> conf = 0.37 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01sn3 place_of_birth! 03cs_xw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 152.000 118.000 0.367 http://example.org/people/person/place_of_birth #11276-0b_dh PRED entity: 0b_dh PRED relation: gender PRED expected values: 05zppz => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #29, 0.89 #35, 0.88 #49), 02zsn (0.44 #28, 0.43 #32, 0.42 #54) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 119 >> proper extension: 0gry51; >> query: (?x11239, 05zppz) <- profession(?x11239, ?x524), ?x524 = 02jknp, people(?x13131, ?x11239) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_dh gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.901 http://example.org/people/person/gender #11275-01cwhp PRED entity: 01cwhp PRED relation: artists! PRED expected values: 02lnbg => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 185): 06j6l (0.39 #355, 0.35 #662, 0.30 #10180), 02lnbg (0.30 #672, 0.28 #365, 0.18 #8347), 016clz (0.28 #8294, 0.19 #20884, 0.18 #10137), 0glt670 (0.28 #348, 0.27 #10173, 0.25 #12015), 0xhtw (0.23 #8306, 0.16 #20896, 0.13 #14140), 0gywn (0.22 #364, 0.22 #12031, 0.20 #7118), 036jv (0.22 #496, 0.10 #803, 0.08 #2338), 01lyv (0.22 #9551, 0.21 #12008, 0.20 #11394), 03_d0 (0.21 #1240, 0.19 #9529, 0.17 #7073), 0155w (0.19 #1333, 0.14 #9622, 0.13 #14228) >> Best rule #355 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01mr2g6; >> query: (?x2461, 06j6l) <- company(?x2461, ?x2299), type_of_union(?x2461, ?x566), artist(?x3265, ?x2461) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #672 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 03f0qd7; *> query: (?x2461, 02lnbg) <- company(?x2461, ?x2299), category(?x2461, ?x134), languages(?x2461, ?x254) *> conf = 0.30 ranks of expected_values: 2 EVAL 01cwhp artists! 02lnbg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.389 http://example.org/music/genre/artists #11274-04zkj5 PRED entity: 04zkj5 PRED relation: profession PRED expected values: 02hrh1q 0np9r => 108 concepts (107 used for prediction) PRED predicted values (max 10 best out of 81): 02hrh1q (0.89 #11700, 0.89 #8487, 0.88 #6297), 01d_h8 (0.55 #2638, 0.52 #3076, 0.52 #2930), 0cbd2 (0.46 #5121, 0.46 #5413, 0.46 #5998), 09jwl (0.37 #8636, 0.36 #5716, 0.36 #5277), 0np9r (0.35 #896, 0.26 #13001, 0.25 #13148), 02jknp (0.32 #2640, 0.31 #3078, 0.29 #2932), 0kyk (0.32 #5141, 0.31 #6018, 0.31 #5433), 015cjr (0.30 #193, 0.30 #5553, 0.17 #2532), 0d8qb (0.30 #5553, 0.12 #1246, 0.07 #954), 0747nrk (0.30 #5553, 0.04 #2834, 0.04 #3418) >> Best rule #11700 for best value: >> intensional similarity = 2 >> extensional distance = 2012 >> proper extension: 04yywz; 06688p; 01l1b90; 05bp8g; 05m63c; 02g8h; 0d_84; 01yznp; 01rrwf6; 02nb2s; ... >> query: (?x7663, 02hrh1q) <- film(?x7663, ?x559), profession(?x7663, ?x987) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 04zkj5 profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 107.000 0.892 http://example.org/people/person/profession EVAL 04zkj5 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 107.000 0.892 http://example.org/people/person/profession #11273-07v64s PRED entity: 07v64s PRED relation: artists PRED expected values: 024dgj => 64 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1199): 03f0fnk (0.75 #10069, 0.71 #6853, 0.60 #4707), 07yg2 (0.62 #10020, 0.60 #4658, 0.57 #6804), 01kd57 (0.60 #4793, 0.50 #3720, 0.43 #8011), 0p76z (0.60 #5201, 0.43 #7347, 0.38 #10563), 016ntp (0.60 #4554, 0.43 #6700, 0.38 #9916), 01qmy04 (0.60 #5313, 0.43 #7459, 0.38 #10675), 016vn3 (0.57 #8444, 0.50 #4153, 0.43 #9516), 0187x8 (0.57 #8211, 0.50 #3920, 0.33 #11428), 0fb2l (0.57 #8413, 0.50 #4122, 0.33 #3050), 01w5n51 (0.57 #8197, 0.44 #11414, 0.33 #688) >> Best rule #10069 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0m0fw; 02qm5j; 052smk; >> query: (?x3734, 03f0fnk) <- artists(?x3734, ?x10144), artists(?x3734, ?x6208), ?x10144 = 016wvy, parent_genre(?x9342, ?x3734), award(?x6208, ?x1479), parent_genre(?x3734, ?x1572), instrumentalists(?x74, ?x6208) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #308 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 0cx7f; *> query: (?x3734, 024dgj) <- artists(?x3734, ?x10144), artists(?x3734, ?x6208), artists(?x3734, ?x5057), artists(?x3734, ?x1749), ?x10144 = 016wvy, parent_genre(?x9342, ?x3734), ?x6208 = 07r4c, ?x5057 = 01w3lzq, artists(?x9342, ?x997), ?x1749 = 01fl3 *> conf = 0.33 ranks of expected_values: 125 EVAL 07v64s artists 024dgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 64.000 28.000 0.750 http://example.org/music/genre/artists #11272-01sbhvd PRED entity: 01sbhvd PRED relation: people! PRED expected values: 041rx => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 53): 041rx (0.38 #81, 0.35 #158, 0.22 #466), 0x67 (0.17 #2551, 0.17 #3398, 0.16 #2936), 033tf_ (0.16 #238, 0.15 #7, 0.14 #700), 0xnvg (0.14 #244, 0.08 #783, 0.08 #629), 01qhm_ (0.13 #160, 0.10 #83, 0.08 #6), 07hwkr (0.10 #89, 0.09 #166, 0.07 #1321), 02w7gg (0.09 #541, 0.08 #1234, 0.07 #310), 09vc4s (0.08 #9, 0.07 #1164, 0.05 #240), 048z7l (0.08 #40, 0.06 #1580, 0.06 #1349), 0dbxy (0.08 #47, 0.05 #278, 0.04 #817) >> Best rule #81 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 01vv6_6; 0m76b; >> query: (?x11200, 041rx) <- profession(?x11200, ?x524), student(?x9318, ?x11200), nationality(?x11200, ?x94), ?x9318 = 0fr9jp >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sbhvd people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.381 http://example.org/people/ethnicity/people #11271-0bk25 PRED entity: 0bk25 PRED relation: combatants! PRED expected values: 0727h => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 59): 0cm2xh (0.40 #78, 0.25 #12, 0.18 #144), 0gfq9 (0.30 #73, 0.25 #7, 0.14 #271), 048n7 (0.30 #90, 0.21 #156, 0.20 #288), 01gjd0 (0.30 #69, 0.18 #135, 0.17 #201), 03gqgt3 (0.30 #123, 0.18 #321, 0.17 #255), 0gjw_ (0.30 #100, 0.16 #166, 0.15 #232), 018w0j (0.30 #103, 0.12 #37, 0.12 #235), 081pw (0.27 #199, 0.25 #265, 0.25 #1), 06k75 (0.20 #82, 0.18 #148, 0.17 #214), 0d06vc (0.20 #71, 0.15 #203, 0.13 #137) >> Best rule #78 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 07ssc; 0f8l9c; 0345h; >> query: (?x14703, 0cm2xh) <- nationality(?x12167, ?x14703), type_of_union(?x12167, ?x566), films(?x12167, ?x4127), ?x566 = 04ztj, film_release_region(?x4127, ?x94) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bk25 combatants! 0727h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 13.000 13.000 0.400 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #11270-015bpl PRED entity: 015bpl PRED relation: featured_film_locations PRED expected values: 0g_wn2 => 54 concepts (32 used for prediction) PRED predicted values (max 10 best out of 31): 02_286 (0.43 #20, 0.17 #2183, 0.16 #1701), 0cv3w (0.25 #310, 0.22 #550, 0.02 #1751), 04jpl (0.14 #9, 0.07 #1209, 0.07 #1449), 0156q (0.14 #41, 0.01 #1481), 04wgh (0.14 #34), 0rh6k (0.12 #241, 0.11 #481, 0.06 #1441), 03rjj (0.11 #486, 0.01 #1446, 0.01 #966), 030qb3t (0.08 #1479, 0.07 #759, 0.07 #999), 080h2 (0.03 #2669, 0.02 #4833, 0.02 #1464), 04lh6 (0.03 #4088, 0.02 #5772) >> Best rule #20 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0992d9; >> query: (?x7989, 02_286) <- film(?x8741, ?x7989), film(?x5545, ?x7989), ?x8741 = 01p85y, award_nominee(?x5545, ?x3101), nominated_for(?x5545, ?x1597) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015bpl featured_film_locations 0g_wn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 32.000 0.429 http://example.org/film/film/featured_film_locations #11269-03rk0 PRED entity: 03rk0 PRED relation: contains! PRED expected values: 02qkt => 238 concepts (170 used for prediction) PRED predicted values (max 10 best out of 274): 02qkt (0.64 #50538, 0.64 #26340, 0.62 #44265), 04_1l0v (0.63 #56015, 0.61 #54223, 0.48 #79319), 09c7w0 (0.60 #53776, 0.58 #55568, 0.58 #134443), 03rk0 (0.50 #5513, 0.33 #3722, 0.33 #2827), 02j71 (0.43 #14337, 0.24 #109336, 0.03 #133543), 02j9z (0.35 #17951, 0.33 #35885, 0.31 #44842), 086g2 (0.33 #3426, 0.24 #149679, 0.08 #142509), 0dg3n1 (0.31 #106803, 0.31 #119349, 0.31 #87983), 07c5l (0.28 #7562, 0.24 #25492, 0.23 #12044), 01hpnh (0.25 #5977, 0.24 #149679, 0.08 #142509) >> Best rule #50538 for best value: >> intensional similarity = 2 >> extensional distance = 57 >> proper extension: 07w5rq; 01q460; 01rr31; 09glw; 01wv24; 01nmgc; 02hwww; 036hnm; 01bdhf; 0fsmy; >> query: (?x2146, 02qkt) <- contains(?x6956, ?x2146), ?x6956 = 0j0k >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rk0 contains! 02qkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 238.000 170.000 0.644 http://example.org/location/location/contains #11268-0kvnn PRED entity: 0kvnn PRED relation: artists! PRED expected values: 02rp117 => 220 concepts (144 used for prediction) PRED predicted values (max 10 best out of 275): 017_qw (0.75 #11886, 0.62 #12822, 0.55 #10642), 01cbwl (0.62 #1912, 0.56 #2223, 0.07 #10932), 016clz (0.59 #28026, 0.50 #316, 0.33 #2184), 06by7 (0.57 #8111, 0.55 #17764, 0.55 #28044), 064t9 (0.54 #16821, 0.52 #17132, 0.50 #23675), 05bt6j (0.54 #28066, 0.30 #8133, 0.29 #18098), 05r6t (0.50 #1953, 0.44 #2264, 0.33 #85), 0cx7f (0.50 #765, 0.33 #141, 0.25 #452), 06j6l (0.40 #985, 0.28 #16857, 0.28 #23711), 025sc50 (0.40 #987, 0.28 #16859, 0.26 #17170) >> Best rule #11886 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 0c_drn; >> query: (?x4387, 017_qw) <- music(?x5927, ?x4387), nominated_for(?x166, ?x5927), nominated_for(?x2107, ?x5927), nominated_for(?x298, ?x5927) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #828 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 02vnpv; *> query: (?x4387, 02rp117) <- artist(?x14211, ?x4387), ?x14211 = 0dd2f, category(?x4387, ?x134) *> conf = 0.25 ranks of expected_values: 30 EVAL 0kvnn artists! 02rp117 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 220.000 144.000 0.754 http://example.org/music/genre/artists #11267-01bzw5 PRED entity: 01bzw5 PRED relation: institution! PRED expected values: 014mlp => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 25): 02h4rq6 (0.87 #99, 0.83 #462, 0.82 #414), 014mlp (0.76 #393, 0.75 #417, 0.72 #514), 02_xgp2 (0.57 #207, 0.54 #134, 0.51 #400), 0bkj86 (0.57 #203, 0.52 #130, 0.51 #105), 03bwzr4 (0.54 #111, 0.53 #185, 0.51 #402), 016t_3 (0.54 #100, 0.48 #391, 0.46 #198), 07s6fsf (0.51 #97, 0.45 #558, 0.43 #388), 04zx3q1 (0.38 #196, 0.34 #123, 0.31 #98), 013zdg (0.26 #395, 0.23 #419, 0.22 #251), 027f2w (0.25 #397, 0.25 #131, 0.23 #204) >> Best rule #99 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 0l2tk; >> query: (?x1276, 02h4rq6) <- major_field_of_study(?x1276, ?x6756), major_field_of_study(?x1276, ?x373), ?x6756 = 0_jm, student(?x1276, ?x3522), disciplines_or_subjects(?x277, ?x373) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #393 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 113 *> proper extension: 02bqy; *> query: (?x1276, 014mlp) <- category(?x1276, ?x134), school(?x2067, ?x1276), student(?x1276, ?x7761), nominated_for(?x7761, ?x1496) *> conf = 0.76 ranks of expected_values: 2 EVAL 01bzw5 institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 153.000 153.000 0.872 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #11266-0gz5hs PRED entity: 0gz5hs PRED relation: actor! PRED expected values: 019nnl => 120 concepts (107 used for prediction) PRED predicted values (max 10 best out of 137): 06y_n (0.37 #13900, 0.21 #13899, 0.09 #15998), 026bfsh (0.12 #2456, 0.06 #3504, 0.06 #4028), 019nnl (0.08 #281, 0.06 #543, 0.02 #1330), 0330r (0.08 #449, 0.06 #711, 0.02 #1498), 07hpv3 (0.04 #16, 0.01 #1590), 0g60z (0.04 #790, 0.02 #11281, 0.02 #12854), 039cq4 (0.04 #3798, 0.04 #6157, 0.03 #3274), 0d68qy (0.04 #298, 0.04 #3706, 0.03 #560), 07c72 (0.04 #309, 0.03 #571, 0.02 #2407), 0pc_l (0.04 #500, 0.03 #762, 0.02 #1024) >> Best rule #13900 for best value: >> intensional similarity = 3 >> extensional distance = 745 >> proper extension: 09d5h; 03jvmp; 0g5lhl7; 05xbx; >> query: (?x1986, ?x9787) <- award_nominee(?x1986, ?x1875), nominated_for(?x1986, ?x9787), actor(?x9787, ?x2390) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #281 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 03mdt; 047cqr; *> query: (?x1986, 019nnl) <- award(?x1986, ?x11272), category(?x1986, ?x134), program(?x1986, ?x1876) *> conf = 0.08 ranks of expected_values: 3 EVAL 0gz5hs actor! 019nnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 107.000 0.367 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11265-017ht PRED entity: 017ht PRED relation: category PRED expected values: 08mbj5d => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #35) >> Best rule #35 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14640, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017ht category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #11264-05pbl56 PRED entity: 05pbl56 PRED relation: language PRED expected values: 02hwhyv => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 42): 04306rv (0.28 #175, 0.15 #118, 0.13 #403), 064_8sq (0.26 #192, 0.21 #306, 0.17 #363), 02bjrlw (0.20 #58, 0.12 #115, 0.10 #973), 0jzc (0.17 #190, 0.06 #133, 0.06 #304), 06nm1 (0.17 #295, 0.15 #181, 0.13 #352), 03_9r (0.11 #237, 0.10 #9, 0.06 #809), 012w70 (0.07 #69, 0.07 #183, 0.04 #240), 04h9h (0.07 #269, 0.04 #440, 0.04 #670), 0653m (0.05 #811, 0.04 #1156, 0.04 #1445), 05zjd (0.04 #423, 0.04 #252, 0.02 #996) >> Best rule #175 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 025twgt; >> query: (?x1595, 04306rv) <- genre(?x1595, ?x5104), film(?x100, ?x1595), ?x5104 = 0bkbm >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 028kj0; *> query: (?x1595, 02hwhyv) <- film_crew_role(?x1595, ?x4177), film_crew_role(?x1595, ?x1171), film_crew_role(?x2160, ?x1171), ?x2160 = 014kq6, ?x4177 = 0263ycg *> conf = 0.03 ranks of expected_values: 16 EVAL 05pbl56 language 02hwhyv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 109.000 109.000 0.283 http://example.org/film/film/language #11263-0ftps PRED entity: 0ftps PRED relation: instrumentalists! PRED expected values: 05148p4 => 129 concepts (96 used for prediction) PRED predicted values (max 10 best out of 123): 05r5c (0.69 #958, 0.69 #875, 0.52 #356), 0342h (0.69 #3673, 0.66 #4626, 0.66 #2882), 05148p4 (0.59 #716, 0.57 #888, 0.55 #1133), 0gkd1 (0.50 #1398, 0.45 #955, 0.44 #1132), 013y1f (0.50 #1398, 0.45 #955, 0.44 #1132), 018vs (0.45 #880, 0.44 #2020, 0.44 #1670), 01qzyz (0.33 #15, 0.05 #957, 0.04 #1746), 01vdm0 (0.30 #4361, 0.30 #695, 0.29 #3051), 02hnl (0.27 #730, 0.27 #902, 0.27 #1345), 0cfdd (0.25 #163, 0.05 #7058, 0.05 #957) >> Best rule #958 for best value: >> intensional similarity = 6 >> extensional distance = 47 >> proper extension: 0bg539; 08n__5; >> query: (?x1407, ?x316) <- role(?x1407, ?x3716), role(?x1407, ?x316), profession(?x1407, ?x1183), ?x316 = 05r5c, role(?x75, ?x3716), performance_role(?x3716, ?x315) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #716 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 02rgz4; 0274ck; 09prnq; 020_4z; *> query: (?x1407, 05148p4) <- type_of_union(?x1407, ?x566), artists(?x1380, ?x1407), ?x1380 = 0dl5d *> conf = 0.59 ranks of expected_values: 3 EVAL 0ftps instrumentalists! 05148p4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 96.000 0.694 http://example.org/music/instrument/instrumentalists #11262-09gkdln PRED entity: 09gkdln PRED relation: ceremony! PRED expected values: 02x1dht 02x17s4 => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 341): 02x1dht (0.60 #1070, 0.45 #2098, 0.43 #1842), 02x17s4 (0.60 #1121, 0.44 #1028, 0.43 #1893), 0f4x7 (0.60 #5166, 0.52 #7233, 0.50 #7491), 0gqy2 (0.58 #5264, 0.56 #7331, 0.54 #7589), 0gq_d (0.58 #5300, 0.55 #7367, 0.52 #7625), 0k611 (0.58 #5215, 0.54 #7282, 0.52 #7540), 018wng (0.58 #5176, 0.52 #7243, 0.50 #7501), 0gqwc (0.56 #5202, 0.54 #7269, 0.52 #7527), 0gvx_ (0.56 #5279, 0.53 #7346, 0.51 #7604), 0gqyl (0.56 #5223, 0.53 #7290, 0.50 #7548) >> Best rule #1070 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 03gwpw2; 04n2r9h; 09bymc; >> query: (?x8964, 02x1dht) <- award_winner(?x8964, ?x8896), award_winner(?x8964, ?x617), ?x617 = 025jfl, award_winner(?x5060, ?x8896), award_nominee(?x8896, ?x806), honored_for(?x8964, ?x1535), film(?x8896, ?x1318), film(?x1709, ?x1535), film_release_region(?x1535, ?x87), award_winner(?x1535, ?x185), award_winner(?x678, ?x8896) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 09gkdln ceremony! 02x17s4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.600 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09gkdln ceremony! 02x1dht CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.600 http://example.org/award/award_category/winners./award/award_honor/ceremony #11261-05fyss PRED entity: 05fyss PRED relation: location PRED expected values: 05k7sb => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 152): 02s838 (0.43 #49811, 0.42 #37760, 0.42 #51418), 02_286 (0.18 #16910, 0.17 #33781, 0.15 #35387), 030qb3t (0.15 #33827, 0.15 #35433, 0.13 #5707), 0cr3d (0.09 #948, 0.08 #145, 0.05 #35495), 059rby (0.08 #819, 0.03 #35366, 0.03 #7246), 013n2h (0.08 #406, 0.02 #1209, 0.01 #2013), 01l69g (0.08 #798), 02sn34 (0.08 #315), 013jz2 (0.08 #86), 0cc56 (0.07 #1664, 0.05 #860, 0.04 #16930) >> Best rule #49811 for best value: >> intensional similarity = 3 >> extensional distance = 1738 >> proper extension: 07m69t; >> query: (?x6071, ?x10563) <- nationality(?x6071, ?x94), ?x94 = 09c7w0, place_of_birth(?x6071, ?x10563) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4125 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 147 *> proper extension: 08f3b1; 045bg; 0453t; 03pm9; 0lrh; 032l1; 040_9; 014635; 02lt8; 017yfz; ... *> query: (?x6071, 05k7sb) <- profession(?x6071, ?x2225), profession(?x6071, ?x353), ?x2225 = 0kyk, ?x353 = 0cbd2 *> conf = 0.03 ranks of expected_values: 18 EVAL 05fyss location 05k7sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 93.000 93.000 0.428 http://example.org/people/person/places_lived./people/place_lived/location #11260-0gtx63s PRED entity: 0gtx63s PRED relation: film_release_region PRED expected values: 03_3d 05b4w => 110 concepts (95 used for prediction) PRED predicted values (max 10 best out of 243): 06mkj (0.92 #2776, 0.92 #2474, 0.91 #3230), 03gj2 (0.91 #1533, 0.89 #1835, 0.87 #3197), 03_3d (0.88 #2725, 0.87 #2423, 0.81 #1817), 035qy (0.86 #1542, 0.86 #3055, 0.85 #3206), 01znc_ (0.83 #3063, 0.83 #1550, 0.83 #3214), 02vzc (0.83 #2771, 0.82 #2469, 0.82 #3074), 05b4w (0.82 #3087, 0.80 #3238, 0.80 #4147), 03spz (0.81 #1905, 0.80 #2511, 0.80 #1603), 05v8c (0.80 #2735, 0.79 #2433, 0.72 #3038), 03rj0 (0.78 #1872, 0.74 #1570, 0.66 #4143) >> Best rule #2776 for best value: >> intensional similarity = 10 >> extensional distance = 62 >> proper extension: 0ndsl1x; >> query: (?x8137, 06mkj) <- film_release_region(?x8137, ?x3749), film_release_region(?x8137, ?x985), film_release_region(?x8137, ?x172), ?x985 = 0k6nt, film_release_region(?x10208, ?x172), film_release_region(?x5142, ?x172), ?x5142 = 0bt3j9, ?x10208 = 09rfpk, ?x3749 = 03ryn, administrative_parent(?x172, ?x551) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #2725 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 62 *> proper extension: 0ndsl1x; *> query: (?x8137, 03_3d) <- film_release_region(?x8137, ?x3749), film_release_region(?x8137, ?x985), film_release_region(?x8137, ?x172), ?x985 = 0k6nt, film_release_region(?x10208, ?x172), film_release_region(?x5142, ?x172), ?x5142 = 0bt3j9, ?x10208 = 09rfpk, ?x3749 = 03ryn, administrative_parent(?x172, ?x551) *> conf = 0.88 ranks of expected_values: 3, 7 EVAL 0gtx63s film_release_region 05b4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 110.000 95.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gtx63s film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 95.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11259-0q9b0 PRED entity: 0q9b0 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #16, 0.81 #47, 0.81 #152), 02nxhr (0.21 #382, 0.05 #68, 0.04 #53), 07c52 (0.21 #382, 0.04 #3, 0.04 #269), 07z4p (0.21 #382, 0.03 #146, 0.03 #126) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 0gbtbm; 07vn_9; >> query: (?x7299, 029j_) <- film_release_region(?x7299, ?x94), ?x94 = 09c7w0, film_crew_role(?x7299, ?x3197), ?x3197 = 02ynfr >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q9b0 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.816 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #11258-01h7xx PRED entity: 01h7xx PRED relation: legislative_sessions PRED expected values: 043djx => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 50): 01gt99 (0.82 #1102, 0.80 #661, 0.80 #659), 01gstn (0.78 #850, 0.67 #793, 0.61 #328), 03ww_x (0.75 #1106, 0.71 #1049, 0.57 #668), 02bqmq (0.75 #1119, 0.71 #1062, 0.57 #681), 060ny2 (0.75 #1134, 0.71 #1077, 0.57 #696), 06r713 (0.75 #1133, 0.71 #1076, 0.47 #1673), 024tkd (0.71 #1081, 0.69 #1138, 0.57 #700), 077g7n (0.69 #1105, 0.64 #1099, 0.64 #1048), 02bqm0 (0.69 #1129, 0.64 #1072, 0.57 #691), 032ft5 (0.69 #1112, 0.64 #1055, 0.50 #1652) >> Best rule #1102 for best value: >> intensional similarity = 41 >> extensional distance = 12 >> proper extension: 02bn_p; 02bqn1; 02gkzs; 02cg7g; 02bqm0; >> query: (?x7944, ?x3669) <- district_represented(?x7944, ?x4776), district_represented(?x7944, ?x4061), district_represented(?x7944, ?x2713), district_represented(?x7944, ?x2623), district_represented(?x7944, ?x961), district_represented(?x7944, ?x448), legislative_sessions(?x2860, ?x7944), district_represented(?x9416, ?x2713), district_represented(?x7973, ?x2713), district_represented(?x6021, ?x2713), district_represented(?x5256, ?x2713), district_represented(?x5005, ?x2713), district_represented(?x3973, ?x2713), district_represented(?x1829, ?x2713), district_represented(?x605, ?x2713), ?x5256 = 01grqd, ?x9416 = 01gsry, ?x448 = 03v1s, state_province_region(?x2021, ?x2713), ?x5005 = 01gstn, ?x7973 = 01gsvb, ?x2860 = 0b3wk, ?x3973 = 01gssm, contains(?x2713, ?x8263), ?x961 = 03s0w, religion(?x2713, ?x109), ?x4061 = 0498y, ?x605 = 077g7n, ?x1829 = 02bp37, contains(?x2623, ?x5844), legislative_sessions(?x3669, ?x7944), vacationer(?x2623, ?x5625), location_of_ceremony(?x566, ?x2623), jurisdiction_of_office(?x3959, ?x2623), ?x3959 = 0f6c3, school(?x260, ?x5844), ?x6021 = 01gsvp, contains(?x4776, ?x2034), origin(?x1953, ?x2623), legislative_sessions(?x5401, ?x7944), location(?x397, ?x4776) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #828 for first EXPECTED value: *> intensional similarity = 39 *> extensional distance = 7 *> proper extension: 01gst_; *> query: (?x7944, 043djx) <- district_represented(?x7944, ?x7518), district_represented(?x7944, ?x4622), district_represented(?x7944, ?x3818), district_represented(?x7944, ?x2713), district_represented(?x7944, ?x2020), district_represented(?x7944, ?x1906), legislative_sessions(?x2860, ?x7944), ?x2713 = 06btq, ?x3818 = 03v0t, ?x4622 = 04tgp, legislative_sessions(?x7973, ?x7944), legislative_sessions(?x2712, ?x7944), ?x7973 = 01gsvb, contains(?x1906, ?x13998), contains(?x1906, ?x13337), contains(?x1906, ?x13203), contains(?x1906, ?x11278), administrative_parent(?x8003, ?x1906), religion(?x1906, ?x109), location(?x5574, ?x1906), legislative_sessions(?x4437, ?x2712), district_represented(?x2712, ?x335), colors(?x11278, ?x1101), state_province_region(?x2228, ?x1906), registering_agency(?x2228, ?x1982), institution(?x865, ?x2228), student(?x2228, ?x6068), ?x4437 = 01gsrl, currency(?x2228, ?x170), ?x2020 = 05k7sb, ?x2860 = 0b3wk, second_level_divisions(?x94, ?x13203), place_of_birth(?x11317, ?x13998), major_field_of_study(?x11278, ?x1154), ?x7518 = 026mj, adjoins(?x1906, ?x279), source(?x13337, ?x958), adjoins(?x1905, ?x1906), ?x1982 = 03z19 *> conf = 0.67 ranks of expected_values: 12 EVAL 01h7xx legislative_sessions 043djx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 38.000 38.000 0.824 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #11257-02js9p PRED entity: 02js9p PRED relation: film PRED expected values: 0c57yj => 83 concepts (37 used for prediction) PRED predicted values (max 10 best out of 408): 0y_9q (0.46 #12520, 0.40 #1789, 0.33 #44719), 0180mw (0.46 #12520, 0.40 #1789, 0.33 #44719), 09m6kg (0.24 #7185, 0.03 #60817), 01_1hw (0.22 #5050, 0.12 #8626, 0.11 #3262), 0cbv4g (0.22 #2706, 0.11 #4494, 0.06 #8070), 0gjc4d3 (0.20 #534, 0.06 #7688, 0.01 #9476), 09sr0 (0.20 #1518, 0.01 #12248, 0.01 #26558), 08mg_b (0.20 #1121, 0.01 #10063), 02z9rr (0.20 #1366), 035gnh (0.20 #1291) >> Best rule #12520 for best value: >> intensional similarity = 3 >> extensional distance = 332 >> proper extension: 03wpmd; 0c01c; 06_bq1; 01kgxf; 015np0; 024jwt; 02js_6; >> query: (?x7014, ?x1868) <- award_winner(?x7014, ?x1384), actor(?x10284, ?x7014), nominated_for(?x7014, ?x1868) >> conf = 0.46 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02js9p film 0c57yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 37.000 0.463 http://example.org/film/actor/film./film/performance/film #11256-09y6pb PRED entity: 09y6pb PRED relation: nominated_for PRED expected values: 014kq6 => 77 concepts (25 used for prediction) PRED predicted values (max 10 best out of 108): 014kq6 (0.53 #1007, 0.04 #819, 0.01 #567), 09y6pb (0.53 #1007), 01s9vc (0.05 #995, 0.03 #492, 0.02 #743), 01kf3_9 (0.05 #809, 0.01 #557), 0fsw_7 (0.05 #908, 0.01 #656), 01kf4tt (0.05 #830, 0.01 #578), 02sg5v (0.05 #775, 0.01 #523), 05css_ (0.04 #915, 0.03 #663, 0.03 #412), 05cj_j (0.04 #801, 0.03 #549, 0.03 #298), 02r_pp (0.04 #900, 0.02 #648, 0.02 #397) >> Best rule #1007 for best value: >> intensional similarity = 3 >> extensional distance = 231 >> proper extension: 02fn5r; >> query: (?x9379, ?x2160) <- nominated_for(?x6244, ?x9379), nominated_for(?x2160, ?x6244), nominated_for(?x688, ?x6244) >> conf = 0.53 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 09y6pb nominated_for 014kq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 25.000 0.533 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #11255-013t9y PRED entity: 013t9y PRED relation: award PRED expected values: 03hkv_r => 149 concepts (124 used for prediction) PRED predicted values (max 10 best out of 277): 0gqzz (0.72 #49392, 0.70 #17122, 0.70 #37436), 04dn09n (0.58 #2032, 0.31 #5616, 0.25 #2431), 03hkv_r (0.48 #2005, 0.26 #5589, 0.22 #2802), 02x1z2s (0.47 #1388, 0.15 #37835, 0.14 #25085), 019f4v (0.39 #1657, 0.34 #6038, 0.32 #4843), 02x17s4 (0.35 #2111, 0.16 #5695, 0.13 #7686), 0gs9p (0.35 #6049, 0.35 #1668, 0.34 #4854), 09sb52 (0.32 #9196, 0.30 #11585, 0.26 #22335), 02x1dht (0.30 #451, 0.19 #2043, 0.14 #5627), 02qyp19 (0.30 #399, 0.18 #5575, 0.14 #2788) >> Best rule #49392 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x6629, ?x2706) <- award_winner(?x2706, ?x6629), award(?x1052, ?x2706) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #2005 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 064177; 06kbb6; *> query: (?x6629, 03hkv_r) <- place_of_birth(?x6629, ?x11731), award(?x6629, ?x601), award_winner(?x1053, ?x6629), ?x601 = 0gr4k *> conf = 0.48 ranks of expected_values: 3 EVAL 013t9y award 03hkv_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 149.000 124.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11254-03h40_7 PRED entity: 03h40_7 PRED relation: profession PRED expected values: 01d_h8 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 77): 02hrh1q (0.88 #3317, 0.87 #3017, 0.84 #15), 01d_h8 (0.84 #3608, 0.84 #3458, 0.84 #3758), 0dxtg (0.60 #2716, 0.56 #1516, 0.51 #3766), 02jknp (0.50 #2710, 0.47 #3760, 0.47 #3460), 03gjzk (0.49 #166, 0.48 #1968, 0.48 #2118), 012t_z (0.33 #6303, 0.26 #13506, 0.25 #13657), 0cbd2 (0.21 #2259, 0.20 #2409, 0.18 #8561), 09jwl (0.19 #1071, 0.17 #3172, 0.17 #6623), 0kyk (0.17 #2433, 0.17 #2283, 0.11 #5133), 02krf9 (0.17 #1379, 0.17 #2130, 0.16 #2730) >> Best rule #3317 for best value: >> intensional similarity = 2 >> extensional distance = 355 >> proper extension: 03xmy1; >> query: (?x10715, 02hrh1q) <- languages(?x10715, ?x254), nominated_for(?x10715, ?x4786) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3608 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 377 *> proper extension: 01qscs; 0jf1b; 015pkc; 01wg982; 01vqrm; 0cm89v; 0bjkpt; 030vmc; 02drd3; 05bnx3j; *> query: (?x10715, 01d_h8) <- produced_by(?x7514, ?x10715), genre(?x7514, ?x225), film(?x147, ?x7514) *> conf = 0.84 ranks of expected_values: 2 EVAL 03h40_7 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.877 http://example.org/people/person/profession #11253-01w60_p PRED entity: 01w60_p PRED relation: artist! PRED expected values: 011k1h => 103 concepts (80 used for prediction) PRED predicted values (max 10 best out of 95): 015_1q (0.26 #686, 0.26 #1624, 0.23 #3100), 0g768 (0.15 #301, 0.14 #1507, 0.13 #3519), 033hn8 (0.14 #1485, 0.12 #279, 0.10 #2424), 03mp8k (0.14 #1535, 0.09 #1669, 0.08 #5559), 016ckq (0.14 #1513, 0.07 #709, 0.05 #3123), 011k1h (0.14 #678, 0.14 #8, 0.14 #142), 01clyr (0.14 #29, 0.14 #163, 0.12 #699), 0181dw (0.14 #38, 0.14 #1512, 0.13 #3524), 0k_kr (0.14 #40, 0.12 #710, 0.11 #442), 017l96 (0.14 #149, 0.12 #685, 0.10 #6455) >> Best rule #686 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0bkf4; 0cj2w; >> query: (?x2169, 015_1q) <- role(?x2169, ?x227), award(?x2169, ?x2420), inductee(?x1091, ?x2169) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #678 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 0bkf4; 0cj2w; *> query: (?x2169, 011k1h) <- role(?x2169, ?x227), award(?x2169, ?x2420), inductee(?x1091, ?x2169) *> conf = 0.14 ranks of expected_values: 6 EVAL 01w60_p artist! 011k1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 103.000 80.000 0.262 http://example.org/music/record_label/artist #11252-05qw5 PRED entity: 05qw5 PRED relation: group PRED expected values: 017lb_ => 125 concepts (31 used for prediction) PRED predicted values (max 10 best out of 31): 07yg2 (0.20 #133, 0.12 #241, 0.06 #350), 01qqwp9 (0.10 #129, 0.08 #564, 0.06 #237), 0123r4 (0.10 #152, 0.06 #369, 0.06 #260), 02r1tx7 (0.10 #124, 0.06 #232, 0.02 #1754), 06mj4 (0.10 #173, 0.06 #281, 0.01 #2239), 01v0sxx (0.10 #193, 0.06 #301, 0.01 #1823), 02ndj5 (0.10 #191, 0.06 #299), 06gcn (0.06 #384, 0.02 #1034, 0.01 #1251), 0b1zz (0.06 #367, 0.02 #1017, 0.01 #1234), 01v0sx2 (0.06 #980, 0.05 #1526, 0.04 #1197) >> Best rule #133 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 01vsy7t; 03f0fnk; 04bgy; 0191h5; 095x_; 01vsqvs; 016wvy; >> query: (?x2120, 07yg2) <- artists(?x9853, ?x2120), artists(?x5934, ?x2120), ?x9853 = 02qm5j, profession(?x2120, ?x220), parent_genre(?x2407, ?x5934) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05qw5 group 017lb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 31.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group #11251-016khd PRED entity: 016khd PRED relation: award_nominee PRED expected values: 03k7bd => 114 concepts (65 used for prediction) PRED predicted values (max 10 best out of 859): 01nms7 (0.81 #119328, 0.81 #51474, 0.81 #98270), 0f4vbz (0.76 #147404, 0.76 #53814, 0.76 #152086), 01swck (0.76 #147404, 0.76 #53814, 0.76 #152086), 01pj5q (0.76 #147404, 0.76 #53814, 0.76 #152086), 015rkw (0.76 #147404, 0.76 #53814, 0.76 #152086), 02t__3 (0.76 #147404, 0.76 #53814, 0.76 #152086), 0h10vt (0.76 #147404, 0.76 #149745, 0.75 #63174), 01wz01 (0.76 #147404, 0.76 #149745, 0.75 #63174), 0169dl (0.76 #147404, 0.76 #149745, 0.75 #63174), 06cgy (0.76 #147404, 0.76 #149745, 0.75 #63174) >> Best rule #119328 for best value: >> intensional similarity = 2 >> extensional distance = 1236 >> proper extension: 06qgvf; 01vvydl; 07s3vqk; 0cnl80; 01vrx3g; 023tp8; 0m2wm; 02zq43; 02lfcm; 07lmxq; ... >> query: (?x851, ?x91) <- award_nominee(?x91, ?x851), film(?x851, ?x1038) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #40172 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 651 *> proper extension: 01vq3nl; *> query: (?x851, 03k7bd) <- actor(?x9188, ?x851), nominated_for(?x851, ?x278) *> conf = 0.01 ranks of expected_values: 849 EVAL 016khd award_nominee 03k7bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 114.000 65.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11250-09gffmz PRED entity: 09gffmz PRED relation: student! PRED expected values: 05qgd9 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 77): 01t0dy (0.33 #217, 0.01 #2327, 0.01 #2854), 0bwfn (0.09 #4493, 0.09 #3439, 0.08 #3966), 065y4w7 (0.07 #541, 0.06 #1069, 0.05 #3178), 09f2j (0.07 #686, 0.03 #12283, 0.03 #7012), 03ksy (0.07 #4851, 0.07 #5378, 0.06 #5905), 04b_46 (0.05 #1282, 0.04 #2864, 0.04 #4445), 021w0_ (0.03 #851, 0.03 #1379, 0.02 #5069), 01q0kg (0.03 #661, 0.03 #1189), 06pwq (0.03 #539, 0.03 #1594, 0.02 #10028), 0fr9jp (0.03 #872, 0.02 #3509, 0.02 #12469) >> Best rule #217 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0q5hw; >> query: (?x1712, 01t0dy) <- award_winner(?x5643, ?x1712), ?x5643 = 0603qp, award(?x1712, ?x688) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09gffmz student! 05qgd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 81.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #11249-01bb1c PRED entity: 01bb1c PRED relation: disciplines_or_subjects PRED expected values: 02xlf => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 134): 02xlf (0.76 #408, 0.72 #443, 0.67 #232), 02vxn (0.43 #1057, 0.42 #1199, 0.40 #1021), 014dfn (0.33 #59, 0.33 #24, 0.25 #94), 0707q (0.22 #274, 0.20 #1056, 0.20 #1198), 0w7c (0.22 #1042, 0.20 #1078, 0.20 #1220), 01jfsb (0.20 #1056, 0.20 #1198, 0.20 #146), 0l67h (0.20 #1056, 0.20 #1198, 0.17 #1629), 08_lx0 (0.20 #1056, 0.20 #1198, 0.17 #1629), 0dwly (0.20 #1056, 0.20 #1198, 0.13 #832), 0j7v_ (0.20 #1056, 0.20 #1198, 0.11 #264) >> Best rule #408 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 04jhhng; >> query: (?x14213, 02xlf) <- disciplines_or_subjects(?x14213, ?x6060), award_winner(?x14213, ?x9982), disciplines_or_subjects(?x575, ?x6060), story_by(?x2037, ?x9982), ?x575 = 040vk98, place_of_birth(?x9982, ?x1860) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bb1c disciplines_or_subjects 02xlf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.765 http://example.org/award/award_category/disciplines_or_subjects #11248-01snm PRED entity: 01snm PRED relation: place! PRED expected values: 01snm => 131 concepts (114 used for prediction) PRED predicted values (max 10 best out of 149): 01snm (0.16 #29383, 0.09 #35574, 0.07 #46914), 05kkh (0.16 #29383, 0.04 #48464), 09c7w0 (0.16 #29383, 0.04 #48464), 02_286 (0.14 #14, 0.09 #35574, 0.07 #46914), 0rk71 (0.14 #282, 0.09 #35574, 0.07 #46914), 0qpqn (0.14 #245, 0.09 #35574, 0.07 #46914), 0pzmf (0.14 #164, 0.09 #35574, 0.07 #46914), 027l4q (0.09 #35574, 0.07 #46914), 06wxw (0.07 #46914, 0.02 #1646, 0.02 #2161), 0p9nv (0.07 #46914) >> Best rule #29383 for best value: >> intensional similarity = 2 >> extensional distance = 266 >> proper extension: 0fngy; >> query: (?x6555, ?x94) <- citytown(?x9620, ?x6555), contains(?x94, ?x9620) >> conf = 0.16 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 01snm place! 01snm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 114.000 0.156 http://example.org/location/hud_county_place/place #11247-0bdwqv PRED entity: 0bdwqv PRED relation: nominated_for PRED expected values: 04glx0 08y2fn 043mk4y 025ts_z => 57 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1596): 06nr2h (0.68 #41266, 0.14 #12688, 0.10 #10176), 0h1x5f (0.50 #1397, 0.45 #12497, 0.40 #7739), 05sy_5 (0.50 #943, 0.36 #12043, 0.30 #10455), 09tqkv2 (0.50 #293, 0.30 #9805, 0.27 #11393), 0bs5vty (0.50 #1440, 0.18 #12540, 0.14 #12688), 02d44q (0.50 #148, 0.18 #11248, 0.10 #9660), 02c638 (0.40 #6650, 0.36 #11408, 0.33 #8235), 03hkch7 (0.40 #6802, 0.36 #11560, 0.33 #8387), 0b6tzs (0.40 #6471, 0.36 #11229, 0.30 #9641), 047d21r (0.40 #6898, 0.36 #11656, 0.30 #10068) >> Best rule #41266 for best value: >> intensional similarity = 3 >> extensional distance = 214 >> proper extension: 0fm3b5; 02qrbbx; >> query: (?x3247, ?x4396) <- award(?x192, ?x3247), nominated_for(?x3247, ?x715), award(?x4396, ?x3247) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #12286 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 099jhq; 0cqh46; 08_vwq; *> query: (?x3247, 043mk4y) <- award(?x12425, ?x3247), award(?x2035, ?x3247), award(?x2033, ?x3247), ?x2033 = 01ycbq, award_nominee(?x496, ?x12425), nominated_for(?x2035, ?x89) *> conf = 0.18 ranks of expected_values: 435, 437, 601, 651 EVAL 0bdwqv nominated_for 025ts_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 29.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bdwqv nominated_for 043mk4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 29.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bdwqv nominated_for 08y2fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 29.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bdwqv nominated_for 04glx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 29.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11246-0r8c8 PRED entity: 0r8c8 PRED relation: place! PRED expected values: 0r8c8 => 102 concepts (58 used for prediction) PRED predicted values (max 10 best out of 161): 0r8bh (0.44 #9300, 0.43 #12917, 0.42 #15507), 0r89d (0.33 #300, 0.17 #1330, 0.06 #1845), 07bcn (0.20 #11368, 0.19 #22234, 0.18 #20679), 0rqf1 (0.17 #1352, 0.04 #2902), 0rnmy (0.17 #1086, 0.01 #4703, 0.01 #5221), 0rj0z (0.17 #1116), 0f2wj (0.14 #7229, 0.12 #14469, 0.02 #22233), 0yc7f (0.06 #1746, 0.06 #2263, 0.04 #2781), 0n6dc (0.06 #1887, 0.06 #2404, 0.04 #2922), 0yc84 (0.06 #1565, 0.06 #2082, 0.04 #2600) >> Best rule #9300 for best value: >> intensional similarity = 5 >> extensional distance = 123 >> proper extension: 0sb1r; 0psxp; 0s3pw; >> query: (?x6367, ?x11966) <- contains(?x11967, ?x6367), adjoins(?x11967, ?x2949), county_seat(?x11967, ?x11966), time_zones(?x11967, ?x2950), currency(?x11967, ?x170) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #8783 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 117 *> proper extension: 01rc6f; *> query: (?x6367, ?x10865) <- contains(?x11967, ?x6367), location_of_ceremony(?x566, ?x11967), contains(?x11967, ?x10865), time_zones(?x11967, ?x2950), source(?x11967, ?x958) *> conf = 0.02 ranks of expected_values: 92 EVAL 0r8c8 place! 0r8c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 102.000 58.000 0.441 http://example.org/location/hud_county_place/place #11245-07sp4l PRED entity: 07sp4l PRED relation: film! PRED expected values: 02j490 => 85 concepts (44 used for prediction) PRED predicted values (max 10 best out of 760): 0187y5 (0.74 #74923, 0.63 #4162, 0.52 #24972), 0522wp (0.63 #4162, 0.48 #74922, 0.43 #56190), 0d_84 (0.33 #44, 0.02 #29178, 0.01 #39583), 02lf1j (0.33 #431), 01vvb4m (0.20 #2604, 0.05 #6768, 0.03 #10930), 0c6qh (0.20 #2496, 0.03 #14983, 0.03 #23305), 0z4s (0.20 #2149, 0.03 #6313, 0.02 #47932), 028k57 (0.20 #2872, 0.03 #7036, 0.02 #13278), 02qx69 (0.20 #2636, 0.03 #6800, 0.02 #13042), 015pxr (0.20 #2430, 0.03 #6594, 0.02 #12836) >> Best rule #74923 for best value: >> intensional similarity = 4 >> extensional distance = 759 >> proper extension: 0gkz15s; >> query: (?x3063, ?x10834) <- nominated_for(?x10834, ?x3063), genre(?x3063, ?x53), film(?x3181, ?x3063), participant(?x10834, ?x6730) >> conf = 0.74 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07sp4l film! 02j490 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 44.000 0.736 http://example.org/film/actor/film./film/performance/film #11244-0d8lm PRED entity: 0d8lm PRED relation: performance_role! PRED expected values: 085jw => 76 concepts (64 used for prediction) PRED predicted values (max 10 best out of 123): 018vs (0.67 #159, 0.62 #1373, 0.56 #1380), 07_l6 (0.67 #159, 0.62 #1373, 0.56 #1380), 01xqw (0.67 #159, 0.62 #1373, 0.56 #1380), 03bx0bm (0.67 #3056, 0.56 #3297, 0.44 #1402), 0342h (0.67 #159, 0.50 #985, 0.50 #904), 02hnl (0.67 #159, 0.50 #1243, 0.50 #1008), 05148p4 (0.67 #159, 0.50 #1312, 0.50 #160), 042v_gx (0.67 #159, 0.50 #160, 0.40 #732), 07y_7 (0.67 #159, 0.50 #160, 0.40 #822), 018j2 (0.67 #159, 0.50 #160, 0.37 #564) >> Best rule #159 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 0l14md; >> query: (?x10811, ?x569) <- instrumentalists(?x10811, ?x562), family(?x9885, ?x10811), family(?x2888, ?x10811), performance_role(?x228, ?x10811), role(?x227, ?x9885), ?x228 = 0l14qv, role(?x2888, ?x2957), role(?x2888, ?x2923), role(?x2888, ?x314), instrumentalists(?x2888, ?x7937), ?x2923 = 02k856, role(?x2888, ?x569), ?x2957 = 01v8y9, ?x314 = 02sgy, profession(?x7937, ?x131), artists(?x1928, ?x7937) >> conf = 0.67 => this is the best rule for 19 predicted values *> Best rule #563 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 3 *> proper extension: 026t6; *> query: (?x10811, ?x1886) <- instrumentalists(?x10811, ?x562), family(?x9885, ?x10811), family(?x2888, ?x10811), performance_role(?x2944, ?x10811), performance_role(?x228, ?x10811), role(?x227, ?x9885), ?x228 = 0l14qv, role(?x2888, ?x2923), role(?x2888, ?x1225), instrumentalists(?x2888, ?x425), role(?x2944, ?x645), performance_role(?x1886, ?x2923), role(?x2944, ?x7449), ?x1225 = 01qbl, group(?x2944, ?x1751), instrumentalists(?x2944, ?x120), ?x7449 = 01vnt4 *> conf = 0.33 ranks of expected_values: 87 EVAL 0d8lm performance_role! 085jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 76.000 64.000 0.667 http://example.org/music/performance_role/guest_performances./music/recording_contribution/performance_role #11243-01mjq PRED entity: 01mjq PRED relation: administrative_parent PRED expected values: 02j71 => 153 concepts (78 used for prediction) PRED predicted values (max 10 best out of 35): 02j71 (0.85 #8804, 0.84 #10185, 0.83 #6877), 0345h (0.50 #303, 0.25 #163, 0.06 #9370), 01mjq (0.33 #35, 0.02 #8104, 0.01 #5801), 02qkt (0.26 #9482, 0.26 #9481, 0.20 #137), 02j9z (0.26 #9482, 0.26 #9481, 0.20 #137), 09c7w0 (0.24 #10312, 0.23 #4526, 0.20 #5490), 03rjj (0.08 #7969, 0.08 #7279, 0.06 #9899), 07ssc (0.08 #2889, 0.06 #4124, 0.06 #2342), 03_3d (0.05 #9349, 0.03 #5494), 0d05w3 (0.04 #878, 0.04 #742, 0.03 #1425) >> Best rule #8804 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 03pn9; >> query: (?x1558, 02j71) <- countries_spoken_in(?x403, ?x1558), adjoins(?x1558, ?x456), adjoins(?x1355, ?x1558), organization(?x1558, ?x127) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mjq administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 78.000 0.855 http://example.org/base/aareas/schema/administrative_area/administrative_parent #11242-0k2cb PRED entity: 0k2cb PRED relation: film! PRED expected values: 05typm 0479b 09xrxq => 81 concepts (30 used for prediction) PRED predicted values (max 10 best out of 860): 04r7jc (0.54 #8325, 0.42 #52042, 0.42 #56207), 0d5wn3 (0.54 #8325, 0.42 #52042, 0.42 #56207), 03nk3t (0.54 #8325, 0.42 #52042, 0.42 #56207), 03975z (0.42 #29144, 0.42 #29143, 0.41 #52043), 03cx282 (0.42 #29144, 0.42 #29143, 0.41 #52043), 016xh5 (0.20 #1084, 0.08 #5246, 0.03 #7327), 016xk5 (0.20 #1243, 0.08 #5405, 0.02 #7486), 02l4rh (0.20 #1235, 0.04 #5397, 0.02 #7478), 0djywgn (0.20 #1486, 0.04 #5648, 0.02 #7729), 06t61y (0.20 #313, 0.04 #4475, 0.02 #6556) >> Best rule #8325 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 07bxqz; >> query: (?x4751, ?x647) <- genre(?x4751, ?x258), nominated_for(?x2880, ?x4751), award_winner(?x4751, ?x647), ?x2880 = 02ppm4q >> conf = 0.54 => this is the best rule for 3 predicted values *> Best rule #40763 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 570 *> proper extension: 0gtsx8c; *> query: (?x4751, 0479b) <- film_release_distribution_medium(?x4751, ?x81), film_release_region(?x4751, ?x94), production_companies(?x4751, ?x382), country(?x4751, ?x512) *> conf = 0.01 ranks of expected_values: 846 EVAL 0k2cb film! 09xrxq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 30.000 0.545 http://example.org/film/actor/film./film/performance/film EVAL 0k2cb film! 0479b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 81.000 30.000 0.545 http://example.org/film/actor/film./film/performance/film EVAL 0k2cb film! 05typm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 30.000 0.545 http://example.org/film/actor/film./film/performance/film #11241-03pp73 PRED entity: 03pp73 PRED relation: award_winner! PRED expected values: 0bxs_d => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 118): 0g55tzk (0.12 #137, 0.05 #1265, 0.05 #1688), 013b2h (0.09 #1349, 0.07 #2336, 0.06 #5862), 05pd94v (0.09 #1271, 0.06 #2258, 0.05 #566), 02rjjll (0.08 #1274, 0.06 #2261, 0.05 #569), 0466p0j (0.08 #1345, 0.06 #2332, 0.05 #5858), 02cg41 (0.08 #1395, 0.06 #2382, 0.04 #4356), 056878 (0.08 #1301, 0.06 #2288, 0.05 #5814), 0gpjbt (0.07 #1298, 0.07 #593, 0.06 #2285), 09qvms (0.06 #1705, 0.06 #13, 0.05 #1987), 03gyp30 (0.06 #1809, 0.05 #1245, 0.05 #2091) >> Best rule #137 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0341n5; >> query: (?x5130, 0g55tzk) <- student(?x5638, ?x5130), award_winner(?x2192, ?x5130), ?x2192 = 0bfvd4, profession(?x5130, ?x1032) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #9310 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1086 *> proper extension: 04cy8rb; 0dky9n; 07fzq3; 09hd6f; *> query: (?x5130, ?x1265) <- place_of_birth(?x5130, ?x479), award_winner(?x2192, ?x5130), ceremony(?x2192, ?x1265) *> conf = 0.04 ranks of expected_values: 54 EVAL 03pp73 award_winner! 0bxs_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 122.000 122.000 0.118 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11240-04sry PRED entity: 04sry PRED relation: award_winner! PRED expected values: 0gvstc3 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 124): 02wzl1d (0.22 #144, 0.12 #676, 0.12 #543), 03gt46z (0.22 #191, 0.12 #723, 0.12 #590), 03gwpw2 (0.22 #142, 0.12 #674, 0.12 #541), 02yxh9 (0.22 #227, 0.12 #759, 0.12 #626), 09g90vz (0.13 #516, 0.10 #1181, 0.10 #383), 0418154 (0.13 #500, 0.07 #1032, 0.06 #1165), 0n8_m93 (0.12 #776, 0.12 #643, 0.11 #244), 02hn5v (0.12 #703, 0.12 #570, 0.09 #836), 09k5jh7 (0.11 #211, 0.10 #344, 0.07 #477), 02pgky2 (0.11 #216, 0.10 #349, 0.06 #748) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02bxjp; >> query: (?x7310, 02wzl1d) <- award_winner(?x372, ?x7310), ?x372 = 02wkmx, award_nominee(?x2444, ?x7310) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #431 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 13 *> proper extension: 01my95; *> query: (?x7310, 0gvstc3) <- participant(?x6278, ?x7310), list(?x7310, ?x5160) *> conf = 0.07 ranks of expected_values: 19 EVAL 04sry award_winner! 0gvstc3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 128.000 128.000 0.222 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11239-07f7jp PRED entity: 07f7jp PRED relation: location PRED expected values: 0c_m3 => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 98): 0c_m3 (0.56 #3223, 0.55 #1613, 0.51 #8862), 02_286 (0.17 #37, 0.14 #12117, 0.14 #12923), 030qb3t (0.13 #7334, 0.13 #4112, 0.13 #8139), 01cx_ (0.08 #163, 0.04 #4192, 0.03 #969), 0rh6k (0.07 #810, 0.06 #5643, 0.06 #2422), 0cr3d (0.07 #3369, 0.06 #7396, 0.06 #4978), 05k7sb (0.06 #109, 0.04 #4138, 0.02 #4942), 0cc56 (0.05 #1670, 0.04 #8919, 0.04 #2475), 09c7w0 (0.05 #1611, 0.04 #7250, 0.04 #4836), 0r0m6 (0.04 #4247, 0.02 #10689, 0.02 #9080) >> Best rule #3223 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 04b19t; >> query: (?x12140, ?x5381) <- profession(?x12140, ?x987), place_of_birth(?x12140, ?x5381), company(?x12140, ?x382) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07f7jp location 0c_m3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.562 http://example.org/people/person/places_lived./people/place_lived/location #11238-0pmhf PRED entity: 0pmhf PRED relation: award_nominee! PRED expected values: 0785v8 => 108 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1401): 05mc99 (0.81 #132502, 0.80 #104608, 0.80 #104609), 06qgvf (0.81 #132502, 0.80 #104608, 0.80 #104609), 046zh (0.25 #2326, 0.25 #1236, 0.15 #37193), 02p65p (0.17 #25, 0.14 #37194, 0.07 #2351), 02d4ct (0.17 #503, 0.14 #37194, 0.03 #14452), 01r93l (0.17 #995, 0.05 #19594, 0.03 #5646), 032_jg (0.17 #171, 0.02 #11794, 0.02 #14120), 06pjs (0.15 #16275, 0.15 #37193, 0.13 #16274), 0161sp (0.15 #37193, 0.13 #16274, 0.12 #62768), 0159h6 (0.14 #37194, 0.13 #2410, 0.03 #18683) >> Best rule #132502 for best value: >> intensional similarity = 2 >> extensional distance = 1288 >> proper extension: 01pbxb; 012d40; 0fvf9q; 0l6qt; 02bfmn; 02rchht; 083chw; 042l3v; 01p7yb; 025h4z; ... >> query: (?x2596, ?x100) <- location(?x2596, ?x1523), award_nominee(?x2596, ?x100) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #37194 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 428 *> proper extension: 03qcq; 0m2wm; 01j5x6; 08m4c8; 07ymr5; 049_zz; 01v3bn; 01pctb; 02qfhb; 03ds83; ... *> query: (?x2596, ?x101) <- participant(?x2596, ?x2908), award_nominee(?x7313, ?x2596), award_nominee(?x101, ?x7313) *> conf = 0.14 ranks of expected_values: 83 EVAL 0pmhf award_nominee! 0785v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 108.000 77.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11237-0k4kk PRED entity: 0k4kk PRED relation: film! PRED expected values: 05qd_ => 66 concepts (56 used for prediction) PRED predicted values (max 10 best out of 52): 086k8 (0.21 #380, 0.18 #605, 0.18 #455), 05qd_ (0.20 #85, 0.20 #9, 0.13 #160), 016tt2 (0.17 #4, 0.16 #80, 0.14 #382), 0g1rw (0.16 #8, 0.13 #159, 0.13 #84), 016tw3 (0.16 #162, 0.15 #314, 0.14 #237), 01795t (0.14 #546, 0.06 #2363, 0.04 #3052), 017jv5 (0.13 #166, 0.12 #318, 0.11 #241), 017s11 (0.12 #229, 0.12 #606, 0.12 #154), 03xq0f (0.11 #533, 0.08 #912, 0.08 #3114), 054g1r (0.11 #563, 0.06 #1169, 0.06 #2380) >> Best rule #380 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 026p_bs; 0d_wms; 042fgh; 025twgf; >> query: (?x1746, 086k8) <- genre(?x1746, ?x53), honored_for(?x1745, ?x1746), language(?x1746, ?x254) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 85 *> proper extension: 075cph; 048rn; 0g5pvv; 08cfr1; 0fxmbn; 0jqb8; 02gqm3; *> query: (?x1746, 05qd_) <- film_art_direction_by(?x1746, ?x9875), genre(?x1746, ?x53), film(?x510, ?x1746) *> conf = 0.20 ranks of expected_values: 2 EVAL 0k4kk film! 05qd_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 56.000 0.207 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #11236-0rd6b PRED entity: 0rd6b PRED relation: location_of_ceremony! PRED expected values: 04ztj => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.90 #45, 0.89 #41, 0.89 #37), 01g63y (0.29 #189, 0.04 #18, 0.03 #38), 0jgjn (0.05 #40, 0.05 #44, 0.04 #48) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 01c1nm; >> query: (?x10726, 04ztj) <- place_of_birth(?x1301, ?x10726), location_of_ceremony(?x7093, ?x10726), gender(?x1301, ?x231) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rd6b location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.899 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #11235-01crd5 PRED entity: 01crd5 PRED relation: country! PRED expected values: 01hp22 071t0 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 47): 071t0 (0.86 #1053, 0.86 #1335, 0.86 #1664), 03hr1p (0.77 #1336, 0.75 #1289, 0.73 #1054), 01lb14 (0.76 #906, 0.73 #1047, 0.73 #1329), 06wrt (0.76 #1048, 0.73 #1330, 0.65 #578), 0w0d (0.68 #1327, 0.68 #1045, 0.67 #1656), 064vjs (0.68 #1061, 0.63 #1672, 0.62 #920), 01hp22 (0.62 #1041, 0.59 #1276, 0.57 #1323), 07bs0 (0.61 #1657, 0.57 #1046, 0.53 #1845), 07jjt (0.59 #911, 0.57 #441, 0.54 #159), 03fyrh (0.57 #1340, 0.57 #1058, 0.56 #353) >> Best rule #1053 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 016zwt; >> query: (?x8593, 071t0) <- country(?x668, ?x8593), ?x668 = 07gyv, combatants(?x4373, ?x8593) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 01crd5 country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.865 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01crd5 country! 01hp22 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 141.000 141.000 0.865 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #11234-0345_ PRED entity: 0345_ PRED relation: film_release_region! PRED expected values: 0fpgp26 => 122 concepts (100 used for prediction) PRED predicted values (max 10 best out of 1359): 08hmch (0.74 #8056, 0.57 #19963, 0.57 #15994), 0fpgp26 (0.73 #9081, 0.58 #34219, 0.58 #48774), 017gm7 (0.71 #8099, 0.56 #33237, 0.54 #1484), 043tvp3 (0.71 #8859, 0.53 #18120, 0.52 #33997), 0bpm4yw (0.69 #8484, 0.60 #33622, 0.60 #16422), 017jd9 (0.69 #8531, 0.58 #33669, 0.56 #1916), 04f52jw (0.68 #8270, 0.55 #33408, 0.55 #20177), 01fmys (0.66 #8185, 0.56 #20092, 0.56 #33323), 0gd0c7x (0.66 #8180, 0.53 #47873, 0.53 #16118), 03nm_fh (0.66 #8544, 0.52 #1929, 0.50 #33682) >> Best rule #8056 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 04vs9; >> query: (?x4954, 08hmch) <- country(?x3127, ?x4954), olympics(?x4954, ?x1931), ?x3127 = 03hr1p >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #9081 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 04vs9; *> query: (?x4954, 0fpgp26) <- country(?x3127, ?x4954), olympics(?x4954, ?x1931), ?x3127 = 03hr1p *> conf = 0.73 ranks of expected_values: 2 EVAL 0345_ film_release_region! 0fpgp26 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 100.000 0.742 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11233-0160nk PRED entity: 0160nk PRED relation: major_field_of_study PRED expected values: 01mkq => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 119): 01mkq (0.68 #616, 0.57 #736, 0.55 #856), 02lp1 (0.61 #612, 0.51 #732, 0.46 #852), 02j62 (0.54 #630, 0.46 #990, 0.46 #750), 05qjt (0.44 #608, 0.37 #728, 0.35 #848), 03g3w (0.41 #627, 0.37 #987, 0.34 #747), 04x_3 (0.41 #626, 0.34 #746, 0.33 #146), 05qfh (0.41 #636, 0.34 #756, 0.33 #876), 0_jm (0.40 #178, 0.31 #58, 0.29 #1498), 01lj9 (0.36 #520, 0.34 #640, 0.30 #880), 041y2 (0.33 #197, 0.29 #677, 0.24 #797) >> Best rule #616 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 0n00; 0d05fv; 01tdnyh; >> query: (?x10572, 01mkq) <- category(?x10572, ?x134), organization(?x10572, ?x5487), organizations_founded(?x122, ?x5487) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0160nk major_field_of_study 01mkq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.678 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11232-0257yf PRED entity: 0257yf PRED relation: ceremony PRED expected values: 01bx35 056878 => 34 concepts (29 used for prediction) PRED predicted values (max 10 best out of 124): 056878 (0.91 #154, 0.90 #538, 0.89 #666), 01bx35 (0.86 #516, 0.86 #132, 0.86 #4), 01mhwk (0.86 #547, 0.86 #163, 0.86 #675), 0jzphpx (0.77 #673, 0.76 #545, 0.75 #417), 01xqqp (0.77 #725, 0.73 #597, 0.73 #981), 05c1t6z (0.18 #1676, 0.18 #1932, 0.18 #1804), 0gvstc3 (0.17 #1692, 0.17 #1948, 0.16 #1820), 02q690_ (0.16 #1722, 0.16 #1978, 0.16 #1850), 0bzm81 (0.16 #1553, 0.16 #1425, 0.14 #1681), 0n8_m93 (0.16 #1643, 0.16 #1515, 0.14 #1771) >> Best rule #154 for best value: >> intensional similarity = 12 >> extensional distance = 41 >> proper extension: 02gx2k; 025mb9; 0248jb; 02v703; 02fm4d; >> query: (?x3033, 056878) <- ceremony(?x3033, ?x6487), ceremony(?x3033, ?x3121), ceremony(?x3033, ?x1480), ceremony(?x3033, ?x1362), ceremony(?x3033, ?x486), ceremony(?x3033, ?x139), ?x3121 = 09n4nb, ?x6487 = 01mh_q, ?x1362 = 019bk0, ?x139 = 05pd94v, ?x486 = 02rjjll, ?x1480 = 01c6qp >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0257yf ceremony 056878 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 29.000 0.907 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0257yf ceremony 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 29.000 0.907 http://example.org/award/award_category/winners./award/award_honor/ceremony #11231-049k07 PRED entity: 049k07 PRED relation: languages PRED expected values: 02h40lc => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 11): 02h40lc (0.28 #470, 0.28 #314, 0.27 #353), 03k50 (0.08 #160, 0.03 #82, 0.03 #1175), 064_8sq (0.04 #327, 0.04 #171, 0.03 #93), 07c9s (0.03 #91, 0.02 #130, 0.02 #169), 06mp7 (0.03 #89, 0.02 #128, 0.01 #167), 02bjrlw (0.02 #157, 0.02 #469, 0.02 #703), 06nm1 (0.02 #123, 0.02 #318, 0.01 #513), 02hxcvy (0.02 #182), 04306rv (0.01 #315, 0.01 #432, 0.01 #471), 09bnf (0.01 #195) >> Best rule #470 for best value: >> intensional similarity = 3 >> extensional distance = 376 >> proper extension: 0h1_w; 03k545; >> query: (?x1773, 02h40lc) <- religion(?x1773, ?x109), nominated_for(?x1773, ?x1861), film(?x1773, ?x141) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049k07 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.280 http://example.org/people/person/languages #11230-01xy5l_ PRED entity: 01xy5l_ PRED relation: film_crew_role! PRED expected values: 047n8xt 014kq6 062zjtt 05pdh86 05nlx4 08s6mr 02q0k7v 0bmfnjs 09p5mwg => 74 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1167): 0ct2tf5 (0.75 #15037, 0.71 #13870, 0.67 #17371), 02mpyh (0.75 #14985, 0.71 #12651, 0.67 #17319), 03nx8mj (0.75 #14489, 0.71 #12155, 0.67 #16823), 016dj8 (0.75 #17082, 0.71 #12414, 0.67 #10080), 03ckwzc (0.75 #14088, 0.71 #11754, 0.67 #9420), 04jpg2p (0.75 #14984, 0.67 #10316, 0.60 #6815), 0d99k_ (0.75 #15150, 0.67 #10482, 0.60 #6981), 0c3z0 (0.75 #14668, 0.67 #10000, 0.60 #6499), 0466s8n (0.75 #15091, 0.67 #10423, 0.60 #6922), 07kh6f3 (0.75 #14435, 0.67 #9767, 0.60 #6266) >> Best rule #15037 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0d2b38; >> query: (?x2472, 0ct2tf5) <- film_crew_role(?x7314, ?x2472), film_crew_role(?x6438, ?x2472), film_crew_role(?x2189, ?x2472), film_release_region(?x2189, ?x87), genre(?x6438, ?x53), language(?x2189, ?x90), film(?x815, ?x2189), ?x7314 = 047vp1n >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #9858 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 02r96rf; *> query: (?x2472, 05pdh86) <- film_crew_role(?x7081, ?x2472), film_crew_role(?x5323, ?x2472), film_crew_role(?x4998, ?x2472), film_crew_role(?x2189, ?x2472), ?x2189 = 02yvct, ?x4998 = 0dzlbx, genre(?x7081, ?x53), ?x5323 = 011yn5, country(?x7081, ?x94) *> conf = 0.67 ranks of expected_values: 26, 35, 73, 318, 342, 374, 423, 642, 728 EVAL 01xy5l_ film_crew_role! 09p5mwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 74.000 60.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01xy5l_ film_crew_role! 0bmfnjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 74.000 60.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01xy5l_ film_crew_role! 02q0k7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 74.000 60.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01xy5l_ film_crew_role! 08s6mr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 74.000 60.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01xy5l_ film_crew_role! 05nlx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 74.000 60.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01xy5l_ film_crew_role! 05pdh86 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 74.000 60.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01xy5l_ film_crew_role! 062zjtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 74.000 60.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01xy5l_ film_crew_role! 014kq6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 74.000 60.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01xy5l_ film_crew_role! 047n8xt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 74.000 60.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11229-086xm PRED entity: 086xm PRED relation: major_field_of_study PRED expected values: 01mkq 062z7 03qsdpk => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 113): 04rjg (0.64 #258, 0.61 #1687, 0.60 #1211), 03qsdpk (0.61 #2263, 0.58 #834, 0.58 #4763), 0w7c (0.61 #2263, 0.58 #834, 0.58 #4763), 01mkq (0.60 #1206, 0.53 #1682, 0.49 #8114), 062z7 (0.55 #264, 0.47 #1217, 0.45 #2169), 02lp1 (0.43 #1678, 0.43 #2154, 0.42 #1202), 05qfh (0.38 #1225, 0.37 #1701, 0.36 #272), 0g26h (0.36 #396, 0.34 #2540, 0.33 #1706), 01540 (0.35 #1249, 0.30 #3154, 0.29 #2916), 06ms6 (0.35 #1208, 0.27 #1684, 0.26 #2518) >> Best rule #258 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01k2wn; 04rwx; 07wrz; 05mv4; 017v71; 01g7_r; 0bwfn; 01rgn3; 01cf5; >> query: (?x3136, 04rjg) <- major_field_of_study(?x3136, ?x8221), student(?x3136, ?x5366), currency(?x3136, ?x170), ?x8221 = 037mh8 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #2263 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 0d5fb; *> query: (?x3136, ?x5614) <- student(?x3136, ?x10363), student(?x5614, ?x10363), colors(?x3136, ?x332), religion(?x10363, ?x7422) *> conf = 0.61 ranks of expected_values: 2, 4, 5 EVAL 086xm major_field_of_study 03qsdpk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 151.000 151.000 0.636 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 086xm major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 151.000 151.000 0.636 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 086xm major_field_of_study 01mkq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 151.000 151.000 0.636 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11228-02qsjt PRED entity: 02qsjt PRED relation: artist! PRED expected values: 0g768 => 146 concepts (104 used for prediction) PRED predicted values (max 10 best out of 99): 015_1q (0.25 #1638, 0.25 #963, 0.25 #18), 03rhqg (0.25 #15, 0.23 #285, 0.20 #1635), 01w40h (0.25 #27, 0.14 #972, 0.14 #297), 03qy3l (0.25 #61, 0.07 #1006, 0.05 #736), 01cszh (0.18 #146, 0.12 #11, 0.08 #3928), 01trtc (0.17 #1150, 0.12 #70, 0.10 #3987), 0mzkr (0.17 #1104, 0.12 #24, 0.08 #1239), 0g768 (0.15 #1655, 0.14 #305, 0.13 #10183), 01dtcb (0.14 #584, 0.12 #44, 0.11 #1124), 011k1h (0.14 #415, 0.14 #955, 0.14 #820) >> Best rule #1638 for best value: >> intensional similarity = 2 >> extensional distance = 86 >> proper extension: 05qw5; 07yg2; 015x1f; 015srx; 08w4pm; 01v0sxx; 0jltp; >> query: (?x6939, 015_1q) <- inductee(?x1091, ?x6939), artists(?x7440, ?x6939) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1655 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 86 *> proper extension: 05qw5; 07yg2; 015x1f; 015srx; 08w4pm; 01v0sxx; 0jltp; *> query: (?x6939, 0g768) <- inductee(?x1091, ?x6939), artists(?x7440, ?x6939) *> conf = 0.15 ranks of expected_values: 8 EVAL 02qsjt artist! 0g768 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 146.000 104.000 0.250 http://example.org/music/record_label/artist #11227-03s0w PRED entity: 03s0w PRED relation: contains PRED expected values: 0t0n5 => 162 concepts (121 used for prediction) PRED predicted values (max 10 best out of 2767): 013crh (0.82 #58573, 0.82 #26359, 0.82 #84935), 0chrx (0.57 #178664, 0.25 #1184, 0.05 #12901), 03s0w (0.52 #172806, 0.49 #240178, 0.25 #87), 09c7w0 (0.52 #172806, 0.49 #240178), 0jpn8 (0.25 #1312, 0.10 #13029, 0.08 #7173), 01y17m (0.25 #398, 0.10 #12115, 0.08 #6259), 065r8g (0.25 #333, 0.10 #12050, 0.08 #6194), 016w7b (0.25 #2387, 0.10 #14104, 0.08 #8248), 0xddr (0.25 #475, 0.10 #12192, 0.08 #6336), 013f9v (0.25 #593, 0.10 #12310, 0.08 #6454) >> Best rule #58573 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 09hzw; >> query: (?x961, ?x310) <- state(?x310, ?x961), adjoins(?x961, ?x1024), country(?x961, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #770 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0t015; *> query: (?x961, 0t0n5) <- contains(?x94, ?x961), contains(?x961, ?x546), ?x546 = 01j_9c *> conf = 0.25 ranks of expected_values: 855 EVAL 03s0w contains 0t0n5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 162.000 121.000 0.825 http://example.org/location/location/contains #11226-032c7m PRED entity: 032c7m PRED relation: sport PRED expected values: 02vx4 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 7): 02vx4 (0.67 #117, 0.67 #109, 0.63 #167), 0z74 (0.27 #778, 0.26 #768, 0.23 #758), 03tmr (0.05 #759, 0.04 #769), 0jm_ (0.04 #761, 0.04 #771), 018jz (0.04 #763, 0.03 #773), 018w8 (0.03 #772), 039yzs (0.01 #775) >> Best rule #117 for best value: >> intensional similarity = 24 >> extensional distance = 13 >> proper extension: 05kjc6; 075q_; 048xg8; 024_ql; >> query: (?x10414, ?x471) <- team(?x530, ?x10414), team(?x203, ?x10414), team(?x63, ?x10414), team(?x60, ?x10414), ?x63 = 02sdk9v, ?x60 = 02nzb8, ?x203 = 0dgrmp, current_club(?x7294, ?x10414), current_club(?x7294, ?x11421), current_club(?x7294, ?x9048), current_club(?x7294, ?x1599), colors(?x1599, ?x3189), colors(?x1599, ?x663), ?x663 = 083jv, sport(?x1599, ?x471), ?x530 = 02_j1w, team(?x8324, ?x1599), colors(?x9048, ?x1101), ?x3189 = 01g5v, ?x471 = 02vx4, ?x1101 = 06fvc, colors(?x11421, ?x8047), current_club(?x4805, ?x11421), ?x4805 = 02rqxc >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 032c7m sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.667 http://example.org/sports/sports_team/sport #11225-016wvy PRED entity: 016wvy PRED relation: category PRED expected values: 08mbj5d => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #36, 0.83 #46, 0.82 #35) >> Best rule #36 for best value: >> intensional similarity = 5 >> extensional distance = 165 >> proper extension: 03fwln; >> query: (?x10144, 08mbj5d) <- nationality(?x10144, ?x512), profession(?x10144, ?x1032), profession(?x10144, ?x220), ?x220 = 016z4k, ?x1032 = 02hrh1q >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016wvy category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.838 http://example.org/common/topic/webpage./common/webpage/category #11224-04gfy7 PRED entity: 04gfy7 PRED relation: languages_spoken PRED expected values: 02hxcvy => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 47): 02h40lc (0.66 #569, 0.56 #286, 0.40 #1227), 064_8sq (0.39 #300, 0.32 #583, 0.22 #394), 04h9h (0.33 #35, 0.25 #82, 0.04 #460), 06nm1 (0.26 #339, 0.22 #292, 0.22 #386), 0880p (0.24 #227, 0.22 #180, 0.14 #132), 06b_j (0.22 #254, 0.18 #207, 0.16 #348), 03hkp (0.22 #154, 0.18 #201, 0.14 #106), 04306rv (0.18 #195, 0.11 #242, 0.11 #148), 06mp7 (0.18 #202, 0.11 #249, 0.10 #955), 0h407 (0.14 #136, 0.12 #514, 0.11 #561) >> Best rule #569 for best value: >> intensional similarity = 6 >> extensional distance = 36 >> proper extension: 078vc; 078ds; 0fk3s; 04czx7; >> query: (?x12951, 02h40lc) <- languages_spoken(?x12951, ?x8098), languages_spoken(?x12951, ?x3592), countries_spoken_in(?x8098, ?x3016), languages(?x3873, ?x8098), language(?x1163, ?x3592), ?x3016 = 0697s >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #595 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 36 *> proper extension: 078vc; 078ds; 0fk3s; 04czx7; *> query: (?x12951, 02hxcvy) <- languages_spoken(?x12951, ?x8098), languages_spoken(?x12951, ?x3592), countries_spoken_in(?x8098, ?x3016), languages(?x3873, ?x8098), language(?x1163, ?x3592), ?x3016 = 0697s *> conf = 0.13 ranks of expected_values: 13 EVAL 04gfy7 languages_spoken 02hxcvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 31.000 31.000 0.658 http://example.org/people/ethnicity/languages_spoken #11223-0gvvm6l PRED entity: 0gvvm6l PRED relation: film_crew_role PRED expected values: 0dxtw => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.74 #199, 0.71 #999, 0.67 #123), 02r96rf (0.72 #194, 0.67 #80, 0.64 #841), 09vw2b7 (0.63 #998, 0.56 #1151, 0.56 #1113), 0dxtw (0.34 #1003, 0.30 #1118, 0.29 #1270), 01vx2h (0.33 #90, 0.33 #204, 0.29 #851), 01pvkk (0.29 #547, 0.27 #91, 0.26 #357), 02ynfr (0.16 #171, 0.16 #513, 0.15 #1009), 0215hd (0.16 #174, 0.15 #1012, 0.15 #1203), 02_n3z (0.16 #154, 0.12 #496, 0.09 #686), 033smt (0.16 #183, 0.07 #525, 0.07 #221) >> Best rule #199 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 0ds3t5x; 0g5qs2k; 0dgst_d; 0ch26b_; 0fpv_3_; 06wbm8q; 04f52jw; 0j43swk; 02fqrf; 05pdh86; ... >> query: (?x8176, 0ch6mp2) <- award_winner(?x8176, ?x8767), film_release_region(?x8176, ?x1917), film_release_region(?x8176, ?x1603), ?x1603 = 06bnz, ?x1917 = 01p1v >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1003 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 153 *> proper extension: 0gkz15s; 09q5w2; 0qm8b; 0bm2g; 07nt8p; 0cw3yd; 0ds2n; 03176f; 01qvz8; 013q0p; ... *> query: (?x8176, 0dxtw) <- award_winner(?x8176, ?x8767), film_format(?x8176, ?x6392), nominated_for(?x68, ?x8176), currency(?x8176, ?x5696) *> conf = 0.34 ranks of expected_values: 4 EVAL 0gvvm6l film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 96.000 0.744 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11222-015fr PRED entity: 015fr PRED relation: organization PRED expected values: 0b6css => 196 concepts (189 used for prediction) PRED predicted values (max 10 best out of 18): 02vk52z (0.90 #1670, 0.88 #2209, 0.88 #266), 0_2v (0.67 #48, 0.62 #26, 0.59 #180), 0b6css (0.67 #54, 0.62 #32, 0.56 #2874), 041288 (0.56 #2874, 0.56 #2919, 0.39 #1796), 018cqq (0.56 #276, 0.53 #55, 0.48 #477), 01rz1 (0.56 #267, 0.48 #581, 0.46 #1160), 02jxk (0.39 #268, 0.33 #582, 0.33 #223), 0j7v_ (0.36 #1785, 0.32 #3478, 0.29 #606), 0gkjy (0.33 #1743, 0.32 #3478, 0.29 #2079), 059dn (0.32 #3478, 0.21 #125, 0.19 #81) >> Best rule #1670 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 059ss; >> query: (?x583, 02vk52z) <- adjoins(?x142, ?x583), organization(?x583, ?x312), contains(?x583, ?x1167) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #54 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 13 *> proper extension: 06mx8; *> query: (?x583, 0b6css) <- contains(?x583, ?x1167), titles(?x583, ?x7081) *> conf = 0.67 ranks of expected_values: 3 EVAL 015fr organization 0b6css CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 196.000 189.000 0.896 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #11221-0451j PRED entity: 0451j PRED relation: type_of_union PRED expected values: 04ztj => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.75 #37, 0.71 #169, 0.70 #173), 01g63y (0.15 #54, 0.14 #10, 0.13 #62), 0jgjn (0.01 #8) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 610 >> proper extension: 05dxl_; 0gry51; 02r99xw; >> query: (?x7610, 04ztj) <- profession(?x7610, ?x1032), profession(?x7610, ?x319), ?x1032 = 02hrh1q, ?x319 = 01d_h8 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0451j type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.755 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11220-02y9bj PRED entity: 02y9bj PRED relation: organization! PRED expected values: 060c4 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.78 #641, 0.73 #760, 0.73 #389), 0dq_5 (0.71 #61, 0.54 #487, 0.52 #128), 07xl34 (0.60 #37, 0.40 #318, 0.38 #78), 02md_2 (0.42 #294, 0.38 #546, 0.35 #492), 05k17c (0.11 #1545, 0.11 #830, 0.11 #1121), 0hm4q (0.11 #1545, 0.09 #315, 0.07 #884), 05c0jwl (0.11 #1545, 0.05 #338, 0.04 #907), 04n1q6 (0.11 #1545, 0.03 #366, 0.03 #272), 08jcfy (0.11 #1545, 0.03 #386, 0.03 #691), 07t3gd (0.03 #1101) >> Best rule #641 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 02zkz7; 02zc7f; 016sd3; 03wv2g; >> query: (?x7071, 060c4) <- school(?x1115, ?x7071), colors(?x7071, ?x3189), team(?x180, ?x1115) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y9bj organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.783 http://example.org/organization/role/leaders./organization/leadership/organization #11219-026kqs9 PRED entity: 026kqs9 PRED relation: honored_for PRED expected values: 03pc89 => 35 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1046): 0d68qy (0.33 #1343, 0.27 #3123, 0.26 #9057), 02rzdcp (0.33 #1389, 0.18 #3169, 0.17 #3762), 03ln8b (0.33 #722, 0.18 #3095, 0.17 #3688), 0fhzwl (0.33 #2878, 0.17 #1692, 0.15 #9406), 03pc89 (0.33 #487, 0.17 #1679, 0.11 #2865), 01b_lz (0.33 #798, 0.14 #4951, 0.12 #10886), 0b6tzs (0.33 #2428, 0.13 #8956, 0.10 #10737), 06nr2h (0.33 #858, 0.11 #2043, 0.09 #3231), 03xf_m (0.33 #982, 0.09 #3355, 0.08 #3948), 04q827 (0.33 #1160, 0.09 #3533, 0.08 #4126) >> Best rule #1343 for best value: >> intensional similarity = 19 >> extensional distance = 4 >> proper extension: 0bzmt8; >> query: (?x6595, 0d68qy) <- ceremony(?x2060, ?x6595), honored_for(?x6595, ?x6669), honored_for(?x6595, ?x4431), honored_for(?x6595, ?x2729), award_winner(?x2729, ?x2799), award_winner(?x6595, ?x538), nominated_for(?x591, ?x2729), currency(?x4431, ?x170), films(?x326, ?x4431), nominated_for(?x484, ?x4431), award_winner(?x2060, ?x8942), nominated_for(?x3662, ?x4431), titles(?x4757, ?x4431), film(?x4279, ?x4431), nominated_for(?x6909, ?x6669), ?x8942 = 01v5h, written_by(?x6669, ?x8268), ?x6909 = 02qyntr, film_festivals(?x6669, ?x3831) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #487 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 1 *> proper extension: 0bz6sb; *> query: (?x6595, 03pc89) <- ceremony(?x7965, ?x6595), honored_for(?x6595, ?x2729), ?x2729 = 02rjv2w, award_winner(?x6595, ?x12809), award(?x8984, ?x7965), award(?x6345, ?x7965), award(?x534, ?x7965), ?x8984 = 0kt_4, nominated_for(?x7965, ?x8496), nominated_for(?x7965, ?x7307), genre(?x8496, ?x53), award(?x8496, ?x1008), ?x6345 = 02gd6x, currency(?x8496, ?x5696), ?x7307 = 011yxy, film(?x1582, ?x534), titles(?x2152, ?x8496), ?x53 = 07s9rl0, profession(?x12809, ?x987) *> conf = 0.33 ranks of expected_values: 5 EVAL 026kqs9 honored_for 03pc89 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 35.000 19.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #11218-015882 PRED entity: 015882 PRED relation: award_nominee PRED expected values: 01jgkj2 => 142 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1089): 01jgkj2 (0.80 #81987, 0.02 #13688, 0.02 #27742), 0lx2l (0.14 #28108, 0.10 #46848, 0.07 #46847), 01kv4mb (0.12 #5136, 0.11 #452, 0.08 #46849), 0pj8m (0.12 #6468, 0.06 #20523, 0.05 #25207), 01w724 (0.12 #5299, 0.06 #19354, 0.05 #24038), 018dyl (0.12 #5681, 0.06 #19736, 0.05 #24420), 0fq117k (0.12 #6328, 0.05 #8671, 0.04 #15697), 0gt_k (0.12 #5098, 0.04 #12125, 0.04 #14467), 02cx90 (0.11 #1015, 0.08 #17412, 0.08 #61920), 02dbp7 (0.11 #1085, 0.07 #22166, 0.03 #10454) >> Best rule #81987 for best value: >> intensional similarity = 3 >> extensional distance = 302 >> proper extension: 0bg539; 0h7pj; >> query: (?x1817, ?x4239) <- award(?x1817, ?x537), award_nominee(?x4239, ?x1817), instrumentalists(?x227, ?x1817) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015882 award_nominee 01jgkj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 51.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11217-0738y5 PRED entity: 0738y5 PRED relation: people! PRED expected values: 0dryh9k => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 60): 0dryh9k (0.66 #929, 0.36 #1157, 0.36 #1613), 041rx (0.35 #1982, 0.33 #1906, 0.29 #2362), 0x67 (0.33 #2140, 0.25 #1379, 0.17 #4117), 02ctzb (0.20 #91, 0.17 #319, 0.14 #243), 033tf_ (0.20 #83, 0.15 #1224, 0.15 #1756), 03ts0c (0.20 #102, 0.14 #178, 0.11 #330), 01qhm_ (0.20 #82, 0.07 #1223, 0.06 #1375), 0xnvg (0.16 #1382, 0.15 #2143, 0.09 #1230), 07hwkr (0.15 #2142, 0.14 #1381, 0.11 #1761), 013xrm (0.14 #172, 0.11 #324, 0.06 #2150) >> Best rule #929 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 0cfywh; >> query: (?x9506, 0dryh9k) <- nationality(?x9506, ?x2146), ?x2146 = 03rk0, place_of_birth(?x9506, ?x13551), people(?x13008, ?x9506) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0738y5 people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.661 http://example.org/people/ethnicity/people #11216-01mv_n PRED entity: 01mv_n PRED relation: profession PRED expected values: 03sbb => 116 concepts (67 used for prediction) PRED predicted values (max 10 best out of 63): 0dxtg (0.40 #14, 0.31 #5895, 0.30 #8394), 09jwl (0.39 #2076, 0.39 #6781, 0.38 #4281), 01d_h8 (0.35 #6, 0.31 #447, 0.31 #7357), 0nbcg (0.29 #4293, 0.28 #2088, 0.28 #6793), 016z4k (0.27 #6767, 0.27 #6179, 0.26 #4267), 0dz3r (0.26 #4265, 0.26 #2060, 0.24 #6765), 03gjzk (0.25 #15, 0.25 #8395, 0.24 #3543), 02jknp (0.25 #8, 0.22 #2654, 0.21 #2507), 0cbd2 (0.23 #1036, 0.22 #1624, 0.21 #1477), 0kyk (0.17 #1057, 0.15 #1498, 0.14 #1645) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0dky9n; >> query: (?x7446, 0dxtg) <- people(?x9771, ?x7446), ?x9771 = 02knxx, place_of_death(?x7446, ?x1523) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1115 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 187 *> proper extension: 042fk; *> query: (?x7446, 03sbb) <- student(?x3424, ?x7446), people(?x9771, ?x7446), location(?x7446, ?x739) *> conf = 0.02 ranks of expected_values: 54 EVAL 01mv_n profession 03sbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 116.000 67.000 0.400 http://example.org/people/person/profession #11215-0hqly PRED entity: 0hqly PRED relation: location PRED expected values: 0r2dp => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 93): 04pry (0.70 #34549, 0.50 #24904, 0.50 #11246), 030qb3t (0.20 #8115, 0.19 #7311, 0.17 #13739), 02_286 (0.20 #840, 0.15 #33781, 0.15 #26549), 0cc56 (0.12 #860, 0.06 #1663, 0.04 #3269), 04jpl (0.08 #17, 0.06 #7245, 0.06 #8049), 0cr3d (0.07 #26657, 0.06 #948, 0.06 #33889), 01cx_ (0.06 #966, 0.03 #163, 0.02 #4178), 027l4q (0.06 #1301, 0.01 #3710, 0.01 #4513), 059rby (0.05 #1622, 0.04 #8048, 0.04 #7244), 0ccvx (0.04 #1025, 0.03 #13074, 0.03 #5040) >> Best rule #34549 for best value: >> intensional similarity = 3 >> extensional distance = 1387 >> proper extension: 07m69t; >> query: (?x11019, ?x12488) <- nationality(?x11019, ?x94), location(?x11019, ?x13801), place_of_birth(?x11019, ?x12488) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hqly location 0r2dp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 91.000 0.703 http://example.org/people/person/places_lived./people/place_lived/location #11214-071jrc PRED entity: 071jrc PRED relation: gender PRED expected values: 05zppz => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #19, 0.89 #23, 0.89 #21), 02zsn (0.57 #59, 0.25 #95, 0.24 #109) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 018ty9; 031bf1; 0c3dzk; >> query: (?x12576, 05zppz) <- nationality(?x12576, ?x94), ?x94 = 09c7w0, profession(?x12576, ?x2265), ?x2265 = 0dgd_ >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 071jrc gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.904 http://example.org/people/person/gender #11213-01b9z4 PRED entity: 01b9z4 PRED relation: place_of_birth PRED expected values: 0xn7b => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 20): 0cr3d (0.28 #2206, 0.25 #1502, 0.04 #9248), 01x73 (0.27 #36619, 0.27 #9859, 0.27 #35914), 030qb3t (0.14 #54, 0.10 #758, 0.05 #2870), 0nq_b (0.14 #589, 0.10 #1293), 01531 (0.11 #2217, 0.08 #1513, 0.02 #4329), 0vm4s (0.10 #970), 02cl1 (0.10 #720), 0ccvx (0.08 #1561, 0.06 #2265, 0.02 #2969), 0yc7f (0.08 #1687, 0.06 #2391), 0rh6k (0.08 #1410, 0.01 #4226, 0.01 #9156) >> Best rule #2206 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01wbg84; 01r42_g; 0436f4; 02lfcm; 02lfl4; 0521rl1; 02lfns; 02lgj6; 01l1sq; 02lf70; ... >> query: (?x9647, 0cr3d) <- award_nominee(?x9647, ?x2965), profession(?x9647, ?x319), ?x2965 = 01dy7j >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01b9z4 place_of_birth 0xn7b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 70.000 0.278 http://example.org/people/person/place_of_birth #11212-040dv PRED entity: 040dv PRED relation: story_by! PRED expected values: 011yg9 => 165 concepts (149 used for prediction) PRED predicted values (max 10 best out of 379): 02qhqz4 (0.33 #69, 0.03 #23535, 0.01 #17123), 08ct6 (0.11 #2217, 0.10 #2558, 0.10 #2899), 01fx4k (0.10 #651, 0.04 #5085, 0.04 #5426), 04jpg2p (0.09 #1642, 0.05 #2324, 0.05 #2665), 01f39b (0.09 #1565, 0.05 #2247, 0.05 #2588), 039zft (0.09 #1562, 0.05 #2244, 0.05 #2585), 085bd1 (0.09 #1456, 0.05 #2138, 0.05 #2479), 01xq8v (0.09 #1618, 0.05 #2300, 0.05 #2641), 0bv8h2 (0.09 #1485, 0.05 #2167, 0.05 #2508), 0dqytn (0.09 #1388, 0.05 #2070, 0.05 #2411) >> Best rule #69 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01vl17; >> query: (?x8981, 02qhqz4) <- story_by(?x7320, ?x8981), film_release_region(?x7320, ?x94), film_crew_role(?x7320, ?x137), film(?x10626, ?x7320), ?x10626 = 0ywqc >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 040dv story_by! 011yg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 149.000 0.333 http://example.org/film/film/story_by #11211-02cw1m PRED entity: 02cw1m PRED relation: group! PRED expected values: 05148p4 => 99 concepts (63 used for prediction) PRED predicted values (max 10 best out of 117): 05148p4 (0.76 #657, 0.74 #2428, 0.74 #817), 05r5c (0.57 #86, 0.26 #807, 0.25 #888), 028tv0 (0.39 #2341, 0.38 #2744, 0.38 #1938), 07y_7 (0.33 #2, 0.26 #803, 0.21 #723), 02sgy (0.29 #85, 0.16 #2816, 0.13 #2009), 02snj9 (0.29 #129, 0.16 #2816, 0.11 #931), 013y1f (0.24 #665, 0.21 #825, 0.19 #906), 06ncr (0.22 #915, 0.16 #2816, 0.16 #1961), 0l14j_ (0.21 #846, 0.19 #686, 0.16 #2816), 01xqw (0.16 #2816, 0.14 #141, 0.13 #2009) >> Best rule #657 for best value: >> intensional similarity = 8 >> extensional distance = 19 >> proper extension: 015cxv; >> query: (?x9589, 05148p4) <- artists(?x1572, ?x9589), artists(?x1000, ?x9589), category(?x9589, ?x134), group(?x745, ?x9589), ?x1572 = 06by7, ?x745 = 01vj9c, artists(?x1000, ?x2930), ?x2930 = 0pkyh >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cw1m group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 63.000 0.762 http://example.org/music/performance_role/regular_performances./music/group_membership/group #11210-02qlkc3 PRED entity: 02qlkc3 PRED relation: award_winner! PRED expected values: 026kq4q => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 96): 03nnm4t (0.14 #497, 0.11 #1202, 0.10 #1343), 026kq4q (0.13 #328, 0.08 #187, 0.05 #469), 02q690_ (0.12 #1193, 0.12 #488, 0.11 #1334), 05c1t6z (0.12 #438, 0.11 #1143, 0.11 #1284), 0gvstc3 (0.12 #457, 0.08 #1303, 0.08 #1444), 07z31v (0.10 #454, 0.07 #1159, 0.06 #1300), 0gx_st (0.09 #1165, 0.09 #1306, 0.09 #1024), 027n06w (0.09 #1201, 0.09 #1342, 0.09 #1483), 07y_p6 (0.08 #239, 0.07 #380, 0.06 #944), 0drtv8 (0.08 #207, 0.07 #348, 0.05 #1194) >> Best rule #497 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 01y8d4; >> query: (?x6087, 03nnm4t) <- award_winner(?x6087, ?x4385), type_of_union(?x6087, ?x566), program_creator(?x8536, ?x6087) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #328 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0b7gr2; *> query: (?x6087, 026kq4q) <- award_nominee(?x6087, ?x4385), award(?x6087, ?x3906), ?x4385 = 03xp8d5 *> conf = 0.13 ranks of expected_values: 2 EVAL 02qlkc3 award_winner! 026kq4q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 92.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11209-01f08r PRED entity: 01f08r PRED relation: exported_to! PRED expected values: 0d05w3 => 180 concepts (119 used for prediction) PRED predicted values (max 10 best out of 217): 05r4w (0.38 #179, 0.29 #1826, 0.24 #2425), 03shp (0.34 #2180, 0.33 #2423, 0.33 #2241), 03rk0 (0.34 #2180, 0.33 #2423, 0.33 #2241), 01z215 (0.34 #2180, 0.33 #2423, 0.33 #2241), 0d05w3 (0.33 #1265, 0.29 #1384, 0.28 #1561), 06q1r (0.25 #222, 0.23 #1575, 0.18 #2468), 0l3h (0.25 #220, 0.18 #750, 0.11 #1396), 047t_ (0.24 #745, 0.15 #1272, 0.14 #2639), 0ctw_b (0.18 #1249, 0.18 #722, 0.16 #1368), 0f8l9c (0.18 #1247, 0.16 #1366, 0.15 #1543) >> Best rule #179 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 06t2t; >> query: (?x3838, 05r4w) <- exported_to(?x94, ?x3838), ?x94 = 09c7w0, contains(?x311, ?x3838), vacationer(?x3838, ?x2499), jurisdiction_of_office(?x3119, ?x3838) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1265 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 0hg5; *> query: (?x3838, 0d05w3) <- exported_to(?x94, ?x3838), nationality(?x51, ?x94), country(?x54, ?x94), combatants(?x94, ?x205) *> conf = 0.33 ranks of expected_values: 5 EVAL 01f08r exported_to! 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 180.000 119.000 0.375 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #11208-0bm2nq PRED entity: 0bm2nq PRED relation: film! PRED expected values: 01wgcvn 023mdt => 71 concepts (48 used for prediction) PRED predicted values (max 10 best out of 748): 024rdh (0.56 #2081, 0.44 #72829, 0.44 #72828), 01q_ph (0.20 #2137, 0.11 #56, 0.04 #4218), 059j1m (0.20 #3552, 0.04 #62420, 0.03 #4162), 073749 (0.20 #2788, 0.02 #11111, 0.02 #15273), 03k48_ (0.20 #3868, 0.01 #24677, 0.01 #32998), 0391jz (0.11 #606, 0.10 #2687), 0p_pd (0.11 #54, 0.09 #4216, 0.02 #43746), 015c4g (0.11 #779, 0.07 #4941, 0.02 #11183), 07b2lv (0.11 #364, 0.04 #62420, 0.03 #4162), 03x400 (0.11 #1157, 0.04 #62420, 0.03 #4162) >> Best rule #2081 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0b76t12; 040b5k; 05g8pg; 0462hhb; 07l4zhn; 09rvcvl; 0bs5vty; >> query: (?x10201, ?x488) <- nominated_for(?x5959, ?x10201), nominated_for(?x488, ?x10201), genre(?x10201, ?x53), film(?x398, ?x10201), ?x5959 = 024rdh >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #5738 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 047p7fr; *> query: (?x10201, 023mdt) <- film(?x398, ?x10201), nominated_for(?x2577, ?x10201), genre(?x10201, ?x809), ?x809 = 0vgkd *> conf = 0.02 ranks of expected_values: 365, 745 EVAL 0bm2nq film! 023mdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 71.000 48.000 0.559 http://example.org/film/actor/film./film/performance/film EVAL 0bm2nq film! 01wgcvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 71.000 48.000 0.559 http://example.org/film/actor/film./film/performance/film #11207-01t6b4 PRED entity: 01t6b4 PRED relation: gender PRED expected values: 05zppz => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #43, 0.85 #51, 0.85 #101), 02zsn (0.40 #104, 0.38 #6, 0.37 #20) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 02nygk; >> query: (?x1285, 05zppz) <- place_of_birth(?x1285, ?x479), nationality(?x1285, ?x94), program_creator(?x10447, ?x1285) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01t6b4 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.861 http://example.org/people/person/gender #11206-014g91 PRED entity: 014g91 PRED relation: profession PRED expected values: 01c72t => 172 concepts (148 used for prediction) PRED predicted values (max 10 best out of 90): 09jwl (0.81 #2122, 0.79 #4228, 0.78 #1521), 02hrh1q (0.75 #465, 0.70 #19552, 0.70 #1066), 01c72t (0.61 #2277, 0.54 #4683, 0.53 #775), 016z4k (0.54 #8115, 0.53 #6615, 0.53 #4813), 0nbcg (0.54 #3188, 0.53 #1534, 0.52 #1234), 0dz3r (0.46 #8563, 0.45 #1053, 0.44 #5711), 0n1h (0.40 #1063, 0.25 #1513, 0.24 #1814), 039v1 (0.38 #1539, 0.32 #1840, 0.31 #2140), 01d_h8 (0.35 #1057, 0.33 #16833, 0.31 #19243), 0dxtg (0.29 #19251, 0.28 #19851, 0.28 #19401) >> Best rule #2122 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 01hrqc; >> query: (?x10879, 09jwl) <- gender(?x10879, ?x231), award_winner(?x5310, ?x10879), nationality(?x10879, ?x94), performance_role(?x10879, ?x316) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #2277 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 067x44; *> query: (?x10879, 01c72t) <- gender(?x10879, ?x231), place_of_death(?x10879, ?x739), artists(?x5985, ?x10879), film_regional_debut_venue(?x2047, ?x739) *> conf = 0.61 ranks of expected_values: 3 EVAL 014g91 profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 172.000 148.000 0.810 http://example.org/people/person/profession #11205-0523v5y PRED entity: 0523v5y PRED relation: place_of_death PRED expected values: 030qb3t => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 20): 030qb3t (0.31 #607, 0.26 #802, 0.25 #997), 0f2wj (0.12 #12, 0.12 #207, 0.07 #597), 0k_p5 (0.06 #88, 0.06 #283, 0.05 #673), 071vr (0.06 #102, 0.06 #492, 0.02 #687), 0cb4j (0.06 #11, 0.06 #401, 0.02 #596), 0mzww (0.06 #103), 06_kh (0.06 #395, 0.04 #980, 0.02 #590), 0cr3d (0.06 #585, 0.03 #4092, 0.02 #5260), 0r540 (0.06 #421, 0.02 #616, 0.02 #1006), 0f2tj (0.06 #292, 0.02 #877, 0.02 #1072) >> Best rule #607 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 05218gr; 04gmp_z; 03wd5tk; 053j4w4; 0fqjks; 051z6mv; 0c4qzm; 05683cn; 0521d_3; 071jv5; >> query: (?x8402, 030qb3t) <- nationality(?x8402, ?x94), award(?x8402, ?x484), ?x484 = 0gq_v, award_nominee(?x12512, ?x8402) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0523v5y place_of_death 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.310 http://example.org/people/deceased_person/place_of_death #11204-0d060g PRED entity: 0d060g PRED relation: olympics PRED expected values: 0kbvb 01f1jy 019n8z => 217 concepts (217 used for prediction) PRED predicted values (max 10 best out of 12): 0jhn7 (0.90 #199, 0.87 #388, 0.83 #255), 06sks6 (0.87 #387, 0.82 #152, 0.79 #156), 0kbvb (0.83 #381, 0.82 #134, 0.80 #192), 0l6vl (0.79 #156, 0.77 #144, 0.77 #202), 0sxrz (0.79 #156, 0.77 #144, 0.77 #202), 0swff (0.79 #156, 0.77 #144, 0.77 #202), 0lbd9 (0.65 #154, 0.60 #212, 0.60 #200), 0sx8l (0.53 #561, 0.40 #81, 0.38 #125), 01f1jy (0.41 #135, 0.40 #193, 0.38 #124), 019n8z (0.40 #201, 0.40 #88, 0.38 #132) >> Best rule #199 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0d0vqn; 035qy; 01znc_; 02vzc; 03h64; >> query: (?x279, 0jhn7) <- olympics(?x279, ?x358), film_release_region(?x10535, ?x279), ?x10535 = 09v42sf >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #381 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 0b90_r; 03_3d; 03rt9; 05qhw; 06mzp; 0k6nt; 09pmkv; 0345h; 06bnz; 03rk0; ... *> query: (?x279, 0kbvb) <- olympics(?x279, ?x358), country(?x1905, ?x279), service_location(?x610, ?x279) *> conf = 0.83 ranks of expected_values: 3, 9, 10 EVAL 0d060g olympics 019n8z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 217.000 217.000 0.900 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0d060g olympics 01f1jy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 217.000 217.000 0.900 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0d060g olympics 0kbvb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 217.000 217.000 0.900 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #11203-06w33f8 PRED entity: 06w33f8 PRED relation: costume_design_by! PRED expected values: 06rmdr => 100 concepts (52 used for prediction) PRED predicted values (max 10 best out of 198): 0f42nz (0.33 #107, 0.25 #305, 0.12 #899), 01c9d (0.25 #392, 0.06 #986, 0.04 #1184), 04fjzv (0.25 #390, 0.06 #984, 0.04 #1182), 0g_zyp (0.25 #379, 0.06 #973, 0.04 #1171), 01rnly (0.25 #378, 0.06 #972, 0.04 #1170), 04x4nv (0.25 #370, 0.06 #964, 0.04 #1162), 01qz5 (0.25 #359, 0.06 #953, 0.04 #1151), 0gg8z1f (0.25 #333, 0.06 #927, 0.04 #1125), 0y_yw (0.25 #325, 0.06 #919, 0.04 #1117), 043t8t (0.25 #289, 0.06 #883, 0.04 #1081) >> Best rule #107 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03wpmd; >> query: (?x1760, 0f42nz) <- costume_design_by(?x195, ?x1760), profession(?x1760, ?x1943), profession(?x5562, ?x1943), ?x5562 = 044f7 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06w33f8 costume_design_by! 06rmdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 52.000 0.333 http://example.org/film/film/costume_design_by #11202-02gnh0 PRED entity: 02gnh0 PRED relation: company! PRED expected values: 021q1c => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 25): 060c4 (0.22 #97, 0.16 #2457, 0.15 #2268), 021q1c (0.11 #58, 0.06 #293, 0.06 #105), 05k17c (0.11 #60, 0.06 #107, 0.05 #295), 021q0l (0.08 #104, 0.05 #621, 0.04 #1378), 0dq_5 (0.08 #2427, 0.07 #2238, 0.02 #2220), 0krdk (0.07 #2416, 0.07 #2227, 0.01 #4583), 0dq3c (0.05 #2411, 0.04 #2222), 01yc02 (0.04 #2418, 0.03 #2229), 04n1q6 (0.04 #12, 0.04 #59, 0.02 #341), 016fly (0.04 #30, 0.04 #77) >> Best rule #97 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 08815; 0dplh; 01mpwj; 04b_46; >> query: (?x8046, 060c4) <- contains(?x94, ?x8046), student(?x8046, ?x4470), major_field_of_study(?x8046, ?x1154), diet(?x4470, ?x3130) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 020923; *> query: (?x8046, 021q1c) <- contains(?x1227, ?x8046), contains(?x94, ?x8046), ?x1227 = 01n7q, currency(?x8046, ?x170), ?x94 = 09c7w0 *> conf = 0.11 ranks of expected_values: 2 EVAL 02gnh0 company! 021q1c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.216 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #11201-05148p4 PRED entity: 05148p4 PRED relation: role! PRED expected values: 0770cd 02dbp7 01l4g5 01wgjj5 => 72 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1176): 0137g1 (0.56 #5126, 0.45 #7636, 0.45 #6799), 05qhnq (0.55 #7812, 0.50 #6139, 0.50 #4467), 01vs4ff (0.55 #7806, 0.50 #1957, 0.46 #1677), 0161sp (0.55 #7645, 0.33 #9316, 0.33 #8897), 023l9y (0.50 #8971, 0.50 #6046, 0.50 #3956), 06x4l_ (0.50 #5969, 0.50 #4297, 0.50 #1793), 04s5_s (0.50 #6265, 0.50 #4175, 0.50 #1668), 0m_v0 (0.50 #6004, 0.50 #1828, 0.45 #7677), 0770cd (0.50 #5926, 0.50 #1750, 0.45 #7599), 03j24kf (0.50 #8975, 0.50 #1874, 0.38 #8139) >> Best rule #5126 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 0bm02; 0l15bq; >> query: (?x1166, 0137g1) <- role(?x1166, ?x2592), role(?x1166, ?x1433), role(?x1166, ?x1225), role(?x6573, ?x1166), award_nominee(?x1388, ?x6573), ?x1225 = 01qbl, role(?x3546, ?x2592), ?x1433 = 0239kh >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #5926 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 0239kh; *> query: (?x1166, 0770cd) <- group(?x1166, ?x11700), group(?x1166, ?x8029), role(?x1166, ?x74), ?x11700 = 017_hq, role(?x212, ?x1166), role(?x3834, ?x1166), artists(?x671, ?x8029), artists(?x1127, ?x3834) *> conf = 0.50 ranks of expected_values: 9, 35, 72, 179 EVAL 05148p4 role! 01wgjj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 72.000 47.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05148p4 role! 01l4g5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 72.000 47.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05148p4 role! 02dbp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 72.000 47.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05148p4 role! 0770cd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 72.000 47.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role #11200-0jyb4 PRED entity: 0jyb4 PRED relation: honored_for! PRED expected values: 0bzjvm => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 118): 0gvstc3 (0.16 #515, 0.05 #1247, 0.04 #2467), 0bvhz9 (0.15 #114, 0.04 #602, 0.03 #1334), 05c1t6z (0.12 #499, 0.05 #11, 0.05 #1231), 03nnm4t (0.11 #551, 0.05 #1283, 0.03 #2381), 0drtv8 (0.10 #55, 0.07 #543, 0.03 #1275), 027hjff (0.10 #47, 0.05 #535, 0.02 #1267), 02q690_ (0.09 #542, 0.04 #1274, 0.03 #3104), 0hr6lkl (0.08 #988, 0.05 #12, 0.05 #2208), 0lp_cd3 (0.07 #505, 0.05 #1237, 0.03 #1725), 0hndn2q (0.06 #1008, 0.03 #520, 0.03 #2228) >> Best rule #515 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 01hvv0; >> query: (?x6215, 0gvstc3) <- nominated_for(?x2135, ?x6215), category(?x6215, ?x134), program(?x2135, ?x531) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #340 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 048rn; *> query: (?x6215, 0bzjvm) <- genre(?x6215, ?x812), produced_by(?x6215, ?x9044), list(?x6215, ?x3004), film(?x2135, ?x6215) *> conf = 0.04 ranks of expected_values: 43 EVAL 0jyb4 honored_for! 0bzjvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 100.000 100.000 0.158 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #11199-09dvgb8 PRED entity: 09dvgb8 PRED relation: crewmember! PRED expected values: 0j_t1 => 88 concepts (68 used for prediction) PRED predicted values (max 10 best out of 213): 033dbw (0.18 #316, 0.12 #636), 0bwfwpj (0.18 #37, 0.12 #357), 06rhz7 (0.18 #218, 0.08 #538), 072x7s (0.18 #65, 0.08 #385), 04gknr (0.18 #34, 0.08 #354), 016017 (0.09 #315, 0.08 #635), 037cr1 (0.09 #304, 0.08 #624), 024lt6 (0.09 #300, 0.08 #620), 07bx6 (0.09 #258, 0.08 #578), 02ylg6 (0.09 #180, 0.08 #500) >> Best rule #316 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 019fnv; >> query: (?x7648, 033dbw) <- nationality(?x7648, ?x94), award(?x7648, ?x500), ?x500 = 0p9sw >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09dvgb8 crewmember! 0j_t1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 68.000 0.182 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #11198-0dw4g PRED entity: 0dw4g PRED relation: award PRED expected values: 02f72n => 115 concepts (112 used for prediction) PRED predicted values (max 10 best out of 307): 02f72n (0.86 #388, 0.80 #389, 0.80 #138), 02sp_v (0.86 #388, 0.78 #7748, 0.77 #28656), 03tcnt (0.86 #388, 0.78 #7748, 0.77 #28656), 02f72_ (0.60 #216, 0.28 #5250, 0.25 #1379), 01ck6h (0.35 #2439, 0.20 #2826, 0.19 #7475), 0gqz2 (0.34 #4724, 0.20 #2788, 0.14 #12856), 03qbnj (0.30 #220, 0.27 #12999, 0.26 #13773), 01c99j (0.30 #213, 0.26 #13766, 0.25 #602), 02f6ym (0.30 #245, 0.25 #634, 0.15 #13798), 02f6xy (0.30 #189, 0.19 #4836, 0.18 #13742) >> Best rule #388 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 09qr6; 01vs_v8; 03fbc; 09hnb; 0478__m; 0fhxv; 03y82t6; 0187x8; >> query: (?x5547, ?x247) <- award_nominee(?x1060, ?x5547), award_winner(?x2634, ?x5547), award_winner(?x247, ?x5547), ?x2634 = 02f72n >> conf = 0.86 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0dw4g award 02f72n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 112.000 0.857 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11197-03khn PRED entity: 03khn PRED relation: location_of_ceremony! PRED expected values: 04ztj => 261 concepts (261 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.84 #237, 0.83 #405, 0.82 #389), 0jgjn (0.11 #184, 0.11 #188, 0.10 #204), 01g63y (0.08 #234, 0.08 #242, 0.08 #130), 01bl8s (0.03 #287, 0.03 #283, 0.02 #467) >> Best rule #237 for best value: >> intensional similarity = 6 >> extensional distance = 23 >> proper extension: 0k9p4; >> query: (?x11237, 04ztj) <- locations(?x4255, ?x11237), mode_of_transportation(?x11237, ?x8731), mode_of_transportation(?x11237, ?x4272), ?x4272 = 07jdr, mode_of_transportation(?x3026, ?x8731), ?x3026 = 0cv3w >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03khn location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 261.000 261.000 0.840 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #11196-041_y PRED entity: 041_y PRED relation: influenced_by PRED expected values: 02kz_ => 140 concepts (50 used for prediction) PRED predicted values (max 10 best out of 296): 03_87 (0.35 #1930, 0.32 #2362, 0.21 #1497), 032l1 (0.33 #521, 0.31 #6148, 0.29 #1820), 02lt8 (0.33 #552, 0.18 #1851, 0.16 #2283), 058vp (0.26 #2345, 0.18 #1913, 0.17 #614), 048cl (0.26 #2394, 0.18 #1962, 0.14 #6290), 03sbs (0.25 #6278, 0.16 #2382, 0.12 #7578), 081k8 (0.24 #1885, 0.21 #6213, 0.21 #1452), 05qmj (0.23 #6249, 0.12 #12128, 0.12 #2596), 084w8 (0.21 #1301, 0.18 #1734, 0.17 #869), 08433 (0.21 #1319, 0.07 #21665, 0.06 #1752) >> Best rule #1930 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 045bg; 01w8sf; 0lrh; 04cbtrw; 073v6; 02yl42; 0lcx; 013pp3; 040_t; 0kp2_; ... >> query: (?x7039, 03_87) <- religion(?x7039, ?x7131), influenced_by(?x7039, ?x4915), ?x4915 = 03f0324, profession(?x7039, ?x353) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1900 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 045bg; 01w8sf; 0lrh; 04cbtrw; 073v6; 02yl42; 0lcx; 013pp3; 040_t; 0kp2_; ... *> query: (?x7039, 02kz_) <- religion(?x7039, ?x7131), influenced_by(?x7039, ?x4915), ?x4915 = 03f0324, profession(?x7039, ?x353) *> conf = 0.18 ranks of expected_values: 14 EVAL 041_y influenced_by 02kz_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 140.000 50.000 0.353 http://example.org/influence/influence_node/influenced_by #11195-02wgk1 PRED entity: 02wgk1 PRED relation: featured_film_locations PRED expected values: 02_286 => 103 concepts (88 used for prediction) PRED predicted values (max 10 best out of 74): 02_286 (0.29 #20, 0.18 #3145, 0.16 #4109), 030qb3t (0.14 #519, 0.14 #39, 0.10 #3164), 01sn3 (0.14 #88, 0.02 #808, 0.01 #3213), 0q_xk (0.14 #153, 0.01 #633, 0.01 #2555), 01n7q (0.14 #30), 04jpl (0.08 #3856, 0.08 #6510, 0.08 #5059), 080h2 (0.07 #2426, 0.05 #2908, 0.05 #2668), 06y57 (0.06 #343, 0.04 #1305, 0.04 #2505), 01_d4 (0.06 #287, 0.04 #1008, 0.03 #2931), 035p3 (0.06 #473, 0.03 #1435, 0.03 #1675) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 06ys2; >> query: (?x4502, 02_286) <- nominated_for(?x3410, ?x4502), nominated_for(?x1733, ?x4502), ?x1733 = 015pkc, award_winner(?x708, ?x3410) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wgk1 featured_film_locations 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 88.000 0.286 http://example.org/film/film/featured_film_locations #11194-02sg5v PRED entity: 02sg5v PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.90 #216, 0.89 #271, 0.88 #407), 02nxhr (0.22 #643, 0.21 #72, 0.10 #47), 07c52 (0.22 #643, 0.08 #58, 0.03 #113), 07z4p (0.22 #643, 0.08 #60, 0.03 #170) >> Best rule #216 for best value: >> intensional similarity = 5 >> extensional distance = 104 >> proper extension: 0gtsx8c; >> query: (?x836, 029j_) <- country(?x836, ?x94), prequel(?x836, ?x6533), ?x94 = 09c7w0, production_companies(?x836, ?x788), film(?x2538, ?x836) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02sg5v film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #11193-0g8_vp PRED entity: 0g8_vp PRED relation: geographic_distribution PRED expected values: 09c7w0 => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 148): 09c7w0 (0.32 #388, 0.32 #1007, 0.30 #930), 0694j (0.17 #216, 0.03 #602, 0.03 #679), 05rh2 (0.17 #232, 0.03 #695, 0.02 #1860), 059s8 (0.17 #228, 0.03 #691, 0.02 #1856), 059t8 (0.17 #227, 0.03 #690, 0.02 #1855), 0d060g (0.14 #1161, 0.13 #1472, 0.11 #2096), 07ssc (0.07 #1094, 0.06 #784, 0.06 #1405), 01n7q (0.07 #331, 0.04 #1104, 0.04 #1182), 0345h (0.07 #1107, 0.04 #1185, 0.04 #2197), 07b_l (0.06 #663, 0.06 #818, 0.05 #974) >> Best rule #388 for best value: >> intensional similarity = 9 >> extensional distance = 20 >> proper extension: 02rbdlq; 0x67; 07hwkr; 07mqps; 03295l; 0222qb; 0dbxy; >> query: (?x5606, 09c7w0) <- people(?x5606, ?x8544), people(?x5606, ?x2383), people(?x5606, ?x917), award_nominee(?x222, ?x8544), film(?x8544, ?x3251), languages_spoken(?x5606, ?x254), written_by(?x1202, ?x8544), friend(?x917, ?x513), gender(?x2383, ?x514) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g8_vp geographic_distribution 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.318 http://example.org/people/ethnicity/geographic_distribution #11192-017gm7 PRED entity: 017gm7 PRED relation: film_release_region PRED expected values: 06mzp 06bnz 03rj0 06f32 016wzw 06t8v 03spz 0165v => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 96): 06bnz (0.76 #503, 0.68 #983, 0.63 #863), 03spz (0.69 #544, 0.62 #904, 0.60 #1024), 03rj0 (0.62 #513, 0.55 #873, 0.53 #993), 06f32 (0.48 #517, 0.39 #877, 0.38 #997), 06mzp (0.44 #490, 0.42 #850, 0.42 #970), 06t8v (0.44 #528, 0.40 #888, 0.39 #1008), 016wzw (0.42 #518, 0.41 #878, 0.39 #998), 077qn (0.33 #537, 0.26 #1017, 0.24 #897), 07t21 (0.28 #501, 0.20 #981, 0.19 #861), 02k54 (0.26 #487, 0.21 #847, 0.19 #967) >> Best rule #503 for best value: >> intensional similarity = 4 >> extensional distance = 170 >> proper extension: 087wc7n; 03bx2lk; 01fmys; 0407yfx; 0407yj_; 0j43swk; 0dyb1; 0gtsxr4; 0gffmn8; 011ycb; ... >> query: (?x1392, 06bnz) <- film_release_region(?x1392, ?x429), film_release_region(?x1392, ?x279), ?x279 = 0d060g, ?x429 = 03rt9 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 46 EVAL 017gm7 film_release_region 0165v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 59.000 59.000 0.756 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gm7 film_release_region 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.756 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gm7 film_release_region 06t8v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.756 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gm7 film_release_region 016wzw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.756 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gm7 film_release_region 06f32 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.756 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gm7 film_release_region 03rj0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.756 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gm7 film_release_region 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.756 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gm7 film_release_region 06mzp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.756 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11191-07tds PRED entity: 07tds PRED relation: institution! PRED expected values: 014mlp 0bkj86 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 12): 014mlp (0.75 #669, 0.74 #261, 0.69 #983), 0bkj86 (0.68 #968, 0.66 #409, 0.58 #250), 07s6fsf (0.53 #314, 0.50 #79, 0.49 #407), 01rr_d (0.42 #99, 0.35 #1011, 0.29 #255), 03mkk4 (0.42 #96, 0.33 #122, 0.29 #148), 022h5x (0.35 #1011, 0.28 #1627, 0.25 #88), 01ysy9 (0.35 #1011, 0.28 #1627, 0.21 #181), 071tyz (0.35 #1011, 0.28 #1627, 0.17 #251), 01gkg3 (0.35 #1011, 0.28 #1627, 0.02 #909), 028dcg (0.26 #204, 0.25 #100, 0.21 #256) >> Best rule #669 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 022xml; >> query: (?x4672, 014mlp) <- student(?x4672, ?x264), major_field_of_study(?x4672, ?x742), fraternities_and_sororities(?x4672, ?x4348) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07tds institution! 0bkj86 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.752 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07tds institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.752 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #11190-0crx5w PRED entity: 0crx5w PRED relation: producer_type PRED expected values: 0ckd1 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.71 #7, 0.70 #4, 0.70 #8) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 03m_k0; 02661h; 0b1s_q; >> query: (?x1541, 0ckd1) <- program(?x1541, ?x8533), award_winner(?x2213, ?x1541), nominated_for(?x678, ?x8533) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0crx5w producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.706 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #11189-02d4ct PRED entity: 02d4ct PRED relation: student! PRED expected values: 01pl14 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 126): 01bm_ (0.20 #244, 0.06 #1292, 0.05 #2340), 065y4w7 (0.12 #1061, 0.10 #2109, 0.05 #24117), 0bwfn (0.11 #1845, 0.08 #11801, 0.08 #14421), 017z88 (0.11 #1653, 0.07 #2701, 0.07 #605), 06182p (0.10 #296, 0.05 #1868, 0.03 #4488), 03ksy (0.10 #105, 0.04 #30497, 0.04 #34689), 0gl5_ (0.10 #242, 0.04 #4434, 0.03 #4958), 01pcj4 (0.10 #367, 0.04 #4035, 0.02 #5607), 02gr81 (0.10 #131, 0.02 #3799), 02fy0z (0.10 #92, 0.02 #3760) >> Best rule #244 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 024rbz; >> query: (?x2374, 01bm_) <- nominated_for(?x2374, ?x2833), ?x2833 = 04jwly >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02d4ct student! 01pl14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 97.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #11188-0sw6y PRED entity: 0sw6y PRED relation: actor! PRED expected values: 05nlzq => 101 concepts (61 used for prediction) PRED predicted values (max 10 best out of 147): 0180mw (0.37 #4504, 0.02 #8378, 0.02 #7862), 05nlzq (0.33 #438, 0.25 #954, 0.17 #1212), 07c72 (0.33 #562, 0.17 #1336, 0.16 #3400), 06cs95 (0.27 #4396, 0.03 #4138, 0.02 #4654), 09g_31 (0.25 #935, 0.17 #1451, 0.17 #1193), 01hvv0 (0.25 #921, 0.17 #1179, 0.08 #5052), 04mx8h4 (0.25 #946, 0.17 #1204, 0.06 #5337), 03y3bp7 (0.25 #817, 0.17 #1075, 0.04 #3139), 024rwx (0.17 #1134, 0.11 #4233, 0.09 #6301), 099pks (0.17 #1127, 0.08 #1643, 0.04 #3191) >> Best rule #4504 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 014zcr; 01tvz5j; 03rs8y; 02nb2s; 04yj5z; 03ds3; 02jt1k; 03n_7k; 01wk7b7; 06mnps; ... >> query: (?x12054, 0180mw) <- actor(?x5219, ?x12054), nominated_for(?x2135, ?x5219), ?x2135 = 06pj8 >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #438 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 024my5; *> query: (?x12054, 05nlzq) <- actor(?x3144, ?x12054), language(?x12054, ?x254), film(?x12054, ?x4766), ?x4766 = 05sw5b, ?x3144 = 015w8_ *> conf = 0.33 ranks of expected_values: 2 EVAL 0sw6y actor! 05nlzq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 61.000 0.366 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11187-01vwyqp PRED entity: 01vwyqp PRED relation: category PRED expected values: 08mbj5d => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.89 #7, 0.88 #12, 0.87 #5) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 07_3qd; 02cw1m; 0fb2l; >> query: (?x3256, 08mbj5d) <- artist(?x2149, ?x3256), artists(?x5300, ?x3256), ?x5300 = 02k_kn >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vwyqp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.893 http://example.org/common/topic/webpage./common/webpage/category #11186-05pcn59 PRED entity: 05pcn59 PRED relation: nominated_for PRED expected values: 05vxdh 02ylg6 01xq8v => 42 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1620): 01k0vq (0.65 #26127, 0.28 #26126, 0.23 #30740), 03nm_fh (0.65 #26127, 0.28 #26126, 0.12 #6843), 0gmcwlb (0.50 #4784, 0.40 #9393, 0.33 #176), 07s846j (0.50 #5191, 0.33 #583, 0.32 #9800), 07w8fz (0.50 #5050, 0.33 #442, 0.32 #9659), 0bmhvpr (0.50 #5155, 0.33 #547, 0.25 #6692), 09gq0x5 (0.43 #9460, 0.33 #4851, 0.33 #243), 0gmgwnv (0.43 #10144, 0.33 #5535, 0.33 #927), 017gl1 (0.43 #9342, 0.33 #125, 0.26 #12417), 026p4q7 (0.42 #9561, 0.33 #4952, 0.33 #344) >> Best rule #26127 for best value: >> intensional similarity = 3 >> extensional distance = 204 >> proper extension: 0m7yy; 02wwsh8; 03ybrwc; 02vl9ln; 0468g4r; >> query: (?x1336, ?x144) <- award_winner(?x1336, ?x2415), award(?x144, ?x1336), award_winner(?x945, ?x2415) >> conf = 0.65 => this is the best rule for 2 predicted values *> Best rule #30740 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 212 *> proper extension: 09v7wsg; *> query: (?x1336, ?x821) <- nominated_for(?x1336, ?x144), award_winner(?x1336, ?x2763), award_winner(?x1336, ?x1335), profession(?x2763, ?x319), nominated_for(?x1335, ?x821) *> conf = 0.23 ranks of expected_values: 220, 748, 776 EVAL 05pcn59 nominated_for 01xq8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 22.000 0.650 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05pcn59 nominated_for 02ylg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 22.000 0.650 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05pcn59 nominated_for 05vxdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 22.000 0.650 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11185-02q5g1z PRED entity: 02q5g1z PRED relation: titles! PRED expected values: 07s9rl0 => 106 concepts (77 used for prediction) PRED predicted values (max 10 best out of 63): 04xvlr (0.72 #1652, 0.72 #1551, 0.70 #1236), 07s9rl0 (0.53 #1548, 0.52 #1653, 0.50 #1131), 060__y (0.49 #1756, 0.39 #1651, 0.32 #1650), 02l7c8 (0.32 #1650, 0.31 #1755, 0.31 #1235), 01z4y (0.29 #1479, 0.22 #1375, 0.18 #2935), 01jfsb (0.21 #3644, 0.20 #20, 0.13 #1983), 07c52 (0.20 #30, 0.18 #442, 0.13 #1890), 024qqx (0.17 #389, 0.15 #596, 0.14 #698), 017fp (0.17 #1154, 0.16 #1676, 0.16 #1571), 07ssc (0.12 #1140, 0.12 #1557, 0.12 #1662) >> Best rule #1652 for best value: >> intensional similarity = 5 >> extensional distance = 184 >> proper extension: 016z9n; 05z43v; 07bxqz; >> query: (?x1753, ?x162) <- genre(?x1753, ?x1509), genre(?x1753, ?x162), ?x162 = 04xvlr, genre(?x6704, ?x1509), ?x6704 = 02wyzmv >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1548 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 184 *> proper extension: 016z9n; 05z43v; 07bxqz; *> query: (?x1753, 07s9rl0) <- genre(?x1753, ?x1509), genre(?x1753, ?x162), ?x162 = 04xvlr, genre(?x6704, ?x1509), ?x6704 = 02wyzmv *> conf = 0.53 ranks of expected_values: 2 EVAL 02q5g1z titles! 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 77.000 0.720 http://example.org/media_common/netflix_genre/titles #11184-02ts3h PRED entity: 02ts3h PRED relation: category PRED expected values: 08mbj5d => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.57 #36, 0.57 #27, 0.56 #23) >> Best rule #36 for best value: >> intensional similarity = 3 >> extensional distance = 233 >> proper extension: 04kjrv; >> query: (?x7140, 08mbj5d) <- participant(?x7140, ?x4819), artists(?x671, ?x4819), profession(?x4819, ?x220) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ts3h category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.570 http://example.org/common/topic/webpage./common/webpage/category #11183-0464pz PRED entity: 0464pz PRED relation: titles! PRED expected values: 07c52 => 100 concepts (82 used for prediction) PRED predicted values (max 10 best out of 74): 07c52 (0.81 #1773, 0.77 #3731, 0.71 #132), 07s9rl0 (0.71 #6890, 0.40 #307, 0.34 #6173), 015w9s (0.53 #353, 0.04 #2821, 0.03 #1790), 04xvlr (0.47 #6893, 0.24 #6176, 0.24 #6278), 01z4y (0.18 #5689, 0.18 #6105, 0.17 #5898), 017fp (0.17 #6913, 0.11 #6400, 0.11 #6298), 01hmnh (0.13 #333, 0.07 #2285, 0.07 #7848), 07ssc (0.12 #6899, 0.11 #7831, 0.10 #5976), 0215n (0.11 #998, 0.11 #1203, 0.10 #1305), 03mqtr (0.10 #351, 0.10 #45, 0.09 #6934) >> Best rule #1773 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 0p_tz; >> query: (?x1653, 07c52) <- award(?x1653, ?x3486), country_of_origin(?x1653, ?x94), titles(?x3381, ?x1653) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0464pz titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 82.000 0.813 http://example.org/media_common/netflix_genre/titles #11182-03r1pr PRED entity: 03r1pr PRED relation: profession PRED expected values: 0cbd2 02jknp => 118 concepts (117 used for prediction) PRED predicted values (max 10 best out of 63): 026sdt1 (0.43 #67, 0.25 #214, 0.12 #802), 02tx6q (0.42 #491, 0.24 #638, 0.19 #785), 0dxtg (0.33 #306, 0.32 #6480, 0.31 #6921), 02jknp (0.29 #1182, 0.27 #6033, 0.27 #2358), 089fss (0.29 #14, 0.11 #1043, 0.11 #1337), 03gjzk (0.26 #6481, 0.25 #6922, 0.24 #6775), 0cbd2 (0.22 #299, 0.22 #2945, 0.22 #3092), 0dgd_ (0.22 #323, 0.12 #176, 0.09 #1352), 09jwl (0.17 #10014, 0.17 #8985, 0.16 #11484), 01c72t (0.17 #1492, 0.16 #1786, 0.15 #1639) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 025_nbr; >> query: (?x2871, 026sdt1) <- crewmember(?x324, ?x2871), profession(?x2871, ?x137), place_of_death(?x2871, ?x4151) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1182 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 104 *> proper extension: 0h1_w; 0f0p0; 0h1m9; 01t07j; 018swb; 01_vfy; 01nrq5; 09qh1; 01fs_4; 01n9d9; ... *> query: (?x2871, 02jknp) <- award_winner(?x2878, ?x2871), place_of_death(?x2871, ?x4151), people(?x13744, ?x2871) *> conf = 0.29 ranks of expected_values: 4, 7 EVAL 03r1pr profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 118.000 117.000 0.429 http://example.org/people/person/profession EVAL 03r1pr profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 118.000 117.000 0.429 http://example.org/people/person/profession #11181-0677j PRED entity: 0677j PRED relation: major_field_of_study PRED expected values: 04rjg 01lj9 => 135 concepts (124 used for prediction) PRED predicted values (max 10 best out of 111): 02j62 (0.67 #27, 0.58 #390, 0.56 #1601), 05qfh (0.67 #33, 0.56 #154, 0.53 #275), 04rjg (0.67 #18, 0.47 #260, 0.46 #2802), 04x_3 (0.56 #144, 0.50 #23, 0.46 #386), 0fdys (0.56 #157, 0.46 #520, 0.46 #399), 01lj9 (0.53 #279, 0.50 #37, 0.44 #158), 01mkq (0.50 #861, 0.47 #1346, 0.44 #2314), 037mh8 (0.50 #66, 0.44 #187, 0.38 #792), 02lp1 (0.44 #132, 0.42 #737, 0.42 #374), 01r4k (0.44 #204, 0.23 #809, 0.21 #446) >> Best rule #27 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 08815; 01mpwj; 0g8rj; 05zl0; >> query: (?x8973, 02j62) <- organization(?x2361, ?x8973), major_field_of_study(?x8973, ?x1327), major_field_of_study(?x8973, ?x254), ?x254 = 02h40lc, ?x1327 = 01lhy >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #18 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 08815; 01mpwj; 0g8rj; 05zl0; *> query: (?x8973, 04rjg) <- organization(?x2361, ?x8973), major_field_of_study(?x8973, ?x1327), major_field_of_study(?x8973, ?x254), ?x254 = 02h40lc, ?x1327 = 01lhy *> conf = 0.67 ranks of expected_values: 3, 6 EVAL 0677j major_field_of_study 01lj9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 135.000 124.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0677j major_field_of_study 04rjg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 135.000 124.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11180-0281y0 PRED entity: 0281y0 PRED relation: location! PRED expected values: 0mdyn => 92 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1920): 016tb7 (0.45 #55357, 0.44 #62904, 0.42 #115731), 022wxh (0.45 #55357, 0.44 #62904, 0.42 #115731), 09fb5 (0.27 #2567, 0.19 #10118, 0.17 #12633), 0q9kd (0.27 #2518, 0.19 #10069, 0.17 #12584), 01ggc9 (0.20 #2056, 0.09 #4572, 0.08 #7090), 03y82t6 (0.20 #962, 0.09 #3478, 0.08 #5996), 03xpsrx (0.20 #545, 0.04 #18159, 0.03 #28221), 078mgh (0.20 #1640, 0.04 #19254, 0.03 #21770), 041c4 (0.20 #1014, 0.04 #18628, 0.03 #21144), 0210hf (0.20 #965, 0.04 #18579, 0.03 #21095) >> Best rule #55357 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 0mn0v; >> query: (?x7769, ?x3694) <- origin(?x3997, ?x7769), location(?x4327, ?x7769), place_of_birth(?x3694, ?x7769) >> conf = 0.45 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0281y0 location! 0mdyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 50.000 0.455 http://example.org/people/person/places_lived./people/place_lived/location #11179-0bz60q PRED entity: 0bz60q PRED relation: profession PRED expected values: 09jwl => 61 concepts (50 used for prediction) PRED predicted values (max 10 best out of 54): 02hrh1q (0.82 #1166, 0.77 #1742, 0.72 #1454), 09jwl (0.50 #1313, 0.31 #304, 0.30 #4757), 01d_h8 (0.49 #2167, 0.40 #871, 0.39 #1015), 02jknp (0.42 #2168, 0.31 #583, 0.30 #4757), 0nbcg (0.34 #1324, 0.18 #459, 0.17 #747), 0dz3r (0.34 #1299, 0.25 #290, 0.20 #434), 016z4k (0.31 #1301, 0.17 #292, 0.15 #436), 01c72t (0.30 #4757, 0.28 #3460, 0.28 #2738), 025352 (0.30 #4757, 0.28 #3460, 0.28 #2738), 015h31 (0.30 #4757, 0.28 #3460, 0.28 #2738) >> Best rule #1166 for best value: >> intensional similarity = 3 >> extensional distance = 288 >> proper extension: 05ty4m; 0134w7; 0bj9k; 0738b8; 0jfx1; 06w6_; 07swvb; 04fzk; 07ldhs; 02k4b2; ... >> query: (?x7000, 02hrh1q) <- award_winner(?x1835, ?x7000), profession(?x7000, ?x353), friend(?x2669, ?x1835) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1313 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 297 *> proper extension: 0411q; 01vvycq; 0137n0; 01kvqc; 016h9b; 015f7; 01cj6y; 09zmys; 0137hn; 0jbyg; ... *> query: (?x7000, 09jwl) <- award_winner(?x4466, ?x7000), profession(?x7000, ?x353), role(?x4466, ?x212) *> conf = 0.50 ranks of expected_values: 2 EVAL 0bz60q profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 61.000 50.000 0.821 http://example.org/people/person/profession #11178-03mr85 PRED entity: 03mr85 PRED relation: genre PRED expected values: 01j1n2 => 73 concepts (34 used for prediction) PRED predicted values (max 10 best out of 88): 05p553 (0.65 #3221, 0.45 #1313, 0.42 #2029), 02kdv5l (0.61 #2266, 0.28 #2624, 0.25 #3338), 04xvlr (0.44 #477, 0.35 #2742, 0.33 #358), 01jfsb (0.38 #2277, 0.32 #2635, 0.31 #3349), 03k9fj (0.31 #2276, 0.20 #3587, 0.20 #2396), 01g6gs (0.31 #258, 0.30 #139, 0.16 #972), 03npn (0.25 #7, 0.07 #2391, 0.07 #3939), 06cvj (0.25 #1669, 0.25 #1908, 0.24 #2028), 06n90 (0.24 #2278, 0.11 #2636, 0.11 #3589), 04xvh5 (0.24 #510, 0.17 #2775, 0.14 #1224) >> Best rule #3221 for best value: >> intensional similarity = 4 >> extensional distance = 695 >> proper extension: 03l6q0; 03cyslc; 02bj22; >> query: (?x12766, 05p553) <- genre(?x12766, ?x1509), nominated_for(?x5079, ?x12766), genre(?x5747, ?x1509), ?x5747 = 0660b9b >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #59 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 02v63m; 0m491; 01svry; 0bwhdbl; 028kj0; *> query: (?x12766, 01j1n2) <- produced_by(?x12766, ?x11751), genre(?x12766, ?x6452), film(?x5079, ?x12766), ?x6452 = 02b5_l *> conf = 0.15 ranks of expected_values: 20 EVAL 03mr85 genre 01j1n2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 73.000 34.000 0.647 http://example.org/film/film/genre #11177-0cvw9 PRED entity: 0cvw9 PRED relation: featured_film_locations! PRED expected values: 08g_jw => 228 concepts (185 used for prediction) PRED predicted values (max 10 best out of 721): 061681 (0.19 #5935, 0.18 #3727, 0.16 #6671), 072x7s (0.18 #3793, 0.14 #17041, 0.14 #11153), 0413cff (0.18 #17299, 0.12 #6259, 0.09 #4051), 04dsnp (0.17 #29506, 0.12 #5954, 0.12 #12578), 0x25q (0.14 #2429, 0.14 #1693, 0.05 #9789), 0dnkmq (0.14 #2161, 0.09 #10993, 0.08 #12465), 050gkf (0.14 #1610, 0.09 #10442, 0.08 #11914), 0d90m (0.14 #1476, 0.09 #10308, 0.08 #11780), 05sy_5 (0.14 #1921, 0.08 #12961, 0.08 #15905), 0c0nhgv (0.14 #2285, 0.08 #12589, 0.08 #15533) >> Best rule #5935 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 09bjv; 0d6hn; 0c1xm; >> query: (?x8297, 061681) <- origin(?x11871, ?x8297), featured_film_locations(?x3257, ?x8297), location(?x2145, ?x8297), capital(?x613, ?x8297) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #10998 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 05r4w; *> query: (?x8297, 08g_jw) <- location(?x13116, ?x8297), contains(?x8297, ?x7574), place_of_death(?x13116, ?x7412), service_location(?x10867, ?x8297) *> conf = 0.05 ranks of expected_values: 278 EVAL 0cvw9 featured_film_locations! 08g_jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 228.000 185.000 0.188 http://example.org/film/film/featured_film_locations #11176-01tnxc PRED entity: 01tnxc PRED relation: award PRED expected values: 09sdmz => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 238): 0ck27z (0.50 #492, 0.18 #17245, 0.15 #11721), 0f4x7 (0.25 #31, 0.18 #17245, 0.15 #25668), 027dtxw (0.25 #4, 0.18 #17245, 0.15 #25668), 09sdmz (0.25 #203, 0.18 #17245, 0.15 #25668), 02x73k6 (0.25 #59, 0.18 #17245, 0.15 #25668), 0gqy2 (0.25 #162, 0.18 #17245, 0.15 #25668), 0bfvd4 (0.25 #113, 0.13 #27273, 0.13 #29681), 0789_m (0.25 #20, 0.06 #2025, 0.06 #11249), 019bnn (0.25 #265, 0.02 #7484, 0.01 #7885), 0gqyl (0.20 #906, 0.18 #17245, 0.15 #25668) >> Best rule #492 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 06gp3f; >> query: (?x8147, 0ck27z) <- award_winner(?x8147, ?x2900), ?x2900 = 02j9lm >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #203 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 03qd_; *> query: (?x8147, 09sdmz) <- award_winner(?x8147, ?x192), film(?x8147, ?x6030), ?x6030 = 0sxgv *> conf = 0.25 ranks of expected_values: 4 EVAL 01tnxc award 09sdmz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 94.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11175-01s1zk PRED entity: 01s1zk PRED relation: artists! PRED expected values: 06cqb => 123 concepts (122 used for prediction) PRED predicted values (max 10 best out of 223): 06by7 (0.53 #1575, 0.50 #2196, 0.43 #4987), 064t9 (0.47 #634, 0.47 #945, 0.45 #324), 016clz (0.30 #1557, 0.25 #1246, 0.24 #2178), 05bt6j (0.29 #355, 0.23 #976, 0.21 #665), 0xhtw (0.25 #1570, 0.22 #2191, 0.19 #4982), 025sc50 (0.24 #6567, 0.23 #981, 0.20 #670), 0155w (0.23 #1660, 0.20 #2281, 0.18 #5072), 03_d0 (0.22 #322, 0.20 #4976, 0.20 #8082), 0gywn (0.21 #6575, 0.20 #368, 0.19 #10245), 02lnbg (0.21 #990, 0.17 #2853, 0.14 #1922) >> Best rule #1575 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 06br6t; >> query: (?x7614, 06by7) <- role(?x7614, ?x227), ?x227 = 0342h, artists(?x2937, ?x7614) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #10245 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 474 *> proper extension: 039cq4; *> query: (?x7614, ?x1000) <- award_winner(?x7614, ?x1751), award(?x1751, ?x724), artists(?x1000, ?x1751) *> conf = 0.19 ranks of expected_values: 17 EVAL 01s1zk artists! 06cqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 123.000 122.000 0.530 http://example.org/music/genre/artists #11174-0265vt PRED entity: 0265vt PRED relation: disciplines_or_subjects PRED expected values: 02xlf => 44 concepts (40 used for prediction) PRED predicted values (max 10 best out of 31): 02xlf (0.82 #123, 0.80 #89, 0.71 #157), 02vxn (0.44 #378, 0.42 #447, 0.41 #412), 0w7c (0.23 #399, 0.22 #433, 0.20 #468), 0707q (0.20 #95, 0.20 #584, 0.20 #549), 0dwly (0.20 #584, 0.20 #549, 0.12 #366), 0l67h (0.20 #584, 0.20 #549, 0.09 #132), 05h83 (0.20 #584, 0.20 #549, 0.09 #306), 05qgc (0.20 #584, 0.20 #549, 0.08 #375), 0j7v_ (0.20 #584, 0.20 #549, 0.06 #361), 08_lx0 (0.20 #584, 0.20 #549, 0.04 #240) >> Best rule #123 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 0265wl; >> query: (?x9285, 02xlf) <- award(?x5346, ?x9285), award(?x1287, ?x9285), award_winner(?x9285, ?x4895), ?x1287 = 09dt7, influenced_by(?x5346, ?x3969), influenced_by(?x2127, ?x5346) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0265vt disciplines_or_subjects 02xlf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 40.000 0.818 http://example.org/award/award_category/disciplines_or_subjects #11173-075fzd PRED entity: 075fzd PRED relation: films PRED expected values: 064q5v => 72 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1562): 06rzwx (0.33 #4060, 0.33 #361, 0.29 #5117), 0p3_y (0.33 #123, 0.25 #1179, 0.25 #652), 0btpm6 (0.33 #382, 0.25 #1438, 0.25 #911), 0_b3d (0.33 #43, 0.25 #1099, 0.25 #572), 0cf8qb (0.33 #395, 0.25 #1451, 0.25 #924), 02q0k7v (0.33 #393, 0.25 #1449, 0.25 #922), 0f4_2k (0.33 #292, 0.25 #1348, 0.25 #821), 06sfk6 (0.33 #228, 0.25 #1284, 0.25 #757), 072x7s (0.33 #71, 0.25 #1127, 0.25 #600), 07j8r (0.33 #126, 0.25 #1182, 0.25 #655) >> Best rule #4060 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 0fzyg; >> query: (?x13930, 06rzwx) <- films(?x13930, ?x7462), nominated_for(?x102, ?x7462), genre(?x7462, ?x53), person(?x7462, ?x3397), award(?x3397, ?x1007), film(?x166, ?x7462), profession(?x3397, ?x131), film_release_distribution_medium(?x7462, ?x81), award_winner(?x2084, ?x3397) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3698 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 07c52; *> query: (?x13930, ?x54) <- films(?x13930, ?x7462), nominated_for(?x102, ?x7462), genre(?x7462, ?x604), person(?x7462, ?x3397), award(?x3397, ?x1007), profession(?x3397, ?x131), artists(?x671, ?x3397), genre(?x54, ?x604) *> conf = 0.01 ranks of expected_values: 1123 EVAL 075fzd films 064q5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 38.000 0.333 http://example.org/film/film_subject/films #11172-0bz6sb PRED entity: 0bz6sb PRED relation: ceremony! PRED expected values: 0k611 0gr07 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 267): 0k611 (0.92 #7333, 0.91 #8790, 0.90 #6120), 0gr07 (0.88 #7913, 0.86 #8885, 0.84 #7428), 01by1l (0.80 #5161, 0.70 #2737, 0.67 #4432), 025mb9 (0.80 #5222, 0.70 #2798, 0.67 #4493), 01c4_6 (0.80 #5147, 0.70 #2723, 0.67 #4418), 02wh75 (0.80 #5099, 0.70 #2675, 0.67 #4370), 02v1m7 (0.80 #5162, 0.70 #2738, 0.67 #4433), 0257pw (0.80 #5327, 0.70 #2903, 0.67 #4598), 02hdky (0.80 #5295, 0.70 #2871, 0.67 #4566), 024_fw (0.80 #5247, 0.70 #2823, 0.67 #4518) >> Best rule #7333 for best value: >> intensional similarity = 11 >> extensional distance = 23 >> proper extension: 02yxh9; >> query: (?x4700, 0k611) <- award_winner(?x4700, ?x3321), award(?x3321, ?x724), award_nominee(?x3321, ?x1930), instance_of_recurring_event(?x4700, ?x3459), ceremony(?x3066, ?x4700), profession(?x3321, ?x2348), ?x3066 = 0gqy2, role(?x1930, ?x316), profession(?x4082, ?x2348), ?x4082 = 016dsy, origin(?x1930, ?x3014) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0bz6sb ceremony! 0gr07 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.920 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bz6sb ceremony! 0k611 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.920 http://example.org/award/award_category/winners./award/award_honor/ceremony #11171-0373qt PRED entity: 0373qt PRED relation: student PRED expected values: 053xw6 => 40 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1216): 0157m (0.09 #249, 0.01 #25330, 0.01 #4429), 01tdnyh (0.05 #7160, 0.04 #9250, 0.03 #17610), 03ft8 (0.05 #6527, 0.04 #12797, 0.03 #8617), 03_dj (0.05 #1996, 0.03 #4086, 0.02 #6176), 09wj5 (0.05 #79, 0.03 #2169, 0.02 #10529), 05np2 (0.05 #1206, 0.02 #7476, 0.02 #9566), 0cj2w (0.05 #1883, 0.02 #8153, 0.02 #10243), 01vwbts (0.05 #813, 0.02 #11263, 0.02 #2903), 01vh18t (0.05 #1618, 0.02 #16248, 0.01 #22519), 0bv7t (0.05 #909, 0.02 #15539, 0.01 #21810) >> Best rule #249 for best value: >> intensional similarity = 2 >> extensional distance = 20 >> proper extension: 01z_jj; 07xhy; >> query: (?x8930, 0157m) <- state_province_region(?x8930, ?x6357), month(?x6357, ?x1459) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0373qt student 053xw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 40.000 28.000 0.091 http://example.org/education/educational_institution/students_graduates./education/education/student #11170-04vcdj PRED entity: 04vcdj PRED relation: actor! PRED expected values: 03y317 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 133): 03ln8b (0.11 #294, 0.03 #1083, 0.03 #1346), 05f4vxd (0.10 #351, 0.03 #1140, 0.03 #1403), 090s_0 (0.07 #3, 0.01 #1844, 0.01 #1581), 05jyb2 (0.07 #57, 0.01 #1898, 0.01 #1635), 0d68qy (0.07 #300, 0.06 #4212, 0.02 #1089), 08jgk1 (0.07 #285, 0.03 #1074, 0.03 #1337), 0kfv9 (0.06 #4212, 0.04 #553, 0.03 #1079), 080dwhx (0.06 #4212, 0.03 #1058, 0.03 #532), 02pqs8l (0.06 #4212, 0.02 #1111, 0.02 #1374), 0b6tzs (0.06 #4212, 0.02 #2631) >> Best rule #294 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 080knyg; 0308kx; >> query: (?x13332, 03ln8b) <- award(?x13332, ?x678), ?x678 = 0cqhk0, award_nominee(?x13332, ?x2841) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04vcdj actor! 03y317 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 71.000 0.105 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11169-0b4lkx PRED entity: 0b4lkx PRED relation: film_release_region PRED expected values: 06mkj => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 5): 09c7w0 (0.05 #871, 0.05 #1302, 0.05 #156), 0345h (0.02 #191, 0.02 #294, 0.02 #396), 0jgd (0.02 #28, 0.01 #565, 0.01 #131), 0d060g (0.02 #83, 0.01 #108, 0.01 #134), 0d0vqn (0.01 #32) >> Best rule #871 for best value: >> intensional similarity = 2 >> extensional distance = 963 >> proper extension: 04m1bm; 064n1pz; 02rb607; 040rmy; 026njb5; 04lqvlr; 04lqvly; 02phtzk; 02hfk5; 07l50vn; ... >> query: (?x8000, 09c7w0) <- film_crew_role(?x8000, ?x1284), nominated_for(?x198, ?x8000) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0b4lkx film_release_region 06mkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 82.000 0.053 http://example.org/film/film/runtime./film/film_cut/film_release_region #11168-01_0f7 PRED entity: 01_0f7 PRED relation: country PRED expected values: 07ssc => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 94): 07ssc (0.35 #74, 0.33 #16, 0.32 #1544), 0f8l9c (0.17 #19, 0.11 #77, 0.10 #1547), 03gj2 (0.17 #22, 0.03 #5166, 0.01 #4405), 04xvlr (0.12 #1588, 0.10 #117, 0.08 #1587), 06l3bl (0.10 #117, 0.08 #1587, 0.07 #2528), 03k9fj (0.10 #117, 0.08 #1587, 0.07 #2528), 0d060g (0.08 #8, 0.07 #184, 0.07 #713), 05qhw (0.08 #15, 0.03 #5166, 0.01 #4405), 03rjj (0.07 #64, 0.04 #1534, 0.03 #300), 0chghy (0.06 #834, 0.06 #892, 0.06 #188) >> Best rule #74 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 03ckwzc; 04gknr; 02qrv7; 05p3738; 02r8hh_; 02725hs; 04z257; 03z106; 02q_4ph; 08tq4x; ... >> query: (?x6531, 07ssc) <- country(?x6531, ?x94), titles(?x162, ?x6531), genre(?x6531, ?x3515), ?x3515 = 082gq >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_0f7 country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.348 http://example.org/film/film/country #11167-0n85g PRED entity: 0n85g PRED relation: artist PRED expected values: 01vv7sc 01wdqrx 01kx_81 01vrkdt 01k_mc 07mvp 024dw0 03j1p2n 019389 02hzz 01wx756 => 87 concepts (64 used for prediction) PRED predicted values (max 10 best out of 819): 048xh (0.50 #2046, 0.16 #45136, 0.14 #28792), 01vw8mh (0.40 #3421, 0.33 #1089, 0.25 #1866), 0dm5l (0.40 #3275, 0.25 #5607, 0.25 #1720), 01vtj38 (0.40 #3591, 0.25 #5923, 0.25 #2036), 01w9wwg (0.40 #3508, 0.25 #5840, 0.11 #13626), 02h9_l (0.40 #3778, 0.25 #6110, 0.11 #13896), 01vsyg9 (0.40 #3478, 0.16 #45136, 0.14 #28792), 0150jk (0.40 #3144, 0.12 #5476, 0.07 #31907), 03xl77 (0.40 #3283, 0.12 #5615, 0.06 #13401), 01vvpjj (0.40 #3242, 0.12 #5574, 0.06 #13360) >> Best rule #2046 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 011k1h; 01txts; >> query: (?x9224, 048xh) <- artist(?x9224, ?x3187), artist(?x9224, ?x1989), award(?x1989, ?x2139), profession(?x1989, ?x131), ?x3187 = 0840vq >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #737 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 03qx_f; *> query: (?x9224, 01wx756) <- organizations_founded(?x11949, ?x9224), artist(?x9224, ?x8332), artist(?x9224, ?x4840), artist(?x9224, ?x2940), ?x4840 = 06m61, artists(?x474, ?x8332), award(?x8332, ?x462), type_of_union(?x2940, ?x566) *> conf = 0.33 ranks of expected_values: 18, 19, 84, 93, 240, 303, 564, 580, 642, 806 EVAL 0n85g artist 01wx756 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 87.000 64.000 0.500 http://example.org/music/record_label/artist EVAL 0n85g artist 02hzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 64.000 0.500 http://example.org/music/record_label/artist EVAL 0n85g artist 019389 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 87.000 64.000 0.500 http://example.org/music/record_label/artist EVAL 0n85g artist 03j1p2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 87.000 64.000 0.500 http://example.org/music/record_label/artist EVAL 0n85g artist 024dw0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 87.000 64.000 0.500 http://example.org/music/record_label/artist EVAL 0n85g artist 07mvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 87.000 64.000 0.500 http://example.org/music/record_label/artist EVAL 0n85g artist 01k_mc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 64.000 0.500 http://example.org/music/record_label/artist EVAL 0n85g artist 01vrkdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 87.000 64.000 0.500 http://example.org/music/record_label/artist EVAL 0n85g artist 01kx_81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 64.000 0.500 http://example.org/music/record_label/artist EVAL 0n85g artist 01wdqrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 64.000 0.500 http://example.org/music/record_label/artist EVAL 0n85g artist 01vv7sc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 87.000 64.000 0.500 http://example.org/music/record_label/artist #11166-01wt4wc PRED entity: 01wt4wc PRED relation: artists! PRED expected values: 01_bkd 03339m 02z7f3 0jrv_ => 123 concepts (40 used for prediction) PRED predicted values (max 10 best out of 232): 01_bkd (0.60 #3025, 0.60 #944, 0.42 #2726), 06by7 (0.60 #911, 0.49 #4186, 0.47 #5079), 015pdg (0.52 #3279, 0.08 #3579, 0.04 #6851), 016clz (0.50 #2083, 0.50 #301, 0.40 #1490), 0xv2x (0.42 #2816, 0.40 #3115, 0.40 #1034), 064t9 (0.42 #9830, 0.42 #3583, 0.41 #6559), 0jrv_ (0.40 #1355, 0.29 #1950, 0.25 #3139), 02lw8j (0.40 #1151, 0.29 #2043, 0.20 #1448), 08jyyk (0.38 #2146, 0.33 #68, 0.27 #2442), 02yy88 (0.33 #193, 0.25 #2271, 0.25 #489) >> Best rule #3025 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 01j59b0; 01fchy; 016lj_; 0c9l1; 01518s; >> query: (?x8012, 01_bkd) <- artists(?x10306, ?x8012), artists(?x9248, ?x8012), ?x9248 = 02t8gf, parent_genre(?x2491, ?x10306) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 27, 54 EVAL 01wt4wc artists! 0jrv_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 123.000 40.000 0.600 http://example.org/music/genre/artists EVAL 01wt4wc artists! 02z7f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 123.000 40.000 0.600 http://example.org/music/genre/artists EVAL 01wt4wc artists! 03339m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 123.000 40.000 0.600 http://example.org/music/genre/artists EVAL 01wt4wc artists! 01_bkd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 40.000 0.600 http://example.org/music/genre/artists #11165-0p7pw PRED entity: 0p7pw PRED relation: nominated_for! PRED expected values: 0gr4k => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 222): 02wwsh8 (0.67 #8902, 0.67 #10543, 0.67 #10778), 04kxsb (0.60 #561, 0.57 #327, 0.53 #795), 0gs9p (0.58 #530, 0.56 #1232, 0.55 #296), 0k611 (0.49 #539, 0.49 #305, 0.45 #1241), 040njc (0.43 #241, 0.43 #475, 0.43 #1177), 0gqy2 (0.36 #6444, 0.36 #354, 0.35 #588), 0gr4k (0.36 #260, 0.35 #494, 0.33 #1196), 027dtxw (0.36 #238, 0.31 #472, 0.27 #706), 02pqp12 (0.31 #1227, 0.30 #993, 0.29 #525), 02rdxsh (0.31 #1220, 0.28 #986, 0.27 #752) >> Best rule #8902 for best value: >> intensional similarity = 4 >> extensional distance = 738 >> proper extension: 06mmr; >> query: (?x9383, ?x3209) <- award(?x9383, ?x3209), award_winner(?x9383, ?x7269), award_nominee(?x1733, ?x7269), award(?x7269, ?x102) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #260 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 0yyts; 05hjnw; 0pd64; 04b2qn; 0yx_w; *> query: (?x9383, 0gr4k) <- nominated_for(?x3209, ?x9383), ?x3209 = 02w9sd7, film_release_region(?x9383, ?x94), currency(?x9383, ?x170) *> conf = 0.36 ranks of expected_values: 7 EVAL 0p7pw nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 85.000 0.666 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11164-07y_7 PRED entity: 07y_7 PRED relation: group PRED expected values: 0134wr => 104 concepts (58 used for prediction) PRED predicted values (max 10 best out of 254): 07mvp (0.70 #2573, 0.60 #3575, 0.57 #1571), 01czx (0.60 #3694, 0.60 #2523, 0.50 #3358), 0dvqq (0.60 #2525, 0.50 #3360, 0.42 #1501), 01wv9xn (0.60 #2516, 0.47 #3687, 0.45 #2683), 027kwc (0.60 #2661, 0.47 #3832, 0.43 #3496), 017lb_ (0.60 #2601, 0.45 #2768, 0.42 #1501), 0123r4 (0.60 #2567, 0.42 #1501, 0.40 #1500), 0bk1p (0.57 #1440, 0.42 #1501, 0.40 #1500), 01s560x (0.57 #1471, 0.40 #1500, 0.40 #3812), 0134wr (0.53 #3767, 0.50 #2596, 0.45 #2763) >> Best rule #2573 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 0l14qv; 0l14md; 05r5c; 02hnl; 06ncr; >> query: (?x75, 07mvp) <- role(?x75, ?x2785), role(?x75, ?x2460), role(?x75, ?x645), role(?x1887, ?x75), role(?x212, ?x75), ?x645 = 028tv0, ?x2785 = 0jtg0, ?x2460 = 01wy6, group(?x75, ?x1751), performance_role(?x75, ?x736) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #3767 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 13 *> proper extension: 028tv0; *> query: (?x75, 0134wr) <- role(?x75, ?x3296), role(?x2328, ?x75), ?x3296 = 07_l6, group(?x75, ?x1751), performance_role(?x1887, ?x75), role(?x894, ?x75), role(?x75, ?x8014), role(?x487, ?x8014) *> conf = 0.53 ranks of expected_values: 10 EVAL 07y_7 group 0134wr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 104.000 58.000 0.700 http://example.org/music/performance_role/regular_performances./music/group_membership/group #11163-02k8k PRED entity: 02k8k PRED relation: country! PRED expected values: 06f41 03_8r 03rbzn => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 52): 03_8r (0.76 #853, 0.75 #2413, 0.74 #437), 06f41 (0.74 #430, 0.63 #170, 0.62 #326), 0194d (0.74 #461, 0.59 #253, 0.59 #201), 01lb14 (0.70 #171, 0.68 #431, 0.67 #223), 01cgz (0.68 #429, 0.67 #637, 0.67 #169), 064vjs (0.65 #445, 0.52 #185, 0.48 #861), 02y8z (0.59 #174, 0.56 #434, 0.52 #226), 09w1n (0.59 #178, 0.52 #230, 0.50 #282), 07jbh (0.59 #447, 0.56 #187, 0.55 #863), 019tzd (0.56 #454, 0.56 #194, 0.49 #662) >> Best rule #853 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 06q1r; >> query: (?x6691, 03_8r) <- adjoins(?x6691, ?x3720), capital(?x6691, ?x13999), film_release_region(?x124, ?x6691) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 21 EVAL 02k8k country! 03rbzn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 154.000 154.000 0.761 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02k8k country! 03_8r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.761 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02k8k country! 06f41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.761 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #11162-06cv1 PRED entity: 06cv1 PRED relation: type_of_union PRED expected values: 01g63y => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 2): 01g63y (0.40 #124, 0.34 #109, 0.33 #133), 0jgjn (0.01 #114) >> Best rule #124 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 01n7qlf; >> query: (?x523, 01g63y) <- celebrity(?x6844, ?x523), type_of_union(?x523, ?x566), gender(?x523, ?x231) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06cv1 type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.398 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11161-06wvfq PRED entity: 06wvfq PRED relation: place_of_birth PRED expected values: 0c8tk => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 97): 0c8tk (0.35 #19018, 0.33 #53554, 0.32 #45098), 04vmp (0.27 #4494, 0.25 #973, 0.24 #5198), 029kpy (0.17 #2390, 0.17 #982, 0.12 #3799), 02_286 (0.16 #19038, 0.10 #21859, 0.09 #33839), 07dfk (0.15 #5993, 0.03 #17263, 0.03 #16559), 0dlv0 (0.12 #3876, 0.12 #4580, 0.11 #2467), 09c6w (0.09 #198, 0.08 #903, 0.07 #1607), 02c7tb (0.09 #539, 0.07 #1948), 01sv6k (0.09 #595, 0.06 #2708, 0.05 #3412), 01_yvy (0.08 #1095, 0.02 #7432, 0.02 #8137) >> Best rule #19018 for best value: >> intensional similarity = 3 >> extensional distance = 286 >> proper extension: 011zf2; 03j43; >> query: (?x9608, ?x4335) <- award(?x9608, ?x10156), location(?x9608, ?x4335), capital(?x9305, ?x4335) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06wvfq place_of_birth 0c8tk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.345 http://example.org/people/person/place_of_birth #11160-0r3tb PRED entity: 0r3tb PRED relation: place_of_death! PRED expected values: 07zhd7 => 114 concepts (44 used for prediction) PRED predicted values (max 10 best out of 860): 012vby (0.08 #3782, 0.08 #546, 0.06 #5295), 017jv5 (0.08 #3782, 0.06 #5295, 0.06 #756), 0b_dh (0.08 #601, 0.07 #1357, 0.07 #2113), 01gz9n (0.08 #520, 0.07 #1276, 0.07 #2032), 03bw6 (0.08 #336, 0.07 #1092, 0.07 #1848), 0gv2r (0.08 #308, 0.07 #1064, 0.07 #1820), 0gv40 (0.08 #199, 0.07 #955, 0.07 #1711), 0bzyh (0.08 #157, 0.07 #913, 0.07 #1669), 09xx0m (0.08 #687, 0.07 #1443, 0.07 #2199), 02qx1m2 (0.08 #516, 0.07 #1272, 0.07 #2028) >> Best rule #3782 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 01tlmw; 0r0m6; 0136jw; 0167q3; 0r2gj; 0b2ds; 0qpqn; 0r15k; 0rqf1; 0r785; ... >> query: (?x8448, ?x1850) <- place_of_death(?x4926, ?x8448), award_nominee(?x1850, ?x4926), award(?x4926, ?x198), county(?x8448, ?x10399) >> conf = 0.08 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0r3tb place_of_death! 07zhd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 44.000 0.079 http://example.org/people/deceased_person/place_of_death #11159-06mxs PRED entity: 06mxs PRED relation: place_of_birth! PRED expected values: 0bdt8 => 187 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1946): 0147dk (0.36 #101693, 0.36 #101692, 0.35 #44332), 0d5_f (0.14 #13040, 0.14 #2609, 0.13 #2608), 05dppk (0.14 #13040, 0.13 #2608, 0.12 #13041), 0kj34 (0.07 #1885, 0.06 #4495, 0.06 #7102), 0fbx6 (0.07 #843, 0.06 #3453, 0.06 #6060), 012g92 (0.07 #2425, 0.06 #5035, 0.06 #7642), 01q8fxx (0.07 #2292, 0.06 #4902, 0.06 #7509), 0csdzz (0.07 #2195, 0.06 #4805, 0.06 #7412), 0gdqy (0.07 #2138, 0.06 #4748, 0.06 #7355), 02hh8j (0.07 #2017, 0.06 #4627, 0.06 #7234) >> Best rule #101693 for best value: >> intensional similarity = 2 >> extensional distance = 54 >> proper extension: 0ftn8; >> query: (?x5168, ?x4217) <- location(?x4217, ?x5168), capital(?x304, ?x5168) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #106908 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 01q0l; *> query: (?x5168, ?x2083) <- capital(?x304, ?x5168), nationality(?x2083, ?x304), contains(?x455, ?x304) *> conf = 0.04 ranks of expected_values: 378 EVAL 06mxs place_of_birth! 0bdt8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 187.000 91.000 0.358 http://example.org/people/person/place_of_birth #11158-0b06q PRED entity: 0b06q PRED relation: category PRED expected values: 08mbj5d => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #60, 0.08 #18, 0.06 #25) >> Best rule #60 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x8009, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b06q category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #11157-01pgk0 PRED entity: 01pgk0 PRED relation: artist! PRED expected values: 0g768 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 108): 0g768 (0.40 #37, 0.17 #742, 0.15 #5819), 0181dw (0.28 #1453, 0.25 #606, 0.17 #3286), 015_1q (0.22 #3969, 0.21 #3264, 0.21 #5097), 03rhqg (0.20 #16, 0.17 #5798, 0.16 #2132), 01trtc (0.20 #73, 0.16 #1484, 0.15 #3176), 011k1h (0.20 #10, 0.13 #3254, 0.13 #3536), 0fb0v (0.20 #7, 0.13 #2123, 0.12 #1418), 02bh8z (0.20 #22, 0.10 #2138, 0.08 #727), 01dtcb (0.20 #47, 0.09 #4419, 0.08 #752), 02p11jq (0.20 #13, 0.08 #5795, 0.08 #718) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02l840; 01vtj38; >> query: (?x11992, 0g768) <- participant(?x4741, ?x11992), producer_type(?x11992, ?x632), vacationer(?x3912, ?x11992), artists(?x671, ?x11992) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pgk0 artist! 0g768 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.400 http://example.org/music/record_label/artist #11156-01kf3_9 PRED entity: 01kf3_9 PRED relation: story_by PRED expected values: 0fx02 => 137 concepts (94 used for prediction) PRED predicted values (max 10 best out of 124): 0fx02 (0.45 #1361, 0.33 #925, 0.33 #60), 03kpvp (0.27 #1300, 0.14 #5434, 0.13 #6521), 0kb3n (0.20 #1226, 0.07 #2747, 0.06 #2965), 03tf_h (0.19 #3475, 0.14 #6739, 0.12 #7825), 0343h (0.13 #2405, 0.09 #3493, 0.08 #3928), 079vf (0.11 #4129, 0.10 #2606, 0.08 #2172), 01y8d4 (0.11 #4047, 0.09 #3612, 0.04 #7745), 011s9r (0.11 #4108, 0.09 #3673, 0.04 #7806), 03_gd (0.10 #1092, 0.05 #1745, 0.04 #2179), 042xh (0.10 #1298, 0.03 #2819, 0.03 #3037) >> Best rule #1361 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01kf5lf; 029v40; >> query: (?x1851, 0fx02) <- film(?x3692, ?x1851), ?x3692 = 03kpvp, currency(?x1851, ?x170), country(?x1851, ?x512) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kf3_9 story_by 0fx02 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 94.000 0.455 http://example.org/film/film/story_by #11155-046zh PRED entity: 046zh PRED relation: vacationer! PRED expected values: 03gh4 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 65): 05qtj (0.21 #917, 0.19 #1159, 0.09 #1522), 03gh4 (0.20 #1168, 0.19 #926, 0.11 #1047), 0cv3w (0.13 #902, 0.12 #176, 0.11 #1144), 035qy (0.12 #153, 0.03 #516, 0.02 #879), 01f08r (0.12 #185, 0.01 #548), 0b90_r (0.11 #1092, 0.10 #850, 0.05 #487), 0f2v0 (0.10 #1150, 0.07 #908, 0.04 #1271), 04jpl (0.06 #856, 0.06 #1098, 0.06 #493), 0160w (0.06 #849, 0.06 #1091, 0.05 #486), 0261m (0.06 #945, 0.05 #1187, 0.04 #1066) >> Best rule #917 for best value: >> intensional similarity = 2 >> extensional distance = 124 >> proper extension: 04d_mtq; >> query: (?x5246, 05qtj) <- vacationer(?x957, ?x5246), type_of_union(?x5246, ?x566) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #1168 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 140 *> proper extension: 02mjmr; 024dgj; *> query: (?x5246, 03gh4) <- participant(?x5246, ?x105), award(?x5246, ?x154), vacationer(?x957, ?x5246) *> conf = 0.20 ranks of expected_values: 2 EVAL 046zh vacationer! 03gh4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 123.000 123.000 0.206 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #11154-0mjn2 PRED entity: 0mjn2 PRED relation: artist! PRED expected values: 0fb0v => 100 concepts (70 used for prediction) PRED predicted values (max 10 best out of 121): 01cszh (0.43 #148, 0.40 #11, 0.33 #970), 017l96 (0.40 #19, 0.29 #841, 0.29 #156), 015_1q (0.36 #1527, 0.35 #1390, 0.31 #2486), 03rhqg (0.32 #1523, 0.29 #1249, 0.29 #153), 0229rs (0.29 #155, 0.27 #977, 0.21 #703), 011k1h (0.29 #832, 0.23 #558, 0.20 #1380), 033hn8 (0.27 #1521, 0.25 #2617, 0.24 #1932), 02p11jq (0.25 #1109, 0.21 #698, 0.20 #972), 0g768 (0.23 #584, 0.22 #447, 0.22 #310), 0mzkr (0.22 #436, 0.18 #1258, 0.15 #573) >> Best rule #148 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 07qnf; 03c3yf; >> query: (?x10263, 01cszh) <- group(?x2798, ?x10263), artists(?x1928, ?x10263), ?x1928 = 0mhfr, ?x2798 = 03qjg, origin(?x10263, ?x1523) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #829 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 016732; *> query: (?x10263, 0fb0v) <- inductee(?x1091, ?x10263), award(?x10263, ?x9828), award(?x10263, ?x4958), ?x4958 = 03qbnj, award(?x1136, ?x9828), ?x1136 = 07c0j *> conf = 0.14 ranks of expected_values: 25 EVAL 0mjn2 artist! 0fb0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 100.000 70.000 0.429 http://example.org/music/record_label/artist #11153-0146pg PRED entity: 0146pg PRED relation: instrumentalists! PRED expected values: 01wy6 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 94): 0342h (0.64 #2931, 0.63 #3275, 0.63 #3966), 05148p4 (0.36 #622, 0.36 #880, 0.35 #1655), 018vs (0.29 #3973, 0.29 #3455, 0.28 #4060), 0l14md (0.29 #180, 0.18 #8, 0.12 #1299), 06ncr (0.21 #215, 0.09 #43, 0.08 #3486), 02hnl (0.18 #34, 0.17 #3477, 0.17 #3650), 0l14j_ (0.18 #53, 0.14 #225, 0.07 #483), 03m5k (0.18 #17, 0.14 #189, 0.04 #2082), 02fsn (0.18 #51, 0.07 #223, 0.04 #567), 03qjg (0.17 #3234, 0.17 #136, 0.16 #2976) >> Best rule #2931 for best value: >> intensional similarity = 3 >> extensional distance = 266 >> proper extension: 03j0br4; 01w524f; 01bpnd; 0k60; 01j590z; >> query: (?x669, 0342h) <- instrumentalists(?x316, ?x669), award(?x669, ?x1079), location(?x669, ?x739) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #218 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 032t2z; *> query: (?x669, 01wy6) <- instrumentalists(?x569, ?x669), ?x569 = 07c6l, artists(?x505, ?x669) *> conf = 0.14 ranks of expected_values: 12 EVAL 0146pg instrumentalists! 01wy6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 141.000 141.000 0.642 http://example.org/music/instrument/instrumentalists #11152-06f0k PRED entity: 06f0k PRED relation: genre PRED expected values: 06n90 => 62 concepts (56 used for prediction) PRED predicted values (max 10 best out of 150): 07s9rl0 (0.97 #1077, 0.83 #1488, 0.71 #417), 0lsxr (0.88 #425, 0.23 #1003, 0.19 #1496), 01z77k (0.56 #276, 0.54 #360, 0.33 #109), 02fgmn (0.46 #479, 0.12 #3063, 0.12 #1057), 01t_vv (0.34 #780, 0.34 #861, 0.33 #618), 0l4h_ (0.33 #47, 0.16 #3309, 0.09 #3888), 06cvj (0.33 #4, 0.16 #3309, 0.09 #3888), 06n90 (0.28 #1171, 0.26 #1417, 0.21 #924), 07ssc (0.25 #416, 0.21 #415, 0.18 #331), 01jfsb (0.25 #428, 0.12 #1088, 0.11 #1499) >> Best rule #1077 for best value: >> intensional similarity = 15 >> extensional distance = 92 >> proper extension: 01qn7n; 0g60z; 06cs95; 03kq98; 02py4c8; 02k_4g; 0464pz; 02bg8v; 0kfv9; 0n2bh; ... >> query: (?x12239, 07s9rl0) <- genre(?x12239, ?x8467), titles(?x512, ?x12239), genre(?x5694, ?x8467), genre(?x4048, ?x8467), genre(?x3376, ?x8467), genre(?x2868, ?x8467), genre(?x2529, ?x8467), genre(?x2207, ?x8467), ?x5694 = 02704ff, country(?x2207, ?x94), ?x2529 = 03m8y5, award(?x2868, ?x1053), film_release_region(?x2868, ?x142), language(?x4048, ?x254), nominated_for(?x2393, ?x3376) >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #1171 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 93 *> proper extension: 09kn9; 05sy2k_; 08cx5g; 02648p; 0828jw; 0ctzf1; 06qwh; 028k2x; 02r2j8; 02q5bx2; ... *> query: (?x12239, 06n90) <- genre(?x12239, ?x8467), titles(?x512, ?x12239), genre(?x10873, ?x8467), genre(?x8551, ?x8467), genre(?x7432, ?x8467), genre(?x4888, ?x8467), ?x10873 = 06cgf, ?x7432 = 01hv3t, film(?x5636, ?x4888), nominated_for(?x1587, ?x8551), nominated_for(?x400, ?x4888), ?x1587 = 02rdyk7 *> conf = 0.28 ranks of expected_values: 8 EVAL 06f0k genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 62.000 56.000 0.968 http://example.org/tv/tv_program/genre #11151-09n5t_ PRED entity: 09n5t_ PRED relation: artists PRED expected values: 0m_v0 015cxv => 63 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1073): 016z1t (0.67 #10047, 0.50 #3626, 0.44 #12186), 01wvxw1 (0.60 #8228, 0.50 #13579, 0.50 #10369), 01cblr (0.60 #6855, 0.50 #5786, 0.50 #4717), 01vrncs (0.60 #13978, 0.50 #5415, 0.43 #10767), 020_4z (0.60 #13770, 0.40 #8419, 0.33 #19120), 0134tg (0.57 #11182, 0.50 #14393, 0.50 #5830), 01pbxb (0.57 #10703, 0.50 #5351, 0.50 #4282), 01vw20_ (0.57 #10942, 0.50 #14153, 0.50 #13082), 04r1t (0.57 #10834, 0.50 #14045, 0.50 #5482), 0fq117k (0.57 #11351, 0.50 #1719, 0.40 #14562) >> Best rule #10047 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 016cjb; >> query: (?x12215, 016z1t) <- artists(?x12215, ?x7544), artists(?x12215, ?x4790), artists(?x12215, ?x2807), artists(?x12215, ?x1992), artists(?x12215, ?x211), role(?x211, ?x212), artist(?x5634, ?x211), influenced_by(?x1573, ?x7544), ?x2807 = 03h_fk5, ?x1992 = 01wz3cx, artist(?x3050, ?x7544), profession(?x4790, ?x220) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3801 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 02w4v; *> query: (?x12215, 015cxv) <- artists(?x12215, ?x7544), artists(?x12215, ?x2807), artists(?x12215, ?x1992), artists(?x12215, ?x211), role(?x211, ?x212), artist(?x9671, ?x211), influenced_by(?x1573, ?x7544), ?x2807 = 03h_fk5, ?x1992 = 01wz3cx, ?x9671 = 041bnw, instrumentalists(?x227, ?x211) *> conf = 0.50 ranks of expected_values: 28, 30 EVAL 09n5t_ artists 015cxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 63.000 28.000 0.667 http://example.org/music/genre/artists EVAL 09n5t_ artists 0m_v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 63.000 28.000 0.667 http://example.org/music/genre/artists #11150-0gz_ PRED entity: 0gz_ PRED relation: influenced_by! PRED expected values: 07kb5 03_87 0h25 => 79 concepts (37 used for prediction) PRED predicted values (max 10 best out of 340): 01hb6v (0.70 #5962, 0.66 #6944, 0.19 #2447), 045bg (0.60 #1992, 0.33 #36, 0.27 #2483), 032l1 (0.45 #2565, 0.33 #607, 0.33 #118), 0399p (0.44 #4719, 0.25 #1780, 0.23 #6675), 0dzkq (0.40 #2080, 0.33 #124, 0.31 #3552), 07h1q (0.40 #2347, 0.20 #7735, 0.19 #2447), 04411 (0.38 #1492, 0.33 #26, 0.19 #4431), 0jcx (0.38 #1583, 0.19 #4522, 0.19 #2447), 07ym0 (0.33 #814, 0.33 #325, 0.25 #1791), 040db (0.33 #564, 0.33 #75, 0.25 #1541) >> Best rule #5962 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0h0p_; 0ddkf; >> query: (?x3712, 01hb6v) <- influenced_by(?x6975, ?x3712), influenced_by(?x7746, ?x6975), ?x7746 = 0h0yt, type_of_union(?x3712, ?x566) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #741 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 081k8; *> query: (?x3712, 03_87) <- influenced_by(?x11097, ?x3712), influenced_by(?x6975, ?x3712), ?x6975 = 05np2, ?x11097 = 02wh0, nationality(?x3712, ?x1353), type_of_union(?x3712, ?x566) *> conf = 0.33 ranks of expected_values: 20, 25, 81 EVAL 0gz_ influenced_by! 0h25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 79.000 37.000 0.696 http://example.org/influence/influence_node/influenced_by EVAL 0gz_ influenced_by! 03_87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 79.000 37.000 0.696 http://example.org/influence/influence_node/influenced_by EVAL 0gz_ influenced_by! 07kb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 79.000 37.000 0.696 http://example.org/influence/influence_node/influenced_by #11149-0c53zb PRED entity: 0c53zb PRED relation: ceremony! PRED expected values: 0gq_v 0gqy2 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 364): 0gqy2 (0.95 #6953, 0.94 #6463, 0.94 #7199), 0gq_v (0.86 #4171, 0.84 #4659, 0.83 #3438), 0gr0m (0.83 #3475, 0.82 #4208, 0.81 #6162), 0gqng (0.82 #4159, 0.81 #4647, 0.81 #5868), 0gr07 (0.82 #4313, 0.81 #4801, 0.81 #5289), 0l8z1 (0.78 #4686, 0.78 #5174, 0.78 #5418), 018wdw (0.77 #10035, 0.77 #9054, 0.74 #7828), 0gqxm (0.77 #10035, 0.77 #9054, 0.74 #7828), 0gqzz (0.77 #10035, 0.77 #9054, 0.74 #7828), 02x201b (0.77 #10035, 0.77 #9054, 0.74 #7828) >> Best rule #6953 for best value: >> intensional similarity = 14 >> extensional distance = 57 >> proper extension: 0fk0xk; >> query: (?x4445, 0gqy2) <- ceremony(?x1323, ?x4445), award_winner(?x4445, ?x3519), award_winner(?x4445, ?x2426), music(?x5220, ?x3519), ceremony(?x1323, ?x11087), award_winner(?x1323, ?x9008), award(?x9008, ?x528), nominated_for(?x200, ?x5220), award(?x300, ?x1323), profession(?x2426, ?x319), role(?x300, ?x227), profession(?x9008, ?x220), participant(?x300, ?x1970), ?x11087 = 073h5b >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0c53zb ceremony! 0gqy2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.949 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c53zb ceremony! 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.949 http://example.org/award/award_category/winners./award/award_honor/ceremony #11148-0fvyg PRED entity: 0fvyg PRED relation: time_zones PRED expected values: 02hcv8 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.74 #560, 0.69 #55, 0.56 #29), 02lcqs (0.32 #291, 0.25 #174, 0.25 #838), 02fqwt (0.29 #300, 0.27 #105, 0.22 #131), 02llzg (0.14 #446, 0.14 #316, 0.09 #329), 02hczc (0.14 #301, 0.11 #366, 0.10 #535), 03bdv (0.08 #331, 0.06 #461, 0.06 #761), 03plfd (0.08 #322, 0.05 #817, 0.03 #218), 052vwh (0.03 #90, 0.03 #454, 0.02 #611), 042g7t (0.03 #544, 0.02 #219, 0.02 #649), 0gsrz4 (0.02 #1218) >> Best rule #560 for best value: >> intensional similarity = 2 >> extensional distance = 173 >> proper extension: 0pbhz; 0jq27; >> query: (?x11246, ?x2674) <- administrative_division(?x11246, ?x11763), time_zones(?x11763, ?x2674) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fvyg time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.739 http://example.org/location/location/time_zones #11147-05zvq6g PRED entity: 05zvq6g PRED relation: nominated_for PRED expected values: 05y0cr => 46 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1434): 0bnzd (0.75 #4234, 0.67 #5813, 0.50 #2656), 0m313 (0.73 #6327, 0.72 #7903, 0.67 #4748), 0gmgwnv (0.72 #8845, 0.44 #5690, 0.41 #7269), 026p4q7 (0.69 #8242, 0.45 #6666, 0.37 #9823), 049xgc (0.68 #7182, 0.62 #8758, 0.44 #5603), 09gq0x5 (0.66 #8142, 0.64 #6566, 0.38 #9723), 01cmp9 (0.66 #8818, 0.50 #2506, 0.44 #5663), 011yqc (0.66 #8096, 0.41 #6520, 0.30 #9677), 011yl_ (0.64 #6839, 0.48 #8415, 0.44 #5260), 011yph (0.64 #6396, 0.31 #7972, 0.22 #4817) >> Best rule #4234 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 09qwmm; 09sb52; 02y_rq5; >> query: (?x1008, 0bnzd) <- award(?x8888, ?x1008), award(?x988, ?x1008), ?x988 = 01tspc6, nominated_for(?x1008, ?x2098), honored_for(?x2245, ?x2098), spouse(?x8888, ?x7261) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6086 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0f4x7; 02z0dfh; 04kxsb; 09qv_s; 02ppm4q; *> query: (?x1008, 05y0cr) <- award(?x988, ?x1008), award_nominee(?x3553, ?x988), nominated_for(?x1008, ?x1199), ?x1199 = 0pv3x, ?x3553 = 0bq2g *> conf = 0.22 ranks of expected_values: 369 EVAL 05zvq6g nominated_for 05y0cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 16.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11146-0736qr PRED entity: 0736qr PRED relation: type_of_union PRED expected values: 04ztj => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #53, 0.72 #21, 0.72 #74), 01g63y (0.48 #73, 0.40 #6, 0.33 #2) >> Best rule #53 for best value: >> intensional similarity = 3 >> extensional distance = 959 >> proper extension: 01mwsnc; 05w6cw; 0131kb; >> query: (?x12551, 04ztj) <- gender(?x12551, ?x231), film(?x12551, ?x3133), cinematography(?x3133, ?x7384) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0736qr type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.736 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11145-08ff1k PRED entity: 08ff1k PRED relation: location_of_ceremony PRED expected values: 0d35y => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 50): 030qb3t (0.59 #1555, 0.57 #1435, 0.09 #257), 0ycht (0.59 #1555, 0.57 #1435, 0.05 #950), 04jpl (0.59 #1555, 0.57 #1435, 0.03 #845), 0cv3w (0.13 #392, 0.06 #1470, 0.05 #1349), 0r62v (0.11 #17, 0.09 #255, 0.04 #614), 02_286 (0.11 #132, 0.03 #849, 0.03 #1327), 0k049 (0.06 #1439, 0.04 #1318, 0.03 #1198), 0d35y (0.05 #1128, 0.04 #650, 0.03 #889), 0fr0t (0.04 #643, 0.03 #882, 0.03 #1002), 035hm (0.04 #675, 0.03 #914, 0.03 #1034) >> Best rule #1555 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 0167v4; >> query: (?x5438, ?x362) <- spouse(?x14459, ?x5438), award(?x5438, ?x1307), location_of_ceremony(?x14459, ?x362) >> conf = 0.59 => this is the best rule for 3 predicted values *> Best rule #1128 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0ff2k; *> query: (?x5438, 0d35y) <- spouse(?x14459, ?x5438), people(?x4322, ?x14459), type_of_union(?x5438, ?x566) *> conf = 0.05 ranks of expected_values: 8 EVAL 08ff1k location_of_ceremony 0d35y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 124.000 124.000 0.585 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #11144-0j4q1 PRED entity: 0j4q1 PRED relation: location! PRED expected values: 016ypb => 102 concepts (25 used for prediction) PRED predicted values (max 10 best out of 760): 0dvld (0.12 #6257, 0.10 #8775, 0.09 #13813), 0dx97 (0.10 #3583, 0.08 #6102, 0.07 #8620), 0134w7 (0.10 #2681, 0.08 #5200, 0.07 #7718), 01lwx (0.10 #4879, 0.08 #7398, 0.07 #9916), 04shbh (0.10 #2689, 0.08 #5208, 0.07 #7726), 02d42t (0.10 #3508, 0.08 #6027, 0.07 #8545), 01vsykc (0.10 #3149, 0.07 #8186, 0.06 #13224), 01c8v0 (0.10 #3301, 0.07 #10857, 0.06 #13376), 0prjs (0.10 #2753, 0.06 #12828, 0.05 #17867), 01pcql (0.10 #3221, 0.05 #18335, 0.04 #5740) >> Best rule #6257 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 0k33p; 0f8j6; >> query: (?x14104, 0dvld) <- contains(?x512, ?x14104), ?x512 = 07ssc, location(?x1040, ?x14104), nominated_for(?x1040, ?x3626), award_winner(?x1265, ?x1040) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #40863 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 168 *> proper extension: 0d6hn; *> query: (?x14104, 016ypb) <- contains(?x512, ?x14104), location(?x1040, ?x14104), currency(?x512, ?x170), combatants(?x151, ?x512), participating_countries(?x358, ?x512) *> conf = 0.02 ranks of expected_values: 675 EVAL 0j4q1 location! 016ypb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 102.000 25.000 0.125 http://example.org/people/person/places_lived./people/place_lived/location #11143-028kb PRED entity: 028kb PRED relation: month! PRED expected values: 0156q 0h7h6 06mxs 071vr => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 314): 0h7h6 (0.90 #52, 0.90 #11, 0.88 #71), 0156q (0.90 #52, 0.90 #11, 0.88 #71), 071vr (0.90 #52, 0.90 #11, 0.88 #71), 06mxs (0.90 #52, 0.90 #11, 0.88 #71), 0l0mk (0.90 #52, 0.90 #11, 0.88 #71), 03czqs (0.90 #52, 0.88 #95, 0.87 #84), 02dtg (0.44 #28, 0.31 #49), 01d26y (0.44 #28), 05jbn (0.37 #91, 0.31 #49), 01b8jj (0.37 #91) >> Best rule #52 for best value: >> intensional similarity = 103 >> extensional distance = 1 >> proper extension: 04wzr; >> query: (?x9905, ?x1646) <- month(?x12674, ?x9905), month(?x11237, ?x9905), month(?x11197, ?x9905), month(?x10610, ?x9905), month(?x9605, ?x9905), month(?x9559, ?x9905), month(?x6959, ?x9905), month(?x6494, ?x9905), month(?x6054, ?x9905), month(?x5267, ?x9905), month(?x4627, ?x9905), month(?x4271, ?x9905), month(?x3501, ?x9905), month(?x3269, ?x9905), month(?x3125, ?x9905), month(?x3026, ?x9905), month(?x2985, ?x9905), month(?x2645, ?x9905), month(?x2611, ?x9905), month(?x2316, ?x9905), month(?x2254, ?x9905), month(?x1523, ?x9905), month(?x1458, ?x9905), month(?x739, ?x9905), month(?x206, ?x9905), ?x12674 = 0g6xq, ?x9605 = 02frhbc, seasonal_months(?x4925, ?x9905), seasonal_months(?x4827, ?x9905), seasonal_months(?x3270, ?x9905), seasonal_months(?x3107, ?x9905), seasonal_months(?x2140, ?x9905), seasonal_months(?x1459, ?x9905), ?x6494 = 02sn34, ?x4271 = 06wjf, ?x11197 = 05l64, ?x3107 = 05lf_, ?x3269 = 0vzm, ?x3026 = 0cv3w, month(?x6960, ?x3270), month(?x1658, ?x3270), month(?x1646, ?x3270), ?x2985 = 03hrz, ?x3501 = 0f2v0, ?x739 = 02_286, ?x2140 = 040fb, ?x4827 = 03_ly, ?x1459 = 04w_7, ?x1523 = 030qb3t, ?x2254 = 0dclg, ?x4925 = 0ll3, ?x2611 = 02h6_6p, place_of_birth(?x275, ?x5267), origin(?x6935, ?x5267), origin(?x4261, ?x5267), citytown(?x3543, ?x5267), dog_breed(?x5267, ?x3095), dog_breed(?x5267, ?x1706), location(?x2580, ?x5267), ?x1658 = 0h7h6, ?x9559 = 07dfk, student(?x1809, ?x275), award_winner(?x275, ?x6633), award_winner(?x275, ?x4333), award_winner(?x275, ?x274), award_winner(?x275, ?x237), jurisdiction_of_office(?x1195, ?x5267), ?x274 = 0cnl80, ?x237 = 04t2l2, award_winner(?x222, ?x275), ?x2645 = 03h64, ?x6633 = 0cl0bk, ?x11237 = 03khn, profession(?x275, ?x319), ?x1458 = 05ywg, ?x6054 = 0fn2g, ?x6960 = 071vr, ?x4333 = 0cnl09, ?x2316 = 06t2t, state(?x5267, ?x4600), ?x206 = 01914, artist(?x2299, ?x4261), ?x10610 = 03902, award_nominee(?x6935, ?x450), ?x3095 = 01_gx_, ?x1195 = 0pqc5, award_winner(?x2580, ?x192), contains(?x94, ?x5267), ?x3125 = 0d6lp, award(?x275, ?x678), location_of_ceremony(?x566, ?x5267), award_nominee(?x2657, ?x275), ?x6959 = 06c62, ?x4627 = 05qtj, locations(?x4368, ?x5267), mode_of_transportation(?x5267, ?x4272), dog_breed(?x8993, ?x1706), dog_breed(?x4356, ?x1706), ?x8993 = 0fsb8, ?x4356 = 06wxw, award(?x4261, ?x2180), ?x2180 = 02v1m7, participant(?x6935, ?x2614) >> conf = 0.90 => this is the best rule for 6 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 028kb month! 071vr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 028kb month! 06mxs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 028kb month! 0h7h6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 028kb month! 0156q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #11142-04wsz PRED entity: 04wsz PRED relation: service_location! PRED expected values: 0cv9b => 129 concepts (45 used for prediction) PRED predicted values (max 10 best out of 179): 07zl6m (0.50 #813, 0.50 #676, 0.43 #1087), 064f29 (0.47 #4167, 0.38 #1699, 0.36 #3480), 077w0b (0.40 #4173, 0.33 #746, 0.33 #609), 018mxj (0.38 #1511, 0.33 #5904, 0.33 #690), 0cv9b (0.38 #4943, 0.33 #691, 0.33 #418), 05b5c (0.36 #3411, 0.33 #4235, 0.33 #808), 0k9ts (0.36 #3375, 0.33 #772, 0.33 #635), 04sv4 (0.33 #4191, 0.33 #764, 0.33 #627), 0p4wb (0.33 #4116, 0.33 #689, 0.31 #4941), 06p8m (0.33 #787, 0.33 #650, 0.29 #1061) >> Best rule #813 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 06bnz; >> query: (?x9122, 07zl6m) <- contains(?x9122, ?x608), service_location(?x555, ?x9122), ?x555 = 01c6k4, partially_contains(?x5903, ?x9122) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4943 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 06qd3; 05b4w; *> query: (?x9122, 0cv9b) <- contains(?x9122, ?x4092), service_location(?x555, ?x9122), ?x555 = 01c6k4, administrative_parent(?x4092, ?x551) *> conf = 0.38 ranks of expected_values: 5 EVAL 04wsz service_location! 0cv9b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 129.000 45.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #11141-0n6mc PRED entity: 0n6mc PRED relation: time_zones PRED expected values: 02lcqs => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 11): 02lcqs (0.92 #886, 0.91 #568, 0.91 #371), 02hcv8 (0.57 #969, 0.57 #584, 0.54 #808), 02hczc (0.48 #2080, 0.44 #1033, 0.42 #1618), 02fqwt (0.44 #159, 0.38 #199, 0.33 #27), 02llzg (0.10 #1130, 0.06 #109, 0.06 #83), 03plfd (0.04 #1136, 0.02 #1786, 0.02 #1866), 03bdv (0.03 #2112, 0.03 #2126, 0.03 #1556), 0gsrz4 (0.02 #1730, 0.02 #1784, 0.02 #1838), 042g7t (0.02 #722, 0.02 #883, 0.01 #1137), 02lcrv (0.02 #718, 0.02 #879, 0.01 #1026) >> Best rule #886 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 0fw4v; >> query: (?x10702, ?x2950) <- source(?x10702, ?x958), ?x958 = 0jbk9, county_seat(?x10702, ?x12655), time_zones(?x12655, ?x2950) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n6mc time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.916 http://example.org/location/location/time_zones #11140-0210f1 PRED entity: 0210f1 PRED relation: award PRED expected values: 06196 => 126 concepts (97 used for prediction) PRED predicted values (max 10 best out of 329): 01yz0x (0.79 #37668, 0.77 #34890, 0.71 #38463), 0265vt (0.75 #5093, 0.75 #3184, 0.73 #4299), 0265wl (0.75 #3184, 0.71 #38463, 0.70 #37667), 01bb1c (0.71 #38463, 0.70 #37667, 0.70 #34892), 0208wk (0.36 #3922, 0.29 #1934, 0.25 #1137), 058bzgm (0.33 #1560, 0.30 #2753, 0.26 #4741), 06196 (0.33 #737, 0.17 #5508, 0.16 #5904), 027x4ws (0.25 #1111, 0.14 #3896, 0.14 #1908), 0c_dx (0.25 #1069, 0.07 #3854, 0.06 #4648), 05x2s (0.20 #2761, 0.17 #1568, 0.14 #4749) >> Best rule #37668 for best value: >> intensional similarity = 6 >> extensional distance = 1889 >> proper extension: 04cy8rb; 01r42_g; 0f830f; 02pp_q_; 08wq0g; 025vry; 08w7vj; 0dky9n; 01qkqwg; 01ky2h; ... >> query: (?x7055, ?x6687) <- award_winner(?x6687, ?x7055), award(?x6055, ?x6687), award(?x5087, ?x6687), ceremony(?x6687, ?x11712), award_winner(?x4879, ?x6055), gender(?x5087, ?x231) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #737 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 05x8n; *> query: (?x7055, 06196) <- award_winner(?x14213, ?x7055), award_winner(?x6687, ?x7055), award_winner(?x1288, ?x7055), award(?x7055, ?x8909), ?x14213 = 01bb1c, ?x1288 = 02662b, ?x6687 = 0262yt *> conf = 0.33 ranks of expected_values: 7 EVAL 0210f1 award 06196 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 126.000 97.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11139-0j5b8 PRED entity: 0j5b8 PRED relation: entity_involved! PRED expected values: 02tvsn => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 75): 0d06vc (0.57 #309, 0.44 #676, 0.35 #1043), 02tvsn (0.40 #175, 0.33 #53, 0.29 #297), 0dr7s (0.33 #45, 0.25 #473, 0.25 #106), 0cm2xh (0.29 #378, 0.15 #1175, 0.14 #1298), 0cwt70 (0.29 #406, 0.15 #1203, 0.14 #1326), 01cpp0 (0.29 #360, 0.12 #727, 0.10 #1094), 02kxjx (0.20 #533, 0.16 #961, 0.15 #1145), 06k75 (0.20 #503, 0.14 #381, 0.07 #2222), 048n7 (0.20 #510, 0.12 #1616, 0.11 #2229), 07j9n (0.17 #1560, 0.07 #2270, 0.06 #2888) >> Best rule #309 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02c4s; 09b6zr; 03f77; 0948xk; >> query: (?x6830, 0d06vc) <- religion(?x6830, ?x1985), entity_involved(?x12777, ?x6830), combatants(?x12777, ?x2152), ?x2152 = 06mkj >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #175 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 01flgk; *> query: (?x6830, 02tvsn) <- entity_involved(?x12673, ?x6830), entity_involved(?x10206, ?x6830), entity_involved(?x10008, ?x6830), ?x10008 = 0cbvg, ?x10206 = 01_3rn, locations(?x12673, ?x455) *> conf = 0.40 ranks of expected_values: 2 EVAL 0j5b8 entity_involved! 02tvsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.571 http://example.org/base/culturalevent/event/entity_involved #11138-05qd_ PRED entity: 05qd_ PRED relation: place_founded PRED expected values: 030qb3t => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 50): 0k_q_ (0.25 #215, 0.25 #149, 0.12 #280), 030qb3t (0.25 #209, 0.14 #1055, 0.13 #1772), 0f2wj (0.25 #205, 0.12 #1442, 0.11 #1507), 0rj4g (0.16 #4823, 0.09 #2738, 0.06 #5412), 0r00l (0.14 #1101, 0.10 #2013, 0.08 #2274), 0fdpd (0.11 #381, 0.08 #642, 0.04 #1293), 02_286 (0.10 #1052, 0.08 #1378, 0.07 #1769), 0f2w0 (0.09 #407, 0.05 #1057, 0.03 #2295), 04jpl (0.07 #721, 0.05 #1111, 0.05 #1177), 06_kh (0.07 #785, 0.04 #1371, 0.04 #1697) >> Best rule #215 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 016tw3; >> query: (?x902, 0k_q_) <- film(?x902, ?x4656), ?x4656 = 02lk60, nominated_for(?x902, ?x1006) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #209 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 016tw3; *> query: (?x902, 030qb3t) <- film(?x902, ?x4656), ?x4656 = 02lk60, nominated_for(?x902, ?x1006) *> conf = 0.25 ranks of expected_values: 2 EVAL 05qd_ place_founded 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 132.000 0.250 http://example.org/organization/organization/place_founded #11137-09ps01 PRED entity: 09ps01 PRED relation: genre PRED expected values: 02l7c8 => 77 concepts (58 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.91 #4823, 0.71 #352, 0.67 #1294), 05p553 (0.50 #4, 0.43 #355, 0.37 #2474), 01jfsb (0.38 #246, 0.36 #1424, 0.36 #2128), 02kdv5l (0.38 #119, 0.33 #2118, 0.30 #1414), 03k9fj (0.38 #128, 0.25 #245, 0.21 #2834), 06n90 (0.38 #130, 0.25 #247, 0.15 #1425), 01hmnh (0.38 #251, 0.17 #17, 0.15 #4131), 02l7c8 (0.33 #16, 0.29 #4838, 0.28 #4955), 0lsxr (0.33 #8, 0.28 #359, 0.20 #711), 0hcr (0.33 #23, 0.12 #6585, 0.06 #3667) >> Best rule #4823 for best value: >> intensional similarity = 3 >> extensional distance = 1106 >> proper extension: 0c0wvx; >> query: (?x4778, 07s9rl0) <- genre(?x4778, ?x162), genre(?x3992, ?x162), ?x3992 = 0pd6l >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0sxlb; *> query: (?x4778, 02l7c8) <- nominated_for(?x4106, ?x4778), film(?x11264, ?x4778), film(?x5500, ?x4778), ?x11264 = 018_lb, award_nominee(?x5500, ?x496) *> conf = 0.33 ranks of expected_values: 8 EVAL 09ps01 genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 77.000 58.000 0.908 http://example.org/film/film/genre #11136-012wgb PRED entity: 012wgb PRED relation: film_release_region! PRED expected values: 0g5qs2k 0c40vxk 0fpkhkz 0cmc26r 0bs8s1p 0g4pl7z => 159 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1305): 08hmch (0.85 #27368, 0.82 #19582, 0.79 #3893), 0bpm4yw (0.85 #27786, 0.82 #20000, 0.65 #78379), 047vnkj (0.85 #27934, 0.79 #3893, 0.73 #20148), 0by1wkq (0.85 #27478, 0.79 #3893, 0.73 #19692), 02yvct (0.85 #27513, 0.79 #3893, 0.73 #19727), 0gd0c7x (0.85 #27486, 0.79 #3893, 0.67 #58619), 05qbckf (0.85 #27483, 0.79 #3893, 0.64 #58616), 053rxgm (0.85 #27385, 0.79 #3893, 0.64 #19599), 027pfg (0.85 #28156, 0.79 #3893, 0.64 #20370), 0btpm6 (0.85 #28208, 0.79 #3893, 0.55 #59341) >> Best rule #27368 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0d0vqn; 04gzd; 05qhw; 0k6nt; 03gj2; 0345h; 015qh; 01pj7; 02vzc; 03rj0; ... >> query: (?x3699, 08hmch) <- film_release_region(?x6175, ?x3699), contains(?x455, ?x3699), ?x455 = 02j9z, ?x6175 = 0gg5kmg >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #19974 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 0f8l9c; *> query: (?x3699, 0cmc26r) <- film_release_region(?x5092, ?x3699), film_release_region(?x1283, ?x3699), ?x1283 = 0cnztc4, ?x5092 = 0gg5qcw, location(?x5758, ?x3699) *> conf = 0.73 ranks of expected_values: 237, 240, 242, 252, 253, 270 EVAL 012wgb film_release_region! 0g4pl7z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 159.000 117.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 012wgb film_release_region! 0bs8s1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 159.000 117.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 012wgb film_release_region! 0cmc26r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 159.000 117.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 012wgb film_release_region! 0fpkhkz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 159.000 117.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 012wgb film_release_region! 0c40vxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 159.000 117.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 012wgb film_release_region! 0g5qs2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 159.000 117.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11135-0194zl PRED entity: 0194zl PRED relation: featured_film_locations PRED expected values: 02jx1 => 70 concepts (59 used for prediction) PRED predicted values (max 10 best out of 40): 02_286 (0.18 #500, 0.16 #1220, 0.16 #740), 030qb3t (0.09 #519, 0.09 #2924, 0.08 #4128), 04jpl (0.08 #9, 0.08 #249, 0.06 #1450), 080h2 (0.05 #1946, 0.04 #2909, 0.03 #3872), 0d6lp (0.04 #72, 0.03 #552, 0.02 #1272), 0vzm (0.04 #74, 0.02 #1996, 0.01 #2959), 017j7y (0.04 #223), 0qpqn (0.04 #160), 05fjf (0.04 #127), 0qr4n (0.04 #80) >> Best rule #500 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 09v8clw; >> query: (?x4963, 02_286) <- film(?x2805, ?x4963), honored_for(?x308, ?x4963), titles(?x53, ?x4963) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0194zl featured_film_locations 02jx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 59.000 0.184 http://example.org/film/film/featured_film_locations #11134-018yj6 PRED entity: 018yj6 PRED relation: film PRED expected values: 02v8kmz => 94 concepts (74 used for prediction) PRED predicted values (max 10 best out of 802): 01gkp1 (0.08 #816, 0.05 #4392, 0.04 #6180), 01shy7 (0.08 #2212, 0.03 #14728, 0.03 #424), 0_b9f (0.06 #67950, 0.03 #76891, 0.03 #2598), 02_06s (0.06 #67950, 0.03 #76891, 0.03 #44703), 0330r (0.06 #67950, 0.03 #76891), 03cv_gy (0.06 #67950, 0.03 #76891), 02pqs8l (0.06 #67950, 0.03 #76891), 01cz7r (0.06 #1323, 0.05 #4899, 0.03 #6687), 047gpsd (0.06 #1189, 0.05 #4765, 0.03 #6553), 058kh7 (0.06 #1577, 0.05 #5153, 0.03 #6941) >> Best rule #816 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 02vmzp; 01zmpg; 01n8_g; 01fwpt; 0kh6b; 02v60l; 019r_1; 04f7c55; 06hx2; 036jp8; ... >> query: (?x8813, 01gkp1) <- nationality(?x8813, ?x94), religion(?x8813, ?x1985), sibling(?x8813, ?x6324) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #8968 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 0c7ct; 013v5j; 015z4j; 024dw0; 030dx5; 0194xc; 04d_mtq; 0b5x23; 02g5bf; 03wdsbz; *> query: (?x8813, 02v8kmz) <- nationality(?x8813, ?x94), sibling(?x8813, ?x6324), type_of_union(?x8813, ?x566) *> conf = 0.01 ranks of expected_values: 727 EVAL 018yj6 film 02v8kmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 94.000 74.000 0.083 http://example.org/film/actor/film./film/performance/film #11133-09q17 PRED entity: 09q17 PRED relation: titles PRED expected values: 023vcd => 65 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1890): 0gc_c_ (0.50 #6649, 0.43 #11271, 0.33 #3571), 042g97 (0.50 #7665, 0.29 #12287, 0.20 #15367), 09fqgj (0.50 #7566, 0.29 #12188, 0.20 #15268), 0d6_s (0.50 #7559, 0.29 #12181, 0.20 #15261), 02g5q1 (0.50 #7366, 0.29 #11988, 0.20 #15068), 03y0pn (0.50 #7209, 0.29 #11831, 0.20 #14911), 05nlx4 (0.50 #7207, 0.29 #11829, 0.20 #14909), 03t95n (0.50 #7140, 0.29 #11762, 0.20 #14842), 07sp4l (0.50 #6582, 0.29 #11204, 0.20 #14284), 015x74 (0.50 #6397, 0.29 #11019, 0.20 #14099) >> Best rule #6649 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 03k9fj; 01hmnh; >> query: (?x7323, 0gc_c_) <- genre(?x11454, ?x7323), titles(?x7323, ?x8987), titles(?x7323, ?x7728), titles(?x7323, ?x7072), ?x11454 = 07vqnc, genre(?x1811, ?x7323), film_release_region(?x7072, ?x94), honored_for(?x6627, ?x7728), nominated_for(?x541, ?x8987) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4470 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 01z4y; *> query: (?x7323, 023vcd) <- genre(?x11454, ?x7323), titles(?x7323, ?x9303), titles(?x7323, ?x8987), ?x9303 = 05567m, ?x8987 = 02tgz4, genre(?x11454, ?x1510), ?x1510 = 01hmnh, program(?x11453, ?x11454) *> conf = 0.33 ranks of expected_values: 180 EVAL 09q17 titles 023vcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 65.000 28.000 0.500 http://example.org/media_common/netflix_genre/titles #11132-026rm_y PRED entity: 026rm_y PRED relation: profession PRED expected values: 02hrh1q => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 49): 02hrh1q (0.89 #2265, 0.88 #315, 0.88 #6165), 01d_h8 (0.45 #606, 0.33 #2706, 0.32 #3606), 0dxtg (0.36 #614, 0.28 #5564, 0.28 #7951), 03gjzk (0.36 #616, 0.28 #7951, 0.28 #8252), 02jknp (0.28 #608, 0.28 #7951, 0.28 #8252), 018gz8 (0.28 #7951, 0.28 #8252, 0.14 #918), 0d1pc (0.28 #7951, 0.28 #8252, 0.10 #502), 0np9r (0.25 #172, 0.25 #22, 0.14 #10224), 09jwl (0.21 #470, 0.21 #2570, 0.20 #3020), 02krf9 (0.15 #628, 0.12 #178, 0.09 #2728) >> Best rule #2265 for best value: >> intensional similarity = 3 >> extensional distance = 700 >> proper extension: 014x77; 0kr5_; 012c6x; 03gm48; 0f0p0; 0h1m9; 02lnhv; 013cr; 04nw9; 02p21g; ... >> query: (?x8740, 02hrh1q) <- award_winner(?x112, ?x8740), place_of_birth(?x8740, ?x863), film(?x8740, ?x2189) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026rm_y profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.889 http://example.org/people/person/profession #11131-0jm4v PRED entity: 0jm4v PRED relation: school PRED expected values: 05krk 0j_sncb 07vyf => 108 concepts (68 used for prediction) PRED predicted values (max 10 best out of 189): 07w0v (0.56 #397, 0.22 #3255, 0.22 #11680), 0bx8pn (0.35 #1743, 0.33 #219, 0.27 #4602), 06pwq (0.33 #392, 0.33 #202, 0.20 #1726), 015q1n (0.33 #1060, 0.31 #870, 0.30 #2012), 01ptt7 (0.33 #223, 0.27 #605, 0.23 #795), 01jq0j (0.33 #502, 0.25 #117, 0.24 #2596), 01jsn5 (0.33 #225, 0.23 #797, 0.22 #3655), 09f2j (0.33 #271, 0.22 #461, 0.21 #1604), 01jt2w (0.33 #324, 0.22 #514, 0.12 #2608), 0pspl (0.33 #244, 0.19 #2861, 0.15 #3674) >> Best rule #397 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01d5z; 0512p; 01yjl; 02c_4; 07l8x; >> query: (?x7158, 07w0v) <- draft(?x7158, ?x4979), school(?x7158, ?x2497), team(?x1348, ?x7158), teams(?x2254, ?x7158), ?x2497 = 0f1nl >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #446 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 01d5z; 0512p; 01yjl; 02c_4; 07l8x; *> query: (?x7158, 07vyf) <- draft(?x7158, ?x4979), school(?x7158, ?x2497), team(?x1348, ?x7158), teams(?x2254, ?x7158), ?x2497 = 0f1nl *> conf = 0.22 ranks of expected_values: 21, 26, 34 EVAL 0jm4v school 07vyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 108.000 68.000 0.556 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jm4v school 0j_sncb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 108.000 68.000 0.556 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jm4v school 05krk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 108.000 68.000 0.556 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #11130-02sdk9v PRED entity: 02sdk9v PRED relation: position! PRED expected values: 03fnmd 040p3y => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 154): 03v9yw (0.82 #151, 0.81 #154, 0.80 #611), 03m6zs (0.82 #151, 0.81 #154, 0.80 #611), 01j95f (0.82 #151, 0.81 #154, 0.80 #611), 0k_l4 (0.82 #151, 0.81 #154, 0.80 #611), 0196bp (0.82 #151, 0.81 #154, 0.80 #611), 02b2np (0.82 #151, 0.81 #154, 0.80 #611), 02029f (0.82 #151, 0.81 #154, 0.80 #611), 01jdxj (0.82 #151, 0.81 #154, 0.80 #611), 085v7 (0.82 #151, 0.81 #154, 0.80 #611), 0kz4w (0.82 #151, 0.81 #154, 0.80 #611) >> Best rule #151 for best value: >> intensional similarity = 14 >> extensional distance = 1 >> proper extension: 02nzb8; >> query: (?x63, ?x209) <- position(?x11748, ?x63), position(?x11337, ?x63), position(?x9215, ?x63), position(?x5641, ?x63), ?x11337 = 01rlz4, position(?x7443, ?x63), position(?x1899, ?x63), position(?x209, ?x63), ?x11748 = 02b0_m, ?x7443 = 09451k, ?x1899 = 01k2yr, team(?x63, ?x3871), ?x9215 = 05hc96, ?x5641 = 03ytj1 >> conf = 0.82 => this is the best rule for 103 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 31, 120 EVAL 02sdk9v position! 040p3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 29.000 29.000 0.819 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position EVAL 02sdk9v position! 03fnmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 29.000 29.000 0.819 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #11129-01w92 PRED entity: 01w92 PRED relation: production_companies! PRED expected values: 01ffx4 => 121 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1023): 09r94m (0.33 #7532, 0.29 #10994, 0.29 #9840), 05vxdh (0.33 #7436, 0.29 #10898, 0.29 #9744), 020bv3 (0.33 #7142, 0.29 #10604, 0.29 #9450), 0glqh5_ (0.33 #7528, 0.29 #10990, 0.29 #9836), 0cz_ym (0.33 #7130, 0.29 #10592, 0.29 #9438), 0ds2l81 (0.33 #7855, 0.29 #11317, 0.29 #10163), 02x3y41 (0.33 #7804, 0.29 #11266, 0.29 #10112), 0b7l4x (0.33 #7601, 0.29 #11063, 0.29 #9909), 05k2xy (0.33 #7173, 0.29 #10635, 0.29 #9481), 02725hs (0.33 #7172, 0.29 #10634, 0.29 #9480) >> Best rule #7532 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01my_c; >> query: (?x3487, 09r94m) <- citytown(?x3487, ?x362), ?x362 = 04jpl, award_nominee(?x3487, ?x2246) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #19977 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0jz9f; 03xsby; *> query: (?x3487, 01ffx4) <- organization(?x4682, ?x3487), ?x4682 = 0dq_5, award_nominee(?x3487, ?x2246) *> conf = 0.06 ranks of expected_values: 728 EVAL 01w92 production_companies! 01ffx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 121.000 96.000 0.333 http://example.org/film/film/production_companies #11128-012d40 PRED entity: 012d40 PRED relation: profession PRED expected values: 0dxtg => 99 concepts (72 used for prediction) PRED predicted values (max 10 best out of 64): 09jwl (0.65 #303, 0.56 #2898, 0.53 #3908), 0dxtg (0.65 #2607, 0.61 #3328, 0.45 #1023), 0nbcg (0.58 #316, 0.44 #2911, 0.42 #3921), 01c72t (0.43 #886, 0.27 #3913, 0.22 #2903), 016z4k (0.39 #292, 0.37 #2887, 0.33 #3897), 0dz3r (0.37 #290, 0.37 #2885, 0.35 #3895), 039v1 (0.28 #321, 0.20 #2916, 0.18 #3926), 0np9r (0.23 #594, 0.17 #2756, 0.17 #2468), 02krf9 (0.21 #3339, 0.21 #2618, 0.17 #1034), 01c8w0 (0.20 #873, 0.06 #3900, 0.06 #2890) >> Best rule #303 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 01l_vgt; >> query: (?x147, 09jwl) <- location_of_ceremony(?x147, ?x1523), artists(?x13968, ?x147), type_of_union(?x147, ?x566) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #2607 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 471 *> proper extension: 021r7r; 05dxl_; 0gry51; *> query: (?x147, 0dxtg) <- profession(?x147, ?x13719), profession(?x147, ?x524), ?x524 = 02jknp, film_crew_role(?x280, ?x13719) *> conf = 0.65 ranks of expected_values: 2 EVAL 012d40 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 72.000 0.649 http://example.org/people/person/profession #11127-05q7cj PRED entity: 05q7cj PRED relation: honored_for PRED expected values: 011ydl 011yg9 => 51 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1057): 0d68qy (0.40 #7865, 0.25 #6084, 0.24 #19746), 0_9l_ (0.33 #571, 0.25 #2351, 0.20 #3536), 0jnwx (0.33 #107, 0.25 #1887, 0.20 #3072), 07j8r (0.33 #149, 0.25 #1929, 0.20 #3114), 050r1z (0.33 #27, 0.25 #1807, 0.20 #2992), 01xq8v (0.33 #451, 0.25 #2231, 0.20 #3416), 0_92w (0.33 #59, 0.25 #1839, 0.20 #3024), 0_9wr (0.33 #412, 0.25 #2192, 0.20 #3377), 02rzdcp (0.33 #7911, 0.17 #6130, 0.13 #19792), 01br2w (0.33 #599, 0.14 #8309, 0.11 #8310) >> Best rule #7865 for best value: >> intensional similarity = 15 >> extensional distance = 13 >> proper extension: 02wzl1d; 058m5m4; 09v0p2c; 03gyp30; 09g90vz; >> query: (?x6861, 0d68qy) <- award_winner(?x6861, ?x1894), award_winner(?x6861, ?x556), award(?x1894, ?x1232), honored_for(?x6861, ?x1209), nominated_for(?x556, ?x174), nominated_for(?x1894, ?x188), participant(?x556, ?x262), ceremony(?x77, ?x6861), people(?x5741, ?x556), written_by(?x7917, ?x556), profession(?x1894, ?x131), ?x5741 = 07bch9, nominated_for(?x618, ?x1209), nominated_for(?x1208, ?x1209), country(?x1209, ?x94) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #22571 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 59 *> proper extension: 073hkh; 05pd94v; 02rjjll; 0bzk8w; 09q_6t; 03gwpw2; 02yv_b; 0ftlkg; 050yyb; 026kq4q; ... *> query: (?x6861, ?x2029) <- award_winner(?x6861, ?x1894), award_winner(?x6861, ?x488), music(?x4707, ?x1894), music(?x1919, ?x1894), award_winner(?x5223, ?x1894), student(?x7545, ?x1894), award(?x1894, ?x1232), film(?x4109, ?x1919), film_release_region(?x4707, ?x94), film_release_region(?x1919, ?x87), titles(?x307, ?x1919), award_nominee(?x100, ?x488), nominated_for(?x1723, ?x4707), award_winner(?x2029, ?x488), profession(?x488, ?x987) *> conf = 0.12 ranks of expected_values: 100, 643 EVAL 05q7cj honored_for 011yg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 51.000 43.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 05q7cj honored_for 011ydl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 43.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #11126-0hkxq PRED entity: 0hkxq PRED relation: nutrient PRED expected values: 0h1wg 07hnp 04kl74p 0h1tg => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 33): 07hnp (0.74 #54, 0.73 #299, 0.67 #301), 04kl74p (0.74 #54, 0.73 #299, 0.67 #302), 0h1tg (0.74 #54, 0.73 #299, 0.56 #300), 0h1wg (0.74 #54, 0.73 #299, 0.56 #285), 08lb68 (0.74 #54, 0.73 #299, 0.44 #303), 01w_3 (0.74 #54, 0.73 #299, 0.33 #248), 0f4k5 (0.74 #54, 0.73 #299, 0.33 #249), 075pwf (0.74 #54, 0.73 #299, 0.33 #246), 0f4l5 (0.74 #54, 0.73 #299, 0.33 #203), 02kd8zw (0.74 #54, 0.73 #299, 0.33 #198) >> Best rule #54 for best value: >> intensional similarity = 130 >> extensional distance = 1 >> proper extension: 01645p; >> query: (?x2701, ?x9619) <- nutrient(?x2701, ?x13944), nutrient(?x2701, ?x13498), nutrient(?x2701, ?x12902), nutrient(?x2701, ?x12868), nutrient(?x2701, ?x12481), nutrient(?x2701, ?x12454), nutrient(?x2701, ?x12083), nutrient(?x2701, ?x11784), nutrient(?x2701, ?x11758), nutrient(?x2701, ?x11592), nutrient(?x2701, ?x11409), nutrient(?x2701, ?x11270), nutrient(?x2701, ?x10891), nutrient(?x2701, ?x10709), nutrient(?x2701, ?x10098), nutrient(?x2701, ?x9949), nutrient(?x2701, ?x9915), nutrient(?x2701, ?x9855), nutrient(?x2701, ?x9840), nutrient(?x2701, ?x9795), nutrient(?x2701, ?x9733), nutrient(?x2701, ?x9490), nutrient(?x2701, ?x9426), nutrient(?x2701, ?x9365), nutrient(?x2701, ?x8487), nutrient(?x2701, ?x8442), nutrient(?x2701, ?x7894), nutrient(?x2701, ?x7720), nutrient(?x2701, ?x7364), nutrient(?x2701, ?x7362), nutrient(?x2701, ?x7219), nutrient(?x2701, ?x6586), nutrient(?x2701, ?x6286), nutrient(?x2701, ?x6160), nutrient(?x2701, ?x6033), nutrient(?x2701, ?x6026), nutrient(?x2701, ?x5549), nutrient(?x2701, ?x5526), nutrient(?x2701, ?x5451), nutrient(?x2701, ?x5374), nutrient(?x2701, ?x5010), nutrient(?x2701, ?x4069), nutrient(?x2701, ?x3901), nutrient(?x2701, ?x3469), nutrient(?x2701, ?x2702), nutrient(?x2701, ?x2018), ?x9915 = 025tkqy, ?x5549 = 025s7j4, ?x12902 = 0fzjh, ?x5010 = 0h1vz, ?x12481 = 027g6p7, ?x11758 = 0q01m, ?x6286 = 02y_3rf, ?x9733 = 0h1tz, ?x3469 = 0h1zw, ?x4069 = 0hqw8p_, ?x6586 = 05gh50, ?x5526 = 09pbb, nutrient(?x9732, ?x2018), nutrient(?x9489, ?x2018), nutrient(?x9005, ?x2018), nutrient(?x8298, ?x2018), nutrient(?x7719, ?x2018), nutrient(?x7057, ?x2018), nutrient(?x6191, ?x2018), nutrient(?x6159, ?x2018), nutrient(?x5009, ?x2018), nutrient(?x4068, ?x2018), nutrient(?x3900, ?x2018), nutrient(?x3468, ?x2018), nutrient(?x1303, ?x2018), nutrient(?x1257, ?x2018), ?x3901 = 0466p20, ?x2702 = 0838f, ?x6191 = 014j1m, ?x9855 = 0d9t0, ?x9365 = 04k8n, ?x9005 = 04zpv, ?x3468 = 0cxn2, ?x10098 = 0h1_c, ?x5009 = 0fjfh, ?x9840 = 02p0tjr, taxonomy(?x2018, ?x939), ?x7719 = 0dj75, nutrient(?x6032, ?x12083), nutrient(?x5373, ?x12083), nutrient(?x1959, ?x12083), ?x10891 = 0g5gq, ?x10709 = 0h1sz, ?x6032 = 01nkt, ?x7364 = 09gvd, ?x13944 = 0f4kp, ?x6159 = 033cnk, ?x1257 = 09728, ?x9490 = 0h1sg, ?x9949 = 02kd0rh, ?x8442 = 02kcv4x, ?x8487 = 014yzm, ?x5373 = 0971v, ?x4068 = 0fbw6, ?x9426 = 0h1yy, nutrient(?x9489, ?x9619), nutrient(?x9489, ?x1304), nutrient(?x9489, ?x1258), ?x11592 = 025sf0_, ?x1303 = 0fj52s, ?x5451 = 05wvs, ?x7057 = 0fbdb, ?x1304 = 08lb68, ?x3900 = 061_f, ?x1258 = 0h1wg, ?x7219 = 0h1vg, ?x11409 = 0h1yf, ?x7720 = 025s7x6, ?x939 = 04n6k, ?x5374 = 025s0zp, ?x11270 = 02kc008, ?x9795 = 05v_8y, ?x6160 = 041r51, ?x12454 = 025rw19, ?x13498 = 07q0m, ?x6026 = 025sf8g, ?x7894 = 0f4hc, ?x6033 = 04zjxcz, ?x7362 = 02kc5rj, ?x12868 = 03d49, ?x9732 = 05z55, ?x11784 = 07zqy, ?x8298 = 037ls6, ?x1959 = 0f25w9 >> conf = 0.74 => this is the best rule for 13 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0hkxq nutrient 0h1tg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.736 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0hkxq nutrient 04kl74p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.736 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0hkxq nutrient 07hnp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.736 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0hkxq nutrient 0h1wg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.736 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #11125-01rgdw PRED entity: 01rgdw PRED relation: school_type PRED expected values: 05jxkf => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.57 #76, 0.55 #4, 0.49 #196), 01rs41 (0.33 #605, 0.29 #701, 0.29 #101), 05pcjw (0.28 #241, 0.27 #145, 0.27 #697), 01_9fk (0.27 #74, 0.26 #314, 0.22 #266), 07tf8 (0.21 #33, 0.20 #57, 0.18 #9), 01_srz (0.10 #123, 0.09 #243, 0.08 #171), 04qbv (0.07 #40, 0.07 #64, 0.03 #568), 04399 (0.07 #86, 0.06 #254, 0.04 #302), 02p0qmm (0.07 #58, 0.03 #1258, 0.03 #1186), 0bwd5 (0.04 #307, 0.04 #139, 0.04 #187) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 03x23q; >> query: (?x5158, 05jxkf) <- colors(?x5158, ?x332), school(?x2820, ?x5158), ?x2820 = 0jmj7, ?x332 = 01l849 >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rgdw school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.567 http://example.org/education/educational_institution/school_type #11124-07nf6 PRED entity: 07nf6 PRED relation: adjoins! PRED expected values: 070zc => 184 concepts (82 used for prediction) PRED predicted values (max 10 best out of 499): 0345h (0.46 #2413, 0.11 #10241, 0.10 #28251), 0f8l9c (0.33 #40, 0.15 #2387, 0.14 #28225), 0154j (0.33 #6, 0.15 #2353, 0.05 #9397), 05qhw (0.33 #27, 0.09 #10202, 0.08 #1591), 01mjq (0.33 #85, 0.08 #1649, 0.08 #2432), 04g61 (0.33 #247, 0.08 #2594, 0.05 #9638), 059j2 (0.33 #64, 0.08 #2411, 0.04 #28249), 0fhnf (0.33 #461, 0.02 #9852, 0.02 #10636), 0k6nt (0.33 #48, 0.02 #9439, 0.02 #10223), 070zc (0.27 #1290, 0.25 #2073, 0.23 #2856) >> Best rule #2413 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 04g61; >> query: (?x10766, 0345h) <- contains(?x10766, ?x4861), adjoins(?x10766, ?x8264), contains(?x8264, ?x11426), ?x11426 = 02m_41 >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1290 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0g7pm; 018_7x; *> query: (?x10766, 070zc) <- contains(?x10766, ?x4861), category(?x10766, ?x134), contains(?x1264, ?x4861), ?x1264 = 0345h *> conf = 0.27 ranks of expected_values: 10 EVAL 07nf6 adjoins! 070zc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 184.000 82.000 0.462 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #11123-0k3j0 PRED entity: 0k3j0 PRED relation: adjoins! PRED expected values: 0k3jq => 116 concepts (72 used for prediction) PRED predicted values (max 10 best out of 442): 0k3jq (0.85 #782, 0.82 #2350, 0.82 #45501), 0k3k1 (0.50 #417, 0.25 #1567, 0.24 #42362), 0k3j0 (0.40 #1489, 0.25 #1567, 0.25 #705), 0m2gz (0.40 #960, 0.25 #1567, 0.24 #47849), 0k3kg (0.25 #1567, 0.25 #244, 0.24 #47849), 0k3jc (0.25 #1567, 0.25 #709, 0.24 #47849), 0mw5x (0.25 #1567, 0.25 #483, 0.24 #47849), 0n5yh (0.25 #1567, 0.25 #246, 0.24 #47849), 0f4y3 (0.25 #1567, 0.24 #47849, 0.24 #42362), 0dc3_ (0.25 #1567, 0.24 #47849, 0.24 #42362) >> Best rule #782 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0n5yh; >> query: (?x12957, ?x6296) <- adjoins(?x12957, ?x11677), adjoins(?x12957, ?x8725), adjoins(?x12957, ?x6296), contains(?x11677, ?x12099), ?x8725 = 0n5_g, contains(?x2020, ?x11677) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k3j0 adjoins! 0k3jq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 72.000 0.846 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #11122-088gzp PRED entity: 088gzp PRED relation: state_province_region PRED expected values: 09f07 => 91 concepts (63 used for prediction) PRED predicted values (max 10 best out of 100): 03rk0 (0.25 #4330, 0.25 #3339, 0.25 #3215), 0dlv0 (0.25 #4330, 0.25 #3339, 0.25 #3215), 086g2 (0.22 #113, 0.03 #236, 0.01 #5822), 059rby (0.13 #867, 0.13 #744, 0.12 #1360), 01n7q (0.11 #881, 0.10 #1374, 0.10 #1869), 01hpnh (0.11 #97, 0.05 #220, 0.01 #5822), 055vr (0.11 #108, 0.03 #231, 0.01 #5822), 07c98 (0.11 #87, 0.02 #210, 0.01 #5822), 05k7sb (0.06 #524, 0.06 #278, 0.05 #401), 05tbn (0.04 #3637, 0.04 #667, 0.04 #2397) >> Best rule #4330 for best value: >> intensional similarity = 4 >> extensional distance = 439 >> proper extension: 014b4h; 02583l; 07lx1s; 02jyr8; 031n8c; 021l5s; 09krm_; 01y9st; 0352gk; 0269kx; ... >> query: (?x13396, ?x9466) <- school_type(?x13396, ?x3092), contains(?x9466, ?x13396), location(?x7517, ?x9466), category(?x13396, ?x134) >> conf = 0.25 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 088gzp state_province_region 09f07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 63.000 0.247 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #11121-05v8c PRED entity: 05v8c PRED relation: olympics PRED expected values: 0l6m5 0lk8j 0jhn7 => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 39): 0jhn7 (0.89 #416, 0.84 #220, 0.76 #1354), 06sks6 (0.82 #413, 0.75 #1234, 0.74 #608), 0kbvb (0.82 #398, 0.72 #750, 0.71 #1219), 0kbws (0.82 #405, 0.72 #757, 0.69 #1226), 0jdk_ (0.79 #219, 0.79 #415, 0.72 #767), 0l998 (0.74 #201, 0.58 #84, 0.57 #397), 0l6m5 (0.71 #401, 0.68 #205, 0.67 #88), 0lgxj (0.68 #221, 0.68 #417, 0.60 #339), 0l6ny (0.64 #400, 0.63 #204, 0.58 #126), 0jkvj (0.63 #230, 0.61 #426, 0.50 #152) >> Best rule #416 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 05b7q; >> query: (?x550, 0jhn7) <- combatants(?x7455, ?x550), country(?x7195, ?x550), combatants(?x550, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 18 EVAL 05v8c olympics 0jhn7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.893 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 05v8c olympics 0lk8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 155.000 155.000 0.893 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 05v8c olympics 0l6m5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 155.000 155.000 0.893 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #11120-0g8rj PRED entity: 0g8rj PRED relation: major_field_of_study PRED expected values: 05qjt 064_8sq 04sh3 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 104): 01mkq (0.63 #2326, 0.59 #1336, 0.57 #2106), 02lp1 (0.54 #2653, 0.54 #1333, 0.53 #2323), 062z7 (0.46 #1345, 0.43 #2665, 0.40 #794), 0g26h (0.46 #2677, 0.43 #2237, 0.39 #3889), 05qjt (0.44 #1329, 0.40 #2319, 0.38 #1989), 0fdys (0.40 #804, 0.35 #2345, 0.34 #1355), 037mh8 (0.37 #1379, 0.33 #828, 0.32 #1489), 06ms6 (0.37 #1338, 0.26 #2328, 0.25 #1448), 01tbp (0.34 #2143, 0.32 #1373, 0.31 #2693), 01540 (0.34 #2694, 0.33 #2364, 0.29 #1374) >> Best rule #2326 for best value: >> intensional similarity = 2 >> extensional distance = 76 >> proper extension: 01nmgc; 019q50; >> query: (?x5486, 01mkq) <- institution(?x620, ?x5486), list(?x5486, ?x2197) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #1329 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 39 *> proper extension: 0f8l9c; 03_c8p; *> query: (?x5486, 05qjt) <- organization(?x5486, ?x5487), company(?x346, ?x5486) *> conf = 0.44 ranks of expected_values: 5, 13, 40 EVAL 0g8rj major_field_of_study 04sh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 100.000 100.000 0.628 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g8rj major_field_of_study 064_8sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 100.000 100.000 0.628 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g8rj major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 100.000 100.000 0.628 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11119-027mdh PRED entity: 027mdh PRED relation: school_type PRED expected values: 05pcjw => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.88 #2424, 0.87 #2006, 0.67 #686), 01rs41 (0.70 #1721, 0.64 #753, 0.64 #731), 05pcjw (0.62 #133, 0.54 #485, 0.53 #749), 01_9fk (0.33 #684, 0.31 #574, 0.30 #46), 01_srz (0.29 #157, 0.24 #201, 0.20 #69), 06cs1 (0.20 #72, 0.18 #116, 0.14 #28), 04qbv (0.10 #3790, 0.08 #652, 0.08 #322), 02p0qmm (0.10 #3790, 0.08 #383, 0.05 #185), 047951 (0.10 #3790, 0.04 #316, 0.03 #778), 02dk5q (0.10 #3790, 0.04 #315, 0.02 #1525) >> Best rule #2424 for best value: >> intensional similarity = 6 >> extensional distance = 209 >> proper extension: 035yzw; >> query: (?x5651, 05jxkf) <- school_type(?x5651, ?x4994), state_province_region(?x5651, ?x1227), school_type(?x5167, ?x4994), school_type(?x1675, ?x4994), ?x5167 = 015cz0, ?x1675 = 01j_cy >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #133 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 0pspl; 0bwfn; *> query: (?x5651, 05pcjw) <- institution(?x865, ?x5651), ?x865 = 02h4rq6, major_field_of_study(?x5651, ?x2606), school_type(?x5651, ?x4994), registering_agency(?x5651, ?x1982), ?x2606 = 062z7 *> conf = 0.62 ranks of expected_values: 3 EVAL 027mdh school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 183.000 183.000 0.877 http://example.org/education/educational_institution/school_type #11118-0225bv PRED entity: 0225bv PRED relation: contains! PRED expected values: 0f2pf9 => 160 concepts (102 used for prediction) PRED predicted values (max 10 best out of 342): 02jx1 (0.34 #47424, 0.18 #84049, 0.14 #44745), 04_1l0v (0.27 #67882, 0.01 #6700, 0.01 #7593), 04ly1 (0.25 #235, 0.17 #1128, 0.09 #2021), 013hxv (0.25 #339, 0.17 #1232, 0.01 #3911), 059rby (0.24 #16987, 0.18 #34851, 0.13 #40211), 0nn83 (0.22 #20543, 0.18 #41979, 0.18 #29474), 01n7q (0.21 #17045, 0.15 #84040, 0.13 #4543), 07ssc (0.19 #47369, 0.10 #83994, 0.08 #44690), 04rrx (0.18 #1912, 0.06 #2805, 0.05 #6378), 05fkf (0.17 #937, 0.05 #17012, 0.04 #2723) >> Best rule #47424 for best value: >> intensional similarity = 4 >> extensional distance = 265 >> proper extension: 0pmp2; 052p7; 096gm; 01w2v; 01s3v; 02k_px; 01ly8d; 01dbxr; 0fngf; 025y67; ... >> query: (?x12485, 02jx1) <- category(?x12485, ?x134), contains(?x2740, ?x12485), featured_film_locations(?x2386, ?x2740), teams(?x2740, ?x2303) >> conf = 0.34 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0225bv contains! 0f2pf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 102.000 0.345 http://example.org/location/location/contains #11117-0blpg PRED entity: 0blpg PRED relation: currency PRED expected values: 09nqf => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.86 #29, 0.84 #43, 0.83 #50), 01nv4h (0.07 #9, 0.07 #16, 0.06 #23), 02l6h (0.04 #60, 0.03 #88), 02gsvk (0.01 #160, 0.01 #139) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 0m313; 016fyc; 034qrh; 03s6l2; 0pc62; 0fgpvf; 0jzw; 0164qt; 06_wqk4; 0p9lw; ... >> query: (?x3988, 09nqf) <- nominated_for(?x166, ?x3988), language(?x3988, ?x254), nominated_for(?x2757, ?x3988), film_release_region(?x3988, ?x87) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0blpg currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.855 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11116-01jwt PRED entity: 01jwt PRED relation: parent_genre! PRED expected values: 03339m => 68 concepts (37 used for prediction) PRED predicted values (max 10 best out of 278): 0d4xmp (0.40 #1042, 0.33 #1567, 0.33 #514), 03339m (0.40 #930, 0.33 #402, 0.33 #138), 05g7tj (0.40 #912, 0.33 #384, 0.33 #120), 066d03 (0.40 #1040, 0.33 #512, 0.33 #248), 0172rj (0.40 #884, 0.33 #356, 0.33 #92), 02yw0y (0.40 #1177, 0.33 #122, 0.20 #914), 016_nr (0.40 #1647, 0.12 #2438, 0.09 #3760), 07ym47 (0.40 #1643, 0.10 #2434, 0.07 #3490), 021vlg (0.33 #1517, 0.33 #464, 0.25 #727), 0jrv_ (0.33 #147, 0.29 #1847, 0.23 #1317) >> Best rule #1042 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0xjl2; >> query: (?x5580, 0d4xmp) <- parent_genre(?x5580, ?x2249), parent_genre(?x6805, ?x5580), parent_genre(?x6349, ?x5580), ?x6349 = 08z0wx, artists(?x6805, ?x562), artists(?x2249, ?x9762), artists(?x2249, ?x8708), artists(?x2249, ?x4877), ?x8708 = 01vn0t_, ?x4877 = 03sww, languages(?x9762, ?x254) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #930 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 0xjl2; *> query: (?x5580, 03339m) <- parent_genre(?x5580, ?x2249), parent_genre(?x6805, ?x5580), parent_genre(?x6349, ?x5580), ?x6349 = 08z0wx, artists(?x6805, ?x562), artists(?x2249, ?x9762), artists(?x2249, ?x8708), artists(?x2249, ?x4877), ?x8708 = 01vn0t_, ?x4877 = 03sww, languages(?x9762, ?x254) *> conf = 0.40 ranks of expected_values: 2 EVAL 01jwt parent_genre! 03339m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 37.000 0.400 http://example.org/music/genre/parent_genre #11115-017lb_ PRED entity: 017lb_ PRED relation: group! PRED expected values: 05qw5 => 104 concepts (46 used for prediction) PRED predicted values (max 10 best out of 152): 01pny5 (0.33 #200, 0.02 #2820), 048tgl (0.13 #1386, 0.04 #4411, 0.04 #4612), 01vswwx (0.09 #702, 0.02 #3929, 0.02 #4533), 0lbj1 (0.09 #607, 0.02 #3834, 0.02 #4438), 01vswx5 (0.09 #700, 0.01 #3323, 0.01 #3927), 01vs14j (0.09 #624, 0.01 #3247, 0.01 #3851), 0fp_v1x (0.09 #610, 0.01 #3233, 0.01 #3837), 01tp5bj (0.09 #644, 0.01 #3871, 0.01 #3670), 05qhnq (0.09 #734, 0.01 #3961, 0.01 #3760), 02bh9 (0.09 #667, 0.01 #3894, 0.01 #3693) >> Best rule #200 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0167xy; >> query: (?x8226, 01pny5) <- category(?x8226, ?x134), artist(?x9492, ?x8226), artist(?x4797, ?x8226), ?x9492 = 03mp8k, ?x4797 = 02p3cr5, group(?x7570, ?x8226), artists(?x2491, ?x8226) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 017lb_ group! 05qw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 46.000 0.333 http://example.org/music/group_member/membership./music/group_membership/group #11114-022fdt PRED entity: 022fdt PRED relation: languages_spoken PRED expected values: 02h40lc 032f6 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 52): 02h40lc (0.89 #784, 0.83 #679, 0.69 #836), 0t_2 (0.62 #376, 0.50 #637, 0.50 #585), 0880p (0.33 #196, 0.33 #92, 0.33 #40), 03hkp (0.33 #169, 0.33 #65, 0.33 #13), 02bv9 (0.33 #438, 0.33 #22, 0.29 #469), 06nm1 (0.33 #165, 0.33 #9, 0.29 #469), 02bjrlw (0.33 #1, 0.29 #469, 0.17 #157), 06mp7 (0.33 #14, 0.29 #469, 0.17 #170), 04306rv (0.33 #5, 0.29 #469, 0.17 #161), 012v8 (0.33 #41, 0.29 #469, 0.17 #197) >> Best rule #784 for best value: >> intensional similarity = 16 >> extensional distance = 26 >> proper extension: 078vc; 078ds; 0fk3s; 04czx7; 0c41n; >> query: (?x12128, 02h40lc) <- languages_spoken(?x12128, ?x10580), language(?x6448, ?x10580), language(?x1810, ?x10580), language(?x148, ?x10580), titles(?x812, ?x6448), nominated_for(?x1313, ?x6448), nominated_for(?x1243, ?x6448), production_companies(?x6448, ?x963), nominated_for(?x72, ?x6448), ?x1243 = 0gr0m, award(?x6448, ?x637), ?x148 = 034qmv, ?x1810 = 02f6g5, ?x1313 = 0gs9p, countries_spoken_in(?x10580, ?x1355), featured_film_locations(?x6448, ?x3951) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11 EVAL 022fdt languages_spoken 032f6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 33.000 33.000 0.893 http://example.org/people/ethnicity/languages_spoken EVAL 022fdt languages_spoken 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.893 http://example.org/people/ethnicity/languages_spoken #11113-0x2p PRED entity: 0x2p PRED relation: draft PRED expected values: 04f4z1k => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 15): 02x2khw (0.86 #154, 0.74 #379, 0.74 #230), 04f4z1k (0.85 #120, 0.79 #241, 0.79 #150), 02rl201 (0.74 #379, 0.71 #170, 0.71 #155), 0g3zpp (0.54 #487, 0.46 #624, 0.46 #441), 092j54 (0.54 #493, 0.46 #630, 0.46 #447), 09l0x9 (0.54 #495, 0.46 #632, 0.44 #449), 05vsb7 (0.50 #486, 0.44 #623, 0.44 #440), 03nt7j (0.48 #492, 0.44 #446, 0.39 #629), 09th87 (0.47 #512, 0.36 #527, 0.34 #360), 0f4vx0 (0.45 #524, 0.42 #433, 0.38 #509) >> Best rule #154 for best value: >> intensional similarity = 14 >> extensional distance = 12 >> proper extension: 07l4z; >> query: (?x2405, 02x2khw) <- team(?x2010, ?x2405), draft(?x2405, ?x8786), draft(?x2405, ?x8499), draft(?x2405, ?x4779), ?x2010 = 02lyr4, draft(?x7725, ?x8499), draft(?x1632, ?x8499), teams(?x2277, ?x2405), season(?x2405, ?x2406), ?x1632 = 0cqt41, ?x2406 = 03c6sl9, ?x8786 = 02pq_x5, ?x4779 = 02z6872, ?x7725 = 07l8x >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #120 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 11 *> proper extension: 01ypc; *> query: (?x2405, 04f4z1k) <- team(?x8520, ?x2405), team(?x2010, ?x2405), draft(?x2405, ?x8499), ?x2010 = 02lyr4, ?x8499 = 02r6gw6, school(?x2405, ?x3416), season(?x2405, ?x2406), team(?x5412, ?x2405), position(?x8521, ?x8520), position(?x8111, ?x8520), position(?x4208, ?x8520), ?x8111 = 07147, ?x8521 = 01v3x8, ?x4208 = 061xq *> conf = 0.85 ranks of expected_values: 2 EVAL 0x2p draft 04f4z1k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.857 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #11112-0fpjd_g PRED entity: 0fpjd_g PRED relation: artists! PRED expected values: 0cx6f => 119 concepts (66 used for prediction) PRED predicted values (max 10 best out of 231): 06by7 (0.53 #16644, 0.51 #1559, 0.51 #3101), 064t9 (0.48 #16635, 0.46 #10173, 0.45 #12018), 0dl5d (0.37 #1557, 0.35 #634, 0.22 #2175), 06j6l (0.33 #48, 0.28 #10210, 0.27 #12055), 0xhtw (0.33 #1554, 0.28 #631, 0.22 #2172), 02x8m (0.31 #17, 0.24 #1556, 0.22 #633), 016clz (0.28 #2161, 0.27 #1851, 0.23 #3085), 05bt6j (0.28 #43, 0.26 #16667, 0.23 #3124), 0glt670 (0.25 #10202, 0.24 #12047, 0.23 #11124), 05w3f (0.24 #1576, 0.17 #653, 0.17 #37) >> Best rule #16644 for best value: >> intensional similarity = 3 >> extensional distance = 732 >> proper extension: 0c7ct; 07qnf; 04r1t; 0167_s; 013v5j; 02r1tx7; 01l_vgt; 01gx5f; 05563d; 0p3r8; ... >> query: (?x1563, 06by7) <- artists(?x6210, ?x1563), artists(?x6210, ?x9321), ?x9321 = 0140t7 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #10470 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 384 *> proper extension: 01pfkw; *> query: (?x1563, ?x505) <- award_nominee(?x1563, ?x8972), artists(?x505, ?x8972), artist(?x2931, ?x1563) *> conf = 0.22 ranks of expected_values: 17 EVAL 0fpjd_g artists! 0cx6f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 119.000 66.000 0.530 http://example.org/music/genre/artists #11111-05lls PRED entity: 05lls PRED relation: artists PRED expected values: 06wvj 0k1wz => 65 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1044): 06wvj (0.67 #5468, 0.57 #6524, 0.43 #7580), 01vvy (0.57 #6369, 0.50 #5313, 0.43 #7425), 0hnlx (0.50 #4271, 0.43 #6383, 0.33 #5327), 0k1wz (0.50 #5157, 0.33 #6213, 0.33 #3047), 01vyp_ (0.43 #7535, 0.33 #5423, 0.33 #147), 06k02 (0.33 #5449, 0.33 #2283, 0.29 #6505), 01gg59 (0.33 #5609, 0.33 #2443, 0.29 #6665), 01hw6wq (0.33 #5455, 0.33 #2289, 0.29 #6511), 01nqfh_ (0.33 #5310, 0.33 #2144, 0.29 #6366), 01pbs9w (0.33 #5791, 0.33 #2625, 0.29 #6847) >> Best rule #5468 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 017_qw; >> query: (?x888, 06wvj) <- artists(?x888, ?x7386), artists(?x888, ?x3774), artists(?x888, ?x2693), ?x2693 = 02ck1, profession(?x3774, ?x1183), origin(?x3774, ?x10334), influenced_by(?x9519, ?x7386) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 05lls artists 0k1wz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 39.000 0.667 http://example.org/music/genre/artists EVAL 05lls artists 06wvj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 39.000 0.667 http://example.org/music/genre/artists #11110-01f62 PRED entity: 01f62 PRED relation: featured_film_locations! PRED expected values: 0gpx6 => 245 concepts (138 used for prediction) PRED predicted values (max 10 best out of 1353): 04dsnp (0.33 #66, 0.22 #5218, 0.18 #8163), 05pxnmb (0.33 #570, 0.09 #8667, 0.09 #7195), 061681 (0.25 #2255, 0.22 #5199, 0.19 #12560), 0413cff (0.25 #18774, 0.11 #5524, 0.10 #20246), 02qpt1w (0.25 #2635, 0.03 #13986, 0.01 #80994), 072x7s (0.22 #5265, 0.20 #18515, 0.18 #7474), 0cc846d (0.22 #5353, 0.14 #20811, 0.09 #8298), 09sh8k (0.22 #5158, 0.10 #20616, 0.09 #8103), 05f4_n0 (0.22 #5461, 0.10 #20919, 0.09 #8406), 047bynf (0.22 #5648, 0.10 #21106, 0.09 #8593) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02cl1; >> query: (?x1649, 04dsnp) <- month(?x1649, ?x2140), capital(?x3454, ?x1649), ?x2140 = 040fb, film_regional_debut_venue(?x5980, ?x1649) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #45648 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 059rby; 02dtg; 0f2wj; 05ksh; 095w_; 07z1m; 01ly5m; 0d0x8; 096g3; 03gh4; ... *> query: (?x1649, ?x689) <- country(?x1649, ?x2152), film_release_region(?x2958, ?x1649), contains(?x3454, ?x1649), country(?x689, ?x2152) *> conf = 0.01 ranks of expected_values: 1342 EVAL 01f62 featured_film_locations! 0gpx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 245.000 138.000 0.333 http://example.org/film/film/featured_film_locations #11109-01z5tr PRED entity: 01z5tr PRED relation: nationality PRED expected values: 09c7w0 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 18): 09c7w0 (0.84 #6317, 0.79 #1, 0.79 #1403), 02jx1 (0.11 #2335, 0.11 #3037, 0.10 #5447), 07ssc (0.09 #3119, 0.09 #3420, 0.08 #3019), 03rk0 (0.06 #5460, 0.06 #9569, 0.06 #9669), 0d060g (0.05 #708, 0.05 #208, 0.05 #3512), 03_3d (0.03 #2810, 0.03 #3511, 0.01 #3311), 0d0vqn (0.02 #110), 0345h (0.02 #4039, 0.02 #7049, 0.02 #3838), 0chghy (0.02 #411, 0.02 #1111, 0.02 #4719), 03rjj (0.02 #5419, 0.02 #7023, 0.02 #3009) >> Best rule #6317 for best value: >> intensional similarity = 2 >> extensional distance = 1519 >> proper extension: 0784v1; >> query: (?x7963, ?x94) <- place_of_birth(?x7963, ?x2850), country(?x2850, ?x94) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01z5tr nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.837 http://example.org/people/person/nationality #11108-07f1x PRED entity: 07f1x PRED relation: medal PRED expected values: 02lpp7 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.78 #23, 0.76 #27, 0.75 #12) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 01z88t; >> query: (?x7747, 02lpp7) <- country(?x171, ?x7747), adjoins(?x7747, ?x1122), combatants(?x7747, ?x390) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07f1x medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.780 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #11107-07ssc PRED entity: 07ssc PRED relation: country_of_origin! PRED expected values: 0ddd0gc 053x8hr 02gjrc 05397h => 229 concepts (185 used for prediction) PRED predicted values (max 10 best out of 606): 03cf9ly (0.22 #7057, 0.18 #7548, 0.17 #7793), 07g9f (0.22 #7029, 0.18 #7520, 0.17 #7765), 01hvv0 (0.22 #6981, 0.18 #7472, 0.17 #7717), 05f7w84 (0.22 #6942, 0.18 #7433, 0.17 #7678), 0ctzf1 (0.22 #6969, 0.17 #5990, 0.12 #6479), 015g28 (0.17 #5921, 0.12 #6410, 0.11 #7146), 02z3cm0 (0.17 #6105, 0.12 #6594, 0.11 #7330), 074j87 (0.17 #6101, 0.12 #6590, 0.11 #7326), 05pbsry (0.17 #6097, 0.12 #6586, 0.11 #7322), 05631 (0.17 #6096, 0.12 #6585, 0.11 #7321) >> Best rule #7057 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 09c7w0; 03rjj; 03_3d; 0d060g; 0d0vqn; 0chghy; 03rt9; >> query: (?x512, 03cf9ly) <- film_release_region(?x66, ?x512), country(?x136, ?x512), country_of_origin(?x2447, ?x512) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07ssc country_of_origin! 05397h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 229.000 185.000 0.222 http://example.org/tv/tv_program/country_of_origin EVAL 07ssc country_of_origin! 02gjrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 229.000 185.000 0.222 http://example.org/tv/tv_program/country_of_origin EVAL 07ssc country_of_origin! 053x8hr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 229.000 185.000 0.222 http://example.org/tv/tv_program/country_of_origin EVAL 07ssc country_of_origin! 0ddd0gc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 229.000 185.000 0.222 http://example.org/tv/tv_program/country_of_origin #11106-08qmfm PRED entity: 08qmfm PRED relation: award PRED expected values: 0fbtbt => 83 concepts (69 used for prediction) PRED predicted values (max 10 best out of 232): 0fbtbt (0.40 #2671, 0.35 #3077, 0.33 #3889), 0cjyzs (0.36 #4168, 0.34 #3762, 0.32 #4574), 09sb52 (0.31 #11410, 0.27 #15065, 0.27 #13847), 0ck27z (0.21 #10244, 0.16 #15117, 0.14 #11462), 02xcb6n (0.19 #813, 0.13 #28027, 0.13 #28026), 02g3gw (0.19 #813, 0.13 #28027, 0.13 #28026), 03ccq3s (0.14 #200, 0.14 #1013, 0.14 #4261), 0gr4k (0.13 #6936, 0.12 #7342, 0.07 #4906), 0gq9h (0.13 #4951, 0.11 #6981, 0.10 #5357), 0cqhk0 (0.13 #10188, 0.10 #15061, 0.09 #11406) >> Best rule #2671 for best value: >> intensional similarity = 3 >> extensional distance = 136 >> proper extension: 04n7njg; 03m_k0; 0br1w; 04snp2; 014dm6; 02661h; 07jrjb; 05hrq4; 017dpj; >> query: (?x9326, 0fbtbt) <- place_of_birth(?x9326, ?x10763), program(?x9326, ?x9327), nominated_for(?x9326, ?x10661) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08qmfm award 0fbtbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 69.000 0.399 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11105-02nczh PRED entity: 02nczh PRED relation: nominated_for! PRED expected values: 0gqwc => 126 concepts (118 used for prediction) PRED predicted values (max 10 best out of 275): 0gqwc (0.69 #1621, 0.69 #1158, 0.68 #19207), 099cng (0.69 #1621, 0.69 #1158, 0.68 #19207), 027571b (0.69 #1621, 0.69 #1158, 0.68 #19207), 09cn0c (0.69 #1621, 0.69 #1158, 0.68 #19207), 0gq9h (0.45 #4458, 0.44 #8850, 0.42 #2607), 019f4v (0.42 #4450, 0.39 #5375, 0.39 #8842), 0gs9p (0.40 #8852, 0.40 #4460, 0.36 #5385), 040njc (0.38 #239, 0.33 #4404, 0.32 #8796), 0k611 (0.37 #4468, 0.34 #5393, 0.33 #8860), 02pqp12 (0.36 #290, 0.27 #4455, 0.23 #2604) >> Best rule #1621 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 05h95s; >> query: (?x6427, ?x1245) <- category(?x6427, ?x134), award(?x6427, ?x1245), award_winner(?x6427, ?x7831), titles(?x53, ?x6427) >> conf = 0.69 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 02nczh nominated_for! 0gqwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 118.000 0.692 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11104-046rfv PRED entity: 046rfv PRED relation: profession PRED expected values: 02hrh1q => 173 concepts (77 used for prediction) PRED predicted values (max 10 best out of 94): 02hrh1q (0.86 #8697, 0.85 #2661, 0.83 #3837), 014kbl (0.70 #1732, 0.50 #2173, 0.47 #2320), 09jwl (0.51 #10025, 0.45 #10907, 0.37 #8555), 0dxtg (0.50 #749, 0.50 #455, 0.40 #1484), 02jknp (0.50 #449, 0.41 #2213, 0.40 #1625), 01d_h8 (0.50 #741, 0.39 #3240, 0.38 #3093), 025352 (0.50 #500, 0.33 #794, 0.33 #353), 028kk_ (0.50 #516, 0.17 #957, 0.12 #1104), 03gjzk (0.40 #1633, 0.29 #2809, 0.26 #4870), 02krf9 (0.40 #1644, 0.25 #2085, 0.24 #2232) >> Best rule #8697 for best value: >> intensional similarity = 4 >> extensional distance = 397 >> proper extension: 01csvq; 01_j71; 057hz; 025ldg; 06tp4h; 01sxd1; >> query: (?x8097, 02hrh1q) <- people(?x13008, ?x8097), gender(?x8097, ?x514), ?x514 = 02zsn, profession(?x8097, ?x1383) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 046rfv profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 77.000 0.857 http://example.org/people/person/profession #11103-02vqhv0 PRED entity: 02vqhv0 PRED relation: film_crew_role PRED expected values: 0215hd 0d2b38 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 44): 01pvkk (0.31 #1126, 0.28 #628, 0.27 #1891), 02rh1dz (0.24 #1125, 0.15 #1217, 0.14 #1463), 0d2b38 (0.23 #641, 0.20 #299, 0.20 #704), 094hwz (0.22 #132, 0.18 #163, 0.17 #631), 02ynfr (0.22 #1130, 0.20 #1468, 0.18 #196), 02_n3z (0.20 #279, 0.18 #153, 0.14 #497), 0215hd (0.18 #1225, 0.17 #1133, 0.16 #1471), 01xy5l_ (0.12 #1220, 0.12 #2070, 0.12 #1466), 02zdwq (0.12 #2070, 0.10 #1946, 0.09 #170), 02vs3x5 (0.12 #2070, 0.10 #1946, 0.09 #171) >> Best rule #1126 for best value: >> intensional similarity = 6 >> extensional distance = 142 >> proper extension: 0gtsx8c; >> query: (?x2024, 01pvkk) <- film_crew_role(?x2024, ?x1284), film_crew_role(?x2024, ?x1171), film_release_distribution_medium(?x2024, ?x81), ?x1171 = 09vw2b7, ?x1284 = 0ch6mp2, crewmember(?x2024, ?x3879) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #641 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 063zky; 056k77g; *> query: (?x2024, 0d2b38) <- genre(?x2024, ?x2540), ?x2540 = 0hcr, film_crew_role(?x2024, ?x1966), profession(?x1109, ?x1966) *> conf = 0.23 ranks of expected_values: 3, 7 EVAL 02vqhv0 film_crew_role 0d2b38 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 75.000 75.000 0.306 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02vqhv0 film_crew_role 0215hd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 75.000 75.000 0.306 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11102-0ggbfwf PRED entity: 0ggbfwf PRED relation: language PRED expected values: 02h40lc => 54 concepts (47 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.95 #1124, 0.92 #1535, 0.91 #535), 064_8sq (0.13 #21, 0.12 #81, 0.12 #199), 06nm1 (0.12 #2773, 0.09 #1544, 0.09 #1367), 04306rv (0.12 #2773, 0.08 #303, 0.08 #1538), 02bjrlw (0.12 #2773, 0.07 #120, 0.07 #239), 06b_j (0.12 #2773, 0.06 #141, 0.05 #260), 0jzc (0.12 #2773, 0.05 #19, 0.03 #1141), 04h9h (0.12 #2773, 0.05 #102, 0.04 #161), 02hxcvy (0.12 #2773, 0.02 #33, 0.02 #742), 03hkp (0.12 #2773, 0.02 #665, 0.02 #782) >> Best rule #1124 for best value: >> intensional similarity = 4 >> extensional distance = 1079 >> proper extension: 06wzvr; >> query: (?x5827, 02h40lc) <- film(?x4968, ?x5827), film_crew_role(?x5827, ?x137), location(?x4968, ?x362), language(?x5827, ?x3592) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ggbfwf language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 47.000 0.955 http://example.org/film/film/language #11101-0jbp0 PRED entity: 0jbp0 PRED relation: influenced_by PRED expected values: 0byfz => 108 concepts (54 used for prediction) PRED predicted values (max 10 best out of 267): 0p_47 (0.17 #544, 0.11 #1418, 0.06 #1855), 01hmk9 (0.15 #658, 0.13 #1532, 0.08 #1969), 014z8v (0.15 #558, 0.10 #1432, 0.10 #1869), 014zfs (0.13 #461, 0.10 #1335, 0.06 #3517), 081lh (0.13 #456, 0.10 #1330, 0.06 #1767), 052hl (0.13 #646, 0.04 #1520, 0.03 #1957), 029_3 (0.11 #555, 0.03 #1429, 0.02 #4483), 032l1 (0.10 #5326, 0.10 #3582, 0.09 #6634), 081k8 (0.09 #6701, 0.08 #8882, 0.08 #7573), 03_87 (0.09 #5440, 0.09 #4568, 0.08 #3696) >> Best rule #544 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 01xwqn; >> query: (?x10398, 0p_47) <- influenced_by(?x10398, ?x11364), participant(?x3466, ?x10398), award_nominee(?x11364, ?x1738) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jbp0 influenced_by 0byfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 54.000 0.170 http://example.org/influence/influence_node/influenced_by #11100-0f8l9c PRED entity: 0f8l9c PRED relation: country! PRED expected values: 02z3r8t 04g9gd 0bpx1k 04nnpw 05mrf_p 07q1m 065_cjc 03q8xj 02fqxm => 269 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1703): 0q9b0 (0.44 #15219, 0.33 #13651, 0.22 #41861), 0292qb (0.44 #15215, 0.33 #13647, 0.22 #41857), 02q56mk (0.44 #14467, 0.33 #12899, 0.22 #41109), 04gknr (0.44 #14234, 0.22 #40876, 0.22 #12666), 09sh8k (0.44 #14115, 0.22 #40757, 0.22 #12547), 0bw20 (0.44 #15202, 0.22 #41844, 0.22 #13634), 03bzjpm (0.40 #7415, 0.33 #15251, 0.22 #16818), 012kyx (0.40 #7284, 0.33 #15120, 0.22 #16687), 0gwlfnb (0.40 #7575, 0.22 #16978, 0.22 #15411), 01k7b0 (0.40 #7303, 0.22 #16706, 0.22 #15139) >> Best rule #15219 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0853g; >> query: (?x789, 0q9b0) <- exported_to(?x789, ?x94), origin(?x3382, ?x789), contains(?x789, ?x790) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #15190 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0853g; *> query: (?x789, 03q8xj) <- exported_to(?x789, ?x94), origin(?x3382, ?x789), contains(?x789, ?x790) *> conf = 0.33 ranks of expected_values: 11, 49, 61, 236, 903, 1305, 1437, 1489, 1616 EVAL 0f8l9c country! 02fqxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 269.000 153.000 0.444 http://example.org/film/film/country EVAL 0f8l9c country! 03q8xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 269.000 153.000 0.444 http://example.org/film/film/country EVAL 0f8l9c country! 065_cjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 269.000 153.000 0.444 http://example.org/film/film/country EVAL 0f8l9c country! 07q1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 269.000 153.000 0.444 http://example.org/film/film/country EVAL 0f8l9c country! 05mrf_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 269.000 153.000 0.444 http://example.org/film/film/country EVAL 0f8l9c country! 04nnpw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 269.000 153.000 0.444 http://example.org/film/film/country EVAL 0f8l9c country! 0bpx1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 269.000 153.000 0.444 http://example.org/film/film/country EVAL 0f8l9c country! 04g9gd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 269.000 153.000 0.444 http://example.org/film/film/country EVAL 0f8l9c country! 02z3r8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 269.000 153.000 0.444 http://example.org/film/film/country #11099-05br2 PRED entity: 05br2 PRED relation: country! PRED expected values: 01cgz => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 56): 0bynt (0.85 #353, 0.85 #923, 0.84 #1265), 06z6r (0.74 #1515, 0.74 #1344, 0.74 #1002), 01cgz (0.62 #300, 0.61 #585, 0.59 #870), 071t0 (0.52 #1335, 0.52 #594, 0.51 #1506), 07gyv (0.42 #520, 0.42 #349, 0.41 #1318), 01lb14 (0.40 #1328, 0.40 #473, 0.39 #1727), 06wrt (0.37 #474, 0.34 #759, 0.33 #1329), 0486tv (0.36 #612, 0.32 #1068, 0.32 #669), 03hr1p (0.36 #481, 0.35 #1336, 0.34 #1507), 06f41 (0.36 #1327, 0.35 #586, 0.35 #1498) >> Best rule #353 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 04thp; >> query: (?x9613, 0bynt) <- official_language(?x9613, ?x254), currency(?x9613, ?x170), administrative_parent(?x9613, ?x551) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #300 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 71 *> proper extension: 02wm6l; *> query: (?x9613, 01cgz) <- form_of_government(?x9613, ?x48), ?x48 = 06cx9 *> conf = 0.62 ranks of expected_values: 3 EVAL 05br2 country! 01cgz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 47.000 47.000 0.849 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #11098-06ncr PRED entity: 06ncr PRED relation: group PRED expected values: 0178kd 03k3b 01lf293 01l_w0 => 96 concepts (66 used for prediction) PRED predicted values (max 10 best out of 682): 014pg1 (0.90 #648, 0.82 #5453, 0.80 #2106), 01v0sxx (0.90 #648, 0.80 #2106, 0.76 #2431), 016m5c (0.90 #648, 0.80 #2106, 0.76 #2431), 02t3ln (0.90 #648, 0.80 #2106, 0.76 #2431), 03qkcn9 (0.90 #648, 0.80 #2106, 0.76 #2431), 01czx (0.90 #648, 0.80 #2106, 0.76 #2431), 06nv27 (0.90 #648, 0.80 #2106, 0.76 #2431), 09jvl (0.90 #648, 0.80 #2106, 0.76 #2431), 02cw1m (0.90 #648, 0.80 #2106, 0.76 #2431), 03c3yf (0.90 #648, 0.80 #2106, 0.76 #2431) >> Best rule #648 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 0342h; >> query: (?x2309, ?x3773) <- role(?x2309, ?x3156), role(?x2309, ?x2206), role(?x2309, ?x1969), group(?x2309, ?x5838), group(?x2309, ?x2395), ?x5838 = 02dw1_, role(?x6039, ?x2309), role(?x248, ?x2309), ?x6039 = 05kms, ?x1969 = 04rzd, group(?x3156, ?x3773), group(?x3156, ?x2901), ?x2901 = 01vrwfv, ?x2206 = 07gql, instrumentalists(?x2309, ?x3492), ?x2395 = 0dvqq, ?x3492 = 01lvcs1 >> conf = 0.90 => this is the best rule for 156 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 11, 14, 33, 119 EVAL 06ncr group 01l_w0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 96.000 66.000 0.902 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 06ncr group 01lf293 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 96.000 66.000 0.902 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 06ncr group 03k3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 96.000 66.000 0.902 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 06ncr group 0178kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 96.000 66.000 0.902 http://example.org/music/performance_role/regular_performances./music/group_membership/group #11097-0nm3n PRED entity: 0nm3n PRED relation: adjoins PRED expected values: 0nm9h 0n5_t => 167 concepts (62 used for prediction) PRED predicted values (max 10 best out of 404): 0nm8n (0.50 #1326, 0.25 #2098, 0.12 #3641), 0nm9y (0.50 #1523, 0.25 #2295, 0.12 #3838), 0n5y4 (0.33 #485, 0.25 #13902, 0.25 #18536), 0k3ll (0.33 #438, 0.18 #47890, 0.18 #43249), 0n5_g (0.33 #393, 0.18 #47890, 0.18 #43249), 0nm42 (0.25 #1873, 0.25 #1101, 0.12 #3416), 0nm6k (0.25 #1084, 0.12 #3399, 0.03 #10810), 0nm3n (0.25 #13902, 0.25 #18536, 0.24 #19309), 0nm9h (0.25 #13902, 0.25 #18536, 0.24 #19309), 0n5_t (0.25 #13902, 0.25 #18536, 0.24 #19309) >> Best rule #1326 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0nm6k; 0nm42; >> query: (?x7330, 0nm8n) <- second_level_divisions(?x94, ?x7330), contains(?x7058, ?x7330), adjoins(?x7330, ?x7954), source(?x7330, ?x958), ?x7058 = 050ks >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13902 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 229 *> proper extension: 0_3cs; 0nvrd; 01cx_; 01jr6; 0n2vl; *> query: (?x7330, ?x12290) <- time_zones(?x7330, ?x2674), adjoins(?x13066, ?x7330), adjoins(?x7954, ?x7330), adjoins(?x7954, ?x12290), source(?x7954, ?x958), contains(?x13066, ?x7600) *> conf = 0.25 ranks of expected_values: 9, 10 EVAL 0nm3n adjoins 0n5_t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 167.000 62.000 0.500 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins EVAL 0nm3n adjoins 0nm9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 167.000 62.000 0.500 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #11096-042xrr PRED entity: 042xrr PRED relation: award_winner! PRED expected values: 0275n3y => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 86): 058m5m4 (0.28 #196, 0.05 #478, 0.05 #55), 05c1t6z (0.28 #156, 0.02 #438, 0.02 #2976), 0275n3y (0.20 #216, 0.16 #75, 0.04 #498), 03nnm4t (0.20 #215, 0.03 #497, 0.02 #1343), 09v0p2c (0.20 #224, 0.01 #7697, 0.01 #9812), 02q690_ (0.12 #206, 0.03 #2039, 0.03 #3026), 0418154 (0.12 #249, 0.02 #4197, 0.02 #1800), 092c5f (0.11 #14, 0.05 #437, 0.04 #2693), 0hndn2q (0.11 #40, 0.02 #4411, 0.02 #4129), 026kq4q (0.11 #46, 0.02 #751, 0.02 #2443) >> Best rule #196 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 02773m2; 02778pf; 0277470; 0284gcb; 02778qt; 02778yp; >> query: (?x4606, 058m5m4) <- award_nominee(?x6324, ?x4606), award_winner(?x4606, ?x3907), ?x6324 = 018ygt >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #216 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 02773m2; 02778pf; 0277470; 0284gcb; 02778qt; 02778yp; *> query: (?x4606, 0275n3y) <- award_nominee(?x6324, ?x4606), award_winner(?x4606, ?x3907), ?x6324 = 018ygt *> conf = 0.20 ranks of expected_values: 3 EVAL 042xrr award_winner! 0275n3y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 123.000 0.280 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11095-04jspq PRED entity: 04jspq PRED relation: executive_produced_by! PRED expected values: 02q3fdr 0fs9vc => 124 concepts (79 used for prediction) PRED predicted values (max 10 best out of 335): 0dyb1 (0.33 #1557, 0.12 #2594, 0.10 #3633), 0407yj_ (0.33 #1557, 0.12 #2594, 0.10 #3633), 02c7k4 (0.33 #1557, 0.12 #2594, 0.10 #3633), 01xdxy (0.12 #2594, 0.10 #8821, 0.10 #14012), 03q0r1 (0.12 #2594, 0.10 #14012, 0.10 #11417), 06fcqw (0.10 #14012, 0.10 #11417, 0.05 #5188), 0fh694 (0.07 #2112, 0.02 #6265, 0.02 #7303), 0bt4g (0.05 #1448, 0.05 #2485, 0.03 #4043), 0mbql (0.05 #1405, 0.05 #2442, 0.03 #4000), 01f7kl (0.05 #1166, 0.05 #2203, 0.03 #3761) >> Best rule #1557 for best value: >> intensional similarity = 2 >> extensional distance = 18 >> proper extension: 03j2gxx; 013km; 02nygk; >> query: (?x6682, ?x1259) <- story_by(?x1259, ?x6682), company(?x6682, ?x3945) >> conf = 0.33 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 04jspq executive_produced_by! 0fs9vc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 79.000 0.327 http://example.org/film/film/executive_produced_by EVAL 04jspq executive_produced_by! 02q3fdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 79.000 0.327 http://example.org/film/film/executive_produced_by #11094-01kp66 PRED entity: 01kp66 PRED relation: type_of_union PRED expected values: 04ztj => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.94 #205, 0.94 #202, 0.93 #85) >> Best rule #205 for best value: >> intensional similarity = 1 >> extensional distance = 3032 >> proper extension: 0qkj7; >> query: (?x4234, 04ztj) <- type_of_union(?x4234, ?x1873) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kp66 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.944 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11093-05m9f9 PRED entity: 05m9f9 PRED relation: nationality PRED expected values: 09c7w0 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.84 #1104, 0.84 #1305, 0.84 #6513), 03rjj (0.15 #205, 0.04 #406, 0.02 #6918), 0d060g (0.11 #107, 0.11 #7, 0.05 #308), 0345h (0.11 #131, 0.11 #31, 0.02 #733), 02jx1 (0.10 #7448, 0.10 #3541, 0.10 #7046), 07ssc (0.08 #817, 0.08 #1018, 0.08 #7028), 03rk0 (0.05 #10970, 0.05 #10870, 0.05 #6156), 0cr3d (0.04 #1003, 0.04 #1405, 0.04 #1204), 0chghy (0.02 #2516, 0.02 #4419, 0.02 #5720), 0f8l9c (0.02 #3329, 0.02 #7437, 0.02 #3730) >> Best rule #1104 for best value: >> intensional similarity = 3 >> extensional distance = 138 >> proper extension: 01jbx1; 02xp18; 024swd; >> query: (?x5166, 09c7w0) <- program(?x5166, ?x493), place_of_birth(?x5166, ?x2850), award(?x5166, ?x4921) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05m9f9 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.843 http://example.org/people/person/nationality #11092-06mzp PRED entity: 06mzp PRED relation: nationality! PRED expected values: 0gdqy => 181 concepts (64 used for prediction) PRED predicted values (max 10 best out of 4061): 07dnx (0.78 #64954, 0.26 #113672, 0.24 #138030), 03_87 (0.78 #64954, 0.06 #26355, 0.05 #30414), 01vsy7t (0.33 #162387, 0.10 #21697, 0.07 #62292), 015n8 (0.26 #113672, 0.24 #138030, 0.12 #19985), 03cdg (0.26 #113672, 0.24 #138030, 0.10 #23886), 02wh0 (0.26 #113672, 0.24 #138030, 0.06 #27823), 03sbs (0.26 #113672, 0.24 #138030, 0.06 #26607), 0j3v (0.26 #113672, 0.24 #138030, 0.06 #24957), 0gz_ (0.26 #113672, 0.24 #138030, 0.06 #25426), 015k7 (0.26 #113672, 0.24 #138030, 0.05 #31050) >> Best rule #64954 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 05bcl; >> query: (?x774, ?x6457) <- contains(?x774, ?x6458), nationality(?x1221, ?x774), place_of_death(?x6457, ?x6458) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #11370 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 03_xj; *> query: (?x774, 0gdqy) <- location(?x2580, ?x774), countries_spoken_in(?x5607, ?x774), ?x5607 = 064_8sq *> conf = 0.17 ranks of expected_values: 51 EVAL 06mzp nationality! 0gdqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 181.000 64.000 0.777 http://example.org/people/person/nationality #11091-011ykb PRED entity: 011ykb PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 24): 0ch6mp2 (0.79 #1052, 0.79 #150, 0.79 #980), 0dxtw (0.42 #984, 0.42 #154, 0.38 #1056), 01vx2h (0.40 #1057, 0.37 #1201, 0.31 #985), 01pvkk (0.29 #1274, 0.28 #1058, 0.28 #2069), 0215hd (0.20 #55, 0.16 #1065, 0.14 #1281), 01xy5l_ (0.20 #50, 0.13 #1060, 0.12 #1276), 089g0h (0.20 #56, 0.13 #1066, 0.13 #1282), 02ynfr (0.20 #1062, 0.19 #990, 0.19 #1206), 02rh1dz (0.17 #81, 0.14 #1055, 0.13 #1199), 015h31 (0.17 #80, 0.10 #1054, 0.09 #1234) >> Best rule #1052 for best value: >> intensional similarity = 3 >> extensional distance = 592 >> proper extension: 0fq27fp; >> query: (?x6472, 0ch6mp2) <- film_crew_role(?x6472, ?x468), currency(?x6472, ?x170), ?x468 = 02r96rf >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011ykb film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.795 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11090-017yxq PRED entity: 017yxq PRED relation: category PRED expected values: 08mbj5d => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #13, 0.80 #22, 0.77 #46) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 133 >> proper extension: 0m19t; >> query: (?x8399, 08mbj5d) <- artists(?x2937, ?x8399), ?x2937 = 0glt670 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017yxq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.822 http://example.org/common/topic/webpage./common/webpage/category #11089-07qnf PRED entity: 07qnf PRED relation: group! PRED expected values: 05148p4 => 47 concepts (45 used for prediction) PRED predicted values (max 10 best out of 120): 05148p4 (0.72 #1007, 0.71 #429, 0.70 #1172), 0l14md (0.70 #417, 0.60 #995, 0.60 #7), 018vs (0.66 #1001, 0.63 #1166, 0.62 #834), 03bx0bm (0.64 #433, 0.60 #1011, 0.60 #1094), 0l14qv (0.52 #87, 0.40 #5, 0.26 #415), 042v_gx (0.40 #8, 0.14 #90, 0.12 #912), 01xqw (0.40 #63, 0.14 #145, 0.08 #821), 04rzd (0.38 #110, 0.20 #28, 0.14 #932), 028tv0 (0.38 #422, 0.37 #1000, 0.37 #1083), 06ncr (0.29 #116, 0.17 #444, 0.16 #938) >> Best rule #1007 for best value: >> intensional similarity = 4 >> extensional distance = 152 >> proper extension: 015cxv; 0qmpd; >> query: (?x997, 05148p4) <- group(?x2798, ?x997), category(?x997, ?x134), role(?x212, ?x2798), role(?x211, ?x2798) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07qnf group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 45.000 0.721 http://example.org/music/performance_role/regular_performances./music/group_membership/group #11088-0466s8n PRED entity: 0466s8n PRED relation: film_crew_role PRED expected values: 01vx2h => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 20): 01vx2h (0.43 #72, 0.40 #41, 0.38 #103), 0dxtw (0.39 #288, 0.39 #412, 0.38 #443), 01xy5l_ (0.25 #12, 0.18 #1871, 0.15 #322), 02ynfr (0.24 #138, 0.18 #417, 0.18 #448), 015h31 (0.20 #38, 0.18 #1871, 0.14 #69), 02rh1dz (0.18 #1871, 0.14 #194, 0.13 #318), 02_n3z (0.18 #1871, 0.10 #311, 0.09 #280), 089fss (0.18 #1871, 0.09 #284, 0.08 #408), 02vs3x5 (0.18 #1871, 0.08 #144, 0.05 #237), 04pyp5 (0.18 #1871, 0.06 #325, 0.06 #418) >> Best rule #72 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02qhqz4; 0pvms; >> query: (?x10225, 01vx2h) <- film(?x1735, ?x10225), film_crew_role(?x10225, ?x1284), ?x1735 = 01l9p, ?x1284 = 0ch6mp2 >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0466s8n film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.429 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11087-02qydsh PRED entity: 02qydsh PRED relation: currency PRED expected values: 09nqf => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.88 #57, 0.84 #36, 0.81 #190), 01nv4h (0.04 #44, 0.03 #79, 0.02 #72), 088n7 (0.01 #168, 0.01 #91) >> Best rule #57 for best value: >> intensional similarity = 7 >> extensional distance = 48 >> proper extension: 08gsvw; 01_mdl; 0pb33; 03twd6; 0c8tkt; 014kq6; 07cz2; 0hx4y; 0x25q; 03r0g9; ... >> query: (?x8794, 09nqf) <- country(?x8794, ?x94), genre(?x8794, ?x1510), genre(?x8794, ?x811), ?x811 = 03k9fj, film_crew_role(?x8794, ?x1966), prequel(?x8292, ?x8794), titles(?x1510, ?x83) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qydsh currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.880 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #11086-087c7 PRED entity: 087c7 PRED relation: category PRED expected values: 08mbj5d => 224 concepts (224 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #78, 0.88 #46, 0.88 #154) >> Best rule #78 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 06thjt; >> query: (?x502, 08mbj5d) <- country(?x502, ?x94), citytown(?x502, ?x503), currency(?x502, ?x170), state(?x503, ?x1755), state_province_region(?x502, ?x335) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087c7 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 224.000 224.000 0.905 http://example.org/common/topic/webpage./common/webpage/category #11085-01pj7 PRED entity: 01pj7 PRED relation: film_release_region! PRED expected values: 0g56t9t 02vxq9m 0gx1bnj 017gl1 0h3xztt 0dtfn 0661m4p 04f52jw 0g5838s 03cw411 0bmhvpr 0hgnl3t 02qyv3h 0g9zljd 0gg8z1f 0g4vmj8 => 176 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1264): 0jjy0 (0.91 #18590, 0.83 #14897, 0.80 #16128), 024mpp (0.90 #15227, 0.87 #16458, 0.78 #18920), 02d44q (0.88 #18586, 0.77 #14893, 0.73 #16124), 04f52jw (0.87 #16308, 0.87 #15077, 0.84 #18770), 01c22t (0.87 #14896, 0.84 #18589, 0.83 #16127), 01vksx (0.87 #14871, 0.84 #18564, 0.83 #16102), 0g4vmj8 (0.87 #16886, 0.83 #15655, 0.72 #9498), 0dtfn (0.84 #18617, 0.83 #14924, 0.80 #16155), 04hwbq (0.84 #8756, 0.80 #14913, 0.77 #16144), 05zlld0 (0.83 #16440, 0.83 #15209, 0.81 #18902) >> Best rule #18590 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0154j; 01ls2; 03rt9; 0hzlz; 0ctw_b; 01p1v; 06f32; >> query: (?x1790, 0jjy0) <- film_release_region(?x5418, ?x1790), olympics(?x1790, ?x418), ?x5418 = 026lgs >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #16308 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 012wgb; *> query: (?x1790, 04f52jw) <- film_release_region(?x2961, ?x1790), ?x2961 = 047p7fr, contains(?x1790, ?x1791) *> conf = 0.87 ranks of expected_values: 4, 7, 8, 23, 24, 27, 42, 44, 62, 78, 86, 98, 104, 109, 160, 167 EVAL 01pj7 film_release_region! 0g4vmj8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 0gg8z1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 0g9zljd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 02qyv3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 0hgnl3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 0bmhvpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 03cw411 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 0g5838s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 04f52jw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 0661m4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 0dtfn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 0h3xztt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 017gl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 0gx1bnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01pj7 film_release_region! 0g56t9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 176.000 83.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11084-019n9w PRED entity: 019n9w PRED relation: campuses! PRED expected values: 019n9w => 226 concepts (149 used for prediction) PRED predicted values (max 10 best out of 346): 019n9w (0.29 #47520, 0.22 #79220, 0.19 #80864), 09f2j (0.29 #47520, 0.22 #79220, 0.19 #80864), 06thjt (0.29 #47520, 0.22 #79220, 0.19 #80864), 0fr9jp (0.22 #79220, 0.02 #78672, 0.02 #73203), 07tgn (0.22 #79220, 0.01 #15850, 0.01 #16943), 01pl14 (0.17 #554, 0.06 #2739, 0.03 #8745), 035gt8 (0.17 #1023, 0.02 #11944, 0.02 #14676), 02km0m (0.17 #761, 0.02 #11682, 0.01 #17691), 03hdz8 (0.10 #80316, 0.08 #79768, 0.06 #69922), 04cnp4 (0.10 #80316, 0.08 #79768, 0.06 #69922) >> Best rule #47520 for best value: >> intensional similarity = 4 >> extensional distance = 181 >> proper extension: 06xpp7; 015ln1; 02sdwt; 02mg7n; >> query: (?x8525, ?x4955) <- student(?x8525, ?x4831), contains(?x94, ?x8525), place_of_death(?x4831, ?x739), student(?x4955, ?x4831) >> conf = 0.29 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 019n9w campuses! 019n9w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 149.000 0.288 http://example.org/education/educational_institution/campuses #11083-03y82t6 PRED entity: 03y82t6 PRED relation: profession PRED expected values: 0np9r => 149 concepts (118 used for prediction) PRED predicted values (max 10 best out of 88): 01d_h8 (0.50 #293, 0.45 #2599, 0.45 #1302), 0dz3r (0.47 #722, 0.45 #3750, 0.44 #7063), 01c72t (0.45 #1895, 0.31 #8816, 0.29 #11126), 03gjzk (0.35 #2607, 0.34 #1310, 0.30 #301), 0dxtg (0.34 #1309, 0.30 #3616, 0.30 #2606), 02jknp (0.30 #1016, 0.23 #1304, 0.23 #3177), 0d1pc (0.30 #911, 0.27 #9516, 0.27 #10383), 012t_z (0.27 #9516, 0.27 #10383, 0.17 #731), 0kyk (0.27 #459, 0.21 #1036, 0.20 #4207), 0np9r (0.23 #3333, 0.17 #10547, 0.15 #3188) >> Best rule #293 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0n839; >> query: (?x4740, 01d_h8) <- profession(?x4740, ?x220), company(?x4740, ?x3265), special_performance_type(?x4740, ?x4832) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3333 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 122 *> proper extension: 04j5fx; *> query: (?x4740, 0np9r) <- profession(?x4740, ?x220), location(?x4740, ?x957), special_performance_type(?x4740, ?x4832) *> conf = 0.23 ranks of expected_values: 10 EVAL 03y82t6 profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 149.000 118.000 0.500 http://example.org/people/person/profession #11082-02mc79 PRED entity: 02mc79 PRED relation: award_nominee PRED expected values: 025hwq => 112 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1075): 030g9z (0.80 #67931, 0.80 #67932, 0.80 #72619), 02tf1y (0.21 #23425, 0.16 #11714, 0.16 #30453), 0bksh (0.12 #1135, 0.04 #3477, 0.03 #5820), 018grr (0.12 #447, 0.03 #51533, 0.02 #7475), 027xbpw (0.12 #756, 0.03 #24181, 0.02 #26523), 027j79k (0.12 #2038, 0.02 #51228, 0.02 #65284), 04t2l2 (0.09 #23465, 0.08 #25807, 0.07 #2382), 05pzdk (0.09 #5943, 0.07 #3600, 0.05 #12972), 06cgy (0.08 #327, 0.06 #5012, 0.04 #2669), 01mh8zn (0.08 #1786, 0.06 #6471, 0.04 #4128) >> Best rule #67931 for best value: >> intensional similarity = 4 >> extensional distance = 275 >> proper extension: 05cv94; 0162c8; 01gp_x; 03kpvp; 0fqyzz; 0b478; 0d02km; 01hrqc; 0gs5q; 025hzx; ... >> query: (?x8071, ?x541) <- produced_by(?x4500, ?x8071), award_nominee(?x5064, ?x8071), award_nominee(?x541, ?x8071), profession(?x5064, ?x319) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1767 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 0hskw; 0d608; *> query: (?x8071, 025hwq) <- award(?x8071, ?x537), produced_by(?x4500, ?x8071), profession(?x8071, ?x1146), ?x1146 = 018gz8 *> conf = 0.04 ranks of expected_values: 127 EVAL 02mc79 award_nominee 025hwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 112.000 39.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11081-0lsxr PRED entity: 0lsxr PRED relation: genre! PRED expected values: 0b2v79 09xbpt 01cssf 0fg04 03ckwzc 0fh694 092vkg 01fmys 0418wg 01dvbd 017z49 07kh6f3 011wtv 0c38gj 01qvz8 013q0p 07sgdw 07bwr 035_2h 0fjyzt 031ldd 01cmp9 0ptx_ 0gg5kmg 035gnh 02825nf 01hq1 05ldxl 060__7 05nyqk 0gm2_0 07tlfx 0crd8q6 07phbc 03k8th => 68 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1622): 07jnt (0.75 #31815, 0.33 #5823, 0.25 #16957), 0170xl (0.71 #38107, 0.50 #15835, 0.50 #14245), 0fvr1 (0.62 #40080, 0.60 #20986, 0.50 #14621), 0yx7h (0.62 #40324, 0.60 #24414, 0.40 #53049), 0g68zt (0.62 #40221, 0.50 #11581, 0.40 #27491), 026n4h6 (0.62 #39986, 0.40 #27256, 0.40 #25666), 0dsvzh (0.60 #27144, 0.50 #41466, 0.50 #39874), 060__7 (0.60 #21961, 0.50 #41055, 0.50 #34686), 0ctb4g (0.60 #21168, 0.50 #11622, 0.40 #27532), 03cvvlg (0.60 #21947, 0.50 #12401, 0.40 #28311) >> Best rule #31815 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 05rwpb; >> query: (?x604, ?x6782) <- split_to(?x13835, ?x604), genre(?x6782, ?x13835), story_by(?x6782, ?x2343), film_release_region(?x6782, ?x87), production_companies(?x6782, ?x5908), nominated_for(?x384, ?x6782), film(?x5542, ?x6782), nominated_for(?x185, ?x6782) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #21961 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 04xvlr; *> query: (?x604, 060__7) <- genre(?x6097, ?x604), genre(?x5667, ?x604), genre(?x2403, ?x604), genre(?x2177, ?x604), ?x6097 = 0y_yw, genre(?x8846, ?x604), honored_for(?x188, ?x5667), nominated_for(?x459, ?x2177), nominated_for(?x112, ?x2177), program(?x129, ?x8846), nominated_for(?x382, ?x5667), ?x2403 = 02rb607 *> conf = 0.60 ranks of expected_values: 8, 24, 26, 47, 55, 60, 79, 122, 168, 198, 266, 339, 378, 390, 487, 528, 683, 687, 696, 708, 709, 773, 839, 840, 847, 864, 980, 1011, 1084, 1154, 1165, 1169, 1306, 1443, 1461 EVAL 0lsxr genre! 03k8th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 07phbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 0crd8q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 07tlfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 0gm2_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 05nyqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 060__7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 05ldxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 01hq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 035gnh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 0gg5kmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 0ptx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 01cmp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 031ldd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 0fjyzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 035_2h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 07bwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 07sgdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 013q0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 01qvz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 0c38gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 011wtv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 07kh6f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 017z49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 01dvbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 0418wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 01fmys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 092vkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 0fh694 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 03ckwzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 0fg04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 01cssf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 09xbpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 68.000 45.000 0.750 http://example.org/film/film/genre EVAL 0lsxr genre! 0b2v79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 68.000 45.000 0.750 http://example.org/film/film/genre #11080-04mx8h4 PRED entity: 04mx8h4 PRED relation: actor PRED expected values: 01tszq 022s1m => 69 concepts (38 used for prediction) PRED predicted values (max 10 best out of 740): 0sw6y (0.33 #1783, 0.26 #3638, 0.07 #4565), 02gf_l (0.33 #1496, 0.21 #3351, 0.07 #4278), 029cpw (0.33 #1479, 0.11 #3334, 0.02 #21875), 024my5 (0.33 #1534, 0.11 #3389, 0.02 #21930), 01nd6v (0.33 #925, 0.05 #3707, 0.04 #4634), 0sw62 (0.22 #1687, 0.16 #3542, 0.04 #4469), 031c2r (0.22 #1792, 0.11 #3647, 0.04 #4574), 019803 (0.22 #1780, 0.11 #3635, 0.02 #8270), 08jtv5 (0.22 #1672, 0.11 #3527, 0.02 #8162), 04hxyv (0.22 #1832, 0.05 #3687, 0.02 #8322) >> Best rule #1783 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 015w8_; 0vhm; 05nlzq; 07vqnc; 043qqt5; 019g8j; >> query: (?x8976, 0sw6y) <- nominated_for(?x3263, ?x8976), ?x3263 = 0cc8l6d, genre(?x8976, ?x1013), actor(?x8976, ?x7117) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1139 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 015w8_; 0vhm; 05nlzq; 07vqnc; 043qqt5; 019g8j; *> query: (?x8976, 01tszq) <- nominated_for(?x3263, ?x8976), ?x3263 = 0cc8l6d, genre(?x8976, ?x1013), actor(?x8976, ?x7117) *> conf = 0.11 ranks of expected_values: 12, 196 EVAL 04mx8h4 actor 022s1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 69.000 38.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 04mx8h4 actor 01tszq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 69.000 38.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11079-043hg PRED entity: 043hg PRED relation: gender PRED expected values: 05zppz => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #29, 0.88 #5, 0.86 #17), 02zsn (0.46 #190, 0.35 #22, 0.31 #4) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 363 >> proper extension: 0h1_w; 054_mz; 012cj0; 02ndbd; 01y_px; 05bxwh; 05h72z; 04q5zw; 09qh1; 09p06; ... >> query: (?x6748, 05zppz) <- award(?x6748, ?x372), award(?x6121, ?x372), ?x6121 = 064lsn, award_winner(?x372, ?x767) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043hg gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.877 http://example.org/people/person/gender #11078-0fdjb PRED entity: 0fdjb PRED relation: genre! PRED expected values: 04fv5b => 47 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1864): 09fc83 (0.57 #17541, 0.50 #13844, 0.50 #10146), 01pvxl (0.57 #23118, 0.33 #24968, 0.33 #13869), 0ptdz (0.57 #16601, 0.33 #14752, 0.33 #3659), 04svwx (0.57 #16609, 0.33 #3667, 0.29 #22156), 0p_rk (0.57 #16177, 0.33 #3235, 0.26 #22190), 011x_4 (0.57 #16146, 0.33 #3204, 0.24 #12942), 0473rc (0.57 #15881, 0.33 #2939, 0.22 #25131), 03z20c (0.57 #15281, 0.33 #2339, 0.17 #24531), 0kvgtf (0.57 #15431, 0.33 #2489, 0.17 #24681), 0h1v19 (0.57 #15243, 0.33 #2301, 0.17 #24493) >> Best rule #17541 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 07qht4; >> query: (?x6277, 09fc83) <- genre(?x9633, ?x6277), genre(?x3848, ?x6277), ?x3848 = 05sy2k_, actor(?x9633, ?x4662), nominated_for(?x686, ?x9633), film(?x4662, ?x408), award_nominee(?x4662, ?x157), award_winner(?x9633, ?x4703), award_nominee(?x450, ?x4662), participant(?x2221, ?x4662) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #8347 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 01jfsb; *> query: (?x6277, 04fv5b) <- genre(?x4696, ?x6277), genre(?x3000, ?x6277), genre(?x485, ?x6277), genre(?x3413, ?x6277), nominated_for(?x6729, ?x485), ?x3000 = 045j3w, film_crew_role(?x485, ?x137), crewmember(?x485, ?x3782), award_winner(?x485, ?x1532), titles(?x162, ?x4696), region(?x485, ?x512), nominated_for(?x6729, ?x6176), ?x6176 = 0gmgwnv *> conf = 0.50 ranks of expected_values: 79 EVAL 0fdjb genre! 04fv5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 47.000 17.000 0.571 http://example.org/film/film/genre #11077-0klh7 PRED entity: 0klh7 PRED relation: type_of_union PRED expected values: 04ztj => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.94 #308, 0.94 #296, 0.94 #46) >> Best rule #308 for best value: >> intensional similarity = 2 >> extensional distance = 2892 >> proper extension: 0bn9sc; 0487c3; 01_4z; 01w923; 080dyk; 09prnq; 05wh0sh; 0fx02; 0gz_; 0bkg4; ... >> query: (?x2849, 04ztj) <- profession(?x2849, ?x319), type_of_union(?x2849, ?x1873) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0klh7 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.942 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11076-015cqh PRED entity: 015cqh PRED relation: group! PRED expected values: 03qjg => 118 concepts (96 used for prediction) PRED predicted values (max 10 best out of 120): 03qjg (0.42 #1575, 0.38 #725, 0.37 #2000), 0l14qv (0.40 #940, 0.39 #1025, 0.33 #5), 05r5c (0.34 #2047, 0.34 #3238, 0.28 #3408), 01vj9c (0.31 #3243, 0.31 #2052, 0.29 #3413), 07y_7 (0.29 #172, 0.22 #427, 0.17 #937), 037c9s (0.29 #339, 0.15 #3742, 0.14 #254), 04rzd (0.22 #539, 0.17 #1644, 0.17 #1814), 042v_gx (0.22 #433, 0.17 #518, 0.15 #3742), 013y1f (0.22 #2065, 0.20 #960, 0.20 #1045), 0l14j_ (0.20 #984, 0.20 #1069, 0.17 #559) >> Best rule #1575 for best value: >> intensional similarity = 7 >> extensional distance = 48 >> proper extension: 0394y; 06gcn; 0qmny; 0cfgd; >> query: (?x8335, 03qjg) <- group(?x10025, ?x8335), artist(?x9492, ?x8335), artists(?x1000, ?x8335), artist(?x9492, ?x6986), artist(?x9492, ?x4960), ?x6986 = 02vgh, award_winner(?x2319, ?x4960) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015cqh group! 03qjg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 96.000 0.420 http://example.org/music/performance_role/regular_performances./music/group_membership/group #11075-05fjf PRED entity: 05fjf PRED relation: state_province_region! PRED expected values: 04f0xq => 208 concepts (162 used for prediction) PRED predicted values (max 10 best out of 776): 05njw (0.40 #2791, 0.20 #5014, 0.17 #5756), 06182p (0.40 #2608, 0.20 #4831, 0.17 #5573), 03pcgf (0.36 #46736, 0.28 #8155, 0.23 #46735), 0xmp9 (0.36 #46736, 0.28 #8155, 0.23 #46735), 0h6l4 (0.36 #46736, 0.28 #8155, 0.23 #46735), 0xn7q (0.36 #46736, 0.28 #8155, 0.23 #46735), 0xt3t (0.36 #46736, 0.28 #8155, 0.23 #46735), 0xkyn (0.36 #46736, 0.28 #8155, 0.23 #46735), 010cw1 (0.36 #46736, 0.28 #8155, 0.23 #46735), 0xszy (0.36 #46736, 0.28 #8155, 0.23 #46735) >> Best rule #2791 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 059rby; 02xry; >> query: (?x6895, 05njw) <- jurisdiction_of_office(?x1159, ?x6895), district_represented(?x176, ?x6895), origin(?x2963, ?x6895) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05fjf state_province_region! 04f0xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 208.000 162.000 0.400 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #11074-0mnm2 PRED entity: 0mnm2 PRED relation: source PRED expected values: 0jbk9 => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #59, 0.93 #56, 0.91 #95) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 0n5_g; >> query: (?x7548, 0jbk9) <- second_level_divisions(?x94, ?x7548), time_zones(?x7548, ?x2674), ?x94 = 09c7w0, ?x2674 = 02hcv8 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mnm2 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.940 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #11073-0x25q PRED entity: 0x25q PRED relation: genre PRED expected values: 06n90 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 148): 07s9rl0 (0.62 #7827, 0.59 #6500, 0.59 #6621), 024qqx (0.53 #3845, 0.52 #6620, 0.52 #6377), 0lsxr (0.45 #9, 0.29 #489, 0.25 #609), 05p553 (0.38 #724, 0.38 #1566, 0.36 #3248), 02n4kr (0.36 #8, 0.15 #248, 0.13 #368), 01hmnh (0.33 #737, 0.32 #377, 0.31 #1339), 06n90 (0.31 #132, 0.29 #492, 0.28 #3376), 02l7c8 (0.29 #6514, 0.29 #4342, 0.28 #2897), 0hn10 (0.27 #10, 0.06 #130, 0.05 #5783), 060__y (0.18 #16, 0.14 #6515, 0.14 #2898) >> Best rule #7827 for best value: >> intensional similarity = 4 >> extensional distance = 1235 >> proper extension: 0k20s; 0cbl95; >> query: (?x3055, 07s9rl0) <- nominated_for(?x2922, ?x3055), nominated_for(?x350, ?x3055), genre(?x3055, ?x811), titles(?x811, ?x148) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #132 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 0140g4; *> query: (?x3055, 06n90) <- nominated_for(?x2922, ?x3055), nominated_for(?x350, ?x3055), nominated_for(?x1807, ?x3055), ?x350 = 05f4m9q *> conf = 0.31 ranks of expected_values: 7 EVAL 0x25q genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 98.000 98.000 0.618 http://example.org/film/film/genre #11072-09b3v PRED entity: 09b3v PRED relation: titles PRED expected values: 01jrbb => 115 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1529): 07bzz7 (0.33 #5330, 0.33 #3801, 0.04 #75681), 027fwmt (0.33 #5926, 0.33 #4397, 0.03 #76277), 017kz7 (0.33 #5718, 0.33 #4189, 0.03 #76069), 02q52q (0.33 #4821, 0.33 #3292, 0.03 #75172), 02py4c8 (0.33 #3149, 0.20 #9265, 0.08 #75029), 041td_ (0.33 #5497, 0.08 #75848, 0.07 #80437), 03h_yy (0.33 #4652, 0.08 #75003, 0.07 #79592), 02s4l6 (0.33 #4891, 0.06 #75242, 0.06 #79831), 03hfmm (0.33 #5824, 0.05 #76175, 0.05 #80764), 03prz_ (0.33 #3886, 0.05 #75766, 0.05 #80355) >> Best rule #5330 for best value: >> intensional similarity = 2 >> extensional distance = 1 >> proper extension: 04t36; >> query: (?x3920, 07bzz7) <- titles(?x3920, ?x4650), ?x4650 = 0fgrm >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09b3v titles 01jrbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 105.000 0.333 http://example.org/media_common/netflix_genre/titles #11071-0d7wh PRED entity: 0d7wh PRED relation: languages_spoken PRED expected values: 083tk => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 54): 0t_2 (0.40 #436, 0.40 #383, 0.39 #596), 064_8sq (0.28 #532, 0.27 #639, 0.27 #372), 02bjrlw (0.28 #532, 0.27 #639, 0.27 #372), 02bv9 (0.28 #532, 0.27 #639, 0.09 #396), 06b_j (0.16 #178, 0.12 #231, 0.11 #391), 06nm1 (0.15 #433, 0.14 #593, 0.14 #915), 01r2l (0.12 #127, 0.06 #928, 0.04 #339), 0880p (0.11 #202, 0.10 #96, 0.09 #415), 03hkp (0.11 #171, 0.10 #65, 0.09 #384), 0k0sv (0.10 #73, 0.08 #1014, 0.08 #799) >> Best rule #436 for best value: >> intensional similarity = 6 >> extensional distance = 53 >> proper extension: 033qxt; >> query: (?x5042, 0t_2) <- people(?x5042, ?x1549), award_nominee(?x1549, ?x2487), languages_spoken(?x5042, ?x254), award_winner(?x112, ?x1549), nominated_for(?x1549, ?x1474), profession(?x2487, ?x1032) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #86 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 8 *> proper extension: 0g96wd; *> query: (?x5042, 083tk) <- people(?x5042, ?x3476), people(?x5042, ?x1549), people(?x5042, ?x981), people(?x5042, ?x488), film(?x1549, ?x994), award_nominee(?x488, ?x1410), student(?x2999, ?x3476), ?x1410 = 01yhvv, nationality(?x981, ?x1310), type_of_union(?x1549, ?x1873) *> conf = 0.10 ranks of expected_values: 11 EVAL 0d7wh languages_spoken 083tk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 22.000 22.000 0.400 http://example.org/people/ethnicity/languages_spoken #11070-02rky4 PRED entity: 02rky4 PRED relation: category PRED expected values: 08mbj5d => 206 concepts (206 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #70, 0.91 #80, 0.91 #65) >> Best rule #70 for best value: >> intensional similarity = 4 >> extensional distance = 156 >> proper extension: 01zn4y; >> query: (?x10368, 08mbj5d) <- citytown(?x10368, ?x6960), currency(?x10368, ?x170), contains(?x6960, ?x1659), place_of_birth(?x1182, ?x6960) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rky4 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 206.000 206.000 0.924 http://example.org/common/topic/webpage./common/webpage/category #11069-05c6073 PRED entity: 05c6073 PRED relation: parent_genre PRED expected values: 06by7 => 74 concepts (54 used for prediction) PRED predicted values (max 10 best out of 221): 06by7 (0.57 #2511, 0.50 #1014, 0.47 #3182), 05w3f (0.33 #1856, 0.33 #856, 0.33 #358), 03lty (0.33 #19, 0.31 #2183, 0.29 #2349), 01pfpt (0.33 #393, 0.25 #1059, 0.25 #726), 017371 (0.33 #273, 0.25 #772, 0.25 #605), 0190_q (0.33 #357, 0.25 #690, 0.22 #1855), 011j5x (0.33 #22, 0.25 #1020, 0.18 #2017), 02w4v (0.33 #197, 0.25 #529, 0.17 #861), 01wqlc (0.33 #381, 0.25 #714, 0.12 #1547), 02r6mf (0.33 #483, 0.25 #816, 0.12 #1649) >> Best rule #2511 for best value: >> intensional similarity = 8 >> extensional distance = 12 >> proper extension: 0g_bh; >> query: (?x12498, 06by7) <- artists(?x12498, ?x9841), artists(?x12498, ?x8199), artists(?x12498, ?x5768), ?x9841 = 02ndj5, group(?x228, ?x8199), artists(?x474, ?x8199), ?x474 = 0m0jc, instrumentalists(?x227, ?x5768) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05c6073 parent_genre 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 54.000 0.571 http://example.org/music/genre/parent_genre #11068-02nxhr PRED entity: 02nxhr PRED relation: film_distribution_medium! PRED expected values: 06_wqk4 014l6_ 02fqrf 033qdy 02dr9j 0bt4g 04180vy => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 1700): 01pj_5 (0.50 #576, 0.50 #233, 0.33 #72), 04k9y6 (0.50 #774, 0.50 #604, 0.33 #100), 0295sy (0.50 #765, 0.50 #595, 0.33 #91), 06gb1w (0.50 #572, 0.40 #833, 0.33 #68), 0bt4g (0.50 #797, 0.40 #833, 0.33 #123), 03kx49 (0.50 #799, 0.36 #161, 0.33 #125), 014l6_ (0.50 #727, 0.36 #161, 0.33 #53), 01r97z (0.50 #684, 0.33 #160, 0.33 #10), 0jqn5 (0.50 #699, 0.33 #158, 0.33 #25), 0dtfn (0.50 #698, 0.33 #24, 0.25 #528) >> Best rule #576 for best value: >> intensional similarity = 73 >> extensional distance = 2 >> proper extension: 0735l; >> query: (?x627, 01pj_5) <- film_release_distribution_medium(?x5829, ?x627), film_release_distribution_medium(?x5767, ?x627), film_release_distribution_medium(?x4441, ?x627), film_release_distribution_medium(?x4041, ?x627), film_release_distribution_medium(?x3812, ?x627), film_release_distribution_medium(?x2097, ?x627), film_release_distribution_medium(?x770, ?x627), film_distribution_medium(?x7917, ?x627), film_distribution_medium(?x7366, ?x627), film_distribution_medium(?x6332, ?x627), film_distribution_medium(?x4551, ?x627), film_distribution_medium(?x4375, ?x627), film_distribution_medium(?x626, ?x627), ?x6332 = 03hxsv, film_release_region(?x4441, ?x5274), film_release_region(?x4441, ?x3277), film_release_region(?x4441, ?x1499), film_release_region(?x4441, ?x1003), film_release_region(?x4441, ?x756), film_release_region(?x4441, ?x512), film_crew_role(?x5829, ?x137), nominated_for(?x558, ?x4441), genre(?x2097, ?x307), nominated_for(?x2022, ?x5829), category(?x5829, ?x134), nominated_for(?x500, ?x2097), ?x1003 = 03gj2, ?x512 = 07ssc, award(?x770, ?x350), country(?x4441, ?x789), film_crew_role(?x5767, ?x2154), nominated_for(?x2551, ?x4041), film(?x4702, ?x4041), country(?x1352, ?x5274), film_release_region(?x3812, ?x1497), award_winner(?x1193, ?x2551), film(?x2551, ?x1318), film(?x1678, ?x4375), award_nominee(?x92, ?x2551), nominated_for(?x1983, ?x5767), currency(?x7366, ?x170), adjoins(?x1355, ?x756), featured_film_locations(?x4551, ?x6930), music(?x4551, ?x669), film(?x629, ?x5767), featured_film_locations(?x4375, ?x3269), film_release_region(?x11839, ?x756), titles(?x571, ?x7366), jurisdiction_of_office(?x182, ?x5274), nominated_for(?x640, ?x4551), film(?x166, ?x4375), award_winner(?x618, ?x2551), language(?x3812, ?x254), country(?x150, ?x756), edited_by(?x4551, ?x4215), form_of_government(?x5274, ?x1926), nominated_for(?x666, ?x7917), ?x1497 = 015qh, film(?x5316, ?x4441), film_crew_role(?x4375, ?x1171), ?x1499 = 01znc_, olympics(?x756, ?x418), production_companies(?x626, ?x847), prequel(?x4551, ?x2947), student(?x3439, ?x4702), type_of_union(?x2551, ?x566), ?x11839 = 072hx4, featured_film_locations(?x7366, ?x1036), ?x3277 = 06t8v, combatants(?x756, ?x1536), ?x1036 = 080h2, titles(?x811, ?x7917), executive_produced_by(?x4375, ?x4060) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #797 for first EXPECTED value: *> intensional similarity = 75 *> extensional distance = 2 *> proper extension: 0dq6p; *> query: (?x627, 0bt4g) <- film_distribution_medium(?x9496, ?x627), film_distribution_medium(?x9213, ?x627), film_distribution_medium(?x6332, ?x627), film_distribution_medium(?x4621, ?x627), film_distribution_medium(?x4502, ?x627), film_distribution_medium(?x2869, ?x627), film_distribution_medium(?x2350, ?x627), film_distribution_medium(?x2006, ?x627), film_distribution_medium(?x1035, ?x627), film_distribution_medium(?x908, ?x627), film_distribution_medium(?x141, ?x627), film_release_region(?x141, ?x2645), film_release_region(?x141, ?x1917), film_release_region(?x141, ?x1892), film_release_region(?x141, ?x1558), film_release_region(?x141, ?x1536), film_release_region(?x141, ?x429), film_release_region(?x141, ?x344), film(?x4704, ?x141), film_crew_role(?x6332, ?x137), crewmember(?x141, ?x666), prequel(?x1035, ?x4392), production_companies(?x1035, ?x738), category(?x6332, ?x134), ?x1558 = 01mjq, film_production_design_by(?x6332, ?x4449), ?x1536 = 06c1y, ?x908 = 01vksx, ?x1917 = 01p1v, award(?x4449, ?x484), nominated_for(?x4449, ?x518), executive_produced_by(?x141, ?x2648), film_release_region(?x2350, ?x3749), film_release_region(?x2350, ?x1646), film(?x13173, ?x4502), film(?x5153, ?x4502), genre(?x4502, ?x53), films(?x13816, ?x4502), nominated_for(?x3019, ?x6332), film_release_region(?x1035, ?x3855), film(?x541, ?x4502), film(?x9084, ?x1035), ?x2645 = 03h64, story_by(?x4502, ?x2993), ?x3019 = 057xs89, genre(?x2350, ?x258), ?x2869 = 03177r, crewmember(?x1035, ?x6166), ?x3749 = 03ryn, film_crew_role(?x4502, ?x2095), film(?x488, ?x2350), language(?x4621, ?x254), film(?x981, ?x6332), ?x344 = 04gzd, award_winner(?x4704, ?x1379), ?x2006 = 031778, film(?x8692, ?x4621), award_nominee(?x5153, ?x1384), month(?x1646, ?x4827), written_by(?x253, ?x8692), film(?x382, ?x6332), ?x429 = 03rt9, ?x1892 = 02vzc, gender(?x4704, ?x514), actor(?x10284, ?x13173), film_release_distribution_medium(?x1035, ?x81), location(?x9084, ?x191), religion(?x5153, ?x492), olympics(?x3855, ?x784), ?x4827 = 03_ly, student(?x12293, ?x13173), produced_by(?x9496, ?x595), profession(?x8692, ?x319), ?x9213 = 0353tm, award_nominee(?x9084, ?x3308) *> conf = 0.50 ranks of expected_values: 5, 7, 13, 14, 25, 152, 824 EVAL 02nxhr film_distribution_medium! 04180vy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 6.000 6.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 02nxhr film_distribution_medium! 0bt4g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 6.000 6.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 02nxhr film_distribution_medium! 02dr9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 02nxhr film_distribution_medium! 033qdy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 6.000 6.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 02nxhr film_distribution_medium! 02fqrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 6.000 6.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 02nxhr film_distribution_medium! 014l6_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 6.000 6.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 02nxhr film_distribution_medium! 06_wqk4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 6.000 6.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #11067-02ndf1 PRED entity: 02ndf1 PRED relation: category PRED expected values: 08mbj5d => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.39 #17, 0.37 #19, 0.36 #10) >> Best rule #17 for best value: >> intensional similarity = 2 >> extensional distance = 317 >> proper extension: 0d193h; 014_lq; 0dw4g; 02cpp; 0b1zz; 0838y; 07r1_; 01w5n51; 0bk1p; 017mbb; ... >> query: (?x11766, 08mbj5d) <- influenced_by(?x11766, ?x1947), award(?x11766, ?x6646) >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ndf1 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.392 http://example.org/common/topic/webpage./common/webpage/category #11066-03qhyn8 PRED entity: 03qhyn8 PRED relation: gender PRED expected values: 05zppz => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #69, 0.85 #57, 0.84 #53), 02zsn (0.46 #198, 0.46 #191, 0.25 #62) >> Best rule #69 for best value: >> intensional similarity = 2 >> extensional distance = 697 >> proper extension: 01ty7ll; 026lj; 017yfz; 03_hd; 0lh0c; 0f2c8g; 0454s1; 0ct9_; 03_js; 04hqbbz; ... >> query: (?x12848, 05zppz) <- place_of_death(?x12848, ?x6959), profession(?x12848, ?x2450) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qhyn8 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.845 http://example.org/people/person/gender #11065-0k2m6 PRED entity: 0k2m6 PRED relation: genre PRED expected values: 06l3bl => 134 concepts (54 used for prediction) PRED predicted values (max 10 best out of 107): 01jfsb (0.60 #2739, 0.53 #5591, 0.52 #2620), 03_9r (0.58 #5103, 0.57 #6296, 0.55 #5222), 03_3d (0.58 #5103, 0.57 #6296, 0.55 #5222), 060__y (0.50 #967, 0.25 #4290, 0.24 #4881), 0jxy (0.46 #2533, 0.39 #2297, 0.35 #519), 03k9fj (0.40 #1197, 0.39 #5590, 0.37 #2264), 02l7c8 (0.40 #4525, 0.38 #16, 0.36 #966), 0hcr (0.38 #2512, 0.35 #498, 0.35 #2276), 0lsxr (0.35 #1904, 0.30 #839, 0.28 #2023), 04xvlr (0.33 #2847, 0.31 #3444, 0.30 #4156) >> Best rule #2739 for best value: >> intensional similarity = 5 >> extensional distance = 73 >> proper extension: 047qxs; 0bw20; >> query: (?x7978, 01jfsb) <- country(?x7978, ?x252), genre(?x7978, ?x225), film_crew_role(?x7978, ?x137), films(?x8435, ?x7978), ?x225 = 02kdv5l >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4192 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 172 *> proper extension: 0m_q0; 0kb07; 0kbhf; 0h3k3f; 0bj25; 0kt_4; 0m_h6; 04wddl; 01k5y0; 0gy4k; *> query: (?x7978, 06l3bl) <- nominated_for(?x1180, ?x7978), nominated_for(?x484, ?x7978), ?x484 = 0gq_v, nominated_for(?x1180, ?x7651), nominated_for(?x1180, ?x2116), award(?x164, ?x1180), ?x2116 = 02c638, ?x7651 = 0h95927 *> conf = 0.14 ranks of expected_values: 25 EVAL 0k2m6 genre 06l3bl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 134.000 54.000 0.600 http://example.org/film/film/genre #11064-05b5c PRED entity: 05b5c PRED relation: service_language PRED expected values: 01gp_d => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 18): 06nm1 (0.50 #202, 0.24 #328, 0.22 #184), 02bjrlw (0.30 #199, 0.11 #163, 0.07 #289), 01r2l (0.20 #207, 0.17 #117, 0.11 #171), 05zjd (0.20 #208, 0.15 #244, 0.11 #352), 02bv9 (0.20 #210, 0.11 #192, 0.11 #174), 06b_j (0.20 #206, 0.11 #170, 0.09 #1189), 01jb8r (0.17 #126, 0.09 #1189, 0.06 #594), 03_9r (0.11 #165, 0.11 #885, 0.10 #201), 02hwhyv (0.11 #175, 0.10 #211, 0.09 #1189), 01gp_d (0.11 #176, 0.10 #212, 0.07 #302) >> Best rule #202 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 02slt7; 077w0b; >> query: (?x13349, 06nm1) <- contact_category(?x13349, ?x6046), service_location(?x13349, ?x789), service_location(?x13349, ?x429), country(?x150, ?x429), ?x789 = 0f8l9c, nationality(?x294, ?x429), participating_countries(?x418, ?x429) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #176 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 03mnk; *> query: (?x13349, 01gp_d) <- contact_category(?x13349, ?x6046), company(?x554, ?x13349), ?x554 = 02211by, category(?x13349, ?x134), industry(?x13349, ?x245) *> conf = 0.11 ranks of expected_values: 10 EVAL 05b5c service_language 01gp_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 171.000 171.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #11063-04wgh PRED entity: 04wgh PRED relation: contains PRED expected values: 054rw => 142 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2665): 01fxg8 (0.25 #4707, 0.07 #7650, 0.07 #10593), 04jr87 (0.25 #3724, 0.07 #6667, 0.07 #9610), 01f1r4 (0.25 #3463, 0.03 #21122, 0.03 #24065), 0bwfn (0.14 #6934, 0.13 #9877, 0.12 #12820), 05hf_5 (0.12 #16850, 0.07 #10964, 0.06 #13907), 01n86 (0.08 #25974, 0.06 #23031, 0.04 #34806), 052nd (0.07 #5933, 0.07 #32424, 0.07 #8876), 06fz_ (0.07 #6918, 0.07 #42241, 0.07 #9861), 01stzp (0.07 #8338, 0.07 #11281, 0.06 #23054), 01trxd (0.07 #8258, 0.07 #11201, 0.06 #22974) >> Best rule #4707 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0r62v; 06c62; >> query: (?x1273, 01fxg8) <- vacationer(?x1273, ?x5246), ?x5246 = 046zh, contains(?x1273, ?x14043) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #285549 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 177 *> proper extension: 065ky; *> query: (?x1273, ?x108) <- contains(?x1273, ?x14462), jurisdiction_of_office(?x1195, ?x14462), jurisdiction_of_office(?x1195, ?x108) *> conf = 0.03 ranks of expected_values: 1473 EVAL 04wgh contains 054rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 142.000 103.000 0.250 http://example.org/location/location/contains #11062-0g9wdmc PRED entity: 0g9wdmc PRED relation: film_release_region PRED expected values: 09c7w0 04v3q 030qb3t => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 100): 09c7w0 (0.93 #4593, 0.92 #8625, 0.92 #9321), 05v8c (0.75 #845, 0.73 #11, 0.48 #2236), 06mzp (0.70 #15, 0.44 #1544, 0.44 #849), 03rj0 (0.68 #46, 0.62 #880, 0.52 #1575), 015qh (0.66 #865, 0.66 #31, 0.37 #2117), 06qd3 (0.64 #28, 0.48 #1557, 0.46 #2253), 06f32 (0.61 #51, 0.53 #885, 0.39 #1580), 0h7x (0.57 #25, 0.38 #1554, 0.38 #2111), 06t8v (0.55 #896, 0.52 #62, 0.37 #1591), 06c1y (0.55 #33, 0.50 #867, 0.28 #2119) >> Best rule #4593 for best value: >> intensional similarity = 3 >> extensional distance = 618 >> proper extension: 04m1bm; 091z_p; 02rb607; 016kz1; 04lqvlr; 02hfk5; 0hv81; 012jfb; 03q8xj; 0gpx6; ... >> query: (?x1803, 09c7w0) <- film_release_region(?x1803, ?x1264), award(?x1803, ?x749), olympics(?x1264, ?x358) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 31, 47 EVAL 0g9wdmc film_release_region 030qb3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 91.000 91.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g9wdmc film_release_region 04v3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 91.000 91.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g9wdmc film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11061-0bt4r4 PRED entity: 0bt4r4 PRED relation: gender PRED expected values: 05zppz => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #13, 0.82 #15, 0.81 #29), 02zsn (0.30 #60, 0.30 #44, 0.28 #62) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 01n4f8; 0pyg6; 0blt6; 03f0r5w; 03cs_xw; >> query: (?x2912, 05zppz) <- award_nominee(?x237, ?x2912), student(?x9307, ?x2912), producer_type(?x2912, ?x632) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bt4r4 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.832 http://example.org/people/person/gender #11060-05w1vf PRED entity: 05w1vf PRED relation: film PRED expected values: 03b1sb => 101 concepts (66 used for prediction) PRED predicted values (max 10 best out of 682): 011yn5 (0.33 #2702, 0.02 #9826, 0.01 #13388), 03f7nt (0.22 #4386, 0.01 #15072, 0.01 #29324), 0cf8qb (0.22 #4897, 0.01 #15583), 02_fz3 (0.22 #4937), 01y9jr (0.20 #1154, 0.17 #2935), 01hr1 (0.20 #44, 0.11 #3606, 0.01 #14292), 0ch3qr1 (0.20 #970, 0.11 #4532), 0ds2l81 (0.20 #1430, 0.10 #48096, 0.01 #24585), 0gfzfj (0.20 #1688, 0.03 #19498, 0.03 #23061), 0661m4p (0.20 #371, 0.03 #18181, 0.03 #21744) >> Best rule #2702 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0p_pd; >> query: (?x11529, 011yn5) <- film(?x11529, ?x7656), film(?x11529, ?x6288), ?x7656 = 011x_4, nominated_for(?x112, ?x6288), genre(?x6288, ?x53) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #15744 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 93 *> proper extension: 0443c; *> query: (?x11529, 03b1sb) <- student(?x3387, ?x11529), student(?x3387, ?x8371), ?x8371 = 024y6w *> conf = 0.01 ranks of expected_values: 540 EVAL 05w1vf film 03b1sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 101.000 66.000 0.333 http://example.org/film/actor/film./film/performance/film #11059-05gnf PRED entity: 05gnf PRED relation: company! PRED expected values: 0frmb1 => 131 concepts (112 used for prediction) PRED predicted values (max 10 best out of 231): 0frmb1 (0.40 #4049, 0.33 #1859, 0.29 #2345), 01w_10 (0.33 #1863, 0.29 #2349, 0.22 #3323), 01xdf5 (0.17 #1710, 0.14 #2196, 0.11 #3413), 0kh6b (0.17 #1774, 0.14 #2260, 0.11 #3477), 01nbq4 (0.17 #1898, 0.14 #2384, 0.11 #3601), 0143wl (0.17 #1827, 0.14 #2313, 0.11 #3530), 02508x (0.17 #1816, 0.14 #2302, 0.11 #3519), 02z6l5f (0.17 #1806, 0.14 #2292, 0.11 #3509), 06y3r (0.17 #7484, 0.06 #11874, 0.04 #15046), 02vq8xn (0.17 #1846, 0.05 #7692, 0.03 #10131) >> Best rule #4049 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 05l71; >> query: (?x6678, 0frmb1) <- company(?x9952, ?x6678), person(?x3775, ?x9952), student(?x2948, ?x9952) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05gnf company! 0frmb1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 112.000 0.400 http://example.org/people/person/employment_history./business/employment_tenure/company #11058-02hxhz PRED entity: 02hxhz PRED relation: nominated_for! PRED expected values: 04ljl_l => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 195): 04ljl_l (0.52 #473, 0.20 #17867, 0.20 #18338), 05b4l5x (0.47 #476, 0.19 #11283, 0.12 #2591), 05p09zm (0.44 #560, 0.20 #16691, 0.20 #16690), 0gq9h (0.34 #2880, 0.29 #8757, 0.28 #3820), 0gs9p (0.34 #2881, 0.27 #8758, 0.26 #3821), 03c7tr1 (0.33 #515, 0.20 #16691, 0.20 #16690), 019f4v (0.32 #2871, 0.27 #1696, 0.26 #3811), 05p1dby (0.30 #548, 0.20 #16691, 0.20 #16690), 0l8z1 (0.26 #1695, 0.16 #4751, 0.16 #5221), 040njc (0.26 #2827, 0.20 #8704, 0.19 #3767) >> Best rule #473 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 01qvz8; >> query: (?x821, 04ljl_l) <- country(?x821, ?x94), nominated_for(?x1105, ?x821), nominated_for(?x541, ?x821), ?x1105 = 07bdd_ >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hxhz nominated_for! 04ljl_l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.519 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #11057-02clgg PRED entity: 02clgg PRED relation: nationality PRED expected values: 09c7w0 => 88 concepts (84 used for prediction) PRED predicted values (max 10 best out of 46): 09c7w0 (0.79 #1101, 0.79 #3314, 0.77 #1403), 0f8l9c (0.31 #1603, 0.03 #1301, 0.03 #4821), 07ssc (0.25 #15, 0.11 #715, 0.10 #215), 0ctw_b (0.25 #27, 0.02 #127, 0.01 #227), 0d0x8 (0.25 #2508), 019k6n (0.25 #2508), 02jx1 (0.10 #2138, 0.10 #3446, 0.10 #3246), 03rk0 (0.06 #7874, 0.05 #7774, 0.05 #8174), 0d060g (0.06 #507, 0.05 #907, 0.05 #107), 03_3d (0.03 #1301, 0.03 #4821, 0.03 #4820) >> Best rule #1101 for best value: >> intensional similarity = 3 >> extensional distance = 673 >> proper extension: 0b79gfg; >> query: (?x8482, 09c7w0) <- gender(?x8482, ?x231), nominated_for(?x8482, ?x7511), producer_type(?x7511, ?x632) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02clgg nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 84.000 0.794 http://example.org/people/person/nationality #11056-028cg00 PRED entity: 028cg00 PRED relation: language PRED expected values: 03115z => 145 concepts (141 used for prediction) PRED predicted values (max 10 best out of 45): 04306rv (0.20 #4, 0.15 #118, 0.14 #176), 06nm1 (0.19 #124, 0.17 #698, 0.17 #1098), 064_8sq (0.17 #77, 0.16 #3128, 0.16 #3013), 02bjrlw (0.12 #58, 0.12 #689, 0.12 #115), 03_9r (0.12 #66, 0.09 #640, 0.08 #3467), 06b_j (0.10 #1857, 0.10 #1282, 0.09 #1972), 0jzc (0.09 #477, 0.08 #75, 0.06 #1912), 04h9h (0.08 #98, 0.06 #1129, 0.05 #7652), 01r2l (0.08 #80, 0.05 #711, 0.05 #252), 012w70 (0.06 #928, 0.05 #7652, 0.05 #1272) >> Best rule #4 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01mgw; >> query: (?x1889, 04306rv) <- genre(?x1889, ?x225), film(?x6211, ?x1889), film_crew_role(?x1889, ?x137), ?x6211 = 012ykt, ?x225 = 02kdv5l >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #495 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 55 *> proper extension: 01qb559; *> query: (?x1889, 03115z) <- genre(?x1889, ?x225), music(?x1889, ?x3805), film_crew_role(?x1889, ?x2095), ?x2095 = 0dxtw, featured_film_locations(?x1889, ?x206), ?x225 = 02kdv5l *> conf = 0.02 ranks of expected_values: 38 EVAL 028cg00 language 03115z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 145.000 141.000 0.200 http://example.org/film/film/language #11055-022qw7 PRED entity: 022qw7 PRED relation: film PRED expected values: 09p4w8 => 79 concepts (39 used for prediction) PRED predicted values (max 10 best out of 539): 02g5q1 (0.40 #1439), 01shy7 (0.20 #422, 0.03 #9367, 0.02 #12945), 01rnly (0.20 #1571, 0.01 #5149, 0.01 #6938), 0872p_c (0.20 #173, 0.01 #5540, 0.01 #50267), 029v40 (0.20 #1623, 0.01 #6990), 03x7hd (0.20 #560, 0.01 #5927), 01f7jt (0.20 #1697), 048yqf (0.20 #1606), 048tv9 (0.20 #1400), 02ljhg (0.20 #1350) >> Best rule #1439 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 08x5c_; >> query: (?x9132, 02g5q1) <- nationality(?x9132, ?x94), film(?x9132, ?x11672), ?x94 = 09c7w0, ?x11672 = 0d99k_ >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 022qw7 film 09p4w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 39.000 0.400 http://example.org/film/actor/film./film/performance/film #11054-01xrlm PRED entity: 01xrlm PRED relation: student PRED expected values: 03z0l6 => 234 concepts (134 used for prediction) PRED predicted values (max 10 best out of 1214): 0tfc (0.12 #4107, 0.08 #10391, 0.04 #14579), 01lwx (0.12 #4079, 0.06 #20833, 0.04 #35491), 0kh6b (0.12 #2712, 0.04 #11090, 0.04 #8996), 063vn (0.12 #2393, 0.04 #10771, 0.04 #8677), 04y9dk (0.12 #2392, 0.04 #10770, 0.04 #8676), 03cd1q (0.12 #4004, 0.04 #12382, 0.04 #10288), 04pqqb (0.12 #2942, 0.04 #11320, 0.04 #9226), 01kx_81 (0.12 #2283, 0.04 #10661, 0.04 #8567), 06ltr (0.12 #3019, 0.04 #9303, 0.04 #13491), 01fx5l (0.12 #3191, 0.04 #9475, 0.04 #13663) >> Best rule #4107 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 04f525m; 02j_j0; >> query: (?x5920, 0tfc) <- category(?x5920, ?x134), state_province_region(?x5920, ?x4221), citytown(?x5920, ?x9969), ?x134 = 08mbj5d, nationality(?x450, ?x4221) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01xrlm student 03z0l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 234.000 134.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #11053-01dtl PRED entity: 01dtl PRED relation: teams! PRED expected values: 095l0 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 160): 0fm2_ (0.17 #37, 0.09 #1117, 0.09 #847), 0b_yz (0.17 #487, 0.04 #2377, 0.04 #2647), 0k33p (0.17 #199, 0.03 #3169, 0.03 #3710), 0k5p1 (0.17 #518, 0.01 #7271, 0.01 #7542), 0gyvgw (0.17 #537, 0.01 #7561, 0.01 #8101), 04jpl (0.12 #549, 0.09 #1089, 0.09 #819), 01vx3m (0.12 #716, 0.09 #1256, 0.09 #986), 01fbb3 (0.12 #754, 0.09 #1294, 0.09 #1024), 013wf1 (0.09 #1308, 0.08 #1578, 0.04 #1848), 0ck6r (0.09 #1278, 0.08 #1548, 0.04 #1818) >> Best rule #37 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 01gxqf; >> query: (?x8195, 0fm2_) <- team(?x8598, ?x8195), team(?x4172, ?x8195), team(?x60, ?x8195), team(?x4172, ?x6477), ?x8598 = 07m69t, team(?x208, ?x6477) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #4252 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 38 *> proper extension: 02279c; 01kckd; 04gkp3; 02b18l; 029q3k; 01j_jh; 06lkg8; 02b168; 0j2jr; *> query: (?x8195, 095l0) <- team(?x4172, ?x8195), team(?x60, ?x8195), team(?x4172, ?x8142), team(?x4172, ?x6477), team(?x4172, ?x6353), ?x8142 = 01xmxj, team(?x1696, ?x6353), sport(?x6477, ?x471) *> conf = 0.03 ranks of expected_values: 68 EVAL 01dtl teams! 095l0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 97.000 97.000 0.167 http://example.org/sports/sports_team_location/teams #11052-0q9kd PRED entity: 0q9kd PRED relation: award PRED expected values: 04ljl_l => 118 concepts (100 used for prediction) PRED predicted values (max 10 best out of 297): 0gs9p (0.41 #6857, 0.40 #8453, 0.38 #2867), 019f4v (0.40 #6845, 0.40 #2855, 0.39 #8441), 02pqp12 (0.37 #2859, 0.26 #6849, 0.24 #8445), 0gr51 (0.33 #2888, 0.27 #4484, 0.24 #6878), 0f4x7 (0.27 #3620, 0.14 #14793, 0.13 #39910), 0gr4k (0.26 #2824, 0.24 #4420, 0.24 #6814), 03hl6lc (0.26 #2966, 0.22 #8779, 0.18 #4562), 04dn09n (0.25 #4429, 0.22 #6823, 0.22 #8779), 02qyp19 (0.25 #2794, 0.22 #8779, 0.15 #4390), 0gqy2 (0.24 #957, 0.22 #8779, 0.16 #14923) >> Best rule #6857 for best value: >> intensional similarity = 3 >> extensional distance = 203 >> proper extension: 0qf43; 0kr5_; 019z7q; 0prjs; 022_lg; 0h1p; 04gcd1; 01_vfy; 03wpmd; 04b19t; ... >> query: (?x71, 0gs9p) <- nominated_for(?x71, ?x1496), film(?x71, ?x10300), film_release_region(?x1496, ?x87) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #8779 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 236 *> proper extension: 042l3v; 01t07j; 07g7h2; 036dyy; 02f93t; 0405l; 0b_dh; *> query: (?x71, ?x143) <- nominated_for(?x71, ?x1496), film(?x71, ?x10300), nominated_for(?x143, ?x1496) *> conf = 0.22 ranks of expected_values: 25 EVAL 0q9kd award 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 118.000 100.000 0.410 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11051-0dgq_kn PRED entity: 0dgq_kn PRED relation: nominated_for! PRED expected values: 05bm4sm => 103 concepts (32 used for prediction) PRED predicted values (max 10 best out of 923): 01ycbq (0.31 #25689, 0.28 #9341, 0.27 #51384), 04shbh (0.31 #25689, 0.28 #9341, 0.27 #51384), 06jzh (0.31 #25689, 0.28 #9341, 0.27 #51384), 03f4w4 (0.31 #25689, 0.28 #9341, 0.27 #51384), 01r9c_ (0.31 #25689, 0.28 #9341, 0.27 #51384), 03_2td (0.31 #25689, 0.28 #9341, 0.27 #51384), 026g801 (0.31 #25689, 0.28 #9341, 0.27 #51384), 0gg9_5q (0.29 #25688, 0.14 #51383, 0.12 #39703), 0146pg (0.18 #21139, 0.09 #9461, 0.08 #58509), 017s11 (0.15 #99, 0.14 #63059, 0.12 #39702) >> Best rule #25689 for best value: >> intensional similarity = 4 >> extensional distance = 193 >> proper extension: 0ckt6; >> query: (?x6007, ?x540) <- executive_produced_by(?x6007, ?x1616), film(?x540, ?x6007), nominated_for(?x163, ?x6007), executive_produced_by(?x1163, ?x163) >> conf = 0.31 => this is the best rule for 7 predicted values *> Best rule #3596 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 0gh65c5; *> query: (?x6007, 05bm4sm) <- film_crew_role(?x6007, ?x1171), nominated_for(?x1443, ?x6007), ?x1171 = 09vw2b7, ?x1443 = 054krc *> conf = 0.06 ranks of expected_values: 57 EVAL 0dgq_kn nominated_for! 05bm4sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 103.000 32.000 0.311 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #11050-02m92h PRED entity: 02m92h PRED relation: profession PRED expected values: 02hrh1q => 67 concepts (58 used for prediction) PRED predicted values (max 10 best out of 45): 02hrh1q (0.88 #1884, 0.88 #1020, 0.86 #588), 09jwl (0.42 #1599, 0.37 #3040, 0.37 #1455), 0np9r (0.28 #593, 0.28 #4466, 0.21 #1889), 0nbcg (0.27 #1611, 0.27 #3052, 0.26 #1467), 0dz3r (0.23 #1586, 0.21 #3027, 0.20 #2883), 016z4k (0.23 #3029, 0.23 #1444, 0.22 #2885), 0cbd2 (0.17 #582, 0.16 #6, 0.14 #438), 01c72t (0.16 #1604, 0.14 #1316, 0.14 #1460), 039v1 (0.14 #1616, 0.13 #1328, 0.13 #1472), 0kyk (0.12 #601, 0.11 #1465, 0.11 #1321) >> Best rule #1884 for best value: >> intensional similarity = 3 >> extensional distance = 808 >> proper extension: 06v8s0; 01sl1q; 06gp3f; 01r42_g; 02zq43; 0p_pd; 03rs8y; 0z4s; 03w1v2; 027dtv3; ... >> query: (?x8519, 02hrh1q) <- gender(?x8519, ?x231), actor(?x2009, ?x8519), profession(?x8519, ?x319) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02m92h profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 58.000 0.884 http://example.org/people/person/profession #11049-0d87hc PRED entity: 0d87hc PRED relation: music PRED expected values: 07qy0b => 120 concepts (61 used for prediction) PRED predicted values (max 10 best out of 105): 0146pg (0.15 #431, 0.12 #220, 0.10 #1063), 06fxnf (0.14 #69, 0.06 #279, 0.06 #490), 01tc9r (0.09 #275, 0.09 #486, 0.08 #696), 02bh9 (0.08 #1104, 0.08 #1946, 0.07 #51), 0cw67g (0.07 #5906, 0.07 #421, 0.07 #11597), 0f7hc (0.07 #5906, 0.07 #421, 0.07 #11597), 0150t6 (0.07 #46, 0.06 #256, 0.06 #467), 04pf4r (0.07 #68, 0.05 #1963, 0.05 #1753), 02jxmr (0.07 #74, 0.04 #4499, 0.03 #1127), 03c_8t (0.07 #210, 0.04 #1052, 0.03 #420) >> Best rule #431 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 0d_wms; >> query: (?x10274, 0146pg) <- honored_for(?x7144, ?x10274), prequel(?x2494, ?x10274), genre(?x10274, ?x239) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #680 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 01k1k4; 0kv2hv; 015x74; 0g5879y; 0kv238; 0315w4; 01s3vk; 072r5v; 04gcyg; 087pfc; ... *> query: (?x10274, 07qy0b) <- nominated_for(?x3458, ?x10274), nominated_for(?x4657, ?x10274), titles(?x2480, ?x10274), ?x3458 = 0gqxm *> conf = 0.04 ranks of expected_values: 21 EVAL 0d87hc music 07qy0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 120.000 61.000 0.147 http://example.org/film/film/music #11048-0ckrgs PRED entity: 0ckrgs PRED relation: film_release_distribution_medium PRED expected values: 07c52 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 6): 029j_ (0.91 #192, 0.89 #137, 0.87 #212), 02nxhr (0.23 #595, 0.22 #318, 0.21 #101), 07c52 (0.23 #595, 0.22 #318, 0.21 #101), 07z4p (0.23 #595, 0.22 #318, 0.21 #101), 0735l (0.19 #611, 0.18 #267, 0.16 #539), 0dq6p (0.19 #611, 0.18 #267, 0.16 #539) >> Best rule #192 for best value: >> intensional similarity = 7 >> extensional distance = 63 >> proper extension: 0d1qmz; 025twgt; >> query: (?x3174, 029j_) <- language(?x3174, ?x2164), prequel(?x3174, ?x297), prequel(?x5633, ?x3174), genre(?x3174, ?x1510), film(?x10231, ?x3174), titles(?x1510, ?x5759), language(?x5759, ?x254) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #595 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1412 *> proper extension: 0dl6fv; *> query: (?x3174, ?x81) <- film(?x2156, ?x3174), film(?x2156, ?x6216), film(?x2156, ?x3344), film(?x2156, ?x2816), award_nominee(?x1285, ?x2156), film_release_distribution_medium(?x2816, ?x81), country(?x3344, ?x94), nominated_for(?x1053, ?x6216) *> conf = 0.23 ranks of expected_values: 3 EVAL 0ckrgs film_release_distribution_medium 07c52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 133.000 0.908 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #11047-025sqz8 PRED entity: 025sqz8 PRED relation: nutrient! PRED expected values: 01645p 07j87 => 57 concepts (53 used for prediction) PRED predicted values (max 10 best out of 12): 01645p (0.89 #305, 0.89 #180, 0.89 #27), 07j87 (0.89 #180, 0.89 #27, 0.89 #157), 0dcfv (0.89 #180, 0.89 #27, 0.89 #157), 06x4c (0.89 #180, 0.89 #27, 0.89 #157), 01sh2 (0.04 #381, 0.02 #498, 0.01 #182), 04k8n (0.04 #381, 0.02 #498), 05wvs (0.04 #381, 0.02 #498), 025rw19 (0.01 #182), 025tkqy (0.01 #182), 014d7f (0.01 #182) >> Best rule #305 for best value: >> intensional similarity = 118 >> extensional distance = 17 >> proper extension: 02kd0rh; >> query: (?x9436, 01645p) <- nutrient(?x10612, ?x9436), nutrient(?x9732, ?x9436), nutrient(?x9005, ?x9436), nutrient(?x8298, ?x9436), nutrient(?x7057, ?x9436), nutrient(?x6191, ?x9436), nutrient(?x6159, ?x9436), nutrient(?x6032, ?x9436), nutrient(?x5009, ?x9436), nutrient(?x4068, ?x9436), nutrient(?x3900, ?x9436), nutrient(?x3468, ?x9436), nutrient(?x2701, ?x9436), nutrient(?x1959, ?x9436), nutrient(?x1303, ?x9436), nutrient(?x1257, ?x9436), ?x3468 = 0cxn2, ?x1303 = 0fj52s, ?x6159 = 033cnk, ?x9005 = 04zpv, ?x3900 = 061_f, ?x7057 = 0fbdb, ?x6032 = 01nkt, nutrient(?x4068, ?x13498), nutrient(?x4068, ?x12902), nutrient(?x4068, ?x12868), nutrient(?x4068, ?x12454), nutrient(?x4068, ?x11784), nutrient(?x4068, ?x11758), nutrient(?x4068, ?x11592), nutrient(?x4068, ?x11270), nutrient(?x4068, ?x10891), nutrient(?x4068, ?x10709), nutrient(?x4068, ?x10195), nutrient(?x4068, ?x10098), nutrient(?x4068, ?x9915), nutrient(?x4068, ?x9855), nutrient(?x4068, ?x9795), nutrient(?x4068, ?x9426), nutrient(?x4068, ?x9365), nutrient(?x4068, ?x8487), nutrient(?x4068, ?x8442), nutrient(?x4068, ?x8413), nutrient(?x4068, ?x8243), nutrient(?x4068, ?x7652), nutrient(?x4068, ?x7364), nutrient(?x4068, ?x7362), nutrient(?x4068, ?x7219), nutrient(?x4068, ?x6586), nutrient(?x4068, ?x6286), nutrient(?x4068, ?x6192), nutrient(?x4068, ?x6033), nutrient(?x4068, ?x5549), nutrient(?x4068, ?x5526), nutrient(?x4068, ?x5451), nutrient(?x4068, ?x5374), nutrient(?x4068, ?x5337), nutrient(?x4068, ?x5010), nutrient(?x4068, ?x3264), nutrient(?x4068, ?x3203), nutrient(?x4068, ?x2018), nutrient(?x4068, ?x1960), nutrient(?x4068, ?x1258), ?x8243 = 014d7f, ?x9426 = 0h1yy, ?x12454 = 025rw19, ?x12868 = 03d49, ?x13498 = 07q0m, ?x3203 = 04kl74p, ?x5374 = 025s0zp, ?x9855 = 0d9t0, ?x10891 = 0g5gq, ?x6586 = 05gh50, ?x7364 = 09gvd, ?x8487 = 014yzm, ?x3264 = 0dcfv, ?x11270 = 02kc008, ?x5010 = 0h1vz, ?x8442 = 02kcv4x, ?x1959 = 0f25w9, ?x7652 = 025s0s0, ?x7362 = 02kc5rj, nutrient(?x8298, ?x13126), nutrient(?x8298, ?x9733), nutrient(?x8298, ?x9619), ?x10612 = 0frq6, ?x9795 = 05v_8y, ?x5337 = 06x4c, ?x10709 = 0h1sz, ?x5526 = 09pbb, nutrient(?x9489, ?x9915), ?x9365 = 04k8n, ?x6191 = 014j1m, ?x2018 = 01sh2, ?x9619 = 0h1tg, nutrient(?x1257, ?x14698), ?x1960 = 07hnp, ?x9489 = 07j87, ?x13126 = 02kc_w5, ?x14698 = 02kb_jm, ?x6033 = 04zjxcz, ?x12902 = 0fzjh, ?x1258 = 0h1wg, ?x11758 = 0q01m, ?x8413 = 02kc4sf, ?x10195 = 0hkwr, ?x7219 = 0h1vg, ?x9733 = 0h1tz, ?x5451 = 05wvs, ?x11592 = 025sf0_, ?x5009 = 0fjfh, ?x6286 = 02y_3rf, ?x9732 = 05z55, ?x6192 = 06jry, ?x11784 = 07zqy, ?x2701 = 0hkxq, ?x10098 = 0h1_c, ?x5549 = 025s7j4 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 025sqz8 nutrient! 07j87 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 53.000 0.895 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 025sqz8 nutrient! 01645p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 53.000 0.895 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #11046-037fqp PRED entity: 037fqp PRED relation: institution! PRED expected values: 0bkj86 => 123 concepts (114 used for prediction) PRED predicted values (max 10 best out of 18): 019v9k (0.68 #155, 0.67 #110, 0.67 #89), 02_xgp2 (0.63 #135, 0.52 #93, 0.51 #221), 0bkj86 (0.57 #88, 0.50 #175, 0.50 #154), 016t_3 (0.56 #126, 0.48 #84, 0.45 #274), 04zx3q1 (0.45 #274, 0.44 #125, 0.42 #146), 071tyz (0.45 #274, 0.42 #146, 0.33 #824), 07s6fsf (0.42 #146, 0.39 #169, 0.39 #148), 02m4yg (0.42 #146, 0.33 #824, 0.31 #871), 01gkg3 (0.42 #146, 0.33 #824, 0.31 #871), 0bjrnt (0.42 #146, 0.31 #871, 0.31 #1061) >> Best rule #155 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 01xrlm; >> query: (?x5581, 019v9k) <- contains(?x94, ?x5581), major_field_of_study(?x5581, ?x12158), ?x12158 = 09s1f, nationality(?x51, ?x94) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #88 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 0177sq; *> query: (?x5581, 0bkj86) <- contains(?x94, ?x5581), major_field_of_study(?x5581, ?x12158), ?x12158 = 09s1f, school(?x2820, ?x5581), colors(?x5581, ?x332) *> conf = 0.57 ranks of expected_values: 3 EVAL 037fqp institution! 0bkj86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 114.000 0.679 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #11045-07c52 PRED entity: 07c52 PRED relation: films PRED expected values: 03s6l2 034qzw 03l6q0 0h21v2 => 65 concepts (63 used for prediction) PRED predicted values (max 10 best out of 646): 04w7rn (0.33 #67, 0.09 #7803, 0.07 #10376), 0g22z (0.20 #3616, 0.09 #8769, 0.09 #8255), 04j14qc (0.18 #8149, 0.14 #10722, 0.05 #18447), 0gd92 (0.18 #8109, 0.14 #10682, 0.05 #18407), 01qbg5 (0.18 #8098, 0.14 #10671, 0.05 #18396), 011yxg (0.18 #8266, 0.13 #11355, 0.09 #8780), 0266s9 (0.15 #10825, 0.12 #8251, 0.12 #10824), 01kt_j (0.15 #10825, 0.12 #8251, 0.12 #10824), 05zr0xl (0.15 #10825, 0.12 #8251, 0.12 #10824), 01fs__ (0.15 #10825, 0.12 #8251, 0.12 #10824) >> Best rule #67 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01hmnh; >> query: (?x2008, 04w7rn) <- titles(?x2008, ?x10661), titles(?x2008, ?x9649), actor(?x10661, ?x585), genre(?x9649, ?x53), films(?x2008, ?x590) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07c52 films 0h21v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 63.000 0.333 http://example.org/film/film_subject/films EVAL 07c52 films 03l6q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 63.000 0.333 http://example.org/film/film_subject/films EVAL 07c52 films 034qzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 63.000 0.333 http://example.org/film/film_subject/films EVAL 07c52 films 03s6l2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 63.000 0.333 http://example.org/film/film_subject/films #11044-04cy8rb PRED entity: 04cy8rb PRED relation: edited_by! PRED expected values: 02rrfzf => 102 concepts (47 used for prediction) PRED predicted values (max 10 best out of 162): 0mbql (0.12 #113, 0.10 #273, 0.07 #913), 0f4yh (0.12 #63, 0.10 #383, 0.08 #543), 0dnqr (0.12 #53, 0.10 #373, 0.08 #533), 02704ff (0.10 #257, 0.10 #417, 0.08 #577), 07bwr (0.10 #248, 0.10 #408, 0.08 #568), 05fgt1 (0.10 #204, 0.10 #364, 0.08 #524), 02r1c18 (0.10 #188, 0.10 #348, 0.08 #508), 01vfqh (0.10 #185, 0.10 #345, 0.08 #505), 0b6tzs (0.10 #180, 0.10 #340, 0.08 #500), 0dfw0 (0.10 #404, 0.08 #564, 0.08 #724) >> Best rule #113 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 06cv1; 0dky9n; 0343h; 052gzr; 06pj8; 0bs1yy; 03q8ch; 02lp3c; 08h79x; 01g1lp; ... >> query: (?x323, 0mbql) <- edited_by(?x5051, ?x323), place_of_birth(?x323, ?x2949), film(?x574, ?x5051) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 06cv1; 0dky9n; 0343h; 052gzr; 06pj8; 0bs1yy; 03q8ch; 02lp3c; 08h79x; 01g1lp; ... *> query: (?x323, 02rrfzf) <- edited_by(?x5051, ?x323), place_of_birth(?x323, ?x2949), film(?x574, ?x5051) *> conf = 0.06 ranks of expected_values: 90 EVAL 04cy8rb edited_by! 02rrfzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 102.000 47.000 0.125 http://example.org/film/film/edited_by #11043-06jzh PRED entity: 06jzh PRED relation: award PRED expected values: 0fq9zdn 0cqgl9 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 243): 09sb52 (0.71 #10867, 0.50 #1243, 0.42 #2446), 02q1tc5 (0.64 #147, 0.13 #21657, 0.12 #24064), 0gqwc (0.51 #475, 0.48 #1277, 0.48 #2480), 094qd5 (0.39 #1247, 0.38 #2450, 0.35 #445), 0gqyl (0.35 #1308, 0.34 #2511, 0.30 #506), 0cqgl9 (0.33 #1392, 0.28 #2595, 0.24 #590), 02z0dfh (0.31 #1278, 0.28 #2481, 0.16 #476), 02y_rq5 (0.29 #2501, 0.27 #496, 0.26 #1298), 05p09zm (0.29 #1727, 0.23 #3732, 0.22 #2128), 02ppm4q (0.28 #1357, 0.28 #2560, 0.19 #555) >> Best rule #10867 for best value: >> intensional similarity = 3 >> extensional distance = 567 >> proper extension: 01v6480; >> query: (?x540, 09sb52) <- award(?x540, ?x2478), award(?x3553, ?x2478), ?x3553 = 0bq2g >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1392 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 01p7yb; 01tvz5j; 0h1nt; 030znt; 02tr7d; 028knk; 028d4v; 019f2f; 05th8t; 02f2dn; ... *> query: (?x540, 0cqgl9) <- award(?x540, ?x2478), ?x2478 = 02x4x18, award_nominee(?x539, ?x540) *> conf = 0.33 ranks of expected_values: 6, 49 EVAL 06jzh award 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 87.000 87.000 0.707 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 06jzh award 0fq9zdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 87.000 87.000 0.707 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11042-0gyfp9c PRED entity: 0gyfp9c PRED relation: film_release_region PRED expected values: 0d0vqn 04gzd 06mzp 0f8l9c 077qn => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 130): 0d0vqn (0.91 #1592, 0.90 #2026, 0.89 #3183), 0f8l9c (0.90 #2040, 0.88 #3197, 0.88 #1461), 06bnz (0.84 #2063, 0.72 #1484, 0.71 #330), 01znc_ (0.81 #2060, 0.75 #182, 0.75 #38), 03_3d (0.75 #2025, 0.75 #3182, 0.75 #147), 05v8c (0.72 #2034, 0.64 #301, 0.56 #3191), 04gzd (0.72 #2029, 0.64 #296, 0.48 #3186), 015qh (0.71 #326, 0.67 #181, 0.63 #2059), 03rk0 (0.71 #339, 0.67 #194, 0.60 #2072), 06mzp (0.65 #883, 0.53 #1460, 0.50 #738) >> Best rule #1592 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 0b76d_m; 0ds35l9; 028_yv; 0c3ybss; 011yrp; 0djb3vw; 0c40vxk; 01vksx; 05z_kps; 047msdk; ... >> query: (?x3226, 0d0vqn) <- film_regional_debut_venue(?x3226, ?x4903), film(?x617, ?x3226), film_release_region(?x3226, ?x87), ?x87 = 05r4w >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 7, 10, 16 EVAL 0gyfp9c film_release_region 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 79.000 79.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gyfp9c film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gyfp9c film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 79.000 79.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gyfp9c film_release_region 04gzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 79.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gyfp9c film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #11041-02h22 PRED entity: 02h22 PRED relation: film_crew_role PRED expected values: 09zzb8 0dxtw => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 35): 09zzb8 (0.78 #2505, 0.74 #2542, 0.74 #253), 02r96rf (0.74 #256, 0.70 #2508, 0.68 #509), 0dxtw (0.38 #2515, 0.36 #2552, 0.35 #119), 01vx2h (0.36 #1175, 0.34 #481, 0.32 #2553), 02ynfr (0.25 #521, 0.22 #268, 0.19 #2557), 0215hd (0.22 #856, 0.22 #819, 0.21 #633), 01xy5l_ (0.22 #266, 0.15 #50, 0.14 #519), 04pyp5 (0.19 #269, 0.14 #89, 0.14 #17), 02rh1dz (0.17 #298, 0.16 #190, 0.15 #1173), 089g0h (0.16 #820, 0.15 #857, 0.14 #2524) >> Best rule #2505 for best value: >> intensional similarity = 5 >> extensional distance = 497 >> proper extension: 035xwd; >> query: (?x5849, 09zzb8) <- film(?x541, ?x5849), titles(?x732, ?x5849), genre(?x5849, ?x53), film_crew_role(?x5849, ?x1171), ?x1171 = 09vw2b7 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 02h22 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 137.000 0.784 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02h22 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.784 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #11040-03bxh PRED entity: 03bxh PRED relation: nationality PRED expected values: 0345h => 175 concepts (173 used for prediction) PRED predicted values (max 10 best out of 62): 09c7w0 (0.86 #12374, 0.79 #10258, 0.79 #3982), 09hrc (0.41 #11262, 0.40 #11159, 0.40 #6573), 04p0c (0.41 #11262, 0.40 #11159, 0.40 #6573), 0345h (0.36 #15987, 0.33 #16088, 0.33 #16493), 07ssc (0.34 #5788, 0.33 #1104, 0.33 #15), 03rjj (0.27 #16391, 0.26 #8068, 0.19 #1494), 04q_g (0.27 #16391, 0.26 #8068, 0.03 #1388), 07kg3 (0.27 #16391, 0.26 #8068, 0.03 #1388), 082fr (0.27 #16391, 0.26 #8068), 0h7x (0.25 #9559, 0.19 #2884, 0.19 #2818) >> Best rule #12374 for best value: >> intensional similarity = 3 >> extensional distance = 1404 >> proper extension: 06151l; 04cy8rb; 023tp8; 0dky9n; 0784v1; 0l56b; 05b4rcb; 05218gr; 07h1tr; 030x48; ... >> query: (?x5600, 09c7w0) <- place_of_birth(?x5600, ?x12642), nationality(?x5600, ?x1310), time_zones(?x12642, ?x2864) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #15987 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2325 *> proper extension: 0cfywh; *> query: (?x5600, ?x1264) <- place_of_birth(?x5600, ?x12642), nationality(?x5600, ?x1310), contains(?x1264, ?x12642) *> conf = 0.36 ranks of expected_values: 4 EVAL 03bxh nationality 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 175.000 173.000 0.858 http://example.org/people/person/nationality #11039-02hn5v PRED entity: 02hn5v PRED relation: award_winner PRED expected values: 03mfqm => 38 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2271): 026ps1 (0.60 #3137, 0.50 #4677, 0.11 #15439), 0gcs9 (0.50 #5059, 0.40 #3519, 0.33 #15821), 02cx90 (0.50 #5282, 0.40 #3742, 0.30 #16044), 0fpjd_g (0.50 #4828, 0.40 #3288, 0.30 #15590), 06fmdb (0.50 #5425, 0.40 #3885, 0.26 #16187), 01htxr (0.40 #4024, 0.33 #5564, 0.22 #16326), 01w60_p (0.40 #3383, 0.33 #4923, 0.22 #15685), 016szr (0.40 #3838, 0.33 #5378, 0.20 #6158), 06rgq (0.40 #4295, 0.33 #5835, 0.15 #16597), 018ndc (0.40 #3539, 0.33 #5079, 0.15 #15841) >> Best rule #3137 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 05pd94v; 01s695; 019bk0; >> query: (?x2707, 026ps1) <- ceremony(?x1313, ?x2707), award_winner(?x2707, ?x5536), nominated_for(?x1313, ?x4559), nominated_for(?x1313, ?x2898), nominated_for(?x1313, ?x1199), award(?x197, ?x1313), film(?x8042, ?x1199), ?x5536 = 01vsgrn, award_winner(?x1313, ?x276), genre(?x1199, ?x53), film_crew_role(?x1199, ?x137), award(?x269, ?x1313), music(?x4559, ?x6399), award_winner(?x2898, ?x3139), country(?x1199, ?x512) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6157 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 4 *> proper extension: 02cg41; *> query: (?x2707, ?x57) <- ceremony(?x1313, ?x2707), award_winner(?x2707, ?x5536), nominated_for(?x1313, ?x4559), nominated_for(?x1313, ?x2898), nominated_for(?x1313, ?x1199), award(?x197, ?x1313), film(?x8042, ?x1199), ?x5536 = 01vsgrn, award_winner(?x1313, ?x276), genre(?x1199, ?x53), film_crew_role(?x1199, ?x137), award(?x269, ?x1313), music(?x4559, ?x6399), award_winner(?x2898, ?x3139), nominated_for(?x57, ?x1199) *> conf = 0.08 ranks of expected_values: 706 EVAL 02hn5v award_winner 03mfqm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 38.000 19.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #11038-01m65sp PRED entity: 01m65sp PRED relation: profession PRED expected values: 016z4k 0cbd2 => 155 concepts (103 used for prediction) PRED predicted values (max 10 best out of 72): 0nbcg (0.65 #3243, 0.64 #4267, 0.64 #2367), 016z4k (0.60 #2778, 0.59 #1756, 0.59 #2486), 0n1h (0.43 #1326, 0.41 #1910, 0.37 #2786), 01c72t (0.41 #898, 0.41 #8066, 0.38 #3089), 01d_h8 (0.35 #11276, 0.33 #5, 0.32 #1027), 0fnpj (0.34 #2103, 0.28 #3125, 0.21 #8102), 03gjzk (0.33 #14, 0.32 #5715, 0.25 #598), 0dxtg (0.33 #13, 0.32 #7327, 0.24 #11284), 0np9r (0.33 #19, 0.23 #9825, 0.22 #6012), 025352 (0.33 #57, 0.22 #1518, 0.15 #1664) >> Best rule #3243 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 025xt8y; 018y2s; 0l12d; 01v_pj6; 012zng; 01vsnff; 01wwvt2; 01tp5bj; 06x4l_; 03xl77; ... >> query: (?x3206, 0nbcg) <- role(?x3206, ?x1166), artists(?x302, ?x3206), ?x302 = 016clz, role(?x3206, ?x227) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #2778 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 01vv7sc; 09qr6; 0j1yf; 0136pk; 03j0br4; 045zr; 0892sx; 0137g1; 0gdh5; 0161sp; ... *> query: (?x3206, 016z4k) <- role(?x3206, ?x1166), participant(?x3206, ?x2352), artists(?x302, ?x3206), profession(?x3206, ?x131) *> conf = 0.60 ranks of expected_values: 2, 19 EVAL 01m65sp profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 155.000 103.000 0.650 http://example.org/people/person/profession EVAL 01m65sp profession 016z4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 155.000 103.000 0.650 http://example.org/people/person/profession #11037-07n68 PRED entity: 07n68 PRED relation: artists! PRED expected values: 016clz 05w3f => 116 concepts (92 used for prediction) PRED predicted values (max 10 best out of 275): 06by7 (0.98 #27670, 0.78 #19600, 0.62 #8109), 0xhtw (0.79 #14009, 0.46 #1570, 0.42 #1259), 016clz (0.67 #4982, 0.67 #1246, 0.57 #12132), 064t9 (0.67 #2817, 0.57 #3127, 0.52 #15557), 05bt6j (0.51 #12172, 0.43 #15588, 0.38 #2848), 0dl5d (0.50 #952, 0.38 #1573, 0.33 #1887), 059kh (0.50 #670, 0.35 #2541, 0.33 #5027), 03lty (0.40 #14021, 0.26 #9978, 0.26 #6870), 0glt670 (0.40 #352, 0.36 #15585, 0.24 #14345), 0bt7w (0.40 #419, 0.18 #27336, 0.17 #1040) >> Best rule #27670 for best value: >> intensional similarity = 8 >> extensional distance = 396 >> proper extension: 01q7cb_; 0lk90; 01p9hgt; 01p45_v; 04bpm6; 01qkqwg; 0zjpz; 09prnq; 01hw6wq; 02qlg7s; ... >> query: (?x13505, 06by7) <- artists(?x9935, ?x13505), artists(?x9935, ?x6225), artists(?x9935, ?x5618), artists(?x9935, ?x1573), parent_genre(?x9935, ?x2996), ?x6225 = 01vng3b, ?x1573 = 03g5jw, ?x5618 = 03d9d6 >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #4982 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 40 *> proper extension: 0285c; 05qw5; 03sww; 02y7sr; *> query: (?x13505, 016clz) <- origin(?x13505, ?x362), artists(?x5934, ?x13505), artists(?x2491, ?x13505), ?x5934 = 05r6t, artists(?x2491, ?x11700), ?x11700 = 017_hq, parent_genre(?x302, ?x2491) *> conf = 0.67 ranks of expected_values: 3, 11 EVAL 07n68 artists! 05w3f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 116.000 92.000 0.977 http://example.org/music/genre/artists EVAL 07n68 artists! 016clz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 92.000 0.977 http://example.org/music/genre/artists #11036-08052t3 PRED entity: 08052t3 PRED relation: film! PRED expected values: 0fsm8c => 82 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1045): 01sl1q (0.16 #2081, 0.06 #1, 0.04 #4164), 07f3xb (0.16 #2321, 0.03 #49933, 0.03 #29129), 025hzx (0.13 #31211, 0.11 #31210, 0.11 #49934), 02w29z (0.12 #1411, 0.08 #5574, 0.06 #9734), 0gnbw (0.12 #1268, 0.08 #5431, 0.03 #7511), 0f5xn (0.12 #970, 0.07 #7213, 0.06 #9293), 0kszw (0.12 #419, 0.07 #6662, 0.06 #8742), 01chc7 (0.12 #560, 0.07 #6803, 0.06 #8883), 0c9xjl (0.12 #972, 0.05 #7215, 0.05 #9295), 0p8r1 (0.12 #4749, 0.08 #8909, 0.07 #6829) >> Best rule #2081 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 0f61tk; >> query: (?x2471, 01sl1q) <- music(?x2471, ?x4020), film(?x6747, ?x2471), production_companies(?x2471, ?x7339), award_nominee(?x10188, ?x6747), ?x10188 = 044mvs >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #4438 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 23 *> proper extension: 0407yfx; 0fpgp26; *> query: (?x2471, 0fsm8c) <- film_release_region(?x2471, ?x2267), film_release_region(?x2471, ?x2236), film_release_region(?x2471, ?x1122), film_release_region(?x2471, ?x550), ?x2267 = 03rj0, ?x1122 = 09pmkv, ?x2236 = 05sb1, ?x550 = 05v8c *> conf = 0.04 ranks of expected_values: 253 EVAL 08052t3 film! 0fsm8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 82.000 38.000 0.158 http://example.org/film/actor/film./film/performance/film #11035-04xhwn PRED entity: 04xhwn PRED relation: place_of_birth PRED expected values: 0r00l => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 98): 01531 (0.25 #1513, 0.02 #33918, 0.02 #43783), 0f2wj (0.23 #19726, 0.20 #13384, 0.18 #16907), 0rd6b (0.23 #19726, 0.20 #13384, 0.18 #16907), 05mph (0.23 #19726, 0.20 #13384, 0.18 #16907), 0cr3d (0.20 #2910, 0.20 #2206, 0.06 #19115), 0cc56 (0.20 #2849, 0.03 #22579, 0.03 #25397), 06wxw (0.20 #2973, 0.03 #12132, 0.01 #29042), 03b12 (0.17 #4632, 0.10 #10269, 0.08 #8155), 0chgzm (0.17 #4535, 0.04 #8058, 0.03 #10172), 0mzvm (0.17 #3654, 0.02 #13518) >> Best rule #1513 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 025h4z; >> query: (?x12566, 01531) <- actor(?x9098, ?x12566), film(?x12566, ?x590), film(?x12566, ?x253), ?x253 = 09m6kg, film_release_distribution_medium(?x590, ?x81) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04xhwn place_of_birth 0r00l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 120.000 0.250 http://example.org/people/person/place_of_birth #11034-07hhnl PRED entity: 07hhnl PRED relation: film_art_direction_by! PRED expected values: 02r_pp 0jwvf 015gm8 => 100 concepts (85 used for prediction) PRED predicted values (max 10 best out of 72): 0ccck7 (0.17 #152, 0.08 #232), 0gw7p (0.17 #121, 0.04 #201), 0k4kk (0.10 #159, 0.04 #170, 0.03 #477), 0286hyp (0.10 #159, 0.03 #477, 0.01 #1991), 015gm8 (0.10 #159, 0.03 #477, 0.01 #1991), 072192 (0.08 #221), 029jt9 (0.08 #220), 0gndh (0.08 #211), 02q_4ph (0.08 #188), 0glnm (0.08 #180) >> Best rule #152 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0584j4n; >> query: (?x4896, 0ccck7) <- award_nominee(?x4896, ?x2716), nominated_for(?x4896, ?x2717), ?x2717 = 0k5g9 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #159 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0584j4n; *> query: (?x4896, ?x1746) <- award_nominee(?x4896, ?x2716), nominated_for(?x4896, ?x2717), nominated_for(?x4896, ?x1746), ?x2717 = 0k5g9 *> conf = 0.10 ranks of expected_values: 5 EVAL 07hhnl film_art_direction_by! 015gm8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 100.000 85.000 0.167 http://example.org/film/film/film_art_direction_by EVAL 07hhnl film_art_direction_by! 0jwvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 85.000 0.167 http://example.org/film/film/film_art_direction_by EVAL 07hhnl film_art_direction_by! 02r_pp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 85.000 0.167 http://example.org/film/film/film_art_direction_by #11033-05v38p PRED entity: 05v38p PRED relation: genre PRED expected values: 02n4kr => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 96): 04xvlr (0.72 #2792, 0.60 #2791, 0.51 #3279), 07ssc (0.60 #2791, 0.51 #3279, 0.51 #4010), 05p553 (0.43 #1460, 0.43 #246, 0.42 #1703), 01hmnh (0.43 #18, 0.38 #139, 0.28 #747), 06cvj (0.39 #245, 0.25 #1459, 0.24 #1702), 03k9fj (0.34 #498, 0.31 #741, 0.30 #1346), 02kdv5l (0.33 #123, 0.32 #1336, 0.30 #488), 01jfsb (0.32 #1347, 0.31 #1833, 0.30 #2440), 060__y (0.27 #259, 0.25 #988, 0.23 #624), 082gq (0.22 #881, 0.12 #638, 0.10 #1123) >> Best rule #2792 for best value: >> intensional similarity = 3 >> extensional distance = 869 >> proper extension: 016ztl; >> query: (?x6445, ?x1403) <- titles(?x1403, ?x6445), film_release_distribution_medium(?x6445, ?x81), genre(?x83, ?x1403) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 09g7vfw; 03z9585; *> query: (?x6445, 02n4kr) <- film(?x2444, ?x6445), film_crew_role(?x6445, ?x137), ?x2444 = 0jfx1 *> conf = 0.19 ranks of expected_values: 13 EVAL 05v38p genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 67.000 67.000 0.723 http://example.org/film/film/genre #11032-05_swj PRED entity: 05_swj PRED relation: award_winner! PRED expected values: 03cd1q => 109 concepts (34 used for prediction) PRED predicted values (max 10 best out of 339): 016890 (0.89 #6433, 0.88 #12866, 0.82 #38599), 021yw7 (0.89 #6433, 0.88 #12866, 0.82 #38599), 03cd1q (0.89 #6433, 0.88 #12866, 0.82 #38599), 027xbpw (0.49 #41817, 0.44 #3216, 0.43 #32162), 0ggjt (0.12 #2126, 0.09 #8559, 0.06 #26245), 03cfjg (0.10 #2171, 0.08 #26290, 0.07 #8604), 01vrnsk (0.10 #5963, 0.06 #12396, 0.05 #14004), 02lfp4 (0.08 #5686, 0.06 #12119, 0.05 #13727), 018dyl (0.08 #5555, 0.05 #11988, 0.05 #13596), 0gt_k (0.08 #5121, 0.05 #11554, 0.05 #13162) >> Best rule #6433 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 0244r8; 09r9m7; >> query: (?x6891, ?x3673) <- music(?x1785, ?x6891), award_winner(?x6891, ?x3673), instrumentalists(?x316, ?x6891) >> conf = 0.89 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 05_swj award_winner! 03cd1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 34.000 0.887 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #11031-016gr2 PRED entity: 016gr2 PRED relation: film PRED expected values: 0dgst_d => 81 concepts (52 used for prediction) PRED predicted values (max 10 best out of 246): 0330r (0.68 #1783, 0.59 #58827, 0.58 #65959), 03177r (0.19 #462, 0.15 #2245, 0.03 #4027), 031786 (0.19 #1271, 0.15 #3054, 0.03 #19609), 02_kd (0.12 #584, 0.10 #2367, 0.03 #60611), 031hcx (0.12 #1270, 0.10 #3053, 0.03 #4835), 031778 (0.12 #313, 0.10 #2096, 0.03 #19609), 03176f (0.12 #705, 0.10 #2488, 0.03 #19609), 03hxsv (0.12 #1114, 0.10 #2897, 0.03 #19609), 07tj4c (0.12 #1693, 0.10 #3476, 0.03 #19609), 03_gz8 (0.12 #1120, 0.10 #2903, 0.03 #19609) >> Best rule #1783 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02tr7d; 015rkw; 06t61y; 065jlv; 02k6rq; 015gw6; 0l6px; 01hkhq; 01ksr1; 02l4pj; ... >> query: (?x1223, ?x1077) <- award_winner(?x1223, ?x374), nominated_for(?x1223, ?x1077), ?x374 = 05cj4r >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #193 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 02tr7d; 015rkw; 06t61y; 065jlv; 02k6rq; 015gw6; 0l6px; 01hkhq; 01ksr1; 02l4pj; ... *> query: (?x1223, 0dgst_d) <- award_winner(?x1223, ?x374), nominated_for(?x1223, ?x1077), ?x374 = 05cj4r *> conf = 0.06 ranks of expected_values: 71 EVAL 016gr2 film 0dgst_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 81.000 52.000 0.675 http://example.org/film/actor/film./film/performance/film #11030-02f6g5 PRED entity: 02f6g5 PRED relation: produced_by PRED expected values: 02r251z => 101 concepts (73 used for prediction) PRED predicted values (max 10 best out of 162): 0pz91 (0.60 #2325, 0.60 #1983, 0.56 #1937), 02r251z (0.60 #2178, 0.56 #1790, 0.11 #1402), 06rq2l (0.22 #1856, 0.13 #2244, 0.01 #3409), 02lf0c (0.19 #2348, 0.03 #4285, 0.02 #3898), 01t6b4 (0.17 #816, 0.06 #2368, 0.04 #2756), 02qzjj (0.17 #1139, 0.06 #2691), 026c1 (0.14 #1936, 0.13 #5424, 0.11 #2324), 02k21g (0.14 #1936, 0.11 #2324, 0.05 #773), 01fyzy (0.14 #1936, 0.11 #2324, 0.05 #773), 03xb2w (0.14 #1936, 0.11 #2324, 0.05 #773) >> Best rule #2325 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 07kb7vh; 095z4q; 0f2sx4; 02tgz4; 087pfc; 0640m69; >> query: (?x1810, ?x1335) <- film(?x1335, ?x1810), production_companies(?x1810, ?x1836), nominated_for(?x298, ?x1810), ?x1335 = 0pz91 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2178 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 07kb7vh; 095z4q; 0f2sx4; 02tgz4; 087pfc; 0640m69; *> query: (?x1810, 02r251z) <- film(?x1335, ?x1810), production_companies(?x1810, ?x1836), nominated_for(?x298, ?x1810), ?x1335 = 0pz91 *> conf = 0.60 ranks of expected_values: 2 EVAL 02f6g5 produced_by 02r251z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 73.000 0.600 http://example.org/film/film/produced_by #11029-02y49 PRED entity: 02y49 PRED relation: influenced_by! PRED expected values: 03hpr => 119 concepts (41 used for prediction) PRED predicted values (max 10 best out of 422): 0j0pf (0.33 #1234, 0.21 #5869, 0.15 #7414), 05jm7 (0.27 #1168, 0.25 #1683, 0.17 #5803), 01dzz7 (0.22 #53, 0.20 #1081, 0.11 #1596), 07lp1 (0.20 #2475, 0.11 #3504, 0.10 #8657), 067xw (0.20 #1312, 0.11 #284, 0.11 #1827), 01zkxv (0.20 #1044, 0.11 #16, 0.10 #6178), 0683n (0.17 #2397, 0.15 #6518, 0.12 #10639), 01hb6v (0.17 #2151, 0.14 #3180, 0.10 #10393), 040db (0.17 #2134, 0.11 #6255, 0.11 #10376), 03f47xl (0.17 #2320, 0.08 #6441, 0.06 #7985) >> Best rule #1234 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 07w21; 09dt7; 05jm7; 04mhl; 018fq; 014ps4; 02mpb; 0gd_s; 0mfc0; 03rx9; ... >> query: (?x8908, 0j0pf) <- influenced_by(?x8908, ?x5004), award(?x8908, ?x1375), profession(?x8908, ?x1032), ?x1375 = 0262zm >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #414 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 01963w; 05x8n; 0210f1; *> query: (?x8908, 03hpr) <- award(?x8908, ?x8909), award(?x8908, ?x1375), profession(?x8908, ?x1032), ?x8909 = 040_9s0, ?x1375 = 0262zm *> conf = 0.11 ranks of expected_values: 25 EVAL 02y49 influenced_by! 03hpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 119.000 41.000 0.333 http://example.org/influence/influence_node/influenced_by #11028-027pdrh PRED entity: 027pdrh PRED relation: people! PRED expected values: 0d7wh => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 28): 02w7gg (0.30 #695, 0.28 #772, 0.27 #926), 041rx (0.15 #620, 0.13 #1544, 0.12 #1005), 0x67 (0.11 #1088, 0.09 #2551, 0.09 #1319), 0d7wh (0.10 #710, 0.07 #941, 0.07 #787), 048z7l (0.07 #40, 0.06 #194, 0.06 #656), 0xnvg (0.07 #13, 0.06 #90, 0.05 #1091), 019kn7 (0.07 #46, 0.06 #200, 0.05 #277), 033tf_ (0.07 #854, 0.06 #1701, 0.06 #1855), 02ctzb (0.06 #169, 0.05 #246, 0.05 #323), 03lmx1 (0.06 #630, 0.02 #861, 0.01 #1477) >> Best rule #695 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 0qdyf; 0fhxv; 02z4b_8; >> query: (?x2572, 02w7gg) <- award(?x2572, ?x1703), award_winner(?x5053, ?x2572), nationality(?x2572, ?x1310), ?x1310 = 02jx1 >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #710 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 103 *> proper extension: 0qdyf; 0fhxv; 02z4b_8; *> query: (?x2572, 0d7wh) <- award(?x2572, ?x1703), award_winner(?x5053, ?x2572), nationality(?x2572, ?x1310), ?x1310 = 02jx1 *> conf = 0.10 ranks of expected_values: 4 EVAL 027pdrh people! 0d7wh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 87.000 0.295 http://example.org/people/ethnicity/people #11027-0b_dy PRED entity: 0b_dy PRED relation: award PRED expected values: 0789_m 0cqh46 04kxsb => 92 concepts (83 used for prediction) PRED predicted values (max 10 best out of 244): 027c95y (0.70 #19960, 0.70 #15169, 0.70 #13571), 027986c (0.70 #19960, 0.70 #15169, 0.70 #13571), 05zr6wv (0.54 #1213, 0.47 #1612, 0.25 #16), 09qvc0 (0.50 #38, 0.33 #836, 0.20 #437), 09qv_s (0.40 #548, 0.33 #947, 0.25 #149), 02x4w6g (0.33 #910, 0.25 #112, 0.20 #511), 099tbz (0.31 #1252, 0.27 #1651, 0.14 #19560), 05pcn59 (0.25 #79, 0.23 #1276, 0.20 #1675), 04dn09n (0.25 #41, 0.20 #440, 0.17 #839), 05ztrmj (0.25 #180, 0.20 #579, 0.17 #978) >> Best rule #19960 for best value: >> intensional similarity = 2 >> extensional distance = 1585 >> proper extension: 030pr; 0207wx; 01wdqrx; 09pjnd; 0hpt3; 09d5h; 0l56b; 03jvmp; 0g5lhl7; 03r1pr; ... >> query: (?x3139, ?x834) <- award_nominee(?x3139, ?x156), award_winner(?x834, ?x3139) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #49 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 02xs5v; *> query: (?x3139, 0cqh46) <- film(?x3139, ?x763), ?x763 = 061681, award_winner(?x156, ?x3139) *> conf = 0.25 ranks of expected_values: 15, 16, 70 EVAL 0b_dy award 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 92.000 83.000 0.703 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0b_dy award 0cqh46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 92.000 83.000 0.703 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0b_dy award 0789_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 92.000 83.000 0.703 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11026-028knk PRED entity: 028knk PRED relation: award_nominee! PRED expected values: 0dn3n => 107 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1173): 01j7z7 (0.81 #65054, 0.81 #85963, 0.81 #127787), 0dn3n (0.81 #65054, 0.81 #85963, 0.81 #127787), 02qgqt (0.22 #4664, 0.18 #113847, 0.04 #44162), 051wwp (0.18 #113847, 0.12 #5809, 0.03 #45307), 0h0wc (0.18 #113847, 0.10 #5196, 0.04 #120817), 02qgyv (0.18 #113847, 0.07 #5140, 0.04 #21404), 0gjvqm (0.18 #113847, 0.07 #4899, 0.04 #120817), 043kzcr (0.18 #113847, 0.07 #5185, 0.04 #120817), 0g8st4 (0.18 #113847, 0.07 #6173, 0.02 #141726), 04bdxl (0.18 #113847, 0.05 #4652, 0.04 #2329) >> Best rule #65054 for best value: >> intensional similarity = 3 >> extensional distance = 873 >> proper extension: 02knnd; 02zyy4; 04rsd2; 01v3bn; 01wz01; 062hgx; 06s6hs; 02lgfh; 03b78r; 01w_10; ... >> query: (?x2028, ?x92) <- location(?x2028, ?x3521), film(?x2028, ?x2052), award_nominee(?x2028, ?x92) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 028knk award_nominee! 0dn3n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 61.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #11025-049dk PRED entity: 049dk PRED relation: student PRED expected values: 01wc7p => 161 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1088): 0432cd (0.29 #5510, 0.17 #13886, 0.15 #20168), 01n1gc (0.29 #4801, 0.17 #13177, 0.15 #19459), 0crqcc (0.29 #5412, 0.17 #13788, 0.15 #20070), 0bt7ws (0.25 #627, 0.20 #2721, 0.02 #50886), 0137n0 (0.25 #181, 0.20 #2275, 0.02 #50440), 0gt3p (0.17 #13906, 0.15 #20188, 0.15 #18094), 02hsgn (0.15 #15481, 0.14 #5011, 0.08 #13387), 03xx9l (0.15 #15984, 0.14 #5514, 0.08 #13890), 0ff3y (0.14 #6260, 0.09 #25106, 0.09 #23012), 0405l (0.14 #6044, 0.09 #24890, 0.09 #22796) >> Best rule #5510 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 07wlf; >> query: (?x1783, 0432cd) <- major_field_of_study(?x1783, ?x12637), major_field_of_study(?x1783, ?x10417), major_field_of_study(?x1783, ?x254), ?x10417 = 01r4k, currency(?x1783, ?x170), ?x254 = 02h40lc, major_field_of_study(?x10332, ?x12637) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 049dk student 01wc7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 161.000 89.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #11024-0154qm PRED entity: 0154qm PRED relation: gender PRED expected values: 02zsn => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.75 #1, 0.73 #37, 0.73 #171), 02zsn (0.73 #34, 0.52 #195, 0.51 #32) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02qgyv; 0jmj; 016zp5; 018ygt; >> query: (?x3281, 05zppz) <- award_winner(?x995, ?x3281), award_nominee(?x1286, ?x3281), ?x1286 = 07vc_9 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 02rmxx; 043hg; *> query: (?x3281, 02zsn) <- award_winner(?x2257, ?x3281), nominated_for(?x2257, ?x4596), ?x4596 = 02d49z *> conf = 0.73 ranks of expected_values: 2 EVAL 0154qm gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 115.000 0.750 http://example.org/people/person/gender #11023-0frmb1 PRED entity: 0frmb1 PRED relation: location PRED expected values: 04ykg => 144 concepts (121 used for prediction) PRED predicted values (max 10 best out of 284): 01j8yr (0.33 #225, 0.06 #17123, 0.05 #21951), 068p2 (0.33 #1038, 0.03 #29213, 0.02 #43719), 0dclg (0.25 #2530, 0.20 #3334, 0.10 #8162), 0xmp9 (0.25 #2294, 0.17 #4707, 0.12 #6315), 0498y (0.25 #2626, 0.17 #5038, 0.12 #6647), 029cr (0.25 #2542, 0.17 #4954, 0.12 #6563), 0rh6k (0.25 #1612, 0.15 #12877, 0.13 #25757), 0ftvg (0.25 #2122, 0.12 #6143, 0.11 #7753), 02_286 (0.21 #33848, 0.19 #19349, 0.19 #37072), 05tbn (0.20 #3405, 0.08 #10648, 0.07 #23525) >> Best rule #225 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 07zl1; >> query: (?x7749, 01j8yr) <- company(?x7749, ?x4257), student(?x9768, ?x7749), type_of_union(?x7749, ?x566), ?x4257 = 01q0kg >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7307 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 036jp8; *> query: (?x7749, 04ykg) <- company(?x7749, ?x1762), student(?x9768, ?x7749), inductee(?x14281, ?x7749) *> conf = 0.11 ranks of expected_values: 35 EVAL 0frmb1 location 04ykg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 144.000 121.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #11022-0gzh PRED entity: 0gzh PRED relation: influenced_by! PRED expected values: 085gk => 128 concepts (85 used for prediction) PRED predicted values (max 10 best out of 468): 0ph2w (0.38 #4263, 0.21 #8370, 0.12 #4776), 02wh0 (0.33 #449, 0.25 #5581, 0.08 #43580), 04411 (0.33 #26, 0.12 #5158, 0.09 #43131), 048cl (0.33 #298, 0.09 #43131, 0.09 #43648), 06myp (0.33 #439, 0.07 #27132, 0.05 #43646), 0c5tl (0.33 #209, 0.05 #43646, 0.05 #15608), 03j43 (0.33 #67, 0.05 #43646, 0.03 #42684), 06g4_ (0.33 #445, 0.05 #43646, 0.03 #27138), 0bt23 (0.33 #448, 0.05 #43646, 0.03 #27141), 018x3 (0.33 #232, 0.05 #43646, 0.03 #37197) >> Best rule #4263 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 02h48; >> query: (?x13698, 0ph2w) <- influenced_by(?x8494, ?x13698), influenced_by(?x6008, ?x13698), ?x6008 = 0bqs56, profession(?x13698, ?x3342), place_of_death(?x8494, ?x5381) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #5617 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02n9k; *> query: (?x13698, 085gk) <- influenced_by(?x5796, ?x13698), nationality(?x13698, ?x94), ?x94 = 09c7w0, organization(?x5796, ?x10530) *> conf = 0.25 ranks of expected_values: 17 EVAL 0gzh influenced_by! 085gk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 128.000 85.000 0.375 http://example.org/influence/influence_node/influenced_by #11021-02h7qr PRED entity: 02h7qr PRED relation: major_field_of_study PRED expected values: 041y2 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 104): 04rjg (0.50 #777, 0.41 #903, 0.32 #1407), 01mkq (0.48 #772, 0.46 #898, 0.32 #1402), 02lp1 (0.37 #768, 0.36 #894, 0.31 #1398), 02j62 (0.37 #788, 0.30 #1166, 0.30 #914), 03g3w (0.35 #784, 0.28 #910, 0.25 #3808), 062z7 (0.33 #785, 0.25 #911, 0.24 #3809), 05qjt (0.26 #764, 0.25 #890, 0.21 #1394), 04x_3 (0.26 #783, 0.23 #909, 0.15 #1413), 0g26h (0.24 #801, 0.23 #1053, 0.22 #1431), 02h40lc (0.22 #760, 0.18 #886, 0.16 #1390) >> Best rule #777 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 01_qgp; 0lk0l; >> query: (?x7394, 04rjg) <- school_type(?x7394, ?x4994), ?x4994 = 07tf8, student(?x7394, ?x7395), contains(?x94, ?x7394) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #838 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 01_qgp; 0lk0l; *> query: (?x7394, 041y2) <- school_type(?x7394, ?x4994), ?x4994 = 07tf8, student(?x7394, ?x7395), contains(?x94, ?x7394) *> conf = 0.20 ranks of expected_values: 16 EVAL 02h7qr major_field_of_study 041y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 139.000 139.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #11020-040j2_ PRED entity: 040j2_ PRED relation: team PRED expected values: 02d02 => 153 concepts (129 used for prediction) PRED predicted values (max 10 best out of 548): 0jm2v (0.56 #12328, 0.49 #11975, 0.29 #381), 0jmk7 (0.56 #12328, 0.49 #11975, 0.18 #4528), 0wsr (0.56 #12328, 0.49 #11975, 0.14 #482), 0jmbv (0.56 #12328, 0.49 #11975, 0.14 #2220), 05g76 (0.56 #12328, 0.49 #11975, 0.12 #739), 0jm3v (0.56 #12328, 0.49 #11975, 0.09 #4238), 02pqcfz (0.56 #12328, 0.49 #11975, 0.07 #2187), 0jmjr (0.56 #12328, 0.49 #11975, 0.03 #7626), 02wvfxz (0.56 #12328, 0.49 #11975, 0.01 #13033), 0jnq8 (0.56 #12328, 0.49 #11975, 0.01 #13033) >> Best rule #12328 for best value: >> intensional similarity = 5 >> extensional distance = 93 >> proper extension: 05c4fys; 02zbjwr; 03m5111; >> query: (?x8110, ?x4804) <- team(?x8110, ?x2174), sport(?x2174, ?x5063), teams(?x6088, ?x2174), teams(?x6088, ?x4804), nationality(?x8110, ?x94) >> conf = 0.56 => this is the best rule for 15 predicted values *> Best rule #8450 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 02qjj7; 054fvj; 0cymln; 054c1; *> query: (?x8110, ?x260) <- team(?x8110, ?x2174), school(?x2174, ?x546), draft(?x2174, ?x1161), school(?x1161, ?x466), draft(?x260, ?x1161) *> conf = 0.09 ranks of expected_values: 81 EVAL 040j2_ team 02d02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 153.000 129.000 0.560 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #11019-01qygl PRED entity: 01qygl PRED relation: state_province_region PRED expected values: 05kkh => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 62): 05kkh (0.76 #12740, 0.33 #11129, 0.27 #12492), 059rby (0.38 #2350, 0.28 #1236, 0.27 #2226), 09c7w0 (0.33 #11129, 0.27 #12492, 0.26 #13859), 0n2k5 (0.33 #11129, 0.27 #12492, 0.26 #13859), 01n7q (0.24 #2240, 0.24 #5955, 0.24 #1620), 081yw (0.18 #800, 0.17 #61, 0.11 #431), 03v0t (0.17 #53, 0.09 #1162, 0.09 #792), 01x73 (0.12 #148, 0.11 #271, 0.05 #9056), 04rrx (0.12 #153, 0.10 #523, 0.09 #1386), 05tbn (0.12 #174, 0.08 #7970, 0.07 #9205) >> Best rule #12740 for best value: >> intensional similarity = 4 >> extensional distance = 344 >> proper extension: 01b_d4; 0g8fs; >> query: (?x8931, ?x177) <- category(?x8931, ?x134), citytown(?x8931, ?x6555), ?x134 = 08mbj5d, state(?x6555, ?x177) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qygl state_province_region 05kkh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.761 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #11018-0cc63l PRED entity: 0cc63l PRED relation: produced_by! PRED expected values: 047d21r => 150 concepts (83 used for prediction) PRED predicted values (max 10 best out of 164): 08g_jw (0.17 #899, 0.06 #2793, 0.05 #3740), 03rz2b (0.17 #256, 0.06 #2150, 0.05 #3097), 04jwjq (0.06 #1951, 0.05 #2898, 0.02 #6686), 01p3ty (0.06 #2124, 0.05 #3071, 0.02 #6859), 09yxcz (0.06 #2788, 0.01 #13206, 0.01 #17941), 03cp4cn (0.03 #11970, 0.03 #16705, 0.01 #27122), 0bwfwpj (0.03 #11455, 0.02 #15243, 0.02 #16190), 05k4my (0.03 #5612, 0.03 #6559, 0.02 #7506), 05qbbfb (0.03 #5313, 0.03 #6260, 0.02 #7207), 02q87z6 (0.03 #5301, 0.03 #6248, 0.02 #7195) >> Best rule #899 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 06gn7r; 0kvsb; 03d8njj; 05xd8x; >> query: (?x5709, 08g_jw) <- student(?x9536, ?x5709), profession(?x5709, ?x524), ?x524 = 02jknp, people(?x5025, ?x5709), ?x5025 = 0dryh9k >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cc63l produced_by! 047d21r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 83.000 0.167 http://example.org/film/film/produced_by #11017-03h2d4 PRED entity: 03h2d4 PRED relation: award PRED expected values: 05ztrmj => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 257): 09sb52 (0.35 #12222, 0.28 #8973, 0.28 #6537), 04kxsb (0.25 #127, 0.10 #5405, 0.08 #12588), 0f4x7 (0.25 #31, 0.09 #6527, 0.09 #843), 0ck27z (0.15 #12274, 0.14 #14711, 0.14 #15117), 0gkts9 (0.14 #982, 0.08 #1794, 0.07 #2200), 05pcn59 (0.13 #6578, 0.12 #894, 0.12 #82), 05ztrmj (0.12 #186, 0.12 #11775, 0.12 #24365), 0gqy2 (0.12 #166, 0.11 #10316, 0.10 #16409), 057xs89 (0.12 #162, 0.09 #6658, 0.06 #9094), 02w9sd7 (0.12 #172, 0.08 #12588, 0.07 #19492) >> Best rule #12222 for best value: >> intensional similarity = 3 >> extensional distance = 1147 >> proper extension: 036hf4; >> query: (?x4286, 09sb52) <- film(?x4286, ?x3035), nominated_for(?x68, ?x3035), award_nominee(?x2373, ?x4286) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #186 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 08w7vj; 03f1zdw; 015wnl; *> query: (?x4286, 05ztrmj) <- nationality(?x4286, ?x512), film(?x4286, ?x3012), ?x3012 = 0ggbhy7 *> conf = 0.12 ranks of expected_values: 7 EVAL 03h2d4 award 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 87.000 0.346 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11016-05qd_ PRED entity: 05qd_ PRED relation: category PRED expected values: 08mbj5d => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #85, 0.84 #89, 0.81 #79) >> Best rule #85 for best value: >> intensional similarity = 2 >> extensional distance = 538 >> proper extension: 015zyd; 05zjtn4; 0dwl2; 01rtm4; 01jssp; 04wlz2; 05krk; 052nd; 0gkkf; 0473m9; ... >> query: (?x902, 08mbj5d) <- organization(?x4682, ?x902), state_province_region(?x902, ?x1227) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qd_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.854 http://example.org/common/topic/webpage./common/webpage/category #11015-027hm_ PRED entity: 027hm_ PRED relation: instrumentalists! PRED expected values: 0342h => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 89): 0342h (0.67 #2718, 0.65 #354, 0.65 #1495), 05r5c (0.49 #973, 0.49 #1762, 0.48 #2023), 05148p4 (0.37 #986, 0.34 #1074, 0.34 #2735), 0680x0 (0.36 #175, 0.31 #1141, 0.26 #965), 018vs (0.34 #2727, 0.29 #2989, 0.29 #3340), 03qjg (0.29 #52, 0.22 #402, 0.18 #665), 06ch55 (0.29 #169, 0.18 #257, 0.10 #959), 02hnl (0.20 #1000, 0.19 #2749, 0.17 #1789), 018j2 (0.18 #214, 0.14 #126, 0.14 #39), 06ncr (0.14 #132, 0.14 #45, 0.12 #922) >> Best rule #2718 for best value: >> intensional similarity = 3 >> extensional distance = 416 >> proper extension: 01yznp; 01wk7b7; >> query: (?x8246, 0342h) <- profession(?x8246, ?x1183), instrumentalists(?x75, ?x8246), ?x1183 = 09jwl >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027hm_ instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.665 http://example.org/music/instrument/instrumentalists #11014-0bfvd4 PRED entity: 0bfvd4 PRED relation: award! PRED expected values: 04sx9_ 01y665 018ygt 01xpxv 033071 02js_6 0cg9f => 35 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2632): 0171cm (0.78 #26278, 0.66 #49277, 0.63 #36133), 031k24 (0.78 #26278, 0.66 #49277, 0.63 #36133), 016gr2 (0.78 #26278, 0.66 #49277, 0.63 #36133), 0170pk (0.64 #10279, 0.54 #13564, 0.50 #3711), 018ygt (0.64 #11650, 0.54 #14935, 0.50 #5082), 0bj9k (0.53 #16927, 0.36 #10357, 0.31 #13642), 048lv (0.50 #3606, 0.46 #13459, 0.45 #10174), 02ldv0 (0.50 #5128, 0.45 #11696, 0.38 #14981), 014gf8 (0.50 #4904, 0.38 #14757, 0.36 #11472), 0170qf (0.50 #3852, 0.36 #10420, 0.33 #16990) >> Best rule #26278 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 09v7wsg; >> query: (?x2192, ?x72) <- ceremony(?x2192, ?x1265), award(?x715, ?x2192), award_winner(?x2192, ?x72) >> conf = 0.78 => this is the best rule for 3 predicted values *> Best rule #11650 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 027dtxw; 0bp_b2; 099jhq; 0fbvqf; 0gqy2; 0bdwqv; 09sdmz; *> query: (?x2192, 018ygt) <- award(?x7981, ?x2192), award(?x5595, ?x2192), ?x7981 = 02bj6k, ceremony(?x2192, ?x1265), film(?x5595, ?x715) *> conf = 0.64 ranks of expected_values: 5, 83, 178, 187, 314, 1164 EVAL 0bfvd4 award! 0cg9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 35.000 16.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvd4 award! 02js_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 35.000 16.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvd4 award! 033071 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 35.000 16.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvd4 award! 01xpxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 16.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvd4 award! 018ygt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 35.000 16.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvd4 award! 01y665 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 35.000 16.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bfvd4 award! 04sx9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 35.000 16.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11013-0bdjd PRED entity: 0bdjd PRED relation: nominated_for! PRED expected values: 0bbxx9b => 105 concepts (22 used for prediction) PRED predicted values (max 10 best out of 821): 01jw4r (0.24 #23323, 0.03 #11138, 0.02 #18134), 01g969 (0.24 #23323), 06jzh (0.24 #23323), 05bm4sm (0.20 #1258, 0.11 #3590, 0.07 #19915), 02cyfz (0.20 #445, 0.11 #2777, 0.06 #5109), 027zz (0.20 #2156, 0.11 #4488, 0.04 #16149), 0146pg (0.20 #120, 0.10 #37444, 0.08 #23443), 0c94fn (0.20 #387, 0.09 #23710, 0.06 #5051), 0b6mgp_ (0.20 #958, 0.08 #24281, 0.06 #5622), 02q_cc (0.20 #160, 0.06 #9489, 0.04 #30483) >> Best rule #23323 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 09g7vfw; 02qyv3h; 0bq6ntw; 043tvp3; 03z9585; >> query: (?x7336, ?x496) <- film_release_region(?x7336, ?x756), ?x756 = 06npd, film(?x496, ?x7336), genre(?x7336, ?x258) >> conf = 0.24 => this is the best rule for 3 predicted values *> Best rule #24145 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 76 *> proper extension: 015bpl; *> query: (?x7336, 0bbxx9b) <- nominated_for(?x6860, ?x7336), nominated_for(?x2209, ?x7336), nominated_for(?x1243, ?x7336), ?x6860 = 018wdw, award(?x324, ?x2209), ceremony(?x1243, ?x78) *> conf = 0.05 ranks of expected_values: 165 EVAL 0bdjd nominated_for! 0bbxx9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 105.000 22.000 0.241 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #11012-018n6m PRED entity: 018n6m PRED relation: award PRED expected values: 02f716 03t5kl 023vrq => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 270): 03t5b6 (0.78 #14079, 0.78 #16037, 0.77 #23473), 02f777 (0.78 #14079, 0.77 #23473, 0.77 #17995), 02f76h (0.78 #14079, 0.77 #23473, 0.77 #17995), 01c99j (0.67 #1785, 0.22 #8433, 0.19 #1003), 02f716 (0.55 #4084, 0.37 #1738, 0.34 #2520), 01bgqh (0.47 #1607, 0.42 #3953, 0.35 #825), 03qbnj (0.43 #1791, 0.27 #1009, 0.20 #4137), 03qbh5 (0.39 #200, 0.38 #982, 0.37 #1764), 01d38g (0.39 #28, 0.26 #1201, 0.19 #6284), 09sb52 (0.31 #29381, 0.28 #28599, 0.26 #30554) >> Best rule #14079 for best value: >> intensional similarity = 4 >> extensional distance = 392 >> proper extension: 01qkqwg; 01jllg1; >> query: (?x4640, ?x2877) <- award_nominee(?x4640, ?x140), artists(?x671, ?x4640), award_nominee(?x959, ?x4640), award_winner(?x2877, ?x4640) >> conf = 0.78 => this is the best rule for 3 predicted values *> Best rule #4084 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 07hgm; 017959; 016l09; *> query: (?x4640, 02f716) <- award(?x4640, ?x2877), artist(?x6474, ?x4640), ?x2877 = 02f5qb *> conf = 0.55 ranks of expected_values: 5, 20, 30 EVAL 018n6m award 023vrq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 128.000 128.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 018n6m award 03t5kl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 128.000 128.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 018n6m award 02f716 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 128.000 128.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #11011-0lx2l PRED entity: 0lx2l PRED relation: participant PRED expected values: 03v1jf => 123 concepts (77 used for prediction) PRED predicted values (max 10 best out of 336): 03v1jf (0.81 #27188, 0.81 #27189, 0.81 #33023), 02t_99 (0.81 #27188, 0.81 #33023, 0.81 #29133), 01vhb0 (0.25 #150, 0.06 #4028, 0.03 #3382), 01ccr8 (0.25 #519, 0.03 #3751, 0.03 #4397), 0kszw (0.25 #169, 0.03 #3401, 0.03 #4047), 01_p6t (0.17 #1031), 01fx2g (0.17 #1003), 019pm_ (0.13 #2774, 0.11 #2127, 0.08 #9896), 02114t (0.10 #1554, 0.10 #2848, 0.04 #5436), 0bksh (0.10 #1625, 0.07 #2272, 0.06 #3565) >> Best rule #27188 for best value: >> intensional similarity = 3 >> extensional distance = 377 >> proper extension: 01l_vgt; 01xyt7; 02cg2v; >> query: (?x2534, ?x1817) <- participant(?x5216, ?x2534), participant(?x1817, ?x2534), award_winner(?x5216, ?x1342) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0lx2l participant 03v1jf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 77.000 0.813 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #11010-02byfd PRED entity: 02byfd PRED relation: type_of_union PRED expected values: 04ztj => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #69, 0.87 #25, 0.86 #57), 01g63y (0.36 #30, 0.31 #58, 0.30 #10), 0jgjn (0.01 #28) >> Best rule #69 for best value: >> intensional similarity = 2 >> extensional distance = 227 >> proper extension: 0cm03; >> query: (?x8893, 04ztj) <- nationality(?x8893, ?x94), location_of_ceremony(?x8893, ?x151) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02byfd type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.873 http://example.org/people/person/spouse_s./people/marriage/type_of_union #11009-07bxqz PRED entity: 07bxqz PRED relation: country PRED expected values: 09c7w0 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 119): 09c7w0 (0.83 #1349, 0.82 #1227, 0.81 #2825), 07ssc (0.31 #936, 0.30 #753, 0.28 #1181), 0345h (0.14 #89, 0.11 #2851, 0.11 #519), 0f8l9c (0.13 #327, 0.12 #204, 0.11 #1738), 03rjj (0.12 #253, 0.07 #129, 0.07 #620), 03_3d (0.09 #69, 0.06 #377, 0.05 #560), 01z4y (0.09 #981, 0.06 #4051, 0.06 #5400), 0ctw_b (0.09 #454, 0.05 #760, 0.05 #515), 0d060g (0.07 #131, 0.06 #2832, 0.05 #2646), 03h64 (0.07 #293, 0.04 #722, 0.03 #1089) >> Best rule #1349 for best value: >> intensional similarity = 4 >> extensional distance = 207 >> proper extension: 02z3r8t; 035xwd; 03ckwzc; 0963mq; 02847m9; 03sxd2; 0661m4p; 07x4qr; 0cc846d; 014zwb; ... >> query: (?x11417, 09c7w0) <- featured_film_locations(?x11417, ?x739), film(?x8375, ?x11417), ?x739 = 02_286, award_winner(?x2016, ?x8375) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07bxqz country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.828 http://example.org/film/film/country #11008-02zhkz PRED entity: 02zhkz PRED relation: actor! PRED expected values: 06cs95 => 82 concepts (80 used for prediction) PRED predicted values (max 10 best out of 88): 028k2x (0.20 #146, 0.02 #3329, 0.01 #5716), 02q5bx2 (0.20 #163), 06zsk51 (0.14 #449, 0.11 #714, 0.01 #1245), 080dwhx (0.14 #272, 0.11 #537, 0.01 #1068), 02rzdcp (0.14 #316, 0.11 #581), 07nnp_ (0.05 #266, 0.01 #1062), 0symg (0.05 #266, 0.01 #1062), 05sxr_ (0.05 #266, 0.01 #1062), 07ghq (0.05 #266, 0.01 #1062), 0ptx_ (0.05 #266, 0.01 #1062) >> Best rule #146 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01520h; >> query: (?x7302, 028k2x) <- film(?x7302, ?x2914), location(?x7302, ?x739), profession(?x7302, ?x955), ?x2914 = 012mrr >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #803 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 01vx5w7; 01w02sy; 01wmgrf; 02vntj; 01wbsdz; 0ddkf; *> query: (?x7302, 06cs95) <- film(?x7302, ?x1184), location(?x7302, ?x739), profession(?x7302, ?x955), ?x955 = 0n1h *> conf = 0.02 ranks of expected_values: 31 EVAL 02zhkz actor! 06cs95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 82.000 80.000 0.200 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #11007-01hc9_ PRED entity: 01hc9_ PRED relation: profession PRED expected values: 0kyk => 138 concepts (110 used for prediction) PRED predicted values (max 10 best out of 86): 02hrh1q (0.86 #16119, 0.73 #5233, 0.72 #15671), 0cbd2 (0.85 #2245, 0.79 #1053, 0.72 #1500), 01d_h8 (0.62 #9698, 0.57 #5970, 0.56 #5225), 0kyk (0.56 #2268, 0.50 #1374, 0.50 #1076), 02jknp (0.55 #11191, 0.46 #8359, 0.46 #5972), 03gjzk (0.51 #11943, 0.44 #8962, 0.41 #6277), 0nbcg (0.43 #480, 0.33 #8681, 0.13 #2717), 016wtf (0.36 #4176, 0.33 #129, 0.26 #598), 09jwl (0.36 #4176, 0.29 #467, 0.26 #598), 05t4q (0.36 #4176, 0.29 #150, 0.26 #598) >> Best rule #16119 for best value: >> intensional similarity = 4 >> extensional distance = 1852 >> proper extension: 02h8hr; 04j5fx; 06nsb9; 01wzs_q; >> query: (?x8841, 02hrh1q) <- location(?x8841, ?x6395), profession(?x8841, ?x987), profession(?x6255, ?x987), ?x6255 = 015pvh >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2268 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 01963w; 048_p; 05x8n; 0210f1; 0fpzt5; 0gppg; 033cw; 0jt86; *> query: (?x8841, 0kyk) <- award(?x8841, ?x10505), award_winner(?x10505, ?x8908), ?x8908 = 02y49 *> conf = 0.56 ranks of expected_values: 4 EVAL 01hc9_ profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 138.000 110.000 0.862 http://example.org/people/person/profession #11006-025vry PRED entity: 025vry PRED relation: religion PRED expected values: 03_gx => 135 concepts (84 used for prediction) PRED predicted values (max 10 best out of 16): 0c8wxp (0.60 #6, 0.29 #186, 0.20 #592), 03_gx (0.42 #1055, 0.29 #2050, 0.29 #194), 0kpl (0.17 #371, 0.13 #281, 0.09 #641), 0kq2 (0.14 #198, 0.04 #424, 0.04 #514), 0n2g (0.10 #238, 0.04 #509, 0.03 #599), 092bf5 (0.07 #287, 0.04 #422, 0.04 #512), 03j6c (0.05 #880, 0.03 #1107, 0.03 #1153), 01lp8 (0.05 #767, 0.04 #1719, 0.03 #906), 0flw86 (0.04 #453, 0.03 #543, 0.03 #907), 019cr (0.02 #1729, 0.02 #1865, 0.01 #1594) >> Best rule #6 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 082db; 043d4; 0c73z; >> query: (?x681, 0c8wxp) <- artists(?x888, ?x681), ?x888 = 05lls, nationality(?x681, ?x1355), profession(?x681, ?x563), ?x1355 = 0h7x >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1055 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 88 *> proper extension: 0p51w; 0dzkq; 03bw6; 02ln1; 02784z; 047g6; 01h2_6; *> query: (?x681, 03_gx) <- people(?x1050, ?x681), nationality(?x681, ?x1355), place_of_death(?x681, ?x682), ?x1050 = 041rx *> conf = 0.42 ranks of expected_values: 2 EVAL 025vry religion 03_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 84.000 0.600 http://example.org/people/person/religion #11005-04wqsm PRED entity: 04wqsm PRED relation: colors PRED expected values: 06fvc => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 20): 06fvc (0.49 #746, 0.48 #765, 0.47 #135), 01g5v (0.39 #995, 0.38 #823, 0.36 #652), 019sc (0.39 #980, 0.37 #732, 0.33 #369), 038hg (0.20 #12, 0.19 #31, 0.17 #107), 01l849 (0.17 #611, 0.17 #591, 0.15 #1052), 088fh (0.17 #611, 0.17 #591, 0.15 #1052), 02rnmb (0.17 #611, 0.17 #591, 0.15 #1052), 0jc_p (0.17 #611, 0.17 #591, 0.15 #1052), 06kqt3 (0.17 #611, 0.17 #591, 0.15 #1052), 09ggk (0.17 #611, 0.17 #591, 0.15 #1052) >> Best rule #746 for best value: >> intensional similarity = 11 >> extensional distance = 210 >> proper extension: 0223bl; 0xbm; 01x4wq; 0k_l4; 0487_; 04ltf; 049n2l; 04mvk7; 06zpgb2; 014nzp; ... >> query: (?x6155, 06fvc) <- team(?x60, ?x6155), sport(?x6155, ?x471), colors(?x6155, ?x663), colors(?x12780, ?x663), colors(?x8901, ?x663), colors(?x260, ?x663), colors(?x6925, ?x663), ?x12780 = 019mdt, teams(?x1658, ?x8901), major_field_of_study(?x6925, ?x254), ?x260 = 01ypc >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wqsm colors 06fvc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.486 http://example.org/sports/sports_team/colors #11004-029q3k PRED entity: 029q3k PRED relation: team! PRED expected values: 07h1h5 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 108): 0f1pyf (0.33 #24, 0.32 #1012, 0.29 #988), 04bsx1 (0.33 #183, 0.29 #988, 0.27 #4928), 0fp_xp (0.33 #54, 0.29 #988, 0.25 #274), 0879xc (0.33 #144, 0.17 #878, 0.14 #364), 0841zn (0.32 #923, 0.17 #878, 0.15 #1098), 0457w0 (0.29 #361, 0.27 #4928, 0.17 #878), 0g7vxv (0.28 #1071, 0.22 #1181, 0.17 #878), 09l9xt (0.27 #906, 0.07 #1893, 0.04 #4292), 071pf2 (0.27 #4928, 0.17 #878, 0.15 #1428), 0d1swh (0.25 #1131, 0.17 #878, 0.15 #1098) >> Best rule #24 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 02gjt4; >> query: (?x9182, 0f1pyf) <- position(?x9182, ?x530), position(?x9182, ?x63), team(?x12564, ?x9182), team(?x7212, ?x9182), ?x12564 = 0bhtzw, ?x530 = 02_j1w, position(?x9182, ?x60), team(?x7234, ?x9182), ?x60 = 02nzb8, ?x63 = 02sdk9v, location(?x7212, ?x7213), place_of_birth(?x7234, ?x12237) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1224 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 43 *> proper extension: 0hqzm6r; *> query: (?x9182, 07h1h5) <- position(?x9182, ?x63), team(?x7212, ?x9182), team(?x7212, ?x928), nationality(?x7212, ?x512), athlete(?x471, ?x7212), position(?x9182, ?x60), ?x512 = 07ssc, ?x471 = 02vx4, gender(?x7212, ?x231) *> conf = 0.04 ranks of expected_values: 63 EVAL 029q3k team! 07h1h5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 79.000 79.000 0.333 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #11003-03hxsv PRED entity: 03hxsv PRED relation: film! PRED expected values: 09y20 015rkw 03y_46 016nff => 94 concepts (37 used for prediction) PRED predicted values (max 10 best out of 995): 09y20 (0.67 #248, 0.33 #2324, 0.06 #18932), 05sq84 (0.67 #235, 0.27 #2311, 0.03 #16843), 03y_46 (0.50 #1013, 0.27 #3089, 0.03 #17621), 016nff (0.50 #1220, 0.27 #3296, 0.03 #17828), 0d5wn3 (0.34 #74758, 0.32 #56068, 0.32 #56067), 0159h6 (0.33 #73, 0.20 #2149, 0.03 #16681), 041c4 (0.33 #891, 0.13 #2967, 0.07 #7119), 025t9b (0.33 #663, 0.13 #2739, 0.03 #6891), 010xjr (0.33 #1670, 0.13 #3746, 0.02 #22430), 01l2fn (0.24 #4414, 0.04 #16870, 0.03 #14794) >> Best rule #248 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 031778; 03177r; 03176f; 031hcx; >> query: (?x6332, 09y20) <- film(?x3861, ?x6332), genre(?x6332, ?x225), nominated_for(?x6332, ?x2006), ?x3861 = 013_vh >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 17 EVAL 03hxsv film! 016nff CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 37.000 0.667 http://example.org/film/actor/film./film/performance/film EVAL 03hxsv film! 03y_46 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 37.000 0.667 http://example.org/film/actor/film./film/performance/film EVAL 03hxsv film! 015rkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 94.000 37.000 0.667 http://example.org/film/actor/film./film/performance/film EVAL 03hxsv film! 09y20 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 37.000 0.667 http://example.org/film/actor/film./film/performance/film #11002-02gkzs PRED entity: 02gkzs PRED relation: district_represented PRED expected values: 04rrd => 38 concepts (36 used for prediction) PRED predicted values (max 10 best out of 160): 04rrd (0.88 #1313, 0.88 #729, 0.88 #694), 059f4 (0.84 #1401, 0.83 #1068, 0.81 #1498), 06btq (0.76 #1413, 0.74 #1112, 0.74 #1111), 05kkh (0.75 #1110, 0.74 #1112, 0.74 #1111), 01n7q (0.75 #1110, 0.74 #1112, 0.74 #1111), 03v0t (0.75 #1110, 0.74 #1112, 0.74 #1111), 04ly1 (0.75 #1110, 0.74 #1112, 0.74 #1111), 07b_l (0.75 #1110, 0.74 #1112, 0.74 #1111), 081yw (0.75 #1110, 0.74 #1112, 0.74 #1111), 06yxd (0.74 #1112, 0.74 #1111, 0.73 #1427) >> Best rule #1313 for best value: >> intensional similarity = 27 >> extensional distance = 30 >> proper extension: 01gssm; 01gsry; 01gssz; >> query: (?x3766, 04rrd) <- legislative_sessions(?x3766, ?x2976), legislative_sessions(?x3766, ?x653), legislative_sessions(?x3766, ?x606), district_represented(?x3766, ?x7405), district_represented(?x3766, ?x4622), district_represented(?x3766, ?x4061), district_represented(?x3766, ?x961), legislative_sessions(?x11605, ?x653), legislative_sessions(?x2357, ?x653), ?x4061 = 0498y, ?x7405 = 07_f2, district_represented(?x653, ?x1426), legislative_sessions(?x2976, ?x355), jurisdiction_of_office(?x2357, ?x94), religion(?x2357, ?x962), legislative_sessions(?x2860, ?x606), ?x1426 = 07z1m, politician(?x8714, ?x11605), jurisdiction_of_office(?x900, ?x4622), capital(?x4622, ?x12941), adjoins(?x961, ?x4105), type_of_union(?x11605, ?x566), religion(?x961, ?x109), ?x109 = 01lp8, contains(?x4622, ?x1505), ?x900 = 0fkvn, state(?x310, ?x961) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02gkzs district_represented 04rrd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 36.000 0.875 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #11001-02ryz24 PRED entity: 02ryz24 PRED relation: film! PRED expected values: 05wjnt => 84 concepts (44 used for prediction) PRED predicted values (max 10 best out of 704): 07r1h (0.69 #14514, 0.67 #68424, 0.65 #85007), 01gb54 (0.41 #14513, 0.39 #6219, 0.38 #58055), 0gv07g (0.34 #91230, 0.31 #89156, 0.31 #87082), 0mdqp (0.33 #39397, 0.18 #58054, 0.17 #51835), 02qgyv (0.20 #383, 0.09 #2455, 0.09 #14515), 014zcr (0.20 #37, 0.09 #2109, 0.08 #60130), 079vf (0.20 #8, 0.09 #2080, 0.03 #4153), 018009 (0.20 #747, 0.09 #2819, 0.03 #6966), 073w14 (0.20 #754, 0.09 #2826, 0.03 #9047), 0sz28 (0.20 #192, 0.09 #2264, 0.03 #8485) >> Best rule #14514 for best value: >> intensional similarity = 3 >> extensional distance = 244 >> proper extension: 06ys2; >> query: (?x2886, ?x6187) <- nominated_for(?x6187, ?x2886), participant(?x6187, ?x2275), award_nominee(?x157, ?x6187) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #21146 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 291 *> proper extension: 01vrwfv; *> query: (?x2886, 05wjnt) <- nominated_for(?x6187, ?x2886), category(?x2886, ?x134), award_winner(?x157, ?x6187) *> conf = 0.02 ranks of expected_values: 323 EVAL 02ryz24 film! 05wjnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 44.000 0.692 http://example.org/film/actor/film./film/performance/film #11000-027f2w PRED entity: 027f2w PRED relation: major_field_of_study PRED expected values: 0193x 04sh3 => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 143): 04rjg (0.79 #817, 0.78 #1291, 0.76 #1052), 01lj9 (0.79 #817, 0.78 #1291, 0.76 #1052), 03g3w (0.79 #817, 0.78 #1291, 0.76 #1052), 04sh3 (0.79 #817, 0.78 #1291, 0.76 #1052), 05qjt (0.79 #817, 0.78 #1291, 0.76 #1172), 02ky346 (0.79 #817, 0.78 #1291, 0.76 #1172), 036hv (0.79 #817, 0.76 #1172, 0.75 #1182), 04x_3 (0.79 #817, 0.76 #1172, 0.71 #1079), 05qt0 (0.79 #817, 0.63 #462, 0.60 #872), 0mkz (0.79 #817, 0.63 #462, 0.58 #464) >> Best rule #817 for best value: >> intensional similarity = 29 >> extensional distance = 2 >> proper extension: 019v9k; >> query: (?x2636, ?x742) <- institution(?x2636, ?x12293), institution(?x2636, ?x11768), institution(?x2636, ?x5754), institution(?x2636, ?x4955), institution(?x2636, ?x4390), institution(?x2636, ?x3439), institution(?x2636, ?x2327), institution(?x2636, ?x2228), institution(?x2636, ?x2079), institution(?x2636, ?x1220), institution(?x2636, ?x481), institution(?x2636, ?x122), ?x2079 = 01bvw5, ?x2228 = 01s0_f, student(?x2636, ?x4292), ?x481 = 052nd, ?x3439 = 03ksy, ?x4955 = 09f2j, ?x12293 = 01pj48, ?x122 = 08815, ?x11768 = 01hc1j, ?x2327 = 07wjk, major_field_of_study(?x4390, ?x3389), major_field_of_study(?x4390, ?x742), ?x3389 = 0mkz, category(?x4390, ?x134), colors(?x4390, ?x663), ?x5754 = 02ln0f, citytown(?x1220, ?x5952) >> conf = 0.79 => this is the best rule for 12 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 45 EVAL 027f2w major_field_of_study 04sh3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 22.000 0.794 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 027f2w major_field_of_study 0193x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 22.000 22.000 0.794 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #10999-02qr69m PRED entity: 02qr69m PRED relation: nominated_for! PRED expected values: 02qyp19 => 73 concepts (69 used for prediction) PRED predicted values (max 10 best out of 210): 040njc (0.68 #899, 0.48 #453, 0.35 #1569), 019f4v (0.67 #941, 0.56 #495, 0.55 #1611), 0gq9h (0.62 #500, 0.61 #946, 0.59 #1616), 0gs9p (0.61 #948, 0.54 #502, 0.50 #56), 0k611 (0.59 #508, 0.57 #954, 0.51 #1624), 04dn09n (0.55 #923, 0.41 #477, 0.33 #254), 0p9sw (0.52 #465, 0.50 #19, 0.41 #1581), 0gs96 (0.52 #302, 0.45 #525, 0.41 #1864), 0f_nbyh (0.50 #8, 0.21 #900, 0.16 #454), 0l8z1 (0.46 #493, 0.39 #939, 0.34 #1609) >> Best rule #899 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 0hmr4; 0gmcwlb; 04m1bm; 09k56b7; 0f4_l; 0661ql3; 019vhk; 040b5k; 011yl_; 0h6r5; ... >> query: (?x2488, 040njc) <- nominated_for(?x6909, ?x2488), honored_for(?x2988, ?x2488), ?x6909 = 02qyntr >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #893 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 0hmr4; 0gmcwlb; 04m1bm; 09k56b7; 0f4_l; 0661ql3; 019vhk; 040b5k; 011yl_; 0h6r5; ... *> query: (?x2488, 02qyp19) <- nominated_for(?x6909, ?x2488), honored_for(?x2988, ?x2488), ?x6909 = 02qyntr *> conf = 0.30 ranks of expected_values: 19 EVAL 02qr69m nominated_for! 02qyp19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 73.000 69.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10998-04_by PRED entity: 04_by PRED relation: profession PRED expected values: 0kyk => 142 concepts (104 used for prediction) PRED predicted values (max 10 best out of 111): 02hrh1q (0.88 #4485, 0.85 #4336, 0.83 #4783), 0dxtg (0.65 #7763, 0.64 #9853, 0.63 #10002), 0kyk (0.55 #3308, 0.53 #2414, 0.50 #5097), 01d_h8 (0.50 #304, 0.42 #9846, 0.41 #7756), 09jwl (0.40 #1509, 0.29 #913, 0.28 #8964), 02jknp (0.35 #7757, 0.35 #9996, 0.34 #9847), 02hv44_ (0.33 #1250, 0.33 #58, 0.32 #14021), 05z96 (0.33 #43, 0.32 #14021, 0.30 #14768), 0q04f (0.33 #100, 0.20 #13871, 0.11 #9691), 01c72t (0.32 #14021, 0.30 #14768, 0.30 #14171) >> Best rule #4485 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 01nczg; 08swgx; 01w7nww; 057hz; 025ldg; 01d0fp; 0c3jz; 06nns1; 02q3bb; 0227vl; ... >> query: (?x10716, 02hrh1q) <- diet(?x10716, ?x3130), gender(?x10716, ?x514), ?x514 = 02zsn, nationality(?x10716, ?x1310), second_level_divisions(?x1310, ?x1156) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3308 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 040_9; *> query: (?x10716, 0kyk) <- story_by(?x9484, ?x10716), influenced_by(?x7861, ?x10716), type_of_union(?x10716, ?x566), languages(?x7861, ?x254) *> conf = 0.55 ranks of expected_values: 3 EVAL 04_by profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 142.000 104.000 0.878 http://example.org/people/person/profession #10997-01n5309 PRED entity: 01n5309 PRED relation: celebrities_impersonated PRED expected values: 016jfw 02y0yt => 122 concepts (104 used for prediction) PRED predicted values (max 10 best out of 130): 09b6zr (0.60 #268, 0.33 #32, 0.29 #387), 0157m (0.40 #249, 0.33 #13, 0.29 #368), 0ph2w (0.40 #267, 0.33 #31, 0.14 #386), 044qx (0.40 #269, 0.33 #33, 0.14 #388), 081lh (0.33 #7, 0.20 #243, 0.14 #362), 01svq8 (0.33 #113, 0.20 #349, 0.14 #468), 0m0nq (0.33 #85, 0.20 #321, 0.14 #440), 01t94_1 (0.33 #80, 0.20 #316, 0.14 #435), 0xnc3 (0.33 #74, 0.20 #310, 0.14 #429), 03f1r6t (0.33 #45, 0.20 #281, 0.14 #400) >> Best rule #268 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 03m6t5; 04s430; >> query: (?x692, 09b6zr) <- celebrities_impersonated(?x692, ?x2817), award(?x2817, ?x678), influenced_by(?x2817, ?x1145) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01n5309 celebrities_impersonated 02y0yt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 104.000 0.600 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated EVAL 01n5309 celebrities_impersonated 016jfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 104.000 0.600 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #10996-0bjv6 PRED entity: 0bjv6 PRED relation: country! PRED expected values: 06z6r => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 51): 06z6r (0.85 #384, 0.84 #996, 0.84 #537), 06f41 (0.82 #65, 0.64 #473, 0.62 #422), 01lb14 (0.74 #66, 0.64 #474, 0.64 #423), 03hr1p (0.71 #72, 0.64 #480, 0.58 #429), 06wrt (0.71 #67, 0.59 #424, 0.58 #475), 0194d (0.71 #95, 0.59 #452, 0.54 #503), 0w0d (0.71 #63, 0.58 #471, 0.55 #420), 064vjs (0.68 #79, 0.56 #436, 0.54 #487), 02y8z (0.63 #69, 0.54 #477, 0.48 #426), 07jbh (0.61 #81, 0.60 #489, 0.55 #438) >> Best rule #384 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 06jnv; >> query: (?x3227, 06z6r) <- capital(?x3227, ?x13383), participating_countries(?x1931, ?x3227), ?x1931 = 0kbws >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bjv6 country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.855 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #10995-04t36 PRED entity: 04t36 PRED relation: genre! PRED expected values: 050xxm 0h3k3f 0hz6mv2 01xlqd 0199wf => 58 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1775): 0cq7kw (0.84 #1755, 0.78 #12281, 0.62 #26323), 01cmp9 (0.84 #1755, 0.78 #12281, 0.62 #26323), 01xlqd (0.84 #1755, 0.78 #12281, 0.62 #26323), 0272_vz (0.84 #1755, 0.78 #12281, 0.62 #26323), 06q8qh (0.84 #1755, 0.78 #12281, 0.62 #26323), 02825cv (0.84 #1755, 0.78 #12281, 0.62 #26323), 017kz7 (0.84 #1755, 0.78 #12281, 0.62 #26323), 06cgf (0.84 #1755, 0.78 #12281, 0.62 #26323), 027ct7c (0.84 #1755, 0.78 #12281, 0.62 #26323), 04jplwp (0.84 #1755, 0.78 #12281, 0.62 #26323) >> Best rule #1755 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 03k9fj; >> query: (?x307, ?x270) <- genre(?x5243, ?x307), genre(?x4249, ?x307), genre(?x2816, ?x307), country(?x4249, ?x94), ?x2816 = 04tz52, ?x5243 = 01pvxl, titles(?x307, ?x270) >> conf = 0.84 => this is the best rule for 18 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 230, 488, 1383, 1558 EVAL 04t36 genre! 0199wf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 29.000 0.841 http://example.org/film/film/genre EVAL 04t36 genre! 01xlqd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 58.000 29.000 0.841 http://example.org/film/film/genre EVAL 04t36 genre! 0hz6mv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 29.000 0.841 http://example.org/film/film/genre EVAL 04t36 genre! 0h3k3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 29.000 0.841 http://example.org/film/film/genre EVAL 04t36 genre! 050xxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 58.000 29.000 0.841 http://example.org/film/film/genre #10994-02fgpf PRED entity: 02fgpf PRED relation: place_of_birth PRED expected values: 02_286 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 112): 02_286 (0.15 #2835, 0.13 #723, 0.11 #5651), 030qb3t (0.10 #2870, 0.07 #5686, 0.07 #9910), 0cc56 (0.09 #737, 0.04 #3553, 0.03 #1441), 04jpl (0.07 #3528, 0.03 #7048, 0.03 #10568), 0c8tk (0.06 #155, 0.02 #3675, 0.01 #4379), 06_kh (0.06 #5, 0.01 #4229, 0.01 #11973), 013yq (0.06 #79, 0.01 #19792), 0xt3t (0.06 #484), 0r3tq (0.06 #430), 0y62n (0.06 #339) >> Best rule #2835 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 03qd_; 01pbwwl; >> query: (?x1894, 02_286) <- award_winner(?x3732, ?x1894), student(?x7545, ?x1894), music(?x188, ?x1894) >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fgpf place_of_birth 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.154 http://example.org/people/person/place_of_birth #10993-02prw4h PRED entity: 02prw4h PRED relation: category PRED expected values: 08mbj5d => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.31 #1, 0.30 #29, 0.30 #9) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 01br2w; 04dsnp; 08r4x3; 035s95; 065z3_x; 03kg2v; 07yvsn; 026qnh6; 09r94m; 0bw20; >> query: (?x1218, 08mbj5d) <- produced_by(?x1218, ?x9439), genre(?x1218, ?x2605), ?x2605 = 03g3w, film(?x1414, ?x1218) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02prw4h category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.314 http://example.org/common/topic/webpage./common/webpage/category #10992-0147dk PRED entity: 0147dk PRED relation: film PRED expected values: 01xbxn 057__d => 130 concepts (90 used for prediction) PRED predicted values (max 10 best out of 957): 01kff7 (0.48 #1781, 0.45 #85471, 0.42 #74789), 03nsm5x (0.48 #1781, 0.45 #85471, 0.42 #74789), 01xbxn (0.48 #1781, 0.45 #85471, 0.42 #74789), 0dll_t2 (0.36 #3562, 0.36 #7123, 0.35 #14246), 07gghl (0.36 #3562, 0.36 #7123, 0.35 #14246), 0f42nz (0.09 #9806, 0.08 #18711, 0.05 #4464), 013q07 (0.07 #355, 0.06 #3917, 0.05 #2136), 03nfnx (0.07 #1394, 0.04 #4956, 0.03 #10298), 02x3lt7 (0.07 #84, 0.03 #10768, 0.03 #7207), 06fcqw (0.07 #1086, 0.02 #8209, 0.02 #11770) >> Best rule #1781 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 02wb6yq; >> query: (?x521, ?x1372) <- award_winner(?x1372, ?x521), artists(?x671, ?x521), participant(?x521, ?x3291) >> conf = 0.48 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0147dk film 057__d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 90.000 0.482 http://example.org/film/actor/film./film/performance/film EVAL 0147dk film 01xbxn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 90.000 0.482 http://example.org/film/actor/film./film/performance/film #10991-017v71 PRED entity: 017v71 PRED relation: major_field_of_study PRED expected values: 02h40lc => 175 concepts (169 used for prediction) PRED predicted values (max 10 best out of 119): 01mkq (0.61 #856, 0.59 #616, 0.59 #1216), 03g3w (0.54 #626, 0.53 #1226, 0.52 #866), 02lp1 (0.46 #612, 0.39 #852, 0.38 #3973), 05qjt (0.45 #1208, 0.44 #1088, 0.44 #608), 05qfh (0.41 #1234, 0.41 #634, 0.39 #874), 04sh3 (0.34 #913, 0.31 #1273, 0.31 #673), 01540 (0.33 #659, 0.31 #1139, 0.29 #1259), 01lj9 (0.30 #877, 0.29 #1237, 0.29 #1117), 02h40lc (0.29 #1084, 0.26 #604, 0.25 #1204), 02_7t (0.28 #1863, 0.27 #183, 0.24 #4024) >> Best rule #856 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 08815; 01mpwj; >> query: (?x5941, 01mkq) <- student(?x5941, ?x1774), major_field_of_study(?x5941, ?x8221), ?x8221 = 037mh8, school_type(?x5941, ?x3205), organization(?x346, ?x5941) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1084 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 46 *> proper extension: 01fpvz; *> query: (?x5941, 02h40lc) <- student(?x5941, ?x1774), major_field_of_study(?x5941, ?x8221), ?x8221 = 037mh8, institution(?x1368, ?x5941), ?x1368 = 014mlp *> conf = 0.29 ranks of expected_values: 9 EVAL 017v71 major_field_of_study 02h40lc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 175.000 169.000 0.614 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10990-081lh PRED entity: 081lh PRED relation: award PRED expected values: 03hl6lc => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 334): 04dn09n (0.72 #60545, 0.70 #59364, 0.70 #58184), 03hkv_r (0.72 #60545, 0.70 #59364, 0.70 #58184), 02x4sn8 (0.72 #60545, 0.70 #59364, 0.70 #58184), 027c924 (0.72 #60545, 0.70 #59364, 0.70 #58184), 027b9ly (0.72 #60545, 0.70 #59364, 0.70 #58184), 09sb52 (0.38 #429, 0.32 #36591, 0.32 #21263), 026mff (0.38 #155, 0.05 #3299, 0.03 #6444), 01by1l (0.37 #2461, 0.28 #20151, 0.27 #3247), 0gr4k (0.36 #16144, 0.32 #18896, 0.30 #1600), 02w9sd7 (0.30 #945, 0.12 #552, 0.12 #4876) >> Best rule #60545 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 01ky2h; 01lcxbb; 01h320; 01t265; 0d0mbj; 0f6lx; 013rds; >> query: (?x986, ?x1198) <- award_winner(?x1198, ?x986), award(?x276, ?x1198) >> conf = 0.72 => this is the best rule for 5 predicted values *> Best rule #5670 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 058kqy; 026c1; 05jcn8; 01twdk; 015njf; 04353; 04vlh5; *> query: (?x986, 03hl6lc) <- film(?x986, ?x3491), film_release_region(?x3491, ?x4743), ?x4743 = 03spz *> conf = 0.24 ranks of expected_values: 28 EVAL 081lh award 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 167.000 167.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10989-04l19_ PRED entity: 04l19_ PRED relation: student! PRED expected values: 014mlp => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 12): 014mlp (0.08 #726, 0.07 #986, 0.07 #706), 019v9k (0.05 #50, 0.03 #130, 0.03 #70), 01gkg3 (0.05 #56, 0.02 #116, 0.01 #136), 028dcg (0.04 #98, 0.02 #438, 0.01 #958), 02_xgp2 (0.03 #514, 0.03 #414, 0.02 #594), 0bkj86 (0.03 #409, 0.03 #369, 0.02 #529), 02h4rq6 (0.02 #83, 0.02 #423, 0.02 #703), 04zx3q1 (0.02 #362, 0.02 #402, 0.02 #522), 02mjs7 (0.01 #125, 0.01 #165, 0.01 #185), 03bwzr4 (0.01 #135) >> Best rule #726 for best value: >> intensional similarity = 4 >> extensional distance = 596 >> proper extension: 019y64; 01xyt7; 01gct2; 019g65; 02cg2v; >> query: (?x6692, 014mlp) <- gender(?x6692, ?x231), type_of_union(?x6692, ?x566), student(?x6919, ?x6692), people(?x3584, ?x6692) >> conf = 0.08 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04l19_ student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.077 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #10988-01rp13 PRED entity: 01rp13 PRED relation: nominated_for! PRED expected values: 09qrn4 => 86 concepts (82 used for prediction) PRED predicted values (max 10 best out of 239): 0gqy2 (0.87 #8051, 0.26 #11783, 0.26 #11082), 0m7yy (0.70 #4201, 0.70 #3033, 0.69 #3500), 0gq9h (0.53 #7991, 0.41 #11022, 0.40 #11489), 0gs9p (0.43 #7993, 0.38 #11491, 0.37 #11024), 019f4v (0.43 #7982, 0.36 #11013, 0.35 #11714), 09qs08 (0.39 #807, 0.35 #574, 0.29 #11896), 0k611 (0.36 #8002, 0.31 #11033, 0.30 #11734), 09qrn4 (0.35 #861, 0.33 #628, 0.22 #1328), 0gr4k (0.34 #7957, 0.21 #11689, 0.21 #11455), 0cqhk0 (0.33 #731, 0.29 #498, 0.20 #1198) >> Best rule #8051 for best value: >> intensional similarity = 3 >> extensional distance = 215 >> proper extension: 0htww; 0gvs1kt; 0j80w; 0jz71; 0gy4k; 0ktx_; 03mr85; >> query: (?x6341, 0gqy2) <- nominated_for(?x870, ?x6341), award(?x11697, ?x870), ?x11697 = 0436zq >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #861 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 02nf2c; 05h95s; *> query: (?x6341, 09qrn4) <- award_winner(?x6341, ?x2554), titles(?x2008, ?x6341), genre(?x6341, ?x258), ?x258 = 05p553 *> conf = 0.35 ranks of expected_values: 8 EVAL 01rp13 nominated_for! 09qrn4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 86.000 82.000 0.871 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10987-0lccn PRED entity: 0lccn PRED relation: inductee! PRED expected values: 0g2c8 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 3): 0g2c8 (0.25 #1, 0.15 #37, 0.14 #91), 06szd3 (0.03 #137, 0.02 #335, 0.02 #686), 0qjfl (0.02 #102) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0197tq; 045zr; >> query: (?x2319, 0g2c8) <- category(?x2319, ?x134), award_nominee(?x2319, ?x1181), ?x1181 = 0b68vs >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lccn inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.250 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #10986-076_74 PRED entity: 076_74 PRED relation: award PRED expected values: 0gqz2 02x17s4 => 107 concepts (80 used for prediction) PRED predicted values (max 10 best out of 315): 04dn09n (0.68 #438, 0.27 #9592, 0.27 #9991), 02x17s4 (0.68 #517, 0.23 #9951, 0.18 #119), 09sb52 (0.53 #2425, 0.31 #14764, 0.31 #13968), 054ks3 (0.50 #1728, 0.44 #3320, 0.41 #2126), 0gqz2 (0.48 #1667, 0.47 #3259, 0.47 #2065), 02x17c2 (0.41 #1805, 0.38 #2203, 0.36 #3397), 0c4z8 (0.41 #1659, 0.33 #3251, 0.31 #2057), 01by1l (0.39 #1699, 0.32 #3291, 0.30 #2097), 03hl6lc (0.37 #570, 0.18 #9724, 0.17 #10123), 0gr51 (0.32 #493, 0.28 #2881, 0.28 #9647) >> Best rule #438 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 01q415; >> query: (?x3862, 04dn09n) <- award(?x3862, ?x384), story_by(?x3938, ?x3862), ?x384 = 03hkv_r >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #517 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 01q415; *> query: (?x3862, 02x17s4) <- award(?x3862, ?x384), story_by(?x3938, ?x3862), ?x384 = 03hkv_r *> conf = 0.68 ranks of expected_values: 2, 5 EVAL 076_74 award 02x17s4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 80.000 0.684 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 076_74 award 0gqz2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 80.000 0.684 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10985-0kq2 PRED entity: 0kq2 PRED relation: religion! PRED expected values: 0sz28 01n4f8 05wjnt 04z0g 04sry 03j0d => 31 concepts (26 used for prediction) PRED predicted values (max 10 best out of 3711): 0mb5x (0.50 #2724, 0.38 #3750, 0.33 #670), 048cl (0.50 #2653, 0.25 #3679, 0.25 #1624), 06myp (0.50 #2919, 0.25 #3945, 0.25 #1890), 03rx9 (0.50 #2835, 0.25 #3861, 0.25 #1806), 081lh (0.50 #2111, 0.25 #3137, 0.25 #1082), 01dhmw (0.50 #2304, 0.25 #3330, 0.25 #1275), 026fd (0.50 #2528, 0.25 #3554, 0.25 #1499), 01pw9v (0.50 #2816, 0.25 #3842, 0.25 #1787), 027l0b (0.50 #2253, 0.25 #3279, 0.25 #1224), 0kp2_ (0.50 #2601, 0.25 #3627, 0.25 #1572) >> Best rule #2724 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 0kpl; 03_gx; >> query: (?x8140, 0mb5x) <- religion(?x7794, ?x8140), religion(?x4353, ?x8140), religion(?x3335, ?x8140), religion(?x2455, ?x8140), film(?x4353, ?x9037), award(?x4353, ?x198), ?x3335 = 0jcx, award_nominee(?x2455, ?x1596), nominated_for(?x2275, ?x9037), country(?x9037, ?x94), student(?x3424, ?x4353), award_winner(?x2186, ?x7794), artist(?x2190, ?x7794) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #586 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 0c8wxp; *> query: (?x8140, 04sry) <- religion(?x4353, ?x8140), religion(?x3335, ?x8140), religion(?x2803, ?x8140), religion(?x2455, ?x8140), film(?x4353, ?x1547), award(?x4353, ?x601), ?x601 = 0gr4k, people(?x1050, ?x4353), film(?x2455, ?x2628), ?x2628 = 06wbm8q, award_nominee(?x2803, ?x1039), place_of_birth(?x3335, ?x14568), produced_by(?x638, ?x2803) *> conf = 0.33 ranks of expected_values: 49, 54, 381, 385, 590, 2287 EVAL 0kq2 religion! 03j0d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 26.000 0.500 http://example.org/people/person/religion EVAL 0kq2 religion! 04sry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 31.000 26.000 0.500 http://example.org/people/person/religion EVAL 0kq2 religion! 04z0g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 26.000 0.500 http://example.org/people/person/religion EVAL 0kq2 religion! 05wjnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 26.000 0.500 http://example.org/people/person/religion EVAL 0kq2 religion! 01n4f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 31.000 26.000 0.500 http://example.org/people/person/religion EVAL 0kq2 religion! 0sz28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 26.000 0.500 http://example.org/people/person/religion #10984-07ym0 PRED entity: 07ym0 PRED relation: organization PRED expected values: 02_l9 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 6): 02_l9 (0.05 #16, 0.05 #40, 0.04 #544), 03lb_v (0.04 #358, 0.02 #550, 0.02 #814), 02hcxm (0.03 #610, 0.02 #250, 0.02 #346), 01prf3 (0.02 #356, 0.01 #500), 07t65 (0.01 #2550), 02vk52z (0.01 #2549) >> Best rule #16 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 016hvl; 0lrh; 073v6; 032l1; 0d5_f; 041c4; 058vp; 040_t; 05np2; 06y8v; ... >> query: (?x8390, 02_l9) <- influenced_by(?x8390, ?x5004), ?x5004 = 081k8, influenced_by(?x1236, ?x8390), people(?x6734, ?x8390), profession(?x8390, ?x353) >> conf = 0.05 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ym0 organization 02_l9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.050 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #10983-01jzyf PRED entity: 01jzyf PRED relation: genre PRED expected values: 04xvlr => 96 concepts (91 used for prediction) PRED predicted values (max 10 best out of 87): 04xvlr (0.52 #6492, 0.52 #6130, 0.52 #5768), 01jfsb (0.40 #252, 0.34 #2417, 0.33 #732), 0219x_ (0.40 #267, 0.33 #747, 0.30 #387), 02kdv5l (0.30 #1203, 0.30 #1323, 0.28 #3009), 02l7c8 (0.30 #3262, 0.29 #6024, 0.29 #3142), 060__y (0.27 #497, 0.22 #136, 0.22 #1562), 03k9fj (0.25 #851, 0.25 #1331, 0.25 #10), 0vgkd (0.25 #9, 0.22 #129, 0.22 #1562), 0hn10 (0.25 #729, 0.20 #249, 0.09 #609), 01t_vv (0.22 #1562, 0.20 #295, 0.17 #775) >> Best rule #6492 for best value: >> intensional similarity = 4 >> extensional distance = 1081 >> proper extension: 09dv8h; 02q5bx2; 07jqjx; >> query: (?x3706, ?x162) <- nominated_for(?x185, ?x3706), titles(?x162, ?x3706), genre(?x3706, ?x53), film(?x396, ?x3706) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jzyf genre 04xvlr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 91.000 0.524 http://example.org/film/film/genre #10982-0jbp0 PRED entity: 0jbp0 PRED relation: language PRED expected values: 02h40lc => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 2): 02h40lc (0.25 #43, 0.20 #94, 0.12 #79), 03_9r (0.02 #45, 0.01 #75, 0.01 #81) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 04bbv7; 07gkgp; 091n7z; >> query: (?x10398, 02h40lc) <- profession(?x10398, ?x1383), category(?x10398, ?x134), ?x1383 = 0np9r >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jbp0 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.250 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #10981-0fgg4 PRED entity: 0fgg4 PRED relation: student! PRED expected values: 08815 06pwq => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 65): 065y4w7 (0.06 #14, 0.06 #541, 0.03 #1068), 08815 (0.06 #2, 0.06 #529, 0.03 #1056), 017z88 (0.06 #82, 0.06 #609, 0.03 #11149), 01w5m (0.06 #105, 0.06 #632, 0.02 #9064), 01rtm4 (0.06 #4, 0.06 #531, 0.01 #3693), 015zyd (0.06 #1, 0.06 #528, 0.01 #12649), 0234_c (0.06 #417, 0.06 #944), 04gd8j (0.06 #368, 0.06 #895), 0cwx_ (0.06 #241, 0.06 #768), 01vs5c (0.06 #181, 0.06 #708) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0g2mbn; 0d02km; 0fqjhm; >> query: (?x4949, 065y4w7) <- award_nominee(?x3274, ?x4949), ?x3274 = 01chc7, film(?x4949, ?x204) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0g2mbn; 0d02km; 0fqjhm; *> query: (?x4949, 08815) <- award_nominee(?x3274, ?x4949), ?x3274 = 01chc7, film(?x4949, ?x204) *> conf = 0.06 ranks of expected_values: 2, 65 EVAL 0fgg4 student! 06pwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 114.000 114.000 0.062 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0fgg4 student! 08815 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.062 http://example.org/education/educational_institution/students_graduates./education/education/student #10980-01xsc9 PRED entity: 01xsc9 PRED relation: actor! PRED expected values: 090s_0 => 139 concepts (108 used for prediction) PRED predicted values (max 10 best out of 69): 03wh49y (0.33 #96), 02gjrc (0.20 #1288, 0.20 #1023, 0.03 #8453), 039c26 (0.20 #845, 0.02 #2703, 0.02 #3233), 02wyzmv (0.20 #1185), 090s_0 (0.12 #1330, 0.05 #6107, 0.02 #2657), 0h63q6t (0.12 #1556), 07g9f (0.12 #1528), 02kk_c (0.12 #1411), 0sw0q (0.10 #2302, 0.05 #2567, 0.04 #3894), 027tbrc (0.06 #1628, 0.06 #1893, 0.02 #3220) >> Best rule #96 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0f0kz; >> query: (?x12020, 03wh49y) <- student(?x9741, ?x12020), film(?x12020, ?x6078), film(?x12020, ?x2734), ?x6078 = 04pk1f, nominated_for(?x198, ?x2734) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1330 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0fx02; *> query: (?x12020, 090s_0) <- student(?x9741, ?x12020), type_of_union(?x12020, ?x566), student(?x9741, ?x11492), ?x11492 = 082xp *> conf = 0.12 ranks of expected_values: 5 EVAL 01xsc9 actor! 090s_0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 139.000 108.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10979-02lmk PRED entity: 02lmk PRED relation: type_of_union PRED expected values: 01g63y => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 2): 01g63y (0.17 #166, 0.17 #61, 0.16 #142), 01bl8s (0.01 #89) >> Best rule #166 for best value: >> intensional similarity = 6 >> extensional distance = 502 >> proper extension: 07hbxm; 01846t; 01pkhw; 0143wl; 04bsx1; >> query: (?x7181, 01g63y) <- profession(?x7181, ?x8290), type_of_union(?x7181, ?x566), nationality(?x7181, ?x1264), adjoins(?x1264, ?x456), featured_film_locations(?x1470, ?x1264), contains(?x1264, ?x196) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lmk type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.173 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10978-01l1b90 PRED entity: 01l1b90 PRED relation: currency PRED expected values: 09nqf => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.56 #19, 0.55 #13, 0.53 #10), 01nv4h (0.05 #35, 0.05 #47, 0.04 #50) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 0grwj; 0337vz; 01wmxfs; 01pw2f1; 0320jz; 0bj9k; 01wxyx1; 026c1; 05dbf; 016z2j; ... >> query: (?x250, 09nqf) <- participant(?x250, ?x2614), participant(?x250, ?x193), people(?x2510, ?x250), category(?x250, ?x134) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l1b90 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.562 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #10977-0kvt9 PRED entity: 0kvt9 PRED relation: contains PRED expected values: 0lfyx => 105 concepts (69 used for prediction) PRED predicted values (max 10 best out of 2049): 0r4h3 (0.75 #26524, 0.74 #61882, 0.74 #67779), 01zlwg6 (0.75 #26524, 0.74 #61882, 0.74 #67779), 0r80l (0.25 #935), 09c7w0 (0.25 #170910, 0.20 #82510, 0.03 #114923), 0kvt9 (0.25 #170910, 0.20 #82510, 0.02 #19174), 01n7q (0.25 #170910, 0.20 #82510, 0.01 #62007), 0288zy (0.06 #8915, 0.04 #3020, 0.04 #5967), 01y9pk (0.05 #12062, 0.05 #15010, 0.02 #41532), 0366c (0.05 #13739, 0.05 #16687, 0.02 #81515), 0bwfn (0.05 #12836, 0.05 #15784, 0.02 #62930) >> Best rule #26524 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 0n5j_; 0jcgs; 0mwl2; 0mxcf; 0nvrd; 0mwh1; 0m2lt; 0kpys; 0jxgx; 0fxyd; ... >> query: (?x9887, ?x8067) <- adjoins(?x9887, ?x2949), administrative_division(?x6834, ?x9887), county(?x8067, ?x9887), contains(?x94, ?x9887) >> conf = 0.75 => this is the best rule for 2 predicted values *> Best rule #20272 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 0cb4j; 03gh4; *> query: (?x9887, 0lfyx) <- contains(?x94, ?x9887), ?x94 = 09c7w0, administrative_division(?x6834, ?x9887) *> conf = 0.02 ranks of expected_values: 1200 EVAL 0kvt9 contains 0lfyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 105.000 69.000 0.745 http://example.org/location/location/contains #10976-05r5c PRED entity: 05r5c PRED relation: role! PRED expected values: 053y0s 01vsxdm 0144l1 01wwvc5 02cx72 02jxmr 01vsy7t 0fhxv 0149xx 01pbs9w 01wwnh2 => 109 concepts (54 used for prediction) PRED predicted values (max 10 best out of 755): 0xsk8 (0.50 #1404, 0.50 #811, 0.25 #2872), 082brv (0.50 #4592, 0.47 #4296, 0.42 #5774), 0144l1 (0.50 #1239, 0.33 #646, 0.31 #3594), 0484q (0.50 #1384, 0.33 #791, 0.31 #3739), 01w8n89 (0.50 #1286, 0.33 #693, 0.30 #3241), 0132k4 (0.50 #779, 0.33 #1372, 0.20 #4318), 0dw3l (0.50 #813, 0.33 #1406, 0.15 #3761), 01mxt_ (0.43 #1933, 0.33 #4882, 0.33 #4290), 07_3qd (0.43 #1793, 0.33 #1204, 0.33 #611), 018gkb (0.43 #2051, 0.33 #1462, 0.31 #3817) >> Best rule #1404 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0680x0; >> query: (?x316, 0xsk8) <- role(?x316, ?x2048), role(?x316, ?x228), ?x228 = 0l14qv, role(?x2620, ?x316), role(?x115, ?x316), ?x2620 = 01kcd, role(?x645, ?x2048) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1239 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 0680x0; *> query: (?x316, 0144l1) <- role(?x316, ?x2048), role(?x316, ?x228), ?x228 = 0l14qv, role(?x2620, ?x316), role(?x115, ?x316), ?x2620 = 01kcd, role(?x645, ?x2048) *> conf = 0.50 ranks of expected_values: 3, 40, 47, 50, 171, 200, 230, 321, 352, 403 EVAL 05r5c role! 01wwnh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05r5c role! 01pbs9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05r5c role! 0149xx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05r5c role! 0fhxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05r5c role! 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05r5c role! 02jxmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05r5c role! 02cx72 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05r5c role! 01wwvc5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05r5c role! 0144l1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05r5c role! 01vsxdm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05r5c role! 053y0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 109.000 54.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #10975-027nb PRED entity: 027nb PRED relation: country! PRED expected values: 01cgz => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 57): 06z6r (0.80 #1800, 0.77 #1857, 0.76 #1971), 03_8r (0.66 #1790, 0.63 #2531, 0.63 #2246), 01cgz (0.61 #755, 0.60 #1781, 0.59 #2237), 071t0 (0.61 #765, 0.59 #1791, 0.56 #993), 01lb14 (0.57 #1099, 0.53 #871, 0.49 #1156), 06f41 (0.47 #756, 0.43 #984, 0.42 #1782), 07gyv (0.44 #1774, 0.43 #1831, 0.41 #349), 09w1n (0.42 #1166, 0.42 #653, 0.35 #881), 03hr1p (0.42 #1792, 0.41 #766, 0.38 #1108), 07jbh (0.41 #1176, 0.41 #1803, 0.40 #1119) >> Best rule #1800 for best value: >> intensional similarity = 5 >> extensional distance = 99 >> proper extension: 077qn; 05r7t; 0165b; >> query: (?x183, 06z6r) <- adjustment_currency(?x183, ?x170), jurisdiction_of_office(?x182, ?x183), countries_spoken_in(?x254, ?x183), olympics(?x183, ?x2966), participating_countries(?x1931, ?x183) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #755 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 035v3; 034tl; *> query: (?x183, 01cgz) <- contains(?x8882, ?x183), contains(?x9729, ?x8882), currency(?x183, ?x170), countries_spoken_in(?x254, ?x183) *> conf = 0.61 ranks of expected_values: 3 EVAL 027nb country! 01cgz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 99.000 0.802 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #10974-01jsn5 PRED entity: 01jsn5 PRED relation: student PRED expected values: 03lh3v => 131 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1279): 02hsgn (0.17 #821, 0.04 #15467, 0.03 #17559), 0n00 (0.17 #546, 0.04 #36113, 0.03 #46574), 0kh6b (0.17 #615, 0.03 #36182, 0.02 #46643), 09r9dp (0.17 #614, 0.02 #8984, 0.02 #11076), 0dqmt0 (0.17 #1239, 0.02 #13793, 0.02 #15885), 01lwx (0.17 #1982, 0.02 #14536, 0.02 #29180), 01tdnyh (0.17 #889, 0.02 #28087, 0.02 #36456), 034bgm (0.17 #415, 0.02 #27613, 0.02 #35982), 0kn3g (0.17 #1668, 0.02 #28866, 0.02 #37235), 0l6qt (0.17 #16, 0.02 #27214, 0.02 #35583) >> Best rule #821 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 053mhx; >> query: (?x2399, 02hsgn) <- contains(?x94, ?x2399), student(?x2399, ?x3070), award_nominee(?x3056, ?x3070), ?x3056 = 01vvb4m >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jsn5 student 03lh3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 77.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #10973-09jg8 PRED entity: 09jg8 PRED relation: symptom_of! PRED expected values: 0gxb2 0hgxh 0f3kl => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 62): 0f3kl (0.60 #467, 0.59 #358, 0.50 #317), 0gxb2 (0.59 #358, 0.50 #104, 0.44 #345), 0hgxh (0.59 #358, 0.44 #117, 0.40 #139), 01cdt5 (0.59 #358, 0.40 #487, 0.40 #167), 04kllm9 (0.44 #117, 0.28 #300, 0.25 #51), 02y0js (0.33 #35, 0.25 #100, 0.19 #828), 01pf6 (0.28 #300, 0.19 #828, 0.19 #433), 0hg45 (0.28 #300, 0.19 #828, 0.19 #433), 0k95h (0.28 #300, 0.19 #828, 0.19 #433), 09969 (0.28 #300, 0.19 #828, 0.19 #433) >> Best rule #467 for best value: >> intensional similarity = 12 >> extensional distance = 8 >> proper extension: 0dcp_; 0h3bn; >> query: (?x9898, 0f3kl) <- symptom_of(?x10717, ?x9898), symptom_of(?x4905, ?x9898), symptom_of(?x3679, ?x9898), ?x3679 = 02tfl8, symptom_of(?x10717, ?x13485), symptom_of(?x10717, ?x13131), symptom_of(?x10717, ?x11739), ?x11739 = 0167bx, ?x13485 = 07s4l, ?x13131 = 0d19y2, symptom_of(?x4905, ?x7006), ?x7006 = 02psvcf >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 09jg8 symptom_of! 0f3kl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.600 http://example.org/medicine/symptom/symptom_of EVAL 09jg8 symptom_of! 0hgxh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.600 http://example.org/medicine/symptom/symptom_of EVAL 09jg8 symptom_of! 0gxb2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.600 http://example.org/medicine/symptom/symptom_of #10972-01wbg84 PRED entity: 01wbg84 PRED relation: location PRED expected values: 0cc56 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 150): 030qb3t (0.22 #5706, 0.21 #1689, 0.20 #6509), 02_286 (0.21 #1643, 0.21 #3249, 0.18 #37), 04jpl (0.15 #820, 0.06 #6443, 0.06 #5640), 0rh6k (0.14 #1610, 0.07 #2413, 0.06 #4019), 01_d4 (0.09 #102, 0.03 #6528, 0.03 #5725), 0498y (0.09 #212, 0.02 #46585), 0dclg (0.09 #117, 0.02 #46702, 0.01 #8150), 0f2tj (0.09 #328, 0.01 #4343), 07h34 (0.09 #195, 0.01 #4210), 01fscv (0.09 #665) >> Best rule #5706 for best value: >> intensional similarity = 3 >> extensional distance = 306 >> proper extension: 05m63c; 09byk; 0285c; 05wjnt; 01_rh4; 02v406; 01gy7r; 039crh; 01twdk; 0dfjb8; ... >> query: (?x368, 030qb3t) <- film(?x368, ?x508), people(?x1446, ?x368), languages(?x368, ?x254) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #860 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 027dtv3; 02tr7d; 02d4ct; 01dy7j; 07s8hms; 02l6dy; 03mp9s; 02624g; 0633p0; *> query: (?x368, 0cc56) <- award_nominee(?x368, ?x7776), award(?x368, ?x401), ?x7776 = 06dn58 *> conf = 0.08 ranks of expected_values: 15 EVAL 01wbg84 location 0cc56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 95.000 95.000 0.224 http://example.org/people/person/places_lived./people/place_lived/location #10971-0jrxx PRED entity: 0jrxx PRED relation: time_zones PRED expected values: 02hcv8 => 198 concepts (198 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.80 #1101, 0.80 #329, 0.79 #209), 02fqwt (0.38 #2083, 0.33 #1, 0.29 #1730), 02lcqs (0.33 #148, 0.32 #1013, 0.29 #109), 02hczc (0.29 #1730, 0.20 #264, 0.20 #383), 02lcrv (0.29 #1730, 0.19 #1861, 0.17 #2449), 042g7t (0.29 #1730, 0.19 #1861, 0.16 #2357), 02llzg (0.15 #306, 0.12 #490, 0.11 #266), 03bdv (0.06 #401, 0.06 #427, 0.05 #228), 05jphn (0.03 #208, 0.02 #262, 0.02 #275), 03plfd (0.03 #1608, 0.03 #1241, 0.03 #1621) >> Best rule #1101 for best value: >> intensional similarity = 3 >> extensional distance = 213 >> proper extension: 027rqbx; 0fb18; 02v3m7; 0nm87; 08xpv_; 041_3z; 0f2pf9; >> query: (?x9290, ?x2674) <- contains(?x9290, ?x11345), time_zones(?x11345, ?x2674), source(?x11345, ?x958) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jrxx time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 198.000 0.800 http://example.org/location/location/time_zones #10970-046qq PRED entity: 046qq PRED relation: award_winner! PRED expected values: 027c95y => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 228): 04ljl_l (0.37 #23505, 0.37 #23504, 0.37 #20086), 05p09zm (0.37 #23505, 0.37 #23504, 0.37 #20086), 07cbcy (0.37 #23505, 0.37 #23504, 0.37 #20086), 0gqy2 (0.37 #23505, 0.37 #23504, 0.37 #20086), 0bdwqv (0.37 #23505, 0.37 #23504, 0.37 #20086), 099jhq (0.37 #23505, 0.37 #23504, 0.37 #20086), 0bfvd4 (0.37 #23505, 0.37 #23504, 0.37 #20086), 0cqh46 (0.37 #23505, 0.37 #23504, 0.37 #20086), 027c95y (0.17 #19231, 0.11 #1435, 0.09 #1864), 0gq9h (0.17 #19231, 0.07 #21368, 0.06 #5208) >> Best rule #23505 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 04lgymt; 02mslq; 04rcr; 02r3zy; 011zf2; 0ggl02; 03g5jw; 05crg7; 01czx; 0288fyj; ... >> query: (?x4277, ?x1312) <- award_winner(?x7515, ?x4277), award(?x4277, ?x1312) >> conf = 0.37 => this is the best rule for 8 predicted values *> Best rule #19231 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1266 *> proper extension: 0grmhb; *> query: (?x4277, ?x591) <- nationality(?x4277, ?x94), award_winner(?x3505, ?x4277), award(?x3505, ?x591) *> conf = 0.17 ranks of expected_values: 9 EVAL 046qq award_winner! 027c95y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 106.000 106.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner #10969-0c3ybss PRED entity: 0c3ybss PRED relation: film_release_region PRED expected values: 0jgd 04g5k => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 120): 03gj2 (0.83 #879, 0.83 #1022, 0.79 #1165), 02vzc (0.80 #903, 0.80 #1046, 0.78 #1189), 0jgd (0.79 #1006, 0.79 #863, 0.75 #1149), 05b4w (0.78 #1057, 0.77 #914, 0.73 #1200), 03rj0 (0.62 #1053, 0.62 #910, 0.59 #1196), 06qd3 (0.59 #890, 0.57 #1176, 0.56 #1319), 01mjq (0.57 #1038, 0.56 #895, 0.53 #1181), 03rk0 (0.54 #1050, 0.53 #907, 0.48 #1193), 016wzw (0.53 #1060, 0.53 #917, 0.51 #1203), 015qh (0.53 #1035, 0.52 #892, 0.48 #1178) >> Best rule #879 for best value: >> intensional similarity = 6 >> extensional distance = 131 >> proper extension: 0ds35l9; 0gtsx8c; 02vp1f_; 03g90h; 01gc7; 0gx1bnj; 0h1cdwq; 0dscrwf; 05p1tzf; 087wc7n; ... >> query: (?x249, 03gj2) <- film_release_region(?x249, ?x550), film_release_region(?x249, ?x390), film_release_region(?x249, ?x87), ?x550 = 05v8c, ?x87 = 05r4w, ?x390 = 0chghy >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1006 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 155 *> proper extension: 0b76d_m; 02vxq9m; 0g5qs2k; 02x3lt7; 0c40vxk; 0401sg; 0c0nhgv; 02yvct; 0fpv_3_; 0661m4p; ... *> query: (?x249, 0jgd) <- film_release_region(?x249, ?x550), film_release_region(?x249, ?x87), ?x550 = 05v8c, ?x87 = 05r4w *> conf = 0.79 ranks of expected_values: 3, 36 EVAL 0c3ybss film_release_region 04g5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 65.000 65.000 0.835 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c3ybss film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 65.000 0.835 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10968-085h1 PRED entity: 085h1 PRED relation: member_states PRED expected values: 027nb 01ls2 04v3q 07ylj 04wgh 035qy 06s6l 0162v 035dk 06t8v 07fj_ 09lxtg 04g61 0167v 03548 0164v 034m8 0165v => 145 concepts (11 used for prediction) PRED predicted values (max 10 best out of 94): 04g61 (0.40 #12, 0.28 #13, 0.14 #71), 035qy (0.40 #10, 0.28 #13, 0.14 #69), 06bnz (0.28 #13, 0.03 #86, 0.03 #98), 01mk6 (0.28 #13, 0.03 #98, 0.03 #84), 05vz3zq (0.28 #13, 0.03 #98, 0.03 #84), 020g9r (0.28 #13), 0hw29 (0.28 #13), 01rdm0 (0.28 #13), 01d8l (0.28 #13), 01fvhp (0.28 #13) >> Best rule #12 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 02jxk; 018cqq; 059dn; >> query: (?x7695, 04g61) <- member_states(?x7695, ?x1790), combatants(?x1790, ?x3918), country(?x3015, ?x1790), combatants(?x326, ?x1790), ?x3015 = 071t0, film_release_region(?x141, ?x1790), currency(?x1790, ?x170) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 26, 27, 41, 42, 43, 50, 56, 71, 72, 73, 74, 77, 86, 87, 90, 92 EVAL 085h1 member_states 0165v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 034m8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 0164v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 03548 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 0167v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 04g61 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 09lxtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 07fj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 06t8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 035dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 0162v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 06s6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 04wgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 04v3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 01ls2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 085h1 member_states 027nb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 145.000 11.000 0.400 http://example.org/user/ktrueman/default_domain/international_organization/member_states #10967-06zrp44 PRED entity: 06zrp44 PRED relation: award_winner PRED expected values: 07bty => 47 concepts (31 used for prediction) PRED predicted values (max 10 best out of 2031): 06pj8 (0.54 #22702, 0.50 #25178, 0.45 #27653), 09889g (0.35 #40724, 0.27 #58054, 0.25 #60530), 0js9s (0.35 #23722, 0.22 #38578, 0.22 #31150), 01f8ld (0.31 #22926, 0.25 #25402, 0.23 #27877), 02vyw (0.31 #23058, 0.20 #37914, 0.14 #57719), 01tdnyh (0.30 #13532, 0.29 #11058, 0.27 #18479), 041jlr (0.30 #16720, 0.08 #37122, 0.06 #46423), 04sry (0.27 #23887, 0.22 #31315, 0.21 #26363), 081lh (0.27 #22455, 0.20 #59592, 0.18 #62066), 06b_0 (0.27 #23945, 0.20 #16522, 0.18 #26421) >> Best rule #22702 for best value: >> intensional similarity = 7 >> extensional distance = 24 >> proper extension: 0gr4k; 019f4v; 0gs9p; 0gr51; 0m57f; >> query: (?x14137, 06pj8) <- award_winner(?x14137, ?x3335), location(?x3335, ?x1264), company(?x3335, ?x5281), nationality(?x3335, ?x10003), influenced_by(?x3335, ?x1857), organizations_founded(?x3335, ?x11768), adjoins(?x10003, ?x2517) >> conf = 0.54 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06zrp44 award_winner 07bty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 31.000 0.538 http://example.org/award/award_category/winners./award/award_honor/award_winner #10966-03b8c4 PRED entity: 03b8c4 PRED relation: school_type PRED expected values: 05pcjw => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.45 #460, 0.43 #172, 0.42 #1135), 01rs41 (0.44 #149, 0.43 #101, 0.39 #125), 05pcjw (0.42 #241, 0.40 #193, 0.39 #121), 01_srz (0.20 #27, 0.08 #483, 0.07 #531), 07tf8 (0.15 #225, 0.13 #33, 0.12 #778), 01_9fk (0.13 #506, 0.12 #603, 0.12 #482), 04399 (0.09 #14, 0.07 #38, 0.05 #62), 0bwd5 (0.06 #163, 0.06 #115, 0.06 #139), 02p0qmm (0.04 #322, 0.03 #779, 0.03 #106), 06cs1 (0.03 #198, 0.03 #222, 0.03 #246) >> Best rule #460 for best value: >> intensional similarity = 3 >> extensional distance = 229 >> proper extension: 0173s9; >> query: (?x12699, 05jxkf) <- colors(?x12699, ?x3189), contains(?x1523, ?x12699), featured_film_locations(?x83, ?x1523) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 08815; 07tgn; 02rff2; 020923; 017y26; 05njyy; 02bq1j; 02zd460; 021s9n; 04ycjk; ... *> query: (?x12699, 05pcjw) <- institution(?x1519, ?x12699), contains(?x94, ?x12699), organization(?x3484, ?x12699), ?x1519 = 013zdg *> conf = 0.42 ranks of expected_values: 3 EVAL 03b8c4 school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 103.000 0.446 http://example.org/education/educational_institution/school_type #10965-0b79gfg PRED entity: 0b79gfg PRED relation: crewmember! PRED expected values: 03tps5 => 81 concepts (27 used for prediction) PRED predicted values (max 10 best out of 301): 0dtfn (0.14 #1247, 0.13 #1546, 0.13 #1845), 01rxyb (0.12 #139, 0.10 #439, 0.05 #1040), 085wqm (0.12 #286, 0.10 #586, 0.05 #1187), 04j14qc (0.12 #258, 0.10 #558, 0.03 #859), 01_0f7 (0.12 #217, 0.10 #517, 0.03 #818), 01cmp9 (0.12 #199, 0.10 #499, 0.03 #800), 0gtxj2q (0.12 #135, 0.10 #435, 0.03 #736), 04g9gd (0.12 #84, 0.10 #384, 0.03 #685), 01j8wk (0.12 #76, 0.10 #376, 0.03 #677), 0cz_ym (0.12 #67, 0.10 #367, 0.03 #668) >> Best rule #1247 for best value: >> intensional similarity = 6 >> extensional distance = 42 >> proper extension: 07h1tr; 026dx; 09dvgb8; 02vxyl5; 025_nbr; >> query: (?x2887, 0dtfn) <- crewmember(?x9872, ?x2887), crewmember(?x2685, ?x2887), crewmember(?x2627, ?x2887), genre(?x9872, ?x162), film_release_region(?x2627, ?x87), nominated_for(?x618, ?x2685) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0b79gfg crewmember! 03tps5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 27.000 0.136 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #10964-012dr7 PRED entity: 012dr7 PRED relation: place_of_death PRED expected values: 0r3tq => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 32): 030qb3t (0.33 #22, 0.18 #1577, 0.17 #9360), 0cc56 (0.20 #407, 0.03 #2740, 0.02 #3129), 02_286 (0.18 #986, 0.17 #1180, 0.17 #792), 0k049 (0.17 #782, 0.06 #9341, 0.06 #3115), 0r3tq (0.17 #733, 0.06 #1704, 0.06 #1899), 071vr (0.17 #686, 0.06 #1852, 0.05 #2241), 068p2 (0.11 #1945, 0.08 #6611, 0.03 #17319), 06_kh (0.10 #1950, 0.06 #1560, 0.06 #1755), 0r00l (0.09 #1135, 0.08 #1329, 0.08 #1523), 01tlmw (0.09 #983, 0.08 #1177, 0.08 #1371) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 015076; >> query: (?x5484, 030qb3t) <- people(?x5855, ?x5484), ?x5855 = 01l2m3, participant(?x5484, ?x6433), nationality(?x5484, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #733 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 022p06; *> query: (?x5484, 0r3tq) <- people(?x5855, ?x5484), ?x5855 = 01l2m3, nationality(?x5484, ?x94), nominated_for(?x5484, ?x10829) *> conf = 0.17 ranks of expected_values: 5 EVAL 012dr7 place_of_death 0r3tq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 156.000 156.000 0.333 http://example.org/people/deceased_person/place_of_death #10963-0kcw2 PRED entity: 0kcw2 PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 20): 0pqc5 (0.63 #189, 0.62 #235, 0.59 #465), 060c4 (0.23 #1268, 0.23 #1291, 0.06 #371), 060bp (0.22 #1266, 0.22 #1289, 0.04 #2509), 0fkvn (0.13 #1684, 0.10 #1799, 0.09 #2075), 0f6c3 (0.12 #1688, 0.11 #1803, 0.10 #1319), 09n5b9 (0.11 #1692, 0.10 #1807, 0.09 #1323), 01q24l (0.10 #106, 0.10 #773, 0.09 #129), 01zq91 (0.04 #1280, 0.04 #1303, 0.01 #2086), 04syw (0.04 #1272, 0.04 #1295, 0.01 #2078), 0dq3c (0.04 #370, 0.03 #1267, 0.03 #1290) >> Best rule #189 for best value: >> intensional similarity = 6 >> extensional distance = 44 >> proper extension: 0rh6k; 0f2r6; 013kcv; 030qb3t; 0f2w0; 01_d4; 0dc95; 0f__1; 0ftxw; 0fvzg; ... >> query: (?x13387, 0pqc5) <- locations(?x8527, ?x13387), team(?x8527, ?x9576), team(?x8527, ?x8728), ?x9576 = 02qk2d5, time_zones(?x13387, ?x2674), ?x8728 = 026xxv_ >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kcw2 jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.630 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #10962-04fhxp PRED entity: 04fhxp PRED relation: profession PRED expected values: 018gz8 => 89 concepts (17 used for prediction) PRED predicted values (max 10 best out of 52): 0dxtg (0.39 #310, 0.29 #754, 0.29 #14), 03gjzk (0.35 #311, 0.23 #755, 0.23 #459), 018gz8 (0.33 #313, 0.27 #461, 0.26 #609), 01d_h8 (0.31 #1635, 0.31 #1043, 0.29 #1191), 02jknp (0.21 #156, 0.20 #1637, 0.19 #1045), 0cbd2 (0.19 #1636, 0.19 #896, 0.17 #1340), 02krf9 (0.16 #322, 0.14 #26, 0.11 #766), 0kyk (0.15 #918, 0.13 #1362, 0.13 #1510), 09jwl (0.14 #19, 0.13 #1352, 0.13 #1204), 02hv44_ (0.14 #57, 0.05 #1390, 0.05 #946) >> Best rule #310 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 0d9_96; 0f87jy; 075npt; 0814k3; >> query: (?x2317, 0dxtg) <- profession(?x2317, ?x1383), gender(?x2317, ?x231), ?x1383 = 0np9r, student(?x3439, ?x2317) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #313 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 145 *> proper extension: 0d9_96; 0f87jy; 075npt; 0814k3; *> query: (?x2317, 018gz8) <- profession(?x2317, ?x1383), gender(?x2317, ?x231), ?x1383 = 0np9r, student(?x3439, ?x2317) *> conf = 0.33 ranks of expected_values: 3 EVAL 04fhxp profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 17.000 0.388 http://example.org/people/person/profession #10961-042d1 PRED entity: 042d1 PRED relation: legislative_sessions PRED expected values: 01grnp 01grmk => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 49): 07p__7 (0.57 #351, 0.44 #1331, 0.21 #1772), 024tkd (0.50 #382, 0.48 #1362, 0.22 #1264), 06f0dc (0.50 #352, 0.48 #1332, 0.22 #1234), 024tcq (0.43 #364, 0.40 #1344, 0.22 #1246), 070m6c (0.43 #349, 0.40 #1329, 0.17 #2113), 070mff (0.43 #380, 0.36 #1360, 0.17 #1850), 02bqm0 (0.40 #1352, 0.36 #372, 0.22 #1254), 02cg7g (0.40 #1349, 0.36 #369, 0.22 #1251), 02bqmq (0.40 #1342, 0.36 #362, 0.22 #1244), 02bn_p (0.36 #1333, 0.36 #353, 0.18 #1774) >> Best rule #351 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 01lct6; >> query: (?x10511, 07p__7) <- profession(?x10511, ?x3342), legislative_sessions(?x10511, ?x7715), people(?x5741, ?x10511), jurisdiction_of_office(?x10511, ?x94), people(?x5741, ?x9276), participant(?x9276, ?x8160) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0424m; 042f1; *> query: (?x10511, 01grmk) <- gender(?x10511, ?x231), ?x231 = 05zppz, taxonomy(?x10511, ?x939), jurisdiction_of_office(?x10511, ?x94), legislative_sessions(?x10511, ?x7715), ?x939 = 04n6k *> conf = 0.20 ranks of expected_values: 30, 31 EVAL 042d1 legislative_sessions 01grmk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 166.000 166.000 0.571 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 042d1 legislative_sessions 01grnp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 166.000 166.000 0.571 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #10960-01jrbb PRED entity: 01jrbb PRED relation: titles! PRED expected values: 09b3v => 78 concepts (48 used for prediction) PRED predicted values (max 10 best out of 70): 07s9rl0 (0.39 #1233, 0.36 #1439, 0.35 #718), 04xvlr (0.36 #1236, 0.30 #1442, 0.22 #2683), 01hmnh (0.33 #129, 0.24 #1051, 0.20 #846), 024qqx (0.24 #183, 0.14 #1728, 0.14 #1932), 07ssc (0.22 #1448, 0.13 #1242, 0.11 #522), 09b3v (0.21 #1074, 0.19 #152, 0.11 #869), 01z4y (0.19 #2508, 0.18 #753, 0.16 #548), 03k9fj (0.18 #4652, 0.17 #4337, 0.17 #4862), 0hcr (0.18 #4652, 0.17 #4337, 0.17 #4862), 05p553 (0.18 #4652, 0.17 #4337, 0.17 #4862) >> Best rule #1233 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 083shs; 0c_j9x; 021y7yw; 026p4q7; 0p4v_; 0_816; 0glnm; 0mcl0; 0pd6l; 03hmt9b; ... >> query: (?x2893, 07s9rl0) <- film(?x2156, ?x2893), film(?x1052, ?x2893), nominated_for(?x1079, ?x2893), ?x1079 = 0l8z1 >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1074 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 100 *> proper extension: 04svwx; *> query: (?x2893, 09b3v) <- genre(?x2893, ?x2540), country(?x2893, ?x94), ?x2540 = 0hcr *> conf = 0.21 ranks of expected_values: 6 EVAL 01jrbb titles! 09b3v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 78.000 48.000 0.390 http://example.org/media_common/netflix_genre/titles #10959-01jr4j PRED entity: 01jr4j PRED relation: currency PRED expected values: 09nqf => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.84 #267, 0.82 #232, 0.81 #436), 01nv4h (0.14 #428, 0.06 #500, 0.05 #205), 02gsvk (0.14 #428, 0.03 #153, 0.03 #335), 02l6h (0.14 #428, 0.02 #509, 0.02 #544), 088n7 (0.01 #547) >> Best rule #267 for best value: >> intensional similarity = 5 >> extensional distance = 110 >> proper extension: 0ptxj; >> query: (?x7149, 09nqf) <- nominated_for(?x5856, ?x7149), genre(?x7149, ?x600), film(?x4240, ?x5856), film(?x1104, ?x7149), film(?x2465, ?x7149) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jr4j currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.839 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10958-07sgfsl PRED entity: 07sgfsl PRED relation: award_winner! PRED expected values: 0cqhk0 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 163): 0cqhk0 (0.73 #469, 0.73 #37, 0.67 #901), 0ck27z (0.25 #3117, 0.25 #2685, 0.16 #4414), 09sb52 (0.17 #4362, 0.13 #6522, 0.12 #3065), 09qs08 (0.15 #14692, 0.15 #13826, 0.09 #144), 09qj50 (0.09 #14259, 0.07 #3457, 0.05 #21174), 09qv3c (0.09 #14259, 0.07 #3457, 0.05 #21174), 09qrn4 (0.09 #14259, 0.07 #3457, 0.05 #21174), 099tbz (0.07 #4379, 0.06 #6539, 0.05 #6107), 01by1l (0.07 #1409, 0.06 #6162, 0.06 #9618), 027c95y (0.06 #1454, 0.03 #1886, 0.03 #2318) >> Best rule #469 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 064nh4k; 0806vbn; 0fn8jc; 07k51gd; >> query: (?x2780, 0cqhk0) <- award_winner(?x6561, ?x2780), ?x6561 = 077yk0, award_nominee(?x1824, ?x2780) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07sgfsl award_winner! 0cqhk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.733 http://example.org/award/award_category/winners./award/award_honor/award_winner #10957-04n65n PRED entity: 04n65n PRED relation: profession PRED expected values: 09jwl => 114 concepts (101 used for prediction) PRED predicted values (max 10 best out of 58): 09jwl (0.81 #1040, 0.77 #2209, 0.74 #3967), 016z4k (0.49 #2194, 0.47 #1755, 0.47 #1609), 01d_h8 (0.33 #151, 0.30 #8059, 0.29 #12590), 01c72t (0.30 #5144, 0.29 #5879, 0.28 #5732), 0dxtg (0.28 #12598, 0.26 #12160, 0.26 #159), 0n1h (0.26 #741, 0.24 #1617, 0.23 #2056), 03gjzk (0.24 #160, 0.22 #9971, 0.22 #10263), 02jknp (0.21 #153, 0.20 #12592, 0.20 #12154), 0fnpj (0.14 #1372, 0.14 #1080, 0.14 #5032), 0kyk (0.13 #467, 0.10 #6765, 0.10 #175) >> Best rule #1040 for best value: >> intensional similarity = 3 >> extensional distance = 179 >> proper extension: 0pgjm; 0770cd; 017vkx; 0dpqk; 0d608; 03dq9; 0dn44; >> query: (?x7201, 09jwl) <- nationality(?x7201, ?x94), profession(?x7201, ?x131), group(?x7201, ?x3390) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04n65n profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 101.000 0.812 http://example.org/people/person/profession #10956-0p_pd PRED entity: 0p_pd PRED relation: profession PRED expected values: 018gz8 => 139 concepts (137 used for prediction) PRED predicted values (max 10 best out of 95): 018gz8 (0.61 #1191, 0.33 #1926, 0.33 #309), 01d_h8 (0.51 #1917, 0.50 #4860, 0.46 #2211), 0cbd2 (0.47 #5743, 0.47 #4714, 0.46 #7360), 0kyk (0.35 #3263, 0.31 #5764, 0.31 #4588), 09jwl (0.35 #1046, 0.28 #3989, 0.26 #1193), 02jknp (0.31 #1919, 0.28 #2213, 0.27 #4274), 0np9r (0.30 #7942, 0.29 #3401, 0.27 #3549), 0dz3r (0.27 #1031, 0.23 #3974, 0.21 #7944), 0d1pc (0.25 #14266, 0.25 #49, 0.13 #5050), 02krf9 (0.25 #14266, 0.19 #1201, 0.19 #2819) >> Best rule #1191 for best value: >> intensional similarity = 2 >> extensional distance = 29 >> proper extension: 054187; >> query: (?x397, 018gz8) <- tv_program(?x397, ?x6884), ?x6884 = 039cq4 >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p_pd profession 018gz8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 137.000 0.613 http://example.org/people/person/profession #10955-03nnm4t PRED entity: 03nnm4t PRED relation: award_winner PRED expected values: 05cj4r 02773nt 04gnbv1 026w_gk 0zcbl => 32 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2194): 0bz5v2 (0.67 #3018, 0.44 #1509, 0.40 #6173), 04crrxr (0.67 #3018, 0.44 #1509, 0.40 #6865), 06msq2 (0.67 #3018, 0.44 #1509, 0.36 #12753), 087qxp (0.67 #3018, 0.44 #1509, 0.29 #18138), 086nl7 (0.67 #3018, 0.44 #1509, 0.29 #18138), 0fvf9q (0.67 #3018, 0.44 #1509, 0.29 #18138), 06pjs (0.67 #3018, 0.44 #1509, 0.28 #3020), 01p85y (0.50 #2730, 0.44 #1509, 0.33 #1221), 033jkj (0.50 #2171, 0.33 #662, 0.20 #6703), 05pzdk (0.44 #1509, 0.40 #6850, 0.33 #8361) >> Best rule #3018 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 092t4b; >> query: (?x5585, ?x9153) <- award_winner(?x5585, ?x9503), award_winner(?x5585, ?x5454), award_winner(?x5585, ?x2813), award_winner(?x5585, ?x906), honored_for(?x5585, ?x2009), ?x2813 = 015c2f, ceremony(?x375, ?x5585), award_winner(?x906, ?x9500), film(?x5454, ?x1420), place_of_birth(?x9503, ?x1131), award_winner(?x9153, ?x9503), student(?x3995, ?x9503), profession(?x9500, ?x353) >> conf = 0.67 => this is the best rule for 7 predicted values *> Best rule #1509 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 1 *> proper extension: 092c5f; *> query: (?x5585, ?x829) <- award_winner(?x5585, ?x10542), award_winner(?x5585, ?x5454), award_winner(?x5585, ?x2813), award_winner(?x5585, ?x2789), award_winner(?x5585, ?x906), honored_for(?x5585, ?x2009), ?x2813 = 015c2f, ceremony(?x375, ?x5585), award_winner(?x906, ?x829), place_of_birth(?x10542, ?x6960), film(?x5454, ?x7432), executive_produced_by(?x1810, ?x2789), ?x7432 = 01hv3t, award_nominee(?x496, ?x2789), profession(?x906, ?x353), country_of_origin(?x2009, ?x94) *> conf = 0.44 ranks of expected_values: 11, 12, 13, 170, 359 EVAL 03nnm4t award_winner 0zcbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 32.000 16.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03nnm4t award_winner 026w_gk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 32.000 16.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03nnm4t award_winner 04gnbv1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 32.000 16.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03nnm4t award_winner 02773nt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 32.000 16.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03nnm4t award_winner 05cj4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 32.000 16.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10954-0c40vxk PRED entity: 0c40vxk PRED relation: film_release_region PRED expected values: 04v3q 01znc_ 012wgb 03ryn 0166b => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 184): 0chghy (0.88 #577, 0.86 #292, 0.86 #1714), 01znc_ (0.88 #604, 0.85 #746, 0.77 #319), 0jgd (0.82 #287, 0.80 #1425, 0.79 #1993), 03rt9 (0.77 #722, 0.75 #295, 0.74 #580), 03rj0 (0.76 #761, 0.75 #334, 0.72 #619), 0ctw_b (0.75 #305, 0.71 #590, 0.71 #732), 016wzw (0.68 #340, 0.65 #767, 0.62 #625), 01p1v (0.68 #613, 0.66 #328, 0.66 #755), 06qd3 (0.64 #315, 0.51 #1737, 0.51 #2874), 06mzp (0.61 #301, 0.56 #586, 0.55 #728) >> Best rule #577 for best value: >> intensional similarity = 9 >> extensional distance = 70 >> proper extension: 0gtsx8c; 02vxq9m; 05p1tzf; 02x3lt7; 017gl1; 08hmch; 0jjy0; 0872p_c; 053rxgm; 0gj8t_b; ... >> query: (?x633, 0chghy) <- film_release_region(?x633, ?x2152), film_release_region(?x633, ?x1892), film_release_region(?x633, ?x1497), film_release_region(?x633, ?x172), ?x1892 = 02vzc, film(?x1478, ?x633), ?x2152 = 06mkj, ?x1497 = 015qh, ?x172 = 0154j >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #604 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 70 *> proper extension: 0gtsx8c; 02vxq9m; 05p1tzf; 02x3lt7; 017gl1; 08hmch; 0jjy0; 0872p_c; 053rxgm; 0gj8t_b; ... *> query: (?x633, 01znc_) <- film_release_region(?x633, ?x2152), film_release_region(?x633, ?x1892), film_release_region(?x633, ?x1497), film_release_region(?x633, ?x172), ?x1892 = 02vzc, film(?x1478, ?x633), ?x2152 = 06mkj, ?x1497 = 015qh, ?x172 = 0154j *> conf = 0.88 ranks of expected_values: 2, 17, 29, 37, 49 EVAL 0c40vxk film_release_region 0166b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 86.000 86.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c40vxk film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 86.000 86.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c40vxk film_release_region 012wgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 86.000 86.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c40vxk film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c40vxk film_release_region 04v3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 86.000 86.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10953-06sks6 PRED entity: 06sks6 PRED relation: olympics! PRED expected values: 05v8c 05v10 02khs 01p1v 035dk 03h2c 0d05q4 0167v 0165b 04hhv 04wlh 034m8 => 60 concepts (58 used for prediction) PRED predicted values (max 10 best out of 54): 09c7w0 (0.88 #604, 0.88 #579, 0.86 #227), 07ssc (0.71 #204, 0.68 #581, 0.66 #606), 03rjj (0.58 #25, 0.57 #580, 0.57 #203), 0163v (0.58 #25, 0.57 #137, 0.50 #112), 03rk0 (0.58 #25, 0.55 #734, 0.48 #175), 0hzlz (0.58 #25, 0.35 #733, 0.34 #150), 04wgh (0.58 #25, 0.35 #733, 0.34 #150), 09pmkv (0.58 #25, 0.35 #733, 0.34 #150), 05b7q (0.58 #25, 0.35 #733, 0.34 #150), 0161c (0.58 #25, 0.35 #733, 0.34 #150) >> Best rule #604 for best value: >> intensional similarity = 8 >> extensional distance = 39 >> proper extension: 015l4k; >> query: (?x2966, 09c7w0) <- sports(?x2966, ?x150), olympics(?x12929, ?x2966), olympics(?x8958, ?x2966), place_of_birth(?x691, ?x12929), film_release_region(?x280, ?x8958), jurisdiction_of_office(?x346, ?x8958), olympics(?x2266, ?x2966), medal(?x2966, ?x422) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #25 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 0kbvv; *> query: (?x2966, ?x94) <- sports(?x2966, ?x4045), olympics(?x12929, ?x2966), olympics(?x8958, ?x2966), olympics(?x2152, ?x2966), olympics(?x429, ?x2966), place_of_birth(?x691, ?x12929), film_release_region(?x280, ?x8958), currency(?x8958, ?x170), country(?x4045, ?x94), ?x2152 = 06mkj, ?x429 = 03rt9 *> conf = 0.58 ranks of expected_values: 13, 14, 15, 16, 19, 24, 26, 27, 28, 29, 30, 31 EVAL 06sks6 olympics! 034m8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 04wlh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 04hhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 0165b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 0167v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 0d05q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 03h2c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 035dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 01p1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 02khs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 05v10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 06sks6 olympics! 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 60.000 58.000 0.878 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #10952-024qwq PRED entity: 024qwq PRED relation: artists! PRED expected values: 05y8n7 => 146 concepts (60 used for prediction) PRED predicted values (max 10 best out of 293): 01lyv (0.50 #5527, 0.28 #6443, 0.26 #1559), 025sc50 (0.40 #8900, 0.34 #1573, 0.31 #1878), 016clz (0.33 #2752, 0.30 #6721, 0.30 #6109), 02vjzr (0.31 #1655, 0.30 #1960, 0.17 #8982), 0xhtw (0.30 #2764, 0.30 #6121, 0.30 #6733), 0glt670 (0.29 #8893, 0.22 #16531, 0.22 #18057), 02lnbg (0.26 #1581, 0.25 #8908, 0.24 #1886), 03_d0 (0.26 #12, 0.22 #622, 0.21 #3980), 0155w (0.25 #711, 0.18 #6205, 0.18 #1627), 0ggx5q (0.25 #8926, 0.19 #1599, 0.18 #11300) >> Best rule #5527 for best value: >> intensional similarity = 4 >> extensional distance = 141 >> proper extension: 07s3vqk; 01lmj3q; 01vrncs; 0137n0; 01kx_81; 01p9hgt; 01wp8w7; 0l12d; 03gr7w; 0136p1; ... >> query: (?x9407, 01lyv) <- award_winner(?x5901, ?x9407), artists(?x3061, ?x9407), artists(?x3061, ?x7620), ?x7620 = 06gcn >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13742 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 370 *> proper extension: 012ljv; 01vvydl; 012d40; 028q6; 0411q; 0hl3d; 032nwy; 026ps1; 0147dk; 03f2_rc; ... *> query: (?x9407, ?x1380) <- award_winner(?x5901, ?x9407), artists(?x3061, ?x9407), artists(?x3061, ?x7620), artists(?x1380, ?x7620) *> conf = 0.03 ranks of expected_values: 270 EVAL 024qwq artists! 05y8n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 146.000 60.000 0.497 http://example.org/music/genre/artists #10951-0ks67 PRED entity: 0ks67 PRED relation: school! PRED expected values: 01yjl => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 93): 0jmj7 (0.65 #1796, 0.61 #1052, 0.60 #1145), 05m_8 (0.17 #1026, 0.17 #1119, 0.17 #189), 01yhm (0.15 #206, 0.12 #299, 0.12 #113), 051vz (0.13 #1046, 0.13 #1139, 0.12 #209), 01slc (0.13 #1081, 0.13 #1174, 0.08 #1825), 0jmm4 (0.12 #259, 0.12 #352, 0.12 #166), 07147 (0.11 #1090, 0.11 #1183, 0.10 #253), 05xvj (0.10 #274, 0.09 #1111, 0.09 #1204), 0cqt41 (0.10 #204, 0.09 #1041, 0.09 #1134), 0jmnl (0.10 #371, 0.09 #464, 0.09 #650) >> Best rule #1796 for best value: >> intensional similarity = 2 >> extensional distance = 189 >> proper extension: 0fht9f; >> query: (?x5807, 0jmj7) <- school(?x5419, ?x5807), team(?x1348, ?x5419) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #1054 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 97 *> proper extension: 0frm7n; *> query: (?x5807, 01yjl) <- school(?x5419, ?x5807), category(?x5807, ?x134), school(?x8542, ?x5807) *> conf = 0.09 ranks of expected_values: 20 EVAL 0ks67 school! 01yjl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 64.000 64.000 0.654 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #10950-05kkh PRED entity: 05kkh PRED relation: location! PRED expected values: 07t2k => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 1781): 03nb5v (0.20 #1319, 0.11 #3829, 0.10 #18886), 06jw0s (0.13 #1145, 0.12 #11184, 0.10 #6165), 09yrh (0.13 #913, 0.11 #3423, 0.09 #8442), 0320jz (0.13 #332, 0.10 #5352, 0.07 #2842), 023s8 (0.13 #2102, 0.08 #19669, 0.08 #24688), 094xh (0.13 #1077, 0.08 #18644, 0.08 #23663), 01p7yb (0.13 #47, 0.07 #2557, 0.06 #5067), 0p_pd (0.13 #48, 0.07 #2558, 0.06 #5068), 01yzhn (0.13 #2123, 0.07 #4633, 0.06 #7143), 0c01c (0.13 #474, 0.07 #2984, 0.06 #5494) >> Best rule #1319 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 09c7w0; 059rby; 02_286; 0hjy; 01n7q; 07z1m; 02xry; 0d0x8; 07b_l; 07h34; ... >> query: (?x177, 03nb5v) <- contains(?x177, ?x14573), source(?x14573, ?x958), jurisdiction_of_office(?x1157, ?x177) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1511 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 09c7w0; 059rby; 02_286; 0hjy; 01n7q; 07z1m; 02xry; 0d0x8; 07b_l; 07h34; ... *> query: (?x177, 07t2k) <- contains(?x177, ?x14573), source(?x14573, ?x958), jurisdiction_of_office(?x1157, ?x177) *> conf = 0.07 ranks of expected_values: 451 EVAL 05kkh location! 07t2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 142.000 142.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location #10949-0drr3 PRED entity: 0drr3 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 125 concepts (82 used for prediction) PRED predicted values (max 10 best out of 42): 09c7w0 (0.91 #275, 0.89 #48, 0.89 #814), 04_1l0v (0.39 #345, 0.31 #232, 0.14 #937), 0d060g (0.35 #454, 0.04 #315, 0.01 #534), 02jx1 (0.15 #341, 0.13 #311, 0.11 #369), 059rby (0.11 #979, 0.10 #118, 0.10 #143), 059j2 (0.09 #340, 0.08 #267, 0.08 #368), 07ssc (0.08 #308, 0.07 #252, 0.06 #295), 03rjj (0.06 #334, 0.04 #315, 0.04 #443), 0f8l9c (0.05 #339, 0.02 #688, 0.02 #161), 01t12z (0.04 #315) >> Best rule #275 for best value: >> intensional similarity = 5 >> extensional distance = 88 >> proper extension: 01531; >> query: (?x11165, 09c7w0) <- adjoins(?x11166, ?x11165), source(?x11165, ?x958), currency(?x11165, ?x170), adjoins(?x11752, ?x11166), administrative_parent(?x11752, ?x335) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0drr3 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 82.000 0.911 http://example.org/location/country/second_level_divisions #10948-04l3_z PRED entity: 04l3_z PRED relation: profession PRED expected values: 01d_h8 02hrh1q 018gz8 0np9r => 138 concepts (128 used for prediction) PRED predicted values (max 10 best out of 80): 01d_h8 (0.86 #2050, 0.80 #1320, 0.80 #6287), 02hrh1q (0.73 #596, 0.69 #12136, 0.66 #14035), 02krf9 (0.57 #1338, 0.40 #2214, 0.34 #1922), 0kyk (0.29 #4261, 0.28 #6601, 0.28 #7039), 018gz8 (0.26 #1474, 0.25 #8195, 0.23 #4102), 012t_z (0.25 #11, 0.10 #14900, 0.09 #17529), 0np9r (0.22 #14479, 0.21 #894, 0.21 #15794), 09jwl (0.19 #2644, 0.18 #2352, 0.17 #3958), 01c72t (0.17 #15797, 0.11 #5132, 0.11 #2357), 02hv44_ (0.17 #5751, 0.16 #2829, 0.16 #7067) >> Best rule #2050 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 01zfmm; 06t8b; 0l9k1; >> query: (?x975, 01d_h8) <- executive_produced_by(?x7248, ?x975), award(?x975, ?x1053), film(?x975, ?x2494), place_of_birth(?x975, ?x1523) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 7 EVAL 04l3_z profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 138.000 128.000 0.860 http://example.org/people/person/profession EVAL 04l3_z profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 138.000 128.000 0.860 http://example.org/people/person/profession EVAL 04l3_z profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 128.000 0.860 http://example.org/people/person/profession EVAL 04l3_z profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 128.000 0.860 http://example.org/people/person/profession #10947-023zl PRED entity: 023zl PRED relation: contains! PRED expected values: 09c7w0 => 161 concepts (104 used for prediction) PRED predicted values (max 10 best out of 426): 09c7w0 (0.88 #15224, 0.74 #73435, 0.74 #8958), 059rby (0.83 #8080, 0.81 #34025, 0.79 #74328), 0d060g (0.70 #76122, 0.69 #48367, 0.14 #2699), 03rjj (0.70 #76122, 0.16 #71652, 0.14 #2696), 0cc56 (0.70 #76122, 0.04 #9024, 0.04 #8129), 05kj_ (0.43 #32275, 0.05 #33170, 0.02 #41230), 01n7q (0.42 #71719, 0.14 #2763, 0.11 #59180), 02jx1 (0.38 #3668, 0.30 #4563, 0.29 #2772), 05tbn (0.26 #62013, 0.08 #33352, 0.05 #73655), 05kr_ (0.25 #48479, 0.02 #52062, 0.02 #63705) >> Best rule #15224 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 026036; >> query: (?x10759, 09c7w0) <- state_province_region(?x10759, ?x335), ?x335 = 059rby, campuses(?x10759, ?x10759), educational_institution(?x10759, ?x10759) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023zl contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 104.000 0.879 http://example.org/location/location/contains #10946-02zyy4 PRED entity: 02zyy4 PRED relation: currency PRED expected values: 09nqf => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.32 #40, 0.27 #43, 0.26 #52), 01nv4h (0.06 #14, 0.02 #47, 0.02 #56) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 348 >> proper extension: 02y_2y; 01h8f; 029pnn; 0154d7; 01vsy9_; 05myd2; 0q1lp; 0ck91; 01c65z; 0pksh; >> query: (?x1678, 09nqf) <- profession(?x1678, ?x319), location(?x1678, ?x4151), film(?x1678, ?x641), ?x319 = 01d_h8 >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zyy4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.320 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #10945-04sylm PRED entity: 04sylm PRED relation: campuses! PRED expected values: 04sylm => 152 concepts (140 used for prediction) PRED predicted values (max 10 best out of 245): 017z88 (0.15 #54082, 0.09 #48068, 0.07 #73), 06182p (0.15 #54082, 0.09 #48068, 0.07 #54083), 01p7x7 (0.15 #54082, 0.09 #48068, 0.07 #54083), 09k9d0 (0.15 #54082, 0.09 #48068, 0.07 #54083), 02301 (0.15 #54082, 0.09 #48068, 0.07 #54083), 06thjt (0.15 #54082, 0.09 #48068, 0.07 #54083), 05njyy (0.15 #54082, 0.09 #48068, 0.07 #54083), 01n951 (0.15 #54082, 0.09 #48068, 0.07 #54083), 023zl (0.15 #54082, 0.09 #48068, 0.07 #54083), 02lv2v (0.15 #54082, 0.09 #48068, 0.07 #54083) >> Best rule #54082 for best value: >> intensional similarity = 4 >> extensional distance = 378 >> proper extension: 07vht; 02185j; 01lvrm; >> query: (?x2767, ?x11824) <- citytown(?x2767, ?x739), major_field_of_study(?x2767, ?x505), citytown(?x11824, ?x739), institution(?x1305, ?x11824) >> conf = 0.15 => this is the best rule for 22 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 12 EVAL 04sylm campuses! 04sylm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 152.000 140.000 0.148 http://example.org/education/educational_institution/campuses #10944-06m_5 PRED entity: 06m_5 PRED relation: medal PRED expected values: 02lq5w => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 3): 02lq67 (0.83 #4, 0.79 #22, 0.76 #121), 02lq5w (0.76 #20, 0.76 #23, 0.76 #122), 02lpp7 (0.74 #24, 0.72 #60, 0.71 #21) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 05r7t; >> query: (?x8420, 02lq67) <- country(?x1967, ?x8420), ?x1967 = 01cgz, origin(?x7951, ?x8420) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 012m_; *> query: (?x8420, 02lq5w) <- contains(?x8420, ?x8838), location(?x4895, ?x8420), official_language(?x8420, ?x5121) *> conf = 0.76 ranks of expected_values: 2 EVAL 06m_5 medal 02lq5w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 160.000 0.833 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #10943-02yr3z PRED entity: 02yr3z PRED relation: school_type PRED expected values: 01rs41 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 20): 01rs41 (0.63 #119, 0.60 #96, 0.59 #855), 05jxkf (0.61 #325, 0.51 #1824, 0.50 #1200), 07tf8 (0.14 #1228, 0.13 #1252, 0.12 #1205), 01_9fk (0.14 #323, 0.12 #898, 0.12 #990), 01_srz (0.12 #94, 0.12 #140, 0.11 #370), 02p0qmm (0.07 #860, 0.04 #929, 0.04 #1137), 01y64 (0.07 #34, 0.06 #126, 0.05 #195), 04qbv (0.06 #130, 0.05 #222, 0.04 #199), 06cs1 (0.05 #97, 0.04 #143, 0.03 #212), 04399 (0.04 #381, 0.04 #151, 0.03 #197) >> Best rule #119 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 0kz2w; 0473m9; 027mdh; 01xk7r; 05nrkb; >> query: (?x6904, 01rs41) <- school_type(?x6904, ?x1044), currency(?x6904, ?x170), category(?x6904, ?x134) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02yr3z school_type 01rs41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.627 http://example.org/education/educational_institution/school_type #10942-0f6lx PRED entity: 0f6lx PRED relation: role PRED expected values: 03qlv7 => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 83): 05r5c (0.56 #528, 0.55 #1568, 0.51 #4064), 0342h (0.42 #5724, 0.40 #316, 0.39 #420), 042v_gx (0.37 #217, 0.30 #1361, 0.27 #1465), 02sgy (0.28 #5726, 0.26 #6, 0.26 #318), 01vdm0 (0.27 #5753, 0.27 #7107, 0.27 #8669), 018vs (0.20 #326, 0.19 #430, 0.19 #14), 05842k (0.20 #287, 0.19 #5487, 0.19 #79), 01vj9c (0.18 #5424, 0.17 #5736, 0.16 #5216), 026t6 (0.18 #5411, 0.16 #8639, 0.16 #8745), 0l14qv (0.17 #4061, 0.16 #8641, 0.16 #3853) >> Best rule #528 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 01vvy; 0pcc0; 06wvj; 04bgy; 0484q; 02r38; >> query: (?x9021, 05r5c) <- type_of_union(?x9021, ?x566), role(?x9021, ?x214), place_of_death(?x9021, ?x739) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #4085 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 144 *> proper extension: 02nfjp; *> query: (?x9021, 03qlv7) <- profession(?x9021, ?x1614), role(?x9021, ?x214), ?x1614 = 01c72t *> conf = 0.05 ranks of expected_values: 38 EVAL 0f6lx role 03qlv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 167.000 167.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role #10941-04__f PRED entity: 04__f PRED relation: people! PRED expected values: 07mqps => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 46): 041rx (0.25 #4, 0.18 #312, 0.16 #1236), 0x67 (0.14 #703, 0.11 #2320, 0.11 #1319), 033tf_ (0.13 #1085, 0.12 #1008, 0.12 #392), 07bch9 (0.13 #254, 0.11 #562, 0.11 #793), 02w7gg (0.11 #387, 0.08 #926, 0.08 #618), 0xnvg (0.08 #1091, 0.07 #1014, 0.07 #167), 02ctzb (0.07 #554, 0.07 #785, 0.06 #15), 01qhm_ (0.07 #1084, 0.06 #1007, 0.03 #3240), 063k3h (0.07 #801, 0.06 #570, 0.06 #262), 048z7l (0.06 #40, 0.04 #656, 0.04 #2427) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 01vsps; 01t94_1; >> query: (?x7958, 041rx) <- award(?x7958, ?x3066), ?x3066 = 0gqy2, celebrities_impersonated(?x3649, ?x7958) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #173 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 033hqf; 0ly5n; 014zn0; 02_01w; 0h326; *> query: (?x7958, 07mqps) <- film(?x7958, ?x689), location(?x7958, ?x8451), celebrities_impersonated(?x3649, ?x7958) *> conf = 0.03 ranks of expected_values: 22 EVAL 04__f people! 07mqps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 108.000 108.000 0.250 http://example.org/people/ethnicity/people #10940-01r9fv PRED entity: 01r9fv PRED relation: artist! PRED expected values: 011k1h => 80 concepts (46 used for prediction) PRED predicted values (max 10 best out of 130): 015_1q (0.29 #158, 0.22 #1559, 0.20 #2119), 0fb0v (0.24 #146, 0.11 #847, 0.10 #6), 011k1h (0.22 #429, 0.13 #1550, 0.13 #1970), 01clyr (0.20 #32, 0.13 #1433, 0.12 #1573), 01t04r (0.20 #64, 0.09 #484, 0.05 #1045), 02y21l (0.20 #95, 0.06 #235, 0.04 #3877), 03rhqg (0.18 #2676, 0.18 #3236, 0.17 #575), 0181dw (0.18 #181, 0.16 #321, 0.14 #601), 0g768 (0.16 #456, 0.14 #2697, 0.13 #3537), 033hn8 (0.16 #433, 0.13 #3234, 0.13 #2674) >> Best rule #158 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 03f1zhf; 048tgl; 01k_0fp; >> query: (?x1544, 015_1q) <- artists(?x1000, ?x1544), gender(?x1544, ?x514), ?x1000 = 0xhtw, origin(?x1544, ?x2850) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #429 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 53 *> proper extension: 089tm; 01t_xp_; 0150jk; 01vsxdm; 0134s5; 0g_g2; 01j59b0; 014_lq; 01q99h; 07bzp; ... *> query: (?x1544, 011k1h) <- artist(?x1543, ?x1544), artists(?x2249, ?x1544), artists(?x1572, ?x1544), ?x1572 = 06by7, ?x2249 = 03lty *> conf = 0.22 ranks of expected_values: 3 EVAL 01r9fv artist! 011k1h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 80.000 46.000 0.294 http://example.org/music/record_label/artist #10939-0d05q4 PRED entity: 0d05q4 PRED relation: olympics PRED expected values: 06sks6 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 41): 06sks6 (0.88 #2782, 0.88 #2905, 0.87 #2659), 0kbws (0.65 #548, 0.64 #1166, 0.64 #671), 0kbvb (0.59 #541, 0.56 #623, 0.56 #130), 0jdk_ (0.59 #561, 0.54 #643, 0.52 #315), 0kbvv (0.56 #560, 0.54 #1178, 0.54 #396), 09n48 (0.54 #373, 0.50 #537, 0.46 #1237), 0sxrz (0.53 #103, 0.53 #227, 0.50 #144), 0jhn7 (0.50 #151, 0.47 #234, 0.47 #192), 018ctl (0.50 #542, 0.47 #788, 0.46 #870), 0swbd (0.44 #545, 0.44 #134, 0.43 #381) >> Best rule #2782 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 05r4w; 03rt9; >> query: (?x4092, 06sks6) <- adjoins(?x3730, ?x4092), currency(?x4092, ?x170), contains(?x3730, ?x5237) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d05q4 olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.882 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #10938-01pcj4 PRED entity: 01pcj4 PRED relation: student PRED expected values: 06fc0b 05ccxr => 140 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1566): 01wg982 (0.25 #366, 0.03 #12846, 0.02 #50286), 0488g9 (0.25 #1899, 0.03 #14379, 0.02 #22699), 02x8mt (0.25 #1602, 0.03 #14082, 0.02 #22402), 01mqnr (0.25 #1412, 0.03 #13892, 0.02 #22212), 01gbb4 (0.25 #1227, 0.03 #13707, 0.02 #22027), 06g2d1 (0.25 #1059, 0.03 #13539, 0.02 #21859), 026m0 (0.25 #1815, 0.03 #47575, 0.02 #18455), 01_x6v (0.25 #363, 0.02 #17003, 0.02 #21163), 09xvf7 (0.25 #2046, 0.02 #18686, 0.01 #39486), 0brkwj (0.25 #1395, 0.02 #18035, 0.01 #38835) >> Best rule #366 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03bmmc; >> query: (?x9879, 01wg982) <- student(?x9879, ?x6558), student(?x9879, ?x4320), major_field_of_study(?x9879, ?x5179), place_of_birth(?x4320, ?x7769), ?x6558 = 0gs1_ >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #51259 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 0jz9f; 0gsg7; 09d5h; 015_1q; 03mdt; 0mzkr; 02hvd; 09mfvx; 043g7l; 073tm9; ... *> query: (?x9879, 06fc0b) <- category(?x9879, ?x134), state_province_region(?x9879, ?x335), ?x335 = 059rby *> conf = 0.01 ranks of expected_values: 1554 EVAL 01pcj4 student 05ccxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 89.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01pcj4 student 06fc0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 140.000 89.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #10937-04jwly PRED entity: 04jwly PRED relation: films! PRED expected values: 018h2 => 111 concepts (50 used for prediction) PRED predicted values (max 10 best out of 57): 0d063v (0.14 #146), 0htp (0.14 #121), 05f4p (0.14 #95), 081pw (0.11 #628, 0.07 #159, 0.05 #2201), 02_h0 (0.11 #883, 0.07 #256, 0.05 #1827), 018h2 (0.11 #805, 0.03 #2534, 0.03 #2220), 04jjy (0.11 #790, 0.01 #1890, 0.01 #5831), 0d1w9 (0.07 #192, 0.04 #819, 0.02 #505), 07jq_ (0.07 #238, 0.03 #1337, 0.03 #1179), 01w1sx (0.07 #247, 0.03 #2289, 0.03 #3705) >> Best rule #146 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0bby9p5; >> query: (?x2833, 0d063v) <- film(?x1414, ?x2833), film(?x8147, ?x2833), award_winner(?x2833, ?x4320), ?x8147 = 01tnxc >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #805 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 02z5x7l; *> query: (?x2833, 018h2) <- nominated_for(?x618, ?x2833), country(?x2833, ?x94), genre(?x2833, ?x714), ?x714 = 0hn10 *> conf = 0.11 ranks of expected_values: 6 EVAL 04jwly films! 018h2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 111.000 50.000 0.143 http://example.org/film/film_subject/films #10936-0824r PRED entity: 0824r PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 19): 0fkvn (0.79 #1025, 0.78 #145, 0.76 #285), 0pqc5 (0.60 #1467, 0.58 #1306, 0.51 #2110), 060c4 (0.51 #1546, 0.49 #1988, 0.47 #1385), 060bp (0.43 #1363, 0.43 #1986, 0.41 #1544), 0789n (0.20 #69, 0.17 #29, 0.16 #290), 01gkgk (0.20 #66, 0.17 #26, 0.11 #207), 01t7n9 (0.16 #297, 0.15 #137, 0.13 #417), 0p5vf (0.14 #592, 0.13 #212, 0.13 #432), 01q24l (0.13 #1313, 0.10 #1133, 0.07 #1474), 02079p (0.12 #1031, 0.08 #391, 0.08 #411) >> Best rule #1025 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 018jmn; >> query: (?x4105, 0fkvn) <- jurisdiction_of_office(?x3959, ?x4105), jurisdiction_of_office(?x3959, ?x728), ?x728 = 059f4 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0824r jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.794 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #10935-0p9sw PRED entity: 0p9sw PRED relation: award! PRED expected values: 081nh 02q9kqf 09thp87 => 54 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2784): 02q9kqf (0.79 #37022, 0.79 #40390, 0.79 #67329), 014zcr (0.60 #26975, 0.56 #6780, 0.50 #10146), 03_gd (0.50 #13627, 0.43 #23725, 0.40 #3530), 03ym1 (0.47 #28595, 0.40 #11766, 0.33 #8400), 0c12h (0.44 #8543, 0.40 #5178, 0.33 #28738), 02kxbwx (0.40 #13637, 0.40 #3540, 0.36 #23735), 0hsmh (0.40 #16390, 0.40 #6293, 0.29 #26488), 02vyw (0.40 #14465, 0.40 #4368, 0.29 #24563), 0184jw (0.40 #5621, 0.36 #25816, 0.33 #29181), 02hfp_ (0.40 #5685, 0.36 #25880, 0.33 #29245) >> Best rule #37022 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x500, ?x902) <- ceremony(?x500, ?x6861), ceremony(?x500, ?x3173), ?x6861 = 05q7cj, award_winner(?x500, ?x902), ?x3173 = 0bzk2h >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 223, 734 EVAL 0p9sw award! 09thp87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 26.000 0.790 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0p9sw award! 02q9kqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 26.000 0.790 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0p9sw award! 081nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 54.000 26.000 0.790 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10934-0pmhf PRED entity: 0pmhf PRED relation: film PRED expected values: 076zy_g => 130 concepts (83 used for prediction) PRED predicted values (max 10 best out of 667): 02jxbw (0.72 #3561, 0.63 #51624, 0.62 #53405), 02d413 (0.72 #3561, 0.63 #51624, 0.62 #53405), 023gxx (0.72 #3561, 0.63 #51624, 0.62 #53405), 06_wqk4 (0.07 #1906, 0.06 #5467, 0.05 #12587), 02md2d (0.06 #78329), 01shy7 (0.05 #422, 0.05 #9323, 0.05 #3983), 0fphf3v (0.05 #3135, 0.05 #4916, 0.05 #6696), 0prrm (0.05 #9756, 0.04 #16877, 0.04 #15096), 06z8s_ (0.04 #129, 0.04 #3690, 0.04 #5470), 034qzw (0.04 #332, 0.04 #9233, 0.03 #23475) >> Best rule #3561 for best value: >> intensional similarity = 3 >> extensional distance = 119 >> proper extension: 01wxyx1; 049qx; 0mm1q; 02ts3h; 0p17j; 03ywyk; >> query: (?x2596, ?x69) <- vacationer(?x792, ?x2596), award(?x2596, ?x401), nominated_for(?x2596, ?x69) >> conf = 0.72 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0pmhf film 076zy_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 83.000 0.718 http://example.org/film/actor/film./film/performance/film #10933-011yth PRED entity: 011yth PRED relation: currency PRED expected values: 09nqf => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.82 #36, 0.81 #29, 0.81 #43), 01nv4h (0.03 #79, 0.03 #9, 0.03 #72), 02l6h (0.01 #137, 0.01 #11, 0.01 #116), 02gsvk (0.01 #132, 0.01 #69) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 171 >> proper extension: 018js4; 01hr1; 01ln5z; 01_mdl; 0872p_c; 05pbl56; 0jdgr; 04t6fk; 0k5g9; 011ysn; ... >> query: (?x1910, 09nqf) <- nominated_for(?x500, ?x1910), nominated_for(?x262, ?x1910), ?x500 = 0p9sw, film(?x1324, ?x1910) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011yth currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.815 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10932-02rqwhl PRED entity: 02rqwhl PRED relation: genre PRED expected values: 03mqtr => 74 concepts (52 used for prediction) PRED predicted values (max 10 best out of 110): 07s9rl0 (0.84 #477, 0.84 #5014, 0.77 #2623), 01jfsb (0.62 #3235, 0.32 #1560, 0.31 #2515), 02kdv5l (0.59 #3225, 0.32 #4658, 0.28 #1550), 02l7c8 (0.44 #493, 0.38 #5030, 0.33 #255), 03k9fj (0.39 #4667, 0.34 #369, 0.26 #3234), 01hmnh (0.28 #3599, 0.21 #4674, 0.17 #376), 04xvlr (0.27 #2624, 0.17 #5253, 0.17 #478), 0lsxr (0.25 #3231, 0.19 #1556, 0.17 #2631), 06cvj (0.24 #480, 0.20 #2746, 0.15 #3584), 06n90 (0.23 #4669, 0.20 #3236, 0.20 #14) >> Best rule #477 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 0d8w2n; >> query: (?x1420, 07s9rl0) <- genre(?x1420, ?x6674), country(?x1420, ?x94), ?x6674 = 01t_vv, language(?x1420, ?x732) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0g56t9t; 03hj5lq; 01jmyj; *> query: (?x1420, 03mqtr) <- genre(?x1420, ?x10928), film(?x157, ?x1420), ?x10928 = 02hmvc, nominated_for(?x1245, ?x1420) *> conf = 0.20 ranks of expected_values: 14 EVAL 02rqwhl genre 03mqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 74.000 52.000 0.844 http://example.org/film/film/genre #10931-01y665 PRED entity: 01y665 PRED relation: nominated_for PRED expected values: 0dr_4 => 118 concepts (61 used for prediction) PRED predicted values (max 10 best out of 406): 0dr_4 (0.30 #14556, 0.30 #92230, 0.29 #24270), 08phg9 (0.30 #14556, 0.30 #92230, 0.29 #24270), 0296vv (0.30 #14556, 0.30 #92230, 0.29 #24270), 06ztvyx (0.30 #14556, 0.29 #24270, 0.27 #4852), 02r858_ (0.04 #2884), 020bv3 (0.04 #8381, 0.03 #9998, 0.02 #29417), 03ln8b (0.04 #47225, 0.03 #24573, 0.02 #11623), 030cx (0.03 #695, 0.02 #12015, 0.02 #13633), 08xvpn (0.03 #1435, 0.02 #4669, 0.02 #6287), 05h43ls (0.03 #380) >> Best rule #14556 for best value: >> intensional similarity = 3 >> extensional distance = 386 >> proper extension: 02fybl; 032nl2; >> query: (?x3039, ?x1597) <- location(?x3039, ?x9699), film(?x3039, ?x1597), participant(?x3039, ?x989) >> conf = 0.30 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 01y665 nominated_for 0dr_4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 61.000 0.305 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10930-0488g9 PRED entity: 0488g9 PRED relation: profession PRED expected values: 0d2b38 => 132 concepts (107 used for prediction) PRED predicted values (max 10 best out of 87): 02hrh1q (0.76 #1013, 0.76 #10597, 0.76 #298), 0cbd2 (0.66 #864, 0.51 #4583, 0.50 #6013), 0np9r (0.37 #9440, 0.31 #11873, 0.26 #12160), 01c72t (0.37 #9440, 0.31 #11873, 0.26 #12160), 09jwl (0.35 #2876, 0.33 #588, 0.31 #3020), 0kyk (0.33 #883, 0.28 #4602, 0.27 #3315), 0nbcg (0.25 #599, 0.25 #2887, 0.21 #3031), 018gz8 (0.22 #1015, 0.21 #443, 0.18 #9167), 016z4k (0.22 #2864, 0.20 #576, 0.19 #3008), 0dz3r (0.20 #574, 0.17 #2862, 0.15 #3006) >> Best rule #1013 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 0lzb8; 05r5w; 02y_2y; 02wr2r; 02_wxh; 01x4r3; 01xwv7; 0sx5w; 01svq8; >> query: (?x11484, 02hrh1q) <- type_of_union(?x11484, ?x566), producer_type(?x11484, ?x632), people(?x3591, ?x11484) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1349 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 014zcr; 05ty4m; 01q_ph; 02lfcm; 0bxtg; 06cv1; 03f2_rc; 0c1pj; 05kfs; 02lk1s; ... *> query: (?x11484, 0d2b38) <- executive_produced_by(?x5946, ?x11484), profession(?x11484, ?x319), written_by(?x7800, ?x11484) *> conf = 0.01 ranks of expected_values: 76 EVAL 0488g9 profession 0d2b38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 132.000 107.000 0.761 http://example.org/people/person/profession #10929-07rzf PRED entity: 07rzf PRED relation: profession PRED expected values: 02hrh1q => 116 concepts (115 used for prediction) PRED predicted values (max 10 best out of 64): 02hrh1q (0.94 #1495, 0.92 #14529, 0.92 #5792), 09jwl (0.71 #7869, 0.22 #5205, 0.17 #13053), 01d_h8 (0.51 #3858, 0.35 #7559, 0.32 #3265), 03gjzk (0.45 #312, 0.34 #3868, 0.31 #1496), 0nbcg (0.39 #7880, 0.15 #5216, 0.12 #13064), 0dz3r (0.37 #7851, 0.11 #5187, 0.09 #13035), 0dxtg (0.34 #3866, 0.30 #7567, 0.29 #10380), 018gz8 (0.31 #462, 0.30 #166, 0.27 #314), 016z4k (0.29 #7853, 0.12 #5189, 0.11 #6521), 02jknp (0.25 #8, 0.25 #7561, 0.23 #452) >> Best rule #1495 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 06b0d2; 05ztm4r; 0q5hw; 01dy7j; 0bt7ws; 033jkj; 06hgym; >> query: (?x11465, 02hrh1q) <- languages(?x11465, ?x254), award(?x11465, ?x6878), award(?x4676, ?x6878), ?x4676 = 04cl1 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07rzf profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 115.000 0.942 http://example.org/people/person/profession #10928-09pxc PRED entity: 09pxc PRED relation: place_of_birth! PRED expected values: 04cw0n4 => 137 concepts (80 used for prediction) PRED predicted values (max 10 best out of 684): 0bn3jg (0.25 #2291, 0.23 #81005, 0.20 #4904), 020bg (0.14 #10368, 0.12 #12981, 0.08 #15594), 03crmd (0.14 #10012, 0.12 #12625, 0.08 #15238), 016hvl (0.14 #8049, 0.12 #10662, 0.02 #169856), 0ngg (0.12 #13045, 0.08 #15658, 0.07 #18271), 041wm (0.12 #12720, 0.08 #15333, 0.07 #17946), 03_fk9 (0.12 #12655, 0.08 #15268, 0.07 #17881), 028pzq (0.12 #12345, 0.08 #14958, 0.07 #17571), 01f2f8 (0.12 #11809, 0.08 #14422, 0.07 #17035), 07vc_9 (0.12 #10665, 0.08 #13278, 0.07 #15891) >> Best rule #2291 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0g7yx; 0c630; >> query: (?x11841, 0bn3jg) <- time_zones(?x11841, ?x2864), category(?x11841, ?x134), contains(?x11842, ?x11841), ?x2864 = 02llzg, ?x11842 = 06w92 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09pxc place_of_birth! 04cw0n4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 80.000 0.250 http://example.org/people/person/place_of_birth #10927-04_tv PRED entity: 04_tv PRED relation: major_field_of_study! PRED expected values: 01t8sr 0f1nl 07tg4 01rc6f => 63 concepts (35 used for prediction) PRED predicted values (max 10 best out of 615): 01w5m (0.67 #8546, 0.65 #14735, 0.50 #15859), 0bwfn (0.67 #8722, 0.61 #14911, 0.50 #5348), 09f2j (0.65 #14793, 0.53 #15917, 0.50 #8604), 065y4w7 (0.60 #3949, 0.58 #8447, 0.50 #6198), 07w0v (0.60 #3956, 0.58 #8454, 0.50 #6205), 0187nd (0.60 #4329, 0.50 #6578, 0.43 #7140), 0gl5_ (0.60 #4200, 0.42 #8698, 0.40 #4762), 07wrz (0.58 #8497, 0.50 #6248, 0.43 #14686), 02zd460 (0.57 #6933, 0.52 #14809, 0.51 #17065), 07t90 (0.57 #6906, 0.50 #6344, 0.43 #14782) >> Best rule #8546 for best value: >> intensional similarity = 10 >> extensional distance = 10 >> proper extension: 04rjg; 01jzxy; 03g3w; 0fdys; 01lj9; 03qsdpk; >> query: (?x1527, 01w5m) <- major_field_of_study(?x620, ?x1527), major_field_of_study(?x1527, ?x4321), major_field_of_study(?x10297, ?x1527), major_field_of_study(?x6193, ?x1527), major_field_of_study(?x4410, ?x1527), ?x4410 = 017j69, school(?x1160, ?x10297), major_field_of_study(?x12530, ?x4321), contains(?x362, ?x6193), ?x12530 = 02cvcd >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #8523 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 10 *> proper extension: 04rjg; 01jzxy; 03g3w; 0fdys; 01lj9; 03qsdpk; *> query: (?x1527, 07tg4) <- major_field_of_study(?x620, ?x1527), major_field_of_study(?x1527, ?x4321), major_field_of_study(?x10297, ?x1527), major_field_of_study(?x6193, ?x1527), major_field_of_study(?x4410, ?x1527), ?x4410 = 017j69, school(?x1160, ?x10297), major_field_of_study(?x12530, ?x4321), contains(?x362, ?x6193), ?x12530 = 02cvcd *> conf = 0.42 ranks of expected_values: 35, 96, 159, 545 EVAL 04_tv major_field_of_study! 01rc6f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 63.000 35.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04_tv major_field_of_study! 07tg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 63.000 35.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04_tv major_field_of_study! 0f1nl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 63.000 35.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04_tv major_field_of_study! 01t8sr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 63.000 35.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10926-0dh73w PRED entity: 0dh73w PRED relation: film_production_design_by! PRED expected values: 04q827 => 111 concepts (64 used for prediction) PRED predicted values (max 10 best out of 175): 02754c9 (0.33 #111, 0.12 #434, 0.11 #595), 0b9rdk (0.33 #100, 0.12 #423, 0.11 #584), 011yrp (0.33 #2, 0.12 #325, 0.11 #486), 0168ls (0.25 #183, 0.12 #345, 0.11 #506), 0h0wd9 (0.12 #473, 0.11 #634, 0.03 #795), 09qycb (0.12 #472, 0.11 #633, 0.03 #794), 02ptczs (0.12 #467, 0.11 #628, 0.03 #789), 01rnly (0.12 #465, 0.11 #626, 0.03 #787), 0hv4t (0.12 #438, 0.11 #599, 0.03 #760), 0286gm1 (0.12 #429, 0.11 #590, 0.03 #751) >> Best rule #111 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 03qhyn8; >> query: (?x4168, 02754c9) <- film_production_design_by(?x3919, ?x4168), award_winner(?x6766, ?x4168), crewmember(?x3919, ?x9769) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dh73w film_production_design_by! 04q827 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 64.000 0.333 http://example.org/film/film/film_production_design_by #10925-02dr9j PRED entity: 02dr9j PRED relation: featured_film_locations PRED expected values: 02_286 => 92 concepts (55 used for prediction) PRED predicted values (max 10 best out of 78): 02_286 (0.25 #20, 0.22 #741, 0.21 #981), 04jpl (0.12 #9, 0.12 #249, 0.10 #730), 030qb3t (0.12 #39, 0.09 #3404, 0.09 #3645), 0rh6k (0.12 #1, 0.06 #722, 0.06 #241), 01_d4 (0.12 #47, 0.06 #1248, 0.05 #1488), 03h64 (0.12 #62, 0.03 #302, 0.02 #783), 02jx1 (0.12 #35, 0.03 #275, 0.02 #756), 056_y (0.12 #98, 0.03 #338, 0.02 #819), 04wgh (0.12 #34, 0.03 #274, 0.02 #755), 06y57 (0.06 #824, 0.06 #343, 0.06 #1064) >> Best rule #20 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 061681; 0125xq; 03yvf2; >> query: (?x7214, 02_286) <- nominated_for(?x6860, ?x7214), nominated_for(?x3508, ?x7214), ?x3508 = 05ztrmj, genre(?x7214, ?x53), ?x6860 = 018wdw >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02dr9j featured_film_locations 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 55.000 0.250 http://example.org/film/film/featured_film_locations #10924-06bnz PRED entity: 06bnz PRED relation: locations! PRED expected values: 081pw => 180 concepts (161 used for prediction) PRED predicted values (max 10 best out of 112): 01gqg3 (0.43 #214, 0.21 #602, 0.20 #1506), 06k75 (0.21 #572, 0.13 #3882, 0.12 #1347), 081pw (0.20 #1422, 0.20 #259, 0.14 #130), 086m1 (0.20 #322, 0.12 #1485, 0.07 #710), 01w1sx (0.17 #91, 0.16 #1512, 0.14 #220), 05t2fh4 (0.17 #122, 0.14 #251, 0.12 #1543), 022840 (0.17 #61, 0.14 #190, 0.10 #319), 024jvz (0.17 #75, 0.14 #204, 0.10 #333), 0k4y6 (0.16 #1495, 0.13 #3568, 0.09 #4604), 0dl4z (0.14 #157, 0.07 #545, 0.07 #2098) >> Best rule #214 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 04wsz; >> query: (?x1603, 01gqg3) <- contains(?x1603, ?x992), service_location(?x555, ?x1603), partially_contains(?x6956, ?x1603) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1422 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 0jcpw; *> query: (?x1603, 081pw) <- contains(?x1603, ?x7184), adjoins(?x344, ?x1603), taxonomy(?x7184, ?x939) *> conf = 0.20 ranks of expected_values: 3 EVAL 06bnz locations! 081pw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 180.000 161.000 0.429 http://example.org/time/event/locations #10923-057xs89 PRED entity: 057xs89 PRED relation: nominated_for PRED expected values: 01hr1 033g4d 02yvct 02xtxw 0900j5 0639bg 02xs6_ 047wh1 026wlxw => 45 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1993): 01pgp6 (0.77 #4600, 0.77 #6134, 0.73 #1534), 05hjnw (0.67 #3799, 0.57 #2265, 0.54 #5331), 02c638 (0.67 #3355, 0.57 #1821, 0.54 #4887), 03hkch7 (0.67 #3503, 0.54 #5035, 0.30 #8101), 0m313 (0.67 #7677, 0.33 #3079, 0.31 #4611), 026p4q7 (0.64 #8002, 0.33 #3404, 0.31 #4936), 0gmcwlb (0.58 #7834, 0.44 #3236, 0.31 #4768), 049xgc (0.58 #8502, 0.33 #3904, 0.31 #5436), 0h03fhx (0.57 #2211, 0.33 #3745, 0.30 #8343), 0h1x5f (0.57 #2879, 0.33 #4413, 0.23 #5945) >> Best rule #4600 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0f4x7; 09sb52; 02x73k6; 05pcn59; 04kxsb; 09sdmz; 099ck7; >> query: (?x3019, ?x408) <- award(?x3101, ?x3019), award(?x2443, ?x3019), award(?x408, ?x3019), ?x3101 = 0dvmd, ?x2443 = 0237fw, nominated_for(?x3019, ?x86) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1833 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 0789_m; *> query: (?x3019, 02yvct) <- award(?x1958, ?x3019), award(?x794, ?x3019), award_nominee(?x241, ?x794), produced_by(?x437, ?x794), ?x1958 = 02wgln, film(?x794, ?x370) *> conf = 0.43 ranks of expected_values: 59, 248, 289, 299, 334, 528, 557, 566 EVAL 057xs89 nominated_for 026wlxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 16.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 057xs89 nominated_for 047wh1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 45.000 16.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 057xs89 nominated_for 02xs6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 16.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 057xs89 nominated_for 0639bg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 45.000 16.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 057xs89 nominated_for 0900j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 45.000 16.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 057xs89 nominated_for 02xtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 45.000 16.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 057xs89 nominated_for 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 45.000 16.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 057xs89 nominated_for 033g4d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 16.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 057xs89 nominated_for 01hr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 45.000 16.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10922-02yy_j PRED entity: 02yy_j PRED relation: profession PRED expected values: 02hrh1q => 81 concepts (42 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.92 #2605, 0.89 #2749, 0.84 #3037), 03gjzk (0.49 #1453, 0.49 #1886, 0.42 #1741), 0kyk (0.48 #3628, 0.33 #4492, 0.33 #4780), 018gz8 (0.47 #1599, 0.47 #1743, 0.44 #1888), 02krf9 (0.40 #23, 0.38 #887, 0.21 #743), 015h31 (0.35 #888, 0.20 #24, 0.14 #1898), 09jwl (0.30 #3042, 0.27 #2898, 0.27 #1024), 0n1h (0.22 #153, 0.17 #585, 0.14 #729), 016z4k (0.22 #148, 0.15 #4326, 0.14 #3030), 01c72t (0.20 #20, 0.11 #4342, 0.11 #164) >> Best rule #2605 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 02h8hr; 0c5vh; >> query: (?x9468, 02hrh1q) <- profession(?x9468, ?x524), person(?x1315, ?x9468), profession(?x5309, ?x524), ?x5309 = 03v40v >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02yy_j profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 42.000 0.915 http://example.org/people/person/profession #10921-04bs3j PRED entity: 04bs3j PRED relation: location PRED expected values: 03dm7 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 275): 0xhj2 (0.65 #8849, 0.65 #30568, 0.61 #27350), 030qb3t (0.40 #83, 0.33 #887, 0.27 #8127), 02_286 (0.28 #841, 0.21 #1645, 0.19 #5668), 01nl79 (0.20 #665, 0.02 #6296, 0.01 #9514), 0b1t1 (0.20 #473, 0.02 #6104, 0.01 #9322), 01x96 (0.19 #84478), 0r0m6 (0.12 #2630, 0.10 #4239, 0.07 #13090), 0cr3d (0.11 #1753, 0.09 #32322, 0.09 #44391), 01n7q (0.10 #2475, 0.06 #31436, 0.06 #14543), 0cc56 (0.08 #4078, 0.07 #2469, 0.07 #6492) >> Best rule #8849 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 094xh; >> query: (?x545, ?x11937) <- place_of_birth(?x545, ?x11937), people(?x1050, ?x545), celebrity(?x7022, ?x545) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #2991 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 018z_c; *> query: (?x545, 03dm7) <- nominated_for(?x545, ?x11806), celebrity(?x7022, ?x545), genre(?x11806, ?x53) *> conf = 0.02 ranks of expected_values: 78 EVAL 04bs3j location 03dm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 153.000 153.000 0.654 http://example.org/people/person/places_lived./people/place_lived/location #10920-015czt PRED entity: 015czt PRED relation: company PRED expected values: 07wg3 => 21 concepts (20 used for prediction) PRED predicted values (max 10 best out of 356): 01j_x (0.67 #1681, 0.60 #1341, 0.50 #1002), 07wj1 (0.67 #1599, 0.60 #1259, 0.33 #579), 060ppp (0.60 #1951, 0.44 #2290, 0.37 #3997), 0300cp (0.60 #1750, 0.44 #2089, 0.37 #3796), 087c7 (0.60 #1706, 0.38 #2045, 0.37 #3408), 01qygl (0.60 #1900, 0.38 #2239, 0.37 #3946), 01s73z (0.60 #1811, 0.38 #2150, 0.35 #2833), 03s7h (0.50 #1968, 0.42 #4014, 0.41 #2990), 02r5dz (0.50 #1772, 0.38 #2111, 0.37 #3818), 019rl6 (0.50 #1863, 0.38 #2202, 0.37 #3909) >> Best rule #1681 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 01cpjx; >> query: (?x11696, 01j_x) <- company(?x11696, ?x12236), company(?x11696, ?x8540), company(?x13983, ?x12236), ?x13983 = 01c83m, company(?x2916, ?x8540), people(?x268, ?x2916), location(?x2916, ?x12461), award(?x2916, ?x591), film(?x2916, ?x1386), nationality(?x2916, ?x512), actor(?x10492, ?x2916) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1347 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 0130xz; 0g686w; *> query: (?x11696, 07wg3) <- company(?x11696, ?x12236), company(?x11696, ?x8540), company(?x13983, ?x12236), ?x13983 = 01c83m, company(?x5869, ?x8540), company(?x2916, ?x8540), people(?x268, ?x2916), location(?x2916, ?x12461), nominated_for(?x2916, ?x6069), nationality(?x2916, ?x512), award(?x2916, ?x591), award_winner(?x3056, ?x5869) *> conf = 0.20 ranks of expected_values: 85 EVAL 015czt company 07wg3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 21.000 20.000 0.667 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #10919-0pdp8 PRED entity: 0pdp8 PRED relation: film_crew_role PRED expected values: 09zzb8 01pvkk => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.75 #1425, 0.73 #741, 0.72 #779), 02r96rf (0.67 #1428, 0.64 #1009, 0.64 #1122), 09vw2b7 (0.67 #1432, 0.61 #1126, 0.61 #1355), 0dxtw (0.38 #1436, 0.37 #1130, 0.36 #979), 01vx2h (0.34 #1437, 0.32 #1018, 0.32 #980), 01pvkk (0.30 #792, 0.30 #754, 0.28 #1663), 02ynfr (0.19 #129, 0.17 #1442, 0.17 #758), 015h31 (0.17 #10, 0.10 #1128, 0.10 #977), 0215hd (0.14 #1445, 0.13 #1026, 0.12 #988), 089g0h (0.12 #1446, 0.11 #1027, 0.11 #989) >> Best rule #1425 for best value: >> intensional similarity = 3 >> extensional distance = 731 >> proper extension: 04lqvlr; 0gh6j94; >> query: (?x2329, 09zzb8) <- film_crew_role(?x2329, ?x1284), film_release_distribution_medium(?x2329, ?x81), ?x1284 = 0ch6mp2 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 0pdp8 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 77.000 0.749 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0pdp8 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.749 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10918-09d11 PRED entity: 09d11 PRED relation: risk_factors PRED expected values: 0217g => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 95): 0c58k (0.80 #1126, 0.73 #930, 0.71 #1624), 0jpmt (0.45 #929, 0.40 #2565, 0.40 #352), 05zppz (0.38 #1798, 0.38 #524, 0.36 #908), 0fltx (0.36 #939, 0.33 #125, 0.31 #2232), 0k95h (0.33 #103, 0.27 #917, 0.27 #1113), 0217g (0.33 #179, 0.25 #705, 0.25 #274), 02y0js (0.33 #49, 0.25 #239, 0.22 #764), 0gk4g (0.33 #53, 0.25 #243, 0.17 #387), 0g02vk (0.33 #72, 0.25 #262, 0.17 #406), 0hg11 (0.33 #54, 0.25 #244, 0.17 #388) >> Best rule #1126 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 01l2m3; 0hg45; 07x16; >> query: (?x6655, 0c58k) <- risk_factors(?x6655, ?x268), symptom_of(?x4905, ?x6655), people(?x268, ?x2916), company(?x2916, ?x8540), award_winner(?x4598, ?x2916) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #179 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 072hv; *> query: (?x6655, 0217g) <- risk_factors(?x5784, ?x6655), symptom_of(?x4905, ?x6655), risk_factors(?x6655, ?x6656), notable_people_with_this_condition(?x6656, ?x8708), ?x8708 = 01vn0t_ *> conf = 0.33 ranks of expected_values: 6 EVAL 09d11 risk_factors 0217g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 65.000 65.000 0.800 http://example.org/medicine/disease/risk_factors #10917-012gk9 PRED entity: 012gk9 PRED relation: prequel! PRED expected values: 015bpl => 84 concepts (43 used for prediction) PRED predicted values (max 10 best out of 34): 0140g4 (0.04 #366, 0.01 #907, 0.01 #1088), 0f4_2k (0.03 #643, 0.03 #823), 014nq4 (0.03 #601, 0.03 #781), 014lc_ (0.03 #722), 0dfw0 (0.01 #989, 0.01 #1350), 0dtfn (0.01 #928, 0.01 #1289), 0315rp (0.01 #1045), 04mcw4 (0.01 #983), 0gffmn8 (0.01 #961), 0dnqr (0.01 #958) >> Best rule #366 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 02754c9; >> query: (?x8971, 0140g4) <- award(?x8971, ?x1312), nominated_for(?x102, ?x8971), ?x1312 = 07cbcy, nominated_for(?x7489, ?x8971) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 012gk9 prequel! 015bpl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 43.000 0.042 http://example.org/film/film/prequel #10916-06w87 PRED entity: 06w87 PRED relation: performance_role! PRED expected values: 0p5mw => 79 concepts (49 used for prediction) PRED predicted values (max 10 best out of 650): 01vrncs (0.50 #529, 0.33 #1053, 0.33 #141), 01vsy95 (0.50 #562, 0.33 #174, 0.29 #1347), 0p5mw (0.50 #673, 0.25 #412, 0.17 #934), 02rn_bj (0.43 #2971, 0.40 #3361, 0.38 #3493), 01r0t_j (0.43 #1403, 0.38 #1925, 0.38 #1664), 050z2 (0.40 #834, 0.38 #1618, 0.33 #2530), 02s6sh (0.40 #899, 0.33 #2595, 0.33 #2463), 01vn35l (0.40 #817, 0.33 #2513, 0.33 #2381), 05qhnq (0.40 #862, 0.33 #342, 0.25 #1646), 04s5_s (0.40 #907, 0.33 #387, 0.25 #1691) >> Best rule #529 for best value: >> intensional similarity = 23 >> extensional distance = 2 >> proper extension: 042v_gx; >> query: (?x736, 01vrncs) <- role(?x8014, ?x736), role(?x5926, ?x736), role(?x5676, ?x736), role(?x3991, ?x736), role(?x1437, ?x736), performance_role(?x736, ?x1969), instrumentalists(?x736, ?x2987), role(?x3401, ?x736), role(?x3166, ?x736), ?x8014 = 0214km, performance_role(?x75, ?x736), ?x5926 = 0cfdd, role(?x5676, ?x8172), role(?x5676, ?x745), role(?x736, ?x228), ?x228 = 0l14qv, profession(?x3401, ?x319), ?x8172 = 06rvn, ?x1437 = 01vdm0, ?x3166 = 0qdyf, ?x745 = 01vj9c, ?x1969 = 04rzd, role(?x211, ?x3991) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #673 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 2 *> proper extension: 04rzd; *> query: (?x736, 0p5mw) <- role(?x8014, ?x736), role(?x227, ?x736), performance_role(?x736, ?x2310), instrumentalists(?x736, ?x2987), role(?x1955, ?x736), role(?x2944, ?x8014), role(?x1332, ?x8014), ?x2944 = 0l14j_, ?x2310 = 0gghm, ?x1332 = 03qlv7, role(?x11186, ?x8014), role(?x5452, ?x8014), role(?x2698, ?x8014), ?x11186 = 01304j, ?x2698 = 09hnb, profession(?x5452, ?x131), artists(?x114, ?x5452), role(?x736, ?x5417), ?x227 = 0342h, ?x5417 = 02w3w *> conf = 0.50 ranks of expected_values: 3 EVAL 06w87 performance_role! 0p5mw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 49.000 0.500 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #10915-01hqhm PRED entity: 01hqhm PRED relation: country PRED expected values: 09c7w0 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.85 #369, 0.84 #431, 0.81 #1169), 07ssc (0.37 #3261, 0.37 #5108, 0.34 #2657), 07s9rl0 (0.18 #2702, 0.07 #3881, 0.06 #1964), 0345h (0.13 #89, 0.12 #3103, 0.12 #1624), 0f8l9c (0.13 #81, 0.11 #757, 0.10 #2660), 0chghy (0.08 #74, 0.06 #504, 0.06 #1058), 0d060g (0.06 #70, 0.06 #2463, 0.05 #3332), 03rjj (0.06 #68, 0.04 #251, 0.04 #2647), 03_3d (0.04 #5670, 0.03 #5547, 0.03 #5609), 03h64 (0.03 #1643, 0.03 #1030, 0.03 #1092) >> Best rule #369 for best value: >> intensional similarity = 3 >> extensional distance = 137 >> proper extension: 02725hs; 0fphgb; 0gjcrrw; 07kb7vh; 0n_hp; 01s9vc; >> query: (?x2090, 09c7w0) <- language(?x2090, ?x254), film_crew_role(?x2090, ?x137), nominated_for(?x4098, ?x2090) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hqhm country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.849 http://example.org/film/film/country #10914-0h0yt PRED entity: 0h0yt PRED relation: location PRED expected values: 09ctj => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 238): 0n9r8 (0.69 #27273, 0.49 #32087, 0.43 #59369), 030qb3t (0.30 #4893, 0.29 #1685, 0.28 #6497), 02_286 (0.22 #77054, 0.18 #39342, 0.17 #26505), 059rby (0.10 #2422, 0.10 #3224, 0.07 #1620), 0cr3d (0.09 #1747, 0.08 #77162, 0.08 #10570), 0cc56 (0.09 #5669, 0.07 #1659, 0.06 #4867), 0rh6k (0.07 #2410, 0.07 #3212, 0.06 #5618), 0hyxv (0.07 #209, 0.03 #8230, 0.02 #15448), 05qtj (0.07 #239, 0.03 #12270, 0.03 #21093), 09ctj (0.07 #758) >> Best rule #27273 for best value: >> intensional similarity = 3 >> extensional distance = 500 >> proper extension: 02wrhj; >> query: (?x7746, ?x6764) <- award_winner(?x472, ?x7746), place_of_birth(?x7746, ?x6764), location(?x7746, ?x362) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #758 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 02tr7d; 065jlv; 01ksr1; *> query: (?x7746, 09ctj) <- award_winner(?x374, ?x7746), award_nominee(?x7746, ?x2493), ?x2493 = 01hkhq *> conf = 0.07 ranks of expected_values: 10 EVAL 0h0yt location 09ctj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 136.000 136.000 0.687 http://example.org/people/person/places_lived./people/place_lived/location #10913-09xvf7 PRED entity: 09xvf7 PRED relation: award PRED expected values: 0gq9h => 82 concepts (78 used for prediction) PRED predicted values (max 10 best out of 282): 07bdd_ (0.52 #4927, 0.51 #1281, 0.50 #6142), 05p1dby (0.42 #1322, 0.41 #2943, 0.41 #2538), 0gq9h (0.35 #1293, 0.34 #5344, 0.33 #4939), 09sb52 (0.24 #9357, 0.21 #10977, 0.21 #12597), 0gqy2 (0.17 #975, 0.10 #2191, 0.08 #14341), 019f4v (0.15 #7763, 0.13 #7358, 0.10 #67), 0ck27z (0.15 #9408, 0.12 #2118, 0.12 #5763), 040njc (0.15 #6489, 0.14 #5274, 0.14 #7704), 0gq_d (0.15 #18227, 0.13 #21873, 0.05 #4274), 018wng (0.15 #18227, 0.13 #21873, 0.04 #4093) >> Best rule #4927 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 0kk9v; >> query: (?x13011, 07bdd_) <- award_nominee(?x13011, ?x788), film(?x788, ?x186), state_province_region(?x788, ?x1227) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1293 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 76 *> proper extension: 01jq34; 0hm0k; 0283xx2; 01zcrv; *> query: (?x13011, 0gq9h) <- award_winner(?x13011, ?x788), film(?x788, ?x186), production_companies(?x1804, ?x788) *> conf = 0.35 ranks of expected_values: 3 EVAL 09xvf7 award 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 78.000 0.516 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10912-05b4w PRED entity: 05b4w PRED relation: country! PRED expected values: 06wrt 03fyrh 01yfj => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 24): 06wrt (0.85 #55, 0.73 #295, 0.72 #223), 03_8r (0.81 #561, 0.79 #225, 0.79 #633), 07gyv (0.70 #220, 0.65 #28, 0.64 #628), 07jbh (0.70 #39, 0.69 #63, 0.68 #111), 07bs0 (0.70 #30, 0.65 #54, 0.58 #246), 01hp22 (0.65 #53, 0.61 #29, 0.60 #557), 03fyrh (0.60 #563, 0.58 #107, 0.58 #59), 07rlg (0.60 #1, 0.58 #49, 0.50 #193), 02vx4 (0.54 #51, 0.48 #27, 0.45 #99), 0dwxr (0.54 #60, 0.48 #36, 0.44 #228) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 04v3q; >> query: (?x2513, 06wrt) <- film_release_region(?x3843, ?x2513), country(?x359, ?x2513), ?x3843 = 080nwsb >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 19 EVAL 05b4w country! 01yfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 168.000 168.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 05b4w country! 03fyrh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 168.000 168.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 05b4w country! 06wrt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #10911-0gr0m PRED entity: 0gr0m PRED relation: award! PRED expected values: 07xr3w 0cqh57 09bxq9 => 53 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2609): 0c3dzk (0.80 #13376, 0.79 #16720, 0.79 #33437), 06hzsx (0.80 #13376, 0.79 #16720, 0.79 #33437), 0627sn (0.80 #13376, 0.79 #16720, 0.79 #33437), 01t07j (0.44 #480, 0.33 #20542, 0.20 #10512), 0693l (0.42 #20916, 0.33 #854, 0.23 #70226), 03_gd (0.40 #10201, 0.25 #20231, 0.23 #70226), 01ts_3 (0.38 #22106, 0.33 #2044, 0.23 #70226), 02kxbwx (0.33 #20241, 0.33 #10211, 0.33 #179), 0c12h (0.33 #11845, 0.33 #1813, 0.30 #5157), 02f93t (0.33 #22744, 0.33 #2682, 0.27 #12714) >> Best rule #13376 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 027dtxw; 040njc; 02g3v6; 02r22gf; 019f4v; 0gs9p; 02qvyrt; 02qyntr; 02x201b; >> query: (?x1243, ?x2466) <- nominated_for(?x1243, ?x3535), award_winner(?x1243, ?x2466), award(?x185, ?x1243), ?x3535 = 0f4yh >> conf = 0.80 => this is the best rule for 3 predicted values *> Best rule #86954 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 211 *> proper extension: 0h53c_5; *> query: (?x1243, ?x669) <- nominated_for(?x1243, ?x3535), award_winner(?x1243, ?x2466), award(?x185, ?x1243), nominated_for(?x669, ?x3535) *> conf = 0.13 ranks of expected_values: 517, 549, 734 EVAL 0gr0m award! 09bxq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 26.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr0m award! 0cqh57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 26.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gr0m award! 07xr3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 26.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10910-025ts_z PRED entity: 025ts_z PRED relation: nominated_for! PRED expected values: 0bdwqv => 78 concepts (68 used for prediction) PRED predicted values (max 10 best out of 209): 0gqy2 (0.57 #2764, 0.18 #4204, 0.16 #2044), 0gq9h (0.46 #2703, 0.25 #5583, 0.24 #4143), 0gs9p (0.39 #2705, 0.21 #4145, 0.21 #5585), 019f4v (0.37 #2694, 0.20 #4134, 0.20 #1974), 0k611 (0.34 #2714, 0.20 #554, 0.20 #5594), 0fbtbt (0.33 #162, 0.19 #13206, 0.19 #10322), 0bdw6t (0.33 #85, 0.19 #13206, 0.19 #10322), 0ck27z (0.33 #72, 0.19 #13206, 0.19 #10322), 0cqhb3 (0.33 #201, 0.19 #13206, 0.19 #10322), 0fbvqf (0.33 #39, 0.10 #2199, 0.04 #16333) >> Best rule #2764 for best value: >> intensional similarity = 3 >> extensional distance = 331 >> proper extension: 02gjrc; >> query: (?x8770, 0gqy2) <- nominated_for(?x880, ?x8770), award(?x2033, ?x880), ?x2033 = 01ycbq >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #370 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 05sw5b; *> query: (?x8770, 0bdwqv) <- film(?x3815, ?x8770), genre(?x8770, ?x53), ?x3815 = 02g5h5 *> conf = 0.12 ranks of expected_values: 57 EVAL 025ts_z nominated_for! 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 78.000 68.000 0.568 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10909-0y_9q PRED entity: 0y_9q PRED relation: language PRED expected values: 02h40lc => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 47): 02h40lc (0.91 #356, 0.90 #1189, 0.90 #830), 064_8sq (0.27 #199, 0.25 #495, 0.24 #435), 04306rv (0.23 #182, 0.20 #537, 0.19 #241), 06nm1 (0.20 #11, 0.11 #365, 0.10 #1257), 0jzc (0.13 #256, 0.12 #552, 0.12 #197), 0653m (0.12 #189, 0.07 #307, 0.06 #130), 02bjrlw (0.11 #119, 0.08 #1367, 0.08 #829), 03_9r (0.11 #128, 0.08 #1316, 0.07 #246), 06b_j (0.10 #318, 0.10 #555, 0.10 #259), 04h9h (0.08 #220, 0.07 #102, 0.06 #4362) >> Best rule #356 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 07bz5; >> query: (?x5304, 02h40lc) <- award(?x5304, ?x289), nominated_for(?x7249, ?x5304), gender(?x7249, ?x231), list(?x5304, ?x3004) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0y_9q language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.910 http://example.org/film/film/language #10908-0bbm7r PRED entity: 0bbm7r PRED relation: honored_for! PRED expected values: 027hjff => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 97): 02q690_ (0.73 #1853, 0.27 #2401, 0.25 #3414), 05c1t6z (0.37 #1811, 0.26 #3372, 0.20 #131), 03nnm4t (0.29 #1861, 0.23 #3422, 0.20 #2101), 0275n3y (0.27 #2401, 0.17 #1862, 0.10 #8764), 027hjff (0.27 #2401, 0.10 #8764, 0.10 #1846), 092t4b (0.27 #2401, 0.10 #8764, 0.09 #9725), 0bvhz9 (0.27 #2401, 0.10 #8764, 0.09 #9725), 0clfdj (0.27 #2401, 0.10 #8764, 0.09 #9725), 073hd1 (0.27 #2401, 0.10 #8764, 0.09 #9725), 09gkdln (0.27 #2401, 0.10 #224, 0.09 #344) >> Best rule #1853 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 0gpjbt; >> query: (?x6023, 02q690_) <- honored_for(?x2292, ?x6023), award_winner(?x2292, ?x3895), ?x3895 = 06jnvs >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2401 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 03y3bp7; 03cf9ly; *> query: (?x6023, ?x2292) <- genre(?x6023, ?x53), category(?x6023, ?x134), nominated_for(?x1867, ?x6023), award_winner(?x2292, ?x1867) *> conf = 0.27 ranks of expected_values: 5 EVAL 0bbm7r honored_for! 027hjff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 107.000 107.000 0.732 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #10907-050g1v PRED entity: 050g1v PRED relation: parent_genre! PRED expected values: 0gg8l => 12 concepts (10 used for prediction) PRED predicted values (max 10 best out of 184): 01n5sn (0.20 #200, 0.07 #466, 0.04 #2148), 07ym47 (0.20 #58, 0.07 #324, 0.04 #2148), 01flzb (0.20 #260, 0.07 #526, 0.04 #2148), 0y3_8 (0.13 #307, 0.08 #574, 0.03 #1110), 0bt7w (0.13 #356, 0.08 #623, 0.03 #1159), 01b4p4 (0.13 #433, 0.08 #700, 0.02 #1236), 0dn16 (0.13 #278, 0.08 #545, 0.02 #1081), 01skxk (0.13 #371, 0.05 #638, 0.02 #1174), 0133k0 (0.13 #468, 0.05 #735, 0.01 #1271), 01_qp_ (0.13 #443, 0.05 #710) >> Best rule #200 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 01fbr2; 01n5sn; 02qcqkl; >> query: (?x13216, 01n5sn) <- parent_genre(?x13216, ?x5717), ?x5717 = 016cjb >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1179 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 255 *> proper extension: 02kx4w; 07s72n; 021vlg; *> query: (?x13216, 0gg8l) <- parent_genre(?x13216, ?x5717), artists(?x5717, ?x12743), artists(?x5717, ?x8583), student(?x5306, ?x8583), film(?x12743, ?x10769), artist(?x5634, ?x12743) *> conf = 0.01 ranks of expected_values: 138 EVAL 050g1v parent_genre! 0gg8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 12.000 10.000 0.200 http://example.org/music/genre/parent_genre #10906-0dnw1 PRED entity: 0dnw1 PRED relation: currency PRED expected values: 09nqf => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.81 #169, 0.81 #162, 0.80 #8), 01nv4h (0.03 #359, 0.02 #261, 0.02 #310), 02l6h (0.01 #249, 0.01 #361, 0.01 #599), 02gsvk (0.01 #286, 0.01 #146, 0.01 #412) >> Best rule #169 for best value: >> intensional similarity = 4 >> extensional distance = 250 >> proper extension: 03t97y; 03twd6; >> query: (?x6094, 09nqf) <- award_winner(?x6094, ?x7556), production_companies(?x6094, ?x902), featured_film_locations(?x6094, ?x739), genre(?x6094, ?x53) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dnw1 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.813 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10905-03z9585 PRED entity: 03z9585 PRED relation: produced_by PRED expected values: 027z0pl => 74 concepts (40 used for prediction) PRED predicted values (max 10 best out of 131): 05mcjs (0.39 #6538, 0.37 #7696, 0.37 #6923), 04pqqb (0.15 #2867, 0.14 #1714, 0.08 #177), 01t6b4 (0.09 #1196, 0.06 #2349, 0.03 #3502), 04wvhz (0.09 #1573, 0.05 #2726, 0.04 #5416), 0184dt (0.09 #2004, 0.06 #851, 0.04 #3157), 06dkzt (0.08 #298, 0.07 #682, 0.02 #7222), 0gg9_5q (0.08 #125, 0.05 #1662, 0.03 #2815), 05nn4k (0.08 #170, 0.03 #2860, 0.01 #6708), 01vhrz (0.08 #315), 027z0pl (0.07 #725, 0.05 #12711, 0.05 #1878) >> Best rule #6538 for best value: >> intensional similarity = 6 >> extensional distance = 283 >> proper extension: 0140g4; 016z9n; 01k5y0; >> query: (?x8193, ?x6673) <- produced_by(?x8193, ?x3528), country(?x8193, ?x94), film(?x2258, ?x8193), written_by(?x8193, ?x6673), profession(?x6673, ?x319), award_winner(?x1434, ?x6673) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #725 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 12 *> proper extension: 01qbg5; *> query: (?x8193, 027z0pl) <- language(?x8193, ?x5671), language(?x8193, ?x254), genre(?x8193, ?x225), film(?x2258, ?x8193), ?x254 = 02h40lc, ?x2258 = 0f4vbz, major_field_of_study(?x1368, ?x5671) *> conf = 0.07 ranks of expected_values: 10 EVAL 03z9585 produced_by 027z0pl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 74.000 40.000 0.386 http://example.org/film/film/produced_by #10904-0bq2g PRED entity: 0bq2g PRED relation: participant PRED expected values: 012x2b => 114 concepts (73 used for prediction) PRED predicted values (max 10 best out of 371): 01hcj2 (0.81 #12839, 0.81 #10269, 0.75 #5134), 01vw20h (0.42 #5135, 0.35 #3210, 0.02 #4805), 06pj8 (0.42 #5135, 0.35 #3210, 0.01 #2707), 02k21g (0.42 #5135, 0.35 #3210), 01q_ph (0.15 #1286, 0.15 #669, 0.03 #12197), 029q_y (0.09 #487, 0.04 #3055, 0.04 #4979), 04bs3j (0.09 #38, 0.04 #1324, 0.01 #2606), 043zg (0.09 #366, 0.03 #4858, 0.03 #2934), 01pllx (0.09 #547, 0.03 #3115, 0.02 #5039), 0gd9k (0.09 #506, 0.01 #3074, 0.01 #3716) >> Best rule #12839 for best value: >> intensional similarity = 3 >> extensional distance = 436 >> proper extension: 04shbh; 01fs_4; 0c2ry; 0333wf; 078jnn; 0mfj2; 031sg0; >> query: (?x3553, ?x793) <- award(?x3553, ?x618), participant(?x793, ?x3553), film(?x3553, ?x144) >> conf = 0.81 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bq2g participant 012x2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 73.000 0.809 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #10903-01dpdh PRED entity: 01dpdh PRED relation: award! PRED expected values: 0137n0 => 34 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2269): 0137n0 (0.83 #16830, 0.78 #40395, 0.78 #26931), 03h_fk5 (0.83 #16830, 0.78 #40395, 0.78 #26931), 01ww2fs (0.78 #40395, 0.77 #16829, 0.76 #60595), 03gr7w (0.77 #16829, 0.76 #60595, 0.73 #13463), 06rgq (0.50 #5815, 0.29 #9180, 0.26 #16831), 018ndc (0.50 #4216, 0.29 #7581, 0.05 #70691), 0197tq (0.50 #3399, 0.26 #16831, 0.21 #10097), 02cx90 (0.43 #7959, 0.33 #4594, 0.21 #10097), 0dl567 (0.43 #7875, 0.33 #4510, 0.10 #14607), 01x0yrt (0.43 #9272, 0.33 #5907, 0.06 #16004) >> Best rule #16830 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 05qck; 058vy5; >> query: (?x2430, ?x3358) <- award_winner(?x2430, ?x3358), profession(?x3358, ?x131), role(?x3358, ?x1466), award_nominee(?x3358, ?x3235) >> conf = 0.83 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01dpdh award! 0137n0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 21.000 0.830 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10902-016z5x PRED entity: 016z5x PRED relation: nominated_for! PRED expected values: 0f4x7 054krc => 107 concepts (94 used for prediction) PRED predicted values (max 10 best out of 226): 027b9j5 (0.67 #15177, 0.66 #15176, 0.66 #16128), 0gq9h (0.61 #2668, 0.38 #772, 0.35 #4090), 040njc (0.56 #2613, 0.33 #717, 0.28 #954), 02pqp12 (0.55 #2664, 0.36 #531, 0.29 #768), 0f4x7 (0.53 #2631, 0.41 #1209, 0.36 #498), 019f4v (0.52 #763, 0.52 #2659, 0.43 #526), 02qyntr (0.50 #2786, 0.36 #653, 0.33 #890), 0gs9p (0.50 #2670, 0.29 #774, 0.29 #537), 04dn09n (0.50 #2641, 0.29 #745, 0.29 #508), 0k611 (0.47 #2679, 0.33 #783, 0.31 #4101) >> Best rule #15177 for best value: >> intensional similarity = 3 >> extensional distance = 973 >> proper extension: 06mmr; >> query: (?x518, ?x4894) <- award(?x518, ?x4894), award(?x157, ?x4894), award_winner(?x4894, ?x262) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2631 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 117 *> proper extension: 0gh8zks; *> query: (?x518, 0f4x7) <- nominated_for(?x2373, ?x518), nominated_for(?x2375, ?x518), ?x2375 = 04kxsb, country(?x518, ?x94) *> conf = 0.53 ranks of expected_values: 5, 18 EVAL 016z5x nominated_for! 054krc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 107.000 94.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 016z5x nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 107.000 94.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10901-047c9l PRED entity: 047c9l PRED relation: award_nominee! PRED expected values: 02bkdn => 114 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1201): 0hvb2 (0.81 #13947, 0.81 #104597, 0.80 #51135), 02bkdn (0.81 #13947, 0.81 #104597, 0.80 #51135), 022yb4 (0.81 #13947, 0.81 #104597, 0.80 #51135), 018ygt (0.17 #1453, 0.13 #99947, 0.03 #15400), 03xpsrx (0.17 #637, 0.13 #99947), 0266r6h (0.17 #1115, 0.04 #8088, 0.04 #12737), 0863x_ (0.17 #1116, 0.04 #8089, 0.04 #12738), 0h27vc (0.17 #1339, 0.04 #8312, 0.04 #12961), 0gkydb (0.17 #636, 0.04 #7609, 0.04 #12258), 0277990 (0.17 #522, 0.04 #7495, 0.03 #14469) >> Best rule #13947 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 02lg9w; >> query: (?x5105, ?x336) <- student(?x888, ?x5105), award_nominee(?x5105, ?x336), actor(?x6726, ?x5105) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 047c9l award_nominee! 02bkdn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 52.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10900-08jfkw PRED entity: 08jfkw PRED relation: gender PRED expected values: 05zppz => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #5, 0.73 #61, 0.72 #107), 02zsn (0.34 #14, 0.33 #20, 0.30 #22) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 044ntk; 012gq6; 034q3l; >> query: (?x7929, 05zppz) <- film(?x7929, ?x10349), place_of_birth(?x7929, ?x9302), ?x10349 = 09qycb >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08jfkw gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.778 http://example.org/people/person/gender #10899-05pdh86 PRED entity: 05pdh86 PRED relation: film_release_region PRED expected values: 0jgd 03rt9 01mjq 06f32 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 112): 09c7w0 (0.93 #7406, 0.93 #5705, 0.92 #4247), 03rt9 (0.90 #615, 0.89 #494, 0.84 #978), 0jgd (0.85 #609, 0.84 #1093, 0.83 #488), 015qh (0.83 #873, 0.70 #994, 0.65 #1843), 01mjq (0.68 #995, 0.64 #1480, 0.62 #874), 01ls2 (0.67 #371, 0.67 #129, 0.61 #976), 06f32 (0.67 #163, 0.56 #526, 0.56 #405), 0h7x (0.64 #1110, 0.57 #1353, 0.56 #505), 077qn (0.50 #908, 0.45 #1029, 0.41 #1635), 07twz (0.50 #68, 0.41 #1036, 0.38 #915) >> Best rule #7406 for best value: >> intensional similarity = 3 >> extensional distance = 1312 >> proper extension: 018js4; 0ckr7s; 0b60sq; 084qpk; 02z9hqn; 0147sh; 06krf3; 03bx2lk; 01dyvs; 075cph; ... >> query: (?x4464, 09c7w0) <- film_release_region(?x4464, ?x985), film_release_region(?x3599, ?x985), ?x3599 = 0kxf1 >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #615 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 02rmd_2; *> query: (?x4464, 03rt9) <- film_release_region(?x4464, ?x2236), film_release_region(?x4464, ?x1475), films(?x7173, ?x4464), ?x1475 = 05qx1, participating_countries(?x1931, ?x2236) *> conf = 0.90 ranks of expected_values: 2, 3, 5, 7 EVAL 05pdh86 film_release_region 06f32 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 85.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05pdh86 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 85.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05pdh86 film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05pdh86 film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.928 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10898-02z3r8t PRED entity: 02z3r8t PRED relation: genre PRED expected values: 01t_vv => 91 concepts (90 used for prediction) PRED predicted values (max 10 best out of 130): 02kdv5l (0.54 #1551, 0.54 #2150, 0.53 #1791), 03k9fj (0.50 #249, 0.44 #1440, 0.42 #1680), 01hmnh (0.50 #255, 0.35 #1446, 0.33 #2642), 060__y (0.50 #135, 0.33 #16, 0.23 #730), 0219x_ (0.50 #144, 0.22 #620, 0.15 #3366), 01jfsb (0.50 #1561, 0.48 #1681, 0.48 #2040), 04xvlr (0.35 #4298, 0.29 #3342, 0.29 #358), 02xlf (0.33 #52, 0.07 #1481, 0.07 #1721), 0lsxr (0.29 #365, 0.25 #127, 0.21 #961), 06cvj (0.27 #717, 0.16 #2390, 0.11 #598) >> Best rule #1551 for best value: >> intensional similarity = 5 >> extensional distance = 113 >> proper extension: 09sh8k; 0gtsx8c; 02vxq9m; 011yxg; 07gp9; 09xbpt; 01k1k4; 0gtv7pk; 0h1cdwq; 061681; ... >> query: (?x755, 02kdv5l) <- film_crew_role(?x755, ?x468), film(?x629, ?x755), prequel(?x755, ?x6450), award_nominee(?x230, ?x629), language(?x755, ?x254) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1962 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 117 *> proper extension: 01br2w; 05dy7p; 02n9bh; 027ct7c; 0bx_hnp; *> query: (?x755, 01t_vv) <- film_crew_role(?x755, ?x1284), film(?x4295, ?x755), ?x1284 = 0ch6mp2, participant(?x4295, ?x1582) *> conf = 0.09 ranks of expected_values: 37 EVAL 02z3r8t genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 91.000 90.000 0.539 http://example.org/film/film/genre #10897-06qd3 PRED entity: 06qd3 PRED relation: participating_countries! PRED expected values: 0blfl => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 38): 0kbws (0.83 #228, 0.81 #5326, 0.80 #5253), 09x3r (0.74 #262, 0.71 #406, 0.69 #586), 016r9z (0.60 #163, 0.53 #271, 0.50 #91), 0blfl (0.58 #277, 0.53 #169, 0.50 #97), 0c_tl (0.39 #237, 0.36 #93, 0.33 #417), 0kbvb (0.30 #1697, 0.28 #2821, 0.28 #3256), 0jhn7 (0.30 #1697, 0.28 #2821, 0.28 #3256), 0kbvv (0.30 #1697, 0.28 #2821, 0.28 #3256), 0jdk_ (0.24 #1516, 0.24 #419, 0.23 #757), 0swff (0.24 #1516, 0.23 #757, 0.23 #1515) >> Best rule #228 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0ctw_b; 0345h; 015qh; 06f32; >> query: (?x1453, 0kbws) <- film_release_region(?x10475, ?x1453), ?x10475 = 047p798, combatants(?x1453, ?x94), organization(?x1453, ?x127) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #277 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 05qhw; 05v8c; 06mzp; 0f8l9c; 04v3q; 035qy; 01znc_; *> query: (?x1453, 0blfl) <- film_release_region(?x10475, ?x1453), film_release_region(?x7494, ?x1453), film_crew_role(?x10475, ?x137), ?x7494 = 0dgrwqr, film_regional_debut_venue(?x10475, ?x6557) *> conf = 0.58 ranks of expected_values: 4 EVAL 06qd3 participating_countries! 0blfl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 184.000 184.000 0.833 http://example.org/olympics/olympic_games/participating_countries #10896-01r2c7 PRED entity: 01r2c7 PRED relation: executive_produced_by! PRED expected values: 057lbk => 158 concepts (109 used for prediction) PRED predicted values (max 10 best out of 450): 02fj8n (0.13 #406, 0.10 #2529, 0.04 #7315), 031778 (0.13 #531, 0.11 #3715, 0.10 #3714), 031786 (0.13 #531), 03hxsv (0.13 #531), 0bv8h2 (0.11 #3715, 0.10 #3714, 0.10 #4247), 01fmys (0.10 #3714, 0.10 #4247, 0.07 #2123), 0642xf3 (0.10 #3714, 0.10 #4247, 0.06 #2122), 04j14qc (0.10 #23390, 0.10 #21795, 0.10 #4246), 0ds3t5x (0.10 #23390, 0.10 #21795, 0.10 #4246), 0mbql (0.10 #378, 0.07 #22859, 0.07 #4780) >> Best rule #406 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 06y3r; >> query: (?x9354, 02fj8n) <- executive_produced_by(?x4235, ?x9354), student(?x6611, ?x9354), nominated_for(?x4235, ?x2006), nominated_for(?x112, ?x4235) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #244 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 06y3r; *> query: (?x9354, 057lbk) <- executive_produced_by(?x4235, ?x9354), student(?x6611, ?x9354), nominated_for(?x4235, ?x2006), nominated_for(?x112, ?x4235) *> conf = 0.03 ranks of expected_values: 142 EVAL 01r2c7 executive_produced_by! 057lbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 158.000 109.000 0.133 http://example.org/film/film/executive_produced_by #10895-06pwq PRED entity: 06pwq PRED relation: company! PRED expected values: 02md_2 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 37): 0dq_5 (0.64 #1378, 0.58 #1535, 0.56 #1888), 01yc02 (0.50 #84, 0.33 #1372, 0.30 #1529), 0dq3c (0.40 #1367, 0.37 #1446, 0.37 #1524), 05_wyz (0.35 #1458, 0.33 #1889, 0.29 #1379), 02211by (0.33 #80, 0.15 #1368, 0.12 #1878), 09d6p2 (0.23 #1459, 0.23 #1890, 0.21 #2789), 01kr6k (0.20 #1386, 0.18 #1896, 0.17 #1465), 01dz7z (0.20 #77, 0.11 #4063, 0.02 #662), 0789n (0.20 #54, 0.11 #4063, 0.02 #639), 021q0l (0.17 #85, 0.13 #2235, 0.13 #2117) >> Best rule #1378 for best value: >> intensional similarity = 2 >> extensional distance = 73 >> proper extension: 03mdt; >> query: (?x581, 0dq_5) <- company(?x233, ?x581), contact_category(?x581, ?x6046) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #596 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 02t4yc; 029qzx; *> query: (?x581, 02md_2) <- student(?x581, ?x1299), school(?x580, ?x581), company(?x233, ?x581) *> conf = 0.15 ranks of expected_values: 15 EVAL 06pwq company! 02md_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 133.000 133.000 0.640 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #10894-01vsnff PRED entity: 01vsnff PRED relation: instrumentalists! PRED expected values: 04rzd => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 117): 026t6 (0.34 #3096, 0.33 #406, 0.31 #1624), 042v_gx (0.34 #3096, 0.31 #1624, 0.30 #3342), 02sgy (0.34 #3096, 0.31 #1624, 0.30 #3342), 01vj9c (0.34 #3096, 0.31 #1624, 0.30 #3342), 03bx0bm (0.33 #406, 0.09 #3097, 0.03 #3833), 02fsn (0.31 #1624, 0.30 #3342, 0.26 #2604), 0bxl5 (0.31 #1624, 0.30 #3342, 0.26 #2604), 0l15bq (0.31 #1624, 0.26 #2604, 0.26 #2850), 0214km (0.31 #1624, 0.26 #2604, 0.26 #2850), 03qjg (0.23 #370, 0.20 #533, 0.18 #939) >> Best rule #3096 for best value: >> intensional similarity = 3 >> extensional distance = 435 >> proper extension: 01vsxdm; 0dm5l; 014hr0; 06br6t; >> query: (?x2187, ?x432) <- role(?x2187, ?x432), role(?x1291, ?x432), performance_role(?x1466, ?x432) >> conf = 0.34 => this is the best rule for 4 predicted values *> Best rule #275 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 0pgjm; 04mn81; 021bk; 018pj3; 01vx5w7; 01vwyqp; 01svw8n; 01ttg5; 0qf11; 016fnb; ... *> query: (?x2187, 04rzd) <- award_nominee(?x2187, ?x3426), award_winner(?x2186, ?x2187), group(?x2187, ?x9791) *> conf = 0.13 ranks of expected_values: 13 EVAL 01vsnff instrumentalists! 04rzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 128.000 128.000 0.335 http://example.org/music/instrument/instrumentalists #10893-0gqy2 PRED entity: 0gqy2 PRED relation: award_winner PRED expected values: 02_fj 016fjj => 43 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1456): 02kxbx3 (0.33 #5631, 0.29 #8066, 0.15 #10502), 0zcbl (0.33 #12176, 0.31 #3957, 0.30 #9741), 0170pk (0.33 #12176, 0.30 #9741, 0.30 #17047), 015c4g (0.33 #12176, 0.30 #9741, 0.30 #17047), 01713c (0.33 #12176, 0.30 #9741, 0.30 #17047), 0bj9k (0.33 #12176, 0.30 #9741, 0.30 #17047), 09fb5 (0.33 #12176, 0.30 #9741, 0.30 #17047), 0170qf (0.33 #12176, 0.30 #9741, 0.30 #17047), 0171cm (0.33 #12176, 0.30 #9741, 0.30 #17047), 016zp5 (0.33 #12176, 0.30 #9741, 0.30 #17047) >> Best rule #5631 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 05ztjjw; >> query: (?x3066, 02kxbx3) <- award_winner(?x3066, ?x92), nominated_for(?x3066, ?x10829), nominated_for(?x3066, ?x9056), crewmember(?x10829, ?x12096), ?x9056 = 09sr0 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3224 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 04ljl_l; 027dtxw; 099jhq; 0cqhk0; 09qvc0; 0cqh46; 0cjyzs; 0bdwqv; 09sdmz; 09qrn4; *> query: (?x3066, 016fjj) <- award_winner(?x3066, ?x92), nominated_for(?x3066, ?x144), award(?x6324, ?x3066), ?x6324 = 018ygt *> conf = 0.23 ranks of expected_values: 148, 1058 EVAL 0gqy2 award_winner 016fjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 43.000 20.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0gqy2 award_winner 02_fj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 20.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #10892-01dq9q PRED entity: 01dq9q PRED relation: award PRED expected values: 02f6yz => 68 concepts (45 used for prediction) PRED predicted values (max 10 best out of 256): 01ckcd (0.81 #1593, 0.79 #8364, 0.77 #8363), 02f5qb (0.65 #153, 0.18 #551, 0.16 #1347), 01by1l (0.51 #111, 0.45 #1305, 0.37 #2103), 01bgqh (0.51 #42, 0.39 #1236, 0.32 #440), 02f72_ (0.49 #224, 0.17 #2614, 0.14 #1020), 02f73p (0.47 #182, 0.15 #2572, 0.12 #1376), 02v1m7 (0.44 #112, 0.12 #510, 0.12 #2502), 03qbh5 (0.40 #200, 0.32 #598, 0.25 #1394), 02f6ym (0.37 #253, 0.21 #651, 0.19 #6770), 02f777 (0.37 #304, 0.12 #1100, 0.08 #17931) >> Best rule #1593 for best value: >> intensional similarity = 4 >> extensional distance = 143 >> proper extension: 01qkqwg; 0191h5; >> query: (?x7407, ?x2855) <- award_nominee(?x7407, ?x5310), artists(?x1572, ?x7407), ?x1572 = 06by7, award_winner(?x2855, ?x7407) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #2703 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 177 *> proper extension: 01wv9xn; 04r1t; 02r1tx7; 0dm5l; 05563d; 07yg2; 05xq9; 0bpk2; 015srx; 02dw1_; ... *> query: (?x7407, 02f6yz) <- artists(?x671, ?x7407), artist(?x2190, ?x7407), group(?x227, ?x7407) *> conf = 0.16 ranks of expected_values: 32 EVAL 01dq9q award 02f6yz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 68.000 45.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10891-01934k PRED entity: 01934k PRED relation: people! PRED expected values: 025hl8 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 41): 02k6hp (0.30 #37, 0.11 #697, 0.05 #169), 0gk4g (0.20 #142, 0.17 #76, 0.16 #1528), 04psf (0.20 #7, 0.08 #73, 0.05 #667), 0dq9p (0.16 #479, 0.13 #347, 0.12 #215), 04p3w (0.10 #143, 0.10 #11, 0.08 #671), 08g5q7 (0.10 #42, 0.08 #108, 0.03 #702), 01l2m3 (0.10 #16, 0.06 #412, 0.06 #544), 01mtqf (0.10 #136, 0.06 #400, 0.03 #1192), 0qcr0 (0.10 #397, 0.09 #1189, 0.08 #661), 02knxx (0.08 #98, 0.07 #362, 0.07 #296) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 03ft8; 0ly5n; >> query: (?x8543, 02k6hp) <- religion(?x8543, ?x1985), place_of_death(?x8543, ?x242), ?x242 = 06_kh >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #402 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 01y8d4; 06y3r; *> query: (?x8543, 025hl8) <- religion(?x8543, ?x1985), place_of_death(?x8543, ?x242), award_winner(?x8543, ?x6807) *> conf = 0.03 ranks of expected_values: 33 EVAL 01934k people! 025hl8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 162.000 162.000 0.300 http://example.org/people/cause_of_death/people #10890-0gx_p PRED entity: 0gx_p PRED relation: gender PRED expected values: 02zsn => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.71 #197, 0.71 #217, 0.71 #207), 02zsn (0.56 #87, 0.49 #12, 0.49 #10) >> Best rule #197 for best value: >> intensional similarity = 2 >> extensional distance = 2357 >> proper extension: 0c7ct; 04zd4m; 07_3qd; 01d494; 01c59k; 01c58j; 0784v1; 09ntbc; 0c11mj; 0453t; ... >> query: (?x6278, 05zppz) <- nationality(?x6278, ?x94), place_of_birth(?x6278, ?x13451) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 469 *> proper extension: 03d9v8; 03t8v3; *> query: (?x6278, ?x231) <- participant(?x406, ?x6278), film(?x6278, ?x670), gender(?x406, ?x231) *> conf = 0.56 ranks of expected_values: 2 EVAL 0gx_p gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.714 http://example.org/people/person/gender #10889-014j1m PRED entity: 014j1m PRED relation: nutrient PRED expected values: 09gwd 0f4hc 025tkqy => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 33): 025tkqy (0.71 #311, 0.71 #298, 0.67 #305), 025sf8g (0.67 #305, 0.67 #285, 0.67 #270), 0f4hc (0.67 #305, 0.57 #307, 0.57 #297), 09gwd (0.67 #305, 0.57 #306, 0.57 #296), 08lb68 (0.67 #305, 0.33 #254, 0.33 #208), 01w_3 (0.67 #305, 0.33 #232, 0.33 #160), 0f4k5 (0.67 #305, 0.33 #233, 0.33 #161), 0466p20 (0.67 #305, 0.33 #242, 0.33 #224), 075pwf (0.67 #305, 0.33 #230, 0.33 #158), 0f4l5 (0.67 #305, 0.33 #146, 0.33 #102) >> Best rule #311 for best value: >> intensional similarity = 79 >> extensional distance = 5 >> proper extension: 0dcfv; >> query: (?x6191, ?x9915) <- nutrient(?x6191, ?x12454), nutrient(?x6191, ?x9436), nutrient(?x6191, ?x9426), nutrient(?x6191, ?x9365), nutrient(?x6191, ?x8243), nutrient(?x6191, ?x7720), nutrient(?x6191, ?x6192), nutrient(?x6191, ?x5549), nutrient(?x6191, ?x5337), nutrient(?x6191, ?x2018), nutrient(?x6191, ?x1258), ?x2018 = 01sh2, nutrient(?x10612, ?x9436), nutrient(?x9732, ?x9436), nutrient(?x9005, ?x9436), nutrient(?x8298, ?x9436), nutrient(?x7719, ?x9436), nutrient(?x7057, ?x9436), nutrient(?x6159, ?x9436), nutrient(?x6032, ?x9436), nutrient(?x5373, ?x9436), nutrient(?x5009, ?x9436), nutrient(?x4068, ?x9436), nutrient(?x3900, ?x9436), nutrient(?x3468, ?x9436), nutrient(?x2701, ?x9436), nutrient(?x1959, ?x9436), nutrient(?x1303, ?x9436), nutrient(?x1257, ?x9436), ?x1257 = 09728, ?x9732 = 05z55, ?x8243 = 014d7f, ?x1303 = 0fj52s, ?x6032 = 01nkt, ?x9005 = 04zpv, ?x2701 = 0hkxq, ?x7057 = 0fbdb, ?x8298 = 037ls6, ?x6285 = 01645p, ?x12454 = 025rw19, ?x5009 = 0fjfh, ?x6192 = 06jry, nutrient(?x3468, ?x14210), nutrient(?x3468, ?x13545), nutrient(?x3468, ?x12336), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x7894), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x6026), nutrient(?x3468, ?x1304), ?x1304 = 08lb68, ?x12336 = 0f4l5, ?x10612 = 0frq6, ?x9915 = 025tkqy, ?x6026 = 025sf8g, ?x5373 = 0971v, ?x14210 = 0f4k5, ?x5337 = 06x4c, ?x13545 = 01w_3, ?x9489 = 07j87, taxonomy(?x9365, ?x939), ?x6159 = 033cnk, ?x4068 = 0fbw6, ?x7894 = 0f4hc, ?x7431 = 09gwd, ?x5549 = 025s7j4, ?x1959 = 0f25w9, ?x3900 = 061_f, ?x939 = 04n6k, ?x7719 = 0dj75, nutrient(?x9005, ?x1258), nutrient(?x5009, ?x1258), nutrient(?x6285, ?x7720), nutrient(?x6285, ?x9426), nutrient(?x10612, ?x7720), nutrient(?x9489, ?x7720), nutrient(?x6032, ?x7720), nutrient(?x6285, ?x1258), nutrient(?x9732, ?x9426) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4 EVAL 014j1m nutrient 025tkqy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 014j1m nutrient 0f4hc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 014j1m nutrient 09gwd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #10888-06mkj PRED entity: 06mkj PRED relation: school! PRED expected values: 0jmk7 => 213 concepts (213 used for prediction) PRED predicted values (max 10 best out of 93): 0jmj7 (0.58 #10954, 0.57 #11612, 0.36 #970), 02d02 (0.27 #1011, 0.10 #10995, 0.09 #11653), 01d6g (0.27 #1014, 0.09 #11656, 0.09 #10998), 04wmvz (0.27 #1021, 0.09 #11663, 0.08 #11005), 0bwjj (0.18 #1017, 0.11 #11678, 0.10 #11001), 01ypc (0.18 #942, 0.08 #11584, 0.08 #10926), 04mjl (0.18 #1005, 0.07 #10989, 0.06 #11647), 03wnh (0.18 #993, 0.06 #10977, 0.05 #11635), 05tfm (0.18 #957, 0.05 #11599, 0.05 #10941), 0487_ (0.18 #1004, 0.04 #11646, 0.04 #10988) >> Best rule #10954 for best value: >> intensional similarity = 2 >> extensional distance = 102 >> proper extension: 02w2bc; 01b1mj; 01wdl3; 01j_06; 01t8sr; 049dk; 02jyr8; 01ptt7; 01jsn5; 0f1nl; ... >> query: (?x2152, 0jmj7) <- contains(?x6304, ?x2152), school(?x4979, ?x2152) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #11678 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 112 *> proper extension: 02gr81; 017j69; 0frm7n; 027mdh; 02zkz7; 08qnnv; 0trv; *> query: (?x2152, ?x799) <- school(?x4979, ?x2152), draft(?x799, ?x4979) *> conf = 0.11 ranks of expected_values: 19 EVAL 06mkj school! 0jmk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 213.000 213.000 0.577 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #10887-03nx8mj PRED entity: 03nx8mj PRED relation: nominated_for! PRED expected values: 05b4l5x 07bdd_ => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 173): 0gq9h (0.30 #780, 0.26 #2455, 0.24 #302), 0f4x7 (0.30 #742, 0.19 #16259, 0.14 #5046), 0gs9p (0.25 #781, 0.18 #2456, 0.18 #9866), 07bdd_ (0.25 #2391, 0.25 #2392, 0.19 #13149), 05b4l5x (0.25 #2391, 0.25 #2392, 0.19 #13149), 04ljl_l (0.25 #2391, 0.25 #2392, 0.19 #16259), 05p09zm (0.25 #2391, 0.25 #2392, 0.19 #16259), 099c8n (0.24 #296, 0.20 #2449, 0.18 #2927), 0k611 (0.23 #790, 0.17 #5094, 0.17 #9875), 0gqy2 (0.22 #840, 0.20 #362, 0.17 #601) >> Best rule #780 for best value: >> intensional similarity = 5 >> extensional distance = 159 >> proper extension: 01b66d; 07c72; 01s81; 0b005; 016tvq; 015pnb; 026y3cf; >> query: (?x4176, 0gq9h) <- nominated_for(?x5197, ?x4176), nominated_for(?x4360, ?x4176), location(?x5197, ?x6226), gender(?x5197, ?x514), celebrities_impersonated(?x692, ?x4360) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #2391 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 245 *> proper extension: 01p9hgt; 01kv4mb; 02fn5r; 0ggjt; 0bhvtc; 03cfjg; 0p_47; 0pmw9; *> query: (?x4176, ?x102) <- nominated_for(?x4176, ?x8562), nominated_for(?x102, ?x8562), award(?x123, ?x102) *> conf = 0.25 ranks of expected_values: 4, 5 EVAL 03nx8mj nominated_for! 07bdd_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 94.000 0.304 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03nx8mj nominated_for! 05b4l5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 94.000 0.304 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10886-05k4my PRED entity: 05k4my PRED relation: film! PRED expected values: 0zcbl => 85 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1084): 0jrqq (0.44 #27050, 0.31 #33292, 0.30 #35373), 086k8 (0.44 #27050, 0.31 #33292, 0.30 #35373), 015rkw (0.10 #282, 0.03 #12766, 0.03 #6524), 0jfx1 (0.10 #406, 0.03 #8728, 0.02 #12890), 015wnl (0.10 #650, 0.02 #36023, 0.02 #8972), 02r_d4 (0.10 #103, 0.02 #41718, 0.01 #8425), 0525b (0.10 #1912, 0.01 #10234, 0.01 #33123), 063b4k (0.10 #2017, 0.01 #10339), 014y6 (0.10 #1830), 0j_c (0.08 #6652, 0.05 #12894, 0.04 #29541) >> Best rule #27050 for best value: >> intensional similarity = 4 >> extensional distance = 297 >> proper extension: 0yx7h; >> query: (?x10422, ?x382) <- genre(?x10422, ?x812), nominated_for(?x102, ?x10422), nominated_for(?x382, ?x10422), ?x812 = 01jfsb >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #76985 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 940 *> proper extension: 0cwrr; 04glx0; 05sy0cv; *> query: (?x10422, ?x193) <- award(?x10422, ?x350), award(?x6334, ?x350), award_winner(?x350, ?x702), film(?x193, ?x6334) *> conf = 0.02 ranks of expected_values: 420 EVAL 05k4my film! 0zcbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 85.000 38.000 0.435 http://example.org/film/actor/film./film/performance/film #10885-0lfgr PRED entity: 0lfgr PRED relation: student PRED expected values: 027kmrb => 101 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1705): 01hc9_ (0.10 #8333, 0.08 #20834, 0.07 #25001), 01l1hr (0.08 #569, 0.05 #10986, 0.03 #2652), 01cv3n (0.08 #88, 0.04 #10505, 0.02 #37588), 02x8z_ (0.08 #768, 0.03 #17435, 0.01 #9102), 024jwt (0.08 #1802, 0.03 #8051, 0.03 #3885), 06jrhz (0.08 #1018, 0.03 #7267, 0.02 #11435), 01my4f (0.08 #1191, 0.03 #7440, 0.02 #17858), 0d3qd0 (0.08 #780, 0.03 #2863, 0.02 #4946), 01_x6v (0.08 #360, 0.03 #2443, 0.02 #4526), 01nr63 (0.08 #1995, 0.01 #29079, 0.01 #12412) >> Best rule #8333 for best value: >> intensional similarity = 2 >> extensional distance = 72 >> proper extension: 030_1_; 03yxwq; 07wj1; 01j_x; 07wh1; 0f1r9; 09c7b; >> query: (?x1809, ?x8841) <- company(?x8841, ?x1809), influenced_by(?x8841, ?x1946) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0lfgr student 027kmrb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 77.000 0.105 http://example.org/education/educational_institution/students_graduates./education/education/student #10884-01mkn_d PRED entity: 01mkn_d PRED relation: award PRED expected values: 0l8z1 => 105 concepts (91 used for prediction) PRED predicted values (max 10 best out of 283): 054krc (0.53 #4960, 0.52 #4554, 0.50 #1306), 0l8z1 (0.42 #4936, 0.41 #4530, 0.39 #1282), 02qvyrt (0.41 #5000, 0.39 #4594, 0.34 #1752), 0gqz2 (0.35 #4547, 0.33 #4953, 0.32 #1299), 0f4x7 (0.33 #31, 0.14 #7339, 0.12 #8151), 04ljl_l (0.33 #3, 0.14 #25175, 0.14 #30049), 054ky1 (0.33 #110, 0.08 #922, 0.04 #8230), 09sb52 (0.31 #8567, 0.31 #17500, 0.30 #11409), 054ks3 (0.29 #1767, 0.26 #5015, 0.25 #1361), 025m8y (0.29 #506, 0.25 #1724, 0.25 #2536) >> Best rule #4960 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 02rgz4; 01vrncs; 07qy0b; 0c_drn; 05mt6w; 01pbwwl; 03f68r6; >> query: (?x6664, 054krc) <- nominated_for(?x6664, ?x10455), music(?x607, ?x6664), film(?x382, ?x10455) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #4936 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 118 *> proper extension: 02rgz4; 01vrncs; 07qy0b; 0c_drn; 05mt6w; 01pbwwl; 03f68r6; *> query: (?x6664, 0l8z1) <- nominated_for(?x6664, ?x10455), music(?x607, ?x6664), film(?x382, ?x10455) *> conf = 0.42 ranks of expected_values: 2 EVAL 01mkn_d award 0l8z1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 91.000 0.533 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10883-0g4vmj8 PRED entity: 0g4vmj8 PRED relation: nominated_for! PRED expected values: 040njc 0fhpv4 => 70 concepts (65 used for prediction) PRED predicted values (max 10 best out of 188): 0gqy2 (0.80 #1720, 0.62 #346, 0.25 #4357), 02x73k6 (0.68 #230, 0.67 #4126, 0.66 #6880), 02wypbh (0.68 #230, 0.67 #4126, 0.66 #6880), 09sdmz (0.64 #368, 0.32 #1742, 0.13 #2200), 0gq9h (0.51 #1665, 0.37 #3957, 0.36 #4647), 027dtxw (0.51 #234, 0.29 #1608, 0.18 #2066), 019f4v (0.43 #1658, 0.32 #3950, 0.31 #3032), 0gs9p (0.41 #1667, 0.33 #3959, 0.33 #3041), 0k611 (0.38 #1676, 0.27 #302, 0.27 #72), 0gr4k (0.33 #1630, 0.24 #4612, 0.21 #6217) >> Best rule #1720 for best value: >> intensional similarity = 4 >> extensional distance = 233 >> proper extension: 0g54xkt; 05jyb2; 01242_; 0k4bc; 02q_x_l; 03cffvv; 0c5qvw; 034hzj; >> query: (?x7275, 0gqy2) <- genre(?x7275, ?x53), nominated_for(?x451, ?x7275), award(?x1865, ?x451), ?x1865 = 03k7bd >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1611 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 233 *> proper extension: 0g54xkt; 05jyb2; 01242_; 0k4bc; 02q_x_l; 03cffvv; 0c5qvw; 034hzj; *> query: (?x7275, 040njc) <- genre(?x7275, ?x53), nominated_for(?x451, ?x7275), award(?x1865, ?x451), ?x1865 = 03k7bd *> conf = 0.30 ranks of expected_values: 13, 43 EVAL 0g4vmj8 nominated_for! 0fhpv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 70.000 65.000 0.804 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0g4vmj8 nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 70.000 65.000 0.804 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10882-043q6n_ PRED entity: 043q6n_ PRED relation: produced_by! PRED expected values: 0bq6ntw 087pfc 0ct2tf5 => 72 concepts (30 used for prediction) PRED predicted values (max 10 best out of 589): 03kg2v (0.40 #1196, 0.04 #3070, 0.03 #6821), 0gm2_0 (0.40 #1775, 0.04 #2712, 0.03 #7400), 03wjm2 (0.40 #1858, 0.03 #7483, 0.02 #14986), 01bl7g (0.40 #1453, 0.02 #7078, 0.02 #3327), 01f6x7 (0.40 #1439, 0.02 #7064, 0.02 #3313), 0dscrwf (0.40 #981, 0.02 #6606, 0.02 #2855), 03z9585 (0.40 #1687, 0.02 #14815, 0.02 #7312), 06dfz1 (0.39 #18752, 0.24 #6562, 0.02 #20629), 0crd8q6 (0.25 #860), 03cp4cn (0.20 #1535, 0.04 #7160, 0.03 #12788) >> Best rule #1196 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03h304l; 0d6484; 03h40_7; >> query: (?x1417, 03kg2v) <- award_nominee(?x10430, ?x1417), award_nominee(?x1417, ?x1104), produced_by(?x1642, ?x1417), ?x10430 = 027z0pl >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2681 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 016tt2; 05qd_; 024rgt; 04cw0j; 02_l96; 016bx2; 01fyzy; 03m9c8; 03rwz3; 025hwq; ... *> query: (?x1417, 087pfc) <- award_nominee(?x541, ?x1417), award_nominee(?x1417, ?x1104), ?x541 = 017s11 *> conf = 0.08 ranks of expected_values: 29, 407 EVAL 043q6n_ produced_by! 0ct2tf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 30.000 0.400 http://example.org/film/film/produced_by EVAL 043q6n_ produced_by! 087pfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 72.000 30.000 0.400 http://example.org/film/film/produced_by EVAL 043q6n_ produced_by! 0bq6ntw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 30.000 0.400 http://example.org/film/film/produced_by #10881-0ftvz PRED entity: 0ftvz PRED relation: category PRED expected values: 08mbj5d => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.81 #7, 0.79 #32, 0.78 #119) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0rs6x; 0j_sncb; 0rkkv; 0rqyx; 0146hc; 09s5q8; 01jq0j; 03l78j; 016sd3; >> query: (?x2624, 08mbj5d) <- contains(?x2623, ?x2624), contains(?x94, ?x2624), ?x94 = 09c7w0, ?x2623 = 02xry >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ftvz category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.815 http://example.org/common/topic/webpage./common/webpage/category #10880-02gsvk PRED entity: 02gsvk PRED relation: currency! PRED expected values: 02w86hz => 8 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1858): 04jwjq (0.72 #1319, 0.68 #1318, 0.64 #3953), 0gg5qcw (0.72 #1319, 0.68 #1318, 0.64 #3953), 0gt14 (0.72 #1319, 0.68 #1318, 0.64 #3953), 0296rz (0.72 #1319, 0.68 #1318, 0.64 #3953), 02jxrw (0.72 #1319, 0.68 #1318, 0.64 #3953), 07l450 (0.72 #1319, 0.68 #1318, 0.64 #3953), 01lsl (0.72 #1319, 0.68 #1318, 0.64 #3953), 02p86pb (0.72 #1319, 0.68 #1318, 0.64 #3953), 09sr0 (0.72 #1319, 0.68 #1318, 0.64 #3953), 02yxbc (0.72 #1319, 0.68 #1318, 0.64 #3953) >> Best rule #1319 for best value: >> intensional similarity = 57 >> extensional distance = 1 >> proper extension: 09nqf; >> query: (?x10674, ?x136) <- currency(?x11607, ?x10674), currency(?x7574, ?x10674), currency(?x11975, ?x10674), category(?x7574, ?x134), citytown(?x7574, ?x8297), currency(?x11114, ?x10674), currency(?x257, ?x10674), genre(?x257, ?x1626), genre(?x257, ?x307), major_field_of_study(?x7574, ?x8925), award(?x257, ?x4443), featured_film_locations(?x257, ?x11801), contains(?x2146, ?x11607), nominated_for(?x2065, ?x257), nominated_for(?x1937, ?x257), major_field_of_study(?x11607, ?x1154), film_release_region(?x257, ?x1264), ?x1626 = 03q4nz, institution(?x734, ?x11607), film(?x656, ?x11114), student(?x11607, ?x10027), genre(?x11114, ?x6674), ?x8925 = 01zc2w, language(?x257, ?x254), country(?x3411, ?x2146), film_release_region(?x7275, ?x2146), film_release_region(?x3784, ?x2146), film_release_region(?x3276, ?x2146), film_release_region(?x2709, ?x2146), film_release_region(?x2656, ?x2146), film_release_region(?x1163, ?x2146), olympics(?x2146, ?x3729), olympics(?x2146, ?x2553), nationality(?x111, ?x2146), ?x307 = 04t36, ?x2709 = 06ztvyx, currency(?x2146, ?x170), ?x7275 = 0g4vmj8, ?x2553 = 016r9z, ?x2656 = 03qnc6q, featured_film_locations(?x11114, ?x739), ?x3784 = 0bmhvpr, adjoins(?x2146, ?x2236), country(?x150, ?x1264), administrative_area_type(?x1264, ?x2792), ?x3729 = 0jdk_, film_release_region(?x7700, ?x1264), country(?x1646, ?x1264), award_winner(?x11114, ?x8073), country(?x4118, ?x1264), country(?x136, ?x1264), adjoins(?x172, ?x1264), ?x1163 = 0c0nhgv, ?x7700 = 0cp08zg, ?x3276 = 0gjc4d3, contains(?x1264, ?x196), ?x4118 = 07jxpf >> conf = 0.72 => this is the best rule for 1479 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 36 EVAL 02gsvk currency! 02w86hz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 8.000 5.000 0.724 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10879-02fqrf PRED entity: 02fqrf PRED relation: language PRED expected values: 02hxcvy => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 56): 012w70 (0.26 #68, 0.05 #591, 0.04 #877), 0459q4 (0.21 #92, 0.03 #3862, 0.03 #615), 03_9r (0.15 #66, 0.07 #1046, 0.06 #299), 064_8sq (0.13 #20, 0.13 #251, 0.13 #1172), 04306rv (0.13 #409, 0.12 #1156, 0.12 #351), 06nm1 (0.13 #67, 0.11 #876, 0.10 #761), 02bjrlw (0.11 #115, 0.10 #173, 0.09 #406), 03115z (0.11 #93, 0.03 #3862), 06b_j (0.10 #311, 0.09 #887, 0.09 #368), 03k50 (0.06 #65, 0.04 #8, 0.03 #413) >> Best rule #68 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0bz3jx; >> query: (?x3498, 012w70) <- language(?x3498, ?x2890), genre(?x3498, ?x604), ?x2890 = 0653m, genre(?x493, ?x604) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #32 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 0bmpm; *> query: (?x3498, 02hxcvy) <- genre(?x3498, ?x53), story_by(?x3498, ?x7106), costume_design_by(?x3498, ?x1500), nominated_for(?x154, ?x3498) *> conf = 0.04 ranks of expected_values: 13 EVAL 02fqrf language 02hxcvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 70.000 70.000 0.255 http://example.org/film/film/language #10878-0152cw PRED entity: 0152cw PRED relation: profession PRED expected values: 05z96 => 88 concepts (62 used for prediction) PRED predicted values (max 10 best out of 68): 09jwl (0.86 #601, 0.80 #1625, 0.79 #455), 0nbcg (0.58 #760, 0.50 #1200, 0.49 #7782), 0dz3r (0.50 #440, 0.46 #294, 0.45 #586), 0dxtg (0.43 #5863, 0.23 #8642, 0.22 #5130), 0n1h (0.36 #448, 0.33 #10, 0.32 #594), 01d_h8 (0.32 #3955, 0.32 #4393, 0.31 #4101), 0kyk (0.31 #5879, 0.17 #174, 0.14 #758), 01c72t (0.28 #6311, 0.23 #314, 0.22 #8945), 03gjzk (0.22 #3963, 0.21 #4109, 0.21 #5131), 02jknp (0.19 #5857, 0.19 #8197, 0.18 #5124) >> Best rule #601 for best value: >> intensional similarity = 6 >> extensional distance = 20 >> proper extension: 01vv126; 0kj34; >> query: (?x872, 09jwl) <- profession(?x872, ?x2659), profession(?x872, ?x1032), participant(?x1208, ?x872), ?x2659 = 039v1, profession(?x10738, ?x1032), ?x10738 = 017f4y >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #5891 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 478 *> proper extension: 079vf; 0grwj; 03qcq; 084w8; 0l6qt; 01xdf5; 083chw; 0hl3d; 07w21; 041h0; ... *> query: (?x872, 05z96) <- profession(?x872, ?x353), profession(?x872, ?x220), profession(?x4936, ?x220), ?x4936 = 03lgg, ?x353 = 0cbd2 *> conf = 0.10 ranks of expected_values: 20 EVAL 0152cw profession 05z96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 88.000 62.000 0.864 http://example.org/people/person/profession #10877-02p0tjr PRED entity: 02p0tjr PRED relation: nutrient! PRED expected values: 0fjfh 05z55 => 53 concepts (52 used for prediction) PRED predicted values (max 10 best out of 17): 0fjfh (0.96 #750, 0.93 #559, 0.93 #548), 05z55 (0.89 #376, 0.89 #369, 0.89 #326), 037ls6 (0.89 #326, 0.89 #168, 0.89 #60), 0971v (0.89 #326, 0.89 #168, 0.89 #60), 09728 (0.89 #326, 0.89 #168, 0.89 #60), 06x4c (0.89 #326, 0.89 #168, 0.89 #60), 0dcfv (0.89 #326, 0.89 #168, 0.89 #60), 01sh2 (0.03 #724, 0.03 #660, 0.03 #778), 04k8n (0.03 #724, 0.03 #660, 0.03 #778), 05wvs (0.03 #724, 0.03 #660, 0.03 #778) >> Best rule #750 for best value: >> intensional similarity = 114 >> extensional distance = 44 >> proper extension: 02y_3rt; >> query: (?x9840, 0fjfh) <- nutrient(?x9005, ?x9840), nutrient(?x7057, ?x9840), nutrient(?x4068, ?x9840), nutrient(?x2701, ?x9840), nutrient(?x1959, ?x9840), nutrient(?x1303, ?x9840), ?x4068 = 0fbw6, nutrient(?x1959, ?x12454), nutrient(?x1959, ?x12083), nutrient(?x1959, ?x11784), nutrient(?x1959, ?x11758), nutrient(?x1959, ?x11592), nutrient(?x1959, ?x11270), nutrient(?x1959, ?x10709), nutrient(?x1959, ?x10098), nutrient(?x1959, ?x9949), nutrient(?x1959, ?x9795), nutrient(?x1959, ?x9733), nutrient(?x1959, ?x9619), nutrient(?x1959, ?x9490), nutrient(?x1959, ?x9365), nutrient(?x1959, ?x8487), nutrient(?x1959, ?x8442), nutrient(?x1959, ?x8413), nutrient(?x1959, ?x7894), nutrient(?x1959, ?x7720), nutrient(?x1959, ?x7652), nutrient(?x1959, ?x7431), nutrient(?x1959, ?x7362), nutrient(?x1959, ?x7219), nutrient(?x1959, ?x7135), nutrient(?x1959, ?x6586), nutrient(?x1959, ?x6192), nutrient(?x1959, ?x6160), nutrient(?x1959, ?x6033), nutrient(?x1959, ?x6026), nutrient(?x1959, ?x5549), nutrient(?x1959, ?x5526), nutrient(?x1959, ?x5451), nutrient(?x1959, ?x5374), nutrient(?x1959, ?x5010), nutrient(?x1959, ?x3469), nutrient(?x1959, ?x3203), nutrient(?x1959, ?x2702), nutrient(?x1959, ?x1960), nutrient(?x1959, ?x1258), nutrient(?x7057, ?x13944), nutrient(?x7057, ?x12902), nutrient(?x7057, ?x12868), nutrient(?x7057, ?x10891), nutrient(?x7057, ?x10195), nutrient(?x7057, ?x9915), nutrient(?x7057, ?x9855), nutrient(?x7057, ?x6286), nutrient(?x7057, ?x3901), nutrient(?x7057, ?x3264), nutrient(?x7057, ?x2018), ?x2018 = 01sh2, ?x10195 = 0hkwr, nutrient(?x9005, ?x13498), nutrient(?x9005, ?x10453), ?x6192 = 06jry, ?x9795 = 05v_8y, ?x6586 = 05gh50, ?x2701 = 0hkxq, ?x5374 = 025s0zp, ?x7362 = 02kc5rj, ?x10709 = 0h1sz, ?x7135 = 025rsfk, ?x9490 = 0h1sg, ?x13944 = 0f4kp, ?x12868 = 03d49, ?x11758 = 0q01m, ?x5526 = 09pbb, ?x6026 = 025sf8g, ?x5010 = 0h1vz, ?x7720 = 025s7x6, ?x6286 = 02y_3rf, ?x9949 = 02kd0rh, ?x6160 = 041r51, ?x11270 = 02kc008, ?x10098 = 0h1_c, ?x9619 = 0h1tg, ?x7894 = 0f4hc, ?x6033 = 04zjxcz, ?x2702 = 0838f, ?x8487 = 014yzm, ?x1303 = 0fj52s, ?x12083 = 01n78x, ?x3203 = 04kl74p, ?x9365 = 04k8n, ?x3469 = 0h1zw, ?x7652 = 025s0s0, ?x13498 = 07q0m, ?x9733 = 0h1tz, ?x8442 = 02kcv4x, ?x7219 = 0h1vg, ?x1960 = 07hnp, ?x12454 = 025rw19, nutrient(?x8298, ?x10453), ?x3901 = 0466p20, ?x9855 = 0d9t0, ?x1258 = 0h1wg, ?x10891 = 0g5gq, ?x11592 = 025sf0_, ?x9915 = 025tkqy, ?x5451 = 05wvs, ?x7431 = 09gwd, ?x8298 = 037ls6, ?x5549 = 025s7j4, ?x3264 = 0dcfv, ?x12902 = 0fzjh, ?x11784 = 07zqy, ?x8413 = 02kc4sf >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02p0tjr nutrient! 05z55 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 52.000 0.957 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 02p0tjr nutrient! 0fjfh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 52.000 0.957 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #10876-03h3x5 PRED entity: 03h3x5 PRED relation: genre PRED expected values: 03k9fj => 92 concepts (78 used for prediction) PRED predicted values (max 10 best out of 99): 07s9rl0 (0.74 #8579, 0.63 #3456, 0.63 #3576), 03k9fj (0.55 #1678, 0.55 #1321, 0.37 #1440), 01hmnh (0.50 #1685, 0.48 #1328, 0.33 #614), 02kdv5l (0.39 #479, 0.36 #717, 0.34 #2622), 01jfsb (0.37 #1560, 0.33 #2632, 0.32 #3707), 04t36 (0.35 #244, 0.19 #1196, 0.14 #1315), 02l7c8 (0.34 #3950, 0.31 #136, 0.31 #3592), 06cvj (0.31 #123, 0.22 #3937, 0.20 #3818), 01zhp (0.28 #1385, 0.25 #1742, 0.09 #1266), 0jxy (0.26 #1355, 0.25 #1712, 0.02 #8624) >> Best rule #8579 for best value: >> intensional similarity = 5 >> extensional distance = 1360 >> proper extension: 0fq27fp; 06n90; >> query: (?x2642, 07s9rl0) <- genre(?x2642, ?x7685), genre(?x11035, ?x7685), genre(?x8017, ?x7685), ?x11035 = 06r1k, actor(?x8017, ?x2594) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1678 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 100 *> proper extension: 04svwx; *> query: (?x2642, 03k9fj) <- genre(?x2642, ?x7685), genre(?x2642, ?x2540), country(?x2642, ?x94), ?x2540 = 0hcr, genre(?x808, ?x7685) *> conf = 0.55 ranks of expected_values: 2 EVAL 03h3x5 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 78.000 0.739 http://example.org/film/film/genre #10875-01w5m PRED entity: 01w5m PRED relation: major_field_of_study PRED expected values: 02ky346 04g7x => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 73): 04rjg (0.42 #636, 0.41 #815, 0.41 #280), 01540 (0.34 #662, 0.33 #751, 0.28 #217), 01tbp (0.34 #840, 0.24 #305, 0.22 #483), 02ky346 (0.31 #278, 0.28 #189, 0.24 #813), 04g7x (0.24 #314, 0.24 #225, 0.17 #849), 04rlf (0.24 #134, 0.12 #223, 0.12 #757), 0_jm (0.23 #1196, 0.19 #2713, 0.16 #2535), 02822 (0.21 #736, 0.21 #647, 0.20 #113), 0dc_v (0.21 #293, 0.20 #204, 0.17 #828), 036hv (0.21 #274, 0.16 #719, 0.16 #630) >> Best rule #636 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 08815; 01rtm4; 01pl14; 052nd; 06pwq; 065y4w7; 01w3v; 07tgn; 01k2wn; 0277jc; ... >> query: (?x3424, 04rjg) <- student(?x3424, ?x117), major_field_of_study(?x3424, ?x254), company(?x920, ?x3424) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #278 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 09c7w0; 03sc8; 01gb54; 07wj1; 01j_x; 07wh1; 0f1r9; *> query: (?x3424, 02ky346) <- company(?x920, ?x3424), company(?x346, ?x3424), influenced_by(?x920, ?x3941) *> conf = 0.31 ranks of expected_values: 4, 5 EVAL 01w5m major_field_of_study 04g7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 65.000 65.000 0.418 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01w5m major_field_of_study 02ky346 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 65.000 65.000 0.418 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10874-0lbfv PRED entity: 0lbfv PRED relation: colors PRED expected values: 01l849 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.39 #287, 0.36 #439, 0.36 #534), 0680m7 (0.33 #19, 0.02 #38, 0.01 #57), 01l849 (0.25 #495, 0.25 #628, 0.25 #685), 019sc (0.19 #539, 0.18 #634, 0.18 #615), 06fvc (0.17 #41, 0.16 #668, 0.16 #630), 036k5h (0.10 #537, 0.09 #670, 0.09 #613), 038hg (0.09 #544, 0.09 #639, 0.09 #297), 04mkbj (0.09 #637, 0.09 #295, 0.08 #694), 088fh (0.09 #44, 0.08 #25, 0.06 #82), 0jc_p (0.08 #137, 0.07 #669, 0.07 #61) >> Best rule #287 for best value: >> intensional similarity = 3 >> extensional distance = 221 >> proper extension: 031q3w; 04s934; 019vv1; 0d5fb; >> query: (?x6505, 083jv) <- student(?x6505, ?x8268), award_winner(?x2168, ?x8268), colors(?x6505, ?x3189) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #495 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 342 *> proper extension: 05zjtn4; 01rtm4; 01jssp; 04wlz2; 05krk; 052nd; 06pwq; 02w2bc; 065y4w7; 01w3v; ... *> query: (?x6505, 01l849) <- institution(?x865, ?x6505), colors(?x6505, ?x3189), currency(?x6505, ?x1099) *> conf = 0.25 ranks of expected_values: 3 EVAL 0lbfv colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 64.000 0.395 http://example.org/education/educational_institution/colors #10873-02ndbd PRED entity: 02ndbd PRED relation: producer_type PRED expected values: 0ckd1 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.82 #9, 0.72 #3, 0.70 #13) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 04n7njg; 03m_k0; 09v6gc9; 09pl3f; 04pg29; 05hrq4; 03yf4d; >> query: (?x856, 0ckd1) <- nominated_for(?x856, ?x3220), program_creator(?x6248, ?x856), profession(?x856, ?x319) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ndbd producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.819 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #10872-02jknp PRED entity: 02jknp PRED relation: profession! PRED expected values: 0q9kd 05ty4m 030pr 02r34n 013cr 05fnl9 064p92m 0jfx1 0p51w 03r1pr 078jt5 01q4qv 0b05xm 09p06 0hw1j 0jrqq 0q59y 03n93 02kxwk 01twdk 0cm89v 0280mv7 0b9l3x 03flwk 0bs8d 016gkf 0mm1q 045cq 014gf8 017r13 03nkts 05cgy8 023n39 04hw4b 06kxk2 03bw6 0969vz 0h5j77 01q9b9 070j61 03dbds 069_0y 0184jw 06t8b 02pv_d 02mc79 01nr36 0jgwf 0kft 023w9s 068g3p 04353 01r2c7 01vh18t 0f7h2g 04258w 04135 01qg7c 03c5f7l 01ypsj 05strv 09xrxq 07jmgz 03hzkq 02465 0b5x23 05w1vf 09g0h 04ch23 059j4x 03f22dp 0pgm3 02t901 06nd8c 03fqv5 01ggbx 03dctt 05f0r8 02qnhk1 => 46 concepts (26 used for prediction) PRED predicted values (max 10 best out of 3633): 03lgg (0.70 #51522, 0.62 #47939, 0.50 #40771), 018grr (0.67 #39908, 0.67 #28668, 0.62 #47076), 01vw8mh (0.67 #40743, 0.67 #28668, 0.60 #29991), 0144l1 (0.67 #39925, 0.50 #50676, 0.50 #47093), 02l840 (0.67 #39581, 0.50 #32412, 0.50 #18077), 049qx (0.67 #40600, 0.50 #47768, 0.50 #19096), 02lk1s (0.67 #28668, 0.62 #46769, 0.60 #28849), 027cxsm (0.67 #28668, 0.62 #46951, 0.60 #29031), 07d370 (0.67 #28668, 0.62 #47518, 0.51 #25083), 0gs5q (0.67 #28668, 0.62 #49031, 0.51 #25083) >> Best rule #51522 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 016z4k; 018gz8; 09jwl; 02krf9; >> query: (?x524, 03lgg) <- profession(?x9685, ?x524), profession(?x7740, ?x524), profession(?x3961, ?x524), profession(?x965, ?x524), award_nominee(?x3961, ?x163), award(?x965, ?x783), ?x9685 = 01bbwp, nominated_for(?x7740, ?x6376) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #28668 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0lgw7; *> query: (?x524, ?x163) <- profession(?x7740, ?x524), profession(?x3961, ?x524), profession(?x532, ?x524), award_nominee(?x3961, ?x163), ?x7740 = 02404v, type_of_union(?x532, ?x566) *> conf = 0.67 ranks of expected_values: 19, 20, 43, 45, 49, 56, 62, 88, 93, 97, 102, 170, 180, 194, 242, 259, 292, 297, 457, 463, 504, 753, 808, 811, 826, 827, 829, 833, 843, 847, 852, 856, 861, 865, 879, 882, 883, 885, 886, 942, 943, 944, 947, 948, 949, 950, 951, 954, 968, 974, 1026, 1103, 1135, 1136, 1141, 1179, 1207, 1266, 1268, 1280, 1301, 1306, 1317, 1320, 1346, 1406, 1563, 1626, 1651, 1721, 1781, 2315, 2611, 2724, 2860 EVAL 02jknp profession! 02qnhk1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 05f0r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03dctt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 01ggbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03fqv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 06nd8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 02t901 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0pgm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03f22dp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 059j4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 04ch23 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 09g0h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 05w1vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0b5x23 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 02465 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03hzkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 07jmgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 09xrxq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 05strv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 01ypsj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03c5f7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 01qg7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 04135 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 04258w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0f7h2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 01vh18t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 01r2c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 04353 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 068g3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 023w9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0kft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0jgwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 01nr36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 02mc79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 02pv_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 06t8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0184jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 069_0y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03dbds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 070j61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 01q9b9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0h5j77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0969vz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03bw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 06kxk2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 04hw4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 023n39 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 05cgy8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03nkts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 017r13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 014gf8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 045cq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0mm1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 016gkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0bs8d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03flwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0b9l3x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0280mv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0cm89v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 01twdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 02kxwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03n93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0q59y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0jrqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0hw1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 09p06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0b05xm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 01q4qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 078jt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 03r1pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0p51w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0jfx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 064p92m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 05fnl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 013cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 02r34n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 030pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 05ty4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 46.000 26.000 0.700 http://example.org/people/person/profession EVAL 02jknp profession! 0q9kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 26.000 0.700 http://example.org/people/person/profession #10871-0134bf PRED entity: 0134bf PRED relation: location! PRED expected values: 0jbp0 => 119 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1274): 0hnp7 (0.50 #1240, 0.04 #11312, 0.04 #13831), 032r1 (0.25 #2315, 0.06 #12387, 0.06 #14906), 0prfz (0.25 #49, 0.06 #10121, 0.04 #25232), 01w02sy (0.25 #596, 0.06 #10668, 0.04 #33334), 03rl84 (0.25 #362, 0.06 #25545, 0.05 #33100), 0465_ (0.25 #1296, 0.06 #26479, 0.04 #34034), 01wy5m (0.25 #984, 0.04 #26167, 0.04 #13575), 0134w7 (0.25 #163, 0.04 #10235, 0.04 #12754), 01vs_v8 (0.25 #403, 0.04 #10475, 0.04 #12994), 016yzz (0.25 #774, 0.04 #10846, 0.04 #13365) >> Best rule #1240 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 07ssc; 02jx1; >> query: (?x12381, 0hnp7) <- contains(?x12381, ?x13278), contains(?x12381, ?x4510), ?x4510 = 04p3c, contains(?x512, ?x12381), ?x13278 = 0138kk >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0134bf location! 0jbp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 58.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #10870-03p9hl PRED entity: 03p9hl PRED relation: award_winner! PRED expected values: 07t_l23 => 142 concepts (124 used for prediction) PRED predicted values (max 10 best out of 228): 02z1nbg (0.35 #1493, 0.33 #627, 0.17 #3221), 0bsjcw (0.33 #18159, 0.32 #1297, 0.32 #3891), 0bfvw2 (0.33 #18159, 0.32 #1297, 0.32 #3891), 0ck27z (0.27 #17818, 0.16 #13926, 0.14 #4849), 0bdwft (0.26 #3527, 0.20 #69, 0.07 #13902), 0gqyl (0.20 #106, 0.15 #3564, 0.15 #1836), 0gqy2 (0.20 #163, 0.05 #1893, 0.05 #2325), 0cqgl9 (0.19 #1054, 0.15 #3648, 0.07 #622), 0bb57s (0.19 #1106, 0.13 #3700, 0.07 #674), 09sb52 (0.18 #4797, 0.15 #1771, 0.14 #8685) >> Best rule #1493 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 03jjzf; >> query: (?x13793, 02z1nbg) <- spouse(?x13793, ?x5335), type_of_union(?x13793, ?x566), film(?x13793, ?x3514), story_by(?x12403, ?x5335) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #3859 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 01gw8b; *> query: (?x13793, 07t_l23) <- actor(?x3725, ?x13793), award(?x13793, ?x3989), award(?x9944, ?x3989), ?x9944 = 02z1yj *> conf = 0.04 ranks of expected_values: 104 EVAL 03p9hl award_winner! 07t_l23 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 142.000 124.000 0.353 http://example.org/award/award_category/winners./award/award_honor/award_winner #10869-0d085 PRED entity: 0d085 PRED relation: award_winner PRED expected values: 0hnjt 01_6dw 06z4wj => 41 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1050): 03m9c8 (0.40 #8898, 0.33 #6436, 0.33 #3974), 0hskw (0.40 #7967, 0.33 #5505, 0.14 #10430), 06z4wj (0.40 #8913, 0.33 #6451, 0.07 #11376), 0b7xl8 (0.40 #9236, 0.33 #6774, 0.05 #11699), 012t1 (0.40 #7584, 0.33 #5122, 0.05 #10047), 052hl (0.33 #6421, 0.33 #1497, 0.20 #11346), 012wg (0.33 #5929, 0.33 #1005, 0.20 #8391), 01vrlqd (0.33 #6630, 0.33 #1706, 0.20 #9092), 01vhrz (0.33 #6909, 0.33 #4447, 0.20 #9371), 0dbpwb (0.33 #6533, 0.33 #4071, 0.20 #8995) >> Best rule #8898 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 02qkk9_; 0bm70b; >> query: (?x5863, 03m9c8) <- award_winner(?x5863, ?x3980), award_winner(?x5863, ?x3947), ?x3947 = 0q59y, gender(?x3980, ?x231), film(?x3980, ?x1586), influenced_by(?x3980, ?x4265) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #8913 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 02qkk9_; 0bm70b; *> query: (?x5863, 06z4wj) <- award_winner(?x5863, ?x3980), award_winner(?x5863, ?x3947), ?x3947 = 0q59y, gender(?x3980, ?x231), film(?x3980, ?x1586), influenced_by(?x3980, ?x4265) *> conf = 0.40 ranks of expected_values: 3, 51, 335 EVAL 0d085 award_winner 06z4wj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 17.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0d085 award_winner 01_6dw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 41.000 17.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0d085 award_winner 0hnjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 41.000 17.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #10868-07f_t4 PRED entity: 07f_t4 PRED relation: film_crew_role PRED expected values: 09zzb8 02_n3z 01pvkk => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 20): 09zzb8 (0.79 #1128, 0.77 #235, 0.75 #1556), 01xy5l_ (0.53 #243, 0.52 #139, 0.43 #113), 02_n3z (0.48 #106, 0.44 #472, 0.43 #236), 01pvkk (0.43 #7, 0.39 #33, 0.35 #137), 02ynfr (0.32 #167, 0.25 #377, 0.21 #1273), 020xn5 (0.22 #136, 0.20 #240, 0.17 #110), 089fss (0.21 #5, 0.17 #31, 0.13 #135), 0ckd1 (0.17 #134, 0.17 #108, 0.17 #238), 05smlt (0.17 #118, 0.17 #248, 0.16 #170), 094hwz (0.17 #296, 0.16 #62, 0.13 #114) >> Best rule #1128 for best value: >> intensional similarity = 3 >> extensional distance = 301 >> proper extension: 0cnztc4; 0g5q34q; 0gh6j94; >> query: (?x7672, 09zzb8) <- film_format(?x7672, ?x909), film_crew_role(?x7672, ?x955), profession(?x123, ?x955) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4 EVAL 07f_t4 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.792 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 07f_t4 film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.792 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 07f_t4 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.792 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10867-0_9l_ PRED entity: 0_9l_ PRED relation: nominated_for! PRED expected values: 03hkv_r => 92 concepts (89 used for prediction) PRED predicted values (max 10 best out of 187): 040njc (0.78 #5536, 0.78 #5092, 0.68 #8192), 027571b (0.68 #8192, 0.67 #5535, 0.67 #5091), 02z1nbg (0.68 #8192, 0.67 #5535, 0.67 #5091), 09cn0c (0.68 #8192, 0.67 #5535, 0.67 #5091), 027c924 (0.68 #8192, 0.67 #5535, 0.67 #5091), 09qwmm (0.37 #24, 0.33 #245, 0.19 #17928), 0f4x7 (0.35 #244, 0.31 #466, 0.31 #688), 0k611 (0.34 #4486, 0.33 #281, 0.28 #4929), 0gqy2 (0.33 #327, 0.32 #4532, 0.23 #4975), 099cng (0.30 #55, 0.23 #276, 0.19 #17928) >> Best rule #5536 for best value: >> intensional similarity = 4 >> extensional distance = 596 >> proper extension: 06w7mlh; 06mmr; >> query: (?x11429, ?x601) <- award_winner(?x11429, ?x488), award(?x11429, ?x601), nominated_for(?x601, ?x89), ceremony(?x601, ?x78) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #17928 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1588 *> proper extension: 06g60w; *> query: (?x11429, ?x375) <- nominated_for(?x4398, ?x11429), award(?x4398, ?x375) *> conf = 0.19 ranks of expected_values: 19 EVAL 0_9l_ nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 92.000 89.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10866-0727h PRED entity: 0727h PRED relation: combatants PRED expected values: 0bk25 => 75 concepts (62 used for prediction) PRED predicted values (max 10 best out of 385): 035qy (0.65 #1506, 0.64 #4516, 0.62 #3557), 06frc (0.65 #1506, 0.64 #4516, 0.54 #3969), 07ssc (0.56 #970, 0.50 #3438, 0.47 #3982), 01xpg (0.50 #3698, 0.46 #1505, 0.46 #3421), 09c7w0 (0.46 #1922, 0.44 #959, 0.40 #5067), 0cdbq (0.45 #1565, 0.36 #2395, 0.33 #4582), 01k6y1 (0.45 #1566, 0.36 #2396, 0.31 #3621), 01tdpv (0.45 #1615, 0.36 #2445, 0.28 #4768), 05qhw (0.44 #969, 0.38 #556, 0.33 #12), 0chghy (0.44 #967, 0.36 #1365, 0.36 #1239) >> Best rule #1506 for best value: >> intensional similarity = 9 >> extensional distance = 9 >> proper extension: 08821; 048n7; 0784z; >> query: (?x13053, ?x8845) <- locations(?x13053, ?x12727), entity_involved(?x13053, ?x8845), contains(?x12727, ?x8958), country(?x1121, ?x8958), countries_spoken_in(?x254, ?x8958), currency(?x8958, ?x170), official_language(?x8845, ?x11038), member_states(?x2106, ?x8958), medal(?x8958, ?x1242) >> conf = 0.65 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0727h combatants 0bk25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 62.000 0.650 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #10865-011s9r PRED entity: 011s9r PRED relation: location PRED expected values: 01sn3 => 128 concepts (94 used for prediction) PRED predicted values (max 10 best out of 254): 01sn3 (0.33 #214, 0.20 #1018, 0.12 #1822), 030qb3t (0.32 #41069, 0.25 #60352, 0.23 #63565), 02_286 (0.32 #60306, 0.31 #41023, 0.29 #63519), 0k33p (0.20 #1285, 0.12 #2089, 0.10 #3695), 0hptm (0.20 #1106, 0.12 #1910, 0.09 #4320), 0xrzh (0.20 #1001, 0.12 #1805, 0.09 #4215), 0ncj8 (0.20 #1012, 0.12 #1816, 0.09 #4226), 052p7 (0.18 #7360, 0.14 #25042, 0.14 #23434), 05ksh (0.18 #7294, 0.08 #23368, 0.08 #24976), 04jpl (0.17 #8056, 0.11 #60286, 0.10 #50642) >> Best rule #214 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01y8d4; >> query: (?x11928, 01sn3) <- peers(?x11928, ?x8209), story_by(?x7425, ?x11928), nationality(?x11928, ?x94), ?x7425 = 042fgh >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011s9r location 01sn3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 94.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #10864-04306rv PRED entity: 04306rv PRED relation: official_language! PRED expected values: 084n_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 337): 0d060g (0.62 #180, 0.59 #718, 0.50 #547), 0hzlz (0.62 #180, 0.59 #718, 0.50 #1800), 0154j (0.62 #180, 0.59 #718, 0.50 #1800), 01mjq (0.62 #180, 0.59 #718, 0.50 #1800), 0k6nt (0.62 #180, 0.59 #718, 0.50 #1800), 0h7x (0.62 #180, 0.59 #718, 0.50 #1800), 082fr (0.62 #180, 0.59 #718, 0.50 #1800), 01ppq (0.62 #180, 0.59 #718, 0.50 #1800), 03pn9 (0.62 #180, 0.59 #718, 0.50 #1800), 07ytt (0.62 #180, 0.59 #718, 0.50 #1800) >> Best rule #180 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 02h40lc; >> query: (?x732, ?x172) <- languages_spoken(?x1423, ?x732), language(?x6097, ?x732), language(?x3745, ?x732), language(?x3599, ?x732), countries_spoken_in(?x732, ?x172), official_language(?x1264, ?x732), ?x3599 = 0kxf1, ?x6097 = 0y_yw, ?x3745 = 03cw411 >> conf = 0.62 => this is the best rule for 11 predicted values *> Best rule #7575 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 53 *> proper extension: 0v0s; *> query: (?x732, ?x94) <- languages_spoken(?x1423, ?x732), people(?x1423, ?x9105), people(?x1423, ?x8450), nationality(?x8450, ?x94), profession(?x9105, ?x967) *> conf = 0.10 ranks of expected_values: 128 EVAL 04306rv official_language! 084n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 83.000 83.000 0.618 http://example.org/location/country/official_language #10863-01wdqrx PRED entity: 01wdqrx PRED relation: profession PRED expected values: 01c72t => 144 concepts (117 used for prediction) PRED predicted values (max 10 best out of 70): 016z4k (0.54 #1013, 0.51 #3610, 0.51 #436), 01d_h8 (0.52 #2459, 0.44 #6353, 0.44 #5054), 01c72t (0.50 #165, 0.38 #6950, 0.36 #6659), 0dxtg (0.42 #2467, 0.37 #5638, 0.37 #6361), 03gjzk (0.42 #2468, 0.34 #6362, 0.34 #5639), 039v1 (0.41 #2631, 0.40 #466, 0.39 #4792), 02jknp (0.30 #2461, 0.24 #6355, 0.24 #5632), 0n1h (0.26 #14872, 0.25 #11, 0.22 #1454), 018gz8 (0.26 #14872, 0.20 #2470, 0.17 #6364), 01xy5l_ (0.26 #14872, 0.02 #464, 0.01 #1041) >> Best rule #1013 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 025xt8y; 01wqflx; 0167v4; >> query: (?x1282, 016z4k) <- origin(?x1282, ?x739), role(?x1282, ?x315), award(?x1282, ?x4958) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 02ht0ln; *> query: (?x1282, 01c72t) <- award(?x1282, ?x10881), artists(?x378, ?x1282), ?x10881 = 026mmy *> conf = 0.50 ranks of expected_values: 3 EVAL 01wdqrx profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 144.000 117.000 0.538 http://example.org/people/person/profession #10862-04vg8 PRED entity: 04vg8 PRED relation: form_of_government PRED expected values: 01fpfn => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 6): 06cx9 (0.43 #19, 0.42 #176, 0.41 #182), 01q20 (0.40 #4, 0.38 #34, 0.30 #112), 01d9r3 (0.34 #186, 0.34 #180, 0.30 #113), 018wl5 (0.33 #14, 0.28 #110, 0.26 #128), 01fpfn (0.30 #87, 0.29 #196, 0.28 #178), 026wp (0.23 #133, 0.16 #242, 0.14 #321) >> Best rule #19 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 09c7w0; 06bnz; >> query: (?x8883, 06cx9) <- jurisdiction_of_office(?x900, ?x8883), jurisdiction_of_office(?x346, ?x8883), ?x346 = 060c4, jurisdiction_of_office(?x900, ?x1024), ?x1024 = 05fhy >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 06frc; *> query: (?x8883, 01fpfn) <- official_language(?x8883, ?x5607), contains(?x789, ?x8883), partially_contains(?x455, ?x789) *> conf = 0.30 ranks of expected_values: 5 EVAL 04vg8 form_of_government 01fpfn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 92.000 0.429 http://example.org/location/country/form_of_government #10861-0l9k1 PRED entity: 0l9k1 PRED relation: gender PRED expected values: 05zppz => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #37, 0.87 #69, 0.87 #59), 02zsn (0.32 #120, 0.29 #124, 0.29 #82) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 037q1z; >> query: (?x11305, 05zppz) <- produced_by(?x5499, ?x11305), location(?x11305, ?x1646), award_winner(?x902, ?x11305) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l9k1 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.887 http://example.org/people/person/gender #10860-06cqb PRED entity: 06cqb PRED relation: parent_genre! PRED expected values: 016cyt => 57 concepts (20 used for prediction) PRED predicted values (max 10 best out of 287): 01ym9b (0.56 #1338, 0.42 #2381, 0.40 #1861), 06hzq3 (0.50 #630, 0.39 #778, 0.37 #779), 03p7rp (0.50 #667, 0.39 #778, 0.37 #779), 0pm85 (0.50 #648, 0.33 #389, 0.20 #909), 0dls3 (0.50 #561, 0.33 #302, 0.20 #822), 03gt7s (0.50 #751, 0.33 #492, 0.03 #2836), 07ffjc (0.50 #622, 0.33 #363, 0.03 #2707), 018ysx (0.40 #982, 0.33 #462, 0.25 #721), 0133_p (0.40 #907, 0.30 #1950, 0.25 #2210), 05w3f (0.40 #810, 0.30 #1853, 0.22 #1039) >> Best rule #1338 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 07gxw; 0190y4; >> query: (?x283, 01ym9b) <- artists(?x283, ?x8143), artists(?x283, ?x6609), parent_genre(?x7280, ?x283), category(?x6609, ?x134), instrumentalists(?x315, ?x8143), people(?x1050, ?x8143), profession(?x6609, ?x131), ?x7280 = 0283d >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2867 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 0g64p; *> query: (?x283, ?x497) <- parent_genre(?x7280, ?x283), parent_genre(?x2491, ?x283), parent_genre(?x7280, ?x497), parent_genre(?x2491, ?x10306), ?x10306 = 09jw2 *> conf = 0.16 ranks of expected_values: 145 EVAL 06cqb parent_genre! 016cyt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 57.000 20.000 0.556 http://example.org/music/genre/parent_genre #10859-025_ql1 PRED entity: 025_ql1 PRED relation: gender PRED expected values: 05zppz => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #7, 0.84 #21, 0.82 #35), 02zsn (0.48 #123, 0.46 #206, 0.46 #201) >> Best rule #7 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 01wg982; 01vsksr; 01wwnh2; >> query: (?x13591, 05zppz) <- profession(?x13591, ?x1614), profession(?x13591, ?x319), category(?x13591, ?x134), ?x1614 = 01c72t, ?x319 = 01d_h8 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025_ql1 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.905 http://example.org/people/person/gender #10858-016wvy PRED entity: 016wvy PRED relation: instrumentalists! PRED expected values: 07_l6 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 124): 07y_7 (0.47 #499, 0.43 #1838, 0.43 #2931), 0dwvl (0.31 #4615, 0.31 #3691, 0.30 #4786), 03qjg (0.30 #1297, 0.22 #2386, 0.22 #1045), 02hnl (0.25 #2536, 0.24 #2875, 0.24 #2285), 0l14qv (0.20 #1254, 0.16 #1086, 0.15 #1002), 026t6 (0.19 #2257, 0.18 #3438, 0.17 #1000), 018j2 (0.17 #1032, 0.15 #449, 0.15 #1116), 0l14md (0.16 #1088, 0.15 #2851, 0.15 #1004), 06ncr (0.15 #455, 0.14 #1206, 0.13 #1290), 03gvt (0.15 #476, 0.12 #809, 0.11 #2906) >> Best rule #499 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 07_3qd; >> query: (?x10144, ?x75) <- nationality(?x10144, ?x512), role(?x10144, ?x75), artists(?x3734, ?x10144), artist(?x8489, ?x10144), ?x8489 = 01cl0d >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #5382 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 401 *> proper extension: 01m7f5r; *> query: (?x10144, ?x645) <- nationality(?x10144, ?x512), profession(?x10144, ?x1183), role(?x10144, ?x74), profession(?x10353, ?x1183), role(?x10353, ?x645) *> conf = 0.05 ranks of expected_values: 46 EVAL 016wvy instrumentalists! 07_l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 135.000 135.000 0.471 http://example.org/music/instrument/instrumentalists #10857-0288fyj PRED entity: 0288fyj PRED relation: award_winner! PRED expected values: 056878 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 99): 0466p0j (0.50 #74, 0.16 #4171, 0.10 #352), 05pd94v (0.30 #2, 0.16 #4171, 0.10 #280), 02rjjll (0.20 #5, 0.16 #4171, 0.11 #283), 056878 (0.20 #31, 0.16 #4171, 0.10 #309), 0gx1673 (0.20 #118, 0.16 #4171, 0.10 #9319), 013b2h (0.16 #4171, 0.14 #356, 0.11 #634), 02cg41 (0.16 #4171, 0.11 #402, 0.10 #9319), 0gpjbt (0.16 #4171, 0.10 #9319, 0.10 #9039), 0hhtgcw (0.16 #4171, 0.10 #9319, 0.10 #9039), 01s695 (0.16 #4171, 0.10 #9319, 0.10 #9039) >> Best rule #74 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01vvydl; 04lgymt; 02l840; 016kjs; 0412f5y; 01vw20h; 03h_0_z; 026yqrr; >> query: (?x2335, 0466p0j) <- award_winner(?x725, ?x2335), award_nominee(?x2335, ?x5536), ?x5536 = 01vsgrn >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01vvydl; 04lgymt; 02l840; 016kjs; 0412f5y; 01vw20h; 03h_0_z; 026yqrr; *> query: (?x2335, 056878) <- award_winner(?x725, ?x2335), award_nominee(?x2335, ?x5536), ?x5536 = 01vsgrn *> conf = 0.20 ranks of expected_values: 4 EVAL 0288fyj award_winner! 056878 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 93.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10856-06hx2 PRED entity: 06hx2 PRED relation: legislative_sessions PRED expected values: 05l2z4 => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 49): 024tkd (0.20 #1165, 0.20 #185, 0.19 #1851), 024tcq (0.20 #1148, 0.20 #168, 0.16 #1393), 06f0dc (0.20 #1136, 0.19 #1822, 0.19 #1871), 07p__7 (0.20 #155, 0.17 #1135, 0.17 #1821), 02bqm0 (0.20 #176, 0.17 #1156, 0.15 #1842), 02cg7g (0.20 #173, 0.17 #1153, 0.15 #1839), 02bqmq (0.20 #166, 0.17 #1146, 0.15 #1832), 070m6c (0.20 #1133, 0.17 #1819, 0.17 #1966), 01gtc0 (0.20 #177, 0.04 #961, 0.03 #2039), 01gtcc (0.20 #169, 0.04 #953, 0.03 #1394) >> Best rule #1165 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0157m; 034rd; 01lct6; >> query: (?x6138, 024tkd) <- profession(?x6138, ?x3342), jurisdiction_of_office(?x6138, ?x94), ?x94 = 09c7w0, location(?x6138, ?x335) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #52 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 015076; *> query: (?x6138, 05l2z4) <- profession(?x6138, ?x3342), sibling(?x11088, ?x6138), people(?x9888, ?x6138), participant(?x11088, ?x543) *> conf = 0.14 ranks of expected_values: 21 EVAL 06hx2 legislative_sessions 05l2z4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 179.000 179.000 0.200 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #10855-01zwy PRED entity: 01zwy PRED relation: organization PRED expected values: 01prf3 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 11): 02hcxm (0.11 #82, 0.11 #58, 0.08 #106), 01r3kd (0.11 #8, 0.03 #565, 0.03 #589), 04m8fy (0.11 #19, 0.01 #526), 02_l9 (0.06 #64, 0.05 #401, 0.04 #112), 01prf3 (0.04 #188, 0.04 #454, 0.03 #429), 03lb_v (0.01 #383, 0.01 #771, 0.01 #795), 03ksy (0.01 #193), 01w3v (0.01 #193), 07t65 (0.01 #2730), 05g9h (0.01 #506) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0m93; 030dr; >> query: (?x8508, 02hcxm) <- profession(?x8508, ?x3802), ?x3802 = 06q2q, religion(?x8508, ?x2694) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #188 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 0cl_m; *> query: (?x8508, 01prf3) <- company(?x8508, ?x3439), religion(?x8508, ?x2694), institution(?x620, ?x3439) *> conf = 0.04 ranks of expected_values: 5 EVAL 01zwy organization 01prf3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 114.000 114.000 0.111 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #10854-02rn00y PRED entity: 02rn00y PRED relation: genre PRED expected values: 05p553 => 136 concepts (134 used for prediction) PRED predicted values (max 10 best out of 107): 05p553 (0.78 #11464, 0.70 #123, 0.69 #242), 09b3v (0.64 #3462, 0.60 #1671, 0.59 #3342), 01jfsb (0.63 #11712, 0.54 #12557, 0.50 #10755), 02kdv5l (0.47 #2507, 0.46 #2388, 0.46 #2745), 01zhp (0.46 #314, 0.40 #195, 0.33 #76), 02l7c8 (0.40 #1687, 0.34 #8488, 0.33 #9203), 060__y (0.39 #733, 0.33 #1330, 0.33 #1688), 06n90 (0.38 #370, 0.32 #2756, 0.29 #3714), 04xvlr (0.33 #1552, 0.33 #1672, 0.31 #837), 04pbhw (0.24 #2560, 0.21 #2798, 0.19 #412) >> Best rule #11464 for best value: >> intensional similarity = 5 >> extensional distance = 750 >> proper extension: 05cj_j; 0prrm; 08cfr1; 03p2xc; 02mc5v; 0291hr; 01lbcqx; 058kh7; 03wy8t; 01xlqd; ... >> query: (?x3455, 05p553) <- genre(?x3455, ?x1510), genre(?x8075, ?x1510), genre(?x4688, ?x1510), ?x8075 = 03nfnx, ?x4688 = 09jcj6 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rn00y genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 134.000 0.775 http://example.org/film/film/genre #10853-049ql1 PRED entity: 049ql1 PRED relation: list PRED expected values: 04k4rt => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.49 #397, 0.44 #82, 0.39 #579), 04k4rt (0.42 #102, 0.36 #95, 0.33 #186), 01pd60 (0.36 #90, 0.29 #391, 0.27 #636), 09g7thr (0.12 #274, 0.12 #295, 0.11 #862) >> Best rule #397 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 01qygl; 01b39j; 0206k5; >> query: (?x11468, 01ptsx) <- industry(?x11468, ?x6575), organization(?x4682, ?x11468), citytown(?x11468, ?x739), taxonomy(?x6575, ?x939) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #102 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 01_4lx; *> query: (?x11468, 04k4rt) <- child(?x11468, ?x12077), child(?x11468, ?x5861), organization(?x4682, ?x5861), place_founded(?x12077, ?x4627), industry(?x12077, ?x245) *> conf = 0.42 ranks of expected_values: 2 EVAL 049ql1 list 04k4rt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 165.000 165.000 0.486 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #10852-02grdc PRED entity: 02grdc PRED relation: award! PRED expected values: 0p_47 => 34 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2353): 0157m (0.78 #36813, 0.78 #36812, 0.76 #13388), 05zjx (0.78 #36813, 0.78 #36812, 0.76 #13388), 0jrny (0.78 #36813, 0.78 #36812, 0.76 #13388), 015qyf (0.78 #36813, 0.78 #36812, 0.76 #13388), 0f6_x (0.78 #36813, 0.78 #36812, 0.76 #13388), 0c2dl (0.78 #36812, 0.76 #13388, 0.76 #10041), 051cc (0.78 #36812, 0.76 #13388, 0.76 #10041), 05bnq3j (0.78 #36812, 0.76 #13388, 0.76 #10041), 0b_dy (0.67 #10896, 0.60 #7549, 0.57 #17589), 0gr36 (0.60 #7492, 0.50 #10839, 0.43 #17532) >> Best rule #36813 for best value: >> intensional similarity = 4 >> extensional distance = 187 >> proper extension: 01c9d1; >> query: (?x594, ?x11364) <- award_winner(?x594, ?x11364), film(?x11364, ?x697), award(?x236, ?x594), ceremony(?x594, ?x139) >> conf = 0.78 => this is the best rule for 5 predicted values *> Best rule #40163 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 204 *> proper extension: 0dt39; 073y53; 05kjlr; 011w54; 06zrp44; 0blst_; 05fmy; 03j2ts; 06_y0kx; *> query: (?x594, ?x269) <- award_winner(?x594, ?x11364), award_winner(?x594, ?x7512), award_winner(?x2523, ?x11364), award_winner(?x2523, ?x269), company(?x7512, ?x5907) *> conf = 0.08 ranks of expected_values: 654 EVAL 02grdc award! 0p_47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 19.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10851-0cw3yd PRED entity: 0cw3yd PRED relation: language PRED expected values: 02h40lc => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.89 #2852, 0.89 #2024, 0.89 #1424), 064_8sq (0.57 #3032, 0.55 #2972, 0.22 #22), 02bjrlw (0.57 #3032, 0.55 #2972, 0.09 #476), 04306rv (0.15 #64, 0.12 #362, 0.11 #5), 06nm1 (0.11 #1196, 0.11 #1433, 0.11 #1078), 05qqm (0.11 #41, 0.05 #218, 0.04 #100), 06b_j (0.07 #82, 0.07 #439, 0.07 #498), 04h9h (0.06 #459, 0.05 #518, 0.04 #931), 03_9r (0.05 #603, 0.05 #2800, 0.05 #1610), 0jzc (0.05 #197, 0.05 #1087, 0.04 #317) >> Best rule #2852 for best value: >> intensional similarity = 3 >> extensional distance = 662 >> proper extension: 02_qt; 05css_; 03nqnnk; 058kh7; 032xky; >> query: (?x2812, 02h40lc) <- country(?x2812, ?x94), featured_film_locations(?x2812, ?x10683), genre(?x2812, ?x53) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cw3yd language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.895 http://example.org/film/film/language #10850-07rfp PRED entity: 07rfp PRED relation: category PRED expected values: 08mbj5d => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #180, 0.82 #179, 0.81 #183) >> Best rule #180 for best value: >> intensional similarity = 5 >> extensional distance = 685 >> proper extension: 02185j; >> query: (?x13915, ?x134) <- citytown(?x13915, ?x9559), citytown(?x12044, ?x9559), contains(?x252, ?x9559), organization(?x4682, ?x12044), category(?x12044, ?x134) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07rfp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.818 http://example.org/common/topic/webpage./common/webpage/category #10849-01k31p PRED entity: 01k31p PRED relation: gender PRED expected values: 05zppz => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 5): 05zppz (0.89 #83, 0.88 #166, 0.88 #164), 02zsn (0.47 #291, 0.47 #288, 0.47 #144), 01lys3 (0.14 #151, 0.14 #87, 0.12 #239), 0d19y2 (0.14 #151, 0.14 #87, 0.12 #239), 014dsx (0.14 #151, 0.14 #87, 0.12 #239) >> Best rule #83 for best value: >> intensional similarity = 7 >> extensional distance = 26 >> proper extension: 016hvl; >> query: (?x13640, 05zppz) <- location(?x13640, ?x13894), profession(?x13640, ?x5805), people(?x4291, ?x13640), ?x5805 = 0fj9f, people(?x4291, ?x6370), nationality(?x6370, ?x1310), religion(?x6370, ?x4641) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k31p gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 178.000 0.893 http://example.org/people/person/gender #10848-025rsfk PRED entity: 025rsfk PRED relation: nutrient! PRED expected values: 061_f 01645p => 55 concepts (51 used for prediction) PRED predicted values (max 10 best out of 7): 061_f (0.93 #437, 0.89 #180, 0.89 #54), 01645p (0.89 #317, 0.89 #312, 0.89 #180), 06x4c (0.89 #180, 0.89 #54, 0.89 #112), 0dcfv (0.89 #180, 0.89 #54, 0.89 #112), 04k8n (0.02 #183, 0.02 #202), 05wvs (0.02 #183, 0.02 #202), 01sh2 (0.02 #183, 0.02 #202) >> Best rule #437 for best value: >> intensional similarity = 114 >> extensional distance = 28 >> proper extension: 014d7f; >> query: (?x7135, 061_f) <- nutrient(?x9732, ?x7135), nutrient(?x7719, ?x7135), nutrient(?x7057, ?x7135), nutrient(?x6191, ?x7135), nutrient(?x6159, ?x7135), nutrient(?x5373, ?x7135), nutrient(?x3468, ?x7135), nutrient(?x2701, ?x7135), ?x6191 = 014j1m, nutrient(?x9732, ?x13944), nutrient(?x9732, ?x13498), nutrient(?x9732, ?x12902), nutrient(?x9732, ?x12454), nutrient(?x9732, ?x12083), nutrient(?x9732, ?x11758), nutrient(?x9732, ?x11409), nutrient(?x9732, ?x11270), nutrient(?x9732, ?x10891), nutrient(?x9732, ?x10709), nutrient(?x9732, ?x10098), nutrient(?x9732, ?x9949), nutrient(?x9732, ?x9733), nutrient(?x9732, ?x9708), nutrient(?x9732, ?x9490), nutrient(?x9732, ?x9436), nutrient(?x9732, ?x9426), nutrient(?x9732, ?x9365), nutrient(?x9732, ?x8442), nutrient(?x9732, ?x7720), nutrient(?x9732, ?x7652), nutrient(?x9732, ?x7364), nutrient(?x9732, ?x7362), nutrient(?x9732, ?x7219), nutrient(?x9732, ?x6586), nutrient(?x9732, ?x6160), nutrient(?x9732, ?x6033), nutrient(?x9732, ?x5526), nutrient(?x9732, ?x5451), nutrient(?x9732, ?x5374), nutrient(?x9732, ?x5010), nutrient(?x9732, ?x2702), nutrient(?x9732, ?x2018), nutrient(?x9732, ?x1960), nutrient(?x9732, ?x1258), ?x13944 = 0f4kp, ?x9490 = 0h1sg, ?x11270 = 02kc008, ?x5374 = 025s0zp, ?x9426 = 0h1yy, ?x2702 = 0838f, ?x8442 = 02kcv4x, ?x7219 = 0h1vg, nutrient(?x6159, ?x13126), nutrient(?x6159, ?x12868), nutrient(?x6159, ?x9795), nutrient(?x6159, ?x9619), nutrient(?x6159, ?x7431), nutrient(?x6159, ?x6192), nutrient(?x6159, ?x6026), nutrient(?x6159, ?x5337), nutrient(?x6159, ?x4069), nutrient(?x6159, ?x3469), nutrient(?x6159, ?x3264), nutrient(?x6159, ?x3203), ?x9619 = 0h1tg, ?x7362 = 02kc5rj, ?x10891 = 0g5gq, ?x1258 = 0h1wg, ?x12083 = 01n78x, ?x9436 = 025sqz8, ?x10709 = 0h1sz, ?x9365 = 04k8n, ?x5373 = 0971v, ?x4069 = 0hqw8p_, ?x5337 = 06x4c, ?x12868 = 03d49, ?x7652 = 025s0s0, ?x11409 = 0h1yf, ?x13498 = 07q0m, ?x9708 = 061xhr, ?x6033 = 04zjxcz, ?x6192 = 06jry, ?x7057 = 0fbdb, ?x7720 = 025s7x6, ?x6160 = 041r51, ?x12454 = 025rw19, ?x5451 = 05wvs, ?x7719 = 0dj75, ?x9795 = 05v_8y, ?x3264 = 0dcfv, ?x9949 = 02kd0rh, nutrient(?x3468, ?x11784), nutrient(?x3468, ?x10195), nutrient(?x3468, ?x6286), ?x5526 = 09pbb, ?x12902 = 0fzjh, ?x5010 = 0h1vz, ?x11784 = 07zqy, nutrient(?x2701, ?x3901), ?x1960 = 07hnp, ?x11758 = 0q01m, ?x2018 = 01sh2, ?x6586 = 05gh50, ?x6286 = 02y_3rf, ?x6026 = 025sf8g, ?x13126 = 02kc_w5, ?x3469 = 0h1zw, ?x7364 = 09gvd, ?x10098 = 0h1_c, ?x3203 = 04kl74p, ?x9733 = 0h1tz, ?x10195 = 0hkwr, ?x7431 = 09gwd, ?x3901 = 0466p20 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 025rsfk nutrient! 01645p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 51.000 0.933 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 025rsfk nutrient! 061_f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 51.000 0.933 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #10847-06f32 PRED entity: 06f32 PRED relation: film_release_region! PRED expected values: 017gm7 04jkpgv 03qnvdl 05z7c 0661ql3 0gz6b6g 06ztvyx 05pdh86 0glqh5_ 0btpm6 0g57wgv 0j8f09z => 188 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1682): 062zm5h (0.88 #35035, 0.82 #25530, 0.80 #30283), 017gm7 (0.85 #23897, 0.83 #32215, 0.82 #29839), 03nm_fh (0.85 #30235, 0.82 #24293, 0.82 #25482), 0407yfx (0.84 #34677, 0.81 #19231, 0.79 #25172), 05pdh86 (0.82 #30202, 0.82 #25449, 0.76 #24260), 04n52p6 (0.82 #29873, 0.74 #25120, 0.72 #34625), 045j3w (0.82 #30029, 0.74 #25276, 0.65 #24087), 02vr3gz (0.82 #8730, 0.79 #24175, 0.78 #30117), 087wc7n (0.81 #34536, 0.79 #25031, 0.76 #23842), 0gffmn8 (0.81 #34799, 0.76 #8660, 0.75 #19353) >> Best rule #35035 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 01ls2; 077qn; >> query: (?x2629, 062zm5h) <- film_release_region(?x7016, ?x2629), film_release_region(?x6168, ?x2629), ?x6168 = 0gj96ln, film_distribution_medium(?x7016, ?x2099) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #23897 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 02jx1; *> query: (?x2629, 017gm7) <- country(?x12372, ?x2629), olympics(?x2629, ?x775), country(?x2340, ?x2629) *> conf = 0.85 ranks of expected_values: 2, 5, 11, 17, 18, 24, 43, 78, 89, 102, 121, 158 EVAL 06f32 film_release_region! 0j8f09z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 0g57wgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 0glqh5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 05pdh86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 06ztvyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 0gz6b6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 05z7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 03qnvdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 04jkpgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06f32 film_release_region! 017gm7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 188.000 96.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10846-0x3r3 PRED entity: 0x3r3 PRED relation: influenced_by PRED expected values: 02wh0 => 117 concepts (63 used for prediction) PRED predicted values (max 10 best out of 329): 05qmj (0.64 #1475, 0.56 #190, 0.50 #5759), 0gz_ (0.57 #1388, 0.50 #5672, 0.47 #8673), 039n1 (0.50 #1607, 0.39 #8033, 0.30 #6319), 026lj (0.45 #472, 0.44 #44, 0.33 #3471), 0j3v (0.44 #60, 0.30 #6057, 0.30 #5629), 0w6w (0.44 #426, 0.21 #8137, 0.20 #8996), 02wh0 (0.40 #11094, 0.39 #8090, 0.28 #8949), 099bk (0.36 #1396, 0.33 #111, 0.29 #7822), 032l1 (0.33 #14663, 0.23 #6514, 0.22 #89), 042q3 (0.32 #6786, 0.30 #6358, 0.25 #8072) >> Best rule #1475 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01d494; 052h3; 01dvtx; 0ct9_; 039n1; >> query: (?x5796, 05qmj) <- interests(?x5796, ?x1858), influenced_by(?x5796, ?x920), company(?x5796, ?x741), place_of_death(?x5796, ?x12697) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #11094 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 084w8; 028p0; 02yl42; 0d5_f; 03f0324; 01tz6vs; 03_87; 01wd02c; 05np2; 016dmx; ... *> query: (?x5796, 02wh0) <- profession(?x5796, ?x8340), influenced_by(?x5796, ?x7250), influenced_by(?x11500, ?x7250), ?x11500 = 0cpvcd *> conf = 0.40 ranks of expected_values: 7 EVAL 0x3r3 influenced_by 02wh0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 117.000 63.000 0.643 http://example.org/influence/influence_node/influenced_by #10845-01t2h2 PRED entity: 01t2h2 PRED relation: religion PRED expected values: 092bf5 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 13): 0c8wxp (0.14 #591, 0.14 #1221, 0.14 #1401), 0kpl (0.09 #55, 0.08 #100, 0.08 #505), 03j6c (0.08 #516, 0.02 #381, 0.02 #3082), 03_gx (0.07 #14, 0.05 #464, 0.05 #1049), 0flw86 (0.03 #497, 0.02 #137, 0.02 #767), 092bf5 (0.03 #151, 0.03 #196, 0.02 #241), 0kq2 (0.02 #63, 0.02 #18, 0.02 #108), 01lp8 (0.02 #1126, 0.02 #946, 0.02 #1171), 0n2g (0.02 #508, 0.01 #58, 0.01 #103), 019cr (0.01 #56, 0.01 #101, 0.01 #146) >> Best rule #591 for best value: >> intensional similarity = 3 >> extensional distance = 623 >> proper extension: 03h_0_z; >> query: (?x1864, 0c8wxp) <- film(?x1864, ?x2889), award_winner(?x5923, ?x1864), award_winner(?x1864, ?x11657) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #151 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 227 *> proper extension: 0hm0k; *> query: (?x1864, 092bf5) <- category(?x1864, ?x134), award_winner(?x5826, ?x1864), language(?x5826, ?x254) *> conf = 0.03 ranks of expected_values: 6 EVAL 01t2h2 religion 092bf5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 104.000 104.000 0.139 http://example.org/people/person/religion #10844-0p_r5 PRED entity: 0p_r5 PRED relation: gender PRED expected values: 05zppz => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #40, 0.84 #78, 0.83 #82), 02zsn (0.45 #8, 0.40 #4, 0.33 #18) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 01r216; 09hd6f; >> query: (?x12975, 05zppz) <- tv_program(?x12975, ?x6884), place_of_birth(?x12975, ?x1860), languages(?x6884, ?x254), titles(?x2008, ?x6884) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p_r5 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.845 http://example.org/people/person/gender #10843-046_v PRED entity: 046_v PRED relation: student! PRED expected values: 03bmmc => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 202): 09r4xx (0.25 #123, 0.11 #650, 0.10 #1177), 01w5m (0.23 #3794, 0.23 #3267, 0.12 #4321), 06thjt (0.21 #925, 0.08 #6195, 0.05 #9884), 07tgn (0.13 #2652, 0.10 #4233, 0.10 #7395), 0bwfn (0.11 #802, 0.09 #15558, 0.09 #10815), 03ksy (0.10 #11700, 0.10 #18024, 0.10 #3268), 065y4w7 (0.10 #1068, 0.08 #12662, 0.07 #14770), 09f2j (0.10 #1213, 0.04 #41644, 0.04 #15442), 02301 (0.08 #5871, 0.05 #601, 0.04 #1655), 05zl0 (0.08 #1783, 0.04 #5999, 0.04 #41644) >> Best rule #123 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 079vf; >> query: (?x10439, 09r4xx) <- place_of_birth(?x10439, ?x739), profession(?x10439, ?x353), story_by(?x5270, ?x10439), ?x5270 = 0bc1yhb >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #7574 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 0drc1; *> query: (?x10439, 03bmmc) <- place_of_death(?x10439, ?x6367), story_by(?x4378, ?x10439), genre(?x4378, ?x225) *> conf = 0.02 ranks of expected_values: 175 EVAL 046_v student! 03bmmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 101.000 101.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #10842-028cg00 PRED entity: 028cg00 PRED relation: film_crew_role PRED expected values: 01pvkk => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 29): 01pvkk (0.43 #9, 0.35 #2472, 0.35 #165), 02vs3x5 (0.29 #19, 0.22 #144, 0.22 #50), 02rh1dz (0.27 #70, 0.26 #635, 0.25 #164), 01xy5l_ (0.21 #229, 0.17 #136, 0.16 #448), 015h31 (0.20 #412, 0.18 #69, 0.18 #634), 0d2b38 (0.20 #648, 0.17 #458, 0.17 #146), 0215hd (0.19 #107, 0.16 #641, 0.16 #451), 02_n3z (0.15 #1034, 0.15 #1068, 0.15 #1067), 094hwz (0.15 #1034, 0.15 #1068, 0.15 #1067), 033smt (0.15 #1034, 0.15 #1068, 0.15 #1067) >> Best rule #9 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 02w9k1c; 08zrbl; >> query: (?x1889, 01pvkk) <- film_crew_role(?x1889, ?x3305), film_crew_role(?x1889, ?x468), ?x3305 = 04pyp5, titles(?x811, ?x1889), ?x468 = 02r96rf, genre(?x1889, ?x225), category(?x1889, ?x134), film_release_region(?x1889, ?x94) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028cg00 film_crew_role 01pvkk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.429 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10841-01z7s_ PRED entity: 01z7s_ PRED relation: profession PRED expected values: 02hrh1q => 83 concepts (57 used for prediction) PRED predicted values (max 10 best out of 57): 02hrh1q (0.90 #5123, 0.90 #3809, 0.90 #3663), 01d_h8 (0.79 #1174, 0.77 #1612, 0.77 #152), 03gjzk (0.71 #1036, 0.70 #160, 0.70 #1474), 018gz8 (0.26 #2060, 0.23 #1038, 0.22 #4834), 09jwl (0.24 #602, 0.20 #4982, 0.17 #894), 0cbd2 (0.22 #1029, 0.18 #4825, 0.17 #153), 0d1pc (0.20 #924, 0.16 #1800, 0.16 #632), 016z4k (0.16 #588, 0.12 #4968, 0.12 #2340), 0nbcg (0.14 #4993, 0.11 #4701, 0.11 #321), 0dz3r (0.14 #4966, 0.11 #2922, 0.11 #2338) >> Best rule #5123 for best value: >> intensional similarity = 4 >> extensional distance = 1493 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 04bdxl; 02s2ft; 05vsxz; 06qgvf; 0grwj; ... >> query: (?x5834, 02hrh1q) <- profession(?x5834, ?x524), film(?x5834, ?x810), gender(?x5834, ?x231), award(?x5834, ?x704) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01z7s_ profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 57.000 0.904 http://example.org/people/person/profession #10840-03s5t PRED entity: 03s5t PRED relation: adjoins PRED expected values: 015jr => 210 concepts (143 used for prediction) PRED predicted values (max 10 best out of 546): 050l8 (0.82 #80720, 0.82 #55339, 0.82 #99177), 015jr (0.82 #80720, 0.82 #55339, 0.81 #80719), 03s5t (0.22 #83797, 0.22 #75339, 0.22 #49189), 01n4w (0.22 #83797, 0.22 #75339, 0.22 #49189), 0vmt (0.22 #83797, 0.22 #75339, 0.22 #49189), 01n7q (0.22 #83797, 0.22 #75339, 0.22 #49189), 05rgl (0.22 #83797, 0.22 #75339, 0.22 #49189), 05fhy (0.22 #83797, 0.22 #75339, 0.22 #49189), 06mz5 (0.22 #83797, 0.22 #75339, 0.22 #49189), 05fjy (0.22 #83797, 0.22 #75339, 0.22 #49189) >> Best rule #80720 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 0n96z; >> query: (?x2768, ?x1138) <- country(?x2768, ?x94), adjoins(?x1138, ?x2768), contains(?x1138, ?x3026), adjoins(?x938, ?x1138) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 03s5t adjoins 015jr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 210.000 143.000 0.823 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #10839-0c9xjl PRED entity: 0c9xjl PRED relation: student! PRED expected values: 04b_46 => 111 concepts (101 used for prediction) PRED predicted values (max 10 best out of 112): 08815 (0.25 #527, 0.05 #1052, 0.05 #1577), 026gvfj (0.12 #636, 0.12 #111, 0.02 #1161), 09f2j (0.12 #684, 0.06 #4359, 0.06 #2259), 033gn8 (0.12 #902, 0.04 #1427, 0.04 #1952), 02gr81 (0.12 #657, 0.04 #1182, 0.04 #1707), 06kknt (0.12 #991, 0.04 #1516, 0.04 #2041), 05nrkb (0.12 #348, 0.02 #11898, 0.02 #13474), 02m0b0 (0.12 #923, 0.02 #1448, 0.02 #1973), 01vg13 (0.12 #744, 0.02 #1269, 0.02 #1794), 02s62q (0.12 #577, 0.02 #1102, 0.02 #1627) >> Best rule #527 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 098n_m; >> query: (?x5470, 08815) <- award(?x5470, ?x2252), profession(?x5470, ?x524), ?x2252 = 02x8n1n, ?x524 = 02jknp >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #6002 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 176 *> proper extension: 01r216; *> query: (?x5470, 04b_46) <- student(?x7545, ?x5470), type_of_union(?x5470, ?x566), written_by(?x6293, ?x5470), major_field_of_study(?x7545, ?x373) *> conf = 0.04 ranks of expected_values: 22 EVAL 0c9xjl student! 04b_46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 111.000 101.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #10838-06npd PRED entity: 06npd PRED relation: combatants! PRED expected values: 03gqgt3 => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 65): 03gqgt3 (0.55 #1226, 0.52 #576, 0.45 #316), 048n7 (0.32 #1974, 0.31 #2039, 0.31 #933), 06k75 (0.29 #80, 0.21 #2096, 0.21 #210), 0gfq9 (0.29 #71, 0.17 #5336, 0.16 #1756), 07_nf (0.23 #1838, 0.23 #2749, 0.22 #2033), 0cm2xh (0.22 #2873, 0.22 #921, 0.21 #206), 01h6pn (0.22 #532, 0.21 #1182, 0.21 #1247), 07j9n (0.20 #354, 0.20 #289, 0.19 #159), 03c3jzx (0.20 #332, 0.17 #527, 0.16 #592), 0d06vc (0.18 #654, 0.17 #849, 0.16 #1174) >> Best rule #1226 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 07jqh; >> query: (?x756, 03gqgt3) <- combatants(?x326, ?x756), combatants(?x326, ?x1536), ?x1536 = 06c1y >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06npd combatants! 03gqgt3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.553 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #10837-01q7q2 PRED entity: 01q7q2 PRED relation: school_type PRED expected values: 01_9fk => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 18): 05pcjw (0.46 #24, 0.30 #369, 0.30 #323), 01_9fk (0.33 #71, 0.31 #209, 0.30 #255), 01rs41 (0.33 #326, 0.29 #671, 0.29 #395), 07tf8 (0.31 #31, 0.29 #77, 0.26 #123), 01_srz (0.13 #1570, 0.08 #325, 0.06 #486), 047951 (0.13 #1570, 0.03 #191, 0.02 #306), 02dk5q (0.13 #1570, 0.02 #604, 0.02 #282), 02p0qmm (0.10 #101, 0.07 #124, 0.04 #699), 04qbv (0.05 #107, 0.04 #130, 0.02 #268), 06cs1 (0.05 #74, 0.03 #212, 0.03 #143) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 09wv__; >> query: (?x8008, 05pcjw) <- student(?x8008, ?x6062), cinematography(?x153, ?x6062), film(?x1019, ?x153), film(?x382, ?x153) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #71 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 03v6t; *> query: (?x8008, 01_9fk) <- major_field_of_study(?x8008, ?x2606), major_field_of_study(?x8008, ?x2601), ?x2601 = 04x_3, ?x2606 = 062z7, contains(?x94, ?x8008) *> conf = 0.33 ranks of expected_values: 2 EVAL 01q7q2 school_type 01_9fk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.462 http://example.org/education/educational_institution/school_type #10836-01mk6 PRED entity: 01mk6 PRED relation: countries_spoken_in! PRED expected values: 01wgr => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 57): 05qqm (0.32 #2353, 0.21 #2522, 0.20 #3867), 02h40lc (0.28 #2242, 0.27 #5892, 0.27 #5327), 06b_j (0.25 #19, 0.08 #2147, 0.08 #75), 04306rv (0.21 #2522, 0.20 #3867, 0.18 #4093), 0880p (0.21 #2522, 0.20 #3867, 0.18 #4093), 0cjk9 (0.21 #2522, 0.20 #3867, 0.18 #4093), 06nm1 (0.20 #1128, 0.19 #232, 0.17 #4493), 0jzc (0.20 #464, 0.18 #576, 0.17 #1136), 064_8sq (0.19 #1978, 0.17 #3100, 0.16 #5455), 02hwhyv (0.15 #138, 0.11 #698, 0.10 #866) >> Best rule #2353 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 05bcl; >> query: (?x7430, ?x10486) <- adjoins(?x2517, ?x7430), capital(?x7430, ?x1458), official_language(?x2517, ?x10486), adjoins(?x344, ?x2517) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #485 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 03pn9; *> query: (?x7430, 01wgr) <- adjoins(?x2517, ?x7430), capital(?x7430, ?x1458), organization(?x7430, ?x312), combatants(?x326, ?x7430) *> conf = 0.02 ranks of expected_values: 49 EVAL 01mk6 countries_spoken_in! 01wgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 151.000 151.000 0.325 http://example.org/language/human_language/countries_spoken_in #10835-0drnwh PRED entity: 0drnwh PRED relation: film_crew_role PRED expected values: 09vw2b7 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.86 #100, 0.79 #68, 0.76 #317), 09vw2b7 (0.81 #99, 0.77 #67, 0.68 #316), 0215hd (0.59 #110, 0.20 #47, 0.15 #78), 01vx2h (0.48 #104, 0.36 #72, 0.34 #633), 0dxtw (0.42 #320, 0.40 #1505, 0.40 #40), 01pvkk (0.37 #260, 0.31 #198, 0.29 #1507), 02_n3z (0.32 #95, 0.13 #63, 0.10 #32), 02rh1dz (0.21 #319, 0.20 #39, 0.17 #102), 015h31 (0.21 #101, 0.13 #69, 0.10 #38), 033smt (0.21 #117, 0.09 #3129, 0.06 #334) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 03whyr; >> query: (?x6679, 0ch6mp2) <- country(?x6679, ?x94), production_companies(?x6679, ?x541), film_crew_role(?x6679, ?x2472), ?x2472 = 01xy5l_ >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #99 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 03whyr; *> query: (?x6679, 09vw2b7) <- country(?x6679, ?x94), production_companies(?x6679, ?x541), film_crew_role(?x6679, ?x2472), ?x2472 = 01xy5l_ *> conf = 0.81 ranks of expected_values: 2 EVAL 0drnwh film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 104.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10834-01ft2l PRED entity: 01ft2l PRED relation: award PRED expected values: 0cqh46 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 287): 04kxsb (0.41 #527, 0.17 #4145, 0.16 #6155), 09sb52 (0.35 #442, 0.33 #11296, 0.33 #10492), 0gqy2 (0.35 #566, 0.15 #2576, 0.13 #10616), 0f4x7 (0.35 #432, 0.14 #10482, 0.14 #11286), 0cqhk0 (0.31 #36, 0.25 #2046, 0.20 #1242), 0ck27z (0.30 #2101, 0.29 #15367, 0.27 #16573), 05pcn59 (0.24 #10532, 0.23 #11336, 0.23 #12140), 04ljl_l (0.24 #405, 0.19 #3, 0.17 #1209), 05p09zm (0.24 #525, 0.15 #9771, 0.15 #6153), 09sdmz (0.24 #607, 0.12 #205, 0.11 #2617) >> Best rule #527 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 04n_g; >> query: (?x3633, 04kxsb) <- participant(?x3633, ?x3183), place_of_birth(?x3633, ?x6960), award(?x3633, ?x3247), ?x3247 = 0bdwqv >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #452 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 04n_g; *> query: (?x3633, 0cqh46) <- participant(?x3633, ?x3183), place_of_birth(?x3633, ?x6960), award(?x3633, ?x3247), ?x3247 = 0bdwqv *> conf = 0.18 ranks of expected_values: 19 EVAL 01ft2l award 0cqh46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 101.000 101.000 0.412 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10833-0ndsl1x PRED entity: 0ndsl1x PRED relation: language PRED expected values: 02h40lc => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.90 #302, 0.90 #844, 0.90 #120), 064_8sq (0.28 #81, 0.14 #502, 0.12 #140), 06nm1 (0.20 #70, 0.11 #912, 0.11 #853), 03_9r (0.16 #69, 0.05 #490, 0.05 #672), 0653m (0.16 #71, 0.04 #191, 0.04 #913), 02bjrlw (0.12 #60, 0.09 #1, 0.07 #481), 012w70 (0.12 #72, 0.03 #553, 0.03 #1094), 04306rv (0.10 #365, 0.10 #485, 0.09 #667), 06b_j (0.09 #23, 0.06 #865, 0.06 #444), 04h9h (0.08 #102, 0.03 #885, 0.03 #644) >> Best rule #302 for best value: >> intensional similarity = 5 >> extensional distance = 122 >> proper extension: 0hgnl3t; >> query: (?x9002, 02h40lc) <- film_release_region(?x9002, ?x1174), film_release_region(?x9002, ?x456), film(?x400, ?x9002), ?x456 = 05qhw, ?x1174 = 047yc >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ndsl1x language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.903 http://example.org/film/film/language #10832-0234_c PRED entity: 0234_c PRED relation: colors PRED expected values: 083jv 0jc_p => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.42 #22, 0.39 #622, 0.39 #382), 01g5v (0.33 #4, 0.28 #1344, 0.28 #1364), 019sc (0.33 #8, 0.19 #688, 0.18 #1408), 01l849 (0.26 #601, 0.26 #761, 0.25 #1141), 06fvc (0.19 #303, 0.17 #383, 0.16 #1083), 067z2v (0.17 #30, 0.06 #510, 0.05 #610), 038hg (0.14 #53, 0.12 #173, 0.12 #73), 036k5h (0.14 #146, 0.12 #106, 0.11 #86), 04mkbj (0.14 #151, 0.10 #511, 0.09 #111), 09q2t (0.11 #155, 0.09 #135, 0.08 #195) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 021l5s; >> query: (?x11036, 083jv) <- colors(?x11036, ?x12067), contains(?x94, ?x11036), ?x12067 = 06kqt3, organization(?x346, ?x11036) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11 EVAL 0234_c colors 0jc_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 119.000 119.000 0.417 http://example.org/education/educational_institution/colors EVAL 0234_c colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.417 http://example.org/education/educational_institution/colors #10831-0dwtp PRED entity: 0dwtp PRED relation: role! PRED expected values: 01vdm0 => 84 concepts (53 used for prediction) PRED predicted values (max 10 best out of 82): 0342h (0.92 #3921, 0.91 #3678, 0.90 #3597), 01v8y9 (0.89 #3358, 0.89 #3321, 0.86 #794), 01vdm0 (0.86 #794, 0.85 #80, 0.85 #2742), 018vs (0.86 #794, 0.85 #80, 0.84 #2404), 0l14j_ (0.86 #794, 0.85 #80, 0.84 #2404), 03q5t (0.86 #794, 0.85 #80, 0.84 #2404), 0mkg (0.86 #794, 0.85 #80, 0.84 #2404), 03gvt (0.86 #794, 0.85 #80, 0.84 #2404), 06rvn (0.86 #794, 0.85 #80, 0.84 #2404), 0680x0 (0.86 #794, 0.85 #80, 0.84 #2404) >> Best rule #3921 for best value: >> intensional similarity = 18 >> extensional distance = 22 >> proper extension: 03bx0bm; >> query: (?x885, 0342h) <- role(?x3215, ?x885), role(?x2764, ?x885), role(?x1482, ?x885), role(?x1166, ?x885), role(?x1997, ?x885), role(?x885, ?x74), ?x3215 = 0bxl5, role(?x1433, ?x1482), role(?x885, ?x868), role(?x248, ?x1166), instrumentalists(?x1166, ?x8921), instrumentalists(?x1166, ?x4646), instrumentalists(?x1166, ?x642), ?x8921 = 016s0m, ?x4646 = 0fhxv, ?x642 = 032t2z, ?x2764 = 01s0ps, ?x1433 = 0239kh >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #794 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 3 *> proper extension: 06rvn; *> query: (?x885, ?x868) <- role(?x5676, ?x885), role(?x4078, ?x885), role(?x3991, ?x885), role(?x2297, ?x885), role(?x2059, ?x885), ?x4078 = 011k_j, ?x2297 = 051hrr, role(?x885, ?x2206), role(?x925, ?x2206), role(?x3418, ?x2206), ?x5676 = 0151b0, role(?x1997, ?x885), ?x2059 = 0dwr4, ?x3991 = 05842k, group(?x2206, ?x1751), role(?x885, ?x868), instrumentalists(?x3418, ?x2835) *> conf = 0.86 ranks of expected_values: 3 EVAL 0dwtp role! 01vdm0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 53.000 0.917 http://example.org/music/performance_role/track_performances./music/track_contribution/role #10830-04_1l0v PRED entity: 04_1l0v PRED relation: time_zones PRED expected values: 02fqwt => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 9): 02fqwt (0.64 #793, 0.58 #722, 0.51 #531), 042g7t (0.64 #793, 0.33 #8, 0.25 #48), 05jphn (0.64 #793, 0.12 #50, 0.12 #40), 02lcrv (0.33 #4, 0.25 #14, 0.20 #24), 02llzg (0.20 #222, 0.19 #312, 0.18 #322), 03plfd (0.11 #227, 0.10 #317, 0.09 #327), 03bdv (0.07 #423, 0.07 #213, 0.06 #866), 0gsrz4 (0.06 #375, 0.05 #475, 0.05 #485), 052vwh (0.03 #650, 0.03 #129, 0.03 #199) >> Best rule #793 for best value: >> intensional similarity = 2 >> extensional distance = 575 >> proper extension: 03khn; >> query: (?x8260, ?x1638) <- adjoins(?x8260, ?x279), time_zones(?x279, ?x1638) >> conf = 0.64 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 04_1l0v time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.636 http://example.org/location/location/time_zones #10829-0686zv PRED entity: 0686zv PRED relation: student! PRED expected values: 0m4yg => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 119): 017z88 (0.12 #82, 0.04 #3757, 0.04 #7432), 07szy (0.12 #40, 0.03 #1615, 0.02 #2665), 078bz (0.12 #77, 0.03 #1652, 0.01 #11627), 026036 (0.12 #392, 0.01 #1967), 02mj7c (0.12 #165, 0.01 #4890, 0.01 #2265), 01ky7c (0.12 #224), 07tg4 (0.11 #611, 0.10 #1136, 0.06 #3236), 015nl4 (0.11 #592, 0.07 #3217, 0.06 #4267), 01s753 (0.11 #1025, 0.01 #2075), 031hxk (0.11 #891) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02qgyv; 01438g; 01wgcvn; 048s0r; 02__7n; >> query: (?x3079, 017z88) <- film(?x3079, ?x1710), award_nominee(?x9289, ?x3079), ?x9289 = 030xr_ >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #1414 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 8 *> proper extension: 09jrf; *> query: (?x3079, 0m4yg) <- student(?x13049, ?x3079), ?x13049 = 0dzbl *> conf = 0.10 ranks of expected_values: 13 EVAL 0686zv student! 0m4yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 82.000 82.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #10828-04t6fk PRED entity: 04t6fk PRED relation: featured_film_locations PRED expected values: 05kj_ => 59 concepts (53 used for prediction) PRED predicted values (max 10 best out of 64): 030qb3t (0.22 #279, 0.13 #1721, 0.09 #3409), 02_286 (0.17 #1702, 0.14 #1942, 0.14 #3630), 04jpl (0.09 #2172, 0.08 #3138, 0.08 #1931), 0cr3d (0.06 #66), 052p7 (0.06 #298, 0.03 #1980, 0.02 #778), 03gh4 (0.06 #355, 0.02 #835, 0.02 #2037), 0rh6k (0.05 #1442, 0.05 #1683, 0.05 #4339), 080h2 (0.05 #1706, 0.04 #1946, 0.04 #3153), 01_d4 (0.03 #1007, 0.03 #4143, 0.03 #2452), 0gkgp (0.03 #1121, 0.02 #641, 0.01 #1843) >> Best rule #279 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 043sct5; >> query: (?x2699, 030qb3t) <- genre(?x2699, ?x258), language(?x2699, ?x2164), ?x2164 = 03_9r, ?x258 = 05p553 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #498 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 48 *> proper extension: 0gzy02; 09q5w2; 0qm8b; 09lcsj; 0kxf1; 0fb7sd; 04cbbz; 03clwtw; 01cycq; 02x3y41; ... *> query: (?x2699, 05kj_) <- genre(?x2699, ?x3515), genre(?x2699, ?x225), film(?x1031, ?x2699), ?x225 = 02kdv5l, ?x3515 = 082gq *> conf = 0.02 ranks of expected_values: 21 EVAL 04t6fk featured_film_locations 05kj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 59.000 53.000 0.222 http://example.org/film/film/featured_film_locations #10827-05w88j PRED entity: 05w88j PRED relation: profession PRED expected values: 02hrh1q => 99 concepts (76 used for prediction) PRED predicted values (max 10 best out of 55): 02hrh1q (0.92 #7614, 0.91 #9702, 0.91 #5081), 03gjzk (0.61 #463, 0.55 #314, 0.33 #2549), 0dxtg (0.54 #461, 0.46 #312, 0.30 #2547), 02jknp (0.52 #455, 0.45 #306, 0.23 #3882), 01d_h8 (0.46 #453, 0.44 #304, 0.34 #3880), 018gz8 (0.24 #316, 0.16 #465, 0.14 #1806), 0np9r (0.22 #320, 0.15 #469, 0.15 #11200), 09jwl (0.21 #7023, 0.21 #1957, 0.21 #616), 0nbcg (0.15 #7035, 0.14 #7929, 0.14 #628), 0dz3r (0.15 #7005, 0.14 #7899, 0.13 #5813) >> Best rule #7614 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 01wjrn; 05wjnt; 05hdf; 0c01c; 01pnn3; 039crh; 02zrv7; 0n8bn; 06_bq1; 04mlh8; ... >> query: (?x9704, 02hrh1q) <- film(?x9704, ?x4098), profession(?x9704, ?x1943), nominated_for(?x9704, ?x782) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05w88j profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 76.000 0.916 http://example.org/people/person/profession #10826-05_5_22 PRED entity: 05_5_22 PRED relation: production_companies PRED expected values: 05rrtf => 108 concepts (100 used for prediction) PRED predicted values (max 10 best out of 60): 06rq1k (0.25 #17, 0.07 #673, 0.06 #427), 09b3v (0.25 #32, 0.03 #2001, 0.03 #1837), 05qd_ (0.20 #256, 0.16 #830, 0.14 #174), 020h2v (0.17 #141, 0.06 #2028, 0.05 #797), 031rp3 (0.17 #157), 016tw3 (0.14 #176, 0.12 #586, 0.10 #1817), 0hpt3 (0.14 #184, 0.05 #512, 0.05 #676), 017s11 (0.12 #1152, 0.12 #1316, 0.11 #1562), 086k8 (0.12 #576, 0.12 #4521, 0.10 #2135), 030_1_ (0.12 #344, 0.10 #262, 0.06 #1575) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02825kb; 02qdrjx; >> query: (?x5201, 06rq1k) <- film(?x7527, ?x5201), ?x7527 = 06crng, titles(?x2480, ?x5201), film_crew_role(?x5201, ?x137) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2026 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 174 *> proper extension: 02_1sj; 02z3r8t; 02v63m; 014zwb; 0c57yj; 038bh3; 047rkcm; 01j5ql; 03cyslc; 03m5y9p; ... *> query: (?x5201, 05rrtf) <- film(?x7527, ?x5201), film_crew_role(?x5201, ?x137), influenced_by(?x7527, ?x2125), featured_film_locations(?x5201, ?x362) *> conf = 0.05 ranks of expected_values: 22 EVAL 05_5_22 production_companies 05rrtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 108.000 100.000 0.250 http://example.org/film/film/production_companies #10825-02b0y3 PRED entity: 02b0y3 PRED relation: team! PRED expected values: 0dgrmp => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 42): 03f0fp (0.86 #1021, 0.84 #1322, 0.84 #306), 0dgrmp (0.80 #202, 0.78 #359, 0.78 #308), 02qvgy (0.52 #1476, 0.50 #1728, 0.01 #1700), 02md_2 (0.51 #1271, 0.48 #1878, 0.48 #1778), 02sf_r (0.11 #487, 0.07 #1199, 0.07 #1249), 01pv51 (0.11 #467, 0.07 #1179, 0.06 #1229), 03558l (0.11 #486, 0.07 #1198, 0.06 #1148), 02_ssl (0.11 #492, 0.07 #1204, 0.06 #1154), 01r3hr (0.09 #1122, 0.07 #1222, 0.07 #1729), 02g_7z (0.08 #1144, 0.07 #1244, 0.07 #1751) >> Best rule #1021 for best value: >> intensional similarity = 11 >> extensional distance = 254 >> proper extension: 02b1yn; >> query: (?x5403, ?x12598) <- position(?x5403, ?x12598), position(?x11153, ?x12598), position(?x4364, ?x12598), team(?x12598, ?x10443), position(?x11748, ?x12598), team(?x7705, ?x5403), position(?x10443, ?x203), current_club(?x676, ?x10443), ?x4364 = 065zf3p, ?x11748 = 02b0_m, ?x11153 = 080_y >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #202 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 13 *> proper extension: 01xn7x1; *> query: (?x5403, ?x203) <- team(?x9411, ?x5403), team(?x7705, ?x5403), position(?x5403, ?x12598), position(?x5403, ?x60), ?x60 = 02nzb8, athlete(?x471, ?x7705), position(?x4364, ?x12598), gender(?x7705, ?x231), team(?x9411, ?x14056), team(?x9411, ?x8885), team(?x203, ?x14056), ?x8885 = 01rlzn *> conf = 0.80 ranks of expected_values: 2 EVAL 02b0y3 team! 0dgrmp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 38.000 38.000 0.856 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #10824-08rr3p PRED entity: 08rr3p PRED relation: nominated_for! PRED expected values: 02ppm4q => 74 concepts (65 used for prediction) PRED predicted values (max 10 best out of 219): 019f4v (0.43 #55, 0.25 #3387, 0.22 #14769), 0gr0m (0.43 #61, 0.22 #3870, 0.22 #3632), 094qd5 (0.43 #37, 0.19 #6429, 0.19 #14292), 0l8z1 (0.29 #3385, 0.25 #4576, 0.22 #14769), 0gqwc (0.29 #62, 0.22 #14769, 0.20 #14770), 02x201b (0.29 #181, 0.09 #10955, 0.05 #15485), 0gq9h (0.28 #3873, 0.27 #3635, 0.27 #4349), 03hl6lc (0.26 #368, 0.11 #5367, 0.10 #5843), 0k611 (0.25 #3406, 0.22 #4597, 0.21 #3883), 0p9sw (0.24 #1449, 0.24 #3353, 0.20 #4544) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 09d3b7; 043n1r5; 0h0wd9; 01gvsn; >> query: (?x2755, 019f4v) <- nominated_for(?x538, ?x2755), ?x538 = 03f2_rc, genre(?x2755, ?x53), film(?x815, ?x2755) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #14292 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1443 *> proper extension: 02xhpl; 02sqkh; 06qwh; 015pnb; *> query: (?x2755, ?x154) <- nominated_for(?x538, ?x2755), award(?x538, ?x154), type_of_union(?x538, ?x566) *> conf = 0.19 ranks of expected_values: 42 EVAL 08rr3p nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 74.000 65.000 0.429 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10823-01clyr PRED entity: 01clyr PRED relation: artist PRED expected values: 01vn35l 016jfw 07mvp 01wg3q 01fh0q 02cw1m 01nz1q6 => 38 concepts (22 used for prediction) PRED predicted values (max 10 best out of 963): 03xhj6 (0.60 #5767, 0.33 #4203, 0.33 #2637), 0134s5 (0.60 #5699, 0.33 #7267, 0.33 #4135), 01vtj38 (0.50 #6761, 0.40 #5976, 0.33 #7544), 01w60_p (0.50 #6375, 0.33 #3243, 0.33 #2460), 0kzy0 (0.50 #4727, 0.33 #3162, 0.33 #815), 01w7nwm (0.50 #6459, 0.33 #980, 0.33 #195), 014_xj (0.50 #7806, 0.33 #9371, 0.29 #8588), 011xhx (0.50 #7821, 0.33 #9386, 0.29 #8603), 02cpp (0.50 #5100, 0.33 #3535, 0.17 #7450), 0p76z (0.50 #5378, 0.33 #3813, 0.17 #7728) >> Best rule #5767 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 01cf93; >> query: (?x5744, 03xhj6) <- artist(?x5744, ?x10257), artist(?x5744, ?x7186), artist(?x5744, ?x6838), artist(?x5744, ?x5745), artist(?x5744, ?x3316), profession(?x5745, ?x220), artists(?x2809, ?x10257), ?x2809 = 05w3f, ?x6838 = 0130sy, group(?x6469, ?x10257), award_nominee(?x380, ?x7186), influenced_by(?x4960, ?x3316) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #5917 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 01cf93; *> query: (?x5744, 07mvp) <- artist(?x5744, ?x10257), artist(?x5744, ?x7186), artist(?x5744, ?x6838), artist(?x5744, ?x5745), artist(?x5744, ?x3316), profession(?x5745, ?x220), artists(?x2809, ?x10257), ?x2809 = 05w3f, ?x6838 = 0130sy, group(?x6469, ?x10257), award_nominee(?x380, ?x7186), influenced_by(?x4960, ?x3316) *> conf = 0.40 ranks of expected_values: 23, 166, 249, 369, 580, 598, 752 EVAL 01clyr artist 01nz1q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 38.000 22.000 0.600 http://example.org/music/record_label/artist EVAL 01clyr artist 02cw1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 38.000 22.000 0.600 http://example.org/music/record_label/artist EVAL 01clyr artist 01fh0q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 38.000 22.000 0.600 http://example.org/music/record_label/artist EVAL 01clyr artist 01wg3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 38.000 22.000 0.600 http://example.org/music/record_label/artist EVAL 01clyr artist 07mvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 38.000 22.000 0.600 http://example.org/music/record_label/artist EVAL 01clyr artist 016jfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 38.000 22.000 0.600 http://example.org/music/record_label/artist EVAL 01clyr artist 01vn35l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 38.000 22.000 0.600 http://example.org/music/record_label/artist #10822-0bzk8w PRED entity: 0bzk8w PRED relation: award_winner PRED expected values: 01vsps 02zft0 => 31 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1192): 01pbs9w (0.50 #3083, 0.47 #6164, 0.14 #16955), 050z2 (0.50 #3083, 0.47 #6164, 0.14 #16955), 022_lg (0.31 #16956, 0.29 #18497, 0.29 #10788), 03s9b (0.31 #16956, 0.29 #18497, 0.03 #13373), 021yc7p (0.30 #3300, 0.25 #1758, 0.25 #217), 0h0wc (0.29 #8071, 0.12 #1907, 0.12 #366), 029m83 (0.29 #18497, 0.26 #18499, 0.02 #13872), 019f2f (0.29 #10788, 0.11 #9246, 0.04 #7705), 0k525 (0.29 #10788, 0.11 #9246, 0.04 #7705), 06rnl9 (0.27 #5046, 0.23 #6587, 0.20 #3506) >> Best rule #3083 for best value: >> intensional similarity = 18 >> extensional distance = 6 >> proper extension: 059x66; 02hn5v; 02yvhx; 03tn9w; 0bzmt8; 0n8_m93; >> query: (?x602, ?x4052) <- ceremony(?x3066, ?x602), ceremony(?x1323, ?x602), ceremony(?x720, ?x602), ceremony(?x484, ?x602), ?x484 = 0gq_v, honored_for(?x602, ?x1744), ?x1323 = 0gqz2, ?x720 = 018wng, ?x3066 = 0gqy2, award_winner(?x602, ?x2530), film_release_region(?x1744, ?x1353), film_release_region(?x1744, ?x1023), film_release_region(?x1744, ?x429), ?x429 = 03rt9, music(?x1744, ?x4052), ?x1023 = 0ctw_b, film(?x2589, ?x1744), ?x1353 = 035qy >> conf = 0.50 => this is the best rule for 2 predicted values *> Best rule #13262 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 33 *> proper extension: 0fv89q; *> query: (?x602, 02zft0) <- ceremony(?x5409, ?x602), ceremony(?x3066, ?x602), ceremony(?x1323, ?x602), ceremony(?x1245, ?x602), ceremony(?x720, ?x602), ceremony(?x484, ?x602), ?x484 = 0gq_v, honored_for(?x602, ?x1744), ?x1323 = 0gqz2, ?x720 = 018wng, ?x3066 = 0gqy2, award_winner(?x602, ?x2530), ?x1245 = 0gqwc, genre(?x1744, ?x53), award(?x382, ?x5409), ceremony(?x5409, ?x2707), award_winner(?x5409, ?x6662), ?x2707 = 02hn5v *> conf = 0.03 ranks of expected_values: 487, 1015 EVAL 0bzk8w award_winner 02zft0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 14.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bzk8w award_winner 01vsps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 14.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10821-04fhn_ PRED entity: 04fhn_ PRED relation: religion PRED expected values: 04pk9 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 12): 0c8wxp (0.14 #141, 0.14 #51, 0.13 #501), 03_gx (0.14 #284, 0.12 #374, 0.12 #419), 092bf5 (0.09 #376, 0.08 #286, 0.08 #241), 0kpl (0.08 #325, 0.07 #2390, 0.07 #3160), 06nzl (0.07 #150, 0.07 #2390, 0.07 #3160), 0kq2 (0.07 #2390, 0.07 #3160, 0.07 #2526), 03j6c (0.07 #2390, 0.07 #3160, 0.07 #2526), 0631_ (0.07 #2390, 0.07 #3160, 0.07 #2526), 0n2g (0.02 #1499, 0.02 #1770, 0.02 #1589), 01lp8 (0.02 #1216, 0.01 #1397, 0.01 #2210) >> Best rule #141 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01vsnff; 02yxwd; 01r93l; 09l3p; 03fbb6; 053xw6; 01nms7; 0ywqc; >> query: (?x3952, 0c8wxp) <- film(?x3952, ?x8084), film(?x3952, ?x7015), award(?x7015, ?x746), ?x8084 = 02cbhg >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1461 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1255 *> proper extension: 0cb77r; 07q1v4; 04b19t; 01wwvc5; 05728w1; 07nv3_; 01309x; 01ycck; 0gv5c; 0bqytm; ... *> query: (?x3952, 04pk9) <- place_of_birth(?x3952, ?x2254), gender(?x3952, ?x231), ?x231 = 05zppz, type_of_union(?x3952, ?x566) *> conf = 0.01 ranks of expected_values: 12 EVAL 04fhn_ religion 04pk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 108.000 108.000 0.143 http://example.org/people/person/religion #10820-0n4mk PRED entity: 0n4mk PRED relation: contains! PRED expected values: 02rtlp5 => 110 concepts (54 used for prediction) PRED predicted values (max 10 best out of 102): 09c7w0 (0.58 #27776, 0.56 #37633, 0.56 #41218), 01n7q (0.36 #24264, 0.32 #27850, 0.21 #32332), 02jx1 (0.28 #32341, 0.19 #41304, 0.08 #22480), 059rby (0.27 #24207, 0.24 #27793, 0.20 #4496), 07ssc (0.21 #32287, 0.15 #41250, 0.12 #22426), 0d060g (0.17 #45701, 0.11 #47494, 0.07 #41231), 0n4m5 (0.16 #43906), 04_1l0v (0.15 #17914, 0.04 #38977, 0.03 #27328), 05k7sb (0.13 #27905, 0.10 #29698, 0.09 #7296), 05tbn (0.11 #4699, 0.11 #3803, 0.11 #29789) >> Best rule #27776 for best value: >> intensional similarity = 5 >> extensional distance = 508 >> proper extension: 080h2; 02d9nr; 0jbs5; 01vqq1; 014wxc; >> query: (?x6335, 09c7w0) <- contains(?x760, ?x6335), location(?x120, ?x760), state(?x553, ?x760), district_represented(?x605, ?x760), vacationer(?x760, ?x10915) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0n4mk contains! 02rtlp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 54.000 0.580 http://example.org/location/location/contains #10819-011k_j PRED entity: 011k_j PRED relation: role! PRED expected values: 04m2zj => 92 concepts (58 used for prediction) PRED predicted values (max 10 best out of 928): 023l9y (0.65 #16208, 0.62 #8675, 0.59 #18554), 03ryks (0.62 #9235, 0.54 #13471, 0.53 #15356), 082brv (0.62 #8732, 0.50 #10617, 0.50 #3555), 0137g1 (0.60 #14229, 0.60 #10466, 0.57 #7640), 01wxdn3 (0.60 #10760, 0.58 #12173, 0.57 #7934), 05qhnq (0.60 #5480, 0.53 #14422, 0.50 #10339), 02l_7y (0.57 #939, 0.56 #4226, 0.47 #3757), 01wl38s (0.57 #7068, 0.50 #4717, 0.50 #4247), 04bpm6 (0.55 #16067, 0.53 #15126, 0.50 #18413), 0161sp (0.53 #14240, 0.50 #4360, 0.45 #16125) >> Best rule #16208 for best value: >> intensional similarity = 22 >> extensional distance = 18 >> proper extension: 01qbl; >> query: (?x4078, 023l9y) <- role(?x227, ?x4078), role(?x4078, ?x4913), role(?x4078, ?x614), role(?x614, ?x2460), role(?x614, ?x2310), role(?x614, ?x1495), role(?x614, ?x1147), role(?x614, ?x74), ?x4913 = 03ndd, group(?x614, ?x5303), group(?x614, ?x4791), ?x74 = 03q5t, ?x1147 = 07kc_, role(?x130, ?x614), ?x2460 = 01wy6, instrumentalists(?x614, ?x8049), ?x4791 = 02t3ln, ?x1495 = 013y1f, ?x5303 = 02mq_y, role(?x2575, ?x614), role(?x1260, ?x2310), ?x8049 = 02vcp0 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #2240 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 04rzd; *> query: (?x4078, 04m2zj) <- role(?x2206, ?x4078), role(?x894, ?x4078), role(?x4078, ?x8172), role(?x4078, ?x7449), role(?x4078, ?x3112), role(?x4078, ?x2725), role(?x4078, ?x228), group(?x4078, ?x3516), performance_role(?x4078, ?x1225), ?x2206 = 07gql, instrumentalists(?x4078, ?x4140), role(?x10989, ?x4078), ?x3112 = 0mbct, ?x894 = 03m5k, ?x228 = 0l14qv, ?x8172 = 06rvn, ?x2725 = 0l1589, role(?x7449, ?x1332), ?x1332 = 03qlv7, role(?x569, ?x1225), artists(?x671, ?x10989) *> conf = 0.33 ranks of expected_values: 83 EVAL 011k_j role! 04m2zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 92.000 58.000 0.650 http://example.org/music/artist/track_contributions./music/track_contribution/role #10818-013w8y PRED entity: 013w8y PRED relation: artists! PRED expected values: 06by7 => 100 concepts (43 used for prediction) PRED predicted values (max 10 best out of 284): 06by7 (0.87 #9409, 0.80 #1227, 0.79 #9105), 02w4v (0.67 #644, 0.33 #3981, 0.33 #342), 0xhtw (0.65 #3650, 0.62 #2741, 0.50 #7892), 05w3f (0.62 #3671, 0.45 #1849, 0.38 #2762), 0dl5d (0.54 #3652, 0.44 #2134, 0.42 #2438), 02yv6b (0.50 #3731, 0.38 #4642, 0.36 #1909), 03lty (0.46 #3661, 0.43 #2752, 0.33 #628), 08jyyk (0.35 #3700, 0.26 #4004, 0.25 #63), 01738f (0.33 #714, 0.24 #2838, 0.10 #1320), 01lyv (0.33 #332, 0.22 #7607, 0.22 #3971) >> Best rule #9409 for best value: >> intensional similarity = 8 >> extensional distance = 90 >> proper extension: 089tm; 01t_xp_; 0150jk; 01vsxdm; 0dtd6; 0frsw; 0fcsd; 014_lq; 07mvp; 09lwrt; ... >> query: (?x8913, 06by7) <- award(?x8913, ?x247), artist(?x7089, ?x8913), artists(?x671, ?x8913), group(?x75, ?x8913), artists(?x671, ?x8156), artists(?x671, ?x5879), ?x8156 = 046p9, ?x5879 = 0167km >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013w8y artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 43.000 0.870 http://example.org/music/genre/artists #10817-01by1l PRED entity: 01by1l PRED relation: award! PRED expected values: 05mt_q 0288fyj 03h_fk5 01vn35l 0k7pf 014488 01n8gr 05vzw3 016fnb 01kd57 01wgfp6 06mt91 01797x 09jm8 => 49 concepts (29 used for prediction) PRED predicted values (max 10 best out of 2353): 09889g (0.84 #9593, 0.79 #76751, 0.79 #12791), 01kv4mb (0.84 #9593, 0.79 #76751, 0.79 #12791), 01vrnsk (0.84 #9593, 0.79 #76751, 0.79 #12791), 02cx90 (0.84 #9593, 0.79 #76751, 0.79 #12791), 045zr (0.84 #9593, 0.79 #76751, 0.79 #12791), 01kstn9 (0.84 #9593, 0.79 #76751, 0.79 #12791), 01lvzbl (0.84 #9593, 0.79 #76751, 0.79 #12791), 01nz1q6 (0.84 #9593, 0.79 #76751, 0.79 #12791), 015mrk (0.84 #9593, 0.79 #76751, 0.79 #12791), 0ddkf (0.84 #9593, 0.79 #76751, 0.79 #12791) >> Best rule #9593 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 01bgqh; 0c4z8; 05q8pss; >> query: (?x2139, ?x538) <- award(?x5547, ?x2139), award(?x2731, ?x2139), award(?x2083, ?x2139), ?x2083 = 02zmh5, award_winner(?x2139, ?x538), ?x5547 = 0dw4g, ?x2731 = 01wwvc5 >> conf = 0.84 => this is the best rule for 20 predicted values *> Best rule #3937 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 02f716; *> query: (?x2139, 01vn35l) <- award(?x5493, ?x2139), award(?x4101, ?x2139), award(?x1684, ?x2139), ?x1684 = 01wv9xn, ?x5493 = 0kr_t, nationality(?x4101, ?x94), award_winner(?x2139, ?x538) *> conf = 0.50 ranks of expected_values: 24, 32, 38, 46, 52, 61, 137, 150, 197, 198, 208, 781, 1360, 1387 EVAL 01by1l award! 09jm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 01797x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 06mt91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 01wgfp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 01kd57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 016fnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 05vzw3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 01n8gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 014488 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 0k7pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 01vn35l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 03h_fk5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 0288fyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01by1l award! 05mt_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 49.000 29.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10816-0ccck7 PRED entity: 0ccck7 PRED relation: production_companies PRED expected values: 016tw3 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 62): 016tw3 (0.34 #416, 0.33 #2584, 0.32 #1832), 05qd_ (0.19 #10, 0.13 #259, 0.10 #176), 086k8 (0.14 #168, 0.10 #1417, 0.09 #334), 016tt2 (0.12 #4, 0.10 #253, 0.08 #87), 0g1rw (0.12 #8, 0.10 #91, 0.05 #1256), 017s11 (0.09 #252, 0.09 #502, 0.08 #86), 054lpb6 (0.09 #2013, 0.08 #2347, 0.08 #763), 01gb54 (0.07 #204, 0.07 #1120, 0.06 #2370), 030_1_ (0.05 #100, 0.05 #1765, 0.05 #183), 01795t (0.05 #687, 0.04 #438, 0.03 #1187) >> Best rule #416 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 015g28; >> query: (?x11218, ?x1104) <- films(?x5069, ?x11218), honored_for(?x5053, ?x11218), film(?x1104, ?x11218) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ccck7 production_companies 016tw3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.336 http://example.org/film/film/production_companies #10815-01f8f7 PRED entity: 01f8f7 PRED relation: genre PRED expected values: 02l7c8 => 63 concepts (62 used for prediction) PRED predicted values (max 10 best out of 103): 02kdv5l (0.71 #2, 0.47 #603, 0.43 #122), 012w70 (0.52 #3737, 0.50 #2647, 0.49 #4579), 0d05w3 (0.52 #3737, 0.50 #2647, 0.49 #4579), 01jfsb (0.43 #1337, 0.42 #1215, 0.30 #254), 082gq (0.43 #150, 0.25 #511, 0.19 #751), 060__y (0.43 #137, 0.18 #978, 0.17 #1098), 04xvlr (0.42 #602, 0.31 #482, 0.29 #1), 03q4nz (0.42 #619, 0.30 #259, 0.29 #18), 02l7c8 (0.38 #737, 0.38 #497, 0.34 #857), 05p553 (0.32 #2049, 0.32 #4824, 0.32 #3015) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 065ym0c; >> query: (?x6788, 02kdv5l) <- nominated_for(?x12715, ?x6788), ?x12715 = 07kfzsg, country(?x6788, ?x2346), ?x2346 = 0d05w3 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #737 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 03ckwzc; 0gtvpkw; 09v9mks; 0353tm; *> query: (?x6788, 02l7c8) <- country(?x6788, ?x1264), country(?x6788, ?x205), film(?x5854, ?x6788), ?x205 = 03rjj, combatants(?x1264, ?x94) *> conf = 0.38 ranks of expected_values: 9 EVAL 01f8f7 genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 63.000 62.000 0.714 http://example.org/film/film/genre #10814-05hdf PRED entity: 05hdf PRED relation: profession PRED expected values: 02hrh1q => 143 concepts (103 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.92 #7919, 0.91 #6426, 0.91 #5231), 09jwl (0.67 #12399, 0.36 #6710, 0.31 #9247), 01d_h8 (0.41 #3731, 0.40 #602, 0.39 #751), 0cbd2 (0.38 #305, 0.25 #1348, 0.23 #1050), 03gjzk (0.36 #761, 0.36 #6710, 0.33 #10590), 0dz3r (0.36 #6710, 0.35 #12381, 0.31 #9247), 01c72t (0.36 #6710, 0.31 #9247, 0.31 #8054), 01c8w0 (0.36 #6710, 0.31 #9247, 0.31 #8054), 0nbcg (0.36 #12412, 0.33 #10590, 0.24 #3311), 016z4k (0.34 #12383, 0.18 #3282, 0.16 #2835) >> Best rule #7919 for best value: >> intensional similarity = 4 >> extensional distance = 418 >> proper extension: 01rr9f; 03f2_rc; 01j5x6; 03lt8g; 0sz28; 0n6f8; 02_hj4; 03xmy1; 0jfx1; 0127m7; ... >> query: (?x2546, 02hrh1q) <- nominated_for(?x2546, ?x9900), participant(?x7670, ?x2546), film(?x2546, ?x2547), profession(?x7670, ?x319) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05hdf profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 103.000 0.917 http://example.org/people/person/profession #10813-025s0zp PRED entity: 025s0zp PRED relation: nutrient! PRED expected values: 0cxn2 => 53 concepts (49 used for prediction) PRED predicted values (max 10 best out of 12): 0frq6 (0.90 #286, 0.90 #72, 0.90 #35), 0cxn2 (0.90 #72, 0.90 #35, 0.90 #28), 06x4c (0.90 #72, 0.90 #35, 0.90 #28), 0dcfv (0.90 #72, 0.90 #35, 0.90 #28), 01sh2 (0.04 #461, 0.03 #185, 0.02 #21), 04k8n (0.04 #461, 0.03 #185, 0.02 #21), 05wvs (0.04 #461, 0.03 #185, 0.02 #21), 025rw19 (0.02 #281, 0.01 #20, 0.01 #53), 025tkqy (0.02 #281, 0.01 #20, 0.01 #53), 014d7f (0.02 #281, 0.01 #20, 0.01 #53) >> Best rule #286 for best value: >> intensional similarity = 113 >> extensional distance = 18 >> proper extension: 06jry; >> query: (?x5374, 0frq6) <- nutrient(?x9732, ?x5374), nutrient(?x9489, ?x5374), nutrient(?x9005, ?x5374), nutrient(?x7719, ?x5374), nutrient(?x7057, ?x5374), nutrient(?x6285, ?x5374), nutrient(?x6191, ?x5374), nutrient(?x6032, ?x5374), nutrient(?x5373, ?x5374), nutrient(?x5009, ?x5374), nutrient(?x3900, ?x5374), nutrient(?x2701, ?x5374), nutrient(?x1303, ?x5374), nutrient(?x1257, ?x5374), ?x2701 = 0hkxq, ?x1257 = 09728, ?x7719 = 0dj75, ?x5373 = 0971v, ?x6032 = 01nkt, nutrient(?x6285, ?x13498), nutrient(?x6285, ?x12868), nutrient(?x6285, ?x12454), nutrient(?x6285, ?x11784), nutrient(?x6285, ?x11592), nutrient(?x6285, ?x11409), nutrient(?x6285, ?x11270), nutrient(?x6285, ?x10891), nutrient(?x6285, ?x10709), nutrient(?x6285, ?x10195), nutrient(?x6285, ?x10098), nutrient(?x6285, ?x9949), nutrient(?x6285, ?x9915), nutrient(?x6285, ?x9855), nutrient(?x6285, ?x9840), nutrient(?x6285, ?x9795), nutrient(?x6285, ?x9733), nutrient(?x6285, ?x9490), nutrient(?x6285, ?x9365), nutrient(?x6285, ?x8487), nutrient(?x6285, ?x8442), nutrient(?x6285, ?x7894), nutrient(?x6285, ?x7362), nutrient(?x6285, ?x7219), nutrient(?x6285, ?x6286), nutrient(?x6285, ?x6033), nutrient(?x6285, ?x6026), nutrient(?x6285, ?x5549), nutrient(?x6285, ?x5526), nutrient(?x6285, ?x5010), nutrient(?x6285, ?x4069), nutrient(?x6285, ?x3203), nutrient(?x6285, ?x2702), nutrient(?x6285, ?x1960), nutrient(?x6285, ?x1304), nutrient(?x6285, ?x1258), ?x5010 = 0h1vz, ?x9949 = 02kd0rh, ?x6286 = 02y_3rf, ?x7057 = 0fbdb, ?x11784 = 07zqy, ?x10098 = 0h1_c, ?x13498 = 07q0m, ?x3203 = 04kl74p, ?x5549 = 025s7j4, ?x8442 = 02kcv4x, ?x3900 = 061_f, ?x11592 = 025sf0_, ?x12868 = 03d49, ?x9855 = 0d9t0, ?x8487 = 014yzm, ?x9365 = 04k8n, ?x9005 = 04zpv, ?x1303 = 0fj52s, nutrient(?x9489, ?x9708), nutrient(?x9489, ?x7431), nutrient(?x9489, ?x7135), nutrient(?x9489, ?x2018), ?x2702 = 0838f, ?x9708 = 061xhr, ?x4069 = 0hqw8p_, ?x10709 = 0h1sz, ?x9915 = 025tkqy, ?x2018 = 01sh2, ?x1960 = 07hnp, ?x6191 = 014j1m, ?x7894 = 0f4hc, ?x6026 = 025sf8g, ?x9733 = 0h1tz, ?x1304 = 08lb68, ?x9840 = 02p0tjr, ?x5009 = 0fjfh, nutrient(?x9732, ?x14210), nutrient(?x9732, ?x13545), nutrient(?x9732, ?x9436), nutrient(?x9732, ?x6517), ?x7135 = 025rsfk, ?x6033 = 04zjxcz, ?x7362 = 02kc5rj, ?x14210 = 0f4k5, ?x7219 = 0h1vg, ?x6517 = 02kd8zw, ?x13545 = 01w_3, ?x9490 = 0h1sg, ?x7431 = 09gwd, ?x9795 = 05v_8y, ?x5526 = 09pbb, ?x12454 = 025rw19, ?x1258 = 0h1wg, ?x11270 = 02kc008, ?x10195 = 0hkwr, ?x9436 = 025sqz8, ?x11409 = 0h1yf, ?x10891 = 0g5gq >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #72 for first EXPECTED value: *> intensional similarity = 117 *> extensional distance = 11 *> proper extension: 02kc008; 0f4kp; *> query: (?x5374, ?x3468) <- nutrient(?x9732, ?x5374), nutrient(?x9489, ?x5374), nutrient(?x9005, ?x5374), nutrient(?x8298, ?x5374), nutrient(?x7719, ?x5374), nutrient(?x7057, ?x5374), nutrient(?x6285, ?x5374), nutrient(?x6191, ?x5374), nutrient(?x6159, ?x5374), nutrient(?x6032, ?x5374), nutrient(?x5373, ?x5374), nutrient(?x5009, ?x5374), nutrient(?x4068, ?x5374), nutrient(?x3900, ?x5374), nutrient(?x2701, ?x5374), nutrient(?x1303, ?x5374), nutrient(?x1257, ?x5374), ?x2701 = 0hkxq, ?x1257 = 09728, ?x7719 = 0dj75, ?x5373 = 0971v, ?x6285 = 01645p, ?x6032 = 01nkt, ?x5009 = 0fjfh, ?x6191 = 014j1m, ?x9489 = 07j87, ?x8298 = 037ls6, ?x9005 = 04zpv, ?x4068 = 0fbw6, ?x1303 = 0fj52s, ?x9732 = 05z55, nutrient(?x6159, ?x14210), nutrient(?x6159, ?x13545), nutrient(?x6159, ?x13498), nutrient(?x6159, ?x13126), nutrient(?x6159, ?x12454), nutrient(?x6159, ?x12083), nutrient(?x6159, ?x11758), nutrient(?x6159, ?x11592), nutrient(?x6159, ?x11409), nutrient(?x6159, ?x10098), nutrient(?x6159, ?x9949), nutrient(?x6159, ?x9915), nutrient(?x6159, ?x9840), nutrient(?x6159, ?x9733), nutrient(?x6159, ?x9619), nutrient(?x6159, ?x9490), nutrient(?x6159, ?x9436), nutrient(?x6159, ?x9426), nutrient(?x6159, ?x9365), nutrient(?x6159, ?x8413), nutrient(?x6159, ?x7720), nutrient(?x6159, ?x7652), nutrient(?x6159, ?x7431), nutrient(?x6159, ?x7362), nutrient(?x6159, ?x7219), nutrient(?x6159, ?x7135), nutrient(?x6159, ?x6517), nutrient(?x6159, ?x6160), nutrient(?x6159, ?x6033), nutrient(?x6159, ?x6026), nutrient(?x6159, ?x5526), nutrient(?x6159, ?x5451), nutrient(?x6159, ?x5337), nutrient(?x6159, ?x5010), nutrient(?x6159, ?x4069), nutrient(?x6159, ?x3469), nutrient(?x6159, ?x3264), nutrient(?x6159, ?x3203), nutrient(?x6159, ?x2702), nutrient(?x6159, ?x2018), nutrient(?x6159, ?x1960), ?x5526 = 09pbb, ?x3264 = 0dcfv, ?x1960 = 07hnp, ?x14210 = 0f4k5, ?x13498 = 07q0m, ?x9365 = 04k8n, nutrient(?x10612, ?x6517), ?x7135 = 025rsfk, ?x7720 = 025s7x6, ?x11758 = 0q01m, ?x7362 = 02kc5rj, ?x9949 = 02kd0rh, ?x9436 = 025sqz8, ?x5010 = 0h1vz, ?x9619 = 0h1tg, ?x9426 = 0h1yy, ?x5451 = 05wvs, ?x9733 = 0h1tz, ?x6033 = 04zjxcz, ?x3900 = 061_f, ?x7431 = 09gwd, ?x4069 = 0hqw8p_, ?x11409 = 0h1yf, ?x3469 = 0h1zw, ?x3203 = 04kl74p, ?x5337 = 06x4c, ?x11592 = 025sf0_, ?x9840 = 02p0tjr, ?x2702 = 0838f, ?x6160 = 041r51, ?x10612 = 0frq6, ?x13126 = 02kc_w5, ?x7652 = 025s0s0, ?x6026 = 025sf8g, ?x13545 = 01w_3, ?x7057 = 0fbdb, ?x12083 = 01n78x, ?x7219 = 0h1vg, ?x10098 = 0h1_c, taxonomy(?x2018, ?x939), ?x9490 = 0h1sg, ?x12454 = 025rw19, ?x8413 = 02kc4sf, nutrient(?x3468, ?x2018), ?x9915 = 025tkqy *> conf = 0.90 ranks of expected_values: 2 EVAL 025s0zp nutrient! 0cxn2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 53.000 49.000 0.900 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #10812-018jcq PRED entity: 018jcq PRED relation: contains! PRED expected values: 017t44 => 103 concepts (95 used for prediction) PRED predicted values (max 10 best out of 175): 09c7w0 (0.69 #43850, 0.68 #15213, 0.65 #8052), 04_1l0v (0.68 #15659, 0.65 #8498, 0.64 #20134), 02j71 (0.31 #23267), 07ssc (0.30 #28667, 0.24 #22402, 0.20 #36720), 02qkt (0.28 #10184, 0.27 #61742, 0.27 #60845), 02jx1 (0.27 #22457, 0.16 #28722, 0.12 #36775), 07c5l (0.20 #3970, 0.16 #10232, 0.14 #11127), 03rk0 (0.18 #30561, 0.15 #18031, 0.12 #25192), 0345h (0.17 #39456, 0.17 #22452, 0.13 #3658), 0d05w3 (0.15 #30574, 0.08 #44890, 0.07 #46679) >> Best rule #43850 for best value: >> intensional similarity = 3 >> extensional distance = 201 >> proper extension: 0xy28; 077qn; 0r04p; 04vg8; 01d8l; 016qwt; 01j2_7; 0nc7s; 0dwh5; 07s3m; ... >> query: (?x8889, 09c7w0) <- jurisdiction_of_office(?x900, ?x8889), contains(?x252, ?x8889), country(?x297, ?x252) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #11796 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 34 *> proper extension: 049yf; 05gqf; 0g3bw; 049wm; 0g3bc; *> query: (?x8889, 017t44) <- contains(?x252, ?x8889), ?x252 = 03_3d *> conf = 0.03 ranks of expected_values: 61 EVAL 018jcq contains! 017t44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 103.000 95.000 0.690 http://example.org/location/location/contains #10811-049xgc PRED entity: 049xgc PRED relation: nominated_for! PRED expected values: 0gs9p 04kxsb 02qvyrt 02x258x 099t8j => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 188): 054krc (0.68 #4832, 0.67 #4831, 0.66 #10296), 027b9ly (0.68 #4832, 0.67 #4831, 0.66 #10296), 0gs9p (0.43 #258, 0.37 #3618, 0.35 #1518), 0l8z1 (0.34 #1511, 0.33 #1721, 0.28 #251), 018wdw (0.26 #1832, 0.25 #1622, 0.11 #782), 0gr4k (0.25 #230, 0.24 #1490, 0.24 #1700), 02qvyrt (0.23 #1546, 0.22 #1756, 0.20 #286), 0gqwc (0.21 #256, 0.20 #3616, 0.17 #4666), 04kxsb (0.20 #1545, 0.20 #285, 0.19 #3645), 0gr42 (0.19 #1540, 0.19 #1750, 0.12 #1120) >> Best rule #4832 for best value: >> intensional similarity = 3 >> extensional distance = 511 >> proper extension: 06mmr; >> query: (?x5648, ?x2257) <- honored_for(?x2294, ?x5648), award(?x5648, ?x2257), nominated_for(?x2257, ?x86) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #258 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 85 *> proper extension: 0d_wms; *> query: (?x5648, 0gs9p) <- honored_for(?x2294, ?x5648), cinematography(?x5648, ?x6062), film(?x7310, ?x5648) *> conf = 0.43 ranks of expected_values: 3, 7, 9, 59, 81 EVAL 049xgc nominated_for! 099t8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 85.000 85.000 0.681 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 049xgc nominated_for! 02x258x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 85.000 85.000 0.681 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 049xgc nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 85.000 85.000 0.681 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 049xgc nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 85.000 0.681 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 049xgc nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 85.000 0.681 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10810-0hg5 PRED entity: 0hg5 PRED relation: countries_spoken_in! PRED expected values: 05zjd => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 55): 02h40lc (0.36 #3302, 0.36 #1872, 0.35 #2972), 0jzc (0.24 #400, 0.23 #290, 0.21 #455), 064_8sq (0.23 #1172, 0.20 #732, 0.20 #347), 04306rv (0.18 #225, 0.13 #170, 0.12 #60), 02ztjwg (0.18 #248, 0.11 #138, 0.09 #853), 012v8 (0.14 #151, 0.13 #261, 0.09 #701), 0k0sb (0.13 #270, 0.08 #160, 0.07 #380), 0cjk9 (0.12 #59, 0.10 #224, 0.07 #829), 02bjrlw (0.11 #111, 0.11 #166, 0.10 #276), 06b_j (0.10 #238, 0.08 #183, 0.07 #18) >> Best rule #3302 for best value: >> intensional similarity = 2 >> extensional distance = 181 >> proper extension: 0h44w; >> query: (?x2756, 02h40lc) <- countries_spoken_in(?x2502, ?x2756), language(?x89, ?x2502) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 03rjj; 0d060g; 0h3y; 0chghy; 03rt9; 07ssc; 015fr; 0ctw_b; 06mkj; 0d05w3; ... *> query: (?x2756, 05zjd) <- country(?x2266, ?x2756), exported_to(?x789, ?x2756), form_of_government(?x2756, ?x1926) *> conf = 0.10 ranks of expected_values: 11 EVAL 0hg5 countries_spoken_in! 05zjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 106.000 106.000 0.361 http://example.org/language/human_language/countries_spoken_in #10809-03cd1q PRED entity: 03cd1q PRED relation: award_winner PRED expected values: 05_swj => 140 concepts (47 used for prediction) PRED predicted values (max 10 best out of 774): 05_swj (0.82 #51508, 0.82 #45068, 0.82 #72436), 09wj5 (0.33 #83, 0.02 #49981, 0.02 #54809), 02ryx0 (0.25 #2612, 0.06 #7440, 0.03 #21929), 016szr (0.25 #2450, 0.06 #7278, 0.02 #21767), 016jll (0.25 #3109, 0.06 #7937, 0.01 #19207), 04jspq (0.25 #2710, 0.06 #7538), 01vvyvk (0.23 #20926, 0.11 #5590, 0.04 #8809), 0lccn (0.23 #20926, 0.05 #21287, 0.02 #38989), 0b68vs (0.23 #20926, 0.04 #8216, 0.03 #9827), 045zr (0.23 #20926, 0.04 #8476, 0.03 #10087) >> Best rule #51508 for best value: >> intensional similarity = 4 >> extensional distance = 487 >> proper extension: 06n7h7; 03ldxq; 0bz5v2; 07ymr5; 049_zz; 01k70_; 06jw0s; 06s6hs; 05sj55; 05wqr1; ... >> query: (?x11469, ?x217) <- gender(?x11469, ?x231), award_winner(?x1362, ?x11469), location(?x11469, ?x8809), award_winner(?x217, ?x11469) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cd1q award_winner 05_swj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 47.000 0.819 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #10808-0m2l9 PRED entity: 0m2l9 PRED relation: profession PRED expected values: 0dxtg => 126 concepts (125 used for prediction) PRED predicted values (max 10 best out of 84): 016z4k (0.64 #149, 0.56 #2909, 0.50 #294), 0dxtg (0.55 #3497, 0.53 #3207, 0.52 #5094), 0cbd2 (0.52 #7564, 0.52 #6834, 0.51 #3491), 039v1 (0.50 #5405, 0.50 #5260, 0.43 #1484), 0dz3r (0.50 #1453, 0.47 #6104, 0.47 #5374), 0kyk (0.47 #752, 0.35 #7585, 0.34 #6855), 01c72t (0.45 #1181, 0.43 #2055, 0.43 #1472), 018gz8 (0.41 #3210, 0.33 #5097, 0.29 #740), 03gjzk (0.38 #3208, 0.31 #5095, 0.29 #6260), 0n1h (0.36 #1170, 0.36 #1461, 0.33 #4647) >> Best rule #149 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01kv4mb; 01w724; 03bnv; 02qwg; 018dyl; 01kd57; 01vrnsk; 0fq117k; 0pj8m; >> query: (?x483, 016z4k) <- award_nominee(?x483, ?x1089), profession(?x483, ?x319), ?x1089 = 01vrncs >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #3497 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 01c58j; 0177s6; 03f47xl; 01_k0d; 0739y; 0c1fs; *> query: (?x483, 0dxtg) <- influenced_by(?x483, ?x5442), award(?x5442, ?x724), location_of_ceremony(?x5442, ?x3026) *> conf = 0.55 ranks of expected_values: 2 EVAL 0m2l9 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 125.000 0.636 http://example.org/people/person/profession #10807-05lwjc PRED entity: 05lwjc PRED relation: parent_genre PRED expected values: 0glt670 06j6l => 50 concepts (44 used for prediction) PRED predicted values (max 10 best out of 166): 06j6l (0.40 #34, 0.33 #198, 0.25 #362), 06by7 (0.29 #1167, 0.28 #1332, 0.27 #1993), 016_nr (0.20 #48, 0.17 #212, 0.12 #376), 0glt670 (0.15 #1344, 0.13 #1840, 0.13 #1179), 03_d0 (0.13 #7242, 0.11 #7075, 0.09 #3631), 064t9 (0.13 #7242, 0.06 #1647, 0.06 #1646), 05r9t (0.12 #393, 0.02 #558, 0.02 #722), 03lty (0.12 #1500, 0.11 #6928, 0.11 #7095), 05r6t (0.12 #2854, 0.12 #4006, 0.11 #3513), 01fh36 (0.11 #7075, 0.04 #5101, 0.04 #4772) >> Best rule #34 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 0glt670; 025sc50; 0gywn; >> query: (?x11787, 06j6l) <- artists(?x11787, ?x10148), artists(?x11787, ?x7553), artists(?x11787, ?x7162), artists(?x11787, ?x5760), artists(?x11787, ?x3756), ?x3756 = 01wgcvn, ?x10148 = 02h9_l, ?x5760 = 01dwrc, ?x7553 = 01wqmm8, ?x7162 = 0ffgh >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 05lwjc parent_genre 06j6l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 44.000 0.400 http://example.org/music/genre/parent_genre EVAL 05lwjc parent_genre 0glt670 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 50.000 44.000 0.400 http://example.org/music/genre/parent_genre #10806-01zfzb PRED entity: 01zfzb PRED relation: production_companies PRED expected values: 031rq5 => 80 concepts (54 used for prediction) PRED predicted values (max 10 best out of 58): 0g1rw (0.32 #3659, 0.30 #917, 0.16 #7), 016tt2 (0.22 #169, 0.17 #335, 0.17 #252), 05qd_ (0.19 #258, 0.19 #175, 0.15 #341), 086k8 (0.12 #1915, 0.12 #168, 0.10 #669), 054lpb6 (0.09 #429, 0.08 #597, 0.06 #2510), 016tw3 (0.08 #1924, 0.08 #2507, 0.07 #678), 01795t (0.07 #855, 0.05 #938, 0.04 #1187), 024rgt (0.07 #439, 0.06 #607, 0.05 #24), 09b3v (0.07 #866, 0.04 #1280, 0.03 #1696), 01gb54 (0.06 #704, 0.06 #1950, 0.04 #2533) >> Best rule #3659 for best value: >> intensional similarity = 4 >> extensional distance = 1087 >> proper extension: 0hgnl3t; >> query: (?x5320, ?x541) <- nominated_for(?x5319, ?x5320), film(?x665, ?x5320), film(?x541, ?x5320), nominated_for(?x1691, ?x5320) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #626 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 203 *> proper extension: 0ds35l9; 02y_lrp; 05jf85; 0bvn25; 0dnvn3; 01k1k4; 034qrh; 05p1tzf; 01sxly; 03s6l2; ... *> query: (?x5320, 031rq5) <- nominated_for(?x5319, ?x5320), titles(?x2480, ?x5320), ?x2480 = 01z4y, genre(?x5320, ?x53) *> conf = 0.04 ranks of expected_values: 19 EVAL 01zfzb production_companies 031rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 80.000 54.000 0.324 http://example.org/film/film/production_companies #10805-0gnjh PRED entity: 0gnjh PRED relation: currency PRED expected values: 09nqf => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.83 #57, 0.82 #71, 0.79 #50), 02gsvk (0.06 #20, 0.01 #132, 0.01 #139), 01nv4h (0.02 #9, 0.02 #156, 0.02 #338) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 181 >> proper extension: 015qsq; 0m313; 016fyc; 034qrh; 0ds33; 0pc62; 0fgpvf; 0209xj; 04fzfj; 0jzw; ... >> query: (?x6604, 09nqf) <- nominated_for(?x484, ?x6604), language(?x6604, ?x254), nominated_for(?x5537, ?x6604), nominated_for(?x3438, ?x6604) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gnjh currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.825 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10804-0j2zj PRED entity: 0j2zj PRED relation: position PRED expected values: 02qvdc => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 26): 02qvdc (0.82 #114, 0.81 #86, 0.81 #112), 02qvgy (0.25 #1, 0.23 #111, 0.21 #107), 02qvkj (0.07 #42, 0.06 #31, 0.06 #10), 01z9v6 (0.03 #35), 08ns5s (0.03 #35), 02_ssl (0.03 #35), 0355dz (0.03 #35), 02wszf (0.03 #35), 02sf_r (0.03 #35), 03558l (0.03 #35) >> Best rule #114 for best value: >> intensional similarity = 13 >> extensional distance = 41 >> proper extension: 0gvt8sz; >> query: (?x9515, ?x5234) <- position(?x9515, ?x2918), team(?x5234, ?x9515), position(?x13661, ?x2918), position(?x10644, ?x2918), position(?x9835, ?x2918), position(?x4426, ?x2918), position(?x2919, ?x2918), ?x4426 = 0c1gj5, ?x9835 = 02hqt6, ?x10644 = 0jnnx, ?x2919 = 0c41y70, team(?x2918, ?x5233), ?x13661 = 0jnr3 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j2zj position 02qvdc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.819 http://example.org/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position #10803-0bpbhm PRED entity: 0bpbhm PRED relation: titles! PRED expected values: 07s9rl0 => 93 concepts (55 used for prediction) PRED predicted values (max 10 best out of 54): 07s9rl0 (0.68 #303, 0.68 #203, 0.53 #305), 01z4y (0.35 #1345, 0.29 #34, 0.27 #1848), 04xvlr (0.29 #206, 0.28 #2121, 0.25 #1618), 0lsxr (0.27 #4148, 0.26 #2217, 0.24 #3337), 05p553 (0.26 #2217, 0.24 #3337, 0.23 #4553), 07c52 (0.16 #2551, 0.10 #5494, 0.10 #5290), 024qqx (0.15 #1289, 0.15 #784, 0.13 #1088), 04t36 (0.14 #8, 0.11 #210, 0.08 #312), 0c3351 (0.12 #655, 0.08 #1159, 0.08 #1059), 07ssc (0.12 #515, 0.12 #816, 0.11 #2026) >> Best rule #303 for best value: >> intensional similarity = 7 >> extensional distance = 26 >> proper extension: 0413cff; >> query: (?x4098, ?x53) <- genre(?x4098, ?x2753), genre(?x4098, ?x604), genre(?x4098, ?x53), ?x53 = 07s9rl0, ?x604 = 0lsxr, country(?x4098, ?x94), ?x2753 = 0219x_ >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bpbhm titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 55.000 0.679 http://example.org/media_common/netflix_genre/titles #10802-01bh6y PRED entity: 01bh6y PRED relation: profession PRED expected values: 02hrh1q => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 74): 02hrh1q (0.92 #765, 0.90 #4516, 0.90 #2715), 0np9r (0.62 #622, 0.48 #472, 0.48 #772), 01d_h8 (0.38 #1656, 0.37 #3606, 0.37 #3306), 03gjzk (0.35 #3901, 0.28 #1366, 0.25 #8418), 018gz8 (0.35 #3901, 0.24 #318, 0.21 #768), 0kyk (0.35 #3901, 0.18 #181, 0.17 #17404), 01445t (0.35 #3901, 0.17 #17404, 0.01 #7226), 0dxtg (0.29 #12917, 0.28 #6915, 0.28 #8266), 02jknp (0.26 #1058, 0.26 #6909, 0.26 #1658), 09jwl (0.23 #3770, 0.22 #1520, 0.20 #2870) >> Best rule #765 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05dxl5; >> query: (?x9604, 02hrh1q) <- award_nominee(?x2589, ?x9604), language(?x9604, ?x254), award_nominee(?x9604, ?x1343) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bh6y profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.917 http://example.org/people/person/profession #10801-032l1 PRED entity: 032l1 PRED relation: languages PRED expected values: 06b_j => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 17): 02h40lc (0.40 #80, 0.29 #314, 0.28 #3161), 064_8sq (0.29 #327, 0.14 #288, 0.10 #405), 06b_j (0.14 #328, 0.14 #289, 0.10 #406), 02bjrlw (0.07 #703, 0.03 #1639, 0.03 #2458), 03k50 (0.05 #706, 0.04 #2305, 0.03 #2383), 04306rv (0.03 #705, 0.03 #1641, 0.02 #2460), 06mp7 (0.03 #557, 0.02 #635, 0.02 #713), 07c9s (0.03 #559, 0.02 #3445, 0.02 #3679), 06nm1 (0.03 #552, 0.02 #708, 0.01 #5773), 0688f (0.02 #731, 0.01 #965) >> Best rule #80 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01dkpb; >> query: (?x3336, 02h40lc) <- type_of_union(?x3336, ?x566), nationality(?x3336, ?x1603), religion(?x3336, ?x2260), ?x2260 = 02rxj >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #328 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0k8y7; 041b4j; *> query: (?x3336, 06b_j) <- type_of_union(?x3336, ?x566), people(?x5590, ?x3336), place_of_birth(?x3336, ?x8745), ?x5590 = 0g6ff *> conf = 0.14 ranks of expected_values: 3 EVAL 032l1 languages 06b_j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 172.000 172.000 0.400 http://example.org/people/person/languages #10800-035xwd PRED entity: 035xwd PRED relation: produced_by PRED expected values: 092kgw => 94 concepts (54 used for prediction) PRED predicted values (max 10 best out of 148): 04pqqb (0.17 #568, 0.07 #1349, 0.07 #958), 02q_cc (0.17 #423, 0.07 #813, 0.04 #5468), 030_3z (0.17 #553, 0.07 #943, 0.04 #2500), 092kgw (0.17 #586, 0.07 #976, 0.02 #4470), 06dkzt (0.17 #690, 0.07 #1080, 0.01 #17729), 01hrqc (0.17 #644, 0.07 #1034), 0bwh6 (0.17 #47, 0.04 #5482, 0.04 #4708), 02lf0c (0.17 #23, 0.03 #1583, 0.02 #4684), 03v1xb (0.17 #303, 0.01 #4577, 0.01 #2250), 06jz0 (0.17 #341, 0.01 #2288) >> Best rule #568 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 05sns6; >> query: (?x796, 04pqqb) <- film(?x7530, ?x796), film(?x2499, ?x796), featured_film_locations(?x796, ?x739), ?x2499 = 0c6qh, award_winner(?x496, ?x7530), ?x739 = 02_286 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #586 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 05sns6; *> query: (?x796, 092kgw) <- film(?x7530, ?x796), film(?x2499, ?x796), featured_film_locations(?x796, ?x739), ?x2499 = 0c6qh, award_winner(?x496, ?x7530), ?x739 = 02_286 *> conf = 0.17 ranks of expected_values: 4 EVAL 035xwd produced_by 092kgw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 54.000 0.167 http://example.org/film/film/produced_by #10799-0fhnf PRED entity: 0fhnf PRED relation: location_of_ceremony! PRED expected values: 04ztj => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.57 #53, 0.56 #45, 0.53 #93), 01g63y (0.03 #70, 0.03 #170, 0.02 #186) >> Best rule #53 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 05bcl; 05fjf; 035hm; >> query: (?x9714, 04ztj) <- adjoins(?x9714, ?x1264), country(?x9714, ?x1355), adjoins(?x13764, ?x9714), time_zones(?x9714, ?x2864), film_release_region(?x66, ?x1264) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fhnf location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 181.000 0.565 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #10798-0bzkgg PRED entity: 0bzkgg PRED relation: honored_for PRED expected values: 01jc6q => 46 concepts (16 used for prediction) PRED predicted values (max 10 best out of 954): 0h03fhx (0.33 #277, 0.12 #1473, 0.09 #2669), 0fpv_3_ (0.33 #140, 0.12 #1336, 0.09 #2532), 0gmgwnv (0.33 #378, 0.12 #1574, 0.09 #2770), 0h95927 (0.33 #452, 0.12 #1648, 0.09 #2844), 0gy7bj4 (0.33 #537, 0.12 #1733, 0.09 #2929), 02vxq9m (0.33 #7, 0.12 #1203, 0.09 #2399), 0j43swk (0.33 #185, 0.12 #1381, 0.09 #2577), 01jc6q (0.20 #598, 0.18 #2989, 0.17 #5995), 05h43ls (0.20 #598, 0.11 #1794, 0.07 #2990), 025scjj (0.18 #2989, 0.17 #5995, 0.17 #1195) >> Best rule #277 for best value: >> intensional similarity = 20 >> extensional distance = 1 >> proper extension: 0n8_m93; >> query: (?x2822, 0h03fhx) <- award_winner(?x2822, ?x9963), award_winner(?x2822, ?x1034), ceremony(?x3617, ?x2822), ceremony(?x1079, ?x2822), ceremony(?x591, ?x2822), ceremony(?x77, ?x2822), award(?x9963, ?x1716), award_winner(?x1033, ?x1034), ?x3617 = 0gvx_, ?x591 = 0f4x7, ?x1079 = 0l8z1, category(?x9963, ?x134), place_of_birth(?x9963, ?x682), award(?x1034, ?x693), ?x77 = 0gqng, nominated_for(?x1034, ?x197), student(?x9318, ?x1034), gender(?x1034, ?x231), film(?x9963, ?x2586), ?x1716 = 02y_rq5 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #598 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 1 *> proper extension: 0n8_m93; *> query: (?x2822, ?x2586) <- award_winner(?x2822, ?x9963), award_winner(?x2822, ?x1034), ceremony(?x3617, ?x2822), ceremony(?x1079, ?x2822), ceremony(?x591, ?x2822), ceremony(?x77, ?x2822), award(?x9963, ?x1716), award_winner(?x1033, ?x1034), ?x3617 = 0gvx_, ?x591 = 0f4x7, ?x1079 = 0l8z1, category(?x9963, ?x134), place_of_birth(?x9963, ?x682), award(?x1034, ?x693), ?x77 = 0gqng, nominated_for(?x1034, ?x197), student(?x9318, ?x1034), gender(?x1034, ?x231), film(?x9963, ?x2586), ?x1716 = 02y_rq5 *> conf = 0.20 ranks of expected_values: 8 EVAL 0bzkgg honored_for 01jc6q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 46.000 16.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #10797-02cg41 PRED entity: 02cg41 PRED relation: ceremony! PRED expected values: 0c4z8 02g8mp 02gx2k 025m8y 01dpdh 023vrq 02ddq4 03r00m 02gm9n => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 200): 02ddq4 (0.80 #1977, 0.77 #2704, 0.75 #1795), 025m8y (0.77 #2597, 0.75 #1688, 0.74 #3144), 02g8mp (0.77 #2584, 0.75 #1675, 0.70 #1857), 023vrq (0.77 #2696, 0.70 #1969, 0.68 #3243), 03t5b6 (0.77 #2647, 0.70 #1920, 0.63 #3194), 0c4z8 (0.75 #1674, 0.70 #1856, 0.69 #2583), 02gm9n (0.75 #1803, 0.69 #2712, 0.63 #3259), 02gx2k (0.69 #2593, 0.68 #3140, 0.62 #1684), 01dpdh (0.60 #1887, 0.54 #2614, 0.53 #3992), 0gqz2 (0.60 #362, 0.53 #3992, 0.52 #2719) >> Best rule #1977 for best value: >> intensional similarity = 17 >> extensional distance = 8 >> proper extension: 02rjjll; 0466p0j; 01xqqp; >> query: (?x9431, 02ddq4) <- ceremony(?x9462, ?x9431), ceremony(?x5765, ?x9431), ceremony(?x3313, ?x9431), ceremony(?x1389, ?x9431), award_winner(?x9431, ?x5536), award_winner(?x9431, ?x3632), award_winner(?x1323, ?x5536), award_nominee(?x140, ?x5536), ?x1389 = 01c427, participant(?x2946, ?x5536), ?x9462 = 01d38t, profession(?x3632, ?x220), origin(?x3632, ?x8322), award_winner(?x158, ?x3632), ?x3313 = 02flpc, ?x5765 = 024_fw, people(?x7185, ?x5536) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 7, 8, 9, 35 EVAL 02cg41 ceremony! 02gm9n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 41.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02cg41 ceremony! 03r00m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 41.000 41.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02cg41 ceremony! 02ddq4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02cg41 ceremony! 023vrq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02cg41 ceremony! 01dpdh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 41.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02cg41 ceremony! 025m8y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02cg41 ceremony! 02gx2k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 41.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02cg41 ceremony! 02g8mp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02cg41 ceremony! 0c4z8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 41.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony #10796-0462hhb PRED entity: 0462hhb PRED relation: nominated_for! PRED expected values: 09sb52 => 90 concepts (83 used for prediction) PRED predicted values (max 10 best out of 199): 09sdmz (0.65 #574, 0.29 #352, 0.17 #11770), 0gqy2 (0.63 #553, 0.33 #2995, 0.30 #2329), 019f4v (0.60 #2932, 0.60 #2266, 0.52 #268), 0gs9p (0.59 #2272, 0.57 #274, 0.57 #2938), 02qyntr (0.54 #2385, 0.52 #387, 0.51 #3051), 0k611 (0.51 #2946, 0.48 #2280, 0.43 #282), 0gs96 (0.50 #77, 0.29 #1409, 0.29 #299), 02y_rq5 (0.50 #61, 0.25 #2059, 0.24 #283), 027571b (0.50 #167, 0.19 #17545, 0.12 #11103), 02ppm4q (0.48 #325, 0.28 #547, 0.26 #2323) >> Best rule #574 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0dgst_d; 0h03fhx; >> query: (?x4756, 09sdmz) <- genre(?x4756, ?x162), nominated_for(?x451, ?x4756), film_release_distribution_medium(?x4756, ?x81), ?x451 = 099jhq >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #250 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 011yr9; 011yg9; 011yhm; *> query: (?x4756, 09sb52) <- titles(?x53, ?x4756), nominated_for(?x618, ?x4756), nominated_for(?x198, ?x4756), ?x198 = 040njc, ?x618 = 09qwmm *> conf = 0.33 ranks of expected_values: 23 EVAL 0462hhb nominated_for! 09sb52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 90.000 83.000 0.651 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10795-025t9b PRED entity: 025t9b PRED relation: location PRED expected values: 04jpl => 78 concepts (76 used for prediction) PRED predicted values (max 10 best out of 136): 04lh6 (0.50 #12069, 0.49 #16092, 0.44 #32989), 04jpl (0.31 #821, 0.21 #4844, 0.21 #2430), 02_286 (0.17 #6474, 0.14 #3255, 0.14 #8082), 030qb3t (0.16 #3301, 0.15 #7324, 0.14 #13762), 0824r (0.14 #216, 0.01 #3434), 0205m3 (0.14 #785), 01fscv (0.14 #666), 0jrxx (0.14 #446), 0cr3d (0.09 #6582, 0.05 #8190, 0.05 #11409), 02jx1 (0.06 #875, 0.05 #1609, 0.01 #8116) >> Best rule #12069 for best value: >> intensional similarity = 2 >> extensional distance = 1050 >> proper extension: 02c4s; 03f0fnk; 0cc63l; 02w5q6; 06sn8m; 039x1k; 02n9k; 032md; 01nkxvx; 030dx5; ... >> query: (?x3872, ?x9026) <- place_of_birth(?x3872, ?x9026), people(?x743, ?x3872) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #821 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 05sq84; 06ltr; 016nff; 053xw6; *> query: (?x3872, 04jpl) <- film(?x3872, ?x7304), nationality(?x3872, ?x1310), ?x7304 = 031hcx *> conf = 0.31 ranks of expected_values: 2 EVAL 025t9b location 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 76.000 0.504 http://example.org/people/person/places_lived./people/place_lived/location #10794-015wnl PRED entity: 015wnl PRED relation: people! PRED expected values: 02w7gg => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 34): 02w7gg (0.33 #2, 0.26 #156, 0.24 #464), 041rx (0.33 #4, 0.12 #928, 0.12 #2391), 03bkbh (0.17 #32, 0.06 #340, 0.04 #648), 018s6c (0.17 #66), 033tf_ (0.13 #315, 0.09 #623, 0.08 #700), 0x67 (0.10 #1011, 0.10 #780, 0.08 #3629), 0d7wh (0.08 #171, 0.06 #556, 0.06 #479), 07hwkr (0.08 #320, 0.06 #628, 0.04 #936), 0xnvg (0.07 #90, 0.07 #706, 0.06 #783), 07bch9 (0.07 #331, 0.05 #639, 0.04 #716) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 09l3p; 0h0yt; 0c_gcr; 016z68; >> query: (?x3780, 02w7gg) <- film(?x3780, ?x2896), ?x2896 = 0645k5, award_winner(?x112, ?x3780) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015wnl people! 02w7gg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.333 http://example.org/people/ethnicity/people #10793-059nf5 PRED entity: 059nf5 PRED relation: colors PRED expected values: 083jv => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.84 #632, 0.82 #2, 0.75 #21), 01l849 (0.56 #77, 0.28 #153, 0.16 #343), 06fvc (0.45 #633, 0.43 #364, 0.41 #269), 01g5v (0.42 #118, 0.42 #251, 0.39 #137), 019sc (0.30 #103, 0.30 #160, 0.29 #638), 02rnmb (0.15 #749, 0.15 #748, 0.15 #747), 0jc_p (0.15 #749, 0.15 #748, 0.15 #747), 088fh (0.15 #749, 0.15 #748, 0.15 #747), 09ggk (0.15 #749, 0.15 #748, 0.15 #747), 06kqt3 (0.15 #749, 0.15 #748, 0.15 #747) >> Best rule #632 for best value: >> intensional similarity = 8 >> extensional distance = 254 >> proper extension: 01ypc; 01jv_6; 01y49; 01ync; 027yf83; 02pjzvh; 025v1sx; 07l4z; 04n7ps6; 02r7lqg; ... >> query: (?x10633, 083jv) <- colors(?x10633, ?x8047), team(?x60, ?x10633), colors(?x13914, ?x8047), colors(?x12231, ?x8047), colors(?x9768, ?x8047), ?x12231 = 09cvbq, contains(?x94, ?x9768), ?x13914 = 01jvgt >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059nf5 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.844 http://example.org/sports/sports_team/colors #10792-02hblj PRED entity: 02hblj PRED relation: location PRED expected values: 07bcn => 85 concepts (58 used for prediction) PRED predicted values (max 10 best out of 100): 0n6dc (0.51 #11259, 0.51 #12064, 0.50 #15281), 030qb3t (0.19 #8125, 0.15 #887, 0.14 #4104), 02_286 (0.16 #33021, 0.15 #7275, 0.14 #23364), 02xry (0.12 #133, 0.05 #937, 0.03 #1741), 0s5cg (0.12 #257, 0.03 #1865, 0.03 #2670), 02cft (0.12 #307, 0.03 #1915, 0.03 #2720), 02j3w (0.12 #229, 0.03 #1837, 0.03 #2642), 01qh7 (0.12 #157, 0.03 #1765, 0.03 #2570), 06y57 (0.12 #256, 0.03 #2669, 0.03 #3473), 059rby (0.10 #820, 0.07 #4037, 0.06 #4841) >> Best rule #11259 for best value: >> intensional similarity = 5 >> extensional distance = 332 >> proper extension: 02wrhj; 02k6rq; 0308kx; >> query: (?x12084, ?x11843) <- type_of_union(?x12084, ?x566), ?x566 = 04ztj, place_of_birth(?x12084, ?x11843), actor(?x8017, ?x12084), film(?x12084, ?x5839) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #20913 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 737 *> proper extension: 05qsxy; *> query: (?x12084, ?x1227) <- type_of_union(?x12084, ?x566), ?x566 = 04ztj, place_of_birth(?x12084, ?x11843), student(?x4555, ?x12084), contains(?x1227, ?x4555) *> conf = 0.06 ranks of expected_values: 15 EVAL 02hblj location 07bcn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 85.000 58.000 0.515 http://example.org/people/person/places_lived./people/place_lived/location #10791-04hqz PRED entity: 04hqz PRED relation: film_release_region! PRED expected values: 053rxgm 0bh8x1y => 137 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1289): 01fmys (0.88 #6652, 0.82 #4084, 0.79 #5368), 06wbm8q (0.88 #4151, 0.77 #10571, 0.74 #5435), 0661m4p (0.85 #4121, 0.82 #5405, 0.79 #6689), 0gj9tn5 (0.85 #4049, 0.79 #5333, 0.76 #6617), 0gd0c7x (0.85 #4079, 0.77 #10499, 0.76 #6647), 062zm5h (0.82 #5763, 0.82 #4479, 0.74 #10899), 0872p_c (0.82 #5262, 0.76 #3978, 0.74 #6546), 05zlld0 (0.82 #4304, 0.79 #6872, 0.79 #5588), 01vksx (0.82 #3947, 0.79 #5231, 0.76 #6515), 0by1wkq (0.82 #4072, 0.79 #5356, 0.76 #6640) >> Best rule #6652 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 05r4w; 0f8l9c; >> query: (?x7413, 01fmys) <- participating_countries(?x418, ?x7413), service_location(?x8082, ?x7413), form_of_government(?x7413, ?x48) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3979 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 06t2t; *> query: (?x7413, 053rxgm) <- film_release_region(?x3076, ?x7413), ?x3076 = 0g5838s, nationality(?x84, ?x7413) *> conf = 0.76 ranks of expected_values: 57, 198 EVAL 04hqz film_release_region! 0bh8x1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 137.000 114.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04hqz film_release_region! 053rxgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 137.000 114.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10790-01nkt PRED entity: 01nkt PRED relation: nutrient PRED expected values: 01sh2 0f4hc 05v_8y 0h1sz 0f4l5 => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 35): 01sh2 (0.71 #335, 0.71 #319, 0.70 #260), 0f4hc (0.66 #308, 0.57 #338, 0.57 #322), 0hkwr (0.66 #308, 0.57 #342, 0.57 #326), 0d9t0 (0.66 #308, 0.57 #341, 0.57 #325), 03d49 (0.66 #308, 0.50 #312, 0.50 #305), 0h1sz (0.66 #308, 0.43 #340, 0.43 #327), 05v_8y (0.66 #308, 0.43 #339, 0.43 #324), 07zqy (0.66 #308, 0.43 #334, 0.43 #328), 061xhr (0.66 #308, 0.43 #323, 0.33 #314), 0466p20 (0.66 #308, 0.33 #262, 0.33 #246) >> Best rule #335 for best value: >> intensional similarity = 85 >> extensional distance = 5 >> proper extension: 0dcfv; >> query: (?x6032, ?x2018) <- nutrient(?x6032, ?x12454), nutrient(?x6032, ?x11592), nutrient(?x6032, ?x9915), nutrient(?x6032, ?x9490), nutrient(?x6032, ?x8243), nutrient(?x6032, ?x6192), nutrient(?x6032, ?x6160), nutrient(?x6032, ?x6026), nutrient(?x6032, ?x5549), nutrient(?x6032, ?x5451), nutrient(?x6032, ?x5337), nutrient(?x6032, ?x3264), ?x5337 = 06x4c, nutrient(?x10612, ?x6026), nutrient(?x9489, ?x6026), nutrient(?x9005, ?x6026), nutrient(?x8298, ?x6026), nutrient(?x7719, ?x6026), nutrient(?x7057, ?x6026), nutrient(?x6285, ?x6026), nutrient(?x6159, ?x6026), nutrient(?x5373, ?x6026), nutrient(?x5009, ?x6026), nutrient(?x4068, ?x6026), nutrient(?x3900, ?x6026), nutrient(?x3468, ?x6026), nutrient(?x2701, ?x6026), nutrient(?x1959, ?x6026), nutrient(?x1303, ?x6026), nutrient(?x1257, ?x6026), nutrient(?x9732, ?x11592), nutrient(?x6191, ?x11592), ?x2701 = 0hkxq, ?x9732 = 05z55, ?x9005 = 04zpv, ?x8298 = 037ls6, ?x6159 = 033cnk, ?x3468 = 0cxn2, ?x6191 = 014j1m, ?x1257 = 09728, ?x7719 = 0dj75, ?x12454 = 025rw19, ?x10612 = 0frq6, ?x4068 = 0fbw6, ?x9915 = 025tkqy, nutrient(?x5009, ?x14618), nutrient(?x5009, ?x12868), nutrient(?x5009, ?x11784), nutrient(?x5009, ?x10709), nutrient(?x5009, ?x10195), nutrient(?x5009, ?x9855), nutrient(?x5009, ?x9795), nutrient(?x5009, ?x7894), nutrient(?x5009, ?x2018), ?x5549 = 025s7j4, ?x10195 = 0hkwr, ?x9855 = 0d9t0, ?x10709 = 0h1sz, ?x5373 = 0971v, ?x8243 = 014d7f, ?x9795 = 05v_8y, taxonomy(?x5451, ?x939), ?x7894 = 0f4hc, ?x9489 = 07j87, ?x12868 = 03d49, ?x6192 = 06jry, ?x3900 = 061_f, ?x6285 = 01645p, ?x14618 = 02y_3rt, nutrient(?x1959, ?x12336), nutrient(?x1959, ?x6517), ?x7057 = 0fbdb, ?x2018 = 01sh2, ?x12336 = 0f4l5, ?x6517 = 02kd8zw, ?x11784 = 07zqy, ?x1303 = 0fj52s, ?x939 = 04n6k, nutrient(?x3468, ?x6160), nutrient(?x2701, ?x11592), nutrient(?x6191, ?x9490), nutrient(?x9732, ?x6160), nutrient(?x3264, ?x9915), nutrient(?x3264, ?x8243), nutrient(?x8298, ?x9490) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6, 7, 13 EVAL 01nkt nutrient 0f4l5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01nkt nutrient 0h1sz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01nkt nutrient 05v_8y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01nkt nutrient 0f4hc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01nkt nutrient 01sh2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.714 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #10789-050yyb PRED entity: 050yyb PRED relation: award_winner PRED expected values: 055c8 => 39 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1271): 0h1p (0.33 #292, 0.25 #3361, 0.23 #4895), 03mfqm (0.33 #962, 0.22 #26093, 0.21 #24554), 0133sq (0.33 #1437, 0.17 #4506, 0.15 #6040), 014zcr (0.33 #29, 0.17 #3098, 0.15 #4632), 02h1rt (0.33 #738, 0.12 #9941, 0.08 #3807), 04ktcgn (0.33 #274, 0.12 #9477, 0.08 #3343), 09swkk (0.33 #772, 0.08 #3841, 0.08 #5375), 05bm4sm (0.33 #882, 0.08 #3951, 0.08 #5485), 02qgqt (0.33 #8, 0.08 #3077, 0.08 #4611), 040rjq (0.33 #1507, 0.08 #4576, 0.08 #6110) >> Best rule #292 for best value: >> intensional similarity = 24 >> extensional distance = 1 >> proper extension: 0bvfqq; >> query: (?x2294, 0h1p) <- ceremony(?x4573, ?x2294), ceremony(?x1703, ?x2294), ceremony(?x1323, ?x2294), ceremony(?x1307, ?x2294), ceremony(?x1079, ?x2294), award_winner(?x2294, ?x9316), award_winner(?x2294, ?x1365), ?x1323 = 0gqz2, ?x1703 = 0k611, honored_for(?x2294, ?x7580), honored_for(?x2294, ?x4502), ?x4573 = 0gq_d, film_crew_role(?x4502, ?x13327), film_crew_role(?x4502, ?x1171), film_crew_role(?x4502, ?x137), ?x1079 = 0l8z1, ?x1171 = 09vw2b7, ?x1307 = 0gq9h, genre(?x7580, ?x53), ?x13327 = 014kbl, ?x137 = 09zzb8, executive_produced_by(?x7580, ?x4060), award_winner(?x1365, ?x538), nominated_for(?x9316, ?x9379) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #16880 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 23 *> proper extension: 0bzk8w; 0bz6l9; 0dthsy; *> query: (?x2294, ?x398) <- ceremony(?x4573, ?x2294), ceremony(?x1703, ?x2294), ceremony(?x1323, ?x2294), ceremony(?x1079, ?x2294), award_winner(?x2294, ?x8070), ?x1323 = 0gqz2, ?x1703 = 0k611, honored_for(?x2294, ?x7580), honored_for(?x2294, ?x4502), ?x4573 = 0gq_d, film_crew_role(?x4502, ?x1171), ?x1079 = 0l8z1, film_crew_role(?x11686, ?x1171), film_crew_role(?x9876, ?x1171), film_crew_role(?x8886, ?x1171), film_crew_role(?x8001, ?x1171), film_crew_role(?x7538, ?x1171), ?x7538 = 035zr0, ?x11686 = 04180vy, ?x8001 = 02qkwl, ?x8886 = 076xkps, ?x9876 = 064ndc, student(?x581, ?x8070), film(?x398, ?x7580) *> conf = 0.06 ranks of expected_values: 282 EVAL 050yyb award_winner 055c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10788-0783m_ PRED entity: 0783m_ PRED relation: people! PRED expected values: 03lmx1 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 29): 0x67 (0.29 #87, 0.14 #1011, 0.11 #241), 041rx (0.17 #4, 0.12 #3007, 0.11 #1313), 033tf_ (0.09 #238, 0.08 #161, 0.08 #700), 0g8_vp (0.08 #22), 0xnvg (0.06 #629, 0.05 #244, 0.05 #706), 02w7gg (0.05 #3544, 0.05 #1927, 0.05 #2004), 07hwkr (0.04 #3015, 0.03 #5171, 0.03 #3785), 07bch9 (0.03 #1178, 0.03 #254, 0.03 #4412), 06v41q (0.03 #106, 0.01 #645, 0.01 #722), 0g96wd (0.03 #141) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 01vw87c; 0c7ct; 03rl84; 044mfr; 01wphh2; 01rmnp; 0gps0z; 01rw116; >> query: (?x2359, 0x67) <- actor(?x5060, ?x2359), profession(?x2359, ?x2348), ?x2348 = 0nbcg >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #707 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 554 *> proper extension: 02wrhj; *> query: (?x2359, 03lmx1) <- actor(?x5060, ?x2359), award_winner(?x5060, ?x822), award(?x5060, ?x678) *> conf = 0.01 ranks of expected_values: 24 EVAL 0783m_ people! 03lmx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 77.000 77.000 0.286 http://example.org/people/ethnicity/people #10787-011ydl PRED entity: 011ydl PRED relation: honored_for! PRED expected values: 05q7cj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 113): 073hkh (0.20 #1, 0.03 #245, 0.02 #2442), 0bvhz9 (0.09 #7570, 0.09 #9037, 0.07 #236), 02glmx (0.07 #190, 0.03 #312, 0.02 #434), 059x66 (0.07 #135, 0.03 #257, 0.02 #2442), 092c5f (0.07 #132, 0.03 #254, 0.02 #2329), 02q690_ (0.03 #2373, 0.03 #4570, 0.03 #3472), 02pgky2 (0.03 #320, 0.02 #442, 0.02 #2442), 050yyb (0.03 #275, 0.02 #397, 0.02 #2442), 0bzmt8 (0.03 #328, 0.02 #450, 0.02 #2442), 0hr6lkl (0.03 #256, 0.02 #378, 0.02 #2209) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 099bhp; >> query: (?x3219, 073hkh) <- film(?x12001, ?x3219), film(?x4681, ?x3219), ?x12001 = 06b4wb, award_nominee(?x4681, ?x71) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2442 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 646 *> proper extension: 07bz5; *> query: (?x3219, ?x78) <- nominated_for(?x4681, ?x3219), award(?x3219, ?x2209), nationality(?x4681, ?x94), ceremony(?x2209, ?x78) *> conf = 0.02 ranks of expected_values: 63 EVAL 011ydl honored_for! 05q7cj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 95.000 95.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #10786-01h1b PRED entity: 01h1b PRED relation: award PRED expected values: 05p1dby => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 288): 05zvj3m (0.70 #25117, 0.70 #20255, 0.70 #40512), 09sb52 (0.38 #13811, 0.32 #20295, 0.32 #14216), 0gq9h (0.35 #8178, 0.20 #5747, 0.17 #6557), 0ck27z (0.31 #11838, 0.27 #8598, 0.26 #12648), 040njc (0.27 #8109, 0.17 #5678, 0.14 #12969), 0cjyzs (0.21 #3751, 0.11 #9827, 0.07 #17118), 02q1tc5 (0.21 #3794, 0.06 #9870, 0.03 #17161), 0fbtbt (0.20 #3878, 0.09 #6713, 0.09 #5903), 0gs9p (0.19 #8180, 0.15 #13040, 0.15 #9800), 019f4v (0.18 #8167, 0.13 #13027, 0.13 #9787) >> Best rule #25117 for best value: >> intensional similarity = 3 >> extensional distance = 1369 >> proper extension: 028q6; 07s3vqk; 0197tq; 05cljf; 0hl3d; 01lmj3q; 09fqtq; 0m2l9; 032nwy; 026ps1; ... >> query: (?x6883, ?x1691) <- nationality(?x6883, ?x94), award_nominee(?x6883, ?x541), award_winner(?x1691, ?x6883) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #8208 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 347 *> proper extension: 024c1b; *> query: (?x6883, 05p1dby) <- produced_by(?x5320, ?x6883), nominated_for(?x5319, ?x5320) *> conf = 0.12 ranks of expected_values: 20 EVAL 01h1b award 05p1dby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 120.000 120.000 0.702 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10785-0gfzfj PRED entity: 0gfzfj PRED relation: music PRED expected values: 05_swj => 66 concepts (37 used for prediction) PRED predicted values (max 10 best out of 114): 023361 (0.33 #150, 0.11 #2056, 0.04 #996), 0146pg (0.33 #10, 0.10 #3185, 0.08 #4034), 0134s5 (0.33 #53, 0.03 #1748, 0.03 #1959), 02bh9 (0.24 #897, 0.19 #1110, 0.18 #1534), 02jxkw (0.19 #776, 0.04 #4801, 0.04 #3955), 01mkn_d (0.14 #541, 0.06 #1180, 0.06 #1604), 0b6yp2 (0.14 #472, 0.06 #1747, 0.06 #1958), 01hw6wq (0.14 #458, 0.03 #1097, 0.03 #1521), 05_swj (0.14 #544, 0.01 #3510, 0.01 #3723), 06fxnf (0.12 #703, 0.10 #2188, 0.09 #3882) >> Best rule #150 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 03h3x5; >> query: (?x10942, 023361) <- language(?x10942, ?x254), genre(?x10942, ?x258), produced_by(?x10942, ?x3673), film(?x1522, ?x10942), ?x254 = 02h40lc, ?x3673 = 021yw7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #544 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 04g73n; *> query: (?x10942, 05_swj) <- film(?x2969, ?x10942), film(?x1537, ?x10942), award_nominee(?x1537, ?x2590), genre(?x10942, ?x258), ?x2969 = 02tqkf, film(?x2590, ?x3133) *> conf = 0.14 ranks of expected_values: 9 EVAL 0gfzfj music 05_swj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 66.000 37.000 0.333 http://example.org/film/film/music #10784-0326tc PRED entity: 0326tc PRED relation: nationality PRED expected values: 02jx1 => 121 concepts (93 used for prediction) PRED predicted values (max 10 best out of 95): 09c7w0 (0.81 #8049, 0.79 #8149, 0.75 #8249), 02jx1 (0.66 #7086, 0.63 #4501, 0.57 #2480), 0cxgc (0.57 #2480, 0.42 #3376, 0.32 #7054), 04jpl (0.57 #2480, 0.42 #3376, 0.32 #7054), 03msf (0.42 #3376, 0.28 #9248, 0.24 #3775), 0j5g9 (0.33 #61, 0.20 #259, 0.04 #694), 03rk0 (0.13 #3219, 0.13 #5509, 0.04 #6998), 06q1r (0.13 #1959, 0.11 #2456, 0.07 #3350), 0d060g (0.11 #3181, 0.10 #1395, 0.10 #5471), 0f8l9c (0.07 #3195, 0.05 #5485, 0.04 #694) >> Best rule #8049 for best value: >> intensional similarity = 5 >> extensional distance = 939 >> proper extension: 0bl60p; 01h2_6; >> query: (?x7972, 09c7w0) <- nationality(?x7972, ?x512), place_of_birth(?x7972, ?x10922), location(?x7972, ?x12268), citytown(?x6432, ?x10922), film_release_region(?x66, ?x512) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #7086 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 597 *> proper extension: 03f1zdw; 01v42g; 027pdrh; 0fv6dr; 0dv1hh; 09m465; 07zr66; 06p0s1; *> query: (?x7972, 02jx1) <- nationality(?x7972, ?x512), nationality(?x5565, ?x512), olympics(?x512, ?x391), contains(?x512, ?x362), ?x5565 = 0mm1q *> conf = 0.66 ranks of expected_values: 2 EVAL 0326tc nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 93.000 0.813 http://example.org/people/person/nationality #10783-0gs1_ PRED entity: 0gs1_ PRED relation: student! PRED expected values: 06182p => 109 concepts (108 used for prediction) PRED predicted values (max 10 best out of 82): 021996 (0.33 #307), 0bwfn (0.12 #5515, 0.09 #2894, 0.08 #17049), 065y4w7 (0.08 #5255, 0.07 #3682, 0.06 #538), 01w5m (0.07 #8491, 0.05 #5346, 0.04 #1677), 08815 (0.07 #1574, 0.05 #526, 0.05 #2622), 09f2j (0.05 #5400, 0.04 #19556, 0.04 #7496), 015nl4 (0.05 #16318, 0.04 #13698, 0.04 #14746), 04b_46 (0.04 #1274, 0.04 #5467, 0.04 #1798), 03ksy (0.04 #5347, 0.04 #7967, 0.04 #11113), 07tg4 (0.04 #4803, 0.04 #3754, 0.03 #6899) >> Best rule #307 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 094xh; >> query: (?x6558, 021996) <- award(?x6558, ?x198), location(?x6558, ?x2256), ?x2256 = 07srw >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #16547 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1066 *> proper extension: 045bs6; 026c0p; 03d63lb; *> query: (?x6558, 06182p) <- profession(?x6558, ?x524), film(?x6558, ?x2423), student(?x5981, ?x6558) *> conf = 0.02 ranks of expected_values: 26 EVAL 0gs1_ student! 06182p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 109.000 108.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #10782-01z215 PRED entity: 01z215 PRED relation: country! PRED expected values: 096f8 0bynt => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 56): 06z6r (0.89 #1153, 0.88 #1041, 0.87 #873), 0bynt (0.85 #4717, 0.85 #4213, 0.85 #1693), 01cgz (0.77 #1136, 0.73 #2033, 0.73 #1753), 01lb14 (0.75 #1755, 0.68 #2035, 0.67 #241), 06f41 (0.74 #801, 0.67 #1025, 0.67 #857), 0w0d (0.72 #1078, 0.71 #798, 0.69 #629), 064vjs (0.68 #818, 0.61 #649, 0.58 #2051), 06wrt (0.67 #242, 0.66 #803, 0.62 #1027), 03hr1p (0.66 #809, 0.65 #1762, 0.63 #2042), 0194d (0.66 #834, 0.62 #1058, 0.60 #1787) >> Best rule #1153 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 09c7w0; 0jgd; 03rjj; 0d060g; 0d0vqn; 0j1z8; 0chghy; 05qhw; 07ssc; 015fr; ... >> query: (?x1781, 06z6r) <- adjoins(?x311, ?x1781), olympics(?x1781, ?x2966), geographic_distribution(?x13008, ?x1781) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #4717 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 127 *> proper extension: 02jxk; *> query: (?x1781, 0bynt) <- member_states(?x7695, ?x1781), ?x7695 = 085h1 *> conf = 0.85 ranks of expected_values: 2, 20 EVAL 01z215 country! 0bynt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 144.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01z215 country! 096f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 144.000 144.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #10781-02189 PRED entity: 02189 PRED relation: entity_involved! PRED expected values: 0f6rc => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 4): 0f6rc (0.33 #23, 0.25 #769, 0.25 #697), 048n7 (0.20 #742, 0.17 #672, 0.17 #539), 0j5ym (0.17 #672, 0.17 #539, 0.17 #538), 0gfq9 (0.12 #1014, 0.12 #745, 0.11 #879) >> Best rule #23 for best value: >> intensional similarity = 2 >> extensional distance = 1 >> proper extension: 049tb; >> query: (?x13317, 0f6rc) <- politician(?x13317, ?x10154), ?x10154 = 04xzm >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02189 entity_involved! 0f6rc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 21.000 21.000 0.333 http://example.org/base/culturalevent/event/entity_involved #10780-040db PRED entity: 040db PRED relation: languages PRED expected values: 06nm1 => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 28): 02h40lc (0.38 #431, 0.38 #1173, 0.35 #1056), 06nm1 (0.20 #123, 0.12 #279, 0.05 #513), 064_8sq (0.12 #288, 0.06 #1069, 0.05 #2005), 02bjrlw (0.12 #274, 0.04 #4565, 0.03 #1172), 04306rv (0.12 #276, 0.03 #588, 0.02 #3514), 0349s (0.12 #305, 0.01 #1164), 04h9h (0.12 #303, 0.01 #1162), 03hkp (0.12 #283, 0.01 #1142), 03k50 (0.05 #511, 0.04 #902, 0.02 #3320), 07c9s (0.05 #520, 0.02 #1574, 0.01 #2003) >> Best rule #431 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 01vrncs; 05qw5; 0hky; 0yxl; 098sx; 02465; >> query: (?x2161, 02h40lc) <- influenced_by(?x2161, ?x9595), influenced_by(?x2161, ?x2625), people(?x6734, ?x9595), influenced_by(?x9595, ?x2994), ?x2625 = 03f70xs >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 04xjp; *> query: (?x2161, 06nm1) <- influenced_by(?x8430, ?x2161), profession(?x2161, ?x353), ?x8430 = 0ct9_, location(?x2161, ?x1649) *> conf = 0.20 ranks of expected_values: 2 EVAL 040db languages 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 169.000 169.000 0.385 http://example.org/people/person/languages #10779-02nwxc PRED entity: 02nwxc PRED relation: profession PRED expected values: 02hrh1q => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 50): 02hrh1q (0.92 #2565, 0.90 #1965, 0.89 #3915), 01d_h8 (0.39 #1356, 0.39 #306, 0.38 #156), 03gjzk (0.35 #4066, 0.29 #1366, 0.27 #1966), 0dxtg (0.32 #4064, 0.30 #7814, 0.29 #3614), 02jknp (0.23 #7208, 0.21 #308, 0.21 #158), 09jwl (0.21 #4520, 0.19 #1670, 0.19 #2120), 0np9r (0.20 #5422, 0.19 #4822, 0.14 #11872), 0d1pc (0.17 #1852, 0.17 #1702, 0.15 #652), 02krf9 (0.15 #4078, 0.10 #778, 0.10 #7828), 0cbd2 (0.14 #12607, 0.13 #8557, 0.13 #9007) >> Best rule #2565 for best value: >> intensional similarity = 3 >> extensional distance = 423 >> proper extension: 0pyg6; 08swgx; 018fmr; 06fc0b; 04bdqk; >> query: (?x5662, 02hrh1q) <- nominated_for(?x5662, ?x6967), film(?x5662, ?x2331), participant(?x722, ?x5662) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02nwxc profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.918 http://example.org/people/person/profession #10778-054ky1 PRED entity: 054ky1 PRED relation: ceremony PRED expected values: 0hndn2q 026kq4q => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 129): 0418154 (0.67 #608, 0.60 #480, 0.12 #6791), 0hndn2q (0.60 #420, 0.50 #548, 0.38 #3075), 026kq4q (0.50 #554, 0.40 #426, 0.38 #3075), 0gpjbt (0.39 #2972, 0.38 #3102, 0.35 #3615), 09n4nb (0.38 #3075, 0.37 #2990, 0.36 #3718), 02cg41 (0.38 #3075, 0.37 #3059, 0.36 #3718), 019bk0 (0.38 #3075, 0.36 #3718, 0.34 #2960), 01mhwk (0.38 #3075, 0.36 #3718, 0.34 #2983), 0bzm__ (0.38 #3075, 0.36 #3718, 0.33 #80), 0dth6b (0.38 #3075, 0.36 #3718, 0.33 #21) >> Best rule #608 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 04dn09n; 054krc; 054ks3; 027s4dn; >> query: (?x2060, 0418154) <- award_winner(?x2060, ?x2524), ceremony(?x2060, ?x5392), ?x5392 = 09p3h7, spouse(?x2524, ?x12584) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #420 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 054knh; *> query: (?x2060, 0hndn2q) <- disciplines_or_subjects(?x2060, ?x373), ceremony(?x2060, ?x2220), ceremony(?x2060, ?x747), ?x2220 = 05zksls, ?x747 = 09q_6t *> conf = 0.60 ranks of expected_values: 2, 3 EVAL 054ky1 ceremony 026kq4q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 60.000 60.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 054ky1 ceremony 0hndn2q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 60.000 60.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony #10777-0170yd PRED entity: 0170yd PRED relation: film! PRED expected values: 01f7dd => 73 concepts (19 used for prediction) PRED predicted values (max 10 best out of 760): 01wbg84 (0.29 #2130, 0.29 #47, 0.03 #4213), 037d35 (0.27 #29155, 0.19 #27072, 0.18 #18742), 04sry (0.14 #3361, 0.14 #1278, 0.05 #5444), 023mdt (0.14 #3662, 0.14 #1579, 0.03 #5745), 01f7dd (0.14 #3293, 0.14 #1210, 0.03 #5376), 015vq_ (0.14 #2798, 0.14 #715, 0.03 #4881), 01gw4f (0.14 #2947, 0.14 #864, 0.03 #5030), 0525b (0.14 #3997, 0.14 #1914, 0.03 #6080), 0428bc (0.14 #3785, 0.14 #1702, 0.03 #5868), 01wk51 (0.14 #3412, 0.14 #1329, 0.03 #5495) >> Best rule #2130 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 08sk8l; >> query: (?x8410, 01wbg84) <- language(?x8410, ?x254), film(?x4992, ?x8410), ?x4992 = 0lkr7, written_by(?x8410, ?x6041) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3293 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 08sk8l; *> query: (?x8410, 01f7dd) <- language(?x8410, ?x254), film(?x4992, ?x8410), ?x4992 = 0lkr7, written_by(?x8410, ?x6041) *> conf = 0.14 ranks of expected_values: 5 EVAL 0170yd film! 01f7dd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 19.000 0.286 http://example.org/film/actor/film./film/performance/film #10776-04pmnt PRED entity: 04pmnt PRED relation: category PRED expected values: 08mbj5d => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.37 #24, 0.32 #34, 0.31 #22) >> Best rule #24 for best value: >> intensional similarity = 7 >> extensional distance = 183 >> proper extension: 0522wp; >> query: (?x6148, 08mbj5d) <- film(?x2549, ?x6148), film(?x2549, ?x10806), film(?x2549, ?x8474), film(?x2549, ?x1202), film(?x815, ?x1202), nominated_for(?x384, ?x10806), ?x8474 = 01dc0c >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04pmnt category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.368 http://example.org/common/topic/webpage./common/webpage/category #10775-0ffjqy PRED entity: 0ffjqy PRED relation: people PRED expected values: 05d1y => 31 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1981): 0g824 (0.50 #900, 0.23 #9537, 0.23 #7809), 0807ml (0.50 #897, 0.17 #6079, 0.15 #9534), 025n3p (0.50 #388, 0.17 #5570, 0.15 #9025), 0c3dzk (0.50 #3419), 01twdk (0.33 #5856, 0.29 #11038, 0.25 #2400), 01vttb9 (0.30 #5182, 0.22 #13819, 0.21 #15547), 01l3mk3 (0.30 #5182, 0.22 #13819, 0.21 #15547), 015wc0 (0.30 #5182, 0.22 #13819, 0.21 #15547), 01_k71 (0.30 #5182, 0.22 #13819, 0.21 #15547), 01jllg1 (0.30 #5182, 0.22 #13819, 0.21 #15547) >> Best rule #900 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0x67; 0xnvg; >> query: (?x12502, 0g824) <- people(?x12502, ?x10412), geographic_distribution(?x12502, ?x94), award_winner(?x1079, ?x10412), origin(?x10412, ?x682), organizations_founded(?x10412, ?x3240), award_winner(?x1821, ?x10412), ceremony(?x1079, ?x78) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ffjqy people 05d1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 18.000 0.500 http://example.org/people/ethnicity/people #10774-09d5h PRED entity: 09d5h PRED relation: program PRED expected values: 01bv8b 0sw0q 03czz87 => 180 concepts (166 used for prediction) PRED predicted values (max 10 best out of 223): 0q9jk (0.33 #119, 0.25 #981, 0.20 #1196), 01fs__ (0.33 #103, 0.25 #965, 0.20 #1180), 016zfm (0.33 #83, 0.25 #945, 0.20 #1160), 017dcd (0.33 #1, 0.25 #863, 0.20 #1078), 017dbx (0.33 #199, 0.25 #1061, 0.20 #1276), 06w7mlh (0.33 #140, 0.25 #1002, 0.20 #1217), 05nlzq (0.33 #145, 0.25 #1007, 0.20 #1222), 03ln8b (0.33 #26, 0.25 #888, 0.20 #1103), 02_1ky (0.33 #175, 0.25 #1037, 0.20 #1252), 05631 (0.33 #206, 0.25 #1068, 0.20 #1283) >> Best rule #119 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0gsg7; >> query: (?x2062, 0q9jk) <- program(?x2062, ?x5236), list(?x2062, ?x7472), actor(?x5236, ?x2588) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09d5h program 03czz87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 166.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program EVAL 09d5h program 0sw0q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 166.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program EVAL 09d5h program 01bv8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 166.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #10773-01_0f7 PRED entity: 01_0f7 PRED relation: film_crew_role PRED expected values: 0dxtw => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 22): 01vx2h (0.60 #10, 0.39 #341, 0.38 #541), 0dxtw (0.52 #9, 0.40 #109, 0.37 #673), 02rh1dz (0.34 #8, 0.16 #339, 0.14 #505), 0d2b38 (0.16 #23, 0.12 #354, 0.11 #554), 0215hd (0.14 #16, 0.14 #547, 0.12 #813), 089g0h (0.13 #17, 0.11 #548, 0.10 #117), 01xy5l_ (0.13 #12, 0.10 #79, 0.10 #145), 015h31 (0.13 #7, 0.10 #338, 0.09 #538), 02_n3z (0.10 #68, 0.10 #134, 0.09 #532), 033smt (0.09 #25, 0.06 #92, 0.06 #158) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 01q2nx; >> query: (?x6531, 01vx2h) <- genre(?x6531, ?x225), ?x225 = 02kdv5l, crewmember(?x6531, ?x1585) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 01q2nx; *> query: (?x6531, 0dxtw) <- genre(?x6531, ?x225), ?x225 = 02kdv5l, crewmember(?x6531, ?x1585) *> conf = 0.52 ranks of expected_values: 2 EVAL 01_0f7 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 65.000 0.596 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10772-0f8l9c PRED entity: 0f8l9c PRED relation: country! PRED expected values: 03hr1p 0194d 01yfj => 303 concepts (303 used for prediction) PRED predicted values (max 10 best out of 12): 0194d (0.87 #488, 0.82 #272, 0.79 #572), 03hr1p (0.85 #782, 0.83 #482, 0.82 #266), 01gqfm (0.73 #285, 0.73 #273, 0.70 #489), 07_53 (0.57 #353, 0.56 #221, 0.48 #485), 02y74 (0.57 #355, 0.55 #283, 0.50 #679), 01yfj (0.42 #299, 0.36 #287, 0.35 #491), 0crlz (0.36 #280, 0.36 #268, 0.33 #304), 03tmr (0.33 #169, 0.30 #481, 0.27 #277), 018jz (0.22 #219, 0.22 #207, 0.20 #135), 06br8 (0.20 #138, 0.14 #198, 0.11 #258) >> Best rule #488 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 05v8c; >> query: (?x789, 0194d) <- film_release_region(?x66, ?x789), service_location(?x555, ?x789), combatants(?x94, ?x789) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6 EVAL 0f8l9c country! 01yfj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 303.000 303.000 0.870 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0f8l9c country! 0194d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 303.000 303.000 0.870 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0f8l9c country! 03hr1p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 303.000 303.000 0.870 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #10771-01f6zc PRED entity: 01f6zc PRED relation: award PRED expected values: 02x8n1n 04kxsb => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 253): 0gr51 (0.80 #493, 0.06 #20696, 0.06 #9602), 027b9j5 (0.70 #21393, 0.70 #17032, 0.70 #19410), 03hl6lc (0.66 #568, 0.04 #10073, 0.04 #12846), 04dn09n (0.49 #437, 0.06 #9546, 0.06 #20640), 02x1dht (0.46 #448, 0.03 #9953, 0.03 #12726), 09sb52 (0.40 #2018, 0.36 #13900, 0.34 #15485), 040njc (0.37 #403, 0.13 #24166, 0.12 #34866), 0gr4k (0.37 #427, 0.13 #24166, 0.12 #34866), 02n9nmz (0.34 #463, 0.13 #24166, 0.12 #34866), 03hkv_r (0.34 #411, 0.05 #12689, 0.05 #14273) >> Best rule #493 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0l6qt; 0h5f5n; 02kxbwx; 0136g9; 05183k; 07s93v; 01gzm2; 0c3ns; 01f7j9; 02fcs2; ... >> query: (?x5316, 0gr51) <- award_nominee(?x2763, ?x5316), award(?x5316, ?x68), ?x68 = 02qyp19 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #121 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 01xsbh; *> query: (?x5316, 04kxsb) <- film(?x5316, ?x10902), type_of_union(?x5316, ?x566), ?x10902 = 02qlp4 *> conf = 0.20 ranks of expected_values: 19, 45 EVAL 01f6zc award 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 106.000 106.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01f6zc award 02x8n1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 106.000 106.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10770-01520h PRED entity: 01520h PRED relation: people! PRED expected values: 041rx => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 34): 041rx (0.16 #158, 0.13 #2083, 0.13 #2391), 0x67 (0.14 #10, 0.12 #1088, 0.10 #1396), 033tf_ (0.14 #7, 0.10 #469, 0.09 #1085), 02ctzb (0.10 #15, 0.04 #323, 0.03 #1324), 0xnvg (0.08 #475, 0.07 #706, 0.06 #244), 02w7gg (0.07 #310, 0.07 #1234, 0.06 #2928), 07hwkr (0.06 #320, 0.04 #2091, 0.04 #1629), 01qhm_ (0.06 #6, 0.03 #83, 0.03 #1084), 07bch9 (0.04 #485, 0.04 #23, 0.04 #1101), 03bkbh (0.04 #32, 0.03 #956, 0.03 #1033) >> Best rule #158 for best value: >> intensional similarity = 4 >> extensional distance = 267 >> proper extension: 0k57l; 01k9lpl; 03chx58; >> query: (?x6755, 041rx) <- profession(?x6755, ?x1146), profession(?x6755, ?x1032), ?x1032 = 02hrh1q, ?x1146 = 018gz8 >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01520h people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.160 http://example.org/people/ethnicity/people #10769-057176 PRED entity: 057176 PRED relation: award PRED expected values: 09qv_s => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 236): 02w9sd7 (0.71 #17212, 0.71 #9203, 0.71 #9604), 027c95y (0.71 #17212, 0.71 #9203, 0.71 #9604), 0gqy2 (0.39 #561, 0.20 #161, 0.10 #961), 09qv_s (0.36 #548, 0.15 #148, 0.12 #27619), 05pcn59 (0.31 #480, 0.16 #11608, 0.15 #1280), 027dtxw (0.29 #404, 0.13 #4, 0.12 #27619), 09sdmz (0.27 #603, 0.24 #203, 0.08 #1403), 099jhq (0.26 #19, 0.19 #419, 0.12 #27619), 057xs89 (0.25 #557, 0.07 #957, 0.07 #1357), 02x4w6g (0.24 #112, 0.16 #11608, 0.15 #512) >> Best rule #17212 for best value: >> intensional similarity = 3 >> extensional distance = 1531 >> proper extension: 02_340; 08cn_n; 0bq4j6; >> query: (?x6979, ?x2915) <- award_nominee(?x6979, ?x843), award_winner(?x2915, ?x6979), award(?x6979, ?x591) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #548 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 040z9; *> query: (?x6979, 09qv_s) <- award_winner(?x6979, ?x843), award(?x6979, ?x2375), ?x2375 = 04kxsb *> conf = 0.36 ranks of expected_values: 4 EVAL 057176 award 09qv_s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 87.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10768-0495ys PRED entity: 0495ys PRED relation: legislative_sessions! PRED expected values: 07t58 => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 6): 07t58 (0.91 #193, 0.90 #194, 0.90 #200), 0x2sv (0.08 #223, 0.05 #216), 0h6dy (0.07 #224, 0.04 #217), 0l_j_ (0.05 #225, 0.04 #218), 030p4s (0.02 #220, 0.02 #227), 0162kb (0.02 #226) >> Best rule #193 for best value: >> intensional similarity = 49 >> extensional distance = 37 >> proper extension: 03rl1g; 043djx; 01gtbb; 01gst_; 01gtcc; 01gtc0; 01gtcq; 01gsvp; 01h7xx; 01gsvb; ... >> query: (?x355, ?x4665) <- legislative_sessions(?x355, ?x3766), legislative_sessions(?x355, ?x3463), legislative_sessions(?x355, ?x952), legislative_sessions(?x355, ?x356), district_represented(?x3766, ?x7405), district_represented(?x3766, ?x6895), district_represented(?x3766, ?x4061), district_represented(?x3766, ?x2977), district_represented(?x3766, ?x2020), district_represented(?x3766, ?x938), district_represented(?x3766, ?x448), legislative_sessions(?x6742, ?x3766), ?x4061 = 0498y, legislative_sessions(?x356, ?x1027), legislative_sessions(?x3766, ?x1137), district_represented(?x3463, ?x4105), district_represented(?x3463, ?x1767), district_represented(?x3463, ?x728), ?x728 = 059f4, gender(?x6742, ?x231), student(?x3821, ?x6742), ?x7405 = 07_f2, legislative_sessions(?x4665, ?x356), legislative_sessions(?x2860, ?x356), district_represented(?x952, ?x13269), district_represented(?x952, ?x3778), district_represented(?x952, ?x1426), district_represented(?x952, ?x760), district_represented(?x952, ?x177), ?x1426 = 07z1m, jurisdiction_of_office(?x900, ?x938), type_of_union(?x6742, ?x566), religion(?x938, ?x9091), ?x9091 = 02t7t, ?x2020 = 05k7sb, contains(?x94, ?x2977), location(?x1285, ?x938), ?x760 = 05fkf, ?x2860 = 0b3wk, contains(?x938, ?x3983), legislative_sessions(?x7961, ?x3463), contains(?x13269, ?x7600), ?x6895 = 05fjf, ?x3778 = 07h34, ?x448 = 03v1s, ?x177 = 05kkh, ?x4105 = 0824r, contains(?x2977, ?x3097), ?x1767 = 04rrd >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0495ys legislative_sessions! 07t58 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.908 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #10767-026390q PRED entity: 026390q PRED relation: genre PRED expected values: 0556j8 04228s => 90 concepts (76 used for prediction) PRED predicted values (max 10 best out of 90): 02l7c8 (0.43 #15, 0.34 #1802, 0.33 #2993), 0219x_ (0.38 #26, 0.26 #264, 0.24 #383), 02kdv5l (0.33 #835, 0.30 #1432, 0.29 #597), 01jfsb (0.31 #844, 0.31 #606, 0.30 #6324), 03k9fj (0.30 #605, 0.25 #843, 0.24 #1440), 01hmnh (0.25 #612, 0.15 #4782, 0.15 #6330), 04xvlr (0.23 #1311, 0.23 #1191, 0.20 #2383), 06cvj (0.21 #2981, 0.21 #3220, 0.20 #241), 0hn10 (0.19 #8, 0.15 #246, 0.09 #365), 082gq (0.19 #3127, 0.19 #1340, 0.19 #1220) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 01gc7; 02rv_dz; 0661ql3; 02mt51; 04smdd; 0sxns; 011yhm; 02_06s; 0llcx; 011ywj; ... >> query: (?x1230, 02l7c8) <- award(?x1230, ?x3435), titles(?x53, ?x1230), award_winner(?x1230, ?x2715), ?x3435 = 03hl6lc >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #280 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 0h3xztt; 0fvr1; 0bpx1k; 01bb9r; 043tz0c; 05r3qc; 0dpl44; 0djkrp; 06t2t2; *> query: (?x1230, 0556j8) <- genre(?x1230, ?x6674), nominated_for(?x68, ?x1230), production_companies(?x1230, ?x788), ?x6674 = 01t_vv *> conf = 0.09 ranks of expected_values: 25, 36 EVAL 026390q genre 04228s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 90.000 76.000 0.429 http://example.org/film/film/genre EVAL 026390q genre 0556j8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 90.000 76.000 0.429 http://example.org/film/film/genre #10766-02_fm2 PRED entity: 02_fm2 PRED relation: film! PRED expected values: 01795t => 80 concepts (62 used for prediction) PRED predicted values (max 10 best out of 62): 04rcl7 (0.51 #1267, 0.46 #2457, 0.46 #2382), 01795t (0.48 #166, 0.48 #92, 0.47 #393), 020h2v (0.21 #569, 0.10 #224, 0.05 #2276), 086k8 (0.19 #1194, 0.19 #823, 0.18 #1120), 03xq0f (0.17 #79, 0.17 #1123, 0.15 #826), 05qd_ (0.17 #83, 0.16 #1127, 0.15 #1425), 01gb54 (0.17 #103, 0.08 #1296, 0.07 #1147), 016tw3 (0.16 #610, 0.16 #2243, 0.15 #1203), 016tt2 (0.15 #1122, 0.15 #1420, 0.14 #751), 0jz9f (0.13 #600, 0.10 #224, 0.10 #526) >> Best rule #1267 for best value: >> intensional similarity = 4 >> extensional distance = 257 >> proper extension: 04dsnp; 091z_p; 05dy7p; 02h22; 064lsn; >> query: (?x218, ?x10685) <- currency(?x218, ?x170), films(?x10489, ?x218), ?x170 = 09nqf, production_companies(?x218, ?x10685) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 0124k9; 0464pz; 0kfv9; 02pqs8l; 0l76z; 03nt59; 08bytj; 01g03q; 01ft14; 06k176; ... *> query: (?x218, 01795t) <- titles(?x3920, ?x218), nominated_for(?x1053, ?x218), child(?x3920, ?x166), state_province_region(?x3920, ?x1227) *> conf = 0.48 ranks of expected_values: 2 EVAL 02_fm2 film! 01795t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 62.000 0.509 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #10765-01pllx PRED entity: 01pllx PRED relation: participant PRED expected values: 01vvb4m => 142 concepts (113 used for prediction) PRED predicted values (max 10 best out of 577): 01vvb4m (0.82 #20456, 0.81 #35171, 0.81 #28128), 08yx9q (0.81 #35171, 0.81 #28128, 0.81 #49257), 03j24kf (0.25 #1603, 0.04 #4161, 0.03 #7359), 01vs_v8 (0.25 #1421, 0.03 #12929, 0.02 #3338), 0h1mt (0.25 #1349, 0.02 #3266, 0.02 #44140), 023v4_ (0.25 #1621, 0.02 #3538, 0.02 #20159), 0mdyn (0.25 #1776, 0.02 #3693, 0.02 #4334), 01wd9lv (0.25 #1705, 0.02 #3622, 0.02 #4263), 0bmh4 (0.25 #1443, 0.02 #3360, 0.02 #4001), 01l1b90 (0.25 #654) >> Best rule #20456 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 05cljf; 02v3yy; >> query: (?x8927, ?x1208) <- participant(?x1208, ?x8927), award_nominee(?x91, ?x8927), produced_by(?x1866, ?x1208) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pllx participant 01vvb4m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 113.000 0.818 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #10764-05wkw PRED entity: 05wkw PRED relation: major_field_of_study PRED expected values: 05qfh => 66 concepts (36 used for prediction) PRED predicted values (max 10 best out of 103): 05qfh (0.81 #3011, 0.81 #3010, 0.79 #1239), 01zc2w (0.81 #3011, 0.81 #3010, 0.79 #1239), 02h40lc (0.44 #531, 0.17 #1064, 0.16 #1508), 03qsdpk (0.40 #392, 0.33 #1101, 0.30 #747), 02822 (0.40 #387, 0.33 #563, 0.25 #1096), 037mh8 (0.36 #1739, 0.23 #1205, 0.21 #2623), 0fdys (0.33 #561, 0.23 #1182, 0.20 #297), 0557q (0.33 #65, 0.17 #1127, 0.11 #1571), 02j62 (0.32 #1709, 0.26 #2593, 0.23 #2858), 06ms6 (0.31 #1163, 0.28 #1697, 0.22 #542) >> Best rule #3011 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 0j0k; >> query: (?x11691, ?x3490) <- taxonomy(?x11691, ?x939), major_field_of_study(?x3490, ?x11691), major_field_of_study(?x3490, ?x4268), major_field_of_study(?x4268, ?x254) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 05wkw major_field_of_study 05qfh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 36.000 0.813 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #10763-06br6t PRED entity: 06br6t PRED relation: origin PRED expected values: 02_286 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 109): 0d6lp (0.33 #1717, 0.29 #1953, 0.25 #2426), 02m77 (0.33 #355, 0.25 #827, 0.02 #6023), 04jpl (0.33 #6, 0.14 #5202, 0.13 #5910), 02ly_ (0.33 #100, 0.07 #3406, 0.03 #5532), 030qb3t (0.25 #978, 0.20 #4994, 0.20 #1214), 0rh7t (0.20 #1287, 0.17 #1759, 0.14 #1995), 02_286 (0.17 #1668, 0.14 #1904, 0.12 #2377), 0r3tb (0.17 #1555, 0.08 #4626, 0.05 #5807), 09c7w0 (0.17 #1417, 0.05 #5197, 0.04 #5905), 01ly5m (0.17 #1474, 0.04 #4545, 0.03 #5490) >> Best rule #1717 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 05crg7; >> query: (?x9757, 0d6lp) <- group(?x227, ?x9757), artists(?x5379, ?x9757), role(?x9757, ?x212), artists(?x5379, ?x9463), artists(?x5379, ?x2784), parent_genre(?x8747, ?x5379), ?x9463 = 01shhf, instrumentalists(?x212, ?x226), instrumentalists(?x716, ?x2784), family(?x1663, ?x212) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1668 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 05crg7; *> query: (?x9757, 02_286) <- group(?x227, ?x9757), artists(?x5379, ?x9757), role(?x9757, ?x212), artists(?x5379, ?x9463), artists(?x5379, ?x2784), parent_genre(?x8747, ?x5379), ?x9463 = 01shhf, instrumentalists(?x212, ?x226), instrumentalists(?x716, ?x2784), family(?x1663, ?x212) *> conf = 0.17 ranks of expected_values: 7 EVAL 06br6t origin 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 79.000 79.000 0.333 http://example.org/music/artist/origin #10762-029jt9 PRED entity: 029jt9 PRED relation: genre PRED expected values: 03g3w => 92 concepts (91 used for prediction) PRED predicted values (max 10 best out of 96): 01jfsb (0.52 #1558, 0.32 #3109, 0.32 #3228), 05p553 (0.45 #2506, 0.42 #3578, 0.40 #9890), 01g6gs (0.41 #495, 0.40 #138, 0.25 #19), 01hmnh (0.31 #849, 0.17 #3114, 0.17 #3233), 06cvj (0.28 #2505, 0.25 #240, 0.25 #2), 04xvlr (0.27 #1310, 0.25 #1786, 0.25 #1072), 082gq (0.25 #1576, 0.20 #624, 0.20 #148), 01t_vv (0.25 #53, 0.17 #1905, 0.12 #291), 060__y (0.22 #372, 0.22 #1205, 0.22 #1086), 0lsxr (0.22 #365, 0.22 #1555, 0.20 #2152) >> Best rule #1558 for best value: >> intensional similarity = 5 >> extensional distance = 90 >> proper extension: 05pdd86; 059lwy; >> query: (?x8941, 01jfsb) <- produced_by(?x8941, ?x8942), genre(?x8941, ?x225), genre(?x8941, ?x53), ?x53 = 07s9rl0, ?x225 = 02kdv5l >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #856 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 0ckr7s; 02w86hz; *> query: (?x8941, 03g3w) <- genre(?x8941, ?x1403), genre(?x8941, ?x811), ?x1403 = 02l7c8, ?x811 = 03k9fj *> conf = 0.13 ranks of expected_values: 35 EVAL 029jt9 genre 03g3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 92.000 91.000 0.522 http://example.org/film/film/genre #10761-04d2yp PRED entity: 04d2yp PRED relation: type_of_union PRED expected values: 04ztj => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #33, 0.87 #29, 0.86 #61), 01g63y (0.25 #2, 0.16 #154, 0.13 #194), 01bl8s (0.02 #11) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 140 >> proper extension: 07zhd7; >> query: (?x11861, 04ztj) <- gender(?x11861, ?x231), people(?x268, ?x11861), award_winner(?x2719, ?x11861) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04d2yp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.873 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10760-0cmt6q PRED entity: 0cmt6q PRED relation: student! PRED expected values: 017zq0 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 52): 03ksy (0.12 #1687, 0.11 #106, 0.09 #2214), 01bm_ (0.11 #246, 0.04 #1827, 0.03 #2354), 02fgdx (0.11 #102, 0.04 #1683, 0.03 #2210), 02kbtf (0.11 #344, 0.03 #2452), 02zcnq (0.11 #146, 0.03 #2254), 065y4w7 (0.08 #1595, 0.06 #2122, 0.03 #8447), 0bwfn (0.07 #802, 0.05 #5018, 0.05 #8708), 017z88 (0.07 #609, 0.04 #1663, 0.03 #2717), 0lfgr (0.07 #570, 0.04 #1624, 0.03 #2151), 07tgn (0.07 #544, 0.03 #2125, 0.01 #27949) >> Best rule #1687 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 027cxsm; 0bczgm; 0bt7ws; 06jnvs; 0cj36c; 0b7gxq; >> query: (?x6532, 03ksy) <- award_nominee(?x6532, ?x237), ?x237 = 04t2l2, gender(?x6532, ?x231) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #1087 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0152cw; 04s430; 01pjr7; 01wqflx; *> query: (?x6532, 017zq0) <- film(?x6532, ?x6480), profession(?x6532, ?x1032), ?x6480 = 02825cv *> conf = 0.04 ranks of expected_values: 28 EVAL 0cmt6q student! 017zq0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 62.000 62.000 0.115 http://example.org/education/educational_institution/students_graduates./education/education/student #10759-0b_71r PRED entity: 0b_71r PRED relation: team PRED expected values: 02pqcfz 026w398 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 227): 02py8_w (0.75 #136, 0.75 #19, 0.74 #155), 026dqjm (0.75 #136, 0.75 #19, 0.74 #155), 0263cyj (0.75 #136, 0.75 #19, 0.74 #155), 03d5m8w (0.75 #136, 0.75 #19, 0.74 #155), 02pyyld (0.75 #136, 0.75 #19, 0.74 #155), 026w398 (0.74 #155, 0.71 #229, 0.67 #30), 02pqcfz (0.74 #155, 0.67 #30, 0.67 #203), 02ptzz0 (0.74 #155, 0.67 #30, 0.67 #203), 03d555l (0.67 #30, 0.67 #203, 0.67 #117), 0jm4b (0.20 #254, 0.20 #232, 0.19 #156) >> Best rule #136 for best value: >> intensional similarity = 30 >> extensional distance = 6 >> proper extension: 0b_756; >> query: (?x10441, ?x6003) <- team(?x10441, ?x9983), team(?x10441, ?x9833), team(?x10441, ?x9576), team(?x10441, ?x8728), team(?x10441, ?x6847), team(?x10441, ?x4938), team(?x10441, ?x2303), ?x9983 = 02q4ntp, ?x4938 = 027yf83, ?x2303 = 02plv57, ?x8728 = 026xxv_, team(?x6848, ?x6847), team(?x1579, ?x6847), team(?x9146, ?x6847), team(?x7378, ?x6847), ?x1579 = 0ctt4z, ?x7378 = 0bzrxn, locations(?x9146, ?x5771), locations(?x9146, ?x5267), locations(?x9146, ?x3983), ?x5267 = 0d9jr, ?x5771 = 0fpzwf, team(?x9146, ?x6003), ?x9576 = 02qk2d5, ?x6848 = 02_ssl, ?x3983 = 0fr0t, team(?x9974, ?x9833), colors(?x6847, ?x332), position(?x6847, ?x13002), ?x9974 = 0b_6pv >> conf = 0.75 => this is the best rule for 5 predicted values *> Best rule #155 for first EXPECTED value: *> intensional similarity = 31 *> extensional distance = 7 *> proper extension: 0b_72t; *> query: (?x10441, ?x6003) <- team(?x10441, ?x9983), team(?x10441, ?x9576), team(?x10441, ?x8728), team(?x10441, ?x6847), team(?x10441, ?x4938), team(?x10441, ?x2303), ?x9983 = 02q4ntp, ?x4938 = 027yf83, ?x2303 = 02plv57, ?x8728 = 026xxv_, team(?x6848, ?x6847), team(?x1579, ?x6847), team(?x9146, ?x6847), team(?x7378, ?x6847), team(?x5897, ?x6847), ?x1579 = 0ctt4z, ?x7378 = 0bzrxn, locations(?x9146, ?x5771), locations(?x9146, ?x5267), ?x5267 = 0d9jr, ?x5771 = 0fpzwf, team(?x9146, ?x6003), ?x9576 = 02qk2d5, team(?x6848, ?x12124), team(?x6848, ?x660), ?x12124 = 0jmgb, locations(?x5897, ?x2879), instance_of_recurring_event(?x9146, ?x10863), ?x2879 = 0ftxw, ?x660 = 0jmdb, ?x10863 = 02jp2w *> conf = 0.74 ranks of expected_values: 6, 7 EVAL 0b_71r team 026w398 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 33.000 33.000 0.753 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_71r team 02pqcfz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 33.000 33.000 0.753 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #10758-0k4bc PRED entity: 0k4bc PRED relation: nominated_for! PRED expected values: 0gs9p => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 157): 0gq_v (0.53 #730, 0.49 #968, 0.49 #493), 0gs9p (0.49 #1012, 0.43 #774, 0.42 #537), 019f4v (0.46 #1002, 0.38 #1950, 0.37 #2424), 0k611 (0.39 #1021, 0.38 #783, 0.37 #546), 0f4x7 (0.35 #974, 0.30 #736, 0.29 #1211), 0gqyl (0.34 #1028, 0.25 #1976, 0.23 #790), 027dtxw (0.33 #4, 0.20 #241, 0.17 #2375), 02r22gf (0.33 #27, 0.20 #264, 0.16 #2398), 02r0csl (0.33 #5, 0.20 #242, 0.14 #2376), 02hsq3m (0.33 #28, 0.20 #265, 0.12 #2873) >> Best rule #730 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 04vq33; >> query: (?x7231, 0gq_v) <- film(?x11277, ?x7231), genre(?x7231, ?x53), film_art_direction_by(?x7231, ?x4896), award_winner(?x3066, ?x11277) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1012 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 152 *> proper extension: 0cwrr; *> query: (?x7231, 0gs9p) <- nominated_for(?x11277, ?x7231), honored_for(?x4445, ?x7231), profession(?x11277, ?x1032), people(?x4322, ?x11277) *> conf = 0.49 ranks of expected_values: 2 EVAL 0k4bc nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.532 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10757-01g42 PRED entity: 01g42 PRED relation: place_of_burial PRED expected values: 018mm4 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 13): 018mm4 (0.10 #71, 0.09 #102, 0.08 #133), 018mmj (0.06 #228, 0.06 #384, 0.06 #166), 0lbp_ (0.06 #109, 0.04 #140, 0.02 #358), 01n7q (0.04 #906, 0.03 #221, 0.02 #159), 0nb1s (0.02 #216, 0.02 #92, 0.02 #372), 018mmw (0.02 #234, 0.02 #390, 0.02 #327), 018mrd (0.02 #240, 0.01 #333), 018mlg (0.02 #148, 0.02 #366, 0.02 #210), 0r04p (0.02 #75, 0.01 #137), 01f38z (0.02 #215, 0.01 #277) >> Best rule #71 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 03h_yfh; >> query: (?x8634, 018mm4) <- people(?x4322, ?x8634), award(?x8634, ?x112), celebrities_impersonated(?x3649, ?x8634) >> conf = 0.10 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01g42 place_of_burial 018mm4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.096 http://example.org/people/deceased_person/place_of_burial #10756-02w9sd7 PRED entity: 02w9sd7 PRED relation: award_winner PRED expected values: 01cj6y => 51 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1578): 02kxbx3 (0.40 #3205, 0.29 #12987, 0.20 #10542), 0170pk (0.33 #347, 0.32 #36693, 0.29 #44032), 07r1h (0.33 #24459, 0.32 #36693, 0.29 #44032), 0bxtg (0.33 #24459, 0.32 #36693, 0.29 #44032), 0dzf_ (0.33 #24459, 0.32 #36693, 0.29 #44032), 0237fw (0.32 #36693, 0.29 #44032, 0.29 #48927), 0f502 (0.32 #36693, 0.29 #44032, 0.29 #48927), 015gjr (0.32 #36693, 0.29 #44032, 0.29 #48927), 0byfz (0.32 #36693, 0.29 #44032, 0.29 #48927), 016zp5 (0.32 #36693, 0.29 #44032, 0.29 #48927) >> Best rule #3205 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 02x1dht; 0gr51; 02x4w6g; 02x8n1n; 02x4x18; 02x4wr9; 02x4sn8; 03hl6lc; >> query: (?x3209, 02kxbx3) <- nominated_for(?x3209, ?x5157), award_winner(?x3209, ?x157), ?x5157 = 05q_dw, award(?x262, ?x3209) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #36693 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 181 *> proper extension: 06196; *> query: (?x3209, ?x2499) <- award(?x407, ?x3209), award(?x5462, ?x3209), award(?x2499, ?x3209), award_winner(?x349, ?x2499), film(?x5462, ?x626) *> conf = 0.32 ranks of expected_values: 28 EVAL 02w9sd7 award_winner 01cj6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 51.000 20.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #10755-0q8p8 PRED entity: 0q8p8 PRED relation: place PRED expected values: 0q8p8 => 103 concepts (42 used for prediction) PRED predicted values (max 10 best out of 77): 0q8p8 (0.11 #15475, 0.08 #18578, 0.05 #21683), 0gyh (0.11 #15475, 0.08 #18578, 0.05 #21683), 0qc7l (0.08 #994, 0.08 #478, 0.03 #2024), 0q6lr (0.08 #877, 0.08 #361), 0q8jl (0.08 #808, 0.08 #292), 0q8s4 (0.08 #626, 0.08 #110), 0fttg (0.08 #894, 0.02 #3987), 0q48z (0.08 #832), 0lphb (0.08 #175), 0d9jr (0.03 #1161, 0.03 #1676, 0.03 #2191) >> Best rule #15475 for best value: >> intensional similarity = 4 >> extensional distance = 248 >> proper extension: 017wh; >> query: (?x11901, ?x2831) <- category(?x11901, ?x134), ?x134 = 08mbj5d, contains(?x11901, ?x2830), contains(?x2831, ?x2830) >> conf = 0.11 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0q8p8 place 0q8p8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 42.000 0.112 http://example.org/location/hud_county_place/place #10754-0dtw1x PRED entity: 0dtw1x PRED relation: film_release_region PRED expected values: 0k6nt 05b4w => 144 concepts (129 used for prediction) PRED predicted values (max 10 best out of 190): 0f8l9c (0.92 #12525, 0.90 #10641, 0.89 #5847), 03h64 (0.90 #8811, 0.89 #4700, 0.88 #4187), 06mkj (0.89 #5889, 0.88 #10683, 0.85 #14109), 0345h (0.89 #8259, 0.87 #10655, 0.86 #5861), 0chghy (0.88 #8744, 0.87 #8402, 0.86 #6004), 03rjj (0.87 #8394, 0.87 #10619, 0.83 #12160), 05qhw (0.87 #10632, 0.80 #12173, 0.78 #4638), 05b4w (0.86 #6068, 0.81 #8295, 0.80 #8808), 015fr (0.85 #10635, 0.85 #8752, 0.84 #8239), 03gj2 (0.85 #8763, 0.84 #10646, 0.84 #8250) >> Best rule #12525 for best value: >> intensional similarity = 8 >> extensional distance = 130 >> proper extension: 0fpkhkz; 05q4y12; 08tq4x; 01sby_; 05ft32; 0g4vmj8; 09rfpk; 09v42sf; 0b85mm; >> query: (?x424, 0f8l9c) <- genre(?x424, ?x258), film_release_region(?x424, ?x1229), film_release_region(?x424, ?x512), film_release_region(?x424, ?x94), ?x512 = 07ssc, ?x94 = 09c7w0, ?x1229 = 059j2, titles(?x13798, ?x424) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #6068 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 26 *> proper extension: 0407yfx; *> query: (?x424, 05b4w) <- film(?x1914, ?x424), crewmember(?x424, ?x425), film_release_region(?x424, ?x2267), film_release_region(?x424, ?x512), film_release_region(?x424, ?x87), film_release_distribution_medium(?x424, ?x81), ?x512 = 07ssc, ?x87 = 05r4w, ?x2267 = 03rj0 *> conf = 0.86 ranks of expected_values: 8, 11 EVAL 0dtw1x film_release_region 05b4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 144.000 129.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dtw1x film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 144.000 129.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10753-028kb PRED entity: 028kb PRED relation: seasonal_months! PRED expected values: 040fv => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 3): 04wzr (0.76 #43, 0.76 #38, 0.73 #18), 028kb (0.76 #43, 0.76 #38, 0.71 #27), 040fv (0.76 #43, 0.76 #38, 0.71 #27) >> Best rule #43 for best value: >> intensional similarity = 85 >> extensional distance = 3 >> proper extension: 0lkm; >> query: (?x9905, ?x2255) <- month(?x12674, ?x9905), month(?x8174, ?x9905), month(?x6458, ?x9905), month(?x6054, ?x9905), month(?x5036, ?x9905), month(?x3106, ?x9905), month(?x2985, ?x9905), month(?x2611, ?x9905), month(?x2316, ?x9905), month(?x2277, ?x9905), month(?x1860, ?x9905), month(?x1036, ?x9905), month(?x206, ?x9905), seasonal_months(?x3107, ?x9905), seasonal_months(?x2140, ?x9905), seasonal_months(?x1650, ?x9905), seasonal_months(?x1459, ?x9905), ?x1860 = 01_d4, ?x8174 = 01lfy, ?x2277 = 013yq, vacationer(?x1036, ?x1093), ?x1459 = 04w_7, place_of_birth(?x9084, ?x1036), ?x206 = 01914, location(?x8718, ?x1036), ?x2985 = 03hrz, featured_film_locations(?x11699, ?x1036), featured_film_locations(?x11148, ?x1036), featured_film_locations(?x7366, ?x1036), featured_film_locations(?x4500, ?x1036), citytown(?x9309, ?x1036), citytown(?x4531, ?x1036), citytown(?x4267, ?x1036), time_zones(?x1036, ?x2950), seasonal_months(?x3107, ?x7298), seasonal_months(?x3107, ?x2255), ?x6054 = 0fn2g, ?x1650 = 06vkl, adjoins(?x1036, ?x10586), teams(?x1036, ?x934), ?x4267 = 08t9df, ?x2316 = 06t2t, ?x6458 = 08966, ?x5036 = 06y57, influenced_by(?x8718, ?x476), ?x12674 = 0g6xq, genre(?x7366, ?x6452), contains(?x279, ?x10586), language(?x11699, ?x254), month(?x1646, ?x3107), award_nominee(?x5087, ?x8718), institution(?x4981, ?x4531), institution(?x1526, ?x4531), mode_of_transportation(?x1036, ?x4272), ?x3106 = 049d1, award(?x8718, ?x575), award_winner(?x1701, ?x9084), state_province_region(?x9309, ?x4600), film(?x1986, ?x11699), profession(?x8718, ?x353), ?x4981 = 03bwzr4, film_distribution_medium(?x7366, ?x81), ?x1526 = 0bkj86, genre(?x11699, ?x53), film(?x5854, ?x7366), colors(?x4531, ?x1101), currency(?x4531, ?x2244), ?x6452 = 02b5_l, award_winner(?x3337, ?x8718), ?x5854 = 04mkft, ?x2611 = 02h6_6p, ?x81 = 029j_, school_type(?x4531, ?x3092), influenced_by(?x576, ?x8718), film(?x1461, ?x7366), ?x2140 = 040fb, film_crew_role(?x11148, ?x137), gender(?x9084, ?x231), ?x231 = 05zppz, ?x4272 = 07jdr, language(?x11148, ?x732), genre(?x11148, ?x225), ?x7298 = 04wzr, country(?x4500, ?x94), nominated_for(?x1336, ?x4500) >> conf = 0.76 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 028kb seasonal_months! 040fv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 12.000 12.000 0.763 http://example.org/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months #10752-0dj0x PRED entity: 0dj0x PRED relation: contains PRED expected values: 0g9k4 => 180 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2503): 0dj0x (0.48 #82459, 0.46 #217913, 0.40 #123683), 02jx1 (0.48 #82459, 0.46 #217913, 0.40 #123683), 07ssc (0.48 #82459, 0.46 #217913, 0.40 #123683), 014jyk (0.25 #1700, 0.15 #19370, 0.11 #10536), 01g0p5 (0.22 #9635, 0.17 #73624, 0.15 #18469), 02gw_w (0.22 #11386, 0.15 #20220, 0.13 #52616), 049kw (0.22 #10560, 0.15 #19394, 0.13 #51790), 0f8j6 (0.22 #11688, 0.15 #20522, 0.11 #8741), 0f485 (0.22 #11337, 0.15 #20171, 0.11 #8390), 0n9dn (0.22 #9512, 0.15 #18346, 0.11 #6565) >> Best rule #82459 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 06c1y; 05rgl; >> query: (?x14064, ?x512) <- contains(?x14064, ?x14110), featured_film_locations(?x6900, ?x14064), location(?x2280, ?x14110), contains(?x512, ?x14110) >> conf = 0.48 => this is the best rule for 3 predicted values *> Best rule #11774 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 02jx1; *> query: (?x14064, 0g9k4) <- place_of_birth(?x5249, ?x14064), state_province_region(?x8833, ?x14064), contains(?x512, ?x14064), ?x512 = 07ssc *> conf = 0.11 ranks of expected_values: 161 EVAL 0dj0x contains 0g9k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 180.000 95.000 0.484 http://example.org/location/location/contains #10751-0286vp PRED entity: 0286vp PRED relation: film! PRED expected values: 044lyq => 56 concepts (39 used for prediction) PRED predicted values (max 10 best out of 666): 0d500h (0.46 #16643, 0.44 #41615, 0.44 #4161), 0h0wc (0.16 #14986, 0.03 #31210, 0.02 #23309), 05cj4r (0.14 #48, 0.07 #4209, 0.05 #6289), 051wwp (0.14 #877, 0.05 #9198, 0.04 #11278), 09fqtq (0.14 #70, 0.04 #4231, 0.03 #6311), 015rkw (0.14 #282, 0.03 #6523, 0.03 #8603), 017gxw (0.09 #3001, 0.07 #921, 0.04 #5082), 01_p6t (0.09 #3102, 0.03 #7263, 0.03 #9343), 03x400 (0.09 #3239, 0.02 #13640, 0.01 #28205), 02114t (0.08 #15198, 0.01 #23521, 0.01 #73469) >> Best rule #16643 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 04xbq3; >> query: (?x6967, ?x11266) <- film(?x12148, ?x6967), nominated_for(?x11266, ?x6967), award(?x12148, ?x3989), ?x3989 = 0bsjcw >> conf = 0.46 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0286vp film! 044lyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 39.000 0.458 http://example.org/film/actor/film./film/performance/film #10750-01nr36 PRED entity: 01nr36 PRED relation: film PRED expected values: 0bh8yn3 => 129 concepts (68 used for prediction) PRED predicted values (max 10 best out of 724): 0gjk1d (0.64 #30306, 0.59 #103373, 0.58 #44563), 07jnt (0.64 #30306, 0.59 #103373, 0.58 #44563), 017jd9 (0.30 #775, 0.03 #43555, 0.02 #50684), 017gl1 (0.24 #141, 0.02 #5489, 0.02 #42921), 017gm7 (0.21 #209, 0.02 #42989, 0.02 #51900), 0ndwt2w (0.09 #996, 0.02 #6344, 0.01 #52687), 03bzyn4 (0.09 #1560), 05h43ls (0.09 #411), 0g3zrd (0.09 #365), 08r4x3 (0.08 #5500, 0.03 #152, 0.03 #51843) >> Best rule #30306 for best value: >> intensional similarity = 3 >> extensional distance = 412 >> proper extension: 01vsykc; 02vtnf; >> query: (?x8491, ?x1209) <- award(?x8491, ?x102), nominated_for(?x8491, ?x1209), participant(?x8491, ?x3210) >> conf = 0.64 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01nr36 film 0bh8yn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 68.000 0.645 http://example.org/film/actor/film./film/performance/film #10749-030jj7 PRED entity: 030jj7 PRED relation: artist PRED expected values: 03xhj6 => 40 concepts (20 used for prediction) PRED predicted values (max 10 best out of 985): 01wg25j (0.50 #1454, 0.40 #3958, 0.33 #619), 0p7h7 (0.50 #1152, 0.40 #3656, 0.33 #317), 03yf3z (0.50 #1824, 0.40 #2659, 0.33 #155), 016sqs (0.50 #2132, 0.40 #2967, 0.33 #463), 09hnb (0.50 #996, 0.40 #3500, 0.31 #4334), 02vr7 (0.50 #1445, 0.40 #3949, 0.25 #2279), 01wp8w7 (0.50 #913, 0.40 #3417, 0.25 #1747), 02f1c (0.50 #1478, 0.40 #3982, 0.23 #4816), 0qf11 (0.50 #1131, 0.40 #3635, 0.23 #4469), 0167xy (0.50 #1580, 0.40 #4084, 0.23 #4918) >> Best rule #1454 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 03rhqg; 01w40h; >> query: (?x5726, 01wg25j) <- category(?x5726, ?x134), artist(?x5726, ?x7477), artist(?x5726, ?x133), ?x133 = 016qtt, participant(?x7477, ?x5246), award(?x7477, ?x4958), award(?x7477, ?x3094), artists(?x505, ?x7477), film(?x7477, ?x4009), ?x3094 = 026mff, ceremony(?x4958, ?x139) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1140 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 03rhqg; 01w40h; *> query: (?x5726, 03xhj6) <- category(?x5726, ?x134), artist(?x5726, ?x7477), artist(?x5726, ?x133), ?x133 = 016qtt, participant(?x7477, ?x5246), award(?x7477, ?x4958), award(?x7477, ?x3094), artists(?x505, ?x7477), film(?x7477, ?x4009), ?x3094 = 026mff, ceremony(?x4958, ?x139) *> conf = 0.25 ranks of expected_values: 72 EVAL 030jj7 artist 03xhj6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 40.000 20.000 0.500 http://example.org/music/record_label/artist #10748-0fngy PRED entity: 0fngy PRED relation: time_zones PRED expected values: 0gsrz4 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 12): 0gsrz4 (0.61 #1069, 0.60 #601, 0.58 #1125), 02llzg (0.43 #95, 0.42 #134, 0.38 #525), 02hcv8 (0.37 #1045, 0.36 #1100, 0.35 #551), 03plfd (0.25 #62, 0.25 #23, 0.16 #140), 02lcqs (0.20 #738, 0.16 #963, 0.16 #977), 02fqwt (0.19 #184, 0.18 #774, 0.18 #1043), 03bdv (0.17 #84, 0.13 #110, 0.12 #163), 052vwh (0.14 #51, 0.08 #77, 0.06 #129), 02hczc (0.12 #185, 0.12 #172, 0.11 #211), 042g7t (0.04 #168, 0.02 #744, 0.02 #626) >> Best rule #1069 for best value: >> intensional similarity = 7 >> extensional distance = 416 >> proper extension: 0mwh1; 027rqbx; 0mwvq; 02v3m7; 0jcky; 0mmpm; 0mw2m; 08xpv_; 041_3z; 039b7_; ... >> query: (?x13378, ?x6582) <- contains(?x13378, ?x3010), contains(?x9251, ?x3010), adjoins(?x910, ?x9251), jurisdiction_of_office(?x182, ?x9251), taxonomy(?x9251, ?x939), time_zones(?x9251, ?x6582), administrative_parent(?x9251, ?x551) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fngy time_zones 0gsrz4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.607 http://example.org/location/location/time_zones #10747-02jx1 PRED entity: 02jx1 PRED relation: nationality! PRED expected values: 05cj4r 02zq43 01j5x6 03kwtb 01p9hgt 02xb2bt 0j_c 016h9b 0b_dy 0psss 03bnv 01vv6_6 09qh1 017vkx 01ycck 03nk3t 06nz46 023l9y 02lfp4 016yvw 02508x 026y23w 04jwp 0hky 0130sy 01p0vf 01v90t 0326tc 0p50v 0kj34 03mv0b 0ct_yc 02tc5y 0133sq 0cgfb 0hcvy 0g7vxv 082xp 0g9zjp 01rnpy 0ck91 01ww_vs 03jj93 06p03s 0525b 03s2dj 01p0w_ 01wskg 016dp0 042xh => 209 concepts (150 used for prediction) PRED predicted values (max 10 best out of 3765): 0d1_f (0.72 #99182, 0.33 #878, 0.31 #348989), 02q42j_ (0.44 #77140, 0.33 #1650, 0.25 #16343), 02g40r (0.44 #77140, 0.33 #2986, 0.25 #17679), 04_1nk (0.44 #77140, 0.33 #1526, 0.25 #16219), 02l4pj (0.44 #161635, 0.42 #18367, 0.38 #253479), 03bnv (0.44 #161635, 0.42 #18367, 0.38 #253479), 081k8 (0.44 #161635, 0.42 #18367, 0.33 #1378), 02fz3w (0.44 #161635, 0.42 #18367, 0.33 #2624), 0326tc (0.44 #161635, 0.42 #18367, 0.33 #2220), 024dw0 (0.44 #161635, 0.42 #18367, 0.33 #2072) >> Best rule #99182 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0l3h; >> query: (?x1310, ?x3444) <- nationality(?x57, ?x1310), adjoins(?x1310, ?x4221), jurisdiction_of_office(?x3444, ?x1310) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #161635 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 0n5d1; *> query: (?x1310, ?x874) <- contains(?x1310, ?x9026), origin(?x1694, ?x1310), location(?x874, ?x9026) *> conf = 0.44 ranks of expected_values: 6, 9, 13, 17, 24, 32, 36, 37, 45, 53, 58, 65, 72, 74, 75, 76, 79, 80, 109, 114, 119, 129, 350, 359, 427, 441, 443, 454, 459, 484, 520, 573, 599, 600, 601, 719, 854, 912, 1258, 2088, 3305, 3638, 3756 EVAL 02jx1 nationality! 042xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 016dp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 01wskg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 01p0w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 03s2dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0525b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 06p03s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 03jj93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 01ww_vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0ck91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 01rnpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0g9zjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 082xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0g7vxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0hcvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0cgfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0133sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 02tc5y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0ct_yc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 03mv0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0kj34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0p50v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0326tc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 01v90t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 01p0vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0130sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0hky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 04jwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 026y23w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 02508x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 016yvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 02lfp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 023l9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 06nz46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 03nk3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 01ycck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 017vkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 09qh1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 01vv6_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 03bnv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0psss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0b_dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 016h9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 0j_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 02xb2bt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 01p9hgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 03kwtb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 01j5x6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 02zq43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 209.000 150.000 0.716 http://example.org/people/person/nationality EVAL 02jx1 nationality! 05cj4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 209.000 150.000 0.716 http://example.org/people/person/nationality #10746-0flpy PRED entity: 0flpy PRED relation: nationality PRED expected values: 09c7w0 => 129 concepts (124 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.88 #7221, 0.88 #8523, 0.84 #1401), 02jx1 (0.20 #1333, 0.18 #3839, 0.16 #633), 07ssc (0.11 #1315, 0.11 #615, 0.11 #4523), 03rk0 (0.09 #2449, 0.09 #1747, 0.06 #11278), 0f8l9c (0.08 #622, 0.08 #722, 0.04 #1723), 05vz3zq (0.07 #470, 0.03 #1570, 0.03 #770), 0d060g (0.07 #2209, 0.06 #1607, 0.06 #207), 0345h (0.06 #231, 0.05 #1531, 0.05 #1031), 03_r3 (0.06 #212, 0.03 #512, 0.02 #1112), 0cdbq (0.04 #463, 0.03 #763, 0.02 #863) >> Best rule #7221 for best value: >> intensional similarity = 3 >> extensional distance = 730 >> proper extension: 07m69t; >> query: (?x6290, 09c7w0) <- place_of_birth(?x6290, ?x11968), category(?x11968, ?x134), source(?x11968, ?x958) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0flpy nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 124.000 0.883 http://example.org/people/person/nationality #10745-01kmd4 PRED entity: 01kmd4 PRED relation: location PRED expected values: 030qb3t => 150 concepts (143 used for prediction) PRED predicted values (max 10 best out of 330): 02_286 (0.54 #46535, 0.37 #52145, 0.31 #58556), 030qb3t (0.40 #2487, 0.30 #24940, 0.29 #52191), 0cr3d (0.33 #945, 0.20 #2548, 0.13 #79362), 0yj9v (0.33 #1452, 0.20 #3055, 0.04 #6262), 0s9z_ (0.33 #1386, 0.20 #2989, 0.04 #6196), 01n7q (0.33 #62, 0.11 #4070, 0.10 #4872), 05tbn (0.33 #186, 0.05 #12826, 0.04 #66538), 02jx1 (0.21 #13699, 0.20 #1673, 0.03 #46569), 0k049 (0.20 #1611, 0.14 #3214, 0.11 #4016), 0nbrp (0.20 #3061, 0.03 #7070, 0.02 #8673) >> Best rule #46535 for best value: >> intensional similarity = 5 >> extensional distance = 624 >> proper extension: 0136g9; 04y9dk; 02cllz; 05y5kf; 015t7v; 018x3; 01vsksr; 01tzm9; 01cspq; 0272kv; ... >> query: (?x7555, 02_286) <- location(?x7555, ?x8317), location(?x7555, ?x205), profession(?x7555, ?x1032), contains(?x94, ?x8317), administrative_parent(?x2856, ?x205) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #2487 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0147dk; 01vsykc; *> query: (?x7555, 030qb3t) <- participant(?x7555, ?x3291), ?x3291 = 01jbx1, location(?x7555, ?x205), profession(?x7555, ?x1032) *> conf = 0.40 ranks of expected_values: 2 EVAL 01kmd4 location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 150.000 143.000 0.543 http://example.org/people/person/places_lived./people/place_lived/location #10744-02v60l PRED entity: 02v60l PRED relation: type_of_union PRED expected values: 04ztj => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.85 #130, 0.85 #150, 0.84 #102), 01g63y (0.49 #17, 0.40 #59, 0.37 #43) >> Best rule #130 for best value: >> intensional similarity = 3 >> extensional distance = 346 >> proper extension: 053yx; 01309x; 01c6l; 01lz4tf; 06c0j; >> query: (?x4611, 04ztj) <- spouse(?x4611, ?x513), gender(?x4611, ?x231), profession(?x4611, ?x319) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v60l type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.853 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10743-03nkts PRED entity: 03nkts PRED relation: student! PRED expected values: 06182p => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 64): 0bwfn (0.09 #802, 0.08 #1329, 0.05 #8708), 04b_46 (0.07 #227, 0.07 #1281, 0.07 #754), 09f2j (0.05 #1213, 0.05 #159, 0.04 #686), 065y4w7 (0.03 #3703, 0.03 #3176, 0.03 #9501), 026gvfj (0.03 #1165, 0.02 #111, 0.02 #1692), 01w3v (0.03 #1069, 0.02 #15, 0.02 #1596), 017z88 (0.03 #1136, 0.02 #1663, 0.02 #6934), 06182p (0.03 #1352, 0.02 #825, 0.01 #11893), 015nl4 (0.03 #3756, 0.03 #3229, 0.03 #11662), 03ksy (0.02 #25932, 0.02 #26459, 0.02 #106) >> Best rule #802 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0h5g_; 044rvb; 01pcq3; 0pz7h; 03d_w3h; 05fnl9; 034np8; 030hcs; 02wgln; 01fh9; ... >> query: (?x6397, 0bwfn) <- award_nominee(?x6397, ?x286), language(?x6397, ?x254), award(?x6397, ?x704), ?x254 = 02h40lc >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #1352 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 06sn8m; *> query: (?x6397, 06182p) <- profession(?x6397, ?x319), award(?x6397, ?x704), language(?x6397, ?x254) *> conf = 0.03 ranks of expected_values: 8 EVAL 03nkts student! 06182p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 70.000 70.000 0.089 http://example.org/education/educational_institution/students_graduates./education/education/student #10742-02q8ms8 PRED entity: 02q8ms8 PRED relation: genre PRED expected values: 05p553 => 66 concepts (60 used for prediction) PRED predicted values (max 10 best out of 98): 05p553 (0.71 #1164, 0.62 #1516, 0.42 #698), 01z4y (0.61 #3733, 0.61 #3851, 0.61 #2097), 01jfsb (0.56 #241, 0.35 #4445, 0.32 #939), 02n4kr (0.21 #238, 0.13 #1285, 0.13 #819), 04xvlr (0.20 #1397, 0.19 #1280, 0.18 #1980), 060__y (0.17 #709, 0.16 #1409, 0.16 #3046), 01hmnh (0.15 #594, 0.14 #5613, 0.14 #1877), 06n90 (0.13 #590, 0.13 #1873, 0.12 #940), 01t_vv (0.12 #1212, 0.11 #1564, 0.10 #746), 03q4nz (0.11 #15, 0.08 #131, 0.07 #2698) >> Best rule #1164 for best value: >> intensional similarity = 5 >> extensional distance = 820 >> proper extension: 0vgkd; >> query: (?x6229, 05p553) <- genre(?x6229, ?x604), genre(?x2498, ?x604), genre(?x1602, ?x604), ?x1602 = 0gxtknx, featured_film_locations(?x2498, ?x1523) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q8ms8 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 60.000 0.709 http://example.org/film/film/genre #10741-07f8wg PRED entity: 07f8wg PRED relation: executive_produced_by! PRED expected values: 02fqxm => 96 concepts (47 used for prediction) PRED predicted values (max 10 best out of 364): 01gwk3 (0.33 #362, 0.02 #12661, 0.02 #16890), 03s5lz (0.08 #593, 0.05 #1121, 0.02 #5868), 0gj96ln (0.08 #878, 0.05 #1406, 0.02 #6153), 047svrl (0.08 #672, 0.05 #1200, 0.02 #1727), 08720 (0.08 #551, 0.05 #1079, 0.02 #1606), 02d003 (0.08 #919, 0.05 #1447, 0.01 #8830), 095z4q (0.08 #895, 0.05 #1423, 0.01 #8806), 02tgz4 (0.08 #999, 0.05 #1527, 0.01 #2581), 02hxhz (0.08 #560, 0.05 #1088, 0.01 #2142), 011yn5 (0.08 #833, 0.02 #1888, 0.01 #2943) >> Best rule #362 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09zw90; >> query: (?x519, 01gwk3) <- executive_produced_by(?x324, ?x519), ?x324 = 07gp9, produced_by(?x518, ?x519) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07f8wg executive_produced_by! 02fqxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 47.000 0.333 http://example.org/film/film/executive_produced_by #10740-01mkq PRED entity: 01mkq PRED relation: major_field_of_study! PRED expected values: 01ptt7 04344j 086xm 017j69 07t90 02zd460 05zl0 03tw2s 02sjgpq 01hx2t 01qqv5 0jhjl 01p896 0160nk 013807 015fs3 01pj48 => 69 concepts (40 used for prediction) PRED predicted values (max 10 best out of 2194): 017j69 (0.71 #7443, 0.60 #5609, 0.60 #5151), 05zl0 (0.67 #7033, 0.67 #6574, 0.57 #7950), 02zd460 (0.62 #13429, 0.54 #16184, 0.51 #16646), 07t90 (0.60 #9739, 0.57 #7906, 0.55 #10197), 05krk (0.60 #5500, 0.57 #7792, 0.50 #13299), 01bzs9 (0.60 #5425, 0.50 #4511, 0.50 #4052), 01kvrz (0.60 #5350, 0.50 #3520, 0.50 #3063), 01k2wn (0.60 #5052, 0.50 #3679, 0.50 #3222), 012mzw (0.60 #6177, 0.50 #6635, 0.43 #8011), 086xm (0.60 #5105, 0.50 #3275, 0.33 #1445) >> Best rule #7443 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 01lhf; >> query: (?x1668, 017j69) <- major_field_of_study(?x1668, ?x742), major_field_of_study(?x4889, ?x1668), major_field_of_study(?x3513, ?x1668), major_field_of_study(?x2497, ?x1668), student(?x3513, ?x2015), organization(?x346, ?x3513), institution(?x865, ?x3513), school(?x799, ?x3513), ?x4889 = 02dq8f, school(?x660, ?x2497), state_province_region(?x2497, ?x3038) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 10, 21, 33, 42, 46, 54, 61, 63, 86, 166, 324, 340, 375 EVAL 01mkq major_field_of_study! 01pj48 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 015fs3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 013807 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 0160nk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 01p896 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 0jhjl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 01qqv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 01hx2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 02sjgpq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 03tw2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 05zl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 02zd460 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 07t90 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 017j69 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 086xm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 04344j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01mkq major_field_of_study! 01ptt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 69.000 40.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10739-0640y35 PRED entity: 0640y35 PRED relation: film_crew_role PRED expected values: 09zzb8 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 31): 02r96rf (0.94 #2377, 0.93 #2442, 0.93 #817), 09zzb8 (0.88 #814, 0.83 #2117, 0.83 #455), 0215hd (0.74 #795, 0.71 #663, 0.70 #371), 0d2b38 (0.67 #88, 0.59 #541, 0.57 #508), 0dxtw (0.62 #822, 0.44 #2093, 0.42 #887), 02_n3z (0.39 #390, 0.36 #781, 0.35 #649), 01pvkk (0.36 #823, 0.30 #3132, 0.28 #2774), 033smt (0.33 #90, 0.18 #412, 0.16 #803), 094hwz (0.33 #12, 0.16 #911, 0.13 #2894), 05smlt (0.33 #17, 0.16 #911, 0.13 #2894) >> Best rule #2377 for best value: >> intensional similarity = 8 >> extensional distance = 798 >> proper extension: 0b76d_m; 0ds35l9; 0d90m; 03qcfvw; 0g56t9t; 09sh8k; 0gtsx8c; 0m313; 02y_lrp; 034qmv; ... >> query: (?x5847, 02r96rf) <- film(?x2437, ?x5847), film_crew_role(?x5847, ?x5136), film_crew_role(?x11619, ?x5136), film_crew_role(?x5945, ?x5136), film_crew_role(?x5128, ?x5136), ?x5945 = 05t0_2v, ?x5128 = 08phg9, ?x11619 = 07l50_1 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #814 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 105 *> proper extension: 0h95zbp; *> query: (?x5847, 09zzb8) <- film_crew_role(?x5847, ?x5136), film_crew_role(?x5847, ?x2091), film_crew_role(?x8646, ?x5136), film_crew_role(?x7107, ?x5136), ?x7107 = 04ghz4m, ?x8646 = 05zvzf3, ?x2091 = 02rh1dz *> conf = 0.88 ranks of expected_values: 2 EVAL 0640y35 film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 115.000 0.938 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10738-0tnkg PRED entity: 0tnkg PRED relation: currency PRED expected values: 09nqf => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.48 #25, 0.45 #44, 0.44 #60) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 305 >> proper extension: 0fm9_; 0mw89; 0mtdx; 0mwh1; 0m7d0; 03fb3t; 0n5gq; 0n5yv; 0zqq8; 0l3n4; ... >> query: (?x13204, 09nqf) <- contains(?x94, ?x13204), contains(?x13204, ?x3178), source(?x13204, ?x958) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tnkg currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.482 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #10737-0d3qd0 PRED entity: 0d3qd0 PRED relation: legislative_sessions PRED expected values: 070m6c => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 37): 070m6c (0.73 #339, 0.50 #413, 0.48 #450), 03rtmz (0.53 #346, 0.43 #556, 0.36 #420), 032ft5 (0.47 #341, 0.43 #556, 0.32 #415), 03ww_x (0.47 #338, 0.43 #556, 0.32 #412), 077g7n (0.43 #556, 0.40 #337, 0.27 #411), 03tcbx (0.43 #556, 0.33 #345, 0.23 #419), 04h1rz (0.43 #556, 0.33 #359, 0.23 #433), 05l2z4 (0.43 #556, 0.33 #336, 0.23 #410), 0495ys (0.43 #556, 0.33 #335, 0.23 #409), 060ny2 (0.43 #556, 0.27 #358, 0.23 #432) >> Best rule #339 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 03txms; >> query: (?x4567, 070m6c) <- jurisdiction_of_office(?x4567, ?x94), legislative_sessions(?x4567, ?x5339), district_represented(?x5339, ?x2256), ?x2256 = 07srw >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d3qd0 legislative_sessions 070m6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.733 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #10736-016ypb PRED entity: 016ypb PRED relation: notable_people_with_this_condition! PRED expected values: 02vrr => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2): 029sk (0.17 #45, 0.13 #67, 0.03 #133), 0h99n (0.03 #252, 0.02 #186, 0.02 #208) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01kwld; 09wj5; 01rh0w; 01v9l67; 0294fd; 015t7v; 02ck7w; 016zp5; 03ym1; 02fgm7; >> query: (?x2922, 029sk) <- award_winner(?x5283, ?x2922), award_winner(?x2762, ?x2922), ?x5283 = 01ps2h8, ?x2762 = 015t56 >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016ypb notable_people_with_this_condition! 02vrr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 92.000 0.167 http://example.org/medicine/disease/notable_people_with_this_condition #10735-02rx2m5 PRED entity: 02rx2m5 PRED relation: films! PRED expected values: 0hjy => 75 concepts (30 used for prediction) PRED predicted values (max 10 best out of 50): 0kbq (0.20 #104, 0.14 #416, 0.03 #571), 06c97 (0.20 #204, 0.14 #360), 0fx2s (0.20 #72, 0.05 #2416, 0.05 #1475), 04gb7 (0.14 #357, 0.06 #667, 0.03 #1762), 0gzh (0.14 #438, 0.02 #593), 081pw (0.07 #2347, 0.07 #1720, 0.06 #1406), 06d4h (0.06 #1760, 0.06 #2387, 0.05 #1446), 0fzyg (0.05 #1771, 0.05 #2398, 0.04 #1457), 03r8gp (0.05 #556, 0.04 #711, 0.03 #1806), 0bq3x (0.05 #1747, 0.04 #2374, 0.04 #1433) >> Best rule #104 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0fvr1; >> query: (?x1866, 0kbq) <- film(?x8099, ?x1866), ?x8099 = 01nms7, nominated_for(?x1162, ?x1866), titles(?x53, ?x1866) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02rx2m5 films! 0hjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 30.000 0.200 http://example.org/film/film_subject/films #10734-07lp1 PRED entity: 07lp1 PRED relation: place_of_birth PRED expected values: 0y2dl => 128 concepts (127 used for prediction) PRED predicted values (max 10 best out of 55): 04n3l (0.33 #73284, 0.32 #22543, 0.28 #50732), 0167q3 (0.25 #255), 06wxw (0.20 #863, 0.05 #2271, 0.03 #4383), 0h1k6 (0.20 #1151), 0hptm (0.12 #1635, 0.10 #3043, 0.09 #3747), 0vzm (0.11 #2231, 0.01 #7865, 0.01 #8569), 02_286 (0.10 #49341, 0.09 #14108, 0.08 #26085), 0f94t (0.06 #1438, 0.05 #2142, 0.05 #2846), 01_d4 (0.06 #1476, 0.05 #2884, 0.04 #3588), 06pr6 (0.06 #1673, 0.05 #3081, 0.04 #3785) >> Best rule #73284 for best value: >> intensional similarity = 4 >> extensional distance = 2259 >> proper extension: 07m69t; >> query: (?x10313, ?x3415) <- location(?x10313, ?x3415), location(?x5798, ?x3415), place_of_birth(?x666, ?x3415), profession(?x5798, ?x319) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07lp1 place_of_birth 0y2dl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 127.000 0.330 http://example.org/people/person/place_of_birth #10733-0fb7c PRED entity: 0fb7c PRED relation: influenced_by! PRED expected values: 05rx__ => 99 concepts (50 used for prediction) PRED predicted values (max 10 best out of 138): 0bqs56 (0.07 #3348, 0.04 #5413, 0.04 #6448), 01j7rd (0.05 #3169, 0.03 #8335, 0.03 #7818), 01xwqn (0.05 #3542, 0.03 #5607, 0.03 #8191), 046lt (0.05 #3207, 0.03 #5272, 0.02 #6307), 0lx2l (0.05 #3187, 0.03 #5252, 0.02 #6287), 02238b (0.05 #3378, 0.03 #5443, 0.02 #6478), 01n5309 (0.05 #3116, 0.03 #5181, 0.02 #6216), 01xdf5 (0.05 #3100, 0.02 #6200, 0.02 #8266), 040db (0.03 #9888, 0.03 #11953, 0.02 #17118), 01xwv7 (0.03 #9205, 0.03 #8689, 0.03 #8172) >> Best rule #3348 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 01wdqrx; 01vw_dv; 02h9_l; 01svq8; >> query: (?x6217, 0bqs56) <- award(?x6217, ?x693), gender(?x6217, ?x231), location(?x6217, ?x1637), person(?x3480, ?x6217) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #7540 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 209 *> proper extension: 070px; *> query: (?x6217, 05rx__) <- film(?x6217, ?x8456), language(?x8456, ?x254), profession(?x6217, ?x319), people(?x6260, ?x6217) *> conf = 0.03 ranks of expected_values: 22 EVAL 0fb7c influenced_by! 05rx__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 99.000 50.000 0.070 http://example.org/influence/influence_node/influenced_by #10732-04mx8h4 PRED entity: 04mx8h4 PRED relation: genre PRED expected values: 05p553 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 79): 07s9rl0 (0.84 #1623, 0.58 #406, 0.56 #3898), 05p553 (0.71 #1141, 0.52 #410, 0.51 #1059), 01z4y (0.49 #1153, 0.38 #422, 0.36 #1071), 025s89p (0.44 #131, 0.33 #50, 0.28 #293), 095bb (0.44 #117, 0.33 #36, 0.25 #279), 0c4xc (0.36 #1177, 0.30 #446, 0.28 #1095), 03k9fj (0.33 #93, 0.28 #255, 0.26 #1634), 0jxy (0.33 #273, 0.26 #354, 0.10 #3927), 01hmnh (0.28 #259, 0.22 #97, 0.16 #1638), 01t_vv (0.24 #1168, 0.23 #681, 0.23 #843) >> Best rule #1623 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 05397h; >> query: (?x8976, 07s9rl0) <- program(?x11453, ?x8976), genre(?x8976, ?x1844), genre(?x11818, ?x1844), ?x11818 = 06k176 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1141 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 97 *> proper extension: 02xhpl; 0hr41p6; *> query: (?x8976, 05p553) <- nominated_for(?x3263, ?x8976), genre(?x8976, ?x10023), genre(?x13288, ?x10023), genre(?x5938, ?x10023), ?x5938 = 05f7w84, actor(?x13288, ?x1896) *> conf = 0.71 ranks of expected_values: 2 EVAL 04mx8h4 genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.836 http://example.org/tv/tv_program/genre #10731-03gh4 PRED entity: 03gh4 PRED relation: district_represented! PRED expected values: 07p__7 02bn_p 03tcbx 04h1rz => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 37): 07p__7 (0.87 #558, 0.83 #1261, 0.80 #336), 02bn_p (0.84 #559, 0.70 #1262, 0.62 #1299), 03tcbx (0.60 #119, 0.39 #563, 0.33 #8), 04h1rz (0.60 #132, 0.33 #21, 0.13 #354), 03rl1g (0.58 #556, 0.54 #1296, 0.53 #1259), 043djx (0.58 #557, 0.54 #1297, 0.51 #1260), 01h7xx (0.52 #580, 0.48 #1320, 0.47 #1283), 01gt99 (0.48 #588, 0.44 #1328, 0.43 #1291), 01gtdd (0.48 #585, 0.43 #1288, 0.42 #1325), 01gst_ (0.45 #562, 0.42 #1302, 0.40 #1265) >> Best rule #558 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 03v1s; 05kj_; 059f4; 0vmt; 03s0w; 01x73; 04rrd; 04rrx; 050l8; 06btq; ... >> query: (?x6226, 07p__7) <- district_represented(?x1829, ?x6226), location(?x2415, ?x6226), ?x1829 = 02bp37 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 03gh4 district_represented! 04h1rz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.871 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 03gh4 district_represented! 03tcbx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.871 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 03gh4 district_represented! 02bn_p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.871 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 03gh4 district_represented! 07p__7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.871 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #10730-0kr_t PRED entity: 0kr_t PRED relation: award_winner! PRED expected values: 019bk0 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 92): 056878 (0.50 #308, 0.33 #170, 0.33 #32), 09n4nb (0.50 #324, 0.33 #186, 0.33 #48), 01xqqp (0.33 #646, 0.33 #370, 0.33 #232), 0gpjbt (0.33 #305, 0.27 #719, 0.22 #581), 01bx35 (0.33 #7, 0.22 #559, 0.18 #697), 05pd94v (0.33 #278, 0.18 #968, 0.17 #140), 0jzphpx (0.33 #39, 0.17 #315, 0.17 #177), 09p30_ (0.33 #83, 0.17 #359, 0.17 #221), 019bk0 (0.19 #1948, 0.17 #292, 0.17 #154), 01s695 (0.18 #693, 0.17 #141, 0.14 #1935) >> Best rule #308 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0gcs9; >> query: (?x5493, 056878) <- award_winner(?x3103, ?x5493), ?x3103 = 03tcnt, artist(?x6672, ?x5493), origin(?x5493, ?x362) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1948 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 04rcr; 02r3zy; 07c0j; 03g5jw; 05crg7; 01czx; 0dvqq; 016fmf; 0249kn; 018ndc; ... *> query: (?x5493, 019bk0) <- group(?x227, ?x5493), award_winner(?x5656, ?x5493), artists(?x302, ?x5493), award(?x5493, ?x247) *> conf = 0.19 ranks of expected_values: 9 EVAL 0kr_t award_winner! 019bk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 86.000 86.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10729-01vdrw PRED entity: 01vdrw PRED relation: profession PRED expected values: 0cbd2 02hv44_ => 154 concepts (90 used for prediction) PRED predicted values (max 10 best out of 101): 02hrh1q (0.85 #7078, 0.84 #12083, 0.84 #10906), 0cbd2 (0.82 #595, 0.77 #2656, 0.76 #2362), 0dxtg (0.62 #161, 0.61 #3839, 0.49 #3692), 02hv44_ (0.40 #1326, 0.40 #57, 0.30 #8535), 0lgw7 (0.40 #1326, 0.39 #4855, 0.33 #7358), 04s2z (0.40 #1326, 0.39 #4855, 0.33 #7358), 0d8qb (0.40 #1326, 0.30 #8535, 0.21 #1031), 01d_h8 (0.39 #3978, 0.38 #3831, 0.37 #3243), 018gz8 (0.36 #3843, 0.23 #6050, 0.23 #1196), 03gjzk (0.34 #3841, 0.32 #1194, 0.31 #3988) >> Best rule #7078 for best value: >> intensional similarity = 4 >> extensional distance = 342 >> proper extension: 04bdxl; 06qgvf; 0l8v5; 06jzh; 0151ns; 08f3b1; 03_vx9; 0blbxk; 03jldb; 01nczg; ... >> query: (?x10974, 02hrh1q) <- people(?x3584, ?x10974), gender(?x10974, ?x514), award(?x10974, ?x7111), ?x514 = 02zsn >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #595 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 017r2; 0n6kf; 014ps4; 0gd_s; *> query: (?x10974, 0cbd2) <- influenced_by(?x10974, ?x6457), gender(?x10974, ?x514), award(?x10974, ?x7111), ?x6457 = 03_87 *> conf = 0.82 ranks of expected_values: 2, 4 EVAL 01vdrw profession 02hv44_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 154.000 90.000 0.849 http://example.org/people/person/profession EVAL 01vdrw profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 154.000 90.000 0.849 http://example.org/people/person/profession #10728-01kt_j PRED entity: 01kt_j PRED relation: nominated_for! PRED expected values: 0b79gfg => 71 concepts (24 used for prediction) PRED predicted values (max 10 best out of 681): 01ft2l (0.48 #37330, 0.45 #30328, 0.45 #34995), 02jr26 (0.48 #37330, 0.45 #30328, 0.45 #34995), 014gf8 (0.48 #37330, 0.45 #30328, 0.45 #34995), 03q5dr (0.45 #30328, 0.45 #34995, 0.45 #41999), 026n9h3 (0.33 #1500, 0.02 #10830, 0.02 #29495), 09d5h (0.16 #39665, 0.15 #44332, 0.14 #48998), 05gnf (0.13 #3783, 0.09 #15445, 0.08 #17778), 0gsg7 (0.10 #2684, 0.09 #5016, 0.07 #9681), 0p_2r (0.10 #2616, 0.03 #14278, 0.02 #16611), 02778pf (0.10 #2489, 0.03 #14151, 0.02 #16484) >> Best rule #37330 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 070g7; 026bfsh; 01f39b; >> query: (?x10595, ?x3192) <- actor(?x10595, ?x3192), award_winner(?x2041, ?x3192), nominated_for(?x2041, ?x337) >> conf = 0.48 => this is the best rule for 3 predicted values *> Best rule #5279 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 02rq7nd; *> query: (?x10595, 0b79gfg) <- genre(?x10595, ?x53), producer_type(?x10595, ?x632), ?x53 = 07s9rl0 *> conf = 0.02 ranks of expected_values: 556 EVAL 01kt_j nominated_for! 0b79gfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 24.000 0.475 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10727-051wwp PRED entity: 051wwp PRED relation: student! PRED expected values: 01q8hj => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 52): 0ks67 (0.25 #189), 09f2j (0.23 #685, 0.03 #30674, 0.03 #2789), 02zd460 (0.13 #1222, 0.12 #1748), 053mhx (0.12 #294, 0.01 #9762, 0.01 #30809), 013807 (0.12 #410), 06rkfs (0.12 #374), 07tds (0.12 #149), 01mpwj (0.12 #107), 015nl4 (0.08 #593, 0.07 #1119, 0.06 #1645), 017hnw (0.08 #1034) >> Best rule #189 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 015wfg; >> query: (?x4928, 0ks67) <- film(?x4928, ?x7947), ?x7947 = 04gcyg >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 051wwp student! 01q8hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #10726-04110lv PRED entity: 04110lv PRED relation: ceremony! PRED expected values: 018wng 0gqzz => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 362): 018wng (0.92 #2172, 0.91 #3602, 0.91 #1933), 0gqzz (0.74 #5722, 0.50 #515, 0.50 #277), 0czp_ (0.74 #5722, 0.19 #3052, 0.19 #2575), 02x201b (0.74 #5722, 0.13 #4292, 0.11 #3746), 02x1z2s (0.28 #2863, 0.26 #2147, 0.23 #716), 03hl6lc (0.28 #2863, 0.26 #2147, 0.23 #716), 02w9sd7 (0.28 #2863, 0.26 #2147, 0.20 #7154), 09cm54 (0.28 #2863, 0.26 #2147, 0.20 #7154), 019f4v (0.28 #2863, 0.24 #1233, 0.23 #716), 04dn09n (0.28 #2863, 0.24 #1219, 0.23 #716) >> Best rule #2172 for best value: >> intensional similarity = 17 >> extensional distance = 23 >> proper extension: 073hgx; >> query: (?x7936, 018wng) <- award_winner(?x7936, ?x276), ceremony(?x3458, ?x7936), ceremony(?x1245, ?x7936), ceremony(?x500, ?x7936), ?x3458 = 0gqxm, honored_for(?x7936, ?x2490), award(?x5043, ?x1245), nominated_for(?x1245, ?x9059), nominated_for(?x1245, ?x6899), nominated_for(?x1245, ?x6269), ?x6899 = 04lhc4, ceremony(?x1245, ?x6344), ?x6269 = 0286gm1, film(?x5043, ?x755), ?x6344 = 0bzm__, award_winner(?x500, ?x902), ?x9059 = 0m_h6 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04110lv ceremony! 0gqzz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.920 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 04110lv ceremony! 018wng CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.920 http://example.org/award/award_category/winners./award/award_honor/ceremony #10725-0326tc PRED entity: 0326tc PRED relation: group PRED expected values: 01_wfj => 114 concepts (33 used for prediction) PRED predicted values (max 10 best out of 59): 0jg77 (0.20 #107, 0.14 #215, 0.13 #543), 02_5x9 (0.20 #11, 0.14 #119, 0.11 #773), 0187x8 (0.20 #61, 0.06 #714), 081wh1 (0.14 #160, 0.11 #814, 0.09 #269), 02hzz (0.14 #175, 0.09 #284, 0.07 #503), 09lwrt (0.14 #155, 0.09 #264, 0.07 #483), 01jcxwp (0.14 #162, 0.09 #271, 0.07 #490), 0838y (0.14 #156, 0.09 #265, 0.07 #484), 01fchy (0.14 #189, 0.07 #517, 0.03 #952), 01v0sx2 (0.12 #550, 0.04 #2068, 0.04 #1419) >> Best rule #107 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 01271h; 03h_fqv; 04kjrv; >> query: (?x7972, 0jg77) <- artists(?x302, ?x7972), role(?x7972, ?x7033), role(?x7972, ?x3161), ?x7033 = 0gkd1, ?x3161 = 01v1d8, ?x302 = 016clz >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0326tc group 01_wfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 33.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group #10724-027gs1_ PRED entity: 027gs1_ PRED relation: award! PRED expected values: 023ny6 => 55 concepts (27 used for prediction) PRED predicted values (max 10 best out of 807): 01q_y0 (0.67 #3028, 0.67 #2242, 0.62 #4262), 030cx (0.67 #2467, 0.62 #4487, 0.50 #3477), 01bv8b (0.60 #258, 0.50 #4296, 0.50 #3286), 0d68qy (0.60 #1252, 0.50 #3271, 0.50 #2261), 0kfpm (0.60 #68, 0.50 #3096, 0.45 #8076), 072kp (0.60 #54, 0.45 #8076, 0.43 #3027), 02czd5 (0.60 #1837, 0.33 #2846, 0.25 #4866), 05f4vxd (0.45 #8076, 0.43 #3027, 0.41 #7066), 01fs__ (0.45 #8076, 0.43 #3027, 0.41 #7066), 03nt59 (0.45 #8076, 0.43 #3027, 0.41 #7066) >> Best rule #3028 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0cqhk0; 0cqhmg; >> query: (?x7510, ?x2293) <- nominated_for(?x7510, ?x10731), nominated_for(?x7510, ?x2293), award(?x201, ?x7510), ?x10731 = 0cs134, ?x2293 = 01q_y0 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3027 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 0cqhk0; 0cqhmg; *> query: (?x7510, ?x631) <- nominated_for(?x7510, ?x10731), nominated_for(?x7510, ?x2293), nominated_for(?x7510, ?x631), award(?x201, ?x7510), ?x10731 = 0cs134, ?x2293 = 01q_y0 *> conf = 0.43 ranks of expected_values: 24 EVAL 027gs1_ award! 023ny6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 55.000 27.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #10723-026bfsh PRED entity: 026bfsh PRED relation: actor PRED expected values: 0j1yf 01wd9lv 03mp9s 01nz1q6 095nx => 57 concepts (24 used for prediction) PRED predicted values (max 10 best out of 661): 015f7 (0.45 #4491, 0.45 #4490, 0.42 #8087), 016ksk (0.45 #4491, 0.45 #4490, 0.42 #8087), 04gycf (0.45 #4491, 0.45 #4490, 0.42 #8087), 01wyz92 (0.45 #4491, 0.45 #4490, 0.42 #8087), 03rl84 (0.25 #151, 0.13 #4642, 0.06 #18102), 06b4wb (0.25 #821, 0.11 #9805, 0.09 #13390), 0sw62 (0.25 #731, 0.11 #15992, 0.09 #11507), 03j9ml (0.25 #834, 0.07 #5325, 0.06 #9818), 0sw6g (0.25 #602, 0.07 #5093, 0.06 #9586), 03xn3s2 (0.25 #514, 0.07 #5005, 0.06 #9498) >> Best rule #4491 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 03czz87; >> query: (?x5529, ?x3707) <- actor(?x5529, ?x4960), actor(?x5529, ?x4062), profession(?x4062, ?x131), program(?x3707, ?x5529), profession(?x3707, ?x319), award_winner(?x2704, ?x4960) >> conf = 0.45 => this is the best rule for 4 predicted values *> Best rule #1043 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 06hwzy; 01b7h8; *> query: (?x5529, 0j1yf) <- program(?x3397, ?x5529), participant(?x3397, ?x1896), artists(?x671, ?x3397), award_nominee(?x3397, ?x4693), award(?x3397, ?x154) *> conf = 0.17 ranks of expected_values: 27, 55 EVAL 026bfsh actor 095nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 24.000 0.452 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 026bfsh actor 01nz1q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 24.000 0.452 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 026bfsh actor 03mp9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 24.000 0.452 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 026bfsh actor 01wd9lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 57.000 24.000 0.452 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 026bfsh actor 0j1yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 57.000 24.000 0.452 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10722-0kz10 PRED entity: 0kz10 PRED relation: parent_genre PRED expected values: 07gxw 08cyft => 74 concepts (54 used for prediction) PRED predicted values (max 10 best out of 218): 06by7 (0.89 #3763, 0.81 #3927, 0.75 #3601), 01243b (0.61 #3289, 0.27 #4590, 0.19 #3614), 011j5x (0.50 #2457, 0.11 #3282, 0.11 #3769), 016clz (0.42 #3265, 0.22 #2440, 0.20 #1794), 07gxw (0.40 #851, 0.33 #1176, 0.33 #38), 05r6t (0.39 #2490, 0.36 #3315, 0.24 #5102), 03_d0 (0.38 #1472, 0.29 #1308, 0.24 #2764), 03lty (0.34 #5066, 0.18 #5722, 0.17 #3441), 0glt670 (0.30 #4427, 0.29 #4589, 0.24 #2764), 01pfpt (0.30 #2598, 0.25 #548, 0.25 #222) >> Best rule #3763 for best value: >> intensional similarity = 9 >> extensional distance = 64 >> proper extension: 028cl7; 017ht; >> query: (?x14233, 06by7) <- parent_genre(?x14233, ?x474), artists(?x474, ?x5126), artists(?x474, ?x3894), artists(?x474, ?x1989), ?x5126 = 03h502k, location(?x1989, ?x2673), artists(?x302, ?x1989), ?x3894 = 01vxlbm, ?x302 = 016clz >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #851 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 01z9l_; *> query: (?x14233, 07gxw) <- parent_genre(?x14233, ?x7220), artists(?x14233, ?x8806), artists(?x14233, ?x8636), ?x8636 = 0k60, artists(?x9012, ?x8806), artist(?x5634, ?x8806), ?x7220 = 0mmp3, ?x9012 = 0hh2s *> conf = 0.40 ranks of expected_values: 5, 20 EVAL 0kz10 parent_genre 08cyft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 74.000 54.000 0.894 http://example.org/music/genre/parent_genre EVAL 0kz10 parent_genre 07gxw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 74.000 54.000 0.894 http://example.org/music/genre/parent_genre #10721-0fy34l PRED entity: 0fy34l PRED relation: film! PRED expected values: 0378zn => 70 concepts (21 used for prediction) PRED predicted values (max 10 best out of 896): 02qjpv5 (0.19 #4166, 0.09 #35402, 0.09 #33319), 0169dl (0.15 #8731, 0.12 #4567, 0.09 #10813), 02pk6x (0.15 #3083, 0.11 #1000, 0.02 #11412), 05dtsb (0.15 #3259, 0.02 #32412, 0.02 #34495), 02qgqt (0.12 #4184, 0.12 #8348, 0.07 #10430), 03cglm (0.12 #5212, 0.08 #9376, 0.07 #11458), 05nzw6 (0.12 #5358, 0.08 #9522, 0.04 #11604), 01r93l (0.12 #4913, 0.08 #9077, 0.04 #11159), 04gc65 (0.12 #6141, 0.08 #10305, 0.04 #12387), 035rnz (0.12 #4859, 0.08 #9023, 0.04 #11105) >> Best rule #4166 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 03mgx6z; 01jr4j; >> query: (?x1948, ?x9439) <- genre(?x1948, ?x4205), genre(?x1948, ?x604), ?x604 = 0lsxr, produced_by(?x1948, ?x9439), ?x4205 = 0c3351 >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #2065 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 0127ps; 02q0v8n; *> query: (?x1948, 0378zn) <- genre(?x1948, ?x4205), genre(?x1948, ?x604), ?x604 = 0lsxr, produced_by(?x1948, ?x9439), ?x4205 = 0c3351, film_crew_role(?x1948, ?x137) *> conf = 0.11 ranks of expected_values: 45 EVAL 0fy34l film! 0378zn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 70.000 21.000 0.188 http://example.org/film/actor/film./film/performance/film #10720-01y2mq PRED entity: 01y2mq PRED relation: artists PRED expected values: 01vw_dv 03f0qd7 => 45 concepts (17 used for prediction) PRED predicted values (max 10 best out of 991): 01vw8mh (0.64 #1083, 0.50 #2601, 0.29 #3682), 01vw_dv (0.64 #1083, 0.33 #1682, 0.33 #1082), 01wgfp6 (0.64 #1083, 0.33 #539, 0.29 #3784), 02vwckw (0.57 #4000, 0.33 #1838, 0.33 #755), 03sww (0.50 #2606, 0.43 #4770, 0.33 #442), 01vvydl (0.50 #2170, 0.33 #6, 0.23 #5412), 01vzx45 (0.50 #2853, 0.33 #689, 0.21 #5017), 01vw26l (0.50 #2471, 0.28 #5719, 0.14 #4635), 026yqrr (0.43 #3813, 0.33 #1651, 0.33 #568), 03f0qd7 (0.43 #4260, 0.33 #2098, 0.33 #1015) >> Best rule #1083 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 0glt670; >> query: (?x13077, ?x4851) <- parent_genre(?x11692, ?x13077), artists(?x13077, ?x11371), artists(?x13077, ?x6144), artists(?x11692, ?x6659), artists(?x11692, ?x4851), ?x6659 = 01vw_dv, ?x6144 = 03h_0_z, ?x11371 = 01wlt3k >> conf = 0.64 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 10 EVAL 01y2mq artists 03f0qd7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 45.000 17.000 0.638 http://example.org/music/genre/artists EVAL 01y2mq artists 01vw_dv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 45.000 17.000 0.638 http://example.org/music/genre/artists #10719-02whj PRED entity: 02whj PRED relation: people! PRED expected values: 0xnvg => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 56): 0x67 (0.44 #3761, 0.43 #4061, 0.40 #5487), 033tf_ (0.37 #607, 0.19 #1357, 0.18 #982), 041rx (0.29 #79, 0.27 #2779, 0.27 #3004), 09vc4s (0.19 #609, 0.10 #234, 0.10 #834), 07bch9 (0.17 #1373, 0.13 #2798, 0.12 #3023), 02w7gg (0.16 #902, 0.10 #6907, 0.09 #9534), 02g7sp (0.13 #843, 0.12 #468, 0.10 #768), 0xnvg (0.13 #838, 0.11 #613, 0.10 #2113), 02ctzb (0.13 #1365, 0.12 #990, 0.08 #2790), 0cn68 (0.11 #57, 0.04 #507, 0.03 #807) >> Best rule #3761 for best value: >> intensional similarity = 4 >> extensional distance = 239 >> proper extension: 03bxh; >> query: (?x1092, 0x67) <- profession(?x1092, ?x131), people(?x7063, ?x1092), location(?x1092, ?x1705), artists(?x505, ?x1092) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #838 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 0pcc0; 0k4gf; 04k15; 082db; 0c73z; *> query: (?x1092, 0xnvg) <- profession(?x1092, ?x131), people(?x7063, ?x1092), instrumentalists(?x227, ?x1092), influenced_by(?x1092, ?x8080) *> conf = 0.13 ranks of expected_values: 8 EVAL 02whj people! 0xnvg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 177.000 177.000 0.444 http://example.org/people/ethnicity/people #10718-074w86 PRED entity: 074w86 PRED relation: film! PRED expected values: 01pb34 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.08 #33, 0.06 #23, 0.06 #28), 09_gdc (0.04 #62, 0.04 #77, 0.03 #57), 01kyvx (0.02 #400, 0.01 #425, 0.01 #410) >> Best rule #33 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 06wzvr; 02qzmz6; 0dqcs3; >> query: (?x4054, 01pb34) <- nominated_for(?x1312, ?x4054), nominated_for(?x688, ?x4054), ?x688 = 05b1610, ?x1312 = 07cbcy, currency(?x4054, ?x170) >> conf = 0.08 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 074w86 film! 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.077 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #10717-042v_gx PRED entity: 042v_gx PRED relation: role! PRED expected values: 01w923 0144l1 0lccn 045zr 03cfjg 03h_fqv 044mfr 01d4cb 0pk41 01wwnh2 => 86 concepts (53 used for prediction) PRED predicted values (max 10 best out of 3744): 018x3 (0.67 #4423, 0.50 #4041, 0.50 #1742), 01lvcs1 (0.67 #3966, 0.50 #4348, 0.42 #9338), 0140t7 (0.62 #5314, 0.60 #7619, 0.44 #6467), 01vs4ff (0.60 #2934, 0.58 #10993, 0.58 #9457), 03ryks (0.60 #2165, 0.46 #12144, 0.44 #7150), 045zr (0.60 #2773, 0.44 #6992, 0.40 #7376), 03h502k (0.60 #2872, 0.40 #2488, 0.36 #8628), 01s21dg (0.60 #2858, 0.33 #9381, 0.33 #7077), 01kd57 (0.60 #2891, 0.33 #9414, 0.33 #593), 01vrncs (0.59 #5750, 0.42 #11896, 0.38 #5782) >> Best rule #4423 for best value: >> intensional similarity = 18 >> extensional distance = 4 >> proper extension: 0l14qv; 018vs; >> query: (?x432, 018x3) <- role(?x4917, ?x432), role(?x3215, ?x432), role(?x2944, ?x432), role(?x2158, ?x432), role(?x894, ?x432), ?x4917 = 06w7v, role(?x9735, ?x432), role(?x8599, ?x432), role(?x2807, ?x432), award_nominee(?x366, ?x2807), ?x9735 = 01wxdn3, ?x3215 = 0bxl5, ?x2158 = 01dnws, ?x894 = 03m5k, award(?x2807, ?x594), award_winner(?x2807, ?x4840), role(?x1524, ?x2944), award_winner(?x2054, ?x8599) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2773 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 3 *> proper extension: 05842k; *> query: (?x432, 045zr) <- role(?x8014, ?x432), role(?x4917, ?x432), role(?x2205, ?x432), role(?x316, ?x432), role(?x74, ?x432), ?x2205 = 0dq630k, role(?x2963, ?x432), role(?x75, ?x74), ?x2963 = 0gcs9, role(?x736, ?x432), role(?x4918, ?x4917), group(?x432, ?x442), role(?x2865, ?x74), ?x4918 = 01mwsnc, ?x8014 = 0214km, ?x316 = 05r5c *> conf = 0.60 ranks of expected_values: 6, 22, 47, 50, 54, 64, 136, 160, 193, 396 EVAL 042v_gx role! 01wwnh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 86.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 042v_gx role! 0pk41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 86.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 042v_gx role! 01d4cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 86.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 042v_gx role! 044mfr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 86.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 042v_gx role! 03h_fqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 86.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 042v_gx role! 03cfjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 86.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 042v_gx role! 045zr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 86.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 042v_gx role! 0lccn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 86.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 042v_gx role! 0144l1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 86.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 042v_gx role! 01w923 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 86.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #10716-0gcpc PRED entity: 0gcpc PRED relation: nominated_for! PRED expected values: 0f4x7 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 171): 0gq_v (0.51 #256, 0.51 #20, 0.41 #492), 040njc (0.47 #479, 0.44 #1423, 0.33 #951), 0gr0m (0.43 #1475, 0.35 #531, 0.27 #2183), 04dn09n (0.43 #1451, 0.34 #507, 0.25 #4283), 0f4x7 (0.40 #497, 0.32 #1441, 0.30 #261), 0gr4k (0.38 #498, 0.38 #1442, 0.29 #262), 02qyntr (0.38 #1594, 0.26 #650, 0.21 #4426), 0gqy2 (0.37 #592, 0.35 #1536, 0.29 #356), 02pqp12 (0.36 #1474, 0.27 #530, 0.20 #4306), 0gqyl (0.32 #549, 0.26 #1493, 0.25 #1021) >> Best rule #256 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 0gcrg; 027ct7c; >> query: (?x4241, 0gq_v) <- film(?x902, ?x4241), film_sets_designed(?x7876, ?x4241), nominated_for(?x1134, ?x4241) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #497 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 07bz5; *> query: (?x4241, 0f4x7) <- list(?x4241, ?x3004), award(?x4241, ?x1107), nominated_for(?x1134, ?x4241) *> conf = 0.40 ranks of expected_values: 5 EVAL 0gcpc nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 68.000 68.000 0.513 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10715-01b_lz PRED entity: 01b_lz PRED relation: nominated_for! PRED expected values: 01fx5l 05gnf => 102 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1032): 0dzf_ (0.80 #39589, 0.79 #90827, 0.79 #60548), 0438pz (0.76 #27945, 0.67 #30273, 0.66 #60549), 0btpx (0.61 #39588, 0.58 #62877, 0.57 #86169), 01zg98 (0.61 #39588, 0.58 #62877, 0.57 #86169), 03sww (0.61 #39588, 0.58 #62877, 0.57 #86169), 038nv6 (0.61 #39588, 0.58 #62877, 0.57 #65206), 012_53 (0.61 #39588, 0.58 #62877, 0.57 #65206), 05gnf (0.35 #41917, 0.34 #37260, 0.32 #51232), 01pcdn (0.33 #1059, 0.14 #88498, 0.09 #4655), 0410cp (0.33 #3210, 0.14 #88498, 0.09 #4655) >> Best rule #39589 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 01h1bf; 02r5qtm; 0431v3; 05pbsry; >> query: (?x3326, ?x2589) <- actor(?x3326, ?x2582), award_winner(?x3326, ?x2589), nominated_for(?x2813, ?x3326), honored_for(?x1265, ?x3326) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #41917 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 02xhpl; 064r97z; 04glx0; 05z43v; 0h95b81; 0ph24; *> query: (?x3326, ?x6678) <- program(?x6678, ?x3326), genre(?x3326, ?x53), honored_for(?x1265, ?x3326), nominated_for(?x2589, ?x3326) *> conf = 0.35 ranks of expected_values: 8 EVAL 01b_lz nominated_for! 05gnf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 102.000 45.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 01b_lz nominated_for! 01fx5l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 45.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10714-012ky3 PRED entity: 012ky3 PRED relation: profession PRED expected values: 01c8w0 => 127 concepts (125 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.91 #16755, 0.77 #17495, 0.71 #8316), 09jwl (0.78 #7432, 0.74 #5356, 0.72 #5653), 0nbcg (0.55 #180, 0.51 #5368, 0.49 #5665), 01c8w0 (0.45 #305, 0.39 #453, 0.27 #1045), 016z4k (0.45 #5488, 0.42 #5786, 0.39 #5637), 0dz3r (0.44 #5338, 0.44 #5635, 0.41 #4002), 01d_h8 (0.32 #8307, 0.31 #11860, 0.31 #13192), 039v1 (0.32 #5373, 0.30 #5670, 0.26 #6559), 0dxtg (0.31 #9944, 0.30 #10240, 0.30 #8315), 0fnpj (0.26 #61, 0.22 #3467, 0.22 #2282) >> Best rule #16755 for best value: >> intensional similarity = 3 >> extensional distance = 2754 >> proper extension: 058w5; >> query: (?x4139, 02hrh1q) <- profession(?x4139, ?x1614), profession(?x1388, ?x1614), ?x1388 = 05mt_q >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #305 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 01l79yc; *> query: (?x4139, 01c8w0) <- award_winner(?x5924, ?x4139), award(?x4139, ?x1079), ?x1079 = 0l8z1 *> conf = 0.45 ranks of expected_values: 4 EVAL 012ky3 profession 01c8w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 127.000 125.000 0.906 http://example.org/people/person/profession #10713-0jgxn PRED entity: 0jgxn PRED relation: films PRED expected values: 0c57yj => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1): 01pv91 (0.02 #8091, 0.02 #9153, 0.02 #9684) >> Best rule #8091 for best value: >> intensional similarity = 9 >> extensional distance = 45 >> proper extension: 019x4f; >> query: (?x13369, 01pv91) <- profession(?x9826, ?x13369), profession(?x3941, ?x13369), profession(?x920, ?x13369), religion(?x9826, ?x8140), influenced_by(?x1737, ?x3941), people(?x1446, ?x9826), company(?x9826, ?x3439), influenced_by(?x920, ?x4033), type_of_union(?x9826, ?x566) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jgxn films 0c57yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 60.000 0.021 http://example.org/film/film_subject/films #10712-07vhb PRED entity: 07vhb PRED relation: colors PRED expected values: 01l849 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.40 #622, 0.40 #582, 0.37 #442), 01l849 (0.29 #1, 0.24 #441, 0.24 #581), 019sc (0.18 #587, 0.18 #667, 0.16 #447), 06fvc (0.17 #663, 0.16 #583, 0.13 #623), 036k5h (0.10 #65, 0.09 #445, 0.09 #645), 04mkbj (0.09 #630, 0.09 #510, 0.08 #110), 038hg (0.09 #592, 0.09 #632, 0.08 #672), 0jc_p (0.08 #564, 0.08 #244, 0.07 #384), 03wkwg (0.08 #75, 0.07 #15, 0.07 #115), 02rnmb (0.08 #73, 0.07 #13, 0.06 #113) >> Best rule #622 for best value: >> intensional similarity = 3 >> extensional distance = 263 >> proper extension: 02kj7g; >> query: (?x5280, 083jv) <- student(?x5280, ?x4490), award_nominee(?x2352, ?x4490), colors(?x5280, ?x3189) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 04m_kpx; 09xzd; *> query: (?x5280, 01l849) <- category(?x5280, ?x134), ?x134 = 08mbj5d, split_to(?x5280, ?x5280) *> conf = 0.29 ranks of expected_values: 2 EVAL 07vhb colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 81.000 0.396 http://example.org/education/educational_institution/colors #10711-0_7w6 PRED entity: 0_7w6 PRED relation: film_release_region PRED expected values: 07ssc 06qd3 02vzc => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 161): 07ssc (0.86 #1169, 0.85 #1746, 0.85 #879), 05b4w (0.84 #634, 0.83 #490, 0.76 #778), 02vzc (0.81 #1202, 0.80 #480, 0.80 #1925), 04gzd (0.78 #442, 0.73 #586, 0.73 #730), 01p1v (0.74 #481, 0.71 #625, 0.63 #769), 047yc (0.73 #603, 0.72 #459, 0.68 #747), 0ctw_b (0.69 #744, 0.64 #600, 0.63 #456), 06f32 (0.67 #492, 0.62 #636, 0.53 #780), 01mjq (0.67 #617, 0.65 #473, 0.60 #761), 06qd3 (0.65 #900, 0.65 #1190, 0.64 #1045) >> Best rule #1169 for best value: >> intensional similarity = 5 >> extensional distance = 94 >> proper extension: 0c3ybss; 0cz8mkh; 08052t3; 07f_7h; 0blpg; >> query: (?x1919, 07ssc) <- titles(?x307, ?x1919), film_release_region(?x1919, ?x2316), film_release_region(?x1919, ?x390), ?x2316 = 06t2t, ?x390 = 0chghy >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 10 EVAL 0_7w6 film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.865 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0_7w6 film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 83.000 83.000 0.865 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0_7w6 film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.865 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10710-05w3y PRED entity: 05w3y PRED relation: contact_category PRED expected values: 03w5xm 02zdwq => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 24): 03w5xm (0.73 #269, 0.73 #266, 0.70 #158), 02zdwq (0.60 #15, 0.50 #24, 0.41 #106), 014dgf (0.29 #35, 0.19 #312, 0.19 #339), 028fjr (0.01 #94), 04192r (0.01 #94), 06hpx2 (0.01 #94), 02h53vq (0.01 #94), 09lq2c (0.01 #94), 033smt (0.01 #94), 014l7h (0.01 #94) >> Best rule #269 for best value: >> intensional similarity = 5 >> extensional distance = 62 >> proper extension: 07tds; 01yfp7; 04f0xq; 0vlf; >> query: (?x6945, 03w5xm) <- service_location(?x6945, ?x279), list(?x6945, ?x5997), nationality(?x199, ?x279), film_release_region(?x66, ?x279), country(?x1036, ?x279) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05w3y contact_category 02zdwq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.734 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category EVAL 05w3y contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.734 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #10709-0hwqz PRED entity: 0hwqz PRED relation: people! PRED expected values: 048z7l => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 43): 041rx (0.63 #152, 0.24 #2748, 0.24 #1412), 033tf_ (0.20 #7, 0.16 #525, 0.15 #1415), 02w7gg (0.20 #2, 0.12 #2672, 0.11 #817), 01qhm_ (0.20 #6, 0.07 #524, 0.06 #1489), 0x67 (0.18 #4311, 0.18 #3349, 0.18 #2977), 0xnvg (0.10 #308, 0.10 #1495, 0.10 #530), 07bch9 (0.08 #837, 0.08 #318, 0.07 #540), 048z7l (0.08 #186, 0.06 #482, 0.06 #556), 09vc4s (0.08 #527, 0.05 #824, 0.05 #83), 013xrm (0.07 #167, 0.04 #685, 0.04 #3358) >> Best rule #152 for best value: >> intensional similarity = 2 >> extensional distance = 164 >> proper extension: 01w3v; 0mcf4; >> query: (?x5884, 041rx) <- religion(?x5884, ?x7131), ?x7131 = 03_gx >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #186 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 164 *> proper extension: 01w3v; 0mcf4; *> query: (?x5884, 048z7l) <- religion(?x5884, ?x7131), ?x7131 = 03_gx *> conf = 0.08 ranks of expected_values: 8 EVAL 0hwqz people! 048z7l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 128.000 128.000 0.627 http://example.org/people/ethnicity/people #10708-05qhw PRED entity: 05qhw PRED relation: film_release_region! PRED expected values: 0djb3vw 09146g 0661ql3 07j8r 01p3ty 04f52jw 0879bpq 040b5k 023gxx 0gh8zks 0dgpwnk 02dpl9 047tsx3 0dlngsd 02prwdh 03yvf2 0dr89x 04pk1f 0cmdwwg 0421v9q 0372j5 05ft32 025ts_z 0gvt53w 0j8f09z 072hx4 => 186 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1551): 04f52jw (0.89 #6578, 0.89 #5521, 0.86 #8692), 0661ql3 (0.87 #3382, 0.82 #5496, 0.82 #20294), 0421v9q (0.82 #5961, 0.78 #9132, 0.76 #2790), 04pk1f (0.82 #1677, 0.79 #5905, 0.74 #3791), 06v9_x (0.82 #1254, 0.71 #2311, 0.64 #5482), 0j3d9tn (0.82 #1575, 0.71 #2632, 0.61 #5803), 0djb3vw (0.82 #1100, 0.61 #5328, 0.57 #2157), 072hx4 (0.79 #6316, 0.76 #3145, 0.73 #2088), 0dlngsd (0.79 #5728, 0.76 #8899, 0.75 #19469), 09v71cj (0.76 #2519, 0.75 #5690, 0.73 #1462) >> Best rule #6578 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 05b7q; >> query: (?x456, 04f52jw) <- combatants(?x326, ?x456), country(?x6265, ?x456), combatants(?x456, ?x151) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 7, 8, 9, 11, 12, 18, 20, 21, 22, 26, 27, 32, 36, 39, 40, 51, 73, 77, 90, 118, 134, 153 EVAL 05qhw film_release_region! 072hx4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0j8f09z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0gvt53w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 025ts_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 05ft32 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0372j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0421v9q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0cmdwwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 04pk1f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0dr89x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 03yvf2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 02prwdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0dlngsd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 047tsx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 02dpl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0dgpwnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0gh8zks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 023gxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 040b5k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0879bpq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 04f52jw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 01p3ty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 07j8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0661ql3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 09146g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qhw film_release_region! 0djb3vw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 186.000 82.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10707-02q8ms8 PRED entity: 02q8ms8 PRED relation: film! PRED expected values: 014x77 06ltr => 60 concepts (34 used for prediction) PRED predicted values (max 10 best out of 529): 035rnz (0.62 #24980, 0.59 #41630, 0.54 #18735), 06cgy (0.08 #2330, 0.02 #14819, 0.02 #60615), 01kb2j (0.08 #18736, 0.04 #8327, 0.04 #49959), 01jfrg (0.08 #18736, 0.04 #8327, 0.04 #49959), 02114t (0.08 #18736, 0.04 #8327, 0.04 #49959), 016z2j (0.08 #18736, 0.04 #8327, 0.04 #49959), 08wjf4 (0.08 #18736, 0.04 #8327, 0.04 #49959), 0blq0z (0.08 #18736, 0.04 #8327, 0.04 #49959), 058s44 (0.08 #18736, 0.04 #49959), 016ypb (0.07 #498, 0.02 #8825, 0.02 #6742) >> Best rule #24980 for best value: >> intensional similarity = 4 >> extensional distance = 840 >> proper extension: 03g9xj; >> query: (?x6229, ?x4039) <- titles(?x811, ?x6229), nominated_for(?x4039, ?x6229), film(?x4039, ?x1644), people(?x3591, ?x4039) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #92 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 0522wp; *> query: (?x6229, 014x77) <- film(?x963, ?x6229), category(?x6229, ?x134), film(?x963, ?x4684), ?x4684 = 03nm_fh *> conf = 0.02 ranks of expected_values: 185, 186 EVAL 02q8ms8 film! 06ltr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 60.000 34.000 0.623 http://example.org/film/actor/film./film/performance/film EVAL 02q8ms8 film! 014x77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 60.000 34.000 0.623 http://example.org/film/actor/film./film/performance/film #10706-03s2y9 PRED entity: 03s2y9 PRED relation: people! PRED expected values: 0gk4g => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 40): 0gk4g (0.14 #1990, 0.13 #802, 0.13 #1858), 04psf (0.10 #7, 0.05 #271, 0.03 #139), 04p3w (0.09 #539, 0.06 #2057, 0.06 #1991), 0dq9p (0.07 #545, 0.07 #2063, 0.07 #2327), 0qcr0 (0.07 #529, 0.07 #1849, 0.06 #2047), 02k6hp (0.06 #367, 0.05 #103, 0.04 #763), 0m32h (0.06 #551, 0.03 #2069, 0.03 #2003), 02y0js (0.05 #2312, 0.05 #2048, 0.05 #1982), 02knxx (0.05 #98, 0.04 #560, 0.04 #362), 01mtqf (0.05 #70, 0.03 #532, 0.01 #994) >> Best rule #1990 for best value: >> intensional similarity = 3 >> extensional distance = 478 >> proper extension: 01l3j; >> query: (?x11625, 0gk4g) <- type_of_union(?x11625, ?x566), place_of_death(?x11625, ?x242), place_of_birth(?x241, ?x242) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s2y9 people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.140 http://example.org/people/cause_of_death/people #10705-07b3r9 PRED entity: 07b3r9 PRED relation: award_nominee! PRED expected values: 03772 => 104 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1101): 03772 (0.82 #53607, 0.81 #44285, 0.81 #67591), 07b3r9 (0.44 #39624, 0.33 #1026, 0.03 #74584), 01xndd (0.15 #3261, 0.06 #7921, 0.05 #14913), 08q3s0 (0.15 #3593, 0.06 #8253, 0.05 #15245), 0h53p1 (0.13 #2952, 0.05 #14604, 0.05 #19264), 0h5jg5 (0.13 #3978, 0.05 #8638, 0.04 #15630), 09hd16 (0.12 #3258, 0.05 #7918, 0.04 #14910), 0h584v (0.12 #3260, 0.04 #7920, 0.04 #10251), 0d7hg4 (0.10 #2908, 0.04 #7568, 0.03 #9899), 047cqr (0.10 #4503, 0.04 #9163, 0.03 #11494) >> Best rule #53607 for best value: >> intensional similarity = 3 >> extensional distance = 354 >> proper extension: 01tnbn; >> query: (?x4383, ?x3571) <- religion(?x4383, ?x1985), nominated_for(?x4383, ?x4384), award_nominee(?x4383, ?x3571) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07b3r9 award_nominee! 03772 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 43.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10704-01vw917 PRED entity: 01vw917 PRED relation: profession PRED expected values: 02hrh1q => 113 concepts (97 used for prediction) PRED predicted values (max 10 best out of 66): 02hrh1q (0.89 #11157, 0.88 #6944, 0.88 #11910), 0dz3r (0.68 #1052, 0.55 #602, 0.50 #2254), 09jwl (0.64 #3783, 0.63 #5294, 0.60 #5897), 0nbcg (0.52 #5910, 0.51 #2285, 0.48 #3645), 016z4k (0.44 #5278, 0.43 #5881, 0.42 #3767), 01d_h8 (0.33 #156, 0.31 #4376, 0.30 #5733), 0dxtg (0.33 #164, 0.29 #13547, 0.28 #11457), 02jknp (0.33 #158, 0.29 #13547, 0.22 #308), 02krf9 (0.33 #178, 0.29 #13547, 0.11 #328), 0n1h (0.30 #1362, 0.29 #912, 0.27 #1812) >> Best rule #11157 for best value: >> intensional similarity = 3 >> extensional distance = 1430 >> proper extension: 031bf1; 031sg0; 05g7q; 0151xv; 05qtcv; 049468; >> query: (?x6576, 02hrh1q) <- film(?x6576, ?x6298), titles(?x53, ?x6298), featured_film_locations(?x6298, ?x479) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vw917 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 97.000 0.890 http://example.org/people/person/profession #10703-0420y PRED entity: 0420y PRED relation: gender PRED expected values: 05zppz => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.93 #79, 0.93 #49, 0.93 #108), 02zsn (0.74 #126, 0.62 #30, 0.57 #173) >> Best rule #79 for best value: >> intensional similarity = 5 >> extensional distance = 72 >> proper extension: 012cph; 082mw; >> query: (?x11830, 05zppz) <- influenced_by(?x10654, ?x11830), influenced_by(?x5796, ?x11830), influenced_by(?x10313, ?x10654), nationality(?x5796, ?x94), ?x10313 = 07lp1 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0420y gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.932 http://example.org/people/person/gender #10702-07hwkr PRED entity: 07hwkr PRED relation: languages_spoken PRED expected values: 0jzc 05qqm 06x8y => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 93): 02h40lc (0.76 #280, 0.62 #348, 0.56 #35), 0h407 (0.20 #100, 0.16 #171, 0.16 #136), 03x42 (0.13 #97, 0.11 #168, 0.11 #133), 0jzc (0.11 #43, 0.09 #288, 0.07 #356), 05f_3 (0.11 #46, 0.06 #291, 0.06 #529), 05qqm (0.11 #58, 0.04 #541, 0.04 #609), 01jb8r (0.11 #204, 0.07 #377, 0.06 #309), 07qv_ (0.11 #192, 0.06 #297, 0.05 #365), 03_9r (0.11 #178, 0.04 #555, 0.03 #657), 0999q (0.11 #189, 0.03 #736, 0.03 #805) >> Best rule #280 for best value: >> intensional similarity = 9 >> extensional distance = 31 >> proper extension: 071x0k; 078vc; 078ds; 04czx7; >> query: (?x3584, 02h40lc) <- languages_spoken(?x3584, ?x5607), language(?x10192, ?x5607), language(?x4844, ?x5607), language(?x2928, ?x5607), ?x4844 = 02hfk5, ?x2928 = 07024, languages(?x380, ?x5607), film_crew_role(?x10192, ?x468), service_language(?x127, ?x5607) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 0fk3s; 0c41n; *> query: (?x3584, 0jzc) <- languages_spoken(?x3584, ?x5671), languages_spoken(?x3584, ?x5607), ?x5607 = 064_8sq, language(?x11296, ?x5671), language(?x6533, ?x5671), ?x11296 = 03k8th, ?x6533 = 02n72k *> conf = 0.11 ranks of expected_values: 4, 6 EVAL 07hwkr languages_spoken 06x8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 28.000 28.000 0.758 http://example.org/people/ethnicity/languages_spoken EVAL 07hwkr languages_spoken 05qqm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 28.000 28.000 0.758 http://example.org/people/ethnicity/languages_spoken EVAL 07hwkr languages_spoken 0jzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 28.000 28.000 0.758 http://example.org/people/ethnicity/languages_spoken #10701-05cljf PRED entity: 05cljf PRED relation: award PRED expected values: 099vwn => 131 concepts (129 used for prediction) PRED predicted values (max 10 best out of 297): 01by1l (0.44 #513, 0.39 #3320, 0.35 #1716), 0gqz2 (0.43 #1685, 0.19 #482, 0.13 #10507), 01bgqh (0.38 #1647, 0.38 #444, 0.27 #3251), 0c4z8 (0.38 #1676, 0.31 #473, 0.20 #6488), 09sb52 (0.35 #2848, 0.27 #4853, 0.27 #7259), 025m8l (0.29 #1723, 0.13 #6535, 0.12 #520), 03qbh5 (0.28 #3410, 0.23 #6618, 0.23 #4212), 02x17c2 (0.28 #1820, 0.13 #6632, 0.12 #617), 099vwn (0.26 #1817, 0.12 #614, 0.08 #3822), 01c427 (0.20 #886, 0.17 #4094, 0.17 #3292) >> Best rule #513 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0840vq; 01vw20h; 0hz_1; >> query: (?x226, 01by1l) <- location(?x226, ?x4978), award_winner(?x139, ?x226), type_of_union(?x226, ?x566), ?x139 = 05pd94v >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1817 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 0d193h; 01cblr; 01v6480; *> query: (?x226, 099vwn) <- category(?x226, ?x134), award(?x226, ?x2585), ?x2585 = 054ks3 *> conf = 0.26 ranks of expected_values: 9 EVAL 05cljf award 099vwn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 131.000 129.000 0.438 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10700-07sgfsl PRED entity: 07sgfsl PRED relation: people! PRED expected values: 0x67 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 36): 0x67 (0.16 #164, 0.14 #626, 0.13 #241), 041rx (0.12 #4, 0.12 #312, 0.12 #81), 033tf_ (0.09 #238, 0.08 #315, 0.07 #546), 048z7l (0.07 #194, 0.04 #348, 0.03 #271), 02ctzb (0.06 #246, 0.06 #323, 0.04 #169), 0g8_vp (0.06 #22, 0.06 #99, 0.02 #253), 07bch9 (0.06 #254, 0.05 #331, 0.03 #408), 0xnvg (0.05 #167, 0.05 #244, 0.05 #706), 02w7gg (0.05 #1157, 0.05 #1234, 0.05 #541), 07hwkr (0.05 #243, 0.04 #320, 0.04 #936) >> Best rule #164 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 0byfz; 03y82t6; 01h1b; 0gr69; >> query: (?x2780, 0x67) <- award_winner(?x822, ?x2780), award_nominee(?x2780, ?x1824), person(?x5639, ?x2780) >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07sgfsl people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.162 http://example.org/people/ethnicity/people #10699-012c6x PRED entity: 012c6x PRED relation: type_of_union PRED expected values: 04ztj => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.89 #21, 0.88 #17, 0.88 #25), 01g63y (0.14 #106, 0.13 #162, 0.12 #102) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 08b8vd; >> query: (?x773, 04ztj) <- nationality(?x773, ?x94), place_of_burial(?x773, ?x7496), profession(?x773, ?x524), film(?x773, ?x1072) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012c6x type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.889 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10698-0fxrk PRED entity: 0fxrk PRED relation: category PRED expected values: 08mbj5d => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.77 #3, 0.64 #22, 0.62 #1) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 018jk2; 018qpq; 0193fp; 01f1ps; 018jkl; 018qd6; 018qt8; >> query: (?x11890, 08mbj5d) <- contains(?x252, ?x11890), ?x252 = 03_3d, country(?x11890, ?x252) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fxrk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.769 http://example.org/common/topic/webpage./common/webpage/category #10697-038w8 PRED entity: 038w8 PRED relation: student! PRED expected values: 05zl0 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 218): 03ksy (0.33 #633, 0.25 #2214, 0.20 #4324), 08815 (0.33 #2, 0.20 #3692, 0.18 #5275), 017hnw (0.33 #509, 0.14 #1563, 0.10 #4199), 01mpwj (0.25 #634, 0.19 #2215, 0.18 #9596), 01fpvz (0.17 #11, 0.14 #5284, 0.13 #5811), 017v3q (0.14 #1826, 0.12 #2353, 0.10 #4463), 02bq1j (0.14 #5440, 0.12 #6494, 0.12 #7021), 05qgd9 (0.12 #6790, 0.11 #9952, 0.08 #7317), 07x4c (0.11 #2894, 0.10 #5004, 0.07 #1313), 06pwq (0.11 #3175, 0.08 #539, 0.08 #7393) >> Best rule #633 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0dq2k; 06c97; >> query: (?x11869, 03ksy) <- people(?x4195, ?x11869), basic_title(?x11869, ?x346), politician(?x8714, ?x11869), people(?x4322, ?x11869), ?x4195 = 02ctzb >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3365 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0424m; 042kg; 08959; *> query: (?x11869, 05zl0) <- nationality(?x11869, ?x94), profession(?x11869, ?x3342), taxonomy(?x11869, ?x939), ?x939 = 04n6k *> conf = 0.11 ranks of expected_values: 11 EVAL 038w8 student! 05zl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 87.000 87.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #10696-061xq PRED entity: 061xq PRED relation: school PRED expected values: 04bfg => 111 concepts (72 used for prediction) PRED predicted values (max 10 best out of 460): 07w0v (0.56 #743, 0.55 #925, 0.50 #1473), 07vyf (0.56 #790, 0.45 #972, 0.43 #1520), 06pwq (0.48 #2749, 0.48 #2565, 0.44 #2383), 01tx9m (0.44 #829, 0.29 #1559, 0.28 #2474), 065y4w7 (0.44 #2202, 0.39 #2385, 0.36 #2934), 06fq2 (0.40 #1957, 0.36 #1592, 0.32 #4152), 01lnyf (0.36 #1525, 0.33 #1890, 0.33 #795), 01j_06 (0.33 #1842, 0.33 #747, 0.29 #1477), 01jq0j (0.33 #845, 0.29 #1575, 0.28 #2490), 025v3k (0.31 #1147, 0.23 #2977, 0.22 #2428) >> Best rule #743 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 06x68; 01d5z; 0cqt41; 01yhm; 01yjl; 01slc; 05xvj; >> query: (?x4208, 07w0v) <- season(?x4208, ?x11834), season(?x4208, ?x9267), season(?x4208, ?x8529), season(?x4208, ?x701), school(?x4208, ?x2522), draft(?x4208, ?x1161), ?x701 = 05kcgsf, ?x9267 = 0dx84s, ?x8529 = 025ygws, colors(?x4208, ?x332), ?x11834 = 02h7s73 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1747 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 13 *> proper extension: 0jmnl; *> query: (?x4208, 04bfg) <- school(?x4208, ?x8706), school(?x4208, ?x4955), draft(?x4208, ?x1161), ?x4955 = 09f2j, currency(?x8706, ?x170), student(?x8706, ?x1817) *> conf = 0.13 ranks of expected_values: 66 EVAL 061xq school 04bfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 111.000 72.000 0.556 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #10695-026dx PRED entity: 026dx PRED relation: award PRED expected values: 019f4v => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 328): 02wkmx (0.84 #7169, 0.82 #7168, 0.80 #4779), 02wypbh (0.84 #7169, 0.82 #7168, 0.80 #4779), 02w_6xj (0.84 #7169, 0.82 #7168, 0.80 #4779), 09d28z (0.84 #7169, 0.82 #7168, 0.80 #4779), 054krc (0.51 #10837, 0.51 #8448, 0.45 #14024), 0l8z1 (0.44 #8426, 0.40 #10815, 0.35 #14002), 0gqz2 (0.42 #6846, 0.37 #12024, 0.36 #12822), 02qvyrt (0.38 #10876, 0.38 #8487, 0.37 #12070), 0gr51 (0.37 #10053, 0.37 #2484, 0.29 #3281), 054ks3 (0.37 #6906, 0.31 #9696, 0.30 #10492) >> Best rule #7169 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 015076; >> query: (?x4703, ?x8364) <- role(?x4703, ?x315), award_winner(?x8364, ?x4703), award(?x697, ?x8364) >> conf = 0.84 => this is the best rule for 4 predicted values *> Best rule #20376 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 259 *> proper extension: 0m32_; *> query: (?x4703, 019f4v) <- film(?x4703, ?x2475), award(?x4703, ?x601) *> conf = 0.35 ranks of expected_values: 11 EVAL 026dx award 019f4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 132.000 132.000 0.843 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10694-013pk3 PRED entity: 013pk3 PRED relation: profession PRED expected values: 02hrh1q 09jwl => 113 concepts (97 used for prediction) PRED predicted values (max 10 best out of 74): 02hrh1q (0.92 #8714, 0.90 #7699, 0.90 #4509), 09jwl (0.65 #6832, 0.64 #9588, 0.63 #9297), 01d_h8 (0.56 #296, 0.55 #3776, 0.53 #2761), 0nbcg (0.51 #6843, 0.49 #9599, 0.49 #753), 02jknp (0.44 #298, 0.29 #1458, 0.28 #3778), 016z4k (0.43 #6819, 0.42 #9575, 0.41 #9284), 0dz3r (0.40 #9573, 0.40 #6672, 0.40 #9282), 039v1 (0.29 #468, 0.25 #6848, 0.23 #9604), 0d1pc (0.26 #772, 0.26 #14069, 0.19 #1352), 02hv44_ (0.26 #14069, 0.05 #9044, 0.05 #8609) >> Best rule #8714 for best value: >> intensional similarity = 3 >> extensional distance = 523 >> proper extension: 031zkw; >> query: (?x7638, 02hrh1q) <- participant(?x7638, ?x488), film(?x7638, ?x2783), profession(?x7638, ?x987) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 013pk3 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 97.000 0.916 http://example.org/people/person/profession EVAL 013pk3 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 97.000 0.916 http://example.org/people/person/profession #10693-09969 PRED entity: 09969 PRED relation: symptom_of! PRED expected values: 0gxb2 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 68): 01j6t0 (0.82 #1136, 0.82 #1064, 0.81 #625), 0brgy (0.67 #836, 0.53 #578, 0.50 #455), 0cjf0 (0.58 #459, 0.56 #340, 0.53 #582), 0gxb2 (0.50 #456, 0.50 #405, 0.40 #579), 01cdt5 (0.38 #544, 0.38 #521, 0.38 #824), 02tfl8 (0.33 #572, 0.33 #27, 0.33 #2), 02y0js (0.33 #22, 0.33 #1, 0.30 #418), 0f3kl (0.33 #18, 0.30 #418, 0.30 #592), 097ns (0.33 #32, 0.30 #418, 0.30 #592), 08g5q7 (0.33 #62, 0.30 #418, 0.30 #592) >> Best rule #1136 for best value: >> intensional similarity = 15 >> extensional distance = 37 >> proper extension: 01g2q; >> query: (?x11307, 01j6t0) <- symptom_of(?x9438, ?x11307), symptom_of(?x9438, ?x13131), symptom_of(?x9438, ?x11739), symptom_of(?x9438, ?x10480), symptom_of(?x9438, ?x8675), symptom_of(?x9438, ?x7260), symptom_of(?x9438, ?x6655), ?x11739 = 0167bx, ?x10480 = 0h1n9, ?x13131 = 0d19y2, ?x6655 = 09d11, ?x8675 = 01gkcc, people(?x7260, ?x6934), ?x6934 = 0cgbf, risk_factors(?x7260, ?x231) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #456 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 10 *> proper extension: 011zdm; 0h1wz; *> query: (?x11307, 0gxb2) <- symptom_of(?x9438, ?x11307), symptom_of(?x6780, ?x11307), ?x9438 = 012qjw, symptom_of(?x6780, ?x13485), symptom_of(?x6780, ?x10480), symptom_of(?x6780, ?x9898), symptom_of(?x6780, ?x7586), risk_factors(?x11307, ?x4195), ?x10480 = 0h1n9, ?x13485 = 07s4l, ?x9898 = 09jg8, ?x7586 = 074m2 *> conf = 0.50 ranks of expected_values: 4 EVAL 09969 symptom_of! 0gxb2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 59.000 59.000 0.821 http://example.org/medicine/symptom/symptom_of #10692-02q690_ PRED entity: 02q690_ PRED relation: ceremony! PRED expected values: 0bfvw2 0fc9js 07kjk7c => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 272): 0gq_v (0.84 #2572, 0.43 #4430, 0.42 #4663), 0fc9js (0.83 #1994, 0.82 #1761, 0.67 #1529), 0gqy2 (0.80 #2665, 0.53 #4523, 0.50 #4989), 0gq_d (0.79 #2699, 0.52 #4557, 0.49 #5257), 0gqyl (0.79 #2628, 0.50 #4486, 0.47 #5186), 0gqwc (0.77 #2607, 0.51 #4465, 0.49 #4931), 018wng (0.77 #2585, 0.49 #4443, 0.47 #4676), 0k611 (0.75 #2620, 0.51 #4478, 0.49 #4944), 0gvx_ (0.75 #2678, 0.50 #4536, 0.48 #5002), 0p9sw (0.75 #2573, 0.49 #4431, 0.47 #4897) >> Best rule #2572 for best value: >> intensional similarity = 14 >> extensional distance = 54 >> proper extension: 0gpjbt; 0fzrtf; 05hmp6; 0n8_m93; >> query: (?x4760, 0gq_v) <- award_winner(?x4760, ?x10317), award_winner(?x4760, ?x8612), ceremony(?x3906, ?x4760), award(?x9892, ?x3906), honored_for(?x4760, ?x4581), nominated_for(?x3906, ?x3751), place_of_birth(?x8612, ?x6987), gender(?x8612, ?x514), film(?x8612, ?x4664), nominated_for(?x264, ?x4581), award_winner(?x9892, ?x1541), profession(?x3751, ?x319), genre(?x4581, ?x53), award_nominee(?x6980, ?x10317) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1994 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 10 *> proper extension: 0bxs_d; *> query: (?x4760, 0fc9js) <- award_winner(?x4760, ?x8612), ceremony(?x3906, ?x4760), award(?x2015, ?x3906), honored_for(?x4760, ?x4581), nominated_for(?x3906, ?x4588), place_of_birth(?x8612, ?x6987), gender(?x8612, ?x514), actor(?x4581, ?x1538), ?x2015 = 06brp0, nominated_for(?x375, ?x4581), nominated_for(?x8612, ?x6176), ?x4588 = 0l76z *> conf = 0.83 ranks of expected_values: 2, 20, 24 EVAL 02q690_ ceremony! 07kjk7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 28.000 28.000 0.839 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02q690_ ceremony! 0fc9js CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 28.000 28.000 0.839 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02q690_ ceremony! 0bfvw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 28.000 28.000 0.839 http://example.org/award/award_category/winners./award/award_honor/ceremony #10691-05xpms PRED entity: 05xpms PRED relation: film PRED expected values: 0f61tk => 99 concepts (81 used for prediction) PRED predicted values (max 10 best out of 285): 08jgk1 (0.58 #51933, 0.47 #19702, 0.47 #17910), 0ds2n (0.12 #524, 0.01 #25599), 08mg_b (0.12 #1122, 0.01 #63798), 01hvjx (0.12 #374, 0.01 #21867), 0266s9 (0.08 #19703, 0.07 #23284, 0.07 #30449), 01gglm (0.06 #1405, 0.01 #4987, 0.01 #3196), 0ch3qr1 (0.06 #976, 0.01 #4558, 0.01 #2767), 03nqnnk (0.06 #1024, 0.01 #56537, 0.01 #51166), 014lc_ (0.06 #2, 0.01 #28659, 0.01 #17913), 04tc1g (0.06 #132, 0.01 #7296, 0.01 #12670) >> Best rule #51933 for best value: >> intensional similarity = 3 >> extensional distance = 847 >> proper extension: 0glmv; 057hz; 01pcql; 02t_v1; 0d05fv; 01twdk; 02sh8y; 02mz_6; 03kxp7; 012gbb; ... >> query: (?x9272, ?x1631) <- film(?x9272, ?x607), nationality(?x9272, ?x94), award_winner(?x1631, ?x9272) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05xpms film 0f61tk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 81.000 0.578 http://example.org/film/actor/film./film/performance/film #10690-01bk1y PRED entity: 01bk1y PRED relation: school_type PRED expected values: 05pcjw => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 22): 05pcjw (0.58 #1, 0.58 #25, 0.47 #409), 01rs41 (0.55 #365, 0.54 #533, 0.53 #605), 05jxkf (0.51 #1444, 0.47 #316, 0.47 #1396), 01_9fk (0.24 #722, 0.23 #938, 0.21 #650), 07tf8 (0.22 #657, 0.20 #873, 0.18 #105), 01_srz (0.13 #411, 0.11 #267, 0.11 #627), 0bwd5 (0.08 #19, 0.05 #115, 0.03 #835), 01jlsn (0.08 #17, 0.03 #1193, 0.03 #833), 04qbv (0.06 #544, 0.05 #616, 0.05 #400), 06cs1 (0.05 #102, 0.05 #30, 0.04 #366) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 04_j5s; >> query: (?x7618, 05pcjw) <- contains(?x335, ?x7618), institution(?x3437, ?x7618), ?x335 = 059rby, ?x3437 = 02_xgp2 >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bk1y school_type 05pcjw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.583 http://example.org/education/educational_institution/school_type #10689-03v_5 PRED entity: 03v_5 PRED relation: place! PRED expected values: 03v_5 => 164 concepts (95 used for prediction) PRED predicted values (max 10 best out of 330): 03v_5 (0.14 #43368, 0.11 #37158, 0.09 #47508), 059rby (0.14 #43368, 0.11 #37158, 0.09 #47508), 09c7w0 (0.14 #43368, 0.11 #37158, 0.09 #47508), 0fplg (0.09 #3098, 0.06 #24256, 0.05 #19097), 02_286 (0.08 #531, 0.08 #14, 0.05 #2595), 0dq16 (0.08 #632, 0.08 #115, 0.05 #2696), 0y617 (0.08 #912, 0.08 #395, 0.04 #4010), 02zp1t (0.08 #942, 0.08 #425, 0.04 #4040), 071cn (0.08 #597, 0.08 #80, 0.04 #3178), 019fh (0.08 #595, 0.04 #3693, 0.04 #4209) >> Best rule #43368 for best value: >> intensional similarity = 4 >> extensional distance = 321 >> proper extension: 0drsm; 0mwh1; 0fxyd; 0nr_q; 0bxqq; 0dc3_; 0n5yv; 0fczy; 0kv2r; 0mwvq; ... >> query: (?x1730, ?x94) <- contains(?x1730, ?x13963), source(?x1730, ?x958), ?x958 = 0jbk9, contains(?x94, ?x13963) >> conf = 0.14 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 03v_5 place! 03v_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 95.000 0.140 http://example.org/location/hud_county_place/place #10688-0gtvrv3 PRED entity: 0gtvrv3 PRED relation: film_release_region PRED expected values: 035qy => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 111): 035qy (0.89 #607, 0.87 #2338, 0.85 #1330), 03_3d (0.88 #1159, 0.86 #1736, 0.85 #1304), 06t2t (0.84 #629, 0.80 #2360, 0.76 #1207), 047yc (0.84 #601, 0.63 #1179, 0.58 #2332), 0k6nt (0.83 #1464, 0.83 #1320, 0.83 #3192), 0b90_r (0.83 #2310, 0.81 #1302, 0.80 #1446), 05qx1 (0.79 #611, 0.51 #1189, 0.50 #467), 05v8c (0.78 #1168, 0.68 #590, 0.67 #2321), 03rt9 (0.77 #2319, 0.76 #1166, 0.74 #1311), 05b4w (0.77 #2795, 0.77 #3515, 0.76 #2363) >> Best rule #607 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 0jjy0; 0872p_c; 0fpkhkz; 05qbckf; 0gd0c7x; 02yvct; 017jd9; 047vnkj; >> query: (?x1463, 035qy) <- film_crew_role(?x1463, ?x468), film_release_region(?x1463, ?x1471), film_release_region(?x1463, ?x1003), ?x1003 = 03gj2, film(?x5898, ?x1463), ?x1471 = 07t21 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gtvrv3 film_release_region 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10687-016ntp PRED entity: 016ntp PRED relation: role PRED expected values: 01vj9c => 112 concepts (55 used for prediction) PRED predicted values (max 10 best out of 114): 0l14qv (0.50 #366, 0.39 #728, 0.38 #818), 026t6 (0.35 #274, 0.29 #184, 0.20 #726), 05842k (0.35 #429, 0.32 #791, 0.32 #881), 05148p4 (0.32 #1536, 0.30 #1535, 0.28 #1717), 03qjg (0.32 #1536, 0.30 #1535, 0.28 #1717), 0l14md (0.32 #1536, 0.30 #1535, 0.28 #1717), 03m5k (0.32 #1536, 0.30 #1535, 0.28 #1717), 07y_7 (0.32 #1536, 0.30 #1535, 0.28 #1717), 01vj9c (0.25 #372, 0.23 #1455, 0.22 #824), 03gvt (0.24 #790, 0.23 #880, 0.20 #428) >> Best rule #366 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 01wl38s; 0kzy0; 01l4zqz; 06k02; 0161sp; 06449; 0p3sf; 0m_v0; 050z2; 02dbp7; ... >> query: (?x3168, 0l14qv) <- role(?x3168, ?x1495), profession(?x3168, ?x1614), ?x1495 = 013y1f, ?x1614 = 01c72t, artists(?x302, ?x3168) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #372 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 01wl38s; 0kzy0; 01l4zqz; 06k02; 0161sp; 06449; 0p3sf; 0m_v0; 050z2; 02dbp7; ... *> query: (?x3168, 01vj9c) <- role(?x3168, ?x1495), profession(?x3168, ?x1614), ?x1495 = 013y1f, ?x1614 = 01c72t, artists(?x302, ?x3168) *> conf = 0.25 ranks of expected_values: 9 EVAL 016ntp role 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 112.000 55.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #10686-03v1w7 PRED entity: 03v1w7 PRED relation: nominated_for PRED expected values: 0ds11z 04pk1f => 105 concepts (49 used for prediction) PRED predicted values (max 10 best out of 589): 04pk1f (0.43 #17765, 0.40 #22613, 0.39 #12921), 02vyyl8 (0.43 #17765, 0.40 #22613, 0.39 #12921), 09g7vfw (0.43 #17765, 0.40 #22613, 0.39 #12921), 04gknr (0.43 #17765, 0.40 #22613, 0.39 #12921), 0ds11z (0.43 #17765, 0.40 #22613, 0.39 #12921), 01q2nx (0.43 #17765, 0.40 #22613, 0.39 #12921), 0m313 (0.33 #1626, 0.03 #8086, 0.02 #6471), 01cmp9 (0.33 #2566, 0.03 #31640, 0.03 #7411), 05fgr_ (0.33 #2825, 0.03 #9285, 0.02 #7670), 05dl1s (0.17 #1527, 0.08 #19382, 0.08 #69444) >> Best rule #17765 for best value: >> intensional similarity = 2 >> extensional distance = 268 >> proper extension: 09ftwr; >> query: (?x6369, ?x485) <- award_winner(?x1307, ?x6369), produced_by(?x485, ?x6369) >> conf = 0.43 => this is the best rule for 6 predicted values ranks of expected_values: 1, 5 EVAL 03v1w7 nominated_for 04pk1f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 49.000 0.430 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 03v1w7 nominated_for 0ds11z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 49.000 0.430 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10685-018wl5 PRED entity: 018wl5 PRED relation: form_of_government! PRED expected values: 0chghy 059j2 0d05q4 0n3g 0jdx => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 532): 016wzw (0.50 #655, 0.50 #565, 0.40 #521), 04hzj (0.50 #609, 0.40 #480, 0.33 #263), 04xn_ (0.50 #598, 0.40 #469, 0.33 #74), 05v8c (0.50 #536, 0.40 #407, 0.33 #12), 01p1v (0.50 #556, 0.40 #427, 0.33 #32), 0jhd (0.50 #620, 0.40 #491, 0.33 #96), 02kcz (0.50 #615, 0.40 #486, 0.33 #91), 0j4b (0.50 #600, 0.40 #471, 0.33 #76), 05tr7 (0.50 #596, 0.40 #467, 0.33 #72), 06sw9 (0.50 #592, 0.40 #463, 0.33 #68) >> Best rule #655 for best value: >> intensional similarity = 123 >> extensional distance = 4 >> proper extension: 06cx9; >> query: (?x1926, ?x2843) <- form_of_government(?x13249, ?x1926), form_of_government(?x10457, ?x1926), form_of_government(?x9874, ?x1926), form_of_government(?x8778, ?x1926), form_of_government(?x5274, ?x1926), form_of_government(?x304, ?x1926), form_of_government(?x279, ?x1926), participating_countries(?x784, ?x5274), combatants(?x1229, ?x5274), religion(?x10457, ?x109), ?x109 = 01lp8, film_release_region(?x11209, ?x304), film_release_region(?x11065, ?x304), film_release_region(?x9432, ?x304), film_release_region(?x7502, ?x304), film_release_region(?x7204, ?x304), film_release_region(?x7114, ?x304), film_release_region(?x6235, ?x304), film_release_region(?x6216, ?x304), film_release_region(?x6095, ?x304), film_release_region(?x5826, ?x304), film_release_region(?x5713, ?x304), film_release_region(?x5317, ?x304), film_release_region(?x3998, ?x304), film_release_region(?x3565, ?x304), film_release_region(?x3491, ?x304), film_release_region(?x3377, ?x304), film_release_region(?x3287, ?x304), film_release_region(?x2441, ?x304), film_release_region(?x2340, ?x304), film_release_region(?x2189, ?x304), film_release_region(?x2155, ?x304), film_release_region(?x2050, ?x304), film_release_region(?x1463, ?x304), film_release_region(?x1392, ?x304), film_release_region(?x409, ?x304), film_release_region(?x186, ?x304), film_release_region(?x66, ?x304), ?x5826 = 0gl02yg, film(?x815, ?x2189), film_release_region(?x3565, ?x142), ?x6095 = 0bq6ntw, ?x2050 = 01fmys, crewmember(?x2189, ?x5664), nominated_for(?x6860, ?x2189), nominated_for(?x2393, ?x2189), nominated_for(?x1243, ?x2189), ?x7204 = 0280061, film(?x2587, ?x7114), ?x11209 = 04fjzv, honored_for(?x2220, ?x2189), country(?x1352, ?x5274), language(?x3998, ?x254), contains(?x10457, ?x1961), ?x9432 = 0gvt53w, film_crew_role(?x2441, ?x137), nominated_for(?x8704, ?x2441), genre(?x2189, ?x53), produced_by(?x2189, ?x459), country(?x1121, ?x304), country(?x471, ?x304), country(?x359, ?x304), award(?x1392, ?x2209), film_release_region(?x7114, ?x2843), ?x186 = 02vxq9m, ?x2843 = 016wzw, nominated_for(?x198, ?x1392), ?x2209 = 0gr42, ?x3377 = 0gj8nq2, category(?x409, ?x134), ?x6235 = 05b6rdt, ?x6860 = 018wdw, ?x142 = 0jgd, ?x3491 = 0gtvpkw, time_zones(?x304, ?x2864), language(?x2189, ?x90), executive_produced_by(?x3998, ?x1387), participating_countries(?x1741, ?x304), ?x5713 = 0cc97st, nominated_for(?x3181, ?x1392), genre(?x2441, ?x225), ?x7502 = 0233bn, production_companies(?x2441, ?x1478), ?x3287 = 026njb5, olympics(?x304, ?x391), titles(?x2480, ?x3565), nominated_for(?x4297, ?x5317), ?x66 = 014lc_, featured_film_locations(?x3565, ?x1523), executive_produced_by(?x2155, ?x3456), ?x1121 = 0bynt, film_crew_role(?x2189, ?x281), ?x2393 = 02x258x, film(?x6980, ?x1463), film(?x2499, ?x2189), organization(?x8778, ?x127), medal(?x5274, ?x1242), production_companies(?x11065, ?x1850), film_crew_role(?x3565, ?x2095), ?x1229 = 059j2, currency(?x9874, ?x170), film(?x274, ?x3565), jurisdiction_of_office(?x182, ?x5274), ?x198 = 040njc, sport(?x59, ?x471), sports(?x358, ?x359), film(?x3462, ?x2189), member_states(?x2106, ?x304), ?x6980 = 0zcbl, written_by(?x11065, ?x2967), film(?x1194, ?x1392), adjoins(?x13249, ?x2291), ?x2340 = 0fpv_3_, produced_by(?x2441, ?x1039), film(?x488, ?x2155), administrative_parent(?x8778, ?x551), ?x6216 = 06fcqw, ?x3181 = 01846t, athlete(?x471, ?x208), service_location(?x610, ?x279), film(?x11797, ?x5317), ?x1243 = 0gr0m, film_release_distribution_medium(?x3565, ?x81) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #414 for first EXPECTED value: *> intensional similarity = 161 *> extensional distance = 3 *> proper extension: 01d9r3; *> query: (?x1926, 059j2) <- form_of_government(?x11774, ?x1926), form_of_government(?x11553, ?x1926), form_of_government(?x10457, ?x1926), form_of_government(?x5274, ?x1926), form_of_government(?x3550, ?x1926), form_of_government(?x3120, ?x1926), form_of_government(?x304, ?x1926), participating_countries(?x784, ?x5274), combatants(?x7430, ?x5274), religion(?x10457, ?x109), ?x109 = 01lp8, film_release_region(?x11209, ?x304), film_release_region(?x10404, ?x304), film_release_region(?x9902, ?x304), film_release_region(?x9529, ?x304), film_release_region(?x9201, ?x304), film_release_region(?x8137, ?x304), film_release_region(?x7678, ?x304), film_release_region(?x7651, ?x304), film_release_region(?x7524, ?x304), film_release_region(?x7493, ?x304), film_release_region(?x7170, ?x304), film_release_region(?x6882, ?x304), film_release_region(?x6661, ?x304), film_release_region(?x6178, ?x304), film_release_region(?x6078, ?x304), film_release_region(?x5929, ?x304), film_release_region(?x5826, ?x304), film_release_region(?x5688, ?x304), film_release_region(?x5220, ?x304), film_release_region(?x5162, ?x304), film_release_region(?x5137, ?x304), film_release_region(?x5016, ?x304), film_release_region(?x4607, ?x304), film_release_region(?x4518, ?x304), film_release_region(?x4453, ?x304), film_release_region(?x4315, ?x304), film_release_region(?x3287, ?x304), film_release_region(?x3268, ?x304), film_release_region(?x3252, ?x304), film_release_region(?x2896, ?x304), film_release_region(?x2717, ?x304), film_release_region(?x2709, ?x304), film_release_region(?x2695, ?x304), film_release_region(?x2501, ?x304), film_release_region(?x2050, ?x304), film_release_region(?x1642, ?x304), film_release_region(?x1602, ?x304), film_release_region(?x1546, ?x304), film_release_region(?x1518, ?x304), film_release_region(?x1470, ?x304), film_release_region(?x1456, ?x304), film_release_region(?x1392, ?x304), film_release_region(?x1219, ?x304), film_release_region(?x1173, ?x304), film_release_region(?x559, ?x304), film_release_region(?x430, ?x304), film_release_region(?x409, ?x304), ?x5826 = 0gl02yg, ?x409 = 0gtv7pk, nationality(?x2083, ?x304), country(?x4045, ?x11774), ?x6661 = 0k7tq, ?x2695 = 047svrl, ?x9902 = 0j8f09z, olympics(?x5274, ?x778), country(?x12943, ?x304), country(?x10585, ?x304), country(?x2867, ?x304), country(?x2315, ?x304), country(?x779, ?x304), country(?x766, ?x304), official_language(?x10457, ?x11341), ?x2717 = 0k5g9, ?x2501 = 040rmy, ?x779 = 096f8, ?x5162 = 0j3d9tn, ?x7493 = 0btpm6, ?x2050 = 01fmys, olympics(?x10457, ?x2966), ?x9201 = 056k77g, ?x3287 = 026njb5, capital(?x5274, ?x1464), olympics(?x304, ?x2630), olympics(?x304, ?x2432), medal(?x304, ?x2132), medal(?x304, ?x1242), ?x5137 = 0kb07, ?x12943 = 01yfj, ?x2867 = 02y8z, country(?x6005, ?x304), ?x6882 = 043tvp3, ?x5016 = 062zm5h, film_crew_role(?x3252, ?x1284), ?x1242 = 02lq5w, ?x2709 = 06ztvyx, ?x1546 = 0d6b7, olympics(?x304, ?x8584), olympics(?x304, ?x7441), olympics(?x304, ?x6893), organization(?x10457, ?x127), ?x4453 = 0dr_9t7, ?x1456 = 0cz8mkh, combatants(?x9203, ?x5274), film_release_region(?x3252, ?x2843), film_release_region(?x3252, ?x1003), contains(?x7273, ?x11553), administrative_parent(?x3120, ?x551), ?x4315 = 0sxkh, organization(?x3120, ?x9102), nominated_for(?x5886, ?x3252), ?x1642 = 0bq8tmw, currency(?x11774, ?x170), ?x7651 = 0h95927, ?x7170 = 02pxst, ?x559 = 05p1tzf, ?x3268 = 02x6dqb, ?x1602 = 0gxtknx, ?x4518 = 0hgnl3t, ?x5929 = 03nqnnk, ?x2315 = 06wrt, ?x7441 = 0ldqf, ?x11209 = 04fjzv, ?x8137 = 0gtx63s, ?x7524 = 01cm8w, ?x5688 = 0dr89x, ?x1003 = 03gj2, ?x1470 = 03twd6, organization(?x304, ?x312), ?x6078 = 04pk1f, ?x2630 = 0swff, ?x10404 = 01s9vc, ?x1518 = 04w7rn, ?x2843 = 016wzw, ?x6893 = 019n8z, ?x4045 = 06z6r, ?x9102 = 041288, ?x784 = 018ctl, ?x430 = 0m2kd, ?x2896 = 0645k5, ?x1284 = 0ch6mp2, film_crew_role(?x6005, ?x5136), ?x8584 = 01f1jf, ?x1392 = 017gm7, countries_within(?x6956, ?x3550), languages(?x3129, ?x11341), ?x766 = 01hp22, ?x7430 = 01mk6, ?x1173 = 0872p_c, ?x7678 = 0gvvf4j, taxonomy(?x11774, ?x939), ?x5136 = 089g0h, ?x1219 = 03bx2lk, ?x10585 = 01gqfm, ?x5886 = 0fq9zdv, ?x9529 = 0gwf191, genre(?x6178, ?x162), ?x2132 = 02lpp7, ?x2432 = 0nbjq, nominated_for(?x286, ?x4607), ?x5220 = 0kbf1 *> conf = 0.40 ranks of expected_values: 14, 39, 84, 277, 284 EVAL 018wl5 form_of_government! 0jdx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 018wl5 form_of_government! 0n3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 018wl5 form_of_government! 0d05q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 018wl5 form_of_government! 059j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 018wl5 form_of_government! 0chghy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 6.000 6.000 0.500 http://example.org/location/country/form_of_government #10684-01jgkj2 PRED entity: 01jgkj2 PRED relation: instrumentalists! PRED expected values: 05r5c => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 123): 05r5c (0.75 #7, 0.53 #262, 0.51 #603), 05148p4 (0.62 #19, 0.40 #615, 0.39 #274), 03gvt (0.30 #341, 0.29 #1281, 0.28 #682), 0l15bq (0.30 #341, 0.29 #1281, 0.28 #682), 026t6 (0.25 #3, 0.21 #88, 0.12 #2571), 03qjg (0.23 #645, 0.21 #134, 0.18 #304), 0l14md (0.21 #91, 0.14 #859, 0.13 #1201), 04rzd (0.16 #290, 0.16 #120, 0.11 #631), 06ch55 (0.12 #80, 0.05 #7438, 0.05 #1104), 01xqw (0.12 #66, 0.05 #7438, 0.04 #662) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0837ql; >> query: (?x9176, 05r5c) <- award_winner(?x567, ?x9176), instrumentalists(?x227, ?x9176), ?x567 = 01d38g >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jgkj2 instrumentalists! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.750 http://example.org/music/instrument/instrumentalists #10683-01xndd PRED entity: 01xndd PRED relation: award_nominee! PRED expected values: 047cqr => 132 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1243): 0697kh (0.82 #16288, 0.81 #16289, 0.81 #127974), 04wvhz (0.82 #16288, 0.81 #16289, 0.81 #127974), 0brkwj (0.82 #16288, 0.81 #127974, 0.81 #144265), 047cqr (0.82 #16288, 0.81 #127974, 0.81 #144265), 09_99w (0.78 #23269, 0.77 #39558, 0.77 #6981), 06jrhz (0.78 #23269, 0.77 #39558, 0.77 #6981), 01xndd (0.27 #151249, 0.23 #37231, 0.09 #5583), 06pj8 (0.27 #151249, 0.08 #12085, 0.06 #7431), 09gb9xh (0.27 #151249, 0.07 #2255, 0.06 #16216), 09b0xs (0.27 #151249, 0.07 #703, 0.04 #14664) >> Best rule #16288 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 07d3x; >> query: (?x4035, ?x1039) <- award_nominee(?x4035, ?x7301), award_nominee(?x4035, ?x1039), award_winner(?x4921, ?x7301), program_creator(?x4084, ?x4035) >> conf = 0.82 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 01xndd award_nominee! 047cqr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 68.000 0.819 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10682-02qdyj PRED entity: 02qdyj PRED relation: citytown PRED expected values: 096gm => 214 concepts (214 used for prediction) PRED predicted values (max 10 best out of 174): 02_286 (0.96 #33483, 0.68 #40845, 0.58 #45260), 07dfk (0.54 #13809, 0.48 #16758, 0.48 #16391), 04jpl (0.44 #3310, 0.29 #5513, 0.29 #2209), 071vr (0.42 #19491, 0.34 #28320, 0.33 #523), 0h7h6 (0.39 #14333, 0.39 #14702, 0.37 #14334), 0hsqf (0.39 #14333, 0.39 #14702, 0.37 #14334), 024bqj (0.39 #14333, 0.39 #14702, 0.37 #14334), 0chgzm (0.39 #14702, 0.37 #14334, 0.34 #17648), 030qb3t (0.35 #13256, 0.27 #17308, 0.23 #12885), 0r00l (0.29 #14614, 0.24 #17560, 0.19 #22714) >> Best rule #33483 for best value: >> intensional similarity = 7 >> extensional distance = 96 >> proper extension: 07t65; 04sylm; 01hb1t; 027kmrb; 03m9c8; 0dbpwb; 05nrkb; 048t8y; 06thjt; 02vptk_; ... >> query: (?x6141, 02_286) <- citytown(?x6141, ?x2474), featured_film_locations(?x6114, ?x2474), featured_film_locations(?x6058, ?x2474), featured_film_locations(?x2490, ?x2474), ?x6058 = 03vyw8, ?x6114 = 01flv_, ?x2490 = 026p4q7 >> conf = 0.96 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02qdyj citytown 096gm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 214.000 214.000 0.959 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #10681-01j5ql PRED entity: 01j5ql PRED relation: genre PRED expected values: 028v3 => 110 concepts (67 used for prediction) PRED predicted values (max 10 best out of 132): 02qfv5d (0.74 #351, 0.73 #7026, 0.72 #7611), 02l7c8 (0.50 #14, 0.31 #5398, 0.30 #2471), 05p553 (0.46 #6679, 0.39 #5153, 0.38 #6562), 02kdv5l (0.41 #4095, 0.38 #235, 0.33 #3745), 04xvlr (0.32 #5385, 0.30 #234, 0.28 #3157), 017fp (0.31 #129, 0.24 #480, 0.15 #3169), 03bxz7 (0.29 #519, 0.24 #168, 0.15 #4028), 03k9fj (0.27 #1063, 0.26 #5512, 0.25 #1645), 09blyk (0.23 #2133, 0.10 #4121, 0.10 #964), 060__y (0.23 #248, 0.23 #3171, 0.22 #3991) >> Best rule #351 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 08cfr1; 0gndh; >> query: (?x6778, ?x600) <- titles(?x600, ?x6778), production_companies(?x6778, ?x3323), genre(?x6778, ?x3515), ?x3515 = 082gq, genre(?x280, ?x600) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #2173 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 187 *> proper extension: 0k20s; *> query: (?x6778, 028v3) <- titles(?x600, ?x6778), country(?x6778, ?x94), genre(?x9614, ?x600), ?x9614 = 01q7h2 *> conf = 0.03 ranks of expected_values: 101 EVAL 01j5ql genre 028v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 110.000 67.000 0.741 http://example.org/film/film/genre #10680-0gzlb9 PRED entity: 0gzlb9 PRED relation: film! PRED expected values: 02y_2y => 67 concepts (15 used for prediction) PRED predicted values (max 10 best out of 745): 04zwtdy (0.42 #18741, 0.40 #12493, 0.40 #18740), 086k8 (0.42 #18741, 0.40 #12493, 0.40 #18740), 02y_2y (0.40 #12493, 0.40 #18740), 0159h6 (0.12 #73, 0.05 #6319, 0.03 #10483), 07r1h (0.12 #1090, 0.03 #3172, 0.03 #5254), 012q4n (0.12 #1138, 0.03 #3220, 0.02 #5302), 0k525 (0.12 #1846, 0.03 #3928, 0.02 #6010), 0bksh (0.12 #856, 0.03 #2938, 0.02 #5020), 03w4sh (0.12 #1147, 0.03 #3229, 0.02 #7393), 026670 (0.12 #1668, 0.03 #3750, 0.01 #5832) >> Best rule #18741 for best value: >> intensional similarity = 4 >> extensional distance = 188 >> proper extension: 0g60z; 0180mw; >> query: (?x8562, ?x4360) <- nominated_for(?x8562, ?x3619), nominated_for(?x102, ?x8562), nominated_for(?x4360, ?x8562), award_nominee(?x71, ?x4360) >> conf = 0.42 => this is the best rule for 2 predicted values *> Best rule #12493 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 175 *> proper extension: 034qmv; 0m2kd; 03s6l2; 0fgpvf; 03rtz1; 02q5g1z; 02rx2m5; 0g3zrd; 0jdgr; 05q54f5; ... *> query: (?x8562, ?x382) <- genre(?x8562, ?x1509), ?x1509 = 060__y, film(?x4360, ?x8562), nominated_for(?x382, ?x8562) *> conf = 0.40 ranks of expected_values: 3 EVAL 0gzlb9 film! 02y_2y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 67.000 15.000 0.420 http://example.org/film/actor/film./film/performance/film #10679-0413cff PRED entity: 0413cff PRED relation: country PRED expected values: 07ssc => 149 concepts (141 used for prediction) PRED predicted values (max 10 best out of 233): 0f8l9c (0.43 #195, 0.15 #1758, 0.15 #311), 035dk (0.41 #1010, 0.38 #1438, 0.38 #1132), 05cgv (0.41 #1010, 0.38 #1438, 0.38 #1132), 088q4 (0.41 #1010, 0.38 #1438, 0.38 #1132), 04v09 (0.38 #1438, 0.33 #1315, 0.33 #1982), 07ssc (0.38 #2242, 0.35 #5891, 0.32 #1938), 081yw (0.31 #1007, 0.22 #1133, 0.21 #1131), 0345h (0.29 #495, 0.29 #790, 0.26 #911), 04_1l0v (0.28 #4371, 0.27 #2774, 0.25 #4978), 0dg3n1 (0.28 #4371, 0.27 #2774, 0.25 #4978) >> Best rule #195 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 01bb9r; 09r94m; 05zvzf3; >> query: (?x5044, 0f8l9c) <- language(?x5044, ?x5359), film_release_region(?x5044, ?x94), genre(?x5044, ?x1014), genre(?x5044, ?x604), genre(?x9614, ?x1014), ?x604 = 0lsxr, ?x5359 = 0jzc, nominated_for(?x4234, ?x9614) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2242 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 98 *> proper extension: 0crc2cp; *> query: (?x5044, 07ssc) <- language(?x5044, ?x254), film_release_region(?x5044, ?x94), featured_film_locations(?x5044, ?x4600), featured_film_locations(?x5044, ?x3432), genre(?x5044, ?x53), teams(?x3432, ?x2433), location(?x1727, ?x4600), state_province_region(?x244, ?x4600) *> conf = 0.38 ranks of expected_values: 6 EVAL 0413cff country 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 149.000 141.000 0.429 http://example.org/film/film/country #10678-0pz91 PRED entity: 0pz91 PRED relation: profession PRED expected values: 01d_h8 018gz8 => 165 concepts (164 used for prediction) PRED predicted values (max 10 best out of 90): 01d_h8 (0.84 #7257, 0.84 #8127, 0.84 #2616), 02jknp (0.70 #2618, 0.61 #4213, 0.55 #4503), 018gz8 (0.57 #160, 0.50 #740, 0.41 #305), 0cbd2 (0.42 #7548, 0.41 #4792, 0.41 #8563), 016z4k (0.41 #6095, 0.36 #11172, 0.32 #8996), 02krf9 (0.41 #314, 0.34 #5801, 0.33 #11604), 0dz3r (0.40 #8994, 0.38 #6093, 0.36 #11170), 0np9r (0.36 #163, 0.34 #5801, 0.33 #11604), 0d1pc (0.34 #5801, 0.33 #11604, 0.29 #9283), 015cjr (0.34 #5801, 0.33 #11604, 0.24 #336) >> Best rule #7257 for best value: >> intensional similarity = 2 >> extensional distance = 347 >> proper extension: 04snp2; 0342vg; 0glyyw; 024t0y; 01b0k1; 03c9pqt; 0g_rs_; 024c1b; >> query: (?x1335, 01d_h8) <- produced_by(?x11980, ?x1335), nominated_for(?x5975, ?x11980) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0pz91 profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 165.000 164.000 0.840 http://example.org/people/person/profession EVAL 0pz91 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 164.000 0.840 http://example.org/people/person/profession #10677-02q7yfq PRED entity: 02q7yfq PRED relation: language PRED expected values: 06nm1 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 37): 064_8sq (0.17 #422, 0.14 #2025, 0.13 #192), 06b_j (0.14 #136, 0.12 #1227, 0.11 #1170), 06nm1 (0.14 #296, 0.13 #1329, 0.13 #1158), 0653m (0.14 #297, 0.06 #1501, 0.06 #1387), 02bjrlw (0.13 #344, 0.11 #172, 0.09 #975), 012w70 (0.12 #298, 0.04 #1388, 0.04 #1217), 04306rv (0.12 #1209, 0.11 #233, 0.11 #1323), 03_9r (0.08 #1556, 0.08 #1613, 0.08 #295), 0459q4 (0.08 #321, 0.03 #1525, 0.02 #1411), 0jzc (0.07 #1167, 0.07 #1224, 0.07 #1338) >> Best rule #422 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 02fn5r; >> query: (?x6806, 064_8sq) <- nominated_for(?x6806, ?x6005), category(?x6005, ?x134), nominated_for(?x154, ?x6005) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #296 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 64 *> proper extension: 0170z3; 02vxq9m; 0czyxs; 01hp5; 061681; 08gsvw; 06z8s_; 033g4d; 02pjc1h; 0pb33; ... *> query: (?x6806, 06nm1) <- genre(?x6806, ?x604), genre(?x6806, ?x225), ?x225 = 02kdv5l, nominated_for(?x154, ?x6806), ?x604 = 0lsxr *> conf = 0.14 ranks of expected_values: 3 EVAL 02q7yfq language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 64.000 0.171 http://example.org/film/film/language #10676-09cdxn PRED entity: 09cdxn PRED relation: type_of_union PRED expected values: 04ztj => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.76 #62, 0.75 #17, 0.73 #74), 01g63y (0.12 #163, 0.12 #2, 0.11 #135) >> Best rule #62 for best value: >> intensional similarity = 3 >> extensional distance = 194 >> proper extension: 0dky9n; 0p51w; 03bw6; 0gm34; 019fnv; 03f68r6; >> query: (?x6115, 04ztj) <- place_of_death(?x6115, ?x242), nominated_for(?x6115, ?x2779), award_winner(?x1243, ?x6115) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09cdxn type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.760 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10675-018js4 PRED entity: 018js4 PRED relation: music PRED expected values: 019x62 => 79 concepts (55 used for prediction) PRED predicted values (max 10 best out of 89): 0150t6 (0.12 #46, 0.08 #1937, 0.07 #257), 06fxnf (0.12 #69, 0.06 #2591, 0.06 #1960), 01mh8zn (0.12 #146, 0.02 #357, 0.01 #1409), 0146pg (0.12 #1061, 0.11 #221, 0.11 #2111), 094tsh6 (0.10 #7155, 0.10 #5260, 0.10 #2101), 0284n42 (0.10 #7155, 0.10 #5260, 0.10 #2101), 02qggqc (0.10 #7155, 0.10 #5260, 0.10 #2101), 02jxkw (0.06 #1192, 0.05 #2663, 0.05 #2032), 02bh9 (0.06 #1314, 0.06 #2993, 0.05 #2782), 02jxmr (0.06 #1965, 0.04 #2386, 0.03 #1337) >> Best rule #46 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 047vnkj; >> query: (?x155, 0150t6) <- nominated_for(?x9391, ?x155), film_release_region(?x155, ?x94), film(?x338, ?x155), ?x9391 = 094tsh6 >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018js4 music 019x62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 55.000 0.125 http://example.org/film/film/music #10674-02ckl3 PRED entity: 02ckl3 PRED relation: institution! PRED expected values: 019v9k => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 21): 02_xgp2 (0.77 #47, 0.76 #404, 0.75 #307), 013zdg (0.77 #47, 0.75 #307, 0.74 #428), 019v9k (0.61 #508, 0.61 #363, 0.61 #291), 0bkj86 (0.61 #123, 0.56 #218, 0.48 #314), 03bwzr4 (0.53 #225, 0.50 #130, 0.50 #37), 016t_3 (0.53 #214, 0.50 #26, 0.46 #358), 027f2w (0.50 #32, 0.33 #125, 0.29 #102), 0bjrnt (0.50 #28, 0.28 #121, 0.27 #169), 01rr_d (0.50 #40, 0.28 #133, 0.24 #110), 02cq61 (0.50 #41, 0.22 #134, 0.20 #182) >> Best rule #47 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03ksy; 0pspl; >> query: (?x11680, ?x3437) <- student(?x11680, ?x11288), student(?x11680, ?x7824), student(?x3437, ?x11288), ?x7824 = 05sj55, contains(?x94, ?x11680) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #508 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 192 *> proper extension: 01rr31; 015fsv; *> query: (?x11680, 019v9k) <- currency(?x11680, ?x170), institution(?x865, ?x11680), ?x865 = 02h4rq6 *> conf = 0.61 ranks of expected_values: 3 EVAL 02ckl3 institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 84.000 0.769 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #10673-03f3yfj PRED entity: 03f3yfj PRED relation: artists! PRED expected values: 05lwjc => 119 concepts (96 used for prediction) PRED predicted values (max 10 best out of 217): 0ggx5q (0.57 #3487, 0.51 #1627, 0.50 #77), 06j6l (0.56 #1599, 0.51 #3459, 0.37 #13383), 06by7 (0.49 #13356, 0.43 #12736, 0.42 #18008), 05bt6j (0.41 #3454, 0.38 #1594, 0.30 #5004), 0y3_8 (0.28 #3458, 0.18 #1598, 0.13 #3148), 02vjzr (0.28 #444, 0.17 #3544, 0.13 #5094), 016clz (0.27 #3105, 0.23 #19853, 0.22 #9306), 02ny8t (0.23 #3543, 0.10 #3233, 0.08 #5093), 0155w (0.21 #13440, 0.14 #10337, 0.14 #9407), 01lyv (0.20 #345, 0.18 #12749, 0.17 #13059) >> Best rule #3487 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 02twdq; >> query: (?x7909, 0ggx5q) <- artists(?x3996, ?x7909), category(?x7909, ?x134), ?x3996 = 02lnbg >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #3610 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 02twdq; *> query: (?x7909, 05lwjc) <- artists(?x3996, ?x7909), category(?x7909, ?x134), ?x3996 = 02lnbg *> conf = 0.12 ranks of expected_values: 21 EVAL 03f3yfj artists! 05lwjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 119.000 96.000 0.573 http://example.org/music/genre/artists #10672-03f4k PRED entity: 03f4k PRED relation: profession PRED expected values: 05vyk => 129 concepts (76 used for prediction) PRED predicted values (max 10 best out of 97): 02hrh1q (0.91 #2983, 0.76 #1648, 0.69 #5651), 09jwl (0.84 #8624, 0.81 #9219, 0.75 #5360), 02jknp (0.67 #1493, 0.28 #1048, 0.24 #3272), 01d_h8 (0.58 #1491, 0.34 #3270, 0.30 #600), 01c8w0 (0.57 #157, 0.55 #751, 0.53 #900), 0dz3r (0.50 #5342, 0.44 #6083, 0.44 #2822), 05vyk (0.48 #1282, 0.47 #985, 0.45 #836), 0cbd2 (0.47 #10542, 0.19 #5940, 0.18 #10690), 016z4k (0.44 #5788, 0.44 #8756, 0.42 #6085), 0dxtg (0.39 #1499, 0.29 #7875, 0.29 #8320) >> Best rule #2983 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 0kr5_; 06t61y; 01n8_g; 01xsbh; 0m31m; 01fdc0; 0gyx4; 01gw4f; 030g9z; 026_dq6; ... >> query: (?x9593, 02hrh1q) <- profession(?x9593, ?x1614), sibling(?x9593, ?x11034), profession(?x5815, ?x1614), ?x5815 = 01l7cxq >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #1282 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 01x66d; 02z81h; 0g7k2g; *> query: (?x9593, 05vyk) <- profession(?x9593, ?x1614), artists(?x5640, ?x9593), artists(?x5640, ?x352), ?x352 = 0hl3d *> conf = 0.48 ranks of expected_values: 7 EVAL 03f4k profession 05vyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 129.000 76.000 0.909 http://example.org/people/person/profession #10671-0jwl2 PRED entity: 0jwl2 PRED relation: actor PRED expected values: 02_p5w 083wr9 => 68 concepts (21 used for prediction) PRED predicted values (max 10 best out of 809): 05fnl9 (0.44 #4715, 0.13 #11925, 0.12 #10219), 02gf_l (0.38 #3316, 0.22 #12490, 0.08 #3669), 0sw62 (0.38 #3504, 0.17 #2587, 0.11 #12678), 02wrhj (0.38 #2886, 0.11 #12060, 0.08 #3669), 031c2r (0.38 #3606, 0.06 #12780, 0.02 #18285), 05b_7n (0.33 #1316, 0.33 #399, 0.08 #6819), 042xrr (0.33 #1287, 0.17 #6790, 0.14 #8624), 01rmnp (0.33 #2530, 0.13 #9868, 0.11 #13538), 01kwh5j (0.33 #2515, 0.13 #9853, 0.11 #13523), 016kft (0.33 #1621, 0.13 #11925, 0.08 #7124) >> Best rule #4715 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 03d34x8; 01rp13; 02_1ky; >> query: (?x4339, 05fnl9) <- actor(?x4339, ?x9493), actor(?x4339, ?x2910), award_winner(?x5585, ?x2910), profession(?x9493, ?x131), ?x5585 = 03nnm4t, place_of_birth(?x9493, ?x13370), language(?x2910, ?x254), film(?x2910, ?x508) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #3657 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 05f7w84; 019g8j; *> query: (?x4339, 083wr9) <- actor(?x4339, ?x12054), actor(?x4339, ?x9493), actor(?x4339, ?x2910), place_of_birth(?x9493, ?x13370), student(?x2909, ?x2910), film(?x2910, ?x508), ?x12054 = 0sw6y, profession(?x2910, ?x1146) *> conf = 0.12 ranks of expected_values: 141, 166 EVAL 0jwl2 actor 083wr9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 68.000 21.000 0.444 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 0jwl2 actor 02_p5w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 68.000 21.000 0.444 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10670-098n_m PRED entity: 098n_m PRED relation: film PRED expected values: 02qcr => 87 concepts (37 used for prediction) PRED predicted values (max 10 best out of 264): 071nw5 (0.64 #39381, 0.62 #51911, 0.61 #62652), 0bnzd (0.64 #39381, 0.62 #51911, 0.61 #62652), 051zy_b (0.09 #580, 0.02 #2370, 0.02 #4161), 07s846j (0.09 #671, 0.02 #2461, 0.02 #4252), 020bv3 (0.03 #2109, 0.03 #3900, 0.01 #50440), 02vrgnr (0.03 #2573, 0.03 #4364, 0.01 #22265), 011ysn (0.03 #2357, 0.03 #4148, 0.01 #30997), 011ycb (0.03 #2648, 0.03 #4439, 0.01 #16969), 03b1sb (0.03 #3295, 0.03 #5086), 034qzw (0.03 #16445, 0.02 #18235, 0.02 #20025) >> Best rule #39381 for best value: >> intensional similarity = 3 >> extensional distance = 860 >> proper extension: 02zq43; 07lmxq; 03m8lq; 01v3s2_; 04cf09; 01wjrn; 049k07; 07ymr5; 02lq10; 05wjnt; ... >> query: (?x5371, ?x6200) <- nominated_for(?x5371, ?x6200), film(?x5371, ?x10201), category(?x10201, ?x134) >> conf = 0.64 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 098n_m film 02qcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 37.000 0.641 http://example.org/film/actor/film./film/performance/film #10669-0yl_3 PRED entity: 0yl_3 PRED relation: institution! PRED expected values: 014mlp => 137 concepts (103 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.87 #278, 0.78 #726, 0.75 #629), 03bwzr4 (0.87 #290, 0.64 #1228, 0.60 #641), 014mlp (0.80 #729, 0.78 #655, 0.77 #679), 019v9k (0.73 #285, 0.69 #733, 0.65 #683), 0bkj86 (0.73 #284, 0.55 #1222, 0.51 #635), 027f2w (0.73 #286, 0.39 #700, 0.37 #637), 016t_3 (0.67 #279, 0.60 #1572, 0.54 #630), 013zdg (0.67 #283, 0.39 #700, 0.33 #7), 0bjrnt (0.47 #282, 0.40 #98, 0.40 #75), 01rr_d (0.47 #293, 0.39 #700, 0.33 #17) >> Best rule #278 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 07tg4; 0pspl; 07tds; 01nnsv; >> query: (?x12374, 02h4rq6) <- major_field_of_study(?x12374, ?x9079), major_field_of_study(?x12374, ?x2605), student(?x12374, ?x1975), institution(?x734, ?x12374), ?x2605 = 03g3w, ?x9079 = 0l5mz >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #729 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 126 *> proper extension: 020vx9; *> query: (?x12374, 014mlp) <- major_field_of_study(?x12374, ?x2981), major_field_of_study(?x12374, ?x2605), student(?x12374, ?x1975), ?x2981 = 02j62, student(?x2605, ?x445) *> conf = 0.80 ranks of expected_values: 3 EVAL 0yl_3 institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 137.000 103.000 0.867 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #10668-019w9j PRED entity: 019w9j PRED relation: country PRED expected values: 03_3d 0h7x 015qh => 40 concepts (37 used for prediction) PRED predicted values (max 10 best out of 330): 03_3d (0.86 #2290, 0.84 #6330, 0.83 #3056), 03rt9 (0.84 #3047, 0.80 #378, 0.72 #568), 015fr (0.83 #3065, 0.83 #2872, 0.82 #5378), 0d0vqn (0.82 #2676, 0.82 #1909, 0.81 #2481), 0k6nt (0.82 #5749, 0.80 #378, 0.75 #5162), 0jgd (0.82 #1903, 0.80 #378, 0.80 #1714), 0b90_r (0.82 #4010, 0.80 #378, 0.79 #2287), 07ylj (0.81 #2499, 0.80 #378, 0.78 #1549), 03gj2 (0.80 #378, 0.79 #2305, 0.72 #568), 01p1v (0.80 #378, 0.78 #1567, 0.75 #5161) >> Best rule #2290 for best value: >> intensional similarity = 47 >> extensional distance = 12 >> proper extension: 03rbzn; >> query: (?x3885, 03_3d) <- country(?x3885, ?x6305), country(?x3885, ?x2188), country(?x3885, ?x1264), country(?x3885, ?x608), country(?x3885, ?x390), country(?x3885, ?x205), ?x390 = 0chghy, ?x1264 = 0345h, olympics(?x3885, ?x778), ?x205 = 03rjj, ?x778 = 0kbvb, country(?x2867, ?x608), administrative_parent(?x608, ?x551), film_release_region(?x5271, ?x608), film_release_region(?x4610, ?x608), film_release_region(?x4047, ?x608), film_release_region(?x3757, ?x608), film_release_region(?x3748, ?x608), film_release_region(?x2746, ?x608), film_release_region(?x2714, ?x608), film_release_region(?x1999, ?x608), film_release_region(?x1293, ?x608), film_release_region(?x1080, ?x608), film_release_region(?x1035, ?x608), film_release_region(?x186, ?x608), ?x2746 = 04f52jw, ?x3748 = 05zlld0, locations(?x6982, ?x608), ?x1080 = 01c22t, ?x4610 = 017jd9, combatants(?x608, ?x1780), ?x1293 = 07g_0c, ?x1999 = 0gd0c7x, ?x4047 = 07s846j, ?x3757 = 02vr3gz, currency(?x6305, ?x170), contains(?x2467, ?x608), ?x2867 = 02y8z, ?x5271 = 047vnkj, capital(?x6305, ?x13440), contains(?x608, ?x6366), ?x1035 = 08hmch, ?x2714 = 0kv238, taxonomy(?x608, ?x939), organization(?x608, ?x127), ?x2188 = 0163v, ?x186 = 02vxq9m >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11, 20 EVAL 019w9j country 015qh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 40.000 37.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 019w9j country 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 40.000 37.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 019w9j country 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 37.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #10667-026lgs PRED entity: 026lgs PRED relation: language PRED expected values: 02h40lc => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 30): 02h40lc (0.96 #3702, 0.95 #679, 0.95 #4608), 0295r (0.23 #3527), 064_8sq (0.23 #76, 0.22 #20, 0.17 #189), 04h9h (0.11 #40, 0.04 #209, 0.04 #603), 0jzc (0.10 #74, 0.04 #1148, 0.04 #695), 03_9r (0.08 #403, 0.06 #65, 0.06 #1139), 02bjrlw (0.08 #170, 0.07 #2044, 0.07 #283), 0653m (0.05 #1423, 0.05 #1653, 0.05 #1140), 03k50 (0.03 #1195, 0.02 #2337, 0.02 #2509), 0349s (0.03 #98, 0.02 #775, 0.02 #1172) >> Best rule #3702 for best value: >> intensional similarity = 4 >> extensional distance = 1115 >> proper extension: 02_1sj; 02z3r8t; 035xwd; 09p35z; 05p3738; 047qxs; 035s95; 03m8y5; 0pvms; 03mh_tp; ... >> query: (?x5418, 02h40lc) <- film(?x1700, ?x5418), film_crew_role(?x5418, ?x137), language(?x5418, ?x732), service_language(?x555, ?x732) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026lgs language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.959 http://example.org/film/film/language #10666-01znc_ PRED entity: 01znc_ PRED relation: organization PRED expected values: 07t65 => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 49): 07t65 (0.92 #569, 0.92 #2016, 0.91 #1631), 018cqq (0.73 #137, 0.67 #73, 0.62 #158), 0b6css (0.65 #220, 0.57 #346, 0.57 #262), 0_2v (0.57 #340, 0.56 #2270, 0.55 #214), 02jxk (0.56 #150, 0.56 #2270, 0.40 #129), 059dn (0.56 #2270, 0.33 #77, 0.27 #141), 041288 (0.37 #2051, 0.37 #1582, 0.36 #2094), 0j7v_ (0.30 #257, 0.27 #131, 0.26 #1677), 0gkjy (0.30 #722, 0.27 #975, 0.27 #2021), 085h1 (0.21 #1696, 0.21 #1695, 0.02 #1064) >> Best rule #569 for best value: >> intensional similarity = 2 >> extensional distance = 46 >> proper extension: 0b90_r; 047lj; 01ls2; 06mzp; 09pmkv; 05qx1; 05v10; 0163v; 06t2t; 0d05w3; ... >> query: (?x1499, 07t65) <- film_release_region(?x1012, ?x1499), ?x1012 = 0bwfwpj >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01znc_ organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.917 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #10665-0ggq0m PRED entity: 0ggq0m PRED relation: artists PRED expected values: 03_0p 012vd6 082db 031x_3 0ftqr 02r38 0459z => 57 concepts (32 used for prediction) PRED predicted values (max 10 best out of 3955): 0h6sv (0.67 #7130, 0.08 #3059, 0.05 #7141), 01w8n89 (0.53 #13557, 0.33 #7436, 0.33 #1314), 03t9sp (0.50 #11331, 0.50 #3171, 0.39 #14394), 02mslq (0.50 #7174, 0.50 #3094, 0.39 #14317), 0163m1 (0.50 #7468, 0.50 #3388, 0.39 #14611), 0197tq (0.50 #7153, 0.50 #3073, 0.33 #14296), 01lvcs1 (0.50 #7424, 0.50 #3344, 0.33 #1302), 01dw_f (0.50 #5743, 0.43 #8803, 0.40 #11863), 024qwq (0.50 #5918, 0.43 #8978, 0.38 #9998), 017lb_ (0.50 #5815, 0.43 #8875, 0.38 #9895) >> Best rule #7130 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 021dvj; 0d6n1; 06q6jz; >> query: (?x597, 0h6sv) <- artists(?x597, ?x7211), artists(?x597, ?x2536), artists(?x597, ?x1583), profession(?x1583, ?x1032), award(?x7211, ?x724), ?x2536 = 06wvj >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #14733 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 01gjqb; *> query: (?x597, 012vd6) <- artists(?x597, ?x5151), parent_genre(?x671, ?x597), award_winner(?x5151, ?x5125), profession(?x5151, ?x563), ?x563 = 01c8w0 *> conf = 0.39 ranks of expected_values: 35, 191, 192, 211, 274, 880 EVAL 0ggq0m artists 0459z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 32.000 0.667 http://example.org/music/genre/artists EVAL 0ggq0m artists 02r38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 57.000 32.000 0.667 http://example.org/music/genre/artists EVAL 0ggq0m artists 0ftqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 32.000 0.667 http://example.org/music/genre/artists EVAL 0ggq0m artists 031x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 32.000 0.667 http://example.org/music/genre/artists EVAL 0ggq0m artists 082db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 57.000 32.000 0.667 http://example.org/music/genre/artists EVAL 0ggq0m artists 012vd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 57.000 32.000 0.667 http://example.org/music/genre/artists EVAL 0ggq0m artists 03_0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 57.000 32.000 0.667 http://example.org/music/genre/artists #10664-07cbs PRED entity: 07cbs PRED relation: profession PRED expected values: 04gc2 => 170 concepts (117 used for prediction) PRED predicted values (max 10 best out of 105): 0fj9f (0.88 #6390, 0.88 #7399, 0.88 #3366), 02hrh1q (0.72 #13121, 0.69 #11968, 0.67 #15859), 01d_h8 (0.62 #2886, 0.57 #438, 0.54 #2166), 0dxtg (0.57 #445, 0.45 #7790, 0.43 #9230), 04gc2 (0.46 #3209, 0.44 #5513, 0.39 #7386), 02jknp (0.43 #439, 0.33 #2887, 0.31 #3895), 03jgz (0.33 #1361, 0.17 #209, 0.14 #353), 09jwl (0.31 #6211, 0.29 #6499, 0.24 #4051), 01c72t (0.30 #12675, 0.22 #1464, 0.21 #4056), 05snw (0.30 #12675, 0.19 #2825, 0.17 #233) >> Best rule #6390 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 021sv1; 08f3b1; 083p7; 02c4s; 0157m; 083pr; 063vn; 0bymv; 0tc7; 0d06m5; ... >> query: (?x5254, 0fj9f) <- religion(?x5254, ?x14017), jurisdiction_of_office(?x5254, ?x94), profession(?x5254, ?x353), basic_title(?x5254, ?x265) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3209 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 0kn4c; 0c_md_; 08959; *> query: (?x5254, 04gc2) <- profession(?x5254, ?x353), basic_title(?x5254, ?x265), place_of_death(?x5254, ?x2298) *> conf = 0.46 ranks of expected_values: 5 EVAL 07cbs profession 04gc2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 170.000 117.000 0.880 http://example.org/people/person/profession #10663-01jpqb PRED entity: 01jpqb PRED relation: school_type PRED expected values: 05jxkf => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.56 #716, 0.55 #1130, 0.55 #601), 05pcjw (0.35 #323, 0.34 #116, 0.30 #415), 07tf8 (0.33 #54, 0.25 #330, 0.21 #491), 01rs41 (0.30 #1338, 0.28 #1614, 0.28 #2698), 02p0qmm (0.09 #308, 0.08 #331, 0.07 #239), 01_srz (0.08 #1336, 0.08 #1612, 0.07 #1382), 01y64 (0.06 #356, 0.05 #241, 0.05 #218), 04399 (0.05 #979, 0.05 #1140, 0.04 #818), 01jlsn (0.03 #131, 0.03 #1995, 0.02 #3244), 06cs1 (0.03 #120, 0.02 #235, 0.02 #212) >> Best rule #716 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 05kj_; >> query: (?x9745, 05jxkf) <- contains(?x94, ?x9745), category(?x9745, ?x134), school(?x2569, ?x9745), draft(?x799, ?x2569) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jpqb school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.556 http://example.org/education/educational_institution/school_type #10662-0ckrnn PRED entity: 0ckrnn PRED relation: language PRED expected values: 02h40lc => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 29): 02h40lc (0.96 #1954, 0.96 #2763, 0.96 #2992), 05qqm (0.33 #39, 0.01 #324), 06nm1 (0.14 #124, 0.12 #295, 0.11 #181), 02bjrlw (0.14 #115, 0.11 #172, 0.08 #286), 06b_j (0.14 #135, 0.11 #192, 0.07 #249), 03_9r (0.05 #1729, 0.05 #1846, 0.04 #3924), 0653m (0.05 #296, 0.03 #1384, 0.03 #1906), 012w70 (0.05 #297, 0.02 #1385, 0.02 #1675), 04h9h (0.04 #326, 0.03 #441, 0.02 #671), 0jzc (0.03 #362, 0.03 #304, 0.02 #533) >> Best rule #1954 for best value: >> intensional similarity = 3 >> extensional distance = 1218 >> proper extension: 0b76d_m; 06w99h3; 0c3ybss; 0yyg4; 090s_0; 0czyxs; 050r1z; 02sg5v; 0kv2hv; 04tc1g; ... >> query: (?x10831, 02h40lc) <- film(?x1149, ?x10831), participant(?x4058, ?x1149), language(?x10831, ?x732) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ckrnn language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.961 http://example.org/film/film/language #10661-0k54q PRED entity: 0k54q PRED relation: film! PRED expected values: 05qd_ => 82 concepts (57 used for prediction) PRED predicted values (max 10 best out of 52): 03xq0f (0.84 #1767, 0.84 #1547, 0.83 #1473), 07k2x (0.47 #849, 0.25 #334, 0.17 #1143), 016tt2 (0.46 #3301, 0.44 #3375, 0.44 #3672), 03rwz3 (0.46 #3301, 0.44 #3375, 0.44 #3672), 0hpt3 (0.46 #3301, 0.44 #3375, 0.44 #3672), 016tw3 (0.33 #85, 0.29 #450, 0.18 #965), 017s11 (0.33 #663, 0.29 #442, 0.16 #2130), 017jv5 (0.33 #15, 0.25 #235, 0.25 #162), 025tlyv (0.33 #132, 0.14 #497, 0.07 #2112), 086k8 (0.29 #883, 0.29 #515, 0.25 #295) >> Best rule #1767 for best value: >> intensional similarity = 6 >> extensional distance = 113 >> proper extension: 0cnztc4; 064n1pz; 0g9zljd; 04nlb94; >> query: (?x5378, 03xq0f) <- country(?x5378, ?x252), genre(?x5378, ?x225), language(?x5378, ?x254), film(?x4800, ?x5378), region(?x5378, ?x512), film_release_region(?x66, ?x252) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #375 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 01gvpz; *> query: (?x5378, 05qd_) <- film(?x10968, ?x5378), film(?x7001, ?x5378), film(?x5202, ?x5378), film(?x4987, ?x5378), country(?x5378, ?x94), award_nominee(?x1039, ?x5202), ?x10968 = 01rw116, actor(?x3144, ?x7001), profession(?x4987, ?x220) *> conf = 0.20 ranks of expected_values: 13 EVAL 0k54q film! 05qd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 82.000 57.000 0.843 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #10660-02fs_d PRED entity: 02fs_d PRED relation: category PRED expected values: 08mbj5d => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #25, 0.91 #15, 0.91 #33) >> Best rule #25 for best value: >> intensional similarity = 4 >> extensional distance = 230 >> proper extension: 012gyf; >> query: (?x6186, 08mbj5d) <- organization(?x346, ?x6186), colors(?x6186, ?x332), institution(?x865, ?x6186), ?x865 = 02h4rq6 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fs_d category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.909 http://example.org/common/topic/webpage./common/webpage/category #10659-0164w8 PRED entity: 0164w8 PRED relation: gender PRED expected values: 05zppz => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 5): 05zppz (0.90 #19, 0.89 #25, 0.89 #15), 02zsn (0.57 #66, 0.29 #4, 0.26 #174), 098s1 (0.12 #47), 0jpmt (0.12 #47), 02ctzb (0.12 #47) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 04flrx; 014dm6; 01qg7c; 07mkj0; 06kkgw; >> query: (?x8288, 05zppz) <- cinematography(?x4591, ?x8288), award(?x8288, ?x1243), film(?x526, ?x4591), nationality(?x8288, ?x94) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0164w8 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.896 http://example.org/people/person/gender #10658-015qqg PRED entity: 015qqg PRED relation: nominated_for! PRED expected values: 03csqj4 => 94 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1002): 0h1mt (0.80 #90982, 0.80 #90983, 0.80 #111981), 06pj8 (0.36 #21423, 0.11 #9761, 0.10 #26090), 0h0wc (0.31 #114316, 0.30 #93317, 0.29 #34989), 03k7bd (0.31 #114316, 0.30 #93317, 0.29 #34989), 0146pg (0.23 #21111, 0.21 #25778, 0.19 #28110), 04qvl7 (0.17 #11681, 0.16 #16345, 0.12 #28010), 01_f_5 (0.17 #1373, 0.14 #3705, 0.12 #8369), 0bytkq (0.17 #655, 0.14 #2987, 0.12 #5319), 016yvw (0.17 #1178, 0.14 #3510, 0.12 #5842), 029m83 (0.17 #1709, 0.14 #4041, 0.12 #6373) >> Best rule #90982 for best value: >> intensional similarity = 5 >> extensional distance = 459 >> proper extension: 02kk_c; >> query: (?x4870, ?x1126) <- award_winner(?x4870, ?x4398), award_winner(?x4870, ?x1126), nominated_for(?x6233, ?x4870), type_of_union(?x6233, ?x566), participant(?x4398, ?x4397) >> conf = 0.80 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015qqg nominated_for! 03csqj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 50.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10657-017j69 PRED entity: 017j69 PRED relation: major_field_of_study PRED expected values: 01mkq 04x_3 011s0 01540 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 99): 05qjt (0.57 #8, 0.30 #620, 0.27 #6440), 04x_3 (0.43 #20, 0.26 #838, 0.25 #2471), 01mkq (0.39 #4711, 0.38 #6242, 0.38 #5834), 0_jm (0.32 #862, 0.30 #6990, 0.29 #5864), 04gb7 (0.32 #2074, 0.17 #2686, 0.16 #2890), 01540 (0.30 #658, 0.27 #4743, 0.25 #2497), 037mh8 (0.29 #1176, 0.25 #1789, 0.23 #2707), 03nfmq (0.29 #28, 0.17 #1152, 0.15 #846), 0w7s (0.29 #78, 0.13 #2529, 0.13 #690), 05qfh (0.28 #2069, 0.24 #4723, 0.24 #6254) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 021q2j; >> query: (?x4410, 05qjt) <- major_field_of_study(?x4410, ?x1527), ?x1527 = 04_tv, currency(?x4410, ?x170) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 021q2j; *> query: (?x4410, 04x_3) <- major_field_of_study(?x4410, ?x1527), ?x1527 = 04_tv, currency(?x4410, ?x170) *> conf = 0.43 ranks of expected_values: 2, 3, 6, 54 EVAL 017j69 major_field_of_study 01540 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 118.000 118.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 017j69 major_field_of_study 011s0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 118.000 118.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 017j69 major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 017j69 major_field_of_study 01mkq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10656-01xdf5 PRED entity: 01xdf5 PRED relation: languages PRED expected values: 02h40lc => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 18): 02h40lc (0.41 #197, 0.33 #1055, 0.29 #1562), 06nm1 (0.05 #201, 0.02 #1254, 0.01 #2307), 04306rv (0.04 #42, 0.03 #159, 0.02 #276), 064_8sq (0.04 #54, 0.03 #1575, 0.03 #2823), 02bjrlw (0.04 #40, 0.03 #196, 0.02 #274), 03_9r (0.04 #44, 0.02 #278, 0.02 #941), 06mp7 (0.04 #50, 0.02 #284, 0.01 #596), 03k50 (0.04 #472, 0.04 #316, 0.03 #433), 0t_2 (0.04 #126, 0.03 #672, 0.03 #438), 0x82 (0.02 #271) >> Best rule #197 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 01xyt7; >> query: (?x236, 02h40lc) <- award_winner(?x882, ?x236), company(?x236, ?x1762), participant(?x237, ?x236) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xdf5 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.410 http://example.org/people/person/languages #10655-02fqxm PRED entity: 02fqxm PRED relation: executive_produced_by PRED expected values: 07f8wg => 62 concepts (46 used for prediction) PRED predicted values (max 10 best out of 68): 04pqqb (0.14 #370, 0.10 #623, 0.03 #1128), 09d5d5 (0.14 #446, 0.10 #699, 0.01 #1204), 0fqyzz (0.14 #345, 0.10 #598, 0.01 #1103), 076_74 (0.14 #346, 0.10 #599), 03y2kr (0.10 #693, 0.01 #1198), 06pj8 (0.07 #1572, 0.06 #1319, 0.06 #1824), 079vf (0.07 #1266, 0.05 #1771, 0.04 #1519), 05hj_k (0.05 #4143, 0.04 #6419, 0.04 #3638), 030_3z (0.05 #1372, 0.03 #1877, 0.03 #1625), 06q8hf (0.04 #4212, 0.04 #6488, 0.03 #8515) >> Best rule #370 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0dx8gj; 06kl78; >> query: (?x12720, 04pqqb) <- genre(?x12720, ?x3250), genre(?x12720, ?x53), film(?x7946, ?x12720), ?x3250 = 0glj9q, ?x53 = 07s9rl0 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1535 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 96 *> proper extension: 07gp9; 01hr1; 06z8s_; 01f7gh; 04n52p6; 0fdv3; 02stbw; 0418wg; 07cz2; 0dr3sl; ... *> query: (?x12720, 07f8wg) <- genre(?x12720, ?x53), production_companies(?x12720, ?x1561), prequel(?x5627, ?x12720), nominated_for(?x154, ?x12720) *> conf = 0.02 ranks of expected_values: 31 EVAL 02fqxm executive_produced_by 07f8wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 62.000 46.000 0.143 http://example.org/film/film/executive_produced_by #10654-01336l PRED entity: 01336l PRED relation: risk_factors! PRED expected values: 0146bp => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 86): 02k6hp (0.64 #763, 0.50 #1165, 0.50 #1102), 02y0js (0.55 #729, 0.43 #1131, 0.43 #1068), 05mdx (0.50 #1146, 0.50 #677, 0.50 #557), 0146bp (0.50 #594, 0.40 #984, 0.31 #1263), 0hg45 (0.45 #768, 0.40 #984, 0.36 #1170), 01_qc_ (0.42 #1022, 0.40 #984, 0.36 #754), 09d11 (0.40 #984, 0.37 #1484, 0.31 #1263), 0gk4g (0.40 #984, 0.36 #735, 0.35 #2158), 01k9gb (0.40 #984, 0.33 #1053, 0.33 #598), 01dcqj (0.40 #984, 0.33 #551, 0.33 #1261) >> Best rule #763 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 05zppz; 0k95h; 0c58k; 0fltx; >> query: (?x9648, 02k6hp) <- risk_factors(?x8523, ?x9648), risk_factors(?x4322, ?x8523), risk_factors(?x8523, ?x8023), people(?x8523, ?x2807), people(?x4322, ?x118), ?x8023 = 0jpmt >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #594 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 4 *> proper extension: 0jpmt; 01hbgs; *> query: (?x9648, 0146bp) <- risk_factors(?x8523, ?x9648), ?x8523 = 0c58k *> conf = 0.50 ranks of expected_values: 4 EVAL 01336l risk_factors! 0146bp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 62.000 62.000 0.636 http://example.org/medicine/disease/risk_factors #10653-012m_ PRED entity: 012m_ PRED relation: nationality! PRED expected values: 05d1y => 165 concepts (78 used for prediction) PRED predicted values (max 10 best out of 4131): 01ckhj (0.40 #20105, 0.40 #16042, 0.29 #32295), 02m7r (0.40 #16905, 0.40 #12842, 0.29 #29095), 0dzlk (0.40 #19821, 0.33 #23884, 0.22 #48266), 016ynj (0.40 #18857, 0.33 #22920, 0.22 #47302), 01xcfy (0.40 #17076, 0.33 #21139, 0.22 #45521), 0mbhr (0.40 #15656, 0.22 #48164, 0.20 #19719), 03_2td (0.40 #19194, 0.20 #15131, 0.17 #23257), 06dv3 (0.40 #16306, 0.20 #12243, 0.17 #20369), 082mw (0.33 #22618, 0.29 #30745, 0.22 #47000), 02l4rh (0.33 #22499, 0.29 #30626, 0.22 #46881) >> Best rule #20105 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0chghy; 07ssc; 0ctw_b; >> query: (?x9006, 01ckhj) <- combatants(?x612, ?x9006), combatants(?x3141, ?x9006), form_of_government(?x9006, ?x6065), ?x612 = 01fc7p, contains(?x9006, ?x5127) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #182855 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 0h3y; 05cgv; 0h7x; 01mjq; 0162v; 03h64; 05r7t; 06m_5; 01p8s; *> query: (?x9006, ?x8299) <- location(?x8299, ?x9006), nationality(?x4724, ?x9006), contains(?x455, ?x9006) *> conf = 0.25 ranks of expected_values: 57 EVAL 012m_ nationality! 05d1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 165.000 78.000 0.400 http://example.org/people/person/nationality #10652-037fqp PRED entity: 037fqp PRED relation: school_type PRED expected values: 04399 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.61 #50, 0.57 #280, 0.54 #303), 05pcjw (0.33 #24, 0.31 #806, 0.27 #369), 01_9fk (0.27 #48, 0.22 #462, 0.21 #508), 07tf8 (0.21 #77, 0.20 #123, 0.18 #169), 047951 (0.10 #30, 0.01 #1571, 0.01 #1663), 01_srz (0.08 #808, 0.06 #555, 0.06 #463), 01jlsn (0.06 #108, 0.04 #545, 0.04 #223), 06cs1 (0.05 #74, 0.05 #120, 0.04 #166), 02p0qmm (0.05 #400, 0.05 #239, 0.04 #423), 0m4mb (0.05 #102, 0.04 #539, 0.04 #217) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 01pl14; 03x23q; >> query: (?x5581, 05jxkf) <- institution(?x865, ?x5581), colors(?x5581, ?x332), ?x332 = 01l849, school(?x2820, ?x5581) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #59 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 01pl14; 03x23q; *> query: (?x5581, 04399) <- institution(?x865, ?x5581), colors(?x5581, ?x332), ?x332 = 01l849, school(?x2820, ?x5581) *> conf = 0.05 ranks of expected_values: 12 EVAL 037fqp school_type 04399 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 127.000 127.000 0.614 http://example.org/education/educational_institution/school_type #10651-04vrxh PRED entity: 04vrxh PRED relation: profession PRED expected values: 0fnpj => 94 concepts (92 used for prediction) PRED predicted values (max 10 best out of 58): 02hrh1q (0.82 #12541, 0.73 #7100, 0.70 #9747), 016z4k (0.53 #297, 0.45 #738, 0.45 #444), 01d_h8 (0.44 #2219, 0.37 #4730, 0.32 #6354), 039v1 (0.39 #1655, 0.37 #1954, 0.36 #2691), 0dxtg (0.33 #13, 0.29 #2227, 0.27 #4738), 03gjzk (0.33 #2229, 0.27 #4740, 0.26 #11057), 0fnpj (0.32 #353, 0.26 #11057, 0.22 #59), 01c72t (0.29 #5340, 0.29 #5044, 0.29 #5191), 0n1h (0.26 #305, 0.26 #11057, 0.26 #158), 05sxg2 (0.26 #11057, 0.02 #2215, 0.02 #3545) >> Best rule #12541 for best value: >> intensional similarity = 3 >> extensional distance = 3027 >> proper extension: 06v8s0; 05d7rk; 04yywz; 06688p; 01l1b90; 05bp8g; 05m63c; 0d_84; 02qjj7; 04rs03; ... >> query: (?x9882, 02hrh1q) <- profession(?x9882, ?x131), profession(?x2908, ?x131), ?x2908 = 0161sp >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #353 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 03k0yw; *> query: (?x9882, 0fnpj) <- instrumentalists(?x1166, ?x9882), award_nominee(?x1989, ?x9882), ?x1166 = 05148p4 *> conf = 0.32 ranks of expected_values: 7 EVAL 04vrxh profession 0fnpj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 94.000 92.000 0.825 http://example.org/people/person/profession #10650-01nqfh_ PRED entity: 01nqfh_ PRED relation: profession PRED expected values: 0dz3r => 90 concepts (72 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.71 #7681, 0.69 #6059, 0.69 #8416), 0nbcg (0.49 #4157, 0.48 #4894, 0.47 #4746), 016z4k (0.43 #151, 0.40 #5606, 0.39 #4720), 0kyk (0.42 #5778, 0.13 #4449, 0.10 #10492), 0dz3r (0.40 #2, 0.40 #149, 0.40 #5604), 01d_h8 (0.36 #3837, 0.35 #6641, 0.34 #6051), 0dxtg (0.33 #6058, 0.32 #7680, 0.30 #7974), 039v1 (0.28 #4751, 0.28 #3719, 0.27 #4899), 03gjzk (0.25 #6060, 0.24 #9301, 0.23 #7682), 02jknp (0.24 #6643, 0.24 #6053, 0.24 #7822) >> Best rule #7681 for best value: >> intensional similarity = 3 >> extensional distance = 1177 >> proper extension: 0l6qt; 03x3qv; 05ty4m; 0lzb8; 05gml8; 04yj5z; 069ld1; 01sp81; 04hpck; 05drq5; ... >> query: (?x562, 02hrh1q) <- student(?x3439, ?x562), profession(?x562, ?x563), nominated_for(?x562, ?x1178) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0fp_v1x; 01vvycq; 01gg59; 04pf4r; 050z2; 09889g; 03h_fqv; 07zft; *> query: (?x562, 0dz3r) <- instrumentalists(?x315, ?x562), music(?x1178, ?x562), ?x315 = 0l14md *> conf = 0.40 ranks of expected_values: 5 EVAL 01nqfh_ profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 72.000 0.712 http://example.org/people/person/profession #10649-02qvzf PRED entity: 02qvzf PRED relation: position! PRED expected values: 0jnr_ 04l5b4 => 23 concepts (12 used for prediction) PRED predicted values (max 10 best out of 209): 0jnmj (0.78 #11, 0.73 #57, 0.73 #56), 0jnlm (0.78 #11, 0.73 #57, 0.73 #56), 04l5b4 (0.78 #11, 0.73 #57, 0.73 #56), 04l590 (0.78 #11, 0.73 #57, 0.73 #56), 0hmt3 (0.78 #11, 0.73 #57, 0.73 #56), 0bszz (0.78 #11, 0.73 #57, 0.73 #56), 0jnl5 (0.78 #11, 0.72 #55, 0.67 #26), 0jnm2 (0.78 #11, 0.72 #55, 0.67 #26), 0j86l (0.33 #51, 0.33 #23, 0.33 #8), 04l58n (0.33 #19, 0.25 #33, 0.17 #115) >> Best rule #11 for best value: >> intensional similarity = 44 >> extensional distance = 1 >> proper extension: 02qvdc; >> query: (?x3724, ?x13608) <- position(?x14123, ?x3724), position(?x13661, ?x3724), position(?x12757, ?x3724), position(?x11826, ?x3724), position(?x11368, ?x3724), position(?x10941, ?x3724), position(?x10713, ?x3724), position(?x10690, ?x3724), position(?x8892, ?x3724), position(?x8270, ?x3724), position(?x8186, ?x3724), position(?x7197, ?x3724), position(?x7174, ?x3724), ?x8892 = 02fp3, ?x10941 = 030ykh, ?x14123 = 04l59s, team(?x3724, ?x14404), team(?x3724, ?x14124), team(?x3724, ?x14015), team(?x3724, ?x13608), team(?x3724, ?x10970), team(?x3724, ?x3298), ?x10713 = 0gx159f, ?x13661 = 0jnr3, ?x12757 = 0hmtk, ?x14124 = 04l590, teams(?x3786, ?x7197), ?x11368 = 032yps, ?x11826 = 0hn2q, ?x14404 = 0jnm2, team(?x13270, ?x7197), team(?x3299, ?x3298), ?x10690 = 0jnng, ?x3299 = 02qvgy, ?x10970 = 0hmt3, sport(?x3298, ?x453), ?x8186 = 0jnm_, team(?x5234, ?x8270), teams(?x1860, ?x14015), ?x7174 = 05pcr, place_of_birth(?x193, ?x1860), featured_film_locations(?x195, ?x1860), adjoins(?x1860, ?x5037), citytown(?x1924, ?x1860) >> conf = 0.78 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 11 EVAL 02qvzf position! 04l5b4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 23.000 12.000 0.775 http://example.org/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position EVAL 02qvzf position! 0jnr_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 23.000 12.000 0.775 http://example.org/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position #10648-06ybb1 PRED entity: 06ybb1 PRED relation: country PRED expected values: 09c7w0 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.92 #430, 0.92 #859, 0.91 #737), 07ssc (0.25 #1731, 0.24 #1914, 0.24 #1853), 0345h (0.13 #150, 0.10 #3518, 0.09 #641), 03rjj (0.11 #251, 0.09 #7, 0.09 #803), 0f8l9c (0.11 #264, 0.09 #20, 0.08 #81), 016wzw (0.08 #109, 0.01 #722, 0.01 #783), 0d060g (0.07 #2211, 0.05 #866, 0.05 #1784), 0chghy (0.07 #135, 0.06 #441, 0.06 #1053), 03_3d (0.07 #130, 0.04 #926, 0.04 #1355), 01z4y (0.06 #4473, 0.06 #3123, 0.06 #3920) >> Best rule #430 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 016kz1; >> query: (?x2165, 09c7w0) <- nominated_for(?x688, ?x2165), ?x688 = 05b1610, genre(?x2165, ?x258), award_winner(?x2165, ?x5338) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06ybb1 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.922 http://example.org/film/film/country #10647-0bjv6 PRED entity: 0bjv6 PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 20): 060bp (0.69 #155, 0.68 #221, 0.67 #23), 0f6c3 (0.39 #535, 0.34 #1085, 0.32 #997), 0pqc5 (0.38 #1104, 0.36 #1965, 0.31 #1148), 0fkvn (0.37 #531, 0.33 #1081, 0.30 #993), 0p5vf (0.36 #1873, 0.36 #1652, 0.34 #1519), 01zq91 (0.36 #1873, 0.36 #1652, 0.34 #1519), 01_fjr (0.36 #1873, 0.36 #1652, 0.34 #1519), 09n5b9 (0.36 #539, 0.32 #1089, 0.29 #1001), 04syw (0.17 #72, 0.15 #1282, 0.14 #1591), 0dq3c (0.15 #904, 0.15 #948, 0.15 #750) >> Best rule #155 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 05r4w; 0jgd; 0154j; 03rjj; 0d060g; 0d0vqn; 0j1z8; 04gzd; 0chghy; 03rt9; ... >> query: (?x3227, 060bp) <- adjoins(?x1353, ?x3227), currency(?x3227, ?x170), country(?x13383, ?x3227) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bjv6 jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.690 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #10646-0ptxj PRED entity: 0ptxj PRED relation: written_by PRED expected values: 06kbb6 => 98 concepts (52 used for prediction) PRED predicted values (max 10 best out of 94): 03s2y9 (0.35 #6405, 0.35 #8428, 0.34 #12144), 0hw1j (0.22 #108, 0.01 #1794, 0.01 #446), 0h953 (0.11 #5058, 0.10 #6743, 0.10 #15179), 0146pg (0.11 #5058, 0.10 #6743, 0.10 #15179), 087yty (0.11 #5058, 0.10 #15179, 0.10 #11805), 05drq5 (0.11 #39, 0.01 #2061), 06b_0 (0.11 #232, 0.01 #570, 0.01 #1244), 0b478 (0.11 #151), 06kbb6 (0.10 #6743, 0.10 #11128, 0.07 #2359), 02vyw (0.05 #778, 0.04 #1116, 0.04 #1452) >> Best rule #6405 for best value: >> intensional similarity = 3 >> extensional distance = 518 >> proper extension: 016ztl; >> query: (?x5212, ?x11625) <- film_release_distribution_medium(?x5212, ?x81), titles(?x8581, ?x5212), film(?x11625, ?x5212) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #6743 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 525 *> proper extension: 0djb3vw; 0gcrg; 0cq8nx; *> query: (?x5212, ?x669) <- film_release_distribution_medium(?x5212, ?x81), nominated_for(?x112, ?x5212), film(?x11625, ?x5212), nominated_for(?x669, ?x5212) *> conf = 0.10 ranks of expected_values: 9 EVAL 0ptxj written_by 06kbb6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 98.000 52.000 0.351 http://example.org/film/film/written_by #10645-01fv4z PRED entity: 01fv4z PRED relation: category PRED expected values: 08mbj5d => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #8, 0.67 #5, 0.60 #2) >> Best rule #8 for best value: >> intensional similarity = 2 >> extensional distance = 15 >> proper extension: 018jmn; >> query: (?x12984, 08mbj5d) <- administrative_parent(?x12984, ?x252), ?x252 = 03_3d >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fv4z category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.824 http://example.org/common/topic/webpage./common/webpage/category #10644-044bn PRED entity: 044bn PRED relation: profession PRED expected values: 02hrh1q => 126 concepts (110 used for prediction) PRED predicted values (max 10 best out of 88): 02hrh1q (0.92 #7170, 0.89 #3293, 0.89 #12238), 01d_h8 (0.53 #8951, 0.52 #9100, 0.52 #9547), 03gjzk (0.50 #11941, 0.44 #10898, 0.44 #7469), 02jknp (0.50 #8, 0.44 #9549, 0.44 #12083), 0cbd2 (0.35 #11784, 0.25 #3435, 0.24 #2988), 09jwl (0.33 #466, 0.28 #4043, 0.28 #3596), 02krf9 (0.25 #27, 0.22 #11953, 0.19 #7481), 0kyk (0.23 #11807, 0.20 #328, 0.17 #1818), 018gz8 (0.22 #2998, 0.17 #10900, 0.17 #12391), 01c72t (0.21 #2110, 0.17 #2557, 0.17 #2706) >> Best rule #7170 for best value: >> intensional similarity = 4 >> extensional distance = 295 >> proper extension: 0fhxv; >> query: (?x11177, 02hrh1q) <- film(?x11177, ?x1973), genre(?x1973, ?x600), cinematography(?x1973, ?x3348), edited_by(?x1973, ?x877) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044bn profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 110.000 0.916 http://example.org/people/person/profession #10643-0m4yg PRED entity: 0m4yg PRED relation: student PRED expected values: 0686zv => 79 concepts (43 used for prediction) PRED predicted values (max 10 best out of 794): 0hky (0.33 #1040, 0.03 #7256, 0.01 #17618), 0h10vt (0.33 #1623, 0.02 #7839, 0.01 #9911), 09jrf (0.33 #2053, 0.02 #8269, 0.01 #10341), 01m7f5r (0.33 #1549, 0.02 #7765, 0.01 #9837), 09gnn (0.33 #1756, 0.02 #7972, 0.01 #18334), 0686zv (0.33 #486, 0.01 #8774, 0.01 #17064), 034bs (0.33 #660), 02c4s (0.33 #240), 037jz (0.25 #3239, 0.05 #5311, 0.01 #9455), 0kh6b (0.09 #4749, 0.05 #6821, 0.03 #8893) >> Best rule #1040 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0dzbl; >> query: (?x9844, 0hky) <- student(?x9844, ?x3575), student(?x9844, ?x3225), student(?x9844, ?x2424), ?x3575 = 02fx3c, film(?x2424, ?x161), profession(?x3225, ?x1032) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #486 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 0dzbl; *> query: (?x9844, 0686zv) <- student(?x9844, ?x3575), student(?x9844, ?x3225), student(?x9844, ?x2424), ?x3575 = 02fx3c, film(?x2424, ?x161), profession(?x3225, ?x1032) *> conf = 0.33 ranks of expected_values: 6 EVAL 0m4yg student 0686zv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 43.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #10642-06mfvc PRED entity: 06mfvc PRED relation: participant! PRED expected values: 04fzk => 124 concepts (67 used for prediction) PRED predicted values (max 10 best out of 138): 033tln (0.14 #311, 0.02 #5522, 0.01 #15284), 078jnn (0.14 #508, 0.01 #5719), 01gkmx (0.14 #561, 0.01 #15534, 0.01 #16184), 05myd2 (0.14 #571), 060j8b (0.14 #423), 072bb1 (0.14 #173), 01vlj1g (0.14 #44), 05b_7n (0.08 #989, 0.07 #1641), 02pzck (0.08 #1245), 0c4f4 (0.07 #1325) >> Best rule #311 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 072bb1; 0bt7ws; 08hsww; 02nwxc; 060j8b; >> query: (?x1987, 033tln) <- award_nominee(?x275, ?x1987), participant(?x1530, ?x1987), ?x275 = 083chw >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2242 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 155 *> proper extension: 02qjj7; 01vv126; 01f492; 026_dq6; 0dq9wx; *> query: (?x1987, 04fzk) <- participant(?x1530, ?x1987), currency(?x1987, ?x170) *> conf = 0.02 ranks of expected_values: 47 EVAL 06mfvc participant! 04fzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 124.000 67.000 0.143 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #10641-058j2 PRED entity: 058j2 PRED relation: company! PRED expected values: 046_v => 203 concepts (99 used for prediction) PRED predicted values (max 10 best out of 317): 0d05fv (0.33 #3963, 0.30 #7841, 0.14 #16803), 0343h (0.33 #991, 0.25 #4382, 0.19 #5595), 02q_cc (0.33 #980, 0.17 #4371, 0.14 #4857), 02nygk (0.33 #241, 0.09 #3874, 0.08 #4601), 0x3r3 (0.19 #5688, 0.17 #1084, 0.14 #4961), 01w_10 (0.18 #3305, 0.18 #2820, 0.10 #12267), 0frmb1 (0.18 #3302, 0.18 #2817, 0.10 #2333), 0d4jl (0.17 #4417, 0.17 #1026, 0.15 #4660), 06pj8 (0.17 #4396, 0.17 #1005, 0.12 #1247), 06y3r (0.17 #10591, 0.14 #8653, 0.12 #13254) >> Best rule #3963 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 09f2j; 045c7b; 01rs59; 019_6d; >> query: (?x6972, 0d05fv) <- state_province_region(?x6972, ?x335), company(?x96, ?x6972), film(?x96, ?x97), person(?x1956, ?x96) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 058j2 company! 046_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 203.000 99.000 0.333 http://example.org/people/person/employment_history./business/employment_tenure/company #10640-02ywhz PRED entity: 02ywhz PRED relation: award_winner PRED expected values: 05hj_k 01337_ => 29 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2235): 0m31m (0.71 #1539, 0.45 #7701, 0.45 #4621), 02q42j_ (0.71 #1539, 0.45 #7701, 0.45 #4621), 0b13g7 (0.71 #1539, 0.45 #7701, 0.45 #4621), 0f4vbz (0.50 #1859, 0.17 #3403, 0.11 #3083), 0151w_ (0.45 #7701, 0.45 #4621, 0.44 #6161), 0jz9f (0.45 #7701, 0.45 #4621, 0.44 #6161), 0170pk (0.45 #7701, 0.45 #4621, 0.44 #6161), 01tspc6 (0.45 #7701, 0.45 #4621, 0.44 #6161), 03t0k1 (0.45 #7701, 0.45 #4621, 0.44 #6161), 0dgskx (0.45 #7701, 0.45 #4621, 0.44 #6161) >> Best rule #1539 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 073h1t; >> query: (?x5761, ?x2654) <- ceremony(?x3617, ?x5761), ceremony(?x3458, ?x5761), ceremony(?x3066, ?x5761), ?x3617 = 0gvx_, ?x3066 = 0gqy2, ?x3458 = 0gqxm, honored_for(?x5761, ?x4159), award_winner(?x4159, ?x2654), film(?x698, ?x4159), nominated_for(?x749, ?x4159), award_winner(?x5761, ?x6426), ?x698 = 0kr5_, film(?x6426, ?x3491), award(?x6709, ?x749), award(?x197, ?x749), ?x6709 = 0jlv5, film(?x752, ?x4159) >> conf = 0.71 => this is the best rule for 3 predicted values *> Best rule #7701 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 14 *> proper extension: 059x66; 0bzm81; 073h9x; 0bc773; 073hgx; 073hd1; 09306z; 04110lv; *> query: (?x5761, ?x2654) <- ceremony(?x3617, ?x5761), ceremony(?x3458, ?x5761), ceremony(?x3066, ?x5761), ceremony(?x591, ?x5761), ?x3617 = 0gvx_, ?x3066 = 0gqy2, ?x3458 = 0gqxm, honored_for(?x5761, ?x4159), award_winner(?x4159, ?x2654), film(?x698, ?x4159), nominated_for(?x68, ?x4159), award_winner(?x5761, ?x1933), genre(?x4159, ?x162), ?x591 = 0f4x7, student(?x1848, ?x698), award(?x4159, ?x143), film(?x698, ?x518) *> conf = 0.45 ranks of expected_values: 17, 32 EVAL 02ywhz award_winner 01337_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 29.000 15.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02ywhz award_winner 05hj_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 29.000 15.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10639-01y8cr PRED entity: 01y8cr PRED relation: place_of_burial PRED expected values: 018mm4 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 10): 018mm4 (0.09 #40, 0.08 #167, 0.07 #135), 018mmj (0.06 #486, 0.06 #455, 0.06 #74), 018mmw (0.03 #80, 0.02 #585, 0.02 #617), 01f38z (0.03 #92, 0.01 #566), 01n7q (0.02 #479, 0.01 #67, 0.01 #448), 0lbp_ (0.02 #236, 0.01 #47, 0.01 #111), 05rgl (0.01 #38, 0.01 #70, 0.01 #133), 0k_q_ (0.01 #71), 018mrd (0.01 #529), 0bvqq (0.01 #138, 0.01 #170) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 019_1h; 015wfg; 03ym1; 0jvtp; >> query: (?x4279, 018mm4) <- award(?x4279, ?x3066), ?x3066 = 0gqy2, student(?x5149, ?x4279), location(?x4279, ?x10584) >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y8cr place_of_burial 018mm4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.087 http://example.org/people/deceased_person/place_of_burial #10638-02yvhx PRED entity: 02yvhx PRED relation: honored_for PRED expected values: 0qm8b => 34 concepts (24 used for prediction) PRED predicted values (max 10 best out of 747): 0l76z (0.33 #270, 0.25 #860, 0.08 #1455), 0hz55 (0.33 #291, 0.12 #8872, 0.07 #11838), 0bnzd (0.33 #417, 0.08 #1602, 0.01 #11246), 01gkp1 (0.33 #282, 0.01 #3545), 01g03q (0.33 #512), 01rf57 (0.33 #239), 015g28 (0.33 #234), 02jxbw (0.25 #963, 0.15 #590, 0.12 #8872), 0170_p (0.25 #621, 0.15 #590, 0.12 #8872), 01xdxy (0.25 #1106, 0.15 #590, 0.08 #11837) >> Best rule #270 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 09p3h7; >> query: (?x5703, 0l76z) <- ceremony(?x5409, ?x5703), ceremony(?x720, ?x5703), award_winner(?x5703, ?x4949), award_winner(?x5703, ?x2596), honored_for(?x5703, ?x253), ceremony(?x720, ?x5761), award_winner(?x5761, ?x4634), award(?x788, ?x5409), award(?x574, ?x720), disciplines_or_subjects(?x720, ?x373), award_nominee(?x100, ?x2596), award_winner(?x458, ?x2596), ?x4634 = 04g3p5, award_winner(?x675, ?x2596), award_nominee(?x1850, ?x788), ?x4949 = 0fgg4 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5914 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 31 *> proper extension: 0fz0c2; 0d__c3; 0dznvw; *> query: (?x5703, ?x573) <- ceremony(?x5409, ?x5703), ceremony(?x2209, ?x5703), ceremony(?x720, ?x5703), award_winner(?x5703, ?x1983), honored_for(?x5703, ?x8063), ceremony(?x720, ?x11984), ceremony(?x720, ?x4388), ceremony(?x720, ?x3254), ceremony(?x720, ?x3173), ceremony(?x720, ?x1819), ?x4388 = 0fz2y7, ?x1819 = 02yv_b, nominated_for(?x1983, ?x573), ?x2209 = 0gr42, ?x3173 = 0bzk2h, nominated_for(?x834, ?x8063), film(?x140, ?x8063), film_crew_role(?x573, ?x137), crewmember(?x508, ?x1983), ?x11984 = 0fzrhn, award(?x382, ?x5409), ?x3254 = 073h9x *> conf = 0.10 ranks of expected_values: 114 EVAL 02yvhx honored_for 0qm8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 34.000 24.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #10637-019sc PRED entity: 019sc PRED relation: colors! PRED expected values: 01q0kg 033x5p 02ln0f 0hsb3 03x1s8 030w19 => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 613): 016sd3 (0.56 #5143, 0.56 #4742, 0.50 #6757), 01b1mj (0.50 #2832, 0.45 #2814, 0.33 #4843), 0gjv_ (0.50 #2977, 0.45 #2814, 0.33 #4988), 01tntf (0.50 #3113, 0.45 #2814, 0.33 #5124), 0pz6q (0.50 #3109, 0.45 #2814, 0.33 #5120), 06b19 (0.50 #3045, 0.45 #2814, 0.33 #5056), 07vk2 (0.50 #2853, 0.45 #2814, 0.33 #4864), 01dthg (0.50 #3009, 0.45 #2814, 0.33 #5020), 07wlt (0.50 #3095, 0.45 #2814, 0.33 #5106), 01s7pm (0.50 #2767, 0.45 #2814, 0.33 #5181) >> Best rule #5143 for best value: >> intensional similarity = 39 >> extensional distance = 7 >> proper extension: 09ggk; >> query: (?x4557, 016sd3) <- colors(?x13707, ?x4557), colors(?x11632, ?x4557), colors(?x8016, ?x4557), colors(?x7660, ?x4557), colors(?x5844, ?x4557), colors(?x5145, ?x4557), colors(?x2730, ?x4557), colors(?x11673, ?x4557), colors(?x11390, ?x4557), colors(?x6074, ?x4557), school_type(?x8016, ?x3092), colors(?x13707, ?x1101), institution(?x1526, ?x8016), currency(?x11632, ?x2244), position(?x11390, ?x60), team(?x2010, ?x6074), student(?x2730, ?x10795), student(?x2730, ?x9977), student(?x2730, ?x771), team(?x5420, ?x11390), major_field_of_study(?x11632, ?x2981), ?x2981 = 02j62, ?x1526 = 0bkj86, child(?x10759, ?x2730), ?x1101 = 06fvc, sport(?x11673, ?x5063), draft(?x6074, ?x1633), citytown(?x5145, ?x1860), school(?x8586, ?x7660), team(?x203, ?x11390), major_field_of_study(?x5145, ?x6756), currency(?x5844, ?x170), people(?x1050, ?x10795), type_of_union(?x9977, ?x566), ?x6756 = 0_jm, institution(?x1519, ?x7660), nominated_for(?x771, ?x1255), category(?x6074, ?x134), school(?x6074, ?x2948) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2814 for first EXPECTED value: *> intensional similarity = 32 *> extensional distance = 2 *> proper extension: 03vtbc; *> query: (?x4557, ?x481) <- colors(?x13707, ?x4557), colors(?x10355, ?x4557), colors(?x8016, ?x4557), colors(?x3416, ?x4557), colors(?x2821, ?x4557), colors(?x2730, ?x4557), colors(?x546, ?x4557), colors(?x8326, ?x4557), colors(?x3674, ?x4557), school_type(?x8016, ?x3092), colors(?x13707, ?x1101), institution(?x620, ?x8016), student(?x2730, ?x434), currency(?x8016, ?x170), colors(?x7704, ?x1101), colors(?x5710, ?x1101), colors(?x7075, ?x1101), colors(?x481, ?x1101), school(?x2820, ?x2821), sport(?x8326, ?x471), ?x7075 = 01d34b, student(?x546, ?x547), student(?x3416, ?x8482), state_province_region(?x546, ?x961), school(?x2569, ?x3416), ?x3674 = 05tg3, ?x7704 = 0cxbth, team(?x60, ?x8326), major_field_of_study(?x2730, ?x2014), contains(?x512, ?x10355), location(?x8482, ?x739), ?x5710 = 050fh *> conf = 0.45 ranks of expected_values: 190, 241, 249, 261, 294, 358 EVAL 019sc colors! 030w19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.556 http://example.org/education/educational_institution/colors EVAL 019sc colors! 03x1s8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.556 http://example.org/education/educational_institution/colors EVAL 019sc colors! 0hsb3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.556 http://example.org/education/educational_institution/colors EVAL 019sc colors! 02ln0f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 20.000 20.000 0.556 http://example.org/education/educational_institution/colors EVAL 019sc colors! 033x5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.556 http://example.org/education/educational_institution/colors EVAL 019sc colors! 01q0kg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.556 http://example.org/education/educational_institution/colors #10636-02p7xc PRED entity: 02p7xc PRED relation: profession PRED expected values: 018gz8 => 80 concepts (41 used for prediction) PRED predicted values (max 10 best out of 81): 01d_h8 (0.63 #3559, 0.56 #1930, 0.55 #302), 01c72t (0.62 #615, 0.60 #1059, 0.60 #467), 09jwl (0.54 #166, 0.53 #610, 0.51 #1054), 02jknp (0.47 #4005, 0.46 #304, 0.46 #4153), 03gjzk (0.41 #310, 0.39 #1938, 0.38 #3863), 0nbcg (0.38 #623, 0.38 #1067, 0.36 #771), 0n1h (0.34 #3269, 0.15 #12, 0.13 #160), 0cbd2 (0.32 #3264, 0.25 #2375, 0.24 #1931), 01c8w0 (0.32 #453, 0.22 #1785, 0.22 #2081), 018gz8 (0.31 #1940, 0.27 #4161, 0.27 #3865) >> Best rule #3559 for best value: >> intensional similarity = 4 >> extensional distance = 425 >> proper extension: 03ldxq; 01xcr4; 0d05fv; 0klw; 07cbs; >> query: (?x9763, 01d_h8) <- religion(?x9763, ?x1985), profession(?x9763, ?x987), profession(?x4724, ?x987), ?x4724 = 019r_1 >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #1940 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 176 *> proper extension: 03xp8d5; *> query: (?x9763, 018gz8) <- category(?x9763, ?x134), profession(?x9763, ?x987), ?x987 = 0dxtg, ?x134 = 08mbj5d *> conf = 0.31 ranks of expected_values: 10 EVAL 02p7xc profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 80.000 41.000 0.635 http://example.org/people/person/profession #10635-0xszy PRED entity: 0xszy PRED relation: featured_film_locations! PRED expected values: 03hj5lq => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 651): 0192hw (0.18 #2444, 0.12 #3181, 0.07 #6866), 0872p_c (0.12 #3026, 0.09 #2289, 0.07 #9660), 04gv3db (0.11 #9167, 0.08 #12852, 0.07 #9904), 033srr (0.10 #6913, 0.06 #12810, 0.06 #13547), 032zq6 (0.09 #2507, 0.07 #6929, 0.06 #3244), 04dsnp (0.09 #2277, 0.06 #3014, 0.06 #14070), 02sg5v (0.09 #2265, 0.06 #3002, 0.05 #8899), 02fqxm (0.09 #2945, 0.06 #3682, 0.05 #9579), 0296rz (0.09 #2891, 0.06 #3628, 0.05 #5102), 05zlld0 (0.09 #2476, 0.06 #3213, 0.05 #4687) >> Best rule #2444 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0_3cs; >> query: (?x10059, 0192hw) <- county_seat(?x10131, ?x10059), time_zones(?x10059, ?x2674), category(?x10059, ?x134), adjoins(?x9233, ?x10059) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #7081 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 0f4y_; *> query: (?x10059, 03hj5lq) <- source(?x10059, ?x958), adjoins(?x10059, ?x9233), featured_film_locations(?x7016, ?x9233), place_of_birth(?x1992, ?x9233) *> conf = 0.03 ranks of expected_values: 155 EVAL 0xszy featured_film_locations! 03hj5lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 177.000 177.000 0.182 http://example.org/film/film/featured_film_locations #10634-07y_7 PRED entity: 07y_7 PRED relation: family PRED expected values: 0d8lm => 98 concepts (96 used for prediction) PRED predicted values (max 10 best out of 124): 085jw (0.33 #27, 0.33 #20, 0.20 #548), 0342h (0.29 #222, 0.18 #430, 0.17 #966), 0fx80y (0.27 #451, 0.25 #77, 0.23 #1352), 05148p4 (0.25 #91, 0.20 #385, 0.14 #229), 01vj9c (0.20 #373, 0.20 #354, 0.17 #172), 0l14md (0.20 #116, 0.17 #1593, 0.16 #1942), 0d8lm (0.20 #371, 0.17 #189, 0.14 #1198), 026t6 (0.20 #113, 0.14 #221, 0.10 #1590), 01kcd (0.14 #878, 0.08 #1031, 0.07 #573), 06ncr (0.12 #1028, 0.11 #698, 0.10 #823) >> Best rule #27 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 06ncr; >> query: (?x75, ?x3156) <- role(?x3156, ?x75), role(?x2725, ?x75), role(?x2460, ?x75), role(?x614, ?x75), role(?x316, ?x75), instrumentalists(?x75, ?x535), role(?x75, ?x212), ?x2460 = 01wy6, ?x2725 = 0l1589, ?x316 = 05r5c, ?x3156 = 085jw, ?x614 = 0mkg >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #371 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 8 *> proper extension: 02sgy; 0dwtp; 05148p4; 07xzm; 02fsn; 01xqw; *> query: (?x75, 0d8lm) <- role(?x2460, ?x75), role(?x745, ?x75), role(?x716, ?x75), instrumentalists(?x75, ?x535), role(?x75, ?x1437), ?x2460 = 01wy6, role(?x1887, ?x75), ?x1437 = 01vdm0, ?x716 = 018vs, ?x745 = 01vj9c *> conf = 0.20 ranks of expected_values: 7 EVAL 07y_7 family 0d8lm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 98.000 96.000 0.333 http://example.org/music/instrument/family #10633-01m7f5r PRED entity: 01m7f5r PRED relation: music! PRED expected values: 02vjp3 => 159 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1057): 016z7s (0.25 #2223, 0.04 #10295, 0.03 #18369), 0btpm6 (0.20 #3766, 0.12 #2757, 0.03 #50454), 02fqrf (0.20 #3364, 0.12 #2355, 0.03 #19510), 02z9rr (0.20 #3804, 0.03 #19950, 0.02 #10867), 0gldyz (0.17 #1948, 0.10 #3966, 0.03 #50454), 02vzpb (0.17 #1921, 0.10 #3939, 0.03 #50454), 02nt3d (0.17 #1638, 0.10 #3656, 0.03 #5674), 0cbv4g (0.17 #1548, 0.10 #3566, 0.03 #5584), 03h4fq7 (0.17 #1530, 0.10 #3548, 0.03 #5566), 0gmblvq (0.17 #1407, 0.10 #3425, 0.03 #5443) >> Best rule #2223 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0150t6; 03bxh; 08c7cz; 03975z; 07zhd7; 07z4fy; >> query: (?x9064, 016z7s) <- music(?x6578, ?x9064), location(?x9064, ?x362), film_release_region(?x6578, ?x94), ?x362 = 04jpl >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #50454 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 200 *> proper extension: 09bx1k; 0bk1p; *> query: (?x9064, ?x508) <- music(?x2973, ?x9064), film(?x4043, ?x2973), film(?x4043, ?x508), language(?x2973, ?x254) *> conf = 0.03 ranks of expected_values: 373 EVAL 01m7f5r music! 02vjp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 159.000 97.000 0.250 http://example.org/film/film/music #10632-0bm2g PRED entity: 0bm2g PRED relation: titles! PRED expected values: 024qqx => 68 concepts (54 used for prediction) PRED predicted values (max 10 best out of 56): 07s9rl0 (0.36 #919, 0.36 #1123, 0.31 #2144), 04xvlr (0.31 #514, 0.28 #4, 0.27 #922), 01z4y (0.20 #2281, 0.17 #2487, 0.17 #3307), 02l7c8 (0.18 #4196, 0.18 #3685, 0.17 #4301), 082gq (0.18 #4196, 0.18 #3685, 0.17 #4301), 060__y (0.18 #4196, 0.18 #3685, 0.17 #4301), 024qqx (0.16 #897, 0.13 #487, 0.13 #385), 01jfsb (0.15 #325, 0.15 #427, 0.14 #2265), 04t36 (0.14 #211, 0.12 #110, 0.10 #8), 07ssc (0.13 #1132, 0.12 #928, 0.11 #520) >> Best rule #919 for best value: >> intensional similarity = 3 >> extensional distance = 209 >> proper extension: 0c5qvw; >> query: (?x2112, 07s9rl0) <- nominated_for(?x1107, ?x2112), award(?x2112, ?x3066), ?x1107 = 019f4v >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #897 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 176 *> proper extension: 04gcyg; 0m_h6; 03bdkd; *> query: (?x2112, 024qqx) <- nominated_for(?x500, ?x2112), film(?x3017, ?x2112), ?x500 = 0p9sw *> conf = 0.16 ranks of expected_values: 7 EVAL 0bm2g titles! 024qqx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 68.000 54.000 0.365 http://example.org/media_common/netflix_genre/titles #10631-015gm8 PRED entity: 015gm8 PRED relation: nominated_for! PRED expected values: 0gr0m => 128 concepts (117 used for prediction) PRED predicted values (max 10 best out of 203): 0gr0m (0.78 #14003, 0.69 #2800, 0.68 #11900), 0gq9h (0.74 #3324, 0.68 #1459, 0.66 #6357), 0k611 (0.71 #1469, 0.52 #3334, 0.41 #4033), 02z1nbg (0.69 #2800, 0.68 #11900, 0.68 #21017), 094qd5 (0.52 #5166, 0.51 #4232, 0.44 #733), 04dn09n (0.49 #3298, 0.39 #1433, 0.38 #6098), 02pqp12 (0.48 #1456, 0.39 #3321, 0.38 #288), 0gs96 (0.48 #2185, 0.42 #1486, 0.35 #1719), 0gqyl (0.44 #308, 0.39 #1476, 0.33 #5208), 03hkv_r (0.39 #6080, 0.39 #6313, 0.23 #1415) >> Best rule #14003 for best value: >> intensional similarity = 5 >> extensional distance = 592 >> proper extension: 06mmr; >> query: (?x11597, ?x1245) <- award(?x11597, ?x3902), award(?x11597, ?x1245), award_winner(?x11597, ?x4349), award_winner(?x3902, ?x396), ceremony(?x1245, ?x78) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015gm8 nominated_for! 0gr0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 117.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10630-04zwjd PRED entity: 04zwjd PRED relation: profession PRED expected values: 01c8w0 => 168 concepts (138 used for prediction) PRED predicted values (max 10 best out of 65): 02hrh1q (0.82 #19140, 0.77 #12915, 0.76 #17215), 09jwl (0.70 #9515, 0.69 #6398, 0.69 #6991), 0dz3r (0.62 #150, 0.43 #6380, 0.38 #8307), 01c8w0 (0.57 #1193, 0.38 #2972, 0.36 #4305), 0nbcg (0.52 #5221, 0.48 #6410, 0.47 #9527), 016z4k (0.51 #5193, 0.43 #744, 0.41 #8458), 0dxtg (0.39 #606, 0.33 #9806, 0.33 #9361), 01d_h8 (0.39 #9353, 0.39 #5640, 0.38 #6532), 03gjzk (0.33 #608, 0.24 #9808, 0.23 #14992), 02jknp (0.31 #9355, 0.26 #11576, 0.25 #9800) >> Best rule #19140 for best value: >> intensional similarity = 4 >> extensional distance = 1943 >> proper extension: 01j5x6; >> query: (?x1940, 02hrh1q) <- location(?x1940, ?x2911), profession(?x1940, ?x1614), profession(?x1231, ?x1614), ?x1231 = 01vrz41 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1193 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 06k02; *> query: (?x1940, 01c8w0) <- award(?x1940, ?x1079), award_winner(?x4038, ?x1940), ?x1079 = 0l8z1, profession(?x1940, ?x1614) *> conf = 0.57 ranks of expected_values: 4 EVAL 04zwjd profession 01c8w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 168.000 138.000 0.822 http://example.org/people/person/profession #10629-01h8sf PRED entity: 01h8sf PRED relation: contains! PRED expected values: 07ssc => 265 concepts (108 used for prediction) PRED predicted values (max 10 best out of 413): 01n7q (0.98 #52086, 0.89 #53878, 0.44 #87950), 02jx1 (0.90 #7173, 0.90 #6363, 0.88 #75316), 07ssc (0.87 #51108, 0.84 #54696, 0.83 #8965), 04jpl (0.84 #31404, 0.69 #40370, 0.57 #47544), 09c7w0 (0.83 #52011, 0.79 #87875, 0.76 #75320), 017cjb (0.55 #3584, 0.38 #6276, 0.33 #7172), 01w0v (0.44 #3792, 0.42 #10071, 0.38 #6484), 0978r (0.40 #5585, 0.39 #3791, 0.33 #6483), 0dbdy (0.38 #8184, 0.26 #18046, 0.20 #21628), 0d060g (0.31 #17048, 0.31 #14362, 0.25 #4495) >> Best rule #52086 for best value: >> intensional similarity = 7 >> extensional distance = 93 >> proper extension: 06_kh; 0r62v; 0r1yc; 0r7fy; 0dc95; 0f04c; 0kpys; 071vr; 0r6rq; 0r2gj; ... >> query: (?x13705, 01n7q) <- category(?x13705, ?x134), contains(?x13201, ?x13705), ?x134 = 08mbj5d, state(?x1339, ?x13201), location(?x1338, ?x1339), time_zones(?x1339, ?x5327), ?x1338 = 09qr6 >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #51108 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 92 *> proper extension: 01y9pk; 02fs_d; *> query: (?x13705, ?x512) <- school_type(?x13705, ?x3092), contains(?x13201, ?x13705), institution(?x620, ?x13705), ?x3092 = 05jxkf, country(?x13201, ?x512), colors(?x13705, ?x332), state(?x1339, ?x13201) *> conf = 0.87 ranks of expected_values: 3 EVAL 01h8sf contains! 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 265.000 108.000 0.979 http://example.org/location/location/contains #10628-0ft7sr PRED entity: 0ft7sr PRED relation: profession PRED expected values: 02ynfr => 99 concepts (96 used for prediction) PRED predicted values (max 10 best out of 88): 02hrh1q (0.88 #2531, 0.87 #2235, 0.86 #3569), 0dxtg (0.43 #310, 0.42 #606, 0.32 #2382), 0kyk (0.41 #5184, 0.39 #3851, 0.38 #7554), 0q04f (0.41 #5184, 0.39 #3851, 0.38 #7554), 01d_h8 (0.37 #2078, 0.37 #2374, 0.35 #5190), 02jknp (0.34 #2376, 0.29 #304, 0.26 #7562), 0d1pc (0.30 #1383, 0.26 #2271, 0.24 #2567), 09jwl (0.29 #316, 0.25 #612, 0.22 #8166), 0cbd2 (0.29 #303, 0.17 #599, 0.13 #8449), 03gjzk (0.26 #2088, 0.26 #1348, 0.24 #4607) >> Best rule #2531 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 01j851; >> query: (?x1779, 02hrh1q) <- spouse(?x1779, ?x8423), special_performance_type(?x8423, ?x9609), profession(?x8423, ?x1032) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1520 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 04kj2v; 05728w1; 0bytkq; 03gyh_z; 0dh73w; 05v1sb; 0d5wn3; 0638kv; 04_1nk; 0cdf37; ... *> query: (?x1779, 02ynfr) <- film_production_design_by(?x3111, ?x1779), profession(?x1779, ?x2450), ?x2450 = 02pjxr, genre(?x3111, ?x53) *> conf = 0.08 ranks of expected_values: 28 EVAL 0ft7sr profession 02ynfr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 99.000 96.000 0.881 http://example.org/people/person/profession #10627-01q4qv PRED entity: 01q4qv PRED relation: award_winner! PRED expected values: 02wkmx => 124 concepts (98 used for prediction) PRED predicted values (max 10 best out of 241): 0gs9p (0.42 #23416, 0.40 #3404, 0.39 #23415), 02pqp12 (0.42 #23416, 0.40 #3404, 0.39 #23415), 019f4v (0.42 #23416, 0.40 #3404, 0.39 #23415), 040njc (0.42 #23416, 0.40 #3404, 0.39 #23415), 04dn09n (0.42 #23416, 0.40 #3404, 0.39 #23415), 02rdyk7 (0.40 #3404, 0.39 #23415, 0.38 #21285), 027c924 (0.27 #3840, 0.19 #1286, 0.19 #860), 09d28z (0.27 #4130, 0.15 #5410, 0.12 #6262), 02w_6xj (0.23 #4068, 0.20 #238, 0.14 #5348), 02wkmx (0.20 #14, 0.19 #3844, 0.14 #2140) >> Best rule #23416 for best value: >> intensional similarity = 4 >> extensional distance = 1267 >> proper extension: 04lgymt; 04rcr; 02r3zy; 0ggl02; 03g5jw; 05crg7; 0dvqq; 016fmf; 0249kn; 01x15dc; ... >> query: (?x3177, ?x1313) <- award_winner(?x7100, ?x3177), award(?x3177, ?x1313), award_winner(?x77, ?x3177), ceremony(?x1313, ?x78) >> conf = 0.42 => this is the best rule for 5 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0b478; *> query: (?x3177, 02wkmx) <- written_by(?x5515, ?x3177), nationality(?x3177, ?x789), ?x789 = 0f8l9c *> conf = 0.20 ranks of expected_values: 10 EVAL 01q4qv award_winner! 02wkmx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 124.000 98.000 0.415 http://example.org/award/award_category/winners./award/award_honor/award_winner #10626-039g82 PRED entity: 039g82 PRED relation: athlete! PRED expected values: 0jm_ => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2): 02vx4 (0.02 #1023, 0.02 #1063, 0.02 #1073), 0jm_ (0.02 #503, 0.02 #573, 0.01 #633) >> Best rule #1023 for best value: >> intensional similarity = 2 >> extensional distance = 3218 >> proper extension: 0g4gr; 01k6y1; 075wq; 03mz5b; 0jvs0; 08lpkq; 037mh8; 06q83; 07h1q; 01cqz5; ... >> query: (?x1784, 02vx4) <- gender(?x1784, ?x231), ?x231 = 05zppz >> conf = 0.02 => this is the best rule for 1 predicted values *> Best rule #503 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1367 *> proper extension: 099bk; 0cm03; 01l79yc; 0frmb1; 019g65; *> query: (?x1784, 0jm_) <- type_of_union(?x1784, ?x566), gender(?x1784, ?x231), student(?x3149, ?x1784) *> conf = 0.02 ranks of expected_values: 2 EVAL 039g82 athlete! 0jm_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.019 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #10625-0bs5k8r PRED entity: 0bs5k8r PRED relation: currency PRED expected values: 0kz1h => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.79 #78, 0.76 #281, 0.75 #351), 02gsvk (0.20 #55, 0.10 #20, 0.10 #435), 02l6h (0.10 #435, 0.09 #46, 0.06 #144), 01nv4h (0.10 #435, 0.05 #37, 0.04 #93), 0kz1h (0.10 #435, 0.04 #47, 0.02 #145), 088n7 (0.10 #435, 0.04 #56, 0.02 #147), 0ptk_ (0.10 #435) >> Best rule #78 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 03g90h; 0pc62; 075wx7_; 01pgp6; 0cz_ym; 0pvms; 05zy2cy; 02ryz24; 04tqtl; 0jwmp; ... >> query: (?x4276, 09nqf) <- film_release_region(?x4276, ?x94), film_crew_role(?x4276, ?x1171), ?x94 = 09c7w0, film_release_distribution_medium(?x4276, ?x81), ?x1171 = 09vw2b7 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #435 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1224 *> proper extension: 0j_tw; 014zwb; 027pfb2; 0gwf191; *> query: (?x4276, ?x170) <- genre(?x4276, ?x1626), genre(?x11114, ?x1626), genre(?x3063, ?x1626), ?x11114 = 02tcgh, currency(?x3063, ?x170) *> conf = 0.10 ranks of expected_values: 5 EVAL 0bs5k8r currency 0kz1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 92.000 0.793 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10624-01grpc PRED entity: 01grpc PRED relation: legislative_sessions! PRED expected values: 0424m => 30 concepts (29 used for prediction) PRED predicted values (max 10 best out of 38): 0fd_1 (0.61 #176, 0.61 #175, 0.61 #174), 03_nq (0.61 #176, 0.61 #175, 0.61 #174), 0424m (0.61 #176, 0.61 #175, 0.61 #174), 0226cw (0.60 #160, 0.50 #130, 0.43 #733), 02hy5d (0.60 #163, 0.50 #133, 0.41 #736), 024_vw (0.60 #168, 0.50 #138, 0.39 #741), 0bymv (0.60 #146, 0.50 #116, 0.31 #404), 012v1t (0.60 #154, 0.50 #124, 0.31 #412), 021sv1 (0.43 #718, 0.41 #748, 0.40 #145), 016lh0 (0.40 #151, 0.27 #380, 0.25 #409) >> Best rule #176 for best value: >> intensional similarity = 38 >> extensional distance = 3 >> proper extension: 070mff; >> query: (?x4812, ?x7891) <- district_represented(?x4812, ?x7405), district_represented(?x4812, ?x6895), district_represented(?x4812, ?x4776), district_represented(?x4812, ?x4754), district_represented(?x4812, ?x4061), district_represented(?x4812, ?x3778), district_represented(?x4812, ?x3670), district_represented(?x4812, ?x1767), district_represented(?x4812, ?x1755), district_represented(?x4812, ?x1426), district_represented(?x4812, ?x760), district_represented(?x4812, ?x335), ?x4776 = 06yxd, ?x4061 = 0498y, legislative_sessions(?x4812, ?x5256), legislative_sessions(?x4812, ?x4787), ?x6895 = 05fjf, ?x1767 = 04rrd, ?x335 = 059rby, ?x3670 = 05tbn, legislative_sessions(?x2860, ?x4787), legislative_sessions(?x7715, ?x4812), ?x760 = 05fkf, ?x1755 = 01x73, legislative_sessions(?x4787, ?x3973), legislative_sessions(?x7891, ?x5256), ?x7405 = 07_f2, ?x4754 = 0g0syc, religion(?x1426, ?x1624), location_of_ceremony(?x566, ?x1426), location(?x8685, ?x1426), jurisdiction_of_office(?x5254, ?x1426), contains(?x1426, ?x347), ?x8685 = 0154d7, adjoins(?x108, ?x1426), ?x1624 = 051kv, ?x3778 = 07h34, currency(?x1426, ?x170) >> conf = 0.61 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 01grpc legislative_sessions! 0424m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 30.000 29.000 0.609 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #10623-01xyt7 PRED entity: 01xyt7 PRED relation: nationality PRED expected values: 09c7w0 => 216 concepts (192 used for prediction) PRED predicted values (max 10 best out of 79): 09c7w0 (0.89 #18290, 0.89 #18189, 0.88 #17179), 0l2vz (0.39 #18188, 0.38 #17984, 0.38 #17180), 06pvr (0.39 #18188, 0.38 #17984, 0.38 #17180), 02jx1 (0.25 #133, 0.18 #4960, 0.18 #5262), 07ssc (0.25 #115, 0.13 #3129, 0.12 #1215), 0f8l9c (0.20 #322, 0.12 #1222, 0.05 #3819), 035qy (0.20 #434, 0.02 #15573, 0.01 #4154), 015fr (0.12 #617, 0.11 #817, 0.06 #1217), 0d060g (0.12 #707, 0.08 #1007, 0.07 #4633), 06mzp (0.12 #721, 0.05 #3819, 0.04 #14571) >> Best rule #18290 for best value: >> intensional similarity = 3 >> extensional distance = 1039 >> proper extension: 078mgh; >> query: (?x5943, ?x94) <- place_of_birth(?x5943, ?x6598), country(?x6598, ?x94), ?x94 = 09c7w0 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xyt7 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 216.000 192.000 0.886 http://example.org/people/person/nationality #10622-046b0s PRED entity: 046b0s PRED relation: nominated_for PRED expected values: 033f8n => 157 concepts (51 used for prediction) PRED predicted values (max 10 best out of 566): 02tgz4 (0.33 #1357, 0.17 #2978, 0.10 #20807), 04qw17 (0.33 #269, 0.17 #1890, 0.06 #52136), 03z20c (0.33 #437, 0.17 #2058, 0.05 #19887), 02b6n9 (0.33 #1412, 0.17 #3033, 0.05 #20862), 029zqn (0.33 #245, 0.17 #1866, 0.05 #19695), 02qlp4 (0.33 #1524, 0.17 #3145, 0.05 #20974), 0dp7wt (0.33 #1218, 0.17 #2839, 0.05 #20668), 071nw5 (0.33 #983, 0.17 #2604, 0.05 #20433), 017jd9 (0.33 #712, 0.17 #2333, 0.05 #20162), 0900j5 (0.33 #538, 0.17 #2159, 0.05 #19988) >> Best rule #1357 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 024rgt; >> query: (?x2548, 02tgz4) <- production_companies(?x349, ?x2548), award_winner(?x7611, ?x2548), ?x7611 = 070j61 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10477 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0gsg7; 0cjdk; 05gnf; 05s34b; *> query: (?x2548, 033f8n) <- category(?x2548, ?x134), child(?x382, ?x2548), award_winner(?x2548, ?x3406) *> conf = 0.06 ranks of expected_values: 316 EVAL 046b0s nominated_for 033f8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 157.000 51.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10621-01t94_1 PRED entity: 01t94_1 PRED relation: people! PRED expected values: 013b6_ => 143 concepts (136 used for prediction) PRED predicted values (max 10 best out of 73): 0x67 (0.27 #4874, 0.21 #465, 0.19 #6166), 02w7gg (0.21 #762, 0.12 #2510, 0.10 #534), 033tf_ (0.14 #386, 0.11 #5099, 0.10 #1222), 0xnvg (0.12 #12, 0.09 #848, 0.08 #5105), 02g7sp (0.12 #17, 0.07 #245, 0.05 #1157), 013xrm (0.12 #1007, 0.11 #2679, 0.10 #2451), 07bch9 (0.11 #1466, 0.10 #1010, 0.10 #1846), 07hwkr (0.09 #2519, 0.08 #771, 0.07 #1911), 063k3h (0.09 #182, 0.07 #410, 0.07 #258), 013b6_ (0.08 #1040, 0.06 #2484, 0.06 #52) >> Best rule #4874 for best value: >> intensional similarity = 4 >> extensional distance = 468 >> proper extension: 024zq; 034ls; 02jyhv; 04m_kpx; >> query: (?x8830, 0x67) <- gender(?x8830, ?x231), category(?x8830, ?x134), people(?x1050, ?x8830), profession(?x8830, ?x987) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1040 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 01h2_6; *> query: (?x8830, 013b6_) <- people(?x10069, ?x8830), influenced_by(?x4066, ?x8830), people(?x1050, ?x8830) *> conf = 0.08 ranks of expected_values: 10 EVAL 01t94_1 people! 013b6_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 143.000 136.000 0.266 http://example.org/people/ethnicity/people #10620-01jgpsh PRED entity: 01jgpsh PRED relation: award_winner! PRED expected values: 04n2r9h => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 90): 0bxs_d (0.25 #115, 0.04 #9451, 0.04 #5077), 0bx6zs (0.25 #127, 0.04 #9451, 0.04 #5077), 03gyp30 (0.09 #399, 0.04 #1386, 0.04 #822), 027hjff (0.09 #339, 0.04 #9451, 0.04 #1326), 0hndn2q (0.09 #181, 0.02 #745, 0.02 #5963), 0gmdkyy (0.09 #171, 0.01 #735, 0.01 #594), 026kq4q (0.09 #187, 0.01 #1597, 0.01 #1315), 03tn9w (0.09 #235, 0.01 #658, 0.01 #517), 0bc773 (0.09 #195, 0.01 #477), 02cg41 (0.07 #408, 0.05 #831, 0.05 #2100) >> Best rule #115 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 030tjk; 03c6vl; >> query: (?x6363, 0bxs_d) <- award_winner(?x6363, ?x7796), ?x7796 = 030tj5, award_winner(?x537, ?x6363) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #327 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 06zmg7m; *> query: (?x6363, 04n2r9h) <- profession(?x6363, ?x1943), profession(?x6363, ?x1146), ?x1146 = 018gz8, ?x1943 = 02krf9 *> conf = 0.02 ranks of expected_values: 56 EVAL 01jgpsh award_winner! 04n2r9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 102.000 102.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10619-0pk1p PRED entity: 0pk1p PRED relation: music PRED expected values: 01ycfv => 93 concepts (50 used for prediction) PRED predicted values (max 10 best out of 98): 023361 (0.25 #150, 0.07 #360, 0.07 #570), 0146pg (0.14 #1485, 0.12 #10, 0.05 #2331), 02g1jh (0.12 #128, 0.04 #338, 0.03 #548), 01sp81 (0.11 #631, 0.07 #6979, 0.07 #8670), 0m68w (0.11 #631, 0.07 #6979, 0.07 #8670), 03ktjq (0.11 #631, 0.07 #8670, 0.06 #8037), 030_1m (0.11 #631, 0.07 #8670, 0.06 #8037), 0h7pj (0.11 #631, 0.07 #8670, 0.06 #9094), 03h610 (0.08 #1552, 0.04 #2822, 0.03 #3033), 02cyfz (0.07 #244, 0.07 #454, 0.06 #665) >> Best rule #150 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 04fzfj; 026n4h6; 0295sy; 037xlx; 02scbv; 02fqxm; >> query: (?x8578, 023361) <- film(?x1561, ?x8578), ?x1561 = 030_1m, nominated_for(?x154, ?x8578), ?x154 = 05b4l5x >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1431 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 91 *> proper extension: 06wzvr; *> query: (?x8578, 01ycfv) <- nominated_for(?x688, ?x8578), nominated_for(?x102, ?x8578), ?x688 = 05b1610, award(?x123, ?x102) *> conf = 0.02 ranks of expected_values: 46 EVAL 0pk1p music 01ycfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 93.000 50.000 0.250 http://example.org/film/film/music #10618-01r3y2 PRED entity: 01r3y2 PRED relation: institution! PRED expected values: 016t_3 019v9k => 130 concepts (98 used for prediction) PRED predicted values (max 10 best out of 22): 019v9k (0.78 #287, 0.77 #216, 0.73 #405), 03bwzr4 (0.73 #222, 0.68 #293, 0.60 #646), 02_xgp2 (0.71 #291, 0.67 #220, 0.61 #244), 0bkj86 (0.65 #122, 0.60 #215, 0.58 #239), 016t_3 (0.63 #211, 0.59 #282, 0.58 #118), 07s6fsf (0.58 #233, 0.52 #70, 0.46 #280), 04zx3q1 (0.50 #210, 0.46 #281, 0.46 #117), 013zdg (0.43 #75, 0.42 #121, 0.34 #285), 027f2w (0.43 #217, 0.38 #124, 0.34 #288), 01rr_d (0.33 #225, 0.28 #2065, 0.22 #86) >> Best rule #287 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 03v6t; 03tw2s; 03cz83; >> query: (?x3090, 019v9k) <- fraternities_and_sororities(?x3090, ?x3697), major_field_of_study(?x3090, ?x10046), major_field_of_study(?x3090, ?x2981), student(?x10046, ?x690), ?x2981 = 02j62 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 01r3y2 institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 98.000 0.780 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01r3y2 institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 130.000 98.000 0.780 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #10617-0466s8n PRED entity: 0466s8n PRED relation: featured_film_locations PRED expected values: 05kj_ => 105 concepts (89 used for prediction) PRED predicted values (max 10 best out of 82): 02_286 (0.18 #20, 0.16 #1464, 0.15 #260), 04jpl (0.12 #9, 0.09 #1453, 0.08 #971), 030qb3t (0.10 #2447, 0.10 #279, 0.10 #2928), 0rh6k (0.08 #241, 0.06 #1445, 0.04 #3132), 0dc95 (0.06 #61, 0.02 #1264, 0.01 #1505), 0sbv7 (0.06 #221, 0.01 #942), 0f2tj (0.06 #123, 0.01 #844), 02ly_ (0.06 #109), 0dclg (0.05 #293, 0.01 #1497, 0.01 #534), 094jv (0.05 #284) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0hz6mv2; >> query: (?x10225, 02_286) <- film_format(?x10225, ?x6392), language(?x10225, ?x254), executive_produced_by(?x10225, ?x12254), film_festivals(?x10225, ?x11147) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #499 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 09lcsj; 057lbk; 07k8rt4; 027pfg; 03q8xj; 04180vy; *> query: (?x10225, 05kj_) <- film_crew_role(?x10225, ?x4305), language(?x10225, ?x254), music(?x10225, ?x565), ?x4305 = 0215hd *> conf = 0.01 ranks of expected_values: 62 EVAL 0466s8n featured_film_locations 05kj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 105.000 89.000 0.176 http://example.org/film/film/featured_film_locations #10616-02q9kqf PRED entity: 02q9kqf PRED relation: profession PRED expected values: 02tx6q => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 57): 02hrh1q (0.68 #3465, 0.66 #8567, 0.65 #8267), 01d_h8 (0.36 #4057, 0.35 #4357, 0.32 #2856), 0dxtg (0.33 #4065, 0.28 #12918, 0.28 #1664), 02tx6q (0.27 #503, 0.21 #1103, 0.21 #953), 02jknp (0.26 #4059, 0.25 #158, 0.25 #4359), 03gjzk (0.25 #4067, 0.24 #2566, 0.24 #5717), 09jwl (0.19 #3771, 0.19 #4671, 0.18 #3320), 02pjxr (0.17 #35, 0.10 #9003, 0.08 #185), 0cbd2 (0.15 #1507, 0.15 #1657, 0.13 #2107), 0dz3r (0.14 #3753, 0.12 #4653, 0.12 #3302) >> Best rule #3465 for best value: >> intensional similarity = 3 >> extensional distance = 1025 >> proper extension: 050z2; 03flwk; 043hg; 047jhq; 025_ql1; >> query: (?x6232, 02hrh1q) <- award_winner(?x637, ?x6232), place_of_birth(?x6232, ?x739), nominated_for(?x637, ?x144) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #503 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 04cy8rb; 0284n42; 076lxv; 027rwmr; 03h26tm; 021yc7p; 09rp4r_; 09pjnd; 0c94fn; 04ktcgn; ... *> query: (?x6232, 02tx6q) <- crewmember(?x1199, ?x6232), award_winner(?x1744, ?x6232), award_nominee(?x1585, ?x6232) *> conf = 0.27 ranks of expected_values: 4 EVAL 02q9kqf profession 02tx6q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 103.000 0.677 http://example.org/people/person/profession #10615-02gx2x PRED entity: 02gx2x PRED relation: languages_spoken PRED expected values: 097kp => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 56): 02h40lc (0.83 #1626, 0.73 #1356, 0.73 #759), 064_8sq (0.67 #667, 0.50 #829, 0.33 #559), 0t_2 (0.58 #1042, 0.50 #229, 0.45 #1798), 06b_j (0.40 #290, 0.33 #128, 0.25 #236), 0880p (0.40 #313, 0.33 #151, 0.25 #259), 04306rv (0.33 #546, 0.33 #114, 0.25 #222), 02bjrlw (0.33 #542, 0.33 #110, 0.25 #218), 06mp7 (0.33 #555, 0.33 #123, 0.25 #231), 032f6 (0.33 #158, 0.33 #103, 0.25 #266), 0swlx (0.33 #159, 0.33 #104, 0.25 #267) >> Best rule #1626 for best value: >> intensional similarity = 11 >> extensional distance = 28 >> proper extension: 078ds; 0fk3s; 04czx7; >> query: (?x13627, 02h40lc) <- languages_spoken(?x13627, ?x7658), countries_spoken_in(?x7658, ?x792), countries_spoken_in(?x7658, ?x172), ?x792 = 0hzlz, service_language(?x10812, ?x7658), service_language(?x9469, ?x7658), official_language(?x9051, ?x7658), ?x9469 = 04sv4, language(?x1470, ?x7658), ?x10812 = 049mr, film_release_region(?x66, ?x172) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #920 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 12 *> proper extension: 04mvp8; *> query: (?x13627, ?x254) <- geographic_distribution(?x13627, ?x13749), people(?x13627, ?x6461), languages_spoken(?x13627, ?x7658), administrative_parent(?x13749, ?x3749), countries_spoken_in(?x7658, ?x10382), countries_spoken_in(?x7658, ?x172), contains(?x14384, ?x13749), contains(?x10382, ?x3407), official_language(?x10382, ?x254), film_release_region(?x4336, ?x172), film_release_region(?x1370, ?x172), form_of_government(?x10382, ?x1926), ?x4336 = 0bpm4yw, ?x1370 = 0gmcwlb *> conf = 0.20 ranks of expected_values: 29 EVAL 02gx2x languages_spoken 097kp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 46.000 46.000 0.833 http://example.org/people/ethnicity/languages_spoken #10614-01k_r5b PRED entity: 01k_r5b PRED relation: award_nominee PRED expected values: 02fn5r => 113 concepts (50 used for prediction) PRED predicted values (max 10 best out of 738): 05sq0m (0.81 #32660, 0.81 #30327, 0.81 #27993), 016sp_ (0.81 #32660, 0.81 #30327, 0.81 #27993), 051m56 (0.81 #32660, 0.81 #30327, 0.81 #27993), 03cfjg (0.81 #32660, 0.81 #30327, 0.81 #27993), 02l840 (0.12 #2491, 0.11 #14155, 0.10 #4824), 02x8z_ (0.10 #1064, 0.03 #8062, 0.03 #12727), 016kjs (0.10 #2561, 0.08 #14225, 0.07 #9560), 01vrt_c (0.10 #2579, 0.07 #9578, 0.06 #14243), 06mt91 (0.10 #3883, 0.07 #6216, 0.06 #15547), 01vw20h (0.09 #15047, 0.08 #3383, 0.08 #5716) >> Best rule #32660 for best value: >> intensional similarity = 3 >> extensional distance = 381 >> proper extension: 09jm8; 016ppr; >> query: (?x5265, ?x2300) <- category(?x5265, ?x134), award_winner(?x1480, ?x5265), award_nominee(?x2300, ?x5265) >> conf = 0.81 => this is the best rule for 4 predicted values *> Best rule #576 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 0lgsq; 03h_fqv; 01j6mff; 0jg77; *> query: (?x5265, 02fn5r) <- category(?x5265, ?x134), award_winner(?x6869, ?x5265), ?x6869 = 01xqqp *> conf = 0.03 ranks of expected_values: 91 EVAL 01k_r5b award_nominee 02fn5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 113.000 50.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10613-06v36 PRED entity: 06v36 PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.87 #311, 0.85 #619, 0.85 #553), 060bp (0.81 #221, 0.76 #67, 0.74 #155), 0pqc5 (0.70 #754, 0.62 #49, 0.52 #2119), 0f6c3 (0.58 #999, 0.57 #866, 0.51 #1131), 0fkvn (0.54 #996, 0.52 #863, 0.46 #1128), 09n5b9 (0.49 #1003, 0.48 #870, 0.43 #1135), 0fj45 (0.41 #239, 0.24 #657, 0.23 #41), 01zq91 (0.26 #2467, 0.24 #102, 0.20 #366), 0p5vf (0.26 #2467, 0.23 #364, 0.23 #386), 0dq3c (0.26 #2467, 0.23 #310, 0.23 #552) >> Best rule #311 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 02k54; 0169t; 019pcs; 07fj_; 04wlh; 0164v; >> query: (?x6437, 060c4) <- organization(?x6437, ?x4753), ?x4753 = 0gkjy, adjoins(?x792, ?x6437), participating_countries(?x1931, ?x6437) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #221 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 01d8l; *> query: (?x6437, 060bp) <- participating_countries(?x1931, ?x6437), jurisdiction_of_office(?x3119, ?x6437), contains(?x2467, ?x6437), ?x3119 = 04syw *> conf = 0.81 ranks of expected_values: 2 EVAL 06v36 jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 142.000 142.000 0.867 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #10612-0gx9rvq PRED entity: 0gx9rvq PRED relation: film_release_region PRED expected values: 03rjj 01p1v 06mkj => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 97): 06mkj (0.88 #190, 0.88 #476, 0.84 #1193), 05qhw (0.86 #438, 0.85 #152, 0.77 #1155), 03rjj (0.86 #432, 0.84 #146, 0.82 #1149), 03spz (0.78 #227, 0.77 #513, 0.66 #1230), 05v8c (0.65 #154, 0.63 #440, 0.55 #1157), 06f32 (0.62 #484, 0.62 #198, 0.47 #1201), 01p1v (0.59 #472, 0.59 #186, 0.44 #1189), 06c1y (0.53 #178, 0.50 #464, 0.40 #573), 016wzw (0.52 #485, 0.52 #199, 0.41 #1202), 06qd3 (0.49 #173, 0.49 #1176, 0.46 #459) >> Best rule #190 for best value: >> intensional similarity = 6 >> extensional distance = 100 >> proper extension: 0gtsx8c; 0c3ybss; 0dscrwf; 05p1tzf; 02x3lt7; 0gkz15s; 087wc7n; 017gl1; 0crfwmx; 08hmch; ... >> query: (?x634, 06mkj) <- film_release_region(?x634, ?x1023), film_release_region(?x634, ?x512), film_release_region(?x634, ?x429), ?x429 = 03rt9, ?x1023 = 0ctw_b, ?x512 = 07ssc >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 7 EVAL 0gx9rvq film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gx9rvq film_release_region 01p1v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 64.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gx9rvq film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10611-01vsy7t PRED entity: 01vsy7t PRED relation: award_winner PRED expected values: 031x_3 => 116 concepts (81 used for prediction) PRED predicted values (max 10 best out of 803): 01hmk9 (0.81 #88775, 0.81 #108149, 0.81 #121074), 019x62 (0.57 #3228, 0.43 #11299, 0.36 #119457), 01kwld (0.25 #4924, 0.25 #1696, 0.17 #9767), 03_wvl (0.25 #5821, 0.25 #2593, 0.17 #10664), 031ydm (0.25 #5566, 0.25 #2338, 0.17 #10409), 044mjy (0.25 #6207, 0.25 #2979, 0.17 #11050), 03x22w (0.25 #5819, 0.25 #2591, 0.17 #10662), 044mrh (0.25 #5703, 0.25 #2475, 0.17 #10546), 044mm6 (0.25 #5065, 0.25 #1837, 0.17 #9908), 044mz_ (0.25 #4843, 0.25 #1615, 0.17 #9686) >> Best rule #88775 for best value: >> intensional similarity = 3 >> extensional distance = 304 >> proper extension: 039cq4; >> query: (?x4620, ?x1136) <- award_winner(?x7183, ?x4620), award_winner(?x1136, ?x4620), influenced_by(?x7183, ?x1145) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #51371 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 91 *> proper extension: 07ymr5; 01gp_x; 079ws; 01y8d4; 05g7q; 0969fd; *> query: (?x4620, 031x_3) <- influenced_by(?x4620, ?x1029), gender(?x4620, ?x231), award_winner(?x1291, ?x4620) *> conf = 0.01 ranks of expected_values: 700 EVAL 01vsy7t award_winner 031x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 81.000 0.813 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #10610-0ph24 PRED entity: 0ph24 PRED relation: honored_for! PRED expected values: 0bxs_d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 106): 05c1t6z (0.35 #1221, 0.29 #858, 0.26 #4489), 0gvstc3 (0.30 #1721, 0.27 #2689, 0.26 #1237), 02q690_ (0.29 #2837, 0.27 #1748, 0.27 #3442), 03nnm4t (0.26 #1273, 0.25 #63, 0.25 #2725), 0lp_cd3 (0.25 #17, 0.22 #138, 0.18 #1711), 0gx_st (0.25 #30, 0.15 #1724, 0.13 #3902), 0gkxgfq (0.25 #92, 0.12 #576, 0.11 #455), 07y_p6 (0.25 #83, 0.12 #1777, 0.09 #2866), 0hn821n (0.25 #114, 0.10 #2776, 0.09 #1324), 0275n3y (0.15 #1758, 0.11 #2000, 0.10 #2847) >> Best rule #1221 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 026bfsh; >> query: (?x11726, 05c1t6z) <- program(?x4566, ?x11726), country_of_origin(?x11726, ?x94), ?x94 = 09c7w0 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1794 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 09dv8h; *> query: (?x11726, 0bxs_d) <- honored_for(?x9450, ?x11726), nominated_for(?x2062, ?x11726), program(?x2062, ?x758), company(?x265, ?x2062) *> conf = 0.12 ranks of expected_values: 13 EVAL 0ph24 honored_for! 0bxs_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 94.000 94.000 0.348 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #10609-02ln0f PRED entity: 02ln0f PRED relation: colors PRED expected values: 019sc => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.42 #162, 0.40 #783, 0.38 #623), 01g5v (0.27 #624, 0.26 #1144, 0.25 #864), 01l849 (0.25 #482, 0.25 #862, 0.25 #622), 019sc (0.18 #868, 0.18 #1148, 0.17 #628), 03wkwg (0.14 #175, 0.12 #15, 0.08 #55), 0jc_p (0.14 #164, 0.10 #204, 0.09 #545), 036k5h (0.12 #205, 0.09 #1146, 0.08 #846), 02rnmb (0.10 #93, 0.08 #173, 0.06 #213), 067z2v (0.09 #189, 0.09 #209, 0.08 #169), 038hg (0.09 #1153, 0.08 #773, 0.08 #393) >> Best rule #162 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 08815; 0f1nl; 04hgpt; 02y9bj; 01qgr3; >> query: (?x5754, 083jv) <- company(?x4486, ?x5754), currency(?x5754, ?x170), school(?x2820, ?x5754) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #868 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 321 *> proper extension: 02fs_d; 07x4c; 0bsnm; 02x9cv; 01n4w_; 02hp70; *> query: (?x5754, 019sc) <- student(?x5754, ?x286), colors(?x5754, ?x1101), institution(?x865, ?x5754) *> conf = 0.18 ranks of expected_values: 4 EVAL 02ln0f colors 019sc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 110.000 0.417 http://example.org/education/educational_institution/colors #10608-01vvyfh PRED entity: 01vvyfh PRED relation: profession PRED expected values: 064xm0 => 113 concepts (108 used for prediction) PRED predicted values (max 10 best out of 74): 09jwl (0.81 #3670, 0.76 #1332, 0.73 #1478), 02hrh1q (0.77 #1619, 0.73 #1181, 0.73 #2349), 0dxtg (0.61 #9224, 0.57 #1180, 0.45 #8054), 0nbcg (0.54 #1345, 0.53 #3683, 0.53 #1491), 0cbd2 (0.51 #3219, 0.49 #2927, 0.47 #6438), 01c72t (0.46 #313, 0.37 #167, 0.31 #460), 03gjzk (0.43 #1182, 0.39 #8056, 0.30 #4251), 018gz8 (0.40 #1184, 0.30 #2060, 0.24 #2791), 039v1 (0.38 #326, 0.36 #3688, 0.34 #619), 0kyk (0.37 #3242, 0.33 #2950, 0.31 #6461) >> Best rule #3670 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 02fybl; >> query: (?x3929, 09jwl) <- nationality(?x3929, ?x1310), profession(?x3929, ?x131), role(?x3929, ?x227) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #645 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 01wbz9; 03f0fnk; 03f0vvr; 01ydzx; 095x_; 02p68d; 01wvxw1; 01nn3m; *> query: (?x3929, 064xm0) <- nationality(?x3929, ?x1310), artists(?x2823, ?x3929), ?x2823 = 02qdgx *> conf = 0.03 ranks of expected_values: 48 EVAL 01vvyfh profession 064xm0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 113.000 108.000 0.813 http://example.org/people/person/profession #10607-026zlh9 PRED entity: 026zlh9 PRED relation: genre PRED expected values: 07s9rl0 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 84): 07s9rl0 (0.95 #2907, 0.84 #123, 0.82 #365), 07ssc (0.52 #3997, 0.52 #4967, 0.52 #4240), 082gq (0.50 #31, 0.13 #1726, 0.13 #1484), 05p553 (0.33 #5820, 0.33 #4972, 0.32 #127), 04xvlr (0.32 #366, 0.25 #608, 0.24 #487), 01jfsb (0.28 #2678, 0.28 #3162, 0.27 #4011), 02kdv5l (0.26 #2667, 0.26 #4000, 0.25 #4970), 0vgkd (0.25 #12, 0.13 #255, 0.08 #618), 04t36 (0.25 #7, 0.08 #129, 0.08 #1823), 06nbt (0.25 #26, 0.08 #269, 0.05 #148) >> Best rule #2907 for best value: >> intensional similarity = 5 >> extensional distance = 1054 >> proper extension: 0fq27fp; 0413cff; 0gh6j94; 0bs8hvm; 015qy1; >> query: (?x6133, 07s9rl0) <- genre(?x6133, ?x1509), genre(?x11385, ?x1509), genre(?x3790, ?x1509), ?x3790 = 07kh6f3, ?x11385 = 01c9d >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026zlh9 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.953 http://example.org/film/film/genre #10606-049rl0 PRED entity: 049rl0 PRED relation: award_winner PRED expected values: 05xbx => 65 concepts (32 used for prediction) PRED predicted values (max 10 best out of 961): 09d5h (0.06 #8407, 0.06 #51837, 0.06 #50217), 0gsg7 (0.06 #8360, 0.06 #51837, 0.06 #50217), 017s11 (0.06 #17880, 0.06 #22745, 0.06 #24367), 06pj8 (0.06 #51837, 0.06 #50217, 0.06 #48596), 0grwj (0.06 #51837, 0.06 #50217, 0.06 #48596), 044f7 (0.06 #51837, 0.06 #50217, 0.06 #48596), 01l1ls (0.06 #51837, 0.06 #50217, 0.06 #48596), 0g51l1 (0.06 #51837, 0.06 #50217, 0.06 #48596), 015cbq (0.06 #51837, 0.06 #50217, 0.06 #48596), 0121rx (0.06 #51837, 0.06 #50217, 0.06 #48596) >> Best rule #8407 for best value: >> intensional similarity = 2 >> extensional distance = 75 >> proper extension: 0grwj; 0kc6x; 065y4w7; 05qd_; 0f721s; 030_1_; 0gsg7; 0g51l1; 09d5h; 06pj8; ... >> query: (?x14380, 09d5h) <- award_winner(?x3486, ?x14380), ?x3486 = 0m7yy >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #51837 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 737 *> proper extension: 09b0xs; 06jrhz; 01vhrz; 0f1jhc; 09gb9xh; *> query: (?x14380, ?x105) <- award_winner(?x3486, ?x14380), award(?x6694, ?x3486), nominated_for(?x7510, ?x6694), genre(?x6694, ?x258), award_winner(?x3486, ?x105) *> conf = 0.06 ranks of expected_values: 24 EVAL 049rl0 award_winner 05xbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 65.000 32.000 0.065 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #10605-01d_s5 PRED entity: 01d_s5 PRED relation: parent_genre PRED expected values: 03mb9 => 58 concepts (40 used for prediction) PRED predicted values (max 10 best out of 207): 016_nr (0.67 #211, 0.57 #375, 0.44 #540), 06by7 (0.56 #2154, 0.35 #2318, 0.31 #1822), 016_rm (0.43 #460, 0.33 #625, 0.33 #296), 02x8m (0.32 #1165, 0.20 #1328, 0.19 #1493), 016clz (0.31 #1483, 0.14 #2142, 0.09 #3625), 06j6l (0.30 #1347, 0.21 #689, 0.19 #1020), 0155w (0.26 #1386, 0.07 #1878, 0.07 #3365), 08cyft (0.25 #1026, 0.15 #1682, 0.14 #695), 05r6t (0.23 #1533, 0.19 #2192, 0.19 #1860), 01243b (0.23 #1507, 0.15 #2166, 0.12 #2330) >> Best rule #211 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 06cp5; 01flzq; 036jv; >> query: (?x7673, 016_nr) <- parent_genre(?x7673, ?x2937), artists(?x7673, ?x6659), artists(?x7673, ?x5760), award_winner(?x139, ?x5760), award_winner(?x9295, ?x5760), ?x6659 = 01vw_dv, ?x9295 = 023vrq >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #328 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 06cp5; 01flzq; 036jv; *> query: (?x7673, ?x474) <- parent_genre(?x7673, ?x2937), artists(?x7673, ?x6659), artists(?x7673, ?x5760), award_winner(?x139, ?x5760), artists(?x474, ?x5760), award_winner(?x9295, ?x5760), ?x6659 = 01vw_dv, ?x9295 = 023vrq *> conf = 0.12 ranks of expected_values: 30 EVAL 01d_s5 parent_genre 03mb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 58.000 40.000 0.667 http://example.org/music/genre/parent_genre #10604-0cnl1c PRED entity: 0cnl1c PRED relation: award_nominee PRED expected values: 05l0j5 => 63 concepts (21 used for prediction) PRED predicted values (max 10 best out of 580): 0bt7ws (0.83 #2331, 0.82 #3200, 0.82 #4662), 0cj36c (0.83 #2331, 0.82 #4662, 0.81 #46637), 0cnl1c (0.82 #1007, 0.71 #3338, 0.32 #9326), 083chw (0.82 #4662, 0.81 #46637, 0.80 #39640), 04zkj5 (0.82 #4662, 0.81 #46637, 0.80 #39640), 043js (0.82 #4662, 0.81 #46637, 0.80 #39640), 05l0j5 (0.73 #1717, 0.65 #4048, 0.18 #34970), 0cj2nl (0.32 #9326, 0.27 #37305, 0.26 #41973), 048wrb (0.32 #9326, 0.27 #37305, 0.26 #41973), 03qmfzx (0.32 #9326, 0.27 #37305, 0.26 #41973) >> Best rule #2331 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0h3mrc; 0cj36c; 04zkj5; >> query: (?x4332, ?x237) <- award_winner(?x9272, ?x4332), award_winner(?x237, ?x4332), actor(?x1631, ?x4332), ?x9272 = 05xpms >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #1717 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0h3mrc; 0cj36c; 04zkj5; *> query: (?x4332, 05l0j5) <- award_winner(?x9272, ?x4332), actor(?x1631, ?x4332), ?x9272 = 05xpms *> conf = 0.73 ranks of expected_values: 7 EVAL 0cnl1c award_nominee 05l0j5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 63.000 21.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10603-016z2j PRED entity: 016z2j PRED relation: location PRED expected values: 02_286 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 136): 02_286 (0.28 #48720, 0.24 #57501, 0.24 #62289), 030qb3t (0.28 #10455, 0.25 #7263, 0.25 #4071), 02xry (0.12 #130, 0.03 #4919, 0.03 #5717), 04jpl (0.10 #48700, 0.09 #62269, 0.06 #15180), 0cr3d (0.10 #39247, 0.06 #17699, 0.06 #75164), 059rby (0.06 #10391, 0.05 #11189, 0.05 #12785), 01531 (0.06 #155, 0.06 #1751, 0.04 #57619), 0rh6k (0.06 #4, 0.03 #4793, 0.03 #39109), 0k049 (0.06 #8, 0.03 #17565, 0.03 #26344), 05fjf (0.06 #328, 0.03 #2722, 0.02 #10703) >> Best rule #48720 for best value: >> intensional similarity = 2 >> extensional distance = 1239 >> proper extension: 0f0y8; 01vvy; 0487c3; 028p0; 0f1vrl; 064p92m; 014dq7; 041mt; 01wj9y9; 06wvj; ... >> query: (?x2373, 02_286) <- location(?x2373, ?x4151), location_of_ceremony(?x413, ?x4151) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016z2j location 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.282 http://example.org/people/person/places_lived./people/place_lived/location #10602-02tgz4 PRED entity: 02tgz4 PRED relation: production_companies PRED expected values: 06rq1k => 91 concepts (62 used for prediction) PRED predicted values (max 10 best out of 90): 016tt2 (0.31 #85, 0.09 #661, 0.09 #1153), 0pz91 (0.16 #164, 0.11 #1894, 0.11 #1562), 086sj (0.16 #164, 0.11 #1894, 0.11 #1562), 086k8 (0.13 #1152, 0.12 #3291, 0.11 #4295), 0c41qv (0.12 #136, 0.09 #386, 0.09 #467), 0g1rw (0.12 #89, 0.07 #339, 0.07 #420), 016tw3 (0.11 #2972, 0.11 #2891, 0.11 #424), 05qd_ (0.11 #4302, 0.11 #3298, 0.11 #3882), 046b0s (0.09 #355, 0.08 #23, 0.07 #436), 054lpb6 (0.09 #2894, 0.09 #2153, 0.08 #2975) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 03h3x5; 074w86; 026hxwx; 04cf_l; 0291ck; >> query: (?x8987, 016tt2) <- nominated_for(?x541, ?x8987), currency(?x8987, ?x170), genre(?x8987, ?x7323), ?x7323 = 09q17 >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #99 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 03h3x5; 074w86; 026hxwx; 04cf_l; 0291ck; *> query: (?x8987, 06rq1k) <- nominated_for(?x541, ?x8987), currency(?x8987, ?x170), genre(?x8987, ?x7323), ?x7323 = 09q17 *> conf = 0.06 ranks of expected_values: 17 EVAL 02tgz4 production_companies 06rq1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 91.000 62.000 0.312 http://example.org/film/film/production_companies #10601-01svw8n PRED entity: 01svw8n PRED relation: type_of_union PRED expected values: 04ztj => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #146, 0.73 #33, 0.73 #242), 01g63y (0.49 #125, 0.46 #362, 0.20 #151) >> Best rule #146 for best value: >> intensional similarity = 2 >> extensional distance = 538 >> proper extension: 01l3j; >> query: (?x3930, 04ztj) <- religion(?x3930, ?x1985), film(?x3930, ?x814) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01svw8n type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.743 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10600-0gcs9 PRED entity: 0gcs9 PRED relation: role PRED expected values: 018vs 01vdm0 => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 116): 01vdm0 (0.42 #618, 0.36 #28, 0.30 #4158), 026t6 (0.37 #593, 0.21 #3, 0.16 #4133), 0l14qv (0.28 #595, 0.26 #2851, 0.26 #885), 0l14md (0.28 #596, 0.07 #4136, 0.07 #6), 0dwtp (0.26 #2851, 0.26 #885, 0.25 #2261), 03bx0bm (0.26 #2851, 0.26 #885, 0.25 #2261), 018vs (0.25 #601, 0.22 #4141, 0.19 #2762), 013y1f (0.24 #623, 0.21 #33, 0.16 #4163), 05148p4 (0.21 #19, 0.17 #609, 0.17 #4149), 07brj (0.20 #612, 0.14 #22, 0.07 #1693) >> Best rule #618 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 03c7ln; 0fp_v1x; 0285c; 01wsl7c; 03j0br4; 03xl77; 01nn6c; 01gx5f; 023l9y; 01l4g5; ... >> query: (?x2963, 01vdm0) <- artists(?x114, ?x2963), role(?x2963, ?x3991), ?x3991 = 05842k >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 0gcs9 role 01vdm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.421 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0gcs9 role 018vs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 149.000 149.000 0.421 http://example.org/music/artist/track_contributions./music/track_contribution/role #10599-04r1t PRED entity: 04r1t PRED relation: artists! PRED expected values: 03jsvl => 78 concepts (64 used for prediction) PRED predicted values (max 10 best out of 283): 03_d0 (0.65 #11989, 0.26 #5218, 0.24 #4911), 03lty (0.53 #2169, 0.49 #8928, 0.43 #4007), 016jny (0.50 #103, 0.33 #1021, 0.29 #409), 03jsvl (0.50 #162, 0.28 #7053, 0.23 #9821), 05bt6j (0.48 #14473, 0.38 #17848, 0.36 #19072), 064t9 (0.43 #6453, 0.43 #17204, 0.42 #17820), 05w3f (0.43 #341, 0.28 #7053, 0.27 #4016), 0126t5 (0.40 #696, 0.28 #7053, 0.23 #9821), 06j6l (0.39 #4946, 0.38 #5253, 0.31 #12024), 016clz (0.38 #18730, 0.38 #10133, 0.38 #10441) >> Best rule #11989 for best value: >> intensional similarity = 5 >> extensional distance = 210 >> proper extension: 028q6; 0146pg; 07q1v4; 01jrz5j; 01kvqc; 02pzc4; 037lyl; 016k62; 01vttb9; 02sjp; ... >> query: (?x1929, 03_d0) <- artists(?x7440, ?x1929), parent_genre(?x8798, ?x7440), artists(?x7440, ?x1282), ?x8798 = 0gg8l, ?x1282 = 01wdqrx >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #162 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 01pbxb; *> query: (?x1929, 03jsvl) <- artists(?x1928, ?x1929), artists(?x1000, ?x1929), artist(?x441, ?x1929), ?x1000 = 0xhtw, influenced_by(?x10670, ?x1929), ?x1928 = 0mhfr *> conf = 0.50 ranks of expected_values: 4 EVAL 04r1t artists! 03jsvl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 78.000 64.000 0.651 http://example.org/music/genre/artists #10598-0443c PRED entity: 0443c PRED relation: profession PRED expected values: 01445t => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 94): 02hrh1q (0.74 #5715, 0.74 #8265, 0.73 #10665), 09jwl (0.48 #3620, 0.39 #5420, 0.31 #6020), 03gjzk (0.47 #2566, 0.44 #2266, 0.36 #1366), 0gl2ny2 (0.45 #4419, 0.45 #4719, 0.44 #4269), 01445t (0.41 #474, 0.31 #1674, 0.28 #624), 01d_h8 (0.39 #1356, 0.36 #2556, 0.34 #2256), 0dxtg (0.37 #2264, 0.36 #2564, 0.30 #1364), 0nbcg (0.35 #3633, 0.26 #6033, 0.25 #3033), 016z4k (0.31 #3604, 0.23 #5404, 0.18 #6754), 0kyk (0.29 #181, 0.24 #1381, 0.22 #631) >> Best rule #5715 for best value: >> intensional similarity = 5 >> extensional distance = 171 >> proper extension: 01ky2h; 01m65sp; 0g2mbn; 0gs6vr; 03j149k; 0227vl; 02tf1y; 09nhvw; 03f4k; 022q4j; ... >> query: (?x13779, 02hrh1q) <- location(?x13779, ?x6987), location(?x13779, ?x739), people(?x2510, ?x13779), ?x739 = 02_286, contains(?x6987, ?x6988) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #474 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 02qjj7; 054fvj; 0cymln; 054c1; 01g0jn; *> query: (?x13779, 01445t) <- location(?x13779, ?x4074), student(?x4955, ?x13779), team(?x13779, ?x8516), place_of_birth(?x4238, ?x4074) *> conf = 0.41 ranks of expected_values: 5 EVAL 0443c profession 01445t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 132.000 132.000 0.740 http://example.org/people/person/profession #10597-0p9sw PRED entity: 0p9sw PRED relation: nominated_for PRED expected values: 08720 0dtfn 0hmm7 0fpv_3_ 026p4q7 0ddt_ 0hfzr 01f8hf 01fx6y 07xvf 0298n7 02zk08 0291ck => 55 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1302): 026p4q7 (0.80 #7403, 0.60 #11665, 0.60 #8823), 03xf_m (0.78 #17051, 0.77 #17050, 0.77 #21316), 0661ql3 (0.78 #17051, 0.77 #17050, 0.77 #21316), 0bcndz (0.78 #17051, 0.77 #17050, 0.77 #21316), 04v8h1 (0.78 #17051, 0.77 #17050, 0.77 #21316), 0h0wd9 (0.78 #17051, 0.77 #17050, 0.77 #21316), 05qm9f (0.78 #17051, 0.77 #17050, 0.77 #21316), 07gp9 (0.78 #17051, 0.77 #17050, 0.77 #21316), 061681 (0.78 #17051, 0.77 #17050, 0.77 #21316), 0cq7tx (0.78 #17051, 0.77 #17050, 0.77 #21316) >> Best rule #7403 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 03hkv_r; 0gr4k; 02hsq3m; 019f4v; 054krc; 0gr42; 0fhpv4; 02qyntr; 099flj; >> query: (?x500, 026p4q7) <- nominated_for(?x500, ?x1916), ceremony(?x500, ?x78), ?x1916 = 0ch26b_ >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 20, 35, 137, 142, 241, 254, 297, 304, 309, 418, 764, 913 EVAL 0p9sw nominated_for 0291ck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 02zk08 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 0298n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 07xvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 01fx6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 01f8hf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 0hfzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 0ddt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 026p4q7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 0fpv_3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 0hmm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 0dtfn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p9sw nominated_for 08720 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 16.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10596-02_0d2 PRED entity: 02_0d2 PRED relation: category PRED expected values: 08mbj5d => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.57 #3, 0.46 #5, 0.43 #2) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 03f3_p3; >> query: (?x6700, 08mbj5d) <- location(?x6700, ?x760), people(?x13213, ?x6700), ?x760 = 05fkf >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_0d2 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.571 http://example.org/common/topic/webpage./common/webpage/category #10595-01twmp PRED entity: 01twmp PRED relation: actor! PRED expected values: 01p4wv => 120 concepts (83 used for prediction) PRED predicted values (max 10 best out of 74): 063ykwt (0.33 #324, 0.17 #589, 0.03 #1649), 02q5bx2 (0.33 #163, 0.17 #693, 0.01 #3608), 02gl58 (0.33 #206, 0.17 #736, 0.01 #3916), 024rwx (0.17 #636, 0.02 #3816, 0.02 #4081), 0cskb (0.17 #728, 0.01 #3908, 0.01 #19882), 05jyb2 (0.17 #588, 0.01 #3768, 0.01 #19882), 028k2x (0.10 #1206, 0.03 #1736, 0.03 #2266), 026bfsh (0.06 #1422, 0.02 #6989, 0.02 #8844), 05f4vxd (0.06 #1414, 0.02 #4064, 0.02 #4595), 017f3m (0.06 #1411, 0.01 #3796, 0.01 #4061) >> Best rule #324 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0hz_1; >> query: (?x9662, 063ykwt) <- location(?x9662, ?x10718), ?x10718 = 0nlh7, profession(?x9662, ?x1032), ?x1032 = 02hrh1q, notable_people_with_this_condition(?x6720, ?x9662) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01twmp actor! 01p4wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 83.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10594-036k5h PRED entity: 036k5h PRED relation: colors! PRED expected values: 02hmw9 => 21 concepts (19 used for prediction) PRED predicted values (max 10 best out of 745): 016sd3 (0.60 #3957, 0.50 #6229, 0.50 #3052), 01s7pm (0.60 #3999, 0.50 #3094, 0.38 #6271), 02rv1w (0.60 #3941, 0.50 #3036, 0.38 #6213), 03np_7 (0.60 #4027, 0.50 #3122, 0.38 #6299), 0yls9 (0.50 #4700, 0.50 #3353, 0.50 #3152), 021996 (0.50 #4769, 0.50 #2967, 0.43 #5228), 02607j (0.50 #4586, 0.50 #2784, 0.43 #5045), 016ndm (0.50 #2805, 0.43 #5066, 0.40 #3710), 0gjv_ (0.50 #2880, 0.43 #5598, 0.40 #3785), 01tntf (0.50 #3032, 0.43 #5750, 0.40 #3937) >> Best rule #3957 for best value: >> intensional similarity = 37 >> extensional distance = 3 >> proper extension: 0jc_p; >> query: (?x3364, 016sd3) <- colors(?x9071, ?x3364), colors(?x7202, ?x3364), colors(?x6894, ?x3364), colors(?x3671, ?x3364), colors(?x3513, ?x3364), school(?x2569, ?x3513), colors(?x4546, ?x3364), student(?x3513, ?x8996), institution(?x2636, ?x3513), institution(?x1200, ?x3513), category(?x3513, ?x134), school_type(?x6894, ?x1044), contains(?x94, ?x3513), major_field_of_study(?x6894, ?x4268), athlete(?x4833, ?x8996), student(?x6894, ?x3633), team(?x2312, ?x4546), team(?x1240, ?x4546), school(?x8586, ?x7202), contains(?x3670, ?x6894), position(?x4546, ?x935), team(?x8996, ?x660), profession(?x8996, ?x1581), ?x4268 = 02822, currency(?x7202, ?x170), ?x2636 = 027f2w, organization(?x346, ?x9071), ?x2312 = 02qpbqj, ?x1200 = 016t_3, major_field_of_study(?x3513, ?x254), ?x935 = 06b1q, state_province_region(?x3671, ?x3302), school(?x4546, ?x735), participant(?x3633, ?x3183), ?x1240 = 023wyl, location(?x3633, ?x13739), fraternities_and_sororities(?x9071, ?x4348) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1118 for first EXPECTED value: *> intensional similarity = 34 *> extensional distance = 1 *> proper extension: 01g5v; *> query: (?x3364, 02hmw9) <- colors(?x6894, ?x3364), colors(?x3513, ?x3364), colors(?x1151, ?x3364), colors(?x388, ?x3364), ?x3513 = 0pspl, major_field_of_study(?x388, ?x1695), school_type(?x388, ?x1507), student(?x1151, ?x1583), student(?x1151, ?x1128), fraternities_and_sororities(?x388, ?x3697), artists(?x505, ?x1128), type_of_union(?x1128, ?x566), currency(?x1151, ?x170), school(?x2820, ?x388), organization(?x6894, ?x5487), ?x1507 = 01_9fk, instrumentalists(?x2309, ?x1583), award(?x1128, ?x567), institution(?x620, ?x6894), major_field_of_study(?x6894, ?x5900), colors(?x1632, ?x3364), ?x2820 = 0jmj7, award_nominee(?x215, ?x1128), role(?x1583, ?x214), major_field_of_study(?x4599, ?x1695), major_field_of_study(?x2606, ?x1695), school(?x685, ?x388), contains(?x2020, ?x1151), ?x2606 = 062z7, artists(?x597, ?x1583), ?x4599 = 07t90, ?x5900 = 0db86, artist(?x2039, ?x1128), major_field_of_study(?x865, ?x1695) *> conf = 0.33 ranks of expected_values: 178 EVAL 036k5h colors! 02hmw9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 21.000 19.000 0.600 http://example.org/education/educational_institution/colors #10593-01mszz PRED entity: 01mszz PRED relation: genre PRED expected values: 0gf28 => 98 concepts (97 used for prediction) PRED predicted values (max 10 best out of 95): 07s9rl0 (0.70 #9542, 0.68 #2814, 0.68 #1958), 02kdv5l (0.64 #125, 0.38 #493, 0.36 #371), 01z4y (0.61 #7829, 0.52 #8319, 0.52 #8686), 01jfsb (0.45 #135, 0.41 #381, 0.38 #8086), 03k9fj (0.45 #134, 0.36 #502, 0.29 #8085), 0556j8 (0.36 #166, 0.28 #1712, 0.07 #288), 04t2t (0.36 #182, 0.02 #3365, 0.02 #550), 02l7c8 (0.33 #3444, 0.32 #3688, 0.32 #9558), 0lsxr (0.28 #1712, 0.27 #131, 0.24 #1598), 06n90 (0.28 #1712, 0.22 #504, 0.17 #8087) >> Best rule #9542 for best value: >> intensional similarity = 3 >> extensional distance = 1441 >> proper extension: 0fq27fp; >> query: (?x6205, 07s9rl0) <- genre(?x6205, ?x258), genre(?x240, ?x258), ?x240 = 02v8kmz >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #3493 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 527 *> proper extension: 04svwx; *> query: (?x6205, 0gf28) <- country(?x6205, ?x94), genre(?x6205, ?x258), ?x258 = 05p553 *> conf = 0.10 ranks of expected_values: 40 EVAL 01mszz genre 0gf28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 98.000 97.000 0.697 http://example.org/film/film/genre #10592-01f1r4 PRED entity: 01f1r4 PRED relation: major_field_of_study PRED expected values: 04rjg 03g3w => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 90): 04rjg (0.56 #235, 0.53 #675, 0.53 #785), 062z7 (0.47 #792, 0.47 #682, 0.44 #572), 04x_3 (0.44 #570, 0.43 #680, 0.37 #460), 05qfh (0.43 #690, 0.42 #580, 0.39 #470), 03g3w (0.42 #791, 0.41 #681, 0.39 #461), 037mh8 (0.38 #277, 0.37 #827, 0.33 #497), 0g26h (0.35 #1576, 0.35 #255, 0.35 #1686), 04sh3 (0.32 #835, 0.29 #725, 0.29 #285), 06ms6 (0.30 #452, 0.29 #12, 0.28 #782), 02_7t (0.29 #714, 0.29 #604, 0.26 #824) >> Best rule #235 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 01prf3; >> query: (?x4099, 04rjg) <- organization(?x4099, ?x5487), citytown(?x4099, ?x957), location_of_ceremony(?x566, ?x957) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 01f1r4 major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 93.000 0.559 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01f1r4 major_field_of_study 04rjg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.559 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10591-0ddt_ PRED entity: 0ddt_ PRED relation: titles! PRED expected values: 024qqx => 69 concepts (53 used for prediction) PRED predicted values (max 10 best out of 59): 01hmnh (0.43 #104, 0.43 #27, 0.29 #131), 07s9rl0 (0.39 #615, 0.29 #309, 0.29 #1952), 04xvlr (0.29 #312, 0.25 #618, 0.18 #3088), 024qqx (0.29 #81, 0.20 #797, 0.18 #491), 01z4y (0.18 #1885, 0.17 #1678, 0.17 #752), 03k9fj (0.18 #3394, 0.18 #5258, 0.18 #1642), 06n90 (0.18 #3394, 0.18 #5258, 0.18 #1642), 02kdv5l (0.18 #3394, 0.18 #5258, 0.18 #1642), 01jfsb (0.14 #328, 0.14 #1558, 0.14 #1148), 07c52 (0.13 #1055, 0.11 #1774, 0.08 #3941) >> Best rule #104 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 042g97; >> query: (?x2899, ?x1510) <- nominated_for(?x2899, ?x1812), genre(?x2899, ?x1510), ?x1510 = 01hmnh >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #81 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 042g97; *> query: (?x2899, 024qqx) <- nominated_for(?x2899, ?x1812), genre(?x2899, ?x1510), ?x1510 = 01hmnh *> conf = 0.29 ranks of expected_values: 4 EVAL 0ddt_ titles! 024qqx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 53.000 0.429 http://example.org/media_common/netflix_genre/titles #10590-04wp63 PRED entity: 04wp63 PRED relation: student! PRED expected values: 065y4w7 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 64): 065y4w7 (0.19 #541, 0.15 #14, 0.10 #1595), 0bwfn (0.10 #275, 0.10 #802, 0.08 #1329), 015zyd (0.08 #2636, 0.03 #4217, 0.02 #5271), 09f2j (0.06 #2794, 0.05 #159, 0.05 #686), 09kvv (0.05 #41, 0.05 #568, 0.04 #1095), 0fnmz (0.05 #101, 0.05 #628, 0.04 #1155), 01w3v (0.05 #15, 0.05 #542, 0.04 #1069), 021w0_ (0.05 #324, 0.05 #851, 0.03 #2432), 06thjt (0.05 #925, 0.04 #1452, 0.03 #1979), 07w0v (0.04 #1074, 0.03 #1601, 0.01 #3709) >> Best rule #541 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 027rfxc; >> query: (?x10262, 065y4w7) <- edited_by(?x1812, ?x10262), award(?x10262, ?x500), film_crew_role(?x1812, ?x137) >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wp63 student! 065y4w7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.190 http://example.org/education/educational_institution/students_graduates./education/education/student #10589-0147dk PRED entity: 0147dk PRED relation: actor! PRED expected values: 050kh5 => 120 concepts (111 used for prediction) PRED predicted values (max 10 best out of 85): 06qv_ (0.11 #211, 0.03 #742, 0.02 #1273), 0fpxp (0.11 #149, 0.03 #680, 0.02 #1211), 08l0x2 (0.11 #145, 0.03 #676, 0.02 #1472), 01f39b (0.11 #102, 0.03 #633), 026y3cf (0.11 #250, 0.01 #1843, 0.01 #2904), 09fc83 (0.11 #90), 016dj8 (0.11 #24704, 0.10 #14602, 0.10 #266), 0ds2n (0.11 #24704, 0.10 #14602, 0.10 #266), 06rhz7 (0.10 #14602, 0.10 #266, 0.10 #18859), 0gfsq9 (0.10 #14602, 0.10 #266, 0.10 #18859) >> Best rule #211 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01j7z7; >> query: (?x521, 06qv_) <- award(?x521, ?x3105), film(?x521, ?x1488), ?x3105 = 01l29r >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0147dk actor! 050kh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 111.000 0.111 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10588-01r9c_ PRED entity: 01r9c_ PRED relation: category PRED expected values: 08mbj5d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.38 #12, 0.36 #9, 0.35 #27) >> Best rule #12 for best value: >> intensional similarity = 5 >> extensional distance = 184 >> proper extension: 05bnp0; 04yywz; 02p65p; 01l1b90; 02g8h; 0159h6; 03qd_; 0htlr; 04hpck; 02r34n; ... >> query: (?x10663, 08mbj5d) <- film(?x10663, ?x4786), profession(?x10663, ?x353), type_of_union(?x10663, ?x566), profession(?x10313, ?x353), ?x10313 = 07lp1 >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r9c_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.376 http://example.org/common/topic/webpage./common/webpage/category #10587-0646qh PRED entity: 0646qh PRED relation: student! PRED expected values: 02bqy => 83 concepts (65 used for prediction) PRED predicted values (max 10 best out of 50): 0bwfn (0.14 #275, 0.06 #3964, 0.06 #2383), 03ksy (0.11 #2741, 0.11 #1160, 0.11 #3795), 0bx8pn (0.06 #572, 0.02 #1626, 0.02 #2153), 053mhx (0.06 #822), 065y4w7 (0.06 #1068, 0.04 #4230, 0.04 #3176), 02tz9z (0.06 #1523), 0ym17 (0.06 #1461), 0g2jl (0.06 #1455), 06thjt (0.06 #1452), 0187nd (0.06 #1420) >> Best rule #275 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 026n998; 0g28b1; 03ckvj9; 03clrng; 03cl8lb; >> query: (?x6868, 0bwfn) <- award_winner(?x6868, ?x4147), award_winner(?x6868, ?x415), ?x415 = 03ckxdg, ?x4147 = 026n3rs >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1763 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 99 *> proper extension: 09v6gc9; 01h1b; *> query: (?x6868, 02bqy) <- award_winner(?x2476, ?x6868), tv_program(?x6868, ?x4721), award_winner(?x2720, ?x2476) *> conf = 0.02 ranks of expected_values: 26 EVAL 0646qh student! 02bqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 83.000 65.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #10586-0k7pf PRED entity: 0k7pf PRED relation: artists! PRED expected values: 064t9 06by7 => 114 concepts (112 used for prediction) PRED predicted values (max 10 best out of 222): 06by7 (0.69 #2534, 0.62 #1278, 0.57 #650), 064t9 (0.57 #641, 0.46 #10387, 0.45 #10701), 017_qw (0.52 #3833, 0.43 #4775, 0.41 #6031), 05w3f (0.50 #39, 0.40 #981, 0.30 #353), 02k_kn (0.43 #696, 0.25 #68, 0.22 #1324), 05bt6j (0.40 #359, 0.36 #673, 0.33 #987), 0155w (0.38 #1366, 0.30 #424, 0.28 #2622), 06j6l (0.35 #1306, 0.33 #992, 0.32 #2562), 02yv6b (0.30 #416, 0.29 #730, 0.27 #1044), 01lyv (0.30 #1291, 0.29 #663, 0.26 #6315) >> Best rule #2534 for best value: >> intensional similarity = 2 >> extensional distance = 79 >> proper extension: 07yg2; 0394y; 08w4pm; >> query: (?x3030, 06by7) <- inductee(?x1091, ?x3030), artist(?x2149, ?x3030) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0k7pf artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 112.000 0.691 http://example.org/music/genre/artists EVAL 0k7pf artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 112.000 0.691 http://example.org/music/genre/artists #10585-04ch23 PRED entity: 04ch23 PRED relation: profession PRED expected values: 02jknp => 88 concepts (46 used for prediction) PRED predicted values (max 10 best out of 59): 03gjzk (0.62 #2189, 0.34 #6105, 0.24 #5380), 02jknp (0.55 #3053, 0.28 #6099, 0.27 #1312), 01d_h8 (0.52 #6098, 0.43 #3052, 0.38 #731), 0kyk (0.40 #1913, 0.39 #1043, 0.35 #318), 0nbcg (0.27 #1480, 0.10 #6267, 0.09 #2786), 09jwl (0.27 #1467, 0.16 #162, 0.16 #6254), 0fj9f (0.23 #1067, 0.13 #1937, 0.11 #777), 0dz3r (0.20 #1452, 0.07 #6239, 0.06 #2758), 018gz8 (0.19 #305, 0.17 #1465, 0.17 #450), 02krf9 (0.17 #2201, 0.13 #3071, 0.11 #6117) >> Best rule #2189 for best value: >> intensional similarity = 7 >> extensional distance = 1013 >> proper extension: 0f1vrl; 043zg; >> query: (?x12071, 03gjzk) <- profession(?x12071, ?x353), profession(?x10716, ?x353), profession(?x7228, ?x353), profession(?x2952, ?x353), influenced_by(?x3542, ?x10716), program(?x2952, ?x3180), ?x7228 = 054187 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3053 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1197 *> proper extension: 012d40; 04yywz; 0337vz; 01j5ts; 01l1b90; 0qf43; 042l3v; 09fb5; 03ckxdg; 0m2l9; ... *> query: (?x12071, 02jknp) <- profession(?x12071, ?x353), profession(?x10716, ?x353), profession(?x7572, ?x353), profession(?x3980, ?x353), influenced_by(?x3542, ?x10716), ?x7572 = 08c7cz, ?x3980 = 016yzz *> conf = 0.55 ranks of expected_values: 2 EVAL 04ch23 profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 46.000 0.621 http://example.org/people/person/profession #10584-02rchht PRED entity: 02rchht PRED relation: award PRED expected values: 01lk0l => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 267): 0gs9p (0.42 #890, 0.37 #1295, 0.07 #1700), 019f4v (0.41 #877, 0.33 #1282, 0.08 #1687), 040njc (0.37 #818, 0.32 #1223, 0.15 #11342), 0bdwft (0.33 #69, 0.04 #4929, 0.04 #4524), 0cqgl9 (0.33 #194, 0.03 #3029, 0.03 #5054), 0gr51 (0.32 #911, 0.19 #1316, 0.14 #11748), 0gq9h (0.31 #888, 0.25 #1293, 0.18 #9316), 02pqp12 (0.29 #881, 0.21 #1286, 0.06 #1691), 04dn09n (0.28 #854, 0.17 #1259, 0.08 #1664), 09sb52 (0.26 #2876, 0.24 #6116, 0.22 #4901) >> Best rule #890 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 030pr; >> query: (?x264, 0gs9p) <- award_nominee(?x163, ?x264), award_winner(?x7606, ?x264), film(?x264, ?x4581) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #9316 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1531 *> proper extension: 0cjdk; 01_8w2; 0gsgr; *> query: (?x264, ?x277) <- award_winner(?x163, ?x264), award_winner(?x277, ?x163) *> conf = 0.18 ranks of expected_values: 20 EVAL 02rchht award 01lk0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 78.000 78.000 0.421 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10583-02yygk PRED entity: 02yygk PRED relation: currency PRED expected values: 09nqf => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.62 #10, 0.58 #16, 0.42 #1), 01nv4h (0.07 #20, 0.07 #35, 0.03 #104) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 01wmxfs; 02tf1y; 01xg_w; >> query: (?x10025, 09nqf) <- people(?x2510, ?x10025), participant(?x2614, ?x10025), ?x2510 = 0x67 >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02yygk currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.625 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #10582-05qbckf PRED entity: 05qbckf PRED relation: film_release_region PRED expected values: 05r4w 0jgd 0b90_r 0154j 03_3d 01znc_ 03ryn => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 98): 03_3d (0.93 #496, 0.88 #1472, 0.85 #618), 01znc_ (0.89 #516, 0.79 #638, 0.79 #1736), 05r4w (0.86 #1466, 0.85 #1832, 0.84 #1588), 0jgd (0.83 #1834, 0.81 #1590, 0.81 #1712), 0154j (0.81 #494, 0.81 #2447, 0.80 #1592), 0b90_r (0.81 #493, 0.79 #1835, 0.79 #1591), 03ryn (0.70 #547, 0.46 #669, 0.43 #1523), 07ylj (0.52 #509, 0.43 #1485, 0.42 #631), 07f1x (0.48 #696, 0.47 #1672, 0.45 #1550), 0d05w3 (0.44 #528, 0.40 #40, 0.25 #1504) >> Best rule #496 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 0ch26b_; 0kv238; 03nsm5x; >> query: (?x1956, 03_3d) <- film_release_region(?x1956, ?x756), film_release_region(?x1956, ?x404), film_crew_role(?x1956, ?x137), ?x756 = 06npd, ?x404 = 047lj >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7 EVAL 05qbckf film_release_region 03ryn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qbckf film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qbckf film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qbckf film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qbckf film_release_region 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qbckf film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qbckf film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10581-04crrxr PRED entity: 04crrxr PRED relation: profession PRED expected values: 0cbd2 02hrh1q => 87 concepts (86 used for prediction) PRED predicted values (max 10 best out of 49): 02hrh1q (0.86 #608, 0.85 #311, 0.78 #4160), 03gjzk (0.61 #461, 0.54 #15, 0.41 #312), 01d_h8 (0.49 #2524, 0.31 #303, 0.31 #6), 02jknp (0.42 #2526, 0.20 #3562, 0.19 #8595), 0np9r (0.27 #614, 0.24 #168, 0.21 #317), 02krf9 (0.23 #26, 0.18 #472, 0.15 #2544), 0cbd2 (0.22 #2525, 0.20 #453, 0.16 #601), 09jwl (0.18 #5348, 0.18 #4460, 0.17 #5200), 0d8qb (0.18 #227, 0.10 #376, 0.08 #79), 015cjr (0.15 #346, 0.12 #197, 0.09 #643) >> Best rule #608 for best value: >> intensional similarity = 3 >> extensional distance = 275 >> proper extension: 0k57l; 030dx5; 045g4l; 0p_r5; 0dszr0; >> query: (?x5447, 02hrh1q) <- nationality(?x5447, ?x94), profession(?x5447, ?x1146), ?x1146 = 018gz8 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 04crrxr profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 86.000 0.859 http://example.org/people/person/profession EVAL 04crrxr profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 87.000 86.000 0.859 http://example.org/people/person/profession #10580-0fh2v5 PRED entity: 0fh2v5 PRED relation: film_crew_role PRED expected values: 02r96rf => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 33): 02r96rf (0.84 #724, 0.75 #37, 0.72 #105), 01vx2h (0.55 #181, 0.52 #732, 0.36 #1045), 0dxtw (0.51 #731, 0.50 #180, 0.39 #1044), 01pvkk (0.32 #733, 0.29 #1393, 0.29 #1255), 01xy5l_ (0.27 #14, 0.25 #48, 0.20 #218), 0215hd (0.23 #221, 0.22 #153, 0.19 #119), 02rh1dz (0.21 #179, 0.20 #730, 0.16 #1910), 089g0h (0.20 #222, 0.17 #324, 0.17 #154), 089fss (0.18 #6, 0.16 #1910, 0.15 #2367), 0d2b38 (0.17 #330, 0.16 #1910, 0.15 #745) >> Best rule #724 for best value: >> intensional similarity = 4 >> extensional distance = 240 >> proper extension: 0cnztc4; >> query: (?x9901, 02r96rf) <- genre(?x9901, ?x225), film_crew_role(?x9901, ?x1284), ?x1284 = 0ch6mp2, ?x225 = 02kdv5l >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fh2v5 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.843 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10579-0d06m5 PRED entity: 0d06m5 PRED relation: people! PRED expected values: 07bch9 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 58): 02ctzb (0.33 #1914, 0.33 #698, 0.28 #1686), 033tf_ (0.33 #7, 0.20 #615, 0.20 #311), 09vc4s (0.33 #9, 0.10 #313, 0.08 #465), 063k3h (0.25 #106, 0.22 #714, 0.19 #1930), 07bch9 (0.25 #1922, 0.22 #1694, 0.21 #1846), 0x67 (0.21 #542, 0.20 #1074, 0.20 #238), 041rx (0.21 #4491, 0.21 #1980, 0.20 #1220), 013xrm (0.20 #323, 0.09 #931, 0.07 #627), 02g7sp (0.14 #549, 0.05 #2373, 0.04 #2602), 0dryh9k (0.14 #1915, 0.09 #2752, 0.08 #2295) >> Best rule #1914 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 09py7; >> query: (?x3445, 02ctzb) <- profession(?x3445, ?x5805), ?x5805 = 0fj9f, people(?x3584, ?x3445) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1922 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 09py7; *> query: (?x3445, 07bch9) <- profession(?x3445, ?x5805), ?x5805 = 0fj9f, people(?x3584, ?x3445) *> conf = 0.25 ranks of expected_values: 5 EVAL 0d06m5 people! 07bch9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 112.000 112.000 0.333 http://example.org/people/ethnicity/people #10578-01vvpjj PRED entity: 01vvpjj PRED relation: artist! PRED expected values: 03rhqg 02bh8z 01cl2y => 154 concepts (105 used for prediction) PRED predicted values (max 10 best out of 113): 03rhqg (0.55 #5335, 0.50 #15, 0.20 #155), 015_1q (0.44 #299, 0.27 #1419, 0.25 #19), 02p11jq (0.25 #5332, 0.21 #1132, 0.20 #712), 0181dw (0.25 #321, 0.20 #181, 0.14 #3961), 01cszh (0.25 #10, 0.19 #290, 0.13 #150), 033hn8 (0.25 #13, 0.13 #153, 0.12 #713), 0229rs (0.25 #17, 0.07 #157, 0.06 #5337), 025t8bv (0.25 #60, 0.03 #900, 0.02 #1320), 011k1h (0.20 #709, 0.14 #1269, 0.14 #1129), 03mp8k (0.20 #206, 0.12 #346, 0.12 #1326) >> Best rule #5335 for best value: >> intensional similarity = 4 >> extensional distance = 214 >> proper extension: 0c9d9; 0c7ct; 04r1t; 05k79; 09prnq; 0167_s; 01gx5f; 03xhj6; 0394y; 018gm9; ... >> query: (?x2440, 03rhqg) <- artist(?x1954, ?x2440), artists(?x2439, ?x2440), artist(?x1954, ?x3175), ?x3175 = 01w7nwm >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1, 18, 32 EVAL 01vvpjj artist! 01cl2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 154.000 105.000 0.546 http://example.org/music/record_label/artist EVAL 01vvpjj artist! 02bh8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 154.000 105.000 0.546 http://example.org/music/record_label/artist EVAL 01vvpjj artist! 03rhqg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 105.000 0.546 http://example.org/music/record_label/artist #10577-01vw_dv PRED entity: 01vw_dv PRED relation: artists! PRED expected values: 01y2mq => 117 concepts (56 used for prediction) PRED predicted values (max 10 best out of 189): 0ggq0m (0.50 #13, 0.09 #8926, 0.08 #8618), 064t9 (0.46 #2779, 0.45 #7388, 0.42 #6773), 06by7 (0.42 #11094, 0.42 #5245, 0.42 #12631), 05bt6j (0.30 #45, 0.22 #12653, 0.22 #11116), 06j6l (0.27 #2815, 0.25 #1279, 0.25 #7424), 08cyft (0.24 #367, 0.20 #59, 0.06 #1288), 016clz (0.23 #15380, 0.22 #12613, 0.22 #11076), 025sc50 (0.22 #1281, 0.22 #2817, 0.21 #6811), 02x8m (0.22 #17222, 0.21 #17223, 0.20 #20), 0xhtw (0.22 #17222, 0.21 #17223, 0.19 #15393) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01tp5bj; >> query: (?x6659, 0ggq0m) <- profession(?x6659, ?x131), instrumentalists(?x1437, ?x6659), ?x1437 = 01vdm0 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #17222 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 965 *> proper extension: 02_5x9; 01qqwp9; 02t3ln; 02mq_y; 0123r4; 0qmpd; 06br6t; 01v27pl; 0h08p; *> query: (?x6659, ?x13077) <- artists(?x11692, ?x6659), parent_genre(?x11692, ?x13077), artists(?x13077, ?x1125) *> conf = 0.22 ranks of expected_values: 18 EVAL 01vw_dv artists! 01y2mq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 117.000 56.000 0.500 http://example.org/music/genre/artists #10576-02vyw PRED entity: 02vyw PRED relation: award_winner! PRED expected values: 02wkmx 04dn09n => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 314): 040njc (0.36 #26719, 0.36 #42416, 0.34 #30113), 02rdyk7 (0.36 #26719, 0.36 #42416, 0.34 #30113), 02qvyrt (0.36 #26719, 0.36 #42416, 0.34 #30113), 0l8z1 (0.17 #7271, 0.07 #32236, 0.05 #38598), 025m8y (0.16 #7302, 0.04 #1789, 0.03 #23419), 054krc (0.15 #7290, 0.07 #32236, 0.05 #38598), 0gqz2 (0.15 #7284, 0.04 #23401, 0.04 #27642), 09d28z (0.12 #10899, 0.10 #720, 0.10 #7081), 02w_6xj (0.11 #10835, 0.09 #7017, 0.09 #656), 09sb52 (0.10 #22517, 0.10 #24637, 0.09 #23789) >> Best rule #26719 for best value: >> intensional similarity = 3 >> extensional distance = 1336 >> proper extension: 0qdwr; >> query: (?x3662, ?x198) <- profession(?x3662, ?x524), award(?x3662, ?x198), award_winner(?x3662, ?x1387) >> conf = 0.36 => this is the best rule for 3 predicted values *> Best rule #11917 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 242 *> proper extension: 04k25; 022_q8; 0gv2r; 06z4wj; 05jjl; *> query: (?x3662, 04dn09n) <- profession(?x3662, ?x524), award_winner(?x289, ?x3662), written_by(?x810, ?x3662) *> conf = 0.10 ranks of expected_values: 11, 23 EVAL 02vyw award_winner! 04dn09n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 122.000 122.000 0.365 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02vyw award_winner! 02wkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 122.000 122.000 0.365 http://example.org/award/award_category/winners./award/award_honor/award_winner #10575-047vnkj PRED entity: 047vnkj PRED relation: film! PRED expected values: 09_gdc => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.13 #13, 0.08 #8, 0.07 #44), 09_gdc (0.09 #2, 0.04 #74, 0.03 #12), 01kyvx (0.06 #63, 0.04 #124, 0.04 #171) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 0401sg; 03bx2lk; 0gd0c7x; 06w839_; 0bq6ntw; >> query: (?x5271, 01pb34) <- genre(?x5271, ?x53), currency(?x5271, ?x170), film_release_region(?x5271, ?x1471), ?x1471 = 07t21 >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 03d34x8; 02rcwq0; 05f4vxd; 0fhzwl; *> query: (?x5271, 09_gdc) <- award_winner(?x5271, ?x2415), honored_for(?x8964, ?x5271), ?x8964 = 09gkdln, nominated_for(?x4767, ?x5271) *> conf = 0.09 ranks of expected_values: 2 EVAL 047vnkj film! 09_gdc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.128 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #10574-03bzyn4 PRED entity: 03bzyn4 PRED relation: prequel! PRED expected values: 05h43ls => 97 concepts (55 used for prediction) PRED predicted values (max 10 best out of 32): 01f85k (0.03 #832, 0.02 #1373), 09v8clw (0.03 #540, 0.01 #1082), 091rc5 (0.03 #449, 0.01 #991), 09wnnb (0.03 #522), 0642ykh (0.03 #477), 063fh9 (0.03 #474), 05t54s (0.02 #1198), 09rx7tx (0.02 #711, 0.01 #1072), 0315rp (0.02 #684, 0.01 #1045), 047csmy (0.02 #636, 0.01 #997) >> Best rule #832 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 0gcrg; >> query: (?x9496, 01f85k) <- genre(?x9496, ?x53), film_crew_role(?x9496, ?x137), ?x53 = 07s9rl0, edited_by(?x9496, ?x323) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03bzyn4 prequel! 05h43ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 55.000 0.029 http://example.org/film/film/prequel #10573-0829rj PRED entity: 0829rj PRED relation: award_winner! PRED expected values: 0bzjgq => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 114): 05hmp6 (0.20 #228, 0.09 #369, 0.03 #651), 0bzjgq (0.18 #6769, 0.17 #7334, 0.11 #119), 0bzm__ (0.18 #6769, 0.17 #7334), 0bz6l9 (0.18 #6769, 0.17 #7334), 0fy6bh (0.11 #47, 0.10 #188, 0.09 #329), 0bzkgg (0.11 #44, 0.09 #326), 0fz0c2 (0.10 #247, 0.09 #388, 0.02 #952), 0fy59t (0.10 #257, 0.09 #398, 0.01 #962), 09pnw5 (0.05 #1372, 0.04 #526, 0.04 #1936), 0hndn2q (0.05 #745, 0.05 #604, 0.04 #1309) >> Best rule #228 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0g1rw; >> query: (?x10925, 05hmp6) <- award_nominee(?x1850, ?x10925), ?x1850 = 017jv5, nominated_for(?x10925, ?x6030) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #6769 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1493 *> proper extension: 0f3zf_; 02jm0n; 01wxyx1; 01wk7b7; 0bytfv; 01v3vp; 049qx; 05typm; 01vzxmq; 04mlh8; ... *> query: (?x10925, ?x8478) <- gender(?x10925, ?x231), nominated_for(?x10925, ?x7370), honored_for(?x8478, ?x7370) *> conf = 0.18 ranks of expected_values: 2 EVAL 0829rj award_winner! 0bzjgq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10572-01wlt3k PRED entity: 01wlt3k PRED relation: award_nominee! PRED expected values: 0837ql => 128 concepts (53 used for prediction) PRED predicted values (max 10 best out of 604): 01wwvc5 (0.81 #120979, 0.81 #120977, 0.81 #120978), 01q32bd (0.81 #120979, 0.81 #120977, 0.81 #120978), 01wlt3k (0.62 #2224, 0.47 #6878, 0.39 #11534), 0837ql (0.62 #1140, 0.40 #5794, 0.37 #12780), 01vsgrn (0.38 #1300, 0.32 #12940, 0.20 #5954), 06mt91 (0.26 #13188, 0.25 #1548, 0.21 #97712), 0288fyj (0.26 #12133, 0.21 #97712, 0.21 #100041), 05mxw33 (0.25 #2230, 0.21 #97712, 0.21 #100041), 05mt_q (0.25 #286, 0.21 #11926, 0.17 #11639), 0412f5y (0.25 #816, 0.21 #12456, 0.17 #11639) >> Best rule #120979 for best value: >> intensional similarity = 3 >> extensional distance = 800 >> proper extension: 03d9d6; 0187x8; 03vhvp; 0cbm64; >> query: (?x11371, ?x2731) <- award_nominee(?x11371, ?x7259), award_nominee(?x11371, ?x2731), artists(?x5630, ?x7259) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #1140 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 02l840; 01q32bd; 01ws9n6; 01vw20h; 026yqrr; 0677ng; *> query: (?x11371, 0837ql) <- award_nominee(?x11371, ?x2732), award_nominee(?x11371, ?x2731), ?x2731 = 01wwvc5, ?x2732 = 01wgxtl, award(?x11371, ?x2139) *> conf = 0.62 ranks of expected_values: 4 EVAL 01wlt3k award_nominee! 0837ql CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 128.000 53.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10571-02h40lc PRED entity: 02h40lc PRED relation: languages! PRED expected values: 01sl1q 04bdxl 01xdf5 049tjg 05ty4m 06jzh 02r_d4 039bp 03qmj9 01pl9g 049k07 03xmy1 02lq10 01w724 015c2f 0klh7 0gr36 01wj18h 01v3bn 01l1hr 02114t 0kh6b 0164nb 0bt7ws 029_3 01trf3 02y_2y 028k57 044k8 0f7hc 0czkbt 03y82t6 03_l8m 0fn5bx 020trj 02t__3 01x4sb 0flpy 0fn8jc 01mqc_ 0h0yt 0232lm 0cbkc 01vh08 01vsqvs 023mdt 04twmk 0427y 08k1lz 03jj93 035wq7 050llt 03_dj 02js_6 => 61 concepts (52 used for prediction) PRED predicted values (max 10 best out of 2447): 021j72 (0.40 #766, 0.33 #77, 0.09 #2143), 0blt6 (0.33 #223, 0.33 #124, 0.14 #910), 0j582 (0.33 #110, 0.29 #896, 0.22 #1190), 01vsqvs (0.33 #162, 0.29 #948, 0.22 #1242), 0738y5 (0.33 #65, 0.20 #1736, 0.20 #754), 0kst7v (0.33 #67, 0.20 #1738, 0.20 #756), 050llt (0.33 #86, 0.20 #775, 0.15 #2348), 04v7k2 (0.33 #91, 0.20 #780, 0.13 #1762), 05nqq3 (0.33 #61, 0.20 #750, 0.13 #1732), 02n1p5 (0.33 #45, 0.20 #734, 0.13 #1716) >> Best rule #766 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 09s02; >> query: (?x254, 021j72) <- languages(?x7517, ?x254), languages(?x3118, ?x254), countries_spoken_in(?x254, ?x126), languages_spoken(?x743, ?x254), people(?x1050, ?x3118), ?x7517 = 03vrnh >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #162 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 064_8sq; *> query: (?x254, 01vsqvs) <- languages(?x4345, ?x254), languages(?x50, ?x254), language(?x5051, ?x254), language(?x2490, ?x254), ?x4345 = 073w14, nominated_for(?x617, ?x5051), ?x2490 = 026p4q7 *> conf = 0.33 ranks of expected_values: 4, 7, 45, 46, 49, 55, 95, 137, 177, 179, 187, 192, 253, 263, 352, 365, 512, 568, 580, 632, 650, 682, 685, 689, 755, 784, 808, 882, 1532, 1560, 1592, 1613, 1622, 1696, 1937, 1983, 1984, 2023, 2043, 2091, 2122, 2137, 2167, 2221, 2377, 2408, 2422 EVAL 02h40lc languages! 02js_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 03_dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 050llt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 035wq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 03jj93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 08k1lz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0427y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 04twmk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 023mdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01vsqvs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01vh08 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0cbkc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0232lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0h0yt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01mqc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0fn8jc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0flpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01x4sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 02t__3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 020trj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0fn5bx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 03_l8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 03y82t6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0czkbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0f7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 044k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 028k57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 02y_2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01trf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 029_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0bt7ws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0164nb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0kh6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 02114t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01l1hr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01v3bn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01wj18h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0gr36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 0klh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 015c2f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01w724 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 02lq10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 03xmy1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 049k07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01pl9g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 03qmj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 039bp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 02r_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 06jzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 05ty4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 049tjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01xdf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 04bdxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 52.000 0.400 http://example.org/people/person/languages EVAL 02h40lc languages! 01sl1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 52.000 0.400 http://example.org/people/person/languages #10570-0154j PRED entity: 0154j PRED relation: olympics PRED expected values: 0l6mp => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 22): 0l6mp (0.78 #54, 0.65 #32, 0.63 #76), 0kbvb (0.76 #26, 0.74 #353, 0.72 #48), 0lbbj (0.74 #353, 0.72 #752, 0.71 #931), 018qb4 (0.74 #353, 0.72 #752, 0.71 #931), 0kbvv (0.74 #353, 0.72 #752, 0.71 #931), 09n48 (0.74 #353, 0.72 #752, 0.71 #931), 0lk8j (0.65 #31, 0.50 #119, 0.50 #53), 018ctl (0.59 #27, 0.58 #115, 0.50 #49), 09x3r (0.56 #50, 0.53 #28, 0.47 #182), 015pkt (0.53 #44, 0.33 #132, 0.33 #66) >> Best rule #54 for best value: >> intensional similarity = 2 >> extensional distance = 16 >> proper extension: 05qtj; >> query: (?x172, 0l6mp) <- film_release_region(?x1252, ?x172), ?x1252 = 02c6d >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0154j olympics 0l6mp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.778 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #10569-017lb_ PRED entity: 017lb_ PRED relation: artist! PRED expected values: 01jv1z => 89 concepts (67 used for prediction) PRED predicted values (max 10 best out of 126): 0181dw (0.56 #2489, 0.25 #584, 0.25 #448), 015_1q (0.50 #3284, 0.38 #562, 0.33 #18), 017l96 (0.43 #2330, 0.40 #1241, 0.20 #153), 03rhqg (0.40 #694, 0.40 #150, 0.38 #558), 04fcjt (0.33 #27, 0.20 #163, 0.12 #571), 043g7l (0.33 #29, 0.18 #2069, 0.18 #3295), 086k8 (0.30 #681, 0.25 #545, 0.17 #273), 016ckq (0.25 #585, 0.24 #2490, 0.20 #721), 01clyr (0.25 #439, 0.21 #1799, 0.17 #303), 0g768 (0.25 #579, 0.20 #715, 0.17 #307) >> Best rule #2489 for best value: >> intensional similarity = 8 >> extensional distance = 107 >> proper extension: 0h7pj; >> query: (?x8226, 0181dw) <- artist(?x4797, ?x8226), award(?x8226, ?x9828), artist(?x4797, ?x10670), artist(?x4797, ?x8583), artist(?x4797, ?x6042), award_winner(?x1480, ?x8583), influenced_by(?x10670, ?x115), ?x6042 = 01wrcxr >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #413 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 0136p1; 01l_vgt; *> query: (?x8226, 01jv1z) <- artists(?x5792, ?x8226), artists(?x3370, ?x8226), artist(?x2149, ?x8226), category(?x8226, ?x134), ?x3370 = 059kh, ?x5792 = 026z9 *> conf = 0.25 ranks of expected_values: 12 EVAL 017lb_ artist! 01jv1z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 89.000 67.000 0.560 http://example.org/music/record_label/artist #10568-03g5jw PRED entity: 03g5jw PRED relation: award_winner! PRED expected values: 05pd94v => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 109): 09n4nb (0.60 #468, 0.33 #888, 0.25 #48), 02rjjll (0.42 #845, 0.21 #1265, 0.20 #285), 056878 (0.40 #452, 0.25 #32, 0.18 #1572), 01bx35 (0.36 #1127, 0.27 #1547, 0.25 #847), 01xqqp (0.25 #935, 0.25 #95, 0.21 #1355), 05pd94v (0.25 #702, 0.25 #2, 0.20 #422), 013b2h (0.25 #919, 0.25 #79, 0.20 #499), 0jzphpx (0.25 #39, 0.20 #459, 0.17 #879), 02cg41 (0.25 #965, 0.20 #405, 0.16 #9945), 0gpjbt (0.25 #869, 0.20 #309, 0.16 #9945) >> Best rule #468 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0dvqq; 04qmr; >> query: (?x1573, 09n4nb) <- award_nominee(?x4593, ?x1573), group(?x227, ?x1573), award_winner(?x5656, ?x1573), ?x4593 = 0478__m >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #702 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 0840vq; 03f1d47; 01w9wwg; *> query: (?x1573, 05pd94v) <- award_nominee(?x5760, ?x1573), award_nominee(?x3682, ?x1573), ?x5760 = 01dwrc, award_nominee(?x1573, ?x9228), artists(?x302, ?x3682) *> conf = 0.25 ranks of expected_values: 6 EVAL 03g5jw award_winner! 05pd94v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 94.000 94.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10567-01pl14 PRED entity: 01pl14 PRED relation: organization! PRED expected values: 060c4 => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 10): 060c4 (0.83 #496, 0.83 #171, 0.83 #535), 07xl34 (0.34 #219, 0.30 #1051, 0.26 #245), 0dq_5 (0.29 #399, 0.25 #555, 0.24 #945), 05k17c (0.25 #20, 0.14 #33, 0.13 #631), 0hm4q (0.14 #34, 0.13 #333, 0.12 #359), 05c0jwl (0.04 #876, 0.04 #1136, 0.04 #135), 08jcfy (0.03 #1104, 0.02 #1195, 0.02 #870), 0dq3c (0.02 #274, 0.01 #378, 0.01 #391), 04n1q6 (0.01 #1098, 0.01 #877, 0.01 #1319), 09d6p2 (0.01 #439) >> Best rule #496 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 03zw80; 02d9nr; >> query: (?x466, 060c4) <- state_province_region(?x466, ?x3908), registering_agency(?x466, ?x1982), contains(?x94, ?x466) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pl14 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.835 http://example.org/organization/role/leaders./organization/leadership/organization #10566-06fxnf PRED entity: 06fxnf PRED relation: nominated_for PRED expected values: 027j9wd => 107 concepts (21 used for prediction) PRED predicted values (max 10 best out of 489): 03twd6 (0.77 #11321, 0.67 #19410, 0.67 #17792), 06fcqw (0.39 #6468, 0.37 #25881, 0.35 #32353), 03k8th (0.39 #6468, 0.37 #25881, 0.35 #32353), 09r94m (0.39 #6468, 0.37 #25881, 0.35 #32353), 05q54f5 (0.39 #6468, 0.37 #25881, 0.35 #32353), 01pv91 (0.39 #6468, 0.37 #25881, 0.35 #32353), 061681 (0.39 #6468, 0.37 #25881, 0.35 #32353), 02_fz3 (0.39 #6468, 0.37 #25881, 0.35 #32353), 03mh94 (0.39 #6468, 0.37 #25881, 0.35 #32353), 06gb1w (0.39 #6468, 0.37 #25881, 0.35 #32353) >> Best rule #11321 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 0lgsq; 01vyp_; 02wb6yq; 02wk4d; 01wf86y; 01vsy9_; >> query: (?x4020, ?x1470) <- instrumentalists(?x75, ?x4020), profession(?x4020, ?x563), award_winner(?x1470, ?x4020) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #6468 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 06449; 05ccxr; *> query: (?x4020, ?x463) <- award_winner(?x1470, ?x4020), music(?x463, ?x4020), award_winner(?x3069, ?x4020) *> conf = 0.39 ranks of expected_values: 17 EVAL 06fxnf nominated_for 027j9wd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 107.000 21.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10565-0l15bq PRED entity: 0l15bq PRED relation: performance_role! PRED expected values: 03wjb7 => 82 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1047): 02rn_bj (0.50 #943, 0.33 #340, 0.27 #4475), 01vn35l (0.33 #1729, 0.33 #278, 0.33 #157), 07r4c (0.33 #1647, 0.33 #196, 0.25 #2255), 04m2zj (0.33 #1666, 0.33 #215, 0.25 #2274), 0274ck (0.33 #131, 0.33 #8, 0.22 #2916), 01w9mnm (0.33 #343, 0.33 #222, 0.22 #3007), 01kd57 (0.33 #311, 0.33 #190, 0.17 #1762), 012x4t (0.33 #1713, 0.33 #262, 0.16 #4742), 03mszl (0.33 #1780, 0.33 #329, 0.15 #4215), 01vs14j (0.33 #1709, 0.33 #258, 0.15 #4144) >> Best rule #943 for best value: >> intensional similarity = 20 >> extensional distance = 2 >> proper extension: 05148p4; >> query: (?x1574, 02rn_bj) <- role(?x1574, ?x1969), role(?x1574, ?x1433), role(?x1574, ?x433), role(?x1574, ?x432), role(?x1574, ?x316), role(?x7033, ?x1574), role(?x4616, ?x1574), role(?x614, ?x1574), role(?x9321, ?x1574), role(?x2784, ?x1574), ?x316 = 05r5c, ?x9321 = 0140t7, ?x7033 = 0gkd1, ?x1433 = 0239kh, role(?x2158, ?x4616), ?x432 = 042v_gx, ?x614 = 0mkg, award(?x2784, ?x1565), role(?x433, ?x74), ?x1969 = 04rzd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #583 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 2 *> proper extension: 013y1f; *> query: (?x1574, 03wjb7) <- role(?x1574, ?x3703), role(?x1574, ?x885), role(?x1574, ?x316), role(?x1574, ?x228), role(?x569, ?x1574), role(?x9321, ?x1574), role(?x7987, ?x1574), role(?x2690, ?x1574), ?x316 = 05r5c, award(?x9321, ?x3926), award(?x9321, ?x2139), location(?x2690, ?x362), ?x2139 = 01by1l, ?x3703 = 02dlh2, ?x7987 = 0j6cj, ?x885 = 0dwtp, ?x3926 = 02f6xy, group(?x228, ?x1573) *> conf = 0.25 ranks of expected_values: 45 EVAL 0l15bq performance_role! 03wjb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 82.000 40.000 0.500 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #10564-01vsnff PRED entity: 01vsnff PRED relation: award PRED expected values: 099vwn => 130 concepts (129 used for prediction) PRED predicted values (max 10 best out of 306): 02x17c2 (0.80 #10403, 0.79 #7201, 0.77 #20805), 01c9f2 (0.80 #10403, 0.79 #7201, 0.77 #20805), 09sb52 (0.42 #8842, 0.32 #21645, 0.31 #24446), 01ckcd (0.33 #332, 0.25 #4332, 0.20 #732), 01c99j (0.33 #1423, 0.22 #10225, 0.15 #7023), 02f6ym (0.33 #1454, 0.19 #10256, 0.17 #254), 03c7tr1 (0.33 #1258, 0.15 #8860, 0.09 #3658), 01bgqh (0.33 #10044, 0.31 #6842, 0.29 #1242), 054ks3 (0.31 #2139, 0.29 #6539, 0.28 #8541), 054krc (0.30 #6486, 0.30 #4886, 0.28 #12489) >> Best rule #10403 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 0m0hw; >> query: (?x2187, ?x247) <- award_winner(?x247, ?x2187), artist(?x2931, ?x2187), film(?x2187, ?x6480) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #2213 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 037hgm; 01hrqc; *> query: (?x2187, 099vwn) <- nominated_for(?x2187, ?x787), role(?x2187, ?x212), role(?x2187, ?x314) *> conf = 0.19 ranks of expected_values: 34 EVAL 01vsnff award 099vwn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 130.000 129.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10563-01q415 PRED entity: 01q415 PRED relation: gender PRED expected values: 05zppz => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #13, 0.89 #23, 0.88 #11), 02zsn (0.30 #50, 0.29 #44, 0.29 #42) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 124 >> proper extension: 081k8; 01vl17; 04093; 019gz; 03_dj; >> query: (?x2248, 05zppz) <- location(?x2248, ?x3634), story_by(?x2057, ?x2248) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q415 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.889 http://example.org/people/person/gender #10562-0lk90 PRED entity: 0lk90 PRED relation: profession PRED expected values: 0cbd2 02hrh1q => 133 concepts (113 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.91 #14494, 0.90 #13004, 0.90 #12259), 09jwl (0.72 #5990, 0.63 #3153, 0.62 #3451), 0dz3r (0.58 #2389, 0.54 #4626, 0.54 #5074), 016z4k (0.56 #901, 0.51 #3137, 0.49 #4032), 01d_h8 (0.54 #4332, 0.43 #6126, 0.42 #8066), 03gjzk (0.36 #4342, 0.31 #5837, 0.29 #2105), 0n1h (0.34 #3593, 0.33 #909, 0.31 #4487), 0d1pc (0.33 #51, 0.29 #799, 0.29 #500), 039v1 (0.30 #6007, 0.28 #934, 0.22 #7647), 0dxtg (0.30 #4340, 0.28 #8074, 0.28 #11659) >> Best rule #14494 for best value: >> intensional similarity = 3 >> extensional distance = 1652 >> proper extension: 02p59ry; 039xcr; 045931; >> query: (?x1093, 02hrh1q) <- profession(?x1093, ?x2348), award(?x1093, ?x1007), film(?x1093, ?x8790) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 18 EVAL 0lk90 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 113.000 0.908 http://example.org/people/person/profession EVAL 0lk90 profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 133.000 113.000 0.908 http://example.org/people/person/profession #10561-0345h PRED entity: 0345h PRED relation: olympics PRED expected values: 0lk8j => 209 concepts (209 used for prediction) PRED predicted values (max 10 best out of 25): 0jdk_ (0.77 #969, 0.74 #1294, 0.74 #301), 0l6mp (0.74 #301, 0.73 #1378, 0.71 #1555), 0c_tl (0.74 #301, 0.73 #1378, 0.71 #1555), 0l998 (0.74 #301, 0.73 #1378, 0.71 #1555), 0l6vl (0.74 #301, 0.73 #1378, 0.71 #1555), 018wrk (0.74 #301, 0.73 #1378, 0.71 #1555), 018ljb (0.74 #301, 0.73 #1378, 0.71 #1555), 0l6ny (0.68 #958, 0.59 #907, 0.59 #1333), 09n48 (0.65 #927, 0.54 #1454, 0.50 #579), 016r9z (0.65 #927, 0.54 #1454, 0.49 #3134) >> Best rule #969 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 0j1z8; >> query: (?x1264, 0jdk_) <- country(?x136, ?x1264), olympics(?x1264, ?x452), exported_to(?x94, ?x1264) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #485 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 05bcl; 0j5g9; *> query: (?x1264, 0lk8j) <- first_level_division_of(?x1646, ?x1264), contains(?x1264, ?x196), adjoins(?x1264, ?x456) *> conf = 0.59 ranks of expected_values: 12 EVAL 0345h olympics 0lk8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 209.000 209.000 0.774 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #10560-01y17m PRED entity: 01y17m PRED relation: institution! PRED expected values: 013zdg 02_xgp2 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 17): 02_xgp2 (0.58 #265, 0.58 #172, 0.54 #321), 07s6fsf (0.54 #55, 0.46 #184, 0.43 #239), 013zdg (0.50 #5, 0.25 #23, 0.23 #188), 04zx3q1 (0.32 #259, 0.31 #166, 0.29 #315), 022h5x (0.28 #1262, 0.28 #1708, 0.28 #1768), 0bjrnt (0.28 #1262, 0.28 #1708, 0.28 #1768), 01ysy9 (0.28 #1262, 0.28 #1708, 0.28 #1768), 02m4yg (0.28 #1262, 0.28 #1708, 0.28 #1768), 071tyz (0.28 #1262, 0.28 #1708, 0.28 #1768), 01gkg3 (0.28 #1262, 0.28 #1708, 0.28 #1768) >> Best rule #265 for best value: >> intensional similarity = 4 >> extensional distance = 147 >> proper extension: 022r38; >> query: (?x3208, 02_xgp2) <- school_type(?x3208, ?x3092), institution(?x4981, ?x3208), major_field_of_study(?x3208, ?x1154), ?x4981 = 03bwzr4 >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01y17m institution! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.584 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01y17m institution! 013zdg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 139.000 139.000 0.584 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #10559-03hy3g PRED entity: 03hy3g PRED relation: profession PRED expected values: 0dxtg => 150 concepts (55 used for prediction) PRED predicted values (max 10 best out of 53): 0dxtg (0.89 #1924, 0.86 #2659, 0.86 #3248), 03gjzk (0.47 #2219, 0.45 #4131, 0.44 #3396), 02krf9 (0.30 #2231, 0.26 #2525, 0.26 #2966), 0cbd2 (0.30 #1624, 0.29 #2800, 0.29 #5153), 0kyk (0.26 #1646, 0.16 #2822, 0.15 #2675), 0np9r (0.25 #19, 0.14 #461, 0.14 #314), 02hv44_ (0.22 #1674, 0.19 #1968, 0.13 #2850), 0dgd_ (0.17 #177, 0.14 #471, 0.14 #324), 09jwl (0.16 #6784, 0.11 #5754, 0.10 #6343), 018gz8 (0.14 #2662, 0.14 #2809, 0.14 #3839) >> Best rule #1924 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 01p1z_; >> query: (?x6356, 0dxtg) <- award_winner(?x8364, ?x6356), award(?x6356, ?x746), gender(?x6356, ?x231), ?x746 = 04dn09n >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hy3g profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 55.000 0.893 http://example.org/people/person/profession #10558-0g51l1 PRED entity: 0g51l1 PRED relation: profession PRED expected values: 02krf9 02pjxr => 96 concepts (53 used for prediction) PRED predicted values (max 10 best out of 52): 02hrh1q (0.70 #2932, 0.70 #1764, 0.68 #1034), 02krf9 (0.29 #7157, 0.28 #7744, 0.28 #7743), 01c72t (0.29 #7157, 0.28 #7744, 0.28 #7743), 025352 (0.29 #7157, 0.28 #7744, 0.28 #7743), 0cbd2 (0.28 #4388, 0.25 #5410, 0.22 #4095), 018gz8 (0.19 #2058, 0.17 #1182, 0.17 #4103), 09jwl (0.18 #2936, 0.17 #6150, 0.17 #6296), 0kyk (0.16 #5431, 0.14 #4409, 0.12 #4116), 0np9r (0.12 #1186, 0.12 #2062, 0.11 #4107), 0nbcg (0.11 #6309, 0.11 #6455, 0.11 #6163) >> Best rule #2932 for best value: >> intensional similarity = 3 >> extensional distance = 772 >> proper extension: 0785v8; 03f1zdw; 011zf2; 03yf3z; 02j9lm; 01z7_f; 01l03w2; 02qw2xb; 027n4zv; 031x_3; ... >> query: (?x1996, 02hrh1q) <- nationality(?x1996, ?x94), award_winner(?x2479, ?x1996), location(?x1996, ?x739) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #7157 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1437 *> proper extension: 017jv5; 035_2h; 039cq4; *> query: (?x1996, ?x319) <- award_winner(?x1996, ?x2479), award_winner(?x3486, ?x2479), profession(?x2479, ?x319) *> conf = 0.29 ranks of expected_values: 2, 51 EVAL 0g51l1 profession 02pjxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 96.000 53.000 0.699 http://example.org/people/person/profession EVAL 0g51l1 profession 02krf9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 53.000 0.699 http://example.org/people/person/profession #10557-09jw2 PRED entity: 09jw2 PRED relation: artists PRED expected values: 01vsy7t 01wqpnm => 74 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1260): 01kcms4 (0.71 #14496, 0.25 #25148, 0.25 #4906), 01tp5bj (0.67 #9778, 0.50 #6582, 0.40 #8713), 01ydzx (0.67 #10187, 0.40 #9122, 0.33 #1664), 048tgl (0.60 #17944, 0.60 #9422, 0.50 #12618), 012zng (0.60 #16112, 0.56 #15048, 0.50 #6524), 01wqpnm (0.60 #9431, 0.50 #7300, 0.44 #15824), 01dw_f (0.60 #9199, 0.50 #7068, 0.33 #15592), 02vr7 (0.60 #9280, 0.50 #7149, 0.33 #15673), 02hzz (0.60 #9260, 0.50 #7129, 0.33 #10325), 0frsw (0.60 #8716, 0.50 #6585, 0.33 #9781) >> Best rule #14496 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 0781g; >> query: (?x10306, 01kcms4) <- parent_genre(?x10306, ?x1572), artists(?x10306, ?x10802), artists(?x10306, ?x1467), artists(?x10306, ?x475), ?x10802 = 01mxnvc, artists(?x3642, ?x475), group(?x227, ?x475), award(?x1467, ?x2634), ?x3642 = 0dls3 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #9431 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 06by7; *> query: (?x10306, 01wqpnm) <- parent_genre(?x14252, ?x10306), artists(?x10306, ?x8149), ?x14252 = 0y2tr, artist(?x8721, ?x8149), ?x8721 = 01cf93, influenced_by(?x4942, ?x8149), profession(?x8149, ?x131) *> conf = 0.60 ranks of expected_values: 6, 16 EVAL 09jw2 artists 01wqpnm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 74.000 34.000 0.714 http://example.org/music/genre/artists EVAL 09jw2 artists 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 74.000 34.000 0.714 http://example.org/music/genre/artists #10556-02mqc4 PRED entity: 02mqc4 PRED relation: award PRED expected values: 0gqwc => 110 concepts (104 used for prediction) PRED predicted values (max 10 best out of 255): 027b9k6 (0.72 #24320, 0.71 #22748, 0.71 #21178), 09sb52 (0.64 #6313, 0.40 #40, 0.40 #2392), 0gqwc (0.46 #1247, 0.19 #6344, 0.12 #12617), 0gqyl (0.41 #1276, 0.17 #6373, 0.16 #884), 0bdw6t (0.40 #104, 0.12 #496, 0.11 #23141), 0cqhmg (0.36 #1134, 0.11 #23141, 0.06 #23926), 0cqhk0 (0.33 #820, 0.21 #2780, 0.19 #3172), 02ppm4q (0.32 #1326, 0.13 #6423, 0.11 #934), 094qd5 (0.30 #1220, 0.12 #6317, 0.11 #23141), 02z0dfh (0.21 #1248, 0.11 #23141, 0.10 #6345) >> Best rule #24320 for best value: >> intensional similarity = 3 >> extensional distance = 1256 >> proper extension: 01wz_ml; 06lxn; >> query: (?x4165, ?x375) <- award_winner(?x4165, ?x820), award_winner(?x375, ?x4165), award(?x374, ?x375) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1247 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 0h1m9; 01mqz0; 01bcq; 01fwf1; 0bw87; 0lfbm; 01wk51; 01tl50z; 0btxr; 02z1yj; ... *> query: (?x4165, 0gqwc) <- film(?x4165, ?x4166), award(?x4165, ?x1132), ?x1132 = 0bdwft *> conf = 0.46 ranks of expected_values: 3 EVAL 02mqc4 award 0gqwc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 104.000 0.723 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10555-0dwz3t PRED entity: 0dwz3t PRED relation: colors PRED expected values: 01g5v => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 16): 083jv (0.84 #2107, 0.82 #1265, 0.80 #1592), 01g5v (0.53 #759, 0.52 #1887, 0.50 #723), 038hg (0.29 #137, 0.18 #2232, 0.17 #1663), 088fh (0.25 #6, 0.23 #1136, 0.20 #78), 02rnmb (0.20 #66, 0.18 #2232, 0.17 #1300), 06kqt3 (0.18 #1810, 0.17 #1300, 0.17 #1663), 01l849 (0.18 #2232, 0.17 #1553, 0.16 #1572), 0jc_p (0.18 #2232, 0.14 #1957, 0.14 #1958), 036k5h (0.14 #1957, 0.14 #1958, 0.11 #185), 09ggk (0.14 #1957, 0.14 #1958, 0.10 #1590) >> Best rule #2107 for best value: >> intensional similarity = 6 >> extensional distance = 263 >> proper extension: 020wyp; 03dkx; 038_0z; >> query: (?x8678, 083jv) <- colors(?x8678, ?x1101), colors(?x10994, ?x1101), colors(?x3387, ?x1101), ?x10994 = 02bvc5, institution(?x1368, ?x3387), ?x1368 = 014mlp >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #759 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 28 *> proper extension: 035l_9; *> query: (?x8678, 01g5v) <- position(?x8678, ?x63), team(?x60, ?x8678), colors(?x8678, ?x4557), teams(?x9969, ?x8678), colors(?x11991, ?x4557), location_of_ceremony(?x566, ?x9969), ?x11991 = 01dwyd, colors(?x546, ?x4557) *> conf = 0.53 ranks of expected_values: 2 EVAL 0dwz3t colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 142.000 142.000 0.838 http://example.org/sports/sports_team/colors #10554-016szr PRED entity: 016szr PRED relation: artist! PRED expected values: 0kk9v => 97 concepts (80 used for prediction) PRED predicted values (max 10 best out of 96): 015_1q (0.29 #695, 0.24 #19, 0.23 #1640), 0181dw (0.14 #717, 0.12 #447, 0.12 #1797), 011k1h (0.14 #10, 0.12 #1766, 0.12 #145), 0g768 (0.12 #3825, 0.12 #1252, 0.12 #3555), 01clyr (0.12 #32, 0.10 #167, 0.07 #3821), 0k_kr (0.12 #43, 0.09 #178, 0.04 #854), 01w40h (0.11 #704, 0.09 #434, 0.08 #2600), 033hn8 (0.11 #1770, 0.11 #3803, 0.11 #3533), 03mp8k (0.10 #1819, 0.09 #1279, 0.08 #2905), 017l96 (0.10 #2860, 0.10 #3537, 0.10 #2995) >> Best rule #695 for best value: >> intensional similarity = 3 >> extensional distance = 117 >> proper extension: 01pfkw; >> query: (?x4850, 015_1q) <- nominated_for(?x4850, ?x1080), award(?x4850, ?x1079), artist(?x2931, ?x4850) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016szr artist! 0kk9v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 80.000 0.286 http://example.org/music/record_label/artist #10553-02jxrw PRED entity: 02jxrw PRED relation: award PRED expected values: 02x2gy0 => 92 concepts (80 used for prediction) PRED predicted values (max 10 best out of 204): 0gqwc (0.24 #59, 0.23 #2782, 0.21 #6712), 099cng (0.24 #1164, 0.24 #998, 0.23 #2783), 094qd5 (0.23 #2782, 0.21 #6712, 0.21 #10181), 0gs96 (0.23 #2782, 0.21 #6712, 0.21 #10181), 027dtxw (0.23 #2782, 0.21 #6712, 0.21 #10181), 09sdmz (0.23 #2782, 0.21 #6712, 0.21 #10181), 099jhq (0.23 #2782, 0.21 #6712, 0.21 #10181), 02z1nbg (0.22 #1067, 0.20 #369, 0.19 #834), 09qwmm (0.21 #26, 0.20 #957, 0.18 #259), 0gr42 (0.17 #1481, 0.07 #2174, 0.06 #1250) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0ds3t5x; 095zlp; 02d44q; 0dgst_d; 02rv_dz; 0g9wdmc; 02q6gfp; 02qr69m; 05c46y6; 0cw3yd; ... >> query: (?x10060, 0gqwc) <- nominated_for(?x1441, ?x10060), ?x1441 = 099cng, film(?x1738, ?x10060), film_release_region(?x10060, ?x94) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #332 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 064n1pz; 0cvkv5; *> query: (?x10060, 02x2gy0) <- nominated_for(?x1441, ?x10060), ?x1441 = 099cng, titles(?x162, ?x10060) *> conf = 0.09 ranks of expected_values: 41 EVAL 02jxrw award 02x2gy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 92.000 80.000 0.242 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #10552-03clwtw PRED entity: 03clwtw PRED relation: prequel PRED expected values: 04cbbz => 57 concepts (47 used for prediction) PRED predicted values (max 10 best out of 2): 02ht1k (0.06 #425), 02stbw (0.06 #403) >> Best rule #425 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0dr_9t7; >> query: (?x7145, 02ht1k) <- film(?x5507, ?x7145), production_companies(?x7145, ?x5908), award_nominee(?x57, ?x5507), ?x5908 = 031rq5 >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03clwtw prequel 04cbbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 47.000 0.059 http://example.org/film/film/prequel #10551-016t0h PRED entity: 016t0h PRED relation: award_winner! PRED expected values: 02f73b => 85 concepts (54 used for prediction) PRED predicted values (max 10 best out of 196): 02f72_ (0.38 #1285, 0.37 #9426, 0.35 #17991), 02f5qb (0.38 #1285, 0.37 #9426, 0.35 #17991), 01c427 (0.38 #1285, 0.37 #9426, 0.35 #17991), 02f6yz (0.38 #1285, 0.37 #9426, 0.35 #17991), 01by1l (0.34 #8254, 0.30 #11679, 0.18 #969), 02v1m7 (0.27 #5683, 0.18 #3112, 0.18 #970), 02f73b (0.24 #1137, 0.15 #5850, 0.13 #6706), 03t5kl (0.23 #3219, 0.07 #14566, 0.05 #5790), 02sp_v (0.22 #5728, 0.18 #1015, 0.17 #159), 01c9jp (0.21 #6608, 0.12 #1039, 0.11 #8324) >> Best rule #1285 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 09qr6; 01vs_v8; 09hnb; 0478__m; 03y82t6; 011z3g; >> query: (?x11749, ?x1389) <- artists(?x302, ?x11749), award_winner(?x2634, ?x11749), ?x2634 = 02f72n, award(?x11749, ?x1389) >> conf = 0.38 => this is the best rule for 4 predicted values *> Best rule #1137 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 09qr6; 01vs_v8; 09hnb; 0478__m; 03y82t6; 011z3g; *> query: (?x11749, 02f73b) <- artists(?x302, ?x11749), award_winner(?x2634, ?x11749), ?x2634 = 02f72n, award(?x11749, ?x1389) *> conf = 0.24 ranks of expected_values: 7 EVAL 016t0h award_winner! 02f73b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 54.000 0.384 http://example.org/award/award_category/winners./award/award_honor/award_winner #10550-01s9vc PRED entity: 01s9vc PRED relation: currency PRED expected values: 09nqf => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.88 #43, 0.85 #183, 0.84 #120), 02l6h (0.12 #771, 0.11 #877, 0.06 #67), 01nv4h (0.12 #771, 0.11 #877, 0.03 #86), 02gsvk (0.12 #771, 0.11 #877, 0.01 #111), 088n7 (0.02 #182, 0.01 #70), 0kz1h (0.01 #110) >> Best rule #43 for best value: >> intensional similarity = 5 >> extensional distance = 49 >> proper extension: 01qvz8; >> query: (?x10404, 09nqf) <- film_crew_role(?x10404, ?x2095), nominated_for(?x10404, ?x1708), film(?x1104, ?x10404), ?x2095 = 0dxtw, titles(?x600, ?x1708) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01s9vc currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.882 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10549-03rtz1 PRED entity: 03rtz1 PRED relation: nominated_for! PRED expected values: 04ljl_l 05b1610 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 251): 0gq9h (0.45 #1959, 0.26 #1248, 0.22 #4803), 0gs9p (0.33 #1960, 0.19 #1723, 0.18 #4804), 019f4v (0.30 #1950, 0.19 #1713, 0.18 #289), 0k611 (0.28 #1969, 0.17 #3865, 0.17 #4813), 0gr4k (0.27 #1923, 0.16 #1212, 0.15 #4767), 05b1610 (0.25 #950, 0.25 #949, 0.20 #9721), 04ljl_l (0.25 #950, 0.25 #949, 0.20 #9721), 05p09zm (0.25 #950, 0.25 #949, 0.20 #9721), 03c7tr1 (0.25 #950, 0.25 #949, 0.08 #1944), 040njc (0.25 #1904, 0.21 #6, 0.20 #9721) >> Best rule #1959 for best value: >> intensional similarity = 3 >> extensional distance = 631 >> proper extension: 0c1j_; >> query: (?x1120, 0gq9h) <- nominated_for(?x154, ?x1120), award(?x538, ?x154), ?x538 = 03f2_rc >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #950 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 245 *> proper extension: 02fn5r; *> query: (?x1120, ?x102) <- nominated_for(?x12393, ?x1120), nominated_for(?x102, ?x12393) *> conf = 0.25 ranks of expected_values: 6, 7 EVAL 03rtz1 nominated_for! 05b1610 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 63.000 63.000 0.453 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03rtz1 nominated_for! 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 63.000 63.000 0.453 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10548-018x3 PRED entity: 018x3 PRED relation: music! PRED expected values: 09hy79 => 133 concepts (125 used for prediction) PRED predicted values (max 10 best out of 113): 01hv3t (0.11 #1756, 0.02 #5816, 0.01 #12921), 021y7yw (0.11 #1256, 0.02 #5316, 0.01 #12421), 05dy7p (0.11 #1251, 0.02 #5311, 0.01 #12416), 07nt8p (0.11 #1231, 0.02 #5291, 0.01 #12396), 0401sg (0.09 #2081, 0.02 #9186, 0.01 #12231), 01s7w3 (0.06 #6963, 0.05 #22188, 0.04 #7978), 06929s (0.05 #2456, 0.02 #5501, 0.02 #7531), 0b9rdk (0.05 #2641, 0.01 #11776, 0.01 #12791), 07bzz7 (0.04 #7635, 0.04 #6620, 0.03 #9665), 0pdp8 (0.04 #6315, 0.03 #14435, 0.01 #36765) >> Best rule #1756 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01q_ph; >> query: (?x5494, 01hv3t) <- artist(?x9224, ?x5494), ?x9224 = 0n85g, influenced_by(?x5494, ?x1029) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018x3 music! 09hy79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 125.000 0.111 http://example.org/film/film/music #10547-03h_fk5 PRED entity: 03h_fk5 PRED relation: profession PRED expected values: 09jwl => 146 concepts (145 used for prediction) PRED predicted values (max 10 best out of 104): 0dxtg (0.77 #5936, 0.76 #5788, 0.61 #8452), 09jwl (0.73 #12019, 0.72 #1351, 0.68 #12614), 01d_h8 (0.58 #5928, 0.56 #5780, 0.44 #4448), 0cbd2 (0.56 #4005, 0.50 #8445, 0.43 #599), 016z4k (0.56 #1336, 0.45 #11114, 0.45 #9629), 0dz3r (0.55 #1630, 0.55 #742, 0.50 #1482), 0fj9f (0.50 #202, 0.44 #1238, 0.25 #1090), 01c72t (0.47 #4762, 0.41 #2837, 0.33 #5058), 02jknp (0.44 #5930, 0.43 #5782, 0.32 #8446), 0kyk (0.43 #622, 0.31 #1214, 0.28 #17934) >> Best rule #5936 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 0884hk; >> query: (?x2807, 0dxtg) <- award_nominee(?x366, ?x2807), story_by(?x861, ?x2807), award(?x2807, ?x594) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #12019 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 470 *> proper extension: 0f0y8; 03c7ln; 0c9d9; 032t2z; 06y9c2; 01q7cb_; 07_3qd; 01w923; 01ky2h; 012zng; ... *> query: (?x2807, 09jwl) <- artists(?x302, ?x2807), instrumentalists(?x227, ?x2807), artist(?x3265, ?x2807) *> conf = 0.73 ranks of expected_values: 2 EVAL 03h_fk5 profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 145.000 0.770 http://example.org/people/person/profession #10546-02qhlwd PRED entity: 02qhlwd PRED relation: film! PRED expected values: 02cllz => 90 concepts (55 used for prediction) PRED predicted values (max 10 best out of 734): 0bs1yy (0.46 #43676, 0.45 #89432, 0.45 #62394), 07nznf (0.15 #58234, 0.15 #64475, 0.15 #33280), 0c6qh (0.14 #412, 0.07 #16639, 0.04 #14971), 01nwwl (0.14 #500, 0.05 #6739, 0.04 #2580), 0h7pj (0.14 #1541, 0.04 #3621, 0.02 #24424), 014zcr (0.14 #37, 0.04 #14596, 0.04 #12516), 0170qf (0.14 #365, 0.04 #4524, 0.03 #6604), 0169dl (0.14 #399, 0.04 #12878, 0.03 #31599), 0h32q (0.14 #772, 0.03 #7011, 0.03 #4931), 01swck (0.14 #798, 0.03 #2878, 0.03 #23681) >> Best rule #43676 for best value: >> intensional similarity = 4 >> extensional distance = 623 >> proper extension: 014_x2; 083shs; 01br2w; 02v8kmz; 02vp1f_; 07xtqq; 0dckvs; 05p1tzf; 060v34; 03s6l2; ... >> query: (?x4188, ?x3042) <- nominated_for(?x3042, ?x4188), titles(?x53, ?x4188), film_release_distribution_medium(?x4188, ?x81), film_crew_role(?x4188, ?x137) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #6646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 160 *> proper extension: 08cx5g; 02qr46y; *> query: (?x4188, 02cllz) <- nominated_for(?x3042, ?x4188), titles(?x3506, ?x4188), titles(?x3506, ?x1002), ?x1002 = 0_b3d *> conf = 0.01 ranks of expected_values: 563 EVAL 02qhlwd film! 02cllz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 55.000 0.463 http://example.org/film/actor/film./film/performance/film #10545-06bss PRED entity: 06bss PRED relation: politician! PRED expected values: 0d075m => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 19): 0d075m (0.56 #147, 0.52 #339, 0.50 #99), 07wbk (0.28 #385, 0.25 #121, 0.25 #49), 07wf9 (0.26 #294, 0.26 #270, 0.25 #318), 07wgm (0.13 #278, 0.12 #350, 0.09 #254), 07wdw (0.09 #247, 0.09 #295, 0.09 #271), 0135dr (0.06 #474), 01fpdh (0.05 #263, 0.04 #311, 0.04 #287), 0d9fz (0.05 #251, 0.04 #299, 0.04 #275), 01swmr (0.05 #242, 0.04 #290, 0.04 #266), 0135cw (0.03 #465) >> Best rule #147 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 03txms; >> query: (?x6742, 0d075m) <- legislative_sessions(?x6742, ?x6139), legislative_sessions(?x6742, ?x4730), legislative_sessions(?x6742, ?x3463), ?x4730 = 02cg7g, religion(?x6742, ?x2769), ?x3463 = 02bqmq, legislative_sessions(?x845, ?x6139) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06bss politician! 0d075m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.556 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #10544-05lb65 PRED entity: 05lb65 PRED relation: award_nominee! PRED expected values: 026zvx7 07z1_q 03x16f => 92 concepts (34 used for prediction) PRED predicted values (max 10 best out of 673): 01wb8bs (0.81 #16218, 0.81 #69513, 0.81 #62561), 05dxl5 (0.81 #16218, 0.81 #69513, 0.81 #62561), 038g2x (0.81 #16218, 0.81 #69513, 0.81 #62561), 07z1_q (0.81 #16218, 0.81 #69513, 0.81 #62561), 048hf (0.81 #16218, 0.81 #69513, 0.81 #62561), 05lb65 (0.71 #1542, 0.61 #6175, 0.59 #3858), 026zvx7 (0.57 #551, 0.47 #2867, 0.44 #5184), 03x16f (0.50 #1900, 0.41 #4216, 0.39 #6533), 017149 (0.28 #55611, 0.27 #6952, 0.18 #50976), 0bgrsl (0.28 #55611, 0.27 #6952, 0.18 #50976) >> Best rule #16218 for best value: >> intensional similarity = 2 >> extensional distance = 436 >> proper extension: 0hwqz; 01tnbn; 036dyy; 03ywyk; 01p0w_; >> query: (?x6851, ?x444) <- participant(?x2129, ?x6851), award_nominee(?x6851, ?x444) >> conf = 0.81 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 7, 8 EVAL 05lb65 award_nominee! 03x16f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 92.000 34.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 05lb65 award_nominee! 07z1_q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 92.000 34.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 05lb65 award_nominee! 026zvx7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 92.000 34.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10543-05b4rcb PRED entity: 05b4rcb PRED relation: film_sets_designed PRED expected values: 06c0ns => 53 concepts (9 used for prediction) PRED predicted values (max 10 best out of 92): 0cwy47 (0.14 #99, 0.12 #191), 048rn (0.12 #229, 0.09 #137), 0h0wd9 (0.09 #178, 0.08 #270), 025scjj (0.09 #176, 0.08 #268), 014knw (0.09 #175, 0.08 #267), 04wddl (0.09 #173, 0.08 #265), 029jt9 (0.09 #171, 0.08 #263), 0kvb6p (0.09 #167, 0.08 #259), 0jqd3 (0.09 #155, 0.08 #247), 0dnw1 (0.09 #149, 0.08 #241) >> Best rule #99 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0584j4n; 051x52f; >> query: (?x2230, 0cwy47) <- nationality(?x2230, ?x94), award_nominee(?x2507, ?x2230), film_sets_designed(?x2230, ?x270) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05b4rcb film_sets_designed 06c0ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 9.000 0.136 http://example.org/film/film_set_designer/film_sets_designed #10542-05drq5 PRED entity: 05drq5 PRED relation: student! PRED expected values: 01w5m => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 65): 09f2j (0.33 #158, 0.07 #684, 0.03 #22250), 01qqv5 (0.33 #334, 0.03 #860), 02607j (0.33 #102, 0.03 #628), 0bwfn (0.17 #800, 0.10 #1326, 0.09 #6060), 08815 (0.07 #528, 0.03 #5788, 0.03 #7366), 04b_46 (0.07 #752, 0.03 #2856, 0.03 #1804), 065y4w7 (0.06 #5800, 0.06 #1592, 0.05 #2644), 01w5m (0.05 #1156, 0.04 #2208, 0.04 #2734), 017z88 (0.05 #7445, 0.03 #9023, 0.03 #8497), 03ksy (0.05 #2735, 0.04 #22197, 0.04 #17463) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 02vyw; >> query: (?x1314, 09f2j) <- award_nominee(?x1314, ?x8408), ?x8408 = 0kb3n, award_winner(?x601, ?x1314) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1156 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 108 *> proper extension: 012cph; 0gv5c; 03f47xl; 064177; 0c4y8; 03cdg; *> query: (?x1314, 01w5m) <- award(?x1314, ?x1107), award(?x1314, ?x601), ?x601 = 0gr4k, nominated_for(?x1107, ?x144) *> conf = 0.05 ranks of expected_values: 8 EVAL 05drq5 student! 01w5m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 78.000 78.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #10541-09ggk PRED entity: 09ggk PRED relation: colors! PRED expected values: 025rcc 0558_1 015wy_ 02p72j => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1445): 016sd3 (0.57 #3139, 0.50 #3600, 0.50 #1293), 0bwfn (0.56 #7861, 0.46 #2311, 0.36 #6002), 01bm_ (0.56 #7861, 0.40 #2068, 0.33 #684), 02xwzh (0.56 #7861, 0.29 #3125, 0.25 #3586), 01s7j5 (0.56 #7861, 0.12 #3695, 0.12 #5538), 017z88 (0.56 #7861, 0.12 #3695, 0.12 #4156), 0fr9jp (0.56 #7861), 09r4xx (0.56 #7861), 01d34b (0.50 #927, 0.46 #2311, 0.36 #6002), 07lx1s (0.50 #955, 0.44 #5105, 0.43 #2801) >> Best rule #3139 for best value: >> intensional similarity = 31 >> extensional distance = 5 >> proper extension: 038hg; >> query: (?x9778, 016sd3) <- colors(?x14319, ?x9778), colors(?x10869, ?x9778), colors(?x7900, ?x9778), colors(?x6132, ?x9778), student(?x10869, ?x8045), colors(?x12141, ?x9778), student(?x6132, ?x1984), list(?x6132, ?x2197), contains(?x94, ?x10869), category(?x10869, ?x134), organization(?x346, ?x10869), state_province_region(?x6132, ?x1310), institution(?x734, ?x6132), organization(?x3484, ?x7900), major_field_of_study(?x7900, ?x1527), citytown(?x7900, ?x5867), position(?x12141, ?x1348), draft(?x12141, ?x2569), ?x3484 = 05k17c, gender(?x1984, ?x231), major_field_of_study(?x6132, ?x2314), ?x1348 = 01pv51, school(?x12141, ?x581), currency(?x6132, ?x1099), award(?x8045, ?x375), major_field_of_study(?x10869, ?x373), participant(?x1984, ?x8222), citytown(?x10869, ?x739), currency(?x14319, ?x170), award_nominee(?x100, ?x8045), type_of_union(?x8045, ?x566) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #401 for first EXPECTED value: *> intensional similarity = 39 *> extensional distance = 1 *> proper extension: 01l849; *> query: (?x9778, 015wy_) <- colors(?x11467, ?x9778), colors(?x10869, ?x9778), colors(?x6505, ?x9778), colors(?x6132, ?x9778), colors(?x4599, ?x9778), colors(?x466, ?x9778), ?x10869 = 03qdm, ?x11467 = 0ghvb, colors(?x9975, ?x9778), ?x6132 = 0hsb3, institution(?x1368, ?x6505), currency(?x6505, ?x1099), ?x4599 = 07t90, major_field_of_study(?x6505, ?x3878), major_field_of_study(?x6505, ?x2605), major_field_of_study(?x6505, ?x1154), ?x1368 = 014mlp, category(?x6505, ?x134), ?x1154 = 02lp1, student(?x6505, ?x3279), student(?x6505, ?x1309), team(?x9974, ?x9975), company(?x1309, ?x14476), student(?x3878, ?x3395), currency(?x892, ?x1099), ?x466 = 01pl14, currency(?x622, ?x1099), currency(?x6034, ?x1099), currency(?x248, ?x1099), major_field_of_study(?x12293, ?x3878), major_field_of_study(?x481, ?x3878), ?x9974 = 0b_6pv, profession(?x3279, ?x987), ?x12293 = 01pj48, award_winner(?x14353, ?x1309), influenced_by(?x1742, ?x3279), ?x481 = 052nd, ?x2605 = 03g3w, team(?x6848, ?x9975) *> conf = 0.33 ranks of expected_values: 203, 279, 280, 542 EVAL 09ggk colors! 02p72j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 18.000 18.000 0.571 http://example.org/education/educational_institution/colors EVAL 09ggk colors! 015wy_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 18.000 18.000 0.571 http://example.org/education/educational_institution/colors EVAL 09ggk colors! 0558_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 18.000 18.000 0.571 http://example.org/education/educational_institution/colors EVAL 09ggk colors! 025rcc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 18.000 18.000 0.571 http://example.org/education/educational_institution/colors #10540-07w5rq PRED entity: 07w5rq PRED relation: major_field_of_study PRED expected values: 02j62 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 119): 01mkq (0.45 #2034, 0.43 #2288, 0.41 #2792), 02j62 (0.38 #2808, 0.37 #2050, 0.37 #2304), 03g3w (0.37 #2046, 0.37 #2804, 0.36 #2300), 02lp1 (0.37 #2030, 0.33 #2788, 0.32 #2284), 04rjg (0.36 #2039, 0.34 #2293, 0.33 #2797), 062z7 (0.32 #2047, 0.29 #2805, 0.28 #2301), 0_jm (0.31 #439, 0.15 #2079, 0.15 #4731), 02_7t (0.28 #446, 0.21 #2086, 0.21 #2844), 05qjt (0.25 #512, 0.25 #2026, 0.22 #2280), 0g26h (0.24 #2063, 0.24 #423, 0.24 #2821) >> Best rule #2034 for best value: >> intensional similarity = 5 >> extensional distance = 89 >> proper extension: 0gl5_; >> query: (?x1961, 01mkq) <- institution(?x1526, ?x1961), category(?x1961, ?x134), citytown(?x1961, ?x13165), ?x1526 = 0bkj86, school_type(?x1961, ?x1962) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2808 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 110 *> proper extension: 0yldt; *> query: (?x1961, 02j62) <- institution(?x1526, ?x1961), category(?x1961, ?x134), citytown(?x1961, ?x13165), ?x1526 = 0bkj86, ?x134 = 08mbj5d *> conf = 0.38 ranks of expected_values: 2 EVAL 07w5rq major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.451 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10539-05h43ls PRED entity: 05h43ls PRED relation: prequel PRED expected values: 03bzyn4 => 106 concepts (40 used for prediction) PRED predicted values (max 10 best out of 11): 02fqxm (0.02 #181), 0m5s5 (0.02 #165), 02scbv (0.02 #120), 063zky (0.02 #105), 03nm_fh (0.02 #89), 05pdh86 (0.02 #81), 05zlld0 (0.02 #62), 0ddt_ (0.02 #49), 0pb33 (0.02 #28), 01hr1 (0.02 #6) >> Best rule #181 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 0dj0m5; 04tc1g; 048qrd; 06ybb1; 02krdz; 02rq8k8; 0n83s; 01mszz; 05b_gq; 02754c9; ... >> query: (?x2586, 02fqxm) <- genre(?x2586, ?x53), award_winner(?x2586, ?x2549), nominated_for(?x688, ?x2586), ?x688 = 05b1610 >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05h43ls prequel 03bzyn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 40.000 0.016 http://example.org/film/film/prequel #10538-01w_10 PRED entity: 01w_10 PRED relation: profession PRED expected values: 0kyk => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 113): 02hrh1q (0.92 #10083, 0.91 #6530, 0.89 #10823), 01d_h8 (0.62 #2819, 0.53 #4152, 0.52 #3856), 015cjr (0.56 #1234, 0.45 #2122, 0.40 #1826), 0dxtg (0.55 #14233, 0.55 #2086, 0.50 #1790), 09jwl (0.50 #759, 0.50 #611, 0.46 #7571), 0nbcg (0.50 #772, 0.50 #624, 0.33 #476), 02jknp (0.50 #748, 0.50 #600, 0.33 #452), 0kyk (0.43 #3139, 0.43 #2991, 0.40 #1954), 0d1pc (0.40 #1975, 0.40 #1531, 0.27 #2271), 018gz8 (0.40 #1793, 0.36 #2089, 0.34 #5051) >> Best rule #10083 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 023tp8; 01qscs; 01q_ph; 0159h6; 04wqr; 04bs3j; 014x77; 09wj5; 03m8lq; 01csvq; ... >> query: (?x8122, 02hrh1q) <- celebrity(?x8122, ?x1400), film(?x8122, ?x2218), people(?x1050, ?x8122) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #3139 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 083pr; 0jcx; 0d06m5; 09bg4l; 0d05fv; 01tdnyh; 0x3r3; 03s9v; 07hyk; 0tfc; *> query: (?x8122, 0kyk) <- company(?x8122, ?x1762), organization(?x8122, ?x4542), location(?x8122, ?x335) *> conf = 0.43 ranks of expected_values: 8 EVAL 01w_10 profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 140.000 140.000 0.917 http://example.org/people/person/profession #10537-037css PRED entity: 037css PRED relation: position PRED expected values: 02nzb8 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 3): 02nzb8 (0.95 #106, 0.90 #172, 0.90 #200), 03f0fp (0.90 #172, 0.90 #200, 0.90 #171), 02md_2 (0.50 #390, 0.31 #395) >> Best rule #106 for best value: >> intensional similarity = 16 >> extensional distance = 69 >> proper extension: 01453; 0223bl; 025txtg; 07r78j; 01kckd; 0cgwt8; 0j47s; 02b1b5; 04gkp3; 01kj5h; ... >> query: (?x14056, ?x60) <- position(?x14056, ?x530), position(?x14056, ?x203), position(?x14056, ?x63), team(?x3031, ?x14056), ?x530 = 02_j1w, team(?x3031, ?x3363), team(?x3031, ?x59), place_of_birth(?x3031, ?x8602), athlete(?x471, ?x3031), team(?x3031, ?x5828), ?x63 = 02sdk9v, teams(?x390, ?x59), team(?x927, ?x59), colors(?x3363, ?x1101), team(?x60, ?x3363), ?x203 = 0dgrmp >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 037css position 02nzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.946 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #10536-06sw9 PRED entity: 06sw9 PRED relation: country! PRED expected values: 01cgz => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 53): 01cgz (0.76 #14, 0.72 #385, 0.69 #438), 071t0 (0.69 #393, 0.69 #446, 0.64 #552), 01lb14 (0.64 #387, 0.56 #440, 0.49 #546), 03hr1p (0.59 #394, 0.53 #447, 0.46 #553), 06f41 (0.57 #386, 0.55 #439, 0.51 #545), 07jbh (0.57 #403, 0.53 #456, 0.49 #562), 07gyv (0.56 #378, 0.55 #431, 0.48 #484), 0194d (0.51 #417, 0.44 #470, 0.41 #576), 0w0d (0.49 #436, 0.49 #383, 0.47 #489), 02y8z (0.49 #390, 0.44 #443, 0.40 #549) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 020d5; >> query: (?x5680, 01cgz) <- form_of_government(?x5680, ?x48), nationality(?x4172, ?x5680), ?x48 = 06cx9 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06sw9 country! 01cgz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.760 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #10535-0b1y_2 PRED entity: 0b1y_2 PRED relation: film! PRED expected values: 09_gdc => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 5): 09_gdc (0.22 #7, 0.03 #63, 0.03 #84), 01pb34 (0.11 #18, 0.05 #28, 0.04 #172), 01kyvx (0.09 #103, 0.09 #67, 0.01 #513), 02t8yb (0.08 #19, 0.03 #14, 0.01 #70), 014kbl (0.03 #20) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0bt3j9; 04j14qc; >> query: (?x2920, 09_gdc) <- nominated_for(?x995, ?x2920), crewmember(?x2920, ?x9769), ?x995 = 099tbz, film_crew_role(?x2920, ?x137) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b1y_2 film! 09_gdc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.222 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #10534-01l_yg PRED entity: 01l_yg PRED relation: nominated_for PRED expected values: 05zr0xl => 93 concepts (30 used for prediction) PRED predicted values (max 10 best out of 262): 0q9jk (0.79 #37327, 0.79 #45444, 0.79 #22715), 0bt4g (0.29 #38951, 0.29 #12981, 0.25 #34079), 01f7jt (0.29 #38951, 0.29 #12981, 0.25 #34079), 03kxj2 (0.29 #38951, 0.29 #12981, 0.25 #34079), 0gvvf4j (0.29 #12981, 0.25 #34079, 0.22 #38950), 04y9mm8 (0.29 #12981, 0.25 #34079, 0.22 #38950), 01pvxl (0.29 #12981, 0.25 #34079, 0.22 #38950), 01f7kl (0.29 #12981, 0.25 #34079, 0.22 #38950), 085bd1 (0.29 #12981, 0.25 #34079, 0.22 #38950), 07xtqq (0.29 #12981, 0.25 #34079, 0.22 #38950) >> Best rule #37327 for best value: >> intensional similarity = 3 >> extensional distance = 1256 >> proper extension: 0hm0k; >> query: (?x9700, ?x8132) <- award_winner(?x8132, ?x9700), award(?x8132, ?x757), titles(?x2008, ?x8132) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #4550 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 036jp8; *> query: (?x9700, 05zr0xl) <- award(?x9700, ?x2071), profession(?x9700, ?x1032), ?x2071 = 0bdw6t *> conf = 0.02 ranks of expected_values: 101 EVAL 01l_yg nominated_for 05zr0xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 93.000 30.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10533-03s0w PRED entity: 03s0w PRED relation: partially_contains PRED expected values: 04yf_ => 145 concepts (112 used for prediction) PRED predicted values (max 10 best out of 35): 0lm0n (0.37 #104, 0.35 #296, 0.33 #449), 0k3nk (0.33 #14, 0.07 #1104, 0.06 #1144), 06c6l (0.33 #31, 0.03 #806, 0.03 #1080), 04yf_ (0.26 #2539, 0.19 #166, 0.18 #127), 05lx3 (0.26 #2539, 0.14 #298, 0.13 #106), 02v3m7 (0.26 #2539, 0.06 #143, 0.05 #220), 02cgp8 (0.14 #294, 0.13 #102, 0.12 #447), 026zt (0.09 #1114, 0.09 #995, 0.05 #1277), 0lcd (0.09 #987, 0.08 #1106, 0.06 #1429), 0p2n (0.09 #1005, 0.07 #1124, 0.04 #2454) >> Best rule #104 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 05kkh; 059rby; 03v1s; 05kj_; 059f4; 05fkf; 05fhy; 04ykg; 06mz5; 01x73; ... >> query: (?x961, 0lm0n) <- district_represented(?x653, ?x961), category(?x961, ?x134), ?x653 = 070m6c, religion(?x961, ?x109) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #2539 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 165 *> proper extension: 059qw; 035p3; *> query: (?x961, ?x4540) <- adjoins(?x3818, ?x961), contains(?x3818, ?x405), partially_contains(?x3818, ?x4540) *> conf = 0.26 ranks of expected_values: 4 EVAL 03s0w partially_contains 04yf_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 112.000 0.367 http://example.org/location/location/partially_contains #10532-04t6fk PRED entity: 04t6fk PRED relation: genre PRED expected values: 04xvlr => 68 concepts (29 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.94 #2352, 0.66 #1054, 0.66 #1996), 01hmnh (0.78 #252, 0.28 #954, 0.22 #1171), 01z4y (0.56 #1406, 0.56 #1641, 0.55 #1760), 03k9fj (0.47 #596, 0.44 #245, 0.31 #947), 02l7c8 (0.45 #367, 0.38 #1422, 0.34 #2130), 0lsxr (0.43 #125, 0.40 #8, 0.24 #827), 01jfsb (0.34 #1772, 0.28 #2833, 0.28 #2007), 060__y (0.33 #251, 0.24 #2114, 0.20 #17), 06cvj (0.27 #1409, 0.22 #2117, 0.22 #1171), 04xvlr (0.27 #353, 0.25 #1055, 0.24 #2114) >> Best rule #2352 for best value: >> intensional similarity = 4 >> extensional distance = 573 >> proper extension: 016ztl; 0267wwv; 0564x; >> query: (?x2699, 07s9rl0) <- music(?x2699, ?x669), genre(?x2699, ?x3515), genre(?x4355, ?x3515), ?x4355 = 08tq4x >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #353 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 09rfpk; *> query: (?x2699, 04xvlr) <- music(?x2699, ?x669), genre(?x2699, ?x3515), ?x3515 = 082gq, story_by(?x2699, ?x2182) *> conf = 0.27 ranks of expected_values: 10 EVAL 04t6fk genre 04xvlr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 68.000 29.000 0.937 http://example.org/film/film/genre #10531-038_0z PRED entity: 038_0z PRED relation: sport PRED expected values: 09xp_ => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 15): 09xp_ (0.87 #225, 0.87 #262, 0.86 #131), 02vx4 (0.73 #180, 0.66 #254, 0.58 #133), 0jm_ (0.70 #124, 0.48 #218, 0.33 #115), 03tmr (0.45 #102, 0.17 #113, 0.17 #56), 018jz (0.28 #220, 0.25 #117, 0.25 #69), 018w8 (0.12 #78, 0.12 #31, 0.11 #74), 039yzs (0.11 #74, 0.04 #204, 0.04 #351), 06f3l (0.11 #74, 0.02 #169, 0.02 #206), 0z74 (0.01 #233), 0486tv (0.01 #111) >> Best rule #225 for best value: >> intensional similarity = 20 >> extensional distance = 52 >> proper extension: 07147; 03m1n; 051wf; >> query: (?x14520, ?x12682) <- team(?x13559, ?x14520), team(?x13559, ?x14238), team(?x13559, ?x13358), team(?x13559, ?x10085), teams(?x390, ?x13358), colors(?x13358, ?x332), ?x332 = 01l849, colors(?x10085, ?x5325), colors(?x10085, ?x4557), colors(?x10085, ?x663), teams(?x2146, ?x14238), teams(?x1023, ?x10085), colors(?x14238, ?x3189), ?x3189 = 01g5v, ?x5325 = 03vtbc, ?x663 = 083jv, ?x4557 = 019sc, sport(?x14238, ?x12682), sport(?x10085, ?x12682), sport(?x13358, ?x12682) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 038_0z sport 09xp_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.870 http://example.org/sports/sports_team/sport #10530-09306z PRED entity: 09306z PRED relation: honored_for PRED expected values: 0sxkh 0k2m6 => 34 concepts (19 used for prediction) PRED predicted values (max 10 best out of 669): 0k2sk (0.33 #59, 0.16 #3569, 0.15 #3570), 077q8x (0.33 #375, 0.04 #2753, 0.04 #3349), 0yx_w (0.33 #521, 0.04 #2899, 0.04 #3495), 01qxc7 (0.33 #266, 0.04 #2644, 0.04 #3240), 01sxly (0.33 #28, 0.04 #2406, 0.04 #3002), 0d68qy (0.18 #9697, 0.06 #10294, 0.06 #10893), 0k4p0 (0.16 #3569, 0.15 #3570, 0.07 #940), 0bdjd (0.16 #3569, 0.15 #3570, 0.04 #2817), 043mk4y (0.16 #3569, 0.15 #3570, 0.02 #10006), 04jpg2p (0.16 #3569, 0.15 #3570, 0.02 #10036) >> Best rule #59 for best value: >> intensional similarity = 20 >> extensional distance = 1 >> proper extension: 073hkh; >> query: (?x7884, 0k2sk) <- award_winner(?x7884, ?x13307), award_winner(?x7884, ?x930), honored_for(?x7884, ?x9761), ceremony(?x4573, ?x7884), ceremony(?x3066, ?x7884), ceremony(?x2222, ?x7884), ceremony(?x1972, ?x7884), ceremony(?x1245, ?x7884), ceremony(?x1243, ?x7884), ?x2222 = 0gs96, ?x3066 = 0gqy2, ?x1972 = 0gqyl, ?x4573 = 0gq_d, nationality(?x13307, ?x94), ?x1243 = 0gr0m, ?x1245 = 0gqwc, ?x930 = 03h26tm, profession(?x13307, ?x1032), film_release_distribution_medium(?x9761, ?x81), genre(?x9761, ?x53) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10740 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 68 *> proper extension: 09q_6t; 02wzl1d; 0g5b0q5; 05zksls; 0hndn2q; 026kq4q; 0drtv8; 09p30_; 026kqs9; 09p2r9; ... *> query: (?x7884, ?x6078) <- award_winner(?x7884, ?x241), honored_for(?x7884, ?x3882), ceremony(?x2222, ?x7884), nominated_for(?x2222, ?x6078), nominated_for(?x2222, ?x4610), nominated_for(?x2222, ?x2943), nominated_for(?x2222, ?x2107), ?x2943 = 0c9k8, ?x4610 = 017jd9, award(?x771, ?x2222), genre(?x6078, ?x258), award_winner(?x2222, ?x12848), ?x2107 = 0260bz, film_release_region(?x6078, ?x87) *> conf = 0.01 ranks of expected_values: 631 EVAL 09306z honored_for 0k2m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 19.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 09306z honored_for 0sxkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 19.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #10529-02jx1 PRED entity: 02jx1 PRED relation: featured_film_locations! PRED expected values: 0194zl => 236 concepts (233 used for prediction) PRED predicted values (max 10 best out of 691): 05z_kps (0.33 #2280, 0.33 #1548, 0.25 #5208), 011yl_ (0.33 #2452, 0.25 #5380, 0.14 #11973), 0413cff (0.33 #2566, 0.25 #5494, 0.14 #31860), 072x7s (0.33 #2307, 0.25 #5235, 0.13 #16956), 04dsnp (0.33 #2260, 0.25 #5188, 0.10 #74759), 0cwy47 (0.33 #2253, 0.25 #5181, 0.10 #12507), 0cc846d (0.33 #2395, 0.25 #5323, 0.08 #15579), 03r0g9 (0.33 #2458, 0.25 #5386, 0.08 #15642), 0639bg (0.33 #2466, 0.25 #5394, 0.08 #15650), 09sh8k (0.33 #2202, 0.25 #5130, 0.07 #16851) >> Best rule #2280 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 04jpl; >> query: (?x1310, 05z_kps) <- contains(?x1310, ?x11732), contains(?x1310, ?x10042), ?x11732 = 0n95v, ?x10042 = 09bkv >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02jx1 featured_film_locations! 0194zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 236.000 233.000 0.333 http://example.org/film/film/featured_film_locations #10528-09tqxt PRED entity: 09tqxt PRED relation: ceremony PRED expected values: 0hr6lkl => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 139): 0bvfqq (0.68 #584, 0.25 #170, 0.22 #2347), 0n8_m93 (0.68 #667, 0.25 #253, 0.17 #805), 0bzm81 (0.68 #573, 0.16 #711, 0.16 #849), 02yxh9 (0.68 #650, 0.16 #788, 0.15 #926), 0bc773 (0.68 #605, 0.16 #743, 0.15 #881), 02yw5r (0.68 #563, 0.16 #701, 0.15 #839), 05q7cj (0.68 #644, 0.16 #782, 0.14 #920), 073h9x (0.68 #601, 0.15 #739, 0.13 #877), 0bz6sb (0.68 #615, 0.14 #753, 0.13 #891), 02yvhx (0.64 #628, 0.25 #214, 0.22 #2347) >> Best rule #584 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 018wng; 0gq_d; >> query: (?x1723, 0bvfqq) <- ceremony(?x1723, ?x472), award_winner(?x472, ?x4701), ?x4701 = 03j24kf >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #430 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 054ky1; 027s4dn; *> query: (?x1723, 0hr6lkl) <- ceremony(?x1723, ?x472), award_winner(?x472, ?x123), ?x123 = 05bnp0, honored_for(?x472, ?x253) *> conf = 0.47 ranks of expected_values: 60 EVAL 09tqxt ceremony 0hr6lkl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 25.000 25.000 0.679 http://example.org/award/award_category/winners./award/award_honor/ceremony #10527-02kfzz PRED entity: 02kfzz PRED relation: written_by PRED expected values: 085pr => 99 concepts (34 used for prediction) PRED predicted values (max 10 best out of 93): 09zw90 (0.15 #10447, 0.14 #675, 0.14 #337), 0d_84 (0.09 #2698, 0.08 #8764, 0.05 #10784), 0kjgl (0.09 #338, 0.07 #8763, 0.07 #8426), 0693l (0.04 #91, 0.03 #3463, 0.03 #1103), 09pl3f (0.04 #184, 0.02 #522, 0.02 #1870), 09pl3s (0.04 #73, 0.02 #411, 0.02 #1759), 032v0v (0.03 #387, 0.02 #49, 0.02 #1061), 0js9s (0.03 #534, 0.02 #196, 0.02 #1882), 0p__8 (0.03 #520, 0.01 #182, 0.01 #4569), 07s93v (0.03 #2407, 0.02 #6452, 0.02 #3419) >> Best rule #10447 for best value: >> intensional similarity = 3 >> extensional distance = 457 >> proper extension: 02y_lrp; 0ds3t5x; 016z5x; 0gjk1d; 07nt8p; 016z9n; 02tqm5; 02vr3gz; 0cmc26r; 0194zl; ... >> query: (?x4089, ?x11526) <- genre(?x4089, ?x53), produced_by(?x4089, ?x11526), award(?x4089, ?x6860) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #2121 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 194 *> proper extension: 0cq8nx; 0c5qvw; *> query: (?x4089, 085pr) <- genre(?x4089, ?x53), award(?x4089, ?x6860), cinematography(?x4089, ?x10704), nominated_for(?x10704, ?x5122) *> conf = 0.01 ranks of expected_values: 86 EVAL 02kfzz written_by 085pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 99.000 34.000 0.154 http://example.org/film/film/written_by #10526-0dzz6g PRED entity: 0dzz6g PRED relation: award PRED expected values: 09cn0c => 73 concepts (62 used for prediction) PRED predicted values (max 10 best out of 251): 02x17s4 (0.26 #2102, 0.25 #2802, 0.25 #5138), 0gr4k (0.26 #2102, 0.25 #2802, 0.25 #5138), 02x4w6g (0.26 #2102, 0.25 #2802, 0.25 #5138), 02w9sd7 (0.26 #2102, 0.25 #2802, 0.25 #5138), 02x4wr9 (0.26 #2102, 0.25 #2802, 0.25 #5138), 02x8n1n (0.26 #2102, 0.25 #2802, 0.25 #5138), 02y_rq5 (0.26 #2102, 0.25 #2802, 0.25 #5138), 0279c15 (0.26 #2102, 0.25 #2802, 0.25 #5138), 099c8n (0.15 #288, 0.14 #754, 0.03 #5842), 09d28z (0.15 #425, 0.09 #1126, 0.08 #891) >> Best rule #2102 for best value: >> intensional similarity = 4 >> extensional distance = 750 >> proper extension: 027tbrc; 0524b41; 06qwh; 023ny6; 06qv_; >> query: (?x3761, ?x601) <- nominated_for(?x601, ?x3761), nominated_for(?x3760, ?x3761), titles(?x1316, ?x3761), award(?x3761, ?x384) >> conf = 0.26 => this is the best rule for 8 predicted values *> Best rule #6776 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1125 *> proper extension: 06cs95; 0557yqh; 08bytj; 03nymk; 0d7vtk; 053x8hr; 07vqnc; 02rkkn1; *> query: (?x3761, ?x1132) <- nominated_for(?x601, ?x3761), nominated_for(?x3760, ?x3761), titles(?x1316, ?x3761), award(?x3760, ?x1132) *> conf = 0.06 ranks of expected_values: 77 EVAL 0dzz6g award 09cn0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 73.000 62.000 0.261 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #10525-045n3p PRED entity: 045n3p PRED relation: type_of_union PRED expected values: 04ztj => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.89 #65, 0.88 #45, 0.83 #105), 01g63y (0.36 #262, 0.26 #253, 0.20 #98), 0jgjn (0.26 #253), 01bl8s (0.05 #23, 0.04 #39, 0.02 #107) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 0jvtp; >> query: (?x14156, 04ztj) <- people(?x5855, ?x14156), nationality(?x14156, ?x2146), languages(?x14156, ?x1882), award(?x14156, ?x4687) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045n3p type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.891 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10524-01qh7 PRED entity: 01qh7 PRED relation: contains PRED expected values: 05bnq8 => 195 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2716): 01hr11 (0.74 #23525, 0.54 #179349, 0.53 #111736), 01_f90 (0.66 #205811, 0.54 #179349, 0.53 #111736), 01qh7 (0.44 #85273, 0.11 #132315, 0.04 #9181), 05k7sb (0.44 #85273, 0.11 #132315, 0.04 #9068), 0k3k1 (0.44 #85273, 0.11 #132315, 0.04 #14701), 09c7w0 (0.44 #85273, 0.11 #132315, 0.03 #155833), 059g4 (0.44 #85273, 0.11 #132315, 0.01 #83528), 02bhj4 (0.25 #981, 0.05 #39211, 0.04 #56852), 07tds (0.25 #595, 0.05 #38825, 0.04 #56466), 017y26 (0.25 #544, 0.05 #38774, 0.04 #56415) >> Best rule #23525 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 03khn; >> query: (?x3007, ?x13089) <- citytown(?x13089, ?x3007), adjoins(?x3052, ?x3007), contains(?x94, ?x13089) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #226392 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 173 *> proper extension: 0jgd; 03_r3; 02k54; 0fm2_; 06mz5; 06bnz; 02vzc; 05b4w; 04kf4; 0d8rs; ... *> query: (?x3007, ?x331) <- contains(?x3007, ?x3439), location_of_ceremony(?x566, ?x3007), major_field_of_study(?x3439, ?x1154), major_field_of_study(?x331, ?x1154) *> conf = 0.02 ranks of expected_values: 1919 EVAL 01qh7 contains 05bnq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 195.000 89.000 0.736 http://example.org/location/location/contains #10523-01snm PRED entity: 01snm PRED relation: place PRED expected values: 01snm => 168 concepts (117 used for prediction) PRED predicted values (max 10 best out of 239): 02_286 (0.17 #14, 0.03 #1045, 0.03 #2076), 0hptm (0.17 #157, 0.02 #4795, 0.02 #6342), 01m9f1 (0.17 #195, 0.01 #14624), 01smm (0.16 #3608, 0.16 #6185, 0.04 #676), 01sn3 (0.16 #3608, 0.03 #1640, 0.03 #2155), 01snm (0.16 #45367, 0.07 #55687, 0.03 #18035), 05kkh (0.16 #45367, 0.07 #55687), 09c7w0 (0.16 #45367, 0.07 #55687), 0n2k5 (0.11 #6701, 0.10 #1547, 0.07 #25766), 0z20d (0.04 #718, 0.03 #1750, 0.02 #6388) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01m9f1; >> query: (?x6555, 02_286) <- place_of_birth(?x1913, ?x6555), location(?x3547, ?x6555), county(?x6555, ?x12764), basic_title(?x1913, ?x346) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #45367 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 238 *> proper extension: 0fngy; *> query: (?x6555, ?x94) <- citytown(?x9620, ?x6555), category(?x9620, ?x134), contains(?x94, ?x9620) *> conf = 0.16 ranks of expected_values: 6 EVAL 01snm place 01snm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 168.000 117.000 0.167 http://example.org/location/hud_county_place/place #10522-013cr PRED entity: 013cr PRED relation: film PRED expected values: 04sh80 => 129 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1185): 05c26ss (0.12 #2414, 0.04 #5984, 0.04 #629), 0f2sx4 (0.12 #1382, 0.04 #8522, 0.03 #12093), 0b3n61 (0.12 #3141, 0.03 #10282, 0.03 #13853), 0bvn25 (0.12 #5405, 0.08 #1835, 0.05 #19688), 05fm6m (0.08 #3102, 0.08 #1317, 0.04 #6672), 034qzw (0.08 #2117, 0.07 #5687, 0.05 #19970), 04gv3db (0.08 #2536, 0.07 #6106, 0.05 #20389), 027j9wd (0.08 #2818, 0.06 #6388, 0.02 #20671), 02ph9tm (0.08 #1098, 0.06 #8238, 0.03 #18950), 09g8vhw (0.08 #2109, 0.04 #12821, 0.03 #3894) >> Best rule #2414 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 010p3; >> query: (?x1401, 05c26ss) <- participant(?x6059, ?x1401), profession(?x1401, ?x1383), religion(?x1401, ?x7131), ?x1383 = 0np9r >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #16026 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 98 *> proper extension: 02s2ft; 02qgqt; 02bfmn; 0byfz; 014zcr; 025h4z; 0z4s; 017149; 0187y5; 016khd; ... *> query: (?x1401, 04sh80) <- film(?x1401, ?x1402), award(?x1401, ?x3066), ?x3066 = 0gqy2, student(?x6894, ?x1401) *> conf = 0.03 ranks of expected_values: 290 EVAL 013cr film 04sh80 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 129.000 68.000 0.125 http://example.org/film/actor/film./film/performance/film #10521-0dmtp PRED entity: 0dmtp PRED relation: contact_category PRED expected values: 03w5xm => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 2): 03w5xm (0.92 #48, 0.92 #43, 0.89 #76), 014dgf (0.33 #14, 0.30 #40, 0.29 #10) >> Best rule #48 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 03mnk; 01yfp7; 04f0xq; 0vlf; >> query: (?x6404, 03w5xm) <- currency(?x6404, ?x170), contact_category(?x6404, ?x6046), company(?x554, ?x6404), list(?x6404, ?x7472), ?x7472 = 01ptsx >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dmtp contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.923 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #10520-06kxk2 PRED entity: 06kxk2 PRED relation: place_of_birth PRED expected values: 01_d4 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 70): 02_286 (0.14 #19, 0.14 #2838, 0.13 #4951), 030qb3t (0.09 #22546, 0.08 #25366, 0.07 #705), 0cc56 (0.05 #738, 0.04 #3557, 0.02 #13421), 01_d4 (0.05 #11341, 0.05 #3590, 0.05 #12750), 0cr3d (0.05 #4322, 0.04 #5732, 0.04 #94), 04f_d (0.04 #1482, 0.04 #73, 0.03 #778), 0qkcb (0.04 #1701, 0.02 #3111, 0.02 #5224), 01sn3 (0.04 #1558, 0.02 #2263, 0.02 #5081), 094jv (0.04 #61, 0.03 #766, 0.02 #1470), 02h98sm (0.04 #699, 0.03 #1404, 0.02 #2813) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 08433; 03ft8; 01vsps; 07db6x; >> query: (?x7130, 02_286) <- place_of_death(?x7130, ?x1523), written_by(?x3369, ?x7130), profession(?x7130, ?x319), award(?x3369, ?x591) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #11341 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 240 *> proper extension: 03j43; 01h4rj; *> query: (?x7130, 01_d4) <- award_winner(?x601, ?x7130), award(?x7130, ?x746), place_of_death(?x7130, ?x1523), type_of_union(?x7130, ?x566) *> conf = 0.05 ranks of expected_values: 4 EVAL 06kxk2 place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 140.000 140.000 0.143 http://example.org/people/person/place_of_birth #10519-05bnp0 PRED entity: 05bnp0 PRED relation: student! PRED expected values: 02_xgp2 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 20): 019v9k (0.19 #63, 0.18 #189, 0.18 #171), 028dcg (0.14 #232, 0.14 #52, 0.12 #214), 02_xgp2 (0.12 #84, 0.10 #138, 0.10 #264), 0bkj86 (0.10 #152, 0.10 #170, 0.10 #260), 016t_3 (0.10 #166, 0.09 #76, 0.09 #184), 02h4rq6 (0.09 #327, 0.09 #57, 0.08 #147), 04zx3q1 (0.07 #74, 0.06 #146, 0.05 #164), 013zdg (0.05 #61, 0.04 #79, 0.03 #133), 01gkg3 (0.04 #68, 0.02 #230, 0.02 #122), 01rr_d (0.03 #159, 0.03 #339, 0.03 #303) >> Best rule #63 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 04cw0j; 04ns3gy; >> query: (?x123, 019v9k) <- student(?x1368, ?x123), award_winner(?x472, ?x123), student(?x122, ?x123) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #84 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 66 *> proper extension: 017yfz; 04m_kpx; *> query: (?x123, 02_xgp2) <- student(?x1368, ?x123), student(?x8925, ?x123), people(?x913, ?x123) *> conf = 0.12 ranks of expected_values: 3 EVAL 05bnp0 student! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 103.000 0.193 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #10518-01qbl PRED entity: 01qbl PRED relation: role! PRED expected values: 0342h => 88 concepts (62 used for prediction) PRED predicted values (max 10 best out of 84): 0dwt5 (0.88 #4883, 0.87 #5051, 0.87 #4108), 0342h (0.87 #5051, 0.87 #4108, 0.86 #4282), 02k856 (0.86 #910, 0.86 #742, 0.85 #1165), 026t6 (0.86 #910, 0.86 #742, 0.85 #1165), 011k_j (0.86 #910, 0.86 #742, 0.85 #1165), 0l14j_ (0.86 #910, 0.86 #742, 0.85 #1165), 06rvn (0.86 #910, 0.86 #742, 0.85 #1165), 0mbct (0.86 #910, 0.86 #742, 0.85 #1165), 0bxl5 (0.83 #2736, 0.80 #3236, 0.79 #830), 018j2 (0.82 #2379, 0.79 #3473, 0.79 #830) >> Best rule #4883 for best value: >> intensional similarity = 20 >> extensional distance = 34 >> proper extension: 03bx0bm; >> query: (?x1225, ?x4769) <- role(?x1660, ?x1225), role(?x3716, ?x1225), role(?x745, ?x1225), role(?x1225, ?x4769), group(?x4769, ?x7653), ?x7653 = 0b_xm, ?x3716 = 03gvt, role(?x6351, ?x4769), group(?x745, ?x498), role(?x4917, ?x745), role(?x1647, ?x745), role(?x10025, ?x745), role(?x9735, ?x745), role(?x2963, ?x745), ?x6351 = 01vsksr, ?x4917 = 06w7v, origin(?x10025, ?x1248), ?x1647 = 05ljv7, ?x9735 = 01wxdn3, ?x2963 = 0gcs9 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #5051 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 36 *> proper extension: 0dq630k; *> query: (?x1225, ?x4769) <- role(?x1660, ?x1225), role(?x1750, ?x1225), role(?x1574, ?x1225), role(?x745, ?x1225), role(?x1225, ?x8172), role(?x1225, ?x4769), group(?x4769, ?x7653), ?x7653 = 0b_xm, ?x745 = 01vj9c, role(?x8172, ?x2059), role(?x6049, ?x4769), role(?x645, ?x1225), ?x1574 = 0l15bq, role(?x4769, ?x74), role(?x1472, ?x4769), ?x2059 = 0dwr4, ?x6049 = 082brv, ?x1472 = 0319l, group(?x1750, ?x442) *> conf = 0.87 ranks of expected_values: 2 EVAL 01qbl role! 0342h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 62.000 0.880 http://example.org/music/performance_role/track_performances./music/track_contribution/role #10517-029_3 PRED entity: 029_3 PRED relation: influenced_by PRED expected values: 0127xk => 168 concepts (100 used for prediction) PRED predicted values (max 10 best out of 406): 01k9lpl (0.25 #745, 0.14 #9445, 0.12 #5963), 01j7rd (0.25 #489, 0.09 #10442, 0.08 #3098), 014z8v (0.23 #1424, 0.20 #5774, 0.19 #9256), 081lh (0.19 #9155, 0.14 #20, 0.14 #1323), 0p_47 (0.17 #1847, 0.17 #543, 0.15 #9243), 01svq8 (0.17 #2163, 0.17 #859, 0.12 #1293), 01hmk9 (0.17 #655, 0.16 #2393, 0.15 #9355), 012gq6 (0.17 #532, 0.11 #5314, 0.09 #19157), 01wp_jm (0.17 #776, 0.10 #9476, 0.08 #16449), 052hl (0.14 #208, 0.14 #1511, 0.08 #5425) >> Best rule #745 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0mbw0; 01x4r3; 04j_gs; >> query: (?x4065, 01k9lpl) <- influenced_by(?x4065, ?x4066), student(?x2522, ?x4065), program(?x4065, ?x1766) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #19157 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 142 *> proper extension: 0chnf; 0716b6; *> query: (?x4065, ?x3917) <- influenced_by(?x4065, ?x5208), category(?x4065, ?x134), influenced_by(?x5208, ?x3917) *> conf = 0.09 ranks of expected_values: 30 EVAL 029_3 influenced_by 0127xk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 168.000 100.000 0.250 http://example.org/influence/influence_node/influenced_by #10516-02qrv7 PRED entity: 02qrv7 PRED relation: genre PRED expected values: 02kdv5l => 119 concepts (35 used for prediction) PRED predicted values (max 10 best out of 106): 07s9rl0 (0.77 #1073, 0.67 #954, 0.65 #3585), 02kdv5l (0.72 #1552, 0.71 #598, 0.70 #1911), 03bxz7 (0.69 #1127, 0.49 #3639, 0.45 #889), 05p553 (0.67 #362, 0.51 #3948, 0.47 #1196), 02l7c8 (0.50 #1208, 0.45 #1805, 0.43 #493), 0lsxr (0.50 #1440, 0.36 #1321, 0.34 #2876), 07ssc (0.48 #2507, 0.14 #1311), 04t36 (0.44 #3471, 0.38 #3591, 0.36 #841), 03k9fj (0.43 #608, 0.26 #1921, 0.25 #2280), 060__y (0.36 #1329, 0.24 #1448, 0.23 #1090) >> Best rule #1073 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 02725hs; 06q8qh; 011yr9; 05q7874; 04lhc4; 0n_hp; >> query: (?x1261, 07s9rl0) <- genre(?x1261, ?x10229), nominated_for(?x835, ?x1261), genre(?x10796, ?x10229), nominated_for(?x1261, ?x2160), ?x10796 = 0dtzkt, film(?x3181, ?x1261) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1552 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 37 *> proper extension: 0c40vxk; *> query: (?x1261, 02kdv5l) <- genre(?x1261, ?x5104), ?x5104 = 0bkbm, film(?x5251, ?x1261), type_of_union(?x5251, ?x566), gender(?x5251, ?x231), award_winner(?x1793, ?x5251) *> conf = 0.72 ranks of expected_values: 2 EVAL 02qrv7 genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 35.000 0.769 http://example.org/film/film/genre #10515-0k33p PRED entity: 0k33p PRED relation: origin! PRED expected values: 01323p 01mxnvc => 180 concepts (108 used for prediction) PRED predicted values (max 10 best out of 495): 01vn35l (0.23 #35746, 0.19 #9703, 0.18 #36259), 01nn6c (0.19 #9703, 0.18 #36259, 0.17 #40857), 05crg7 (0.17 #1070, 0.08 #4132, 0.06 #2090), 01vsyjy (0.15 #7659), 01vsyg9 (0.15 #7659), 06nv27 (0.12 #4300, 0.11 #2768, 0.11 #7366), 03t9sp (0.12 #49, 0.10 #559, 0.07 #1579), 01vrnsk (0.12 #299, 0.10 #809, 0.06 #2849), 01vsl3_ (0.12 #104, 0.10 #614, 0.06 #2654), 07c0j (0.12 #31, 0.10 #541, 0.06 #2581) >> Best rule #35746 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 01xd9; >> query: (?x9878, ?x7237) <- location(?x7237, ?x9878), instrumentalists(?x75, ?x7237), award_nominee(?x7238, ?x7237), artists(?x474, ?x7237) >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k33p origin! 01mxnvc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 108.000 0.229 http://example.org/music/artist/origin EVAL 0k33p origin! 01323p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 108.000 0.229 http://example.org/music/artist/origin #10514-0g60z PRED entity: 0g60z PRED relation: actor PRED expected values: 05l4yg => 83 concepts (56 used for prediction) PRED predicted values (max 10 best out of 883): 01d8yn (0.39 #25820, 0.36 #10140, 0.36 #27664), 03c6vl (0.39 #25820, 0.36 #10140, 0.36 #27664), 015p37 (0.39 #25820, 0.36 #10140, 0.36 #27664), 037gjc (0.39 #25820, 0.36 #10140, 0.36 #27664), 036c_0 (0.39 #25820, 0.36 #10140, 0.36 #27664), 02rhfsc (0.36 #10140, 0.36 #27664, 0.36 #22128), 047c9l (0.36 #10140, 0.36 #27664, 0.36 #22128), 03wbzp (0.36 #10140, 0.36 #27664, 0.36 #22128), 01hxs4 (0.36 #10140, 0.36 #27664, 0.36 #22128), 030hbp (0.36 #10140, 0.36 #27664, 0.36 #22128) >> Best rule #25820 for best value: >> intensional similarity = 3 >> extensional distance = 174 >> proper extension: 0gfzgl; 01f3p_; 01hvv0; 07wqr6; 0cskb; 0123qq; >> query: (?x337, ?x820) <- nominated_for(?x820, ?x337), genre(?x337, ?x53), type_of_union(?x820, ?x566) >> conf = 0.39 => this is the best rule for 5 predicted values *> Best rule #8296 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 96 *> proper extension: 09v38qj; *> query: (?x337, ?x4408) <- actor(?x337, ?x8431), honored_for(?x2126, ?x337), award_nominee(?x4408, ?x8431) *> conf = 0.10 ranks of expected_values: 36 EVAL 0g60z actor 05l4yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 83.000 56.000 0.391 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10513-01dthg PRED entity: 01dthg PRED relation: major_field_of_study PRED expected values: 02h40lc => 156 concepts (133 used for prediction) PRED predicted values (max 10 best out of 119): 01mkq (0.53 #3797, 0.52 #3919, 0.50 #1845), 04rjg (0.52 #4658, 0.47 #264, 0.42 #2216), 02j62 (0.52 #1617, 0.50 #153, 0.46 #1495), 02lp1 (0.46 #3915, 0.44 #1841, 0.41 #1475), 03g3w (0.44 #1857, 0.39 #1613, 0.38 #2223), 062z7 (0.41 #272, 0.38 #150, 0.35 #3932), 04gb7 (0.34 #1020, 0.33 #1142, 0.25 #166), 01lj9 (0.34 #1870, 0.33 #3822, 0.32 #2236), 04x_3 (0.34 #1490, 0.31 #1856, 0.30 #1978), 05qfh (0.32 #1501, 0.30 #1989, 0.27 #2477) >> Best rule #3797 for best value: >> intensional similarity = 4 >> extensional distance = 130 >> proper extension: 0ks67; >> query: (?x6908, 01mkq) <- institution(?x3437, ?x6908), ?x3437 = 02_xgp2, major_field_of_study(?x6908, ?x742), currency(?x6908, ?x1099) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1590 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 64 *> proper extension: 01j_9c; 02w2bc; 065y4w7; 0288zy; 07tgn; 04rwx; 09kvv; 0bx8pn; 01jsn5; 07vht; ... *> query: (?x6908, 02h40lc) <- contains(?x1310, ?x6908), institution(?x1368, ?x6908), category(?x6908, ?x134), ?x1368 = 014mlp, company(?x5510, ?x6908) *> conf = 0.21 ranks of expected_values: 20 EVAL 01dthg major_field_of_study 02h40lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 156.000 133.000 0.530 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10512-03rbj2 PRED entity: 03rbj2 PRED relation: award! PRED expected values: 02tq2r 021j72 03t8v3 => 36 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2548): 05g3ss (0.73 #3335, 0.70 #60049, 0.69 #36691), 03vrnh (0.73 #3335, 0.70 #60049, 0.69 #36691), 03hfxx (0.73 #3335, 0.69 #36691, 0.69 #43364), 09l3p (0.28 #4550, 0.09 #7885, 0.08 #14556), 028knk (0.24 #3859, 0.10 #7194, 0.08 #10529), 016k6x (0.24 #4789, 0.09 #14795, 0.09 #11459), 018ygt (0.24 #5181, 0.07 #15187, 0.07 #21858), 0d6d2 (0.24 #5690, 0.07 #15696, 0.07 #22367), 0dvld (0.22 #5084, 0.09 #15090, 0.09 #21761), 03ym1 (0.22 #5013, 0.08 #11683, 0.08 #15019) >> Best rule #3335 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03r8tl; 0b6k___; 0b6jkkg; 03r8v_; >> query: (?x4687, ?x7517) <- award(?x111, ?x4687), nominated_for(?x4687, ?x657), award_winner(?x4687, ?x7517), ?x657 = 04jwjq >> conf = 0.73 => this is the best rule for 3 predicted values *> Best rule #2974 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 03r8tl; 0b6k___; 0b6jkkg; 03r8v_; *> query: (?x4687, 021j72) <- award(?x111, ?x4687), nominated_for(?x4687, ?x657), award_winner(?x4687, ?x7517), ?x657 = 04jwjq *> conf = 0.17 ranks of expected_values: 77, 82 EVAL 03rbj2 award! 03t8v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 36.000 18.000 0.729 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03rbj2 award! 021j72 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 36.000 18.000 0.729 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03rbj2 award! 02tq2r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 36.000 18.000 0.729 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10511-01tntf PRED entity: 01tntf PRED relation: school_type PRED expected values: 01rs41 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 19): 01rs41 (0.58 #437, 0.53 #317, 0.53 #245), 05jxkf (0.48 #940, 0.46 #1084, 0.46 #1156), 05pcjw (0.44 #481, 0.43 #337, 0.43 #505), 01_9fk (0.21 #938, 0.21 #1082, 0.21 #1154), 01_srz (0.17 #99, 0.13 #243, 0.12 #339), 07tf8 (0.15 #1161, 0.15 #1089, 0.14 #561), 06cs1 (0.10 #6, 0.08 #54, 0.07 #102), 04qbv (0.07 #112, 0.03 #424, 0.03 #496), 0bwd5 (0.04 #403, 0.04 #739, 0.04 #571), 04399 (0.04 #254, 0.04 #1094, 0.04 #302) >> Best rule #437 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 031n8c; 032d52; >> query: (?x10178, 01rs41) <- contains(?x1227, ?x10178), currency(?x10178, ?x170), religion(?x1227, ?x109), state_province_region(?x99, ?x1227) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tntf school_type 01rs41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.579 http://example.org/education/educational_institution/school_type #10510-01750n PRED entity: 01750n PRED relation: artists PRED expected values: 03h_yfh => 69 concepts (27 used for prediction) PRED predicted values (max 10 best out of 3226): 019f9z (0.67 #12565, 0.61 #15832, 0.60 #6038), 07s3vqk (0.67 #11974, 0.60 #5447, 0.50 #15241), 0407f (0.67 #12245, 0.60 #5718, 0.50 #15512), 016376 (0.67 #12927, 0.60 #6400, 0.44 #16194), 020_4z (0.67 #12906, 0.60 #6379, 0.39 #16173), 01kx_81 (0.67 #12049, 0.60 #5522, 0.39 #15316), 012vd6 (0.67 #12446, 0.44 #15713, 0.40 #5919), 02jq1 (0.60 #5931, 0.56 #12458, 0.39 #15725), 01wd9lv (0.60 #6016, 0.56 #12543, 0.39 #15810), 015xp4 (0.60 #5901, 0.56 #12428, 0.33 #15695) >> Best rule #12565 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 016cjb; >> query: (?x14481, 019f9z) <- artists(?x14481, ?x8362), parent_genre(?x9043, ?x14481), ?x8362 = 01wg25j, artists(?x9043, ?x3168), role(?x3168, ?x2309), ?x2309 = 06ncr >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #11962 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 7 *> proper extension: 017323; *> query: (?x14481, ?x158) <- parent_genre(?x14481, ?x7440), ?x7440 = 0155w, artists(?x14481, ?x8362), role(?x8362, ?x2764), place_of_birth(?x8362, ?x13996), role(?x158, ?x2764), group(?x2764, ?x3207), people(?x2510, ?x8362), role(?x74, ?x2764) *> conf = 0.08 ranks of expected_values: 882 EVAL 01750n artists 03h_yfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 69.000 27.000 0.667 http://example.org/music/genre/artists #10509-0hz6mv2 PRED entity: 0hz6mv2 PRED relation: film_crew_role PRED expected values: 0ch6mp2 01pvkk => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 30): 09zzb8 (0.75 #391, 0.70 #352, 0.70 #704), 0ch6mp2 (0.71 #165, 0.70 #869, 0.70 #712), 02r96rf (0.71 #238, 0.69 #1101, 0.69 #1140), 09vw2b7 (0.60 #359, 0.58 #398, 0.57 #164), 01vx2h (0.57 #248, 0.43 #209, 0.40 #482), 01pvkk (0.43 #132, 0.41 #796, 0.41 #679), 0dxtw (0.38 #286, 0.35 #1255, 0.33 #442), 02rh1dz (0.35 #1255, 0.20 #51, 0.14 #207), 0215hd (0.25 #529, 0.21 #607, 0.18 #882), 02ynfr (0.25 #409, 0.16 #1633, 0.14 #1830) >> Best rule #391 for best value: >> intensional similarity = 9 >> extensional distance = 10 >> proper extension: 03hp2y1; >> query: (?x9565, 09zzb8) <- country(?x9565, ?x512), country(?x9565, ?x94), language(?x9565, ?x254), film(?x1104, ?x9565), ?x1104 = 016tw3, genre(?x9565, ?x1014), ?x94 = 09c7w0, ?x512 = 07ssc, executive_produced_by(?x9565, ?x12825) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 5 *> proper extension: 0dgst_d; *> query: (?x9565, 0ch6mp2) <- country(?x9565, ?x94), film_release_region(?x9565, ?x3699), film_release_region(?x9565, ?x1353), film_release_region(?x9565, ?x583), film_release_region(?x9565, ?x304), film_release_region(?x9565, ?x87), ?x87 = 05r4w, ?x304 = 0d0vqn, ?x1353 = 035qy, genre(?x9565, ?x1014), ?x3699 = 012wgb, executive_produced_by(?x9565, ?x12825), ?x583 = 015fr *> conf = 0.71 ranks of expected_values: 2, 6 EVAL 0hz6mv2 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 102.000 102.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0hz6mv2 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10508-0k049 PRED entity: 0k049 PRED relation: contains! PRED expected values: 030qb3t => 134 concepts (129 used for prediction) PRED predicted values (max 10 best out of 381): 02jx1 (0.38 #72355, 0.36 #30369, 0.30 #19650), 03h64 (0.38 #72355, 0.21 #41982), 07ssc (0.36 #30369, 0.30 #19650, 0.22 #3603), 0chghy (0.36 #30369, 0.30 #19650, 0.11 #3594), 0d060g (0.36 #30369, 0.30 #19650, 0.11 #41100), 0f8l9c (0.36 #30369, 0.30 #19650, 0.09 #5404), 06bnz (0.36 #30369, 0.30 #19650, 0.04 #13500), 0h7x (0.36 #30369, 0.30 #19650, 0.02 #25992), 03gj2 (0.36 #30369, 0.30 #19650), 02_286 (0.33 #42, 0.20 #1828, 0.09 #97368) >> Best rule #72355 for best value: >> intensional similarity = 4 >> extensional distance = 186 >> proper extension: 0k3p; >> query: (?x191, ?x94) <- citytown(?x574, ?x191), location(?x3536, ?x191), film(?x3536, ?x428), nationality(?x3536, ?x94) >> conf = 0.38 => this is the best rule for 2 predicted values *> Best rule #4564 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 0qjfl; *> query: (?x191, 030qb3t) <- contains(?x2949, ?x191), contains(?x1227, ?x191), ?x1227 = 01n7q, ?x2949 = 0kpys *> conf = 0.21 ranks of expected_values: 14 EVAL 0k049 contains! 030qb3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 134.000 129.000 0.379 http://example.org/location/location/contains #10507-0214km PRED entity: 0214km PRED relation: role! PRED expected values: 01m3x5p 01mwsnc => 61 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1157): 050z2 (0.73 #11485, 0.68 #15585, 0.67 #10123), 0137g1 (0.73 #11420, 0.67 #5083, 0.60 #4180), 01wxdn3 (0.67 #6723, 0.67 #5366, 0.60 #4463), 082brv (0.67 #10199, 0.67 #6581, 0.60 #4321), 04bpm6 (0.67 #6397, 0.64 #11377, 0.62 #8659), 05qhnq (0.67 #5266, 0.57 #7981, 0.55 #11603), 0285c (0.67 #5959, 0.50 #8219, 0.50 #2795), 01w272y (0.67 #6476, 0.50 #2861, 0.41 #3161), 03ryks (0.60 #4353, 0.50 #13417, 0.50 #8875), 01vs4ff (0.60 #4356, 0.50 #8878, 0.50 #8425) >> Best rule #11485 for best value: >> intensional similarity = 24 >> extensional distance = 9 >> proper extension: 0dwsp; >> query: (?x8014, 050z2) <- role(?x4917, ?x8014), role(?x4311, ?x8014), role(?x3215, ?x8014), role(?x1212, ?x8014), role(?x868, ?x8014), ?x1212 = 07xzm, role(?x3657, ?x8014), role(?x4917, ?x7033), role(?x4917, ?x2059), role(?x4917, ?x1482), ?x7033 = 0gkd1, role(?x1399, ?x4311), artists(?x302, ?x3657), role(?x4429, ?x4311), role(?x8282, ?x4917), ?x3215 = 0bxl5, ?x4429 = 0g33q, artist(?x2149, ?x3657), role(?x885, ?x4311), ?x2059 = 0dwr4, ?x1482 = 02g9p4, ?x8282 = 01q_wyj, ?x868 = 0dwvl, instrumentalists(?x4311, ?x562) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #3161 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 2 *> proper extension: 01vj9c; *> query: (?x8014, ?x654) <- role(?x4311, ?x8014), role(?x1212, ?x8014), role(?x894, ?x8014), role(?x745, ?x8014), role(?x614, ?x8014), ?x1212 = 07xzm, ?x4311 = 01xqw, role(?x2747, ?x8014), role(?x925, ?x8014), ?x614 = 0mkg, award_winner(?x342, ?x2747), music(?x924, ?x925), role(?x745, ?x5926), role(?x745, ?x4769), role(?x745, ?x4425), role(?x10025, ?x745), role(?x9735, ?x745), role(?x654, ?x745), ?x4769 = 0dwt5, ?x10025 = 02yygk, ?x5926 = 0cfdd, ?x9735 = 01wxdn3, award(?x2747, ?x2561), group(?x745, ?x498), ?x894 = 03m5k, ?x342 = 01s695, ?x4425 = 0979zs *> conf = 0.41 ranks of expected_values: 112, 664 EVAL 0214km role! 01mwsnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 61.000 53.000 0.727 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0214km role! 01m3x5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 53.000 0.727 http://example.org/music/artist/track_contributions./music/track_contribution/role #10506-01z_g6 PRED entity: 01z_g6 PRED relation: type_of_union PRED expected values: 04ztj => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.79 #25, 0.76 #37, 0.74 #17), 01g63y (0.20 #50, 0.16 #70, 0.16 #58), 0jgjn (0.01 #24) >> Best rule #25 for best value: >> intensional similarity = 2 >> extensional distance = 126 >> proper extension: 05fg2; >> query: (?x5065, 04ztj) <- award_winner(?x686, ?x5065), student(?x1368, ?x5065) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01z_g6 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.789 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10505-025rxjq PRED entity: 025rxjq PRED relation: film! PRED expected values: 09_gdc => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.14 #8, 0.14 #3, 0.05 #25), 09_gdc (0.14 #2, 0.02 #177, 0.02 #47), 01kyvx (0.01 #471, 0.01 #456, 0.01 #461) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 011x_4; 014bpd; 0g_zyp; >> query: (?x7819, 01pb34) <- film(?x7624, ?x7819), genre(?x7819, ?x53), ?x7624 = 01pjr7, nominated_for(?x384, ?x7819) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02x8fs; 02825cv; *> query: (?x7819, 09_gdc) <- film(?x7624, ?x7819), genre(?x7819, ?x53), ?x7624 = 01pjr7, country(?x7819, ?x94) *> conf = 0.14 ranks of expected_values: 2 EVAL 025rxjq film! 09_gdc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.143 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #10504-05bnx3j PRED entity: 05bnx3j PRED relation: producer_type PRED expected values: 0ckd1 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.77 #4, 0.70 #2, 0.70 #9) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 03fykz; 0bvg70; 03ktjq; 02f9wb; 01wd9lv; 023jq1; 055sjw; 02t_8z; >> query: (?x12500, 0ckd1) <- award_winner(?x6673, ?x12500), program(?x12500, ?x1434), student(?x6501, ?x12500) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05bnx3j producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.765 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #10503-05f0r8 PRED entity: 05f0r8 PRED relation: place_of_burial PRED expected values: 018mmj => 79 concepts (78 used for prediction) PRED predicted values (max 10 best out of 11): 018mmj (0.07 #106, 0.07 #138, 0.06 #234), 018mm4 (0.06 #136, 0.06 #200, 0.06 #104), 018mmw (0.04 #80, 0.04 #112, 0.03 #176), 0lbp_ (0.04 #47, 0.02 #207, 0.01 #239), 01f38z (0.03 #285, 0.02 #411, 0.02 #252), 01n7q (0.02 #131, 0.02 #195, 0.02 #227), 018mrd (0.02 #54, 0.01 #660, 0.01 #693), 030qb3t (0.02 #382), 0nb1s (0.02 #286, 0.01 #412), 018mlg (0.01 #502, 0.01 #629, 0.01 #183) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 106 >> proper extension: 02pp_q_; 08433; 057d89; 01g4zr; 01xcqc; 03ft8; 01c58j; 0177s6; 01t07j; 014dq7; ... >> query: (?x13945, 018mmj) <- profession(?x13945, ?x987), place_of_death(?x13945, ?x1523), people(?x268, ?x13945), ?x987 = 0dxtg >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05f0r8 place_of_burial 018mmj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 78.000 0.074 http://example.org/people/deceased_person/place_of_burial #10502-027b9ly PRED entity: 027b9ly PRED relation: award_winner PRED expected values: 06pj8 0js9s 01ts_3 => 45 concepts (13 used for prediction) PRED predicted values (max 10 best out of 897): 0js9s (0.28 #3904, 0.20 #1445, 0.07 #8823), 0151w_ (0.28 #2647, 0.08 #5106, 0.07 #7566), 06pj8 (0.28 #2890, 0.07 #7809, 0.06 #10270), 0693l (0.23 #3129, 0.06 #8048, 0.06 #10509), 01f8ld (0.23 #3114, 0.06 #8033, 0.06 #10494), 01d8yn (0.20 #805, 0.10 #3264, 0.03 #5723), 0c00lh (0.20 #1207, 0.10 #3666, 0.03 #6125), 02bfxb (0.20 #737, 0.10 #3196, 0.03 #5655), 01q415 (0.20 #459, 0.10 #2918, 0.03 #5377), 0gnbw (0.20 #1604, 0.08 #24604, 0.07 #4918) >> Best rule #3904 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 0cc8l6d; >> query: (?x5516, 0js9s) <- nominated_for(?x5516, ?x4329), award_winner(?x4329, ?x4328), award_winner(?x5516, ?x9149), award_winner(?x1198, ?x9149), ?x1198 = 02pqp12 >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 27 EVAL 027b9ly award_winner 01ts_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 45.000 13.000 0.275 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027b9ly award_winner 0js9s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 13.000 0.275 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027b9ly award_winner 06pj8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 45.000 13.000 0.275 http://example.org/award/award_category/winners./award/award_honor/award_winner #10501-012fvq PRED entity: 012fvq PRED relation: campuses! PRED expected values: 012fvq => 204 concepts (113 used for prediction) PRED predicted values (max 10 best out of 309): 0qlnr (0.14 #318, 0.11 #864, 0.06 #2503), 0pspl (0.14 #97, 0.11 #643, 0.06 #2282), 01k2wn (0.14 #19, 0.11 #565, 0.03 #4389), 01swxv (0.14 #74, 0.11 #620, 0.02 #7174), 02tz9z (0.14 #475, 0.11 #1021, 0.02 #11399), 01yqqv (0.07 #1437, 0.06 #1983, 0.06 #2530), 01_s9q (0.07 #1278, 0.06 #1824, 0.06 #2371), 04rwx (0.07 #1127, 0.06 #1673, 0.05 #2766), 0jpn8 (0.07 #1417, 0.06 #1963, 0.05 #3056), 01rgn3 (0.07 #1385, 0.06 #1931, 0.05 #3024) >> Best rule #318 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 01k2wn; 01swxv; 02tz9z; >> query: (?x3576, 0qlnr) <- major_field_of_study(?x3576, ?x254), currency(?x3576, ?x170), colors(?x3576, ?x3364), school_type(?x3576, ?x1044), ?x254 = 02h40lc >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #45902 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 202 *> proper extension: 03q6zc; *> query: (?x3576, ?x331) <- institution(?x1368, ?x3576), state_province_region(?x3576, ?x3670), ?x1368 = 014mlp, country(?x3670, ?x94), contains(?x3670, ?x331) *> conf = 0.01 ranks of expected_values: 188 EVAL 012fvq campuses! 012fvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 204.000 113.000 0.143 http://example.org/education/educational_institution/campuses #10500-0mrf1 PRED entity: 0mrf1 PRED relation: contains! PRED expected values: 07b_l => 100 concepts (54 used for prediction) PRED predicted values (max 10 best out of 112): 04_1l0v (0.60 #451, 0.59 #13919, 0.40 #17510), 09c7w0 (0.60 #3, 0.55 #13471, 0.46 #40422), 03v0t (0.29 #15497, 0.29 #1130, 0.27 #8316), 04ych (0.29 #961, 0.14 #6351, 0.12 #10840), 07b_l (0.27 #42214, 0.26 #9203, 0.26 #11896), 0msck (0.27 #42214, 0.14 #1794, 0.14 #37720), 05fjf (0.20 #22819, 0.12 #29104, 0.12 #27309), 01n7q (0.16 #32404, 0.15 #44991, 0.15 #42293), 04ykg (0.16 #4574, 0.14 #5473, 0.12 #1879), 02qkt (0.15 #37169, 0.12 #39867) >> Best rule #451 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 04ly1; 0vbk; 05mph; >> query: (?x13388, 04_1l0v) <- time_zones(?x13388, ?x1638), adjoins(?x14231, ?x13388), contains(?x14231, ?x10465), ?x1638 = 02fqwt, ?x10465 = 0_z91, time_zones(?x14231, ?x1638) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #42214 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 207 *> proper extension: 0n6mc; *> query: (?x13388, ?x3634) <- source(?x13388, ?x958), adjoins(?x14231, ?x13388), county(?x10465, ?x14231), ?x958 = 0jbk9, contains(?x3634, ?x10465), second_level_divisions(?x94, ?x14231), ?x94 = 09c7w0 *> conf = 0.27 ranks of expected_values: 5 EVAL 0mrf1 contains! 07b_l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 100.000 54.000 0.600 http://example.org/location/location/contains #10499-01lfvj PRED entity: 01lfvj PRED relation: time_zones PRED expected values: 02llzg => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 7): 02llzg (0.50 #17, 0.46 #288, 0.45 #248), 02hcv8 (0.27 #133, 0.25 #709, 0.24 #198), 02lcqs (0.22 #96, 0.21 #109, 0.20 #174), 02fqwt (0.20 #196, 0.16 #79, 0.12 #341), 02hczc (0.07 #80, 0.07 #197, 0.05 #316), 03bdv (0.04 #214, 0.03 #777, 0.03 #556), 03plfd (0.01 #781) >> Best rule #17 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0hb37; >> query: (?x1356, 02llzg) <- category(?x1356, ?x134), contains(?x10706, ?x1356), contains(?x205, ?x1356), ?x10706 = 0bzty, contains(?x205, ?x14190), ?x14190 = 0d8zt, administrative_parent(?x205, ?x551) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lfvj time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.500 http://example.org/location/location/time_zones #10498-02mc79 PRED entity: 02mc79 PRED relation: award_nominee! PRED expected values: 025hwq => 82 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1018): 02tf1y (0.35 #9331, 0.18 #18661, 0.18 #23327), 086k8 (0.20 #14058, 0.06 #46712, 0.05 #42046), 025hwq (0.20 #1758, 0.15 #104956, 0.13 #48984), 07myb2 (0.20 #2172, 0.15 #104956, 0.13 #48984), 05qd_ (0.17 #14175, 0.15 #104956, 0.13 #48984), 016tt2 (0.15 #104956, 0.13 #14107, 0.13 #48984), 024rgt (0.15 #104956, 0.13 #48984, 0.11 #14547), 03rwz3 (0.15 #104956, 0.13 #48984, 0.11 #48983), 031rq5 (0.15 #104956, 0.13 #48984, 0.11 #48983), 02mc79 (0.15 #104956, 0.13 #48984, 0.10 #1791) >> Best rule #9331 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 01j5ts; 023tp8; 0p_pd; 0159h6; 02kxbwx; 032_jg; 0151w_; 0h1mt; 0sz28; 01kvqc; ... >> query: (?x8071, ?x8897) <- profession(?x8071, ?x319), sibling(?x8897, ?x8071), award_nominee(?x8071, ?x541) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1758 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01fwpt; 030dx5; 045g4l; 02cvp8; 0p_r5; *> query: (?x8071, 025hwq) <- profession(?x8071, ?x1146), sibling(?x8897, ?x8071), ?x1146 = 018gz8 *> conf = 0.20 ranks of expected_values: 3 EVAL 02mc79 award_nominee! 025hwq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 49.000 0.354 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10497-01gtcq PRED entity: 01gtcq PRED relation: legislative_sessions! PRED expected values: 042fk => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 27): 02hy5d (0.60 #538, 0.50 #191, 0.45 #478), 024_vw (0.60 #543, 0.50 #196, 0.45 #483), 0rlz (0.57 #547, 0.55 #487, 0.53 #516), 0835q (0.57 #547, 0.55 #487, 0.53 #516), 042fk (0.57 #547, 0.55 #487, 0.53 #516), 01mvpv (0.57 #547, 0.55 #487, 0.53 #516), 042f1 (0.57 #547, 0.55 #487, 0.53 #516), 0bymv (0.53 #521, 0.50 #174, 0.36 #461), 012v1t (0.53 #529, 0.50 #182, 0.36 #469), 021sv1 (0.53 #520, 0.44 #725, 0.40 #789) >> Best rule #538 for best value: >> intensional similarity = 37 >> extensional distance = 13 >> proper extension: 02bn_p; 02bqn1; 02bp37; 02bqmq; 02cg7g; >> query: (?x5252, 02hy5d) <- legislative_sessions(?x7973, ?x5252), legislative_sessions(?x6021, ?x5252), legislative_sessions(?x3669, ?x5252), legislative_sessions(?x2860, ?x3669), district_represented(?x5252, ?x7058), district_represented(?x5252, ?x4622), district_represented(?x5252, ?x2020), district_represented(?x5252, ?x1906), district_represented(?x5252, ?x1767), district_represented(?x5252, ?x760), legislative_sessions(?x3973, ?x6021), ?x1767 = 04rrd, ?x4622 = 04tgp, district_represented(?x3669, ?x1025), jurisdiction_of_office(?x900, ?x760), legislative_sessions(?x5401, ?x5252), place_of_birth(?x2543, ?x760), religion(?x760, ?x109), contains(?x760, ?x5837), legislative_sessions(?x5742, ?x7973), location(?x5479, ?x760), location(?x120, ?x760), contains(?x7058, ?x7417), ?x1906 = 04rrx, currency(?x7417, ?x170), peers(?x2835, ?x120), ?x2020 = 05k7sb, time_zones(?x760, ?x2674), adjoins(?x7058, ?x6842), instrumentalists(?x1332, ?x120), profession(?x120, ?x1183), artist(?x4483, ?x5479), teams(?x5837, ?x3798), award_nominee(?x527, ?x5479), role(?x120, ?x214), religion(?x7058, ?x962), profession(?x5479, ?x131) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #547 for first EXPECTED value: *> intensional similarity = 37 *> extensional distance = 13 *> proper extension: 02bn_p; 02bqn1; 02bp37; 02bqmq; 02cg7g; *> query: (?x5252, ?x5742) <- legislative_sessions(?x7973, ?x5252), legislative_sessions(?x6021, ?x5252), legislative_sessions(?x3669, ?x5252), legislative_sessions(?x2860, ?x3669), district_represented(?x5252, ?x7058), district_represented(?x5252, ?x4622), district_represented(?x5252, ?x2020), district_represented(?x5252, ?x1906), district_represented(?x5252, ?x1767), district_represented(?x5252, ?x760), legislative_sessions(?x3973, ?x6021), ?x1767 = 04rrd, ?x4622 = 04tgp, district_represented(?x3669, ?x1025), jurisdiction_of_office(?x900, ?x760), legislative_sessions(?x5401, ?x5252), place_of_birth(?x2543, ?x760), religion(?x760, ?x109), contains(?x760, ?x5837), legislative_sessions(?x5742, ?x7973), location(?x5479, ?x760), location(?x120, ?x760), contains(?x7058, ?x7417), ?x1906 = 04rrx, currency(?x7417, ?x170), peers(?x2835, ?x120), ?x2020 = 05k7sb, time_zones(?x760, ?x2674), adjoins(?x7058, ?x6842), instrumentalists(?x1332, ?x120), profession(?x120, ?x1183), artist(?x4483, ?x5479), teams(?x5837, ?x3798), award_nominee(?x527, ?x5479), role(?x120, ?x214), religion(?x7058, ?x962), profession(?x5479, ?x131) *> conf = 0.57 ranks of expected_values: 5 EVAL 01gtcq legislative_sessions! 042fk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 29.000 29.000 0.600 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #10496-0c8br PRED entity: 0c8br PRED relation: nationality PRED expected values: 09c7w0 => 135 concepts (80 used for prediction) PRED predicted values (max 10 best out of 29): 09c7w0 (0.86 #2015, 0.83 #2719, 0.82 #2919), 059rby (0.40 #6951), 02jx1 (0.23 #633, 0.20 #133, 0.19 #734), 07ssc (0.22 #716, 0.14 #1827, 0.14 #615), 0f8l9c (0.10 #122, 0.06 #322, 0.05 #522), 03rt9 (0.09 #613, 0.04 #714, 0.03 #2329), 0d060g (0.07 #1819, 0.07 #7460, 0.06 #7259), 0chghy (0.06 #210, 0.04 #812, 0.03 #1215), 06m_5 (0.06 #383, 0.06 #483, 0.05 #583), 012m_ (0.06 #391, 0.01 #1398) >> Best rule #2015 for best value: >> intensional similarity = 4 >> extensional distance = 131 >> proper extension: 0blt6; 028r4y; 06jrhz; 06sn8m; 0ccqd7; 0bq4j6; 03c9pqt; 02k76g; >> query: (?x8034, 09c7w0) <- profession(?x8034, ?x353), student(?x7278, ?x8034), place_of_birth(?x8034, ?x739), ?x739 = 02_286 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c8br nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 80.000 0.865 http://example.org/people/person/nationality #10495-083q7 PRED entity: 083q7 PRED relation: organizations_founded PRED expected values: 04k4l => 172 concepts (158 used for prediction) PRED predicted values (max 10 best out of 113): 01g7_r (0.45 #3948, 0.08 #9329, 0.08 #9835), 05zl0 (0.45 #3948, 0.08 #9329, 0.08 #9835), 01k2wn (0.45 #3948, 0.08 #9329, 0.08 #9835), 01rz1 (0.30 #3149, 0.17 #4768, 0.03 #6089), 034h1h (0.30 #4792, 0.03 #4185), 05f4p (0.20 #680, 0.20 #478, 0.17 #1085), 01v9b1 (0.20 #705, 0.20 #503, 0.17 #1110), 0_00 (0.20 #693, 0.14 #1401, 0.05 #3022), 03lb_v (0.18 #4859), 02_l9 (0.18 #4859) >> Best rule #3948 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 01vvydl; 03qncl3; >> query: (?x1159, ?x1103) <- profession(?x1159, ?x2606), company(?x1159, ?x1103), organizations_founded(?x1159, ?x13997), nationality(?x1159, ?x94) >> conf = 0.45 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 083q7 organizations_founded 04k4l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 158.000 0.449 http://example.org/organization/organization_founder/organizations_founded #10494-03gkn5 PRED entity: 03gkn5 PRED relation: company PRED expected values: 03ksy => 177 concepts (70 used for prediction) PRED predicted values (max 10 best out of 166): 01w5m (0.43 #3477, 0.41 #3837, 0.18 #4018), 065y4w7 (0.40 #914, 0.40 #733, 0.12 #3081), 07wrz (0.36 #4005, 0.24 #4730, 0.20 #5633), 09c7w0 (0.33 #1, 0.26 #4154, 0.25 #543), 01rs59 (0.33 #126, 0.25 #668, 0.20 #1209), 05zl0 (0.24 #4601, 0.14 #4058, 0.11 #6952), 07tds (0.20 #790, 0.17 #1512, 0.12 #2053), 01stzp (0.19 #2692, 0.06 #9944, 0.05 #3596), 09f2j (0.18 #3142, 0.09 #4044, 0.09 #4587), 06rq1k (0.17 #5626, 0.06 #9789, 0.05 #3276) >> Best rule #3477 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 04xm_; >> query: (?x3520, 01w5m) <- company(?x3520, ?x5621), major_field_of_study(?x5621, ?x5179), student(?x5621, ?x525), ?x5179 = 04gb7, currency(?x5621, ?x170) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4562 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 083q7; 01d494; 0d4jl; 0dzkq; 041c4; 0bv7t; 0x3r3; 05fyss; 06crk; 03f47xl; ... *> query: (?x3520, 03ksy) <- company(?x3520, ?x5621), major_field_of_study(?x5621, ?x254), student(?x5621, ?x525), school(?x3333, ?x5621), draft(?x3333, ?x1161) *> conf = 0.15 ranks of expected_values: 12 EVAL 03gkn5 company 03ksy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 177.000 70.000 0.429 http://example.org/people/person/employment_history./business/employment_tenure/company #10493-01wgfp6 PRED entity: 01wgfp6 PRED relation: profession PRED expected values: 0nbcg => 156 concepts (93 used for prediction) PRED predicted values (max 10 best out of 75): 0dz3r (0.67 #590, 0.58 #1326, 0.55 #2209), 016z4k (0.57 #1475, 0.50 #592, 0.49 #1328), 01d_h8 (0.54 #1918, 0.40 #3982, 0.40 #4424), 0nbcg (0.52 #6658, 0.51 #6806, 0.50 #619), 0n1h (0.50 #12, 0.37 #12677, 0.28 #1483), 01c72t (0.43 #2082, 0.29 #8867, 0.28 #8128), 0db79 (0.37 #12677), 0dxtg (0.37 #12543, 0.30 #1926, 0.29 #4432), 03gjzk (0.36 #1927, 0.30 #4433, 0.29 #3991), 02jknp (0.28 #1920, 0.20 #12537, 0.18 #12685) >> Best rule #590 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0770cd; >> query: (?x5901, 0dz3r) <- award(?x5901, ?x3835), place_of_birth(?x5901, ?x2552), ?x3835 = 01cky2 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #6658 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 402 *> proper extension: 0f0y8; 0c9d9; 0c7ct; 06y9c2; 01q7cb_; 01w923; 012zng; 0zjpz; 09prnq; 02jg92; ... *> query: (?x5901, 0nbcg) <- artist(?x2149, ?x5901), type_of_union(?x5901, ?x566), profession(?x5901, ?x1032), artists(?x671, ?x5901) *> conf = 0.52 ranks of expected_values: 4 EVAL 01wgfp6 profession 0nbcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 156.000 93.000 0.667 http://example.org/people/person/profession #10492-02scbv PRED entity: 02scbv PRED relation: honored_for PRED expected values: 01mszz => 143 concepts (88 used for prediction) PRED predicted values (max 10 best out of 148): 06ybb1 (0.84 #2352, 0.83 #1881, 0.82 #3608), 0cwfgz (0.84 #2352, 0.83 #1881, 0.82 #3608), 07b1gq (0.84 #2352, 0.83 #1881, 0.82 #3608), 01mszz (0.71 #108, 0.59 #3136, 0.57 #3925), 01771z (0.67 #471, 0.66 #1566, 0.59 #3136), 02scbv (0.59 #3136, 0.57 #125, 0.57 #3925), 0cf08 (0.17 #6284, 0.14 #6285, 0.04 #13007), 0dfw0 (0.17 #401, 0.12 #559, 0.06 #1654), 08984j (0.14 #6285), 0dtfn (0.12 #344, 0.12 #502, 0.06 #814) >> Best rule #2352 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 0dr_9t7; 0bj25; >> query: (?x6918, ?x188) <- honored_for(?x188, ?x6918), genre(?x6918, ?x225), nominated_for(?x154, ?x6918), film_release_region(?x6918, ?x94) >> conf = 0.84 => this is the best rule for 3 predicted values *> Best rule #108 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 01771z; 0q9sg; 04cbbz; 0cwfgz; 06c0ns; *> query: (?x6918, 01mszz) <- honored_for(?x6918, ?x188), genre(?x6918, ?x225), nominated_for(?x2165, ?x6918), ?x2165 = 06ybb1 *> conf = 0.71 ranks of expected_values: 4 EVAL 02scbv honored_for 01mszz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 143.000 88.000 0.835 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #10491-03sww PRED entity: 03sww PRED relation: artist! PRED expected values: 03rhqg => 95 concepts (63 used for prediction) PRED predicted values (max 10 best out of 99): 033hn8 (0.25 #13, 0.11 #4017, 0.11 #2636), 01cf93 (0.25 #58, 0.09 #334, 0.07 #4062), 0mzkr (0.25 #25, 0.08 #2234, 0.07 #2924), 04fcjt (0.25 #29, 0.06 #719, 0.03 #1547), 01t04r (0.25 #64, 0.05 #4068, 0.05 #3515), 0gh4g0 (0.25 #6, 0.01 #1386, 0.01 #6082), 015_1q (0.21 #2228, 0.20 #2918, 0.20 #4990), 0g768 (0.17 #313, 0.15 #727, 0.12 #589), 03rhqg (0.16 #3466, 0.15 #4986, 0.15 #4019), 03mp8k (0.15 #204, 0.11 #618, 0.09 #756) >> Best rule #13 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01jcxwp; 0bsj9; >> query: (?x4877, 033hn8) <- artists(?x13938, ?x4877), artists(?x2249, ?x4877), ?x13938 = 04f73rc, ?x2249 = 03lty, award(?x4877, ?x2456) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3466 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 453 *> proper extension: 0326tc; 04m2zj; *> query: (?x4877, 03rhqg) <- artists(?x13938, ?x4877), parent_genre(?x12808, ?x13938), ?x12808 = 03fpx *> conf = 0.16 ranks of expected_values: 9 EVAL 03sww artist! 03rhqg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 95.000 63.000 0.250 http://example.org/music/record_label/artist #10490-043tg PRED entity: 043tg PRED relation: interests PRED expected values: 0x0w => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 15): 02jcc (0.59 #181, 0.51 #256, 0.42 #196), 04s0m (0.41 #234, 0.41 #264, 0.37 #204), 05qt0 (0.36 #157, 0.33 #7, 0.32 #232), 05r79 (0.32 #229, 0.29 #184, 0.27 #259), 0x0w (0.24 #192, 0.22 #267, 0.16 #207), 0gt_hv (0.20 #30, 0.18 #240, 0.18 #195), 04rjg (0.14 #170, 0.09 #230, 0.08 #260), 06ms6 (0.11 #646, 0.10 #707, 0.07 #168), 05qfh (0.11 #201, 0.08 #261, 0.06 #186), 097df (0.07 #178, 0.06 #193, 0.05 #268) >> Best rule #181 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0399p; >> query: (?x8232, 02jcc) <- interests(?x8232, ?x3561), influenced_by(?x8232, ?x3712), peers(?x8232, ?x7495), influenced_by(?x916, ?x7495) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #192 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 0399p; *> query: (?x8232, 0x0w) <- interests(?x8232, ?x3561), influenced_by(?x8232, ?x3712), peers(?x8232, ?x7495), influenced_by(?x916, ?x7495) *> conf = 0.24 ranks of expected_values: 5 EVAL 043tg interests 0x0w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 164.000 164.000 0.588 http://example.org/user/alexander/philosophy/philosopher/interests #10489-01b66t PRED entity: 01b66t PRED relation: nominated_for! PRED expected values: 026dg51 => 77 concepts (56 used for prediction) PRED predicted values (max 10 best out of 926): 026n998 (0.80 #32552, 0.80 #127895, 0.79 #125569), 02kmx6 (0.66 #53478, 0.65 #60452, 0.10 #3372), 033m23 (0.61 #32553, 0.56 #44176, 0.52 #65104), 070w7s (0.60 #16275, 0.59 #20927, 0.58 #11623), 02_2v2 (0.60 #16275, 0.59 #20927, 0.58 #11623), 057d89 (0.60 #16275, 0.59 #20927, 0.58 #11623), 026dg51 (0.60 #16275, 0.59 #20927, 0.58 #11623), 026dd2b (0.40 #4217, 0.33 #1892, 0.26 #20928), 025vldk (0.33 #1550, 0.26 #20928, 0.24 #16276), 04gtdnh (0.33 #892, 0.26 #20928, 0.24 #16276) >> Best rule #32552 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 0ddd0gc; 01b66d; 06y_n; 01kt_j; 01fszq; >> query: (?x4721, ?x415) <- award_winner(?x4721, ?x415), actor(?x4721, ?x7835), producer_type(?x4721, ?x632) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #16275 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 0cpz4k; *> query: (?x4721, ?x2176) <- country_of_origin(?x4721, ?x94), tv_program(?x2176, ?x4721), award_nominee(?x438, ?x2176) *> conf = 0.60 ranks of expected_values: 7 EVAL 01b66t nominated_for! 026dg51 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 56.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10488-06q1r PRED entity: 06q1r PRED relation: exported_to PRED expected values: 0154j => 286 concepts (258 used for prediction) PRED predicted values (max 10 best out of 70): 0j4b (0.36 #621, 0.33 #40, 0.29 #1256), 06s_2 (0.33 #51, 0.17 #419, 0.12 #1267), 06tw8 (0.30 #566, 0.21 #2465, 0.20 #3417), 07fsv (0.27 #622, 0.20 #1521, 0.15 #1573), 0h3y (0.25 #691, 0.23 #1009, 0.22 #1747), 0jdx (0.25 #729, 0.23 #1047, 0.17 #1785), 07ssc (0.21 #3231, 0.20 #217, 0.17 #3757), 04sj3 (0.20 #578, 0.18 #631, 0.14 #472), 07dzf (0.20 #564, 0.18 #617, 0.14 #1673), 0d05w3 (0.20 #235, 0.12 #3775, 0.10 #2769) >> Best rule #621 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0hptm; >> query: (?x6401, 0j4b) <- jurisdiction_of_office(?x3444, ?x6401), teams(?x6401, ?x8106), location(?x488, ?x6401) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #3225 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 0n3g; 0853g; *> query: (?x6401, 0154j) <- exported_to(?x6401, ?x1229), film_release_region(?x3151, ?x1229), ?x3151 = 0gtsxr4 *> conf = 0.09 ranks of expected_values: 27 EVAL 06q1r exported_to 0154j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 286.000 258.000 0.364 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #10487-02k76g PRED entity: 02k76g PRED relation: gender PRED expected values: 05zppz => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #45, 0.83 #68, 0.82 #17), 02zsn (0.49 #55, 0.46 #156, 0.23 #137) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 206 >> proper extension: 01vvycq; >> query: (?x12969, 05zppz) <- profession(?x12969, ?x987), profession(?x12969, ?x353), ?x353 = 0cbd2, ?x987 = 0dxtg >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02k76g gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.865 http://example.org/people/person/gender #10486-06y9bd PRED entity: 06y9bd PRED relation: award_winner! PRED expected values: 0bq_mx => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 110): 02q690_ (0.17 #9806, 0.16 #205, 0.12 #625), 09pj68 (0.17 #9806, 0.07 #104, 0.04 #524), 09qvms (0.17 #9806, 0.06 #3513, 0.05 #3653), 027hjff (0.17 #9806, 0.05 #337, 0.05 #3417), 0275n3y (0.17 #9806, 0.05 #3434, 0.04 #5674), 03gwpw2 (0.17 #9806, 0.02 #5609, 0.02 #1269), 0bq_mx (0.17 #6582, 0.17 #8685, 0.17 #8544), 027n06w (0.17 #6582, 0.17 #8685, 0.17 #8544), 03nnm4t (0.11 #213, 0.10 #1613, 0.10 #493), 05c1t6z (0.11 #295, 0.11 #1555, 0.10 #1695) >> Best rule #9806 for best value: >> intensional similarity = 2 >> extensional distance = 1699 >> proper extension: 01nzs7; 0kc9f; >> query: (?x10160, ?x762) <- nominated_for(?x10160, ?x5808), honored_for(?x762, ?x5808) >> conf = 0.17 => this is the best rule for 6 predicted values *> Best rule #6582 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1171 *> proper extension: 025cn2; *> query: (?x10160, ?x4781) <- award_winner(?x10160, ?x9439), nationality(?x10160, ?x94), award_winner(?x4781, ?x9439) *> conf = 0.17 ranks of expected_values: 7 EVAL 06y9bd award_winner! 0bq_mx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 94.000 94.000 0.174 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10485-0bdwft PRED entity: 0bdwft PRED relation: award! PRED expected values: 0159h6 01vwllw 02kxwk 0h96g 03cn92 01x4sb 01skmp 0kftt 019l3m 019l68 031sg0 => 47 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2742): 0hsn_ (0.78 #62823, 0.76 #39678, 0.76 #39679), 0h1mt (0.78 #62823, 0.76 #39678, 0.76 #39679), 01kp66 (0.78 #62823, 0.76 #39678, 0.76 #39679), 039wsf (0.78 #62823, 0.76 #39678, 0.76 #39679), 01dbhb (0.78 #62823, 0.76 #39678, 0.76 #39679), 01hkhq (0.78 #62823, 0.76 #39678, 0.76 #39679), 02kxwk (0.55 #7823, 0.14 #62822, 0.14 #66128), 025mb_ (0.50 #2562, 0.36 #9174, 0.14 #12479), 01_p6t (0.50 #1658, 0.27 #8270, 0.14 #62822), 0pyww (0.50 #1373, 0.18 #7985, 0.08 #11290) >> Best rule #62823 for best value: >> intensional similarity = 4 >> extensional distance = 235 >> proper extension: 02r0csl; 026mg3; 0p9sw; 02r22gf; 02hsq3m; 0dzfdw; 0gqzz; 03x3wf; 0gr0m; 02gx2k; ... >> query: (?x1132, ?x1126) <- award(?x1343, ?x1132), ceremony(?x1132, ?x1265), award_nominee(?x444, ?x1343), award_winner(?x1132, ?x1126) >> conf = 0.78 => this is the best rule for 6 predicted values *> Best rule #7823 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0bfvw2; 0cqhk0; 0bdw1g; 09sb52; 0gqyl; 0bdx29; 09qs08; 0gkts9; 0cqgl9; *> query: (?x1132, 02kxwk) <- award(?x1343, ?x1132), ceremony(?x1132, ?x1265), ?x1343 = 030znt, award(?x715, ?x1132) *> conf = 0.55 ranks of expected_values: 7, 33, 55, 126, 139, 151, 199, 593, 749, 777, 1646 EVAL 0bdwft award! 031sg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwft award! 019l68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwft award! 019l3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwft award! 0kftt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwft award! 01skmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwft award! 01x4sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwft award! 03cn92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwft award! 0h96g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwft award! 02kxwk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwft award! 01vwllw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdwft award! 0159h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 47.000 20.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10484-0cjcbg PRED entity: 0cjcbg PRED relation: category PRED expected values: 08mbj5d => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #52, 0.10 #9, 0.09 #7) >> Best rule #52 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x11272, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cjcbg category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #10483-02_n7 PRED entity: 02_n7 PRED relation: source PRED expected values: 0jbk9 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.88 #27, 0.85 #33, 0.85 #32) >> Best rule #27 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 01j8yr; 0d9jr; 0r6rq; 010v8k; 0s6jm; 013nv_; 0r3wm; 02mf7; 058cm; 0jbrr; >> query: (?x6316, 0jbk9) <- administrative_division(?x6316, ?x3589), category(?x6316, ?x134), contains(?x94, ?x6316), currency(?x3589, ?x170) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_n7 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.881 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #10482-030hbp PRED entity: 030hbp PRED relation: award_nominee PRED expected values: 011_3s => 113 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1207): 040t74 (0.81 #51357, 0.81 #91041, 0.80 #58363), 031296 (0.81 #51357, 0.81 #91041, 0.80 #58363), 0gcdzz (0.81 #51357, 0.81 #91041, 0.80 #58363), 011_3s (0.81 #91041, 0.80 #58363, 0.80 #58362), 01pgzn_ (0.47 #7498, 0.39 #495, 0.13 #58364), 07h565 (0.47 #8335, 0.35 #1332, 0.13 #58364), 03_6y (0.44 #7780, 0.42 #777, 0.13 #58364), 023kzp (0.44 #8392, 0.42 #1389, 0.07 #6056), 0278x6s (0.44 #8195, 0.39 #1192, 0.13 #58364), 0btpx (0.44 #8863, 0.35 #1860, 0.13 #58364) >> Best rule #51357 for best value: >> intensional similarity = 3 >> extensional distance = 355 >> proper extension: 04wqr; 03m8lq; 0162c8; 01pctb; 03ds83; 01hrqc; >> query: (?x10491, ?x4586) <- participant(?x2216, ?x10491), award_nominee(?x4586, ?x10491), student(?x7447, ?x4586) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #91041 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 775 *> proper extension: 08xz51; *> query: (?x10491, ?x336) <- award_winner(?x715, ?x10491), award_winner(?x7573, ?x10491), award_nominee(?x336, ?x10491) *> conf = 0.81 ranks of expected_values: 4 EVAL 030hbp award_nominee 011_3s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 113.000 47.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10481-017c87 PRED entity: 017c87 PRED relation: type_of_union PRED expected values: 01g63y => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.89 #29, 0.84 #37, 0.84 #61), 01g63y (0.34 #38, 0.30 #62, 0.25 #30) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 0157m; 01tnbn; 02yy8; >> query: (?x8665, 04ztj) <- student(?x3439, ?x8665), religion(?x8665, ?x2694), spouse(?x8665, ?x548) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 01j5x6; 0241wg; 0k8y7; 0c7xjb; 02n9k; *> query: (?x8665, 01g63y) <- people(?x1050, ?x8665), languages(?x8665, ?x254), spouse(?x8665, ?x548) *> conf = 0.34 ranks of expected_values: 2 EVAL 017c87 type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 131.000 0.891 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10480-0gq9h PRED entity: 0gq9h PRED relation: nominated_for PRED expected values: 0n0bp 072x7s 0fpv_3_ 0c9k8 0jswp 0qm9n 07cdz 051zy_b 0hfzr 0g9lm2 08nvyr 0cqnss 05znxx 027ct7c 05v38p 0p_tz 011yxy 04165w 02vnmc9 0bkq7 0h3k3f 02p86pb 0h1x5f => 62 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1229): 0m313 (0.79 #10508, 0.78 #21032, 0.78 #26283), 09gq0x5 (0.79 #10508, 0.78 #21225, 0.78 #26283), 0bdjd (0.79 #10508, 0.78 #26283, 0.77 #21020), 0hfzr (0.79 #10508, 0.78 #26283, 0.77 #21020), 09m6kg (0.79 #10508, 0.78 #26283, 0.77 #21020), 0b_5d (0.79 #10508, 0.78 #26283, 0.77 #21020), 0bl1_ (0.79 #10508, 0.78 #26283, 0.77 #21020), 07xtqq (0.79 #10508, 0.78 #26283, 0.77 #21020), 0hmr4 (0.79 #10508, 0.78 #26283, 0.77 #21020), 05sbv3 (0.79 #10508, 0.78 #26283, 0.77 #21020) >> Best rule #10508 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0f4x7; 02pqp12; 054krc; 04kxsb; 0gqy2; 02qyntr; >> query: (?x1307, ?x144) <- nominated_for(?x1307, ?x5873), ?x5873 = 0cq86w, award(?x144, ?x1307), ceremony(?x1307, ?x1084) >> conf = 0.79 => this is the best rule for 15 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 17, 18, 19, 22, 24, 30, 33, 35, 57, 60, 85, 95, 109, 116, 118, 119, 131, 139, 143, 159, 196, 212 EVAL 0gq9h nominated_for 0h1x5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 02p86pb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0h3k3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0bkq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 02vnmc9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 04165w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 011yxy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0p_tz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 05v38p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 027ct7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 05znxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0cqnss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 08nvyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0g9lm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0hfzr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 051zy_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 07cdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0qm9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0jswp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0c9k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0fpv_3_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 072x7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq9h nominated_for 0n0bp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 62.000 32.000 0.790 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10479-0cqt90 PRED entity: 0cqt90 PRED relation: student! PRED expected values: 01jtp7 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 215): 02zd460 (0.21 #1218, 0.20 #168, 0.03 #11718), 01qrb2 (0.20 #348, 0.07 #1398, 0.03 #2973), 08qnnv (0.20 #212, 0.03 #1262, 0.03 #2837), 01jssp (0.20 #5, 0.03 #1055, 0.03 #2630), 06bw5 (0.20 #185, 0.03 #1235, 0.02 #4385), 0cwx_ (0.14 #1289, 0.05 #3914, 0.03 #6014), 02301 (0.14 #1124, 0.01 #47859), 08815 (0.11 #527, 0.10 #4202, 0.07 #4727), 0bwfn (0.10 #4473, 0.09 #39652, 0.09 #2373), 03ksy (0.10 #4306, 0.09 #2206, 0.06 #49992) >> Best rule #1218 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 02cg2v; >> query: (?x3884, 02zd460) <- student(?x4672, ?x3884), religion(?x3884, ?x1985), featured_film_locations(?x5277, ?x4672), major_field_of_study(?x4672, ?x742) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2156 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 054_mz; 0168cl; 08hp53; 081nh; 03pvt; 0f8pz; 05hj_k; 024swd; 04g3p5; 02gyl0; ... *> query: (?x3884, 01jtp7) <- student(?x4672, ?x3884), profession(?x3884, ?x967), ?x967 = 012t_z, award(?x3884, ?x102) *> conf = 0.03 ranks of expected_values: 82 EVAL 0cqt90 student! 01jtp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 153.000 153.000 0.207 http://example.org/education/educational_institution/students_graduates./education/education/student #10478-014hdb PRED entity: 014hdb PRED relation: award_winner! PRED expected values: 0m7yy => 123 concepts (102 used for prediction) PRED predicted values (max 10 best out of 231): 0m7yy (0.72 #2325, 0.36 #1039, 0.04 #19885), 09v92_x (0.64 #430, 0.58 #429, 0.50 #276), 0gqng (0.58 #429, 0.47 #2146, 0.47 #1718), 09v51c2 (0.50 #318, 0.07 #4715, 0.07 #18422), 027cyf7 (0.47 #1494, 0.02 #10061, 0.02 #13915), 02wkmx (0.27 #1731, 0.22 #445, 0.06 #6014), 0gq9h (0.25 #2652, 0.19 #4364, 0.17 #3508), 0gq6s3 (0.22 #457, 0.13 #1743), 03nqnk3 (0.20 #1850, 0.11 #564, 0.08 #6133), 0gs9p (0.18 #6079, 0.17 #80, 0.11 #8220) >> Best rule #2325 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 01j53q; >> query: (?x10271, 0m7yy) <- award_winner(?x10271, ?x6678), program(?x6678, ?x4223), award_winner(?x6678, ?x1686), award_winner(?x4223, ?x3446) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014hdb award_winner! 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 102.000 0.719 http://example.org/award/award_category/winners./award/award_honor/award_winner #10477-052hl PRED entity: 052hl PRED relation: award PRED expected values: 054ks3 01l29r => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 321): 0gr51 (0.71 #38532, 0.70 #36943, 0.70 #41314), 03hl6lc (0.71 #38532, 0.70 #36943, 0.70 #41314), 02g3ft (0.71 #38532, 0.70 #36943, 0.70 #41314), 01l29r (0.71 #38532, 0.70 #36943, 0.70 #41314), 0gs9p (0.39 #9609, 0.37 #10404, 0.37 #9212), 09sb52 (0.36 #19896, 0.33 #16322, 0.33 #15527), 019f4v (0.35 #9596, 0.34 #10391, 0.32 #9199), 040njc (0.34 #9540, 0.32 #10335, 0.32 #9143), 0gq9h (0.33 #11593, 0.32 #13181, 0.31 #6827), 02pqp12 (0.22 #9600, 0.21 #9203, 0.21 #10395) >> Best rule #38532 for best value: >> intensional similarity = 3 >> extensional distance = 1531 >> proper extension: 06w2sn5; 03fbc; 01n8gr; 0163m1; 016bx2; 01lw3kh; 01dq9q; 016lmg; 03vhvp; 01yndb; ... >> query: (?x6771, ?x1429) <- award(?x6771, ?x601), award_nominee(?x6771, ?x4060), award_winner(?x1429, ?x6771) >> conf = 0.71 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 29 EVAL 052hl award 01l29r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 150.000 150.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 052hl award 054ks3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 150.000 150.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10476-0blt6 PRED entity: 0blt6 PRED relation: award PRED expected values: 02g2wv => 121 concepts (104 used for prediction) PRED predicted values (max 10 best out of 265): 09sb52 (0.35 #2452, 0.34 #20543, 0.34 #17327), 0fbtbt (0.30 #3045, 0.25 #1035, 0.20 #3850), 05pcn59 (0.28 #4102, 0.27 #3297, 0.24 #2493), 0cjyzs (0.27 #2919, 0.23 #909, 0.20 #3724), 05p09zm (0.22 #4143, 0.20 #5349, 0.20 #4947), 02q1tc5 (0.20 #3766, 0.06 #951, 0.06 #14620), 0cqhk0 (0.19 #9686, 0.16 #14108, 0.15 #16922), 0gkvb7 (0.19 #429, 0.17 #1233, 0.07 #2841), 03c7tr1 (0.18 #2470, 0.18 #4079, 0.17 #3274), 05b4l5x (0.17 #2418, 0.16 #6841, 0.16 #4831) >> Best rule #2452 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 01817f; 07jrjb; 01vxqyl; >> query: (?x3583, 09sb52) <- profession(?x3583, ?x987), nominated_for(?x3583, ?x1542), celebrity(?x3583, ?x4126) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #41414 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2255 *> proper extension: 01j7pt; *> query: (?x3583, ?x6463) <- nominated_for(?x3583, ?x11615), nominated_for(?x6463, ?x11615), award_winner(?x6463, ?x2258) *> conf = 0.13 ranks of expected_values: 18 EVAL 0blt6 award 02g2wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 121.000 104.000 0.350 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10475-0170yd PRED entity: 0170yd PRED relation: titles! PRED expected values: 07s9rl0 => 74 concepts (42 used for prediction) PRED predicted values (max 10 best out of 53): 07s9rl0 (0.51 #207, 0.51 #411, 0.46 #104), 04xvlr (0.26 #414, 0.22 #107, 0.20 #1972), 01z4y (0.19 #548, 0.18 #36, 0.17 #1066), 017fp (0.19 #434, 0.14 #127, 0.09 #1158), 0219x_ (0.19 #2797, 0.18 #3734, 0.17 #615), 0lsxr (0.19 #2797, 0.18 #3734, 0.17 #615), 07ssc (0.16 #420, 0.15 #318, 0.11 #730), 01jfsb (0.15 #20, 0.15 #328, 0.14 #226), 07c52 (0.11 #1476, 0.11 #1894, 0.11 #1791), 024qqx (0.10 #698, 0.08 #1319, 0.08 #801) >> Best rule #207 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 0j8f09z; >> query: (?x8410, 07s9rl0) <- nominated_for(?x2532, ?x8410), award(?x8410, ?x1770), ?x2532 = 02x4wr9 >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0170yd titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 42.000 0.514 http://example.org/media_common/netflix_genre/titles #10474-028rk PRED entity: 028rk PRED relation: celebrities_impersonated! PRED expected values: 03m6t5 => 198 concepts (140 used for prediction) PRED predicted values (max 10 best out of 8): 03m6t5 (0.75 #115, 0.43 #227, 0.36 #163), 0pz04 (0.25 #184, 0.25 #120, 0.17 #64), 03d_zl4 (0.25 #118, 0.17 #182, 0.17 #70), 0d608 (0.25 #119, 0.10 #135, 0.09 #167), 04s430 (0.17 #181, 0.17 #61, 0.12 #117), 018grr (0.17 #58, 0.12 #114, 0.09 #146), 0f7hc (0.07 #228, 0.01 #645, 0.01 #653), 01n5309 (0.03 #387, 0.02 #666, 0.02 #535) >> Best rule #115 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 042kg; >> query: (?x2663, 03m6t5) <- person(?x6773, ?x2663), religion(?x2663, ?x2591), ?x6773 = 05t54s, company(?x2663, ?x94) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028rk celebrities_impersonated! 03m6t5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 140.000 0.750 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #10473-01dtcb PRED entity: 01dtcb PRED relation: child PRED expected values: 01clyr => 189 concepts (130 used for prediction) PRED predicted values (max 10 best out of 298): 0c41qv (0.33 #225, 0.25 #541, 0.20 #858), 05s_k6 (0.33 #272, 0.25 #588, 0.20 #905), 031rq5 (0.25 #5605, 0.15 #3864, 0.12 #5447), 013x0b (0.25 #1908, 0.13 #4769, 0.12 #5085), 05b0f7 (0.20 #907, 0.20 #749, 0.14 #1544), 016tw3 (0.18 #2712, 0.17 #1123, 0.14 #1440), 03sb38 (0.18 #2765, 0.17 #1176, 0.14 #1493), 032j_n (0.18 #2795, 0.15 #3909, 0.11 #7708), 07y2b (0.18 #2826, 0.14 #1396, 0.13 #5047), 0dwcl (0.17 #1241, 0.14 #4418, 0.14 #1558) >> Best rule #225 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 016tt2; >> query: (?x7793, 0c41qv) <- child(?x7793, ?x7840), service_location(?x7793, ?x551), child(?x1104, ?x7793), category(?x7840, ?x134), organizations_founded(?x1125, ?x7840) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3815 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 013x0b; 073tm9; 0181hw; 02b07b; *> query: (?x7793, ?x5021) <- industry(?x7793, ?x3368), artist(?x7793, ?x6124), artist(?x5021, ?x6124), artist(?x2931, ?x6124), ?x2931 = 03rhqg *> conf = 0.04 ranks of expected_values: 131 EVAL 01dtcb child 01clyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 189.000 130.000 0.333 http://example.org/organization/organization/child./organization/organization_relationship/child #10472-078sj4 PRED entity: 078sj4 PRED relation: film! PRED expected values: 0169dl 06z8gn => 64 concepts (26 used for prediction) PRED predicted values (max 10 best out of 503): 0csdzz (0.32 #51815, 0.27 #18654, 0.27 #20727), 0c6qh (0.23 #4553, 0.06 #31091, 0.03 #37308), 08cn4_ (0.15 #4576, 0.11 #2504, 0.03 #49742), 0169dl (0.15 #4540, 0.04 #6612, 0.03 #8684), 02xwgr (0.15 #5072, 0.03 #49742, 0.03 #53888), 046zh (0.15 #5073, 0.02 #9217, 0.02 #13364), 044rvb (0.15 #4244, 0.01 #22902, 0.01 #27046), 0pnf3 (0.15 #5881, 0.01 #7953), 028r4y (0.15 #5108), 0154qm (0.14 #556, 0.11 #2628, 0.04 #6772) >> Best rule #51815 for best value: >> intensional similarity = 3 >> extensional distance = 1018 >> proper extension: 09fb5; 05gnf; 06ys2; >> query: (?x2814, ?x10634) <- nominated_for(?x10634, ?x2814), place_of_birth(?x10634, ?x4627), award_winner(?x4781, ?x10634) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #4540 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 06z8s_; 0418wg; 01bb9r; 07w8fz; 011ysn; 0bmhvpr; 06_x996; 01hqk; 029k4p; 02704ff; ... *> query: (?x2814, 0169dl) <- film(?x286, ?x2814), film_release_distribution_medium(?x2814, ?x81), nominated_for(?x112, ?x2814), ?x286 = 014zcr *> conf = 0.15 ranks of expected_values: 4 EVAL 078sj4 film! 06z8gn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 26.000 0.323 http://example.org/film/actor/film./film/performance/film EVAL 078sj4 film! 0169dl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 64.000 26.000 0.323 http://example.org/film/actor/film./film/performance/film #10471-04tqtl PRED entity: 04tqtl PRED relation: film! PRED expected values: 06qgvf 0168dy => 73 concepts (52 used for prediction) PRED predicted values (max 10 best out of 756): 06cv1 (0.73 #58037, 0.68 #47672, 0.64 #62186), 089kpp (0.49 #58036, 0.47 #37309, 0.47 #2072), 01mkn_d (0.49 #58036, 0.47 #2072, 0.46 #12435), 0693l (0.12 #10363, 0.04 #524, 0.04 #2596), 05hj_k (0.12 #33162, 0.12 #18653, 0.11 #41454), 06q8hf (0.12 #33162, 0.12 #18653, 0.11 #41454), 079vf (0.09 #8, 0.07 #2080, 0.07 #4153), 01f6zc (0.06 #938, 0.06 #3010, 0.05 #5083), 041c4 (0.06 #889, 0.06 #2961, 0.05 #5034), 024bbl (0.06 #831, 0.06 #2903, 0.05 #4976) >> Best rule #58037 for best value: >> intensional similarity = 3 >> extensional distance = 777 >> proper extension: 0g5qs2k; 01hvjx; >> query: (?x3093, ?x8716) <- nominated_for(?x8716, ?x3093), participant(?x8716, ?x1117), film(?x395, ?x3093) >> conf = 0.73 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04tqtl film! 0168dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 52.000 0.730 http://example.org/film/actor/film./film/performance/film EVAL 04tqtl film! 06qgvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 52.000 0.730 http://example.org/film/actor/film./film/performance/film #10470-0j5q3 PRED entity: 0j5q3 PRED relation: student! PRED expected values: 0fr9jp => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 101): 0m4yg (0.14 #892, 0.14 #365, 0.03 #3527), 031ns1 (0.10 #1045, 0.05 #518, 0.02 #3680), 0bwfn (0.07 #1329, 0.05 #17667, 0.05 #13451), 065y4w7 (0.05 #541, 0.05 #14, 0.05 #1068), 07tg4 (0.05 #613, 0.05 #86, 0.03 #5356), 015nl4 (0.05 #594, 0.05 #67, 0.03 #18513), 02hmw9 (0.05 #764, 0.05 #237, 0.02 #3399), 0h6rm (0.05 #671, 0.05 #144, 0.01 #5414), 027xq5 (0.05 #1048, 0.05 #521), 01f6ss (0.05 #1044, 0.05 #517) >> Best rule #892 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0159h6; 0f2df; 01fwj8; 05dbf; 071ynp; 06mnps; 0k269; 07fpm3; 06ltr; 04ld94; ... >> query: (?x7056, 0m4yg) <- award(?x7056, ?x1007), people(?x3715, ?x7056), ?x3715 = 03lmx1, nationality(?x7056, ?x94) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #6142 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 331 *> proper extension: 04cr6qv; *> query: (?x7056, 0fr9jp) <- people(?x743, ?x7056), film(?x7056, ?x2886), participant(?x7056, ?x1736) *> conf = 0.03 ranks of expected_values: 24 EVAL 0j5q3 student! 0fr9jp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 105.000 105.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #10469-016gr2 PRED entity: 016gr2 PRED relation: award_nominee! PRED expected values: 02d45s => 67 concepts (26 used for prediction) PRED predicted values (max 10 best out of 665): 01sp81 (0.81 #13859, 0.81 #60063, 0.81 #60064), 03v3xp (0.81 #13859, 0.81 #60063, 0.81 #60064), 09fqtq (0.81 #13859, 0.81 #60063, 0.81 #60064), 02d45s (0.81 #60063, 0.81 #60064, 0.81 #13858), 016gr2 (0.75 #242, 0.26 #57753, 0.18 #41581), 0170s4 (0.26 #57753, 0.18 #48510, 0.16 #2310), 020_95 (0.26 #57753, 0.16 #2310, 0.02 #19741), 02__7n (0.26 #57753, 0.02 #3933, 0.02 #8552), 01y665 (0.26 #57753, 0.02 #19148, 0.01 #28389), 02ct_k (0.26 #57753, 0.01 #20505, 0.01 #13574) >> Best rule #13859 for best value: >> intensional similarity = 3 >> extensional distance = 991 >> proper extension: 038rzr; 0kvqv; 01tnbn; 0flpy; 07g7h2; 01m3b1t; 03d1y3; 0237jb; 01933d; 036dyy; ... >> query: (?x1223, ?x926) <- award_nominee(?x1223, ?x926), award_winner(?x472, ?x926), student(?x2999, ?x1223) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #60063 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1663 *> proper extension: 02v49c; *> query: (?x1223, ?x72) <- award_nominee(?x1223, ?x926), award_nominee(?x1223, ?x72), film(?x926, ?x1797), award(?x926, ?x102) *> conf = 0.81 ranks of expected_values: 4 EVAL 016gr2 award_nominee! 02d45s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 67.000 26.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10468-0dq6p PRED entity: 0dq6p PRED relation: film_distribution_medium! PRED expected values: 03g90h 0ds33 0f4yh 0243cq 0k54q 0k_9j => 6 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1861): 08hmch (0.50 #555, 0.50 #376, 0.43 #735), 0cz8mkh (0.50 #564, 0.50 #385, 0.43 #744), 03y0pn (0.50 #660, 0.50 #481, 0.43 #840), 03hxsv (0.50 #648, 0.50 #469, 0.43 #828), 0d4htf (0.50 #634, 0.50 #455, 0.43 #814), 0hv8w (0.50 #633, 0.50 #454, 0.43 #813), 03bzyn4 (0.50 #533, 0.50 #505, 0.33 #684), 01pj_5 (0.50 #612, 0.43 #792, 0.43 #535), 06_sc3 (0.50 #498, 0.36 #710, 0.33 #677), 02wgk1 (0.50 #434, 0.33 #613, 0.33 #257) >> Best rule #555 for best value: >> intensional similarity = 119 >> extensional distance = 4 >> proper extension: 07z4p; >> query: (?x2007, 08hmch) <- film_distribution_medium(?x8072, ?x2007), film_distribution_medium(?x7366, ?x2007), film_distribution_medium(?x6018, ?x2007), film_distribution_medium(?x2869, ?x2007), film_distribution_medium(?x1915, ?x2007), genre(?x7366, ?x6625), ?x6625 = 01585b, film_release_region(?x1915, ?x2629), film_release_region(?x1915, ?x1917), film_release_region(?x1915, ?x1603), film_release_region(?x1915, ?x1453), film_release_region(?x1915, ?x774), film_release_region(?x1915, ?x756), film_release_region(?x1915, ?x583), film_release_region(?x1915, ?x512), genre(?x1915, ?x571), film_release_region(?x11351, ?x756), film_release_region(?x6422, ?x756), film_release_region(?x5877, ?x756), film_release_region(?x3784, ?x756), film_release_region(?x2471, ?x756), film_release_region(?x1178, ?x756), country(?x8190, ?x756), country(?x3598, ?x756), country(?x11735, ?x512), country(?x7982, ?x512), country(?x7700, ?x512), country(?x5976, ?x512), country(?x5331, ?x512), country(?x4860, ?x512), country(?x2939, ?x512), country(?x2840, ?x512), region(?x54, ?x512), member_states(?x7695, ?x1917), ?x7982 = 016mhd, service_location(?x5072, ?x1917), contains(?x512, ?x362), religion(?x512, ?x492), teams(?x774, ?x11564), country(?x8072, ?x279), olympics(?x774, ?x2043), olympics(?x774, ?x867), nationality(?x7400, ?x512), ?x4860 = 0bmch_x, ?x1178 = 053rxgm, first_level_division_of(?x6401, ?x512), contains(?x774, ?x1220), film_release_region(?x6587, ?x512), film_release_region(?x6168, ?x512), film_release_region(?x3619, ?x512), film_release_region(?x3268, ?x512), film_release_region(?x2958, ?x512), film_release_region(?x2655, ?x512), film_release_region(?x1859, ?x512), film_release_region(?x428, ?x512), ?x2471 = 08052t3, ?x2043 = 0lv1x, film_release_region(?x499, ?x512), ?x1859 = 0m491, organization(?x512, ?x127), ?x2655 = 0fpmrm3, official_language(?x2629, ?x2890), ?x867 = 0l6ny, ?x5331 = 09r94m, ?x8190 = 09_9n, official_language(?x774, ?x90), ?x6587 = 07s3m4g, ?x11351 = 02wtp6, ?x6168 = 0gj96ln, combatants(?x1536, ?x756), ?x11735 = 02x2jl_, ?x428 = 0h1cdwq, ?x3784 = 0bmhvpr, combatants(?x326, ?x2629), country(?x2044, ?x1917), country(?x1352, ?x1917), country(?x471, ?x512), combatants(?x613, ?x512), ?x7700 = 0cp08zg, titles(?x512, ?x582), film_crew_role(?x6018, ?x137), ?x471 = 02vx4, edited_by(?x6018, ?x4215), titles(?x1510, ?x2869), currency(?x1603, ?x170), ?x3619 = 0fphgb, nationality(?x889, ?x1603), service_location(?x11636, ?x2629), ?x2958 = 0b_5d, country(?x1339, ?x512), film(?x2383, ?x6018), country(?x359, ?x774), administrative_area_type(?x774, ?x2792), film_release_region(?x5721, ?x1603), ?x3598 = 03rbzn, ?x583 = 015fr, olympics(?x512, ?x3971), film(?x382, ?x2869), ?x7400 = 082mw, country(?x9042, ?x512), film(?x488, ?x2869), country(?x3304, ?x774), ?x5721 = 01d259, location(?x399, ?x512), ?x3268 = 02x6dqb, ?x613 = 0bq0p9, ?x2939 = 08k40m, participating_countries(?x7429, ?x512), ?x6422 = 02qk3fk, entity_involved(?x9351, ?x2629), ?x5976 = 02q7fl9, combatants(?x612, ?x512), contains(?x6956, ?x1453), ?x2840 = 0f4vx, combatants(?x2629, ?x5114), ?x1352 = 0w0d, ?x5877 = 02qyv3h, film(?x609, ?x6018), ?x2044 = 06f41 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #710 for first EXPECTED value: *> intensional similarity = 119 *> extensional distance = 4 *> proper extension: 07z4p; *> query: (?x2007, ?x54) <- film_distribution_medium(?x8072, ?x2007), film_distribution_medium(?x7366, ?x2007), film_distribution_medium(?x6018, ?x2007), film_distribution_medium(?x2869, ?x2007), film_distribution_medium(?x1915, ?x2007), genre(?x7366, ?x6625), ?x6625 = 01585b, film_release_region(?x1915, ?x2629), film_release_region(?x1915, ?x1917), film_release_region(?x1915, ?x1603), film_release_region(?x1915, ?x1453), film_release_region(?x1915, ?x774), film_release_region(?x1915, ?x756), film_release_region(?x1915, ?x583), film_release_region(?x1915, ?x512), genre(?x1915, ?x571), film_release_region(?x11351, ?x756), film_release_region(?x6422, ?x756), film_release_region(?x5877, ?x756), film_release_region(?x3784, ?x756), film_release_region(?x2471, ?x756), film_release_region(?x1178, ?x756), country(?x8190, ?x756), country(?x3598, ?x756), country(?x11735, ?x512), country(?x7982, ?x512), country(?x7700, ?x512), country(?x5976, ?x512), country(?x5331, ?x512), country(?x4860, ?x512), country(?x2939, ?x512), country(?x2840, ?x512), region(?x54, ?x512), member_states(?x7695, ?x1917), ?x7982 = 016mhd, service_location(?x5072, ?x1917), contains(?x512, ?x362), religion(?x512, ?x492), teams(?x774, ?x11564), country(?x8072, ?x279), olympics(?x774, ?x2043), olympics(?x774, ?x867), nationality(?x7400, ?x512), ?x4860 = 0bmch_x, ?x1178 = 053rxgm, first_level_division_of(?x6401, ?x512), contains(?x774, ?x1220), film_release_region(?x6587, ?x512), film_release_region(?x6168, ?x512), film_release_region(?x3619, ?x512), film_release_region(?x3268, ?x512), film_release_region(?x2958, ?x512), film_release_region(?x2655, ?x512), film_release_region(?x1859, ?x512), film_release_region(?x428, ?x512), ?x2471 = 08052t3, ?x2043 = 0lv1x, film_release_region(?x499, ?x512), ?x1859 = 0m491, organization(?x512, ?x127), ?x2655 = 0fpmrm3, official_language(?x2629, ?x2890), ?x867 = 0l6ny, ?x5331 = 09r94m, ?x8190 = 09_9n, official_language(?x774, ?x90), ?x6587 = 07s3m4g, ?x11351 = 02wtp6, ?x6168 = 0gj96ln, combatants(?x1536, ?x756), ?x11735 = 02x2jl_, ?x428 = 0h1cdwq, ?x3784 = 0bmhvpr, combatants(?x326, ?x2629), country(?x2044, ?x1917), country(?x1352, ?x1917), country(?x471, ?x512), combatants(?x613, ?x512), ?x7700 = 0cp08zg, titles(?x512, ?x582), film_crew_role(?x6018, ?x137), ?x471 = 02vx4, edited_by(?x6018, ?x4215), titles(?x1510, ?x2869), currency(?x1603, ?x170), ?x3619 = 0fphgb, nationality(?x889, ?x1603), service_location(?x11636, ?x2629), ?x2958 = 0b_5d, country(?x1339, ?x512), film(?x2383, ?x6018), country(?x359, ?x774), administrative_area_type(?x774, ?x2792), film_release_region(?x5721, ?x1603), ?x3598 = 03rbzn, ?x583 = 015fr, olympics(?x512, ?x3971), film(?x382, ?x2869), ?x7400 = 082mw, country(?x9042, ?x512), film(?x488, ?x2869), country(?x3304, ?x774), ?x5721 = 01d259, location(?x399, ?x512), ?x3268 = 02x6dqb, ?x613 = 0bq0p9, ?x2939 = 08k40m, participating_countries(?x7429, ?x512), ?x6422 = 02qk3fk, entity_involved(?x9351, ?x2629), ?x5976 = 02q7fl9, combatants(?x612, ?x512), contains(?x6956, ?x1453), ?x2840 = 0f4vx, combatants(?x2629, ?x5114), ?x1352 = 0w0d, ?x5877 = 02qyv3h, film(?x609, ?x6018), ?x2044 = 06f41 *> conf = 0.36 ranks of expected_values: 33, 146, 147, 154, 179, 256 EVAL 0dq6p film_distribution_medium! 0k_9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 5.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0dq6p film_distribution_medium! 0k54q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 6.000 5.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0dq6p film_distribution_medium! 0243cq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 5.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0dq6p film_distribution_medium! 0f4yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 5.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0dq6p film_distribution_medium! 0ds33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 5.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0dq6p film_distribution_medium! 03g90h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 5.000 0.500 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #10467-03zv9 PRED entity: 03zv9 PRED relation: team PRED expected values: 049bp4 03x6m 049n3s => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 875): 0284h6 (0.42 #153, 0.38 #154, 0.38 #152), 016gp5 (0.42 #153, 0.38 #154, 0.38 #152), 01j95f (0.42 #153, 0.38 #154, 0.38 #152), 0j2pg (0.42 #153, 0.38 #154, 0.38 #152), 0182r9 (0.42 #153, 0.38 #154, 0.38 #152), 01xn7x1 (0.42 #153, 0.38 #154, 0.38 #152), 01dtl (0.42 #153, 0.38 #154, 0.38 #152), 01z1r (0.42 #153, 0.38 #154, 0.38 #152), 01rl_3 (0.42 #153, 0.38 #154, 0.38 #152), 014nzp (0.42 #153, 0.38 #154, 0.38 #152) >> Best rule #153 for best value: >> intensional similarity = 23 >> extensional distance = 1 >> proper extension: 0h69c; >> query: (?x5471, ?x1899) <- team(?x5471, ?x10389), team(?x5471, ?x5914), team(?x5471, ?x3552), team(?x5471, ?x979), colors(?x3552, ?x3621), colors(?x3552, ?x3189), team(?x1898, ?x5914), sport(?x10389, ?x471), colors(?x979, ?x663), colors(?x10478, ?x3621), teams(?x13218, ?x3552), team(?x63, ?x10389), ?x10478 = 06thjt, country(?x471, ?x94), sport(?x1899, ?x471), athlete(?x471, ?x208), olympics(?x471, ?x358), sports(?x584, ?x471), ?x3189 = 01g5v, vacationer(?x739, ?x1898), gender(?x1898, ?x231), ?x663 = 083jv, profession(?x1898, ?x1032) >> conf = 0.42 => this is the best rule for 287 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 60, 64, 216 EVAL 03zv9 team 049n3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 8.000 8.000 0.417 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team EVAL 03zv9 team 03x6m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 8.000 8.000 0.417 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team EVAL 03zv9 team 049bp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 8.000 8.000 0.417 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #10466-026njb5 PRED entity: 026njb5 PRED relation: film_regional_debut_venue PRED expected values: 018cvf => 110 concepts (106 used for prediction) PRED predicted values (max 10 best out of 29): 018cvf (0.50 #16, 0.33 #49, 0.29 #214), 015hr (0.29 #212, 0.26 #178, 0.25 #112), 0gg7gsl (0.16 #240, 0.08 #273, 0.07 #439), 02_286 (0.15 #167, 0.08 #235, 0.06 #101), 07zmj (0.10 #461, 0.09 #228, 0.07 #194), 04_m9gk (0.08 #256, 0.06 #289, 0.04 #188), 0fpkxfd (0.08 #249, 0.04 #282, 0.01 #1074), 0j63cyr (0.08 #378, 0.07 #707, 0.07 #411), 04jpl (0.07 #165, 0.06 #99, 0.05 #233), 0kfhjq0 (0.06 #380, 0.06 #213, 0.06 #446) >> Best rule #16 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0ds6bmk; 03np63f; >> query: (?x3287, 018cvf) <- language(?x3287, ?x254), award(?x3287, ?x941), film_release_region(?x3287, ?x2513), film_release_region(?x3287, ?x304), ?x941 = 0fq9zdn, ?x2513 = 05b4w, ?x304 = 0d0vqn >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026njb5 film_regional_debut_venue 018cvf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 106.000 0.500 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #10465-02lfcm PRED entity: 02lfcm PRED relation: actor! PRED expected values: 017f3m => 104 concepts (91 used for prediction) PRED predicted values (max 10 best out of 50): 080dwhx (0.33 #1062, 0.07 #798, 0.02 #4238), 026b33f (0.12 #40, 0.07 #832, 0.07 #1096), 06zsk51 (0.07 #974, 0.03 #1238), 0qmk5 (0.07 #1277), 06y_n (0.04 #1516, 0.03 #1781, 0.02 #2311), 019nnl (0.04 #1339, 0.03 #1604), 05p9_ql (0.03 #1190, 0.02 #6618), 02_1q9 (0.03 #1061, 0.02 #5826, 0.01 #5031), 034vds (0.03 #1303), 0147w8 (0.03 #1288) >> Best rule #1062 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 01dy7j; 03zyvw; 040981l; >> query: (?x447, 080dwhx) <- award_nominee(?x3688, ?x447), award_nominee(?x1169, ?x447), ?x1169 = 02lfns, award_winner(?x5459, ?x3688) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02lfcm actor! 017f3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 91.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10464-027tbrc PRED entity: 027tbrc PRED relation: program! PRED expected values: 0g5lhl7 => 71 concepts (60 used for prediction) PRED predicted values (max 10 best out of 50): 0gsg7 (0.26 #710, 0.26 #1153, 0.23 #823), 05gnf (0.25 #721, 0.24 #775, 0.23 #1164), 03mdt (0.15 #331, 0.14 #440, 0.13 #769), 09d5h (0.15 #1154, 0.14 #765, 0.14 #711), 0g5lhl7 (0.14 #114, 0.09 #222, 0.09 #168), 0cjdk (0.13 #1156, 0.12 #767, 0.12 #329), 07c52 (0.11 #708, 0.10 #652, 0.03 #1966), 02hmvw (0.10 #149, 0.09 #257, 0.06 #203), 0187wh (0.10 #133, 0.09 #187, 0.06 #241), 03lpbx (0.08 #247, 0.07 #139, 0.06 #193) >> Best rule #710 for best value: >> intensional similarity = 4 >> extensional distance = 130 >> proper extension: 02rq7nd; >> query: (?x2447, 0gsg7) <- genre(?x2447, ?x53), languages(?x2447, ?x254), program(?x6092, ?x2447), nominated_for(?x2448, ?x2447) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #114 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 017dcd; 09kn9; 015g28; 08cx5g; 02648p; 0dr1c2; 0ctzf1; 028k2x; 06r4f; 03g9xj; ... *> query: (?x2447, 0g5lhl7) <- actor(?x2447, ?x2045), genre(?x2447, ?x811), ?x811 = 03k9fj, program(?x6092, ?x2447) *> conf = 0.14 ranks of expected_values: 5 EVAL 027tbrc program! 0g5lhl7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 71.000 60.000 0.258 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #10463-02n9nmz PRED entity: 02n9nmz PRED relation: award! PRED expected values: 06_x996 01chpn => 52 concepts (11 used for prediction) PRED predicted values (max 10 best out of 602): 0cq806 (0.71 #2858, 0.62 #4869, 0.43 #7892), 0ywrc (0.71 #2319, 0.50 #4330, 0.36 #7353), 0209hj (0.62 #4087, 0.57 #2076, 0.55 #9124), 07xtqq (0.62 #4057, 0.57 #2046, 0.45 #9094), 0c0zq (0.62 #4914, 0.57 #2903, 0.40 #9951), 04v8x9 (0.57 #2052, 0.50 #4063, 0.35 #9100), 0bj25 (0.57 #2857, 0.38 #4868, 0.33 #1850), 0cq7kw (0.57 #2452, 0.38 #4463, 0.29 #7486), 0bs4r (0.50 #4628, 0.43 #2617, 0.33 #5634), 07g1sm (0.50 #4723, 0.43 #2712, 0.33 #1705) >> Best rule #2858 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 040njc; 019f4v; 0gq9h; 0gs9p; >> query: (?x1180, 0cq806) <- nominated_for(?x1180, ?x5712), award(?x5351, ?x1180), ?x5712 = 0k4p0, ?x5351 = 0c00lh >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1409 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 04dn09n; *> query: (?x1180, 06_x996) <- nominated_for(?x1180, ?x5712), nominated_for(?x1180, ?x2613), award(?x361, ?x1180), ?x5712 = 0k4p0, ?x361 = 0h5f5n, ?x2613 = 02q56mk *> conf = 0.33 ranks of expected_values: 64, 233 EVAL 02n9nmz award! 01chpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 52.000 11.000 0.714 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02n9nmz award! 06_x996 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 52.000 11.000 0.714 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #10462-0f8l9c PRED entity: 0f8l9c PRED relation: contains PRED expected values: 04gdr => 303 concepts (145 used for prediction) PRED predicted values (max 10 best out of 2991): 01c6zg (0.85 #243444, 0.84 #228957, 0.84 #394154), 01b85 (0.83 #170995, 0.83 #266632, 0.83 #179690), 0d8h4 (0.83 #170995, 0.83 #266632, 0.83 #179690), 05c17 (0.83 #170995, 0.83 #266632, 0.83 #179690), 02wzv (0.83 #170995, 0.83 #266632, 0.83 #179690), 0mhl6 (0.83 #170995, 0.83 #266632, 0.83 #179690), 0mhlq (0.83 #170995, 0.83 #266632, 0.83 #179690), 0pbhz (0.74 #191282, 0.73 #379661, 0.73 #373864), 0fwdr (0.73 #379661, 0.73 #373864, 0.58 #66653), 09hzc (0.73 #379661, 0.73 #373864, 0.58 #66653) >> Best rule #243444 for best value: >> intensional similarity = 2 >> extensional distance = 45 >> proper extension: 0lwkz; >> query: (?x789, ?x790) <- time_zones(?x789, ?x2864), administrative_parent(?x790, ?x789) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #187141 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 40 *> proper extension: 02s838; *> query: (?x789, 04gdr) <- vacationer(?x789, ?x444), origin(?x3382, ?x789) *> conf = 0.02 ranks of expected_values: 2682 EVAL 0f8l9c contains 04gdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 303.000 145.000 0.848 http://example.org/location/location/contains #10461-0z07 PRED entity: 0z07 PRED relation: state_province_region PRED expected values: 07b_l => 185 concepts (183 used for prediction) PRED predicted values (max 10 best out of 161): 01n7q (0.67 #141, 0.38 #2372, 0.35 #7086), 07b_l (0.61 #11676, 0.60 #11552, 0.46 #10181), 09c7w0 (0.61 #11676, 0.60 #11552, 0.46 #10181), 059rby (0.42 #5706, 0.34 #5211, 0.33 #7447), 0mq17 (0.35 #11304, 0.31 #11180, 0.29 #5702), 0mr_8 (0.35 #11304, 0.31 #11180, 0.29 #5702), 05fly (0.19 #5207, 0.09 #577, 0.08 #825), 0f2s6 (0.17 #20991, 0.06 #1982, 0.05 #2354), 01n4w (0.17 #43, 0.04 #11223, 0.02 #13207), 0vmt (0.17 #13, 0.02 #4103, 0.02 #8453) >> Best rule #141 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 065y4w7; 09f2j; >> query: (?x11188, 01n7q) <- citytown(?x11188, ?x9713), company(?x346, ?x11188), category(?x11188, ?x134), location(?x10317, ?x9713), ?x10317 = 0341n5 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #11676 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 138 *> proper extension: 05zjtn4; 02rg_4; 02897w; 0g8fs; *> query: (?x11188, ?x94) <- citytown(?x11188, ?x9713), administrative_division(?x9713, ?x9712), contains(?x94, ?x9712), source(?x9712, ?x958), contains(?x9713, ?x4211) *> conf = 0.61 ranks of expected_values: 2 EVAL 0z07 state_province_region 07b_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 185.000 183.000 0.667 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #10460-0427y PRED entity: 0427y PRED relation: religion PRED expected values: 03_gx => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 23): 03_gx (0.58 #420, 0.20 #1187, 0.14 #511), 0kpl (0.19 #281, 0.19 #416, 0.17 #643), 0c8wxp (0.17 #999, 0.16 #954, 0.16 #187), 0n2g (0.13 #148, 0.09 #646, 0.07 #556), 092bf5 (0.11 #106, 0.05 #197, 0.05 #513), 0kq2 (0.10 #289, 0.09 #651, 0.07 #561), 051kv (0.06 #95, 0.03 #140, 0.03 #231), 01lp8 (0.03 #453, 0.03 #2213, 0.03 #1174), 03j6c (0.03 #202, 0.02 #3182, 0.02 #3000), 04pk9 (0.03 #201, 0.02 #743, 0.02 #381) >> Best rule #420 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 0lrh; 073v6; 085pr; 052h3; 03f0324; 058vp; 04hcw; 02ln1; 07h1q; 0h25; ... >> query: (?x9596, 03_gx) <- influenced_by(?x2534, ?x9596), people(?x1050, ?x9596), ?x1050 = 041rx >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0427y religion 03_gx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.576 http://example.org/people/person/religion #10459-014hr0 PRED entity: 014hr0 PRED relation: award PRED expected values: 024_41 => 92 concepts (85 used for prediction) PRED predicted values (max 10 best out of 279): 0gqz2 (0.50 #484, 0.34 #2902, 0.22 #8141), 09sb52 (0.50 #2459, 0.28 #18182, 0.28 #18988), 025m8y (0.50 #503, 0.25 #2115, 0.23 #3727), 01bgqh (0.45 #4879, 0.36 #10926, 0.34 #3267), 054krc (0.44 #3715, 0.29 #2909, 0.25 #2103), 0l8z1 (0.35 #3691, 0.33 #2079, 0.23 #2885), 0fhpv4 (0.33 #2210, 0.20 #8464, 0.20 #8465), 025m98 (0.33 #236, 0.20 #8465, 0.18 #17738), 02hgm4 (0.33 #138, 0.06 #2959, 0.06 #6989), 03ncb2 (0.33 #308, 0.05 #3532, 0.03 #3129) >> Best rule #484 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03n0q5; 01wd9vs; >> query: (?x2897, 0gqz2) <- award_nominee(?x669, ?x2897), award_winner(?x6311, ?x2897), ?x669 = 0146pg, award(?x2897, ?x2139) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #14915 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 458 *> proper extension: 0l56b; *> query: (?x2897, ?x594) <- award_winner(?x6399, ?x2897), award_winner(?x594, ?x6399), artists(?x888, ?x6399) *> conf = 0.22 ranks of expected_values: 27 EVAL 014hr0 award 024_41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 92.000 85.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10458-02f77y PRED entity: 02f77y PRED relation: award! PRED expected values: 0lk90 01vrt_c 033wx9 016fnb 043zg => 44 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2473): 0478__m (0.79 #33614, 0.79 #36976, 0.77 #40340), 01w61th (0.79 #33614, 0.79 #36976, 0.77 #40340), 0kr_t (0.60 #8332, 0.57 #11693, 0.50 #4971), 0137g1 (0.60 #7473, 0.57 #14195, 0.50 #4112), 0dvqq (0.60 #7351, 0.57 #10712, 0.50 #3990), 046p9 (0.60 #9078, 0.50 #5717, 0.43 #15800), 043zg (0.60 #8288, 0.50 #4927, 0.43 #11649), 01pfr3 (0.60 #6816, 0.50 #3455, 0.43 #10177), 016t0h (0.60 #9867, 0.50 #6506, 0.43 #13228), 01vsgrn (0.57 #15067, 0.50 #21789, 0.50 #18428) >> Best rule #33614 for best value: >> intensional similarity = 6 >> extensional distance = 144 >> proper extension: 0fhpv4; >> query: (?x6416, ?x883) <- award(?x7781, ?x6416), award(?x1896, ?x6416), artist(?x6474, ?x7781), participant(?x1896, ?x4397), artists(?x302, ?x1896), award_winner(?x6416, ?x883) >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #8288 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 02f716; *> query: (?x6416, 043zg) <- award(?x7781, ?x6416), award(?x1462, ?x6416), ?x7781 = 089pg7, friend(?x1462, ?x6577), instrumentalists(?x227, ?x1462) *> conf = 0.60 ranks of expected_values: 7, 11, 25, 246, 366 EVAL 02f77y award! 043zg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 44.000 14.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f77y award! 016fnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 44.000 14.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f77y award! 033wx9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 14.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f77y award! 01vrt_c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 44.000 14.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f77y award! 0lk90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 44.000 14.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10457-01v3k2 PRED entity: 01v3k2 PRED relation: major_field_of_study PRED expected values: 05qt0 => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 109): 02j62 (0.34 #9162, 0.33 #32, 0.32 #2533), 03g3w (0.33 #28, 0.27 #2654, 0.26 #9158), 0fdys (0.33 #41, 0.27 #791, 0.27 #666), 05qt0 (0.33 #58, 0.13 #9256, 0.13 #10634), 064_8sq (0.33 #49, 0.10 #674, 0.09 #1049), 01mkq (0.30 #4267, 0.30 #9146, 0.26 #3142), 02lp1 (0.29 #4263, 0.28 #3138, 0.28 #2513), 062z7 (0.28 #2530, 0.25 #9159, 0.23 #8783), 04rjg (0.28 #4272, 0.26 #9151, 0.25 #2522), 05qjt (0.23 #508, 0.23 #2509, 0.22 #4259) >> Best rule #9162 for best value: >> intensional similarity = 4 >> extensional distance = 461 >> proper extension: 03bwzr4; >> query: (?x9108, 02j62) <- major_field_of_study(?x9108, ?x8925), major_field_of_study(?x2313, ?x8925), major_field_of_study(?x2314, ?x8925), ?x2313 = 07wrz >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 01k3s2; 01s753; *> query: (?x9108, 05qt0) <- contains(?x4221, ?x9108), colors(?x9108, ?x8047), ?x8047 = 038hg, currency(?x9108, ?x1099), category(?x9108, ?x134) *> conf = 0.33 ranks of expected_values: 4 EVAL 01v3k2 major_field_of_study 05qt0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 167.000 167.000 0.341 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10456-014x77 PRED entity: 014x77 PRED relation: film PRED expected values: 02q8ms8 => 125 concepts (82 used for prediction) PRED predicted values (max 10 best out of 954): 02b6n9 (0.20 #1563, 0.17 #3342, 0.06 #6900), 01xbxn (0.20 #1384, 0.17 #3163, 0.06 #4942), 0cbv4g (0.20 #908, 0.17 #2687, 0.06 #4466), 03mh94 (0.20 #63, 0.17 #1842, 0.06 #3621), 01y9r2 (0.20 #1336, 0.17 #3115, 0.04 #23128), 04vr_f (0.20 #168, 0.17 #1947, 0.03 #12621), 047wh1 (0.20 #878, 0.17 #2657, 0.03 #13331), 0g7pm1 (0.20 #1194, 0.17 #2973, 0.03 #13647), 0fzm0g (0.20 #1772, 0.17 #3551, 0.03 #17783), 0cc7hmk (0.20 #288, 0.17 #2067, 0.03 #16299) >> Best rule #1563 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02lfcm; 0gy6z9; 02bj6k; >> query: (?x548, 02b6n9) <- film(?x548, ?x7012), ?x7012 = 09hy79, type_of_union(?x548, ?x566) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 014x77 film 02q8ms8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 82.000 0.200 http://example.org/film/actor/film./film/performance/film #10455-02d45s PRED entity: 02d45s PRED relation: film PRED expected values: 0292qb => 94 concepts (41 used for prediction) PRED predicted values (max 10 best out of 306): 01shy7 (0.06 #421, 0.05 #3989, 0.05 #7557), 02z3r8t (0.04 #108, 0.03 #3676, 0.03 #1892), 08r4x3 (0.04 #1938, 0.04 #154, 0.03 #7290), 03bx2lk (0.04 #1967, 0.03 #183, 0.03 #5535), 02x2jl_ (0.04 #37465, 0.02 #1750, 0.02 #3534), 011ywj (0.04 #37465, 0.02 #17487, 0.02 #15703), 0h7t36 (0.04 #37465, 0.01 #12384), 011yth (0.04 #37465, 0.01 #298, 0.01 #2082), 011yqc (0.04 #37465, 0.01 #232, 0.01 #2016), 09m6kg (0.04 #37465, 0.01 #31, 0.01 #1815) >> Best rule #421 for best value: >> intensional similarity = 3 >> extensional distance = 167 >> proper extension: 03n93; 019n7x; >> query: (?x10866, 01shy7) <- nationality(?x10866, ?x94), award_nominee(?x10866, ?x262), participant(?x10866, ?x4782) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02d45s film 0292qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 41.000 0.059 http://example.org/film/actor/film./film/performance/film #10454-042f1 PRED entity: 042f1 PRED relation: people! PRED expected values: 02ctzb => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 49): 041rx (0.49 #7135, 0.30 #4208, 0.28 #4433), 02ctzb (0.44 #1740, 0.43 #465, 0.40 #615), 033tf_ (0.22 #982, 0.22 #7138, 0.20 #607), 01qhm_ (0.22 #1581, 0.13 #2558, 0.10 #1356), 0x67 (0.21 #8719, 0.19 #8794, 0.17 #8193), 02w7gg (0.17 #4356, 0.11 #4881, 0.11 #6307), 07mqps (0.17 #19, 0.12 #919, 0.10 #1369), 06v41q (0.17 #28, 0.07 #628, 0.06 #928), 013xrm (0.15 #395, 0.07 #4449, 0.06 #4224), 07hwkr (0.13 #612, 0.12 #762, 0.12 #162) >> Best rule #7135 for best value: >> intensional similarity = 6 >> extensional distance = 712 >> proper extension: 07h1q; 02784z; 071jv5; 015c1b; >> query: (?x9765, 041rx) <- people(?x7185, ?x9765), people(?x7185, ?x5742), people(?x7185, ?x3969), influenced_by(?x117, ?x3969), location_of_ceremony(?x3969, ?x12160), notable_people_with_this_condition(?x12882, ?x5742) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1740 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0g4gr; 037mh8; *> query: (?x9765, 02ctzb) <- gender(?x9765, ?x231), ?x231 = 05zppz, taxonomy(?x9765, ?x939), ?x939 = 04n6k *> conf = 0.44 ranks of expected_values: 2 EVAL 042f1 people! 02ctzb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 159.000 159.000 0.494 http://example.org/people/ethnicity/people #10453-011s9r PRED entity: 011s9r PRED relation: story_by! PRED expected values: 024mxd => 161 concepts (147 used for prediction) PRED predicted values (max 10 best out of 223): 013q07 (0.20 #69, 0.08 #1085, 0.07 #1423), 05sy_5 (0.20 #215, 0.05 #2585, 0.03 #4952), 01f8hf (0.20 #167, 0.05 #2537, 0.02 #6595), 01gwk3 (0.20 #226, 0.05 #2596, 0.02 #6654), 0bth54 (0.20 #19, 0.05 #2389, 0.02 #6447), 024mxd (0.10 #13529, 0.10 #3047, 0.10 #2031), 0mbql (0.08 #906, 0.08 #567, 0.05 #2599), 0f3m1 (0.08 #950, 0.08 #611, 0.05 #2643), 0k_9j (0.08 #940, 0.08 #601, 0.05 #2633), 0dfw0 (0.08 #852, 0.08 #513, 0.05 #2545) >> Best rule #69 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0p__8; >> query: (?x11928, 013q07) <- story_by(?x1072, ?x11928), nationality(?x11928, ?x279), ?x279 = 0d060g, gender(?x11928, ?x231) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #13529 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 124 *> proper extension: 027l0b; 0cj2w; *> query: (?x11928, ?x1072) <- place_of_birth(?x11928, ?x1658), award_winner(?x11928, ?x8209), profession(?x11928, ?x353), story_by(?x1072, ?x8209) *> conf = 0.10 ranks of expected_values: 6 EVAL 011s9r story_by! 024mxd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 161.000 147.000 0.200 http://example.org/film/film/story_by #10452-013q0p PRED entity: 013q0p PRED relation: language PRED expected values: 02h40lc => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 36): 02h40lc (0.90 #539, 0.90 #782, 0.90 #903), 04306rv (0.40 #5, 0.22 #123, 0.17 #64), 03_9r (0.20 #10, 0.17 #69, 0.14 #187), 0cjk9 (0.20 #4, 0.11 #122), 064_8sq (0.17 #81, 0.15 #1651, 0.14 #741), 012w70 (0.17 #72, 0.06 #369, 0.05 #190), 01gp_d (0.17 #95, 0.02 #273, 0.02 #333), 06nm1 (0.13 #367, 0.13 #426, 0.12 #730), 02bjrlw (0.10 #538, 0.09 #476, 0.09 #659), 06b_j (0.08 #379, 0.06 #1167, 0.06 #1107) >> Best rule #539 for best value: >> intensional similarity = 4 >> extensional distance = 249 >> proper extension: 04dsnp; 0hv81; 072r5v; 02wk7b; 02yy9r; >> query: (?x4717, 02h40lc) <- film(?x2789, ?x4717), featured_film_locations(?x4717, ?x3026), film_release_region(?x4717, ?x94), genre(?x4717, ?x225) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013q0p language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.904 http://example.org/film/film/language #10451-02lfwp PRED entity: 02lfwp PRED relation: student! PRED expected values: 07tg4 0c_zj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 107): 065y4w7 (0.25 #14, 0.06 #4230, 0.06 #6338), 0bwfn (0.08 #6599, 0.08 #3437, 0.07 #8707), 015nl4 (0.07 #13769, 0.03 #1121, 0.02 #33270), 03ksy (0.07 #3795, 0.07 #6957, 0.07 #10646), 07tg4 (0.06 #13788, 0.03 #8518, 0.02 #6410), 07tds (0.05 #676, 0.03 #1203, 0.03 #1730), 01rtm4 (0.05 #531, 0.03 #1058, 0.02 #2639), 01g0p5 (0.05 #734, 0.03 #1788, 0.03 #2315), 017hnw (0.05 #1036, 0.03 #2090, 0.03 #2617), 0hd7j (0.05 #675, 0.03 #1729, 0.03 #2256) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01wyy_; >> query: (?x11965, 065y4w7) <- program(?x11965, ?x11818), ?x11818 = 06k176, profession(?x11965, ?x319) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #13788 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 394 *> proper extension: 0784v1; 07m69t; *> query: (?x11965, 07tg4) <- nationality(?x11965, ?x1310), ?x1310 = 02jx1 *> conf = 0.06 ranks of expected_values: 5 EVAL 02lfwp student! 0c_zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 105.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 02lfwp student! 07tg4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 105.000 105.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #10450-023slg PRED entity: 023slg PRED relation: student! PRED expected values: 031n5b => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 82): 09f2j (0.14 #2267, 0.06 #3321, 0.06 #5956), 04sylm (0.13 #2711, 0.08 #1657, 0.07 #4292), 02m0sc (0.08 #1401, 0.03 #5617, 0.01 #10360), 01q7q2 (0.08 #1347, 0.03 #5563, 0.01 #10306), 017rbx (0.08 #1396, 0.03 #5612, 0.01 #10355), 0k__z (0.08 #1362, 0.03 #5578, 0.01 #10321), 02g839 (0.08 #7930, 0.07 #8457, 0.07 #4241), 03ksy (0.08 #1687, 0.07 #2214, 0.07 #2741), 01t0dy (0.08 #1798, 0.07 #2852, 0.04 #4433), 01d34b (0.08 #1837, 0.07 #2891, 0.04 #4472) >> Best rule #2267 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 03qd_; >> query: (?x11916, 09f2j) <- role(?x11916, ?x716), ?x716 = 018vs, profession(?x11916, ?x655), group(?x11916, ?x4783), place_of_birth(?x11916, ?x3052) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2988 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 13 *> proper extension: 07q1v4; 01x1fq; *> query: (?x11916, 031n5b) <- role(?x11916, ?x716), role(?x11916, ?x228), ?x228 = 0l14qv, artists(?x505, ?x11916), ?x505 = 03_d0, role(?x74, ?x716) *> conf = 0.07 ranks of expected_values: 14 EVAL 023slg student! 031n5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 135.000 135.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #10449-0bytkq PRED entity: 0bytkq PRED relation: people! PRED expected values: 0222qb => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 31): 041rx (0.15 #389, 0.11 #928, 0.11 #851), 0x67 (0.09 #2552, 0.09 #2629, 0.09 #2243), 02w7gg (0.07 #849, 0.07 #926, 0.07 #1003), 02ctzb (0.07 #15, 0.05 #92, 0.05 #169), 013xrm (0.07 #20, 0.05 #97, 0.05 #174), 07hwkr (0.07 #12, 0.05 #166, 0.03 #320), 033tf_ (0.07 #469, 0.06 #854, 0.06 #777), 0xnvg (0.06 #398, 0.05 #475, 0.04 #1322), 0222qb (0.05 #198, 0.04 #275, 0.03 #352), 01qhm_ (0.04 #468, 0.04 #391, 0.03 #622) >> Best rule #389 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 01zcrv; >> query: (?x3080, 041rx) <- nominated_for(?x3080, ?x2852), nominated_for(?x4060, ?x2852), ?x4060 = 05hj_k >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #198 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 03cp7b3; *> query: (?x3080, 0222qb) <- profession(?x3080, ?x2450), award_winner(?x8284, ?x3080), ?x2450 = 02pjxr, genre(?x8284, ?x571) *> conf = 0.05 ranks of expected_values: 9 EVAL 0bytkq people! 0222qb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 83.000 83.000 0.151 http://example.org/people/ethnicity/people #10448-0k20s PRED entity: 0k20s PRED relation: nominated_for! PRED expected values: 040njc => 99 concepts (63 used for prediction) PRED predicted values (max 10 best out of 247): 0k611 (0.77 #11485, 0.68 #14778, 0.67 #11484), 019f4v (0.72 #991, 0.62 #3570, 0.62 #4507), 04dn09n (0.68 #972, 0.56 #504, 0.50 #34), 09d28z (0.68 #14778, 0.67 #11484, 0.67 #14777), 02z13jg (0.68 #14778, 0.67 #11484, 0.67 #14777), 054knh (0.68 #14778, 0.67 #11484, 0.67 #14777), 02rdxsh (0.68 #14778, 0.67 #11484, 0.67 #14777), 02pqp12 (0.66 #996, 0.37 #3575, 0.37 #4043), 040njc (0.64 #944, 0.50 #1414, 0.48 #3991), 03hkv_r (0.53 #1422, 0.46 #2126, 0.43 #2361) >> Best rule #11485 for best value: >> intensional similarity = 4 >> extensional distance = 645 >> proper extension: 07bz5; >> query: (?x11110, ?x77) <- nominated_for(?x3177, ?x11110), award(?x11110, ?x77), award_winner(?x77, ?x2086), ceremony(?x77, ?x78) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #944 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 45 *> proper extension: 09gq0x5; 0y_9q; *> query: (?x11110, 040njc) <- titles(?x789, ?x11110), nominated_for(?x2379, ?x11110), nominated_for(?x1313, ?x11110), ?x1313 = 0gs9p, ?x2379 = 02qvyrt, nominated_for(?x3177, ?x11110) *> conf = 0.64 ranks of expected_values: 9 EVAL 0k20s nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 99.000 63.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10447-08xvpn PRED entity: 08xvpn PRED relation: currency PRED expected values: 09nqf => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.78 #106, 0.78 #64, 0.77 #190), 02l6h (0.20 #4, 0.02 #53, 0.02 #46), 01nv4h (0.03 #44, 0.03 #170, 0.03 #114), 0ptk_ (0.01 #52) >> Best rule #106 for best value: >> intensional similarity = 3 >> extensional distance = 251 >> proper extension: 05f67hw; >> query: (?x9801, 09nqf) <- films(?x3359, ?x9801), produced_by(?x9801, ?x846), award_nominee(?x846, ?x847) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08xvpn currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.783 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10446-051wwp PRED entity: 051wwp PRED relation: gender PRED expected values: 05zppz => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #19, 0.84 #15, 0.84 #13), 02zsn (0.53 #49, 0.42 #2, 0.31 #22) >> Best rule #19 for best value: >> intensional similarity = 2 >> extensional distance = 377 >> proper extension: 0g_rs_; >> query: (?x4928, 05zppz) <- produced_by(?x8367, ?x4928), profession(?x4928, ?x319) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051wwp gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.844 http://example.org/people/person/gender #10445-06fqlk PRED entity: 06fqlk PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #41, 0.85 #31, 0.83 #62), 02nxhr (0.08 #7, 0.05 #32, 0.04 #153), 07z4p (0.08 #10, 0.03 #246, 0.02 #266), 07c52 (0.04 #38, 0.03 #244, 0.03 #69) >> Best rule #41 for best value: >> intensional similarity = 5 >> extensional distance = 103 >> proper extension: 034qrh; 026p_bs; 02qrv7; 0p3_y; 01kf4tt; 0946bb; 0kvgtf; 02rq8k8; 07nxvj; 015qqg; ... >> query: (?x6489, 029j_) <- language(?x6489, ?x732), film(?x157, ?x6489), ?x732 = 04306rv, genre(?x6489, ?x225), award_nominee(?x157, ?x91) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06fqlk film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #10444-04gqr PRED entity: 04gqr PRED relation: country! PRED expected values: 07gyv => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 53): 01cgz (0.63 #1604, 0.63 #1445, 0.63 #1021), 06f41 (0.60 #227, 0.59 #174, 0.58 #333), 064vjs (0.56 #189, 0.55 #242, 0.53 #348), 07gyv (0.55 #219, 0.53 #325, 0.52 #272), 03hr1p (0.55 #287, 0.48 #234, 0.47 #181), 07jbh (0.53 #191, 0.49 #562, 0.48 #403), 02y8z (0.53 #178, 0.48 #284, 0.45 #549), 07bs0 (0.53 #172, 0.43 #543, 0.43 #225), 06wrt (0.52 #281, 0.48 #228, 0.47 #546), 0w0d (0.51 #542, 0.50 #383, 0.49 #1019) >> Best rule #1604 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 027rn; 05r4w; 09c7w0; 0160w; 0b90_r; 0154j; 03rjj; 03_3d; 0h3y; 0d0vqn; ... >> query: (?x4120, 01cgz) <- currency(?x4120, ?x170), taxonomy(?x4120, ?x939), country(?x1121, ?x4120) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #219 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 068cn; 082pc; *> query: (?x4120, 07gyv) <- currency(?x4120, ?x170), adjoins(?x4120, ?x291), locations(?x11216, ?x4120) *> conf = 0.55 ranks of expected_values: 4 EVAL 04gqr country! 07gyv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 142.000 142.000 0.633 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #10443-0z07 PRED entity: 0z07 PRED relation: company! PRED expected values: 0krdk => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 40): 0krdk (0.81 #406, 0.74 #317, 0.70 #672), 02211by (0.57 #92, 0.36 #89, 0.20 #137), 01yc02 (0.53 #408, 0.44 #1031, 0.41 #319), 0dq3c (0.47 #402, 0.47 #624, 0.44 #313), 09d6p2 (0.47 #418, 0.43 #462, 0.36 #89), 05_wyz (0.46 #461, 0.44 #417, 0.43 #639), 01cpkt (0.43 #83, 0.36 #89, 0.10 #4290), 0130xz (0.43 #85, 0.36 #89, 0.10 #4290), 01kr6k (0.36 #89, 0.29 #381, 0.29 #115), 04192r (0.36 #89, 0.29 #129, 0.22 #262) >> Best rule #406 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 061v5m; 0537b; >> query: (?x11188, 0krdk) <- company(?x4682, ?x11188), company(?x346, ?x11188), industry(?x11188, ?x1605), ?x4682 = 0dq_5, ?x346 = 060c4 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0z07 company! 0krdk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.806 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #10442-032t2z PRED entity: 032t2z PRED relation: gender PRED expected values: 05zppz => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 3): 05zppz (0.89 #25, 0.88 #45, 0.86 #53), 02zsn (0.49 #156, 0.46 #236, 0.46 #220), 0jpmt (0.12 #131) >> Best rule #25 for best value: >> intensional similarity = 6 >> extensional distance = 94 >> proper extension: 011_vz; 017mbb; >> query: (?x642, 05zppz) <- artists(?x2809, ?x642), role(?x642, ?x228), artists(?x2809, ?x6475), artists(?x2809, ?x6406), ?x6475 = 07mvp, ?x6406 = 01386_ >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 032t2z gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.885 http://example.org/people/person/gender #10441-04bp0l PRED entity: 04bp0l PRED relation: languages PRED expected values: 02h40lc => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.91 #200, 0.91 #212, 0.90 #234), 0t_2 (0.33 #6, 0.30 #210, 0.17 #409), 03_9r (0.04 #335, 0.04 #346), 06nm1 (0.02 #192, 0.02 #336, 0.02 #347), 064_8sq (0.02 #194, 0.01 #294, 0.01 #338), 02bv9 (0.02 #196), 04306rv (0.02 #190), 02bjrlw (0.02 #188) >> Best rule #200 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 01xr2s; 04f6hhm; 02q5bx2; >> query: (?x14684, 02h40lc) <- genre(?x14684, ?x5728), nominated_for(?x6678, ?x14684), program(?x6678, ?x6322), languages(?x6322, ?x254) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bp0l languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.909 http://example.org/tv/tv_program/languages #10440-01_xtx PRED entity: 01_xtx PRED relation: award PRED expected values: 0f4x7 02x4w6g => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 243): 027c95y (0.73 #1196, 0.70 #31850, 0.68 #1993), 0ck27z (0.61 #488, 0.21 #9247, 0.15 #16014), 05ztrmj (0.25 #179, 0.17 #6152, 0.14 #2968), 063y_ky (0.25 #127, 0.12 #31451, 0.09 #2518), 02x8n1n (0.25 #117, 0.09 #515, 0.07 #797), 05q5t0b (0.25 #158, 0.07 #797, 0.07 #955), 03c7tr1 (0.23 #2846, 0.21 #2050, 0.19 #3244), 0gqwc (0.23 #870, 0.16 #2862, 0.16 #1667), 05b4l5x (0.21 #5581, 0.20 #5183, 0.20 #4785), 094qd5 (0.20 #840, 0.13 #3230, 0.13 #6016) >> Best rule #1196 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 05r5w; 01wc7p; >> query: (?x3865, ?x2915) <- participant(?x3865, ?x4782), languages(?x3865, ?x5607), award_winner(?x2915, ?x3865) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1624 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 04nw9; 016vg8; 0gmtm; 026r8q; 0g476; *> query: (?x3865, 0f4x7) <- participant(?x3865, ?x4782), religion(?x3865, ?x1985), award_winner(?x2915, ?x3865) *> conf = 0.14 ranks of expected_values: 16, 26 EVAL 01_xtx award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 100.000 100.000 0.728 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01_xtx award 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 100.000 100.000 0.728 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10439-07m2y PRED entity: 07m2y PRED relation: group PRED expected values: 01lf293 => 74 concepts (26 used for prediction) PRED predicted values (max 10 best out of 826): 05563d (0.78 #3069, 0.75 #2120, 0.71 #1929), 02vnpv (0.75 #2625, 0.70 #3766, 0.67 #4716), 07mvp (0.75 #2544, 0.67 #4635, 0.67 #1403), 0134wr (0.67 #1432, 0.67 #1243, 0.64 #4283), 01q99h (0.67 #1397, 0.67 #1208, 0.62 #2538), 03qkcn9 (0.67 #1505, 0.67 #1316, 0.62 #2646), 01vrwfv (0.67 #1353, 0.67 #1164, 0.62 #2494), 01v0sxx (0.67 #1469, 0.67 #1280, 0.62 #2610), 06nv27 (0.67 #4611, 0.64 #4230, 0.62 #2520), 02vgh (0.67 #1409, 0.62 #2930, 0.62 #2550) >> Best rule #3069 for best value: >> intensional similarity = 17 >> extensional distance = 7 >> proper extension: 05148p4; >> query: (?x9413, 05563d) <- instrumentalists(?x9413, ?x2945), role(?x9413, ?x1166), role(?x212, ?x9413), ?x2945 = 01271h, instrumentalists(?x1166, ?x7252), instrumentalists(?x1166, ?x6067), group(?x1166, ?x6475), group(?x1166, ?x2005), ?x7252 = 017g21, ?x6067 = 018y81, role(?x75, ?x1166), role(?x1166, ?x432), role(?x248, ?x1166), ?x6475 = 07mvp, ?x2005 = 05k79, role(?x1166, ?x74), role(?x925, ?x1166) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1440 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 4 *> proper extension: 0l14md; *> query: (?x9413, 01lf293) <- instrumentalists(?x9413, ?x6469), instrumentalists(?x9413, ?x2945), role(?x9413, ?x2888), role(?x314, ?x9413), type_of_union(?x2945, ?x566), ?x314 = 02sgy, award_winner(?x10574, ?x2945), participant(?x2945, ?x6208), artists(?x302, ?x2945), ?x2888 = 02fsn, artist(?x2299, ?x6208), role(?x2945, ?x227), gender(?x6469, ?x231), gender(?x6208, ?x514), role(?x6208, ?x74), role(?x9413, ?x1332) *> conf = 0.50 ranks of expected_values: 54 EVAL 07m2y group 01lf293 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 74.000 26.000 0.778 http://example.org/music/performance_role/regular_performances./music/group_membership/group #10438-01lp8 PRED entity: 01lp8 PRED relation: taxonomy PRED expected values: 04n6k => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.44 #28, 0.44 #27, 0.42 #34) >> Best rule #28 for best value: >> intensional similarity = 12 >> extensional distance = 14 >> proper extension: 0b06q; 06yyp; 042s9; >> query: (?x109, ?x939) <- religion(?x3357, ?x109), religion(?x1351, ?x109), religion(?x961, ?x109), religion(?x521, ?x109), contains(?x8260, ?x961), adjoins(?x1603, ?x3357), medal(?x3357, ?x422), jurisdiction_of_office(?x900, ?x961), contains(?x961, ?x310), country(?x1037, ?x3357), location_of_ceremony(?x566, ?x1351), taxonomy(?x1351, ?x939) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lp8 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.438 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #10437-06pcz0 PRED entity: 06pcz0 PRED relation: nationality PRED expected values: 09c7w0 => 92 concepts (88 used for prediction) PRED predicted values (max 10 best out of 107): 09c7w0 (0.89 #4441, 0.86 #1007, 0.80 #705), 03v0t (0.33 #8166, 0.33 #5448), 03rjj (0.25 #3129, 0.24 #7060, 0.03 #3231), 0d060g (0.25 #3129, 0.06 #2126, 0.05 #208), 02_286 (0.25 #3129, 0.01 #6859), 059rby (0.25 #3129, 0.01 #6859), 02jx1 (0.24 #7060, 0.10 #4976, 0.10 #5077), 07ssc (0.24 #7060, 0.09 #518, 0.08 #216), 03rk0 (0.24 #7060, 0.06 #951, 0.06 #8010), 0h7x (0.24 #7060, 0.03 #3231, 0.03 #5044) >> Best rule #4441 for best value: >> intensional similarity = 3 >> extensional distance = 1334 >> proper extension: 07m69t; >> query: (?x11437, 09c7w0) <- place_of_birth(?x11437, ?x5037), source(?x5037, ?x958), location(?x1593, ?x5037) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06pcz0 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 88.000 0.894 http://example.org/people/person/nationality #10436-0661ql3 PRED entity: 0661ql3 PRED relation: nominated_for! PRED expected values: 05ztjjw 0gq_v 04dn09n 099c8n => 85 concepts (80 used for prediction) PRED predicted values (max 10 best out of 201): 0gr0m (0.77 #5895, 0.74 #2618, 0.67 #5894), 0p9sw (0.77 #5895, 0.74 #2618, 0.67 #5894), 02r22gf (0.77 #5895, 0.74 #2618, 0.67 #5894), 099c8n (0.52 #51, 0.25 #487, 0.22 #3105), 0gs9p (0.49 #2455, 0.37 #3985, 0.37 #3111), 0gq_v (0.47 #17, 0.40 #2415, 0.32 #3945), 0k611 (0.43 #2462, 0.34 #2900, 0.32 #3118), 0gr4k (0.35 #2422, 0.24 #4388, 0.23 #6574), 04dn09n (0.33 #2430, 0.26 #3960, 0.26 #3086), 0gs96 (0.33 #78, 0.28 #2476, 0.23 #4006) >> Best rule #5895 for best value: >> intensional similarity = 4 >> extensional distance = 645 >> proper extension: 06w7mlh; 07bz5; >> query: (?x2394, ?x1243) <- award(?x2394, ?x1243), nominated_for(?x748, ?x2394), ceremony(?x1243, ?x78), award_winner(?x1243, ?x2466) >> conf = 0.77 => this is the best rule for 3 predicted values *> Best rule #51 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 62 *> proper extension: 011yxg; 020fcn; 029zqn; 0g3zrd; 026p4q7; 04jwly; 07w8fz; 0prh7; 01y9r2; 04jplwp; ... *> query: (?x2394, 099c8n) <- film_crew_role(?x2394, ?x137), nominated_for(?x2393, ?x2394), country(?x2394, ?x94), ?x2393 = 02x258x *> conf = 0.52 ranks of expected_values: 4, 6, 9, 30 EVAL 0661ql3 nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 80.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0661ql3 nominated_for! 04dn09n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 80.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0661ql3 nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 85.000 80.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0661ql3 nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 85.000 80.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10435-0k_p5 PRED entity: 0k_p5 PRED relation: contains! PRED expected values: 030qb3t => 82 concepts (72 used for prediction) PRED predicted values (max 10 best out of 197): 030qb3t (0.19 #992, 0.11 #3672, 0.09 #5459), 07ssc (0.17 #48270, 0.17 #49163, 0.16 #50952), 02jx1 (0.15 #48324, 0.15 #49217, 0.13 #52794), 0cb4j (0.15 #3607, 0.12 #5394, 0.05 #9861), 0k_s5 (0.15 #1581, 0.09 #4261, 0.07 #6048), 07z1m (0.14 #5450, 0.06 #3663, 0.04 #47435), 0kpzy (0.13 #3939, 0.03 #20018, 0.02 #21806), 04_1l0v (0.12 #24571, 0.09 #27251, 0.06 #40647), 059rby (0.11 #2699, 0.11 #1806, 0.10 #4486), 05k7sb (0.09 #21571, 0.09 #19783, 0.09 #22466) >> Best rule #992 for best value: >> intensional similarity = 2 >> extensional distance = 25 >> proper extension: 018mm4; 0fr9jp; >> query: (?x5895, 030qb3t) <- contains(?x2949, ?x5895), ?x2949 = 0kpys >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k_p5 contains! 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 72.000 0.185 http://example.org/location/location/contains #10434-086qd PRED entity: 086qd PRED relation: category PRED expected values: 08mbj5d => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.87 #33, 0.85 #6, 0.85 #41) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 281 >> proper extension: 01lcxbb; 0f6lx; 013rds; >> query: (?x2138, 08mbj5d) <- award_winner(?x462, ?x2138), artists(?x671, ?x2138), origin(?x2138, ?x5774) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 086qd category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.866 http://example.org/common/topic/webpage./common/webpage/category #10433-01jsn5 PRED entity: 01jsn5 PRED relation: school! PRED expected values: 0jm3b => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 87): 05m_8 (0.17 #1047, 0.17 #1569, 0.17 #612), 0713r (0.17 #555, 0.15 #207, 0.15 #642), 02d02 (0.15 #238, 0.12 #673, 0.12 #586), 01ypc (0.15 #175, 0.08 #1567, 0.08 #784), 01slc (0.15 #1097, 0.15 #662, 0.15 #575), 01d5z (0.15 #619, 0.15 #532, 0.11 #1750), 051vz (0.13 #1588, 0.13 #805, 0.13 #1066), 01yjl (0.12 #636, 0.12 #549, 0.12 #1071), 05g76 (0.12 #629, 0.10 #542, 0.08 #455), 061xq (0.12 #1075, 0.10 #1597, 0.10 #814) >> Best rule #1047 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 017j69; >> query: (?x2399, 05m_8) <- institution(?x620, ?x2399), currency(?x2399, ?x170), school(?x1823, ?x2399), school(?x8542, ?x2399) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1828 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 06mkj; 0d05w3; *> query: (?x2399, ?x660) <- contains(?x94, ?x2399), school(?x12852, ?x2399), draft(?x660, ?x12852) *> conf = 0.11 ranks of expected_values: 36 EVAL 01jsn5 school! 0jm3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 157.000 157.000 0.174 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #10432-016z43 PRED entity: 016z43 PRED relation: titles! PRED expected values: 0djd22 => 57 concepts (38 used for prediction) PRED predicted values (max 10 best out of 52): 01z4y (0.33 #134, 0.21 #2248, 0.20 #3263), 09b3v (0.33 #48, 0.08 #148, 0.05 #950), 04xvlr (0.33 #203, 0.24 #1106, 0.23 #1207), 0l4h_ (0.25 #181), 0219x_ (0.22 #2824, 0.21 #2926, 0.21 #2620), 01t_vv (0.22 #2824, 0.21 #2926, 0.21 #2620), 0556j8 (0.22 #2824, 0.21 #2926, 0.21 #2620), 05p553 (0.22 #2824, 0.21 #2926, 0.21 #2620), 07ssc (0.22 #209, 0.11 #1012, 0.10 #2122), 01jfsb (0.16 #519, 0.15 #419, 0.14 #821) >> Best rule #134 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 02_1sj; 01738w; >> query: (?x12401, 01z4y) <- titles(?x53, ?x12401), film(?x806, ?x12401), ?x806 = 03qd_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2052 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 767 *> proper extension: 04gknr; 064q5v; 03ffcz; *> query: (?x12401, 0djd22) <- titles(?x53, ?x12401), award_winner(?x12401, ?x4563), film(?x806, ?x12401) *> conf = 0.01 ranks of expected_values: 42 EVAL 016z43 titles! 0djd22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 57.000 38.000 0.333 http://example.org/media_common/netflix_genre/titles #10431-0clfdj PRED entity: 0clfdj PRED relation: honored_for PRED expected values: 017z49 03176f => 25 concepts (21 used for prediction) PRED predicted values (max 10 best out of 877): 02rcwq0 (0.57 #4454, 0.17 #6239, 0.14 #6532), 01b_lz (0.50 #2568, 0.33 #3756, 0.33 #197), 0fhzwl (0.50 #2869, 0.33 #4057, 0.33 #498), 05lfwd (0.50 #2715, 0.33 #3903, 0.33 #344), 0cs134 (0.50 #2927, 0.33 #4115, 0.33 #556), 030k94 (0.50 #2556, 0.33 #3744, 0.33 #185), 0d68qy (0.43 #4303, 0.35 #5493, 0.31 #6684), 02rzdcp (0.43 #4349, 0.22 #6134, 0.22 #5539), 03d34x8 (0.43 #4269, 0.17 #6054, 0.14 #6650), 0l76z (0.33 #3831, 0.33 #864, 0.08 #8585) >> Best rule #4454 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 09p2r9; >> query: (?x472, 02rcwq0) <- ceremony(?x451, ?x472), award_winner(?x472, ?x9314), award_winner(?x472, ?x1950), award_nominee(?x1950, ?x1739), nominated_for(?x9314, ?x2973), award_winner(?x1950, ?x7045), award_nominee(?x9314, ?x4580), people(?x1423, ?x9314), gender(?x9314, ?x514), ?x4580 = 026l37, honored_for(?x472, ?x253), film(?x9314, ?x1721), profession(?x1950, ?x1032) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2963 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 02q690_; *> query: (?x472, ?x8367) <- ceremony(?x451, ?x472), award_winner(?x472, ?x9314), award_winner(?x472, ?x8674), award_winner(?x472, ?x1950), award_winner(?x472, ?x374), award_nominee(?x1950, ?x1739), nominated_for(?x9314, ?x2973), award_winner(?x1950, ?x7045), award_nominee(?x9314, ?x1486), people(?x1446, ?x9314), gender(?x9314, ?x514), ?x8674 = 01jmv8, award_nominee(?x539, ?x9314), ?x1446 = 033tf_, award_winner(?x8367, ?x374), participant(?x9314, ?x10103) *> conf = 0.20 ranks of expected_values: 126, 396 EVAL 0clfdj honored_for 03176f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 25.000 21.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0clfdj honored_for 017z49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 25.000 21.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #10430-0jm3v PRED entity: 0jm3v PRED relation: draft PRED expected values: 025tn92 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 16): 025tn92 (0.82 #155, 0.78 #188, 0.78 #171), 038981 (0.75 #109, 0.73 #157, 0.70 #190), 04f4z1k (0.50 #47, 0.40 #256, 0.40 #79), 047dpm0 (0.50 #48, 0.40 #80, 0.40 #64), 02rl201 (0.50 #36, 0.40 #68, 0.40 #52), 02x2khw (0.50 #35, 0.40 #51, 0.33 #244), 02pq_rp (0.40 #71, 0.37 #856, 0.37 #248), 02z6872 (0.40 #73, 0.37 #250, 0.36 #858), 02r6gw6 (0.40 #76, 0.36 #861, 0.34 #301), 03nt7j (0.40 #247, 0.33 #519, 0.32 #177) >> Best rule #155 for best value: >> intensional similarity = 7 >> extensional distance = 20 >> proper extension: 0jmnl; >> query: (?x799, 025tn92) <- school(?x799, ?x6814), draft(?x799, ?x2569), ?x2569 = 038c0q, fraternities_and_sororities(?x6814, ?x3697), category(?x6814, ?x134), ?x3697 = 0325pb, major_field_of_study(?x6814, ?x1154) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jm3v draft 025tn92 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.818 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #10429-04ltlj PRED entity: 04ltlj PRED relation: film! PRED expected values: 016zp5 => 67 concepts (32 used for prediction) PRED predicted values (max 10 best out of 637): 0hpz8 (0.33 #1843), 02vyw (0.10 #24955, 0.10 #18717), 04_by (0.09 #10398, 0.08 #12478), 01q_ph (0.07 #29114, 0.07 #33274, 0.03 #2136), 0h0wc (0.07 #29114, 0.07 #33274, 0.02 #27455), 0mdqp (0.07 #29114, 0.07 #33274, 0.02 #16756), 02g0mx (0.07 #29114, 0.07 #33274, 0.02 #2603), 0f7hc (0.07 #29114, 0.07 #33274, 0.01 #15387), 01h1b (0.07 #29114, 0.07 #33274, 0.01 #9522), 01kt17 (0.07 #29114, 0.07 #33274) >> Best rule #1843 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0jqzt; >> query: (?x11276, 0hpz8) <- titles(?x13390, ?x11276), language(?x11276, ?x254), film_release_region(?x11276, ?x94), ?x13390 = 02blr4 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5133 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 104 *> proper extension: 0gtv7pk; 0gd0c7x; 0gfsq9; 0gj8nq2; 09fc83; 02qr3k8; 0gvvf4j; 0dp7wt; 06_sc3; 0dgq80b; *> query: (?x11276, 016zp5) <- genre(?x11276, ?x571), film(?x4859, ?x11276), ?x571 = 03npn, award_nominee(?x931, ?x4859) *> conf = 0.03 ranks of expected_values: 92 EVAL 04ltlj film! 016zp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 67.000 32.000 0.333 http://example.org/film/actor/film./film/performance/film #10428-03mv0b PRED entity: 03mv0b PRED relation: nationality PRED expected values: 02jx1 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 75): 09c7w0 (0.80 #1001, 0.79 #1201, 0.78 #1301), 07ssc (0.78 #7329, 0.75 #5323, 0.14 #8834), 02jx1 (0.44 #2106, 0.21 #8532, 0.15 #533), 03rk0 (0.19 #446, 0.18 #946, 0.15 #2554), 077qn (0.14 #8834, 0.13 #8935, 0.11 #9236), 06c1y (0.14 #8834, 0.11 #9236, 0.11 #8131), 0d05w3 (0.10 #450, 0.07 #950, 0.04 #550), 0d060g (0.07 #507, 0.07 #807, 0.06 #1810), 0h7x (0.07 #835, 0.03 #1436, 0.03 #1335), 0f8l9c (0.05 #1423, 0.05 #322, 0.04 #1524) >> Best rule #1001 for best value: >> intensional similarity = 6 >> extensional distance = 42 >> proper extension: 01vb403; 011_3s; 0dpqk; 016gkf; 01wc7p; 01h1b; 01pjr7; 03m2fg; 030g9z; 0c1j_; ... >> query: (?x9579, 09c7w0) <- award_winner(?x8459, ?x9579), profession(?x9579, ?x1146), profession(?x9579, ?x319), ?x1146 = 018gz8, type_of_union(?x9579, ?x11744), ?x319 = 01d_h8 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2106 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 107 *> proper extension: 03zqc1; 03m9c8; 01dbgw; *> query: (?x9579, ?x94) <- award_winner(?x8459, ?x9579), award_winner(?x8459, ?x4240), award_winner(?x8459, ?x3644), nationality(?x4240, ?x94), participant(?x4240, ?x3002), ?x3644 = 057hz *> conf = 0.44 ranks of expected_values: 3 EVAL 03mv0b nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 104.000 0.795 http://example.org/people/person/nationality #10427-05nrkb PRED entity: 05nrkb PRED relation: student PRED expected values: 0mbs8 => 85 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1761): 01pcdn (0.33 #825, 0.17 #7048, 0.03 #9124), 075npt (0.33 #1973, 0.17 #8196, 0.03 #10272), 013pp3 (0.33 #916, 0.17 #7139, 0.03 #9215), 04z0g (0.33 #1000, 0.17 #7223, 0.03 #9299), 01wz01 (0.33 #687, 0.17 #6910, 0.03 #8986), 015wfg (0.33 #730, 0.17 #6953, 0.03 #9029), 01jqr_5 (0.33 #383, 0.17 #6606, 0.03 #8682), 022s1m (0.33 #2029, 0.17 #8252, 0.03 #10328), 01m3b1t (0.33 #1244, 0.17 #7467, 0.03 #9543), 01z7_f (0.33 #720, 0.17 #6943, 0.03 #9019) >> Best rule #825 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0ks67; >> query: (?x9479, 01pcdn) <- student(?x9479, ?x4586), student(?x9479, ?x3557), student(?x9479, ?x2681), ?x3557 = 01qr1_, award_nominee(?x4586, ?x336), participant(?x3307, ?x2681) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05nrkb student 0mbs8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 38.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #10426-06chf PRED entity: 06chf PRED relation: film PRED expected values: 03q5db 017n9 => 154 concepts (123 used for prediction) PRED predicted values (max 10 best out of 555): 04kzqz (0.59 #26305, 0.59 #15613, 0.55 #31237), 04f6df0 (0.55 #31237, 0.17 #9038, 0.16 #18903), 05z43v (0.55 #31237, 0.16 #18903, 0.14 #23016), 063ykwt (0.55 #31237, 0.16 #18903, 0.14 #23016), 03tbg6 (0.33 #10684, 0.32 #8216, 0.22 #43566), 01cssf (0.33 #10684, 0.32 #8216, 0.22 #43566), 07tlfx (0.33 #10684, 0.32 #8216, 0.22 #43566), 0b6l1st (0.33 #10684, 0.32 #8216, 0.22 #35349), 02q0k7v (0.33 #10684, 0.32 #8216, 0.22 #35349), 07_k0c0 (0.17 #9038, 0.06 #9861, 0.05 #18902) >> Best rule #26305 for best value: >> intensional similarity = 3 >> extensional distance = 198 >> proper extension: 0405l; >> query: (?x2803, ?x1230) <- award_winner(?x1230, ?x2803), profession(?x2803, ?x319), film(?x2803, ?x1038) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06chf film 017n9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 123.000 0.595 http://example.org/film/director/film EVAL 06chf film 03q5db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 123.000 0.595 http://example.org/film/director/film #10425-01pcrw PRED entity: 01pcrw PRED relation: location PRED expected values: 04jpl => 141 concepts (94 used for prediction) PRED predicted values (max 10 best out of 265): 030qb3t (0.33 #83, 0.27 #2493, 0.26 #33824), 04jpl (0.28 #20099, 0.14 #12064, 0.13 #48222), 02_286 (0.23 #41814, 0.23 #4053, 0.22 #28957), 0k049 (0.17 #8, 0.05 #5630, 0.05 #6434), 0c_m3 (0.17 #271, 0.03 #40170, 0.02 #11515), 0179qv (0.17 #770, 0.02 #3180, 0.02 #3983), 059rby (0.09 #819, 0.08 #1622, 0.05 #22512), 0fhsz (0.09 #1436, 0.08 #2239), 0ftn8 (0.09 #1345, 0.08 #2148), 03902 (0.09 #1324, 0.08 #2127) >> Best rule #83 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01q32bd; >> query: (?x3083, 030qb3t) <- participant(?x2562, ?x3083), profession(?x3083, ?x353), religion(?x3083, ?x1985), ?x2562 = 01trhmt >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #20099 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 157 *> proper extension: 07m69t; *> query: (?x3083, 04jpl) <- nationality(?x3083, ?x512), ?x512 = 07ssc, location(?x3083, ?x6959) *> conf = 0.28 ranks of expected_values: 2 EVAL 01pcrw location 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 94.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #10424-06wjf PRED entity: 06wjf PRED relation: month PRED expected values: 02xx5 => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 1): 02xx5 (0.92 #57, 0.90 #52, 0.90 #58) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0177z; 0d9jr; >> query: (?x4271, 02xx5) <- month(?x4271, ?x9905), mode_of_transportation(?x4271, ?x4272), ?x9905 = 028kb >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06wjf month 02xx5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 187.000 0.918 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #10423-0hzlz PRED entity: 0hzlz PRED relation: country! PRED expected values: 0581vn8 034hzj => 201 concepts (134 used for prediction) PRED predicted values (max 10 best out of 1840): 01m13b (0.35 #5259, 0.34 #20598, 0.33 #8667), 049mql (0.26 #5755, 0.22 #4051, 0.19 #21094), 0bmch_x (0.26 #5901, 0.21 #12719, 0.19 #21240), 0dscrwf (0.26 #5180, 0.17 #3476, 0.17 #11998), 023g6w (0.25 #21852, 0.22 #9921, 0.22 #6513), 03rz2b (0.22 #5550, 0.18 #10664, 0.17 #12368), 0401sg (0.22 #5205, 0.17 #15432, 0.17 #12023), 0cp08zg (0.22 #6379, 0.17 #16606, 0.17 #13197), 04z4j2 (0.22 #6658, 0.17 #13476, 0.16 #21997), 0fjyzt (0.22 #6001, 0.17 #12819, 0.16 #21340) >> Best rule #5259 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 01f08r; >> query: (?x792, 01m13b) <- exported_to(?x8742, ?x792), location(?x1235, ?x792), jurisdiction_of_office(?x182, ?x792) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #6573 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 01f08r; *> query: (?x792, 0581vn8) <- exported_to(?x8742, ?x792), location(?x1235, ?x792), jurisdiction_of_office(?x182, ?x792) *> conf = 0.09 ranks of expected_values: 510, 668 EVAL 0hzlz country! 034hzj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 201.000 134.000 0.348 http://example.org/film/film/country EVAL 0hzlz country! 0581vn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 201.000 134.000 0.348 http://example.org/film/film/country #10422-0f4l5 PRED entity: 0f4l5 PRED relation: nutrient! PRED expected values: 01nkt 04zpv => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 24): 01nkt (0.95 #450, 0.95 #442, 0.94 #312), 0fjfh (0.94 #810, 0.94 #796, 0.94 #710), 04zpv (0.92 #845, 0.92 #842, 0.92 #779), 0fbdb (0.91 #714, 0.91 #691, 0.91 #916), 0fj52s (0.90 #970, 0.90 #939, 0.90 #822), 061_f (0.89 #977, 0.89 #898, 0.89 #870), 014j1m (0.89 #582, 0.89 #374, 0.89 #356), 0hkxq (0.87 #927, 0.87 #906, 0.87 #964), 0971v (0.86 #407, 0.86 #348, 0.86 #335), 0fbw6 (0.85 #943, 0.83 #924, 0.83 #909) >> Best rule #450 for best value: >> intensional similarity = 132 >> extensional distance = 35 >> proper extension: 04kl74p; 0h1zw; 025sf8g; 06jry; 09gwd; 0h1tg; 02p0tjr; 02kc_w5; >> query: (?x12336, ?x6032) <- nutrient(?x10612, ?x12336), nutrient(?x9732, ?x12336), nutrient(?x6159, ?x12336), nutrient(?x3468, ?x12336), nutrient(?x1959, ?x12336), ?x1959 = 0f25w9, ?x3468 = 0cxn2, ?x6159 = 033cnk, nutrient(?x9732, ?x14210), nutrient(?x9732, ?x13545), nutrient(?x9732, ?x13498), nutrient(?x9732, ?x12902), nutrient(?x9732, ?x12454), nutrient(?x9732, ?x12083), nutrient(?x9732, ?x11758), nutrient(?x9732, ?x11592), nutrient(?x9732, ?x11409), nutrient(?x9732, ?x11270), nutrient(?x9732, ?x10891), nutrient(?x9732, ?x10709), nutrient(?x9732, ?x10098), nutrient(?x9732, ?x9949), nutrient(?x9732, ?x9915), nutrient(?x9732, ?x9733), nutrient(?x9732, ?x9708), nutrient(?x9732, ?x9490), nutrient(?x9732, ?x9436), nutrient(?x9732, ?x9426), nutrient(?x9732, ?x9365), nutrient(?x9732, ?x8442), nutrient(?x9732, ?x8413), nutrient(?x9732, ?x7894), nutrient(?x9732, ?x7720), nutrient(?x9732, ?x7652), nutrient(?x9732, ?x7364), nutrient(?x9732, ?x7362), nutrient(?x9732, ?x7219), nutrient(?x9732, ?x7135), nutrient(?x9732, ?x6586), nutrient(?x9732, ?x6517), nutrient(?x9732, ?x6160), nutrient(?x9732, ?x6033), nutrient(?x9732, ?x5549), nutrient(?x9732, ?x5526), nutrient(?x9732, ?x5451), nutrient(?x9732, ?x5374), nutrient(?x9732, ?x5010), nutrient(?x9732, ?x2702), nutrient(?x9732, ?x2018), nutrient(?x9732, ?x1960), nutrient(?x9732, ?x1304), nutrient(?x9732, ?x1258), ?x9733 = 0h1tz, ?x6033 = 04zjxcz, ?x11409 = 0h1yf, ?x7364 = 09gvd, ?x13498 = 07q0m, ?x6517 = 02kd8zw, ?x6586 = 05gh50, ?x11592 = 025sf0_, ?x7720 = 025s7x6, ?x14210 = 0f4k5, ?x8413 = 02kc4sf, ?x9915 = 025tkqy, ?x8442 = 02kcv4x, ?x9426 = 0h1yy, ?x5010 = 0h1vz, ?x10709 = 0h1sz, ?x10098 = 0h1_c, nutrient(?x9489, ?x9708), nutrient(?x8298, ?x9708), nutrient(?x7719, ?x9708), nutrient(?x7057, ?x9708), nutrient(?x6191, ?x9708), nutrient(?x4068, ?x9708), nutrient(?x3900, ?x9708), ?x3900 = 061_f, ?x10612 = 0frq6, ?x1304 = 08lb68, nutrient(?x9005, ?x11270), nutrient(?x6285, ?x11270), nutrient(?x6032, ?x11270), nutrient(?x5373, ?x11270), nutrient(?x5009, ?x11270), nutrient(?x2701, ?x11270), nutrient(?x1303, ?x11270), nutrient(?x1257, ?x11270), ?x7362 = 02kc5rj, ?x9949 = 02kd0rh, ?x2018 = 01sh2, ?x4068 = 0fbw6, ?x8298 = 037ls6, ?x7219 = 0h1vg, ?x5549 = 025s7j4, ?x10891 = 0g5gq, ?x6285 = 01645p, ?x9436 = 025sqz8, ?x1960 = 07hnp, ?x1257 = 09728, ?x9489 = 07j87, ?x5373 = 0971v, ?x5526 = 09pbb, ?x2702 = 0838f, ?x9005 = 04zpv, ?x7057 = 0fbdb, ?x7652 = 025s0s0, ?x12083 = 01n78x, ?x5009 = 0fjfh, ?x1303 = 0fj52s, ?x11758 = 0q01m, ?x9490 = 0h1sg, ?x7719 = 0dj75, ?x7135 = 025rsfk, ?x9365 = 04k8n, ?x2701 = 0hkxq, ?x6032 = 01nkt, ?x6160 = 041r51, ?x12454 = 025rw19, ?x5374 = 025s0zp, ?x12902 = 0fzjh, ?x6191 = 014j1m, ?x13545 = 01w_3, ?x5451 = 05wvs, nutrient(?x6032, ?x1258), nutrient(?x5373, ?x7894), nutrient(?x10612, ?x1258), nutrient(?x1959, ?x7894), nutrient(?x1959, ?x1258), nutrient(?x7719, ?x11270), nutrient(?x1303, ?x7894), nutrient(?x1257, ?x7894), nutrient(?x6285, ?x1258) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0f4l5 nutrient! 04zpv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 30.000 30.000 0.946 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0f4l5 nutrient! 01nkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.946 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #10421-05bpg3 PRED entity: 05bpg3 PRED relation: film PRED expected values: 0b7l4x => 128 concepts (101 used for prediction) PRED predicted values (max 10 best out of 901): 03nt59 (0.46 #64161, 0.35 #74855, 0.35 #94465), 07h9gp (0.20 #265, 0.01 #14523), 07w8fz (0.14 #2296, 0.04 #30810, 0.01 #53978), 034qzw (0.14 #2115, 0.03 #7462, 0.02 #11027), 03mh_tp (0.14 #2290, 0.02 #30804, 0.01 #9420), 0gmgwnv (0.14 #2857, 0.02 #31371, 0.01 #36717), 01k60v (0.14 #2524, 0.02 #31038, 0.01 #4306), 025rxjq (0.14 #3136, 0.01 #12048, 0.01 #15612), 080lkt7 (0.14 #2564, 0.01 #31078), 0320fn (0.14 #2441) >> Best rule #64161 for best value: >> intensional similarity = 3 >> extensional distance = 678 >> proper extension: 02lf0c; 01hxs4; 02r5w9; 06v_gh; 0c3ns; 01_j71; 0kvqv; 02bvt; 03ys2f; 03ysmg; ... >> query: (?x5410, ?x6070) <- location(?x5410, ?x739), award_nominee(?x5410, ?x1909), award_winner(?x6070, ?x5410) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #9947 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 157 *> proper extension: 02d9k; 01vv126; 034ls; 01f492; 07pzc; 01g0jn; 02lm0t; *> query: (?x5410, 0b7l4x) <- location(?x5410, ?x739), currency(?x5410, ?x170), participant(?x5410, ?x10915) *> conf = 0.02 ranks of expected_values: 274 EVAL 05bpg3 film 0b7l4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 128.000 101.000 0.462 http://example.org/film/actor/film./film/performance/film #10420-03f1zdw PRED entity: 03f1zdw PRED relation: award_winner! PRED expected values: 09cm54 => 84 concepts (83 used for prediction) PRED predicted values (max 10 best out of 175): 05pcn59 (0.37 #11104, 0.37 #14949, 0.36 #29052), 02x4w6g (0.37 #11104, 0.37 #14949, 0.36 #29052), 0bfvd4 (0.37 #11104, 0.37 #14949, 0.36 #29052), 02w9sd7 (0.37 #11104, 0.37 #14949, 0.36 #29052), 09qvf4 (0.25 #205, 0.11 #1059, 0.10 #632), 09cn0c (0.25 #314, 0.11 #1168, 0.10 #741), 027dtxw (0.20 #431, 0.12 #4, 0.07 #16658), 094qd5 (0.20 #470, 0.12 #43, 0.07 #16658), 0gqwc (0.12 #73, 0.10 #500, 0.07 #16658), 027571b (0.12 #271, 0.10 #698, 0.07 #16658) >> Best rule #11104 for best value: >> intensional similarity = 3 >> extensional distance = 1379 >> proper extension: 01wp8w7; 04nw9; 017vkx; 01wn718; 01wgfp6; 01m3b1t; 01933d; 01dhpj; 07sbk; 03y3dk; ... >> query: (?x1222, ?x591) <- award_winner(?x72, ?x1222), award_nominee(?x1222, ?x57), award(?x1222, ?x591) >> conf = 0.37 => this is the best rule for 4 predicted values *> Best rule #522 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01tspc6; 0170pk; 0171cm; 0m31m; 0lpjn; 0bq2g; 03y_46; 0dgskx; *> query: (?x1222, 09cm54) <- award_winner(?x72, ?x1222), award_nominee(?x2653, ?x1222), ?x2653 = 03t0k1 *> conf = 0.10 ranks of expected_values: 34 EVAL 03f1zdw award_winner! 09cm54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 84.000 83.000 0.368 http://example.org/award/award_category/winners./award/award_honor/award_winner #10419-02vnmc9 PRED entity: 02vnmc9 PRED relation: nominated_for! PRED expected values: 0gq9h => 92 concepts (86 used for prediction) PRED predicted values (max 10 best out of 323): 0gq9h (0.47 #2883, 0.44 #5470, 0.44 #1943), 0gs9p (0.47 #1945, 0.41 #2885, 0.38 #5707), 0bfvd4 (0.41 #321, 0.39 #556, 0.38 #1027), 0gr4k (0.38 #2849, 0.31 #5671, 0.30 #1909), 09qwmm (0.37 #1910, 0.35 #2850, 0.24 #4967), 040njc (0.37 #1889, 0.32 #5651, 0.32 #4946), 04dn09n (0.37 #1918, 0.31 #4975, 0.31 #2858), 07kjk7c (0.36 #423, 0.35 #658, 0.33 #1129), 02pqp12 (0.35 #1940, 0.27 #4997, 0.26 #5702), 0gq_v (0.35 #5429, 0.29 #2842, 0.27 #5664) >> Best rule #2883 for best value: >> intensional similarity = 3 >> extensional distance = 152 >> proper extension: 0cvkv5; 0cq8nx; 0j8f09z; >> query: (?x7750, 0gq9h) <- nominated_for(?x1245, ?x7750), ?x1245 = 0gqwc, genre(?x7750, ?x53) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vnmc9 nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 86.000 0.474 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10418-01jb26 PRED entity: 01jb26 PRED relation: type_of_union PRED expected values: 01g63y => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.78 #69, 0.76 #81, 0.76 #77), 01g63y (0.44 #329, 0.25 #410, 0.23 #46), 01bl8s (0.25 #410) >> Best rule #69 for best value: >> intensional similarity = 4 >> extensional distance = 282 >> proper extension: 04bs3j; 02j8nx; 022g44; 02hhtj; 0d608; 033jj1; 0n839; >> query: (?x5268, 04ztj) <- nationality(?x5268, ?x94), film(?x5268, ?x7444), profession(?x5268, ?x1041), ?x1041 = 03gjzk >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #329 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2422 *> proper extension: 034rd; 01nbq4; 04j5fx; 01k31p; *> query: (?x5268, ?x566) <- location(?x5268, ?x1523), location(?x3580, ?x1523), contains(?x94, ?x1523), type_of_union(?x3580, ?x566) *> conf = 0.44 ranks of expected_values: 2 EVAL 01jb26 type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.778 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10417-051m56 PRED entity: 051m56 PRED relation: gender PRED expected values: 05zppz => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.77 #29, 0.75 #9, 0.75 #59), 02zsn (0.31 #2, 0.30 #112, 0.29 #18) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 336 >> proper extension: 01qvgl; 0kp2_; 017l4; 04m2zj; 0hr3g; 023slg; >> query: (?x8832, 05zppz) <- profession(?x8832, ?x220), place_of_birth(?x8832, ?x859), instrumentalists(?x227, ?x8832) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051m56 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.772 http://example.org/people/person/gender #10416-09xp_ PRED entity: 09xp_ PRED relation: athlete PRED expected values: 06whf => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2001): 0f2zc (0.33 #224, 0.25 #1220, 0.25 #655), 01jqr_5 (0.33 #158, 0.25 #1154, 0.25 #589), 03n69x (0.33 #174, 0.25 #1170, 0.22 #1310), 071h5c (0.33 #115, 0.25 #687, 0.18 #141), 07zr66 (0.33 #101, 0.25 #673, 0.18 #141), 04bsx1 (0.33 #98, 0.25 #670, 0.18 #141), 0czmk1 (0.33 #90, 0.25 #662, 0.18 #141), 09m465 (0.33 #87, 0.25 #659, 0.18 #141), 0dv1hh (0.33 #86, 0.25 #658, 0.18 #141), 0fp_xp (0.33 #75, 0.25 #647, 0.18 #141) >> Best rule #224 for best value: >> intensional similarity = 40 >> extensional distance = 1 >> proper extension: 0jm_; >> query: (?x12682, 0f2zc) <- sport(?x14441, ?x12682), sport(?x14238, ?x12682), sport(?x13932, ?x12682), sport(?x13358, ?x12682), sport(?x12490, ?x12682), colors(?x13358, ?x332), ?x332 = 01l849, athlete(?x12682, ?x4895), teams(?x390, ?x13358), teams(?x2146, ?x14238), teams(?x4221, ?x13932), teams(?x1310, ?x13932), team(?x13559, ?x14441), contains(?x4221, ?x4220), colors(?x12490, ?x7179), colors(?x12490, ?x3364), ?x3364 = 036k5h, colors(?x11722, ?x7179), colors(?x7392, ?x7179), ?x7392 = 0ylsr, colors(?x14238, ?x3189), featured_film_locations(?x708, ?x1310), contains(?x1310, ?x892), location(?x981, ?x1310), featured_film_locations(?x1903, ?x4221), time_zones(?x1310, ?x5327), country(?x4221, ?x512), ?x11722 = 019vv1, place_of_birth(?x3528, ?x1310), organization(?x4682, ?x13559), locations(?x14038, ?x1310), origin(?x1694, ?x1310), film_release_region(?x3986, ?x390), adjoins(?x1023, ?x390), contains(?x390, ?x901), ?x3986 = 0jymd, location(?x4468, ?x390), vacationer(?x390, ?x2275), contains(?x6956, ?x2146), adjoins(?x2236, ?x2146) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #569 for first EXPECTED value: *> intensional similarity = 34 *> extensional distance = 1 *> proper extension: 07bs0; *> query: (?x12682, ?x652) <- country(?x12682, ?x512), ?x512 = 07ssc, athlete(?x12682, ?x10562), athlete(?x12682, ?x4895), olympics(?x12682, ?x2553), film(?x4895, ?x7947), gender(?x4895, ?x231), ?x2553 = 016r9z, genre(?x7947, ?x812), music(?x7947, ?x4866), location(?x4895, ?x8420), place_of_birth(?x10562, ?x6885), nationality(?x10562, ?x6401), profession(?x4895, ?x353), profession(?x10562, ?x5805), film_release_distribution_medium(?x7947, ?x81), ?x812 = 01jfsb, ?x231 = 05zppz, featured_film_locations(?x9805, ?x8420), language(?x7947, ?x254), contains(?x6401, ?x4030), contains(?x6304, ?x8420), form_of_government(?x6401, ?x6065), location(?x1857, ?x6885), country(?x4045, ?x6401), produced_by(?x7947, ?x2761), exported_to(?x6401, ?x94), vacationer(?x6401, ?x848), citytown(?x4390, ?x6885), profession(?x652, ?x5805), religion(?x10562, ?x8140), olympics(?x6401, ?x2043), jurisdiction_of_office(?x3119, ?x6401), country(?x3287, ?x6401) *> conf = 0.18 ranks of expected_values: 1466 EVAL 09xp_ athlete 06whf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 23.000 0.333 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #10415-07mgr PRED entity: 07mgr PRED relation: location_of_ceremony! PRED expected values: 03lmzl => 237 concepts (200 used for prediction) PRED predicted values (max 10 best out of 158): 012_53 (0.17 #568, 0.10 #822, 0.04 #2854), 02m30v (0.12 #1270, 0.05 #6611, 0.05 #2032), 02v406 (0.06 #1117, 0.06 #1371, 0.04 #2641), 0gdqy (0.06 #1240, 0.06 #1494, 0.04 #2764), 0c9c0 (0.06 #1082, 0.06 #1336, 0.04 #2606), 06wvj (0.06 #1075, 0.06 #1329, 0.04 #2599), 06x58 (0.06 #1057, 0.06 #1311, 0.04 #2581), 03lt8g (0.06 #1039, 0.06 #1293, 0.04 #2563), 01nglk (0.06 #1257, 0.05 #2019, 0.03 #3289), 01f9mq (0.06 #1256, 0.05 #2018, 0.03 #3288) >> Best rule #568 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0d6nx; >> query: (?x10537, 012_53) <- administrative_division(?x10537, ?x9230), capital(?x3918, ?x10537), category(?x10537, ?x134), country(?x10537, ?x205) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07mgr location_of_ceremony! 03lmzl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 237.000 200.000 0.167 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #10414-0345_ PRED entity: 0345_ PRED relation: time_zones PRED expected values: 02fqwt => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 12): 02fqwt (0.58 #1210, 0.20 #1, 0.13 #885), 02hcv8 (0.33 #3, 0.33 #1199, 0.28 #1330), 02llzg (0.21 #108, 0.21 #121, 0.20 #212), 03plfd (0.14 #23, 0.11 #114, 0.11 #127), 02lcqs (0.14 #889, 0.14 #83, 0.13 #5), 02hczc (0.13 #2, 0.10 #886, 0.08 #80), 042g7t (0.09 #24, 0.07 #11, 0.06 #310), 03bdv (0.09 #474, 0.09 #227, 0.08 #45), 05jphn (0.07 #13, 0.02 #26, 0.01 #52), 0gsrz4 (0.06 #606, 0.06 #645, 0.06 #671) >> Best rule #1210 for best value: >> intensional similarity = 4 >> extensional distance = 458 >> proper extension: 0mrf1; >> query: (?x4954, ?x1638) <- adjoins(?x6691, ?x4954), adjoins(?x3720, ?x4954), contains(?x6691, ?x13999), time_zones(?x3720, ?x1638) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0345_ time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.583 http://example.org/location/location/time_zones #10413-09yhzs PRED entity: 09yhzs PRED relation: location PRED expected values: 030qb3t => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 75): 030qb3t (0.32 #13717, 0.22 #35374, 0.16 #4895), 02_286 (0.31 #4047, 0.28 #35328, 0.25 #8057), 04jpl (0.10 #35308, 0.08 #54558, 0.07 #4027), 0cr3d (0.07 #9769, 0.06 #36238, 0.06 #29019), 0m27n (0.06 #53739, 0.03 #77804, 0.03 #85025), 09c7w0 (0.06 #53739, 0.03 #77804, 0.03 #85025), 0cc56 (0.05 #8077, 0.05 #4067, 0.05 #4869), 01cx_ (0.05 #13797, 0.04 #4173, 0.03 #35454), 059rby (0.05 #35307, 0.04 #54557, 0.03 #13650), 01n7q (0.04 #35354, 0.04 #63, 0.03 #4073) >> Best rule #13717 for best value: >> intensional similarity = 2 >> extensional distance = 847 >> proper extension: 04hcw; >> query: (?x3027, 030qb3t) <- location(?x3027, ?x4419), administrative_division(?x4419, ?x938) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09yhzs location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.320 http://example.org/people/person/places_lived./people/place_lived/location #10412-02_xgp2 PRED entity: 02_xgp2 PRED relation: student PRED expected values: 05bnp0 => 25 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1293): 07m77x (0.91 #1942, 0.50 #2322, 0.33 #598), 08p1gp (0.91 #1942, 0.50 #2356, 0.33 #632), 05fg2 (0.91 #1942, 0.40 #3049, 0.33 #1112), 06g4_ (0.91 #1942, 0.37 #3668, 0.33 #3647), 02r34n (0.91 #1942, 0.37 #3668, 0.33 #3911), 01hbq0 (0.91 #1942, 0.37 #3668, 0.33 #4098), 03swmf (0.91 #1942, 0.37 #3668, 0.33 #4055), 04pp9s (0.91 #1942, 0.37 #3668, 0.33 #4066), 0d0l91 (0.91 #1942, 0.37 #3668, 0.33 #4088), 01_rh4 (0.91 #1942, 0.37 #3668, 0.33 #3511) >> Best rule #1942 for best value: >> intensional similarity = 24 >> extensional distance = 2 >> proper extension: 022h5x; >> query: (?x3437, ?x123) <- institution(?x3437, ?x14069), institution(?x3437, ?x13618), institution(?x3437, ?x6545), institution(?x3437, ?x5621), institution(?x3437, ?x4338), institution(?x3437, ?x3485), institution(?x3437, ?x1665), institution(?x3437, ?x546), student(?x3437, ?x5790), major_field_of_study(?x3437, ?x254), student(?x1368, ?x5790), contains(?x94, ?x14069), institution(?x1368, ?x2175), student(?x1368, ?x123), major_field_of_study(?x1368, ?x373), student(?x3485, ?x4480), ?x4338 = 0bqxw, people(?x1050, ?x5790), ?x5621 = 01vs5c, ?x6545 = 01ky7c, ?x2175 = 01ptt7, ?x546 = 01j_9c, company(?x346, ?x1665), category(?x13618, ?x134) >> conf = 0.91 => this is the best rule for 166 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 23 EVAL 02_xgp2 student 05bnp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 25.000 24.000 0.913 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #10411-03rz2b PRED entity: 03rz2b PRED relation: genre PRED expected values: 03q4nz => 73 concepts (64 used for prediction) PRED predicted values (max 10 best out of 154): 03rk0 (0.63 #1742, 0.62 #2558, 0.61 #4073), 03q4nz (0.53 #133, 0.25 #17, 0.11 #3158), 01jfsb (0.50 #11, 0.44 #1056, 0.36 #359), 02kdv5l (0.50 #2, 0.38 #1047, 0.35 #4545), 01chg (0.42 #147, 0.25 #31, 0.03 #6525), 04t36 (0.37 #120, 0.25 #4, 0.13 #3726), 04xvlr (0.29 #1626, 0.28 #2442, 0.26 #2677), 03k9fj (0.28 #4553, 0.22 #4084, 0.20 #3499), 0lsxr (0.25 #1168, 0.23 #1284, 0.23 #935), 01hmnh (0.25 #16, 0.25 #4090, 0.17 #1061) >> Best rule #1742 for best value: >> intensional similarity = 5 >> extensional distance = 343 >> proper extension: 03kq98; >> query: (?x2882, ?x2146) <- titles(?x2146, ?x2882), titles(?x53, ?x2882), ?x53 = 07s9rl0, nominated_for(?x7965, ?x2882), award(?x534, ?x7965) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #133 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 01f8gz; 030z4z; *> query: (?x2882, 03q4nz) <- language(?x2882, ?x10323), language(?x2882, ?x1882), ?x1882 = 03k50, genre(?x2882, ?x53), award_winner(?x2882, ?x10061), languages_spoken(?x7838, ?x10323) *> conf = 0.53 ranks of expected_values: 2 EVAL 03rz2b genre 03q4nz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 64.000 0.626 http://example.org/film/film/genre #10410-03mh94 PRED entity: 03mh94 PRED relation: genre PRED expected values: 06n90 => 63 concepts (60 used for prediction) PRED predicted values (max 10 best out of 84): 07s9rl0 (0.75 #120, 0.62 #954, 0.59 #2980), 01hmnh (0.49 #731, 0.41 #374, 0.24 #255), 04t36 (0.35 #363, 0.14 #720, 0.08 #2628), 01jfsb (0.31 #1084, 0.31 #1561, 0.31 #1441), 02kdv5l (0.30 #717, 0.29 #360, 0.29 #241), 060__y (0.25 #135, 0.17 #492, 0.14 #611), 01t_vv (0.25 #172, 0.11 #53, 0.08 #3032), 01zhp (0.24 #790, 0.12 #433, 0.03 #1505), 06n90 (0.24 #370, 0.19 #727, 0.15 #489), 0lsxr (0.23 #604, 0.23 #485, 0.18 #1200) >> Best rule #120 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0gkz15s; 0cc7hmk; 06lpmt; 06nr2h; 0cbv4g; 05tgks; 03vyw8; 01y9r2; 0sxlb; 0322yj; >> query: (?x463, 07s9rl0) <- nominated_for(?x4563, ?x463), film(?x7981, ?x463), film(?x3651, ?x463), ?x7981 = 02bj6k, nominated_for(?x3651, ?x4639) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #370 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 04svwx; *> query: (?x463, 06n90) <- genre(?x463, ?x2540), genre(?x463, ?x1403), ?x2540 = 0hcr, ?x1403 = 02l7c8 *> conf = 0.24 ranks of expected_values: 9 EVAL 03mh94 genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 63.000 60.000 0.750 http://example.org/film/film/genre #10409-02v570 PRED entity: 02v570 PRED relation: language PRED expected values: 02h40lc => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.96 #3420, 0.96 #4018, 0.96 #1993), 06nm1 (0.20 #11, 0.18 #127, 0.17 #418), 06b_j (0.20 #23, 0.15 #197, 0.14 #81), 04306rv (0.20 #5, 0.15 #1525, 0.14 #63), 0jzc (0.20 #20, 0.08 #194, 0.06 #311), 071fb (0.20 #18, 0.06 #425, 0.05 #542), 064_8sq (0.17 #1955, 0.15 #4159, 0.15 #2487), 03_9r (0.14 #68, 0.09 #126, 0.08 #184), 04h9h (0.14 #100, 0.09 #158, 0.08 #216), 03hkp (0.13 #247, 0.11 #422, 0.09 #597) >> Best rule #3420 for best value: >> intensional similarity = 4 >> extensional distance = 468 >> proper extension: 014_x2; 015qsq; 0d90m; 03qcfvw; 09sh8k; 034qmv; 0g22z; 018js4; 02vxq9m; 0b2v79; ... >> query: (?x7462, 02h40lc) <- country(?x7462, ?x94), featured_film_locations(?x7462, ?x739), nominated_for(?x102, ?x7462), language(?x7462, ?x8650) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v570 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.962 http://example.org/film/film/language #10408-02r6gw6 PRED entity: 02r6gw6 PRED relation: school PRED expected values: 01hhvg 06fq2 => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 689): 015q1n (0.67 #1644, 0.59 #1337, 0.33 #1529), 065y4w7 (0.62 #1700, 0.59 #1337, 0.55 #1221), 0lyjf (0.62 #1743, 0.59 #1337, 0.55 #1221), 06pwq (0.60 #1354, 0.50 #1927, 0.50 #1817), 07w0v (0.59 #1337, 0.55 #1221, 0.50 #1471), 05krk (0.59 #1337, 0.55 #1221, 0.50 #1234), 01jsk6 (0.59 #1337, 0.55 #1221, 0.50 #1211), 01pl14 (0.59 #1337, 0.50 #1695, 0.50 #1235), 01vs5c (0.59 #1337, 0.50 #1752, 0.50 #1292), 0g8rj (0.59 #1337, 0.50 #1751, 0.50 #1172) >> Best rule #1644 for best value: >> intensional similarity = 65 >> extensional distance = 4 >> proper extension: 06439y; >> query: (?x8499, 015q1n) <- school(?x8499, ?x7439), school(?x8499, ?x6856), school(?x8499, ?x3779), draft(?x8995, ?x8499), draft(?x8111, ?x8499), draft(?x4487, ?x8499), draft(?x3333, ?x8499), draft(?x1438, ?x8499), draft(?x580, ?x8499), institution(?x1368, ?x6856), institution(?x1200, ?x6856), team(?x2010, ?x4487), school(?x4546, ?x6856), student(?x6856, ?x6700), citytown(?x6856, ?x11246), school(?x9049, ?x3779), ?x1200 = 016t_3, school(?x8995, ?x2711), school(?x8995, ?x735), fraternities_and_sororities(?x7439, ?x3697), major_field_of_study(?x3779, ?x9111), major_field_of_study(?x3779, ?x1668), ?x9049 = 0jmm4, school(?x8542, ?x6856), sport(?x8995, ?x5063), school(?x4979, ?x3779), school(?x4487, ?x4780), institution(?x620, ?x3779), colors(?x4487, ?x663), team(?x10434, ?x8995), school_type(?x6856, ?x1507), school(?x3333, ?x2150), draft(?x11420, ?x8542), draft(?x5483, ?x8542), draft(?x5419, ?x8542), draft(?x2398, ?x8542), team(?x8110, ?x8111), position(?x4546, ?x1792), position(?x4546, ?x1240), ?x11420 = 0jmhr, ?x1240 = 023wyl, category(?x1438, ?x134), ?x4979 = 0f4vx0, school(?x8111, ?x4904), ?x735 = 065y4w7, fraternities_and_sororities(?x2711, ?x4348), ?x2398 = 0jmfb, currency(?x6856, ?x170), teams(?x659, ?x4487), school(?x580, ?x4916), team(?x261, ?x1438), team(?x706, ?x4546), ?x4916 = 019dwp, ?x1792 = 05zm34, ?x2150 = 07w3r, major_field_of_study(?x13316, ?x9111), major_field_of_study(?x6856, ?x2014), ?x5483 = 0jml5, organization(?x5510, ?x6856), interests(?x7341, ?x9111), ?x13316 = 01stzp, ?x1368 = 014mlp, ?x5419 = 0jmmn, teams(?x5771, ?x1438), ?x1668 = 01mkq >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1337 for first EXPECTED value: *> intensional similarity = 73 *> extensional distance = 2 *> proper extension: 0g3zpp; *> query: (?x8499, ?x331) <- school(?x8499, ?x6856), school(?x8499, ?x3779), school(?x8499, ?x3777), draft(?x8111, ?x8499), draft(?x7357, ?x8499), draft(?x7060, ?x8499), draft(?x4487, ?x8499), institution(?x4981, ?x6856), institution(?x1200, ?x6856), team(?x2010, ?x4487), school(?x8902, ?x6856), school(?x2820, ?x6856), currency(?x3779, ?x170), ?x8902 = 01c_d, major_field_of_study(?x6856, ?x7403), major_field_of_study(?x6856, ?x2606), ?x3777 = 012vwb, school(?x4856, ?x3779), colors(?x4487, ?x4557), colors(?x4487, ?x663), ?x4981 = 03bwzr4, contains(?x94, ?x3779), ?x2820 = 0jmj7, major_field_of_study(?x7596, ?x2606), major_field_of_study(?x6732, ?x2606), major_field_of_study(?x6056, ?x2606), major_field_of_study(?x5750, ?x2606), major_field_of_study(?x4955, ?x2606), major_field_of_study(?x4672, ?x2606), major_field_of_study(?x4599, ?x2606), major_field_of_study(?x4296, ?x2606), major_field_of_study(?x3424, ?x2606), major_field_of_study(?x3416, ?x2606), major_field_of_study(?x263, ?x2606), major_field_of_study(?x3995, ?x2606), team(?x2066, ?x7060), ?x4557 = 019sc, school(?x4979, ?x3779), ?x6056 = 05zl0, ?x3416 = 02183k, ?x3995 = 0fdys, ?x4672 = 07tds, ?x6732 = 0gdm1, draft(?x799, ?x4979), ?x4599 = 07t90, teams(?x1523, ?x7357), student(?x2606, ?x677), ?x1200 = 016t_3, ?x263 = 01rtm4, school(?x4979, ?x1011), school(?x4979, ?x331), ?x4955 = 09f2j, registering_agency(?x3779, ?x1982), ?x7596 = 012mzw, school(?x7357, ?x3387), ?x4296 = 07vyf, ?x663 = 083jv, institution(?x8398, ?x3387), institution(?x620, ?x3387), school(?x7060, ?x581), colors(?x3387, ?x1101), team(?x8110, ?x8111), ?x620 = 07s6fsf, ?x1011 = 07w0v, student(?x3387, ?x2136), school(?x8111, ?x2522), ?x8398 = 028dcg, position(?x4856, ?x180), organization(?x346, ?x3387), ?x3424 = 01w5m, position_s(?x4856, ?x706), taxonomy(?x7403, ?x939), ?x5750 = 01nnsv *> conf = 0.59 ranks of expected_values: 17, 118 EVAL 02r6gw6 school 06fq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 18.000 18.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 02r6gw6 school 01hhvg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 18.000 18.000 0.667 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #10407-0mnlq PRED entity: 0mnlq PRED relation: time_zones PRED expected values: 02hcv8 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.86 #347, 0.86 #211, 0.84 #545), 02lcqs (0.41 #57, 0.33 #71, 0.28 #97), 02fqwt (0.18 #439, 0.17 #106, 0.17 #771), 02hczc (0.10 #107, 0.10 #414, 0.10 #560), 02llzg (0.10 #296, 0.09 #324, 0.08 #390), 03bdv (0.03 #867, 0.03 #1101, 0.03 #893), 03plfd (0.02 #555, 0.02 #330, 0.02 #396), 0gsrz4 (0.02 #674, 0.02 #619, 0.02 #633), 042g7t (0.01 #303, 0.01 #331, 0.01 #275), 05jphn (0.01 #277) >> Best rule #347 for best value: >> intensional similarity = 4 >> extensional distance = 280 >> proper extension: 0mw89; 0mw93; 0m7fm; 0n5fl; 0mxcf; 0m2lt; 0cr3d; 01qh7; 01cx_; 0d22f; ... >> query: (?x12515, ?x2674) <- adjoins(?x12267, ?x12515), currency(?x12267, ?x170), time_zones(?x12267, ?x2674), ?x170 = 09nqf >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mnlq time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.857 http://example.org/location/location/time_zones #10406-03k7bd PRED entity: 03k7bd PRED relation: award PRED expected values: 09sb52 => 93 concepts (70 used for prediction) PRED predicted values (max 10 best out of 276): 09sb52 (0.44 #439, 0.41 #11239, 0.37 #839), 0bfvd4 (0.33 #113, 0.30 #913, 0.22 #3313), 0ck27z (0.33 #91, 0.22 #491, 0.16 #13291), 0cqhk0 (0.33 #35, 0.22 #435, 0.13 #27603), 0cqh46 (0.33 #50, 0.16 #3250, 0.13 #27603), 0bs0bh (0.33 #102, 0.13 #902, 0.11 #502), 0bp_b2 (0.33 #18, 0.13 #818, 0.11 #418), 08_vwq (0.33 #267, 0.13 #1067, 0.11 #667), 04ljl_l (0.33 #3, 0.13 #3203, 0.11 #403), 09qrn4 (0.33 #235, 0.11 #635, 0.07 #28004) >> Best rule #439 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 049g_xj; 043js; 016yvw; 031k24; 02clgg; 01jw4r; >> query: (?x1865, 09sb52) <- award_nominee(?x1865, ?x949), ?x949 = 05zbm4, nominated_for(?x1865, ?x1866) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03k7bd award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 70.000 0.444 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10405-02tn0_ PRED entity: 02tn0_ PRED relation: program PRED expected values: 0l76z => 130 concepts (120 used for prediction) PRED predicted values (max 10 best out of 134): 0828jw (0.67 #76, 0.07 #5803, 0.07 #5630), 05z43v (0.34 #6943, 0.21 #12163, 0.12 #12337), 03cf9ly (0.22 #163, 0.07 #3977, 0.07 #2761), 01rf57 (0.22 #50, 0.07 #3864, 0.07 #2648), 04xbq3 (0.18 #642, 0.03 #1681, 0.02 #2721), 0l76z (0.17 #1614, 0.13 #229, 0.13 #3870), 02qjv1p (0.12 #631, 0.04 #2710, 0.04 #3926), 08bytj (0.12 #619, 0.04 #2698, 0.04 #3914), 03cv_gy (0.12 #588, 0.04 #2667, 0.04 #3883), 063zky (0.12 #597, 0.04 #2676, 0.01 #5978) >> Best rule #76 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 09hd6f; >> query: (?x9785, 0828jw) <- award_winner(?x9785, ?x2803), award_nominee(?x9785, ?x2802), ?x2802 = 0h53p1 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1614 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 02ndbd; *> query: (?x9785, 0l76z) <- award_nominee(?x2802, ?x9785), film(?x9785, ?x155), program(?x9785, ?x2026) *> conf = 0.17 ranks of expected_values: 6 EVAL 02tn0_ program 0l76z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 130.000 120.000 0.667 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/program #10404-027ydt PRED entity: 027ydt PRED relation: fraternities_and_sororities PRED expected values: 0325pb => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 3): 035tlh (0.33 #26, 0.25 #86, 0.24 #29), 0325pb (0.32 #13, 0.28 #46, 0.24 #25), 04m8fy (0.09 #172, 0.08 #3, 0.07 #9) >> Best rule #26 for best value: >> intensional similarity = 7 >> extensional distance = 31 >> proper extension: 07w0v; 027xx3; >> query: (?x6584, 035tlh) <- colors(?x6584, ?x663), school_type(?x6584, ?x3092), institution(?x1200, ?x6584), institution(?x865, ?x6584), ?x663 = 083jv, ?x1200 = 016t_3, ?x865 = 02h4rq6 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 017j69; *> query: (?x6584, 0325pb) <- colors(?x6584, ?x663), institution(?x1771, ?x6584), major_field_of_study(?x6584, ?x10391), category(?x6584, ?x134), ?x1771 = 019v9k, ?x10391 = 02jfc *> conf = 0.32 ranks of expected_values: 2 EVAL 027ydt fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 179.000 179.000 0.333 http://example.org/education/university/fraternities_and_sororities #10403-0277jc PRED entity: 0277jc PRED relation: major_field_of_study PRED expected values: 037mh8 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 104): 01mkq (0.64 #1017, 0.55 #767, 0.39 #641), 02lp1 (0.57 #1013, 0.45 #763, 0.31 #637), 037mh8 (0.56 #2627, 0.37 #695, 0.33 #1071), 02j62 (0.55 #1033, 0.45 #783, 0.42 #908), 04rjg (0.51 #1022, 0.40 #772, 0.39 #897), 05qjt (0.43 #1009, 0.40 #759, 0.35 #884), 03g3w (0.43 #1029, 0.39 #653, 0.36 #904), 01lj9 (0.42 #1042, 0.39 #792, 0.36 #666), 062z7 (0.42 #1030, 0.35 #905, 0.32 #654), 0fdys (0.37 #1041, 0.32 #665, 0.29 #791) >> Best rule #1017 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 0373qt; >> query: (?x1220, 01mkq) <- list(?x1220, ?x2197), institution(?x2636, ?x1220), student(?x1220, ?x1221) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #2627 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 164 *> proper extension: 02g839; 02d9nr; 053mhx; 0fr9jp; 019vsw; 05bjp6; 0194_r; 0dbns; 0dzbl; *> query: (?x1220, ?x8221) <- contains(?x774, ?x1220), student(?x1220, ?x1221), student(?x8221, ?x1221) *> conf = 0.56 ranks of expected_values: 3 EVAL 0277jc major_field_of_study 037mh8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.642 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10402-01p4vl PRED entity: 01p4vl PRED relation: award_nominee PRED expected values: 014488 => 127 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1171): 032wdd (0.82 #67396, 0.81 #106902, 0.81 #144086), 08swgx (0.81 #106902, 0.81 #144086, 0.81 #44150), 014488 (0.81 #106902, 0.81 #144086, 0.81 #44150), 05cx7x (0.81 #106902, 0.81 #144086, 0.81 #44150), 028r4y (0.15 #153382, 0.14 #65071, 0.14 #81342), 02p65p (0.15 #153382, 0.11 #27, 0.08 #4673), 0dvmd (0.15 #153382, 0.08 #42517, 0.07 #81343), 017149 (0.15 #153382, 0.08 #41924, 0.04 #109324), 02qgyv (0.15 #153382, 0.07 #7467, 0.07 #42323), 02ck7w (0.15 #153382, 0.07 #43064, 0.04 #66309) >> Best rule #67396 for best value: >> intensional similarity = 3 >> extensional distance = 467 >> proper extension: 01wdqrx; 01k98nm; >> query: (?x7830, ?x2763) <- award_nominee(?x2763, ?x7830), profession(?x7830, ?x987), participant(?x2108, ?x2763) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #106902 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 697 *> proper extension: 01pbxb; 0411q; 05cljf; 032nwy; 0168cl; 025xt8y; 018y2s; 04dqdk; 058s57; 010hn; ... *> query: (?x7830, ?x221) <- award_nominee(?x2352, ?x7830), award_nominee(?x221, ?x7830), nationality(?x7830, ?x94), artists(?x671, ?x2352) *> conf = 0.81 ranks of expected_values: 3 EVAL 01p4vl award_nominee 014488 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 127.000 79.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10401-06hzsx PRED entity: 06hzsx PRED relation: cinematography! PRED expected values: 026p_bs => 144 concepts (93 used for prediction) PRED predicted values (max 10 best out of 347): 0jvt9 (0.81 #1390, 0.80 #695, 0.77 #4169), 0kbhf (0.12 #200, 0.10 #1242, 0.06 #547), 0jymd (0.12 #132, 0.06 #827, 0.06 #479), 03cw411 (0.11 #817, 0.08 #1859, 0.04 #3596), 084qpk (0.11 #719, 0.04 #3845, 0.04 #4193), 0b_5d (0.06 #99, 0.06 #794, 0.06 #446), 0bj25 (0.06 #294, 0.06 #989, 0.06 #641), 0bbgvp (0.06 #341, 0.06 #1036, 0.06 #688), 06x77g (0.06 #300, 0.06 #995, 0.06 #647), 09d3b7 (0.06 #291, 0.06 #986, 0.06 #638) >> Best rule #1390 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 09myny; >> query: (?x7118, ?x3294) <- place_of_death(?x7118, ?x1523), nominated_for(?x7118, ?x3294), cinematography(?x5499, ?x7118), profession(?x7118, ?x2265) >> conf = 0.81 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06hzsx cinematography! 026p_bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 93.000 0.809 http://example.org/film/film/cinematography #10400-01ln5z PRED entity: 01ln5z PRED relation: currency PRED expected values: 09nqf => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.87 #8, 0.83 #22, 0.83 #71), 01nv4h (0.16 #288, 0.04 #72, 0.03 #79), 02l6h (0.16 #288, 0.03 #32, 0.02 #53), 088n7 (0.16 #288, 0.01 #112) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 0g5pv3; 03l6q0; 02_qt; 03mgx6z; 0g5pvv; 01g3gq; 042fgh; 02mc5v; 07ghq; 01_1hw; >> query: (?x549, 09nqf) <- film(?x1104, ?x549), film(?x548, ?x549), featured_film_locations(?x549, ?x1273), prequel(?x1074, ?x549) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ln5z currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.868 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10399-02pt27 PRED entity: 02pt27 PRED relation: instrumentalists! PRED expected values: 06w7v => 130 concepts (89 used for prediction) PRED predicted values (max 10 best out of 113): 0342h (0.83 #2014, 0.83 #1930, 0.81 #881), 018vs (0.59 #276, 0.53 #452, 0.52 #1939), 05r5c (0.55 #271, 0.51 #1411, 0.50 #885), 01rhl (0.32 #2013, 0.31 #2190, 0.30 #4479), 02hnl (0.26 #911, 0.26 #2136, 0.25 #2312), 06w7v (0.26 #422, 0.24 #510, 0.22 #685), 0l14qv (0.24 #268, 0.21 #532, 0.15 #794), 0l14md (0.22 #884, 0.21 #270, 0.20 #95), 018j2 (0.22 #915, 0.19 #389, 0.18 #477), 026t6 (0.21 #177, 0.19 #353, 0.19 #616) >> Best rule #2014 for best value: >> intensional similarity = 4 >> extensional distance = 143 >> proper extension: 04m2zj; >> query: (?x9693, ?x227) <- instrumentalists(?x1166, ?x9693), artists(?x597, ?x9693), role(?x9693, ?x227), ?x227 = 0342h >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #422 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 29 *> proper extension: 0m2l9; 01gf5h; 01vrncs; 02whj; 0137n0; 01kx_81; 01wp8w7; 0zjpz; 02jg92; 014q2g; ... *> query: (?x9693, 06w7v) <- role(?x9693, ?x227), artists(?x7440, ?x9693), ?x7440 = 0155w, profession(?x9693, ?x131), nationality(?x9693, ?x1310) *> conf = 0.26 ranks of expected_values: 6 EVAL 02pt27 instrumentalists! 06w7v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 130.000 89.000 0.834 http://example.org/music/instrument/instrumentalists #10398-01s21dg PRED entity: 01s21dg PRED relation: award PRED expected values: 02sp_v => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 280): 01bgqh (0.34 #5587, 0.33 #1231, 0.27 #43), 09sb52 (0.31 #3209, 0.31 #3605, 0.30 #10337), 05pcn59 (0.30 #3645, 0.30 #3249, 0.28 #2061), 01c99j (0.28 #1408, 0.17 #2596, 0.12 #220), 05p09zm (0.26 #912, 0.26 #2100, 0.23 #3288), 02f6ym (0.23 #1439, 0.14 #2627, 0.13 #37622), 0cqhk0 (0.22 #1621, 0.09 #6373, 0.09 #26965), 05zr6wv (0.21 #809, 0.18 #3185, 0.17 #3581), 054ks3 (0.20 #5682, 0.18 #8454, 0.17 #2910), 02f5qb (0.20 #1339, 0.15 #2527, 0.14 #5695) >> Best rule #5587 for best value: >> intensional similarity = 3 >> extensional distance = 203 >> proper extension: 02r3zy; 0dvqq; 03fbc; 0163m1; 01yzl2; 03d9d6; 01dwrc; 025cn2; 07bzp; 01dq9q; ... >> query: (?x4741, 01bgqh) <- award_winner(?x1232, ?x4741), award_nominee(?x4741, ?x1282), origin(?x4741, ?x2277) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #5702 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 203 *> proper extension: 02r3zy; 0dvqq; 03fbc; 0163m1; 01yzl2; 03d9d6; 01dwrc; 025cn2; 07bzp; 01dq9q; ... *> query: (?x4741, 02sp_v) <- award_winner(?x1232, ?x4741), award_nominee(?x4741, ?x1282), origin(?x4741, ?x2277) *> conf = 0.10 ranks of expected_values: 62 EVAL 01s21dg award 02sp_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 113.000 113.000 0.341 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10397-01t_vv PRED entity: 01t_vv PRED relation: genre! PRED expected values: 030k94 05sy2k_ 030p35 => 50 concepts (25 used for prediction) PRED predicted values (max 10 best out of 375): 05397h (0.67 #3476, 0.40 #1861, 0.33 #255), 01h72l (0.64 #5408, 0.50 #571, 0.38 #4335), 07gbf (0.62 #4211, 0.44 #5016, 0.40 #2327), 020qr4 (0.60 #1075, 0.40 #2149, 0.40 #1880), 04f6hhm (0.60 #943, 0.40 #1213, 0.38 #4171), 053x8hr (0.60 #995, 0.40 #1265, 0.33 #195), 019nnl (0.55 #5391, 0.50 #4318, 0.50 #554), 0584r4 (0.50 #4325, 0.50 #561, 0.45 #5398), 06y_n (0.50 #4486, 0.50 #722, 0.45 #5559), 03nymk (0.50 #4439, 0.50 #675, 0.36 #5512) >> Best rule #3476 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0djd22; 06q7n; >> query: (?x6674, 05397h) <- genre(?x5808, ?x6674), genre(?x5386, ?x6674), genre(?x3630, ?x6674), nominated_for(?x3082, ?x3630), ?x5808 = 05lfwd, actor(?x5386, ?x300), nominated_for(?x678, ?x5386), program(?x2819, ?x3630) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #581 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 01z4y; *> query: (?x6674, 030k94) <- genre(?x11203, ?x6674), genre(?x9749, ?x6674), genre(?x7551, ?x6674), genre(?x5561, ?x6674), genre(?x5386, ?x6674), genre(?x4588, ?x6674), genre(?x2078, ?x6674), ?x4588 = 0l76z, ?x2078 = 03ln8b, ?x7551 = 014gjp, ?x5386 = 0vjr, ?x9749 = 01_2n, ?x5561 = 0431v3, ?x11203 = 0123qq *> conf = 0.50 ranks of expected_values: 13, 67, 141 EVAL 01t_vv genre! 030p35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 50.000 25.000 0.667 http://example.org/tv/tv_program/genre EVAL 01t_vv genre! 05sy2k_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 50.000 25.000 0.667 http://example.org/tv/tv_program/genre EVAL 01t_vv genre! 030k94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 50.000 25.000 0.667 http://example.org/tv/tv_program/genre #10396-02bj22 PRED entity: 02bj22 PRED relation: film_crew_role PRED expected values: 09zzb8 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 25): 09zzb8 (0.77 #1205, 0.77 #1131, 0.73 #1278), 01vx2h (0.49 #83, 0.43 #192, 0.40 #338), 0dxtw (0.41 #1214, 0.40 #483, 0.38 #1140), 015h31 (0.38 #8, 0.24 #1021, 0.19 #80), 01pvkk (0.33 #84, 0.28 #1650, 0.28 #1142), 033smt (0.25 #28, 0.24 #1021, 0.09 #100), 02ynfr (0.24 #1021, 0.20 #1146, 0.20 #489), 089g0h (0.24 #1021, 0.16 #92, 0.13 #1150), 0d2b38 (0.24 #1021, 0.13 #207, 0.12 #26), 02_n3z (0.24 #1021, 0.12 #74, 0.09 #1206) >> Best rule #1205 for best value: >> intensional similarity = 4 >> extensional distance = 665 >> proper extension: 0crh5_f; 02phtzk; 0h95zbp; 03q8xj; 0gh6j94; >> query: (?x9193, 09zzb8) <- language(?x9193, ?x254), film_crew_role(?x9193, ?x1171), genre(?x9193, ?x239), ?x1171 = 09vw2b7 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bj22 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10395-02q3fdr PRED entity: 02q3fdr PRED relation: actor PRED expected values: 0pz7h => 133 concepts (104 used for prediction) PRED predicted values (max 10 best out of 62): 0ckm4x (0.43 #242, 0.29 #1111, 0.25 #987), 066l3y (0.43 #204, 0.25 #18, 0.21 #1073), 02gf_l (0.40 #87, 0.20 #397, 0.12 #646), 07cn2c (0.33 #135, 0.30 #321, 0.12 #1066), 05v954 (0.30 #392, 0.25 #20, 0.20 #951), 0cpjgj (0.29 #208, 0.25 #22, 0.21 #1077), 084x96 (0.29 #247, 0.10 #992, 0.08 #1116), 03k545 (0.25 #50, 0.17 #174, 0.14 #236), 01bh6y (0.25 #41, 0.17 #165, 0.14 #227), 01nms7 (0.25 #31, 0.17 #155, 0.14 #217) >> Best rule #242 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02z9hqn; 02z5x7l; >> query: (?x5936, 0ckm4x) <- genre(?x5936, ?x53), actor(?x5936, ?x489), country(?x5936, ?x252), nominated_for(?x13075, ?x5936) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02q3fdr actor 0pz7h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 104.000 0.429 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #10394-044qx PRED entity: 044qx PRED relation: people! PRED expected values: 01ddth => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 42): 0gk4g (0.33 #10, 0.26 #1440, 0.25 #1375), 0qcr0 (0.33 #196, 0.13 #1366, 0.12 #1821), 04psf (0.33 #72, 0.03 #2347, 0.03 #332), 02knxx (0.13 #227, 0.11 #1007, 0.10 #1332), 02k6hp (0.13 #231, 0.08 #1271, 0.07 #2376), 01qqwn (0.13 #255, 0.02 #1295, 0.02 #1425), 0dq9p (0.12 #927, 0.11 #1122, 0.11 #732), 0m32h (0.09 #543, 0.07 #478, 0.06 #283), 04p3w (0.08 #1246, 0.07 #336, 0.07 #3651), 02y0js (0.08 #3642, 0.08 #1302, 0.07 #4617) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 030pr; >> query: (?x4240, 0gk4g) <- nominated_for(?x4240, ?x4241), award_winner(?x591, ?x4240), ?x4241 = 0gcpc >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 044qx people! 01ddth CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 129.000 0.333 http://example.org/people/cause_of_death/people #10393-043js PRED entity: 043js PRED relation: award_nominee PRED expected values: 0cnl80 => 117 concepts (29 used for prediction) PRED predicted values (max 10 best out of 754): 0cmt6q (0.82 #4642, 0.79 #2321, 0.65 #3802), 04t2l2 (0.82 #4642, 0.79 #2321, 0.59 #2361), 04twmk (0.82 #4642, 0.79 #2321, 0.28 #55689), 0cnl80 (0.76 #2367, 0.03 #18608, 0.02 #30209), 05xpms (0.65 #4293, 0.02 #32135, 0.02 #20534), 05l0j5 (0.65 #4029, 0.02 #20270, 0.02 #31871), 0cl0bk (0.59 #3827, 0.02 #20068, 0.02 #33992), 043js (0.53 #2901, 0.28 #55689, 0.18 #580), 0lpjn (0.45 #623, 0.03 #7585, 0.02 #21505), 01g23m (0.45 #909, 0.02 #49636, 0.01 #51957) >> Best rule #4642 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 08wq0g; 0cj36c; 05xpms; >> query: (?x2657, ?x237) <- profession(?x2657, ?x1032), award_nominee(?x4333, ?x2657), award_nominee(?x237, ?x2657), ?x4333 = 0cnl09 >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #2367 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 08wq0g; 0cj36c; 05xpms; *> query: (?x2657, 0cnl80) <- profession(?x2657, ?x1032), award_nominee(?x4333, ?x2657), ?x4333 = 0cnl09 *> conf = 0.76 ranks of expected_values: 4 EVAL 043js award_nominee 0cnl80 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 29.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10392-07j94 PRED entity: 07j94 PRED relation: nominated_for! PRED expected values: 02qyp19 => 120 concepts (112 used for prediction) PRED predicted values (max 10 best out of 165): 02qt02v (0.67 #4707, 0.67 #8741, 0.66 #6052), 019f4v (0.62 #49, 0.41 #273, 0.40 #2515), 0p9sw (0.53 #18, 0.28 #2034, 0.25 #466), 0gr0m (0.49 #53, 0.29 #2519, 0.28 #2069), 0gq_v (0.42 #17, 0.30 #2483, 0.30 #2033), 02qyp19 (0.39 #225, 0.16 #7621, 0.16 #2467), 0gr4k (0.35 #2040, 0.33 #24, 0.29 #2490), 0l8z1 (0.33 #47, 0.32 #2063, 0.22 #2513), 0f4x7 (0.31 #247, 0.31 #2489, 0.31 #23), 054krc (0.29 #61, 0.21 #2077, 0.21 #2527) >> Best rule #4707 for best value: >> intensional similarity = 4 >> extensional distance = 465 >> proper extension: 08cfr1; >> query: (?x4530, ?x899) <- production_companies(?x4530, ?x7690), award(?x4530, ?x899), film(?x2279, ?x4530), country(?x4530, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #225 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 133 *> proper extension: 0hv81; *> query: (?x4530, 02qyp19) <- award_winner(?x4530, ?x3873), nominated_for(?x846, ?x4530), nominated_for(?x1862, ?x4530), ?x1862 = 0gr51 *> conf = 0.39 ranks of expected_values: 6 EVAL 07j94 nominated_for! 02qyp19 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 120.000 112.000 0.671 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10391-0p__8 PRED entity: 0p__8 PRED relation: film PRED expected values: 02qydsh => 112 concepts (62 used for prediction) PRED predicted values (max 10 best out of 978): 0kbwb (0.60 #83870, 0.51 #14274, 0.44 #17844), 01k1k4 (0.60 #83870, 0.44 #17844, 0.43 #28551), 039cq4 (0.60 #83870, 0.44 #17844, 0.36 #99931), 02f6g5 (0.11 #280, 0.04 #10985, 0.03 #23477), 02ny6g (0.11 #598, 0.03 #22011, 0.03 #23795), 0f2sx4 (0.11 #1380, 0.03 #12085, 0.03 #6732), 0640m69 (0.11 #1756, 0.03 #12461, 0.03 #7108), 02hxhz (0.11 #121, 0.03 #10826, 0.03 #5473), 074w86 (0.11 #669, 0.03 #11374, 0.03 #6021), 02ph9tm (0.11 #1095, 0.03 #11800, 0.03 #6447) >> Best rule #83870 for best value: >> intensional similarity = 3 >> extensional distance = 931 >> proper extension: 025p38; 01nrq5; 0bkmf; 050llt; >> query: (?x5940, ?x408) <- award_winner(?x401, ?x5940), film(?x5940, ?x146), nominated_for(?x5940, ?x408) >> conf = 0.60 => this is the best rule for 3 predicted values *> Best rule #1493 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0byfz; 02_l96; 0gn30; 01fyzy; 0h7pj; 0cbm64; *> query: (?x5940, 02qydsh) <- award_winner(?x1312, ?x5940), award_nominee(?x815, ?x5940), ?x1312 = 07cbcy *> conf = 0.05 ranks of expected_values: 70 EVAL 0p__8 film 02qydsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 112.000 62.000 0.595 http://example.org/film/actor/film./film/performance/film #10390-0bp_b2 PRED entity: 0bp_b2 PRED relation: award! PRED expected values: 01rnxn 048q6x 01hmk9 022yb4 01d0b1 06bzwt => 46 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2774): 021vwt (0.80 #26565, 0.70 #43179, 0.70 #43177), 0hz_1 (0.80 #26565, 0.70 #43179, 0.70 #43177), 016k6x (0.62 #21355, 0.33 #1432, 0.25 #18032), 0d6d2 (0.54 #22251, 0.33 #2328, 0.25 #15608), 0zcbl (0.54 #21919, 0.33 #1996, 0.25 #15276), 0170pk (0.54 #20360, 0.33 #7077, 0.07 #36968), 018ygt (0.54 #21744, 0.25 #15101, 0.12 #25065), 03ym1 (0.50 #14933, 0.46 #21576, 0.33 #8293), 02xs5v (0.50 #15579, 0.33 #8939, 0.33 #2299), 0sw6g (0.50 #15578, 0.33 #8938, 0.33 #2298) >> Best rule #26565 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 09v7wsg; >> query: (?x435, ?x879) <- award_winner(?x435, ?x879), nominated_for(?x435, ?x337), ceremony(?x435, ?x5585), ?x5585 = 03nnm4t >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #12367 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0fbvqf; 0ck27z; *> query: (?x435, 022yb4) <- award(?x9538, ?x435), award(?x9132, ?x435), nationality(?x9132, ?x94), nominated_for(?x435, ?x337), ?x9538 = 03v0vd *> conf = 0.50 ranks of expected_values: 22, 74, 235, 544, 1004, 2656 EVAL 0bp_b2 award! 06bzwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 46.000 18.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bp_b2 award! 01d0b1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 18.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bp_b2 award! 022yb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 46.000 18.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bp_b2 award! 01hmk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 18.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bp_b2 award! 048q6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 18.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bp_b2 award! 01rnxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 18.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10389-01hmnh PRED entity: 01hmnh PRED relation: genre! PRED expected values: 02pqs8l 07gbf => 71 concepts (48 used for prediction) PRED predicted values (max 10 best out of 534): 05nlzq (0.67 #4391, 0.50 #3867, 0.50 #3604), 025x1t (0.67 #4430, 0.33 #3906, 0.33 #3643), 0fhzwl (0.60 #2535, 0.40 #5958, 0.40 #5430), 06r1k (0.50 #6004, 0.50 #4425, 0.50 #1528), 01rf57 (0.50 #5856, 0.50 #1380, 0.40 #5328), 07gbf (0.50 #5450, 0.50 #1502, 0.40 #2555), 0q6g3 (0.50 #6031, 0.50 #1555, 0.40 #3137), 099pks (0.50 #4309, 0.50 #3785, 0.40 #5626), 02v5xg (0.50 #5952, 0.50 #1476, 0.40 #3058), 02rhwjr (0.50 #6046, 0.50 #1570, 0.40 #3152) >> Best rule #4391 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0pr6f; >> query: (?x1510, 05nlzq) <- genre(?x11477, ?x1510), genre(?x11454, ?x1510), program(?x11453, ?x11454), ?x11477 = 043qqt5 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5450 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 06nbt; 01t_vv; *> query: (?x1510, 07gbf) <- genre(?x6332, ?x1510), genre(?x2184, ?x1510), titles(?x8581, ?x6332), genre(?x419, ?x1510), ?x2184 = 0fvr1 *> conf = 0.50 ranks of expected_values: 6, 72 EVAL 01hmnh genre! 07gbf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 71.000 48.000 0.667 http://example.org/tv/tv_program/genre EVAL 01hmnh genre! 02pqs8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 71.000 48.000 0.667 http://example.org/tv/tv_program/genre #10388-027vps PRED entity: 027vps PRED relation: award PRED expected values: 0gr4k => 120 concepts (104 used for prediction) PRED predicted values (max 10 best out of 273): 0gs9p (0.71 #21669, 0.69 #17655, 0.69 #19662), 02wwsh8 (0.71 #21669, 0.69 #17655, 0.69 #19662), 0gr4k (0.51 #433, 0.46 #32, 0.36 #3641), 03hl6lc (0.45 #977, 0.36 #1378, 0.36 #175), 02qyp19 (0.36 #803, 0.36 #1204, 0.20 #3610), 09sb52 (0.34 #12480, 0.28 #13683, 0.26 #15288), 02pqp12 (0.33 #2073, 0.33 #469, 0.28 #68), 07bdd_ (0.31 #8827, 0.31 #8489, 0.18 #8828), 02n9nmz (0.28 #67, 0.27 #468, 0.21 #6084), 03hkv_r (0.27 #416, 0.26 #3624, 0.24 #6032) >> Best rule #21669 for best value: >> intensional similarity = 3 >> extensional distance = 1026 >> proper extension: 01qkqwg; 08xz51; >> query: (?x8225, ?x1313) <- award_winner(?x11428, ?x8225), award_nominee(?x788, ?x8225), award_winner(?x1313, ?x8225) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #433 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 012vct; *> query: (?x8225, 0gr4k) <- written_by(?x951, ?x8225), award(?x8225, ?x1307), ?x1307 = 0gq9h, profession(?x8225, ?x319) *> conf = 0.51 ranks of expected_values: 3 EVAL 027vps award 0gr4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 104.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10387-01vsyg9 PRED entity: 01vsyg9 PRED relation: profession PRED expected values: 0nbcg => 121 concepts (112 used for prediction) PRED predicted values (max 10 best out of 84): 02hrh1q (0.80 #2206, 0.77 #10857, 0.74 #5872), 01d_h8 (0.62 #443, 0.44 #4250, 0.44 #4543), 0nbcg (0.59 #3834, 0.57 #906, 0.57 #3394), 016z4k (0.48 #587, 0.45 #2341, 0.45 #1464), 0n1h (0.40 #157, 0.31 #449, 0.24 #595), 0cbd2 (0.38 #1905, 0.32 #1321, 0.31 #1029), 0fj9f (0.37 #4151, 0.06 #1075, 0.05 #1367), 0dxtg (0.31 #451, 0.30 #4258, 0.29 #4551), 0fnpj (0.31 #496, 0.22 #1519, 0.20 #204), 02jknp (0.31 #445, 0.21 #4252, 0.20 #4545) >> Best rule #2206 for best value: >> intensional similarity = 3 >> extensional distance = 129 >> proper extension: 01vb403; 06chvn; 02rk45; 0dqcm; 0gdqy; >> query: (?x5623, 02hrh1q) <- type_of_union(?x5623, ?x1873), award_winner(?x2930, ?x5623), ?x1873 = 01g63y >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3834 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 262 *> proper extension: 01vzz1c; *> query: (?x5623, 0nbcg) <- artists(?x505, ?x5623), role(?x5623, ?x227), profession(?x5623, ?x131) *> conf = 0.59 ranks of expected_values: 3 EVAL 01vsyg9 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 121.000 112.000 0.802 http://example.org/people/person/profession #10386-03548 PRED entity: 03548 PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.90 #7, 0.89 #23, 0.88 #13) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 07ytt; >> query: (?x6572, 0hzc9wc) <- countries_within(?x2467, ?x6572), taxonomy(?x6572, ?x939), ?x939 = 04n6k >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03548 administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.900 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #10385-0symg PRED entity: 0symg PRED relation: film! PRED expected values: 02wgln => 82 concepts (24 used for prediction) PRED predicted values (max 10 best out of 859): 0405l (0.46 #43591, 0.42 #29057, 0.40 #22830), 0h5g_ (0.40 #6300, 0.40 #4224, 0.33 #8375), 014g22 (0.33 #2791, 0.20 #6943, 0.20 #4867), 0bksh (0.33 #2928, 0.20 #7080, 0.20 #5004), 016ks_ (0.33 #2858, 0.20 #7010, 0.20 #4934), 0170s4 (0.33 #2472, 0.20 #6624, 0.20 #4548), 02mjf2 (0.33 #2848, 0.20 #7000, 0.20 #4924), 01g257 (0.33 #2328, 0.20 #6480, 0.20 #4404), 01hmb_ (0.33 #3779, 0.20 #7931, 0.20 #5855), 01z_g6 (0.33 #2980, 0.20 #7132, 0.20 #5056) >> Best rule #43591 for best value: >> intensional similarity = 5 >> extensional distance = 285 >> proper extension: 0cwrr; >> query: (?x11027, ?x11079) <- nominated_for(?x11079, ?x11027), category(?x11027, ?x134), type_of_union(?x11079, ?x1873), award(?x11079, ?x1587), location(?x11079, ?x739) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #16919 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 38 *> proper extension: 06w99h3; 018f8; 01kff7; 01f7kl; 027rpym; 01f6x7; 01bl7g; 0p9tm; 02q5bx2; 012gk9; ... *> query: (?x11027, 02wgln) <- genre(?x11027, ?x8280), genre(?x11027, ?x162), ?x8280 = 0hfjk, titles(?x162, ?x10147), titles(?x162, ?x9222), ?x9222 = 06zsk51, ?x10147 = 04z4j2 *> conf = 0.03 ranks of expected_values: 317 EVAL 0symg film! 02wgln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 82.000 24.000 0.462 http://example.org/film/actor/film./film/performance/film #10384-03818y PRED entity: 03818y PRED relation: student PRED expected values: 06pjs => 170 concepts (125 used for prediction) PRED predicted values (max 10 best out of 743): 051cc (0.17 #3572, 0.17 #1478, 0.10 #7760), 0f5xn (0.17 #3042, 0.17 #948, 0.10 #7230), 046zh (0.17 #906, 0.10 #7188, 0.09 #11376), 01vw37m (0.17 #1092, 0.10 #7374, 0.09 #11562), 0306ds (0.10 #4596, 0.09 #8784, 0.04 #36007), 02vntj (0.10 #4892, 0.09 #9080, 0.03 #55149), 0892sx (0.10 #4613, 0.09 #8801, 0.03 #25554), 015v3r (0.10 #4688, 0.09 #8876, 0.03 #25629), 02cx72 (0.10 #4791, 0.09 #8979, 0.02 #36202), 01ft2l (0.10 #4766, 0.09 #8954, 0.02 #65496) >> Best rule #3572 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 04qhdf; >> query: (?x10217, 051cc) <- currency(?x10217, ?x170), citytown(?x10217, ?x2277), state_province_region(?x10217, ?x3038), ?x2277 = 013yq >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #85861 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 129 *> proper extension: 01j53q; *> query: (?x10217, ?x5343) <- citytown(?x10217, ?x2277), place_of_birth(?x5343, ?x2277), film(?x5343, ?x2644), county_seat(?x13275, ?x2277) *> conf = 0.01 ranks of expected_values: 646 EVAL 03818y student 06pjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 170.000 125.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #10383-01xr2s PRED entity: 01xr2s PRED relation: category PRED expected values: 08mbj5d => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.45 #8, 0.45 #10, 0.45 #9) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 09v38qj; >> query: (?x2042, 08mbj5d) <- program(?x4155, ?x2042), honored_for(?x6238, ?x2042), actor(?x2042, ?x4470), country_of_origin(?x2042, ?x94) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xr2s category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.451 http://example.org/common/topic/webpage./common/webpage/category #10382-04q01mn PRED entity: 04q01mn PRED relation: language PRED expected values: 02h40lc => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.92 #120, 0.90 #2, 0.90 #598), 04306rv (0.21 #4418, 0.18 #123, 0.18 #183), 06nm1 (0.21 #4418, 0.18 #129, 0.17 #368), 064_8sq (0.21 #4418, 0.17 #2344, 0.17 #200), 06b_j (0.21 #4418, 0.10 #82, 0.09 #141), 02bjrlw (0.21 #4418, 0.10 #3454, 0.09 #1370), 0jzc (0.21 #4418, 0.08 #79, 0.08 #138), 03_9r (0.21 #4418, 0.06 #10, 0.06 #1677), 04h9h (0.21 #4418, 0.06 #43, 0.06 #341), 032f6 (0.21 #4418, 0.06 #56, 0.05 #115) >> Best rule #120 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 08720; 02c638; 03hkch7; 0ctb4g; 03cw411; 0dt8xq; 02825nf; 01hq1; 08zrbl; 03m5y9p; ... >> query: (?x13884, 02h40lc) <- category(?x13884, ?x134), films(?x8435, ?x13884), titles(?x53, ?x13884), production_companies(?x13884, ?x2548) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04q01mn language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.923 http://example.org/film/film/language #10381-03rt9 PRED entity: 03rt9 PRED relation: country! PRED expected values: 0g5879y 034r25 => 189 concepts (121 used for prediction) PRED predicted values (max 10 best out of 1717): 0gxtknx (0.68 #30462, 0.20 #7008, 0.18 #12085), 0_b3d (0.68 #30462, 0.18 #11989, 0.18 #16924), 03cfkrw (0.68 #30462, 0.18 #12544, 0.13 #14236), 07j8r (0.68 #30462, 0.11 #15612, 0.10 #17307), 02p76f9 (0.68 #30462, 0.10 #8109, 0.09 #13186), 0yxf4 (0.68 #30462, 0.10 #7870, 0.09 #12947), 03h0byn (0.68 #30462), 016ks5 (0.68 #30462), 01m13b (0.39 #15375, 0.36 #8606, 0.35 #25531), 023g6w (0.36 #9850, 0.22 #16619, 0.22 #43696) >> Best rule #30462 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 05bcl; >> query: (?x429, ?x1002) <- nationality(?x6356, ?x429), taxonomy(?x429, ?x939), film(?x6356, ?x1002) >> conf = 0.68 => this is the best rule for 8 predicted values *> Best rule #12244 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 9 *> proper extension: 06rny; *> query: (?x429, 0g5879y) <- split_to(?x3699, ?x429), contains(?x3699, ?x3198) *> conf = 0.18 ranks of expected_values: 163, 240 EVAL 03rt9 country! 034r25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 189.000 121.000 0.675 http://example.org/film/film/country EVAL 03rt9 country! 0g5879y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 189.000 121.000 0.675 http://example.org/film/film/country #10380-0d35y PRED entity: 0d35y PRED relation: teams PRED expected values: 0jml5 => 254 concepts (254 used for prediction) PRED predicted values (max 10 best out of 282): 02d02 (0.17 #905, 0.06 #3059, 0.06 #2341), 02fp3 (0.17 #904, 0.06 #3058, 0.06 #2340), 02c_4 (0.17 #879, 0.06 #3033, 0.06 #2315), 027yf83 (0.17 #451, 0.06 #2605, 0.05 #3323), 0j6tr (0.17 #686, 0.06 #2840, 0.04 #3917), 01lpwh (0.17 #599, 0.06 #2753, 0.04 #3830), 07l4z (0.17 #548, 0.06 #2702, 0.04 #3779), 0cgwt8 (0.17 #477, 0.06 #2631, 0.04 #3708), 0bwjj (0.17 #935, 0.06 #3089, 0.04 #3807), 0j2zj (0.17 #929, 0.06 #3083, 0.04 #3801) >> Best rule #905 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0f04v; >> query: (?x4419, 02d02) <- administrative_division(?x4419, ?x938), location(?x2226, ?x4419), dog_breed(?x4419, ?x1706), adjoins(?x4419, ?x9331) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d35y teams 0jml5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 254.000 254.000 0.167 http://example.org/sports/sports_team_location/teams #10379-0kr_t PRED entity: 0kr_t PRED relation: category PRED expected values: 08mbj5d => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.89 #11, 0.88 #14, 0.88 #13) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 03g5jw; 05crg7; 0249kn; 018ndc; 017j6; 0163m1; 0hvbj; 01fmz6; 0dw4g; 016890; ... >> query: (?x5493, 08mbj5d) <- group(?x227, ?x5493), award(?x5493, ?x247), award_nominee(?x1573, ?x5493) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kr_t category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.895 http://example.org/common/topic/webpage./common/webpage/category #10378-01nxzv PRED entity: 01nxzv PRED relation: nominated_for PRED expected values: 05zr0xl => 88 concepts (40 used for prediction) PRED predicted values (max 10 best out of 266): 0m63c (0.40 #25924, 0.36 #4858, 0.30 #19442), 01gkp1 (0.36 #746, 0.15 #51860, 0.15 #58342), 07tw_b (0.36 #4858, 0.30 #19442, 0.29 #24302), 02qhqz4 (0.36 #4858, 0.30 #19442, 0.29 #24302), 043t8t (0.27 #722, 0.01 #15302, 0.01 #7201), 030cx (0.18 #697, 0.02 #2316, 0.02 #3935), 02ntb8 (0.18 #770, 0.02 #4008), 033fqh (0.18 #774, 0.01 #7253, 0.01 #8874), 01shy7 (0.18 #389, 0.01 #3627, 0.01 #6868), 024lt6 (0.18 #1450) >> Best rule #25924 for best value: >> intensional similarity = 3 >> extensional distance = 639 >> proper extension: 02hsgn; >> query: (?x11808, ?x7693) <- film(?x11808, ?x7693), award_winner(?x11808, ?x1119), award_winner(?x7693, ?x2156) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nxzv nominated_for 05zr0xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 40.000 0.399 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10377-05p92jn PRED entity: 05p92jn PRED relation: award PRED expected values: 0cqhk0 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 249): 0cqhk0 (0.50 #1249, 0.14 #15354, 0.13 #16972), 05zvj3m (0.33 #93, 0.20 #497, 0.14 #15354), 0gr51 (0.33 #100, 0.20 #504, 0.14 #15354), 02qyp19 (0.33 #1, 0.20 #405, 0.14 #15354), 03hl6lc (0.33 #178, 0.20 #582, 0.14 #15354), 09qs08 (0.33 #144, 0.20 #548, 0.14 #15354), 0cjyzs (0.30 #1318, 0.14 #15354, 0.13 #16972), 0gqwc (0.20 #478, 0.14 #15354, 0.12 #7273), 094qd5 (0.20 #448, 0.14 #15354, 0.12 #7273), 02y_rq5 (0.20 #499, 0.14 #15354, 0.12 #7273) >> Best rule #1249 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 05xpms; >> query: (?x6622, 0cqhk0) <- award_nominee(?x6622, ?x3924), nominated_for(?x6622, ?x86), ?x3924 = 0h3mrc >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05p92jn award 0cqhk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10376-0kfv9 PRED entity: 0kfv9 PRED relation: honored_for! PRED expected values: 02q690_ 027n06w => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 83): 073hd1 (0.33 #80, 0.01 #764, 0.01 #4070), 0gvstc3 (0.29 #368, 0.23 #938, 0.23 #1166), 05c1t6z (0.28 #923, 0.27 #125, 0.27 #353), 02q690_ (0.27 #1078, 0.26 #850, 0.26 #964), 0lp_cd3 (0.25 #359, 0.17 #929, 0.16 #587), 03nnm4t (0.23 #858, 0.22 #972, 0.21 #1086), 09g90vz (0.18 #215, 0.07 #899, 0.07 #1013), 05zksls (0.16 #4561, 0.09 #141, 0.08 #6274), 0418154 (0.16 #4561, 0.09 #201, 0.08 #6274), 0hr3c8y (0.16 #4561, 0.08 #6274, 0.08 #6273) >> Best rule #80 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0h6r5; >> query: (?x1849, 073hd1) <- nominated_for(?x435, ?x1849), nominated_for(?x1991, ?x1849), ?x1991 = 02lf70 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1078 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 92 *> proper extension: 0gbtbm; 02gl58; 02py9yf; *> query: (?x1849, 02q690_) <- actor(?x1849, ?x369), honored_for(?x2126, ?x1849), nominated_for(?x435, ?x1849) *> conf = 0.27 ranks of expected_values: 4, 13 EVAL 0kfv9 honored_for! 027n06w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 68.000 68.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0kfv9 honored_for! 02q690_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 68.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #10375-0b2qtl PRED entity: 0b2qtl PRED relation: genre PRED expected values: 07s9rl0 060__y => 98 concepts (87 used for prediction) PRED predicted values (max 10 best out of 108): 02kdv5l (0.88 #3747, 0.50 #2, 0.48 #119), 07s9rl0 (0.82 #1522, 0.80 #586, 0.78 #3512), 01jfsb (0.60 #130, 0.50 #364, 0.46 #13), 03k9fj (0.38 #831, 0.36 #714, 0.36 #1182), 06n90 (0.38 #14, 0.25 #3759, 0.25 #833), 05p553 (0.37 #2461, 0.37 #2929, 0.37 #7144), 01hmnh (0.30 #837, 0.30 #720, 0.29 #18), 0lsxr (0.28 #126, 0.20 #3754, 0.18 #4339), 04pbhw (0.25 #53, 0.14 #872, 0.14 #755), 060__y (0.22 #1070, 0.22 #4347, 0.21 #3528) >> Best rule #3747 for best value: >> intensional similarity = 5 >> extensional distance = 521 >> proper extension: 0cks1m; 02gqm3; 04svwx; >> query: (?x5096, 02kdv5l) <- genre(?x5096, ?x1626), genre(?x6839, ?x1626), genre(?x5849, ?x1626), ?x5849 = 02h22, ?x6839 = 0dr1c2 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1522 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 216 *> proper extension: 011yfd; 0k2m6; 0j8f09z; *> query: (?x5096, 07s9rl0) <- nominated_for(?x1313, ?x5096), language(?x5096, ?x90), country(?x5096, ?x94), ?x1313 = 0gs9p *> conf = 0.82 ranks of expected_values: 2, 10 EVAL 0b2qtl genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 98.000 87.000 0.878 http://example.org/film/film/genre EVAL 0b2qtl genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 87.000 0.878 http://example.org/film/film/genre #10374-0trv PRED entity: 0trv PRED relation: major_field_of_study PRED expected values: 05qjt 0g4gr 0_jm => 171 concepts (158 used for prediction) PRED predicted values (max 10 best out of 117): 02lp1 (0.67 #128, 0.55 #245, 0.50 #4351), 01lj9 (0.59 #270, 0.56 #153, 0.39 #504), 062z7 (0.52 #494, 0.46 #11175, 0.40 #4366), 03g3w (0.50 #259, 0.45 #493, 0.45 #1195), 01tbp (0.44 #173, 0.41 #758, 0.39 #407), 02jfc (0.44 #195, 0.33 #78, 0.30 #663), 02ky346 (0.44 #132, 0.33 #15, 0.23 #2124), 0db86 (0.44 #165, 0.33 #48, 0.18 #1101), 0_jm (0.42 #7096, 0.33 #171, 0.33 #54), 01540 (0.42 #525, 0.34 #2166, 0.33 #57) >> Best rule #128 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 065y4w7; 07w0v; 07szy; 03tw2s; 01jq0j; 027ybp; >> query: (?x8706, 02lp1) <- institution(?x1771, ?x8706), ?x1771 = 019v9k, currency(?x8706, ?x170), school(?x10279, ?x8706), ?x10279 = 04wmvz, colors(?x8706, ?x332) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7096 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 166 *> proper extension: 01v3k2; *> query: (?x8706, 0_jm) <- major_field_of_study(?x8706, ?x4321), major_field_of_study(?x9522, ?x4321), major_field_of_study(?x3416, ?x4321), major_field_of_study(?x3248, ?x4321), ?x9522 = 01yqqv, major_field_of_study(?x620, ?x4321), ?x3416 = 02183k, school_type(?x3248, ?x3092) *> conf = 0.42 ranks of expected_values: 9, 11, 17 EVAL 0trv major_field_of_study 0_jm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 171.000 158.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0trv major_field_of_study 0g4gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 171.000 158.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0trv major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 171.000 158.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10373-0315rp PRED entity: 0315rp PRED relation: genre PRED expected values: 02kdv5l => 92 concepts (79 used for prediction) PRED predicted values (max 10 best out of 108): 07s9rl0 (0.81 #7154, 0.74 #7869, 0.65 #477), 02kdv5l (0.62 #122, 0.58 #360, 0.51 #4530), 024qqx (0.54 #8345, 0.50 #4408, 0.49 #6556), 02l7c8 (0.53 #5615, 0.40 #15, 0.29 #8598), 05p553 (0.52 #4054, 0.44 #838, 0.42 #243), 01hmnh (0.51 #4663, 0.48 #4782, 0.42 #5020), 02xlf (0.25 #290, 0.10 #1123, 0.09 #528), 0lsxr (0.23 #724, 0.22 #1439, 0.21 #5848), 04xvh5 (0.22 #509, 0.17 #390, 0.16 #628), 0hcr (0.21 #4788, 0.17 #3237, 0.17 #4669) >> Best rule #7154 for best value: >> intensional similarity = 6 >> extensional distance = 1129 >> proper extension: 02d413; 0g22z; 0sxg4; 01br2w; 028_yv; 027qgy; 02v8kmz; 047q2k1; 0c0yh4; 0yyg4; ... >> query: (?x8397, 07s9rl0) <- country(?x8397, ?x94), genre(?x8397, ?x811), genre(?x7693, ?x811), genre(?x4664, ?x811), ?x4664 = 0fqt1ns, ?x7693 = 0m63c >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #122 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 0prrm; 043tvp3; *> query: (?x8397, 02kdv5l) <- country(?x8397, ?x94), genre(?x8397, ?x811), executive_produced_by(?x8397, ?x846), edited_by(?x8397, ?x4215), prequel(?x8397, ?x2878) *> conf = 0.62 ranks of expected_values: 2 EVAL 0315rp genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 79.000 0.807 http://example.org/film/film/genre #10372-0dkv90 PRED entity: 0dkv90 PRED relation: film! PRED expected values: 04f525m => 81 concepts (67 used for prediction) PRED predicted values (max 10 best out of 56): 086k8 (0.29 #76, 0.26 #150, 0.23 #2), 03xq0f (0.23 #4, 0.21 #78, 0.17 #152), 024rgt (0.23 #19, 0.21 #93, 0.13 #167), 05qd_ (0.23 #1045, 0.15 #1790, 0.14 #2088), 016tw3 (0.18 #454, 0.16 #232, 0.16 #2016), 024rdh (0.16 #406, 0.10 #703, 0.08 #554), 017s11 (0.13 #2157, 0.13 #2083, 0.13 #2454), 0jz9f (0.13 #223, 0.10 #519, 0.10 #445), 024rbz (0.10 #678, 0.10 #455, 0.09 #381), 01gb54 (0.10 #250, 0.07 #1366, 0.07 #1141) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 040b5k; >> query: (?x7789, 086k8) <- nominated_for(?x2489, ?x7789), nominated_for(?x507, ?x7789), ?x2489 = 02x2gy0, ?x507 = 02g3v6 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #379 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 02qhqz4; *> query: (?x7789, 04f525m) <- film_crew_role(?x7789, ?x468), genre(?x7789, ?x1626), film_release_region(?x7789, ?x94), ?x1626 = 03q4nz *> conf = 0.02 ranks of expected_values: 44 EVAL 0dkv90 film! 04f525m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 81.000 67.000 0.286 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #10371-016yr0 PRED entity: 016yr0 PRED relation: category PRED expected values: 08mbj5d => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.32 #30, 0.32 #26, 0.31 #31) >> Best rule #30 for best value: >> intensional similarity = 3 >> extensional distance = 1248 >> proper extension: 01wbsdz; >> query: (?x4327, 08mbj5d) <- profession(?x4327, ?x319), award_nominee(?x5645, ?x4327), location(?x4327, ?x7769) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016yr0 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.318 http://example.org/common/topic/webpage./common/webpage/category #10370-0163kf PRED entity: 0163kf PRED relation: artist! PRED expected values: 016ckq => 134 concepts (92 used for prediction) PRED predicted values (max 10 best out of 110): 03rhqg (0.38 #154, 0.27 #432, 0.18 #1405), 01cl2y (0.31 #168, 0.13 #446, 0.07 #724), 0fb0v (0.25 #146, 0.25 #7, 0.14 #285), 01w40h (0.25 #27, 0.14 #305, 0.12 #166), 0mzkr (0.25 #24, 0.12 #163, 0.10 #302), 04fcjt (0.25 #28, 0.10 #445, 0.10 #306), 02zn1b (0.25 #148, 0.10 #426, 0.08 #4595), 016ckq (0.25 #42, 0.10 #737, 0.10 #320), 01dtcb (0.25 #46, 0.09 #1714, 0.08 #4595), 033hn8 (0.24 #291, 0.16 #847, 0.14 #708) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 01vsy3q; 01wg25j; >> query: (?x12102, 03rhqg) <- artist(?x2241, ?x12102), people(?x2510, ?x12102), ?x2241 = 02p11jq >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01vrz41; 012x4t; *> query: (?x12102, 016ckq) <- award_winner(?x5059, ?x12102), award_winner(?x2186, ?x12102), ?x5059 = 01vt9p3, award_nominee(?x1128, ?x12102) *> conf = 0.25 ranks of expected_values: 8 EVAL 0163kf artist! 016ckq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 134.000 92.000 0.375 http://example.org/music/record_label/artist #10369-06n90 PRED entity: 06n90 PRED relation: disciplines_or_subjects! PRED expected values: 01tgwv => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 125): 039yzf (0.60 #1674, 0.56 #2918, 0.50 #1008), 040v3t (0.60 #1669, 0.50 #1003, 0.44 #2913), 0208wk (0.50 #2724, 0.50 #1099, 0.50 #1004), 040_9s0 (0.50 #2717, 0.50 #1092, 0.50 #997), 01yz0x (0.50 #1067, 0.50 #972, 0.44 #2882), 02r6nbc (0.50 #1082, 0.50 #987, 0.40 #1653), 01f7d (0.50 #1106, 0.50 #1011, 0.40 #1677), 02r771y (0.50 #1135, 0.50 #1040, 0.40 #1706), 03mv9j (0.50 #1032, 0.40 #1698, 0.38 #2752), 04jhhng (0.50 #1035, 0.40 #1701, 0.33 #463) >> Best rule #1674 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0707q; >> query: (?x1013, 039yzf) <- disciplines_or_subjects(?x12418, ?x1013), disciplines_or_subjects(?x10747, ?x1013), disciplines_or_subjects(?x1375, ?x1013), ?x1375 = 0262zm, award(?x9284, ?x12418), award(?x476, ?x12418), award(?x4987, ?x10747), ?x9284 = 0gd_s, ?x476 = 07w21, film(?x4987, ?x1842), profession(?x4987, ?x220) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1110 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 05hgj; *> query: (?x1013, 01tgwv) <- disciplines_or_subjects(?x11579, ?x1013), disciplines_or_subjects(?x1375, ?x1013), award(?x11262, ?x1375), award(?x9738, ?x1375), award(?x8863, ?x1375), ?x11262 = 0fvt2, ?x8863 = 0fpzt5, ?x11579 = 058bzgm, ?x9738 = 03rx9, award_winner(?x1375, ?x1727) *> conf = 0.50 ranks of expected_values: 13 EVAL 06n90 disciplines_or_subjects! 01tgwv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 68.000 68.000 0.600 http://example.org/award/award_category/disciplines_or_subjects #10368-095bb PRED entity: 095bb PRED relation: genre! PRED expected values: 019nnl 015w8_ 0vhm 03nymk => 42 concepts (27 used for prediction) PRED predicted values (max 10 best out of 244): 019nnl (0.75 #3166, 0.50 #4023, 0.50 #2308), 0584r4 (0.62 #3174, 0.50 #2316, 0.50 #2030), 06y_n (0.62 #3349, 0.50 #2491, 0.50 #2205), 05f7w84 (0.60 #1823, 0.60 #1248, 0.50 #3537), 020qr4 (0.60 #1723, 0.60 #1148, 0.50 #3437), 05h95s (0.60 #1285, 0.50 #3574, 0.50 #2145), 0cwrr (0.60 #1158, 0.50 #2018, 0.50 #873), 09g_31 (0.60 #1882, 0.50 #3596, 0.50 #1022), 017dtf (0.60 #1349, 0.50 #2209, 0.43 #2781), 0ctzf1 (0.60 #1566, 0.50 #992, 0.40 #1852) >> Best rule #3166 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 01z4y; 0c4xc; >> query: (?x7685, 019nnl) <- genre(?x9340, ?x7685), genre(?x8846, ?x7685), genre(?x5583, ?x7685), tv_program(?x8339, ?x8846), place_of_birth(?x8339, ?x739), ?x5583 = 099pks, actor(?x9340, ?x7266), ?x7266 = 02gf_l, languages(?x9340, ?x254), nominated_for(?x3263, ?x8846) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 24, 26, 57 EVAL 095bb genre! 03nymk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 42.000 27.000 0.750 http://example.org/tv/tv_program/genre EVAL 095bb genre! 0vhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 42.000 27.000 0.750 http://example.org/tv/tv_program/genre EVAL 095bb genre! 015w8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 42.000 27.000 0.750 http://example.org/tv/tv_program/genre EVAL 095bb genre! 019nnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 27.000 0.750 http://example.org/tv/tv_program/genre #10367-027mdh PRED entity: 027mdh PRED relation: citytown PRED expected values: 0d6lp => 184 concepts (162 used for prediction) PRED predicted values (max 10 best out of 379): 02_286 (0.32 #23960, 0.29 #44246, 0.27 #21012), 01_d4 (0.25 #406, 0.15 #2614, 0.10 #1510), 0chrx (0.25 #554, 0.10 #1658, 0.08 #2762), 01qh7 (0.25 #62, 0.10 #1902, 0.06 #3742), 0y617 (0.25 #312, 0.10 #2152, 0.06 #3992), 0rh6k (0.23 #2577, 0.10 #1473, 0.09 #12518), 030qb3t (0.20 #7756, 0.20 #6652, 0.20 #1132), 0ftvz (0.20 #788, 0.08 #2996, 0.07 #3364), 02dtg (0.20 #1480, 0.08 #2584, 0.06 #8472), 07bcn (0.14 #43860, 0.07 #55302, 0.05 #4916) >> Best rule #23960 for best value: >> intensional similarity = 6 >> extensional distance = 93 >> proper extension: 02bh8z; 07l1c; 077w0b; 018_q8; 0sxdg; 01dfb6; 01_4lx; 07_dn; 02l48d; 026v5; >> query: (?x5651, 02_286) <- currency(?x5651, ?x170), state_province_region(?x5651, ?x1227), ?x170 = 09nqf, contains(?x1227, ?x191), location(?x397, ?x1227), vacationer(?x1227, ?x2658) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #4485 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 0k8z; *> query: (?x5651, 0d6lp) <- state_province_region(?x5651, ?x1227), currency(?x5651, ?x170), ?x170 = 09nqf, ?x1227 = 01n7q, category(?x5651, ?x134), organization(?x346, ?x5651) *> conf = 0.12 ranks of expected_values: 11 EVAL 027mdh citytown 0d6lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 184.000 162.000 0.316 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #10366-0b2qtl PRED entity: 0b2qtl PRED relation: films! PRED expected values: 0cbvg => 89 concepts (42 used for prediction) PRED predicted values (max 10 best out of 46): 0cm2xh (0.20 #47, 0.06 #203, 0.05 #674), 081pw (0.20 #630, 0.06 #316, 0.05 #473), 07_nf (0.06 #694, 0.02 #2751, 0.02 #853), 0chghy (0.06 #317, 0.06 #160), 0fzyg (0.06 #367, 0.03 #681, 0.03 #524), 0bq3x (0.06 #343, 0.03 #1927, 0.03 #500), 0fx2s (0.06 #229, 0.03 #2757, 0.03 #700), 0fy91 (0.06 #449), 0flry (0.06 #410), 075k5 (0.06 #397) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 017kct; >> query: (?x5096, 0cm2xh) <- film(?x9808, ?x5096), ?x9808 = 0141kz, genre(?x5096, ?x162), language(?x5096, ?x90) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #712 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 155 *> proper extension: 0c0wvx; *> query: (?x5096, 0cbvg) <- genre(?x5096, ?x3515), ?x3515 = 082gq *> conf = 0.01 ranks of expected_values: 41 EVAL 0b2qtl films! 0cbvg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 89.000 42.000 0.200 http://example.org/film/film_subject/films #10365-017zq0 PRED entity: 017zq0 PRED relation: institution! PRED expected values: 07s6fsf => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 21): 02_xgp2 (0.63 #100, 0.59 #145, 0.54 #190), 03bwzr4 (0.62 #102, 0.60 #192, 0.58 #147), 016t_3 (0.49 #92, 0.48 #137, 0.47 #47), 0bkj86 (0.47 #141, 0.40 #186, 0.40 #51), 07s6fsf (0.43 #135, 0.42 #180, 0.40 #157), 04zx3q1 (0.37 #46, 0.33 #136, 0.30 #181), 027f2w (0.30 #142, 0.30 #52, 0.26 #187), 013zdg (0.30 #50, 0.25 #140, 0.24 #95), 03mkk4 (0.29 #815, 0.23 #54, 0.22 #99), 028dcg (0.29 #815, 0.20 #62, 0.16 #1452) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 015fsv; >> query: (?x1440, 02_xgp2) <- major_field_of_study(?x1440, ?x4268), institution(?x865, ?x1440), fraternities_and_sororities(?x1440, ?x4348), ?x4348 = 035tlh >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #135 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 02hp6p; *> query: (?x1440, 07s6fsf) <- institution(?x865, ?x1440), student(?x1440, ?x9815), fraternities_and_sororities(?x1440, ?x3697), film(?x9815, ?x3752) *> conf = 0.43 ranks of expected_values: 5 EVAL 017zq0 institution! 07s6fsf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 114.000 114.000 0.635 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #10364-07ylj PRED entity: 07ylj PRED relation: form_of_government PRED expected values: 06cx9 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 5): 01fpfn (0.39 #48, 0.39 #53, 0.39 #193), 06cx9 (0.39 #351, 0.38 #191, 0.37 #246), 018wl5 (0.38 #12, 0.35 #17, 0.35 #2), 01q20 (0.35 #4, 0.33 #24, 0.32 #9), 026wp (0.12 #5, 0.10 #20, 0.09 #15) >> Best rule #48 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 0j5g9; >> query: (?x1203, 01fpfn) <- official_language(?x1203, ?x2502), administrative_parent(?x1203, ?x551), teams(?x1203, ?x12184) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #351 for first EXPECTED value: *> intensional similarity = 1 *> extensional distance = 183 *> proper extension: 02wm6l; *> query: (?x1203, 06cx9) <- form_of_government(?x1203, ?x6377) *> conf = 0.39 ranks of expected_values: 2 EVAL 07ylj form_of_government 06cx9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 127.000 0.390 http://example.org/location/country/form_of_government #10363-0h1tz PRED entity: 0h1tz PRED relation: nutrient! PRED expected values: 0fbw6 => 59 concepts (55 used for prediction) PRED predicted values (max 10 best out of 6): 0fbw6 (0.89 #15, 0.89 #95, 0.88 #71), 06x4c (0.89 #15, 0.89 #95, 0.88 #71), 0dcfv (0.89 #15, 0.89 #95, 0.88 #71), 04k8n (0.03 #446), 05wvs (0.03 #446), 01sh2 (0.03 #446) >> Best rule #15 for best value: >> intensional similarity = 117 >> extensional distance = 10 >> proper extension: 09pbb; >> query: (?x9733, ?x3264) <- nutrient(?x10612, ?x9733), nutrient(?x9732, ?x9733), nutrient(?x9489, ?x9733), nutrient(?x9005, ?x9733), nutrient(?x8298, ?x9733), nutrient(?x7719, ?x9733), nutrient(?x7057, ?x9733), nutrient(?x6285, ?x9733), nutrient(?x6159, ?x9733), nutrient(?x6032, ?x9733), nutrient(?x5373, ?x9733), nutrient(?x5009, ?x9733), nutrient(?x3900, ?x9733), nutrient(?x3468, ?x9733), nutrient(?x2701, ?x9733), nutrient(?x1959, ?x9733), nutrient(?x1303, ?x9733), nutrient(?x1257, ?x9733), ?x5009 = 0fjfh, ?x9005 = 04zpv, ?x1257 = 09728, nutrient(?x8298, ?x13944), nutrient(?x8298, ?x13126), nutrient(?x8298, ?x12902), nutrient(?x8298, ?x12454), nutrient(?x8298, ?x11784), nutrient(?x8298, ?x11758), nutrient(?x8298, ?x11409), nutrient(?x8298, ?x11270), nutrient(?x8298, ?x10891), nutrient(?x8298, ?x10453), nutrient(?x8298, ?x10195), nutrient(?x8298, ?x10098), nutrient(?x8298, ?x9915), nutrient(?x8298, ?x9619), nutrient(?x8298, ?x9490), nutrient(?x8298, ?x9436), nutrient(?x8298, ?x9426), nutrient(?x8298, ?x9365), nutrient(?x8298, ?x8442), nutrient(?x8298, ?x8413), nutrient(?x8298, ?x7894), nutrient(?x8298, ?x7720), nutrient(?x8298, ?x7652), nutrient(?x8298, ?x7431), nutrient(?x8298, ?x7364), nutrient(?x8298, ?x7362), nutrient(?x8298, ?x7219), nutrient(?x8298, ?x7135), nutrient(?x8298, ?x6586), nutrient(?x8298, ?x6160), nutrient(?x8298, ?x6033), nutrient(?x8298, ?x6026), nutrient(?x8298, ?x5549), nutrient(?x8298, ?x5451), nutrient(?x8298, ?x5337), nutrient(?x8298, ?x5010), nutrient(?x8298, ?x4069), nutrient(?x8298, ?x3203), nutrient(?x8298, ?x2702), nutrient(?x8298, ?x1960), ?x11270 = 02kc008, ?x9436 = 025sqz8, ?x6160 = 041r51, ?x6285 = 01645p, ?x2702 = 0838f, ?x9426 = 0h1yy, ?x6026 = 025sf8g, ?x7362 = 02kc5rj, ?x5549 = 025s7j4, ?x10453 = 075pwf, ?x10195 = 0hkwr, ?x1960 = 07hnp, ?x11758 = 0q01m, ?x12902 = 0fzjh, ?x8413 = 02kc4sf, ?x6033 = 04zjxcz, ?x10891 = 0g5gq, ?x9365 = 04k8n, ?x3468 = 0cxn2, ?x9489 = 07j87, ?x10612 = 0frq6, ?x7720 = 025s7x6, ?x7719 = 0dj75, ?x7364 = 09gvd, ?x6586 = 05gh50, ?x13944 = 0f4kp, ?x11784 = 07zqy, ?x5373 = 0971v, ?x7894 = 0f4hc, ?x7135 = 025rsfk, nutrient(?x4068, ?x12454), nutrient(?x3264, ?x12454), ?x2701 = 0hkxq, ?x9915 = 025tkqy, ?x3900 = 061_f, ?x5337 = 06x4c, ?x5451 = 05wvs, ?x11409 = 0h1yf, ?x13126 = 02kc_w5, ?x8442 = 02kcv4x, ?x4069 = 0hqw8p_, ?x9490 = 0h1sg, ?x7219 = 0h1vg, ?x4068 = 0fbw6, ?x7057 = 0fbdb, ?x7431 = 09gwd, ?x3203 = 04kl74p, ?x5010 = 0h1vz, ?x9732 = 05z55, ?x7652 = 025s0s0, ?x1303 = 0fj52s, ?x6032 = 01nkt, ?x1959 = 0f25w9, ?x9619 = 0h1tg, ?x10098 = 0h1_c, ?x6159 = 033cnk >> conf = 0.89 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0h1tz nutrient! 0fbw6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 55.000 0.887 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #10362-09g8vhw PRED entity: 09g8vhw PRED relation: prequel PRED expected values: 03tps5 => 121 concepts (80 used for prediction) PRED predicted values (max 10 best out of 35): 051ys82 (0.05 #908, 0.05 #1636, 0.04 #1090), 06_wqk4 (0.05 #908, 0.05 #1636, 0.04 #1090), 02q7yfq (0.05 #908, 0.05 #1636, 0.04 #1090), 0fdv3 (0.03 #578, 0.03 #760, 0.03 #1124), 0dfw0 (0.03 #638, 0.03 #820, 0.03 #1184), 07g1sm (0.03 #850, 0.03 #1032, 0.03 #1396), 0dyb1 (0.02 #1505, 0.01 #1869, 0.01 #4415), 0x25q (0.02 #1506, 0.01 #2596), 0g5pv3 (0.02 #1478), 05nlx4 (0.02 #1762, 0.01 #2125, 0.01 #2307) >> Best rule #908 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0m313; 03s6l2; 0hmr4; 017gl1; 092vkg; 0bshwmp; 09q5w2; 0dtfn; 02q5g1z; 0fdv3; ... >> query: (?x2075, ?x857) <- executive_produced_by(?x2075, ?x3744), honored_for(?x857, ?x2075), currency(?x2075, ?x170), film(?x902, ?x2075) >> conf = 0.05 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 09g8vhw prequel 03tps5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 80.000 0.048 http://example.org/film/film/prequel #10361-03h2c PRED entity: 03h2c PRED relation: olympics PRED expected values: 06sks6 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 41): 06sks6 (0.90 #1093, 0.89 #928, 0.88 #1421), 0kbws (0.58 #301, 0.53 #260, 0.52 #342), 0kbvb (0.44 #294, 0.37 #171, 0.36 #335), 0kbvv (0.42 #313, 0.39 #190, 0.37 #231), 018ctl (0.37 #172, 0.35 #213, 0.35 #295), 09n48 (0.37 #167, 0.35 #208, 0.35 #290), 0swbd (0.37 #175, 0.33 #216, 0.28 #298), 0jdk_ (0.35 #191, 0.33 #314, 0.30 #232), 0jhn7 (0.24 #315, 0.24 #192, 0.21 #233), 0swff (0.24 #187, 0.22 #228, 0.17 #310) >> Best rule #1093 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 04w4s; 06ryl; 03gyl; 06s0l; 035yg; 04vs9; 04ty8; 04hvw; >> query: (?x3720, 06sks6) <- member_states(?x7695, ?x3720), country(?x471, ?x3720), currency(?x3720, ?x170) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03h2c olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.904 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #10360-06chf PRED entity: 06chf PRED relation: award PRED expected values: 019f4v => 153 concepts (137 used for prediction) PRED predicted values (max 10 best out of 299): 027b9ly (0.72 #38037, 0.71 #44850, 0.71 #38036), 02g3ft (0.72 #38037, 0.71 #44850, 0.71 #38036), 0gq9h (0.55 #3679, 0.45 #5280, 0.41 #10483), 019f4v (0.54 #3669, 0.47 #5270, 0.46 #12873), 0cjyzs (0.35 #15314, 0.35 #10111, 0.34 #8510), 0f_nbyh (0.33 #811, 0.29 #1611, 0.25 #3612), 07bdd_ (0.32 #1667, 0.23 #6070, 0.23 #6470), 09sb52 (0.31 #24857, 0.31 #25657, 0.31 #26057), 0gr51 (0.31 #5703, 0.28 #12905, 0.26 #10905), 0gr4k (0.29 #3635, 0.26 #5637, 0.24 #12839) >> Best rule #38037 for best value: >> intensional similarity = 3 >> extensional distance = 1268 >> proper extension: 0khth; 06z4wj; 04k05; 07k2d; 06lxn; >> query: (?x2803, ?x5516) <- award_winner(?x5516, ?x2803), award_winner(?x9785, ?x2803), award(?x2179, ?x5516) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #3669 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 07nznf; 0kr5_; 03_gd; 02kxbwx; 0151w_; 0bwh6; 01gzm2; 052gzr; 0h1p; 04y8r; ... *> query: (?x2803, 019f4v) <- produced_by(?x1230, ?x2803), film(?x2803, ?x1077), honored_for(?x3254, ?x1230) *> conf = 0.54 ranks of expected_values: 4 EVAL 06chf award 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 153.000 137.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10359-080knyg PRED entity: 080knyg PRED relation: profession PRED expected values: 02hrh1q => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 65): 02hrh1q (0.90 #315, 0.89 #1515, 0.88 #5265), 03gjzk (0.35 #1666, 0.29 #316, 0.24 #3466), 01d_h8 (0.34 #456, 0.33 #906, 0.31 #1056), 0dxtg (0.32 #1664, 0.31 #614, 0.30 #914), 09jwl (0.31 #1370, 0.29 #470, 0.28 #6451), 018gz8 (0.28 #6451, 0.25 #10352, 0.24 #318), 0kyk (0.28 #6451, 0.25 #10352, 0.15 #631), 0nbcg (0.28 #6451, 0.23 #1383, 0.17 #483), 02jknp (0.28 #6451, 0.20 #8259, 0.19 #458), 089fss (0.28 #6451, 0.07 #17, 0.02 #3767) >> Best rule #315 for best value: >> intensional similarity = 2 >> extensional distance = 47 >> proper extension: 02h8hr; 0l_dv; >> query: (?x2360, 02hrh1q) <- actor(?x5060, ?x2360), person(?x5639, ?x2360) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 080knyg profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.898 http://example.org/people/person/profession #10358-0lbd9 PRED entity: 0lbd9 PRED relation: sports PRED expected values: 01cgz => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 40): 06wrt (0.88 #346, 0.86 #792, 0.86 #754), 01cgz (0.83 #420, 0.82 #791, 0.81 #753), 01sgl (0.76 #150, 0.74 #112, 0.74 #299), 07_53 (0.75 #137, 0.71 #99, 0.67 #286), 03krj (0.75 #143, 0.71 #105, 0.58 #292), 0486tv (0.64 #805, 0.63 #545, 0.62 #359), 06z68 (0.62 #131, 0.57 #93, 0.36 #318), 019w9j (0.62 #130, 0.57 #92, 0.33 #429), 07jbh (0.62 #132, 0.57 #94, 0.33 #281), 03_8r (0.57 #88, 0.50 #275, 0.50 #126) >> Best rule #346 for best value: >> intensional similarity = 12 >> extensional distance = 14 >> proper extension: 0l6vl; 0l98s; 0l6ny; 09x3r; 0lv1x; 0lk8j; 0nbjq; 0lgxj; 018ljb; 0jkvj; >> query: (?x6464, 06wrt) <- sports(?x6464, ?x3659), sports(?x6464, ?x171), olympics(?x94, ?x6464), ?x3659 = 0dwxr, ?x171 = 0d1tm, olympics(?x2315, ?x6464), olympics(?x1355, ?x6464), olympics(?x1273, ?x6464), countries_spoken_in(?x5359, ?x1273), ?x1355 = 0h7x, form_of_government(?x1273, ?x1926), medal(?x6464, ?x422) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #420 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 16 *> proper extension: 0l998; *> query: (?x6464, 01cgz) <- sports(?x6464, ?x4876), sports(?x6464, ?x3659), sports(?x6464, ?x2885), olympics(?x94, ?x6464), sports(?x4255, ?x3659), olympics(?x1892, ?x6464), ?x4876 = 0d1t3, sports(?x775, ?x3659), olympics(?x3659, ?x1931), ?x2885 = 07jjt, ?x4255 = 0lgxj, ?x1892 = 02vzc *> conf = 0.83 ranks of expected_values: 2 EVAL 0lbd9 sports 01cgz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 62.000 62.000 0.875 http://example.org/olympics/olympic_games/sports #10357-03q5t PRED entity: 03q5t PRED relation: role PRED expected values: 025cbm 01vnt4 => 91 concepts (58 used for prediction) PRED predicted values (max 10 best out of 97): 05842k (0.90 #4804, 0.88 #4428, 0.88 #3597), 07xzm (0.83 #2432, 0.80 #3089, 0.78 #1946), 026t6 (0.83 #4093, 0.79 #5120, 0.78 #1860), 0l15bq (0.82 #3562, 0.80 #2161, 0.80 #1973), 0dwtp (0.82 #4745, 0.81 #3443, 0.81 #4839), 07_l6 (0.82 #4745, 0.81 #3443, 0.81 #4839), 02hnl (0.82 #4745, 0.81 #3443, 0.81 #4839), 025cbm (0.82 #4745, 0.81 #3443, 0.81 #4839), 01vnt4 (0.82 #4745, 0.81 #3443, 0.81 #4839), 05148p4 (0.79 #368, 0.76 #88, 0.75 #3457) >> Best rule #4804 for best value: >> intensional similarity = 16 >> extensional distance = 27 >> proper extension: 07m2y; >> query: (?x74, 05842k) <- role(?x74, ?x2059), role(?x74, ?x894), role(?x74, ?x614), role(?x645, ?x74), instrumentalists(?x74, ?x9134), role(?x74, ?x4311), ?x894 = 03m5k, role(?x433, ?x74), role(?x4917, ?x2059), place_of_birth(?x9134, ?x854), role(?x614, ?x5921), profession(?x9134, ?x1614), instrumentalists(?x614, ?x317), ?x5921 = 0g2ff, ?x4917 = 06w7v, role(?x2575, ?x614) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #4745 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 27 *> proper extension: 03t22m; *> query: (?x74, ?x433) <- role(?x74, ?x4769), role(?x74, ?x1437), role(?x74, ?x745), role(?x1166, ?x74), role(?x645, ?x74), instrumentalists(?x74, ?x642), group(?x74, ?x4715), group(?x645, ?x10737), group(?x645, ?x8335), group(?x645, ?x4995), ?x1437 = 01vdm0, ?x10737 = 0b1hw, ?x1166 = 05148p4, ?x745 = 01vj9c, role(?x74, ?x4311), role(?x679, ?x645), ?x4995 = 01fmz6, ?x8335 = 015cqh, role(?x433, ?x74), role(?x4769, ?x212) *> conf = 0.82 ranks of expected_values: 8, 9 EVAL 03q5t role 01vnt4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 91.000 58.000 0.897 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 03q5t role 025cbm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 91.000 58.000 0.897 http://example.org/music/performance_role/track_performances./music/track_contribution/role #10356-02hrh0_ PRED entity: 02hrh0_ PRED relation: place_of_death! PRED expected values: 07z4fy => 174 concepts (144 used for prediction) PRED predicted values (max 10 best out of 732): 01h320 (0.11 #3909, 0.06 #5419, 0.05 #6175), 01dvtx (0.11 #3936, 0.05 #6202, 0.05 #6957), 03csqj4 (0.11 #4438, 0.05 #6704, 0.05 #7459), 01h5f8 (0.11 #3652, 0.05 #7431, 0.04 #9700), 042f1 (0.11 #3527, 0.05 #7306, 0.04 #9575), 025l5 (0.11 #3212, 0.05 #6991, 0.04 #9260), 01kstn9 (0.11 #3154, 0.05 #6933, 0.04 #9202), 0ggjt (0.11 #3138, 0.05 #6917, 0.04 #9186), 03h_fk5 (0.11 #3126, 0.05 #6905, 0.04 #9174), 09dt7 (0.11 #3062, 0.04 #9866, 0.04 #10622) >> Best rule #3909 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0d23k; >> query: (?x5193, 01h320) <- administrative_division(?x5193, ?x6226), citytown(?x7910, ?x5193), featured_film_locations(?x7199, ?x6226), film(?x609, ?x7199) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02hrh0_ place_of_death! 07z4fy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 174.000 144.000 0.111 http://example.org/people/deceased_person/place_of_death #10355-0gzy02 PRED entity: 0gzy02 PRED relation: country PRED expected values: 07ssc => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 126): 0cdbq (0.60 #3746, 0.38 #1272), 07ssc (0.41 #2659, 0.38 #2355, 0.37 #2416), 0b90_r (0.41 #2659, 0.38 #2355, 0.37 #2416), 03rjj (0.41 #2659, 0.38 #2355, 0.37 #2416), 02jx1 (0.41 #2659, 0.38 #2355, 0.37 #2416), 0345h (0.11 #2927, 0.10 #2806, 0.09 #3592), 0f8l9c (0.09 #2496, 0.09 #564, 0.09 #686), 0d060g (0.07 #68, 0.04 #2908, 0.04 #5264), 0d05w3 (0.07 #103, 0.02 #163, 0.02 #2943), 03_3d (0.05 #2907, 0.04 #5263, 0.04 #4779) >> Best rule #3746 for best value: >> intensional similarity = 4 >> extensional distance = 1258 >> proper extension: 03g9xj; >> query: (?x327, ?x4492) <- nominated_for(?x3483, ?x327), nationality(?x3483, ?x4492), nationality(?x3483, ?x94), ?x94 = 09c7w0 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2659 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 836 *> proper extension: 06krf3; 02vr3gz; 06929s; 043n0v_; 064q5v; 06823p; 0p_tz; 03lfd_; 07vfy4; 01bjbk; *> query: (?x327, ?x94) <- film(?x9000, ?x327), award(?x327, ?x1443), nationality(?x9000, ?x94), type_of_union(?x9000, ?x566) *> conf = 0.41 ranks of expected_values: 2 EVAL 0gzy02 country 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.596 http://example.org/film/film/country #10354-06g2d1 PRED entity: 06g2d1 PRED relation: film PRED expected values: 0dzlbx => 108 concepts (59 used for prediction) PRED predicted values (max 10 best out of 739): 05sy_5 (0.75 #3575, 0.64 #25023, 0.64 #14299), 02qr3k8 (0.07 #3074, 0.07 #4862, 0.03 #10224), 04gv3db (0.07 #753, 0.03 #7902, 0.02 #20414), 0888c3 (0.07 #1413, 0.01 #42521, 0.01 #40734), 02ht1k (0.07 #630, 0.01 #68551), 02stbw (0.07 #384), 01719t (0.05 #5593, 0.03 #66133, 0.03 #84010), 0f7hw (0.05 #6918), 011ywj (0.05 #17520, 0.02 #40755, 0.02 #42542), 017jd9 (0.05 #16866, 0.02 #38313, 0.02 #40101) >> Best rule #3575 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 012dr7; >> query: (?x6085, ?x4788) <- award(?x6085, ?x591), nominated_for(?x6085, ?x4788), ?x591 = 0f4x7 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #13362 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 288 *> proper extension: 05hdf; 01twdk; 0bkmf; *> query: (?x6085, 0dzlbx) <- film(?x6085, ?x3517), award_winner(?x6079, ?x6085), participant(?x971, ?x6085) *> conf = 0.01 ranks of expected_values: 532 EVAL 06g2d1 film 0dzlbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 108.000 59.000 0.749 http://example.org/film/actor/film./film/performance/film #10353-03hpr PRED entity: 03hpr PRED relation: award_winner! PRED expected values: 02664f => 152 concepts (104 used for prediction) PRED predicted values (max 10 best out of 326): 02662b (0.50 #1288, 0.37 #18892, 0.36 #42082), 039yzf (0.50 #1288, 0.37 #18892, 0.36 #42082), 0265wl (0.50 #1288, 0.37 #18892, 0.36 #42082), 0262x6 (0.50 #1288, 0.37 #18892, 0.36 #42082), 0p9sw (0.33 #453, 0.20 #882, 0.01 #33940), 02q1tc5 (0.33 #3152, 0.30 #4011, 0.18 #7875), 01yz0x (0.30 #173, 0.23 #18462, 0.11 #42081), 02r22gf (0.29 #464, 0.17 #893), 0265vt (0.23 #18462, 0.20 #319, 0.11 #42081), 040vk98 (0.23 #18462, 0.20 #30, 0.11 #42081) >> Best rule #1288 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 01yznp; 094wz7q; 02h1rt; 02xc1w4; 051z6rz; 02vxyl5; >> query: (?x10275, ?x1288) <- profession(?x10275, ?x353), award(?x10275, ?x1288), crewmember(?x280, ?x10275) >> conf = 0.50 => this is the best rule for 4 predicted values *> Best rule #215 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0399p; *> query: (?x10275, 02664f) <- influenced_by(?x10275, ?x2161), ?x2161 = 040db, location(?x10275, ?x4090) *> conf = 0.10 ranks of expected_values: 30 EVAL 03hpr award_winner! 02664f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 152.000 104.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #10352-049mr PRED entity: 049mr PRED relation: organization! PRED expected values: 0dq_5 => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 19): 0dq_5 (0.80 #225, 0.77 #197, 0.76 #130), 060c4 (0.18 #286, 0.16 #466, 0.16 #565), 0142rn (0.18 #286, 0.11 #135, 0.10 #562), 05_wyz (0.18 #286, 0.11 #135, 0.10 #562), 01yc02 (0.18 #286, 0.11 #135, 0.10 #562), 0krdk (0.18 #286, 0.11 #135, 0.10 #562), 0g686w (0.18 #286, 0.10 #562, 0.09 #215), 07xl34 (0.05 #160, 0.05 #475, 0.04 #574), 0dq3c (0.03 #176, 0.02 #340, 0.01 #247), 0fj45 (0.02 #340) >> Best rule #225 for best value: >> intensional similarity = 12 >> extensional distance = 59 >> proper extension: 016tt2; >> query: (?x10812, 0dq_5) <- service_location(?x10812, ?x94), ?x94 = 09c7w0, service_language(?x10812, ?x7658), languages(?x9173, ?x7658), languages_spoken(?x3584, ?x7658), people(?x3584, ?x4992), countries_spoken_in(?x7658, ?x3749), ?x4992 = 0lkr7, ?x3749 = 03ryn, ?x9173 = 01x53m, industry(?x10812, ?x1605), language(?x1470, ?x7658) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049mr organization! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.803 http://example.org/organization/role/leaders./organization/leadership/organization #10351-02r1ysd PRED entity: 02r1ysd PRED relation: nominated_for! PRED expected values: 03ccq3s 09qrn4 => 101 concepts (92 used for prediction) PRED predicted values (max 10 best out of 244): 09v7wsg (0.79 #2873, 0.73 #1435, 0.71 #2872), 0cjyzs (0.52 #800, 0.50 #322, 0.40 #561), 0gq9h (0.49 #12743, 0.46 #13221, 0.37 #13460), 0gs9p (0.42 #12745, 0.39 #13223, 0.34 #13940), 019f4v (0.42 #12734, 0.39 #13212, 0.33 #13451), 09qj50 (0.41 #755, 0.30 #277, 0.23 #516), 09qv3c (0.41 #281, 0.38 #759, 0.31 #520), 0fbtbt (0.39 #1597, 0.28 #1357, 0.27 #1118), 03ccq3s (0.36 #383, 0.34 #861, 0.33 #622), 0cqhk0 (0.36 #270, 0.29 #748, 0.27 #509) >> Best rule #2873 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0p_tz; >> query: (?x6726, ?x7510) <- country_of_origin(?x6726, ?x94), award(?x6726, ?x7510), nominated_for(?x5105, ?x6726), nominated_for(?x7510, ?x631) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #383 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 02xhpl; *> query: (?x6726, 03ccq3s) <- honored_for(?x762, ?x6726), nominated_for(?x5105, ?x6726), genre(?x6726, ?x258), ?x258 = 05p553 *> conf = 0.36 ranks of expected_values: 9, 11 EVAL 02r1ysd nominated_for! 09qrn4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 101.000 92.000 0.789 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02r1ysd nominated_for! 03ccq3s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 101.000 92.000 0.789 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10350-06t2t PRED entity: 06t2t PRED relation: location_of_ceremony! PRED expected values: 04ztj => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.89 #17, 0.89 #41, 0.88 #37), 0jgjn (0.08 #40, 0.05 #76, 0.05 #72), 01g63y (0.05 #18, 0.05 #98, 0.05 #22), 01bl8s (0.05 #19, 0.04 #39, 0.04 #43) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 080h2; 0ply0; 0f2v0; 0f2rq; 0947l; 0h3tv; >> query: (?x2316, 04ztj) <- month(?x2316, ?x1650), vacationer(?x2316, ?x1207), teams(?x2316, ?x10636) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06t2t location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.895 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #10349-071nw5 PRED entity: 071nw5 PRED relation: nominated_for! PRED expected values: 03hkv_r 04dn09n 02x17s4 => 94 concepts (87 used for prediction) PRED predicted values (max 10 best out of 204): 0gq9h (0.66 #1218, 0.64 #1450, 0.61 #986), 0gs9p (0.61 #1220, 0.56 #1452, 0.54 #988), 03hkv_r (0.59 #943, 0.43 #1175, 0.43 #1407), 02n9nmz (0.57 #982, 0.39 #1214, 0.38 #1446), 019f4v (0.52 #979, 0.52 #1211, 0.49 #1443), 04dn09n (0.48 #962, 0.41 #1194, 0.40 #1426), 0k611 (0.46 #996, 0.38 #1228, 0.36 #1460), 0gq_v (0.45 #716, 0.33 #1180, 0.33 #1412), 02x17s4 (0.43 #1018, 0.25 #1482, 0.24 #9745), 0f4x7 (0.43 #1186, 0.40 #1418, 0.37 #954) >> Best rule #1218 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 0f4vx; >> query: (?x6200, 0gq9h) <- written_by(?x6200, ?x5371), nominated_for(?x601, ?x6200), ?x601 = 0gr4k, country(?x6200, ?x94) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #943 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 0ch26b_; 0cw3yd; *> query: (?x6200, 03hkv_r) <- written_by(?x6200, ?x5371), nominated_for(?x601, ?x6200), ?x601 = 0gr4k, film_crew_role(?x6200, ?x137) *> conf = 0.59 ranks of expected_values: 3, 6, 9 EVAL 071nw5 nominated_for! 02x17s4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 94.000 87.000 0.658 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 071nw5 nominated_for! 04dn09n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 87.000 0.658 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 071nw5 nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 87.000 0.658 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10348-043js PRED entity: 043js PRED relation: award_nominee! PRED expected values: 0cnl80 => 100 concepts (31 used for prediction) PRED predicted values (max 10 best out of 746): 083chw (0.81 #55544, 0.81 #55543, 0.81 #37027), 0cnl1c (0.81 #55544, 0.81 #55543, 0.81 #37027), 0cms7f (0.81 #55544, 0.81 #55543, 0.81 #37027), 0blbxk (0.81 #55544, 0.81 #55543, 0.81 #37027), 01jw4r (0.81 #55544, 0.81 #55543, 0.81 #37027), 03k7bd (0.81 #55544, 0.81 #55543, 0.81 #37027), 0cnl80 (0.41 #2357, 0.15 #55545, 0.02 #11614), 043js (0.36 #2893, 0.29 #27770, 0.15 #55545), 05xpms (0.36 #4288, 0.29 #27770, 0.15 #55545), 05l0j5 (0.36 #4017, 0.29 #27770, 0.15 #55545) >> Best rule #55544 for best value: >> intensional similarity = 3 >> extensional distance = 1271 >> proper extension: 02k6rq; 07_grx; 076df9; 03h40_7; 0bm9xk; >> query: (?x2657, ?x275) <- award_nominee(?x2657, ?x275), location(?x2657, ?x739), award_nominee(?x275, ?x221) >> conf = 0.81 => this is the best rule for 6 predicted values *> Best rule #2357 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 0cj2nl; 0cmt6q; *> query: (?x2657, 0cnl80) <- profession(?x2657, ?x1032), award_nominee(?x2657, ?x3924), ?x3924 = 0h3mrc *> conf = 0.41 ranks of expected_values: 7 EVAL 043js award_nominee! 0cnl80 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 100.000 31.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10347-015bwt PRED entity: 015bwt PRED relation: award_winner! PRED expected values: 01d38g => 88 concepts (80 used for prediction) PRED predicted values (max 10 best out of 243): 01c427 (0.38 #16392, 0.37 #29771, 0.37 #29770), 03qbh5 (0.38 #16392, 0.37 #29771, 0.37 #29770), 02f73p (0.38 #16392, 0.37 #29771, 0.37 #29770), 02f764 (0.38 #16392, 0.37 #29771, 0.37 #29770), 01cky2 (0.33 #1054, 0.25 #3645, 0.13 #2778), 02f777 (0.33 #306, 0.25 #737, 0.13 #3759), 02f77y (0.33 #260, 0.25 #691, 0.09 #1553), 02f6yz (0.33 #316, 0.25 #747, 0.09 #1609), 099vwn (0.33 #214, 0.25 #645, 0.09 #1507), 024fxq (0.25 #818, 0.03 #2542, 0.02 #10309) >> Best rule #16392 for best value: >> intensional similarity = 3 >> extensional distance = 412 >> proper extension: 012ljv; 01vyp_; 01l9v7n; 04ls53; 01pbs9w; 09r9m7; 01l79yc; 02wb6d; 023361; 0g476; ... >> query: (?x11455, ?x1389) <- award(?x11455, ?x1389), artists(?x671, ?x11455), award_winner(?x6869, ?x11455) >> conf = 0.38 => this is the best rule for 4 predicted values *> Best rule #891 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0ffgh; *> query: (?x11455, 01d38g) <- award_nominee(?x11455, ?x2614), artists(?x11787, ?x11455), origin(?x11455, ?x94), ?x11787 = 05lwjc *> conf = 0.11 ranks of expected_values: 23 EVAL 015bwt award_winner! 01d38g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 88.000 80.000 0.376 http://example.org/award/award_category/winners./award/award_honor/award_winner #10346-034ks PRED entity: 034ks PRED relation: influenced_by! PRED expected values: 03vrp => 129 concepts (33 used for prediction) PRED predicted values (max 10 best out of 356): 02wh0 (0.40 #966, 0.30 #3544, 0.16 #5608), 039n1 (0.40 #908, 0.30 #3486, 0.16 #5550), 099bk (0.40 #664, 0.30 #3242, 0.16 #5306), 043s3 (0.40 #669, 0.30 #3247, 0.16 #5311), 028p0 (0.40 #553, 0.20 #3131, 0.14 #1584), 034ks (0.40 #918, 0.20 #3496, 0.14 #1949), 0dzkq (0.31 #4765, 0.15 #9927, 0.15 #7864), 047g6 (0.30 #3574, 0.20 #996, 0.16 #5638), 03sbs (0.30 #3380, 0.20 #802, 0.16 #5444), 0x3r3 (0.23 #4880, 0.10 #5916, 0.10 #10042) >> Best rule #966 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0gz_; >> query: (?x9903, 02wh0) <- profession(?x9903, ?x11056), profession(?x9903, ?x3802), ?x11056 = 05snw, influenced_by(?x5797, ?x9903), influenced_by(?x9903, ?x3712), ?x3802 = 06q2q >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #4321 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0c1pj; 01cwhp; 01wj18h; 0d4jl; 01s7z0; *> query: (?x9903, 03vrp) <- profession(?x9903, ?x3801), religion(?x9903, ?x1985), company(?x9903, ?x11488), organization(?x4095, ?x11488), ?x1985 = 0c8wxp *> conf = 0.08 ranks of expected_values: 90 EVAL 034ks influenced_by! 03vrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 129.000 33.000 0.400 http://example.org/influence/influence_node/influenced_by #10345-01nrq5 PRED entity: 01nrq5 PRED relation: student! PRED expected values: 019dwp => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 172): 017j69 (0.25 #1199, 0.04 #2780, 0.03 #18063), 0bwfn (0.17 #275, 0.05 #29264, 0.05 #52456), 065y4w7 (0.17 #14, 0.05 #9500, 0.04 #11608), 06thjt (0.14 #925, 0.10 #3560, 0.09 #4087), 06182p (0.14 #825, 0.05 #11892, 0.04 #11365), 02kbtf (0.14 #871, 0.03 #3506, 0.02 #4560), 02sdwt (0.14 #929, 0.02 #5672, 0.01 #17266), 01w5m (0.12 #1686, 0.08 #2740, 0.05 #28040), 03ksy (0.12 #1160, 0.06 #24351, 0.06 #14862), 09f2j (0.12 #1213, 0.04 #22293, 0.04 #29148) >> Best rule #1199 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01pcbg; >> query: (?x3261, 017j69) <- film(?x3261, ?x8112), nominated_for(?x3261, ?x9098), ?x8112 = 04g73n >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #46381 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 679 *> proper extension: 0f1pyf; 0bw7ly; 01vw917; 03h40_7; 03c_8t; *> query: (?x3261, ?x4916) <- place_of_birth(?x3261, ?x5775), gender(?x3261, ?x231), category(?x5775, ?x134), contains(?x5775, ?x4916) *> conf = 0.01 ranks of expected_values: 154 EVAL 01nrq5 student! 019dwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 174.000 174.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #10344-08433 PRED entity: 08433 PRED relation: influenced_by! PRED expected values: 019z7q 026fd 0821j => 165 concepts (62 used for prediction) PRED predicted values (max 10 best out of 458): 040db (0.50 #74, 0.30 #574, 0.16 #19120), 0683n (0.40 #828, 0.18 #1831, 0.14 #3333), 0pqzh (0.38 #443, 0.10 #943, 0.09 #1946), 0mb5x (0.30 #825, 0.18 #1828, 0.14 #3330), 01v_0b (0.30 #970, 0.18 #1973, 0.09 #1471), 014ps4 (0.30 #801, 0.14 #3306, 0.12 #301), 07g2b (0.30 #516, 0.08 #19062, 0.07 #14030), 0n6kf (0.27 #1689, 0.20 #686, 0.18 #1187), 013pp3 (0.27 #1720, 0.20 #717, 0.18 #1218), 02yl42 (0.25 #131, 0.21 #3136, 0.18 #3638) >> Best rule #74 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 046_v; >> query: (?x1029, 040db) <- influenced_by(?x8753, ?x1029), nationality(?x1029, ?x94), type_of_union(?x1029, ?x566), ?x8753 = 0yxl >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1527 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01v_0b; *> query: (?x1029, 019z7q) <- profession(?x1029, ?x353), influenced_by(?x1029, ?x3541), ?x3541 = 040_9, location(?x1029, ?x1025) *> conf = 0.18 ranks of expected_values: 37, 59, 167 EVAL 08433 influenced_by! 0821j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 165.000 62.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 08433 influenced_by! 026fd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 165.000 62.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 08433 influenced_by! 019z7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 165.000 62.000 0.500 http://example.org/influence/influence_node/influenced_by #10343-029ql PRED entity: 029ql PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.80 #199, 0.79 #1189, 0.79 #1289), 07ssc (0.37 #5651, 0.17 #114, 0.09 #510), 0n2k5 (0.33 #9721), 05kkh (0.33 #9721), 0chghy (0.20 #10, 0.02 #3677, 0.02 #4965), 02jx1 (0.13 #3303, 0.12 #4690, 0.12 #4096), 03rk0 (0.06 #9766, 0.05 #10063, 0.05 #9865), 0d060g (0.05 #1493, 0.05 #205, 0.05 #3674), 03rjj (0.03 #698, 0.02 #8334, 0.02 #10221), 0f8l9c (0.03 #1111, 0.03 #517, 0.02 #616) >> Best rule #199 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 03f1zhf; >> query: (?x6331, 09c7w0) <- profession(?x6331, ?x1041), participant(?x6331, ?x703), ?x1041 = 03gjzk >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 029ql nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.803 http://example.org/people/person/nationality #10342-0b90_r PRED entity: 0b90_r PRED relation: film_release_region! PRED expected values: 0dtfn 05qbckf 0gd0c7x 0dr3sl 0g5838s 023gxx 0192hw 0jwmp 02fqrf 05zlld0 0gyh2wm 02mt51 0gy2y8r 02rmd_2 0b44shh 0bc1yhb 02gs6r 064lsn 04cppj 02bg55 07pd_j 035zr0 0m63c 0cp08zg 03nsm5x 0ds2l81 05zvzf3 0g57wgv 0dw4b0 047p798 0564x 049w1q => 245 concepts (219 used for prediction) PRED predicted values (max 10 best out of 1091): 0gd0c7x (0.86 #47882, 0.85 #28368, 0.83 #41377), 05zlld0 (0.85 #28546, 0.81 #48060, 0.75 #49144), 0dtfn (0.85 #28310, 0.77 #20721, 0.77 #41319), 0g5838s (0.81 #28475, 0.77 #41484, 0.72 #47989), 0g4vmj8 (0.81 #28951, 0.72 #48465, 0.62 #14855), 05qbckf (0.80 #41375, 0.77 #28366, 0.77 #94499), 0bc1yhb (0.77 #41745, 0.75 #14640, 0.73 #28736), 0dr3sl (0.77 #28452, 0.75 #47966, 0.71 #41461), 02bg55 (0.77 #28882, 0.75 #14786, 0.69 #48396), 0h95927 (0.77 #28987, 0.74 #41996, 0.69 #48501) >> Best rule #47882 for best value: >> intensional similarity = 2 >> extensional distance = 34 >> proper extension: 06wjf; >> query: (?x151, 0gd0c7x) <- film_release_region(?x1919, ?x151), ?x1919 = 0_7w6 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 20, 21, 23, 25, 40, 41, 46, 54, 56, 57, 60, 78, 86, 90, 91, 104, 146, 167, 220, 235 EVAL 0b90_r film_release_region! 049w1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0564x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 047p798 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0dw4b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0g57wgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0ds2l81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 03nsm5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0cp08zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0m63c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 035zr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 07pd_j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 02bg55 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 04cppj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 064lsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 02gs6r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0bc1yhb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0b44shh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 02rmd_2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0gy2y8r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 02mt51 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0gyh2wm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 05zlld0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 02fqrf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0jwmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0192hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 023gxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0g5838s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0dr3sl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0gd0c7x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 05qbckf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b90_r film_release_region! 0dtfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 245.000 219.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10341-04_1l0v PRED entity: 04_1l0v PRED relation: contains PRED expected values: 04ych 04rrd 05k7sb 05tbn 026mj => 108 concepts (47 used for prediction) PRED predicted values (max 10 best out of 2969): 09s5q8 (0.65 #26123, 0.61 #75474, 0.60 #84186), 01jq0j (0.65 #26123, 0.61 #75474, 0.60 #84186), 03l78j (0.65 #26123, 0.61 #75474, 0.60 #84186), 0146hc (0.65 #26123, 0.61 #75474, 0.60 #84186), 04ftdq (0.65 #26123, 0.61 #75474, 0.60 #84186), 021q2j (0.65 #26123, 0.61 #75474, 0.60 #84186), 03bmmc (0.65 #26123, 0.61 #75474, 0.60 #84186), 01t0dy (0.65 #26123, 0.61 #75474, 0.60 #84186), 09k9d0 (0.65 #26123, 0.61 #75474, 0.60 #84186), 01p7x7 (0.65 #26123, 0.61 #75474, 0.60 #84186) >> Best rule #26123 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0jhwd; >> query: (?x8260, ?x5145) <- contains(?x8260, ?x3818), state_province_region(?x5145, ?x3818), country(?x3818, ?x94), major_field_of_study(?x5145, ?x742) >> conf = 0.65 => this is the best rule for 207 predicted values *> Best rule #34830 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 24 *> proper extension: 065ky; *> query: (?x8260, ?x1144) <- contains(?x8260, ?x7058), contains(?x8260, ?x3818), contains(?x8260, ?x760), currency(?x760, ?x170), religion(?x3818, ?x109), adjoins(?x1144, ?x7058) *> conf = 0.60 ranks of expected_values: 1121, 1122, 1123, 1125, 1127 EVAL 04_1l0v contains 026mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 108.000 47.000 0.652 http://example.org/location/location/contains EVAL 04_1l0v contains 05tbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 108.000 47.000 0.652 http://example.org/location/location/contains EVAL 04_1l0v contains 05k7sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 108.000 47.000 0.652 http://example.org/location/location/contains EVAL 04_1l0v contains 04rrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 108.000 47.000 0.652 http://example.org/location/location/contains EVAL 04_1l0v contains 04ych CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 108.000 47.000 0.652 http://example.org/location/location/contains #10340-06w7v PRED entity: 06w7v PRED relation: group PRED expected values: 02mq_y => 74 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1687): 02vnpv (0.73 #2914, 0.71 #1253, 0.68 #5885), 0134wr (0.71 #1754, 0.71 #1201, 0.60 #1018), 0gr69 (0.71 #1184, 0.60 #1001, 0.57 #1737), 02dw1_ (0.67 #3378, 0.62 #2082, 0.58 #5420), 047cx (0.64 #2805, 0.57 #1513, 0.55 #2989), 01fchy (0.60 #1046, 0.57 #1229, 0.50 #861), 0p8h0 (0.57 #1276, 0.50 #908, 0.44 #2564), 02t3ln (0.57 #1145, 0.50 #777, 0.43 #1698), 0khth (0.57 #1696, 0.45 #2804, 0.43 #1512), 017_hq (0.57 #1257, 0.45 #3102, 0.43 #1810) >> Best rule #2914 for best value: >> intensional similarity = 19 >> extensional distance = 9 >> proper extension: 0l14md; >> query: (?x4917, 02vnpv) <- role(?x3161, ?x4917), role(?x2048, ?x4917), ?x3161 = 01v1d8, instrumentalists(?x4917, ?x1656), role(?x4917, ?x75), ?x2048 = 018j2, role(?x211, ?x4917), ?x75 = 07y_7, role(?x4917, ?x1437), role(?x4917, ?x1267), role(?x8539, ?x1437), role(?x4428, ?x1437), role(?x3834, ?x1437), role(?x1437, ?x74), ?x4428 = 02jxmr, location(?x3834, ?x1426), ?x1267 = 07brj, artist(?x2149, ?x3834), category(?x8539, ?x134) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1888 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 6 *> proper extension: 042v_gx; *> query: (?x4917, 02mq_y) <- role(?x8014, ?x4917), role(?x3161, ?x4917), role(?x2957, ?x4917), role(?x211, ?x4917), role(?x3161, ?x3112), role(?x3161, ?x2309), role(?x1473, ?x3161), group(?x4917, ?x3207), ?x2309 = 06ncr, ?x2957 = 01v8y9, ?x8014 = 0214km, instrumentalists(?x4917, ?x3403), group(?x3161, ?x3682), ?x3112 = 0mbct, role(?x2306, ?x3161), role(?x4917, ?x75), ?x1473 = 0g2dz, award_winner(?x342, ?x3403) *> conf = 0.38 ranks of expected_values: 44 EVAL 06w7v group 02mq_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 74.000 41.000 0.727 http://example.org/music/performance_role/regular_performances./music/group_membership/group #10339-014z8v PRED entity: 014z8v PRED relation: film PRED expected values: 03l6q0 => 119 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1023): 03kx49 (0.40 #1340, 0.22 #3128, 0.20 #4916), 027fwmt (0.20 #1591, 0.11 #3379, 0.10 #5167), 042g97 (0.20 #1765, 0.11 #3553, 0.10 #5341), 09cxm4 (0.20 #1429, 0.11 #3217, 0.10 #5005), 03cffvv (0.20 #1742, 0.11 #3530, 0.10 #5318), 0dc7hc (0.20 #1588, 0.11 #3376, 0.10 #5164), 02ctc6 (0.20 #522, 0.11 #2310, 0.10 #4098), 01d259 (0.20 #986, 0.11 #2774, 0.10 #4562), 03h3x5 (0.20 #422, 0.11 #2210, 0.10 #3998), 02bqvs (0.20 #1495, 0.11 #3283, 0.10 #5071) >> Best rule #1340 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0p_47; 01hmk9; 01k9lpl; >> query: (?x4112, 03kx49) <- profession(?x4112, ?x353), influenced_by(?x2127, ?x4112), influenced_by(?x2125, ?x4112), ?x2127 = 01j7rd, ?x2125 = 0126rp >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #125716 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 791 *> proper extension: 049tjg; 0785v8; 019_1h; 0f6_dy; 02xb2bt; 03q1vd; 050t68; 015wfg; 03q95r; 05typm; ... *> query: (?x4112, 03l6q0) <- actor(?x4275, ?x4112), film(?x4112, ?x5024), genre(?x5024, ?x258) *> conf = 0.01 ranks of expected_values: 957 EVAL 014z8v film 03l6q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 119.000 87.000 0.400 http://example.org/film/actor/film./film/performance/film #10338-0pz6q PRED entity: 0pz6q PRED relation: major_field_of_study PRED expected values: 037mh8 => 172 concepts (166 used for prediction) PRED predicted values (max 10 best out of 118): 03g3w (0.73 #659, 0.58 #1542, 0.50 #406), 05qjt (0.73 #639, 0.50 #386, 0.46 #766), 04rjg (0.73 #652, 0.50 #399, 0.46 #1535), 02j62 (0.64 #663, 0.58 #1546, 0.53 #1168), 01mkq (0.64 #647, 0.50 #394, 0.45 #4808), 037mh8 (0.62 #449, 0.55 #702, 0.46 #1585), 01lj9 (0.55 #673, 0.36 #2061, 0.30 #2565), 062z7 (0.50 #407, 0.45 #660, 0.33 #1543), 04sh3 (0.50 #457, 0.42 #1593, 0.36 #710), 0fdys (0.45 #672, 0.38 #419, 0.32 #1177) >> Best rule #659 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 08815; 07tgn; 07tg4; 07tk7; 01y06y; >> query: (?x9988, 03g3w) <- student(?x9988, ?x12592), student(?x9988, ?x3993), contains(?x1264, ?x9988), type_of_union(?x3993, ?x566), organization(?x3993, ?x13641), interests(?x12592, ?x713) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #449 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 05t7c1; *> query: (?x9988, 037mh8) <- student(?x9988, ?x3993), contains(?x1264, ?x9988), influenced_by(?x3993, ?x9600), interests(?x3993, ?x8405), ?x9600 = 039n1 *> conf = 0.62 ranks of expected_values: 6 EVAL 0pz6q major_field_of_study 037mh8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 172.000 166.000 0.727 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10337-025vldk PRED entity: 025vldk PRED relation: profession PRED expected values: 0cbd2 0dxtg => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 49): 0dxtg (0.88 #764, 0.87 #914, 0.83 #1064), 02hrh1q (0.75 #1965, 0.70 #6615, 0.68 #2565), 03gjzk (0.71 #616, 0.65 #1366, 0.65 #1216), 0d8qb (0.45 #531, 0.04 #981, 0.03 #2031), 01d_h8 (0.35 #606, 0.33 #3456, 0.32 #6606), 015cjr (0.32 #501, 0.25 #51, 0.05 #651), 02krf9 (0.23 #1228, 0.21 #628, 0.20 #928), 0cbd2 (0.23 #1057, 0.23 #1357, 0.22 #607), 02jknp (0.22 #4358, 0.21 #6608, 0.21 #5558), 018gz8 (0.21 #618, 0.18 #1368, 0.15 #1068) >> Best rule #764 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 011s9r; >> query: (?x7229, 0dxtg) <- nationality(?x7229, ?x94), tv_program(?x7229, ?x6080), award_winner(?x7229, ?x415) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 025vldk profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.879 http://example.org/people/person/profession EVAL 025vldk profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 94.000 94.000 0.879 http://example.org/people/person/profession #10336-015fr PRED entity: 015fr PRED relation: adjoins PRED expected values: 06nnj => 193 concepts (134 used for prediction) PRED predicted values (max 10 best out of 504): 07twz (0.85 #50573, 0.84 #49805, 0.83 #75844), 015fr (0.57 #1557, 0.12 #19179, 0.11 #41400), 0f8l9c (0.27 #2334, 0.24 #3100, 0.17 #6931), 06bnz (0.22 #3913, 0.19 #4680, 0.17 #7743), 01p1v (0.21 #1632, 0.07 #2399, 0.06 #29115), 06mzp (0.20 #2333, 0.18 #3099, 0.17 #3865), 0345h (0.19 #12319, 0.19 #9255, 0.19 #4659), 05rgl (0.19 #6224, 0.14 #5459, 0.13 #7757), 0d05w3 (0.18 #13907, 0.18 #14674, 0.15 #38426), 0j3b (0.17 #3887, 0.14 #6184, 0.14 #5419) >> Best rule #50573 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 01c6yz; 0fqxw; 0d8h4; >> query: (?x583, ?x4737) <- administrative_parent(?x12465, ?x583), adjoins(?x583, ?x142), adjoins(?x4737, ?x583) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1935 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 02wmy; *> query: (?x583, 06nnj) <- official_language(?x583, ?x6753), contains(?x12315, ?x583), ?x12315 = 06n3y *> conf = 0.07 ranks of expected_values: 63 EVAL 015fr adjoins 06nnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 193.000 134.000 0.846 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #10335-03f5mt PRED entity: 03f5mt PRED relation: role PRED expected values: 0l14qv => 62 concepts (61 used for prediction) PRED predicted values (max 10 best out of 111): 018j2 (0.83 #2870, 0.83 #735, 0.83 #313), 06w7v (0.83 #735, 0.83 #313, 0.82 #1897), 07xzm (0.83 #735, 0.83 #313, 0.82 #1897), 01wy6 (0.82 #1417, 0.71 #785, 0.71 #1583), 0l14qv (0.79 #2771, 0.78 #1061, 0.77 #1694), 0l14j_ (0.78 #1111, 0.77 #1744, 0.75 #1007), 02hnl (0.75 #425, 0.75 #1616, 0.73 #4206), 028tv0 (0.75 #425, 0.73 #2021, 0.71 #1912), 0gkd1 (0.75 #425, 0.71 #106, 0.71 #1583), 03gvt (0.75 #425, 0.71 #106, 0.69 #421) >> Best rule #2870 for best value: >> intensional similarity = 21 >> extensional distance = 22 >> proper extension: 0dwt5; >> query: (?x8957, ?x2048) <- role(?x3214, ?x8957), role(?x2048, ?x8957), role(?x1166, ?x8957), role(?x2048, ?x212), group(?x2048, ?x997), role(?x4052, ?x2048), ?x4052 = 050z2, instrumentalists(?x2048, ?x10039), role(?x3991, ?x2048), role(?x2460, ?x2048), ?x10039 = 0ftqr, ?x1166 = 05148p4, ?x3991 = 05842k, ?x2460 = 01wy6, instrumentalists(?x212, ?x226), ?x3214 = 02snj9, role(?x11689, ?x212), ?x11689 = 06p03s, role(?x433, ?x212), ?x997 = 07qnf, role(?x645, ?x2048) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #2771 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 22 *> proper extension: 0dwt5; *> query: (?x8957, 0l14qv) <- role(?x3214, ?x8957), role(?x2048, ?x8957), role(?x1166, ?x8957), role(?x2048, ?x212), group(?x2048, ?x997), role(?x4052, ?x2048), ?x4052 = 050z2, instrumentalists(?x2048, ?x10039), role(?x3991, ?x2048), role(?x2460, ?x2048), ?x10039 = 0ftqr, ?x1166 = 05148p4, ?x3991 = 05842k, ?x2460 = 01wy6, instrumentalists(?x212, ?x226), ?x3214 = 02snj9, role(?x11689, ?x212), ?x11689 = 06p03s, role(?x433, ?x212), ?x997 = 07qnf, role(?x645, ?x2048) *> conf = 0.79 ranks of expected_values: 5 EVAL 03f5mt role 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 61.000 0.829 http://example.org/music/performance_role/regular_performances./music/group_membership/role #10334-01w92 PRED entity: 01w92 PRED relation: award_winner PRED expected values: 0381pn => 102 concepts (66 used for prediction) PRED predicted values (max 10 best out of 451): 03jvmp (0.58 #104922, 0.44 #48424, 0.43 #54878), 0f721s (0.33 #6665, 0.33 #211, 0.27 #9891), 05gnf (0.33 #1104, 0.22 #7558, 0.18 #10784), 09d5h (0.33 #319, 0.22 #6773, 0.18 #9999), 05s34b (0.33 #1607, 0.11 #8061, 0.09 #11287), 01p5yn (0.33 #660, 0.11 #7114, 0.09 #10340), 0g5lhl7 (0.27 #106538, 0.22 #6906, 0.18 #10132), 07k2d (0.27 #106538, 0.22 #8045, 0.11 #20960), 01w92 (0.27 #106538, 0.18 #10253, 0.17 #11869), 0hm0k (0.27 #106538, 0.11 #7484, 0.09 #10710) >> Best rule #104922 for best value: >> intensional similarity = 3 >> extensional distance = 1260 >> proper extension: 09v6gc9; >> query: (?x3487, ?x2246) <- award_winner(?x3381, ?x3487), award_nominee(?x2246, ?x3487), award_winner(?x715, ?x2246) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #51651 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 02yl42; 048_p; *> query: (?x3487, ?x105) <- award_winner(?x3486, ?x3487), award_winner(?x3486, ?x8508), award_winner(?x3486, ?x105), ?x8508 = 01zwy *> conf = 0.01 ranks of expected_values: 349 EVAL 01w92 award_winner 0381pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 102.000 66.000 0.579 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #10333-04xrx PRED entity: 04xrx PRED relation: award PRED expected values: 054ks3 02f6ym => 120 concepts (98 used for prediction) PRED predicted values (max 10 best out of 308): 05pcn59 (0.30 #5173, 0.28 #2037, 0.28 #2821), 02f6xy (0.26 #193, 0.19 #20386, 0.16 #3329), 05b4l5x (0.23 #1966, 0.19 #2358, 0.18 #2750), 02f73p (0.23 #181, 0.19 #20386, 0.15 #965), 02f6ym (0.21 #1031, 0.19 #247, 0.19 #20386), 02f71y (0.20 #960, 0.16 #176, 0.12 #6448), 054ks3 (0.20 #3271, 0.19 #135, 0.19 #20386), 01c92g (0.19 #92, 0.15 #3228, 0.14 #4404), 02f73b (0.19 #20386, 0.18 #1059, 0.16 #275), 05zr6wv (0.19 #20386, 0.17 #9817, 0.17 #5113) >> Best rule #5173 for best value: >> intensional similarity = 3 >> extensional distance = 129 >> proper extension: 032xhg; 01rr9f; 01kwld; 0l12d; 018grr; 0443y3; 07ss8_; 01cwhp; 0154qm; 04gycf; ... >> query: (?x2614, 05pcn59) <- profession(?x2614, ?x220), vacationer(?x2983, ?x2614), award_nominee(?x2614, ?x527) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #1031 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 01l_vgt; *> query: (?x2614, 02f6ym) <- artists(?x671, ?x2614), ?x671 = 064t9, participant(?x3101, ?x2614) *> conf = 0.21 ranks of expected_values: 5, 7 EVAL 04xrx award 02f6ym CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 120.000 98.000 0.298 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04xrx award 054ks3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 120.000 98.000 0.298 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10332-01k3qj PRED entity: 01k3qj PRED relation: profession PRED expected values: 0dz3r => 169 concepts (113 used for prediction) PRED predicted values (max 10 best out of 86): 02hrh1q (0.85 #9211, 0.83 #7135, 0.83 #2680), 09jwl (0.77 #6100, 0.76 #8919, 0.73 #12628), 0dz3r (0.65 #2964, 0.63 #1038, 0.55 #742), 0dxtg (0.57 #310, 0.38 #1939, 0.37 #2235), 016z4k (0.56 #3262, 0.54 #3856, 0.52 #6084), 0np9r (0.51 #1651, 0.43 #170, 0.17 #614), 01d_h8 (0.43 #302, 0.31 #7274, 0.30 #450), 02jknp (0.43 #304, 0.25 #452, 0.24 #1933), 03gjzk (0.40 #460, 0.22 #15744, 0.21 #6392), 0cbd2 (0.38 #1932, 0.35 #2228, 0.17 #599) >> Best rule #9211 for best value: >> intensional similarity = 4 >> extensional distance = 373 >> proper extension: 02zq43; 02r99xw; >> query: (?x7578, 02hrh1q) <- languages(?x7578, ?x254), profession(?x7578, ?x1614), gender(?x7578, ?x514), people(?x12136, ?x7578) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #2964 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 98 *> proper extension: 01k5t_3; 015mrk; 0415mzy; 01vs73g; 01wwnh2; *> query: (?x7578, 0dz3r) <- category(?x7578, ?x134), artists(?x2937, ?x7578), profession(?x7578, ?x1614), ?x2937 = 0glt670 *> conf = 0.65 ranks of expected_values: 3 EVAL 01k3qj profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 169.000 113.000 0.851 http://example.org/people/person/profession #10331-0hv8w PRED entity: 0hv8w PRED relation: film_format PRED expected values: 0cj16 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 4): 0cj16 (0.20 #33, 0.18 #199, 0.17 #239), 017fx5 (0.17 #24, 0.16 #29, 0.09 #145), 07fb8_ (0.16 #142, 0.16 #202, 0.16 #187), 01dc60 (0.02 #50, 0.02 #60, 0.02 #130) >> Best rule #33 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 04tng0; 0b85mm; >> query: (?x5473, 0cj16) <- nominated_for(?x1107, ?x5473), film_release_region(?x5473, ?x2984), film_release_region(?x5473, ?x252), ?x252 = 03_3d, ?x2984 = 082fr >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hv8w film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.205 http://example.org/film/film/film_format #10330-0y_pg PRED entity: 0y_pg PRED relation: nominated_for! PRED expected values: 0k611 03hl6lc => 81 concepts (72 used for prediction) PRED predicted values (max 10 best out of 216): 0gs9p (0.71 #527, 0.49 #993, 0.42 #1459), 0k611 (0.58 #536, 0.42 #769, 0.35 #1002), 04dn09n (0.56 #499, 0.36 #965, 0.34 #732), 019f4v (0.54 #750, 0.46 #983, 0.44 #517), 040njc (0.53 #471, 0.42 #704, 0.33 #937), 02qyntr (0.50 #175, 0.42 #641, 0.40 #874), 0gqy2 (0.50 #117, 0.38 #583, 0.33 #1049), 0gr4k (0.40 #957, 0.28 #724, 0.25 #25), 02pqp12 (0.40 #522, 0.32 #755, 0.28 #988), 03hl6lc (0.40 #591, 0.18 #1523, 0.17 #1057) >> Best rule #527 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0p_qr; >> query: (?x7922, 0gs9p) <- film(?x2763, ?x7922), award(?x7922, ?x1862), genre(?x7922, ?x53), ?x1862 = 0gr51 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #536 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 0p_qr; *> query: (?x7922, 0k611) <- film(?x2763, ?x7922), award(?x7922, ?x1862), genre(?x7922, ?x53), ?x1862 = 0gr51 *> conf = 0.58 ranks of expected_values: 2, 10 EVAL 0y_pg nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 81.000 72.000 0.711 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0y_pg nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 72.000 0.711 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10329-09v478h PRED entity: 09v478h PRED relation: award! PRED expected values: 012ykt => 57 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2367): 069_0y (0.50 #9014, 0.50 #5630, 0.33 #2247), 0pksh (0.50 #6661, 0.33 #10045, 0.33 #3278), 02mz_6 (0.50 #5486, 0.33 #8870, 0.33 #2103), 012d40 (0.50 #3402, 0.33 #6786, 0.33 #19), 01f7v_ (0.50 #4559, 0.33 #7943, 0.33 #1176), 03_2y (0.50 #6258, 0.33 #9642, 0.33 #2875), 0342vg (0.50 #5789, 0.33 #9173, 0.33 #2406), 01t2h2 (0.33 #476, 0.25 #3859, 0.24 #6767), 02p59ry (0.33 #2040, 0.25 #5423, 0.24 #6767), 0451j (0.33 #2214, 0.25 #5597, 0.17 #8981) >> Best rule #9014 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 09v4bym; 07kfzsg; >> query: (?x11115, 069_0y) <- nominated_for(?x11115, ?x9216), nominated_for(?x11115, ?x7293), ?x7293 = 027m67, film_release_region(?x9216, ?x142), language(?x9216, ?x254), genre(?x9216, ?x53), ?x53 = 07s9rl0 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6767 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 09v92_x; *> query: (?x11115, ?x5501) <- nominated_for(?x11115, ?x9216), nominated_for(?x11115, ?x7293), nominated_for(?x11115, ?x6219), ?x7293 = 027m67, ?x9216 = 08j7lh, genre(?x6219, ?x53), film(?x5501, ?x6219) *> conf = 0.24 ranks of expected_values: 13 EVAL 09v478h award! 012ykt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 57.000 15.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10328-04sskp PRED entity: 04sskp PRED relation: actor PRED expected values: 0htlr => 76 concepts (28 used for prediction) PRED predicted values (max 10 best out of 489): 016ggh (0.59 #929, 0.57 #1859, 0.57 #1858), 06mnbn (0.59 #929, 0.57 #1859, 0.57 #1858), 0f6_x (0.59 #929, 0.57 #1859, 0.57 #1858), 01ckhj (0.59 #929, 0.57 #1859, 0.57 #1858), 03s2dj (0.20 #864, 0.17 #2723, 0.17 #1793), 01cwcr (0.20 #569, 0.17 #2428, 0.17 #1498), 01520h (0.20 #532, 0.17 #2391, 0.17 #1461), 01ggc9 (0.20 #767, 0.17 #2626, 0.17 #1696), 01x0sy (0.20 #719, 0.17 #2578, 0.17 #1648), 01vh18t (0.20 #712, 0.17 #2571, 0.17 #1641) >> Best rule #929 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 09fc83; 02q5bx2; 0h63q6t; >> query: (?x8062, ?x2531) <- genre(?x8062, ?x1510), ?x1510 = 01hmnh, film(?x2531, ?x8062), film(?x731, ?x8062), languages(?x731, ?x732) >> conf = 0.59 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 04sskp actor 0htlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 28.000 0.588 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10327-073hmq PRED entity: 073hmq PRED relation: ceremony! PRED expected values: 0f4x7 0gr51 0gqxm 0gq_d => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 345): 0gr51 (0.91 #3215, 0.91 #4423, 0.90 #4665), 0gq_d (0.91 #6197, 0.90 #5228, 0.90 #5955), 0f4x7 (0.88 #4378, 0.88 #4137, 0.88 #5831), 0gqxm (0.75 #1570, 0.75 #9452, 0.73 #8968), 0gqzz (0.75 #9452, 0.73 #8968, 0.50 #1007), 02x201b (0.75 #9452, 0.73 #8968, 0.20 #5572), 0czp_ (0.75 #9452, 0.73 #8968, 0.15 #6541), 019f4v (0.31 #4603, 0.30 #3396, 0.30 #485), 04dn09n (0.31 #4603, 0.30 #3396, 0.30 #485), 054krc (0.31 #4603, 0.30 #3396, 0.30 #485) >> Best rule #3215 for best value: >> intensional similarity = 20 >> extensional distance = 21 >> proper extension: 0bc773; >> query: (?x1601, 0gr51) <- ceremony(?x1313, ?x1601), ceremony(?x1243, ?x1601), ceremony(?x1079, ?x1601), ceremony(?x601, ?x1601), ceremony(?x500, ?x1601), ?x1243 = 0gr0m, ?x601 = 0gr4k, honored_for(?x1601, ?x3157), award_winner(?x1601, ?x8652), ?x500 = 0p9sw, ?x1313 = 0gs9p, nominated_for(?x112, ?x3157), film_release_region(?x3157, ?x142), film_release_region(?x3157, ?x94), ?x1079 = 0l8z1, ?x94 = 09c7w0, production_companies(?x3157, ?x541), award(?x3157, ?x3233), country(?x471, ?x142), produced_by(?x4111, ?x8652) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 073hmq ceremony! 0gq_d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073hmq ceremony! 0gqxm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073hmq ceremony! 0gr51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073hmq ceremony! 0f4x7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony #10326-025t8bv PRED entity: 025t8bv PRED relation: artist PRED expected values: 011zf2 0dw4g => 78 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1449): 02vgh (0.60 #2985, 0.43 #3818, 0.41 #5821), 01s560x (0.57 #4078, 0.56 #5739, 0.41 #5821), 01q99h (0.57 #3765, 0.44 #5426, 0.41 #5821), 046p9 (0.57 #3921, 0.44 #5582, 0.41 #5821), 01wj18h (0.57 #3538, 0.44 #5199, 0.41 #5821), 019g40 (0.43 #3429, 0.41 #5821, 0.40 #2596), 0c9d9 (0.43 #3341, 0.41 #5821, 0.40 #2508), 01vvyfh (0.43 #3594, 0.41 #5821, 0.40 #2761), 09889g (0.43 #3681, 0.41 #5821, 0.40 #1185), 01fh0q (0.43 #3985, 0.41 #5821, 0.40 #3152) >> Best rule #2985 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 03rhqg; >> query: (?x9120, 02vgh) <- artist(?x9120, ?x6996), artist(?x9120, ?x5883), artist(?x9120, ?x1381), award_winner(?x1381, ?x9830), instrumentalists(?x227, ?x5883), award_winner(?x2461, ?x1381), award(?x9830, ?x2139), origin(?x1381, ?x4627), ?x6996 = 0132k4, role(?x5883, ?x75) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2890 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 03rhqg; *> query: (?x9120, 0dw4g) <- artist(?x9120, ?x6996), artist(?x9120, ?x5883), artist(?x9120, ?x1381), award_winner(?x1381, ?x9830), instrumentalists(?x227, ?x5883), award_winner(?x2461, ?x1381), award(?x9830, ?x2139), origin(?x1381, ?x4627), ?x6996 = 0132k4, role(?x5883, ?x75) *> conf = 0.20 ranks of expected_values: 238, 891 EVAL 025t8bv artist 0dw4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 78.000 51.000 0.600 http://example.org/music/record_label/artist EVAL 025t8bv artist 011zf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 78.000 51.000 0.600 http://example.org/music/record_label/artist #10325-04mn81 PRED entity: 04mn81 PRED relation: profession PRED expected values: 04f2zj => 117 concepts (94 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.89 #4351, 0.86 #5363, 0.81 #3043), 01d_h8 (0.47 #3035, 0.39 #5211, 0.37 #870), 09lbv (0.42 #161, 0.15 #738, 0.12 #1315), 039v1 (0.40 #754, 0.38 #3931, 0.38 #3787), 03gjzk (0.32 #3044, 0.27 #4352, 0.26 #5220), 0dxtg (0.29 #877, 0.28 #3042, 0.27 #5218), 01c72t (0.29 #6529, 0.28 #6384, 0.28 #6819), 012t_z (0.28 #11269, 0.15 #10, 0.08 #1598), 02jknp (0.20 #872, 0.18 #12427, 0.18 #12571), 0d1pc (0.20 #2357, 0.20 #1635, 0.16 #1924) >> Best rule #4351 for best value: >> intensional similarity = 3 >> extensional distance = 271 >> proper extension: 0m2wm; 03m8lq; 01j5x6; 01pctb; 0d02km; 019n7x; >> query: (?x1989, 02hrh1q) <- award_nominee(?x1989, ?x4594), participant(?x2562, ?x1989), people(?x2510, ?x1989) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #668 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 03qd_; 021bk; 02qfhb; *> query: (?x1989, 04f2zj) <- role(?x1989, ?x227), award_nominee(?x2732, ?x1989), people(?x2510, ?x1989) *> conf = 0.14 ranks of expected_values: 12 EVAL 04mn81 profession 04f2zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 117.000 94.000 0.886 http://example.org/people/person/profession #10324-01gwck PRED entity: 01gwck PRED relation: school_type PRED expected values: 05jxkf => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 21): 05jxkf (0.60 #100, 0.47 #196, 0.44 #436), 01rs41 (0.42 #125, 0.32 #149, 0.32 #269), 05pcjw (0.38 #73, 0.36 #145, 0.33 #169), 07tf8 (0.23 #201, 0.20 #105, 0.14 #441), 01_9fk (0.16 #194, 0.13 #410, 0.12 #74), 04qbv (0.14 #16, 0.12 #40, 0.11 #64), 01_srz (0.07 #555, 0.05 #892, 0.05 #627), 01y64 (0.04 #156, 0.04 #180, 0.04 #228), 04399 (0.04 #230, 0.03 #302, 0.03 #326), 02p0qmm (0.03 #1044, 0.03 #1333, 0.03 #2790) >> Best rule #100 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 017j69; 02zd460; 0g8rj; 01t0dy; 03tw2s; 015fsv; 01yqqv; 013807; >> query: (?x12728, 05jxkf) <- major_field_of_study(?x12728, ?x10391), citytown(?x12728, ?x9605), ?x10391 = 02jfc >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gwck school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.600 http://example.org/education/educational_institution/school_type #10323-09qvc0 PRED entity: 09qvc0 PRED relation: award! PRED expected values: 02lq10 09r9dp 03xkps 0p_47 0kjgl 01nxzv 02js_6 => 54 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2639): 0l786 (0.75 #12012, 0.25 #18658, 0.25 #2046), 02bj6k (0.66 #59801, 0.40 #5596, 0.38 #12241), 0h7pj (0.66 #59801, 0.25 #2526, 0.20 #5847), 0pnf3 (0.66 #59801, 0.17 #16612, 0.15 #39871), 0j1yf (0.66 #59801, 0.11 #59804, 0.06 #27058), 0byfz (0.62 #10013, 0.50 #47, 0.40 #3368), 02t_zq (0.60 #3891, 0.50 #570, 0.17 #17182), 07r1h (0.58 #15067, 0.46 #18391, 0.25 #11745), 0z4s (0.58 #13375, 0.42 #16699, 0.25 #10053), 0170pk (0.50 #10403, 0.42 #13725, 0.38 #17049) >> Best rule #12012 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0789_m; 02w9sd7; >> query: (?x693, 0l786) <- award(?x4266, ?x693), award(?x368, ?x693), ?x4266 = 015qt5, actor(?x493, ?x368), film(?x368, ?x508) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6512 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0bp_b2; 0bdw6t; 0gqy2; *> query: (?x693, 02js_6) <- award(?x8179, ?x693), category_of(?x693, ?x2758), ?x8179 = 01mqnr, nominated_for(?x693, ?x631) *> conf = 0.40 ranks of expected_values: 49, 172, 320, 380, 381, 535 EVAL 09qvc0 award! 02js_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 54.000 19.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qvc0 award! 01nxzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 19.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qvc0 award! 0kjgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 19.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qvc0 award! 0p_47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 19.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qvc0 award! 03xkps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 19.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qvc0 award! 09r9dp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 54.000 19.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qvc0 award! 02lq10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 19.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10322-04jwjq PRED entity: 04jwjq PRED relation: genre PRED expected values: 07s9rl0 02l7c8 => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 83): 07s9rl0 (0.69 #241, 0.68 #4690, 0.64 #2045), 02l7c8 (0.62 #257, 0.50 #137, 0.50 #17), 05p553 (0.54 #245, 0.50 #5, 0.39 #847), 03k50 (0.53 #1683, 0.50 #2165, 0.48 #3967), 02kdv5l (0.47 #363, 0.28 #1325, 0.27 #3609), 03k9fj (0.34 #372, 0.25 #4701, 0.23 #3618), 01jfsb (0.32 #1335, 0.30 #3139, 0.30 #2298), 01t_vv (0.25 #174, 0.15 #294, 0.08 #2098), 01j1n2 (0.25 #60, 0.15 #300, 0.04 #2104), 01hmnh (0.24 #379, 0.17 #4708, 0.15 #3986) >> Best rule #241 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 09fn1w; 021pqy; 030z4z; 07vfy4; >> query: (?x657, 07s9rl0) <- film(?x1445, ?x657), genre(?x657, ?x3741), ?x3741 = 01chg >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04jwjq genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.692 http://example.org/film/film/genre EVAL 04jwjq genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.692 http://example.org/film/film/genre #10321-099cng PRED entity: 099cng PRED relation: award! PRED expected values: 02x0dzw 04wx2v => 43 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2561): 09l3p (0.79 #10052, 0.69 #36870, 0.69 #33517), 0h1nt (0.79 #10052, 0.69 #36870, 0.69 #33517), 015q43 (0.79 #10052, 0.69 #36870, 0.69 #33517), 01l9p (0.79 #10052, 0.69 #36870, 0.69 #33517), 016k6x (0.19 #1434, 0.16 #4784, 0.10 #8135), 014zcr (0.18 #52, 0.18 #3402, 0.17 #6753), 0jmj (0.18 #1216, 0.16 #4566, 0.15 #46926), 02kxwk (0.18 #1221, 0.16 #4571, 0.12 #26814), 0170qf (0.18 #3350, 0.17 #6701, 0.16 #580), 03h_9lg (0.18 #3350, 0.17 #6701, 0.15 #46926) >> Best rule #10052 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 06196; >> query: (?x1441, ?x1244) <- award(?x385, ?x1441), award(?x396, ?x1441), award_winner(?x1441, ?x1244), ceremony(?x1441, ?x472) >> conf = 0.79 => this is the best rule for 4 predicted values *> Best rule #26814 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 197 *> proper extension: 02qwdhq; 03hj5vf; 02pzxlw; 02p_04b; 0fm3kw; 0fqpg6b; *> query: (?x1441, ?x989) <- award(?x385, ?x1441), award(?x988, ?x1441), nominated_for(?x1441, ?x308), award_nominee(?x989, ?x988) *> conf = 0.12 ranks of expected_values: 262, 430 EVAL 099cng award! 04wx2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 14.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099cng award! 02x0dzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 14.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10320-0blq0z PRED entity: 0blq0z PRED relation: award PRED expected values: 02x73k6 0279c15 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 261): 0279c15 (0.71 #11457, 0.68 #18963, 0.67 #23706), 05pcn59 (0.41 #869, 0.36 #1264, 0.31 #474), 027dtxw (0.34 #399, 0.17 #4, 0.14 #794), 05zr6wv (0.33 #807, 0.30 #1202, 0.24 #412), 057xs89 (0.26 #1341, 0.25 #946, 0.24 #551), 02x73k6 (0.21 #453, 0.09 #4346, 0.08 #848), 0bfvd4 (0.21 #507, 0.06 #4062, 0.05 #2877), 01by1l (0.20 #3665, 0.13 #2085, 0.11 #9986), 05p09zm (0.20 #911, 0.18 #1306, 0.09 #3281), 05zvj3m (0.20 #881, 0.17 #1276, 0.07 #486) >> Best rule #11457 for best value: >> intensional similarity = 3 >> extensional distance = 1196 >> proper extension: 04cy8rb; 01r42_g; 02pp_q_; 08wq0g; 01qkqwg; 08m4c8; 03jvmp; 0275_pj; 0g5lhl7; 06rnl9; ... >> query: (?x2670, ?x2535) <- award_nominee(?x72, ?x2670), award_winner(?x2670, ?x989), award_winner(?x2535, ?x2670) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 0blq0z award 0279c15 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.710 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0blq0z award 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 82.000 82.000 0.710 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10319-02f6yz PRED entity: 02f6yz PRED relation: award! PRED expected values: 01cblr 01dq9q 089pg7 => 39 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2039): 089tm (0.82 #43634, 0.81 #43636, 0.79 #43633), 0178kd (0.82 #43634, 0.81 #43636, 0.79 #43633), 01v0sx2 (0.82 #43634, 0.81 #43636, 0.79 #43633), 0dtd6 (0.67 #3884, 0.62 #7240, 0.58 #13952), 07r1_ (0.67 #15477, 0.54 #18834, 0.53 #22190), 01xzb6 (0.67 #14965, 0.50 #11609, 0.50 #4897), 01vs_v8 (0.62 #10652, 0.58 #14008, 0.54 #17365), 0478__m (0.62 #11392, 0.58 #14748, 0.47 #21461), 046p9 (0.62 #12427, 0.50 #15783, 0.50 #9071), 01wf86y (0.62 #12255, 0.42 #15611, 0.31 #18968) >> Best rule #43634 for best value: >> intensional similarity = 5 >> extensional distance = 77 >> proper extension: 02q3s; >> query: (?x8994, ?x4842) <- award_winner(?x8994, ?x4842), award_winner(?x8994, ?x2395), artist(?x6672, ?x2395), award(?x4842, ?x724), artist(?x5836, ?x4842) >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #12213 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 02f705; *> query: (?x8994, 01dq9q) <- award(?x11749, ?x8994), award(?x1060, ?x8994), group(?x2798, ?x1060), group(?x227, ?x1060), ?x2798 = 03qjg, ?x227 = 0342h, ?x11749 = 016t0h *> conf = 0.38 ranks of expected_values: 62, 67, 95 EVAL 02f6yz award! 089pg7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 39.000 18.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6yz award! 01dq9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 39.000 18.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6yz award! 01cblr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 39.000 18.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10318-014cw2 PRED entity: 014cw2 PRED relation: artists! PRED expected values: 016jny => 109 concepts (44 used for prediction) PRED predicted values (max 10 best out of 251): 06by7 (0.63 #10347, 0.57 #334, 0.56 #7533), 05bt6j (0.56 #7556, 0.48 #9744, 0.44 #11628), 064t9 (0.48 #10338, 0.42 #11596, 0.42 #7524), 016jny (0.40 #108, 0.30 #420, 0.20 #1672), 0jmwg (0.40 #114, 0.08 #1991, 0.07 #9188), 0xhtw (0.35 #642, 0.34 #954, 0.28 #1581), 02w4v (0.34 #358, 0.21 #2236, 0.21 #1297), 05w3f (0.29 #4736, 0.28 #664, 0.22 #9113), 06j6l (0.29 #5687, 0.27 #4435, 0.26 #8185), 01lyv (0.29 #347, 0.21 #7858, 0.20 #35) >> Best rule #10347 for best value: >> intensional similarity = 5 >> extensional distance = 371 >> proper extension: 015882; 0frsw; 01k98nm; 04gycf; 0cbm64; 02h9_l; >> query: (?x13413, 06by7) <- artists(?x302, ?x13413), instrumentalists(?x227, ?x13413), artists(?x302, ?x2697), parent_genre(?x301, ?x302), ?x2697 = 033wx9 >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #108 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 02t3ln; *> query: (?x13413, 016jny) <- artists(?x13412, ?x13413), ?x13412 = 05tcx0, category(?x13413, ?x134), ?x134 = 08mbj5d *> conf = 0.40 ranks of expected_values: 4 EVAL 014cw2 artists! 016jny CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 44.000 0.633 http://example.org/music/genre/artists #10317-0qmny PRED entity: 0qmny PRED relation: group! PRED expected values: 05148p4 => 67 concepts (66 used for prediction) PRED predicted values (max 10 best out of 73): 05148p4 (0.79 #1340, 0.75 #1252, 0.60 #724), 018vs (0.71 #1334, 0.67 #1246, 0.31 #2127), 03bx0bm (0.64 #1346, 0.64 #1258, 0.60 #730), 0l14md (0.64 #1327, 0.64 #1239, 0.40 #711), 028tv0 (0.47 #1245, 0.40 #1333, 0.40 #717), 0l14qv (0.40 #1325, 0.36 #1237, 0.25 #269), 03qjg (0.31 #1280, 0.30 #752, 0.29 #1368), 0l14j_ (0.30 #756, 0.23 #844, 0.21 #1372), 07y_7 (0.19 #1322, 0.14 #530, 0.11 #1234), 05r5c (0.17 #1328, 0.17 #1240, 0.11 #1504) >> Best rule #1340 for best value: >> intensional similarity = 6 >> extensional distance = 40 >> proper extension: 0fcsd; 02vgh; 0qmpd; 0pqp3; 02vnpv; >> query: (?x8637, 05148p4) <- artists(?x9013, ?x8637), artists(?x1380, ?x8637), ?x1380 = 0dl5d, group(?x227, ?x8637), artists(?x9013, ?x4983), award(?x4983, ?x724) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qmny group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 66.000 0.786 http://example.org/music/performance_role/regular_performances./music/group_membership/group #10316-051z6rz PRED entity: 051z6rz PRED relation: gender PRED expected values: 05zppz => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #41, 0.83 #43, 0.80 #35), 02zsn (0.35 #20, 0.31 #24, 0.30 #22) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 533 >> proper extension: 05f260; >> query: (?x6166, 05zppz) <- award(?x6166, ?x2209), nominated_for(?x2209, ?x951), ?x951 = 0cwy47 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051z6rz gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.836 http://example.org/people/person/gender #10315-01c7y PRED entity: 01c7y PRED relation: languages_spoken! PRED expected values: 0fqp6zk => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 74): 07hwkr (0.80 #982, 0.53 #1605, 0.51 #1743), 078vc (0.64 #527, 0.37 #346, 0.33 #596), 059_w (0.50 #233, 0.33 #372, 0.33 #26), 0bhsnb (0.50 #206, 0.33 #414, 0.33 #68), 0c41n (0.50 #276, 0.33 #415, 0.33 #69), 0fk3s (0.50 #269, 0.33 #408, 0.33 #62), 03x1x (0.50 #257, 0.33 #396, 0.33 #50), 0g8_vp (0.50 #226, 0.33 #365, 0.33 #19), 0dryh9k (0.37 #346, 0.33 #84, 0.24 #1178), 02sch9 (0.37 #346, 0.18 #516, 0.17 #2151) >> Best rule #982 for best value: >> intensional similarity = 9 >> extensional distance = 23 >> proper extension: 0swlx; >> query: (?x11341, 07hwkr) <- official_language(?x10457, ?x11341), languages_spoken(?x12951, ?x11341), people(?x12951, ?x6677), languages_spoken(?x12951, ?x13017), languages(?x2873, ?x13017), award_nominee(?x6677, ?x1538), nominated_for(?x6677, ?x6439), politician(?x8714, ?x6677), countries_spoken_in(?x13017, ?x792) >> conf = 0.80 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01c7y languages_spoken! 0fqp6zk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 32.000 0.800 http://example.org/people/ethnicity/languages_spoken #10314-01g6gs PRED entity: 01g6gs PRED relation: genre! PRED expected values: 0glnm 0fy66 0kbhf 0ft18 0kt_4 0k5px => 35 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1836): 0jqd3 (0.75 #17551, 0.71 #15725, 0.67 #12077), 0qmjd (0.64 #7300, 0.59 #7301, 0.52 #5473), 03s6l2 (0.60 #5562, 0.50 #11036, 0.50 #9213), 0y_pg (0.60 #6870, 0.50 #12344, 0.50 #3218), 052_mn (0.60 #6894, 0.50 #12368, 0.50 #3242), 0c9k8 (0.60 #7794, 0.50 #9619, 0.50 #4142), 02p86pb (0.60 #8846, 0.50 #10671, 0.50 #5194), 0pd6l (0.60 #7974, 0.50 #9799, 0.50 #4322), 0cwy47 (0.60 #7449, 0.50 #9274, 0.50 #3797), 0cz_ym (0.60 #7605, 0.50 #9430, 0.50 #3953) >> Best rule #17551 for best value: >> intensional similarity = 13 >> extensional distance = 6 >> proper extension: 0bkbm; >> query: (?x1805, 0jqd3) <- genre(?x9484, ?x1805), genre(?x5857, ?x1805), genre(?x5080, ?x1805), genre(?x1822, ?x1805), award(?x1822, ?x1243), film_release_region(?x1822, ?x94), costume_design_by(?x5080, ?x12104), nominated_for(?x2794, ?x9484), award_winner(?x5080, ?x786), ?x5857 = 05css_, film(?x1119, ?x9484), award(?x185, ?x1243), award_winner(?x1243, ?x5528) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #5473 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 03bxz7; *> query: (?x1805, ?x1708) <- genre(?x9484, ?x1805), genre(?x9234, ?x1805), genre(?x6680, ?x1805), genre(?x6137, ?x1805), genre(?x5857, ?x1805), genre(?x5080, ?x1805), genre(?x1822, ?x1805), award(?x1822, ?x198), film_release_region(?x1822, ?x94), costume_design_by(?x5080, ?x12104), nominated_for(?x2794, ?x9484), award_winner(?x5080, ?x786), ?x6680 = 01k7b0, cinematography(?x9234, ?x12576), ?x6137 = 06cm5, list(?x9234, ?x3004), nominated_for(?x1708, ?x5857) *> conf = 0.52 ranks of expected_values: 130, 422, 646, 721, 1081, 1087 EVAL 01g6gs genre! 0k5px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 16.000 0.750 http://example.org/film/film/genre EVAL 01g6gs genre! 0kt_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 16.000 0.750 http://example.org/film/film/genre EVAL 01g6gs genre! 0ft18 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 35.000 16.000 0.750 http://example.org/film/film/genre EVAL 01g6gs genre! 0kbhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 35.000 16.000 0.750 http://example.org/film/film/genre EVAL 01g6gs genre! 0fy66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 16.000 0.750 http://example.org/film/film/genre EVAL 01g6gs genre! 0glnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 35.000 16.000 0.750 http://example.org/film/film/genre #10313-016gkf PRED entity: 016gkf PRED relation: profession PRED expected values: 02jknp => 78 concepts (54 used for prediction) PRED predicted values (max 10 best out of 63): 02jknp (0.71 #1001, 0.69 #1285, 0.67 #149), 03gjzk (0.67 #297, 0.53 #3849, 0.43 #439), 0dgd_ (0.40 #27, 0.05 #1021, 0.05 #1305), 0np9r (0.33 #302, 0.30 #1864, 0.28 #2006), 0cbd2 (0.33 #290, 0.30 #432, 0.25 #858), 02krf9 (0.33 #166, 0.18 #3860, 0.17 #1018), 09jwl (0.32 #442, 0.22 #726, 0.22 #3426), 0lgw7 (0.20 #44, 0.01 #1038, 0.01 #1322), 0nbcg (0.16 #738, 0.15 #4858, 0.11 #5995), 0dz3r (0.14 #4832, 0.11 #5969, 0.10 #570) >> Best rule #1001 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 0q9kd; 0fvf9q; 02p65p; 054_mz; 0kr5_; 0jf1b; 03_gd; 02kxbwx; 02q_cc; 02ndbd; ... >> query: (?x5370, 02jknp) <- award(?x5370, ?x198), ?x198 = 040njc, profession(?x5370, ?x319) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016gkf profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 54.000 0.706 http://example.org/people/person/profession #10312-028d4v PRED entity: 028d4v PRED relation: profession PRED expected values: 0dxtg => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 58): 0dxtg (0.38 #162, 0.30 #606, 0.28 #458), 09jwl (0.38 #758, 0.31 #166, 0.27 #610), 02jknp (0.33 #8, 0.31 #156, 0.25 #10363), 01d_h8 (0.33 #598, 0.33 #4002, 0.32 #2670), 0nbcg (0.31 #771, 0.15 #623, 0.14 #475), 01c72t (0.25 #10363, 0.25 #763, 0.15 #171), 03gjzk (0.25 #10363, 0.25 #15, 0.24 #2679), 02krf9 (0.25 #10363, 0.17 #26, 0.15 #174), 0np9r (0.25 #10363, 0.15 #168, 0.15 #6977), 0kyk (0.25 #10363, 0.15 #177, 0.14 #473) >> Best rule #162 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 03qd_; 0pgjm; 021bk; 02qx69; 01pcbg; 073749; 028k57; 051wwp; 07y8l9; 07m77x; ... >> query: (?x2383, 0dxtg) <- film(?x2383, ?x3833), award_nominee(?x806, ?x2383), ?x3833 = 02ht1k >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028d4v profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.385 http://example.org/people/person/profession #10311-0vbk PRED entity: 0vbk PRED relation: contains PRED expected values: 0qt85 => 187 concepts (128 used for prediction) PRED predicted values (max 10 best out of 2811): 0841v (0.51 #141082, 0.51 #85234, 0.48 #38205), 0kpw3 (0.33 #2572, 0.06 #26082, 0.05 #29021), 0vbk (0.26 #358609, 0.03 #144021, 0.02 #176359), 09c7w0 (0.26 #358609, 0.02 #94052), 0f25y (0.17 #1372, 0.05 #16067, 0.04 #19005), 02482c (0.17 #1283, 0.05 #15978, 0.04 #18916), 0djd3 (0.17 #890, 0.05 #15585, 0.04 #18523), 0xtz9 (0.17 #2727, 0.05 #17422, 0.04 #20360), 02jztz (0.17 #1930, 0.05 #16625, 0.04 #19563), 0n4yq (0.17 #1888, 0.05 #16583, 0.04 #19521) >> Best rule #141082 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 0g14f; >> query: (?x4758, ?x13100) <- contains(?x4758, ?x7812), country(?x4758, ?x94), state_province_region(?x13100, ?x4758) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #176359 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 112 *> proper extension: 0195pd; 01vsb_; *> query: (?x4758, ?x108) <- contains(?x8260, ?x4758), state_province_region(?x8120, ?x4758), contains(?x8260, ?x108) *> conf = 0.02 ranks of expected_values: 1839 EVAL 0vbk contains 0qt85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 187.000 128.000 0.510 http://example.org/location/location/contains #10310-02_t2t PRED entity: 02_t2t PRED relation: student! PRED expected values: 0bsnm => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 134): 017z88 (0.12 #1133, 0.08 #5867, 0.03 #12179), 02g839 (0.12 #1076, 0.07 #5810, 0.05 #4232), 07wjk (0.12 #6374, 0.09 #7952, 0.06 #2166), 0bwfn (0.07 #1326, 0.07 #24470, 0.06 #274), 01qd_r (0.06 #280, 0.02 #6066, 0.01 #7118), 0778_3 (0.06 #2600, 0.02 #8386, 0.02 #6808), 015nl4 (0.05 #24262, 0.03 #29523, 0.03 #26366), 01w5m (0.05 #1156, 0.04 #5890, 0.04 #20618), 09f2j (0.05 #1210, 0.04 #5944, 0.04 #24354), 01g0p5 (0.05 #1258, 0.04 #5992, 0.03 #206) >> Best rule #1133 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 028q6; 01kvqc; 0kvrb; 06x4l_; 012ky3; 016szr; 02ryx0; 0bvzp; 02p2zq; 01vttb9; ... >> query: (?x8319, 017z88) <- role(?x8319, ?x316), award_nominee(?x8319, ?x6380), student(?x481, ?x8319), ?x316 = 05r5c >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02_t2t student! 0bsnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 88.000 0.122 http://example.org/education/educational_institution/students_graduates./education/education/student #10309-05zx7xk PRED entity: 05zx7xk PRED relation: nominated_for PRED expected values: 01dvbd => 45 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1337): 01dvbd (0.68 #15976, 0.68 #15975, 0.64 #9585), 049xgc (0.67 #4070, 0.17 #8866, 0.17 #7268), 0m313 (0.60 #3204, 0.19 #8000, 0.19 #9598), 0gmcwlb (0.60 #3375, 0.18 #6573, 0.18 #8171), 011yl_ (0.60 #3723, 0.16 #5320, 0.14 #8519), 07w8fz (0.60 #3654, 0.16 #5251, 0.14 #8450), 016mhd (0.60 #4418, 0.11 #9214, 0.11 #10812), 027r9t (0.60 #4289, 0.09 #5886, 0.09 #9085), 0209xj (0.53 #3285, 0.18 #6387, 0.16 #9586), 02yvct (0.53 #3513, 0.16 #8309, 0.15 #9907) >> Best rule #15976 for best value: >> intensional similarity = 3 >> extensional distance = 215 >> proper extension: 0fqnzts; >> query: (?x13311, ?x9258) <- award(?x9258, ?x13311), award(?x1616, ?x13311), nominated_for(?x68, ?x9258) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05zx7xk nominated_for 01dvbd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 10.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10308-0dcqh PRED entity: 0dcqh PRED relation: symptom_of! PRED expected values: 01j6t0 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 29): 01j6t0 (0.82 #1360, 0.82 #1261, 0.82 #602), 0cjf0 (0.67 #405, 0.66 #1247, 0.60 #978), 0hgxh (0.45 #650, 0.33 #34, 0.24 #624), 0brgy (0.41 #339, 0.41 #1186, 0.35 #1392), 0j5fv (0.41 #339, 0.38 #746, 0.33 #97), 02tfl8 (0.41 #339, 0.33 #118, 0.33 #45), 012qjw (0.38 #1187, 0.35 #1393, 0.33 #426), 01pf6 (0.35 #1406, 0.25 #289, 0.24 #1284), 0hg45 (0.35 #1406, 0.25 #286, 0.24 #1284), 0f3kl (0.33 #136, 0.33 #63, 0.24 #624) >> Best rule #1360 for best value: >> intensional similarity = 11 >> extensional distance = 37 >> proper extension: 07s4l; >> query: (?x12870, 01j6t0) <- symptom_of(?x13487, ?x12870), symptom_of(?x13487, ?x11126), symptom_of(?x13487, ?x6260), symptom_of(?x13487, ?x4906), symptom_of(?x13487, ?x4322), symptom_of(?x13487, ?x4291), ?x4291 = 07jwr, ?x11126 = 0hg45, ?x6260 = 0dq9p, ?x4322 = 0gk4g, ?x4906 = 0hg11 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dcqh symptom_of! 01j6t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.821 http://example.org/medicine/symptom/symptom_of #10307-03gqgt3 PRED entity: 03gqgt3 PRED relation: locations PRED expected values: 04wsz => 61 concepts (43 used for prediction) PRED predicted values (max 10 best out of 383): 02j9z (0.43 #568, 0.38 #753, 0.21 #1306), 047yc (0.43 #3143, 0.16 #3515, 0.15 #3887), 01z215 (0.43 #3143, 0.15 #3887, 0.13 #4447), 03spz (0.43 #3143, 0.12 #6109, 0.12 #2124), 09c7w0 (0.40 #927, 0.22 #1110, 0.17 #375), 0156q (0.35 #2070, 0.29 #2812, 0.22 #4666), 0d05w3 (0.29 #4258, 0.22 #2406, 0.21 #1845), 03rk0 (0.29 #4258, 0.21 #1845, 0.16 #3515), 03shp (0.29 #4258, 0.21 #1845, 0.16 #3515), 0f8l9c (0.28 #1129, 0.20 #1682, 0.19 #1868) >> Best rule #568 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 0py8j; 0cbvg; 07j9n; 03jv8d; >> query: (?x13022, 02j9z) <- combatants(?x13022, ?x2152), combatants(?x13022, ?x456), ?x2152 = 06mkj, film_release_region(?x8657, ?x456), film_release_region(?x6931, ?x456), film_release_region(?x5578, ?x456), film_release_region(?x4464, ?x456), ?x4464 = 05pdh86, ?x8657 = 030z4z, ?x5578 = 0ddj0x, ?x6931 = 09v3jyg >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #5510 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 44 *> proper extension: 08821; 0784z; 01tffp; *> query: (?x13022, 04wsz) <- combatants(?x13022, ?x2152), film_release_region(?x6218, ?x2152), film_release_region(?x6078, ?x2152), film_release_region(?x1535, ?x2152), film_release_region(?x607, ?x2152), ?x1535 = 02r1c18, medal(?x2152, ?x422), ?x607 = 02x3lt7, ?x6078 = 04pk1f, film(?x1548, ?x6218) *> conf = 0.11 ranks of expected_values: 65 EVAL 03gqgt3 locations 04wsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 61.000 43.000 0.429 http://example.org/time/event/locations #10306-02029f PRED entity: 02029f PRED relation: current_club! PRED expected values: 03z8bw => 140 concepts (93 used for prediction) PRED predicted values (max 10 best out of 55): 02s9vc (0.40 #381, 0.35 #564, 0.29 #319), 02ltg3 (0.40 #367, 0.26 #1032, 0.22 #641), 03_44z (0.33 #57, 0.25 #115, 0.20 #388), 033nzk (0.33 #31, 0.25 #89, 0.20 #120), 03ys48 (0.33 #46, 0.25 #104, 0.20 #135), 03_qj1 (0.33 #11, 0.13 #855, 0.12 #554), 03y_f8 (0.32 #1028, 0.26 #847, 0.23 #788), 03dj48 (0.31 #412, 0.17 #984, 0.14 #320), 02pp1 (0.29 #323, 0.29 #264, 0.20 #385), 03d8m4 (0.23 #400, 0.17 #972, 0.17 #158) >> Best rule #381 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 02b2np; 011v3; 04ltf; 03x6m; 0j46b; 01rly6; 0ckf6; 0mmd6; >> query: (?x8537, 02s9vc) <- team(?x982, ?x8537), colors(?x8537, ?x663), position(?x8537, ?x63), ?x63 = 02sdk9v, teams(?x14446, ?x8537), location(?x982, ?x362), current_club(?x8102, ?x8537) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #168 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 0ly8z; *> query: (?x8537, 03z8bw) <- team(?x8576, ?x8537), team(?x203, ?x8537), team(?x60, ?x8537), current_club(?x8102, ?x8537), ?x203 = 0dgrmp, team(?x8576, ?x8673), ?x8102 = 03_qrp, team(?x3586, ?x8673), position(?x62, ?x60) *> conf = 0.17 ranks of expected_values: 15 EVAL 02029f current_club! 03z8bw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 140.000 93.000 0.400 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #10305-0djd22 PRED entity: 0djd22 PRED relation: titles PRED expected values: 0ktpx 016z43 => 69 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1789): 05_5rjx (0.50 #5199, 0.33 #2091, 0.27 #11414), 02s4l6 (0.50 #4965, 0.33 #1857, 0.17 #12734), 01qbg5 (0.50 #5738, 0.33 #2630, 0.17 #13507), 02prwdh (0.50 #5454, 0.33 #2346, 0.17 #13223), 03hkch7 (0.50 #5092, 0.33 #1984, 0.17 #12861), 0d8w2n (0.50 #6186, 0.33 #3078, 0.11 #21752), 046f3p (0.50 #5787, 0.33 #2679, 0.11 #21752), 093l8p (0.50 #5777, 0.33 #2669, 0.11 #21752), 02nczh (0.50 #5609, 0.33 #2501, 0.11 #21752), 0194zl (0.50 #5377, 0.33 #2269, 0.11 #21752) >> Best rule #5199 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 0hn10; >> query: (?x3155, 05_5rjx) <- titles(?x3155, ?x6493), titles(?x3155, ?x3116), titles(?x3155, ?x83), titles(?x3155, ?x69), ?x69 = 02d413, film_release_region(?x83, ?x94), film(?x965, ?x83), film(?x450, ?x3116), currency(?x3116, ?x170), genre(?x6493, ?x53) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3079 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 07s9rl0; *> query: (?x3155, 016z43) <- genre(?x13282, ?x3155), genre(?x5808, ?x3155), genre(?x5594, ?x3155), genre(?x1280, ?x3155), titles(?x3155, ?x1493), titles(?x3155, ?x557), ?x5808 = 05lfwd, ?x557 = 03h_yy, ?x13282 = 05397h, ?x5594 = 01fx1l, ?x1493 = 05j82v, nominated_for(?x588, ?x1280) *> conf = 0.33 ranks of expected_values: 63, 412 EVAL 0djd22 titles 016z43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 69.000 15.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 0djd22 titles 0ktpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 69.000 15.000 0.500 http://example.org/media_common/netflix_genre/titles #10304-0gztl PRED entity: 0gztl PRED relation: list PRED expected values: 01ptsx => 207 concepts (207 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.71 #103, 0.67 #124, 0.66 #131), 04k4rt (0.62 #81, 0.58 #60, 0.58 #53), 01pd60 (0.56 #83, 0.49 #132, 0.48 #55), 09g7thr (0.30 #585, 0.22 #973, 0.19 #1045) >> Best rule #103 for best value: >> intensional similarity = 5 >> extensional distance = 36 >> proper extension: 02bh8z; 0sxdg; 01_4lx; >> query: (?x1604, 01ptsx) <- company(?x1491, ?x1604), currency(?x1604, ?x170), ?x170 = 09nqf, ?x1491 = 0krdk, citytown(?x1604, ?x5719) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gztl list 01ptsx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 207.000 207.000 0.711 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #10303-01vt5c_ PRED entity: 01vt5c_ PRED relation: artists! PRED expected values: 03w94xt => 131 concepts (53 used for prediction) PRED predicted values (max 10 best out of 252): 064t9 (0.83 #10524, 0.69 #311, 0.68 #5416), 06j6l (0.77 #5450, 0.54 #345, 0.41 #8757), 06by7 (0.57 #1821, 0.53 #2723, 0.48 #2422), 0gywn (0.54 #353, 0.44 #5458, 0.37 #8765), 02lnbg (0.38 #354, 0.31 #1255, 0.27 #2156), 02x8m (0.38 #317, 0.24 #617, 0.23 #5422), 0y3_8 (0.38 #1245, 0.23 #344, 0.15 #644), 016_nr (0.35 #1570, 0.12 #669, 0.11 #970), 05bt6j (0.33 #1842, 0.32 #2744, 0.32 #13858), 01fm07 (0.31 #420, 0.18 #720, 0.13 #1621) >> Best rule #10524 for best value: >> intensional similarity = 5 >> extensional distance = 420 >> proper extension: 0123r4; >> query: (?x7951, 064t9) <- artists(?x3562, ?x7951), artists(?x3562, ?x10740), artists(?x3562, ?x1989), ?x1989 = 04mn81, ?x10740 = 016ppr >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1391 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 017vkx; 0dzc16; 0415mzy; *> query: (?x7951, 03w94xt) <- artists(?x3916, ?x7951), award(?x7951, ?x724), ?x3916 = 08cyft *> conf = 0.03 ranks of expected_values: 188 EVAL 01vt5c_ artists! 03w94xt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 131.000 53.000 0.834 http://example.org/music/genre/artists #10302-02qvdc PRED entity: 02qvdc PRED relation: team PRED expected values: 04l5d0 => 32 concepts (16 used for prediction) PRED predicted values (max 10 best out of 967): 03b3j (0.80 #11365, 0.80 #10426, 0.67 #5739), 03gqb0k (0.80 #11353, 0.80 #10414, 0.67 #5727), 0289q (0.80 #11418, 0.73 #10479, 0.67 #5792), 0fht9f (0.80 #11307, 0.73 #10368, 0.50 #5681), 06x76 (0.80 #11855, 0.67 #10916, 0.67 #6229), 04l5d0 (0.74 #2800, 0.74 #2799, 0.69 #3735), 02hqt6 (0.74 #2800, 0.74 #2799, 0.69 #3735), 0jnmj (0.74 #2800, 0.74 #2799, 0.69 #3735), 0j8cb (0.74 #2800, 0.74 #2799, 0.69 #3735), 0jnlm (0.74 #2800, 0.74 #2799, 0.69 #3735) >> Best rule #11365 for best value: >> intensional similarity = 19 >> extensional distance = 13 >> proper extension: 02vkdwz; 06b1q; 047g8h; 023wyl; 02g_6j; 05zm34; 01_9c1; 02g_7z; 08ns5s; >> query: (?x5234, 03b3j) <- team(?x5234, ?x12734), team(?x5234, ?x11473), team(?x5234, ?x10941), team(?x5234, ?x10755), team(?x5234, ?x10644), position(?x3299, ?x5234), teams(?x659, ?x10755), sport(?x10644, ?x453), teams(?x5719, ?x11473), athlete(?x453, ?x11825), team(?x13270, ?x10941), teams(?x1036, ?x10644), organization(?x4682, ?x13270), colors(?x10755, ?x13863), category(?x12734, ?x134), adjoins(?x3300, ?x5719), locations(?x3797, ?x5719), dog_breed(?x5719, ?x1706), jurisdiction_of_office(?x1195, ?x5719) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2800 for first EXPECTED value: *> intensional similarity = 48 *> extensional distance = 1 *> proper extension: 02qvl7; *> query: (?x5234, ?x10142) <- team(?x5234, ?x14404), team(?x5234, ?x14258), team(?x5234, ?x14123), team(?x5234, ?x14035), team(?x5234, ?x13860), team(?x5234, ?x13608), team(?x5234, ?x12757), team(?x5234, ?x12734), team(?x5234, ?x12541), team(?x5234, ?x11473), team(?x5234, ?x10950), team(?x5234, ?x10755), team(?x5234, ?x10713), team(?x5234, ?x9515), team(?x5234, ?x8892), team(?x5234, ?x8037), team(?x5234, ?x6640), team(?x5234, ?x5380), team(?x5234, ?x5233), team(?x5234, ?x3723), ?x10755 = 0jbqf, ?x10713 = 0gx159f, ?x12541 = 04l58n, position(?x10142, ?x5234), position(?x9835, ?x5234), position(?x9547, ?x5234), position(?x8541, ?x5234), ?x8037 = 0jnrk, ?x9547 = 04l5d0, ?x10950 = 0jnr_, ?x8892 = 02fp3, ?x14258 = 0bszz, ?x12757 = 0hmtk, ?x11473 = 0jnpv, ?x13608 = 0jnl5, sport(?x9515, ?x453), ?x9835 = 02hqt6, ?x5233 = 0j5m6, ?x14123 = 04l59s, ?x5380 = 0b6p3qf, ?x8541 = 0jnpc, ?x6640 = 0gvt8sz, ?x3723 = 0hn6d, ?x13860 = 06x6s, colors(?x12734, ?x332), ?x14404 = 0jnm2, team(?x13270, ?x14035), team(?x2918, ?x10142) *> conf = 0.74 ranks of expected_values: 6 EVAL 02qvdc team 04l5d0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 32.000 16.000 0.800 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #10301-01pqy_ PRED entity: 01pqy_ PRED relation: location PRED expected values: 0d05q4 => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 234): 02hrh0_ (0.70 #94825, 0.47 #120537, 0.47 #57850), 030qb3t (0.33 #7316, 0.31 #8120, 0.27 #12942), 02_286 (0.26 #36197, 0.22 #20931, 0.21 #15307), 0d9y6 (0.25 #268, 0.02 #4285, 0.02 #8305), 0cc56 (0.14 #860, 0.08 #5682, 0.07 #15327), 0r62v (0.14 #850, 0.02 #4868, 0.02 #21745), 0h8d (0.14 #997), 01531 (0.13 #1765, 0.06 #2568, 0.04 #4979), 0rsjf (0.11 #8841, 0.07 #28930, 0.07 #26519), 0cr3d (0.08 #6574, 0.07 #62010, 0.07 #27468) >> Best rule #94825 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 07h1h5; 076psv; 0g69lg; 07vfqj; 07m69t; 03975z; 05vzql; 09myny; 01vzz1c; 01g5kv; >> query: (?x5197, ?x5193) <- location(?x5197, ?x6226), place_of_birth(?x5197, ?x5193) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pqy_ location 0d05q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 167.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #10300-0ckcvk PRED entity: 0ckcvk PRED relation: profession PRED expected values: 05vyk => 89 concepts (39 used for prediction) PRED predicted values (max 10 best out of 51): 01c72t (0.92 #1948, 0.77 #912, 0.76 #1504), 0nbcg (0.87 #2400, 0.52 #1068, 0.45 #2252), 09jwl (0.70 #2239, 0.55 #2387, 0.47 #2091), 02hrh1q (0.67 #3270, 0.63 #2974, 0.62 #3714), 05vyk (0.40 #390, 0.40 #94, 0.32 #686), 0dz3r (0.37 #2222, 0.36 #2370, 0.34 #2074), 016z4k (0.33 #2076, 0.32 #2224, 0.31 #2520), 01d_h8 (0.31 #4150, 0.29 #2670, 0.28 #2966), 0dxtg (0.29 #4157, 0.29 #2677, 0.28 #2973), 039v1 (0.26 #2257, 0.21 #2405, 0.13 #2109) >> Best rule #1948 for best value: >> intensional similarity = 5 >> extensional distance = 325 >> proper extension: 0c9d9; 0fp_v1x; 01vvy; 01pr_j6; 0bg539; 05k2s_; 01w923; 0p5mw; 0kvrb; 0lgm5; ... >> query: (?x9727, 01c72t) <- profession(?x9727, ?x563), profession(?x8476, ?x563), profession(?x6382, ?x563), ?x8476 = 012201, ?x6382 = 01wd9lv >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #390 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 09h_q; 0h6sv; *> query: (?x9727, 05vyk) <- award_winner(?x5765, ?x9727), profession(?x9727, ?x563), award(?x8129, ?x5765), ?x8129 = 01dhpj *> conf = 0.40 ranks of expected_values: 5 EVAL 0ckcvk profession 05vyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 89.000 39.000 0.920 http://example.org/people/person/profession #10299-0f2s6 PRED entity: 0f2s6 PRED relation: dog_breed PRED expected values: 01t032 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 1): 01t032 (0.34 #9, 0.26 #18, 0.26 #15) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 0qy5v; 0__wm; >> query: (?x9713, 01t032) <- source(?x9713, ?x958), location(?x954, ?x9713), jurisdiction_of_office(?x1195, ?x9713) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2s6 dog_breed 01t032 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.341 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #10298-02bhj4 PRED entity: 02bhj4 PRED relation: school_type PRED expected values: 06cs1 => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 17): 05jxkf (0.56 #338, 0.54 #485, 0.54 #317), 01_9fk (0.28 #337, 0.28 #316, 0.24 #484), 07tf8 (0.19 #342, 0.17 #321, 0.17 #489), 06cs1 (0.16 #589, 0.14 #150, 0.04 #213), 04399 (0.16 #589, 0.07 #53, 0.06 #74), 04qbv (0.16 #589, 0.04 #602, 0.03 #244), 01y64 (0.12 #9, 0.05 #114, 0.05 #135), 0m4mb (0.11 #29, 0.02 #533, 0.02 #1612), 01rs62 (0.11 #36), 02p0qmm (0.05 #301, 0.04 #638, 0.04 #469) >> Best rule #338 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 0frm7n; >> query: (?x7202, 05jxkf) <- category(?x7202, ?x134), school(?x2820, ?x7202), school(?x8586, ?x7202), draft(?x660, ?x8586) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #589 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 191 *> proper extension: 0fht9f; *> query: (?x7202, ?x1507) <- school(?x2820, ?x7202), school(?x2820, ?x5357), school_type(?x5357, ?x1507) *> conf = 0.16 ranks of expected_values: 4 EVAL 02bhj4 school_type 06cs1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 157.000 157.000 0.556 http://example.org/education/educational_institution/school_type #10297-0lyjf PRED entity: 0lyjf PRED relation: institution! PRED expected values: 014mlp 022h5x => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 18): 03bwzr4 (0.83 #221, 0.80 #145, 0.71 #49), 014mlp (0.81 #156, 0.78 #443, 0.77 #424), 019v9k (0.80 #139, 0.75 #215, 0.74 #445), 02_xgp2 (0.77 #219, 0.70 #143, 0.66 #162), 04zx3q1 (0.50 #211, 0.47 #135, 0.40 #78), 027f2w (0.42 #216, 0.37 #140, 0.34 #159), 03mkk4 (0.38 #104, 0.35 #85, 0.34 #180), 022h5x (0.34 #169, 0.29 #54, 0.28 #2313), 0bjrnt (0.30 #81, 0.23 #138, 0.20 #272), 01rr_d (0.30 #91, 0.23 #148, 0.16 #781) >> Best rule #221 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 0bqxw; 0jpn8; >> query: (?x4904, 03bwzr4) <- fraternities_and_sororities(?x4904, ?x3697), institution(?x1200, ?x4904), ?x1200 = 016t_3, major_field_of_study(?x4904, ?x1154) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #156 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 017j69; *> query: (?x4904, 014mlp) <- school(?x2574, ?x4904), student(?x4904, ?x5338), position(?x2574, ?x180), participant(?x5338, ?x3083) *> conf = 0.81 ranks of expected_values: 2, 8 EVAL 0lyjf institution! 022h5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 161.000 161.000 0.827 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0lyjf institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 161.000 161.000 0.827 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #10296-0fqt1ns PRED entity: 0fqt1ns PRED relation: film! PRED expected values: 03m3nzf => 92 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1102): 01vsn38 (0.33 #1851, 0.04 #18481, 0.03 #30953), 04fzk (0.17 #704, 0.13 #6939, 0.11 #9018), 02lymt (0.17 #851, 0.13 #7086, 0.11 #9165), 048lv (0.17 #219, 0.12 #2297, 0.05 #8533), 0309lm (0.17 #1603, 0.12 #3681, 0.03 #18233), 02lhm2 (0.17 #963, 0.08 #5119, 0.03 #23830), 01fyzy (0.17 #1059, 0.07 #7294, 0.05 #9373), 0p8r1 (0.17 #582, 0.05 #58784, 0.04 #46313), 057_yx (0.17 #1837, 0.04 #18467, 0.04 #28861), 02_p5w (0.17 #642, 0.03 #46373, 0.02 #11035) >> Best rule #1851 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02ny6g; >> query: (?x4664, 01vsn38) <- genre(?x4664, ?x53), production_companies(?x4664, ?x541), film(?x318, ?x4664), ?x318 = 02g8h >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fqt1ns film! 03m3nzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 77.000 0.333 http://example.org/film/actor/film./film/performance/film #10295-06r4f PRED entity: 06r4f PRED relation: country_of_origin PRED expected values: 09c7w0 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 72): 09c7w0 (0.91 #322, 0.90 #389, 0.88 #646), 03_3d (0.29 #258, 0.27 #58, 0.22 #25), 0d060g (0.18 #70, 0.18 #48, 0.17 #81), 07ssc (0.17 #86, 0.14 #108, 0.14 #20), 03rjj (0.14 #958, 0.04 #201, 0.04 #245), 03rt9 (0.14 #958, 0.02 #869, 0.02 #307), 02jx1 (0.14 #958, 0.02 #869, 0.02 #321), 0d0vqn (0.14 #958, 0.02 #869, 0.02 #802), 04jpl (0.14 #958, 0.01 #563), 05v8c (0.04 #253, 0.01 #992, 0.01 #534) >> Best rule #322 for best value: >> intensional similarity = 5 >> extensional distance = 52 >> proper extension: 080dwhx; 019nnl; 0ddd0gc; 08jgk1; 0464pz; 0kfv9; 03ln8b; 0gfzgl; 02hct1; 0d68qy; ... >> query: (?x9327, 09c7w0) <- program_creator(?x9327, ?x1683), genre(?x9327, ?x53), languages(?x9327, ?x254), program(?x4299, ?x9327), tv_program(?x5432, ?x9327) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06r4f country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.907 http://example.org/tv/tv_program/country_of_origin #10294-0j5fv PRED entity: 0j5fv PRED relation: symptom_of PRED expected values: 04psf 0k95h 09d11 02k6hp => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 83): 0167bx (0.67 #450, 0.62 #351, 0.62 #220), 01n3bm (0.67 #447, 0.55 #651, 0.50 #747), 011zdm (0.62 #220, 0.50 #111, 0.44 #442), 01gkcc (0.62 #220, 0.43 #321, 0.43 #285), 02k6hp (0.56 #491, 0.50 #541, 0.50 #158), 07jwr (0.50 #224, 0.50 #179, 0.50 #138), 0hg11 (0.50 #182, 0.50 #141, 0.39 #315), 0gk4g (0.50 #139, 0.44 #513, 0.44 #472), 0dq9p (0.50 #145, 0.43 #317, 0.43 #276), 09d11 (0.50 #466, 0.43 #278, 0.39 #315) >> Best rule #450 for best value: >> intensional similarity = 22 >> extensional distance = 7 >> proper extension: 02tfl8; 0hgxh; >> query: (?x6780, 0167bx) <- symptom_of(?x6780, ?x14562), symptom_of(?x6780, ?x13231), symptom_of(?x6780, ?x11392), symptom_of(?x6780, ?x7586), symptom_of(?x6780, ?x1158), risk_factors(?x11392, ?x268), symptom_of(?x10717, ?x14562), symptom_of(?x9509, ?x14562), people(?x13231, ?x7385), risk_factors(?x7586, ?x231), risk_factors(?x6655, ?x13231), ?x9509 = 0gxb2, people(?x268, ?x269), ?x10717 = 0cjf0, risk_factors(?x13231, ?x8524), people(?x7586, ?x11410), nationality(?x7385, ?x512), ?x231 = 05zppz, people(?x1158, ?x2511), award(?x7385, ?x591), symptom_of(?x9438, ?x6655), artists(?x3108, ?x2511) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #491 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 7 *> proper extension: 0hg45; *> query: (?x6780, 02k6hp) <- symptom_of(?x6780, ?x14562), symptom_of(?x6780, ?x13231), symptom_of(?x6780, ?x11392), symptom_of(?x6780, ?x7586), risk_factors(?x11392, ?x268), symptom_of(?x9509, ?x14562), people(?x13231, ?x7385), people(?x13231, ?x4309), risk_factors(?x7586, ?x231), risk_factors(?x6655, ?x13231), film(?x7385, ?x1072), symptom_of(?x9509, ?x4322), people(?x268, ?x269), risk_factors(?x14001, ?x7586), ?x1072 = 01_mdl, ?x4322 = 0gk4g, nationality(?x4309, ?x512) *> conf = 0.56 ranks of expected_values: 5, 10, 16, 40 EVAL 0j5fv symptom_of 02k6hp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 20.000 20.000 0.667 http://example.org/medicine/symptom/symptom_of EVAL 0j5fv symptom_of 09d11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 20.000 20.000 0.667 http://example.org/medicine/symptom/symptom_of EVAL 0j5fv symptom_of 0k95h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 20.000 20.000 0.667 http://example.org/medicine/symptom/symptom_of EVAL 0j5fv symptom_of 04psf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 20.000 20.000 0.667 http://example.org/medicine/symptom/symptom_of #10293-0p7h7 PRED entity: 0p7h7 PRED relation: category PRED expected values: 08mbj5d => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #35, 0.86 #28, 0.85 #26) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 463 >> proper extension: 016qtt; 05cljf; 01vw87c; 01pfr3; 0m2l9; 02mslq; 0kzy0; 0152cw; 01w61th; 01kwlwp; ... >> query: (?x4609, 08mbj5d) <- artists(?x378, ?x4609), award_winner(?x1088, ?x4609), artist(?x2241, ?x4609) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p7h7 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.862 http://example.org/common/topic/webpage./common/webpage/category #10292-03l6bs PRED entity: 03l6bs PRED relation: contains! PRED expected values: 0824r => 139 concepts (96 used for prediction) PRED predicted values (max 10 best out of 222): 0824r (0.82 #10743, 0.79 #45670, 0.79 #59103), 059rby (0.25 #8076, 0.25 #13448, 0.24 #8971), 0fw2y (0.20 #45671), 05tbn (0.17 #2013, 0.15 #1118, 0.14 #6490), 02jx1 (0.17 #35907, 0.16 #60980, 0.14 #29639), 07b_l (0.15 #39624, 0.14 #40519, 0.12 #19917), 02_286 (0.14 #13471, 0.13 #8099, 0.13 #8994), 01n7q (0.13 #44851, 0.12 #4554, 0.11 #17087), 04_1l0v (0.12 #20146, 0.09 #39853, 0.08 #40748), 05k7sb (0.11 #17142, 0.08 #1922, 0.07 #132) >> Best rule #10743 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 02_2kg; 043q2z; 02bf58; >> query: (?x6006, ?x4105) <- currency(?x6006, ?x170), school_type(?x6006, ?x3205), ?x3205 = 01rs41, state_province_region(?x6006, ?x4105) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03l6bs contains! 0824r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 96.000 0.821 http://example.org/location/location/contains #10291-034_7s PRED entity: 034_7s PRED relation: district_represented PRED expected values: 059t8 059ts => 11 concepts (7 used for prediction) PRED predicted values (max 10 best out of 77): 059rby (0.86 #499, 0.86 #424, 0.84 #340), 05k7sb (0.83 #443, 0.77 #283, 0.77 #366), 081mh (0.77 #290, 0.69 #373, 0.57 #450), 059t8 (0.71 #60, 0.53 #492, 0.51 #501), 0h5qxv (0.71 #60, 0.51 #501, 0.51 #487), 05rh2 (0.71 #60, 0.51 #501, 0.51 #487), 059ts (0.71 #60, 0.51 #501, 0.51 #487), 087r4 (0.71 #60, 0.51 #501, 0.51 #487), 0847q (0.71 #60, 0.51 #487, 0.51 #482), 03gh4 (0.71 #308, 0.63 #391, 0.52 #468) >> Best rule #499 for best value: >> intensional similarity = 74 >> extensional distance = 40 >> proper extension: 03rl1g; 01gtbb; 01gst_; 01gtc0; 01gtcq; 01gsvp; 01gsvb; 01gt99; >> query: (?x11190, ?x335) <- legislative_sessions(?x11189, ?x11190), legislative_sessions(?x10543, ?x11190), district_represented(?x10543, ?x11542), district_represented(?x10543, ?x10063), district_represented(?x10543, ?x9370), district_represented(?x10543, ?x7468), district_represented(?x10543, ?x3824), district_represented(?x10543, ?x3474), district_represented(?x10543, ?x1905), legislative_sessions(?x13634, ?x11190), contains(?x7468, ?x1036), capital(?x3824, ?x1275), contains(?x279, ?x3824), district_represented(?x11189, ?x13765), district_represented(?x11189, ?x12125), contains(?x9370, ?x2541), adjoins(?x3824, ?x1274), adjoins(?x4198, ?x3824), legislative_sessions(?x3099, ?x11189), politician(?x3098, ?x3099), state_province_region(?x10100, ?x9370), contains(?x3824, ?x3825), state_province_region(?x1914, ?x7468), legislative_sessions(?x13950, ?x11189), adjoins(?x953, ?x7468), student(?x2327, ?x3099), taxonomy(?x3824, ?x939), ?x1274 = 04ykg, contains(?x12971, ?x3474), ?x939 = 04n6k, place_of_birth(?x3099, ?x1658), geographic_distribution(?x14365, ?x9370), adjoins(?x6842, ?x9370), capital(?x11542, ?x12635), location(?x8720, ?x7468), contains(?x3474, ?x5678), state_province_region(?x12356, ?x11542), location(?x10626, ?x9370), gender(?x3099, ?x231), category(?x13634, ?x134), partially_contains(?x11542, ?x10954), state(?x8823, ?x12125), profession(?x3099, ?x5805), ?x5805 = 0fj9f, contains(?x390, ?x12125), contains(?x10063, ?x1411), adjoins(?x1905, ?x335), vacationer(?x7468, ?x4884), state_province_region(?x1306, ?x1905), contains(?x11542, ?x12135), currency(?x1905, ?x2244), contains(?x1905, ?x1196), ?x335 = 059rby, administrative_division(?x13571, ?x13765), adjoins(?x11993, ?x1905), religion(?x1905, ?x1985), religion(?x1905, ?x962), religion(?x1905, ?x492), location(?x927, ?x12125), state(?x12755, ?x1905), contains(?x12125, ?x10889), adjoins(?x12854, ?x12125), featured_film_locations(?x5313, ?x10063), ?x492 = 0flw86, time_zones(?x1905, ?x2674), adjoins(?x11542, ?x7058), partially_contains(?x7468, ?x6195), ?x1985 = 0c8wxp, ?x231 = 05zppz, jurisdiction_of_office(?x900, ?x12125), state_province_region(?x12737, ?x3824), state(?x6224, ?x3474), ?x900 = 0fkvn, ?x962 = 05sfs >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #60 for first EXPECTED value: *> intensional similarity = 75 *> extensional distance = 1 *> proper extension: 04fhps; *> query: (?x11190, ?x9370) <- legislative_sessions(?x11189, ?x11190), legislative_sessions(?x10543, ?x11190), legislative_sessions(?x8777, ?x11190), ?x10543 = 03h_f4, ?x8777 = 01gvxh, legislative_sessions(?x13634, ?x11190), legislative_sessions(?x12796, ?x11190), ?x12796 = 0h6dy, district_represented(?x11190, ?x7468), district_represented(?x11190, ?x3474), district_represented(?x11190, ?x1905), contains(?x1905, ?x14472), contains(?x1905, ?x13670), contains(?x1905, ?x13458), contains(?x1905, ?x1196), state_province_region(?x2327, ?x1905), adjoins(?x1274, ?x1905), adjoins(?x479, ?x1905), adjoins(?x335, ?x1905), adjoins(?x177, ?x1905), adjoins(?x1905, ?x1275), location(?x917, ?x1196), contains(?x1274, ?x3204), category(?x479, ?x134), jurisdiction_of_office(?x1195, ?x479), place_of_birth(?x478, ?x479), citytown(?x2228, ?x479), religion(?x1274, ?x10107), religion(?x1274, ?x2769), religion(?x1274, ?x2591), religion(?x1274, ?x962), religion(?x1274, ?x109), location(?x1461, ?x1274), ?x10107 = 05w5d, institution(?x865, ?x13670), ?x962 = 05sfs, currency(?x13670, ?x2244), ?x7468 = 015jr, ?x134 = 08mbj5d, time_zones(?x14472, ?x2674), ?x3474 = 05j49, jurisdiction_of_office(?x1157, ?x177), ?x2591 = 0631_, district_represented(?x176, ?x177), jurisdiction_of_office(?x900, ?x177), district_represented(?x605, ?x1274), contains(?x177, ?x388), administrative_division(?x7328, ?x1274), ?x13634 = 0l_j_, featured_film_locations(?x1015, ?x479), ?x2769 = 019cr, location(?x932, ?x177), location(?x115, ?x479), taxonomy(?x1274, ?x939), organization(?x5510, ?x13458), ?x5510 = 07xl34, place_of_birth(?x2025, ?x1196), country(?x1274, ?x94), major_field_of_study(?x13458, ?x1668), contains(?x335, ?x322), state_province_region(?x11648, ?x1274), state_province_region(?x166, ?x335), state(?x2850, ?x335), citytown(?x10884, ?x335), major_field_of_study(?x13670, ?x1695), location(?x101, ?x335), student(?x2327, ?x1422), district_represented(?x11189, ?x9370), place_of_birth(?x669, ?x335), district_represented(?x3463, ?x335), institution(?x620, ?x2327), major_field_of_study(?x2327, ?x742), ?x109 = 01lp8, ?x3463 = 02bqmq, adjoins(?x448, ?x177) *> conf = 0.71 ranks of expected_values: 4, 7 EVAL 034_7s district_represented 059ts CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 11.000 7.000 0.857 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 034_7s district_represented 059t8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 11.000 7.000 0.857 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #10290-04f0xq PRED entity: 04f0xq PRED relation: state_province_region PRED expected values: 05fjf => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 45): 01n7q (0.44 #387, 0.37 #510, 0.31 #18), 059rby (0.29 #742, 0.26 #4069, 0.26 #3328), 03v0t (0.12 #1037, 0.09 #1284, 0.08 #1407), 07b_l (0.11 #542, 0.09 #2512, 0.08 #3250), 05kkh (0.11 #617, 0.08 #2, 0.07 #125), 04rrx (0.10 #768, 0.08 #891, 0.08 #1014), 059_c (0.08 #1494, 0.08 #1740, 0.06 #2110), 081yw (0.08 #1045, 0.08 #2400, 0.07 #1907), 01x73 (0.08 #25, 0.07 #148, 0.07 #271), 07z1m (0.08 #22, 0.07 #145, 0.05 #1499) >> Best rule #387 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 0z90c; >> query: (?x7471, 01n7q) <- company(?x4792, ?x7471), company(?x1491, ?x7471), ?x4792 = 05_wyz, contact_category(?x7471, ?x897), ?x1491 = 0krdk >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #196 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 12 *> proper extension: 0300cp; 0168nq; 019rl6; 04fv0k; 060ppp; 0dq23; 01frpd; *> query: (?x7471, 05fjf) <- company(?x4792, ?x7471), company(?x1491, ?x7471), company(?x265, ?x7471), industry(?x7471, ?x13047), ?x265 = 0dq3c, ?x1491 = 0krdk, ?x4792 = 05_wyz *> conf = 0.07 ranks of expected_values: 14 EVAL 04f0xq state_province_region 05fjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 79.000 79.000 0.444 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #10289-0k__z PRED entity: 0k__z PRED relation: school! PRED expected values: 0jmj7 => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 93): 0jmj7 (0.65 #5240, 0.62 #2167, 0.61 #3190), 05m_8 (0.23 #2142, 0.22 #2514, 0.17 #3165), 01ync (0.20 #225, 0.11 #3535, 0.11 #3629), 0jmnl (0.20 #278, 0.06 #371, 0.06 #3254), 01y3c (0.20 #196, 0.06 #3172, 0.06 #2149), 0jm7n (0.20 #267, 0.04 #2220, 0.04 #3243), 0jmfb (0.20 #210, 0.02 #3186, 0.02 #1605), 07l8x (0.18 #345, 0.13 #2577, 0.11 #2205), 01slc (0.15 #3220, 0.14 #3499, 0.13 #3593), 05xvj (0.14 #2599, 0.13 #2227, 0.11 #3535) >> Best rule #5240 for best value: >> intensional similarity = 3 >> extensional distance = 191 >> proper extension: 0fht9f; >> query: (?x8363, 0jmj7) <- school(?x700, ?x8363), school(?x700, ?x4296), contains(?x94, ?x4296) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k__z school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.648 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #10288-027pfg PRED entity: 027pfg PRED relation: film! PRED expected values: 0k269 0hsn_ => 80 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1008): 058frd (0.57 #85120, 0.47 #2076, 0.46 #76816), 03v1w7 (0.47 #2076, 0.46 #76816, 0.44 #47752), 07rd7 (0.47 #2076, 0.46 #76816, 0.44 #47752), 02bh9 (0.47 #2076, 0.46 #76816, 0.44 #47752), 0sz28 (0.14 #2267, 0.14 #190, 0.02 #4342), 0lpjn (0.14 #2551, 0.05 #8777, 0.03 #12928), 0c0k1 (0.14 #3579, 0.05 #16031, 0.03 #30563), 01nm3s (0.14 #684, 0.04 #4836, 0.03 #17289), 06cgy (0.14 #248, 0.04 #4400, 0.03 #6476), 03yj_0n (0.14 #610, 0.04 #4762, 0.02 #64361) >> Best rule #85120 for best value: >> intensional similarity = 4 >> extensional distance = 998 >> proper extension: 03kq98; 01xr2s; 03ln8b; 01b_lz; 0d66j2; 0gj50; 02r5qtm; 02rcwq0; 01vnbh; 0vjr; ... >> query: (?x6932, ?x6086) <- nominated_for(?x6086, ?x6932), place_of_birth(?x6086, ?x13182), nominated_for(?x112, ?x6932), film(?x6086, ?x9452) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #6833 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 67 *> proper extension: 0gtsx8c; *> query: (?x6932, 0k269) <- film_release_region(?x6932, ?x2316), film_release_region(?x6932, ?x1790), film_release_region(?x6932, ?x456), ?x1790 = 01pj7, ?x2316 = 06t2t, ?x456 = 05qhw *> conf = 0.04 ranks of expected_values: 86 EVAL 027pfg film! 0hsn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 43.000 0.573 http://example.org/film/actor/film./film/performance/film EVAL 027pfg film! 0k269 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 80.000 43.000 0.573 http://example.org/film/actor/film./film/performance/film #10287-0d5_f PRED entity: 0d5_f PRED relation: type_of_union PRED expected values: 04ztj => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.79 #103, 0.78 #47, 0.78 #127), 01g63y (0.47 #38, 0.33 #7, 0.33 #5), 01bl8s (0.03 #73, 0.02 #85, 0.02 #57) >> Best rule #103 for best value: >> intensional similarity = 5 >> extensional distance = 134 >> proper extension: 01p7yb; 02r_d4; 06n7h7; 03ldxq; 0d0vj4; 016kjs; 02r34n; 03gr7w; 03k7bd; 01_x6v; ... >> query: (?x4301, 04ztj) <- nationality(?x4301, ?x304), location(?x4301, ?x4627), student(?x4100, ?x4301), profession(?x4301, ?x353), student(?x9110, ?x4301) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d5_f type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.794 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10286-02xc1w4 PRED entity: 02xc1w4 PRED relation: award_winner! PRED expected values: 0gvstc3 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 117): 02jp5r (0.29 #68, 0.18 #208, 0.16 #348), 0bvfqq (0.28 #3501, 0.17 #6024, 0.17 #6023), 0gvstc3 (0.28 #3501, 0.17 #6024, 0.17 #6023), 073h1t (0.24 #26, 0.21 #166, 0.19 #306), 073h9x (0.24 #49, 0.18 #189, 0.16 #329), 09gkdln (0.17 #6024, 0.17 #6023, 0.05 #121), 03nnm4t (0.17 #6024, 0.17 #6023, 0.05 #73), 04n2r9h (0.17 #6024, 0.17 #6023, 0.05 #44), 05c1t6z (0.17 #6024, 0.17 #6023, 0.04 #1135), 0hn821n (0.17 #6024, 0.17 #6023, 0.03 #1250) >> Best rule #68 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 03hpr; >> query: (?x5664, 02jp5r) <- crewmember(?x2189, ?x5664), film_release_region(?x2189, ?x1264), film_distribution_medium(?x2189, ?x81), ?x1264 = 0345h >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3501 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1171 *> proper extension: 0f721s; 06jntd; 0283xx2; *> query: (?x5664, ?x1764) <- award_winner(?x5663, ?x5664), award_winner(?x3845, ?x5664), honored_for(?x1764, ?x5663), nominated_for(?x2156, ?x3845) *> conf = 0.28 ranks of expected_values: 3 EVAL 02xc1w4 award_winner! 0gvstc3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 99.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10285-047vnfs PRED entity: 047vnfs PRED relation: specialization_of PRED expected values: 04_tv => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 047vnfs specialization_of 04_tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/people/profession/specialization_of #10284-0g9wd99 PRED entity: 0g9wd99 PRED relation: award! PRED expected values: 07w21 014dq7 0h25 018zvb => 61 concepts (35 used for prediction) PRED predicted values (max 10 best out of 2958): 01vdrw (0.80 #90899, 0.64 #107734, 0.63 #117831), 0gyx4 (0.67 #14720, 0.31 #18085, 0.20 #4620), 01vs_v8 (0.59 #30876, 0.45 #37609, 0.35 #27509), 0gd5z (0.50 #10760, 0.40 #7394, 0.33 #20858), 0c921 (0.50 #16130, 0.40 #6030, 0.31 #19495), 01cspq (0.50 #15983, 0.40 #5883, 0.31 #19348), 06l6nj (0.50 #16495, 0.40 #6395, 0.31 #19860), 0c12h (0.50 #15286, 0.23 #18651, 0.20 #5186), 05ldnp (0.50 #14362, 0.23 #17727, 0.20 #4262), 02kxbwx (0.50 #13644, 0.23 #17009, 0.20 #3544) >> Best rule #90899 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 0262s1; >> query: (?x11471, ?x10974) <- award(?x5335, ?x11471), influenced_by(?x5335, ?x2994), spouse(?x13793, ?x5335), award_winner(?x11471, ?x10974) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #6827 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 040vk98; 0c_dx; 04hddx; *> query: (?x11471, 07w21) <- award(?x5335, ?x11471), award(?x118, ?x11471), ?x5335 = 013pp3, influenced_by(?x117, ?x118), disciplines_or_subjects(?x11471, ?x5864), gender(?x118, ?x231) *> conf = 0.40 ranks of expected_values: 25, 124, 128, 753 EVAL 0g9wd99 award! 018zvb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 61.000 35.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0g9wd99 award! 0h25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 35.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0g9wd99 award! 014dq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 61.000 35.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0g9wd99 award! 07w21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 61.000 35.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10283-02m77 PRED entity: 02m77 PRED relation: capital! PRED expected values: 025ndl => 235 concepts (212 used for prediction) PRED predicted values (max 10 best out of 119): 07ssc (0.80 #3226, 0.73 #1480, 0.56 #942), 0cdbq (0.22 #1014, 0.11 #879, 0.09 #3970), 0gtzp (0.14 #268, 0.11 #1076, 0.11 #941), 0f8l9c (0.14 #155, 0.11 #963, 0.11 #828), 09c7w0 (0.12 #674, 0.11 #943, 0.11 #808), 02jx1 (0.12 #571, 0.11 #974, 0.10 #1108), 014tss (0.12 #628, 0.11 #1031, 0.10 #1165), 03rt9 (0.12 #552, 0.10 #1089, 0.08 #1627), 0193qj (0.12 #764, 0.08 #1705, 0.06 #2780), 03_3d (0.12 #679, 0.08 #1620, 0.06 #2695) >> Best rule #3226 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0ftn8; >> query: (?x6885, ?x512) <- location(?x1997, ?x6885), country(?x6885, ?x512), capital(?x6401, ?x6885), profession(?x1997, ?x220) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #6359 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 0fn2g; *> query: (?x6885, 025ndl) <- place_of_birth(?x9680, ?x6885), capital(?x6401, ?x6885), religion(?x9680, ?x1985) *> conf = 0.03 ranks of expected_values: 84 EVAL 02m77 capital! 025ndl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 235.000 212.000 0.800 http://example.org/location/country/capital #10282-02vm9nd PRED entity: 02vm9nd PRED relation: ceremony PRED expected values: 0gkxgfq => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 139): 05c1t6z (0.67 #293, 0.56 #710, 0.56 #432), 02q690_ (0.67 #342, 0.52 #481, 0.49 #759), 0gx_st (0.67 #315, 0.46 #454, 0.42 #732), 0gvstc3 (0.58 #312, 0.50 #451, 0.47 #729), 03nnm4t (0.50 #351, 0.48 #490, 0.46 #629), 0hn821n (0.50 #407, 0.31 #546, 0.30 #685), 0bxs_d (0.42 #391, 0.27 #530, 0.26 #669), 07z31v (0.42 #309, 0.23 #448, 0.22 #587), 0gpjbt (0.33 #3922, 0.33 #4200, 0.33 #3783), 0bx6zs (0.33 #403, 0.25 #542, 0.24 #681) >> Best rule #293 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0cqhk0; 09qvc0; 09qv3c; 0cjyzs; 09qs08; 03ccq3s; 09qvf4; 09qrn4; 027gs1_; >> query: (?x2750, 05c1t6z) <- ceremony(?x2750, ?x2751), nominated_for(?x2750, ?x7904), actor(?x7904, ?x665), ?x665 = 02r_d4, award(?x703, ?x2750) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #105 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 04fgzb0; *> query: (?x2750, 0gkxgfq) <- ceremony(?x2750, ?x2751), nominated_for(?x2750, ?x7904), ?x7904 = 0fpxp, award(?x703, ?x2750), profession(?x703, ?x524) *> conf = 0.33 ranks of expected_values: 12 EVAL 02vm9nd ceremony 0gkxgfq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 46.000 46.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony #10281-010t4v PRED entity: 010t4v PRED relation: contains! PRED expected values: 09c7w0 => 149 concepts (102 used for prediction) PRED predicted values (max 10 best out of 348): 09c7w0 (0.79 #8054, 0.75 #84985, 0.74 #85880), 0mmpz (0.33 #655, 0.25 #1549, 0.12 #2443), 059rby (0.30 #82318, 0.15 #4495, 0.11 #43855), 01n7q (0.22 #43913, 0.20 #60906, 0.20 #45702), 07ssc (0.21 #73383, 0.15 #59071, 0.15 #57281), 02jx1 (0.20 #73438, 0.17 #72543, 0.12 #82385), 0d1xx (0.17 #586, 0.12 #1480, 0.06 #2374), 0mmpm (0.17 #733, 0.09 #22366, 0.09 #3578), 04jpl (0.14 #72478, 0.12 #82320, 0.04 #34019), 0kpys (0.12 #44016, 0.12 #12704, 0.10 #16282) >> Best rule #8054 for best value: >> intensional similarity = 5 >> extensional distance = 66 >> proper extension: 0xn7b; >> query: (?x9973, 09c7w0) <- source(?x9973, ?x958), ?x958 = 0jbk9, location(?x9972, ?x9973), actor(?x6706, ?x9972), category(?x9972, ?x134) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 010t4v contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 102.000 0.794 http://example.org/location/location/contains #10280-01g23m PRED entity: 01g23m PRED relation: type_of_union PRED expected values: 01g63y => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 1): 01g63y (0.41 #40, 0.37 #16, 0.36 #79) >> Best rule #40 for best value: >> intensional similarity = 2 >> extensional distance = 129 >> proper extension: 01n7qlf; >> query: (?x4005, 01g63y) <- type_of_union(?x4005, ?x566), celebrity(?x400, ?x4005) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01g23m type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.412 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10279-0cjdk PRED entity: 0cjdk PRED relation: company! PRED expected values: 054fvj => 183 concepts (158 used for prediction) PRED predicted values (max 10 best out of 143): 0fvf9q (0.25 #247, 0.14 #1225, 0.08 #3671), 01w_10 (0.22 #2112, 0.20 #2602, 0.20 #2356), 0frmb1 (0.20 #2598, 0.20 #2352, 0.17 #3333), 01xdf5 (0.14 #1227, 0.10 #2449, 0.10 #2203), 054fvj (0.14 #1413, 0.10 #2635, 0.10 #2389), 0b80__ (0.14 #1317, 0.08 #3763, 0.08 #3519), 0b1f49 (0.14 #1292, 0.08 #3738, 0.07 #4961), 04jspq (0.13 #5514, 0.08 #3558, 0.08 #4781), 07f7jp (0.12 #7079, 0.10 #7568, 0.10 #2675), 0glyyw (0.12 #7015, 0.10 #7504, 0.08 #4079) >> Best rule #247 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 016tt2; >> query: (?x2554, 0fvf9q) <- award_winner(?x5592, ?x2554), state_province_region(?x2554, ?x1227), service_language(?x2554, ?x254), nominated_for(?x2554, ?x2436) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1413 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 05qd_; *> query: (?x2554, 054fvj) <- award_winner(?x5592, ?x2554), state_province_region(?x2554, ?x1227), citytown(?x2554, ?x1523), company(?x8314, ?x2554) *> conf = 0.14 ranks of expected_values: 5 EVAL 0cjdk company! 054fvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 183.000 158.000 0.250 http://example.org/people/person/employment_history./business/employment_tenure/company #10278-06_sc3 PRED entity: 06_sc3 PRED relation: prequel PRED expected values: 0401sg => 77 concepts (53 used for prediction) PRED predicted values (max 10 best out of 69): 080dfr7 (0.17 #530, 0.11 #710, 0.01 #4690), 06_sc3 (0.17 #512, 0.11 #692, 0.01 #3395), 0dr3sl (0.04 #1666, 0.03 #2026, 0.03 #2206), 01cssf (0.03 #1090, 0.03 #1270, 0.02 #1450), 05zlld0 (0.03 #1142, 0.03 #1322, 0.02 #1682), 014lc_ (0.03 #1081, 0.03 #1261, 0.02 #1621), 065dc4 (0.03 #1150, 0.03 #1330, 0.02 #1690), 04cf_l (0.03 #1235, 0.03 #1415, 0.02 #1955), 013q0p (0.03 #1171, 0.03 #1351, 0.02 #1891), 0bmssv (0.03 #1156, 0.03 #1336, 0.02 #1876) >> Best rule #530 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0gtv7pk; 0401sg; 080dfr7; >> query: (?x8234, 080dfr7) <- film(?x3717, ?x8234), film(?x558, ?x8234), film(?x609, ?x8234), ?x558 = 0151ns, award_nominee(?x3717, ?x931), film_distribution_medium(?x8234, ?x81) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06_sc3 prequel 0401sg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 53.000 0.167 http://example.org/film/film/prequel #10277-0vfs8 PRED entity: 0vfs8 PRED relation: source PRED expected values: 0jbk9 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.82 #10, 0.81 #21, 0.75 #50) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 204 >> proper extension: 029jpy; 0r04p; 0bxbb; 0l1pj; 034lk7; 0r0ss; 0fttg; 0jpy_; 0135p7; 01p726; ... >> query: (?x8115, 0jbk9) <- contains(?x94, ?x8115), time_zones(?x8115, ?x2674), ?x94 = 09c7w0, place_of_birth(?x8149, ?x8115) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0vfs8 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.816 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #10276-02x73k6 PRED entity: 02x73k6 PRED relation: nominated_for PRED expected values: 01jc6q 0blpg 0h6r5 09q23x 01rwpj 0k4p0 0sxlb => 42 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1488): 026p4q7 (0.78 #6429, 0.21 #9475, 0.17 #10997), 04b2qn (0.76 #7614, 0.69 #12187, 0.68 #13712), 02b6n9 (0.76 #7614, 0.69 #12187, 0.68 #13712), 0b4lkx (0.76 #7614, 0.69 #12187, 0.68 #13712), 01jc6q (0.76 #7614, 0.69 #12187, 0.68 #13712), 05rfst (0.76 #7614, 0.69 #12187, 0.68 #13712), 0g4vmj8 (0.76 #7614, 0.69 #12187, 0.68 #13712), 03c_cxn (0.76 #7614, 0.69 #12187, 0.68 #13712), 0dr_9t7 (0.76 #7614, 0.69 #12187, 0.68 #13712), 024lt6 (0.76 #7614, 0.69 #12187, 0.68 #13712) >> Best rule #6429 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 099c8n; >> query: (?x1033, 026p4q7) <- award(?x197, ?x1033), nominated_for(?x1033, ?x6176), ?x6176 = 0gmgwnv >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #7614 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 099c8n; *> query: (?x1033, ?x197) <- award(?x197, ?x1033), nominated_for(?x1033, ?x6176), ?x6176 = 0gmgwnv *> conf = 0.76 ranks of expected_values: 5, 71, 74, 82, 111, 165, 991 EVAL 02x73k6 nominated_for 0sxlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 42.000 14.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x73k6 nominated_for 0k4p0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 42.000 14.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x73k6 nominated_for 01rwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 42.000 14.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x73k6 nominated_for 09q23x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 42.000 14.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x73k6 nominated_for 0h6r5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 42.000 14.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x73k6 nominated_for 0blpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 14.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x73k6 nominated_for 01jc6q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 42.000 14.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10275-01jzyf PRED entity: 01jzyf PRED relation: film! PRED expected values: 06lht1 => 127 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1181): 02kxbx3 (0.46 #20776, 0.40 #135090, 0.40 #31168), 04qvl7 (0.46 #20776, 0.40 #135090, 0.40 #31168), 01fh9 (0.46 #20776, 0.39 #126780, 0.38 #126779), 02kxbwx (0.40 #135090, 0.40 #31168, 0.39 #68581), 01wbg84 (0.40 #4201, 0.33 #6278, 0.29 #8356), 01swck (0.40 #4952, 0.25 #798, 0.17 #7029), 01nm3s (0.40 #4843, 0.25 #689, 0.17 #6920), 03n_7k (0.29 #8706, 0.17 #6628, 0.02 #33643), 02s529 (0.25 #1960, 0.20 #6114, 0.17 #8191), 01jmv8 (0.25 #1499, 0.20 #5653, 0.17 #7730) >> Best rule #20776 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 0dnvn3; 02vrgnr; >> query: (?x3706, ?x185) <- film(?x826, ?x3706), award_winner(?x3706, ?x185), film_release_distribution_medium(?x3706, ?x81), honored_for(?x3706, ?x898) >> conf = 0.46 => this is the best rule for 3 predicted values *> Best rule #17507 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 076zy_g; *> query: (?x3706, 06lht1) <- films(?x12672, ?x3706), film(?x396, ?x3706), nominated_for(?x2393, ?x3706), ?x2393 = 02x258x *> conf = 0.03 ranks of expected_values: 337 EVAL 01jzyf film! 06lht1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 127.000 65.000 0.462 http://example.org/film/actor/film./film/performance/film #10274-0g8fs PRED entity: 0g8fs PRED relation: student PRED expected values: 02drd3 => 123 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1612): 02lt8 (0.25 #2768, 0.15 #6952, 0.11 #11135), 0d3k14 (0.25 #1854, 0.08 #8130, 0.06 #20681), 0h0wc (0.25 #392, 0.08 #6668, 0.04 #15035), 0453t (0.25 #338, 0.08 #6614, 0.04 #14981), 02g3w (0.25 #1899, 0.08 #8175, 0.04 #16542), 087qxp (0.25 #1313, 0.08 #7589, 0.04 #15956), 02q4mt (0.25 #1949, 0.08 #8225, 0.04 #16592), 08ff1k (0.25 #942, 0.08 #7218, 0.04 #15585), 03pp73 (0.25 #888, 0.08 #7164, 0.04 #15531), 05y5fw (0.25 #872, 0.08 #7148, 0.04 #15515) >> Best rule #2768 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 07wrz; 07tds; 0g8rj; 0hsb3; 07x4c; 06thjt; >> query: (?x9691, 02lt8) <- colors(?x9691, ?x332), contains(?x94, ?x9691), major_field_of_study(?x9691, ?x1154), organizations_founded(?x9765, ?x9691) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #35476 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 01hr11; *> query: (?x9691, 02drd3) <- contains(?x94, ?x9691), major_field_of_study(?x9691, ?x1154), ?x1154 = 02lp1, ?x94 = 09c7w0 *> conf = 0.01 ranks of expected_values: 738 EVAL 0g8fs student 02drd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 123.000 87.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #10273-01rxyb PRED entity: 01rxyb PRED relation: film_crew_role PRED expected values: 02r96rf => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 33): 02r96rf (0.70 #819, 0.69 #887, 0.69 #785), 01vx2h (0.46 #894, 0.36 #248, 0.36 #792), 0215hd (0.33 #51, 0.23 #357, 0.18 #289), 0d2b38 (0.33 #58, 0.14 #228, 0.13 #908), 089g0h (0.22 #52, 0.18 #358, 0.12 #1107), 02rh1dz (0.22 #43, 0.18 #893, 0.17 #9), 02ynfr (0.21 #796, 0.19 #898, 0.18 #82), 01xy5l_ (0.18 #216, 0.16 #250, 0.13 #114), 015h31 (0.17 #8, 0.12 #212, 0.11 #42), 094hwz (0.17 #13, 0.11 #47, 0.09 #251) >> Best rule #819 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 02vqhv0; 01q2nx; 0bxsk; 056xkh; 047p798; 0jqzt; 09v8clw; >> query: (?x4375, 02r96rf) <- film_format(?x4375, ?x909), language(?x4375, ?x254), film_crew_role(?x4375, ?x137) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rxyb film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.701 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10272-06m_5 PRED entity: 06m_5 PRED relation: form_of_government PRED expected values: 06cx9 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 5): 06cx9 (0.43 #376, 0.42 #306, 0.41 #286), 01d9r3 (0.38 #289, 0.38 #309, 0.37 #379), 018wl5 (0.38 #52, 0.36 #7, 0.34 #367), 01q20 (0.35 #43, 0.33 #18, 0.32 #458), 026wp (0.09 #190, 0.08 #165, 0.08 #5) >> Best rule #376 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 084n_; 020p1; >> query: (?x8420, 06cx9) <- organization(?x8420, ?x127), contains(?x6304, ?x8420), form_of_government(?x8420, ?x4763) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06m_5 form_of_government 06cx9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.428 http://example.org/location/country/form_of_government #10271-02ynfr PRED entity: 02ynfr PRED relation: profession! PRED expected values: 0ft7sr => 57 concepts (32 used for prediction) PRED predicted values (max 10 best out of 4109): 06chf (0.60 #13543, 0.33 #60168, 0.31 #68649), 026dx (0.56 #69335, 0.42 #60854, 0.40 #14229), 0dpqk (0.50 #69434, 0.50 #60953, 0.40 #14328), 021yw7 (0.50 #60450, 0.44 #68931, 0.40 #13825), 01_x6v (0.50 #60021, 0.44 #68502, 0.40 #13396), 05wm88 (0.50 #63160, 0.44 #71641, 0.40 #16535), 02b29 (0.50 #61582, 0.44 #70063, 0.40 #14957), 015pxr (0.50 #59945, 0.44 #68426, 0.40 #13320), 06cv1 (0.50 #67944, 0.42 #59463, 0.23 #93386), 05vtbl (0.50 #62694, 0.40 #16069, 0.38 #71175) >> Best rule #13543 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 02jknp; 02krf9; >> query: (?x3197, 06chf) <- profession(?x11358, ?x3197), profession(?x5532, ?x3197), profession(?x2400, ?x3197), award_winner(?x9703, ?x2400), award_nominee(?x1145, ?x2400), film_art_direction_by(?x6077, ?x5532), award_nominee(?x2507, ?x5532), ?x11358 = 026xt5c >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #13204 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 02jknp; 02krf9; *> query: (?x3197, 0ft7sr) <- profession(?x11358, ?x3197), profession(?x5532, ?x3197), profession(?x2400, ?x3197), award_winner(?x9703, ?x2400), award_nominee(?x1145, ?x2400), film_art_direction_by(?x6077, ?x5532), award_nominee(?x2507, ?x5532), ?x11358 = 026xt5c *> conf = 0.20 ranks of expected_values: 1149 EVAL 02ynfr profession! 0ft7sr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 32.000 0.600 http://example.org/people/person/profession #10270-07lmxq PRED entity: 07lmxq PRED relation: award_nominee! PRED expected values: 0g8st4 => 81 concepts (21 used for prediction) PRED predicted values (max 10 best out of 517): 0g8st4 (0.81 #25538, 0.81 #48762, 0.81 #37148), 0fthdk (0.81 #25538, 0.81 #48762, 0.81 #37148), 05th8t (0.81 #25538, 0.81 #48762, 0.81 #37148), 07lmxq (0.45 #4746, 0.16 #48763, 0.14 #41794), 02bkdn (0.32 #5032, 0.16 #48763, 0.14 #41794), 02qgyv (0.32 #5135, 0.16 #48763, 0.14 #39472), 0h0wc (0.32 #5191, 0.16 #48763, 0.14 #39472), 01_p6t (0.32 #5986, 0.16 #48763, 0.14 #39472), 05dbf (0.27 #5116, 0.02 #26012, 0.02 #28334), 01gq0b (0.27 #5036, 0.01 #23610, 0.01 #28254) >> Best rule #25538 for best value: >> intensional similarity = 3 >> extensional distance = 1120 >> proper extension: 04nw9; 06449; 011hdn; 03dbww; >> query: (?x539, ?x540) <- film(?x539, ?x2878), award_nominee(?x539, ?x540), gender(?x539, ?x231) >> conf = 0.81 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 07lmxq award_nominee! 0g8st4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 21.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10269-02h9_l PRED entity: 02h9_l PRED relation: award_winner! PRED expected values: 01c427 01cw7s => 135 concepts (102 used for prediction) PRED predicted values (max 10 best out of 293): 01cky2 (0.57 #1916, 0.18 #24368, 0.15 #7955), 03qbnj (0.43 #6038, 0.38 #37985, 0.38 #3880), 01c4_6 (0.43 #6038, 0.38 #37985, 0.38 #3880), 031b3h (0.43 #6038, 0.38 #3880, 0.36 #5606), 02f764 (0.43 #6038, 0.38 #3880, 0.35 #16839), 01cw51 (0.43 #1863, 0.15 #7902, 0.14 #11356), 02f73p (0.33 #185, 0.20 #616, 0.12 #2771), 02f705 (0.33 #152, 0.20 #583, 0.12 #2738), 02f72_ (0.29 #1951, 0.25 #7990, 0.17 #11444), 01by1l (0.29 #1837, 0.23 #16953, 0.17 #21271) >> Best rule #1916 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 015bwt; >> query: (?x10148, 01cky2) <- artists(?x1952, ?x10148), artists(?x671, ?x10148), award_winner(?x4796, ?x10148), ?x671 = 064t9, ?x1952 = 021_z5 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #5258 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 012gq6; 03xx9l; *> query: (?x10148, 01c427) <- person(?x3480, ?x10148), type_of_union(?x10148, ?x566), award(?x10148, ?x1565), ceremony(?x1565, ?x1480), ?x1480 = 01c6qp *> conf = 0.15 ranks of expected_values: 21, 91 EVAL 02h9_l award_winner! 01cw7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 135.000 102.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02h9_l award_winner! 01c427 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 135.000 102.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner #10268-03z509 PRED entity: 03z509 PRED relation: award PRED expected values: 0bdx29 => 106 concepts (74 used for prediction) PRED predicted values (max 10 best out of 234): 0bsjcw (0.73 #404, 0.73 #403, 0.72 #6842), 0ck27z (0.49 #2506, 0.47 #1300, 0.28 #6934), 0bdwft (0.45 #472, 0.27 #1678, 0.14 #3691), 09sb52 (0.40 #3260, 0.29 #4869, 0.29 #2858), 0gqyl (0.38 #1715, 0.18 #509, 0.18 #3728), 0gqwc (0.36 #478, 0.29 #1684, 0.19 #6513), 0cqgl9 (0.36 #595, 0.29 #1801, 0.12 #191), 02z0dfh (0.29 #1685, 0.18 #479, 0.09 #3698), 03qgjwc (0.27 #1792, 0.18 #586, 0.12 #182), 05zr6wv (0.27 #822, 0.12 #4845, 0.11 #11670) >> Best rule #404 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 078jt5; 01my4f; >> query: (?x4380, ?x375) <- nominated_for(?x4380, ?x6706), ?x6706 = 02qkq0, award_winner(?x375, ?x4380), award(?x374, ?x375) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1316 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 02bkdn; 0347xl; 040t74; 02jtjz; 02xv8m; 04bcb1; 02vqpx8; 03wbzp; 05cl2w; 07qcbw; ... *> query: (?x4380, 0bdx29) <- nominated_for(?x4380, ?x6706), place_of_birth(?x4380, ?x739), actor(?x6706, ?x8431), ?x8431 = 022yb4 *> conf = 0.17 ranks of expected_values: 25 EVAL 03z509 award 0bdx29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 106.000 74.000 0.733 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10267-05_k56 PRED entity: 05_k56 PRED relation: executive_produced_by! PRED expected values: 0407yfx => 112 concepts (78 used for prediction) PRED predicted values (max 10 best out of 231): 01jrbb (0.43 #530, 0.42 #2121, 0.29 #1060), 0dyb1 (0.43 #530, 0.42 #2121, 0.29 #1060), 02c7k4 (0.43 #530, 0.42 #2121, 0.29 #1060), 01xdxy (0.23 #1061, 0.08 #5835, 0.06 #4243), 03whyr (0.23 #1061, 0.06 #11667, 0.06 #12197), 02fj8n (0.10 #405, 0.08 #1996, 0.03 #935), 0407yfx (0.10 #114, 0.07 #644, 0.05 #1175), 03t79f (0.10 #307, 0.03 #837, 0.03 #1898), 09gdh6k (0.07 #939, 0.05 #2000, 0.05 #409), 01bn3l (0.07 #958, 0.05 #2019, 0.05 #428) >> Best rule #530 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 079vf; >> query: (?x1052, ?x2893) <- story_by(?x2893, ?x1052), executive_produced_by(?x1080, ?x1052), award_winner(?x1052, ?x3456) >> conf = 0.43 => this is the best rule for 3 predicted values *> Best rule #114 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 079vf; *> query: (?x1052, 0407yfx) <- story_by(?x2893, ?x1052), executive_produced_by(?x1080, ?x1052), award_winner(?x1052, ?x3456) *> conf = 0.10 ranks of expected_values: 7 EVAL 05_k56 executive_produced_by! 0407yfx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 112.000 78.000 0.435 http://example.org/film/film/executive_produced_by #10266-02qtywd PRED entity: 02qtywd PRED relation: award_winner! PRED expected values: 03ncb2 => 78 concepts (60 used for prediction) PRED predicted values (max 10 best out of 242): 025mb9 (0.37 #15452, 0.37 #9013, 0.36 #2146), 02nhxf (0.37 #15452, 0.37 #9013, 0.36 #2146), 01bgqh (0.16 #1759, 0.12 #9443, 0.10 #22321), 01c99j (0.12 #9443, 0.10 #22321, 0.09 #24041), 0l8z1 (0.12 #9443, 0.10 #22321, 0.09 #24041), 025m98 (0.12 #9443, 0.10 #22321, 0.09 #24041), 02f72_ (0.12 #9443, 0.10 #22321, 0.09 #24041), 02gdjb (0.12 #9443, 0.10 #22321, 0.09 #24041), 02f73b (0.12 #9443, 0.10 #22321, 0.09 #24041), 02f72n (0.12 #9443, 0.10 #22321, 0.09 #24041) >> Best rule #15452 for best value: >> intensional similarity = 2 >> extensional distance = 1462 >> proper extension: 07mvp; 07k2d; >> query: (?x11533, ?x1827) <- award_winner(?x217, ?x11533), award(?x11533, ?x1827) >> conf = 0.37 => this is the best rule for 2 predicted values *> Best rule #6867 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 785 *> proper extension: 03d9d6; 09lwrt; 01w5n51; 0187x8; 016lmg; 04qzm; *> query: (?x11533, ?x724) <- award_nominee(?x1181, ?x11533), artists(?x671, ?x1181), award(?x1181, ?x724) *> conf = 0.08 ranks of expected_values: 34 EVAL 02qtywd award_winner! 03ncb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 78.000 60.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #10265-01zkxv PRED entity: 01zkxv PRED relation: award PRED expected values: 058bzgm => 132 concepts (113 used for prediction) PRED predicted values (max 10 best out of 297): 040vk98 (0.72 #32744, 0.71 #37537, 0.71 #2026), 0265vt (0.62 #2319, 0.20 #322, 0.19 #20364), 02664f (0.46 #2214, 0.40 #217, 0.16 #15175), 0265wl (0.46 #2233, 0.20 #236, 0.16 #15175), 0262zm (0.42 #2080, 0.40 #83, 0.16 #15175), 058bzgm (0.40 #369, 0.22 #1997, 0.20 #22361), 05x2s (0.40 #377, 0.16 #15175, 0.15 #17171), 01by1l (0.38 #11291, 0.18 #3707, 0.14 #511), 09sb52 (0.30 #8425, 0.28 #9224, 0.26 #31187), 01bgqh (0.29 #442, 0.25 #841, 0.21 #3638) >> Best rule #32744 for best value: >> intensional similarity = 3 >> extensional distance = 1561 >> proper extension: 0kk9v; 05v1sb; 02fgm7; 025vwmy; >> query: (?x576, ?x8880) <- award_nominee(?x576, ?x12009), award_winner(?x8880, ?x576), award(?x476, ?x8880) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #369 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0821j; *> query: (?x576, 058bzgm) <- award_nominee(?x576, ?x12009), award(?x576, ?x11263), ?x11263 = 01tgwv, influenced_by(?x576, ?x1287) *> conf = 0.40 ranks of expected_values: 6 EVAL 01zkxv award 058bzgm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 132.000 113.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10264-0jgk3 PRED entity: 0jgk3 PRED relation: source PRED expected values: 0jbk9 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #62, 0.92 #85, 0.92 #22) >> Best rule #62 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 0mx4_; 0mw93; 0m7fm; 0n5fl; 0fr59; 0mxcf; 0mx6c; 0m2lt; 0mlyw; 0p0cw; ... >> query: (?x8219, 0jbk9) <- adjoins(?x8219, ?x9290), currency(?x8219, ?x170), ?x170 = 09nqf, county(?x8127, ?x9290) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgk3 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.940 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #10263-03d9d6 PRED entity: 03d9d6 PRED relation: group! PRED expected values: 02hnl => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 68): 02hnl (0.78 #706, 0.77 #281, 0.76 #1047), 01vj9c (0.27 #1117, 0.23 #1032, 0.22 #436), 0l14qv (0.25 #685, 0.23 #1111, 0.22 #1026), 03qjg (0.23 #300, 0.22 #1151, 0.22 #470), 04rzd (0.16 #709, 0.12 #1135, 0.11 #1050), 06ncr (0.14 #1142, 0.14 #716, 0.12 #1057), 013y1f (0.14 #449, 0.13 #1130, 0.12 #1045), 07gql (0.14 #459, 0.07 #1055, 0.07 #289), 07c6l (0.14 #433, 0.07 #1029, 0.06 #1114), 0l14j_ (0.14 #729, 0.11 #1155, 0.11 #1070) >> Best rule #706 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 07qnf; 03xhj6; 018gm9; 06nv27; 02vgh; 01kcms4; 06gcn; 08w4pm; 02cw1m; 0fb2l; >> query: (?x5618, 02hnl) <- group(?x227, ?x5618), artists(?x3061, ?x5618), ?x3061 = 05bt6j >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03d9d6 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.784 http://example.org/music/performance_role/regular_performances./music/group_membership/group #10262-0gj8nq2 PRED entity: 0gj8nq2 PRED relation: genre PRED expected values: 01hmnh => 124 concepts (71 used for prediction) PRED predicted values (max 10 best out of 101): 07s9rl0 (0.92 #6986, 0.80 #5781, 0.79 #4939), 082gq (0.75 #2192, 0.74 #1590, 0.50 #390), 03k9fj (0.58 #7964, 0.50 #851, 0.42 #611), 0lsxr (0.51 #3255, 0.38 #2412, 0.37 #1929), 04xvlr (0.50 #362, 0.33 #3491, 0.28 #2164), 04xvh5 (0.50 #394, 0.21 #1594, 0.21 #994), 06n90 (0.42 #612, 0.39 #2657, 0.39 #2897), 05p553 (0.38 #4460, 0.36 #844, 0.33 #6868), 06l3bl (0.38 #398, 0.17 #2200, 0.14 #1598), 01hmnh (0.36 #977, 0.32 #3868, 0.29 #2902) >> Best rule #6986 for best value: >> intensional similarity = 6 >> extensional distance = 337 >> proper extension: 0kb57; 06nr2h; 0m_h6; >> query: (?x3377, 07s9rl0) <- film(?x574, ?x3377), genre(?x3377, ?x571), films(?x11988, ?x3377), genre(?x485, ?x571), ?x485 = 0ds11z, titles(?x571, ?x708) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #977 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 12 *> proper extension: 03_wm6; *> query: (?x3377, 01hmnh) <- film_crew_role(?x3377, ?x2095), genre(?x3377, ?x812), genre(?x3377, ?x571), currency(?x3377, ?x170), country(?x3377, ?x94), ?x571 = 03npn, ?x2095 = 0dxtw, ?x812 = 01jfsb *> conf = 0.36 ranks of expected_values: 10 EVAL 0gj8nq2 genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 124.000 71.000 0.923 http://example.org/film/film/genre #10261-0175yg PRED entity: 0175yg PRED relation: parent_genre PRED expected values: 0155w => 49 concepts (41 used for prediction) PRED predicted values (max 10 best out of 160): 06by7 (0.59 #2005, 0.58 #1341, 0.58 #1839), 02w4v (0.50 #528, 0.38 #692, 0.33 #31), 0gywn (0.50 #372, 0.25 #205, 0.18 #1324), 0155w (0.44 #2487, 0.26 #5324, 0.21 #3156), 017371 (0.33 #107, 0.25 #604, 0.12 #768), 0283d (0.25 #401, 0.03 #3394, 0.03 #2226), 016jny (0.24 #3657, 0.10 #1656, 0.09 #1823), 0xhtw (0.21 #3156, 0.13 #4987, 0.12 #5155), 03lty (0.18 #5008, 0.15 #4840, 0.15 #6345), 03_d0 (0.18 #1324, 0.14 #6159, 0.11 #5501) >> Best rule #2005 for best value: >> intensional similarity = 7 >> extensional distance = 25 >> proper extension: 0781g; >> query: (?x12114, 06by7) <- artists(?x12114, ?x5385), artists(?x12114, ?x5141), artists(?x7440, ?x5385), ?x7440 = 0155w, artist(?x2190, ?x5385), award(?x5141, ?x4912), group(?x227, ?x5385) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #2487 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 42 *> proper extension: 01skxk; 04_sqm; *> query: (?x12114, ?x7440) <- artists(?x12114, ?x5385), artists(?x7440, ?x5385), artists(?x7329, ?x5385), artists(?x7440, ?x5312), artists(?x7440, ?x5140), ?x5140 = 015xp4, ?x5312 = 094xh, group(?x227, ?x5385), ?x7329 = 016jny *> conf = 0.44 ranks of expected_values: 4 EVAL 0175yg parent_genre 0155w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 49.000 41.000 0.593 http://example.org/music/genre/parent_genre #10260-0cz_ym PRED entity: 0cz_ym PRED relation: nominated_for! PRED expected values: 0gq_v => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 202): 0gq9h (0.43 #2370, 0.41 #5604, 0.40 #6066), 0gs9p (0.36 #5606, 0.36 #6068, 0.35 #5375), 0gq_v (0.32 #1405, 0.31 #2329, 0.30 #5563), 0k611 (0.31 #5615, 0.30 #2381, 0.30 #6077), 0gr0m (0.29 #2367, 0.27 #1443, 0.25 #5601), 0gqy2 (0.28 #2427, 0.26 #5661, 0.25 #6123), 0gs96 (0.27 #1473, 0.24 #2397, 0.22 #5631), 0p9sw (0.27 #2330, 0.24 #5564, 0.23 #6026), 04dn09n (0.27 #2344, 0.25 #5578, 0.25 #5347), 0l8z1 (0.27 #2361, 0.23 #5595, 0.22 #2823) >> Best rule #2370 for best value: >> intensional similarity = 3 >> extensional distance = 280 >> proper extension: 075cph; >> query: (?x1877, 0gq9h) <- honored_for(?x762, ?x1877), nominated_for(?x100, ?x1877), production_companies(?x1877, ?x1104) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1405 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 192 *> proper extension: 072r5v; *> query: (?x1877, 0gq_v) <- nominated_for(?x100, ?x1877), genre(?x1877, ?x162), ?x162 = 04xvlr *> conf = 0.32 ranks of expected_values: 3 EVAL 0cz_ym nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 96.000 0.426 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10259-046488 PRED entity: 046488 PRED relation: nominated_for! PRED expected values: 018db8 => 95 concepts (34 used for prediction) PRED predicted values (max 10 best out of 732): 03xsby (0.16 #4674, 0.15 #2337, 0.12 #32719), 06g2d1 (0.15 #77127, 0.13 #25707, 0.11 #58432), 02lhm2 (0.15 #77127, 0.13 #25707, 0.11 #58432), 0hvb2 (0.15 #77127, 0.13 #25707, 0.11 #58432), 05vsxz (0.15 #77127, 0.13 #25707, 0.11 #58432), 01vw37m (0.15 #77127, 0.13 #25707, 0.11 #58432), 01ksr1 (0.15 #77127, 0.13 #25707, 0.11 #58432), 0b_dy (0.15 #77127, 0.13 #25707, 0.11 #58432), 026r8q (0.15 #77127, 0.13 #25707, 0.11 #58432), 05yh_t (0.15 #77127, 0.13 #25707, 0.11 #58432) >> Best rule #4674 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 0ds3t5x; 095zlp; 01h7bb; 0bth54; 017gl1; 04vr_f; 0c0nhgv; 011yqc; 02rv_dz; 072x7s; ... >> query: (?x4993, ?x1850) <- film_release_region(?x4993, ?x94), production_companies(?x4993, ?x1850), nominated_for(?x1162, ?x4993), ?x1162 = 099c8n >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #25707 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 176 *> proper extension: 05h95s; 05fgr_; 07s8z_l; *> query: (?x4993, ?x269) <- category(?x4993, ?x134), titles(?x53, ?x4993), award_winner(?x4993, ?x1850), award_nominee(?x1850, ?x269) *> conf = 0.13 ranks of expected_values: 58 EVAL 046488 nominated_for! 018db8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 95.000 34.000 0.155 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10258-035yn8 PRED entity: 035yn8 PRED relation: crewmember PRED expected values: 021yc7p => 108 concepts (77 used for prediction) PRED predicted values (max 10 best out of 40): 0c94fn (0.09 #249, 0.06 #105, 0.04 #199), 0b6mgp_ (0.07 #22, 0.04 #116, 0.04 #402), 09dvgb8 (0.07 #34, 0.02 #128, 0.02 #175), 0284n42 (0.07 #718, 0.05 #1148, 0.04 #814), 02xc1w4 (0.06 #121, 0.06 #168, 0.05 #265), 092ys_y (0.05 #208, 0.04 #114, 0.04 #258), 06rnl9 (0.05 #204, 0.04 #16, 0.04 #254), 04ktcgn (0.05 #345, 0.04 #678, 0.04 #12), 05bm4sm (0.05 #312, 0.02 #645, 0.02 #406), 02q9kqf (0.04 #236, 0.03 #2587, 0.03 #2057) >> Best rule #249 for best value: >> intensional similarity = 5 >> extensional distance = 55 >> proper extension: 0crh5_f; 02825cv; 04z_3pm; 07ghq; 02wtp6; >> query: (?x1744, 0c94fn) <- film_release_region(?x1744, ?x4743), film_release_region(?x1744, ?x550), category(?x1744, ?x134), ?x550 = 05v8c, nationality(?x2724, ?x4743) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 0m_mm; 0k5g9; 0kb07; 0gy4k; *> query: (?x1744, 021yc7p) <- nominated_for(?x484, ?x1744), film_release_region(?x1744, ?x87), ?x87 = 05r4w, ?x484 = 0gq_v *> conf = 0.04 ranks of expected_values: 14 EVAL 035yn8 crewmember 021yc7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 108.000 77.000 0.088 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #10257-07dfk PRED entity: 07dfk PRED relation: state_province_region! PRED expected values: 09k0h5 => 203 concepts (158 used for prediction) PRED predicted values (max 10 best out of 779): 03pnvq (0.43 #21681, 0.43 #25420, 0.13 #3738), 02vnb_ (0.43 #21681, 0.43 #25420), 07gqbk (0.43 #21681, 0.43 #25420), 06q07 (0.23 #103925, 0.13 #3738, 0.09 #11964), 01bfjy (0.23 #103925, 0.13 #3738, 0.09 #11964), 081g_l (0.23 #103925, 0.13 #3738, 0.09 #11964), 03_c8p (0.23 #103925, 0.13 #3738, 0.09 #11964), 01qckn (0.23 #103925, 0.13 #3738, 0.09 #11964), 05b0f7 (0.23 #103925, 0.13 #3738, 0.09 #11964), 02b07b (0.23 #103925, 0.13 #3738, 0.09 #11964) >> Best rule #21681 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0dyjz; 0fxrk; 0htx8; 0d9rp; >> query: (?x9559, ?x3636) <- contains(?x252, ?x9559), administrative_parent(?x5076, ?x9559), citytown(?x3636, ?x5076) >> conf = 0.43 => this is the best rule for 3 predicted values *> Best rule #103925 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 259 *> proper extension: 0m2gk; 0nh57; 0cc1v; 0nt4s; 0f4zv; *> query: (?x9559, ?x4079) <- contains(?x9559, ?x8951), citytown(?x4079, ?x8951) *> conf = 0.23 ranks of expected_values: 13 EVAL 07dfk state_province_region! 09k0h5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 203.000 158.000 0.428 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #10256-02kdv5l PRED entity: 02kdv5l PRED relation: genre! PRED expected values: 01gc7 01hr1 01ln5z 0bth54 0c40vxk 04tc1g 0crfwmx 08hmch 0k2sk 053rxgm 02qrv7 03fts 05p3738 02qhqz4 040b5k 0crh5_f 0j43swk 032016 0bmc4cm 0jvt9 02rrfzf 0f4yh 03r0g9 0435vm 05c9zr 0d61px 01rxyb 07k8rt4 09fn1w 01f8hf 01jwxx 048rn 0bc1yhb 01f6x7 0glqh5_ 0dh8v4 01bl7g 01kf5lf 0gw7p 05qbbfb 0g5pvv 0287477 03n3gl 026hxwx 04y9mm8 06rzwx 03y0pn 07xvf 0pd64 048tv9 0315rp 0hhggmy 025twgf 0gy0l_ 05567m 02_nsc 0dcz8_ 085wqm 02qlp4 0fztbq => 49 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1434): 02q3fdr (0.67 #19219, 0.38 #31976, 0.33 #6457), 0564x (0.67 #19752, 0.38 #32509, 0.33 #6990), 07nt8p (0.62 #30040, 0.50 #10190, 0.38 #31458), 048scx (0.60 #15711, 0.57 #25636, 0.50 #8622), 05p3738 (0.60 #15787, 0.50 #10115, 0.50 #8698), 0pd6l (0.60 #16101, 0.50 #9012, 0.43 #26026), 0f4k49 (0.60 #16227, 0.50 #9138, 0.33 #6304), 0bx0l (0.60 #15856, 0.50 #8767, 0.33 #5933), 0gmgwnv (0.60 #16431, 0.50 #9342, 0.33 #5092), 0dx8gj (0.57 #26010, 0.50 #23175, 0.50 #8996) >> Best rule #19219 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 03q4nz; 0hcr; 0jxy; >> query: (?x225, 02q3fdr) <- genre(?x7975, ?x225), genre(?x7554, ?x225), genre(?x6839, ?x225), genre(?x4089, ?x225), genre(?x1707, ?x225), ?x6839 = 0dr1c2, film_release_region(?x1707, ?x94), film(?x2373, ?x1707), nominated_for(?x102, ?x4089), award(?x7554, ?x77), ?x94 = 09c7w0, film_crew_role(?x1707, ?x137), music(?x7975, ?x6907) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #15787 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 02p0szs; *> query: (?x225, 05p3738) <- genre(?x7265, ?x225), genre(?x6798, ?x225), genre(?x4038, ?x225), genre(?x2326, ?x225), genre(?x1185, ?x225), produced_by(?x4038, ?x10715), nominated_for(?x556, ?x1185), cinematography(?x6798, ?x7249), film_release_region(?x1185, ?x87), film(?x525, ?x2326), ?x7265 = 04tng0 *> conf = 0.60 ranks of expected_values: 5, 11, 13, 14, 43, 44, 132, 133, 137, 141, 145, 157, 162, 212, 225, 239, 240, 279, 290, 296, 303, 304, 364, 365, 371, 377, 378, 397, 400, 401, 411, 413, 414, 417, 428, 430, 441, 470, 480, 538, 641, 649, 684, 707, 708, 713, 825, 835, 838, 846, 912, 1039, 1075, 1083, 1089, 1105, 1216, 1302, 1354, 1380 EVAL 02kdv5l genre! 0fztbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 02qlp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 085wqm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0dcz8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 02_nsc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 05567m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0gy0l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 025twgf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0hhggmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0315rp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 048tv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0pd64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 07xvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 03y0pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 06rzwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 04y9mm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 026hxwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 03n3gl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0287477 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0g5pvv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 05qbbfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0gw7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 01kf5lf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 01bl7g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0dh8v4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0glqh5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 01f6x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0bc1yhb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 048rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 01jwxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 01f8hf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 09fn1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 07k8rt4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 01rxyb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0d61px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 05c9zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0435vm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 03r0g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0f4yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 02rrfzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0jvt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0bmc4cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 032016 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0j43swk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0crh5_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 040b5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 02qhqz4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 05p3738 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 03fts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 02qrv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 053rxgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0k2sk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 08hmch CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0crfwmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 04tc1g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0c40vxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 0bth54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 01ln5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 01hr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 49.000 29.000 0.667 http://example.org/film/film/genre EVAL 02kdv5l genre! 01gc7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 49.000 29.000 0.667 http://example.org/film/film/genre #10255-04s934 PRED entity: 04s934 PRED relation: student PRED expected values: 0kp2_ => 139 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1619): 07s93v (0.20 #2340, 0.17 #4432, 0.14 #6524), 0mb0 (0.20 #3885, 0.17 #5977, 0.14 #8069), 01wz01 (0.20 #2785, 0.17 #4877, 0.14 #6969), 0205dx (0.20 #2922, 0.17 #5014, 0.14 #7106), 02r_d4 (0.20 #2176, 0.17 #4268, 0.14 #6360), 02p5hf (0.20 #3858, 0.17 #5950, 0.14 #8042), 0pz04 (0.20 #3504, 0.17 #5596, 0.14 #7688), 028knk (0.20 #2396, 0.17 #4488, 0.14 #6580), 0py5b (0.20 #2027, 0.08 #14579, 0.06 #16671), 01cj6y (0.20 #730, 0.07 #25834, 0.07 #27930) >> Best rule #2340 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0473m9; 02jmst; >> query: (?x6396, 07s93v) <- category(?x6396, ?x134), school_type(?x6396, ?x13709), ?x134 = 08mbj5d, state_province_region(?x6396, ?x1227), citytown(?x6396, ?x3125), ?x3125 = 0d6lp >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #13729 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 06pwq; 065y4w7; 01k2wn; 04rwx; 022lly; 01q2sk; 07vyf; 0yls9; 01bm_; 02gn8s; ... *> query: (?x6396, 0kp2_) <- category(?x6396, ?x134), colors(?x6396, ?x9464), school_type(?x6396, ?x13709), student(?x6396, ?x2200), ?x9464 = 03wkwg, citytown(?x6396, ?x3125) *> conf = 0.08 ranks of expected_values: 195 EVAL 04s934 student 0kp2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 139.000 42.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #10254-02ywhz PRED entity: 02ywhz PRED relation: ceremony! PRED expected values: 0gq_v 0gq9h => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 356): 0gq9h (0.89 #3899, 0.87 #5096, 0.87 #4377), 0gq_v (0.83 #4341, 0.82 #3863, 0.80 #4102), 0gqzz (0.74 #5527, 0.55 #761, 0.50 #280), 0czp_ (0.74 #5527, 0.23 #2837, 0.15 #4518), 02x201b (0.74 #5527, 0.13 #4808, 0.11 #7930), 02x258x (0.37 #6009, 0.35 #6010, 0.34 #4568), 04dn09n (0.37 #6009, 0.35 #6010, 0.34 #4568), 09sb52 (0.37 #6009, 0.35 #6010, 0.34 #4568), 09qwmm (0.37 #6009, 0.35 #6010, 0.34 #4568), 040njc (0.37 #6009, 0.35 #6010, 0.34 #4568) >> Best rule #3899 for best value: >> intensional similarity = 17 >> extensional distance = 42 >> proper extension: 0bzknt; >> query: (?x5761, 0gq9h) <- ceremony(?x1862, ?x5761), ceremony(?x591, ?x5761), honored_for(?x5761, ?x303), award_winner(?x5761, ?x1933), language(?x303, ?x90), ?x1862 = 0gr51, film_crew_role(?x303, ?x137), award(?x986, ?x591), ceremony(?x591, ?x11428), award_winner(?x591, ?x496), ?x11428 = 0dznvw, nominated_for(?x591, ?x3255), nominated_for(?x591, ?x2550), ?x986 = 081lh, ?x3255 = 0_816, ?x2550 = 07j8r, award_nominee(?x496, ?x495) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02ywhz ceremony! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.886 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02ywhz ceremony! 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.886 http://example.org/award/award_category/winners./award/award_honor/ceremony #10253-0cpllql PRED entity: 0cpllql PRED relation: genre PRED expected values: 06n90 => 83 concepts (63 used for prediction) PRED predicted values (max 10 best out of 94): 05p553 (0.63 #589, 0.61 #472, 0.59 #707), 07s9rl0 (0.59 #2460, 0.58 #4103, 0.58 #4810), 06n90 (0.43 #832, 0.31 #246, 0.29 #12), 04pbhw (0.30 #874, 0.19 #288, 0.14 #54), 02l7c8 (0.27 #6590, 0.27 #6825, 0.26 #4824), 0lsxr (0.25 #243, 0.20 #3759, 0.19 #6349), 02n4kr (0.23 #3758, 0.14 #6348, 0.12 #4110), 0btmb (0.22 #905, 0.09 #1724, 0.08 #1958), 060__y (0.19 #367, 0.19 #250, 0.18 #1187), 0219x_ (0.19 #375, 0.13 #961, 0.12 #258) >> Best rule #589 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 087wc7n; >> query: (?x626, 05p553) <- film(?x1382, ?x626), genre(?x626, ?x10185), ?x10185 = 01zhp >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #832 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 81 *> proper extension: 0436yk; 08fbnx; 0dr1c2; 063y9fp; *> query: (?x626, 06n90) <- film(?x1382, ?x626), genre(?x626, ?x1510), genre(?x626, ?x225), ?x1510 = 01hmnh, ?x225 = 02kdv5l *> conf = 0.43 ranks of expected_values: 3 EVAL 0cpllql genre 06n90 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 63.000 0.629 http://example.org/film/film/genre #10252-04bdqk PRED entity: 04bdqk PRED relation: type_of_union PRED expected values: 04ztj => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.86 #1, 0.85 #25, 0.85 #57), 01g63y (0.40 #2, 0.35 #38, 0.33 #54) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 03xmy1; 0993r; 02t_99; 043zg; >> query: (?x10521, 04ztj) <- spouse(?x10521, ?x10522), award(?x10521, ?x154), ?x154 = 05b4l5x >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bdqk type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.857 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10251-09146g PRED entity: 09146g PRED relation: currency PRED expected values: 09nqf => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.89 #85, 0.87 #120, 0.87 #134), 02l6h (0.04 #144, 0.03 #193, 0.02 #116), 01nv4h (0.03 #37, 0.02 #226, 0.02 #373), 02gsvk (0.02 #153, 0.01 #174, 0.01 #188), 088n7 (0.01 #161, 0.01 #84) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 01hr1; 0c_j9x; 0dnqr; 03l6q0; 0277j40; 026fs38; 02p76f9; 025s1wg; >> query: (?x1904, 09nqf) <- prequel(?x2709, ?x1904), award_winner(?x1904, ?x3069), nominated_for(?x398, ?x1904), genre(?x1904, ?x225) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09146g currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.894 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10250-07xvf PRED entity: 07xvf PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #93, 0.85 #52, 0.84 #62), 07z4p (0.09 #5, 0.04 #35, 0.03 #137), 02nxhr (0.05 #12, 0.05 #78, 0.05 #17), 07c52 (0.03 #54, 0.03 #33, 0.03 #79) >> Best rule #93 for best value: >> intensional similarity = 4 >> extensional distance = 352 >> proper extension: 09fc83; 032xky; >> query: (?x7373, 029j_) <- featured_film_locations(?x7373, ?x1061), film(?x4400, ?x7373), award_winner(?x4453, ?x4400), category(?x4400, ?x134) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07xvf film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #10249-0hcvy PRED entity: 0hcvy PRED relation: influenced_by! PRED expected values: 042v2 07lp1 => 159 concepts (52 used for prediction) PRED predicted values (max 10 best out of 390): 0j0pf (0.25 #207, 0.12 #724, 0.08 #1240), 01hb6v (0.17 #1127, 0.12 #11979, 0.12 #3192), 040rjq (0.14 #2037, 0.05 #18573, 0.05 #5134), 040db (0.13 #3690, 0.13 #8341, 0.08 #15060), 014ps4 (0.13 #3926, 0.08 #1345, 0.08 #3410), 0p8jf (0.13 #3726, 0.08 #3210, 0.07 #11884), 0gd5z (0.12 #604, 0.12 #87, 0.08 #1120), 0mb0 (0.12 #947, 0.12 #430, 0.08 #1463), 049gc (0.12 #743, 0.12 #226, 0.08 #1259), 01zkxv (0.12 #533, 0.12 #16, 0.07 #3630) >> Best rule #207 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 07d3x; 01lc5; >> query: (?x11271, 0j0pf) <- profession(?x11271, ?x319), story_by(?x4458, ?x11271), influenced_by(?x11271, ?x2162), notable_people_with_this_condition(?x7374, ?x11271) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #11784 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 93 *> proper extension: 01h2_6; *> query: (?x11271, 07lp1) <- influenced_by(?x11271, ?x7334), religion(?x11271, ?x1985), peers(?x7334, ?x3941) *> conf = 0.08 ranks of expected_values: 38, 57 EVAL 0hcvy influenced_by! 07lp1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 159.000 52.000 0.250 http://example.org/influence/influence_node/influenced_by EVAL 0hcvy influenced_by! 042v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 159.000 52.000 0.250 http://example.org/influence/influence_node/influenced_by #10248-043y95 PRED entity: 043y95 PRED relation: teams! PRED expected values: 03676 => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 118): 0h3y (0.09 #7, 0.04 #2438, 0.04 #815), 01nqj (0.09 #221, 0.04 #815, 0.04 #814), 07f5x (0.09 #181, 0.04 #815, 0.04 #814), 06srk (0.09 #171, 0.04 #815, 0.04 #814), 02kcz (0.09 #165, 0.04 #815, 0.04 #814), 088q4 (0.09 #102, 0.04 #815, 0.04 #814), 0hdx8 (0.09 #193, 0.04 #815, 0.04 #814), 01nln (0.09 #157, 0.04 #815, 0.04 #814), 035dk (0.09 #65, 0.04 #815, 0.04 #814), 06s_2 (0.04 #815, 0.04 #814, 0.04 #813) >> Best rule #7 for best value: >> intensional similarity = 11 >> extensional distance = 9 >> proper extension: 03yl2t; 044l47; 0449sw; 03_qj1; 03zmc7; 03_qrp; 04k3jt; 04h5tx; 04n8xs; >> query: (?x8836, 0h3y) <- team(?x530, ?x8836), team(?x203, ?x8836), team(?x63, ?x8836), team(?x60, ?x8836), ?x60 = 02nzb8, ?x530 = 02_j1w, ?x203 = 0dgrmp, ?x63 = 02sdk9v, team(?x1142, ?x8836), ?x1142 = 0356lc, sport(?x8836, ?x471) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 043y95 teams! 03676 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 46.000 0.091 http://example.org/sports/sports_team_location/teams #10247-01dw4q PRED entity: 01dw4q PRED relation: award_winner! PRED expected values: 09p2r9 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 109): 0275n3y (0.20 #74, 0.10 #10081, 0.08 #1194), 09pj68 (0.20 #104, 0.10 #10081, 0.06 #664), 03nnm4t (0.11 #353, 0.10 #10081, 0.07 #913), 07y9ts (0.11 #347, 0.10 #10081, 0.06 #487), 058m5m4 (0.10 #10081, 0.08 #754, 0.06 #474), 09qftb (0.10 #10081, 0.06 #672, 0.05 #1092), 07y_p6 (0.10 #10081, 0.06 #657, 0.04 #937), 09gkdln (0.10 #10081, 0.06 #681, 0.04 #5721), 056878 (0.07 #1291, 0.05 #1151, 0.04 #3111), 09g90vz (0.07 #963, 0.06 #543, 0.06 #683) >> Best rule #74 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0443y3; >> query: (?x444, 0275n3y) <- award_nominee(?x444, ?x3956), vacationer(?x739, ?x444), ?x3956 = 05dxl5 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1072 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 02l840; 0478__m; 016fnb; 0g824; *> query: (?x444, 09p2r9) <- award_nominee(?x444, ?x10004), vacationer(?x739, ?x444), spouse(?x4229, ?x10004) *> conf = 0.05 ranks of expected_values: 20 EVAL 01dw4q award_winner! 09p2r9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 98.000 98.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10246-02qtywd PRED entity: 02qtywd PRED relation: award PRED expected values: 03ncb2 => 126 concepts (106 used for prediction) PRED predicted values (max 10 best out of 290): 03qbnj (0.78 #6855, 0.78 #7663, 0.78 #7259), 03tk6z (0.78 #6855, 0.78 #7663, 0.78 #7259), 03qbh5 (0.40 #204, 0.25 #2220, 0.21 #5849), 03ncb2 (0.40 #308, 0.15 #32677, 0.13 #41146), 09sb52 (0.34 #14155, 0.32 #12139, 0.29 #2863), 0c4z8 (0.31 #2088, 0.31 #1281, 0.26 #7331), 01bgqh (0.31 #1252, 0.27 #2059, 0.26 #6898), 01ck6h (0.26 #1330, 0.14 #2137, 0.12 #4154), 054ks3 (0.23 #2157, 0.22 #2560, 0.21 #5786), 02f6xy (0.21 #1409, 0.20 #200, 0.16 #2216) >> Best rule #6855 for best value: >> intensional similarity = 3 >> extensional distance = 202 >> proper extension: 02mslq; 0kzy0; 02whj; 0lgsq; 01qvgl; 01bpc9; 01ky2h; 01vvpjj; 0137g1; 01l9v7n; ... >> query: (?x11533, ?x2139) <- role(?x11533, ?x227), award_winner(?x2139, ?x11533), award_winner(?x486, ?x11533) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #308 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0197tq; *> query: (?x11533, 03ncb2) <- award_winner(?x11533, ?x2662), role(?x11533, ?x227), ?x2662 = 045zr *> conf = 0.40 ranks of expected_values: 4 EVAL 02qtywd award 03ncb2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 126.000 106.000 0.783 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10245-0svqs PRED entity: 0svqs PRED relation: award PRED expected values: 0gr07 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 265): 099tbz (0.72 #26679, 0.70 #10505, 0.69 #26678), 0gq9h (0.18 #2500, 0.14 #18591, 0.13 #2904), 0ck27z (0.18 #16163, 0.15 #15354, 0.15 #7363), 027dtxw (0.18 #16163, 0.15 #15354, 0.15 #22230), 027cyf7 (0.18 #16163, 0.15 #15354, 0.15 #22230), 03qgjwc (0.18 #16163, 0.15 #15354, 0.15 #22230), 0gqyl (0.18 #16163, 0.15 #15354, 0.15 #22230), 099cng (0.18 #16163, 0.15 #15354, 0.15 #22230), 02ppm4q (0.18 #16163, 0.15 #15354, 0.15 #22230), 09td7p (0.18 #16163, 0.15 #15354, 0.15 #22230) >> Best rule #26679 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 099ks0; >> query: (?x4923, ?x401) <- award_winner(?x401, ?x4923), award(?x56, ?x401) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1051 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 0dszr0; 0678gl; *> query: (?x4923, 0gr07) <- profession(?x4923, ?x1383), ?x1383 = 0np9r, religion(?x4923, ?x962) *> conf = 0.01 ranks of expected_values: 229 EVAL 0svqs award 0gr07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 88.000 88.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10244-072x7s PRED entity: 072x7s PRED relation: genre PRED expected values: 01f9r0 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 114): 05p553 (0.46 #1175, 0.36 #2230, 0.35 #1409), 02kdv5l (0.35 #119, 0.34 #236, 0.34 #353), 03k9fj (0.34 #1066, 0.28 #1885, 0.28 #1300), 02l7c8 (0.31 #601, 0.29 #718, 0.29 #3882), 01hmnh (0.25 #252, 0.21 #1891, 0.21 #1774), 082gq (0.25 #29, 0.20 #967, 0.19 #732), 060__y (0.25 #602, 0.17 #719, 0.16 #954), 03npn (0.22 #475, 0.17 #358, 0.12 #241), 06n90 (0.20 #364, 0.20 #481, 0.19 #247), 0lsxr (0.20 #2820, 0.19 #2117, 0.18 #2938) >> Best rule #1175 for best value: >> intensional similarity = 4 >> extensional distance = 131 >> proper extension: 02d413; 015qsq; 0b2v79; 034qrh; 018f8; 0k5g9; 0qmd5; 084302; 01rwyq; 0f4yh; ... >> query: (?x1685, 05p553) <- country(?x1685, ?x94), nominated_for(?x4318, ?x1685), featured_film_locations(?x1685, ?x362), influenced_by(?x4318, ?x7183) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1245 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 131 *> proper extension: 02d413; 015qsq; 0b2v79; 034qrh; 018f8; 0k5g9; 0qmd5; 084302; 01rwyq; 0f4yh; ... *> query: (?x1685, 01f9r0) <- country(?x1685, ?x94), nominated_for(?x4318, ?x1685), featured_film_locations(?x1685, ?x362), influenced_by(?x4318, ?x7183) *> conf = 0.05 ranks of expected_values: 70 EVAL 072x7s genre 01f9r0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 76.000 76.000 0.459 http://example.org/film/film/genre #10243-0gdqy PRED entity: 0gdqy PRED relation: award PRED expected values: 09ly2r6 => 134 concepts (112 used for prediction) PRED predicted values (max 10 best out of 322): 0789r6 (0.73 #30002, 0.71 #34472, 0.71 #30001), 02vl9ln (0.71 #30001, 0.71 #30409, 0.71 #30408), 03ybrwc (0.71 #30001, 0.71 #30409, 0.71 #30408), 0gs9p (0.70 #7377, 0.69 #8188, 0.64 #4132), 019f4v (0.67 #7364, 0.67 #8175, 0.64 #4119), 02pqp12 (0.64 #4123, 0.59 #8179, 0.59 #7368), 040njc (0.57 #7305, 0.53 #8116, 0.45 #4060), 0gr51 (0.43 #8208, 0.41 #7397, 0.36 #4152), 0gr4k (0.39 #7330, 0.39 #8141, 0.36 #4085), 0gqy2 (0.38 #19220, 0.10 #1382, 0.09 #16381) >> Best rule #30002 for best value: >> intensional similarity = 4 >> extensional distance = 1163 >> proper extension: 015zql; >> query: (?x10354, ?x13075) <- award_winner(?x13075, ?x10354), profession(?x10354, ?x319), award_winner(?x1365, ?x10354), award(?x286, ?x13075) >> conf = 0.73 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gdqy award 09ly2r6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 112.000 0.726 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10242-0gjc4d3 PRED entity: 0gjc4d3 PRED relation: film_release_region PRED expected values: 04gzd 015fr 035qy 01znc_ 06t2t => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 171): 05qhw (0.89 #1684, 0.83 #316, 0.82 #620), 035qy (0.88 #1703, 0.82 #2311, 0.81 #2615), 015fr (0.87 #1687, 0.79 #623, 0.79 #471), 01znc_ (0.85 #799, 0.85 #1711, 0.72 #951), 02vzc (0.85 #809, 0.84 #2329, 0.79 #2785), 0b90_r (0.84 #1676, 0.74 #308, 0.74 #2284), 04gzd (0.83 #1680, 0.57 #312, 0.56 #616), 06t2t (0.80 #1731, 0.71 #2339, 0.70 #363), 03spz (0.80 #1765, 0.70 #397, 0.68 #701), 05v8c (0.76 #1686, 0.58 #2598, 0.58 #2294) >> Best rule #1684 for best value: >> intensional similarity = 7 >> extensional distance = 90 >> proper extension: 0gtsx8c; >> query: (?x3276, 05qhw) <- film_release_region(?x3276, ?x2146), film_release_region(?x3276, ?x512), film_release_region(?x3276, ?x279), ?x279 = 0d060g, film(?x262, ?x3276), ?x2146 = 03rk0, ?x512 = 07ssc >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1703 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 90 *> proper extension: 0gtsx8c; *> query: (?x3276, 035qy) <- film_release_region(?x3276, ?x2146), film_release_region(?x3276, ?x512), film_release_region(?x3276, ?x279), ?x279 = 0d060g, film(?x262, ?x3276), ?x2146 = 03rk0, ?x512 = 07ssc *> conf = 0.88 ranks of expected_values: 2, 3, 4, 7, 8 EVAL 0gjc4d3 film_release_region 06t2t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 74.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gjc4d3 film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gjc4d3 film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gjc4d3 film_release_region 015fr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gjc4d3 film_release_region 04gzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 74.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10241-06pvr PRED entity: 06pvr PRED relation: contains PRED expected values: 0r771 => 108 concepts (45 used for prediction) PRED predicted values (max 10 best out of 2751): 020923 (0.66 #78835, 0.61 #90516, 0.45 #119712), 013807 (0.66 #78835, 0.61 #90516, 0.45 #119712), 02jmst (0.66 #78835, 0.61 #90516, 0.45 #119712), 021s9n (0.66 #78835, 0.61 #90516, 0.45 #119712), 07vfz (0.66 #78835, 0.61 #90516, 0.45 #119712), 02bb47 (0.66 #78835, 0.61 #90516, 0.45 #119712), 027xx3 (0.66 #78835, 0.61 #90516, 0.45 #119712), 06rny (0.65 #14598), 0r6c4 (0.64 #49637, 0.61 #90516, 0.60 #8056), 0l2lk (0.64 #49637, 0.60 #52557, 0.40 #9644) >> Best rule #78835 for best value: >> intensional similarity = 5 >> extensional distance = 101 >> proper extension: 03rt9; 035dk; 01w0v; 0m7d0; 03shp; 0nvt9; 0j0k; 0glh3; 0127c4; >> query: (?x2632, ?x3021) <- contains(?x2632, ?x10916), contains(?x2632, ?x5288), citytown(?x10436, ?x10916), contains(?x10916, ?x3021), major_field_of_study(?x5288, ?x254) >> conf = 0.66 => this is the best rule for 7 predicted values *> Best rule #1708 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 09c7w0; *> query: (?x2632, 0r771) <- location(?x3662, ?x2632), contains(?x2632, ?x10657), contains(?x2632, ?x3125), ?x10657 = 0qymv, ?x3125 = 0d6lp *> conf = 0.33 ranks of expected_values: 852 EVAL 06pvr contains 0r771 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 108.000 45.000 0.663 http://example.org/location/location/contains #10240-0ffgh PRED entity: 0ffgh PRED relation: profession PRED expected values: 0n1h => 135 concepts (102 used for prediction) PRED predicted values (max 10 best out of 68): 039v1 (0.46 #317, 0.40 #602, 0.32 #745), 01d_h8 (0.37 #2705, 0.37 #5548, 0.36 #1142), 0n1h (0.32 #1006, 0.30 #864, 0.29 #722), 01c72t (0.30 #4000, 0.29 #6987, 0.29 #11808), 03gjzk (0.29 #11808, 0.28 #5557, 0.27 #2714), 0dxtg (0.29 #11808, 0.27 #5556, 0.25 #11251), 0d1pc (0.29 #11808, 0.15 #2322, 0.14 #1611), 02jknp (0.25 #6, 0.23 #2423, 0.20 #3559), 09lbv (0.15 #586, 0.11 #301, 0.07 #729), 025352 (0.14 #340, 0.11 #5031, 0.09 #625) >> Best rule #317 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 02fybl; >> query: (?x7162, 039v1) <- participant(?x7162, ?x1522), role(?x7162, ?x1466), location(?x7162, ?x1860) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1006 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 96 *> proper extension: 0m0hw; *> query: (?x7162, 0n1h) <- participant(?x7162, ?x1522), artist(?x5666, ?x7162), award_winner(?x2238, ?x7162) *> conf = 0.32 ranks of expected_values: 3 EVAL 0ffgh profession 0n1h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 135.000 102.000 0.457 http://example.org/people/person/profession #10239-0f485 PRED entity: 0f485 PRED relation: place_of_death! PRED expected values: 03_80b => 140 concepts (60 used for prediction) PRED predicted values (max 10 best out of 381): 0dx97 (0.25 #990, 0.11 #5528, 0.09 #6285), 0kn3g (0.25 #1262, 0.11 #5800, 0.09 #6557), 0kn4c (0.25 #800, 0.11 #5338, 0.09 #6095), 016ghw (0.25 #1499, 0.11 #6037, 0.09 #6794), 011zwl (0.25 #1482, 0.11 #6020, 0.09 #6777), 01b0k1 (0.25 #1451, 0.11 #5989, 0.09 #6746), 02vkvcz (0.25 #1435, 0.11 #5973, 0.09 #6730), 047g6 (0.25 #1429, 0.11 #5967, 0.09 #6724), 01tw31 (0.25 #1337, 0.11 #5875, 0.09 #6632), 06myp (0.25 #1335, 0.11 #5873, 0.09 #6630) >> Best rule #990 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 04jpl; >> query: (?x13447, 0dx97) <- contains(?x13447, ?x10852), contains(?x13447, ?x10042), ?x10042 = 09bkv, contains(?x362, ?x13447), ?x10852 = 0nbfm >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f485 place_of_death! 03_80b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 60.000 0.250 http://example.org/people/deceased_person/place_of_death #10238-0y3_8 PRED entity: 0y3_8 PRED relation: artists PRED expected values: 0gs6vr 04b7xr 01vs73g => 54 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1416): 0415mzy (0.60 #4576, 0.44 #10721, 0.43 #5599), 0478__m (0.60 #4477, 0.44 #10622, 0.43 #5500), 01vs_v8 (0.60 #4258, 0.44 #10403, 0.43 #5281), 0191h5 (0.57 #6758, 0.50 #9831, 0.40 #4711), 0136p1 (0.56 #10380, 0.44 #13452, 0.43 #5258), 01l_vgt (0.56 #11541, 0.44 #10517, 0.43 #5395), 01w8n89 (0.50 #9519, 0.43 #6446, 0.40 #4399), 01gg59 (0.50 #2370, 0.43 #7491, 0.38 #8515), 01gx5f (0.50 #9495, 0.43 #6422, 0.30 #12568), 06br6t (0.50 #10060, 0.43 #6987, 0.20 #13133) >> Best rule #4576 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 0ggx5q; >> query: (?x3243, 0415mzy) <- artists(?x3243, ?x10502), artists(?x3243, ?x7476), artists(?x3243, ?x7088), artists(?x3243, ?x4394), ?x7088 = 019x62, artists(?x7220, ?x7476), ?x7220 = 0mmp3, category(?x10502, ?x134), ?x4394 = 049qx >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #10918 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 02cqny; *> query: (?x3243, 01vs73g) <- artists(?x3243, ?x7476), artists(?x3243, ?x7222), artists(?x3243, ?x7088), ?x7088 = 019x62, artists(?x7220, ?x7476), music(?x155, ?x7222), group(?x75, ?x7476), parent_genre(?x2439, ?x7220) *> conf = 0.44 ranks of expected_values: 21, 25, 73 EVAL 0y3_8 artists 01vs73g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 54.000 27.000 0.600 http://example.org/music/genre/artists EVAL 0y3_8 artists 04b7xr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 54.000 27.000 0.600 http://example.org/music/genre/artists EVAL 0y3_8 artists 0gs6vr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 54.000 27.000 0.600 http://example.org/music/genre/artists #10237-0163r3 PRED entity: 0163r3 PRED relation: film PRED expected values: 01hvjx => 129 concepts (90 used for prediction) PRED predicted values (max 10 best out of 479): 06y_n (0.07 #28643, 0.07 #37596, 0.06 #78767), 02y_lrp (0.06 #14, 0.04 #5385, 0.01 #25076), 0crfwmx (0.06 #151, 0.02 #5522), 01shy7 (0.06 #25486, 0.03 #79191, 0.03 #2215), 02825cv (0.04 #26205, 0.02 #49479, 0.02 #38739), 07bzz7 (0.04 #11632, 0.04 #13422, 0.03 #890), 016z9n (0.04 #20062, 0.02 #132840, 0.01 #77346), 027fwmt (0.04 #6964, 0.03 #1593, 0.03 #21285), 01718w (0.04 #6771, 0.03 #1400, 0.03 #3191), 03nfnx (0.04 #13936, 0.03 #1404, 0.02 #28256) >> Best rule #28643 for best value: >> intensional similarity = 3 >> extensional distance = 140 >> proper extension: 01pw2f1; 0c01c; 01_rh4; 039crh; 0g2mbn; 01kmd4; 0q1lp; 03k48_; 01507p; 01rzxl; ... >> query: (?x6716, ?x9787) <- location(?x6716, ?x5381), religion(?x6716, ?x7300), actor(?x9787, ?x6716) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #27227 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 140 *> proper extension: 01pw2f1; 0c01c; 01_rh4; 039crh; 0g2mbn; 01kmd4; 0q1lp; 03k48_; 01507p; 01rzxl; ... *> query: (?x6716, 01hvjx) <- location(?x6716, ?x5381), religion(?x6716, ?x7300), actor(?x9787, ?x6716) *> conf = 0.01 ranks of expected_values: 308 EVAL 0163r3 film 01hvjx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 129.000 90.000 0.071 http://example.org/film/actor/film./film/performance/film #10236-02bv9 PRED entity: 02bv9 PRED relation: official_language! PRED expected values: 0c4b8 => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 327): 0hzlz (0.62 #184, 0.61 #560, 0.59 #559), 05bmq (0.62 #184, 0.61 #560, 0.59 #559), 03ryn (0.62 #184, 0.61 #560, 0.59 #559), 03gk2 (0.62 #184, 0.59 #559, 0.58 #370), 0d060g (0.50 #383, 0.33 #8, 0.25 #194), 01nln (0.50 #496, 0.33 #121, 0.25 #307), 06dfg (0.50 #497, 0.33 #122, 0.25 #308), 07z5n (0.50 #427, 0.33 #52, 0.25 #238), 03_xj (0.50 #475, 0.33 #100, 0.25 #286), 0366c (0.50 #550, 0.33 #175, 0.25 #361) >> Best rule #184 for best value: >> intensional similarity = 19 >> extensional distance = 1 >> proper extension: 02h40lc; >> query: (?x7658, ?x172) <- countries_spoken_in(?x7658, ?x9458), countries_spoken_in(?x7658, ?x3749), countries_spoken_in(?x7658, ?x172), ?x9458 = 05bmq, languages(?x5597, ?x7658), service_language(?x6945, ?x7658), languages_spoken(?x12278, ?x7658), languages_spoken(?x5269, ?x7658), ?x12278 = 05l3g_, ?x6945 = 05w3y, language(?x4054, ?x7658), official_language(?x9051, ?x7658), languages(?x419, ?x7658), people(?x5269, ?x9301), people(?x5269, ?x1126), ?x1126 = 0h1mt, ?x3749 = 03ryn, spouse(?x2841, ?x9301), ?x4054 = 074w86 >> conf = 0.62 => this is the best rule for 4 predicted values *> Best rule #290 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 0x82; *> query: (?x7658, 0c4b8) <- countries_spoken_in(?x7658, ?x10382), countries_spoken_in(?x7658, ?x9458), ?x9458 = 05bmq, languages(?x5597, ?x7658), official_language(?x9051, ?x7658), language(?x11682, ?x7658), language(?x4054, ?x7658), language(?x1470, ?x7658), nominated_for(?x688, ?x4054), film_release_distribution_medium(?x4054, ?x81), currency(?x10382, ?x170), films(?x11817, ?x1470), genre(?x1470, ?x53), award_winner(?x1470, ?x4020), administrative_parent(?x10382, ?x551), film(?x4285, ?x11682) *> conf = 0.50 ranks of expected_values: 11 EVAL 02bv9 official_language! 0c4b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 40.000 40.000 0.618 http://example.org/location/country/official_language #10235-016z9n PRED entity: 016z9n PRED relation: language PRED expected values: 02h40lc => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 32): 02h40lc (0.94 #61, 0.92 #240, 0.92 #479), 064_8sq (0.13 #22, 0.13 #260, 0.13 #379), 06nm1 (0.11 #607, 0.09 #488, 0.08 #70), 04306rv (0.10 #183, 0.10 #362, 0.09 #124), 02bjrlw (0.10 #60, 0.08 #358, 0.07 #597), 06b_j (0.06 #201, 0.05 #82, 0.05 #619), 03_9r (0.06 #10, 0.04 #3884, 0.04 #3705), 04h9h (0.04 #162, 0.04 #400, 0.03 #700), 0jzc (0.04 #20, 0.03 #437, 0.03 #317), 03hkp (0.04 #15, 0.02 #432, 0.02 #551) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 187 >> proper extension: 0661m4p; 0gjc4d3; 01q2nx; >> query: (?x2336, 02h40lc) <- film(?x777, ?x2336), film(?x777, ?x153), featured_film_locations(?x2336, ?x108) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016z9n language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.942 http://example.org/film/film/language #10234-02vkvcz PRED entity: 02vkvcz PRED relation: people! PRED expected values: 0d7wh => 117 concepts (36 used for prediction) PRED predicted values (max 10 best out of 42): 02w7gg (0.34 #1927, 0.31 #772, 0.30 #1003), 0d7wh (0.24 #633, 0.22 #710, 0.19 #556), 041rx (0.19 #1698, 0.17 #1621, 0.16 #2006), 02ctzb (0.15 #169, 0.12 #323, 0.12 #400), 0xnvg (0.13 #475, 0.06 #1707, 0.06 #2092), 033tf_ (0.09 #2086, 0.08 #2240, 0.08 #1701), 0222qb (0.08 #198, 0.07 #275, 0.06 #352), 0x67 (0.07 #2012, 0.06 #1550, 0.06 #2089), 0dryh9k (0.07 #1633, 0.02 #2018, 0.02 #1710), 07bch9 (0.06 #485, 0.04 #1101, 0.04 #2179) >> Best rule #1927 for best value: >> intensional similarity = 3 >> extensional distance = 193 >> proper extension: 0184jc; 05vsxz; 07fq1y; 0l6qt; 0lbj1; 0byfz; 0qf43; 0h0jz; 08w7vj; 01sp81; ... >> query: (?x12364, 02w7gg) <- nationality(?x12364, ?x1310), ?x1310 = 02jx1, nominated_for(?x12364, ?x6616) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #633 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 01fwf1; 03975z; 01xsc9; *> query: (?x12364, 0d7wh) <- place_of_birth(?x12364, ?x362), award(?x12364, ?x2222), ?x362 = 04jpl, nominated_for(?x2222, ?x80) *> conf = 0.24 ranks of expected_values: 2 EVAL 02vkvcz people! 0d7wh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 36.000 0.344 http://example.org/people/ethnicity/people #10233-04tc1g PRED entity: 04tc1g PRED relation: nominated_for! PRED expected values: 07bdd_ => 92 concepts (88 used for prediction) PRED predicted values (max 10 best out of 187): 07bdd_ (0.74 #1000, 0.73 #1237, 0.33 #763), 04ljl_l (0.43 #951, 0.43 #1188, 0.26 #4505), 05b4l5x (0.43 #1191, 0.41 #954, 0.26 #4505), 0gq9h (0.35 #1721, 0.30 #2195, 0.30 #2669), 03c7tr1 (0.34 #994, 0.34 #1231, 0.17 #757), 05p1dby (0.33 #1029, 0.31 #1266, 0.26 #4505), 019f4v (0.29 #1712, 0.27 #1475, 0.26 #2660), 04dn09n (0.27 #1693, 0.25 #745, 0.24 #2641), 0gqxm (0.26 #4505, 0.26 #5691, 0.25 #5692), 0gs9p (0.26 #1722, 0.25 #774, 0.23 #11445) >> Best rule #1000 for best value: >> intensional similarity = 5 >> extensional distance = 56 >> proper extension: 016kz1; >> query: (?x887, 07bdd_) <- music(?x887, ?x4020), nominated_for(?x1312, ?x887), nominated_for(?x688, ?x887), ?x688 = 05b1610, award(?x269, ?x1312) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04tc1g nominated_for! 07bdd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 88.000 0.741 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10232-015q1n PRED entity: 015q1n PRED relation: institution! PRED expected values: 02h4rq6 02_xgp2 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 17): 02_xgp2 (0.84 #62, 0.80 #26, 0.79 #44), 02h4rq6 (0.82 #349, 0.82 #330, 0.82 #275), 027f2w (0.47 #41, 0.46 #59, 0.41 #23), 013zdg (0.38 #40, 0.35 #76, 0.35 #22), 022h5x (0.37 #274, 0.30 #199, 0.25 #87), 0bjrnt (0.37 #274, 0.30 #199, 0.21 #57), 02m4yg (0.37 #274, 0.30 #199, 0.11 #29), 071tyz (0.37 #274, 0.30 #199, 0.07 #168), 01ysy9 (0.37 #274, 0.30 #199, 0.07 #35), 01kxxq (0.37 #274, 0.05 #178, 0.04 #269) >> Best rule #62 for best value: >> intensional similarity = 2 >> extensional distance = 55 >> proper extension: 03_c8p; >> query: (?x6271, 02_xgp2) <- organization(?x5510, ?x6271), organization(?x6271, ?x5487) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 015q1n institution! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.842 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 015q1n institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.842 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #10231-0g69lg PRED entity: 0g69lg PRED relation: gender PRED expected values: 05zppz => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #1, 0.84 #5, 0.84 #25), 02zsn (0.28 #72, 0.27 #58, 0.27 #62) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 03m_k0; >> query: (?x6765, 05zppz) <- award(?x6765, ?x9640), ?x9640 = 0gkr9q, profession(?x6765, ?x987) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g69lg gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.857 http://example.org/people/person/gender #10230-03hxsv PRED entity: 03hxsv PRED relation: film! PRED expected values: 04mkft => 106 concepts (64 used for prediction) PRED predicted values (max 10 best out of 58): 016tt2 (0.55 #2422, 0.20 #3, 0.18 #441), 01795t (0.37 #235, 0.12 #2728, 0.08 #1553), 05qd_ (0.34 #1031, 0.20 #7, 0.19 #592), 054g1r (0.25 #252, 0.10 #398, 0.10 #2745), 06jntd (0.20 #29, 0.08 #1639, 0.07 #1345), 016tw3 (0.18 #301, 0.16 #1252, 0.16 #740), 017s11 (0.15 #440, 0.14 #2934, 0.14 #880), 05s_k6 (0.13 #62, 0.09 #135, 0.06 #208), 03xsby (0.13 #14, 0.07 #87, 0.06 #160), 0g1rw (0.11 #884, 0.11 #1103, 0.10 #444) >> Best rule #2422 for best value: >> intensional similarity = 4 >> extensional distance = 223 >> proper extension: 0h95zbp; >> query: (?x6332, 016tt2) <- film_crew_role(?x6332, ?x137), film(?x609, ?x6332), film(?x609, ?x6500), ?x6500 = 026hxwx >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1644 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 179 *> proper extension: 0d8w2n; *> query: (?x6332, 04mkft) <- film_distribution_medium(?x6332, ?x81), film(?x382, ?x6332), production_companies(?x485, ?x382) *> conf = 0.09 ranks of expected_values: 12 EVAL 03hxsv film! 04mkft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 106.000 64.000 0.551 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #10229-01q0kg PRED entity: 01q0kg PRED relation: state_province_region PRED expected values: 01n7q => 186 concepts (151 used for prediction) PRED predicted values (max 10 best out of 124): 01n7q (0.72 #5444, 0.67 #1358, 0.67 #1252), 059rby (0.44 #251, 0.40 #128, 0.36 #374), 09c7w0 (0.33 #5445, 0.31 #6066, 0.23 #1359), 071vr (0.31 #12497, 0.31 #7182, 0.29 #11509), 02xry (0.14 #1397, 0.10 #1026, 0.05 #7096), 05k7sb (0.12 #3120, 0.10 #772, 0.10 #4978), 07b_l (0.10 #1161, 0.09 #1532, 0.07 #2643), 05tbn (0.06 #4007, 0.06 #4131, 0.06 #9830), 03v0t (0.06 #6119, 0.05 #1164, 0.05 #1041), 05kkh (0.06 #8547, 0.06 #8920, 0.05 #9412) >> Best rule #5444 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 0gy1_; 0ky6d; >> query: (?x4257, ?x1227) <- company(?x7749, ?x4257), citytown(?x4257, ?x6960), contains(?x1227, ?x6960), state(?x581, ?x1227) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q0kg state_province_region 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 151.000 0.723 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #10228-02w9k1c PRED entity: 02w9k1c PRED relation: film_crew_role PRED expected values: 0263ycg => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 24): 01vx2h (0.38 #128, 0.35 #186, 0.34 #98), 0dxtw (0.37 #1186, 0.36 #628, 0.36 #1215), 02ynfr (0.21 #12, 0.17 #424, 0.17 #483), 02rh1dz (0.15 #96, 0.12 #184, 0.11 #126), 0d2b38 (0.12 #138, 0.11 #196, 0.11 #108), 015h31 (0.12 #125, 0.11 #95, 0.10 #183), 089g0h (0.12 #279, 0.11 #1221, 0.11 #986), 089fss (0.11 #4, 0.07 #1152, 0.07 #1182), 094hwz (0.07 #130, 0.06 #100, 0.06 #188), 033smt (0.05 #1080, 0.05 #1228, 0.05 #935) >> Best rule #128 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 0522wp; >> query: (?x5819, 01vx2h) <- region(?x5819, ?x512), film(?x609, ?x5819), ?x609 = 03xq0f >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 037xlx; 03prz_; 06bc59; 0k419; *> query: (?x5819, 0263ycg) <- country(?x5819, ?x205), currency(?x5819, ?x1099), film(?x609, ?x5819), ?x205 = 03rjj *> conf = 0.04 ranks of expected_values: 12 EVAL 02w9k1c film_crew_role 0263ycg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 93.000 93.000 0.375 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10227-02fy0z PRED entity: 02fy0z PRED relation: major_field_of_study PRED expected values: 029g_vk => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 111): 02lp1 (0.56 #246, 0.42 #1182, 0.41 #831), 01mkq (0.51 #250, 0.50 #484, 0.46 #835), 062z7 (0.43 #495, 0.40 #261, 0.36 #846), 04rjg (0.41 #489, 0.39 #957, 0.38 #255), 03g3w (0.41 #494, 0.35 #1430, 0.33 #962), 05qjt (0.40 #242, 0.33 #476, 0.29 #944), 05qfh (0.36 #269, 0.33 #503, 0.29 #35), 01r4k (0.33 #313, 0.29 #79, 0.16 #898), 0g4gr (0.33 #966, 0.21 #147, 0.16 #1200), 01tbp (0.31 #290, 0.26 #1226, 0.26 #875) >> Best rule #246 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 08815; 01jssp; 06pwq; 01w3v; 07w0v; 04rwx; 03v6t; 01j_cy; 07szy; 0lfgr; ... >> query: (?x3149, 02lp1) <- currency(?x3149, ?x170), major_field_of_study(?x3149, ?x2601), institution(?x1368, ?x3149), ?x2601 = 04x_3 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #8791 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 546 *> proper extension: 02bb47; 020923; 04b_46; 04ycjk; 0pz6q; 04_j5s; *> query: (?x3149, ?x254) <- organization(?x346, ?x3149), institution(?x1368, ?x3149), major_field_of_study(?x1368, ?x254) *> conf = 0.05 ranks of expected_values: 72 EVAL 02fy0z major_field_of_study 029g_vk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 109.000 109.000 0.556 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10226-0151zx PRED entity: 0151zx PRED relation: nationality PRED expected values: 07ssc => 139 concepts (78 used for prediction) PRED predicted values (max 10 best out of 65): 09c7w0 (0.79 #2087, 0.78 #6359, 0.78 #6856), 07ssc (0.40 #15, 0.37 #411, 0.34 #1604), 03rk0 (0.12 #2728, 0.11 #2629, 0.11 #4018), 0345h (0.08 #4571, 0.04 #926, 0.04 #2017), 0f8l9c (0.08 #4571, 0.04 #3500, 0.03 #4094), 0chghy (0.08 #4571, 0.03 #2484, 0.03 #7755), 03_3d (0.08 #4571, 0.03 #2484, 0.03 #7755), 03rt9 (0.08 #4571, 0.03 #2484, 0.03 #7755), 0d0vqn (0.08 #4571, 0.03 #2484, 0.03 #7755), 0h7x (0.08 #4571, 0.03 #2484, 0.03 #7755) >> Best rule #2087 for best value: >> intensional similarity = 4 >> extensional distance = 375 >> proper extension: 02mslq; 011zf2; 03yf3z; 0b6yp2; 015wfg; 03h610; 094xh; 0fpjyd; 01m5m5b; >> query: (?x8983, 09c7w0) <- gender(?x8983, ?x231), category(?x8983, ?x134), student(?x9844, ?x8983), nationality(?x8983, ?x1310) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0byfz; 0f0kz; 031y07; *> query: (?x8983, 07ssc) <- profession(?x8983, ?x1032), film(?x8983, ?x8984), film(?x8983, ?x1386), ?x8984 = 0kt_4, award_winner(?x1386, ?x669) *> conf = 0.40 ranks of expected_values: 2 EVAL 0151zx nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 139.000 78.000 0.785 http://example.org/people/person/nationality #10225-030_1m PRED entity: 030_1m PRED relation: industry PRED expected values: 02vxn => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 44): 02vxn (0.59 #1058, 0.56 #962, 0.53 #626), 020mfr (0.30 #305, 0.12 #1556, 0.09 #1844), 01mw1 (0.25 #289, 0.16 #1540, 0.11 #769), 07c52 (0.20 #4, 0.11 #148, 0.10 #196), 02jjt (0.13 #392, 0.12 #872, 0.11 #152), 0g4gr (0.12 #103, 0.04 #391, 0.04 #439), 011s0 (0.11 #154, 0.08 #250, 0.03 #826), 0sydc (0.09 #369, 0.08 #273, 0.04 #465), 03qh03g (0.09 #389, 0.06 #725, 0.05 #1206), 04rlf (0.07 #926, 0.06 #1457, 0.05 #1553) >> Best rule #1058 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0338lq; 04f525m; 024rbz; 05d6kv; 08wjc1; 09mfvx; 034f0d; 024rdh; 081bls; 07k2x; ... >> query: (?x1561, 02vxn) <- production_companies(?x69, ?x1561), film(?x1561, ?x5730), genre(?x5730, ?x53) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030_1m industry 02vxn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.592 http://example.org/business/business_operation/industry #10224-01d26y PRED entity: 01d26y PRED relation: place_of_birth! PRED expected values: 08n9ng => 229 concepts (146 used for prediction) PRED predicted values (max 10 best out of 1970): 0hz_1 (0.40 #185510, 0.34 #331843, 0.34 #371044), 05sdxx (0.20 #4578, 0.11 #9803, 0.10 #15028), 05txrz (0.20 #3487, 0.11 #8712, 0.10 #13937), 03_6y (0.20 #3278, 0.11 #8503, 0.10 #13728), 036hf4 (0.20 #4499, 0.11 #9724, 0.10 #14949), 03c_8t (0.20 #5196, 0.04 #57446, 0.04 #60058), 03dn9v (0.20 #4872, 0.04 #57122, 0.04 #59734), 01m7pwq (0.20 #4659, 0.04 #56909, 0.04 #59521), 05z_p6 (0.20 #4360, 0.04 #56610, 0.04 #59222), 077yk0 (0.20 #3962, 0.04 #56212, 0.04 #58824) >> Best rule #185510 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 087vz; >> query: (?x10586, ?x8596) <- location(?x8596, ?x10586), nationality(?x8596, ?x94), notable_people_with_this_condition(?x9933, ?x8596), place_of_birth(?x8596, ?x10718) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #86219 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 0xn7b; *> query: (?x10586, ?x764) <- adjoins(?x1036, ?x10586), citytown(?x4531, ?x10586), location(?x10423, ?x1036), location(?x764, ?x1036), nationality(?x10423, ?x94) *> conf = 0.02 ranks of expected_values: 1705 EVAL 01d26y place_of_birth! 08n9ng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 229.000 146.000 0.402 http://example.org/people/person/place_of_birth #10223-04q827 PRED entity: 04q827 PRED relation: film_production_design_by PRED expected values: 0dh73w => 92 concepts (49 used for prediction) PRED predicted values (max 10 best out of 18): 05b2gsm (0.04 #79, 0.04 #111, 0.04 #142), 03csqj4 (0.03 #58, 0.02 #89, 0.02 #121), 059x0w (0.03 #348, 0.02 #510, 0.01 #283), 0mz73 (0.03 #348, 0.02 #510, 0.01 #283), 055c8 (0.03 #348, 0.02 #510, 0.01 #283), 0cdf37 (0.02 #364, 0.02 #396, 0.02 #267), 04_1nk (0.02 #362, 0.02 #234, 0.02 #459), 02vxyl5 (0.02 #890, 0.02 #1050, 0.02 #858), 02x2t07 (0.02 #213, 0.02 #1529, 0.01 #244), 0bytkq (0.02 #516, 0.02 #322, 0.02 #901) >> Best rule #79 for best value: >> intensional similarity = 5 >> extensional distance = 50 >> proper extension: 0p_qr; 0yx1m; >> query: (?x10806, 05b2gsm) <- nominated_for(?x1716, ?x10806), nominated_for(?x704, ?x10806), ?x1716 = 02y_rq5, film(?x382, ?x10806), award_winner(?x704, ?x72) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #324 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 230 *> proper extension: 0dnvn3; 01b195; 0pdp8; 0bby9p5; 0bbw2z6; 035w2k; 0127ps; 023g6w; *> query: (?x10806, 0dh73w) <- written_by(?x10806, ?x3260), film_crew_role(?x10806, ?x137), language(?x10806, ?x254), award_winner(?x10806, ?x3186) *> conf = 0.01 ranks of expected_values: 14 EVAL 04q827 film_production_design_by 0dh73w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 92.000 49.000 0.038 http://example.org/film/film/film_production_design_by #10222-0b44shh PRED entity: 0b44shh PRED relation: country PRED expected values: 09c7w0 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 125): 09c7w0 (0.82 #3060, 0.78 #4051, 0.78 #6160), 0d060g (0.43 #4860, 0.11 #4673, 0.10 #132), 06q1r (0.43 #4860), 07ssc (0.26 #824, 0.25 #1761, 0.24 #1508), 0345h (0.20 #275, 0.14 #1961, 0.14 #2466), 0f8l9c (0.17 #827, 0.15 #1575, 0.14 #1764), 03rjj (0.11 #4673, 0.10 #5539, 0.10 #4672), 0154j (0.11 #4673, 0.10 #5539, 0.10 #4672), 06bnz (0.11 #4673, 0.10 #5539, 0.10 #4672), 03gj2 (0.11 #4673, 0.10 #5539, 0.10 #4672) >> Best rule #3060 for best value: >> intensional similarity = 4 >> extensional distance = 428 >> proper extension: 03p2xc; >> query: (?x5109, 09c7w0) <- titles(?x53, ?x5109), film(?x2033, ?x5109), genre(?x5109, ?x258), ?x258 = 05p553 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b44shh country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.816 http://example.org/film/film/country #10221-0260bz PRED entity: 0260bz PRED relation: genre PRED expected values: 04xvh5 => 70 concepts (68 used for prediction) PRED predicted values (max 10 best out of 86): 01jfsb (0.62 #365, 0.33 #2027, 0.32 #957), 05p553 (0.37 #949, 0.35 #2611, 0.34 #2492), 0lsxr (0.36 #361, 0.26 #125, 0.20 #1071), 02kdv5l (0.33 #2017, 0.31 #473, 0.31 #119), 02l7c8 (0.29 #2859, 0.27 #4161, 0.27 #3095), 03k9fj (0.25 #1907, 0.25 #482, 0.25 #2026), 082gq (0.23 #1092, 0.21 #855, 0.19 #1687), 09blyk (0.21 #383, 0.12 #5922, 0.05 #619), 060__y (0.18 #843, 0.18 #1080, 0.17 #724), 01hmnh (0.18 #2033, 0.17 #1914, 0.16 #2506) >> Best rule #365 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 07s3m4g; 04svwx; >> query: (?x2107, 01jfsb) <- genre(?x2107, ?x600), country(?x2107, ?x94), ?x600 = 02n4kr >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #5922 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1314 *> proper extension: 01f3p_; 03g9xj; 0cskb; *> query: (?x2107, ?x53) <- nominated_for(?x10866, ?x2107), nationality(?x10866, ?x94), film(?x10866, ?x12964), genre(?x12964, ?x53) *> conf = 0.12 ranks of expected_values: 15 EVAL 0260bz genre 04xvh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 70.000 68.000 0.618 http://example.org/film/film/genre #10220-0xn5b PRED entity: 0xn5b PRED relation: featured_film_locations! PRED expected values: 03hj5lq => 98 concepts (89 used for prediction) PRED predicted values (max 10 best out of 531): 04dsnp (0.25 #66, 0.20 #803, 0.05 #3014), 047csmy (0.20 #1133, 0.12 #396, 0.05 #3344), 0ds2n (0.20 #968, 0.05 #3179, 0.03 #4653), 0bl1_ (0.20 #1080, 0.05 #3291, 0.03 #4765), 035xwd (0.20 #786, 0.05 #2997, 0.03 #4471), 0hmr4 (0.20 #781, 0.05 #2992, 0.03 #4466), 01_1hw (0.20 #1352, 0.03 #5775, 0.03 #7250), 0jyx6 (0.20 #813, 0.03 #5236, 0.03 #6711), 01lsl (0.20 #1371, 0.02 #3582, 0.02 #13903), 07nnp_ (0.20 #1464, 0.02 #3675, 0.02 #5149) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 04jpl; 0c_m3; 01b8w_; >> query: (?x5464, 04dsnp) <- contains(?x94, ?x5464), place_of_birth(?x1922, ?x5464), award_nominee(?x1922, ?x4053), ?x4053 = 01pkhw >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #448 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 04jpl; 0c_m3; 01b8w_; *> query: (?x5464, 03hj5lq) <- contains(?x94, ?x5464), place_of_birth(?x1922, ?x5464), award_nominee(?x1922, ?x4053), ?x4053 = 01pkhw *> conf = 0.12 ranks of expected_values: 30 EVAL 0xn5b featured_film_locations! 03hj5lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 98.000 89.000 0.250 http://example.org/film/film/featured_film_locations #10219-04cppj PRED entity: 04cppj PRED relation: film_release_region PRED expected values: 0b90_r 03_3d 07ssc 03gj2 02vzc 016wzw => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 161): 09c7w0 (0.94 #2693, 0.94 #5687, 0.94 #2544), 03gj2 (0.89 #473, 0.88 #1071, 0.83 #622), 03_3d (0.87 #604, 0.85 #306, 0.80 #1951), 07ssc (0.85 #316, 0.84 #913, 0.84 #166), 0d060g (0.85 #307, 0.80 #1054, 0.77 #754), 02vzc (0.85 #497, 0.83 #1993, 0.83 #348), 0b90_r (0.83 #304, 0.83 #1051, 0.74 #453), 04gzd (0.75 #310, 0.61 #1057, 0.56 #459), 03rk0 (0.73 #353, 0.57 #1100, 0.54 #502), 047yc (0.71 #327, 0.57 #1074, 0.52 #476) >> Best rule #2693 for best value: >> intensional similarity = 3 >> extensional distance = 287 >> proper extension: 0b60sq; 04dsnp; 02q3fdr; 02yy9r; >> query: (?x6516, 09c7w0) <- currency(?x6516, ?x170), film_release_region(?x6516, ?x87), executive_produced_by(?x6516, ?x595) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #473 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 52 *> proper extension: 04zyhx; 03z9585; 0hhggmy; 0fpgp26; *> query: (?x6516, 03gj2) <- film_release_region(?x6516, ?x3277), film_release_region(?x6516, ?x1499), film_release_region(?x6516, ?x774), ?x1499 = 01znc_, ?x3277 = 06t8v, ?x774 = 06mzp *> conf = 0.89 ranks of expected_values: 2, 3, 4, 6, 7, 11 EVAL 04cppj film_release_region 016wzw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 86.000 86.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04cppj film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04cppj film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04cppj film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04cppj film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04cppj film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10218-01hrqc PRED entity: 01hrqc PRED relation: place_of_birth PRED expected values: 0rvty => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 173): 0cr3d (0.12 #94, 0.10 #2206, 0.06 #8542), 030qb3t (0.12 #54, 0.08 #4982, 0.07 #12726), 0h1k6 (0.12 #445, 0.05 #3261, 0.04 #3965), 0n90z (0.12 #685, 0.05 #3501, 0.04 #4205), 04lh6 (0.11 #1741, 0.05 #7373, 0.04 #9485), 02_286 (0.10 #23955, 0.09 #21139, 0.09 #19731), 0d6lp (0.07 #818, 0.05 #1522, 0.03 #4338), 05ksh (0.07 #741, 0.03 #4261, 0.03 #14117), 0c_m3 (0.07 #901, 0.03 #4421, 0.02 #7237), 0167q3 (0.07 #959, 0.03 #4479, 0.02 #8703) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02zrv7; 01ccr8; >> query: (?x7571, 0cr3d) <- nominated_for(?x7571, ?x7299), participant(?x7571, ?x5240), performance_role(?x7571, ?x228) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01hrqc place_of_birth 0rvty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 145.000 145.000 0.125 http://example.org/people/person/place_of_birth #10217-09fb5 PRED entity: 09fb5 PRED relation: award_winner! PRED expected values: 027cyf7 => 100 concepts (98 used for prediction) PRED predicted values (max 10 best out of 240): 09sb52 (0.37 #22577, 0.36 #35551, 0.31 #34714), 099jhq (0.37 #22577, 0.36 #35551, 0.31 #34714), 0gqy2 (0.37 #22577, 0.36 #35551, 0.31 #34714), 024dzn (0.37 #22577, 0.36 #35551, 0.31 #34714), 027cyf7 (0.15 #23833, 0.11 #32201, 0.11 #22996), 0ck27z (0.15 #23833, 0.09 #15557, 0.09 #15975), 05p09zm (0.15 #23833, 0.07 #2209, 0.06 #4299), 0fq9zdn (0.15 #23833, 0.07 #471, 0.05 #36388), 027571b (0.15 #23833, 0.07 #680, 0.04 #3607), 0cqhk0 (0.15 #23833, 0.06 #15504, 0.06 #4634) >> Best rule #22577 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 014hr0; >> query: (?x406, ?x451) <- award_winner(?x406, ?x1870), award(?x406, ?x451) >> conf = 0.37 => this is the best rule for 4 predicted values *> Best rule #23833 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1519 *> proper extension: 0280mv7; 035_2h; 039cq4; 01j53q; 08849; *> query: (?x406, ?x941) <- award_winner(?x406, ?x3907), award_winner(?x941, ?x3907) *> conf = 0.15 ranks of expected_values: 5 EVAL 09fb5 award_winner! 027cyf7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 100.000 98.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #10216-0n96z PRED entity: 0n96z PRED relation: place_of_birth! PRED expected values: 01pbwwl => 162 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1633): 0dsb_yy (0.20 #995, 0.08 #21878, 0.07 #27098), 066yfh (0.20 #2438, 0.08 #23321, 0.06 #36371), 02g40r (0.20 #2180, 0.08 #23063, 0.06 #36113), 02q42j_ (0.20 #1229, 0.08 #22112, 0.06 #35162), 04_1nk (0.20 #1135, 0.08 #22018, 0.06 #35068), 02pq9yv (0.20 #679, 0.08 #21562, 0.06 #34612), 01pcvn (0.17 #3781, 0.14 #6391, 0.08 #16833), 03jg5t (0.17 #4207, 0.14 #6817, 0.08 #17259), 0bd2n4 (0.17 #3325, 0.14 #5935, 0.08 #16377), 0g8st4 (0.17 #3997, 0.10 #9217, 0.08 #14439) >> Best rule #995 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 05bcl; >> query: (?x14093, 0dsb_yy) <- time_zones(?x14093, ?x5327), adjoins(?x11888, ?x14093), country(?x14093, ?x512), ?x5327 = 03bdv >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0n96z place_of_birth! 01pbwwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 162.000 114.000 0.200 http://example.org/people/person/place_of_birth #10215-0gqwc PRED entity: 0gqwc PRED relation: award_winner PRED expected values: 01gvyp 0g476 04rfq => 65 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1989): 01hkhq (0.67 #12670, 0.60 #15103, 0.57 #27262), 0151w_ (0.50 #17209, 0.44 #34234, 0.42 #39096), 02vyw (0.50 #17803, 0.33 #10507, 0.25 #34828), 0c921 (0.50 #18967, 0.25 #35992, 0.22 #11671), 0js9s (0.50 #18453, 0.25 #35478, 0.22 #11157), 081lh (0.44 #9911, 0.40 #17207, 0.32 #39094), 0gjvqm (0.44 #12397, 0.36 #2432, 0.36 #2431), 0lpjn (0.40 #3026, 0.36 #2432, 0.36 #2431), 0h1nt (0.40 #14827, 0.36 #2432, 0.36 #2431), 0h32q (0.40 #962, 0.36 #2431, 0.33 #26750) >> Best rule #12670 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0bsjcw; >> query: (?x1245, 01hkhq) <- ceremony(?x1245, ?x78), award(?x2891, ?x1245), award(?x719, ?x1245), ?x719 = 01csvq, film(?x2891, ?x2547) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4489 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 02qkk9_; *> query: (?x1245, 0g476) <- ceremony(?x1245, ?x78), award_winner(?x1245, ?x6852), ?x6852 = 0lfbm *> conf = 0.20 ranks of expected_values: 171, 172 EVAL 0gqwc award_winner 04rfq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 42.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0gqwc award_winner 0g476 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 65.000 42.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0gqwc award_winner 01gvyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 65.000 42.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #10214-026_dq6 PRED entity: 026_dq6 PRED relation: vacationer! PRED expected values: 0cv3w => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 111): 0cv3w (0.26 #926, 0.22 #1298, 0.21 #1546), 05qtj (0.26 #941, 0.18 #1561, 0.17 #2057), 02_286 (0.17 #139, 0.12 #387, 0.12 #263), 06c62 (0.17 #210, 0.12 #334, 0.08 #4189), 0f8l9c (0.17 #141, 0.08 #637, 0.05 #762), 05fkf (0.17 #140, 0.08 #636, 0.05 #761), 0hjy (0.17 #144, 0.08 #640, 0.05 #765), 0f2v0 (0.16 #3420, 0.13 #932, 0.12 #1552), 04jpl (0.14 #2742, 0.12 #1498, 0.11 #3490), 0b90_r (0.13 #4106, 0.13 #872, 0.12 #1244) >> Best rule #926 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 07ldhs; >> query: (?x9374, 0cv3w) <- participant(?x12047, ?x9374), profession(?x9374, ?x1032), participant(?x9374, ?x2352), currency(?x9374, ?x170) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026_dq6 vacationer! 0cv3w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.261 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #10213-0239kh PRED entity: 0239kh PRED relation: role! PRED expected values: 0163r3 => 67 concepts (44 used for prediction) PRED predicted values (max 10 best out of 900): 050z2 (0.67 #4386, 0.67 #3917, 0.62 #7194), 05qhnq (0.67 #5909, 0.60 #3573, 0.56 #8714), 04bpm6 (0.67 #5672, 0.60 #3336, 0.50 #15964), 018x3 (0.67 #5855, 0.50 #2123, 0.45 #464), 016ntp (0.67 #5744, 0.50 #2012, 0.45 #464), 02s6sh (0.62 #7441, 0.60 #3233, 0.50 #5566), 01vsnff (0.60 #3356, 0.56 #8497, 0.50 #4289), 082brv (0.60 #3533, 0.50 #7274, 0.50 #5869), 01wxdn3 (0.60 #3674, 0.50 #7415, 0.50 #4138), 01vn35l (0.60 #2924, 0.50 #3855, 0.50 #1066) >> Best rule #4386 for best value: >> intensional similarity = 20 >> extensional distance = 4 >> proper extension: 04rzd; >> query: (?x1433, 050z2) <- role(?x1433, ?x8172), role(?x1433, ?x5926), role(?x1433, ?x1663), role(?x1433, ?x316), role(?x1433, ?x314), role(?x1433, ?x227), role(?x1433, ?x1268), ?x8172 = 06rvn, role(?x1663, ?x960), ?x316 = 05r5c, role(?x1663, ?x315), role(?x6996, ?x1433), role(?x569, ?x1433), ?x227 = 0342h, ?x5926 = 0cfdd, ?x960 = 04q7r, role(?x569, ?x614), ?x6996 = 0132k4, role(?x75, ?x314), role(?x217, ?x314) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5890 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 4 *> proper extension: 013y1f; *> query: (?x1433, 0163r3) <- role(?x1433, ?x8172), role(?x1433, ?x3716), role(?x1433, ?x2764), role(?x1433, ?x1267), role(?x1433, ?x615), role(?x1433, ?x432), role(?x1433, ?x314), role(?x1433, ?x1268), role(?x3991, ?x1433), ?x432 = 042v_gx, ?x2764 = 01s0ps, ?x615 = 0dwsp, role(?x8172, ?x1969), role(?x8172, ?x1432), ?x314 = 02sgy, ?x1432 = 0395lw, role(?x9321, ?x8172), ?x1267 = 07brj, ?x3991 = 05842k, ?x9321 = 0140t7, ?x1969 = 04rzd, ?x3716 = 03gvt, role(?x8172, ?x2157), role(?x1260, ?x1433) *> conf = 0.50 ranks of expected_values: 32 EVAL 0239kh role! 0163r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 67.000 44.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #10212-06jzh PRED entity: 06jzh PRED relation: profession PRED expected values: 02hrh1q => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.90 #1515, 0.90 #3765, 0.89 #2115), 03gjzk (0.50 #16, 0.30 #3316, 0.30 #3466), 01d_h8 (0.46 #1956, 0.42 #3306, 0.42 #3456), 0dxtg (0.38 #614, 0.32 #1214, 0.30 #1964), 09jwl (0.33 #320, 0.26 #3020, 0.25 #1370), 0np9r (0.27 #5251, 0.26 #5402, 0.25 #12459), 0d1pc (0.27 #5251, 0.26 #5402, 0.25 #12459), 02krf9 (0.27 #5251, 0.26 #5402, 0.25 #12459), 0d8qb (0.26 #5402, 0.25 #12910, 0.25 #11258), 0dz3r (0.26 #1952, 0.23 #3302, 0.23 #3452) >> Best rule #1515 for best value: >> intensional similarity = 3 >> extensional distance = 157 >> proper extension: 06dv3; 014zcr; 0m2wm; 01q_ph; 027dtv3; 09wj5; 01vvycq; 0187y5; 05zbm4; 0h1mt; ... >> query: (?x540, 02hrh1q) <- award_nominee(?x540, ?x539), location(?x540, ?x5719), participant(?x540, ?x2108) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06jzh profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.899 http://example.org/people/person/profession #10211-027m5wv PRED entity: 027m5wv PRED relation: executive_produced_by PRED expected values: 05hj_k => 84 concepts (63 used for prediction) PRED predicted values (max 10 best out of 97): 05hj_k (0.31 #1102, 0.26 #851, 0.12 #5634), 0glyyw (0.07 #188, 0.06 #439, 0.05 #690), 06pj8 (0.06 #5591, 0.04 #2318, 0.03 #1311), 079vf (0.06 #253, 0.04 #2, 0.04 #2265), 03v1xb (0.05 #5789), 0b7xl8 (0.05 #5789), 052hl (0.05 #5789), 01wd9lv (0.05 #5789), 0bvg70 (0.05 #5789), 01pctb (0.05 #5789) >> Best rule #1102 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 03m8y5; >> query: (?x6081, 05hj_k) <- film(?x166, ?x6081), country(?x6081, ?x94), film(?x1384, ?x6081), ?x166 = 0jz9f >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027m5wv executive_produced_by 05hj_k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 63.000 0.311 http://example.org/film/film/executive_produced_by #10210-0kszw PRED entity: 0kszw PRED relation: award_winner! PRED expected values: 09gq0x5 => 104 concepts (76 used for prediction) PRED predicted values (max 10 best out of 406): 04sskp (0.45 #65869, 0.44 #2271, 0.44 #18166), 04jpg2p (0.45 #65869, 0.44 #2271, 0.42 #40878), 0_9l_ (0.45 #65869, 0.44 #2271, 0.42 #40878), 04pk1f (0.19 #5678, 0.16 #7949, 0.15 #32928), 04ltlj (0.19 #5678, 0.16 #7949, 0.15 #32928), 031hcx (0.19 #5678, 0.16 #7949, 0.15 #32928), 0prhz (0.19 #5678, 0.16 #7949, 0.15 #32928), 050xxm (0.19 #5678, 0.16 #7949, 0.15 #32928), 09tkzy (0.19 #5678, 0.16 #7949, 0.15 #32928), 0ds11z (0.19 #5678, 0.16 #7949, 0.15 #32928) >> Best rule #65869 for best value: >> intensional similarity = 2 >> extensional distance = 1545 >> proper extension: 01nqfh_; 0dky9n; 024rbz; 01nzs7; 0kk9v; 027_tg; 03cp7b3; 01j7pt; 01nc3rh; 0f3zsq; ... >> query: (?x2531, ?x8062) <- award_winner(?x1508, ?x2531), nominated_for(?x2531, ?x8062) >> conf = 0.45 => this is the best rule for 3 predicted values *> Best rule #39742 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1052 *> proper extension: 03qcq; 0197tq; 026ps1; 02l840; 03f5spx; 01jrz5j; 016kjs; 01wbgdv; 01l4zqz; 01wcp_g; ... *> query: (?x2531, ?x1813) <- award_nominee(?x1223, ?x2531), location(?x2531, ?x6764), award_winner(?x1813, ?x1223) *> conf = 0.06 ranks of expected_values: 24 EVAL 0kszw award_winner! 09gq0x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 104.000 76.000 0.453 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #10209-0fzrhn PRED entity: 0fzrhn PRED relation: award_winner PRED expected values: 0byfz => 38 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1649): 02cqbx (0.67 #2421, 0.57 #3963, 0.44 #7048), 07zhd7 (0.43 #4591, 0.33 #7676, 0.33 #6133), 076lxv (0.33 #6262, 0.33 #4719, 0.33 #1635), 02sj1x (0.33 #6697, 0.33 #5154, 0.29 #3612), 02wb6d (0.33 #2583, 0.33 #1041, 0.29 #4125), 0520r2x (0.33 #1555, 0.32 #1541, 0.29 #3097), 07h1tr (0.33 #392, 0.29 #3476, 0.22 #6561), 05qd_ (0.33 #1662, 0.29 #3204, 0.22 #6289), 0dck27 (0.33 #297, 0.27 #7710, 0.27 #3083), 057bc6m (0.33 #1209, 0.22 #5835, 0.17 #2751) >> Best rule #2421 for best value: >> intensional similarity = 19 >> extensional distance = 4 >> proper extension: 0c53zb; 0c53vt; 0d__c3; >> query: (?x11984, 02cqbx) <- ceremony(?x3617, ?x11984), ceremony(?x2222, ?x11984), ceremony(?x1245, ?x11984), award_winner(?x11984, ?x7118), award_winner(?x11984, ?x2109), honored_for(?x11984, ?x8984), ?x1245 = 0gqwc, nominated_for(?x198, ?x8984), ?x2222 = 0gs96, award_winner(?x1243, ?x7118), ?x3617 = 0gvx_, place_of_birth(?x2109, ?x6960), film(?x269, ?x8984), profession(?x2109, ?x7630), award_winner(?x2109, ?x2110), ?x7630 = 026sdt1, nationality(?x2109, ?x94), type_of_union(?x2109, ?x566), people(?x4195, ?x2109) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1541 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 0dznvw; *> query: (?x11984, ?x269) <- ceremony(?x4573, ?x11984), ceremony(?x3617, ?x11984), ceremony(?x2222, ?x11984), ceremony(?x1245, ?x11984), ceremony(?x591, ?x11984), award_winner(?x11984, ?x7118), award_winner(?x11984, ?x2109), honored_for(?x11984, ?x12679), honored_for(?x11984, ?x8984), ?x1245 = 0gqwc, nominated_for(?x198, ?x8984), ?x2222 = 0gs96, ?x2109 = 0c6g29, nominated_for(?x269, ?x8984), ?x3617 = 0gvx_, award_winner(?x1243, ?x7118), genre(?x8984, ?x53), place_of_burial(?x7118, ?x3691), ?x591 = 0f4x7, country(?x12679, ?x94), ?x4573 = 0gq_d *> conf = 0.32 ranks of expected_values: 20 EVAL 0fzrhn award_winner 0byfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 38.000 14.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10208-0rh6k PRED entity: 0rh6k PRED relation: dog_breed PRED expected values: 01_gx_ => 238 concepts (238 used for prediction) PRED predicted values (max 10 best out of 1): 01_gx_ (0.43 #21, 0.41 #20, 0.40 #11) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0135k2; 01zqy6t; >> query: (?x108, 01_gx_) <- citytown(?x127, ?x108), adjoins(?x1426, ?x108), teams(?x108, ?x662) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rh6k dog_breed 01_gx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 238.000 238.000 0.433 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #10207-02pp1 PRED entity: 02pp1 PRED relation: current_club PRED expected values: 0hvjr => 143 concepts (91 used for prediction) PRED predicted values (max 10 best out of 214): 0xbm (0.50 #1059, 0.43 #1958, 0.33 #168), 06l22 (0.50 #1095, 0.43 #1994, 0.33 #204), 03x6m (0.50 #1562, 0.40 #1413, 0.29 #2161), 0y54 (0.43 #2096, 0.29 #1946, 0.25 #1047), 01gjlw (0.43 #2117, 0.25 #771, 0.21 #1188), 080_y (0.40 #1446, 0.33 #254, 0.25 #1145), 0138mv (0.40 #1417, 0.33 #1566, 0.25 #819), 03tck1 (0.33 #263, 0.25 #1154, 0.25 #1006), 03x726 (0.33 #1621, 0.25 #1023, 0.20 #1472), 02k9k9 (0.33 #1609, 0.25 #1011, 0.20 #1460) >> Best rule #1059 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 02ltg3; >> query: (?x11309, 0xbm) <- current_club(?x11309, ?x8585), current_club(?x11309, ?x5710), current_club(?x978, ?x5710), ?x8585 = 04ltf, team(?x63, ?x5710), team(?x60, ?x11309), sport(?x5710, ?x471), ?x978 = 03y_f8, team(?x208, ?x5710) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1510 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 03z8bw; *> query: (?x11309, 0hvjr) <- current_club(?x11309, ?x6670), current_club(?x11309, ?x5710), colors(?x11309, ?x1101), team(?x208, ?x5710), team(?x6523, ?x6670), position(?x6670, ?x203), ?x203 = 0dgrmp, colors(?x481, ?x1101), teams(?x12603, ?x6670) *> conf = 0.17 ranks of expected_values: 78 EVAL 02pp1 current_club 0hvjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 143.000 91.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #10206-01y888 PRED entity: 01y888 PRED relation: contains! PRED expected values: 06mzp => 183 concepts (88 used for prediction) PRED predicted values (max 10 best out of 392): 09c7w0 (0.81 #59103, 0.77 #17014, 0.74 #4479), 06mzp (0.75 #24176, 0.69 #53728, 0.63 #17011), 02jx1 (0.62 #10831, 0.29 #52919, 0.28 #16202), 059rby (0.46 #42107, 0.45 #43897, 0.44 #46584), 07ssc (0.41 #10776, 0.28 #52864, 0.16 #24208), 04jpl (0.40 #24198, 0.29 #30465, 0.27 #33151), 03rjj (0.26 #2696, 0.17 #905, 0.03 #4486), 02_286 (0.24 #13473, 0.22 #14368, 0.22 #15263), 0345h (0.22 #2768, 0.13 #977, 0.10 #52914), 01n7q (0.21 #23358, 0.16 #24254, 0.14 #26045) >> Best rule #59103 for best value: >> intensional similarity = 6 >> extensional distance = 366 >> proper extension: 02_2kg; >> query: (?x4031, 09c7w0) <- contains(?x10610, ?x4031), location_of_ceremony(?x566, ?x10610), location(?x3931, ?x10610), school_type(?x4031, ?x3092), place_of_death(?x2161, ?x10610), ?x566 = 04ztj >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #24176 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 124 *> proper extension: 018mlg; *> query: (?x4031, ?x774) <- contains(?x10610, ?x4031), jurisdiction_of_office(?x1195, ?x10610), citytown(?x4230, ?x10610), ?x1195 = 0pqc5, country(?x10610, ?x774) *> conf = 0.75 ranks of expected_values: 2 EVAL 01y888 contains! 06mzp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 88.000 0.807 http://example.org/location/location/contains #10205-049n7 PRED entity: 049n7 PRED relation: colors PRED expected values: 083jv => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.99 #971, 0.99 #951, 0.97 #428), 01l849 (0.85 #176, 0.50 #40, 0.33 #20), 06fvc (0.60 #61, 0.43 #99, 0.39 #235), 01g5v (0.40 #81, 0.39 #1126, 0.32 #1283), 019sc (0.40 #85, 0.37 #278, 0.36 #297), 02rnmb (0.40 #72, 0.35 #246, 0.32 #227), 0jc_p (0.33 #5, 0.25 #44, 0.21 #738), 03vtbc (0.23 #599, 0.21 #203, 0.20 #86), 036k5h (0.21 #738, 0.20 #1201, 0.19 #330), 07plts (0.19 #330, 0.19 #329, 0.19 #328) >> Best rule #971 for best value: >> intensional similarity = 8 >> extensional distance = 223 >> proper extension: 01lpx8; >> query: (?x1160, 083jv) <- colors(?x1160, ?x12067), colors(?x13542, ?x12067), colors(?x11185, ?x12067), colors(?x6123, ?x12067), ?x11185 = 01n4w_, contains(?x512, ?x6123), category(?x6123, ?x134), ?x13542 = 0mmd6 >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049n7 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.987 http://example.org/sports/sports_team/colors #10204-0xxc PRED entity: 0xxc PRED relation: major_field_of_study PRED expected values: 01lj9 => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 111): 01mkq (0.47 #646, 0.45 #772, 0.38 #1024), 02j62 (0.47 #662, 0.44 #1545, 0.44 #1797), 02lp1 (0.33 #1777, 0.30 #1525, 0.29 #2787), 04rjg (0.32 #1534, 0.30 #1786, 0.28 #2290), 062z7 (0.32 #1794, 0.31 #1542, 0.29 #659), 03g3w (0.32 #1793, 0.29 #784, 0.27 #1541), 01tbp (0.29 #819, 0.25 #693, 0.25 #63), 05qjt (0.27 #1521, 0.25 #1773, 0.24 #2783), 01lj9 (0.25 #42, 0.21 #1555, 0.20 #168), 036hv (0.25 #11, 0.20 #137, 0.16 #641) >> Best rule #646 for best value: >> intensional similarity = 5 >> extensional distance = 49 >> proper extension: 01jssp; 01pq4w; 017cy9; 06b19; 02f4s3; 0187nd; >> query: (?x10205, 01mkq) <- organization(?x5510, ?x10205), institution(?x1368, ?x10205), ?x5510 = 07xl34, ?x1368 = 014mlp, currency(?x10205, ?x2244) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 036k0s; *> query: (?x10205, 01lj9) <- contains(?x9370, ?x10205), contains(?x279, ?x10205), ?x9370 = 059t8, category(?x10205, ?x134), ?x134 = 08mbj5d, ?x279 = 0d060g *> conf = 0.25 ranks of expected_values: 9 EVAL 0xxc major_field_of_study 01lj9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 156.000 156.000 0.471 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10203-03k545 PRED entity: 03k545 PRED relation: film PRED expected values: 03cp4cn => 73 concepts (49 used for prediction) PRED predicted values (max 10 best out of 658): 0ds1glg (0.25 #1238, 0.06 #6575, 0.04 #13693), 03vfr_ (0.25 #1634, 0.04 #14089, 0.03 #19429), 01rwpj (0.25 #2643, 0.04 #24001, 0.02 #25780), 0194zl (0.25 #840, 0.03 #23977, 0.02 #25756), 09jcj6 (0.25 #793, 0.03 #23930, 0.02 #25709), 0h3xztt (0.25 #172, 0.03 #23309, 0.02 #25088), 02t_h3 (0.25 #1750, 0.03 #24887, 0.02 #26666), 072zl1 (0.25 #1274, 0.02 #24411, 0.02 #26190), 0djkrp (0.25 #1520, 0.02 #24657, 0.01 #26436), 01w8g3 (0.25 #660, 0.02 #23797, 0.01 #25576) >> Best rule #1238 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02114t; 017gxw; >> query: (?x11470, 0ds1glg) <- nationality(?x11470, ?x94), film(?x11470, ?x3283), award_winner(?x3499, ?x11470), ?x3283 = 06gjk9 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2878 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0c9c0; 0f0kz; 07s8hms; 02yxwd; 016k6x; 04sry; *> query: (?x11470, 03cp4cn) <- nationality(?x11470, ?x94), film(?x11470, ?x1916), award_winner(?x3499, ?x11470), ?x1916 = 0ch26b_ *> conf = 0.12 ranks of expected_values: 66 EVAL 03k545 film 03cp4cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 73.000 49.000 0.250 http://example.org/film/actor/film./film/performance/film #10202-01w7nwm PRED entity: 01w7nwm PRED relation: artists! PRED expected values: 01fm07 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 230): 064t9 (0.66 #5271, 0.57 #1869, 0.54 #2487), 06by7 (0.59 #16103, 0.45 #23, 0.43 #5280), 0gywn (0.46 #5316, 0.25 #2532, 0.23 #5007), 025sc50 (0.42 #5308, 0.32 #5617, 0.26 #4999), 01lyv (0.36 #36, 0.24 #8385, 0.20 #9932), 05bt6j (0.31 #1900, 0.25 #16125, 0.24 #5302), 02lnbg (0.30 #1915, 0.25 #5317, 0.24 #2533), 0ggx5q (0.28 #1934, 0.22 #5336, 0.22 #2552), 02x8m (0.26 #5277, 0.12 #16100, 0.12 #9916), 03_d0 (0.25 #5269, 0.25 #2794, 0.23 #4341) >> Best rule #5271 for best value: >> intensional similarity = 3 >> extensional distance = 150 >> proper extension: 01v27pl; >> query: (?x3175, 064t9) <- artists(?x3319, ?x3175), ?x3319 = 06j6l, category(?x3175, ?x134) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #5383 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 150 *> proper extension: 01v27pl; *> query: (?x3175, 01fm07) <- artists(?x3319, ?x3175), ?x3319 = 06j6l, category(?x3175, ?x134) *> conf = 0.07 ranks of expected_values: 46 EVAL 01w7nwm artists! 01fm07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 119.000 119.000 0.664 http://example.org/music/genre/artists #10201-016ks5 PRED entity: 016ks5 PRED relation: titles! PRED expected values: 02xh1 => 84 concepts (50 used for prediction) PRED predicted values (max 10 best out of 56): 04xvlr (0.36 #507, 0.33 #204, 0.31 #405), 02l7c8 (0.35 #1614, 0.33 #4970, 0.26 #302), 0219x_ (0.35 #1614, 0.33 #4970, 0.26 #302), 02xh1 (0.35 #1614, 0.33 #4970, 0.26 #302), 01jfsb (0.26 #725, 0.14 #4072, 0.13 #2646), 0lsxr (0.26 #302, 0.25 #504, 0.24 #4155), 01hmnh (0.25 #125, 0.14 #1035, 0.14 #25), 024qqx (0.25 #179, 0.14 #79, 0.10 #1089), 01z4y (0.22 #1044, 0.22 #639, 0.19 #1444), 017fp (0.14 #424, 0.13 #324, 0.11 #223) >> Best rule #507 for best value: >> intensional similarity = 2 >> extensional distance = 134 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x6149, 04xvlr) <- titles(?x512, ?x6149), ?x512 = 07ssc >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1614 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 479 *> proper extension: 016kz1; *> query: (?x6149, ?x2753) <- music(?x6149, ?x9408), genre(?x6149, ?x2753), titles(?x2753, ?x994), award(?x6149, ?x834) *> conf = 0.35 ranks of expected_values: 4 EVAL 016ks5 titles! 02xh1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 84.000 50.000 0.360 http://example.org/media_common/netflix_genre/titles #10200-01wx756 PRED entity: 01wx756 PRED relation: artist! PRED expected values: 0n85g => 125 concepts (97 used for prediction) PRED predicted values (max 10 best out of 124): 01clyr (0.43 #561, 0.30 #827, 0.25 #960), 0g768 (0.38 #1629, 0.33 #1230, 0.29 #565), 01dtcb (0.29 #1505, 0.22 #707, 0.20 #840), 0181dw (0.29 #1368, 0.22 #6424, 0.14 #570), 043g7l (0.29 #559, 0.13 #1224, 0.11 #692), 011k1h (0.25 #143, 0.21 #1606, 0.14 #542), 0mzkr (0.25 #22, 0.17 #288, 0.12 #1485), 01trtc (0.25 #66, 0.17 #332, 0.12 #1529), 03mp8k (0.25 #60, 0.17 #326, 0.09 #1789), 04t53l (0.25 #8, 0.17 #274, 0.03 #1737) >> Best rule #561 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 016t0h; 012x1l; >> query: (?x11827, 01clyr) <- artist(?x3887, ?x11827), artist(?x2931, ?x11827), artists(?x505, ?x11827), ?x3887 = 02bh8z, ?x2931 = 03rhqg >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1253 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 09lwrt; *> query: (?x11827, 0n85g) <- artist(?x3887, ?x11827), artists(?x505, ?x11827), ?x3887 = 02bh8z, instrumentalists(?x227, ?x11827) *> conf = 0.13 ranks of expected_values: 28 EVAL 01wx756 artist! 0n85g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 125.000 97.000 0.429 http://example.org/music/record_label/artist #10199-03tp4 PRED entity: 03tp4 PRED relation: people PRED expected values: 08959 => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 03tp4 people 08959 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/people/cause_of_death/people #10198-01rr_d PRED entity: 01rr_d PRED relation: student PRED expected values: 083pr => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1652): 059y0 (0.50 #2035, 0.33 #2949, 0.33 #1581), 05fg2 (0.50 #2085, 0.33 #1404, 0.29 #3002), 013pp3 (0.50 #1943, 0.33 #1489, 0.29 #3087), 04z0g (0.43 #3329, 0.33 #2866, 0.33 #2638), 0b78hw (0.43 #3302, 0.33 #2839, 0.33 #2611), 024jwt (0.40 #2485, 0.33 #2946, 0.33 #2718), 014vk4 (0.33 #2962, 0.33 #2734, 0.33 #1366), 06y7d (0.33 #2960, 0.33 #2732, 0.33 #1364), 01tdnyh (0.33 #2851, 0.33 #2623, 0.33 #1255), 0969fd (0.33 #2945, 0.33 #2717, 0.33 #1349) >> Best rule #2035 for best value: >> intensional similarity = 29 >> extensional distance = 2 >> proper extension: 0bkj86; >> query: (?x7636, 059y0) <- institution(?x7636, ?x10889), institution(?x7636, ?x9823), institution(?x7636, ?x9181), institution(?x7636, ?x6548), institution(?x7636, ?x5750), institution(?x7636, ?x5035), institution(?x7636, ?x3424), institution(?x7636, ?x1391), institution(?x7636, ?x481), ?x9181 = 012lzr, ?x1391 = 05f7s1, student(?x7636, ?x13011), student(?x7636, ?x1984), ?x3424 = 01w5m, colors(?x6548, ?x663), student(?x9823, ?x2092), major_field_of_study(?x7636, ?x2981), school_type(?x6548, ?x4994), ?x2981 = 02j62, ?x10889 = 01hl_w, ?x5750 = 01nnsv, organization(?x5510, ?x9823), currency(?x9823, ?x1099), school_type(?x5035, ?x3092), citytown(?x6548, ?x1841), people(?x6720, ?x1984), major_field_of_study(?x5035, ?x2921), student(?x7660, ?x13011), ?x481 = 052nd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #917 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 1 *> proper extension: 014mlp; *> query: (?x7636, ?x1620) <- institution(?x7636, ?x9823), institution(?x7636, ?x9181), institution(?x7636, ?x7596), institution(?x7636, ?x5178), institution(?x7636, ?x5035), institution(?x7636, ?x3513), institution(?x7636, ?x2327), institution(?x7636, ?x735), institution(?x7636, ?x481), institution(?x7636, ?x122), ?x9181 = 012lzr, ?x9823 = 0gk7z, student(?x7636, ?x1984), ?x122 = 08815, ?x5035 = 01bcwk, ?x3513 = 0pspl, student(?x5178, ?x3445), student(?x5178, ?x1620), organization(?x3484, ?x5178), ?x2327 = 07wjk, ?x3445 = 0d06m5, ?x735 = 065y4w7, place_of_birth(?x1984, ?x2474), nationality(?x1984, ?x279), politician(?x12007, ?x1984), currency(?x481, ?x2244), major_field_of_study(?x481, ?x254), company(?x1913, ?x5178), state_province_region(?x481, ?x6842), ?x7596 = 012mzw *> conf = 0.17 ranks of expected_values: 194 EVAL 01rr_d student 083pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 25.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #10197-07jnt PRED entity: 07jnt PRED relation: film! PRED expected values: 01t9_0 => 86 concepts (74 used for prediction) PRED predicted values (max 10 best out of 58): 03xq0f (0.86 #75, 0.84 #438, 0.55 #726), 031rq5 (0.43 #2316), 05qd_ (0.23 #224, 0.16 #79, 0.15 #514), 016tw3 (0.16 #2252, 0.15 #3490, 0.14 #3778), 016tt2 (0.15 #74, 0.14 #437, 0.13 #147), 03rwz3 (0.14 #403, 0.04 #908, 0.04 #1052), 06jntd (0.09 #752, 0.03 #246, 0.03 #391), 01gb54 (0.09 #99, 0.09 #462, 0.08 #244), 0g1rw (0.09 #1233, 0.07 #1959, 0.07 #4649), 061dn_ (0.08 #22, 0.06 #889, 0.06 #817) >> Best rule #75 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 011yxg; 0ds11z; 02hxhz; 0c00zd0; 031778; 01_1pv; 026p4q7; 048htn; 03h3x5; 0cn_b8; ... >> query: (?x6782, 03xq0f) <- film(?x382, ?x6782), nominated_for(?x185, ?x6782), language(?x6782, ?x254), region(?x6782, ?x94) >> conf = 0.86 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07jnt film! 01t9_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 74.000 0.860 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #10196-07bzz7 PRED entity: 07bzz7 PRED relation: film_release_region PRED expected values: 03rjj 015fr => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 230): 0jgd (0.85 #1466, 0.85 #3257, 0.81 #3909), 02jx1 (0.85 #2602, 0.83 #2929, 0.83 #3579), 0chghy (0.85 #2615, 0.85 #2451, 0.84 #3429), 03rjj (0.84 #3422, 0.84 #4888, 0.84 #3912), 0154j (0.83 #3421, 0.82 #3911, 0.80 #3259), 035qy (0.81 #3293, 0.81 #3945, 0.79 #3455), 05qhw (0.81 #4900, 0.81 #3924, 0.80 #5063), 01znc_ (0.80 #3302, 0.77 #3464, 0.75 #3954), 015fr (0.79 #3437, 0.77 #1484, 0.77 #3927), 03rt9 (0.77 #1480, 0.71 #3271, 0.71 #3923) >> Best rule #1466 for best value: >> intensional similarity = 6 >> extensional distance = 46 >> proper extension: 0djb3vw; 0d6b7; >> query: (?x5139, 0jgd) <- music(?x5139, ?x3321), award_nominee(?x3321, ?x4701), film_release_region(?x5139, ?x1264), ?x1264 = 0345h, instrumentalists(?x228, ?x3321), artists(?x378, ?x4701) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #3422 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 110 *> proper extension: 0gtsx8c; *> query: (?x5139, 03rjj) <- film_release_region(?x5139, ?x2267), film_release_region(?x5139, ?x1892), film_release_region(?x5139, ?x1229), film_release_region(?x5139, ?x94), ?x2267 = 03rj0, ?x94 = 09c7w0, ?x1229 = 059j2, ?x1892 = 02vzc, country(?x5139, ?x1310) *> conf = 0.84 ranks of expected_values: 4, 9 EVAL 07bzz7 film_release_region 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 88.000 88.000 0.854 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07bzz7 film_release_region 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 88.000 0.854 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10195-01dvtx PRED entity: 01dvtx PRED relation: gender PRED expected values: 05zppz => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #37, 0.92 #57, 0.91 #43), 02zsn (0.53 #234, 0.34 #128, 0.31 #62) >> Best rule #37 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 0gt_k; >> query: (?x4003, 05zppz) <- nationality(?x4003, ?x94), people(?x5540, ?x4003), profession(?x4003, ?x353), company(?x4003, ?x2313), influenced_by(?x1737, ?x4003) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dvtx gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.923 http://example.org/people/person/gender #10194-07szy PRED entity: 07szy PRED relation: major_field_of_study PRED expected values: 0g26h 037mh8 01400v => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 82): 037mh8 (0.70 #149, 0.67 #347, 0.62 #644), 03nfmq (0.50 #324, 0.50 #126, 0.42 #225), 0193x (0.50 #322, 0.50 #124, 0.35 #421), 04sh3 (0.48 #651, 0.42 #255, 0.40 #156), 0g26h (0.46 #1119, 0.43 #624, 0.42 #327), 04g7x (0.42 #351, 0.40 #153, 0.35 #450), 0dc_v (0.42 #328, 0.40 #130, 0.33 #625), 04gb7 (0.40 #131, 0.33 #230, 0.29 #626), 0l5mz (0.33 #649, 0.33 #253, 0.30 #154), 01jzxy (0.33 #313, 0.33 #214, 0.30 #115) >> Best rule #149 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 05qd_; >> query: (?x1681, 037mh8) <- company(?x920, ?x1681), organizations_founded(?x1681, ?x5487), organization(?x346, ?x1681) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 39 EVAL 07szy major_field_of_study 01400v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 110.000 110.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07szy major_field_of_study 037mh8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07szy major_field_of_study 0g26h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 110.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10193-02dwj PRED entity: 02dwj PRED relation: language PRED expected values: 06b_j => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 35): 064_8sq (0.18 #5111, 0.16 #1020, 0.15 #784), 06nm1 (0.18 #5111, 0.13 #10, 0.13 #476), 02bjrlw (0.18 #5111, 0.11 #349, 0.10 #940), 06b_j (0.18 #5111, 0.08 #312, 0.07 #961), 03_9r (0.18 #5111, 0.06 #417, 0.05 #2067), 0jzc (0.18 #5111, 0.06 #427, 0.05 #958), 01r2l (0.18 #5111, 0.01 #82, 0.01 #314), 04306rv (0.13 #943, 0.13 #352, 0.11 #1883), 04h9h (0.07 #42, 0.04 #216, 0.04 #981), 012w70 (0.05 #302, 0.02 #2011, 0.02 #3068) >> Best rule #5111 for best value: >> intensional similarity = 4 >> extensional distance = 1502 >> proper extension: 04bp0l; >> query: (?x5228, ?x90) <- nominated_for(?x2507, ?x5228), award_winner(?x2111, ?x2507), nominated_for(?x2507, ?x10599), language(?x10599, ?x90) >> conf = 0.18 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 02dwj language 06b_j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 99.000 0.181 http://example.org/film/film/language #10192-0bksh PRED entity: 0bksh PRED relation: award_nominee! PRED expected values: 01pllx => 138 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1132): 01r93l (0.83 #34891, 0.82 #16282, 0.82 #41869), 04bdxl (0.83 #34891, 0.82 #16282, 0.82 #41869), 01gkmx (0.83 #34891, 0.82 #16282, 0.82 #41869), 01pllx (0.83 #34891, 0.82 #16282, 0.82 #41869), 01rr9f (0.25 #94, 0.09 #23356, 0.06 #32659), 01hxs4 (0.25 #184, 0.07 #23446, 0.05 #28098), 016tb7 (0.25 #828, 0.07 #24090, 0.05 #3153), 016tbr (0.25 #2114, 0.07 #25376, 0.05 #4439), 0j1yf (0.20 #2325, 0.20 #25587, 0.18 #34890), 0bksh (0.20 #3455, 0.07 #132553, 0.06 #12760) >> Best rule #34891 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 066m4g; 012x4t; 0q5hw; 03cxsvl; 0c408_; 01t_wfl; >> query: (?x4782, ?x91) <- friend(?x1896, ?x4782), award_nominee(?x794, ?x4782), award_nominee(?x4782, ?x91) >> conf = 0.83 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 0bksh award_nominee! 01pllx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 138.000 85.000 0.826 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10191-0gj8nq2 PRED entity: 0gj8nq2 PRED relation: film_release_region PRED expected values: 03_3d 07ssc 01p8s => 74 concepts (63 used for prediction) PRED predicted values (max 10 best out of 131): 035qy (0.93 #263, 0.93 #144, 0.91 #382), 07ssc (0.86 #725, 0.86 #129, 0.84 #367), 03_3d (0.86 #123, 0.84 #242, 0.82 #361), 06qd3 (0.65 #386, 0.62 #267, 0.60 #148), 03ryn (0.60 #181, 0.56 #300, 0.53 #419), 06mzp (0.59 #730, 0.56 #253, 0.54 #372), 09pmkv (0.56 #377, 0.55 #139, 0.53 #258), 0h7x (0.54 #383, 0.53 #264, 0.52 #145), 01pj7 (0.49 #393, 0.45 #155, 0.44 #274), 02k54 (0.45 #130, 0.44 #249, 0.44 #368) >> Best rule #263 for best value: >> intensional similarity = 6 >> extensional distance = 43 >> proper extension: 03twd6; 0fq7dv_; 0dr3sl; >> query: (?x3377, 035qy) <- film_release_region(?x3377, ?x1023), film_release_region(?x3377, ?x404), film_release_region(?x3377, ?x172), ?x1023 = 0ctw_b, ?x404 = 047lj, ?x172 = 0154j >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #725 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 127 *> proper extension: 0ds3t5x; 0g5qs2k; 0c40vxk; 04969y; 01vksx; 0872p_c; 053rxgm; 05z_kps; 0gtvrv3; 0fpkhkz; ... *> query: (?x3377, 07ssc) <- film_release_region(?x3377, ?x4743), film_release_region(?x3377, ?x1558), film_release_region(?x3377, ?x1023), nationality(?x226, ?x1023), ?x1558 = 01mjq, ?x4743 = 03spz *> conf = 0.86 ranks of expected_values: 2, 3, 58 EVAL 0gj8nq2 film_release_region 01p8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 74.000 63.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gj8nq2 film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 63.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gj8nq2 film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 63.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10190-0fzm0g PRED entity: 0fzm0g PRED relation: film_crew_role PRED expected values: 02r96rf => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 31): 02r96rf (0.69 #312, 0.66 #727, 0.66 #1418), 01vx2h (0.45 #319, 0.33 #528, 0.33 #562), 01pvkk (0.32 #320, 0.31 #147, 0.29 #1391), 02rh1dz (0.20 #9, 0.18 #318, 0.17 #43), 015h31 (0.20 #8, 0.13 #178, 0.13 #2384), 02ynfr (0.20 #324, 0.17 #567, 0.17 #911), 0263ycg (0.17 #84, 0.13 #2384, 0.09 #2697), 020xn5 (0.17 #41, 0.13 #2384, 0.09 #2697), 0215hd (0.15 #326, 0.14 #1432, 0.14 #1535), 0d2b38 (0.13 #2384, 0.13 #333, 0.11 #298) >> Best rule #312 for best value: >> intensional similarity = 5 >> extensional distance = 319 >> proper extension: 014_x2; 0m2kd; 060v34; 04fzfj; 03ckwzc; 04gknr; 01vfqh; 09cr8; 015x74; 05qbckf; ... >> query: (?x12934, 02r96rf) <- currency(?x12934, ?x170), film_crew_role(?x12934, ?x2095), ?x2095 = 0dxtw, genre(?x12934, ?x53), film(?x3079, ?x12934) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fzm0g film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.685 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10189-01hxs4 PRED entity: 01hxs4 PRED relation: award PRED expected values: 09qrn4 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 249): 0bfvd4 (0.39 #515, 0.09 #9761, 0.08 #12173), 09sb52 (0.37 #6069, 0.32 #4863, 0.32 #14913), 0ck27z (0.32 #8934, 0.32 #9336, 0.29 #10944), 05pcn59 (0.27 #883, 0.25 #1285, 0.24 #6109), 05p09zm (0.26 #2132, 0.23 #1328, 0.21 #926), 05zr6wv (0.20 #17, 0.17 #4439, 0.16 #6047), 03c7tr1 (0.20 #56, 0.15 #2066, 0.14 #1664), 02x4x18 (0.20 #131, 0.13 #34175, 0.09 #5759), 02x8n1n (0.20 #118, 0.12 #520, 0.03 #6148), 09qwmm (0.20 #33, 0.10 #5661, 0.07 #7269) >> Best rule #515 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 07ddz9; >> query: (?x917, 0bfvd4) <- gender(?x917, ?x231), award(?x917, ?x435), ?x435 = 0bp_b2 >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #639 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 07ddz9; *> query: (?x917, 09qrn4) <- gender(?x917, ?x231), award(?x917, ?x435), ?x435 = 0bp_b2 *> conf = 0.18 ranks of expected_values: 16 EVAL 01hxs4 award 09qrn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 107.000 107.000 0.393 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10188-012mzw PRED entity: 012mzw PRED relation: category PRED expected values: 08mbj5d => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #39, 0.90 #43, 0.90 #32) >> Best rule #39 for best value: >> intensional similarity = 2 >> extensional distance = 189 >> proper extension: 0fht9f; 0frm7n; >> query: (?x7596, 08mbj5d) <- school(?x1239, ?x7596), team(?x1114, ?x1239) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012mzw category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.906 http://example.org/common/topic/webpage./common/webpage/category #10187-05jg58 PRED entity: 05jg58 PRED relation: artists PRED expected values: 07g2v 0mgcr 01wvxw1 => 61 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1199): 01w8n89 (0.80 #14208, 0.64 #8855, 0.56 #15276), 01gx5f (0.67 #3488, 0.55 #6695, 0.50 #8831), 01vw20_ (0.64 #7718, 0.55 #6650, 0.53 #10923), 01y_rz (0.55 #7349, 0.50 #9485, 0.50 #8417), 01386_ (0.55 #6979, 0.50 #4840, 0.43 #9115), 0ycp3 (0.50 #3806, 0.50 #2738, 0.45 #7013), 01vrt_c (0.50 #3276, 0.47 #9687, 0.38 #4344), 011_vz (0.50 #8312, 0.45 #7244, 0.43 #9380), 01shhf (0.50 #4063, 0.45 #7270, 0.38 #5131), 014_lq (0.50 #3674, 0.45 #6881, 0.38 #4742) >> Best rule #14208 for best value: >> intensional similarity = 9 >> extensional distance = 28 >> proper extension: 03w94xt; >> query: (?x8187, 01w8n89) <- artists(?x8187, ?x4261), artists(?x8187, ?x3740), instrumentalists(?x1969, ?x3740), ?x1969 = 04rzd, artists(?x7329, ?x3740), artists(?x1572, ?x3740), influenced_by(?x1573, ?x4261), ?x7329 = 016jny, ?x1572 = 06by7 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #7145 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 9 *> proper extension: 0xhtw; 03lty; 016jny; 0155w; 09nwwf; *> query: (?x8187, 01wvxw1) <- artists(?x8187, ?x3740), artists(?x8187, ?x3118), artists(?x8187, ?x1060), ?x3740 = 0fpj4lx, parent_genre(?x8187, ?x302), award(?x1060, ?x3045), ?x3045 = 02sp_v, participant(?x970, ?x3118), vacationer(?x362, ?x3118) *> conf = 0.45 ranks of expected_values: 49, 138, 505 EVAL 05jg58 artists 01wvxw1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 61.000 23.000 0.800 http://example.org/music/genre/artists EVAL 05jg58 artists 0mgcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 23.000 0.800 http://example.org/music/genre/artists EVAL 05jg58 artists 07g2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 61.000 23.000 0.800 http://example.org/music/genre/artists #10186-0b05xm PRED entity: 0b05xm PRED relation: profession PRED expected values: 02jknp => 58 concepts (53 used for prediction) PRED predicted values (max 10 best out of 39): 02hrh1q (0.90 #6926, 0.78 #7220, 0.71 #2218), 02jknp (0.56 #2359, 0.54 #448, 0.40 #1036), 02krf9 (0.38 #172, 0.33 #613, 0.31 #466), 0cbd2 (0.27 #4706, 0.26 #5589, 0.25 #6031), 018gz8 (0.22 #1191, 0.19 #1926, 0.19 #1338), 09jwl (0.16 #4575, 0.16 #5017, 0.16 #5311), 0np9r (0.14 #1195, 0.13 #1930, 0.13 #1342), 0nbcg (0.11 #2823, 0.11 #4588, 0.11 #5030), 0dz3r (0.10 #5002, 0.10 #4560, 0.10 #5149), 0kyk (0.10 #469, 0.10 #3262, 0.09 #1204) >> Best rule #6926 for best value: >> intensional similarity = 3 >> extensional distance = 2760 >> proper extension: 07j8kh; >> query: (?x3570, 02hrh1q) <- profession(?x3570, ?x1041), profession(?x11433, ?x1041), ?x11433 = 01bj6y >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2359 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1174 *> proper extension: 01c59k; 0bs1yy; 05h72z; 01n9d9; 03nqbvz; 01s7qqw; 0627sn; 0cc63l; 03crcpt; 027t8fw; ... *> query: (?x3570, 02jknp) <- profession(?x3570, ?x1041), profession(?x12274, ?x1041), ?x12274 = 066yfh *> conf = 0.56 ranks of expected_values: 2 EVAL 0b05xm profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 53.000 0.904 http://example.org/people/person/profession #10185-06t8v PRED entity: 06t8v PRED relation: country! PRED expected values: 01dys 01hp22 07bs0 06wrt 09f6b => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 34): 06wrt (0.75 #11, 0.74 #215, 0.69 #113), 01cgz (0.71 #10, 0.69 #112, 0.65 #1472), 07bs0 (0.71 #9, 0.57 #145, 0.54 #111), 03rbzn (0.71 #15, 0.56 #117, 0.53 #423), 02y8z (0.67 #12, 0.64 #114, 0.58 #216), 01sgl (0.67 #26, 0.56 #298, 0.49 #128), 01gqfm (0.67 #30, 0.54 #132, 0.44 #234), 07jjt (0.62 #14, 0.49 #218, 0.46 #116), 01hp22 (0.58 #6, 0.56 #108, 0.54 #414), 0486tv (0.58 #24, 0.49 #534, 0.47 #432) >> Best rule #11 for best value: >> intensional similarity = 2 >> extensional distance = 22 >> proper extension: 012wgb; 056_y; >> query: (?x3277, 06wrt) <- film_release_region(?x6492, ?x3277), ?x6492 = 0ds6bmk >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 9, 21, 26 EVAL 06t8v country! 09f6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 147.000 147.000 0.750 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06t8v country! 06wrt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.750 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06t8v country! 07bs0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 147.000 0.750 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06t8v country! 01hp22 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 147.000 147.000 0.750 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06t8v country! 01dys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 147.000 147.000 0.750 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #10184-0935jw PRED entity: 0935jw PRED relation: film PRED expected values: 0k54q => 86 concepts (40 used for prediction) PRED predicted values (max 10 best out of 710): 05sw5b (0.24 #9772, 0.04 #15145, 0.03 #27682), 0435vm (0.20 #648, 0.17 #2440, 0.14 #4231), 087pfc (0.20 #1533, 0.17 #3325, 0.14 #5116), 05qbckf (0.20 #309, 0.17 #2101, 0.14 #3892), 03bzjpm (0.20 #1317, 0.17 #3109, 0.14 #4900), 02xbyr (0.20 #806, 0.17 #2598, 0.14 #4389), 09jcj6 (0.20 #800, 0.17 #2592, 0.14 #4383), 06gjk9 (0.20 #538, 0.17 #2330, 0.14 #4121), 062zm5h (0.20 #859, 0.17 #2651, 0.14 #4442), 065_cjc (0.20 #1198, 0.17 #2990, 0.14 #4781) >> Best rule #9772 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 0sz28; 02lkcc; 02jt1k; 0fsm8c; 0170pk; 0pmhf; 02g3mn; 0glmv; 01n7qlf; 02g5h5; ... >> query: (?x12649, 05sw5b) <- place_of_birth(?x12649, ?x362), profession(?x12649, ?x1383), film(?x12649, ?x10327), film(?x7372, ?x10327), ?x7372 = 03wy70 >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #27803 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 213 *> proper extension: 044f7; 0c8hct; 066l3y; 06jrhz; 05v954; 06h7l7; 09wlpl; 027j79k; 06vqdf; 075npt; ... *> query: (?x12649, 0k54q) <- place_of_birth(?x12649, ?x362), profession(?x12649, ?x4725), profession(?x12649, ?x1383), ?x1383 = 0np9r, profession(?x8160, ?x4725), ?x8160 = 02dlfh *> conf = 0.02 ranks of expected_values: 347 EVAL 0935jw film 0k54q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 86.000 40.000 0.243 http://example.org/film/actor/film./film/performance/film #10183-01jbx1 PRED entity: 01jbx1 PRED relation: nationality PRED expected values: 09c7w0 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 33): 09c7w0 (0.86 #1502, 0.85 #4414, 0.85 #1201), 0kpys (0.33 #8831, 0.31 #8127), 030qb3t (0.33 #8831, 0.31 #8127), 01n7q (0.33 #8831, 0.31 #8127), 07ssc (0.30 #10443, 0.13 #415, 0.12 #215), 0345h (0.30 #10443, 0.07 #331, 0.05 #931), 02jx1 (0.25 #233, 0.22 #633, 0.16 #1033), 06q1r (0.07 #377, 0.07 #477, 0.06 #577), 0hzlz (0.07 #323, 0.07 #423, 0.04 #1123), 06mzp (0.07 #321, 0.07 #421, 0.04 #1121) >> Best rule #1502 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 01t6b4; 0162c8; 01pcmd; 06chf; 04x4s2; 0b1f49; 0603qp; 05gp3x; 02vqpx8; 070j61; ... >> query: (?x3291, 09c7w0) <- place_of_birth(?x3291, ?x8618), people(?x2510, ?x3291), program(?x3291, ?x11033) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jbx1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.857 http://example.org/people/person/nationality #10182-03q8ch PRED entity: 03q8ch PRED relation: nominated_for PRED expected values: 0cc5qkt => 125 concepts (35 used for prediction) PRED predicted values (max 10 best out of 330): 0f4yh (0.80 #48531, 0.80 #38826, 0.78 #50149), 02mmwk (0.40 #8096, 0.38 #11336, 0.38 #4854), 0hx4y (0.40 #8096, 0.38 #11336, 0.38 #4854), 04mcw4 (0.40 #8096, 0.38 #11336, 0.38 #4854), 0315rp (0.40 #8096, 0.38 #11336, 0.38 #4854), 011xg5 (0.40 #8096, 0.38 #11336, 0.38 #4854), 0292qb (0.40 #8096, 0.38 #11336, 0.38 #4854), 012mrr (0.40 #8096, 0.38 #11336, 0.38 #4854), 01flv_ (0.40 #8096, 0.38 #11336, 0.38 #4854), 02rb84n (0.40 #8096, 0.38 #4854, 0.37 #4857) >> Best rule #48531 for best value: >> intensional similarity = 3 >> extensional distance = 927 >> proper extension: 06n7h7; 03ldxq; 049_zz; 02pt6k_; 01k70_; 02qssrm; 05yjhm; >> query: (?x4215, ?x3535) <- nominated_for(?x4215, ?x1685), award_winner(?x3535, ?x4215), place_of_birth(?x4215, ?x739) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3777 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0jgwf; *> query: (?x4215, 0cc5qkt) <- edited_by(?x9154, ?x4215), film(?x157, ?x9154), award_winner(?x1747, ?x4215) *> conf = 0.06 ranks of expected_values: 101 EVAL 03q8ch nominated_for 0cc5qkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 125.000 35.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10181-0f8l9c PRED entity: 0f8l9c PRED relation: film_release_region! PRED expected values: 04q24zv => 285 concepts (285 used for prediction) PRED predicted values (max 10 best out of 583): 043h78 (0.29 #1850, 0.29 #1717, 0.29 #1184), 03z106 (0.29 #1654, 0.25 #2320, 0.25 #2186), 02psgq (0.29 #1677, 0.25 #2343, 0.14 #2742), 0dkv90 (0.25 #2240, 0.18 #2640, 0.12 #3837), 015ynm (0.25 #2244, 0.14 #1845, 0.14 #1712), 025ts_z (0.18 #2647, 0.14 #2114, 0.14 #1848), 05fcbk7 (0.18 #2568, 0.14 #2035, 0.14 #1769), 03cyslc (0.14 #2093, 0.14 #1827, 0.14 #1694), 08sfxj (0.14 #2073, 0.14 #1807, 0.14 #1674), 05c46y6 (0.14 #2031, 0.14 #1765, 0.14 #1632) >> Best rule #1850 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 0cgm9; >> query: (?x789, 043h78) <- entity_involved(?x9939, ?x789), partially_contains(?x455, ?x789) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f8l9c film_release_region! 04q24zv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 285.000 285.000 0.286 http://example.org/film/film/runtime./film/film_cut/film_release_region #10180-02kxwk PRED entity: 02kxwk PRED relation: profession PRED expected values: 02jknp => 87 concepts (53 used for prediction) PRED predicted values (max 10 best out of 45): 01d_h8 (0.38 #6, 0.34 #1623, 0.33 #594), 0dxtg (0.38 #14, 0.34 #308, 0.28 #1337), 03gjzk (0.36 #309, 0.23 #1044, 0.23 #1191), 02jknp (0.31 #8, 0.27 #596, 0.24 #1625), 018gz8 (0.31 #17, 0.27 #458, 0.26 #311), 09jwl (0.16 #4135, 0.16 #2518, 0.16 #3841), 0cbd2 (0.14 #5593, 0.14 #2359, 0.12 #7211), 0d1pc (0.12 #931, 0.08 #196, 0.08 #49), 0nbcg (0.11 #3852, 0.11 #4146, 0.11 #7381), 0dz3r (0.11 #4118, 0.11 #3824, 0.10 #6030) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 04h07s; 08_438; >> query: (?x4367, 01d_h8) <- film(?x4367, ?x148), ?x148 = 034qmv, profession(?x4367, ?x1032) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 04h07s; 08_438; *> query: (?x4367, 02jknp) <- film(?x4367, ?x148), ?x148 = 034qmv, profession(?x4367, ?x1032) *> conf = 0.31 ranks of expected_values: 4 EVAL 02kxwk profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 53.000 0.385 http://example.org/people/person/profession #10179-059m45 PRED entity: 059m45 PRED relation: film PRED expected values: 0kvgxk => 84 concepts (40 used for prediction) PRED predicted values (max 10 best out of 489): 0gvsh7l (0.49 #30429, 0.49 #28639, 0.48 #32219), 01fx1l (0.49 #30429, 0.49 #28639, 0.48 #32219), 03wj4r8 (0.20 #1457, 0.07 #3246, 0.02 #5035), 04x4vj (0.20 #773, 0.04 #4351, 0.01 #16881), 04sh80 (0.20 #1748, 0.02 #5326), 011yd2 (0.20 #356, 0.02 #3934), 0gwf191 (0.20 #1571), 026f__m (0.20 #1343), 0hv8w (0.20 #944), 011yth (0.14 #2089, 0.06 #3878, 0.01 #9247) >> Best rule #30429 for best value: >> intensional similarity = 3 >> extensional distance = 949 >> proper extension: 012x4t; 01wwvc5; 0bt4r4; 01309x; 01pp3p; 01c6l; 037d35; 012vct; 01zwy; 013tcv; ... >> query: (?x7034, ?x5594) <- location(?x7034, ?x6987), award_nominee(?x1129, ?x7034), nominated_for(?x7034, ?x5594) >> conf = 0.49 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 059m45 film 0kvgxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 40.000 0.488 http://example.org/film/actor/film./film/performance/film #10178-0181hw PRED entity: 0181hw PRED relation: industry PRED expected values: 02jjt => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 113): 02jjt (0.68 #974, 0.33 #467, 0.31 #560), 03qh03g (0.64 #1063, 0.25 #96, 0.23 #971), 01mw1 (0.59 #1290, 0.43 #1659, 0.38 #1751), 020mfr (0.45 #1304, 0.39 #1673, 0.34 #1765), 01mf0 (0.25 #812, 0.09 #1687, 0.09 #766), 07c52 (0.25 #95, 0.07 #463, 0.06 #556), 029g_vk (0.16 #1852, 0.15 #2037, 0.15 #2083), 0sydc (0.12 #814, 0.09 #399, 0.08 #1090), 0hz28 (0.11 #1087, 0.05 #2240, 0.04 #765), 011s0 (0.11 #654, 0.09 #746, 0.08 #792) >> Best rule #974 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 0c_j5d; 04qhdf; 02rr_z4; >> query: (?x8170, 02jjt) <- industry(?x8170, ?x8681), industry(?x12227, ?x8681), industry(?x3887, ?x8681), ?x12227 = 07s363, ?x3887 = 02bh8z >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0181hw industry 02jjt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.677 http://example.org/business/business_operation/industry #10177-03_x5t PRED entity: 03_x5t PRED relation: student! PRED expected values: 0g4gr => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 12): 02822 (0.04 #341, 0.04 #466, 0.03 #1706), 03qsdpk (0.02 #346, 0.02 #471, 0.02 #1711), 01zc2w (0.02 #358, 0.01 #483, 0.01 #1723), 0fdys (0.02 #153, 0.01 #464, 0.01 #1953), 041y2 (0.02 #51, 0.01 #113, 0.01 #237), 05qfh (0.02 #337, 0.01 #462, 0.01 #586), 0w7c (0.01 #1717, 0.01 #1966, 0.01 #539), 062z7 (0.01 #146), 03g3w (0.01 #456, 0.01 #1945, 0.01 #1696), 02vxn (0.01 #439, 0.01 #749, 0.01 #314) >> Best rule #341 for best value: >> intensional similarity = 2 >> extensional distance = 186 >> proper extension: 01bpnd; >> query: (?x10371, 02822) <- languages(?x10371, ?x254), participant(?x2352, ?x10371) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_x5t student! 0g4gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 92.000 0.043 http://example.org/education/field_of_study/students_majoring./education/education/student #10176-05lfwd PRED entity: 05lfwd PRED relation: nominated_for! PRED expected values: 06czyr => 88 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1065): 06czyr (0.87 #2322, 0.84 #6965, 0.83 #13930), 04pp9s (0.87 #2322, 0.84 #6965, 0.83 #13930), 06s6hs (0.87 #2322, 0.84 #6965, 0.83 #13930), 03q5dr (0.59 #23220, 0.57 #37153, 0.57 #30185), 014zcr (0.57 #42, 0.10 #4685, 0.04 #69712), 06pj8 (0.25 #39477, 0.17 #51088, 0.07 #5072), 0261g5l (0.25 #39477, 0.17 #51088, 0.03 #83605), 05mvd62 (0.25 #39477, 0.17 #51088), 03mdt (0.22 #12313, 0.12 #9992, 0.12 #14636), 0bsb4j (0.21 #539, 0.03 #5182, 0.03 #7504) >> Best rule #2322 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 09xbpt; 03s6l2; 078sj4; 01bb9r; 07w8fz; 06_x996; 0h03fhx; 029k4p; 0hz55; 0gg5qcw; ... >> query: (?x5808, ?x849) <- award_winner(?x5808, ?x849), award_winner(?x5808, ?x848), award(?x848, ?x8250), vacationer(?x4627, ?x848), ?x8250 = 0cqhb3 >> conf = 0.87 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 05lfwd nominated_for! 06czyr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 43.000 0.867 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10175-01nyl PRED entity: 01nyl PRED relation: organization PRED expected values: 0gkjy 0b6css => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 49): 0gkjy (0.82 #70, 0.73 #91, 0.56 #1075), 0b6css (0.67 #73, 0.56 #94, 0.56 #1075), 01rz1 (0.39 #106, 0.32 #1414, 0.30 #127), 04k4l (0.32 #235, 0.32 #130, 0.32 #1414), 0j7v_ (0.32 #1414, 0.31 #68, 0.29 #447), 0_2v (0.32 #1414, 0.28 #381, 0.28 #782), 018cqq (0.32 #1414, 0.23 #137, 0.19 #116), 02jxk (0.32 #1414, 0.20 #107, 0.18 #128), 059dn (0.32 #1414, 0.06 #141, 0.06 #120), 085h1 (0.32 #1414, 0.04 #12, 0.02 #369) >> Best rule #70 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 07p7g; >> query: (?x7871, 0gkjy) <- administrative_parent(?x7871, ?x551), contains(?x2467, ?x7871), ?x2467 = 0dg3n1 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01nyl organization 0b6css CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.822 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 01nyl organization 0gkjy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.822 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #10174-046f3p PRED entity: 046f3p PRED relation: film! PRED expected values: 02t__3 07nx9j 07rzf => 84 concepts (38 used for prediction) PRED predicted values (max 10 best out of 929): 020_95 (0.63 #58244, 0.44 #74886, 0.40 #60328), 04xn2m (0.44 #74886, 0.40 #60328, 0.40 #58243), 025jfl (0.44 #74886, 0.40 #60328, 0.40 #58243), 0gyx4 (0.20 #772, 0.06 #11169, 0.05 #4931), 04954 (0.20 #1306, 0.05 #58247, 0.04 #79048), 01pkhw (0.20 #698, 0.04 #31203, 0.03 #9015), 04wp3s (0.20 #976, 0.04 #31203, 0.02 #3056), 071ywj (0.20 #509, 0.04 #31203, 0.02 #15065), 0ksrf8 (0.20 #992, 0.04 #31203, 0.01 #13468), 048lv (0.20 #219, 0.03 #6457, 0.03 #10616) >> Best rule #58244 for best value: >> intensional similarity = 4 >> extensional distance = 746 >> proper extension: 06ys2; >> query: (?x7664, ?x5454) <- nominated_for(?x5454, ?x7664), award_winner(?x5157, ?x5454), award_nominee(?x5454, ?x1208), participant(?x872, ?x1208) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #16637 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 194 *> proper extension: 02vxq9m; 05p1tzf; 0fh694; 092vkg; 02prw4h; 0dgst_d; 047msdk; 0gmcwlb; 02rqwhl; 075wx7_; ... *> query: (?x7664, ?x1250) <- nominated_for(?x1033, ?x7664), award(?x4400, ?x1033), award(?x1250, ?x1033), ?x4400 = 02mjf2, nominated_for(?x1250, ?x2090) *> conf = 0.02 ranks of expected_values: 319, 347 EVAL 046f3p film! 07rzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 38.000 0.628 http://example.org/film/actor/film./film/performance/film EVAL 046f3p film! 07nx9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 38.000 0.628 http://example.org/film/actor/film./film/performance/film EVAL 046f3p film! 02t__3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 38.000 0.628 http://example.org/film/actor/film./film/performance/film #10173-0hv4t PRED entity: 0hv4t PRED relation: genre PRED expected values: 02n4kr => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 95): 04xvlr (0.61 #6489, 0.56 #721, 0.54 #6488), 02n4kr (0.61 #6489, 0.56 #721, 0.54 #6488), 02xh1 (0.61 #6489, 0.56 #721, 0.54 #6488), 05p553 (0.34 #3848, 0.34 #6975, 0.34 #7216), 02l7c8 (0.32 #255, 0.32 #4580, 0.31 #2058), 02kdv5l (0.32 #7696, 0.27 #6129, 0.27 #7093), 03k9fj (0.21 #251, 0.21 #6742, 0.21 #6138), 060__y (0.19 #616, 0.19 #1097, 0.19 #256), 082gq (0.17 #270, 0.16 #150, 0.15 #6037), 03bxz7 (0.16 #175, 0.13 #295, 0.12 #655) >> Best rule #6489 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 02xhwm; >> query: (?x6653, ?x812) <- titles(?x812, ?x6653), genre(?x2009, ?x812) >> conf = 0.61 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0hv4t genre 02n4kr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 81.000 0.613 http://example.org/film/film/genre #10172-04qk12 PRED entity: 04qk12 PRED relation: genre PRED expected values: 02l7c8 => 80 concepts (75 used for prediction) PRED predicted values (max 10 best out of 112): 07ssc (0.79 #1075, 0.77 #955, 0.66 #1435), 02l7c8 (0.42 #732, 0.39 #613, 0.38 #6606), 05p553 (0.41 #7671, 0.37 #3355, 0.34 #5274), 01jfsb (0.38 #6963, 0.37 #7322, 0.37 #7203), 02kdv5l (0.35 #7430, 0.35 #478, 0.27 #5989), 03k9fj (0.32 #250, 0.28 #369, 0.28 #7559), 0lsxr (0.22 #7199, 0.20 #7318, 0.19 #6959), 017fp (0.20 #970, 0.20 #850, 0.17 #1329), 02n4kr (0.20 #7, 0.17 #5876, 0.14 #7317), 01hmnh (0.19 #137, 0.17 #375, 0.15 #256) >> Best rule #1075 for best value: >> intensional similarity = 6 >> extensional distance = 164 >> proper extension: 0c0yh4; 01wb95; 064r97z; 0y_yw; 05z43v; 09tkzy; 01fwzk; 02p86pb; 01gvsn; >> query: (?x8555, ?x512) <- genre(?x8555, ?x162), genre(?x8555, ?x53), ?x53 = 07s9rl0, titles(?x512, ?x8555), film(?x374, ?x8555), ?x162 = 04xvlr >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #732 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 113 *> proper extension: 097zcz; *> query: (?x8555, 02l7c8) <- genre(?x8555, ?x1509), genre(?x8555, ?x53), ?x53 = 07s9rl0, currency(?x8555, ?x170), ?x170 = 09nqf, ?x1509 = 060__y *> conf = 0.42 ranks of expected_values: 2 EVAL 04qk12 genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 75.000 0.787 http://example.org/film/film/genre #10171-0fhpv4 PRED entity: 0fhpv4 PRED relation: nominated_for PRED expected values: 09k56b7 02c638 0h03fhx 0g4vmj8 => 57 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1525): 09q5w2 (0.78 #24795, 0.77 #24794, 0.77 #21691), 08zrbl (0.78 #24795, 0.77 #24794, 0.77 #21691), 01mgw (0.76 #10407, 0.67 #11957, 0.66 #13505), 011yqc (0.71 #9497, 0.67 #11047, 0.63 #12595), 0dr_4 (0.71 #9512, 0.63 #11062, 0.57 #12610), 0ywrc (0.67 #5095, 0.53 #9744, 0.50 #11294), 011yg9 (0.67 #5532, 0.50 #11731, 0.50 #3983), 0jqn5 (0.65 #9488, 0.56 #4839, 0.43 #11038), 0mcl0 (0.65 #9855, 0.53 #11405, 0.46 #12953), 09p3_s (0.65 #10124, 0.50 #2377, 0.47 #11674) >> Best rule #24795 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 0fqnzts; >> query: (?x3889, ?x2006) <- ceremony(?x3889, ?x472), award(?x2006, ?x3889), award(?x84, ?x3889), nominated_for(?x507, ?x2006) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #5327 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 027dtxw; *> query: (?x3889, 0h03fhx) <- nominated_for(?x3889, ?x6932), nominated_for(?x3889, ?x972), award_winner(?x3889, ?x669), nominated_for(?x1587, ?x972), ?x1587 = 02rdyk7, ?x6932 = 027pfg *> conf = 0.56 ranks of expected_values: 19, 67, 68, 205 EVAL 0fhpv4 nominated_for 0g4vmj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 57.000 21.000 0.775 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fhpv4 nominated_for 0h03fhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 57.000 21.000 0.775 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fhpv4 nominated_for 02c638 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 57.000 21.000 0.775 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fhpv4 nominated_for 09k56b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 57.000 21.000 0.775 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10170-025mb9 PRED entity: 025mb9 PRED relation: award! PRED expected values: 01k98nm => 47 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1997): 0ddkf (0.85 #10109, 0.85 #3369, 0.82 #13482), 01vttb9 (0.85 #10109, 0.85 #3369, 0.82 #13482), 01k98nm (0.85 #10109, 0.85 #3369, 0.82 #13482), 02z4b_8 (0.71 #12175, 0.60 #8803, 0.33 #5433), 0gdh5 (0.60 #7497, 0.57 #10869, 0.33 #4127), 0dl567 (0.60 #7885, 0.57 #11257, 0.33 #4515), 0b68vs (0.60 #7021, 0.43 #10393, 0.33 #3651), 04xrx (0.60 #7440, 0.43 #10812, 0.33 #4070), 0g824 (0.60 #8599, 0.43 #11971, 0.33 #5229), 016s0m (0.60 #9306, 0.43 #12678, 0.33 #5936) >> Best rule #10109 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 01c427; >> query: (?x4012, ?x568) <- ceremony(?x4012, ?x5656), award_winner(?x4012, ?x4574), award_winner(?x4012, ?x3234), award_winner(?x4012, ?x568), ?x3234 = 01k98nm, ?x5656 = 0466p0j, award_winner(?x1381, ?x4574) >> conf = 0.85 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 025mb9 award! 01k98nm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 47.000 22.000 0.852 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10169-01hx2t PRED entity: 01hx2t PRED relation: major_field_of_study PRED expected values: 01mkq => 193 concepts (103 used for prediction) PRED predicted values (max 10 best out of 116): 01mkq (0.76 #855, 0.75 #2175, 0.70 #1575), 02j62 (0.71 #510, 0.67 #750, 0.56 #5085), 03g3w (0.64 #507, 0.60 #747, 0.45 #2187), 05qfh (0.60 #756, 0.59 #876, 0.57 #516), 04x_3 (0.59 #866, 0.57 #626, 0.52 #986), 05qjt (0.57 #488, 0.53 #728, 0.43 #2168), 04rjg (0.55 #2180, 0.52 #2421, 0.51 #1940), 01lj9 (0.53 #880, 0.50 #640, 0.50 #520), 02_7t (0.47 #783, 0.36 #2343, 0.33 #4393), 02ky346 (0.45 #1696, 0.45 #1936, 0.43 #2176) >> Best rule #855 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 01w3v; 03ksy; 02rg_4; 07tds; 0gl5_; >> query: (?x8479, 01mkq) <- major_field_of_study(?x8479, ?x6870), school(?x1010, ?x8479), ?x6870 = 01540, fraternities_and_sororities(?x8479, ?x4348), colors(?x8479, ?x3315), currency(?x8479, ?x170) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hx2t major_field_of_study 01mkq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 103.000 0.765 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10168-01rzqj PRED entity: 01rzqj PRED relation: award_nominee PRED expected values: 03_1pg => 109 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1014): 03_1pg (0.81 #123732, 0.81 #72376, 0.80 #77045), 0m2wm (0.53 #4729, 0.03 #14066, 0.02 #37413), 06mmb (0.53 #5224, 0.03 #14561, 0.02 #37908), 016xh5 (0.53 #6092, 0.02 #15429, 0.02 #38776), 0h5g_ (0.53 #4756, 0.02 #14093, 0.02 #37440), 0159h6 (0.47 #4755, 0.04 #14092, 0.02 #37439), 09y20 (0.47 #4992, 0.03 #14329, 0.03 #126067), 05vsxz (0.47 #4677, 0.03 #14014, 0.03 #84057), 02cllz (0.47 #5198, 0.03 #14535, 0.02 #37882), 0993r (0.47 #5348, 0.03 #14685, 0.02 #38032) >> Best rule #123732 for best value: >> intensional similarity = 3 >> extensional distance = 1444 >> proper extension: 012t1; 0244r8; 076_74; 01ycck; 0280mv7; 09pl3f; 0d_skg; 01p0vf; 08t7nz; 081l_; ... >> query: (?x3366, ?x1039) <- profession(?x3366, ?x524), award_nominee(?x1039, ?x3366), type_of_union(?x3366, ?x566) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rzqj award_nominee 03_1pg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 55.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10167-0zm1 PRED entity: 0zm1 PRED relation: people! PRED expected values: 0g6ff => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 42): 06mvq (0.25 #34, 0.02 #881, 0.02 #1035), 041rx (0.24 #697, 0.21 #389, 0.20 #81), 0g6ff (0.20 #98, 0.17 #175, 0.10 #252), 03ts0c (0.20 #103, 0.17 #180, 0.05 #950), 0x67 (0.19 #626, 0.12 #857, 0.11 #472), 02ctzb (0.14 #400, 0.11 #477, 0.07 #785), 013xrm (0.10 #251, 0.08 #944, 0.08 #2022), 03bkbh (0.10 #263, 0.03 #648, 0.02 #1418), 09zyn5 (0.10 #304, 0.02 #997, 0.01 #1151), 033tf_ (0.08 #2317, 0.07 #777, 0.07 #854) >> Best rule #34 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0d5_f; 0c1fs; >> query: (?x4292, 06mvq) <- influenced_by(?x9610, ?x4292), influenced_by(?x5262, ?x4292), ?x9610 = 0c4y8, student(?x8223, ?x5262), influenced_by(?x4292, ?x2610) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #98 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 032l1; 058vp; 03_87; *> query: (?x4292, 0g6ff) <- influenced_by(?x5262, ?x4292), influenced_by(?x986, ?x4292), ?x5262 = 080r3, gender(?x4292, ?x231), influenced_by(?x4292, ?x2610), award_nominee(?x986, ?x4562) *> conf = 0.20 ranks of expected_values: 3 EVAL 0zm1 people! 0g6ff CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 120.000 0.250 http://example.org/people/ethnicity/people #10166-03qjg PRED entity: 03qjg PRED relation: role PRED expected values: 06w87 0dq630k => 68 concepts (54 used for prediction) PRED predicted values (max 10 best out of 142): 02hnl (0.87 #2237, 0.85 #517, 0.83 #1359), 013y1f (0.87 #2237, 0.85 #517, 0.83 #294), 05r5c (0.87 #2237, 0.85 #517, 0.83 #294), 02sgy (0.85 #517, 0.83 #1865, 0.83 #294), 02k84w (0.85 #517, 0.83 #294, 0.83 #145), 0gkd1 (0.85 #517, 0.83 #294, 0.83 #145), 0l14j_ (0.85 #517, 0.83 #294, 0.83 #145), 02k856 (0.85 #517, 0.83 #294, 0.83 #145), 018j2 (0.85 #517, 0.83 #294, 0.83 #145), 0jtg0 (0.85 #517, 0.83 #294, 0.83 #145) >> Best rule #2237 for best value: >> intensional similarity = 10 >> extensional distance = 21 >> proper extension: 01c3q; >> query: (?x2798, ?x645) <- role(?x300, ?x2798), role(?x645, ?x2798), role(?x2798, ?x3991), role(?x2798, ?x2059), group(?x645, ?x1684), role(?x211, ?x3991), role(?x366, ?x2798), ?x1684 = 01wv9xn, ?x2059 = 0dwr4, group(?x2798, ?x997) >> conf = 0.87 => this is the best rule for 3 predicted values *> Best rule #147 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x2798, ?x3328) <- role(?x2963, ?x2798), role(?x565, ?x2798), role(?x2944, ?x2798), role(?x2785, ?x2798), role(?x2798, ?x75), instrumentalists(?x2798, ?x3569), ?x3569 = 011hdn, ?x2944 = 0l14j_, group(?x2798, ?x9706), role(?x3328, ?x2785), ?x9706 = 01fchy, music(?x9978, ?x565), ?x2963 = 0gcs9 *> conf = 0.64 ranks of expected_values: 47, 64 EVAL 03qjg role 0dq630k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 68.000 54.000 0.866 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 03qjg role 06w87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 68.000 54.000 0.866 http://example.org/music/performance_role/regular_performances./music/group_membership/role #10165-05tk7y PRED entity: 05tk7y PRED relation: nationality PRED expected values: 015fr => 82 concepts (78 used for prediction) PRED predicted values (max 10 best out of 38): 09c7w0 (0.78 #101, 0.76 #801, 0.74 #1201), 02jx1 (0.43 #33, 0.35 #4807, 0.35 #4706), 07ssc (0.34 #5711, 0.33 #4909, 0.24 #15), 0d060g (0.34 #5711, 0.33 #4909, 0.05 #7), 03rt9 (0.34 #5711, 0.33 #4909, 0.05 #13), 0j5g9 (0.34 #5711, 0.33 #4909, 0.05 #62), 0345h (0.34 #5711, 0.33 #4909, 0.03 #4205), 03_3d (0.07 #206, 0.03 #4205, 0.03 #1806), 03rk0 (0.06 #5857, 0.06 #6357, 0.06 #6457), 0chghy (0.03 #4205, 0.02 #1110, 0.02 #2812) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 04n7njg; >> query: (?x1550, 09c7w0) <- actor(?x5810, ?x1550), nominated_for(?x1550, ?x2029), category(?x1550, ?x134) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4205 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1884 *> proper extension: 079vf; 05d7rk; 04yywz; 01l1b90; 05m63c; 02g8h; 0d_84; 01yznp; 033hqf; 04bs3j; ... *> query: (?x1550, ?x94) <- film(?x1550, ?x2029), film_release_region(?x2029, ?x94) *> conf = 0.03 ranks of expected_values: 30 EVAL 05tk7y nationality 015fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 82.000 78.000 0.778 http://example.org/people/person/nationality #10164-0gs6vr PRED entity: 0gs6vr PRED relation: category PRED expected values: 08mbj5d => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #44, 0.90 #19, 0.87 #64) >> Best rule #44 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 01pfkw; >> query: (?x6577, 08mbj5d) <- participant(?x6577, ?x4080), artist(?x4079, ?x6577), award_winner(?x1361, ?x4080), artists(?x302, ?x4080) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gs6vr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.906 http://example.org/common/topic/webpage./common/webpage/category #10163-042z_g PRED entity: 042z_g PRED relation: place_of_birth PRED expected values: 030qb3t => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 21): 0cc56 (0.20 #33, 0.01 #13412, 0.01 #5666), 03l2n (0.20 #169), 02_286 (0.08 #5652, 0.07 #13398, 0.07 #40157), 030qb3t (0.05 #13433, 0.04 #40192, 0.04 #5687), 01_d4 (0.05 #2178, 0.04 #2882, 0.03 #13445), 0cr3d (0.04 #798, 0.04 #1502, 0.03 #13473), 094jv (0.03 #765, 0.02 #1469, 0.01 #5694), 0dclg (0.03 #782, 0.02 #1486, 0.01 #40216), 0ctw_b (0.02 #4225, 0.02 #14789), 01531 (0.02 #5034, 0.02 #2217, 0.02 #3625) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02h3tp; >> query: (?x5099, 0cc56) <- film(?x5099, ?x7922), profession(?x5099, ?x1032), ?x7922 = 0y_pg >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #13433 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1525 *> proper extension: 02qggqc; 0p51w; 0b79gfg; 0b6yp2; 03q8ch; 0fpjyd; 03bw6; 0frnff; 03f68r6; 0bn3jg; *> query: (?x5099, 030qb3t) <- nominated_for(?x5099, ?x2336), nationality(?x5099, ?x94), ?x94 = 09c7w0 *> conf = 0.05 ranks of expected_values: 4 EVAL 042z_g place_of_birth 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 75.000 0.200 http://example.org/people/person/place_of_birth #10162-0bwx3 PRED entity: 0bwx3 PRED relation: student! PRED expected values: 01mpwj => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 189): 07tk7 (0.17 #441, 0.06 #2545, 0.03 #8857), 01mpwj (0.15 #6944, 0.10 #2736, 0.06 #1158), 02cw8s (0.13 #3752, 0.13 #3226, 0.01 #21637), 023znp (0.12 #1170, 0.10 #2748, 0.06 #5378), 01w5m (0.11 #9047, 0.09 #15885, 0.09 #10625), 02kj7g (0.10 #3145, 0.06 #1567, 0.03 #6827), 07tgn (0.07 #4225, 0.07 #12641, 0.06 #15271), 0bwfn (0.07 #48146, 0.07 #46041, 0.07 #6060), 07wrz (0.07 #4796, 0.06 #2166, 0.05 #2692), 01stzp (0.07 #5244, 0.04 #9452, 0.03 #13134) >> Best rule #441 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03sbs; 03s9v; 039n1; >> query: (?x5811, 07tk7) <- nationality(?x5811, ?x94), influenced_by(?x11499, ?x5811), ?x11499 = 06jkm, country(?x54, ?x94) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #6944 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 01lct6; *> query: (?x5811, 01mpwj) <- student(?x3439, ?x5811), profession(?x5811, ?x353), ?x3439 = 03ksy *> conf = 0.15 ranks of expected_values: 2 EVAL 0bwx3 student! 01mpwj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 127.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #10161-01_d4 PRED entity: 01_d4 PRED relation: category PRED expected values: 08mbj5d => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.81 #78, 0.79 #50, 0.78 #76) >> Best rule #78 for best value: >> intensional similarity = 2 >> extensional distance = 131 >> proper extension: 0_565; >> query: (?x1860, 08mbj5d) <- county_seat(?x6410, ?x1860), county(?x5037, ?x6410) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_d4 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.812 http://example.org/common/topic/webpage./common/webpage/category #10160-0bqch PRED entity: 0bqch PRED relation: influenced_by! PRED expected values: 018x3 => 75 concepts (25 used for prediction) PRED predicted values (max 10 best out of 322): 045bg (0.25 #36, 0.10 #1066, 0.07 #551), 0683n (0.20 #855, 0.15 #1370, 0.12 #4977), 040db (0.13 #591, 0.12 #6774, 0.11 #4713), 0399p (0.13 #845, 0.12 #2905, 0.06 #4967), 0lcx (0.13 #668, 0.10 #1183, 0.06 #2728), 058vp (0.13 #752, 0.10 #1267, 0.05 #6935), 01x1cn2 (0.13 #7215, 0.11 #9792, 0.11 #10309), 027y_ (0.13 #7215, 0.11 #9792, 0.11 #10309), 01vsqvs (0.13 #7215, 0.11 #9792, 0.11 #10309), 07h1q (0.12 #2984, 0.07 #5046, 0.07 #924) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04xzm; 019fz; >> query: (?x11335, 045bg) <- location(?x11335, ?x12144), profession(?x11335, ?x12779), type_of_union(?x11335, ?x566), ?x12779 = 01l5t6 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #6930 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 167 *> proper extension: 04r1t; 07yg2; 05xq9; 0134tg; 07mvp; 03c3yf; 0qmny; 033s6; 07hgm; 016vn3; ... *> query: (?x11335, 018x3) <- influenced_by(?x10075, ?x11335), influenced_by(?x2538, ?x10075), artist(?x2149, ?x2538) *> conf = 0.02 ranks of expected_values: 175 EVAL 0bqch influenced_by! 018x3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 75.000 25.000 0.250 http://example.org/influence/influence_node/influenced_by #10159-01pj7 PRED entity: 01pj7 PRED relation: medal PRED expected values: 02lq67 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.83 #16, 0.82 #21, 0.81 #17) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 01d8l; >> query: (?x1790, 02lq67) <- medal(?x1790, ?x1242), combatants(?x326, ?x1790), combatants(?x756, ?x1790) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pj7 medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.829 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #10158-0b_fw PRED entity: 0b_fw PRED relation: religion PRED expected values: 0c8wxp => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 25): 0c8wxp (0.24 #546, 0.24 #727, 0.21 #772), 03_gx (0.20 #59, 0.16 #960, 0.15 #1865), 0631_ (0.20 #53, 0.07 #188, 0.06 #548), 0kq2 (0.17 #108, 0.14 #153, 0.07 #243), 0kpl (0.16 #640, 0.15 #866, 0.13 #1002), 03j6c (0.07 #651, 0.04 #1419, 0.04 #1691), 019cr (0.07 #236, 0.06 #461, 0.06 #416), 01lp8 (0.07 #226, 0.04 #271, 0.03 #993), 0n2g (0.05 #689, 0.04 #869, 0.04 #824), 02rsw (0.05 #700, 0.03 #835, 0.01 #790) >> Best rule #546 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 01pl9g; 03n93; 0hnp7; 06hx2; 02s58t; >> query: (?x2167, 0c8wxp) <- people(?x6260, ?x2167), type_of_union(?x2167, ?x566), participant(?x1568, ?x2167) >> conf = 0.24 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_fw religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.245 http://example.org/people/person/religion #10157-0kfhjq0 PRED entity: 0kfhjq0 PRED relation: film_regional_debut_venue! PRED expected values: 024lt6 => 36 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1756): 0cnztc4 (0.60 #1602, 0.40 #1776, 0.33 #2298), 05p1tzf (0.58 #351, 0.58 #350, 0.52 #3512), 0gvvm6l (0.58 #351, 0.58 #350, 0.52 #3512), 01shy7 (0.58 #351, 0.58 #350, 0.52 #3512), 0dt8xq (0.58 #351, 0.58 #350, 0.50 #172), 07pd_j (0.58 #351, 0.58 #350, 0.50 #172), 03lfd_ (0.58 #351, 0.58 #350, 0.50 #172), 0j_tw (0.58 #351, 0.58 #350, 0.50 #172), 0372j5 (0.58 #350, 0.44 #3508, 0.37 #702), 09v42sf (0.50 #1225, 0.40 #1923, 0.40 #1749) >> Best rule #1602 for best value: >> intensional similarity = 30 >> extensional distance = 3 >> proper extension: 07751; >> query: (?x6557, 0cnztc4) <- film_regional_debut_venue(?x10246, ?x6557), film_regional_debut_venue(?x9902, ?x6557), film_regional_debut_venue(?x7629, ?x6557), film_regional_debut_venue(?x5347, ?x6557), film_release_region(?x10246, ?x512), nominated_for(?x1312, ?x10246), nominated_for(?x2393, ?x9902), film_release_region(?x5347, ?x774), film_release_region(?x5347, ?x344), film_festivals(?x9902, ?x13775), film(?x3013, ?x7629), film_release_region(?x9902, ?x1790), film_release_region(?x9902, ?x1353), film_release_region(?x9902, ?x390), film(?x617, ?x9902), award(?x708, ?x2393), category(?x7629, ?x134), nominated_for(?x2393, ?x2490), production_companies(?x7629, ?x541), ?x390 = 0chghy, ?x512 = 07ssc, ?x1790 = 01pj7, ?x1353 = 035qy, written_by(?x10246, ?x1335), ?x2490 = 026p4q7, production_companies(?x5347, ?x2549), award(?x523, ?x2393), ?x344 = 04gzd, film_release_distribution_medium(?x10246, ?x81), ?x774 = 06mzp >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1233 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 2 *> proper extension: 018cvf; *> query: (?x6557, ?x1392) <- film_regional_debut_venue(?x10246, ?x6557), film_regional_debut_venue(?x9902, ?x6557), film_regional_debut_venue(?x7629, ?x6557), film_regional_debut_venue(?x5347, ?x6557), film_release_region(?x10246, ?x94), nominated_for(?x1312, ?x10246), nominated_for(?x2456, ?x9902), nominated_for(?x2393, ?x9902), nominated_for(?x1162, ?x9902), film_release_region(?x5347, ?x429), film_festivals(?x9902, ?x13775), film(?x3013, ?x7629), film_release_region(?x9902, ?x3227), film(?x617, ?x9902), ?x2393 = 02x258x, production_companies(?x5347, ?x541), film_crew_role(?x5347, ?x137), nominated_for(?x3508, ?x7629), executive_produced_by(?x10246, ?x3880), nominated_for(?x157, ?x5347), award(?x9902, ?x11466), crewmember(?x5347, ?x1933), country(?x13383, ?x3227), film_release_region(?x1392, ?x3227), ?x429 = 03rt9, contains(?x6304, ?x3227), award(?x324, ?x2456), member_states(?x7695, ?x3227), ?x1162 = 099c8n, ?x6304 = 02qkt *> conf = 0.09 ranks of expected_values: 394 EVAL 0kfhjq0 film_regional_debut_venue! 024lt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 36.000 23.000 0.600 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #10156-027b9j5 PRED entity: 027b9j5 PRED relation: award_winner PRED expected values: 0b_dy => 38 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1932): 02qgqt (0.57 #2472, 0.33 #17, 0.28 #27020), 0bj9k (0.57 #2865, 0.33 #410, 0.10 #5320), 040z9 (0.57 #4073, 0.33 #1618, 0.10 #6528), 0cf2h (0.57 #3827, 0.33 #1372, 0.10 #6282), 0kjgl (0.43 #4165, 0.33 #1710, 0.20 #6620), 01vvb4m (0.43 #3109, 0.33 #654, 0.14 #17190), 06cgy (0.43 #2757, 0.33 #302, 0.14 #17190), 039bp (0.43 #2662, 0.33 #207, 0.10 #5117), 0bl2g (0.43 #2514, 0.33 #59, 0.10 #4969), 0d6d2 (0.43 #4214, 0.33 #1759, 0.10 #6669) >> Best rule #2472 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 027986c; 02z13jg; 04kxsb; 027c95y; >> query: (?x4894, 02qgqt) <- award(?x1077, ?x4894), award_winner(?x4894, ?x851), nominated_for(?x112, ?x1077), ?x851 = 016khd >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #3124 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 027986c; 02z13jg; 04kxsb; 027c95y; *> query: (?x4894, 0b_dy) <- award(?x1077, ?x4894), award_winner(?x4894, ?x851), nominated_for(?x112, ?x1077), ?x851 = 016khd *> conf = 0.29 ranks of expected_values: 53 EVAL 027b9j5 award_winner 0b_dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 38.000 16.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner #10155-09k5jh7 PRED entity: 09k5jh7 PRED relation: honored_for PRED expected values: 09hy79 0581vn8 => 48 concepts (18 used for prediction) PRED predicted values (max 10 best out of 907): 05f4vxd (0.50 #1474, 0.40 #2651, 0.40 #2062), 08phg9 (0.50 #895, 0.40 #2070, 0.33 #307), 0fhzwl (0.50 #1668, 0.40 #2256, 0.30 #2845), 0d68qy (0.40 #2500, 0.40 #1911, 0.35 #3087), 02rzdcp (0.40 #1957, 0.25 #1369, 0.25 #782), 07l50vn (0.33 #326, 0.25 #914, 0.20 #2089), 047d21r (0.30 #2564, 0.07 #6087, 0.06 #9022), 09gq0x5 (0.30 #2451, 0.07 #5974, 0.06 #8909), 0b76kw1 (0.30 #2466, 0.07 #5989, 0.06 #8924), 063ykwt (0.25 #806, 0.20 #2570, 0.20 #1981) >> Best rule #1474 for best value: >> intensional similarity = 15 >> extensional distance = 2 >> proper extension: 09gkdln; >> query: (?x6108, 05f4vxd) <- honored_for(?x6108, ?x7635), honored_for(?x6108, ?x4086), honored_for(?x6108, ?x2189), ceremony(?x2577, ?x6108), award_winner(?x6108, ?x815), ?x2189 = 02yvct, ?x4086 = 06_x996, nominated_for(?x2577, ?x696), executive_produced_by(?x696, ?x3662), currency(?x696, ?x170), film(?x6275, ?x696), nominated_for(?x1770, ?x696), ?x1770 = 09cm54, award_winner(?x2577, ?x396), award(?x7635, ?x834) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1175 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 09g90vz; *> query: (?x6108, ?x144) <- honored_for(?x6108, ?x7635), honored_for(?x6108, ?x6900), honored_for(?x6108, ?x6448), honored_for(?x6108, ?x2742), ?x7635 = 08nhfc1, award_winner(?x6108, ?x8740), ceremony(?x1162, ?x6108), ?x2742 = 05c46y6, nominated_for(?x143, ?x6900), nominated_for(?x1162, ?x1508), nominated_for(?x1162, ?x144), award(?x6448, ?x68), ?x1508 = 09z2b7, ?x8740 = 026rm_y, titles(?x812, ?x6448), nominated_for(?x4968, ?x6900), genre(?x6900, ?x1316) *> conf = 0.01 ranks of expected_values: 821, 857 EVAL 09k5jh7 honored_for 0581vn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 18.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 09k5jh7 honored_for 09hy79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 18.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #10154-018s6c PRED entity: 018s6c PRED relation: people PRED expected values: 016k62 0dqmt0 0g_rs_ => 30 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1779): 03rx9 (0.50 #8282, 0.50 #4828, 0.40 #10011), 04z0g (0.50 #7734, 0.50 #4280, 0.33 #6007), 0lrh (0.50 #7294, 0.50 #3840, 0.33 #5567), 052hl (0.50 #4392, 0.33 #7846, 0.33 #6119), 05xpv (0.50 #4696, 0.33 #8150, 0.33 #6423), 02z1yj (0.50 #4855, 0.33 #8309, 0.33 #6582), 0427y (0.50 #4811, 0.33 #8265, 0.33 #6538), 01twdk (0.50 #4129, 0.33 #7583, 0.33 #5856), 01vrt_c (0.50 #3608, 0.33 #7062, 0.33 #5335), 01mqz0 (0.50 #3661, 0.33 #7115, 0.33 #5388) >> Best rule #8282 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 013b6_; >> query: (?x13001, 03rx9) <- languages_spoken(?x13001, ?x12272), languages_spoken(?x13001, ?x3966), ?x12272 = 0880p, people(?x13001, ?x4295), language(?x6245, ?x3966), language(?x2370, ?x3966), languages(?x3583, ?x3966), official_language(?x4743, ?x3966), award_winner(?x2370, ?x1197), ?x6245 = 02ph9tm >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018s6c people 0g_rs_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 22.000 0.500 http://example.org/people/ethnicity/people EVAL 018s6c people 0dqmt0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 22.000 0.500 http://example.org/people/ethnicity/people EVAL 018s6c people 016k62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 22.000 0.500 http://example.org/people/ethnicity/people #10153-02pfymy PRED entity: 02pfymy PRED relation: child! PRED expected values: 09k0h5 => 198 concepts (135 used for prediction) PRED predicted values (max 10 best out of 82): 0l8sx (0.25 #13, 0.14 #850, 0.08 #1432), 06q07 (0.25 #129, 0.10 #1298, 0.06 #1803), 049ql1 (0.22 #1155, 0.20 #2080, 0.20 #488), 09k0h5 (0.20 #244, 0.17 #580, 0.14 #2096), 03d6fyn (0.20 #366, 0.14 #867, 0.14 #784), 025txrl (0.20 #407, 0.14 #908, 0.14 #825), 0kx4m (0.20 #344, 0.14 #845, 0.14 #679), 07733f (0.20 #330, 0.07 #1753, 0.06 #1920), 06p8m (0.17 #655, 0.10 #1320, 0.07 #1742), 027lf1 (0.14 #902, 0.03 #3952, 0.02 #4807) >> Best rule #13 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 086k8; >> query: (?x10436, 0l8sx) <- child(?x10436, ?x14600), citytown(?x10436, ?x9559), organization(?x4682, ?x14600), industry(?x14600, ?x2271), state_province_region(?x10436, ?x1227), ?x1227 = 01n7q, contains(?x252, ?x9559) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #244 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 06py2; *> query: (?x10436, 09k0h5) <- citytown(?x10436, ?x10916), citytown(?x10436, ?x9559), ?x10916 = 0r6cx, citytown(?x11641, ?x9559), place_founded(?x11641, ?x11227), category(?x9559, ?x134), location(?x2306, ?x9559), profession(?x2306, ?x131) *> conf = 0.20 ranks of expected_values: 4 EVAL 02pfymy child! 09k0h5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 198.000 135.000 0.250 http://example.org/organization/organization/child./organization/organization_relationship/child #10152-03rjj PRED entity: 03rjj PRED relation: contains PRED expected values: 0fjsl 0hb37 0d99m 03qhnx => 237 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2760): 04q_g (0.85 #123911, 0.85 #152728, 0.82 #242073), 01rxw2 (0.85 #123911, 0.85 #152728, 0.82 #242073), 052gtg (0.82 #175788, 0.79 #31694, 0.74 #109501), 07mgr (0.81 #80681, 0.80 #92208, 0.75 #259374), 0fjsl (0.81 #80681, 0.02 #262256), 07ytt (0.80 #92208, 0.75 #259374, 0.10 #35931), 0hb37 (0.74 #109501, 0.20 #22851, 0.05 #80481), 0d99m (0.74 #109501), 01z9j2 (0.25 #28753, 0.10 #37397, 0.09 #40278), 01vc3y (0.25 #28713, 0.10 #37357, 0.09 #40238) >> Best rule #123911 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 04w58; >> query: (?x205, ?x2856) <- country(?x150, ?x205), countries_spoken_in(?x90, ?x205), administrative_parent(?x2856, ?x205) >> conf = 0.85 => this is the best rule for 2 predicted values *> Best rule #80681 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 02j71; *> query: (?x205, ?x13881) <- administrative_parent(?x12203, ?x205), administrative_parent(?x7190, ?x205), capital(?x7190, ?x12198), administrative_division(?x13881, ?x12203) *> conf = 0.81 ranks of expected_values: 5, 7, 8 EVAL 03rjj contains 03qhnx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 237.000 96.000 0.853 http://example.org/location/location/contains EVAL 03rjj contains 0d99m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 237.000 96.000 0.853 http://example.org/location/location/contains EVAL 03rjj contains 0hb37 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 237.000 96.000 0.853 http://example.org/location/location/contains EVAL 03rjj contains 0fjsl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 237.000 96.000 0.853 http://example.org/location/location/contains #10151-05ft32 PRED entity: 05ft32 PRED relation: language PRED expected values: 02h40lc => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 47): 02h40lc (0.95 #1596, 0.94 #1003, 0.90 #354), 064_8sq (0.13 #373, 0.13 #1022, 0.12 #728), 04306rv (0.11 #535, 0.08 #1423, 0.08 #712), 06nm1 (0.09 #777, 0.09 #835, 0.09 #1190), 02bjrlw (0.08 #531, 0.07 #236, 0.06 #1002), 06b_j (0.07 #552, 0.06 #729, 0.05 #22), 03_9r (0.05 #245, 0.05 #2430, 0.04 #3782), 04h9h (0.05 #277, 0.04 #394, 0.04 #453), 0jzc (0.04 #2481, 0.04 #549, 0.03 #726), 032f6 (0.04 #2481, 0.04 #290, 0.03 #113) >> Best rule #1596 for best value: >> intensional similarity = 3 >> extensional distance = 1128 >> proper extension: 02_1sj; 02z3r8t; 035xwd; 09p35z; 03ckwzc; 0963mq; 03t97y; 05p3738; 047qxs; 035s95; ... >> query: (?x6761, 02h40lc) <- film(?x3273, ?x6761), film_crew_role(?x6761, ?x468), language(?x6761, ?x3966) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05ft32 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.948 http://example.org/film/film/language #10150-0163r3 PRED entity: 0163r3 PRED relation: award PRED expected values: 0l8z1 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 287): 025m8y (0.80 #4368, 0.78 #8738, 0.77 #7546), 025mbn (0.80 #4368, 0.78 #8738, 0.77 #7546), 0l8z1 (0.39 #459, 0.08 #8402, 0.07 #39325), 0c4z8 (0.36 #4040, 0.26 #7218, 0.24 #7616), 0ck27z (0.36 #9224, 0.33 #10018, 0.30 #10812), 03qbh5 (0.32 #4168, 0.23 #2580, 0.22 #3374), 03qbnj (0.25 #4196, 0.16 #226, 0.15 #2608), 01ckcd (0.23 #2711, 0.13 #1123, 0.13 #4299), 0cqhk0 (0.23 #9172, 0.22 #9966, 0.20 #10760), 01c92g (0.23 #4064, 0.20 #94, 0.17 #2476) >> Best rule #4368 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 01lf293; 017959; >> query: (?x6716, ?x1854) <- award(?x6716, ?x724), ?x724 = 01bgqh, award_winner(?x1854, ?x6716) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #459 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 0bk1p; 01njxvw; *> query: (?x6716, 0l8z1) <- award(?x6716, ?x2379), category(?x6716, ?x134), ?x2379 = 02qvyrt *> conf = 0.39 ranks of expected_values: 3 EVAL 0163r3 award 0l8z1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 114.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10149-0hskw PRED entity: 0hskw PRED relation: type_of_union PRED expected values: 04ztj => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.90 #17, 0.88 #5, 0.86 #97), 01g63y (0.31 #62, 0.30 #70, 0.30 #38), 01bl8s (0.01 #15) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 03qkgyl; 0b66qd; >> query: (?x2733, 04ztj) <- profession(?x2733, ?x319), spouse(?x2739, ?x2733), ?x319 = 01d_h8 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hskw type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.898 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10148-08141d PRED entity: 08141d PRED relation: profession PRED expected values: 0cbd2 => 83 concepts (61 used for prediction) PRED predicted values (max 10 best out of 49): 02hrh1q (0.92 #2847, 0.90 #5828, 0.89 #5679), 0dxtg (0.40 #14, 0.29 #2697, 0.24 #4187), 01d_h8 (0.40 #6, 0.26 #7012, 0.26 #5670), 03gjzk (0.40 #16, 0.23 #2699, 0.19 #2400), 09jwl (0.40 #20, 0.16 #5535, 0.15 #2404), 015h31 (0.40 #28, 0.04 #2711, 0.01 #2263), 018gz8 (0.24 #614, 0.22 #2701, 0.21 #763), 02jknp (0.20 #8, 0.19 #5075, 0.18 #4181), 02krf9 (0.20 #27, 0.12 #2710, 0.08 #3604), 016z4k (0.20 #4, 0.11 #4475, 0.10 #3134) >> Best rule #2847 for best value: >> intensional similarity = 4 >> extensional distance = 347 >> proper extension: 02756j; >> query: (?x12799, 02hrh1q) <- place_of_birth(?x12799, ?x1523), film(?x12799, ?x218), gender(?x12799, ?x514), ?x514 = 02zsn >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #6864 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1271 *> proper extension: 070m12; *> query: (?x12799, 0cbd2) <- place_of_birth(?x12799, ?x1523), citytown(?x234, ?x1523), teams(?x1523, ?x705) *> conf = 0.12 ranks of expected_values: 17 EVAL 08141d profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 83.000 61.000 0.920 http://example.org/people/person/profession #10147-07wlf PRED entity: 07wlf PRED relation: registering_agency PRED expected values: 03z19 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.77 #21, 0.72 #31, 0.70 #28) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 015zyd; 04wlz2; 06pwq; 0kz2w; 01k2wn; 0473m9; 06jk5_; 017d77; 033q4k; 04rwx; ... >> query: (?x2760, 03z19) <- currency(?x2760, ?x170), major_field_of_study(?x2760, ?x254), country(?x2760, ?x94) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07wlf registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.774 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #10146-01vdm0 PRED entity: 01vdm0 PRED relation: role! PRED expected values: 01gf5h 0l12d 06k02 0136pk 01vn35l 01271h 0gcs9 0p3sf 01w7nww 01w272y 05y7hc 01wf86y 01k47c 01jgkj2 01x1fq 01w20rx => 82 concepts (59 used for prediction) PRED predicted values (max 10 best out of 948): 0161sp (0.71 #7411, 0.62 #8109, 0.60 #10557), 03c7ln (0.60 #10464, 0.60 #4528, 0.57 #7318), 06x4l_ (0.60 #10554, 0.60 #4618, 0.57 #7408), 01w806h (0.60 #4634, 0.57 #7424, 0.50 #11967), 03j24kf (0.60 #4690, 0.55 #10974, 0.50 #6433), 0m_v0 (0.60 #4652, 0.50 #8837, 0.50 #6395), 01vsnff (0.60 #4595, 0.50 #8432, 0.50 #6338), 0197tq (0.60 #4530, 0.50 #6273, 0.50 #3485), 01bczm (0.60 #4721, 0.50 #6464, 0.50 #3676), 01vsl3_ (0.60 #4614, 0.50 #6357, 0.50 #3569) >> Best rule #7411 for best value: >> intensional similarity = 18 >> extensional distance = 5 >> proper extension: 0gkd1; >> query: (?x1437, 0161sp) <- role(?x1969, ?x1437), role(?x1166, ?x1437), role(?x569, ?x1437), role(?x8722, ?x1437), role(?x7794, ?x1437), role(?x4186, ?x1437), role(?x1715, ?x1437), ?x1166 = 05148p4, ?x1969 = 04rzd, ?x1715 = 04bpm6, award(?x7794, ?x1323), award_winner(?x2585, ?x7794), ?x569 = 07c6l, profession(?x4186, ?x131), instrumentalists(?x1437, ?x226), award_winner(?x2186, ?x7794), artists(?x505, ?x8722), student(?x6130, ?x7794) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #8107 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 6 *> proper extension: 025cbm; *> query: (?x1437, 01vn35l) <- role(?x6039, ?x1437), role(?x1166, ?x1437), role(?x569, ?x1437), role(?x2765, ?x1437), group(?x1166, ?x6635), role(?x1437, ?x315), instrumentalists(?x1166, ?x2170), ?x6635 = 015cxv, ?x2170 = 0144l1, role(?x645, ?x6039), role(?x569, ?x3214), ?x2765 = 01w724 *> conf = 0.50 ranks of expected_values: 13, 15, 16, 22, 27, 40, 48, 61, 68, 169, 188, 191, 238, 244, 336, 358 EVAL 01vdm0 role! 01w20rx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 01x1fq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 01jgkj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 01k47c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 01wf86y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 05y7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 01w272y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 01w7nww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 0p3sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 0gcs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 01271h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 01vn35l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 0136pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 06k02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 0l12d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vdm0 role! 01gf5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 82.000 59.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #10145-02x02kb PRED entity: 02x02kb PRED relation: film PRED expected values: 01p3ty => 131 concepts (78 used for prediction) PRED predicted values (max 10 best out of 529): 0f42nz (0.45 #17030, 0.42 #27778, 0.38 #9866), 0168ls (0.25 #3825, 0.03 #32483), 0199wf (0.25 #5245, 0.02 #26739), 02_qt (0.25 #4217, 0.01 #52576, 0.01 #57949), 0kt_4 (0.25 #5097), 0gl3hr (0.25 #4683), 04jwjq (0.14 #16212, 0.12 #9048, 0.09 #26960), 052_mn (0.12 #10360, 0.11 #28272, 0.09 #17524), 0h2zvzr (0.12 #10398, 0.09 #17562, 0.07 #28310), 02w86hz (0.12 #9568, 0.09 #16732, 0.06 #14941) >> Best rule #17030 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 03fwln; >> query: (?x12608, 0f42nz) <- languages(?x12608, ?x1882), film(?x12608, ?x11114), nationality(?x12608, ?x2146), profession(?x12608, ?x1032), ?x2146 = 03rk0 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #27287 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 55 *> proper extension: 05d7rk; 03m2fg; 02n1gr; 02c_wc; 06wvfq; 03f02ct; 03j367r; 07rn0z; 01k6nm; 07jmnh; ... *> query: (?x12608, 01p3ty) <- profession(?x12608, ?x1032), nationality(?x12608, ?x2146), ?x1032 = 02hrh1q, film(?x12608, ?x11114), ?x2146 = 03rk0 *> conf = 0.09 ranks of expected_values: 16 EVAL 02x02kb film 01p3ty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 131.000 78.000 0.455 http://example.org/film/actor/film./film/performance/film #10144-0277c3 PRED entity: 0277c3 PRED relation: award PRED expected values: 01cw7s => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 291): 01by1l (0.43 #4912, 0.42 #512, 0.36 #4112), 09sb52 (0.35 #24440, 0.25 #21640, 0.24 #28440), 01bgqh (0.34 #4842, 0.29 #8042, 0.28 #7642), 02f6xy (0.28 #5000, 0.19 #4200, 0.17 #8200), 02f71y (0.27 #4983, 0.15 #4183, 0.14 #6183), 0c4z8 (0.26 #471, 0.25 #9271, 0.25 #4871), 023vrq (0.26 #4323, 0.15 #7923, 0.12 #2723), 01c427 (0.25 #84, 0.24 #4884, 0.16 #4084), 02f79n (0.25 #339, 0.15 #4339, 0.11 #7139), 02581q (0.25 #7, 0.04 #6407, 0.04 #8807) >> Best rule #4912 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 0hvbj; 016890; 01dwrc; 015bwt; >> query: (?x6124, 01by1l) <- award_nominee(?x6124, ?x1128), artists(?x3562, ?x6124), artist(?x2931, ?x6124), ?x3562 = 025sc50 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #5062 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 95 *> proper extension: 0hvbj; 016890; 01dwrc; 015bwt; *> query: (?x6124, 01cw7s) <- award_nominee(?x6124, ?x1128), artists(?x3562, ?x6124), artist(?x2931, ?x6124), ?x3562 = 025sc50 *> conf = 0.13 ranks of expected_values: 44 EVAL 0277c3 award 01cw7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 120.000 120.000 0.433 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10143-0crqcc PRED entity: 0crqcc PRED relation: people! PRED expected values: 041rx => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 25): 041rx (0.18 #4, 0.17 #158, 0.15 #312), 0x67 (0.12 #2012, 0.12 #2859, 0.11 #2705), 033tf_ (0.08 #1239, 0.08 #777, 0.07 #7), 02w7gg (0.06 #1003, 0.06 #772, 0.06 #1234), 0xnvg (0.05 #1245, 0.05 #783, 0.05 #13), 048z7l (0.05 #40, 0.04 #348, 0.04 #425), 07hwkr (0.04 #936, 0.04 #628, 0.03 #2630), 01qhm_ (0.03 #1238, 0.03 #776, 0.02 #1315), 07bch9 (0.03 #793, 0.03 #1255, 0.03 #1024), 09vc4s (0.02 #779, 0.02 #1549, 0.02 #1241) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 174 >> proper extension: 0k_mt; >> query: (?x7044, 041rx) <- gender(?x7044, ?x231), written_by(?x9755, ?x7044), location(?x7044, ?x3014) >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0crqcc people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.176 http://example.org/people/ethnicity/people #10142-0407yj_ PRED entity: 0407yj_ PRED relation: film_release_region PRED expected values: 0hzlz 03rj0 03ryn => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 118): 09c7w0 (0.92 #1210, 0.92 #5372, 0.92 #4566), 03spz (0.79 #1014, 0.69 #745, 0.66 #879), 03rj0 (0.73 #711, 0.69 #845, 0.68 #980), 0ctw_b (0.69 #688, 0.69 #822, 0.58 #2165), 03rk0 (0.69 #709, 0.69 #843, 0.48 #978), 06f32 (0.65 #716, 0.59 #850, 0.49 #985), 06qd3 (0.62 #967, 0.58 #698, 0.57 #2041), 01mjq (0.59 #970, 0.54 #701, 0.53 #2178), 03ryn (0.58 #734, 0.48 #868, 0.39 #1003), 0hzlz (0.58 #685, 0.41 #819, 0.34 #954) >> Best rule #1210 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 031t2d; 0ckrgs; 0gffmn8; 05_5_22; 05nlx4; 01bn3l; 0b3n61; 056xkh; 0gyv0b4; 09v8clw; >> query: (?x2933, 09c7w0) <- prequel(?x2933, ?x3854), film_release_region(?x2933, ?x1892), film_release_region(?x1364, ?x1892), ?x1364 = 047msdk >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #711 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 24 *> proper extension: 07gp9; 04pk1f; 0cmf0m0; *> query: (?x2933, 03rj0) <- prequel(?x2933, ?x3854), film_release_region(?x2933, ?x1892), film_release_region(?x2933, ?x550), ?x1892 = 02vzc, ?x550 = 05v8c *> conf = 0.73 ranks of expected_values: 3, 9, 10 EVAL 0407yj_ film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 75.000 75.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0407yj_ film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 75.000 75.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0407yj_ film_release_region 0hzlz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 75.000 75.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10141-0jksm PRED entity: 0jksm PRED relation: institution! PRED expected values: 02h4rq6 03bwzr4 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 21): 02h4rq6 (0.80 #190, 0.77 #72, 0.76 #48), 03bwzr4 (0.73 #60, 0.70 #155, 0.70 #202), 019v9k (0.72 #197, 0.71 #55, 0.68 #127), 014mlp (0.71 #75, 0.69 #99, 0.66 #51), 016t_3 (0.65 #26, 0.64 #49, 0.59 #121), 0bkj86 (0.61 #31, 0.59 #54, 0.56 #149), 04zx3q1 (0.52 #24, 0.48 #142, 0.47 #47), 027f2w (0.46 #56, 0.44 #33, 0.41 #128), 02m4yg (0.39 #214, 0.09 #204, 0.09 #86), 01ysy9 (0.39 #214, 0.07 #45, 0.07 #140) >> Best rule #190 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 0373qg; 0ym17; >> query: (?x13695, 02h4rq6) <- major_field_of_study(?x13695, ?x4100), major_field_of_study(?x13695, ?x1154), ?x1154 = 02lp1, major_field_of_study(?x734, ?x4100) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0jksm institution! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.798 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0jksm institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.798 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #10140-0klh7 PRED entity: 0klh7 PRED relation: people! PRED expected values: 041rx => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 49): 01g7zj (0.33 #50, 0.05 #350, 0.02 #875), 025rpb0 (0.33 #44, 0.02 #494, 0.02 #1544), 041rx (0.31 #154, 0.23 #3154, 0.23 #2779), 033tf_ (0.23 #157, 0.23 #307, 0.20 #232), 09kr66 (0.23 #192, 0.05 #567, 0.03 #642), 0x67 (0.21 #1360, 0.18 #310, 0.18 #4285), 02w7gg (0.18 #302, 0.14 #77, 0.13 #2327), 0g48m4 (0.17 #5), 0xnvg (0.15 #463, 0.14 #313, 0.10 #1363), 07hwkr (0.15 #462, 0.13 #612, 0.10 #687) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01xllf; >> query: (?x2849, 01g7zj) <- film(?x2849, ?x4880), people(?x9428, ?x2849), ?x4880 = 029k4p, profession(?x2849, ?x319) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #154 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 01tvz5j; 04bs3j; *> query: (?x2849, 041rx) <- film(?x2849, ?x1230), people(?x11132, ?x2849), type_of_union(?x2849, ?x1873), ?x11132 = 022dp5 *> conf = 0.31 ranks of expected_values: 3 EVAL 0klh7 people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 131.000 131.000 0.333 http://example.org/people/ethnicity/people #10139-0g2jl PRED entity: 0g2jl PRED relation: student PRED expected values: 01pjr7 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1392): 09v6tz (0.08 #1339, 0.05 #7607, 0.04 #11785), 0p8jf (0.08 #478, 0.05 #6746, 0.04 #10924), 0ff3y (0.06 #8334, 0.05 #12512, 0.05 #14601), 0d3k14 (0.05 #16475, 0.05 #1851, 0.03 #8119), 06hx2 (0.05 #15692, 0.05 #1068, 0.03 #7336), 0194xc (0.05 #1639, 0.04 #16263, 0.03 #7907), 01n1gc (0.05 #611, 0.03 #15235, 0.03 #6879), 01x4r3 (0.05 #1607, 0.03 #16231, 0.03 #7875), 03ft8 (0.05 #257, 0.03 #14881, 0.03 #6525), 01pqy_ (0.05 #896, 0.03 #15520, 0.03 #7164) >> Best rule #1339 for best value: >> intensional similarity = 2 >> extensional distance = 37 >> proper extension: 0d06m5; 0d05fv; >> query: (?x10576, 09v6tz) <- list(?x10576, ?x2197), organization(?x10576, ?x5487) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g2jl student 01pjr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 61.000 0.077 http://example.org/education/educational_institution/students_graduates./education/education/student #10138-0330r PRED entity: 0330r PRED relation: nominated_for! PRED expected values: 03ccq3s => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 182): 027gs1_ (0.79 #1406, 0.71 #1405, 0.69 #2110), 0gq9h (0.36 #6854, 0.34 #7088, 0.34 #7322), 0fbtbt (0.33 #157, 0.27 #1563, 0.26 #1327), 0bdx29 (0.33 #81, 0.23 #10544, 0.22 #1251), 0fbvqf (0.33 #37, 0.23 #10544, 0.22 #1207), 0bdw6t (0.33 #82, 0.23 #10544, 0.21 #1252), 0bp_b2 (0.33 #17, 0.23 #10544, 0.20 #11958), 0gkts9 (0.33 #121, 0.23 #10544, 0.20 #11958), 0ck27z (0.33 #70, 0.23 #10544, 0.20 #11958), 0cqhb3 (0.33 #195, 0.23 #10544, 0.20 #11958) >> Best rule #1406 for best value: >> intensional similarity = 3 >> extensional distance = 119 >> proper extension: 0300ml; 02rq7nd; >> query: (?x9541, ?x2016) <- award(?x9541, ?x2016), genre(?x9541, ?x258), award(?x201, ?x2016) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #608 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 100 *> proper extension: 020qr4; 045qmr; 0dk0dj; 047m_w; *> query: (?x9541, 03ccq3s) <- genre(?x9541, ?x11891), genre(?x4409, ?x11891), ?x4409 = 03tps5 *> conf = 0.25 ranks of expected_values: 28 EVAL 0330r nominated_for! 03ccq3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 68.000 68.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10137-0c6qh PRED entity: 0c6qh PRED relation: location PRED expected values: 05mph 0gx1l => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 214): 02_286 (0.22 #67885, 0.20 #20801, 0.20 #37), 02xry (0.20 #131, 0.08 #930, 0.04 #38452), 013yq (0.20 #117, 0.06 #3310, 0.05 #8104), 0d9y6 (0.20 #264, 0.04 #2659, 0.01 #3457), 01qh7 (0.20 #155, 0.03 #4147, 0.02 #5745), 059rby (0.14 #38337, 0.06 #16788, 0.05 #4008), 0rh6k (0.12 #2399, 0.04 #3996, 0.03 #5594), 01_d4 (0.08 #2495, 0.08 #899, 0.04 #16872), 0f2wj (0.08 #833, 0.05 #1631, 0.02 #16806), 0f2rq (0.08 #1076, 0.03 #8264, 0.02 #11458) >> Best rule #67885 for best value: >> intensional similarity = 2 >> extensional distance = 1594 >> proper extension: 0tfc; >> query: (?x2499, 02_286) <- location(?x2499, ?x3908), jurisdiction_of_office(?x900, ?x3908) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #38636 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 447 *> proper extension: 0kvnn; 0x3r3; 0bwx3; 05gpy; 05n19y; 03f3_p3; 027y_; 0bn8fw; 054fvj; 018zvb; ... *> query: (?x2499, 05mph) <- location(?x2499, ?x3908), state(?x1248, ?x3908) *> conf = 0.02 ranks of expected_values: 71 EVAL 0c6qh location 0gx1l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.219 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0c6qh location 05mph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 116.000 116.000 0.219 http://example.org/people/person/places_lived./people/place_lived/location #10136-0hmm7 PRED entity: 0hmm7 PRED relation: award PRED expected values: 027c924 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 180): 0gr51 (0.27 #11665, 0.26 #10497, 0.26 #11431), 0gq9h (0.27 #11665, 0.26 #10497, 0.26 #11431), 019f4v (0.27 #11665, 0.26 #10497, 0.26 #11431), 02qyntr (0.27 #11665, 0.26 #10497, 0.26 #11431), 04kxsb (0.27 #11665, 0.26 #10497, 0.26 #11431), 02pqp12 (0.27 #11665, 0.26 #10497, 0.26 #11431), 04dn09n (0.27 #11665, 0.26 #10497, 0.25 #14234), 0gs9p (0.26 #11431, 0.22 #4666, 0.21 #997), 0f4x7 (0.26 #11431, 0.22 #4666, 0.14 #957), 0gr4k (0.26 #11431, 0.22 #4666, 0.10 #958) >> Best rule #11665 for best value: >> intensional similarity = 3 >> extensional distance = 858 >> proper extension: 02d413; 0ds35l9; 015qsq; 0d90m; 03qcfvw; 0m313; 02y_lrp; 0g22z; 083shs; 02vxq9m; ... >> query: (?x2047, ?x746) <- award_winner(?x2047, ?x1119), nominated_for(?x746, ?x2047), award(?x2047, ?x1587) >> conf = 0.27 => this is the best rule for 7 predicted values *> Best rule #11431 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 843 *> proper extension: 04glx0; 07bz5; *> query: (?x2047, ?x591) <- award_winner(?x2047, ?x1119), award(?x2047, ?x1587), award_winner(?x591, ?x1119) *> conf = 0.26 ranks of expected_values: 16 EVAL 0hmm7 award 027c924 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 93.000 93.000 0.272 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #10135-06bnz PRED entity: 06bnz PRED relation: official_language PRED expected values: 06b_j => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 48): 06b_j (0.55 #3304, 0.42 #5510, 0.38 #5509), 02h40lc (0.52 #3260, 0.35 #574, 0.31 #2070), 0cjk9 (0.38 #5509, 0.37 #4186, 0.36 #4451), 0880p (0.38 #5509, 0.37 #4186, 0.36 #4451), 064_8sq (0.22 #104, 0.16 #2833, 0.16 #4334), 06nm1 (0.17 #492, 0.16 #1108, 0.14 #2560), 04306rv (0.14 #1589, 0.12 #1413, 0.12 #621), 02bjrlw (0.13 #177, 0.12 #309, 0.08 #793), 06mp7 (0.13 #187, 0.12 #319, 0.08 #803), 0jzc (0.12 #4332, 0.12 #2038, 0.10 #4993) >> Best rule #3304 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 03_xj; 0h44w; >> query: (?x1603, ?x5671) <- countries_spoken_in(?x5671, ?x1603), language(?x582, ?x5671), ?x582 = 01sxly >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06bnz official_language 06b_j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 193.000 0.548 http://example.org/location/country/official_language #10134-01yc02 PRED entity: 01yc02 PRED relation: company PRED expected values: 03v52f 01pf21 => 37 concepts (32 used for prediction) PRED predicted values (max 10 best out of 826): 01npw8 (0.71 #3580, 0.62 #3881, 0.60 #4185), 01c6k4 (0.71 #3027, 0.54 #4839, 0.50 #3932), 05njw (0.71 #3253, 0.50 #4158, 0.50 #2045), 02630g (0.71 #3148, 0.50 #4053, 0.50 #1940), 03sc8 (0.60 #2500, 0.57 #3405, 0.57 #3105), 01pf21 (0.60 #4118, 0.57 #3513, 0.57 #3213), 03mnk (0.60 #2476, 0.50 #1873, 0.50 #969), 035nm (0.60 #2603, 0.50 #2000, 0.50 #1096), 0lwkh (0.57 #3576, 0.57 #3276, 0.50 #4181), 0cv9b (0.57 #3346, 0.57 #3046, 0.50 #3951) >> Best rule #3580 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 09d6p2; >> query: (?x1907, 01npw8) <- company(?x1907, ?x13900), company(?x1907, ?x11558), company(?x1907, ?x7633), company(?x1907, ?x3920), citytown(?x7633, ?x674), state_province_region(?x13900, ?x1755), company(?x346, ?x7633), ?x346 = 060c4, ?x3920 = 09b3v, citytown(?x13900, ?x4074), service_location(?x11558, ?x94), contact_category(?x13900, ?x3231), state_province_region(?x11558, ?x1227), industry(?x11558, ?x14634) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4118 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 8 *> proper extension: 028fjr; *> query: (?x1907, 01pf21) <- company(?x1907, ?x13900), company(?x1907, ?x11558), company(?x1907, ?x7633), company(?x1907, ?x3920), company(?x1907, ?x3793), citytown(?x7633, ?x674), state_province_region(?x13900, ?x1755), company(?x346, ?x7633), ?x346 = 060c4, contact_category(?x3793, ?x897), service_location(?x13900, ?x94), category(?x11558, ?x134), production_companies(?x148, ?x3920), citytown(?x13900, ?x4074), citytown(?x3920, ?x11930) *> conf = 0.60 ranks of expected_values: 6, 24 EVAL 01yc02 company 01pf21 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 37.000 32.000 0.714 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 01yc02 company 03v52f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 37.000 32.000 0.714 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #10133-0ftxw PRED entity: 0ftxw PRED relation: place_of_birth! PRED expected values: 049gc 07g7h2 => 189 concepts (150 used for prediction) PRED predicted values (max 10 best out of 2313): 02ts3h (0.39 #91271, 0.38 #101700, 0.36 #153852), 049gc (0.39 #91271, 0.38 #101700, 0.36 #153852), 06l9n8 (0.39 #91271, 0.38 #101700, 0.36 #153852), 012cj0 (0.39 #91271, 0.38 #101700, 0.36 #153852), 01qklj (0.39 #91271, 0.38 #101700, 0.36 #153852), 0677ng (0.33 #4113, 0.20 #6720, 0.12 #17153), 064jjy (0.33 #4324, 0.20 #6931, 0.12 #17364), 01mqc_ (0.33 #4157, 0.20 #6764, 0.12 #17197), 02mqc4 (0.33 #3428, 0.20 #6035, 0.12 #16468), 01j7rd (0.33 #2996, 0.20 #5603, 0.12 #16036) >> Best rule #91271 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 0_vn7; 0dyl9; >> query: (?x2879, ?x772) <- category(?x2879, ?x134), location(?x772, ?x2879), contains(?x94, ?x2879), dog_breed(?x2879, ?x1706) >> conf = 0.39 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0ftxw place_of_birth! 07g7h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 189.000 150.000 0.395 http://example.org/people/person/place_of_birth EVAL 0ftxw place_of_birth! 049gc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 189.000 150.000 0.395 http://example.org/people/person/place_of_birth #10132-011kn2 PRED entity: 011kn2 PRED relation: major_field_of_study PRED expected values: 01tbp => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 119): 02j62 (0.49 #774, 0.48 #1022, 0.43 #1270), 02lp1 (0.48 #1004, 0.41 #1252, 0.30 #756), 062z7 (0.40 #1019, 0.35 #1267, 0.31 #771), 03g3w (0.37 #646, 0.35 #1018, 0.33 #1266), 05qfh (0.33 #1028, 0.28 #1276, 0.25 #780), 01540 (0.30 #1054, 0.25 #1302, 0.17 #806), 04gb7 (0.29 #665, 0.24 #1409, 0.20 #45), 05qjt (0.28 #1000, 0.28 #1248, 0.26 #752), 0g26h (0.28 #1035, 0.26 #1283, 0.24 #1407), 037mh8 (0.28 #1061, 0.25 #813, 0.25 #69) >> Best rule #774 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 01c0cc; 0j_sncb; 02bqy; 0k9wp; 01bk1y; 01qd_r; 013nky; 0p7tb; >> query: (?x13458, 02j62) <- major_field_of_study(?x13458, ?x2014), currency(?x13458, ?x2244), contains(?x279, ?x13458), ?x2014 = 04rjg >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1053 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 013719; *> query: (?x13458, 01tbp) <- major_field_of_study(?x13458, ?x1668), currency(?x13458, ?x2244), category(?x13458, ?x134), ?x1668 = 01mkq *> conf = 0.26 ranks of expected_values: 13 EVAL 011kn2 major_field_of_study 01tbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 112.000 112.000 0.487 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10131-01gfq4 PRED entity: 01gfq4 PRED relation: artist PRED expected values: 0135xb => 75 concepts (55 used for prediction) PRED predicted values (max 10 best out of 999): 0qf3p (0.50 #1811, 0.33 #980, 0.25 #10121), 06gcn (0.50 #2212, 0.25 #25757, 0.24 #29082), 0gbwp (0.50 #1934, 0.25 #3594, 0.20 #2764), 047cx (0.50 #1997, 0.20 #2827, 0.19 #3657), 07yg2 (0.40 #2778, 0.21 #4438, 0.17 #1948), 0g824 (0.38 #3773, 0.33 #452, 0.18 #14574), 01k23t (0.38 #3882, 0.20 #14683, 0.20 #16346), 03xhj6 (0.33 #1967, 0.33 #306, 0.26 #4457), 0cg9y (0.33 #1795, 0.33 #134, 0.26 #4285), 01vn35l (0.33 #1840, 0.33 #179, 0.19 #3500) >> Best rule #1811 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0mzkr; 01clyr; 0n85g; >> query: (?x3888, 0qf3p) <- artist(?x3888, ?x10924), artist(?x3888, ?x5494), category(?x3888, ?x134), ?x5494 = 018x3, nationality(?x10924, ?x252), award(?x10924, ?x1479), profession(?x10924, ?x319), artists(?x474, ?x10924) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2172 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 0mzkr; 01clyr; 0n85g; *> query: (?x3888, 0135xb) <- artist(?x3888, ?x10924), artist(?x3888, ?x5494), category(?x3888, ?x134), ?x5494 = 018x3, nationality(?x10924, ?x252), award(?x10924, ?x1479), profession(?x10924, ?x319), artists(?x474, ?x10924) *> conf = 0.17 ranks of expected_values: 299 EVAL 01gfq4 artist 0135xb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 75.000 55.000 0.500 http://example.org/music/record_label/artist #10130-02bfxb PRED entity: 02bfxb PRED relation: nationality PRED expected values: 0ctw_b => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 78): 0ctw_b (0.80 #2906, 0.45 #2204, 0.40 #9423), 09c7w0 (0.74 #3107, 0.74 #1703, 0.73 #3608), 07ssc (0.40 #9423, 0.36 #8217, 0.17 #2118), 0chghy (0.40 #9423, 0.05 #5110, 0.05 #3808), 0d060g (0.36 #8217, 0.06 #407, 0.05 #808), 06q1r (0.36 #8217, 0.03 #2180, 0.03 #2881), 02jx1 (0.16 #2136, 0.12 #1635, 0.12 #1234), 0f8l9c (0.07 #2125, 0.05 #5110, 0.05 #3808), 03rk0 (0.06 #9769, 0.05 #9869, 0.05 #2149), 03rt9 (0.05 #5110, 0.05 #3808, 0.05 #801) >> Best rule #2906 for best value: >> intensional similarity = 2 >> extensional distance = 555 >> proper extension: 01qx13; >> query: (?x3434, ?x1023) <- place_of_birth(?x3434, ?x11743), country(?x11743, ?x1023) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bfxb nationality 0ctw_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.801 http://example.org/people/person/nationality #10129-0g2mbn PRED entity: 0g2mbn PRED relation: student! PRED expected values: 09s5q8 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 104): 01jq0j (0.12 #248, 0.04 #1829, 0.03 #2356), 06xpp7 (0.12 #177, 0.04 #1758, 0.03 #2285), 0cwx_ (0.12 #241, 0.04 #1822, 0.03 #2876), 01w5m (0.07 #8537, 0.06 #9591, 0.03 #21188), 0bwfn (0.07 #2383, 0.06 #7653, 0.06 #18195), 0gl5_ (0.05 #771, 0.04 #4460, 0.04 #6568), 078bz (0.05 #604, 0.04 #1131, 0.03 #2712), 0lfgr (0.05 #570, 0.04 #1097, 0.02 #3205), 03k7dn (0.05 #960, 0.04 #1487, 0.02 #3595), 02gr81 (0.05 #659, 0.04 #1186, 0.02 #3294) >> Best rule #248 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0187y5; 014gf8; 0g824; 01d1st; >> query: (?x5153, 01jq0j) <- award_nominee(?x5153, ?x2763), ?x2763 = 019pm_, category(?x5153, ?x134) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #4419 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 0c7ct; 0p3r8; 02w5q6; 0261x8t; 02p68d; 010p3; 0163t3; 02_wxh; 0sx5w; 01rzxl; *> query: (?x5153, 09s5q8) <- program(?x5153, ?x3626), nationality(?x5153, ?x94), category(?x5153, ?x134) *> conf = 0.02 ranks of expected_values: 63 EVAL 0g2mbn student! 09s5q8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 93.000 93.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #10128-0ct2tf5 PRED entity: 0ct2tf5 PRED relation: produced_by PRED expected values: 043q6n_ => 87 concepts (50 used for prediction) PRED predicted values (max 10 best out of 139): 025n3p (0.10 #8142, 0.10 #16295, 0.10 #18624), 01kgxf (0.10 #8142, 0.10 #17460, 0.10 #12023), 02xnjd (0.08 #273, 0.06 #2600, 0.06 #4539), 03ktjq (0.08 #201, 0.05 #2916, 0.04 #4080), 05prs8 (0.08 #54, 0.04 #5482, 0.02 #2769), 02bfxb (0.08 #114, 0.04 #1277, 0.03 #1664), 01rlxt (0.08 #193, 0.03 #4072, 0.02 #7559), 02779r4 (0.08 #232, 0.03 #5660, 0.02 #2947), 029m83 (0.07 #663, 0.01 #3377), 03kpvp (0.05 #2452, 0.05 #1675, 0.04 #2064) >> Best rule #8142 for best value: >> intensional similarity = 3 >> extensional distance = 193 >> proper extension: 02k_4g; 019nnl; 08jgk1; 01b_lz; 02pqs8l; 01s81; 030cx; 02kk_c; 0vjr; 0828jw; ... >> query: (?x9421, ?x2858) <- award_winner(?x9421, ?x2858), award(?x2858, ?x401), ?x401 = 05zr6wv >> conf = 0.10 => this is the best rule for 2 predicted values *> Best rule #1938 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 62 *> proper extension: 03l6q0; 02rrfzf; *> query: (?x9421, ?x1417) <- prequel(?x3700, ?x9421), film_release_distribution_medium(?x9421, ?x81), award_winner(?x9421, ?x2858), nominated_for(?x1417, ?x3700) *> conf = 0.04 ranks of expected_values: 12 EVAL 0ct2tf5 produced_by 043q6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 87.000 50.000 0.104 http://example.org/film/film/produced_by #10127-01tzm9 PRED entity: 01tzm9 PRED relation: location PRED expected values: 059rby => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 97): 030qb3t (0.22 #11327, 0.22 #19360, 0.22 #13737), 02_286 (0.19 #25739, 0.18 #10478, 0.18 #39397), 04jpl (0.17 #17, 0.08 #54638, 0.06 #11261), 0cr3d (0.10 #1751, 0.08 #54766, 0.07 #7373), 06y9v (0.07 #156), 0cc56 (0.07 #4876, 0.05 #10498, 0.05 #2466), 05k7sb (0.06 #2518, 0.05 #7337, 0.05 #912), 059rby (0.04 #4032, 0.04 #19293, 0.04 #13670), 01n7q (0.04 #11307, 0.04 #1669, 0.03 #7291), 0r0m6 (0.04 #5037, 0.03 #10659, 0.03 #11462) >> Best rule #11327 for best value: >> intensional similarity = 3 >> extensional distance = 355 >> proper extension: 0b5x23; >> query: (?x7353, 030qb3t) <- location(?x7353, ?x13284), people(?x743, ?x7353), languages(?x7353, ?x254) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #4032 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 140 *> proper extension: 01yznp; 03h_fqv; 05kh_; 01vb6z; 016dp0; *> query: (?x7353, 059rby) <- profession(?x7353, ?x987), film(?x7353, ?x2869), influenced_by(?x7353, ?x4988) *> conf = 0.04 ranks of expected_values: 8 EVAL 01tzm9 location 059rby CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 93.000 93.000 0.218 http://example.org/people/person/places_lived./people/place_lived/location #10126-0crh5_f PRED entity: 0crh5_f PRED relation: film_regional_debut_venue PRED expected values: 0d35y 0d9jr => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 28): 015hr (0.43 #39, 0.23 #357, 0.20 #558), 0prpt (0.40 #80, 0.32 #685, 0.29 #369), 018cvf (0.30 #1103, 0.30 #70, 0.29 #1132), 0kfhjq0 (0.19 #445, 0.13 #674, 0.13 #846), 0gg7gsl (0.15 #551, 0.14 #350, 0.14 #32), 07751 (0.14 #34, 0.14 #410, 0.10 #63), 03nn7l2 (0.14 #52, 0.03 #370, 0.03 #457), 07zmj (0.10 #83, 0.09 #889, 0.09 #343), 0h7h6 (0.10 #749, 0.04 #607, 0.03 #722), 0bmj62v (0.10 #749, 0.03 #422, 0.02 #622) >> Best rule #39 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 053tj7; >> query: (?x2954, 015hr) <- film_release_region(?x2954, ?x1355), film_release_distribution_medium(?x2954, ?x2008), film_regional_debut_venue(?x2954, ?x659), film_release_distribution_medium(?x511, ?x2008), ?x1355 = 0h7x, ?x511 = 0dscrwf, film_distribution_medium(?x2954, ?x2099) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0crh5_f film_regional_debut_venue 0d9jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 133.000 0.429 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 0crh5_f film_regional_debut_venue 0d35y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 133.000 0.429 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #10125-02x0bdb PRED entity: 02x0bdb PRED relation: place_of_death PRED expected values: 030qb3t => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 33): 06_kh (0.33 #5, 0.04 #3313, 0.04 #394), 030qb3t (0.22 #217, 0.17 #411, 0.16 #606), 02_286 (0.17 #402, 0.12 #597, 0.10 #3125), 0k049 (0.08 #392, 0.07 #198, 0.07 #1364), 0f2wj (0.07 #790, 0.05 #596, 0.04 #1373), 04jpl (0.04 #202, 0.03 #4482, 0.03 #785), 0r00l (0.04 #357, 0.02 #940, 0.02 #1329), 0cc56 (0.04 #212, 0.02 #601, 0.01 #406), 0281rp (0.04 #314, 0.01 #897), 0rnmy (0.04 #237, 0.01 #1598, 0.01 #1792) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01934k; >> query: (?x6807, 06_kh) <- award_nominee(?x6808, ?x6807), nationality(?x6807, ?x94), ?x6808 = 02l0sf >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #217 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 08b8vd; 01y8cr; 06nz46; 015njf; 02t_w8; 01p4r3; 03y2kr; 04vt98; 027r0_f; 02zfg3; *> query: (?x6807, 030qb3t) <- people(?x9771, ?x6807), type_of_union(?x6807, ?x566), ?x9771 = 02knxx *> conf = 0.22 ranks of expected_values: 2 EVAL 02x0bdb place_of_death 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.333 http://example.org/people/deceased_person/place_of_death #10124-02lxrv PRED entity: 02lxrv PRED relation: genre PRED expected values: 0vgkd 03bxz7 => 98 concepts (80 used for prediction) PRED predicted values (max 10 best out of 97): 01z4y (0.61 #8443, 0.61 #7035, 0.57 #2460), 017fp (0.57 #2460, 0.55 #2578, 0.55 #8442), 02l7c8 (0.50 #365, 0.38 #8103, 0.37 #7284), 02kdv5l (0.33 #705, 0.33 #1173, 0.32 #1992), 01jfsb (0.32 #1181, 0.32 #7514, 0.32 #2000), 03k9fj (0.30 #712, 0.30 #1063, 0.29 #1765), 0219x_ (0.26 #24, 0.22 #375, 0.11 #8113), 03npn (0.26 #5, 0.13 #942, 0.11 #122), 0lsxr (0.26 #475, 0.21 #7511, 0.21 #593), 01hmnh (0.24 #133, 0.21 #1772, 0.21 #2006) >> Best rule #8443 for best value: >> intensional similarity = 4 >> extensional distance = 1218 >> proper extension: 024rwx; 0ctzf1; 09g_31; >> query: (?x5890, ?x2480) <- titles(?x2480, ?x5890), titles(?x2480, ?x6940), genre(?x631, ?x2480), nominated_for(?x384, ?x6940) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 04y9mm8; 09p5mwg; 09qljs; 06cgf; *> query: (?x5890, 0vgkd) <- genre(?x5890, ?x271), film(?x376, ?x5890), film(?x7980, ?x5890), ?x271 = 01q03 *> conf = 0.21 ranks of expected_values: 14, 22 EVAL 02lxrv genre 03bxz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 98.000 80.000 0.614 http://example.org/film/film/genre EVAL 02lxrv genre 0vgkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 98.000 80.000 0.614 http://example.org/film/film/genre #10123-02lj6p PRED entity: 02lj6p PRED relation: film PRED expected values: 03nx8mj => 92 concepts (58 used for prediction) PRED predicted values (max 10 best out of 525): 06z8s_ (0.44 #73274, 0.36 #87572, 0.35 #94721), 02qr3k8 (0.06 #3074, 0.05 #20944, 0.03 #26306), 03q0r1 (0.06 #635, 0.05 #7783, 0.03 #13144), 04sh80 (0.06 #3533, 0.03 #1746), 06lpmt (0.06 #683, 0.02 #7831, 0.02 #11405), 0gzy02 (0.06 #1831, 0.02 #19701, 0.02 #28638), 09qycb (0.06 #1643, 0.02 #21300, 0.02 #8791), 04x4vj (0.06 #772), 03fts (0.06 #226), 01shy7 (0.05 #7570, 0.05 #3996, 0.04 #11144) >> Best rule #73274 for best value: >> intensional similarity = 3 >> extensional distance = 1310 >> proper extension: 04cy8rb; 07lmxq; 0f830f; 08wq0g; 06n7h7; 03ldxq; 0bz5v2; 04cf09; 049k07; 07hbxm; ... >> query: (?x8619, ?x876) <- award_nominee(?x2422, ?x8619), nominated_for(?x8619, ?x876), film(?x2422, ?x349) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #696 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 044ntk; 012gq6; 01ft2l; 0fb7c; 02f6s3; 02661h; 028mc6; 02wr6r; 0127xk; 01kkx2; ... *> query: (?x8619, 03nx8mj) <- award(?x8619, ?x5235), place_of_birth(?x8619, ?x1860), ?x5235 = 09qrn4 *> conf = 0.03 ranks of expected_values: 174 EVAL 02lj6p film 03nx8mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 92.000 58.000 0.444 http://example.org/film/actor/film./film/performance/film #10122-01vtj38 PRED entity: 01vtj38 PRED relation: film PRED expected values: 07kb7vh => 128 concepts (86 used for prediction) PRED predicted values (max 10 best out of 925): 01k60v (0.64 #53705, 0.62 #37594, 0.60 #23272), 01xbxn (0.20 #1394, 0.14 #3184, 0.04 #4974), 0fphf3v (0.20 #1362, 0.14 #3152, 0.03 #24634), 09g7vfw (0.20 #553, 0.14 #2343, 0.03 #20244), 02ctc6 (0.20 #522, 0.14 #2312, 0.03 #82347), 049mql (0.20 #684, 0.14 #2474, 0.03 #82347), 09fqgj (0.20 #1661, 0.14 #3451, 0.02 #14192), 0f8j13 (0.20 #1565, 0.14 #3355, 0.02 #6936), 0287477 (0.20 #1067, 0.14 #2857, 0.02 #6438), 07nxnw (0.20 #1211, 0.14 #3001, 0.02 #8372) >> Best rule #53705 for best value: >> intensional similarity = 3 >> extensional distance = 412 >> proper extension: 04bdxl; 02g8h; 0d_84; 0prfz; 0l8v5; 0h5g_; 04wqr; 01rr9f; 06cv1; 05hj0n; ... >> query: (?x7331, ?x4448) <- participant(?x7331, ?x338), gender(?x338, ?x231), nominated_for(?x7331, ?x4448) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #23958 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 179 *> proper extension: 0grwj; 027dtv3; 05k2s_; 0343h; 02g87m; 01n4f8; 01kj0p; 03jqw5; 07swvb; 0hqcy; ... *> query: (?x7331, 07kb7vh) <- award_nominee(?x12724, ?x7331), award(?x7331, ?x528), participant(?x4397, ?x7331) *> conf = 0.01 ranks of expected_values: 860 EVAL 01vtj38 film 07kb7vh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 86.000 0.643 http://example.org/film/actor/film./film/performance/film #10121-081pw PRED entity: 081pw PRED relation: combatants PRED expected values: 0154j 0f8l9c 02psqkz => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 290): 043870 (0.55 #830, 0.38 #514, 0.33 #303), 03gk2 (0.38 #443, 0.36 #759, 0.25 #126), 0f8l9c (0.38 #433, 0.33 #222, 0.30 #644), 014tss (0.36 #787, 0.33 #260, 0.25 #154), 0dv0z (0.36 #798, 0.25 #482, 0.25 #165), 02psqkz (0.33 #562, 0.25 #351, 0.20 #2060), 03x1x (0.33 #284, 0.18 #811, 0.18 #1350), 02fp48 (0.33 #103, 0.10 #736, 0.08 #949), 0g78xc (0.27 #757, 0.25 #441, 0.17 #230), 024pcx (0.27 #800, 0.25 #484, 0.12 #1657) >> Best rule #830 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0845v; 0dr7s; >> query: (?x326, 043870) <- locations(?x326, ?x6304), combatants(?x326, ?x3728), country(?x4355, ?x3728), taxonomy(?x326, ?x939), contains(?x6304, ?x172) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #433 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 086m1; 07j9n; 01gqg3; *> query: (?x326, 0f8l9c) <- locations(?x326, ?x1144), combatants(?x326, ?x3728), combatants(?x326, ?x756), country(?x4355, ?x3728), taxonomy(?x326, ?x939), film_release_region(?x303, ?x756) *> conf = 0.38 ranks of expected_values: 3, 6, 56 EVAL 081pw combatants 02psqkz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 64.000 0.545 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 081pw combatants 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 64.000 0.545 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants EVAL 081pw combatants 0154j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 64.000 64.000 0.545 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #10120-0ds1glg PRED entity: 0ds1glg PRED relation: film_release_region PRED expected values: 03_3d 07ssc 06mkj 03h64 => 52 concepts (50 used for prediction) PRED predicted values (max 10 best out of 131): 03h64 (0.90 #335, 0.87 #475, 0.87 #615), 06mkj (0.89 #326, 0.87 #606, 0.87 #466), 07ssc (0.87 #571, 0.86 #431, 0.86 #711), 03_3d (0.81 #284, 0.75 #424, 0.75 #704), 03rj0 (0.73 #49, 0.73 #329, 0.66 #469), 015qh (0.73 #33, 0.73 #313, 0.66 #453), 0ctw_b (0.73 #20, 0.68 #300, 0.67 #440), 01mjq (0.73 #35, 0.62 #455, 0.61 #595), 01ls2 (0.64 #8, 0.60 #288, 0.52 #428), 06t8v (0.64 #66, 0.58 #346, 0.50 #766) >> Best rule #335 for best value: >> intensional similarity = 7 >> extensional distance = 71 >> proper extension: 0gtsx8c; 02vxq9m; 0gx1bnj; 0ds3t5x; 0dscrwf; 02x3lt7; 01vksx; 017gl1; 08hmch; 0h3xztt; ... >> query: (?x7126, 03h64) <- film_release_region(?x7126, ?x2843), film_release_region(?x7126, ?x583), film_release_region(?x7126, ?x344), ?x344 = 04gzd, ?x583 = 015fr, film(?x919, ?x7126), ?x2843 = 016wzw >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0ds1glg film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 50.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds1glg film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 50.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds1glg film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 50.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds1glg film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 50.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10119-0ntwb PRED entity: 0ntwb PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 104 concepts (36 used for prediction) PRED predicted values (max 10 best out of 18): 09c7w0 (0.85 #420, 0.85 #409, 0.85 #393), 03v0t (0.13 #405, 0.09 #392, 0.09 #357), 05mph (0.03 #302), 0vbk (0.03 #302), 05fky (0.03 #302), 0824r (0.03 #302), 0498y (0.03 #302), 04ly1 (0.03 #302), 07h34 (0.03 #302), 07b_l (0.03 #302) >> Best rule #420 for best value: >> intensional similarity = 8 >> extensional distance = 181 >> proper extension: 0nm8n; >> query: (?x9368, ?x94) <- source(?x9368, ?x958), adjoins(?x9368, ?x13667), adjoins(?x9368, ?x8624), currency(?x13667, ?x170), ?x958 = 0jbk9, second_level_divisions(?x94, ?x13667), ?x94 = 09c7w0, county(?x3964, ?x8624) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ntwb second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 36.000 0.852 http://example.org/location/country/second_level_divisions #10118-0chghy PRED entity: 0chghy PRED relation: vacationer PRED expected values: 01p4vl => 236 concepts (173 used for prediction) PRED predicted values (max 10 best out of 193): 01xyt7 (0.20 #807, 0.11 #3030, 0.10 #3544), 016fnb (0.17 #2152, 0.15 #3007, 0.14 #8489), 0lk90 (0.15 #2929, 0.14 #3443, 0.13 #4129), 01yf85 (0.15 #3058, 0.14 #3572, 0.13 #4258), 01vs_v8 (0.14 #383, 0.11 #2948, 0.11 #554), 0f4vbz (0.14 #384, 0.11 #555, 0.10 #726), 0c6qh (0.14 #391, 0.10 #733, 0.09 #1759), 05r5w (0.14 #3494, 0.13 #4180, 0.12 #8462), 03lt8g (0.13 #4130, 0.10 #3444, 0.10 #707), 0bksh (0.12 #8493, 0.10 #788, 0.09 #12260) >> Best rule #807 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 022b_; >> query: (?x390, 01xyt7) <- jurisdiction_of_office(?x3959, ?x390), films(?x390, ?x5835) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3562 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 0h3tv; *> query: (?x390, 01p4vl) <- teams(?x390, ?x59), vacationer(?x390, ?x5514), celebrity(?x3244, ?x5514) *> conf = 0.10 ranks of expected_values: 23 EVAL 0chghy vacationer 01p4vl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 236.000 173.000 0.200 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #10117-0dcz8_ PRED entity: 0dcz8_ PRED relation: nominated_for! PRED expected values: 0gqxm 05ztrmj => 115 concepts (107 used for prediction) PRED predicted values (max 10 best out of 227): 0gr42 (0.31 #91, 0.22 #808, 0.18 #569), 02g3v6 (0.31 #22, 0.18 #500, 0.17 #1217), 0p9sw (0.28 #738, 0.23 #21, 0.20 #1694), 0gq9h (0.25 #14644, 0.25 #14883, 0.23 #21815), 05p1dby (0.23 #84, 0.13 #562, 0.11 #1279), 05p09zm (0.23 #96, 0.08 #574, 0.07 #813), 02r22gf (0.22 #746, 0.16 #1702, 0.15 #29), 0gq6s3 (0.22 #25102, 0.19 #23665, 0.19 #25103), 0gr51 (0.22 #25102, 0.19 #25103, 0.19 #25344), 02qyp19 (0.22 #25102, 0.19 #25103, 0.19 #25344) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 014lc_; >> query: (?x9715, 0gr42) <- story_by(?x9715, ?x12856), prequel(?x9715, ?x6528), country(?x9715, ?x94), category(?x9715, ?x134) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #135 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 014lc_; *> query: (?x9715, 05ztrmj) <- story_by(?x9715, ?x12856), prequel(?x9715, ?x6528), country(?x9715, ?x94), category(?x9715, ?x134) *> conf = 0.15 ranks of expected_values: 28, 57 EVAL 0dcz8_ nominated_for! 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 115.000 107.000 0.308 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0dcz8_ nominated_for! 0gqxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 115.000 107.000 0.308 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10116-03qnc6q PRED entity: 03qnc6q PRED relation: film_release_region PRED expected values: 0chghy 06qd3 07t21 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 127): 0chghy (0.89 #3226, 0.86 #1886, 0.84 #1484), 03rt9 (0.81 #2023, 0.80 #2291, 0.79 #1755), 04gzd (0.70 #1750, 0.68 #2286, 0.67 #2018), 047yc (0.66 #2034, 0.64 #156, 0.63 #2302), 06qd3 (0.63 #2444, 0.62 #2176, 0.58 #3516), 05qx1 (0.60 #33, 0.54 #1509, 0.51 #2045), 03ryn (0.60 #68, 0.45 #202, 0.44 #336), 06f32 (0.58 #2063, 0.55 #1795, 0.55 #2331), 047lj (0.55 #143, 0.44 #2289, 0.44 #1753), 09pmkv (0.50 #1899, 0.49 #1497, 0.48 #1765) >> Best rule #3226 for best value: >> intensional similarity = 5 >> extensional distance = 170 >> proper extension: 0gtsx8c; 0gtv7pk; 087wc7n; 0crfwmx; 06v9_x; 0661m4p; 0879bpq; 05q4y12; 0gtsxr4; 0gffmn8; ... >> query: (?x2656, 0chghy) <- film_release_region(?x2656, ?x3699), film_release_region(?x2656, ?x1023), ?x1023 = 0ctw_b, geographic_distribution(?x7322, ?x3699), vacationer(?x3699, ?x1017) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 18 EVAL 03qnc6q film_release_region 07t21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 115.000 115.000 0.890 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03qnc6q film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 115.000 0.890 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03qnc6q film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.890 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10115-0272kv PRED entity: 0272kv PRED relation: crewmember! PRED expected values: 02vjp3 => 114 concepts (97 used for prediction) PRED predicted values (max 10 best out of 4): 0b2qtl (0.02 #16127, 0.02 #18384, 0.02 #18385), 01f69m (0.02 #16127, 0.02 #18384, 0.02 #25155), 015qsq (0.02 #16127, 0.02 #18384, 0.02 #25155), 02q7yfq (0.01 #567) >> Best rule #16127 for best value: >> intensional similarity = 4 >> extensional distance = 956 >> proper extension: 0gsg7; 09d5h; 0cjdk; 05gnf; 01zcrv; >> query: (?x9363, ?x89) <- nominated_for(?x9363, ?x5096), nominated_for(?x9363, ?x89), honored_for(?x8150, ?x5096), award_winner(?x8480, ?x9363) >> conf = 0.02 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0272kv crewmember! 02vjp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 97.000 0.022 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #10114-023p7l PRED entity: 023p7l PRED relation: currency PRED expected values: 09nqf => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.78 #148, 0.78 #120, 0.78 #99), 088n7 (0.10 #21, 0.09 #28, 0.07 #14), 01nv4h (0.03 #37, 0.02 #177, 0.02 #226), 02l6h (0.01 #39, 0.01 #179), 02gsvk (0.01 #41) >> Best rule #148 for best value: >> intensional similarity = 4 >> extensional distance = 848 >> proper extension: 0gtsx8c; 047svrl; 07kb7vh; 0hgnl3t; 07k2mq; 01gglm; >> query: (?x3759, 09nqf) <- production_companies(?x3759, ?x2156), film(?x556, ?x3759), award_nominee(?x2156, ?x1285), film(?x2156, ?x148) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023p7l currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.785 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10113-02cft PRED entity: 02cft PRED relation: mode_of_transportation PRED expected values: 01bjv => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 3): 01bjv (0.86 #43, 0.85 #97, 0.85 #37), 0k4j (0.07 #95, 0.06 #17, 0.06 #68), 06d_3 (0.05 #144, 0.05 #36, 0.04 #183) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 02_286; 0fhp9; 030qb3t; 0156q; 01f62; 0h7h6; 01_d4; 0dclg; 052p7; 02h6_6p; ... >> query: (?x6357, 01bjv) <- location(?x489, ?x6357), film_release_region(?x6394, ?x6357), mode_of_transportation(?x6357, ?x4272) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cft mode_of_transportation 01bjv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 194.000 0.862 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #10112-0g1rw PRED entity: 0g1rw PRED relation: film PRED expected values: 0gzy02 0sxmx 027j9wd 0gl3hr 0fsd9t 06bc59 06pyc2 => 117 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1677): 02q52q (0.72 #21021, 0.70 #12011, 0.68 #18017), 0gt1k (0.72 #21021, 0.70 #12011, 0.68 #18017), 083skw (0.72 #21021, 0.70 #12011, 0.68 #18017), 0gxfz (0.72 #21021, 0.01 #49553), 0h21v2 (0.70 #12011, 0.68 #18017, 0.68 #28528), 04z257 (0.70 #12011, 0.68 #18017, 0.68 #28528), 033qdy (0.70 #12011, 0.68 #18017, 0.68 #28528), 03wbqc4 (0.70 #12011, 0.68 #18017, 0.68 #28528), 0q9sg (0.70 #12011, 0.68 #18017, 0.68 #28528), 03r0g9 (0.70 #12011, 0.68 #18017, 0.68 #28528) >> Best rule #21021 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 09d5h; 05xbx; >> query: (?x788, ?x1804) <- award_nominee(?x788, ?x1172), film(?x788, ?x186), nominated_for(?x788, ?x1804) >> conf = 0.72 => this is the best rule for 4 predicted values *> Best rule #1536 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 03y3dk; *> query: (?x788, 0gzy02) <- award_nominee(?x788, ?x9363), award(?x788, ?x500), ?x9363 = 0272kv *> conf = 0.25 ranks of expected_values: 18, 103, 296, 836, 1271, 1397, 1573 EVAL 0g1rw film 06pyc2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 117.000 33.000 0.719 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0g1rw film 06bc59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 117.000 33.000 0.719 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0g1rw film 0fsd9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 117.000 33.000 0.719 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0g1rw film 0gl3hr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 117.000 33.000 0.719 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0g1rw film 027j9wd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 117.000 33.000 0.719 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0g1rw film 0sxmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 117.000 33.000 0.719 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 0g1rw film 0gzy02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 117.000 33.000 0.719 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #10111-0f8j13 PRED entity: 0f8j13 PRED relation: currency PRED expected values: 09nqf => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.85 #57, 0.85 #71, 0.84 #92), 01nv4h (0.26 #351, 0.20 #2, 0.07 #23), 0kz1h (0.26 #351, 0.02 #68, 0.01 #89), 02l6h (0.01 #298, 0.01 #228, 0.01 #109), 02gsvk (0.01 #111) >> Best rule #57 for best value: >> intensional similarity = 7 >> extensional distance = 39 >> proper extension: 02r1c18; 0gxtknx; 03pc89; 0cqr0q; >> query: (?x9478, 09nqf) <- genre(?x9478, ?x811), genre(?x9478, ?x809), ?x809 = 0vgkd, country(?x9478, ?x94), ?x94 = 09c7w0, genre(?x8075, ?x811), ?x8075 = 03nfnx >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f8j13 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.854 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10110-0fvyg PRED entity: 0fvyg PRED relation: teams PRED expected values: 0j8cb => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 168): 0cqt41 (0.14 #389, 0.04 #748, 0.03 #5774), 0jmk7 (0.14 #661, 0.04 #1020, 0.03 #2097), 0jnq8 (0.14 #587, 0.04 #946, 0.03 #2023), 0jmjr (0.14 #580, 0.04 #939, 0.03 #2016), 04mjl (0.14 #515, 0.04 #874, 0.03 #1951), 02pqcfz (0.14 #441, 0.04 #800, 0.03 #1877), 04112r (0.14 #410, 0.04 #769, 0.03 #1846), 07k53y (0.14 #371, 0.04 #730, 0.03 #1807), 0hmtk (0.14 #675, 0.04 #1034, 0.03 #2111), 05g76 (0.14 #394, 0.04 #753, 0.03 #1830) >> Best rule #389 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0cc56; 049kw; >> query: (?x11246, 0cqt41) <- location(?x5645, ?x11246), participant(?x5645, ?x7804), award_nominee(?x9384, ?x5645), ?x9384 = 05mlqj >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fvyg teams 0j8cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 137.000 0.143 http://example.org/sports/sports_team_location/teams #10109-0dnqr PRED entity: 0dnqr PRED relation: film! PRED expected values: 0c0k1 => 102 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1286): 06mr6 (0.72 #62465, 0.67 #52053, 0.49 #68715), 04wp63 (0.72 #62465, 0.67 #52053, 0.49 #68715), 0146pg (0.49 #68715, 0.47 #24987, 0.46 #62464), 0b6mgp_ (0.47 #24987, 0.46 #62464, 0.44 #20821), 06j8wx (0.29 #962, 0.02 #21783, 0.01 #9291), 0343h (0.27 #41643, 0.19 #77046, 0.19 #79129), 03h_9lg (0.14 #133, 0.09 #2214, 0.03 #31367), 017lqp (0.14 #1612, 0.08 #9941, 0.06 #18270), 01nwwl (0.14 #504, 0.07 #12997, 0.04 #42147), 02xs5v (0.14 #1407, 0.06 #3488, 0.04 #9736) >> Best rule #62465 for best value: >> intensional similarity = 4 >> extensional distance = 334 >> proper extension: 0gydcp7; 0gy2y8r; 047rkcm; >> query: (?x2947, ?x5869) <- nominated_for(?x5869, ?x2947), language(?x2947, ?x254), executive_produced_by(?x2947, ?x4552), film(?x5869, ?x195) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #5672 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 08sk8l; *> query: (?x2947, 0c0k1) <- film(?x3181, ?x2947), cinematography(?x2947, ?x11915), edited_by(?x2947, ?x1387) *> conf = 0.08 ranks of expected_values: 37 EVAL 0dnqr film! 0c0k1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 102.000 47.000 0.719 http://example.org/film/actor/film./film/performance/film #10108-01g1lp PRED entity: 01g1lp PRED relation: award_winner! PRED expected values: 02wkmx => 117 concepts (115 used for prediction) PRED predicted values (max 10 best out of 277): 05p1dby (0.37 #29973, 0.37 #31687, 0.37 #31686), 0gs9p (0.37 #29973, 0.37 #31687, 0.37 #31686), 02x4sn8 (0.37 #29973, 0.37 #31687, 0.37 #31686), 02x4wr9 (0.37 #29973, 0.37 #31687, 0.37 #31686), 0789r6 (0.37 #29973, 0.37 #31687, 0.37 #31686), 07bdd_ (0.31 #2205, 0.25 #2633, 0.19 #3062), 0gq9h (0.29 #2997, 0.29 #77, 0.14 #505), 027c924 (0.29 #11, 0.17 #867, 0.16 #4720), 02pqp12 (0.29 #70, 0.13 #926, 0.13 #4779), 0m7yy (0.28 #2746, 0.15 #2318, 0.07 #606) >> Best rule #29973 for best value: >> intensional similarity = 3 >> extensional distance = 1402 >> proper extension: 086k8; 04lgymt; 017s11; 016tt2; 04rcr; 0g1rw; 0kx4m; 05qd_; 03h26tm; 016tw3; ... >> query: (?x7855, ?x350) <- award_winner(?x1104, ?x7855), award(?x7855, ?x350), award_winner(?x7855, ?x1039) >> conf = 0.37 => this is the best rule for 5 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 06cv1; 02kxbwx; 052gzr; 02kxbx3; 02lp3c; 0jgwf; 0kft; 03qhyn8; *> query: (?x7855, 02wkmx) <- profession(?x7855, ?x319), award_nominee(?x7855, ?x1104), edited_by(?x3781, ?x7855) *> conf = 0.14 ranks of expected_values: 31 EVAL 01g1lp award_winner! 02wkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 117.000 115.000 0.371 http://example.org/award/award_category/winners./award/award_honor/award_winner #10107-0cbgl PRED entity: 0cbgl PRED relation: place_of_death PRED expected values: 0r02m => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 97): 04jpl (0.17 #784, 0.08 #6240, 0.07 #4097), 05qtj (0.17 #2983, 0.07 #3373, 0.07 #9802), 0d35y (0.17 #840, 0.06 #2788), 0r02m (0.15 #3504, 0.07 #8960), 030qb3t (0.15 #14626, 0.15 #16766, 0.14 #16571), 02_286 (0.15 #3322, 0.14 #2153, 0.13 #5662), 0fvwg (0.11 #1273, 0.07 #2442, 0.04 #3415), 0f8j6 (0.11 #1748, 0.04 #3501, 0.03 #4086), 03v0t (0.09 #8570, 0.08 #18686, 0.07 #18882), 0d6lp (0.09 #1797, 0.07 #3550, 0.07 #2186) >> Best rule #784 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02cj_f; 0p9gg; >> query: (?x14008, 04jpl) <- people(?x6821, ?x14008), award_winner(?x12729, ?x14008), profession(?x14008, ?x353), ?x6821 = 06z5s >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #3504 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 047g6; *> query: (?x14008, ?x13255) <- company(?x14008, ?x8525), influenced_by(?x10578, ?x14008), student(?x8525, ?x3495), citytown(?x8525, ?x13255) *> conf = 0.15 ranks of expected_values: 4 EVAL 0cbgl place_of_death 0r02m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 170.000 170.000 0.167 http://example.org/people/deceased_person/place_of_death #10106-017fp PRED entity: 017fp PRED relation: genre! PRED expected values: 03mnn0 0dtzkt => 43 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1746): 07s846j (0.80 #3527, 0.74 #8817, 0.73 #15871), 0cmc26r (0.80 #3527, 0.74 #8817, 0.73 #15871), 0f4k49 (0.80 #3527, 0.74 #8817, 0.73 #15871), 02r1c18 (0.80 #3527, 0.74 #8817, 0.73 #15871), 0yzvw (0.80 #3527, 0.74 #8817, 0.73 #15871), 0ywrc (0.80 #3527, 0.74 #8817, 0.73 #15871), 0qm9n (0.80 #3527, 0.74 #8817, 0.73 #15871), 03s6l2 (0.80 #3527, 0.74 #8817, 0.73 #15871), 0209hj (0.80 #3527, 0.74 #8817, 0.73 #15871), 08nhfc1 (0.80 #3527, 0.74 #8817, 0.73 #15871) >> Best rule #3527 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 07s9rl0; >> query: (?x1316, ?x89) <- titles(?x1316, ?x7635), titles(?x1316, ?x4047), titles(?x1316, ?x3116), titles(?x1316, ?x253), titles(?x1316, ?x89), ?x4047 = 07s846j, ?x3116 = 0qmd5, genre(?x195, ?x1316), ?x7635 = 08nhfc1, ?x253 = 09m6kg >> conf = 0.80 => this is the best rule for 35 predicted values *> Best rule #10467 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 02kdv5l; 01g6gs; 082gq; 04xvh5; 03bxz7; *> query: (?x1316, 0dtzkt) <- genre(?x9993, ?x1316), genre(?x5051, ?x1316), genre(?x1803, ?x1316), genre(?x861, ?x1316), film_release_region(?x1803, ?x87), ?x9993 = 0kb1g, film(?x793, ?x861), crewmember(?x5051, ?x9769) *> conf = 0.12 ranks of expected_values: 1387, 1515 EVAL 017fp genre! 0dtzkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 14.000 0.797 http://example.org/film/film/genre EVAL 017fp genre! 03mnn0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 14.000 0.797 http://example.org/film/film/genre #10105-0488g PRED entity: 0488g PRED relation: district_represented! PRED expected values: 02bqm0 => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 50): 02bqm0 (0.68 #74, 0.65 #24, 0.61 #174), 03rl1g (0.63 #151, 0.59 #101, 0.58 #401), 02bqmq (0.61 #164, 0.61 #64, 0.59 #14), 043djx (0.61 #155, 0.59 #105, 0.58 #405), 02bqn1 (0.56 #952, 0.55 #801, 0.53 #7), 02cg7g (0.56 #952, 0.55 #801, 0.50 #21), 02gkzs (0.56 #952, 0.55 #801, 0.47 #18), 03rtmz (0.56 #952, 0.55 #801, 0.34 #163), 02glc4 (0.56 #952, 0.55 #801, 0.32 #179), 03tcbx (0.56 #952, 0.55 #801, 0.29 #162) >> Best rule #74 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0gj4fx; >> query: (?x1782, 02bqm0) <- district_represented(?x6933, ?x1782), ?x6933 = 024tkd, contains(?x1782, ?x1783) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0488g district_represented! 02bqm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.684 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #10104-0bth54 PRED entity: 0bth54 PRED relation: genre PRED expected values: 02kdv5l 01zhp => 88 concepts (59 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.80 #1173, 0.79 #2111, 0.76 #2931), 02kdv5l (0.75 #355, 0.67 #3, 0.62 #1293), 02l7c8 (0.36 #2945, 0.32 #3534, 0.31 #3299), 05p553 (0.35 #357, 0.35 #2818, 0.35 #6812), 0lsxr (0.33 #10, 0.22 #479, 0.22 #362), 03npn (0.33 #8, 0.13 #2001, 0.13 #1298), 02n4kr (0.28 #4582, 0.17 #713, 0.14 #5755), 04xvlr (0.28 #1174, 0.20 #2932, 0.19 #588), 0hcr (0.27 #256, 0.15 #4595, 0.14 #1429), 060__y (0.22 #1188, 0.19 #2712, 0.19 #2946) >> Best rule #1173 for best value: >> intensional similarity = 4 >> extensional distance = 133 >> proper extension: 0m313; 083shs; 011yrp; 011yxg; 095zlp; 0ds11z; 01h7bb; 011yph; 05jzt3; 0b6tzs; ... >> query: (?x573, 07s9rl0) <- nominated_for(?x1162, ?x573), language(?x573, ?x254), film_crew_role(?x573, ?x137), ?x1162 = 099c8n >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #355 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 02825nf; *> query: (?x573, 02kdv5l) <- nominated_for(?x3508, ?x573), nominated_for(?x800, ?x573), ?x3508 = 05ztrmj *> conf = 0.75 ranks of expected_values: 2, 32 EVAL 0bth54 genre 01zhp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 88.000 59.000 0.800 http://example.org/film/film/genre EVAL 0bth54 genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 59.000 0.800 http://example.org/film/film/genre #10103-01vrt_c PRED entity: 01vrt_c PRED relation: instrumentalists! PRED expected values: 0342h => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 85): 0342h (0.52 #272, 0.50 #4010, 0.46 #3654), 05r5c (0.48 #276, 0.34 #632, 0.34 #1967), 05148p4 (0.31 #3671, 0.30 #289, 0.28 #4027), 03qjg (0.30 #320, 0.14 #4058, 0.12 #676), 018vs (0.28 #3663, 0.21 #4019, 0.18 #6698), 0l14md (0.17 #275, 0.13 #1165, 0.11 #1521), 0l14qv (0.17 #95, 0.11 #3655, 0.09 #629), 02hnl (0.15 #3685, 0.14 #659, 0.14 #4041), 0mkg (0.13 #279, 0.04 #546, 0.03 #635), 026t6 (0.09 #448, 0.09 #181, 0.08 #92) >> Best rule #272 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0lgsq; 01r9fv; 05qw5; 0161c2; 07r4c; 01wf86y; 016s0m; >> query: (?x1206, 0342h) <- artists(?x302, ?x1206), award(?x1206, ?x1479), ?x1479 = 01ckbq >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrt_c instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.522 http://example.org/music/instrument/instrumentalists #10102-01zfmm PRED entity: 01zfmm PRED relation: gender PRED expected values: 05zppz => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #51, 0.87 #67, 0.87 #37), 02zsn (0.29 #158, 0.29 #116, 0.29 #128) >> Best rule #51 for best value: >> intensional similarity = 2 >> extensional distance = 190 >> proper extension: 0j582; 0ksf29; 04cbtrw; 0gg9_5q; 01mwsnc; 04bgy; 0d_skg; 017l4; 08gf93; 024t0y; ... >> query: (?x2789, 05zppz) <- executive_produced_by(?x1810, ?x2789), type_of_union(?x2789, ?x566) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01zfmm gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.875 http://example.org/people/person/gender #10101-07fj_ PRED entity: 07fj_ PRED relation: olympics PRED expected values: 0l998 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 37): 0jdk_ (0.60 #1514, 0.58 #1079, 0.56 #240), 0l6m5 (0.52 #154, 0.48 #701, 0.47 #1065), 0lgxj (0.50 #205, 0.42 #1334, 0.39 #1443), 0l998 (0.41 #187, 0.36 #1207, 0.36 #1316), 0lbd9 (0.41 #209, 0.35 #245, 0.34 #100), 0l6ny (0.38 #700, 0.38 #1064, 0.38 #1209), 0lv1x (0.35 #195, 0.33 #1324, 0.31 #1142), 0ldqf (0.35 #213, 0.30 #1342, 0.30 #724), 0blg2 (0.35 #197, 0.29 #1326, 0.28 #1072), 0jkvj (0.32 #1089, 0.32 #1379, 0.31 #1343) >> Best rule #1514 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 06qd3; 06f32; 05r7t; >> query: (?x4521, 0jdk_) <- country(?x2978, ?x4521), olympics(?x4521, ?x584), ?x2978 = 03_8r >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #187 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 0cdbq; *> query: (?x4521, 0l998) <- locations(?x12789, ?x4521), participating_countries(?x1931, ?x4521), nationality(?x10965, ?x4521) *> conf = 0.41 ranks of expected_values: 4 EVAL 07fj_ olympics 0l998 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 111.000 0.596 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #10100-0kftt PRED entity: 0kftt PRED relation: award_winner! PRED expected values: 02py7pj => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 284): 024dzn (0.44 #1722, 0.44 #1290, 0.41 #3441), 03tk6z (0.44 #1722, 0.44 #1290, 0.41 #3441), 02lp0w (0.44 #1722, 0.44 #1290, 0.41 #3441), 01by1l (0.37 #1833, 0.26 #541, 0.25 #971), 09sb52 (0.18 #14644, 0.17 #17219, 0.16 #6489), 02hgm4 (0.16 #23187, 0.15 #34780, 0.15 #36068), 05q8pss (0.16 #23187, 0.15 #34780, 0.15 #36068), 0fc9js (0.16 #23187, 0.15 #34780, 0.15 #36068), 026m9w (0.16 #23187, 0.15 #34780, 0.15 #36068), 09lvl1 (0.16 #23187, 0.15 #34780, 0.15 #36068) >> Best rule #1722 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 01nrgq; >> query: (?x8423, ?x1245) <- award_winner(?x8423, ?x1993), inductee(?x11145, ?x8423), award(?x8423, ?x1245) >> conf = 0.44 => this is the best rule for 3 predicted values *> Best rule #23187 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 998 *> proper extension: 0280mv7; *> query: (?x8423, ?x4386) <- award_winner(?x1993, ?x8423), people(?x5269, ?x1993), award_winner(?x4386, ?x1993) *> conf = 0.16 ranks of expected_values: 12 EVAL 0kftt award_winner! 02py7pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 117.000 117.000 0.443 http://example.org/award/award_category/winners./award/award_honor/award_winner #10099-0789r6 PRED entity: 0789r6 PRED relation: award_winner PRED expected values: 01ts_3 => 51 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1249): 0dbbz (0.62 #9406, 0.33 #2026, 0.25 #6946), 02kxbx3 (0.50 #10611, 0.18 #22914, 0.13 #13072), 04sry (0.39 #11453, 0.25 #8993, 0.16 #13914), 06b_0 (0.38 #9051, 0.33 #14761, 0.33 #22142), 05kfs (0.38 #7512, 0.28 #9972, 0.10 #22275), 0bwh6 (0.38 #7647, 0.17 #10107, 0.13 #22410), 0k_mt (0.33 #2128, 0.25 #7048, 0.25 #4588), 081lh (0.33 #10029, 0.25 #7569, 0.11 #22332), 0js9s (0.33 #11289, 0.14 #23592, 0.12 #8829), 01g1lp (0.33 #14761, 0.33 #22142, 0.32 #22141) >> Best rule #9406 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 04dn09n; 019f4v; 02rdyk7; 02qt02v; >> query: (?x13075, 0dbbz) <- award_winner(?x13075, ?x10354), award_winner(?x11779, ?x10354), award_winner(?x10354, ?x1365), student(?x12823, ?x10354), ?x11779 = 03ybrwc >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #31984 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 154 *> proper extension: 02x2gy0; 02qwdhq; 03hj5vf; 02qrbbx; *> query: (?x13075, ?x7068) <- award(?x5555, ?x13075), award(?x286, ?x13075), nominated_for(?x13075, ?x534), film(?x7068, ?x5555), titles(?x53, ?x5555) *> conf = 0.23 ranks of expected_values: 41 EVAL 0789r6 award_winner 01ts_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 51.000 14.000 0.625 http://example.org/award/award_category/winners./award/award_honor/award_winner #10098-01gvxv PRED entity: 01gvxv PRED relation: location PRED expected values: 0824r => 145 concepts (143 used for prediction) PRED predicted values (max 10 best out of 183): 0fw2y (0.73 #22437, 0.70 #48885, 0.70 #44076), 030qb3t (0.57 #32936, 0.52 #11298, 0.40 #81), 059rby (0.13 #15, 0.12 #36877, 0.06 #23253), 01n7q (0.13 #61, 0.11 #36923, 0.07 #9675), 01_d4 (0.13 #100, 0.04 #23338, 0.04 #9714), 07_f2 (0.13 #349, 0.04 #5957, 0.04 #6758), 0cr3d (0.09 #9757, 0.09 #64257, 0.07 #48225), 01cx_ (0.09 #33016, 0.04 #8973, 0.03 #8172), 04jpl (0.09 #64130, 0.09 #20848, 0.08 #22453), 0d6lp (0.07 #166, 0.06 #967, 0.05 #9780) >> Best rule #22437 for best value: >> intensional similarity = 4 >> extensional distance = 291 >> proper extension: 041mt; 0lgm5; 0kvnn; 03f6fl0; 081k8; 01k3qj; 0448r; 0g7k2g; 0dw6b; 02s58t; ... >> query: (?x11577, ?x2680) <- location(?x11577, ?x108), nationality(?x11577, ?x94), languages(?x11577, ?x254), place_of_birth(?x11577, ?x2680) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #101792 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2270 *> proper extension: 026lj; 01wj9y9; 0mj0c; 017yfz; 01_k1z; 024zq; 07c37; 04jwp; 05gpy; 034ls; ... *> query: (?x11577, ?x94) <- location(?x11577, ?x6088), gender(?x11577, ?x514), contains(?x94, ?x6088) *> conf = 0.03 ranks of expected_values: 108 EVAL 01gvxv location 0824r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 145.000 143.000 0.730 http://example.org/people/person/places_lived./people/place_lived/location #10097-0fcgd PRED entity: 0fcgd PRED relation: partially_contains! PRED expected values: 068cn => 92 concepts (51 used for prediction) PRED predicted values (max 10 best out of 121): 01qtj9 (0.43 #2233, 0.39 #197, 0.38 #395), 0lfyd (0.38 #395, 0.37 #2335, 0.34 #712), 06mzp (0.36 #1118, 0.29 #1724, 0.25 #296), 03v0t (0.33 #153, 0.29 #253, 0.25 #352), 05tbn (0.33 #151, 0.29 #251, 0.25 #350), 081mh (0.33 #142, 0.29 #242, 0.25 #341), 0498y (0.33 #156, 0.29 #256, 0.25 #355), 01n7q (0.29 #222, 0.20 #1045, 0.20 #944), 02j9z (0.27 #2645, 0.21 #1739, 0.15 #2445), 01qcz7 (0.26 #2232, 0.25 #2231, 0.25 #2333) >> Best rule #2233 for best value: >> intensional similarity = 8 >> extensional distance = 15 >> proper extension: 026zt; 02m4d; 0lm0n; 02v3m7; 05g56; 065ky; 04ykz; >> query: (?x14686, ?x5291) <- partially_contains(?x10972, ?x14686), adjoins(?x10972, ?x10971), adjoins(?x10972, ?x5291), location_of_ceremony(?x566, ?x10972), capital(?x5291, ?x13478), administrative_parent(?x10971, ?x774), ?x566 = 04ztj, adjoins(?x5291, ?x10972) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #496 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0f60c; *> query: (?x14686, ?x172) <- contains(?x8154, ?x14686), partially_contains(?x3277, ?x8154), partially_contains(?x789, ?x8154), adjoins(?x172, ?x789), contains(?x789, ?x790), contains(?x455, ?x3277), administrative_parent(?x789, ?x551), adjoins(?x1003, ?x3277) *> conf = 0.08 ranks of expected_values: 83 EVAL 0fcgd partially_contains! 068cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 92.000 51.000 0.429 http://example.org/location/location/partially_contains #10096-01kyvx PRED entity: 01kyvx PRED relation: film PRED expected values: 02gs6r 056k77g => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 71): 01pvxl (0.33 #262, 0.33 #49, 0.25 #120), 0fgrm (0.33 #39, 0.25 #110, 0.17 #252), 0cq7tx (0.33 #37, 0.25 #108, 0.17 #250), 023p7l (0.33 #30, 0.25 #101, 0.17 #243), 0ds5_72 (0.33 #280), 044g_k (0.33 #222), 02825kb (0.25 #127, 0.17 #269, 0.01 #1843), 028kj0 (0.17 #284), 0dnkmq (0.17 #283), 056xkh (0.17 #282) >> Best rule #262 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 09_gdc; 01pb34; >> query: (?x296, 01pvxl) <- film(?x296, ?x596), special_performance_type(?x13457, ?x296), special_performance_type(?x7764, ?x296), genre(?x596, ?x225), nominated_for(?x1053, ?x596), gender(?x7764, ?x514), actor(?x10018, ?x13457) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kyvx film 056k77g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 44.000 0.333 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film EVAL 01kyvx film 02gs6r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 44.000 0.333 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #10095-04t6fk PRED entity: 04t6fk PRED relation: film! PRED expected values: 06pj8 => 94 concepts (63 used for prediction) PRED predicted values (max 10 best out of 156): 01f7j9 (0.36 #8808, 0.36 #5224, 0.34 #6329), 0qf43 (0.20 #5), 03q8ch (0.16 #3298), 01wyy_ (0.14 #1375, 0.10 #4949, 0.10 #5776), 06pj8 (0.10 #3071, 0.09 #598, 0.06 #3622), 08t7nz (0.10 #1099, 0.08 #11562, 0.07 #3574), 0d608 (0.09 #6052, 0.02 #6051, 0.02 #8807), 0f0kz (0.09 #6052, 0.02 #6051, 0.02 #8807), 02kxbx3 (0.08 #911, 0.05 #2287, 0.05 #3110), 06chf (0.07 #354, 0.03 #1453, 0.03 #2004) >> Best rule #8808 for best value: >> intensional similarity = 4 >> extensional distance = 473 >> proper extension: 08fbnx; 02r2j8; >> query: (?x2699, ?x2182) <- film(?x1031, ?x2699), titles(?x2480, ?x2699), written_by(?x2699, ?x2182), genre(?x2699, ?x225) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #3071 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 154 *> proper extension: 0c40vxk; 03mh_tp; 09rsjpv; 02rmd_2; 0prrm; 0b7l4x; 043tvp3; 0dgrwqr; 02q0k7v; *> query: (?x2699, 06pj8) <- film(?x1104, ?x2699), genre(?x2699, ?x225), edited_by(?x2699, ?x4215), film(?x1031, ?x2699) *> conf = 0.10 ranks of expected_values: 5 EVAL 04t6fk film! 06pj8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 63.000 0.364 http://example.org/film/director/film #10094-02qr3k8 PRED entity: 02qr3k8 PRED relation: film! PRED expected values: 01y_px 0ywqc => 50 concepts (32 used for prediction) PRED predicted values (max 10 best out of 734): 0gn30 (0.33 #932, 0.25 #2978, 0.20 #5024), 01ry0f (0.33 #836, 0.25 #2882, 0.20 #4928), 019_1h (0.33 #164, 0.25 #2210, 0.20 #4256), 0f6_x (0.33 #8797, 0.04 #19029, 0.04 #21075), 01tnbn (0.33 #9236, 0.04 #21514, 0.03 #19468), 0154d7 (0.33 #9663, 0.02 #19895, 0.02 #21941), 01vy_v8 (0.33 #8904, 0.01 #19136, 0.01 #23227), 0gr36 (0.33 #8669, 0.01 #18901), 01rnxn (0.33 #6634), 04bdlg (0.25 #3937, 0.20 #5983) >> Best rule #932 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 04cbbz; >> query: (?x7415, 0gn30) <- film(?x7076, ?x7415), film(?x5440, ?x7415), film(?x3627, ?x7415), ?x5440 = 016z51, place_of_birth(?x7076, ?x739), participant(?x3628, ?x3627), gender(?x3627, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #14031 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 053tj7; *> query: (?x7415, 0ywqc) <- person(?x7415, ?x11159), genre(?x7415, ?x571), location(?x11159, ?x279), type_of_union(?x11159, ?x566) *> conf = 0.02 ranks of expected_values: 342, 709 EVAL 02qr3k8 film! 0ywqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 50.000 32.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 02qr3k8 film! 01y_px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 32.000 0.333 http://example.org/film/actor/film./film/performance/film #10093-0b13g7 PRED entity: 0b13g7 PRED relation: executive_produced_by! PRED expected values: 0gjk1d 02704ff => 118 concepts (105 used for prediction) PRED predicted values (max 10 best out of 370): 02704ff (0.33 #325, 0.01 #2945, 0.01 #3468), 0gjk1d (0.33 #58, 0.01 #2678, 0.01 #3201), 0bt4g (0.11 #2515, 0.03 #5131, 0.02 #3038), 0mbql (0.11 #2472, 0.03 #5088, 0.02 #2995), 01f7kl (0.11 #2231, 0.03 #4847, 0.02 #2754), 0ggbhy7 (0.10 #12046, 0.10 #10475, 0.04 #8905), 09r94m (0.10 #12046, 0.10 #10475, 0.04 #8905), 072zl1 (0.10 #12046, 0.10 #10475, 0.04 #8905), 05vxdh (0.10 #12046, 0.10 #10475, 0.04 #8905), 02vqsll (0.10 #12046, 0.10 #10475, 0.04 #8905) >> Best rule #325 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02q42j_; >> query: (?x3568, 02704ff) <- produced_by(?x4159, ?x3568), produced_by(?x2881, ?x3568), ?x2881 = 0bpx1k, ?x4159 = 011yr9 >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0b13g7 executive_produced_by! 02704ff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 105.000 0.333 http://example.org/film/film/executive_produced_by EVAL 0b13g7 executive_produced_by! 0gjk1d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 105.000 0.333 http://example.org/film/film/executive_produced_by #10092-0jmm4 PRED entity: 0jmm4 PRED relation: sport PRED expected values: 018w8 => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 8): 018w8 (0.84 #130, 0.83 #112, 0.80 #103), 0jm_ (0.32 #265, 0.29 #247, 0.21 #165), 02vx4 (0.32 #282, 0.31 #309, 0.25 #291), 018jz (0.27 #249, 0.27 #267, 0.23 #212), 039yzs (0.24 #205, 0.20 #196, 0.15 #151), 03tmr (0.19 #236, 0.19 #163, 0.17 #181), 09xp_ (0.03 #277, 0.03 #222, 0.02 #231), 0z74 (0.01 #233, 0.01 #297) >> Best rule #130 for best value: >> intensional similarity = 12 >> extensional distance = 29 >> proper extension: 01jvgt; >> query: (?x9049, 018w8) <- position(?x9049, ?x4570), team(?x4570, ?x11805), team(?x4570, ?x9760), team(?x4570, ?x5756), team(?x4570, ?x4571), team(?x4570, ?x2820), ?x4571 = 0jm6n, ?x11805 = 0jm5b, school(?x9049, ?x388), ?x5756 = 0jm4b, ?x2820 = 0jmj7, ?x9760 = 0bwjj >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jmm4 sport 018w8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.839 http://example.org/sports/sports_team/sport #10091-0gyv0b4 PRED entity: 0gyv0b4 PRED relation: genre PRED expected values: 01jfsb => 73 concepts (71 used for prediction) PRED predicted values (max 10 best out of 90): 01jfsb (0.62 #131, 0.48 #974, 0.41 #854), 05p553 (0.44 #364, 0.40 #606, 0.37 #726), 03k9fj (0.39 #973, 0.38 #613, 0.36 #853), 02l7c8 (0.31 #4820, 0.30 #4578, 0.28 #3858), 0jtdp (0.30 #374, 0.02 #1936, 0.02 #736), 01hmnh (0.28 #620, 0.27 #740, 0.26 #980), 06n90 (0.27 #855, 0.25 #975, 0.19 #615), 04xvlr (0.18 #4806, 0.18 #4564, 0.15 #1324), 0219x_ (0.17 #387, 0.11 #1349, 0.09 #5071), 060__y (0.17 #4821, 0.16 #4579, 0.14 #1099) >> Best rule #131 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 01cx_; >> query: (?x10446, 01jfsb) <- split_to(?x4375, ?x10446), film_crew_role(?x4375, ?x137) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gyv0b4 genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 71.000 0.625 http://example.org/film/film/genre #10090-0fkzq PRED entity: 0fkzq PRED relation: jurisdiction_of_office PRED expected values: 03v1s => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 980): 07z1m (0.68 #2262, 0.68 #1810, 0.55 #2721), 059f4 (0.68 #2262, 0.68 #1810, 0.55 #2721), 03s5t (0.68 #2262, 0.68 #1810, 0.55 #2721), 06mz5 (0.68 #2262, 0.68 #1810, 0.55 #2721), 05kr_ (0.68 #2262, 0.68 #1810, 0.55 #2721), 015jr (0.68 #2262, 0.68 #1810, 0.55 #2721), 06nrt (0.68 #2262, 0.68 #1810, 0.55 #2721), 04rrx (0.68 #2262, 0.68 #1810, 0.55 #2721), 0498y (0.68 #2262, 0.68 #1810, 0.55 #2721), 05kj_ (0.68 #1810, 0.55 #2721, 0.52 #3178) >> Best rule #2262 for best value: >> intensional similarity = 22 >> extensional distance = 2 >> proper extension: 09n5b9; >> query: (?x12303, ?x938) <- jurisdiction_of_office(?x12303, ?x7518), jurisdiction_of_office(?x12303, ?x7405), jurisdiction_of_office(?x12303, ?x4105), jurisdiction_of_office(?x12303, ?x2256), jurisdiction_of_office(?x12303, ?x901), jurisdiction_of_office(?x12303, ?x760), jurisdiction_of_office(?x12303, ?x335), ?x335 = 059rby, state(?x10868, ?x901), capital(?x901, ?x13479), ?x7405 = 07_f2, country(?x901, ?x390), ?x760 = 05fkf, ?x7518 = 026mj, ?x4105 = 0824r, country(?x2256, ?x94), religion(?x2256, ?x8613), capital(?x2256, ?x674), ?x8613 = 04pk9, district_represented(?x845, ?x2256), ?x845 = 07p__7, adjoins(?x2256, ?x938) >> conf = 0.68 => this is the best rule for 22 predicted values *> Best rule #1835 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 2 *> proper extension: 09n5b9; *> query: (?x12303, 03v1s) <- jurisdiction_of_office(?x12303, ?x7518), jurisdiction_of_office(?x12303, ?x7405), jurisdiction_of_office(?x12303, ?x4105), jurisdiction_of_office(?x12303, ?x2256), jurisdiction_of_office(?x12303, ?x901), jurisdiction_of_office(?x12303, ?x760), jurisdiction_of_office(?x12303, ?x335), ?x335 = 059rby, state(?x10868, ?x901), capital(?x901, ?x13479), ?x7405 = 07_f2, country(?x901, ?x390), ?x760 = 05fkf, ?x7518 = 026mj, ?x4105 = 0824r, country(?x2256, ?x94), religion(?x2256, ?x8613), capital(?x2256, ?x674), ?x8613 = 04pk9, district_represented(?x845, ?x2256), ?x845 = 07p__7, adjoins(?x2256, ?x938) *> conf = 0.50 ranks of expected_values: 48 EVAL 0fkzq jurisdiction_of_office 03v1s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 21.000 21.000 0.681 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #10089-0bq2g PRED entity: 0bq2g PRED relation: location PRED expected values: 04jpl => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 162): 0cc56 (0.09 #21685, 0.07 #2458, 0.06 #4861), 0r0m6 (0.08 #215, 0.08 #2618, 0.08 #3419), 0cr3d (0.08 #142, 0.07 #1744, 0.07 #2545), 01n7q (0.08 #61, 0.07 #2464, 0.07 #862), 04jpl (0.07 #16838, 0.06 #68109, 0.06 #15236), 059rby (0.06 #3219, 0.05 #6423, 0.05 #7224), 013yq (0.05 #116, 0.05 #13733, 0.05 #4922), 0d6lp (0.05 #165, 0.04 #7374, 0.04 #3369), 02jx1 (0.05 #69, 0.03 #2472, 0.03 #3273), 01qh7 (0.05 #154, 0.02 #1756, 0.02 #2557) >> Best rule #21685 for best value: >> intensional similarity = 2 >> extensional distance = 349 >> proper extension: 014dq7; 05qw5; 041mt; 01wj9y9; 04g865; 01m65sp; 0mj0c; 02lt8; 019r_1; 01_k1z; ... >> query: (?x3553, 0cc56) <- location(?x3553, ?x739), ?x739 = 02_286 >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #16838 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 223 *> proper extension: 023jq1; *> query: (?x3553, 04jpl) <- award_winner(?x988, ?x3553), languages(?x3553, ?x254) *> conf = 0.07 ranks of expected_values: 5 EVAL 0bq2g location 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 110.000 110.000 0.085 http://example.org/people/person/places_lived./people/place_lived/location #10088-01w1kyf PRED entity: 01w1kyf PRED relation: profession PRED expected values: 0kyk => 105 concepts (86 used for prediction) PRED predicted values (max 10 best out of 62): 02jknp (0.46 #1625, 0.40 #154, 0.28 #3095), 03gjzk (0.36 #1631, 0.23 #5747, 0.22 #8687), 0cbd2 (0.21 #1624, 0.16 #3241, 0.15 #2653), 09jwl (0.20 #4869, 0.19 #3399, 0.19 #2223), 018gz8 (0.18 #1633, 0.15 #751, 0.15 #898), 0np9r (0.15 #9134, 0.14 #10310, 0.14 #2372), 02krf9 (0.15 #1643, 0.10 #5612, 0.09 #172), 0nbcg (0.14 #30, 0.13 #4882, 0.13 #3412), 01c72t (0.14 #22, 0.10 #4874, 0.10 #2228), 0dz3r (0.14 #3384, 0.13 #4854, 0.12 #4266) >> Best rule #1625 for best value: >> intensional similarity = 3 >> extensional distance = 679 >> proper extension: 01wj9y9; 0454s1; >> query: (?x5094, 02jknp) <- profession(?x5094, ?x987), type_of_union(?x5094, ?x566), ?x987 = 0dxtg >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1646 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 679 *> proper extension: 01wj9y9; 0454s1; *> query: (?x5094, 0kyk) <- profession(?x5094, ?x987), type_of_union(?x5094, ?x566), ?x987 = 0dxtg *> conf = 0.13 ranks of expected_values: 11 EVAL 01w1kyf profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 105.000 86.000 0.458 http://example.org/people/person/profession #10087-01n7qlf PRED entity: 01n7qlf PRED relation: film PRED expected values: 05650n => 97 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1062): 0b3n61 (0.15 #22840, 0.03 #132480, 0.02 #21050), 029k4p (0.14 #837, 0.11 #4418, 0.05 #11578), 03tbg6 (0.11 #23137, 0.03 #132480, 0.02 #35669), 0pc62 (0.11 #21575, 0.03 #132480, 0.01 #14415), 027r9t (0.10 #6618, 0.07 #1247, 0.06 #3037), 03m4mj (0.10 #5573, 0.07 #202, 0.05 #10943), 049mql (0.10 #6056, 0.05 #11426, 0.04 #7846), 02vjp3 (0.10 #6671, 0.05 #12041, 0.03 #13831), 0dzlbx (0.10 #6223, 0.05 #11593, 0.03 #13383), 03vfr_ (0.09 #23125, 0.03 #132480, 0.03 #30286) >> Best rule #22840 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 03k545; >> query: (?x3611, 0b3n61) <- film(?x3611, ?x6719), film(?x6324, ?x6719), ?x6324 = 018ygt, language(?x6719, ?x254) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #31446 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 132 *> proper extension: 018y2s; 01k5t_3; 0blbxk; 0fsm8c; 03j0br4; 0blq0z; 01wgcvn; 0dl567; 01kh2m1; 0dzc16; ... *> query: (?x3611, 05650n) <- place_of_birth(?x3611, ?x1523), profession(?x3611, ?x1183), film(?x3611, ?x4766), ?x1183 = 09jwl *> conf = 0.01 ranks of expected_values: 520 EVAL 01n7qlf film 05650n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 97.000 74.000 0.153 http://example.org/film/actor/film./film/performance/film #10086-04ycjk PRED entity: 04ycjk PRED relation: currency PRED expected values: 09nqf => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.79 #170, 0.78 #177, 0.76 #79), 01nv4h (0.15 #129, 0.14 #150, 0.13 #157), 02l6h (0.05 #68, 0.04 #145, 0.04 #356), 0ptk_ (0.03 #67, 0.03 #74, 0.03 #215), 02gsvk (0.03 #77, 0.01 #196), 0kz1h (0.02 #132, 0.02 #76, 0.02 #301) >> Best rule #170 for best value: >> intensional similarity = 5 >> extensional distance = 311 >> proper extension: 03zw80; 03x1s8; >> query: (?x7065, 09nqf) <- contains(?x3052, ?x7065), contains(?x94, ?x7065), organization(?x3484, ?x7065), ?x94 = 09c7w0, location(?x1322, ?x3052) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ycjk currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.792 http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency #10085-01jfrg PRED entity: 01jfrg PRED relation: type_of_union PRED expected values: 04ztj => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.88 #33, 0.87 #85, 0.87 #101), 01g63y (0.33 #54, 0.29 #110, 0.28 #150) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 02778yp; 06czyr; 05_2h8; >> query: (?x6113, 04ztj) <- award_winner(?x4084, ?x6113), award(?x6113, ?x686), location_of_ceremony(?x6113, ?x8569) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jfrg type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.875 http://example.org/people/person/spouse_s./people/marriage/type_of_union #10084-035dk PRED entity: 035dk PRED relation: olympics PRED expected values: 06sks6 0jhn7 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 39): 06sks6 (0.87 #3310, 0.87 #3154, 0.87 #3115), 0kbvv (0.50 #805, 0.44 #765, 0.43 #1468), 0kbvb (0.47 #788, 0.46 #748, 0.44 #358), 0jdk_ (0.45 #806, 0.42 #376, 0.35 #1469), 09n48 (0.45 #784, 0.39 #354, 0.38 #744), 018ctl (0.43 #1484, 0.42 #781, 0.42 #1602), 0lbd9 (0.39 #1995, 0.38 #2582, 0.38 #2701), 0lbbj (0.39 #1995, 0.38 #2582, 0.38 #2701), 0swbd (0.39 #361, 0.36 #791, 0.31 #1025), 0jhn7 (0.33 #377, 0.31 #807, 0.30 #767) >> Best rule #3310 for best value: >> intensional similarity = 3 >> extensional distance = 159 >> proper extension: 01nty; 034tl; >> query: (?x2051, 06sks6) <- countries_spoken_in(?x254, ?x2051), olympics(?x2051, ?x1081), country(?x471, ?x2051) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 035dk olympics 0jhn7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 158.000 158.000 0.870 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 035dk olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.870 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #10083-06w99h3 PRED entity: 06w99h3 PRED relation: film! PRED expected values: 07r_dg => 69 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1001): 0jfx1 (0.46 #14546, 0.44 #51948, 0.43 #72725), 02pq9yv (0.46 #14546, 0.44 #51948, 0.43 #72725), 0p8r1 (0.24 #8896, 0.20 #10974, 0.18 #6819), 09yhzs (0.18 #2590, 0.14 #4668, 0.12 #512), 0pz91 (0.18 #2289, 0.14 #4367, 0.03 #56103), 02gf_l (0.13 #9577, 0.11 #11655, 0.03 #7500), 02_p5w (0.13 #8956, 0.07 #11034, 0.03 #6879), 0f0kz (0.12 #513, 0.09 #2591, 0.07 #4669), 08vr94 (0.12 #674, 0.09 #2752, 0.07 #4830), 04sry (0.12 #1273, 0.09 #3351, 0.07 #5429) >> Best rule #14546 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 02hfk5; >> query: (?x224, ?x2444) <- film(?x902, ?x224), film_crew_role(?x224, ?x4305), nominated_for(?x2444, ?x224), ?x4305 = 0215hd >> conf = 0.46 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 06w99h3 film! 07r_dg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 37.000 0.455 http://example.org/film/actor/film./film/performance/film #10082-03sbs PRED entity: 03sbs PRED relation: interests PRED expected values: 09xq9d => 86 concepts (51 used for prediction) PRED predicted values (max 10 best out of 13): 05r79 (0.30 #42, 0.29 #136, 0.29 #16), 05qt0 (0.29 #20, 0.20 #79, 0.20 #93), 0gt_hv (0.20 #79, 0.20 #93, 0.20 #174), 09xq9d (0.20 #79, 0.20 #93, 0.20 #174), 097df (0.15 #227, 0.14 #24, 0.05 #144), 04rjg (0.15 #227, 0.09 #43, 0.08 #56), 05qfh (0.15 #227, 0.09 #44, 0.08 #57), 06ms6 (0.15 #227, 0.05 #122, 0.05 #135), 04g7x (0.15 #227, 0.03 #115, 0.03 #142), 06mq7 (0.04 #77, 0.04 #91, 0.03 #145) >> Best rule #42 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 0399p; >> query: (?x7250, 05r79) <- gender(?x7250, ?x231), interests(?x7250, ?x713), influenced_by(?x7250, ?x6015), influenced_by(?x9284, ?x6015), ?x9284 = 0gd_s >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #79 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 24 *> proper extension: 0jrg; *> query: (?x7250, ?x713) <- gender(?x7250, ?x231), influenced_by(?x7250, ?x7251), influenced_by(?x7250, ?x3712), influenced_by(?x7250, ?x712), ?x3712 = 0gz_, interests(?x712, ?x713), profession(?x7251, ?x7290) *> conf = 0.20 ranks of expected_values: 4 EVAL 03sbs interests 09xq9d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 51.000 0.304 http://example.org/user/alexander/philosophy/philosopher/interests #10081-05683cn PRED entity: 05683cn PRED relation: award_winner! PRED expected values: 0dznvw => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 128): 0fk0xk (0.29 #219, 0.25 #501, 0.19 #783), 0fv89q (0.25 #546, 0.18 #4795, 0.17 #5501), 0dznvw (0.18 #4795, 0.17 #5501, 0.17 #7053), 0fz0c2 (0.14 #811, 0.14 #247, 0.14 #952), 0c53zb (0.14 #766, 0.14 #202, 0.12 #484), 0bz6l9 (0.14 #756, 0.14 #192, 0.12 #474), 0fy6bh (0.14 #752, 0.14 #188, 0.12 #470), 0d__c3 (0.14 #266, 0.12 #548, 0.10 #830), 0c53vt (0.14 #253, 0.12 #535, 0.05 #817), 0dthsy (0.14 #349, 0.09 #913, 0.08 #1054) >> Best rule #219 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0579tg2; >> query: (?x9875, 0fk0xk) <- gender(?x9875, ?x231), award_nominee(?x9875, ?x8401), ?x8401 = 057bc6m >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #4795 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1176 *> proper extension: 0lzkm; *> query: (?x9875, ?x11428) <- gender(?x9875, ?x231), award_winner(?x9875, ?x12186), award_winner(?x11428, ?x12186) *> conf = 0.18 ranks of expected_values: 3 EVAL 05683cn award_winner! 0dznvw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 99.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10080-0hndn2q PRED entity: 0hndn2q PRED relation: ceremony! PRED expected values: 054ky1 => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 346): 0gqy2 (0.50 #11571, 0.50 #11826, 0.46 #12335), 02xcb6n (0.50 #212, 0.39 #1532, 0.25 #255), 0gkr9q (0.50 #221, 0.39 #1532, 0.25 #255), 0fbtbt (0.50 #160, 0.39 #1532, 0.25 #255), 0cjyzs (0.50 #75, 0.25 #255, 0.21 #1533), 027gs1_ (0.50 #193, 0.25 #255, 0.21 #1533), 0bdwft (0.50 #47, 0.25 #255, 0.21 #1533), 0bfvd4 (0.50 #81, 0.25 #255, 0.21 #1533), 0bdwqv (0.50 #123, 0.25 #255, 0.21 #1533), 09qrn4 (0.50 #165, 0.25 #255, 0.21 #1533) >> Best rule #11571 for best value: >> intensional similarity = 8 >> extensional distance = 120 >> proper extension: 073hkh; 0clfdj; 0bzk8w; 09q_6t; 03gwpw2; 02wzl1d; 02yw5r; 09qvms; 092c5f; 05c1t6z; ... >> query: (?x2515, 0gqy2) <- honored_for(?x2515, ?x385), award_winner(?x2515, ?x7048), award_winner(?x2515, ?x3571), award_winner(?x2515, ?x3186), award_winner(?x2107, ?x3186), award_winner(?x3571, ?x1039), award(?x3186, ?x451), place_of_birth(?x7048, ?x12892) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2120 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 09p30_; *> query: (?x2515, 054ky1) <- honored_for(?x2515, ?x385), award_winner(?x2515, ?x8070), award_winner(?x2515, ?x3186), award_winner(?x2515, ?x1179), award_winner(?x2107, ?x3186), ?x1179 = 05m883, award_winner(?x384, ?x8070) *> conf = 0.40 ranks of expected_values: 59 EVAL 0hndn2q ceremony! 054ky1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 51.000 51.000 0.500 http://example.org/award/award_category/winners./award/award_honor/ceremony #10079-0mm0p PRED entity: 0mm0p PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 84 concepts (53 used for prediction) PRED predicted values (max 10 best out of 6): 09c7w0 (0.92 #47, 0.91 #36, 0.90 #24), 081yw (0.13 #403, 0.13 #379, 0.12 #35), 0mlzk (0.13 #379, 0.03 #691, 0.03 #645), 0mlvc (0.03 #691), 03rt9 (0.02 #525, 0.02 #541, 0.02 #572), 02jx1 (0.01 #546) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 0cb4j; 0mlxt; >> query: (?x8547, 09c7w0) <- time_zones(?x8547, ?x2950), currency(?x8547, ?x170), ?x2950 = 02lcqs, ?x170 = 09nqf >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mm0p second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 53.000 0.915 http://example.org/location/country/second_level_divisions #10078-01933d PRED entity: 01933d PRED relation: location_of_ceremony PRED expected values: 0k_q_ => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 96): 0k049 (0.33 #4, 0.08 #3059, 0.08 #1999), 02_286 (0.17 #717, 0.09 #2008, 0.07 #366), 035hm (0.14 #193, 0.11 #311, 0.07 #429), 04lh6 (0.14 #192, 0.11 #310, 0.07 #428), 0ycht (0.14 #465, 0.09 #933, 0.07 #1286), 0cv3w (0.13 #5559, 0.13 #2736, 0.12 #3912), 059rby (0.11 #712, 0.06 #2357, 0.06 #2710), 06y57 (0.11 #291, 0.05 #877, 0.03 #1230), 027rn (0.11 #236, 0.05 #822, 0.03 #1175), 06kx2 (0.09 #931, 0.06 #1402, 0.06 #814) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 03xmy1; >> query: (?x8103, 0k049) <- location(?x8103, ?x11000), location_of_ceremony(?x8103, ?x957), ?x11000 = 0r3tq >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #381 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 06c0j; 02m30v; *> query: (?x8103, 0k_q_) <- people(?x9771, ?x8103), location_of_ceremony(?x8103, ?x957), spouse(?x12525, ?x8103) *> conf = 0.07 ranks of expected_values: 27 EVAL 01933d location_of_ceremony 0k_q_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 183.000 183.000 0.333 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #10077-05w1vf PRED entity: 05w1vf PRED relation: actor! PRED expected values: 039c26 => 100 concepts (44 used for prediction) PRED predicted values (max 10 best out of 92): 02py4c8 (0.15 #540, 0.01 #2922, 0.01 #2392), 034fl9 (0.12 #442, 0.02 #1502, 0.01 #2558), 02r2j8 (0.12 #414), 0gbtbm (0.12 #340), 02gjrc (0.08 #754, 0.02 #2606), 02qkq0 (0.08 #653, 0.01 #1449), 0124k9 (0.08 #549, 0.01 #2931), 03ffcz (0.08 #650), 026bfsh (0.06 #1421, 0.03 #2477, 0.02 #1949), 026y3cf (0.03 #1044, 0.02 #3159, 0.01 #2629) >> Best rule #540 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01l1hr; 01wb8bs; 02js_6; >> query: (?x11529, 02py4c8) <- film(?x11529, ?x1910), film(?x11529, ?x1230), ?x1910 = 011yth, nominated_for(?x68, ?x1230) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #2959 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 597 *> proper extension: 076df9; *> query: (?x11529, 039c26) <- gender(?x11529, ?x231), actor(?x9636, ?x11529), titles(?x2008, ?x9636) *> conf = 0.02 ranks of expected_values: 33 EVAL 05w1vf actor! 039c26 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 100.000 44.000 0.154 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10076-03m_k0 PRED entity: 03m_k0 PRED relation: award PRED expected values: 03hl6lc => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 268): 03hl6lc (0.73 #22304, 0.71 #1195, 0.70 #22303), 0cjyzs (0.32 #4085, 0.32 #3289, 0.32 #3687), 05zr6wv (0.30 #4794, 0.06 #2405, 0.06 #6386), 09sb52 (0.28 #4818, 0.22 #5216, 0.21 #6410), 03ccq3s (0.25 #196, 0.16 #992, 0.14 #1391), 040njc (0.25 #7, 0.14 #2794, 0.14 #405), 019f4v (0.25 #64, 0.14 #462, 0.12 #2851), 02rdyk7 (0.25 #89, 0.14 #487, 0.12 #21904), 0gs9p (0.25 #77, 0.14 #475, 0.12 #2864), 02pqp12 (0.25 #68, 0.14 #466, 0.11 #17921) >> Best rule #22304 for best value: >> intensional similarity = 3 >> extensional distance = 2274 >> proper extension: 06lxn; >> query: (?x3058, ?x746) <- award_winner(?x746, ?x3058), award(?x6771, ?x746), award_nominee(?x6771, ?x4060) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03m_k0 award 03hl6lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10075-04ztj PRED entity: 04ztj PRED relation: location_of_ceremony PRED expected values: 059_c 05ywg 0fq8f 01x73 05k7sb 02wt0 06t2t 01ly5m 0cv3w 04n3l 0843m 0rj0z 0gv10 068p2 0177z 0cpyv 05jbn 0n3g 03gh4 0f04v 0l35f 04hqz 0qr8z 0jhz_ 09949m 0f1sm 0fhnf 0dzt9 0typ5 0rd6b 0dqyw 0dr31 0b_yz 03khn 0tz41 0cy41 0kc40 0yyh 04jt2 0nm9h 0jpy_ 0d1y7 05bkf 019fv4 0qx1w 061k5 0fcyj 02hvkf 0dhd5 0dfcn 021lkq 0zz6w 0ng8v => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 460): 03gh4 (0.33 #2, 0.25 #12, 0.25 #6), 0kwgs (0.33 #3, 0.25 #13, 0.25 #7), 0nqv1 (0.25 #14), 01r32 (0.25 #11), 0h44w (0.10 #15, 0.01 #9), 01k6y1 (0.10 #15, 0.01 #9), 0d05w3 (0.10 #15, 0.01 #9), 06mkj (0.10 #15, 0.01 #9), 0h7x (0.10 #15, 0.01 #9), 059j2 (0.10 #15, 0.01 #9) >> Best rule #2 for best value: >> intensional similarity = 21 >> extensional distance = 1 >> proper extension: 01g63y; >> query: (?x566, 03gh4) <- type_of_union(?x8966, ?x566), type_of_union(?x7222, ?x566), type_of_union(?x6693, ?x566), type_of_union(?x5223, ?x566), type_of_union(?x4408, ?x566), type_of_union(?x3546, ?x566), type_of_union(?x1285, ?x566), type_of_union(?x912, ?x566), type_of_union(?x96, ?x566), location_of_ceremony(?x566, ?x1957), ?x4408 = 04yqlk, ?x8966 = 01qqtr, nominated_for(?x1285, ?x10595), instrumentalists(?x228, ?x7222), award_winner(?x1231, ?x5223), profession(?x96, ?x353), film(?x96, ?x97), tv_program(?x912, ?x589), currency(?x1957, ?x170), award(?x6693, ?x3906), award_nominee(?x3546, ?x2841) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 161, 181, 210, 221, 247, 253, 275, 318, 357, 358, 363, 377, 378, 390, 401, 406, 423, 429, 444 EVAL 04ztj location_of_ceremony 0ng8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0zz6w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 021lkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0dfcn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0dhd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 02hvkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0fcyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 061k5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0qx1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 019fv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 05bkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0d1y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0jpy_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0nm9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 04jt2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0yyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0kc40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0cy41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0tz41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 03khn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0b_yz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0dr31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0dqyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0rd6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0typ5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0dzt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0fhnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0f1sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 09949m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0jhz_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0qr8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 04hqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0l35f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0f04v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 03gh4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0n3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 05jbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0cpyv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0177z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 068p2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0gv10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0rj0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0843m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 04n3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0cv3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 01ly5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 06t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 02wt0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 05k7sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 01x73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 0fq8f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 05ywg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony EVAL 04ztj location_of_ceremony 059_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.333 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #10074-0l6wj PRED entity: 0l6wj PRED relation: religion PRED expected values: 0c8wxp => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 13): 0kpl (0.19 #100, 0.12 #505, 0.11 #730), 0c8wxp (0.11 #276, 0.11 #1536, 0.11 #1626), 03_gx (0.11 #419, 0.10 #554, 0.10 #464), 0kq2 (0.07 #153, 0.07 #108, 0.06 #513), 03j6c (0.04 #66, 0.03 #1011, 0.03 #1236), 092bf5 (0.04 #61, 0.03 #556, 0.03 #421), 06nzl (0.04 #60, 0.02 #285, 0.01 #600), 051kv (0.04 #50, 0.01 #140, 0.01 #185), 0n2g (0.03 #733, 0.02 #913, 0.02 #418), 01lp8 (0.02 #91, 0.01 #1396, 0.01 #991) >> Best rule #100 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 09jd9; >> query: (?x8333, 0kpl) <- story_by(?x3003, ?x8333), award(?x8333, ?x1107), place_of_death(?x8333, ?x242) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #276 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 96 *> proper extension: 02xnjd; *> query: (?x8333, 0c8wxp) <- gender(?x8333, ?x231), award_nominee(?x5537, ?x8333), film(?x5537, ?x721) *> conf = 0.11 ranks of expected_values: 2 EVAL 0l6wj religion 0c8wxp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.190 http://example.org/people/person/religion #10073-0cyn3 PRED entity: 0cyn3 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 151 concepts (81 used for prediction) PRED predicted values (max 10 best out of 12): 09c7w0 (0.89 #248, 0.88 #594, 0.88 #100), 059rby (0.23 #305, 0.20 #701, 0.14 #800), 04n3l (0.23 #305, 0.14 #800, 0.11 #24), 0cyn3 (0.23 #305, 0.11 #24, 0.10 #955), 0fm9_ (0.23 #305, 0.11 #24, 0.10 #955), 02jx1 (0.07 #48, 0.06 #358, 0.06 #795), 07ssc (0.03 #322, 0.03 #505, 0.02 #542), 0d060g (0.02 #450, 0.02 #502, 0.01 #539), 0h7x (0.02 #84, 0.01 #96, 0.01 #120), 03rt9 (0.01 #832) >> Best rule #248 for best value: >> intensional similarity = 4 >> extensional distance = 125 >> proper extension: 02cl1; 0nh1v; 0mnyn; >> query: (?x11938, 09c7w0) <- county(?x13062, ?x11938), currency(?x11938, ?x170), contains(?x335, ?x11938), source(?x11938, ?x958) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cyn3 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 81.000 0.890 http://example.org/location/country/second_level_divisions #10072-0btyf5z PRED entity: 0btyf5z PRED relation: film! PRED expected values: 03xq0f => 79 concepts (70 used for prediction) PRED predicted values (max 10 best out of 56): 03xq0f (0.89 #300, 0.57 #1040, 0.16 #596), 05qd_ (0.25 #8, 0.18 #304, 0.18 #156), 086k8 (0.21 #150, 0.20 #298, 0.20 #816), 017s11 (0.16 #225, 0.13 #447, 0.13 #1409), 016tw3 (0.15 #1861, 0.15 #454, 0.13 #3507), 01795t (0.11 #387, 0.10 #17, 0.09 #91), 0g1rw (0.10 #599, 0.07 #1561, 0.07 #2082), 04mkft (0.09 #1071, 0.06 #109, 0.06 #331), 017jv5 (0.09 #162, 0.07 #606, 0.06 #2163), 01gb54 (0.08 #324, 0.06 #2928, 0.06 #176) >> Best rule #300 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 07kb7vh; >> query: (?x1932, 03xq0f) <- nominated_for(?x451, ?x1932), film_distribution_medium(?x1932, ?x2099), country(?x1932, ?x94), ?x2099 = 0735l >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0btyf5z film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 70.000 0.893 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #10071-07j8r PRED entity: 07j8r PRED relation: titles! PRED expected values: 01jfsb 02qfv5d => 86 concepts (47 used for prediction) PRED predicted values (max 10 best out of 64): 07s9rl0 (0.55 #205, 0.45 #614, 0.45 #1), 04xvlr (0.36 #821, 0.32 #617, 0.31 #310), 01z4y (0.20 #2087, 0.18 #1677, 0.18 #4355), 02l7c8 (0.19 #4113, 0.17 #3391, 0.15 #2154), 0lsxr (0.19 #4113, 0.17 #3391, 0.15 #2154), 017fp (0.18 #227, 0.14 #636, 0.12 #738), 03mqtr (0.16 #249, 0.11 #658, 0.10 #351), 01jfsb (0.15 #1661, 0.13 #1351, 0.13 #1967), 07c52 (0.15 #2185, 0.14 #1876, 0.11 #3111), 01hmnh (0.14 #434, 0.11 #128, 0.09 #3828) >> Best rule #205 for best value: >> intensional similarity = 5 >> extensional distance = 36 >> proper extension: 07xtqq; 0209xj; 0_b3d; 092vkg; 0c0nhgv; 0gmcwlb; 05j82v; 011yth; 02c638; 0yzvw; ... >> query: (?x2550, 07s9rl0) <- country(?x2550, ?x252), nominated_for(?x3209, ?x2550), nominated_for(?x2375, ?x2550), ?x2375 = 04kxsb, ?x3209 = 02w9sd7 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1661 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 310 *> proper extension: 0jym0; 03f7xg; 02nczh; 07bxqz; 016z43; *> query: (?x2550, 01jfsb) <- nominated_for(?x68, ?x2550), award_winner(?x2550, ?x7068), featured_film_locations(?x2550, ?x362), titles(?x512, ?x2550) *> conf = 0.15 ranks of expected_values: 8, 22 EVAL 07j8r titles! 02qfv5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 86.000 47.000 0.553 http://example.org/media_common/netflix_genre/titles EVAL 07j8r titles! 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 86.000 47.000 0.553 http://example.org/media_common/netflix_genre/titles #10070-03rjj PRED entity: 03rjj PRED relation: location! PRED expected values: 02sdx => 244 concepts (180 used for prediction) PRED predicted values (max 10 best out of 2208): 0g7k2g (0.33 #1741, 0.09 #238967, 0.07 #115711), 012201 (0.33 #1717, 0.07 #115711, 0.06 #399950), 020bg (0.33 #2442, 0.07 #115711, 0.06 #399950), 03crmd (0.33 #2116, 0.07 #115711, 0.06 #399950), 04lg6 (0.33 #1901, 0.07 #115711, 0.06 #399950), 0prfz (0.25 #22687, 0.25 #15140, 0.13 #42809), 09yrh (0.25 #16005, 0.20 #13490, 0.19 #41157), 023kzp (0.25 #16307, 0.13 #64101, 0.11 #71649), 01p7yb (0.25 #15138, 0.13 #42807, 0.10 #62932), 0139q5 (0.25 #17081, 0.11 #29658, 0.10 #22112) >> Best rule #1741 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0947l; >> query: (?x205, 0g7k2g) <- contains(?x205, ?x8475), ?x8475 = 05p7tx, vacationer(?x205, ?x1890) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #238967 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 03v_5; 0f2nf; 0d739; 01p726; *> query: (?x205, ?x8600) <- contains(?x205, ?x8475), school_type(?x8475, ?x9240), company(?x8600, ?x8475) *> conf = 0.09 ranks of expected_values: 487 EVAL 03rjj location! 02sdx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 244.000 180.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #10069-0ctzf1 PRED entity: 0ctzf1 PRED relation: actor PRED expected values: 029cpw 0935jw => 76 concepts (43 used for prediction) PRED predicted values (max 10 best out of 799): 01nsyf (0.33 #1737, 0.29 #2661, 0.08 #4509), 04f62k (0.33 #1800, 0.14 #2724, 0.08 #4572), 04j5fx (0.33 #1735, 0.14 #2659, 0.08 #4507), 02g5h5 (0.33 #304, 0.08 #4000, 0.08 #4925), 015p37 (0.33 #805, 0.08 #4501, 0.08 #5426), 03_1pg (0.33 #403, 0.08 #4099, 0.08 #5024), 03rwng (0.33 #455, 0.08 #4151, 0.08 #5076), 02g9z1 (0.33 #859, 0.08 #4555, 0.08 #5480), 03j367r (0.33 #825, 0.08 #4521, 0.08 #5446), 04wf_b (0.33 #693, 0.08 #4389, 0.08 #5314) >> Best rule #1737 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 0gxr1c; >> query: (?x7325, 01nsyf) <- genre(?x7325, ?x1510), genre(?x7325, ?x812), actor(?x7325, ?x8876), country_of_origin(?x7325, ?x252), ?x1510 = 01hmnh, ?x252 = 03_3d, profession(?x8876, ?x319), ?x812 = 01jfsb >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10719 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 22 *> proper extension: 05sy0cv; 017dbx; 05631; *> query: (?x7325, 029cpw) <- languages(?x7325, ?x254), genre(?x7325, ?x10023), genre(?x7325, ?x1510), ?x10023 = 0pr6f, genre(?x7566, ?x1510), ?x7566 = 05h95s *> conf = 0.08 ranks of expected_values: 124 EVAL 0ctzf1 actor 0935jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 43.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 0ctzf1 actor 029cpw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 76.000 43.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10068-076tw54 PRED entity: 076tw54 PRED relation: film! PRED expected values: 016tw3 => 131 concepts (121 used for prediction) PRED predicted values (max 10 best out of 85): 09tlc8 (0.64 #7513, 0.63 #6143, 0.63 #4241), 054lpb6 (0.64 #7513, 0.63 #6143, 0.63 #4241), 017s11 (0.58 #2046, 0.42 #838, 0.41 #381), 016tw3 (0.38 #389, 0.38 #846, 0.33 #161), 086k8 (0.38 #2, 0.25 #77, 0.21 #305), 05qd_ (0.21 #312, 0.19 #1298, 0.18 #1448), 03xq0f (0.20 #1144, 0.20 #918, 0.18 #687), 01795t (0.19 #1157, 0.18 #931, 0.16 #1382), 016tt2 (0.17 #231, 0.16 #917, 0.15 #1672), 061dn_ (0.17 #174, 0.08 #859, 0.07 #251) >> Best rule #7513 for best value: >> intensional similarity = 6 >> extensional distance = 923 >> proper extension: 02rrfzf; >> query: (?x13292, ?x1478) <- production_companies(?x13292, ?x1478), genre(?x13292, ?x53), production_companies(?x7629, ?x1478), film_release_region(?x7629, ?x87), ?x87 = 05r4w, film(?x1478, ?x633) >> conf = 0.64 => this is the best rule for 2 predicted values *> Best rule #389 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 32 *> proper extension: 0j43swk; 06yykb; *> query: (?x13292, 016tw3) <- production_companies(?x13292, ?x1478), genre(?x13292, ?x53), ?x1478 = 054lpb6, ?x53 = 07s9rl0, country(?x13292, ?x94), film(?x3780, ?x13292) *> conf = 0.38 ranks of expected_values: 4 EVAL 076tw54 film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 131.000 121.000 0.644 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #10067-040nwr PRED entity: 040nwr PRED relation: actor! PRED expected values: 050kh5 => 117 concepts (67 used for prediction) PRED predicted values (max 10 best out of 152): 050kh5 (0.12 #504, 0.08 #768, 0.08 #240), 01f3p_ (0.08 #52, 0.02 #1636, 0.02 #7183), 02r5qtm (0.08 #68, 0.02 #1652), 026bfsh (0.07 #1681, 0.06 #8021, 0.05 #7757), 03gvm3t (0.07 #1723, 0.03 #3308, 0.02 #3044), 0828jw (0.07 #1689, 0.02 #5387, 0.02 #5651), 0gfzgl (0.05 #1617, 0.04 #3202, 0.02 #2938), 03ln8b (0.05 #1615, 0.04 #2936, 0.03 #6369), 01vnbh (0.05 #1676, 0.02 #2997, 0.01 #3525), 0d7vtk (0.05 #1777, 0.01 #6531) >> Best rule #504 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 025p38; 02wmbg; 05vzql; 08s0m7; 04v7k2; >> query: (?x12675, 050kh5) <- nationality(?x12675, ?x2146), languages(?x12675, ?x254), special_performance_type(?x12675, ?x4832), ?x2146 = 03rk0 >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 040nwr actor! 050kh5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 67.000 0.118 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #10066-0kz2w PRED entity: 0kz2w PRED relation: institution! PRED expected values: 0bkj86 019v9k => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 19): 03bwzr4 (0.79 #280, 0.71 #343, 0.62 #31), 0bkj86 (0.78 #148, 0.76 #171, 0.75 #26), 0bjrnt (0.75 #24, 0.44 #146, 0.41 #169), 019v9k (0.73 #500, 0.71 #276, 0.68 #459), 04zx3q1 (0.62 #22, 0.52 #271, 0.51 #334), 027f2w (0.62 #28, 0.50 #277, 0.50 #150), 07s6fsf (0.52 #270, 0.50 #21, 0.49 #333), 01rr_d (0.50 #34, 0.26 #179, 0.26 #466), 013zdg (0.38 #274, 0.33 #337, 0.33 #87), 028dcg (0.38 #36, 0.22 #77, 0.19 #98) >> Best rule #280 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 03v6t; 07vht; 0bqxw; 01l8t8; >> query: (?x1043, 03bwzr4) <- currency(?x1043, ?x170), organization(?x1043, ?x5487), major_field_of_study(?x1043, ?x742) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #148 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 02301; 01mpwj; 015cz0; 05zl0; 01q8hj; 01stzp; *> query: (?x1043, 0bkj86) <- major_field_of_study(?x1043, ?x8221), major_field_of_study(?x1043, ?x2605), ?x2605 = 03g3w, ?x8221 = 037mh8 *> conf = 0.78 ranks of expected_values: 2, 4 EVAL 0kz2w institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 130.000 0.788 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0kz2w institution! 0bkj86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.788 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #10065-016l09 PRED entity: 016l09 PRED relation: artist! PRED expected values: 03rhqg 03vtfp => 86 concepts (47 used for prediction) PRED predicted values (max 10 best out of 122): 0fb0v (0.56 #1239, 0.33 #417, 0.30 #554), 015_1q (0.38 #156, 0.34 #4132, 0.28 #5094), 03rhqg (0.32 #1248, 0.32 #1933, 0.31 #837), 033hn8 (0.25 #150, 0.22 #424, 0.20 #561), 043g7l (0.25 #167, 0.14 #4143, 0.12 #4967), 017l96 (0.23 #840, 0.22 #292, 0.20 #703), 01cl2y (0.23 #851, 0.17 #988, 0.12 #1810), 01dtcb (0.22 #457, 0.20 #731, 0.20 #594), 012x8m (0.22 #542, 0.20 #679, 0.20 #131), 0mzkr (0.22 #299, 0.12 #1943, 0.12 #162) >> Best rule #1239 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 033wx9; 01vsyg9; >> query: (?x9791, 0fb0v) <- award(?x9791, ?x3365), artist(?x382, ?x9791), award(?x2040, ?x3365), ?x2040 = 0dtd6, award(?x382, ?x500) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1248 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 033wx9; 01vsyg9; *> query: (?x9791, 03rhqg) <- award(?x9791, ?x3365), artist(?x382, ?x9791), award(?x2040, ?x3365), ?x2040 = 0dtd6, award(?x382, ?x500) *> conf = 0.32 ranks of expected_values: 3, 14 EVAL 016l09 artist! 03vtfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 86.000 47.000 0.559 http://example.org/music/record_label/artist EVAL 016l09 artist! 03rhqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 47.000 0.559 http://example.org/music/record_label/artist #10064-08nz99 PRED entity: 08nz99 PRED relation: nationality PRED expected values: 09c7w0 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.86 #301, 0.86 #7512, 0.85 #201), 02jx1 (0.17 #433, 0.16 #635, 0.12 #534), 07ssc (0.17 #415, 0.14 #516, 0.14 #2918), 03rk0 (0.06 #11959, 0.05 #11759, 0.05 #12559), 0d060g (0.05 #4512, 0.05 #3110, 0.04 #6516), 0h7x (0.04 #435, 0.02 #5841, 0.02 #6945), 0345h (0.04 #6138, 0.03 #532, 0.03 #6640), 03rt9 (0.03 #514, 0.02 #2916, 0.02 #5719), 0f8l9c (0.03 #624, 0.03 #4727, 0.03 #6631), 0ctw_b (0.03 #729, 0.03 #829, 0.02 #1029) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 06t8b; >> query: (?x11373, 09c7w0) <- written_by(?x3221, ?x11373), type_of_union(?x11373, ?x566), producer_type(?x11373, ?x632) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08nz99 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.864 http://example.org/people/person/nationality #10063-0bdwft PRED entity: 0bdwft PRED relation: award_winner PRED expected values: 019l68 => 49 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1532): 0chw_ (0.50 #6810, 0.33 #4360, 0.29 #9260), 0154qm (0.43 #8053, 0.38 #5603, 0.33 #704), 0h0wc (0.38 #5430, 0.35 #36735, 0.34 #24491), 01j5ts (0.35 #36735, 0.34 #24491, 0.33 #2449), 018417 (0.35 #36735, 0.34 #24491, 0.33 #4861), 05dbf (0.35 #36735, 0.34 #24491, 0.33 #2910), 0f4vbz (0.35 #36735, 0.34 #24491, 0.33 #460), 01dbk6 (0.35 #36735, 0.34 #24491, 0.33 #3668), 014g22 (0.35 #36735, 0.34 #24491, 0.33 #904), 01w1kyf (0.35 #36735, 0.34 #24491, 0.33 #3596) >> Best rule #6810 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 02z0dfh; 02x4x18; 02ppm4q; 0cqgl9; >> query: (?x1132, 0chw_) <- award(?x10127, ?x1132), award(?x4103, ?x1132), award(?x241, ?x1132), ?x241 = 01j5ts, ?x4103 = 02jsgf, location(?x10127, ?x2850) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1912 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 0gqyl; *> query: (?x1132, 019l68) <- award(?x7615, ?x1132), award(?x4103, ?x1132), award(?x241, ?x1132), ?x241 = 01j5ts, ?x4103 = 02jsgf, ?x7615 = 039x1k *> conf = 0.33 ranks of expected_values: 77 EVAL 0bdwft award_winner 019l68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 49.000 16.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #10062-099bhp PRED entity: 099bhp PRED relation: genre PRED expected values: 0hcr => 54 concepts (49 used for prediction) PRED predicted values (max 10 best out of 88): 07s9rl0 (0.74 #3739, 0.67 #1566, 0.66 #1808), 03k9fj (0.60 #612, 0.39 #2547, 0.33 #132), 02l7c8 (0.45 #2793, 0.38 #737, 0.31 #2431), 01hmnh (0.40 #619, 0.33 #19, 0.30 #499), 0hcr (0.40 #505, 0.33 #25, 0.30 #625), 0bj8m2 (0.40 #531, 0.17 #2173, 0.09 #1324), 02kdv5l (0.33 #3, 0.30 #603, 0.29 #2297), 0vgkd (0.33 #131, 0.25 #251, 0.21 #731), 04t36 (0.33 #126, 0.25 #246, 0.20 #486), 06nbt (0.33 #147, 0.25 #267, 0.17 #2173) >> Best rule #3739 for best value: >> intensional similarity = 6 >> extensional distance = 1357 >> proper extension: 0fq27fp; 0cbl95; >> query: (?x10072, 07s9rl0) <- genre(?x10072, ?x258), split_to(?x2480, ?x258), genre(?x1015, ?x258), genre(?x974, ?x258), ?x974 = 04kkz8, ?x1015 = 04dsnp >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #505 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 0b60sq; 016ztl; *> query: (?x10072, 0hcr) <- genre(?x10072, ?x7685), film(?x10258, ?x10072), ?x10258 = 093h7p, genre(?x11035, ?x7685), ?x11035 = 06r1k *> conf = 0.40 ranks of expected_values: 5 EVAL 099bhp genre 0hcr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 54.000 49.000 0.740 http://example.org/film/film/genre #10061-02d6c PRED entity: 02d6c PRED relation: contains! PRED expected values: 09c7w0 => 142 concepts (100 used for prediction) PRED predicted values (max 10 best out of 367): 09c7w0 (0.80 #897, 0.73 #39349, 0.70 #31299), 07b_l (0.36 #4691, 0.19 #3797, 0.18 #43147), 01n7q (0.31 #17066, 0.31 #14383, 0.27 #8123), 04_1l0v (0.25 #5814, 0.17 #29956, 0.17 #37111), 0nrqh (0.25 #400, 0.10 #1294, 0.08 #3082), 0d060g (0.17 #51873, 0.06 #19684, 0.06 #59921), 07ssc (0.16 #48326, 0.16 #69796, 0.15 #87686), 0kpys (0.16 #8226, 0.15 #11804, 0.13 #22533), 02jx1 (0.13 #48380, 0.12 #69850, 0.11 #87740), 059rby (0.11 #6278, 0.06 #38472, 0.06 #48314) >> Best rule #897 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 01j_9c; 02yr1q; 03fgm; 0ghtf; >> query: (?x10662, 09c7w0) <- contains(?x13427, ?x10662), contains(?x961, ?x10662), ?x961 = 03s0w, category(?x10662, ?x134), time_zones(?x13427, ?x1638) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d6c contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 100.000 0.800 http://example.org/location/location/contains #10060-0mpbx PRED entity: 0mpbx PRED relation: location! PRED expected values: 01w1kyf => 172 concepts (128 used for prediction) PRED predicted values (max 10 best out of 3451): 03r1pr (0.51 #62932, 0.49 #62931, 0.48 #50342), 014635 (0.22 #770, 0.08 #3287, 0.06 #5804), 01q9b9 (0.22 #1506, 0.08 #4023, 0.06 #6540), 012v1t (0.17 #3734, 0.12 #6251, 0.11 #1217), 02lt8 (0.17 #3313, 0.12 #5830, 0.10 #36035), 027zz (0.17 #4703, 0.12 #7220, 0.05 #9737), 0x3r3 (0.17 #3698, 0.10 #11249, 0.06 #21318), 0sx5w (0.17 #4658, 0.06 #7175, 0.06 #27312), 01_x6v (0.17 #2951, 0.06 #5468, 0.06 #23088), 06wvj (0.12 #5502, 0.11 #468, 0.10 #8019) >> Best rule #62932 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 0cb4j; >> query: (?x11240, ?x3308) <- place_of_birth(?x3308, ?x11240), profession(?x3308, ?x319), currency(?x11240, ?x170) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #80554 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 82 *> proper extension: 0195pd; 01s3v; 01c1nm; 013t2y; 012ts; 01vskn; 01hvzr; *> query: (?x11240, ?x51) <- place_of_birth(?x2871, ?x11240), country(?x11240, ?x94), nationality(?x51, ?x94) *> conf = 0.01 ranks of expected_values: 2924 EVAL 0mpbx location! 01w1kyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 128.000 0.507 http://example.org/people/person/places_lived./people/place_lived/location #10059-085pr PRED entity: 085pr PRED relation: written_by! PRED expected values: 02kfzz => 126 concepts (93 used for prediction) PRED predicted values (max 10 best out of 268): 0b4lkx (0.49 #12531, 0.35 #1978, 0.34 #3959), 0p9tm (0.49 #12531, 0.35 #1978, 0.34 #3959), 02ph9tm (0.05 #1743, 0.02 #425, 0.02 #9000), 01lbcqx (0.04 #544, 0.04 #4503, 0.03 #7141), 03wy8t (0.04 #595, 0.04 #1254, 0.03 #1913), 018f8 (0.04 #72, 0.03 #3370, 0.02 #4031), 08ct6 (0.04 #315, 0.02 #974, 0.02 #4933), 04sh80 (0.04 #654, 0.02 #1972), 01d2v1 (0.04 #639, 0.02 #1957), 01rnly (0.04 #1246, 0.03 #2565, 0.03 #3885) >> Best rule #12531 for best value: >> intensional similarity = 3 >> extensional distance = 192 >> proper extension: 04t2l2; 0byfz; 0qf43; 014zcr; 0h5f5n; 01q_ph; 0159h6; 0bxtg; 0c1pj; 04r7jc; ... >> query: (?x3527, ?x7846) <- nationality(?x3527, ?x94), written_by(?x518, ?x3527), award_winner(?x7846, ?x3527) >> conf = 0.49 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 085pr written_by! 02kfzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 93.000 0.494 http://example.org/film/film/written_by #10058-03hpkp PRED entity: 03hpkp PRED relation: school! PRED expected values: 02x2khw => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 20): 0f4vx0 (0.17 #431, 0.16 #511, 0.16 #451), 02qw1zx (0.16 #425, 0.13 #385, 0.13 #545), 02pq_x5 (0.14 #561, 0.11 #437, 0.10 #317), 02rl201 (0.14 #561, 0.10 #24, 0.10 #4), 02x2khw (0.14 #561, 0.10 #423, 0.09 #383), 02z6872 (0.14 #561, 0.09 #310, 0.08 #490), 02pq_rp (0.14 #561, 0.08 #428, 0.08 #388), 04f4z1k (0.14 #561, 0.07 #438, 0.06 #518), 02r6gw6 (0.14 #561, 0.06 #394, 0.06 #434), 047dpm0 (0.14 #561, 0.06 #399, 0.06 #439) >> Best rule #431 for best value: >> intensional similarity = 2 >> extensional distance = 141 >> proper extension: 0fht9f; >> query: (?x10303, 0f4vx0) <- school(?x8111, ?x10303), sport(?x8111, ?x5063) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #561 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 173 *> proper extension: 0frm7n; *> query: (?x10303, ?x1161) <- category(?x10303, ?x134), ?x134 = 08mbj5d, school(?x8111, ?x10303), draft(?x8111, ?x1161) *> conf = 0.14 ranks of expected_values: 5 EVAL 03hpkp school! 02x2khw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 153.000 153.000 0.175 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #10057-026g4l_ PRED entity: 026g4l_ PRED relation: profession PRED expected values: 05sxg2 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 49): 02hrh1q (0.72 #5975, 0.70 #2548, 0.70 #2399), 0dxtg (0.54 #162, 0.52 #907, 0.52 #609), 02jknp (0.50 #901, 0.48 #603, 0.47 #1050), 02hv44_ (0.50 #207, 0.26 #8197, 0.14 #58), 03gjzk (0.47 #462, 0.43 #760, 0.40 #909), 0cbd2 (0.38 #155, 0.26 #8197, 0.14 #6), 05sxg2 (0.29 #1, 0.26 #8197, 0.17 #150), 09jwl (0.26 #8197, 0.19 #2851, 0.19 #3298), 0kyk (0.26 #8197, 0.17 #179, 0.09 #6289), 02krf9 (0.26 #8197, 0.15 #921, 0.14 #474) >> Best rule #5975 for best value: >> intensional similarity = 4 >> extensional distance = 1564 >> proper extension: 01dq9q; >> query: (?x5714, 02hrh1q) <- award_nominee(?x5714, ?x3692), award_nominee(?x5714, ?x3381), film(?x3692, ?x186), award_winner(?x3381, ?x3487) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0fvf9q; 05qd_; 04cw0j; 01_6dw; 03m9c8; *> query: (?x5714, 05sxg2) <- award_nominee(?x7274, ?x5714), award_nominee(?x6535, ?x5714), ?x7274 = 0dbpwb, award_nominee(?x496, ?x6535) *> conf = 0.29 ranks of expected_values: 7 EVAL 026g4l_ profession 05sxg2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 75.000 75.000 0.720 http://example.org/people/person/profession #10056-01v9724 PRED entity: 01v9724 PRED relation: influenced_by! PRED expected values: 084w8 0282x 048cl 03cdg => 125 concepts (27 used for prediction) PRED predicted values (max 10 best out of 361): 05jm7 (0.50 #626, 0.29 #3075, 0.25 #3564), 040db (0.38 #3502, 0.36 #3013, 0.25 #1055), 0p8jf (0.29 #3048, 0.25 #3537, 0.14 #8815), 0d4jl (0.29 #3053, 0.25 #3542, 0.14 #8815), 02wh0 (0.28 #8750, 0.14 #8814, 0.14 #8815), 02xyl (0.25 #969, 0.21 #3418, 0.19 #3907), 06hmd (0.25 #3639, 0.21 #3150, 0.14 #8814), 067xw (0.25 #1250, 0.14 #3208, 0.12 #3697), 042xh (0.25 #973, 0.14 #8815, 0.13 #3917), 048cl (0.25 #8607, 0.08 #2242, 0.05 #4689) >> Best rule #626 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 07g2b; 06jcc; >> query: (?x5435, 05jm7) <- influenced_by(?x10978, ?x5435), influenced_by(?x3969, ?x5435), profession(?x3969, ?x353), ?x10978 = 02ghq, influenced_by(?x117, ?x3969) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8607 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 04k15; *> query: (?x5435, 048cl) <- influenced_by(?x7828, ?x5435), influenced_by(?x3969, ?x5435), profession(?x3969, ?x353), influenced_by(?x1752, ?x7828), organization(?x3969, ?x8603) *> conf = 0.25 ranks of expected_values: 10, 27, 56, 237 EVAL 01v9724 influenced_by! 03cdg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 125.000 27.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 01v9724 influenced_by! 048cl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 125.000 27.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 01v9724 influenced_by! 0282x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 125.000 27.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 01v9724 influenced_by! 084w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 125.000 27.000 0.500 http://example.org/influence/influence_node/influenced_by #10055-069ld1 PRED entity: 069ld1 PRED relation: place_of_birth PRED expected values: 0lhn5 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 31): 01531 (0.27 #31697, 0.27 #19721, 0.27 #35218), 0cr3d (0.09 #798, 0.06 #94, 0.04 #7841), 02_286 (0.08 #14810, 0.07 #38053, 0.07 #14106), 030qb3t (0.06 #54, 0.04 #38088, 0.03 #33863), 0fvyg (0.06 #444, 0.03 #1148), 0y1rf (0.06 #434, 0.03 #1138), 049kw (0.06 #427, 0.03 #1131), 05r7t (0.06 #240, 0.03 #944), 068p2 (0.06 #162, 0.03 #866), 01cx_ (0.06 #109, 0.01 #7856, 0.01 #12082) >> Best rule #31697 for best value: >> intensional similarity = 2 >> extensional distance = 2301 >> proper extension: 0qkj7; >> query: (?x890, ?x3014) <- location(?x890, ?x3014), gender(?x890, ?x231) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 069ld1 place_of_birth 0lhn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 65.000 0.275 http://example.org/people/person/place_of_birth #10054-0h3mrc PRED entity: 0h3mrc PRED relation: profession PRED expected values: 018gz8 => 106 concepts (99 used for prediction) PRED predicted values (max 10 best out of 46): 01d_h8 (0.56 #152, 0.49 #882, 0.49 #444), 02jknp (0.52 #2637, 0.42 #1607, 0.33 #154), 018gz8 (0.42 #1607, 0.38 #15, 0.28 #7302), 0cbd2 (0.42 #1607, 0.28 #7302, 0.28 #8472), 0np9r (0.42 #1607, 0.28 #7302, 0.28 #8472), 09jwl (0.42 #1607, 0.28 #7302, 0.28 #8472), 015cjr (0.42 #1607, 0.28 #7302, 0.28 #8472), 08z956 (0.42 #1607, 0.28 #7302, 0.28 #8472), 0kyk (0.42 #1607, 0.28 #8472, 0.26 #11831), 0nbcg (0.14 #7331, 0.13 #3826, 0.13 #5870) >> Best rule #152 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01jbx1; >> query: (?x3924, 01d_h8) <- program(?x3924, ?x1631), languages(?x3924, ?x254), ?x254 = 02h40lc >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1607 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 159 *> proper extension: 02f9wb; 02c0mv; 08xz51; 06vqdf; 023jq1; 08f3yq; *> query: (?x3924, ?x1032) <- program(?x3924, ?x1631), award_winner(?x274, ?x3924), profession(?x274, ?x1032) *> conf = 0.42 ranks of expected_values: 3 EVAL 0h3mrc profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 99.000 0.556 http://example.org/people/person/profession #10053-02tjl3 PRED entity: 02tjl3 PRED relation: country PRED expected values: 0d060g => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.82 #1910, 0.80 #1539, 0.79 #1788), 07ssc (0.31 #1986, 0.26 #139, 0.22 #2173), 0345h (0.20 #28, 0.13 #150, 0.12 #642), 0d060g (0.20 #9, 0.08 #70, 0.06 #1732), 03_3d (0.14 #745, 0.08 #69, 0.04 #5111), 0f8l9c (0.13 #142, 0.10 #1989, 0.09 #2176), 07s9rl0 (0.11 #2031, 0.07 #3019, 0.07 #3509), 0chghy (0.06 #135, 0.06 #627, 0.05 #811), 03rjj (0.05 #683, 0.04 #1976, 0.04 #129), 0ctw_b (0.04 #146, 0.02 #761, 0.02 #1993) >> Best rule #1910 for best value: >> intensional similarity = 4 >> extensional distance = 551 >> proper extension: 0gcrg; >> query: (?x5520, 09c7w0) <- nominated_for(?x9781, ?x5520), film_crew_role(?x5520, ?x137), participant(?x4295, ?x9781), profession(?x4295, ?x1032) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 09sh8k; 0fdv3; 0dfw0; *> query: (?x5520, 0d060g) <- nominated_for(?x9781, ?x5520), film(?x2317, ?x5520), genre(?x5520, ?x2605), ?x9781 = 0f276 *> conf = 0.20 ranks of expected_values: 4 EVAL 02tjl3 country 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 86.000 0.817 http://example.org/film/film/country #10052-0bvzp PRED entity: 0bvzp PRED relation: student! PRED expected values: 03ksy => 120 concepts (109 used for prediction) PRED predicted values (max 10 best out of 182): 017z88 (0.25 #82, 0.15 #1657, 0.14 #5857), 02_gzx (0.20 #909, 0.13 #3009, 0.07 #3534), 02_jjm (0.20 #1003, 0.04 #1528, 0.03 #3103), 0bwfn (0.11 #24952, 0.08 #32827, 0.08 #26002), 09f2j (0.09 #5934, 0.09 #6459, 0.08 #1734), 02g839 (0.09 #3175, 0.08 #12101, 0.08 #13677), 02cw8s (0.08 #1645, 0.05 #5845, 0.04 #6370), 01t0dy (0.08 #1792, 0.02 #3892, 0.02 #12818), 04sylm (0.07 #5851, 0.06 #6376, 0.04 #6901), 01g0p5 (0.07 #2832, 0.04 #4932, 0.03 #5982) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01dhpj; 0ckcvk; >> query: (?x6399, 017z88) <- award(?x6399, ?x8141), ?x8141 = 024_41, award_nominee(?x6399, ?x5125) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3781 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 02bf2s; *> query: (?x6399, 03ksy) <- award(?x6399, ?x594), student(?x12028, ?x6399), inductee(?x9953, ?x6399) *> conf = 0.06 ranks of expected_values: 14 EVAL 0bvzp student! 03ksy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 120.000 109.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #10051-07z1m PRED entity: 07z1m PRED relation: contains PRED expected values: 0mn0v 0mnsf 034lk7 => 147 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2788): 01n4w_ (0.76 #159888, 0.50 #63953, 0.48 #26162), 0rh6k (0.65 #17442, 0.64 #11628, 0.63 #37789), 0fr61 (0.65 #17442, 0.64 #11628, 0.63 #37789), 0cc07 (0.64 #11628, 0.63 #37789, 0.62 #81395), 0cv1h (0.64 #11628, 0.63 #37789, 0.62 #81395), 0dq23 (0.50 #63953, 0.48 #26162, 0.47 #159887), 077w0b (0.50 #63953, 0.48 #26162, 0.47 #159887), 02630g (0.50 #63953, 0.48 #26162, 0.47 #159887), 01skqzw (0.50 #63953, 0.48 #26162, 0.47 #159887), 05xbx (0.50 #63953, 0.48 #26162, 0.47 #159887) >> Best rule #159888 for best value: >> intensional similarity = 2 >> extensional distance = 116 >> proper extension: 0195pd; >> query: (?x1426, ?x12450) <- state_province_region(?x12450, ?x1426), contains(?x94, ?x12450) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #206405 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 174 *> proper extension: 06p5g; *> query: (?x1426, ?x94) <- contains(?x1426, ?x11559), contains(?x94, ?x11559), taxonomy(?x1426, ?x939) *> conf = 0.49 ranks of expected_values: 15, 1365 EVAL 07z1m contains 034lk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 147.000 111.000 0.755 http://example.org/location/location/contains EVAL 07z1m contains 0mnsf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 147.000 111.000 0.755 http://example.org/location/location/contains EVAL 07z1m contains 0mn0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 111.000 0.755 http://example.org/location/location/contains #10050-01yj2 PRED entity: 01yj2 PRED relation: contains! PRED expected values: 01tjt2 => 151 concepts (125 used for prediction) PRED predicted values (max 10 best out of 358): 0c4b8 (0.66 #37645, 0.65 #34955, 0.57 #18821), 09c7w0 (0.60 #16135, 0.59 #30480, 0.58 #21518), 07ssc (0.60 #39439, 0.60 #38575, 0.56 #111989), 0d060g (0.50 #13, 0.09 #17041, 0.08 #25113), 02jx1 (0.46 #38629, 0.25 #5460, 0.11 #94156), 04_1l0v (0.40 #21965, 0.39 #30927, 0.36 #39890), 02qkt (0.40 #55019, 0.21 #91728, 0.16 #8407), 01tjt2 (0.27 #98549, 0.13 #5135, 0.02 #38543), 01yj2 (0.27 #98549, 0.07 #4952, 0.02 #38543), 05kr_ (0.25 #125, 0.05 #17153, 0.04 #9978) >> Best rule #37645 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 0gclb; 07p7g; >> query: (?x8751, ?x5738) <- capital(?x5738, ?x8751), capital(?x792, ?x8751), country(?x171, ?x792) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #98549 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 399 *> proper extension: 06pvr; 01w0v; 0290rb; 0g14f; 037n3; 01zk9d; 07sb1; *> query: (?x8751, ?x792) <- contains(?x8751, ?x9861), contains(?x792, ?x9861), institution(?x865, ?x9861) *> conf = 0.27 ranks of expected_values: 8 EVAL 01yj2 contains! 01tjt2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 151.000 125.000 0.658 http://example.org/location/location/contains #10049-07t3x8 PRED entity: 07t3x8 PRED relation: nationality PRED expected values: 03rk0 => 82 concepts (74 used for prediction) PRED predicted values (max 10 best out of 24): 03rk0 (0.87 #346, 0.83 #646, 0.75 #5627), 09c7w0 (0.74 #3315, 0.74 #2712, 0.73 #2409), 01hpnh (0.32 #5628, 0.28 #5224, 0.27 #5122), 0dlv0 (0.25 #2913, 0.25 #4720), 07ssc (0.12 #415, 0.12 #1420, 0.11 #1520), 02jx1 (0.10 #3146, 0.09 #1438, 0.09 #6361), 06q1r (0.06 #477, 0.04 #577, 0.02 #2384), 0h7x (0.06 #135, 0.05 #235, 0.02 #1339), 03gyl (0.06 #166, 0.05 #266), 0d060g (0.05 #1211, 0.05 #2013, 0.04 #5129) >> Best rule #346 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 0265z9l; 0b5x23; >> query: (?x10221, 03rk0) <- gender(?x10221, ?x231), award(?x10221, ?x4687), ?x4687 = 03rbj2, type_of_union(?x10221, ?x566) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07t3x8 nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 74.000 0.872 http://example.org/people/person/nationality #10048-06gbnc PRED entity: 06gbnc PRED relation: languages_spoken PRED expected values: 02h40lc => 26 concepts (26 used for prediction) PRED predicted values (max 10 best out of 55): 02h40lc (0.89 #444, 0.43 #389, 0.39 #554), 0t_2 (0.47 #564, 0.45 #674, 0.44 #343), 06nm1 (0.33 #9, 0.24 #230, 0.22 #285), 064_8sq (0.33 #19, 0.20 #350, 0.19 #571), 02bjrlw (0.33 #1, 0.20 #57, 0.11 #388), 06b_j (0.33 #20, 0.14 #407, 0.14 #241), 06mp7 (0.33 #14, 0.11 #676, 0.10 #235), 0880p (0.33 #43, 0.11 #595, 0.11 #817), 04306rv (0.33 #5, 0.10 #226, 0.09 #667), 0k0sv (0.33 #21, 0.10 #242, 0.09 #297) >> Best rule #444 for best value: >> intensional similarity = 7 >> extensional distance = 26 >> proper extension: 078vc; 078ds; 0fk3s; 04czx7; >> query: (?x6736, 02h40lc) <- languages_spoken(?x6736, ?x9617), language(?x4452, ?x9617), ?x4452 = 034r25, languages(?x12479, ?x9617), ?x12479 = 0bkq_8, official_language(?x9328, ?x9617), ?x9328 = 024pcx >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06gbnc languages_spoken 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 26.000 26.000 0.893 http://example.org/people/ethnicity/languages_spoken #10047-0cq7tx PRED entity: 0cq7tx PRED relation: music PRED expected values: 05pq9 => 146 concepts (68 used for prediction) PRED predicted values (max 10 best out of 144): 012201 (0.25 #360, 0.04 #4135, 0.04 #1617), 0146pg (0.20 #1057, 0.13 #3995, 0.12 #4626), 04bpm6 (0.17 #446, 0.04 #3383, 0.03 #5062), 0pkgt (0.17 #614, 0.02 #3341, 0.02 #5230), 089z0z (0.17 #625, 0.02 #3562, 0.02 #5241), 02sj1x (0.15 #2785, 0.12 #5722, 0.11 #5931), 015wc0 (0.14 #1432, 0.14 #1013, 0.14 #804), 03c_8t (0.14 #1047, 0.14 #838, 0.04 #3984), 02qfhb (0.14 #922, 0.02 #3022, 0.02 #3859), 076psv (0.11 #10284, 0.10 #1257, 0.09 #3985) >> Best rule #360 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0jsf6; >> query: (?x4404, 012201) <- award_winner(?x4404, ?x4423), executive_produced_by(?x4404, ?x6369), film_sets_designed(?x4423, ?x951), award_winner(?x1793, ?x4423) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cq7tx music 05pq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 146.000 68.000 0.250 http://example.org/film/film/music #10046-088q1s PRED entity: 088q1s PRED relation: combatants! PRED expected values: 03w6sj => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 93): 081pw (0.82 #1809, 0.79 #1685, 0.62 #1057), 048n7 (0.65 #1619, 0.63 #1555, 0.62 #2056), 02kxjx (0.62 #2056, 0.62 #2305, 0.60 #2804), 0gjw_ (0.57 #529, 0.50 #402, 0.42 #964), 0c3mz (0.57 #535, 0.50 #408, 0.40 #472), 01gjd0 (0.57 #500, 0.33 #935, 0.31 #1059), 08qz1l (0.50 #973, 0.43 #538, 0.37 #1661), 01fc7p (0.50 #934, 0.43 #499, 0.33 #126), 03gqgt3 (0.45 #2489, 0.37 #1737, 0.36 #1861), 0dl4z (0.43 #503, 0.33 #67, 0.31 #1062) >> Best rule #1809 for best value: >> intensional similarity = 9 >> extensional distance = 20 >> proper extension: 05b4w; >> query: (?x11095, 081pw) <- combatants(?x12789, ?x11095), form_of_government(?x11095, ?x1926), combatants(?x12789, ?x8687), combatants(?x12789, ?x390), combatants(?x12789, ?x94), ?x8687 = 059z0, ?x94 = 09c7w0, ?x390 = 0chghy, locations(?x12789, ?x291) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #537 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 09c7w0; *> query: (?x11095, 03w6sj) <- combatants(?x12789, ?x11095), form_of_government(?x11095, ?x6065), ?x12789 = 02h2z_, entity_involved(?x9203, ?x11095), form_of_government(?x6401, ?x6065), form_of_government(?x4402, ?x6065), contains(?x6401, ?x4030), official_language(?x4402, ?x254) *> conf = 0.43 ranks of expected_values: 11 EVAL 088q1s combatants! 03w6sj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 106.000 106.000 0.818 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #10045-01l9v7n PRED entity: 01l9v7n PRED relation: music! PRED expected values: 03h3x5 0yx1m => 119 concepts (60 used for prediction) PRED predicted values (max 10 best out of 988): 02scbv (0.23 #4037, 0.22 #5047, 0.06 #2717), 06c0ns (0.23 #4037, 0.22 #5047, 0.05 #4739), 059lwy (0.23 #4037, 0.22 #5047, 0.03 #7751), 02n72k (0.23 #4037, 0.22 #5047, 0.02 #12777), 01kf4tt (0.23 #4037, 0.22 #5047, 0.02 #12359), 014kq6 (0.23 #4037, 0.22 #5047, 0.02 #14339), 0fsw_7 (0.23 #4037, 0.22 #5047), 01771z (0.23 #4037, 0.22 #5047), 06ybb1 (0.23 #4037, 0.22 #5047), 02qrv7 (0.23 #4037, 0.22 #5047) >> Best rule #4037 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 02bn75; >> query: (?x3134, ?x835) <- award(?x3134, ?x1323), ?x1323 = 0gqz2, music(?x1262, ?x3134), nominated_for(?x835, ?x1262) >> conf = 0.23 => this is the best rule for 14 predicted values *> Best rule #2276 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 03f2_rc; 0146pg; 01vrncs; 02fgpf; 01r6jt2; 01gg59; 01tc9r; 016szr; 01l1rw; 01wd9lv; ... *> query: (?x3134, 03h3x5) <- award(?x3134, ?x1323), ?x1323 = 0gqz2, people(?x3591, ?x3134), music(?x188, ?x3134) *> conf = 0.11 ranks of expected_values: 23 EVAL 01l9v7n music! 0yx1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 60.000 0.227 http://example.org/film/film/music EVAL 01l9v7n music! 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 119.000 60.000 0.227 http://example.org/film/film/music #10044-0fr9jp PRED entity: 0fr9jp PRED relation: student PRED expected values: 03nb5v 0j5q3 => 109 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1103): 05bnp0 (0.33 #2083, 0.10 #10371, 0.03 #22805), 015qq1 (0.33 #3945, 0.08 #12233, 0.03 #14305), 04t969 (0.33 #3339, 0.08 #11627, 0.03 #24061), 01vwbts (0.33 #2877, 0.08 #11165, 0.02 #25672), 0hgqq (0.33 #2908, 0.06 #11196, 0.02 #13268), 01pqy_ (0.33 #2963, 0.06 #11251, 0.02 #13323), 02cyfz (0.33 #2403, 0.06 #10691, 0.02 #12763), 09v6tz (0.33 #3401, 0.06 #11689, 0.02 #13761), 07s8hms (0.33 #2688, 0.06 #10976, 0.02 #23410), 02vyw (0.33 #2646, 0.06 #10934, 0.02 #25441) >> Best rule #2083 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 09f2j; >> query: (?x9318, 05bnp0) <- student(?x9318, ?x13962), student(?x9318, ?x10415), student(?x9318, ?x1774), ?x10415 = 06jz0, people(?x1050, ?x1774), ?x13962 = 033db3 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fr9jp student 0j5q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 65.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0fr9jp student 03nb5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 65.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #10043-01jw67 PRED entity: 01jw67 PRED relation: genre PRED expected values: 02l7c8 => 78 concepts (74 used for prediction) PRED predicted values (max 10 best out of 116): 05p553 (0.52 #5695, 0.42 #609, 0.41 #488), 02l7c8 (0.40 #5707, 0.39 #500, 0.38 #258), 03k9fj (0.39 #374, 0.27 #1100, 0.24 #1221), 01jfsb (0.35 #7279, 0.31 #859, 0.30 #2917), 02kdv5l (0.33 #365, 0.30 #1212, 0.29 #2907), 04xvlr (0.30 #1454, 0.29 #1575, 0.22 #243), 060__y (0.24 #1470, 0.23 #1591, 0.17 #3769), 01hmnh (0.22 #381, 0.15 #5466, 0.15 #623), 0lsxr (0.21 #7275, 0.21 #734, 0.19 #1582), 06n90 (0.19 #5704, 0.17 #376, 0.13 #1223) >> Best rule #5695 for best value: >> intensional similarity = 4 >> extensional distance = 1095 >> proper extension: 014_x2; 0ds35l9; 0d90m; 03qcfvw; 0g56t9t; 09sh8k; 0m313; 02y_lrp; 034qmv; 018js4; ... >> query: (?x6222, 05p553) <- genre(?x6222, ?x307), film(?x2173, ?x6222), genre(?x8320, ?x307), ?x8320 = 09cxm4 >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #5707 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1095 *> proper extension: 014_x2; 0ds35l9; 0d90m; 03qcfvw; 0g56t9t; 09sh8k; 0m313; 02y_lrp; 034qmv; 018js4; ... *> query: (?x6222, 02l7c8) <- genre(?x6222, ?x307), film(?x2173, ?x6222), genre(?x8320, ?x307), ?x8320 = 09cxm4 *> conf = 0.40 ranks of expected_values: 2 EVAL 01jw67 genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 74.000 0.515 http://example.org/film/film/genre #10042-03zqc1 PRED entity: 03zqc1 PRED relation: award_nominee! PRED expected values: 03w4sh => 118 concepts (77 used for prediction) PRED predicted values (max 10 best out of 974): 038g2x (0.81 #9239, 0.81 #173168, 0.81 #4620), 030znt (0.76 #173169, 0.76 #175480, 0.76 #94678), 03zqc1 (0.59 #2404, 0.50 #93, 0.35 #76203), 03w4sh (0.41 #3788, 0.29 #9240, 0.19 #8406), 0d810y (0.29 #9240, 0.27 #143154, 0.16 #8260), 08pth9 (0.29 #9240, 0.27 #143154, 0.16 #7978), 09btt1 (0.29 #9240, 0.27 #143154, 0.16 #7979), 04mz10g (0.29 #9240, 0.27 #143154, 0.16 #7214), 04y79_n (0.29 #9240, 0.27 #143154, 0.16 #7215), 0bbvr84 (0.29 #9240, 0.27 #143154, 0.16 #154696) >> Best rule #9239 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 03x3qv; 086sj; 05ry0p; >> query: (?x516, ?x4976) <- award_nominee(?x516, ?x4976), award_nominee(?x7842, ?x4976), ?x7842 = 048hf >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #3788 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 026zvx7; 0308kx; 03w4sh; 048hf; 02s_qz; 06hgym; 04vmqg; *> query: (?x516, 03w4sh) <- award_nominee(?x516, ?x4976), ?x4976 = 05683p, award(?x516, ?x618) *> conf = 0.41 ranks of expected_values: 4 EVAL 03zqc1 award_nominee! 03w4sh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 118.000 77.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10041-03bnv PRED entity: 03bnv PRED relation: artists! PRED expected values: 06by7 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 216): 06by7 (0.54 #642, 0.52 #1264, 0.46 #10576), 016clz (0.45 #1868, 0.30 #4353, 0.30 #4041), 02lnbg (0.41 #2545, 0.32 #5340, 0.30 #7200), 0glt670 (0.39 #5321, 0.33 #6561, 0.32 #6871), 05bt6j (0.38 #664, 0.29 #1907, 0.28 #2529), 025sc50 (0.38 #5331, 0.33 #6571, 0.31 #6881), 06j6l (0.34 #5329, 0.32 #6569, 0.31 #2223), 02w4v (0.33 #44, 0.21 #1598, 0.13 #7805), 0xhtw (0.29 #1880, 0.25 #10571, 0.19 #4365), 0ggx5q (0.29 #5359, 0.28 #2564, 0.26 #5049) >> Best rule #642 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01mwsnc; 01k_0fp; 01nz1q6; >> query: (?x3321, 06by7) <- instrumentalists(?x228, ?x3321), artists(?x284, ?x3321), type_of_appearance(?x3321, ?x3429) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bnv artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.538 http://example.org/music/genre/artists #10040-0bsj9 PRED entity: 0bsj9 PRED relation: artists! PRED expected values: 06bpt_ => 76 concepts (50 used for prediction) PRED predicted values (max 10 best out of 256): 06by7 (0.83 #12414, 0.72 #13342, 0.68 #13032), 064t9 (0.63 #13643, 0.46 #6201, 0.46 #14881), 016clz (0.54 #3099, 0.44 #6502, 0.44 #7432), 05r6t (0.40 #392, 0.32 #11547, 0.29 #1010), 05bt6j (0.35 #13364, 0.34 #13054, 0.33 #3447), 02yv6b (0.34 #5359, 0.34 #4123, 0.32 #8462), 0dl5d (0.32 #3733, 0.28 #9002, 0.27 #7137), 0glt670 (0.32 #2208, 0.31 #5610, 0.24 #6849), 05w3f (0.31 #4061, 0.27 #5297, 0.27 #7155), 02t8gf (0.29 #1070, 0.25 #1379, 0.20 #452) >> Best rule #12414 for best value: >> intensional similarity = 6 >> extensional distance = 416 >> proper extension: 07s3vqk; 0lbj1; 01vrx3g; 02mslq; 06cc_1; 0kzy0; 01vvycq; 025xt8y; 03f5spx; 01q7cb_; ... >> query: (?x12246, 06by7) <- artist(?x4483, ?x12246), artists(?x1000, ?x12246), artists(?x1000, ?x12880), artists(?x1000, ?x10813), ?x10813 = 0ycfj, ?x12880 = 011xhx >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #422 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0285c; *> query: (?x12246, 06bpt_) <- artist(?x4483, ?x12246), artists(?x10930, ?x12246), artists(?x1000, ?x12246), ?x1000 = 0xhtw, origin(?x12246, ?x739), ?x10930 = 0jrv_ *> conf = 0.20 ranks of expected_values: 29 EVAL 0bsj9 artists! 06bpt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 76.000 50.000 0.830 http://example.org/music/genre/artists #10039-07zrf PRED entity: 07zrf PRED relation: language! PRED expected values: 03z106 => 64 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1725): 01jw67 (0.77 #8621, 0.54 #8622, 0.52 #27618), 02q87z6 (0.77 #8621, 0.54 #8622), 0946bb (0.77 #8621, 0.50 #5692, 0.40 #10870), 019vhk (0.77 #8621, 0.33 #440, 0.25 #5612), 07sgdw (0.77 #8621, 0.33 #773, 0.25 #5945), 015whm (0.77 #8621, 0.33 #620, 0.25 #5792), 03phtz (0.77 #8621, 0.33 #1709, 0.25 #6881), 06c0ns (0.77 #8621, 0.33 #1172, 0.25 #6344), 059lwy (0.77 #8621, 0.33 #1143, 0.25 #6315), 037xlx (0.77 #8621, 0.33 #944, 0.25 #6116) >> Best rule #8621 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 01lqm; >> query: (?x393, ?x188) <- language(?x6918, ?x393), language(?x1602, ?x393), language(?x810, ?x393), ?x810 = 0jzw, film(?x2415, ?x1602), genre(?x1602, ?x258), film_release_region(?x1602, ?x87), honored_for(?x6918, ?x188), award_nominee(?x1582, ?x2415), country(?x1602, ?x512) >> conf = 0.77 => this is the best rule for 16 predicted values *> Best rule #4055 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 064_8sq; *> query: (?x393, 03z106) <- language(?x1602, ?x393), language(?x810, ?x393), ?x810 = 0jzw, film(?x2415, ?x1602), ?x2415 = 0170s4, film_crew_role(?x1602, ?x468), genre(?x1602, ?x258), film_release_region(?x1602, ?x279), countries_spoken_in(?x393, ?x7747), ?x279 = 0d060g *> conf = 0.50 ranks of expected_values: 350 EVAL 07zrf language! 03z106 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 21.000 0.767 http://example.org/film/film/language #10038-04jpg2p PRED entity: 04jpg2p PRED relation: film! PRED expected values: 02_p8v 0h0yt => 72 concepts (37 used for prediction) PRED predicted values (max 10 best out of 826): 02bh9 (0.66 #58105, 0.65 #45650, 0.64 #47727), 03v1w7 (0.42 #35273, 0.42 #41499, 0.40 #49804), 07rd7 (0.42 #35273, 0.42 #41499, 0.40 #49804), 025hzx (0.42 #35273, 0.42 #41499, 0.40 #49804), 03mfqm (0.42 #35273, 0.42 #41499, 0.40 #49804), 03h26tm (0.38 #22824, 0.34 #51880, 0.34 #51879), 0h5g_ (0.25 #4222, 0.20 #6297, 0.14 #2148), 0159h6 (0.25 #4221, 0.20 #6296, 0.03 #41498), 02fz3w (0.25 #5723, 0.20 #7798, 0.03 #41498), 05sq84 (0.25 #4383, 0.20 #6458, 0.03 #10608) >> Best rule #58105 for best value: >> intensional similarity = 3 >> extensional distance = 847 >> proper extension: 01f3p_; >> query: (?x8570, ?x2531) <- nominated_for(?x2531, ?x8570), participant(?x1371, ?x2531), film(?x2531, ?x485) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #5487 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 03p2xc; 03wjm2; *> query: (?x8570, 0h0yt) <- film(?x1549, ?x8570), country(?x8570, ?x94), ?x1549 = 09y20, produced_by(?x8570, ?x4314) *> conf = 0.12 ranks of expected_values: 103, 328 EVAL 04jpg2p film! 0h0yt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 72.000 37.000 0.665 http://example.org/film/actor/film./film/performance/film EVAL 04jpg2p film! 02_p8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 72.000 37.000 0.665 http://example.org/film/actor/film./film/performance/film #10037-026mfbr PRED entity: 026mfbr PRED relation: titles! PRED expected values: 01z4y => 148 concepts (87 used for prediction) PRED predicted values (max 10 best out of 77): 01z4y (0.50 #36, 0.43 #3926, 0.31 #1364), 07s9rl0 (0.35 #2149, 0.35 #6882, 0.33 #306), 05p553 (0.25 #305, 0.18 #3582, 0.18 #2148), 011ys5 (0.25 #305, 0.18 #3582, 0.18 #2148), 0556j8 (0.25 #305, 0.18 #3582, 0.18 #2148), 01hmnh (0.23 #534, 0.23 #637, 0.22 #1355), 024qqx (0.23 #690, 0.17 #895, 0.17 #587), 04xvlr (0.22 #7294, 0.21 #3997, 0.20 #717), 04btyz (0.20 #284, 0.07 #386, 0.03 #3971), 0hfjk (0.20 #282, 0.05 #997, 0.04 #3558) >> Best rule #36 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 034qzw; >> query: (?x750, 01z4y) <- film(?x5019, ?x750), category(?x750, ?x134), film_crew_role(?x750, ?x281), film_release_distribution_medium(?x750, ?x81), ?x5019 = 04fcx7 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026mfbr titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 87.000 0.500 http://example.org/media_common/netflix_genre/titles #10036-02jkkv PRED entity: 02jkkv PRED relation: nominated_for! PRED expected values: 014zcr => 97 concepts (29 used for prediction) PRED predicted values (max 10 best out of 730): 0q9kd (0.72 #23386, 0.48 #65480, 0.46 #67818), 0d_skg (0.72 #23386, 0.48 #65480, 0.46 #67818), 027pdrh (0.38 #28066, 0.34 #25724, 0.26 #21047), 01j5ws (0.34 #58466, 0.24 #32742, 0.23 #35080), 011yrp (0.25 #4676, 0.15 #11692, 0.12 #14032), 02p65p (0.24 #32742, 0.23 #35080, 0.20 #58465), 0f5xn (0.24 #32742, 0.23 #35080, 0.20 #58465), 095b70 (0.24 #32742, 0.23 #35080, 0.20 #58465), 032zg9 (0.24 #32742, 0.23 #35080, 0.20 #58465), 04fhxp (0.24 #32742, 0.23 #35080, 0.20 #58465) >> Best rule #23386 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 064lsn; 0581vn8; >> query: (?x9361, ?x71) <- genre(?x9361, ?x53), produced_by(?x9361, ?x71), nominated_for(?x601, ?x9361), ?x601 = 0gr4k >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #21090 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 97 *> proper extension: 064lsn; 0581vn8; *> query: (?x9361, 014zcr) <- genre(?x9361, ?x53), produced_by(?x9361, ?x71), nominated_for(?x601, ?x9361), ?x601 = 0gr4k *> conf = 0.02 ranks of expected_values: 224 EVAL 02jkkv nominated_for! 014zcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 97.000 29.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #10035-01c1px PRED entity: 01c1px PRED relation: profession PRED expected values: 0dxtg => 103 concepts (73 used for prediction) PRED predicted values (max 10 best out of 74): 02hrh1q (0.78 #4799, 0.77 #2623, 0.75 #3783), 0dxtg (0.70 #12, 0.68 #1752, 0.67 #2042), 09jwl (0.53 #162, 0.50 #452, 0.50 #307), 02krf9 (0.50 #25, 0.31 #2055, 0.30 #1765), 0nbcg (0.47 #174, 0.40 #319, 0.35 #464), 0cbd2 (0.42 #731, 0.42 #586, 0.30 #1021), 01c72t (0.40 #312, 0.35 #457, 0.26 #167), 0dz3r (0.35 #292, 0.26 #147, 0.23 #437), 039v1 (0.30 #324, 0.27 #469, 0.26 #179), 0n1h (0.30 #300, 0.27 #445, 0.21 #155) >> Best rule #4799 for best value: >> intensional similarity = 3 >> extensional distance = 813 >> proper extension: 01njxvw; >> query: (?x8050, 02hrh1q) <- people(?x9428, ?x8050), nationality(?x8050, ?x94), nominated_for(?x8050, ?x8837) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0250f; 01vl17; 01p8r8; 0jnb0; *> query: (?x8050, 0dxtg) <- profession(?x8050, ?x8310), profession(?x8050, ?x524), ?x524 = 02jknp, ?x8310 = 0196pc *> conf = 0.70 ranks of expected_values: 2 EVAL 01c1px profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 73.000 0.777 http://example.org/people/person/profession #10034-01kf3_9 PRED entity: 01kf3_9 PRED relation: film_release_region PRED expected values: 09c7w0 => 136 concepts (135 used for prediction) PRED predicted values (max 10 best out of 172): 09c7w0 (0.78 #3412, 0.73 #7000, 0.73 #8257), 07ssc (0.45 #11127, 0.45 #13105, 0.44 #14005), 0f8l9c (0.45 #11127, 0.45 #13105, 0.44 #14005), 0345h (0.31 #22456, 0.29 #14545, 0.26 #7044), 02jx1 (0.31 #22456, 0.29 #14545), 059j2 (0.30 #7042, 0.29 #8299, 0.25 #3454), 0d0vqn (0.30 #7009, 0.28 #8266, 0.25 #3421), 0k6nt (0.30 #7033, 0.28 #8290, 0.25 #3445), 05r4w (0.28 #6999, 0.27 #8256, 0.20 #3411), 03rjj (0.28 #7005, 0.27 #8262, 0.20 #12933) >> Best rule #3412 for best value: >> intensional similarity = 5 >> extensional distance = 57 >> proper extension: 064q5v; >> query: (?x1851, 09c7w0) <- film(?x2507, ?x1851), country(?x1851, ?x789), country(?x1851, ?x512), ?x789 = 0f8l9c, ?x512 = 07ssc >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kf3_9 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 135.000 0.780 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10033-0n6f8 PRED entity: 0n6f8 PRED relation: people! PRED expected values: 07bch9 => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 68): 033tf_ (0.27 #238, 0.25 #546, 0.21 #469), 03bkbh (0.25 #186, 0.17 #109, 0.05 #802), 02w7gg (0.25 #156, 0.08 #8704, 0.08 #1773), 0x67 (0.25 #1858, 0.21 #4861, 0.20 #4476), 041rx (0.23 #3546, 0.22 #3469, 0.21 #1159), 0xnvg (0.20 #1476, 0.17 #1322, 0.15 #1014), 048z7l (0.19 #810, 0.08 #348, 0.08 #2581), 07hwkr (0.18 #243, 0.17 #89, 0.12 #2014), 01qhm_ (0.17 #699, 0.12 #930, 0.12 #622), 02ctzb (0.12 #631, 0.12 #169, 0.12 #1093) >> Best rule #238 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01vwllw; 0391jz; 0205dx; 0bqs56; 060j8b; 06rgq; 023s8; >> query: (?x1299, 033tf_) <- participant(?x1299, ?x2626), film(?x1299, ?x861), student(?x1771, ?x1299) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1332 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 0134w7; 06tp4h; 016tbr; 0c1j_; *> query: (?x1299, 07bch9) <- film(?x1299, ?x861), vacationer(?x4627, ?x1299), friend(?x1299, ?x4536) *> conf = 0.12 ranks of expected_values: 14 EVAL 0n6f8 people! 07bch9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 174.000 174.000 0.273 http://example.org/people/ethnicity/people #10032-017_hq PRED entity: 017_hq PRED relation: artists! PRED expected values: 01756d => 95 concepts (43 used for prediction) PRED predicted values (max 10 best out of 285): 017_qw (0.55 #10295, 0.30 #4707, 0.06 #4087), 0xhtw (0.53 #5902, 0.48 #6832, 0.45 #325), 08jyyk (0.43 #5332, 0.12 #3472, 0.12 #6881), 05r6t (0.35 #700, 0.33 #4107, 0.33 #1009), 06j6l (0.35 #11518, 0.31 #7173, 0.31 #1595), 0y3_8 (0.33 #2521, 0.33 #2831, 0.30 #4072), 0dl5d (0.32 #6835, 0.29 #4976, 0.26 #1257), 03lty (0.32 #5913, 0.30 #336, 0.28 #5603), 0ggx5q (0.32 #76, 0.23 #696, 0.21 #1005), 02yv6b (0.28 #1646, 0.25 #3814, 0.22 #6295) >> Best rule #10295 for best value: >> intensional similarity = 6 >> extensional distance = 197 >> proper extension: 0c_mvb; 06wvj; 07qy0b; 01mkn_d; 04qr6d; 0dr5y; 01nc3rh; 05f2jk; 0czhv7; 02_33l; ... >> query: (?x11700, 017_qw) <- artists(?x3370, ?x11700), artists(?x3061, ?x11700), artists(?x3370, ?x5508), artists(?x3061, ?x9087), ?x5508 = 0jn5l, origin(?x9087, ?x362) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #3096 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 50 *> proper extension: 02bh9; *> query: (?x11700, ?x114) <- award(?x11700, ?x1565), artists(?x3370, ?x11700), artists(?x1572, ?x11700), ?x3370 = 059kh, parent_genre(?x114, ?x1572) *> conf = 0.07 ranks of expected_values: 101 EVAL 017_hq artists! 01756d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 95.000 43.000 0.553 http://example.org/music/genre/artists #10031-02g3v6 PRED entity: 02g3v6 PRED relation: award! PRED expected values: 0ft7sr => 39 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2258): 026lyl4 (0.67 #10133, 0.25 #3271, 0.22 #6649), 02vxyl5 (0.38 #3210, 0.33 #6588, 0.09 #9966), 0gl88b (0.38 #533, 0.33 #3911, 0.09 #7289), 0bytfv (0.38 #1016, 0.22 #4394, 0.06 #7772), 06cv1 (0.33 #3490, 0.03 #6868, 0.02 #10245), 0bytkq (0.25 #849, 0.22 #4227, 0.06 #7605), 0c6g29 (0.25 #547, 0.22 #3925, 0.06 #7303), 0ft7sr (0.25 #452, 0.22 #3830, 0.06 #7208), 0js9s (0.25 #8669, 0.15 #37167, 0.13 #43924), 02kxbx3 (0.25 #7743, 0.14 #11120, 0.12 #14498) >> Best rule #10133 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 047xyn; >> query: (?x507, ?x5613) <- award_winner(?x507, ?x5613), award_winner(?x507, ?x4691), award(?x4691, ?x143), crewmember(?x972, ?x4691) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #452 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 027h4yd; *> query: (?x507, 0ft7sr) <- award(?x13091, ?x507), people(?x743, ?x13091), costume_design_by(?x2380, ?x13091) *> conf = 0.25 ranks of expected_values: 8 EVAL 02g3v6 award! 0ft7sr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 39.000 14.000 0.672 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10030-0lyjf PRED entity: 0lyjf PRED relation: school! PRED expected values: 051vz => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 160): 04wmvz (0.25 #139, 0.13 #1737, 0.12 #1889), 02d02 (0.25 #130, 0.12 #1424, 0.12 #2108), 05m_8 (0.23 #1069, 0.22 #1373, 0.21 #2057), 051vz (0.20 #96, 0.18 #400, 0.18 #1542), 0bwjj (0.20 #135, 0.14 #1429, 0.11 #1733), 0jmm4 (0.20 #133, 0.12 #1427, 0.11 #1731), 06x68 (0.20 #83, 0.12 #2518, 0.12 #1605), 01slc (0.18 #1719, 0.18 #1871, 0.18 #1643), 07l4z (0.15 #131, 0.14 #1729, 0.14 #1425), 0713r (0.15 #106, 0.14 #1400, 0.13 #1704) >> Best rule #139 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 0ks67; >> query: (?x4904, 04wmvz) <- school(?x465, ?x4904), student(?x4904, ?x11208), student(?x4904, ?x8206), award_nominee(?x8609, ?x11208), athlete(?x5063, ?x8206) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #96 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 0ks67; *> query: (?x4904, 051vz) <- school(?x465, ?x4904), student(?x4904, ?x11208), student(?x4904, ?x8206), award_nominee(?x8609, ?x11208), athlete(?x5063, ?x8206) *> conf = 0.20 ranks of expected_values: 4 EVAL 0lyjf school! 051vz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 201.000 201.000 0.250 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #10029-01lw3kh PRED entity: 01lw3kh PRED relation: category PRED expected values: 08mbj5d => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #28, 0.83 #35, 0.83 #31) >> Best rule #28 for best value: >> intensional similarity = 2 >> extensional distance = 351 >> proper extension: 0123r4; >> query: (?x6237, 08mbj5d) <- artists(?x671, ?x6237), ?x671 = 064t9 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lw3kh category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.853 http://example.org/common/topic/webpage./common/webpage/category #10028-02d45s PRED entity: 02d45s PRED relation: award_nominee PRED expected values: 016gr2 => 101 concepts (50 used for prediction) PRED predicted values (max 10 best out of 853): 016gr2 (0.82 #7019, 0.81 #7018, 0.81 #60841), 02tr7d (0.40 #5029, 0.23 #7021, 0.03 #7372), 02d45s (0.33 #2198, 0.29 #84244, 0.29 #86586), 02l4pj (0.33 #779, 0.28 #5457, 0.23 #7021), 02qgqt (0.33 #20, 0.11 #4698, 0.05 #28100), 03mp9s (0.33 #1588, 0.09 #6266, 0.05 #3927), 05k2s_ (0.33 #270, 0.06 #4948, 0.02 #2609), 02x7vq (0.33 #1301, 0.06 #5979, 0.01 #29381), 02mt4k (0.33 #1155, 0.05 #5833, 0.01 #15196), 015rkw (0.31 #5050, 0.23 #7021, 0.04 #7393) >> Best rule #7019 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 026v437; >> query: (?x10866, ?x1223) <- award_nominee(?x1223, ?x10866), award_winner(?x1223, ?x473), ?x473 = 09fqtq >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d45s award_nominee 016gr2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 50.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #10027-0hn4h PRED entity: 0hn4h PRED relation: place_of_birth! PRED expected values: 0pksh => 172 concepts (53 used for prediction) PRED predicted values (max 10 best out of 347): 03d9wk (0.33 #5215, 0.17 #13054, 0.17 #10441), 0kbg6 (0.33 #5199, 0.17 #13038, 0.17 #10425), 09jd9 (0.33 #5193, 0.17 #13032, 0.17 #10419), 026sb55 (0.33 #5176, 0.17 #13015, 0.17 #10402), 06lhbl (0.33 #5149, 0.17 #12988, 0.17 #10375), 03f4w4 (0.33 #5099, 0.17 #12938, 0.17 #10325), 01b0k1 (0.33 #5098, 0.17 #12937, 0.17 #10324), 0935jw (0.33 #5097, 0.17 #12936, 0.17 #10323), 02vkvcz (0.33 #5065, 0.17 #12904, 0.17 #10291), 07zhd7 (0.33 #5037, 0.17 #12876, 0.17 #10263) >> Best rule #5215 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 04jpl; >> query: (?x14362, 03d9wk) <- capital(?x5776, ?x14362), combatants(?x5776, ?x1023), combatants(?x5776, ?x550), combatants(?x5776, ?x94), ?x1023 = 0ctw_b, combatants(?x2629, ?x5776), ?x550 = 05v8c, country(?x14362, ?x8593), ?x94 = 09c7w0 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hn4h place_of_birth! 0pksh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 53.000 0.333 http://example.org/people/person/place_of_birth #10026-027lf1 PRED entity: 027lf1 PRED relation: state_province_region PRED expected values: 03v0t => 235 concepts (235 used for prediction) PRED predicted values (max 10 best out of 102): 059rby (0.55 #16764, 0.52 #9802, 0.50 #9552), 01n7q (0.50 #1501, 0.46 #4974, 0.42 #9176), 04rrd (0.33 #273, 0.25 #397, 0.23 #5328), 05fkf (0.33 #134, 0.23 #5328, 0.18 #14643), 03v0t (0.27 #25843, 0.26 #15889, 0.25 #21970), 09c7w0 (0.27 #25843, 0.26 #15889, 0.25 #21970), 01w65s (0.27 #25843, 0.26 #15889, 0.25 #21970), 07b_l (0.25 #545, 0.23 #5328, 0.18 #14643), 081yw (0.25 #679, 0.23 #5328, 0.18 #14643), 071vr (0.25 #494, 0.20 #989, 0.20 #988) >> Best rule #16764 for best value: >> intensional similarity = 5 >> extensional distance = 95 >> proper extension: 07ccs; >> query: (?x11273, 059rby) <- citytown(?x11273, ?x6960), citytown(?x10368, ?x6960), category(?x11273, ?x134), currency(?x10368, ?x170), service_location(?x10368, ?x94) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #25843 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 395 *> proper extension: 0lfgr; 022xml; 0q19t; 02rg_4; 02897w; 022jr5; 01hnb; 02h7qr; 03l78j; 02bpy_; ... *> query: (?x11273, ?x94) <- citytown(?x11273, ?x6960), citytown(?x10368, ?x6960), category(?x11273, ?x134), currency(?x10368, ?x170), contains(?x94, ?x6960) *> conf = 0.27 ranks of expected_values: 5 EVAL 027lf1 state_province_region 03v0t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 235.000 235.000 0.546 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #10025-01tbp PRED entity: 01tbp PRED relation: major_field_of_study! PRED expected values: 03bwzr4 => 68 concepts (55 used for prediction) PRED predicted values (max 10 best out of 16): 03bwzr4 (0.83 #350, 0.80 #367, 0.78 #322), 0bkj86 (0.78 #291, 0.71 #254, 0.67 #363), 02mjs7 (0.57 #35, 0.52 #141, 0.48 #195), 07s6fsf (0.56 #304, 0.53 #231, 0.52 #141), 027f2w (0.56 #304, 0.53 #231, 0.52 #141), 028dcg (0.56 #304, 0.53 #231, 0.52 #141), 022h5x (0.56 #304, 0.52 #141, 0.50 #119), 03mkk4 (0.56 #304, 0.52 #141, 0.48 #195), 0bjrnt (0.56 #304, 0.52 #141, 0.48 #195), 01rr_d (0.56 #304, 0.52 #141, 0.48 #195) >> Best rule #350 for best value: >> intensional similarity = 12 >> extensional distance = 10 >> proper extension: 02ky346; 06ms6; 0db86; 01540; 02jfc; >> query: (?x6859, 03bwzr4) <- major_field_of_study(?x7618, ?x6859), major_field_of_study(?x6056, ?x6859), major_field_of_study(?x1681, ?x6859), major_field_of_study(?x1675, ?x6859), currency(?x7618, ?x170), institution(?x620, ?x7618), taxonomy(?x6859, ?x939), ?x1681 = 07szy, student(?x1675, ?x1875), contains(?x94, ?x1675), ?x6056 = 05zl0, major_field_of_study(?x734, ?x6859) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tbp major_field_of_study! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 55.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #10024-0hcs3 PRED entity: 0hcs3 PRED relation: team PRED expected values: 04mjl => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 359): 0x2p (0.33 #41, 0.29 #1441, 0.20 #741), 04mjl (0.33 #149, 0.14 #1549, 0.08 #3152), 02__x (0.33 #107, 0.14 #1507, 0.08 #3152), 051vz (0.33 #37, 0.14 #1437, 0.08 #3152), 0jm74 (0.25 #491, 0.20 #841, 0.17 #1191), 0jm5b (0.25 #638, 0.20 #988, 0.17 #1338), 026xxv_ (0.25 #542, 0.20 #892, 0.17 #1242), 0fbtm7 (0.25 #533, 0.08 #2984, 0.05 #5443), 01ypc (0.20 #705, 0.17 #1055, 0.14 #1755), 084l5 (0.20 #777, 0.17 #1127, 0.14 #1827) >> Best rule #41 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 040j2_; >> query: (?x12323, 0x2p) <- athlete(?x5063, ?x12323), ?x5063 = 018jz, team(?x12323, ?x7399), place_of_birth(?x12323, ?x1860), nationality(?x12323, ?x94), season(?x7399, ?x2406) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 040j2_; *> query: (?x12323, 04mjl) <- athlete(?x5063, ?x12323), ?x5063 = 018jz, team(?x12323, ?x7399), place_of_birth(?x12323, ?x1860), nationality(?x12323, ?x94), season(?x7399, ?x2406) *> conf = 0.33 ranks of expected_values: 2 EVAL 0hcs3 team 04mjl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.333 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #10023-0k6nt PRED entity: 0k6nt PRED relation: medal PRED expected values: 02lq5w => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 1): 02lq5w (0.86 #22, 0.84 #11, 0.83 #9) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0193qj; >> query: (?x985, 02lq5w) <- olympics(?x985, ?x391), combatants(?x390, ?x985), combatants(?x2391, ?x985) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k6nt medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.857 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #10022-02g87m PRED entity: 02g87m PRED relation: award_winner PRED expected values: 06mmb => 99 concepts (38 used for prediction) PRED predicted values (max 10 best out of 573): 01x_d8 (0.82 #45187, 0.82 #54874, 0.82 #54873), 0c01c (0.82 #45187, 0.82 #54873, 0.82 #56489), 02g87m (0.51 #12910, 0.39 #24208, 0.31 #220), 06mmb (0.51 #12910, 0.39 #24208, 0.28 #48414), 048lv (0.38 #209, 0.16 #54875, 0.02 #11504), 028knk (0.38 #313, 0.16 #54875, 0.01 #11608), 0c3jz (0.31 #868, 0.16 #54875, 0.01 #12163), 0301yj (0.31 #1531, 0.16 #54875), 0ksrf8 (0.28 #48414, 0.27 #50031, 0.27 #50030), 02xv8m (0.28 #48414, 0.27 #50031, 0.27 #50030) >> Best rule #45187 for best value: >> intensional similarity = 3 >> extensional distance = 1108 >> proper extension: 012ljv; 028q6; 0fvf9q; 0l6qt; 06j0md; 0197tq; 0411q; 06gp3f; 02rchht; 04cy8rb; ... >> query: (?x1460, ?x1461) <- nationality(?x1460, ?x94), award_winner(?x1336, ?x1460), award_winner(?x1461, ?x1460) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #12910 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 406 *> proper extension: 01wz01; 039cq4; *> query: (?x1460, ?x2559) <- award_winner(?x1460, ?x8691), award_winner(?x2559, ?x8691), participant(?x8341, ?x8691) *> conf = 0.51 ranks of expected_values: 4 EVAL 02g87m award_winner 06mmb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 38.000 0.816 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #10021-03hh89 PRED entity: 03hh89 PRED relation: gender PRED expected values: 02zsn => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #15, 0.73 #135, 0.72 #75), 02zsn (0.29 #22, 0.29 #60, 0.29 #78) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 586 >> proper extension: 0564mx; >> query: (?x5446, 05zppz) <- nationality(?x5446, ?x390), award_nominee(?x5446, ?x3927), written_by(?x86, ?x3927) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #22 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 721 *> proper extension: 05ty4m; 05cj4r; 0436f4; 01rr9f; 03f2_rc; 01gvr1; 01mvth; 03qd_; 04bd8y; 015grj; ... *> query: (?x5446, 02zsn) <- film(?x5446, ?x86), award_nominee(?x5446, ?x3927), award_winner(?x5446, ?x6622) *> conf = 0.29 ranks of expected_values: 2 EVAL 03hh89 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.728 http://example.org/people/person/gender #10020-0l5mz PRED entity: 0l5mz PRED relation: major_field_of_study! PRED expected values: 07tgn => 72 concepts (48 used for prediction) PRED predicted values (max 10 best out of 972): 07szy (0.80 #6279, 0.71 #4577, 0.67 #5142), 01w5m (0.78 #5211, 0.73 #6914, 0.71 #4646), 01w3v (0.73 #6818, 0.60 #3982, 0.58 #8525), 0gl5_ (0.71 #4795, 0.56 #5360, 0.50 #6497), 07w0v (0.70 #6258, 0.50 #2288, 0.43 #4556), 03ksy (0.66 #8056, 0.62 #7945, 0.62 #7485), 02zd460 (0.64 #7371, 0.64 #6987, 0.62 #8128), 0dzst (0.64 #7169, 0.60 #4333, 0.50 #3202), 0bwfn (0.64 #16209, 0.57 #4820, 0.50 #3687), 01mpwj (0.64 #6916, 0.50 #6350, 0.50 #2949) >> Best rule #6279 for best value: >> intensional similarity = 13 >> extensional distance = 8 >> proper extension: 02h40lc; 02j62; 0_jm; >> query: (?x9079, 07szy) <- major_field_of_study(?x5750, ?x9079), major_field_of_study(?x5486, ?x9079), major_field_of_study(?x3821, ?x9079), major_field_of_study(?x1526, ?x9079), major_field_of_study(?x1200, ?x9079), ?x1526 = 0bkj86, ?x1200 = 016t_3, school(?x2067, ?x5750), institution(?x620, ?x5750), organization(?x346, ?x5750), category(?x3821, ?x134), ?x5486 = 0g8rj, school_type(?x5750, ?x1044) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #6820 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 9 *> proper extension: 04gb7; *> query: (?x9079, 07tgn) <- major_field_of_study(?x2999, ?x9079), major_field_of_study(?x581, ?x9079), major_field_of_study(?x1526, ?x9079), institution(?x1526, ?x11614), institution(?x1526, ?x5539), institution(?x1526, ?x5288), institution(?x1526, ?x3913), ?x581 = 06pwq, ?x5288 = 02zd460, ?x11614 = 07tk7, ?x5539 = 01b_d4, ?x3913 = 01b1pf, student(?x1526, ?x476), ?x2999 = 07tg4 *> conf = 0.64 ranks of expected_values: 11 EVAL 0l5mz major_field_of_study! 07tgn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 72.000 48.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #10019-02yygk PRED entity: 02yygk PRED relation: profession PRED expected values: 0dz3r => 124 concepts (120 used for prediction) PRED predicted values (max 10 best out of 125): 0nbcg (0.56 #2807, 0.56 #175, 0.56 #1785), 0dz3r (0.56 #1758, 0.54 #1172, 0.54 #294), 016z4k (0.55 #882, 0.52 #296, 0.51 #1174), 039v1 (0.39 #1790, 0.38 #2812, 0.34 #1058), 01d_h8 (0.38 #2200, 0.37 #1616, 0.33 #6295), 0n1h (0.37 #1181, 0.31 #889, 0.30 #742), 0dxtg (0.35 #7770, 0.32 #9234, 0.29 #10697), 01c72t (0.34 #1337, 0.32 #5872, 0.31 #4117), 0np9r (0.29 #2942, 0.27 #6876, 0.25 #15360), 0fnpj (0.28 #2690, 0.17 #1814, 0.16 #5616) >> Best rule #2807 for best value: >> intensional similarity = 2 >> extensional distance = 191 >> proper extension: 01w9ph_; 01w03jv; >> query: (?x10025, 0nbcg) <- artists(?x505, ?x10025), group(?x10025, ?x8335) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1758 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 133 *> proper extension: 09g0h; *> query: (?x10025, 0dz3r) <- nationality(?x10025, ?x94), role(?x10025, ?x716), group(?x10025, ?x8335) *> conf = 0.56 ranks of expected_values: 2 EVAL 02yygk profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 120.000 0.565 http://example.org/people/person/profession #10018-02fn5r PRED entity: 02fn5r PRED relation: award_winner! PRED expected values: 01s695 01c6qp => 122 concepts (110 used for prediction) PRED predicted values (max 10 best out of 121): 01c6qp (0.22 #18, 0.17 #7861, 0.11 #149), 056878 (0.22 #30, 0.17 #7861, 0.10 #2912), 05pd94v (0.17 #7861, 0.11 #2, 0.11 #1050), 01bx35 (0.17 #7861, 0.11 #1447, 0.10 #2626), 01s695 (0.17 #7861, 0.10 #527, 0.10 #1837), 026kq4q (0.11 #173, 0.09 #304, 0.02 #5937), 0hr3c8y (0.11 #140, 0.03 #6166, 0.03 #6035), 0hndn2q (0.11 #168, 0.03 #299, 0.02 #6587), 073hkh (0.11 #132, 0.03 #263, 0.01 #6551), 0gmdkyy (0.11 #159, 0.03 #290, 0.01 #12711) >> Best rule #18 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 01p9hgt; 01kv4mb; 0ggjt; 0bhvtc; 03cfjg; 0p_47; 0pmw9; >> query: (?x2638, 01c6qp) <- award(?x2638, ?x341), nominated_for(?x1413, ?x2638) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 02fn5r award_winner! 01c6qp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 110.000 0.222 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02fn5r award_winner! 01s695 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 122.000 110.000 0.222 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #10017-03hl6lc PRED entity: 03hl6lc PRED relation: award! PRED expected values: 081lh 021bk 0169dl 02ld6x 03m_k0 07h07 02f93t 0dbbz => 47 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2794): 03m_k0 (0.80 #13328, 0.80 #60002, 0.75 #63335), 01_f_5 (0.80 #13328, 0.80 #60002, 0.75 #63335), 05drq5 (0.80 #13328, 0.80 #60002, 0.75 #63335), 052hl (0.80 #13328, 0.80 #60002, 0.75 #63335), 0jw67 (0.80 #13328, 0.80 #60002, 0.75 #63335), 02f93t (0.67 #19322, 0.60 #9325, 0.50 #12657), 0693l (0.60 #7504, 0.56 #17501, 0.50 #4172), 07h07 (0.60 #7763, 0.50 #4431, 0.33 #21094), 081lh (0.56 #16886, 0.40 #6889, 0.25 #13554), 01t07j (0.56 #17130, 0.40 #7133, 0.25 #13798) >> Best rule #13328 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 054ks3; >> query: (?x3435, ?x1314) <- award(?x12523, ?x3435), award(?x11705, ?x3435), ?x12523 = 09xx0m, award_winner(?x3435, ?x1314), award_winner(?x78, ?x11705), nominated_for(?x3435, ?x69) >> conf = 0.80 => this is the best rule for 5 predicted values ranks of expected_values: 1, 6, 8, 9, 38, 81, 115, 599 EVAL 03hl6lc award! 0dbbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 47.000 20.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hl6lc award! 02f93t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 47.000 20.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hl6lc award! 07h07 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 47.000 20.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hl6lc award! 03m_k0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 20.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hl6lc award! 02ld6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 47.000 20.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hl6lc award! 0169dl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 47.000 20.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hl6lc award! 021bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 20.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hl6lc award! 081lh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 47.000 20.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10016-02jxmr PRED entity: 02jxmr PRED relation: award PRED expected values: 0gqz2 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 299): 0gqz2 (0.46 #2076, 0.40 #479, 0.37 #878), 0c4z8 (0.36 #2067, 0.24 #8054, 0.24 #9251), 01by1l (0.34 #8093, 0.30 #10088, 0.29 #10487), 09sb52 (0.33 #6029, 0.28 #15606, 0.28 #8423), 01bgqh (0.27 #2039, 0.24 #12815, 0.23 #10420), 02x17c2 (0.22 #2210, 0.20 #1012, 0.16 #1411), 099vwn (0.19 #2207, 0.10 #211, 0.09 #1009), 03qbh5 (0.19 #9380, 0.18 #2196, 0.18 #8183), 05pcn59 (0.18 #6069, 0.16 #8463, 0.13 #9660), 04njml (0.17 #3292, 0.16 #498, 0.16 #897) >> Best rule #2076 for best value: >> intensional similarity = 2 >> extensional distance = 119 >> proper extension: 01cblr; >> query: (?x4428, 0gqz2) <- award(?x4428, ?x2585), ?x2585 = 054ks3 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jxmr award 0gqz2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.463 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10015-028kb PRED entity: 028kb PRED relation: seasonal_months PRED expected values: 040fv => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 2): 040fv (0.78 #31, 0.71 #19, 0.67 #29), 028kb (0.78 #31, 0.71 #19, 0.64 #22) >> Best rule #31 for best value: >> intensional similarity = 107 >> extensional distance = 4 >> proper extension: 05lf_; >> query: (?x9905, ?x1459) <- month(?x10143, ?x9905), month(?x9605, ?x9905), month(?x8977, ?x9905), month(?x8252, ?x9905), month(?x6959, ?x9905), month(?x6703, ?x9905), month(?x6494, ?x9905), month(?x6458, ?x9905), month(?x6054, ?x9905), month(?x4627, ?x9905), month(?x4271, ?x9905), month(?x3373, ?x9905), month(?x3269, ?x9905), month(?x3106, ?x9905), month(?x2474, ?x9905), month(?x1860, ?x9905), month(?x1649, ?x9905), month(?x1523, ?x9905), month(?x863, ?x9905), month(?x739, ?x9905), month(?x362, ?x9905), ?x3106 = 049d1, location_of_ceremony(?x566, ?x6458), ?x1523 = 030qb3t, ?x1860 = 01_d4, month(?x6458, ?x6303), month(?x6458, ?x4925), month(?x6458, ?x4869), month(?x6458, ?x1650), month(?x6458, ?x1459), capital(?x7406, ?x6458), ?x566 = 04ztj, ?x8252 = 0k3p, ?x1650 = 06vkl, citytown(?x9227, ?x6458), ?x4627 = 05qtj, ?x6494 = 02sn34, ?x739 = 02_286, ?x6054 = 0fn2g, ?x4925 = 0ll3, ?x362 = 04jpl, ?x4869 = 02xx5, location(?x14208, ?x6458), ?x6959 = 06c62, ?x6703 = 0f04v, place_of_death(?x5148, ?x6458), location(?x1410, ?x2474), ?x1649 = 01f62, place_of_birth(?x3069, ?x8977), location(?x862, ?x863), mode_of_transportation(?x863, ?x4272), ?x3269 = 0vzm, place_of_death(?x8938, ?x863), citytown(?x481, ?x2474), ?x4271 = 06wjf, administrative_parent(?x863, ?x1355), country(?x7108, ?x1355), country(?x4876, ?x1355), country(?x3345, ?x1355), country(?x2978, ?x1355), ?x1410 = 01yhvv, organization(?x1355, ?x127), taxonomy(?x1355, ?x939), first_level_division_of(?x7182, ?x1355), ?x4876 = 0d1t3, ?x9605 = 02frhbc, film_release_region(?x7336, ?x1355), film_release_region(?x6270, ?x1355), film_release_region(?x5576, ?x1355), film_release_region(?x5067, ?x1355), film_release_region(?x2783, ?x1355), film_release_region(?x2738, ?x1355), film_release_region(?x2471, ?x1355), film_release_region(?x1859, ?x1355), film_release_region(?x1370, ?x1355), film_release_region(?x1364, ?x1355), film_release_region(?x66, ?x1355), seasonal_months(?x9905, ?x3107), ?x2738 = 0h1v19, time_zones(?x2474, ?x2674), ?x1364 = 047msdk, contains(?x1355, ?x2637), ?x3345 = 09qgm, olympics(?x1355, ?x8189), olympics(?x1355, ?x7688), ?x7688 = 0jkvj, ?x1859 = 0m491, nationality(?x9320, ?x1355), medal(?x1355, ?x422), ?x7336 = 0bdjd, ?x2783 = 0879bpq, teams(?x2474, ?x3073), ?x9320 = 0c921, ?x7108 = 0194d, ?x66 = 014lc_, ?x5067 = 01rwpj, ?x2471 = 08052t3, ?x6270 = 0g9zljd, olympics(?x1355, ?x4424), ?x8189 = 015l4k, ?x6303 = 0lkm, ?x1370 = 0gmcwlb, ?x4424 = 0blfl, ?x3373 = 0ply0, ?x10143 = 0h3tv, ?x5576 = 0gbfn9, ?x2978 = 03_8r >> conf = 0.78 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 028kb seasonal_months 040fv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.780 http://example.org/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months #10014-0233qs PRED entity: 0233qs PRED relation: artists PRED expected values: 01wbl_r 09z1lg => 63 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1102): 0127s7 (0.62 #1618, 0.50 #2698, 0.32 #4857), 043zg (0.50 #2643, 0.50 #1563, 0.21 #4802), 01s7ns (0.50 #3134, 0.50 #2054, 0.21 #5293), 01wv9p (0.50 #2519, 0.50 #1439, 0.08 #11159), 011z3g (0.50 #1681, 0.38 #5999, 0.38 #2761), 03f5spx (0.50 #1135, 0.38 #2215, 0.32 #4374), 01dwrc (0.50 #1602, 0.38 #2682, 0.32 #4841), 09k2t1 (0.50 #1243, 0.38 #2323, 0.31 #5561), 01vtj38 (0.50 #1740, 0.38 #2820, 0.28 #6058), 025ldg (0.50 #1449, 0.38 #2529, 0.28 #5767) >> Best rule #1618 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 05bt6j; >> query: (?x12611, 0127s7) <- artists(?x12611, ?x6659), artists(?x12611, ?x883), participant(?x5996, ?x6659), origin(?x6659, ?x2277), ?x883 = 01w61th, award_nominee(?x2562, ?x6659), type_of_union(?x6659, ?x566) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2308 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0827d; 064t9; 025sc50; 02lnbg; 0g293; 08vlns; *> query: (?x12611, 01wbl_r) <- artists(?x12611, ?x6659), artists(?x12611, ?x4062), artists(?x12611, ?x883), participant(?x5996, ?x6659), origin(?x6659, ?x2277), ?x883 = 01w61th, award_nominee(?x2562, ?x6659), ?x4062 = 0bqsy *> conf = 0.25 ranks of expected_values: 177, 181 EVAL 0233qs artists 09z1lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 63.000 24.000 0.625 http://example.org/music/genre/artists EVAL 0233qs artists 01wbl_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 63.000 24.000 0.625 http://example.org/music/genre/artists #10013-01wg982 PRED entity: 01wg982 PRED relation: profession PRED expected values: 0dxtg => 134 concepts (88 used for prediction) PRED predicted values (max 10 best out of 61): 0dxtg (0.83 #1728, 0.82 #2729, 0.81 #2157), 016z4k (0.48 #1863, 0.45 #4153, 0.44 #6310), 03gjzk (0.44 #2301, 0.43 #2587, 0.42 #3016), 0dz3r (0.43 #1289, 0.42 #4007, 0.42 #6308), 039v1 (0.33 #32, 0.31 #604, 0.30 #461), 04f2zj (0.33 #92, 0.08 #664, 0.07 #1951), 0cbd2 (0.27 #2151, 0.26 #1722, 0.25 #2723), 02krf9 (0.25 #3312, 0.23 #5608, 0.17 #2597), 0n1h (0.23 #1869, 0.22 #2012, 0.21 #1583), 0kyk (0.22 #1027, 0.13 #3602, 0.12 #884) >> Best rule #1728 for best value: >> intensional similarity = 4 >> extensional distance = 185 >> proper extension: 0b_c7; 03wpmd; 0884hk; 05cgy8; 06q5t7; >> query: (?x2408, 0dxtg) <- place_of_birth(?x2408, ?x11721), written_by(?x10651, ?x2408), gender(?x2408, ?x231), award(?x2408, ?x9462) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wg982 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 88.000 0.829 http://example.org/people/person/profession #10012-08052t3 PRED entity: 08052t3 PRED relation: film_release_region PRED expected values: 0jgd 03gj2 0345h 06bnz 0163v 03spz => 83 concepts (80 used for prediction) PRED predicted values (max 10 best out of 180): 03gj2 (0.91 #415, 0.91 #1080, 0.91 #814), 0345h (0.90 #156, 0.86 #821, 0.86 #1620), 06bnz (0.88 #431, 0.87 #1096, 0.86 #1362), 0jgd (0.84 #402, 0.83 #136, 0.83 #1600), 03spz (0.82 #873, 0.80 #474, 0.79 #1405), 01ls2 (0.76 #8, 0.67 #141, 0.61 #407), 0ctw_b (0.70 #1081, 0.70 #416, 0.68 #1614), 01p1v (0.67 #836, 0.67 #38, 0.65 #437), 06t8v (0.67 #457, 0.62 #1122, 0.61 #1388), 06f32 (0.58 #845, 0.58 #446, 0.57 #180) >> Best rule #415 for best value: >> intensional similarity = 7 >> extensional distance = 67 >> proper extension: 0gtsx8c; 0gtvrv3; >> query: (?x2471, 03gj2) <- film_release_region(?x2471, ?x2146), film_release_region(?x2471, ?x1497), film_release_region(?x2471, ?x172), ?x1497 = 015qh, film_crew_role(?x2471, ?x137), ?x2146 = 03rk0, ?x172 = 0154j >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 26 EVAL 08052t3 film_release_region 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 80.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08052t3 film_release_region 0163v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 83.000 80.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08052t3 film_release_region 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 80.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08052t3 film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 80.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08052t3 film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 80.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08052t3 film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 80.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #10011-01vvb4m PRED entity: 01vvb4m PRED relation: film PRED expected values: 02725hs 0b2km_ => 117 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1027): 03l6q0 (0.20 #538, 0.04 #7622, 0.04 #5851), 05fm6m (0.20 #1308, 0.03 #38500, 0.03 #34958), 0qm8b (0.20 #240, 0.03 #17950, 0.02 #5553), 03h_yy (0.20 #73, 0.02 #7157, 0.02 #5386), 0g22z (0.20 #15, 0.02 #7099, 0.02 #5328), 03q0r1 (0.20 #632, 0.02 #18342, 0.01 #39596), 05dss7 (0.20 #1151, 0.02 #18861, 0.01 #22403), 038bh3 (0.20 #776, 0.02 #18486, 0.01 #22028), 016z9n (0.20 #365, 0.02 #101315, 0.01 #94230), 03p2xc (0.20 #1235, 0.01 #31342, 0.01 #45512) >> Best rule #538 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0652ty; >> query: (?x3056, 03l6q0) <- film(?x3056, ?x11909), profession(?x3056, ?x319), ?x11909 = 0ptdz >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3369 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 03yj_0n; 0cjsxp; 073749; 05yh_t; 01vw917; 01cyjx; *> query: (?x3056, 0b2km_) <- film(?x3056, ?x7482), film(?x3056, ?x7107), ?x7107 = 04ghz4m, produced_by(?x7482, ?x1285) *> conf = 0.10 ranks of expected_values: 54 EVAL 01vvb4m film 0b2km_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 117.000 95.000 0.200 http://example.org/film/actor/film./film/performance/film EVAL 01vvb4m film 02725hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 95.000 0.200 http://example.org/film/actor/film./film/performance/film #10010-0qpqn PRED entity: 0qpqn PRED relation: contains! PRED expected values: 0qpn9 => 160 concepts (128 used for prediction) PRED predicted values (max 10 best out of 297): 0vmt (0.82 #73286, 0.82 #42902, 0.81 #60771), 04_1l0v (0.42 #40666, 0.40 #41561, 0.36 #15642), 07ssc (0.35 #112631, 0.24 #66167, 0.20 #101018), 02qkt (0.32 #69162, 0.32 #70950, 0.26 #32520), 01n7q (0.26 #29571, 0.25 #42085, 0.23 #59954), 02jx1 (0.22 #112686, 0.17 #66222, 0.15 #101073), 06pvr (0.21 #24296, 0.21 #18932, 0.19 #12677), 02_286 (0.21 #42945, 0.10 #935, 0.07 #13448), 059rby (0.20 #912, 0.17 #42922, 0.17 #112619), 05fjf (0.19 #12884, 0.09 #57566, 0.09 #20033) >> Best rule #73286 for best value: >> intensional similarity = 3 >> extensional distance = 206 >> proper extension: 0tln7; 0174qm; 016gb5; 054y8; >> query: (?x9331, ?x938) <- contains(?x94, ?x9331), category(?x9331, ?x134), state(?x9331, ?x938) >> conf = 0.82 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qpqn contains! 0qpn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 128.000 0.822 http://example.org/location/location/contains #10009-0d8w2n PRED entity: 0d8w2n PRED relation: currency PRED expected values: 09nqf => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.83 #78, 0.82 #190, 0.82 #92), 01nv4h (0.25 #912, 0.12 #386, 0.11 #9), 02l6h (0.25 #912, 0.12 #386, 0.03 #263), 02gsvk (0.12 #386, 0.02 #427, 0.02 #532), 0kz1h (0.12 #386, 0.01 #180, 0.01 #229), 088n7 (0.12 #386), 0ptk_ (0.12 #386) >> Best rule #78 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 04mcw4; >> query: (?x12395, 09nqf) <- film_distribution_medium(?x12395, ?x2099), films(?x2286, ?x12395), film_distribution_medium(?x4409, ?x2099), titles(?x53, ?x12395), film_crew_role(?x4409, ?x137) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d8w2n currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.829 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #10008-04vmp PRED entity: 04vmp PRED relation: place_of_death! PRED expected values: 061zc_ 02g5bf 0cm19f => 122 concepts (92 used for prediction) PRED predicted values (max 10 best out of 791): 02x02kb (0.20 #680, 0.10 #25797, 0.04 #6577), 0kt64b (0.20 #439, 0.10 #25797, 0.04 #6336), 07csf4 (0.11 #788, 0.06 #1525, 0.06 #2262), 0gs7x (0.11 #1379, 0.06 #2116, 0.06 #2853), 02hh8j (0.11 #1233, 0.06 #1970, 0.06 #2707), 01rgr (0.11 #1223, 0.06 #1960, 0.06 #2697), 0knjh (0.11 #1172, 0.06 #1909, 0.06 #2646), 01vh096 (0.11 #1164, 0.06 #1901, 0.06 #2638), 0ct9_ (0.11 #1145, 0.06 #1882, 0.06 #2619), 07ym0 (0.11 #1140, 0.06 #1877, 0.06 #2614) >> Best rule #680 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02hwww; >> query: (?x7412, 02x02kb) <- contains(?x12040, ?x7412), contains(?x2146, ?x7412), ?x2146 = 03rk0, ?x12040 = 055vr >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #28748 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 96 *> proper extension: 0136jw; 0hkpn; 08314; *> query: (?x7412, ?x111) <- place_of_death(?x13873, ?x7412), place_of_death(?x11786, ?x7412), gender(?x13873, ?x231), award_winner(?x4687, ?x11786), award_winner(?x4687, ?x111) *> conf = 0.03 ranks of expected_values: 608, 618 EVAL 04vmp place_of_death! 0cm19f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 92.000 0.200 http://example.org/people/deceased_person/place_of_death EVAL 04vmp place_of_death! 02g5bf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 122.000 92.000 0.200 http://example.org/people/deceased_person/place_of_death EVAL 04vmp place_of_death! 061zc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 122.000 92.000 0.200 http://example.org/people/deceased_person/place_of_death #10007-04nl83 PRED entity: 04nl83 PRED relation: film_crew_role PRED expected values: 02r96rf => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 25): 02r96rf (0.71 #479, 0.66 #788, 0.65 #377), 01vx2h (0.44 #486, 0.36 #384, 0.33 #452), 02ynfr (0.20 #48, 0.19 #490, 0.17 #388), 02rh1dz (0.19 #485, 0.11 #383, 0.11 #794), 0d2b38 (0.18 #92, 0.14 #126, 0.12 #877), 01xy5l_ (0.16 #114, 0.13 #80, 0.13 #865), 0215hd (0.16 #85, 0.16 #357, 0.15 #870), 02vs3x5 (0.14 #192, 0.12 #56, 0.08 #362), 089g0h (0.14 #120, 0.14 #871, 0.13 #86), 04pyp5 (0.12 #49, 0.08 #185, 0.07 #491) >> Best rule #479 for best value: >> intensional similarity = 4 >> extensional distance = 293 >> proper extension: 0d_2fb; 0gs973; >> query: (?x534, 02r96rf) <- genre(?x534, ?x53), film_crew_role(?x534, ?x2095), ?x2095 = 0dxtw, production_companies(?x534, ?x2549) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04nl83 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.705 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #10006-02rdyk7 PRED entity: 02rdyk7 PRED relation: nominated_for PRED expected values: 03hkch7 05mrf_p 04b2qn => 50 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1225): 049xgc (0.82 #5394, 0.75 #12992, 0.64 #6914), 0pv3x (0.82 #4712, 0.73 #7752, 0.69 #10790), 09gq0x5 (0.80 #12398, 0.73 #4800, 0.67 #7840), 064lsn (0.78 #12154, 0.74 #1519, 0.65 #16712), 09cr8 (0.78 #12154, 0.74 #1519, 0.64 #19753), 0h6r5 (0.78 #12154, 0.74 #1519, 0.64 #19753), 0hmm7 (0.78 #12154, 0.74 #1519, 0.64 #19753), 0yxf4 (0.78 #12154, 0.74 #1519, 0.64 #7064), 0kvgnq (0.78 #12154, 0.74 #1519, 0.63 #16711), 0m313 (0.75 #13685, 0.70 #12166, 0.64 #6088) >> Best rule #5394 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 0p9sw; 02r22gf; 019f4v; 0k611; >> query: (?x1587, 049xgc) <- nominated_for(?x1587, ?x5930), nominated_for(?x1587, ?x3471), nominated_for(?x1587, ?x1118), ?x1118 = 0_92w, ?x3471 = 07cyl, nominated_for(?x1554, ?x5930), award_winner(?x1587, ?x986) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #3473 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0f4x7; 02w9sd7; *> query: (?x1587, 03hkch7) <- nominated_for(?x1587, ?x8176), nominated_for(?x1587, ?x5930), nominated_for(?x1587, ?x2840), ?x5930 = 07cw4, ?x2840 = 0f4vx, genre(?x8176, ?x53) *> conf = 0.60 ranks of expected_values: 52, 94, 320 EVAL 02rdyk7 nominated_for 04b2qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 50.000 15.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02rdyk7 nominated_for 05mrf_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 50.000 15.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02rdyk7 nominated_for 03hkch7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 50.000 15.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10005-057lbk PRED entity: 057lbk PRED relation: nominated_for! PRED expected values: 05zr6wv => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 212): 02hsq3m (0.45 #5310, 0.44 #5550, 0.34 #8670), 0gr42 (0.44 #1050, 0.32 #3450, 0.31 #5610), 02g3v6 (0.38 #5542, 0.33 #4342, 0.31 #2182), 0p9sw (0.36 #3381, 0.33 #5061, 0.30 #6741), 02r22gf (0.35 #2669, 0.31 #5549, 0.29 #5309), 057xs89 (0.33 #1321, 0.33 #1081, 0.32 #5401), 03m73lj (0.33 #834, 0.27 #3474, 0.25 #1554), 02x258x (0.33 #1299, 0.15 #6099, 0.15 #2739), 063y_ky (0.33 #1541, 0.15 #1781, 0.14 #3701), 018wdw (0.32 #3541, 0.25 #5701, 0.22 #8581) >> Best rule #5310 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 05567m; >> query: (?x4378, 02hsq3m) <- genre(?x4378, ?x225), film_distribution_medium(?x4378, ?x2099), music(?x4378, ?x3042), film_format(?x4378, ?x909), nominated_for(?x9780, ?x4378) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #38645 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1588 *> proper extension: 06g60w; *> query: (?x4378, ?x401) <- nominated_for(?x9780, ?x4378), award(?x9780, ?x401) *> conf = 0.19 ranks of expected_values: 33 EVAL 057lbk nominated_for! 05zr6wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 167.000 167.000 0.452 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #10004-0c2ry PRED entity: 0c2ry PRED relation: place_of_birth PRED expected values: 02_n7 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 153): 01snm (0.33 #1647, 0.05 #5874, 0.04 #7284), 0t_3w (0.20 #2509, 0.14 #3213, 0.07 #4622), 03kjh (0.20 #2738, 0.06 #5556, 0.05 #6261), 0fvxg (0.14 #3365, 0.07 #4774, 0.06 #5479), 0chrx (0.14 #3121, 0.07 #4530, 0.03 #8758), 030qb3t (0.13 #6394, 0.11 #7803, 0.10 #22590), 0n9r8 (0.11 #3767, 0.05 #5882, 0.04 #6587), 0f2wj (0.11 #3538, 0.02 #12696, 0.02 #20442), 06pr6 (0.11 #3783, 0.02 #12941, 0.01 #15759), 0sc6p (0.11 #4186) >> Best rule #1647 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02s58t; >> query: (?x4057, 01snm) <- participant(?x4057, ?x9862), participant(?x4057, ?x4058), award_winner(?x4058, ?x1850), type_of_union(?x4058, ?x566), ?x9862 = 022q4j >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c2ry place_of_birth 02_n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 139.000 139.000 0.333 http://example.org/people/person/place_of_birth #10003-0308kx PRED entity: 0308kx PRED relation: profession PRED expected values: 02hrh1q => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 46): 02hrh1q (0.89 #465, 0.87 #9618, 0.87 #1515), 03gjzk (0.35 #1066, 0.33 #2416, 0.24 #2116), 01d_h8 (0.33 #2106, 0.33 #2856, 0.31 #6757), 0dxtg (0.32 #2114, 0.32 #1064, 0.31 #914), 0np9r (0.26 #10954, 0.20 #2272, 0.20 #1972), 0cbd2 (0.26 #10954, 0.17 #1207, 0.15 #4507), 0d1pc (0.26 #10954, 0.12 #352, 0.07 #52), 015cjr (0.26 #10954, 0.07 #51, 0.06 #351), 02jknp (0.23 #2108, 0.21 #2858, 0.21 #8110), 09jwl (0.18 #3470, 0.17 #7222, 0.16 #8422) >> Best rule #465 for best value: >> intensional similarity = 3 >> extensional distance = 293 >> proper extension: 0fs9jn; >> query: (?x4149, 02hrh1q) <- student(?x1043, ?x4149), film(?x4149, ?x1048), currency(?x1043, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0308kx profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.892 http://example.org/people/person/profession #10002-01vrkdt PRED entity: 01vrkdt PRED relation: place_of_birth PRED expected values: 030qb3t => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 102): 02_286 (0.08 #4947, 0.08 #1427, 0.08 #65504), 030qb3t (0.06 #1462, 0.05 #10615, 0.05 #4278), 0cr3d (0.05 #26143, 0.05 #28960, 0.04 #45156), 0cc56 (0.05 #33, 0.02 #4961, 0.02 #10594), 01_d4 (0.04 #3586, 0.04 #2882, 0.04 #72597), 0dclg (0.04 #5006, 0.03 #4302, 0.03 #10639), 094jv (0.03 #765, 0.02 #3581, 0.02 #4285), 04lh6 (0.03 #1741, 0.01 #5965, 0.01 #6669), 013kcv (0.02 #23, 0.02 #9880, 0.01 #9176), 0d6hn (0.02 #413, 0.02 #1117, 0.01 #10270) >> Best rule #4947 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 012ljv; 028q6; 0hl3d; 032nwy; 025vry; 07q1v4; 01jrz5j; 014zfs; 0244r8; 01kvqc; ... >> query: (?x3962, 02_286) <- profession(?x3962, ?x1614), award_winner(?x1854, ?x3962), artists(?x302, ?x3962), ?x1614 = 01c72t >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #1462 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 77 *> proper extension: 09bx1k; *> query: (?x3962, 030qb3t) <- award_winner(?x702, ?x3962), artists(?x302, ?x3962), award_winner(?x7317, ?x3962), gender(?x3962, ?x514) *> conf = 0.06 ranks of expected_values: 2 EVAL 01vrkdt place_of_birth 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.077 http://example.org/people/person/place_of_birth #10001-0byq0v PRED entity: 0byq0v PRED relation: sport PRED expected values: 02vx4 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 20): 02vx4 (0.88 #352, 0.88 #652, 0.88 #643), 0z74 (0.49 #840, 0.27 #986, 0.23 #976), 0jm_ (0.12 #671, 0.11 #509, 0.10 #500), 03tmr (0.11 #498, 0.10 #507, 0.10 #669), 018jz (0.10 #808, 0.10 #673, 0.10 #817), 018w8 (0.06 #672, 0.05 #501, 0.05 #510), 039yzs (0.03 #847, 0.03 #837, 0.03 #819), 0194d (0.03 #74, 0.02 #261, 0.02 #94), 0486tv (0.03 #74, 0.02 #261, 0.02 #94), 06z6r (0.03 #74, 0.02 #261, 0.02 #94) >> Best rule #352 for best value: >> intensional similarity = 21 >> extensional distance = 40 >> proper extension: 02b2np; 085v7; 0196bp; 03gr14; >> query: (?x12207, 02vx4) <- position(?x12207, ?x530), position(?x12207, ?x63), position(?x12207, ?x60), position(?x12207, ?x203), ?x203 = 0dgrmp, ?x530 = 02_j1w, current_club(?x6180, ?x12207), ?x60 = 02nzb8, colors(?x12207, ?x4557), position(?x13495, ?x63), position(?x12878, ?x63), position(?x7699, ?x63), position(?x6964, ?x63), position(?x4148, ?x63), position(?x11736, ?x63), ?x13495 = 03zb6t, ?x12878 = 03fhj1, ?x6964 = 047fwlg, ?x11736 = 03z2rz, ?x4148 = 017znw, ?x7699 = 05gsd2 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0byq0v sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.881 http://example.org/sports/sports_team/sport #10000-0j1yf PRED entity: 0j1yf PRED relation: actor! PRED expected values: 026bfsh => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 126): 039cq4 (0.17 #129, 0.10 #22983, 0.09 #11356), 026bfsh (0.12 #362, 0.10 #3530, 0.08 #1418), 07s846j (0.09 #11356, 0.07 #31186, 0.07 #31185), 02zv4b (0.06 #1610, 0.04 #2138, 0.04 #4251), 04vrxh (0.05 #265), 09px1w (0.05 #265), 05gnf (0.05 #265), 091yn0 (0.05 #265), 05drr9 (0.05 #265), 04mn81 (0.05 #265) >> Best rule #129 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 07ymr5; >> query: (?x1896, 039cq4) <- award_nominee(?x6447, ?x1896), award_winner(?x1896, ?x1989), ?x6447 = 091yn0 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #362 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 05mt_q; 01v40wd; 01vw8mh; 0bs1g5r; 02vwckw; *> query: (?x1896, 026bfsh) <- award_nominee(?x959, ?x1896), award(?x1896, ?x5799), ?x5799 = 03t5n3 *> conf = 0.12 ranks of expected_values: 2 EVAL 0j1yf actor! 026bfsh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.167 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #9999-058kh7 PRED entity: 058kh7 PRED relation: titles! PRED expected values: 01z4y => 72 concepts (64 used for prediction) PRED predicted values (max 10 best out of 55): 01z4y (0.47 #660, 0.45 #2229, 0.42 #2124), 07s9rl0 (0.41 #832, 0.41 #728, 0.38 #310), 02l7c8 (0.33 #4709, 0.26 #1353, 0.26 #3558), 04xvlr (0.30 #4608, 0.27 #2406, 0.25 #107), 03mqtr (0.27 #356, 0.17 #566, 0.15 #461), 01t_vv (0.26 #1353, 0.23 #309, 0.22 #3027), 07ssc (0.25 #215, 0.18 #320, 0.17 #530), 05p553 (0.23 #309, 0.22 #3027, 0.21 #3239), 06cvj (0.23 #309, 0.22 #3027, 0.21 #3239), 01jfsb (0.15 #3891, 0.14 #2733, 0.13 #3154) >> Best rule #660 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 0963mq; 0416y94; 02rv_dz; 020bv3; 03m8y5; 0pvms; 01gkp1; 0qf2t; 03c_cxn; 011yn5; ... >> query: (?x9646, 01z4y) <- featured_film_locations(?x9646, ?x1860), genre(?x9646, ?x6674), film(?x12602, ?x9646), ?x6674 = 01t_vv, film(?x397, ?x9646) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 058kh7 titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 64.000 0.474 http://example.org/media_common/netflix_genre/titles #9998-0j8p6 PRED entity: 0j8p6 PRED relation: contains! PRED expected values: 059ss => 150 concepts (71 used for prediction) PRED predicted values (max 10 best out of 264): 05j49 (0.88 #34069, 0.87 #35863, 0.86 #41245), 09c7w0 (0.81 #47527, 0.77 #51117, 0.73 #10753), 0jcpw (0.71 #54702, 0.70 #49319, 0.66 #31378), 059ss (0.52 #46629, 0.33 #53804, 0.31 #40349), 0j8p6 (0.52 #46629, 0.33 #53804, 0.31 #40349), 05kr_ (0.43 #17154, 0.36 #25227, 0.36 #12669), 07ssc (0.38 #14368, 0.11 #54733, 0.11 #55629), 0345h (0.31 #7248, 0.08 #18005, 0.08 #18903), 059g4 (0.26 #29149, 0.25 #1357, 0.17 #2252), 07c5l (0.25 #1289, 0.17 #2184, 0.09 #29081) >> Best rule #34069 for best value: >> intensional similarity = 4 >> extensional distance = 146 >> proper extension: 0_3cs; 0fhp9; 01mc11; 0wh3; 0fvxz; 0pmpl; 01f62; 010dft; 016v46; 0pzpz; ... >> query: (?x6224, ?x3474) <- administrative_division(?x6224, ?x3474), time_zones(?x6224, ?x12963), contains(?x3474, ?x5678), contains(?x279, ?x6224) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #46629 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 205 *> proper extension: 01zk9d; *> query: (?x6224, ?x279) <- citytown(?x5679, ?x6224), contains(?x279, ?x5679), major_field_of_study(?x5679, ?x10417), organization(?x346, ?x5679) *> conf = 0.52 ranks of expected_values: 4 EVAL 0j8p6 contains! 059ss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 150.000 71.000 0.883 http://example.org/location/location/contains #9997-09lxtg PRED entity: 09lxtg PRED relation: participating_countries! PRED expected values: 0kbws => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 40): 0kbws (0.82 #756, 0.82 #1422, 0.72 #717), 018ctl (0.52 #241, 0.46 #358, 0.46 #397), 09x3r (0.36 #245, 0.35 #11, 0.32 #518), 016r9z (0.23 #21, 0.21 #60, 0.19 #255), 0sx8l (0.22 #247, 0.22 #1068, 0.21 #52), 0blfl (0.20 #262, 0.18 #1083, 0.18 #418), 0jdk_ (0.18 #1016, 0.18 #1095, 0.16 #664), 0kbvb (0.18 #1016, 0.18 #1095, 0.16 #664), 0jkvj (0.18 #1016, 0.18 #1095, 0.16 #664), 0lbbj (0.18 #1016, 0.18 #1095, 0.16 #664) >> Best rule #756 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 0j11; >> query: (?x4569, 0kbws) <- administrative_parent(?x4569, ?x551), participating_countries(?x418, ?x4569), adjustment_currency(?x4569, ?x170) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09lxtg participating_countries! 0kbws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.823 http://example.org/olympics/olympic_games/participating_countries #9996-0j43swk PRED entity: 0j43swk PRED relation: film_release_region PRED expected values: 015fr 05b4w => 79 concepts (77 used for prediction) PRED predicted values (max 10 best out of 104): 015fr (0.81 #716, 0.80 #1562, 0.79 #857), 05b4w (0.77 #898, 0.76 #757, 0.75 #1603), 01mjq (0.60 #740, 0.59 #881, 0.57 #1586), 06qd3 (0.53 #735, 0.52 #1863, 0.49 #1581), 016wzw (0.53 #759, 0.50 #1605, 0.50 #900), 06mzp (0.52 #721, 0.49 #1003, 0.47 #1567), 01ls2 (0.51 #854, 0.48 #713, 0.47 #1559), 06t8v (0.48 #769, 0.47 #1615, 0.47 #910), 0h7x (0.44 #732, 0.43 #1014, 0.39 #873), 05qx1 (0.43 #738, 0.42 #879, 0.41 #1584) >> Best rule #716 for best value: >> intensional similarity = 5 >> extensional distance = 174 >> proper extension: 0gtsx8c; 0c3ybss; 0c40vxk; 0401sg; 087wc7n; 0crfwmx; 08hmch; 0jjy0; 0gj8t_b; 0cz8mkh; ... >> query: (?x3035, 015fr) <- film(?x194, ?x3035), film_release_region(?x3035, ?x4743), film_release_region(?x3035, ?x789), ?x789 = 0f8l9c, ?x4743 = 03spz >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0j43swk film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 77.000 0.807 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0j43swk film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 77.000 0.807 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9995-0ds33 PRED entity: 0ds33 PRED relation: nominated_for! PRED expected values: 05ztjjw => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 186): 02g3ft (0.66 #5958, 0.66 #10542, 0.66 #10774), 04ljl_l (0.54 #461, 0.35 #3, 0.17 #4585), 0gr0m (0.52 #4636, 0.21 #1199, 0.21 #4406), 0gq9h (0.34 #4409, 0.33 #5326, 0.31 #8076), 0gs9p (0.32 #4410, 0.30 #287, 0.29 #3951), 03c7tr1 (0.31 #500, 0.16 #42, 0.12 #10775), 019f4v (0.31 #4400, 0.28 #5317, 0.27 #1422), 05p1dby (0.28 #76, 0.24 #534, 0.19 #16735), 0k611 (0.26 #4649, 0.25 #5336, 0.24 #4419), 0gq_v (0.25 #4599, 0.25 #4369, 0.24 #5286) >> Best rule #5958 for best value: >> intensional similarity = 4 >> extensional distance = 514 >> proper extension: 04m1bm; 091z_p; 02rb607; 02n9bh; 04lqvlr; 04lqvly; 02hfk5; 064lsn; 0g9zljd; 0gpx6; ... >> query: (?x508, ?x1429) <- language(?x508, ?x254), nominated_for(?x154, ?x508), award(?x508, ?x1429), film_crew_role(?x508, ?x137) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #10775 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x508, ?x500) <- award(?x508, ?x1429), award(?x2878, ?x1429), award(?x2878, ?x500) *> conf = 0.12 ranks of expected_values: 59 EVAL 0ds33 nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 83.000 83.000 0.661 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9994-0ddjy PRED entity: 0ddjy PRED relation: currency PRED expected values: 09nqf => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.92 #50, 0.86 #232, 0.86 #176), 01nv4h (0.04 #72, 0.03 #471, 0.02 #779), 02gsvk (0.02 #202, 0.01 #496, 0.01 #664), 02l6h (0.02 #494), 088n7 (0.01 #441), 0kz1h (0.01 #124) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 0d90m; 01k1k4; 0ds11z; 02hxhz; 01vksx; 0bwfwpj; 08hmch; 0k2sk; 0dtfn; 0jqn5; ... >> query: (?x2366, 09nqf) <- language(?x2366, ?x254), film_distribution_medium(?x2366, ?x81), nominated_for(?x669, ?x2366), crewmember(?x2366, ?x1643) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ddjy currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.922 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #9993-0dtd6 PRED entity: 0dtd6 PRED relation: group! PRED expected values: 0140t7 => 83 concepts (45 used for prediction) PRED predicted values (max 10 best out of 134): 01vtqml (0.14 #876, 0.07 #1075, 0.06 #1276), 01k23t (0.14 #746, 0.07 #1146, 0.06 #1347), 03kwtb (0.14 #619, 0.07 #1019, 0.06 #1220), 01304j (0.14 #788, 0.06 #1389, 0.02 #2592), 02whj (0.14 #817, 0.01 #5028), 01wg6y (0.14 #967), 023322 (0.11 #1379, 0.07 #1178, 0.05 #2382), 048tgl (0.09 #1577, 0.07 #2380, 0.06 #3584), 0285c (0.09 #1428, 0.03 #3435, 0.03 #2632), 06cc_1 (0.07 #1008, 0.06 #1809, 0.06 #1209) >> Best rule #876 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 05563d; >> query: (?x2040, 01vtqml) <- group(?x2944, ?x2040), group(?x227, ?x2040), ?x2944 = 0l14j_, artist(?x2039, ?x2040), artists(?x1000, ?x2040), ?x1000 = 0xhtw, ?x227 = 0342h >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dtd6 group! 0140t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 45.000 0.143 http://example.org/music/group_member/membership./music/group_membership/group #9992-01wp_jm PRED entity: 01wp_jm PRED relation: influenced_by! PRED expected values: 02g8h 06crng => 112 concepts (36 used for prediction) PRED predicted values (max 10 best out of 428): 049fgvm (0.33 #260, 0.11 #1266, 0.09 #9576), 01xdf5 (0.33 #3, 0.11 #1009, 0.08 #2520), 01j7rd (0.33 #69, 0.09 #9576, 0.07 #16135), 01s7qqw (0.33 #205, 0.08 #11295, 0.07 #16135), 03g5_y (0.33 #308, 0.07 #1314, 0.05 #2320), 05ty4m (0.33 #7, 0.07 #1013, 0.05 #2524), 02_j7t (0.33 #66, 0.04 #1072, 0.03 #2078), 0pz7h (0.33 #23, 0.04 #1029, 0.03 #2035), 02_wxh (0.33 #368, 0.04 #1374, 0.03 #2380), 04h07s (0.33 #174, 0.04 #1180, 0.03 #2186) >> Best rule #260 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0p_47; >> query: (?x10101, 049fgvm) <- profession(?x10101, ?x1183), ?x1183 = 09jwl, influenced_by(?x6692, ?x10101), ?x6692 = 04l19_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #17145 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 333 *> proper extension: 014_lq; 07r1_; *> query: (?x10101, ?x2817) <- influenced_by(?x10101, ?x9024), influenced_by(?x10101, ?x986), influenced_by(?x2817, ?x9024), award(?x2817, ?x678), award(?x986, ?x68) *> conf = 0.04 ranks of expected_values: 142, 143 EVAL 01wp_jm influenced_by! 06crng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 112.000 36.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 01wp_jm influenced_by! 02g8h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 112.000 36.000 0.333 http://example.org/influence/influence_node/influenced_by #9991-02jfc PRED entity: 02jfc PRED relation: student PRED expected values: 0d0l91 => 70 concepts (34 used for prediction) PRED predicted values (max 10 best out of 235): 0br1w (0.50 #558, 0.25 #1507, 0.22 #2459), 06c0j (0.25 #1658, 0.25 #709, 0.25 #471), 0kn4c (0.25 #1212, 0.25 #25, 0.24 #4305), 04z0g (0.25 #366, 0.20 #2981, 0.17 #3456), 01zwy (0.25 #409, 0.20 #884, 0.12 #1596), 059y0 (0.25 #451, 0.20 #926, 0.12 #1638), 01tdnyh (0.25 #353, 0.20 #828, 0.12 #1540), 0bkg4 (0.25 #322, 0.20 #797, 0.12 #1509), 0jcx (0.25 #303, 0.20 #778, 0.12 #1490), 0d5_f (0.25 #100, 0.20 #813, 0.12 #1287) >> Best rule #558 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 06ms6; >> query: (?x10391, 0br1w) <- major_field_of_study(?x6417, ?x10391), major_field_of_study(?x5750, ?x10391), major_field_of_study(?x5486, ?x10391), school_type(?x5486, ?x3092), ?x6417 = 01t0dy, student(?x10391, ?x287), school(?x662, ?x5486), major_field_of_study(?x734, ?x10391), ?x5750 = 01nnsv >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02jfc student 0d0l91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 34.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/student #9990-0qb7t PRED entity: 0qb7t PRED relation: taxonomy PRED expected values: 04n6k => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.03 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14821, 04n6k) <- >> conf = 0.03 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qb7t taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.030 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #9989-01rly6 PRED entity: 01rly6 PRED relation: sport PRED expected values: 02vx4 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 7): 02vx4 (0.87 #569, 0.87 #522, 0.87 #503), 018w8 (0.15 #580, 0.11 #664, 0.10 #691), 0jm_ (0.14 #579, 0.11 #699, 0.10 #663), 018jz (0.12 #701, 0.10 #253, 0.10 #329), 03tmr (0.12 #697, 0.10 #249, 0.08 #959), 039yzs (0.06 #255, 0.05 #703, 0.05 #331), 09xp_ (0.02 #330, 0.01 #648, 0.01 #964) >> Best rule #569 for best value: >> intensional similarity = 10 >> extensional distance = 108 >> proper extension: 0f6cl2; >> query: (?x11139, 02vx4) <- position(?x11139, ?x203), position(?x11139, ?x63), position(?x11139, ?x60), ?x60 = 02nzb8, ?x63 = 02sdk9v, ?x203 = 0dgrmp, colors(?x11139, ?x663), colors(?x10196, ?x663), colors(?x216, ?x663), current_club(?x4805, ?x10196) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rly6 sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.873 http://example.org/sports/sports_team/sport #9988-03bwzr4 PRED entity: 03bwzr4 PRED relation: major_field_of_study PRED expected values: 04_tv 01tbp 037mh8 06q83 01bt59 023907r 0w7s => 55 concepts (49 used for prediction) PRED predicted values (max 10 best out of 104): 0fdys (0.82 #1144, 0.78 #995, 0.74 #673), 037mh8 (0.82 #1163, 0.78 #1014, 0.69 #1389), 04gb7 (0.80 #1072, 0.74 #673, 0.71 #922), 03g3w (0.74 #673, 0.73 #1140, 0.67 #991), 02vxn (0.74 #673, 0.63 #1274, 0.63 #1198), 01lhf (0.74 #673, 0.63 #1274, 0.63 #1198), 064_8sq (0.74 #673, 0.63 #1274, 0.63 #1198), 06b_j (0.74 #673, 0.63 #1274, 0.63 #1198), 03r8gp (0.74 #673, 0.63 #1274, 0.63 #1198), 04g51 (0.67 #855, 0.62 #1576, 0.59 #373) >> Best rule #1144 for best value: >> intensional similarity = 12 >> extensional distance = 9 >> proper extension: 0bjrnt; >> query: (?x4981, 0fdys) <- institution(?x4981, ?x388), colors(?x388, ?x3364), major_field_of_study(?x4981, ?x2606), major_field_of_study(?x7920, ?x2606), major_field_of_study(?x3178, ?x2606), major_field_of_study(?x2606, ?x373), school(?x685, ?x388), student(?x2606, ?x677), ?x7920 = 01p79b, ?x3178 = 01vc5m, student(?x388, ?x643), school(?x387, ?x388) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1163 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 9 *> proper extension: 0bjrnt; *> query: (?x4981, 037mh8) <- institution(?x4981, ?x388), colors(?x388, ?x3364), major_field_of_study(?x4981, ?x2606), major_field_of_study(?x7920, ?x2606), major_field_of_study(?x3178, ?x2606), major_field_of_study(?x2606, ?x373), school(?x685, ?x388), student(?x2606, ?x677), ?x7920 = 01p79b, ?x3178 = 01vc5m, student(?x388, ?x643), school(?x387, ?x388) *> conf = 0.82 ranks of expected_values: 2, 11, 14, 28, 29, 42, 56 EVAL 03bwzr4 major_field_of_study 0w7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 55.000 49.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 03bwzr4 major_field_of_study 023907r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 55.000 49.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 03bwzr4 major_field_of_study 01bt59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 55.000 49.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 03bwzr4 major_field_of_study 06q83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 55.000 49.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 03bwzr4 major_field_of_study 037mh8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 55.000 49.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 03bwzr4 major_field_of_study 01tbp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 55.000 49.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 03bwzr4 major_field_of_study 04_tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 55.000 49.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #9987-0qf3p PRED entity: 0qf3p PRED relation: category PRED expected values: 08mbj5d => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #57, 0.83 #72, 0.82 #30) >> Best rule #57 for best value: >> intensional similarity = 5 >> extensional distance = 351 >> proper extension: 03c7ln; 0197tq; 0fp_v1x; 01pfr3; 02mslq; 06cc_1; 0c7ct; 0kzy0; 0168cl; 01vvycq; ... >> query: (?x2600, 08mbj5d) <- artists(?x11737, ?x2600), artists(?x671, ?x2600), artists(?x11737, ?x5883), ?x5883 = 01wgjj5, ?x671 = 064t9 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qf3p category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.853 http://example.org/common/topic/webpage./common/webpage/category #9986-050t68 PRED entity: 050t68 PRED relation: profession PRED expected values: 02hrh1q => 85 concepts (84 used for prediction) PRED predicted values (max 10 best out of 62): 02hrh1q (0.88 #7516, 0.88 #6466, 0.87 #2715), 01d_h8 (0.35 #4056, 0.34 #4356, 0.33 #1056), 03gjzk (0.34 #2266, 0.26 #4216, 0.26 #8553), 0dxtg (0.32 #2264, 0.30 #4214, 0.28 #1064), 02jknp (0.26 #8553, 0.24 #4058, 0.24 #4358), 0np9r (0.26 #8553, 0.20 #3622, 0.20 #3472), 02krf9 (0.26 #8553, 0.19 #628, 0.15 #2278), 018gz8 (0.26 #8553, 0.15 #1368, 0.15 #1518), 0cbd2 (0.26 #8553, 0.14 #1057, 0.12 #907), 021wpb (0.26 #8553, 0.10 #54, 0.07 #204) >> Best rule #7516 for best value: >> intensional similarity = 2 >> extensional distance = 1891 >> proper extension: 033hqf; 025p38; 01q7cb_; 02lq10; 05hdf; 08b8vd; 01mt1fy; 0dfjb8; 04cr6qv; 015lhm; ... >> query: (?x3932, 02hrh1q) <- film(?x3932, ?x2336), nominated_for(?x591, ?x2336) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050t68 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 84.000 0.876 http://example.org/people/person/profession #9985-02psqkz PRED entity: 02psqkz PRED relation: capital PRED expected values: 06c62 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 183): 04jpl (0.43 #1077, 0.38 #1434, 0.33 #958), 0156q (0.33 #132, 0.25 #251, 0.17 #966), 07dfk (0.33 #45, 0.17 #879, 0.14 #1834), 0k3p (0.29 #1107, 0.17 #868, 0.13 #2181), 096gm (0.25 #261, 0.12 #2527, 0.12 #2407), 06c62 (0.22 #954, 0.12 #2414, 0.12 #2505), 05qtj (0.22 #954, 0.12 #1909, 0.12 #2505), 01n43d (0.22 #954, 0.12 #1909, 0.12 #2505), 0947l (0.22 #954, 0.12 #1909, 0.12 #2505), 019fv4 (0.22 #954, 0.12 #2505, 0.10 #5258) >> Best rule #1077 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 02jx1; 049nq; >> query: (?x3918, 04jpl) <- capital(?x3918, ?x9660), form_of_government(?x3918, ?x6065), ?x6065 = 01q20, location(?x1279, ?x9660), vacationer(?x9660, ?x5246), award_winner(?x618, ?x5246), participant(?x286, ?x5246) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #954 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 0160w; *> query: (?x3918, ?x1705) <- capital(?x3918, ?x9660), form_of_government(?x3918, ?x6065), form_of_government(?x3918, ?x4763), ?x6065 = 01q20, location(?x3709, ?x9660), ?x4763 = 01fpfn, location(?x3709, ?x1705), contains(?x205, ?x9660) *> conf = 0.22 ranks of expected_values: 6 EVAL 02psqkz capital 06c62 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 128.000 128.000 0.429 http://example.org/location/country/capital #9984-0537b PRED entity: 0537b PRED relation: company! PRED expected values: 0142rn => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 37): 05_wyz (0.53 #612, 0.50 #1212, 0.50 #412), 09d6p2 (0.39 #453, 0.37 #853, 0.35 #173), 01kr6k (0.30 #421, 0.30 #181, 0.28 #621), 04192r (0.25 #35, 0.20 #115, 0.18 #2124), 0142rn (0.25 #20, 0.18 #2124, 0.18 #60), 021q1c (0.24 #1886, 0.23 #2088, 0.18 #2124), 07t3gd (0.19 #1897, 0.18 #2124, 0.18 #1923), 02y6fz (0.18 #2124, 0.18 #1923, 0.18 #1922), 09lq2c (0.18 #2124, 0.18 #1923, 0.18 #1922), 014l7h (0.18 #2124, 0.18 #1923, 0.18 #1922) >> Best rule #612 for best value: >> intensional similarity = 7 >> extensional distance = 34 >> proper extension: 01_lh1; >> query: (?x6676, 05_wyz) <- company(?x4682, ?x6676), company(?x346, ?x6676), company(?x265, ?x6676), ?x265 = 0dq3c, ?x4682 = 0dq_5, organization(?x346, ?x99), jurisdiction_of_office(?x346, ?x47) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 049ql1; *> query: (?x6676, 0142rn) <- state_province_region(?x6676, ?x3818), industry(?x6676, ?x6575), ?x6575 = 029g_vk, organization(?x4682, ?x6676) *> conf = 0.25 ranks of expected_values: 5 EVAL 0537b company! 0142rn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 109.000 109.000 0.528 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #9983-02k84w PRED entity: 02k84w PRED relation: group PRED expected values: 01cblr => 85 concepts (40 used for prediction) PRED predicted values (max 10 best out of 842): 02dw1_ (0.69 #2995, 0.67 #3546, 0.67 #1708), 05563d (0.67 #1677, 0.62 #3881, 0.61 #909), 06nv27 (0.67 #1697, 0.62 #2984, 0.61 #909), 0134wr (0.67 #1198, 0.61 #909, 0.60 #830), 0gr69 (0.67 #1180, 0.61 #909, 0.60 #812), 01fchy (0.67 #1226, 0.61 #909, 0.60 #858), 01q99h (0.67 #1166, 0.61 #909, 0.60 #798), 02cw1m (0.67 #1223, 0.61 #909, 0.50 #1588), 047cx (0.61 #909, 0.60 #773, 0.55 #2610), 0123r4 (0.61 #909, 0.60 #797, 0.55 #2634) >> Best rule #2995 for best value: >> intensional similarity = 15 >> extensional distance = 11 >> proper extension: 0mkg; 03qjg; >> query: (?x1886, 02dw1_) <- role(?x2923, ?x1886), role(?x2377, ?x1886), role(?x315, ?x1886), instrumentalists(?x1886, ?x5623), ?x2377 = 01bns_, artist(?x1954, ?x5623), currency(?x5623, ?x170), role(?x4917, ?x1886), role(?x736, ?x2923), ?x315 = 0l14md, type_of_union(?x5623, ?x1873), ?x1873 = 01g63y, role(?x211, ?x4917), role(?x868, ?x4917), group(?x1886, ?x5303) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #909 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 3 *> proper extension: 05148p4; *> query: (?x1886, ?x997) <- role(?x2377, ?x1886), role(?x2048, ?x1886), role(?x212, ?x1886), instrumentalists(?x1886, ?x5623), ?x2377 = 01bns_, ?x5623 = 01vsyg9, role(?x6049, ?x1886), group(?x2048, ?x997), role(?x2157, ?x2048), role(?x1166, ?x2048), role(?x316, ?x1886), ?x2157 = 011_6p, role(?x211, ?x2048), instrumentalists(?x2048, ?x367), role(?x736, ?x2048), ?x212 = 026t6, ?x1166 = 05148p4 *> conf = 0.61 ranks of expected_values: 38 EVAL 02k84w group 01cblr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 85.000 40.000 0.692 http://example.org/music/performance_role/regular_performances./music/group_membership/group #9982-016xh5 PRED entity: 016xh5 PRED relation: award_winner! PRED expected values: 04kxsb => 127 concepts (125 used for prediction) PRED predicted values (max 10 best out of 237): 09sb52 (0.39 #905, 0.35 #41, 0.34 #32847), 063y_ky (0.34 #32847, 0.31 #43217, 0.31 #26792), 027dtxw (0.14 #868, 0.11 #436, 0.10 #4), 02pqp12 (0.12 #1800, 0.08 #3096, 0.06 #2232), 0gq9h (0.12 #1807, 0.07 #3103, 0.05 #28090), 099tbz (0.12 #2651, 0.09 #11725, 0.07 #5675), 0bfvd4 (0.11 #980, 0.10 #116, 0.05 #46245), 027c95y (0.11 #1455, 0.10 #3183, 0.07 #4047), 05p09zm (0.11 #1422, 0.09 #1854, 0.06 #6606), 02z1nbg (0.10 #195, 0.08 #3220, 0.07 #627) >> Best rule #905 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 01yhvv; 05tk7y; >> query: (?x6122, 09sb52) <- award_nominee(?x6122, ?x1222), ?x1222 = 03f1zdw, award(?x6122, ?x704) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #46245 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2309 *> proper extension: 0338lq; 024qwq; *> query: (?x6122, ?x618) <- award_nominee(?x5454, ?x6122), award(?x5454, ?x618) *> conf = 0.05 ranks of expected_values: 54 EVAL 016xh5 award_winner! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 127.000 125.000 0.393 http://example.org/award/award_category/winners./award/award_honor/award_winner #9981-069d68 PRED entity: 069d68 PRED relation: sibling PRED expected values: 069d71 => 121 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1): 013rds (0.10 #460, 0.09 #576) >> Best rule #460 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 03_x5t; 03h8_g; 01mskc3; >> query: (?x8395, 013rds) <- location(?x8395, ?x3501), ?x3501 = 0f2v0, gender(?x8395, ?x231), profession(?x8395, ?x1581), nationality(?x8395, ?x94) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 069d68 sibling 069d71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 50.000 0.100 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #9980-0fpj9pm PRED entity: 0fpj9pm PRED relation: artists! PRED expected values: 0pm85 => 157 concepts (86 used for prediction) PRED predicted values (max 10 best out of 266): 06by7 (0.65 #3474, 0.52 #12887, 0.49 #17275), 064t9 (0.56 #11938, 0.52 #2524, 0.49 #5035), 02yv6b (0.35 #1355, 0.23 #3866, 0.21 #3553), 0glt670 (0.33 #11967, 0.30 #6635, 0.20 #4436), 05bt6j (0.31 #4752, 0.31 #2555, 0.28 #12909), 06j6l (0.31 #1303, 0.28 #11974, 0.28 #6642), 05w3f (0.31 #1293, 0.22 #979, 0.21 #39), 025sc50 (0.30 #11976, 0.28 #6644, 0.22 #4759), 01lyv (0.29 #35, 0.27 #15720, 0.21 #20746), 0155w (0.29 #109, 0.25 #422, 0.23 #1363) >> Best rule #3474 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 0kr_t; >> query: (?x7236, 06by7) <- award(?x7236, ?x247), ?x247 = 02wh75, artists(?x302, ?x7236) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #10359 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 114 *> proper extension: 031x_3; *> query: (?x7236, ?x301) <- performance_role(?x7236, ?x212), artists(?x2996, ?x7236), parent_genre(?x301, ?x2996) *> conf = 0.06 ranks of expected_values: 115 EVAL 0fpj9pm artists! 0pm85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 157.000 86.000 0.646 http://example.org/music/genre/artists #9979-0c9c0 PRED entity: 0c9c0 PRED relation: film PRED expected values: 01sbv9 => 106 concepts (78 used for prediction) PRED predicted values (max 10 best out of 783): 07nxvj (0.21 #694), 035xwd (0.14 #115, 0.04 #1900, 0.04 #3685), 01xbxn (0.14 #1389, 0.04 #3174, 0.04 #4959), 02vjp3 (0.14 #1296, 0.02 #10222), 04jpg2p (0.14 #1458, 0.01 #21095, 0.01 #28235), 04ltlj (0.14 #1715), 027pfg (0.14 #1220), 0418wg (0.08 #2185, 0.07 #3970, 0.07 #400), 06z8s_ (0.08 #1914, 0.07 #3699, 0.07 #129), 09xbpt (0.08 #1832, 0.07 #3617, 0.07 #47) >> Best rule #694 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 01wz01; >> query: (?x2790, 07nxvj) <- film(?x2790, ?x485), award_winner(?x9561, ?x2790), ?x9561 = 0h10vt >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #6984 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 079ws; 0969fd; *> query: (?x2790, 01sbv9) <- nationality(?x2790, ?x512), award_winner(?x2790, ?x262), influenced_by(?x2790, ?x8460) *> conf = 0.02 ranks of expected_values: 317 EVAL 0c9c0 film 01sbv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 106.000 78.000 0.214 http://example.org/film/actor/film./film/performance/film #9978-01w60_p PRED entity: 01w60_p PRED relation: artists! PRED expected values: 0h08p => 122 concepts (121 used for prediction) PRED predicted values (max 10 best out of 227): 064t9 (0.58 #2470, 0.51 #17217, 0.49 #8304), 017_qw (0.36 #6204, 0.24 #983, 0.11 #10195), 0glt670 (0.34 #8332, 0.28 #9560, 0.23 #11096), 025sc50 (0.32 #2507, 0.28 #8341, 0.25 #9569), 016clz (0.30 #5226, 0.28 #5840, 0.26 #3076), 0xhtw (0.29 #2781, 0.25 #5238, 0.24 #5852), 01lyv (0.27 #3105, 0.25 #2798, 0.25 #9246), 05bt6j (0.26 #2501, 0.25 #17248, 0.25 #3115), 07sbbz2 (0.23 #2772, 0.20 #8, 0.20 #622), 02w4v (0.22 #2809, 0.19 #3116, 0.16 #5266) >> Best rule #2470 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 0411q; >> query: (?x2169, 064t9) <- award(?x2169, ?x4018), award_nominee(?x215, ?x2169), ?x4018 = 03qbh5 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1133 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 0dtd6; 0178_w; 07r1_; *> query: (?x2169, 0h08p) <- award(?x2169, ?x4488), award(?x2169, ?x4018), ?x4488 = 02gdjb, award_winner(?x4018, ?x217) *> conf = 0.04 ranks of expected_values: 96 EVAL 01w60_p artists! 0h08p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 122.000 121.000 0.576 http://example.org/music/genre/artists #9977-01wgxtl PRED entity: 01wgxtl PRED relation: vacationer! PRED expected values: 0f2v0 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 46): 03gh4 (0.13 #956, 0.12 #1581, 0.11 #1206), 05qtj (0.09 #1197, 0.08 #2322, 0.05 #3950), 0f2v0 (0.09 #1188, 0.05 #1063, 0.05 #2313), 0b90_r (0.07 #1128, 0.05 #2253, 0.04 #1378), 04jpl (0.07 #9, 0.06 #1134, 0.06 #134), 0cv3w (0.06 #2307, 0.05 #1182, 0.04 #932), 0jbs5 (0.05 #475, 0.02 #1100, 0.02 #850), 07fr_ (0.04 #1221, 0.02 #971, 0.02 #596), 0160w (0.04 #877, 0.03 #1127, 0.03 #2252), 06c62 (0.03 #1212, 0.03 #2337, 0.02 #962) >> Best rule #956 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 01hrqc; >> query: (?x2732, 03gh4) <- award_nominee(?x2732, ?x827), participant(?x2732, ?x10777), artists(?x671, ?x2732) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #1188 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 114 *> proper extension: 01lz4tf; *> query: (?x2732, 0f2v0) <- participant(?x2614, ?x2732), participant(?x2614, ?x250), artist(?x3265, ?x2614) *> conf = 0.09 ranks of expected_values: 3 EVAL 01wgxtl vacationer! 0f2v0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 105.000 0.134 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #9976-016jny PRED entity: 016jny PRED relation: parent_genre! PRED expected values: 04z1v0 0jrgr 05tcx0 => 51 concepts (28 used for prediction) PRED predicted values (max 10 best out of 284): 05jg58 (0.60 #354, 0.20 #610, 0.14 #1123), 0xv2x (0.40 #639, 0.40 #383, 0.29 #1152), 06cp5 (0.40 #587, 0.40 #331, 0.29 #1100), 0dl5d (0.40 #528, 0.29 #1041, 0.29 #784), 03lty (0.40 #536, 0.29 #1049, 0.25 #1818), 0dls3 (0.40 #557, 0.29 #1070, 0.25 #1839), 05r6t (0.40 #580, 0.29 #1093, 0.25 #1862), 01h0kx (0.40 #641, 0.29 #1154, 0.25 #1923), 016jny (0.40 #598, 0.29 #1111, 0.25 #1880), 0g_bh (0.38 #1389, 0.33 #2158, 0.25 #1645) >> Best rule #354 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 0dls3; >> query: (?x7329, 05jg58) <- artists(?x7329, ?x8556), artists(?x7329, ?x4790), artists(?x7329, ?x3280), award_nominee(?x2443, ?x3280), award_winner(?x4835, ?x3280), gender(?x3280, ?x514), ?x8556 = 01wqflx, artist(?x2931, ?x4790) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #670 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 0xhtw; 06by7; 02w4v; *> query: (?x7329, 0jrgr) <- artists(?x7329, ?x11446), artists(?x7329, ?x3280), ?x11446 = 016t00, origin(?x3280, ?x4627), nominated_for(?x3280, ?x1490), film(?x3280, ?x4067), award_winner(?x2443, ?x3280) *> conf = 0.20 ranks of expected_values: 75, 90, 161 EVAL 016jny parent_genre! 05tcx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 51.000 28.000 0.600 http://example.org/music/genre/parent_genre EVAL 016jny parent_genre! 0jrgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 51.000 28.000 0.600 http://example.org/music/genre/parent_genre EVAL 016jny parent_genre! 04z1v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 28.000 0.600 http://example.org/music/genre/parent_genre #9975-09bkv PRED entity: 09bkv PRED relation: contains! PRED expected values: 07ssc => 160 concepts (97 used for prediction) PRED predicted values (max 10 best out of 409): 024pcx (0.86 #49182, 0.85 #20568, 0.85 #19672), 07ssc (0.75 #84075, 0.65 #22358, 0.65 #21494), 09c7w0 (0.69 #72419, 0.58 #55443, 0.57 #50976), 059rby (0.45 #79590, 0.16 #65287, 0.15 #66180), 036wy (0.39 #16096, 0.29 #2552, 0.20 #6125), 01n7q (0.32 #47472, 0.20 #49261, 0.18 #22436), 012wyq (0.25 #22359, 0.22 #86729, 0.22 #86728), 09bkv (0.25 #22359, 0.22 #86729, 0.22 #86728), 03rjj (0.22 #2691, 0.16 #9844, 0.14 #903), 02_286 (0.19 #66202, 0.19 #65309, 0.17 #70671) >> Best rule #49182 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 0bmm4; 0fsmy; 0bsl6; 0f2yw; 0cf0s; 0fr_v; >> query: (?x10042, ?x9328) <- capital(?x9328, ?x10042), contains(?x362, ?x10042), adjoins(?x9328, ?x1611) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #84075 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 339 *> proper extension: 02jx1; 0zc6f; 0dbdy; 0jcg8; 07w4j; 0jt5zcn; 06y9v; 0p5wz; 05bcl; 0143hl; ... *> query: (?x10042, 07ssc) <- contains(?x1310, ?x10042), contains(?x362, ?x10042), location(?x361, ?x362), state_province_region(?x963, ?x1310), nationality(?x57, ?x1310) *> conf = 0.75 ranks of expected_values: 2 EVAL 09bkv contains! 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 97.000 0.857 http://example.org/location/location/contains #9974-050ks PRED entity: 050ks PRED relation: religion PRED expected values: 01y0s9 => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 21): 01y0s9 (0.64 #244, 0.63 #124, 0.57 #52), 0flw86 (0.42 #865, 0.40 #987, 0.39 #1059), 058x5 (0.38 #242, 0.34 #170, 0.30 #50), 092bf5 (0.33 #32, 0.29 #728, 0.29 #632), 02t7t (0.30 #61, 0.30 #133, 0.26 #397), 03j6c (0.13 #371, 0.09 #731, 0.09 #1383), 0n2g (0.08 #30, 0.04 #102, 0.03 #198), 0kpl (0.04 #365, 0.04 #101, 0.03 #629), 06yyp (0.04 #372, 0.04 #108, 0.03 #204), 01spm (0.04 #117, 0.04 #141, 0.02 #381) >> Best rule #244 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 059_c; 01x73; 05fky; >> query: (?x7058, 01y0s9) <- religion(?x7058, ?x109), ?x109 = 01lp8, state(?x9624, ?x7058) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050ks religion 01y0s9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 171.000 0.643 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #9973-0g9z_32 PRED entity: 0g9z_32 PRED relation: person PRED expected values: 0d06m5 => 72 concepts (20 used for prediction) PRED predicted values (max 10 best out of 186): 06c97 (0.20 #103, 0.07 #290, 0.03 #667), 0157m (0.16 #31, 0.10 #218), 09b6zr (0.14 #266, 0.12 #79), 06c0j (0.08 #181, 0.05 #368), 0jw67 (0.08 #72, 0.05 #259), 0d3k14 (0.08 #170, 0.03 #357, 0.01 #734), 0f7fy (0.08 #124, 0.03 #311), 0127s7 (0.08 #108, 0.03 #295), 028rk (0.08 #56, 0.03 #243), 042kg (0.08 #173, 0.02 #360) >> Best rule #103 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 0413cff; 05_61y; >> query: (?x7311, 06c97) <- language(?x7311, ?x3966), person(?x7311, ?x2669), featured_film_locations(?x7311, ?x739), languages_spoken(?x1050, ?x3966) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #69 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0413cff; 05_61y; *> query: (?x7311, 0d06m5) <- language(?x7311, ?x3966), person(?x7311, ?x2669), featured_film_locations(?x7311, ?x739), languages_spoken(?x1050, ?x3966) *> conf = 0.04 ranks of expected_values: 48 EVAL 0g9z_32 person 0d06m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 72.000 20.000 0.200 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #9972-0jyb4 PRED entity: 0jyb4 PRED relation: film_regional_debut_venue PRED expected values: 04jpl => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 16): 015hr (0.25 #49, 0.09 #220, 0.08 #324), 018cvf (0.10 #291, 0.10 #641, 0.09 #187), 0prpt (0.08 #653, 0.08 #303, 0.08 #338), 0kfhjq0 (0.05 #325, 0.04 #429, 0.04 #360), 0gg7gsl (0.04 #213, 0.03 #317, 0.03 #421), 07zmj (0.03 #306, 0.03 #656, 0.03 #237), 0j63cyr (0.03 #288, 0.03 #219, 0.03 #638), 02_286 (0.03 #173, 0.03 #627, 0.02 #347), 07751 (0.03 #634, 0.02 #423, 0.02 #354), 04jpl (0.02 #171, 0.01 #345, 0.01 #414) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0pv2t; >> query: (?x6215, 015hr) <- nominated_for(?x6755, ?x6215), ?x6755 = 01520h, nominated_for(?x198, ?x6215) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 180 *> proper extension: 0gx1bnj; 026p_bs; 0jjy0; 0gj8t_b; 03bx2lk; 053tj7; 0j_tw; 0661m4p; 07f_7h; 0gffmn8; ... *> query: (?x6215, 04jpl) <- produced_by(?x6215, ?x9044), film_release_region(?x6215, ?x304), ?x304 = 0d0vqn *> conf = 0.02 ranks of expected_values: 10 EVAL 0jyb4 film_regional_debut_venue 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 75.000 75.000 0.250 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #9971-01hwc6 PRED entity: 01hwc6 PRED relation: genre! PRED expected values: 0kvgtf 013q0p 0ckt6 => 29 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1890): 02mc5v (0.67 #10774, 0.60 #8909, 0.57 #12640), 013q0p (0.62 #13059, 0.56 #11192, 0.33 #830), 05p1tzf (0.62 #13059, 0.56 #11192, 0.33 #77), 026mfbr (0.60 #5706, 0.50 #1974, 0.33 #110), 02v5_g (0.50 #10142, 0.43 #12008, 0.40 #8277), 02v63m (0.50 #9511, 0.43 #11377, 0.40 #7646), 01pj_5 (0.50 #10106, 0.43 #11972, 0.40 #8241), 0bwhdbl (0.50 #10781, 0.43 #12647, 0.40 #8916), 02fwfb (0.50 #3174, 0.40 #6906, 0.33 #14370), 05r3qc (0.50 #2973, 0.40 #6705, 0.33 #1109) >> Best rule #10774 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 0gf28; >> query: (?x1729, 02mc5v) <- genre(?x4847, ?x1729), genre(?x3317, ?x1729), genre(?x1728, ?x1729), ?x3317 = 03l6q0, prequel(?x4847, ?x559), titles(?x2480, ?x1728), film(?x1678, ?x1728), film_crew_role(?x4847, ?x137), nominated_for(?x102, ?x4847), film(?x665, ?x4847) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #13059 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 5 *> proper extension: 09q17; *> query: (?x1729, ?x559) <- genre(?x4847, ?x1729), genre(?x3317, ?x1729), genre(?x1728, ?x1729), ?x3317 = 03l6q0, prequel(?x4847, ?x559), titles(?x2480, ?x1728), film(?x1678, ?x1728), film_crew_role(?x4847, ?x137), country(?x1728, ?x94) *> conf = 0.62 ranks of expected_values: 2, 73, 319 EVAL 01hwc6 genre! 0ckt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 29.000 14.000 0.667 http://example.org/film/film/genre EVAL 01hwc6 genre! 013q0p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 29.000 14.000 0.667 http://example.org/film/film/genre EVAL 01hwc6 genre! 0kvgtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 29.000 14.000 0.667 http://example.org/film/film/genre #9970-02x_h0 PRED entity: 02x_h0 PRED relation: award PRED expected values: 01c9dd 023vrq => 107 concepts (105 used for prediction) PRED predicted values (max 10 best out of 269): 0ck27z (0.33 #92, 0.10 #29111, 0.10 #26690), 01bgqh (0.30 #4476, 0.27 #11730, 0.26 #8506), 09sb52 (0.26 #4071, 0.25 #444, 0.24 #26639), 03t5kl (0.25 #1032, 0.15 #33052, 0.15 #32648), 03qbh5 (0.22 #8667, 0.21 #4637, 0.18 #12294), 023vrq (0.22 #1131, 0.15 #33052, 0.15 #32648), 05p09zm (0.21 #526, 0.15 #1332, 0.15 #28212), 03c7tr1 (0.19 #27405, 0.18 #28616, 0.17 #462), 01c427 (0.19 #27405, 0.18 #28616, 0.16 #890), 01cw7s (0.19 #27405, 0.18 #28616, 0.15 #28212) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 095b70; >> query: (?x5479, 0ck27z) <- participant(?x6659, ?x5479), participant(?x5479, ?x2562), ?x6659 = 01vw_dv >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1131 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 122 *> proper extension: 0m19t; 017lb_; *> query: (?x5479, 023vrq) <- artist(?x4483, ?x5479), artists(?x2937, ?x5479), ?x2937 = 0glt670 *> conf = 0.22 ranks of expected_values: 6, 12 EVAL 02x_h0 award 023vrq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 107.000 105.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x_h0 award 01c9dd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 107.000 105.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9969-0d4jl PRED entity: 0d4jl PRED relation: company PRED expected values: 01j_x => 102 concepts (67 used for prediction) PRED predicted values (max 10 best out of 102): 09c7w0 (0.11 #2486, 0.05 #5938, 0.03 #5361), 02f8zw (0.09 #305, 0.06 #1069, 0.05 #1260), 01jq4b (0.09 #470, 0.06 #1043, 0.02 #2382), 03y7ml (0.09 #320, 0.01 #2998), 02jx1 (0.09 #2677, 0.05 #6129, 0.03 #5553), 07ssc (0.09 #2677, 0.05 #6129, 0.03 #5553), 0lbfv (0.08 #2678, 0.02 #8427, 0.01 #11875), 07wrz (0.08 #2521, 0.03 #5973, 0.03 #1757), 01t9_0 (0.08 #724), 01q0kg (0.08 #636) >> Best rule #2486 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 0cl_m; >> query: (?x3279, 09c7w0) <- religion(?x3279, ?x1985), nationality(?x3279, ?x512), student(?x6505, ?x3279), company(?x3279, ?x3424) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #1131 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 03j90; *> query: (?x3279, 01j_x) <- award_winner(?x10270, ?x3279), influenced_by(?x3279, ?x6457), influenced_by(?x3279, ?x5004), ?x5004 = 081k8, gender(?x6457, ?x231) *> conf = 0.06 ranks of expected_values: 15 EVAL 0d4jl company 01j_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 102.000 67.000 0.108 http://example.org/people/person/employment_history./business/employment_tenure/company #9968-02w3w PRED entity: 02w3w PRED relation: role PRED expected values: 0319l => 63 concepts (43 used for prediction) PRED predicted values (max 10 best out of 97): 04rzd (0.84 #1376, 0.83 #2312, 0.83 #2690), 01vj9c (0.84 #1376, 0.83 #2312, 0.83 #2690), 01dnws (0.84 #1376, 0.83 #2312, 0.83 #2690), 07gql (0.84 #1376, 0.83 #2312, 0.83 #2690), 0192l (0.84 #1376, 0.83 #2312, 0.83 #2690), 085jw (0.84 #1376, 0.83 #2312, 0.83 #2690), 02w3w (0.78 #982, 0.69 #1446, 0.65 #2498), 0l14j_ (0.77 #1422, 0.76 #1980, 0.70 #1192), 0myk8 (0.77 #1423, 0.62 #179, 0.59 #1981), 0l14qv (0.75 #1566, 0.75 #826, 0.75 #645) >> Best rule #1376 for best value: >> intensional similarity = 17 >> extensional distance = 11 >> proper extension: 0g2ff; >> query: (?x5417, ?x2206) <- role(?x5417, ?x4583), role(?x5417, ?x1166), role(?x5417, ?x316), ?x316 = 05r5c, role(?x5417, ?x745), ?x4583 = 0bmnm, role(?x2206, ?x5417), role(?x565, ?x1166), group(?x1166, ?x12506), group(?x1166, ?x9791), role(?x1166, ?x2764), role(?x248, ?x1166), ?x9791 = 016l09, role(?x1166, ?x74), ?x2764 = 01s0ps, ?x12506 = 01518s, instrumentalists(?x1166, ?x130) >> conf = 0.84 => this is the best rule for 6 predicted values *> Best rule #821 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 6 *> proper extension: 0342h; 05148p4; 018j2; *> query: (?x5417, ?x894) <- role(?x5417, ?x1482), role(?x5417, ?x1267), group(?x5417, ?x2906), instrumentalists(?x5417, ?x9298), role(?x894, ?x1267), role(?x885, ?x1267), instrumentalists(?x1267, ?x1521), role(?x745, ?x5417), ?x1482 = 02g9p4, role(?x1267, ?x74), ?x885 = 0dwtp, ?x9298 = 016j2t, role(?x2908, ?x1267), role(?x565, ?x1267), ?x2908 = 0161sp, ?x565 = 01wl38s *> conf = 0.64 ranks of expected_values: 28 EVAL 02w3w role 0319l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 63.000 43.000 0.840 http://example.org/music/performance_role/regular_performances./music/group_membership/role #9967-026mx4 PRED entity: 026mx4 PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 22): 0fkvn (0.47 #258, 0.45 #305, 0.45 #351), 060c4 (0.40 #2015, 0.34 #2291, 0.13 #278), 0f6c3 (0.40 #239, 0.39 #262, 0.38 #309), 09n5b9 (0.37 #243, 0.36 #266, 0.35 #313), 060bp (0.35 #2013, 0.29 #2289, 0.13 #278), 0fj45 (0.13 #278, 0.12 #602, 0.12 #601), 0fkzq (0.11 #248, 0.11 #571, 0.11 #548), 01t7n9 (0.08 #250, 0.08 #273, 0.08 #320), 0789n (0.08 #241, 0.08 #264, 0.08 #311), 0pqc5 (0.07 #536, 0.07 #722, 0.07 #769) >> Best rule #258 for best value: >> intensional similarity = 7 >> extensional distance = 62 >> proper extension: 01_c4; >> query: (?x13193, 0fkvn) <- category(?x13193, ?x134), administrative_parent(?x13193, ?x2146), contains(?x2146, ?x7412), jurisdiction_of_office(?x182, ?x2146), place_of_birth(?x491, ?x7412), location(?x1806, ?x7412), place_of_death(?x2145, ?x7412) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026mx4 jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.469 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #9966-02x1dht PRED entity: 02x1dht PRED relation: award! PRED expected values: 02rv_dz => 66 concepts (19 used for prediction) PRED predicted values (max 10 best out of 991): 011yhm (0.57 #1689, 0.50 #2706, 0.26 #16303), 0_b9f (0.57 #1493, 0.50 #2510, 0.25 #5569), 0404j37 (0.50 #665, 0.38 #3720, 0.25 #5760), 0bmhn (0.50 #931, 0.31 #5006, 0.29 #7046), 0hmr4 (0.50 #65, 0.29 #1084, 0.25 #2101), 0209hj (0.50 #62, 0.25 #5157, 0.25 #4137), 0b_5d (0.50 #292, 0.25 #4367, 0.24 #6407), 0pd4f (0.50 #431, 0.19 #4506, 0.18 #6546), 0m313 (0.44 #5101, 0.43 #1025, 0.38 #3061), 0c0zq (0.43 #1918, 0.38 #3954, 0.38 #2935) >> Best rule #1689 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 09qwmm; 0gqwc; >> query: (?x899, 011yhm) <- nominated_for(?x899, ?x898), award_winner(?x899, ?x286), ?x898 = 0344gc, ceremony(?x899, ?x762), award(?x800, ?x899) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1164 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 09qwmm; 0gqwc; *> query: (?x899, 02rv_dz) <- nominated_for(?x899, ?x898), award_winner(?x899, ?x286), ?x898 = 0344gc, ceremony(?x899, ?x762), award(?x800, ?x899) *> conf = 0.29 ranks of expected_values: 31 EVAL 02x1dht award! 02rv_dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 66.000 19.000 0.571 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9965-03ts0c PRED entity: 03ts0c PRED relation: people PRED expected values: 0lcx => 45 concepts (12 used for prediction) PRED predicted values (max 10 best out of 3282): 01twdk (0.50 #14311, 0.43 #10900, 0.20 #4079), 06crk (0.50 #6003, 0.29 #11120, 0.29 #9414), 016z2j (0.43 #8826, 0.38 #17358, 0.38 #15651), 046zh (0.43 #9266, 0.38 #16091, 0.31 #19502), 05dbf (0.43 #7103, 0.33 #284, 0.05 #8525), 0161sp (0.40 #3795, 0.33 #5499, 0.29 #10616), 01817f (0.40 #4045, 0.33 #5749, 0.29 #10866), 01wvxw1 (0.40 #4538, 0.33 #6242, 0.29 #11359), 01xg_w (0.40 #4760, 0.33 #6464, 0.29 #11581), 0fb1q (0.40 #3840, 0.33 #5544, 0.29 #10661) >> Best rule #14311 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 048z7l; >> query: (?x6734, 01twdk) <- people(?x6734, ?x10965), people(?x6734, ?x6830), people(?x6734, ?x598), profession(?x598, ?x11127), profession(?x2693, ?x11127), organizations_founded(?x6830, ?x10739), location(?x10965, ?x6959), place_of_death(?x598, ?x4627), actor(?x11482, ?x10965), ?x2693 = 02ck1, place_of_birth(?x771, ?x4627) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03ts0c people 0lcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 45.000 12.000 0.500 http://example.org/people/ethnicity/people #9964-012d40 PRED entity: 012d40 PRED relation: artists! PRED expected values: 025g__ => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 147): 064t9 (0.41 #6295, 0.39 #7551, 0.32 #13831), 06by7 (0.38 #7560, 0.36 #13840, 0.35 #6304), 06j6l (0.24 #6332, 0.21 #7588, 0.17 #8530), 0glt670 (0.22 #6324, 0.15 #7580, 0.14 #8522), 025sc50 (0.21 #6334, 0.15 #7590, 0.15 #7276), 016clz (0.19 #13822, 0.17 #7542, 0.15 #6286), 05bt6j (0.18 #6327, 0.17 #13863, 0.17 #7583), 0gywn (0.18 #6342, 0.16 #7598, 0.12 #8540), 01lyv (0.17 #6317, 0.16 #7573, 0.15 #4432), 0xhtw (0.16 #13835, 0.11 #7555, 0.09 #12265) >> Best rule #6295 for best value: >> intensional similarity = 3 >> extensional distance = 505 >> proper extension: 01pbxb; 01wl38s; 0168cl; 025xt8y; 03f5spx; 01gf5h; 0770cd; 01wbl_r; 09k2t1; 0kvrb; ... >> query: (?x147, 064t9) <- award_nominee(?x400, ?x147), award(?x147, ?x3508), artists(?x13968, ?x147) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1721 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 139 *> proper extension: 05bp8g; 02h8hr; 012vf6; 0gs6vr; 03b78r; 0jpdn; 02v92l; 01xllf; 0dt645q; 0mbhr; ... *> query: (?x147, 025g__) <- film(?x147, ?x6205), special_performance_type(?x147, ?x4832), film(?x8796, ?x6205) *> conf = 0.03 ranks of expected_values: 60 EVAL 012d40 artists! 025g__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 102.000 102.000 0.414 http://example.org/music/genre/artists #9963-01c333 PRED entity: 01c333 PRED relation: registering_agency PRED expected values: 03z19 => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.83 #7, 0.83 #10, 0.82 #11) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 036921; 032d52; >> query: (?x3044, 03z19) <- country(?x3044, ?x94), school_type(?x3044, ?x3205), currency(?x3044, ?x170), organization(?x346, ?x3044) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c333 registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.833 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #9962-02flpq PRED entity: 02flpq PRED relation: ceremony PRED expected values: 01s695 => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 126): 0gpjbt (0.93 #542, 0.88 #413, 0.64 #284), 02rjjll (0.89 #521, 0.84 #392, 0.58 #651), 0466p0j (0.88 #585, 0.85 #456, 0.64 #327), 01s695 (0.86 #390, 0.80 #519, 0.54 #649), 05pd94v (0.86 #518, 0.84 #389, 0.64 #260), 0gx1673 (0.52 #625, 0.50 #496, 0.50 #367), 05c1t6z (0.19 #789, 0.12 #2079, 0.12 #1692), 0gvstc3 (0.18 #805, 0.10 #2095, 0.10 #1708), 02q690_ (0.17 #833, 0.11 #1478, 0.11 #1865), 03nnm4t (0.16 #842, 0.11 #1487, 0.10 #1874) >> Best rule #542 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 03nl5k; >> query: (?x7534, 0gpjbt) <- award(?x954, ?x7534), ceremony(?x7534, ?x2186), award_winner(?x2054, ?x954), ?x2186 = 056878 >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #390 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 72 *> proper extension: 02flpc; 02flqd; *> query: (?x7534, 01s695) <- award(?x2335, ?x7534), award(?x954, ?x7534), ceremony(?x7534, ?x725), place_of_birth(?x2335, ?x4954), ?x725 = 01bx35, profession(?x954, ?x131) *> conf = 0.86 ranks of expected_values: 4 EVAL 02flpq ceremony 01s695 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 37.000 37.000 0.926 http://example.org/award/award_category/winners./award/award_honor/ceremony #9961-04m8fy PRED entity: 04m8fy PRED relation: fraternities_and_sororities! PRED expected values: 0cwx_ => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 114): 0187nd (0.50 #789), 0jkhr (0.50 #769), 02zc7f (0.50 #768), 01rgdw (0.50 #749), 04hgpt (0.50 #743), 07t90 (0.50 #741), 0m9_5 (0.50 #736), 012vwb (0.50 #732), 01r3y2 (0.50 #726), 01jswq (0.50 #720) >> Best rule #789 for best value: >> intensional similarity = 14 >> extensional distance = 2 >> proper extension: 0325pb; 035tlh; >> query: (?x10424, 0187nd) <- fraternities_and_sororities(?x7418, ?x10424), fraternities_and_sororities(?x5288, ?x10424), fraternities_and_sororities(?x2970, ?x10424), fraternities_and_sororities(?x2682, ?x10424), institution(?x1368, ?x7418), currency(?x7418, ?x170), colors(?x7418, ?x332), ?x170 = 09nqf, school_type(?x7418, ?x1044), country(?x7418, ?x94), ?x5288 = 02zd460, ?x2970 = 04344j, major_field_of_study(?x2682, ?x6756), ?x6756 = 0_jm >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #770 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 0325pb; 035tlh; *> query: (?x10424, 0cwx_) <- fraternities_and_sororities(?x7418, ?x10424), fraternities_and_sororities(?x5288, ?x10424), fraternities_and_sororities(?x2970, ?x10424), fraternities_and_sororities(?x2682, ?x10424), institution(?x1368, ?x7418), currency(?x7418, ?x170), colors(?x7418, ?x332), ?x170 = 09nqf, school_type(?x7418, ?x1044), country(?x7418, ?x94), ?x5288 = 02zd460, ?x2970 = 04344j, major_field_of_study(?x2682, ?x6756), ?x6756 = 0_jm *> conf = 0.25 ranks of expected_values: 48 EVAL 04m8fy fraternities_and_sororities! 0cwx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 18.000 18.000 0.500 http://example.org/education/university/fraternities_and_sororities #9960-0347xl PRED entity: 0347xl PRED relation: type_of_union PRED expected values: 04ztj => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.71 #149, 0.70 #183, 0.70 #195), 01g63y (0.46 #182, 0.45 #203, 0.45 #169) >> Best rule #149 for best value: >> intensional similarity = 2 >> extensional distance = 1714 >> proper extension: 01v6480; >> query: (?x3289, 04ztj) <- film(?x3289, ?x2973), award(?x3289, ?x686) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0347xl type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.707 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9959-01csrl PRED entity: 01csrl PRED relation: location_of_ceremony PRED expected values: 0qr8z => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 22): 0k049 (0.20 #243, 0.02 #3594, 0.02 #2877), 03gh4 (0.10 #540, 0.02 #1258, 0.01 #4011), 071vr (0.10 #546), 0xmqf (0.04 #831, 0.01 #1191), 0cv3w (0.04 #989, 0.03 #3625, 0.03 #3864), 0b90_r (0.03 #837, 0.02 #957, 0.01 #2876), 0r62v (0.03 #851, 0.02 #971, 0.01 #2050), 0l38x (0.03 #940, 0.02 #1060, 0.01 #1181), 0fr0t (0.03 #880, 0.02 #1000, 0.01 #1121), 01cx_ (0.03 #870, 0.02 #990, 0.01 #1111) >> Best rule #243 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02knnd; 044bn; >> query: (?x2417, 0k049) <- film(?x2417, ?x9185), profession(?x2417, ?x1032), ?x9185 = 01lsl >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01csrl location_of_ceremony 0qr8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 148.000 0.200 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #9958-0392kz PRED entity: 0392kz PRED relation: film PRED expected values: 02vw1w2 0cks1m => 108 concepts (37 used for prediction) PRED predicted values (max 10 best out of 747): 0fgrm (0.48 #9741, 0.05 #52729, 0.04 #38399), 02v5xg (0.47 #30448, 0.29 #30447, 0.17 #3583), 02825kb (0.35 #10182, 0.04 #53170, 0.03 #38840), 02z5x7l (0.33 #1208, 0.15 #11952, 0.09 #10162), 0dh8v4 (0.33 #941, 0.15 #11685, 0.07 #8104), 06xkst (0.29 #30447, 0.12 #3582, 0.11 #3581), 017dtf (0.29 #30447, 0.11 #3581, 0.11 #60895), 031kyy (0.29 #30447, 0.11 #3581, 0.11 #60895), 08cl7s (0.29 #30447, 0.11 #3581, 0.11 #60895), 03d8jd1 (0.27 #8888, 0.05 #21424, 0.04 #25007) >> Best rule #9741 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 032xhg; 032w8h; 08vr94; 0fby2t; 03dpqd; 023v4_; 0h27vc; 01gbb4; 018yj6; 01xpxv; ... >> query: (?x10231, 0fgrm) <- film(?x10231, ?x297), film(?x296, ?x297), category(?x297, ?x134), special_performance_type(?x256, ?x296), profession(?x3853, ?x296) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #10957 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 08p1gp; *> query: (?x10231, 02vw1w2) <- profession(?x10231, ?x1383), gender(?x10231, ?x514), special_performance_type(?x10231, ?x296), ?x296 = 01kyvx *> conf = 0.19 ranks of expected_values: 12, 44 EVAL 0392kz film 0cks1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 108.000 37.000 0.478 http://example.org/film/actor/film./film/performance/film EVAL 0392kz film 02vw1w2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 108.000 37.000 0.478 http://example.org/film/actor/film./film/performance/film #9957-0fcsd PRED entity: 0fcsd PRED relation: artists! PRED expected values: 0xhtw => 91 concepts (61 used for prediction) PRED predicted values (max 10 best out of 292): 016clz (0.74 #15599, 0.58 #4054, 0.50 #316), 0xhtw (0.66 #5936, 0.58 #6874, 0.52 #5002), 064t9 (0.64 #6558, 0.56 #15919, 0.55 #11551), 025sc50 (0.45 #673, 0.33 #50, 0.32 #1607), 06j6l (0.45 #671, 0.31 #6592, 0.30 #982), 0ggq0m (0.43 #11237, 0.42 #7180, 0.10 #4997), 05w3f (0.41 #1283, 0.33 #6894, 0.33 #5956), 03lty (0.35 #6884, 0.33 #5946, 0.29 #5634), 0glt670 (0.35 #664, 0.29 #1598, 0.28 #3467), 0gywn (0.33 #6602, 0.33 #58, 0.26 #4419) >> Best rule #15599 for best value: >> intensional similarity = 7 >> extensional distance = 281 >> proper extension: 01cv3n; 01tp5bj; 06x4l_; 01m65sp; 02bh9; 037hgm; 01vswwx; 01kd57; 03xnq9_; 02bgmr; ... >> query: (?x4461, 016clz) <- artists(?x1380, ?x4461), artists(?x1380, ?x8849), artists(?x1380, ?x5057), artists(?x1380, ?x2242), ?x2242 = 09prnq, artist(?x2931, ?x8849), ?x5057 = 01w3lzq >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #5936 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 74 *> proper extension: 02t3ln; *> query: (?x4461, 0xhtw) <- group(?x1166, ?x4461), ?x1166 = 05148p4, artists(?x1380, ?x4461), artists(?x1380, ?x7896), artists(?x1380, ?x764), ?x7896 = 03k3b, performance_role(?x764, ?x228) *> conf = 0.66 ranks of expected_values: 2 EVAL 0fcsd artists! 0xhtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 61.000 0.739 http://example.org/music/genre/artists #9956-0dwh5 PRED entity: 0dwh5 PRED relation: contains! PRED expected values: 01n7q => 100 concepts (40 used for prediction) PRED predicted values (max 10 best out of 441): 01n7q (0.83 #4543, 0.79 #970, 0.77 #25038), 07c5l (0.42 #5360, 0.08 #29015, 0.08 #29913), 0kpys (0.21 #1072, 0.21 #3752, 0.19 #8218), 02qkt (0.17 #30765, 0.17 #28070, 0.17 #28968), 04_1l0v (0.17 #26383, 0.17 #23696, 0.10 #28173), 0cb4j (0.16 #927, 0.14 #1820, 0.13 #2713), 05kj_ (0.13 #15237, 0.11 #18816, 0.11 #20606), 0l2xl (0.12 #4899, 0.05 #31314, 0.05 #29517), 0l2vz (0.12 #4740, 0.04 #8313, 0.02 #12784), 0d060g (0.12 #5372, 0.11 #6265, 0.09 #10733) >> Best rule #4543 for best value: >> intensional similarity = 6 >> extensional distance = 22 >> proper extension: 0r1jr; 0f04c; 0l2hf; 0l34j; 0gjcy; 0l2vz; 0r5wt; 0r5y9; 0kpzy; 0gdk0; ... >> query: (?x14094, 01n7q) <- source(?x14094, ?x958), ?x958 = 0jbk9, contains(?x6815, ?x14094), contains(?x2632, ?x14094), ?x2632 = 06pvr, contains(?x1227, ?x6815) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dwh5 contains! 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 40.000 0.833 http://example.org/location/location/contains #9955-05yh_t PRED entity: 05yh_t PRED relation: nominated_for PRED expected values: 0180mw => 90 concepts (31 used for prediction) PRED predicted values (max 10 best out of 217): 0f7hw (0.28 #12963, 0.28 #14586, 0.25 #34044), 04ghz4m (0.28 #12963, 0.28 #14586, 0.25 #34044), 01cssf (0.28 #12963, 0.28 #14586, 0.25 #34044), 0180mw (0.14 #2658, 0.12 #4278, 0.09 #43775), 0g60z (0.09 #16210, 0.09 #43775, 0.09 #1661), 030p35 (0.09 #16210, 0.09 #43775, 0.09 #2339), 0gxsh4 (0.09 #16210, 0.09 #43775, 0.06 #3182), 0gvsh7l (0.09 #16210, 0.07 #1275, 0.03 #2895), 0vjr (0.09 #16210, 0.07 #855, 0.01 #10576), 02sqkh (0.09 #16210, 0.02 #3958) >> Best rule #12963 for best value: >> intensional similarity = 3 >> extensional distance = 689 >> proper extension: 079vf; 0c01c; 02wr2r; 06_bq1; 01d6jf; 05g7q; >> query: (?x5729, ?x638) <- award_winner(?x192, ?x5729), gender(?x5729, ?x231), film(?x5729, ?x638) >> conf = 0.28 => this is the best rule for 3 predicted values *> Best rule #2658 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 04264n; 03pp73; 06sn8m; 03swmf; *> query: (?x5729, 0180mw) <- award(?x5729, ?x435), people(?x2510, ?x5729), ?x435 = 0bp_b2 *> conf = 0.14 ranks of expected_values: 4 EVAL 05yh_t nominated_for 0180mw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 31.000 0.281 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9954-04gxp2 PRED entity: 04gxp2 PRED relation: educational_institution PRED expected values: 04gxp2 => 98 concepts (45 used for prediction) PRED predicted values (max 10 best out of 133): 02bq1j (0.25 #155, 0.23 #7552, 0.21 #21049), 07szy (0.25 #35, 0.23 #7552, 0.21 #21049), 04gxp2 (0.23 #7552, 0.21 #21049, 0.17 #21050), 08815 (0.09 #541, 0.03 #3240, 0.02 #4318), 0ymf1 (0.09 #1077, 0.02 #4315, 0.01 #5933), 02ckl3 (0.09 #980, 0.02 #4218, 0.01 #5836), 017v71 (0.09 #720, 0.02 #4497, 0.01 #5576), 032r4n (0.09 #1029, 0.01 #5885, 0.01 #6963), 02zd460 (0.05 #1238, 0.03 #3396, 0.02 #3935), 02fgdx (0.05 #1176, 0.03 #3334, 0.02 #4952) >> Best rule #155 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 07szy; 02bq1j; >> query: (?x13215, 02bq1j) <- student(?x13215, ?x9684), institution(?x1519, ?x13215), ?x9684 = 0c_md_, currency(?x13215, ?x170) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #7552 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 79 *> proper extension: 01dnnt; *> query: (?x13215, ?x1681) <- student(?x13215, ?x9684), basic_title(?x9684, ?x265), student(?x1681, ?x9684), jurisdiction_of_office(?x265, ?x94) *> conf = 0.23 ranks of expected_values: 3 EVAL 04gxp2 educational_institution 04gxp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 45.000 0.250 http://example.org/education/educational_institution_campus/educational_institution #9953-03f70xs PRED entity: 03f70xs PRED relation: influenced_by! PRED expected values: 01wd02c 037jz => 128 concepts (60 used for prediction) PRED predicted values (max 10 best out of 465): 07lp1 (0.50 #1419, 0.43 #2935, 0.33 #2429), 03qcq (0.50 #1010, 0.33 #506, 0.17 #2020), 041mt (0.33 #73, 0.25 #1586, 0.25 #1082), 0683n (0.33 #333, 0.25 #1846, 0.23 #3363), 014ps4 (0.33 #306, 0.25 #1819, 0.19 #4347), 08433 (0.33 #534, 0.25 #1038, 0.17 #13627), 01vsl3_ (0.33 #603, 0.25 #1107, 0.17 #2117), 03j24kf (0.33 #688, 0.25 #1192, 0.17 #2202), 084w8 (0.33 #2, 0.25 #1515, 0.15 #3032), 01v_0b (0.33 #474, 0.25 #1987, 0.15 #3504) >> Best rule #1419 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 08433; >> query: (?x2625, 07lp1) <- influenced_by(?x9519, ?x2625), influenced_by(?x8004, ?x2625), influenced_by(?x2120, ?x2625), ?x2120 = 05qw5, artists(?x1000, ?x8004), ?x1000 = 0xhtw, profession(?x9519, ?x319) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13626 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 167 *> proper extension: 07scx; *> query: (?x2625, ?x117) <- influenced_by(?x8004, ?x2625), influenced_by(?x2845, ?x2625), influenced_by(?x8149, ?x8004), peers(?x4608, ?x8004), influenced_by(?x117, ?x2845) *> conf = 0.12 ranks of expected_values: 77, 119 EVAL 03f70xs influenced_by! 037jz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 128.000 60.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 03f70xs influenced_by! 01wd02c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 128.000 60.000 0.500 http://example.org/influence/influence_node/influenced_by #9952-0hnf5vm PRED entity: 0hnf5vm PRED relation: award! PRED expected values: 0mdqp => 45 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2727): 01_x6v (0.75 #74113, 0.69 #64005, 0.69 #53898), 01_x6d (0.75 #74113, 0.69 #64005, 0.69 #53898), 0bz60q (0.33 #5391, 0.18 #37052, 0.18 #16840), 086nl7 (0.33 #4636, 0.18 #37052, 0.18 #16840), 03xp8d5 (0.33 #4607, 0.08 #28185, 0.08 #31554), 021yw7 (0.33 #4373, 0.05 #11111, 0.05 #34689), 01kws3 (0.33 #4942, 0.04 #15048, 0.04 #18416), 0gz5hs (0.33 #3876, 0.02 #13982, 0.02 #17350), 04s04 (0.33 #5709, 0.02 #15815, 0.02 #19183), 03xpf_7 (0.33 #4178, 0.02 #14284, 0.02 #17652) >> Best rule #74113 for best value: >> intensional similarity = 4 >> extensional distance = 235 >> proper extension: 0ddd9; 02flpc; >> query: (?x3646, ?x2390) <- award(?x4360, ?x3646), award_winner(?x3646, ?x2390), participant(?x4360, ?x5625), participant(?x3627, ?x4360) >> conf = 0.75 => this is the best rule for 2 predicted values *> Best rule #10272 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 0bsjcw; 0gr07; 0bb57s; *> query: (?x3646, 0mdqp) <- award(?x4360, ?x3646), award_nominee(?x4328, ?x4360), ?x4328 = 01z7_f, nominated_for(?x4360, ?x2177) *> conf = 0.16 ranks of expected_values: 69 EVAL 0hnf5vm award! 0mdqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 45.000 23.000 0.747 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9951-07xvf PRED entity: 07xvf PRED relation: film_crew_role PRED expected values: 01vx2h => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 32): 01vx2h (0.66 #488, 0.44 #1585, 0.42 #909), 02ynfr (0.25 #492, 0.24 #750, 0.24 #236), 089fss (0.20 #5, 0.12 #3069, 0.10 #1582), 01xy5l_ (0.17 #490, 0.13 #362, 0.13 #394), 015h31 (0.17 #487, 0.12 #3069, 0.12 #908), 0215hd (0.16 #916, 0.15 #175, 0.15 #367), 0d2b38 (0.15 #502, 0.15 #182, 0.14 #923), 089g0h (0.15 #917, 0.12 #3069, 0.12 #496), 02vs3x5 (0.12 #3069, 0.09 #468, 0.08 #52), 04pyp5 (0.12 #3069, 0.09 #1267, 0.08 #2081) >> Best rule #488 for best value: >> intensional similarity = 5 >> extensional distance = 108 >> proper extension: 01q2nx; >> query: (?x7373, 01vx2h) <- film_crew_role(?x7373, ?x2091), film_crew_role(?x7373, ?x137), ?x2091 = 02rh1dz, film_crew_role(?x9599, ?x137), ?x9599 = 07l450 >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07xvf film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.664 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9950-01hw6wq PRED entity: 01hw6wq PRED relation: type_of_union PRED expected values: 04ztj => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.73 #77, 0.73 #73, 0.72 #97), 01g63y (0.33 #6, 0.20 #451, 0.19 #225), 0jgjn (0.20 #451, 0.19 #225), 01bl8s (0.20 #451, 0.19 #225) >> Best rule #77 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 02rgz4; 01nqfh_; 0k4gf; 04k15; 0jn5l; 082db; 03_f0; 0hr3g; 063tn; 03f4k; ... >> query: (?x2363, 04ztj) <- profession(?x2363, ?x131), music(?x10130, ?x2363), instrumentalists(?x227, ?x2363), genre(?x10130, ?x225) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hw6wq type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.730 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9949-0948xk PRED entity: 0948xk PRED relation: people! PRED expected values: 03lmx1 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 58): 02ctzb (0.39 #632, 0.29 #1017, 0.29 #940), 07bch9 (0.33 #717, 0.33 #640, 0.29 #2027), 063k3h (0.33 #31, 0.24 #1418, 0.22 #2882), 07hwkr (0.33 #12, 0.12 #2940, 0.10 #937), 03lmx1 (0.25 #477, 0.16 #862, 0.12 #168), 033tf_ (0.22 #701, 0.18 #1703, 0.18 #1626), 02g7sp (0.20 #95, 0.10 #249, 0.07 #2715), 02w7gg (0.20 #4867, 0.19 #5098, 0.17 #6794), 0x67 (0.20 #5799, 0.19 #1012, 0.17 #6725), 041rx (0.16 #8105, 0.16 #8414, 0.15 #3317) >> Best rule #632 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 06c0j; >> query: (?x9680, 02ctzb) <- profession(?x9680, ?x2659), jurisdiction_of_office(?x9680, ?x512), award_winner(?x3846, ?x9680), basic_title(?x9680, ?x182), nationality(?x111, ?x512) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #477 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 011zwl; *> query: (?x9680, 03lmx1) <- nationality(?x9680, ?x6401), nationality(?x9680, ?x512), ?x6401 = 06q1r, student(?x892, ?x9680), ?x512 = 07ssc *> conf = 0.25 ranks of expected_values: 5 EVAL 0948xk people! 03lmx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 162.000 162.000 0.389 http://example.org/people/ethnicity/people #9948-09p30_ PRED entity: 09p30_ PRED relation: honored_for PRED expected values: 0l76z => 37 concepts (32 used for prediction) PRED predicted values (max 10 best out of 641): 049xgc (0.40 #2679, 0.17 #13533, 0.17 #17653), 04b2qn (0.40 #2802, 0.10 #12943, 0.10 #12352), 04q827 (0.40 #2899, 0.05 #6424, 0.04 #5835), 03xf_m (0.40 #2725, 0.05 #6250, 0.04 #5661), 04nl83 (0.40 #2368, 0.05 #5893, 0.04 #5304), 05zr0xl (0.33 #1066, 0.25 #3999, 0.22 #4585), 0524b41 (0.33 #1002, 0.25 #3935, 0.22 #4521), 0ddd0gc (0.33 #664, 0.25 #3597, 0.22 #4183), 0gvsh7l (0.33 #1059, 0.25 #3992, 0.22 #4578), 0l76z (0.33 #271, 0.25 #1444, 0.20 #2029) >> Best rule #2679 for best value: >> intensional similarity = 13 >> extensional distance = 3 >> proper extension: 050yyb; 09p2r9; >> query: (?x6238, 049xgc) <- award_winner(?x6238, ?x8134), award_winner(?x6238, ?x2551), award_winner(?x6238, ?x1179), award_winner(?x6238, ?x1119), ?x1179 = 05m883, film(?x8134, ?x1444), award(?x1119, ?x112), nationality(?x1119, ?x94), film(?x2551, ?x1318), award_nominee(?x1119, ?x71), award_winner(?x6236, ?x8134), award_winner(?x1770, ?x1119), award_nominee(?x2551, ?x525) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #271 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 09p3h7; *> query: (?x6238, 0l76z) <- award_winner(?x6238, ?x8375), award_winner(?x6238, ?x1119), ?x1119 = 039bp, honored_for(?x6238, ?x6048), nationality(?x8375, ?x94), film(?x2307, ?x6048), nominated_for(?x1245, ?x6048), nominated_for(?x1585, ?x6048), country(?x6048, ?x279), titles(?x53, ?x6048), ?x1245 = 0gqwc, languages(?x8375, ?x254) *> conf = 0.33 ranks of expected_values: 10 EVAL 09p30_ honored_for 0l76z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 37.000 32.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #9947-0nm9h PRED entity: 0nm9h PRED relation: adjoins PRED expected values: 0nm3n => 164 concepts (67 used for prediction) PRED predicted values (max 10 best out of 450): 0nm42 (0.40 #3418, 0.25 #2646, 0.20 #4191), 0nm3n (0.33 #3862, 0.25 #50287, 0.25 #50286), 0n5_t (0.33 #3862, 0.25 #50287, 0.25 #50286), 0nm9h (0.33 #3862, 0.25 #50287, 0.24 #39445), 0n5xb (0.25 #3019, 0.25 #2247, 0.20 #3791), 0nm8n (0.25 #2871, 0.20 #4416, 0.20 #3643), 0nm9y (0.25 #3069, 0.20 #4614, 0.20 #3841), 0d060g (0.25 #782, 0.18 #29387, 0.08 #42543), 059f4 (0.25 #806, 0.18 #29387, 0.08 #42543), 0694j (0.25 #1068, 0.18 #29387, 0.08 #42543) >> Best rule #3418 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0nm9y; >> query: (?x12290, 0nm42) <- contains(?x7058, ?x12290), time_zones(?x12290, ?x2674), ?x7058 = 050ks, adjoins(?x7954, ?x12290), adjoins(?x7330, ?x7954) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3862 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0nm9y; *> query: (?x12290, ?x7330) <- contains(?x7058, ?x12290), time_zones(?x12290, ?x2674), ?x7058 = 050ks, adjoins(?x7954, ?x12290), adjoins(?x7330, ?x7954) *> conf = 0.33 ranks of expected_values: 2 EVAL 0nm9h adjoins 0nm3n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 164.000 67.000 0.400 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #9946-02dpl9 PRED entity: 02dpl9 PRED relation: titles! PRED expected values: 01hmnh => 79 concepts (61 used for prediction) PRED predicted values (max 10 best out of 75): 07c52 (0.85 #955, 0.64 #29, 0.21 #130), 07ssc (0.80 #1140, 0.61 #212, 0.24 #10), 09b3v (0.45 #150, 0.05 #1897, 0.05 #1998), 07s9rl0 (0.41 #3283, 0.41 #3181, 0.40 #3488), 03mdt (0.40 #44, 0.28 #145, 0.06 #970), 04xvlr (0.32 #206, 0.31 #1134, 0.28 #1852), 06n90 (0.25 #3282, 0.24 #3487, 0.24 #3178), 0g5lhl7 (0.24 #39, 0.11 #140, 0.05 #1169), 01jfsb (0.21 #2684, 0.15 #635, 0.14 #3404), 01z4y (0.21 #5373, 0.19 #4140, 0.18 #5475) >> Best rule #955 for best value: >> intensional similarity = 4 >> extensional distance = 160 >> proper extension: 01qn7n; 0g60z; 080dwhx; 06cs95; 072kp; 039fgy; 02k_4g; 07hpv3; 02nf2c; 019nnl; ... >> query: (?x3897, 07c52) <- titles(?x5607, ?x3897), major_field_of_study(?x5607, ?x90), major_field_of_study(?x122, ?x5607), major_field_of_study(?x865, ?x5607) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #127 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 0464pz; 0l76z; *> query: (?x3897, 01hmnh) <- titles(?x789, ?x3897), company(?x346, ?x789), titles(?x789, ?x11110), country(?x11110, ?x291) *> conf = 0.19 ranks of expected_values: 11 EVAL 02dpl9 titles! 01hmnh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 79.000 61.000 0.846 http://example.org/media_common/netflix_genre/titles #9945-0f8j13 PRED entity: 0f8j13 PRED relation: film! PRED expected values: 015pkc 0bksh 037gjc 01fx2g => 96 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1151): 09wj5 (0.27 #2175, 0.05 #116297, 0.03 #64383), 01l2fn (0.27 #2336, 0.05 #116297, 0.03 #64383), 01tsbmv (0.27 #3969, 0.05 #8122, 0.04 #18508), 0170pk (0.27 #2355, 0.03 #12739, 0.02 #25204), 07h5d (0.22 #20769, 0.20 #58152, 0.20 #76843), 0b_dy (0.21 #12990, 0.03 #8836, 0.03 #66461), 06lvlf (0.18 #3125, 0.03 #13509, 0.02 #15587), 02cllz (0.18 #2480, 0.02 #122526, 0.02 #25329), 014q2g (0.18 #2544, 0.02 #25393, 0.01 #29546), 023n39 (0.18 #3273, 0.01 #36505, 0.01 #115416) >> Best rule #2175 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01vksx; 03y0pn; 04165w; >> query: (?x9478, 09wj5) <- film_format(?x9478, ?x909), film(?x2444, ?x9478), genre(?x9478, ?x271), ?x2444 = 0jfx1 >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #8582 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 017gm7; 0gd0c7x; 031778; 0661ql3; 02ryz24; 03176f; 05qbbfb; 01mgw; *> query: (?x9478, 015pkc) <- film_format(?x9478, ?x909), film(?x7352, ?x9478), genre(?x9478, ?x811), film(?x395, ?x9478), ?x811 = 03k9fj *> conf = 0.07 ranks of expected_values: 116, 229 EVAL 0f8j13 film! 01fx2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 59.000 0.273 http://example.org/film/actor/film./film/performance/film EVAL 0f8j13 film! 037gjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 59.000 0.273 http://example.org/film/actor/film./film/performance/film EVAL 0f8j13 film! 0bksh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 96.000 59.000 0.273 http://example.org/film/actor/film./film/performance/film EVAL 0f8j13 film! 015pkc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 96.000 59.000 0.273 http://example.org/film/actor/film./film/performance/film #9944-01vvydl PRED entity: 01vvydl PRED relation: award_nominee! PRED expected values: 07pzc => 119 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1341): 01vsgrn (0.82 #23244, 0.81 #102285, 0.81 #55789), 05vzw3 (0.82 #23244, 0.81 #102285, 0.81 #55789), 01w9k25 (0.82 #23244, 0.81 #102285, 0.81 #55789), 07pzc (0.82 #23244, 0.81 #102285, 0.81 #55789), 01wwvc5 (0.27 #2923, 0.25 #5247, 0.09 #19194), 02l840 (0.27 #2480, 0.19 #4804, 0.17 #18751), 03bxwtd (0.27 #3009, 0.03 #172018, 0.02 #14632), 0837ql (0.25 #5788, 0.18 #3464, 0.05 #12761), 01wgxtl (0.25 #5248, 0.10 #19195, 0.09 #2924), 01wd9lv (0.25 #1463, 0.06 #10759, 0.06 #6111) >> Best rule #23244 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 01pcq3; 03d_w3h; 0svqs; 01_p6t; 01f7dd; 02624g; 02q3bb; 0hz_1; >> query: (?x140, ?x527) <- award_winner(?x1827, ?x140), award_nominee(?x140, ?x527), diet(?x140, ?x3130) >> conf = 0.82 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 01vvydl award_nominee! 07pzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 119.000 74.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9943-01q3_2 PRED entity: 01q3_2 PRED relation: artists! PRED expected values: 06by7 02qdgx => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 220): 06by7 (0.76 #647, 0.74 #1584, 0.61 #22), 0xhtw (0.49 #1579, 0.38 #1266, 0.32 #4079), 016clz (0.38 #1567, 0.34 #630, 0.32 #1254), 02yv6b (0.38 #725, 0.31 #1662, 0.18 #413), 0155w (0.31 #733, 0.20 #1670, 0.18 #421), 017_qw (0.29 #3189, 0.21 #2876, 0.19 #6312), 03lty (0.25 #1278, 0.21 #4091, 0.17 #1591), 06j6l (0.25 #9107, 0.25 #49, 0.23 #8795), 0glt670 (0.25 #4416, 0.24 #5040, 0.20 #9100), 05w3f (0.24 #664, 0.21 #352, 0.21 #39) >> Best rule #647 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 02r3zy; 03g5jw; 0dvqq; 0gr69; 03vhvp; >> query: (?x9731, 06by7) <- award(?x9731, ?x9828), award_nominee(?x9731, ?x568), ?x9828 = 01ckcd >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 25 EVAL 01q3_2 artists! 02qdgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 104.000 104.000 0.759 http://example.org/music/genre/artists EVAL 01q3_2 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.759 http://example.org/music/genre/artists #9942-0nlh7 PRED entity: 0nlh7 PRED relation: contains PRED expected values: 01k3s2 => 172 concepts (139 used for prediction) PRED predicted values (max 10 best out of 2294): 0pmpl (0.10 #276937, 0.08 #229789, 0.08 #109011), 0nlh7 (0.10 #276937, 0.08 #229789, 0.08 #271043), 016w7b (0.10 #23031, 0.05 #37761, 0.03 #28923), 052nd (0.08 #23621, 0.05 #20676, 0.02 #38351), 0778_3 (0.08 #25893, 0.05 #22948, 0.02 #40623), 022jr5 (0.08 #24309, 0.05 #39039, 0.01 #95017), 05gm16l (0.08 #24997, 0.02 #39727, 0.01 #95705), 01k3s2 (0.08 #300505, 0.07 #318179, 0.07 #332915), 01n7rc (0.08 #229789, 0.08 #271043, 0.02 #48819), 02gw_w (0.07 #11393, 0.04 #85048, 0.04 #82102) >> Best rule #276937 for best value: >> intensional similarity = 4 >> extensional distance = 257 >> proper extension: 05fly; >> query: (?x10718, ?x1637) <- location(?x6217, ?x10718), place_of_birth(?x6217, ?x1637), country(?x10718, ?x279), type_of_union(?x6217, ?x566) >> conf = 0.10 => this is the best rule for 2 predicted values *> Best rule #300505 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 308 *> proper extension: 0rj0z; *> query: (?x10718, ?x4342) <- location(?x8256, ?x10718), location(?x6217, ?x10718), category(?x10718, ?x134), profession(?x6217, ?x319), student(?x4342, ?x8256) *> conf = 0.08 ranks of expected_values: 8 EVAL 0nlh7 contains 01k3s2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 172.000 139.000 0.099 http://example.org/location/location/contains #9941-0fq5j PRED entity: 0fq5j PRED relation: time_zones PRED expected values: 0gsrz4 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.34 #42, 0.32 #432, 0.31 #146), 02lcqs (0.22 #148, 0.14 #356, 0.14 #369), 02llzg (0.16 #82, 0.15 #30, 0.11 #264), 02fqwt (0.12 #40, 0.10 #430, 0.10 #456), 03bdv (0.09 #175, 0.09 #123, 0.08 #19), 02hczc (0.08 #41, 0.04 #379, 0.04 #392), 03plfd (0.06 #23, 0.05 #75, 0.03 #88), 0gsrz4 (0.06 #21, 0.04 #73, 0.01 #411), 052vwh (0.06 #64, 0.03 #90, 0.02 #103) >> Best rule #42 for best value: >> intensional similarity = 5 >> extensional distance = 143 >> proper extension: 0njvn; 0wh3; 04ykg; 03s5t; 01n4w; 0d0x8; 07b_l; 05fky; 0nj07; 0n5yv; ... >> query: (?x14579, 02hcv8) <- contains(?x1174, ?x14579), contains(?x9122, ?x1174), adjoins(?x1174, ?x4092), partially_contains(?x5903, ?x9122), organization(?x4092, ?x127) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 47 *> proper extension: 06tw8; 06cmp; 0hkt6; *> query: (?x14579, 0gsrz4) <- contains(?x1174, ?x14579), contains(?x9122, ?x1174), contains(?x9122, ?x608), ?x608 = 02k54, locations(?x326, ?x9122), service_location(?x555, ?x9122) *> conf = 0.06 ranks of expected_values: 8 EVAL 0fq5j time_zones 0gsrz4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 102.000 102.000 0.345 http://example.org/location/location/time_zones #9940-02ldmw PRED entity: 02ldmw PRED relation: major_field_of_study PRED expected values: 05qjc => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 111): 04rjg (0.67 #21, 0.35 #525, 0.35 #777), 01lj9 (0.67 #42, 0.26 #546, 0.24 #924), 02_7t (0.56 #68, 0.35 #824, 0.32 #572), 02lp1 (0.56 #12, 0.34 #2910, 0.32 #516), 03g3w (0.56 #28, 0.32 #784, 0.32 #658), 041y2 (0.56 #82, 0.21 #838, 0.20 #964), 01mkq (0.48 #520, 0.44 #898, 0.44 #16), 02j62 (0.47 #662, 0.44 #32, 0.44 #788), 062z7 (0.44 #29, 0.32 #659, 0.32 #533), 02jfc (0.44 #87, 0.32 #843, 0.29 #591) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 017j69; >> query: (?x7744, 04rjg) <- student(?x7744, ?x5915), currency(?x7744, ?x170), cast_members(?x905, ?x5915), profession(?x5915, ?x1032) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7438 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 329 *> proper extension: 06pwq; 0kz2w; 0gkkf; 027mdh; 0ymcz; *> query: (?x7744, ?x2605) <- school_type(?x7744, ?x3092), school_type(?x11318, ?x3092), school_type(?x9110, ?x3092), ?x11318 = 02ldkf, major_field_of_study(?x9110, ?x2605) *> conf = 0.05 ranks of expected_values: 97 EVAL 02ldmw major_field_of_study 05qjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 114.000 114.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #9939-03mnn0 PRED entity: 03mnn0 PRED relation: genre PRED expected values: 017fp => 116 concepts (94 used for prediction) PRED predicted values (max 10 best out of 180): 07s9rl0 (0.98 #9789, 0.93 #10147, 0.91 #5003), 05p553 (0.96 #7518, 0.95 #7637, 0.75 #8114), 02kdv5l (0.93 #7754, 0.93 #7873, 0.43 #8351), 01hmnh (0.88 #7413, 0.77 #6933, 0.40 #6335), 0hcr (0.78 #6341, 0.78 #6699, 0.72 #6103), 01jfsb (0.78 #8362, 0.66 #8844, 0.52 #7884), 03g3w (0.69 #6223, 0.65 #5984, 0.34 #7180), 03bxz7 (0.68 #6612, 0.56 #1844, 0.50 #1606), 04t36 (0.50 #2270, 0.50 #1675, 0.48 #7282), 03k9fj (0.48 #6687, 0.48 #6329, 0.47 #6927) >> Best rule #9789 for best value: >> intensional similarity = 7 >> extensional distance = 865 >> proper extension: 02d413; 014_x2; 018js4; 0sxg4; 083shs; 0140g4; 01jc6q; 08lr6s; 0ds3t5x; 095zlp; ... >> query: (?x6125, 07s9rl0) <- genre(?x6125, ?x8681), film(?x5362, ?x6125), genre(?x2920, ?x8681), genre(?x1218, ?x8681), ?x1218 = 02prw4h, ?x2920 = 0b1y_2, genre(?x9188, ?x8681) >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #6213 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 92 *> proper extension: 0qm8b; 02qr69m; 07w8fz; 034r25; 043n0v_; 017180; 02z0f6l; 0b4lkx; 07tlfx; 09rfpk; *> query: (?x6125, 017fp) <- genre(?x6125, ?x8681), film_crew_role(?x6125, ?x137), major_field_of_study(?x8681, ?x2164), genre(?x9501, ?x8681), genre(?x8495, ?x8681), film(?x92, ?x8495), produced_by(?x9501, ?x8019), nominated_for(?x2156, ?x8495) *> conf = 0.28 ranks of expected_values: 17 EVAL 03mnn0 genre 017fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 116.000 94.000 0.984 http://example.org/film/film/genre #9938-01rr31 PRED entity: 01rr31 PRED relation: category PRED expected values: 08mbj5d => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #56, 0.91 #19, 0.91 #37) >> Best rule #56 for best value: >> intensional similarity = 4 >> extensional distance = 318 >> proper extension: 0ymc8; 0ymb6; 0ym69; >> query: (?x4845, 08mbj5d) <- currency(?x4845, ?x170), state_province_region(?x4845, ?x13030), currency(?x54, ?x170), currency(?x65, ?x170) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rr31 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.909 http://example.org/common/topic/webpage./common/webpage/category #9937-0824r PRED entity: 0824r PRED relation: location! PRED expected values: 04t969 01gvxv 06nsb9 => 188 concepts (127 used for prediction) PRED predicted values (max 10 best out of 1542): 03nb5v (0.15 #16409, 0.15 #3837, 0.14 #8866), 06jw0s (0.14 #11203, 0.10 #31316, 0.10 #36344), 02xyl (0.11 #7543, 0.03 #12516, 0.03 #10002), 094xh (0.10 #8621, 0.10 #3592, 0.09 #13650), 023s8 (0.10 #12165, 0.10 #4622, 0.09 #17194), 0cgbf (0.10 #11451, 0.10 #3908, 0.09 #16480), 0gs5q (0.10 #11831, 0.06 #26916, 0.06 #29430), 0c01c (0.10 #10532, 0.06 #30645, 0.06 #15561), 02ghq (0.10 #4703, 0.07 #12246, 0.07 #9732), 01yzhn (0.10 #4643, 0.07 #12186, 0.06 #27271) >> Best rule #16409 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 01914; >> query: (?x4105, 03nb5v) <- state_province_region(?x5581, ?x4105), adjoins(?x961, ?x4105), location_of_ceremony(?x566, ?x4105) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #173483 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 146 *> proper extension: 049nq; *> query: (?x4105, ?x4039) <- contains(?x4105, ?x14503), place_of_birth(?x4039, ?x14503), administrative_parent(?x4105, ?x94) *> conf = 0.06 ranks of expected_values: 45 EVAL 0824r location! 06nsb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 188.000 127.000 0.152 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0824r location! 01gvxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 188.000 127.000 0.152 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0824r location! 04t969 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 188.000 127.000 0.152 http://example.org/people/person/places_lived./people/place_lived/location #9936-03jl0_ PRED entity: 03jl0_ PRED relation: program PRED expected values: 095sx6 => 122 concepts (42 used for prediction) PRED predicted values (max 10 best out of 259): 04glx0 (0.43 #3280, 0.30 #4502, 0.27 #4991), 097h2 (0.43 #3331, 0.30 #4553, 0.27 #5042), 03g9xj (0.33 #170, 0.20 #4078, 0.10 #7992), 03y317 (0.33 #154, 0.20 #4062, 0.10 #7976), 0vhm (0.33 #78, 0.12 #6677, 0.10 #3986), 0cskb (0.33 #176, 0.12 #6775, 0.10 #4084), 028k2x (0.33 #126, 0.12 #6725, 0.10 #4034), 03bww6 (0.33 #116, 0.10 #4024, 0.07 #10139), 025x1t (0.33 #200, 0.10 #4108, 0.06 #6799), 0123qq (0.33 #198, 0.10 #4106, 0.06 #6797) >> Best rule #3280 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0kc6x; 0gsgr; >> query: (?x8760, 04glx0) <- company(?x8314, ?x8760), ?x8314 = 014l7h, category(?x8760, ?x134), citytown(?x8760, ?x12585), company(?x7153, ?x8760), profession(?x7153, ?x319) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4154 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 01nzs7; 0b275x; 0kctd; *> query: (?x8760, ?x419) <- program(?x8760, ?x8759), category(?x8760, ?x134), genre(?x8759, ?x571), genre(?x8759, ?x258), ?x571 = 03npn, genre(?x86, ?x258), genre(?x419, ?x258) *> conf = 0.06 ranks of expected_values: 221 EVAL 03jl0_ program 095sx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 122.000 42.000 0.429 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #9935-032q8q PRED entity: 032q8q PRED relation: student! PRED expected values: 03qsdpk => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 12): 03qsdpk (0.15 #160, 0.14 #36, 0.13 #284), 0557q (0.04 #362), 05qjc (0.04 #348), 05lls (0.04 #317), 02822 (0.04 #778, 0.04 #527, 0.04 #967), 01zc2w (0.02 #606, 0.02 #669, 0.02 #921), 0w7c (0.02 #852, 0.02 #414, 0.02 #663), 041y2 (0.02 #423, 0.02 #485), 03g3w (0.02 #455, 0.01 #957, 0.01 #831), 0fdys (0.02 #965, 0.01 #1028, 0.01 #587) >> Best rule #160 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 02qw2xb; >> query: (?x6398, 03qsdpk) <- award_nominee(?x6398, ?x2360), award_nominee(?x6398, ?x823), ?x823 = 064nh4k, ?x2360 = 080knyg >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 032q8q student! 03qsdpk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.154 http://example.org/education/field_of_study/students_majoring./education/education/student #9934-05bnq8 PRED entity: 05bnq8 PRED relation: contains! PRED expected values: 01qh7 => 107 concepts (61 used for prediction) PRED predicted values (max 10 best out of 195): 09c7w0 (0.83 #15255, 0.79 #12564, 0.78 #11668), 05k7sb (0.75 #11665, 0.73 #48459, 0.72 #50254), 0d060g (0.51 #34088, 0.47 #44863, 0.46 #18840), 02jx1 (0.40 #2777, 0.33 #1880, 0.15 #14441), 01qh7 (0.33 #189, 0.25 #1085, 0.22 #1982), 01cx_ (0.28 #4683, 0.26 #7377, 0.07 #3785), 059rby (0.28 #11685, 0.26 #12581, 0.26 #8995), 01n7q (0.25 #974, 0.12 #22508, 0.11 #9053), 030qb3t (0.25 #997, 0.11 #1894, 0.06 #9076), 0978r (0.22 #2000, 0.20 #2897, 0.06 #10078) >> Best rule #15255 for best value: >> intensional similarity = 8 >> extensional distance = 50 >> proper extension: 02bjhv; 037njl; 02h30z; >> query: (?x9827, 09c7w0) <- school_type(?x9827, ?x3205), school_type(?x9827, ?x1044), ?x1044 = 05pcjw, school_type(?x12869, ?x3205), school_type(?x1103, ?x3205), ?x1103 = 01k2wn, institution(?x1200, ?x12869), category(?x9827, ?x134) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #189 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 03ksy; *> query: (?x9827, 01qh7) <- student(?x9827, ?x11077), school_type(?x9827, ?x3205), category(?x9827, ?x134), ?x11077 = 0d__g, school_type(?x10421, ?x3205), school_type(?x3576, ?x3205), ?x3576 = 012fvq, ?x10421 = 02qw_v *> conf = 0.33 ranks of expected_values: 5 EVAL 05bnq8 contains! 01qh7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 107.000 61.000 0.827 http://example.org/location/location/contains #9933-02j490 PRED entity: 02j490 PRED relation: nationality PRED expected values: 09c7w0 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 82): 09c7w0 (0.90 #401, 0.81 #201, 0.79 #101), 07ssc (0.30 #10029, 0.11 #2520, 0.11 #1418), 0f8l9c (0.30 #10029, 0.03 #522, 0.03 #1224), 02jx1 (0.12 #4043, 0.11 #4444, 0.11 #3742), 03rjj (0.11 #405, 0.05 #1207, 0.04 #805), 03rk0 (0.10 #3352, 0.08 #3956, 0.08 #4959), 0chghy (0.08 #10, 0.03 #310, 0.03 #610), 0d060g (0.05 #908, 0.05 #1008, 0.05 #3012), 06q1r (0.04 #177, 0.03 #277, 0.02 #5994), 0345h (0.03 #1333, 0.03 #3137, 0.02 #4944) >> Best rule #401 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 047hpm; 04cr6qv; 0gs6vr; 02jyhv; 09nhvw; 022q4j; >> query: (?x10897, 09c7w0) <- film(?x10897, ?x4093), people(?x3591, ?x10897), ?x3591 = 0xnvg >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02j490 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.899 http://example.org/people/person/nationality #9932-03g90h PRED entity: 03g90h PRED relation: film_crew_role PRED expected values: 0263ycg => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 32): 02r96rf (0.80 #1328, 0.78 #612, 0.76 #1555), 09zzb8 (0.79 #2136, 0.78 #1326, 0.78 #2760), 01vx2h (0.71 #75, 0.62 #107, 0.55 #139), 01xy5l_ (0.63 #398, 0.57 #655, 0.55 #785), 0d2b38 (0.59 #408, 0.58 #795, 0.57 #665), 01pvkk (0.57 #76, 0.38 #108, 0.37 #460), 0dxtw (0.51 #1464, 0.50 #1529, 0.49 #1335), 02rh1dz (0.29 #73, 0.27 #329, 0.27 #618), 0263ycg (0.25 #18, 0.10 #659, 0.08 #210), 02ynfr (0.24 #272, 0.23 #464, 0.20 #1341) >> Best rule #1328 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 083shs; 0340hj; 047qxs; 0c34mt; 032zq6; 01hqk; 0prh7; 05tgks; 0bl3nn; 0dp7wt; ... >> query: (?x280, 02r96rf) <- country(?x280, ?x94), film_crew_role(?x280, ?x1171), story_by(?x280, ?x3806), ?x1171 = 09vw2b7 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #18 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 05h43ls; *> query: (?x280, 0263ycg) <- country(?x280, ?x94), produced_by(?x280, ?x3806), film_distribution_medium(?x280, ?x81), ?x81 = 029j_, film_crew_role(?x280, ?x5136), ?x5136 = 089g0h *> conf = 0.25 ranks of expected_values: 9 EVAL 03g90h film_crew_role 0263ycg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 118.000 118.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9931-042v_gx PRED entity: 042v_gx PRED relation: role! PRED expected values: 01wy6 => 81 concepts (71 used for prediction) PRED predicted values (max 10 best out of 78): 0342h (0.91 #3969, 0.89 #3220, 0.86 #2189), 05r5c (0.85 #943, 0.83 #2626, 0.83 #1893), 018vs (0.85 #943, 0.83 #2626, 0.83 #1893), 02k856 (0.85 #943, 0.83 #2626, 0.83 #1893), 06w7v (0.85 #943, 0.83 #2626, 0.83 #1893), 0gkd1 (0.85 #943, 0.83 #2626, 0.83 #1893), 01qzyz (0.85 #943, 0.83 #2626, 0.83 #1893), 0j862 (0.85 #943, 0.83 #2626, 0.83 #1893), 042v_gx (0.83 #3375, 0.83 #2044, 0.78 #2932), 037c9s (0.76 #797, 0.70 #578, 0.69 #71) >> Best rule #3969 for best value: >> intensional similarity = 14 >> extensional distance = 30 >> proper extension: 01dnws; >> query: (?x432, 0342h) <- role(?x7987, ?x432), role(?x3160, ?x432), role(?x2908, ?x432), role(?x1652, ?x432), role(?x3716, ?x432), award_winner(?x247, ?x3160), role(?x4769, ?x432), award(?x2908, ?x2322), ?x4769 = 0dwt5, award_nominee(?x369, ?x1652), instrumentalists(?x3716, ?x7882), role(?x432, ?x1332), category(?x7987, ?x134), ?x7882 = 01z9_x >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #215 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 018vs; *> query: (?x432, ?x74) <- role(?x4239, ?x432), role(?x3657, ?x432), role(?x3160, ?x432), role(?x5676, ?x432), role(?x1147, ?x432), ?x3160 = 01w806h, award_nominee(?x4239, ?x565), artists(?x671, ?x4239), group(?x432, ?x442), role(?x74, ?x5676), award_nominee(?x366, ?x4239), ?x3657 = 01w8n89, ?x1147 = 07kc_, role(?x433, ?x432) *> conf = 0.59 ranks of expected_values: 45 EVAL 042v_gx role! 01wy6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 81.000 71.000 0.906 http://example.org/music/performance_role/regular_performances./music/group_membership/role #9930-071rlr PRED entity: 071rlr PRED relation: position PRED expected values: 02_j1w => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 4): 02sdk9v (0.88 #112, 0.88 #107, 0.87 #154), 02_j1w (0.84 #72, 0.84 #51, 0.83 #58), 03f0fp (0.50 #258, 0.37 #279), 02md_2 (0.50 #258) >> Best rule #112 for best value: >> intensional similarity = 11 >> extensional distance = 142 >> proper extension: 044l47; 03j722; 01kj5h; 0303jw; 01tqfs; 03fmw_; 046k81; 03zbg0; 01cw24; 04h5_c; ... >> query: (?x12325, ?x63) <- position(?x12325, ?x203), position(?x12325, ?x63), position(?x12325, ?x60), ?x203 = 0dgrmp, ?x63 = 02sdk9v, team(?x530, ?x12325), ?x60 = 02nzb8, ?x530 = 02_j1w, position(?x12325, ?x60), team(?x63, ?x12325), team(?x203, ?x12325) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #72 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 60 *> proper extension: 04112r; 048xg8; 07245g; 0kq9l; 04btgp; 08vk_r; 047g98; 0dkb83; 0498yf; *> query: (?x12325, ?x530) <- position(?x12325, ?x203), position(?x12325, ?x63), ?x203 = 0dgrmp, ?x63 = 02sdk9v, team(?x530, ?x12325), current_club(?x4972, ?x12325), current_club(?x4972, ?x9273), current_club(?x4972, ?x5993), colors(?x5993, ?x1101), colors(?x5993, ?x663), ?x663 = 083jv, sport(?x9273, ?x471), ?x1101 = 06fvc, teams(?x8181, ?x9273), teams(?x10753, ?x5993) *> conf = 0.84 ranks of expected_values: 2 EVAL 071rlr position 02_j1w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 45.000 45.000 0.875 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #9929-0789r6 PRED entity: 0789r6 PRED relation: award! PRED expected values: 03rz2b => 55 concepts (28 used for prediction) PRED predicted values (max 10 best out of 981): 0g9lm2 (0.45 #3065, 0.45 #2473, 0.38 #4088), 0hfzr (0.40 #415, 0.36 #1437, 0.33 #4503), 09gq0x5 (0.40 #175, 0.36 #1197, 0.32 #2219), 0c0zq (0.40 #902, 0.36 #1924, 0.32 #2946), 07xtqq (0.35 #32, 0.32 #2076, 0.32 #1054), 0m313 (0.35 #6, 0.32 #1028, 0.27 #2050), 03hmt9b (0.35 #394, 0.32 #1416, 0.27 #2438), 0hmr4 (0.35 #66, 0.32 #1088, 0.27 #2110), 0gmcwlb (0.30 #125, 0.28 #3191, 0.27 #2169), 0404j37 (0.30 #667, 0.27 #1689, 0.23 #2711) >> Best rule #3065 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 099cng; 027b9k6; 027571b; 09cn0c; >> query: (?x13075, ?x4359) <- award(?x286, ?x13075), nominated_for(?x13075, ?x4359), ?x4359 = 0g9lm2, award_winner(?x13075, ?x3960), produced_by(?x2719, ?x3960) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #3066 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 099cng; 027b9k6; 027571b; 09cn0c; *> query: (?x13075, ?x2719) <- award(?x286, ?x13075), nominated_for(?x13075, ?x4359), ?x4359 = 0g9lm2, award_winner(?x13075, ?x3960), produced_by(?x2719, ?x3960) *> conf = 0.13 ranks of expected_values: 111 EVAL 0789r6 award! 03rz2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 55.000 28.000 0.455 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9928-096lf_ PRED entity: 096lf_ PRED relation: award_winner PRED expected values: 031296 => 96 concepts (48 used for prediction) PRED predicted values (max 10 best out of 355): 039g82 (0.82 #9697, 0.82 #46868, 0.82 #12929), 031296 (0.82 #9697, 0.82 #46868, 0.82 #12929), 096lf_ (0.21 #77569, 0.16 #69490, 0.05 #72721), 026_dcw (0.21 #77569, 0.05 #72721), 0crx5w (0.21 #77569, 0.05 #72721), 05fnl9 (0.21 #77569, 0.01 #3489), 02t_st (0.21 #77569), 06msq2 (0.21 #77569), 01pcbg (0.21 #77569), 0gsg7 (0.21 #77569) >> Best rule #9697 for best value: >> intensional similarity = 2 >> extensional distance = 934 >> proper extension: 0f6lx; >> query: (?x10086, ?x1784) <- place_of_birth(?x10086, ?x12048), award_winner(?x1784, ?x10086) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 096lf_ award_winner 031296 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 48.000 0.818 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #9927-0345h PRED entity: 0345h PRED relation: nationality! PRED expected values: 017r2 08m4c8 0bqytm 03bxh 0522wp 0hr3g 07h1q 02my3z => 182 concepts (121 used for prediction) PRED predicted values (max 10 best out of 4104): 03bxh (0.31 #19982, 0.24 #223793, 0.17 #483555), 042q3 (0.31 #19982, 0.24 #223793, 0.17 #483555), 07h1q (0.31 #19982, 0.24 #223793, 0.17 #483555), 017r2 (0.31 #19982, 0.24 #223793, 0.17 #483555), 07dnx (0.31 #19982, 0.24 #223793, 0.11 #26701), 0bqytm (0.31 #19982, 0.17 #483555), 06cgy (0.31 #19982, 0.11 #24372, 0.11 #20376), 0dzlk (0.31 #19982, 0.11 #27485, 0.11 #23489), 05p92jn (0.31 #19982, 0.11 #25995, 0.11 #21999), 025b5y (0.31 #19982, 0.11 #25691, 0.11 #21695) >> Best rule #19982 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 0fdys; 022dp5; 012f86; >> query: (?x1264, ?x380) <- split_to(?x5540, ?x1264), people(?x5540, ?x380) >> conf = 0.31 => this is the best rule for 26 predicted values ranks of expected_values: 1, 3, 4, 6, 15, 31, 2947 EVAL 0345h nationality! 02my3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 182.000 121.000 0.308 http://example.org/people/person/nationality EVAL 0345h nationality! 07h1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 182.000 121.000 0.308 http://example.org/people/person/nationality EVAL 0345h nationality! 0hr3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 182.000 121.000 0.308 http://example.org/people/person/nationality EVAL 0345h nationality! 0522wp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 182.000 121.000 0.308 http://example.org/people/person/nationality EVAL 0345h nationality! 03bxh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 121.000 0.308 http://example.org/people/person/nationality EVAL 0345h nationality! 0bqytm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 182.000 121.000 0.308 http://example.org/people/person/nationality EVAL 0345h nationality! 08m4c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 182.000 121.000 0.308 http://example.org/people/person/nationality EVAL 0345h nationality! 017r2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 182.000 121.000 0.308 http://example.org/people/person/nationality #9926-01npcy7 PRED entity: 01npcy7 PRED relation: gender PRED expected values: 05zppz => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.75 #11, 0.72 #220, 0.72 #223), 02zsn (0.63 #67, 0.56 #16, 0.55 #222) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 01my4f; >> query: (?x9482, 05zppz) <- spouse(?x9482, ?x8638), film(?x8638, ?x1487), participant(?x3999, ?x8638), people(?x1446, ?x8638) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01npcy7 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.750 http://example.org/people/person/gender #9925-0k1bs PRED entity: 0k1bs PRED relation: artists! PRED expected values: 07sbbz2 0gywn => 179 concepts (72 used for prediction) PRED predicted values (max 10 best out of 263): 064t9 (0.56 #3995, 0.49 #6452, 0.48 #21796), 0155w (0.50 #5925, 0.28 #3166, 0.27 #6234), 07sbbz2 (0.50 #314, 0.24 #5830, 0.21 #3071), 0dl5d (0.48 #1856, 0.44 #2163, 0.43 #14117), 016clz (0.47 #1536, 0.39 #6751, 0.38 #3068), 01fh36 (0.44 #2229, 0.43 #1922, 0.38 #391), 0xhtw (0.43 #14117, 0.43 #14116, 0.42 #5838), 02l96k (0.43 #14117, 0.43 #14116, 0.25 #408), 025tm81 (0.43 #14117, 0.43 #14116, 0.12 #390), 03lty (0.38 #332, 0.33 #26, 0.24 #8305) >> Best rule #3995 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0k29f; >> query: (?x6456, 064t9) <- religion(?x6456, ?x1985), profession(?x6456, ?x955), category(?x6456, ?x134), ?x955 = 0n1h >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #314 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 089tm; 0394y; 07bzp; 07m4c; 03c3yf; *> query: (?x6456, 07sbbz2) <- artist(?x3050, ?x6456), artists(?x7083, ?x6456), ?x3050 = 0229rs, ?x7083 = 02yv6b *> conf = 0.50 ranks of expected_values: 3, 21 EVAL 0k1bs artists! 0gywn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 179.000 72.000 0.562 http://example.org/music/genre/artists EVAL 0k1bs artists! 07sbbz2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 179.000 72.000 0.562 http://example.org/music/genre/artists #9924-03jxw PRED entity: 03jxw PRED relation: influenced_by! PRED expected values: 0lrh => 178 concepts (63 used for prediction) PRED predicted values (max 10 best out of 451): 07g2b (0.33 #522, 0.17 #1027, 0.16 #2036), 0d4jl (0.33 #620, 0.12 #30821, 0.12 #17679), 013pp3 (0.32 #2238, 0.30 #2743, 0.17 #724), 01vdrw (0.32 #2455, 0.30 #2960, 0.12 #30821), 01hc9_ (0.26 #2375, 0.25 #2880, 0.13 #28291), 0n6kf (0.26 #2208, 0.25 #2713, 0.12 #30821), 05gpy (0.26 #9594, 0.25 #8583, 0.23 #6564), 0lrh (0.22 #21214, 0.19 #23743, 0.19 #24756), 08433 (0.22 #21214, 0.19 #23743, 0.19 #24756), 07h1q (0.21 #2420, 0.20 #2925, 0.17 #906) >> Best rule #522 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 040_9; 0c5tl; >> query: (?x10090, 07g2b) <- profession(?x10090, ?x353), influenced_by(?x8441, ?x10090), influenced_by(?x10090, ?x2162), ?x8441 = 0c1fs >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #21214 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 116 *> proper extension: 01h2_6; *> query: (?x10090, ?x1029) <- influenced_by(?x10090, ?x2162), influenced_by(?x2208, ?x10090), peers(?x1029, ?x2208), religion(?x2208, ?x1985) *> conf = 0.22 ranks of expected_values: 8 EVAL 03jxw influenced_by! 0lrh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 178.000 63.000 0.333 http://example.org/influence/influence_node/influenced_by #9923-0hn821n PRED entity: 0hn821n PRED relation: ceremony! PRED expected values: 09qj50 0fbvqf 07kjk7c => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 307): 0gqy2 (0.81 #4273, 0.78 #4029, 0.77 #4517), 0gkts9 (0.80 #1831, 0.80 #1587, 0.78 #1342), 09qvf4 (0.80 #1613, 0.67 #878, 0.67 #633), 0gqyl (0.80 #3991, 0.72 #4235, 0.70 #4479), 0gqwc (0.76 #4214, 0.73 #3970, 0.73 #4458), 0gq9h (0.76 #4215, 0.69 #4459, 0.69 #3971), 0gs9p (0.76 #4216, 0.69 #4460, 0.69 #3972), 0gq_d (0.75 #4308, 0.73 #4064, 0.73 #4552), 0k611 (0.75 #4227, 0.72 #3983, 0.72 #4471), 0gvx_ (0.75 #4287, 0.70 #4531, 0.70 #4043) >> Best rule #4273 for best value: >> intensional similarity = 17 >> extensional distance = 65 >> proper extension: 073hkh; 0bzk8w; 02yw5r; 059x66; 073hmq; 0bzm81; 0dth6b; 02yv_b; 073h1t; 0bvfqq; ... >> query: (?x10010, 0gqy2) <- ceremony(?x8660, ?x10010), ceremony(?x4386, ?x10010), ceremony(?x3906, ?x10010), award_winner(?x4386, ?x635), award_winner(?x10010, ?x496), honored_for(?x10010, ?x493), nominated_for(?x3906, ?x5060), nominated_for(?x3906, ?x2293), award(?x364, ?x3906), award(?x4385, ?x4386), award(?x1725, ?x4386), instance_of_recurring_event(?x10010, ?x2758), award(?x687, ?x8660), ?x4385 = 03xp8d5, nominated_for(?x201, ?x2293), genre(?x5060, ?x53), influenced_by(?x1725, ?x1726) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1905 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 8 *> proper extension: 07z31v; *> query: (?x10010, 07kjk7c) <- ceremony(?x5235, ?x10010), ceremony(?x4386, ?x10010), ceremony(?x2016, ?x10010), ceremony(?x686, ?x10010), ?x4386 = 0fc9js, award_winner(?x10010, ?x3709), award_winner(?x10010, ?x496), ?x2016 = 0cjyzs, honored_for(?x10010, ?x4083), award(?x4083, ?x3722), nationality(?x3709, ?x94), ?x686 = 0bdw1g, award_winner(?x4083, ?x525), written_by(?x2565, ?x496), award_nominee(?x1065, ?x3709), award(?x496, ?x401), award_nominee(?x496, ?x495), type_of_union(?x3709, ?x566), ?x5235 = 09qrn4, film_crew_role(?x2565, ?x137) *> conf = 0.70 ranks of expected_values: 19, 22, 30 EVAL 0hn821n ceremony! 07kjk7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 28.000 28.000 0.806 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0hn821n ceremony! 0fbvqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 28.000 28.000 0.806 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0hn821n ceremony! 09qj50 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 28.000 28.000 0.806 http://example.org/award/award_category/winners./award/award_honor/ceremony #9922-01fx5l PRED entity: 01fx5l PRED relation: award_nominee! PRED expected values: 032w8h => 84 concepts (38 used for prediction) PRED predicted values (max 10 best out of 753): 07s8r0 (0.81 #83859, 0.81 #41928, 0.80 #53576), 01fx5l (0.50 #3781, 0.42 #6111, 0.40 #10769), 032w8h (0.42 #2691, 0.33 #9679, 0.33 #5021), 01nwwl (0.20 #9978, 0.16 #76871, 0.15 #11647), 01w7nwm (0.18 #88521, 0.16 #76871, 0.15 #11647), 01f2q5 (0.18 #88521, 0.16 #76871, 0.15 #11647), 01w272y (0.18 #88521, 0.16 #76871, 0.15 #11647), 0770cd (0.18 #88521, 0.16 #76871, 0.15 #11647), 04fzk (0.18 #88521, 0.16 #76871, 0.15 #11647), 016yvw (0.18 #88521, 0.04 #12906) >> Best rule #83859 for best value: >> intensional similarity = 4 >> extensional distance = 1700 >> proper extension: 03b78r; 024y6w; 0knjh; >> query: (?x6282, ?x1641) <- award_nominee(?x6282, ?x7269), award_nominee(?x6282, ?x1641), nationality(?x6282, ?x94), location(?x7269, ?x2235) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #2691 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 015pkc; 0k269; 015vq_; 02yxwd; 029_l; *> query: (?x6282, 032w8h) <- award_nominee(?x7269, ?x6282), award_nominee(?x1733, ?x6282), ?x7269 = 0gnbw, participant(?x1733, ?x2763) *> conf = 0.42 ranks of expected_values: 3 EVAL 01fx5l award_nominee! 032w8h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 38.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9921-0fvf9q PRED entity: 0fvf9q PRED relation: executive_produced_by! PRED expected values: 0g9lm2 0dlngsd => 112 concepts (103 used for prediction) PRED predicted values (max 10 best out of 222): 049xgc (0.14 #846, 0.02 #3477, 0.02 #5579), 0gg5qcw (0.14 #812), 02phtzk (0.14 #777), 09p35z (0.14 #557), 07s846j (0.04 #11047, 0.04 #8939, 0.03 #2632), 0gkz3nz (0.04 #11047, 0.04 #8939, 0.03 #2632), 0gvs1kt (0.04 #11047, 0.03 #2632, 0.03 #3683), 01hv3t (0.04 #11047, 0.03 #2632, 0.03 #3683), 076tq0z (0.04 #11047, 0.03 #2632, 0.03 #3683), 095zlp (0.04 #11047, 0.03 #2632, 0.03 #3683) >> Best rule #846 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0dvmd; 0cjsxp; 01gb54; 0dvld; >> query: (?x163, 049xgc) <- award_winner(?x164, ?x163), nominated_for(?x163, ?x1753), ?x1753 = 02q5g1z >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fvf9q executive_produced_by! 0dlngsd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 103.000 0.143 http://example.org/film/film/executive_produced_by EVAL 0fvf9q executive_produced_by! 0g9lm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 103.000 0.143 http://example.org/film/film/executive_produced_by #9920-01wmxfs PRED entity: 01wmxfs PRED relation: profession PRED expected values: 018gz8 => 100 concepts (99 used for prediction) PRED predicted values (max 10 best out of 54): 01d_h8 (0.46 #1568, 0.42 #1994, 0.41 #432), 0dz3r (0.44 #1138, 0.40 #2842, 0.34 #3268), 016z4k (0.42 #1140, 0.38 #3270, 0.37 #572), 0n1h (0.26 #1148, 0.19 #3278, 0.17 #2852), 039v1 (0.24 #2872, 0.23 #600, 0.18 #4008), 02jknp (0.24 #5404, 0.24 #5830, 0.23 #1570), 0d1pc (0.23 #1040, 0.21 #756, 0.20 #472), 01c72t (0.21 #2861, 0.17 #3287, 0.16 #3997), 012t_z (0.17 #297, 0.09 #439, 0.06 #1575), 05sxg2 (0.17 #285, 0.03 #427, 0.02 #1847) >> Best rule #1568 for best value: >> intensional similarity = 3 >> extensional distance = 212 >> proper extension: 04vrxh; >> query: (?x828, 01d_h8) <- award(?x828, ?x112), currency(?x828, ?x170), award_winner(?x4836, ?x828) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #2003 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 287 *> proper extension: 06w2sn5; 01vvyfh; 036px; 012z8_; 0bv7t; 02x_h0; 01wgfp6; 0b_j2; 0ffgh; 0677ng; ... *> query: (?x828, 018gz8) <- award(?x828, ?x112), currency(?x828, ?x170), award_winner(?x567, ?x828) *> conf = 0.16 ranks of expected_values: 11 EVAL 01wmxfs profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 100.000 99.000 0.463 http://example.org/people/person/profession #9919-0hc8h PRED entity: 0hc8h PRED relation: contains! PRED expected values: 0cv5l => 70 concepts (43 used for prediction) PRED predicted values (max 10 best out of 317): 0cv5l (0.88 #8093, 0.87 #13487, 0.87 #17086), 02jx1 (0.81 #34179, 0.81 #30581, 0.80 #32382), 07ssc (0.79 #9027, 0.77 #9924, 0.76 #15322), 09c7w0 (0.66 #19792, 0.66 #26090, 0.65 #27890), 02xry (0.54 #7359, 0.26 #12753, 0.21 #13653), 0chghy (0.46 #4520, 0.20 #10813, 0.19 #11712), 0345h (0.41 #5478, 0.24 #14471, 0.18 #18970), 01n7q (0.36 #19867, 0.17 #27965, 0.15 #32461), 059rby (0.35 #7216, 0.31 #8115, 0.17 #12610), 0ht8h (0.33 #427, 0.25 #3124, 0.08 #4924) >> Best rule #8093 for best value: >> intensional similarity = 6 >> extensional distance = 24 >> proper extension: 02_286; 01mc11; 0rkkv; 0n1rj; 0ggh3; >> query: (?x12767, ?x13888) <- state(?x12767, ?x13888), administrative_parent(?x10338, ?x13888), location_of_ceremony(?x566, ?x13888), ?x566 = 04ztj, country(?x13888, ?x1310), place_of_death(?x1975, ?x13888) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hc8h contains! 0cv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 43.000 0.885 http://example.org/location/location/contains #9918-014lc_ PRED entity: 014lc_ PRED relation: genre PRED expected values: 06n90 => 87 concepts (84 used for prediction) PRED predicted values (max 10 best out of 99): 07s9rl0 (0.85 #6362, 0.80 #6122, 0.76 #7203), 02kdv5l (0.74 #2044, 0.64 #5764, 0.62 #363), 05p553 (0.50 #365, 0.47 #6847, 0.46 #1566), 06n90 (0.42 #372, 0.39 #2053, 0.35 #732), 02l7c8 (0.42 #6016, 0.36 #7217, 0.27 #6376), 01hmnh (0.32 #2178, 0.31 #1097, 0.28 #2898), 0hcr (0.25 #2424, 0.24 #2544, 0.23 #1704), 02n4kr (0.23 #3370, 0.22 #4450, 0.17 #2290), 0jxy (0.22 #2446, 0.21 #2566, 0.19 #525), 03npn (0.20 #2049, 0.12 #2769, 0.11 #5769) >> Best rule #6362 for best value: >> intensional similarity = 4 >> extensional distance = 1101 >> proper extension: 07bwr; >> query: (?x66, 07s9rl0) <- genre(?x66, ?x604), film(?x65, ?x66), genre(?x7087, ?x604), ?x7087 = 0bnzd >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #372 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 0340hj; 018nnz; 02_sr1; *> query: (?x66, 06n90) <- film(?x3558, ?x66), executive_produced_by(?x66, ?x1683), film(?x65, ?x66), film_release_distribution_medium(?x66, ?x81) *> conf = 0.42 ranks of expected_values: 4 EVAL 014lc_ genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 84.000 0.853 http://example.org/film/film/genre #9917-03ryn PRED entity: 03ryn PRED relation: country! PRED expected values: 0gx1bnj => 192 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1844): 02q_4ph (0.48 #23917, 0.44 #37586, 0.43 #37587), 07kdkfj (0.48 #23917, 0.44 #37586, 0.43 #37587), 0192hw (0.48 #23917, 0.43 #37587, 0.34 #78600), 01m13b (0.46 #10396, 0.35 #27480, 0.33 #22355), 04z4j2 (0.43 #3258, 0.29 #6674, 0.27 #8382), 0ddbjy4 (0.43 #3208, 0.27 #8332, 0.18 #16876), 09hy79 (0.43 #2882, 0.27 #8006, 0.14 #6298), 0639bg (0.43 #2303, 0.27 #7427, 0.14 #5719), 0d_2fb (0.43 #2062, 0.27 #7186, 0.14 #5478), 049mql (0.38 #10893, 0.29 #22852, 0.29 #2352) >> Best rule #23917 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 02j9z; 0dg3n1; 0j0k; 04swx; >> query: (?x3749, ?x3257) <- contains(?x3749, ?x12783), contains(?x3749, ?x11382), administrative_parent(?x11793, ?x12783), featured_film_locations(?x3257, ?x11382), adjoins(?x390, ?x3749) >> conf = 0.48 => this is the best rule for 3 predicted values *> Best rule #12002 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 0chghy; 02k54; 06qd3; 07t21; *> query: (?x3749, 0gx1bnj) <- film_release_region(?x6543, ?x3749), film_release_region(?x2340, ?x3749), film_release_region(?x1904, ?x3749), ?x6543 = 0421v9q, film_crew_role(?x2340, ?x468), ?x1904 = 09146g *> conf = 0.19 ranks of expected_values: 328 EVAL 03ryn country! 0gx1bnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 192.000 87.000 0.475 http://example.org/film/film/country #9916-017cy9 PRED entity: 017cy9 PRED relation: school_type PRED expected values: 07tf8 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.55 #292, 0.46 #364, 0.46 #412), 01_9fk (0.28 #290, 0.21 #410, 0.20 #362), 01rs41 (0.28 #581, 0.24 #677, 0.24 #845), 05pcjw (0.24 #577, 0.24 #841, 0.23 #481), 07tf8 (0.19 #297, 0.17 #273, 0.16 #153), 01_srz (0.06 #219, 0.06 #363, 0.06 #411), 04399 (0.05 #134, 0.04 #422, 0.03 #374), 02dk5q (0.05 #127, 0.02 #583, 0.02 #559), 02p0qmm (0.04 #490, 0.04 #706, 0.04 #514), 01y64 (0.03 #564, 0.03 #900, 0.02 #780) >> Best rule #292 for best value: >> intensional similarity = 2 >> extensional distance = 109 >> proper extension: 0frm7n; >> query: (?x4780, 05jxkf) <- school(?x4779, ?x4780), school(?x4487, ?x4780) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #297 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 109 *> proper extension: 0frm7n; *> query: (?x4780, 07tf8) <- school(?x4779, ?x4780), school(?x4487, ?x4780) *> conf = 0.19 ranks of expected_values: 5 EVAL 017cy9 school_type 07tf8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 96.000 96.000 0.550 http://example.org/education/educational_institution/school_type #9915-02__ww PRED entity: 02__ww PRED relation: student! PRED expected values: 02ldmw => 84 concepts (68 used for prediction) PRED predicted values (max 10 best out of 23): 08815 (0.17 #2, 0.02 #5799, 0.02 #12650), 033gn8 (0.17 #378, 0.01 #6175, 0.01 #4594), 017hnw (0.17 #509), 0bwfn (0.05 #1856, 0.05 #9234, 0.05 #12396), 015nl4 (0.03 #4283, 0.03 #16406, 0.03 #17987), 09f2j (0.03 #4375, 0.03 #4902, 0.02 #3321), 065y4w7 (0.03 #10027, 0.03 #12135, 0.03 #8973), 017z88 (0.03 #1136, 0.03 #3244, 0.03 #1663), 03ksy (0.03 #11700, 0.03 #9592, 0.03 #5376), 01w5m (0.03 #9591, 0.03 #8537, 0.03 #5375) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 02jt1k; >> query: (?x11925, 08815) <- location(?x11925, ?x9394), actor(?x7175, ?x11925), ?x7175 = 02_1kl >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02__ww student! 02ldmw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 68.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #9914-02lfp4 PRED entity: 02lfp4 PRED relation: nationality PRED expected values: 02jx1 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.73 #1520, 0.71 #2121, 0.71 #1420), 07ssc (0.38 #5023, 0.31 #2020, 0.18 #116), 0345h (0.38 #5023, 0.31 #2020, 0.04 #233), 02jx1 (0.28 #134, 0.25 #7225, 0.12 #335), 0978r (0.25 #7225), 0d060g (0.08 #7, 0.05 #611, 0.05 #712), 0f8l9c (0.06 #224, 0.02 #6946, 0.02 #1941), 03rk0 (0.05 #8871, 0.05 #8671, 0.05 #9171), 04jpl (0.05 #503, 0.05 #604, 0.04 #705), 03rt9 (0.04 #315, 0.01 #1432, 0.01 #2033) >> Best rule #1520 for best value: >> intensional similarity = 3 >> extensional distance = 718 >> proper extension: 023jq1; 08849; >> query: (?x4951, 09c7w0) <- award_winner(?x4951, ?x669), profession(?x4951, ?x987), student(?x11987, ?x4951) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #134 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 37 *> proper extension: 02r1tx7; 01qqwp9; 0phx4; 06nv27; 06gcn; 08w4pm; 0kj34; 01w5gg6; 01_wfj; 0pqp3; ... *> query: (?x4951, 02jx1) <- origin(?x4951, ?x362), ?x362 = 04jpl *> conf = 0.28 ranks of expected_values: 4 EVAL 02lfp4 nationality 02jx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 97.000 0.731 http://example.org/people/person/nationality #9913-01jfrg PRED entity: 01jfrg PRED relation: place_of_birth PRED expected values: 03l2n => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 126): 0fw2f (0.28 #33109, 0.28 #39451, 0.28 #38041), 081mh (0.28 #33109, 0.28 #39451, 0.28 #38041), 05kkh (0.28 #33109, 0.28 #39451, 0.28 #38041), 02_286 (0.28 #33109, 0.28 #39451, 0.28 #38041), 030qb3t (0.08 #4282, 0.07 #54, 0.07 #11327), 01_d4 (0.07 #66, 0.04 #12749, 0.04 #5703), 0cr3d (0.07 #2209, 0.04 #32498, 0.04 #56449), 01531 (0.03 #105, 0.03 #32509, 0.02 #35328), 0ccvx (0.03 #153, 0.02 #5790, 0.02 #2973), 0hptm (0.03 #225, 0.02 #17135, 0.02 #19955) >> Best rule #33109 for best value: >> intensional similarity = 3 >> extensional distance = 454 >> proper extension: 0dszr0; >> query: (?x6113, ?x177) <- actor(?x4084, ?x6113), location(?x6113, ?x177), nominated_for(?x686, ?x4084) >> conf = 0.28 => this is the best rule for 4 predicted values *> Best rule #6510 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 106 *> proper extension: 0c01c; 01fs_4; 033jkj; 02y_2y; 01wc7p; 0dx_q; 03kxp7; 02dlfh; 0chw_; 0jbp0; *> query: (?x6113, 03l2n) <- actor(?x4084, ?x6113), participant(?x3039, ?x6113), award_winner(?x8787, ?x6113) *> conf = 0.02 ranks of expected_values: 63 EVAL 01jfrg place_of_birth 03l2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 125.000 125.000 0.282 http://example.org/people/person/place_of_birth #9912-03c_cxn PRED entity: 03c_cxn PRED relation: edited_by PRED expected values: 03crcpt => 92 concepts (52 used for prediction) PRED predicted values (max 10 best out of 22): 0gd9k (0.05 #79, 0.05 #108, 0.01 #414), 03crcpt (0.05 #45, 0.02 #318, 0.01 #595), 02kxbwx (0.05 #92, 0.02 #336, 0.01 #121), 02qggqc (0.05 #90, 0.02 #793, 0.02 #703), 02kxbx3 (0.05 #99, 0.01 #128, 0.01 #343), 0bs1yy (0.05 #98, 0.01 #560), 022_q8 (0.03 #579, 0.02 #179, 0.02 #364), 03q8ch (0.03 #865, 0.03 #896, 0.03 #562), 027pdrh (0.02 #312, 0.01 #589, 0.01 #467), 0jz9f (0.02 #179, 0.02 #364, 0.01 #790) >> Best rule #79 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 02z3r8t; 03ckwzc; 02rmd_2; 043sct5; 04nm0n0; 02z2mr7; 02q8ms8; 03cyslc; >> query: (?x5107, 0gd9k) <- film_festivals(?x5107, ?x9189), ?x9189 = 04grdgy, titles(?x512, ?x5107), genre(?x5107, ?x53) >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #45 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 0bh8drv; *> query: (?x5107, 03crcpt) <- nominated_for(?x1008, ?x5107), titles(?x512, ?x5107), film(?x166, ?x5107), ?x1008 = 05zvq6g *> conf = 0.05 ranks of expected_values: 2 EVAL 03c_cxn edited_by 03crcpt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 52.000 0.053 http://example.org/film/film/edited_by #9911-035ktt PRED entity: 035ktt PRED relation: school! PRED expected values: 0jmj7 => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 91): 0jmj7 (0.69 #680, 0.69 #587, 0.68 #1424), 05m_8 (0.18 #375, 0.12 #561, 0.11 #1398), 0bwjj (0.17 #75, 0.06 #540, 0.06 #633), 0jmhr (0.17 #86, 0.04 #458, 0.03 #1481), 051vz (0.13 #395, 0.09 #581, 0.09 #1418), 01slc (0.13 #431, 0.09 #896, 0.09 #1454), 07147 (0.13 #440, 0.09 #626, 0.07 #1463), 06x68 (0.12 #379, 0.10 #658, 0.08 #1402), 01yjl (0.12 #403, 0.09 #868, 0.09 #682), 0713r (0.12 #409, 0.09 #595, 0.09 #688) >> Best rule #680 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 01y17m; 02l1fn; 015fsv; 01yqqv; 01nhgd; >> query: (?x5596, 0jmj7) <- currency(?x5596, ?x170), school(?x8901, ?x5596), contains(?x94, ?x5596), draft(?x8901, ?x1161) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035ktt school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 171.000 0.691 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9910-02dpl9 PRED entity: 02dpl9 PRED relation: nominated_for! PRED expected values: 02g3v6 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 210): 0l8z1 (0.38 #294, 0.35 #3186, 0.31 #1981), 054krc (0.34 #3204, 0.32 #312, 0.29 #1999), 0gr0m (0.31 #302, 0.27 #3194, 0.26 #1989), 0gq9h (0.31 #4643, 0.30 #1992, 0.30 #3197), 02qvyrt (0.29 #340, 0.26 #3232, 0.22 #4678), 0gq_v (0.29 #1948, 0.28 #4599, 0.28 #261), 0k611 (0.28 #316, 0.28 #3208, 0.27 #4654), 040njc (0.28 #730, 0.21 #3140, 0.19 #248), 0p9sw (0.28 #262, 0.27 #1949, 0.27 #3154), 019f4v (0.27 #4634, 0.27 #1983, 0.27 #3188) >> Best rule #294 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 07gbf; >> query: (?x3897, 0l8z1) <- nominated_for(?x9891, ?x3897), titles(?x789, ?x3897), music(?x915, ?x9891), role(?x9891, ?x228) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1227 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 174 *> proper extension: 018nnz; 02r9p0c; 031f_m; 0gfzfj; *> query: (?x3897, 02g3v6) <- film(?x8587, ?x3897), genre(?x3897, ?x1013), ?x1013 = 06n90, film(?x5959, ?x3897) *> conf = 0.14 ranks of expected_values: 43 EVAL 02dpl9 nominated_for! 02g3v6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 72.000 72.000 0.381 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9909-0bdjd PRED entity: 0bdjd PRED relation: film_release_region PRED expected values: 0chghy => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 114): 0chghy (0.81 #319, 0.79 #627, 0.76 #1706), 035qy (0.81 #342, 0.76 #650, 0.65 #1729), 015fr (0.81 #325, 0.74 #633, 0.64 #1712), 0d060g (0.75 #314, 0.73 #622, 0.63 #1701), 06bnz (0.70 #352, 0.68 #660, 0.58 #1739), 0b90_r (0.69 #619, 0.67 #311, 0.59 #1698), 06t2t (0.67 #677, 0.67 #369, 0.51 #1756), 03rt9 (0.67 #631, 0.65 #323, 0.53 #1710), 0154j (0.63 #620, 0.63 #1699, 0.61 #312), 03rk0 (0.61 #363, 0.58 #671, 0.31 #1750) >> Best rule #319 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 07l50vn; >> query: (?x7336, 0chghy) <- genre(?x7336, ?x258), film_release_region(?x7336, ?x4737), ?x4737 = 07twz, nominated_for(?x198, ?x7336) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bdjd film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.807 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9908-015nl4 PRED entity: 015nl4 PRED relation: student PRED expected values: 01pkhw 05kwx2 => 57 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1291): 07f7jp (0.18 #3962, 0.05 #12101, 0.03 #26351), 0m32_ (0.18 #2455, 0.03 #6524, 0.02 #10594), 01hkhq (0.15 #26459, 0.15 #38674, 0.13 #30532), 0d3k14 (0.09 #11979, 0.09 #3840, 0.06 #26229), 0ff3y (0.09 #12186, 0.07 #10152, 0.06 #26436), 05bnp0 (0.09 #2045, 0.07 #10184, 0.05 #8150), 02hsgn (0.09 #2830, 0.07 #10969, 0.05 #25219), 01n1gc (0.09 #2623, 0.05 #8728, 0.05 #25012), 03_nq (0.09 #3555, 0.05 #30017, 0.05 #11694), 0683n (0.09 #3452, 0.05 #11591, 0.04 #17698) >> Best rule #3962 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0473m9; >> query: (?x2486, 07f7jp) <- major_field_of_study(?x2486, ?x7070), ?x7070 = 0mg1w, institution(?x1368, ?x2486) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #67171 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 278 *> proper extension: 02_jjm; *> query: (?x2486, ?x374) <- student(?x2486, ?x7047), student(?x2486, ?x473), profession(?x7047, ?x319), nationality(?x473, ?x6307), award_winner(?x473, ?x374) *> conf = 0.02 ranks of expected_values: 1070 EVAL 015nl4 student 05kwx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 52.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 015nl4 student 01pkhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 52.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student #9907-058bzgm PRED entity: 058bzgm PRED relation: award! PRED expected values: 01zkxv => 53 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2194): 07w21 (0.71 #30464, 0.67 #27089, 0.67 #16967), 018fq (0.67 #18361, 0.67 #3375, 0.64 #31858), 0210f1 (0.67 #18927, 0.58 #29049, 0.57 #32424), 0gd_s (0.67 #19533, 0.58 #29655, 0.57 #33030), 01zkxv (0.67 #3375, 0.50 #27123, 0.43 #30498), 0klw (0.67 #3375, 0.33 #4802, 0.29 #31796), 01f7j9 (0.67 #3375, 0.10 #91117, 0.08 #91118), 0c3kw (0.64 #30811, 0.58 #27436, 0.57 #24062), 0fpzt5 (0.58 #29554, 0.57 #32929, 0.50 #19432), 0mfc0 (0.57 #33072, 0.57 #22949, 0.50 #29697) >> Best rule #30464 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 0265wl; >> query: (?x11579, 07w21) <- award(?x11262, ?x11579), award(?x5506, ?x11579), ?x5506 = 048_p, student(?x2999, ?x11262), category(?x11262, ?x134), currency(?x2999, ?x1099) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3375 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 040_9s0; *> query: (?x11579, ?x4895) <- award(?x13644, ?x11579), award(?x11262, ?x11579), award(?x5506, ?x11579), award(?x3963, ?x11579), ?x3963 = 02g75, profession(?x5506, ?x353), award_nominee(?x11262, ?x4895), ?x13644 = 042xh *> conf = 0.67 ranks of expected_values: 5 EVAL 058bzgm award! 01zkxv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 27.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9906-0p8jf PRED entity: 0p8jf PRED relation: nationality PRED expected values: 09c7w0 => 154 concepts (144 used for prediction) PRED predicted values (max 10 best out of 46): 09c7w0 (0.88 #301, 0.86 #6316, 0.86 #5207), 0d060g (0.40 #13856, 0.11 #3109, 0.11 #6013), 0b90_r (0.40 #13856, 0.01 #1605), 068p2 (0.30 #5307, 0.28 #6416, 0.25 #5509), 05tbn (0.30 #5307, 0.28 #6416, 0.25 #5509), 07ssc (0.27 #515, 0.24 #1016, 0.21 #816), 03rt9 (0.25 #113, 0.11 #6013, 0.10 #6718), 02jx1 (0.16 #1435, 0.14 #2235, 0.14 #1735), 0345h (0.11 #6013, 0.10 #6718, 0.08 #731), 03rjj (0.11 #6013, 0.10 #6718, 0.08 #205) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0pgjm; 0bytfv; 03zyvw; 01kp66; 033m23; >> query: (?x2993, 09c7w0) <- award(?x2993, ?x575), student(?x6894, ?x2993), profession(?x2993, ?x353), ?x6894 = 0cwx_ >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p8jf nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 144.000 0.875 http://example.org/people/person/nationality #9905-01vn0t_ PRED entity: 01vn0t_ PRED relation: profession PRED expected values: 0dz3r 09jwl => 106 concepts (51 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.81 #4252, 0.79 #5569, 0.78 #5131), 09jwl (0.81 #2502, 0.80 #4549, 0.77 #2650), 0nbcg (0.75 #1200, 0.68 #1492, 0.61 #2663), 0dz3r (0.55 #732, 0.53 #2925, 0.53 #2633), 01c72t (0.50 #462, 0.48 #1776, 0.46 #1630), 039v1 (0.44 #621, 0.39 #4567, 0.38 #475), 01d_h8 (0.42 #2782, 0.37 #3074, 0.33 #3804), 025352 (0.29 #351, 0.27 #935, 0.25 #497), 0n1h (0.29 #303, 0.27 #887, 0.23 #1909), 0dxtg (0.28 #2790, 0.28 #5422, 0.26 #3082) >> Best rule #4252 for best value: >> intensional similarity = 4 >> extensional distance = 193 >> proper extension: 04sx9_; >> query: (?x8708, 02hrh1q) <- type_of_union(?x8708, ?x1873), award_nominee(?x7088, ?x8708), nationality(?x8708, ?x512), ?x1873 = 01g63y >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #2502 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 07_3qd; *> query: (?x8708, 09jwl) <- artists(?x671, ?x8708), role(?x8708, ?x316), artist(?x2149, ?x8708), ?x671 = 064t9 *> conf = 0.81 ranks of expected_values: 2, 4 EVAL 01vn0t_ profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 51.000 0.810 http://example.org/people/person/profession EVAL 01vn0t_ profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 51.000 0.810 http://example.org/people/person/profession #9904-0149xx PRED entity: 0149xx PRED relation: type_of_union PRED expected values: 04ztj => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.79 #78, 0.79 #98, 0.78 #126), 01g63y (0.11 #303, 0.10 #287, 0.10 #327) >> Best rule #78 for best value: >> intensional similarity = 3 >> extensional distance = 297 >> proper extension: 017r2; 04jwp; 03_js; 042d1; 0bt23; 0hcvy; 01hkck; 042fk; >> query: (?x5125, 04ztj) <- student(?x11822, ?x5125), profession(?x5125, ?x563), people(?x4959, ?x5125) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0149xx type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.793 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9903-05cx7x PRED entity: 05cx7x PRED relation: award_nominee PRED expected values: 023kzp => 91 concepts (50 used for prediction) PRED predicted values (max 10 best out of 845): 023kzp (0.85 #11637, 0.83 #9308, 0.82 #11636), 0z4s (0.85 #11637, 0.83 #9308, 0.82 #11636), 016vg8 (0.83 #9308, 0.82 #11636, 0.81 #88412), 014488 (0.83 #9308, 0.82 #11636, 0.81 #88412), 05cx7x (0.65 #3993, 0.56 #6320, 0.43 #8648), 02p65p (0.31 #9336, 0.30 #7008, 0.24 #2353), 02qgqt (0.31 #9329, 0.30 #7001, 0.19 #88414), 0187y5 (0.31 #9441, 0.27 #7113, 0.14 #81432), 02wgln (0.31 #9728, 0.27 #7400, 0.14 #81432), 01kb2j (0.28 #10512, 0.27 #8184, 0.19 #88414) >> Best rule #11637 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 02p65p; 01yb09; 0flw6; 0bqdvt; 0278x6s; >> query: (?x7487, ?x5022) <- award_nominee(?x5925, ?x7487), award_nominee(?x5022, ?x7487), ?x5925 = 023kzp, award_winner(?x72, ?x5022) >> conf = 0.85 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 05cx7x award_nominee 023kzp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 50.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9902-01mjq PRED entity: 01mjq PRED relation: time_zones PRED expected values: 02llzg => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 13): 02llzg (0.63 #1925, 0.33 #134, 0.32 #108), 02hcv8 (0.40 #471, 0.35 #1914, 0.33 #887), 03plfd (0.33 #10, 0.16 #114, 0.14 #387), 02fqwt (0.22 #833, 0.20 #1288, 0.20 #625), 02lcqs (0.22 #473, 0.17 #96, 0.16 #1214), 042g7t (0.12 #193, 0.10 #310, 0.09 #219), 02hczc (0.12 #1016, 0.12 #834, 0.11 #626), 03bdv (0.10 #279, 0.08 #1228, 0.07 #487), 0gsrz4 (0.09 #658, 0.07 #528, 0.06 #1243), 052vwh (0.05 #155, 0.05 #181, 0.04 #233) >> Best rule #1925 for best value: >> intensional similarity = 2 >> extensional distance = 579 >> proper extension: 0mxcf; 0p0cw; 0r5wt; 0d8jf; 0mpbj; 0yls9; 0l2l3; 0nht0; 0p03t; 0fc2c; ... >> query: (?x1558, ?x2864) <- adjoins(?x1264, ?x1558), time_zones(?x1264, ?x2864) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mjq time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.630 http://example.org/location/location/time_zones #9901-01hx2t PRED entity: 01hx2t PRED relation: contains! PRED expected values: 09c7w0 0d234 => 202 concepts (85 used for prediction) PRED predicted values (max 10 best out of 302): 09c7w0 (0.83 #29561, 0.83 #50166, 0.78 #53750), 02frhbc (0.33 #3215, 0.15 #5900, 0.13 #50163), 04rrx (0.25 #1021, 0.14 #6391, 0.12 #10867), 04_1l0v (0.21 #58226, 0.11 #13428), 01n7q (0.20 #9028, 0.18 #8133, 0.16 #57406), 059rby (0.20 #56453, 0.14 #8076, 0.11 #1810), 081yw (0.18 #3857, 0.12 #1172, 0.08 #11018), 041_3z (0.18 #13373, 0.15 #12478), 05kkh (0.14 #6274, 0.12 #904, 0.08 #9855), 02xry (0.14 #8218, 0.12 #9113, 0.11 #1952) >> Best rule #29561 for best value: >> intensional similarity = 5 >> extensional distance = 81 >> proper extension: 016sd3; >> query: (?x8479, 09c7w0) <- colors(?x8479, ?x3315), contains(?x726, ?x8479), school(?x3089, ?x8479), school(?x1010, ?x8479), category(?x726, ?x134) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 13 EVAL 01hx2t contains! 0d234 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 202.000 85.000 0.831 http://example.org/location/location/contains EVAL 01hx2t contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 85.000 0.831 http://example.org/location/location/contains #9900-0q9sg PRED entity: 0q9sg PRED relation: language PRED expected values: 02h40lc => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.95 #3576, 0.94 #2873, 0.94 #3284), 04306rv (0.64 #1342, 0.60 #3750, 0.59 #813), 02bjrlw (0.64 #1342, 0.60 #3750, 0.59 #813), 06mp7 (0.64 #1342, 0.60 #3750, 0.59 #813), 0295r (0.64 #1342, 0.60 #3750, 0.59 #813), 06nm1 (0.41 #1870, 0.33 #69, 0.20 #243), 07zrf (0.41 #1870, 0.09 #409, 0.07 #467), 064_8sq (0.20 #254, 0.15 #2069, 0.15 #3304), 03k50 (0.20 #241, 0.03 #705, 0.02 #1176), 05zjd (0.12 #373, 0.06 #605, 0.03 #721) >> Best rule #3576 for best value: >> intensional similarity = 3 >> extensional distance = 870 >> proper extension: 053tj7; >> query: (?x4538, 02h40lc) <- language(?x4538, ?x5671), genre(?x4538, ?x53), produced_by(?x4538, ?x4495) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q9sg language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.952 http://example.org/film/film/language #9899-02cllz PRED entity: 02cllz PRED relation: award_winner! PRED expected values: 09bymc => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 123): 027hjff (0.24 #335, 0.04 #1308, 0.03 #4227), 092c5f (0.22 #14, 0.03 #2655, 0.03 #3767), 0clfdj (0.11 #4, 0.06 #1255, 0.04 #282), 092t4b (0.11 #52, 0.05 #1303, 0.04 #330), 02wzl1d (0.11 #11, 0.03 #1262, 0.03 #2652), 0hhtgcw (0.11 #84, 0.02 #1335, 0.02 #640), 0bvhz9 (0.10 #406, 0.02 #2769, 0.01 #3881), 0fqpc7d (0.10 #175, 0.03 #592, 0.03 #1009), 0g5b0q5 (0.10 #159, 0.02 #2661, 0.02 #1271), 09gkdln (0.08 #398, 0.05 #676, 0.04 #3873) >> Best rule #335 for best value: >> intensional similarity = 2 >> extensional distance = 47 >> proper extension: 01fsyp; >> query: (?x2457, 027hjff) <- award_winner(?x5592, ?x2457), ?x5592 = 0275n3y >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #397 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 47 *> proper extension: 01fsyp; *> query: (?x2457, 09bymc) <- award_winner(?x5592, ?x2457), ?x5592 = 0275n3y *> conf = 0.04 ranks of expected_values: 40 EVAL 02cllz award_winner! 09bymc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 99.000 99.000 0.245 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9898-01r97z PRED entity: 01r97z PRED relation: nominated_for! PRED expected values: 04ljl_l 07bdd_ => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 226): 07bdd_ (0.67 #756, 0.55 #51, 0.25 #14809), 07cbcy (0.55 #62, 0.45 #767, 0.25 #14809), 04ljl_l (0.47 #708, 0.36 #3, 0.22 #19515), 05b4l5x (0.47 #711, 0.27 #6, 0.25 #14809), 0gq9h (0.40 #2647, 0.40 #2882, 0.29 #1236), 0gq_v (0.36 #2605, 0.36 #2840, 0.21 #489), 0gs9p (0.33 #2649, 0.33 #2884, 0.28 #3354), 019f4v (0.31 #2638, 0.31 #2873, 0.28 #3813), 02hsq3m (0.30 #499, 0.28 #1910, 0.25 #3085), 02g3v6 (0.30 #491, 0.19 #3547, 0.18 #3077) >> Best rule #756 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 02y_lrp; 03cvwkr; 0bshwmp; 069q4f; 026n4h6; 06rmdr; 05h43ls; 082scv; 07sp4l; 02krdz; ... >> query: (?x770, 07bdd_) <- award(?x770, ?x4317), nominated_for(?x541, ?x770), nominated_for(?x4317, ?x2084), ?x2084 = 048qrd >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01r97z nominated_for! 07bdd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.673 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01r97z nominated_for! 04ljl_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.673 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9897-07jnt PRED entity: 07jnt PRED relation: film_regional_debut_venue PRED expected values: 018cvf => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 30): 0prpt (0.34 #61, 0.27 #229, 0.23 #597), 018cvf (0.31 #585, 0.30 #351, 0.30 #519), 0kfhjq0 (0.12 #249, 0.11 #350, 0.11 #283), 07751 (0.11 #43, 0.10 #278, 0.10 #647), 07zmj (0.11 #64, 0.08 #400, 0.08 #198), 0j63cyr (0.11 #316, 0.10 #349, 0.10 #181), 0gg7gsl (0.09 #175, 0.07 #142, 0.06 #276), 02_286 (0.09 #36, 0.07 #572, 0.06 #640), 04jpl (0.09 #34, 0.04 #202, 0.03 #370), 04_m9gk (0.04 #191, 0.04 #292, 0.03 #258) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0g5q34q; 0gh6j94; >> query: (?x6782, 0prpt) <- film_regional_debut_venue(?x6782, ?x5416), country(?x6782, ?x94), film_release_region(?x6782, ?x87), films(?x4450, ?x6782) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #585 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 165 *> proper extension: 0cnztc4; 0crh5_f; 043sct5; *> query: (?x6782, 018cvf) <- film_regional_debut_venue(?x6782, ?x5416), country(?x6782, ?x94), film_release_region(?x6782, ?x87) *> conf = 0.31 ranks of expected_values: 2 EVAL 07jnt film_regional_debut_venue 018cvf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.343 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #9896-025tkqy PRED entity: 025tkqy PRED relation: nutrient! PRED expected values: 0f25w9 0fjfh 014j1m => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 71): 0fjfh (0.93 #388, 0.92 #448, 0.92 #442), 014j1m (0.91 #428, 0.91 #422, 0.90 #332), 0f25w9 (0.90 #120, 0.90 #162, 0.89 #89), 04k8n (0.04 #225, 0.04 #67, 0.04 #66), 01sh2 (0.04 #225, 0.04 #67, 0.04 #66), 05wvs (0.04 #225, 0.04 #67, 0.04 #66), 0f4kp (0.04 #67, 0.04 #66, 0.04 #48), 0fzjh (0.04 #67, 0.04 #66, 0.04 #48), 025rw19 (0.04 #67, 0.04 #66, 0.04 #48), 0q01m (0.04 #67, 0.04 #66, 0.04 #48) >> Best rule #388 for best value: >> intensional similarity = 116 >> extensional distance = 25 >> proper extension: 02y_3rf; >> query: (?x9915, 0fjfh) <- nutrient(?x9732, ?x9915), nutrient(?x9005, ?x9915), nutrient(?x8298, ?x9915), nutrient(?x7057, ?x9915), nutrient(?x6032, ?x9915), nutrient(?x5373, ?x9915), nutrient(?x5337, ?x9915), nutrient(?x4068, ?x9915), nutrient(?x3900, ?x9915), nutrient(?x3468, ?x9915), nutrient(?x2701, ?x9915), nutrient(?x1303, ?x9915), ?x7057 = 0fbdb, ?x6032 = 01nkt, nutrient(?x5373, ?x13944), nutrient(?x5373, ?x13498), nutrient(?x5373, ?x13126), nutrient(?x5373, ?x12902), nutrient(?x5373, ?x12083), nutrient(?x5373, ?x11758), nutrient(?x5373, ?x11409), nutrient(?x5373, ?x11270), nutrient(?x5373, ?x10709), nutrient(?x5373, ?x10098), nutrient(?x5373, ?x9795), nutrient(?x5373, ?x9733), nutrient(?x5373, ?x9619), nutrient(?x5373, ?x9490), nutrient(?x5373, ?x9436), nutrient(?x5373, ?x9426), nutrient(?x5373, ?x9365), nutrient(?x5373, ?x8487), nutrient(?x5373, ?x8442), nutrient(?x5373, ?x8243), nutrient(?x5373, ?x7894), nutrient(?x5373, ?x7720), nutrient(?x5373, ?x7431), nutrient(?x5373, ?x7364), nutrient(?x5373, ?x7362), nutrient(?x5373, ?x7219), nutrient(?x5373, ?x7135), nutrient(?x5373, ?x6192), nutrient(?x5373, ?x6160), nutrient(?x5373, ?x6033), nutrient(?x5373, ?x6026), nutrient(?x5373, ?x5549), nutrient(?x5373, ?x5526), nutrient(?x5373, ?x5451), nutrient(?x5373, ?x5374), nutrient(?x5373, ?x5010), nutrient(?x5373, ?x3469), nutrient(?x5373, ?x1960), nutrient(?x5373, ?x1304), nutrient(?x5373, ?x1258), ?x11270 = 02kc008, ?x9436 = 025sqz8, ?x9619 = 0h1tg, ?x9795 = 05v_8y, ?x8487 = 014yzm, ?x11409 = 0h1yf, ?x1304 = 08lb68, ?x5010 = 0h1vz, ?x2701 = 0hkxq, ?x1258 = 0h1wg, ?x12083 = 01n78x, ?x6192 = 06jry, ?x8442 = 02kcv4x, ?x12902 = 0fzjh, ?x7431 = 09gwd, ?x7894 = 0f4hc, ?x3469 = 0h1zw, ?x1303 = 0fj52s, nutrient(?x5337, ?x2702), nutrient(?x5337, ?x2018), ?x10709 = 0h1sz, nutrient(?x9732, ?x10891), nutrient(?x9732, ?x9949), nutrient(?x9732, ?x9708), nutrient(?x9732, ?x6586), ?x5451 = 05wvs, ?x9708 = 061xhr, ?x5526 = 09pbb, ?x3468 = 0cxn2, ?x13498 = 07q0m, ?x9490 = 0h1sg, ?x10891 = 0g5gq, ?x5374 = 025s0zp, ?x1960 = 07hnp, ?x9426 = 0h1yy, ?x6026 = 025sf8g, ?x7720 = 025s7x6, ?x2018 = 01sh2, ?x13944 = 0f4kp, ?x9005 = 04zpv, ?x11758 = 0q01m, ?x4068 = 0fbw6, ?x9365 = 04k8n, ?x2702 = 0838f, nutrient(?x6191, ?x8243), ?x6191 = 014j1m, ?x7364 = 09gvd, nutrient(?x1959, ?x13126), ?x6586 = 05gh50, ?x7219 = 0h1vg, ?x7362 = 02kc5rj, ?x9949 = 02kd0rh, ?x5549 = 025s7j4, ?x6033 = 04zjxcz, ?x9733 = 0h1tz, ?x1959 = 0f25w9, ?x6160 = 041r51, ?x10098 = 0h1_c, nutrient(?x8298, ?x11784), ?x7135 = 025rsfk, ?x11784 = 07zqy, ?x3900 = 061_f >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 025tkqy nutrient! 014j1m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.926 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 025tkqy nutrient! 0fjfh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.926 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 025tkqy nutrient! 0f25w9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.926 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #9895-04f0xq PRED entity: 04f0xq PRED relation: organization! PRED expected values: 0dq_5 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 36): 0dq_5 (0.82 #191, 0.81 #55, 0.81 #116), 060c4 (0.48 #913, 0.44 #348, 0.42 #954), 0dq3c (0.26 #993, 0.25 #866, 0.25 #471), 0krdk (0.26 #993, 0.25 #866, 0.25 #471), 01kr6k (0.25 #866, 0.25 #471, 0.24 #952), 05_wyz (0.19 #994, 0.19 #992, 0.18 #45), 07xl34 (0.17 #890, 0.10 #835, 0.10 #1106), 04192r (0.04 #894, 0.04 #893, 0.04 #136), 09lq2c (0.04 #894, 0.04 #893, 0.04 #136), 033smt (0.04 #894, 0.04 #893, 0.04 #136) >> Best rule #191 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 0p4wb; 05w3y; >> query: (?x7471, 0dq_5) <- service_location(?x7471, ?x94), category(?x7471, ?x134), ?x134 = 08mbj5d, list(?x7471, ?x5997), ?x5997 = 04k4rt >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04f0xq organization! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.818 http://example.org/organization/role/leaders./organization/leadership/organization #9894-05fx6 PRED entity: 05fx6 PRED relation: artists PRED expected values: 070b4 => 54 concepts (37 used for prediction) PRED predicted values (max 10 best out of 994): 01323p (0.60 #1793, 0.32 #19251, 0.22 #8333), 012zng (0.60 #2315, 0.29 #11039, 0.25 #5588), 01wgjj5 (0.60 #1631, 0.23 #19089, 0.22 #8171), 01w02sy (0.52 #6545, 0.37 #6546, 0.28 #11993), 01j59b0 (0.50 #474, 0.25 #5928, 0.23 #19024), 02r3zy (0.50 #65, 0.25 #5519, 0.22 #7697), 019389 (0.50 #717, 0.20 #12716, 0.17 #10530), 0ycp3 (0.50 #618, 0.18 #19168, 0.17 #10431), 03t9sp (0.41 #18674, 0.40 #1216, 0.35 #25226), 03fbc (0.41 #18755, 0.40 #3478, 0.33 #8927) >> Best rule #1793 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 01b4p4; >> query: (?x14438, 01323p) <- parent_genre(?x7960, ?x14438), parent_genre(?x14438, ?x5934), ?x5934 = 05r6t, parent_genre(?x7960, ?x12498), parent_genre(?x7960, ?x9013), parent_genre(?x7960, ?x2996), artists(?x9013, ?x4646), ?x4646 = 0fhxv, artists(?x2996, ?x498), ?x12498 = 05c6073, parent_genre(?x2542, ?x2996), ?x2542 = 03xnwz >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4098 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 05c6073; *> query: (?x14438, 070b4) <- parent_genre(?x10969, ?x14438), parent_genre(?x7960, ?x14438), ?x7960 = 05y8n7, artists(?x10969, ?x7706), artists(?x10969, ?x4936), artist(?x9671, ?x7706), artist(?x9121, ?x4936), location(?x7706, ?x4253), film(?x4936, ?x5721) *> conf = 0.40 ranks of expected_values: 18 EVAL 05fx6 artists 070b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 54.000 37.000 0.600 http://example.org/music/genre/artists #9893-014lc_ PRED entity: 014lc_ PRED relation: film! PRED expected values: 0jbp0 => 74 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1115): 0gn30 (0.33 #946, 0.06 #5094, 0.04 #7168), 012d40 (0.33 #15, 0.04 #12461, 0.04 #24909), 0dpqk (0.33 #892, 0.03 #15413, 0.03 #17489), 05w1vf (0.33 #1879, 0.03 #20550, 0.02 #24699), 0m68w (0.33 #1957, 0.01 #16478, 0.01 #18554), 015g_7 (0.33 #1497, 0.01 #16018, 0.01 #18094), 02hy9p (0.33 #1426, 0.01 #15947, 0.01 #18023), 01vzx45 (0.33 #1318, 0.01 #15839, 0.01 #17915), 05hj_k (0.33 #699, 0.01 #15220, 0.01 #17296), 05183k (0.24 #10371) >> Best rule #946 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01mszz; >> query: (?x66, 0gn30) <- film(?x3183, ?x66), film(?x3558, ?x66), ?x3183 = 0fb1q >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #12123 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 01cssf; 01qb5d; 017gl1; 01_mdl; 04n52p6; 05qbckf; 03177r; 0hx4y; 0dyb1; 03r0g9; ... *> query: (?x66, 0jbp0) <- film_release_region(?x66, ?x94), prequel(?x5128, ?x66), story_by(?x66, ?x5431), film(?x65, ?x66) *> conf = 0.05 ranks of expected_values: 150 EVAL 014lc_ film! 0jbp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 74.000 40.000 0.333 http://example.org/film/actor/film./film/performance/film #9892-0pyg6 PRED entity: 0pyg6 PRED relation: artists! PRED expected values: 025sc50 => 129 concepts (93 used for prediction) PRED predicted values (max 10 best out of 233): 06by7 (0.58 #22279, 0.50 #13002, 0.48 #13929), 025sc50 (0.46 #977, 0.32 #13031, 0.30 #13958), 0glt670 (0.43 #968, 0.36 #1587, 0.35 #2825), 017_qw (0.39 #5627, 0.14 #5318, 0.13 #8408), 016clz (0.37 #932, 0.29 #4952, 0.25 #4643), 05bt6j (0.35 #13025, 0.35 #971, 0.33 #13952), 02lnbg (0.35 #985, 0.27 #2842, 0.23 #13039), 05w3f (0.22 #656, 0.12 #2512, 0.09 #1893), 01lyv (0.21 #13633, 0.20 #11779, 0.18 #16727), 0155w (0.19 #1961, 0.19 #724, 0.15 #6907) >> Best rule #22279 for best value: >> intensional similarity = 3 >> extensional distance = 666 >> proper extension: 04r1t; 02r1tx7; 05563d; 07m4c; 0qmny; >> query: (?x2194, 06by7) <- artists(?x671, ?x2194), artists(?x671, ?x7581), ?x7581 = 01wf86y >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #977 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 06y9c2; 01q7cb_; 0285c; 0gs6vr; *> query: (?x2194, 025sc50) <- artists(?x505, ?x2194), participant(?x11924, ?x2194), participant(?x2387, ?x2194) *> conf = 0.46 ranks of expected_values: 2 EVAL 0pyg6 artists! 025sc50 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 93.000 0.582 http://example.org/music/genre/artists #9891-07wjk PRED entity: 07wjk PRED relation: list PRED expected values: 09g7thr => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 5): 09g7thr (0.62 #71, 0.61 #57, 0.59 #64), 01ptsx (0.12 #518, 0.09 #742, 0.09 #637), 04k4rt (0.08 #517, 0.07 #636, 0.07 #741), 01pd60 (0.07 #519, 0.07 #715, 0.07 #743), 026cl_m (0.03 #101, 0.03 #123, 0.03 #158) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 03v6t; 07vht; 0bqxw; >> query: (?x2327, 09g7thr) <- major_field_of_study(?x2327, ?x742), organization(?x2327, ?x5487), organization(?x346, ?x2327), citytown(?x2327, ?x1658) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07wjk list 09g7thr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.619 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #9890-01q7h2 PRED entity: 01q7h2 PRED relation: currency PRED expected values: 09nqf => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.78 #8, 0.77 #92, 0.77 #71), 01nv4h (0.03 #9, 0.02 #65, 0.02 #37), 02l6h (0.01 #39, 0.01 #53) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 260 >> proper extension: 05f67hw; >> query: (?x9614, 09nqf) <- language(?x9614, ?x254), films(?x5179, ?x9614), produced_by(?x9614, ?x163) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q7h2 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.779 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #9889-029fbr PRED entity: 029fbr PRED relation: artists PRED expected values: 01516r => 60 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1837): 0191h5 (0.55 #11423, 0.50 #3877, 0.35 #21119), 01y_rz (0.50 #4178, 0.33 #10649, 0.32 #12798), 01wy61y (0.50 #3597, 0.33 #10068, 0.27 #12217), 01wwvt2 (0.50 #3416, 0.28 #9887, 0.23 #12036), 0l8g0 (0.50 #3791, 0.22 #10262, 0.21 #7024), 012zng (0.50 #3366, 0.17 #9837, 0.15 #20608), 01tp5bj (0.50 #3423, 0.17 #9894, 0.15 #15277), 07rnh (0.50 #4064, 0.14 #12684, 0.12 #15918), 01shhf (0.44 #10570, 0.36 #12719, 0.33 #4099), 01386_ (0.44 #10279, 0.36 #12428, 0.26 #18895) >> Best rule #11423 for best value: >> intensional similarity = 10 >> extensional distance = 18 >> proper extension: 02w4v; 08cyft; >> query: (?x10969, 0191h5) <- artists(?x10969, ?x7706), artists(?x10969, ?x3399), artists(?x10307, ?x3399), artists(?x8031, ?x3399), artists(?x2249, ?x3399), ?x10307 = 0k345, ?x8031 = 01738f, location(?x7706, ?x4253), ?x2249 = 03lty, ?x4253 = 0ccvx >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #3978 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 0k345; *> query: (?x10969, 01516r) <- artists(?x10969, ?x9603), artists(?x10969, ?x8864), artists(?x10969, ?x3399), ?x3399 = 01gx5f, gender(?x9603, ?x231), role(?x9603, ?x716), role(?x9603, ?x316), ?x316 = 05r5c, ?x8864 = 070b4, ?x716 = 018vs *> conf = 0.33 ranks of expected_values: 43 EVAL 029fbr artists 01516r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 60.000 28.000 0.550 http://example.org/music/genre/artists #9888-095zlp PRED entity: 095zlp PRED relation: genre PRED expected values: 04xvlr => 72 concepts (71 used for prediction) PRED predicted values (max 10 best out of 115): 018h2 (0.52 #4182, 0.52 #4062, 0.52 #5853), 04jjy (0.52 #4182, 0.52 #4062, 0.52 #5853), 0d63kt (0.52 #4182, 0.52 #4062, 0.52 #5853), 02l7c8 (0.49 #611, 0.39 #254, 0.33 #492), 05p553 (0.47 #123, 0.40 #4, 0.38 #1315), 01hmnh (0.36 #1567, 0.16 #2644, 0.14 #4796), 02kdv5l (0.33 #6212, 0.32 #1432, 0.30 #2629), 01jfsb (0.33 #1442, 0.30 #1203, 0.30 #2639), 04xvlr (0.30 #1, 0.27 #596, 0.24 #477), 03k9fj (0.25 #1561, 0.21 #2638, 0.20 #1202) >> Best rule #4182 for best value: >> intensional similarity = 3 >> extensional distance = 1086 >> proper extension: 0c3ybss; 09xbpt; 047gn4y; 0dnvn3; 03h_yy; 03s6l2; 04kkz8; 09gdm7q; 02v63m; 03s5lz; ... >> query: (?x414, ?x714) <- film(?x374, ?x414), nominated_for(?x163, ?x414), titles(?x714, ?x414) >> conf = 0.52 => this is the best rule for 3 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 02d003; 0g9z_32; *> query: (?x414, 04xvlr) <- film(?x2353, ?x414), ?x2353 = 02qgyv, produced_by(?x414, ?x163) *> conf = 0.30 ranks of expected_values: 9 EVAL 095zlp genre 04xvlr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 72.000 71.000 0.522 http://example.org/film/film/genre #9887-0fy6bh PRED entity: 0fy6bh PRED relation: award_winner PRED expected values: 05v1sb 0fqjks 0gqrb => 45 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1729): 0c0tzp (0.50 #7638, 0.38 #13803, 0.35 #21473), 081nh (0.50 #9541, 0.33 #14143, 0.33 #1873), 02cqbx (0.50 #10074, 0.33 #14676, 0.33 #2406), 0520r2x (0.50 #9214, 0.33 #13816, 0.33 #1546), 0fqjks (0.43 #3066, 0.38 #13803, 0.35 #21473), 05v1sb (0.43 #3066, 0.38 #13803, 0.35 #21473), 057bc6m (0.43 #3066, 0.38 #13803, 0.35 #21473), 05x2t7 (0.43 #3066, 0.35 #41420, 0.33 #1822), 04vzv4 (0.43 #3066, 0.35 #41420, 0.33 #2233), 053vcrp (0.43 #3066, 0.35 #41420, 0.23 #18403) >> Best rule #7638 for best value: >> intensional similarity = 16 >> extensional distance = 2 >> proper extension: 0dth6b; >> query: (?x3029, 0c0tzp) <- award_winner(?x3029, ?x9363), award_winner(?x3029, ?x4423), ceremony(?x1703, ?x3029), ceremony(?x1323, ?x3029), honored_for(?x3029, ?x2368), honored_for(?x3029, ?x1746), ?x4423 = 076psv, type_of_union(?x9363, ?x566), nominated_for(?x510, ?x1746), location(?x9363, ?x7190), film_release_region(?x1746, ?x94), ?x1703 = 0k611, award(?x115, ?x1323), award_winner(?x1323, ?x1934), produced_by(?x3330, ?x9363), nominated_for(?x1708, ?x2368) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3066 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 1 *> proper extension: 0d__c3; *> query: (?x3029, ?x4251) <- award_winner(?x3029, ?x12398), award_winner(?x3029, ?x9363), award_winner(?x3029, ?x4423), award_winner(?x3029, ?x2068), ceremony(?x1972, ?x3029), honored_for(?x3029, ?x1746), produced_by(?x3330, ?x9363), ?x2068 = 0gl88b, award_winner(?x12398, ?x4251), award(?x9363, ?x1105), award_nominee(?x199, ?x4423), crewmember(?x3986, ?x12398), genre(?x1746, ?x53), film_sets_designed(?x4423, ?x951), nominated_for(?x4423, ?x6680), nominated_for(?x1972, ?x86) *> conf = 0.43 ranks of expected_values: 5, 6, 232 EVAL 0fy6bh award_winner 0gqrb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 45.000 36.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0fy6bh award_winner 0fqjks CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 45.000 36.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0fy6bh award_winner 05v1sb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 45.000 36.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9886-0c1ps1 PRED entity: 0c1ps1 PRED relation: nationality PRED expected values: 09c7w0 => 80 concepts (76 used for prediction) PRED predicted values (max 10 best out of 13): 09c7w0 (0.82 #694, 0.82 #397, 0.79 #3081), 02jx1 (0.12 #527, 0.12 #131, 0.11 #230), 07ssc (0.12 #14, 0.09 #1899, 0.09 #3094), 03rk0 (0.05 #7297, 0.05 #6603, 0.05 #7197), 0d060g (0.05 #4176, 0.05 #1492, 0.05 #1691), 06q1r (0.04 #76, 0.01 #4544, 0.01 #4944), 03_3d (0.03 #1491, 0.02 #698, 0.01 #7257), 0345h (0.02 #7282, 0.02 #6588, 0.02 #7480), 0f8l9c (0.02 #7273, 0.02 #7471, 0.02 #2104), 0chghy (0.02 #1894, 0.02 #1694, 0.02 #3089) >> Best rule #694 for best value: >> intensional similarity = 3 >> extensional distance = 518 >> proper extension: 018fwv; >> query: (?x10469, 09c7w0) <- place_of_birth(?x10469, ?x2850), nationality(?x10469, ?x205), actor(?x1849, ?x10469) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c1ps1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 76.000 0.821 http://example.org/people/person/nationality #9885-02glc4 PRED entity: 02glc4 PRED relation: legislative_sessions! PRED expected values: 02hy5d 0194xc => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 734): 02hy5d (0.79 #506, 0.78 #389, 0.77 #530), 0194xc (0.77 #531, 0.76 #303, 0.69 #139), 012v1t (0.76 #303, 0.69 #139, 0.67 #382), 016lh0 (0.76 #303, 0.69 #139, 0.65 #229), 0d06m5 (0.76 #303, 0.69 #139, 0.65 #229), 03txms (0.76 #303, 0.69 #139, 0.65 #229), 02mjmr (0.76 #303, 0.69 #139, 0.65 #229), 01lct6 (0.69 #139, 0.64 #398, 0.62 #68), 06hx2 (0.69 #139, 0.64 #398, 0.52 #326), 0dq2k (0.16 #648, 0.15 #699, 0.15 #673) >> Best rule #506 for best value: >> intensional similarity = 55 >> extensional distance = 12 >> proper extension: 032ft5; >> query: (?x5339, 02hy5d) <- legislative_sessions(?x845, ?x5339), legislative_sessions(?x653, ?x5339), legislative_sessions(?x355, ?x5339), district_represented(?x845, ?x7518), district_represented(?x845, ?x6895), district_represented(?x845, ?x6521), district_represented(?x845, ?x4776), district_represented(?x845, ?x4758), district_represented(?x845, ?x4622), district_represented(?x845, ?x3634), district_represented(?x845, ?x3038), district_represented(?x845, ?x2831), district_represented(?x845, ?x2713), district_represented(?x845, ?x2049), district_represented(?x845, ?x2020), district_represented(?x845, ?x1767), district_represented(?x845, ?x1351), district_represented(?x845, ?x1227), district_represented(?x845, ?x1138), district_represented(?x845, ?x961), district_represented(?x845, ?x953), district_represented(?x845, ?x760), legislative_sessions(?x5339, ?x1829), legislative_sessions(?x5339, ?x952), ?x2049 = 050l8, district_represented(?x5339, ?x4754), ?x6895 = 05fjf, ?x4622 = 04tgp, ?x760 = 05fkf, ?x1138 = 059_c, ?x4776 = 06yxd, legislative_sessions(?x2860, ?x5339), ?x2831 = 0gyh, ?x4758 = 0vbk, ?x1351 = 06mz5, ?x6521 = 05mph, ?x7518 = 026mj, ?x3634 = 07b_l, ?x1227 = 01n7q, ?x355 = 0495ys, legislative_sessions(?x11605, ?x845), legislative_sessions(?x7961, ?x845), ?x3038 = 0d0x8, legislative_sessions(?x6742, ?x5339), ?x11605 = 024_vw, ?x2713 = 06btq, state_province_region(?x546, ?x961), ?x1767 = 04rrd, ?x953 = 0hjy, ?x1829 = 02bp37, ?x653 = 070m6c, ?x952 = 06f0dc, jurisdiction_of_office(?x7961, ?x6252), contains(?x961, ?x310), ?x2020 = 05k7sb >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02glc4 legislative_sessions! 0194xc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.786 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 02glc4 legislative_sessions! 02hy5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.786 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #9884-0pz91 PRED entity: 0pz91 PRED relation: currency PRED expected values: 09nqf => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.40 #10, 0.35 #4, 0.32 #91), 01nv4h (0.11 #5, 0.04 #104, 0.03 #20) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 030g9z; 01vhrz; >> query: (?x1335, 09nqf) <- award_winner(?x541, ?x1335), participant(?x364, ?x1335), produced_by(?x821, ?x1335) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pz91 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.404 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #9883-0c3xw46 PRED entity: 0c3xw46 PRED relation: film! PRED expected values: 0170s4 => 65 concepts (40 used for prediction) PRED predicted values (max 10 best out of 756): 057_yx (0.22 #1838, 0.02 #5996, 0.01 #22631), 035rnz (0.22 #692, 0.02 #6929, 0.02 #9008), 016yzz (0.15 #2762, 0.01 #23555, 0.01 #21476), 024bbl (0.11 #835, 0.08 #2914, 0.03 #79013), 01z7_f (0.11 #754, 0.08 #2833, 0.02 #6991), 01713c (0.11 #256, 0.08 #2335), 06cgy (0.11 #251, 0.04 #14806, 0.02 #4409), 01chc7 (0.11 #557, 0.03 #4715, 0.02 #6794), 015wnl (0.11 #647, 0.03 #6884, 0.02 #21440), 0dvmd (0.11 #525, 0.03 #6762, 0.02 #4683) >> Best rule #1838 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0b2v79; 02x0fs9; >> query: (?x3812, 057_yx) <- film_release_region(?x3812, ?x87), film(?x2374, ?x3812), genre(?x3812, ?x239), ?x2374 = 02d4ct >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #4554 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 0c3ybss; 0c40vxk; 053rxgm; 07qg8v; 0cz8mkh; 0fq7dv_; 0ch26b_; 0cc5mcj; 08052t3; 0kv238; ... *> query: (?x3812, 0170s4) <- film_release_region(?x3812, ?x404), film(?x1896, ?x3812), genre(?x3812, ?x239), ?x404 = 047lj *> conf = 0.02 ranks of expected_values: 196 EVAL 0c3xw46 film! 0170s4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 65.000 40.000 0.222 http://example.org/film/actor/film./film/performance/film #9882-027ct7c PRED entity: 027ct7c PRED relation: currency PRED expected values: 09nqf => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.80 #176, 0.78 #36, 0.77 #22), 01nv4h (0.03 #170, 0.03 #254, 0.03 #198), 02l6h (0.02 #256, 0.01 #116, 0.01 #123), 02gsvk (0.02 #146, 0.01 #174, 0.01 #230), 088n7 (0.01 #168) >> Best rule #176 for best value: >> intensional similarity = 4 >> extensional distance = 426 >> proper extension: 0j_tw; >> query: (?x5533, 09nqf) <- genre(?x5533, ?x53), film(?x4307, ?x5533), film_crew_role(?x5533, ?x1284), film_release_distribution_medium(?x5533, ?x81) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027ct7c currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.801 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #9881-01v27pl PRED entity: 01v27pl PRED relation: artists! PRED expected values: 064t9 => 56 concepts (37 used for prediction) PRED predicted values (max 10 best out of 220): 064t9 (0.94 #1870, 0.88 #2179, 0.86 #2800), 025sc50 (0.80 #2837, 0.79 #2476, 0.79 #2216), 06by7 (0.80 #9318, 0.68 #9628, 0.65 #9939), 02lnbg (0.62 #2225, 0.60 #3156, 0.60 #2846), 0gywn (0.60 #987, 0.58 #3776, 0.56 #5945), 0glt670 (0.55 #4380, 0.48 #3759, 0.47 #1898), 05bt6j (0.40 #1283, 0.38 #1238, 0.29 #2522), 09nwwf (0.40 #1066, 0.33 #448, 0.25 #757), 02ny8t (0.40 #1373, 0.33 #135, 0.09 #1991), 0155w (0.40 #1036, 0.25 #727, 0.24 #6306) >> Best rule #1870 for best value: >> intensional similarity = 15 >> extensional distance = 30 >> proper extension: 01vvycq; 0136p1; 0pyg6; 07ss8_; 047sxrj; 01trhmt; 01wwvc5; 02wb6yq; 01vvyfh; 0gbwp; ... >> query: (?x11667, 064t9) <- artists(?x14382, ?x11667), artists(?x10319, ?x11667), artists(?x5876, ?x11667), artists(?x3319, ?x11667), ?x3319 = 06j6l, artists(?x14382, ?x4842), ?x5876 = 0ggx5q, artists(?x10319, ?x10180), artists(?x10319, ?x6651), artists(?x10319, ?x4123), category(?x11667, ?x134), ?x4123 = 01wv9p, ?x6651 = 019f9z, ?x4842 = 0hvbj, ?x10180 = 020hyj >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v27pl artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 37.000 0.938 http://example.org/music/genre/artists #9880-033cw PRED entity: 033cw PRED relation: category PRED expected values: 08mbj5d => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.48 #8, 0.35 #10, 0.31 #55) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 06lxn; >> query: (?x10056, 08mbj5d) <- award_winner(?x1375, ?x10056), influenced_by(?x5086, ?x10056), ceremony(?x1375, ?x11712) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033cw category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.478 http://example.org/common/topic/webpage./common/webpage/category #9879-06pj8 PRED entity: 06pj8 PRED relation: award PRED expected values: 0gs9p 054ky1 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 333): 02rdyk7 (0.71 #13038, 0.71 #27261, 0.70 #48993), 02g3ft (0.71 #13038, 0.71 #27261, 0.70 #48993), 02qt02v (0.71 #13038, 0.71 #27261, 0.70 #48993), 02g3gw (0.71 #13038, 0.71 #27261, 0.70 #48993), 027c924 (0.71 #13038, 0.71 #27261, 0.70 #48993), 02w_6xj (0.71 #13038, 0.71 #27261, 0.70 #48993), 09d28z (0.71 #13038, 0.71 #27261, 0.70 #48993), 0468g4r (0.71 #13038, 0.71 #27261, 0.70 #48993), 0m7yy (0.71 #13038, 0.71 #27261, 0.70 #48993), 0fbtbt (0.38 #6546, 0.32 #21954, 0.31 #12867) >> Best rule #13038 for best value: >> intensional similarity = 2 >> extensional distance = 141 >> proper extension: 0d4fqn; 02773m2; 02778pf; 0277470; 0f721s; 0284gcb; 0crx5w; 06v_gh; 09gffmz; 0g5lhl7; ... >> query: (?x2135, ?x198) <- award_winner(?x198, ?x2135), program(?x2135, ?x531) >> conf = 0.71 => this is the best rule for 9 predicted values *> Best rule #24964 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 270 *> proper extension: 0454s1; 05bht9; *> query: (?x2135, 0gs9p) <- film(?x2135, ?x1452), profession(?x2135, ?x319) *> conf = 0.37 ranks of expected_values: 12, 110 EVAL 06pj8 award 054ky1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 162.000 162.000 0.709 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 06pj8 award 0gs9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 162.000 162.000 0.709 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9878-02kxbwx PRED entity: 02kxbwx PRED relation: profession PRED expected values: 0dxtg => 132 concepts (128 used for prediction) PRED predicted values (max 10 best out of 74): 0dxtg (0.89 #1629, 0.86 #4134, 0.85 #5751), 02hrh1q (0.77 #3398, 0.75 #3546, 0.74 #1925), 03gjzk (0.46 #5165, 0.44 #6048, 0.44 #5606), 0cbd2 (0.28 #7364, 0.28 #5893, 0.27 #5598), 05sxg2 (0.27 #15300, 0.26 #16183, 0.03 #3829), 02krf9 (0.26 #4442, 0.25 #4736, 0.23 #5471), 09jwl (0.19 #9288, 0.18 #165, 0.18 #9876), 0kyk (0.16 #4004, 0.15 #5916, 0.13 #7093), 018gz8 (0.16 #5608, 0.16 #6050, 0.15 #4579), 0dz3r (0.13 #9272, 0.12 #10301, 0.11 #11625) >> Best rule #1629 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 06w38l; >> query: (?x826, 0dxtg) <- award_winner(?x762, ?x826), award(?x826, ?x1862), ?x1862 = 0gr51 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02kxbwx profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 128.000 0.889 http://example.org/people/person/profession #9877-02mslq PRED entity: 02mslq PRED relation: profession PRED expected values: 09jwl => 145 concepts (81 used for prediction) PRED predicted values (max 10 best out of 89): 02hrh1q (0.82 #1817, 0.77 #765, 0.77 #1517), 09jwl (0.74 #320, 0.73 #5434, 0.72 #6186), 0dz3r (0.60 #152, 0.45 #2707, 0.43 #5416), 0nbcg (0.58 #783, 0.51 #5447, 0.50 #2136), 01c72t (0.56 #2429, 0.45 #2580, 0.37 #3181), 016z4k (0.51 #2709, 0.49 #4363, 0.48 #454), 0dxtg (0.36 #6480, 0.34 #614, 0.31 #1216), 01d_h8 (0.34 #1508, 0.32 #6472, 0.29 #756), 02jknp (0.31 #608, 0.28 #1210, 0.26 #1510), 039v1 (0.31 #5452, 0.29 #6204, 0.28 #7558) >> Best rule #1817 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 01l2fn; 027r8p; 063472; 058nh2; 015dcj; 05vk_d; >> query: (?x547, 02hrh1q) <- award(?x547, ?x4488), nationality(?x547, ?x94), notable_people_with_this_condition(?x13845, ?x547) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #320 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 01vng3b; 0130sy; 021r7r; 01w9ph_; 01wg25j; 01nrz4; *> query: (?x547, 09jwl) <- artists(?x505, ?x547), artist(?x8721, ?x547), ?x8721 = 01cf93, role(?x547, ?x214) *> conf = 0.74 ranks of expected_values: 2 EVAL 02mslq profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 81.000 0.824 http://example.org/people/person/profession #9876-02mpyh PRED entity: 02mpyh PRED relation: nominated_for! PRED expected values: 040njc => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 193): 027c924 (0.68 #11553, 0.67 #7773, 0.66 #11552), 0gq9h (0.57 #276, 0.50 #2942, 0.44 #498), 0k611 (0.50 #286, 0.44 #508, 0.43 #2952), 099tbz (0.50 #265, 0.20 #10441, 0.19 #20887), 0f_nbyh (0.50 #229, 0.16 #1117, 0.12 #451), 0gs9p (0.44 #500, 0.36 #2944, 0.29 #278), 040njc (0.43 #228, 0.38 #450, 0.35 #2894), 09sb52 (0.43 #253, 0.31 #475, 0.25 #1556), 0gr0m (0.40 #2939, 0.36 #273, 0.33 #51), 0p9sw (0.40 #2906, 0.36 #240, 0.25 #462) >> Best rule #11553 for best value: >> intensional similarity = 3 >> extensional distance = 986 >> proper extension: 06mmr; >> query: (?x8574, ?x3889) <- award(?x8574, ?x3889), nominated_for(?x3889, ?x414), award(?x84, ?x3889) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #228 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 07vn_9; *> query: (?x8574, 040njc) <- film_crew_role(?x8574, ?x2154), ?x2154 = 01vx2h, nominated_for(?x384, ?x8574), ?x384 = 03hkv_r *> conf = 0.43 ranks of expected_values: 7 EVAL 02mpyh nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 100.000 100.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9875-03f02ct PRED entity: 03f02ct PRED relation: profession PRED expected values: 01d_h8 => 113 concepts (63 used for prediction) PRED predicted values (max 10 best out of 50): 01d_h8 (0.67 #1930, 0.67 #1782, 0.50 #6), 0dxtg (0.60 #1937, 0.58 #1789, 0.31 #7711), 015cjr (0.36 #49, 0.14 #493, 0.11 #197), 03gjzk (0.26 #1938, 0.23 #1790, 0.20 #7712), 02krf9 (0.21 #1950, 0.21 #1802, 0.10 #7724), 09jwl (0.19 #2534, 0.17 #3274, 0.17 #7420), 0fj9f (0.14 #54, 0.10 #942, 0.09 #646), 0cbd2 (0.14 #1783, 0.14 #1931, 0.14 #7409), 018gz8 (0.14 #3420, 0.14 #2384, 0.13 #5345), 0d1pc (0.14 #494, 0.09 #3454, 0.09 #3750) >> Best rule #1930 for best value: >> intensional similarity = 4 >> extensional distance = 485 >> proper extension: 06y9bd; 06w58f; 04dyqk; 04kwbt; >> query: (?x10570, 01d_h8) <- nationality(?x10570, ?x2146), profession(?x10570, ?x524), award(?x10570, ?x1937), ?x524 = 02jknp >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f02ct profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 63.000 0.674 http://example.org/people/person/profession #9874-0bw6y PRED entity: 0bw6y PRED relation: award PRED expected values: 09qj50 => 85 concepts (72 used for prediction) PRED predicted values (max 10 best out of 282): 094qd5 (0.61 #1250, 0.60 #1652, 0.54 #848), 02y_rq5 (0.54 #897, 0.47 #495, 0.39 #2505), 0cqgl9 (0.51 #994, 0.47 #592, 0.27 #1396), 0gqyl (0.45 #1309, 0.44 #1711, 0.44 #505), 09qwmm (0.44 #1642, 0.43 #1240, 0.35 #2446), 0bfvw2 (0.44 #417, 0.41 #819, 0.31 #4839), 09sb52 (0.41 #7276, 0.38 #844, 0.38 #442), 099cng (0.38 #1692, 0.36 #1290, 0.25 #2496), 02ppm4q (0.38 #556, 0.35 #958, 0.30 #2566), 02z0dfh (0.35 #877, 0.31 #475, 0.25 #2485) >> Best rule #1250 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0159h6; 0c4f4; 01csvq; 0h1mt; 0h1m9; 0n6f8; 049g_xj; 01l2fn; 01l9p; 06x58; ... >> query: (?x6744, 094qd5) <- award(?x6744, ?x1245), ?x1245 = 0gqwc, participant(?x4360, ?x6744), profession(?x6744, ?x1032) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #45 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02s58t; *> query: (?x6744, 09qj50) <- nationality(?x6744, ?x94), type_of_union(?x6744, ?x566), participant(?x6744, ?x9862), ?x9862 = 022q4j *> conf = 0.12 ranks of expected_values: 33 EVAL 0bw6y award 09qj50 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 85.000 72.000 0.614 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9873-0hfzr PRED entity: 0hfzr PRED relation: films! PRED expected values: 06d4h => 69 concepts (38 used for prediction) PRED predicted values (max 10 best out of 59): 06d4h (0.07 #1428, 0.07 #2043, 0.06 #3125), 0cm2xh (0.05 #46, 0.03 #1893, 0.03 #354), 0fzyg (0.05 #3136, 0.05 #207, 0.05 #1439), 05489 (0.05 #1898, 0.05 #2052, 0.04 #1437), 07s2s (0.04 #3180, 0.04 #1483, 0.03 #405), 0bq3x (0.04 #3112, 0.04 #1107, 0.04 #1415), 01vq3 (0.04 #1426, 0.04 #3123, 0.03 #1887), 04gb7 (0.04 #1430, 0.03 #2045, 0.03 #1891), 07jq_ (0.04 #1466, 0.03 #3163, 0.02 #1927), 07c52 (0.04 #2020, 0.04 #1866, 0.04 #1405) >> Best rule #1428 for best value: >> intensional similarity = 3 >> extensional distance = 260 >> proper extension: 05f67hw; >> query: (?x4216, 06d4h) <- language(?x4216, ?x254), films(?x326, ?x4216), produced_by(?x4216, ?x2135) >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hfzr films! 06d4h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 38.000 0.069 http://example.org/film/film_subject/films #9872-084qpk PRED entity: 084qpk PRED relation: film! PRED expected values: 01wg982 => 83 concepts (56 used for prediction) PRED predicted values (max 10 best out of 99): 058kqy (0.53 #7369, 0.53 #7916, 0.49 #1363), 032v0v (0.17 #41, 0.05 #1131, 0.02 #3045), 04jspq (0.17 #157, 0.01 #6979), 01z7s_ (0.17 #413), 04g865 (0.14 #618, 0.01 #2531), 04k25 (0.14 #614), 06pj8 (0.11 #1957, 0.04 #2779, 0.03 #7963), 01m4yn (0.10 #14486, 0.09 #14759, 0.09 #14213), 0j_c (0.09 #880, 0.04 #2521, 0.04 #1152), 013zyw (0.09 #956, 0.02 #1501, 0.01 #2597) >> Best rule #7369 for best value: >> intensional similarity = 4 >> extensional distance = 422 >> proper extension: 02z3r8t; 02vqhv0; 035s95; 05fgt1; 03m8y5; 0pvms; 0gtvpkw; 04grkmd; 0c57yj; 024mpp; ... >> query: (?x814, ?x815) <- genre(?x814, ?x812), film(?x523, ?x814), written_by(?x814, ?x815), titles(?x812, ?x80) >> conf = 0.53 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 084qpk film! 01wg982 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 56.000 0.533 http://example.org/film/director/film #9871-088tb PRED entity: 088tb PRED relation: major_field_of_study! PRED expected values: 01r3y2 01qrb2 02hp70 => 48 concepts (30 used for prediction) PRED predicted values (max 10 best out of 688): 07szy (0.78 #1795, 0.67 #2961, 0.63 #7667), 02zd460 (0.69 #4279, 0.67 #7815, 0.67 #3109), 08815 (0.67 #1755, 0.65 #4679, 0.62 #5269), 03ksy (0.67 #7741, 0.63 #8913, 0.62 #6563), 05zl0 (0.67 #1979, 0.62 #4315, 0.60 #810), 01w3v (0.67 #7641, 0.62 #4105, 0.58 #2935), 07t90 (0.67 #1915, 0.62 #4251, 0.58 #3081), 01j_cy (0.67 #3544, 0.60 #1209, 0.60 #625), 065y4w7 (0.67 #3518, 0.60 #1183, 0.42 #2934), 06pwq (0.63 #7638, 0.53 #8223, 0.52 #9977) >> Best rule #1795 for best value: >> intensional similarity = 14 >> extensional distance = 7 >> proper extension: 05qjt; 06ms6; 062z7; 05qfh; >> query: (?x866, 07szy) <- major_field_of_study(?x4981, ?x866), major_field_of_study(?x1526, ?x866), major_field_of_study(?x865, ?x866), ?x1526 = 0bkj86, student(?x866, ?x11018), ?x865 = 02h4rq6, major_field_of_study(?x3424, ?x866), major_field_of_study(?x1011, ?x866), ?x4981 = 03bwzr4, major_field_of_study(?x866, ?x2014), ?x3424 = 01w5m, school(?x260, ?x1011), ?x260 = 01ypc, student(?x1011, ?x400) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1847 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 7 *> proper extension: 05qjt; 06ms6; 062z7; 05qfh; *> query: (?x866, 01r3y2) <- major_field_of_study(?x4981, ?x866), major_field_of_study(?x1526, ?x866), major_field_of_study(?x865, ?x866), ?x1526 = 0bkj86, student(?x866, ?x11018), ?x865 = 02h4rq6, major_field_of_study(?x3424, ?x866), major_field_of_study(?x1011, ?x866), ?x4981 = 03bwzr4, major_field_of_study(?x866, ?x2014), ?x3424 = 01w5m, school(?x260, ?x1011), ?x260 = 01ypc, student(?x1011, ?x400) *> conf = 0.44 ranks of expected_values: 48, 115, 367 EVAL 088tb major_field_of_study! 02hp70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 48.000 30.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 088tb major_field_of_study! 01qrb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 30.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 088tb major_field_of_study! 01r3y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 48.000 30.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #9870-09r9dp PRED entity: 09r9dp PRED relation: nominated_for PRED expected values: 0d68qy => 110 concepts (54 used for prediction) PRED predicted values (max 10 best out of 403): 02rzdcp (0.78 #8107, 0.78 #55153, 0.62 #2118), 07l50_1 (0.31 #63269, 0.30 #22703, 0.28 #25948), 07_k0c0 (0.31 #63269, 0.30 #22703, 0.28 #25948), 07kh6f3 (0.31 #63269, 0.30 #22703, 0.28 #25948), 02qydsh (0.30 #22703, 0.28 #25948, 0.28 #37308), 03z106 (0.30 #22703, 0.28 #25948, 0.28 #37308), 07sc6nw (0.30 #22703, 0.28 #25948, 0.28 #37308), 05r3qc (0.30 #22703, 0.28 #25948, 0.28 #37308), 080dwhx (0.25 #59, 0.03 #24385, 0.03 #45479), 03ln8b (0.15 #1923, 0.12 #69763, 0.08 #74634) >> Best rule #8107 for best value: >> intensional similarity = 3 >> extensional distance = 193 >> proper extension: 02wb6yq; 06t8b; >> query: (?x3789, ?x3310) <- award_winner(?x944, ?x3789), participant(?x3789, ?x545), award_winner(?x3310, ?x3789) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3615 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 69 *> proper extension: 05km8z; *> query: (?x3789, 0d68qy) <- award_winner(?x944, ?x3789), student(?x254, ?x3789) *> conf = 0.04 ranks of expected_values: 20 EVAL 09r9dp nominated_for 0d68qy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 110.000 54.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9869-01vt5c_ PRED entity: 01vt5c_ PRED relation: location PRED expected values: 0c8tk => 137 concepts (119 used for prediction) PRED predicted values (max 10 best out of 212): 06m_5 (0.63 #13655), 07z1m (0.33 #78, 0.08 #1684, 0.02 #10519), 013d7t (0.33 #259, 0.08 #1865), 0846v (0.33 #164, 0.08 #1770), 01n4w (0.33 #152, 0.08 #1758), 02_286 (0.26 #61893, 0.25 #1642, 0.25 #45823), 030qb3t (0.25 #45869, 0.22 #61939, 0.19 #69975), 0cr3d (0.17 #947, 0.08 #70037, 0.08 #1750), 0f2v0 (0.17 #985, 0.06 #2591, 0.04 #5000), 0162v (0.17 #906, 0.03 #4118, 0.02 #4921) >> Best rule #13655 for best value: >> intensional similarity = 4 >> extensional distance = 183 >> proper extension: 024yxd; >> query: (?x7951, ?x8420) <- award(?x7951, ?x724), location(?x7951, ?x362), profession(?x7951, ?x131), origin(?x7951, ?x8420) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #4240 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 0c7ct; 01w8n89; 017b2p; *> query: (?x7951, 0c8tk) <- artists(?x3916, ?x7951), artists(?x284, ?x7951), ?x3916 = 08cyft, category(?x7951, ?x134), parent_genre(?x283, ?x284) *> conf = 0.03 ranks of expected_values: 59 EVAL 01vt5c_ location 0c8tk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 137.000 119.000 0.631 http://example.org/people/person/places_lived./people/place_lived/location #9868-0dt645q PRED entity: 0dt645q PRED relation: film PRED expected values: 0bh72t => 88 concepts (53 used for prediction) PRED predicted values (max 10 best out of 498): 026q3s3 (0.33 #204, 0.25 #1990, 0.20 #3777), 02vw1w2 (0.33 #214, 0.21 #2000, 0.14 #3787), 0dd6bf (0.33 #1233, 0.12 #3019, 0.10 #6592), 031f_m (0.33 #1579, 0.12 #3365, 0.09 #5152), 0dh8v4 (0.17 #2726, 0.14 #4513, 0.10 #6299), 02z9hqn (0.17 #1915, 0.11 #3702, 0.07 #5488), 07ng9k (0.17 #1992, 0.11 #3779, 0.07 #5565), 02z5x7l (0.14 #4779, 0.12 #2992, 0.10 #6565), 02qm_f (0.14 #3728, 0.03 #18019, 0.02 #19805), 03d8jd1 (0.14 #5294, 0.03 #16012, 0.02 #23158) >> Best rule #204 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 03fghg; >> query: (?x10418, 026q3s3) <- profession(?x10418, ?x1032), nationality(?x10418, ?x252), film(?x10418, ?x10642), gender(?x10418, ?x231), ?x10642 = 05vc35 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6537 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 40 *> proper extension: 019vgs; 03k1vm; 0b9f7t; 01kym3; *> query: (?x10418, 0bh72t) <- profession(?x10418, ?x1383), gender(?x10418, ?x231), ?x1383 = 0np9r, film(?x10418, ?x1628), special_performance_type(?x10418, ?x296) *> conf = 0.02 ranks of expected_values: 201 EVAL 0dt645q film 0bh72t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 88.000 53.000 0.333 http://example.org/film/actor/film./film/performance/film #9867-035nm PRED entity: 035nm PRED relation: company! PRED expected values: 05_wyz => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 36): 060c4 (0.79 #4592, 0.74 #2443, 0.73 #4677), 02y6fz (0.70 #190, 0.22 #4506, 0.19 #568), 01yc02 (0.54 #217, 0.49 #1186, 0.47 #681), 02211by (0.50 #171, 0.35 #339, 0.33 #255), 05_wyz (0.47 #690, 0.47 #2751, 0.47 #1195), 09d6p2 (0.41 #691, 0.35 #395, 0.33 #1196), 0142rn (0.29 #276, 0.26 #612, 0.23 #360), 09lq2c (0.22 #4506, 0.20 #195, 0.15 #447), 04192r (0.22 #4506, 0.19 #289, 0.18 #331), 01rk91 (0.22 #4506, 0.13 #3958, 0.12 #3368) >> Best rule #4592 for best value: >> intensional similarity = 3 >> extensional distance = 165 >> proper extension: 0f8l9c; 059j2; 03rj0; 01_8w2; 04hzj; 05c74; 0175rc; >> query: (?x9476, 060c4) <- company(?x265, ?x9476), jurisdiction_of_office(?x265, ?x94), basic_title(?x966, ?x265) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #690 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 046b0s; *> query: (?x9476, 05_wyz) <- category(?x9476, ?x134), company(?x4682, ?x9476), industry(?x9476, ?x10787), citytown(?x9476, ?x479), ?x4682 = 0dq_5 *> conf = 0.47 ranks of expected_values: 5 EVAL 035nm company! 05_wyz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 157.000 157.000 0.790 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #9866-09wj5 PRED entity: 09wj5 PRED relation: location PRED expected values: 030qb3t => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 193): 0cy07 (0.42 #95635, 0.42 #98045, 0.42 #82778), 030qb3t (0.23 #17763, 0.23 #11334, 0.22 #6512), 02_286 (0.23 #61921, 0.19 #17717, 0.18 #57098), 0cc56 (0.08 #1663, 0.08 #2467, 0.07 #3271), 059rby (0.08 #1623, 0.05 #3231, 0.05 #11268), 06y57 (0.08 #255, 0.03 #43399, 0.02 #1862), 0cr3d (0.06 #1751, 0.06 #82922, 0.06 #13807), 0r0m6 (0.06 #1824, 0.04 #3432, 0.03 #6647), 01n7q (0.05 #16939, 0.05 #1669, 0.04 #12921), 013yq (0.05 #6548, 0.04 #3333, 0.02 #10567) >> Best rule #95635 for best value: >> intensional similarity = 2 >> extensional distance = 2474 >> proper extension: 07h1h5; 023l9y; 04m2zj; 04hqbbz; 0kbn5; 05dxl_; >> query: (?x629, ?x13829) <- profession(?x629, ?x1032), place_of_birth(?x629, ?x13829) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #17763 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 441 *> proper extension: 05m63c; 02qjj7; 033hqf; 01q7cb_; 01pw2f1; 01pl9g; 02d9k; 0285c; 02lf1j; 01vv126; ... *> query: (?x629, 030qb3t) <- nationality(?x629, ?x1310), location(?x629, ?x362), participant(?x1017, ?x629) *> conf = 0.23 ranks of expected_values: 2 EVAL 09wj5 location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 135.000 0.418 http://example.org/people/person/places_lived./people/place_lived/location #9865-01ls2 PRED entity: 01ls2 PRED relation: country! PRED expected values: 02vx4 07bs0 02y8z 07jjt 07jbh => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 40): 06f41 (0.83 #48, 0.70 #128, 0.61 #248), 07jbh (0.72 #61, 0.63 #101, 0.59 #141), 0194d (0.64 #153, 0.61 #73, 0.49 #313), 064vjs (0.61 #59, 0.57 #139, 0.53 #259), 02y8z (0.61 #51, 0.57 #131, 0.49 #251), 01gqfm (0.61 #76, 0.48 #156, 0.43 #276), 0dwxr (0.61 #57, 0.43 #137, 0.43 #1481), 07jjt (0.58 #93, 0.56 #53, 0.50 #133), 02vx4 (0.56 #43, 0.43 #1481, 0.39 #123), 07bs0 (0.55 #127, 0.50 #47, 0.49 #287) >> Best rule #48 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 03gj2; 01znc_; 02vzc; 03h64; >> query: (?x410, 06f41) <- country(?x150, ?x410), film_release_region(?x6603, ?x410), ?x6603 = 094g2z >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #61 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 03gj2; 01znc_; 02vzc; 03h64; *> query: (?x410, 07jbh) <- country(?x150, ?x410), film_release_region(?x6603, ?x410), ?x6603 = 094g2z *> conf = 0.72 ranks of expected_values: 2, 5, 8, 9, 10 EVAL 01ls2 country! 07jbh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01ls2 country! 07jjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 109.000 109.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01ls2 country! 02y8z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 109.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01ls2 country! 07bs0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 109.000 109.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01ls2 country! 02vx4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 109.000 109.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #9864-01f6zc PRED entity: 01f6zc PRED relation: religion PRED expected values: 01lp8 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 17): 0c8wxp (0.35 #96, 0.24 #367, 0.22 #728), 0kpl (0.09 #778, 0.06 #552, 0.05 #913), 03_gx (0.08 #1506, 0.07 #2859, 0.07 #2453), 06nzl (0.04 #105, 0.02 #195, 0.02 #602), 0n2g (0.04 #781, 0.02 #193, 0.02 #238), 092bf5 (0.03 #241, 0.03 #423, 0.03 #196), 03j6c (0.03 #1874, 0.02 #291, 0.02 #473), 019cr (0.02 #101, 0.01 #643, 0.01 #1184), 02rsw (0.02 #114), 01lp8 (0.02 #723, 0.02 #362, 0.02 #453) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 0cbm64; >> query: (?x5316, 0c8wxp) <- award_nominee(?x5316, ?x2763), award(?x5316, ?x2325), ?x2325 = 05p09zm >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #723 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 387 *> proper extension: 03f1zhf; 01vv6xv; *> query: (?x5316, 01lp8) <- participant(?x5316, ?x914), people(?x743, ?x5316) *> conf = 0.02 ranks of expected_values: 10 EVAL 01f6zc religion 01lp8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 103.000 103.000 0.354 http://example.org/people/person/religion #9863-0638kv PRED entity: 0638kv PRED relation: place_of_birth PRED expected values: 0f2nf => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 131): 030qb3t (0.25 #705, 0.17 #1463, 0.14 #2167), 04swd (0.25 #3133, 0.09 #3839, 0.04 #5951), 01zrs_ (0.25 #616), 02m77 (0.25 #252), 02_286 (0.20 #9878, 0.18 #11287, 0.17 #17634), 0f2w0 (0.20 #767, 0.09 #3585, 0.04 #5697), 01sn3 (0.17 #1558, 0.04 #10713, 0.04 #5784), 04ly1 (0.17 #1549, 0.04 #5775, 0.03 #6479), 07dfk (0.14 #2472, 0.01 #13036, 0.01 #12332), 0cr3d (0.12 #2911, 0.10 #5025, 0.07 #5729) >> Best rule #705 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 026lj; 0tfc; >> query: (?x4830, ?x1523) <- student(?x4390, ?x4830), place_of_death(?x4830, ?x1523), gender(?x4830, ?x231), ?x4390 = 0h6rm >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #7390 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 06j0md; 0265v21; 05fg2; 02d4ct; 02l5rm; 02ct_k; *> query: (?x4830, 0f2nf) <- award_winner(?x484, ?x4830), student(?x122, ?x4830), ?x122 = 08815, profession(?x4830, ?x319) *> conf = 0.06 ranks of expected_values: 19 EVAL 0638kv place_of_birth 0f2nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 149.000 149.000 0.250 http://example.org/people/person/place_of_birth #9862-04zl8 PRED entity: 04zl8 PRED relation: film! PRED expected values: 016tt2 => 50 concepts (44 used for prediction) PRED predicted values (max 10 best out of 48): 086k8 (0.38 #2, 0.16 #1326, 0.16 #1844), 05qd_ (0.38 #8, 0.13 #1406, 0.13 #1259), 016tw3 (0.15 #303, 0.14 #1261, 0.13 #742), 041c4 (0.13 #1177, 0.06 #1917, 0.06 #1916), 0dpqk (0.13 #1177, 0.06 #1917, 0.06 #1916), 016tt2 (0.13 #76, 0.12 #149, 0.12 #1327), 03xq0f (0.12 #443, 0.11 #370, 0.10 #223), 0g1rw (0.09 #300, 0.08 #80, 0.07 #739), 017jv5 (0.09 #307, 0.05 #746, 0.04 #1412), 0jz9f (0.08 #74, 0.07 #294, 0.07 #733) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 09lxv9; >> query: (?x5317, 086k8) <- nominated_for(?x4297, ?x5317), nominated_for(?x10747, ?x5317), ?x10747 = 0262s1 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0pvms; *> query: (?x5317, 016tt2) <- written_by(?x5317, ?x4987), language(?x5317, ?x254), special_performance_type(?x4987, ?x4832) *> conf = 0.13 ranks of expected_values: 6 EVAL 04zl8 film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 50.000 44.000 0.375 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #9861-0fbtbt PRED entity: 0fbtbt PRED relation: ceremony PRED expected values: 05c1t6z 0gvstc3 => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 131): 05c1t6z (0.92 #408, 0.88 #540, 0.86 #277), 0gvstc3 (0.83 #558, 0.83 #426, 0.79 #295), 0lp_cd3 (0.72 #1708, 0.25 #1314, 0.21 #4858), 05pd94v (0.68 #1052, 0.46 #1841, 0.35 #2760), 0gpjbt (0.65 #1079, 0.48 #1868, 0.38 #2393), 0466p0j (0.63 #1121, 0.46 #1910, 0.36 #2435), 02rjjll (0.62 #1055, 0.46 #1844, 0.35 #2369), 09n4nb (0.61 #1096, 0.47 #1885, 0.36 #2804), 02cg41 (0.61 #1168, 0.46 #1957, 0.36 #2482), 056878 (0.60 #1081, 0.46 #1870, 0.36 #2789) >> Best rule #408 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 09v82c0; >> query: (?x4921, 05c1t6z) <- ceremony(?x4921, ?x2292), ?x2292 = 0gx_st, nominated_for(?x4921, ?x337), award(?x2803, ?x4921), award_winner(?x2213, ?x2803) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0fbtbt ceremony 0gvstc3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.917 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0fbtbt ceremony 05c1t6z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.917 http://example.org/award/award_category/winners./award/award_honor/ceremony #9860-0p76z PRED entity: 0p76z PRED relation: artist! PRED expected values: 023rwm => 101 concepts (94 used for prediction) PRED predicted values (max 10 best out of 133): 01clyr (0.40 #844, 0.33 #164, 0.29 #2341), 03rhqg (0.36 #2327, 0.33 #150, 0.33 #14), 03qx_f (0.33 #1021, 0.33 #69, 0.29 #1429), 01trtc (0.33 #1156, 0.33 #340, 0.25 #476), 0n85g (0.33 #58, 0.29 #1418, 0.21 #2371), 05cl8y (0.33 #52, 0.29 #1412, 0.20 #732), 056252 (0.33 #40, 0.20 #720, 0.17 #992), 01x7jb (0.33 #75, 0.20 #755, 0.17 #1027), 01dtcb (0.33 #178, 0.17 #1130, 0.14 #4804), 0f38nv (0.33 #245, 0.07 #2422, 0.06 #2558) >> Best rule #844 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 01309x; >> query: (?x10145, 01clyr) <- artist(?x5634, ?x10145), artists(?x7329, ?x10145), artists(?x7083, ?x10145), ?x7329 = 016jny, ?x7083 = 02yv6b, origin(?x10145, ?x9588), artist(?x5634, ?x5916), ?x5916 = 02cpp >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #818 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 01309x; *> query: (?x10145, 023rwm) <- artist(?x5634, ?x10145), artists(?x7329, ?x10145), artists(?x7083, ?x10145), ?x7329 = 016jny, ?x7083 = 02yv6b, origin(?x10145, ?x9588), artist(?x5634, ?x5916), ?x5916 = 02cpp *> conf = 0.20 ranks of expected_values: 19 EVAL 0p76z artist! 023rwm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 101.000 94.000 0.400 http://example.org/music/record_label/artist #9859-0czmk1 PRED entity: 0czmk1 PRED relation: team PRED expected values: 023fb => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 443): 023fb (0.85 #1886, 0.85 #1885, 0.84 #2427), 01x4wq (0.85 #1886, 0.85 #1885, 0.84 #3508), 0j2jr (0.85 #1886, 0.85 #1885, 0.84 #3508), 027pwl (0.85 #1886, 0.85 #1885, 0.84 #3508), 06zpgb2 (0.40 #730, 0.11 #3509, 0.08 #1805), 0j46b (0.40 #454, 0.11 #3509, 0.07 #2613), 0cnk2q (0.40 #271, 0.09 #1079, 0.07 #2430), 025czw (0.33 #138, 0.20 #408, 0.11 #3509), 01cwm1 (0.33 #117, 0.11 #927, 0.11 #3509), 01rly6 (0.33 #191, 0.11 #3509, 0.09 #1269) >> Best rule #1886 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 05_6_y; 0bn9sc; 0487c3; 02d9k; 09ntbc; 083qy7; 07nv3_; 02vl_pz; 0135nb; 05s_c38; ... >> query: (?x9697, ?x13211) <- team(?x9697, ?x13211), gender(?x9697, ?x231), profession(?x9697, ?x7623), team(?x9779, ?x13211) >> conf = 0.85 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 0czmk1 team 023fb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.847 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #9858-0ptdz PRED entity: 0ptdz PRED relation: film! PRED expected values: 02p65p => 113 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1249): 0tc7 (0.22 #6635, 0.02 #29519, 0.02 #31599), 01gkmx (0.17 #3666, 0.17 #1586, 0.09 #9906), 0bxtg (0.17 #2157, 0.17 #77, 0.06 #97788), 0bl2g (0.17 #2135, 0.17 #55, 0.06 #97788), 01j5ws (0.17 #2594, 0.17 #514, 0.06 #97788), 019vgs (0.17 #2740, 0.17 #660, 0.06 #97788), 0p8r1 (0.17 #2665, 0.17 #585, 0.06 #15146), 05nzw6 (0.17 #3271, 0.17 #1191, 0.05 #32395), 04t7ts (0.17 #2291, 0.17 #211, 0.04 #8531), 02p65p (0.17 #2101, 0.17 #21, 0.04 #8341) >> Best rule #6635 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 0bz3jx; 07ghq; 0m5s5; >> query: (?x11909, 0tc7) <- currency(?x11909, ?x170), genre(?x11909, ?x11523), genre(?x11909, ?x6674), film(?x2726, ?x11909), featured_film_locations(?x11909, ?x739), ?x11523 = 07s2s, genre(?x273, ?x6674) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #2101 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 03q0r1; *> query: (?x11909, 02p65p) <- currency(?x11909, ?x170), genre(?x11909, ?x53), film(?x5410, ?x11909), ?x5410 = 05bpg3, film(?x1104, ?x11909) *> conf = 0.17 ranks of expected_values: 10 EVAL 0ptdz film! 02p65p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 113.000 68.000 0.222 http://example.org/film/actor/film./film/performance/film #9857-01gkgk PRED entity: 01gkgk PRED relation: basic_title! PRED expected values: 03txms 03_nq => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 754): 07cbs (0.50 #351, 0.50 #155, 0.50 #90), 042d1 (0.50 #372, 0.50 #111, 0.43 #437), 03_nq (0.50 #105, 0.40 #301, 0.33 #366), 083pr (0.43 #402, 0.33 #337, 0.25 #469), 08f3b1 (0.38 #459, 0.27 #861, 0.22 #728), 0fd_1 (0.38 #492, 0.20 #1162, 0.20 #826), 01mvpv (0.33 #388, 0.33 #62, 0.29 #453), 042f1 (0.33 #771, 0.33 #370, 0.29 #435), 083q7 (0.33 #330, 0.29 #395, 0.25 #462), 02yy8 (0.33 #385, 0.29 #450, 0.25 #517) >> Best rule #351 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 060bp; >> query: (?x2358, 07cbs) <- basic_title(?x11605, ?x2358), basic_title(?x9334, ?x2358), basic_title(?x5978, ?x2358), student(?x5426, ?x11605), legislative_sessions(?x11605, ?x1137), district_represented(?x1137, ?x335), gender(?x5978, ?x231), legislative_sessions(?x2860, ?x1137), legislative_sessions(?x9334, ?x355), entity_involved(?x8416, ?x5978), profession(?x5978, ?x2225), currency(?x5426, ?x170), politician(?x8714, ?x9334), politician(?x10510, ?x5978) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #105 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 2 *> proper extension: 060c4; *> query: (?x2358, 03_nq) <- basic_title(?x11605, ?x2358), basic_title(?x5978, ?x2358), basic_title(?x2357, ?x2358), student(?x4016, ?x11605), legislative_sessions(?x11605, ?x2976), legislative_sessions(?x11605, ?x1137), district_represented(?x1137, ?x6226), district_represented(?x1137, ?x961), ?x5978 = 0424m, ?x6226 = 03gh4, ?x961 = 03s0w, legislative_sessions(?x2860, ?x1137), place_of_birth(?x11605, ?x739), student(?x865, ?x11605), profession(?x2357, ?x353), award_winner(?x5631, ?x2357), jurisdiction_of_office(?x2358, ?x94), legislative_sessions(?x2976, ?x355) *> conf = 0.50 ranks of expected_values: 3, 48 EVAL 01gkgk basic_title! 03_nq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 21.000 21.000 0.500 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title EVAL 01gkgk basic_title! 03txms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 21.000 21.000 0.500 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #9856-06k02 PRED entity: 06k02 PRED relation: artists! PRED expected values: 029h7y 03mb9 03w94xt 02w6s3 => 103 concepts (52 used for prediction) PRED predicted values (max 10 best out of 191): 03_d0 (0.72 #2125, 0.38 #614, 0.30 #916), 064t9 (0.71 #8180, 0.43 #11, 0.43 #6061), 06by7 (0.43 #6373, 0.43 #4859, 0.42 #4556), 05bt6j (0.29 #43, 0.28 #647, 0.27 #8212), 06j6l (0.29 #47, 0.25 #8216, 0.24 #6097), 0ggx5q (0.29 #73, 0.16 #8242, 0.15 #6123), 016ybr (0.29 #122, 0.03 #8291, 0.03 #726), 05lls (0.27 #919, 0.18 #1826, 0.14 #1221), 01lyv (0.24 #3056, 0.19 #6386, 0.17 #3964), 016clz (0.24 #306, 0.23 #4843, 0.23 #12105) >> Best rule #2125 for best value: >> intensional similarity = 3 >> extensional distance = 189 >> proper extension: 0h08p; >> query: (?x2306, 03_d0) <- artists(?x597, ?x2306), artists(?x597, ?x4184), ?x4184 = 01m3x5p >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #493 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 07j8kh; 09z1lg; *> query: (?x2306, 03w94xt) <- artists(?x10290, ?x2306), award_winner(?x1079, ?x2306), ?x10290 = 03ckfl9 *> conf = 0.12 ranks of expected_values: 41, 107, 116 EVAL 06k02 artists! 02w6s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 52.000 0.723 http://example.org/music/genre/artists EVAL 06k02 artists! 03w94xt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 103.000 52.000 0.723 http://example.org/music/genre/artists EVAL 06k02 artists! 03mb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 103.000 52.000 0.723 http://example.org/music/genre/artists EVAL 06k02 artists! 029h7y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 103.000 52.000 0.723 http://example.org/music/genre/artists #9855-06d4h PRED entity: 06d4h PRED relation: films PRED expected values: 023gxx 01rwyq 0hfzr 04q01mn => 50 concepts (19 used for prediction) PRED predicted values (max 10 best out of 714): 091rc5 (0.33 #2251, 0.29 #2752, 0.20 #1247), 02d413 (0.33 #1, 0.25 #502, 0.20 #1005), 04lhc4 (0.33 #339, 0.25 #840, 0.20 #1343), 01q7h2 (0.33 #445, 0.25 #946, 0.20 #1449), 04x4vj (0.33 #224, 0.25 #725, 0.20 #1228), 0170th (0.33 #123, 0.25 #624, 0.20 #1127), 01b195 (0.33 #104, 0.25 #605, 0.20 #1108), 0296vv (0.33 #398, 0.25 #899, 0.20 #1402), 016y_f (0.33 #215, 0.25 #716, 0.20 #1219), 03lv4x (0.33 #209, 0.25 #710, 0.20 #1213) >> Best rule #2251 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 07s2s; >> query: (?x5069, 091rc5) <- films(?x5069, ?x2107), film(?x100, ?x2107), film_crew_role(?x2107, ?x1171), award_nominee(?x100, ?x4767), titles(?x162, ?x2107), film(?x2135, ?x2107), ?x4767 = 0205dx, language(?x2107, ?x254), production_companies(?x2107, ?x2246), ?x1171 = 09vw2b7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4722 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 17 *> proper extension: 0g9pc; *> query: (?x5069, 0hfzr) <- films(?x5069, ?x11483), films(?x5069, ?x9801), films(?x5069, ?x2107), film(?x100, ?x2107), film_crew_role(?x2107, ?x137), award_nominee(?x100, ?x101), nominated_for(?x100, ?x2029), genre(?x11483, ?x53), ?x2029 = 020bv3, nominated_for(?x384, ?x9801), produced_by(?x11483, ?x9363), award_winner(?x2107, ?x2135) *> conf = 0.16 ranks of expected_values: 89, 143, 281, 318 EVAL 06d4h films 04q01mn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 50.000 19.000 0.333 http://example.org/film/film_subject/films EVAL 06d4h films 0hfzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 50.000 19.000 0.333 http://example.org/film/film_subject/films EVAL 06d4h films 01rwyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 50.000 19.000 0.333 http://example.org/film/film_subject/films EVAL 06d4h films 023gxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 50.000 19.000 0.333 http://example.org/film/film_subject/films #9854-07db6x PRED entity: 07db6x PRED relation: award PRED expected values: 0gr4k => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 300): 07bdd_ (0.59 #471, 0.53 #7761, 0.50 #7356), 05p1dby (0.59 #512, 0.39 #7397, 0.39 #8207), 040njc (0.54 #5678, 0.53 #3653, 0.52 #6893), 0gr51 (0.38 #910, 0.35 #1315, 0.29 #2125), 019f4v (0.38 #5737, 0.37 #6952, 0.32 #3712), 0gs9p (0.37 #5749, 0.35 #6964, 0.31 #3724), 0gr4k (0.29 #2058, 0.28 #2463, 0.27 #12994), 04dn09n (0.24 #2069, 0.24 #13005, 0.23 #3689), 09sb52 (0.22 #30012, 0.20 #31632, 0.19 #32037), 0f_nbyh (0.22 #4465, 0.21 #5680, 0.20 #6895) >> Best rule #471 for best value: >> intensional similarity = 2 >> extensional distance = 20 >> proper extension: 0c41qv; >> query: (?x13015, 07bdd_) <- award_nominee(?x13015, ?x574), ?x574 = 016tt2 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #2058 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 08433; 0171lb; 01vsps; 012wg; 015njf; 03qjlz; 0282x; 0bs8d; 0hky; 0gv2r; ... *> query: (?x13015, 0gr4k) <- place_of_death(?x13015, ?x682), written_by(?x6100, ?x13015), profession(?x13015, ?x524), award(?x13015, ?x1307) *> conf = 0.29 ranks of expected_values: 7 EVAL 07db6x award 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 109.000 109.000 0.591 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9853-06kqt3 PRED entity: 06kqt3 PRED relation: colors! PRED expected values: 01f6ss => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 976): 07lx1s (0.60 #2420, 0.43 #3375, 0.38 #4334), 01hjy5 (0.60 #2671, 0.33 #757, 0.29 #3626), 016sd3 (0.50 #4680, 0.50 #1331, 0.45 #6593), 01jq34 (0.50 #4352, 0.50 #1003, 0.44 #5311), 02vnp2 (0.50 #4638, 0.50 #1289, 0.44 #5597), 01tntf (0.50 #1308, 0.43 #3698, 0.40 #2743), 01b1mj (0.50 #975, 0.43 #3365, 0.40 #2410), 0gjv_ (0.50 #1141, 0.43 #3531, 0.40 #2576), 0pz6q (0.50 #1300, 0.43 #3690, 0.40 #2735), 016ndm (0.50 #2026, 0.43 #3457, 0.40 #2502) >> Best rule #2420 for best value: >> intensional similarity = 37 >> extensional distance = 3 >> proper extension: 036k5h; >> query: (?x12067, 07lx1s) <- colors(?x13166, ?x12067), colors(?x1160, ?x12067), position(?x1160, ?x10822), position(?x1160, ?x7533), position(?x1160, ?x5727), position(?x1160, ?x4244), season(?x1160, ?x10017), season(?x1160, ?x9498), ?x4244 = 028c_8, colors(?x11185, ?x12067), colors(?x6123, ?x12067), team(?x10822, ?x4243), ?x9498 = 027pwzc, institution(?x865, ?x11185), registering_agency(?x11185, ?x1982), currency(?x11185, ?x170), ?x10017 = 026fmqm, draft(?x1160, ?x10600), draft(?x1160, ?x1633), draft(?x1160, ?x1161), ?x4243 = 0713r, ?x5727 = 02wszf, ?x1161 = 02x2khw, contains(?x512, ?x6123), ?x1633 = 02rl201, team(?x2918, ?x13166), ?x10600 = 04f4z1k, organization(?x346, ?x11185), school(?x1160, ?x7338), school(?x1160, ?x735), ?x346 = 060c4, student(?x11185, ?x10593), ?x7533 = 01yvvn, ?x735 = 065y4w7, teams(?x1658, ?x13166), major_field_of_study(?x7338, ?x2606), ?x2606 = 062z7 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #464 for first EXPECTED value: *> intensional similarity = 37 *> extensional distance = 1 *> proper extension: 01l849; *> query: (?x12067, 01f6ss) <- colors(?x13860, ?x12067), colors(?x13542, ?x12067), colors(?x1160, ?x12067), position(?x1160, ?x10822), position(?x1160, ?x5727), position(?x1160, ?x4244), season(?x1160, ?x10017), ?x4244 = 028c_8, ?x10822 = 017drs, school(?x1160, ?x1428), school(?x1160, ?x581), sport(?x1160, ?x5063), ?x5727 = 02wszf, colors(?x9803, ?x12067), colors(?x1506, ?x12067), ?x1428 = 01j_06, draft(?x1160, ?x8499), draft(?x1160, ?x1633), ?x10017 = 026fmqm, current_club(?x4406, ?x13542), team(?x6873, ?x13542), team(?x2918, ?x13860), team(?x60, ?x13542), draft(?x10279, ?x1633), draft(?x7725, ?x1633), draft(?x1823, ?x1633), position(?x13542, ?x203), ?x9803 = 02h659, category(?x1160, ?x134), contains(?x94, ?x1506), ?x7725 = 07l8x, major_field_of_study(?x581, ?x742), sport(?x13860, ?x453), ?x1823 = 01yhm, ?x8499 = 02r6gw6, state_province_region(?x581, ?x1227), ?x10279 = 04wmvz *> conf = 0.33 ranks of expected_values: 297 EVAL 06kqt3 colors! 01f6ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 21.000 21.000 0.600 http://example.org/education/educational_institution/colors #9852-01___w PRED entity: 01___w PRED relation: profession! PRED expected values: 01mr2g6 => 39 concepts (21 used for prediction) PRED predicted values (max 10 best out of 4115): 01nz1q6 (0.67 #24802, 0.46 #41770, 0.33 #16322), 05bnp0 (0.67 #21230, 0.38 #38198, 0.33 #12750), 0dpqk (0.50 #22825, 0.50 #18585, 0.46 #39793), 05wm88 (0.50 #25030, 0.50 #20790, 0.46 #41998), 015pxr (0.50 #21816, 0.50 #17576, 0.41 #51510), 02b29 (0.50 #23453, 0.50 #19213, 0.38 #40421), 015njf (0.50 #22761, 0.50 #18521, 0.38 #39729), 056wb (0.50 #25452, 0.50 #23189, 0.38 #40157), 0drdv (0.50 #25110, 0.50 #20870, 0.36 #54804), 01bbwp (0.50 #24412, 0.50 #20172, 0.36 #54106) >> Best rule #24802 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 02jknp; 0n1h; 0dxtg; >> query: (?x3970, 01nz1q6) <- profession(?x6166, ?x3970), profession(?x3969, ?x3970), ?x6166 = 051z6rz, people(?x7185, ?x3969), people(?x4322, ?x3969), influenced_by(?x117, ?x3969), location(?x3969, ?x1025) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2720 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 016fly; *> query: (?x3970, 01mr2g6) <- profession(?x3969, ?x3970), company(?x3970, ?x892), student(?x892, ?x6037), major_field_of_study(?x892, ?x742), influenced_by(?x2485, ?x6037), school_type(?x892, ?x3092) *> conf = 0.33 ranks of expected_values: 830 EVAL 01___w profession! 01mr2g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 21.000 0.667 http://example.org/people/person/profession #9851-059rby PRED entity: 059rby PRED relation: state! PRED expected values: 0y1rf => 150 concepts (147 used for prediction) PRED predicted values (max 10 best out of 491): 0cc56 (0.27 #15208, 0.17 #11326, 0.17 #12816), 0ccvx (0.27 #15208, 0.17 #11326, 0.17 #12816), 0ycht (0.27 #15208, 0.17 #11326, 0.17 #12816), 0cymp (0.27 #15208, 0.17 #11326, 0.17 #12816), 01mb87 (0.27 #15208, 0.17 #11326, 0.17 #12816), 0y617 (0.17 #11326, 0.17 #12816, 0.17 #5661), 0n6dc (0.17 #11326, 0.17 #12816, 0.17 #5661), 0g5rg (0.17 #11326, 0.17 #12816, 0.17 #5661), 01531 (0.17 #11326, 0.17 #12816, 0.17 #5661), 0y2dl (0.17 #11326, 0.17 #12816, 0.17 #5661) >> Best rule #15208 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 0mgfs; >> query: (?x335, ?x12931) <- contains(?x335, ?x12931), location_of_ceremony(?x566, ?x12931), country(?x335, ?x94) >> conf = 0.27 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 059rby state! 0y1rf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 147.000 0.268 http://example.org/base/biblioness/bibs_location/state #9850-01mwsnc PRED entity: 01mwsnc PRED relation: participant PRED expected values: 0jfx1 => 139 concepts (48 used for prediction) PRED predicted values (max 10 best out of 231): 0jfx1 (0.14 #805, 0.08 #2735, 0.03 #5951), 01rh0w (0.14 #738, 0.08 #2668, 0.03 #5884), 02wb6yq (0.14 #4085, 0.10 #6658, 0.10 #7301), 03bnv (0.11 #3448, 0.03 #6020, 0.03 #7951), 0484q (0.09 #4337, 0.09 #1763, 0.07 #6910), 09889g (0.09 #4206, 0.09 #1632, 0.07 #6779), 06w2sn5 (0.09 #3957, 0.09 #1383, 0.07 #6530), 0f4vbz (0.09 #1432, 0.08 #2719, 0.08 #4649), 049qx (0.09 #1589, 0.07 #5449, 0.07 #6092), 0227vl (0.09 #4402, 0.07 #6975, 0.07 #7618) >> Best rule #805 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 01vw8mh; 01w5gg6; 013rds; >> query: (?x4918, 0jfx1) <- film(?x4918, ?x1619), artists(?x283, ?x4918), gender(?x4918, ?x231), ?x231 = 05zppz, ?x283 = 06cqb >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mwsnc participant 0jfx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 48.000 0.143 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #9849-0rp46 PRED entity: 0rp46 PRED relation: place PRED expected values: 0rp46 => 114 concepts (87 used for prediction) PRED predicted values (max 10 best out of 150): 0s5cg (0.34 #24224, 0.23 #12366, 0.18 #21645), 05qtj (0.20 #2577, 0.18 #27833, 0.15 #8759), 0rp46 (0.20 #2577, 0.18 #27833, 0.15 #8759), 01_d4 (0.20 #2577, 0.18 #27833, 0.15 #8759), 0h7h6 (0.20 #2577, 0.18 #27833, 0.15 #8759), 0h1k6 (0.06 #1351, 0.03 #1866, 0.02 #3412), 02_286 (0.06 #1045, 0.02 #2591, 0.02 #4136), 0r0m6 (0.06 #1126, 0.02 #4217, 0.01 #9369), 06wxw (0.06 #1131, 0.01 #7828, 0.01 #9374), 0jrq9 (0.05 #17005, 0.05 #22677, 0.05 #24740) >> Best rule #24224 for best value: >> intensional similarity = 4 >> extensional distance = 211 >> proper extension: 0104lr; >> query: (?x3259, ?x5037) <- source(?x3259, ?x958), category(?x3259, ?x134), location(?x5336, ?x3259), place_of_birth(?x5336, ?x5037) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #2577 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 0fhp9; 01f62; 013yq; 02h6_6p; *> query: (?x3259, ?x1658) <- location(?x5336, ?x3259), administrative_division(?x3259, ?x11986), company(?x5336, ?x8641), location(?x5336, ?x1658) *> conf = 0.20 ranks of expected_values: 3 EVAL 0rp46 place 0rp46 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 87.000 0.336 http://example.org/location/hud_county_place/place #9848-04w391 PRED entity: 04w391 PRED relation: profession PRED expected values: 02hrh1q => 88 concepts (87 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.89 #2249, 0.89 #1057, 0.89 #2100), 0np9r (0.70 #1362, 0.50 #319, 0.16 #5385), 01d_h8 (0.42 #602, 0.42 #1049, 0.41 #900), 0dxtg (0.34 #1801, 0.29 #6718, 0.27 #2993), 03gjzk (0.29 #909, 0.28 #1058, 0.26 #2995), 0d1pc (0.27 #200, 0.25 #1243, 0.23 #1541), 0cbd2 (0.27 #1795, 0.16 #5073, 0.16 #4775), 09jwl (0.24 #2999, 0.23 #4191, 0.18 #4042), 0dz3r (0.20 #2982, 0.15 #4174, 0.13 #1194), 018gz8 (0.20 #315, 0.18 #1060, 0.17 #911) >> Best rule #2249 for best value: >> intensional similarity = 3 >> extensional distance = 215 >> proper extension: 012_53; >> query: (?x3999, 02hrh1q) <- film(?x3999, ?x755), participant(?x6835, ?x3999), country(?x755, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04w391 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 87.000 0.889 http://example.org/people/person/profession #9847-01p3ty PRED entity: 01p3ty PRED relation: film! PRED expected values: 03wpmd 02x02kb => 61 concepts (37 used for prediction) PRED predicted values (max 10 best out of 909): 0241wg (0.61 #52011, 0.56 #27040, 0.56 #49929), 04b19t (0.55 #33284, 0.51 #29123, 0.41 #41606), 099ks0 (0.50 #10398, 0.46 #4160, 0.43 #2080), 016zp5 (0.17 #979, 0.07 #9297, 0.06 #11377), 0372kf (0.17 #923, 0.04 #7162, 0.03 #9241), 015pkc (0.16 #2358, 0.15 #4438, 0.10 #8596), 03fw4y (0.11 #47848, 0.01 #62411), 044rvb (0.11 #2182, 0.10 #4262, 0.07 #8420), 04fzk (0.09 #11106, 0.07 #13185, 0.07 #6947), 01520h (0.08 #1190, 0.07 #7429, 0.06 #11588) >> Best rule #52011 for best value: >> intensional similarity = 4 >> extensional distance = 710 >> proper extension: 0fpxp; >> query: (?x2617, ?x8073) <- nominated_for(?x1937, ?x2617), nominated_for(?x8073, ?x2617), religion(?x8073, ?x492), film(?x8073, ?x657) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #62411 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 841 *> proper extension: 04glx0; *> query: (?x2617, ?x1445) <- award_winner(?x2617, ?x3129), nominated_for(?x2618, ?x2617), award(?x2617, ?x1937), award_winner(?x1937, ?x1445) *> conf = 0.01 ranks of expected_values: 672, 718 EVAL 01p3ty film! 02x02kb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 37.000 0.608 http://example.org/film/actor/film./film/performance/film EVAL 01p3ty film! 03wpmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 37.000 0.608 http://example.org/film/actor/film./film/performance/film #9846-0fpj4lx PRED entity: 0fpj4lx PRED relation: origin PRED expected values: 030qb3t => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 94): 02_286 (0.32 #9681, 0.10 #8735, 0.07 #18421), 0d9jr (0.20 #98, 0.07 #806, 0.05 #1042), 0c_m3 (0.20 #101, 0.05 #1045, 0.03 #1517), 0dclg (0.17 #280, 0.07 #752, 0.04 #1224), 0y62n (0.17 #388), 04jpl (0.14 #1894, 0.09 #950, 0.06 #3310), 0jgx (0.08 #546, 0.04 #1254, 0.03 #1490), 09bjv (0.08 #481, 0.04 #1189, 0.03 #1425), 0l3q2 (0.08 #680, 0.03 #1624), 02dtg (0.07 #718, 0.04 #1190, 0.03 #2842) >> Best rule #9681 for best value: >> intensional similarity = 4 >> extensional distance = 322 >> proper extension: 01l_vgt; 01vw917; >> query: (?x3740, ?x739) <- artist(?x2299, ?x3740), artists(?x302, ?x3740), place_of_birth(?x3740, ?x739), gender(?x3740, ?x231) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #3102 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 107 *> proper extension: 05683p; 02ldv0; 03m6_z; 02fybl; 04954; 01r4zfk; *> query: (?x3740, 030qb3t) <- gender(?x3740, ?x231), profession(?x3740, ?x1032), role(?x3740, ?x227), ?x1032 = 02hrh1q, ?x231 = 05zppz *> conf = 0.06 ranks of expected_values: 12 EVAL 0fpj4lx origin 030qb3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 123.000 123.000 0.324 http://example.org/music/artist/origin #9845-01bb9r PRED entity: 01bb9r PRED relation: language PRED expected values: 02h40lc => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 30): 02h40lc (0.96 #1870, 0.95 #1519, 0.95 #2394), 064_8sq (0.15 #313, 0.14 #1304, 0.14 #1597), 04306rv (0.13 #5, 0.12 #645, 0.12 #1228), 06nm1 (0.13 #303, 0.11 #477, 0.11 #361), 0653m (0.10 #12, 0.06 #128, 0.05 #536), 02bjrlw (0.10 #641, 0.08 #1224, 0.08 #175), 06b_j (0.07 #1070, 0.06 #1480, 0.06 #662), 03_9r (0.05 #534, 0.05 #302, 0.05 #1000), 04h9h (0.05 #216, 0.04 #392, 0.04 #858), 012w70 (0.04 #363, 0.04 #129, 0.03 #13) >> Best rule #1870 for best value: >> intensional similarity = 4 >> extensional distance = 658 >> proper extension: 0gbfn9; >> query: (?x2955, 02h40lc) <- film(?x382, ?x2955), film(?x3961, ?x2955), language(?x2955, ?x5359), film(?x286, ?x2955) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bb9r language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.959 http://example.org/film/film/language #9844-0947l PRED entity: 0947l PRED relation: mode_of_transportation PRED expected values: 025t3bg => 275 concepts (275 used for prediction) PRED predicted values (max 10 best out of 3): 025t3bg (0.88 #40, 0.82 #118, 0.80 #154), 06d_3 (0.06 #108, 0.04 #281, 0.04 #75), 0k4j (0.04 #280, 0.04 #74, 0.04 #83) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 09b83; >> query: (?x8956, 025t3bg) <- citytown(?x5695, ?x8956), mode_of_transportation(?x8956, ?x4272), administrative_division(?x8956, ?x9792), contains(?x205, ?x8956) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0947l mode_of_transportation 025t3bg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 275.000 275.000 0.875 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #9843-07t65 PRED entity: 07t65 PRED relation: organization! PRED expected values: 01znc_ 07z5n 04j53 05qkp 088q4 03shp 03__y 077qn 06ryl 0166v 06tw8 016zwt 0164v 01ppq 04vs9 0162b 04hvw => 63 concepts (34 used for prediction) PRED predicted values (max 10 best out of 163): 04vs9 (0.67 #819, 0.60 #535, 0.50 #960), 0166v (0.67 #765, 0.60 #481, 0.50 #906), 0164v (0.67 #816, 0.60 #532, 0.33 #957), 06tw8 (0.67 #781, 0.60 #497, 0.33 #922), 07z5n (0.67 #872, 0.33 #731, 0.33 #164), 04hvw (0.50 #981, 0.50 #840, 0.40 #556), 088q4 (0.50 #885, 0.50 #744, 0.40 #460), 06ryl (0.50 #905, 0.33 #764, 0.33 #197), 05qkp (0.50 #882, 0.33 #741, 0.33 #174), 0162b (0.50 #967, 0.33 #259, 0.23 #1251) >> Best rule #819 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 041288; >> query: (?x312, 04vs9) <- organization(?x10451, ?x312), organization(?x7360, ?x312), organization(?x3683, ?x312), ?x7360 = 0fv4v, film_release_region(?x186, ?x3683), currency(?x10451, ?x170) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 25, 31 EVAL 07t65 organization! 04hvw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 0162b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 04vs9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 01ppq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 0164v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 016zwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 06tw8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 0166v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 06ryl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 03__y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 03shp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 088q4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 05qkp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 04j53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 07z5n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07t65 organization! 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 34.000 0.667 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #9842-05vz3zq PRED entity: 05vz3zq PRED relation: olympics PRED expected values: 015l4k => 240 concepts (240 used for prediction) PRED predicted values (max 10 best out of 30): 0kbvb (0.83 #420, 0.74 #1334, 0.73 #942), 0jdk_ (0.83 #437, 0.74 #619, 0.74 #1142), 06sks6 (0.78 #435, 0.77 #957, 0.76 #1663), 0kbws (0.76 #1131, 0.72 #1340, 0.70 #1446), 0l6m5 (0.76 #527, 0.72 #423, 0.67 #240), 0jhn7 (0.74 #1352, 0.74 #1143, 0.72 #1458), 018ctl (0.72 #421, 0.50 #343, 0.43 #943), 0l6ny (0.67 #422, 0.61 #1468, 0.56 #1336), 09n48 (0.61 #419, 0.42 #1437, 0.40 #941), 0swff (0.61 #433, 0.42 #1437, 0.38 #875) >> Best rule #420 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01mjq; >> query: (?x5114, 0kbvb) <- olympics(?x5114, ?x452), contains(?x5114, ?x8745), ?x452 = 0sx7r, organization(?x5114, ?x312) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1437 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 01z215; 05b7q; 01d8l; *> query: (?x5114, ?x358) <- combatants(?x5114, ?x1353), medal(?x5114, ?x422), combatants(?x326, ?x5114), olympics(?x1353, ?x358) *> conf = 0.42 ranks of expected_values: 19 EVAL 05vz3zq olympics 015l4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 240.000 240.000 0.833 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #9841-04g73n PRED entity: 04g73n PRED relation: film! PRED expected values: 054g1r => 90 concepts (80 used for prediction) PRED predicted values (max 10 best out of 45): 054g1r (0.61 #184, 0.47 #109, 0.32 #336), 09b3v (0.47 #1050, 0.47 #752, 0.47 #1503), 04rcl7 (0.47 #1050, 0.47 #752, 0.47 #1503), 03xq0f (0.40 #5, 0.13 #80, 0.11 #456), 016tt2 (0.30 #4, 0.18 #455, 0.12 #3533), 05s_k6 (0.30 #63, 0.01 #514, 0.01 #1113), 086k8 (0.17 #379, 0.17 #678, 0.16 #976), 016tw3 (0.17 #2190, 0.16 #2414, 0.16 #537), 017s11 (0.13 #529, 0.13 #1882, 0.13 #903), 05qd_ (0.13 #460, 0.13 #983, 0.13 #3389) >> Best rule #184 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 08cx5g; >> query: (?x8112, 054g1r) <- award_winner(?x8112, ?x6664), titles(?x3920, ?x8112), nominated_for(?x2156, ?x8112), company(?x2426, ?x3920) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04g73n film! 054g1r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 80.000 0.611 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #9840-0ggx5q PRED entity: 0ggx5q PRED relation: parent_genre! PRED expected values: 064t9 => 46 concepts (32 used for prediction) PRED predicted values (max 10 best out of 296): 0y3_8 (0.50 #307, 0.44 #1639, 0.40 #839), 059kh (0.50 #309, 0.40 #841, 0.33 #2442), 01ym9b (0.50 #1105, 0.33 #40, 0.27 #2171), 01h0kx (0.50 #396, 0.33 #130, 0.25 #662), 0grjmv (0.50 #386, 0.33 #120, 0.25 #652), 07ym47 (0.50 #1123, 0.27 #2189, 0.25 #2457), 0dn16 (0.40 #810, 0.33 #1610, 0.33 #1344), 01cbwl (0.40 #834, 0.33 #1368, 0.33 #36), 016_nr (0.33 #2462, 0.33 #1128, 0.30 #1927), 03xnwz (0.33 #28, 0.25 #560, 0.25 #294) >> Best rule #307 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 06by7; >> query: (?x5876, 0y3_8) <- artists(?x5876, ?x8839), artists(?x5876, ?x7331), artists(?x5876, ?x6042), artists(?x5876, ?x4394), artists(?x5876, ?x1093), ?x1093 = 0lk90, ?x7331 = 01vtj38, ?x8839 = 01x0yrt, artist(?x4797, ?x6042), ?x4394 = 049qx >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #276 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 06by7; *> query: (?x5876, 064t9) <- artists(?x5876, ?x8839), artists(?x5876, ?x7331), artists(?x5876, ?x6042), artists(?x5876, ?x4394), artists(?x5876, ?x1093), ?x1093 = 0lk90, ?x7331 = 01vtj38, ?x8839 = 01x0yrt, artist(?x4797, ?x6042), ?x4394 = 049qx *> conf = 0.25 ranks of expected_values: 24 EVAL 0ggx5q parent_genre! 064t9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 46.000 32.000 0.500 http://example.org/music/genre/parent_genre #9839-01slc PRED entity: 01slc PRED relation: school PRED expected values: 09s5q8 => 66 concepts (52 used for prediction) PRED predicted values (max 10 best out of 783): 01jq0j (0.60 #1339, 0.33 #3824, 0.33 #283), 06fq2 (0.50 #2061, 0.50 #476, 0.37 #4198), 0f1nl (0.50 #556, 0.40 #1789, 0.40 #1436), 09f2j (0.50 #772, 0.38 #2894, 0.30 #4850), 01qgr3 (0.50 #992, 0.25 #816, 0.25 #640), 012vwb (0.46 #2874, 0.40 #2161, 0.40 #1633), 03tw2s (0.40 #1862, 0.40 #1509, 0.35 #4529), 065y4w7 (0.40 #2120, 0.38 #2833, 0.38 #3009), 01lnyf (0.40 #1643, 0.33 #234, 0.33 #58), 01jsk6 (0.40 #1742, 0.33 #333, 0.20 #2270) >> Best rule #1339 for best value: >> intensional similarity = 21 >> extensional distance = 3 >> proper extension: 05m_8; >> query: (?x7060, 01jq0j) <- draft(?x7060, ?x8499), season(?x7060, ?x8529), season(?x7060, ?x3431), teams(?x1860, ?x7060), season(?x4208, ?x8529), season(?x2174, ?x8529), season(?x1438, ?x8529), season(?x700, ?x8529), ?x700 = 06x68, ?x8499 = 02r6gw6, ?x1438 = 0512p, ?x2174 = 051vz, team(?x2010, ?x7060), ?x3431 = 025ygqm, school(?x7060, ?x4410), school(?x7060, ?x4257), school(?x7060, ?x2830), ?x4208 = 061xq, ?x4257 = 01q0kg, service_location(?x4410, ?x94), major_field_of_study(?x2830, ?x742) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1323 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 3 *> proper extension: 05m_8; *> query: (?x7060, 09s5q8) <- draft(?x7060, ?x8499), season(?x7060, ?x8529), season(?x7060, ?x3431), teams(?x1860, ?x7060), season(?x4208, ?x8529), season(?x2174, ?x8529), season(?x1438, ?x8529), season(?x700, ?x8529), ?x700 = 06x68, ?x8499 = 02r6gw6, ?x1438 = 0512p, ?x2174 = 051vz, team(?x2010, ?x7060), ?x3431 = 025ygqm, school(?x7060, ?x4410), school(?x7060, ?x4257), school(?x7060, ?x2830), ?x4208 = 061xq, ?x4257 = 01q0kg, service_location(?x4410, ?x94), major_field_of_study(?x2830, ?x742) *> conf = 0.20 ranks of expected_values: 64 EVAL 01slc school 09s5q8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 66.000 52.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9838-0yfp PRED entity: 0yfp PRED relation: profession PRED expected values: 0dxtg => 124 concepts (82 used for prediction) PRED predicted values (max 10 best out of 94): 0dxtg (0.84 #452, 0.80 #9361, 0.77 #1474), 01d_h8 (0.67 #445, 0.49 #3219, 0.45 #1467), 02jknp (0.51 #446, 0.44 #1468, 0.35 #9355), 05z96 (0.37 #439, 0.30 #6427, 0.20 #772), 0q04f (0.37 #439, 0.30 #6427, 0.11 #682), 03gjzk (0.35 #14, 0.33 #3227, 0.32 #9362), 09jwl (0.30 #18, 0.23 #164, 0.22 #310), 018gz8 (0.29 #3229, 0.26 #2791, 0.25 #16), 01c72t (0.25 #23, 0.19 #169, 0.19 #315), 0nbcg (0.20 #30, 0.16 #4703, 0.15 #176) >> Best rule #452 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 098n_m; 0237jb; >> query: (?x973, 0dxtg) <- award(?x973, ?x2341), profession(?x973, ?x353), ?x2341 = 02x17s4 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0yfp profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 82.000 0.837 http://example.org/people/person/profession #9837-0164nb PRED entity: 0164nb PRED relation: program PRED expected values: 06hwzy => 131 concepts (118 used for prediction) PRED predicted values (max 10 best out of 25): 06hwzy (0.27 #56, 0.24 #232, 0.23 #81), 0gvvf4j (0.24 #252, 0.02 #303), 01h1bf (0.20 #233, 0.09 #82, 0.07 #132), 01j7mr (0.18 #83, 0.11 #234, 0.09 #58), 01b7h8 (0.14 #93, 0.09 #244, 0.05 #68), 0275kr (0.12 #20, 0.04 #246), 0304nh (0.11 #235, 0.09 #84, 0.09 #59), 039cq4 (0.11 #238, 0.05 #87, 0.02 #187), 026bfsh (0.09 #85, 0.09 #60, 0.04 #236), 02zv4b (0.09 #53, 0.05 #78, 0.02 #229) >> Best rule #56 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 09k2t1; 01wj9y9; 02v60l; 0261x8t; 010p3; 02_wxh; 02h9_l; 01mbwlb; >> query: (?x3817, 06hwzy) <- religion(?x3817, ?x1985), location(?x3817, ?x1227), person(?x3480, ?x3817) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0164nb program 06hwzy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 118.000 0.273 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #9836-02j69w PRED entity: 02j69w PRED relation: film! PRED expected values: 016tw3 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 58): 086k8 (0.33 #2, 0.20 #743, 0.20 #520), 017s11 (0.29 #77, 0.18 #151, 0.15 #4521), 01795t (0.29 #92, 0.18 #166, 0.11 #4890), 016tw3 (0.21 #307, 0.20 #603, 0.19 #2603), 03xq0f (0.19 #227, 0.18 #153, 0.13 #1116), 05qd_ (0.18 #157, 0.16 #305, 0.15 #2601), 054g1r (0.14 #109, 0.11 #4890, 0.09 #183), 030_1m (0.14 #88, 0.11 #4890, 0.09 #162), 032dg7 (0.14 #121, 0.11 #4890, 0.09 #195), 016tt2 (0.13 #4894, 0.13 #4968, 0.12 #2373) >> Best rule #2 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 014zwb; 0888c3; >> query: (?x4694, 086k8) <- genre(?x4694, ?x53), film(?x3258, ?x4694), ?x3258 = 02qx69, film_crew_role(?x4694, ?x137), film(?x7621, ?x4694) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #307 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 028_yv; 01vksx; 0c0nhgv; 0dr_4; 03cw411; 0n04r; 0cmc26r; 064lsn; 03cp4cn; 0k7tq; ... *> query: (?x4694, 016tw3) <- genre(?x4694, ?x53), film_regional_debut_venue(?x4694, ?x6557), film_crew_role(?x4694, ?x137), film_release_region(?x4694, ?x94), produced_by(?x4694, ?x7621), films(?x1083, ?x4694) *> conf = 0.21 ranks of expected_values: 4 EVAL 02j69w film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 105.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #9835-0bl60p PRED entity: 0bl60p PRED relation: gender PRED expected values: 05zppz => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #13, 0.72 #9, 0.72 #157), 02zsn (0.52 #121, 0.52 #140, 0.50 #29) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 28 >> proper extension: 05v954; >> query: (?x7730, 05zppz) <- place_of_birth(?x7730, ?x108), ?x108 = 0rh6k >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bl60p gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.733 http://example.org/people/person/gender #9834-025s1wg PRED entity: 025s1wg PRED relation: film! PRED expected values: 014zcr => 72 concepts (54 used for prediction) PRED predicted values (max 10 best out of 810): 06cv1 (0.18 #41433, 0.18 #39361, 0.12 #58008), 0p8r1 (0.17 #582, 0.10 #4726, 0.06 #17156), 019vgs (0.17 #657, 0.05 #4801, 0.02 #17231), 015pvh (0.17 #1098, 0.05 #5242, 0.02 #17672), 046chh (0.17 #1178, 0.04 #9465, 0.03 #3249), 01swck (0.17 #797, 0.03 #2868, 0.03 #9084), 01nm3s (0.17 #686, 0.03 #4830, 0.03 #8973), 01p7yb (0.17 #51, 0.03 #8338, 0.02 #12481), 04gr35 (0.17 #1739, 0.02 #5883), 01z7_f (0.17 #753, 0.02 #11112, 0.01 #35970) >> Best rule #41433 for best value: >> intensional similarity = 4 >> extensional distance = 476 >> proper extension: 0564x; >> query: (?x11066, ?x523) <- genre(?x11066, ?x225), film_release_distribution_medium(?x11066, ?x81), written_by(?x11066, ?x523), language(?x11066, ?x254) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #36 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 01vfqh; 03fts; 031t2d; 03kxj2; 0407yj_; 02rrfzf; 01jzyf; 03q0r1; 06sfk6; 04x4vj; *> query: (?x11066, 014zcr) <- film(?x8022, ?x11066), genre(?x11066, ?x225), ?x8022 = 02661h, film(?x166, ?x11066) *> conf = 0.08 ranks of expected_values: 30 EVAL 025s1wg film! 014zcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 72.000 54.000 0.179 http://example.org/film/actor/film./film/performance/film #9833-0g5lhl7 PRED entity: 0g5lhl7 PRED relation: company! PRED expected values: 014l7h => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 44): 0dq_5 (0.74 #2045, 0.73 #1819, 0.66 #2180), 014l7h (0.74 #1289, 0.62 #882, 0.62 #1062), 0krdk (0.63 #2034, 0.56 #1086, 0.54 #1808), 05_wyz (0.60 #2046, 0.38 #1820, 0.38 #2181), 060c4 (0.57 #2031, 0.48 #2166, 0.44 #1083), 02k13d (0.50 #193, 0.33 #553, 0.22 #958), 0dq3c (0.44 #1082, 0.42 #2165, 0.38 #1804), 01yc02 (0.38 #2171, 0.35 #1810, 0.31 #1088), 01kr6k (0.33 #161, 0.25 #296, 0.19 #1828), 015czt (0.33 #172, 0.03 #2470, 0.03 #2515) >> Best rule #2045 for best value: >> intensional similarity = 2 >> extensional distance = 33 >> proper extension: 07vfj; 061v5m; >> query: (?x2776, 0dq_5) <- company(?x5161, ?x2776), ?x5161 = 09d6p2 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1289 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 03jl0_; 0hm10; *> query: (?x2776, 014l7h) <- program(?x2776, ?x4063), genre(?x4063, ?x53), company(?x900, ?x2776) *> conf = 0.74 ranks of expected_values: 2 EVAL 0g5lhl7 company! 014l7h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 158.000 158.000 0.743 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #9832-0gkvb7 PRED entity: 0gkvb7 PRED relation: award! PRED expected values: 012x4t 029_3 036px 0p__8 03d_zl4 01hmk9 01vtj38 => 43 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2719): 01l1ls (0.78 #26658, 0.72 #43314, 0.71 #43312), 02__7n (0.40 #5419, 0.16 #23325, 0.16 #46646), 025mb_ (0.40 #5912, 0.13 #12576, 0.12 #15908), 01_j71 (0.40 #4260, 0.08 #10924, 0.08 #14256), 09yrh (0.40 #4611, 0.08 #21275, 0.08 #14607), 01hcj2 (0.40 #6020, 0.05 #12684, 0.05 #16016), 03xp8d5 (0.33 #1227, 0.20 #7888, 0.20 #4557), 02xs0q (0.33 #981, 0.20 #4311, 0.16 #23325), 05bnq3j (0.33 #1316, 0.20 #4646, 0.16 #23325), 06msq2 (0.33 #1240, 0.20 #4570, 0.16 #23325) >> Best rule #26658 for best value: >> intensional similarity = 5 >> extensional distance = 148 >> proper extension: 02r0d0; >> query: (?x537, ?x248) <- award_winner(?x537, ?x804), award_winner(?x537, ?x248), nationality(?x804, ?x94), award(?x804, ?x458), category_of(?x537, ?x2758) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2101 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 0fc9js; *> query: (?x537, 01vtj38) <- award(?x8924, ?x537), award(?x8071, ?x537), award(?x6363, ?x537), ?x6363 = 01jgpsh, ceremony(?x537, ?x4760), nationality(?x8924, ?x94), ?x8071 = 02mc79 *> conf = 0.33 ranks of expected_values: 22, 28, 169, 258, 1055, 2286, 2706 EVAL 0gkvb7 award! 01vtj38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 43.000 17.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gkvb7 award! 01hmk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 17.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gkvb7 award! 03d_zl4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 17.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gkvb7 award! 0p__8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 43.000 17.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gkvb7 award! 036px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 17.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gkvb7 award! 029_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 43.000 17.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gkvb7 award! 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 17.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9831-0g39h PRED entity: 0g39h PRED relation: state_province_region! PRED expected values: 01c57n => 229 concepts (130 used for prediction) PRED predicted values (max 10 best out of 759): 01dbns (0.25 #383, 0.20 #1885, 0.17 #3387), 0sxdg (0.25 #436, 0.17 #3440, 0.12 #7198), 04zwc (0.25 #405, 0.17 #3409, 0.07 #5664), 013fn (0.25 #642, 0.17 #3646, 0.07 #5901), 069vt (0.25 #556, 0.17 #3560, 0.07 #5815), 012lzr (0.25 #439, 0.17 #3443, 0.07 #5698), 01bcwk (0.23 #21056, 0.23 #16545, 0.22 #24063), 01q58t (0.23 #21056, 0.23 #16545, 0.22 #24063), 01l53f (0.23 #21056, 0.23 #16545, 0.22 #24063), 01qrcx (0.23 #21056, 0.23 #16545, 0.22 #24063) >> Best rule #383 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 06mtq; >> query: (?x9725, 01dbns) <- jurisdiction_of_office(?x10118, ?x9725), administrative_parent(?x9725, ?x390), ?x10118 = 0p5vf, capital(?x9725, ?x11731) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g39h state_province_region! 01c57n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 229.000 130.000 0.250 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #9830-0f8l9c PRED entity: 0f8l9c PRED relation: combatants PRED expected values: 06bnz 024pcx => 291 concepts (233 used for prediction) PRED predicted values (max 10 best out of 357): 0ctw_b (0.83 #7453, 0.82 #4712, 0.82 #7452), 0g78xc (0.82 #7452, 0.82 #5272, 0.82 #7454), 0f8l9c (0.75 #1856, 0.53 #3819, 0.49 #4155), 024pcx (0.54 #1107, 0.44 #1500, 0.25 #8984), 06bnz (0.45 #1863, 0.44 #1359, 0.41 #1527), 06v9sf (0.33 #355, 0.29 #523, 0.25 #8984), 05v8c (0.26 #2355, 0.26 #2243, 0.25 #8984), 07f1x (0.26 #2277, 0.25 #8984, 0.25 #1887), 06qd3 (0.25 #8984, 0.22 #2251, 0.21 #12012), 06c1y (0.25 #8984, 0.22 #2252, 0.21 #12012) >> Best rule #7453 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 07t65; >> query: (?x789, ?x1023) <- combatants(?x6407, ?x789), combatants(?x1023, ?x789), combatants(?x612, ?x6407), country(?x972, ?x1023) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1107 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 025ndl; 0g78xc; 07l75; 0dv0z; 01fvhp; 01s47p; *> query: (?x789, 024pcx) <- combatants(?x94, ?x789), combatants(?x10119, ?x789), ?x10119 = 07j9n *> conf = 0.54 ranks of expected_values: 4, 5 EVAL 0f8l9c combatants 024pcx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 291.000 233.000 0.828 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants EVAL 0f8l9c combatants 06bnz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 291.000 233.000 0.828 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #9829-06nsb9 PRED entity: 06nsb9 PRED relation: location PRED expected values: 0824r => 72 concepts (65 used for prediction) PRED predicted values (max 10 best out of 101): 02_286 (0.40 #841, 0.33 #11285, 0.31 #16106), 030qb3t (0.25 #8115, 0.25 #16151, 0.24 #8919), 05tbn (0.25 #187, 0.20 #991, 0.03 #9024), 0cc56 (0.20 #861, 0.06 #8894, 0.04 #23359), 0xl08 (0.20 #1125), 0cr3d (0.15 #10587, 0.09 #15409, 0.07 #23446), 0n5j_ (0.09 #5624, 0.04 #16873, 0.04 #12052), 01cx_ (0.08 #4982, 0.06 #3375, 0.05 #8195), 04jpl (0.08 #13676, 0.08 #15282, 0.07 #1624), 0d6lp (0.07 #1774, 0.07 #2577, 0.04 #8200) >> Best rule #841 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0m66w; >> query: (?x13108, 02_286) <- profession(?x13108, ?x5716), location(?x13108, ?x1189), profession(?x7372, ?x5716), ?x7372 = 03wy70, ?x1189 = 0xkq4 >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06nsb9 location 0824r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 65.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #9828-07pzc PRED entity: 07pzc PRED relation: award_nominee PRED expected values: 01vvydl => 157 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1012): 01vvydl (0.81 #58617, 0.80 #166457, 0.80 #63307), 02l840 (0.23 #4849, 0.18 #2504, 0.12 #40018), 06mt91 (0.23 #6252, 0.18 #3907, 0.10 #1563), 04lgymt (0.18 #2447, 0.15 #4792, 0.14 #18859), 026yqrr (0.18 #3805, 0.15 #6150, 0.12 #20217), 016kjs (0.18 #2574, 0.15 #4919, 0.08 #40088), 0288fyj (0.18 #2844, 0.15 #5189, 0.06 #40358), 01w9k25 (0.18 #4475, 0.15 #6820, 0.04 #16199), 01vw20h (0.12 #19816, 0.10 #22161, 0.10 #40918), 05vzw3 (0.12 #19848, 0.10 #22193, 0.04 #85497) >> Best rule #58617 for best value: >> intensional similarity = 4 >> extensional distance = 171 >> proper extension: 01nfys; >> query: (?x9167, ?x140) <- gender(?x9167, ?x231), profession(?x9167, ?x131), award_nominee(?x140, ?x9167), participant(?x2237, ?x9167) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07pzc award_nominee 01vvydl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 76.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9827-019fz PRED entity: 019fz PRED relation: basic_title PRED expected values: 02079p => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 11): 060c4 (0.33 #165, 0.25 #21, 0.20 #93), 0dq3c (0.27 #92, 0.25 #20, 0.19 #146), 0fkvn (0.25 #22, 0.19 #166, 0.13 #94), 0789n (0.25 #28, 0.11 #64, 0.07 #190), 09d6p2 (0.11 #135, 0.10 #171, 0.03 #567), 060bp (0.10 #163, 0.03 #559, 0.03 #595), 01gkgk (0.07 #78, 0.04 #186, 0.03 #204), 02079p (0.05 #173, 0.03 #245, 0.02 #299), 0p5vf (0.05 #174, 0.02 #372, 0.01 #570), 0fj45 (0.02 #322, 0.02 #430) >> Best rule #165 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 07_m9_; 082xp; 06c0j; >> query: (?x12258, 060c4) <- type_of_union(?x12258, ?x11744), organizations_founded(?x12258, ?x4672), profession(?x12258, ?x5805), ?x5805 = 0fj9f >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #173 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 07_m9_; 082xp; 06c0j; *> query: (?x12258, 02079p) <- type_of_union(?x12258, ?x11744), organizations_founded(?x12258, ?x4672), profession(?x12258, ?x5805), ?x5805 = 0fj9f *> conf = 0.05 ranks of expected_values: 8 EVAL 019fz basic_title 02079p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 112.000 112.000 0.333 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #9826-011xhx PRED entity: 011xhx PRED relation: group! PRED expected values: 05842k => 81 concepts (50 used for prediction) PRED predicted values (max 10 best out of 118): 03bx0bm (0.71 #198, 0.62 #459, 0.60 #1419), 05148p4 (0.71 #1239, 0.70 #1065, 0.68 #1413), 03qjg (0.38 #482, 0.36 #308, 0.29 #221), 0l14qv (0.29 #179, 0.27 #266, 0.25 #1226), 05r5c (0.29 #181, 0.27 #268, 0.23 #1054), 06ncr (0.29 #212, 0.27 #299, 0.23 #473), 01vj9c (0.29 #186, 0.26 #1494, 0.24 #1059), 0l14j_ (0.29 #225, 0.20 #51, 0.18 #312), 013y1f (0.27 #288, 0.23 #462, 0.15 #1248), 06w7v (0.23 #508, 0.18 #334, 0.14 #247) >> Best rule #198 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 05563d; 015srx; >> query: (?x12880, 03bx0bm) <- artists(?x378, ?x12880), ?x378 = 07sbbz2, artist(?x441, ?x12880), group(?x645, ?x12880), ?x645 = 028tv0 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #524 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 11 *> proper extension: 089tm; 017j6; 07bzp; 07mvp; 03c3yf; 016376; *> query: (?x12880, ?x74) <- artists(?x378, ?x12880), ?x378 = 07sbbz2, artist(?x441, ?x12880), group(?x645, ?x12880), role(?x3703, ?x645), role(?x74, ?x645), ?x3703 = 02dlh2 *> conf = 0.10 ranks of expected_values: 54 EVAL 011xhx group! 05842k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 81.000 50.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #9825-0ck91 PRED entity: 0ck91 PRED relation: person! PRED expected values: 043q4d => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 3): 043q4d (0.08 #23, 0.06 #31, 0.04 #39), 026h21_ (0.01 #74, 0.01 #105, 0.01 #112), 0c5lg (0.01 #139, 0.01 #89) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0p_pd; 02qjj7; 01vrncs; 016z2j; 02w4fkq; 0blt6; 01zg98; 039crh; 01s3kv; 0lkr7; ... >> query: (?x11601, 043q4d) <- location(?x11601, ?x4151), ?x4151 = 0r0m6, profession(?x11601, ?x319), languages(?x11601, ?x254) >> conf = 0.08 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ck91 person! 043q4d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.077 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #9824-023ny6 PRED entity: 023ny6 PRED relation: award PRED expected values: 027gs1_ => 101 concepts (81 used for prediction) PRED predicted values (max 10 best out of 169): 0m7yy (0.75 #2714, 0.43 #1069, 0.42 #4588), 0cjyzs (0.62 #317, 0.60 #552, 0.57 #82), 09qv3c (0.60 #511, 0.57 #41, 0.56 #276), 09qj50 (0.54 #235, 0.54 #470, 0.50 #507), 027gs1_ (0.54 #235, 0.54 #470, 0.50 #180), 09qrn4 (0.54 #235, 0.54 #470, 0.50 #628), 0cqhk0 (0.44 #265, 0.43 #30, 0.40 #500), 09qvc0 (0.30 #503, 0.25 #268, 0.24 #1909), 09qvf4 (0.25 #615, 0.22 #2021, 0.22 #2258), 0cqhmg (0.20 #683, 0.19 #448, 0.14 #213) >> Best rule #2714 for best value: >> intensional similarity = 5 >> extensional distance = 74 >> proper extension: 0cwrr; 02hct1; 01j7mr; 02md2d; 05_z42; 0bbm7r; 04glx0; 02qkq0; 021gzd; 0dl6fv; ... >> query: (?x9951, 0m7yy) <- nominated_for(?x2417, ?x9951), award(?x9951, ?x2603), award(?x758, ?x2603), award_winner(?x2603, ?x1057), ?x758 = 0kfpm >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #235 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 072kp; 01q_y0; 01bv8b; 01s81; 0l76z; 05f4vxd; 02r1ysd; 01lv85; 014gjp; 0q9jk; ... *> query: (?x9951, ?x757) <- nominated_for(?x2417, ?x9951), award(?x9951, ?x2603), ?x2603 = 09qs08, titles(?x2008, ?x9951), nominated_for(?x757, ?x9951) *> conf = 0.54 ranks of expected_values: 5 EVAL 023ny6 award 027gs1_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 81.000 0.750 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9823-01j7mr PRED entity: 01j7mr PRED relation: languages PRED expected values: 02h40lc => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.89 #266, 0.89 #244, 0.88 #277), 0t_2 (0.33 #28, 0.20 #6, 0.18 #39), 03_9r (0.05 #312, 0.04 #411, 0.04 #345), 06nm1 (0.04 #104, 0.04 #126, 0.03 #203), 064_8sq (0.02 #315, 0.01 #205, 0.01 #216), 02bv9 (0.01 #108, 0.01 #130, 0.01 #141), 04306rv (0.01 #102, 0.01 #124, 0.01 #135), 02bjrlw (0.01 #100, 0.01 #122, 0.01 #133), 07qv_ (0.01 #153), 05zjd (0.01 #151) >> Best rule #266 for best value: >> intensional similarity = 3 >> extensional distance = 157 >> proper extension: 02gjrc; >> query: (?x3626, 02h40lc) <- nominated_for(?x537, ?x3626), actor(?x3626, ?x5153), nationality(?x5153, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j7mr languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.887 http://example.org/tv/tv_program/languages #9822-01m1dzc PRED entity: 01m1dzc PRED relation: role PRED expected values: 0gghm => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 121): 05r5c (0.40 #1892, 0.39 #2206, 0.39 #3879), 02sgy (0.34 #530, 0.29 #215, 0.23 #3877), 01vdm0 (0.27 #2230, 0.27 #3903, 0.25 #1812), 018j2 (0.27 #1361, 0.27 #629, 0.26 #1466), 04rzd (0.27 #1361, 0.27 #629, 0.26 #1466), 018vs (0.21 #222, 0.20 #537, 0.16 #3884), 01vj9c (0.20 #15, 0.16 #3886, 0.14 #1899), 02w3w (0.20 #90, 0.12 #195, 0.09 #4710), 03m5k (0.20 #19, 0.03 #4292, 0.03 #228), 02k856 (0.20 #66, 0.03 #4292, 0.03 #275) >> Best rule #1892 for best value: >> intensional similarity = 2 >> extensional distance = 257 >> proper extension: 02fybl; 01m7f5r; >> query: (?x4102, 05r5c) <- role(?x4102, ?x227), location(?x4102, ?x3778) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #264 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 0zjpz; 09prnq; 01w8n89; 0fpj4lx; 01vsyjy; 0dw3l; 04mx7s; 01vs4f3; 01t8399; 01tw31; *> query: (?x4102, 0gghm) <- instrumentalists(?x1969, ?x4102), artist(?x3006, ?x4102), ?x1969 = 04rzd *> conf = 0.05 ranks of expected_values: 30 EVAL 01m1dzc role 0gghm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 117.000 117.000 0.398 http://example.org/music/artist/track_contributions./music/track_contribution/role #9821-01j5x6 PRED entity: 01j5x6 PRED relation: nationality PRED expected values: 02jx1 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 69): 09c7w0 (0.81 #1309, 0.76 #5623, 0.75 #101), 02jx1 (0.33 #6326, 0.33 #10349, 0.23 #4248), 07ssc (0.33 #6326, 0.33 #10349, 0.15 #4230), 0cxgc (0.33 #6326, 0.33 #10349, 0.01 #5522), 04jpl (0.33 #6326, 0.33 #10349), 03rk0 (0.09 #2860, 0.08 #4562, 0.07 #6572), 0d060g (0.06 #5427, 0.05 #814, 0.05 #5327), 03rjj (0.03 #510, 0.03 #2819, 0.03 #5425), 0f8l9c (0.03 #527, 0.03 #829, 0.02 #929), 0h7x (0.02 #4250, 0.02 #11551, 0.02 #5455) >> Best rule #1309 for best value: >> intensional similarity = 3 >> extensional distance = 179 >> proper extension: 03ds3; 01j4ls; 01pw2f1; 02_j7t; 0c01c; 047hpm; 01jbx1; 01_rh4; 01fs_4; 039crh; ... >> query: (?x891, 09c7w0) <- profession(?x891, ?x1032), participant(?x2258, ?x891), actor(?x8870, ?x891) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #6326 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1040 *> proper extension: 0dj5q; 07h1q; 02cg2v; 0cfywh; *> query: (?x891, ?x362) <- people(?x743, ?x891), place_of_birth(?x891, ?x10922), contains(?x362, ?x10922) *> conf = 0.33 ranks of expected_values: 2 EVAL 01j5x6 nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 116.000 0.807 http://example.org/people/person/nationality #9820-01j_cy PRED entity: 01j_cy PRED relation: student PRED expected values: 095nx => 118 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1621): 05dtwm (0.33 #966, 0.03 #15561, 0.02 #17646), 04fcx7 (0.33 #869, 0.03 #15464, 0.02 #17549), 03d1y3 (0.33 #1275, 0.03 #15870), 044gyq (0.33 #548, 0.03 #15143), 014zfs (0.33 #157, 0.03 #14752), 0cbgl (0.11 #16674, 0.06 #20844, 0.06 #22929), 019vgs (0.10 #2711, 0.09 #8966, 0.08 #11051), 037d35 (0.10 #3139, 0.05 #9394, 0.04 #11479), 033w9g (0.10 #2855, 0.05 #9110, 0.04 #11195), 025b3k (0.10 #3730, 0.05 #9985, 0.04 #12070) >> Best rule #966 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01jt2w; >> query: (?x1675, 05dtwm) <- student(?x1675, ?x6068), major_field_of_study(?x1675, ?x254), school(?x580, ?x1675), ?x6068 = 025j1t >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01j_cy student 095nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 98.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #9819-012ljv PRED entity: 012ljv PRED relation: music! PRED expected values: 0g83dv => 94 concepts (40 used for prediction) PRED predicted values (max 10 best out of 679): 02mpyh (0.75 #5055, 0.73 #5054, 0.73 #6067), 0415ggl (0.75 #5055, 0.73 #5054, 0.73 #6067), 01s7w3 (0.04 #8957, 0.04 #11987, 0.04 #12997), 02rrfzf (0.04 #4368, 0.04 #5380, 0.03 #6392), 02ht1k (0.03 #4411, 0.03 #5423, 0.02 #7445), 09d3b7 (0.03 #6908, 0.02 #9938, 0.02 #8928), 07bzz7 (0.03 #6594, 0.02 #9624, 0.02 #11644), 0401sg (0.03 #6117, 0.02 #9147, 0.01 #7127), 09d38d (0.02 #5023, 0.02 #6035, 0.02 #7047), 0h3k3f (0.02 #4887, 0.02 #5899, 0.02 #6911) >> Best rule #5055 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 02rgz4; 01nqfh_; 07qy0b; 02qfhb; 07y8l9; 01mkn_d; 0gv07g; 01m7f5r; 01nc3rh; 06zd1c; ... >> query: (?x84, ?x8084) <- nominated_for(?x84, ?x8084), genre(?x8084, ?x53), music(?x3850, ?x84) >> conf = 0.75 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 012ljv music! 0g83dv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 40.000 0.748 http://example.org/film/film/music #9818-030znt PRED entity: 030znt PRED relation: film PRED expected values: 0422v0 => 113 concepts (78 used for prediction) PRED predicted values (max 10 best out of 436): 03ln8b (0.62 #23229, 0.60 #21442, 0.60 #58962), 02qkq0 (0.62 #23229, 0.60 #21442, 0.60 #58962), 02k_4g (0.62 #23229, 0.60 #21442, 0.60 #58962), 064ndc (0.62 #23229, 0.60 #21442, 0.60 #58962), 024hbv (0.62 #23229, 0.60 #21442, 0.60 #58962), 01cvtf (0.62 #23229, 0.60 #21442, 0.60 #58962), 02md2d (0.62 #23229, 0.60 #21442, 0.60 #58962), 02pqs8l (0.62 #23229, 0.60 #21442, 0.60 #58962), 083shs (0.38 #1806), 03l6q0 (0.17 #541, 0.03 #7687, 0.01 #23770) >> Best rule #23229 for best value: >> intensional similarity = 3 >> extensional distance = 696 >> proper extension: 049tjg; 01wjrn; 02lq10; 05wjnt; 05hdf; 01pnn3; 039crh; 01lqnff; 0418ft; 0q1lp; ... >> query: (?x1343, ?x782) <- film(?x1343, ?x293), nominated_for(?x1343, ?x782), people(?x2510, ?x1343) >> conf = 0.62 => this is the best rule for 8 predicted values *> Best rule #8926 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 01bcq; 031sg0; 039wsf; *> query: (?x1343, 0422v0) <- award(?x1343, ?x2041), location(?x1343, ?x859), ?x2041 = 0bdx29 *> conf = 0.03 ranks of expected_values: 140 EVAL 030znt film 0422v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 113.000 78.000 0.619 http://example.org/film/actor/film./film/performance/film #9817-05zr0xl PRED entity: 05zr0xl PRED relation: nominated_for! PRED expected values: 01l_yg 01nxzv => 84 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1032): 03cws8h (0.78 #46598, 0.77 #51260, 0.77 #46597), 0b7t3p (0.78 #46598, 0.77 #51260, 0.77 #46597), 02wk_43 (0.77 #51260, 0.77 #46597, 0.59 #13980), 0f721s (0.68 #30289, 0.67 #25630, 0.66 #34948), 0404wqb (0.48 #20969, 0.47 #20970, 0.44 #55924), 01y665 (0.25 #643, 0.04 #7632, 0.04 #16952), 03mcwq3 (0.25 #524, 0.04 #7513, 0.03 #14504), 03y9ccy (0.25 #781, 0.03 #17090, 0.02 #21752), 02xc1w4 (0.25 #1259, 0.02 #24559, 0.02 #47858), 069nzr (0.25 #1119, 0.02 #8108, 0.02 #12769) >> Best rule #46598 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 01j95; >> query: (?x8533, ?x9892) <- award_winner(?x8533, ?x9892), program(?x9892, ?x9541), profession(?x9892, ?x987) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #51263 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 147 *> proper extension: 03ffcz; *> query: (?x8533, ?x636) <- award_winner(?x8533, ?x9892), award_nominee(?x9892, ?x636), program(?x9892, ?x9541) *> conf = 0.20 ranks of expected_values: 40, 671 EVAL 05zr0xl nominated_for! 01nxzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 39.000 0.781 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 05zr0xl nominated_for! 01l_yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 84.000 39.000 0.781 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9816-04n_g PRED entity: 04n_g PRED relation: type_of_union PRED expected values: 01g63y => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 2): 01g63y (0.30 #85, 0.28 #91, 0.24 #70), 01bl8s (0.02 #20, 0.02 #5, 0.01 #17) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 234 >> proper extension: 01n7qlf; >> query: (?x3891, 01g63y) <- place_of_birth(?x3891, ?x739), participant(?x3891, ?x7632), film(?x3891, ?x3137), type_of_union(?x3891, ?x566) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04n_g type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.297 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9815-05zvj3m PRED entity: 05zvj3m PRED relation: award! PRED expected values: 01vw26l 0fby2t 05txrz 0pz04 029pnn 015p37 05wm88 => 38 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2146): 01h1b (0.72 #20037, 0.71 #20036, 0.71 #23378), 04t2l2 (0.72 #20037, 0.71 #20036, 0.71 #23378), 06cgy (0.52 #6680, 0.50 #3718, 0.33 #379), 026r8q (0.52 #6680, 0.50 #5447, 0.33 #2108), 086sj (0.52 #6680, 0.50 #4479, 0.33 #1140), 09yrh (0.52 #6680, 0.33 #1282, 0.25 #7962), 0c1pj (0.52 #6680, 0.33 #118, 0.25 #6798), 06lvlf (0.52 #6680, 0.33 #1716, 0.25 #8396), 034x61 (0.52 #6680, 0.33 #187, 0.25 #6867), 0tc7 (0.52 #6680, 0.33 #617, 0.25 #7297) >> Best rule #20037 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 0d085; >> query: (?x1691, ?x11233) <- award_winner(?x1691, ?x11233), award_winner(?x1691, ?x2534), participant(?x2534, ?x1817), award_nominee(?x11233, ?x2275) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #6680 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09sb52; *> query: (?x1691, ?x1554) <- award(?x5541, ?x1691), ?x5541 = 01pk3z, nominated_for(?x1691, ?x4920), nominated_for(?x1554, ?x4920) *> conf = 0.52 ranks of expected_values: 41, 45, 52, 83, 148, 1239, 1748 EVAL 05zvj3m award! 05wm88 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 38.000 17.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zvj3m award! 015p37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 38.000 17.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zvj3m award! 029pnn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 38.000 17.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zvj3m award! 0pz04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 38.000 17.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zvj3m award! 05txrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 38.000 17.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zvj3m award! 0fby2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 38.000 17.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zvj3m award! 01vw26l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 38.000 17.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9814-018m5q PRED entity: 018m5q PRED relation: campuses PRED expected values: 018m5q => 176 concepts (105 used for prediction) PRED predicted values (max 10 best out of 261): 0k2h6 (0.20 #44306, 0.19 #35001, 0.17 #386), 07tl0 (0.20 #44306, 0.19 #35001, 0.17 #38834), 0677j (0.20 #44306, 0.19 #35001, 0.17 #38834), 02hmw9 (0.20 #44306, 0.19 #35001, 0.17 #38834), 013nky (0.20 #44306, 0.19 #35001, 0.17 #38834), 01f2xy (0.20 #44306, 0.19 #35001, 0.17 #38834), 01s753 (0.20 #44306, 0.19 #35001, 0.17 #38834), 0159r9 (0.20 #44306, 0.19 #35001, 0.17 #38834), 01k8q5 (0.20 #44306, 0.19 #35001, 0.17 #38834), 07tg4 (0.20 #44306, 0.19 #35001, 0.17 #38834) >> Best rule #44306 for best value: >> intensional similarity = 5 >> extensional distance = 332 >> proper extension: 06klyh; >> query: (?x3671, ?x2999) <- school_type(?x3671, ?x5931), citytown(?x3671, ?x3301), citytown(?x2999, ?x3301), contains(?x1310, ?x3671), contains(?x455, ?x2999) >> conf = 0.20 => this is the best rule for 14 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 13 EVAL 018m5q campuses 018m5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 176.000 105.000 0.197 http://example.org/education/educational_institution/campuses #9813-0fm3kw PRED entity: 0fm3kw PRED relation: nominated_for PRED expected values: 04q24zv 0g5838s => 50 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1305): 04q24zv (0.50 #406, 0.43 #3594, 0.40 #2000), 0g5838s (0.50 #454, 0.40 #2048, 0.38 #8425), 0g9lm2 (0.46 #8631, 0.27 #7036, 0.21 #24584), 0gvvm6l (0.36 #7618, 0.23 #9213, 0.20 #2836), 0dmn0x (0.36 #7794, 0.23 #9389, 0.20 #3012), 06823p (0.31 #8999, 0.29 #4216, 0.27 #7404), 04qw17 (0.31 #8234, 0.27 #6639, 0.10 #24187), 0j43swk (0.31 #8419, 0.18 #6824, 0.13 #25968), 011yhm (0.31 #9003, 0.13 #24956, 0.13 #26552), 04lhc4 (0.31 #9042, 0.12 #26591, 0.12 #24995) >> Best rule #406 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 0gq6s3; 0fm3b5; >> query: (?x7774, 04q24zv) <- nominated_for(?x7774, ?x7735), nominated_for(?x7774, ?x3757), nominated_for(?x7774, ?x1786), nominated_for(?x7774, ?x534), ?x7735 = 0gpx6, ?x534 = 04nl83, ?x1786 = 091z_p, ?x3757 = 02vr3gz >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0fm3kw nominated_for 0g5838s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 18.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fm3kw nominated_for 04q24zv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 18.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9812-01yndb PRED entity: 01yndb PRED relation: artists! PRED expected values: 0827d 0175yg => 108 concepts (45 used for prediction) PRED predicted values (max 10 best out of 206): 06by7 (0.75 #330, 0.70 #10211, 0.64 #638), 064t9 (0.51 #11745, 0.47 #4024, 0.47 #5258), 0xhtw (0.48 #325, 0.29 #633, 0.21 #10206), 0glt670 (0.30 #4053, 0.29 #3745, 0.28 #42), 0gywn (0.29 #675, 0.28 #59, 0.26 #3762), 025sc50 (0.28 #51, 0.27 #4062, 0.23 #5296), 059kh (0.28 #50, 0.10 #2210, 0.09 #1592), 05bt6j (0.27 #11777, 0.26 #10234, 0.24 #13627), 016jny (0.27 #414, 0.13 #722, 0.12 #6587), 016clz (0.26 #13586, 0.25 #312, 0.24 #4323) >> Best rule #330 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 089tm; 067mj; 05crg7; 01czx; 0134s5; 0g_g2; 07bzp; 07mvp; 0gr69; 03c3yf; ... >> query: (?x9144, 06by7) <- award(?x9144, ?x2420), artists(?x7083, ?x9144), ?x7083 = 02yv6b >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #516 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 089tm; 067mj; 05crg7; 01czx; 0134s5; 0g_g2; 07bzp; 07mvp; 0gr69; 03c3yf; ... *> query: (?x9144, 0175yg) <- award(?x9144, ?x2420), artists(?x7083, ?x9144), ?x7083 = 02yv6b *> conf = 0.07 ranks of expected_values: 44, 51 EVAL 01yndb artists! 0175yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 108.000 45.000 0.750 http://example.org/music/genre/artists EVAL 01yndb artists! 0827d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 108.000 45.000 0.750 http://example.org/music/genre/artists #9811-0trv PRED entity: 0trv PRED relation: school_type PRED expected values: 07tf8 => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 21): 01_9fk (0.38 #25, 0.32 #324, 0.28 #853), 05pcjw (0.36 #208, 0.32 #254, 0.30 #231), 01rs41 (0.34 #96, 0.32 #2743, 0.32 #165), 07tf8 (0.29 #31, 0.27 #8, 0.23 #422), 01y64 (0.09 #471, 0.09 #80, 0.06 #195), 04qbv (0.09 #15, 0.04 #38, 0.04 #61), 01_srz (0.09 #2742, 0.08 #578, 0.08 #3065), 01jlsn (0.06 #85, 0.04 #1465, 0.03 #729), 0m4mb (0.06 #79, 0.03 #401, 0.03 #1459), 02dk5q (0.06 #351, 0.05 #604, 0.04 #443) >> Best rule #25 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 0ks67; >> query: (?x8706, 01_9fk) <- school(?x580, ?x8706), institution(?x865, ?x8706), school(?x4171, ?x8706), major_field_of_study(?x8706, ?x10046), ?x10046 = 041y2 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 0ks67; *> query: (?x8706, 07tf8) <- school(?x580, ?x8706), institution(?x865, ?x8706), school(?x4171, ?x8706), major_field_of_study(?x8706, ?x10046), ?x10046 = 041y2 *> conf = 0.29 ranks of expected_values: 4 EVAL 0trv school_type 07tf8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 190.000 190.000 0.375 http://example.org/education/educational_institution/school_type #9810-02dth1 PRED entity: 02dth1 PRED relation: type_of_union PRED expected values: 04ztj => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.86 #81, 0.85 #93, 0.85 #97), 01g63y (0.21 #38, 0.18 #46, 0.16 #66), 0jgjn (0.03 #32, 0.01 #40) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 183 >> proper extension: 0yfp; 01vsl3_; 07z1_q; 03bnv; 01fs_4; 0btyl; 01cbt3; 03d_zl4; 0mb5x; 023w9s; ... >> query: (?x4204, 04ztj) <- people(?x268, ?x4204), film(?x4204, ?x887), award(?x4204, ?x435) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02dth1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.859 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9809-026390q PRED entity: 026390q PRED relation: nominated_for! PRED expected values: 040njc => 83 concepts (72 used for prediction) PRED predicted values (max 10 best out of 208): 0gq9h (0.75 #508, 0.73 #282, 0.71 #734), 027b9k6 (0.67 #3847, 0.67 #5205, 0.66 #9958), 027b9ly (0.67 #3847, 0.67 #5205, 0.66 #9958), 019f4v (0.60 #276, 0.58 #728, 0.55 #502), 040njc (0.49 #232, 0.45 #684, 0.43 #910), 0p9sw (0.48 #471, 0.45 #697, 0.43 #923), 0gr4k (0.45 #3192, 0.42 #251, 0.40 #929), 0gqy2 (0.43 #564, 0.42 #338, 0.38 #790), 0f4x7 (0.42 #250, 0.39 #476, 0.33 #3191), 0gqyl (0.40 #296, 0.29 #4526, 0.27 #522) >> Best rule #508 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 072x7s; 0p3_y; 0htww; 0n04r; 09r94m; 02h22; 0gndh; 0298n7; 03pc89; 0581vn8; ... >> query: (?x1230, 0gq9h) <- nominated_for(?x1703, ?x1230), ?x1703 = 0k611, genre(?x1230, ?x53), films(?x11683, ?x1230) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #232 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 43 *> proper extension: 0kb57; 01fwzk; *> query: (?x1230, 040njc) <- nominated_for(?x1703, ?x1230), nominated_for(?x1245, ?x1230), ?x1703 = 0k611, genre(?x1230, ?x53), ?x1245 = 0gqwc *> conf = 0.49 ranks of expected_values: 5 EVAL 026390q nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 83.000 72.000 0.752 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9808-02_340 PRED entity: 02_340 PRED relation: executive_produced_by! PRED expected values: 06q8qh => 126 concepts (81 used for prediction) PRED predicted values (max 10 best out of 170): 025ts_z (0.09 #4198, 0.07 #6330, 0.05 #7927), 0bt4g (0.05 #4151, 0.04 #5750, 0.04 #6283), 0mbql (0.05 #4107, 0.04 #5706, 0.04 #6239), 01f7kl (0.05 #3862, 0.04 #5461, 0.04 #5994), 01bn3l (0.04 #4158, 0.03 #2559, 0.03 #5757), 0gmblvq (0.04 #3951, 0.03 #2352, 0.03 #5550), 07p62k (0.04 #3848, 0.03 #2249, 0.03 #5447), 01f7jt (0.04 #4242, 0.03 #2643, 0.03 #5841), 016y_f (0.04 #3977, 0.03 #2378, 0.03 #5576), 0k2sk (0.04 #3776, 0.03 #2177, 0.03 #5375) >> Best rule #4198 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 06j0md; >> query: (?x6579, 025ts_z) <- award_nominee(?x1416, ?x6579), program(?x6579, ?x9188), award_winner(?x3617, ?x6579), participant(?x1416, ?x2352) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02_340 executive_produced_by! 06q8qh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 81.000 0.089 http://example.org/film/film/executive_produced_by #9807-0rh6k PRED entity: 0rh6k PRED relation: place_of_death! PRED expected values: 083q7 => 237 concepts (223 used for prediction) PRED predicted values (max 10 best out of 776): 0bdlj (0.17 #1085, 0.11 #3333, 0.07 #4832), 03cd1q (0.17 #1372, 0.11 #3620, 0.07 #5119), 06h7l7 (0.17 #1078, 0.11 #3326, 0.07 #4825), 09p06 (0.17 #890, 0.11 #3138, 0.07 #4637), 04zd4m (0.17 #787, 0.11 #3035, 0.07 #4534), 039n1 (0.17 #1241, 0.11 #3489, 0.07 #5737), 07_m9_ (0.17 #952, 0.11 #3200, 0.07 #5448), 017r2 (0.17 #800, 0.11 #3048, 0.07 #5296), 03gt0c5 (0.17 #1456, 0.11 #3704, 0.07 #5952), 016ghw (0.11 #2985, 0.08 #4483, 0.07 #5233) >> Best rule #1085 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 019fv4; >> query: (?x108, 0bdlj) <- citytown(?x127, ?x108), religion(?x108, ?x109), location(?x236, ?x108) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0rh6k place_of_death! 083q7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 237.000 223.000 0.167 http://example.org/people/deceased_person/place_of_death #9806-06bng PRED entity: 06bng PRED relation: award PRED expected values: 040vk98 => 149 concepts (136 used for prediction) PRED predicted values (max 10 best out of 296): 040vk98 (0.77 #4052, 0.76 #3248, 0.55 #1637), 0262x6 (0.55 #3536, 0.53 #4340, 0.40 #1925), 0262yt (0.52 #3486, 0.43 #4290, 0.40 #1875), 02662b (0.48 #3296, 0.43 #4100, 0.40 #1685), 02664f (0.47 #4241, 0.41 #3437, 0.40 #1826), 0265wl (0.45 #3456, 0.37 #4260, 0.25 #2649), 0262zm (0.40 #4107, 0.38 #3303, 0.35 #1692), 01tgwv (0.31 #3581, 0.23 #4385, 0.23 #2816), 045xh (0.28 #3594, 0.23 #4398, 0.23 #2816), 039yzf (0.25 #1957, 0.23 #2816, 0.21 #3568) >> Best rule #4052 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 04mhl; 0b0pf; 048_p; 0mfc0; 0jt86; >> query: (?x8433, 040vk98) <- award(?x8433, ?x9285), profession(?x8433, ?x2225), student(?x4955, ?x8433), ?x9285 = 0265vt >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06bng award 040vk98 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 136.000 0.767 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9805-040p_q PRED entity: 040p_q PRED relation: major_field_of_study PRED expected values: 0jjw => 58 concepts (46 used for prediction) PRED predicted values (max 10 best out of 134): 0g26h (0.50 #295, 0.40 #469, 0.29 #557), 02j62 (0.50 #285, 0.38 #634, 0.37 #1244), 0g4gr (0.50 #286, 0.25 #635, 0.20 #460), 06ms6 (0.43 #537, 0.42 #713, 0.40 #449), 05qfh (0.40 #464, 0.39 #902, 0.33 #1163), 02lp1 (0.40 #443, 0.17 #88, 0.15 #87), 03g3w (0.39 #894, 0.33 #1155, 0.33 #980), 0_jm (0.38 #655, 0.33 #45, 0.29 #568), 037mh8 (0.33 #1187, 0.33 #1012, 0.33 #926), 062z7 (0.33 #1156, 0.33 #981, 0.33 #895) >> Best rule #295 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 0_jm; 02_7t; >> query: (?x9093, 0g26h) <- major_field_of_study(?x10910, ?x9093), major_field_of_study(?x8095, ?x9093), major_field_of_study(?x2775, ?x9093), ?x8095 = 02mp0g, school(?x660, ?x2775), institution(?x1368, ?x2775), institution(?x865, ?x2775), major_field_of_study(?x2014, ?x9093), organization(?x5510, ?x2775), ?x10910 = 013807, ?x1368 = 014mlp, ?x865 = 02h4rq6, student(?x2775, ?x1447) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #88 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 062z7; *> query: (?x9093, ?x254) <- major_field_of_study(?x9386, ?x9093), major_field_of_study(?x8095, ?x9093), major_field_of_study(?x2775, ?x9093), major_field_of_study(?x735, ?x9093), ?x8095 = 02mp0g, ?x2775 = 078bz, contains(?x94, ?x735), school(?x580, ?x735), major_field_of_study(?x9093, ?x2014), major_field_of_study(?x735, ?x254), organization(?x346, ?x9386), student(?x735, ?x65) *> conf = 0.17 ranks of expected_values: 48 EVAL 040p_q major_field_of_study 0jjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 58.000 46.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #9804-0hvvf PRED entity: 0hvvf PRED relation: nominated_for! PRED expected values: 040njc => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 184): 0gr0m (0.71 #1143, 0.69 #4340, 0.67 #1142), 0k611 (0.54 #980, 0.51 #1210, 0.51 #67), 040njc (0.51 #7, 0.46 #920, 0.42 #1150), 0gq_v (0.46 #933, 0.40 #20, 0.39 #249), 02qyntr (0.44 #1313, 0.32 #1083, 0.28 #170), 02pqp12 (0.42 #1198, 0.33 #968, 0.30 #55), 0p9sw (0.42 #934, 0.39 #21, 0.28 #1164), 0gr4k (0.40 #939, 0.38 #1169, 0.35 #26), 04kxsb (0.37 #1230, 0.27 #87, 0.21 #1000), 099c8n (0.34 #1196, 0.19 #738, 0.19 #966) >> Best rule #1143 for best value: >> intensional similarity = 3 >> extensional distance = 169 >> proper extension: 06mmr; >> query: (?x7765, ?x1243) <- award(?x7765, ?x1243), nominated_for(?x1243, ?x5818), ?x5818 = 0ktpx >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 81 *> proper extension: 07bz5; *> query: (?x7765, 040njc) <- award(?x7765, ?x1033), list(?x7765, ?x3004), award_winner(?x7765, ?x1119) *> conf = 0.51 ranks of expected_values: 3 EVAL 0hvvf nominated_for! 040njc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 53.000 53.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9803-06_6j3 PRED entity: 06_6j3 PRED relation: profession PRED expected values: 0nbcg => 123 concepts (69 used for prediction) PRED predicted values (max 10 best out of 72): 0dxtg (0.86 #5013, 0.46 #1484, 0.37 #2513), 01d_h8 (0.48 #5005, 0.37 #2946, 0.31 #6035), 03gjzk (0.42 #1485, 0.35 #1044, 0.33 #2661), 02jknp (0.41 #5007, 0.20 #8830, 0.20 #9712), 018gz8 (0.40 #605, 0.39 #1487, 0.37 #1046), 0nbcg (0.27 #9881, 0.26 #9440, 0.25 #9293), 0cbd2 (0.24 #5006, 0.21 #2800, 0.20 #154), 016z4k (0.24 #9267, 0.23 #9414, 0.22 #8385), 0dz3r (0.23 #9853, 0.22 #9412, 0.22 #9265), 0kyk (0.17 #5027, 0.16 #616, 0.15 #3997) >> Best rule #5013 for best value: >> intensional similarity = 4 >> extensional distance = 277 >> proper extension: 0b_c7; 04qr6d; 0133sq; >> query: (?x4632, 0dxtg) <- religion(?x4632, ?x2769), profession(?x4632, ?x1383), profession(?x5779, ?x1383), ?x5779 = 066l3y >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #9881 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 784 *> proper extension: 0ggl02; 09b3v; 0163m1; 0jvs0; 02pt7h_; 0gr69; 0cbm64; *> query: (?x4632, 0nbcg) <- gender(?x4632, ?x231), ?x231 = 05zppz, category(?x4632, ?x134) *> conf = 0.27 ranks of expected_values: 6 EVAL 06_6j3 profession 0nbcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 123.000 69.000 0.864 http://example.org/people/person/profession #9802-05txrz PRED entity: 05txrz PRED relation: profession PRED expected values: 02hrh1q 0np9r => 104 concepts (89 used for prediction) PRED predicted values (max 10 best out of 74): 02hrh1q (0.89 #160, 0.88 #9129, 0.88 #7953), 02jknp (0.59 #1624, 0.57 #1183, 0.55 #2800), 0cbd2 (0.48 #2358, 0.46 #4563, 0.46 #3240), 03gjzk (0.47 #1778, 0.47 #1337, 0.47 #308), 0kyk (0.32 #3262, 0.31 #2527, 0.31 #2968), 0np9r (0.28 #166, 0.19 #460, 0.17 #607), 09jwl (0.25 #3839, 0.24 #3692, 0.24 #605), 0dz3r (0.21 #3824, 0.21 #3677, 0.12 #5148), 0nbcg (0.19 #3852, 0.18 #3705, 0.14 #618), 02krf9 (0.17 #1642, 0.17 #2818, 0.16 #1348) >> Best rule #160 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 04t2l2; 03qmj9; 032w8h; 05wjnt; 08vr94; 0fby2t; 086nl7; 08hsww; 025b5y; 0cmt6q; ... >> query: (?x4371, 02hrh1q) <- film(?x4371, ?x365), ?x365 = 0bvn25, profession(?x4371, ?x319) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 05txrz profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 104.000 89.000 0.889 http://example.org/people/person/profession EVAL 05txrz profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 89.000 0.889 http://example.org/people/person/profession #9801-0660b9b PRED entity: 0660b9b PRED relation: film_crew_role PRED expected values: 09zzb8 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.98 #1522, 0.74 #994, 0.74 #1086), 0dxtw (0.55 #159, 0.50 #790, 0.49 #1064), 01pvkk (0.41 #791, 0.36 #340, 0.35 #250), 033smt (0.36 #232, 0.12 #2389, 0.11 #803), 015h31 (0.32 #217, 0.25 #7, 0.20 #1062), 02rh1dz (0.27 #218, 0.27 #158, 0.25 #8), 02_n3z (0.27 #212, 0.27 #152, 0.20 #92), 02vs3x5 (0.25 #19, 0.12 #2389, 0.11 #1366), 02ynfr (0.24 #794, 0.22 #1068, 0.20 #734), 0263ycg (0.23 #225, 0.20 #45, 0.12 #2389) >> Best rule #1522 for best value: >> intensional similarity = 5 >> extensional distance = 892 >> proper extension: 0fq27fp; >> query: (?x5747, 09zzb8) <- genre(?x5747, ?x258), film_crew_role(?x5747, ?x1078), profession(?x199, ?x1078), film_crew_role(?x3012, ?x1078), ?x3012 = 0ggbhy7 >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0660b9b film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.983 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9800-0h2zvzr PRED entity: 0h2zvzr PRED relation: language PRED expected values: 03k50 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 35): 03k50 (0.61 #3463, 0.60 #3051, 0.03 #353), 07c9s (0.61 #3463, 0.60 #3051, 0.02 #363), 09bnf (0.61 #3463, 0.60 #3051), 09s02 (0.61 #3463, 0.60 #3051), 0999q (0.61 #3463, 0.60 #3051), 064_8sq (0.16 #79, 0.15 #308, 0.14 #194), 06nm1 (0.13 #240, 0.12 #1052, 0.11 #1568), 04306rv (0.08 #2883, 0.08 #874, 0.08 #2769), 02bjrlw (0.08 #405, 0.07 #578, 0.07 #871), 06b_j (0.07 #1408, 0.07 #1294, 0.07 #1466) >> Best rule #3463 for best value: >> intensional similarity = 5 >> extensional distance = 1249 >> proper extension: 04xbq3; >> query: (?x8381, ?x254) <- film(?x9537, ?x8381), film(?x8380, ?x8381), languages(?x8380, ?x254), profession(?x8380, ?x1032), type_of_union(?x9537, ?x566) >> conf = 0.61 => this is the best rule for 5 predicted values ranks of expected_values: 1 EVAL 0h2zvzr language 03k50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.605 http://example.org/film/film/language #9799-03j63k PRED entity: 03j63k PRED relation: award PRED expected values: 0bdw1g => 81 concepts (75 used for prediction) PRED predicted values (max 10 best out of 206): 0fbvqf (0.50 #1916, 0.44 #2151, 0.41 #1681), 0bdx29 (0.44 #7018, 0.43 #7017, 0.43 #2348), 0bdwft (0.44 #7018, 0.43 #7017, 0.43 #2348), 0bfvw2 (0.44 #7018, 0.43 #7017, 0.43 #2348), 07t_l23 (0.44 #7018, 0.43 #7017, 0.43 #2348), 0bdw6t (0.36 #1963, 0.36 #1728, 0.33 #2198), 0ck27z (0.36 #1950, 0.31 #2421, 0.30 #2185), 0cqhb3 (0.32 #2068, 0.32 #1833, 0.28 #2539), 0bdw1g (0.32 #1674, 0.30 #2144, 0.23 #1909), 0gkts9 (0.32 #2001, 0.26 #2236, 0.23 #1766) >> Best rule #1916 for best value: >> intensional similarity = 7 >> extensional distance = 20 >> proper extension: 07gbf; >> query: (?x7254, 0fbvqf) <- genre(?x7254, ?x53), nominated_for(?x4921, ?x7254), nominated_for(?x2041, ?x7254), languages(?x7254, ?x254), ?x254 = 02h40lc, ?x4921 = 0fbtbt, ?x2041 = 0bdx29 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1674 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 20 *> proper extension: 0g60z; 080dwhx; 039fgy; 02k_4g; 0kfv9; 03d34x8; 01xr2s; 030k94; 039c26; 02rzdcp; ... *> query: (?x7254, 0bdw1g) <- genre(?x7254, ?x53), nominated_for(?x2041, ?x7254), languages(?x7254, ?x254), award(?x7254, ?x3486), ?x2041 = 0bdx29, titles(?x512, ?x7254) *> conf = 0.32 ranks of expected_values: 9 EVAL 03j63k award 0bdw1g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 81.000 75.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9798-05zpghd PRED entity: 05zpghd PRED relation: film_festivals PRED expected values: 04_m9gk => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 13): 04_m9gk (0.10 #13, 0.08 #34, 0.06 #139), 04grdgy (0.10 #9, 0.02 #597, 0.02 #282), 0kfhjq0 (0.08 #68, 0.03 #278, 0.03 #152), 09rwjly (0.05 #50, 0.02 #260, 0.02 #743), 03nn7l2 (0.04 #80, 0.02 #248, 0.02 #290), 0hrcs29 (0.04 #78, 0.01 #204, 0.01 #624), 0bmj62v (0.03 #243, 0.02 #201, 0.02 #327), 0j63cyr (0.02 #234, 0.02 #843, 0.01 #1074), 0bx_f_t (0.02 #121, 0.01 #226, 0.01 #184), 0gg7gsl (0.02 #736, 0.02 #841, 0.01 #925) >> Best rule #13 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 043tz0c; 02ntb8; 033fqh; 03hj5lq; 02nt3d; 095z4q; 07gghl; 026f__m; >> query: (?x5534, 04_m9gk) <- film(?x906, ?x5534), film_crew_role(?x5534, ?x137), ?x137 = 09zzb8, award_nominee(?x906, ?x5690), ?x5690 = 0h27vc >> conf = 0.10 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05zpghd film_festivals 04_m9gk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.100 http://example.org/film/film/film_festivals #9797-04hvw PRED entity: 04hvw PRED relation: participating_countries! PRED expected values: 0kbws => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 42): 0kbws (0.73 #589, 0.72 #56, 0.71 #425), 018ctl (0.57 #295, 0.45 #336, 0.43 #254), 09n48 (0.49 #290, 0.38 #331, 0.34 #700), 0lgxj (0.43 #316, 0.38 #603, 0.35 #357), 09x3r (0.39 #299, 0.34 #340, 0.34 #586), 0sx8l (0.29 #301, 0.22 #55, 0.20 #342), 0blfl (0.25 #317, 0.19 #71, 0.18 #276), 016r9z (0.21 #309, 0.19 #350, 0.19 #63), 0c_tl (0.16 #311, 0.12 #270, 0.12 #352), 06sks6 (0.14 #312, 0.11 #353, 0.10 #722) >> Best rule #589 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 027rn; 09c7w0; 0160w; 0b90_r; 0154j; 03rjj; 03_3d; 0h3y; 0d0vqn; 0chghy; ... >> query: (?x11774, 0kbws) <- taxonomy(?x11774, ?x939), country(?x4045, ?x11774), ?x4045 = 06z6r >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04hvw participating_countries! 0kbws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.735 http://example.org/olympics/olympic_games/participating_countries #9796-034ls PRED entity: 034ls PRED relation: type_of_union PRED expected values: 04ztj => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #265, 0.87 #157, 0.86 #145), 01g63y (0.33 #605, 0.16 #230, 0.16 #518), 0jgjn (0.02 #224, 0.01 #260), 01bl8s (0.01 #267) >> Best rule #265 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 01gj8_; 04jwp; 01s1zk; 06bng; 041wm; 019fz; 01w03jv; 01k31p; >> query: (?x7540, 04ztj) <- profession(?x7540, ?x5805), location(?x7540, ?x7058), ?x5805 = 0fj9f >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034ls type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.877 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9795-0hvb2 PRED entity: 0hvb2 PRED relation: film PRED expected values: 0209hj => 78 concepts (45 used for prediction) PRED predicted values (max 10 best out of 557): 0g60z (0.63 #5341, 0.57 #24925, 0.47 #44507), 0gxsh4 (0.63 #5341, 0.57 #24925, 0.47 #44507), 02h2vv (0.63 #5341, 0.47 #44507, 0.44 #49852), 01vnbh (0.63 #5341, 0.47 #44507, 0.44 #49852), 09cr8 (0.40 #7401, 0.24 #9181, 0.06 #46288), 05sy_5 (0.23 #8171, 0.11 #9951, 0.01 #17073), 02c638 (0.22 #3893, 0.20 #5674, 0.06 #28486), 03m8y5 (0.20 #402, 0.10 #2182, 0.06 #28486), 0c0zq (0.20 #1554, 0.10 #3334, 0.06 #46288), 0ds3t5x (0.20 #53, 0.10 #1833, 0.06 #46288) >> Best rule #5341 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0c4f4; 014488; 073x6y; 01nr36; 022411; >> query: (?x1870, ?x337) <- award_nominee(?x5626, ?x1870), ?x5626 = 014gf8, nominated_for(?x1870, ?x337) >> conf = 0.63 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 0hvb2 film 0209hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 45.000 0.632 http://example.org/film/actor/film./film/performance/film #9794-06f0dc PRED entity: 06f0dc PRED relation: district_represented PRED expected values: 059f4 04rrx 04tgp 050ks 0gj4fx => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 284): 059f4 (0.91 #582, 0.91 #202, 0.90 #561), 04tgp (0.91 #202, 0.90 #327, 0.87 #306), 04rrx (0.91 #202, 0.90 #327, 0.87 #306), 050ks (0.91 #202, 0.90 #327, 0.87 #306), 01n4w (0.91 #202, 0.90 #327, 0.87 #306), 0gj4fx (0.91 #202, 0.90 #327, 0.87 #306), 05kr_ (0.68 #265, 0.62 #264, 0.61 #603), 05rgl (0.68 #265, 0.62 #264, 0.61 #603), 0694j (0.68 #265, 0.62 #264, 0.61 #603), 0pmq2 (0.68 #265, 0.57 #80, 0.56 #580) >> Best rule #582 for best value: >> intensional similarity = 33 >> extensional distance = 20 >> proper extension: 01grnp; 01gsvp; 01grp0; 01gtdd; 01grmk; >> query: (?x952, 059f4) <- district_represented(?x952, ?x6521), district_represented(?x952, ?x3086), district_represented(?x952, ?x3038), district_represented(?x952, ?x2713), district_represented(?x952, ?x938), district_represented(?x952, ?x335), district_represented(?x952, ?x177), legislative_sessions(?x355, ?x952), ?x335 = 059rby, religion(?x3086, ?x962), ?x962 = 05sfs, adjoins(?x938, ?x2982), contains(?x94, ?x3086), location(?x1376, ?x6521), legislative_sessions(?x652, ?x952), contains(?x938, ?x3983), district_represented(?x759, ?x177), location(?x5507, ?x3086), state_province_region(?x388, ?x177), contains(?x177, ?x1629), ?x759 = 043djx, location(?x1817, ?x938), jurisdiction_of_office(?x900, ?x6521), award(?x1817, ?x537), religion(?x177, ?x492), contains(?x2982, ?x659), state_province_region(?x2150, ?x6521), artists(?x378, ?x1817), ?x2713 = 06btq, legislative_sessions(?x2860, ?x952), location(?x932, ?x177), state(?x859, ?x6521), ?x3038 = 0d0x8 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6 EVAL 06f0dc district_represented 0gj4fx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 34.000 34.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 06f0dc district_represented 050ks CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 06f0dc district_represented 04tgp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 06f0dc district_represented 04rrx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 06f0dc district_represented 059f4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #9793-026db_ PRED entity: 026db_ PRED relation: industry PRED expected values: 0hz28 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 45): 01mw1 (0.19 #673, 0.13 #2113, 0.12 #1681), 020mfr (0.17 #689, 0.11 #1697, 0.11 #2370), 029g_vk (0.14 #587, 0.11 #923, 0.11 #1835), 02vxn (0.14 #482, 0.11 #674, 0.07 #3555), 0vg8 (0.12 #339, 0.09 #627, 0.09 #291), 03qh03g (0.12 #1109, 0.10 #773, 0.10 #2021), 0191_7 (0.11 #184, 0.11 #232, 0.10 #568), 015p1m (0.11 #172, 0.11 #220, 0.09 #316), 02h400t (0.11 #218, 0.09 #314, 0.08 #362), 01mf0 (0.09 #703, 0.08 #751, 0.07 #1663) >> Best rule #673 for best value: >> intensional similarity = 6 >> extensional distance = 45 >> proper extension: 01w92; >> query: (?x13730, 01mw1) <- state_province_region(?x13730, ?x3670), organization(?x4682, ?x13730), ?x4682 = 0dq_5, contains(?x3670, ?x331), location_of_ceremony(?x566, ?x3670), featured_film_locations(?x6493, ?x3670) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1758 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 70 *> proper extension: 018p5f; *> query: (?x13730, 0hz28) <- citytown(?x13730, ?x2254), organization(?x4682, ?x13730), ?x4682 = 0dq_5, location(?x120, ?x2254), dog_breed(?x2254, ?x1706) *> conf = 0.06 ranks of expected_values: 19 EVAL 026db_ industry 0hz28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 141.000 141.000 0.191 http://example.org/business/business_operation/industry #9792-0h25 PRED entity: 0h25 PRED relation: award PRED expected values: 0g9wd99 => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 326): 0gr4k (0.25 #2469, 0.14 #845, 0.10 #47945), 04njml (0.25 #2538, 0.09 #5380, 0.08 #32890), 0gqwc (0.22 #4947, 0.16 #19972, 0.15 #26468), 01bgqh (0.22 #2885, 0.14 #23188, 0.14 #23594), 02h3d1 (0.20 #2619, 0.06 #5461, 0.03 #7897), 019bnn (0.18 #2300, 0.13 #4736, 0.12 #11639), 01c99j (0.18 #23373, 0.18 #23779, 0.09 #20125), 09sb52 (0.18 #26434, 0.17 #39833, 0.17 #34149), 01by1l (0.17 #2955, 0.13 #4579, 0.12 #6609), 05b4l5x (0.15 #19903, 0.11 #26399, 0.11 #34114) >> Best rule #2469 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 05pq9; 012wg; 06h7l7; 01vrlqd; 02fgp0; 039xcr; 0164y7; 0f3nn; >> query: (?x10500, 0gr4k) <- place_of_birth(?x10500, ?x7184), profession(?x10500, ?x6421), people(?x10199, ?x10500), ?x6421 = 02hv44_ >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1185 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 07xr3w; *> query: (?x10500, 0g9wd99) <- place_of_birth(?x10500, ?x7184), profession(?x10500, ?x987), ?x7184 = 06pr6, gender(?x10500, ?x514) *> conf = 0.14 ranks of expected_values: 16 EVAL 0h25 award 0g9wd99 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 170.000 170.000 0.250 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9791-02nwxc PRED entity: 02nwxc PRED relation: film PRED expected values: 0320fn 03vyw8 => 117 concepts (86 used for prediction) PRED predicted values (max 10 best out of 702): 01g03q (0.64 #33924, 0.62 #7143, 0.62 #16071), 07g9f (0.64 #33924, 0.62 #76769, 0.38 #141045), 03np63f (0.18 #1374, 0.03 #3159, 0.01 #12087), 01gkp1 (0.18 #816, 0.02 #4387, 0.02 #6173), 0pc62 (0.18 #94, 0.01 #39373, 0.01 #7237), 01d2v1 (0.18 #1710), 02qr3k8 (0.11 #17358, 0.03 #61986, 0.02 #129835), 051zy_b (0.09 #579, 0.03 #5936, 0.02 #4150), 02nt3d (0.09 #1082, 0.03 #2867, 0.02 #6439), 0b3n61 (0.09 #1357, 0.02 #8500, 0.02 #10285) >> Best rule #33924 for best value: >> intensional similarity = 3 >> extensional distance = 399 >> proper extension: 01wxyx1; 01wk7b7; 049qx; 01vzxmq; 03m6pk; >> query: (?x5662, ?x6967) <- participant(?x5662, ?x447), gender(?x5662, ?x514), nominated_for(?x5662, ?x6967) >> conf = 0.64 => this is the best rule for 2 predicted values *> Best rule #24261 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 375 *> proper extension: 031zkw; 033p3_; 015076; *> query: (?x5662, 03vyw8) <- participant(?x5662, ?x447), award_winner(?x1670, ?x5662), award(?x56, ?x1670) *> conf = 0.02 ranks of expected_values: 180, 625 EVAL 02nwxc film 03vyw8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 117.000 86.000 0.643 http://example.org/film/actor/film./film/performance/film EVAL 02nwxc film 0320fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 117.000 86.000 0.643 http://example.org/film/actor/film./film/performance/film #9790-01v3ht PRED entity: 01v3ht PRED relation: organization! PRED expected values: 07xl34 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 9): 060c4 (0.69 #41, 0.64 #262, 0.64 #171), 07xl34 (0.33 #76, 0.21 #193, 0.21 #89), 0dq_5 (0.24 #152, 0.19 #113, 0.18 #360), 0hm4q (0.14 #73, 0.07 #112, 0.06 #320), 05k17c (0.10 #202, 0.10 #332, 0.10 #280), 05c0jwl (0.06 #70, 0.05 #252, 0.05 #226), 08jcfy (0.02 #155, 0.02 #103, 0.02 #142), 04n1q6 (0.01 #279, 0.01 #45, 0.01 #240), 02wlwtm (0.01 #52) >> Best rule #41 for best value: >> intensional similarity = 5 >> extensional distance = 91 >> proper extension: 01jssp; 04wlz2; 02cttt; 01bzw5; 07lx1s; 01bvw5; 01ptt7; 07wjk; 01swxv; 01dq5z; ... >> query: (?x4220, 060c4) <- colors(?x4220, ?x3621), colors(?x4220, ?x3189), ?x3189 = 01g5v, state_province_region(?x4220, ?x4221), colors(?x179, ?x3621) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 109 *> proper extension: 0277jc; 05f7s1; 07w5rq; 01n1pp; 05t7c1; 03x83_; 0c_zj; 015cz0; 09hgk; 057bxr; ... *> query: (?x4220, 07xl34) <- contains(?x4221, ?x4220), institution(?x1368, ?x4220), category(?x4220, ?x134), official_language(?x4221, ?x254) *> conf = 0.33 ranks of expected_values: 2 EVAL 01v3ht organization! 07xl34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 77.000 0.688 http://example.org/organization/role/leaders./organization/leadership/organization #9789-019gz PRED entity: 019gz PRED relation: location PRED expected values: 04jpl => 167 concepts (139 used for prediction) PRED predicted values (max 10 best out of 287): 02cft (0.47 #10760, 0.40 #8348, 0.28 #13978), 04jpl (0.43 #4839, 0.27 #32190, 0.22 #6448), 0r1jr (0.25 #3348, 0.20 #4152, 0.06 #13807), 05qtj (0.25 #2650, 0.06 #12302, 0.05 #58959), 02_286 (0.24 #16125, 0.20 #53931, 0.19 #65995), 02m77 (0.22 #5956, 0.15 #9176, 0.12 #12392), 07ssc (0.13 #7235, 0.11 #7236, 0.11 #6457), 03rt9 (0.13 #7235, 0.11 #20108, 0.10 #37809), 0cr3d (0.12 #18644, 0.10 #47604, 0.09 #34732), 05l5n (0.12 #18600, 0.08 #20208, 0.05 #31469) >> Best rule #10760 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 018dnt; 01vvpjj; 059t6d; >> query: (?x11410, 02cft) <- people(?x7322, ?x11410), ?x7322 = 03bkbh, nationality(?x11410, ?x429), ?x429 = 03rt9, location(?x11410, ?x1591) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #4839 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02xb2bt; *> query: (?x11410, 04jpl) <- nationality(?x11410, ?x512), nationality(?x11410, ?x429), ?x429 = 03rt9, ?x512 = 07ssc *> conf = 0.43 ranks of expected_values: 2 EVAL 019gz location 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 167.000 139.000 0.467 http://example.org/people/person/places_lived./people/place_lived/location #9788-04wsz PRED entity: 04wsz PRED relation: contains PRED expected values: 0j1z8 => 147 concepts (77 used for prediction) PRED predicted values (max 10 best out of 2721): 01w2v (0.72 #111373, 0.60 #64480, 0.55 #143612), 02w6bq (0.72 #111373, 0.60 #64480, 0.55 #143612), 013g3 (0.72 #111373, 0.60 #64480, 0.55 #143612), 05sb1 (0.69 #70342, 0.66 #61549, 0.64 #96719), 06tw8 (0.69 #70342, 0.66 #61549, 0.64 #96719), 04gqr (0.69 #70342, 0.66 #61549, 0.64 #96719), 01znc_ (0.69 #70342, 0.66 #61549, 0.64 #96719), 0jdd (0.69 #70342, 0.66 #61549, 0.64 #96719), 0j1z8 (0.69 #70342, 0.66 #61549, 0.64 #96719), 0161c (0.66 #61549, 0.64 #96719, 0.64 #225695) >> Best rule #111373 for best value: >> intensional similarity = 6 >> extensional distance = 23 >> proper extension: 05fkf; 04ykg; 0488g; 05tbn; 04ly1; 081yw; >> query: (?x9122, ?x6366) <- contains(?x9122, ?x6841), contains(?x9122, ?x608), citytown(?x1142, ?x608), contains(?x608, ?x6366), teams(?x608, ?x13273), organization(?x6841, ?x127) >> conf = 0.72 => this is the best rule for 3 predicted values *> Best rule #70342 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 02613; *> query: (?x9122, ?x311) <- contains(?x9122, ?x6841), contains(?x9122, ?x1780), locations(?x326, ?x9122), exported_to(?x1780, ?x2346), currency(?x6841, ?x170), adjoins(?x6841, ?x311) *> conf = 0.69 ranks of expected_values: 9 EVAL 04wsz contains 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 147.000 77.000 0.724 http://example.org/location/location/contains #9787-02xtxw PRED entity: 02xtxw PRED relation: nominated_for! PRED expected values: 05zr6wv 057xs89 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 208): 05zr6wv (0.69 #2402, 0.69 #2883, 0.69 #1441), 027gs1_ (0.50 #189, 0.08 #1630, 0.07 #2831), 09qs08 (0.50 #110, 0.06 #14408, 0.06 #1551), 09qv3c (0.50 #41, 0.06 #14408, 0.06 #1722), 09qvf4 (0.50 #149, 0.06 #14408, 0.05 #1590), 0gs9p (0.33 #305, 0.25 #3908, 0.24 #3668), 0gq9h (0.32 #303, 0.31 #3906, 0.27 #8468), 019f4v (0.28 #294, 0.25 #3897, 0.23 #4858), 099c8n (0.27 #2218, 0.21 #1017, 0.20 #1257), 0gr4k (0.26 #266, 0.19 #3869, 0.18 #7710) >> Best rule #2402 for best value: >> intensional similarity = 3 >> extensional distance = 298 >> proper extension: 02vr3gz; 0c1sgd3; 07l50vn; 07xvf; >> query: (?x3559, ?x401) <- film_crew_role(?x3559, ?x468), ?x468 = 02r96rf, award(?x3559, ?x401) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 27 EVAL 02xtxw nominated_for! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 73.000 73.000 0.693 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02xtxw nominated_for! 05zr6wv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.693 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9786-06ms6 PRED entity: 06ms6 PRED relation: major_field_of_study! PRED expected values: 03ksy 012fvq 07tds 020ddc 0jpkw => 65 concepts (34 used for prediction) PRED predicted values (max 10 best out of 659): 08815 (0.80 #8082, 0.78 #7003, 0.75 #9699), 01mpwj (0.71 #6032, 0.70 #8189, 0.67 #9806), 03ksy (0.71 #4953, 0.67 #7109, 0.67 #6570), 0g8rj (0.71 #5023, 0.60 #3946, 0.50 #9335), 07tgn (0.70 #8094, 0.67 #9711, 0.67 #7015), 07tds (0.67 #9849, 0.67 #7153, 0.64 #8771), 0bwfn (0.67 #12663, 0.67 #6733, 0.60 #4039), 07wjk (0.67 #9757, 0.67 #7061, 0.60 #3288), 01j_9c (0.67 #7010, 0.60 #3777, 0.60 #3237), 01f1r4 (0.67 #6591, 0.60 #3357, 0.57 #6052) >> Best rule #8082 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 01lhy; >> query: (?x1695, 08815) <- major_field_of_study(?x9200, ?x1695), major_field_of_study(?x2999, ?x1695), ?x2999 = 07tg4, major_field_of_study(?x373, ?x1695), major_field_of_study(?x734, ?x1695), student(?x1695, ?x4053), state_province_region(?x9200, ?x760), citytown(?x9200, ?x6158), location(?x120, ?x760), profession(?x4053, ?x1032) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #4953 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 0dc_v; *> query: (?x1695, 03ksy) <- major_field_of_study(?x6056, ?x1695), major_field_of_study(?x5750, ?x1695), major_field_of_study(?x2999, ?x1695), ?x2999 = 07tg4, student(?x1695, ?x3806), major_field_of_study(?x1695, ?x2606), ?x6056 = 05zl0, major_field_of_study(?x5750, ?x9079), ?x9079 = 0l5mz, school(?x2067, ?x5750), major_field_of_study(?x122, ?x2606) *> conf = 0.71 ranks of expected_values: 3, 6, 75, 218, 356 EVAL 06ms6 major_field_of_study! 0jpkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 65.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 06ms6 major_field_of_study! 020ddc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 65.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 06ms6 major_field_of_study! 07tds CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 65.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 06ms6 major_field_of_study! 012fvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 65.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 06ms6 major_field_of_study! 03ksy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #9785-0133sq PRED entity: 0133sq PRED relation: producer_type PRED expected values: 0ckd1 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.23 #2, 0.19 #21, 0.19 #7) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 02drd3; >> query: (?x10854, 0ckd1) <- produced_by(?x2539, ?x10854), genre(?x2539, ?x2540), ?x2540 = 0hcr, film_release_distribution_medium(?x2539, ?x81) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0133sq producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.227 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #9784-02cft PRED entity: 02cft PRED relation: citytown! PRED expected values: 01m_zd => 197 concepts (177 used for prediction) PRED predicted values (max 10 best out of 661): 011xy1 (0.71 #105120, 0.56 #54974, 0.51 #105119), 0blbx (0.56 #54974, 0.51 #105119, 0.48 #31526), 0373qt (0.35 #4849, 0.06 #93795, 0.05 #78433), 01nds (0.25 #4617, 0.15 #7852, 0.14 #11895), 02975m (0.20 #2340, 0.20 #1532, 0.12 #10425), 01l50r (0.20 #2299, 0.20 #1491, 0.12 #10384), 032j_n (0.20 #2165, 0.20 #1357, 0.12 #10250), 07l1c (0.20 #1942, 0.20 #1134, 0.12 #10027), 0dn_w (0.20 #1580, 0.12 #4812, 0.08 #7238), 01z_jj (0.20 #1557, 0.12 #4789, 0.08 #7215) >> Best rule #105120 for best value: >> intensional similarity = 3 >> extensional distance = 211 >> proper extension: 07sb1; >> query: (?x6357, ?x8694) <- citytown(?x11652, ?x6357), contains(?x6357, ?x8694), institution(?x620, ?x8694) >> conf = 0.71 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02cft citytown! 01m_zd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 197.000 177.000 0.707 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #9783-0ddfph PRED entity: 0ddfph PRED relation: student! PRED expected values: 088gzp => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 67): 03w1lf (0.11 #351, 0.06 #1405, 0.05 #3514), 088gzp (0.07 #513, 0.03 #1040, 0.03 #1567), 07wjk (0.06 #1644, 0.02 #4280, 0.02 #4808), 02hwww (0.06 #968, 0.05 #441, 0.03 #2550), 050xpd (0.04 #988, 0.03 #1515, 0.02 #2570), 0bwfn (0.04 #7128, 0.04 #16617, 0.04 #20833), 02cbvn (0.04 #136), 01w5m (0.03 #9595, 0.03 #6958, 0.03 #4322), 065y4w7 (0.03 #6867, 0.03 #7395, 0.03 #30585), 015nl4 (0.03 #22733, 0.03 #32219, 0.03 #32746) >> Best rule #351 for best value: >> intensional similarity = 5 >> extensional distance = 55 >> proper extension: 087z12; 07jmnh; 03d63lb; >> query: (?x13417, 03w1lf) <- nationality(?x13417, ?x2146), profession(?x13417, ?x1032), ?x1032 = 02hrh1q, ?x2146 = 03rk0, film(?x13417, ?x2617) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #513 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 55 *> proper extension: 087z12; 07jmnh; 03d63lb; *> query: (?x13417, 088gzp) <- nationality(?x13417, ?x2146), profession(?x13417, ?x1032), ?x1032 = 02hrh1q, ?x2146 = 03rk0, film(?x13417, ?x2617) *> conf = 0.07 ranks of expected_values: 2 EVAL 0ddfph student! 088gzp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 110.000 0.105 http://example.org/education/educational_institution/students_graduates./education/education/student #9782-06f32 PRED entity: 06f32 PRED relation: olympics PRED expected values: 0kbws => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 40): 0kbws (0.78 #1386, 0.78 #365, 0.76 #521), 0kbvb (0.78 #358, 0.71 #514, 0.71 #672), 0jdk_ (0.67 #376, 0.62 #1003, 0.57 #532), 09n48 (0.59 #549, 0.57 #1256, 0.57 #629), 018ctl (0.56 #790, 0.54 #1339, 0.50 #594), 0l6mp (0.56 #447, 0.44 #369, 0.41 #996), 0jhn7 (0.52 #808, 0.50 #455, 0.50 #104), 0swbd (0.50 #401, 0.50 #362, 0.49 #1620), 0lgxj (0.50 #105, 0.49 #1371, 0.49 #2198), 0sxrz (0.50 #99, 0.39 #450, 0.36 #607) >> Best rule #1386 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 0hzlz; 09pmkv; 0d0kn; >> query: (?x2629, 0kbws) <- film_release_region(?x1785, ?x2629), ?x1785 = 0gj9tn5, olympics(?x2629, ?x775), form_of_government(?x2629, ?x48) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06f32 olympics 0kbws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.778 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #9781-02s9vc PRED entity: 02s9vc PRED relation: current_club PRED expected values: 0175tv => 64 concepts (43 used for prediction) PRED predicted values (max 10 best out of 736): 04ltf (0.55 #1059, 0.35 #1201, 0.33 #349), 049f05 (0.50 #532, 0.33 #248, 0.24 #1384), 0xbm (0.36 #1012, 0.33 #302, 0.29 #1154), 01634x (0.36 #1066, 0.33 #72, 0.25 #640), 06l22 (0.36 #1044, 0.25 #618, 0.24 #1186), 0y54 (0.33 #292, 0.25 #576, 0.18 #1002), 0cttx (0.33 #410, 0.25 #694, 0.18 #1120), 045xx (0.33 #344, 0.25 #628, 0.18 #1338), 01l0__ (0.33 #238, 0.25 #522, 0.12 #1232), 025txtg (0.33 #152, 0.25 #436, 0.12 #1288) >> Best rule #1059 for best value: >> intensional similarity = 11 >> extensional distance = 9 >> proper extension: 02ltg3; 035qgm; 02bh_v; 03dj48; 01352_; 03_44z; 02w64f; >> query: (?x9926, 04ltf) <- current_club(?x9926, ?x13947), current_club(?x9926, ?x5207), current_club(?x9926, ?x2971), position(?x9926, ?x63), colors(?x5207, ?x3189), team(?x11941, ?x9926), position(?x2971, ?x60), colors(?x2971, ?x663), team(?x11781, ?x5207), ?x11781 = 02y0dd, team(?x7026, ?x13947) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1850 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 28 *> proper extension: 03y_f8; 032jlh; *> query: (?x9926, ?x59) <- current_club(?x9926, ?x5207), position(?x9926, ?x203), colors(?x5207, ?x3189), position(?x5207, ?x60), sport(?x9926, ?x471), position(?x7423, ?x203), position(?x59, ?x203), team(?x203, ?x348), ?x7423 = 03j79x *> conf = 0.01 ranks of expected_values: 473 EVAL 02s9vc current_club 0175tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 64.000 43.000 0.545 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #9780-03_hd PRED entity: 03_hd PRED relation: location PRED expected values: 04jpl => 152 concepts (109 used for prediction) PRED predicted values (max 10 best out of 262): 09c7w0 (0.33 #3, 0.25 #807, 0.14 #3219), 0fhp9 (0.33 #43, 0.25 #847, 0.11 #4867), 07ssc (0.33 #26, 0.25 #830, 0.11 #4850), 0h7x (0.33 #74, 0.25 #878, 0.11 #4898), 04jpl (0.30 #6449, 0.12 #4037, 0.11 #45082), 01xd9 (0.25 #889, 0.20 #5713, 0.14 #7321), 0dclg (0.25 #4137, 0.06 #9766, 0.04 #11375), 09hzc (0.24 #11258, 0.20 #31386, 0.20 #16894), 02h6_6p (0.20 #1739, 0.12 #4151, 0.03 #21858), 0345h (0.20 #1675, 0.12 #4087, 0.03 #12934) >> Best rule #3 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 032r1; >> query: (?x4547, 09c7w0) <- influenced_by(?x12216, ?x4547), influenced_by(?x4055, ?x4547), religion(?x4547, ?x2694), ?x4055 = 034bs, influenced_by(?x4547, ?x712), ?x12216 = 047g6 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6449 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 04kj2v; *> query: (?x4547, 04jpl) <- student(?x2142, ?x4547), ?x2142 = 0dplh, profession(?x4547, ?x10210) *> conf = 0.30 ranks of expected_values: 5 EVAL 03_hd location 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 152.000 109.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #9779-03f0fnk PRED entity: 03f0fnk PRED relation: gender PRED expected values: 05zppz => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #95, 0.88 #119, 0.88 #127), 02zsn (0.55 #291, 0.45 #52, 0.43 #104) >> Best rule #95 for best value: >> intensional similarity = 3 >> extensional distance = 215 >> proper extension: 0d4jl; 0c5tl; 08304; >> query: (?x4712, 05zppz) <- influenced_by(?x8718, ?x4712), profession(?x4712, ?x220), student(?x2775, ?x4712) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f0fnk gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.889 http://example.org/people/person/gender #9778-02jx1 PRED entity: 02jx1 PRED relation: olympics PRED expected values: 0lk8j => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 37): 06sks6 (0.90 #5023, 0.88 #4911, 0.87 #4986), 0kbvb (0.68 #1267, 0.65 #1044, 0.61 #1230), 0kbws (0.66 #2828, 0.65 #1865, 0.65 #1273), 0kbvv (0.58 #1060, 0.57 #1023, 0.55 #2319), 09n48 (0.52 #1892, 0.50 #1559, 0.50 #114), 0swbd (0.52 #1011, 0.50 #122, 0.48 #1160), 018ctl (0.52 #1008, 0.50 #119, 0.48 #1934), 0jdk_ (0.52 #1173, 0.52 #1284, 0.50 #135), 0jhn7 (0.50 #1062, 0.50 #136, 0.42 #1285), 0l6mp (0.50 #127, 0.43 #386, 0.40 #608) >> Best rule #5023 for best value: >> intensional similarity = 2 >> extensional distance = 160 >> proper extension: 027nb; 01z88t; 056vv; 05qkp; 0jdd; 07bxhl; 07dvs; 047t_; 06ryl; 06sff; ... >> query: (?x1310, 06sks6) <- currency(?x1310, ?x170), olympics(?x1310, ?x1608) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #126 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 09c7w0; 07ssc; *> query: (?x1310, 0lk8j) <- nationality(?x2608, ?x1310), contains(?x1310, ?x892), ?x2608 = 01hb6v *> conf = 0.25 ranks of expected_values: 34 EVAL 02jx1 olympics 0lk8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 200.000 200.000 0.901 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #9777-0137g1 PRED entity: 0137g1 PRED relation: award_winner! PRED expected values: 01c4_6 => 148 concepts (117 used for prediction) PRED predicted values (max 10 best out of 277): 01by1l (0.52 #13333, 0.37 #14185, 0.36 #23448), 01bgqh (0.45 #9424, 0.28 #13263, 0.20 #14115), 01c4_6 (0.36 #23448, 0.36 #23447, 0.36 #26010), 01ck6v (0.36 #23448, 0.36 #23447, 0.36 #26010), 02f72n (0.36 #23448, 0.36 #23447, 0.36 #26010), 02f5qb (0.36 #23448, 0.36 #23447, 0.36 #26010), 02f705 (0.36 #23448, 0.36 #23447, 0.36 #26010), 02f79n (0.36 #23448, 0.36 #23447, 0.36 #26010), 03qbnj (0.27 #9608, 0.17 #13447, 0.12 #17281), 02f73p (0.23 #7005, 0.19 #1886, 0.12 #2314) >> Best rule #13333 for best value: >> intensional similarity = 5 >> extensional distance = 153 >> proper extension: 01wwvd2; 01wyq0w; >> query: (?x2784, 01by1l) <- award_winner(?x4892, ?x2784), award(?x3321, ?x4892), award(?x1231, ?x4892), ?x3321 = 03bnv, ?x1231 = 01vrz41 >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #23448 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 277 *> proper extension: 03_0p; 02ryx0; 0kp2_; 0cj2w; *> query: (?x2784, ?x10169) <- instrumentalists(?x227, ?x2784), award(?x2784, ?x10169), role(?x2784, ?x212), award_winner(?x10169, ?x521) *> conf = 0.36 ranks of expected_values: 3 EVAL 0137g1 award_winner! 01c4_6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 148.000 117.000 0.523 http://example.org/award/award_category/winners./award/award_honor/award_winner #9776-05zjx PRED entity: 05zjx PRED relation: film PRED expected values: 0243cq => 81 concepts (75 used for prediction) PRED predicted values (max 10 best out of 543): 01vfqh (0.33 #203, 0.11 #1993, 0.03 #98462), 03m8y5 (0.33 #407, 0.11 #2197, 0.01 #61271), 07yvsn (0.33 #558, 0.05 #2348), 0qf2t (0.17 #833, 0.11 #2623), 041td_ (0.17 #1107, 0.05 #2897, 0.03 #98462), 031778 (0.17 #316, 0.05 #2106, 0.03 #3896), 085bd1 (0.17 #451, 0.05 #2241, 0.03 #4031), 031786 (0.17 #1276, 0.05 #3066, 0.01 #42446), 04fzfj (0.17 #105, 0.05 #1895), 08720 (0.17 #90, 0.05 #1880) >> Best rule #203 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 071ynp; 02f8lw; 04z542; 01jmv8; >> query: (?x7598, 01vfqh) <- award_nominee(?x7598, ?x5330), ?x5330 = 02f2p7, award_nominee(?x1871, ?x7598) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05zjx film 0243cq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 75.000 0.333 http://example.org/film/actor/film./film/performance/film #9775-019vhk PRED entity: 019vhk PRED relation: nominated_for! PRED expected values: 0gr0m 02x258x => 87 concepts (69 used for prediction) PRED predicted values (max 10 best out of 220): 09cm54 (0.69 #5620, 0.68 #1297, 0.67 #5619), 027986c (0.69 #5620, 0.68 #1297, 0.67 #5619), 04dn09n (0.59 #1757, 0.57 #1541, 0.50 #2189), 0gr0m (0.56 #482, 0.46 #1563, 0.31 #1779), 027dtxw (0.50 #3, 0.40 #219, 0.37 #1516), 0gr4k (0.41 #1750, 0.41 #2182, 0.35 #4991), 0gqy2 (0.41 #535, 0.34 #1616, 0.30 #8107), 02x1dht (0.40 #37, 0.37 #685, 0.32 #253), 054krc (0.38 #489, 0.35 #1570, 0.31 #1786), 09sb52 (0.38 #459, 0.35 #27, 0.32 #675) >> Best rule #5620 for best value: >> intensional similarity = 4 >> extensional distance = 507 >> proper extension: 06mmr; >> query: (?x2852, ?x1107) <- honored_for(?x6238, ?x2852), award(?x2852, ?x1107), award(?x276, ?x1107), nominated_for(?x1107, ?x144) >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #482 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 37 *> proper extension: 064lsn; *> query: (?x2852, 0gr0m) <- honored_for(?x6238, ?x2852), nominated_for(?x1198, ?x2852), nominated_for(?x637, ?x2852), ?x1198 = 02pqp12, ?x637 = 02r22gf *> conf = 0.56 ranks of expected_values: 4, 41 EVAL 019vhk nominated_for! 02x258x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 87.000 69.000 0.686 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019vhk nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 69.000 0.686 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9774-03x1s8 PRED entity: 03x1s8 PRED relation: colors PRED expected values: 019sc => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 18): 019sc (0.50 #7, 0.46 #26, 0.20 #159), 083jv (0.38 #552, 0.38 #58, 0.37 #913), 01g5v (0.33 #60, 0.31 #307, 0.31 #497), 06fvc (0.19 #116, 0.19 #306, 0.19 #59), 06kqt3 (0.17 #16, 0.08 #35, 0.04 #73), 01jnf1 (0.15 #29, 0.08 #10, 0.05 #181), 036k5h (0.14 #43, 0.12 #62, 0.11 #100), 038hg (0.09 #163, 0.09 #144, 0.09 #923), 09ggk (0.09 #167, 0.07 #585, 0.06 #699), 04mkbj (0.09 #351, 0.08 #446, 0.08 #921) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 07w3r; 02_cx_; 017y6l; 05x_5; 030w19; >> query: (?x12126, 019sc) <- colors(?x12126, ?x5845), ?x5845 = 067z2v, currency(?x12126, ?x170), category(?x12126, ?x134) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x1s8 colors 019sc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.500 http://example.org/education/educational_institution/colors #9773-03fnjv PRED entity: 03fnjv PRED relation: category PRED expected values: 08mbj5d => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #70, 0.10 #69, 0.09 #68) >> Best rule #70 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x7898, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03fnjv category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #9772-07rhpg PRED entity: 07rhpg PRED relation: award_winner! PRED expected values: 09sb52 => 92 concepts (90 used for prediction) PRED predicted values (max 10 best out of 195): 09sb52 (0.75 #474, 0.75 #41, 0.18 #3066), 02ppm4q (0.44 #433, 0.31 #22907, 0.30 #36739), 04kxsb (0.17 #560, 0.17 #127, 0.16 #14693), 094qd5 (0.17 #478, 0.17 #45, 0.11 #23772), 027dtxw (0.16 #14693, 0.15 #21609, 0.15 #21176), 0bfvd4 (0.16 #14693, 0.15 #21609, 0.15 #21176), 09cm54 (0.16 #14693, 0.15 #21609, 0.15 #21176), 02x4w6g (0.16 #14693, 0.15 #21609, 0.15 #21176), 0ck27z (0.11 #23772, 0.11 #10030, 0.11 #7006), 0f4x7 (0.11 #23772, 0.09 #22042, 0.08 #464) >> Best rule #474 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 02cgb8; >> query: (?x7952, 09sb52) <- award_winner(?x7952, ?x2556), ?x2556 = 0171cm, type_of_union(?x7952, ?x566) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07rhpg award_winner! 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 90.000 0.750 http://example.org/award/award_category/winners./award/award_honor/award_winner #9771-0yz30 PRED entity: 0yz30 PRED relation: place PRED expected values: 0yz30 => 79 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1): 0n25q (0.06 #3093, 0.05 #5158) >> Best rule #3093 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 0mn0v; >> query: (?x14177, ?x14240) <- county(?x14177, ?x14240), source(?x14177, ?x958), place_of_birth(?x7414, ?x14177) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0yz30 place 0yz30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 38.000 0.059 http://example.org/location/hud_county_place/place #9770-02h3d1 PRED entity: 02h3d1 PRED relation: award_winner PRED expected values: 0pkgt => 42 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1419): 02fgpf (0.69 #4902, 0.50 #7743, 0.40 #10195), 077rj (0.69 #4902, 0.50 #6211, 0.39 #26968), 01vrz41 (0.69 #4902, 0.39 #46595, 0.39 #44141), 04x4s2 (0.69 #4902, 0.39 #46595, 0.39 #44141), 013_vh (0.69 #4902, 0.39 #46595, 0.39 #44141), 024yxd (0.69 #4902, 0.39 #46595, 0.39 #44141), 0pkgt (0.69 #4902, 0.39 #26968, 0.38 #9805), 01vlj1g (0.69 #4902, 0.38 #9805, 0.38 #12258), 02t__l (0.69 #4902, 0.38 #9805, 0.38 #12258), 03xx9l (0.69 #4902, 0.38 #9805, 0.37 #7353) >> Best rule #4902 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 025mbn; >> query: (?x3467, ?x163) <- award(?x6025, ?x3467), award(?x163, ?x3467), award_winner(?x3467, ?x6382), award_winner(?x3467, ?x3770), ?x6025 = 018gqj, ?x6382 = 01wd9lv, origin(?x3770, ?x362) >> conf = 0.69 => this is the best rule for 10 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7 EVAL 02h3d1 award_winner 0pkgt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 42.000 19.000 0.692 http://example.org/award/award_category/winners./award/award_honor/award_winner #9769-01c72t PRED entity: 01c72t PRED relation: profession! PRED expected values: 0f0y8 07s3vqk 0146pg 01x66d 0k4gf 01wdqrx 012x4t 06wvj 04xrx 02pzc4 01lcxbb 01gx5f 049qx 03f6fl0 052hl 04kjrv 0f_y9 01vz80y 01lqnff 0xsk8 06rgq 020hh3 04qr6d 02f1c 01d_h 0kj34 03975z 01x1fq 01wdcxk 01p8r8 016wvy 014g91 01njxvw 0459z => 43 concepts (26 used for prediction) PRED predicted values (max 10 best out of 3909): 05wm88 (0.71 #39037, 0.67 #27203, 0.50 #23258), 02b29 (0.71 #37572, 0.67 #25738, 0.50 #21793), 01vsl3_ (0.71 #28376, 0.57 #36265, 0.50 #23668), 0137n0 (0.71 #27922, 0.50 #23668, 0.50 #20032), 0gv5c (0.71 #36825, 0.50 #24991, 0.50 #21046), 019z7q (0.71 #35718, 0.50 #23884, 0.50 #19939), 02184q (0.71 #38509, 0.50 #22730, 0.50 #18785), 026ck (0.71 #39159, 0.50 #23380, 0.50 #19435), 01vw8mh (0.71 #29055, 0.50 #21165, 0.43 #36944), 01j6mff (0.71 #30509, 0.33 #26564, 0.33 #6841) >> Best rule #39037 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0dxtg; 02hv44_; >> query: (?x1614, 05wm88) <- profession(?x10656, ?x1614), profession(?x8600, ?x1614), profession(?x4324, ?x1614), award(?x4324, ?x591), nominated_for(?x4324, ?x825), ?x10656 = 01z0lb, location(?x8600, ?x4627) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #30195 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 016z4k; 0n1h; *> query: (?x1614, 06rgq) <- profession(?x8600, ?x1614), profession(?x4324, ?x1614), profession(?x3737, ?x1614), award(?x4324, ?x591), nominated_for(?x4324, ?x825), ?x3737 = 01q32bd, artists(?x888, ?x8600) *> conf = 0.71 ranks of expected_values: 11, 50, 57, 65, 207, 228, 244, 279, 322, 433, 552, 574, 575, 981, 1028, 1032, 1053, 1104, 1443, 1709, 1722, 1735, 1978, 2013, 2064, 2301, 2320, 3349, 3488, 3590, 3611 EVAL 01c72t profession! 0459z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01njxvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 014g91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 016wvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01p8r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01wdcxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01x1fq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 03975z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 0kj34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01d_h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 02f1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 04qr6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 020hh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 06rgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 0xsk8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01lqnff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01vz80y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 0f_y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 04kjrv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 052hl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 03f6fl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 049qx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01gx5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01lcxbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 02pzc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 04xrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 06wvj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01wdqrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 0k4gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 01x66d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 0146pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 07s3vqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 43.000 26.000 0.714 http://example.org/people/person/profession EVAL 01c72t profession! 0f0y8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.714 http://example.org/people/person/profession #9768-03tn9w PRED entity: 03tn9w PRED relation: ceremony! PRED expected values: 0gr0m 0k611 0gr51 => 50 concepts (49 used for prediction) PRED predicted values (max 10 best out of 335): 0k611 (0.91 #7317, 0.90 #2962, 0.89 #2235), 0gr0m (0.88 #1500, 0.87 #2711, 0.84 #4646), 0gr51 (0.86 #3450, 0.86 #4661, 0.86 #4419), 0gqxm (0.75 #11851, 0.74 #1451, 0.74 #10398), 0gqzz (0.75 #11851, 0.74 #1451, 0.74 #10398), 0czp_ (0.75 #11851, 0.74 #1451, 0.74 #10398), 02x201b (0.75 #11851, 0.74 #1451, 0.74 #10398), 040njc (0.44 #968, 0.43 #4840, 0.37 #484), 02qvyrt (0.44 #968, 0.43 #4840, 0.37 #484), 02qyntr (0.44 #968, 0.43 #4840, 0.37 #484) >> Best rule #7317 for best value: >> intensional similarity = 15 >> extensional distance = 63 >> proper extension: 0fv89q; 0fzrhn; >> query: (?x6686, 0k611) <- ceremony(?x1079, ?x6686), honored_for(?x6686, ?x1386), honored_for(?x6686, ?x833), film(?x3447, ?x833), award_winner(?x6686, ?x669), nominated_for(?x1079, ?x9993), nominated_for(?x1079, ?x4431), nominated_for(?x1079, ?x1199), ceremony(?x1079, ?x7144), ?x7144 = 02yxh9, award_winner(?x1079, ?x84), ?x4431 = 0pd4f, ?x1199 = 0pv3x, award(?x1386, ?x1429), ?x9993 = 0kb1g >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 03tn9w ceremony! 0gr51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 49.000 0.908 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03tn9w ceremony! 0k611 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 49.000 0.908 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03tn9w ceremony! 0gr0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 49.000 0.908 http://example.org/award/award_category/winners./award/award_honor/ceremony #9767-07m4c PRED entity: 07m4c PRED relation: artist! PRED expected values: 03rhqg => 78 concepts (46 used for prediction) PRED predicted values (max 10 best out of 106): 03rhqg (0.50 #576, 0.25 #296, 0.23 #3949), 01cszh (0.33 #1131, 0.33 #991, 0.16 #1402), 01cl0d (0.33 #54, 0.04 #4969, 0.04 #5389), 015_1q (0.26 #2546, 0.25 #438, 0.25 #158), 02p11jq (0.25 #713, 0.25 #433, 0.20 #1133), 04fcjt (0.25 #728, 0.25 #448, 0.20 #1148), 01t04r (0.25 #764, 0.25 #484, 0.20 #904), 0g768 (0.25 #456, 0.25 #176, 0.16 #1402), 0mzkr (0.25 #444, 0.25 #304, 0.16 #1402), 06x2ww (0.25 #328, 0.17 #1028, 0.17 #608) >> Best rule #576 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 01bpc9; 016s_5; >> query: (?x7544, 03rhqg) <- artists(?x12215, ?x7544), artists(?x10318, ?x7544), artists(?x1928, ?x7544), ?x1928 = 0mhfr, ?x10318 = 03jsvl, artist(?x3050, ?x7544), ?x12215 = 09n5t_ >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07m4c artist! 03rhqg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 46.000 0.500 http://example.org/music/record_label/artist #9766-0142rn PRED entity: 0142rn PRED relation: company PRED expected values: 0537b 0py9b 03s7h => 27 concepts (15 used for prediction) PRED predicted values (max 10 best out of 349): 03s7h (0.79 #2576, 0.75 #1912, 0.73 #2908), 019rl6 (0.71 #330, 0.67 #1478, 0.60 #1147), 01qygl (0.71 #330, 0.60 #1185, 0.50 #2510), 0537b (0.71 #330, 0.60 #1132, 0.50 #1793), 04sv4 (0.71 #330, 0.60 #1197, 0.50 #1858), 01c6k4 (0.71 #330, 0.50 #2325, 0.50 #1331), 01dfb6 (0.71 #330, 0.50 #1869, 0.50 #1539), 0k8z (0.71 #330, 0.50 #1733, 0.40 #1072), 06pwq (0.71 #330, 0.39 #661, 0.36 #3657), 0mgkg (0.71 #330, 0.39 #661, 0.36 #2518) >> Best rule #2576 for best value: >> intensional similarity = 17 >> extensional distance = 12 >> proper extension: 0dq3c; 02y6fz; 01kr6k; 09lq2c; >> query: (?x6403, 03s7h) <- company(?x6403, ?x13314), company(?x6403, ?x9968), list(?x13314, ?x7472), service_location(?x9968, ?x2146), service_location(?x9968, ?x512), service_location(?x9968, ?x205), ?x2146 = 03rk0, category(?x9968, ?x134), film_release_region(?x3471, ?x205), film_release_region(?x511, ?x205), second_level_divisions(?x205, ?x7191), olympics(?x205, ?x391), ?x512 = 07ssc, ?x511 = 0dscrwf, administrative_parent(?x2856, ?x205), contains(?x205, ?x1356), ?x3471 = 07cyl >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 26 EVAL 0142rn company 03s7h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 27.000 15.000 0.786 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0142rn company 0py9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 27.000 15.000 0.786 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0142rn company 0537b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 27.000 15.000 0.786 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #9765-0b1y_2 PRED entity: 0b1y_2 PRED relation: film! PRED expected values: 0f7hc => 86 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1179): 0f7hc (0.74 #66558, 0.51 #66557, 0.46 #85277), 04xn2m (0.51 #66557, 0.46 #85277, 0.46 #87357), 021yc7p (0.51 #66557, 0.46 #85277, 0.46 #87357), 05qd_ (0.51 #66557, 0.46 #85277, 0.46 #87357), 01gb54 (0.51 #66557, 0.46 #85277, 0.46 #87357), 0d6484 (0.46 #79038, 0.44 #33282, 0.40 #74878), 027y151 (0.46 #79038, 0.40 #74878, 0.40 #2080), 016yvw (0.11 #950, 0.10 #3030, 0.05 #15509), 0h5g_ (0.08 #74, 0.08 #2154, 0.06 #6315), 0154qm (0.08 #561, 0.08 #2641, 0.06 #4720) >> Best rule #66558 for best value: >> intensional similarity = 4 >> extensional distance = 561 >> proper extension: 08hmch; 065zlr; 07x4qr; 0gy2y8r; 0ggbfwf; 027j9wd; 0456zg; >> query: (?x2920, ?x11519) <- nominated_for(?x11519, ?x2920), film_crew_role(?x2920, ?x137), film(?x879, ?x2920), participant(?x11519, ?x3122) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b1y_2 film! 0f7hc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 52.000 0.736 http://example.org/film/actor/film./film/performance/film #9764-09pl3f PRED entity: 09pl3f PRED relation: award_winner! PRED expected values: 0jt3qpk => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 115): 0bxs_d (0.17 #115, 0.10 #679, 0.08 #820), 05c1t6z (0.15 #156, 0.12 #4950, 0.11 #3117), 0hndn2q (0.14 #322, 0.08 #40, 0.06 #4270), 0gvstc3 (0.13 #3136, 0.10 #5533, 0.09 #2008), 027n06w (0.13 #3175, 0.09 #5008, 0.09 #2047), 0bq_mx (0.13 #1120, 0.12 #838, 0.10 #3235), 02q690_ (0.13 #2039, 0.12 #5000, 0.11 #2885), 02wzl1d (0.12 #857, 0.08 #11, 0.08 #152), 0drtv8 (0.12 #912, 0.08 #207, 0.07 #1194), 03nnm4t (0.11 #5009, 0.10 #2048, 0.09 #2894) >> Best rule #115 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 06pj8; 01_x6v; 098n5; 021yw7; 01d8yn; 0br1w; 01xndd; 03xp8d5; 044f7; 056wb; >> query: (?x6001, 0bxs_d) <- story_by(?x3748, ?x6001), program_creator(?x11895, ?x6001), award_winner(?x5277, ?x6001) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #325 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 01s7z0; *> query: (?x6001, 0jt3qpk) <- nationality(?x6001, ?x94), executive_produced_by(?x6543, ?x6001), program_creator(?x11895, ?x6001) *> conf = 0.07 ranks of expected_values: 36 EVAL 09pl3f award_winner! 0jt3qpk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 135.000 135.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9763-01k165 PRED entity: 01k165 PRED relation: profession PRED expected values: 060m4 => 166 concepts (117 used for prediction) PRED predicted values (max 10 best out of 96): 01d_h8 (0.91 #12896, 0.89 #9639, 0.50 #154), 02hrh1q (0.83 #609, 0.75 #757, 0.68 #2981), 0kyk (0.69 #8184, 0.50 #1217, 0.33 #31), 0cbd2 (0.64 #1193, 0.37 #8160, 0.21 #2233), 04gc2 (0.61 #1972, 0.58 #2713, 0.58 #2565), 0dxtg (0.45 #9647, 0.44 #12904, 0.33 #904), 02jknp (0.41 #12898, 0.40 #9641, 0.38 #1046), 02krf9 (0.33 #176, 0.25 #622, 0.17 #918), 0d8qb (0.33 #80, 0.18 #376, 0.14 #8233), 016fly (0.33 #75, 0.17 #7043, 0.12 #1855) >> Best rule #12896 for best value: >> intensional similarity = 7 >> extensional distance = 1071 >> proper extension: 06151l; 01l1b90; 0qf43; 04rs03; 07f8wg; 02pp_q_; 0415svh; 02kxbwx; 03h_9lg; 067jsf; ... >> query: (?x3099, 01d_h8) <- profession(?x3099, ?x5805), profession(?x6442, ?x5805), profession(?x4334, ?x5805), profession(?x1365, ?x5805), ?x4334 = 02xfrd, ?x6442 = 08d6bd, ?x1365 = 0bwh6 >> conf = 0.91 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01k165 profession 060m4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 166.000 117.000 0.909 http://example.org/people/person/profession #9762-03hxsv PRED entity: 03hxsv PRED relation: story_by PRED expected values: 042xh => 101 concepts (58 used for prediction) PRED predicted values (max 10 best out of 91): 0343h (0.20 #234, 0.09 #18, 0.08 #882), 042xh (0.13 #431, 0.07 #1735, 0.03 #3687), 079vf (0.05 #2172, 0.05 #2, 0.04 #1956), 046_v (0.05 #173, 0.04 #821, 0.02 #2343), 079ws (0.05 #131, 0.04 #2301, 0.01 #2085), 07nznf (0.05 #1, 0.02 #2171, 0.02 #649), 0jpdn (0.05 #156, 0.02 #2326, 0.02 #804), 063b4k (0.05 #206, 0.02 #2376), 01wd02c (0.05 #119, 0.01 #7928, 0.01 #2289), 02gn9g (0.05 #212, 0.01 #2382) >> Best rule #234 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 018nnz; >> query: (?x6332, 0343h) <- nominated_for(?x2006, ?x6332), genre(?x6332, ?x225), film(?x382, ?x6332), film_distribution_medium(?x6332, ?x81) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #431 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 018nnz; *> query: (?x6332, 042xh) <- nominated_for(?x2006, ?x6332), genre(?x6332, ?x225), film(?x382, ?x6332), film_distribution_medium(?x6332, ?x81) *> conf = 0.13 ranks of expected_values: 2 EVAL 03hxsv story_by 042xh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 58.000 0.200 http://example.org/film/film/story_by #9761-06pwq PRED entity: 06pwq PRED relation: major_field_of_study PRED expected values: 062z7 02j62 01tbp 01400v => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 89): 02j62 (0.62 #2691, 0.46 #4115, 0.45 #2334), 062z7 (0.58 #373, 0.50 #2689, 0.50 #551), 01tbp (0.50 #42, 0.44 #2714, 0.35 #2357), 02h40lc (0.44 #538, 0.41 #716, 0.41 #627), 03nfmq (0.44 #558, 0.41 #736, 0.41 #647), 02_7t (0.38 #2718, 0.33 #402, 0.30 #5122), 03qsdpk (0.32 #2702, 0.25 #386, 0.25 #30), 0dc_v (0.31 #561, 0.29 #739, 0.29 #650), 0_jm (0.31 #5116, 0.30 #7344, 0.29 #6810), 041y2 (0.26 #2724, 0.25 #408, 0.24 #4148) >> Best rule #2691 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 08815; 01jssp; 05krk; 065y4w7; 07szy; 0bx8pn; 01jq34; 0f1nl; 07wlf; 03ksy; ... >> query: (?x581, 02j62) <- school(?x580, ?x581), list(?x581, ?x2197), student(?x581, ?x1299) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 22 EVAL 06pwq major_field_of_study 01400v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 157.000 157.000 0.618 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 06pwq major_field_of_study 01tbp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.618 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 06pwq major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.618 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 06pwq major_field_of_study 062z7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.618 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #9760-0m77m PRED entity: 0m77m PRED relation: influenced_by! PRED expected values: 0gd5z => 135 concepts (70 used for prediction) PRED predicted values (max 10 best out of 395): 0n6kf (0.50 #192, 0.25 #2767, 0.17 #3799), 013pp3 (0.50 #222, 0.20 #2797, 0.17 #3829), 03qcq (0.50 #1, 0.20 #2576, 0.13 #3608), 019z7q (0.50 #25, 0.12 #2084, 0.11 #1570), 01v_0b (0.50 #484, 0.11 #2029, 0.10 #3059), 07lp1 (0.35 #2993, 0.23 #4025, 0.20 #3509), 02yl42 (0.25 #2710, 0.24 #2194, 0.23 #3742), 0ff3y (0.25 #507, 0.22 #2052, 0.07 #5150), 0d4jl (0.25 #117, 0.15 #2692, 0.13 #3724), 040db (0.25 #76, 0.15 #2651, 0.13 #3683) >> Best rule #192 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 084w8; 02kz_; >> query: (?x1235, 0n6kf) <- award_winner(?x921, ?x1235), influenced_by(?x10974, ?x1235), gender(?x1235, ?x514), ?x10974 = 01vdrw >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3694 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 058vp; *> query: (?x1235, 0gd5z) <- influenced_by(?x1235, ?x3336), influenced_by(?x1235, ?x1236), influenced_by(?x5261, ?x1235), ?x3336 = 032l1, influenced_by(?x1236, ?x2240) *> conf = 0.10 ranks of expected_values: 83 EVAL 0m77m influenced_by! 0gd5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 135.000 70.000 0.500 http://example.org/influence/influence_node/influenced_by #9759-0dvld PRED entity: 0dvld PRED relation: nationality PRED expected values: 07ssc => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 70): 09c7w0 (0.82 #2499, 0.80 #3888, 0.80 #1702), 07ssc (0.33 #2712, 0.33 #11035, 0.30 #9643), 0h924 (0.33 #11035), 0d060g (0.30 #9643, 0.05 #3794, 0.05 #4588), 0f8l9c (0.30 #9643, 0.03 #9345, 0.02 #5992), 0345h (0.30 #9643, 0.03 #9345, 0.02 #6001), 06mkj (0.30 #9643, 0.03 #9345, 0.01 #246), 05qhw (0.30 #9643, 0.03 #9345), 03rk0 (0.11 #3436, 0.08 #5520, 0.08 #5916), 0chghy (0.03 #410, 0.03 #510, 0.03 #9345) >> Best rule #2499 for best value: >> intensional similarity = 2 >> extensional distance = 349 >> proper extension: 014dq7; 041mt; 01wj9y9; 01m65sp; 0mj0c; 02lt8; 019r_1; 01_k1z; 012v1t; 07t2k; ... >> query: (?x5951, 09c7w0) <- location(?x5951, ?x739), ?x739 = 02_286 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #2712 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 394 *> proper extension: 0784v1; *> query: (?x5951, 07ssc) <- nationality(?x5951, ?x1310), ?x1310 = 02jx1 *> conf = 0.33 ranks of expected_values: 2 EVAL 0dvld nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.818 http://example.org/people/person/nationality #9758-027r9t PRED entity: 027r9t PRED relation: nominated_for! PRED expected values: 02qvyrt => 71 concepts (62 used for prediction) PRED predicted values (max 10 best out of 214): 0gq9h (0.53 #955, 0.45 #4782, 0.41 #5457), 02pqp12 (0.45 #1627, 0.33 #952, 0.27 #2527), 0gs9p (0.43 #957, 0.39 #1632, 0.38 #5009), 019f4v (0.40 #948, 0.35 #1623, 0.34 #2523), 02qyntr (0.38 #1742, 0.33 #1067, 0.26 #2642), 027dtxw (0.37 #1578, 0.27 #903, 0.19 #4955), 0f4x7 (0.37 #924, 0.26 #1599, 0.26 #5426), 0gqwc (0.33 #954, 0.31 #2529, 0.27 #7428), 0gqy2 (0.33 #1010, 0.28 #1685, 0.26 #2585), 0gr4k (0.30 #4752, 0.29 #1600, 0.28 #5427) >> Best rule #955 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 0m313; 0ds3t5x; 0fh694; 092vkg; 0jyx6; 0pv3x; 0jym0; 016z9n; 07j8r; 0j_t1; ... >> query: (?x7141, 0gq9h) <- nominated_for(?x2880, ?x7141), nominated_for(?x1254, ?x7141), ?x2880 = 02ppm4q, nominated_for(?x396, ?x7141), ?x1254 = 02z0dfh >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1661 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 100 *> proper extension: 0ds35l9; 0sxg4; 02vxq9m; 01jc6q; 09m6kg; 095zlp; 01sxly; 011yph; 0209hj; 0b6tzs; ... *> query: (?x7141, 02qvyrt) <- nominated_for(?x2880, ?x7141), nominated_for(?x1254, ?x7141), ?x2880 = 02ppm4q, nominated_for(?x396, ?x7141), award(?x91, ?x1254) *> conf = 0.23 ranks of expected_values: 48 EVAL 027r9t nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 71.000 62.000 0.533 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9757-04lhc4 PRED entity: 04lhc4 PRED relation: edited_by PRED expected values: 027pdrh => 101 concepts (65 used for prediction) PRED predicted values (max 10 best out of 20): 027pdrh (0.12 #10, 0.02 #667, 0.01 #636), 03q8ch (0.05 #71, 0.04 #279, 0.04 #547), 03nqbvz (0.04 #43, 0.03 #251, 0.02 #132), 06t8b (0.04 #988, 0.02 #1169, 0.02 #297), 02qggqc (0.03 #569, 0.03 #477, 0.03 #61), 04cy8rb (0.03 #88, 0.02 #208, 0.02 #475), 02kxbx3 (0.03 #99, 0.02 #159, 0.01 #189), 08h79x (0.03 #284, 0.03 #76, 0.02 #552), 0343h (0.03 #65, 0.02 #333, 0.02 #393), 02lp3c (0.03 #75, 0.02 #104, 0.02 #462) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 056xkh; >> query: (?x6899, 027pdrh) <- produced_by(?x6899, ?x71), film(?x1188, ?x6899), ?x71 = 0q9kd >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04lhc4 edited_by 027pdrh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 65.000 0.125 http://example.org/film/film/edited_by #9756-02c7lt PRED entity: 02c7lt PRED relation: place_of_death PRED expected values: 030qb3t => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 52): 04jpl (0.17 #2736, 0.07 #3127, 0.05 #397), 030qb3t (0.14 #7245, 0.13 #2167, 0.13 #4508), 0k049 (0.14 #784, 0.13 #2148, 0.13 #1563), 02_286 (0.10 #4499, 0.09 #7236, 0.07 #9771), 06_kh (0.10 #1565, 0.06 #1955, 0.06 #2150), 0r3w7 (0.09 #958, 0.06 #1737, 0.04 #2127), 0978r (0.06 #244, 0.02 #2777), 0f2wj (0.05 #402, 0.04 #988, 0.03 #9770), 03l2n (0.05 #456, 0.04 #1042, 0.02 #2016), 0fhp9 (0.05 #404, 0.04 #990) >> Best rule #2736 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 032t2z; 015njf; 01v9724; 016bx2; 06qn87; 015dnt; 063_t; 04vt98; 02tn0_; 02r38; ... >> query: (?x11284, 04jpl) <- nationality(?x11284, ?x1310), profession(?x11284, ?x524), ?x1310 = 02jx1, people(?x1158, ?x11284) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #7245 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 386 *> proper extension: 03_0p; *> query: (?x11284, 030qb3t) <- gender(?x11284, ?x514), people(?x1158, ?x11284), award(?x11284, ?x686) *> conf = 0.14 ranks of expected_values: 2 EVAL 02c7lt place_of_death 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.175 http://example.org/people/deceased_person/place_of_death #9755-02fy0z PRED entity: 02fy0z PRED relation: institution! PRED expected values: 02h4rq6 => 108 concepts (58 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.84 #417, 0.83 #112, 0.82 #135), 03bwzr4 (0.80 #14, 0.63 #123, 0.58 #146), 016t_3 (0.80 #4, 0.61 #113, 0.49 #91), 0bkj86 (0.70 #8, 0.53 #73, 0.48 #117), 02_xgp2 (0.60 #12, 0.58 #144, 0.56 #99), 07s6fsf (0.60 #1, 0.53 #110, 0.44 #393), 04zx3q1 (0.50 #2, 0.32 #111, 0.31 #67), 027f2w (0.50 #9, 0.30 #1151, 0.29 #74), 013zdg (0.40 #7, 0.31 #116, 0.30 #1151), 0bjrnt (0.30 #1151, 0.30 #6, 0.26 #529) >> Best rule #417 for best value: >> intensional similarity = 4 >> extensional distance = 150 >> proper extension: 049dk; 027mdh; 02l1fn; 01yqqv; 01stj9; >> query: (?x3149, 02h4rq6) <- school(?x7060, ?x3149), major_field_of_study(?x3149, ?x8221), major_field_of_study(?x5167, ?x8221), ?x5167 = 015cz0 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fy0z institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 58.000 0.842 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9754-01pq5j7 PRED entity: 01pq5j7 PRED relation: award PRED expected values: 02f5qb => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 320): 03qbh5 (0.37 #996, 0.25 #2982, 0.22 #599), 05zr6wv (0.33 #414, 0.21 #3194, 0.18 #3591), 05p09zm (0.33 #518, 0.18 #12830, 0.15 #3298), 09sb52 (0.28 #438, 0.23 #21091, 0.22 #12750), 03c7tr1 (0.28 #455, 0.15 #12767, 0.10 #3235), 02f777 (0.28 #700, 0.12 #3083, 0.12 #4274), 057xs89 (0.28 #554, 0.10 #3334, 0.10 #12866), 01ckcd (0.25 #3109, 0.21 #4300, 0.14 #9461), 01ck6h (0.24 #1310, 0.22 #2502, 0.19 #2899), 01c92g (0.24 #95, 0.19 #889, 0.16 #13995) >> Best rule #996 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 05pdbs; 0ggl02; 016732; 03cd1q; 02qtywd; >> query: (?x5225, 03qbh5) <- award_winner(?x4958, ?x5225), award(?x5225, ?x724), ?x4958 = 03qbnj >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #2932 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 01vsy3q; *> query: (?x5225, 02f5qb) <- artists(?x505, ?x5225), influenced_by(?x5225, ?x3176), award_winner(?x1232, ?x5225) *> conf = 0.23 ranks of expected_values: 12 EVAL 01pq5j7 award 02f5qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 120.000 120.000 0.370 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9753-0lhql PRED entity: 0lhql PRED relation: contains! PRED expected values: 02xry => 157 concepts (130 used for prediction) PRED predicted values (max 10 best out of 283): 02xry (0.65 #102022, 0.64 #112762, 0.61 #66214), 02qkt (0.59 #30765, 0.53 #29869, 0.53 #22712), 06pvr (0.38 #5536, 0.17 #17165, 0.14 #16271), 02j9z (0.33 #22394, 0.31 #30447, 0.29 #29551), 01n7q (0.32 #16183, 0.31 #5448, 0.30 #34971), 04_1l0v (0.30 #46079, 0.25 #76509, 0.24 #61293), 0j0k (0.27 #30796, 0.24 #38848, 0.24 #25428), 059rby (0.25 #19, 0.12 #95773, 0.11 #9866), 05k7sb (0.25 #132, 0.08 #42185, 0.08 #5503), 07ssc (0.20 #58193, 0.19 #68033, 0.19 #67139) >> Best rule #102022 for best value: >> intensional similarity = 3 >> extensional distance = 408 >> proper extension: 0k696; >> query: (?x4144, ?x2623) <- adjoins(?x4144, ?x6084), contains(?x2623, ?x6084), state_province_region(?x3314, ?x2623) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lhql contains! 02xry CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 130.000 0.649 http://example.org/location/location/contains #9752-07s6tbm PRED entity: 07s6tbm PRED relation: award PRED expected values: 0cjyzs 027gs1_ => 72 concepts (56 used for prediction) PRED predicted values (max 10 best out of 243): 02q1tc5 (0.71 #556, 0.64 #150, 0.16 #14617), 0cjyzs (0.35 #2543, 0.33 #3761, 0.24 #2949), 0fbtbt (0.33 #2670, 0.28 #3888, 0.25 #3076), 027qq9b (0.27 #209, 0.18 #615, 0.16 #14617), 0ck27z (0.26 #4965, 0.21 #9025, 0.13 #6589), 09sb52 (0.24 #10597, 0.24 #6537, 0.23 #12221), 01by1l (0.20 #2143, 0.08 #925, 0.07 #1331), 02p_7cr (0.16 #14617, 0.14 #15430, 0.12 #21120), 02p_04b (0.16 #14617, 0.14 #15430, 0.12 #21120), 02pzxlw (0.16 #14617, 0.14 #15430, 0.12 #21120) >> Best rule #556 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 01wk7b7; >> query: (?x1341, 02q1tc5) <- nominated_for(?x1341, ?x589), nominated_for(?x7426, ?x589), ?x7426 = 025vl4m >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2543 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 207 *> proper extension: 04rtpt; *> query: (?x1341, 0cjyzs) <- program(?x1341, ?x8775), producer_type(?x8775, ?x632) *> conf = 0.35 ranks of expected_values: 2, 62 EVAL 07s6tbm award 027gs1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 72.000 56.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07s6tbm award 0cjyzs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 56.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9751-03q2t9 PRED entity: 03q2t9 PRED relation: award PRED expected values: 01d38g 02f6xy => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 251): 0gqz2 (0.49 #875, 0.47 #1272, 0.11 #4845), 054ks3 (0.44 #1330, 0.43 #933, 0.17 #4903), 01by1l (0.38 #509, 0.33 #2494, 0.32 #1303), 02x17c2 (0.38 #1007, 0.36 #1404, 0.07 #8947), 02f5qb (0.35 #550, 0.12 #2535, 0.12 #8887), 0c4z8 (0.33 #1263, 0.33 #866, 0.22 #2454), 01bgqh (0.29 #2425, 0.27 #440, 0.24 #4807), 02f6ym (0.27 #648, 0.15 #2633, 0.09 #7397), 02f72_ (0.27 #619, 0.10 #8956, 0.09 #2604), 02f73p (0.27 #582, 0.10 #8919, 0.09 #2567) >> Best rule #875 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 0lbj1; 05ty4m; 0146pg; 01vvycq; 01gf5h; 01wbgdv; 0lgsq; 01vrz41; 02r4qs; 01bpc9; ... >> query: (?x5456, 0gqz2) <- award(?x5456, ?x2238), ?x2238 = 025m8l, profession(?x5456, ?x131) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0g7k2g; *> query: (?x5456, 02f6xy) <- artists(?x671, ?x5456), location(?x5456, ?x3689), company(?x5456, ?x12827) *> conf = 0.23 ranks of expected_values: 11, 41 EVAL 03q2t9 award 02f6xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 70.000 70.000 0.493 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03q2t9 award 01d38g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 70.000 70.000 0.493 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9750-03ncb2 PRED entity: 03ncb2 PRED relation: ceremony PRED expected values: 01c6qp 056878 013b2h => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 127): 01c6qp (0.95 #141, 0.90 #522, 0.89 #395), 056878 (0.89 #407, 0.87 #153, 0.87 #534), 013b2h (0.85 #451, 0.84 #197, 0.83 #578), 0gx1673 (0.51 #234, 0.49 #1377, 0.48 #869), 0bc773 (0.33 #2795, 0.21 #3558, 0.20 #3686), 05c1t6z (0.19 #1662, 0.18 #1535, 0.18 #2043), 02q690_ (0.18 #1580, 0.17 #1707, 0.17 #1834), 0gvstc3 (0.18 #1679, 0.17 #1552, 0.16 #2060), 03nnm4t (0.16 #1589, 0.15 #1716, 0.15 #1843), 0n8_m93 (0.16 #1502, 0.14 #1629, 0.14 #1756) >> Best rule #141 for best value: >> intensional similarity = 7 >> extensional distance = 53 >> proper extension: 02gx2k; 025mb9; 0248jb; 02fm4d; 024_dt; >> query: (?x8409, 01c6qp) <- ceremony(?x8409, ?x6487), ceremony(?x8409, ?x1362), ceremony(?x8409, ?x342), ?x342 = 01s695, award(?x2698, ?x8409), ?x6487 = 01mh_q, ?x1362 = 019bk0 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 03ncb2 ceremony 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.945 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03ncb2 ceremony 056878 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.945 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03ncb2 ceremony 01c6qp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.945 http://example.org/award/award_category/winners./award/award_honor/ceremony #9749-05xvj PRED entity: 05xvj PRED relation: school PRED expected values: 01jq0j 015fsv => 90 concepts (69 used for prediction) PRED predicted values (max 10 best out of 682): 07w0v (0.98 #543, 0.73 #542, 0.67 #1820), 06pwq (0.98 #543, 0.73 #542, 0.52 #5444), 03tw2s (0.98 #543, 0.73 #542, 0.50 #1011), 02pptm (0.98 #543, 0.73 #542, 0.50 #1766), 01jq0j (0.98 #543, 0.73 #542, 0.40 #4634), 01pl14 (0.98 #543, 0.73 #542, 0.33 #2174), 0bx8pn (0.98 #543, 0.73 #542, 0.33 #1650), 01q0kg (0.98 #543, 0.73 #542, 0.33 #420), 021w0_ (0.98 #543, 0.73 #542, 0.33 #544), 01ptt7 (0.98 #543, 0.73 #542, 0.33 #1654) >> Best rule #543 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 0jmj7; >> query: (?x12042, ?x466) <- draft(?x12042, ?x4779), colors(?x12042, ?x663), school(?x12042, ?x10666), school(?x12042, ?x6925), school(?x12042, ?x4904), school(?x12042, ?x4556), draft(?x10279, ?x4779), school(?x4779, ?x388), school(?x10279, ?x8851), school(?x10279, ?x8706), school(?x10279, ?x466), ?x10666 = 01dzg0, ?x8706 = 0trv, ?x8851 = 021w0_, ?x4556 = 01lnyf, currency(?x6925, ?x170), ?x4904 = 0lyjf >> conf = 0.98 => this is the best rule for 23 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 78 EVAL 05xvj school 015fsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 90.000 69.000 0.984 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 05xvj school 01jq0j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 69.000 0.984 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9748-0bsxd3 PRED entity: 0bsxd3 PRED relation: program! PRED expected values: 06mmb => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 108): 016ks_ (0.67 #283, 0.47 #662, 0.10 #1708), 03f3yfj (0.33 #256, 0.33 #68, 0.33 #567), 0gps0z (0.33 #80, 0.33 #567, 0.25 #363), 0261x8t (0.33 #60, 0.33 #567, 0.25 #343), 016ksk (0.33 #34, 0.33 #567, 0.25 #317), 04gycf (0.33 #32, 0.33 #567, 0.25 #315), 01wyz92 (0.33 #31, 0.33 #567, 0.25 #314), 024t0y (0.33 #92, 0.33 #567, 0.25 #375), 035wq7 (0.33 #91, 0.33 #567, 0.25 #374), 03h8_g (0.33 #88, 0.33 #567, 0.25 #371) >> Best rule #283 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 0cpz4k; >> query: (?x13044, ?x4463) <- program(?x6678, ?x13044), languages(?x13044, ?x254), genre(?x13044, ?x13313), ?x254 = 02h40lc, ?x6678 = 05gnf, genre(?x6496, ?x13313), actor(?x13044, ?x4463), ?x6496 = 05r1_t >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #18 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 06hwzy; *> query: (?x13044, 06mmb) <- program(?x6678, ?x13044), languages(?x13044, ?x254), genre(?x13044, ?x13313), ?x254 = 02h40lc, ?x13313 = 06ntj, country_of_origin(?x13044, ?x94), nominated_for(?x6678, ?x337), award_winner(?x1265, ?x6678), award_winner(?x6678, ?x1686) *> conf = 0.33 ranks of expected_values: 27 EVAL 0bsxd3 program! 06mmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 66.000 66.000 0.667 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #9747-06wbm8q PRED entity: 06wbm8q PRED relation: film! PRED expected values: 03h_9lg => 66 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1122): 056ws9 (0.49 #29158, 0.49 #16661, 0.45 #22909), 0p8r1 (0.19 #13082, 0.14 #8917, 0.11 #586), 01vsn38 (0.17 #87472, 0.11 #1855, 0.06 #14351), 0f4vbz (0.17 #87472, 0.11 #363, 0.06 #12859), 0bl2g (0.17 #87472, 0.11 #55, 0.04 #64563), 06qgjh (0.17 #87472, 0.11 #1471, 0.04 #64563), 0fby2t (0.17 #87472, 0.11 #754, 0.04 #2838), 04mhxx (0.17 #87472, 0.11 #1713, 0.04 #3797), 08vr94 (0.17 #87472, 0.11 #677, 0.04 #2761), 02clgg (0.17 #87472, 0.11 #1478, 0.04 #13974) >> Best rule #29158 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 014lc_; 053rxgm; 0d6b7; 0432_5; 026lgs; 0hv8w; 031ldd; 02825cv; 035zr0; 02825nf; ... >> query: (?x2628, ?x5970) <- film_release_region(?x2628, ?x2267), nominated_for(?x5970, ?x2628), ?x2267 = 03rj0 >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #33455 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 243 *> proper extension: 0d_2fb; 0gs973; 01f39b; 08sk8l; 05ch98; 033pf1; 02qdrjx; 03whyr; 0h63q6t; 0ptdz; ... *> query: (?x2628, 03h_9lg) <- film(?x2455, ?x2628), genre(?x2628, ?x1510), ?x1510 = 01hmnh *> conf = 0.03 ranks of expected_values: 250 EVAL 06wbm8q film! 03h_9lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 66.000 46.000 0.486 http://example.org/film/actor/film./film/performance/film #9746-0gkz15s PRED entity: 0gkz15s PRED relation: language PRED expected values: 02h40lc => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 53): 02h40lc (0.90 #63, 0.90 #544, 0.89 #724), 064_8sq (0.15 #324, 0.15 #564, 0.13 #1224), 06nm1 (0.12 #11, 0.12 #553, 0.11 #792), 04306rv (0.12 #5, 0.09 #1027, 0.09 #1088), 03_9r (0.12 #10, 0.08 #671, 0.07 #434), 0653m (0.10 #12, 0.10 #133, 0.07 #73), 012w70 (0.10 #13, 0.04 #2032, 0.03 #974), 02bjrlw (0.08 #303, 0.07 #1023, 0.06 #425), 06b_j (0.07 #84, 0.07 #325, 0.07 #684), 03k50 (0.07 #70, 0.05 #130, 0.04 #2032) >> Best rule #63 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 0ds1glg; >> query: (?x781, 02h40lc) <- film_release_region(?x781, ?x2146), written_by(?x781, ?x10064), ?x2146 = 03rk0 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gkz15s language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.902 http://example.org/film/film/language #9745-0f2v0 PRED entity: 0f2v0 PRED relation: dog_breed PRED expected values: 0km5c => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 1): 0km5c (0.47 #11, 0.44 #29, 0.40 #8) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0jgx; >> query: (?x3501, 0km5c) <- locations(?x12451, ?x3501), jurisdiction_of_office(?x1195, ?x3501), origin(?x4995, ?x3501) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2v0 dog_breed 0km5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.474 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #9744-02kbtf PRED entity: 02kbtf PRED relation: major_field_of_study PRED expected values: 062z7 01400v => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 123): 062z7 (0.46 #29, 0.37 #154, 0.32 #905), 03g3w (0.43 #153, 0.41 #1404, 0.40 #653), 05qjt (0.41 #383, 0.38 #633, 0.29 #1384), 04rjg (0.40 #146, 0.39 #1397, 0.37 #646), 01mkq (0.37 #391, 0.37 #141, 0.36 #1895), 02lp1 (0.34 #2142, 0.32 #1891, 0.32 #2268), 03qsdpk (0.31 #49, 0.14 #1928, 0.14 #674), 0w7c (0.31 #61, 0.12 #561, 0.11 #1437), 01lj9 (0.30 #166, 0.29 #416, 0.28 #666), 037mh8 (0.28 #695, 0.27 #445, 0.25 #1446) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 026gvfj; >> query: (?x9307, 062z7) <- student(?x9307, ?x5099), major_field_of_study(?x9307, ?x2981), award_nominee(?x5099, ?x525), ?x525 = 017149 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1, 56 EVAL 02kbtf major_field_of_study 01400v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 112.000 112.000 0.462 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02kbtf major_field_of_study 062z7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.462 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #9743-0135p7 PRED entity: 0135p7 PRED relation: source PRED expected values: 0jbk9 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #53, 0.88 #11, 0.82 #29) >> Best rule #53 for best value: >> intensional similarity = 1 >> extensional distance = 514 >> proper extension: 010bnr; >> query: (?x12881, 0jbk9) <- place(?x12881, ?x12881) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0135p7 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #9742-0820xz PRED entity: 0820xz PRED relation: contains! PRED expected values: 019rg5 => 161 concepts (71 used for prediction) PRED predicted values (max 10 best out of 338): 019rg5 (0.79 #59163, 0.79 #21499, 0.70 #54676), 09c7w0 (0.75 #18814, 0.72 #16124, 0.72 #43021), 04jpl (0.58 #19729, 0.38 #4500, 0.29 #6292), 02jx1 (0.39 #19794, 0.35 #4565, 0.33 #15313), 059rby (0.31 #61874, 0.26 #46625, 0.25 #48418), 07ssc (0.26 #15258, 0.24 #19739, 0.18 #11675), 0fngy (0.25 #818, 0.01 #20525), 07tp2 (0.25 #504, 0.01 #20211), 019pcs (0.25 #223), 0978r (0.23 #1998, 0.14 #7374, 0.10 #8269) >> Best rule #59163 for best value: >> intensional similarity = 6 >> extensional distance = 352 >> proper extension: 014b4h; 0283sdr; >> query: (?x3132, ?x910) <- organization(?x5510, ?x3132), contains(?x13481, ?x3132), institution(?x865, ?x3132), location(?x11018, ?x13481), country(?x13481, ?x910), major_field_of_study(?x865, ?x254) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0820xz contains! 019rg5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 71.000 0.787 http://example.org/location/location/contains #9741-01r4zfk PRED entity: 01r4zfk PRED relation: religion PRED expected values: 0c8wxp => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 18): 0c8wxp (0.18 #1176, 0.16 #1312, 0.16 #456), 0kpl (0.17 #190, 0.08 #1045, 0.08 #1135), 03_gx (0.10 #149, 0.08 #194, 0.08 #2177), 0kq2 (0.05 #153, 0.03 #198, 0.02 #378), 04pk9 (0.05 #155, 0.02 #335, 0.01 #2048), 03j6c (0.05 #1823, 0.05 #1733, 0.04 #1868), 092bf5 (0.04 #556, 0.03 #1006, 0.03 #1186), 01lp8 (0.04 #991, 0.02 #1488, 0.02 #2164), 02rxj (0.03 #187, 0.03 #277, 0.03 #232), 0flw86 (0.02 #1804, 0.02 #1849, 0.02 #1714) >> Best rule #1176 for best value: >> intensional similarity = 4 >> extensional distance = 277 >> proper extension: 022769; 01hkhq; 01bcq; 0265z9l; >> query: (?x8784, 0c8wxp) <- languages(?x8784, ?x254), nationality(?x8784, ?x94), film(?x8784, ?x887), people(?x5741, ?x8784) >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r4zfk religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.179 http://example.org/people/person/religion #9740-038_0z PRED entity: 038_0z PRED relation: teams! PRED expected values: 05sb1 => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 136): 0chghy (0.33 #10, 0.25 #1643, 0.25 #1370), 0ctw_b (0.25 #299, 0.22 #1088, 0.20 #2174), 03rk0 (0.25 #1156, 0.22 #1088, 0.19 #2177), 0261m (0.25 #740, 0.22 #1088, 0.19 #2177), 0f1sm (0.25 #1008, 0.20 #2094, 0.17 #2914), 0dc95 (0.25 #1711, 0.17 #2531, 0.12 #3894), 0d6lp (0.25 #1455, 0.15 #4727, 0.12 #5542), 068p2 (0.20 #2302, 0.17 #3116, 0.15 #4755), 05bcl (0.20 #2017, 0.14 #3381, 0.04 #6923), 04f_d (0.17 #3057, 0.11 #4421, 0.08 #4696) >> Best rule #10 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 020wyp; >> query: (?x14520, 0chghy) <- team(?x13559, ?x14520), ?x13559 = 021q23, colors(?x14520, ?x8047), colors(?x14520, ?x332), ?x332 = 01l849, ?x8047 = 038hg >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 038_0z teams! 05sb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 60.000 0.333 http://example.org/sports/sports_team_location/teams #9739-05prs8 PRED entity: 05prs8 PRED relation: produced_by! PRED expected values: 04k9y6 0gy0n => 90 concepts (51 used for prediction) PRED predicted values (max 10 best out of 454): 08720 (0.31 #5602, 0.30 #9342, 0.30 #6537), 05fgt1 (0.06 #2080, 0.02 #3947, 0.01 #14230), 01q2nx (0.06 #21485, 0.05 #3735, 0.04 #5603), 03wjm2 (0.06 #21485, 0.03 #4653, 0.02 #7456), 03cp4cn (0.06 #21485, 0.03 #4330, 0.02 #7133), 03kg2v (0.06 #21485, 0.03 #3995, 0.02 #6798), 047vnkj (0.06 #21485, 0.02 #4227, 0.02 #5160), 03z9585 (0.06 #21485, 0.02 #4481, 0.02 #5414), 0bbw2z6 (0.06 #21485, 0.02 #4173, 0.02 #6976), 01bl7g (0.06 #21485, 0.02 #4249, 0.02 #7052) >> Best rule #5602 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 0bs1yy; >> query: (?x1533, ?x485) <- nominated_for(?x1533, ?x485), executive_produced_by(?x1077, ?x1533), place_of_birth(?x1533, ?x12875) >> conf = 0.31 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05prs8 produced_by! 0gy0n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 51.000 0.311 http://example.org/film/film/produced_by EVAL 05prs8 produced_by! 04k9y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 51.000 0.311 http://example.org/film/film/produced_by #9738-0gj8t_b PRED entity: 0gj8t_b PRED relation: film_release_region PRED expected values: 01ls2 07ssc => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 138): 07ssc (0.86 #563, 0.85 #425, 0.82 #701), 0k6nt (0.82 #710, 0.81 #1124, 0.80 #434), 01znc_ (0.81 #586, 0.77 #724, 0.76 #862), 01mjq (0.62 #588, 0.58 #1140, 0.57 #726), 01ls2 (0.61 #422, 0.55 #560, 0.46 #1112), 047yc (0.58 #575, 0.56 #437, 0.50 #851), 06qd3 (0.54 #583, 0.51 #859, 0.51 #1135), 05qx1 (0.51 #585, 0.46 #447, 0.40 #1137), 047lj (0.51 #559, 0.41 #421, 0.36 #1111), 06mzp (0.50 #844, 0.49 #706, 0.45 #568) >> Best rule #563 for best value: >> intensional similarity = 8 >> extensional distance = 67 >> proper extension: 0h1cdwq; 05p1tzf; 087wc7n; 08hmch; 05z_kps; 047msdk; 017gm7; 0gxtknx; 0bq8tmw; 04n52p6; ... >> query: (?x1202, 07ssc) <- film(?x815, ?x1202), film_release_region(?x1202, ?x2513), film_release_region(?x1202, ?x1790), film_release_region(?x1202, ?x1353), ?x1353 = 035qy, ?x2513 = 05b4w, ?x1790 = 01pj7, genre(?x1202, ?x53) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 0gj8t_b film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.855 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gj8t_b film_release_region 01ls2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 48.000 48.000 0.855 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9737-053ksp PRED entity: 053ksp PRED relation: profession PRED expected values: 0kyk => 88 concepts (73 used for prediction) PRED predicted values (max 10 best out of 66): 01d_h8 (0.65 #594, 0.53 #1476, 0.50 #153), 02jknp (0.56 #596, 0.46 #1478, 0.41 #155), 0cbd2 (0.43 #154, 0.42 #301, 0.23 #595), 09jwl (0.41 #900, 0.18 #18, 0.18 #4429), 03gjzk (0.37 #602, 0.37 #1484, 0.26 #161), 016z4k (0.31 #886, 0.11 #4415, 0.11 #6179), 0dz3r (0.30 #884, 0.13 #4413, 0.11 #6177), 0nbcg (0.28 #913, 0.13 #4442, 0.12 #6206), 018gz8 (0.27 #1486, 0.12 #1780, 0.11 #604), 0kyk (0.25 #323, 0.19 #176, 0.13 #1205) >> Best rule #594 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 01r216; >> query: (?x10381, 01d_h8) <- written_by(?x2903, ?x10381), award_winner(?x2707, ?x10381), award(?x10381, ?x384) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #323 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 04107; 09jd9; *> query: (?x10381, 0kyk) <- award_winner(?x601, ?x10381), story_by(?x161, ?x10381), award(?x10381, ?x384) *> conf = 0.25 ranks of expected_values: 10 EVAL 053ksp profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 88.000 73.000 0.651 http://example.org/people/person/profession #9736-01p726 PRED entity: 01p726 PRED relation: origin! PRED expected values: 01w5n51 => 123 concepts (63 used for prediction) PRED predicted values (max 10 best out of 298): 03flwk (0.06 #13944, 0.06 #12911, 0.06 #8264), 02p68d (0.06 #868, 0.02 #2417, 0.02 #3450), 06nv27 (0.04 #3834, 0.02 #7449, 0.02 #13646), 06s7rd (0.04 #2425, 0.03 #2942, 0.02 #3975), 01vrt_c (0.04 #2097, 0.03 #2614, 0.02 #3647), 01d1st (0.03 #2881, 0.02 #3914, 0.02 #4431), 02t3ln (0.03 #3299, 0.03 #1749, 0.02 #2266), 05crg7 (0.03 #1598, 0.02 #4182, 0.02 #2115), 01wf86y (0.03 #1879, 0.02 #4463, 0.02 #4980), 01vvyc_ (0.03 #1795, 0.02 #4379, 0.02 #4896) >> Best rule #13944 for best value: >> intensional similarity = 3 >> extensional distance = 217 >> proper extension: 0hzlz; >> query: (?x12912, ?x5100) <- contains(?x12912, ?x1103), place_of_birth(?x5100, ?x12912), award(?x5100, ?x198) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01p726 origin! 01w5n51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 63.000 0.060 http://example.org/music/artist/origin #9735-04x1_w PRED entity: 04x1_w PRED relation: student! PRED expected values: 017hnw => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 58): 0373qt (0.12 #853, 0.10 #1380), 011xy1 (0.12 #845, 0.10 #1372), 015nl4 (0.09 #594, 0.07 #1121, 0.04 #3756), 0bwfn (0.09 #275, 0.06 #3437, 0.06 #2383), 04b_46 (0.09 #227, 0.03 #3389, 0.03 #2335), 09f2j (0.09 #159, 0.03 #5429, 0.03 #1740), 017z88 (0.09 #82, 0.03 #5352, 0.03 #9041), 07tg4 (0.09 #86, 0.01 #9572, 0.01 #15899), 06pwq (0.09 #12, 0.01 #2120, 0.01 #10014), 01w3vc (0.09 #451) >> Best rule #853 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 06whf; 0f1pyf; 01tw31; >> query: (?x7402, 0373qt) <- nationality(?x7402, ?x429), location(?x7402, ?x108), ?x429 = 03rt9 >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04x1_w student! 017hnw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 87.000 0.121 http://example.org/education/educational_institution/students_graduates./education/education/student #9734-09g90vz PRED entity: 09g90vz PRED relation: ceremony! PRED expected values: 0ck27z 09sdmz 0cqhmg => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 311): 0cqhmg (0.67 #2245, 0.60 #1740, 0.50 #2495), 09sdmz (0.67 #2154, 0.60 #1649, 0.50 #2404), 0ck27z (0.60 #1572, 0.50 #2327, 0.50 #2077), 0gkts9 (0.53 #2882, 0.47 #3386, 0.47 #3134), 0gqy2 (0.50 #8420, 0.50 #8167, 0.49 #7915), 0gq_d (0.49 #8204, 0.48 #8457, 0.48 #7952), 0k611 (0.48 #8372, 0.48 #8119, 0.47 #7867), 0gqwc (0.48 #8359, 0.48 #8106, 0.47 #7854), 0gvx_ (0.48 #8435, 0.47 #8182, 0.46 #7930), 0gqyl (0.47 #8127, 0.47 #8380, 0.46 #7875) >> Best rule #2245 for best value: >> intensional similarity = 18 >> extensional distance = 4 >> proper extension: 0hr3c8y; 027hjff; 0g55tzk; >> query: (?x9306, 0cqhmg) <- award_winner(?x9306, ?x6561), award_winner(?x9306, ?x5707), honored_for(?x9306, ?x5060), honored_for(?x9306, ?x4932), honored_for(?x9306, ?x2528), ?x2528 = 0d68qy, award_winner(?x4932, ?x3366), genre(?x4932, ?x225), ceremony(?x1111, ?x9306), nominated_for(?x2077, ?x4932), titles(?x2008, ?x4932), nominated_for(?x435, ?x4932), actor(?x10661, ?x2077), award_nominee(?x843, ?x2077), award_nominee(?x1342, ?x5707), ?x1111 = 0cqh6z, nationality(?x6561, ?x279), category(?x5060, ?x134) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 09g90vz ceremony! 0cqhmg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09g90vz ceremony! 09sdmz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09g90vz ceremony! 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony #9733-0cqgl9 PRED entity: 0cqgl9 PRED relation: award! PRED expected values: 06jzh 01vwllw 0h32q 0bw87 01skmp 0kjrx 025mb_ 030hbp => 50 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2646): 0dvld (0.82 #10002, 0.81 #16670, 0.79 #26674), 020_95 (0.82 #10002, 0.81 #16670, 0.79 #26674), 0c3p7 (0.82 #10002, 0.81 #16670, 0.79 #26674), 01bj6y (0.82 #10002, 0.81 #16670, 0.79 #26674), 0794g (0.45 #7558, 0.17 #10893, 0.11 #4224), 0f502 (0.45 #7879, 0.12 #31222, 0.11 #4545), 0pz91 (0.36 #6985, 0.17 #56694, 0.14 #33344), 0lx2l (0.36 #7324, 0.11 #3990, 0.11 #30667), 019pm_ (0.36 #7397, 0.08 #10732, 0.06 #30740), 0jmj (0.33 #11211, 0.33 #1209, 0.20 #14544) >> Best rule #10002 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 05b4l5x; 05zr6wv; 03c7tr1; 07bdd_; 05pcn59; 05p1dby; 05ztrmj; 0hnf5vm; >> query: (?x3722, ?x5454) <- award(?x2221, ?x3722), award(?x1343, ?x3722), ?x2221 = 026c1, nominated_for(?x1343, ?x293), award_winner(?x3722, ?x5454) >> conf = 0.82 => this is the best rule for 4 predicted values *> Best rule #8989 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 05b4l5x; 05zr6wv; 03c7tr1; 07bdd_; 05pcn59; 05p1dby; 05ztrmj; 0hnf5vm; *> query: (?x3722, 0kjrx) <- award(?x2221, ?x3722), award(?x1343, ?x3722), ?x2221 = 026c1, nominated_for(?x1343, ?x293), award_winner(?x3722, ?x5454) *> conf = 0.27 ranks of expected_values: 47, 103, 296, 452, 544, 579, 748, 782 EVAL 0cqgl9 award! 030hbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 50.000 18.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqgl9 award! 025mb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 18.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqgl9 award! 0kjrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 50.000 18.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqgl9 award! 01skmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 18.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqgl9 award! 0bw87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 18.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqgl9 award! 0h32q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 18.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqgl9 award! 01vwllw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 18.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqgl9 award! 06jzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 50.000 18.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9732-0bqtx PRED entity: 0bqtx PRED relation: combatants PRED expected values: 0bq0p9 => 73 concepts (43 used for prediction) PRED predicted values (max 10 best out of 409): 09c7w0 (0.57 #755, 0.53 #2795, 0.49 #3170), 01h3dj (0.50 #446, 0.33 #70, 0.30 #1714), 01mk6 (0.50 #445, 0.32 #501, 0.11 #3167), 05vz3zq (0.36 #811, 0.32 #501, 0.25 #683), 0bq0p9 (0.33 #1658, 0.33 #390, 0.33 #14), 0f8l9c (0.33 #392, 0.33 #16, 0.32 #501), 06v36 (0.33 #251, 0.33 #250, 0.16 #2405), 02psqkz (0.33 #425, 0.32 #501, 0.27 #1693), 05qhw (0.33 #386, 0.32 #501, 0.20 #1654), 0c4b8 (0.33 #62, 0.32 #501, 0.20 #1706) >> Best rule #755 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 0kbq; >> query: (?x11802, 09c7w0) <- combatants(?x11802, ?x279), locations(?x11802, ?x792), films(?x11802, ?x4351), titles(?x53, ?x4351), film_crew_role(?x4351, ?x1284), nominated_for(?x601, ?x4351) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1658 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 28 *> proper extension: 01fc7p; 02kxg_; *> query: (?x11802, 0bq0p9) <- combatants(?x11802, ?x512), combatants(?x11802, ?x279), ?x512 = 07ssc, time_zones(?x279, ?x1638), country(?x150, ?x279), film_release_region(?x66, ?x279), contains(?x279, ?x481), olympics(?x279, ?x358) *> conf = 0.33 ranks of expected_values: 5 EVAL 0bqtx combatants 0bq0p9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 43.000 0.571 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #9731-013nky PRED entity: 013nky PRED relation: institution! PRED expected values: 04zx3q1 => 185 concepts (142 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.73 #582, 0.72 #1473, 0.70 #1673), 02_xgp2 (0.69 #557, 0.68 #958, 0.63 #590), 019v9k (0.69 #407, 0.65 #586, 0.64 #429), 03bwzr4 (0.61 #413, 0.57 #592, 0.56 #458), 016t_3 (0.50 #426, 0.50 #26, 0.49 #449), 04zx3q1 (0.50 #24, 0.40 #180, 0.39 #581), 07s6fsf (0.44 #401, 0.42 #580, 0.38 #1049), 027f2w (0.32 #587, 0.30 #2812, 0.30 #453), 01rr_d (0.31 #216, 0.30 #2812, 0.28 #194), 013zdg (0.30 #2812, 0.26 #384, 0.25 #585) >> Best rule #582 for best value: >> intensional similarity = 5 >> extensional distance = 93 >> proper extension: 0fnmz; 01pq4w; 0m9_5; 0g2jl; 0ym17; >> query: (?x10197, 02h4rq6) <- student(?x10197, ?x3476), institution(?x1526, ?x10197), category(?x10197, ?x134), religion(?x3476, ?x2694), ?x1526 = 0bkj86 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 07tg4; 05zl0; *> query: (?x10197, 04zx3q1) <- student(?x10197, ?x3476), institution(?x1368, ?x10197), category(?x10197, ?x134), ?x3476 = 0n00, ?x1368 = 014mlp *> conf = 0.50 ranks of expected_values: 6 EVAL 013nky institution! 04zx3q1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 185.000 142.000 0.726 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9730-0394y PRED entity: 0394y PRED relation: group! PRED expected values: 0l14md 028tv0 => 107 concepts (76 used for prediction) PRED predicted values (max 10 best out of 123): 0l14md (0.73 #1490, 0.71 #1140, 0.69 #1403), 018vs (0.72 #1410, 0.69 #1672, 0.68 #1235), 03bx0bm (0.63 #3081, 0.63 #3168, 0.62 #2906), 013y1f (0.50 #28, 0.44 #376, 0.39 #811), 028tv0 (0.48 #1409, 0.47 #1496, 0.45 #1933), 0l14qv (0.48 #1226, 0.45 #1401, 0.41 #1663), 05r5c (0.43 #1928, 0.40 #443, 0.39 #791), 04rzd (0.38 #1690, 0.36 #1952, 0.34 #1428), 06w7v (0.33 #421, 0.22 #856, 0.22 #769), 07y_7 (0.33 #2, 0.22 #2444, 0.22 #1660) >> Best rule #1490 for best value: >> intensional similarity = 8 >> extensional distance = 28 >> proper extension: 012vm6; >> query: (?x4642, 0l14md) <- artists(?x1572, ?x4642), artist(?x2190, ?x4642), group(?x2798, ?x4642), group(?x1166, ?x4642), ?x2798 = 03qjg, ?x1166 = 05148p4, artists(?x1572, ?x10243), ?x10243 = 01k_0fp >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 0394y group! 028tv0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 76.000 0.733 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0394y group! 0l14md CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 76.000 0.733 http://example.org/music/performance_role/regular_performances./music/group_membership/group #9729-01jsk6 PRED entity: 01jsk6 PRED relation: school! PRED expected values: 02c_4 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 86): 05m_8 (0.21 #691, 0.21 #261, 0.17 #950), 051vz (0.21 #280, 0.17 #710, 0.14 #797), 0jmm4 (0.21 #327, 0.11 #1120, 0.10 #757), 0cqt41 (0.20 #17, 0.17 #275, 0.16 #705), 06rpd (0.20 #70, 0.17 #328, 0.12 #775), 01slc (0.20 #54, 0.15 #829, 0.15 #1001), 05g76 (0.20 #20, 0.14 #278, 0.12 #775), 07l8x (0.17 #750, 0.17 #320, 0.12 #775), 01yhm (0.17 #277, 0.14 #707, 0.13 #794), 02d02 (0.17 #323, 0.14 #753, 0.12 #775) >> Best rule #691 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 05kj_; >> query: (?x10945, 05m_8) <- school(?x4979, ?x10945), school(?x4979, ?x3779), draft(?x799, ?x4979), ?x3779 = 01pq4w >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #319 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 06mkj; 0d05w3; *> query: (?x10945, 02c_4) <- school(?x4979, ?x10945), school(?x2569, ?x10945), ?x4979 = 0f4vx0, school(?x2569, ?x10666), colors(?x10666, ?x663) *> conf = 0.14 ranks of expected_values: 24 EVAL 01jsk6 school! 02c_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 126.000 126.000 0.207 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9728-08vlns PRED entity: 08vlns PRED relation: artists PRED expected values: 09z1lg => 50 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1137): 03f5spx (0.67 #2197, 0.60 #1127, 0.50 #4339), 011z3g (0.67 #2738, 0.60 #1668, 0.50 #4880), 01vtj38 (0.67 #2797, 0.60 #1727, 0.50 #4939), 025ldg (0.67 #2508, 0.60 #1438, 0.50 #4650), 09qr6 (0.67 #2230, 0.60 #1160, 0.50 #4372), 01dwrc (0.67 #2659, 0.60 #1589, 0.50 #4801), 06mt91 (0.67 #2746, 0.60 #1676, 0.50 #4888), 09889g (0.67 #2586, 0.60 #1516, 0.50 #4728), 01jfr3y (0.67 #2668, 0.60 #1598, 0.50 #4810), 02wb6yq (0.67 #2412, 0.60 #1342, 0.50 #4554) >> Best rule #2197 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 05bt6j; >> query: (?x12082, 03f5spx) <- artists(?x12082, ?x5364), artists(?x12082, ?x883), ?x883 = 01w61th, award_winner(?x4799, ?x5364), award(?x5364, ?x154), award_nominee(?x5364, ?x286), gender(?x5364, ?x514), celebrity(?x6151, ?x5364) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4281 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 0233qs; *> query: (?x12082, ?x286) <- artists(?x12082, ?x11709), artists(?x12082, ?x5364), artists(?x12082, ?x883), ?x883 = 01w61th, ?x11709 = 03f0qd7, profession(?x5364, ?x131), location(?x5364, ?x1523), award_nominee(?x5364, ?x286) *> conf = 0.30 ranks of expected_values: 208 EVAL 08vlns artists 09z1lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 50.000 15.000 0.667 http://example.org/music/genre/artists #9727-016zp5 PRED entity: 016zp5 PRED relation: award_nominee PRED expected values: 01r93l => 75 concepts (41 used for prediction) PRED predicted values (max 10 best out of 989): 01v9l67 (0.82 #13914, 0.81 #11595, 0.80 #71896), 01846t (0.82 #13914, 0.81 #11595, 0.80 #71896), 015t56 (0.82 #13914, 0.81 #11595, 0.80 #71896), 01r93l (0.82 #13914, 0.80 #71896, 0.06 #10259), 02bfmn (0.62 #9314, 0.36 #11633, 0.02 #95096), 016zp5 (0.62 #10555, 0.33 #12874, 0.27 #85815), 0hvb2 (0.46 #11989, 0.06 #9670, 0.04 #35180), 03_6y (0.46 #12364, 0.06 #10045, 0.02 #95096), 01mqc_ (0.46 #13258, 0.02 #17897, 0.02 #20217), 01pgzn_ (0.44 #12089, 0.06 #9770, 0.03 #16728) >> Best rule #13914 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 02bfmn; 0c4f4; 0f0kz; 014488; 03_6y; 016vg8; 01z7s_; 073x6y; 01d1st; 05cx7x; ... >> query: (?x5495, ?x1194) <- award_nominee(?x2762, ?x5495), award_nominee(?x1194, ?x5495), ?x2762 = 015t56 >> conf = 0.82 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 016zp5 award_nominee 01r93l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 41.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9726-0cfdd PRED entity: 0cfdd PRED relation: role PRED expected values: 02snj9 => 77 concepts (55 used for prediction) PRED predicted values (max 10 best out of 123): 0mkg (0.87 #3622, 0.73 #5223, 0.72 #3851), 0g2dz (0.83 #2491, 0.82 #5900, 0.82 #5899), 0l15bq (0.82 #5900, 0.82 #3505, 0.82 #5899), 028tv0 (0.80 #2842, 0.77 #3287, 0.74 #4197), 03bx0bm (0.80 #3643, 0.72 #3872, 0.72 #5586), 07gql (0.80 #3659, 0.70 #5260, 0.70 #778), 02sgy (0.78 #777, 0.75 #1458, 0.74 #5434), 026t6 (0.78 #777, 0.75 #1458, 0.72 #555), 042v_gx (0.76 #5209, 0.75 #1458, 0.73 #3620), 018j2 (0.75 #1458, 0.73 #3654, 0.72 #555) >> Best rule #3622 for best value: >> intensional similarity = 21 >> extensional distance = 13 >> proper extension: 03bx0bm; >> query: (?x5926, 0mkg) <- role(?x3215, ?x5926), role(?x1148, ?x5926), role(?x894, ?x5926), role(?x316, ?x5926), role(?x5926, ?x8172), group(?x1148, ?x9706), role(?x1473, ?x5926), role(?x7794, ?x1148), role(?x5623, ?x1148), role(?x1292, ?x1148), role(?x1148, ?x3112), ?x3215 = 0bxl5, award_nominee(?x7794, ?x2219), ?x1292 = 03kwtb, ?x3112 = 0mbct, ?x5623 = 01vsyg9, role(?x8172, ?x615), ?x1473 = 0g2dz, ?x316 = 05r5c, group(?x5926, ?x1945), role(?x74, ?x894) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #778 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 2 *> proper extension: 0342h; *> query: (?x5926, ?x74) <- role(?x1148, ?x5926), role(?x432, ?x5926), role(?x227, ?x5926), role(?x212, ?x5926), role(?x5926, ?x8172), ?x1148 = 02qjv, instrumentalists(?x5926, ?x140), group(?x5926, ?x1945), role(?x1467, ?x5926), ?x8172 = 06rvn, ?x212 = 026t6, currency(?x140, ?x170), artists(?x2937, ?x140), ?x1945 = 02_5x9, ?x432 = 042v_gx, award_nominee(?x527, ?x140), group(?x227, ?x379), role(?x219, ?x227), family(?x2923, ?x227), role(?x211, ?x227), role(?x74, ?x227) *> conf = 0.70 ranks of expected_values: 47 EVAL 0cfdd role 02snj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 77.000 55.000 0.867 http://example.org/music/performance_role/regular_performances./music/group_membership/role #9725-0bq0p9 PRED entity: 0bq0p9 PRED relation: jurisdiction_of_office! PRED expected values: 0fj45 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.71 #1361, 0.69 #1775, 0.69 #2189), 060bp (0.64 #1290, 0.62 #1036, 0.60 #1773), 0p5vf (0.50 #105, 0.43 #151, 0.38 #220), 04syw (0.43 #1273, 0.37 #950, 0.28 #1250), 0f6c3 (0.40 #31, 0.25 #215, 0.25 #192), 0789n (0.38 #194, 0.33 #102, 0.29 #148), 0fj45 (0.33 #112, 0.29 #158, 0.26 #963), 01_fjr (0.33 #18, 0.21 #1059, 0.20 #41), 01zq91 (0.26 #429, 0.21 #1059, 0.17 #107), 01gkgk (0.25 #190, 0.21 #1059, 0.20 #29) >> Best rule #1361 for best value: >> intensional similarity = 5 >> extensional distance = 61 >> proper extension: 0jdd; >> query: (?x613, 060c4) <- official_language(?x613, ?x9113), capital(?x613, ?x8297), countries_spoken_in(?x9113, ?x279), organization(?x613, ?x4230), languages_spoken(?x5025, ?x9113) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #112 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 03rk0; *> query: (?x613, 0fj45) <- combatants(?x613, ?x94), combatants(?x10413, ?x613), ?x10413 = 0gjw_, capital(?x613, ?x8297), nationality(?x6961, ?x613) *> conf = 0.33 ranks of expected_values: 7 EVAL 0bq0p9 jurisdiction_of_office! 0fj45 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 103.000 103.000 0.714 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #9724-017j6 PRED entity: 017j6 PRED relation: artists! PRED expected values: 04n7jdv => 94 concepts (47 used for prediction) PRED predicted values (max 10 best out of 252): 06j6l (0.62 #948, 0.61 #1249, 0.58 #1550), 064t9 (0.59 #914, 0.56 #1215, 0.53 #12363), 0gywn (0.48 #1259, 0.47 #958, 0.43 #1560), 0xhtw (0.42 #3329, 0.40 #3933, 0.38 #3631), 025sc50 (0.36 #950, 0.36 #1251, 0.35 #348), 05bt6j (0.36 #40, 0.31 #4863, 0.30 #1847), 01flzq (0.29 #412, 0.04 #8247, 0.04 #6441), 059kh (0.29 #2456, 0.22 #2154, 0.18 #1853), 011j5x (0.26 #2137, 0.25 #2439, 0.15 #4249), 03lty (0.26 #3943, 0.25 #3641, 0.23 #2736) >> Best rule #948 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 032nwy; 02mslq; 01wbgdv; 01kx_81; 09qr6; 012x4t; 03t9sp; 05crg7; 04mn81; 01wwvt2; ... >> query: (?x3390, 06j6l) <- category(?x3390, ?x134), artists(?x1127, ?x3390), ?x1127 = 02x8m, award(?x3390, ?x1565) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2692 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 01gx5f; 0fpj4lx; 01w524f; 0kxbc; 0484q; 012ycy; *> query: (?x3390, 04n7jdv) <- artists(?x5934, ?x3390), artist(?x3240, ?x3390), ?x5934 = 05r6t *> conf = 0.04 ranks of expected_values: 132 EVAL 017j6 artists! 04n7jdv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 94.000 47.000 0.621 http://example.org/music/genre/artists #9723-0w7s PRED entity: 0w7s PRED relation: major_field_of_study! PRED expected values: 03bwzr4 => 60 concepts (41 used for prediction) PRED predicted values (max 10 best out of 19): 014mlp (0.80 #683, 0.80 #255, 0.80 #160), 03bwzr4 (0.80 #263, 0.78 #129, 0.71 #108), 02_xgp2 (0.78 #128, 0.75 #419, 0.71 #107), 0bkj86 (0.67 #84, 0.65 #279, 0.60 #163), 03mkk4 (0.52 #118, 0.44 #273, 0.42 #78), 07s6fsf (0.52 #118, 0.44 #273, 0.42 #78), 028dcg (0.52 #118, 0.44 #273, 0.42 #78), 027f2w (0.52 #118, 0.44 #273, 0.42 #78), 01rr_d (0.52 #118, 0.44 #273, 0.42 #78), 013zdg (0.52 #118, 0.44 #273, 0.42 #78) >> Best rule #683 for best value: >> intensional similarity = 9 >> extensional distance = 83 >> proper extension: 01h788; >> query: (?x11820, 014mlp) <- major_field_of_study(?x4296, ?x11820), major_field_of_study(?x1771, ?x11820), colors(?x4296, ?x8271), company(?x4486, ?x4296), student(?x4296, ?x3927), institution(?x1771, ?x12293), institution(?x1771, ?x8191), ?x8191 = 0bsnm, ?x12293 = 01pj48 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #263 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 13 *> proper extension: 0g4gr; 0g26h; 03qsdpk; 041y2; 09s1f; 06mq7; *> query: (?x11820, 03bwzr4) <- major_field_of_study(?x9768, ?x11820), major_field_of_study(?x4750, ?x11820), major_field_of_study(?x4296, ?x11820), major_field_of_study(?x1681, ?x11820), ?x4296 = 07vyf, school_type(?x9768, ?x1507), student(?x1681, ?x1580), institution(?x620, ?x1681), school(?x1883, ?x1681), school(?x3674, ?x9768), ?x1883 = 02qw1zx, company(?x5652, ?x4750) *> conf = 0.80 ranks of expected_values: 2 EVAL 0w7s major_field_of_study! 03bwzr4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 60.000 41.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #9722-081mh PRED entity: 081mh PRED relation: contains PRED expected values: 010z5n => 89 concepts (75 used for prediction) PRED predicted values (max 10 best out of 2627): 081mh (0.24 #208683, 0.03 #12105, 0.02 #205744), 09c7w0 (0.24 #208683), 0bwfn (0.20 #1044, 0.09 #3982, 0.06 #33371), 021q2j (0.20 #1258, 0.09 #4196, 0.06 #33585), 03bmmc (0.20 #775, 0.09 #3713, 0.06 #33102), 02lwv5 (0.20 #1741, 0.09 #4679, 0.06 #34068), 0ccvx (0.20 #545, 0.09 #3483, 0.06 #32872), 04ftdq (0.20 #1242, 0.09 #4180, 0.06 #33569), 01t0dy (0.20 #844, 0.09 #3782, 0.06 #33171), 0f94t (0.20 #95, 0.09 #3033, 0.06 #32422) >> Best rule #208683 for best value: >> intensional similarity = 2 >> extensional distance = 895 >> proper extension: 01m3dv; 0lm0n; 0fngy; 037n3; 01zk9d; 0gslw; >> query: (?x2977, ?x94) <- contains(?x2977, ?x5775), contains(?x94, ?x5775) >> conf = 0.24 => this is the best rule for 2 predicted values *> Best rule #13956 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 37 *> proper extension: 03rz4; *> query: (?x2977, 010z5n) <- partially_contains(?x2977, ?x12511), taxonomy(?x12511, ?x939) *> conf = 0.03 ranks of expected_values: 1504 EVAL 081mh contains 010z5n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 89.000 75.000 0.242 http://example.org/location/location/contains #9721-01tkqy PRED entity: 01tkqy PRED relation: profession! PRED expected values: 054k_8 => 55 concepts (14 used for prediction) PRED predicted values (max 10 best out of 4066): 015pxr (0.71 #26026, 0.39 #38738, 0.33 #4840), 052hl (0.71 #27623, 0.39 #40335, 0.33 #6437), 021yw7 (0.71 #26531, 0.35 #39243, 0.33 #5345), 0mdqp (0.71 #25612, 0.35 #38324, 0.33 #4426), 03b78r (0.71 #27839, 0.33 #6653, 0.33 #2416), 02v49c (0.71 #28262, 0.33 #7076, 0.33 #2839), 02633g (0.71 #28068, 0.33 #6882, 0.33 #2645), 04fcx7 (0.71 #27044, 0.33 #5858, 0.33 #1621), 04cl1 (0.71 #26925, 0.33 #5739, 0.33 #1502), 017yxq (0.71 #28193, 0.33 #7007, 0.33 #2770) >> Best rule #26026 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 02jknp; 03gjzk; 018gz8; >> query: (?x13719, 015pxr) <- profession(?x6098, ?x13719), profession(?x1666, ?x13719), profession(?x147, ?x13719), ?x147 = 012d40, place_of_birth(?x6098, ?x7771), film(?x6098, ?x5247), location(?x6098, ?x7412), award(?x1666, ?x112) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6032 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 02hrh1q; *> query: (?x13719, 054k_8) <- profession(?x12256, ?x13719), profession(?x10841, ?x13719), profession(?x6098, ?x13719), profession(?x1666, ?x13719), profession(?x147, ?x13719), ?x147 = 012d40, ?x6098 = 02tq2r, ?x1666 = 028lc8, ?x12256 = 044pqn, currency(?x10841, ?x170) *> conf = 0.33 ranks of expected_values: 1243 EVAL 01tkqy profession! 054k_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 14.000 0.714 http://example.org/people/person/profession #9720-01f7gh PRED entity: 01f7gh PRED relation: film_crew_role PRED expected values: 09vw2b7 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 25): 09vw2b7 (0.73 #142, 0.68 #278, 0.67 #210), 01pvkk (0.64 #45, 0.29 #283, 0.29 #1211), 0dxtw (0.39 #1210, 0.39 #282, 0.39 #763), 0215hd (0.33 #17, 0.23 #85, 0.14 #1320), 02_n3z (0.33 #1, 0.15 #69, 0.09 #137), 06qc5 (0.18 #61, 0.02 #1020, 0.02 #1158), 015h31 (0.15 #76, 0.10 #451, 0.10 #383), 02rh1dz (0.15 #384, 0.15 #247, 0.14 #452), 0d2b38 (0.14 #160, 0.13 #638, 0.13 #228), 089g0h (0.13 #154, 0.13 #222, 0.13 #632) >> Best rule #142 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 044g_k; 09g8vhw; 02725hs; 0fphgb; 0gjcrrw; 07kb7vh; 0n_hp; >> query: (?x1430, 09vw2b7) <- nominated_for(?x4680, ?x1430), film_crew_role(?x1430, ?x1284), nominated_for(?x507, ?x1430), ?x1284 = 0ch6mp2 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f7gh film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.732 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9719-05bnp0 PRED entity: 05bnp0 PRED relation: student! PRED expected values: 05qgc => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 43): 03qsdpk (0.56 #96, 0.14 #336, 0.12 #396), 02822 (0.26 #331, 0.22 #511, 0.19 #271), 05qfh (0.18 #87, 0.05 #267, 0.05 #147), 04gb7 (0.18 #94, 0.03 #755, 0.02 #574), 0w7c (0.10 #342, 0.09 #282, 0.08 #522), 0fdys (0.09 #389, 0.07 #569, 0.07 #149), 03g3w (0.09 #381, 0.07 #742, 0.07 #561), 0mg1w (0.07 #165, 0.04 #345, 0.03 #285), 02vxn (0.06 #484, 0.05 #304, 0.05 #124), 062z7 (0.06 #382, 0.04 #202, 0.04 #562) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 04xfb; 09k0f; 042q3; >> query: (?x123, 03qsdpk) <- type_of_union(?x123, ?x1873), student(?x10705, ?x123), films(?x10705, ?x1331) >> conf = 0.56 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05bnp0 student! 05qgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.559 http://example.org/education/field_of_study/students_majoring./education/education/student #9718-01htxr PRED entity: 01htxr PRED relation: award PRED expected values: 02f6xy => 151 concepts (134 used for prediction) PRED predicted values (max 10 best out of 315): 01c92g (0.79 #7624, 0.78 #22874, 0.78 #46157), 09sb52 (0.45 #12076, 0.41 #20503, 0.37 #9669), 0f4x7 (0.42 #1235, 0.30 #2038, 0.29 #4846), 054ks3 (0.29 #943, 0.21 #7362, 0.21 #541), 0c4z8 (0.26 #873, 0.22 #16520, 0.22 #14514), 04kxsb (0.26 #1329, 0.20 #124, 0.16 #2132), 05pcn59 (0.24 #9709, 0.24 #1687, 0.20 #8105), 03t5kl (0.22 #4238, 0.21 #4639, 0.18 #5842), 02f6xy (0.21 #4213, 0.21 #4614, 0.21 #600), 03qbnj (0.21 #4244, 0.21 #4645, 0.20 #3040) >> Best rule #7624 for best value: >> intensional similarity = 3 >> extensional distance = 141 >> proper extension: 016qtt; 01vvydl; 012d40; 07s3vqk; 0lbj1; 01vw87c; 01vrx3g; 0147dk; 03f2_rc; 0152cw; ... >> query: (?x6207, ?x537) <- award_winner(?x537, ?x6207), artists(?x505, ?x6207), film(?x6207, ?x1295) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #4213 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 0lk90; 033wx9; 01wgxtl; 014q2g; 01vw20_; 01w02sy; 039bpc; 01q32bd; 01pfkw; 0c7xjb; ... *> query: (?x6207, 02f6xy) <- participant(?x3884, ?x6207), artist(?x3265, ?x6207), award_nominee(?x6207, ?x1660) *> conf = 0.21 ranks of expected_values: 9 EVAL 01htxr award 02f6xy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 151.000 134.000 0.790 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9717-0btpm6 PRED entity: 0btpm6 PRED relation: production_companies PRED expected values: 02hvd => 115 concepts (68 used for prediction) PRED predicted values (max 10 best out of 71): 03xq0f (0.31 #5622), 01gb54 (0.17 #120, 0.08 #1762, 0.07 #1352), 016tw3 (0.12 #4388, 0.11 #2478, 0.11 #669), 05qd_ (0.11 #2476, 0.11 #1159, 0.10 #1242), 024rgt (0.10 #599, 0.08 #107, 0.06 #1010), 054lpb6 (0.10 #343, 0.09 #2481, 0.09 #4970), 01795t (0.10 #350, 0.08 #761, 0.07 #1500), 017s11 (0.09 #906, 0.09 #1070, 0.08 #4379), 016tt2 (0.09 #2470, 0.08 #1646, 0.08 #1318), 0kx4m (0.09 #994, 0.03 #1651, 0.03 #583) >> Best rule #5622 for best value: >> intensional similarity = 4 >> extensional distance = 745 >> proper extension: 011yfd; 05y0cr; 0cq8nx; 06zn1c; 05dl1s; >> query: (?x7493, ?x609) <- award(?x7493, ?x451), nominated_for(?x277, ?x7493), award(?x123, ?x451), film(?x609, ?x7493) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #203 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 05p1tzf; 0gtvrv3; 03qnvdl; 04n52p6; 05qbckf; 0gd0c7x; 08052t3; 0kv238; 05zlld0; 05c26ss; ... *> query: (?x7493, 02hvd) <- film_crew_role(?x7493, ?x137), film_release_region(?x7493, ?x5482), film_release_region(?x7493, ?x279), ?x279 = 0d060g, ?x5482 = 04g5k *> conf = 0.04 ranks of expected_values: 28 EVAL 0btpm6 production_companies 02hvd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 115.000 68.000 0.312 http://example.org/film/film/production_companies #9716-062zm5h PRED entity: 062zm5h PRED relation: story_by PRED expected values: 046_v => 90 concepts (54 used for prediction) PRED predicted values (max 10 best out of 67): 03qcq (0.17 #2, 0.01 #1508), 04hw4b (0.15 #553, 0.04 #1199, 0.04 #768), 0184dt (0.15 #466, 0.04 #1112, 0.04 #681), 01vz80y (0.11 #861, 0.08 #2371, 0.06 #9091), 014nvr (0.11 #328, 0.01 #1837), 0hcvy (0.11 #398), 02nygk (0.08 #640, 0.04 #855, 0.03 #3447), 09pl3f (0.08 #534, 0.02 #965, 0.02 #1180), 016z2j (0.07 #3453, 0.02 #9087, 0.02 #9307), 02114t (0.07 #3453, 0.02 #9087, 0.02 #9307) >> Best rule #2 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 06pyc2; >> query: (?x5016, 03qcq) <- film_format(?x5016, ?x10390), film(?x6221, ?x5016), film(?x2670, ?x5016), award_winner(?x2670, ?x72), ?x6221 = 015p3p >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #817 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 01gc7; 0bmhvpr; 07s846j; 0hgnl3t; 0h03fhx; 0dlngsd; 02prwdh; 0gtt5fb; 0h63gl9; 0gwjw0c; ... *> query: (?x5016, 046_v) <- film_release_region(?x5016, ?x4059), film_release_region(?x5016, ?x390), ?x390 = 0chghy, ?x4059 = 077qn, film(?x7587, ?x5016) *> conf = 0.04 ranks of expected_values: 20 EVAL 062zm5h story_by 046_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 90.000 54.000 0.167 http://example.org/film/film/story_by #9715-0gs9p PRED entity: 0gs9p PRED relation: nominated_for PRED expected values: 0209xj 0jzw 0b73_1d 0bcndz 0jym0 0f4_l 012mrr 0jswp 07s846j 0m_q0 049xgc 0286gm1 0k4bc 0h95927 08zrbl 0b4lkx 04wddl 01z452 0c0zq 01fx4k => 57 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1280): 07cyl (0.77 #21632, 0.77 #22986, 0.75 #4481), 0hfzr (0.77 #21632, 0.77 #22986, 0.68 #36524), 0cf08 (0.77 #21632, 0.77 #22986, 0.68 #36524), 042y1c (0.77 #21632, 0.77 #22986, 0.68 #36524), 0bj25 (0.77 #21632, 0.77 #22986, 0.68 #36524), 01jc6q (0.77 #21632, 0.77 #22986, 0.68 #36524), 0bx0l (0.77 #21632, 0.77 #22986, 0.68 #36524), 0c9k8 (0.77 #21632, 0.77 #22986, 0.68 #36524), 0gw7p (0.77 #21632, 0.77 #22986, 0.68 #36524), 0cq7tx (0.77 #21632, 0.77 #22986, 0.68 #36524) >> Best rule #21632 for best value: >> intensional similarity = 4 >> extensional distance = 119 >> proper extension: 0gkvb7; 02p_7cr; 0cqhk0; 0bdw1g; 09qvc0; 09qj50; 047byns; 0cqh6z; 0ck27z; 0cjyzs; ... >> query: (?x1313, ?x197) <- award(?x269, ?x1313), nominated_for(?x1313, ?x144), ceremony(?x1313, ?x78), award(?x197, ?x1313) >> conf = 0.77 => this is the best rule for 14 predicted values *> Best rule #8843 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 02qyp19; 027dtxw; 040njc; 0f4x7; 02pqp12; 0gq9h; 0k611; 0gr51; 04kxsb; 02ppm4q; *> query: (?x1313, 049xgc) <- award(?x269, ?x1313), nominated_for(?x1313, ?x2550), ceremony(?x1313, ?x78), ?x2550 = 07j8r *> conf = 0.77 ranks of expected_values: 15, 18, 19, 20, 23, 25, 26, 31, 36, 38, 43, 44, 49, 54, 72, 105, 132, 271, 284, 287 EVAL 0gs9p nominated_for 01fx4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0c0zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 01z452 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 04wddl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0b4lkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 08zrbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0h95927 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0k4bc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0286gm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 049xgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0m_q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 07s846j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0jswp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 012mrr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0f4_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0jym0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0bcndz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0b73_1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0jzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gs9p nominated_for 0209xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 57.000 29.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9714-034m8 PRED entity: 034m8 PRED relation: member_states! PRED expected values: 085h1 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 8): 085h1 (0.73 #103, 0.71 #43, 0.71 #11), 018cqq (0.34 #10, 0.30 #42, 0.30 #22), 059dn (0.29 #12, 0.25 #24, 0.25 #4), 02jxk (0.27 #9, 0.27 #21, 0.21 #77), 041288 (0.09 #105, 0.08 #155, 0.07 #150), 07t65 (0.09 #105, 0.08 #155, 0.07 #150), 02vk52z (0.09 #105, 0.08 #155, 0.07 #150), 04k4l (0.07 #150) >> Best rule #103 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 059ss; >> query: (?x9459, 085h1) <- adjoins(?x9459, ?x583), organization(?x9459, ?x127), contains(?x9459, ?x13946) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034m8 member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.730 http://example.org/user/ktrueman/default_domain/international_organization/member_states #9713-0f40w PRED entity: 0f40w PRED relation: film! PRED expected values: 02f2dn => 81 concepts (23 used for prediction) PRED predicted values (max 10 best out of 695): 0bl2g (0.31 #2131, 0.04 #14590, 0.02 #10437), 0dvld (0.26 #3133, 0.03 #11439, 0.03 #32210), 01vb6z (0.14 #10382, 0.10 #45687), 011zd3 (0.14 #373, 0.09 #26996, 0.06 #2449), 01pqy_ (0.14 #922, 0.08 #12459, 0.01 #7151), 05dbf (0.14 #364, 0.04 #19051, 0.02 #31517), 0zcbl (0.14 #1216, 0.03 #3292, 0.02 #7445), 015pvh (0.14 #1097, 0.03 #3173, 0.02 #15632), 02qgyv (0.14 #383, 0.03 #2459, 0.01 #39841), 07rzf (0.14 #1876, 0.03 #3952, 0.01 #16411) >> Best rule #2131 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 09fb5; >> query: (?x2288, 0bl2g) <- nominated_for(?x4782, ?x2288), film(?x4782, ?x3601), participant(?x1896, ?x4782), ?x3601 = 0830vk >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #29525 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 259 *> proper extension: 03j63k; *> query: (?x2288, 02f2dn) <- titles(?x3613, ?x2288), nominated_for(?x1007, ?x2288), titles(?x3613, ?x4136), ?x4136 = 02jr6k *> conf = 0.02 ranks of expected_values: 356 EVAL 0f40w film! 02f2dn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 81.000 23.000 0.314 http://example.org/film/actor/film./film/performance/film #9712-0c73g PRED entity: 0c73g PRED relation: influenced_by PRED expected values: 0k4gf => 123 concepts (69 used for prediction) PRED predicted values (max 10 best out of 370): 081k8 (0.40 #592, 0.33 #157, 0.13 #27011), 0hqgp (0.36 #2608, 0.21 #13491, 0.20 #15665), 03_f0 (0.33 #1572, 0.20 #702, 0.15 #4181), 028p0 (0.33 #31, 0.20 #466, 0.13 #27011), 07ym0 (0.33 #279, 0.20 #714, 0.08 #2890), 0420y (0.25 #3014, 0.13 #27011, 0.13 #27010), 041mt (0.22 #2232, 0.10 #6148, 0.10 #5711), 03f70xs (0.22 #2243, 0.10 #6159, 0.10 #5722), 05qmj (0.20 #628, 0.17 #2804, 0.13 #12380), 043d4 (0.20 #674, 0.17 #1544, 0.11 #2412) >> Best rule #592 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 032l1; >> query: (?x13248, 081k8) <- influenced_by(?x13248, ?x10605), profession(?x13248, ?x1614), nationality(?x13248, ?x1264), ?x10605 = 0h336 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3940 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 0g7k2g; *> query: (?x13248, 0k4gf) <- artists(?x11193, ?x13248), artists(?x888, ?x13248), ?x11193 = 06q6jz, instrumentalists(?x316, ?x13248), profession(?x13248, ?x1614), ?x888 = 05lls *> conf = 0.08 ranks of expected_values: 95 EVAL 0c73g influenced_by 0k4gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 123.000 69.000 0.400 http://example.org/influence/influence_node/influenced_by #9711-073v6 PRED entity: 073v6 PRED relation: award_winner! PRED expected values: 0ddd9 => 140 concepts (115 used for prediction) PRED predicted values (max 10 best out of 324): 01l78d (0.50 #5017, 0.06 #30538, 0.04 #17058), 0265vt (0.35 #5052, 0.08 #10322, 0.07 #9352), 01ppdy (0.27 #2066, 0.20 #1636, 0.17 #2926), 01yz0x (0.24 #4905, 0.12 #604, 0.11 #1034), 0ddd9 (0.20 #56, 0.11 #916, 0.11 #9087), 05f4m9q (0.20 #14, 0.11 #874, 0.03 #3884), 0196kn (0.20 #406, 0.11 #1266, 0.03 #4276), 02wkmx (0.20 #15, 0.06 #30538, 0.05 #28402), 02rdyk7 (0.20 #92, 0.04 #28479, 0.04 #8263), 0gqng (0.20 #2, 0.02 #10324, 0.01 #28389) >> Best rule #5017 for best value: >> intensional similarity = 5 >> extensional distance = 44 >> proper extension: 061dn_; 03m9c8; 0dbpwb; >> query: (?x3325, 01l78d) <- award_winner(?x11471, ?x3325), award(?x9854, ?x11471), award(?x4808, ?x11471), ?x9854 = 0gthm, nationality(?x4808, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 041jlr; *> query: (?x3325, 0ddd9) <- influenced_by(?x3325, ?x8768), award_winner(?x7111, ?x3325), location(?x3325, ?x2474), ?x8768 = 07dnx *> conf = 0.20 ranks of expected_values: 5 EVAL 073v6 award_winner! 0ddd9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 140.000 115.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #9710-05842k PRED entity: 05842k PRED relation: role PRED expected values: 05ljv7 => 91 concepts (71 used for prediction) PRED predicted values (max 10 best out of 50): 05842k (0.88 #3200, 0.87 #2111, 0.86 #2659), 07y_7 (0.88 #2231, 0.86 #140, 0.86 #472), 01vj9c (0.87 #2980, 0.86 #2633, 0.86 #140), 042v_gx (0.87 #2978, 0.86 #140, 0.86 #2928), 0l14qv (0.86 #140, 0.86 #472, 0.86 #471), 05r5c (0.86 #140, 0.86 #472, 0.86 #471), 0gkd1 (0.86 #140, 0.86 #472, 0.86 #471), 01v1d8 (0.86 #140, 0.86 #472, 0.86 #471), 0395lw (0.86 #140, 0.86 #472, 0.86 #471), 01v8y9 (0.86 #140, 0.86 #472, 0.86 #471) >> Best rule #3200 for best value: >> intensional similarity = 10 >> extensional distance = 32 >> proper extension: 0192l; >> query: (?x3991, 05842k) <- role(?x4078, ?x3991), role(?x2888, ?x3991), role(?x1473, ?x3991), performance_role(?x1433, ?x4078), role(?x7172, ?x4078), role(?x1166, ?x2888), ?x1473 = 0g2dz, family(?x2888, ?x9885), group(?x2888, ?x3109), group(?x3991, ?x5493) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1968 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 11 *> proper extension: 0jtg0; *> query: (?x3991, ?x3703) <- role(?x10738, ?x3991), role(?x8035, ?x3991), role(?x2908, ?x3991), role(?x2698, ?x3991), role(?x9413, ?x3991), role(?x3716, ?x3991), role(?x2309, ?x3991), performance_role(?x1466, ?x3716), ?x2908 = 0161sp, ?x2309 = 06ncr, group(?x3716, ?x7653), award(?x2698, ?x1079), role(?x10738, ?x3703), role(?x3239, ?x9413), artists(?x671, ?x8035), role(?x3716, ?x868) *> conf = 0.67 ranks of expected_values: 32 EVAL 05842k role 05ljv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 91.000 71.000 0.882 http://example.org/music/performance_role/track_performances./music/track_contribution/role #9709-0c9c0 PRED entity: 0c9c0 PRED relation: award PRED expected values: 03hkv_r => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 295): 027986c (0.72 #39514, 0.70 #28734, 0.69 #39513), 05zr6wv (0.50 #815, 0.18 #2411, 0.16 #8399), 05b4l5x (0.43 #6, 0.10 #2799, 0.10 #804), 0gr51 (0.35 #5284, 0.28 #4087, 0.25 #3687), 04dn09n (0.33 #5230, 0.26 #4033, 0.22 #7625), 0gq9h (0.33 #8856, 0.32 #9255, 0.23 #6062), 05p09zm (0.29 #120, 0.20 #2514, 0.15 #38714), 0gkvb7 (0.29 #27, 0.18 #29932, 0.14 #1224), 02f716 (0.29 #173, 0.07 #2966, 0.07 #2567), 02f73b (0.29 #282, 0.07 #2676, 0.06 #3075) >> Best rule #39514 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 025p38; 025vry; 0dky9n; 067jsf; 01ky2h; 0l56b; 01nrq5; 01lcxbb; 01h320; 0kk9v; ... >> query: (?x2790, ?x1336) <- award_winner(?x1336, ?x2790), award(?x241, ?x1336) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #5204 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 168 *> proper extension: 027d5g5; *> query: (?x2790, 03hkv_r) <- award_nominee(?x262, ?x2790), award_winner(?x834, ?x2790), written_by(?x5070, ?x2790) *> conf = 0.25 ranks of expected_values: 15 EVAL 0c9c0 award 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 113.000 113.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9708-0q9zc PRED entity: 0q9zc PRED relation: religion PRED expected values: 03_gx => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 25): 0c8wxp (0.50 #1678, 0.42 #1766, 0.42 #1898), 03_gx (0.28 #1113, 0.23 #277, 0.17 #1641), 0kq2 (0.17 #281, 0.11 #1117, 0.06 #1161), 0n2g (0.10 #1112, 0.07 #276, 0.05 #1508), 092bf5 (0.10 #103, 0.09 #191, 0.07 #1247), 01lp8 (0.09 #177, 0.05 #1233, 0.04 #3086), 03j6c (0.07 #3105, 0.07 #2840, 0.04 #196), 0flw86 (0.05 #3087, 0.05 #2822, 0.04 #1630), 051kv (0.05 #137, 0.04 #181, 0.03 #1149), 04pk9 (0.05 #151, 0.03 #1647, 0.03 #1691) >> Best rule #1678 for best value: >> intensional similarity = 3 >> extensional distance = 320 >> proper extension: 0byfz; 01tvz5j; 01mvth; 02g87m; 0m31m; 01xcfy; 02rmfm; 01gv_f; 015vq_; 02dth1; ... >> query: (?x8375, 0c8wxp) <- award_nominee(?x1711, ?x8375), religion(?x8375, ?x2694), film(?x8375, ?x9755) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1113 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 125 *> proper extension: 07g2b; 0hnlx; 019z7q; 0k4gf; 04jzj; 0m77m; 045bg; 016hvl; 028p0; 02p21g; ... *> query: (?x8375, 03_gx) <- location(?x8375, ?x1131), influenced_by(?x545, ?x8375), religion(?x8375, ?x2694) *> conf = 0.28 ranks of expected_values: 2 EVAL 0q9zc religion 03_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 137.000 0.500 http://example.org/people/person/religion #9707-095p3z PRED entity: 095p3z PRED relation: award_winner! PRED expected values: 0fz20l => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 137): 013b2h (0.11 #359, 0.09 #919, 0.09 #1059), 019bk0 (0.09 #156, 0.07 #996, 0.06 #716), 02cg41 (0.08 #965, 0.08 #1105, 0.08 #545), 01s695 (0.08 #983, 0.07 #843, 0.07 #1123), 0d__c3 (0.08 #124, 0.05 #544, 0.04 #684), 01bx35 (0.07 #147, 0.07 #847, 0.07 #987), 01xqqp (0.07 #935, 0.07 #1075, 0.07 #375), 0466p0j (0.07 #915, 0.06 #1055, 0.06 #495), 0gpjbt (0.07 #309, 0.06 #869, 0.06 #1009), 01c6qp (0.06 #719, 0.06 #999, 0.06 #159) >> Best rule #359 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 03h610; >> query: (?x9127, 013b2h) <- award_nominee(?x3811, ?x9127), music(?x4970, ?x9127), music(?x2612, ?x3811) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #1681 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 167 *> proper extension: 0fp_v1x; 02mslq; 03ds3; 07q1v4; 0p5mw; 0b82vw; 0bs1yy; 0b6yp2; 0134s5; 03h4mp; ... *> query: (?x9127, ?x78) <- music(?x4970, ?x9127), award(?x9127, ?x1323), ceremony(?x1323, ?x78) *> conf = 0.02 ranks of expected_values: 79 EVAL 095p3z award_winner! 0fz20l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 96.000 96.000 0.110 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9706-05r5c PRED entity: 05r5c PRED relation: role PRED expected values: 0214km => 91 concepts (88 used for prediction) PRED predicted values (max 10 best out of 50): 02w4b (0.82 #727, 0.82 #708, 0.82 #990), 018vs (0.82 #990, 0.81 #1295, 0.81 #1252), 03m5k (0.82 #990, 0.81 #1295, 0.81 #1252), 02k84w (0.82 #990, 0.81 #1295, 0.81 #1252), 0319l (0.82 #990, 0.81 #1295, 0.81 #1252), 01v1d8 (0.82 #990, 0.81 #1295, 0.81 #1252), 02k856 (0.82 #990, 0.81 #1295, 0.81 #1252), 01xqw (0.82 #990, 0.81 #1295, 0.81 #1252), 01wy6 (0.82 #990, 0.81 #1295, 0.81 #1252), 01399x (0.82 #990, 0.81 #1295, 0.81 #1252) >> Best rule #727 for best value: >> intensional similarity = 8 >> extensional distance = 15 >> proper extension: 0979zs; >> query: (?x316, ?x3418) <- role(?x2690, ?x316), role(?x3418, ?x316), role(?x1436, ?x316), role(?x75, ?x316), role(?x228, ?x1436), participant(?x2690, ?x8146), ?x3418 = 02w4b, instrumentalists(?x75, ?x672) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #163 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 0342h; 02sgy; 042v_gx; 03qjg; *> query: (?x316, 0214km) <- role(?x2963, ?x316), instrumentalists(?x316, ?x4593), ?x2963 = 0gcs9, role(?x316, ?x75), award_nominee(?x4593, ?x1573), student(?x6611, ?x4593) *> conf = 0.67 ranks of expected_values: 19 EVAL 05r5c role 0214km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 91.000 88.000 0.824 http://example.org/music/performance_role/track_performances./music/track_contribution/role #9705-0r1yc PRED entity: 0r1yc PRED relation: jurisdiction_of_office! PRED expected values: 01q24l => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 23): 0fkvn (0.62 #70, 0.40 #4, 0.38 #444), 0f6c3 (0.62 #73, 0.40 #7, 0.32 #447), 09n5b9 (0.54 #77, 0.40 #11, 0.26 #451), 01q24l (0.48 #277, 0.23 #101, 0.23 #982), 060bp (0.38 #684, 0.38 #441, 0.27 #1828), 04syw (0.38 #689, 0.32 #446, 0.10 #1195), 0789n (0.38 #75, 0.13 #692, 0.10 #53), 0fj45 (0.36 #702, 0.32 #459, 0.06 #1208), 060c4 (0.32 #1830, 0.23 #69, 0.19 #1302), 01t7n9 (0.31 #84, 0.20 #18, 0.13 #701) >> Best rule #70 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0f8l9c; >> query: (?x1226, 0fkvn) <- jurisdiction_of_office(?x1195, ?x1226), jurisdiction_of_office(?x1365, ?x1226), award_winner(?x289, ?x1365) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #277 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0r679; 0135g; 0qy5v; 0r4z7; 0pc56; 0r4h3; 0r03f; 0r066; *> query: (?x1226, 01q24l) <- contains(?x1227, ?x1226), ?x1227 = 01n7q, category(?x1226, ?x134), jurisdiction_of_office(?x1195, ?x1226) *> conf = 0.48 ranks of expected_values: 4 EVAL 0r1yc jurisdiction_of_office! 01q24l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 142.000 142.000 0.615 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #9704-030k94 PRED entity: 030k94 PRED relation: nominated_for! PRED expected values: 0blbxk => 103 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1346): 0f721s (0.80 #30319, 0.79 #16327, 0.78 #18659), 03q5dr (0.63 #79296, 0.62 #37315, 0.58 #86294), 03mdt (0.27 #10039, 0.20 #7706, 0.10 #26366), 06pj8 (0.27 #33083, 0.11 #2766, 0.07 #40080), 05gnf (0.25 #10780, 0.20 #60636, 0.16 #6115), 030znt (0.25 #269, 0.11 #2602, 0.07 #51577), 018z_c (0.25 #968, 0.11 #3301, 0.05 #19627), 025mb_ (0.25 #1893, 0.04 #8889, 0.04 #11222), 05w88j (0.25 #1978, 0.04 #11307, 0.04 #67636), 03061d (0.25 #2237, 0.04 #67636, 0.03 #90961) >> Best rule #30319 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0g60z; 080dwhx; 02py4c8; 0kfpm; 02k_4g; 0cwrr; 0358x_; 0ddd0gc; 08jgk1; 0464pz; ... >> query: (?x3169, ?x1394) <- award(?x3169, ?x435), genre(?x3169, ?x53), honored_for(?x2292, ?x3169), award_winner(?x3169, ?x1394) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #23325 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 02825cv; *> query: (?x3169, ?x275) <- nominated_for(?x2657, ?x3169), award_nominee(?x2657, ?x4702), award_nominee(?x2657, ?x275), ?x4702 = 01kwsg *> conf = 0.09 ranks of expected_values: 156 EVAL 030k94 nominated_for! 0blbxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 103.000 50.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9703-09ld6g PRED entity: 09ld6g PRED relation: profession PRED expected values: 02hrh1q => 132 concepts (84 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.94 #9562, 0.91 #9711, 0.90 #3592), 0dxtg (0.58 #3591, 0.57 #5232, 0.53 #6126), 03gjzk (0.49 #3593, 0.41 #5830, 0.40 #6575), 02jknp (0.45 #1498, 0.44 #604, 0.33 #157), 01d_h8 (0.39 #1347, 0.38 #4031, 0.37 #7013), 0np9r (0.29 #5239, 0.29 #6580, 0.28 #6879), 0cbd2 (0.24 #8657, 0.23 #4480, 0.22 #4778), 09jwl (0.22 #2701, 0.21 #3148, 0.18 #3447), 0kyk (0.20 #1669, 0.18 #1967, 0.17 #3308), 0n1h (0.17 #2098, 0.15 #2694, 0.15 #2396) >> Best rule #9562 for best value: >> intensional similarity = 5 >> extensional distance = 498 >> proper extension: 02zq43; 03f2_rc; 015grj; 03lt8g; 0sz28; 0n6f8; 03xmy1; 01vs_v8; 03j0br4; 043js; ... >> query: (?x13716, 02hrh1q) <- gender(?x13716, ?x231), profession(?x13716, ?x1146), languages(?x13716, ?x254), profession(?x6707, ?x1146), ?x6707 = 03d_zl4 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09ld6g profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 84.000 0.940 http://example.org/people/person/profession #9702-0l14j_ PRED entity: 0l14j_ PRED relation: role! PRED expected values: 01v1d8 => 91 concepts (54 used for prediction) PRED predicted values (max 10 best out of 82): 06ncr (0.84 #603, 0.83 #1826, 0.82 #3367), 018vs (0.84 #603, 0.83 #1826, 0.82 #3367), 03qjg (0.84 #603, 0.83 #1826, 0.82 #3367), 04rzd (0.84 #603, 0.83 #1826, 0.82 #3367), 0dwtp (0.84 #603, 0.83 #1826, 0.82 #3367), 03t22m (0.84 #603, 0.83 #1826, 0.82 #3367), 05kms (0.84 #603, 0.83 #1826, 0.82 #3367), 03qmg1 (0.84 #603, 0.83 #1826, 0.82 #3367), 0bmnm (0.84 #603, 0.83 #1826, 0.82 #3367), 0l14v3 (0.84 #603, 0.83 #1826, 0.82 #3367) >> Best rule #603 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 042v_gx; >> query: (?x2944, ?x716) <- role(?x2944, ?x1332), role(?x2785, ?x2944), role(?x1750, ?x2944), ?x1332 = 03qlv7, group(?x2944, ?x5303), instrumentalists(?x2944, ?x120), group(?x2785, ?x1945), ?x5303 = 02mq_y, role(?x2944, ?x716), ?x1750 = 02hnl >> conf = 0.84 => this is the best rule for 13 predicted values *> Best rule #497 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 0l14md; 05r5c; *> query: (?x2944, 01v1d8) <- role(?x2944, ?x4311), role(?x2944, ?x1332), role(?x4917, ?x2944), role(?x2785, ?x2944), ?x1332 = 03qlv7, group(?x2944, ?x2073), instrumentalists(?x2944, ?x120), ?x2785 = 0jtg0, ?x2073 = 01czx, ?x4917 = 06w7v, instrumentalists(?x4311, ?x562) *> conf = 0.67 ranks of expected_values: 34 EVAL 0l14j_ role! 01v1d8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 91.000 54.000 0.836 http://example.org/music/performance_role/regular_performances./music/group_membership/role #9701-029zqn PRED entity: 029zqn PRED relation: film_crew_role PRED expected values: 0dxtw => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 25): 01vx2h (0.45 #181, 0.44 #215, 0.35 #354), 0dxtw (0.39 #214, 0.39 #627, 0.38 #180), 02ynfr (0.19 #529, 0.18 #82, 0.17 #185), 02rh1dz (0.18 #179, 0.17 #213, 0.12 #110), 0215hd (0.16 #703, 0.15 #463, 0.13 #771), 01xy5l_ (0.15 #698, 0.13 #458, 0.13 #183), 089g0h (0.13 #704, 0.12 #464, 0.12 #1396), 0d2b38 (0.12 #710, 0.11 #778, 0.11 #195), 02_n3z (0.11 #69, 0.10 #687, 0.09 #998), 015h31 (0.09 #109, 0.09 #178, 0.09 #212) >> Best rule #181 for best value: >> intensional similarity = 5 >> extensional distance = 199 >> proper extension: 034qbx; 056xkh; 047p798; >> query: (?x1734, 01vx2h) <- film_crew_role(?x1734, ?x2178), film_crew_role(?x1734, ?x468), film(?x1299, ?x1734), ?x2178 = 01pvkk, ?x468 = 02r96rf >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #214 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 204 *> proper extension: 0fq27fp; *> query: (?x1734, 0dxtw) <- film_crew_role(?x1734, ?x2178), film_crew_role(?x1734, ?x468), ?x2178 = 01pvkk, ?x468 = 02r96rf, genre(?x1734, ?x53) *> conf = 0.39 ranks of expected_values: 2 EVAL 029zqn film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.453 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9700-01v40wd PRED entity: 01v40wd PRED relation: artists! PRED expected values: 0glt670 => 116 concepts (67 used for prediction) PRED predicted values (max 10 best out of 233): 0glt670 (0.79 #982, 0.73 #356, 0.30 #3175), 06by7 (0.72 #14440, 0.55 #17258, 0.51 #5347), 064t9 (0.69 #327, 0.55 #3146, 0.50 #17249), 025sc50 (0.58 #366, 0.36 #53, 0.32 #992), 06j6l (0.38 #364, 0.36 #990, 0.32 #7882), 01flzq (0.36 #1060, 0.17 #18175, 0.12 #434), 036jv (0.32 #1134, 0.19 #508, 0.12 #195), 05bt6j (0.30 #3178, 0.29 #2238, 0.26 #3491), 016clz (0.30 #17240, 0.29 #2197, 0.28 #1257), 0ggx5q (0.28 #81, 0.27 #394, 0.27 #3213) >> Best rule #982 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 01wj5hp; >> query: (?x3893, 0glt670) <- artist(?x3265, ?x3893), award(?x3893, ?x9295), location(?x3893, ?x2850), ?x9295 = 023vrq >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v40wd artists! 0glt670 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 67.000 0.786 http://example.org/music/genre/artists #9699-0412f5y PRED entity: 0412f5y PRED relation: award_winner! PRED expected values: 05pd94v => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 101): 05pd94v (0.23 #2, 0.10 #562, 0.09 #702), 02rjjll (0.15 #5, 0.11 #285, 0.10 #565), 0gx1673 (0.15 #119, 0.06 #399, 0.06 #679), 019bk0 (0.14 #156, 0.09 #296, 0.08 #16), 01s695 (0.12 #143, 0.10 #563, 0.08 #703), 056878 (0.12 #172, 0.08 #592, 0.08 #32), 02cg41 (0.12 #125, 0.10 #265, 0.09 #685), 013b2h (0.11 #639, 0.11 #779, 0.08 #359), 01c6qp (0.10 #579, 0.09 #719, 0.08 #159), 01bx35 (0.10 #147, 0.09 #567, 0.08 #707) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0147dk; 02l840; 016kjs; 04mn81; 01wwvc5; 01wgxtl; 0126y2; 04qmr; 01q32bd; 01ws9n6; ... >> query: (?x3607, 05pd94v) <- award_nominee(?x4476, ?x3607), award(?x3607, ?x2139), ?x4476 = 01vw20h >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0412f5y award_winner! 05pd94v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.231 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9698-01jtp7 PRED entity: 01jtp7 PRED relation: company! PRED expected values: 05k17c => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 35): 05k17c (0.28 #1985, 0.19 #2789, 0.16 #4725), 0dq_5 (0.25 #1955, 0.21 #1197, 0.21 #1244), 0krdk (0.24 #1944, 0.20 #1233, 0.19 #1186), 060c4 (0.22 #1940, 0.20 #1229, 0.19 #1182), 0dq3c (0.15 #1939, 0.14 #2, 0.13 #898), 01yc02 (0.14 #9, 0.13 #1946, 0.12 #1274), 02zdwq (0.14 #26, 0.12 #1274, 0.10 #74), 01kr6k (0.14 #28, 0.12 #1274, 0.10 #76), 05_wyz (0.13 #1956, 0.12 #1198, 0.12 #1245), 021q1c (0.12 #201, 0.12 #578, 0.11 #390) >> Best rule #1985 for best value: >> intensional similarity = 5 >> extensional distance = 230 >> proper extension: 033hn8; 01_8w2; 0j47s; 0fvppk; 059yj; 01m_zd; 02h758; >> query: (?x2166, ?x3484) <- organization(?x3484, ?x2166), organization(?x3484, ?x12277), organization(?x3484, ?x11693), state_province_region(?x12277, ?x1227), ?x11693 = 02p8454 >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jtp7 company! 05k17c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.279 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #9697-0f4_l PRED entity: 0f4_l PRED relation: film_regional_debut_venue PRED expected values: 0prpt => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 23): 0prpt (0.12 #165, 0.07 #336, 0.05 #677), 018cvf (0.09 #153, 0.06 #324, 0.05 #665), 015hr (0.06 #117, 0.05 #151, 0.05 #799), 0gg7gsl (0.05 #76, 0.03 #110, 0.02 #622), 0j63cyr (0.05 #82, 0.01 #321, 0.01 #1791), 07zmj (0.03 #339, 0.02 #168, 0.02 #202), 07751 (0.03 #317, 0.02 #1207, 0.02 #214), 02_286 (0.03 #345, 0.02 #379, 0.02 #515), 0cmd3zy (0.02 #444, 0.01 #341), 0kfhjq0 (0.02 #426, 0.01 #358, 0.01 #1282) >> Best rule #165 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0dkv90; >> query: (?x2177, 0prpt) <- titles(?x53, ?x2177), film(?x3117, ?x2177), nominated_for(?x112, ?x2177), film_release_region(?x2177, ?x94) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f4_l film_regional_debut_venue 0prpt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.116 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #9696-0pqp3 PRED entity: 0pqp3 PRED relation: artist! PRED expected values: 011k11 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 124): 011k11 (0.80 #1969, 0.38 #1001, 0.29 #725), 086k8 (0.75 #2627, 0.33 #1, 0.14 #830), 03rhqg (0.69 #4713, 0.43 #843, 0.43 #705), 0g768 (0.67 #588, 0.46 #2109, 0.44 #2938), 0mzkr (0.62 #1129, 0.33 #162, 0.31 #2097), 01clyr (0.56 #3763, 0.38 #999, 0.33 #170), 0181dw (0.54 #2114, 0.27 #2943, 0.25 #455), 01w40h (0.38 #1132, 0.35 #2100, 0.29 #718), 01cl0d (0.34 #3784, 0.19 #3646, 0.16 #1988), 015_1q (0.33 #18, 0.29 #847, 0.25 #432) >> Best rule #1969 for best value: >> intensional similarity = 8 >> extensional distance = 23 >> proper extension: 07_3qd; 0144l1; 0cg9y; 02b25y; 03f0vvr; 02f1c; 01vsy9_; >> query: (?x11107, 011k11) <- artists(?x1380, ?x11107), category(?x11107, ?x134), artist(?x13540, ?x11107), artist(?x13540, ?x10670), artist(?x13540, ?x9087), ?x9087 = 0kj34, ?x10670 = 0167xy, ?x134 = 08mbj5d >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pqp3 artist! 011k11 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.800 http://example.org/music/record_label/artist #9695-02lpp7 PRED entity: 02lpp7 PRED relation: medal! PRED expected values: 0b90_r 0d060g 0j1z8 01ls2 07ylj 04j53 06t8v 0161c 0jgx 04g5k 07f1x => 3 concepts (3 used for prediction) PRED predicted values (max 10 best out of 432): 0b90_r (0.78 #131, 0.74 #127, 0.59 #122), 07f1x (0.78 #131, 0.74 #127, 0.59 #122), 01ls2 (0.78 #131, 0.74 #127, 0.55 #57), 06t8v (0.78 #131, 0.74 #127, 0.55 #57), 07ylj (0.78 #131, 0.74 #127, 0.52 #60), 09pmkv (0.78 #131, 0.74 #127, 0.46 #120), 0161c (0.78 #131, 0.74 #127, 0.46 #120), 047yc (0.78 #131, 0.74 #127, 0.46 #120), 06t2t (0.78 #131, 0.74 #127, 0.41 #65), 05r7t (0.78 #131, 0.74 #127, 0.34 #61) >> Best rule #131 for best value: >> intensional similarity = 333 >> extensional distance = 1 >> proper extension: 02lq5w; >> query: (?x2132, ?x1203) <- medal(?x6974, ?x2132), medal(?x4569, ?x2132), medal(?x4521, ?x2132), medal(?x3730, ?x2132), medal(?x3728, ?x2132), medal(?x3635, ?x2132), medal(?x3357, ?x2132), medal(?x2629, ?x2132), medal(?x1558, ?x2132), medal(?x1497, ?x2132), medal(?x1471, ?x2132), medal(?x1023, ?x2132), medal(?x985, ?x2132), medal(?x792, ?x2132), medal(?x789, ?x2132), medal(?x774, ?x2132), medal(?x756, ?x2132), medal(?x583, ?x2132), medal(?x429, ?x2132), medal(?x404, ?x2132), medal(?x390, ?x2132), medal(?x304, ?x2132), medal(?x252, ?x2132), medal(?x142, ?x2132), medal(?x94, ?x2132), ?x1023 = 0ctw_b, ?x3357 = 04w8f, ?x252 = 03_3d, ?x792 = 0hzlz, ?x1497 = 015qh, ?x3635 = 019pcs, ?x3728 = 087vz, medal(?x8189, ?x2132), medal(?x7441, ?x2132), medal(?x6893, ?x2132), medal(?x6464, ?x2132), medal(?x5176, ?x2132), medal(?x4424, ?x2132), medal(?x4255, ?x2132), medal(?x3110, ?x2132), medal(?x2553, ?x2132), medal(?x2496, ?x2132), medal(?x2233, ?x2132), medal(?x2134, ?x2132), medal(?x1741, ?x2132), medal(?x1277, ?x2132), medal(?x418, ?x2132), ?x3110 = 0kbvv, ?x2233 = 0l6mp, ?x756 = 06npd, ?x6974 = 01nln, ?x4521 = 07fj_, ?x7441 = 0ldqf, sports(?x6464, ?x4876), sports(?x6464, ?x2867), sports(?x6464, ?x471), ?x404 = 047lj, ?x4424 = 0blfl, ?x471 = 02vx4, ?x94 = 09c7w0, ?x2496 = 0sxrz, locations(?x6464, ?x6959), ?x418 = 09n48, ?x6893 = 019n8z, ?x142 = 0jgd, film_release_region(?x11395, ?x390), film_release_region(?x10346, ?x390), film_release_region(?x9900, ?x390), film_release_region(?x8580, ?x390), film_release_region(?x8258, ?x390), film_release_region(?x7887, ?x390), film_release_region(?x7864, ?x390), film_release_region(?x7494, ?x390), film_release_region(?x7016, ?x390), film_release_region(?x7009, ?x390), film_release_region(?x6661, ?x390), film_release_region(?x6621, ?x390), film_release_region(?x6376, ?x390), film_release_region(?x6321, ?x390), film_release_region(?x6078, ?x390), film_release_region(?x5704, ?x390), film_release_region(?x5013, ?x390), film_release_region(?x4690, ?x390), film_release_region(?x4453, ?x390), film_release_region(?x4448, ?x390), film_release_region(?x3986, ?x390), film_release_region(?x3981, ?x390), film_release_region(?x3958, ?x390), film_release_region(?x3938, ?x390), film_release_region(?x3745, ?x390), film_release_region(?x3599, ?x390), film_release_region(?x3524, ?x390), film_release_region(?x3482, ?x390), film_release_region(?x3471, ?x390), film_release_region(?x3453, ?x390), film_release_region(?x3226, ?x390), film_release_region(?x3157, ?x390), film_release_region(?x3151, ?x390), film_release_region(?x3053, ?x390), film_release_region(?x2709, ?x390), film_release_region(?x2627, ?x390), film_release_region(?x2318, ?x390), film_release_region(?x2163, ?x390), film_release_region(?x1743, ?x390), film_release_region(?x1518, ?x390), film_release_region(?x1498, ?x390), film_release_region(?x1451, ?x390), film_release_region(?x1392, ?x390), film_release_region(?x1283, ?x390), film_release_region(?x1259, ?x390), film_release_region(?x1108, ?x390), film_release_region(?x1080, ?x390), film_release_region(?x972, ?x390), film_release_region(?x951, ?x390), film_release_region(?x908, ?x390), film_release_region(?x622, ?x390), film_release_region(?x559, ?x390), film_release_region(?x86, ?x390), ?x622 = 0fq27fp, country(?x10158, ?x390), country(?x8234, ?x390), country(?x7481, ?x390), country(?x6627, ?x390), country(?x5871, ?x390), country(?x4820, ?x390), country(?x3573, ?x390), country(?x3219, ?x390), country(?x1808, ?x390), country(?x1295, ?x390), contains(?x390, ?x10889), ?x11395 = 05ypj5, nationality(?x5282, ?x390), nationality(?x3031, ?x390), nationality(?x1738, ?x390), nationality(?x556, ?x390), olympics(?x2316, ?x6464), olympics(?x1203, ?x6464), olympics(?x151, ?x6464), ?x1471 = 07t21, ?x7887 = 04z_3pm, ?x1558 = 01mjq, ?x8189 = 015l4k, ?x1451 = 04zyhx, exported_to(?x390, ?x4164), ?x2134 = 0blg2, ?x951 = 0cwy47, ?x304 = 0d0vqn, country(?x5177, ?x390), country(?x4673, ?x390), country(?x4503, ?x390), country(?x3554, ?x390), country(?x2884, ?x390), service_location(?x5072, ?x390), ?x559 = 05p1tzf, ?x2629 = 06f32, administrative_parent(?x8506, ?x390), ?x4503 = 06z68, ?x789 = 0f8l9c, ?x1392 = 017gm7, award_winner(?x3453, ?x1656), ?x3986 = 0jymd, ?x908 = 01vksx, ?x1498 = 04jkpgv, ?x4876 = 0d1t3, ?x3524 = 06r2_, featured_film_locations(?x6627, ?x1523), films(?x8278, ?x1295), nominated_for(?x3499, ?x3226), ?x4690 = 0gkz3nz, olympics(?x774, ?x7775), olympics(?x774, ?x7688), ?x3958 = 0gyh2wm, ?x4255 = 0lgxj, adjoins(?x390, ?x9654), combatants(?x390, ?x3142), combatants(?x390, ?x613), combatants(?x390, ?x550), form_of_government(?x390, ?x6065), ?x2627 = 0gz6b6g, genre(?x1808, ?x811), film_release_region(?x5347, ?x774), film_release_region(?x4024, ?x774), ?x2316 = 06t2t, countries_spoken_in(?x5607, ?x774), ?x7494 = 0dgrwqr, executive_produced_by(?x1295, ?x6883), ?x3142 = 03b79, ?x3745 = 03cw411, film_release_region(?x10327, ?x550), film_release_region(?x5305, ?x550), film_release_region(?x2878, ?x550), team(?x3031, ?x59), organization(?x774, ?x127), nominated_for(?x400, ?x6627), ?x2884 = 09wz9, film(?x2818, ?x1808), ?x4453 = 0dr_9t7, genre(?x1295, ?x258), ?x8580 = 0hhggmy, gender(?x3031, ?x231), ?x1277 = 0swbd, ?x3482 = 017z49, ?x3730 = 03shp, film(?x1537, ?x6627), ?x1080 = 01c22t, ?x2867 = 02y8z, production_companies(?x7481, ?x1914), ?x811 = 03k9fj, country(?x5291, ?x774), combatants(?x326, ?x390), countries_within(?x6956, ?x550), nominated_for(?x1053, ?x2709), ?x4024 = 0n04r, ?x5305 = 012s1d, ?x613 = 0bq0p9, ?x3599 = 0kxf1, film_crew_role(?x4820, ?x7591), ?x1518 = 04w7rn, ?x2878 = 0hx4y, award_nominee(?x1739, ?x1738), award_nominee(?x1222, ?x1738), ?x10327 = 03vfr_, ?x3471 = 07cyl, ?x10346 = 0dw4b0, ?x8258 = 05ldxl, film_release_region(?x886, ?x550), ?x151 = 0b90_r, ?x7775 = 01f1kd, ?x3499 = 03qgjwc, ?x972 = 017gl1, country(?x4893, ?x774), ?x4673 = 07jbh, ?x6621 = 0h63gl9, award(?x3226, ?x2183), form_of_government(?x550, ?x48), ?x1222 = 03f1zdw, nominated_for(?x6627, ?x6628), administrative_parent(?x390, ?x551), taxonomy(?x550, ?x939), nominated_for(?x68, ?x3573), ?x7688 = 0jkvj, award_winner(?x834, ?x1738), ?x3151 = 0gtsxr4, ?x7016 = 07g1sm, film(?x4371, ?x2709), genre(?x3226, ?x53), award_winner(?x3573, ?x3574), currency(?x3157, ?x170), nominated_for(?x384, ?x3219), film_crew_role(?x5871, ?x2095), produced_by(?x1808, ?x5781), ?x4569 = 09lxtg, ?x3938 = 024mpp, languages(?x5597, ?x5607), genre(?x3573, ?x307), written_by(?x1295, ?x7624), jurisdiction_of_office(?x3444, ?x9654), language(?x787, ?x5607), nationality(?x1221, ?x774), major_field_of_study(?x122, ?x5607), award_winner(?x5282, ?x628), film_release_region(?x9209, ?x985), ?x6661 = 0k7tq, ?x5704 = 0h95zbp, award(?x1738, ?x451), film(?x4051, ?x1295), vacationer(?x390, ?x5514), film(?x382, ?x4820), country(?x1352, ?x985), ?x4371 = 05txrz, film_format(?x7481, ?x6392), language(?x1808, ?x254), ?x5177 = 06zgc, medal(?x550, ?x422), ?x886 = 0kv2hv, written_by(?x5871, ?x3736), ?x1739 = 015rkw, ?x1259 = 04hwbq, friend(?x5514, ?x10277), ?x3554 = 035d1m, sports(?x1741, ?x2752), service_language(?x896, ?x5607), country(?x8174, ?x985), ?x429 = 03rt9, ?x1108 = 0jjy0, official_language(?x2468, ?x5607), ?x2318 = 06v9_x, ?x3981 = 047tsx3, ?x7009 = 0bs8s1p, ?x2095 = 0dxtw, production_companies(?x3219, ?x1104), produced_by(?x6627, ?x521), member_states(?x7416, ?x774), film_production_design_by(?x6321, ?x6096), ?x6078 = 04pk1f, production_companies(?x6321, ?x9997), people(?x5741, ?x556), ?x1283 = 0cnztc4, ?x5347 = 02ylg6, ?x86 = 0ds35l9, ?x2553 = 016r9z, genre(?x2709, ?x714), company(?x265, ?x5072), titles(?x3506, ?x3157), ?x8234 = 06_sc3, film(?x609, ?x2709), ?x9209 = 0crs0b8, geographic_distribution(?x1571, ?x390), major_field_of_study(?x10889, ?x947), ?x5597 = 02pk6x, profession(?x5282, ?x1032), written_by(?x3053, ?x1052), nominated_for(?x143, ?x3157), ?x254 = 02h40lc, languages_spoken(?x1176, ?x5607), category(?x10158, ?x134), ?x6376 = 01f85k, ?x7591 = 0d2b38, contains(?x774, ?x1220), ?x2163 = 0j6b5, ?x4448 = 01k60v, ?x5176 = 0sx92, ?x5013 = 011ycb, ?x9900 = 0qmfk, ?x583 = 015fr, award(?x3157, ?x2222), ?x787 = 08gsvw, production_companies(?x3053, ?x2156), ?x551 = 02j71, ?x1743 = 0c8tkt, ?x7864 = 0cbn7c, titles(?x2480, ?x1295), ?x2752 = 09_94 >> conf = 0.78 => this is the best rule for 23 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 7, 14, 24, 32, 72, 92 EVAL 02lpp7 medal! 07f1x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 04g5k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 0161c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 06t8v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 04j53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 07ylj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 01ls2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 0d060g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 02lpp7 medal! 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.782 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #9694-0146bp PRED entity: 0146bp PRED relation: risk_factors PRED expected values: 01336l => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 95): 0jpmt (0.57 #495, 0.57 #465, 0.52 #1934), 02ctzb (0.57 #829, 0.50 #396, 0.43 #448), 06v41q (0.56 #440), 05zppz (0.50 #1845, 0.50 #339, 0.46 #494), 0x67 (0.50 #343, 0.46 #494, 0.40 #216), 025rpb0 (0.50 #209), 0c58k (0.48 #1668, 0.46 #494, 0.44 #2049), 012jc (0.46 #494, 0.44 #1119, 0.43 #915), 0fltx (0.46 #494, 0.41 #819, 0.37 #611), 0k95h (0.46 #494, 0.41 #819, 0.33 #1436) >> Best rule #495 for best value: >> intensional similarity = 21 >> extensional distance = 5 >> proper extension: 01bcp7; 0dcqh; >> query: (?x13891, ?x8023) <- risk_factors(?x13891, ?x13662), risk_factors(?x13891, ?x8524), symptom_of(?x4905, ?x13891), people(?x13662, ?x1128), risk_factors(?x11307, ?x8524), risk_factors(?x8523, ?x8524), ?x11307 = 09969, symptom_of(?x4905, ?x13560), symptom_of(?x4905, ?x11659), symptom_of(?x4905, ?x4291), people(?x8523, ?x2807), risk_factors(?x8523, ?x8023), ?x4291 = 07jwr, artist(?x2039, ?x1128), notable_people_with_this_condition(?x13560, ?x190), artists(?x505, ?x1128), ?x8023 = 0jpmt, symptom_of(?x6780, ?x11659), award_nominee(?x215, ?x1128), award_winner(?x567, ?x1128), ?x6780 = 0j5fv >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #494 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 5 *> proper extension: 01bcp7; 0dcqh; *> query: (?x13891, ?x9648) <- risk_factors(?x13891, ?x13662), risk_factors(?x13891, ?x8524), symptom_of(?x4905, ?x13891), people(?x13662, ?x1128), risk_factors(?x11307, ?x8524), risk_factors(?x8523, ?x8524), ?x11307 = 09969, symptom_of(?x4905, ?x13560), symptom_of(?x4905, ?x11659), symptom_of(?x4905, ?x4291), people(?x8523, ?x2807), risk_factors(?x8523, ?x9648), risk_factors(?x8523, ?x8023), ?x4291 = 07jwr, artist(?x2039, ?x1128), notable_people_with_this_condition(?x13560, ?x190), artists(?x505, ?x1128), ?x8023 = 0jpmt, symptom_of(?x6780, ?x11659), award_nominee(?x215, ?x1128), award_winner(?x567, ?x1128), ?x6780 = 0j5fv *> conf = 0.46 ranks of expected_values: 11 EVAL 0146bp risk_factors 01336l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 43.000 43.000 0.571 http://example.org/medicine/disease/risk_factors #9693-06kb_ PRED entity: 06kb_ PRED relation: influenced_by! PRED expected values: 0l99s 0ff2k => 115 concepts (47 used for prediction) PRED predicted values (max 10 best out of 354): 067xw (0.40 #282, 0.13 #5604, 0.12 #5376), 0ldd (0.40 #498, 0.12 #5592, 0.06 #10690), 07lp1 (0.38 #1941, 0.25 #2450, 0.20 #2960), 06jcc (0.38 #1838, 0.25 #2347, 0.20 #310), 0683n (0.38 #1863, 0.23 #10527, 0.19 #5429), 034bs (0.38 #1682, 0.23 #10346, 0.19 #5248), 0d4jl (0.33 #625, 0.25 #5210, 0.25 #1644), 05qzv (0.33 #11100, 0.25 #1927, 0.19 #5493), 013pp3 (0.33 #730, 0.17 #1239, 0.13 #5604), 0pqzh (0.33 #1471, 0.17 #962, 0.13 #5604) >> Best rule #282 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 037jz; >> query: (?x5040, 067xw) <- influenced_by(?x7828, ?x5040), influenced_by(?x5334, ?x5040), ?x7828 = 014ps4, nationality(?x5040, ?x1310), ?x5334 = 06hmd >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1817 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01v9724; 02mpb; *> query: (?x5040, 0l99s) <- influenced_by(?x7828, ?x5040), ?x7828 = 014ps4, place_of_burial(?x5040, ?x4435), profession(?x5040, ?x353) *> conf = 0.12 ranks of expected_values: 120, 134 EVAL 06kb_ influenced_by! 0ff2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 115.000 47.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 06kb_ influenced_by! 0l99s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 115.000 47.000 0.400 http://example.org/influence/influence_node/influenced_by #9692-0b25vg PRED entity: 0b25vg PRED relation: award PRED expected values: 0gqyl 099t8j 05ztrmj => 100 concepts (95 used for prediction) PRED predicted values (max 10 best out of 289): 0gqyl (0.76 #1289, 0.62 #1685, 0.15 #30916), 099t8j (0.71 #28142, 0.71 #18224, 0.71 #18621), 0gqwc (0.65 #1262, 0.44 #1658, 0.20 #74), 09sdmz (0.40 #199, 0.17 #595, 0.15 #30916), 0gqy2 (0.40 #158, 0.15 #30916, 0.13 #27744), 02w9sd7 (0.40 #164, 0.15 #30916, 0.13 #27744), 099jhq (0.40 #19, 0.15 #30916, 0.13 #27744), 099ck7 (0.40 #259, 0.15 #30916, 0.13 #27744), 04kxsb (0.40 #121, 0.15 #30916, 0.13 #30917), 027dtxw (0.40 #4, 0.13 #27744, 0.13 #30917) >> Best rule #1289 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 01g257; 043kzcr; 0154qm; >> query: (?x10482, 0gqyl) <- award(?x10482, ?x3499), award(?x10482, ?x2880), ?x2880 = 02ppm4q, ?x3499 = 03qgjwc >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 35 EVAL 0b25vg award 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 100.000 95.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0b25vg award 099t8j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 95.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0b25vg award 0gqyl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 95.000 0.765 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9691-0bs4r PRED entity: 0bs4r PRED relation: films! PRED expected values: 081pw => 89 concepts (38 used for prediction) PRED predicted values (max 10 best out of 47): 081pw (0.07 #1894, 0.07 #160, 0.06 #1423), 0cm2xh (0.07 #47, 0.04 #361, 0.04 #678), 05489 (0.07 #209, 0.06 #1472, 0.05 #366), 0d1w9 (0.07 #193, 0.03 #1927, 0.02 #2085), 0fx2s (0.06 #1493, 0.05 #2122, 0.05 #73), 07_nf (0.05 #224, 0.03 #1958, 0.03 #1487), 01vq3 (0.05 #41, 0.04 #355, 0.04 #513), 01cgz (0.05 #19, 0.03 #333, 0.02 #491), 06d4h (0.04 #357, 0.04 #1463, 0.04 #515), 018h2 (0.04 #494, 0.03 #1442, 0.03 #336) >> Best rule #1894 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 018js4; 0147sh; 01_mdl; 018f8; 072x7s; 02rx2m5; 0htww; 0fy66; 0pd57; 01k60v; ... >> query: (?x6069, 081pw) <- film_release_region(?x6069, ?x94), nominated_for(?x1703, ?x6069), ?x1703 = 0k611 >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bs4r films! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 38.000 0.074 http://example.org/film/film_subject/films #9690-03xk1_ PRED entity: 03xk1_ PRED relation: gender PRED expected values: 05zppz => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #9, 0.76 #5, 0.74 #31), 02zsn (0.51 #63, 0.50 #4, 0.50 #2) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 85 >> proper extension: 01515w; >> query: (?x10336, 05zppz) <- award(?x10336, ?x112), film(?x10336, ?x7161), profession(?x10336, ?x1032), ?x112 = 027dtxw >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xk1_ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.908 http://example.org/people/person/gender #9689-01zlh5 PRED entity: 01zlh5 PRED relation: people! PRED expected values: 0gk4g => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 35): 0gk4g (0.14 #1462, 0.14 #1660, 0.14 #1066), 0qcr0 (0.09 #133, 0.08 #1, 0.08 #1057), 0dq9p (0.09 #1073, 0.08 #677, 0.07 #1667), 01l2m3 (0.08 #148, 0.03 #1072, 0.03 #1402), 04p3w (0.07 #1067, 0.06 #143, 0.06 #1463), 02y0js (0.06 #662, 0.06 #728, 0.05 #1652), 019dmc (0.06 #182, 0.02 #710, 0.01 #776), 0m32h (0.05 #155, 0.04 #1079, 0.03 #683), 032s66 (0.05 #181, 0.02 #115, 0.02 #379), 051_y (0.05 #180, 0.02 #114, 0.01 #774) >> Best rule #1462 for best value: >> intensional similarity = 3 >> extensional distance = 482 >> proper extension: 03j43; 0gm34; 03f68r6; >> query: (?x8205, 0gk4g) <- type_of_union(?x8205, ?x566), place_of_death(?x8205, ?x242), location(?x241, ?x242) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01zlh5 people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.140 http://example.org/people/cause_of_death/people #9688-0wr_s PRED entity: 0wr_s PRED relation: place PRED expected values: 0wr_s => 74 concepts (32 used for prediction) PRED predicted values (max 10 best out of 7): 0wqwj (0.10 #990, 0.10 #475), 0wq36 (0.10 #975, 0.10 #460), 043yj (0.10 #932, 0.10 #417), 0ws0h (0.10 #720, 0.10 #205), 0wq3z (0.10 #635, 0.10 #120), 0qkyj (0.10 #885), 0wp9b (0.10 #544) >> Best rule #990 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0wp9b; 0qkyj; 0yx74; >> query: (?x13807, 0wqwj) <- time_zones(?x13807, ?x1638), ?x1638 = 02fqwt, contains(?x4622, ?x13807), ?x4622 = 04tgp >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0wr_s place 0wr_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 32.000 0.100 http://example.org/location/hud_county_place/place #9687-07_53 PRED entity: 07_53 PRED relation: country PRED expected values: 0d060g 0h3y 02k54 0d05w3 => 40 concepts (39 used for prediction) PRED predicted values (max 10 best out of 316): 0d0vqn (0.90 #4535, 0.85 #3590, 0.82 #3405), 0f8l9c (0.89 #4358, 0.89 #4169, 0.87 #3981), 0d060g (0.86 #4718, 0.86 #4533, 0.85 #6873), 0d05w3 (0.86 #4768, 0.81 #4583, 0.80 #3079), 07t21 (0.81 #4746, 0.81 #4561, 0.78 #5910), 01p1v (0.81 #4756, 0.76 #4571, 0.75 #380), 06c1y (0.79 #1131, 0.75 #2300, 0.75 #380), 035qy (0.79 #1131, 0.75 #380, 0.73 #383), 03gj2 (0.79 #1131, 0.75 #380, 0.73 #383), 03shp (0.79 #1131, 0.75 #380, 0.73 #383) >> Best rule #4535 for best value: >> intensional similarity = 59 >> extensional distance = 19 >> proper extension: 01sgl; >> query: (?x6150, 0d0vqn) <- country(?x6150, ?x1603), country(?x6150, ?x1499), country(?x6150, ?x1264), country(?x6150, ?x205), country(?x6150, ?x142), country(?x6150, ?x47), currency(?x47, ?x170), olympics(?x47, ?x778), form_of_government(?x47, ?x48), ?x142 = 0jgd, ?x1264 = 0345h, contains(?x7273, ?x47), ?x778 = 0kbvb, administrative_area_type(?x47, ?x2792), ?x205 = 03rjj, film_release_region(?x11313, ?x1499), film_release_region(?x10860, ?x1499), film_release_region(?x9002, ?x1499), film_release_region(?x8580, ?x1499), film_release_region(?x7016, ?x1499), film_release_region(?x6661, ?x1499), film_release_region(?x5791, ?x1499), film_release_region(?x4514, ?x1499), film_release_region(?x4024, ?x1499), film_release_region(?x3000, ?x1499), film_release_region(?x2342, ?x1499), film_release_region(?x2155, ?x1499), film_release_region(?x1927, ?x1499), film_release_region(?x1701, ?x1499), film_release_region(?x1259, ?x1499), film_release_region(?x984, ?x1499), film_release_region(?x972, ?x1499), film_release_region(?x781, ?x1499), ?x8580 = 0hhggmy, medal(?x1499, ?x422), ?x9002 = 0ndsl1x, ?x7273 = 07c5l, ?x3000 = 045j3w, ?x781 = 0gkz15s, jurisdiction_of_office(?x182, ?x1499), ?x6661 = 0k7tq, ?x5791 = 03mgx6z, participating_countries(?x418, ?x1499), ?x11313 = 0by17xn, organization(?x1499, ?x127), ?x1603 = 06bnz, ?x10860 = 049w1q, ?x972 = 017gl1, olympics(?x1499, ?x584), ?x7016 = 07g1sm, ?x4024 = 0n04r, ?x4514 = 06tpmy, ?x2155 = 0407yfx, ?x1927 = 0by1wkq, ?x2342 = 0ct5zc, ?x1259 = 04hwbq, ?x984 = 0m_mm, country(?x8809, ?x1499), ?x1701 = 0bh8yn3 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #4718 for first EXPECTED value: *> intensional similarity = 59 *> extensional distance = 19 *> proper extension: 03rbzn; *> query: (?x6150, 0d060g) <- country(?x6150, ?x1603), country(?x6150, ?x1499), country(?x6150, ?x1264), country(?x6150, ?x205), country(?x6150, ?x142), country(?x6150, ?x47), currency(?x47, ?x170), olympics(?x47, ?x778), form_of_government(?x47, ?x48), ?x142 = 0jgd, ?x1264 = 0345h, contains(?x7273, ?x47), ?x778 = 0kbvb, administrative_area_type(?x47, ?x2792), ?x205 = 03rjj, film_release_region(?x11313, ?x1499), film_release_region(?x10860, ?x1499), film_release_region(?x9002, ?x1499), film_release_region(?x8580, ?x1499), film_release_region(?x7016, ?x1499), film_release_region(?x6661, ?x1499), film_release_region(?x6215, ?x1499), film_release_region(?x5791, ?x1499), film_release_region(?x4514, ?x1499), film_release_region(?x4024, ?x1499), film_release_region(?x3000, ?x1499), film_release_region(?x2342, ?x1499), film_release_region(?x2155, ?x1499), film_release_region(?x1927, ?x1499), film_release_region(?x1259, ?x1499), film_release_region(?x1118, ?x1499), film_release_region(?x972, ?x1499), film_release_region(?x781, ?x1499), ?x8580 = 0hhggmy, medal(?x1499, ?x422), ?x9002 = 0ndsl1x, ?x7273 = 07c5l, ?x3000 = 045j3w, ?x781 = 0gkz15s, jurisdiction_of_office(?x182, ?x1499), ?x6661 = 0k7tq, ?x5791 = 03mgx6z, participating_countries(?x418, ?x1499), ?x11313 = 0by17xn, organization(?x1499, ?x127), ?x1603 = 06bnz, ?x10860 = 049w1q, ?x972 = 017gl1, olympics(?x1499, ?x584), ?x7016 = 07g1sm, ?x4024 = 0n04r, ?x4514 = 06tpmy, ?x2155 = 0407yfx, ?x1927 = 0by1wkq, ?x2342 = 0ct5zc, ?x1259 = 04hwbq, ?x1118 = 0_92w, ?x6215 = 0jyb4, taxonomy(?x47, ?x939) *> conf = 0.86 ranks of expected_values: 3, 4, 25, 33 EVAL 07_53 country 0d05w3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 40.000 39.000 0.905 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07_53 country 02k54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 40.000 39.000 0.905 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07_53 country 0h3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 40.000 39.000 0.905 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07_53 country 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 40.000 39.000 0.905 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #9686-06j8wx PRED entity: 06j8wx PRED relation: gender PRED expected values: 05zppz => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #13, 0.72 #166, 0.71 #174), 02zsn (0.52 #91, 0.42 #4, 0.40 #2) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 366 >> proper extension: 09gffmz; 070w7s; 03bx_5q; 03ckvj9; 02qlkc3; 02m92h; 03wh8pq; 05cqhl; 0cj2w; >> query: (?x5422, 05zppz) <- award_winner(?x926, ?x5422), profession(?x5422, ?x987), ?x987 = 0dxtg >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06j8wx gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.799 http://example.org/people/person/gender #9685-011xhx PRED entity: 011xhx PRED relation: award PRED expected values: 01d38t => 50 concepts (34 used for prediction) PRED predicted values (max 10 best out of 216): 01by1l (0.62 #5784, 0.39 #7406, 0.35 #1328), 01bgqh (0.45 #1258, 0.35 #5714, 0.31 #3688), 02f5qb (0.41 #3802, 0.24 #5828, 0.23 #6233), 01ckcd (0.38 #742, 0.32 #3577, 0.30 #337), 054ks3 (0.35 #1358, 0.25 #7436, 0.19 #953), 01c427 (0.35 #1300, 0.17 #3325, 0.17 #7378), 01c9jp (0.31 #595, 0.28 #3430, 0.20 #190), 01c99j (0.30 #1442, 0.13 #7520, 0.13 #5898), 02f716 (0.30 #3823, 0.23 #583, 0.22 #5849), 02f72_ (0.29 #3875, 0.21 #5901, 0.20 #6306) >> Best rule #5784 for best value: >> intensional similarity = 8 >> extensional distance = 304 >> proper extension: 04lgymt; 0jdhp; 01x15dc; 01vd7hn; 02_jkc; 01wyq0w; 010xjr; 05mxw33; 03cd1q; 02qtywd; >> query: (?x12880, 01by1l) <- award(?x12880, ?x3631), award(?x13142, ?x3631), award(?x9868, ?x3631), award(?x9791, ?x3631), ?x9791 = 016l09, group(?x227, ?x9868), artist(?x3888, ?x13142), ?x3888 = 01gfq4 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #736 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 089tm; 017j6; 05563d; 015srx; 07bzp; 03c3yf; 0134pk; *> query: (?x12880, 01d38t) <- artists(?x378, ?x12880), ?x378 = 07sbbz2, artist(?x441, ?x12880), group(?x227, ?x12880) *> conf = 0.23 ranks of expected_values: 17 EVAL 011xhx award 01d38t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 50.000 34.000 0.618 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9684-0jmjr PRED entity: 0jmjr PRED relation: school PRED expected values: 01j_cy => 67 concepts (59 used for prediction) PRED predicted values (max 10 best out of 206): 0bx8pn (0.55 #1161, 0.54 #1914, 0.50 #785), 0j_sncb (0.38 #1742, 0.36 #1365, 0.36 #989), 0dzst (0.36 #1472, 0.36 #1284, 0.33 #1660), 015q1n (0.36 #1241, 0.35 #2940, 0.33 #1617), 01ptt7 (0.33 #412, 0.31 #1918, 0.31 #1730), 06pwq (0.31 #1898, 0.29 #2654, 0.27 #1145), 065y4w7 (0.29 #7777, 0.28 #7392, 0.27 #1334), 07t90 (0.29 #3094, 0.24 #2904, 0.24 #2523), 01jsn5 (0.29 #3056, 0.24 #2485, 0.23 #1732), 07w0v (0.27 #1149, 0.26 #5678, 0.24 #8738) >> Best rule #1161 for best value: >> intensional similarity = 12 >> extensional distance = 9 >> proper extension: 0jmnl; >> query: (?x9937, 0bx8pn) <- draft(?x9937, ?x8586), draft(?x9937, ?x8542), draft(?x9937, ?x4979), ?x8542 = 09th87, ?x4979 = 0f4vx0, team(?x10097, ?x9937), school(?x9937, ?x621), people(?x2510, ?x10097), location(?x10097, ?x1523), ?x8586 = 038981, ?x2510 = 0x67, sport(?x9937, ?x4833) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2665 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 15 *> proper extension: 0jm9w; 0jm7n; *> query: (?x9937, 01j_cy) <- draft(?x9937, ?x8542), draft(?x9937, ?x4979), ?x8542 = 09th87, position(?x9937, ?x1348), team(?x10097, ?x9937), team(?x4747, ?x9937), draft(?x8228, ?x4979), ?x1348 = 01pv51, ?x8228 = 0jmcv, school(?x4979, ?x1201), category(?x1201, ?x134), institution(?x620, ?x1201), citytown(?x1201, ?x13766) *> conf = 0.18 ranks of expected_values: 37 EVAL 0jmjr school 01j_cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 67.000 59.000 0.545 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9683-04f7c55 PRED entity: 04f7c55 PRED relation: artist! PRED expected values: 015_1q => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 119): 033hn8 (0.33 #14, 0.19 #860, 0.12 #578), 016ckq (0.33 #44, 0.12 #890, 0.12 #608), 01cszh (0.33 #11, 0.12 #575, 0.11 #716), 03rhqg (0.27 #1144, 0.24 #1003, 0.17 #2413), 015_1q (0.25 #302, 0.25 #3827, 0.24 #4109), 01clyr (0.25 #175, 0.19 #880, 0.12 #598), 0g768 (0.25 #320, 0.17 #461, 0.14 #7372), 01cl2y (0.25 #313, 0.16 #1441, 0.14 #1159), 0n85g (0.25 #205, 0.16 #1756, 0.14 #4012), 0mzkr (0.25 #167, 0.12 #872, 0.12 #590) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01zmpg; >> query: (?x5691, 033hn8) <- instrumentalists(?x212, ?x5691), religion(?x5691, ?x109), people(?x1423, ?x5691), sibling(?x5691, ?x5514) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #302 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 017f4y; *> query: (?x5691, 015_1q) <- instrumentalists(?x212, ?x5691), role(?x5691, ?x227), place_of_birth(?x5691, ?x5719), ?x5719 = 0f2rq *> conf = 0.25 ranks of expected_values: 5 EVAL 04f7c55 artist! 015_1q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 144.000 144.000 0.333 http://example.org/music/record_label/artist #9682-0404j37 PRED entity: 0404j37 PRED relation: film_crew_role PRED expected values: 09vw2b7 0dxtw => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 31): 09zzb8 (0.75 #331, 0.75 #1685, 0.74 #661), 02r96rf (0.71 #3048, 0.66 #400, 0.65 #1688), 09vw2b7 (0.64 #668, 0.64 #1692, 0.64 #3052), 0dxtw (0.39 #144, 0.39 #936, 0.38 #342), 02ynfr (0.25 #1585, 0.19 #1700, 0.18 #940), 01xy5l_ (0.25 #1585, 0.16 #47, 0.12 #212), 015h31 (0.25 #1585, 0.14 #142, 0.12 #208), 089g0h (0.25 #1585, 0.12 #679, 0.12 #316), 02_n3z (0.25 #1585, 0.09 #2283, 0.09 #596), 089fss (0.25 #1585, 0.09 #3877, 0.08 #1691) >> Best rule #331 for best value: >> intensional similarity = 4 >> extensional distance = 152 >> proper extension: 02y_lrp; 01k1k4; 0ds33; 02x3lt7; 01r97z; 0_b3d; 02prw4h; 01vfqh; 01kff7; 07qg8v; ... >> query: (?x6448, 09zzb8) <- award(?x6448, ?x198), film_crew_role(?x6448, ?x2178), nominated_for(?x198, ?x144), ?x2178 = 01pvkk >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #668 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 251 *> proper extension: 047qxs; 03m8y5; 0mbql; *> query: (?x6448, 09vw2b7) <- featured_film_locations(?x6448, ?x3951), film(?x3036, ?x6448), film_crew_role(?x6448, ?x1284) *> conf = 0.64 ranks of expected_values: 3, 4 EVAL 0404j37 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 118.000 118.000 0.753 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0404j37 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 118.000 118.000 0.753 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9681-0hsn_ PRED entity: 0hsn_ PRED relation: type_of_union PRED expected values: 01g63y => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 01g63y (0.35 #1, 0.32 #13, 0.31 #10) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 01p7yb; 0159h6; 03f2_rc; 014x77; 01gvr1; 01mqz0; 028knk; 05dbf; 01hkhq; 0kszw; ... >> query: (?x8734, 01g63y) <- spouse(?x3980, ?x8734), award(?x8734, ?x749), ?x749 = 094qd5 >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hsn_ type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.355 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9680-073hgx PRED entity: 073hgx PRED relation: ceremony! PRED expected values: 0gqng 0p9sw 0gqwc 0gs9p 0gr42 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 338): 0gqng (0.91 #1223, 0.90 #1468, 0.85 #2206), 0gqwc (0.90 #3713, 0.89 #2980, 0.88 #2738), 0p9sw (0.89 #2944, 0.88 #2702, 0.88 #2460), 0gs9p (0.88 #1272, 0.88 #2739, 0.85 #2981), 0gr42 (0.81 #807, 0.80 #319, 0.79 #3004), 0gqzz (0.77 #5848, 0.76 #3417, 0.56 #39), 02x201b (0.77 #5848, 0.76 #3417, 0.15 #1394), 0czp_ (0.77 #5848, 0.76 #3417, 0.13 #3661), 03hkv_r (0.33 #3906, 0.32 #1709, 0.32 #1464), 054krc (0.33 #3906, 0.32 #1709, 0.32 #1464) >> Best rule #1223 for best value: >> intensional similarity = 15 >> extensional distance = 31 >> proper extension: 073hkh; 02yw5r; 0bzm81; 073h1t; 02hn5v; 0bz6l9; 0bc773; 0bzm__; 073hd1; 09306z; ... >> query: (?x7038, 0gqng) <- honored_for(?x7038, ?x2370), honored_for(?x7038, ?x2151), ceremony(?x2222, ?x7038), ceremony(?x1862, ?x7038), ceremony(?x1079, ?x7038), titles(?x53, ?x2151), ?x2222 = 0gs96, ?x1079 = 0l8z1, award(?x2370, ?x2915), award(?x2151, ?x112), genre(?x2370, ?x162), award_winner(?x7038, ?x777), ?x1862 = 0gr51, award_winner(?x2915, ?x157), film_release_region(?x2151, ?x87) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 073hgx ceremony! 0gr42 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073hgx ceremony! 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073hgx ceremony! 0gqwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073hgx ceremony! 0p9sw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073hgx ceremony! 0gqng CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony #9679-042v_gx PRED entity: 042v_gx PRED relation: instrumentalists PRED expected values: 03gr7w 03xnq9_ => 83 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1391): 01vsy95 (0.68 #9164, 0.68 #4274, 0.63 #10991), 01wz3cx (0.68 #9164, 0.63 #10991, 0.58 #4276), 01r0t_j (0.68 #9164, 0.63 #10991, 0.58 #4276), 014q2g (0.68 #4274, 0.63 #9163, 0.58 #16486), 01vsyg9 (0.68 #4274, 0.63 #9163, 0.58 #16486), 01vsnff (0.68 #4274, 0.63 #9163, 0.58 #9162), 0161sp (0.68 #4274, 0.63 #9163, 0.58 #9162), 01wp8w7 (0.68 #4274, 0.63 #9163, 0.58 #9162), 01vsl3_ (0.68 #4274, 0.63 #9163, 0.58 #9162), 01vsy7t (0.68 #4274, 0.63 #9163, 0.58 #9162) >> Best rule #9164 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0bxl5; >> query: (?x432, ?x1089) <- role(?x3214, ?x432), role(?x315, ?x432), role(?x2923, ?x432), role(?x3399, ?x432), role(?x3166, ?x432), ?x315 = 0l14md, performance_role(?x1089, ?x432), ?x3214 = 02snj9, ?x3399 = 01gx5f, ?x2923 = 02k856, artist(?x2149, ?x3166) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #4366 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 2 *> proper extension: 0342h; *> query: (?x432, 03gr7w) <- role(?x2205, ?x432), role(?x74, ?x432), role(?x11186, ?x432), role(?x9735, ?x432), role(?x8599, ?x432), role(?x2638, ?x432), role(?x300, ?x432), ?x2205 = 0dq630k, ?x300 = 01vw87c, ?x9735 = 01wxdn3, ?x11186 = 01304j, profession(?x8599, ?x131), ?x2638 = 02fn5r, instrumentalists(?x432, ?x133), ?x133 = 016qtt *> conf = 0.50 ranks of expected_values: 127, 758 EVAL 042v_gx instrumentalists 03xnq9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 83.000 63.000 0.682 http://example.org/music/instrument/instrumentalists EVAL 042v_gx instrumentalists 03gr7w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 83.000 63.000 0.682 http://example.org/music/instrument/instrumentalists #9678-01hkhq PRED entity: 01hkhq PRED relation: film PRED expected values: 05fcbk7 09wnnb => 130 concepts (101 used for prediction) PRED predicted values (max 10 best out of 835): 047myg9 (0.55 #71207, 0.55 #56964, 0.48 #106808), 02mt51 (0.25 #666, 0.02 #57630), 03bxp5 (0.25 #1080, 0.01 #66946, 0.01 #77627), 0prrm (0.20 #2638, 0.04 #11538, 0.04 #25781), 03wy8t (0.20 #3360, 0.04 #12260, 0.04 #14040), 013q07 (0.17 #3916, 0.08 #7476, 0.07 #11036), 0f40w (0.17 #3922, 0.08 #7482, 0.04 #11042), 027pfg (0.12 #1219, 0.10 #2999, 0.02 #11899), 02p76f9 (0.12 #1423, 0.08 #4983, 0.04 #8543), 04165w (0.12 #1313, 0.06 #6653, 0.03 #64086) >> Best rule #71207 for best value: >> intensional similarity = 2 >> extensional distance = 355 >> proper extension: 05_pkf; 0405l; 0bkq_8; >> query: (?x2493, ?x4027) <- languages(?x2493, ?x254), nominated_for(?x2493, ?x4027) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2241 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01s7z0; *> query: (?x2493, 05fcbk7) <- company(?x2493, ?x2486), religion(?x2493, ?x2694), actor(?x6023, ?x2493) *> conf = 0.10 ranks of expected_values: 90, 500 EVAL 01hkhq film 09wnnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 130.000 101.000 0.554 http://example.org/film/actor/film./film/performance/film EVAL 01hkhq film 05fcbk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 130.000 101.000 0.554 http://example.org/film/actor/film./film/performance/film #9677-02qx5h PRED entity: 02qx5h PRED relation: nationality PRED expected values: 09c7w0 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 66): 09c7w0 (0.82 #3102, 0.82 #3303, 0.82 #401), 02jx1 (0.33 #33, 0.26 #733, 0.21 #6712), 05tbn (0.25 #6211, 0.25 #6010, 0.24 #4707), 07ssc (0.21 #6712, 0.19 #715, 0.14 #1415), 0d060g (0.21 #6712, 0.14 #107, 0.12 #807), 0j5g9 (0.21 #6712, 0.05 #762, 0.02 #662), 03rk0 (0.09 #1846, 0.09 #2146, 0.09 #2346), 0345h (0.07 #1031, 0.04 #831, 0.03 #2701), 0h7x (0.06 #1035, 0.03 #835, 0.03 #2701), 03gj2 (0.04 #1026, 0.03 #826, 0.03 #2701) >> Best rule #3102 for best value: >> intensional similarity = 4 >> extensional distance = 1162 >> proper extension: 01gct2; 06mm1x; >> query: (?x12788, 09c7w0) <- student(?x6787, ?x12788), institution(?x865, ?x6787), major_field_of_study(?x6787, ?x742), currency(?x6787, ?x170) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qx5h nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.820 http://example.org/people/person/nationality #9676-0dcfv PRED entity: 0dcfv PRED relation: nutrient PRED expected values: 0838f 025sf8g => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 63): 0q01m (0.87 #1012, 0.84 #190, 0.83 #949), 09gvd (0.87 #985, 0.84 #190, 0.83 #922), 0h1yy (0.87 #994, 0.84 #190, 0.83 #931), 02kc4sf (0.87 #990, 0.84 #190, 0.83 #927), 025s0s0 (0.87 #987, 0.84 #190, 0.83 #924), 02kc5rj (0.87 #984, 0.84 #190, 0.83 #921), 0h1vg (0.87 #983, 0.84 #190, 0.83 #920), 025sf0_ (0.87 #1011, 0.84 #190, 0.83 #948), 0h1_c (0.87 #1004, 0.84 #190, 0.83 #941), 05wvs (0.87 #974, 0.84 #190, 0.83 #911) >> Best rule #1012 for best value: >> intensional similarity = 61 >> extensional distance = 13 >> proper extension: 0f25w9; >> query: (?x3264, 0q01m) <- nutrient(?x3264, ?x12454), nutrient(?x3264, ?x9915), nutrient(?x3264, ?x8243), nutrient(?x3264, ?x6192), nutrient(?x3264, ?x5549), nutrient(?x3264, ?x5337), nutrient(?x3264, ?x2018), nutrient(?x9005, ?x5337), nutrient(?x8298, ?x5337), nutrient(?x7719, ?x5337), nutrient(?x7057, ?x5337), nutrient(?x6191, ?x5337), nutrient(?x6159, ?x5337), nutrient(?x6032, ?x5337), nutrient(?x5009, ?x5337), nutrient(?x4068, ?x5337), nutrient(?x3900, ?x5337), nutrient(?x3468, ?x5337), nutrient(?x2701, ?x5337), nutrient(?x1303, ?x5337), nutrient(?x1257, ?x5337), ?x6192 = 06jry, ?x12454 = 025rw19, taxonomy(?x2018, ?x939), ?x6191 = 014j1m, ?x939 = 04n6k, nutrient(?x9732, ?x2018), nutrient(?x9489, ?x2018), ?x5549 = 025s7j4, ?x6032 = 01nkt, ?x9489 = 07j87, ?x7057 = 0fbdb, nutrient(?x6285, ?x8243), nutrient(?x5373, ?x8243), ?x8298 = 037ls6, ?x1257 = 09728, ?x6285 = 01645p, ?x5009 = 0fjfh, ?x1303 = 0fj52s, ?x6159 = 033cnk, ?x3468 = 0cxn2, ?x5373 = 0971v, ?x9005 = 04zpv, nutrient(?x10612, ?x9915), ?x3900 = 061_f, ?x2701 = 0hkxq, ?x9732 = 05z55, ?x10612 = 0frq6, ?x4068 = 0fbw6, ?x7719 = 0dj75, taxonomy(?x5337, ?x939), nutrient(?x6191, ?x2018), nutrient(?x8298, ?x2018), nutrient(?x6032, ?x9915), nutrient(?x1303, ?x9915), nutrient(?x1303, ?x2018), nutrient(?x4068, ?x9915), nutrient(?x4068, ?x2018), nutrient(?x9005, ?x2018), nutrient(?x3900, ?x9915), nutrient(?x9732, ?x9915) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #190 for first EXPECTED value: *> intensional similarity = 75 *> extensional distance = 5 *> proper extension: 01nkt; *> query: (?x3264, ?x1258) <- nutrient(?x3264, ?x9915), nutrient(?x3264, ?x8243), nutrient(?x3264, ?x6192), nutrient(?x3264, ?x5549), nutrient(?x3264, ?x5337), nutrient(?x3264, ?x2018), ?x8243 = 014d7f, ?x6192 = 06jry, nutrient(?x9732, ?x2018), nutrient(?x9489, ?x2018), nutrient(?x9005, ?x2018), nutrient(?x8298, ?x2018), nutrient(?x7719, ?x2018), nutrient(?x7057, ?x2018), nutrient(?x6191, ?x2018), nutrient(?x6159, ?x2018), nutrient(?x5009, ?x2018), nutrient(?x4068, ?x2018), nutrient(?x3900, ?x2018), nutrient(?x3468, ?x2018), nutrient(?x2701, ?x2018), nutrient(?x1303, ?x2018), nutrient(?x1257, ?x2018), ?x9005 = 04zpv, ?x3468 = 0cxn2, ?x8298 = 037ls6, ?x6191 = 014j1m, ?x2701 = 0hkxq, ?x4068 = 0fbw6, ?x7719 = 0dj75, ?x9915 = 025tkqy, ?x7057 = 0fbdb, ?x5337 = 06x4c, ?x1303 = 0fj52s, ?x1257 = 09728, ?x3900 = 061_f, ?x9732 = 05z55, ?x6159 = 033cnk, ?x5009 = 0fjfh, taxonomy(?x2018, ?x939), nutrient(?x9489, ?x13498), nutrient(?x9489, ?x12902), nutrient(?x9489, ?x12083), nutrient(?x9489, ?x11758), nutrient(?x9489, ?x10891), nutrient(?x9489, ?x10709), nutrient(?x9489, ?x10195), nutrient(?x9489, ?x9949), nutrient(?x9489, ?x9490), nutrient(?x9489, ?x7894), nutrient(?x9489, ?x7720), nutrient(?x9489, ?x7431), nutrient(?x9489, ?x7364), nutrient(?x9489, ?x6586), nutrient(?x9489, ?x5374), nutrient(?x9489, ?x2702), nutrient(?x9489, ?x1258), ?x7894 = 0f4hc, ?x5549 = 025s7j4, ?x2702 = 0838f, ?x12902 = 0fzjh, ?x7720 = 025s7x6, ?x7431 = 09gwd, ?x10195 = 0hkwr, ?x5374 = 025s0zp, ?x13498 = 07q0m, ?x7364 = 09gvd, ?x9949 = 02kd0rh, ?x939 = 04n6k, ?x12083 = 01n78x, ?x10891 = 0g5gq, ?x10709 = 0h1sz, ?x6586 = 05gh50, ?x9490 = 0h1sg, ?x11758 = 0q01m *> conf = 0.84 ranks of expected_values: 19, 36 EVAL 0dcfv nutrient 025sf8g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 43.000 43.000 0.867 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0dcfv nutrient 0838f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 43.000 43.000 0.867 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #9675-03_dj PRED entity: 03_dj PRED relation: influenced_by! PRED expected values: 0gd5z => 115 concepts (29 used for prediction) PRED predicted values (max 10 best out of 374): 0683n (0.43 #841, 0.12 #1349, 0.12 #3891), 06jcc (0.33 #307, 0.12 #1323, 0.12 #9154), 07lp1 (0.29 #919, 0.12 #9154, 0.12 #6611), 0n6kf (0.29 #695, 0.12 #9154, 0.12 #6611), 0399p (0.29 #831, 0.12 #9154, 0.12 #6611), 01v_0b (0.29 #986, 0.12 #9154, 0.12 #6611), 05qzv (0.29 #906, 0.12 #9154, 0.12 #6611), 01x53m (0.29 #874, 0.12 #9154, 0.12 #6611), 0m77m (0.29 #542, 0.03 #1050, 0.02 #3592), 05jm7 (0.15 #1154, 0.14 #3186, 0.12 #9154) >> Best rule #841 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 012cph; 045bg; 0lrh; 032l1; 06whf; >> query: (?x12345, 0683n) <- influenced_by(?x1029, ?x12345), profession(?x12345, ?x353), place_of_death(?x12345, ?x3699), ?x1029 = 08433 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 012cph; 045bg; 0lrh; 032l1; 06whf; *> query: (?x12345, 0gd5z) <- influenced_by(?x1029, ?x12345), profession(?x12345, ?x353), place_of_death(?x12345, ?x3699), ?x1029 = 08433 *> conf = 0.14 ranks of expected_values: 12 EVAL 03_dj influenced_by! 0gd5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 115.000 29.000 0.429 http://example.org/influence/influence_node/influenced_by #9674-030hbp PRED entity: 030hbp PRED relation: type_of_union PRED expected values: 01g63y => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.87 #81, 0.87 #57, 0.86 #77), 01g63y (0.38 #86, 0.36 #106, 0.36 #94) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 01x1cn2; 011lvx; 01bj6y; 049468; >> query: (?x10491, 04ztj) <- spouse(?x1815, ?x10491), student(?x7744, ?x10491), award(?x10491, ?x678) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #86 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 178 *> proper extension: 04bs3j; 0htlr; 0456xp; 04shbh; 0prjs; 01mqz0; 03xmy1; 03rl84; 02fb1n; 01vhb0; ... *> query: (?x10491, 01g63y) <- spouse(?x1815, ?x10491), participant(?x2216, ?x10491), film(?x10491, ?x4643) *> conf = 0.38 ranks of expected_values: 2 EVAL 030hbp type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.871 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9673-01nrnm PRED entity: 01nrnm PRED relation: student PRED expected values: 02tn0_ => 156 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1645): 09v6tz (0.21 #3433, 0.04 #49460, 0.04 #13893), 01vwbts (0.20 #811, 0.11 #7087, 0.07 #2903), 02cyfz (0.14 #2425, 0.05 #48452, 0.05 #8701), 0ff3y (0.14 #4161, 0.05 #50188, 0.04 #14621), 03ft8 (0.14 #2348, 0.05 #48375, 0.04 #12808), 0cp9f9 (0.14 #3519, 0.04 #49546, 0.03 #55825), 01pqy_ (0.14 #2989, 0.04 #49016, 0.03 #55295), 01wwvt2 (0.14 #2456, 0.04 #48483, 0.03 #54762), 026m0 (0.14 #3915, 0.04 #49942, 0.03 #56221), 023v4_ (0.14 #2952, 0.02 #48979, 0.01 #53163) >> Best rule #3433 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0473m9; 01w5m; 09f2j; 01jt2w; >> query: (?x6120, 09v6tz) <- institution(?x1200, ?x6120), category(?x6120, ?x134), major_field_of_study(?x6120, ?x373), currency(?x6120, ?x1099), ?x373 = 02vxn >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #56491 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 026036; *> query: (?x6120, ?x1039) <- student(?x6120, ?x2803), program(?x2803, ?x2026), producer_type(?x2803, ?x632), award_nominee(?x2803, ?x1039) *> conf = 0.02 ranks of expected_values: 907 EVAL 01nrnm student 02tn0_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 156.000 73.000 0.214 http://example.org/education/educational_institution/students_graduates./education/education/student #9672-04bd8y PRED entity: 04bd8y PRED relation: film PRED expected values: 0315rp => 76 concepts (57 used for prediction) PRED predicted values (max 10 best out of 513): 0g60z (0.58 #28501, 0.37 #89072, 0.34 #39191), 01rnly (0.25 #1563, 0.08 #5125), 03cffvv (0.15 #3516, 0.08 #5297, 0.07 #7078), 01jrbb (0.13 #5814, 0.08 #2252, 0.03 #60568), 023vcd (0.13 #6973, 0.01 #10535, 0.01 #12316), 08mg_b (0.13 #12468, 0.12 #1118, 0.02 #11804), 02z9rr (0.13 #12468, 0.12 #1360, 0.01 #10265), 0kvbl6 (0.13 #12468, 0.12 #1114, 0.01 #10019), 01msrb (0.13 #12468, 0.12 #780, 0.01 #9685), 07gghl (0.13 #12468, 0.01 #11858) >> Best rule #28501 for best value: >> intensional similarity = 3 >> extensional distance = 1049 >> proper extension: 04bdxl; 06qgvf; 02bfmn; 0cnl80; 05ty4m; 0m2wm; 02zq43; 0l8v5; 032xhg; 054_mz; ... >> query: (?x820, ?x337) <- award_nominee(?x820, ?x1870), film(?x820, ?x1450), nominated_for(?x820, ?x337) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04bd8y film 0315rp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 57.000 0.584 http://example.org/film/actor/film./film/performance/film #9671-021w0_ PRED entity: 021w0_ PRED relation: major_field_of_study PRED expected values: 04rlf => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 121): 01mkq (0.55 #4143, 0.50 #16, 0.42 #2767), 02lp1 (0.55 #4139, 0.42 #2512, 0.39 #3764), 0g26h (0.46 #2544, 0.44 #294, 0.41 #4796), 02j62 (0.43 #4158, 0.43 #12294, 0.42 #4033), 04rjg (0.39 #4023, 0.37 #4148, 0.33 #12284), 062z7 (0.38 #4155, 0.35 #2528, 0.34 #3530), 03g3w (0.38 #4154, 0.33 #2778, 0.32 #6030), 02_7t (0.38 #317, 0.31 #2567, 0.28 #4819), 0_jm (0.35 #435, 0.32 #2560, 0.32 #560), 01tbp (0.34 #2562, 0.26 #4189, 0.26 #4439) >> Best rule #4143 for best value: >> intensional similarity = 5 >> extensional distance = 98 >> proper extension: 023zl; >> query: (?x8851, 01mkq) <- institution(?x4981, ?x8851), institution(?x1368, ?x8851), category(?x8851, ?x134), ?x1368 = 014mlp, ?x4981 = 03bwzr4 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #322 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0yldt; *> query: (?x8851, 04rlf) <- institution(?x1368, ?x8851), child(?x3360, ?x8851), category(?x8851, ?x134), citytown(?x8851, ?x8468) *> conf = 0.12 ranks of expected_values: 37 EVAL 021w0_ major_field_of_study 04rlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 184.000 184.000 0.550 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #9670-059j2 PRED entity: 059j2 PRED relation: contains PRED expected values: 0lvng => 233 concepts (136 used for prediction) PRED predicted values (max 10 best out of 2835): 0fqyc (0.86 #201547, 0.85 #204468, 0.84 #181101), 0k3p (0.83 #242447, 0.81 #61339, 0.77 #108074), 02_vs (0.83 #242447, 0.81 #61339, 0.61 #350537), 0h095 (0.75 #292114, 0.74 #227842, 0.33 #9189), 0flsf (0.75 #292114, 0.74 #227842, 0.06 #61085), 0qjd (0.61 #350537, 0.60 #379746, 0.10 #33937), 0154j (0.61 #350537, 0.10 #43813, 0.02 #131441), 059j2 (0.51 #265815, 0.36 #309642, 0.10 #43813), 049nq (0.51 #265815), 0lvng (0.34 #338852, 0.03 #129527, 0.03 #149976) >> Best rule #201547 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 036wy; 0166c7; >> query: (?x1229, ?x3407) <- contains(?x1229, ?x2351), time_zones(?x1229, ?x2864), administrative_parent(?x3407, ?x1229) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #338852 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 02ly_; *> query: (?x1229, ?x7363) <- contains(?x1229, ?x13675), location(?x2580, ?x1229), citytown(?x7363, ?x13675) *> conf = 0.34 ranks of expected_values: 10 EVAL 059j2 contains 0lvng CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 233.000 136.000 0.858 http://example.org/location/location/contains #9669-0cs134 PRED entity: 0cs134 PRED relation: nominated_for! PRED expected values: 09qvf4 => 103 concepts (95 used for prediction) PRED predicted values (max 10 best out of 189): 0m7yy (0.73 #5361, 0.70 #6996, 0.69 #8861), 09qrn4 (0.64 #2027, 0.53 #1328, 0.49 #2960), 09qv3c (0.63 #1205, 0.51 #1904, 0.51 #2837), 03c7tr1 (0.56 #4940, 0.19 #6063, 0.19 #5828), 03ccq3s (0.47 #2938, 0.46 #2005, 0.42 #1306), 0fbtbt (0.45 #1557, 0.37 #3888, 0.32 #3655), 0bdx29 (0.45 #1480, 0.26 #2179, 0.24 #6145), 09qvf4 (0.44 #2010, 0.36 #2943, 0.32 #1311), 0fbvqf (0.40 #1435, 0.27 #3766, 0.26 #2134), 0bdw1g (0.40 #1429, 0.25 #3760, 0.24 #2128) >> Best rule #5361 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 0cwrr; 04glx0; >> query: (?x10731, ?x757) <- genre(?x10731, ?x53), honored_for(?x762, ?x10731), award(?x10731, ?x757), nominated_for(?x1630, ?x10731) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2010 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 37 *> proper extension: 026wlxw; *> query: (?x10731, 09qvf4) <- nominated_for(?x2603, ?x10731), nominated_for(?x1630, ?x10731), award(?x7511, ?x2603), award(?x1057, ?x2603), ?x7511 = 01lv85 *> conf = 0.44 ranks of expected_values: 8 EVAL 0cs134 nominated_for! 09qvf4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 103.000 95.000 0.725 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9668-057176 PRED entity: 057176 PRED relation: film PRED expected values: 05p1qyh => 91 concepts (52 used for prediction) PRED predicted values (max 10 best out of 558): 05q_dw (0.67 #1788, 0.57 #17874, 0.50 #10724), 05p1qyh (0.50 #10724, 0.48 #25026, 0.48 #25025), 020bv3 (0.39 #2107, 0.02 #5681, 0.02 #48579), 0bvn25 (0.29 #50, 0.02 #34012, 0.01 #8986), 065_cjc (0.29 #1195), 02c638 (0.28 #2127, 0.01 #85803), 03ydlnj (0.17 #3184), 03bx2lk (0.14 #185, 0.06 #1973, 0.03 #3760), 06gb1w (0.14 #733, 0.06 #2521, 0.02 #4308), 01qb5d (0.14 #138, 0.06 #1926, 0.02 #3713) >> Best rule #1788 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0794g; 07swvb; 086nl7; >> query: (?x6979, ?x5157) <- award_nominee(?x9084, ?x6979), award_winner(?x5157, ?x6979), ?x9084 = 036hf4 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #10724 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 669 *> proper extension: 016qtt; 05ty4m; 05cj4r; 02zq43; 0436f4; 03f2_rc; 01gvr1; 03qd_; 01j5x6; 015grj; ... *> query: (?x6979, ?x2362) <- award_nominee(?x843, ?x6979), people(?x1050, ?x6979), nominated_for(?x6979, ?x2362) *> conf = 0.50 ranks of expected_values: 2 EVAL 057176 film 05p1qyh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 52.000 0.667 http://example.org/film/actor/film./film/performance/film #9667-02g839 PRED entity: 02g839 PRED relation: student PRED expected values: 03mszl => 115 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1578): 026m0 (0.33 #1802, 0.03 #16293, 0.02 #36993), 0gs1_ (0.33 #1118, 0.02 #36309, 0.01 #48729), 01_x6d (0.33 #749, 0.02 #86944, 0.02 #82803), 09xvf7 (0.33 #2035, 0.01 #16526, 0.01 #20666), 0brkwj (0.33 #1385, 0.01 #15876, 0.01 #20016), 083chw (0.09 #2096, 0.08 #4166, 0.05 #6236), 01l1hr (0.09 #2635, 0.08 #4705, 0.04 #15056), 0d3qd0 (0.09 #2844, 0.08 #4914, 0.04 #6984), 02nrdp (0.09 #3732, 0.08 #5802, 0.04 #7872), 0gpprt (0.09 #3576, 0.08 #5646, 0.04 #7716) >> Best rule #1802 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01qd_r; >> query: (?x1151, 026m0) <- student(?x1151, ?x5587), student(?x1151, ?x2390), citytown(?x1151, ?x3052), type_of_union(?x5587, ?x566), ?x2390 = 01_x6v >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7530 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 08qnnv; 01d34b; 07x4c; 015y3j; 033gn8; *> query: (?x1151, 03mszl) <- student(?x1151, ?x8341), profession(?x8341, ?x220), program(?x8341, ?x9788) *> conf = 0.02 ranks of expected_values: 632 EVAL 02g839 student 03mszl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 115.000 91.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #9666-04088s0 PRED entity: 04088s0 PRED relation: team! PRED expected values: 0b_6v_ 0b_756 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 15): 0b_6pv (0.75 #249, 0.67 #399, 0.67 #279), 0bzrsh (0.75 #248, 0.61 #398, 0.60 #203), 0b_6mr (0.75 #251, 0.60 #206, 0.60 #191), 0b_6_l (0.67 #404, 0.67 #284, 0.62 #254), 0b_6v_ (0.67 #275, 0.61 #395, 0.60 #185), 0b_6x2 (0.67 #391, 0.60 #181, 0.56 #271), 0b_756 (0.62 #250, 0.61 #400, 0.60 #205), 0b_6lb (0.62 #247, 0.61 #397, 0.60 #172), 0b_770 (0.62 #255, 0.60 #180, 0.50 #120), 0b_6q5 (0.61 #403, 0.60 #163, 0.56 #298) >> Best rule #249 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 03y9p40; >> query: (?x5032, 0b_6pv) <- team(?x13209, ?x5032), team(?x6002, ?x5032), ?x13209 = 0b_734, colors(?x5032, ?x663), team(?x6002, ?x5551), colors(?x7618, ?x663), ?x5551 = 02pjzvh, sport(?x5032, ?x12913), colors(?x9172, ?x663), institution(?x620, ?x7618), position_s(?x9172, ?x180) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #275 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 7 *> proper extension: 02plv57; 03d5m8w; *> query: (?x5032, 0b_6v_) <- team(?x13209, ?x5032), team(?x8992, ?x5032), team(?x6802, ?x5032), team(?x5897, ?x5032), ?x5897 = 0b_6rk, sport(?x5032, ?x12913), team(?x13209, ?x11789), team(?x13209, ?x9909), ?x6802 = 0br1x_, ?x9909 = 026wlnm, locations(?x8992, ?x674), ?x11789 = 02pyyld *> conf = 0.67 ranks of expected_values: 5, 7 EVAL 04088s0 team! 0b_756 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 121.000 121.000 0.750 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 04088s0 team! 0b_6v_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 121.000 121.000 0.750 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #9665-095b70 PRED entity: 095b70 PRED relation: type_of_union PRED expected values: 04ztj => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.73 #9, 0.72 #150, 0.72 #13), 01g63y (0.47 #250, 0.43 #145, 0.19 #10) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 0n6f8; >> query: (?x5996, 04ztj) <- participant(?x1660, ?x5996), award_winner(?x3624, ?x5996), vacationer(?x10450, ?x5996) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 095b70 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.726 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9664-01xlqd PRED entity: 01xlqd PRED relation: category PRED expected values: 08mbj5d => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.35 #11, 0.34 #9, 0.31 #2) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 207 >> proper extension: 0522wp; >> query: (?x9832, 08mbj5d) <- film(?x902, ?x9832), film_distribution_medium(?x9832, ?x81), film(?x902, ?x7760), nominated_for(?x2794, ?x7760) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xlqd category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.354 http://example.org/common/topic/webpage./common/webpage/category #9663-02gx2k PRED entity: 02gx2k PRED relation: ceremony PRED expected values: 0gpjbt 0466p0j 02cg41 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 122): 0gpjbt (0.92 #23, 0.91 #149, 0.91 #653), 02cg41 (0.91 #867, 0.91 #615, 0.88 #111), 0466p0j (0.89 #570, 0.89 #318, 0.88 #66), 05c1t6z (0.12 #2783, 0.11 #2279, 0.10 #2405), 02q690_ (0.11 #2827, 0.10 #2323, 0.10 #2449), 0gvstc3 (0.10 #2799, 0.09 #2295, 0.08 #2421), 03nnm4t (0.10 #2836, 0.10 #2332, 0.10 #2458), 0gx_st (0.09 #2298, 0.09 #2802, 0.09 #2424), 0n8_m93 (0.09 #4663, 0.09 #2876, 0.07 #3380), 0bzm81 (0.09 #4663, 0.09 #2788, 0.07 #3292) >> Best rule #23 for best value: >> intensional similarity = 9 >> extensional distance = 50 >> proper extension: 02581q; 02wh75; 026mg3; 01d38g; 02g8mp; 01c4_6; 02nhxf; 025m8y; 01by1l; 02v1m7; ... >> query: (?x1584, 0gpjbt) <- ceremony(?x1584, ?x725), ceremony(?x1584, ?x486), ceremony(?x1584, ?x342), ceremony(?x1584, ?x139), ?x725 = 01bx35, ?x139 = 05pd94v, ?x342 = 01s695, award(?x1322, ?x1584), ?x486 = 02rjjll >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 02gx2k ceremony 02cg41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.923 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02gx2k ceremony 0466p0j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.923 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02gx2k ceremony 0gpjbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.923 http://example.org/award/award_category/winners./award/award_honor/ceremony #9662-0tyql PRED entity: 0tyql PRED relation: currency PRED expected values: 09nqf => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.85 #4, 0.85 #2, 0.78 #1) >> Best rule #4 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 0v0d9; 0t_07; 0hz35; 0d739; 01m7mv; 01m8dg; 01m2n1; >> query: (?x1589, 09nqf) <- contains(?x2020, ?x1589), ?x2020 = 05k7sb, time_zones(?x1589, ?x2674), ?x2674 = 02hcv8, category(?x1589, ?x134) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tyql currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.852 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #9661-07bwr PRED entity: 07bwr PRED relation: country PRED expected values: 07ssc => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 40): 07ssc (0.64 #136, 0.30 #500, 0.29 #1287), 0chghy (0.52 #3894, 0.37 #4867, 0.37 #3652), 0d0vqn (0.37 #4867, 0.37 #3652, 0.03 #2431), 0f8l9c (0.27 #139, 0.14 #503, 0.10 #2939), 0345h (0.26 #511, 0.14 #328, 0.13 #2031), 01z4y (0.25 #1332, 0.15 #181, 0.07 #545), 0154j (0.10 #65, 0.03 #2431, 0.03 #489), 0ctw_b (0.09 #143, 0.03 #872, 0.03 #2431), 03rjj (0.09 #490, 0.06 #247, 0.04 #368), 03_3d (0.07 #308, 0.04 #6446, 0.04 #1097) >> Best rule #136 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0gjk1d; 02_kd; 02t_h3; >> query: (?x5066, 07ssc) <- titles(?x2480, ?x5066), film(?x157, ?x5066), executive_produced_by(?x5066, ?x5973), ?x5973 = 02q42j_ >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07bwr country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.636 http://example.org/film/film/country #9660-05d8vw PRED entity: 05d8vw PRED relation: artists! PRED expected values: 02mscn => 135 concepts (72 used for prediction) PRED predicted values (max 10 best out of 230): 025sc50 (0.75 #1287, 0.52 #3759, 0.49 #4377), 06by7 (0.70 #19810, 0.60 #20120, 0.53 #20739), 0glt670 (0.47 #1278, 0.47 #5295, 0.41 #1587), 05bt6j (0.42 #354, 0.37 #18596, 0.36 #663), 02lnbg (0.42 #1295, 0.32 #3767, 0.25 #6548), 0ggx5q (0.39 #1314, 0.29 #3786, 0.26 #4404), 02x8m (0.32 #4963, 0.28 #4345, 0.27 #6508), 03_d0 (0.30 #939, 0.28 #4956, 0.27 #4338), 0m0jc (0.29 #627, 0.25 #318, 0.16 #1554), 0155w (0.25 #4433, 0.23 #5051, 0.20 #107) >> Best rule #1287 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 01kx_81; 07ss8_; 01x1cn2; 0qf3p; 01vsykc; 01vvyfh; 012z8_; 0ffgh; 0dt1cm; 0gps0z; >> query: (?x2055, 025sc50) <- artists(?x3928, ?x2055), artist(?x6474, ?x2055), ?x3928 = 0gywn, currency(?x2055, ?x170) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3336 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 028q6; 01cv3n; *> query: (?x2055, 02mscn) <- artists(?x671, ?x2055), award(?x2055, ?x567), religion(?x2055, ?x10107), origin(?x2055, ?x4733) *> conf = 0.04 ranks of expected_values: 101 EVAL 05d8vw artists! 02mscn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 135.000 72.000 0.750 http://example.org/music/genre/artists #9659-02jkkv PRED entity: 02jkkv PRED relation: film! PRED expected values: 014zcr => 126 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1072): 06t8b (0.45 #118391, 0.43 #110083, 0.42 #124622), 0q9kd (0.20 #10387, 0.13 #27000, 0.11 #108006), 0d_skg (0.20 #10387, 0.13 #27000, 0.11 #108006), 055c8 (0.18 #8849, 0.09 #19234, 0.07 #23386), 024bbl (0.18 #9143, 0.09 #19528, 0.07 #23680), 01w1kyf (0.17 #905, 0.11 #7136, 0.11 #5058), 035kl6 (0.17 #1799, 0.11 #8030, 0.11 #5952), 01x209s (0.17 #1141, 0.11 #7372, 0.11 #5294), 01tspc6 (0.17 #161, 0.11 #6392, 0.11 #4314), 01dbk6 (0.17 #953, 0.11 #7184, 0.11 #5106) >> Best rule #118391 for best value: >> intensional similarity = 4 >> extensional distance = 821 >> proper extension: 07bz5; >> query: (?x9361, ?x5364) <- award(?x9361, ?x384), nominated_for(?x5364, ?x9361), location(?x5364, ?x1523), award(?x5364, ?x154) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #49891 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 182 *> proper extension: 01h1bf; 02kk_c; *> query: (?x9361, 014zcr) <- award_winner(?x9361, ?x7903), nominated_for(?x5364, ?x9361), producer_type(?x7903, ?x632) *> conf = 0.05 ranks of expected_values: 183 EVAL 02jkkv film! 014zcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 126.000 69.000 0.447 http://example.org/film/actor/film./film/performance/film #9658-01gx5f PRED entity: 01gx5f PRED relation: artists! PRED expected values: 0hdf8 0y4f8 => 87 concepts (31 used for prediction) PRED predicted values (max 10 best out of 293): 064t9 (0.51 #3556, 0.49 #5914, 0.34 #4441), 0xhtw (0.50 #1488, 0.38 #2671, 0.34 #5035), 0p9xd (0.50 #1621, 0.25 #442, 0.24 #4726), 01fh36 (0.50 #1552, 0.25 #373, 0.23 #2735), 025sc50 (0.49 #3590, 0.25 #5948, 0.14 #1813), 0m0jc (0.44 #2367, 0.20 #1186, 0.19 #4143), 06j6l (0.38 #3588, 0.33 #43, 0.25 #5946), 05w3f (0.33 #1506, 0.31 #2689, 0.25 #327), 0jrv_ (0.33 #1638, 0.25 #459, 0.24 #4726), 016jny (0.33 #95, 0.25 #389, 0.23 #2751) >> Best rule #3556 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 01vvydl; 01l1b90; 09qr6; 04xrx; 0126y2; 015mrk; 0840vq; 0gy6z9; 016ksk; 01q32bd; ... >> query: (?x3399, 064t9) <- artists(?x2937, ?x3399), type_of_union(?x3399, ?x566), ?x2937 = 0glt670 >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #1536 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 06nv27; 02ndj5; *> query: (?x3399, 0hdf8) <- artists(?x10969, ?x3399), artists(?x505, ?x3399), ?x10969 = 029fbr, artists(?x505, ?x12753), artists(?x505, ?x7211), ?x12753 = 0f8grf, ?x7211 = 0135xb *> conf = 0.33 ranks of expected_values: 12, 201 EVAL 01gx5f artists! 0y4f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 87.000 31.000 0.508 http://example.org/music/genre/artists EVAL 01gx5f artists! 0hdf8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 87.000 31.000 0.508 http://example.org/music/genre/artists #9657-06nns1 PRED entity: 06nns1 PRED relation: location PRED expected values: 059rby => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 124): 01cx_ (0.70 #45801, 0.56 #23299, 0.55 #14459), 02_286 (0.25 #26551, 0.22 #13692, 0.17 #40213), 01n7q (0.18 #63, 0.09 #2472, 0.05 #4078), 0cr3d (0.13 #13799, 0.09 #12192, 0.08 #12995), 01531 (0.09 #157, 0.09 #3369, 0.05 #5778), 0cc56 (0.09 #57, 0.06 #13712, 0.05 #26571), 059rby (0.09 #16, 0.06 #1622, 0.06 #9654), 0k049 (0.09 #8, 0.05 #4023, 0.05 #4826), 0rh6k (0.09 #807, 0.05 #13659, 0.04 #16071), 0r0m6 (0.09 #217, 0.04 #2626, 0.03 #4232) >> Best rule #45801 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 07m69t; >> query: (?x5853, ?x3052) <- place_of_birth(?x5853, ?x3052), location(?x5853, ?x1523) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 09fb5; 06r3p2; *> query: (?x5853, 059rby) <- profession(?x5853, ?x1032), film(?x5853, ?x5323), ?x5323 = 011yn5 *> conf = 0.09 ranks of expected_values: 7 EVAL 06nns1 location 059rby CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 102.000 102.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #9656-01qz5 PRED entity: 01qz5 PRED relation: award PRED expected values: 0gq9h => 95 concepts (91 used for prediction) PRED predicted values (max 10 best out of 201): 019f4v (0.43 #52, 0.19 #515, 0.13 #977), 054knh (0.35 #694, 0.31 #1156, 0.30 #1157), 02pqp12 (0.35 #694, 0.31 #1156, 0.29 #231), 0gs9p (0.35 #694, 0.31 #1156, 0.29 #231), 0k611 (0.35 #694, 0.31 #1156, 0.29 #231), 02qvyrt (0.35 #694, 0.31 #1156, 0.29 #231), 040njc (0.35 #694, 0.31 #1156, 0.29 #231), 0gqy2 (0.35 #694, 0.31 #1156, 0.29 #231), 02qyntr (0.35 #694, 0.31 #1156, 0.29 #231), 02r22gf (0.35 #694, 0.31 #1156, 0.29 #231) >> Best rule #52 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 02h22; >> query: (?x8188, 019f4v) <- nominated_for(?x7965, ?x8188), nominated_for(?x1703, ?x8188), ?x7965 = 054knh, titles(?x162, ?x8188), ?x1703 = 0k611 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #694 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 04h4c9; *> query: (?x8188, ?x198) <- nominated_for(?x7965, ?x8188), nominated_for(?x198, ?x8188), ?x7965 = 054knh, award_winner(?x8188, ?x5495), currency(?x8188, ?x170) *> conf = 0.35 ranks of expected_values: 11 EVAL 01qz5 award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 95.000 91.000 0.429 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9655-0969fd PRED entity: 0969fd PRED relation: student! PRED expected values: 03ksy => 119 concepts (87 used for prediction) PRED predicted values (max 10 best out of 226): 017z88 (0.33 #82, 0.11 #608, 0.08 #1660), 07wrz (0.13 #1114, 0.10 #2166, 0.03 #4271), 07tgn (0.13 #1069, 0.07 #7908, 0.05 #2647), 03ksy (0.13 #1158, 0.06 #15367, 0.06 #20627), 01w5m (0.13 #4314, 0.09 #3157, 0.07 #10522), 09f2j (0.11 #1737, 0.06 #10154, 0.06 #11733), 0217m9 (0.11 #697, 0.03 #1749, 0.02 #10166), 0342z_ (0.11 #981, 0.03 #2033, 0.01 #5190), 0bwfn (0.11 #3957, 0.11 #3431, 0.09 #29738), 04b_46 (0.11 #3909, 0.09 #3383, 0.05 #12327) >> Best rule #82 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 016k62; >> query: (?x10677, 017z88) <- nationality(?x10677, ?x94), award_winner(?x8129, ?x10677), ?x8129 = 01dhpj, profession(?x10677, ?x353) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1158 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0hcvy; *> query: (?x10677, 03ksy) <- influenced_by(?x10677, ?x3541), ?x3541 = 040_9, profession(?x10677, ?x353) *> conf = 0.13 ranks of expected_values: 4 EVAL 0969fd student! 03ksy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 119.000 87.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #9654-0nmj PRED entity: 0nmj PRED relation: category PRED expected values: 08mbj5d => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #2, 0.78 #59, 0.78 #12) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01j_9c; 02yr1q; 03fgm; >> query: (?x10350, 08mbj5d) <- contains(?x961, ?x10350), contains(?x94, ?x10350), ?x94 = 09c7w0, ?x961 = 03s0w >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nmj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.800 http://example.org/common/topic/webpage./common/webpage/category #9653-02633g PRED entity: 02633g PRED relation: currency PRED expected values: 09nqf => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.37 #28, 0.35 #22, 0.32 #13), 01nv4h (0.03 #26, 0.03 #11, 0.03 #14) >> Best rule #28 for best value: >> intensional similarity = 3 >> extensional distance = 140 >> proper extension: 02dlfh; >> query: (?x8065, 09nqf) <- influenced_by(?x8065, ?x1145), film(?x8065, ?x2318), profession(?x8065, ?x319) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02633g currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.373 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #9652-0djlxb PRED entity: 0djlxb PRED relation: nominated_for! PRED expected values: 02x8n1n 02x4wr9 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 177): 0gq9h (0.52 #533, 0.39 #2178, 0.34 #2648), 0gs9p (0.49 #535, 0.33 #2180, 0.32 #2650), 019f4v (0.46 #525, 0.33 #2170, 0.30 #1230), 057xs89 (0.43 #117, 0.25 #8933, 0.24 #9639), 02hsq3m (0.43 #30, 0.12 #265, 0.11 #2145), 0gr4k (0.40 #497, 0.21 #5433, 0.21 #1202), 0f4x7 (0.37 #496, 0.25 #8933, 0.24 #9639), 0gqy2 (0.36 #590, 0.25 #8933, 0.24 #2235), 04dn09n (0.35 #506, 0.25 #8933, 0.25 #2151), 0k611 (0.35 #544, 0.30 #1249, 0.29 #2189) >> Best rule #533 for best value: >> intensional similarity = 3 >> extensional distance = 159 >> proper extension: 0yx7h; 0194zl; 01k5y0; >> query: (?x3275, 0gq9h) <- nominated_for(?x1972, ?x3275), ?x1972 = 0gqyl, film(?x2437, ?x3275) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #8933 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 849 *> proper extension: 08j7lh; *> query: (?x3275, ?x112) <- language(?x3275, ?x254), award_winner(?x3275, ?x4835), nominated_for(?x1254, ?x3275), award(?x4835, ?x112) *> conf = 0.25 ranks of expected_values: 34, 75 EVAL 0djlxb nominated_for! 02x4wr9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 75.000 75.000 0.522 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0djlxb nominated_for! 02x8n1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 75.000 75.000 0.522 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9651-0bwhdbl PRED entity: 0bwhdbl PRED relation: executive_produced_by PRED expected values: 05hj_k => 105 concepts (60 used for prediction) PRED predicted values (max 10 best out of 132): 05hj_k (0.20 #1601, 0.17 #1350, 0.17 #1100), 02z6l5f (0.14 #2374, 0.14 #870, 0.10 #1873), 034bgm (0.14 #821, 0.13 #1572, 0.08 #1321), 06pj8 (0.14 #807, 0.10 #3563, 0.10 #1810), 02q_cc (0.14 #780, 0.08 #1280, 0.07 #3536), 030_3z (0.14 #860, 0.08 #1360, 0.07 #1611), 02q42j_ (0.14 #889, 0.08 #1389, 0.07 #1640), 0b13g7 (0.14 #838, 0.08 #1338, 0.07 #1589), 01kp66 (0.14 #855, 0.08 #1355, 0.07 #1606), 0glyyw (0.10 #1943, 0.09 #5702, 0.09 #2444) >> Best rule #1601 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 03l6q0; 01k0vq; >> query: (?x8130, 05hj_k) <- genre(?x8130, ?x6452), production_companies(?x8130, ?x10884), executive_produced_by(?x8130, ?x7324), ?x6452 = 02b5_l, film(?x5490, ?x8130), award_winner(?x1670, ?x5490) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bwhdbl executive_produced_by 05hj_k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 60.000 0.200 http://example.org/film/film/executive_produced_by #9650-03lgg PRED entity: 03lgg PRED relation: student! PRED expected values: 0kw4j => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 163): 011xy1 (0.11 #318, 0.08 #845, 0.01 #2953), 078bz (0.08 #604, 0.01 #15361), 08htt0 (0.08 #1020), 03bmmc (0.08 #723), 0bwfn (0.07 #2383, 0.07 #3437, 0.06 #29790), 01w5m (0.07 #3267, 0.05 #11699, 0.05 #9591), 07tgn (0.05 #9503, 0.05 #1071, 0.03 #14774), 023znp (0.05 #2227, 0.03 #8024, 0.01 #4335), 02g839 (0.05 #6876, 0.05 #1079, 0.04 #18471), 03ksy (0.05 #9592, 0.05 #13281, 0.05 #11173) >> Best rule #318 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 06nv27; >> query: (?x4936, 011xy1) <- artists(?x10969, ?x4936), artists(?x302, ?x4936), ?x10969 = 029fbr, artists(?x302, ?x1989), ?x1989 = 04mn81 >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03lgg student! 0kw4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 118.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #9649-0d68qy PRED entity: 0d68qy PRED relation: honored_for! PRED expected values: 03gt46z 09v0p2c 03gyp30 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 77): 0gvstc3 (0.28 #24, 0.22 #1114, 0.22 #1441), 0lp_cd3 (0.17 #14, 0.15 #1104, 0.15 #450), 0gx_st (0.17 #25, 0.12 #1115, 0.11 #1442), 0hn821n (0.17 #103, 0.06 #1193, 0.06 #1520), 04n2r9h (0.17 #31, 0.06 #1121, 0.06 #1448), 09pj68 (0.11 #80, 0.08 #189, 0.06 #516), 07y9ts (0.11 #48, 0.07 #1138, 0.07 #1465), 09qvms (0.11 #7, 0.05 #443, 0.05 #1097), 03gwpw2 (0.11 #5, 0.05 #1095, 0.05 #1422), 09gkdln (0.11 #95, 0.04 #5000, 0.04 #1948) >> Best rule #24 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 07s8z_l; >> query: (?x2528, 0gvstc3) <- award_winner(?x2528, ?x832), honored_for(?x5585, ?x2528), ?x5585 = 03nnm4t >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #6541 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1221 *> proper extension: 0sxg4; 01ln5z; 0fr63l; 04mzf8; 0407yfx; 03h3x5; 0kv238; 0cw3yd; 05g8pg; 04lqvly; ... *> query: (?x2528, ?x3609) <- nominated_for(?x678, ?x2528), nominated_for(?x5690, ?x2528), award_winner(?x3609, ?x5690) *> conf = 0.09 ranks of expected_values: 20, 22, 23 EVAL 0d68qy honored_for! 03gyp30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 77.000 77.000 0.278 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0d68qy honored_for! 09v0p2c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 77.000 77.000 0.278 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0d68qy honored_for! 03gt46z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 77.000 77.000 0.278 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #9648-0mfj2 PRED entity: 0mfj2 PRED relation: location PRED expected values: 0h7h6 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 195): 018lbg (0.52 #17710, 0.47 #40240, 0.47 #72439), 03pzf (0.25 #525, 0.04 #6965, 0.01 #15819), 030qb3t (0.24 #12157, 0.23 #16182, 0.19 #25035), 0h7h6 (0.23 #6530, 0.02 #12969, 0.02 #8946), 02_286 (0.22 #1647, 0.22 #5672, 0.20 #842), 0106dv (0.20 #1309, 0.11 #2114, 0.05 #22540), 0r0f7 (0.20 #1217, 0.11 #2022, 0.05 #22540), 01_d4 (0.20 #907, 0.11 #1712, 0.04 #12176), 0s6jm (0.20 #1232, 0.11 #2037), 01n7q (0.11 #1673, 0.07 #7308, 0.07 #12137) >> Best rule #17710 for best value: >> intensional similarity = 3 >> extensional distance = 350 >> proper extension: 0p3r8; 044mfr; 02w5q6; 026_dq6; 019n7x; 0dq9wx; 01my95; >> query: (?x8858, ?x12589) <- nationality(?x8858, ?x279), place_of_birth(?x8858, ?x12589), participant(?x3917, ?x8858) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #6530 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 92 *> proper extension: 02y8bn; *> query: (?x8858, 0h7h6) <- nationality(?x8858, ?x279), type_of_union(?x8858, ?x566), ?x566 = 04ztj, ?x279 = 0d060g *> conf = 0.23 ranks of expected_values: 4 EVAL 0mfj2 location 0h7h6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 116.000 0.517 http://example.org/people/person/places_lived./people/place_lived/location #9647-0bqytm PRED entity: 0bqytm PRED relation: cinematography! PRED expected values: 04vr_f => 82 concepts (28 used for prediction) PRED predicted values (max 10 best out of 339): 03wy8t (0.05 #1320, 0.05 #1996, 0.04 #2672), 03cw411 (0.05 #1139, 0.05 #1815, 0.04 #2491), 0kbhf (0.05 #1891, 0.04 #2229, 0.03 #3243), 083skw (0.05 #1776, 0.04 #2114, 0.03 #3128), 0jymd (0.05 #1825, 0.04 #2163, 0.03 #1149), 084qpk (0.04 #2394, 0.04 #2056, 0.03 #1042), 0jvt9 (0.04 #2478, 0.03 #3154, 0.03 #1126), 057__d (0.03 #7111, 0.03 #7110, 0.03 #5419), 019vhk (0.03 #7110, 0.03 #5419, 0.02 #5418), 02yy9r (0.03 #1357, 0.03 #2033, 0.02 #2709) >> Best rule #1320 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 087yty; >> query: (?x5014, 03wy8t) <- award(?x5014, ?x1243), award_winner(?x7941, ?x5014), cinematography(?x8373, ?x5014), film_release_region(?x8373, ?x87) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bqytm cinematography! 04vr_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 28.000 0.054 http://example.org/film/film/cinematography #9646-02yr3z PRED entity: 02yr3z PRED relation: organization! PRED expected values: 060c4 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 10): 060c4 (0.86 #54, 0.83 #314, 0.83 #249), 0dq_5 (0.41 #282, 0.41 #529, 0.41 #555), 05k17c (0.25 #33, 0.20 #7, 0.19 #332), 07xl34 (0.23 #1090, 0.21 #1311, 0.20 #232), 0hm4q (0.05 #1087, 0.05 #1126, 0.05 #1412), 05c0jwl (0.03 #1292, 0.03 #967, 0.03 #1188), 09d6p2 (0.03 #62, 0.01 #218, 0.01 #257), 08jcfy (0.02 #1078, 0.02 #974, 0.02 #1013), 04n1q6 (0.01 #747, 0.01 #1007), 0dq3c (0.01 #274) >> Best rule #54 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 05cwl_; >> query: (?x6904, 060c4) <- currency(?x6904, ?x170), school_type(?x6904, ?x1044), ?x1044 = 05pcjw, registering_agency(?x6904, ?x1982) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02yr3z organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.861 http://example.org/organization/role/leaders./organization/leadership/organization #9645-015l4k PRED entity: 015l4k PRED relation: participating_countries PRED expected values: 07ssc => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 331): 09c7w0 (0.85 #967, 0.80 #184, 0.78 #977), 0d060g (0.85 #967, 0.80 #184, 0.78 #977), 05b4w (0.85 #967, 0.80 #184, 0.78 #977), 0f8l9c (0.85 #967, 0.80 #184, 0.78 #977), 03rjj (0.85 #967, 0.80 #184, 0.78 #977), 06mzp (0.85 #967, 0.80 #184, 0.78 #977), 0h7x (0.85 #967, 0.80 #184, 0.78 #977), 0d0vqn (0.85 #967, 0.80 #184, 0.78 #977), 01mk6 (0.85 #967, 0.80 #184, 0.78 #977), 059j2 (0.85 #967, 0.80 #184, 0.68 #971) >> Best rule #967 for best value: >> intensional similarity = 70 >> extensional distance = 1 >> proper extension: 018ctl; >> query: (?x8189, ?x205) <- olympics(?x3309, ?x8189), olympics(?x1175, ?x8189), olympics(?x7430, ?x8189), olympics(?x2984, ?x8189), olympics(?x2513, ?x8189), olympics(?x1229, ?x8189), olympics(?x789, ?x8189), olympics(?x205, ?x8189), olympics(?x94, ?x8189), medal(?x8189, ?x1242), sports(?x8189, ?x8190), sports(?x8189, ?x2752), sports(?x8189, ?x2631), ?x94 = 09c7w0, ?x789 = 0f8l9c, olympics(?x2752, ?x418), country(?x2752, ?x1892), country(?x2752, ?x1603), country(?x2752, ?x1471), country(?x2752, ?x1264), sports(?x7775, ?x2752), sports(?x6893, ?x2752), sports(?x5176, ?x2752), ?x8190 = 09_9n, ?x1471 = 07t21, ?x3309 = 09w1n, ?x1264 = 0345h, ?x1229 = 059j2, ?x5176 = 0sx92, ?x1175 = 02_5h, film_release_region(?x11209, ?x2984), film_release_region(?x11065, ?x2984), film_release_region(?x7692, ?x2984), film_release_region(?x7265, ?x2984), film_release_region(?x5825, ?x2984), film_release_region(?x5317, ?x2984), film_release_region(?x3524, ?x2984), film_release_region(?x3514, ?x2984), film_release_region(?x3392, ?x2984), film_release_region(?x2104, ?x2984), participating_countries(?x1608, ?x7430), combatants(?x3141, ?x7430), contains(?x2984, ?x2985), film_release_region(?x324, ?x7430), ?x11209 = 04fjzv, ?x6893 = 019n8z, olympics(?x2984, ?x2233), olympics(?x2984, ?x775), ?x3514 = 04vh83, ?x775 = 0l998, ?x7265 = 04tng0, ?x3524 = 06r2_, ?x7775 = 01f1kd, ?x5825 = 067ghz, ?x11065 = 0n08r, ?x5317 = 04zl8, country(?x2631, ?x2346), ?x2513 = 05b4w, ?x2104 = 0j_tw, ?x1892 = 02vzc, combatants(?x7430, ?x1353), film(?x2182, ?x7692), executive_produced_by(?x7692, ?x846), ?x2346 = 0d05w3, ?x3141 = 03bxbql, ?x3392 = 0jwmp, ?x2233 = 0l6mp, ?x1603 = 06bnz, jurisdiction_of_office(?x182, ?x7430), film_release_region(?x6100, ?x2984) >> conf = 0.85 => this is the best rule for 12 predicted values *> Best rule #977 for first EXPECTED value: *> intensional similarity = 70 *> extensional distance = 1 *> proper extension: 018ctl; *> query: (?x8189, ?x87) <- olympics(?x3309, ?x8189), olympics(?x1175, ?x8189), olympics(?x7430, ?x8189), olympics(?x2984, ?x8189), olympics(?x2513, ?x8189), olympics(?x1229, ?x8189), olympics(?x789, ?x8189), olympics(?x94, ?x8189), medal(?x8189, ?x1242), sports(?x8189, ?x8190), sports(?x8189, ?x2752), sports(?x8189, ?x2631), ?x94 = 09c7w0, ?x789 = 0f8l9c, olympics(?x2752, ?x418), country(?x2752, ?x1892), country(?x2752, ?x1603), country(?x2752, ?x1471), country(?x2752, ?x1264), sports(?x7775, ?x2752), sports(?x6893, ?x2752), sports(?x5176, ?x2752), ?x8190 = 09_9n, ?x1471 = 07t21, ?x3309 = 09w1n, ?x1264 = 0345h, ?x1229 = 059j2, ?x5176 = 0sx92, ?x1175 = 02_5h, film_release_region(?x11209, ?x2984), film_release_region(?x11065, ?x2984), film_release_region(?x7692, ?x2984), film_release_region(?x7265, ?x2984), film_release_region(?x5825, ?x2984), film_release_region(?x5317, ?x2984), film_release_region(?x3524, ?x2984), film_release_region(?x3514, ?x2984), film_release_region(?x3392, ?x2984), film_release_region(?x2104, ?x2984), participating_countries(?x1608, ?x7430), combatants(?x3141, ?x7430), contains(?x2984, ?x2985), film_release_region(?x324, ?x7430), ?x11209 = 04fjzv, ?x6893 = 019n8z, olympics(?x2984, ?x2233), olympics(?x2984, ?x775), ?x3514 = 04vh83, ?x775 = 0l998, ?x7265 = 04tng0, ?x3524 = 06r2_, ?x7775 = 01f1kd, ?x5825 = 067ghz, ?x11065 = 0n08r, ?x5317 = 04zl8, country(?x2631, ?x2346), country(?x2631, ?x87), ?x2513 = 05b4w, ?x2104 = 0j_tw, ?x1892 = 02vzc, combatants(?x7430, ?x1353), film(?x2182, ?x7692), executive_produced_by(?x7692, ?x846), ?x2346 = 0d05w3, ?x3141 = 03bxbql, ?x3392 = 0jwmp, ?x2233 = 0l6mp, ?x1603 = 06bnz, jurisdiction_of_office(?x182, ?x7430), film_release_region(?x6100, ?x2984) *> conf = 0.78 ranks of expected_values: 23 EVAL 015l4k participating_countries 07ssc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 32.000 32.000 0.846 http://example.org/olympics/olympic_games/participating_countries #9644-035sc2 PRED entity: 035sc2 PRED relation: profession PRED expected values: 01d_h8 => 105 concepts (58 used for prediction) PRED predicted values (max 10 best out of 62): 01d_h8 (0.71 #1466, 0.52 #1028, 0.49 #1612), 018gz8 (0.33 #14, 0.25 #1036, 0.19 #2350), 0cbd2 (0.30 #7455, 0.22 #2781, 0.19 #2051), 02krf9 (0.26 #2360, 0.26 #1776, 0.25 #1630), 0kyk (0.20 #7475, 0.16 #2071, 0.13 #3385), 09jwl (0.17 #4544, 0.17 #3813, 0.17 #8340), 0np9r (0.16 #1040, 0.13 #2354, 0.13 #1770), 0nbcg (0.11 #4557, 0.11 #3826, 0.11 #8353), 0dz3r (0.11 #4530, 0.10 #3799, 0.09 #5406), 016z4k (0.11 #4532, 0.11 #3801, 0.10 #8328) >> Best rule #1466 for best value: >> intensional similarity = 3 >> extensional distance = 338 >> proper extension: 07nznf; 05bnp0; 0kr5_; 03qd_; 03gm48; 05_k56; 016gr2; 05drq5; 0162c8; 07s93v; ... >> query: (?x8680, 01d_h8) <- profession(?x8680, ?x524), student(?x6611, ?x8680), ?x524 = 02jknp >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035sc2 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 58.000 0.706 http://example.org/people/person/profession #9643-0cmdwwg PRED entity: 0cmdwwg PRED relation: film_release_region PRED expected values: 0154j 05qhw 015qh 02vzc => 72 concepts (62 used for prediction) PRED predicted values (max 10 best out of 111): 09c7w0 (0.93 #7475, 0.92 #4785, 0.92 #7624), 06mkj (0.86 #1101, 0.83 #353, 0.82 #3345), 0154j (0.84 #751, 0.84 #452, 0.76 #1051), 05qhw (0.84 #462, 0.82 #761, 0.79 #612), 02vzc (0.78 #1096, 0.76 #348, 0.74 #3340), 06bnz (0.72 #341, 0.71 #640, 0.67 #1089), 03rt9 (0.69 #312, 0.65 #611, 0.61 #461), 03spz (0.68 #539, 0.66 #838, 0.66 #390), 01mjq (0.65 #488, 0.63 #787, 0.52 #1087), 03rj0 (0.61 #506, 0.61 #805, 0.50 #3349) >> Best rule #7475 for best value: >> intensional similarity = 3 >> extensional distance = 1314 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; 018js4; ... >> query: (?x6394, 09c7w0) <- film_release_region(?x6394, ?x1264), partially_contains(?x1264, ?x8154), country(?x136, ?x1264) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #751 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 02qyv3h; 065ym0c; *> query: (?x6394, 0154j) <- film_release_region(?x6394, ?x7747), film_regional_debut_venue(?x6394, ?x6601), ?x7747 = 07f1x *> conf = 0.84 ranks of expected_values: 3, 4, 5, 14 EVAL 0cmdwwg film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 62.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cmdwwg film_release_region 015qh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 72.000 62.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cmdwwg film_release_region 05qhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 62.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cmdwwg film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 62.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9642-04mp9q PRED entity: 04mp9q PRED relation: colors PRED expected values: 083jv => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 16): 083jv (0.40 #1006, 0.40 #1092, 0.39 #1070), 06fvc (0.33 #183, 0.29 #43, 0.27 #366), 01g5v (0.29 #44, 0.26 #447, 0.24 #184), 019sc (0.26 #208, 0.25 #88, 0.24 #188), 04d18d (0.20 #39, 0.19 #281, 0.14 #59), 02rnmb (0.19 #281, 0.18 #322, 0.10 #1139), 038hg (0.13 #1134, 0.11 #396, 0.11 #336), 088fh (0.13 #1134, 0.10 #67, 0.10 #1139), 01l849 (0.13 #1134, 0.10 #1139, 0.10 #1138), 0jc_p (0.13 #1134, 0.10 #1139, 0.10 #1138) >> Best rule #1006 for best value: >> intensional similarity = 8 >> extensional distance = 372 >> proper extension: 01k6zy; >> query: (?x5524, 083jv) <- sport(?x5524, ?x471), sport(?x3810, ?x471), sport(?x2074, ?x471), team(?x3047, ?x3810), athlete(?x471, ?x208), colors(?x2074, ?x3189), team(?x8194, ?x3810), ?x3189 = 01g5v >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mp9q colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.401 http://example.org/sports/sports_team/colors #9641-09yxcz PRED entity: 09yxcz PRED relation: film! PRED expected values: 07f0tw => 98 concepts (65 used for prediction) PRED predicted values (max 10 best out of 915): 01zh29 (0.50 #1411, 0.10 #3494, 0.04 #5576), 0tj9 (0.50 #2022, 0.07 #4105, 0.02 #6187), 015npr (0.45 #77064, 0.44 #77063, 0.43 #95815), 02x2097 (0.45 #77064, 0.44 #77063, 0.43 #95815), 02x20c9 (0.44 #77063, 0.43 #95815, 0.42 #16663), 08y7b9 (0.33 #1943, 0.10 #4026, 0.02 #6108), 01zp33 (0.33 #1304, 0.07 #3387, 0.02 #5469), 0bxy67 (0.17 #1775, 0.07 #3858, 0.02 #5940), 0241wg (0.17 #533, 0.07 #2616, 0.02 #17196), 07jmnh (0.17 #1961, 0.05 #4044, 0.03 #14458) >> Best rule #1411 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0f42nz; >> query: (?x10774, 01zh29) <- produced_by(?x10774, ?x2065), titles(?x3741, ?x10774), ?x3741 = 01chg, genre(?x10774, ?x53) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09yxcz film! 07f0tw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 65.000 0.500 http://example.org/film/actor/film./film/performance/film #9640-02r3zy PRED entity: 02r3zy PRED relation: award PRED expected values: 02f72n 02f73b => 107 concepts (88 used for prediction) PRED predicted values (max 10 best out of 258): 01d38t (0.80 #6276, 0.80 #7061, 0.79 #5491), 02gdjb (0.43 #1389, 0.38 #1781, 0.12 #5310), 0c4z8 (0.42 #8307, 0.39 #9091, 0.37 #7915), 01c99j (0.38 #1787, 0.29 #1395, 0.27 #8455), 01c9jp (0.33 #2144, 0.31 #6458, 0.29 #8811), 02f72n (0.33 #2104, 0.29 #6025, 0.29 #6418), 054ks3 (0.33 #9943, 0.28 #8375, 0.26 #9159), 03qbnj (0.31 #8069, 0.29 #8461, 0.29 #1401), 054krc (0.31 #9891, 0.14 #1263, 0.12 #1655), 0gqz2 (0.30 #9884, 0.25 #1648, 0.15 #7924) >> Best rule #6276 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 04rcr; 07c0j; 03g5jw; 05crg7; 0dvqq; 03fbc; 0249kn; 018ndc; 017j6; 04qmr; ... >> query: (?x1060, ?x2180) <- group(?x1495, ?x1060), award_nominee(?x217, ?x1060), award_winner(?x2180, ?x1060), role(?x130, ?x1495) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01w5n51; *> query: (?x1060, 02f72n) <- group(?x2798, ?x1060), award_nominee(?x217, ?x1060), ?x2798 = 03qjg, award(?x1060, ?x247) *> conf = 0.33 ranks of expected_values: 6, 24 EVAL 02r3zy award 02f73b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 107.000 88.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02r3zy award 02f72n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 107.000 88.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9639-02rq7nd PRED entity: 02rq7nd PRED relation: category PRED expected values: 08mbj5d => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.43 #8, 0.43 #14, 0.43 #11) >> Best rule #8 for best value: >> intensional similarity = 6 >> extensional distance = 42 >> proper extension: 0464pz; >> query: (?x14197, 08mbj5d) <- award(?x14197, ?x14350), languages(?x14197, ?x254), genre(?x14197, ?x53), ?x254 = 02h40lc, honored_for(?x13189, ?x14197), ?x53 = 07s9rl0 >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rq7nd category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.432 http://example.org/common/topic/webpage./common/webpage/category #9638-0362q0 PRED entity: 0362q0 PRED relation: film PRED expected values: 03l6q0 => 114 concepts (20 used for prediction) PRED predicted values (max 10 best out of 270): 0ckt6 (0.55 #5808, 0.39 #9957, 0.38 #15766), 04tqtl (0.05 #257, 0.01 #1917, 0.01 #6065), 03wy8t (0.03 #757, 0.02 #1587, 0.01 #2417), 02pg45 (0.03 #462, 0.01 #2122), 084qpk (0.03 #52, 0.01 #1712), 0372j5 (0.03 #585), 02c7k4 (0.03 #547), 01pgp6 (0.03 #142), 0bz3jx (0.02 #4712, 0.02 #2223, 0.02 #5541), 04sh80 (0.02 #2476, 0.02 #816, 0.01 #4965) >> Best rule #5808 for best value: >> intensional similarity = 4 >> extensional distance = 213 >> proper extension: 03g62; >> query: (?x5394, ?x12899) <- profession(?x5394, ?x524), nationality(?x5394, ?x94), film(?x5394, ?x1728), nominated_for(?x5394, ?x12899) >> conf = 0.55 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0362q0 film 03l6q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 20.000 0.548 http://example.org/film/director/film #9637-02d6n_ PRED entity: 02d6n_ PRED relation: gender PRED expected values: 05zppz => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #50, 0.72 #70, 0.71 #160), 02zsn (0.46 #162, 0.39 #19, 0.39 #12) >> Best rule #50 for best value: >> intensional similarity = 3 >> extensional distance = 939 >> proper extension: 043q6n_; >> query: (?x11220, 05zppz) <- student(?x4296, ?x11220), major_field_of_study(?x4296, ?x1154), company(?x4486, ?x4296) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d6n_ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.728 http://example.org/people/person/gender #9636-01nfys PRED entity: 01nfys PRED relation: film PRED expected values: 08k40m 07yvsn => 83 concepts (71 used for prediction) PRED predicted values (max 10 best out of 586): 0277j40 (0.33 #1219, 0.03 #114191, 0.03 #87425), 04cv9m (0.33 #700, 0.03 #87425, 0.01 #31029), 0gg5kmg (0.33 #1073, 0.03 #87425, 0.01 #2857), 04x4vj (0.33 #770, 0.03 #87425, 0.01 #38235), 01jzyf (0.33 #608, 0.03 #87425), 0j43swk (0.33 #498, 0.03 #87425), 09txzv (0.33 #253, 0.03 #87425), 01shy7 (0.07 #5775, 0.06 #9343, 0.06 #12911), 03bzjpm (0.05 #3094, 0.04 #6662, 0.03 #4878), 03bx2lk (0.05 #1968, 0.03 #3752, 0.03 #5536) >> Best rule #1219 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01z7_f; >> query: (?x9161, 0277j40) <- award_nominee(?x10464, ?x9161), people(?x5056, ?x9161), ?x10464 = 09xrxq >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nfys film 07yvsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 71.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 01nfys film 08k40m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 71.000 0.333 http://example.org/film/actor/film./film/performance/film #9635-014ps4 PRED entity: 014ps4 PRED relation: student! PRED expected values: 07ccs => 110 concepts (94 used for prediction) PRED predicted values (max 10 best out of 187): 017j69 (0.25 #144, 0.14 #1196, 0.14 #670), 03ksy (0.23 #4840, 0.23 #4314, 0.15 #5892), 01w5m (0.20 #3787, 0.14 #1157, 0.14 #631), 07tgn (0.14 #5277, 0.14 #1069, 0.14 #543), 01k7xz (0.14 #1118, 0.14 #592, 0.12 #1644), 020ddc (0.14 #1372, 0.14 #846, 0.12 #1898), 0ymf1 (0.14 #1576, 0.14 #1050, 0.12 #2102), 0lk0l (0.14 #1016, 0.12 #2068, 0.11 #3120), 0dzbl (0.14 #5760, 0.08 #5234, 0.08 #4708), 0bwfn (0.13 #9743, 0.12 #10270, 0.08 #33424) >> Best rule #144 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02ghq; >> query: (?x7828, 017j69) <- influenced_by(?x7828, ?x6055), influenced_by(?x7828, ?x5435), ?x5435 = 01v9724, award(?x7828, ?x575), ?x6055 = 0g5ff >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 014ps4 student! 07ccs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 94.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #9634-018jz PRED entity: 018jz PRED relation: athlete PRED expected values: 0f2zc => 94 concepts (54 used for prediction) PRED predicted values (max 10 best out of 133): 0f2zc (0.33 #1413, 0.33 #347, 0.33 #214), 01jqr_5 (0.33 #1349, 0.33 #283, 0.33 #150), 02cg2v (0.33 #265, 0.25 #664, 0.17 #1597), 04g9sq (0.33 #264, 0.25 #663, 0.17 #1596), 095nx (0.33 #262, 0.25 #661, 0.17 #1594), 02lm0t (0.33 #257, 0.25 #656, 0.17 #1589), 01jz6d (0.33 #256, 0.25 #655, 0.17 #1588), 02_nkp (0.33 #255, 0.25 #654, 0.17 #1587), 049sb (0.33 #250, 0.25 #649, 0.17 #1582), 01pj3h (0.33 #243, 0.25 #642, 0.17 #1575) >> Best rule #1413 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 03tmr; >> query: (?x5063, 0f2zc) <- sport(?x6823, ?x5063), athlete(?x5063, ?x8206), athlete(?x5063, ?x8110), team(?x2010, ?x6823), nationality(?x8110, ?x94), teams(?x4144, ?x6823), gender(?x8206, ?x231), type_of_union(?x8206, ?x566) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018jz athlete 0f2zc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 54.000 0.333 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #9633-02y49 PRED entity: 02y49 PRED relation: profession PRED expected values: 0cbd2 => 123 concepts (106 used for prediction) PRED predicted values (max 10 best out of 97): 0cbd2 (0.93 #7659, 0.90 #2656, 0.88 #742), 0kyk (0.67 #765, 0.56 #324, 0.55 #471), 0dxtg (0.65 #4280, 0.55 #2221, 0.47 #2810), 01d_h8 (0.41 #2213, 0.36 #12367, 0.35 #8540), 018gz8 (0.34 #2224, 0.20 #2813, 0.19 #3842), 02jknp (0.33 #6922, 0.33 #6627, 0.33 #4274), 025352 (0.30 #7652, 0.09 #1087, 0.08 #3001), 09jwl (0.29 #1471, 0.27 #6178, 0.23 #2226), 03gjzk (0.29 #2222, 0.27 #6178, 0.24 #11788), 0np9r (0.18 #1492, 0.14 #1639, 0.12 #6788) >> Best rule #7659 for best value: >> intensional similarity = 5 >> extensional distance = 515 >> proper extension: 06y9c2; 09byk; 012_53; 01pcrw; 02j8nx; 08n9ng; 0kh6b; 0bkg4; 027hnjh; 015lhm; ... >> query: (?x8908, 0cbd2) <- profession(?x8908, ?x3746), profession(?x10978, ?x3746), profession(?x6400, ?x3746), ?x6400 = 06lbp, ?x10978 = 02ghq >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y49 profession 0cbd2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 106.000 0.926 http://example.org/people/person/profession #9632-027lfrs PRED entity: 027lfrs PRED relation: nationality PRED expected values: 03rk0 => 118 concepts (97 used for prediction) PRED predicted values (max 10 best out of 73): 09c7w0 (0.85 #4073, 0.85 #3666, 0.85 #4275), 03rk0 (0.82 #6213, 0.78 #4071, 0.75 #402), 0f8l9c (0.70 #123, 0.59 #323, 0.05 #1539), 02j71 (0.63 #2640), 055vr (0.34 #4072, 0.31 #2537), 07ssc (0.29 #3665, 0.23 #216, 0.10 #116), 02jx1 (0.29 #3665, 0.15 #234, 0.10 #3083), 0j0k (0.27 #4684, 0.26 #3255, 0.23 #4993), 02qkt (0.27 #4684, 0.26 #3255, 0.23 #4993), 06k5_ (0.10 #3152, 0.03 #2638, 0.03 #3151) >> Best rule #4073 for best value: >> intensional similarity = 4 >> extensional distance = 679 >> proper extension: 02mslq; 05typm; 02x8kk; 02x8mt; 03c_8t; >> query: (?x14055, 09c7w0) <- place_of_birth(?x14055, ?x11134), adjoins(?x12193, ?x11134), contains(?x11134, ?x12923), category(?x12193, ?x134) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #6213 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1320 *> proper extension: 06lgq8; *> query: (?x14055, ?x2146) <- place_of_birth(?x14055, ?x11134), contains(?x11134, ?x12923), contains(?x2146, ?x11134), countries_spoken_in(?x254, ?x2146) *> conf = 0.82 ranks of expected_values: 2 EVAL 027lfrs nationality 03rk0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 97.000 0.852 http://example.org/people/person/nationality #9631-03tbg6 PRED entity: 03tbg6 PRED relation: production_companies PRED expected values: 046b0s => 106 concepts (81 used for prediction) PRED predicted values (max 10 best out of 65): 03xq0f (0.33 #990, 0.33 #3148, 0.33 #1489), 05qd_ (0.14 #9, 0.11 #5713, 0.10 #1084), 016tw3 (0.14 #11, 0.10 #5715, 0.10 #836), 0g1rw (0.14 #7, 0.05 #1247, 0.05 #5711), 01795t (0.14 #103, 0.07 #1261, 0.05 #1345), 09b3v (0.11 #114, 0.05 #1272, 0.05 #444), 0kk9v (0.11 #116, 0.04 #1274, 0.03 #1358), 05rrtf (0.11 #139, 0.03 #3781, 0.03 #551), 01mkn_d (0.10 #4466, 0.09 #4881, 0.04 #2569), 016tt2 (0.10 #1078, 0.09 #415, 0.09 #168) >> Best rule #990 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 0hv81; >> query: (?x10455, ?x382) <- honored_for(?x10455, ?x5135), film(?x382, ?x10455), language(?x10455, ?x254), award_winner(?x10455, ?x510) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #848 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 108 *> proper extension: 08720; 0kv238; 0ckrgs; 02fqrf; 0gh65c5; 07tw_b; 057lbk; 012s1d; 02ph9tm; 05n6sq; ... *> query: (?x10455, 046b0s) <- film(?x478, ?x10455), film_crew_role(?x10455, ?x2095), ?x2095 = 0dxtw, category(?x10455, ?x134) *> conf = 0.05 ranks of expected_values: 18 EVAL 03tbg6 production_companies 046b0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 106.000 81.000 0.333 http://example.org/film/film/production_companies #9630-0bq2g PRED entity: 0bq2g PRED relation: special_performance_type PRED expected values: 01pb34 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 4): 01pb34 (0.29 #13, 0.25 #3, 0.23 #8), 09_gdc (0.08 #7, 0.06 #22, 0.05 #17), 01kyvx (0.08 #6, 0.02 #413, 0.01 #637), 02t8yb (0.03 #29, 0.02 #59, 0.02 #84) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 0q5hw; 01s21dg; >> query: (?x3553, 01pb34) <- award_winner(?x3553, ?x989), friend(?x3553, ?x1424), celebrity(?x3553, ?x8291) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bq2g special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.286 http://example.org/film/actor/film./film/performance/special_performance_type #9629-05mrf_p PRED entity: 05mrf_p PRED relation: nominated_for! PRED expected values: 02rdyk7 => 106 concepts (96 used for prediction) PRED predicted values (max 10 best out of 219): 09ly2r6 (0.68 #14594, 0.68 #13873, 0.66 #13872), 0gq9h (0.33 #5324, 0.30 #11302, 0.29 #2693), 0k611 (0.32 #313, 0.28 #552, 0.27 #74), 040njc (0.30 #485, 0.24 #16510, 0.22 #5268), 0gs9p (0.29 #2695, 0.29 #5326, 0.28 #543), 019f4v (0.28 #5316, 0.26 #2685, 0.26 #294), 02qvyrt (0.26 #336, 0.24 #16510, 0.22 #20337), 0gr4k (0.26 #266, 0.24 #16510, 0.21 #5288), 02qyntr (0.26 #420, 0.21 #5442, 0.20 #2811), 04kxsb (0.26 #335, 0.18 #2726, 0.16 #5357) >> Best rule #14594 for best value: >> intensional similarity = 3 >> extensional distance = 986 >> proper extension: 097h2; 019g8j; 0147w8; 02rq7nd; >> query: (?x5074, ?x1254) <- award(?x5074, ?x1254), nominated_for(?x1254, ?x144), award(?x91, ?x1254) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #16510 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1088 *> proper extension: 0c3xpwy; *> query: (?x5074, ?x1079) <- award_winner(?x5074, ?x10634), nominated_for(?x10634, ?x695), award(?x10634, ?x1079) *> conf = 0.24 ranks of expected_values: 19 EVAL 05mrf_p nominated_for! 02rdyk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 106.000 96.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9628-03rz2b PRED entity: 03rz2b PRED relation: award PRED expected values: 0789r6 => 72 concepts (59 used for prediction) PRED predicted values (max 10 best out of 184): 054knh (0.34 #236, 0.29 #472, 0.27 #5415), 0b6k___ (0.29 #386, 0.25 #622, 0.24 #857), 0b6jkkg (0.29 #393, 0.25 #629, 0.24 #864), 03r8tl (0.25 #552, 0.24 #787, 0.21 #316), 0789r6 (0.20 #4708, 0.14 #12957, 0.13 #12484), 0gq9h (0.14 #63, 0.10 #3123, 0.08 #5242), 027c95y (0.14 #118, 0.10 #1060, 0.09 #1295), 0gs9p (0.14 #65, 0.09 #3125, 0.08 #1007), 0gs96 (0.14 #90, 0.09 #1738, 0.09 #3150), 0gr0m (0.14 #60, 0.09 #3120, 0.07 #5239) >> Best rule #236 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 04jwjq; 0209hj; 02pxmgz; 01p3ty; 08g_jw; >> query: (?x2882, ?x7965) <- country(?x2882, ?x2146), ?x2146 = 03rk0, film(?x7531, ?x2882), nominated_for(?x7965, ?x2882), film(?x10061, ?x2882) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #4708 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 700 *> proper extension: 05h95s; *> query: (?x2882, ?x13075) <- titles(?x53, ?x2882), award_winner(?x2882, ?x10061), award_nominee(?x1208, ?x10061), award_winner(?x13075, ?x10061) *> conf = 0.20 ranks of expected_values: 5 EVAL 03rz2b award 0789r6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 72.000 59.000 0.342 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9627-0r6cx PRED entity: 0r6cx PRED relation: adjoins PRED expected values: 0f04v => 171 concepts (91 used for prediction) PRED predicted values (max 10 best out of 465): 0f04v (0.85 #16231, 0.84 #24731, 0.84 #28598), 0r6cx (0.25 #536, 0.24 #69548, 0.21 #64141), 0r6c4 (0.24 #69548, 0.20 #1443, 0.08 #5304), 0l2xl (0.23 #12366, 0.10 #2679, 0.10 #1908), 0mhdz (0.21 #37097, 0.20 #51002, 0.13 #24732), 0kpzy (0.20 #1838, 0.12 #4153, 0.10 #2609), 0l2vz (0.17 #4081, 0.15 #2537, 0.10 #1766), 02dtg (0.12 #11620, 0.08 #4661, 0.07 #6206), 059rby (0.10 #17020, 0.09 #23201, 0.07 #22427), 0dc95 (0.10 #2447, 0.10 #1676, 0.08 #3991) >> Best rule #16231 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 018f94; >> query: (?x10916, ?x6703) <- adjoins(?x6703, ?x10916), category(?x10916, ?x134), citytown(?x6404, ?x6703) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r6cx adjoins 0f04v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 91.000 0.849 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #9626-0bjqh PRED entity: 0bjqh PRED relation: student PRED expected values: 01k8rb 03pvt 05kh_ => 163 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1357): 02jsgf (0.17 #679, 0.10 #6958, 0.09 #9051), 017r13 (0.17 #1093, 0.10 #7372, 0.09 #9465), 02nwxc (0.17 #994, 0.10 #7273, 0.08 #3087), 06y9c2 (0.17 #87, 0.10 #6366, 0.08 #2180), 01gv_f (0.17 #622, 0.10 #6901, 0.07 #4808), 0chsq (0.17 #63, 0.10 #6342, 0.07 #4249), 073v6 (0.17 #526, 0.09 #8898, 0.07 #4712), 02ndbd (0.13 #4299, 0.10 #6392, 0.09 #8485), 06hx2 (0.09 #22001, 0.03 #24095, 0.03 #26188), 0d3k14 (0.09 #22786, 0.03 #29066, 0.02 #18599) >> Best rule #679 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 02xwzh; >> query: (?x1924, 02jsgf) <- state_province_region(?x1924, ?x3818), colors(?x1924, ?x332), ?x3818 = 03v0t, student(?x1924, ?x2426) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #597 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 02xwzh; *> query: (?x1924, 03pvt) <- state_province_region(?x1924, ?x3818), colors(?x1924, ?x332), ?x3818 = 03v0t, student(?x1924, ?x2426) *> conf = 0.08 ranks of expected_values: 27 EVAL 0bjqh student 05kh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 163.000 102.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0bjqh student 03pvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 163.000 102.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0bjqh student 01k8rb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 163.000 102.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #9625-07rd7 PRED entity: 07rd7 PRED relation: type_of_union PRED expected values: 01g63y => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 2): 01g63y (0.38 #22, 0.31 #112, 0.28 #142), 01bl8s (0.01 #71, 0.01 #74) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 04bs3j; 022_lg; 01x1cn2; 01gbbz; 03f0fnk; 0484q; 06crng; 02js_6; >> query: (?x4314, 01g63y) <- award(?x4314, ?x1053), spouse(?x2531, ?x4314), influenced_by(?x4314, ?x3028) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07rd7 type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.375 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9624-02r34n PRED entity: 02r34n PRED relation: student! PRED expected values: 013807 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 231): 0lfgr (0.25 #569, 0.03 #4778, 0.03 #6882), 015nl4 (0.20 #67, 0.06 #14270, 0.05 #21111), 02j416 (0.20 #431), 013807 (0.18 #1462, 0.09 #3566, 0.05 #3040), 07w0v (0.18 #1598, 0.04 #3176, 0.02 #10015), 08815 (0.12 #2106, 0.09 #12627, 0.07 #9997), 03ksy (0.12 #2210, 0.08 #6945, 0.07 #8523), 01qd_r (0.12 #807, 0.04 #3437, 0.03 #6594), 02bq1j (0.12 #2271, 0.04 #9110, 0.03 #5954), 0k__z (0.12 #834, 0.02 #5569, 0.02 #14511) >> Best rule #569 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 015n8; >> query: (?x1188, 0lfgr) <- religion(?x1188, ?x14146), nationality(?x1188, ?x94), ?x14146 = 01hng3 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1462 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 05j0wc; *> query: (?x1188, 013807) <- profession(?x1188, ?x1383), student(?x3995, ?x1188), ?x1383 = 0np9r, student(?x1200, ?x1188) *> conf = 0.18 ranks of expected_values: 4 EVAL 02r34n student! 013807 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 125.000 125.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #9623-0ghvb PRED entity: 0ghvb PRED relation: contains! PRED expected values: 09c7w0 => 194 concepts (81 used for prediction) PRED predicted values (max 10 best out of 260): 09c7w0 (0.81 #10745, 0.80 #16116, 0.79 #60891), 059rby (0.22 #27778, 0.22 #26882, 0.12 #60908), 01n7q (0.15 #27835, 0.15 #26939, 0.12 #4553), 0d060g (0.13 #15231, 0.11 #18815, 0.09 #19710), 05tbn (0.11 #9174, 0.10 #10069, 0.10 #11860), 02_286 (0.10 #27801, 0.10 #26905, 0.06 #60931), 05k7sb (0.10 #1922, 0.08 #4608, 0.07 #5503), 04ly1 (0.10 #2026, 0.05 #23516, 0.04 #16349), 02jx1 (0.10 #34109, 0.09 #23366, 0.07 #49328), 04rrx (0.08 #4602, 0.07 #6392, 0.07 #5497) >> Best rule #10745 for best value: >> intensional similarity = 5 >> extensional distance = 66 >> proper extension: 02t4yc; 02xpy5; 02zc7f; >> query: (?x11467, 09c7w0) <- fraternities_and_sororities(?x11467, ?x3697), colors(?x11467, ?x332), organization(?x346, ?x11467), currency(?x11467, ?x170), contains(?x961, ?x11467) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ghvb contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 81.000 0.809 http://example.org/location/location/contains #9622-0401sg PRED entity: 0401sg PRED relation: film_release_region PRED expected values: 03_3d 0chghy 047yc 06qd3 06mkj => 109 concepts (106 used for prediction) PRED predicted values (max 10 best out of 149): 0chghy (0.90 #5562, 0.89 #4564, 0.89 #6275), 06mkj (0.90 #4601, 0.88 #3178, 0.88 #6312), 03_3d (0.89 #1994, 0.82 #999, 0.79 #2851), 07ssc (0.85 #2861, 0.85 #4997, 0.84 #4568), 0d060g (0.79 #4559, 0.77 #5557, 0.76 #4988), 04gzd (0.78 #1998, 0.71 #2855, 0.66 #2427), 01p1v (0.75 #1890, 0.69 #3031, 0.68 #2461), 015qh (0.74 #2451, 0.73 #2022, 0.73 #2879), 06t8v (0.73 #918, 0.67 #1344, 0.56 #2912), 047yc (0.72 #2440, 0.70 #2297, 0.69 #3152) >> Best rule #5562 for best value: >> intensional similarity = 10 >> extensional distance = 144 >> proper extension: 0gh8zks; >> query: (?x664, 0chghy) <- film_release_region(?x664, ?x1264), film_release_region(?x664, ?x1023), film_release_region(?x664, ?x608), film_release_region(?x664, ?x456), ?x1023 = 0ctw_b, film_release_region(?x3854, ?x608), ?x1264 = 0345h, ?x3854 = 03q0r1, country(?x150, ?x456), combatants(?x326, ?x456) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 10, 14 EVAL 0401sg film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 106.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0401sg film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 109.000 106.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0401sg film_release_region 047yc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 109.000 106.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0401sg film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 106.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0401sg film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 106.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9621-01vq3 PRED entity: 01vq3 PRED relation: films PRED expected values: 026gyn_ 0jwmp => 57 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1665): 07cyl (0.40 #1690, 0.25 #1180, 0.13 #5271), 02yvct (0.40 #2658, 0.24 #7767, 0.20 #2146), 01rnly (0.33 #959, 0.25 #1469, 0.20 #2490), 04tqtl (0.33 #656, 0.25 #1166, 0.20 #1676), 0bmhn (0.33 #975, 0.25 #1485, 0.20 #1995), 0pd64 (0.33 #896, 0.25 #1406, 0.20 #1916), 07g1sm (0.33 #860, 0.25 #1370, 0.20 #1880), 0jsf6 (0.33 #819, 0.25 #1329, 0.20 #1839), 0y_yw (0.33 #807, 0.25 #1317, 0.20 #1827), 0gw7p (0.33 #796, 0.25 #1306, 0.20 #1816) >> Best rule #1690 for best value: >> intensional similarity = 17 >> extensional distance = 3 >> proper extension: 05vtw; >> query: (?x5011, 07cyl) <- films(?x5011, ?x7881), films(?x5011, ?x4241), films(?x5011, ?x3601), films(?x5011, ?x2958), music(?x2958, ?x12188), film_release_region(?x2958, ?x7430), film_release_region(?x2958, ?x4698), film_release_region(?x2958, ?x1649), ?x7430 = 01mk6, place_of_birth(?x5342, ?x4698), nominated_for(?x198, ?x2958), film(?x541, ?x3601), crewmember(?x7881, ?x2871), currency(?x4241, ?x170), production_companies(?x80, ?x541), month(?x1649, ?x1459), award_nominee(?x519, ?x541) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2042 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 3 *> proper extension: 05vtw; *> query: (?x5011, ?x80) <- films(?x5011, ?x7881), films(?x5011, ?x4241), films(?x5011, ?x3601), films(?x5011, ?x2958), music(?x2958, ?x12188), film_release_region(?x2958, ?x7430), film_release_region(?x2958, ?x4698), film_release_region(?x2958, ?x1649), ?x7430 = 01mk6, place_of_birth(?x5342, ?x4698), nominated_for(?x198, ?x2958), film(?x541, ?x3601), crewmember(?x7881, ?x2871), currency(?x4241, ?x170), production_companies(?x80, ?x541), month(?x1649, ?x1459), award_nominee(?x519, ?x541) *> conf = 0.02 ranks of expected_values: 473, 940 EVAL 01vq3 films 0jwmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 18.000 0.400 http://example.org/film/film_subject/films EVAL 01vq3 films 026gyn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 18.000 0.400 http://example.org/film/film_subject/films #9620-0dz46 PRED entity: 0dz46 PRED relation: award PRED expected values: 040_9s0 => 162 concepts (143 used for prediction) PRED predicted values (max 10 best out of 281): 01cd7p (0.70 #57002, 0.68 #37600, 0.68 #2426), 047xyn (0.70 #57002, 0.68 #37600, 0.68 #2426), 040_9s0 (0.57 #1531, 0.52 #3149, 0.43 #1936), 0j6j8 (0.57 #1545, 0.43 #1950, 0.25 #737), 040vk98 (0.55 #2859, 0.25 #29, 0.18 #4073), 01yz0x (0.52 #3007, 0.50 #177, 0.29 #1794), 0265vt (0.50 #326, 0.41 #3156, 0.15 #4370), 0262x6 (0.50 #318, 0.32 #3148, 0.14 #1935), 0265wl (0.50 #238, 0.30 #3068, 0.14 #1855), 0208wk (0.50 #347, 0.29 #1964, 0.29 #1559) >> Best rule #57002 for best value: >> intensional similarity = 3 >> extensional distance = 2274 >> proper extension: 089tm; 01pfr3; 04lgymt; 017s11; 025jfl; 0kx4m; 03h26tm; 04f525m; 02r3zy; 016tw3; ... >> query: (?x8997, ?x4879) <- award(?x8997, ?x11084), award_winner(?x11084, ?x9519), award_winner(?x4879, ?x8997) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #1531 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 09jd9; *> query: (?x8997, 040_9s0) <- award(?x8997, ?x11084), ?x11084 = 02tzwd, nationality(?x8997, ?x94), award_winner(?x4879, ?x8997) *> conf = 0.57 ranks of expected_values: 3 EVAL 0dz46 award 040_9s0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 162.000 143.000 0.701 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9619-01dwrc PRED entity: 01dwrc PRED relation: award PRED expected values: 03r00m => 76 concepts (51 used for prediction) PRED predicted values (max 10 best out of 246): 023vrq (0.80 #5489, 0.77 #10981, 0.77 #17263), 01ckcd (0.58 #1500, 0.37 #3068, 0.21 #8628), 02f5qb (0.58 #1327, 0.26 #2895, 0.19 #6666), 02f72_ (0.52 #1396, 0.33 #220, 0.24 #2964), 02f716 (0.52 #1348, 0.24 #2916, 0.21 #8628), 03tcnt (0.42 #1338, 0.21 #8628, 0.19 #2906), 02f77l (0.39 #1421, 0.22 #2989, 0.21 #8628), 02f73p (0.39 #1357, 0.21 #8628, 0.21 #2925), 02f73b (0.35 #1453, 0.21 #8628, 0.19 #6666), 02sp_v (0.33 #158, 0.18 #942, 0.18 #8627) >> Best rule #5489 for best value: >> intensional similarity = 3 >> extensional distance = 228 >> proper extension: 024y6w; >> query: (?x5760, ?x2180) <- award_winner(?x2180, ?x5760), artists(?x671, ?x5760), ?x671 = 064t9 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #8628 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 499 *> proper extension: 07qy0b; 01wbsdz; 01mkn_d; 01w5gg6; 089kpp; *> query: (?x5760, ?x2855) <- award_nominee(?x4983, ?x5760), artists(?x474, ?x5760), award_winner(?x2855, ?x4983), award(?x4983, ?x724) *> conf = 0.21 ranks of expected_values: 24 EVAL 01dwrc award 03r00m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 76.000 51.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9618-079kr PRED entity: 079kr PRED relation: group! PRED expected values: 0l14md => 107 concepts (85 used for prediction) PRED predicted values (max 10 best out of 121): 018vs (0.80 #1041, 0.69 #1301, 0.67 #1129), 0l14md (0.76 #1382, 0.72 #2149, 0.71 #860), 03bx0bm (0.67 #1140, 0.67 #620, 0.62 #4307), 028tv0 (0.50 #182, 0.47 #1387, 0.45 #1814), 07y_7 (0.50 #342, 0.33 #172, 0.31 #1290), 0l14qv (0.44 #1293, 0.40 #773, 0.38 #1208), 05r5c (0.44 #1296, 0.33 #604, 0.33 #178), 01wy6 (0.43 #550, 0.33 #210, 0.18 #1374), 03qjg (0.36 #3210, 0.34 #3727, 0.33 #386), 0l14j_ (0.33 #390, 0.33 #220, 0.25 #1338) >> Best rule #1041 for best value: >> intensional similarity = 8 >> extensional distance = 13 >> proper extension: 05xq9; 0l8g0; 07rnh; 012x1l; >> query: (?x12497, 018vs) <- group(?x2309, ?x12497), artists(?x5934, ?x12497), artists(?x302, ?x12497), ?x302 = 016clz, role(?x74, ?x2309), role(?x2309, ?x4917), ?x5934 = 05r6t, ?x4917 = 06w7v >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1382 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 15 *> proper extension: 0g_g2; 0dw4g; 011xhx; *> query: (?x12497, 0l14md) <- group(?x227, ?x12497), group(?x2492, ?x12497), artist(?x5744, ?x12497), artist(?x5744, ?x12266), artist(?x5744, ?x10461), artist(?x5744, ?x6947), artist(?x5744, ?x4182), artist(?x5744, ?x3869), ?x6947 = 01vrnsk, ?x4182 = 07yg2, ?x10461 = 01vvybv, artists(?x1000, ?x12266), role(?x3869, ?x2764) *> conf = 0.76 ranks of expected_values: 2 EVAL 079kr group! 0l14md CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 85.000 0.800 http://example.org/music/performance_role/regular_performances./music/group_membership/group #9617-026g4l_ PRED entity: 026g4l_ PRED relation: award_winner! PRED expected values: 01l29r => 119 concepts (89 used for prediction) PRED predicted values (max 10 best out of 201): 05p1dby (0.53 #538, 0.44 #968, 0.24 #2688), 0gq9h (0.39 #16347, 0.37 #19359, 0.37 #20220), 0m7yy (0.29 #2760, 0.28 #1040, 0.13 #610), 01l29r (0.25 #166, 0.17 #1026, 0.13 #596), 01lj_c (0.25 #294, 0.11 #1154, 0.09 #24955), 03hkv_r (0.25 #17, 0.09 #24955, 0.07 #25386), 05b1610 (0.25 #39, 0.08 #15054, 0.07 #469), 0gr4k (0.25 #33, 0.07 #3473, 0.04 #4333), 07bdd_ (0.23 #2646, 0.20 #496, 0.17 #926), 040njc (0.14 #3448, 0.09 #4308, 0.08 #4739) >> Best rule #538 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 05xbx; >> query: (?x5714, 05p1dby) <- award_nominee(?x3381, ?x5714), award_winner(?x7285, ?x5714), titles(?x3381, ?x715) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0dbpwb; *> query: (?x5714, 01l29r) <- award_nominee(?x6534, ?x5714), award_winner(?x7285, ?x5714), ?x6534 = 01_6dw *> conf = 0.25 ranks of expected_values: 4 EVAL 026g4l_ award_winner! 01l29r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 119.000 89.000 0.533 http://example.org/award/award_category/winners./award/award_honor/award_winner #9616-06b4wb PRED entity: 06b4wb PRED relation: actor! PRED expected values: 043qqt5 => 77 concepts (26 used for prediction) PRED predicted values (max 10 best out of 131): 05nlzq (0.20 #447, 0.06 #712, 0.02 #1239), 043qqt5 (0.20 #488, 0.06 #753, 0.02 #1280), 063zky (0.20 #370, 0.06 #635, 0.01 #1162), 05f7w84 (0.11 #105, 0.09 #1161, 0.08 #1424), 026bfsh (0.11 #95, 0.05 #2205, 0.04 #2996), 0vhm (0.11 #89, 0.03 #1145, 0.03 #1408), 019g8j (0.11 #228, 0.02 #1284, 0.02 #1547), 01bv8b (0.11 #40, 0.02 #1096, 0.01 #833), 0431v3 (0.11 #96), 01h72l (0.10 #302, 0.06 #1094, 0.05 #1357) >> Best rule #447 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 030h95; 06lj1m; 015vq_; 03ktjq; 018_lb; >> query: (?x12001, 05nlzq) <- film(?x12001, ?x1076), profession(?x12001, ?x1032), ?x1076 = 0k2sk >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #488 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 030h95; 06lj1m; 015vq_; 03ktjq; 018_lb; *> query: (?x12001, 043qqt5) <- film(?x12001, ?x1076), profession(?x12001, ?x1032), ?x1076 = 0k2sk *> conf = 0.20 ranks of expected_values: 2 EVAL 06b4wb actor! 043qqt5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 26.000 0.200 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #9615-073749 PRED entity: 073749 PRED relation: nationality PRED expected values: 09c7w0 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 99): 09c7w0 (0.80 #601, 0.80 #1504, 0.78 #1103), 0d060g (0.44 #1203, 0.40 #7, 0.31 #6539), 07ssc (0.11 #215, 0.09 #3636, 0.09 #1218), 02jx1 (0.11 #3554, 0.11 #3654, 0.10 #1236), 03rk0 (0.06 #9300, 0.05 #9500, 0.05 #9400), 03_3d (0.04 #7243, 0.03 #908, 0.03 #7244), 0chghy (0.04 #7243, 0.03 #7244, 0.02 #3631), 0345h (0.04 #7243, 0.03 #7244, 0.02 #933), 0f8l9c (0.04 #7243, 0.03 #7244, 0.02 #2919), 03rt9 (0.04 #7243, 0.03 #7244, 0.02 #2919) >> Best rule #601 for best value: >> intensional similarity = 3 >> extensional distance = 169 >> proper extension: 012zng; 01vv126; 02r3cn; >> query: (?x4107, 09c7w0) <- location(?x4107, ?x335), category(?x4107, ?x134), partially_contains(?x335, ?x10954) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 073749 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.801 http://example.org/people/person/nationality #9614-0fpj4lx PRED entity: 0fpj4lx PRED relation: film PRED expected values: 0dzlbx => 98 concepts (91 used for prediction) PRED predicted values (max 10 best out of 422): 01shy7 (0.11 #2216, 0.09 #5800, 0.09 #4008), 056xkh (0.11 #3394, 0.09 #6978, 0.09 #5186), 0bxsk (0.09 #6587, 0.07 #8379, 0.05 #10171), 01d259 (0.09 #6366, 0.07 #8158, 0.05 #9950), 02z3r8t (0.07 #10860, 0.02 #16236, 0.01 #46700), 0f42nz (0.05 #11662, 0.03 #17038, 0.03 #15246), 0ndwt2w (0.05 #11754, 0.02 #17130, 0.01 #35050), 017jd9 (0.05 #11533, 0.02 #16909, 0.01 #34829), 0f4_l (0.05 #11102, 0.02 #16478), 017gl1 (0.05 #10895, 0.02 #16271) >> Best rule #2216 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01q7cb_; 01gf5h; 01gx5f; 01w8n89; 0dw3l; 01y_rz; >> query: (?x3740, 01shy7) <- artists(?x8031, ?x3740), artists(?x302, ?x3740), ?x302 = 016clz, ?x8031 = 01738f, profession(?x3740, ?x220) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #11605 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 02756j; *> query: (?x3740, 0dzlbx) <- diet(?x3740, ?x3130), gender(?x3740, ?x231), student(?x3439, ?x3740), place_of_birth(?x3740, ?x739) *> conf = 0.02 ranks of expected_values: 72 EVAL 0fpj4lx film 0dzlbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 98.000 91.000 0.111 http://example.org/film/actor/film./film/performance/film #9613-04mlh8 PRED entity: 04mlh8 PRED relation: actor! PRED expected values: 016ztl => 94 concepts (72 used for prediction) PRED predicted values (max 10 best out of 31): 016ztl (0.30 #48, 0.27 #80, 0.26 #113), 02q3fdr (0.19 #112, 0.18 #47, 0.17 #79), 0b60sq (0.15 #66, 0.14 #132, 0.12 #99), 05pyrb (0.12 #46, 0.09 #208, 0.07 #78), 031f_m (0.09 #187, 0.09 #219, 0.08 #251), 02vw1w2 (0.07 #71, 0.07 #104, 0.07 #233), 0dd6bf (0.07 #215, 0.07 #247, 0.06 #279), 03d8jd1 (0.06 #64, 0.05 #96, 0.05 #129), 07ng9k (0.06 #38, 0.05 #200, 0.04 #264), 05dfy_ (0.06 #54, 0.03 #184, 0.02 #216) >> Best rule #48 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 06v8s0; 081jbk; 066l3y; 09wlpl; 044_7j; >> query: (?x7288, 016ztl) <- nationality(?x7288, ?x94), gender(?x7288, ?x231), actor(?x5938, ?x7288), actor(?x5286, ?x7288) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mlh8 actor! 016ztl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 72.000 0.303 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #9612-03_9r PRED entity: 03_9r PRED relation: languages! PRED expected values: 01qnfc => 88 concepts (25 used for prediction) PRED predicted values (max 10 best out of 941): 0448r (0.60 #4306, 0.50 #8182, 0.50 #7536), 0bdt8 (0.60 #4229, 0.40 #8105, 0.40 #7459), 01q8fxx (0.60 #4469, 0.40 #8345, 0.40 #7699), 03crmd (0.60 #4434, 0.40 #8310, 0.40 #7664), 02pk6x (0.60 #4189, 0.40 #8065, 0.40 #5479), 026rm_y (0.60 #4337, 0.40 #5627, 0.33 #464), 01j5sv (0.40 #8331, 0.40 #7685, 0.40 #5100), 01ps2h8 (0.40 #8046, 0.40 #4170, 0.33 #942), 0dqcm (0.40 #8241, 0.40 #4365, 0.33 #1137), 02lf70 (0.40 #7846, 0.40 #3970, 0.33 #742) >> Best rule #4306 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 04306rv; 064_8sq; >> query: (?x2164, 0448r) <- major_field_of_study(?x1368, ?x2164), languages(?x147, ?x2164), languages_spoken(?x9648, ?x2164), language(?x3000, ?x2164), language(?x696, ?x2164), ?x696 = 0209xj, service_language(?x555, ?x2164), film_release_region(?x3000, ?x87) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_9r languages! 01qnfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 25.000 0.600 http://example.org/people/person/languages #9611-0mhdz PRED entity: 0mhdz PRED relation: county PRED expected values: 0l2xl => 96 concepts (50 used for prediction) PRED predicted values (max 10 best out of 40): 0l2xl (0.46 #1381, 0.25 #66, 0.22 #460), 06pvr (0.19 #6724, 0.18 #197, 0.12 #789), 01n7q (0.19 #6724, 0.18 #197, 0.12 #789), 09c7w0 (0.19 #6724, 0.18 #197, 0.12 #789), 0l2vz (0.17 #26, 0.14 #223, 0.11 #815), 0kq1l (0.17 #59, 0.10 #651, 0.07 #848), 0kpys (0.17 #1000, 0.03 #7132, 0.03 #4365), 0kpzy (0.11 #836, 0.08 #47, 0.06 #441), 0cb4j (0.11 #989, 0.03 #4354, 0.03 #4158), 0l34j (0.08 #24, 0.05 #616, 0.04 #813) >> Best rule #1381 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05rgl; >> query: (?x12143, ?x7964) <- adjoins(?x12143, ?x6703), source(?x6703, ?x958), county(?x6703, ?x7964) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mhdz county 0l2xl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 50.000 0.462 http://example.org/location/hud_county_place/county #9610-019rl6 PRED entity: 019rl6 PRED relation: place_founded PRED expected values: 0r6cx => 140 concepts (115 used for prediction) PRED predicted values (max 10 best out of 75): 0r6ff (0.44 #194, 0.42 #775, 0.39 #3323), 030qb3t (0.23 #851, 0.20 #335, 0.18 #399), 0f2wj (0.23 #847, 0.12 #1819, 0.10 #2675), 02_286 (0.20 #2676, 0.17 #1820, 0.17 #1236), 0r6c4 (0.14 #190, 0.11 #320, 0.11 #255), 080h2 (0.14 #139, 0.11 #204, 0.10 #333), 0c75w (0.14 #181, 0.11 #246, 0.10 #375), 0f04c (0.14 #148, 0.11 #213, 0.07 #470), 0r679 (0.14 #155, 0.11 #220, 0.07 #477), 07dfk (0.14 #2978, 0.12 #1598, 0.12 #2848) >> Best rule #194 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 08t9df; >> query: (?x7218, ?x11315) <- industry(?x7218, ?x12987), citytown(?x7218, ?x11315), ?x12987 = 01mf0, place_founded(?x7218, ?x581), category(?x7218, ?x134) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1681 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 35 *> proper extension: 0jhjl; 0jksm; *> query: (?x7218, ?x3794) <- citytown(?x7218, ?x11315), category(?x7218, ?x134), list(?x7218, ?x7472), adjoins(?x6703, ?x11315), adjoins(?x3794, ?x11315), location(?x5809, ?x6703) *> conf = 0.01 ranks of expected_values: 75 EVAL 019rl6 place_founded 0r6cx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 140.000 115.000 0.444 http://example.org/organization/organization/place_founded #9609-05sy2k_ PRED entity: 05sy2k_ PRED relation: honored_for! PRED expected values: 0hhtgcw => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 61): 0gvstc3 (0.33 #515, 0.29 #759, 0.25 #1003), 0hhtgcw (0.29 #805, 0.25 #1049, 0.20 #1537), 02q690_ (0.25 #2006, 0.17 #4570, 0.17 #4937), 0lp_cd3 (0.22 #1359, 0.22 #1237, 0.17 #2335), 09qvms (0.22 #1229, 0.11 #2205, 0.11 #1351), 0hr3c8y (0.22 #1348, 0.10 #2446, 0.10 #1836), 05c1t6z (0.17 #4772, 0.16 #6237, 0.16 #7091), 03nnm4t (0.17 #2381, 0.17 #2015, 0.15 #2747), 0g5b0q5 (0.17 #502, 0.14 #746, 0.12 #990), 09g90vz (0.17 #596, 0.14 #840, 0.12 #1084) >> Best rule #515 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 05f4vxd; 0dsx3f; >> query: (?x3848, 0gvstc3) <- program(?x8817, ?x3848), genre(?x3848, ?x11671), genre(?x3848, ?x1510), ?x11671 = 07qht4, program(?x10407, ?x3848), titles(?x1510, ?x83), genre(?x12964, ?x1510), ?x12964 = 04hk0w >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #805 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 5 *> proper extension: 09v38qj; *> query: (?x3848, 0hhtgcw) <- program(?x8817, ?x3848), genre(?x3848, ?x11671), genre(?x3848, ?x571), ?x11671 = 07qht4, child(?x1908, ?x8817), genre(?x3111, ?x571), genre(?x1498, ?x571), ?x3111 = 0g68zt, ?x1498 = 04jkpgv *> conf = 0.29 ranks of expected_values: 2 EVAL 05sy2k_ honored_for! 0hhtgcw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #9608-04264n PRED entity: 04264n PRED relation: people! PRED expected values: 01_qc_ => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 42): 0gk4g (0.24 #76, 0.20 #274, 0.20 #934), 0dq9p (0.14 #875, 0.13 #1073, 0.13 #1007), 04p3w (0.12 #143, 0.12 #341, 0.11 #869), 0qcr0 (0.12 #67, 0.10 #793, 0.09 #661), 0dcsx (0.08 #345, 0.06 #147, 0.06 #279), 01l2m3 (0.08 #82, 0.04 #412, 0.04 #1270), 02y0js (0.07 #1256, 0.05 #860, 0.05 #1454), 06z5s (0.06 #157, 0.06 #223, 0.06 #355), 01tf_6 (0.06 #163, 0.06 #229, 0.06 #361), 0gg4h (0.06 #168, 0.06 #234, 0.05 #696) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 081nh; 02sj1x; 03h4mp; 01pr6q7; 0hqcy; 0638kv; 022p06; 02__94; 03_bcg; 03bw6; ... >> query: (?x3465, 0gk4g) <- gender(?x3465, ?x231), award(?x3465, ?x435), place_of_burial(?x3465, ?x3691), ?x3691 = 018mmj >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #160 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 04wqr; 0h1m9; 02knnd; 03fvqg; 0bmh4; 01nrq5; 01v3bn; 0ly5n; 0k8y7; 0klw; ... *> query: (?x3465, 01_qc_) <- film(?x3465, ?x5843), location(?x3465, ?x739), place_of_burial(?x3465, ?x3691), nominated_for(?x484, ?x5843) *> conf = 0.06 ranks of expected_values: 11 EVAL 04264n people! 01_qc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 133.000 133.000 0.240 http://example.org/people/cause_of_death/people #9607-0n04r PRED entity: 0n04r PRED relation: genre PRED expected values: 07s9rl0 => 90 concepts (87 used for prediction) PRED predicted values (max 10 best out of 117): 07s9rl0 (0.88 #4250, 0.84 #365, 0.80 #1212), 01jfsb (0.61 #7167, 0.59 #3773, 0.53 #2791), 02kdv5l (0.41 #3764, 0.32 #1335, 0.30 #1578), 05p553 (0.37 #2917, 0.35 #2674, 0.35 #4985), 02l7c8 (0.34 #501, 0.31 #3656, 0.30 #985), 01hmnh (0.30 #3779, 0.18 #2687, 0.16 #5363), 03k9fj (0.26 #3772, 0.26 #1101, 0.23 #5356), 060__y (0.25 #259, 0.24 #17, 0.20 #381), 03bxz7 (0.24 #56, 0.20 #177, 0.18 #420), 04xvlr (0.22 #1698, 0.21 #2307, 0.21 #3642) >> Best rule #4250 for best value: >> intensional similarity = 4 >> extensional distance = 779 >> proper extension: 0415ggl; >> query: (?x4024, 07s9rl0) <- film_crew_role(?x4024, ?x137), genre(?x4024, ?x604), genre(?x5331, ?x604), ?x5331 = 09r94m >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n04r genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 87.000 0.882 http://example.org/film/film/genre #9606-09gffmz PRED entity: 09gffmz PRED relation: gender PRED expected values: 05zppz => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #45, 0.85 #9, 0.85 #65), 02zsn (0.30 #68, 0.29 #78, 0.26 #86) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 194 >> proper extension: 05hjmd; >> query: (?x1712, 05zppz) <- award_winner(?x1712, ?x1711), produced_by(?x430, ?x1712), award_winner(?x8295, ?x1712) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09gffmz gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.852 http://example.org/people/person/gender #9605-0295r PRED entity: 0295r PRED relation: service_language! PRED expected values: 01c6k4 => 34 concepts (31 used for prediction) PRED predicted values (max 10 best out of 146): 01c6k4 (0.67 #727, 0.62 #871, 0.60 #582), 064f29 (0.62 #929, 0.56 #1074, 0.50 #496), 06q07 (0.60 #658, 0.50 #803, 0.50 #514), 069b85 (0.50 #1001, 0.50 #568, 0.44 #1146), 018mxj (0.50 #875, 0.50 #442, 0.44 #1020), 0p4wb (0.50 #730, 0.50 #441, 0.40 #585), 04sv4 (0.50 #520, 0.40 #664, 0.38 #953), 05b5c (0.50 #567, 0.40 #711, 0.38 #1000), 0gvbw (0.50 #457, 0.40 #601, 0.38 #890), 05w3y (0.50 #498, 0.40 #642, 0.38 #931) >> Best rule #727 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 06b_j; >> query: (?x7791, 01c6k4) <- languages_spoken(?x7790, ?x7791), languages(?x10870, ?x7791), languages(?x1888, ?x7791), official_language(?x985, ?x7791), language(?x1866, ?x7791), people(?x7790, ?x11155), story_by(?x11073, ?x10870), influenced_by(?x10870, ?x5004), nominated_for(?x1888, ?x2749), award(?x11155, ?x591), geographic_distribution(?x7790, ?x304) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0295r service_language! 01c6k4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 31.000 0.667 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #9604-0g0syc PRED entity: 0g0syc PRED relation: contains! PRED expected values: 05tbn => 45 concepts (42 used for prediction) PRED predicted values (max 10 best out of 8): 09c7w0 (0.90 #32741, 0.90 #31828, 0.86 #34568), 04_1l0v (0.80 #35016, 0.79 #35929, 0.78 #36842), 029jpy (0.46 #38223, 0.33 #216, 0.25 #23837), 059g4 (0.46 #38223, 0.12 #14078, 0.06 #24084), 05rgl (0.20 #6481, 0.17 #11015, 0.05 #29217), 0d060g (0.14 #37322), 0jcpw (0.05 #38093), 059f4 (0.04 #36433, 0.03 #37351, 0.03 #33691) >> Best rule #32741 for best value: >> intensional similarity = 54 >> extensional distance = 29 >> proper extension: 05fhy; >> query: (?x4754, 09c7w0) <- district_represented(?x6933, ?x4754), district_represented(?x5256, ?x4754), district_represented(?x4812, ?x4754), district_represented(?x4730, ?x4754), district_represented(?x2976, ?x4754), district_represented(?x1829, ?x4754), district_represented(?x952, ?x4754), district_represented(?x606, ?x4754), district_represented(?x605, ?x4754), district_represented(?x176, ?x4754), ?x952 = 06f0dc, legislative_sessions(?x606, ?x5977), legislative_sessions(?x606, ?x3463), ?x3463 = 02bqmq, district_represented(?x2976, ?x7405), district_represented(?x2976, ?x5575), district_represented(?x2976, ?x4622), district_represented(?x2976, ?x3670), district_represented(?x2976, ?x2977), ?x6933 = 024tkd, ?x5575 = 05fjy, legislative_sessions(?x11605, ?x2976), legislative_sessions(?x2357, ?x2976), legislative_sessions(?x652, ?x2976), ?x7405 = 07_f2, legislative_sessions(?x4567, ?x4730), ?x11605 = 024_vw, district_represented(?x4730, ?x4105), legislative_sessions(?x5742, ?x4812), ?x2357 = 0bymv, legislative_sessions(?x2860, ?x4812), ?x2977 = 081mh, legislative_sessions(?x5256, ?x4437), ?x3670 = 05tbn, legislative_sessions(?x176, ?x759), ?x2860 = 0b3wk, ?x4105 = 0824r, district_represented(?x1829, ?x12828), district_represented(?x1829, ?x3908), district_represented(?x1829, ?x3086), district_represented(?x1829, ?x2831), district_represented(?x176, ?x7058), ?x3086 = 0846v, ?x2831 = 0gyh, ?x5977 = 06r713, legislative_sessions(?x13086, ?x176), ?x4622 = 04tgp, ?x7058 = 050ks, ?x3908 = 04ly1, ?x605 = 077g7n, ?x12828 = 0gj4fx, ?x652 = 021sv1, legislative_sessions(?x9046, ?x4437), legislative_sessions(?x7715, ?x4812) >> conf = 0.90 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g0syc contains! 05tbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 45.000 42.000 0.903 http://example.org/location/location/contains #9603-01f2xy PRED entity: 01f2xy PRED relation: major_field_of_study PRED expected values: 02ky346 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 101): 03g3w (0.75 #153, 0.33 #28, 0.33 #2406), 02j62 (0.50 #157, 0.42 #1157, 0.40 #2158), 062z7 (0.50 #154, 0.29 #2407, 0.28 #2155), 04rjg (0.44 #2399, 0.42 #146, 0.33 #21), 01mkq (0.42 #141, 0.37 #2394, 0.31 #1141), 02_7t (0.42 #192, 0.24 #2445, 0.17 #1693), 02jfc (0.42 #211, 0.10 #2464, 0.08 #3467), 05qjt (0.33 #133, 0.25 #2386, 0.25 #1133), 01lj9 (0.33 #42, 0.25 #167, 0.20 #2420), 0h5k (0.33 #149, 0.18 #8142, 0.10 #2402) >> Best rule #153 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 01b1pf; >> query: (?x7355, 03g3w) <- institution(?x7817, ?x7355), institution(?x1368, ?x7355), organization(?x2361, ?x7355), ?x1368 = 014mlp, ?x7817 = 02cq61 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #142 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 01b1pf; *> query: (?x7355, 02ky346) <- institution(?x7817, ?x7355), institution(?x1368, ?x7355), organization(?x2361, ?x7355), ?x1368 = 014mlp, ?x7817 = 02cq61 *> conf = 0.25 ranks of expected_values: 21 EVAL 01f2xy major_field_of_study 02ky346 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 140.000 140.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #9602-04bgy PRED entity: 04bgy PRED relation: artists! PRED expected values: 06j6l => 138 concepts (62 used for prediction) PRED predicted values (max 10 best out of 253): 0ggx5q (0.57 #686, 0.47 #2212, 0.25 #76), 06j6l (0.57 #655, 0.37 #2181, 0.25 #45), 05w3f (0.50 #950, 0.40 #340, 0.25 #8552), 025sc50 (0.43 #657, 0.42 #2183, 0.25 #47), 02lnbg (0.43 #666, 0.37 #2192, 0.25 #56), 0dl5d (0.40 #934, 0.40 #324, 0.26 #3073), 02qdgx (0.40 #341, 0.22 #3090, 0.20 #951), 02k_kn (0.33 #3117, 0.25 #8309, 0.25 #3422), 07sbbz2 (0.31 #1533, 0.25 #8, 0.25 #8552), 017_qw (0.31 #1585, 0.25 #7695, 0.23 #7084) >> Best rule #686 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 086qd; 0161c2; 0gbwp; 043zg; 017b2p; >> query: (?x6469, 0ggx5q) <- profession(?x6469, ?x4773), profession(?x6469, ?x131), type_of_union(?x6469, ?x566), ?x4773 = 0d1pc, ?x131 = 0dz3r, artists(?x671, ?x6469) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #655 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 086qd; 0161c2; 0gbwp; 043zg; 017b2p; *> query: (?x6469, 06j6l) <- profession(?x6469, ?x4773), profession(?x6469, ?x131), type_of_union(?x6469, ?x566), ?x4773 = 0d1pc, ?x131 = 0dz3r, artists(?x671, ?x6469) *> conf = 0.57 ranks of expected_values: 2 EVAL 04bgy artists! 06j6l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 62.000 0.571 http://example.org/music/genre/artists #9601-0d0vqn PRED entity: 0d0vqn PRED relation: participating_countries! PRED expected values: 0c_tl => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 36): 09n48 (0.64 #687, 0.62 #619, 0.62 #584), 0c_tl (0.50 #121, 0.36 #190, 0.29 #703), 06sks6 (0.30 #122, 0.29 #704, 0.27 #191), 0jdk_ (0.27 #137, 0.26 #2795, 0.26 #2760), 0swff (0.27 #137, 0.26 #2795, 0.26 #2760), 0jhn7 (0.27 #137, 0.26 #2795, 0.26 #2760), 0kbvv (0.27 #137, 0.26 #2795, 0.26 #2760), 0swbd (0.27 #137, 0.26 #2795, 0.26 #2760), 0jkvj (0.27 #137, 0.26 #2795, 0.26 #2760), 0124ld (0.27 #137, 0.26 #2795, 0.26 #2760) >> Best rule #687 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 084n_; >> query: (?x304, 09n48) <- country(?x1009, ?x304), organization(?x304, ?x127), nationality(?x2083, ?x304) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #121 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01rhrd; *> query: (?x304, 0c_tl) <- olympics(?x304, ?x4424), country(?x150, ?x304), ?x4424 = 0blfl *> conf = 0.50 ranks of expected_values: 2 EVAL 0d0vqn participating_countries! 0c_tl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 189.000 189.000 0.643 http://example.org/olympics/olympic_games/participating_countries #9600-0t_gg PRED entity: 0t_gg PRED relation: time_zones PRED expected values: 02hcv8 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.85 #107, 0.69 #16, 0.57 #224), 02lcqs (0.38 #174, 0.37 #44, 0.37 #31), 02fqwt (0.25 #144, 0.23 #131, 0.21 #235), 02llzg (0.22 #95, 0.19 #82, 0.16 #69), 02hczc (0.21 #846, 0.08 #67, 0.08 #197), 02lcrv (0.21 #846, 0.04 #72, 0.04 #85), 052vwh (0.17 #12, 0.01 #285), 03bdv (0.06 #214, 0.05 #461, 0.05 #487), 042g7t (0.04 #76, 0.04 #89, 0.02 #492), 03plfd (0.02 #218, 0.01 #283, 0.01 #504) >> Best rule #107 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0k3kg; 0k3kv; 0k3gj; 0k3hn; 0k3l5; 0k3k1; 0k3gw; 0k3jq; 0k3j0; 0k3jc; ... >> query: (?x4989, 02hcv8) <- source(?x4989, ?x958), ?x958 = 0jbk9, contains(?x2020, ?x4989), ?x2020 = 05k7sb >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0t_gg time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.848 http://example.org/location/location/time_zones #9599-0tgcy PRED entity: 0tgcy PRED relation: location! PRED expected values: 018gkb => 88 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1376): 06g60w (0.50 #32772, 0.49 #32773, 0.48 #25210), 027rfxc (0.50 #32772, 0.49 #32773, 0.48 #25210), 023kzp (0.38 #1217, 0.17 #6258, 0.12 #11300), 05x2t7 (0.25 #372, 0.17 #5413, 0.12 #10455), 01kph_c (0.25 #975, 0.17 #6016, 0.08 #11058), 016tbr (0.25 #2078, 0.17 #7119, 0.08 #12161), 033jkj (0.25 #887, 0.17 #5928, 0.08 #10970), 067sqt (0.25 #2309, 0.17 #7350, 0.08 #12392), 0c0k1 (0.25 #1769, 0.17 #6810, 0.08 #11852), 013zyw (0.25 #1179, 0.17 #6220, 0.08 #11262) >> Best rule #32772 for best value: >> intensional similarity = 5 >> extensional distance = 145 >> proper extension: 01p726; >> query: (?x10228, ?x4863) <- place_of_birth(?x8469, ?x10228), place_of_birth(?x4863, ?x10228), time_zones(?x10228, ?x2674), ?x2674 = 02hcv8, award(?x8469, ?x1703) >> conf = 0.50 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0tgcy location! 018gkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 54.000 0.496 http://example.org/people/person/places_lived./people/place_lived/location #9598-049n2l PRED entity: 049n2l PRED relation: current_club! PRED expected values: 02bh_v => 102 concepts (47 used for prediction) PRED predicted values (max 10 best out of 38): 01_lhg (0.64 #185, 0.41 #331, 0.25 #8), 032jlh (0.27 #203, 0.20 #85, 0.18 #349), 01352_ (0.25 #27, 0.20 #115, 0.20 #56), 035qgm (0.23 #253, 0.17 #372, 0.15 #283), 03yl2t (0.20 #63, 0.18 #478, 0.17 #778), 03ylxn (0.20 #83, 0.12 #952, 0.12 #347), 0cnk2q (0.20 #60, 0.12 #952, 0.10 #650), 03d8m4 (0.20 #69, 0.12 #952, 0.10 #1397), 02rqxc (0.20 #68, 0.12 #952, 0.08 #244), 03_qj1 (0.20 #70, 0.12 #952, 0.07 #660) >> Best rule #185 for best value: >> intensional similarity = 11 >> extensional distance = 9 >> proper extension: 049msk; >> query: (?x9048, 01_lhg) <- position(?x9048, ?x530), position(?x9048, ?x203), position(?x9048, ?x63), position(?x9048, ?x60), ?x60 = 02nzb8, ?x203 = 0dgrmp, current_club(?x7294, ?x9048), team(?x5471, ?x9048), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x5471 = 03zv9 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #167 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 9 *> proper extension: 02psgvg; *> query: (?x9048, 02bh_v) <- position(?x9048, ?x530), position(?x9048, ?x63), position(?x9048, ?x60), ?x60 = 02nzb8, team(?x5191, ?x9048), team(?x5191, ?x7247), ?x530 = 02_j1w, ?x7247 = 04991x, ?x63 = 02sdk9v, nationality(?x5191, ?x205) *> conf = 0.18 ranks of expected_values: 12 EVAL 049n2l current_club! 02bh_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 102.000 47.000 0.636 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #9597-04165w PRED entity: 04165w PRED relation: film_crew_role PRED expected values: 02r96rf 09vw2b7 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 34): 09vw2b7 (0.66 #44, 0.59 #786, 0.55 #340), 02r96rf (0.62 #782, 0.62 #336, 0.58 #485), 0dxtw (0.35 #790, 0.33 #48, 0.30 #344), 01vx2h (0.31 #345, 0.30 #791, 0.29 #494), 01pvkk (0.27 #792, 0.22 #13, 0.21 #829), 0215hd (0.16 #57, 0.15 #353, 0.12 #502), 02ynfr (0.15 #796, 0.13 #54, 0.13 #350), 0d2b38 (0.13 #360, 0.11 #64, 0.11 #509), 089g0h (0.13 #58, 0.12 #354, 0.10 #503), 01xy5l_ (0.12 #348, 0.11 #52, 0.11 #460) >> Best rule #44 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 01vksx; 03hkch7; 05hjnw; 011yn5; 0h95927; 04b2qn; 0170xl; >> query: (?x7580, 09vw2b7) <- film(?x398, ?x7580), nominated_for(?x2853, ?x7580), ?x2853 = 09qv_s, genre(?x7580, ?x53) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04165w film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.657 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 04165w film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.657 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9596-0gztl PRED entity: 0gztl PRED relation: state_province_region PRED expected values: 07b_l => 191 concepts (188 used for prediction) PRED predicted values (max 10 best out of 108): 07b_l (0.85 #8665, 0.34 #1612, 0.32 #23294), 059rby (0.52 #4710, 0.52 #1367, 0.51 #12006), 09c7w0 (0.34 #1612, 0.32 #23294, 0.27 #20062), 01n7q (0.29 #1504, 0.27 #7569, 0.26 #2621), 0ms1n (0.28 #16096, 0.25 #14733), 05k7sb (0.15 #5973, 0.11 #8572, 0.07 #16621), 0f2rq (0.14 #1736, 0.08 #6066, 0.07 #6685), 081yw (0.12 #309, 0.12 #185, 0.12 #432), 03v0t (0.12 #1665, 0.08 #4759, 0.08 #301), 0d0x8 (0.11 #8585, 0.05 #15149, 0.05 #9328) >> Best rule #8665 for best value: >> intensional similarity = 5 >> extensional distance = 86 >> proper extension: 0k9wp; >> query: (?x1604, ?x3634) <- citytown(?x1604, ?x5719), location(?x400, ?x5719), contains(?x5719, ?x3387), state(?x5719, ?x3634), county_seat(?x11836, ?x5719) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gztl state_province_region 07b_l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 188.000 0.854 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #9595-03bxwtd PRED entity: 03bxwtd PRED relation: award_nominee PRED expected values: 01pfkw => 111 concepts (49 used for prediction) PRED predicted values (max 10 best out of 805): 023p29 (0.81 #93432, 0.80 #107451, 0.80 #65396), 09mq4m (0.81 #93432, 0.80 #107451, 0.80 #65396), 01vw20h (0.65 #5728, 0.17 #26746, 0.14 #88759), 016kjs (0.53 #4900, 0.14 #88759, 0.10 #25918), 03j3pg9 (0.47 #6758, 0.14 #88759, 0.06 #27776), 02l840 (0.41 #4830, 0.17 #25848, 0.14 #88759), 01wgxtl (0.41 #5277, 0.14 #88759, 0.07 #26295), 01wlt3k (0.41 #6903, 0.05 #27921, 0.02 #74637), 06mt91 (0.35 #6227, 0.14 #88759, 0.09 #27245), 01wwvc5 (0.35 #5276, 0.14 #88759, 0.06 #26294) >> Best rule #93432 for best value: >> intensional similarity = 2 >> extensional distance = 523 >> proper extension: 01wj5hp; 04qzm; 09jm8; 016ppr; >> query: (?x3062, ?x527) <- artists(?x302, ?x3062), award_nominee(?x527, ?x3062) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #88759 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 513 *> proper extension: 047c9l; 04n32; 01c7p_; *> query: (?x3062, ?x140) <- award_nominee(?x3062, ?x6268), artists(?x302, ?x3062), award_nominee(?x140, ?x6268) *> conf = 0.14 ranks of expected_values: 51 EVAL 03bxwtd award_nominee 01pfkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 111.000 49.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9594-0140g4 PRED entity: 0140g4 PRED relation: language PRED expected values: 02h40lc => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 57): 02h40lc (0.90 #1598, 0.90 #3140, 0.90 #1716), 04306rv (0.40 #300, 0.13 #595, 0.12 #1423), 06nm1 (0.33 #306, 0.16 #424, 0.16 #720), 06b_j (0.27 #318, 0.23 #436, 0.20 #82), 064_8sq (0.16 #612, 0.15 #1618, 0.15 #2149), 03x42 (0.15 #227, 0.03 #7161), 02bjrlw (0.13 #591, 0.11 #1419, 0.10 #770), 07zrf (0.08 #121, 0.07 #239, 0.04 #712), 03_9r (0.07 #2852, 0.07 #2433, 0.07 #1488), 0653m (0.07 #602, 0.07 #307, 0.07 #2198) >> Best rule #1598 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 025twgt; >> query: (?x188, 02h40lc) <- genre(?x188, ?x53), music(?x188, ?x3134), nominated_for(?x4538, ?x188), artists(?x4910, ?x3134) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0140g4 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.904 http://example.org/film/film/language #9593-02v8kmz PRED entity: 02v8kmz PRED relation: language PRED expected values: 02h40lc => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 43): 02h40lc (0.90 #301, 0.90 #420, 0.89 #1800), 064_8sq (0.20 #140, 0.13 #677, 0.13 #558), 06b_j (0.20 #141, 0.11 #381, 0.07 #441), 03_9r (0.15 #249, 0.07 #128, 0.05 #487), 04306rv (0.13 #123, 0.10 #184, 0.09 #363), 0jzc (0.11 #138, 0.07 #378, 0.05 #319), 02bjrlw (0.10 #300, 0.08 #240, 0.07 #656), 06nm1 (0.10 #728, 0.09 #369, 0.09 #607), 03k50 (0.07 #127, 0.03 #367, 0.02 #664), 012w70 (0.06 #312, 0.03 #490, 0.02 #1626) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 03f7xg; >> query: (?x240, 02h40lc) <- film(?x192, ?x240), genre(?x240, ?x53), film_crew_role(?x240, ?x137), film_production_design_by(?x240, ?x6096) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v8kmz language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.903 http://example.org/film/film/language #9592-0g3zrd PRED entity: 0g3zrd PRED relation: language PRED expected values: 02h40lc => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 30): 02h40lc (0.91 #120, 0.91 #238, 0.90 #537), 0295r (0.20 #29), 064_8sq (0.17 #81, 0.14 #557, 0.13 #618), 04306rv (0.15 #123, 0.09 #420, 0.09 #300), 06nm1 (0.13 #426, 0.11 #247, 0.10 #668), 02bjrlw (0.10 #119, 0.08 #597, 0.07 #536), 06b_j (0.08 #438, 0.06 #141, 0.06 #680), 0jzc (0.05 #435, 0.05 #79, 0.04 #677), 03_9r (0.04 #667, 0.04 #3715, 0.04 #3775), 0653m (0.04 #307, 0.04 #846, 0.04 #1084) >> Best rule #120 for best value: >> intensional similarity = 4 >> extensional distance = 142 >> proper extension: 02q52q; 0gxfz; 0glnm; 02q_x_l; 04wddl; >> query: (?x2331, 02h40lc) <- genre(?x2331, ?x53), film(?x10834, ?x2331), participant(?x10834, ?x6730), costume_design_by(?x2331, ?x6327) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g3zrd language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.910 http://example.org/film/film/language #9591-06lpmt PRED entity: 06lpmt PRED relation: genre PRED expected values: 02l7c8 => 73 concepts (45 used for prediction) PRED predicted values (max 10 best out of 101): 02l7c8 (0.70 #248, 0.63 #1997, 0.32 #2934), 01z4y (0.61 #4907, 0.61 #466, 0.53 #4089), 06cvj (0.60 #236, 0.23 #819, 0.21 #1287), 02kdv5l (0.58 #2218, 0.45 #2335, 0.35 #934), 03k9fj (0.50 #1176, 0.50 #942, 0.47 #2226), 01jfsb (0.42 #3747, 0.35 #2227, 0.35 #1644), 0gf28 (0.31 #411, 0.16 #878, 0.14 #62), 0l4h_ (0.31 #420, 0.14 #71, 0.07 #537), 06n90 (0.27 #2228, 0.24 #944, 0.23 #2461), 01t_vv (0.25 #169, 0.21 #518, 0.20 #285) >> Best rule #248 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0d8w2n; >> query: (?x4130, 02l7c8) <- genre(?x4130, ?x12008), ?x12008 = 0gsy3b, featured_film_locations(?x4130, ?x739), film_release_distribution_medium(?x4130, ?x81) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06lpmt genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 45.000 0.700 http://example.org/film/film/genre #9590-031x_3 PRED entity: 031x_3 PRED relation: performance_role PRED expected values: 0j210 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 35): 03bx0bm (0.42 #362, 0.41 #319, 0.09 #233), 0l14md (0.22 #307, 0.18 #350, 0.03 #955), 013y1f (0.20 #19, 0.11 #363, 0.10 #320), 05r5c (0.20 #7, 0.09 #50, 0.09 #308), 026t6 (0.19 #303, 0.10 #346, 0.04 #88), 0l14qv (0.17 #305, 0.11 #348, 0.02 #391), 0342h (0.10 #347, 0.07 #304, 0.04 #89), 0l15bq (0.07 #321, 0.06 #364, 0.01 #580), 02sgy (0.05 #306, 0.04 #349, 0.04 #91), 02snj9 (0.05 #335, 0.03 #378) >> Best rule #362 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 0p5mw; 01ccr8; >> query: (?x8583, 03bx0bm) <- nationality(?x8583, ?x94), award(?x8583, ?x159), performance_role(?x8583, ?x14713) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 031x_3 performance_role 0j210 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.417 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #9589-03j70t PRED entity: 03j70t PRED relation: current_club! PRED expected values: 02s2lg => 73 concepts (61 used for prediction) PRED predicted values (max 10 best out of 30): 03ylxn (0.07 #115, 0.06 #25, 0.06 #265), 03y_f8 (0.05 #1293, 0.05 #1473, 0.05 #1233), 03yl2t (0.05 #4, 0.04 #274, 0.04 #304), 033nzk (0.04 #272, 0.04 #572, 0.04 #632), 03xh50 (0.04 #312, 0.04 #42, 0.04 #162), 02ltg3 (0.04 #877, 0.04 #577, 0.03 #277), 03d8m4 (0.04 #1060, 0.04 #730, 0.03 #940), 032jlh (0.04 #477, 0.04 #207, 0.03 #267), 03ys48 (0.04 #78, 0.04 #648, 0.03 #228), 03dj48 (0.04 #1343, 0.04 #863, 0.03 #383) >> Best rule #115 for best value: >> intensional similarity = 14 >> extensional distance = 73 >> proper extension: 02gys2; 03j722; 01kj5h; 0303jw; 01tqfs; 03fmw_; 03zbg0; 0dy6c9; 03fn34; 02b1l_; ... >> query: (?x11178, 03ylxn) <- team(?x530, ?x11178), team(?x203, ?x11178), team(?x63, ?x11178), ?x63 = 02sdk9v, position(?x11178, ?x60), ?x203 = 0dgrmp, ?x530 = 02_j1w, ?x60 = 02nzb8, position(?x11178, ?x60), position(?x11178, ?x63), position(?x11178, ?x63), position(?x11178, ?x530), position(?x11178, ?x530), position(?x11178, ?x203) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #396 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 92 *> proper extension: 01j95f; 023fb; 01cwm1; 04x8cp; *> query: (?x11178, 02s2lg) <- team(?x530, ?x11178), team(?x203, ?x11178), team(?x63, ?x11178), ?x63 = 02sdk9v, position(?x11178, ?x60), ?x203 = 0dgrmp, ?x530 = 02_j1w, ?x60 = 02nzb8, position(?x11178, ?x203), position(?x11178, ?x63), position(?x11178, ?x203), position(?x11178, ?x530), position(?x11178, ?x530) *> conf = 0.02 ranks of expected_values: 26 EVAL 03j70t current_club! 02s2lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 73.000 61.000 0.067 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #9588-0cg9y PRED entity: 0cg9y PRED relation: artist! PRED expected values: 033hn8 => 94 concepts (55 used for prediction) PRED predicted values (max 10 best out of 113): 0g768 (0.37 #3889, 0.13 #1229, 0.13 #5753), 03rhqg (0.35 #4273, 0.21 #4672, 0.15 #5738), 033hn8 (0.26 #4271, 0.14 #1611, 0.13 #2143), 043g7l (0.18 #2157, 0.17 #1625, 0.16 #2290), 01cszh (0.17 #3869, 0.13 #1209, 0.10 #1741), 03mp8k (0.16 #2190, 0.16 #1658, 0.15 #4318), 011k1h (0.16 #4666, 0.12 #3070, 0.12 #3469), 017l96 (0.15 #152, 0.11 #19, 0.10 #3345), 0181dw (0.15 #4692, 0.13 #1234, 0.13 #3495), 01xyqk (0.14 #2336, 0.06 #1538, 0.05 #207) >> Best rule #3889 for best value: >> intensional similarity = 5 >> extensional distance = 270 >> proper extension: 0f0y8; 01lmj3q; 01vvycq; 0150jk; 0152cw; 07qnf; 01vrt_c; 03t9sp; 058s57; 04r1t; ... >> query: (?x2347, 0g768) <- artist(?x3265, ?x2347), artist(?x3265, ?x9731), artist(?x3265, ?x5310), ?x5310 = 012vd6, profession(?x9731, ?x131) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #4271 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 335 *> proper extension: 01vvydl; 0c9d9; 01pfr3; 06cc_1; 04rcr; 0kzy0; 01gf5h; 01wbgdv; 07c0j; 018y2s; ... *> query: (?x2347, 033hn8) <- artist(?x3265, ?x2347), artist(?x3265, ?x6986), artist(?x3265, ?x5310), artists(?x505, ?x5310), ?x6986 = 02vgh *> conf = 0.26 ranks of expected_values: 3 EVAL 0cg9y artist! 033hn8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 55.000 0.371 http://example.org/music/record_label/artist #9587-050f0s PRED entity: 050f0s PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 32): 0ch6mp2 (0.73 #1035, 0.71 #1517, 0.70 #1294), 02r96rf (0.71 #223, 0.62 #4, 0.61 #1513), 09zzb8 (0.71 #1028, 0.70 #1510, 0.70 #220), 09vw2b7 (0.62 #1034, 0.59 #226, 0.59 #1516), 01vx2h (0.44 #48, 0.37 #231, 0.35 #524), 0dxtw (0.41 #230, 0.35 #120, 0.34 #1520), 01pvkk (0.27 #1522, 0.27 #232, 0.27 #1040), 02rh1dz (0.19 #46, 0.17 #229, 0.16 #119), 02ynfr (0.17 #16, 0.17 #52, 0.16 #235), 01xy5l_ (0.16 #15, 0.10 #1803, 0.10 #1042) >> Best rule #1035 for best value: >> intensional similarity = 4 >> extensional distance = 742 >> proper extension: 01br2w; 0djb3vw; 04dsnp; 091z_p; 05dy7p; 02rb607; 040rmy; 02n9bh; 04lqvly; 02hfk5; ... >> query: (?x1965, 0ch6mp2) <- genre(?x1965, ?x258), currency(?x1965, ?x170), nominated_for(?x574, ?x1965), film_crew_role(?x1965, ?x955) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050f0s film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.726 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9586-052h3 PRED entity: 052h3 PRED relation: influenced_by PRED expected values: 032r1 => 145 concepts (57 used for prediction) PRED predicted values (max 10 best out of 331): 03sbs (0.53 #5410, 0.43 #5842, 0.40 #4544), 05qmj (0.50 #5380, 0.49 #5812, 0.36 #4514), 048cl (0.38 #2825, 0.25 #5422, 0.20 #5854), 03_87 (0.33 #633, 0.17 #9286, 0.17 #1065), 03_hd (0.33 #565, 0.17 #997, 0.13 #3157), 0hky (0.33 #624, 0.17 #1056, 0.13 #3216), 04jvt (0.33 #763, 0.17 #1195, 0.13 #3355), 03_dj (0.33 #842, 0.17 #1274, 0.13 #3434), 02kz_ (0.33 #169, 0.13 #3193, 0.12 #1465), 040_9 (0.33 #98, 0.07 #3122, 0.06 #9183) >> Best rule #5410 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0399p; >> query: (?x3711, 03sbs) <- influenced_by(?x3711, ?x712), gender(?x3711, ?x231), interests(?x3711, ?x6978), nationality(?x3711, ?x94) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #835 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 03hnd; 034bs; *> query: (?x3711, 032r1) <- student(?x3424, ?x3711), profession(?x3711, ?x9081), ?x9081 = 0d8qb, influenced_by(?x3711, ?x712), place_of_death(?x3711, ?x739) *> conf = 0.17 ranks of expected_values: 53 EVAL 052h3 influenced_by 032r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 145.000 57.000 0.531 http://example.org/influence/influence_node/influenced_by #9585-01wd9lv PRED entity: 01wd9lv PRED relation: award PRED expected values: 0gq9h 02sp_v 01cky2 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 293): 02v1m7 (0.81 #390, 0.74 #23738, 0.73 #7393), 025mbn (0.81 #390, 0.74 #23738, 0.73 #7393), 02h3d1 (0.81 #390, 0.74 #23738, 0.73 #7393), 02qvyrt (0.40 #1287, 0.36 #7122, 0.34 #5177), 0c4z8 (0.40 #5515, 0.22 #5904, 0.21 #2014), 03qbh5 (0.38 #5641, 0.30 #4085, 0.25 #973), 025m8y (0.36 #1261, 0.25 #2428, 0.23 #7096), 025m8l (0.35 #111, 0.17 #2446, 0.17 #5169), 0gq9h (0.35 #14861, 0.34 #12137, 0.15 #45137), 0cjyzs (0.32 #10995, 0.26 #4380, 0.25 #1657) >> Best rule #390 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 03cd1q; >> query: (?x6382, ?x594) <- student(?x1151, ?x6382), award_winner(?x594, ?x6382), ?x1151 = 02g839 >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #14861 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 347 *> proper extension: 024c1b; *> query: (?x6382, 0gq9h) <- produced_by(?x9801, ?x6382), nominated_for(?x105, ?x9801) *> conf = 0.35 ranks of expected_values: 9, 40, 43 EVAL 01wd9lv award 01cky2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 135.000 135.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01wd9lv award 02sp_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 135.000 135.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01wd9lv award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 135.000 135.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9584-07szy PRED entity: 07szy PRED relation: school! PRED expected values: 01xvb => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 84): 01d6g (0.27 #230, 0.10 #811, 0.10 #479), 05m_8 (0.21 #666, 0.18 #1745, 0.17 #2078), 01yhm (0.21 #681, 0.17 #764, 0.13 #2093), 0jmm4 (0.21 #729, 0.13 #1227, 0.09 #1476), 0bwjj (0.18 #233, 0.14 #731, 0.10 #2143), 0487_ (0.18 #222, 0.12 #139, 0.11 #720), 051vz (0.18 #683, 0.17 #766, 0.16 #1762), 0jmnl (0.18 #746, 0.14 #829, 0.11 #1244), 05g49 (0.18 #705, 0.11 #1203, 0.10 #788), 07l8x (0.17 #308, 0.15 #474, 0.14 #1802) >> Best rule #230 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 05kj_; >> query: (?x1681, 01d6g) <- contains(?x94, ?x1681), time_zones(?x1681, ?x2674), school(?x465, ?x1681) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1754 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 74 *> proper extension: 0fht9f; *> query: (?x1681, 01xvb) <- school(?x5822, ?x1681), position(?x5822, ?x180) *> conf = 0.04 ranks of expected_values: 75 EVAL 07szy school! 01xvb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 105.000 105.000 0.273 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9583-06whf PRED entity: 06whf PRED relation: influenced_by! PRED expected values: 0mb5x => 159 concepts (75 used for prediction) PRED predicted values (max 10 best out of 447): 06whf (0.60 #662, 0.33 #3164, 0.25 #4168), 040db (0.60 #576, 0.25 #9584, 0.25 #4082), 073v6 (0.50 #117, 0.33 #3120, 0.25 #2619), 013pp3 (0.50 #217, 0.25 #2719, 0.22 #3220), 0lrh (0.50 #104, 0.22 #3107, 0.20 #605), 01vdrw (0.50 #433, 0.22 #3436, 0.20 #934), 045bg (0.50 #35, 0.22 #3038, 0.12 #2537), 01hc9_ (0.50 #355, 0.12 #2857, 0.12 #31546), 0g72r (0.50 #487, 0.12 #2989, 0.11 #3490), 034bs (0.40 #653, 0.33 #3155, 0.25 #2654) >> Best rule #662 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03_dj; >> query: (?x4265, 06whf) <- influenced_by(?x1029, ?x4265), nationality(?x4265, ?x429), place_of_death(?x4265, ?x4627), ?x429 = 03rt9 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #329 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 032l1; 03f0324; *> query: (?x4265, 0mb5x) <- influenced_by(?x4265, ?x1236), religion(?x4265, ?x2694), influenced_by(?x9173, ?x4265), ?x9173 = 01x53m *> conf = 0.25 ranks of expected_values: 21 EVAL 06whf influenced_by! 0mb5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 159.000 75.000 0.600 http://example.org/influence/influence_node/influenced_by #9582-03gyvwg PRED entity: 03gyvwg PRED relation: actor PRED expected values: 044_7j => 89 concepts (78 used for prediction) PRED predicted values (max 10 best out of 72): 044_7j (0.40 #109, 0.29 #251, 0.25 #959), 05j0wc (0.38 #336, 0.20 #901, 0.20 #193), 05v954 (0.31 #663, 0.28 #1155, 0.22 #1084), 0ckm4x (0.25 #418, 0.23 #702, 0.22 #1194), 0cpjgj (0.25 #381, 0.20 #452, 0.20 #168), 08p1gp (0.25 #409, 0.20 #480, 0.20 #196), 08141d (0.25 #349, 0.20 #134, 0.14 #276), 0chrwb (0.22 #1000, 0.20 #860, 0.20 #436), 0678gl (0.20 #496, 0.20 #212, 0.18 #1271), 055t01 (0.20 #487, 0.20 #203, 0.13 #911) >> Best rule #109 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 02pb2bp; 02gs6r; 05pyrb; >> query: (?x11154, 044_7j) <- currency(?x11154, ?x170), film(?x7764, ?x11154), genre(?x11154, ?x225), actor(?x11154, ?x12318), gender(?x7764, ?x514), nationality(?x7764, ?x252), special_performance_type(?x7764, ?x296), language(?x11154, ?x2164) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03gyvwg actor 044_7j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 78.000 0.400 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #9581-02hp6p PRED entity: 02hp6p PRED relation: organization! PRED expected values: 060c4 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 16): 060c4 (0.85 #301, 0.85 #366, 0.84 #288), 0dq_5 (0.40 #35, 0.38 #321, 0.34 #360), 07xl34 (0.24 #349, 0.22 #765, 0.22 #1168), 05k17c (0.23 #46, 0.22 #241, 0.21 #72), 0hm4q (0.07 #905, 0.07 #1074, 0.07 #970), 05c0jwl (0.05 #902, 0.05 #733, 0.05 #746), 08jcfy (0.03 #740, 0.03 #753, 0.03 #805), 0dq3c (0.02 #1978, 0.01 #313, 0.01 #352), 01t7n9 (0.02 #1978), 02079p (0.02 #1978) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 02kth6; 01c333; 01j_5k; 017y6l; 01xk7r; 02m0sc; 019c57; >> query: (?x11654, 060c4) <- colors(?x11654, ?x663), country(?x11654, ?x94), student(?x11654, ?x5059), institution(?x620, ?x11654) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hp6p organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.852 http://example.org/organization/role/leaders./organization/leadership/organization #9580-0g5qs2k PRED entity: 0g5qs2k PRED relation: film! PRED expected values: 016tw3 => 66 concepts (50 used for prediction) PRED predicted values (max 10 best out of 58): 03xq0f (0.58 #1415, 0.22 #5, 0.17 #228), 016tt2 (0.22 #4, 0.19 #449, 0.17 #746), 04f525m (0.22 #10, 0.06 #455, 0.06 #381), 086k8 (0.22 #225, 0.19 #892, 0.17 #1336), 01795t (0.17 #463, 0.14 #760, 0.13 #389), 05qd_ (0.17 #83, 0.16 #1047, 0.16 #1121), 017s11 (0.17 #300, 0.14 #967, 0.13 #893), 016tw3 (0.14 #2478, 0.14 #1271, 0.12 #1949), 03xsby (0.13 #239, 0.11 #16, 0.06 #387), 07swvb (0.13 #2389, 0.08 #1560, 0.05 #3678) >> Best rule #1415 for best value: >> intensional similarity = 4 >> extensional distance = 206 >> proper extension: 0522wp; >> query: (?x504, 03xq0f) <- film(?x10958, ?x504), film_distribution_medium(?x504, ?x81), film(?x10958, ?x7692), nominated_for(?x68, ?x7692) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #2478 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 861 *> proper extension: 03f7xg; 04vq33; *> query: (?x504, 016tw3) <- film(?x4128, ?x504), award_nominee(?x4128, ?x450), produced_by(?x504, ?x10540) *> conf = 0.14 ranks of expected_values: 8 EVAL 0g5qs2k film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 66.000 50.000 0.577 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #9579-02q4mt PRED entity: 02q4mt PRED relation: producer_type PRED expected values: 0ckd1 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.30 #11, 0.19 #3, 0.18 #1) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 450 >> proper extension: 04bs3j; 0277470; 0f1vrl; 0bt4r4; 02j8nx; 015f7; 07lwsz; 0blt6; 07fvf1; 014g22; ... >> query: (?x11873, 0ckd1) <- type_of_union(?x11873, ?x566), profession(?x11873, ?x1041), ?x1041 = 03gjzk >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q4mt producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.299 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #9578-094qd5 PRED entity: 094qd5 PRED relation: award! PRED expected values: 0f4vbz 01fwk3 057hz 0bw87 => 53 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2445): 016nff (0.88 #16526, 0.83 #16525, 0.80 #6609), 0bw87 (0.88 #16526, 0.83 #16525, 0.80 #6609), 02jsgf (0.67 #17644, 0.43 #20948, 0.40 #14337), 01j5ts (0.60 #13263, 0.25 #6652, 0.25 #3347), 02d42t (0.50 #17910, 0.50 #4687, 0.40 #14603), 014zcr (0.50 #29800, 0.45 #33105, 0.33 #52), 020_95 (0.50 #18088, 0.43 #21392, 0.40 #14781), 01skmp (0.50 #18425, 0.40 #15118, 0.38 #28341), 0f4vbz (0.50 #17095, 0.40 #13788, 0.38 #27011), 03zqc1 (0.50 #16634, 0.40 #13327, 0.38 #26550) >> Best rule #16526 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0gkts9; >> query: (?x749, ?x2028) <- nominated_for(?x749, ?x306), award_winner(?x749, ?x2589), award_winner(?x749, ?x2028), ?x2589 = 019f2f, award_winner(?x8890, ?x2028) >> conf = 0.88 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 9, 147, 152 EVAL 094qd5 award! 0bw87 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 53.000 26.000 0.882 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 094qd5 award! 057hz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 26.000 0.882 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 094qd5 award! 01fwk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 26.000 0.882 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 094qd5 award! 0f4vbz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 53.000 26.000 0.882 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9577-03vrv9 PRED entity: 03vrv9 PRED relation: profession PRED expected values: 0dxtg => 109 concepts (61 used for prediction) PRED predicted values (max 10 best out of 70): 0dxtg (0.67 #5233, 0.65 #4942, 0.61 #5087), 01445t (0.50 #601, 0.40 #1326, 0.34 #1616), 03gjzk (0.45 #1463, 0.42 #6829, 0.32 #1173), 09jwl (0.40 #17, 0.30 #4657, 0.18 #6108), 0dz3r (0.40 #2, 0.26 #4642, 0.10 #5658), 0nbcg (0.40 #30, 0.23 #4670, 0.17 #3945), 0kyk (0.40 #28, 0.19 #2348, 0.19 #2203), 016z4k (0.40 #4, 0.18 #4644, 0.12 #2324), 0d1pc (0.40 #49, 0.14 #2659, 0.10 #2804), 012t_z (0.40 #11, 0.08 #2621, 0.07 #2766) >> Best rule #5233 for best value: >> intensional similarity = 5 >> extensional distance = 403 >> proper extension: 01vvycq; 05drq5; 02_4fn; 047q2wc; 01pp3p; 0522wp; 02m92h; 03mv0b; 03s2y9; 04dz_y7; ... >> query: (?x11282, 0dxtg) <- profession(?x11282, ?x524), profession(?x11282, ?x319), ?x524 = 02jknp, ?x319 = 01d_h8, gender(?x11282, ?x231) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03vrv9 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 61.000 0.669 http://example.org/people/person/profession #9576-0kz2w PRED entity: 0kz2w PRED relation: state_province_region PRED expected values: 05k7sb => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 68): 059rby (0.25 #1975, 0.22 #1236, 0.22 #5297), 01n7q (0.19 #1373, 0.19 #1250, 0.18 #4573), 05tbn (0.14 #174, 0.14 #51, 0.11 #2268), 07b_l (0.10 #296, 0.10 #419, 0.07 #665), 03v0t (0.10 #1038, 0.10 #545, 0.10 #176), 0d0x8 (0.10 #167, 0.10 #44, 0.07 #783), 05kkh (0.10 #2, 0.08 #987, 0.06 #2711), 05k7sb (0.10 #154, 0.07 #3479, 0.06 #1016), 0rh6k (0.10 #1, 0.05 #740, 0.05 #124), 05fkf (0.07 #257, 0.06 #380, 0.05 #626) >> Best rule #1975 for best value: >> intensional similarity = 2 >> extensional distance = 83 >> proper extension: 0c_j5d; 0gztl; 04qhdf; 03mnk; 0k8z; 0168nq; 05xbx; 01zpmq; 04htfd; 07l1c; ... >> query: (?x1043, 059rby) <- company(?x346, ?x1043), currency(?x1043, ?x170) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #154 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 031n8c; 036921; *> query: (?x1043, 05k7sb) <- company(?x346, ?x1043), currency(?x1043, ?x170), school_type(?x1043, ?x1044) *> conf = 0.10 ranks of expected_values: 8 EVAL 0kz2w state_province_region 05k7sb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 92.000 92.000 0.247 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #9575-04fhps PRED entity: 04fhps PRED relation: legislative_sessions! PRED expected values: 04lgybj => 10 concepts (10 used for prediction) PRED predicted values (max 10 best out of 55): 04lgybj (0.76 #280, 0.69 #469, 0.65 #407), 04fhps (0.76 #280, 0.69 #469, 0.65 #407), 03rtmz (0.61 #243, 0.56 #432, 0.52 #281), 07p__7 (0.56 #232, 0.52 #281, 0.51 #476), 024tcq (0.56 #246, 0.52 #281, 0.49 #435), 02bqn1 (0.56 #236, 0.52 #281, 0.49 #425), 060ny2 (0.56 #263, 0.51 #452, 0.41 #390), 070m6c (0.52 #281, 0.51 #475, 0.51 #419), 02bqm0 (0.52 #281, 0.50 #255, 0.49 #444), 070mff (0.52 #281, 0.50 #265, 0.49 #454) >> Best rule #280 for best value: >> intensional similarity = 129 >> extensional distance = 16 >> proper extension: 03rl1g; 077g7n; 070m6c; 07p__7; 06f0dc; 02bn_p; 02bqn1; 02bqmq; 024tcq; 02bqm0; ... >> query: (?x11189, ?x3473) <- legislative_sessions(?x10543, ?x11189), district_represented(?x11189, ?x7468), legislative_sessions(?x10543, ?x3473), legislative_sessions(?x8776, ?x11189), contains(?x7468, ?x4780), contains(?x279, ?x7468), legislative_sessions(?x14471, ?x10543), adjoins(?x7468, ?x4600), adjoins(?x7468, ?x953), major_field_of_study(?x4780, ?x373), featured_film_locations(?x1721, ?x7468), district_represented(?x10543, ?x1905), film_release_region(?x11209, ?x279), film_release_region(?x9900, ?x279), film_release_region(?x8381, ?x279), film_release_region(?x7897, ?x279), film_release_region(?x7275, ?x279), film_release_region(?x6168, ?x279), film_release_region(?x6121, ?x279), film_release_region(?x6078, ?x279), film_release_region(?x5092, ?x279), film_release_region(?x4998, ?x279), film_release_region(?x4668, ?x279), film_release_region(?x4446, ?x279), film_release_region(?x4422, ?x279), film_release_region(?x3958, ?x279), film_release_region(?x3377, ?x279), film_release_region(?x3035, ?x279), film_release_region(?x2340, ?x279), film_release_region(?x2318, ?x279), film_release_region(?x1988, ?x279), film_release_region(?x1916, ?x279), film_release_region(?x1701, ?x279), film_release_region(?x1602, ?x279), film_release_region(?x1421, ?x279), film_release_region(?x1370, ?x279), film_release_region(?x1002, ?x279), film_release_region(?x972, ?x279), film_release_region(?x324, ?x279), film_release_region(?x141, ?x279), film_release_region(?x80, ?x279), film_release_region(?x66, ?x279), ?x66 = 014lc_, combatants(?x279, ?x7430), combatants(?x279, ?x792), combatants(?x279, ?x390), countries_spoken_in(?x393, ?x279), ?x6121 = 064lsn, service_location(?x9968, ?x279), ?x141 = 0gtsx8c, ?x2318 = 06v9_x, olympics(?x279, ?x2630), nationality(?x12004, ?x279), nationality(?x10184, ?x279), nationality(?x6804, ?x279), nationality(?x5940, ?x279), nationality(?x3325, ?x279), ?x4446 = 0db94w, adjoins(?x279, ?x1144), country_of_origin(?x11895, ?x279), ?x9968 = 0k9ts, ?x8381 = 0h2zvzr, ?x6078 = 04pk1f, ?x1916 = 0ch26b_, time_zones(?x279, ?x1638), ?x792 = 0hzlz, ?x12004 = 02_01w, ?x2630 = 0swff, film_release_region(?x1064, ?x279), ?x1370 = 0gmcwlb, ?x4998 = 0dzlbx, ?x3958 = 0gyh2wm, ?x6168 = 0gj96ln, ?x7897 = 03np63f, category(?x4780, ?x134), country(?x2474, ?x279), country(?x4876, ?x279), country(?x4673, ?x279), country(?x3015, ?x279), country(?x2884, ?x279), ?x4668 = 0bh8x1y, ?x1988 = 09k56b7, combatants(?x326, ?x279), ?x373 = 02vxn, ?x1701 = 0bh8yn3, geographic_distribution(?x1571, ?x279), ?x2340 = 0fpv_3_, ?x2884 = 09wz9, ?x3035 = 0j43swk, ?x80 = 0b76d_m, ?x1002 = 0_b3d, ?x1421 = 07qg8v, ?x11209 = 04fjzv, ?x7430 = 01mk6, country(?x7462, ?x279), country(?x4963, ?x279), country(?x136, ?x279), institution(?x734, ?x4780), ?x5092 = 0gg5qcw, ?x1144 = 0j3b, ?x4963 = 0194zl, ?x4422 = 06zn2v2, ?x972 = 017gl1, ?x7462 = 02v570, contains(?x4600, ?x1087), ?x324 = 07gp9, locations(?x13643, ?x279), ?x3377 = 0gj8nq2, administrative_parent(?x5244, ?x953), entity_involved(?x3278, ?x279), religion(?x1905, ?x109), ?x9900 = 0qmfk, state(?x11121, ?x953), member_states(?x7416, ?x279), ?x1602 = 0gxtknx, ?x7275 = 0g4vmj8, district_represented(?x605, ?x4600), ?x3325 = 073v6, ?x136 = 09sh8k, ?x11895 = 03cf9ly, adjoins(?x177, ?x1905), ?x390 = 0chghy, ?x5940 = 0p__8, ?x10184 = 04rg6, ?x4673 = 07jbh, exported_to(?x252, ?x279), ?x3015 = 071t0, ?x4876 = 0d1t3, ?x6804 = 022q4l9 >> conf = 0.76 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 04fhps legislative_sessions! 04lgybj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 10.000 10.000 0.760 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #9574-0838y PRED entity: 0838y PRED relation: artist! PRED expected values: 01trtc => 116 concepts (78 used for prediction) PRED predicted values (max 10 best out of 113): 015_1q (0.38 #1412, 0.33 #994, 0.33 #437), 03rhqg (0.37 #990, 0.28 #1408, 0.24 #1825), 033hn8 (0.25 #153, 0.22 #1267, 0.22 #988), 0n85g (0.25 #201, 0.20 #479, 0.14 #3263), 0g768 (0.25 #36, 0.15 #1290, 0.15 #1151), 01trtc (0.22 #350, 0.20 #628, 0.19 #767), 011k1h (0.20 #566, 0.20 #427, 0.19 #2236), 017l96 (0.20 #575, 0.19 #714, 0.17 #1411), 0k_kr (0.20 #600, 0.19 #739, 0.13 #461), 0229rs (0.19 #992, 0.17 #1410, 0.17 #1827) >> Best rule #1412 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 01wv9xn; 01vrwfv; 01q99h; 07bzp; 07mvp; 03c3yf; 01w5n51; 0187x8; 01lf293; 033s6; ... >> query: (?x6818, 015_1q) <- group(?x2798, ?x6818), group(?x227, ?x6818), artist(?x1954, ?x6818), ?x227 = 0342h, ?x2798 = 03qjg, award(?x6818, ?x2180) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #350 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0167xy; *> query: (?x6818, 01trtc) <- influenced_by(?x6818, ?x4942), group(?x680, ?x6818), origin(?x4942, ?x3052), group(?x227, ?x4942) *> conf = 0.22 ranks of expected_values: 6 EVAL 0838y artist! 01trtc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 116.000 78.000 0.379 http://example.org/music/record_label/artist #9573-02_fj PRED entity: 02_fj PRED relation: award PRED expected values: 0gqy2 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 304): 03x3wf (0.78 #13570, 0.72 #31538, 0.71 #23948), 0c4z8 (0.27 #12442, 0.19 #11644, 0.17 #13240), 09sb52 (0.27 #20393, 0.23 #26384, 0.23 #22789), 0gqy2 (0.25 #161, 0.21 #8541, 0.14 #2555), 04kxsb (0.25 #122, 0.13 #2516, 0.11 #4511), 02w9sd7 (0.25 #167, 0.10 #4556, 0.08 #2561), 08_vwq (0.25 #266, 0.06 #33139, 0.06 #4655), 054krc (0.22 #8865, 0.09 #15252, 0.09 #11660), 040njc (0.19 #9984, 0.17 #12779, 0.14 #17967), 0gq9h (0.19 #10052, 0.17 #5662, 0.16 #18035) >> Best rule #13570 for best value: >> intensional similarity = 3 >> extensional distance = 463 >> proper extension: 06lxn; >> query: (?x3017, ?x724) <- artists(?x505, ?x3017), award_winner(?x724, ?x3017), artist(?x3240, ?x3017) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 06c0j; *> query: (?x3017, 0gqy2) <- profession(?x3017, ?x319), spouse(?x3017, ?x3870), celebrities_impersonated(?x3649, ?x3017) *> conf = 0.25 ranks of expected_values: 4 EVAL 02_fj award 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 100.000 100.000 0.783 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9572-02hblj PRED entity: 02hblj PRED relation: language PRED expected values: 02h40lc => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 3): 02h40lc (0.87 #45, 0.86 #57, 0.82 #28), 03_9r (0.09 #37, 0.05 #41, 0.03 #59), 03k50 (0.08 #8, 0.07 #11, 0.05 #17) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 066l3y; 05v954; 09wlpl; 044_7j; 0bn8fw; 05z775; 055t01; 084x96; >> query: (?x12084, 02h40lc) <- actor(?x5936, ?x12084), genre(?x5936, ?x53), profession(?x12084, ?x319), ?x53 = 07s9rl0 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hblj language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.872 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #9571-0gys2jp PRED entity: 0gys2jp PRED relation: language PRED expected values: 03115z => 84 concepts (74 used for prediction) PRED predicted values (max 10 best out of 43): 064_8sq (0.46 #691, 0.36 #1592, 0.23 #353), 012w70 (0.41 #739, 0.33 #10, 0.27 #232), 0459q4 (0.33 #33, 0.19 #425, 0.19 #480), 06nm1 (0.27 #1582, 0.24 #681, 0.22 #176), 04306rv (0.23 #1577, 0.22 #676, 0.17 #1180), 06b_j (0.23 #354, 0.14 #77, 0.12 #132), 02bjrlw (0.19 #673, 0.18 #1574, 0.14 #58), 04h9h (0.17 #318, 0.14 #96, 0.12 #151), 0t_2 (0.16 #740, 0.02 #1470, 0.02 #1412), 0jzc (0.14 #74, 0.12 #129, 0.11 #184) >> Best rule #691 for best value: >> intensional similarity = 6 >> extensional distance = 66 >> proper extension: 0b76d_m; 011yrp; 07gp9; 02d44q; 0jjy0; 0h3xztt; 053rxgm; 047msdk; 03twd6; 02r8hh_; ... >> query: (?x11701, 064_8sq) <- language(?x11701, ?x5974), language(?x11701, ?x2164), film_release_region(?x11701, ?x583), titles(?x2164, ?x3135), countries_spoken_in(?x5974, ?x1122), ?x583 = 015fr >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #481 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 41 *> proper extension: 0p7qm; *> query: (?x11701, 03115z) <- language(?x11701, ?x5974), language(?x11701, ?x2890), ?x2890 = 0653m, official_language(?x2645, ?x5974), film(?x4835, ?x11701) *> conf = 0.12 ranks of expected_values: 13 EVAL 0gys2jp language 03115z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 84.000 74.000 0.456 http://example.org/film/film/language #9570-023p33 PRED entity: 023p33 PRED relation: currency PRED expected values: 09nqf => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.79 #246, 0.78 #302, 0.78 #120), 02gsvk (0.14 #34, 0.08 #104, 0.08 #111), 01nv4h (0.06 #23, 0.04 #163, 0.03 #184), 088n7 (0.03 #56, 0.02 #126, 0.02 #70), 0kz1h (0.03 #54), 02l6h (0.03 #130, 0.02 #60, 0.02 #95) >> Best rule #246 for best value: >> intensional similarity = 4 >> extensional distance = 591 >> proper extension: 04cf_l; 0c5qvw; >> query: (?x2097, 09nqf) <- nominated_for(?x500, ?x2097), genre(?x2097, ?x307), production_companies(?x2097, ?x3920), child(?x3920, ?x166) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023p33 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.788 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #9569-0myk8 PRED entity: 0myk8 PRED relation: role PRED expected values: 042v_gx => 67 concepts (49 used for prediction) PRED predicted values (max 10 best out of 110): 0342h (0.84 #1852, 0.83 #2308, 0.83 #3479), 01dnws (0.84 #1852, 0.83 #2308, 0.83 #571), 026t6 (0.83 #4876, 0.81 #3125, 0.76 #4409), 013y1f (0.83 #3862, 0.80 #4049, 0.80 #3977), 0dwtp (0.80 #2213, 0.78 #1757, 0.75 #3139), 021bmf (0.80 #2247, 0.78 #1791, 0.73 #461), 0l14md (0.79 #4513, 0.76 #3950, 0.76 #4412), 02k84w (0.78 #1779, 0.70 #2235, 0.67 #1080), 05r5c (0.76 #4065, 0.74 #3603, 0.73 #5461), 042v_gx (0.76 #3952, 0.73 #4414, 0.72 #3482) >> Best rule #1852 for best value: >> intensional similarity = 28 >> extensional distance = 7 >> proper extension: 026t6; >> query: (?x2956, ?x716) <- role(?x2956, ?x2158), role(?x2956, ?x1663), role(?x1663, ?x5676), role(?x1663, ?x3112), role(?x1663, ?x1662), role(?x1663, ?x1225), role(?x1663, ?x960), ?x1662 = 02bxd, role(?x2957, ?x2956), role(?x716, ?x2956), role(?x228, ?x2956), ?x960 = 04q7r, ?x3112 = 0mbct, ?x5676 = 0151b0, ?x2957 = 01v8y9, instrumentalists(?x228, ?x10802), performance_role(?x6208, ?x228), role(?x228, ?x645), group(?x228, ?x8078), group(?x228, ?x4010), ?x10802 = 01mxnvc, role(?x642, ?x228), role(?x780, ?x2158), ?x1225 = 01qbl, ?x8078 = 0134wr, ?x6208 = 07r4c, ?x645 = 028tv0, origin(?x4010, ?x1860) >> conf = 0.84 => this is the best rule for 2 predicted values *> Best rule #3952 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 23 *> proper extension: 0dwsp; 05148p4; 0bxl5; 0dwt5; *> query: (?x2956, 042v_gx) <- role(?x2956, ?x1663), role(?x2956, ?x1495), role(?x1663, ?x5676), role(?x1663, ?x3112), role(?x1663, ?x1662), role(?x1663, ?x960), ?x1662 = 02bxd, role(?x1166, ?x2956), ?x960 = 04q7r, ?x3112 = 0mbct, ?x5676 = 0151b0, ?x1495 = 013y1f, role(?x75, ?x1166), role(?x5494, ?x1166), role(?x5208, ?x1166), role(?x214, ?x1166), instrumentalists(?x1166, ?x5623), ?x5494 = 018x3, group(?x1166, ?x2567), ?x5623 = 01vsyg9, ?x5208 = 01s7qqw, ?x2567 = 02r1tx7 *> conf = 0.76 ranks of expected_values: 10 EVAL 0myk8 role 042v_gx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 67.000 49.000 0.836 http://example.org/music/performance_role/track_performances./music/track_contribution/role #9568-0156q PRED entity: 0156q PRED relation: month PRED expected values: 028kb => 376 concepts (376 used for prediction) PRED predicted values (max 10 best out of 1): 028kb (0.90 #102, 0.89 #96, 0.89 #112) >> Best rule #102 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 06t2t; 0fn2g; 0h3tv; 0g6xq; >> query: (?x1646, 028kb) <- month(?x1646, ?x7298), month(?x1646, ?x3270), ?x7298 = 04wzr, ?x3270 = 05cw8 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0156q month 028kb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 376.000 376.000 0.896 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #9567-01vdm0 PRED entity: 01vdm0 PRED relation: role! PRED expected values: 0dwsp 0l14j_ => 74 concepts (62 used for prediction) PRED predicted values (max 10 best out of 49): 0dwsp (0.86 #107, 0.86 #106, 0.84 #1935), 0l14md (0.86 #107, 0.86 #106, 0.84 #1314), 0dq630k (0.86 #107, 0.86 #106, 0.84 #1314), 01vj9c (0.86 #107, 0.86 #106, 0.84 #452), 042v_gx (0.86 #107, 0.86 #106, 0.84 #452), 02pprs (0.86 #107, 0.86 #106, 0.84 #452), 0l1589 (0.86 #107, 0.86 #106, 0.84 #452), 01kcd (0.86 #107, 0.86 #106, 0.84 #452), 0859_ (0.86 #107, 0.86 #106, 0.84 #452), 01qzyz (0.86 #107, 0.86 #106, 0.83 #312) >> Best rule #107 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 0214km; >> query: (?x1437, ?x5676) <- role(?x5141, ?x1437), role(?x4693, ?x1437), role(?x2698, ?x1437), role(?x925, ?x1437), role(?x565, ?x1437), role(?x1437, ?x5676), role(?x1437, ?x716), ?x925 = 07q1v4, award_winner(?x4317, ?x4693), ?x716 = 018vs, role(?x316, ?x1437), award_winner(?x139, ?x5141), ?x2698 = 09hnb, artists(?x1000, ?x565), award_nominee(?x4693, ?x3397) >> conf = 0.86 => this is the best rule for 10 predicted values ranks of expected_values: 1, 12 EVAL 01vdm0 role! 0l14j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 74.000 62.000 0.860 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 01vdm0 role! 0dwsp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 62.000 0.860 http://example.org/music/performance_role/track_performances./music/track_contribution/role #9566-08gg47 PRED entity: 08gg47 PRED relation: film! PRED expected values: 01yfm8 => 70 concepts (39 used for prediction) PRED predicted values (max 10 best out of 777): 0z4s (0.58 #62557, 0.46 #29182, 0.45 #41696), 09thp87 (0.46 #29182, 0.45 #41696, 0.45 #54213), 0241jw (0.25 #296, 0.03 #10716, 0.01 #2380), 02ck7w (0.25 #942, 0.03 #13448, 0.02 #11362), 09wj5 (0.20 #101, 0.04 #10521, 0.02 #12607), 03ym1 (0.20 #1015, 0.03 #11435, 0.03 #15605), 0js9s (0.20 #1158, 0.02 #11578, 0.01 #13664), 015t56 (0.15 #471, 0.05 #35438, 0.02 #10891), 0154qm (0.15 #563, 0.04 #2647, 0.03 #10983), 0f0kz (0.15 #517, 0.03 #15107, 0.03 #2601) >> Best rule #62557 for best value: >> intensional similarity = 5 >> extensional distance = 1039 >> proper extension: 02sqkh; >> query: (?x3304, ?x450) <- titles(?x1316, ?x3304), nominated_for(?x8415, ?x3304), nominated_for(?x450, ?x3304), award_winner(?x972, ?x8415), film(?x450, ?x518) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #3380 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 047gn4y; 07g_0c; 049mql; 05nyqk; *> query: (?x3304, 01yfm8) <- film_crew_role(?x3304, ?x3305), country(?x3304, ?x774), ?x3305 = 04pyp5, film_release_region(?x66, ?x774) *> conf = 0.03 ranks of expected_values: 153 EVAL 08gg47 film! 01yfm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 70.000 39.000 0.583 http://example.org/film/actor/film./film/performance/film #9565-047n8xt PRED entity: 047n8xt PRED relation: nominated_for! PRED expected values: 02y_rq5 => 123 concepts (106 used for prediction) PRED predicted values (max 10 best out of 214): 0f4x7 (0.62 #26, 0.47 #7047, 0.28 #4941), 099c8n (0.62 #55, 0.33 #1693, 0.30 #7780), 0gq_v (0.61 #4935, 0.31 #13595, 0.29 #18512), 0fq9zdv (0.55 #1104, 0.54 #1338, 0.38 #870), 0gq9h (0.50 #7082, 0.45 #4976, 0.41 #13636), 0gs9p (0.50 #63, 0.42 #4978, 0.41 #7084), 03hl6lc (0.50 #128, 0.30 #7149, 0.20 #23644), 02qyp19 (0.50 #1, 0.25 #703, 0.22 #7022), 09qv_s (0.50 #113, 0.25 #347, 0.20 #1517), 099ck7 (0.50 #175, 0.25 #409, 0.17 #7196) >> Best rule #26 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 03hkch7; >> query: (?x2121, 0f4x7) <- nominated_for(?x899, ?x2121), genre(?x2121, ?x1316), production_companies(?x2121, ?x9041), ?x899 = 02x1dht, ?x1316 = 017fp >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1009 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 0fgpvf; 05z_kps; 09p0ct; 021y7yw; 026njb5; 0g83dv; 0g9lm2; 0462hhb; 02w9k1c; 089j8p; ... *> query: (?x2121, 02y_rq5) <- nominated_for(?x941, ?x2121), genre(?x2121, ?x53), country(?x2121, ?x390), ?x941 = 0fq9zdn, film_crew_role(?x2121, ?x137) *> conf = 0.14 ranks of expected_values: 61 EVAL 047n8xt nominated_for! 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 123.000 106.000 0.625 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9564-01w20rx PRED entity: 01w20rx PRED relation: role PRED expected values: 01vdm0 => 107 concepts (86 used for prediction) PRED predicted values (max 10 best out of 77): 05r5c (0.36 #1059, 0.21 #954, 0.21 #1269), 0342h (0.33 #1055, 0.26 #950, 0.25 #740), 01vdm0 (0.24 #1083, 0.15 #978, 0.14 #2241), 02sgy (0.21 #1057, 0.16 #742, 0.15 #952), 026t6 (0.19 #423, 0.15 #1053, 0.13 #318), 042v_gx (0.19 #1060, 0.18 #745, 0.16 #955), 018vs (0.16 #1065, 0.14 #435, 0.14 #15), 05842k (0.16 #1130, 0.10 #1340, 0.10 #2288), 013y1f (0.14 #1088, 0.09 #563, 0.08 #983), 0l14qv (0.13 #1056, 0.10 #531, 0.10 #426) >> Best rule #1059 for best value: >> intensional similarity = 4 >> extensional distance = 256 >> proper extension: 02fybl; 09g0h; >> query: (?x10628, 05r5c) <- profession(?x10628, ?x131), nationality(?x10628, ?x94), ?x94 = 09c7w0, role(?x10628, ?x745) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1083 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 256 *> proper extension: 02fybl; 09g0h; *> query: (?x10628, 01vdm0) <- profession(?x10628, ?x131), nationality(?x10628, ?x94), ?x94 = 09c7w0, role(?x10628, ?x745) *> conf = 0.24 ranks of expected_values: 3 EVAL 01w20rx role 01vdm0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 107.000 86.000 0.360 http://example.org/music/artist/track_contributions./music/track_contribution/role #9563-01wxdn3 PRED entity: 01wxdn3 PRED relation: artists! PRED expected values: 05w3f => 123 concepts (47 used for prediction) PRED predicted values (max 10 best out of 252): 03lty (0.88 #6609, 0.26 #4097, 0.22 #5321), 0xhtw (0.78 #6597, 0.43 #1581, 0.41 #4711), 06by7 (0.63 #10059, 0.51 #11000, 0.51 #11629), 08jyyk (0.50 #1005, 0.35 #4136, 0.29 #1632), 02yv6b (0.50 #412, 0.33 #4794, 0.22 #6680), 05w3f (0.50 #350, 0.27 #5359, 0.22 #5988), 02w4v (0.50 #1297, 0.17 #982, 0.16 #3174), 059kh (0.45 #3805, 0.44 #5057, 0.43 #1926), 05r6t (0.43 #1647, 0.41 #2584, 0.35 #4151), 064t9 (0.35 #11933, 0.32 #13190, 0.32 #3142) >> Best rule #6609 for best value: >> intensional similarity = 5 >> extensional distance = 49 >> proper extension: 01vtqml; 03lgg; 01k47c; 01wkmgb; 01y_rz; 01t8399; 01vsn38; >> query: (?x9735, 03lty) <- artists(?x3167, ?x9735), role(?x9735, ?x227), profession(?x9735, ?x319), parent_genre(?x6349, ?x3167), ?x6349 = 08z0wx >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #350 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 09prnq; 01mwsnc; *> query: (?x9735, 05w3f) <- artists(?x302, ?x9735), role(?x9735, ?x4917), profession(?x9735, ?x655), ?x655 = 0gbbt, ?x4917 = 06w7v *> conf = 0.50 ranks of expected_values: 6 EVAL 01wxdn3 artists! 05w3f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 123.000 47.000 0.882 http://example.org/music/genre/artists #9562-01nhgd PRED entity: 01nhgd PRED relation: contains! PRED expected values: 09c7w0 => 135 concepts (72 used for prediction) PRED predicted values (max 10 best out of 273): 09c7w0 (0.81 #4477, 0.78 #12528, 0.77 #15211), 0n2q0 (0.56 #33095, 0.31 #59048, 0.31 #63524), 059rby (0.55 #22382, 0.48 #28643, 0.47 #29537), 07ssc (0.52 #54603, 0.50 #57289, 0.18 #44757), 02_286 (0.40 #8095, 0.34 #9885, 0.17 #11673), 01n7q (0.30 #59126, 0.19 #9920, 0.14 #14390), 04_1l0v (0.30 #62628), 02jx1 (0.29 #54658, 0.28 #57344, 0.15 #47495), 03rt9 (0.24 #8971, 0.05 #56387), 07h34 (0.20 #8283, 0.05 #7388, 0.03 #4472) >> Best rule #4477 for best value: >> intensional similarity = 7 >> extensional distance = 19 >> proper extension: 07szy; 01ptt7; 02fgdx; 012mzw; 02f4s3; 01jsk6; >> query: (?x13680, 09c7w0) <- institution(?x9054, ?x13680), institution(?x1368, ?x13680), institution(?x865, ?x13680), currency(?x13680, ?x170), ?x1368 = 014mlp, ?x9054 = 022h5x, ?x865 = 02h4rq6 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nhgd contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 72.000 0.810 http://example.org/location/location/contains #9561-012cph PRED entity: 012cph PRED relation: influenced_by! PRED expected values: 0821j => 178 concepts (76 used for prediction) PRED predicted values (max 10 best out of 354): 014ps4 (0.60 #3901, 0.17 #5953, 0.16 #12628), 040db (0.40 #3665, 0.39 #15472, 0.31 #9312), 01v_0b (0.40 #4074, 0.33 #5100, 0.31 #9721), 07lp1 (0.40 #4007, 0.33 #5033, 0.23 #9654), 084w8 (0.40 #3593, 0.31 #9240, 0.26 #15400), 01vdrw (0.40 #4034, 0.26 #15841, 0.23 #9681), 034bs (0.40 #3744, 0.25 #7852, 0.17 #15551), 041mt (0.40 #3666, 0.23 #9313, 0.17 #15473), 0gd5z (0.38 #7784, 0.20 #3676, 0.17 #15483), 042v2 (0.38 #8050, 0.20 #3942, 0.17 #5994) >> Best rule #3901 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01v9724; >> query: (?x1030, 014ps4) <- influenced_by(?x8389, ?x1030), ?x8389 = 0683n, type_of_union(?x1030, ?x566), place_of_burial(?x1030, ?x7496) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #9593 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 032l1; 0dzkq; 02lt8; 081k8; 02zjd; 03_87; 043tg; 03jxw; *> query: (?x1030, 0821j) <- place_of_death(?x1030, ?x739), influenced_by(?x8389, ?x1030), type_of_union(?x1030, ?x566), ?x8389 = 0683n *> conf = 0.08 ranks of expected_values: 178 EVAL 012cph influenced_by! 0821j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 178.000 76.000 0.600 http://example.org/influence/influence_node/influenced_by #9560-088q4 PRED entity: 088q4 PRED relation: teams PRED expected values: 038_3y => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 200): 03_qrp (0.20 #166, 0.14 #525, 0.08 #1243), 038zh6 (0.12 #2144, 0.09 #1067, 0.05 #5375), 02fbb5 (0.09 #949, 0.08 #1667, 0.06 #2026), 023zd7 (0.09 #879, 0.06 #1956, 0.05 #3033), 03dj48 (0.09 #964, 0.06 #2041, 0.05 #3118), 0329nn (0.09 #815, 0.06 #1892, 0.05 #2969), 02bh_v (0.09 #932, 0.06 #2009, 0.05 #3086), 020wyp (0.09 #1050, 0.06 #2127, 0.05 #2845), 0cnk2q (0.09 #719, 0.06 #1796, 0.05 #2514), 02pp1 (0.09 #988, 0.06 #2065, 0.05 #3501) >> Best rule #166 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 05cgv; 035dk; 04v09; >> query: (?x3432, 03_qrp) <- featured_film_locations(?x5044, ?x3432), country(?x150, ?x3432), ?x5044 = 0413cff >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 088q4 teams 038_3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 117.000 0.200 http://example.org/sports/sports_team_location/teams #9559-017mbb PRED entity: 017mbb PRED relation: artist! PRED expected values: 026s90 => 86 concepts (70 used for prediction) PRED predicted values (max 10 best out of 128): 03rhqg (0.50 #975, 0.40 #153, 0.25 #427), 01t04r (0.40 #200, 0.25 #474, 0.25 #63), 015_1q (0.30 #1803, 0.24 #1391, 0.23 #1253), 01cf93 (0.25 #467, 0.25 #56, 0.20 #604), 01cl2y (0.25 #29, 0.23 #988, 0.20 #166), 011k1h (0.25 #421, 0.20 #832, 0.20 #558), 01jv1z (0.25 #416, 0.20 #553, 0.20 #142), 033hn8 (0.25 #14, 0.20 #151, 0.19 #2484), 017l96 (0.25 #430, 0.20 #567, 0.15 #978), 04fcjt (0.25 #28, 0.20 #165, 0.12 #439) >> Best rule #975 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 02whj; 01gx5f; 01vsy3q; 0kxbc; 01386_; 0167xy; 01t8399; 01nrz4; >> query: (?x9206, 03rhqg) <- artists(?x2249, ?x9206), ?x2249 = 03lty, artist(?x4483, ?x9206), artist(?x4483, ?x11700), ?x11700 = 017_hq >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4389 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 185 *> proper extension: 03qd_; 0bg539; 03cs_z7; 0pgjm; 021bk; 01wwvd2; 0pmw9; 08n__5; 02ldv0; 03m6pk; ... *> query: (?x9206, ?x1954) <- role(?x9206, ?x227), award(?x9206, ?x2877), award(?x5935, ?x2877), artist(?x1954, ?x5935) *> conf = 0.04 ranks of expected_values: 89 EVAL 017mbb artist! 026s90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 86.000 70.000 0.500 http://example.org/music/record_label/artist #9558-03wjb7 PRED entity: 03wjb7 PRED relation: gender PRED expected values: 05zppz => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #63, 0.81 #67, 0.81 #97), 02zsn (0.55 #182, 0.51 #159, 0.29 #58) >> Best rule #63 for best value: >> intensional similarity = 4 >> extensional distance = 182 >> proper extension: 0z4s; 0bg539; 0pgjm; 07ymr5; 0783m_; 0f0kz; 03l3jy; 05b_7n; 01_p6t; 02ldv0; ... >> query: (?x8403, 05zppz) <- profession(?x8403, ?x1183), ?x1183 = 09jwl, student(?x12669, ?x8403), nationality(?x8403, ?x1310) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03wjb7 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.810 http://example.org/people/person/gender #9557-02583l PRED entity: 02583l PRED relation: institution! PRED expected values: 01rr_d => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.94 #1109, 0.60 #868, 0.58 #315), 014mlp (0.69 #2027, 0.61 #870, 0.61 #1111), 013zdg (0.58 #175, 0.55 #223, 0.50 #199), 019v9k (0.55 #1115, 0.53 #874, 0.52 #971), 02_xgp2 (0.45 #349, 0.43 #325, 0.43 #85), 03bwzr4 (0.42 #1121, 0.41 #351, 0.38 #471), 0bkj86 (0.41 #344, 0.32 #608, 0.31 #464), 07s6fsf (0.40 #1, 0.33 #121, 0.33 #25), 016t_3 (0.36 #1110, 0.36 #316, 0.34 #2366), 027f2w (0.29 #82, 0.20 #10, 0.20 #346) >> Best rule #1109 for best value: >> intensional similarity = 5 >> extensional distance = 345 >> proper extension: 08qnnv; 0gl5_; 01p896; >> query: (?x1306, 02h4rq6) <- institution(?x1305, ?x1306), institution(?x1305, ?x9989), institution(?x1305, ?x4672), ?x4672 = 07tds, ?x9989 = 01d650 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #306 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 0373qt; 013719; *> query: (?x1306, 01rr_d) <- organization(?x3484, ?x1306), state_province_region(?x1306, ?x1905), institution(?x1305, ?x1306), currency(?x1306, ?x2244) *> conf = 0.26 ranks of expected_values: 11 EVAL 02583l institution! 01rr_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 183.000 183.000 0.937 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9556-029ghl PRED entity: 029ghl PRED relation: award_winner! PRED expected values: 06krf3 => 157 concepts (102 used for prediction) PRED predicted values (max 10 best out of 250): 0hvvf (0.15 #88550, 0.07 #104446, 0.07 #104447), 01qbg5 (0.15 #88550, 0.07 #104446, 0.07 #104447), 02k_4g (0.15 #88550, 0.01 #25050), 06krf3 (0.07 #104446, 0.07 #104447, 0.07 #99904), 0hv4t (0.07 #104446, 0.07 #104447, 0.07 #99904), 04j13sx (0.07 #104446, 0.07 #104447, 0.07 #99904), 047csmy (0.07 #1733, 0.05 #5138, 0.03 #95361), 01l_pn (0.07 #1763, 0.03 #5168, 0.02 #2898), 05qbbfb (0.07 #1813, 0.03 #95361, 0.02 #5218), 0n83s (0.07 #1724, 0.03 #95361, 0.02 #5129) >> Best rule #88550 for best value: >> intensional similarity = 3 >> extensional distance = 1156 >> proper extension: 014l4w; 07k2d; >> query: (?x9301, ?x782) <- award_winner(?x5821, ?x9301), award(?x9301, ?x154), award_winner(?x782, ?x5821) >> conf = 0.15 => this is the best rule for 3 predicted values *> Best rule #104446 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1350 *> proper extension: 01w92; 01p5yn; 02vyh; 035_2h; 04glx0; 039cq4; 04rqd; *> query: (?x9301, ?x6013) <- award_winner(?x5821, ?x9301), nominated_for(?x5821, ?x6013), nominated_for(?x112, ?x6013) *> conf = 0.07 ranks of expected_values: 4 EVAL 029ghl award_winner! 06krf3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 157.000 102.000 0.148 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #9555-0407f PRED entity: 0407f PRED relation: place_of_death PRED expected values: 013yq => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 58): 030qb3t (0.16 #6425, 0.15 #5260, 0.14 #410), 02_286 (0.12 #789, 0.11 #1177, 0.10 #4281), 05jbn (0.12 #847, 0.05 #265, 0.05 #459), 0k049 (0.09 #9316, 0.08 #5241, 0.07 #1167), 06_kh (0.07 #1169, 0.07 #5, 0.05 #9512), 0fhp9 (0.06 #1372, 0.05 #2342, 0.04 #1954), 06yxd (0.06 #9702, 0.05 #6015, 0.03 #10285), 01ktz1 (0.06 #9702, 0.05 #6015, 0.03 #10285), 0978r (0.05 #242, 0.04 #630, 0.04 #1212), 04n3l (0.05 #243, 0.04 #1213, 0.03 #1407) >> Best rule #6425 for best value: >> intensional similarity = 3 >> extensional distance = 167 >> proper extension: 07_grx; 05hjmd; 0bm9xk; >> query: (?x3316, 030qb3t) <- award_nominee(?x3316, ?x1060), gender(?x3316, ?x231), people(?x6260, ?x3316) >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0407f place_of_death 013yq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 132.000 0.160 http://example.org/people/deceased_person/place_of_death #9554-0q8sw PRED entity: 0q8sw PRED relation: place! PRED expected values: 0q8sw => 83 concepts (32 used for prediction) PRED predicted values (max 10 best out of 45): 0fttg (0.06 #378, 0.05 #894, 0.03 #16023), 0lphb (0.06 #175, 0.05 #691, 0.01 #6705), 0qc7l (0.06 #478, 0.05 #994, 0.01 #6705), 0q6lr (0.06 #361, 0.05 #877, 0.01 #6705), 0q48z (0.06 #316, 0.05 #832, 0.01 #6705), 0q8jl (0.06 #292, 0.05 #808, 0.01 #6705), 0q8s4 (0.06 #110, 0.05 #626, 0.01 #6705), 0fw1y (0.01 #1528, 0.01 #2044), 0fw2f (0.01 #1481, 0.01 #1997), 0fw3f (0.01 #1438, 0.01 #1954) >> Best rule #378 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 01wdl3; 02jyr8; 0q8s4; 0lphb; 0k696; 0q8jl; 0q48z; 0325dj; 0q8p8; 0q6lr; ... >> query: (?x9556, 0fttg) <- contains(?x9555, ?x9556), contains(?x2831, ?x9556), contains(?x94, ?x9556), time_zones(?x9555, ?x1638), ?x2831 = 0gyh, ?x94 = 09c7w0 >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #6705 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 222 *> proper extension: 0f2tj; *> query: (?x9556, ?x1201) <- time_zones(?x9556, ?x1638), state(?x9556, ?x2831), contains(?x2831, ?x1201) *> conf = 0.01 ranks of expected_values: 42 EVAL 0q8sw place! 0q8sw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 83.000 32.000 0.059 http://example.org/location/hud_county_place/place #9553-01wbsdz PRED entity: 01wbsdz PRED relation: location PRED expected values: 013yq => 134 concepts (117 used for prediction) PRED predicted values (max 10 best out of 219): 02_286 (0.56 #37, 0.36 #2446, 0.26 #73919), 030qb3t (0.36 #1689, 0.34 #7310, 0.32 #3295), 0cr3d (0.20 #948, 0.08 #17812, 0.07 #49132), 059rby (0.18 #1622, 0.10 #819, 0.06 #5637), 0k049 (0.18 #1614, 0.09 #5629, 0.07 #9644), 01n7q (0.18 #1669, 0.06 #35397, 0.06 #9699), 02jx1 (0.11 #71, 0.07 #2480, 0.07 #4086), 0ccvx (0.11 #222, 0.07 #2631, 0.05 #17086), 05fkf (0.11 #38, 0.07 #2447, 0.05 #42562), 02dtg (0.11 #24, 0.07 #2433, 0.05 #3236) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0p_47; >> query: (?x5882, 02_286) <- film(?x5882, ?x8979), participant(?x6144, ?x5882), instrumentalists(?x1166, ?x5882), location(?x5882, ?x6453) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #922 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 04lgymt; *> query: (?x5882, 013yq) <- award_nominee(?x5479, ?x5882), ?x5479 = 02x_h0, gender(?x5882, ?x231) *> conf = 0.10 ranks of expected_values: 18 EVAL 01wbsdz location 013yq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 134.000 117.000 0.556 http://example.org/people/person/places_lived./people/place_lived/location #9552-035d1m PRED entity: 035d1m PRED relation: country PRED expected values: 0ctw_b 05cgv 03shp => 34 concepts (33 used for prediction) PRED predicted values (max 10 best out of 454): 015qh (0.86 #1439, 0.80 #1619, 0.75 #542), 0jdx (0.86 #1439, 0.72 #1440, 0.66 #1623), 0d0vqn (0.83 #2708, 0.80 #1619, 0.79 #1618), 059j2 (0.80 #1619, 0.79 #1618, 0.75 #542), 01znc_ (0.80 #1619, 0.79 #1618, 0.75 #542), 0b90_r (0.80 #1619, 0.77 #2884, 0.75 #542), 06c1y (0.80 #1619, 0.75 #542, 0.75 #2731), 04gzd (0.80 #1619, 0.75 #542, 0.75 #2174), 02vzc (0.80 #1619, 0.75 #542, 0.75 #2201), 05r4w (0.80 #1619, 0.75 #542, 0.75 #536) >> Best rule #1439 for best value: >> intensional similarity = 52 >> extensional distance = 3 >> proper extension: 06z6r; >> query: (?x3554, ?x7833) <- olympics(?x3554, ?x778), country(?x3554, ?x13525), country(?x3554, ?x3277), country(?x3554, ?x3227), country(?x3554, ?x2756), country(?x3554, ?x2346), country(?x3554, ?x1603), country(?x3554, ?x1471), country(?x3554, ?x1264), country(?x3554, ?x789), country(?x3554, ?x404), country(?x3554, ?x94), ?x2346 = 0d05w3, ?x789 = 0f8l9c, ?x2756 = 0hg5, sports(?x2966, ?x3554), adjustment_currency(?x3227, ?x170), ?x2966 = 06sks6, country(?x3309, ?x3227), country(?x1967, ?x3227), country(?x1121, ?x3227), ?x3277 = 06t8v, film_release_region(?x9902, ?x3227), film_release_region(?x6270, ?x3227), film_release_region(?x5162, ?x3227), film_release_region(?x3491, ?x3227), film_release_region(?x2340, ?x3227), ?x2340 = 0fpv_3_, adjoins(?x3227, ?x9485), adjoins(?x3227, ?x7833), ?x1603 = 06bnz, ?x1967 = 01cgz, ?x1471 = 07t21, ?x1264 = 0345h, ?x3309 = 09w1n, country(?x3641, ?x7833), organization(?x7833, ?x127), country(?x13383, ?x3227), ?x9902 = 0j8f09z, teams(?x3227, ?x6566), ?x1121 = 0bynt, ?x404 = 047lj, location_of_ceremony(?x566, ?x13525), time_zones(?x9485, ?x2864), ?x94 = 09c7w0, ?x3491 = 0gtvpkw, jurisdiction_of_office(?x3444, ?x13525), ?x3641 = 03fyrh, ?x5162 = 0j3d9tn, jurisdiction_of_office(?x182, ?x13525), ?x6270 = 0g9zljd, organization(?x13525, ?x3750) >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #2950 for first EXPECTED value: *> intensional similarity = 53 *> extensional distance = 11 *> proper extension: 06wrt; *> query: (?x3554, 03shp) <- olympics(?x3554, ?x3729), country(?x3554, ?x2756), country(?x3554, ?x2346), country(?x3554, ?x1558), country(?x3554, ?x789), country(?x3554, ?x456), country(?x3554, ?x205), ?x2346 = 0d05w3, ?x789 = 0f8l9c, jurisdiction_of_office(?x182, ?x2756), contains(?x455, ?x2756), organization(?x2756, ?x312), ?x1558 = 01mjq, film_release_region(?x9839, ?x456), film_release_region(?x8867, ?x456), film_release_region(?x8176, ?x456), film_release_region(?x8025, ?x456), film_release_region(?x6528, ?x456), film_release_region(?x6516, ?x456), film_release_region(?x5016, ?x456), film_release_region(?x4290, ?x456), film_release_region(?x2933, ?x456), film_release_region(?x2896, ?x456), film_release_region(?x2598, ?x456), film_release_region(?x2094, ?x456), film_release_region(?x1707, ?x456), film_release_region(?x1470, ?x456), film_release_region(?x1263, ?x456), film_release_region(?x1202, ?x456), film_release_region(?x299, ?x456), ?x8176 = 0gvvm6l, ?x1263 = 0dgst_d, ?x8867 = 03lfd_, combatants(?x456, ?x151), ?x2933 = 0407yj_, jurisdiction_of_office(?x346, ?x456), ?x2094 = 05z7c, ?x299 = 01gc7, ?x1202 = 0gj8t_b, ?x3729 = 0jdk_, ?x205 = 03rjj, ?x4290 = 0gtxj2q, taxonomy(?x2756, ?x939), ?x6528 = 0dc_ms, ?x5016 = 062zm5h, ?x2896 = 0645k5, adjoins(?x344, ?x456), ?x1707 = 04n52p6, genre(?x6516, ?x258), ?x9839 = 0gy7bj4, ?x8025 = 03nsm5x, ?x2598 = 07f_7h, ?x1470 = 03twd6 *> conf = 0.77 ranks of expected_values: 23, 27, 39 EVAL 035d1m country 03shp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 34.000 33.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 035d1m country 05cgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 34.000 33.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 035d1m country 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 34.000 33.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #9551-047bynf PRED entity: 047bynf PRED relation: film! PRED expected values: 06wm0z => 104 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1082): 03dbds (0.17 #79180, 0.15 #93766, 0.15 #104189), 05zbm4 (0.15 #2235, 0.02 #43902, 0.02 #27233), 0gpprt (0.15 #5692, 0.09 #9858, 0.09 #11941), 01713c (0.15 #4422, 0.09 #8588, 0.07 #12754), 04yj5z (0.14 #122, 0.08 #2205, 0.02 #41789), 03y_46 (0.14 #1019, 0.06 #19766, 0.03 #46852), 018swb (0.14 #342, 0.06 #8674, 0.06 #10757), 046qq (0.14 #743, 0.05 #4909, 0.04 #15324), 05dbf (0.14 #365, 0.05 #19112, 0.03 #23278), 0169dl (0.14 #401, 0.04 #23314, 0.03 #77497) >> Best rule #79180 for best value: >> intensional similarity = 5 >> extensional distance = 317 >> proper extension: 02rrfzf; 0jqb8; 0199wf; >> query: (?x6636, ?x7621) <- country(?x6636, ?x94), ?x94 = 09c7w0, currency(?x6636, ?x170), film(?x7621, ?x6636), written_by(?x2128, ?x7621) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 047bynf film! 06wm0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 56.000 0.167 http://example.org/film/actor/film./film/performance/film #9550-05q4y12 PRED entity: 05q4y12 PRED relation: currency PRED expected values: 09nqf => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.77 #99, 0.77 #204, 0.76 #15), 01nv4h (0.05 #170, 0.03 #72, 0.03 #93), 02l6h (0.04 #25, 0.04 #81, 0.03 #46), 02gsvk (0.03 #132, 0.02 #181, 0.02 #160), 0kz1h (0.01 #75), 088n7 (0.01 #168) >> Best rule #99 for best value: >> intensional similarity = 4 >> extensional distance = 221 >> proper extension: 04cf_l; >> query: (?x2788, 09nqf) <- titles(?x2480, ?x2788), ?x2480 = 01z4y, genre(?x2788, ?x258), ?x258 = 05p553 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05q4y12 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.767 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #9549-05jbn PRED entity: 05jbn PRED relation: location! PRED expected values: 0137n0 0n6f8 016h4r => 207 concepts (151 used for prediction) PRED predicted values (max 10 best out of 2537): 03mszl (0.34 #90113, 0.33 #2503, 0.33 #77598), 016qtt (0.33 #2503, 0.33 #77598, 0.32 #232782), 0170pk (0.33 #304, 0.04 #77902, 0.03 #52875), 0137hn (0.33 #1326, 0.02 #78924, 0.02 #220270), 016jfw (0.33 #1234, 0.02 #78832, 0.02 #220270), 016h9b (0.33 #546, 0.02 #78144, 0.02 #220270), 0294fd (0.33 #805, 0.02 #78403, 0.01 #150991), 03h_fk5 (0.29 #50067, 0.26 #12513, 0.25 #3032), 01kstn9 (0.29 #50067, 0.26 #12513, 0.19 #32543), 0ggjt (0.29 #50067, 0.26 #12513, 0.19 #32543) >> Best rule #90113 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 012wyq; 01zk9d; >> query: (?x4978, ?x7753) <- origin(?x7753, ?x4978), contains(?x4978, ?x1506), student(?x5280, ?x7753) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #15018 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0rnmy; *> query: (?x4978, ?x366) <- place_of_death(?x2807, ?x4978), origin(?x2807, ?x5381), award_nominee(?x2807, ?x366) *> conf = 0.04 ranks of expected_values: 1290, 1315, 1468 EVAL 05jbn location! 016h4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 207.000 151.000 0.337 http://example.org/people/person/places_lived./people/place_lived/location EVAL 05jbn location! 0n6f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 207.000 151.000 0.337 http://example.org/people/person/places_lived./people/place_lived/location EVAL 05jbn location! 0137n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 207.000 151.000 0.337 http://example.org/people/person/places_lived./people/place_lived/location #9548-0kvbl6 PRED entity: 0kvbl6 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 3): 029j_ (0.99 #157, 0.98 #53, 0.86 #73), 07c52 (0.04 #22, 0.03 #26, 0.03 #126), 07z4p (0.02 #316, 0.02 #144, 0.02 #148) >> Best rule #157 for best value: >> intensional similarity = 3 >> extensional distance = 619 >> proper extension: 02rrfzf; 02q0k7v; 04hk0w; >> query: (?x6334, 029j_) <- produced_by(?x6334, ?x2451), nominated_for(?x7761, ?x6334), film_release_distribution_medium(?x6334, ?x627) >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kvbl6 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.990 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #9547-0342h PRED entity: 0342h PRED relation: role PRED expected values: 01qbl 0979zs => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 45): 02sgy (0.82 #501, 0.82 #40, 0.81 #1001), 018j2 (0.82 #501, 0.82 #40, 0.81 #1001), 01hww_ (0.82 #501, 0.82 #40, 0.81 #1001), 01xqw (0.82 #501, 0.82 #40, 0.81 #1001), 03qlv7 (0.82 #501, 0.82 #40, 0.81 #1001), 011k_j (0.82 #501, 0.82 #40, 0.81 #1001), 05ljv7 (0.82 #501, 0.82 #40, 0.81 #1001), 011_6p (0.82 #501, 0.82 #40, 0.81 #1001), 02dlh2 (0.82 #501, 0.82 #40, 0.81 #1001), 01qbl (0.82 #501, 0.82 #40, 0.81 #1001) >> Best rule #501 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 02g9p4; 05ljv7; >> query: (?x227, ?x214) <- role(?x219, ?x227), role(?x74, ?x227), instrumentalists(?x227, ?x7581), instrumentalists(?x227, ?x3401), ?x7581 = 01wf86y, profession(?x3401, ?x319), role(?x214, ?x227) >> conf = 0.82 => this is the best rule for 16 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 10, 15 EVAL 0342h role 0979zs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 73.000 73.000 0.822 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 0342h role 01qbl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 73.000 73.000 0.822 http://example.org/music/performance_role/track_performances./music/track_contribution/role #9546-02kk_c PRED entity: 02kk_c PRED relation: nominated_for! PRED expected values: 07kjk7c => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 188): 099c8n (0.44 #1023, 0.19 #11872, 0.18 #12113), 0gq9h (0.37 #11878, 0.36 #12119, 0.33 #12360), 0gs9p (0.34 #11880, 0.33 #12121, 0.29 #12362), 019f4v (0.33 #1020, 0.33 #537, 0.33 #11869), 09sb52 (0.33 #517, 0.33 #35, 0.22 #1000), 03hl6lc (0.33 #1098, 0.33 #133, 0.12 #11947), 0k611 (0.33 #557, 0.28 #11889, 0.27 #12130), 0gr51 (0.33 #80, 0.28 #1045, 0.22 #13261), 0gqyl (0.33 #564, 0.28 #1047, 0.20 #12137), 040njc (0.33 #972, 0.27 #11821, 0.26 #12062) >> Best rule #1023 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01242_; >> query: (?x4881, 099c8n) <- honored_for(?x747, ?x4881), award_winner(?x747, ?x2461), ?x2461 = 01cwhp >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #435 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 02qjv1p; *> query: (?x4881, 07kjk7c) <- program(?x3381, ?x4881), nominated_for(?x1039, ?x4881), award_winner(?x4881, ?x496), ?x496 = 0bxtg *> conf = 0.25 ranks of expected_values: 31 EVAL 02kk_c nominated_for! 07kjk7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 72.000 72.000 0.444 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9545-02qfk4j PRED entity: 02qfk4j PRED relation: edited_by! PRED expected values: 02w86hz => 46 concepts (14 used for prediction) No prediction ranks of expected_values: EVAL 02qfk4j edited_by! 02w86hz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 14.000 0.000 http://example.org/film/film/edited_by #9544-0blt6 PRED entity: 0blt6 PRED relation: gender PRED expected values: 05zppz => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #43, 0.84 #69, 0.83 #143), 02zsn (0.57 #20, 0.57 #2, 0.55 #10) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 0564mx; >> query: (?x3583, 05zppz) <- award_nominee(?x940, ?x3583), producer_type(?x3583, ?x632), place_of_birth(?x3583, ?x739) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0blt6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.850 http://example.org/people/person/gender #9543-0dn3n PRED entity: 0dn3n PRED relation: student! PRED expected values: 041y2 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 13): 041y2 (0.06 #51, 0.04 #113, 0.02 #175), 02822 (0.04 #217, 0.04 #837, 0.04 #527), 03qsdpk (0.02 #842, 0.02 #1214, 0.02 #160), 0fdys (0.02 #525), 02vxn (0.02 #128, 0.01 #190), 01zc2w (0.02 #172, 0.01 #854, 0.01 #1598), 0mg1w (0.02 #169, 0.01 #913), 040p_q (0.02 #173), 0g26h (0.02 #156), 05qfh (0.01 #833, 0.01 #1205, 0.01 #1639) >> Best rule #51 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 019vgs; >> query: (?x3070, 041y2) <- award_nominee(?x496, ?x3070), location(?x3070, ?x335), ?x496 = 0bxtg >> conf = 0.06 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dn3n student! 041y2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.062 http://example.org/education/field_of_study/students_majoring./education/education/student #9542-0322yj PRED entity: 0322yj PRED relation: language PRED expected values: 02h40lc => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.96 #1702, 0.96 #5355, 0.96 #3141), 04306rv (0.23 #850, 0.22 #963, 0.22 #680), 02bjrlw (0.15 #451, 0.14 #959, 0.13 #846), 06nm1 (0.15 #349, 0.15 #405, 0.14 #800), 03_9r (0.12 #180, 0.11 #10, 0.09 #66), 03hkp (0.11 #15, 0.04 #241, 0.03 #917), 05qqm (0.11 #39, 0.03 #489, 0.02 #884), 0jzc (0.09 #76, 0.08 #133, 0.08 #865), 0653m (0.09 #68, 0.08 #125, 0.06 #182), 0t_2 (0.06 #184, 0.02 #2229, 0.01 #2399) >> Best rule #1702 for best value: >> intensional similarity = 4 >> extensional distance = 331 >> proper extension: 015qsq; 0pv2t; 03m4mj; 06rmdr; 0jym0; 0kvgtf; 0jsqk; 048rn; 04t9c0; 0yyn5; ... >> query: (?x12437, 02h40lc) <- featured_film_locations(?x12437, ?x739), film(?x2135, ?x12437), language(?x12437, ?x5607), film(?x496, ?x12437) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0322yj language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.964 http://example.org/film/film/language #9541-0bcp9b PRED entity: 0bcp9b PRED relation: language PRED expected values: 02h40lc 06b_j => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.97 #1245, 0.97 #621, 0.96 #2268), 06nm1 (0.16 #629, 0.14 #236, 0.14 #405), 02bjrlw (0.15 #227, 0.11 #1, 0.11 #283), 0jzc (0.09 #301, 0.05 #132, 0.05 #19), 06b_j (0.08 #640, 0.07 #1376, 0.07 #1433), 03_9r (0.08 #9, 0.08 #122, 0.06 #572), 0653m (0.05 #237, 0.05 #687, 0.04 #1197), 012w70 (0.05 #12, 0.04 #350, 0.03 #1198), 04h9h (0.04 #659, 0.04 #266, 0.04 #96), 03hkp (0.04 #296, 0.04 #127, 0.03 #183) >> Best rule #1245 for best value: >> intensional similarity = 4 >> extensional distance = 270 >> proper extension: 0gy30w; >> query: (?x7628, 02h40lc) <- film(?x2551, ?x7628), crewmember(?x7628, ?x6232), film(?x1104, ?x7628), language(?x7628, ?x732) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 0bcp9b language 06b_j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 121.000 121.000 0.974 http://example.org/film/film/language EVAL 0bcp9b language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.974 http://example.org/film/film/language #9540-02gdjb PRED entity: 02gdjb PRED relation: award! PRED expected values: 02dbp7 018gqj 01wg6y => 47 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2220): 0134pk (0.80 #16090, 0.60 #6094, 0.50 #12758), 02x8z_ (0.80 #33332, 0.79 #46667, 0.76 #26666), 03d2k (0.79 #46667, 0.76 #26666, 0.75 #23330), 0133x7 (0.79 #46667, 0.76 #26666, 0.75 #23330), 01lf293 (0.79 #46667, 0.76 #26666, 0.75 #23330), 0frsw (0.70 #13985, 0.60 #3989, 0.50 #10653), 0kr_t (0.70 #14922, 0.50 #11590, 0.40 #4926), 016l09 (0.70 #16069, 0.40 #6073, 0.33 #2741), 0dvqq (0.60 #13948, 0.40 #3952, 0.38 #10616), 012x1l (0.60 #6530, 0.38 #13194, 0.33 #3198) >> Best rule #16090 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 02f72n; 02f5qb; 02f716; 02f73p; 02f72_; >> query: (?x4488, 0134pk) <- award(?x7955, ?x4488), award(?x6891, ?x4488), award(?x2040, ?x4488), award_winner(?x4866, ?x6891), ?x2040 = 0dtd6, award_winner(?x1079, ?x7955) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #7961 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 01bgqh; 01c92g; 01ck6h; 03qpp9; *> query: (?x4488, 02dbp7) <- award(?x7211, ?x4488), ceremony(?x4488, ?x139), ?x7211 = 0135xb *> conf = 0.17 ranks of expected_values: 248, 304, 711 EVAL 02gdjb award! 01wg6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 14.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02gdjb award! 018gqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 14.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02gdjb award! 02dbp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 47.000 14.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9539-027r9t PRED entity: 027r9t PRED relation: nominated_for! PRED expected values: 02qgqt => 70 concepts (31 used for prediction) PRED predicted values (max 10 best out of 763): 01n5309 (0.41 #37268, 0.39 #23293, 0.34 #37267), 01797x (0.39 #23293, 0.34 #37267, 0.28 #27952), 05wjnt (0.39 #23293, 0.34 #37267, 0.28 #27952), 03rl84 (0.39 #23293, 0.34 #37267, 0.28 #27952), 086k8 (0.20 #18692, 0.14 #25681, 0.14 #23351), 05qd_ (0.16 #18806, 0.11 #28124, 0.10 #25795), 017s11 (0.15 #18733, 0.14 #9316, 0.14 #2329), 016tt2 (0.14 #18742, 0.10 #25731, 0.10 #28060), 0dvmd (0.12 #657, 0.05 #7644, 0.05 #9973), 0146pg (0.12 #7107, 0.10 #9436, 0.10 #2449) >> Best rule #37268 for best value: >> intensional similarity = 4 >> extensional distance = 413 >> proper extension: 04xbq3; >> query: (?x7141, ?x843) <- nominated_for(?x68, ?x7141), film(?x843, ?x7141), award_nominee(?x843, ?x157), honored_for(?x1819, ?x7141) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #48913 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 735 *> proper extension: 05h95s; *> query: (?x7141, ?x157) <- titles(?x53, ?x7141), award_winner(?x7141, ?x396), award_nominee(?x396, ?x157) *> conf = 0.10 ranks of expected_values: 14 EVAL 027r9t nominated_for! 02qgqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 70.000 31.000 0.411 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9538-04t2t PRED entity: 04t2t PRED relation: genre! PRED expected values: 01dyvs 09146g 01l_pn 056k77g 03k8th => 51 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1859): 06rzwx (0.81 #16680, 0.80 #16679, 0.72 #25946), 06x43v (0.81 #16680, 0.80 #16679, 0.72 #25946), 033g4d (0.81 #16680, 0.80 #16679, 0.72 #25946), 01rxyb (0.81 #16680, 0.80 #16679, 0.72 #25946), 065ym0c (0.81 #16680, 0.80 #16679, 0.72 #25946), 08j7lh (0.81 #16680, 0.80 #16679, 0.72 #25946), 02gpkt (0.81 #16680, 0.80 #16679, 0.72 #25946), 0bmc4cm (0.81 #16680, 0.80 #16679, 0.72 #25946), 0mb8c (0.81 #16680, 0.80 #16679, 0.72 #25946), 01mgw (0.80 #16679, 0.72 #25946, 0.58 #3705) >> Best rule #16680 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 07s9rl0; 03k9fj; 02l7c8; 0hfjk; >> query: (?x7160, ?x7514) <- genre(?x9175, ?x7160), genre(?x148, ?x7160), ?x148 = 034qmv, titles(?x7160, ?x7514), film_crew_role(?x9175, ?x137), film_release_region(?x9175, ?x94), ?x137 = 09zzb8, language(?x7514, ?x254) >> conf = 0.81 => this is the best rule for 9 predicted values *> Best rule #12699 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 0jxy; *> query: (?x7160, 056k77g) <- genre(?x9175, ?x7160), genre(?x8465, ?x7160), genre(?x148, ?x7160), award(?x9175, ?x7215), country(?x9175, ?x2346), nominated_for(?x5039, ?x9175), production_companies(?x148, ?x2156), ?x8465 = 05dfy_, film(?x2156, ?x297) *> conf = 0.67 ranks of expected_values: 14, 98, 175, 242, 1019 EVAL 04t2t genre! 03k8th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.809 http://example.org/film/film/genre EVAL 04t2t genre! 056k77g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 51.000 21.000 0.809 http://example.org/film/film/genre EVAL 04t2t genre! 01l_pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 21.000 0.809 http://example.org/film/film/genre EVAL 04t2t genre! 09146g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 51.000 21.000 0.809 http://example.org/film/film/genre EVAL 04t2t genre! 01dyvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 51.000 21.000 0.809 http://example.org/film/film/genre #9537-05cc1 PRED entity: 05cc1 PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 20): 060bp (0.67 #155, 0.64 #639, 0.64 #419), 0pqc5 (0.37 #1633, 0.36 #1655, 0.36 #1699), 0dq3c (0.36 #1519, 0.22 #46, 0.17 #618), 0f6c3 (0.32 #601, 0.29 #909, 0.26 #1041), 0fkvn (0.29 #597, 0.28 #1015, 0.28 #905), 09n5b9 (0.27 #605, 0.25 #913, 0.23 #1045), 04syw (0.19 #358, 0.18 #292, 0.17 #534), 0p5vf (0.13 #144, 0.12 #254, 0.12 #122), 01zq91 (0.12 #146, 0.12 #168, 0.10 #256), 0fj45 (0.10 #195, 0.09 #701, 0.09 #371) >> Best rule #155 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 06qd3; 0162v; 03rj0; 03gyl; 06sw9; 06s9y; 04vs9; 01nqj; >> query: (?x6827, 060bp) <- currency(?x6827, ?x170), country(?x2978, ?x6827), ?x2978 = 03_8r >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05cc1 jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #9536-03xp8d5 PRED entity: 03xp8d5 PRED relation: gender PRED expected values: 05zppz => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #83, 0.87 #109, 0.87 #69), 02zsn (0.31 #152, 0.31 #156, 0.30 #160) >> Best rule #83 for best value: >> intensional similarity = 2 >> extensional distance = 212 >> proper extension: 04zd4m; 014dq7; 0c5tl; 05gpy; 0l99s; 03h2p5; 0ldd; 02gnlz; >> query: (?x4385, 05zppz) <- story_by(?x1965, ?x4385), profession(?x4385, ?x319) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xp8d5 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.888 http://example.org/people/person/gender #9535-017vb_ PRED entity: 017vb_ PRED relation: company! PRED expected values: 014l7h => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 32): 014l7h (0.60 #171, 0.57 #266, 0.50 #692), 0dq_5 (0.53 #1323, 0.53 #1293, 0.48 #1717), 0krdk (0.46 #1895, 0.42 #1847, 0.42 #1706), 060c4 (0.40 #477, 0.40 #97, 0.38 #1891), 0dq3c (0.30 #476, 0.29 #239, 0.26 #1890), 05_wyz (0.28 #1294, 0.26 #1577, 0.25 #1718), 02k13d (0.25 #345, 0.20 #488, 0.18 #630), 01yc02 (0.22 #1284, 0.21 #1897, 0.21 #1993), 02211by (0.17 #1703, 0.15 #1562, 0.14 #1609), 09d6p2 (0.14 #257, 0.14 #1625, 0.12 #1719) >> Best rule #171 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 0cjdk; >> query: (?x10016, 014l7h) <- category(?x10016, ?x134), program(?x10016, ?x14197), organization(?x4682, ?x10016), service_language(?x10016, ?x254), ?x254 = 02h40lc, nominated_for(?x14647, ?x14197), genre(?x14197, ?x53), award(?x14197, ?x14350) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017vb_ company! 014l7h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.600 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #9534-0bzrxn PRED entity: 0bzrxn PRED relation: team PRED expected values: 02plv57 02qk2d5 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 14): 026wlnm (0.83 #77, 0.81 #110, 0.79 #174), 02qk2d5 (0.76 #109, 0.75 #76, 0.75 #68), 02plv57 (0.66 #170, 0.65 #89, 0.64 #57), 026dqjm (0.58 #72, 0.57 #113, 0.56 #105), 03d555l (0.56 #105, 0.50 #74, 0.50 #26), 0263cyj (0.56 #105, 0.48 #108, 0.41 #172), 02pyyld (0.56 #105, 0.48 #112, 0.41 #176), 03d5m8w (0.56 #105, 0.45 #54, 0.42 #78), 01jvgt (0.05 #375, 0.05 #358, 0.05 #416), 070xg (0.05 #375, 0.05 #358, 0.05 #416) >> Best rule #77 for best value: >> intensional similarity = 15 >> extensional distance = 10 >> proper extension: 0br1x_; >> query: (?x7378, 026wlnm) <- team(?x7378, ?x10846), team(?x7378, ?x10171), team(?x7378, ?x9983), team(?x7378, ?x8528), ?x10846 = 02pzy52, ?x10171 = 026w398, team(?x12798, ?x9983), team(?x12451, ?x9983), team(?x11210, ?x9983), team(?x10673, ?x9983), ?x11210 = 0b_6q5, ?x8528 = 091tgz, ?x12798 = 0b_770, ?x10673 = 0b_6mr, ?x12451 = 0b_6xf >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #109 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 19 *> proper extension: 0cc8q3; 05g_nr; 0b_756; 0b_770; 0b_734; *> query: (?x7378, 02qk2d5) <- team(?x7378, ?x10846), team(?x7378, ?x6003), team(?x5755, ?x10846), position(?x10846, ?x4747), team(?x12451, ?x10846), team(?x8992, ?x10846), team(?x3797, ?x10846), ?x3797 = 0b_6zk, ?x6003 = 02py8_w, sport(?x10846, ?x12913), ?x8992 = 0b_6s7, ?x12451 = 0b_6xf *> conf = 0.76 ranks of expected_values: 2, 3 EVAL 0bzrxn team 02qk2d5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 65.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0bzrxn team 02plv57 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 65.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #9533-06bw5 PRED entity: 06bw5 PRED relation: educational_institution PRED expected values: 06bw5 => 214 concepts (137 used for prediction) PRED predicted values (max 10 best out of 286): 01gwck (0.20 #16717, 0.14 #1026, 0.12 #1565), 06bw5 (0.20 #16717, 0.07 #47460, 0.06 #52854), 025rcc (0.20 #16717, 0.07 #47460, 0.06 #52854), 01n6r0 (0.14 #684, 0.12 #1223, 0.04 #3381), 07wrz (0.11 #1675, 0.03 #3834, 0.03 #4373), 01k2wn (0.11 #1637, 0.03 #3796, 0.03 #4335), 02hft3 (0.11 #1661, 0.03 #3820, 0.03 #4359), 01rgn3 (0.11 #1903, 0.02 #7836, 0.02 #9453), 015y3j (0.11 #1884, 0.02 #9973, 0.02 #10513), 02fgdx (0.11 #1714, 0.02 #9803, 0.02 #10343) >> Best rule #16717 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 01hnb; >> query: (?x5777, ?x5887) <- citytown(?x5777, ?x9605), currency(?x5777, ?x170), citytown(?x5887, ?x9605), county_seat(?x11062, ?x9605) >> conf = 0.20 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 06bw5 educational_institution 06bw5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 214.000 137.000 0.199 http://example.org/education/educational_institution_campus/educational_institution #9532-041y2 PRED entity: 041y2 PRED relation: major_field_of_study! PRED expected values: 02h4rq6 => 56 concepts (44 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.88 #289, 0.82 #327, 0.82 #196), 02_xgp2 (0.88 #296, 0.82 #334, 0.78 #314), 04zx3q1 (0.73 #213, 0.67 #269, 0.64 #195), 0bjrnt (0.60 #64, 0.50 #160, 0.45 #661), 028dcg (0.46 #230, 0.46 #399, 0.45 #97), 07s6fsf (0.46 #230, 0.46 #399, 0.45 #97), 013zdg (0.46 #230, 0.46 #399, 0.45 #97), 027f2w (0.46 #230, 0.46 #399, 0.45 #97), 02cq61 (0.46 #230, 0.46 #399, 0.45 #97), 03mkk4 (0.45 #661, 0.45 #660, 0.45 #97) >> Best rule #289 for best value: >> intensional similarity = 9 >> extensional distance = 14 >> proper extension: 01tbp; >> query: (?x10046, 02h4rq6) <- major_field_of_study(?x10045, ?x10046), major_field_of_study(?x4794, ?x10046), major_field_of_study(?x1011, ?x10046), category(?x10045, ?x134), student(?x4794, ?x1485), student(?x10046, ?x690), contains(?x335, ?x4794), featured_film_locations(?x253, ?x4794), ?x1011 = 07w0v >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 041y2 major_field_of_study! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 44.000 0.875 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #9531-07y9w5 PRED entity: 07y9w5 PRED relation: crewmember PRED expected values: 094tsh6 => 74 concepts (62 used for prediction) PRED predicted values (max 10 best out of 28): 0284n42 (0.09 #324, 0.07 #49, 0.03 #417), 092ys_y (0.08 #19, 0.06 #339, 0.03 #109), 0b79gfg (0.08 #337, 0.03 #430, 0.02 #886), 03m49ly (0.07 #353, 0.04 #33, 0.03 #399), 095zvfg (0.06 #356, 0.05 #81, 0.03 #310), 027y151 (0.06 #358, 0.04 #38, 0.03 #312), 051z6rz (0.06 #347, 0.04 #27, 0.03 #208), 0c94fn (0.06 #330, 0.02 #879, 0.02 #468), 0g9zcgx (0.05 #350, 0.04 #30, 0.03 #211), 04ktcgn (0.05 #331, 0.04 #192, 0.03 #424) >> Best rule #324 for best value: >> intensional similarity = 4 >> extensional distance = 265 >> proper extension: 0d90m; 011yrp; 08720; 0fr63l; 02qm_f; 01c22t; 020fcn; 01f7gh; 0ch26b_; 09g8vhw; ... >> query: (?x1477, 0284n42) <- film(?x368, ?x1477), country(?x1477, ?x94), language(?x1477, ?x254), crewmember(?x1477, ?x929) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #357 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 265 *> proper extension: 0d90m; 011yrp; 08720; 0fr63l; 02qm_f; 01c22t; 020fcn; 01f7gh; 0ch26b_; 09g8vhw; ... *> query: (?x1477, 094tsh6) <- film(?x368, ?x1477), country(?x1477, ?x94), language(?x1477, ?x254), crewmember(?x1477, ?x929) *> conf = 0.03 ranks of expected_values: 14 EVAL 07y9w5 crewmember 094tsh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 74.000 62.000 0.090 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #9530-03x3qv PRED entity: 03x3qv PRED relation: place_of_birth PRED expected values: 02_286 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 43): 0cr3d (0.09 #1502, 0.05 #2206, 0.04 #12063), 02_286 (0.07 #22551, 0.07 #36636, 0.07 #38750), 01531 (0.04 #809, 0.02 #4329, 0.02 #1513), 0bxbr (0.04 #919, 0.02 #1623), 0k_p5 (0.04 #917, 0.02 #1621), 06yxd (0.04 #875, 0.02 #1579), 0xrzh (0.04 #840, 0.02 #1544), 0fw2y (0.04 #796, 0.02 #1500), 01cx_ (0.04 #813, 0.02 #2221, 0.02 #4333), 0r00l (0.04 #1191) >> Best rule #1502 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 048lv; 027f7dj; 043kzcr; 016fjj; 01wgcvn; 0c3jz; 06wm0z; 04z542; 01cwkq; >> query: (?x336, 0cr3d) <- award(?x336, ?x678), award_nominee(?x1871, ?x336), ?x1871 = 02bkdn >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #22551 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1860 *> proper extension: 01l3j; 0443c; *> query: (?x336, 02_286) <- nationality(?x336, ?x94), ?x94 = 09c7w0, type_of_union(?x336, ?x566) *> conf = 0.07 ranks of expected_values: 2 EVAL 03x3qv place_of_birth 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.087 http://example.org/people/person/place_of_birth #9529-05drq5 PRED entity: 05drq5 PRED relation: film PRED expected values: 03ntbmw => 98 concepts (42 used for prediction) PRED predicted values (max 10 best out of 328): 01_mdl (0.76 #830, 0.53 #4980, 0.43 #4979), 0hvvf (0.76 #830, 0.43 #4979, 0.41 #3320), 0bz3jx (0.05 #563, 0.02 #2223, 0.02 #3883), 011yhm (0.05 #567, 0.02 #1397, 0.01 #2227), 02704ff (0.05 #485, 0.02 #1315, 0.01 #2145), 01jzyf (0.05 #304, 0.02 #1134, 0.01 #1964), 02r1c18 (0.05 #118, 0.02 #948, 0.01 #1778), 01vfqh (0.05 #99, 0.02 #929, 0.01 #1759), 0b73_1d (0.05 #53, 0.02 #883, 0.01 #1713), 03wy8t (0.02 #757, 0.01 #2417, 0.01 #3247) >> Best rule #830 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0qf43; 014zcr; 0c1pj; 05kfs; 02kxbwx; 081lh; 0151w_; 0prjs; 06pj8; 0c3ns; ... >> query: (?x1314, ?x1072) <- award(?x1314, ?x1198), written_by(?x1072, ?x1314), profession(?x1314, ?x319), ?x1198 = 02pqp12 >> conf = 0.76 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 05drq5 film 03ntbmw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 42.000 0.763 http://example.org/film/director/film #9528-027pfg PRED entity: 027pfg PRED relation: film_release_region PRED expected values: 0jgd 0154j 0j1z8 01ls2 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 84): 09c7w0 (0.92 #3978, 0.92 #5596, 0.83 #995), 0jgd (0.84 #1245, 0.84 #625, 0.83 #749), 0154j (0.78 #750, 0.78 #998, 0.76 #626), 0d060g (0.78 #752, 0.76 #1000, 0.75 #628), 01znc_ (0.75 #649, 0.74 #1021, 0.73 #773), 047yc (0.49 #639, 0.49 #763, 0.47 #1011), 06f32 (0.49 #667, 0.48 #1287, 0.47 #1411), 01ls2 (0.47 #633, 0.46 #757, 0.45 #1005), 05qx1 (0.42 #399, 0.40 #772, 0.40 #1020), 047lj (0.32 #632, 0.32 #1004, 0.32 #756) >> Best rule #3978 for best value: >> intensional similarity = 4 >> extensional distance = 1010 >> proper extension: 0170z3; 014lc_; 02d413; 0b76d_m; 014_x2; 0d90m; 03qcfvw; 0g56t9t; 09sh8k; 0m313; ... >> query: (?x6932, 09c7w0) <- film_release_region(?x6932, ?x429), participating_countries(?x418, ?x429), contains(?x429, ?x1788), nominated_for(?x112, ?x6932) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1245 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 202 *> proper extension: 023gxx; 09g7vfw; 024mpp; 043sct5; 04nm0n0; 04yg13l; 047vnkj; 0h95zbp; 03mgx6z; 02qk3fk; ... *> query: (?x6932, 0jgd) <- film_release_region(?x6932, ?x429), ?x429 = 03rt9, film(?x541, ?x6932) *> conf = 0.84 ranks of expected_values: 2, 3, 8, 16 EVAL 027pfg film_release_region 01ls2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 65.000 65.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 027pfg film_release_region 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 65.000 65.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 027pfg film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 65.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 027pfg film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 65.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9527-06lht1 PRED entity: 06lht1 PRED relation: award PRED expected values: 0f4x7 => 79 concepts (75 used for prediction) PRED predicted values (max 10 best out of 246): 05zr6wv (0.40 #418, 0.13 #2022, 0.12 #2423), 09sb52 (0.35 #7259, 0.34 #6457, 0.33 #41), 0cqgl9 (0.33 #190, 0.20 #591, 0.13 #992), 0gqyl (0.33 #105, 0.20 #506, 0.13 #26875), 0bdwft (0.33 #69, 0.20 #470, 0.09 #871), 02z0dfh (0.33 #76, 0.20 #477, 0.09 #878), 05p09zm (0.20 #523, 0.12 #2127, 0.11 #2528), 05pcn59 (0.20 #483, 0.12 #2087, 0.11 #2488), 05ztrmj (0.20 #583, 0.09 #2187, 0.09 #2588), 07cbcy (0.20 #480, 0.09 #2084, 0.08 #2485) >> Best rule #418 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 018grr; 05txrz; >> query: (?x4966, 05zr6wv) <- film(?x4966, ?x6175), film(?x4966, ?x750), ?x750 = 026mfbr, film_release_region(?x6175, ?x87) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #6447 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 544 *> proper extension: 02lfl4; 0h1mt; 02lf70; 0b_7k; 02_fj; 0k7pf; 059gkk; 03np3w; 02fx3c; 02vyw; ... *> query: (?x4966, 0f4x7) <- film(?x4966, ?x750), award_winner(?x2988, ?x4966), written_by(?x750, ?x2101) *> conf = 0.14 ranks of expected_values: 19 EVAL 06lht1 award 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 79.000 75.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9526-02qsqmq PRED entity: 02qsqmq PRED relation: film! PRED expected values: 06mnbn 06crng => 76 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1194): 015wnl (0.48 #4811, 0.14 #2730, 0.03 #6892), 0f5xn (0.36 #15539, 0.05 #7213, 0.05 #17621), 065jlv (0.29 #2394, 0.29 #313, 0.06 #4475), 0134w7 (0.29 #2242, 0.29 #161, 0.06 #4323), 0prjs (0.29 #2299, 0.29 #218, 0.04 #4380), 01kwsg (0.22 #15408, 0.02 #5001, 0.02 #29978), 0171cm (0.21 #12911, 0.05 #19157, 0.05 #14569), 02bj6k (0.18 #15954, 0.04 #5547, 0.02 #49257), 0kszw (0.17 #12905, 0.05 #14569, 0.04 #17070), 05xf75 (0.16 #5653, 0.14 #3572, 0.01 #16060) >> Best rule #4811 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 023cjg; >> query: (?x5746, 015wnl) <- film(?x3011, ?x5746), film(?x3011, ?x3012), award(?x3011, ?x704), ?x3012 = 0ggbhy7 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #7551 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 56 *> proper extension: 047gn4y; 06_wqk4; 09p0ct; 02r79_h; 075wx7_; 02725hs; 05fgt1; 05zy2cy; 0b1y_2; 06_x996; ... *> query: (?x5746, 06crng) <- film_crew_role(?x5746, ?x2472), film_crew_role(?x5746, ?x1171), ?x1171 = 09vw2b7, titles(?x512, ?x5746), ?x2472 = 01xy5l_, film_release_region(?x5746, ?x94) *> conf = 0.02 ranks of expected_values: 654, 878 EVAL 02qsqmq film! 06crng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 46.000 0.480 http://example.org/film/actor/film./film/performance/film EVAL 02qsqmq film! 06mnbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 46.000 0.480 http://example.org/film/actor/film./film/performance/film #9525-0d3k14 PRED entity: 0d3k14 PRED relation: location PRED expected values: 01cx_ => 245 concepts (210 used for prediction) PRED predicted values (max 10 best out of 401): 0p9z5 (0.52 #31303), 030qb3t (0.40 #12921, 0.28 #46646, 0.26 #141412), 02_286 (0.38 #30536, 0.38 #9666, 0.33 #11270), 059rby (0.38 #8842, 0.25 #1619, 0.20 #12854), 01cx_ (0.33 #4974, 0.25 #10593, 0.25 #9791), 01snm (0.33 #318, 0.14 #7539, 0.12 #10750), 0dclg (0.25 #9745, 0.05 #26601, 0.04 #67559), 0f2rq (0.21 #68247, 0.20 #85913, 0.20 #87518), 013n2h (0.20 #3614, 0.12 #22876, 0.11 #27694), 04jpl (0.19 #30516, 0.17 #5632, 0.17 #4027) >> Best rule #31303 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 02x8kk; >> query: (?x11088, ?x9863) <- location(?x11088, ?x108), sibling(?x11088, ?x9569), place_of_birth(?x9569, ?x9863), student(?x3439, ?x9569) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #4974 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 02yy8; *> query: (?x11088, 01cx_) <- student(?x3485, ?x11088), religion(?x11088, ?x1985), politician(?x8714, ?x11088), ?x3485 = 01mpwj *> conf = 0.33 ranks of expected_values: 5 EVAL 0d3k14 location 01cx_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 245.000 210.000 0.524 http://example.org/people/person/places_lived./people/place_lived/location #9524-02k6rq PRED entity: 02k6rq PRED relation: profession PRED expected values: 02hrh1q => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 57): 02hrh1q (0.89 #465, 0.88 #1815, 0.88 #5115), 01d_h8 (0.32 #4956, 0.31 #8257, 0.31 #4206), 0dxtg (0.28 #1964, 0.28 #6614, 0.28 #4364), 02jknp (0.28 #9302, 0.21 #4208, 0.21 #8259), 0np9r (0.28 #9302, 0.20 #4672, 0.14 #11875), 03gjzk (0.24 #7217, 0.23 #5566, 0.23 #3916), 09jwl (0.21 #920, 0.18 #7821, 0.17 #2720), 0cbd2 (0.15 #5257, 0.15 #1957, 0.15 #6607), 0nbcg (0.14 #933, 0.12 #7834, 0.12 #5883), 018gz8 (0.14 #5118, 0.13 #1818, 0.12 #4668) >> Best rule #465 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0184jc; 01y_px; 05kwx2; >> query: (?x2045, 02hrh1q) <- award_nominee(?x2173, ?x2045), award_nominee(?x1739, ?x2045), award_winner(?x926, ?x2173), ?x1739 = 015rkw >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02k6rq profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.889 http://example.org/people/person/profession #9523-04v89z PRED entity: 04v89z PRED relation: music PRED expected values: 020fgy => 71 concepts (50 used for prediction) PRED predicted values (max 10 best out of 88): 02wb6d (0.25 #337, 0.05 #758, 0.03 #1602), 07zhd7 (0.25 #413, 0.01 #834), 04vt98 (0.09 #1054, 0.09 #1053, 0.09 #1265), 05728w1 (0.09 #1054, 0.09 #1053, 0.09 #1265), 01b9ck (0.09 #1054, 0.09 #1053, 0.09 #1265), 02sj1x (0.07 #476, 0.04 #2590, 0.03 #687), 02cyfz (0.07 #454, 0.03 #1721, 0.02 #876), 0146pg (0.06 #1697, 0.06 #852, 0.06 #1064), 01jpmpv (0.04 #475, 0.04 #686, 0.03 #1530), 02bn75 (0.04 #564, 0.03 #775, 0.03 #2042) >> Best rule #337 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0k4f3; 0m_q0; >> query: (?x8217, 02wb6d) <- nominated_for(?x9296, ?x8217), nominated_for(?x2778, ?x8217), language(?x8217, ?x254), nationality(?x9296, ?x512), ?x2778 = 05728w1 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2062 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 199 *> proper extension: 016z5x; 09q5w2; 0bm2g; 016z9n; 02tqm5; 0ptxj; 0y_9q; 01zfzb; 026zlh9; 05fm6m; ... *> query: (?x8217, 020fgy) <- nominated_for(?x9296, ?x8217), award(?x8217, ?x1243), people(?x9771, ?x9296), country(?x8217, ?x94) *> conf = 0.03 ranks of expected_values: 20 EVAL 04v89z music 020fgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 71.000 50.000 0.250 http://example.org/film/film/music #9522-03c74_8 PRED entity: 03c74_8 PRED relation: season! PRED expected values: 05xvj => 7 concepts (7 used for prediction) PRED predicted values (max 10 best out of 358): 01yhm (0.77 #173, 0.77 #139, 0.73 #180), 05xvj (0.69 #156, 0.69 #165, 0.68 #94), 0cqt41 (0.69 #138, 0.69 #165, 0.68 #94), 03m1n (0.69 #165, 0.68 #94, 0.64 #55), 04mjl (0.69 #165, 0.68 #94, 0.64 #55), 01d6g (0.69 #165, 0.68 #94, 0.64 #55), 0512p (0.69 #165, 0.68 #94, 0.64 #55), 05m_8 (0.69 #165, 0.68 #94, 0.64 #55), 07l4z (0.69 #165, 0.68 #94, 0.64 #55), 05g76 (0.69 #165, 0.68 #94, 0.64 #55) >> Best rule #173 for best value: >> intensional similarity = 181 >> extensional distance = 11 >> proper extension: 025ygws; >> query: (?x8923, ?x1823) <- season(?x8894, ?x8923), season(?x7060, ?x8923), season(?x4208, ?x8923), season(?x3333, ?x8923), season(?x1010, ?x8923), season(?x260, ?x8923), ?x7060 = 01slc, ?x1010 = 01d5z, position(?x260, ?x5727), position(?x260, ?x4244), season(?x260, ?x9498), season(?x260, ?x9267), season(?x260, ?x8517), season(?x260, ?x3431), season(?x260, ?x2406), colors(?x260, ?x4557), colors(?x260, ?x1101), ?x4557 = 019sc, ?x3431 = 025ygqm, draft(?x260, ?x8786), draft(?x260, ?x8499), draft(?x260, ?x4779), draft(?x260, ?x1161), school(?x260, ?x5621), school(?x260, ?x3948), school(?x260, ?x3777), school(?x260, ?x1681), school(?x260, ?x1428), school(?x260, ?x1011), school(?x260, ?x466), ?x2406 = 03c6sl9, team(?x2010, ?x260), ?x1161 = 02x2khw, team(?x11844, ?x260), ?x4244 = 028c_8, ?x2010 = 02lyr4, ?x9498 = 027pwzc, ?x8786 = 02pq_x5, ?x3333 = 01yjl, school(?x4171, ?x5621), school(?x8901, ?x5621), school(?x7725, ?x5621), school(?x1438, ?x5621), school(?x684, ?x5621), school_type(?x1428, ?x4994), major_field_of_study(?x5621, ?x2981), ?x684 = 01ct6, draft(?x10939, ?x4779), draft(?x7399, ?x4779), draft(?x7357, ?x4779), draft(?x6823, ?x4779), draft(?x2405, ?x4779), draft(?x2174, ?x4779), draft(?x1823, ?x4779), draft(?x580, ?x4779), institution(?x8398, ?x1681), institution(?x7636, ?x1681), institution(?x3437, ?x1681), institution(?x2636, ?x1681), institution(?x1771, ?x1681), institution(?x1200, ?x1681), ?x3777 = 012vwb, ?x2981 = 02j62, ?x1438 = 0512p, ?x1823 = 01yhm, student(?x1681, ?x1580), company(?x920, ?x1681), fraternities_and_sororities(?x1681, ?x3697), major_field_of_study(?x1428, ?x4321), ?x4994 = 07tf8, school(?x4779, ?x388), ?x1200 = 016t_3, ?x4321 = 0g26h, ?x3437 = 02_xgp2, ?x2405 = 0x2p, major_field_of_study(?x1681, ?x10391), major_field_of_study(?x1681, ?x8925), major_field_of_study(?x1681, ?x5900), major_field_of_study(?x1681, ?x3995), major_field_of_study(?x1681, ?x3490), major_field_of_study(?x1681, ?x2014), major_field_of_study(?x1681, ?x1668), ?x3697 = 0325pb, ?x3995 = 0fdys, citytown(?x5621, ?x13702), ?x7725 = 07l8x, category(?x1681, ?x134), ?x134 = 08mbj5d, ?x7636 = 01rr_d, ?x5900 = 0db86, ?x7399 = 06wpc, ?x4208 = 061xq, school_type(?x466, ?x3092), institution(?x1390, ?x5621), ?x11844 = 0h69c, ?x7357 = 04mjl, student(?x466, ?x3134), company(?x3131, ?x1681), ?x3490 = 05qfh, ?x10939 = 0x0d, contains(?x94, ?x1428), ?x10391 = 02jfc, ?x9267 = 0dx84s, ?x8499 = 02r6gw6, ?x6823 = 07l8f, currency(?x5621, ?x170), company(?x3520, ?x466), student(?x5621, ?x525), organization(?x346, ?x1681), colors(?x13795, ?x1101), colors(?x12706, ?x1101), colors(?x12414, ?x1101), colors(?x12072, ?x1101), colors(?x11195, ?x1101), colors(?x10990, ?x1101), colors(?x9835, ?x1101), colors(?x8912, ?x1101), colors(?x8826, ?x1101), colors(?x7136, ?x1101), colors(?x4802, ?x1101), colors(?x2677, ?x1101), colors(?x2011, ?x1101), colors(?x13707, ?x1101), colors(?x12761, ?x1101), colors(?x11632, ?x1101), colors(?x9344, ?x1101), colors(?x8463, ?x1101), colors(?x8363, ?x1101), colors(?x6417, ?x1101), colors(?x6223, ?x1101), ?x4171 = 092j54, ?x1771 = 019v9k, ?x9344 = 02nq10, ?x12072 = 0346qt, ?x2011 = 04913k, ?x8363 = 0k__z, ?x4802 = 019lty, ?x6417 = 01t0dy, ?x2014 = 04rjg, ?x12761 = 0225v9, ?x8925 = 01zc2w, ?x2677 = 0g701n, ?x170 = 09nqf, ?x13707 = 024cg8, ?x2636 = 027f2w, ?x8463 = 04cnp4, student(?x1011, ?x400), ?x12706 = 03j0ss, ?x580 = 05m_8, ?x9835 = 02hqt6, ?x12414 = 035tjy, ?x13795 = 044p4_, colors(?x8894, ?x8271), ?x1668 = 01mkq, ?x7136 = 0jm74, major_field_of_study(?x466, ?x947), ?x11195 = 0kwv2, ?x10990 = 0329gm, ?x8398 = 028dcg, ?x2174 = 051vz, contains(?x1274, ?x3948), ?x11632 = 0mbwf, sport(?x8894, ?x5063), school(?x8894, ?x735), major_field_of_study(?x3948, ?x1858), ?x8901 = 07l4z, contains(?x3269, ?x1011), ?x735 = 065y4w7, list(?x1681, ?x2197), school(?x1883, ?x466), organization(?x1011, ?x5487), major_field_of_study(?x1011, ?x866), ?x6223 = 05d9y_, ?x8826 = 03x6w8, ?x8517 = 0285r5d, ?x947 = 036hv, student(?x3948, ?x1068), ?x5727 = 02wszf, ?x8912 = 01lpx8, colors(?x3948, ?x332), ?x3092 = 05jxkf >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #156 for first EXPECTED value: *> intensional similarity = 181 *> extensional distance = 11 *> proper extension: 025ygws; *> query: (?x8923, 05xvj) <- season(?x8894, ?x8923), season(?x7060, ?x8923), season(?x4208, ?x8923), season(?x3333, ?x8923), season(?x1010, ?x8923), season(?x260, ?x8923), ?x7060 = 01slc, ?x1010 = 01d5z, position(?x260, ?x5727), position(?x260, ?x4244), season(?x260, ?x9498), season(?x260, ?x9267), season(?x260, ?x8517), season(?x260, ?x3431), season(?x260, ?x2406), colors(?x260, ?x4557), colors(?x260, ?x1101), ?x4557 = 019sc, ?x3431 = 025ygqm, draft(?x260, ?x8786), draft(?x260, ?x8499), draft(?x260, ?x4779), draft(?x260, ?x1161), school(?x260, ?x5621), school(?x260, ?x3948), school(?x260, ?x3777), school(?x260, ?x1681), school(?x260, ?x1428), school(?x260, ?x1011), school(?x260, ?x466), ?x2406 = 03c6sl9, team(?x2010, ?x260), ?x1161 = 02x2khw, team(?x11844, ?x260), ?x4244 = 028c_8, ?x2010 = 02lyr4, ?x9498 = 027pwzc, ?x8786 = 02pq_x5, ?x3333 = 01yjl, school(?x4171, ?x5621), school(?x8901, ?x5621), school(?x7725, ?x5621), school(?x1438, ?x5621), school(?x684, ?x5621), school_type(?x1428, ?x4994), major_field_of_study(?x5621, ?x2981), ?x684 = 01ct6, draft(?x10939, ?x4779), draft(?x7399, ?x4779), draft(?x7357, ?x4779), draft(?x6823, ?x4779), draft(?x2405, ?x4779), draft(?x2174, ?x4779), draft(?x1823, ?x4779), draft(?x580, ?x4779), institution(?x8398, ?x1681), institution(?x7636, ?x1681), institution(?x3437, ?x1681), institution(?x2636, ?x1681), institution(?x1771, ?x1681), institution(?x1200, ?x1681), ?x3777 = 012vwb, ?x2981 = 02j62, ?x1438 = 0512p, ?x1823 = 01yhm, student(?x1681, ?x1580), company(?x920, ?x1681), fraternities_and_sororities(?x1681, ?x3697), major_field_of_study(?x1428, ?x4321), ?x4994 = 07tf8, school(?x4779, ?x388), ?x1200 = 016t_3, ?x4321 = 0g26h, ?x3437 = 02_xgp2, ?x2405 = 0x2p, major_field_of_study(?x1681, ?x10391), major_field_of_study(?x1681, ?x8925), major_field_of_study(?x1681, ?x5900), major_field_of_study(?x1681, ?x3995), major_field_of_study(?x1681, ?x3490), major_field_of_study(?x1681, ?x2014), major_field_of_study(?x1681, ?x1668), ?x3697 = 0325pb, ?x3995 = 0fdys, citytown(?x5621, ?x13702), ?x7725 = 07l8x, category(?x1681, ?x134), ?x134 = 08mbj5d, ?x7636 = 01rr_d, ?x5900 = 0db86, ?x7399 = 06wpc, ?x4208 = 061xq, school_type(?x466, ?x3092), institution(?x1390, ?x5621), ?x11844 = 0h69c, ?x7357 = 04mjl, student(?x466, ?x3134), company(?x3131, ?x1681), ?x3490 = 05qfh, ?x10939 = 0x0d, contains(?x94, ?x1428), ?x10391 = 02jfc, ?x9267 = 0dx84s, ?x8499 = 02r6gw6, ?x6823 = 07l8f, currency(?x5621, ?x170), company(?x3520, ?x466), student(?x5621, ?x525), organization(?x346, ?x1681), colors(?x13795, ?x1101), colors(?x12706, ?x1101), colors(?x12414, ?x1101), colors(?x12072, ?x1101), colors(?x11195, ?x1101), colors(?x10990, ?x1101), colors(?x9835, ?x1101), colors(?x8912, ?x1101), colors(?x8826, ?x1101), colors(?x7136, ?x1101), colors(?x4802, ?x1101), colors(?x2677, ?x1101), colors(?x2011, ?x1101), colors(?x13707, ?x1101), colors(?x12761, ?x1101), colors(?x11632, ?x1101), colors(?x9344, ?x1101), colors(?x8463, ?x1101), colors(?x8363, ?x1101), colors(?x6417, ?x1101), colors(?x6223, ?x1101), ?x4171 = 092j54, ?x1771 = 019v9k, ?x9344 = 02nq10, ?x12072 = 0346qt, ?x2011 = 04913k, ?x8363 = 0k__z, ?x4802 = 019lty, ?x6417 = 01t0dy, ?x2014 = 04rjg, ?x12761 = 0225v9, ?x8925 = 01zc2w, ?x2677 = 0g701n, ?x170 = 09nqf, ?x13707 = 024cg8, ?x2636 = 027f2w, ?x8463 = 04cnp4, student(?x1011, ?x400), ?x12706 = 03j0ss, ?x580 = 05m_8, ?x9835 = 02hqt6, ?x12414 = 035tjy, ?x13795 = 044p4_, colors(?x8894, ?x8271), ?x1668 = 01mkq, ?x7136 = 0jm74, major_field_of_study(?x466, ?x947), ?x11195 = 0kwv2, ?x10990 = 0329gm, ?x8398 = 028dcg, ?x2174 = 051vz, contains(?x1274, ?x3948), ?x11632 = 0mbwf, sport(?x8894, ?x5063), school(?x8894, ?x735), major_field_of_study(?x3948, ?x1858), ?x8901 = 07l4z, contains(?x3269, ?x1011), ?x735 = 065y4w7, list(?x1681, ?x2197), school(?x1883, ?x466), organization(?x1011, ?x5487), major_field_of_study(?x1011, ?x866), ?x6223 = 05d9y_, ?x8826 = 03x6w8, ?x8517 = 0285r5d, ?x947 = 036hv, student(?x3948, ?x1068), ?x5727 = 02wszf, ?x8912 = 01lpx8, colors(?x3948, ?x332), ?x3092 = 05jxkf *> conf = 0.69 ranks of expected_values: 2 EVAL 03c74_8 season! 05xvj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 7.000 7.000 0.769 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #9521-03x3wf PRED entity: 03x3wf PRED relation: award_winner PRED expected values: 0127gn 01vsy9_ => 58 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1753): 014zfs (0.75 #7552, 0.32 #14889, 0.18 #22227), 016sp_ (0.50 #5414, 0.19 #75818, 0.04 #17642), 0137n0 (0.50 #5137, 0.12 #56248, 0.12 #46468), 016srn (0.50 #5569, 0.12 #56248, 0.12 #46468), 02fn5r (0.50 #5445, 0.10 #73370, 0.08 #17673), 01n8gr (0.50 #5612, 0.10 #73370, 0.04 #17840), 036px (0.50 #5802, 0.04 #18030, 0.03 #22921), 02vyw (0.42 #15456, 0.24 #17903, 0.24 #22794), 0bkf4 (0.38 #29348, 0.34 #95379, 0.34 #44023), 0pz91 (0.38 #7595, 0.16 #14932, 0.09 #22270) >> Best rule #7552 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 05b1610; 07bdd_; 0cc8l6d; >> query: (?x1088, 014zfs) <- award_winner(?x1088, ?x7183), award_winner(?x1088, ?x3017), influenced_by(?x8065, ?x7183), influenced_by(?x2817, ?x7183), ?x8065 = 02633g, nationality(?x3017, ?x94), ?x2817 = 0q5hw >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #8485 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 05b1610; 07bdd_; 0cc8l6d; *> query: (?x1088, 0127gn) <- award_winner(?x1088, ?x7183), award_winner(?x1088, ?x3017), influenced_by(?x8065, ?x7183), influenced_by(?x2817, ?x7183), ?x8065 = 02633g, nationality(?x3017, ?x94), ?x2817 = 0q5hw *> conf = 0.12 ranks of expected_values: 130, 739 EVAL 03x3wf award_winner 01vsy9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 40.000 0.750 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 03x3wf award_winner 0127gn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 58.000 40.000 0.750 http://example.org/award/award_category/winners./award/award_honor/award_winner #9520-08fbnx PRED entity: 08fbnx PRED relation: film! PRED expected values: 042gr4 => 73 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1031): 03q64h (0.33 #8293, 0.33 #6208, 0.33 #2039), 042gr4 (0.33 #10386, 0.33 #2048, 0.25 #4132), 01qvtwm (0.33 #6158, 0.04 #37427, 0.03 #29090), 03cz9_ (0.33 #6147, 0.03 #29079, 0.03 #37416), 01vvb4m (0.25 #2607, 0.17 #6777, 0.14 #10946), 0f5xn (0.25 #3056, 0.17 #7226, 0.14 #11395), 0ywqc (0.25 #3876, 0.17 #8046, 0.14 #12215), 02w29z (0.25 #3500, 0.17 #7670, 0.14 #11839), 02cllz (0.25 #2494, 0.17 #6664, 0.14 #10833), 01l9p (0.25 #2364, 0.17 #6534, 0.14 #10703) >> Best rule #8293 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 056k77g; 03cwwl; >> query: (?x4770, 03q64h) <- genre(?x4770, ?x6459), genre(?x4770, ?x1626), genre(?x4770, ?x1013), ?x1013 = 06n90, film(?x10418, ?x4770), ?x1626 = 03q4nz, genre(?x3344, ?x6459), genre(?x2050, ?x6459), film_release_region(?x2050, ?x3277), film_release_region(?x2050, ?x142), ?x3344 = 02rrfzf, ?x3277 = 06t8v, ?x142 = 0jgd >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10386 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 076xkdz; *> query: (?x4770, 042gr4) <- genre(?x4770, ?x6459), genre(?x4770, ?x2540), genre(?x4770, ?x1013), genre(?x4770, ?x225), ?x1013 = 06n90, ?x225 = 02kdv5l, actor(?x4770, ?x6414), genre(?x10192, ?x6459), genre(?x3344, ?x6459), genre(?x2339, ?x6459), ?x2540 = 0hcr, ?x2339 = 0d_2fb, ?x10192 = 01sbv9, ?x3344 = 02rrfzf *> conf = 0.33 ranks of expected_values: 2 EVAL 08fbnx film! 042gr4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 29.000 0.333 http://example.org/film/actor/film./film/performance/film #9519-0kbvb PRED entity: 0kbvb PRED relation: sports PRED expected values: 07jjt => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 33): 0crlz (0.78 #409, 0.77 #504, 0.77 #672), 07jjt (0.77 #504, 0.77 #672, 0.76 #280), 0486tv (0.77 #504, 0.77 #672, 0.76 #280), 06z6r (0.77 #504, 0.77 #672, 0.76 #280), 07jbh (0.77 #504, 0.77 #672, 0.76 #280), 0194d (0.77 #504, 0.77 #672, 0.76 #280), 035d1m (0.77 #504, 0.77 #672, 0.76 #280), 07bs0 (0.67 #421, 0.67 #141, 0.67 #84), 01sgl (0.67 #421, 0.67 #141, 0.67 #84), 07rlg (0.67 #421, 0.67 #141, 0.67 #84) >> Best rule #409 for best value: >> intensional similarity = 64 >> extensional distance = 7 >> proper extension: 09x3r; 0blg2; 0lbd9; >> query: (?x778, 0crlz) <- olympics(?x7287, ?x778), olympics(?x6305, ?x778), olympics(?x4302, ?x778), olympics(?x1892, ?x778), olympics(?x1353, ?x778), olympics(?x910, ?x778), olympics(?x512, ?x778), olympics(?x142, ?x778), sports(?x778, ?x4045), ?x142 = 0jgd, combatants(?x7455, ?x7287), jurisdiction_of_office(?x182, ?x6305), film_release_region(?x10860, ?x1353), film_release_region(?x6516, ?x1353), film_release_region(?x5704, ?x1353), film_release_region(?x5644, ?x1353), film_release_region(?x5418, ?x1353), film_release_region(?x5067, ?x1353), film_release_region(?x4998, ?x1353), film_release_region(?x4841, ?x1353), film_release_region(?x4684, ?x1353), film_release_region(?x4336, ?x1353), film_release_region(?x4290, ?x1353), film_release_region(?x3854, ?x1353), film_release_region(?x3757, ?x1353), film_release_region(?x2958, ?x1353), film_release_region(?x1803, ?x1353), film_release_region(?x1602, ?x1353), film_release_region(?x607, ?x1353), olympics(?x6305, ?x1931), contains(?x7287, ?x11116), ?x4841 = 0k4fz, ?x5067 = 01rwpj, contains(?x6304, ?x6305), ?x4336 = 0bpm4yw, olympics(?x6733, ?x778), ?x6733 = 01sgl, member_states(?x2106, ?x1353), nationality(?x1068, ?x1353), ?x5418 = 026lgs, ?x4998 = 0dzlbx, ?x3757 = 02vr3gz, ?x5704 = 0h95zbp, ?x1803 = 0g9wdmc, locations(?x9939, ?x1353), ?x4290 = 0gtxj2q, ?x2958 = 0b_5d, organization(?x4302, ?x127), ?x182 = 060bp, ?x1931 = 0kbws, combatants(?x172, ?x1353), ?x607 = 02x3lt7, ?x1892 = 02vzc, adjoins(?x3951, ?x4302), ?x3854 = 03q0r1, taxonomy(?x910, ?x939), ?x4045 = 06z6r, ?x512 = 07ssc, ?x4684 = 03nm_fh, ?x10860 = 049w1q, ?x5644 = 0dll_t2, ?x6516 = 04cppj, ?x1602 = 0gxtknx, administrative_area_type(?x1353, ?x2792) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #504 for first EXPECTED value: *> intensional similarity = 64 *> extensional distance = 9 *> proper extension: 01f1jy; *> query: (?x778, ?x171) <- olympics(?x7287, ?x778), olympics(?x4302, ?x778), olympics(?x2152, ?x778), olympics(?x1790, ?x778), olympics(?x1558, ?x778), olympics(?x910, ?x778), olympics(?x429, ?x778), olympics(?x142, ?x778), sports(?x778, ?x171), jurisdiction_of_office(?x182, ?x910), combatants(?x13876, ?x7287), film_release_region(?x9657, ?x142), film_release_region(?x8955, ?x142), film_release_region(?x8137, ?x142), film_release_region(?x7832, ?x142), film_release_region(?x6446, ?x142), film_release_region(?x5791, ?x142), film_release_region(?x5588, ?x142), film_release_region(?x5499, ?x142), film_release_region(?x5315, ?x142), film_release_region(?x3745, ?x142), film_release_region(?x3603, ?x142), film_release_region(?x3292, ?x142), film_release_region(?x2342, ?x142), film_release_region(?x1386, ?x142), film_release_region(?x1173, ?x142), film_release_region(?x1170, ?x142), film_release_region(?x972, ?x142), film_release_region(?x299, ?x142), ?x9657 = 07jqjx, ?x1386 = 0dtfn, currency(?x4302, ?x170), sports(?x778, ?x359), countries_spoken_in(?x254, ?x910), ?x5315 = 0glqh5_, entity_involved(?x13684, ?x4302), ?x13876 = 0hw29, ?x8137 = 0gtx63s, combatants(?x4302, ?x1780), film_release_region(?x886, ?x142), ?x7832 = 0fphf3v, ?x2152 = 06mkj, ?x5499 = 0gt1k, ?x5791 = 03mgx6z, ?x8955 = 0g4pl7z, ?x3745 = 03cw411, contains(?x6304, ?x4302), ?x1170 = 09gdm7q, country(?x1557, ?x142), ?x1173 = 0872p_c, organization(?x910, ?x127), film_release_region(?x3603, ?x1061), ?x5588 = 0gtt5fb, combatants(?x326, ?x1790), ?x972 = 017gl1, ?x2342 = 0ct5zc, ?x3292 = 0gvs1kt, ?x6446 = 089j8p, nationality(?x294, ?x429), ?x299 = 01gc7, ?x1061 = 04v3q, country(?x103, ?x1558), country(?x1591, ?x429), medal(?x4302, ?x422) *> conf = 0.77 ranks of expected_values: 2 EVAL 0kbvb sports 07jjt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 28.000 28.000 0.778 http://example.org/user/jg/default_domain/olympic_games/sports #9518-01j_cy PRED entity: 01j_cy PRED relation: major_field_of_study PRED expected values: 0193x 04rlf => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 101): 0g26h (0.54 #253, 0.44 #471, 0.40 #1016), 02ky346 (0.54 #231, 0.35 #667, 0.33 #1103), 05qjt (0.50 #1206, 0.49 #1097, 0.47 #1315), 02_7t (0.46 #273, 0.41 #491, 0.38 #1036), 01lj9 (0.46 #250, 0.38 #1667, 0.38 #904), 03g3w (0.45 #894, 0.43 #1657, 0.42 #1548), 01540 (0.38 #269, 0.38 #2122, 0.35 #705), 01r4k (0.38 #290, 0.35 #726, 0.26 #1162), 0db86 (0.38 #262, 0.29 #1461, 0.27 #153), 02jfc (0.38 #289, 0.24 #725, 0.21 #1052) >> Best rule #253 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 023zl; >> query: (?x1675, 0g26h) <- major_field_of_study(?x1675, ?x6859), major_field_of_study(?x1675, ?x254), institution(?x620, ?x1675), ?x254 = 02h40lc, ?x6859 = 01tbp >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #246 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 023zl; *> query: (?x1675, 0193x) <- major_field_of_study(?x1675, ?x6859), major_field_of_study(?x1675, ?x254), institution(?x620, ?x1675), ?x254 = 02h40lc, ?x6859 = 01tbp *> conf = 0.23 ranks of expected_values: 19, 33 EVAL 01j_cy major_field_of_study 04rlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 98.000 98.000 0.538 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01j_cy major_field_of_study 0193x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 98.000 98.000 0.538 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #9517-03mnk PRED entity: 03mnk PRED relation: service_language PRED expected values: 02h40lc => 219 concepts (219 used for prediction) PRED predicted values (max 10 best out of 21): 02h40lc (0.92 #569, 0.91 #2756, 0.90 #2040), 06nm1 (0.44 #111, 0.24 #1161, 0.21 #1056), 064_8sq (0.22 #115, 0.21 #1817, 0.17 #220), 04306rv (0.22 #108, 0.17 #213, 0.17 #24), 02bv9 (0.17 #36, 0.11 #120, 0.08 #225), 05zjd (0.13 #244, 0.11 #118, 0.08 #643), 03_9r (0.11 #110, 0.09 #173, 0.09 #1160), 01r2l (0.11 #117, 0.09 #1188, 0.08 #642), 06b_j (0.11 #116, 0.06 #1061, 0.06 #1145), 02hwhyv (0.11 #121, 0.06 #1066, 0.06 #1150) >> Best rule #569 for best value: >> intensional similarity = 6 >> extensional distance = 22 >> proper extension: 0196bp; >> query: (?x3230, 02h40lc) <- organization(?x4682, ?x3230), contact_category(?x3230, ?x6046), contact_category(?x3230, ?x3231), ?x3231 = 014dgf, contact_category(?x3387, ?x6046), currency(?x3387, ?x170) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mnk service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 219.000 219.000 0.917 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #9516-01l03w2 PRED entity: 01l03w2 PRED relation: profession PRED expected values: 09jwl => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 56): 02hrh1q (0.73 #6947, 0.73 #6647, 0.73 #5744), 09jwl (0.69 #3637, 0.68 #3939, 0.66 #2125), 0nbcg (0.48 #3952, 0.47 #3650, 0.46 #1836), 016z4k (0.47 #604, 0.45 #2109, 0.44 #1807), 0dz3r (0.42 #2258, 0.42 #1053, 0.40 #1203), 01d_h8 (0.30 #6336, 0.30 #5735, 0.29 #9641), 01c72t (0.29 #3642, 0.28 #3944, 0.28 #3793), 039v1 (0.28 #9484, 0.28 #1502, 0.28 #3655), 0dxtg (0.28 #9484, 0.28 #1502, 0.28 #9635), 02jknp (0.28 #9484, 0.28 #1502, 0.28 #9635) >> Best rule #6947 for best value: >> intensional similarity = 3 >> extensional distance = 1253 >> proper extension: 04g_wd; >> query: (?x4635, 02hrh1q) <- award_nominee(?x4102, ?x4635), location(?x4635, ?x1426), gender(?x4102, ?x231) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #3637 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 515 *> proper extension: 0f0y8; 053y0s; 03c7ln; 0c9d9; 02rgz4; 01nqfh_; 01vvy; 032t2z; 06y9c2; 0274ck; ... *> query: (?x4635, 09jwl) <- artists(?x8798, ?x4635), instrumentalists(?x2048, ?x4635), nationality(?x4635, ?x94) *> conf = 0.69 ranks of expected_values: 2 EVAL 01l03w2 profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.735 http://example.org/people/person/profession #9515-025snf PRED entity: 025snf PRED relation: program PRED expected values: 01hvv0 => 45 concepts (27 used for prediction) PRED predicted values (max 10 best out of 289): 0170k0 (0.67 #3179, 0.50 #640, 0.40 #3180), 043qqt5 (0.67 #3179, 0.45 #5096, 0.40 #3180), 0vhm (0.67 #3179, 0.40 #3180, 0.40 #1301), 028k2x (0.67 #3179, 0.40 #3180, 0.40 #1348), 0ctzf1 (0.67 #3179, 0.40 #3180, 0.36 #5628), 015w8_ (0.67 #3179, 0.40 #3180, 0.36 #5628), 05nlzq (0.67 #3179, 0.40 #3180, 0.36 #5628), 07c72 (0.67 #3179, 0.40 #3180, 0.36 #5628), 01h72l (0.67 #3179, 0.40 #3180, 0.36 #5628), 019nnl (0.67 #3179, 0.40 #3180, 0.36 #5628) >> Best rule #3179 for best value: >> intensional similarity = 15 >> extensional distance = 4 >> proper extension: 02qbjm; 07zlqp; 01fkr_; >> query: (?x12812, ?x802) <- program(?x12812, ?x11454), program(?x12812, ?x8444), category(?x12812, ?x134), actor(?x11454, ?x3785), program(?x14653, ?x8444), ?x14653 = 09bv45, tv_program(?x1182, ?x11454), languages(?x11454, ?x254), genre(?x11454, ?x1510), ?x254 = 02h40lc, genre(?x8444, ?x225), genre(?x97, ?x1510), country_of_origin(?x11454, ?x94), disciplines_or_subjects(?x1288, ?x1510), genre(?x802, ?x1510) >> conf = 0.67 => this is the best rule for 70 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 23 EVAL 025snf program 01hvv0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 45.000 27.000 0.667 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #9514-024n3z PRED entity: 024n3z PRED relation: award_winner! PRED expected values: 092c5f => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 79): 092c5f (0.76 #296, 0.03 #860, 0.03 #1142), 09p30_ (0.25 #85, 0.14 #226, 0.02 #1213), 09qvms (0.12 #13, 0.07 #154, 0.05 #436), 058m5m4 (0.12 #55, 0.07 #196, 0.03 #1042), 0g55tzk (0.12 #137, 0.07 #278, 0.02 #983), 092_25 (0.12 #72, 0.07 #213, 0.02 #918), 0418154 (0.12 #108, 0.07 #249, 0.02 #531), 0g5b0q5 (0.12 #20, 0.07 #161, 0.02 #443), 0fqpc7d (0.12 #36, 0.07 #177, 0.02 #1305), 09pj68 (0.12 #105, 0.07 #246, 0.01 #528) >> Best rule #296 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 02fgm7; >> query: (?x2727, 092c5f) <- award_nominee(?x1424, ?x2727), award_winner(?x2727, ?x629), ?x1424 = 01rh0w >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024n3z award_winner! 092c5f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.765 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9513-01h1bf PRED entity: 01h1bf PRED relation: award PRED expected values: 0m7yy => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 190): 0m7yy (0.59 #3658, 0.49 #5303, 0.46 #602), 02pz3j5 (0.56 #350, 0.09 #4346, 0.09 #4816), 02q1tc5 (0.56 #345, 0.09 #4341, 0.09 #4811), 02p_04b (0.44 #407, 0.08 #4403, 0.08 #4873), 02pzxlw (0.44 #370, 0.07 #12222, 0.07 #12694), 02py_sj (0.44 #439, 0.07 #12222, 0.07 #12694), 0l8z1 (0.39 #2402, 0.13 #8513, 0.07 #12274), 02p_7cr (0.33 #259, 0.08 #4725, 0.08 #5195), 027qq9b (0.33 #379, 0.07 #12222, 0.07 #12694), 054krc (0.27 #2420, 0.10 #8531, 0.05 #12292) >> Best rule #3658 for best value: >> intensional similarity = 5 >> extensional distance = 73 >> proper extension: 02gl58; >> query: (?x3075, 0m7yy) <- award_winner(?x3075, ?x9038), award_winner(?x3075, ?x6171), category(?x9038, ?x134), country_of_origin(?x3075, ?x94), nominated_for(?x6171, ?x4891) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01h1bf award 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.587 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9512-01gw4f PRED entity: 01gw4f PRED relation: gender PRED expected values: 02zsn => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.92 #28, 0.90 #56, 0.85 #22), 05zppz (0.78 #3, 0.77 #45, 0.75 #71) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 01gw8b; >> query: (?x4867, 02zsn) <- nationality(?x4867, ?x94), award(?x4867, ?x1245), actor(?x6415, ?x4867), ?x1245 = 0gqwc >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gw4f gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.919 http://example.org/people/person/gender #9511-01c7qd PRED entity: 01c7qd PRED relation: profession PRED expected values: 0cbd2 => 122 concepts (114 used for prediction) PRED predicted values (max 10 best out of 90): 02hrh1q (0.78 #7628, 0.73 #9567, 0.70 #7777), 0dxtg (0.77 #1208, 0.70 #1953, 0.66 #2699), 01c72t (0.75 #472, 0.71 #323, 0.65 #771), 09jwl (0.59 #7033, 0.57 #5390, 0.56 #7931), 01d_h8 (0.58 #1200, 0.48 #1945, 0.44 #2691), 0cbd2 (0.51 #3290, 0.51 #2393, 0.49 #3439), 02jknp (0.44 #1202, 0.42 #1947, 0.37 #2693), 0nbcg (0.44 #1376, 0.44 #6299, 0.43 #7944), 01c8w0 (0.40 #307, 0.30 #9, 0.26 #1054), 03gjzk (0.39 #1210, 0.33 #1955, 0.29 #2402) >> Best rule #7628 for best value: >> intensional similarity = 3 >> extensional distance = 813 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 04bdxl; 06qgvf; 0grwj; 05bnp0; 07fq1y; 02qgqt; ... >> query: (?x9834, 02hrh1q) <- nationality(?x9834, ?x94), nominated_for(?x9834, ?x2901), people(?x1050, ?x9834) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3290 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 181 *> proper extension: 05gpy; *> query: (?x9834, 0cbd2) <- story_by(?x6048, ?x9834), nominated_for(?x143, ?x6048), nominated_for(?x1585, ?x6048) *> conf = 0.51 ranks of expected_values: 6 EVAL 01c7qd profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 114.000 0.777 http://example.org/people/person/profession #9510-09kn9 PRED entity: 09kn9 PRED relation: program! PRED expected values: 0kctd => 88 concepts (82 used for prediction) PRED predicted values (max 10 best out of 53): 07y2b (0.38 #39, 0.18 #208, 0.17 #264), 0187wh (0.33 #306, 0.33 #250, 0.30 #138), 0gsg7 (0.28 #2877, 0.25 #2933, 0.22 #2141), 05gnf (0.26 #2889, 0.21 #3677, 0.20 #2945), 0cjdk (0.22 #61, 0.22 #1185, 0.21 #1467), 09d5h (0.17 #2878, 0.14 #3666, 0.14 #2934), 03mdt (0.15 #962, 0.13 #1469, 0.12 #1637), 0b275x (0.12 #524, 0.12 #18, 0.12 #580), 07c52 (0.12 #2197, 0.12 #1125, 0.11 #2820), 0ljc_ (0.12 #28, 0.11 #85, 0.10 #141) >> Best rule #39 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 06qxh; >> query: (?x1843, 07y2b) <- genre(?x1843, ?x1013), genre(?x1843, ?x811), tv_program(?x1799, ?x1843), ?x811 = 03k9fj, titles(?x2008, ?x1843), ?x1013 = 06n90, nominated_for(?x1799, ?x723) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #815 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 25 *> proper extension: 08cl7s; *> query: (?x1843, 0kctd) <- genre(?x1843, ?x1844), genre(?x1843, ?x811), ?x811 = 03k9fj, country_of_origin(?x1843, ?x94), languages(?x1843, ?x254), actor(?x1843, ?x3808), genre(?x9142, ?x1844), ?x9142 = 02q_x_l *> conf = 0.11 ranks of expected_values: 14 EVAL 09kn9 program! 0kctd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 88.000 82.000 0.375 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #9509-04b7xr PRED entity: 04b7xr PRED relation: profession PRED expected values: 09jwl 0nbcg => 98 concepts (90 used for prediction) PRED predicted values (max 10 best out of 64): 09jwl (0.76 #18, 0.74 #312, 0.74 #2079), 02hrh1q (0.69 #10334, 0.69 #8863, 0.65 #9451), 0nbcg (0.53 #766, 0.51 #2092, 0.51 #1061), 0n1h (0.53 #10, 0.28 #304, 0.25 #1040), 01c72t (0.46 #611, 0.35 #23, 0.31 #3414), 03gjzk (0.37 #1928, 0.22 #7835, 0.22 #7688), 0dxtg (0.36 #1926, 0.25 #7833, 0.25 #9009), 039v1 (0.32 #2097, 0.30 #3427, 0.29 #183), 01d_h8 (0.29 #4, 0.28 #9001, 0.27 #10325), 0fnpj (0.27 #5755, 0.23 #354, 0.18 #60) >> Best rule #18 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 0lbj1; 01vvycq; 01vrz41; 015_30; 02b25y; 0gcs9; 01n8gr; 02qwg; 0fhxv; 03j24kf; ... >> query: (?x6942, 09jwl) <- award(?x6942, ?x2585), award(?x6942, ?x1801), artists(?x378, ?x6942), ?x1801 = 01c92g, ?x2585 = 054ks3 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 04b7xr profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 90.000 0.765 http://example.org/people/person/profession EVAL 04b7xr profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 90.000 0.765 http://example.org/people/person/profession #9508-026fd PRED entity: 026fd PRED relation: profession PRED expected values: 0dxtg => 147 concepts (145 used for prediction) PRED predicted values (max 10 best out of 73): 0dxtg (0.88 #447, 0.87 #12, 0.85 #3782), 03gjzk (0.66 #4798, 0.51 #2043, 0.49 #883), 0cbd2 (0.47 #7691, 0.47 #1311, 0.46 #9432), 018gz8 (0.47 #2915, 0.36 #885, 0.35 #2770), 0kyk (0.42 #1332, 0.35 #462, 0.33 #5102), 0np9r (0.24 #2919, 0.17 #3064, 0.16 #2774), 02hv44_ (0.22 #489, 0.15 #344, 0.15 #1504), 0d1pc (0.22 #772, 0.20 #1932, 0.18 #6862), 09jwl (0.20 #1902, 0.19 #6832, 0.19 #307), 05z96 (0.15 #7724, 0.13 #8305, 0.12 #5114) >> Best rule #447 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 08433; >> query: (?x5898, 0dxtg) <- written_by(?x3306, ?x5898), influenced_by(?x5898, ?x6504), student(?x2327, ?x5898) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026fd profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 145.000 0.878 http://example.org/people/person/profession #9507-017vkx PRED entity: 017vkx PRED relation: nationality PRED expected values: 02jx1 => 142 concepts (119 used for prediction) PRED predicted values (max 10 best out of 36): 02jx1 (0.92 #8730, 0.88 #1190, 0.80 #10619), 09c7w0 (0.85 #8531, 0.83 #10620, 0.83 #9723), 03rk0 (0.24 #6884, 0.06 #11658, 0.06 #11757), 0f8l9c (0.14 #4878, 0.11 #417, 0.08 #6860), 03rjj (0.13 #4862, 0.08 #6844, 0.03 #698), 06q1r (0.11 #1165, 0.10 #571, 0.05 #2653), 0345h (0.09 #6869, 0.03 #1119, 0.02 #2310), 0d060g (0.07 #1297, 0.06 #2882, 0.05 #1594), 0chghy (0.06 #703, 0.03 #802, 0.03 #5362), 03_3d (0.05 #6845, 0.02 #7342, 0.02 #7441) >> Best rule #8730 for best value: >> intensional similarity = 4 >> extensional distance = 1247 >> proper extension: 02784z; 047g6; 0bhtzw; >> query: (?x3856, ?x1310) <- nationality(?x3856, ?x512), place_of_birth(?x3856, ?x12491), gender(?x3856, ?x231), country(?x12491, ?x1310) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017vkx nationality 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 119.000 0.923 http://example.org/people/person/nationality #9506-0hskw PRED entity: 0hskw PRED relation: gender PRED expected values: 05zppz => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #57, 0.88 #51, 0.88 #49), 02zsn (0.49 #251, 0.47 #26, 0.44 #44) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 246 >> proper extension: 032md; >> query: (?x2733, 05zppz) <- film(?x2733, ?x5584), profession(?x2733, ?x106), nominated_for(?x746, ?x5584) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hskw gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.883 http://example.org/people/person/gender #9505-054ks3 PRED entity: 054ks3 PRED relation: ceremony PRED expected values: 09q_6t => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 127): 0gpjbt (0.75 #783, 0.67 #405, 0.57 #657), 09n4nb (0.75 #799, 0.67 #421, 0.57 #673), 0466p0j (0.75 #825, 0.67 #447, 0.57 #699), 01c6qp (0.75 #774, 0.67 #396, 0.47 #2412), 01mh_q (0.75 #836, 0.67 #458, 0.45 #2474), 0jzphpx (0.75 #792, 0.67 #414, 0.37 #2430), 02cg41 (0.67 #489, 0.62 #867, 0.57 #741), 013b2h (0.67 #451, 0.62 #829, 0.43 #2467), 01xqqp (0.67 #463, 0.62 #841, 0.41 #2479), 05pd94v (0.67 #380, 0.57 #632, 0.50 #758) >> Best rule #783 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 01dpdh; 026mff; >> query: (?x2585, 0gpjbt) <- award_winner(?x2585, ?x248), award(?x5906, ?x2585), award(?x3358, ?x2585), ?x3358 = 01n8gr, artists(?x474, ?x5906), participant(?x970, ?x5906) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3403 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 192 *> proper extension: 05f4m9q; 05q8pss; 02g2wv; 02f77l; 02f6yz; 0641kkh; *> query: (?x2585, ?x342) <- award_winner(?x2585, ?x3442), award(?x5906, ?x2585), artists(?x474, ?x5906), award_nominee(?x793, ?x3442), award_winner(?x342, ?x5906) *> conf = 0.33 ranks of expected_values: 86 EVAL 054ks3 ceremony 09q_6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 56.000 56.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony #9504-082scv PRED entity: 082scv PRED relation: genre PRED expected values: 0219x_ => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 94): 02kdv5l (0.55 #123, 0.52 #244, 0.40 #2), 05p553 (0.42 #246, 0.41 #1940, 0.37 #730), 03k9fj (0.40 #12, 0.40 #617, 0.38 #254), 01jfsb (0.34 #134, 0.33 #255, 0.32 #8968), 01hmnh (0.30 #1107, 0.29 #623, 0.27 #1470), 04xvlr (0.29 #1816, 0.26 #2421, 0.21 #4478), 06n90 (0.25 #135, 0.20 #14, 0.19 #1466), 06cvj (0.24 #1939, 0.10 #3391, 0.10 #3149), 060__y (0.23 #1832, 0.21 #2437, 0.19 #1953), 082gq (0.20 #31, 0.13 #515, 0.12 #2451) >> Best rule #123 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 0cn_b8; 0cwfgz; 02wgbb; >> query: (?x2826, 02kdv5l) <- nominated_for(?x2827, ?x2826), nominated_for(?x1105, ?x2826), nominated_for(?x102, ?x2826), ?x102 = 04ljl_l, ?x1105 = 07bdd_ >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1842 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 331 *> proper extension: 0170z3; 014_x2; 0sxg4; 01br2w; 0140g4; 0m2kd; 0fgpvf; 08r4x3; 0h3xztt; 01719t; ... *> query: (?x2826, 0219x_) <- nominated_for(?x2827, ?x2826), nominated_for(?x102, ?x2826), titles(?x53, ?x2826), ?x53 = 07s9rl0 *> conf = 0.16 ranks of expected_values: 15 EVAL 082scv genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 86.000 86.000 0.545 http://example.org/film/film/genre #9503-051kd PRED entity: 051kd PRED relation: actor PRED expected values: 0dt645q => 74 concepts (54 used for prediction) PRED predicted values (max 10 best out of 771): 03cz4j (0.33 #2734, 0.25 #3665, 0.20 #5527), 01vs8ng (0.33 #2779, 0.25 #3710, 0.20 #5572), 01rddlc (0.33 #2701, 0.25 #3632, 0.20 #5494), 0f8grf (0.33 #2754, 0.25 #3685, 0.20 #5547), 0309lm (0.33 #1643, 0.20 #4436, 0.11 #11885), 01pgzn_ (0.33 #1110, 0.20 #3903, 0.08 #15080), 030x48 (0.33 #1179, 0.20 #3972, 0.08 #22600), 062hgx (0.33 #1279, 0.20 #4072, 0.06 #11521), 03nb5v (0.33 #1452, 0.20 #4245, 0.06 #11694), 01p8r8 (0.33 #1694, 0.20 #4487, 0.06 #11936) >> Best rule #2734 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 02rhwjr; >> query: (?x13769, 03cz4j) <- genre(?x13769, ?x2540), genre(?x13769, ?x1013), genre(?x13769, ?x53), program(?x14343, ?x13769), ?x53 = 07s9rl0, languages(?x13769, ?x2164), country_of_origin(?x13769, ?x252), actor(?x13769, ?x256), ?x2540 = 0hcr, ?x1013 = 06n90 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10093 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 12 *> proper extension: 02v5xg; *> query: (?x13769, 0dt645q) <- genre(?x13769, ?x5937), genre(?x13769, ?x2540), ?x2540 = 0hcr, ?x5937 = 0jxy, languages(?x13769, ?x2164), major_field_of_study(?x10333, ?x2164), language(?x136, ?x2164), ?x10333 = 02gkxp, languages(?x5314, ?x2164), ?x5314 = 0283ph *> conf = 0.07 ranks of expected_values: 70 EVAL 051kd actor 0dt645q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 74.000 54.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #9502-0dtfn PRED entity: 0dtfn PRED relation: award PRED expected values: 0262s1 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 205): 0gs9p (0.62 #520, 0.41 #2117, 0.38 #2282), 0gq9h (0.58 #518, 0.43 #2115, 0.38 #2282), 0p9sw (0.41 #2072, 0.38 #475, 0.16 #2758), 019f4v (0.38 #2282, 0.35 #2106, 0.35 #2968), 040njc (0.38 #2282, 0.35 #2968, 0.28 #7308), 0gqy2 (0.38 #2282, 0.35 #2968, 0.28 #7308), 0l8z1 (0.38 #2282, 0.35 #2968, 0.28 #7308), 02qyntr (0.38 #2282, 0.35 #2968, 0.28 #7308), 0gr42 (0.38 #2282, 0.35 #2968, 0.28 #7308), 02g3v6 (0.38 #2282, 0.35 #2968, 0.28 #7308) >> Best rule #520 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0cq7tx; >> query: (?x1386, 0gs9p) <- film(?x2916, ?x1386), award(?x1386, ?x1703), ?x1703 = 0k611, list(?x1386, ?x3004) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #202 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0199wf; *> query: (?x1386, 0262s1) <- film(?x8685, ?x1386), ?x8685 = 0154d7, genre(?x1386, ?x225) *> conf = 0.29 ranks of expected_values: 11 EVAL 0dtfn award 0262s1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 115.000 115.000 0.615 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9501-0qpjt PRED entity: 0qpjt PRED relation: place_of_death! PRED expected values: 09r8l => 91 concepts (62 used for prediction) PRED predicted values (max 10 best out of 264): 0p9gg (0.12 #723, 0.07 #1479, 0.03 #2235), 02wlk (0.12 #694, 0.07 #1450, 0.03 #2206), 04n_g (0.12 #154, 0.07 #910, 0.03 #1666), 02l0xc (0.03 #2202, 0.02 #2958, 0.02 #3714), 01w724 (0.03 #1616, 0.02 #2372, 0.02 #3128), 028lc8 (0.03 #1565, 0.02 #2321, 0.02 #3077), 09dt7 (0.03 #1554), 02drd3 (0.02 #2953, 0.02 #3709, 0.01 #4465), 0jnb0 (0.02 #2913, 0.02 #3669, 0.01 #4425), 01xpxv (0.02 #2891, 0.02 #3647, 0.01 #4403) >> Best rule #723 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 07vyf; >> query: (?x9010, 0p9gg) <- contains(?x938, ?x9010), contains(?x94, ?x9010), ?x94 = 09c7w0, ?x938 = 0vmt, category(?x9010, ?x134) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qpjt place_of_death! 09r8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 62.000 0.125 http://example.org/people/deceased_person/place_of_death #9500-01399x PRED entity: 01399x PRED relation: role! PRED expected values: 0f0qfz => 49 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1069): 050z2 (0.82 #4269, 0.82 #3988, 0.75 #2087), 023l9y (0.69 #4960, 0.67 #4487, 0.54 #5435), 0770cd (0.62 #1974, 0.55 #3875, 0.50 #3401), 04bpm6 (0.62 #4818, 0.58 #4345, 0.48 #10992), 04s5_s (0.60 #1406, 0.50 #2357, 0.50 #1889), 05qhnq (0.55 #4112, 0.50 #6007, 0.50 #3160), 01wxdn3 (0.54 #5161, 0.50 #4688, 0.50 #2312), 06x4l_ (0.54 #4872, 0.50 #4399, 0.50 #2023), 0137g1 (0.54 #4865, 0.50 #4392, 0.46 #5340), 01w806h (0.54 #4889, 0.50 #4416, 0.40 #3467) >> Best rule #4269 for best value: >> intensional similarity = 31 >> extensional distance = 9 >> proper extension: 05r5c; 0mkg; 04rzd; >> query: (?x9219, ?x4052) <- role(?x4913, ?x9219), role(?x7033, ?x9219), role(?x614, ?x9219), role(?x74, ?x9219), ?x4913 = 03ndd, role(?x9219, ?x227), role(?x1231, ?x9219), ?x74 = 03q5t, role(?x4052, ?x614), role(?x1715, ?x614), role(?x3418, ?x614), role(?x2592, ?x614), role(?x2158, ?x614), role(?x745, ?x614), ?x7033 = 0gkd1, instrumentalists(?x614, ?x2799), group(?x614, ?x7544), role(?x614, ?x8014), role(?x614, ?x1574), ?x8014 = 0214km, ?x1574 = 0l15bq, ?x2799 = 01vsl3_, ?x745 = 01vj9c, role(?x2575, ?x614), ?x3418 = 02w4b, ?x2158 = 01dnws, ?x2592 = 0j871, ?x1715 = 04bpm6, ?x7544 = 07m4c, role(?x214, ?x614), ?x4052 = 050z2 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #670 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 2 *> proper extension: 0l14qv; *> query: (?x9219, 0f0qfz) <- role(?x4913, ?x9219), role(?x716, ?x9219), role(?x7033, ?x9219), role(?x614, ?x9219), role(?x228, ?x9219), role(?x74, ?x9219), ?x4913 = 03ndd, role(?x9219, ?x227), role(?x4855, ?x9219), ?x74 = 03q5t, ?x614 = 0mkg, ?x7033 = 0gkd1, ?x716 = 018vs, ?x4855 = 01l4g5, role(?x9219, ?x3991), ?x3991 = 05842k, role(?x228, ?x3215), role(?x228, ?x2764), role(?x228, ?x1662), ?x1662 = 02bxd, role(?x228, ?x2309), role(?x130, ?x228), group(?x228, ?x10502), ?x2309 = 06ncr, ?x10502 = 016vn3, ?x3215 = 0bxl5, group(?x9219, ?x5838), performance_role(?x568, ?x228), role(?x868, ?x228), ?x2764 = 01s0ps *> conf = 0.25 ranks of expected_values: 170 EVAL 01399x role! 0f0qfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 49.000 38.000 0.818 http://example.org/music/artist/track_contributions./music/track_contribution/role #9499-01ls2 PRED entity: 01ls2 PRED relation: organization PRED expected values: 02vk52z => 109 concepts (106 used for prediction) PRED predicted values (max 10 best out of 50): 02vk52z (0.91 #133, 0.88 #243, 0.86 #332), 0b6css (0.62 #54, 0.49 #142, 0.44 #120), 0_2v (0.58 #48, 0.57 #136, 0.50 #114), 018cqq (0.58 #55, 0.54 #143, 0.53 #121), 01rz1 (0.54 #46, 0.50 #112, 0.49 #178), 0gkjy (0.41 #249, 0.35 #338, 0.35 #646), 041288 (0.38 #655, 0.36 #1163, 0.36 #1009), 02jxk (0.35 #47, 0.33 #201, 0.32 #113), 0j7v_ (0.32 #1791, 0.26 #998, 0.25 #1241), 059dn (0.32 #1791, 0.16 #37, 0.15 #59) >> Best rule #133 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 09pmkv; 059j2; 0163v; 05b4w; 03ryn; 0jgx; 04hqz; >> query: (?x410, 02vk52z) <- film_release_region(?x428, ?x410), ?x428 = 0h1cdwq, form_of_government(?x410, ?x4763) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ls2 organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 106.000 0.914 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #9498-023vcd PRED entity: 023vcd PRED relation: film! PRED expected values: 06z8gn => 111 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1078): 0gn30 (0.40 #3023, 0.03 #13416, 0.02 #75795), 04yywz (0.40 #2097, 0.03 #8333, 0.02 #14570), 02k21g (0.20 #791, 0.11 #4947, 0.08 #9105), 0151w_ (0.20 #162, 0.06 #4318, 0.04 #6397), 063g7l (0.20 #1892, 0.06 #10206, 0.03 #33075), 03v0vd (0.20 #1627, 0.05 #12019, 0.03 #16178), 0f7h2v (0.20 #464, 0.03 #15015, 0.03 #21250), 01nm3s (0.20 #686, 0.03 #17315, 0.02 #38109), 08vr94 (0.20 #673, 0.03 #19381, 0.02 #58890), 069nzr (0.20 #899, 0.02 #27924, 0.02 #11291) >> Best rule #3023 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 012mrr; >> query: (?x10246, 0gn30) <- award_winner(?x10246, ?x1335), film(?x10361, ?x10246), film(?x1065, ?x10246), type_of_union(?x1065, ?x566), ?x10361 = 0234pg >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #16064 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 64 *> proper extension: 028_yv; 011yrp; 01vksx; 0cwy47; 047msdk; 0jqn5; 0fpkhkz; 04jkpgv; 0dr_4; 01f8gz; ... *> query: (?x10246, 06z8gn) <- award_winner(?x10246, ?x1335), film(?x794, ?x10246), film_regional_debut_venue(?x10246, ?x6557), film_crew_role(?x10246, ?x137) *> conf = 0.02 ranks of expected_values: 811 EVAL 023vcd film! 06z8gn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 111.000 66.000 0.400 http://example.org/film/actor/film./film/performance/film #9497-02mx98 PRED entity: 02mx98 PRED relation: instrumentalists! PRED expected values: 02hnl => 117 concepts (104 used for prediction) PRED predicted values (max 10 best out of 127): 0342h (0.82 #1619, 0.77 #357, 0.69 #3323), 05r5c (0.77 #444, 0.77 #361, 0.74 #624), 018vs (0.69 #443, 0.69 #366, 0.69 #894), 05148p4 (0.63 #555, 0.59 #465, 0.58 #1005), 02hnl (0.56 #442, 0.54 #2686, 0.49 #623), 0l14md (0.56 #442, 0.54 #2686, 0.49 #623), 042v_gx (0.56 #442, 0.54 #2686, 0.49 #623), 02sgy (0.56 #442, 0.54 #2686, 0.49 #623), 0l14qv (0.36 #2148, 0.32 #4931, 0.32 #625), 02dlh2 (0.36 #2148, 0.32 #4931, 0.32 #625) >> Best rule #1619 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 01yzl2; >> query: (?x8114, 0342h) <- instrumentalists(?x212, ?x8114), artists(?x302, ?x8114), nationality(?x8114, ?x94), ?x302 = 016clz >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #442 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 0bg539; *> query: (?x8114, ?x314) <- instrumentalists(?x212, ?x8114), role(?x8114, ?x716), role(?x8114, ?x316), role(?x8114, ?x314), ?x316 = 05r5c, ?x716 = 018vs *> conf = 0.56 ranks of expected_values: 5 EVAL 02mx98 instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 117.000 104.000 0.821 http://example.org/music/instrument/instrumentalists #9496-024dw0 PRED entity: 024dw0 PRED relation: artist! PRED expected values: 0n85g => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 124): 01clyr (0.33 #175, 0.26 #2431, 0.25 #316), 015_1q (0.25 #584, 0.24 #1571, 0.23 #2699), 0n85g (0.25 #345, 0.22 #486, 0.15 #2037), 0mzkr (0.25 #308, 0.17 #26, 0.12 #872), 03rhqg (0.24 #2413, 0.20 #721, 0.19 #3259), 0181dw (0.22 #466, 0.17 #1594, 0.15 #6389), 01w40h (0.21 #1157, 0.18 #1016, 0.17 #29), 0k_kr (0.21 #1173, 0.12 #1455, 0.12 #2301), 0g768 (0.21 #2435, 0.17 #3987, 0.16 #3846), 01cszh (0.20 #716, 0.08 #575, 0.08 #3113) >> Best rule #175 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0137hn; >> query: (?x7506, 01clyr) <- role(?x7506, ?x1750), location(?x7506, ?x9929), sibling(?x10282, ?x7506), artists(?x1380, ?x7506) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #345 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 0gbwp; *> query: (?x7506, 0n85g) <- type_of_union(?x7506, ?x566), sibling(?x7506, ?x10282), artists(?x1380, ?x7506), ?x566 = 04ztj, instrumentalists(?x212, ?x7506) *> conf = 0.25 ranks of expected_values: 3 EVAL 024dw0 artist! 0n85g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 157.000 157.000 0.333 http://example.org/music/record_label/artist #9495-03g5_y PRED entity: 03g5_y PRED relation: currency PRED expected values: 09nqf => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.49 #1, 0.38 #19, 0.35 #25), 01nv4h (0.03 #14, 0.03 #17, 0.03 #5) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 0pyg6; 02mjf2; 0205dx; 02hhtj; >> query: (?x7872, 09nqf) <- profession(?x7872, ?x319), participant(?x1735, ?x7872), ?x319 = 01d_h8 >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03g5_y currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.494 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #9494-03f1zdw PRED entity: 03f1zdw PRED relation: award PRED expected values: 027dtxw => 96 concepts (79 used for prediction) PRED predicted values (max 10 best out of 269): 09qv_s (0.70 #14731, 0.70 #15130, 0.69 #23492), 0ck27z (0.62 #1284, 0.18 #11544, 0.14 #25484), 027dtxw (0.30 #4, 0.23 #402, 0.15 #20306), 01by1l (0.23 #906, 0.11 #9265, 0.10 #12849), 0cqh46 (0.20 #50, 0.15 #448, 0.15 #20306), 0bdwqv (0.20 #167, 0.15 #565, 0.14 #25484), 09qwmm (0.18 #11544, 0.15 #431, 0.15 #20306), 094qd5 (0.18 #11544, 0.15 #441, 0.15 #20306), 0gqwc (0.18 #11544, 0.15 #471, 0.15 #20306), 02ppm4q (0.18 #11544, 0.15 #20306, 0.14 #25484) >> Best rule #14731 for best value: >> intensional similarity = 2 >> extensional distance = 1294 >> proper extension: 01_8w2; 018_q8; 0gsgr; 0kc8y; >> query: (?x1222, ?x591) <- award_winner(?x1222, ?x72), award_winner(?x591, ?x1222) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0csdzz; *> query: (?x1222, 027dtxw) <- nominated_for(?x1222, ?x1813), ?x1813 = 09gq0x5, award(?x1222, ?x591) *> conf = 0.30 ranks of expected_values: 3 EVAL 03f1zdw award 027dtxw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 79.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9493-02fgpf PRED entity: 02fgpf PRED relation: award_winner! PRED expected values: 073hgx => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 131): 01s695 (0.25 #3, 0.10 #2796, 0.10 #668), 0gpjbt (0.19 #29, 0.15 #162, 0.09 #2822), 01bx35 (0.17 #7981, 0.12 #7, 0.12 #805), 056878 (0.17 #7981, 0.10 #2825, 0.07 #3490), 02yxh9 (0.17 #7981, 0.04 #11307, 0.04 #11441), 02ywhz (0.17 #7981, 0.04 #11307, 0.04 #11441), 073hgx (0.17 #7981, 0.04 #11307, 0.04 #11441), 013b2h (0.13 #2869, 0.12 #209, 0.10 #741), 05zksls (0.12 #35, 0.04 #11307, 0.04 #11441), 02yw5r (0.12 #12, 0.04 #11307, 0.04 #11441) >> Best rule #3 for best value: >> intensional similarity = 2 >> extensional distance = 14 >> proper extension: 02ht0ln; >> query: (?x1894, 01s695) <- award(?x1894, ?x10881), ?x10881 = 026mmy >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #7981 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1342 *> proper extension: 01j53q; *> query: (?x1894, ?x3254) <- award_winner(?x8661, ?x1894), award_winner(?x3254, ?x8661), award_winner(?x1323, ?x8661) *> conf = 0.17 ranks of expected_values: 7 EVAL 02fgpf award_winner! 073hgx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 112.000 112.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9492-09jvl PRED entity: 09jvl PRED relation: artists! PRED expected values: 0jmwg => 108 concepts (75 used for prediction) PRED predicted values (max 10 best out of 268): 016clz (0.85 #17106, 0.56 #2808, 0.55 #5917), 06by7 (0.61 #9047, 0.60 #7803, 0.59 #1889), 0jmwg (0.60 #1354, 0.40 #1042, 0.33 #1665), 0xhtw (0.50 #2197, 0.49 #4995, 0.46 #18346), 064t9 (0.46 #14632, 0.45 #5615, 0.44 #22394), 03lty (0.41 #18064, 0.33 #5007, 0.33 #2209), 0dl5d (0.36 #18055, 0.24 #4998, 0.23 #7490), 059kh (0.33 #50, 0.32 #5340, 0.31 #2853), 011j5x (0.33 #34, 0.28 #2837, 0.26 #5946), 016ybr (0.33 #439, 0.25 #749, 0.20 #1059) >> Best rule #17106 for best value: >> intensional similarity = 7 >> extensional distance = 245 >> proper extension: 01cv3n; 06x4l_; 01m65sp; 02bh9; 037hgm; 01vswwx; 01kd57; 03xnq9_; 02bgmr; 05y7hc; ... >> query: (?x10938, 016clz) <- artists(?x5934, ?x10938), artists(?x5934, ?x10864), artists(?x5934, ?x9603), ?x10864 = 057xn_m, parent_genre(?x2542, ?x5934), ?x2542 = 03xnwz, ?x9603 = 012ycy >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1354 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 01tv3x2; *> query: (?x10938, 0jmwg) <- artist(?x3874, ?x10938), ?x3874 = 015kg1, artists(?x8481, ?x10938), artists(?x5934, ?x10938), artists(?x2995, ?x10938), ?x5934 = 05r6t, ?x2995 = 01cbwl, parent_genre(?x8481, ?x7808) *> conf = 0.60 ranks of expected_values: 3 EVAL 09jvl artists! 0jmwg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 75.000 0.846 http://example.org/music/genre/artists #9491-0b_75k PRED entity: 0b_75k PRED relation: locations PRED expected values: 099ty => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 468): 0fsb8 (0.57 #1163, 0.55 #2195, 0.50 #991), 0f2r6 (0.45 #2082, 0.43 #2426, 0.40 #362), 0ftxw (0.43 #1093, 0.38 #1437, 0.38 #1265), 071cn (0.43 #1104, 0.36 #2136, 0.33 #3168), 030qb3t (0.40 #3992, 0.40 #3819, 0.33 #4338), 04f_d (0.40 #2624, 0.36 #2280, 0.33 #3140), 0f2rq (0.36 #2161, 0.36 #2505, 0.36 #2333), 0lphb (0.36 #2518, 0.36 #2346, 0.33 #2690), 0d9jr (0.36 #2501, 0.33 #3189, 0.33 #953), 029cr (0.35 #3667, 0.33 #2633, 0.32 #3322) >> Best rule #1163 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 0b_6_l; >> query: (?x6583, 0fsb8) <- team(?x6583, ?x9909), team(?x6583, ?x6003), team(?x6583, ?x4369), locations(?x6583, ?x2277), ?x6003 = 02py8_w, ?x4369 = 02pqcfz, ?x9909 = 026wlnm, teams(?x2277, ?x2405), featured_film_locations(?x2362, ?x2277), location(?x624, ?x2277) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2625 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 13 *> proper extension: 0b_6jz; *> query: (?x6583, 099ty) <- team(?x6583, ?x9833), team(?x6583, ?x6003), locations(?x6583, ?x5893), locations(?x6583, ?x1860), team(?x1348, ?x6003), location(?x558, ?x5893), place_of_birth(?x193, ?x1860), ?x9833 = 03y9p40, citytown(?x1924, ?x1860), featured_film_locations(?x195, ?x1860) *> conf = 0.27 ranks of expected_values: 36 EVAL 0b_75k locations 099ty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 71.000 71.000 0.571 http://example.org/time/event/locations #9490-0zjpz PRED entity: 0zjpz PRED relation: artist! PRED expected values: 0mzkr => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 119): 015_1q (0.28 #1280, 0.26 #1140, 0.23 #2540), 01f_3w (0.25 #34, 0.19 #1154, 0.17 #1294), 0g768 (0.25 #37, 0.17 #2837, 0.17 #2557), 03rhqg (0.21 #1696, 0.19 #2536, 0.18 #1836), 03qx_f (0.21 #1753, 0.12 #493, 0.10 #633), 0n85g (0.20 #342, 0.18 #1882, 0.17 #1322), 01clyr (0.20 #313, 0.17 #2553, 0.16 #2693), 011k1h (0.19 #1130, 0.17 #1270, 0.12 #4070), 01cf93 (0.18 #477, 0.14 #617, 0.11 #1037), 033hn8 (0.17 #1274, 0.15 #6876, 0.15 #1134) >> Best rule #1280 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 03f2_rc; >> query: (?x1970, 015_1q) <- artists(?x1000, ?x1970), participant(?x1970, ?x3034), spouse(?x9276, ?x1970), artist(?x4868, ?x1970) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #4646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 03rl84; 03xnq9_; *> query: (?x1970, 0mzkr) <- artists(?x1000, ?x1970), participant(?x1970, ?x3034), profession(?x1970, ?x2348), ?x2348 = 0nbcg *> conf = 0.12 ranks of expected_values: 18 EVAL 0zjpz artist! 0mzkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 136.000 136.000 0.276 http://example.org/music/record_label/artist #9489-05yh_t PRED entity: 05yh_t PRED relation: type_of_union PRED expected values: 04ztj => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.80 #9, 0.75 #37, 0.75 #25), 01g63y (0.45 #237, 0.26 #2, 0.15 #30) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 301 >> proper extension: 09byk; 01q7cb_; 02v406; 0dfjb8; 01h8f; 013zyw; 0f2c8g; 01d5vk; 01fxck; 0154d7; ... >> query: (?x5729, 04ztj) <- film(?x5729, ?x638), profession(?x5729, ?x524), ?x524 = 02jknp >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05yh_t type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.802 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9488-02mw6c PRED entity: 02mw6c PRED relation: organizations_founded! PRED expected values: 0cw10 => 155 concepts (70 used for prediction) PRED predicted values (max 10 best out of 18): 03cdg (0.25 #550, 0.03 #1683, 0.03 #1798), 07cbs (0.05 #1290, 0.05 #1405, 0.03 #1633), 034rd (0.05 #1408, 0.03 #2097, 0.03 #2326), 04411 (0.03 #2968, 0.03 #1826, 0.03 #1710), 03kdl (0.03 #2540, 0.02 #3451, 0.01 #3565), 0jcx (0.02 #3218, 0.02 #3446, 0.01 #3674), 06y3r (0.01 #6920, 0.01 #7268, 0.01 #5198), 01hdht (0.01 #5226), 044kwr (0.01 #5223), 05hjmd (0.01 #5220) >> Best rule #550 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 0g5lhl7; >> query: (?x11350, 03cdg) <- company(?x900, ?x11350), ?x900 = 0fkvn >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02mw6c organizations_founded! 0cw10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 70.000 0.250 http://example.org/organization/organization_founder/organizations_founded #9487-02qkwl PRED entity: 02qkwl PRED relation: language PRED expected values: 02h40lc => 118 concepts (115 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.92 #1252, 0.91 #3109, 0.91 #2513), 04306rv (0.20 #182, 0.17 #301, 0.14 #478), 064_8sq (0.18 #672, 0.15 #1272, 0.15 #972), 03_9r (0.17 #306, 0.10 #247, 0.10 #187), 06nm1 (0.14 #70, 0.14 #902, 0.14 #661), 06b_j (0.14 #82, 0.12 #319, 0.10 #200), 02hxc3j (0.14 #66, 0.03 #421, 0.01 #1077), 0k0sv (0.14 #24), 02bjrlw (0.12 #119, 0.11 #829, 0.09 #651), 0jzc (0.12 #316, 0.10 #257, 0.10 #197) >> Best rule #1252 for best value: >> intensional similarity = 5 >> extensional distance = 231 >> proper extension: 02_qt; 03nqnnk; 02q8ms8; >> query: (?x8001, 02h40lc) <- featured_film_locations(?x8001, ?x1036), film_release_region(?x8001, ?x94), film(?x3999, ?x8001), country(?x8001, ?x1264), participant(?x6835, ?x3999) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qkwl language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 115.000 0.918 http://example.org/film/film/language #9486-0b90_r PRED entity: 0b90_r PRED relation: nationality! PRED expected values: 06x58 083qy7 016yr0 => 209 concepts (109 used for prediction) PRED predicted values (max 10 best out of 4067): 041mt (0.24 #198918, 0.22 #150204, 0.20 #223276), 07y_r (0.20 #23878, 0.15 #52293, 0.13 #60413), 03y3dk (0.20 #22963, 0.15 #51378, 0.13 #59498), 01qq_lp (0.20 #21445, 0.15 #49860, 0.13 #57980), 059xvg (0.17 #70063, 0.17 #1050, 0.16 #90361), 0p__8 (0.17 #70858, 0.17 #1845, 0.16 #91156), 01pcq3 (0.17 #197, 0.14 #52970, 0.14 #8318), 0841zn (0.17 #2459, 0.14 #55232, 0.14 #10580), 055t01 (0.17 #3753, 0.14 #56526, 0.14 #11874), 01rzqj (0.17 #956, 0.14 #53729, 0.14 #9077) >> Best rule #198918 for best value: >> intensional similarity = 2 >> extensional distance = 36 >> proper extension: 01nhhz; >> query: (?x151, ?x2208) <- location(?x2208, ?x151), nationality(?x2167, ?x151) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #64953 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 035v3; *> query: (?x151, ?x192) <- form_of_government(?x151, ?x6377), featured_film_locations(?x1820, ?x151), award_winner(?x1820, ?x192) *> conf = 0.15 ranks of expected_values: 197, 3083, 3920 EVAL 0b90_r nationality! 016yr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 109.000 0.239 http://example.org/people/person/nationality EVAL 0b90_r nationality! 083qy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 209.000 109.000 0.239 http://example.org/people/person/nationality EVAL 0b90_r nationality! 06x58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 209.000 109.000 0.239 http://example.org/people/person/nationality #9485-04tgp PRED entity: 04tgp PRED relation: taxonomy PRED expected values: 04n6k => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.91 #18, 0.90 #13, 0.90 #11) >> Best rule #18 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 0vmt; 03s0w; 059_c; 0gyh; 04s7y; 015jr; 059t8; 0j95; 059s8; 0847q; >> query: (?x4622, 04n6k) <- district_represented(?x176, ?x4622), state_province_region(?x2821, ?x4622), adjoins(?x2831, ?x4622) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04tgp taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.911 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #9484-09r8l PRED entity: 09r8l PRED relation: artist! PRED expected values: 033hn8 => 99 concepts (76 used for prediction) PRED predicted values (max 10 best out of 110): 015_1q (0.28 #439, 0.27 #5208, 0.22 #4366), 03rhqg (0.22 #5204, 0.21 #715, 0.19 #435), 017l96 (0.18 #18, 0.15 #718, 0.13 #438), 011k1h (0.16 #5199, 0.15 #850, 0.13 #10), 01cszh (0.15 #571, 0.06 #5200, 0.06 #851), 01w40h (0.14 #728, 0.12 #448, 0.11 #1008), 043g7l (0.14 #591, 0.10 #5220, 0.08 #4378), 01clyr (0.13 #33, 0.11 #173, 0.09 #873), 0fb0v (0.13 #7, 0.08 #847, 0.07 #1127), 0g768 (0.12 #597, 0.12 #7187, 0.12 #5646) >> Best rule #439 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 07mvp; >> query: (?x3957, 015_1q) <- award_winner(?x3957, ?x1270), artists(?x2664, ?x3957), ?x2664 = 01lyv >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #433 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 67 *> proper extension: 07mvp; *> query: (?x3957, 033hn8) <- award_winner(?x3957, ?x1270), artists(?x2664, ?x3957), ?x2664 = 01lyv *> conf = 0.12 ranks of expected_values: 12 EVAL 09r8l artist! 033hn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 99.000 76.000 0.275 http://example.org/music/record_label/artist #9483-01cdt5 PRED entity: 01cdt5 PRED relation: symptom_of PRED expected values: 04p3w 011zdm 0dcp_ => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 83): 0h1n9 (0.82 #629, 0.60 #465, 0.55 #587), 0dcp_ (0.67 #254, 0.29 #485, 0.23 #39), 0d19y2 (0.64 #636, 0.55 #554, 0.50 #472), 09jg8 (0.64 #545, 0.55 #627, 0.50 #187), 074m2 (0.56 #374, 0.50 #97, 0.40 #209), 0167bx (0.55 #634, 0.55 #592, 0.50 #470), 01n3bm (0.55 #588, 0.50 #105, 0.45 #630), 01gkcc (0.54 #210, 0.50 #184, 0.40 #209), 04psf (0.54 #210, 0.40 #209, 0.40 #489), 072hv (0.54 #210, 0.40 #209, 0.38 #226) >> Best rule #629 for best value: >> intensional similarity = 15 >> extensional distance = 9 >> proper extension: 02y0js; >> query: (?x13487, 0h1n9) <- symptom_of(?x13487, ?x10199), symptom_of(?x13487, ?x4906), symptom_of(?x13487, ?x3680), symptom_of(?x4905, ?x10199), risk_factors(?x10199, ?x8523), risk_factors(?x10199, ?x8023), ?x4905 = 01j6t0, ?x8023 = 0jpmt, risk_factors(?x6483, ?x4906), symptom_of(?x13373, ?x3680), symptom_of(?x3679, ?x3680), risk_factors(?x7260, ?x8523), ?x3679 = 02tfl8, ?x13373 = 0f3kl, ?x7260 = 01_qc_ >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #254 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 4 *> proper extension: 01l2m3; 0dq9p; *> query: (?x13487, 0dcp_) <- symptom_of(?x13487, ?x14024), symptom_of(?x13487, ?x10199), symptom_of(?x13487, ?x6710), symptom_of(?x13487, ?x4959), symptom_of(?x13487, ?x4906), symptom_of(?x13099, ?x10199), symptom_of(?x4905, ?x10199), people(?x13099, ?x8858), symptom_of(?x9118, ?x14024), risk_factors(?x6483, ?x4906), symptom_of(?x9118, ?x11739), symptom_of(?x9118, ?x10480), symptom_of(?x4905, ?x9510), symptom_of(?x13373, ?x4959), ?x13373 = 0f3kl, symptom_of(?x10069, ?x6710), ?x11739 = 0167bx, ?x10480 = 0h1n9, people(?x10069, ?x6261), ?x6261 = 015gy7, ?x9510 = 0hgxh *> conf = 0.67 ranks of expected_values: 2, 18, 31 EVAL 01cdt5 symptom_of 0dcp_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 18.000 18.000 0.818 http://example.org/medicine/symptom/symptom_of EVAL 01cdt5 symptom_of 011zdm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 18.000 18.000 0.818 http://example.org/medicine/symptom/symptom_of EVAL 01cdt5 symptom_of 04p3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 18.000 18.000 0.818 http://example.org/medicine/symptom/symptom_of #9482-025ttz4 PRED entity: 025ttz4 PRED relation: institution! PRED expected values: 02_xgp2 => 197 concepts (96 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.80 #422, 0.78 #398, 0.77 #246), 019v9k (0.72 #429, 0.72 #405, 0.68 #253), 02_xgp2 (0.69 #110, 0.60 #257, 0.58 #357), 014mlp (0.65 #249, 0.63 #1954, 0.62 #1128), 016t_3 (0.56 #423, 0.54 #399, 0.53 #472), 027f2w (0.56 #107, 0.31 #254, 0.29 #354), 0bkj86 (0.51 #252, 0.50 #352, 0.48 #453), 07s6fsf (0.48 #244, 0.40 #662, 0.40 #344), 04zx3q1 (0.36 #245, 0.34 #345, 0.33 #446), 013zdg (0.32 #251, 0.25 #597, 0.23 #452) >> Best rule #422 for best value: >> intensional similarity = 5 >> extensional distance = 108 >> proper extension: 027mdh; >> query: (?x9018, 02h4rq6) <- institution(?x4981, ?x9018), category(?x9018, ?x134), currency(?x9018, ?x5696), ?x4981 = 03bwzr4, major_field_of_study(?x9018, ?x1668) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #110 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 038czx; *> query: (?x9018, 02_xgp2) <- currency(?x9018, ?x5696), major_field_of_study(?x9018, ?x9111), contains(?x456, ?x9018), ?x9111 = 04sh3 *> conf = 0.69 ranks of expected_values: 3 EVAL 025ttz4 institution! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 197.000 96.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9481-01gb54 PRED entity: 01gb54 PRED relation: film PRED expected values: 0g56t9t 0c3zjn7 07s3m4g => 129 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1593): 01pv91 (0.79 #30713, 0.77 #30712, 0.74 #1537), 08fn5b (0.79 #30713, 0.77 #30712, 0.74 #1537), 047csmy (0.79 #30713, 0.77 #30712, 0.71 #32251), 06ztvyx (0.79 #30713, 0.77 #30712, 0.71 #32251), 0cmf0m0 (0.79 #30713, 0.77 #30712, 0.71 #32251), 06_x996 (0.74 #1537, 0.74 #1536, 0.71 #24570), 02q7fl9 (0.74 #1537, 0.74 #1536, 0.71 #24570), 0cc5qkt (0.74 #1537, 0.74 #1536, 0.71 #24570), 06gjk9 (0.74 #1537, 0.74 #1536, 0.71 #24570), 01flv_ (0.74 #1537, 0.74 #1536, 0.71 #24570) >> Best rule #30713 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 03czrpj; 054g1r; 0kc9f; >> query: (?x4564, ?x2920) <- nominated_for(?x4564, ?x2920), film_release_region(?x2920, ?x94), film(?x4564, ?x253) >> conf = 0.79 => this is the best rule for 5 predicted values *> Best rule #1537 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 06q07; *> query: (?x4564, ?x4167) <- production_companies(?x4167, ?x4564), artist(?x4564, ?x6471), film(?x3051, ?x4167) *> conf = 0.74 ranks of expected_values: 11, 20 EVAL 01gb54 film 07s3m4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 129.000 105.000 0.792 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 01gb54 film 0c3zjn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 129.000 105.000 0.792 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 01gb54 film 0g56t9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 105.000 0.792 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #9480-01wb8bs PRED entity: 01wb8bs PRED relation: profession PRED expected values: 02hrh1q => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 60): 02hrh1q (0.88 #3615, 0.88 #465, 0.88 #5715), 01d_h8 (0.38 #756, 0.38 #306, 0.34 #456), 03gjzk (0.37 #1366, 0.36 #1816, 0.33 #616), 0dxtg (0.35 #1364, 0.32 #1814, 0.31 #614), 0np9r (0.28 #8551, 0.20 #172, 0.14 #11573), 0d1pc (0.28 #8551, 0.12 #52, 0.10 #952), 02jknp (0.25 #758, 0.23 #308, 0.22 #5858), 09jwl (0.24 #1520, 0.19 #4820, 0.18 #4670), 0cbd2 (0.17 #1207, 0.16 #1657, 0.16 #1057), 02krf9 (0.16 #1378, 0.15 #628, 0.15 #1828) >> Best rule #3615 for best value: >> intensional similarity = 3 >> extensional distance = 873 >> proper extension: 02t_99; >> query: (?x3955, 02hrh1q) <- award_nominee(?x3955, ?x516), location(?x3955, ?x503), film(?x3955, ?x1877) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wb8bs profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.879 http://example.org/people/person/profession #9479-06j0md PRED entity: 06j0md PRED relation: award_winner! PRED expected values: 0bx6zs => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 103): 026kq4q (0.22 #187, 0.10 #11284, 0.04 #892), 0gx_st (0.20 #2398, 0.17 #9872, 0.12 #1024), 09qvms (0.20 #2398, 0.17 #9872, 0.05 #5090), 092c5f (0.20 #2398, 0.17 #9872, 0.04 #3963), 092t4b (0.20 #2398, 0.17 #9872, 0.04 #5129), 09p30_ (0.17 #9872, 0.12 #85, 0.03 #649), 0bx6zs (0.17 #9872, 0.10 #11284, 0.08 #409), 07z31v (0.17 #9872, 0.09 #1018, 0.07 #736), 0bxs_d (0.17 #9872, 0.08 #397, 0.07 #1384), 07y_p6 (0.17 #9872, 0.08 #380, 0.06 #1367) >> Best rule #187 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02pp_q_; >> query: (?x201, 026kq4q) <- producer_type(?x201, ?x632), award_nominee(?x5677, ?x201), award_nominee(?x2285, ?x201), ?x2285 = 0721cy, nationality(?x5677, ?x94) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #9872 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1699 *> proper extension: 0kc9f; *> query: (?x201, ?x5459) <- nominated_for(?x201, ?x4535), honored_for(?x5459, ?x4535) *> conf = 0.17 ranks of expected_values: 7 EVAL 06j0md award_winner! 0bx6zs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 109.000 109.000 0.222 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9478-0gjc4d3 PRED entity: 0gjc4d3 PRED relation: film_format PRED expected values: 017fx5 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 3): 0cj16 (0.33 #3, 0.18 #71, 0.16 #157), 07fb8_ (0.21 #213, 0.17 #207, 0.17 #201), 017fx5 (0.14 #42, 0.11 #24, 0.10 #30) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0fpmrm3; >> query: (?x3276, 0cj16) <- film(?x3842, ?x3276), film_release_region(?x3276, ?x2645), ?x2645 = 03h64, ?x3842 = 0cjsxp, film_crew_role(?x3276, ?x468) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 42 *> proper extension: 01f39b; *> query: (?x3276, 017fx5) <- story_by(?x3276, ?x7106), genre(?x3276, ?x1510), ?x1510 = 01hmnh, language(?x3276, ?x254), produced_by(?x3276, ?x2533) *> conf = 0.14 ranks of expected_values: 3 EVAL 0gjc4d3 film_format 017fx5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.333 http://example.org/film/film/film_format #9477-04pk1f PRED entity: 04pk1f PRED relation: film_release_region PRED expected values: 05qhw 0k6nt => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 95): 05qhw (0.85 #1816, 0.84 #2651, 0.82 #2929), 0k6nt (0.80 #2658, 0.79 #2936, 0.79 #1823), 05b4w (0.79 #1856, 0.77 #2691, 0.75 #2969), 04gzd (0.62 #1812, 0.58 #2647, 0.55 #2925), 0ctw_b (0.60 #1824, 0.59 #2659, 0.59 #2937), 015qh (0.58 #1836, 0.55 #2671, 0.53 #2949), 01p1v (0.58 #1845, 0.53 #2680, 0.52 #2958), 03rk0 (0.56 #1849, 0.51 #2684, 0.48 #2962), 06t8v (0.51 #1868, 0.49 #2703, 0.47 #2981), 01ls2 (0.50 #1814, 0.48 #2649, 0.47 #2927) >> Best rule #1816 for best value: >> intensional similarity = 6 >> extensional distance = 147 >> proper extension: 0g56t9t; 0gtsx8c; 02vxq9m; 0c3ybss; 0gx1bnj; 0ds3t5x; 0dckvs; 0g5qs2k; 0dscrwf; 0djb3vw; ... >> query: (?x6078, 05qhw) <- film_release_region(?x6078, ?x2316), film_release_region(?x6078, ?x304), film_release_region(?x6078, ?x279), ?x2316 = 06t2t, ?x304 = 0d0vqn, ?x279 = 0d060g >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04pk1f film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04pk1f film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9476-0ggbhy7 PRED entity: 0ggbhy7 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 23): 09vw2b7 (0.61 #549, 0.59 #1506, 0.58 #1232), 0dxtw (0.44 #43, 0.35 #1236, 0.34 #1510), 01pvkk (0.33 #78, 0.29 #1100, 0.29 #1168), 0215hd (0.33 #51, 0.18 #2626, 0.13 #1107), 0d2b38 (0.22 #58, 0.18 #2626, 0.12 #160), 015h31 (0.22 #41, 0.18 #2626, 0.10 #143), 02_n3z (0.22 #35, 0.18 #2626, 0.08 #171), 033smt (0.22 #60, 0.18 #2626, 0.07 #162), 02ynfr (0.18 #2626, 0.15 #1515, 0.15 #558), 02rh1dz (0.18 #2626, 0.11 #42, 0.11 #144) >> Best rule #549 for best value: >> intensional similarity = 4 >> extensional distance = 576 >> proper extension: 047gn4y; 04kkz8; 08hmch; 02v63m; 0c00zd0; 0m491; 01j8wk; 0gydcp7; 065zlr; 07x4qr; ... >> query: (?x3012, 09vw2b7) <- film(?x1414, ?x3012), nominated_for(?x3568, ?x3012), film_crew_role(?x3012, ?x137), production_companies(?x3012, ?x3331) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ggbhy7 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.609 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9475-01w02sy PRED entity: 01w02sy PRED relation: artists! PRED expected values: 05r6t 016jny => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 234): 06by7 (0.63 #22430, 0.55 #956, 0.51 #7178), 064t9 (0.62 #1258, 0.57 #6858, 0.55 #3748), 0glt670 (0.43 #1287, 0.32 #6887, 0.28 #3777), 025sc50 (0.35 #1297, 0.31 #6897, 0.30 #675), 05bt6j (0.31 #1912, 0.31 #3780, 0.26 #1290), 02lnbg (0.30 #683, 0.30 #6905, 0.29 #1305), 0ggx5q (0.30 #703, 0.26 #1325, 0.26 #3815), 017_qw (0.30 #8465, 0.12 #19047, 0.12 #25275), 06j6l (0.29 #1295, 0.29 #6895, 0.28 #3474), 0xhtw (0.28 #951, 0.27 #22425, 0.24 #7173) >> Best rule #22430 for best value: >> intensional similarity = 3 >> extensional distance = 620 >> proper extension: 02t3ln; 02mq_y; 06br6t; >> query: (?x3118, 06by7) <- artists(?x8187, ?x3118), artists(?x8187, ?x3740), ?x3740 = 0fpj4lx >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #730 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 01vvycq; 02l840; 01q7cb_; 0lk90; 09qr6; 06w2sn5; 0j1yf; 0285c; 01vsnff; 07ss8_; ... *> query: (?x3118, 016jny) <- profession(?x3118, ?x131), celebrity(?x3118, ?x7375), artist(?x1693, ?x3118) *> conf = 0.13 ranks of expected_values: 23, 25 EVAL 01w02sy artists! 016jny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 131.000 131.000 0.625 http://example.org/music/genre/artists EVAL 01w02sy artists! 05r6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 131.000 131.000 0.625 http://example.org/music/genre/artists #9474-05sb1 PRED entity: 05sb1 PRED relation: taxonomy PRED expected values: 04n6k => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.84 #11, 0.80 #28, 0.79 #22) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 06npd; 06mzp; 03gj2; 05cgv; 0h7x; 01znc_; 01mjq; 077qn; >> query: (?x2236, 04n6k) <- countries_within(?x6956, ?x2236), adjoins(?x2236, ?x2146), nationality(?x822, ?x2236) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05sb1 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.837 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #9473-01tsbmv PRED entity: 01tsbmv PRED relation: category PRED expected values: 08mbj5d => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.37 #23, 0.33 #62, 0.32 #53) >> Best rule #23 for best value: >> intensional similarity = 2 >> extensional distance = 858 >> proper extension: 07h1q; >> query: (?x11684, 08mbj5d) <- people(?x6736, ?x11684), geographic_distribution(?x6736, ?x512) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tsbmv category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.374 http://example.org/common/topic/webpage./common/webpage/category #9472-0d6lp PRED entity: 0d6lp PRED relation: featured_film_locations! PRED expected values: 0cbn7c => 245 concepts (224 used for prediction) PRED predicted values (max 10 best out of 843): 0m5s5 (0.50 #660, 0.08 #13660, 0.07 #17993), 06gb1w (0.25 #310, 0.13 #3920, 0.12 #6087), 0192hw (0.25 #227, 0.08 #14672, 0.08 #13950), 032zq6 (0.25 #290, 0.08 #14013, 0.08 #13290), 0bv8h2 (0.25 #253, 0.08 #13253, 0.07 #32502), 0k2sk (0.25 #72, 0.07 #17405, 0.07 #32502), 0c0nhgv (0.25 #75, 0.07 #17408, 0.05 #8019), 0x25q (0.25 #216, 0.07 #17549, 0.04 #33440), 051zy_b (0.25 #969, 0.07 #32502, 0.07 #3857), 08phg9 (0.25 #377, 0.07 #32502, 0.05 #85216) >> Best rule #660 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 035p3; >> query: (?x3125, 0m5s5) <- featured_film_locations(?x836, ?x3125), adjoins(?x3125, ?x3677), ?x836 = 02sg5v >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d6lp featured_film_locations! 0cbn7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 245.000 224.000 0.500 http://example.org/film/film/featured_film_locations #9471-02l48d PRED entity: 02l48d PRED relation: company! PRED expected values: 0dq3c => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 42): 060c4 (0.84 #1805, 0.69 #5822, 0.68 #6274), 0krdk (0.82 #1809, 0.80 #2170, 0.77 #2576), 0dq3c (0.62 #47, 0.60 #948, 0.60 #273), 01yc02 (0.53 #595, 0.53 #505, 0.52 #1000), 05_wyz (0.53 #829, 0.50 #964, 0.50 #739), 09d6p2 (0.45 #920, 0.40 #605, 0.38 #785), 0142rn (0.33 #206, 0.28 #5549, 0.28 #5548), 02211by (0.33 #184, 0.28 #5549, 0.28 #5548), 09lq2c (0.28 #5549, 0.28 #5548, 0.25 #75), 06hpx2 (0.28 #5549, 0.28 #5548, 0.21 #7404) >> Best rule #1805 for best value: >> intensional similarity = 5 >> extensional distance = 36 >> proper extension: 03mdt; 0gy1_; >> query: (?x12013, 060c4) <- company(?x7176, ?x12013), state_province_region(?x12013, ?x335), company(?x7176, ?x11946), contact_category(?x12013, ?x897), ?x11946 = 0135cw >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 01dfb6; 05njw; 0dq23; *> query: (?x12013, 0dq3c) <- company(?x4682, ?x12013), place_founded(?x12013, ?x4271), place_of_birth(?x4169, ?x4271), ?x4682 = 0dq_5, administrative_parent(?x4271, ?x2346) *> conf = 0.62 ranks of expected_values: 3 EVAL 02l48d company! 0dq3c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 203.000 203.000 0.842 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #9470-05r5c PRED entity: 05r5c PRED relation: role PRED expected values: 0myk8 0192l => 94 concepts (84 used for prediction) PRED predicted values (max 10 best out of 65): 042v_gx (0.81 #1499, 0.81 #2847, 0.81 #2059), 07gql (0.81 #1499, 0.81 #2847, 0.81 #2059), 02fsn (0.81 #1499, 0.81 #2847, 0.81 #2059), 03q5t (0.81 #1499, 0.81 #2847, 0.81 #2059), 0dwt5 (0.81 #1499, 0.81 #2847, 0.81 #2059), 07_l6 (0.81 #1499, 0.81 #2847, 0.81 #2059), 0g33q (0.81 #1499, 0.81 #2847, 0.81 #2059), 018j2 (0.81 #1499, 0.81 #2847, 0.81 #2059), 03qlv7 (0.81 #1499, 0.81 #2847, 0.81 #2059), 03t22m (0.81 #1499, 0.81 #2847, 0.81 #2059) >> Best rule #1499 for best value: >> intensional similarity = 6 >> extensional distance = 23 >> proper extension: 028tv0; >> query: (?x316, ?x228) <- role(?x74, ?x316), role(?x5691, ?x316), performance_role(?x2836, ?x316), role(?x228, ?x316), profession(?x5691, ?x220), group(?x5691, ?x9228) >> conf = 0.81 => this is the best rule for 16 predicted values *> Best rule #612 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 03f5mt; *> query: (?x316, ?x1750) <- instrumentalists(?x316, ?x10625), instrumentalists(?x316, ?x2698), instrumentalists(?x316, ?x654), ?x2698 = 09hnb, role(?x74, ?x316), profession(?x654, ?x131), instrumentalists(?x1750, ?x10625) *> conf = 0.54 ranks of expected_values: 45, 57 EVAL 05r5c role 0192l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 94.000 84.000 0.812 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 05r5c role 0myk8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 94.000 84.000 0.812 http://example.org/music/performance_role/regular_performances./music/group_membership/role #9469-02wbnv PRED entity: 02wbnv PRED relation: category PRED expected values: 08mbj5d => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.87 #141, 0.86 #125, 0.86 #139) >> Best rule #141 for best value: >> intensional similarity = 5 >> extensional distance = 413 >> proper extension: 01fy2s; >> query: (?x14310, 08mbj5d) <- citytown(?x14310, ?x6960), organization(?x4682, ?x14310), contains(?x6960, ?x1659), citytown(?x10368, ?x6960), contains(?x1227, ?x10368) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wbnv category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.870 http://example.org/common/topic/webpage./common/webpage/category #9468-04vrxh PRED entity: 04vrxh PRED relation: award_winner! PRED expected values: 02cg41 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 99): 02rjjll (0.17 #8320, 0.15 #287, 0.14 #569), 02cg41 (0.17 #8320, 0.14 #972, 0.12 #690), 0gpjbt (0.17 #8320, 0.13 #875, 0.10 #10718), 02q690_ (0.17 #8320, 0.04 #488, 0.03 #2885), 05c1t6z (0.17 #8320, 0.03 #2835, 0.03 #438), 0gvstc3 (0.17 #8320, 0.03 #6520, 0.03 #457), 019bk0 (0.16 #862, 0.10 #580, 0.09 #721), 01c6qp (0.14 #583, 0.12 #301, 0.12 #1993), 013b2h (0.14 #1913, 0.13 #2054, 0.13 #926), 05pd94v (0.12 #1976, 0.12 #1835, 0.12 #284) >> Best rule #8320 for best value: >> intensional similarity = 2 >> extensional distance = 1364 >> proper extension: 0f721s; 01p5yn; 05s34b; >> query: (?x9882, ?x486) <- award_winner(?x9882, ?x1896), award_winner(?x486, ?x1896) >> conf = 0.17 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 04vrxh award_winner! 02cg41 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.173 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9467-01snm PRED entity: 01snm PRED relation: location! PRED expected values: 0m66w => 149 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2083): 029ql (0.76 #153208, 0.56 #37673, 0.50 #90418), 01wwvd2 (0.54 #55255, 0.47 #271240, 0.47 #12557), 0ckcvk (0.47 #271240, 0.47 #12557, 0.47 #278775), 016376 (0.29 #138136, 0.28 #143161, 0.28 #87905), 023kzp (0.09 #13769, 0.09 #16281, 0.08 #6234), 0hnp7 (0.09 #37674, 0.04 #90419, 0.03 #6258), 022q4j (0.09 #37674, 0.04 #90419, 0.03 #7018), 0187y5 (0.09 #37674, 0.04 #90419, 0.03 #5127), 01ty7ll (0.09 #37674, 0.04 #90419, 0.02 #7616), 06c0j (0.09 #37674, 0.04 #90419) >> Best rule #153208 for best value: >> intensional similarity = 3 >> extensional distance = 211 >> proper extension: 0kdqw; 01p726; 0tnkg; >> query: (?x6555, ?x4451) <- place_of_birth(?x4451, ?x6555), citytown(?x5754, ?x6555), location(?x4451, ?x4356) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #18782 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 0sg6b; 0xynl; 0p9z5; 015cj9; 03kjh; 0235n9; *> query: (?x6555, 0m66w) <- place_of_birth(?x8900, ?x6555), gender(?x8900, ?x231), place_of_burial(?x8900, ?x11261) *> conf = 0.06 ranks of expected_values: 46 EVAL 01snm location! 0m66w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 149.000 122.000 0.756 http://example.org/people/person/places_lived./people/place_lived/location #9466-0d1tm PRED entity: 0d1tm PRED relation: country PRED expected values: 0163v => 37 concepts (36 used for prediction) PRED predicted values (max 10 best out of 328): 07t21 (0.87 #3092, 0.86 #3670, 0.86 #2891), 06mkj (0.84 #5964, 0.84 #190, 0.83 #1515), 035qy (0.84 #190, 0.83 #1515, 0.82 #5191), 0b90_r (0.84 #190, 0.83 #1515, 0.82 #3834), 03h64 (0.84 #190, 0.83 #1515, 0.75 #1384), 03spz (0.84 #190, 0.83 #1515, 0.75 #2093), 01znc_ (0.84 #190, 0.83 #1515, 0.75 #2093), 05r4w (0.84 #190, 0.83 #1515, 0.75 #2093), 0k6nt (0.84 #190, 0.83 #1515, 0.73 #1514), 03rk0 (0.84 #190, 0.83 #1515, 0.73 #1514) >> Best rule #3092 for best value: >> intensional similarity = 43 >> extensional distance = 13 >> proper extension: 03hr1p; >> query: (?x171, 07t21) <- sports(?x2131, ?x171), country(?x171, ?x789), country(?x171, ?x304), ?x304 = 0d0vqn, country(?x11839, ?x789), country(?x7470, ?x789), country(?x4786, ?x789), country(?x4127, ?x789), country(?x1498, ?x789), film_release_region(?x9839, ?x789), film_release_region(?x8477, ?x789), film_release_region(?x5825, ?x789), film_release_region(?x3958, ?x789), film_release_region(?x3742, ?x789), film_release_region(?x2471, ?x789), film_release_region(?x2151, ?x789), film_release_region(?x504, ?x789), country(?x4503, ?x789), ?x504 = 0g5qs2k, nationality(?x317, ?x789), service_location(?x555, ?x789), country(?x4627, ?x789), adjoins(?x789, ?x774), combatants(?x1140, ?x789), ?x7470 = 02yxbc, ?x2471 = 08052t3, ?x4127 = 049mql, contains(?x789, ?x790), ?x8477 = 078mm1, ?x5825 = 067ghz, ?x2131 = 0lk8j, ?x3958 = 0gyh2wm, ?x3742 = 02w86hz, ?x9839 = 0gy7bj4, combatants(?x789, ?x151), ?x4503 = 06z68, film(?x166, ?x2151), nominated_for(?x1498, ?x4007), production_companies(?x4786, ?x1478), location(?x981, ?x789), adjustment_currency(?x789, ?x170), film(?x382, ?x11839), film_crew_role(?x2151, ?x137) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #190 for first EXPECTED value: *> intensional similarity = 55 *> extensional distance = 1 *> proper extension: 06z6r; *> query: (?x171, ?x291) <- sports(?x7441, ?x171), sports(?x4255, ?x171), sports(?x3729, ?x171), sports(?x2966, ?x171), sports(?x2369, ?x171), sports(?x2233, ?x171), sports(?x1081, ?x171), sports(?x775, ?x171), sports(?x391, ?x171), country(?x171, ?x8449), country(?x171, ?x1603), country(?x171, ?x1355), country(?x171, ?x1229), country(?x171, ?x789), country(?x171, ?x583), country(?x171, ?x456), country(?x171, ?x429), country(?x171, ?x421), country(?x171, ?x390), country(?x171, ?x304), country(?x171, ?x252), country(?x171, ?x205), country(?x171, ?x172), ?x304 = 0d0vqn, ?x789 = 0f8l9c, ?x2966 = 06sks6, ?x8449 = 02k1b, ?x3729 = 0jdk_, ?x1603 = 06bnz, ?x775 = 0l998, ?x2233 = 0l6mp, ?x390 = 0chghy, ?x4255 = 0lgxj, ?x1081 = 0l6m5, ?x205 = 03rjj, ?x172 = 0154j, ?x1229 = 059j2, sports(?x7441, ?x2867), ?x421 = 03_r3, ?x429 = 03rt9, ?x252 = 03_3d, sports(?x7441, ?x4876), sports(?x7441, ?x779), ?x2867 = 02y8z, ?x779 = 096f8, olympics(?x1353, ?x391), olympics(?x291, ?x391), ?x1353 = 035qy, ?x4876 = 0d1t3, sports(?x391, ?x1352), ?x2369 = 0lbbj, ?x583 = 015fr, ?x1355 = 0h7x, ?x456 = 05qhw, medal(?x391, ?x422) *> conf = 0.84 ranks of expected_values: 13 EVAL 0d1tm country 0163v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 37.000 36.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #9465-01qdjm PRED entity: 01qdjm PRED relation: gender PRED expected values: 05zppz => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #91, 0.83 #51, 0.83 #41), 02zsn (0.32 #24, 0.31 #12, 0.31 #58) >> Best rule #91 for best value: >> intensional similarity = 3 >> extensional distance = 289 >> proper extension: 03c7ln; 0fp_v1x; 06y9c2; 07_3qd; 0ftps; 01p45_v; 012zng; 0zjpz; 02jg92; 01vv126; ... >> query: (?x2747, 05zppz) <- role(?x2747, ?x2309), role(?x75, ?x2309), group(?x2309, ?x1751) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qdjm gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.838 http://example.org/people/person/gender #9464-0778p PRED entity: 0778p PRED relation: major_field_of_study PRED expected values: 062z7 => 215 concepts (215 used for prediction) PRED predicted values (max 10 best out of 118): 02j62 (0.44 #532, 0.39 #13303, 0.38 #2158), 0g26h (0.44 #545, 0.31 #1420, 0.30 #2171), 04rjg (0.41 #1146, 0.34 #2650, 0.33 #1021), 01mkq (0.41 #2645, 0.36 #2142, 0.35 #2519), 02lp1 (0.39 #2515, 0.38 #2641, 0.38 #1137), 05qjt (0.36 #883, 0.30 #1008, 0.30 #2637), 05qfh (0.33 #538, 0.21 #2667, 0.20 #2541), 03g3w (0.30 #2657, 0.29 #2531, 0.28 #2154), 062z7 (0.29 #1154, 0.28 #2658, 0.27 #2029), 01lj9 (0.24 #1167, 0.21 #1292, 0.21 #1042) >> Best rule #532 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 027xx3; >> query: (?x3543, 02j62) <- school_type(?x3543, ?x11041), institution(?x865, ?x3543), contains(?x94, ?x3543), ?x11041 = 04399 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1154 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 0b1xl; 01nnsv; 0gl5_; 0g2jl; *> query: (?x3543, 062z7) <- citytown(?x3543, ?x5267), school_type(?x3543, ?x1044), ?x1044 = 05pcjw, colors(?x3543, ?x663) *> conf = 0.29 ranks of expected_values: 9 EVAL 0778p major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 215.000 215.000 0.444 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #9463-04cw0n4 PRED entity: 04cw0n4 PRED relation: profession PRED expected values: 0dgd_ => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 128): 0dgd_ (0.89 #482, 0.88 #782, 0.88 #1082), 02hrh1q (0.62 #1365, 0.61 #2265, 0.60 #2865), 01d_h8 (0.30 #1206, 0.29 #1656, 0.28 #1806), 02jknp (0.29 #458, 0.28 #158, 0.26 #308), 0dxtg (0.27 #1214, 0.27 #1514, 0.27 #1664), 03gjzk (0.20 #1216, 0.19 #1516, 0.19 #1366), 02krf9 (0.16 #328, 0.14 #178, 0.11 #478), 09jwl (0.16 #1520, 0.16 #3170, 0.16 #3320), 0cbd2 (0.12 #2107, 0.12 #2557, 0.12 #2707), 0nbcg (0.11 #1233, 0.11 #1533, 0.10 #3934) >> Best rule #482 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 0f3zf_; 04g865; 0693l; 06g60w; 087yty; 087v17; 026v_78; 070bjw; 07mkj0; 0f3zsq; ... >> query: (?x13048, 0dgd_) <- nationality(?x13048, ?x94), gender(?x13048, ?x231), ?x231 = 05zppz, cinematography(?x9993, ?x13048), ?x94 = 09c7w0 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cw0n4 profession 0dgd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.886 http://example.org/people/person/profession #9462-02lvtb PRED entity: 02lvtb PRED relation: artists! PRED expected values: 0m40d => 99 concepts (51 used for prediction) PRED predicted values (max 10 best out of 238): 06by7 (0.81 #12483, 0.55 #644, 0.55 #1578), 016clz (0.38 #2498, 0.35 #1562, 0.30 #6861), 0xhtw (0.35 #1885, 0.35 #2821, 0.31 #2197), 05bt6j (0.31 #2224, 0.28 #12505, 0.23 #13127), 0mhfr (0.30 #647, 0.29 #5319, 0.29 #5630), 06j6l (0.30 #671, 0.28 #6593, 0.26 #11576), 025sc50 (0.29 #6595, 0.22 #7532, 0.20 #11578), 02w4v (0.27 #5339, 0.26 #5028, 0.26 #4094), 03lty (0.27 #2833, 0.27 #2521, 0.26 #1897), 0glt670 (0.27 #7522, 0.27 #6585, 0.20 #11568) >> Best rule #12483 for best value: >> intensional similarity = 5 >> extensional distance = 478 >> proper extension: 089tm; 01t_xp_; 01pfr3; 01nqfh_; 0150jk; 07qnf; 02r3zy; 07c0j; 01k5t_3; 067mj; ... >> query: (?x5119, 06by7) <- artists(?x2664, ?x5119), artists(?x2664, ?x6467), artists(?x2664, ?x4609), participant(?x6467, ?x1213), ?x4609 = 0p7h7 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #148 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0lsw9; *> query: (?x5119, 0m40d) <- artists(?x8138, ?x5119), gender(?x5119, ?x231), ?x8138 = 0161rf *> conf = 0.17 ranks of expected_values: 26 EVAL 02lvtb artists! 0m40d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 99.000 51.000 0.810 http://example.org/music/genre/artists #9461-0ccqd7 PRED entity: 0ccqd7 PRED relation: actor! PRED expected values: 09g_31 => 93 concepts (46 used for prediction) PRED predicted values (max 10 best out of 114): 034fl9 (0.33 #178, 0.01 #4957, 0.01 #3894), 02r2j8 (0.33 #150), 05f7w84 (0.09 #2230, 0.07 #1964, 0.07 #2495), 09g_31 (0.07 #2289, 0.07 #1491, 0.06 #2023), 0jwl2 (0.07 #1398, 0.06 #1930, 0.05 #2461), 024rwx (0.05 #1698, 0.05 #1431, 0.05 #1963), 015w8_ (0.05 #1371, 0.05 #1903, 0.05 #2169), 01h72l (0.05 #1895, 0.05 #2161, 0.04 #1363), 06cs95 (0.03 #537, 0.03 #802, 0.03 #1067), 06r1k (0.03 #747, 0.03 #1012, 0.03 #1277) >> Best rule #178 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01vwllw; >> query: (?x9894, 034fl9) <- location(?x9894, ?x739), ?x739 = 02_286, film(?x9894, ?x5513), ?x5513 = 0d4htf >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2289 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 103 *> proper extension: 06v8s0; 0chrwb; 05dxl5; 07cn2c; 030_3z; 081jbk; 066l3y; 09fp45; 03crcpt; 05v954; ... *> query: (?x9894, 09g_31) <- language(?x9894, ?x254), ?x254 = 02h40lc, profession(?x9894, ?x1032), nationality(?x9894, ?x94) *> conf = 0.07 ranks of expected_values: 4 EVAL 0ccqd7 actor! 09g_31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 46.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #9460-0yxf4 PRED entity: 0yxf4 PRED relation: nominated_for! PRED expected values: 02r0csl 040njc => 63 concepts (57 used for prediction) PRED predicted values (max 10 best out of 192): 02rdyk7 (0.75 #2244, 0.74 #60, 0.74 #733), 02w_6xj (0.67 #3813, 0.67 #4038, 0.66 #2243), 040njc (0.65 #229, 0.54 #5, 0.52 #1351), 0k611 (0.57 #1409, 0.53 #2531, 0.52 #1857), 0gr4k (0.45 #2492, 0.44 #2042, 0.43 #1818), 099c8n (0.44 #1396, 0.32 #1172, 0.28 #50), 0gqy2 (0.44 #110, 0.37 #2578, 0.37 #1904), 0f4x7 (0.41 #2491, 0.41 #1817, 0.38 #23), 0p9sw (0.41 #18, 0.38 #2036, 0.36 #915), 04kxsb (0.41 #85, 0.32 #1431, 0.32 #758) >> Best rule #2244 for best value: >> intensional similarity = 3 >> extensional distance = 220 >> proper extension: 06mmr; >> query: (?x6616, ?x1587) <- award(?x6616, ?x1587), nominated_for(?x1587, ?x3496), ?x3496 = 011ysn >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #229 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 011yfd; *> query: (?x6616, 040njc) <- nominated_for(?x1313, ?x6616), ?x1313 = 0gs9p, titles(?x512, ?x6616), film_release_region(?x66, ?x512) *> conf = 0.65 ranks of expected_values: 3, 22 EVAL 0yxf4 nominated_for! 040njc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 63.000 57.000 0.748 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0yxf4 nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 63.000 57.000 0.748 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9459-02qgqt PRED entity: 02qgqt PRED relation: award PRED expected values: 09sb52 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 238): 0f4x7 (0.72 #22368, 0.71 #22367, 0.70 #23545), 09qv_s (0.72 #22368, 0.71 #22367, 0.70 #23545), 02x4w6g (0.72 #22368, 0.71 #22367, 0.70 #23545), 027c95y (0.72 #22368, 0.71 #22367, 0.70 #23545), 027986c (0.72 #22368, 0.71 #22367, 0.70 #23545), 09sb52 (0.71 #1216, 0.50 #823, 0.37 #4746), 03qgjwc (0.43 #174, 0.15 #21974, 0.13 #24330), 02y_rq5 (0.43 #92, 0.15 #21974, 0.13 #24330), 057xs89 (0.36 #1331, 0.33 #546, 0.15 #21974), 0gqwc (0.29 #71, 0.17 #856, 0.15 #21974) >> Best rule #22368 for best value: >> intensional similarity = 3 >> extensional distance = 1563 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 012ljv; 02s2ft; 05vsxz; 0grwj; 05bnp0; ... >> query: (?x157, ?x2853) <- award_nominee(?x92, ?x157), award_winner(?x2853, ?x157), award(?x123, ?x2853) >> conf = 0.72 => this is the best rule for 5 predicted values *> Best rule #1216 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 06dv3; 01qscs; 09fb5; 0bxtg; 01wmxfs; 03f1zdw; 0170pk; 0jfx1; 01vvb4m; 0flw6; ... *> query: (?x157, 09sb52) <- award_nominee(?x92, ?x157), award_winner(?x2853, ?x157), ?x2853 = 09qv_s *> conf = 0.71 ranks of expected_values: 6 EVAL 02qgqt award 09sb52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 99.000 99.000 0.725 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9458-038g2x PRED entity: 038g2x PRED relation: award_winner! PRED expected values: 09qvms => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 71): 09qvms (0.47 #13, 0.06 #154, 0.04 #718), 09g90vz (0.05 #124, 0.05 #265, 0.04 #829), 058m5m4 (0.05 #55, 0.04 #196, 0.03 #760), 09gkdln (0.05 #122, 0.04 #263, 0.03 #404), 03nnm4t (0.05 #74, 0.03 #356, 0.03 #215), 07y9ts (0.05 #68, 0.02 #350, 0.01 #2324), 09qftb (0.05 #113, 0.02 #1523, 0.02 #1664), 07y_p6 (0.05 #98, 0.01 #380, 0.01 #239), 09pj68 (0.05 #105, 0.01 #810, 0.01 #951), 03gyp30 (0.05 #258, 0.03 #1386, 0.03 #1809) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 01dw4q; 03zqc1; 06b0d2; 03lt8g; 030znt; 0gd_b_; 07z1_q; 04psyp; 05dxl5; 05683p; ... >> query: (?x2578, 09qvms) <- award_nominee(?x10004, ?x2578), award_nominee(?x2578, ?x2129), ?x10004 = 04vmqg >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 038g2x award_winner! 09qvms CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.474 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9457-07tlg PRED entity: 07tlg PRED relation: citytown PRED expected values: 0978r => 147 concepts (83 used for prediction) PRED predicted values (max 10 best out of 170): 0978r (0.75 #10706, 0.70 #24749, 0.66 #24377), 01w0v (0.45 #24750, 0.30 #25494, 0.30 #10707), 02jx1 (0.30 #10707, 0.30 #10705, 0.29 #24748), 02_286 (0.25 #753, 0.24 #2597, 0.19 #2966), 04jpl (0.23 #4433, 0.21 #4804, 0.21 #5175), 013wf1 (0.21 #2951, 0.05 #14029, 0.05 #26600), 030qb3t (0.16 #2242, 0.12 #2979, 0.08 #2610), 05l5n (0.11 #4463, 0.11 #3725, 0.10 #6315), 0fm2_ (0.09 #1496, 0.04 #3710, 0.02 #4079), 01cx_ (0.06 #10031, 0.05 #11143, 0.05 #10403) >> Best rule #10706 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 014b4h; 02583l; 01nmgc; 0b5hj5; 06b7s9; 0ch280; 02bf58; 02vkzcx; >> query: (?x12396, ?x3301) <- contains(?x3301, ?x12396), institution(?x1368, ?x12396), place_of_death(?x7296, ?x3301), teams(?x3301, ?x10112) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tlg citytown 0978r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 83.000 0.746 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #9456-0d4htf PRED entity: 0d4htf PRED relation: film_crew_role PRED expected values: 01vx2h => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 31): 0dxtw (0.48 #152, 0.44 #331, 0.43 #367), 01vx2h (0.43 #153, 0.38 #332, 0.37 #368), 01pvkk (0.30 #154, 0.30 #226, 0.30 #333), 02rh1dz (0.21 #151, 0.19 #330, 0.18 #366), 02ynfr (0.19 #230, 0.18 #158, 0.18 #514), 0215hd (0.15 #517, 0.12 #340, 0.12 #376), 089g0h (0.12 #341, 0.12 #234, 0.12 #377), 015h31 (0.11 #329, 0.11 #365, 0.11 #42), 0d2b38 (0.11 #347, 0.11 #383, 0.11 #240), 01xy5l_ (0.11 #120, 0.11 #512, 0.10 #191) >> Best rule #152 for best value: >> intensional similarity = 3 >> extensional distance = 139 >> proper extension: 03ckwzc; 0963mq; 03t97y; 0jjy0; 07g_0c; 02847m9; 03sxd2; 02vqhv0; 085ccd; 09lcsj; ... >> query: (?x5513, 0dxtw) <- genre(?x5513, ?x239), crewmember(?x5513, ?x3879), featured_film_locations(?x5513, ?x739) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 139 *> proper extension: 03ckwzc; 0963mq; 03t97y; 0jjy0; 07g_0c; 02847m9; 03sxd2; 02vqhv0; 085ccd; 09lcsj; ... *> query: (?x5513, 01vx2h) <- genre(?x5513, ?x239), crewmember(?x5513, ?x3879), featured_film_locations(?x5513, ?x739) *> conf = 0.43 ranks of expected_values: 2 EVAL 0d4htf film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.475 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9455-01pk3z PRED entity: 01pk3z PRED relation: film PRED expected values: 0h1cdwq => 94 concepts (48 used for prediction) PRED predicted values (max 10 best out of 673): 0f4_l (0.33 #348, 0.03 #85590, 0.02 #9263), 02825cv (0.20 #2921, 0.03 #6487, 0.02 #4704), 08952r (0.20 #2498, 0.01 #6064), 011ysn (0.17 #564, 0.10 #2347, 0.03 #5913), 033qdy (0.17 #1171, 0.10 #2954), 011ycb (0.17 #853, 0.03 #6202, 0.02 #7985), 0277j40 (0.17 #1220, 0.03 #6569, 0.02 #10135), 04x4vj (0.17 #771, 0.03 #6120, 0.01 #20384), 0gmd3k7 (0.17 #1105, 0.03 #6454, 0.01 #10020), 056xkh (0.17 #1594, 0.03 #85590, 0.02 #10509) >> Best rule #348 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0170qf; 01xv77; >> query: (?x5541, 0f4_l) <- award_nominee(?x5541, ?x8134), award(?x5541, ?x704), ?x8134 = 0kjrx >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pk3z film 0h1cdwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 48.000 0.333 http://example.org/film/actor/film./film/performance/film #9454-01qckn PRED entity: 01qckn PRED relation: category PRED expected values: 08mbj5d => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #165, 0.84 #164, 0.84 #145) >> Best rule #165 for best value: >> intensional similarity = 6 >> extensional distance = 527 >> proper extension: 0ymbl; 0gkkf; 07tl0; 01k8q5; 018m5q; 0c_zj; 01sjz_; 01f2xy; 0677j; 01clyb; ... >> query: (?x10312, ?x134) <- organization(?x4682, ?x10312), citytown(?x10312, ?x5036), organization(?x4682, ?x3578), company(?x346, ?x3578), category(?x3578, ?x134), ?x134 = 08mbj5d >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qckn category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.845 http://example.org/common/topic/webpage./common/webpage/category #9453-04p3w PRED entity: 04p3w PRED relation: symptom_of! PRED expected values: 01cdt5 => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 67): 01cdt5 (0.67 #131, 0.60 #175, 0.50 #245), 0cjf0 (0.53 #934, 0.42 #793, 0.39 #843), 02tfl8 (0.38 #135, 0.33 #416, 0.33 #91), 0gxb2 (0.38 #143, 0.32 #976, 0.30 #187), 0brgy (0.37 #350, 0.33 #511, 0.32 #976), 012qjw (0.33 #563, 0.33 #541, 0.33 #512), 01pf6 (0.33 #40, 0.23 #530, 0.23 #974), 0hg45 (0.33 #37, 0.23 #530, 0.23 #974), 0hgxh (0.32 #976, 0.23 #530, 0.23 #974), 0j5fv (0.29 #560, 0.26 #319, 0.25 #692) >> Best rule #131 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0gk4g; 01dcqj; 01l2m3; >> query: (?x4659, 01cdt5) <- people(?x4659, ?x7684), people(?x4659, ?x7454), people(?x4659, ?x2426), type_of_union(?x7454, ?x566), symptom_of(?x4905, ?x4659), profession(?x7684, ?x1032), place_of_birth(?x7684, ?x12931), award_winner(?x782, ?x7684), place_of_burial(?x7684, ?x1879), location(?x2426, ?x1860), award_nominee(?x2426, ?x1377) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04p3w symptom_of! 01cdt5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.667 http://example.org/medicine/symptom/symptom_of #9452-0bynt PRED entity: 0bynt PRED relation: sports! PRED expected values: 0l6ny => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 20): 0sxrz (0.83 #429, 0.83 #993, 0.82 #1216), 0l6ny (0.83 #429, 0.83 #993, 0.82 #1216), 018wrk (0.83 #429, 0.83 #993, 0.82 #1216), 0blg2 (0.83 #429, 0.83 #993, 0.82 #1216), 0swff (0.53 #21, 0.39 #327, 0.31 #840), 0swbd (0.53 #21, 0.39 #327, 0.31 #835), 09n48 (0.53 #21, 0.39 #327, 0.28 #831), 0sx7r (0.53 #21, 0.39 #327, 0.28 #832), 019n8z (0.53 #21, 0.39 #327, 0.25 #844), 018ctl (0.53 #21, 0.39 #327, 0.25 #833) >> Best rule #429 for best value: >> intensional similarity = 12 >> extensional distance = 13 >> proper extension: 04lgq; >> query: (?x1121, ?x358) <- sports(?x5395, ?x1121), sports(?x2233, ?x1121), sports(?x2131, ?x1121), sports(?x778, ?x1121), ?x5395 = 018qb4, sports(?x778, ?x171), olympics(?x47, ?x778), olympics(?x2885, ?x2131), medal(?x2131, ?x422), olympics(?x279, ?x2233), sports(?x358, ?x1121), ?x2885 = 07jjt >> conf = 0.83 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0bynt sports! 0l6ny CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.832 http://example.org/olympics/olympic_games/sports #9451-0p9tm PRED entity: 0p9tm PRED relation: costume_design_by PRED expected values: 02cqbx => 79 concepts (63 used for prediction) PRED predicted values (max 10 best out of 21): 02cqbx (0.05 #131, 0.02 #299, 0.02 #159), 03mfqm (0.03 #273, 0.03 #329, 0.03 #161), 0gl88b (0.03 #120, 0.03 #5, 0.01 #232), 09x8ms (0.03 #143), 0c6g29 (0.03 #122), 0bytfv (0.02 #724, 0.02 #868, 0.02 #322), 03y1mlp (0.02 #341, 0.02 #30, 0.02 #483), 02mxbd (0.02 #272, 0.02 #384, 0.02 #45), 026lyl4 (0.02 #51, 0.01 #79, 0.01 #138), 03gt0c5 (0.02 #55, 0.01 #83, 0.01 #113) >> Best rule #131 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 0jqb8; >> query: (?x7846, 02cqbx) <- language(?x7846, ?x254), film_release_region(?x7846, ?x94), genre(?x7846, ?x225), list(?x7846, ?x3004) >> conf = 0.05 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p9tm costume_design_by 02cqbx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 63.000 0.051 http://example.org/film/film/costume_design_by #9450-01pllx PRED entity: 01pllx PRED relation: award PRED expected values: 09qrn4 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 279): 01bgqh (0.39 #447, 0.20 #10167, 0.19 #4092), 05p09zm (0.37 #2149, 0.31 #2959, 0.28 #3769), 01by1l (0.35 #517, 0.20 #10237, 0.18 #4162), 01c92g (0.35 #502, 0.14 #97, 0.10 #10222), 05pcn59 (0.34 #2106, 0.33 #6966, 0.29 #7371), 0c4z8 (0.30 #476, 0.14 #71, 0.11 #4121), 07cbcy (0.29 #78, 0.15 #888, 0.11 #2913), 03qbh5 (0.26 #611, 0.14 #206, 0.14 #10331), 05b4l5x (0.23 #2031, 0.19 #1626, 0.18 #2841), 01ck6h (0.22 #527, 0.14 #122, 0.11 #10247) >> Best rule #447 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 012x4t; 01vsl3_; 0gcs9; >> query: (?x8927, 01bgqh) <- type_of_union(?x8927, ?x566), participant(?x8927, ?x989), inductee(?x11145, ?x8927) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #4290 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 89 *> proper extension: 01wz_ml; 0hcs3; *> query: (?x8927, 09qrn4) <- inductee(?x11145, ?x8927), location(?x8927, ?x335) *> conf = 0.05 ranks of expected_values: 134 EVAL 01pllx award 09qrn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 135.000 135.000 0.391 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9449-0161h5 PRED entity: 0161h5 PRED relation: participant! PRED expected values: 01fs_4 => 87 concepts (32 used for prediction) PRED predicted values (max 10 best out of 311): 01fs_4 (0.81 #14025, 0.81 #15940, 0.80 #14026), 01vs_v8 (0.12 #152, 0.07 #3338, 0.05 #4612), 015f7 (0.12 #243, 0.05 #7890, 0.02 #13630), 03lt8g (0.12 #70, 0.04 #3256, 0.04 #7717), 0227vl (0.12 #536, 0.04 #3722, 0.04 #8183), 0127s7 (0.12 #394, 0.04 #3580, 0.03 #6767), 015lhm (0.12 #378, 0.03 #2927, 0.02 #3564), 0bbf1f (0.12 #206, 0.02 #7853, 0.02 #13593), 02p21g (0.12 #105, 0.02 #7752, 0.02 #3291), 0151w_ (0.12 #64, 0.02 #3250, 0.02 #13451) >> Best rule #14025 for best value: >> intensional similarity = 4 >> extensional distance = 250 >> proper extension: 01xyt7; >> query: (?x10929, ?x3628) <- participant(?x10929, ?x3868), participant(?x10929, ?x3628), student(?x10478, ?x3868), people(?x5042, ?x10929) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0161h5 participant! 01fs_4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 32.000 0.810 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #9448-0jjw PRED entity: 0jjw PRED relation: major_field_of_study PRED expected values: 04rjg 040p_q => 54 concepts (47 used for prediction) PRED predicted values (max 10 best out of 128): 04rjg (0.82 #527, 0.82 #615, 0.82 #1677), 02j62 (0.38 #466, 0.38 #554, 0.35 #728), 02vxn (0.33 #266, 0.33 #91, 0.33 #3), 02h40lc (0.33 #90, 0.33 #2, 0.31 #442), 03g3w (0.33 #375, 0.33 #111, 0.31 #463), 0fdys (0.33 #120, 0.33 #32, 0.25 #384), 06ms6 (0.33 #102, 0.31 #454, 0.29 #366), 03qsdpk (0.33 #127, 0.25 #215, 0.23 #655), 05qdh (0.33 #138, 0.25 #226, 0.17 #313), 041y2 (0.33 #63, 0.25 #239, 0.11 #176) >> Best rule #527 for best value: >> intensional similarity = 8 >> extensional distance = 24 >> proper extension: 036hv; 02lp1; 01mkq; 06ms6; 04rjg; 0h5k; 03g3w; 062z7; 0193x; 05qfh; ... >> query: (?x3440, ?x2014) <- major_field_of_study(?x4955, ?x3440), ?x4955 = 09f2j, major_field_of_study(?x2014, ?x3440), major_field_of_study(?x3386, ?x3440), student(?x3386, ?x9503), institution(?x3386, ?x3424), ?x9503 = 04ns3gy, ?x3424 = 01w5m >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 60 EVAL 0jjw major_field_of_study 040p_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 54.000 47.000 0.825 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study EVAL 0jjw major_field_of_study 04rjg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 47.000 0.825 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #9447-076xkps PRED entity: 076xkps PRED relation: film! PRED expected values: 01ycbq 01chc7 => 104 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1018): 01f7dd (0.22 #1209, 0.20 #3290, 0.02 #15785), 072vj (0.13 #43725, 0.13 #33316, 0.12 #60380), 03ym1 (0.11 #1013, 0.10 #3094, 0.06 #7256), 09wj5 (0.11 #100, 0.10 #2181, 0.06 #6343), 015wnl (0.11 #649, 0.10 #2730, 0.05 #11059), 0js9s (0.11 #1156, 0.10 #3237, 0.05 #7399), 0294fd (0.11 #717, 0.10 #2798, 0.05 #6960), 0241jw (0.11 #295, 0.10 #2376, 0.05 #6538), 016fjj (0.11 #634, 0.10 #2715, 0.04 #13125), 02ck7w (0.11 #940, 0.10 #3021, 0.04 #13431) >> Best rule #1209 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0yyg4; 0191n; 07nnp_; >> query: (?x8886, 01f7dd) <- currency(?x8886, ?x170), film(?x382, ?x8886), film(?x230, ?x8886), ?x230 = 02bfmn, country(?x8886, ?x94) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #10737 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 81 *> proper extension: 0d90m; 03qcfvw; 04fzfj; 01qb5d; 0_b3d; 048scx; 03t97y; 07sc6nw; 09p0ct; 0cz8mkh; ... *> query: (?x8886, 01ycbq) <- film_crew_role(?x8886, ?x2154), titles(?x571, ?x8886), genre(?x8886, ?x812), ?x2154 = 01vx2h, ?x812 = 01jfsb *> conf = 0.02 ranks of expected_values: 296, 477 EVAL 076xkps film! 01chc7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 104.000 67.000 0.222 http://example.org/film/actor/film./film/performance/film EVAL 076xkps film! 01ycbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 104.000 67.000 0.222 http://example.org/film/actor/film./film/performance/film #9446-03fwln PRED entity: 03fwln PRED relation: people! PRED expected values: 02sch9 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 57): 0x67 (0.44 #164, 0.40 #703, 0.38 #780), 0dryh9k (0.35 #1556, 0.34 #2249, 0.09 #3173), 041rx (0.23 #466, 0.22 #851, 0.20 #543), 033tf_ (0.19 #777, 0.18 #1470, 0.16 #931), 04mvp8 (0.18 #298, 0.13 #683, 0.04 #2300), 025rpb0 (0.17 #122, 0.08 #507, 0.08 #353), 0fk1z (0.17 #151, 0.08 #536, 0.08 #382), 03x1x (0.17 #133, 0.07 #672, 0.01 #1981), 03295l (0.15 #332, 0.09 #1333, 0.08 #1410), 07bch9 (0.11 #177, 0.09 #254, 0.08 #2333) >> Best rule #164 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 016qtt; 03f1d47; >> query: (?x10783, 0x67) <- profession(?x10783, ?x220), program(?x10783, ?x12165), ?x220 = 016z4k, film(?x10783, ?x5247) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1575 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 58 *> proper extension: 0cmpn; *> query: (?x10783, 02sch9) <- award_winner(?x10156, ?x10783), nationality(?x10783, ?x2146), ?x2146 = 03rk0, award_winner(?x10156, ?x14044), profession(?x14044, ?x1032) *> conf = 0.10 ranks of expected_values: 13 EVAL 03fwln people! 02sch9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 91.000 91.000 0.444 http://example.org/people/ethnicity/people #9445-015_30 PRED entity: 015_30 PRED relation: artists! PRED expected values: 02w4v => 117 concepts (58 used for prediction) PRED predicted values (max 10 best out of 228): 017_qw (0.72 #1307, 0.19 #996, 0.17 #4106), 05bt6j (0.46 #1598, 0.43 #1909, 0.22 #10931), 06j6l (0.40 #48, 0.35 #1914, 0.34 #3469), 03_d0 (0.40 #12, 0.35 #634, 0.25 #1878), 0gywn (0.40 #58, 0.31 #1924, 0.29 #5034), 0ggq0m (0.40 #324, 0.19 #1257, 0.10 #635), 05lls (0.40 #326, 0.09 #1259, 0.05 #8413), 0glt670 (0.37 #3461, 0.35 #5327, 0.34 #5016), 02vjzr (0.37 #1690, 0.22 #2001, 0.11 #4489), 025sc50 (0.29 #5026, 0.28 #1605, 0.28 #5337) >> Best rule #1307 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 07q1v4; 07j8kh; >> query: (?x1800, 017_qw) <- award(?x1800, ?x1443), ?x1443 = 054krc, artists(?x671, ?x1800), location(?x1800, ?x2850) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #4709 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 199 *> proper extension: 01nqfh_; 0bkg4; 01s7qqw; 018d6l; 01mr2g6; 03wjb7; 01k47c; 01wxdn3; 0517bc; *> query: (?x1800, 02w4v) <- artists(?x671, ?x1800), category(?x1800, ?x134), student(?x7545, ?x1800) *> conf = 0.13 ranks of expected_values: 28 EVAL 015_30 artists! 02w4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 117.000 58.000 0.719 http://example.org/music/genre/artists #9444-01m7f5r PRED entity: 01m7f5r PRED relation: student! PRED expected values: 07tg4 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 194): 07tgn (0.75 #7892, 0.10 #1067, 0.08 #54087), 07tg4 (0.26 #2711, 0.11 #5861, 0.11 #611), 017z88 (0.17 #6907, 0.12 #3757, 0.12 #15832), 01g0p5 (0.14 #3357, 0.07 #9132, 0.06 #6507), 0m4yg (0.11 #889, 0.08 #54087, 0.07 #23989), 011xy1 (0.11 #843, 0.08 #54087, 0.06 #8193), 07tk7 (0.11 #966, 0.08 #54087, 0.04 #3066), 0k2h6 (0.11 #914, 0.08 #54087, 0.04 #3014), 02mw6c (0.11 #954, 0.08 #54087, 0.02 #6204), 09f2j (0.10 #14334, 0.10 #5409, 0.09 #14859) >> Best rule #7892 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 03k545; 09jd9; >> query: (?x9064, 07tgn) <- nationality(?x9064, ?x512), student(?x13049, ?x9064), student(?x13049, ?x6037), ?x6037 = 0hky >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2711 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 0126rp; *> query: (?x9064, 07tg4) <- profession(?x9064, ?x1614), people(?x5042, ?x9064), ?x5042 = 0d7wh, location(?x9064, ?x362) *> conf = 0.26 ranks of expected_values: 2 EVAL 01m7f5r student! 07tg4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 126.000 0.745 http://example.org/education/educational_institution/students_graduates./education/education/student #9443-047msdk PRED entity: 047msdk PRED relation: film_release_region PRED expected values: 0d0vqn 03gj2 0h7h6 07twz => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 123): 0d0vqn (0.93 #1003, 0.92 #719, 0.91 #2436), 03gj2 (0.89 #3164, 0.88 #3022, 0.88 #2452), 0d060g (0.85 #718, 0.80 #2435, 0.78 #3147), 07f1x (0.75 #248, 0.40 #2536, 0.38 #1676), 03rj0 (0.66 #2482, 0.66 #3052, 0.66 #3194), 047yc (0.58 #737, 0.57 #3024, 0.56 #3166), 016wzw (0.55 #2487, 0.51 #3057, 0.51 #3199), 04gzd (0.54 #3151, 0.54 #3009, 0.53 #2439), 015qh (0.53 #2465, 0.52 #748, 0.52 #3177), 01p1v (0.52 #2474, 0.51 #3186, 0.51 #3044) >> Best rule #1003 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 011yrp; 07gp9; 02x3lt7; 03hjv97; 04969y; 01vksx; 017gl1; 0m_mm; 02d44q; 04hwbq; ... >> query: (?x1364, 0d0vqn) <- titles(?x2480, ?x1364), award(?x1364, ?x899), film_release_region(?x1364, ?x1355), ?x1355 = 0h7x >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 21, 52 EVAL 047msdk film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 103.000 103.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047msdk film_release_region 0h7h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 103.000 103.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047msdk film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047msdk film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9442-01wmjkb PRED entity: 01wmjkb PRED relation: currency PRED expected values: 09nqf => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.50 #1, 0.43 #22, 0.42 #4), 01nv4h (0.09 #80, 0.09 #29, 0.08 #5) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01vvzb1; >> query: (?x8341, 09nqf) <- artists(?x671, ?x8341), location(?x8341, ?x11843), ?x11843 = 0n6dc, category(?x8341, ?x134) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wmjkb currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.500 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #9441-05cl8y PRED entity: 05cl8y PRED relation: industry PRED expected values: 02vxn => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 45): 01mw1 (0.67 #2026, 0.44 #1790, 0.30 #565), 02vxn (0.65 #1744, 0.60 #1932, 0.53 #1461), 020mfr (0.40 #2041, 0.27 #1805, 0.25 #580), 02jjt (0.36 #290, 0.27 #1750, 0.24 #2033), 03qh03g (0.33 #5, 0.27 #287, 0.25 #52), 01mf0 (0.21 #1348, 0.11 #1177, 0.06 #1819), 029g_vk (0.19 #1329, 0.18 #293, 0.18 #1470), 0hz28 (0.18 #311, 0.11 #546, 0.11 #1177), 0sydc (0.18 #314, 0.11 #549, 0.11 #1177), 07c52 (0.17 #4142, 0.16 #3718, 0.11 #1177) >> Best rule #2026 for best value: >> intensional similarity = 5 >> extensional distance = 87 >> proper extension: 049vhf; 01tlrp; 03_kl4; >> query: (?x8559, 01mw1) <- industry(?x8559, ?x8681), industry(?x7793, ?x8681), artist(?x7793, ?x10039), ?x10039 = 0ftqr, child(?x7793, ?x4483) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1744 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 76 *> proper extension: 02rr_z4; *> query: (?x8559, 02vxn) <- industry(?x8559, ?x8681), industry(?x14234, ?x8681), industry(?x6082, ?x8681), film(?x6082, ?x2685), ?x2685 = 0g5879y, organization(?x4682, ?x14234) *> conf = 0.65 ranks of expected_values: 2 EVAL 05cl8y industry 02vxn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 165.000 165.000 0.674 http://example.org/business/business_operation/industry #9440-04tc1g PRED entity: 04tc1g PRED relation: genre PRED expected values: 02kdv5l => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 89): 02kdv5l (0.75 #243, 0.45 #483, 0.44 #363), 07s9rl0 (0.73 #6726, 0.70 #6967, 0.59 #4684), 01z4y (0.61 #5765, 0.52 #4804, 0.51 #3242), 01jfsb (0.50 #252, 0.39 #372, 0.36 #6617), 01hmnh (0.33 #17, 0.18 #497, 0.18 #841), 02n4kr (0.33 #8, 0.14 #368, 0.14 #1689), 02xlf (0.33 #53, 0.03 #2094, 0.02 #2214), 02l7c8 (0.33 #2896, 0.29 #2776, 0.29 #4698), 0lsxr (0.26 #609, 0.26 #1090, 0.25 #970), 06cvj (0.21 #2885, 0.20 #2765, 0.11 #364) >> Best rule #243 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 084qpk; 031t2d; 04tqtl; 049mql; 03ct7jd; 076zy_g; >> query: (?x887, 02kdv5l) <- nominated_for(?x382, ?x887), film(?x1672, ?x887), genre(?x887, ?x258), ?x1672 = 02_hj4 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04tc1g genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.750 http://example.org/film/film/genre #9439-0pz04 PRED entity: 0pz04 PRED relation: award PRED expected values: 05zvj3m => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 213): 05p09zm (0.50 #523, 0.38 #925, 0.17 #121), 07cbcy (0.33 #478, 0.25 #880, 0.17 #76), 05zvj3m (0.33 #492, 0.25 #894, 0.17 #90), 05b1610 (0.33 #439, 0.25 #841, 0.17 #37), 0gqy2 (0.33 #564, 0.25 #966, 0.17 #162), 04ljl_l (0.33 #405, 0.25 #807, 0.17 #3), 09sb52 (0.32 #6872, 0.31 #4460, 0.23 #9284), 047byns (0.25 #1256, 0.12 #25327, 0.01 #1658), 02grdc (0.25 #1236, 0.02 #5658, 0.02 #3246), 07bdd_ (0.17 #465, 0.17 #63, 0.13 #26132) >> Best rule #523 for best value: >> intensional similarity = 2 >> extensional distance = 4 >> proper extension: 01n5309; >> query: (?x8145, 05p09zm) <- award_nominee(?x5940, ?x8145), celebrities_impersonated(?x8145, ?x986) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #492 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 4 *> proper extension: 01n5309; *> query: (?x8145, 05zvj3m) <- award_nominee(?x5940, ?x8145), celebrities_impersonated(?x8145, ?x986) *> conf = 0.33 ranks of expected_values: 3 EVAL 0pz04 award 05zvj3m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9438-040_9 PRED entity: 040_9 PRED relation: influenced_by PRED expected values: 0l99s => 135 concepts (48 used for prediction) PRED predicted values (max 10 best out of 321): 01v9724 (0.43 #1495, 0.33 #1055, 0.29 #4123), 02lt8 (0.43 #1437, 0.33 #558, 0.25 #4503), 032l1 (0.40 #3157, 0.33 #967, 0.22 #8864), 0465_ (0.40 #3267, 0.33 #638, 0.17 #1077), 040_9 (0.33 #537, 0.30 #3166, 0.11 #2293), 03_87 (0.33 #641, 0.29 #4148, 0.25 #4586), 081k8 (0.33 #594, 0.24 #10247, 0.23 #9808), 02wh0 (0.33 #822, 0.20 #3451, 0.17 #13983), 01tz6vs (0.33 #615, 0.20 #3244, 0.14 #1494), 014635 (0.33 #548, 0.20 #3177, 0.11 #2304) >> Best rule #1495 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 04xjp; >> query: (?x3541, 01v9724) <- influenced_by(?x12382, ?x3541), influenced_by(?x11271, ?x3541), influenced_by(?x587, ?x3541), profession(?x3541, ?x353), location(?x12382, ?x4350), ?x11271 = 0hcvy, award(?x587, ?x921) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #14038 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 151 *> proper extension: 07rd7; 0167xy; 055yr; *> query: (?x3541, ?x2845) <- influenced_by(?x117, ?x3541), influenced_by(?x3541, ?x3542), peers(?x11554, ?x3542), influenced_by(?x117, ?x2845) *> conf = 0.10 ranks of expected_values: 82 EVAL 040_9 influenced_by 0l99s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 135.000 48.000 0.429 http://example.org/influence/influence_node/influenced_by #9437-026mmy PRED entity: 026mmy PRED relation: award! PRED expected values: 02pv_d => 43 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2777): 018db8 (0.75 #26974, 0.70 #26972, 0.69 #47215), 0m_v0 (0.70 #26972, 0.69 #47215, 0.69 #26973), 01x15dc (0.70 #26972, 0.69 #47215, 0.69 #50591), 0c00lh (0.70 #26972, 0.69 #26973, 0.69 #50591), 036dyy (0.70 #26972, 0.68 #53966, 0.68 #50590), 01l3mk3 (0.62 #5673, 0.10 #12417, 0.08 #9044), 02zft0 (0.62 #5130, 0.09 #11874, 0.06 #28735), 01x6v6 (0.62 #5334, 0.08 #8705, 0.07 #12078), 01tc9r (0.62 #4458, 0.08 #7829, 0.07 #40467), 02z4b_8 (0.60 #2067, 0.25 #12182, 0.12 #5438) >> Best rule #26974 for best value: >> intensional similarity = 5 >> extensional distance = 100 >> proper extension: 02r9qt; >> query: (?x10881, ?x793) <- award_winner(?x10881, ?x9719), award_winner(?x10881, ?x793), participant(?x794, ?x793), participant(?x793, ?x843), type_of_union(?x9719, ?x566) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #9078 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 02rdxsh; *> query: (?x10881, 02pv_d) <- award(?x6048, ?x10881), nominated_for(?x10881, ?x3854), film_release_region(?x6048, ?x1499), music(?x6048, ?x3410) *> conf = 0.12 ranks of expected_values: 426 EVAL 026mmy award! 02pv_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 20.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9436-0k6bt PRED entity: 0k6bt PRED relation: country PRED expected values: 0f8l9c => 123 concepts (31 used for prediction) PRED predicted values (max 10 best out of 63): 0154j (0.40 #174, 0.40 #93, 0.36 #1416), 09c7w0 (0.29 #972, 0.27 #1332, 0.22 #1964), 03rjj (0.13 #181, 0.07 #267, 0.05 #2680), 02j71 (0.13 #2321, 0.08 #611, 0.07 #1869), 059j2 (0.08 #205, 0.08 #291, 0.05 #2680), 02qkt (0.08 #2320, 0.06 #609, 0.04 #1506), 07ssc (0.07 #628, 0.07 #538, 0.06 #1707), 0345h (0.05 #466, 0.05 #2680, 0.04 #910), 05qhw (0.05 #2680, 0.04 #1057, 0.02 #2769), 0f8l9c (0.05 #2680, 0.02 #1597, 0.02 #2231) >> Best rule #174 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 0fydw; 0k424; >> query: (?x13513, ?x172) <- contains(?x172, ?x13513), ?x172 = 0154j >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2680 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1103 *> proper extension: 0160w; 0f4y_; 05kr_; 0mn0v; 0ml25; 06q1r; 0nh0f; 0f04v; 0f2tj; 0mnsf; ... *> query: (?x13513, ?x2756) <- time_zones(?x13513, ?x2864), time_zones(?x5994, ?x2864), time_zones(?x2756, ?x2864), form_of_government(?x2756, ?x1926), institution(?x865, ?x5994), country(?x2266, ?x2756), olympics(?x2756, ?x2966) *> conf = 0.05 ranks of expected_values: 10 EVAL 0k6bt country 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 123.000 31.000 0.400 http://example.org/base/biblioness/bibs_location/country #9435-048_p PRED entity: 048_p PRED relation: influenced_by! PRED expected values: 0j0pf => 88 concepts (31 used for prediction) PRED predicted values (max 10 best out of 180): 0j0pf (0.26 #1240, 0.25 #207, 0.24 #1757), 05jm7 (0.25 #141, 0.19 #1174, 0.17 #1691), 01dzz7 (0.17 #53, 0.15 #569, 0.15 #1086), 0821j (0.10 #874, 0.08 #358, 0.07 #1391), 014ps4 (0.08 #312, 0.07 #1345, 0.07 #1862), 03772 (0.08 #203, 0.07 #1236, 0.07 #1753), 01k56k (0.08 #497, 0.07 #1530, 0.07 #2047), 067xw (0.08 #285, 0.07 #1318, 0.07 #1835), 04cbtrw (0.08 #108, 0.05 #624, 0.04 #1141), 0jt90f5 (0.08 #81, 0.05 #597, 0.04 #1114) >> Best rule #1240 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 01dhmw; 0b0pf; 0g5ff; 0210f1; 0mfc0; 033cw; 03hpr; >> query: (?x5506, 0j0pf) <- award(?x5506, ?x8880), award(?x5506, ?x3337), ?x8880 = 0262x6, award_winner(?x14213, ?x5506), award_winner(?x3337, ?x476) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 048_p influenced_by! 0j0pf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 31.000 0.259 http://example.org/influence/influence_node/influenced_by #9434-03l78j PRED entity: 03l78j PRED relation: institution! PRED expected values: 02h4rq6 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 20): 02h4rq6 (0.76 #945, 0.72 #68, 0.67 #113), 016t_3 (0.46 #69, 0.41 #136, 0.40 #91), 03bwzr4 (0.45 #79, 0.39 #301, 0.39 #866), 02_xgp2 (0.44 #77, 0.43 #864, 0.42 #1132), 0bkj86 (0.41 #73, 0.39 #95, 0.33 #452), 03mkk4 (0.29 #831, 0.12 #98, 0.11 #364), 01gkg3 (0.29 #831, 0.10 #14, 0.06 #1882), 02m4yg (0.29 #831, 0.06 #1882, 0.06 #59), 04zx3q1 (0.22 #89, 0.21 #67, 0.20 #854), 013zdg (0.20 #72, 0.17 #383, 0.17 #271) >> Best rule #945 for best value: >> intensional similarity = 4 >> extensional distance = 423 >> proper extension: 02fs_d; >> query: (?x9066, 02h4rq6) <- institution(?x1771, ?x9066), institution(?x1771, ?x2522), ?x2522 = 022lly, student(?x1771, ?x744) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03l78j institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.765 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9433-01p0w_ PRED entity: 01p0w_ PRED relation: role PRED expected values: 03bx0bm => 185 concepts (156 used for prediction) PRED predicted values (max 10 best out of 119): 03bx0bm (0.53 #84, 0.50 #467, 0.45 #1863), 018vs (0.32 #2665, 0.30 #3047, 0.30 #3112), 026t6 (0.32 #2665, 0.30 #3047, 0.30 #3112), 0l14md (0.32 #2665, 0.30 #3047, 0.30 #3112), 02snj9 (0.32 #2665, 0.30 #3047, 0.30 #3112), 028tv0 (0.20 #2613, 0.19 #3060, 0.18 #2995), 02hnl (0.20 #1870, 0.20 #600, 0.17 #918), 01vdm0 (0.14 #1273, 0.12 #5156, 0.11 #4521), 03qjg (0.12 #2642, 0.12 #3024, 0.12 #3089), 042v_gx (0.12 #454, 0.09 #1216, 0.08 #835) >> Best rule #84 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0d608; >> query: (?x12422, 03bx0bm) <- friend(?x7571, ?x12422), role(?x12422, ?x227), role(?x219, ?x227) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p0w_ role 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 185.000 156.000 0.533 http://example.org/music/group_member/membership./music/group_membership/role #9432-03g3w PRED entity: 03g3w PRED relation: genre! PRED expected values: 049mql 01jwxx 029jt9 06pyc2 => 97 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1784): 02kfzz (0.71 #48957, 0.50 #27494, 0.50 #22128), 040b5k (0.62 #52332, 0.50 #21925, 0.40 #32657), 0168ls (0.60 #55688, 0.50 #52109, 0.50 #25280), 0p7qm (0.60 #55921, 0.50 #25513, 0.50 #23724), 07f_t4 (0.60 #56756, 0.50 #26348, 0.50 #24559), 0415ggl (0.60 #33163, 0.50 #52838, 0.50 #22431), 011yfd (0.60 #32843, 0.50 #52518, 0.50 #22111), 041td_ (0.60 #33281, 0.50 #22549, 0.44 #54745), 039zft (0.60 #29552, 0.50 #13456, 0.44 #54592), 049mql (0.60 #32866, 0.50 #56120, 0.40 #36443) >> Best rule #48957 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 0c3351; >> query: (?x2605, 02kfzz) <- genre(?x9978, ?x2605), genre(?x8000, ?x2605), genre(?x7161, ?x2605), genre(?x4197, ?x2605), ?x7161 = 0bw20, nominated_for(?x398, ?x8000), film_crew_role(?x9978, ?x137), award(?x8000, ?x289), genre(?x4881, ?x2605), nominated_for(?x68, ?x4197) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #32866 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 017fp; *> query: (?x2605, 049mql) <- genre(?x7161, ?x2605), genre(?x5152, ?x2605), genre(?x3979, ?x2605), genre(?x3759, ?x2605), production_companies(?x3759, ?x2156), country(?x7161, ?x94), film_crew_role(?x3979, ?x137), ?x5152 = 08sfxj, film_release_distribution_medium(?x3979, ?x81) *> conf = 0.60 ranks of expected_values: 10, 395, 647, 920 EVAL 03g3w genre! 06pyc2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 97.000 58.000 0.714 http://example.org/film/film/genre EVAL 03g3w genre! 029jt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 97.000 58.000 0.714 http://example.org/film/film/genre EVAL 03g3w genre! 01jwxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 97.000 58.000 0.714 http://example.org/film/film/genre EVAL 03g3w genre! 049mql CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 97.000 58.000 0.714 http://example.org/film/film/genre #9431-0j862 PRED entity: 0j862 PRED relation: role! PRED expected values: 028tv0 => 42 concepts (41 used for prediction) PRED predicted values (max 10 best out of 114): 05148p4 (0.85 #1464, 0.85 #1374, 0.84 #1347), 01vj9c (0.84 #1347, 0.84 #1568, 0.82 #785), 04rzd (0.84 #1347, 0.84 #1568, 0.82 #785), 028tv0 (0.84 #1347, 0.84 #1568, 0.82 #785), 03qmg1 (0.84 #1347, 0.84 #1568, 0.82 #785), 013y1f (0.80 #931, 0.78 #817, 0.78 #781), 018j2 (0.80 #940, 0.78 #826, 0.74 #223), 07y_7 (0.80 #1016, 0.74 #223, 0.73 #1351), 02sgy (0.78 #681, 0.74 #1240, 0.74 #223), 0g2dz (0.78 #706, 0.71 #104, 0.70 #109) >> Best rule #1464 for best value: >> intensional similarity = 27 >> extensional distance = 11 >> proper extension: 0bmnm; >> query: (?x7772, ?x1166) <- role(?x2957, ?x7772), role(?x2460, ?x7772), role(?x2206, ?x7772), role(?x1750, ?x7772), role(?x1466, ?x7772), ?x1750 = 02hnl, performance_role(?x1332, ?x2460), instrumentalists(?x2460, ?x11689), instrumentalists(?x2460, ?x7121), instrumentalists(?x2460, ?x3503), role(?x3991, ?x2460), role(?x1495, ?x2460), ?x1466 = 03bx0bm, ?x3991 = 05842k, ?x11689 = 06p03s, ?x1495 = 013y1f, vacationer(?x390, ?x3503), artists(?x3319, ?x3503), ?x3319 = 06j6l, place_of_birth(?x3503, ?x5302), participant(?x9084, ?x3503), role(?x7772, ?x1166), role(?x4052, ?x2957), ?x2206 = 07gql, group(?x2460, ?x3516), ?x1166 = 05148p4, ?x7121 = 04kjrv >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1347 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 9 *> proper extension: 0l14md; 028tv0; 05148p4; 02fsn; *> query: (?x7772, ?x645) <- role(?x5417, ?x7772), role(?x2460, ?x7772), role(?x1750, ?x7772), ?x1750 = 02hnl, ?x2460 = 01wy6, role(?x7772, ?x1969), role(?x7772, ?x645), role(?x10239, ?x1969), role(?x1969, ?x4583), role(?x1969, ?x2785), role(?x2675, ?x1969), role(?x2158, ?x1969), role(?x75, ?x1969), ?x4583 = 0bmnm, group(?x1969, ?x5858), group(?x1969, ?x5279), ?x10239 = 01p95y0, ?x5279 = 06nv27, ?x2158 = 01dnws, instrumentalists(?x1969, ?x4101), ?x2785 = 0jtg0, role(?x367, ?x1969), ?x4101 = 01vd7hn, ?x2675 = 020w2, ?x5417 = 02w3w, ?x75 = 07y_7, ?x5858 = 013w2r *> conf = 0.84 ranks of expected_values: 4 EVAL 0j862 role! 028tv0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 42.000 41.000 0.846 http://example.org/music/performance_role/regular_performances./music/group_membership/role #9430-011ydl PRED entity: 011ydl PRED relation: nominated_for! PRED expected values: 0gqy2 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 205): 019f4v (0.73 #279, 0.61 #969, 0.57 #1199), 0gr42 (0.67 #3221, 0.66 #4839, 0.66 #4378), 0gr0m (0.47 #284, 0.36 #974, 0.34 #1204), 0gs96 (0.43 #312, 0.24 #1002, 0.23 #1232), 04dn09n (0.43 #951, 0.38 #1181, 0.36 #261), 0gqy2 (0.42 #345, 0.37 #1035, 0.36 #1265), 0f4x7 (0.41 #943, 0.37 #253, 0.37 #1173), 0p9sw (0.35 #248, 0.27 #1168, 0.25 #938), 0l8z1 (0.35 #277, 0.24 #1197, 0.24 #967), 0gqyl (0.34 #302, 0.32 #992, 0.28 #1222) >> Best rule #279 for best value: >> intensional similarity = 5 >> extensional distance = 90 >> proper extension: 0m313; 083shs; 0bth54; 0n0bp; 0209hj; 017gl1; 0m_mm; 09q5w2; 0_92w; 020fcn; ... >> query: (?x3219, 019f4v) <- nominated_for(?x1313, ?x3219), nominated_for(?x484, ?x3219), ?x1313 = 0gs9p, genre(?x3219, ?x53), ?x484 = 0gq_v >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #345 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 90 *> proper extension: 0m313; 083shs; 0bth54; 0n0bp; 0209hj; 017gl1; 0m_mm; 09q5w2; 0_92w; 020fcn; ... *> query: (?x3219, 0gqy2) <- nominated_for(?x1313, ?x3219), nominated_for(?x484, ?x3219), ?x1313 = 0gs9p, genre(?x3219, ?x53), ?x484 = 0gq_v *> conf = 0.42 ranks of expected_values: 6 EVAL 011ydl nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 67.000 0.728 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9429-01tkqy PRED entity: 01tkqy PRED relation: film_crew_role! PRED expected values: 033f8n => 53 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1725): 06znpjr (0.78 #11209, 0.50 #4834, 0.47 #17585), 01gwk3 (0.78 #11032, 0.50 #4657, 0.43 #17408), 047wh1 (0.78 #10862, 0.50 #4487, 0.40 #17238), 024l2y (0.78 #10396, 0.50 #4021, 0.40 #16772), 05f4_n0 (0.78 #10734, 0.50 #4359, 0.37 #17110), 03z20c (0.78 #10564, 0.50 #4189, 0.33 #16940), 06fqlk (0.78 #11039, 0.50 #4664, 0.33 #17415), 0fdv3 (0.78 #10415, 0.50 #4040, 0.33 #16791), 0dp7wt (0.78 #11199, 0.50 #4824, 0.33 #2274), 020fcn (0.78 #10338, 0.50 #3963, 0.33 #1413) >> Best rule #11209 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 0dxtw; 01vx2h; >> query: (?x13719, 06znpjr) <- film_crew_role(?x8979, ?x13719), film_crew_role(?x2052, ?x13719), ?x8979 = 08c6k9, nominated_for(?x3066, ?x2052), nominated_for(?x2532, ?x2052), ?x3066 = 0gqy2, film_festivals(?x2052, ?x9080), award(?x276, ?x2532) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #10818 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 0dxtw; 01vx2h; *> query: (?x13719, 033f8n) <- film_crew_role(?x8979, ?x13719), film_crew_role(?x2052, ?x13719), ?x8979 = 08c6k9, nominated_for(?x3066, ?x2052), nominated_for(?x2532, ?x2052), ?x3066 = 0gqy2, film_festivals(?x2052, ?x9080), award(?x276, ?x2532) *> conf = 0.44 ranks of expected_values: 705 EVAL 01tkqy film_crew_role! 033f8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 19.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9428-02qjj7 PRED entity: 02qjj7 PRED relation: student! PRED expected values: 014mlp => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 14): 014mlp (0.12 #26, 0.11 #486, 0.09 #86), 07s6fsf (0.12 #21, 0.07 #41, 0.06 #61), 013zdg (0.05 #88, 0.04 #108, 0.01 #208), 019v9k (0.04 #150, 0.02 #350, 0.02 #230), 028dcg (0.03 #498, 0.02 #398, 0.02 #478), 02h4rq6 (0.03 #143, 0.02 #123, 0.02 #483), 0bkj86 (0.03 #149, 0.02 #489, 0.02 #1181), 03mkk4 (0.02 #493, 0.02 #1181, 0.01 #693), 02_xgp2 (0.02 #794, 0.02 #1181, 0.02 #894), 016t_3 (0.02 #1181, 0.01 #484, 0.01 #964) >> Best rule #26 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02cg2v; >> query: (?x445, 014mlp) <- nationality(?x445, ?x94), participant(?x444, ?x445), gender(?x445, ?x231), team(?x445, ?x8902) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qjj7 student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.125 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #9427-013423 PRED entity: 013423 PRED relation: artist! PRED expected values: 04gm7n => 111 concepts (63 used for prediction) PRED predicted values (max 10 best out of 99): 015_1q (0.30 #714, 0.25 #853, 0.23 #1131), 017l96 (0.20 #156, 0.13 #713, 0.12 #1547), 03rhqg (0.18 #849, 0.18 #1544, 0.15 #1127), 01clyr (0.17 #588, 0.10 #1561, 0.09 #1144), 0g768 (0.14 #174, 0.14 #2959, 0.13 #870), 01w40h (0.14 #584, 0.11 #1140, 0.10 #1557), 0181dw (0.12 #2127, 0.12 #2546, 0.11 #4079), 01trtc (0.12 #71, 0.10 #1879, 0.10 #2995), 02p11jq (0.11 #151, 0.10 #569, 0.09 #2796), 0mzkr (0.11 #581, 0.09 #1276, 0.09 #163) >> Best rule #714 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 07s3vqk; 0197tq; 03f2_rc; 0b68vs; 0137n0; 012x4t; 015_30; 086qd; 01trhmt; 01wwvc5; ... >> query: (?x6418, 015_1q) <- award_nominee(?x3235, ?x6418), location(?x6418, ?x1131), award(?x6418, ?x724), ?x724 = 01bgqh >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #96 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 01dwrc; *> query: (?x6418, 04gm7n) <- award_nominee(?x3235, ?x6418), award(?x6418, ?x724), participant(?x3235, ?x4397), ?x4397 = 0gyx4 *> conf = 0.04 ranks of expected_values: 48 EVAL 013423 artist! 04gm7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 111.000 63.000 0.296 http://example.org/music/record_label/artist #9426-07h34 PRED entity: 07h34 PRED relation: partially_contains PRED expected values: 02cgp8 => 193 concepts (152 used for prediction) PRED predicted values (max 10 best out of 37): 02cgp8 (0.27 #61, 0.16 #248, 0.13 #98), 05lx3 (0.14 #326, 0.13 #64, 0.12 #629), 026zt (0.13 #1232, 0.13 #1270, 0.06 #1307), 04ykz (0.13 #863, 0.12 #977, 0.12 #635), 0lcd (0.11 #1224, 0.10 #1262, 0.09 #127), 0k3nk (0.11 #200, 0.10 #237, 0.08 #274), 0fb18 (0.11 #1267, 0.02 #4125, 0.01 #5759), 0p2n (0.08 #1241, 0.03 #3048, 0.03 #3987), 06c6l (0.07 #216, 0.06 #253, 0.06 #290), 09glw (0.06 #1227, 0.04 #130, 0.04 #168) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0g0syc; >> query: (?x3778, 02cgp8) <- district_represented(?x11142, ?x3778), district_represented(?x176, ?x3778), ?x11142 = 01grq1, ?x176 = 03rl1g >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07h34 partially_contains 02cgp8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 152.000 0.267 http://example.org/location/location/partially_contains #9425-015bpl PRED entity: 015bpl PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.81 #96, 0.81 #121, 0.81 #156), 02nxhr (0.09 #2, 0.07 #12, 0.07 #7), 07z4p (0.03 #20, 0.02 #145, 0.02 #210), 07c52 (0.02 #223, 0.02 #173, 0.02 #248) >> Best rule #96 for best value: >> intensional similarity = 4 >> extensional distance = 1230 >> proper extension: 0fq27fp; 07l50vn; 0cvkv5; 09rfh9; >> query: (?x7989, 029j_) <- genre(?x7989, ?x811), currency(?x7989, ?x170), genre(?x6167, ?x811), ?x6167 = 05r3qc >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015bpl film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.811 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #9424-015srx PRED entity: 015srx PRED relation: group! PRED expected values: 05148p4 => 86 concepts (77 used for prediction) PRED predicted values (max 10 best out of 120): 0342h (0.91 #3441, 0.90 #1240, 0.90 #3530), 05148p4 (0.81 #1343, 0.79 #1078, 0.78 #1431), 02hnl (0.80 #1264, 0.79 #557, 0.77 #3465), 018vs (0.67 #630, 0.64 #541, 0.64 #2481), 03qjg (0.57 #576, 0.47 #1283, 0.25 #2076), 0l14qv (0.36 #534, 0.27 #623, 0.25 #1418), 06ncr (0.33 #39, 0.27 #656, 0.24 #1098), 07c6l (0.33 #10, 0.16 #1422, 0.13 #627), 07gql (0.33 #37, 0.13 #654, 0.12 #1449), 05r5c (0.29 #536, 0.26 #2036, 0.26 #2388) >> Best rule #3441 for best value: >> intensional similarity = 5 >> extensional distance = 182 >> proper extension: 089tm; 01pfr3; 0m19t; 07qnf; 07c0j; 02_5x9; 02r1tx7; 03fbc; 01vrwfv; 0249kn; ... >> query: (?x5793, 0342h) <- group(?x1466, ?x5793), artists(?x378, ?x5793), role(?x1466, ?x74), role(?x7238, ?x1466), ?x7238 = 0fq117k >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #1343 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 29 *> proper extension: 04r1t; 04qmr; 01cblr; 07h76; 017lb_; 01jkqfz; *> query: (?x5793, 05148p4) <- group(?x1466, ?x5793), artists(?x671, ?x5793), ?x1466 = 03bx0bm, artists(?x671, ?x4360), artists(?x671, ?x2926), ?x2926 = 016pns, religion(?x4360, ?x7300) *> conf = 0.81 ranks of expected_values: 2 EVAL 015srx group! 05148p4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 77.000 0.908 http://example.org/music/performance_role/regular_performances./music/group_membership/group #9423-0d29z PRED entity: 0d29z PRED relation: geographic_distribution PRED expected values: 0ctw_b 03rk0 => 22 concepts (21 used for prediction) PRED predicted values (max 10 best out of 876): 0697s (0.33 #176, 0.33 #24, 0.29 #278), 0j1z8 (0.33 #156, 0.33 #4, 0.29 #258), 03rk0 (0.33 #169, 0.29 #271, 0.29 #635), 06m_5 (0.33 #194, 0.29 #296, 0.29 #635), 05sb1 (0.33 #19, 0.25 #69, 0.20 #121), 0ctw_b (0.33 #8, 0.25 #58, 0.20 #110), 05b4w (0.33 #21, 0.25 #71, 0.20 #123), 05cgv (0.33 #11, 0.25 #61, 0.20 #113), 047yc (0.33 #9, 0.25 #59, 0.20 #111), 03h64 (0.33 #23, 0.25 #73, 0.20 #125) >> Best rule #176 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 06gbnc; 01rv7x; 04mvp8; >> query: (?x9148, 0697s) <- geographic_distribution(?x9148, ?x1229), film_release_region(?x5139, ?x1229), film_release_region(?x5016, ?x1229), film_release_region(?x3081, ?x1229), film_release_region(?x1988, ?x1229), country(?x4876, ?x1229), olympics(?x1229, ?x3729), olympics(?x1229, ?x418), ?x3729 = 0jdk_, ?x5139 = 07bzz7, second_level_divisions(?x1229, ?x3408), ?x3081 = 023gxx, ?x4876 = 0d1t3, combatants(?x151, ?x1229), ?x5016 = 062zm5h, ?x1988 = 09k56b7, ?x418 = 09n48 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #169 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 4 *> proper extension: 06gbnc; 01rv7x; 04mvp8; *> query: (?x9148, 03rk0) <- geographic_distribution(?x9148, ?x1229), film_release_region(?x5139, ?x1229), film_release_region(?x5016, ?x1229), film_release_region(?x3081, ?x1229), film_release_region(?x1988, ?x1229), country(?x4876, ?x1229), olympics(?x1229, ?x3729), olympics(?x1229, ?x418), ?x3729 = 0jdk_, ?x5139 = 07bzz7, second_level_divisions(?x1229, ?x3408), ?x3081 = 023gxx, ?x4876 = 0d1t3, combatants(?x151, ?x1229), ?x5016 = 062zm5h, ?x1988 = 09k56b7, ?x418 = 09n48 *> conf = 0.33 ranks of expected_values: 3, 6 EVAL 0d29z geographic_distribution 03rk0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 21.000 0.333 http://example.org/people/ethnicity/geographic_distribution EVAL 0d29z geographic_distribution 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 22.000 21.000 0.333 http://example.org/people/ethnicity/geographic_distribution #9422-0qmk5 PRED entity: 0qmk5 PRED relation: titles! PRED expected values: 07c52 => 62 concepts (44 used for prediction) PRED predicted values (max 10 best out of 58): 07c52 (0.70 #1284, 0.70 #30, 0.69 #763), 07s9rl0 (0.28 #4062, 0.27 #3958, 0.25 #4166), 03mdt (0.26 #313, 0.25 #206, 0.16 #254), 01z4y (0.20 #3678, 0.18 #3161, 0.15 #3890), 04xvlr (0.18 #3961, 0.17 #4065, 0.16 #4482), 01z77k (0.10 #794, 0.07 #2256, 0.07 #2671), 0215n (0.09 #915, 0.09 #1123, 0.08 #602), 024qqx (0.09 #3935, 0.06 #4246, 0.05 #4350), 07ssc (0.09 #3967, 0.09 #4071, 0.08 #4488), 01jfsb (0.08 #4289, 0.08 #3145, 0.08 #4498) >> Best rule #1284 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 0hr41p6; >> query: (?x11336, 07c52) <- nominated_for(?x435, ?x11336), program(?x8231, ?x11336), genre(?x11336, ?x571), genre(?x124, ?x571) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qmk5 titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 44.000 0.700 http://example.org/media_common/netflix_genre/titles #9421-0d0mbj PRED entity: 0d0mbj PRED relation: profession PRED expected values: 09jwl 02hv44_ 025352 => 134 concepts (60 used for prediction) PRED predicted values (max 10 best out of 94): 0cbd2 (0.94 #4972, 0.77 #3511, 0.68 #2635), 02hrh1q (0.85 #2934, 0.83 #7318, 0.83 #5709), 09jwl (0.78 #5568, 0.72 #4838, 0.50 #8492), 0nbcg (0.72 #8503, 0.67 #176, 0.53 #322), 0dxtg (0.50 #597, 0.46 #2203, 0.44 #1911), 016z4k (0.50 #4, 0.42 #150, 0.40 #296), 01d_h8 (0.41 #3364, 0.36 #3803, 0.35 #4095), 0dz3r (0.40 #4821, 0.34 #878, 0.33 #8475), 02hv44_ (0.30 #1807, 0.26 #2829, 0.26 #1077), 03gjzk (0.29 #599, 0.23 #3665, 0.21 #8342) >> Best rule #4972 for best value: >> intensional similarity = 5 >> extensional distance = 201 >> proper extension: 01h8f; 0dszr0; >> query: (?x6961, 0cbd2) <- profession(?x6961, ?x3746), student(?x2142, ?x6961), location(?x6961, ?x1310), profession(?x10000, ?x3746), ?x10000 = 03j0d >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #5568 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 262 *> proper extension: 01nqfh_; 02bfxb; 0bkg4; 01l4g5; 01r4hry; 03wjb7; *> query: (?x6961, 09jwl) <- profession(?x6961, ?x955), student(?x2142, ?x6961), profession(?x3737, ?x955), profession(?x226, ?x955), ?x3737 = 01q32bd, ?x226 = 05cljf *> conf = 0.78 ranks of expected_values: 3, 9, 20 EVAL 0d0mbj profession 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 134.000 60.000 0.941 http://example.org/people/person/profession EVAL 0d0mbj profession 02hv44_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 134.000 60.000 0.941 http://example.org/people/person/profession EVAL 0d0mbj profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 60.000 0.941 http://example.org/people/person/profession #9420-01wgxtl PRED entity: 01wgxtl PRED relation: artists! PRED expected values: 01flzq 036jv => 111 concepts (66 used for prediction) PRED predicted values (max 10 best out of 233): 06by7 (0.61 #1882, 0.60 #642, 0.52 #1572), 0xhtw (0.46 #637, 0.34 #1567, 0.29 #1877), 025sc50 (0.43 #981, 0.38 #3151, 0.37 #3771), 016clz (0.43 #625, 0.41 #1865, 0.39 #935), 05bt6j (0.36 #1904, 0.31 #1594, 0.30 #4384), 02lnbg (0.35 #2230, 0.33 #3160, 0.32 #3780), 06j6l (0.35 #3459, 0.34 #3769, 0.34 #3149), 0ggx5q (0.31 #2249, 0.30 #4651, 0.29 #3179), 0gywn (0.31 #2849, 0.30 #4651, 0.27 #8431), 016_v3 (0.30 #431, 0.17 #121, 0.16 #17987) >> Best rule #1882 for best value: >> intensional similarity = 2 >> extensional distance = 64 >> proper extension: 0frsw; 016fmf; 0b1zz; 081wh1; 089pg7; 017_hq; 016t0h; 016vj5; 0jg77; >> query: (?x2732, 06by7) <- award(?x2732, ?x4892), ?x4892 = 02f72_ >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #4651 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 104 *> proper extension: 01l1b90; 019g40; 0zjpz; 024dgj; 04f7c55; 01vng3b; 06tp4h; 04kjrv; 02jyhv; 01wvxw1; ... *> query: (?x2732, ?x2937) <- participant(?x7547, ?x2732), artist(?x1124, ?x2732), artists(?x2937, ?x7547) *> conf = 0.30 ranks of expected_values: 13, 15 EVAL 01wgxtl artists! 036jv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 111.000 66.000 0.606 http://example.org/music/genre/artists EVAL 01wgxtl artists! 01flzq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 111.000 66.000 0.606 http://example.org/music/genre/artists #9419-01gp_d PRED entity: 01gp_d PRED relation: service_language! PRED expected values: 05b5c => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 150): 064f29 (0.67 #923, 0.60 #493, 0.50 #206), 018mxj (0.67 #869, 0.60 #439, 0.33 #1300), 0p4wb (0.50 #1299, 0.50 #725, 0.50 #151), 069b85 (0.50 #995, 0.50 #278, 0.42 #1426), 04sv4 (0.50 #230, 0.40 #661, 0.40 #517), 05b5c (0.50 #277, 0.40 #708, 0.40 #564), 0gvbw (0.50 #167, 0.40 #598, 0.40 #454), 07zl6m (0.50 #282, 0.40 #713, 0.40 #425), 0dmtp (0.50 #205, 0.40 #636, 0.40 #348), 05w3y (0.50 #925, 0.40 #495, 0.33 #65) >> Best rule #923 for best value: >> intensional similarity = 15 >> extensional distance = 4 >> proper extension: 06nm1; >> query: (?x9936, 064f29) <- language(?x6605, ?x9936), language(?x6578, ?x9936), language(?x2943, ?x9936), film_crew_role(?x6578, ?x468), ?x6605 = 012kyx, film(?x541, ?x6578), nominated_for(?x3508, ?x6578), language(?x6578, ?x2164), ?x2164 = 03_9r, country(?x6578, ?x94), film(?x902, ?x2943), ?x902 = 05qd_, award_winner(?x2943, ?x406), titles(?x53, ?x2943), nominated_for(?x112, ?x2943) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #277 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 04306rv; *> query: (?x9936, 05b5c) <- language(?x6605, ?x9936), language(?x6578, ?x9936), ?x6578 = 01y9jr, official_language(?x1892, ?x9936), service_language(?x555, ?x9936), country(?x6605, ?x94), nominated_for(?x1429, ?x6605), film(?x5942, ?x6605), film_release_distribution_medium(?x6605, ?x81), countries_spoken_in(?x9936, ?x304), currency(?x6605, ?x1099), ?x94 = 09c7w0, film_release_region(?x1259, ?x1892), ?x1259 = 04hwbq *> conf = 0.50 ranks of expected_values: 6 EVAL 01gp_d service_language! 05b5c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 40.000 40.000 0.667 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #9418-01v1d8 PRED entity: 01v1d8 PRED relation: role! PRED expected values: 0l14j_ => 64 concepts (43 used for prediction) PRED predicted values (max 10 best out of 107): 05r5c (0.91 #3603, 0.80 #1904, 0.79 #3074), 0l14md (0.86 #3073, 0.83 #2007, 0.82 #2639), 02sgy (0.80 #2530, 0.80 #1896, 0.80 #1796), 018vs (0.80 #2530, 0.77 #3183, 0.76 #2001), 04rzd (0.80 #2530, 0.76 #2001, 0.70 #1933), 0dwt5 (0.80 #1975, 0.80 #1869, 0.74 #2719), 042v_gx (0.80 #1799, 0.76 #2001, 0.75 #2756), 01vj9c (0.80 #1911, 0.75 #2440, 0.75 #2121), 05148p4 (0.79 #3510, 0.76 #2552, 0.75 #3297), 02fsn (0.78 #1739, 0.75 #2055, 0.70 #1892) >> Best rule #3603 for best value: >> intensional similarity = 20 >> extensional distance = 32 >> proper extension: 0dwr4; 0gghm; 0bxl5; >> query: (?x3161, 05r5c) <- role(?x3215, ?x3161), role(?x2297, ?x3161), role(?x1225, ?x3161), role(?x645, ?x3161), role(?x7112, ?x3215), role(?x2765, ?x3215), role(?x3161, ?x314), performance_role(?x1574, ?x1225), instrumentalists(?x3161, ?x3494), ?x7112 = 0133x7, role(?x3160, ?x3161), ?x314 = 02sgy, role(?x1225, ?x432), role(?x75, ?x2297), performance_role(?x1817, ?x1225), award_nominee(?x3293, ?x3494), role(?x679, ?x645), role(?x3215, ?x433), ?x2765 = 01w724, ?x433 = 025cbm >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #1953 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 8 *> proper extension: 0mkg; *> query: (?x3161, 0l14j_) <- role(?x4913, ?x3161), role(?x3215, ?x3161), role(?x1750, ?x3161), role(?x1225, ?x3161), ?x3215 = 0bxl5, role(?x1225, ?x4078), role(?x314, ?x3161), group(?x3161, ?x3682), instrumentalists(?x3161, ?x140), ?x4078 = 011k_j, role(?x1660, ?x1225), ?x1750 = 02hnl, performance_role(?x1817, ?x1225), performance_role(?x1089, ?x314), ?x4913 = 03ndd, role(?x217, ?x314), instrumentalists(?x314, ?x133) *> conf = 0.70 ranks of expected_values: 28 EVAL 01v1d8 role! 0l14j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 64.000 43.000 0.912 http://example.org/music/performance_role/regular_performances./music/group_membership/role #9417-03c7ln PRED entity: 03c7ln PRED relation: role PRED expected values: 02sgy => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 113): 0342h (0.57 #282, 0.51 #1391, 0.50 #190), 01vj9c (0.36 #290, 0.36 #186, 0.26 #93), 02sgy (0.36 #284, 0.33 #192, 0.30 #1393), 03qjg (0.36 #186, 0.30 #1762, 0.26 #93), 02hnl (0.36 #186, 0.30 #1762, 0.26 #93), 0gkd1 (0.36 #186, 0.30 #1762, 0.26 #93), 01vdm0 (0.33 #122, 0.29 #307, 0.28 #583), 0l14qv (0.24 #3255, 0.24 #3160, 0.24 #3351), 05148p4 (0.22 #113, 0.14 #298, 0.13 #1310), 0dq630k (0.21 #319, 0.17 #227, 0.13 #411) >> Best rule #282 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 082brv; 03mszl; >> query: (?x211, 0342h) <- artists(?x9750, ?x211), ?x9750 = 016zgj, role(?x211, ?x212), gender(?x211, ?x231) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #284 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 082brv; 03mszl; *> query: (?x211, 02sgy) <- artists(?x9750, ?x211), ?x9750 = 016zgj, role(?x211, ?x212), gender(?x211, ?x231) *> conf = 0.36 ranks of expected_values: 3 EVAL 03c7ln role 02sgy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.571 http://example.org/music/artist/track_contributions./music/track_contribution/role #9416-01v3s2_ PRED entity: 01v3s2_ PRED relation: profession PRED expected values: 0dxtg => 111 concepts (86 used for prediction) PRED predicted values (max 10 best out of 59): 018gz8 (0.83 #461, 0.78 #313, 0.67 #17), 0dxtg (0.50 #162, 0.37 #5196, 0.33 #458), 0np9r (0.44 #316, 0.42 #464, 0.20 #6386), 01d_h8 (0.37 #1338, 0.36 #5188, 0.35 #4744), 09jwl (0.36 #4905, 0.33 #167, 0.33 #611), 03gjzk (0.34 #5197, 0.26 #6233, 0.25 #7417), 0cbd2 (0.33 #451, 0.33 #7, 0.22 #303), 0nbcg (0.31 #623, 0.26 #3733, 0.24 #4917), 016z4k (0.31 #596, 0.22 #4890, 0.21 #3706), 02jknp (0.30 #1340, 0.24 #748, 0.24 #1044) >> Best rule #461 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0pz7h; 08vr94; 04s430; >> query: (?x905, 018gz8) <- cast_members(?x905, ?x906), gender(?x905, ?x231), profession(?x905, ?x1032) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #162 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 01m65sp; *> query: (?x905, 0dxtg) <- people(?x10798, ?x905), gender(?x905, ?x231), ?x10798 = 019kn7 *> conf = 0.50 ranks of expected_values: 2 EVAL 01v3s2_ profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 86.000 0.833 http://example.org/people/person/profession #9415-01sp81 PRED entity: 01sp81 PRED relation: nationality PRED expected values: 06v36 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.77 #7626, 0.76 #4853, 0.73 #6834), 07ssc (0.33 #609, 0.24 #15, 0.19 #114), 03rk0 (0.09 #1332, 0.08 #1926, 0.06 #4005), 0f8l9c (0.05 #22, 0.04 #121, 0.03 #1309), 04xn_ (0.05 #73), 0d060g (0.05 #205, 0.04 #7632, 0.04 #1789), 03rt9 (0.04 #112, 0.02 #1300, 0.02 #904), 0345h (0.03 #3991, 0.02 #922, 0.02 #1912), 03rjj (0.02 #3965, 0.02 #1490, 0.02 #302), 0chghy (0.02 #208, 0.02 #1099, 0.02 #1990) >> Best rule #7626 for best value: >> intensional similarity = 2 >> extensional distance = 3559 >> proper extension: 01nvdc; 03cxqp5; >> query: (?x926, 09c7w0) <- nationality(?x926, ?x1310), country_of_origin(?x2777, ?x1310) >> conf = 0.77 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01sp81 nationality 06v36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 84.000 0.772 http://example.org/people/person/nationality #9414-02rmd_2 PRED entity: 02rmd_2 PRED relation: language PRED expected values: 02h40lc => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 48): 02h40lc (0.92 #1805, 0.90 #664, 0.89 #2828), 06nm1 (0.25 #72, 0.18 #433, 0.18 #314), 012w70 (0.25 #74, 0.18 #435, 0.12 #316), 02bjrlw (0.25 #62, 0.17 #364, 0.10 #3065), 064_8sq (0.20 #564, 0.18 #145, 0.16 #864), 04306rv (0.18 #128, 0.17 #368, 0.14 #488), 03_9r (0.18 #133, 0.07 #493, 0.07 #1571), 0t_2 (0.11 #377, 0.09 #198, 0.06 #856), 06b_j (0.11 #506, 0.09 #1165, 0.08 #1045), 03115z (0.09 #460, 0.09 #161, 0.06 #341) >> Best rule #1805 for best value: >> intensional similarity = 5 >> extensional distance = 151 >> proper extension: 02pg45; 0295sy; 0286gm1; 025s1wg; >> query: (?x4372, 02h40lc) <- edited_by(?x4372, ?x7984), film(?x3462, ?x4372), genre(?x4372, ?x239), film(?x6532, ?x4372), award_nominee(?x6532, ?x237) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rmd_2 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.915 http://example.org/film/film/language #9413-0kftt PRED entity: 0kftt PRED relation: award PRED expected values: 0bfvw2 0bdwft => 112 concepts (94 used for prediction) PRED predicted values (max 10 best out of 299): 03x3wf (0.71 #21658, 0.71 #22060, 0.70 #34098), 01by1l (0.47 #2919, 0.45 #2518, 0.40 #3320), 05p09zm (0.45 #2129, 0.32 #4535, 0.32 #1728), 01bgqh (0.45 #2449, 0.44 #1246, 0.42 #3251), 03qbh5 (0.42 #2612, 0.38 #1409, 0.36 #3414), 0f4x7 (0.41 #1635, 0.23 #2036, 0.16 #3640), 09sb52 (0.40 #7259, 0.37 #14879, 0.34 #20495), 05zr6wv (0.36 #1621, 0.32 #2022, 0.21 #4428), 054ks3 (0.33 #142, 0.32 #2548, 0.27 #3350), 0c4z8 (0.33 #72, 0.29 #2478, 0.27 #3280) >> Best rule #21658 for best value: >> intensional similarity = 3 >> extensional distance = 1200 >> proper extension: 01w92; 026v1z; >> query: (?x8423, ?x1088) <- award_winner(?x8423, ?x1993), award_nominee(?x8423, ?x772), award_winner(?x1088, ?x8423) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1272 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0g476; *> query: (?x8423, 0bdwft) <- award(?x8423, ?x4382), film(?x8423, ?x4404), ?x4382 = 03tk6z *> conf = 0.12 ranks of expected_values: 89, 139 EVAL 0kftt award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 112.000 94.000 0.710 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0kftt award 0bfvw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 112.000 94.000 0.710 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9412-0bkf72 PRED entity: 0bkf72 PRED relation: place_of_birth PRED expected values: 02_286 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 61): 01_d4 (0.17 #66, 0.04 #6402, 0.04 #2178), 02_286 (0.10 #3539, 0.09 #14099, 0.09 #13395), 0cr3d (0.07 #2206, 0.06 #2910, 0.05 #4318), 09c7w0 (0.06 #1409, 0.03 #9153, 0.03 #2817), 030qb3t (0.05 #758, 0.04 #16246, 0.04 #1462), 02dtg (0.03 #714, 0.01 #3530, 0.01 #13386), 0rh6k (0.03 #5634, 0.03 #1410, 0.02 #7042), 094jv (0.03 #1469, 0.03 #5693, 0.02 #7101), 0b1t1 (0.03 #1774, 0.02 #1070, 0.01 #5294), 01m1zk (0.03 #1556, 0.02 #852) >> Best rule #66 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 086k8; >> query: (?x8590, 01_d4) <- award(?x8590, ?x198), award_nominee(?x8590, ?x4383), ?x4383 = 07b3r9 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #3539 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 133 *> proper extension: 0jt90f5; 02xp18; 0br1w; 014dm6; 02661h; 07jrjb; 05hrq4; 017dpj; *> query: (?x8590, 02_286) <- award(?x8590, ?x198), program(?x8590, ?x4384), type_of_union(?x8590, ?x566) *> conf = 0.10 ranks of expected_values: 2 EVAL 0bkf72 place_of_birth 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.167 http://example.org/people/person/place_of_birth #9411-027hq5f PRED entity: 027hq5f PRED relation: profession PRED expected values: 02hrh1q => 72 concepts (36 used for prediction) PRED predicted values (max 10 best out of 72): 02hrh1q (1.00 #1484, 0.91 #2516, 0.91 #1926), 0dxtg (0.52 #601, 0.50 #454, 0.46 #1483), 02jknp (0.46 #1477, 0.43 #448, 0.34 #595), 018gz8 (0.40 #605, 0.14 #1634, 0.13 #1487), 0np9r (0.36 #167, 0.33 #20, 0.28 #314), 03gjzk (0.33 #14, 0.30 #456, 0.30 #603), 02krf9 (0.33 #26, 0.14 #1497, 0.14 #615), 0kyk (0.16 #618, 0.10 #1794, 0.10 #4712), 09jwl (0.16 #4730, 0.16 #5025, 0.15 #4288), 015cjr (0.14 #196, 0.11 #343, 0.08 #638) >> Best rule #1484 for best value: >> intensional similarity = 5 >> extensional distance = 611 >> proper extension: 04rs03; 02pp_q_; 0415svh; 067jsf; 01pr_j6; 01g4zr; 0162c8; 06w33f8; 0177s6; 01t07j; ... >> query: (?x13348, 02hrh1q) <- profession(?x13348, ?x5716), profession(?x13348, ?x319), profession(?x8917, ?x5716), ?x8917 = 0kt64b, ?x319 = 01d_h8 >> conf = 1.00 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027hq5f profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 36.000 0.997 http://example.org/people/person/profession #9410-0738b8 PRED entity: 0738b8 PRED relation: location PRED expected values: 02xry => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 93): 030qb3t (0.26 #13683, 0.23 #2481, 0.21 #37691), 0cc56 (0.15 #55, 0.14 #855, 0.05 #37665), 01n7q (0.15 #4061, 0.05 #13663, 0.04 #7262), 04jpl (0.10 #37626, 0.08 #25622, 0.08 #46428), 0cr3d (0.09 #10543, 0.08 #46553, 0.07 #44952), 07z1m (0.08 #77, 0.07 #877, 0.05 #4077), 07b_l (0.08 #183, 0.07 #983, 0.04 #4183), 0rh6k (0.08 #4, 0.07 #804, 0.03 #4004), 01sn3 (0.08 #211, 0.07 #1011, 0.02 #3411), 03v0t (0.08 #195, 0.07 #995, 0.02 #4195) >> Best rule #13683 for best value: >> intensional similarity = 3 >> extensional distance = 421 >> proper extension: 05m63c; 01vw87c; 02g8h; 0d_84; 01ty7ll; 033hqf; 0htlr; 03_vx9; 01q7cb_; 0456xp; ... >> query: (?x2437, 030qb3t) <- location(?x2437, ?x335), film(?x2437, ?x428), participant(?x2437, ?x545) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #4129 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 239 *> proper extension: 02nb2s; 08433; 02r34n; 02jt1k; 02lq10; 04gcd1; 04fhxp; 0jt90f5; 02mxw0; 0m32_; ... *> query: (?x2437, 02xry) <- location(?x2437, ?x1755), film(?x2437, ?x428), district_represented(?x176, ?x1755) *> conf = 0.04 ranks of expected_values: 24 EVAL 0738b8 location 02xry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 85.000 85.000 0.258 http://example.org/people/person/places_lived./people/place_lived/location #9409-0jzc PRED entity: 0jzc PRED relation: official_language! PRED expected values: 01n6c => 81 concepts (74 used for prediction) PRED predicted values (max 10 best out of 295): 0hzlz (0.62 #343, 0.56 #685, 0.56 #1033), 01znc_ (0.62 #343, 0.56 #685, 0.56 #1033), 0d060g (0.62 #343, 0.56 #685, 0.56 #1033), 01z215 (0.62 #343, 0.56 #685, 0.56 #1033), 047yc (0.62 #343, 0.56 #685, 0.56 #1033), 0j1z8 (0.62 #343, 0.56 #685, 0.56 #1033), 07dzf (0.62 #343, 0.56 #685, 0.56 #1033), 03shp (0.62 #343, 0.56 #685, 0.54 #858), 07fj_ (0.62 #343, 0.56 #685, 0.54 #858), 027jk (0.62 #343, 0.56 #685, 0.54 #858) >> Best rule #343 for best value: >> intensional similarity = 13 >> extensional distance = 1 >> proper extension: 02h40lc; >> query: (?x5359, ?x279) <- languages(?x7341, ?x5359), language(?x10024, ?x5359), language(?x5001, ?x5359), language(?x2892, ?x5359), language(?x2368, ?x5359), language(?x924, ?x5359), ?x5001 = 09q23x, countries_spoken_in(?x5359, ?x279), ?x2368 = 075cph, ?x924 = 04gknr, ?x2892 = 05q54f5, ?x10024 = 0b2km_, official_language(?x1756, ?x5359) >> conf = 0.62 => this is the best rule for 11 predicted values *> Best rule #911 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 5 *> proper extension: 0999q; *> query: (?x5359, 01n6c) <- languages(?x7341, ?x5359), countries_spoken_in(?x5359, ?x10451), countries_spoken_in(?x5359, ?x4092), countries_spoken_in(?x5359, ?x3730), countries_spoken_in(?x5359, ?x3016), ?x3016 = 0697s, member_states(?x7695, ?x10451), languages_spoken(?x1176, ?x5359), currency(?x10451, ?x170), film_release_region(?x186, ?x4092), taxonomy(?x4092, ?x939), contains(?x3730, ?x5237) *> conf = 0.14 ranks of expected_values: 113 EVAL 0jzc official_language! 01n6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 81.000 74.000 0.618 http://example.org/location/country/official_language #9408-0160w PRED entity: 0160w PRED relation: medal PRED expected values: 02lq67 => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 2): 02lq5w (0.81 #110, 0.81 #148, 0.79 #78), 02lq67 (0.79 #109, 0.76 #103, 0.75 #167) >> Best rule #110 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 03f2w; >> query: (?x126, 02lq5w) <- country(?x3693, ?x126), olympics(?x126, ?x778), organization(?x126, ?x127) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #109 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 03f2w; *> query: (?x126, 02lq67) <- country(?x3693, ?x126), olympics(?x126, ?x778), organization(?x126, ?x127) *> conf = 0.79 ranks of expected_values: 2 EVAL 0160w medal 02lq67 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 161.000 161.000 0.811 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #9407-01_wfj PRED entity: 01_wfj PRED relation: artists! PRED expected values: 0hdf8 0bt7w => 85 concepts (47 used for prediction) PRED predicted values (max 10 best out of 278): 06by7 (0.82 #5298, 0.79 #2499, 0.72 #4676), 03lty (0.61 #8113, 0.57 #648, 0.50 #1266), 05bt6j (0.58 #4074, 0.55 #4697, 0.39 #5319), 016jny (0.57 #725, 0.50 #1343, 0.33 #415), 02yv6b (0.50 #409, 0.40 #1337, 0.30 #3818), 064t9 (0.49 #4356, 0.49 #3424, 0.48 #14010), 08jyyk (0.43 #687, 0.40 #1305, 0.35 #2854), 09nwwf (0.43 #757, 0.33 #447, 0.32 #2615), 03339m (0.43 #789, 0.30 #1407, 0.22 #1098), 0cx7f (0.38 #4794, 0.37 #2308, 0.36 #4171) >> Best rule #5298 for best value: >> intensional similarity = 10 >> extensional distance = 65 >> proper extension: 0ftps; 016h9b; 050z2; 0f0qfz; 01vs4ff; 01k47c; 023322; 01t8399; 018gkb; >> query: (?x9999, 06by7) <- artists(?x1380, ?x9999), artists(?x302, ?x9999), ?x1380 = 0dl5d, artists(?x302, ?x10745), artists(?x302, ?x7210), artists(?x302, ?x4237), role(?x7210, ?x227), ?x10745 = 01s560x, location(?x4237, ?x8771), parent_genre(?x301, ?x302) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #690 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 5 *> proper extension: 0pkyh; 01w8n89; *> query: (?x9999, 0hdf8) <- artists(?x1380, ?x9999), artists(?x1000, ?x9999), artists(?x302, ?x9999), ?x1380 = 0dl5d, ?x302 = 016clz, category(?x9999, ?x134), ?x134 = 08mbj5d, artist(?x441, ?x9999), ?x1000 = 0xhtw *> conf = 0.29 ranks of expected_values: 22, 84 EVAL 01_wfj artists! 0bt7w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 85.000 47.000 0.821 http://example.org/music/genre/artists EVAL 01_wfj artists! 0hdf8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 85.000 47.000 0.821 http://example.org/music/genre/artists #9406-05lnk0 PRED entity: 05lnk0 PRED relation: profession PRED expected values: 0cbd2 => 63 concepts (51 used for prediction) PRED predicted values (max 10 best out of 56): 0dxtg (0.98 #1505, 0.55 #3892, 0.54 #760), 0cbd2 (0.96 #1051, 0.52 #455, 0.51 #753), 0n1h (0.84 #907, 0.24 #609, 0.22 #758), 02hrh1q (0.71 #314, 0.69 #164, 0.62 #5087), 01d_h8 (0.63 #2094, 0.57 #3884, 0.53 #3437), 02jknp (0.43 #2096, 0.42 #1499, 0.38 #3886), 09jwl (0.41 #915, 0.16 #7479, 0.16 #1660), 03gjzk (0.41 #2104, 0.36 #1507, 0.34 #3447), 0nbcg (0.35 #928, 0.11 #4211, 0.10 #7492), 016z4k (0.34 #899, 0.08 #1644, 0.08 #4031) >> Best rule #1505 for best value: >> intensional similarity = 5 >> extensional distance = 975 >> proper extension: 09gffmz; 06gn7r; 01lct6; >> query: (?x7627, 0dxtg) <- profession(?x7627, ?x8310), profession(?x12948, ?x8310), profession(?x8309, ?x8310), ?x8309 = 01vl17, ?x12948 = 02gnj2 >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #1051 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 448 *> proper extension: 0grwj; 084w8; 0l6qt; 0hl3d; 06y9c2; 0152cw; 0m77m; 09dt7; 01q415; 06k02; ... *> query: (?x7627, 0cbd2) <- nationality(?x7627, ?x94), profession(?x7627, ?x8310), profession(?x8209, ?x8310), ?x8209 = 01y8d4 *> conf = 0.96 ranks of expected_values: 2 EVAL 05lnk0 profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 51.000 0.979 http://example.org/people/person/profession #9405-0h7pj PRED entity: 0h7pj PRED relation: actor! PRED expected values: 04x4gj => 131 concepts (104 used for prediction) PRED predicted values (max 10 best out of 75): 030cx (0.37 #10350, 0.27 #10349, 0.09 #23372), 06sfk6 (0.27 #10349, 0.10 #18583, 0.09 #17786), 0ds33 (0.27 #10349, 0.10 #18583, 0.09 #17786), 0gbtbm (0.25 #341, 0.07 #606, 0.06 #871), 07nnp_ (0.10 #18583, 0.09 #17786, 0.09 #16724), 0pk1p (0.10 #18583, 0.09 #17786, 0.09 #16724), 03rtz1 (0.10 #18583, 0.09 #17786, 0.09 #16724), 0180mw (0.07 #1180, 0.04 #3567, 0.04 #2242), 05631 (0.07 #1317, 0.03 #2114, 0.02 #3704), 080dwhx (0.07 #536, 0.06 #801, 0.03 #10089) >> Best rule #10350 for best value: >> intensional similarity = 3 >> extensional distance = 421 >> proper extension: 0f721s; 0283xx2; 03lpbx; >> query: (?x8898, ?x4535) <- award_winner(?x693, ?x8898), award_winner(?x4535, ?x8898), genre(?x4535, ?x258) >> conf = 0.37 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0h7pj actor! 04x4gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 104.000 0.366 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #9404-022s1m PRED entity: 022s1m PRED relation: actor! PRED expected values: 04mx8h4 => 98 concepts (35 used for prediction) PRED predicted values (max 10 best out of 165): 05f7w84 (0.50 #632, 0.14 #3002, 0.14 #1684), 0jwl2 (0.33 #72, 0.25 #335, 0.14 #861), 04mx8h4 (0.33 #701, 0.08 #1753, 0.06 #1490), 015w8_ (0.17 #571, 0.09 #2150, 0.08 #1623), 0vhm (0.17 #616, 0.06 #4483, 0.05 #2195), 01hvv0 (0.17 #677, 0.06 #4483, 0.05 #1729), 03y3bp7 (0.17 #569, 0.03 #1358, 0.03 #1621), 080dwhx (0.14 #795, 0.12 #1058, 0.06 #4483), 0d68qy (0.14 #826, 0.12 #1089, 0.06 #4483), 02_1q9 (0.14 #794, 0.12 #1057, 0.06 #4483) >> Best rule #632 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 044_7j; >> query: (?x12851, 05f7w84) <- gender(?x12851, ?x231), language(?x12851, ?x254), ?x231 = 05zppz, actor(?x8628, ?x12851), ?x8628 = 09g_31 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #701 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 044_7j; *> query: (?x12851, 04mx8h4) <- gender(?x12851, ?x231), language(?x12851, ?x254), ?x231 = 05zppz, actor(?x8628, ?x12851), ?x8628 = 09g_31 *> conf = 0.33 ranks of expected_values: 3 EVAL 022s1m actor! 04mx8h4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 35.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #9403-07lwsz PRED entity: 07lwsz PRED relation: gender PRED expected values: 05zppz => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #17, 0.86 #27, 0.83 #5), 02zsn (0.28 #32, 0.28 #42, 0.27 #30) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 189 >> proper extension: 0gg9_5q; 01mwsnc; 04bgy; 017l4; 03y2kr; 0jpdn; 02465; 024t0y; 01g04k; >> query: (?x3571, 05zppz) <- type_of_union(?x3571, ?x566), profession(?x3571, ?x319), executive_produced_by(?x8770, ?x3571) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07lwsz gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.874 http://example.org/people/person/gender #9402-016kft PRED entity: 016kft PRED relation: award PRED expected values: 04kxsb 0bdwqv => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 229): 0cqhb3 (0.73 #30908, 0.71 #28099, 0.71 #15260), 09sb52 (0.33 #4053, 0.32 #3250, 0.32 #8069), 0cqhk0 (0.18 #1641, 0.15 #2042, 0.15 #1239), 0gqy2 (0.13 #4175, 0.13 #14858, 0.13 #5781), 0fbtbt (0.13 #14858, 0.11 #1032, 0.09 #2236), 04kxsb (0.13 #14858, 0.09 #3333, 0.09 #4136), 0gq9h (0.13 #14858, 0.09 #8907, 0.08 #7302), 0gs9p (0.13 #14858, 0.08 #8909, 0.07 #19273), 019f4v (0.13 #14858, 0.07 #8896, 0.07 #867), 0gr4k (0.13 #14858, 0.07 #8863, 0.07 #19273) >> Best rule #30908 for best value: >> intensional similarity = 3 >> extensional distance = 2278 >> proper extension: 06lxn; >> query: (?x9359, ?x8250) <- award_winner(?x8250, ?x9359), award(?x1676, ?x8250), award_nominee(?x968, ?x1676) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #14858 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1535 *> proper extension: 0kcd5; *> query: (?x9359, ?x143) <- award_winner(?x8250, ?x9359), nominated_for(?x9359, ?x4231), nominated_for(?x143, ?x4231) *> conf = 0.13 ranks of expected_values: 6, 37 EVAL 016kft award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 96.000 96.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 016kft award 04kxsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 96.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9401-0285c PRED entity: 0285c PRED relation: instrumentalists! PRED expected values: 018vs => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 122): 0342h (0.80 #772, 0.75 #346, 0.75 #260), 02hnl (0.72 #202, 0.64 #714, 0.44 #1365), 018vs (0.55 #352, 0.49 #949, 0.47 #2911), 05148p4 (0.50 #360, 0.45 #957, 0.44 #2919), 0l14md (0.44 #1365, 0.43 #945, 0.43 #2987), 028tv0 (0.44 #1365, 0.43 #2987, 0.41 #342), 06w87 (0.33 #3592, 0.30 #768, 0.30 #2989), 05842k (0.31 #3333, 0.30 #768, 0.30 #2989), 01vj9c (0.31 #3333, 0.30 #768, 0.30 #2989), 03qjg (0.30 #391, 0.27 #561, 0.27 #903) >> Best rule #772 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0jfx1; >> query: (?x1955, 0342h) <- profession(?x1955, ?x2659), role(?x1955, ?x212), people(?x3584, ?x1955), ?x2659 = 039v1 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #352 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 019389; *> query: (?x1955, 018vs) <- profession(?x1955, ?x2659), profession(?x1955, ?x2348), ?x2348 = 0nbcg, ?x2659 = 039v1, performance_role(?x1955, ?x212) *> conf = 0.55 ranks of expected_values: 3 EVAL 0285c instrumentalists! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 121.000 121.000 0.805 http://example.org/music/instrument/instrumentalists #9400-05zl0 PRED entity: 05zl0 PRED relation: citytown PRED expected values: 0ljsz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 181): 09c7w0 (0.23 #11051, 0.21 #29097, 0.20 #26887), 02_286 (0.18 #8119, 0.18 #9224, 0.18 #8488), 094jv (0.06 #22101, 0.06 #34, 0.06 #8473), 0430_ (0.06 #22101), 0b2lw (0.06 #22101), 04f_d (0.06 #22101), 01qh7 (0.06 #62, 0.05 #2640, 0.05 #430), 01zqy6t (0.06 #343, 0.05 #711, 0.05 #1079), 0f2nf (0.06 #208, 0.05 #576, 0.05 #944), 0rh6k (0.06 #1, 0.05 #369, 0.04 #1106) >> Best rule #11051 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 01zn4y; >> query: (?x6056, ?x94) <- category(?x6056, ?x134), company(?x346, ?x6056), contains(?x94, ?x6056) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #3572 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 0194_r; *> query: (?x6056, 0ljsz) <- company(?x1159, ?x6056), student(?x6056, ?x445), state_province_region(?x6056, ?x6895) *> conf = 0.02 ranks of expected_values: 94 EVAL 05zl0 citytown 0ljsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 98.000 98.000 0.230 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #9399-0pmp2 PRED entity: 0pmp2 PRED relation: contains! PRED expected values: 0d060g => 177 concepts (71 used for prediction) PRED predicted values (max 10 best out of 331): 0d060g (0.93 #22416, 0.81 #40325, 0.78 #10752), 07ssc (0.82 #21539, 0.47 #2717, 0.42 #44837), 04jpl (0.76 #2707, 0.56 #23322, 0.45 #7189), 09c7w0 (0.73 #39431, 0.70 #16131, 0.68 #24200), 04ykg (0.61 #4559, 0.18 #18002, 0.16 #20691), 02jx1 (0.59 #2772, 0.57 #21594, 0.38 #23387), 02_286 (0.58 #12587, 0.43 #17961, 0.39 #20650), 059rby (0.46 #12564, 0.34 #17938, 0.31 #20627), 05k7sb (0.38 #31496, 0.06 #3713, 0.04 #27916), 0pmp2 (0.38 #56462, 0.35 #57360, 0.27 #48391) >> Best rule #22416 for best value: >> intensional similarity = 5 >> extensional distance = 86 >> proper extension: 0pmpl; 04s7y; 01kxnd; 059ss; 0694j; 015jr; 06nrt; 059t8; 0jpkw; 0j95; ... >> query: (?x2453, 0d060g) <- contains(?x6842, ?x2453), adjoins(?x335, ?x6842), jurisdiction_of_office(?x12303, ?x6842), contains(?x6842, ?x481), ?x481 = 052nd >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pmp2 contains! 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 71.000 0.932 http://example.org/location/location/contains #9398-0gsg7 PRED entity: 0gsg7 PRED relation: award_winner! PRED expected values: 03gwpw2 => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 119): 05c1t6z (0.33 #15, 0.20 #710, 0.14 #1127), 04n2r9h (0.25 #1296, 0.22 #5049, 0.21 #5327), 09gkdln (0.25 #398, 0.20 #4290, 0.20 #2066), 02q690_ (0.25 #1733, 0.09 #6042, 0.07 #15912), 0h_9252 (0.20 #4228, 0.12 #4784, 0.10 #7565), 09q_6t (0.19 #4317, 0.03 #15855, 0.03 #18774), 0fqpc7d (0.14 #3928, 0.13 #4206, 0.10 #2399), 0fz0c2 (0.13 #4275, 0.08 #3441, 0.08 #3302), 03gwpw2 (0.12 #1399, 0.12 #1260, 0.11 #5013), 0c53zb (0.12 #1590, 0.08 #2980, 0.08 #3397) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 05gnf; >> query: (?x1762, 05c1t6z) <- program(?x1762, ?x50), award_winner(?x2062, ?x1762), ?x2062 = 09d5h >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1399 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 022tfp; *> query: (?x1762, 03gwpw2) <- program(?x1762, ?x6415), program(?x1762, ?x623), country_of_origin(?x623, ?x94), film_crew_role(?x6415, ?x9094) *> conf = 0.12 ranks of expected_values: 9 EVAL 0gsg7 award_winner! 03gwpw2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 182.000 182.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9397-01jbx1 PRED entity: 01jbx1 PRED relation: student! PRED expected values: 01bzw5 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 211): 065y4w7 (0.17 #14, 0.07 #5789, 0.06 #9464), 02s62q (0.17 #52, 0.05 #1102, 0.04 #1627), 01jt2w (0.17 #281, 0.05 #1331, 0.02 #3431), 0bwfn (0.12 #1848, 0.09 #2898, 0.08 #9723), 01w5m (0.10 #630, 0.07 #13758, 0.04 #30037), 08815 (0.10 #527, 0.05 #4727, 0.05 #15756), 09f2j (0.10 #682, 0.05 #20113, 0.04 #6457), 017v71 (0.10 #718, 0.04 #2293, 0.02 #3343), 02d9nr (0.10 #811, 0.04 #2386, 0.01 #5536), 01lhdt (0.10 #783, 0.04 #2358, 0.01 #13911) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 076df9; >> query: (?x3291, 065y4w7) <- program_creator(?x11033, ?x3291), category(?x3291, ?x134), actor(?x9076, ?x3291) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jbx1 student! 01bzw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 105.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #9396-07hgm PRED entity: 07hgm PRED relation: artists! PRED expected values: 06by7 => 74 concepts (60 used for prediction) PRED predicted values (max 10 best out of 281): 06by7 (0.70 #1237, 0.67 #2454, 0.67 #4590), 016clz (0.53 #1526, 0.52 #2135, 0.50 #2439), 03_d0 (0.47 #11580, 0.33 #620, 0.22 #4582), 0gywn (0.44 #662, 0.27 #11622, 0.25 #1271), 0ggx5q (0.40 #1291, 0.22 #682, 0.21 #1595), 0y3_8 (0.35 #1261, 0.34 #1565, 0.31 #2478), 06j6l (0.34 #11613, 0.33 #653, 0.31 #4615), 0xhtw (0.33 #625, 0.29 #4587, 0.20 #5197), 0fd3y (0.33 #315, 0.29 #11, 0.23 #923), 02qdgx (0.33 #645, 0.10 #2471, 0.10 #3080) >> Best rule #1237 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 03t9sp; 0136p1; 01tp5bj; 016fmf; 0dm5l; 01l_vgt; 03xhj6; 016fnb; 02bgmr; 01wgjj5; ... >> query: (?x9497, 06by7) <- artists(?x3370, ?x9497), artists(?x671, ?x9497), ?x3370 = 059kh, origin(?x9497, ?x739), ?x671 = 064t9 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07hgm artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 60.000 0.700 http://example.org/music/genre/artists #9395-07ccs PRED entity: 07ccs PRED relation: student PRED expected values: 014ps4 => 141 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1485): 01q415 (0.18 #6614, 0.12 #12888, 0.04 #25435), 024y6w (0.18 #7726, 0.08 #9817, 0.06 #14000), 016kb7 (0.18 #7635, 0.06 #13909, 0.04 #26456), 08k1lz (0.17 #10102, 0.14 #1737, 0.12 #3828), 0d3k14 (0.14 #1854, 0.12 #3945, 0.10 #16493), 010hn (0.14 #368, 0.12 #2459, 0.08 #8733), 01h5f8 (0.14 #1913, 0.12 #4004, 0.08 #10278), 02p8v8 (0.14 #1661, 0.12 #3752, 0.08 #10026), 01hbq0 (0.14 #2057, 0.12 #4148, 0.08 #10422), 01zfmm (0.14 #441, 0.12 #2532, 0.08 #8806) >> Best rule #6614 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 03np_7; >> query: (?x6333, 01q415) <- state_province_region(?x6333, ?x3634), student(?x6333, ?x5350), ?x3634 = 07b_l >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #7624 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 03np_7; *> query: (?x6333, 014ps4) <- state_province_region(?x6333, ?x3634), student(?x6333, ?x5350), ?x3634 = 07b_l *> conf = 0.09 ranks of expected_values: 212 EVAL 07ccs student 014ps4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 141.000 114.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student #9394-0jmnl PRED entity: 0jmnl PRED relation: school PRED expected values: 01pl14 => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 591): 0bx8pn (0.50 #767, 0.43 #953, 0.40 #1140), 01pl14 (0.43 #935, 0.33 #749, 0.26 #1117), 0j_sncb (0.43 #969, 0.33 #783, 0.26 #1117), 06pwq (0.33 #1124, 0.33 #751, 0.29 #1310), 01jsk6 (0.33 #911, 0.33 #167, 0.29 #1097), 01ptt7 (0.33 #1144, 0.33 #27, 0.24 #1702), 065y4w7 (0.33 #753, 0.31 #3735, 0.29 #5783), 0pspl (0.33 #794, 0.29 #980, 0.26 #1117), 0225bv (0.33 #919, 0.29 #1105, 0.26 #1117), 07ccs (0.33 #847, 0.29 #1033, 0.26 #1117) >> Best rule #767 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 0jmj7; 0jm4b; 0jm64; >> query: (?x13777, 0bx8pn) <- team(?x4834, ?x13777), draft(?x13777, ?x12852), draft(?x13777, ?x8586), draft(?x13777, ?x4979), ?x12852 = 06439y, school(?x13777, ?x9200), ?x8586 = 038981, ?x9200 = 0dzst, ?x4979 = 0f4vx0, athlete(?x4833, ?x4834) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #935 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 0jmm4; *> query: (?x13777, 01pl14) <- school(?x13777, ?x9200), ?x9200 = 0dzst, draft(?x13777, ?x4979), draft(?x10409, ?x4979), draft(?x2398, ?x4979), ?x2398 = 0jmfb, ?x10409 = 0jmh7, school(?x4979, ?x331) *> conf = 0.43 ranks of expected_values: 2 EVAL 0jmnl school 01pl14 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 50.000 50.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9393-017mbb PRED entity: 017mbb PRED relation: award PRED expected values: 02f77l => 91 concepts (80 used for prediction) PRED predicted values (max 10 best out of 254): 02f6xy (0.58 #2624, 0.24 #1816, 0.22 #6664), 01by1l (0.55 #14257, 0.47 #2537, 0.42 #19109), 01c9jp (0.51 #3825, 0.40 #3421, 0.32 #5845), 02f72_ (0.50 #633, 0.37 #6693, 0.31 #1037), 0c4z8 (0.45 #1688, 0.45 #2496, 0.31 #880), 01bgqh (0.45 #2467, 0.38 #6507, 0.37 #3679), 02f716 (0.42 #6640, 0.29 #13512, 0.28 #3812), 054ks3 (0.41 #1759, 0.38 #951, 0.34 #2567), 03tcnt (0.39 #6630, 0.25 #570, 0.21 #11885), 02f73p (0.39 #3823, 0.35 #6651, 0.33 #11906) >> Best rule #2624 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 01wwvd2; >> query: (?x9206, 02f6xy) <- award(?x9206, ?x2877), role(?x9206, ?x227), award(?x3834, ?x2877), ?x3834 = 01wzlxj >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #659 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 0qf11; *> query: (?x9206, 02f77l) <- artists(?x3061, ?x9206), artists(?x1572, ?x9206), ?x3061 = 05bt6j, artist(?x13110, ?x9206), ?x1572 = 06by7, ?x13110 = 03vtrv *> conf = 0.25 ranks of expected_values: 23 EVAL 017mbb award 02f77l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 91.000 80.000 0.579 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9392-02pxst PRED entity: 02pxst PRED relation: film! PRED expected values: 02dbn2 => 96 concepts (25 used for prediction) PRED predicted values (max 10 best out of 981): 016ggh (0.08 #12279, 0.06 #18525, 0.03 #37264), 0f5xn (0.08 #970, 0.05 #7216, 0.03 #3052), 01q_ph (0.07 #25041, 0.04 #47945, 0.03 #50027), 0lpjn (0.07 #10889, 0.05 #17135, 0.03 #35874), 0170qf (0.07 #8696, 0.05 #368, 0.03 #21188), 0j_c (0.07 #19148, 0.02 #6656), 01nwwl (0.06 #10913, 0.05 #2585, 0.05 #6749), 016xh5 (0.06 #11492, 0.04 #17738, 0.03 #19820), 025j1t (0.05 #9405, 0.03 #21897, 0.02 #5241), 01vsn38 (0.05 #49743, 0.05 #51825, 0.02 #24757) >> Best rule #12279 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 02qr46y; >> query: (?x7170, 016ggh) <- titles(?x512, ?x7170), ?x512 = 07ssc, nominated_for(?x2379, ?x7170), nominated_for(?x2306, ?x7170) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #5020 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 02pb2bp; 0dh8v4; 02r9p0c; 03gyvwg; *> query: (?x7170, 02dbn2) <- film(?x1104, ?x7170), language(?x7170, ?x2164), film(?x2306, ?x7170), ?x2164 = 03_9r *> conf = 0.02 ranks of expected_values: 562 EVAL 02pxst film! 02dbn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 96.000 25.000 0.079 http://example.org/film/actor/film./film/performance/film #9391-02w86hz PRED entity: 02w86hz PRED relation: film_release_region PRED expected values: 09pmkv 05sb1 => 92 concepts (87 used for prediction) PRED predicted values (max 10 best out of 182): 0d0vqn (0.90 #3097, 0.90 #4300, 0.90 #4643), 059j2 (0.88 #4331, 0.85 #4674, 0.84 #4159), 05r4w (0.86 #2576, 0.83 #4635, 0.83 #4120), 03rjj (0.86 #4640, 0.84 #4297, 0.79 #6189), 0345h (0.83 #4333, 0.82 #2617, 0.81 #3130), 0chghy (0.83 #4305, 0.81 #4648, 0.80 #6197), 03h64 (0.83 #4198, 0.77 #4370, 0.77 #2654), 035qy (0.81 #4335, 0.74 #2619, 0.74 #3132), 03gj2 (0.81 #4666, 0.80 #4323, 0.76 #4151), 0k6nt (0.81 #4665, 0.79 #4150, 0.78 #4493) >> Best rule #3097 for best value: >> intensional similarity = 6 >> extensional distance = 82 >> proper extension: 053tj7; 0gh6j94; 0hz6mv2; >> query: (?x3742, 0d0vqn) <- film_release_region(?x3742, ?x2152), film_release_region(?x3742, ?x1892), ?x2152 = 06mkj, film_format(?x3742, ?x6392), country(?x8190, ?x1892), ?x8190 = 09_9n >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #4326 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 143 *> proper extension: 0djb3vw; 0h95zbp; *> query: (?x3742, 09pmkv) <- film_release_region(?x3742, ?x2152), film_release_region(?x3742, ?x512), ?x2152 = 06mkj, genre(?x3742, ?x225), film_crew_role(?x3742, ?x468), ?x468 = 02r96rf, ?x512 = 07ssc *> conf = 0.39 ranks of expected_values: 42, 55 EVAL 02w86hz film_release_region 05sb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 92.000 87.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02w86hz film_release_region 09pmkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 92.000 87.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9390-084qpk PRED entity: 084qpk PRED relation: music PRED expected values: 089kpp => 94 concepts (77 used for prediction) PRED predicted values (max 10 best out of 88): 01hw6wq (0.33 #37, 0.19 #662, 0.02 #1500), 04pf4r (0.33 #67, 0.05 #692, 0.04 #1530), 02bh9 (0.17 #258, 0.07 #466, 0.06 #1513), 0b6yp2 (0.17 #259, 0.02 #1094, 0.02 #4039), 02ryx0 (0.17 #317, 0.02 #1152, 0.01 #1572), 0146pg (0.14 #1472, 0.07 #2101, 0.07 #5674), 01x1fq (0.10 #798, 0.02 #5210, 0.01 #5838), 01m4yn (0.08 #4825, 0.07 #7770, 0.07 #11978), 07q1v4 (0.07 #430, 0.05 #639, 0.02 #848), 0csdzz (0.07 #601, 0.02 #8591, 0.02 #4799) >> Best rule #37 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 03bx2lk; >> query: (?x814, 01hw6wq) <- film(?x6569, ?x814), film(?x3117, ?x814), film(?x3056, ?x814), ?x3056 = 01vvb4m, ?x6569 = 03q43g, written_by(?x2177, ?x3117) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2505 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 193 *> proper extension: 0ddfwj1; 07xtqq; 087wc7n; 0jjy0; 0f40w; 08rr3p; 011ycb; 01l_pn; 01y9jr; 07pd_j; ... *> query: (?x814, 089kpp) <- film(?x3117, ?x814), film(?x3056, ?x814), award_winner(?x401, ?x3056), executive_produced_by(?x4880, ?x3117), film(?x3117, ?x2177) *> conf = 0.02 ranks of expected_values: 55 EVAL 084qpk music 089kpp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 94.000 77.000 0.333 http://example.org/film/film/music #9389-07vhb PRED entity: 07vhb PRED relation: institution! PRED expected values: 014mlp => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 20): 014mlp (0.80 #133, 0.77 #68, 0.76 #111), 016t_3 (0.55 #131, 0.54 #66, 0.52 #109), 0bkj86 (0.50 #71, 0.47 #136, 0.42 #114), 07s6fsf (0.44 #64, 0.42 #129, 0.40 #107), 04zx3q1 (0.38 #65, 0.36 #2, 0.35 #130), 013zdg (0.31 #70, 0.29 #135, 0.28 #113), 027f2w (0.29 #9, 0.24 #201, 0.24 #222), 03mkk4 (0.29 #11, 0.21 #74, 0.18 #161), 01rr_d (0.21 #15, 0.18 #1412, 0.17 #78), 0bjrnt (0.21 #219, 0.18 #1412, 0.16 #1367) >> Best rule #133 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 05krk; 01pl14; 06pwq; 01w3v; 0kz2w; 07szy; 0bx8pn; 07wrz; 07vht; 07tg4; ... >> query: (?x5280, 014mlp) <- major_field_of_study(?x5280, ?x1695), ?x1695 = 06ms6, institution(?x865, ?x5280) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07vhb institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9388-06nvzg PRED entity: 06nvzg PRED relation: organization! PRED expected values: 060c4 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 27): 060c4 (0.76 #262, 0.76 #314, 0.75 #288), 0dq_5 (0.34 #568, 0.34 #699, 0.34 #582), 05k17c (0.20 #1199, 0.14 #847, 0.11 #85), 07xl34 (0.20 #1199, 0.14 #847, 0.09 #1196), 0dq3c (0.20 #1199, 0.14 #847, 0.05 #573), 09d6p2 (0.05 #573, 0.02 #192, 0.01 #231), 028fjr (0.05 #573), 04192r (0.05 #573), 06hpx2 (0.05 #573), 02h53vq (0.05 #573) >> Best rule #262 for best value: >> intensional similarity = 7 >> extensional distance = 74 >> proper extension: 015zyd; 0g2c8; 0473m9; 06jk5_; 04rwx; 037s9x; 031n8c; 02s62q; 07w3r; 07wrz; ... >> query: (?x12371, 060c4) <- currency(?x12371, ?x170), state_province_region(?x12371, ?x335), registering_agency(?x12371, ?x1982), country(?x12371, ?x94), ?x94 = 09c7w0, ?x170 = 09nqf, ?x1982 = 03z19 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06nvzg organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.763 http://example.org/organization/role/leaders./organization/leadership/organization #9387-0sxg4 PRED entity: 0sxg4 PRED relation: nominated_for! PRED expected values: 040njc => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 192): 0k611 (0.65 #753, 0.58 #982, 0.47 #3731), 040njc (0.60 #922, 0.58 #693, 0.58 #3671), 03hkv_r (0.60 #1617, 0.58 #701, 0.58 #1846), 0l8z1 (0.53 #736, 0.38 #1652, 0.36 #965), 054krc (0.53 #749, 0.36 #978, 0.34 #1665), 02qyntr (0.52 #3837, 0.51 #1088, 0.47 #859), 099c8n (0.51 #740, 0.44 #1656, 0.43 #1885), 0gr0m (0.49 #741, 0.34 #1657, 0.33 #512), 02qvyrt (0.44 #776, 0.40 #1005, 0.34 #3754), 0gqy2 (0.43 #343, 0.37 #801, 0.35 #7443) >> Best rule #753 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 0fpv_3_; 09tkzy; 02p86pb; >> query: (?x161, 0k611) <- nominated_for(?x1180, ?x161), nominated_for(?x1107, ?x161), film(?x374, ?x161), ?x1180 = 02n9nmz, ?x1107 = 019f4v >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #922 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 53 *> proper extension: 03q8xj; *> query: (?x161, 040njc) <- nominated_for(?x2375, ?x161), nominated_for(?x601, ?x161), ?x2375 = 04kxsb, ?x601 = 0gr4k, nominated_for(?x374, ?x161) *> conf = 0.60 ranks of expected_values: 2 EVAL 0sxg4 nominated_for! 040njc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.651 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9386-01wy61y PRED entity: 01wy61y PRED relation: artists! PRED expected values: 03_d0 01kcty => 132 concepts (65 used for prediction) PRED predicted values (max 10 best out of 310): 06j6l (0.87 #7956, 0.83 #10991, 0.80 #8562), 09nwwf (0.71 #5305, 0.50 #1953, 0.45 #1346), 025sc50 (0.54 #1566, 0.52 #2783, 0.38 #958), 03_d0 (0.53 #4571, 0.47 #4877, 0.46 #6098), 0gywn (0.50 #5531, 0.50 #965, 0.47 #4615), 02yv6b (0.50 #1916, 0.33 #701, 0.32 #3438), 0155w (0.50 #3446, 0.33 #709, 0.27 #3141), 05w3f (0.43 #1857, 0.33 #642, 0.27 #3379), 05bt6j (0.42 #10380, 0.35 #14027, 0.31 #16152), 01fh36 (0.36 #1904, 0.27 #3426, 0.27 #3121) >> Best rule #7956 for best value: >> intensional similarity = 8 >> extensional distance = 111 >> proper extension: 01l1b90; 01wbl_r; 05d8vw; 086qd; 01364q; 01pgzn_; 0126y2; 030155; 044gyq; 01q32bd; ... >> query: (?x4162, 06j6l) <- artists(?x1127, ?x4162), artists(?x1127, ?x10539), artists(?x1127, ?x8032), artists(?x1127, ?x7597), ?x7597 = 03c3yf, ?x8032 = 0xsk8, type_of_union(?x4162, ?x566), ?x10539 = 028qyn >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #4571 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 32 *> proper extension: 032nwy; 02mslq; 01qkqwg; 04mn81; 03j0br4; 09hnb; 01w724; 01w7nww; 01vsykc; 01w8n89; ... *> query: (?x4162, 03_d0) <- artists(?x5934, ?x4162), artists(?x1127, ?x4162), ?x1127 = 02x8m, instrumentalists(?x74, ?x4162), place_of_birth(?x4162, ?x4163), parent_genre(?x2407, ?x5934) *> conf = 0.53 ranks of expected_values: 4, 187 EVAL 01wy61y artists! 01kcty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 132.000 65.000 0.867 http://example.org/music/genre/artists EVAL 01wy61y artists! 03_d0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 65.000 0.867 http://example.org/music/genre/artists #9385-045bs6 PRED entity: 045bs6 PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #11, 0.77 #23, 0.76 #19), 02zsn (0.46 #92, 0.42 #36, 0.40 #30) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 073v6; >> query: (?x2025, 05zppz) <- nationality(?x2025, ?x279), ?x279 = 0d060g, student(?x3439, ?x2025), religion(?x2025, ?x1985) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045bs6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.850 http://example.org/people/person/gender #9384-05b6s5j PRED entity: 05b6s5j PRED relation: genre PRED expected values: 066wd => 105 concepts (96 used for prediction) PRED predicted values (max 10 best out of 132): 07s9rl0 (0.95 #4137, 0.94 #6746, 0.92 #5398), 05p553 (0.91 #5485, 0.83 #5654, 0.58 #3214), 06n90 (0.86 #2797, 0.71 #2884, 0.44 #5325), 03k9fj (0.82 #2289, 0.41 #5323, 0.40 #515), 066wd (0.76 #2023, 0.69 #1515, 0.50 #142), 01htzx (0.68 #2295, 0.43 #2888, 0.40 #2801), 01z4y (0.58 #5499, 0.54 #5668, 0.40 #270), 0hcr (0.52 #5331, 0.50 #523, 0.35 #2297), 0c4xc (0.40 #5524, 0.38 #5693, 0.33 #3253), 0lsxr (0.40 #261, 0.25 #429, 0.24 #1103) >> Best rule #4137 for best value: >> intensional similarity = 8 >> extensional distance = 64 >> proper extension: 08l0x2; 06w7mlh; 07wqr6; 0h63q6t; >> query: (?x11414, 07s9rl0) <- languages(?x11414, ?x254), program(?x11100, ?x11414), genre(?x11414, ?x225), program(?x11249, ?x11414), genre(?x4538, ?x225), genre(?x924, ?x225), ?x924 = 04gknr, ?x4538 = 0q9sg >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #2023 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 28 *> proper extension: 03wh49y; *> query: (?x11414, ?x53) <- program_creator(?x11414, ?x12775), genre(?x11414, ?x225), program_creator(?x8818, ?x12775), gender(?x12775, ?x231), ?x231 = 05zppz, genre(?x8818, ?x53), religion(?x12775, ?x1985) *> conf = 0.76 ranks of expected_values: 5 EVAL 05b6s5j genre 066wd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 105.000 96.000 0.955 http://example.org/tv/tv_program/genre #9383-04chyn PRED entity: 04chyn PRED relation: institution! PRED expected values: 02h4rq6 03bwzr4 => 149 concepts (86 used for prediction) PRED predicted values (max 10 best out of 21): 02h4rq6 (0.82 #332, 0.80 #284, 0.79 #590), 03bwzr4 (0.72 #60, 0.63 #224, 0.62 #295), 019v9k (0.71 #243, 0.66 #218, 0.64 #289), 07s6fsf (0.67 #47, 0.54 #282, 0.52 #211), 02_xgp2 (0.56 #105, 0.56 #58, 0.54 #480), 0bkj86 (0.47 #217, 0.46 #100, 0.44 #53), 013zdg (0.33 #216, 0.25 #287, 0.22 #52), 04zx3q1 (0.28 #470, 0.28 #212, 0.26 #95), 027f2w (0.26 #102, 0.25 #244, 0.24 #219), 01rr_d (0.21 #181, 0.19 #1976, 0.19 #1189) >> Best rule #332 for best value: >> intensional similarity = 7 >> extensional distance = 123 >> proper extension: 02gr81; 09f2j; 027mdh; 0gl5_; 01r3w7; 022fj_; 01p896; 01c57n; >> query: (?x3248, 02h4rq6) <- category(?x3248, ?x134), institution(?x1200, ?x3248), major_field_of_study(?x3248, ?x1527), major_field_of_study(?x10104, ?x1527), major_field_of_study(?x8363, ?x1527), ?x8363 = 0k__z, ?x10104 = 0177sq >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04chyn institution! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 86.000 0.816 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 04chyn institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 86.000 0.816 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9382-018vs PRED entity: 018vs PRED relation: role! PRED expected values: 01wsl7c 0gcs9 01wwnh2 => 79 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1101): 02s6sh (0.67 #6377, 0.56 #5977, 0.50 #3978), 0j6cj (0.60 #1899, 0.56 #6299, 0.44 #11497), 0l12d (0.60 #1665, 0.50 #8063, 0.44 #6065), 04mx7s (0.60 #2711, 0.48 #398, 0.44 #5910), 017f4y (0.60 #1972, 0.44 #6372, 0.33 #11570), 01w9mnm (0.60 #1920, 0.44 #6320, 0.33 #5920), 01vv7sc (0.56 #6031, 0.44 #5631, 0.33 #32), 01vn35l (0.56 #5711, 0.40 #3310, 0.40 #2512), 045zr (0.50 #3699, 0.44 #6098, 0.43 #4099), 01vsyjy (0.48 #398, 0.40 #1872, 0.38 #2798) >> Best rule #6377 for best value: >> intensional similarity = 9 >> extensional distance = 7 >> proper extension: 0l15bq; >> query: (?x716, 02s6sh) <- role(?x2157, ?x716), role(?x1225, ?x716), role(?x433, ?x716), role(?x211, ?x716), role(?x716, ?x75), ?x433 = 025cbm, role(?x645, ?x716), ?x2157 = 011_6p, ?x1225 = 01qbl >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #6117 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 7 *> proper extension: 0l15bq; *> query: (?x716, 0gcs9) <- role(?x2157, ?x716), role(?x1225, ?x716), role(?x433, ?x716), role(?x211, ?x716), role(?x716, ?x75), ?x433 = 025cbm, role(?x645, ?x716), ?x2157 = 011_6p, ?x1225 = 01qbl *> conf = 0.44 ranks of expected_values: 48, 49, 110 EVAL 018vs role! 01wwnh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 79.000 63.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 018vs role! 0gcs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 79.000 63.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 018vs role! 01wsl7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 79.000 63.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #9381-02f2dn PRED entity: 02f2dn PRED relation: nationality PRED expected values: 07ssc => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 58): 09c7w0 (0.77 #6048, 0.74 #595, 0.72 #6147), 07ssc (0.33 #510, 0.09 #1998, 0.09 #114), 03rk0 (0.07 #1234, 0.06 #4605, 0.05 #1631), 0d060g (0.04 #7342, 0.04 #2584, 0.04 #1295), 03rjj (0.03 #1586, 0.03 #104, 0.03 #203), 0chghy (0.03 #1586, 0.03 #10, 0.02 #1795), 0f8l9c (0.03 #1586, 0.03 #22, 0.02 #3988), 0345h (0.03 #1586, 0.02 #1617, 0.02 #4591), 03rt9 (0.03 #1586, 0.02 #409, 0.01 #7348), 0d05w3 (0.03 #1586, 0.02 #1238, 0.01 #148) >> Best rule #6048 for best value: >> intensional similarity = 2 >> extensional distance = 2033 >> proper extension: 07_grx; 0bm9xk; >> query: (?x2646, 09c7w0) <- nationality(?x2646, ?x1310), award_nominee(?x2646, ?x396) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #510 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 394 *> proper extension: 07_3qd; 0784v1; 0fv6dr; 09lhln; 0bw7ly; 0djvzd; 07m69t; 0dv1hh; 09m465; 07zr66; *> query: (?x2646, 07ssc) <- nationality(?x2646, ?x1310), ?x1310 = 02jx1 *> conf = 0.33 ranks of expected_values: 2 EVAL 02f2dn nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.771 http://example.org/people/person/nationality #9380-096lf_ PRED entity: 096lf_ PRED relation: award_nominee! PRED expected values: 031296 => 117 concepts (42 used for prediction) PRED predicted values (max 10 best out of 770): 031296 (0.81 #91007, 0.81 #77003, 0.81 #81673), 06chf (0.21 #81672, 0.04 #624, 0.03 #98009), 0sz28 (0.21 #81672, 0.01 #11909, 0.01 #14242), 046qq (0.21 #81672), 01vvb4m (0.21 #81672), 01xcfy (0.21 #81672), 021yc7p (0.21 #81672), 06msq2 (0.15 #5705, 0.12 #3372, 0.11 #1039), 01xdf5 (0.13 #4703, 0.06 #2370, 0.04 #37), 055sjw (0.11 #2080, 0.09 #4413, 0.08 #6746) >> Best rule #91007 for best value: >> intensional similarity = 3 >> extensional distance = 1192 >> proper extension: 080knyg; 01vx5w7; >> query: (?x10086, ?x1784) <- film(?x10086, ?x1586), award_nominee(?x10086, ?x1784), film_release_distribution_medium(?x1586, ?x81) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 096lf_ award_nominee! 031296 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 42.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9379-0407yfx PRED entity: 0407yfx PRED relation: executive_produced_by PRED expected values: 05_k56 => 73 concepts (36 used for prediction) PRED predicted values (max 10 best out of 134): 05hj_k (0.19 #3850, 0.18 #4353, 0.18 #4603), 06pj8 (0.19 #2305, 0.16 #2556, 0.09 #3808), 06q8hf (0.19 #3918, 0.18 #4421, 0.18 #4671), 079vf (0.15 #2252, 0.05 #1752, 0.04 #7014), 0697kh (0.12 #183, 0.09 #433, 0.01 #1183), 0fz27v (0.11 #716, 0.01 #7228, 0.01 #7478), 0glyyw (0.10 #1937, 0.05 #7199, 0.05 #7449), 0343h (0.09 #2292, 0.08 #2543, 0.05 #542), 01twdk (0.09 #362, 0.05 #1112, 0.04 #1612), 03c9pqt (0.08 #1995, 0.07 #2245, 0.04 #1245) >> Best rule #3850 for best value: >> intensional similarity = 4 >> extensional distance = 235 >> proper extension: 083shs; 0170_p; 09p35z; 02hxhz; 0b73_1d; 02qm_f; 048scx; 0k2sk; 02v63m; 02prw4h; ... >> query: (?x2155, 05hj_k) <- executive_produced_by(?x2155, ?x3456), award_winner(?x3455, ?x3456), award_winner(?x4224, ?x3456), film(?x2156, ?x2155) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #783 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 03h3x5; 02c7k4; *> query: (?x2155, 05_k56) <- genre(?x2155, ?x2540), nominated_for(?x3911, ?x2155), ?x2540 = 0hcr, ?x3911 = 02x1z2s *> conf = 0.07 ranks of expected_values: 11 EVAL 0407yfx executive_produced_by 05_k56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 73.000 36.000 0.190 http://example.org/film/film/executive_produced_by #9378-011xg5 PRED entity: 011xg5 PRED relation: film! PRED expected values: 016khd => 120 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1261): 06pj8 (0.72 #153860, 0.71 #68603, 0.71 #158019), 03r1pr (0.47 #70683, 0.47 #153859, 0.47 #83159), 06rnl9 (0.47 #70683, 0.47 #153859, 0.47 #83159), 027rwmr (0.47 #70683, 0.47 #153859, 0.47 #83159), 0146pg (0.47 #70683, 0.47 #153859, 0.47 #83159), 01vy_v8 (0.40 #733, 0.05 #29834, 0.05 #19443), 02yxwd (0.22 #2823, 0.06 #6981, 0.06 #4901), 0h5g_ (0.20 #74, 0.11 #2153, 0.04 #33331), 0c0k1 (0.20 #1505, 0.06 #7742, 0.06 #5662), 09l3p (0.20 #748, 0.06 #153861, 0.05 #124755) >> Best rule #153860 for best value: >> intensional similarity = 4 >> extensional distance = 867 >> proper extension: 042zrm; 03bzyn4; 01bjbk; 06pyc2; >> query: (?x8349, ?x2135) <- genre(?x8349, ?x53), nominated_for(?x2135, ?x8349), film(?x2135, ?x1743), award_winner(?x2135, ?x798) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #10532 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 056xkh; *> query: (?x8349, 016khd) <- genre(?x8349, ?x53), film(?x382, ?x8349), film(?x3558, ?x8349), produced_by(?x8349, ?x846) *> conf = 0.03 ranks of expected_values: 486 EVAL 011xg5 film! 016khd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 120.000 76.000 0.722 http://example.org/film/actor/film./film/performance/film #9377-0d060g PRED entity: 0d060g PRED relation: second_level_divisions PRED expected values: 01x42h => 195 concepts (164 used for prediction) PRED predicted values (max 10 best out of 600): 015jr (0.22 #25089, 0.22 #7380, 0.17 #27055), 0j95 (0.22 #25089, 0.22 #7380, 0.17 #27055), 059t8 (0.22 #25089, 0.22 #7380, 0.17 #27055), 06nrt (0.22 #25089, 0.22 #7380, 0.17 #27055), 059s8 (0.22 #25089, 0.22 #7380, 0.17 #27055), 05kr_ (0.22 #25089, 0.22 #7380, 0.17 #27055), 05j49 (0.22 #25089, 0.22 #7380, 0.17 #27055), 0694j (0.22 #25089, 0.22 #7380, 0.17 #67875), 059ss (0.22 #25089, 0.22 #7380, 0.17 #67875), 04s7y (0.22 #25089, 0.22 #7380, 0.17 #67875) >> Best rule #25089 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0gslw; >> query: (?x279, ?x3474) <- contains(?x279, ?x6842), contains(?x279, ?x5678), state_province_region(?x6091, ?x6842), contains(?x3474, ?x5678) >> conf = 0.22 => this is the best rule for 25 predicted values No rule for expected values ranks of expected_values: EVAL 0d060g second_level_divisions 01x42h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 195.000 164.000 0.224 http://example.org/location/country/second_level_divisions #9376-02psqkz PRED entity: 02psqkz PRED relation: combatants PRED expected values: 015qh => 126 concepts (100 used for prediction) PRED predicted values (max 10 best out of 341): 0154j (0.84 #3088, 0.83 #4990, 0.83 #2785), 07ssc (0.83 #4990, 0.83 #2785, 0.83 #3394), 06npd (0.83 #4990, 0.83 #2785, 0.83 #3394), 015qh (0.83 #4990, 0.83 #2785, 0.83 #3394), 03bxbql (0.83 #4990, 0.83 #2785, 0.83 #3394), 087vz (0.79 #1021, 0.72 #1249, 0.50 #2520), 035qy (0.79 #1004, 0.67 #1232, 0.50 #325), 0chghy (0.71 #992, 0.67 #1220, 0.65 #3095), 015fr (0.64 #995, 0.61 #1223, 0.42 #846), 05vz3zq (0.64 #1029, 0.56 #1257, 0.50 #350) >> Best rule #3088 for best value: >> intensional similarity = 8 >> extensional distance = 35 >> proper extension: 06npd; 02vzc; >> query: (?x3918, ?x279) <- capital(?x3918, ?x9660), combatants(?x279, ?x3918), film_release_region(?x6270, ?x279), film_release_region(?x5255, ?x279), ?x5255 = 01sby_, contains(?x279, ?x481), ?x6270 = 0g9zljd, combatants(?x3278, ?x3918) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #4990 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 45 *> proper extension: 0285m87; 03f4n1; *> query: (?x3918, ?x172) <- capital(?x3918, ?x9660), combatants(?x279, ?x3918), combatants(?x172, ?x3918), contains(?x279, ?x9061), combatants(?x94, ?x279), adjoins(?x479, ?x279), category(?x9061, ?x134) *> conf = 0.83 ranks of expected_values: 4 EVAL 02psqkz combatants 015qh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 126.000 100.000 0.840 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #9375-0vm39 PRED entity: 0vm39 PRED relation: locations! PRED expected values: 0b_6s7 => 115 concepts (79 used for prediction) PRED predicted values (max 10 best out of 117): 0b_6lb (0.28 #2826, 0.20 #719, 0.19 #591), 0b_6xf (0.28 #2826, 0.18 #3217, 0.17 #2181), 0gx1673 (0.28 #2826, 0.18 #3217, 0.17 #2181), 0hhtgcw (0.28 #2826, 0.18 #3217, 0.17 #2181), 0466p0j (0.28 #2826, 0.18 #3217, 0.17 #2181), 0lk8j (0.28 #2826, 0.18 #3217, 0.17 #2181), 0l6ny (0.28 #2826, 0.18 #3217, 0.17 #2181), 0b_6mr (0.28 #346, 0.24 #602, 0.18 #3217), 0b_6qj (0.25 #325, 0.24 #581, 0.22 #709), 0bzrsh (0.22 #337, 0.21 #593, 0.20 #721) >> Best rule #2826 for best value: >> intensional similarity = 4 >> extensional distance = 137 >> proper extension: 03rz4; >> query: (?x8969, ?x867) <- locations(?x11210, ?x8969), locations(?x11210, ?x1523), vacationer(?x1523, ?x793), locations(?x867, ?x1523) >> conf = 0.28 => this is the best rule for 7 predicted values *> Best rule #323 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 03l2n; 05jbn; 0lphb; 06c62; 04gxf; 07mgr; *> query: (?x8969, 0b_6s7) <- locations(?x11210, ?x8969), administrative_division(?x8969, ?x8968), location(?x7224, ?x8969), category(?x8969, ?x134) *> conf = 0.22 ranks of expected_values: 13 EVAL 0vm39 locations! 0b_6s7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 115.000 79.000 0.281 http://example.org/time/event/locations #9374-0nht0 PRED entity: 0nht0 PRED relation: time_zones PRED expected values: 02fqwt => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.86 #173, 0.85 #256, 0.85 #66), 02fqwt (0.74 #414, 0.65 #532, 0.57 #690), 02lcqs (0.33 #99, 0.22 #484, 0.21 #164), 02hczc (0.10 #161, 0.10 #481, 0.10 #122), 03bdv (0.09 #725, 0.07 #1150, 0.06 #1098), 02llzg (0.07 #992, 0.06 #654, 0.06 #1096), 042g7t (0.05 #50, 0.01 #880, 0.01 #907), 03plfd (0.02 #715, 0.02 #879, 0.02 #686), 0gsrz4 (0.02 #684, 0.02 #713, 0.02 #740), 052vwh (0.01 #1000, 0.01 #1104, 0.01 #1156) >> Best rule #173 for best value: >> intensional similarity = 5 >> extensional distance = 228 >> proper extension: 0nvrd; 0d6lp; 0fr61; 0l30v; 0mwxl; 0fc_9; 0l2q3; 0mrq3; 0mmpz; 0fkhz; ... >> query: (?x6898, ?x2674) <- adjoins(?x9546, ?x6898), adjoins(?x10566, ?x9546), time_zones(?x9546, ?x2674), currency(?x6898, ?x170), second_level_divisions(?x94, ?x9546) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #414 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 307 *> proper extension: 02dtg; 02_286; 0wh3; 0cc56; 01_d4; 0dc95; 0f04c; 01qh7; 01531; 013m43; ... *> query: (?x6898, ?x1638) <- adjoins(?x9546, ?x6898), adjoins(?x12942, ?x9546), source(?x6898, ?x958), contains(?x1274, ?x6898), time_zones(?x12942, ?x1638) *> conf = 0.74 ranks of expected_values: 2 EVAL 0nht0 time_zones 02fqwt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.858 http://example.org/location/location/time_zones #9373-034qrh PRED entity: 034qrh PRED relation: film! PRED expected values: 084m3 => 82 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1108): 02w29z (0.46 #22835, 0.45 #6226, 0.44 #43594), 0gv07g (0.41 #39440, 0.40 #41517, 0.34 #74733), 0gn30 (0.25 #946, 0.11 #5096, 0.04 #23781), 0tc7 (0.25 #393, 0.02 #25303, 0.01 #29456), 0m8_v (0.25 #733, 0.01 #4883), 02v406 (0.25 #726, 0.01 #4876), 034q3l (0.25 #1526, 0.01 #9829), 01hbq0 (0.25 #2020), 09n70c (0.25 #1732), 019l68 (0.25 #1555) >> Best rule #22835 for best value: >> intensional similarity = 3 >> extensional distance = 230 >> proper extension: 0gyy53; 01gglm; >> query: (?x437, ?x794) <- titles(?x2480, ?x437), nominated_for(?x794, ?x437), film_format(?x437, ?x909) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #5450 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 81 *> proper extension: 06zn1c; *> query: (?x437, 084m3) <- nominated_for(?x1312, ?x437), nominated_for(?x794, ?x437), ?x1312 = 07cbcy *> conf = 0.02 ranks of expected_values: 221 EVAL 034qrh film! 084m3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 82.000 41.000 0.457 http://example.org/film/actor/film./film/performance/film #9372-05strv PRED entity: 05strv PRED relation: award PRED expected values: 0gq9h => 90 concepts (89 used for prediction) PRED predicted values (max 10 best out of 253): 02xcb6n (0.70 #27559, 0.70 #10127, 0.70 #11748), 0gq9h (0.50 #78, 0.34 #3723, 0.33 #4534), 09sb52 (0.38 #5712, 0.27 #8142, 0.23 #10573), 0gr4k (0.30 #1248, 0.26 #2868, 0.25 #3273), 04dn09n (0.28 #1259, 0.22 #3284, 0.22 #2879), 040njc (0.27 #3653, 0.26 #4464, 0.25 #4059), 0cjyzs (0.27 #1727, 0.26 #512, 0.13 #5373), 0gr51 (0.27 #1316, 0.22 #3341, 0.22 #2936), 019f4v (0.25 #67, 0.18 #3712, 0.17 #4118), 0p9sw (0.25 #23, 0.15 #4051, 0.13 #15805) >> Best rule #27559 for best value: >> intensional similarity = 2 >> extensional distance = 2276 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01vsxdm; 01wv9xn; 0frsw; 016fmf; 01vrwfv; 0134s5; 02lbrd; ... >> query: (?x10151, ?x8660) <- award(?x10151, ?x4921), award_winner(?x8660, ?x10151) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #78 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 086k8; *> query: (?x10151, 0gq9h) <- award_winner(?x687, ?x10151), nominated_for(?x10151, ?x522), ?x522 = 01h7bb *> conf = 0.50 ranks of expected_values: 2 EVAL 05strv award 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 89.000 0.701 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9371-01qxc7 PRED entity: 01qxc7 PRED relation: film_crew_role PRED expected values: 09zzb8 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 32): 09zzb8 (0.75 #470, 0.74 #2757, 0.73 #1597), 02_n3z (0.28 #468, 0.20 #35, 0.12 #2956), 0215hd (0.28 #468, 0.16 #469, 0.16 #450), 033smt (0.28 #468, 0.12 #2956, 0.07 #124), 0ckd1 (0.28 #468, 0.12 #2956, 0.03 #171), 015h31 (0.20 #41, 0.15 #107, 0.14 #74), 0d2b38 (0.20 #56, 0.14 #89, 0.12 #290), 02ynfr (0.19 #581, 0.18 #1609, 0.17 #2006), 02rh1dz (0.17 #9, 0.15 #276, 0.14 #376), 01xy5l_ (0.14 #245, 0.14 #178, 0.14 #211) >> Best rule #470 for best value: >> intensional similarity = 4 >> extensional distance = 258 >> proper extension: 0bs8hvm; >> query: (?x4489, 09zzb8) <- titles(?x1510, ?x4489), country(?x4489, ?x94), film_crew_role(?x4489, ?x2178), ?x2178 = 01pvkk >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qxc7 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.754 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9370-03ktjq PRED entity: 03ktjq PRED relation: produced_by! PRED expected values: 027gy0k 063_j5 => 125 concepts (85 used for prediction) PRED predicted values (max 10 best out of 534): 060v34 (0.39 #37214, 0.22 #51167, 0.11 #63266), 01s7w3 (0.11 #799, 0.05 #2659, 0.04 #3589), 08xvpn (0.11 #835, 0.05 #2695, 0.04 #3625), 072x7s (0.11 #144, 0.05 #2004, 0.04 #2934), 011xg5 (0.11 #754, 0.05 #2614, 0.04 #3544), 0h21v2 (0.11 #534, 0.05 #2394, 0.04 #3324), 0cc5qkt (0.11 #310, 0.05 #2170, 0.04 #3100), 0hx4y (0.11 #248, 0.05 #2108, 0.04 #3038), 02rb84n (0.11 #154, 0.05 #2014, 0.04 #2944), 0jqn5 (0.11 #129, 0.05 #1989, 0.04 #2919) >> Best rule #37214 for best value: >> intensional similarity = 3 >> extensional distance = 336 >> proper extension: 02g8h; 0kr5_; 012t1; 0ksf29; 04b19t; 034bgm; 04g865; 03tf_h; 09p06; 0br1w; ... >> query: (?x5781, ?x570) <- produced_by(?x1185, ?x5781), profession(?x5781, ?x319), nominated_for(?x5781, ?x570) >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03ktjq produced_by! 063_j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 85.000 0.391 http://example.org/film/film/produced_by EVAL 03ktjq produced_by! 027gy0k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 85.000 0.391 http://example.org/film/film/produced_by #9369-011yn5 PRED entity: 011yn5 PRED relation: nominated_for! PRED expected values: 0f_nbyh 019f4v 0k611 09sdmz => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 196): 027b9j5 (0.67 #4372, 0.67 #7824, 0.67 #8286), 027c95y (0.67 #4372, 0.67 #7824, 0.67 #8286), 04kxsb (0.51 #781, 0.25 #91, 0.20 #17264), 0gs9p (0.45 #750, 0.35 #4201, 0.32 #3741), 0k611 (0.45 #759, 0.30 #4210, 0.27 #3750), 040njc (0.42 #697, 0.28 #4148, 0.25 #3688), 019f4v (0.40 #742, 0.34 #4193, 0.31 #3733), 09sb52 (0.40 #723, 0.25 #33, 0.25 #8285), 0gqyl (0.38 #765, 0.25 #8285, 0.22 #4216), 02x17s4 (0.35 #780, 0.14 #2620, 0.14 #1240) >> Best rule #4372 for best value: >> intensional similarity = 4 >> extensional distance = 453 >> proper extension: 0cwrr; 04glx0; 05fgr_; 07bz5; >> query: (?x5323, ?x591) <- award_winner(?x5323, ?x406), nominated_for(?x3069, ?x5323), award(?x5323, ?x591), honored_for(?x5349, ?x5323) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #759 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 0209xj; 0gjk1d; 016z9n; 0gmgwnv; 0170xl; *> query: (?x5323, 0k611) <- award_winner(?x5323, ?x406), nominated_for(?x3069, ?x5323), nominated_for(?x2853, ?x5323), ?x2853 = 09qv_s *> conf = 0.45 ranks of expected_values: 5, 7, 16, 32 EVAL 011yn5 nominated_for! 09sdmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 89.000 89.000 0.674 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 011yn5 nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 89.000 89.000 0.674 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 011yn5 nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 89.000 89.000 0.674 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 011yn5 nominated_for! 0f_nbyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 89.000 89.000 0.674 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9368-03188 PRED entity: 03188 PRED relation: country! PRED expected values: 0bynt => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 55): 0bynt (0.88 #739, 0.86 #1411, 0.86 #851), 03_8r (0.67 #528, 0.66 #1032, 0.64 #1424), 01cgz (0.63 #855, 0.62 #183, 0.60 #519), 071t0 (0.56 #1425, 0.55 #1033, 0.54 #1145), 01lb14 (0.44 #1417, 0.43 #521, 0.43 #1361), 07gyv (0.43 #1239, 0.43 #959, 0.42 #1015), 07jbh (0.40 #35, 0.36 #987, 0.36 #1435), 01gqfm (0.40 #52, 0.23 #276, 0.23 #780), 06f41 (0.38 #520, 0.38 #1024, 0.36 #968), 0486tv (0.37 #545, 0.36 #1441, 0.34 #1833) >> Best rule #739 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 027nb; 04v3q; 06t2t; 01n6c; 03h64; 0697s; 04vs9; >> query: (?x11593, 0bynt) <- currency(?x11593, ?x170), official_language(?x11593, ?x254), organization(?x11593, ?x127) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03188 country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.878 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #9367-0k2h6 PRED entity: 0k2h6 PRED relation: school_type PRED expected values: 07tf8 => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.85 #326, 0.76 #395, 0.73 #372), 07tf8 (0.80 #515, 0.23 #446, 0.21 #630), 01rs41 (0.59 #902, 0.29 #1247, 0.29 #1363), 05pcjw (0.54 #898, 0.29 #438, 0.25 #1474), 0bpgx (0.30 #204, 0.02 #1516, 0.01 #2254), 01_9fk (0.15 #669, 0.15 #1152, 0.15 #1083), 02dk5q (0.15 #191, 0.02 #1503, 0.01 #2241), 01jlsn (0.15 #361, 0.14 #292, 0.10 #2329), 01_srz (0.10 #2908, 0.10 #2329, 0.08 #1245), 04qbv (0.10 #2908, 0.10 #2329, 0.08 #2932) >> Best rule #326 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 01y9st; 01v3k2; >> query: (?x10355, 05jxkf) <- school_type(?x10355, ?x5931), currency(?x10355, ?x1099), colors(?x10355, ?x3621), contains(?x512, ?x10355) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 06klyh; *> query: (?x10355, 07tf8) <- school_type(?x10355, ?x5931), contains(?x512, ?x10355), school_type(?x6548, ?x5931), ?x6548 = 0yls9 *> conf = 0.80 ranks of expected_values: 2 EVAL 0k2h6 school_type 07tf8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 180.000 180.000 0.846 http://example.org/education/educational_institution/school_type #9366-01pcql PRED entity: 01pcql PRED relation: film PRED expected values: 062zm5h => 144 concepts (57 used for prediction) PRED predicted values (max 10 best out of 707): 031hcx (0.11 #3066, 0.07 #8440, 0.05 #4857), 02qr3k8 (0.11 #3081, 0.02 #12037, 0.02 #8455), 09tkzy (0.11 #3260, 0.02 #8634, 0.02 #12216), 0ndwt2w (0.07 #1001, 0.04 #2792, 0.03 #11748), 011ywj (0.07 #3228, 0.06 #12184, 0.05 #8602), 03177r (0.07 #2256, 0.05 #4047, 0.05 #7630), 031778 (0.07 #2107, 0.05 #7481, 0.03 #32558), 017kct (0.07 #2373, 0.05 #7747, 0.02 #32824), 031786 (0.07 #3067, 0.05 #4858, 0.04 #1276), 04jpg2p (0.07 #3255, 0.04 #1464, 0.04 #8629) >> Best rule #3066 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 03dpqd; >> query: (?x3661, 031hcx) <- film(?x3661, ?x3111), award_winner(?x2880, ?x3661), ?x2880 = 02ppm4q >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #6232 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 132 *> proper extension: 022769; 01hkhq; 03ym1; 026rm_y; *> query: (?x3661, 062zm5h) <- award_winner(?x3661, ?x8307), gender(?x3661, ?x514), place_of_birth(?x3661, ?x10273), languages(?x3661, ?x254) *> conf = 0.01 ranks of expected_values: 398 EVAL 01pcql film 062zm5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 144.000 57.000 0.107 http://example.org/film/actor/film./film/performance/film #9365-01jgpsh PRED entity: 01jgpsh PRED relation: person! PRED expected values: 0g9lm2 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 11): 0dtw1x (0.09 #3, 0.07 #284, 0.06 #144), 0bx_hnp (0.03 #134, 0.02 #275, 0.01 #839), 05_5_22 (0.02 #238, 0.02 #97, 0.01 #591), 03nqnnk (0.02 #141, 0.01 #2148), 0421ng (0.02 #141), 02847m9 (0.02 #79, 0.01 #713, 0.01 #430), 058kh7 (0.02 #203), 0g9lm2 (0.01 #727, 0.01 #515, 0.01 #868), 037q31 (0.01 #115), 03mnn0 (0.01 #108) >> Best rule #3 for best value: >> intensional similarity = 2 >> extensional distance = 9 >> proper extension: 04sd0; >> query: (?x6363, 0dtw1x) <- artists(?x2480, ?x6363), ?x2480 = 01z4y >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #727 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 344 *> proper extension: 012gbb; 03k545; *> query: (?x6363, 0g9lm2) <- film(?x6363, ?x5020), religion(?x6363, ?x1985), award_winner(?x537, ?x6363) *> conf = 0.01 ranks of expected_values: 8 EVAL 01jgpsh person! 0g9lm2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 114.000 114.000 0.091 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #9364-0pk41 PRED entity: 0pk41 PRED relation: role PRED expected values: 042v_gx => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 106): 02sgy (0.46 #1041, 0.37 #1144, 0.33 #6), 05r5c (0.40 #1249, 0.39 #1870, 0.38 #2700), 042v_gx (0.33 #1044, 0.26 #1147, 0.24 #526), 013y1f (0.31 #931, 0.31 #517, 0.26 #413), 0l15bq (0.31 #931, 0.31 #517, 0.26 #413), 03bx0bm (0.31 #931, 0.31 #517, 0.26 #413), 018vs (0.28 #1049, 0.22 #14, 0.20 #427), 01vdm0 (0.27 #2724, 0.25 #341, 0.24 #859), 0214km (0.22 #99, 0.10 #2379, 0.10 #2483), 05842k (0.21 #492, 0.19 #1941, 0.18 #2771) >> Best rule #1041 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 02fybl; >> query: (?x9246, 02sgy) <- profession(?x9246, ?x2659), ?x2659 = 039v1, role(?x9246, ?x227) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1044 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 121 *> proper extension: 02fybl; *> query: (?x9246, 042v_gx) <- profession(?x9246, ?x2659), ?x2659 = 039v1, role(?x9246, ?x227) *> conf = 0.33 ranks of expected_values: 3 EVAL 0pk41 role 042v_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 130.000 0.455 http://example.org/music/artist/track_contributions./music/track_contribution/role #9363-0cy8v PRED entity: 0cy8v PRED relation: location! PRED expected values: 0p8jf => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 268): 02g3w (0.04 #4776, 0.04 #2257, 0.03 #7295), 0jt90f5 (0.04 #2943, 0.04 #424, 0.03 #5462), 02t__3 (0.04 #3743, 0.02 #1224, 0.01 #13819), 03fwln (0.04 #4680, 0.02 #2161, 0.01 #14756), 01wp8w7 (0.04 #2779, 0.02 #260, 0.01 #7817), 032q8q (0.04 #3822, 0.02 #1303, 0.01 #6341), 01wg982 (0.04 #2957, 0.02 #438, 0.01 #5476), 02sjf5 (0.03 #7759, 0.02 #2721, 0.02 #202), 0fpzt5 (0.03 #11881, 0.02 #21957, 0.02 #14400), 02lt8 (0.03 #5835, 0.02 #3316, 0.02 #797) >> Best rule #4776 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 01cx_; 01m1_t; 0mn8t; 0mb2b; 0f2nf; 0_j_z; >> query: (?x10343, 02g3w) <- time_zones(?x10343, ?x2674), county(?x10343, ?x12680), currency(?x10343, ?x170), ?x2674 = 02hcv8 >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cy8v location! 0p8jf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 65.000 0.040 http://example.org/people/person/places_lived./people/place_lived/location #9362-027rpym PRED entity: 027rpym PRED relation: nominated_for PRED expected values: 0bcndz => 101 concepts (42 used for prediction) PRED predicted values (max 10 best out of 256): 0bcndz (0.84 #6520, 0.81 #4763, 0.80 #4262), 08984j (0.11 #201, 0.01 #6220, 0.01 #6470), 01kf3_9 (0.07 #1305, 0.06 #4566, 0.05 #6323), 0fsw_7 (0.07 #1402, 0.05 #4663, 0.05 #6420), 02qrv7 (0.07 #1286, 0.05 #4547, 0.04 #6304), 0g5pvv (0.07 #1418, 0.05 #4679, 0.04 #6436), 05css_ (0.06 #3417, 0.05 #2664, 0.05 #5172), 0q9sg (0.06 #128, 0.03 #4891, 0.03 #6397), 0140g4 (0.06 #4, 0.02 #4516, 0.02 #2510), 0cf08 (0.06 #207, 0.01 #3466) >> Best rule #6520 for best value: >> intensional similarity = 3 >> extensional distance = 226 >> proper extension: 02fn5r; >> query: (?x4865, ?x1745) <- nominated_for(?x4865, ?x4300), nominated_for(?x1745, ?x4865), nominated_for(?x484, ?x1745) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027rpym nominated_for 0bcndz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 42.000 0.840 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #9361-0dq9p PRED entity: 0dq9p PRED relation: people PRED expected values: 06dl_ 09cdxn 0bvzp 01zwy 0gthm 014zn0 => 74 concepts (48 used for prediction) PRED predicted values (max 10 best out of 3265): 03rx9 (0.87 #4961, 0.20 #5378, 0.20 #4139), 014z8v (0.87 #4961, 0.20 #5093, 0.20 #3854), 021j72 (0.87 #4961, 0.20 #5424, 0.20 #4185), 015076 (0.87 #4961, 0.20 #5454, 0.20 #4215), 01w9ph_ (0.87 #4961, 0.20 #5283, 0.20 #4044), 0pj8m (0.87 #4961, 0.20 #5281, 0.20 #4042), 042d1 (0.87 #4961, 0.20 #5410, 0.20 #4171), 03_js (0.87 #4961, 0.20 #5334, 0.20 #4095), 01lwx (0.87 #4961, 0.20 #5508, 0.20 #4269), 0j3v (0.87 #4961, 0.20 #5032, 0.20 #3793) >> Best rule #4961 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 098s1; >> query: (?x6260, ?x158) <- risk_factors(?x10613, ?x6260), symptom_of(?x6260, ?x12536), symptom_of(?x13373, ?x12536), symptom_of(?x5855, ?x12536), symptom_of(?x13373, ?x4959), symptom_of(?x13373, ?x3680), ?x3680 = 025hl8, people(?x5855, ?x158), ?x4959 = 01dcqj >> conf = 0.87 => this is the best rule for 29 predicted values *> Best rule #1158 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 0dcsx; *> query: (?x6260, 014zn0) <- people(?x6260, ?x11956), people(?x6260, ?x8774), people(?x6260, ?x8450), people(?x6260, ?x6239), people(?x6260, ?x2401), award_nominee(?x2400, ?x2401), place_of_burial(?x6239, ?x11261), nominated_for(?x6239, ?x3268), place_of_birth(?x8450, ?x8451), ?x8774 = 05xpv, politician(?x1912, ?x11956), jurisdiction_of_office(?x11956, ?x94) *> conf = 0.33 ranks of expected_values: 37, 628, 1246 EVAL 0dq9p people 014zn0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 74.000 48.000 0.869 http://example.org/people/cause_of_death/people EVAL 0dq9p people 0gthm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 74.000 48.000 0.869 http://example.org/people/cause_of_death/people EVAL 0dq9p people 01zwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 48.000 0.869 http://example.org/people/cause_of_death/people EVAL 0dq9p people 0bvzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 48.000 0.869 http://example.org/people/cause_of_death/people EVAL 0dq9p people 09cdxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 48.000 0.869 http://example.org/people/cause_of_death/people EVAL 0dq9p people 06dl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 74.000 48.000 0.869 http://example.org/people/cause_of_death/people #9360-0py9b PRED entity: 0py9b PRED relation: industry PRED expected values: 01mf0 => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 46): 01mw1 (0.75 #4049, 0.67 #47, 0.50 #1), 02vxn (0.55 #4464, 0.48 #2808, 0.38 #3452), 020mfr (0.50 #62, 0.45 #4064, 0.33 #16), 029g_vk (0.45 #333, 0.44 #977, 0.44 #195), 02wbm (0.29 #105, 0.10 #1071, 0.09 #243), 01mf0 (0.27 #6121, 0.23 #4509, 0.22 #214), 0hz28 (0.27 #6121, 0.23 #4509, 0.22 #213), 019z7b (0.27 #6121, 0.23 #4509, 0.22 #193), 06xw2 (0.27 #6121, 0.23 #4509, 0.22 #219), 01mfj (0.27 #6121, 0.23 #4509, 0.12 #772) >> Best rule #4049 for best value: >> intensional similarity = 5 >> extensional distance = 78 >> proper extension: 01qxs3; 01tlrp; 0dwcl; 07733f; 03_kl4; 043fz4; 01yf92; 070ny; >> query: (?x7970, 01mw1) <- industry(?x7970, ?x14555), industry(?x9469, ?x14555), industry(?x5072, ?x14555), ?x9469 = 04sv4, service_location(?x5072, ?x94) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6121 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 232 *> proper extension: 05h4t7; 06rq1k; 05d6kv; 01p5yn; 0278rq7; 09glbnt; 04rtpt; 081bls; 07k2x; 025hwq; ... *> query: (?x7970, ?x245) <- industry(?x7970, ?x14555), industry(?x9469, ?x14555), service_location(?x9469, ?x94), industry(?x9469, ?x245) *> conf = 0.27 ranks of expected_values: 6 EVAL 0py9b industry 01mf0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 169.000 169.000 0.750 http://example.org/business/business_operation/industry #9359-016ybr PRED entity: 016ybr PRED relation: artists PRED expected values: 03f6fl0 017lb_ => 59 concepts (16 used for prediction) PRED predicted values (max 10 best out of 3253): 089pg7 (0.60 #3881, 0.50 #4944, 0.44 #6006), 03d9d6 (0.60 #3697, 0.50 #4760, 0.44 #5822), 0bdxs5 (0.60 #8229, 0.50 #1850, 0.40 #7166), 0lk90 (0.60 #7510, 0.50 #1131, 0.40 #6447), 016fmf (0.60 #3403, 0.50 #1275, 0.40 #2338), 01s21dg (0.60 #2543, 0.50 #1480, 0.40 #3608), 01vrt_c (0.60 #3267, 0.50 #1139, 0.40 #2202), 033wx9 (0.60 #3401, 0.50 #1273, 0.40 #2336), 011z3g (0.60 #2719, 0.50 #1656, 0.40 #3784), 01s7ns (0.60 #3085, 0.50 #2022, 0.40 #4150) >> Best rule #3881 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 016clz; 01cbwl; >> query: (?x8386, 089pg7) <- artists(?x8386, ?x9228), artists(?x8386, ?x3126), artists(?x8386, ?x680), ?x680 = 01cv3n, parent_genre(?x8386, ?x671), award(?x9228, ?x1389), ?x3126 = 0161c2, artist(?x3265, ?x9228) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3636 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 016clz; 01cbwl; *> query: (?x8386, 03f6fl0) <- artists(?x8386, ?x9228), artists(?x8386, ?x3126), artists(?x8386, ?x680), ?x680 = 01cv3n, parent_genre(?x8386, ?x671), award(?x9228, ?x1389), ?x3126 = 0161c2, artist(?x3265, ?x9228) *> conf = 0.40 ranks of expected_values: 174, 579 EVAL 016ybr artists 017lb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 16.000 0.600 http://example.org/music/genre/artists EVAL 016ybr artists 03f6fl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 59.000 16.000 0.600 http://example.org/music/genre/artists #9358-0126rp PRED entity: 0126rp PRED relation: nationality PRED expected values: 07ssc => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 74): 09c7w0 (0.82 #9828, 0.82 #1005, 0.81 #1205), 02jx1 (0.65 #1137, 0.38 #333, 0.25 #33), 0yl27 (0.52 #9928, 0.28 #9929, 0.26 #8023), 07ssc (0.46 #804, 0.44 #1119, 0.43 #803), 0bq0p9 (0.46 #804, 0.43 #803, 0.40 #2506), 03rt9 (0.46 #804, 0.40 #2506, 0.08 #514), 06q1r (0.46 #804, 0.40 #2506, 0.07 #678), 05bcl (0.28 #9929, 0.26 #8023, 0.14 #260), 0j5g9 (0.28 #9929, 0.26 #8023, 0.01 #5877), 0345h (0.17 #702, 0.06 #5142, 0.06 #6550) >> Best rule #9828 for best value: >> intensional similarity = 3 >> extensional distance = 1745 >> proper extension: 07m69t; >> query: (?x2125, 09c7w0) <- location(?x2125, ?x14206), contains(?x4070, ?x14206), place_of_death(?x4496, ?x4070) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #804 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 01s7qqw; 03g5_y; 01xwqn; *> query: (?x2125, ?x429) <- influenced_by(?x2125, ?x7679), influenced_by(?x2125, ?x3917), ?x3917 = 0p_47, nationality(?x7679, ?x613), nationality(?x7679, ?x429), combatants(?x612, ?x613) *> conf = 0.46 ranks of expected_values: 4 EVAL 0126rp nationality 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 122.000 122.000 0.821 http://example.org/people/person/nationality #9357-01c83m PRED entity: 01c83m PRED relation: entity_involved! PRED expected values: 0gfq9 => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 2): 02cnqk (0.20 #186, 0.17 #252, 0.14 #318), 0gfhg1y (0.17 #240, 0.14 #306, 0.12 #373) >> Best rule #186 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0130xz; 0g686w; >> query: (?x13983, 02cnqk) <- company(?x13983, ?x13471), company(?x13983, ?x12236), ?x13471 = 01j_x, company(?x12235, ?x12236), company(?x11696, ?x12236), ?x12235 = 01cpkt, ?x11696 = 015czt >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01c83m entity_involved! 0gfq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 29.000 29.000 0.200 http://example.org/base/culturalevent/event/entity_involved #9356-05l0j5 PRED entity: 05l0j5 PRED relation: film PRED expected values: 09p35z => 80 concepts (45 used for prediction) PRED predicted values (max 10 best out of 570): 0bvn25 (0.20 #3632, 0.17 #5423, 0.15 #7214), 02825cv (0.13 #4726, 0.11 #6517, 0.11 #1144), 06fpsx (0.13 #4922, 0.11 #6713, 0.11 #1340), 0888c3 (0.11 #1416, 0.09 #3207, 0.07 #4998), 03nfnx (0.11 #1404, 0.09 #3195, 0.07 #4986), 0ds5_72 (0.11 #1458, 0.09 #3249, 0.07 #5040), 0gldyz (0.11 #1658, 0.09 #3449, 0.07 #5240), 0830vk (0.11 #594, 0.09 #2385, 0.07 #4176), 0ds2l81 (0.11 #1439, 0.09 #3230, 0.07 #5021), 03fts (0.11 #227, 0.09 #2018, 0.07 #3809) >> Best rule #3632 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 04t2l2; 08wq0g; 043js; 0cnl09; 08hsww; 0cmt6q; 0cj36c; >> query: (?x7752, 0bvn25) <- award_nominee(?x7663, ?x7752), award_nominee(?x3852, ?x7752), award_nominee(?x274, ?x7752), ?x7663 = 04zkj5, award_nominee(?x237, ?x274), ?x3852 = 0bt7ws >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05l0j5 film 09p35z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 45.000 0.200 http://example.org/film/actor/film./film/performance/film #9355-011ysn PRED entity: 011ysn PRED relation: nominated_for! PRED expected values: 0l8z1 => 71 concepts (61 used for prediction) PRED predicted values (max 10 best out of 210): 054krc (0.33 #65, 0.28 #296, 0.27 #2839), 0l8z1 (0.31 #2825, 0.24 #7397, 0.22 #282), 02qyntr (0.28 #404, 0.21 #1560, 0.20 #173), 0gr51 (0.28 #303, 0.20 #72, 0.19 #4927), 019f4v (0.28 #4908, 0.28 #2827, 0.24 #3520), 0fhpv4 (0.27 #133, 0.24 #7397, 0.22 #364), 0gr42 (0.27 #83, 0.22 #314, 0.19 #12944), 05ztrmj (0.27 #127, 0.22 #358, 0.13 #589), 0gq_v (0.27 #2794, 0.26 #4875, 0.22 #251), 02qvyrt (0.24 #1477, 0.24 #7397, 0.22 #321) >> Best rule #65 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 09q5w2; >> query: (?x3496, 054krc) <- nominated_for(?x3069, ?x3496), ?x3069 = 0150t6, film_crew_role(?x3496, ?x137) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2825 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 345 *> proper extension: 0cvkv5; *> query: (?x3496, 0l8z1) <- nominated_for(?x3069, ?x3496), music(?x224, ?x3069), award_winner(?x522, ?x3069) *> conf = 0.31 ranks of expected_values: 2 EVAL 011ysn nominated_for! 0l8z1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 61.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9354-0gt3p PRED entity: 0gt3p PRED relation: location PRED expected values: 0gjcy => 150 concepts (148 used for prediction) PRED predicted values (max 10 best out of 179): 01vqq1 (0.75 #66729, 0.73 #29742, 0.70 #76380), 02_286 (0.46 #2450, 0.38 #1646, 0.34 #40233), 030qb3t (0.29 #20980, 0.28 #40279, 0.17 #5710), 0h7h6 (0.21 #4913, 0.20 #8127, 0.03 #40286), 0cr3d (0.13 #13003, 0.10 #7378, 0.10 #68486), 04jpl (0.12 #20914, 0.11 #40213, 0.07 #12071), 052p7 (0.10 #4950, 0.10 #8164, 0.02 #931), 06y57 (0.10 #255, 0.02 #40451, 0.01 #59746), 029cr (0.10 #129), 080h2 (0.08 #4877, 0.07 #8091, 0.01 #24168) >> Best rule #66729 for best value: >> intensional similarity = 3 >> extensional distance = 1218 >> proper extension: 07h1h5; >> query: (?x7759, ?x10858) <- location(?x7759, ?x3125), place_of_birth(?x7759, ?x10858), citytown(?x1168, ?x3125) >> conf = 0.75 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gt3p location 0gjcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 148.000 0.751 http://example.org/people/person/places_lived./people/place_lived/location #9353-03mgx6z PRED entity: 03mgx6z PRED relation: film_release_region PRED expected values: 05r4w 03rjj 06bnz 06t2t 016wzw => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 184): 09c7w0 (0.93 #9853, 0.92 #9989, 0.92 #10126), 0f8l9c (0.91 #3025, 0.90 #2064, 0.88 #3435), 05r4w (0.89 #3011, 0.88 #3421, 0.88 #3284), 06bnz (0.86 #1807, 0.84 #1671, 0.82 #3588), 03rjj (0.82 #3014, 0.82 #3560, 0.81 #3424), 06t2t (0.81 #3056, 0.80 #3466, 0.79 #1685), 015qh (0.78 #713, 0.67 #849, 0.67 #29), 07t21 (0.78 #848, 0.67 #28, 0.57 #301), 047yc (0.71 #157, 0.60 #976, 0.59 #2068), 03rk0 (0.69 #2090, 0.67 #862, 0.61 #3051) >> Best rule #9853 for best value: >> intensional similarity = 9 >> extensional distance = 1316 >> proper extension: 0900j5; 0bh72t; 015qy1; >> query: (?x5791, 09c7w0) <- film_release_region(?x5791, ?x1790), film_release_region(?x6175, ?x1790), film_release_region(?x4998, ?x1790), film_release_region(?x4610, ?x1790), ?x4998 = 0dzlbx, ?x4610 = 017jd9, olympics(?x1790, ?x418), medal(?x1790, ?x1242), ?x6175 = 0gg5kmg >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #3011 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 72 *> proper extension: 0h1cdwq; 05p1tzf; 087wc7n; 0gj8t_b; 0gj9tn5; 0g9wdmc; 03qnc6q; 0gyfp9c; 0c3xw46; 05c26ss; ... *> query: (?x5791, 05r4w) <- film_release_region(?x5791, ?x1790), film_release_region(?x5791, ?x583), film_release_region(?x5791, ?x456), ?x1790 = 01pj7, ?x583 = 015fr, genre(?x5791, ?x604), genre(?x493, ?x604), ?x456 = 05qhw *> conf = 0.89 ranks of expected_values: 3, 4, 5, 6, 14 EVAL 03mgx6z film_release_region 016wzw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 86.000 86.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03mgx6z film_release_region 06t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03mgx6z film_release_region 06bnz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03mgx6z film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03mgx6z film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9352-0n1s0 PRED entity: 0n1s0 PRED relation: film! PRED expected values: 024rgt => 71 concepts (58 used for prediction) PRED predicted values (max 10 best out of 66): 017jv5 (0.25 #90, 0.12 #165, 0.06 #240), 016tw3 (0.20 #11, 0.19 #236, 0.14 #386), 05qd_ (0.20 #9, 0.17 #534, 0.16 #759), 03xsby (0.20 #16, 0.12 #166, 0.03 #3116), 0338lq (0.20 #7, 0.06 #157, 0.02 #983), 034f0d (0.20 #33, 0.06 #183), 086k8 (0.16 #1281, 0.16 #1432, 0.16 #377), 017s11 (0.15 #303, 0.14 #378, 0.13 #603), 0g1rw (0.12 #233, 0.07 #533, 0.07 #833), 016tt2 (0.12 #1283, 0.12 #1586, 0.12 #1056) >> Best rule #90 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 09fb5; >> query: (?x5984, 017jv5) <- nominated_for(?x1554, ?x5984), nominated_for(?x398, ?x5984), ?x398 = 0bl2g, film(?x1554, ?x195), award_nominee(?x400, ?x1554) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #470 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 284 *> proper extension: 0gffmn8; *> query: (?x5984, 024rgt) <- genre(?x5984, ?x53), music(?x5984, ?x4029), film(?x318, ?x5984), executive_produced_by(?x5984, ?x3223) *> conf = 0.05 ranks of expected_values: 31 EVAL 0n1s0 film! 024rgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 71.000 58.000 0.250 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #9351-06czyr PRED entity: 06czyr PRED relation: nominated_for PRED expected values: 05lfwd => 120 concepts (73 used for prediction) PRED predicted values (max 10 best out of 679): 05lfwd (0.80 #913, 0.78 #110391, 0.78 #103897), 04vr_f (0.12 #3404, 0.05 #8273, 0.03 #19640), 07g9f (0.10 #1466, 0.09 #113639, 0.08 #108768), 0872p_c (0.10 #163, 0.04 #3408), 04b2qn (0.10 #1226, 0.02 #4471, 0.01 #30449), 0d4htf (0.10 #869, 0.02 #4114), 01f3p_ (0.09 #113639, 0.08 #108768, 0.01 #5378), 02_1q9 (0.09 #113639, 0.08 #108768), 0b1y_2 (0.08 #3686, 0.01 #29664, 0.01 #8555), 03ln8b (0.06 #1925, 0.05 #10040, 0.04 #5170) >> Best rule #913 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 066m4g; >> query: (?x5599, 05lfwd) <- award(?x5599, ?x1058), award_nominee(?x5593, ?x5599), ?x5593 = 025b5y >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06czyr nominated_for 05lfwd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 73.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9350-04jn6y7 PRED entity: 04jn6y7 PRED relation: genre PRED expected values: 07s9rl0 => 88 concepts (82 used for prediction) PRED predicted values (max 10 best out of 116): 07s9rl0 (0.89 #5323, 0.86 #242, 0.73 #3990), 02l7c8 (0.54 #136, 0.33 #1341, 0.32 #979), 02kdv5l (0.47 #5083, 0.42 #2297, 0.39 #1451), 01hmnh (0.43 #17, 0.38 #620, 0.24 #981), 03k9fj (0.41 #614, 0.29 #3031, 0.29 #11), 05p553 (0.40 #366, 0.37 #2662, 0.37 #486), 060__y (0.38 #137, 0.21 #1584, 0.20 #4005), 06n90 (0.35 #5092, 0.22 #856, 0.21 #1460), 04xvlr (0.31 #123, 0.29 #243, 0.24 #3991), 082gq (0.29 #30, 0.19 #4260, 0.18 #2807) >> Best rule #5323 for best value: >> intensional similarity = 4 >> extensional distance = 607 >> proper extension: 016kz1; 02q3fdr; 05dl1s; >> query: (?x12693, 07s9rl0) <- genre(?x12693, ?x600), music(?x12693, ?x3910), genre(?x2107, ?x600), ?x2107 = 0260bz >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04jn6y7 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 82.000 0.885 http://example.org/film/film/genre #9349-04x_3 PRED entity: 04x_3 PRED relation: major_field_of_study! PRED expected values: 04zx3q1 => 61 concepts (39 used for prediction) PRED predicted values (max 10 best out of 15): 014mlp (0.86 #450, 0.85 #254, 0.84 #272), 04zx3q1 (0.75 #183, 0.74 #270, 0.73 #203), 01rr_d (0.69 #375, 0.62 #83, 0.59 #133), 027f2w (0.69 #375, 0.62 #83, 0.59 #133), 01ysy9 (0.68 #393, 0.67 #320, 0.62 #83), 013zdg (0.62 #83, 0.59 #133, 0.57 #201), 02cq61 (0.62 #83, 0.59 #133, 0.57 #201), 071tyz (0.62 #83, 0.59 #133, 0.57 #201), 0bjrnt (0.60 #119, 0.60 #104, 0.50 #81), 022h5x (0.50 #81, 0.46 #131, 0.42 #199) >> Best rule #450 for best value: >> intensional similarity = 9 >> extensional distance = 47 >> proper extension: 029g_vk; >> query: (?x2601, 014mlp) <- major_field_of_study(?x13695, ?x2601), major_field_of_study(?x3424, ?x2601), major_field_of_study(?x2013, ?x2601), major_field_of_study(?x1154, ?x2601), institution(?x865, ?x2013), colors(?x13695, ?x663), student(?x3424, ?x117), student(?x2601, ?x2873), fraternities_and_sororities(?x3424, ?x4348) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #183 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 6 *> proper extension: 05qfh; *> query: (?x2601, 04zx3q1) <- major_field_of_study(?x7660, ?x2601), major_field_of_study(?x3424, ?x2601), major_field_of_study(?x2013, ?x2601), major_field_of_study(?x1154, ?x2601), institution(?x11690, ?x2013), ?x3424 = 01w5m, ?x11690 = 01ysy9, taxonomy(?x2601, ?x939), ?x7660 = 01qd_r, student(?x2013, ?x1197) *> conf = 0.75 ranks of expected_values: 2 EVAL 04x_3 major_field_of_study! 04zx3q1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 61.000 39.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #9348-07h1q PRED entity: 07h1q PRED relation: nationality PRED expected values: 0345h => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 77): 0345h (0.84 #14658, 0.84 #13346, 0.84 #14961), 09c7w0 (0.76 #9425, 0.74 #11130, 0.74 #10929), 03gk2 (0.60 #8822, 0.45 #9323, 0.44 #10028), 09krp (0.50 #10027, 0.39 #15871, 0.37 #4005), 0h7x (0.43 #735, 0.33 #1536, 0.33 #1036), 0f8l9c (0.29 #922, 0.25 #122, 0.21 #9626), 03b79 (0.25 #255, 0.17 #555, 0.06 #1756), 03rt9 (0.21 #9626, 0.20 #313, 0.19 #4910), 07ssc (0.21 #9626, 0.19 #1716, 0.19 #4910), 01mk6 (0.21 #9626, 0.19 #4910, 0.14 #880) >> Best rule #14658 for best value: >> intensional similarity = 3 >> extensional distance = 1370 >> proper extension: 02s2ft; 02bfmn; 0bxtg; 017149; 01pcq3; 066m4g; 0207wx; 05k2s_; 01qvgl; 05prs8; ... >> query: (?x10110, ?x1264) <- place_of_birth(?x10110, ?x8977), country(?x8977, ?x1264), gender(?x10110, ?x231) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07h1q nationality 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.840 http://example.org/people/person/nationality #9347-0p50v PRED entity: 0p50v PRED relation: award PRED expected values: 02qyp19 07kjk7c => 118 concepts (88 used for prediction) PRED predicted values (max 10 best out of 280): 04dn09n (0.70 #33967, 0.69 #30730, 0.69 #16573), 02x17s4 (0.50 #529, 0.26 #933, 0.21 #3762), 0gr51 (0.44 #504, 0.37 #3737, 0.31 #6567), 02n9nmz (0.44 #473, 0.23 #3706, 0.23 #877), 03hkv_r (0.38 #420, 0.28 #3653, 0.26 #824), 0gqy2 (0.33 #165, 0.26 #10269, 0.16 #12125), 04kxsb (0.33 #126, 0.11 #10230, 0.10 #8614), 024_dt (0.33 #378), 02qyp19 (0.31 #405, 0.21 #3638, 0.19 #809), 02x1dht (0.31 #458, 0.16 #3691, 0.16 #862) >> Best rule #33967 for best value: >> intensional similarity = 3 >> extensional distance = 1955 >> proper extension: 04lgymt; 0ggl02; 03j43; 0288fyj; 0hwd8; 01x15dc; 04n_g; 0163m1; 01vd7hn; 01l03w2; ... >> query: (?x8268, ?x746) <- award(?x8268, ?x601), gender(?x8268, ?x231), award_winner(?x746, ?x8268) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #405 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 01kt17; *> query: (?x8268, 02qyp19) <- award(?x8268, ?x7606), written_by(?x2168, ?x8268), ?x7606 = 01l78d *> conf = 0.31 ranks of expected_values: 9, 11 EVAL 0p50v award 07kjk7c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 118.000 88.000 0.696 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0p50v award 02qyp19 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 118.000 88.000 0.696 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9346-0431v3 PRED entity: 0431v3 PRED relation: titles! PRED expected values: 07c52 => 64 concepts (12 used for prediction) PRED predicted values (max 10 best out of 27): 07c52 (0.94 #235, 0.79 #132, 0.79 #337), 0215n (0.16 #485, 0.06 #588, 0.04 #1206), 03mdt (0.13 #657, 0.11 #969, 0.11 #1071), 01z77k (0.08 #572, 0.08 #881, 0.03 #469), 0hn10 (0.06 #16, 0.03 #221, 0.03 #323), 07s9rl0 (0.06 #924, 0.06 #821, 0.06 #512), 01z4y (0.04 #923, 0.02 #1026, 0.02 #1129), 01t_vv (0.04 #923, 0.02 #1026, 0.02 #1129), 03k9fj (0.04 #923, 0.02 #1026, 0.02 #1129), 0vgkd (0.04 #923, 0.02 #1026, 0.02 #1129) >> Best rule #235 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 072kp; 02nf2c; 0124k9; 08jgk1; 03ln8b; 01q_y0; 0d68qy; 01bv8b; 01b9w3; 01s81; ... >> query: (?x5561, 07c52) <- nominated_for(?x7510, ?x5561), ?x7510 = 027gs1_, titles(?x11493, ?x5561), genre(?x5561, ?x53) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0431v3 titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 12.000 0.938 http://example.org/media_common/netflix_genre/titles #9345-0c8qq PRED entity: 0c8qq PRED relation: film! PRED expected values: 015vql => 73 concepts (23 used for prediction) PRED predicted values (max 10 best out of 905): 0cg9f (0.70 #12513, 0.56 #27109, 0.47 #27108), 03h4mp (0.56 #27109, 0.47 #27108, 0.46 #29194), 03_bcg (0.56 #27109, 0.47 #27108, 0.46 #29194), 0gr36 (0.25 #2584, 0.12 #4670, 0.02 #8840), 0h0wc (0.25 #425, 0.07 #10852, 0.06 #4596), 0h32q (0.25 #776, 0.06 #4947, 0.04 #11203), 01tcf7 (0.25 #199, 0.06 #4370, 0.02 #10626), 015dqj (0.25 #1709, 0.06 #5880, 0.02 #12136), 03k7bd (0.25 #298, 0.06 #4469, 0.02 #10725), 0psss (0.19 #4733, 0.12 #2647, 0.01 #25585) >> Best rule #12513 for best value: >> intensional similarity = 6 >> extensional distance = 53 >> proper extension: 09p7fh; >> query: (?x3311, ?x5693) <- nominated_for(?x5693, ?x3311), nominated_for(?x1245, ?x3311), nominated_for(?x601, ?x3311), ?x1245 = 0gqwc, ?x601 = 0gr4k, film(?x5693, ?x1763) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c8qq film! 015vql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 23.000 0.704 http://example.org/film/actor/film./film/performance/film #9344-07w8fz PRED entity: 07w8fz PRED relation: nominated_for! PRED expected values: 016z2j => 66 concepts (38 used for prediction) PRED predicted values (max 10 best out of 590): 031k24 (0.27 #83921, 0.26 #27976, 0.25 #39632), 0736qr (0.27 #83921, 0.26 #27976, 0.25 #39632), 03jldb (0.27 #83921, 0.26 #27976, 0.25 #39632), 06t8b (0.17 #18651, 0.16 #16320, 0.15 #13989), 024t0y (0.17 #18651, 0.16 #16320, 0.15 #13989), 024bbl (0.08 #1038, 0.06 #76929, 0.04 #3370), 0h0wc (0.08 #527, 0.06 #76929, 0.03 #56479), 0bxtg (0.08 #83, 0.06 #76929, 0.02 #60697), 01gq0b (0.08 #377, 0.06 #76929, 0.02 #35346), 0210hf (0.08 #1053, 0.06 #76929) >> Best rule #83921 for best value: >> intensional similarity = 3 >> extensional distance = 1063 >> proper extension: 01jc6q; 01vksx; 0jqn5; 03fts; 01f8gz; 09k56b7; 0jym0; 02stbw; 09p7fh; 0kv238; ... >> query: (?x3133, ?x286) <- film(?x286, ?x3133), nominated_for(?x68, ?x3133), titles(?x53, ?x3133) >> conf = 0.27 => this is the best rule for 3 predicted values *> Best rule #76929 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 901 *> proper extension: 0bx_hnp; 0267wwv; 09rfpk; *> query: (?x3133, ?x968) <- nominated_for(?x969, ?x3133), film_crew_role(?x3133, ?x137), award_nominee(?x968, ?x969) *> conf = 0.06 ranks of expected_values: 69 EVAL 07w8fz nominated_for! 016z2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 66.000 38.000 0.273 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9343-048cl PRED entity: 048cl PRED relation: influenced_by PRED expected values: 01v9724 039n1 => 159 concepts (59 used for prediction) PRED predicted values (max 10 best out of 355): 02wh0 (0.55 #3387, 0.50 #2097, 0.46 #4243), 0372p (0.50 #2690, 0.50 #1830, 0.38 #3976), 032l1 (0.50 #7801, 0.33 #89, 0.25 #947), 0448r (0.50 #3698, 0.33 #259, 0.25 #1117), 05qmj (0.43 #1480, 0.40 #2769, 0.38 #4055), 039n1 (0.38 #2041, 0.27 #3331, 0.20 #2901), 07kb5 (0.33 #15, 0.29 #1305, 0.25 #873), 084nh (0.33 #389, 0.25 #3828, 0.25 #1247), 04xjp (0.33 #56, 0.25 #914, 0.18 #3065), 0379s (0.33 #78, 0.25 #936, 0.18 #3087) >> Best rule #3387 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 06myp; 0420y; >> query: (?x7509, 02wh0) <- influenced_by(?x3428, ?x7509), influenced_by(?x2608, ?x7509), ?x3428 = 0dzkq, religion(?x7509, ?x962), profession(?x2608, ?x2225) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2041 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 03j43; 099bk; 02ln1; 0ct9_; 02wh0; 01h2_6; *> query: (?x7509, 039n1) <- influenced_by(?x3428, ?x7509), ?x3428 = 0dzkq, religion(?x7509, ?x962), interests(?x7509, ?x1695) *> conf = 0.38 ranks of expected_values: 6, 12 EVAL 048cl influenced_by 039n1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 159.000 59.000 0.545 http://example.org/influence/influence_node/influenced_by EVAL 048cl influenced_by 01v9724 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 159.000 59.000 0.545 http://example.org/influence/influence_node/influenced_by #9342-02sj1x PRED entity: 02sj1x PRED relation: profession PRED expected values: 01c8w0 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 82): 02hrh1q (0.78 #7365, 0.73 #6465, 0.70 #6615), 01c72t (0.69 #1375, 0.64 #3775, 0.64 #4075), 01d_h8 (0.62 #6, 0.43 #1056, 0.42 #606), 01c8w0 (0.46 #909, 0.38 #1809, 0.31 #1359), 09jwl (0.44 #3170, 0.43 #2270, 0.41 #2720), 0nbcg (0.40 #1383, 0.38 #3183, 0.37 #3033), 0dxtg (0.34 #5414, 0.32 #4964, 0.30 #1214), 02jknp (0.33 #1208, 0.27 #1058, 0.26 #458), 0dz3r (0.31 #1352, 0.26 #3152, 0.26 #2702), 05vyk (0.28 #16051, 0.18 #996, 0.16 #1446) >> Best rule #7365 for best value: >> intensional similarity = 2 >> extensional distance = 477 >> proper extension: 0q1lp; >> query: (?x3519, 02hrh1q) <- religion(?x3519, ?x7131), nominated_for(?x3519, ?x4179) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #909 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 0cyhq; *> query: (?x3519, 01c8w0) <- music(?x6680, ?x3519), language(?x6680, ?x254), film_art_direction_by(?x6680, ?x4251) *> conf = 0.46 ranks of expected_values: 4 EVAL 02sj1x profession 01c8w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 146.000 146.000 0.779 http://example.org/people/person/profession #9341-05v8c PRED entity: 05v8c PRED relation: olympics PRED expected values: 06sks6 => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 41): 06sks6 (0.88 #4506, 0.87 #3559, 0.87 #4424), 0kbws (0.80 #547, 0.75 #1080, 0.71 #711), 0kbvb (0.72 #540, 0.64 #704, 0.63 #1155), 0kbvv (0.72 #559, 0.64 #723, 0.62 #1420), 09n48 (0.64 #536, 0.54 #700, 0.53 #1069), 0jdk_ (0.60 #560, 0.57 #1380, 0.56 #1011), 0swbd (0.56 #544, 0.52 #667, 0.45 #1487), 018ctl (0.56 #541, 0.50 #746, 0.48 #582), 0jhn7 (0.56 #561, 0.44 #233, 0.43 #725), 0sxrz (0.47 #308, 0.45 #349, 0.44 #226) >> Best rule #4506 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 027nb; 0169t; 02khs; 01n6c; 0hg5; 04w4s; 0jdd; 07bxhl; 02lx0; 0h8d; ... >> query: (?x550, 06sks6) <- contains(?x6304, ?x550), country(?x1121, ?x550), jurisdiction_of_office(?x265, ?x550) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v8c olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.876 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #9340-06hdk PRED entity: 06hdk PRED relation: jurisdiction_of_office! PRED expected values: 01q24l => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 20): 0f6c3 (0.53 #192, 0.48 #215, 0.44 #353), 09n5b9 (0.50 #196, 0.46 #219, 0.40 #357), 0fkvn (0.50 #188, 0.46 #211, 0.39 #349), 0pqc5 (0.30 #580, 0.26 #649, 0.25 #603), 060c4 (0.29 #992, 0.25 #256, 0.20 #670), 060bp (0.29 #254, 0.25 #990, 0.20 #668), 0fkzq (0.18 #201, 0.15 #224, 0.15 #362), 0789n (0.15 #194, 0.11 #217, 0.08 #332), 01t7n9 (0.12 #203, 0.11 #226, 0.08 #364), 04syw (0.09 #260, 0.05 #996, 0.04 #536) >> Best rule #192 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 01914; 0fw4v; >> query: (?x9187, 0f6c3) <- category(?x9187, ?x134), administrative_parent(?x9187, ?x9186), country(?x9187, ?x1229), time_zones(?x9187, ?x2864), ?x134 = 08mbj5d >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #589 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 163 *> proper extension: 0f2tj; *> query: (?x9187, 01q24l) <- category(?x9187, ?x134), ?x134 = 08mbj5d, state(?x9187, ?x9186), time_zones(?x9187, ?x2864), adjoins(?x3407, ?x9186) *> conf = 0.06 ranks of expected_values: 12 EVAL 06hdk jurisdiction_of_office! 01q24l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 155.000 155.000 0.529 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #9339-01h7xx PRED entity: 01h7xx PRED relation: district_represented PRED expected values: 059rby 01x73 07b_l 07h34 => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 333): 01x73 (0.95 #1190, 0.95 #1146, 0.94 #164), 059rby (0.94 #1310, 0.94 #164, 0.93 #1181), 07h34 (0.94 #164, 0.91 #122, 0.89 #421), 07b_l (0.85 #252, 0.78 #611, 0.71 #441), 01n7q (0.85 #252, 0.71 #802, 0.67 #598), 0g0syc (0.85 #252, 0.64 #820, 0.56 #616), 050l8 (0.71 #808, 0.56 #604, 0.52 #979), 05fky (0.71 #818, 0.56 #614, 0.52 #989), 05fjy (0.71 #821, 0.56 #617, 0.52 #992), 059_c (0.71 #801, 0.56 #597, 0.50 #473) >> Best rule #1190 for best value: >> intensional similarity = 38 >> extensional distance = 39 >> proper extension: 01grnp; 01gst9; 01gsry; >> query: (?x7944, 01x73) <- legislative_sessions(?x2712, ?x7944), district_represented(?x7944, ?x6895), district_represented(?x7944, ?x3908), district_represented(?x7944, ?x3670), district_represented(?x7944, ?x961), district_represented(?x10638, ?x3670), district_represented(?x5339, ?x3670), district_represented(?x3766, ?x3670), district_represented(?x3540, ?x3670), district_represented(?x2861, ?x3670), district_represented(?x2019, ?x3670), legislative_sessions(?x2860, ?x7944), ?x2861 = 03tcbx, contains(?x3670, ?x12296), contains(?x3670, ?x4982), place_of_death(?x8970, ?x3670), location(?x395, ?x3670), religion(?x961, ?x109), ?x3540 = 024tcq, location_of_ceremony(?x566, ?x3670), administrative_division(?x4362, ?x961), contains(?x94, ?x961), award_winner(?x395, ?x919), contains(?x961, ?x310), ?x10638 = 01grmk, location(?x1299, ?x3908), contains(?x3908, ?x4161), source(?x12296, ?x958), ?x5339 = 02glc4, ?x2019 = 01gtbb, place_of_birth(?x4080, ?x4982), school(?x1161, ?x4161), state_province_region(?x3172, ?x3908), award_nominee(?x395, ?x192), ?x6895 = 05fjf, category(?x4982, ?x134), partially_contains(?x3908, ?x4540), ?x3766 = 02gkzs >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 01h7xx district_represented 07h34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.951 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01h7xx district_represented 07b_l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.951 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01h7xx district_represented 01x73 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.951 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01h7xx district_represented 059rby CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.951 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #9338-07ssc PRED entity: 07ssc PRED relation: combatants PRED expected values: 05vz3zq => 217 concepts (178 used for prediction) PRED predicted values (max 10 best out of 343): 035qy (0.83 #3638, 0.83 #1843, 0.83 #4045), 015qh (0.83 #3638, 0.83 #1843, 0.83 #4045), 027qpc (0.83 #3638, 0.83 #1843, 0.83 #4045), 01h3dj (0.83 #3638, 0.83 #1843, 0.83 #4045), 05vz3zq (0.65 #1870, 0.54 #2335, 0.50 #1812), 07ssc (0.50 #1787, 0.42 #2310, 0.40 #1845), 024pcx (0.44 #1424, 0.26 #6065, 0.22 #9423), 0dv0z (0.38 #1422, 0.26 #6065, 0.24 #7508), 01fvhp (0.38 #1427, 0.26 #6065, 0.24 #7508), 07l75 (0.38 #1411, 0.26 #6065, 0.24 #7508) >> Best rule #3638 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 03b79; 01k6y1; 024pcx; >> query: (?x512, ?x151) <- nationality(?x12565, ?x512), combatants(?x151, ?x512), location(?x12565, ?x1264) >> conf = 0.83 => this is the best rule for 4 predicted values *> Best rule #1870 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 0b90_r; 0154j; 03rjj; 0d060g; 05qhw; 015fr; 0f8l9c; 03gj2; 0ctw_b; 059j2; ... *> query: (?x512, 05vz3zq) <- film_release_region(?x3491, ?x512), combatants(?x151, ?x512), ?x3491 = 0gtvpkw *> conf = 0.65 ranks of expected_values: 5 EVAL 07ssc combatants 05vz3zq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 217.000 178.000 0.831 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #9337-01385g PRED entity: 01385g PRED relation: award_winner! PRED expected values: 027c95y => 127 concepts (91 used for prediction) PRED predicted values (max 10 best out of 212): 0789_m (0.36 #38375, 0.34 #23714, 0.32 #6467), 03hl6lc (0.33 #176, 0.04 #5349, 0.03 #4487), 0cjyzs (0.33 #537, 0.03 #33737, 0.03 #34168), 07kjk7c (0.33 #290), 09sb52 (0.11 #18150, 0.10 #13835, 0.10 #12973), 099tbz (0.11 #920, 0.05 #18166, 0.05 #13851), 0f4x7 (0.10 #3049, 0.10 #2618, 0.07 #1325), 027c95y (0.09 #3175, 0.09 #2744, 0.05 #13951), 0gs9p (0.09 #5252, 0.06 #6977, 0.05 #1373), 019f4v (0.08 #5239, 0.06 #6964, 0.05 #4377) >> Best rule #38375 for best value: >> intensional similarity = 4 >> extensional distance = 1859 >> proper extension: 06msq2; 05b49tt; 060pl5; 03qhyn8; >> query: (?x12677, ?x458) <- profession(?x12677, ?x524), award_winner(?x850, ?x12677), nationality(?x12677, ?x1310), award(?x12677, ?x458) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #3175 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 199 *> proper extension: 0131kb; 01l3j; *> query: (?x12677, 027c95y) <- type_of_union(?x12677, ?x566), film(?x12677, ?x3549), people(?x9933, ?x12677) *> conf = 0.09 ranks of expected_values: 8 EVAL 01385g award_winner! 027c95y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 127.000 91.000 0.363 http://example.org/award/award_category/winners./award/award_honor/award_winner #9336-073v6 PRED entity: 073v6 PRED relation: influenced_by! PRED expected values: 04cbtrw => 167 concepts (62 used for prediction) PRED predicted values (max 10 best out of 473): 03f47xl (0.50 #1796, 0.38 #3845, 0.08 #3331), 0683n (0.40 #1873, 0.38 #3922, 0.33 #335), 05qzv (0.40 #1938, 0.31 #3987, 0.09 #2961), 01v_0b (0.33 #480, 0.31 #4067, 0.30 #2018), 040db (0.33 #76, 0.31 #3663, 0.20 #1614), 01vdrw (0.33 #440, 0.23 #4027, 0.20 #5564), 0399p (0.33 #325, 0.23 #3912, 0.20 #1863), 02kz_ (0.33 #219, 0.23 #3806, 0.10 #1757), 03qcq (0.33 #1, 0.20 #5125, 0.08 #6150), 019z7q (0.33 #25, 0.15 #3612, 0.13 #5149) >> Best rule #1796 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 040db; 0379s; 032l1; 07ym0; 0ct9_; 03jxw; 0113sg; >> query: (?x3325, 03f47xl) <- influenced_by(?x3325, ?x2162), influenced_by(?x2608, ?x3325), place_of_death(?x3325, ?x9863), ?x2162 = 04xjp >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1644 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 040db; 0379s; 032l1; 07ym0; 0ct9_; 03jxw; 0113sg; *> query: (?x3325, 04cbtrw) <- influenced_by(?x3325, ?x2162), influenced_by(?x2608, ?x3325), place_of_death(?x3325, ?x9863), ?x2162 = 04xjp *> conf = 0.10 ranks of expected_values: 68 EVAL 073v6 influenced_by! 04cbtrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 167.000 62.000 0.500 http://example.org/influence/influence_node/influenced_by #9335-01y665 PRED entity: 01y665 PRED relation: student! PRED expected values: 07wjk => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 68): 02zd460 (0.12 #170), 0bwfn (0.11 #802, 0.06 #275, 0.05 #8180), 07wjk (0.09 #1644), 08815 (0.09 #529, 0.02 #6853, 0.02 #2110), 015nl4 (0.07 #594, 0.04 #6918, 0.03 #7445), 033gn8 (0.07 #905, 0.01 #7229, 0.01 #21999), 09f2j (0.06 #159, 0.02 #32850, 0.02 #23361), 0cwx_ (0.06 #241, 0.02 #768, 0.01 #2349), 0lyjf (0.06 #157, 0.01 #2265), 01bzs9 (0.06 #460) >> Best rule #170 for best value: >> intensional similarity = 2 >> extensional distance = 15 >> proper extension: 012x2b; >> query: (?x3039, 02zd460) <- film(?x3039, ?x5128), ?x5128 = 08phg9 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #1644 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 148 *> proper extension: 0dky9n; 063vn; 059xvg; 09fp45; 0841zn; 0k29f; 055t01; 03c_8t; 07glc4; 07q68q; *> query: (?x3039, 07wjk) <- nationality(?x3039, ?x279), ?x279 = 0d060g *> conf = 0.09 ranks of expected_values: 3 EVAL 01y665 student! 07wjk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.118 http://example.org/education/educational_institution/students_graduates./education/education/student #9334-018qql PRED entity: 018qql PRED relation: award PRED expected values: 09qrn4 => 135 concepts (112 used for prediction) PRED predicted values (max 10 best out of 276): 0f4x7 (0.33 #435, 0.30 #1647, 0.25 #3263), 054ky1 (0.30 #1725, 0.20 #109, 0.17 #3745), 0bdwqv (0.28 #5020, 0.08 #3000, 0.08 #9060), 09sb52 (0.25 #9333, 0.24 #7313, 0.23 #17010), 05b4l5x (0.25 #1218, 0.20 #6, 0.17 #3642), 0ck27z (0.25 #4940, 0.10 #40091, 0.10 #31606), 05pcn59 (0.24 #7353, 0.24 #9373, 0.23 #10989), 05p09zm (0.24 #11031, 0.24 #9415, 0.22 #7395), 0gqy2 (0.22 #8244, 0.22 #7840, 0.21 #9860), 09qvc0 (0.22 #4888, 0.10 #1656, 0.09 #2060) >> Best rule #435 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0161h5; >> query: (?x13648, 0f4x7) <- award_winner(?x2071, ?x13648), profession(?x13648, ?x1032), friend(?x3628, ?x13648), ?x3628 = 08b8vd >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5895 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 04nw9; 02m7r; 01wj92r; 01h320; 0l786; 01zwy; 01v5h; 0cyhq; 018qpb; 0c3dzk; *> query: (?x13648, 09qrn4) <- award_winner(?x2071, ?x13648), place_of_burial(?x13648, ?x3691), people(?x7322, ?x13648) *> conf = 0.08 ranks of expected_values: 108 EVAL 018qql award 09qrn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 135.000 112.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9333-05nyqk PRED entity: 05nyqk PRED relation: genre PRED expected values: 0lsxr => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 85): 07s9rl0 (0.74 #6064, 0.61 #4000, 0.60 #4121), 05p553 (0.58 #4245, 0.38 #2061, 0.37 #2304), 02l7c8 (0.30 #3529, 0.29 #4015, 0.29 #4500), 03k9fj (0.26 #4253, 0.26 #980, 0.25 #1222), 017fp (0.25 #257, 0.22 #136, 0.09 #4135), 04xvh5 (0.25 #277, 0.22 #156, 0.09 #1850), 060__y (0.24 #501, 0.23 #622, 0.21 #1832), 01hmnh (0.22 #139, 0.21 #502, 0.19 #623), 02n4kr (0.22 #129, 0.17 #250, 0.13 #2429), 03g3w (0.22 #146, 0.17 #267, 0.08 #1114) >> Best rule #6064 for best value: >> intensional similarity = 3 >> extensional distance = 1362 >> proper extension: 0c0wvx; >> query: (?x9199, 07s9rl0) <- genre(?x9199, ?x225), genre(?x1077, ?x225), ?x1077 = 09q5w2 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1824 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 153 *> proper extension: 07f_7h; *> query: (?x9199, 0lsxr) <- film_format(?x9199, ?x909), ?x909 = 07fb8_, film_crew_role(?x9199, ?x137) *> conf = 0.21 ranks of expected_values: 13 EVAL 05nyqk genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 70.000 70.000 0.738 http://example.org/film/film/genre #9332-01v80y PRED entity: 01v80y PRED relation: award_nominee PRED expected values: 06cgy => 105 concepts (40 used for prediction) PRED predicted values (max 10 best out of 990): 06cgy (0.81 #91315, 0.80 #21070, 0.02 #19056), 03gm48 (0.29 #65553, 0.27 #88973), 01541z (0.29 #65553, 0.02 #63655, 0.02 #61314), 04wx2v (0.29 #65553, 0.02 #4363), 02mxw0 (0.29 #65553), 02b29 (0.27 #88973), 05gnf (0.27 #88973), 0klh7 (0.27 #88973), 02w0dc0 (0.27 #88973), 04m_zp (0.10 #3273, 0.03 #19660, 0.03 #26684) >> Best rule #91315 for best value: >> intensional similarity = 3 >> extensional distance = 1095 >> proper extension: 07sgfsl; >> query: (?x8950, ?x1554) <- award_winner(?x78, ?x8950), profession(?x8950, ?x319), award_nominee(?x1554, ?x8950) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v80y award_nominee 06cgy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 40.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9331-01t9qj_ PRED entity: 01t9qj_ PRED relation: award PRED expected values: 054ky1 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 301): 02py7pj (0.71 #16613, 0.71 #22697, 0.71 #22291), 09sb52 (0.31 #12195, 0.24 #14221, 0.23 #15843), 0f4x7 (0.31 #436, 0.28 #3274, 0.27 #2869), 0gkvb7 (0.27 #27, 0.15 #23508, 0.13 #2054), 01bgqh (0.25 #1664, 0.21 #3691, 0.18 #1258), 01by1l (0.23 #1734, 0.20 #3761, 0.17 #5381), 05zr6wv (0.21 #827, 0.09 #5690, 0.08 #4070), 0gqwc (0.19 #17831, 0.15 #23508, 0.13 #33235), 0bdw1g (0.19 #17831, 0.06 #34452, 0.02 #3686), 09qrn4 (0.18 #240, 0.08 #3483, 0.07 #1455) >> Best rule #16613 for best value: >> intensional similarity = 3 >> extensional distance = 1229 >> proper extension: 0khth; 014l4w; 07k2d; >> query: (?x8006, ?x8459) <- award_winner(?x8459, ?x8006), award(?x8006, ?x3247), award_winner(?x6807, ?x8006) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 033hqf; 044qx; 013sg6; 01l3j; *> query: (?x8006, 054ky1) <- place_of_death(?x8006, ?x13207), nationality(?x8006, ?x94), celebrities_impersonated(?x3649, ?x8006) *> conf = 0.10 ranks of expected_values: 41 EVAL 01t9qj_ award 054ky1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 103.000 103.000 0.715 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9330-0161c2 PRED entity: 0161c2 PRED relation: instrumentalists! PRED expected values: 026t6 02sgy => 147 concepts (103 used for prediction) PRED predicted values (max 10 best out of 120): 018vs (0.43 #3411, 0.40 #1966, 0.40 #96), 05148p4 (0.42 #3419, 0.39 #2399, 0.37 #1974), 03qjg (0.40 #134, 0.33 #219, 0.21 #3449), 026t6 (0.30 #88, 0.21 #768, 0.20 #173), 018j2 (0.20 #121, 0.13 #206, 0.12 #1991), 04rzd (0.20 #120, 0.13 #205, 0.10 #3435), 06ncr (0.20 #127, 0.13 #212, 0.08 #3442), 0l14qv (0.15 #2385, 0.11 #4171, 0.11 #3150), 0l14md (0.15 #2387, 0.13 #177, 0.13 #3407), 01wy6 (0.13 #215, 0.10 #130, 0.06 #300) >> Best rule #3411 for best value: >> intensional similarity = 4 >> extensional distance = 209 >> proper extension: 02l_7y; 0lsw9; 01wt4wc; 04m2zj; 02pt27; 01tw31; 01pny5; >> query: (?x3126, 018vs) <- artists(?x302, ?x3126), artists(?x302, ?x8560), ?x8560 = 02y7sr, instrumentalists(?x227, ?x3126) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #88 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 04bgy; 095x_; 01k_0fp; 01nn3m; *> query: (?x3126, 026t6) <- artists(?x8386, ?x3126), type_of_union(?x3126, ?x566), ?x8386 = 016ybr *> conf = 0.30 ranks of expected_values: 4, 25 EVAL 0161c2 instrumentalists! 02sgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 147.000 103.000 0.431 http://example.org/music/instrument/instrumentalists EVAL 0161c2 instrumentalists! 026t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 147.000 103.000 0.431 http://example.org/music/instrument/instrumentalists #9329-0372j5 PRED entity: 0372j5 PRED relation: genre PRED expected values: 05p553 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 102): 07s9rl0 (0.71 #248, 0.70 #1355, 0.67 #5912), 09q17 (0.53 #10846, 0.52 #13190, 0.51 #9983), 024qqx (0.53 #10846, 0.52 #13190, 0.51 #9983), 03k9fj (0.50 #752, 0.40 #875, 0.37 #2352), 01jfsb (0.47 #507, 0.40 #14, 0.36 #261), 05p553 (0.42 #2590, 0.42 #128, 0.39 #2344), 01hmnh (0.42 #143, 0.37 #882, 0.34 #4925), 02kdv5l (0.42 #742, 0.35 #1480, 0.34 #988), 0lsxr (0.40 #10, 0.34 #4925, 0.26 #1241), 02n4kr (0.40 #9, 0.27 #502, 0.26 #871) >> Best rule #248 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 04nlb94; >> query: (?x6751, 07s9rl0) <- film(?x382, ?x6751), language(?x6751, ?x4442), ?x4442 = 06mp7 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2590 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 137 *> proper extension: 0sxg4; 02hxhz; 07y9w5; 01719t; 0340hj; 035s95; 0d_2fb; 012mrr; 0gyy53; 03mh_tp; ... *> query: (?x6751, 05p553) <- film(?x709, ?x6751), film(?x541, ?x6751), film_crew_role(?x6751, ?x137), ?x541 = 017s11 *> conf = 0.42 ranks of expected_values: 6 EVAL 0372j5 genre 05p553 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 121.000 121.000 0.714 http://example.org/film/film/genre #9328-027jw0c PRED entity: 027jw0c PRED relation: place_founded PRED expected values: 02z0j => 211 concepts (33 used for prediction) PRED predicted values (max 10 best out of 86): 030qb3t (0.50 #209, 0.33 #13, 0.25 #143), 0f2wj (0.33 #9, 0.25 #205, 0.25 #139), 02h6_6p (0.15 #2196, 0.05 #991, 0.03 #1791), 0r00l (0.14 #586, 0.13 #1118, 0.13 #1051), 0vzm (0.11 #417, 0.05 #949, 0.03 #1349), 0qcrj (0.11 #457, 0.03 #1389), 06pwq (0.11 #400, 0.03 #1332), 02_286 (0.08 #1135, 0.07 #537, 0.07 #1335), 080h2 (0.08 #470, 0.07 #538, 0.07 #604), 01n7q (0.08 #471, 0.07 #605, 0.03 #1537) >> Best rule #209 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 025tlyv; >> query: (?x9997, 030qb3t) <- film(?x9997, ?x2434), film(?x9997, ?x1315), ?x1315 = 053tj7, film_release_region(?x2434, ?x252), film_release_region(?x2434, ?x205), ?x205 = 03rjj, executive_produced_by(?x2434, ?x8563), ?x252 = 03_3d >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027jw0c place_founded 02z0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 211.000 33.000 0.500 http://example.org/organization/organization/place_founded #9327-0fc9js PRED entity: 0fc9js PRED relation: ceremony PRED expected values: 02q690_ => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 130): 02q690_ (0.74 #972, 0.60 #712, 0.33 #61), 0gpjbt (0.48 #1850, 0.36 #4065, 0.34 #4456), 09n4nb (0.47 #1866, 0.35 #4081, 0.34 #4472), 0466p0j (0.46 #1892, 0.35 #4107, 0.33 #4498), 05pd94v (0.46 #1824, 0.33 #4039, 0.33 #2214), 02cg41 (0.46 #1939, 0.35 #4154, 0.33 #4545), 02rjjll (0.46 #1827, 0.34 #4042, 0.33 #4433), 056878 (0.46 #1852, 0.34 #4067, 0.34 #4458), 01c6qp (0.45 #1840, 0.33 #4055, 0.33 #4446), 01mh_q (0.43 #1904, 0.32 #4119, 0.31 #4510) >> Best rule #972 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 09v82c0; >> query: (?x4386, 02q690_) <- nominated_for(?x4386, ?x5698), award(?x236, ?x4386), ceremony(?x4386, ?x1265), ?x1265 = 05c1t6z >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fc9js ceremony 02q690_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.742 http://example.org/award/award_category/winners./award/award_honor/ceremony #9326-0d060g PRED entity: 0d060g PRED relation: film_release_region! PRED expected values: 0ds35l9 0h1cdwq 0872p_c 0cnztc4 0gj9qxr 0fq7dv_ 0j6b5 0crc2cp 0gvs1kt 0184tc 08tq4x 080lkt7 0dt8xq 0284b56 0gg5kmg 02q8ms8 05dss7 0280061 01mgw 0gh6j94 0m63c 024lt6 027r7k => 218 concepts (168 used for prediction) PRED predicted values (max 10 best out of 1560): 024mpp (0.86 #14044, 0.78 #29830, 0.75 #20358), 0h1cdwq (0.86 #13719, 0.75 #29505, 0.75 #20033), 045j3w (0.86 #13956, 0.71 #20270, 0.68 #32899), 0872p_c (0.84 #29571, 0.82 #13785, 0.79 #20099), 047msdk (0.82 #13802, 0.79 #32745, 0.79 #20116), 087pfc (0.82 #14573, 0.79 #20887, 0.78 #30359), 01c22t (0.82 #13781, 0.78 #29567, 0.75 #20095), 0dt8xq (0.82 #14188, 0.75 #20502, 0.72 #29974), 0ds35l9 (0.82 #13687, 0.75 #29473, 0.71 #20001), 0dtfn (0.81 #29590, 0.80 #5383, 0.79 #32747) >> Best rule #14044 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 047yc; >> query: (?x279, 024mpp) <- film_release_region(?x5347, ?x279), film_release_region(?x4615, ?x279), ?x4615 = 0dlngsd, ?x5347 = 02ylg6 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #13719 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 047yc; *> query: (?x279, 0h1cdwq) <- film_release_region(?x5347, ?x279), film_release_region(?x4615, ?x279), ?x4615 = 0dlngsd, ?x5347 = 02ylg6 *> conf = 0.86 ranks of expected_values: 2, 4, 8, 9, 11, 15, 17, 22, 24, 27, 33, 63, 65, 68, 74, 118, 138, 150, 163, 171, 199, 228, 660 EVAL 0d060g film_release_region! 027r7k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 024lt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0m63c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0gh6j94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 01mgw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0280061 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 05dss7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 02q8ms8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0gg5kmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0284b56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0dt8xq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 080lkt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 08tq4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0184tc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0gvs1kt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0crc2cp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0j6b5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0fq7dv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0gj9qxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0cnztc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0872p_c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0h1cdwq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d060g film_release_region! 0ds35l9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 218.000 168.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9325-01bbwp PRED entity: 01bbwp PRED relation: profession PRED expected values: 0n1h => 97 concepts (43 used for prediction) PRED predicted values (max 10 best out of 65): 01d_h8 (0.73 #2684, 0.69 #1838, 0.68 #1556), 03gjzk (0.64 #3113, 0.63 #1139, 0.63 #575), 0nbcg (0.48 #1011, 0.12 #5104, 0.10 #5386), 0dz3r (0.36 #989, 0.10 #5082, 0.08 #1976), 039v1 (0.32 #1016, 0.17 #29, 0.08 #5109), 0np9r (0.29 #2130, 0.29 #3540, 0.28 #3683), 0n1h (0.25 #996, 0.09 #432, 0.09 #291), 0fnpj (0.18 #1040, 0.06 #2027, 0.05 #335), 01c72t (0.17 #18, 0.12 #5098, 0.11 #1005), 025352 (0.17 #52, 0.06 #757, 0.05 #898) >> Best rule #2684 for best value: >> intensional similarity = 5 >> extensional distance = 168 >> proper extension: 03wpmd; 081_zm; 03cxsvl; 03_80b; 035sc2; 04qr6d; 03d8njj; 0894_x; 045n3p; >> query: (?x9685, 01d_h8) <- religion(?x9685, ?x2694), profession(?x9685, ?x524), ?x524 = 02jknp, religion(?x4112, ?x2694), people(?x5855, ?x4112) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #996 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 42 *> proper extension: 01w61th; 04dqdk; 01l1sq; 0j1yf; 01vsnff; 0lccn; 01pgzn_; 04xrx; 0137g1; 01vsl3_; ... *> query: (?x9685, 0n1h) <- religion(?x9685, ?x2694), type_of_union(?x9685, ?x566), profession(?x9685, ?x220), gender(?x9685, ?x231), ?x220 = 016z4k *> conf = 0.25 ranks of expected_values: 7 EVAL 01bbwp profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 43.000 0.729 http://example.org/people/person/profession #9324-01rmnp PRED entity: 01rmnp PRED relation: profession PRED expected values: 0np9r 025352 => 134 concepts (62 used for prediction) PRED predicted values (max 10 best out of 63): 0np9r (0.90 #763, 0.82 #169, 0.74 #615), 09jwl (0.84 #1207, 0.79 #4030, 0.78 #2244), 016z4k (0.57 #301, 0.54 #1932, 0.53 #2973), 0dz3r (0.57 #1190, 0.52 #299, 0.49 #4013), 01d_h8 (0.43 #2379, 0.38 #2082, 0.37 #3124), 039v1 (0.39 #4047, 0.36 #1224, 0.33 #5086), 0n1h (0.33 #1052, 0.29 #1644, 0.28 #1792), 0fnpj (0.33 #357, 0.21 #1248, 0.19 #4368), 01c72t (0.32 #7009, 0.32 #5667, 0.32 #6115), 0dxtg (0.28 #2387, 0.25 #2090, 0.25 #3132) >> Best rule #763 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 01kwh5j; >> query: (?x9263, 0np9r) <- profession(?x9263, ?x1032), special_performance_type(?x9263, ?x296), ?x296 = 01kyvx >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 20 EVAL 01rmnp profession 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 134.000 62.000 0.900 http://example.org/people/person/profession EVAL 01rmnp profession 0np9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 62.000 0.900 http://example.org/people/person/profession #9323-0478__m PRED entity: 0478__m PRED relation: award PRED expected values: 0c4z8 => 111 concepts (102 used for prediction) PRED predicted values (max 10 best out of 288): 02f77y (0.79 #13970, 0.77 #13969, 0.73 #34945), 02g3gj (0.79 #13970, 0.77 #13969, 0.73 #34945), 02f6yz (0.57 #691, 0.16 #26784, 0.14 #3019), 01c427 (0.47 #2800, 0.28 #3964, 0.28 #3188), 01ckcd (0.43 #708, 0.16 #26784, 0.15 #3424), 03tcnt (0.43 #550, 0.16 #26784, 0.13 #33389), 02f77l (0.43 #631, 0.16 #26784, 0.13 #33389), 09sb52 (0.34 #20999, 0.32 #22165, 0.26 #8189), 0c4z8 (0.30 #10159, 0.30 #3951, 0.26 #3175), 01c9jp (0.29 #570, 0.21 #4062, 0.16 #26784) >> Best rule #13970 for best value: >> intensional similarity = 3 >> extensional distance = 516 >> proper extension: 03j90; >> query: (?x4593, ?x2634) <- award_winner(?x2634, ?x4593), award(?x9868, ?x2634), group(?x227, ?x9868) >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #10159 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 393 *> proper extension: 01vw87c; 01pfr3; 0152cw; 01v0sx2; 01j4ls; 01r9fv; 01wv9xn; 0dtd6; 0frsw; 01dw9z; ... *> query: (?x4593, 0c4z8) <- award(?x4593, ?x4958), award(?x8166, ?x4958), ?x8166 = 0bs1g5r *> conf = 0.30 ranks of expected_values: 9 EVAL 0478__m award 0c4z8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 111.000 102.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9322-0gl02yg PRED entity: 0gl02yg PRED relation: nominated_for! PRED expected values: 09v8db5 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 198): 07kfzsg (0.74 #8075, 0.68 #10691, 0.68 #949), 019f4v (0.45 #4567, 0.30 #529, 0.30 #3378), 0gq9h (0.37 #4576, 0.36 #538, 0.33 #1961), 0gs9p (0.35 #4578, 0.34 #3389, 0.32 #3865), 0k611 (0.29 #4587, 0.28 #1261, 0.27 #549), 054krc (0.28 #4583, 0.17 #3394, 0.16 #3870), 04dn09n (0.28 #4548, 0.23 #3359, 0.23 #2408), 040njc (0.26 #4519, 0.24 #3330, 0.24 #1904), 0gr0m (0.25 #772, 0.22 #1247, 0.22 #4573), 0gq_v (0.25 #4532, 0.23 #7856, 0.22 #6905) >> Best rule #8075 for best value: >> intensional similarity = 5 >> extensional distance = 815 >> proper extension: 06mmr; >> query: (?x5826, ?x12715) <- award(?x5826, ?x12715), nominated_for(?x12715, ?x6788), nominated_for(?x12715, ?x6376), edited_by(?x6788, ?x9086), film_release_region(?x6376, ?x151) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #9267 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 850 *> proper extension: 03d17dg; *> query: (?x5826, ?x5923) <- award_winner(?x5826, ?x1864), profession(?x1864, ?x1032), ?x1032 = 02hrh1q, award(?x1864, ?x5923) *> conf = 0.23 ranks of expected_values: 13 EVAL 0gl02yg nominated_for! 09v8db5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 77.000 77.000 0.742 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9321-042zrm PRED entity: 042zrm PRED relation: film_release_region PRED expected values: 0jgd 03rjj 030qb3t => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 241): 0f8l9c (0.88 #2381, 0.88 #3222, 0.88 #1036), 05r4w (0.85 #1012, 0.81 #6068, 0.81 #1853), 03rjj (0.83 #343, 0.81 #848, 0.80 #1858), 03h64 (0.81 #1087, 0.81 #413, 0.78 #1928), 0jgd (0.81 #1014, 0.78 #340, 0.78 #2359), 0k6nt (0.81 #366, 0.80 #3226, 0.79 #1881), 015fr (0.81 #356, 0.75 #1030, 0.74 #1871), 05qhw (0.77 #1027, 0.71 #1868, 0.67 #6083), 0154j (0.77 #1016, 0.70 #2361, 0.69 #1857), 05b4w (0.76 #1925, 0.74 #915, 0.72 #410) >> Best rule #2381 for best value: >> intensional similarity = 5 >> extensional distance = 150 >> proper extension: 0dckvs; 02x3lt7; 0m491; 0gvrws1; 0j_tw; 0fpv_3_; 01shy7; 0b_5d; 023gxx; 0ywrc; ... >> query: (?x8236, 0f8l9c) <- produced_by(?x8236, ?x1039), film_release_region(?x8236, ?x1264), film_release_region(?x8236, ?x1229), ?x1229 = 059j2, country(?x1646, ?x1264) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #343 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0g5879y; *> query: (?x8236, 03rjj) <- produced_by(?x8236, ?x1039), crewmember(?x8236, ?x9391), film_release_region(?x8236, ?x252), ?x252 = 03_3d *> conf = 0.83 ranks of expected_values: 3, 5, 90 EVAL 042zrm film_release_region 030qb3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 145.000 145.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 042zrm film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 145.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 042zrm film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 145.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9320-02bn_p PRED entity: 02bn_p PRED relation: legislative_sessions! PRED expected values: 02bp37 04gp1d 02cg7g => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 43): 0495ys (0.88 #232, 0.88 #140, 0.87 #188), 02glc4 (0.88 #232, 0.88 #140, 0.87 #188), 02bp37 (0.88 #232, 0.88 #140, 0.87 #188), 024tkd (0.88 #232, 0.88 #140, 0.87 #188), 02cg7g (0.88 #232, 0.88 #140, 0.87 #188), 02gkzs (0.88 #232, 0.88 #140, 0.87 #188), 03ww_x (0.88 #232, 0.88 #140, 0.87 #188), 02bqmq (0.88 #232, 0.88 #140, 0.87 #188), 02bn_p (0.81 #456, 0.81 #454, 0.80 #1184), 04gp1d (0.81 #456, 0.81 #454, 0.80 #1184) >> Best rule #232 for best value: >> intensional similarity = 40 >> extensional distance = 2 >> proper extension: 077g7n; >> query: (?x1027, ?x355) <- legislative_sessions(?x2976, ?x1027), legislative_sessions(?x653, ?x1027), legislative_sessions(?x2860, ?x1027), ?x2976 = 03rtmz, district_represented(?x1027, ?x6895), district_represented(?x1027, ?x5575), district_represented(?x1027, ?x4758), district_represented(?x1027, ?x3634), district_represented(?x1027, ?x3086), district_represented(?x1027, ?x2977), district_represented(?x1027, ?x2831), district_represented(?x1027, ?x1906), district_represented(?x1027, ?x1025), district_represented(?x1027, ?x728), ?x728 = 059f4, ?x6895 = 05fjf, ?x4758 = 0vbk, capital(?x5575, ?x9341), jurisdiction_of_office(?x10093, ?x5575), jurisdiction_of_office(?x900, ?x5575), ?x900 = 0fkvn, legislative_sessions(?x9334, ?x1027), legislative_sessions(?x2357, ?x1027), legislative_sessions(?x1027, ?x606), legislative_sessions(?x1027, ?x355), ?x606 = 03ww_x, contains(?x94, ?x5575), adjoins(?x5575, ?x10378), ?x653 = 070m6c, ?x2860 = 0b3wk, ?x1906 = 04rrx, ?x3086 = 0846v, ?x3634 = 07b_l, religion(?x5575, ?x109), ?x9334 = 02hy5d, ?x2977 = 081mh, ?x10093 = 09n5b9, ?x1025 = 04ych, ?x2357 = 0bymv, ?x2831 = 0gyh >> conf = 0.88 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 5, 10 EVAL 02bn_p legislative_sessions! 02cg7g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 34.000 34.000 0.884 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions EVAL 02bn_p legislative_sessions! 04gp1d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 34.000 34.000 0.884 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions EVAL 02bn_p legislative_sessions! 02bp37 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 34.000 34.000 0.884 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #9319-0840vq PRED entity: 0840vq PRED relation: award PRED expected values: 02f72n => 107 concepts (95 used for prediction) PRED predicted values (max 10 best out of 282): 02f71y (0.66 #581, 0.59 #181, 0.16 #6001), 02f73b (0.52 #685, 0.48 #285, 0.20 #1885), 09sb52 (0.47 #11241, 0.24 #22841, 0.24 #14441), 02f5qb (0.45 #554, 0.37 #154, 0.25 #1754), 0c4z8 (0.45 #2070, 0.39 #1670, 0.21 #2870), 03qbh5 (0.38 #604, 0.37 #1804, 0.35 #2204), 03qbnj (0.38 #632, 0.33 #232, 0.29 #2232), 02f716 (0.38 #575, 0.30 #175, 0.18 #8176), 02f6ym (0.34 #656, 0.33 #256, 0.18 #1856), 02f73p (0.34 #586, 0.26 #186, 0.19 #2186) >> Best rule #581 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 016lmg; >> query: (?x3187, 02f71y) <- award(?x3187, ?x8458), ?x8458 = 02f777, award_nominee(?x2925, ?x3187), artists(?x671, ?x3187) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 01vvycq; 01w61th; 03t9sp; 01wj18h; 0bqsy; 0hvbj; 01dwrc; 01vzx45; 07sbk; 01lqf49; ... *> query: (?x3187, 02f72n) <- award(?x3187, ?x8458), origin(?x3187, ?x4627), artists(?x671, ?x3187), ?x8458 = 02f777 *> conf = 0.30 ranks of expected_values: 15 EVAL 0840vq award 02f72n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 107.000 95.000 0.655 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9318-02zfdp PRED entity: 02zfdp PRED relation: award_winner! PRED expected values: 09p2r9 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 90): 09qvms (0.17 #154, 0.10 #7333, 0.05 #1282), 01c6qp (0.12 #1006, 0.06 #1993, 0.05 #2839), 013b2h (0.11 #1067, 0.07 #785, 0.07 #2054), 0clfdj (0.10 #7333, 0.08 #145, 0.06 #568), 09gkdln (0.10 #7333, 0.08 #263, 0.04 #686), 05c1t6z (0.10 #7333, 0.08 #156, 0.04 #2412), 03nnm4t (0.10 #7333, 0.08 #215, 0.03 #2471), 09p2r9 (0.10 #7333, 0.08 #234, 0.02 #657), 02wzl1d (0.10 #7333, 0.08 #152, 0.02 #4241), 0hr3c8y (0.10 #7333, 0.06 #433, 0.06 #292) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 02p65p; 07s8r0; 0306ds; 05th8t; 01ksr1; 026l37; 053y4h; 0c3p7; 0g8st4; 03zz8b; >> query: (?x9152, 09qvms) <- award_nominee(?x4043, ?x9152), actor(?x3303, ?x9152), ?x4043 = 06t74h >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #7333 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1997 *> proper extension: 030_1_; 0fb0v; 04n65n; 018p5f; *> query: (?x9152, ?x944) <- award_nominee(?x6314, ?x9152), award(?x6314, ?x618), award_winner(?x944, ?x6314) *> conf = 0.10 ranks of expected_values: 8 EVAL 02zfdp award_winner! 09p2r9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 81.000 81.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9317-011yxy PRED entity: 011yxy PRED relation: nominated_for! PRED expected values: 094qd5 0gq9h => 102 concepts (91 used for prediction) PRED predicted values (max 10 best out of 212): 09d28z (0.69 #7198, 0.68 #7197, 0.67 #7432), 027c924 (0.69 #7198, 0.68 #7197, 0.67 #7432), 09cn0c (0.69 #7198, 0.68 #7197, 0.67 #7432), 0gq_v (0.65 #1412, 0.33 #3966, 0.33 #4198), 0gq9h (0.54 #1453, 0.53 #989, 0.48 #4007), 019f4v (0.50 #1446, 0.49 #982, 0.41 #4232), 0k611 (0.46 #1462, 0.36 #4248, 0.36 #7736), 03hl6lc (0.41 #1749, 0.22 #1053, 0.19 #589), 04dn09n (0.39 #964, 0.39 #1660, 0.30 #500), 040njc (0.38 #935, 0.36 #471, 0.36 #1399) >> Best rule #7198 for best value: >> intensional similarity = 4 >> extensional distance = 492 >> proper extension: 07bz5; >> query: (?x7307, ?x1243) <- nominated_for(?x2530, ?x7307), award(?x7307, ?x1243), honored_for(?x602, ?x7307), award(?x185, ?x1243) >> conf = 0.69 => this is the best rule for 3 predicted values *> Best rule #1453 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 101 *> proper extension: 0m313; 0b2v79; 01gc7; 011yxg; 0ds11z; 0n0bp; 0209hj; 0fg04; 05jzt3; 017gl1; ... *> query: (?x7307, 0gq9h) <- nominated_for(?x2222, ?x7307), honored_for(?x602, ?x7307), film(?x6957, ?x7307), ?x2222 = 0gs96 *> conf = 0.54 ranks of expected_values: 5, 24 EVAL 011yxy nominated_for! 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 102.000 91.000 0.689 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 011yxy nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 102.000 91.000 0.689 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9316-0hsb3 PRED entity: 0hsb3 PRED relation: colors PRED expected values: 019sc => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.38 #1654, 0.37 #1578, 0.36 #1901), 01g5v (0.27 #1903, 0.26 #1580, 0.25 #1656), 019sc (0.18 #1907, 0.18 #1660, 0.17 #1584), 06fvc (0.15 #1902, 0.15 #1655, 0.15 #1579), 04mkbj (0.12 #29, 0.10 #276, 0.09 #86), 088fh (0.12 #633, 0.06 #899, 0.05 #1013), 03wkwg (0.10 #281, 0.09 #91, 0.09 #566), 038hg (0.09 #1665, 0.09 #1912, 0.08 #449), 036k5h (0.09 #860, 0.09 #1905, 0.08 #784), 02rnmb (0.09 #165, 0.07 #51, 0.06 #298) >> Best rule #1654 for best value: >> intensional similarity = 3 >> extensional distance = 332 >> proper extension: 02zkz7; 02jx_v; >> query: (?x6132, 083jv) <- category(?x6132, ?x134), colors(?x6132, ?x332), state_province_region(?x6132, ?x1310) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1907 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 442 *> proper extension: 0q19t; 03zw80; 021l5s; 01y9st; 0352gk; 0269kx; 01hnb; 057wlm; 06l32y; 016sd3; ... *> query: (?x6132, 019sc) <- contains(?x362, ?x6132), colors(?x6132, ?x332) *> conf = 0.18 ranks of expected_values: 3 EVAL 0hsb3 colors 019sc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 142.000 142.000 0.377 http://example.org/education/educational_institution/colors #9315-016khd PRED entity: 016khd PRED relation: gender PRED expected values: 05zppz => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #130, 0.72 #220, 0.71 #216), 02zsn (0.50 #46, 0.49 #30, 0.49 #18) >> Best rule #130 for best value: >> intensional similarity = 2 >> extensional distance = 1294 >> proper extension: 01wxdn3; >> query: (?x851, 05zppz) <- student(?x1809, ?x851), place_of_birth(?x851, ?x108) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016khd gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.722 http://example.org/people/person/gender #9314-02gr81 PRED entity: 02gr81 PRED relation: school! PRED expected values: 05m_8 => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 91): 07l8x (0.50 #155, 0.40 #246, 0.22 #519), 04wmvz (0.33 #532, 0.25 #168, 0.20 #259), 05g76 (0.25 #384, 0.25 #111, 0.20 #566), 07147 (0.25 #156, 0.22 #520, 0.20 #247), 0cqt41 (0.25 #108, 0.22 #472, 0.20 #199), 051wf (0.25 #180, 0.22 #544, 0.20 #271), 043vc (0.25 #127, 0.22 #491, 0.20 #218), 04mjl (0.25 #425, 0.22 #516, 0.15 #880), 05xvj (0.25 #450, 0.20 #268, 0.15 #814), 07l4z (0.25 #159, 0.20 #250, 0.12 #432) >> Best rule #155 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 08815; 09f2j; >> query: (?x4209, 07l8x) <- school(?x260, ?x4209), institution(?x620, ?x4209), student(?x4209, ?x123), ?x123 = 05bnp0, draft(?x260, ?x1161) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #457 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 027mdh; *> query: (?x4209, 05m_8) <- school(?x4208, ?x4209), institution(?x1305, ?x4209), ?x1305 = 02mjs7, school(?x1161, ?x4209), draft(?x4208, ?x1633) *> conf = 0.22 ranks of expected_values: 16 EVAL 02gr81 school! 05m_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 178.000 178.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9313-0333t PRED entity: 0333t PRED relation: films! PRED expected values: 07_nf => 86 concepts (30 used for prediction) PRED predicted values (max 10 best out of 39): 081pw (0.21 #474, 0.20 #632, 0.07 #159), 07_nf (0.17 #67, 0.06 #538, 0.06 #696), 0d3k14 (0.17 #94), 0fx2s (0.09 #229, 0.05 #387, 0.03 #861), 0cm2xh (0.05 #676, 0.04 #518, 0.02 #835), 0kbq (0.05 #576, 0.04 #734, 0.02 #1050), 06d4h (0.05 #1303, 0.04 #2091, 0.03 #988), 07jq_ (0.04 #711, 0.04 #553, 0.02 #870), 01w1sx (0.04 #562, 0.04 #720, 0.03 #879), 05489 (0.04 #366, 0.04 #208, 0.03 #997) >> Best rule #474 for best value: >> intensional similarity = 3 >> extensional distance = 137 >> proper extension: 0192hw; >> query: (?x9838, 081pw) <- genre(?x9838, ?x3515), ?x3515 = 082gq, country(?x9838, ?x94) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 035s95; *> query: (?x9838, 07_nf) <- titles(?x53, ?x9838), film(?x10050, ?x9838), currency(?x9838, ?x170), ?x10050 = 01hmb_ *> conf = 0.17 ranks of expected_values: 2 EVAL 0333t films! 07_nf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 30.000 0.209 http://example.org/film/film_subject/films #9312-0cqhb3 PRED entity: 0cqhb3 PRED relation: nominated_for PRED expected values: 02rzdcp 030p35 01fx1l => 48 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1326): 02rcwq0 (0.60 #781, 0.20 #2363, 0.07 #7109), 02rzdcp (0.40 #2068, 0.40 #486, 0.16 #5230), 02k_4g (0.40 #1687, 0.40 #105, 0.16 #4849), 0ddd0gc (0.40 #1777, 0.40 #195, 0.13 #3358), 02qkq0 (0.40 #2623, 0.40 #1041, 0.11 #5785), 02md2d (0.40 #2218, 0.40 #636, 0.11 #5380), 030p35 (0.40 #2293, 0.40 #711, 0.11 #5455), 03_8kz (0.40 #2965, 0.40 #1383, 0.11 #6127), 01cvtf (0.40 #3092, 0.40 #1510, 0.05 #6254), 01fx1l (0.40 #866, 0.20 #2448, 0.13 #4029) >> Best rule #781 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0bdx29; 0fbtbt; 0gkr9q; >> query: (?x8250, 02rcwq0) <- award_winner(?x8250, ?x368), award(?x286, ?x8250), nominated_for(?x8250, ?x493), ?x493 = 080dwhx >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2068 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0m7yy; *> query: (?x8250, 02rzdcp) <- award_winner(?x8250, ?x368), award(?x2009, ?x8250), award(?x1849, ?x8250), ?x1849 = 0kfv9, ?x2009 = 03d34x8 *> conf = 0.40 ranks of expected_values: 2, 7, 10 EVAL 0cqhb3 nominated_for 01fx1l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 48.000 17.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0cqhb3 nominated_for 030p35 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 48.000 17.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0cqhb3 nominated_for 02rzdcp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 48.000 17.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9311-03l3jy PRED entity: 03l3jy PRED relation: nationality PRED expected values: 09c7w0 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 66): 09c7w0 (0.77 #5753, 0.77 #5654, 0.75 #100), 0gx1l (0.27 #7147), 048fz (0.27 #7147), 0kpys (0.27 #7147), 06mkj (0.14 #46, 0.03 #5554), 05r7t (0.14 #77), 02jx1 (0.12 #2614, 0.12 #231, 0.11 #330), 07ssc (0.09 #7756, 0.09 #2992, 0.09 #1206), 03rk0 (0.06 #7192, 0.05 #7291, 0.05 #7786), 0d060g (0.05 #1793, 0.05 #1693, 0.04 #6853) >> Best rule #5753 for best value: >> intensional similarity = 2 >> extensional distance = 2033 >> proper extension: 01pbxb; 04qvl7; 05cljf; 042l3v; 0m2l9; 01zkxv; 0jf1b; 01w61th; 01kwlwp; 0207wx; ... >> query: (?x4389, 09c7w0) <- award_nominee(?x4389, ?x1596), nationality(?x4389, ?x1453) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03l3jy nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.771 http://example.org/people/person/nationality #9310-012gbb PRED entity: 012gbb PRED relation: profession PRED expected values: 02hrh1q => 169 concepts (163 used for prediction) PRED predicted values (max 10 best out of 93): 02hrh1q (0.92 #4670, 0.91 #10374, 0.90 #13524), 01d_h8 (0.51 #4211, 0.50 #607, 0.46 #7215), 0cbd2 (0.50 #307, 0.40 #1808, 0.40 #908), 03gjzk (0.40 #166, 0.33 #767, 0.30 #3769), 09jwl (0.38 #11579, 0.37 #15480, 0.37 #13979), 05z96 (0.35 #1845, 0.12 #645, 0.10 #945), 01c72t (0.33 #25, 0.31 #3328, 0.27 #3478), 0kyk (0.33 #331, 0.30 #932, 0.22 #3034), 0d1pc (0.33 #52, 0.29 #503, 0.23 #5007), 025352 (0.33 #61, 0.17 #361, 0.06 #1712) >> Best rule #4670 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 03mp9s; 02js9p; >> query: (?x8240, 02hrh1q) <- type_of_union(?x8240, ?x566), award(?x8240, ?x1245), location(?x8240, ?x1646), ?x1245 = 0gqwc >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012gbb profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 163.000 0.919 http://example.org/people/person/profession #9309-01hxs4 PRED entity: 01hxs4 PRED relation: currency PRED expected values: 09nqf => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.53 #4, 0.52 #13, 0.51 #7), 01nv4h (0.01 #59) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 0dxmyh; >> query: (?x917, 09nqf) <- friend(?x917, ?x8898), participant(?x8898, ?x2763), film(?x8898, ?x814) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hxs4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.528 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #9308-01clyb PRED entity: 01clyb PRED relation: institution! PRED expected values: 014mlp 02_xgp2 => 147 concepts (123 used for prediction) PRED predicted values (max 10 best out of 24): 014mlp (0.82 #466, 0.81 #490, 0.80 #538), 019v9k (0.76 #469, 0.75 #493, 0.74 #590), 02h4rq6 (0.76 #535, 0.75 #463, 0.74 #487), 02_xgp2 (0.60 #545, 0.52 #473, 0.51 #497), 03bwzr4 (0.57 #547, 0.44 #2064, 0.44 #451), 07s6fsf (0.53 #533, 0.35 #1016, 0.35 #1089), 016t_3 (0.49 #440, 0.47 #536, 0.47 #464), 04zx3q1 (0.37 #534, 0.35 #1114, 0.33 #26), 01rr_d (0.35 #1114, 0.33 #42, 0.29 #1977), 013zdg (0.35 #1114, 0.33 #32, 0.29 #613) >> Best rule #466 for best value: >> intensional similarity = 6 >> extensional distance = 77 >> proper extension: 08815; 05krk; 01j_9c; 02w2bc; 07tgn; 01k2wn; 0lfgr; 07vk2; 01jtp7; 07w4j; ... >> query: (?x10348, 014mlp) <- organization(?x2361, ?x10348), currency(?x10348, ?x1099), major_field_of_study(?x10348, ?x2981), student(?x10348, ?x6673), ?x2981 = 02j62, institution(?x1526, ?x10348) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 01clyb institution! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 147.000 123.000 0.823 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01clyb institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 123.000 0.823 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9307-02dlh2 PRED entity: 02dlh2 PRED relation: role PRED expected values: 0bm02 => 85 concepts (49 used for prediction) PRED predicted values (max 10 best out of 91): 05842k (0.91 #1942, 0.90 #3418, 0.89 #2934), 05r5c (0.90 #3642, 0.88 #4012, 0.86 #3552), 0bxl5 (0.86 #2368, 0.85 #2181, 0.78 #2928), 04rzd (0.85 #4325, 0.83 #4037, 0.83 #3949), 02fsn (0.84 #1290, 0.84 #2414, 0.83 #3358), 02pprs (0.83 #1941, 0.82 #825, 0.82 #4106), 0151b0 (0.83 #1941, 0.82 #825, 0.82 #3159), 011k_j (0.80 #2310, 0.78 #4474, 0.78 #3058), 05148p4 (0.79 #2987, 0.78 #2709, 0.74 #3082), 0mkg (0.79 #2229, 0.77 #3839, 0.77 #2136) >> Best rule #1942 for best value: >> intensional similarity = 18 >> extensional distance = 9 >> proper extension: 0dwsp; 018vs; 0dwt5; >> query: (?x3703, ?x2764) <- performance_role(?x5480, ?x3703), performance_role(?x1750, ?x3703), role(?x2764, ?x3703), role(?x745, ?x3703), role(?x432, ?x3703), role(?x227, ?x5480), role(?x3703, ?x615), role(?x2698, ?x2764), ?x432 = 042v_gx, ?x1750 = 02hnl, role(?x2764, ?x3716), role(?x645, ?x3703), role(?x5480, ?x4913), ?x3716 = 03gvt, ?x227 = 0342h, ?x745 = 01vj9c, ?x4913 = 03ndd, ?x2698 = 09hnb >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #365 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x3703, ?x1268) <- performance_role(?x5480, ?x3703), performance_role(?x315, ?x3703), role(?x4311, ?x3703), role(?x2764, ?x3703), role(?x1663, ?x3703), role(?x432, ?x3703), role(?x314, ?x3703), role(?x2798, ?x5480), role(?x2158, ?x5480), ?x2764 = 01s0ps, performance_role(?x1260, ?x3703), ?x1663 = 01w4dy, ?x314 = 02sgy, ?x315 = 0l14md, role(?x3703, ?x2059), ?x432 = 042v_gx, ?x2158 = 01dnws, ?x2798 = 03qjg, ?x2059 = 0dwr4, role(?x5480, ?x1268), ?x4311 = 01xqw *> conf = 0.70 ranks of expected_values: 50 EVAL 02dlh2 role 0bm02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 85.000 49.000 0.909 http://example.org/music/performance_role/track_performances./music/track_contribution/role #9306-035gnh PRED entity: 035gnh PRED relation: film! PRED expected values: 02jm0n => 75 concepts (34 used for prediction) PRED predicted values (max 10 best out of 789): 04h6mm (0.56 #47776, 0.44 #41542, 0.44 #29083), 04zwtdy (0.44 #41542, 0.43 #37388, 0.43 #16620), 086k8 (0.44 #41542, 0.43 #37388, 0.43 #16620), 0pz91 (0.15 #211, 0.07 #2287, 0.07 #6442), 06rq2l (0.11 #1574, 0.03 #3650, 0.03 #11962), 0gn30 (0.10 #3021, 0.09 #7176, 0.09 #9255), 049dyj (0.09 #175, 0.03 #2251, 0.03 #6406), 0mdqp (0.09 #118, 0.03 #4273, 0.02 #29201), 0863x_ (0.09 #839, 0.01 #4994, 0.01 #34074), 0btpx (0.07 #3547, 0.07 #7702, 0.06 #9781) >> Best rule #47776 for best value: >> intensional similarity = 4 >> extensional distance = 804 >> proper extension: 0n2bh; 01h1bf; 03y3bp7; 01f3p_; 02sqkh; 02kk_c; 05gnf; 028k2x; 05sy0cv; 03g9xj; ... >> query: (?x7428, ?x4901) <- nominated_for(?x4901, ?x7428), profession(?x4901, ?x319), film(?x4901, ?x7415), ?x319 = 01d_h8 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2362 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 71 *> proper extension: 060v34; 0dj0m5; 02hxhz; 0872p_c; 026n4h6; 048qrd; 06ybb1; 05h43ls; 03z20c; 02x6dqb; ... *> query: (?x7428, 02jm0n) <- nominated_for(?x1105, ?x7428), nominated_for(?x513, ?x7428), ?x1105 = 07bdd_, award_winner(?x513, ?x917) *> conf = 0.03 ranks of expected_values: 62 EVAL 035gnh film! 02jm0n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 75.000 34.000 0.563 http://example.org/film/actor/film./film/performance/film #9305-0172rj PRED entity: 0172rj PRED relation: parent_genre! PRED expected values: 08z0wx => 76 concepts (32 used for prediction) PRED predicted values (max 10 best out of 278): 06cp5 (0.44 #2210, 0.40 #1145, 0.38 #1946), 05jt_ (0.40 #1172, 0.38 #1973, 0.33 #1437), 0173b0 (0.40 #1225, 0.33 #1490, 0.33 #425), 04f73rc (0.40 #1295, 0.33 #1560, 0.33 #495), 01738f (0.40 #1165, 0.33 #1430, 0.33 #365), 0b_6yv (0.40 #1284, 0.33 #1549, 0.33 #484), 04_sqm (0.40 #1257, 0.33 #1522, 0.33 #457), 02t8gf (0.40 #1188, 0.33 #1453, 0.33 #388), 0g_bh (0.38 #1979, 0.33 #2778, 0.31 #3308), 0xv2x (0.38 #1998, 0.33 #2262, 0.25 #2797) >> Best rule #2210 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 05jg58; >> query: (?x8011, 06cp5) <- artists(?x8011, ?x9463), artists(?x8011, ?x1955), ?x1955 = 0285c, group(?x2888, ?x9463), ?x2888 = 02fsn >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #342 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 03lty; *> query: (?x8011, 08z0wx) <- artists(?x8011, ?x12121), artists(?x8011, ?x9463), artists(?x8011, ?x8165), artists(?x8011, ?x3933), artists(?x8011, ?x1955), parent_genre(?x11973, ?x8011), ?x9463 = 01shhf, ?x3933 = 01vtqml, ?x8165 = 01516r, ?x1955 = 0285c, profession(?x12121, ?x131) *> conf = 0.33 ranks of expected_values: 24 EVAL 0172rj parent_genre! 08z0wx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 76.000 32.000 0.444 http://example.org/music/genre/parent_genre #9304-06jntd PRED entity: 06jntd PRED relation: child! PRED expected values: 03rwz3 => 105 concepts (98 used for prediction) PRED predicted values (max 10 best out of 59): 03rwz3 (0.33 #126, 0.33 #42, 0.29 #209), 03phgz (0.33 #40, 0.14 #207, 0.12 #290), 017s11 (0.33 #87, 0.14 #170, 0.12 #253), 09b3v (0.24 #696, 0.18 #444, 0.17 #780), 01gb54 (0.14 #196, 0.03 #781, 0.03 #948), 049ql1 (0.12 #319, 0.08 #1243, 0.07 #1660), 03d6fyn (0.12 #280, 0.05 #1621, 0.05 #1537), 025txrl (0.12 #321, 0.03 #907, 0.03 #1245), 0l8sx (0.12 #681, 0.12 #429, 0.11 #512), 086k8 (0.12 #418, 0.11 #501, 0.10 #838) >> Best rule #126 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 030_1m; >> query: (?x4800, 03rwz3) <- film(?x4800, ?x6184), film(?x4800, ?x5570), film(?x4800, ?x994), ?x6184 = 02jxbw, ?x5570 = 0295sy, titles(?x1510, ?x994) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06jntd child! 03rwz3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 98.000 0.333 http://example.org/organization/organization/child./organization/organization_relationship/child #9303-04fhps PRED entity: 04fhps PRED relation: district_represented PRED expected values: 05kr_ 06nrt => 15 concepts (15 used for prediction) PRED predicted values (max 10 best out of 371): 059rby (0.91 #827, 0.90 #632, 0.89 #569), 01x73 (0.90 #647, 0.89 #584, 0.89 #521), 05fjf (0.90 #679, 0.89 #487, 0.85 #357), 0694j (0.90 #694, 0.84 #500, 0.80 #370), 05tbn (0.87 #731, 0.86 #472, 0.84 #601), 05kr_ (0.86 #120, 0.81 #180, 0.74 #244), 06nrt (0.86 #120, 0.81 #180, 0.74 #244), 05k7sb (0.85 #847, 0.84 #460, 0.83 #652), 05fky (0.83 #628, 0.83 #565, 0.70 #886), 050l8 (0.82 #759, 0.82 #306, 0.77 #627) >> Best rule #827 for best value: >> intensional similarity = 67 >> extensional distance = 45 >> proper extension: 04h1rz; >> query: (?x11189, 059rby) <- district_represented(?x11189, ?x13765), district_represented(?x11189, ?x9370), district_represented(?x11189, ?x7468), district_represented(?x11189, ?x3824), district_represented(?x11189, ?x3474), legislative_sessions(?x11189, ?x11190), country(?x3824, ?x279), legislative_sessions(?x12796, ?x11189), district_represented(?x11190, ?x1905), administrative_division(?x13571, ?x13765), taxonomy(?x3824, ?x939), state_province_region(?x10205, ?x9370), partially_contains(?x13765, ?x10954), adjoins(?x6842, ?x9370), category(?x12796, ?x134), contains(?x3824, ?x1275), adjoins(?x4198, ?x3824), state_province_region(?x4780, ?x7468), contains(?x7468, ?x8916), contains(?x7468, ?x1036), contains(?x12971, ?x3474), adjoins(?x7468, ?x953), institution(?x2636, ?x4780), institution(?x734, ?x4780), legislative_sessions(?x3099, ?x11189), ?x734 = 04zx3q1, contains(?x3474, ?x5679), administrative_division(?x6224, ?x3474), location(?x3236, ?x8916), vacationer(?x1036, ?x1093), time_zones(?x4198, ?x1638), place_of_birth(?x199, ?x8916), currency(?x10205, ?x2244), featured_film_locations(?x136, ?x1036), contains(?x4198, ?x7067), district_represented(?x6933, ?x4198), district_represented(?x1829, ?x4198), district_represented(?x845, ?x4198), major_field_of_study(?x4780, ?x1154), major_field_of_study(?x4780, ?x254), ?x254 = 02h40lc, ?x10954 = 0lm0n, location(?x8720, ?x7468), ?x1829 = 02bp37, month(?x1036, ?x1459), time_zones(?x8916, ?x2950), school(?x4487, ?x4780), school_type(?x5679, ?x3092), religion(?x4198, ?x109), ?x6933 = 024tkd, teams(?x1036, ?x934), citytown(?x4267, ?x1036), location(?x764, ?x1036), jurisdiction_of_office(?x900, ?x4198), ?x845 = 07p__7, colors(?x5679, ?x332), ?x2636 = 027f2w, ?x1154 = 02lp1, ?x6842 = 0694j, country(?x150, ?x279), countries_spoken_in(?x393, ?x279), nationality(?x483, ?x279), service_location(?x13476, ?x279), film_release_region(?x11074, ?x279), ?x11074 = 0jqzt, medal(?x279, ?x422), ?x13476 = 069b85 >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #120 for first EXPECTED value: *> intensional similarity = 78 *> extensional distance = 1 *> proper extension: 01gvxh; *> query: (?x11189, ?x1905) <- district_represented(?x11189, ?x14386), district_represented(?x11189, ?x14129), district_represented(?x11189, ?x13765), district_represented(?x11189, ?x12125), district_represented(?x11189, ?x11542), district_represented(?x11189, ?x10544), district_represented(?x11189, ?x10063), district_represented(?x11189, ?x9370), district_represented(?x11189, ?x7468), district_represented(?x11189, ?x3824), district_represented(?x11189, ?x3474), legislative_sessions(?x11189, ?x11190), legislative_sessions(?x11189, ?x10543), legislative_sessions(?x11189, ?x8777), legislative_sessions(?x11189, ?x3473), ?x3824 = 04s7y, ?x14129 = 087r4, legislative_sessions(?x3099, ?x11189), legislative_sessions(?x13634, ?x11189), legislative_sessions(?x8776, ?x11189), ?x10543 = 03h_f4, ?x3099 = 01k165, ?x3474 = 05j49, ?x7468 = 015jr, ?x11542 = 059s8, ?x9370 = 059t8, contains(?x390, ?x12125), location(?x927, ?x12125), contains(?x12125, ?x14636), contains(?x12125, ?x10889), contains(?x12125, ?x8823), adjoins(?x12854, ?x12125), ?x13634 = 0l_j_, major_field_of_study(?x10889, ?x2981), institution(?x3437, ?x10889), institution(?x1771, ?x10889), institution(?x865, ?x10889), ?x1771 = 019v9k, organization(?x5510, ?x10889), ?x10544 = 059ts, currency(?x10889, ?x7888), place_of_birth(?x2443, ?x8823), location_of_ceremony(?x566, ?x8823), ?x865 = 02h4rq6, ?x13765 = 05rh2, student(?x10889, ?x10520), category(?x14636, ?x134), ?x566 = 04ztj, ?x2981 = 02j62, ?x134 = 08mbj5d, ?x10063 = 0j95, ?x3437 = 02_xgp2, ?x3473 = 04lgybj, origin(?x6368, ?x8823), district_represented(?x8777, ?x9311), district_represented(?x8777, ?x1905), ?x8776 = 0x2sv, profession(?x927, ?x7623), gender(?x927, ?x231), ?x14386 = 0h5qxv, ?x5510 = 07xl34, adjoins(?x8506, ?x12854), capital(?x12854, ?x8963), jurisdiction_of_office(?x900, ?x12854), ?x9311 = 06nrt, ?x11190 = 034_7s, ?x231 = 05zppz, film_release_region(?x8955, ?x390), film_release_region(?x2933, ?x390), film_release_region(?x1108, ?x390), ?x2933 = 0407yj_, ?x1108 = 0jjy0, geographic_distribution(?x1571, ?x390), combatants(?x94, ?x390), service_location(?x555, ?x390), country(?x308, ?x390), organization(?x390, ?x127), ?x8955 = 0g4pl7z *> conf = 0.86 ranks of expected_values: 6, 7 EVAL 04fhps district_represented 06nrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 15.000 15.000 0.915 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04fhps district_represented 05kr_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 15.000 15.000 0.915 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #9302-0kvnn PRED entity: 0kvnn PRED relation: award PRED expected values: 0gqz2 => 206 concepts (206 used for prediction) PRED predicted values (max 10 best out of 306): 054krc (0.51 #11456, 0.49 #15516, 0.43 #17140), 0gqz2 (0.46 #13073, 0.42 #11449, 0.38 #17133), 01by1l (0.42 #4579, 0.36 #3361, 0.30 #32999), 0gqy2 (0.40 #978, 0.17 #1790, 0.10 #9910), 0bdwqv (0.40 #986, 0.08 #9918, 0.08 #11136), 026mfs (0.38 #2972, 0.30 #2160, 0.17 #4190), 0l8z1 (0.38 #11432, 0.35 #15492, 0.33 #13056), 02qvyrt (0.38 #15556, 0.35 #11496, 0.35 #17180), 054ks3 (0.37 #13135, 0.30 #17195, 0.29 #11511), 01bgqh (0.33 #3697, 0.30 #4103, 0.29 #3291) >> Best rule #11456 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 037lyl; 09bx1k; 089kpp; >> query: (?x4387, 054krc) <- student(?x1513, ?x4387), artists(?x2996, ?x4387), music(?x5927, ?x4387), place_of_birth(?x4387, ?x8451) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #13073 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 0b6yp2; *> query: (?x4387, 0gqz2) <- student(?x1513, ?x4387), nationality(?x4387, ?x94), music(?x5927, ?x4387), ?x94 = 09c7w0 *> conf = 0.46 ranks of expected_values: 2 EVAL 0kvnn award 0gqz2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 206.000 0.509 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9301-07w0v PRED entity: 07w0v PRED relation: school! PRED expected values: 05l71 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 73): 0jmj7 (0.75 #599, 0.69 #3264, 0.68 #2760), 061xq (0.45 #243, 0.11 #1467, 0.11 #2521), 02d02 (0.40 #123, 0.18 #627, 0.14 #411), 05tfm (0.29 #373, 0.20 #85, 0.11 #2521), 07l4z (0.27 #268, 0.20 #124, 0.17 #1492), 05g76 (0.25 #160, 0.18 #304, 0.12 #448), 05m_8 (0.22 #1442, 0.21 #578, 0.21 #1082), 0jmm4 (0.21 #631, 0.20 #127, 0.11 #2521), 051vz (0.21 #378, 0.18 #1458, 0.18 #594), 01y49 (0.21 #377, 0.11 #2521, 0.07 #3458) >> Best rule #599 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 06mkj; 0d05w3; >> query: (?x1011, 0jmj7) <- school(?x465, ?x1011), organization(?x1011, ?x5487), contains(?x94, ?x1011) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2521 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 112 *> proper extension: 05kj_; *> query: (?x1011, ?x799) <- school(?x8542, ?x1011), draft(?x799, ?x8542) *> conf = 0.11 ranks of expected_values: 42 EVAL 07w0v school! 05l71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 134.000 134.000 0.750 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9300-09s02 PRED entity: 09s02 PRED relation: languages! PRED expected values: 01x2tm8 04cmrt 02qnyr7 => 36 concepts (12 used for prediction) PRED predicted values (max 10 best out of 3022): 01x2tm8 (0.71 #3730, 0.62 #4375, 0.60 #2438), 06kl0k (0.60 #2473, 0.57 #3765, 0.50 #4410), 0jrqq (0.60 #2150, 0.50 #862, 0.43 #3442), 0738y5 (0.57 #3741, 0.40 #2449, 0.38 #4386), 05vzql (0.50 #4435, 0.50 #1854, 0.50 #1210), 04cmrt (0.50 #1895, 0.50 #1251, 0.43 #3831), 02wmbg (0.50 #1098, 0.43 #3678, 0.40 #2386), 09ld6g (0.50 #1283, 0.43 #3863, 0.40 #2571), 040wdl (0.50 #1382, 0.40 #3224, 0.33 #2671), 084z0w (0.50 #909, 0.40 #2197, 0.29 #3489) >> Best rule #3730 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 09bnf; >> query: (?x13017, 01x2tm8) <- languages(?x12914, ?x13017), languages(?x11170, ?x13017), languages(?x8380, ?x13017), languages(?x8097, ?x13017), location(?x12914, ?x2146), profession(?x12914, ?x524), ?x8097 = 046rfv, ?x524 = 02jknp, film(?x11170, ?x8074), ?x8380 = 09r_wb, contains(?x2146, ?x1391), administrative_parent(?x2146, ?x551), genre(?x8074, ?x53) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 765 EVAL 09s02 languages! 02qnyr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 12.000 0.714 http://example.org/people/person/languages EVAL 09s02 languages! 04cmrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 36.000 12.000 0.714 http://example.org/people/person/languages EVAL 09s02 languages! 01x2tm8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 12.000 0.714 http://example.org/people/person/languages #9299-01m42d0 PRED entity: 01m42d0 PRED relation: film PRED expected values: 0gzy02 => 116 concepts (89 used for prediction) PRED predicted values (max 10 best out of 710): 04954r (0.25 #5978, 0.25 #617, 0.20 #7765), 018f8 (0.25 #183, 0.20 #3757, 0.20 #1970), 0bm2g (0.25 #338, 0.20 #3912, 0.20 #2125), 0gl3hr (0.25 #1097, 0.20 #4671, 0.20 #2884), 0fy66 (0.25 #599, 0.20 #4173, 0.20 #2386), 01kf4tt (0.20 #2190, 0.12 #5764, 0.10 #7551), 02qr3k8 (0.20 #4862, 0.10 #8436, 0.08 #20947), 0pd57 (0.20 #4274, 0.10 #7848, 0.03 #27508), 015whm (0.12 #6010, 0.10 #7797, 0.07 #9584), 02r_pp (0.12 #6238, 0.10 #8025, 0.07 #9812) >> Best rule #5978 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 012d40; 0187y5; 01w1kyf; >> query: (?x8010, 04954r) <- film(?x8010, ?x5187), ?x5187 = 02sfnv, award(?x8010, ?x2071), nationality(?x8010, ?x94) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #23278 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 06c97; *> query: (?x8010, 0gzy02) <- nationality(?x8010, ?x94), people(?x13213, ?x8010), celebrities_impersonated(?x3649, ?x8010) *> conf = 0.04 ranks of expected_values: 163 EVAL 01m42d0 film 0gzy02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 116.000 89.000 0.250 http://example.org/film/actor/film./film/performance/film #9298-0kpw3 PRED entity: 0kpw3 PRED relation: contains! PRED expected values: 07b_l => 82 concepts (23 used for prediction) PRED predicted values (max 10 best out of 122): 09c7w0 (0.92 #6277, 0.88 #7175, 0.87 #8071), 05tbn (0.86 #2015, 0.65 #2911, 0.16 #3808), 01n7q (0.38 #3662, 0.21 #4557, 0.17 #6352), 04_1l0v (0.29 #11653, 0.05 #6723, 0.04 #7621), 059rby (0.28 #3604, 0.15 #5397, 0.12 #7192), 0kpys (0.24 #2868, 0.06 #3583, 0.05 #9860), 02jx1 (0.19 #9051, 0.19 #9948, 0.17 #11745), 07ssc (0.18 #17073, 0.17 #18867, 0.14 #9893), 07b_l (0.11 #3806, 0.06 #4701, 0.06 #5599), 08xpv_ (0.08 #2629, 0.06 #3525, 0.02 #4422) >> Best rule #6277 for best value: >> intensional similarity = 5 >> extensional distance = 970 >> proper extension: 04ykg; 03s5t; 01n4w; 0d0x8; 07b_l; 05fky; >> query: (?x13669, 09c7w0) <- contains(?x6521, ?x13669), adjoins(?x3634, ?x6521), featured_film_locations(?x4880, ?x3634), ?x4880 = 029k4p, contains(?x3634, ?x216) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #3806 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 438 *> proper extension: 05zjtn4; 06_kh; 0fm9_; 0cb4j; 02_286; 0288zy; 02cttt; 01hhvg; 0r62v; 0f94t; ... *> query: (?x13669, 07b_l) <- contains(?x6521, ?x13669), location(?x1376, ?x6521), religion(?x6521, ?x109), award(?x1376, ?x4418), ?x4418 = 02664f *> conf = 0.11 ranks of expected_values: 9 EVAL 0kpw3 contains! 07b_l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 82.000 23.000 0.918 http://example.org/location/location/contains #9297-0436kgz PRED entity: 0436kgz PRED relation: location_of_ceremony PRED expected values: 056_y => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 77): 0cv3w (0.19 #1217, 0.17 #625, 0.14 #744), 02_286 (0.12 #1195, 0.07 #2495, 0.07 #2258), 030qb3t (0.09 #1201, 0.08 #473, 0.07 #372), 0d1qn (0.08 #473, 0.04 #710, 0.01 #387), 0k049 (0.08 #714, 0.07 #477, 0.07 #595), 0r0m6 (0.07 #640, 0.07 #759, 0.06 #1232), 0ggyr (0.07 #91, 0.02 #564, 0.01 #445), 059rby (0.06 #1191, 0.06 #362, 0.03 #2254), 0r62v (0.05 #1199, 0.04 #607, 0.03 #726), 0b90_r (0.05 #594, 0.05 #713, 0.04 #1186) >> Best rule #1217 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 0457w0; >> query: (?x6658, 0cv3w) <- location_of_ceremony(?x6658, ?x362), contains(?x362, ?x639), vacationer(?x362, ?x827) >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0436kgz location_of_ceremony 056_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 107.000 0.185 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #9296-06c44 PRED entity: 06c44 PRED relation: influenced_by! PRED expected values: 01vvy => 173 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1143): 0683n (0.50 #851, 0.23 #30114, 0.23 #16247), 03_87 (0.50 #10006, 0.18 #7951, 0.18 #3847), 013pp3 (0.50 #734, 0.14 #7912, 0.12 #29997), 041_y (0.50 #794, 0.09 #7972, 0.07 #15160), 048cl (0.45 #3885, 0.32 #7989, 0.31 #4911), 058vp (0.41 #7926, 0.27 #3822, 0.25 #4335), 07h1q (0.33 #7073, 0.33 #408, 0.25 #920), 045bg (0.33 #4134, 0.32 #7725, 0.25 #16455), 041jlr (0.33 #361, 0.25 #873, 0.13 #12313), 0hgqq (0.33 #193, 0.13 #12313, 0.13 #15906) >> Best rule #851 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01tz6vs; >> query: (?x6204, 0683n) <- influenced_by(?x862, ?x6204), people(?x5540, ?x6204), profession(?x6204, ?x353), people(?x4322, ?x6204), diet(?x6204, ?x3130) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4612 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 05qmj; *> query: (?x6204, ?x84) <- influenced_by(?x11097, ?x6204), ?x11097 = 02wh0, profession(?x6204, ?x1614), profession(?x968, ?x1614), profession(?x84, ?x1614), ?x968 = 015grj *> conf = 0.02 ranks of expected_values: 943 EVAL 06c44 influenced_by! 01vvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 173.000 73.000 0.500 http://example.org/influence/influence_node/influenced_by #9295-0d66j2 PRED entity: 0d66j2 PRED relation: nominated_for! PRED expected values: 03xkps => 77 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1042): 01ggc9 (0.77 #2338, 0.67 #4676, 0.65 #28063), 023s8 (0.77 #2338, 0.67 #4676, 0.65 #28063), 0gsg7 (0.38 #16369, 0.33 #11691, 0.32 #30402), 030hbp (0.33 #2095, 0.14 #28064, 0.11 #58471), 0hvb2 (0.33 #373, 0.14 #28064, 0.11 #58471), 02bkdn (0.33 #374, 0.14 #28064, 0.11 #58471), 02mqc4 (0.33 #894, 0.14 #28064, 0.11 #58471), 040t74 (0.33 #724, 0.14 #28064, 0.11 #58471), 0335fp (0.33 #1702, 0.14 #28064, 0.11 #58471), 04bcb1 (0.33 #1020, 0.14 #28064, 0.11 #58471) >> Best rule #2338 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0g60z; >> query: (?x3610, ?x2900) <- actor(?x3610, ?x10161), actor(?x3610, ?x2900), nominated_for(?x435, ?x3610), honored_for(?x3609, ?x3610), ?x10161 = 01ggc9 >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #5478 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 01qn7n; 0cskb; *> query: (?x3610, 03xkps) <- actor(?x3610, ?x2900), languages(?x3610, ?x254), genre(?x3610, ?x6674), ?x6674 = 01t_vv *> conf = 0.03 ranks of expected_values: 317 EVAL 0d66j2 nominated_for! 03xkps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 45.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9294-01fxck PRED entity: 01fxck PRED relation: nationality PRED expected values: 09c7w0 => 120 concepts (97 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.99 #7747, 0.89 #7449, 0.89 #1785), 07ssc (0.42 #6769, 0.11 #1402, 0.09 #9254), 03rk0 (0.28 #6800, 0.12 #4908, 0.09 #3812), 02jx1 (0.17 #6787, 0.13 #528, 0.12 #428), 0d060g (0.10 #303, 0.10 #204, 0.09 #502), 0f8l9c (0.09 #6776, 0.05 #1211, 0.04 #913), 06q1r (0.05 #6831, 0.01 #8220, 0.01 #9216), 03_3d (0.05 #6760, 0.03 #8249, 0.01 #9245), 01xbgx (0.04 #80, 0.04 #179, 0.02 #675), 0345h (0.04 #1021, 0.04 #1814, 0.03 #1517) >> Best rule #7747 for best value: >> intensional similarity = 4 >> extensional distance = 815 >> proper extension: 05218gr; 0bl60p; >> query: (?x7814, 09c7w0) <- place_of_birth(?x7814, ?x3689), nationality(?x7814, ?x205), location(?x3688, ?x3689), dog_breed(?x3689, ?x1706) >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fxck nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 97.000 0.994 http://example.org/people/person/nationality #9293-03k50 PRED entity: 03k50 PRED relation: languages! PRED expected values: 084z0w 0dfjb8 08bqy9 0265z9l 04y0yc 03f02ct 08s0m7 047s_cr 0tj9 045hz5 => 57 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1882): 0dfjb8 (0.57 #5741, 0.33 #1500, 0.33 #894), 07f3xb (0.50 #3703, 0.33 #1281, 0.25 #4309), 02wmbg (0.43 #5893, 0.33 #2257, 0.33 #1652), 0jrqq (0.43 #5662, 0.33 #1421, 0.33 #815), 09ld6g (0.43 #6055, 0.33 #1814, 0.33 #1208), 08s0m7 (0.43 #6047, 0.33 #1200, 0.09 #7258), 012d40 (0.38 #7880, 0.36 #8487, 0.33 #7274), 0448r (0.36 #7086, 0.33 #7693, 0.33 #1634), 0bdt8 (0.36 #7015, 0.33 #7622, 0.33 #1563), 084z0w (0.33 #1468, 0.33 #862, 0.29 #5709) >> Best rule #5741 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 0999q; 09s02; 09bnf; >> query: (?x1882, 0dfjb8) <- languages(?x10074, ?x1882), languages(?x8380, ?x1882), languages(?x656, ?x1882), location(?x656, ?x13813), nationality(?x10074, ?x2146), ?x8380 = 09r_wb, special_performance_type(?x656, ?x4832), profession(?x10074, ?x319) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 10, 11, 12, 31, 94, 586, 588 EVAL 03k50 languages! 045hz5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 57.000 24.000 0.571 http://example.org/people/person/languages EVAL 03k50 languages! 0tj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 57.000 24.000 0.571 http://example.org/people/person/languages EVAL 03k50 languages! 047s_cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 24.000 0.571 http://example.org/people/person/languages EVAL 03k50 languages! 08s0m7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 57.000 24.000 0.571 http://example.org/people/person/languages EVAL 03k50 languages! 03f02ct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 24.000 0.571 http://example.org/people/person/languages EVAL 03k50 languages! 04y0yc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 24.000 0.571 http://example.org/people/person/languages EVAL 03k50 languages! 0265z9l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 57.000 24.000 0.571 http://example.org/people/person/languages EVAL 03k50 languages! 08bqy9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 57.000 24.000 0.571 http://example.org/people/person/languages EVAL 03k50 languages! 0dfjb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 24.000 0.571 http://example.org/people/person/languages EVAL 03k50 languages! 084z0w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 57.000 24.000 0.571 http://example.org/people/person/languages #9292-03tcbx PRED entity: 03tcbx PRED relation: district_represented PRED expected values: 03gh4 => 31 concepts (25 used for prediction) PRED predicted values (max 10 best out of 62): 0498y (0.83 #1096, 0.82 #608, 0.81 #1153), 04ych (0.83 #1074, 0.82 #608, 0.81 #1131), 04rrd (0.82 #608, 0.82 #1369, 0.81 #1194), 03gh4 (0.82 #608, 0.81 #993, 0.78 #877), 059f4 (0.82 #608, 0.79 #1358, 0.78 #1354), 05kkh (0.82 #608, 0.78 #1354, 0.75 #395), 07z1m (0.82 #608, 0.78 #1354, 0.75 #395), 0gyh (0.82 #608, 0.78 #1086, 0.77 #1143), 05fjy (0.82 #608, 0.78 #876, 0.75 #395), 05fky (0.82 #608, 0.75 #759, 0.75 #395) >> Best rule #1096 for best value: >> intensional similarity = 29 >> extensional distance = 16 >> proper extension: 01h7xx; >> query: (?x2861, 0498y) <- legislative_sessions(?x6728, ?x2861), legislative_sessions(?x3540, ?x2861), district_represented(?x2861, ?x6895), district_represented(?x2861, ?x2977), district_represented(?x2861, ?x1755), district_represented(?x2861, ?x961), contains(?x2977, ?x3097), adjoins(?x2977, ?x177), district_represented(?x9416, ?x1755), legislative_sessions(?x5266, ?x2861), religion(?x1755, ?x2591), religion(?x1755, ?x1363), ?x1363 = 058x5, location(?x6113, ?x2977), ?x961 = 03s0w, contains(?x1755, ?x13745), ?x6895 = 05fjf, legislative_sessions(?x2861, ?x6743), country(?x1755, ?x94), ?x9416 = 01gsry, origin(?x680, ?x1755), legislative_sessions(?x652, ?x3540), source(?x13745, ?x958), ?x2591 = 0631_, jurisdiction_of_office(?x900, ?x2977), contains(?x3448, ?x1755), district_represented(?x6728, ?x938), student(?x1151, ?x680), contains(?x938, ?x3983) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #608 for first EXPECTED value: *> intensional similarity = 34 *> extensional distance = 5 *> proper extension: 070mff; *> query: (?x2861, ?x726) <- legislative_sessions(?x6139, ?x2861), legislative_sessions(?x3765, ?x2861), legislative_sessions(?x2976, ?x2861), legislative_sessions(?x1829, ?x2861), legislative_sessions(?x606, ?x2861), legislative_sessions(?x355, ?x2861), district_represented(?x2861, ?x6895), district_represented(?x2861, ?x4754), district_represented(?x2861, ?x2977), district_represented(?x2861, ?x2020), ?x2977 = 081mh, ?x6895 = 05fjf, ?x4754 = 0g0syc, district_represented(?x1829, ?x12828), district_represented(?x1829, ?x938), district_represented(?x1829, ?x726), ?x6139 = 060ny2, ?x606 = 03ww_x, ?x12828 = 0gj4fx, ?x355 = 0495ys, ?x3765 = 04gp1d, legislative_sessions(?x2861, ?x1027), ?x938 = 0vmt, location(?x237, ?x2020), contains(?x2020, ?x10440), contains(?x2020, ?x3439), legislative_sessions(?x5266, ?x2861), currency(?x10440, ?x170), ?x2976 = 03rtmz, major_field_of_study(?x3439, ?x11206), ?x11206 = 05b6c, state_province_region(?x1520, ?x2020), religion(?x2020, ?x109), institution(?x620, ?x3439) *> conf = 0.82 ranks of expected_values: 4 EVAL 03tcbx district_represented 03gh4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 31.000 25.000 0.833 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #9291-02x4wr9 PRED entity: 02x4wr9 PRED relation: award! PRED expected values: 03hy3g 06t8b 04353 02yy_j => 57 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2213): 04sry (0.80 #16779, 0.66 #53700, 0.65 #50339), 0gpprt (0.80 #16779, 0.66 #53700, 0.65 #50339), 02f93t (0.67 #22817, 0.23 #26172, 0.22 #12746), 0gyx4 (0.58 #21379, 0.33 #1244, 0.28 #24734), 0184jw (0.58 #22384, 0.33 #2249, 0.23 #25739), 03hy3g (0.58 #21977, 0.33 #1842, 0.22 #11906), 06mn7 (0.58 #21363, 0.33 #1228, 0.20 #24718), 02vyw (0.58 #21138, 0.33 #1003, 0.20 #24493), 0js9s (0.50 #22034, 0.33 #1899, 0.22 #15321), 02hfp_ (0.50 #22446, 0.33 #2311, 0.22 #15733) >> Best rule #16779 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02y_rq5; 02x4w6g; 02x8n1n; 02x17s4; 0279c15; 02w9sd7; >> query: (?x2532, ?x777) <- nominated_for(?x2532, ?x3761), nominated_for(?x2532, ?x2903), ?x3761 = 0dzz6g, film_crew_role(?x2903, ?x137), award_winner(?x2532, ?x777) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #21977 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 03nqnk3; *> query: (?x2532, 03hy3g) <- award(?x6239, ?x2532), award(?x523, ?x2532), ?x6239 = 0c12h, edited_by(?x814, ?x523), type_of_union(?x523, ?x566) *> conf = 0.58 ranks of expected_values: 6, 109, 200, 265 EVAL 02x4wr9 award! 02yy_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 57.000 18.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4wr9 award! 04353 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 57.000 18.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4wr9 award! 06t8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 18.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4wr9 award! 03hy3g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 57.000 18.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9290-0kctd PRED entity: 0kctd PRED relation: program PRED expected values: 09kn9 => 87 concepts (29 used for prediction) PRED predicted values (max 10 best out of 282): 015g28 (0.50 #293, 0.10 #2453, 0.10 #2213), 0124k9 (0.50 #259, 0.10 #2419, 0.10 #2179), 0bx_hnp (0.50 #414, 0.10 #2574, 0.10 #2334), 043qqt5 (0.25 #439, 0.22 #4042, 0.16 #4763), 08cx5g (0.25 #294, 0.12 #5338, 0.07 #5820), 0jq2r (0.25 #370, 0.09 #5414, 0.07 #4454), 02rkkn1 (0.25 #458, 0.08 #1658, 0.06 #1898), 01ft14 (0.25 #419, 0.08 #1619, 0.06 #1859), 08bytj (0.25 #364, 0.08 #1564, 0.06 #1804), 0524b41 (0.25 #352, 0.08 #1552, 0.06 #1792) >> Best rule #293 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0g5lhl7; 03mdt; >> query: (?x11493, 015g28) <- titles(?x11493, ?x9082), genre(?x9082, ?x53), ?x53 = 07s9rl0, program(?x11493, ?x8316) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6007 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 03h2c3; *> query: (?x11493, ?x273) <- program(?x11493, ?x5561), genre(?x5561, ?x6674), actor(?x5561, ?x1065), genre(?x273, ?x6674) *> conf = 0.04 ranks of expected_values: 212 EVAL 0kctd program 09kn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 87.000 29.000 0.500 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #9289-03ww_x PRED entity: 03ww_x PRED relation: legislative_sessions PRED expected values: 02gkzs => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 41): 0495ys (0.92 #173, 0.87 #999, 0.86 #476), 02gkzs (0.92 #173, 0.86 #476, 0.85 #866), 077g7n (0.92 #173, 0.86 #476, 0.85 #866), 02bn_p (0.92 #173, 0.86 #476, 0.85 #866), 02bqm0 (0.92 #173, 0.86 #476, 0.85 #866), 02bqn1 (0.92 #173, 0.86 #476, 0.85 #866), 02cg7g (0.92 #173, 0.86 #476, 0.85 #866), 03ww_x (0.83 #916, 0.83 #871, 0.80 #1085), 01gsvb (0.41 #1458, 0.40 #824, 0.40 #823), 01gsvp (0.40 #824, 0.40 #823, 0.39 #44) >> Best rule #173 for best value: >> intensional similarity = 48 >> extensional distance = 1 >> proper extension: 03rtmz; >> query: (?x606, ?x605) <- legislative_sessions(?x606, ?x6728), legislative_sessions(?x606, ?x6139), legislative_sessions(?x606, ?x5977), legislative_sessions(?x606, ?x5339), legislative_sessions(?x606, ?x3540), legislative_sessions(?x606, ?x3463), legislative_sessions(?x606, ?x2976), legislative_sessions(?x606, ?x2861), legislative_sessions(?x606, ?x1830), legislative_sessions(?x606, ?x1028), legislative_sessions(?x606, ?x952), legislative_sessions(?x606, ?x653), legislative_sessions(?x11605, ?x606), legislative_sessions(?x9334, ?x606), legislative_sessions(?x4821, ?x606), legislative_sessions(?x1027, ?x606), legislative_sessions(?x605, ?x606), legislative_sessions(?x355, ?x606), district_represented(?x606, ?x6226), district_represented(?x606, ?x4754), district_represented(?x606, ?x2020), district_represented(?x606, ?x1906), district_represented(?x606, ?x448), district_represented(?x606, ?x335), ?x6139 = 060ny2, ?x6226 = 03gh4, ?x3540 = 024tcq, ?x653 = 070m6c, ?x5977 = 06r713, ?x335 = 059rby, ?x3463 = 02bqmq, ?x355 = 0495ys, ?x2020 = 05k7sb, ?x5339 = 02glc4, ?x448 = 03v1s, legislative_sessions(?x2860, ?x606), ?x1027 = 02bn_p, ?x11605 = 024_vw, ?x6728 = 070mff, ?x952 = 06f0dc, ?x1830 = 03z5xd, ?x4821 = 02bqm0, district_represented(?x2976, ?x961), ?x4754 = 0g0syc, ?x9334 = 02hy5d, ?x2861 = 03tcbx, ?x1906 = 04rrx, ?x1028 = 032ft5 >> conf = 0.92 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 03ww_x legislative_sessions 02gkzs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 41.000 0.917 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #9288-01xvb PRED entity: 01xvb PRED relation: school PRED expected values: 07szy => 68 concepts (65 used for prediction) PRED predicted values (max 10 best out of 189): 065y4w7 (0.44 #389, 0.43 #1716, 0.40 #770), 01rc6f (0.33 #1272, 0.25 #2604, 0.21 #1840), 01pl14 (0.31 #1333, 0.22 #385, 0.20 #2095), 07w0v (0.29 #7441, 0.29 #1910, 0.23 #7822), 05krk (0.27 #2094, 0.22 #384, 0.21 #2856), 0bx8pn (0.23 #1352, 0.21 #6878, 0.20 #7834), 01vs5c (0.22 #7519, 0.18 #3516, 0.18 #3325), 0g8rj (0.22 #467, 0.16 #2939, 0.15 #1415), 05x_5 (0.22 #498, 0.13 #2208, 0.12 #2398), 07ccs (0.21 #2004, 0.14 #7045, 0.13 #7047) >> Best rule #389 for best value: >> intensional similarity = 12 >> extensional distance = 7 >> proper extension: 084l5; 05g49; >> query: (?x1239, 065y4w7) <- position_s(?x1239, ?x2247), position_s(?x1239, ?x2147), position_s(?x1239, ?x1792), position_s(?x1239, ?x180), team(?x10287, ?x1239), ?x2147 = 04nfpk, position(?x1239, ?x1240), ?x1792 = 05zm34, ?x1240 = 023wyl, ?x2247 = 01_9c1, school(?x1239, ?x4904), ?x180 = 01r3hr >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #7449 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 61 *> proper extension: 01ypc; 05m_8; 03lpp_; 06x68; 01d5z; 049n7; 0512p; 0cqt41; 01yhm; 05g76; ... *> query: (?x1239, 07szy) <- school(?x1239, ?x4904), draft(?x1239, ?x465), team(?x1517, ?x1239), team(?x1517, ?x4170), team(?x1517, ?x2114), colors(?x4170, ?x4557), team(?x10361, ?x4170), school(?x4170, ?x546), team(?x11323, ?x2114) *> conf = 0.14 ranks of expected_values: 27 EVAL 01xvb school 07szy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 68.000 65.000 0.444 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9287-09qycb PRED entity: 09qycb PRED relation: genre PRED expected values: 082gq => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 91): 02kdv5l (0.53 #231, 0.41 #461, 0.27 #3577), 03k9fj (0.44 #469, 0.33 #9, 0.29 #239), 01jfsb (0.41 #240, 0.33 #815, 0.30 #585), 0gf28 (0.40 #751, 0.15 #2536, 0.10 #3177), 01hmnh (0.38 #475, 0.19 #2667, 0.16 #3591), 02l7c8 (0.37 #5431, 0.36 #2201, 0.34 #1624), 01t_vv (0.33 #51, 0.20 #166, 0.19 #741), 0hn10 (0.33 #8, 0.20 #123, 0.06 #2775), 04228s (0.33 #73, 0.20 #188, 0.05 #763), 0lsxr (0.24 #237, 0.20 #812, 0.20 #582) >> Best rule #231 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 0dq626; 033srr; >> query: (?x10349, 02kdv5l) <- film(?x4277, ?x10349), genre(?x10349, ?x53), ?x4277 = 046qq >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #3026 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 526 *> proper extension: 01cgz; *> query: (?x10349, 082gq) <- films(?x326, ?x10349), films(?x326, ?x2423), film(?x450, ?x2423) *> conf = 0.19 ranks of expected_values: 13 EVAL 09qycb genre 082gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 77.000 77.000 0.529 http://example.org/film/film/genre #9286-05wkw PRED entity: 05wkw PRED relation: student PRED expected values: 02k21g => 88 concepts (62 used for prediction) PRED predicted values (max 10 best out of 447): 036px (0.33 #93, 0.20 #1280, 0.20 #1041), 07myb2 (0.22 #3061, 0.22 #2824, 0.15 #5435), 06c0j (0.22 #3086, 0.22 #2849, 0.15 #5460), 04z0g (0.22 #2982, 0.22 #2745, 0.15 #5356), 049dyj (0.20 #1204, 0.20 #965, 0.12 #2396), 03gr7w (0.20 #1219, 0.17 #1456, 0.12 #2411), 01_x6v (0.20 #1228, 0.17 #1465, 0.12 #2420), 03_l8m (0.20 #1301, 0.17 #1538, 0.12 #2493), 02rn_bj (0.20 #1352, 0.17 #1589, 0.12 #2544), 0j6cj (0.20 #1343, 0.17 #1580, 0.12 #2535) >> Best rule #93 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 011s0; >> query: (?x11691, 036px) <- industry(?x12044, ?x11691), major_field_of_study(?x11691, ?x373), industry(?x1104, ?x373), film(?x1104, ?x86), taxonomy(?x11691, ?x939), award_nominee(?x846, ?x1104), production_companies(?x253, ?x1104) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05wkw student 02k21g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 62.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #9285-01k0vq PRED entity: 01k0vq PRED relation: film! PRED expected values: 032_jg 0c01c 03l3jy => 94 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1004): 0c01c (0.50 #425, 0.23 #4578, 0.08 #58153), 03l3jy (0.50 #766, 0.23 #4919, 0.03 #6996), 01pcbg (0.25 #580, 0.15 #4733, 0.03 #45691), 03v1jf (0.25 #925, 0.08 #5078, 0.01 #7155), 0266r6h (0.25 #836, 0.08 #4989, 0.01 #7066), 0347db (0.25 #1244, 0.08 #5397), 0863x_ (0.20 #2914, 0.08 #4990, 0.02 #13296), 0mdqp (0.20 #2196, 0.08 #4272, 0.02 #20885), 063g7l (0.20 #3968, 0.08 #6044, 0.02 #22657), 0315q3 (0.20 #2896, 0.08 #4972, 0.01 #7049) >> Best rule #425 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0gtsx8c; >> query: (?x7579, 0c01c) <- executive_produced_by(?x7579, ?x2648), prequel(?x7579, ?x6684), ?x2648 = 034bgm, film(?x1460, ?x7579) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 53 EVAL 01k0vq film! 03l3jy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 37.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 01k0vq film! 0c01c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 37.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 01k0vq film! 032_jg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 94.000 37.000 0.500 http://example.org/film/actor/film./film/performance/film #9284-0gr42 PRED entity: 0gr42 PRED relation: category_of PRED expected values: 0g_w => 62 concepts (32 used for prediction) PRED predicted values (max 10 best out of 3): 0g_w (0.79 #194, 0.76 #152, 0.67 #131), 0c4ys (0.38 #606, 0.36 #651, 0.25 #540), 0gcf2r (0.26 #300, 0.24 #344, 0.24 #390) >> Best rule #194 for best value: >> intensional similarity = 7 >> extensional distance = 22 >> proper extension: 0gr07; >> query: (?x2209, 0g_w) <- ceremony(?x2209, ?x6323), ceremony(?x2209, ?x3579), ceremony(?x2209, ?x1084), ?x1084 = 02yw5r, ?x3579 = 0bc773, ceremony(?x4573, ?x6323), ?x4573 = 0gq_d >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gr42 category_of 0g_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 32.000 0.792 http://example.org/award/award_category/category_of #9283-06bvp PRED entity: 06bvp PRED relation: films PRED expected values: 04w7rn => 76 concepts (34 used for prediction) PRED predicted values (max 10 best out of 559): 011yxg (0.43 #2659, 0.08 #7952, 0.06 #10070), 03bdkd (0.40 #2077, 0.12 #5782, 0.11 #6311), 029jt9 (0.33 #444, 0.25 #1502, 0.20 #2031), 0kvb6p (0.33 #440, 0.25 #1498, 0.20 #2027), 02ctc6 (0.33 #155, 0.25 #1213, 0.20 #1742), 0hfzr (0.25 #1265, 0.12 #4969, 0.11 #6558), 03cw411 (0.25 #1241, 0.12 #4945, 0.05 #6534), 0ds11z (0.25 #1081, 0.11 #6374, 0.06 #4785), 07xtqq (0.25 #1078, 0.11 #6371, 0.06 #4782), 0kb07 (0.25 #1320, 0.06 #5024, 0.06 #6351) >> Best rule #2659 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 08b3m; >> query: (?x9829, 011yxg) <- films(?x9829, ?x1746), nominated_for(?x510, ?x1746), genre(?x1746, ?x10848), genre(?x1746, ?x53), language(?x1746, ?x254), film(?x8002, ?x1746), ?x53 = 07s9rl0, ?x10848 = 0jdm8, nominated_for(?x484, ?x1746) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #14360 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 67 *> proper extension: 06c62; *> query: (?x9829, 04w7rn) <- films(?x9829, ?x1746), nominated_for(?x510, ?x1746), genre(?x1746, ?x53), film_release_region(?x1746, ?x94), currency(?x1746, ?x170), film(?x8002, ?x1746), ?x53 = 07s9rl0, ?x170 = 09nqf *> conf = 0.01 ranks of expected_values: 491 EVAL 06bvp films 04w7rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 34.000 0.429 http://example.org/film/film_subject/films #9282-054kmq PRED entity: 054kmq PRED relation: team PRED expected values: 0xbm => 71 concepts (54 used for prediction) PRED predicted values (max 10 best out of 489): 0xbm (0.86 #5324, 0.84 #5057, 0.84 #6653), 02s2ys (0.33 #700, 0.11 #8250, 0.09 #1597), 02qhlm (0.33 #154, 0.09 #1597, 0.09 #9054), 02k9k9 (0.33 #216, 0.09 #1597, 0.09 #9055), 035qgm (0.33 #157, 0.09 #1597, 0.09 #9055), 06ybz_ (0.33 #138, 0.09 #1597, 0.09 #9055), 049dzz (0.33 #172, 0.09 #1597, 0.06 #2835), 02hzx8 (0.33 #162, 0.09 #1597, 0.06 #2825), 025rpyx (0.33 #252, 0.09 #1597, 0.03 #7982), 02b0_6 (0.20 #345, 0.17 #611, 0.15 #878) >> Best rule #5324 for best value: >> intensional similarity = 7 >> extensional distance = 44 >> proper extension: 06sy4c; 0gtgp6; >> query: (?x12447, ?x12043) <- team(?x12447, ?x12043), profession(?x12447, ?x7623), current_club(?x11564, ?x12043), team(?x12447, ?x7294), position(?x12043, ?x530), team(?x530, ?x9756), ?x9756 = 051gjr >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 054kmq team 0xbm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 54.000 0.862 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #9281-0661ql3 PRED entity: 0661ql3 PRED relation: award PRED expected values: 02g3ft => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 185): 040njc (0.27 #12172, 0.27 #12171, 0.26 #6308), 054krc (0.27 #12172, 0.27 #12171, 0.26 #6308), 0fhpv4 (0.27 #12172, 0.27 #12171, 0.26 #6308), 02pqp12 (0.27 #12172, 0.27 #12171, 0.26 #6308), 0gq9h (0.27 #12172, 0.27 #12171, 0.26 #6308), 02qyntr (0.27 #12172, 0.27 #12171, 0.26 #6308), 019f4v (0.27 #12172, 0.27 #12171, 0.26 #6308), 0gr51 (0.27 #12172, 0.27 #12171, 0.26 #6308), 02qvyrt (0.27 #12172, 0.27 #12171, 0.26 #6308), 0l8z1 (0.27 #12172, 0.27 #12171, 0.26 #6308) >> Best rule #12172 for best value: >> intensional similarity = 3 >> extensional distance = 953 >> proper extension: 0g60z; 02_1q9; 080dwhx; 02_1rq; 03kq98; 072kp; 039fgy; 0kfpm; 02k_4g; 0358x_; ... >> query: (?x2394, ?x1198) <- nominated_for(?x748, ?x2394), nominated_for(?x1198, ?x2394), award(?x2394, ?x500) >> conf = 0.27 => this is the best rule for 16 predicted values *> Best rule #1642 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 148 *> proper extension: 0bmpm; *> query: (?x2394, 02g3ft) <- nominated_for(?x748, ?x2394), nominated_for(?x68, ?x2394), award(?x2394, ?x500), crewmember(?x2394, ?x2887) *> conf = 0.09 ranks of expected_values: 45 EVAL 0661ql3 award 02g3ft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 101.000 101.000 0.269 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9280-014zwb PRED entity: 014zwb PRED relation: costume_design_by PRED expected values: 02w0dc0 => 94 concepts (89 used for prediction) PRED predicted values (max 10 best out of 15): 03mfqm (0.17 #18, 0.04 #158, 0.03 #244), 0bytfv (0.17 #39, 0.04 #180, 0.03 #266), 05x2t7 (0.15 #90, 0.08 #62, 0.01 #146), 03y1mlp (0.06 #114, 0.03 #429, 0.02 #142), 02cqbx (0.02 #299, 0.02 #983, 0.02 #1100), 03gt0c5 (0.02 #167, 0.02 #224, 0.02 #339), 02mxbd (0.02 #444, 0.02 #840, 0.02 #612), 02w0dc0 (0.02 #766, 0.02 #883, 0.02 #912), 02pqgt8 (0.02 #835, 0.02 #181, 0.02 #979), 026lyl4 (0.02 #192, 0.01 #278) >> Best rule #18 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 03mh_tp; >> query: (?x3071, 03mfqm) <- film(?x6360, ?x3071), film(?x2317, ?x3071), ?x2317 = 04fhxp, award_winner(?x368, ?x6360), award_winner(?x873, ?x6360) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #766 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 395 *> proper extension: 01br2w; 02v8kmz; 0dnvn3; 0dckvs; 0ds11z; 0pc62; 0dqytn; 0170_p; 0209hj; 026mfbr; ... *> query: (?x3071, 02w0dc0) <- film_release_distribution_medium(?x3071, ?x81), film(?x1774, ?x3071), film_crew_role(?x3071, ?x1966), profession(?x1109, ?x1966) *> conf = 0.02 ranks of expected_values: 8 EVAL 014zwb costume_design_by 02w0dc0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 94.000 89.000 0.167 http://example.org/film/film/costume_design_by #9279-05ft32 PRED entity: 05ft32 PRED relation: film_release_region PRED expected values: 05qhw 0k6nt 05b4w => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 131): 0chghy (0.85 #815, 0.84 #495, 0.84 #655), 05r4w (0.84 #485, 0.84 #645, 0.82 #805), 0k6nt (0.83 #510, 0.81 #670, 0.79 #830), 0jgd (0.81 #487, 0.80 #807, 0.80 #326), 03_3d (0.80 #329, 0.78 #490, 0.76 #650), 0154j (0.80 #488, 0.77 #648, 0.76 #808), 05qhw (0.78 #660, 0.76 #820, 0.75 #500), 05b4w (0.76 #552, 0.72 #712, 0.72 #872), 01znc_ (0.73 #528, 0.71 #688, 0.71 #848), 03rt9 (0.68 #499, 0.67 #659, 0.65 #819) >> Best rule #815 for best value: >> intensional similarity = 6 >> extensional distance = 232 >> proper extension: 08hmch; 0crc2cp; 0bc1yhb; 0gh6j94; >> query: (?x6761, 0chghy) <- film_release_region(?x6761, ?x2152), film_release_region(?x6761, ?x1229), film_release_region(?x6761, ?x583), ?x2152 = 06mkj, ?x583 = 015fr, origin(?x8152, ?x1229) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #510 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 159 *> proper extension: 014lc_; 0b76d_m; 0ds35l9; 02vxq9m; 02vp1f_; 011yrp; 07gp9; 0ddfwj1; 0ds3t5x; 0gtv7pk; ... *> query: (?x6761, 0k6nt) <- film_release_region(?x6761, ?x2152), film_release_region(?x6761, ?x583), ?x2152 = 06mkj, ?x583 = 015fr, nominated_for(?x2183, ?x6761) *> conf = 0.83 ranks of expected_values: 3, 7, 8 EVAL 05ft32 film_release_region 05b4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 61.000 61.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05ft32 film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 61.000 61.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05ft32 film_release_region 05qhw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 61.000 61.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9278-0dsx3f PRED entity: 0dsx3f PRED relation: award PRED expected values: 0m7yy => 137 concepts (131 used for prediction) PRED predicted values (max 10 best out of 243): 0m7yy (0.85 #367, 0.80 #3886, 0.67 #837), 0gkr9q (0.55 #1410, 0.47 #5397, 0.47 #5396), 0cqhb3 (0.55 #1410, 0.47 #5397, 0.47 #5396), 0bdw6t (0.55 #1410, 0.47 #5397, 0.47 #5396), 0bdw1g (0.55 #1410, 0.47 #5397, 0.47 #5396), 02pz3j5 (0.45 #1290, 0.43 #1525, 0.11 #2229), 02q1tc5 (0.41 #1285, 0.39 #1520, 0.11 #2224), 0cjyzs (0.40 #82, 0.23 #317, 0.23 #10315), 027gs1_ (0.40 #181, 0.20 #5578, 0.19 #3700), 027qq9b (0.36 #1319, 0.35 #1554, 0.11 #2258) >> Best rule #367 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 045r_9; >> query: (?x6322, 0m7yy) <- award(?x6322, ?x8660), nominated_for(?x686, ?x6322), award_winner(?x6322, ?x6678), ?x6678 = 05gnf >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dsx3f award 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 131.000 0.846 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9277-0c1ps1 PRED entity: 0c1ps1 PRED relation: type_of_union PRED expected values: 04ztj => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.72 #49, 0.71 #53, 0.71 #85), 01g63y (0.19 #297, 0.13 #54, 0.12 #2), 0jgjn (0.19 #297), 01bl8s (0.19 #297) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 1306 >> proper extension: 02784z; 05p606; >> query: (?x10469, 04ztj) <- gender(?x10469, ?x231), film(?x10469, ?x2350), ?x231 = 05zppz >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c1ps1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.720 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9276-0sw0q PRED entity: 0sw0q PRED relation: program! PRED expected values: 09d5h => 103 concepts (97 used for prediction) PRED predicted values (max 10 best out of 50): 05gnf (0.40 #14, 0.37 #71, 0.30 #128), 09d5h (0.26 #117, 0.24 #174, 0.21 #60), 0gsg7 (0.26 #402, 0.24 #173, 0.22 #459), 0cjdk (0.19 #233, 0.18 #290, 0.16 #863), 03mdt (0.14 #1380, 0.12 #979, 0.12 #1438), 07c52 (0.11 #1030, 0.03 #3610, 0.02 #4882), 01fsyp (0.09 #164, 0.06 #393, 0.05 #107), 01nrq5 (0.08 #573, 0.08 #515, 0.05 #2177), 0187wh (0.07 #254, 0.07 #311, 0.06 #884), 0g5lhl7 (0.06 #2640, 0.05 #978, 0.05 #750) >> Best rule #14 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01f39b; >> query: (?x9098, 05gnf) <- actor(?x9098, ?x5715), actor(?x9098, ?x3261), type_of_union(?x5715, ?x566), film(?x3261, ?x6214), ?x6214 = 0k5fg >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #117 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 02k_4g; 01b66d; 01fszq; *> query: (?x9098, 09d5h) <- genre(?x9098, ?x258), program(?x9097, ?x9098), nominated_for(?x3261, ?x9098), people(?x4659, ?x3261), film(?x3261, ?x1734) *> conf = 0.26 ranks of expected_values: 2 EVAL 0sw0q program! 09d5h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 97.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #9275-05r7t PRED entity: 05r7t PRED relation: service_location! PRED expected values: 01hlwv => 136 concepts (115 used for prediction) PRED predicted values (max 10 best out of 136): 018mxj (0.43 #1098, 0.30 #1642, 0.28 #2594), 01c6k4 (0.40 #2590, 0.30 #1094, 0.24 #1638), 0k9ts (0.35 #1179, 0.27 #907, 0.21 #1723), 064f29 (0.28 #1284, 0.27 #876, 0.26 #1148), 07zl6m (0.27 #948, 0.26 #1220, 0.19 #1492), 0p4wb (0.27 #825, 0.22 #1097, 0.20 #1233), 04sv4 (0.27 #899, 0.22 #1171, 0.18 #1715), 069b85 (0.27 #944, 0.18 #1760, 0.17 #1216), 05w3y (0.27 #878, 0.17 #1150, 0.16 #1286), 0dmtp (0.27 #875, 0.17 #1147, 0.16 #1283) >> Best rule #1098 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 02j71; >> query: (?x6559, 018mxj) <- adjustment_currency(?x6559, ?x170), service_location(?x8931, ?x6559), administrative_parent(?x8428, ?x6559) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #928 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 02jx1; *> query: (?x6559, 01hlwv) <- contains(?x7273, ?x6559), origin(?x7547, ?x6559), country(?x766, ?x6559) *> conf = 0.13 ranks of expected_values: 30 EVAL 05r7t service_location! 01hlwv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 136.000 115.000 0.435 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #9274-084qpk PRED entity: 084qpk PRED relation: film! PRED expected values: 06151l 01g969 => 100 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1077): 0f5xn (0.31 #7169, 0.07 #3032, 0.06 #964), 09y20 (0.19 #6452, 0.06 #247, 0.02 #89205), 06cv1 (0.18 #59992, 0.17 #57922, 0.17 #20686), 016ywr (0.16 #6501, 0.02 #8569, 0.02 #29257), 0c0k1 (0.11 #3568, 0.06 #13910, 0.06 #11841), 0klh7 (0.11 #486, 0.04 #6691, 0.02 #8759), 06q8hf (0.11 #64131, 0.10 #72409, 0.09 #49647), 05hj_k (0.11 #64131, 0.10 #72409, 0.09 #49647), 01f873 (0.08 #3952, 0.04 #16363, 0.02 #20501), 0f0kz (0.08 #12923, 0.07 #10854, 0.07 #14992) >> Best rule #7169 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 02qhqz4; 062zjtt; 03wh49y; 0bbm7r; 093l8p; 0f7hw; >> query: (?x814, 0f5xn) <- film(?x8898, ?x814), country(?x814, ?x94), film(?x8898, ?x8631), ?x8631 = 01_1hw >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #4161 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 0522wp; *> query: (?x814, 06151l) <- category(?x814, ?x134), film(?x3462, ?x814), film(?x3462, ?x11619), ?x11619 = 07l50_1 *> conf = 0.01 ranks of expected_values: 773 EVAL 084qpk film! 01g969 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 67.000 0.312 http://example.org/film/actor/film./film/performance/film EVAL 084qpk film! 06151l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 100.000 67.000 0.312 http://example.org/film/actor/film./film/performance/film #9273-01r9md PRED entity: 01r9md PRED relation: film PRED expected values: 02qdrjx => 109 concepts (77 used for prediction) PRED predicted values (max 10 best out of 625): 0340hj (0.47 #2027, 0.41 #41180, 0.40 #237), 0g0x9c (0.20 #1365, 0.13 #3155, 0.02 #6735), 024mpp (0.20 #649, 0.13 #2439, 0.01 #40038), 04sntd (0.20 #490, 0.13 #2280, 0.01 #29138), 0dnvn3 (0.20 #55, 0.07 #1845, 0.01 #28703), 083shs (0.13 #1809, 0.10 #19, 0.01 #28667), 01pvxl (0.13 #2698, 0.10 #908, 0.01 #11648), 0298n7 (0.13 #3139, 0.10 #1349), 06gb1w (0.10 #735, 0.07 #2525, 0.05 #4315), 0d90m (0.10 #8, 0.07 #1798, 0.05 #3588) >> Best rule #2027 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 04fzk; 0g2mbn; 07nx9j; >> query: (?x13173, 0340hj) <- film(?x13173, ?x4502), nationality(?x13173, ?x1023), ?x4502 = 02wgk1, gender(?x13173, ?x514) >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01r9md film 02qdrjx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 77.000 0.467 http://example.org/film/actor/film./film/performance/film #9272-03bnv PRED entity: 03bnv PRED relation: award PRED expected values: 02f5qb => 138 concepts (122 used for prediction) PRED predicted values (max 10 best out of 288): 054krc (0.41 #5222, 0.14 #41479, 0.10 #15492), 03qbh5 (0.39 #8891, 0.30 #1386, 0.28 #3756), 0c4z8 (0.39 #8761, 0.27 #9551, 0.27 #2046), 0gqz2 (0.37 #5215, 0.18 #35947, 0.16 #34366), 02qvyrt (0.37 #5259, 0.16 #34366, 0.13 #48198), 054ks3 (0.33 #5274, 0.31 #3299, 0.26 #3694), 0l8z1 (0.32 #5198, 0.16 #34366, 0.13 #48198), 01ck6h (0.31 #3279, 0.28 #3674, 0.22 #1304), 09sb52 (0.30 #28876, 0.30 #28481, 0.30 #9916), 02f5qb (0.25 #547, 0.20 #8842, 0.12 #3312) >> Best rule #5222 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 012ljv; 09r9m7; 07mvp; 05ccxr; 0164y7; >> query: (?x3321, 054krc) <- award_winner(?x3321, ?x1089), award_winner(?x2139, ?x3321), music(?x5139, ?x3321) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #547 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 07_3qd; 01rmnp; *> query: (?x3321, 02f5qb) <- role(?x3321, ?x1574), artist(?x2149, ?x3321), ?x1574 = 0l15bq *> conf = 0.25 ranks of expected_values: 10 EVAL 03bnv award 02f5qb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 138.000 122.000 0.414 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9271-0jm4b PRED entity: 0jm4b PRED relation: colors PRED expected values: 083jv => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.81 #1179, 0.65 #1220, 0.65 #1240), 06fvc (0.35 #1221, 0.35 #1241, 0.34 #1180), 01l849 (0.34 #1030, 0.33 #39, 0.30 #1098), 02rnmb (0.34 #1030, 0.30 #1027, 0.24 #547), 0jc_p (0.34 #1030, 0.25 #175, 0.25 #61), 09ggk (0.34 #1030, 0.22 #554, 0.21 #1095), 03vtbc (0.34 #1030, 0.22 #554, 0.21 #653), 01jnf1 (0.33 #11, 0.25 #68, 0.13 #1054), 019sc (0.30 #795, 0.30 #848, 0.30 #293), 088fh (0.30 #1027, 0.15 #1034, 0.15 #1033) >> Best rule #1179 for best value: >> intensional similarity = 22 >> extensional distance = 271 >> proper extension: 04088s0; 026xxv_; 01lpx8; 0263cyj; 02fbb5; 03dkx; >> query: (?x5756, 083jv) <- colors(?x5756, ?x3189), colors(?x13989, ?x3189), colors(?x13580, ?x3189), colors(?x12042, ?x3189), colors(?x10443, ?x3189), colors(?x5850, ?x3189), colors(?x5551, ?x3189), colors(?x4369, ?x3189), colors(?x2067, ?x3189), ?x5551 = 02pjzvh, colors(?x6644, ?x3189), colors(?x2959, ?x3189), ?x12042 = 05xvj, ?x5850 = 037mjv, team(?x4244, ?x2067), ?x4244 = 028c_8, institution(?x620, ?x2959), team(?x12339, ?x4369), ?x13580 = 01_1kk, ?x10443 = 03j6_5, ?x13989 = 0d9qmn, citytown(?x6644, ?x12655) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jm4b colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.813 http://example.org/sports/sports_team/colors #9270-014_lq PRED entity: 014_lq PRED relation: artist! PRED expected values: 01dtcb => 80 concepts (67 used for prediction) PRED predicted values (max 10 best out of 119): 03x9yr (0.33 #137, 0.12 #842, 0.08 #701), 03qy3l (0.33 #65, 0.08 #629, 0.06 #770), 03b8gh (0.33 #123, 0.08 #687, 0.06 #828), 0g768 (0.25 #461, 0.25 #320, 0.19 #743), 04t53l (0.25 #431, 0.19 #713, 0.17 #572), 0fb0v (0.25 #289, 0.14 #148, 0.12 #712), 02nddq (0.25 #545, 0.12 #827, 0.12 #404), 015_1q (0.24 #1289, 0.21 #3406, 0.21 #1712), 03rhqg (0.24 #2838, 0.23 #2697, 0.22 #3120), 01dtcb (0.17 #1176, 0.14 #1882, 0.12 #2870) >> Best rule #137 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01ww_vs; >> query: (?x5329, 03x9yr) <- artists(?x9831, ?x5329), ?x9831 = 0xv2x, influenced_by(?x5329, ?x646) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1176 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 0lbj1; 01vrz41; 0fhxv; 01xzb6; 0ddkf; 02z4b_8; *> query: (?x5329, 01dtcb) <- award(?x5329, ?x4892), artists(?x1572, ?x5329), ?x4892 = 02f72_, ?x1572 = 06by7 *> conf = 0.17 ranks of expected_values: 10 EVAL 014_lq artist! 01dtcb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 80.000 67.000 0.333 http://example.org/music/record_label/artist #9269-02825nf PRED entity: 02825nf PRED relation: genre PRED expected values: 0lsxr => 102 concepts (100 used for prediction) PRED predicted values (max 10 best out of 98): 07s9rl0 (0.68 #728, 0.66 #2791, 0.66 #4614), 01z4y (0.61 #9351, 0.52 #7408, 0.51 #5707), 01jfsb (0.49 #4141, 0.33 #497, 0.33 #12), 03k9fj (0.38 #4140, 0.38 #132, 0.35 #11), 02l7c8 (0.31 #3902, 0.29 #5844, 0.29 #7302), 06n90 (0.28 #4142, 0.21 #619, 0.19 #863), 04xvlr (0.22 #729, 0.21 #2792, 0.20 #2914), 0lsxr (0.22 #3408, 0.21 #1947, 0.21 #2069), 06cvj (0.21 #3889, 0.10 #2551, 0.09 #5831), 01hmnh (0.20 #139, 0.19 #5969, 0.18 #5238) >> Best rule #728 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 0416y94; 07w8fz; 071nw5; 02z0f6l; 01gglm; >> query: (?x7629, 07s9rl0) <- film_crew_role(?x7629, ?x1171), written_by(?x7629, ?x4371), films(?x14068, ?x7629), film(?x3013, ?x7629) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #3408 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 367 *> proper extension: 04kzqz; 0d1qmz; 015g28; 0bz3jx; 0291hr; *> query: (?x7629, 0lsxr) <- currency(?x7629, ?x170), films(?x14068, ?x7629), film(?x4371, ?x7629), award_nominee(?x123, ?x4371) *> conf = 0.22 ranks of expected_values: 8 EVAL 02825nf genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 102.000 100.000 0.677 http://example.org/film/film/genre #9268-03whyr PRED entity: 03whyr PRED relation: production_companies PRED expected values: 09b3v => 74 concepts (56 used for prediction) PRED predicted values (max 10 best out of 96): 054g1r (0.34 #2809, 0.33 #2232, 0.33 #2561), 04rtpt (0.29 #791, 0.05 #1205, 0.03 #626), 016tw3 (0.24 #755, 0.09 #2244, 0.09 #1417), 01gb54 (0.20 #37, 0.18 #826, 0.18 #780), 016tt2 (0.20 #4, 0.12 #335, 0.10 #1327), 030_1_ (0.20 #17, 0.09 #760, 0.05 #2249), 02j_j0 (0.20 #47, 0.06 #1617, 0.04 #3516), 03sb38 (0.20 #54, 0.04 #1624, 0.03 #1459), 05nn2c (0.18 #771, 0.03 #1021, 0.02 #689), 0c_j5d (0.17 #89, 0.14 #172, 0.10 #584) >> Best rule #2809 for best value: >> intensional similarity = 4 >> extensional distance = 620 >> proper extension: 0d6b7; 040rmy; 0gcrg; 027ct7c; 0gpx6; >> query: (?x9524, ?x2156) <- genre(?x9524, ?x225), film_crew_role(?x9524, ?x137), film(?x2156, ?x9524), music(?x9524, ?x4727) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #610 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 0b60sq; *> query: (?x9524, 09b3v) <- genre(?x9524, ?x1510), ?x1510 = 01hmnh, written_by(?x9524, ?x1052), film(?x2156, ?x9524), story_by(?x9524, ?x8210) *> conf = 0.07 ranks of expected_values: 25 EVAL 03whyr production_companies 09b3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 74.000 56.000 0.336 http://example.org/film/film/production_companies #9267-04zyhx PRED entity: 04zyhx PRED relation: currency PRED expected values: 09nqf => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.77 #78, 0.76 #204, 0.76 #64), 02l6h (0.11 #11, 0.06 #95, 0.04 #109), 01nv4h (0.03 #184, 0.03 #93, 0.03 #205), 0kz1h (0.02 #47, 0.01 #54, 0.01 #61) >> Best rule #78 for best value: >> intensional similarity = 6 >> extensional distance = 79 >> proper extension: 0c_j9x; 0pd6l; 01_1hw; >> query: (?x1451, 09nqf) <- language(?x1451, ?x732), ?x732 = 04306rv, film(?x4630, ?x1451), titles(?x2753, ?x1451), film_release_distribution_medium(?x1451, ?x81), award(?x4630, ?x375) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04zyhx currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.765 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #9266-03f2w PRED entity: 03f2w PRED relation: country! PRED expected values: 06f41 => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 55): 0bynt (0.87 #1173, 0.85 #894, 0.84 #1118), 071t0 (0.79 #466, 0.78 #1132, 0.77 #410), 01cgz (0.74 #456, 0.72 #400, 0.70 #345), 03_8r (0.73 #965, 0.72 #1076, 0.71 #1131), 01lb14 (0.72 #347, 0.72 #458, 0.72 #402), 03hr1p (0.67 #467, 0.67 #411, 0.65 #356), 06wrt (0.67 #128, 0.65 #459, 0.65 #403), 0w0d (0.67 #13, 0.65 #454, 0.63 #398), 019tzd (0.67 #153, 0.57 #208, 0.52 #263), 01hp22 (0.67 #118, 0.56 #8, 0.55 #338) >> Best rule #1173 for best value: >> intensional similarity = 5 >> extensional distance = 103 >> proper extension: 05rznz; >> query: (?x11872, 0bynt) <- official_language(?x11872, ?x732), organization(?x11872, ?x312), ?x312 = 07t65, major_field_of_study(?x2014, ?x732), language(?x148, ?x732) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #457 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 41 *> proper extension: 05v8c; *> query: (?x11872, 06f41) <- olympics(?x11872, ?x2233), ?x2233 = 0l6mp, film_release_region(?x204, ?x11872), medal(?x11872, ?x422), organization(?x11872, ?x312), country(?x4045, ?x11872) *> conf = 0.65 ranks of expected_values: 12 EVAL 03f2w country! 06f41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 54.000 54.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #9265-01kx_81 PRED entity: 01kx_81 PRED relation: profession PRED expected values: 09jwl => 113 concepts (98 used for prediction) PRED predicted values (max 10 best out of 84): 09jwl (0.82 #3823, 0.81 #4411, 0.81 #894), 01c72t (0.62 #3533, 0.42 #1630, 0.34 #2946), 016z4k (0.59 #2049, 0.46 #587, 0.44 #2341), 039v1 (0.59 #765, 0.39 #618, 0.39 #3840), 0dxtg (0.46 #4114, 0.41 #12, 0.32 #2496), 03gjzk (0.45 #4115, 0.30 #4555, 0.30 #4994), 0cbd2 (0.38 #1467, 0.30 #1905, 0.29 #5), 02jknp (0.36 #4108, 0.29 #6, 0.26 #444), 0n1h (0.33 #1034, 0.28 #2056, 0.26 #13907), 09lbv (0.28 #164, 0.13 #895, 0.11 #3824) >> Best rule #3823 for best value: >> intensional similarity = 3 >> extensional distance = 205 >> proper extension: 02fybl; 09g0h; >> query: (?x1291, 09jwl) <- role(?x1291, ?x432), role(?x1291, ?x227), group(?x432, ?x442) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kx_81 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 98.000 0.816 http://example.org/people/person/profession #9264-0cqnss PRED entity: 0cqnss PRED relation: film_release_region PRED expected values: 02vzc => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 102): 0f8l9c (0.32 #4756, 0.31 #4934, 0.30 #8491), 06mkj (0.27 #8535, 0.26 #8183, 0.24 #1130), 07ssc (0.26 #8483, 0.25 #8131, 0.23 #2838), 05r4w (0.26 #8463, 0.24 #8111, 0.22 #7583), 02vzc (0.25 #8529, 0.25 #1124, 0.24 #8177), 059j2 (0.25 #8504, 0.24 #8152, 0.24 #1099), 0k6nt (0.25 #8495, 0.24 #8143, 0.23 #1090), 0chghy (0.25 #8476, 0.24 #1071, 0.24 #8124), 03rjj (0.25 #8468, 0.24 #8116, 0.23 #2823), 0345h (0.24 #8506, 0.23 #8154, 0.22 #7626) >> Best rule #4756 for best value: >> intensional similarity = 5 >> extensional distance = 764 >> proper extension: 07s8z_l; >> query: (?x4970, ?x94) <- award_winner(?x4970, ?x9170), award_winner(?x4970, ?x9127), titles(?x307, ?x4970), nationality(?x9127, ?x94), award_winner(?x1443, ?x9170) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #8529 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1128 *> proper extension: 05f67hw; *> query: (?x4970, 02vzc) <- film_release_region(?x4970, ?x94), language(?x4970, ?x254), ?x254 = 02h40lc *> conf = 0.25 ranks of expected_values: 5 EVAL 0cqnss film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 78.000 0.316 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9263-02krf9 PRED entity: 02krf9 PRED relation: profession! PRED expected values: 03rs8y 07lmxq 012c6x 02773m2 0crx5w 01c59k 08hp53 0g51l1 05ldnp 01wyy_ 03y9ccy 08qvhv 030_3z 03f0r5w 051wwp 025vw4t 01tt43d 05d1dy 0jsg0m 0250f 08cn_n 08xwck 09p0q 0grmhb 03w9sgh 04vt98 0428bc 01w0yrc 04j0s3 0b7gr2 090gk3 01b3bp => 34 concepts (16 used for prediction) PRED predicted values (max 10 best out of 3881): 052hl (0.75 #21975, 0.62 #25959, 0.60 #10024), 0q9t7 (0.75 #22504, 0.60 #10553, 0.50 #26488), 01pjr7 (0.75 #22249, 0.60 #10298, 0.50 #26233), 02mz_6 (0.75 #22119, 0.60 #10168, 0.50 #26103), 0b57p6 (0.71 #19431, 0.62 #23415, 0.50 #27399), 05jcn8 (0.71 #16902, 0.54 #11949, 0.40 #4951), 01g4zr (0.71 #16198, 0.50 #20182, 0.40 #8231), 04gcd1 (0.71 #16541, 0.50 #20525, 0.40 #4590), 081nh (0.71 #16582, 0.40 #4631, 0.38 #20566), 06m6z6 (0.62 #25046, 0.62 #21062, 0.60 #5127) >> Best rule #21975 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0cbd2; >> query: (?x1943, 052hl) <- profession(?x2803, ?x1943), profession(?x828, ?x1943), profession(?x495, ?x1943), profession(?x275, ?x1943), award_nominee(?x2802, ?x2803), ?x275 = 083chw, award_nominee(?x495, ?x221), award(?x828, ?x112) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #21349 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0cbd2; *> query: (?x1943, 03f0r5w) <- profession(?x2803, ?x1943), profession(?x828, ?x1943), profession(?x495, ?x1943), profession(?x275, ?x1943), award_nominee(?x2802, ?x2803), ?x275 = 083chw, award_nominee(?x495, ?x221), award(?x828, ?x112) *> conf = 0.62 ranks of expected_values: 15, 65, 98, 103, 107, 111, 133, 149, 253, 263, 370, 469, 501, 578, 612, 624, 706, 819, 848, 902, 1035, 1060, 1066, 1294, 1404, 1492, 1535, 1816, 2029, 2122, 2469, 2568 EVAL 02krf9 profession! 01b3bp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 090gk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 0b7gr2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 04j0s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 01w0yrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 0428bc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 04vt98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 03w9sgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 0grmhb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 09p0q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 08xwck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 08cn_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 0250f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 0jsg0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 05d1dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 01tt43d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 025vw4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 051wwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 03f0r5w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 030_3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 08qvhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 03y9ccy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 01wyy_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 05ldnp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 0g51l1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 08hp53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 01c59k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 0crx5w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 02773m2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 012c6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 07lmxq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 16.000 0.750 http://example.org/people/person/profession EVAL 02krf9 profession! 03rs8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 16.000 0.750 http://example.org/people/person/profession #9262-0chrx PRED entity: 0chrx PRED relation: dog_breed PRED expected values: 0km5c => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 1): 0km5c (0.88 #4, 0.47 #6, 0.42 #2) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 02hrh0_; >> query: (?x8451, 0km5c) <- location(?x8450, ?x8451), place_of_birth(?x5769, ?x8451), award(?x8450, ?x112), dog_breed(?x8451, ?x3095) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0chrx dog_breed 0km5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.878 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #9261-0nh1v PRED entity: 0nh1v PRED relation: time_zones PRED expected values: 02fqwt => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 8): 02fqwt (0.91 #316, 0.91 #289, 0.89 #144), 02hcv8 (0.56 #685, 0.56 #711, 0.55 #698), 02lcqs (0.27 #227, 0.24 #726, 0.21 #778), 02hczc (0.10 #93, 0.10 #237, 0.08 #579), 02llzg (0.05 #1313, 0.05 #1380, 0.05 #1141), 03bdv (0.04 #935, 0.04 #883, 0.03 #1078), 02lcrv (0.02 #309), 042g7t (0.01 #432) >> Best rule #316 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 0l_q9; 0g_wn2; 0lg0r; >> query: (?x7640, ?x1638) <- county_seat(?x7640, ?x11299), contains(?x1274, ?x7640), source(?x7640, ?x958), time_zones(?x11299, ?x1638) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nh1v time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.910 http://example.org/location/location/time_zones #9260-0ptj2 PRED entity: 0ptj2 PRED relation: place_of_birth! PRED expected values: 01x2_q => 158 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1561): 0nk72 (0.25 #1761, 0.03 #30498, 0.02 #38340), 032v0v (0.20 #2900, 0.17 #5512, 0.14 #10737), 04k15 (0.20 #3348, 0.14 #11185, 0.14 #8573), 01h2_6 (0.17 #7701, 0.14 #10314, 0.12 #15538), 07h1q (0.17 #7327, 0.14 #9940, 0.12 #15164), 0l9k1 (0.17 #7514, 0.14 #10127, 0.12 #15351), 0277c3 (0.17 #6482, 0.14 #9095, 0.12 #14319), 0bqytm (0.17 #6254, 0.14 #8867, 0.12 #14091), 018dyl (0.17 #6078, 0.14 #8691, 0.12 #13915), 0hskw (0.17 #5748, 0.14 #8361, 0.12 #13585) >> Best rule #1761 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03pbf; 0cpyv; >> query: (?x5115, 0nk72) <- category(?x5115, ?x134), contains(?x1264, ?x5115), place_of_death(?x8418, ?x5115), ?x1264 = 0345h >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ptj2 place_of_birth! 01x2_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 77.000 0.250 http://example.org/people/person/place_of_birth #9259-02hhtj PRED entity: 02hhtj PRED relation: profession PRED expected values: 02hrh1q => 111 concepts (106 used for prediction) PRED predicted values (max 10 best out of 66): 02hrh1q (0.91 #10089, 0.90 #7315, 0.90 #4541), 0dxtg (0.88 #12572, 0.74 #12718, 0.69 #1765), 02jknp (0.73 #1029, 0.55 #7600, 0.52 #7746), 09jwl (0.50 #5129, 0.47 #12869, 0.35 #8925), 0nbcg (0.45 #905, 0.41 #2045, 0.36 #175), 018gz8 (0.43 #1184, 0.36 #1038, 0.30 #4543), 0d1pc (0.41 #2045, 0.30 #924, 0.28 #2531), 016z4k (0.41 #2045, 0.20 #5115, 0.20 #880), 01c979 (0.41 #2045, 0.02 #2271, 0.02 #4753), 028kk_ (0.41 #2045, 0.02 #2556, 0.02 #3286) >> Best rule #10089 for best value: >> intensional similarity = 3 >> extensional distance = 425 >> proper extension: 01hkhq; >> query: (?x5881, 02hrh1q) <- film(?x5881, ?x1702), participant(?x2281, ?x5881), award_nominee(?x2281, ?x192) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hhtj profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 106.000 0.906 http://example.org/people/person/profession #9258-0dgq80b PRED entity: 0dgq80b PRED relation: language PRED expected values: 02h40lc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.94 #119, 0.94 #2059, 0.94 #2877), 03_9r (0.18 #9, 0.14 #3581, 0.08 #596), 0jzc (0.18 #19, 0.06 #3989, 0.04 #1724), 01r2l (0.18 #24, 0.06 #3989, 0.03 #611), 06nm1 (0.15 #303, 0.15 #244, 0.14 #597), 064_8sq (0.14 #1372, 0.14 #255, 0.14 #196), 04306rv (0.14 #3581, 0.11 #180, 0.10 #239), 0999q (0.14 #3581), 07c9s (0.14 #3581), 02bjrlw (0.09 #176, 0.09 #1, 0.09 #294) >> Best rule #119 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 03_wm6; 04y9mm8; >> query: (?x10623, 02h40lc) <- genre(?x10623, ?x571), currency(?x10623, ?x170), language(?x10623, ?x1310), ?x571 = 03npn >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dgq80b language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.944 http://example.org/film/film/language #9257-03rk0 PRED entity: 03rk0 PRED relation: service_location! PRED expected values: 01c6k4 => 239 concepts (239 used for prediction) PRED predicted values (max 10 best out of 153): 01c6k4 (0.50 #676, 0.36 #1884, 0.35 #2152), 06_9lg (0.46 #5197, 0.31 #15548, 0.21 #1438), 018mxj (0.42 #680, 0.33 #1486, 0.32 #4708), 064f29 (0.33 #729, 0.31 #1267, 0.31 #1132), 077w0b (0.33 #735, 0.31 #1138, 0.29 #467), 07zl6m (0.33 #800, 0.29 #532, 0.23 #2008), 04fv0k (0.33 #755, 0.23 #2633, 0.22 #2767), 05b5c (0.29 #527, 0.26 #2271, 0.26 #2137), 069b85 (0.29 #528, 0.25 #796, 0.22 #2808), 0dmtp (0.29 #460, 0.23 #1936, 0.21 #2472) >> Best rule #676 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 06mx8; >> query: (?x2146, 01c6k4) <- contains(?x2146, ?x1391), titles(?x2146, ?x4444), film(?x12209, ?x4444) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rk0 service_location! 01c6k4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 239.000 239.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #9256-0c41qv PRED entity: 0c41qv PRED relation: production_companies! PRED expected values: 014_x2 03clwtw 09bw4_ => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1294): 09bw4_ (0.37 #22444, 0.17 #2052, 0.08 #6541), 02ywwy (0.37 #22444, 0.05 #3367, 0.04 #20199), 014l6_ (0.37 #22444, 0.03 #68452, 0.02 #46364), 08rr3p (0.37 #22444), 0b3n61 (0.27 #5337, 0.17 #6459, 0.12 #3092), 03nfnx (0.27 #5369, 0.17 #6491, 0.12 #3124), 09146g (0.27 #4694, 0.17 #5816, 0.12 #2449), 03clwtw (0.25 #3017, 0.18 #5262, 0.12 #8628), 07024 (0.25 #5928, 0.18 #4806, 0.09 #17149), 011wtv (0.25 #6108, 0.17 #1619, 0.09 #4986) >> Best rule #22444 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 037bm2; >> query: (?x7339, ?x204) <- industry(?x7339, ?x373), company(?x4854, ?x7339), produced_by(?x204, ?x4854) >> conf = 0.37 => this is the best rule for 4 predicted values ranks of expected_values: 1, 8, 874 EVAL 0c41qv production_companies! 09bw4_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.370 http://example.org/film/film/production_companies EVAL 0c41qv production_companies! 03clwtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 117.000 117.000 0.370 http://example.org/film/film/production_companies EVAL 0c41qv production_companies! 014_x2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 117.000 117.000 0.370 http://example.org/film/film/production_companies #9255-0qdyf PRED entity: 0qdyf PRED relation: artists! PRED expected values: 0dl5d 02yv6b => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 257): 064t9 (0.50 #10814, 0.46 #13590, 0.46 #1863), 016clz (0.36 #929, 0.33 #2471, 0.28 #8646), 03_d0 (0.34 #3095, 0.29 #4021, 0.29 #4948), 06j6l (0.33 #5912, 0.28 #13625, 0.27 #48), 05bt6j (0.32 #967, 0.30 #16394, 0.30 #8994), 025sc50 (0.27 #50, 0.25 #10851, 0.24 #13627), 0gywn (0.27 #57, 0.23 #13634, 0.21 #14251), 01lyv (0.23 #12377, 0.22 #13610, 0.21 #14227), 0ggx5q (0.22 #10879, 0.18 #78, 0.15 #9953), 02lnbg (0.22 #10859, 0.18 #58, 0.14 #2524) >> Best rule #10814 for best value: >> intensional similarity = 3 >> extensional distance = 129 >> proper extension: 0pcc0; 041mt; 06wvj; 0130sy; 01vsqvs; 063tn; >> query: (?x3166, 064t9) <- profession(?x3166, ?x1183), languages(?x3166, ?x254), artists(?x474, ?x3166) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8970 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 01l79yc; *> query: (?x3166, 0dl5d) <- nationality(?x3166, ?x1310), artists(?x474, ?x3166), ?x1310 = 02jx1 *> conf = 0.22 ranks of expected_values: 11, 20 EVAL 0qdyf artists! 02yv6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 136.000 136.000 0.504 http://example.org/music/genre/artists EVAL 0qdyf artists! 0dl5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 136.000 136.000 0.504 http://example.org/music/genre/artists #9254-011ypx PRED entity: 011ypx PRED relation: film_crew_role PRED expected values: 09zzb8 09vw2b7 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.78 #70, 0.75 #345, 0.74 #1138), 09vw2b7 (0.72 #351, 0.65 #1283, 0.65 #1317), 02r96rf (0.68 #1280, 0.68 #1314, 0.67 #348), 01vx2h (0.34 #1287, 0.34 #1321, 0.33 #1148), 02rh1dz (0.17 #79, 0.13 #354, 0.12 #182), 015h31 (0.17 #9, 0.09 #181, 0.09 #1696), 0215hd (0.15 #1293, 0.14 #1327, 0.14 #1051), 089g0h (0.14 #327, 0.14 #362, 0.12 #1294), 0d2b38 (0.14 #196, 0.14 #368, 0.14 #230), 01xy5l_ (0.13 #1289, 0.13 #1323, 0.13 #1150) >> Best rule #70 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0209xj; >> query: (?x5927, 09zzb8) <- nominated_for(?x899, ?x5927), nominated_for(?x5927, ?x2107), ?x899 = 02x1dht, genre(?x5927, ?x53) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 011ypx film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 011ypx film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9253-045g4l PRED entity: 045g4l PRED relation: profession PRED expected values: 02hrh1q => 92 concepts (57 used for prediction) PRED predicted values (max 10 best out of 81): 02hrh1q (0.89 #2101, 0.88 #4338, 0.87 #4785), 0dxtg (0.74 #908, 0.58 #2100, 0.53 #5380), 03gjzk (0.49 #2102, 0.45 #4339, 0.43 #4786), 02jknp (0.43 #455, 0.29 #1051, 0.26 #3884), 01d_h8 (0.38 #2092, 0.38 #3882, 0.34 #4776), 0np9r (0.35 #2107, 0.31 #4344, 0.31 #4940), 0cbd2 (0.27 #603, 0.27 #5224, 0.27 #4628), 0kyk (0.23 #5247, 0.22 #3906, 0.22 #924), 02krf9 (0.20 #2113, 0.15 #5393, 0.14 #4946), 0fj9f (0.18 #204, 0.14 #502, 0.13 #651) >> Best rule #2101 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 0q9kd; 04t2l2; 0mdqp; 081lh; 013cr; 02p21g; 01n4f8; 034np8; 0gz5hs; 0126rp; ... >> query: (?x10901, 02hrh1q) <- location(?x10901, ?x2850), religion(?x10901, ?x7131), profession(?x10901, ?x1146), ?x1146 = 018gz8 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045g4l profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 57.000 0.892 http://example.org/people/person/profession #9252-05vsb7 PRED entity: 05vsb7 PRED relation: draft! PRED expected values: 01y49 06rny => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 124): 01y49 (0.50 #565, 0.47 #69, 0.46 #688), 05gg4 (0.50 #575, 0.47 #69, 0.46 #688), 05g49 (0.50 #579, 0.47 #69, 0.46 #688), 01c_d (0.50 #599, 0.47 #69, 0.46 #688), 06rny (0.47 #69, 0.46 #688, 0.46 #687), 0289q (0.47 #69, 0.46 #688, 0.46 #687), 0fbftr (0.47 #69, 0.46 #688, 0.46 #687), 04czgbh (0.47 #69, 0.46 #688, 0.46 #687), 02wvfxz (0.47 #69, 0.46 #688, 0.46 #687), 02wvf2s (0.47 #69, 0.46 #688, 0.46 #687) >> Best rule #565 for best value: >> intensional similarity = 45 >> extensional distance = 4 >> proper extension: 02pq_x5; >> query: (?x465, 01y49) <- draft(?x7643, ?x465), draft(?x6976, ?x465), draft(?x1576, ?x465), draft(?x729, ?x465), school(?x465, ?x4410), school(?x465, ?x735), team(?x935, ?x6976), school(?x6976, ?x7338), team(?x180, ?x7643), colors(?x7643, ?x4557), school(?x7643, ?x1675), team(?x706, ?x1576), institution(?x8398, ?x4410), institution(?x1771, ?x4410), institution(?x865, ?x4410), student(?x4410, ?x5620), student(?x4410, ?x1379), organization(?x346, ?x4410), ?x865 = 02h4rq6, ?x735 = 065y4w7, major_field_of_study(?x4410, ?x2605), major_field_of_study(?x4410, ?x2008), major_field_of_study(?x4410, ?x1154), ?x2008 = 07c52, student(?x2605, ?x445), team(?x11323, ?x1576), major_field_of_study(?x9399, ?x2605), major_field_of_study(?x4750, ?x2605), major_field_of_study(?x3199, ?x2605), major_field_of_study(?x741, ?x2605), sport(?x6976, ?x1083), ?x8398 = 028dcg, ?x741 = 01w3v, award_nominee(?x1065, ?x1379), ?x4750 = 04hgpt, ?x1154 = 02lp1, nominated_for(?x5620, ?x6884), ?x3199 = 0373qg, category(?x729, ?x134), profession(?x1379, ?x1032), ?x7338 = 01qgr3, student(?x1771, ?x744), ?x9399 = 02z6fs, ?x4557 = 019sc, major_field_of_study(?x1771, ?x90) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 05vsb7 draft! 06rny CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 17.000 17.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 05vsb7 draft! 01y49 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 17.000 17.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #9251-0_rwf PRED entity: 0_rwf PRED relation: contains! PRED expected values: 06mz5 => 41 concepts (20 used for prediction) PRED predicted values (max 10 best out of 140): 09c7w0 (0.80 #2691, 0.79 #899, 0.78 #3), 01n7q (0.44 #8152, 0.41 #7246, 0.40 #9056), 0vmt (0.32 #951, 0.28 #5431, 0.28 #55), 050l8 (0.24 #9879, 0.15 #11683, 0.15 #1926), 05fjy (0.24 #9879, 0.15 #11683, 0.11 #319), 0846v (0.24 #9879, 0.15 #11683, 0.07 #12586), 0b90_r (0.24 #9879, 0.15 #11683, 0.05 #8972), 0m27n (0.21 #1303, 0.17 #407, 0.15 #2199), 07srw (0.19 #5521, 0.14 #3729, 0.14 #6417), 04_1l0v (0.18 #4931, 0.14 #6723, 0.06 #15731) >> Best rule #2691 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 010dft; 0qr4n; 01vsl; 0100mt; 0x335; 010h9y; 0fw3f; >> query: (?x9414, 09c7w0) <- source(?x9414, ?x958), ?x958 = 0jbk9, time_zones(?x9414, ?x2088), ?x2088 = 02hczc, place(?x9414, ?x9414) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #12586 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 331 *> proper extension: 0fw4v; 0lg0r; *> query: (?x9414, ?x2256) <- category(?x9414, ?x134), source(?x9414, ?x958), ?x958 = 0jbk9, time_zones(?x9414, ?x2088), time_zones(?x11669, ?x2088), time_zones(?x3987, ?x2088), time_zones(?x2256, ?x2088), time_zones(?x726, ?x2088), locations(?x4803, ?x11669), taxonomy(?x2256, ?x939), contains(?x2256, ?x1699), religion(?x2256, ?x109), state(?x3987, ?x2982), adjoins(?x1227, ?x726), contains(?x726, ?x727), jurisdiction_of_office(?x900, ?x726) *> conf = 0.07 ranks of expected_values: 16 EVAL 0_rwf contains! 06mz5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 41.000 20.000 0.800 http://example.org/location/location/contains #9250-04z1v0 PRED entity: 04z1v0 PRED relation: parent_genre PRED expected values: 016jny => 86 concepts (68 used for prediction) PRED predicted values (max 10 best out of 133): 06by7 (0.86 #5425, 0.78 #6245, 0.67 #1653), 05w3f (0.75 #1826, 0.33 #354, 0.24 #1992), 011j5x (0.40 #676, 0.33 #186, 0.25 #1333), 016clz (0.40 #658, 0.25 #1315, 0.12 #1970), 03lty (0.35 #8055, 0.33 #183, 0.20 #673), 02w4v (0.33 #31, 0.23 #1505, 0.18 #1179), 064t9 (0.33 #339, 0.14 #9522, 0.12 #1811), 07v64s (0.33 #365, 0.14 #9522, 0.06 #1837), 03_d0 (0.25 #500, 0.23 #2956, 0.23 #4926), 0190_q (0.25 #516, 0.20 #842, 0.19 #1825) >> Best rule #5425 for best value: >> intensional similarity = 7 >> extensional distance = 67 >> proper extension: 01gbcf; 018ysx; 028cl7; 017ht; >> query: (?x10721, 06by7) <- parent_genre(?x10721, ?x6173), parent_genre(?x9342, ?x6173), parent_genre(?x6714, ?x6173), artists(?x6173, ?x4642), ?x9342 = 0grjmv, artists(?x6714, ?x475), ?x4642 = 0394y >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #6299 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 74 *> proper extension: 0jf1v; *> query: (?x10721, 016jny) <- parent_genre(?x10721, ?x6173), parent_genre(?x9342, ?x6173), artists(?x6173, ?x4642), artists(?x9342, ?x7612), parent_genre(?x11242, ?x9342), ?x4642 = 0394y, ?x7612 = 01w5n51 *> conf = 0.13 ranks of expected_values: 25 EVAL 04z1v0 parent_genre 016jny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 86.000 68.000 0.855 http://example.org/music/genre/parent_genre #9249-030znt PRED entity: 030znt PRED relation: student! PRED expected values: 0gl5_ => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 36): 0bwfn (0.21 #275, 0.06 #1329, 0.06 #4491), 04b_46 (0.14 #227, 0.02 #3389, 0.02 #2862), 0kz2w (0.07 #21, 0.01 #1602), 01jvxb (0.07 #258), 07vjm (0.07 #228), 0ks67 (0.07 #189), 053mhx (0.07 #822, 0.01 #5038, 0.01 #3457), 03qdm (0.07 #936), 015ln1 (0.07 #724), 027kp3 (0.07 #680) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 01dw4q; 058ncz; 03zqc1; 035gjq; 03lt8g; 05lb87; 038g2x; 04psyp; 01wb8bs; 05dxl5; ... >> query: (?x1343, 0bwfn) <- award_winner(?x2129, ?x1343), award_nominee(?x1116, ?x1343), ?x2129 = 0443y3 >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #1298 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 459 *> proper extension: 0gv40; *> query: (?x1343, 0gl5_) <- award_nominee(?x444, ?x1343), award_winner(?x7085, ?x1343), people(?x2510, ?x1343) *> conf = 0.01 ranks of expected_values: 36 EVAL 030znt student! 0gl5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 91.000 91.000 0.214 http://example.org/education/educational_institution/students_graduates./education/education/student #9248-048yqf PRED entity: 048yqf PRED relation: language PRED expected values: 02h40lc => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 43): 02h40lc (0.96 #1106, 0.94 #3091, 0.91 #1398), 064_8sq (0.33 #79, 0.33 #21, 0.22 #137), 012w70 (0.33 #128, 0.33 #70, 0.09 #419), 0653m (0.28 #127, 0.20 #69, 0.12 #418), 0459q4 (0.22 #152, 0.20 #94, 0.04 #443), 04306rv (0.20 #63, 0.15 #179, 0.12 #237), 06nm1 (0.18 #533, 0.16 #417, 0.16 #1114), 02bjrlw (0.17 #1, 0.10 #524, 0.10 #349), 0jzc (0.13 #77, 0.11 #135, 0.05 #251), 06b_j (0.11 #719, 0.09 #429, 0.09 #1593) >> Best rule #1106 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 0gtv7pk; 0h1cdwq; 02sg5v; 03t97y; 03qnvdl; 0436yk; 018nnz; 01kf3_9; 028cg00; 0661m4p; ... >> query: (?x9914, 02h40lc) <- genre(?x9914, ?x225), prequel(?x9914, ?x8787), language(?x9914, ?x2164), production_companies(?x9914, ?x7339) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 048yqf language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.960 http://example.org/film/film/language #9247-0mnrb PRED entity: 0mnrb PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 117 concepts (67 used for prediction) PRED predicted values (max 10 best out of 39): 09c7w0 (0.92 #177, 0.91 #154, 0.91 #118), 04_1l0v (0.44 #283, 0.36 #543, 0.28 #402), 07z1m (0.25 #270, 0.21 #117, 0.15 #321), 03rjj (0.18 #14, 0.14 #2, 0.05 #131), 020d5 (0.11 #12, 0.07 #24, 0.03 #714), 05fjf (0.04 #801, 0.04 #816, 0.01 #803), 0694j (0.04 #801, 0.04 #816, 0.01 #803), 05tbn (0.04 #801, 0.04 #816, 0.01 #803), 04rrx (0.04 #801, 0.04 #816, 0.01 #803), 05kkh (0.04 #801, 0.04 #816, 0.01 #803) >> Best rule #177 for best value: >> intensional similarity = 5 >> extensional distance = 81 >> proper extension: 0mnzd; 0fxyd; 0n5fz; 0n5df; 0k3gj; 0nm6k; 0k3l5; 0jrtv; 0k3ll; 0dcdp; ... >> query: (?x14006, 09c7w0) <- time_zones(?x14006, ?x2674), source(?x14006, ?x958), ?x2674 = 02hcv8, ?x958 = 0jbk9, county_seat(?x14006, ?x9846) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mnrb second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 67.000 0.916 http://example.org/location/country/second_level_divisions #9246-01vsqvs PRED entity: 01vsqvs PRED relation: instrumentalists! PRED expected values: 03q5t => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 108): 0342h (0.73 #1920, 0.73 #3403, 0.70 #1833), 05148p4 (0.60 #108, 0.40 #195, 0.33 #1936), 0l14md (0.40 #182, 0.40 #95, 0.33 #8), 0mkg (0.40 #98, 0.07 #2450, 0.06 #2013), 02hnl (0.33 #34, 0.23 #1949, 0.20 #1775), 03qjg (0.30 #1966, 0.22 #1879, 0.21 #3449), 04rzd (0.30 #994, 0.20 #907, 0.20 #298), 018vs (0.29 #1231, 0.27 #1928, 0.26 #2015), 018j2 (0.20 #995, 0.20 #908, 0.20 #299), 026t6 (0.20 #873, 0.20 #351, 0.20 #264) >> Best rule #1920 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 015cxv; >> query: (?x9179, 0342h) <- artists(?x11746, ?x9179), artists(?x3108, ?x9179), ?x3108 = 02w4v, artists(?x11746, ?x8579), ?x8579 = 01vs4f3 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #88 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 032t2z; 01vsl3_; *> query: (?x9179, 03q5t) <- people(?x10900, ?x9179), nationality(?x9179, ?x1264), artists(?x9063, ?x9179), ?x9063 = 0cx7f, profession(?x9179, ?x1032) *> conf = 0.20 ranks of expected_values: 17 EVAL 01vsqvs instrumentalists! 03q5t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 158.000 158.000 0.733 http://example.org/music/instrument/instrumentalists #9245-037hgm PRED entity: 037hgm PRED relation: role PRED expected values: 02g9p4 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 119): 0342h (0.47 #190, 0.46 #1681, 0.44 #1372), 03bx0bm (0.38 #1265, 0.36 #1698, 0.34 #207), 0l14md (0.33 #69, 0.25 #497, 0.22 #130), 013y1f (0.25 #497, 0.12 #309, 0.12 #308), 05r5c (0.22 #131, 0.22 #70, 0.21 #443), 02sgy (0.22 #68, 0.20 #1429, 0.19 #496), 01vj9c (0.20 #1429, 0.19 #496, 0.17 #433), 042v_gx (0.20 #1429, 0.19 #496, 0.17 #433), 01dnws (0.20 #1429, 0.19 #496, 0.17 #433), 0l14qv (0.17 #6, 0.11 #67, 0.09 #315) >> Best rule #190 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 024dgj; 02rn_bj; 03193l; >> query: (?x4759, 0342h) <- student(?x3416, ?x4759), profession(?x4759, ?x131), role(?x4759, ?x716), ?x131 = 0dz3r >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1738 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 268 *> proper extension: 04mn81; 011_vz; 017mbb; *> query: (?x4759, ?x74) <- role(?x4759, ?x716), artists(?x302, ?x4759), role(?x74, ?x716) *> conf = 0.03 ranks of expected_values: 75 EVAL 037hgm role 02g9p4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 130.000 130.000 0.474 http://example.org/music/group_member/membership./music/group_membership/role #9244-0bhvtc PRED entity: 0bhvtc PRED relation: artist! PRED expected values: 0bfp0l => 47 concepts (42 used for prediction) PRED predicted values (max 10 best out of 78): 015_1q (0.22 #852, 0.21 #1130, 0.19 #1688), 03rhqg (0.15 #1685, 0.14 #849, 0.14 #1824), 0g768 (0.12 #1845, 0.12 #1706, 0.10 #1148), 033hn8 (0.11 #1822, 0.11 #1683, 0.09 #847), 011k1h (0.11 #1680, 0.10 #1819, 0.09 #844), 017l96 (0.10 #1687, 0.09 #1826, 0.09 #1129), 0181dw (0.10 #875, 0.10 #1850, 0.10 #1153), 01trtc (0.09 #1184, 0.08 #906, 0.08 #1742), 043g7l (0.09 #1142, 0.08 #1700, 0.07 #864), 01w40h (0.08 #861, 0.08 #1139, 0.07 #1697) >> Best rule #852 for best value: >> intensional similarity = 2 >> extensional distance = 274 >> proper extension: 01vvydl; 07s3vqk; 0197tq; 0411q; 0lbj1; 01lmj3q; 01q_ph; 026ps1; 0147dk; 03f2_rc; ... >> query: (?x3202, 015_1q) <- award_winner(?x3202, ?x1413), artist(?x2241, ?x3202) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #938 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 274 *> proper extension: 01vvydl; 07s3vqk; 0197tq; 0411q; 0lbj1; 01lmj3q; 01q_ph; 026ps1; 0147dk; 03f2_rc; ... *> query: (?x3202, 0bfp0l) <- award_winner(?x3202, ?x1413), artist(?x2241, ?x3202) *> conf = 0.03 ranks of expected_values: 35 EVAL 0bhvtc artist! 0bfp0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 47.000 42.000 0.225 http://example.org/music/record_label/artist #9243-01f7v_ PRED entity: 01f7v_ PRED relation: profession PRED expected values: 0dxtg => 109 concepts (80 used for prediction) PRED predicted values (max 10 best out of 49): 0dxtg (0.90 #1196, 0.83 #3565, 0.82 #3713), 02hrh1q (0.70 #10675, 0.69 #8600, 0.68 #11713), 03gjzk (0.60 #1198, 0.48 #1346, 0.47 #2086), 0cbd2 (0.29 #3559, 0.29 #3707, 0.28 #4004), 0dgd_ (0.25 #30, 0.14 #326, 0.12 #474), 0lgw7 (0.25 #47, 0.06 #491, 0.05 #787), 02krf9 (0.24 #2839, 0.24 #2395, 0.24 #470), 02pjxr (0.20 #181, 0.14 #329, 0.06 #4475), 026sdt1 (0.20 #216, 0.14 #364, 0.04 #660), 09jwl (0.18 #9493, 0.17 #11570, 0.16 #10383) >> Best rule #1196 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 03ft8; 0jt90f5; 0282x; >> query: (?x4169, 0dxtg) <- profession(?x4169, ?x319), executive_produced_by(?x3886, ?x4169), written_by(?x1625, ?x4169) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f7v_ profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 80.000 0.896 http://example.org/people/person/profession #9242-0f4yh PRED entity: 0f4yh PRED relation: award PRED expected values: 02g3ft => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 182): 02hsq3m (0.40 #28, 0.18 #719, 0.07 #3713), 0gs9p (0.27 #19351, 0.27 #19350, 0.25 #11748), 019f4v (0.27 #19351, 0.27 #19350, 0.25 #11748), 0gq9h (0.27 #19351, 0.27 #19350, 0.25 #11748), 027dtxw (0.27 #19351, 0.27 #19350, 0.25 #11748), 0gq_v (0.27 #19351, 0.27 #19350, 0.25 #11748), 040njc (0.27 #19351, 0.27 #19350, 0.25 #11748), 0gr0m (0.27 #19351, 0.27 #19350, 0.25 #11748), 02qvyrt (0.27 #19351, 0.27 #19350, 0.25 #11748), 02qyntr (0.27 #19351, 0.27 #19350, 0.25 #11748) >> Best rule #28 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 06mmr; >> query: (?x3535, 02hsq3m) <- award_winner(?x3535, ?x1643), ?x1643 = 09pjnd, award(?x3535, ?x500) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2601 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 85 *> proper extension: 0g56t9t; 0g22z; 016fyc; 0ds33; 016z5x; 0bth54; 0pc62; 0fg04; 084qpk; 0164qt; ... *> query: (?x3535, 02g3ft) <- country(?x3535, ?x94), production_companies(?x3535, ?x847), edited_by(?x3535, ?x1387), nominated_for(?x112, ?x3535) *> conf = 0.09 ranks of expected_values: 33 EVAL 0f4yh award 02g3ft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 116.000 116.000 0.400 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9241-07tj4c PRED entity: 07tj4c PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 28): 0ch6mp2 (0.76 #2186, 0.75 #2415, 0.73 #2262), 09vw2b7 (0.66 #2185, 0.64 #2414, 0.60 #2261), 02r96rf (0.66 #2181, 0.64 #2410, 0.61 #3216), 094hwz (0.50 #17, 0.04 #740, 0.04 #1161), 0dxtw (0.40 #2190, 0.39 #2419, 0.34 #3225), 01vx2h (0.33 #13, 0.33 #2191, 0.30 #2420), 01pvkk (0.33 #52, 0.29 #2421, 0.28 #2192), 02rh1dz (0.33 #11, 0.12 #734, 0.11 #2418), 02ynfr (0.17 #2425, 0.17 #2196, 0.17 #18), 04pyp5 (0.17 #19, 0.07 #2426, 0.07 #856) >> Best rule #2186 for best value: >> intensional similarity = 3 >> extensional distance = 716 >> proper extension: 0h95zbp; 0g5q34q; 0gh6j94; >> query: (?x11001, 0ch6mp2) <- film_crew_role(?x11001, ?x137), film_release_distribution_medium(?x11001, ?x81), ?x137 = 09zzb8 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tj4c film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.765 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9240-015bpl PRED entity: 015bpl PRED relation: prequel PRED expected values: 012gk9 => 74 concepts (39 used for prediction) PRED predicted values (max 10 best out of 15): 0m5s5 (0.17 #346, 0.14 #710), 0dc_ms (0.03 #1020, 0.02 #1201), 02wgk1 (0.03 #991, 0.02 #1172), 05zlld0 (0.03 #970, 0.02 #1151), 0x25q (0.03 #960, 0.02 #1141), 0hx4y (0.03 #956, 0.02 #1137), 03177r (0.03 #955, 0.02 #1136), 0fztbq (0.02 #1264), 012s1d (0.02 #1188), 047wh1 (0.02 #1187) >> Best rule #346 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02bg8v; >> query: (?x7989, 0m5s5) <- film(?x5545, ?x7989), film(?x2033, ?x7989), country(?x7989, ?x94), ?x5545 = 017khj, language(?x7989, ?x254), nominated_for(?x2033, ?x253) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015bpl prequel 012gk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 39.000 0.167 http://example.org/film/film/prequel #9239-06sn8m PRED entity: 06sn8m PRED relation: film PRED expected values: 063y9fp => 95 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1031): 03whyr (0.33 #1572, 0.04 #14120, 0.04 #3364), 0h1x5f (0.33 #1585, 0.04 #3377, 0.02 #6963), 0h03fhx (0.33 #780, 0.04 #2572, 0.02 #6158), 0gj8t_b (0.33 #181, 0.04 #1973, 0.02 #16313), 0pvms (0.33 #413, 0.04 #2205, 0.02 #9376), 0c00zd0 (0.33 #260, 0.04 #2052, 0.02 #9223), 09rsjpv (0.33 #575, 0.04 #2367, 0.02 #9538), 0gvrws1 (0.33 #321, 0.04 #2113, 0.02 #9284), 099bhp (0.11 #10584, 0.10 #8791, 0.09 #5206), 024rwx (0.10 #21510, 0.09 #23303, 0.09 #19717) >> Best rule #1572 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 05fnl9; >> query: (?x6962, 03whyr) <- award(?x6962, ?x8250), profession(?x6962, ?x1032), ?x8250 = 0cqhb3, language(?x6962, ?x254) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5117 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 0678gl; *> query: (?x6962, 063y9fp) <- profession(?x6962, ?x1032), ?x1032 = 02hrh1q, actor(?x10826, ?x6962), film_release_region(?x10826, ?x94) *> conf = 0.06 ranks of expected_values: 21 EVAL 06sn8m film 063y9fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 95.000 71.000 0.333 http://example.org/film/actor/film./film/performance/film #9238-0d0l91 PRED entity: 0d0l91 PRED relation: student! PRED expected values: 02jfc => 89 concepts (61 used for prediction) PRED predicted values (max 10 best out of 97): 02822 (0.18 #279, 0.12 #93, 0.10 #714), 0w7c (0.18 #42, 0.12 #104, 0.06 #290), 040p_q (0.18 #49, 0.12 #111, 0.01 #669), 062z7 (0.09 #332, 0.09 #22, 0.06 #84), 0fdys (0.09 #29, 0.07 #525, 0.07 #649), 03g3w (0.09 #331, 0.06 #83, 0.06 #517), 01zc2w (0.09 #48, 0.06 #110, 0.04 #358), 0h5k (0.09 #17, 0.06 #79, 0.03 #265), 01r2l (0.09 #40, 0.06 #102, 0.02 #350), 06nm1 (0.09 #18, 0.06 #80, 0.02 #328) >> Best rule #279 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 01z7_f; >> query: (?x11562, 02822) <- location(?x11562, ?x739), people(?x1050, ?x11562), student(?x1200, ?x11562), actor(?x7230, ?x11562) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #177 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 0cgfb; *> query: (?x11562, 02jfc) <- profession(?x11562, ?x1032), profession(?x11562, ?x967), ?x1032 = 02hrh1q, ?x967 = 012t_z, category(?x11562, ?x134) *> conf = 0.04 ranks of expected_values: 20 EVAL 0d0l91 student! 02jfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 89.000 61.000 0.182 http://example.org/education/field_of_study/students_majoring./education/education/student #9237-02f1c PRED entity: 02f1c PRED relation: profession PRED expected values: 01c72t => 93 concepts (85 used for prediction) PRED predicted values (max 10 best out of 69): 0nbcg (0.67 #26, 0.53 #2158, 0.51 #168), 0dxtg (0.49 #3138, 0.33 #436, 0.29 #6128), 02jknp (0.48 #3133, 0.24 #857, 0.23 #7259), 01c72t (0.45 #729, 0.35 #1013, 0.30 #19), 039v1 (0.33 #31, 0.32 #173, 0.31 #2163), 03gjzk (0.32 #3139, 0.27 #2427, 0.26 #4849), 0fnpj (0.30 #55, 0.22 #197, 0.15 #623), 09lbv (0.28 #7823, 0.13 #11091, 0.07 #15), 029bkp (0.28 #7823, 0.13 #11091, 0.07 #185), 0q04f (0.28 #7823, 0.13 #11091, 0.02 #519) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 0lbj1; 032t2z; 01vvycq; 01wcp_g; 0285c; 0zjpz; 018pj3; 02b25y; 0161sp; 01gg59; ... >> query: (?x8799, 0nbcg) <- profession(?x8799, ?x11254), artist(?x2931, ?x8799), ?x11254 = 04f2zj >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #729 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 01nqfh_; *> query: (?x8799, 01c72t) <- profession(?x8799, ?x131), award_winner(?x2165, ?x8799), instrumentalists(?x227, ?x8799) *> conf = 0.45 ranks of expected_values: 4 EVAL 02f1c profession 01c72t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 85.000 0.667 http://example.org/people/person/profession #9236-01679d PRED entity: 01679d PRED relation: role PRED expected values: 03q5t 0979zs => 68 concepts (46 used for prediction) PRED predicted values (max 10 best out of 102): 018vs (0.90 #1136, 0.86 #2706, 0.85 #406), 07m2y (0.90 #1136, 0.86 #2706, 0.85 #406), 0979zs (0.86 #2706, 0.85 #406, 0.83 #3439), 01vdm0 (0.86 #3469, 0.86 #2525, 0.85 #3365), 01s0ps (0.85 #406, 0.83 #3439, 0.83 #2078), 03q5t (0.85 #406, 0.83 #3439, 0.83 #2078), 0gkd1 (0.81 #513, 0.75 #207, 0.73 #1855), 01v1d8 (0.81 #513, 0.75 #207, 0.72 #725), 0dwsp (0.81 #513, 0.75 #207, 0.72 #725), 02dlh2 (0.81 #513, 0.75 #207, 0.72 #725) >> Best rule #1136 for best value: >> intensional similarity = 23 >> extensional distance = 5 >> proper extension: 0dwsp; >> query: (?x2253, ?x9413) <- role(?x9413, ?x2253), role(?x3991, ?x2253), role(?x3296, ?x2253), ?x3991 = 05842k, role(?x2253, ?x1267), role(?x2253, ?x614), ?x614 = 0mkg, role(?x4741, ?x2253), role(?x1997, ?x2253), celebrity(?x4741, ?x4819), award(?x4741, ?x1232), origin(?x4741, ?x2277), role(?x3409, ?x9413), award_winner(?x2186, ?x4741), role(?x9413, ?x1332), role(?x2253, ?x1436), instrumentalists(?x3296, ?x1399), ?x1267 = 07brj, ?x1332 = 03qlv7, award_nominee(?x4741, ?x1282), ?x3409 = 0680x0, ?x2186 = 056878, type_of_union(?x1997, ?x566) >> conf = 0.90 => this is the best rule for 2 predicted values *> Best rule #2706 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 12 *> proper extension: 0dwvl; 06w7v; *> query: (?x2253, ?x9413) <- role(?x9413, ?x2253), role(?x3991, ?x2253), role(?x3296, ?x2253), ?x3991 = 05842k, role(?x2253, ?x1267), role(?x2253, ?x614), ?x614 = 0mkg, role(?x4741, ?x2253), celebrity(?x4741, ?x4819), award(?x4741, ?x1232), origin(?x4741, ?x2277), role(?x214, ?x9413), award_winner(?x1362, ?x4741), role(?x9413, ?x1332), role(?x2253, ?x1436), instrumentalists(?x3296, ?x1399), ?x1267 = 07brj, ?x1332 = 03qlv7, ?x1399 = 011zf2, role(?x645, ?x3296), group(?x3296, ?x3109), role(?x885, ?x3296) *> conf = 0.86 ranks of expected_values: 3, 6 EVAL 01679d role 0979zs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 46.000 0.898 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 01679d role 03q5t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 68.000 46.000 0.898 http://example.org/music/performance_role/track_performances./music/track_contribution/role #9235-02drd3 PRED entity: 02drd3 PRED relation: people! PRED expected values: 0qcr0 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 28): 0gk4g (0.13 #1396, 0.12 #1858, 0.11 #1462), 0dq9p (0.09 #1601, 0.08 #83, 0.07 #1865), 0c58k (0.08 #96, 0.01 #558), 0qcr0 (0.08 #1387, 0.06 #1453, 0.06 #1849), 02y0js (0.05 #1586, 0.05 #1850, 0.05 #1454), 04p3w (0.05 #1595, 0.03 #1859, 0.03 #1397), 02k6hp (0.05 #1489, 0.04 #1621, 0.03 #1885), 02knxx (0.04 #1418, 0.03 #1880, 0.03 #1484), 01psyx (0.03 #1497, 0.02 #837, 0.02 #1629), 0m32h (0.03 #1475, 0.03 #1871, 0.03 #1409) >> Best rule #1396 for best value: >> intensional similarity = 2 >> extensional distance = 275 >> proper extension: 0dky9n; >> query: (?x12439, 0gk4g) <- place_of_death(?x12439, ?x6960), nominated_for(?x12439, ?x4927) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #1387 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 275 *> proper extension: 0dky9n; *> query: (?x12439, 0qcr0) <- place_of_death(?x12439, ?x6960), nominated_for(?x12439, ?x4927) *> conf = 0.08 ranks of expected_values: 4 EVAL 02drd3 people! 0qcr0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 100.000 100.000 0.134 http://example.org/people/cause_of_death/people #9234-01svw8n PRED entity: 01svw8n PRED relation: category PRED expected values: 08mbj5d => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #17, 0.84 #5, 0.83 #20) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 330 >> proper extension: 0197tq; 0411q; 05cljf; 01lmj3q; 0m2l9; 026ps1; 06cc_1; 04rcr; 01vvycq; 02l840; ... >> query: (?x3930, 08mbj5d) <- award_nominee(?x748, ?x3930), award_winner(?x4837, ?x3930), artist(?x8489, ?x3930) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01svw8n category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.852 http://example.org/common/topic/webpage./common/webpage/category #9233-02pqcfz PRED entity: 02pqcfz PRED relation: sport PRED expected values: 039yzs => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 9): 018w8 (0.79 #202, 0.77 #184, 0.76 #238), 039yzs (0.70 #124, 0.67 #79, 0.63 #259), 02vx4 (0.57 #664, 0.53 #701, 0.53 #565), 0jm_ (0.31 #483, 0.29 #274, 0.27 #138), 03tmr (0.29 #82, 0.22 #109, 0.15 #444), 018jz (0.24 #485, 0.23 #375, 0.21 #494), 06f3l (0.11 #117, 0.08 #171, 0.05 #252), 09xp_ (0.04 #340, 0.02 #413, 0.02 #449), 0z74 (0.02 #451, 0.02 #460, 0.01 #497) >> Best rule #202 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 0jmdb; >> query: (?x4369, 018w8) <- team(?x12339, ?x4369), position(?x4369, ?x5755), position(?x4369, ?x1348), ?x5755 = 0355dz, teams(?x1523, ?x4369), ?x1348 = 01pv51 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 03by7wc; *> query: (?x4369, 039yzs) <- position(?x4369, ?x6848), position(?x9576, ?x6848), position(?x6128, ?x6848), ?x6128 = 0jm64, ?x9576 = 02qk2d5, team(?x8527, ?x4369), ?x8527 = 0b_6v_ *> conf = 0.70 ranks of expected_values: 2 EVAL 02pqcfz sport 039yzs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.786 http://example.org/sports/sports_team/sport #9232-03wh8kl PRED entity: 03wh8kl PRED relation: award PRED expected values: 03ccq3s => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 233): 03ccq3s (0.50 #199, 0.43 #604, 0.18 #11748), 09sb52 (0.43 #1661, 0.25 #8142, 0.24 #8547), 0fbtbt (0.37 #1448, 0.35 #3068, 0.35 #2258), 027gs1_ (0.21 #690, 0.18 #11748, 0.18 #11342), 0cqhk0 (0.16 #3241, 0.16 #17014, 0.13 #17825), 09qrn4 (0.16 #3241, 0.16 #17014, 0.13 #17825), 09qv3c (0.16 #3241, 0.16 #17014, 0.13 #17825), 09qvc0 (0.16 #3241, 0.16 #17014, 0.12 #27546), 09qvf4 (0.16 #3241, 0.16 #17014, 0.12 #27546), 09qs08 (0.16 #3241, 0.16 #17014, 0.12 #27546) >> Best rule #199 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02bvt; 0988cp; 03wh8pq; 06w58f; >> query: (?x7095, 03ccq3s) <- award_nominee(?x7095, ?x10011), award_nominee(?x7095, ?x201), ?x10011 = 02wk_43, ?x201 = 06j0md >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03wh8kl award 03ccq3s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9231-023gxx PRED entity: 023gxx PRED relation: films! PRED expected values: 06d4h => 72 concepts (30 used for prediction) PRED predicted values (max 10 best out of 66): 01cgz (0.40 #173, 0.04 #18, 0.02 #1895), 06d4h (0.12 #42, 0.10 #197, 0.06 #2390), 018jz (0.10 #196, 0.01 #821), 07s2s (0.08 #98, 0.04 #2446, 0.02 #3232), 081pw (0.07 #2351, 0.03 #2507, 0.03 #2822), 018w8 (0.07 #193, 0.01 #661), 01vq3 (0.06 #507, 0.06 #351, 0.04 #2388), 0bxg3 (0.06 #546, 0.06 #390, 0.01 #2427), 0fzyg (0.05 #2401, 0.04 #53, 0.03 #520), 05489 (0.05 #2399, 0.03 #988, 0.03 #674) >> Best rule #173 for best value: >> intensional similarity = 2 >> extensional distance = 28 >> proper extension: 01cgz; >> query: (?x3081, 01cgz) <- films(?x1083, ?x3081), athlete(?x1083, ?x445) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 030cx; *> query: (?x3081, 06d4h) <- nominated_for(?x2596, ?x3081), film(?x2596, ?x3532), ?x3532 = 04ydr95, award_nominee(?x2596, ?x100) *> conf = 0.12 ranks of expected_values: 2 EVAL 023gxx films! 06d4h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 30.000 0.400 http://example.org/film/film_subject/films #9230-0lk90 PRED entity: 0lk90 PRED relation: award PRED expected values: 02f77y => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 277): 09sb52 (0.37 #2061, 0.34 #2869, 0.30 #4081), 05pcn59 (0.35 #2909, 0.30 #4121, 0.27 #6545), 01by1l (0.30 #14252, 0.29 #13040, 0.29 #12232), 01c427 (0.25 #488, 0.17 #1296, 0.15 #3720), 02f72_ (0.25 #633, 0.15 #1441, 0.10 #7097), 02f5qb (0.25 #559, 0.14 #3791, 0.13 #2579), 02f77y (0.25 #665, 0.11 #1473, 0.10 #7129), 01bgqh (0.24 #12971, 0.23 #6911, 0.22 #20647), 03qbh5 (0.22 #3437, 0.21 #13133, 0.20 #5861), 05zr6wv (0.21 #2845, 0.18 #4057, 0.18 #2441) >> Best rule #2061 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 01rr9f; 09yrh; 0gn30; 06_bq1; >> query: (?x1093, 09sb52) <- award_winner(?x1093, ?x3602), celebrity(?x1093, ?x6035) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #665 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 02twdq; 016vn3; 01v27pl; *> query: (?x1093, 02f77y) <- artists(?x996, ?x1093), ?x996 = 0dn16 *> conf = 0.25 ranks of expected_values: 7 EVAL 0lk90 award 02f77y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 131.000 131.000 0.370 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9229-0ff2k PRED entity: 0ff2k PRED relation: influenced_by! PRED expected values: 0kp2_ => 157 concepts (110 used for prediction) PRED predicted values (max 10 best out of 499): 05ty4m (0.38 #5656, 0.33 #6682, 0.32 #8223), 017yfz (0.33 #158, 0.25 #1185, 0.25 #672), 01vsy95 (0.33 #121, 0.25 #1148, 0.25 #635), 01t07j (0.33 #60, 0.25 #1087, 0.25 #574), 05jm7 (0.29 #16053, 0.27 #17081, 0.15 #6301), 0c00lh (0.25 #2277, 0.21 #7412, 0.15 #3818), 018009 (0.25 #1196, 0.07 #4277, 0.06 #5818), 09qh1 (0.25 #645, 0.04 #21565, 0.01 #19127), 02nygk (0.25 #1021, 0.01 #19503, 0.01 #21558), 016_mj (0.21 #4163, 0.19 #6730, 0.19 #5704) >> Best rule #5656 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 05ty4m; 0pz91; 05jm7; 041c4; 01vb6z; 01hmk9; 0mb5x; 023w9s; >> query: (?x11598, 05ty4m) <- influenced_by(?x2465, ?x11598), written_by(?x3643, ?x11598), category(?x11598, ?x134), type_of_union(?x11598, ?x566) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #6431 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 06bng; *> query: (?x11598, 0kp2_) <- story_by(?x6078, ?x11598), production_companies(?x6078, ?x382), crewmember(?x6078, ?x3782), influenced_by(?x2465, ?x11598) *> conf = 0.05 ranks of expected_values: 194 EVAL 0ff2k influenced_by! 0kp2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 157.000 110.000 0.375 http://example.org/influence/influence_node/influenced_by #9228-026036 PRED entity: 026036 PRED relation: student PRED expected values: 05hrq4 0428bc => 87 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1637): 03ft8 (0.36 #4422, 0.20 #2339, 0.13 #6505), 018yj6 (0.33 #1523, 0.02 #14024, 0.02 #16108), 02qssrm (0.33 #1069, 0.02 #13570, 0.02 #15654), 0ff3y (0.27 #6226, 0.20 #8309, 0.20 #4143), 0405l (0.20 #3927, 0.18 #6010, 0.13 #8093), 01c7qd (0.20 #3757, 0.18 #5840, 0.13 #7923), 041_y (0.20 #3296, 0.18 #5379, 0.13 #7462), 01_6dw (0.20 #3207, 0.18 #5290, 0.13 #7373), 0gt3p (0.20 #3417, 0.18 #5500, 0.13 #7583), 03c6v3 (0.20 #3902, 0.18 #5985, 0.13 #8068) >> Best rule #4422 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 06kknt; >> query: (?x10386, 03ft8) <- student(?x10386, ?x10360), student(?x10386, ?x9156), executive_produced_by(?x1692, ?x10360), place_of_birth(?x10360, ?x739), place_of_death(?x10360, ?x1523), student(?x1200, ?x9156) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #14194 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 49 *> proper extension: 03p7gb; 05njyy; 02l1fn; 026ssfj; 021q2j; 01l8t8; 04_j5s; 036921; 09k9d0; 035gt8; ... *> query: (?x10386, 0428bc) <- school_type(?x10386, ?x13611), state_province_region(?x10386, ?x335), ?x335 = 059rby, school_type(?x5390, ?x13611), school_type(?x4824, ?x13611), student(?x4824, ?x1169), colors(?x5390, ?x3315) *> conf = 0.04 ranks of expected_values: 416, 989 EVAL 026036 student 0428bc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 28.000 0.364 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 026036 student 05hrq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 87.000 28.000 0.364 http://example.org/education/educational_institution/students_graduates./education/education/student #9227-02ryz24 PRED entity: 02ryz24 PRED relation: film! PRED expected values: 05qd_ => 74 concepts (41 used for prediction) PRED predicted values (max 10 best out of 46): 05qd_ (0.33 #157, 0.17 #305, 0.17 #9), 03xq0f (0.20 #301, 0.12 #79, 0.11 #450), 01795t (0.17 #18, 0.11 #166, 0.07 #314), 054g1r (0.17 #34, 0.11 #182, 0.06 #2899), 086k8 (0.16 #298, 0.15 #2264, 0.15 #2639), 016tw3 (0.14 #233, 0.13 #2725, 0.13 #381), 016tt2 (0.14 #226, 0.11 #2266, 0.11 #2564), 017s11 (0.14 #373, 0.12 #2265, 0.12 #299), 04mkft (0.12 #109, 0.11 #183, 0.09 #331), 0fqy4p (0.11 #176, 0.04 #250, 0.02 #398) >> Best rule #157 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 03h_yy; 0dzlbx; 063_j5; 03hp2y1; >> query: (?x2886, 05qd_) <- nominated_for(?x2373, ?x2886), ?x2373 = 016z2j, film(?x436, ?x2886), film_crew_role(?x2886, ?x137) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ryz24 film! 05qd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 41.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #9226-02scbv PRED entity: 02scbv PRED relation: honored_for! PRED expected values: 01mszz => 153 concepts (78 used for prediction) PRED predicted values (max 10 best out of 283): 074rg9 (0.85 #7425, 0.84 #6031, 0.83 #7269), 0946bb (0.85 #7425, 0.84 #6031, 0.83 #7269), 01771z (0.76 #2473, 0.75 #2317, 0.65 #2472), 01mszz (0.75 #724, 0.58 #2474, 0.52 #7891), 02scbv (0.58 #2474, 0.52 #7891, 0.38 #740), 0dfw0 (0.18 #1163, 0.13 #1627, 0.12 #2244), 01srq2 (0.17 #589, 0.04 #3065, 0.03 #4455), 01vfqh (0.17 #486, 0.04 #2962, 0.03 #4352), 0fdv3 (0.14 #1117, 0.13 #1581, 0.12 #2198), 042g97 (0.13 #1697, 0.10 #2314, 0.09 #2469) >> Best rule #7425 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 0g60z; 02xhpl; 0hv81; 0180mw; 0q9jk; >> query: (?x6918, ?x188) <- honored_for(?x6918, ?x188), award_winner(?x6918, ?x800), nominated_for(?x154, ?x6918), honored_for(?x2165, ?x6918) >> conf = 0.85 => this is the best rule for 2 predicted values *> Best rule #724 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 074rg9; *> query: (?x6918, 01mszz) <- honored_for(?x6918, ?x6746), genre(?x6918, ?x812), ?x812 = 01jfsb, ?x6746 = 059lwy *> conf = 0.75 ranks of expected_values: 4 EVAL 02scbv honored_for! 01mszz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 153.000 78.000 0.853 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #9225-02bj22 PRED entity: 02bj22 PRED relation: country PRED expected values: 09c7w0 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 83): 09c7w0 (0.84 #737, 0.82 #1353, 0.82 #1292), 07ssc (0.26 #1061, 0.22 #3280, 0.21 #3897), 0chghy (0.17 #74, 0.12 #135, 0.09 #319), 0345h (0.14 #702, 0.13 #3291, 0.13 #1009), 0d060g (0.11 #437, 0.10 #1299, 0.10 #1360), 0f8l9c (0.10 #4331, 0.09 #1740, 0.09 #5129), 03_3d (0.07 #866, 0.07 #1790, 0.06 #3949), 01hmnh (0.06 #3632, 0.06 #2833, 0.06 #3695), 03h64 (0.05 #721, 0.04 #414, 0.04 #1091), 03rjj (0.03 #2840, 0.03 #3085, 0.03 #3146) >> Best rule #737 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 0jqb8; >> query: (?x9193, 09c7w0) <- language(?x9193, ?x254), film(?x2564, ?x9193), genre(?x9193, ?x239), ?x239 = 06cvj >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bj22 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.836 http://example.org/film/film/country #9224-05p1dby PRED entity: 05p1dby PRED relation: nominated_for PRED expected values: 03qcfvw 0dc7hc 06bc59 => 47 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1807): 0419kt (0.68 #23250, 0.66 #21698, 0.65 #29450), 04fv5b (0.68 #23250, 0.66 #21698, 0.65 #29450), 03hkch7 (0.54 #3546, 0.21 #6643, 0.18 #8195), 07w8fz (0.54 #3547, 0.19 #8196, 0.18 #6644), 011yl_ (0.54 #3616, 0.19 #8265, 0.17 #9815), 07024 (0.54 #3520, 0.16 #20148, 0.16 #20146), 0_816 (0.54 #3568, 0.16 #20148, 0.16 #20146), 019vhk (0.54 #3502, 0.14 #20558, 0.13 #9701), 0yx_w (0.54 #4436, 0.11 #7533, 0.10 #10635), 0yzbg (0.54 #4168, 0.10 #7265, 0.10 #8817) >> Best rule #23250 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 06196; 0fqnzts; >> query: (?x2022, ?x351) <- award(?x6187, ?x2022), film(?x6187, ?x634), award(?x351, ?x2022) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 05f4m9q; 07bdd_; *> query: (?x2022, 03qcfvw) <- award(?x4397, ?x2022), nominated_for(?x2022, ?x5277), ?x4397 = 0gyx4, ?x5277 = 047csmy *> conf = 0.50 ranks of expected_values: 50, 1129, 1435 EVAL 05p1dby nominated_for 06bc59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 20.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05p1dby nominated_for 0dc7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 20.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05p1dby nominated_for 03qcfvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 47.000 20.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9223-0gxtknx PRED entity: 0gxtknx PRED relation: film_crew_role PRED expected values: 09zzb8 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 31): 09zzb8 (0.73 #667, 0.71 #890, 0.70 #519), 09vw2b7 (0.67 #7, 0.65 #673, 0.64 #525), 0dxtw (0.45 #677, 0.44 #529, 0.43 #307), 01vx2h (0.37 #678, 0.36 #530, 0.35 #345), 01pvkk (0.30 #902, 0.29 #679, 0.27 #531), 02rh1dz (0.21 #306, 0.20 #343, 0.19 #676), 02ynfr (0.19 #313, 0.18 #276, 0.17 #683), 0215hd (0.17 #20, 0.16 #94, 0.15 #464), 089g0h (0.17 #21, 0.14 #95, 0.12 #465), 01xy5l_ (0.17 #15, 0.11 #89, 0.10 #52) >> Best rule #667 for best value: >> intensional similarity = 4 >> extensional distance = 284 >> proper extension: 01q2nx; 04ynx7; >> query: (?x1602, 09zzb8) <- language(?x1602, ?x254), crewmember(?x1602, ?x5664), film_crew_role(?x1602, ?x468), film(?x494, ?x1602) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gxtknx film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.731 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9222-0138t4 PRED entity: 0138t4 PRED relation: contains! PRED expected values: 02jx1 => 186 concepts (75 used for prediction) PRED predicted values (max 10 best out of 313): 0978r (0.75 #64534, 0.71 #45699, 0.71 #53769), 09c7w0 (0.70 #62744, 0.66 #63641, 0.64 #60952), 02jx1 (0.69 #27872, 0.61 #56458, 0.61 #35850), 059rby (0.46 #34970, 0.39 #18848, 0.27 #56479), 01n7q (0.40 #5451, 0.29 #9036, 0.25 #3659), 02qkt (0.36 #16486, 0.29 #50179, 0.29 #20070), 05l5n (0.33 #7287, 0.33 #1016, 0.29 #9080), 0jt5zcn (0.33 #7307, 0.29 #9100, 0.25 #9997), 02j9z (0.33 #28, 0.18 #16168, 0.12 #20651), 04_1l0v (0.31 #56010, 0.31 #48834, 0.27 #59599) >> Best rule #64534 for best value: >> intensional similarity = 5 >> extensional distance = 190 >> proper extension: 0c5x_; 01p896; >> query: (?x10627, ?x3301) <- student(?x10627, ?x2648), state_province_region(?x10627, ?x3301), school_type(?x10627, ?x4994), film(?x2648, ?x4269), institution(?x1526, ?x10627) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #27872 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 0ymc8; *> query: (?x10627, 02jx1) <- currency(?x10627, ?x1099), organization(?x2361, ?x10627), ?x1099 = 01nv4h, contains(?x512, ?x10627) *> conf = 0.69 ranks of expected_values: 3 EVAL 0138t4 contains! 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 186.000 75.000 0.750 http://example.org/location/location/contains #9221-02qyntr PRED entity: 02qyntr PRED relation: award! PRED expected values: 04cy8rb 03qhyn8 => 64 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2353): 0bn3jg (0.81 #54053, 0.62 #77709, 0.20 #33774), 0hsmh (0.71 #16450, 0.60 #13072, 0.44 #26582), 0127m7 (0.60 #10781, 0.60 #4027, 0.57 #14159), 081lh (0.60 #10361, 0.60 #3607, 0.54 #37387), 0c12h (0.60 #11951, 0.57 #15329, 0.54 #38977), 02f93t (0.60 #12832, 0.57 #16210, 0.54 #39858), 0gs1_ (0.60 #12033, 0.57 #15411, 0.46 #39059), 06b_0 (0.60 #12355, 0.57 #15733, 0.46 #39381), 0184jw (0.60 #12395, 0.57 #15773, 0.46 #39421), 02hfp_ (0.60 #12459, 0.57 #15837, 0.46 #39485) >> Best rule #54053 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 09qwmm; 09qv_s; >> query: (?x6909, ?x3042) <- nominated_for(?x6909, ?x9452), nominated_for(?x6909, ?x1877), ?x9452 = 0c0zq, nominated_for(?x100, ?x1877), award_winner(?x6909, ?x3042) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #70954 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 136 *> proper extension: 026mg3; *> query: (?x6909, ?x72) <- nominated_for(?x6909, ?x1813), nominated_for(?x6909, ?x1386), nominated_for(?x72, ?x1813), nominated_for(?x1386, ?x1812) *> conf = 0.12 ranks of expected_values: 1000, 1455 EVAL 02qyntr award! 03qhyn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 23.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qyntr award! 04cy8rb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 23.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9220-058nh2 PRED entity: 058nh2 PRED relation: profession PRED expected values: 0dxtg 0kyk => 89 concepts (42 used for prediction) PRED predicted values (max 10 best out of 62): 0dxtg (0.84 #306, 0.83 #159, 0.82 #1482), 03gjzk (0.40 #895, 0.38 #1483, 0.38 #1924), 018gz8 (0.29 #897, 0.21 #2661, 0.14 #3249), 0cbd2 (0.28 #1623, 0.27 #1476, 0.26 #1917), 02krf9 (0.23 #2230, 0.22 #1789, 0.22 #2524), 09jwl (0.22 #1046, 0.21 #2369, 0.20 #2075), 0nbcg (0.21 #765, 0.20 #618, 0.16 #1059), 0np9r (0.17 #19, 0.15 #5605, 0.14 #901), 0kyk (0.14 #1057, 0.14 #1204, 0.13 #763), 01c72t (0.13 #2374, 0.13 #1051, 0.13 #2080) >> Best rule #306 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 0jf1b; 01q4qv; 013t9y; 06b_0; 0c921; >> query: (?x5273, 0dxtg) <- award(?x5273, ?x198), written_by(?x518, ?x5273), ?x198 = 040njc, nationality(?x5273, ?x512) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 058nh2 profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 89.000 42.000 0.840 http://example.org/people/person/profession EVAL 058nh2 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 42.000 0.840 http://example.org/people/person/profession #9219-06w839_ PRED entity: 06w839_ PRED relation: film_release_region PRED expected values: 05r4w 0k6nt 0345h 0h7x 01mjq => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 146): 09c7w0 (0.93 #4098, 0.93 #4239, 0.93 #6498), 06t2t (0.91 #1884, 0.85 #333, 0.84 #756), 05r4w (0.91 #849, 0.88 #990, 0.85 #1695), 07ssc (0.88 #1847, 0.83 #578, 0.81 #3121), 0345h (0.85 #1858, 0.84 #3132, 0.84 #2849), 02vzc (0.85 #324, 0.82 #2725, 0.81 #2866), 0k6nt (0.81 #2844, 0.80 #725, 0.79 #3127), 0d060g (0.77 #289, 0.76 #1840, 0.72 #3678), 0h7x (0.71 #450, 0.69 #309, 0.57 #591), 01mjq (0.71 #457, 0.62 #316, 0.60 #739) >> Best rule #4098 for best value: >> intensional similarity = 7 >> extensional distance = 287 >> proper extension: 0170z3; 09sh8k; 09xbpt; 0bvn25; 0czyxs; 034qrh; 060v34; 0cpllql; 0fgpvf; 026mfbr; ... >> query: (?x3088, 09c7w0) <- film_release_region(?x3088, ?x304), film(?x318, ?x3088), category(?x3088, ?x134), film_release_region(?x5016, ?x304), film_release_region(?x1518, ?x304), ?x1518 = 04w7rn, ?x5016 = 062zm5h >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #849 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 30 *> proper extension: 0gtv7pk; 02x3lt7; 0gj8t_b; 0gtvrv3; 0bq8tmw; 0cc7hmk; 09k56b7; 0407yfx; 02yvct; 0bby9p5; ... *> query: (?x3088, 05r4w) <- film_release_region(?x3088, ?x344), film_release_region(?x3088, ?x304), film(?x6190, ?x3088), category(?x3088, ?x134), ?x304 = 0d0vqn, ?x344 = 04gzd, award_winner(?x6190, ?x3446) *> conf = 0.91 ranks of expected_values: 3, 5, 7, 9, 10 EVAL 06w839_ film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 78.000 78.000 0.934 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06w839_ film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 78.000 78.000 0.934 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06w839_ film_release_region 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 78.000 78.000 0.934 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06w839_ film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 78.000 0.934 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06w839_ film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.934 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9218-0bh8x1y PRED entity: 0bh8x1y PRED relation: film_festivals PRED expected values: 0gg7gsl => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 18): 0bmj62v (0.11 #51, 0.09 #251, 0.04 #151), 03nn7l2 (0.11 #56, 0.04 #256, 0.02 #156), 03wf1p2 (0.10 #253, 0.01 #313, 0.01 #173), 04_m9gk (0.10 #252, 0.05 #52, 0.04 #332), 0kfhjq0 (0.08 #245, 0.05 #165, 0.05 #205), 04grdgy (0.08 #248, 0.05 #48, 0.03 #188), 0gg7gsl (0.08 #241, 0.05 #161, 0.05 #201), 0j63cyr (0.07 #243, 0.05 #163, 0.05 #203), 09rwjly (0.07 #247, 0.05 #47, 0.02 #147), 05f5rsr (0.06 #250) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 07s3m4g; >> query: (?x4668, 0bmj62v) <- genre(?x4668, ?x53), film(?x10629, ?x4668), ?x10629 = 0fvppk, film_release_region(?x4668, ?x87) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 238 *> proper extension: 0h3y; 042rnl; 02z13jg; 03kwtb; 01_vfy; 05whq_9; 01q4qv; 01ycck; 01f7v_; 01c6l; ... *> query: (?x4668, 0gg7gsl) <- film_festivals(?x4668, ?x7988), film_festivals(?x9501, ?x7988), language(?x9501, ?x90), film_release_region(?x9501, ?x87) *> conf = 0.08 ranks of expected_values: 7 EVAL 0bh8x1y film_festivals 0gg7gsl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 86.000 86.000 0.105 http://example.org/film/film/film_festivals #9217-04sylm PRED entity: 04sylm PRED relation: student PRED expected values: 01lw3kh 01wwnh2 => 105 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1412): 01wyq0w (0.33 #1500, 0.02 #34860, 0.02 #9840), 02vntj (0.07 #4872, 0.06 #2787, 0.04 #6957), 0ff3y (0.06 #16657, 0.04 #41677, 0.03 #54187), 0gs1_ (0.06 #3215, 0.04 #5300, 0.03 #9470), 016fjj (0.06 #2678, 0.02 #29783, 0.02 #4763), 0306ds (0.04 #4578, 0.04 #15003, 0.04 #6663), 015wc0 (0.04 #5858, 0.04 #7943, 0.03 #30878), 01l1rw (0.04 #5166, 0.04 #7251, 0.03 #13506), 03rs8y (0.04 #4216, 0.04 #6301, 0.03 #12556), 021bk (0.04 #4523, 0.03 #14948, 0.03 #2438) >> Best rule #1500 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 031n5b; >> query: (?x2767, 01wyq0w) <- student(?x2767, ?x5556), student(?x2767, ?x5356), major_field_of_study(?x2767, ?x505), ?x5356 = 06h2w, gender(?x5556, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04sylm student 01wwnh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 67.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 04sylm student 01lw3kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 67.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #9216-02dr9j PRED entity: 02dr9j PRED relation: titles! PRED expected values: 0h9qh => 87 concepts (47 used for prediction) PRED predicted values (max 10 best out of 65): 07s9rl0 (0.40 #1, 0.35 #1648, 0.35 #102), 01jfsb (0.40 #20, 0.35 #102, 0.21 #1030), 03k9fj (0.35 #102, 0.26 #3194, 0.23 #1236), 02kdv5l (0.35 #102, 0.21 #1030, 0.19 #4532), 02l7c8 (0.35 #102, 0.21 #1030, 0.19 #4532), 0jdm8 (0.35 #102, 0.21 #1030, 0.19 #4532), 0g092b (0.35 #102, 0.21 #1030, 0.19 #4532), 03q4nz (0.35 #102, 0.21 #1030, 0.19 #4532), 04xvlr (0.33 #932, 0.26 #727, 0.24 #1651), 01hmnh (0.29 #1160, 0.24 #3118, 0.21 #2497) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0571m; 02d478; 0gy0n; >> query: (?x7214, 07s9rl0) <- genre(?x7214, ?x53), award_winner(?x7214, ?x2891), film_crew_role(?x7214, ?x137), ?x2891 = 01xcfy >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2225 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 288 *> proper extension: 02vw1w2; *> query: (?x7214, 0h9qh) <- genre(?x7214, ?x811), ?x811 = 03k9fj, film_release_distribution_medium(?x7214, ?x81) *> conf = 0.01 ranks of expected_values: 65 EVAL 02dr9j titles! 0h9qh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 87.000 47.000 0.400 http://example.org/media_common/netflix_genre/titles #9215-047rgpy PRED entity: 047rgpy PRED relation: profession! PRED expected values: 01trhmt 0415mzy => 27 concepts (15 used for prediction) PRED predicted values (max 10 best out of 4182): 02fybl (0.67 #19278, 0.67 #15043, 0.50 #6571), 09889g (0.67 #18545, 0.67 #14310, 0.50 #5838), 03f7m4h (0.67 #19692, 0.67 #15457, 0.40 #11222), 02dbp7 (0.67 #14169, 0.56 #18404, 0.50 #21178), 02l840 (0.67 #12908, 0.56 #17143, 0.50 #4436), 03f1zhf (0.67 #15929, 0.56 #20164, 0.50 #7457), 01vsy7t (0.67 #14185, 0.56 #18420, 0.40 #9950), 0473q (0.67 #15068, 0.56 #19303, 0.40 #10833), 0ddkf (0.67 #14938, 0.56 #19173, 0.40 #10703), 02cx90 (0.67 #14077, 0.56 #18312, 0.40 #9842) >> Best rule #19278 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 05sxg2; 01c72t; >> query: (?x12718, 02fybl) <- profession(?x10539, ?x12718), profession(?x6792, ?x12718), profession(?x140, ?x12718), ?x10539 = 028qyn, student(?x7545, ?x6792), award_winner(?x6792, ?x1751), location(?x140, ?x1523) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #10284 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 02dsz; *> query: (?x12718, 0415mzy) <- profession(?x10539, ?x12718), profession(?x6268, ?x12718), award(?x10539, ?x724), ?x6268 = 026yqrr, organizations_founded(?x10539, ?x8489) *> conf = 0.40 ranks of expected_values: 288, 294 EVAL 047rgpy profession! 0415mzy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 15.000 0.667 http://example.org/people/person/profession EVAL 047rgpy profession! 01trhmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 15.000 0.667 http://example.org/people/person/profession #9214-0z4_0 PRED entity: 0z4_0 PRED relation: teams PRED expected values: 03y9p40 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 213): 0jm6n (0.20 #87, 0.03 #1882, 0.01 #3319), 05g3v (0.20 #40, 0.03 #1835, 0.01 #3272), 0bwjj (0.20 #216, 0.02 #3088, 0.01 #3448), 0j2zj (0.20 #210, 0.02 #3082, 0.01 #3442), 02wvfxl (0.20 #101, 0.02 #2973, 0.01 #3333), 01d5z (0.20 #18, 0.02 #2890, 0.01 #3250), 04cxw5b (0.14 #525, 0.12 #884, 0.08 #1243), 0cqt41 (0.03 #2543, 0.03 #2902, 0.03 #3622), 0jnlm (0.03 #2146, 0.03 #2505, 0.02 #2864), 026dqjm (0.03 #2104, 0.03 #2463, 0.02 #2822) >> Best rule #87 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0k049; 01cx_; 0f2tj; >> query: (?x13702, 0jm6n) <- place_of_birth(?x9038, ?x13702), award_nominee(?x690, ?x9038), organization(?x9038, ?x10424), gender(?x9038, ?x514) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0z4_0 teams 03y9p40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 158.000 0.200 http://example.org/sports/sports_team_location/teams #9213-02r0csl PRED entity: 02r0csl PRED relation: award! PRED expected values: 0k4p0 => 47 concepts (15 used for prediction) PRED predicted values (max 10 best out of 739): 0hfzr (0.80 #4420, 0.67 #6426, 0.53 #7429), 017jd9 (0.50 #1456, 0.47 #6473, 0.44 #10488), 0c0zq (0.50 #4902, 0.47 #6908, 0.33 #7911), 0209hj (0.50 #4076, 0.47 #6082, 0.30 #7085), 07s846j (0.50 #4406, 0.43 #7415, 0.40 #6412), 0pv3x (0.50 #1109, 0.40 #7129, 0.40 #6126), 05hjnw (0.50 #1493, 0.40 #4504, 0.33 #6510), 0mcl0 (0.50 #4387, 0.40 #6393, 0.29 #5390), 0cq806 (0.50 #4858, 0.40 #6864, 0.25 #1847), 04j4tx (0.50 #1411, 0.36 #5425, 0.30 #4422) >> Best rule #4420 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0gq_v; 0gr4k; 04dn09n; 019f4v; 0gr0m; 0gq9h; 02qyntr; >> query: (?x143, 0hfzr) <- nominated_for(?x143, ?x11429), nominated_for(?x143, ?x2376), ?x2376 = 042y1c, award(?x251, ?x143), ?x11429 = 0_9l_ >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2572 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 02g3v6; 02hsq3m; 02z0dfh; *> query: (?x143, 0k4p0) <- nominated_for(?x143, ?x5213), nominated_for(?x143, ?x1496), award(?x251, ?x143), ?x5213 = 01s3vk, film(?x7815, ?x1496) *> conf = 0.17 ranks of expected_values: 238 EVAL 02r0csl award! 0k4p0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 47.000 15.000 0.800 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9212-0cbd2 PRED entity: 0cbd2 PRED relation: profession! PRED expected values: 01yk13 0lk90 05prs8 04xjp 0f4vbz 01_x6v 03r1pr 01m65sp 085pr 03hnd 0p_47 04yt7 049qx 04107 051wwp 06hmd 04crrxr 06jrhz 0g5ff 06hx2 05cgy8 0kp2_ 0210f1 025vldk 0f_y9 0b7gxq 05lnk0 0h0yt 01w9ph_ 01j5sd 06bng 01zwy 02y49 063tn 0c4y8 010xjr 01c7qd 0fxky3 085q5 02784z 0h336 03j90 01vdrw 042kg 085gk 01h2_6 08141d 063b4k 06101p 01kym3 09jd9 => 39 concepts (24 used for prediction) PRED predicted values (max 10 best out of 3709): 026dx (0.78 #57856, 0.62 #54089, 0.54 #22602), 021yw7 (0.67 #38659, 0.60 #61265, 0.56 #57496), 01vsy7t (0.67 #42755, 0.60 #35221, 0.54 #22602), 0l12d (0.67 #41840, 0.60 #34306, 0.50 #30540), 01tv3x2 (0.67 #43348, 0.60 #35814, 0.50 #32048), 0zjpz (0.67 #41928, 0.60 #34394, 0.48 #60276), 02fybl (0.67 #43516, 0.60 #35982, 0.44 #58585), 014q2g (0.67 #42168, 0.60 #34634, 0.44 #57237), 03bnv (0.67 #42323, 0.60 #34789, 0.44 #41438), 01vrkdt (0.67 #42522, 0.60 #34988, 0.44 #41438) >> Best rule #57856 for best value: >> intensional similarity = 10 >> extensional distance = 7 >> proper extension: 01c72t; >> query: (?x353, 026dx) <- profession(?x9930, ?x353), profession(?x7613, ?x353), profession(?x6236, ?x353), profession(?x3867, ?x353), profession(?x523, ?x353), award_nominee(?x100, ?x9930), instrumentalists(?x227, ?x3867), ?x523 = 06cv1, participant(?x6236, ?x338), award_nominee(?x7613, ?x709) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #38741 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 018gz8; *> query: (?x353, 0p_47) <- profession(?x9930, ?x353), profession(?x6399, ?x353), profession(?x6236, ?x353), profession(?x3867, ?x353), ?x9930 = 06l9n8, artists(?x1000, ?x3867), award_winner(?x2897, ?x6399), award_winner(?x8965, ?x6236) *> conf = 0.67 ranks of expected_values: 76, 103, 183, 224, 240, 243, 255, 257, 258, 259, 275, 276, 277, 278, 280, 281, 298, 303, 372, 413, 580, 607, 631, 645, 653, 711, 802, 837, 972, 1051, 1217, 1467, 1508, 1584, 1740, 1775, 1783, 1984, 1988, 2097, 2243, 2365, 2367, 3317, 3436, 3475, 3598 EVAL 0cbd2 profession! 09jd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 01kym3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 06101p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 063b4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 08141d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 01h2_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 085gk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 042kg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 01vdrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 03j90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0h336 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 02784z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 085q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0fxky3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 01c7qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 010xjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0c4y8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 063tn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 02y49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 01zwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 06bng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 01j5sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 01w9ph_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0h0yt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 05lnk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0b7gxq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0f_y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 025vldk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0210f1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0kp2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 05cgy8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 06hx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0g5ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 06jrhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 04crrxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 06hmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 051wwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 04107 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 049qx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 04yt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0p_47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 03hnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 085pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 01m65sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 03r1pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 01_x6v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0f4vbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 04xjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 05prs8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 0lk90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 24.000 0.778 http://example.org/people/person/profession EVAL 0cbd2 profession! 01yk13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 24.000 0.778 http://example.org/people/person/profession #9211-06z6r PRED entity: 06z6r PRED relation: sports! PRED expected values: 018wrk 0ldqf => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 25): 0jdk_ (0.90 #702, 0.87 #625, 0.85 #579), 0kbvb (0.85 #571, 0.83 #548, 0.82 #524), 0l998 (0.84 #664, 0.76 #164, 0.76 #662), 0nbjq (0.84 #664, 0.76 #164, 0.76 #662), 0sxrz (0.76 #164, 0.76 #662, 0.76 #660), 018wrk (0.76 #164, 0.76 #662, 0.76 #660), 06sks6 (0.76 #164, 0.76 #662, 0.76 #660), 0kbws (0.67 #713, 0.61 #70, 0.50 #187), 0ldqf (0.67 #372, 0.64 #514, 0.62 #490), 09n48 (0.61 #70, 0.57 #379, 0.50 #187) >> Best rule #702 for best value: >> intensional similarity = 32 >> extensional distance = 18 >> proper extension: 07_53; >> query: (?x4045, 0jdk_) <- country(?x4045, ?x8420), country(?x4045, ?x7032), country(?x4045, ?x304), country(?x4045, ?x291), sports(?x391, ?x4045), ?x391 = 0l6vl, olympics(?x291, ?x2966), adjustment_currency(?x291, ?x170), film_release_region(?x10860, ?x304), film_release_region(?x7379, ?x304), film_release_region(?x6480, ?x304), film_release_region(?x5270, ?x304), film_release_region(?x4545, ?x304), film_release_region(?x4514, ?x304), film_release_region(?x3986, ?x304), film_release_region(?x634, ?x304), ?x4545 = 05p09dd, country(?x5168, ?x304), ?x10860 = 049w1q, nationality(?x2083, ?x304), adjoins(?x291, ?x9035), ?x6480 = 02825cv, ?x5270 = 0bc1yhb, ?x634 = 0gx9rvq, ?x7379 = 032clf, ?x2966 = 06sks6, form_of_government(?x7032, ?x48), ?x3986 = 0jymd, contains(?x8420, ?x8838), olympics(?x4045, ?x775), ?x4514 = 06tpmy, administrative_parent(?x7032, ?x551) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #164 for first EXPECTED value: *> intensional similarity = 39 *> extensional distance = 2 *> proper extension: 07gyv; *> query: (?x4045, ?x358) <- country(?x4045, ?x4752), country(?x4045, ?x3912), country(?x4045, ?x3016), country(?x4045, ?x1925), country(?x4045, ?x1122), country(?x4045, ?x429), country(?x4045, ?x291), ?x291 = 0h3y, ?x3016 = 0697s, ?x3912 = 04w58, country(?x343, ?x1122), geographic_distribution(?x13008, ?x1122), ?x13008 = 04mvp8, sports(?x358, ?x4045), second_level_divisions(?x429, ?x1788), film_release_region(?x8682, ?x429), film_release_region(?x8193, ?x429), film_release_region(?x6520, ?x429), film_release_region(?x6095, ?x429), film_release_region(?x5576, ?x429), film_release_region(?x4430, ?x429), film_release_region(?x3292, ?x429), film_release_region(?x1080, ?x429), film_release_region(?x1002, ?x429), film_release_region(?x385, ?x429), ?x5576 = 0gbfn9, ?x385 = 0ds3t5x, ?x6095 = 0bq6ntw, ?x8193 = 03z9585, ?x3292 = 0gvs1kt, ?x8682 = 0bmfnjs, ?x1002 = 0_b3d, ?x4430 = 043sct5, ?x6520 = 02bg55, film_release_region(?x124, ?x1122), countries_within(?x6956, ?x1122), ?x1080 = 01c22t, jurisdiction_of_office(?x3444, ?x1925), form_of_government(?x4752, ?x48) *> conf = 0.76 ranks of expected_values: 6, 9 EVAL 06z6r sports! 0ldqf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 43.000 43.000 0.900 http://example.org/user/jg/default_domain/olympic_games/sports EVAL 06z6r sports! 018wrk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 43.000 43.000 0.900 http://example.org/user/jg/default_domain/olympic_games/sports #9210-0ddkf PRED entity: 0ddkf PRED relation: inductee! PRED expected values: 0g2c8 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 3): 0g2c8 (0.36 #10, 0.31 #1, 0.20 #55), 06szd3 (0.03 #201, 0.03 #237, 0.03 #174), 0qjfl (0.02 #21, 0.02 #184, 0.02 #57) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 04r1t; 07yg2; 05xq9; 07m4c; 0qmny; >> query: (?x6877, 0g2c8) <- artists(?x284, ?x6877), influenced_by(?x5345, ?x6877), artist(?x3265, ?x6877) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ddkf inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.365 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #9209-0ply0 PRED entity: 0ply0 PRED relation: origin! PRED expected values: 04gycf => 198 concepts (78 used for prediction) PRED predicted values (max 10 best out of 651): 019g40 (0.33 #58, 0.10 #1084, 0.06 #13937), 012xdf (0.20 #32907, 0.20 #31877, 0.20 #37540), 02s2wq (0.20 #32907, 0.20 #37540, 0.18 #5650), 018ndc (0.20 #1144, 0.17 #1657, 0.11 #3200), 04gycf (0.18 #17480), 0j1yf (0.18 #17480), 01d1st (0.17 #1836, 0.11 #3379, 0.10 #1323), 01wf86y (0.12 #2382, 0.05 #3924, 0.05 #3411), 01vvyc_ (0.12 #2298, 0.05 #3840, 0.05 #3327), 04n2vgk (0.11 #9664, 0.09 #5546, 0.06 #2464) >> Best rule #58 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02xry; >> query: (?x3373, 019g40) <- origin(?x10091, ?x3373), origin(?x4842, ?x3373), ?x4842 = 0hvbj, instrumentalists(?x212, ?x10091), artists(?x302, ?x10091) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #17480 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 0r3tb; 014kj2; *> query: (?x3373, ?x1896) <- origin(?x4842, ?x3373), award(?x4842, ?x724), group(?x1896, ?x4842), artists(?x671, ?x4842) *> conf = 0.18 ranks of expected_values: 5 EVAL 0ply0 origin! 04gycf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 198.000 78.000 0.333 http://example.org/music/artist/origin #9208-099flj PRED entity: 099flj PRED relation: nominated_for PRED expected values: 0bmhvpr 026qnh6 05zy3sc => 51 concepts (4 used for prediction) PRED predicted values (max 10 best out of 1136): 0g9lm2 (0.78 #3812, 0.67 #2232, 0.33 #651), 0j8f09z (0.78 #6323, 0.77 #6322, 0.17 #2990), 0462hhb (0.67 #3891, 0.67 #2311, 0.33 #730), 04qw17 (0.67 #3422, 0.67 #1842, 0.33 #261), 095zlp (0.67 #1634, 0.44 #3214, 0.33 #53), 011yhm (0.67 #2602, 0.44 #4182, 0.33 #1021), 02qr69m (0.67 #1934, 0.44 #3514, 0.33 #353), 02mt51 (0.67 #2178, 0.44 #3758, 0.33 #597), 089j8p (0.56 #4167, 0.50 #2587, 0.33 #1006), 021y7yw (0.50 #1930, 0.44 #3510, 0.33 #349) >> Best rule #3812 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 0fq9zdn; 05zvq6g; 0fq9zdv; >> query: (?x11466, 0g9lm2) <- nominated_for(?x11466, ?x7009), nominated_for(?x11466, ?x3292), ?x7009 = 0bs8s1p, film_release_region(?x3292, ?x1229), film_release_region(?x3292, ?x151), ?x1229 = 059j2, ?x151 = 0b90_r >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2584 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 09qwmm; 02pqp12; 099cng; *> query: (?x11466, 05zy3sc) <- nominated_for(?x11466, ?x7009), nominated_for(?x11466, ?x825), ?x7009 = 0bs8s1p, ceremony(?x11466, ?x1442), edited_by(?x825, ?x826), executive_produced_by(?x825, ?x2135) *> conf = 0.33 ranks of expected_values: 94, 260, 545 EVAL 099flj nominated_for 05zy3sc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 51.000 4.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099flj nominated_for 026qnh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 4.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099flj nominated_for 0bmhvpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 51.000 4.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9207-03j6c PRED entity: 03j6c PRED relation: religion! PRED expected values: 05kr_ => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1455): 01n4w (0.67 #1784, 0.62 #1171, 0.61 #2191), 0rh6k (0.67 #511, 0.58 #716, 0.57 #613), 05fjf (0.67 #589, 0.57 #691, 0.56 #1202), 07z1m (0.67 #535, 0.57 #637, 0.50 #1148), 026mj (0.67 #593, 0.57 #695, 0.50 #287), 05kkh (0.62 #1739, 0.57 #2146, 0.56 #1126), 0vmt (0.62 #1752, 0.57 #2159, 0.56 #1139), 02xry (0.57 #1778, 0.56 #1165, 0.52 #2185), 01x73 (0.57 #1767, 0.56 #1154, 0.52 #2174), 0d0x8 (0.57 #1785, 0.56 #1172, 0.52 #2192) >> Best rule #1784 for best value: >> intensional similarity = 10 >> extensional distance = 19 >> proper extension: 058x5; 0631_; 01y0s9; 019cr; 021_0p; 04pk9; 05w5d; >> query: (?x8967, 01n4w) <- religion(?x8829, ?x8967), religion(?x7504, ?x8967), religion(?x512, ?x8967), people(?x5025, ?x7504), profession(?x8829, ?x131), gender(?x7504, ?x514), contains(?x512, ?x362), service_location(?x555, ?x512), jurisdiction_of_office(?x1328, ?x512), film_release_region(?x66, ?x512) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #749 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 10 *> proper extension: 042s9; *> query: (?x8967, 05kr_) <- religion(?x8975, ?x8967), religion(?x8829, ?x8967), religion(?x7039, ?x8967), religion(?x94, ?x8967), diet(?x8829, ?x3130), profession(?x8829, ?x131), influenced_by(?x5335, ?x7039), student(?x2313, ?x5335), location(?x8975, ?x6250) *> conf = 0.50 ranks of expected_values: 25 EVAL 03j6c religion! 05kr_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 33.000 33.000 0.667 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #9206-01csrl PRED entity: 01csrl PRED relation: award PRED expected values: 0gqyl => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 233): 0bdw1g (0.72 #37987, 0.71 #13335, 0.68 #25861), 0cqhk0 (0.42 #37, 0.15 #16200, 0.14 #12967), 09sb52 (0.33 #15800, 0.30 #6506, 0.30 #20244), 09qvf4 (0.29 #210, 0.06 #13140, 0.05 #16373), 0cqhmg (0.29 #362, 0.05 #6827, 0.04 #10060), 0gkts9 (0.26 #168, 0.06 #13098, 0.05 #16331), 0gqy2 (0.25 #2992, 0.24 #1780, 0.20 #3396), 0f4x7 (0.23 #1647, 0.23 #2859, 0.20 #2455), 0ck27z (0.22 #16255, 0.21 #17063, 0.21 #17871), 05pcn59 (0.20 #8163, 0.20 #9779, 0.19 #7759) >> Best rule #37987 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 099ks0; 06lxn; >> query: (?x2417, ?x686) <- award_winner(?x686, ?x2417), award(?x1343, ?x686) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #8187 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 379 *> proper extension: 06cv1; 01hxs4; 035gjq; 01b9ck; 04bpm6; 01t07j; 0721cy; 04cbtrw; 01271h; 01vsykc; ... *> query: (?x2417, 0gqyl) <- nominated_for(?x2417, ?x9951), nationality(?x2417, ?x94), award(?x2417, ?x757), participant(?x2416, ?x2417) *> conf = 0.11 ranks of expected_values: 29 EVAL 01csrl award 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 115.000 115.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9205-01ycfv PRED entity: 01ycfv PRED relation: location PRED expected values: 02_286 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 185): 02_286 (0.18 #4863, 0.17 #9690, 0.13 #1646), 04lh6 (0.14 #436, 0.08 #1240, 0.04 #2045), 0cr3d (0.14 #145, 0.05 #3362, 0.05 #14625), 01n7q (0.14 #63, 0.05 #6498, 0.04 #867), 030qb3t (0.13 #36281, 0.11 #32260, 0.11 #38694), 04jpl (0.11 #4826, 0.10 #9653, 0.10 #56306), 0cc56 (0.08 #9710, 0.07 #4883, 0.06 #7296), 059rby (0.07 #16, 0.05 #6451, 0.04 #4037), 0rw2x (0.07 #717, 0.04 #1521, 0.02 #2326), 03b12 (0.07 #519, 0.04 #1323, 0.02 #2128) >> Best rule #4863 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 0969fd; >> query: (?x9408, 02_286) <- award_winner(?x646, ?x9408), student(?x2909, ?x9408), people(?x4322, ?x9408) >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ycfv location 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.179 http://example.org/people/person/places_lived./people/place_lived/location #9204-07gghl PRED entity: 07gghl PRED relation: production_companies PRED expected values: 046b0s => 126 concepts (86 used for prediction) PRED predicted values (max 10 best out of 78): 01gb54 (0.20 #119, 0.20 #37, 0.12 #1351), 05nn2c (0.20 #110, 0.20 #28, 0.05 #357), 05qd_ (0.20 #9, 0.14 #173, 0.14 #1159), 016tt2 (0.20 #85, 0.14 #167, 0.12 #250), 0c41qv (0.20 #137, 0.14 #219, 0.06 #1123), 054lpb6 (0.17 #507, 0.14 #178, 0.14 #753), 016tw3 (0.14 #175, 0.14 #1325, 0.12 #2566), 09tlc8 (0.14 #229), 0kx4m (0.12 #337, 0.09 #911, 0.05 #1158), 030_1_ (0.12 #263, 0.06 #3566, 0.06 #3151) >> Best rule #119 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 026f__m; >> query: (?x6627, 01gb54) <- honored_for(?x6627, ?x7728), honored_for(?x6627, ?x887), ?x887 = 04tc1g, country(?x6627, ?x94), film(?x3054, ?x7728) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #926 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 018nnz; 02n72k; *> query: (?x6627, 046b0s) <- nominated_for(?x6627, ?x886), executive_produced_by(?x6627, ?x521), film(?x1538, ?x6627), award_nominee(?x1538, ?x286) *> conf = 0.06 ranks of expected_values: 25 EVAL 07gghl production_companies 046b0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 126.000 86.000 0.200 http://example.org/film/film/production_companies #9203-04rjg PRED entity: 04rjg PRED relation: major_field_of_study! PRED expected values: 07wjk 01vc5m 02183k 07vfj 01f1r4 07t90 04hgpt 0677j => 119 concepts (89 used for prediction) PRED predicted values (max 10 best out of 533): 01w5m (0.71 #9111, 0.70 #13383, 0.69 #15760), 01nnsv (0.67 #7759, 0.50 #5384, 0.45 #13931), 07wjk (0.62 #15717, 0.60 #6219, 0.57 #9068), 09kvv (0.60 #6204, 0.50 #5254, 0.50 #4779), 03qdm (0.60 #6055, 0.50 #5580, 0.36 #14127), 080z7 (0.60 #5861, 0.40 #6336, 0.36 #13933), 03fgm (0.57 #9359, 0.50 #5560, 0.50 #5085), 086xm (0.57 #9093, 0.50 #13365, 0.50 #4819), 0gjv_ (0.56 #11576, 0.50 #4928, 0.50 #3978), 0lfgr (0.55 #13804, 0.50 #14754, 0.50 #7632) >> Best rule #9111 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 01jzxy; >> query: (?x2014, 01w5m) <- major_field_of_study(?x732, ?x2014), major_field_of_study(?x5941, ?x2014), major_field_of_study(?x2999, ?x2014), major_field_of_study(?x734, ?x2014), ?x5941 = 017v71, student(?x2999, ?x164) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #15717 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 11 *> proper extension: 0193x; *> query: (?x2014, 07wjk) <- major_field_of_study(?x8221, ?x2014), major_field_of_study(?x4390, ?x2014), major_field_of_study(?x3044, ?x2014), major_field_of_study(?x2999, ?x2014), major_field_of_study(?x734, ?x2014), ?x2999 = 07tg4, company(?x12441, ?x4390), major_field_of_study(?x481, ?x8221), currency(?x4390, ?x1099), currency(?x3044, ?x170) *> conf = 0.62 ranks of expected_values: 3, 12, 17, 22, 56, 58, 101, 150 EVAL 04rjg major_field_of_study! 0677j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 119.000 89.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rjg major_field_of_study! 04hgpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 119.000 89.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rjg major_field_of_study! 07t90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 119.000 89.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rjg major_field_of_study! 01f1r4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 119.000 89.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rjg major_field_of_study! 07vfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 119.000 89.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rjg major_field_of_study! 02183k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 119.000 89.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rjg major_field_of_study! 01vc5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 119.000 89.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rjg major_field_of_study! 07wjk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 89.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #9202-01518s PRED entity: 01518s PRED relation: artist! PRED expected values: 09zcbg => 81 concepts (77 used for prediction) PRED predicted values (max 10 best out of 121): 03rhqg (0.84 #5338, 0.20 #3798, 0.20 #6321), 01trtc (0.50 #214, 0.29 #1894, 0.25 #1334), 033hn8 (0.33 #1414, 0.33 #14, 0.26 #2256), 023rwm (0.33 #2, 0.26 #2244, 0.25 #1402), 011k1h (0.33 #10, 0.18 #990, 0.17 #3232), 0n85g (0.33 #64, 0.18 #1044, 0.17 #1464), 03mp8k (0.33 #68, 0.14 #1888, 0.12 #628), 0g768 (0.29 #1998, 0.29 #2560, 0.25 #3820), 015_1q (0.27 #1140, 0.22 #860, 0.22 #4362), 04t53l (0.25 #568, 0.25 #148, 0.18 #988) >> Best rule #5338 for best value: >> intensional similarity = 8 >> extensional distance = 138 >> proper extension: 013pk3; >> query: (?x12506, 03rhqg) <- artist(?x11715, ?x12506), artist(?x11715, ?x7188), artist(?x11715, ?x379), ?x7188 = 0gr69, award(?x379, ?x4892), artists(?x378, ?x379), group(?x227, ?x379), ?x4892 = 02f72_ >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #398 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 048tgl; *> query: (?x12506, 09zcbg) <- artists(?x13652, ?x12506), artists(?x9248, ?x12506), artists(?x3753, ?x12506), artists(?x1572, ?x12506), ?x9248 = 02t8gf, ?x1572 = 06by7, ?x3753 = 01_bkd, parent_genre(?x13652, ?x8747), artists(?x8747, ?x7972), parent_genre(?x8747, ?x301), category(?x12506, ?x134), ?x7972 = 0326tc *> conf = 0.17 ranks of expected_values: 21 EVAL 01518s artist! 09zcbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 81.000 77.000 0.843 http://example.org/music/record_label/artist #9201-09r9m7 PRED entity: 09r9m7 PRED relation: type_of_union PRED expected values: 04ztj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.76 #65, 0.75 #77, 0.75 #73), 01g63y (0.37 #353, 0.13 #138, 0.11 #266) >> Best rule #65 for best value: >> intensional similarity = 3 >> extensional distance = 239 >> proper extension: 01h4rj; >> query: (?x5772, 04ztj) <- award_winner(?x1443, ?x5772), place_of_death(?x5772, ?x739), nominated_for(?x1443, ?x155) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09r9m7 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.763 http://example.org/people/person/spouse_s./people/marriage/type_of_union #9200-0c3xpwy PRED entity: 0c3xpwy PRED relation: titles! PRED expected values: 07c52 => 75 concepts (60 used for prediction) PRED predicted values (max 10 best out of 96): 07c52 (0.73 #1373, 0.69 #1268, 0.68 #1478), 07s9rl0 (0.27 #6155, 0.26 #5222, 0.26 #5948), 04xvlr (0.27 #6155, 0.25 #417, 0.22 #1558), 01hmnh (0.27 #6155, 0.25 #130, 0.17 #233), 07ssc (0.27 #6155, 0.25 #113, 0.17 #216), 01z77k (0.27 #6155, 0.17 #267, 0.10 #1509), 017fp (0.25 #437, 0.10 #1578, 0.09 #5245), 03mqtr (0.25 #459, 0.06 #1600, 0.05 #5993), 09b3v (0.24 #979, 0.03 #2123, 0.03 #1186), 024qqx (0.18 #1217, 0.18 #2154, 0.15 #3520) >> Best rule #1373 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 01qn7n; 05sy2k_; 099pks; 08l0x2; 01yb1y; 06r4f; 01_2n; 06qxh; 02gl58; 02py9yf; ... >> query: (?x5663, 07c52) <- genre(?x5663, ?x4205), program(?x11526, ?x5663), genre(?x4021, ?x4205), ?x4021 = 0btbyn >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c3xpwy titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 60.000 0.733 http://example.org/media_common/netflix_genre/titles #9199-066yfh PRED entity: 066yfh PRED relation: profession PRED expected values: 01d_h8 => 77 concepts (36 used for prediction) PRED predicted values (max 10 best out of 53): 01d_h8 (0.73 #6, 0.70 #882, 0.67 #1174), 02hrh1q (0.73 #1329, 0.68 #3813, 0.67 #4544), 0dxtg (0.68 #13, 0.61 #889, 0.61 #1181), 0cbd2 (0.28 #3214, 0.28 #5263, 0.28 #5262), 0n1h (0.28 #3214, 0.28 #5263, 0.28 #5262), 012t_z (0.28 #3214, 0.28 #5263, 0.28 #5262), 020xn5 (0.28 #3214, 0.28 #5263, 0.28 #5262), 0dz3r (0.28 #3214, 0.28 #5263, 0.28 #5262), 05sxg2 (0.28 #3214, 0.28 #5263, 0.28 #5262), 047rgpy (0.28 #3214, 0.28 #5263, 0.28 #5262) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 165 >> proper extension: 02qjj7; 03p01x; >> query: (?x12274, 01d_h8) <- profession(?x12274, ?x1041), profession(?x12274, ?x524), ?x524 = 02jknp, ?x1041 = 03gjzk >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 066yfh profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 36.000 0.731 http://example.org/people/person/profession #9198-01k_r5b PRED entity: 01k_r5b PRED relation: award_nominee! PRED expected values: 02fn5r => 103 concepts (43 used for prediction) PRED predicted values (max 10 best out of 796): 01lmj3q (0.82 #23222, 0.82 #69650, 0.81 #69649), 02cx90 (0.82 #23222, 0.82 #69650, 0.81 #69649), 01k_r5b (0.67 #3557, 0.56 #1234, 0.29 #83585), 02f1c (0.29 #83585, 0.28 #4647, 0.27 #76617), 01l03w2 (0.29 #83585, 0.28 #4647, 0.27 #76617), 01x15dc (0.29 #83585, 0.28 #4647, 0.27 #76617), 0m_v0 (0.29 #83585, 0.28 #4647, 0.27 #76617), 015882 (0.29 #83585, 0.28 #4647, 0.27 #76617), 0bhvtc (0.29 #83585, 0.28 #4647, 0.27 #76617), 026ps1 (0.29 #83585, 0.28 #4647, 0.27 #76617) >> Best rule #23222 for best value: >> intensional similarity = 3 >> extensional distance = 303 >> proper extension: 03d9d6; 0cbm64; >> query: (?x5265, ?x4239) <- award_nominee(?x5265, ?x4239), instrumentalists(?x227, ?x5265), award_nominee(?x4239, ?x565) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #83585 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1012 *> proper extension: 014l4w; 0b_dh; *> query: (?x5265, ?x367) <- award_winner(?x5265, ?x7258), award_winner(?x367, ?x7258), award_winner(?x1480, ?x5265) *> conf = 0.29 ranks of expected_values: 15 EVAL 01k_r5b award_nominee! 02fn5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 103.000 43.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9197-02lf0c PRED entity: 02lf0c PRED relation: profession PRED expected values: 01d_h8 => 119 concepts (100 used for prediction) PRED predicted values (max 10 best out of 79): 01d_h8 (0.89 #3262, 0.89 #5485, 0.87 #3855), 02hrh1q (0.87 #8752, 0.82 #9048, 0.75 #9344), 0dxtg (0.73 #457, 0.72 #3269, 0.69 #5344), 0q04f (0.45 #543, 0.44 #247, 0.40 #395), 0cbd2 (0.40 #303, 0.33 #155, 0.31 #1931), 018gz8 (0.36 #608, 0.23 #2384, 0.21 #2236), 02hv44_ (0.33 #205, 0.30 #353, 0.27 #501), 02krf9 (0.33 #3727, 0.31 #1062, 0.30 #4616), 01c72t (0.30 #319, 0.22 #171, 0.20 #23), 0kyk (0.27 #1953, 0.22 #3137, 0.22 #2989) >> Best rule #3262 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 04b19t; >> query: (?x595, 01d_h8) <- film(?x595, ?x8788), film_crew_role(?x8788, ?x137), produced_by(?x1311, ?x595) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lf0c profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 100.000 0.891 http://example.org/people/person/profession #9196-08952r PRED entity: 08952r PRED relation: genre PRED expected values: 0ltv => 79 concepts (42 used for prediction) PRED predicted values (max 10 best out of 91): 07s9rl0 (0.65 #3131, 0.64 #3010, 0.59 #3373), 01z4y (0.59 #3130, 0.58 #963, 0.50 #3251), 03k9fj (0.38 #251, 0.24 #1215, 0.24 #1696), 01jfsb (0.35 #3263, 0.33 #1336, 0.32 #2900), 02l7c8 (0.33 #2302, 0.33 #2662, 0.33 #136), 0lsxr (0.29 #248, 0.19 #1332, 0.19 #489), 06cvj (0.21 #2289, 0.21 #2529, 0.21 #2409), 04xvlr (0.19 #3011, 0.18 #3132, 0.16 #3495), 01t_vv (0.18 #1018, 0.16 #2221, 0.16 #2701), 01hmnh (0.17 #258, 0.16 #1222, 0.16 #1823) >> Best rule #3131 for best value: >> intensional similarity = 4 >> extensional distance = 694 >> proper extension: 01fs__; >> query: (?x4304, 07s9rl0) <- award_winner(?x4304, ?x2101), nominated_for(?x1336, ?x4304), language(?x4304, ?x254), titles(?x2480, ?x4304) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08952r genre 0ltv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 42.000 0.645 http://example.org/film/film/genre #9195-0c0zq PRED entity: 0c0zq PRED relation: nominated_for! PRED expected values: 02x1dht 02pqp12 0gs9p 02w9sd7 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 170): 019f4v (0.68 #1517, 0.67 #1516, 0.67 #8226), 0f4x7 (0.68 #1517, 0.67 #1516, 0.67 #8226), 02qvyrt (0.68 #1517, 0.67 #1516, 0.67 #8226), 03hl6lc (0.68 #1517, 0.67 #1516, 0.67 #8226), 027b9ly (0.68 #1517, 0.67 #1516, 0.67 #8226), 02w_6xj (0.68 #1517, 0.67 #1516, 0.67 #8226), 027b9j5 (0.68 #1517, 0.67 #1516, 0.67 #8226), 0gs9p (0.34 #1350, 0.33 #699, 0.32 #1134), 0gq_v (0.27 #1314, 0.26 #1098, 0.26 #1748), 0gqy2 (0.24 #1185, 0.23 #1401, 0.22 #1835) >> Best rule #1517 for best value: >> intensional similarity = 3 >> extensional distance = 511 >> proper extension: 02rq7nd; >> query: (?x9452, ?x1162) <- award(?x9452, ?x1162), honored_for(?x1084, ?x9452), nominated_for(?x1162, ?x144) >> conf = 0.68 => this is the best rule for 7 predicted values *> Best rule #1350 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 511 *> proper extension: 02rq7nd; *> query: (?x9452, 0gs9p) <- award(?x9452, ?x1162), honored_for(?x1084, ?x9452), nominated_for(?x1162, ?x144) *> conf = 0.34 ranks of expected_values: 8, 14, 34, 66 EVAL 0c0zq nominated_for! 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 73.000 73.000 0.683 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0c0zq nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 73.000 73.000 0.683 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0c0zq nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 73.000 73.000 0.683 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0c0zq nominated_for! 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 73.000 73.000 0.683 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9194-0hmm7 PRED entity: 0hmm7 PRED relation: nominated_for! PRED expected values: 0p9sw => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 200): 027c95y (0.67 #2108, 0.66 #21312, 0.66 #19672), 02rdyk7 (0.67 #2108, 0.66 #21312, 0.66 #19672), 0gs9p (0.61 #2639, 0.60 #1232, 0.44 #764), 0k611 (0.53 #1241, 0.51 #2648, 0.50 #773), 040njc (0.51 #2584, 0.45 #1177, 0.42 #943), 0f4x7 (0.43 #2603, 0.41 #1196, 0.36 #1899), 0gq_v (0.43 #2832, 0.42 #722, 0.41 #2363), 0p9sw (0.39 #2598, 0.38 #1191, 0.33 #957), 0gr0m (0.37 #2635, 0.36 #1228, 0.31 #5912), 0gqy2 (0.36 #1289, 0.36 #2696, 0.33 #1055) >> Best rule #2108 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 08cfr1; >> query: (?x2047, ?x1587) <- titles(?x600, ?x2047), award(?x2047, ?x1587), film(?x2046, ?x2047), film_production_design_by(?x2047, ?x6096) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #2598 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 81 *> proper extension: 07bz5; *> query: (?x2047, 0p9sw) <- award_winner(?x2047, ?x1119), list(?x2047, ?x3004), award(?x2047, ?x1587) *> conf = 0.39 ranks of expected_values: 8 EVAL 0hmm7 nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 129.000 129.000 0.666 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9193-04rvy8 PRED entity: 04rvy8 PRED relation: place_of_death PRED expected values: 030qb3t => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 27): 0f2wj (0.14 #12, 0.04 #1184, 0.04 #3519), 030qb3t (0.08 #3529, 0.05 #802, 0.05 #9179), 0k049 (0.04 #1175, 0.04 #2151, 0.03 #1761), 04jpl (0.04 #592, 0.04 #202, 0.03 #585), 02_286 (0.04 #208, 0.03 #1771, 0.03 #2355), 0281rp (0.04 #314, 0.02 #704), 06_kh (0.04 #200, 0.02 #2735, 0.01 #785), 03l2n (0.04 #261), 0fhp9 (0.04 #209), 04llb (0.03 #535) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 05g8ky; >> query: (?x9357, 0f2wj) <- type_of_union(?x9357, ?x566), location_of_ceremony(?x9357, ?x957), ?x957 = 0r62v, ?x566 = 04ztj >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #3529 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 334 *> proper extension: 0bbxx9b; 0b6mgp_; *> query: (?x9357, 030qb3t) <- award(?x9357, ?x1307), award_nominee(?x8645, ?x9357), nominated_for(?x1307, ?x6030), ?x6030 = 0sxgv *> conf = 0.08 ranks of expected_values: 2 EVAL 04rvy8 place_of_death 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 108.000 0.143 http://example.org/people/deceased_person/place_of_death #9192-02kxx1 PRED entity: 02kxx1 PRED relation: institution! PRED expected values: 016t_3 => 185 concepts (185 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.85 #47, 0.66 #425, 0.65 #625), 019v9k (0.67 #52, 0.64 #430, 0.60 #452), 02_xgp2 (0.65 #56, 0.55 #434, 0.54 #167), 016t_3 (0.58 #48, 0.52 #70, 0.49 #137), 07s6fsf (0.52 #45, 0.38 #423, 0.37 #134), 04zx3q1 (0.43 #46, 0.37 #1247, 0.33 #135), 027f2w (0.38 #53, 0.32 #164, 0.31 #98), 0bjrnt (0.37 #1247, 0.18 #50, 0.18 #428), 013zdg (0.31 #96, 0.30 #51, 0.29 #185), 01rr_d (0.23 #60, 0.20 #105, 0.19 #438) >> Best rule #47 for best value: >> intensional similarity = 6 >> extensional distance = 58 >> proper extension: 027mdh; >> query: (?x11870, 02h4rq6) <- organization(?x5510, ?x11870), institution(?x4981, ?x11870), institution(?x1526, ?x11870), ?x1526 = 0bkj86, school_type(?x11870, ?x3092), ?x4981 = 03bwzr4 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 58 *> proper extension: 027mdh; *> query: (?x11870, 016t_3) <- organization(?x5510, ?x11870), institution(?x4981, ?x11870), institution(?x1526, ?x11870), ?x1526 = 0bkj86, school_type(?x11870, ?x3092), ?x4981 = 03bwzr4 *> conf = 0.58 ranks of expected_values: 4 EVAL 02kxx1 institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 185.000 185.000 0.850 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9191-019fz PRED entity: 019fz PRED relation: profession PRED expected values: 03sbb => 123 concepts (74 used for prediction) PRED predicted values (max 10 best out of 101): 04gc2 (0.81 #2633, 0.25 #5804, 0.22 #617), 02hrh1q (0.69 #10391, 0.60 #1310, 0.54 #4479), 039v1 (0.62 #8536, 0.18 #2340, 0.16 #1908), 09jwl (0.53 #8519, 0.37 #2323, 0.33 #2755), 035y33 (0.51 #9509, 0.02 #2711, 0.01 #5882), 05snw (0.50 #233, 0.46 #953, 0.21 #5763), 01d_h8 (0.48 #1734, 0.46 #1590, 0.44 #438), 0dxtg (0.48 #9377, 0.47 #6640, 0.45 #6352), 012t_z (0.46 #2460, 0.43 #6495, 0.30 #1740), 0nbcg (0.37 #8531, 0.29 #2335, 0.24 #2767) >> Best rule #2633 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 083p7; 083pr; 01jqr_5; 0d06m5; 01n1gc; 0fwy0h; 0dq2k; 012gx2; 06hx2; 06bss; ... >> query: (?x12258, 04gc2) <- profession(?x12258, ?x9682), profession(?x13098, ?x9682), type_of_union(?x12258, ?x11744), ?x13098 = 042fk >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #5763 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 01w5n51; *> query: (?x12258, ?x353) <- peers(?x12258, ?x5978), influenced_by(?x5978, ?x5254), influenced_by(?x5254, ?x7251), profession(?x5254, ?x353) *> conf = 0.21 ranks of expected_values: 24 EVAL 019fz profession 03sbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 123.000 74.000 0.810 http://example.org/people/person/profession #9190-04q24zv PRED entity: 04q24zv PRED relation: film_crew_role PRED expected values: 09vw2b7 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 29): 09vw2b7 (0.64 #1244, 0.63 #1208, 0.58 #1684), 01vx2h (0.50 #845, 0.48 #736, 0.29 #1249), 0dxtw (0.43 #10, 0.41 #844, 0.38 #1248), 01pvkk (0.30 #483, 0.30 #411, 0.29 #1214), 0215hd (0.25 #91, 0.25 #418, 0.22 #309), 02rh1dz (0.21 #734, 0.20 #843, 0.10 #1247), 015h31 (0.20 #733, 0.20 #842, 0.07 #1062), 0d2b38 (0.19 #860, 0.17 #751, 0.15 #98), 089g0h (0.18 #419, 0.17 #92, 0.16 #310), 02ynfr (0.18 #1254, 0.16 #1218, 0.15 #850) >> Best rule #1244 for best value: >> intensional similarity = 4 >> extensional distance = 578 >> proper extension: 07kb7vh; >> query: (?x2797, 09vw2b7) <- nominated_for(?x533, ?x2797), film(?x1414, ?x2797), film_crew_role(?x2797, ?x137), ?x137 = 09zzb8 >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04q24zv film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.636 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9189-02725hs PRED entity: 02725hs PRED relation: film_crew_role PRED expected values: 0dxtw 0d2b38 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 29): 0dxtw (0.71 #8, 0.54 #68, 0.52 #1273), 01pvkk (0.43 #129, 0.40 #669, 0.40 #489), 02rh1dz (0.29 #7, 0.22 #37, 0.22 #1272), 015h31 (0.24 #1574, 0.23 #1271, 0.12 #3479), 02ynfr (0.22 #42, 0.21 #1277, 0.20 #1580), 0215hd (0.20 #1582, 0.17 #1279, 0.17 #1826), 0d2b38 (0.19 #1589, 0.16 #471, 0.15 #81), 089g0h (0.17 #1583, 0.14 #1280, 0.14 #1006), 02vs3x5 (0.14 #19, 0.12 #3479, 0.11 #49), 089fss (0.12 #3479, 0.11 #34, 0.10 #394) >> Best rule #8 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 04tng0; >> query: (?x2289, 0dxtw) <- genre(?x2289, ?x3515), language(?x2289, ?x90), ?x3515 = 082gq, nominated_for(?x2289, ?x2288), film(?x166, ?x2289), film_crew_role(?x2289, ?x137) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 02725hs film_crew_role 0d2b38 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 125.000 125.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02725hs film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9188-09h_q PRED entity: 09h_q PRED relation: location PRED expected values: 06pr6 => 181 concepts (174 used for prediction) PRED predicted values (max 10 best out of 251): 06pr6 (0.25 #1142, 0.12 #101679, 0.09 #2742), 0156q (0.25 #87, 0.06 #4087, 0.05 #8090), 01ly5m (0.25 #147, 0.04 #16954, 0.03 #12152), 030qb3t (0.24 #67336, 0.23 #72938, 0.22 #77739), 0ky0b (0.18 #32021, 0.03 #72055, 0.03 #87262), 06y9v (0.17 #1755, 0.06 #5758, 0.05 #8158), 0c_m3 (0.14 #3469, 0.06 #5872, 0.04 #17876), 04vmp (0.13 #31572, 0.03 #78009, 0.02 #67606), 04jpl (0.12 #4017, 0.11 #72873, 0.10 #77674), 04ych (0.12 #4052, 0.02 #35280, 0.02 #23263) >> Best rule #1142 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05wh0sh; >> query: (?x8080, 06pr6) <- student(?x12475, ?x8080), influenced_by(?x1092, ?x8080), ?x12475 = 02_jjm, profession(?x8080, ?x563) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09h_q location 06pr6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 174.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #9187-01r_t_ PRED entity: 01r_t_ PRED relation: people! PRED expected values: 0m32h => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 38): 01n3bm (0.20 #43, 0.09 #109, 0.05 #175), 01mtqf (0.20 #4, 0.09 #70, 0.03 #268), 01l2m3 (0.20 #16, 0.04 #214, 0.03 #1864), 02y0js (0.18 #68, 0.06 #1916, 0.05 #3302), 0gk4g (0.13 #604, 0.13 #2254, 0.13 #472), 0dq9p (0.09 #1931, 0.07 #1271, 0.07 #1799), 04p3w (0.09 #209, 0.07 #473, 0.06 #1265), 0qcr0 (0.08 #1255, 0.08 #595, 0.08 #133), 07jwr (0.06 #141, 0.02 #1989, 0.02 #1659), 01_qc_ (0.04 #226, 0.03 #160, 0.03 #1876) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03nyts; >> query: (?x6146, 01n3bm) <- profession(?x6146, ?x524), place_of_death(?x6146, ?x9559), ?x9559 = 07dfk, location(?x6146, ?x13893) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1277 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 239 *> proper extension: 0dky9n; *> query: (?x6146, 0m32h) <- award_winner(?x6147, ?x6146), nominated_for(?x6147, ?x2163), place_of_death(?x6146, ?x9559) *> conf = 0.04 ranks of expected_values: 11 EVAL 01r_t_ people! 0m32h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 140.000 140.000 0.200 http://example.org/people/cause_of_death/people #9186-011yrp PRED entity: 011yrp PRED relation: produced_by PRED expected values: 081lh => 120 concepts (64 used for prediction) PRED predicted values (max 10 best out of 208): 01tt43d (0.38 #13572, 0.34 #21724, 0.30 #11243), 0jz9f (0.19 #3879, 0.13 #3878, 0.11 #1163), 04pqqb (0.14 #5818, 0.13 #3878, 0.12 #13184), 03qhyn8 (0.14 #5818, 0.13 #3878, 0.12 #13184), 06pj8 (0.13 #3557, 0.05 #1230, 0.03 #10922), 02jkkv (0.13 #3878, 0.11 #1163, 0.10 #18620), 02q_cc (0.11 #3523, 0.05 #3134, 0.04 #5851), 0grrq8 (0.10 #4818, 0.07 #5594, 0.06 #7145), 0bwh6 (0.08 #1210, 0.06 #7028, 0.04 #4701), 054_mz (0.08 #1955, 0.06 #791, 0.04 #2343) >> Best rule #13572 for best value: >> intensional similarity = 4 >> extensional distance = 260 >> proper extension: 0d1qmz; 015g28; 016ztl; 0bz3jx; 0291hr; >> query: (?x303, ?x6426) <- films(?x326, ?x303), film(?x6426, ?x303), language(?x303, ?x90), genre(?x303, ?x53) >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 011yrp produced_by 081lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 64.000 0.379 http://example.org/film/film/produced_by #9185-016m5c PRED entity: 016m5c PRED relation: award PRED expected values: 01d38t => 116 concepts (92 used for prediction) PRED predicted values (max 10 best out of 306): 01ckcd (0.51 #11273, 0.50 #338, 0.47 #12488), 01by1l (0.40 #2948, 0.38 #14693, 0.38 #3758), 01bgqh (0.40 #2878, 0.38 #3688, 0.37 #1663), 02f5qb (0.38 #11092, 0.37 #10687, 0.35 #12307), 01ckrr (0.38 #638, 0.24 #13193, 0.23 #5093), 02f716 (0.37 #13948, 0.37 #10708, 0.31 #1393), 054ks3 (0.37 #1763, 0.29 #3788, 0.27 #5003), 02f77l (0.35 #11192, 0.35 #14027, 0.34 #10787), 02f6yz (0.35 #11256, 0.34 #10851, 0.33 #321), 01c9jp (0.35 #11126, 0.33 #191, 0.33 #12341) >> Best rule #11273 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0150jk; 0dtd6; 01vrwfv; 01rm8b; 0mgcr; 013w2r; 01q99h; 081wh1; 07r1_; 01jcxwp; ... >> query: (?x12228, 01ckcd) <- group(?x227, ?x12228), artists(?x1000, ?x12228), artist(?x11912, ?x12228), award(?x12228, ?x11068) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #11267 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 0150jk; 0dtd6; 01vrwfv; 01rm8b; 0mgcr; 013w2r; 01q99h; 081wh1; 07r1_; 01jcxwp; ... *> query: (?x12228, 01d38t) <- group(?x227, ?x12228), artists(?x1000, ?x12228), artist(?x11912, ?x12228), award(?x12228, ?x11068) *> conf = 0.35 ranks of expected_values: 11 EVAL 016m5c award 01d38t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 116.000 92.000 0.514 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9184-0kbn5 PRED entity: 0kbn5 PRED relation: nationality PRED expected values: 09c7w0 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 92): 09c7w0 (0.87 #601, 0.83 #501, 0.82 #201), 03rjj (0.40 #4955, 0.40 #4853, 0.38 #4245), 0d060g (0.40 #4955, 0.40 #4853, 0.38 #4245), 0n5dt (0.34 #802, 0.33 #2020, 0.32 #1313), 05fjf (0.34 #802, 0.33 #2020, 0.32 #1313), 02jx1 (0.12 #2461, 0.09 #1752, 0.09 #5490), 07ssc (0.11 #1734, 0.10 #2443, 0.10 #1329), 03rk0 (0.05 #5101, 0.05 #1664, 0.05 #5001), 0345h (0.04 #1242, 0.04 #1038, 0.03 #2356), 0chghy (0.04 #110, 0.01 #5356, 0.01 #2426) >> Best rule #601 for best value: >> intensional similarity = 4 >> extensional distance = 208 >> proper extension: 0dky9n; 05218gr; 01l3j; >> query: (?x9653, 09c7w0) <- place_of_death(?x9653, ?x10988), contains(?x12221, ?x10988), time_zones(?x10988, ?x2674), county(?x1214, ?x12221) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kbn5 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.867 http://example.org/people/person/nationality #9183-015cxv PRED entity: 015cxv PRED relation: artist! PRED expected values: 02swsm => 70 concepts (42 used for prediction) PRED predicted values (max 10 best out of 126): 015_1q (0.39 #1582, 0.33 #162, 0.29 #2150), 043g7l (0.33 #316, 0.33 #174, 0.14 #458), 033hn8 (0.33 #14, 0.29 #440, 0.19 #1718), 027f3ys (0.33 #250, 0.17 #392, 0.08 #960), 015kg1 (0.33 #21, 0.04 #1583, 0.03 #1867), 03mp8k (0.29 #494, 0.17 #352, 0.11 #2624), 0n85g (0.29 #490, 0.16 #1342, 0.15 #2194), 0g768 (0.26 #1316, 0.25 #890, 0.18 #748), 03rhqg (0.25 #1010, 0.24 #1862, 0.24 #1436), 01dtcb (0.22 #616, 0.19 #1468, 0.19 #2604) >> Best rule #1582 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 01qkqwg; >> query: (?x6635, 015_1q) <- artists(?x3108, ?x6635), artists(?x1572, ?x6635), ?x3108 = 02w4v, ?x1572 = 06by7, award_winner(?x2704, ?x6635) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #4220 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 396 *> proper extension: 03c7ln; 0c7ct; 07_3qd; 013v5j; 01tp5bj; 02wb6yq; 06nv27; 04cr6qv; 03xnq9_; 01wgjj5; ... *> query: (?x6635, 02swsm) <- artists(?x3108, ?x6635), artists(?x3108, ?x7018), artists(?x3108, ?x2824), artists(?x3108, ?x1322), ?x7018 = 01sxd1, ?x2824 = 02w4fkq, instrumentalists(?x227, ?x1322) *> conf = 0.03 ranks of expected_values: 94 EVAL 015cxv artist! 02swsm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 70.000 42.000 0.391 http://example.org/music/record_label/artist #9182-01lv85 PRED entity: 01lv85 PRED relation: nominated_for! PRED expected values: 02kxwk => 79 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1053): 05zbm4 (0.60 #35003, 0.53 #18671, 0.52 #30336), 04h07s (0.54 #14003, 0.51 #16338, 0.46 #11668), 05gnf (0.27 #25671, 0.20 #16337, 0.19 #67675), 050023 (0.21 #71, 0.08 #11740, 0.07 #14075), 06pj8 (0.16 #5101, 0.07 #432, 0.06 #2766), 026dcvf (0.14 #72, 0.08 #11741, 0.07 #14076), 02778pf (0.14 #155, 0.08 #4824, 0.06 #2489), 0p_2r (0.14 #282, 0.08 #4951, 0.06 #2616), 026dd2b (0.14 #1898, 0.07 #15902, 0.07 #13567), 026dg51 (0.14 #176, 0.07 #11845, 0.05 #14180) >> Best rule #35003 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 01f39b; >> query: (?x7511, ?x879) <- actor(?x7511, ?x879), titles(?x2008, ?x7511), award_winner(?x8870, ?x879), film(?x879, ?x1255) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #58338 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 164 *> proper extension: 02gjrc; *> query: (?x7511, ?x201) <- nominated_for(?x2016, ?x7511), genre(?x7511, ?x258), award_winner(?x2016, ?x201) *> conf = 0.03 ranks of expected_values: 304 EVAL 01lv85 nominated_for! 02kxwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 79.000 44.000 0.604 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9181-040_t PRED entity: 040_t PRED relation: influenced_by PRED expected values: 014dq7 02kz_ => 179 concepts (66 used for prediction) PRED predicted values (max 10 best out of 335): 042q3 (0.50 #3363, 0.50 #1218, 0.12 #21485), 032l1 (0.50 #948, 0.43 #3951, 0.33 #3093), 0bk5r (0.50 #1019, 0.17 #3164, 0.12 #5311), 02wh0 (0.42 #3381, 0.33 #1236, 0.25 #1665), 06myp (0.42 #3373, 0.17 #1228, 0.12 #2086), 048cl (0.38 #1516, 0.25 #1945, 0.17 #4520), 034bs (0.38 #1834, 0.25 #1405, 0.06 #4722), 015n8 (0.33 #1264, 0.25 #3409, 0.12 #2122), 03sbs (0.33 #3220, 0.22 #4508, 0.17 #1075), 084w8 (0.29 #3865, 0.20 #5581, 0.20 #5154) >> Best rule #3363 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0453t; 0jcx; 06whf; 0c5tl; 0bk5r; 06c44; 04hcw; 0c1fs; 07dnx; 04xm_; >> query: (?x6319, 042q3) <- influenced_by(?x6319, ?x2240), profession(?x6319, ?x353), student(?x3439, ?x6319), ?x2240 = 0j3v >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1886 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0d4jl; 058vp; 01vdrw; *> query: (?x6319, 02kz_) <- influenced_by(?x6319, ?x2240), profession(?x6319, ?x11999), ?x11999 = 015btn, religion(?x6319, ?x4641) *> conf = 0.25 ranks of expected_values: 14, 144 EVAL 040_t influenced_by 02kz_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 179.000 66.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 040_t influenced_by 014dq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 179.000 66.000 0.500 http://example.org/influence/influence_node/influenced_by #9180-066m4g PRED entity: 066m4g PRED relation: nationality PRED expected values: 09c7w0 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 16): 09c7w0 (0.82 #101, 0.81 #401, 0.74 #901), 0d060g (0.32 #5411, 0.31 #2404, 0.31 #4909), 02jx1 (0.15 #333, 0.09 #833, 0.09 #4441), 07ssc (0.11 #315, 0.08 #6226, 0.08 #4123), 03rk0 (0.06 #7258, 0.05 #7560, 0.05 #7760), 0chghy (0.03 #310, 0.02 #810, 0.02 #2414), 03_3d (0.02 #1607, 0.01 #7318, 0.01 #7820), 03rjj (0.02 #405, 0.02 #2108, 0.02 #2409), 0d04z6 (0.02 #271), 0jgd (0.02 #202) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 034x61; 021vwt; 02zyy4; 08m4c8; 09f0bj; 08yx9q; 0k2mxq; 095b70; 050zr4; >> query: (?x849, 09c7w0) <- award_nominee(?x849, ?x8167), place_of_birth(?x849, ?x5771), ?x8167 = 01_njt >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 066m4g nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.818 http://example.org/people/person/nationality #9179-01vrz41 PRED entity: 01vrz41 PRED relation: participant! PRED expected values: 01vsgrn => 94 concepts (59 used for prediction) PRED predicted values (max 10 best out of 298): 01pcvn (0.78 #7622, 0.03 #7367, 0.02 #1653), 01vsl3_ (0.46 #1909, 0.44 #3816, 0.35 #6987), 0bmh4 (0.46 #1909, 0.44 #3816, 0.35 #6987), 01wgxtl (0.08 #829, 0.04 #2738, 0.02 #9718), 0gs6vr (0.08 #1066, 0.03 #3610, 0.02 #1702), 0bdxs5 (0.08 #1167, 0.03 #3711, 0.02 #6882), 07g2v (0.08 #245, 0.03 #9771), 01vsgrn (0.08 #1006, 0.02 #9895, 0.02 #7356), 06mt91 (0.08 #1079, 0.02 #7429, 0.02 #2988), 02bc74 (0.08 #1264, 0.01 #7614) >> Best rule #7622 for best value: >> intensional similarity = 2 >> extensional distance = 150 >> proper extension: 0p3r8; 06tp4h; 02jyhv; 09nhvw; 0cgfb; 01pgk0; >> query: (?x1231, ?x2647) <- artist(?x1954, ?x1231), participant(?x1231, ?x2647) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1006 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 0dvqq; 01vw20_; 04qmr; 0dl567; 05vzw3; 03y82t6; 0kr_t; 0415mzy; 0dw4g; 01vs73g; ... *> query: (?x1231, 01vsgrn) <- award_nominee(?x1231, ?x4593), ?x4593 = 0478__m, artists(?x671, ?x1231) *> conf = 0.08 ranks of expected_values: 8 EVAL 01vrz41 participant! 01vsgrn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 94.000 59.000 0.776 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #9178-018db8 PRED entity: 018db8 PRED relation: film PRED expected values: 0jjy0 046488 => 107 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1213): 0_9l_ (0.22 #1732, 0.05 #8876, 0.04 #3518), 011yqc (0.20 #2018, 0.04 #100027, 0.03 #130395), 0cz_ym (0.16 #2079, 0.04 #100027, 0.03 #130395), 0btbyn (0.12 #2446, 0.04 #100027, 0.03 #130395), 016ywb (0.11 #1235, 0.08 #3021, 0.02 #19096), 01shy7 (0.11 #421, 0.08 #3993, 0.07 #30785), 03bzjpm (0.11 #1312, 0.07 #12028, 0.07 #6670), 0pc62 (0.11 #94, 0.05 #5452, 0.04 #10810), 02qzh2 (0.11 #691, 0.04 #66087, 0.04 #14289), 09g8vhw (0.11 #324, 0.04 #66087, 0.04 #55370) >> Best rule #1732 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0d3k14; >> query: (?x793, 0_9l_) <- celebrity(?x1424, ?x793), sibling(?x793, ?x11259), award_winner(?x451, ?x793) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #100027 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1215 *> proper extension: 0dbpyd; 0jz9f; 0520r2x; 0cb77r; 02bfmn; 0l8v5; 03ckxdg; 026dcvf; 054_mz; 02lf0c; ... *> query: (?x793, ?x253) <- award_nominee(?x262, ?x793), award_winner(?x1077, ?x793), nominated_for(?x262, ?x253) *> conf = 0.04 ranks of expected_values: 352 EVAL 018db8 film 046488 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 107.000 80.000 0.222 http://example.org/film/actor/film./film/performance/film EVAL 018db8 film 0jjy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 80.000 0.222 http://example.org/film/actor/film./film/performance/film #9177-06q83 PRED entity: 06q83 PRED relation: major_field_of_study! PRED expected values: 016t_3 03bwzr4 => 51 concepts (46 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.88 #290, 0.86 #266, 0.77 #636), 016t_3 (0.82 #355, 0.78 #155, 0.71 #264), 02_xgp2 (0.78 #164, 0.70 #364, 0.62 #297), 03bwzr4 (0.67 #165, 0.64 #274, 0.61 #365), 04zx3q1 (0.64 #263, 0.62 #287, 0.61 #354), 0bkj86 (0.57 #269, 0.56 #293, 0.56 #160), 07s6fsf (0.53 #308, 0.41 #44, 0.39 #285), 022h5x (0.53 #308, 0.39 #285, 0.34 #309), 03mkk4 (0.50 #55, 0.41 #44, 0.33 #11), 028dcg (0.50 #62, 0.41 #44, 0.33 #18) >> Best rule #290 for best value: >> intensional similarity = 11 >> extensional distance = 14 >> proper extension: 04g51; 0299ct; >> query: (?x9444, 014mlp) <- major_field_of_study(?x13670, ?x9444), major_field_of_study(?x5280, ?x9444), ?x5280 = 07vhb, colors(?x13670, ?x4557), institution(?x9054, ?x13670), institution(?x865, ?x13670), institution(?x9054, ?x3948), institution(?x9054, ?x3821), ?x865 = 02h4rq6, ?x3821 = 0kw4j, ?x3948 = 025v3k >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #355 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 31 *> proper extension: 02h40lc; 0h5k; 06b_j; 0_jm; 01tbp; 02_7t; 01zc2w; 01bt59; 02jfc; 01r4k; ... *> query: (?x9444, 016t_3) <- major_field_of_study(?x6894, ?x9444), major_field_of_study(?x5280, ?x9444), major_field_of_study(?x3543, ?x9444), institution(?x865, ?x5280), student(?x5280, ?x1942), major_field_of_study(?x5280, ?x11691), major_field_of_study(?x5280, ?x742), colors(?x3543, ?x663), ?x11691 = 05wkw, ?x742 = 05qjt, category(?x6894, ?x134) *> conf = 0.82 ranks of expected_values: 2, 4 EVAL 06q83 major_field_of_study! 03bwzr4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 46.000 0.875 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 06q83 major_field_of_study! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 51.000 46.000 0.875 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #9176-04svwx PRED entity: 04svwx PRED relation: genre PRED expected values: 07s9rl0 06n90 => 138 concepts (94 used for prediction) PRED predicted values (max 10 best out of 109): 06n90 (0.87 #3529, 0.77 #5415, 0.65 #4826), 07s9rl0 (0.71 #3163, 0.56 #4105, 0.54 #3047), 03k9fj (0.67 #3280, 0.67 #1650, 0.67 #1534), 01jfsb (0.65 #5414, 0.62 #7414, 0.55 #7884), 0lsxr (0.42 #7410, 0.33 #2001, 0.27 #8113), 03q4nz (0.37 #4359, 0.35 #6128, 0.35 #3650), 0bj8m2 (0.29 #2155, 0.25 #865, 0.22 #4859), 04t2t (0.25 #874, 0.20 #1461, 0.17 #1694), 01zhp (0.23 #10554, 0.17 #2067, 0.15 #11026), 082gq (0.22 #4131, 0.19 #5547, 0.17 #5785) >> Best rule #3529 for best value: >> intensional similarity = 11 >> extensional distance = 13 >> proper extension: 0872p_c; 05qbckf; 0f40w; 0d_wms; 0dzlbx; 0bh8tgs; 026lgs; 05r3qc; 0kvbl6; 02vjp3; ... >> query: (?x12093, 06n90) <- genre(?x12093, ?x5937), genre(?x6840, ?x5937), genre(?x6610, ?x5937), genre(?x3844, ?x5937), genre(?x1419, ?x5937), ?x1419 = 02vw1w2, genre(?x419, ?x5937), ?x3844 = 02_qt, ?x6840 = 02z5x7l, person(?x12093, ?x4944), actor(?x6610, ?x6414) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04svwx genre 06n90 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 94.000 0.867 http://example.org/film/film/genre EVAL 04svwx genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 94.000 0.867 http://example.org/film/film/genre #9175-03crmd PRED entity: 03crmd PRED relation: film PRED expected values: 01ffx4 => 148 concepts (50 used for prediction) PRED predicted values (max 10 best out of 942): 034qmv (0.13 #1806, 0.04 #3597, 0.03 #39422), 02yvct (0.13 #2143, 0.02 #20053, 0.02 #25431), 0h6r5 (0.10 #6053, 0.09 #7844, 0.07 #13217), 02qr3k8 (0.07 #4873, 0.07 #3082, 0.05 #15619), 0yyn5 (0.07 #4546, 0.03 #15292, 0.01 #24252), 016ywb (0.07 #4822, 0.02 #10195, 0.02 #26319), 01y9r2 (0.07 #10303, 0.02 #37173, 0.02 #26427), 095zlp (0.07 #3642, 0.02 #26930, 0.02 #14388), 02qk3fk (0.07 #4715, 0.02 #15461), 027m5wv (0.07 #4640) >> Best rule #1806 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 012d40; 09byk; 0bdt8; 08qxx9; 0cbkc; >> query: (?x10520, 034qmv) <- languages(?x10520, ?x732), nationality(?x10520, ?x205), film(?x10520, ?x11001), ?x732 = 04306rv >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03crmd film 01ffx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 50.000 0.133 http://example.org/film/actor/film./film/performance/film #9174-018ygt PRED entity: 018ygt PRED relation: currency PRED expected values: 09nqf => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.53 #16, 0.50 #79, 0.44 #97), 01nv4h (0.06 #8, 0.03 #80, 0.02 #155) >> Best rule #16 for best value: >> intensional similarity = 2 >> extensional distance = 30 >> proper extension: 025504; >> query: (?x6324, 09nqf) <- program(?x6324, ?x2528), category(?x6324, ?x134) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018ygt currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.531 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #9173-08cn_n PRED entity: 08cn_n PRED relation: award PRED expected values: 05b1610 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 247): 03y8cbv (0.72 #23043, 0.69 #8082, 0.68 #10508), 0gs9p (0.39 #2907, 0.36 #2099, 0.27 #79), 019f4v (0.35 #2894, 0.35 #2086, 0.23 #874), 07bdd_ (0.34 #65, 0.15 #3297, 0.14 #2489), 040njc (0.34 #2836, 0.33 #2028, 0.28 #3240), 0gq9h (0.33 #3309, 0.33 #2501, 0.28 #2905), 09sb52 (0.30 #5292, 0.26 #7717, 0.25 #6100), 05b1610 (0.27 #38, 0.12 #442, 0.09 #2462), 0gr51 (0.23 #100, 0.22 #2928, 0.21 #2120), 02pqp12 (0.22 #2898, 0.22 #2090, 0.17 #878) >> Best rule #23043 for best value: >> intensional similarity = 3 >> extensional distance = 2323 >> proper extension: 06lxn; >> query: (?x8118, ?x10550) <- award_winner(?x10550, ?x8118), award(?x2595, ?x10550), gender(?x2595, ?x231) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 0854hr; *> query: (?x8118, 05b1610) <- award(?x8118, ?x350), nominated_for(?x8118, ?x903), ?x350 = 05f4m9q *> conf = 0.27 ranks of expected_values: 8 EVAL 08cn_n award 05b1610 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 80.000 80.000 0.717 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9172-03_r3 PRED entity: 03_r3 PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.92 #34, 0.88 #23, 0.87 #63) >> Best rule #34 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 077qn; >> query: (?x421, 0hzc9wc) <- country(?x668, ?x421), participating_countries(?x418, ?x421), ?x668 = 07gyv >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_r3 administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.915 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #9171-02bxd PRED entity: 02bxd PRED relation: role! PRED expected values: 0l14j_ => 54 concepts (46 used for prediction) PRED predicted values (max 10 best out of 112): 05r5c (0.88 #2015, 0.87 #2585, 0.85 #2358), 01vdm0 (0.84 #109, 0.84 #444, 0.84 #1437), 03qlv7 (0.84 #109, 0.84 #444, 0.84 #1437), 05842k (0.84 #109, 0.84 #444, 0.84 #1437), 05148p4 (0.84 #109, 0.84 #444, 0.82 #896), 0l14v3 (0.84 #109, 0.84 #444, 0.82 #896), 0l14j_ (0.84 #109, 0.84 #444, 0.82 #896), 01vj9c (0.82 #1343, 0.78 #1124, 0.77 #4039), 0bxl5 (0.82 #1397, 0.71 #738, 0.70 #1287), 0dwt5 (0.80 #1302, 0.75 #1331, 0.75 #976) >> Best rule #2015 for best value: >> intensional similarity = 30 >> extensional distance = 23 >> proper extension: 0342h; 042v_gx; 05148p4; 07xzm; 0xzly; 011_6p; 0dq630k; 0jtg0; 01v8y9; 0bxl5; ... >> query: (?x1662, 05r5c) <- role(?x227, ?x1662), role(?x3296, ?x1662), role(?x1574, ?x1662), role(?x228, ?x1662), role(?x212, ?x1662), ?x228 = 0l14qv, ?x212 = 026t6, instrumentalists(?x3296, ?x3890), instrumentalists(?x3296, ?x3774), role(?x3215, ?x3296), role(?x3161, ?x3296), role(?x1473, ?x3296), role(?x1267, ?x3296), role(?x2888, ?x3296), role(?x2157, ?x3296), ?x3774 = 04k15, ?x1473 = 0g2dz, ?x3215 = 0bxl5, ?x3161 = 01v1d8, ?x1574 = 0l15bq, ?x2157 = 011_6p, ?x2888 = 02fsn, ?x3890 = 01gg59, role(?x1662, ?x1332), role(?x1267, ?x4913), role(?x1267, ?x4471), group(?x3296, ?x3109), ?x4471 = 026g73, role(?x565, ?x1267), ?x4913 = 03ndd >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #109 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 1 *> proper extension: 0l14md; *> query: (?x1662, ?x1166) <- role(?x315, ?x1662), role(?x5480, ?x1662), role(?x3296, ?x1662), role(?x2459, ?x1662), role(?x1432, ?x1662), role(?x228, ?x1662), role(?x212, ?x1662), role(?x75, ?x1662), ?x228 = 0l14qv, ?x212 = 026t6, ?x3296 = 07_l6, ?x75 = 07y_7, ?x1432 = 0395lw, role(?x1662, ?x2048), ?x2048 = 018j2, role(?x1662, ?x3991), role(?x1662, ?x1166), ?x3991 = 05842k, role(?x316, ?x2459), ?x5480 = 01w4c9, group(?x315, ?x7868), family(?x615, ?x315), role(?x1147, ?x315), ?x1147 = 07kc_, role(?x4162, ?x2459), instrumentalists(?x315, ?x226), performance_role(?x115, ?x315), ?x7868 = 0knhk *> conf = 0.84 ranks of expected_values: 7 EVAL 02bxd role! 0l14j_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 54.000 46.000 0.880 http://example.org/music/performance_role/track_performances./music/track_contribution/role #9170-016tw3 PRED entity: 016tw3 PRED relation: company! PRED expected values: 06pj8 => 131 concepts (107 used for prediction) PRED predicted values (max 10 best out of 109): 06pj8 (0.25 #37, 0.10 #1258, 0.09 #1502), 0b80__ (0.25 #95, 0.10 #1316, 0.08 #1805), 02q_cc (0.25 #12, 0.08 #4654, 0.07 #5145), 030_3z (0.25 #90, 0.04 #4732, 0.04 #5223), 06q8hf (0.13 #4543, 0.12 #4788, 0.11 #5033), 05hj_k (0.13 #4471, 0.12 #4716, 0.11 #4961), 0glyyw (0.11 #900, 0.10 #1388, 0.09 #1632), 07f7jp (0.11 #964, 0.10 #1452, 0.09 #1696), 06y3r (0.11 #909, 0.07 #2375, 0.07 #2130), 081nh (0.11 #776, 0.07 #2242, 0.07 #1997) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 017s11; 030_1_; >> query: (?x1104, 06pj8) <- production_companies(?x2699, ?x1104), ?x2699 = 04t6fk, award_winner(?x3600, ?x1104) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016tw3 company! 06pj8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 107.000 0.250 http://example.org/people/person/employment_history./business/employment_tenure/company #9169-02k21g PRED entity: 02k21g PRED relation: cast_members! PRED expected values: 07ymr5 => 114 concepts (71 used for prediction) PRED predicted values (max 10 best out of 3): 02k21g (0.69 #6, 0.60 #3), 07ymr5 (0.50 #2, 0.46 #5), 0pz7h (0.38 #4, 0.30 #1) >> Best rule #6 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 04s430; >> query: (?x4490, 02k21g) <- cast_members(?x5620, ?x4490), nationality(?x5620, ?x94) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 8 *> proper extension: 01v3s2_; 07ymr5; 04h07s; 030wkp; 06cddt; *> query: (?x4490, 07ymr5) <- cast_members(?x5620, ?x4490), ?x5620 = 05drr9 *> conf = 0.50 ranks of expected_values: 2 EVAL 02k21g cast_members! 07ymr5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 71.000 0.692 http://example.org/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members #9168-01p896 PRED entity: 01p896 PRED relation: organization! PRED expected values: 060c4 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 27): 060c4 (0.80 #41, 0.79 #626, 0.78 #327), 07xl34 (0.39 #193, 0.33 #128, 0.31 #583), 0dq_5 (0.26 #100, 0.24 #152, 0.23 #842), 05k17c (0.13 #807, 0.13 #1432, 0.11 #20), 0hm4q (0.13 #807, 0.13 #1432, 0.07 #138), 05c0jwl (0.13 #807, 0.13 #1432, 0.06 #5), 04n1q6 (0.13 #807, 0.13 #1432, 0.05 #1472), 08jcfy (0.13 #807, 0.13 #1432, 0.02 #1040), 0dq3c (0.05 #1472, 0.04 #1590, 0.01 #92), 028fjr (0.05 #1472, 0.04 #1590) >> Best rule #41 for best value: >> intensional similarity = 6 >> extensional distance = 44 >> proper extension: 0gy3w; >> query: (?x9912, 060c4) <- school_type(?x9912, ?x3092), currency(?x9912, ?x170), ?x170 = 09nqf, major_field_of_study(?x9912, ?x6756), institution(?x865, ?x9912), ?x6756 = 0_jm >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p896 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.804 http://example.org/organization/role/leaders./organization/leadership/organization #9167-02_1q9 PRED entity: 02_1q9 PRED relation: nominated_for! PRED expected values: 02py_sj => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 161): 02py_sj (0.73 #441, 0.64 #675, 0.60 #909), 09qs08 (0.30 #1046, 0.25 #1280, 0.19 #3152), 0fbtbt (0.27 #3199, 0.24 #2731, 0.23 #4837), 0gq9h (0.25 #8721, 0.25 #8955, 0.21 #9190), 027gs1_ (0.25 #3225, 0.24 #2757, 0.22 #1119), 0cjyzs (0.23 #3124, 0.22 #1018, 0.22 #1486), 0gs9p (0.23 #8723, 0.22 #8957, 0.17 #8489), 09qv3c (0.22 #977, 0.19 #1211, 0.18 #3083), 0fbvqf (0.22 #3080, 0.20 #2612, 0.18 #4718), 0bdx29 (0.22 #3126, 0.18 #4764, 0.18 #2658) >> Best rule #441 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0147w8; >> query: (?x416, 02py_sj) <- nominated_for(?x2872, ?x416), actor(?x416, ?x7595), ?x2872 = 02pz3j5, award_nominee(?x7595, ?x100) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_1q9 nominated_for! 02py_sj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.727 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9166-0f2nf PRED entity: 0f2nf PRED relation: place_of_birth! PRED expected values: 0638kv => 187 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1798): 02qtywd (0.25 #2320, 0.20 #4925, 0.03 #38792), 0h5jg5 (0.25 #1511, 0.20 #4116, 0.03 #37983), 054187 (0.25 #1495, 0.20 #4100, 0.03 #37967), 08q3s0 (0.25 #1096, 0.20 #3701, 0.03 #37568), 09v6gc9 (0.25 #1037, 0.20 #3642, 0.03 #37509), 0h584v (0.25 #793, 0.20 #3398, 0.03 #37265), 0884hk (0.25 #790, 0.20 #3395, 0.03 #37262), 05b4rcb (0.25 #406, 0.20 #3011, 0.03 #36878), 06w33f8 (0.25 #302, 0.20 #2907, 0.03 #36774), 05cv94 (0.25 #229, 0.20 #2834, 0.03 #36701) >> Best rule #2320 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01x73; >> query: (?x9336, 02qtywd) <- time_zones(?x9336, ?x2674), contains(?x9336, ?x5178), ?x2674 = 02hcv8, ?x5178 = 02bq1j >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #213632 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 190 *> proper extension: 0l4vc; *> query: (?x9336, ?x1620) <- citytown(?x5178, ?x9336), organization(?x3484, ?x5178), student(?x5178, ?x1620) *> conf = 0.09 ranks of expected_values: 160 EVAL 0f2nf place_of_birth! 0638kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 187.000 106.000 0.250 http://example.org/people/person/place_of_birth #9165-05mlqj PRED entity: 05mlqj PRED relation: award_nominee PRED expected values: 07q0g5 => 94 concepts (35 used for prediction) PRED predicted values (max 10 best out of 678): 07q0g5 (0.82 #14013, 0.81 #77075, 0.81 #21021), 016yr0 (0.82 #14013, 0.81 #77075, 0.81 #21021), 05mlqj (0.57 #4340, 0.50 #6676, 0.43 #2004), 02x0dzw (0.15 #79411, 0.15 #81747, 0.07 #4241), 04wvhz (0.15 #79411, 0.07 #2546, 0.06 #4882), 016vg8 (0.15 #79411, 0.06 #8113, 0.03 #12783), 0dvmd (0.15 #79411, 0.03 #17045, 0.03 #12372), 04w391 (0.15 #79411, 0.03 #12589, 0.02 #17262), 01vw37m (0.15 #79411, 0.02 #13132, 0.01 #48166), 05qd_ (0.15 #79411, 0.01 #21201, 0.01 #46891) >> Best rule #14013 for best value: >> intensional similarity = 3 >> extensional distance = 682 >> proper extension: 0dvqq; 0kr_t; 0cj2k3; 01w9k25; >> query: (?x9384, ?x4327) <- award_nominee(?x9384, ?x7367), student(?x6760, ?x7367), award_nominee(?x4327, ?x9384) >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 05mlqj award_nominee 07q0g5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 35.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9164-05zvzf3 PRED entity: 05zvzf3 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 30): 0ch6mp2 (0.86 #488, 0.81 #1323, 0.79 #263), 09vw2b7 (0.71 #166, 0.68 #1033, 0.67 #102), 01vx2h (0.48 #428, 0.38 #139, 0.38 #1038), 02ynfr (0.25 #79, 0.24 #111, 0.24 #175), 01xy5l_ (0.20 #173, 0.15 #269, 0.15 #45), 02rh1dz (0.19 #74, 0.18 #106, 0.17 #138), 0d2b38 (0.19 #86, 0.15 #278, 0.14 #310), 04pyp5 (0.15 #48, 0.13 #16, 0.13 #1509), 089fss (0.15 #165, 0.13 #1509, 0.12 #69), 02_n3z (0.13 #1, 0.13 #1509, 0.11 #225) >> Best rule #488 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 03mh_tp; >> query: (?x8646, 0ch6mp2) <- film_festivals(?x8646, ?x11147), film_crew_role(?x8646, ?x4305), film_crew_role(?x5074, ?x4305), ?x5074 = 05mrf_p >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 77 *> proper extension: 03_wm6; 06y611; *> query: (?x8646, 09vw2b7) <- language(?x8646, ?x5607), film_crew_role(?x8646, ?x468), ?x468 = 02r96rf, ?x5607 = 064_8sq *> conf = 0.71 ranks of expected_values: 2 EVAL 05zvzf3 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.864 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9163-0340hj PRED entity: 0340hj PRED relation: language PRED expected values: 064_8sq => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 44): 06b_j (0.20 #22, 0.11 #196, 0.09 #138), 01r2l (0.20 #24, 0.08 #82, 0.06 #4048), 064_8sq (0.16 #253, 0.15 #1303, 0.14 #1774), 06nm1 (0.14 #126, 0.13 #242, 0.11 #1292), 04306rv (0.12 #1226, 0.11 #1406, 0.09 #236), 0653m (0.09 #127, 0.08 #69, 0.06 #4048), 02bjrlw (0.09 #1223, 0.07 #1403, 0.07 #175), 03_9r (0.07 #357, 0.06 #241, 0.06 #4048), 0jzc (0.06 #4048, 0.05 #3697, 0.05 #135), 012w70 (0.06 #4048, 0.05 #3697, 0.03 #651) >> Best rule #22 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 06ys2; >> query: (?x1511, 06b_j) <- nominated_for(?x5467, ?x1511), nominated_for(?x4106, ?x1511), nominated_for(?x1733, ?x1511), ?x4106 = 04fzk, ?x1733 = 015pkc, award_winner(?x5467, ?x395) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 100 *> proper extension: 07gp9; 01vksx; 0jqn5; 024l2y; 09k56b7; 0ddjy; 02ll45; 04pk1f; 05nlx4; 01hv3t; ... *> query: (?x1511, 064_8sq) <- genre(?x1511, ?x53), nominated_for(?x640, ?x1511), nominated_for(?x1733, ?x1511), ?x640 = 02hsq3m *> conf = 0.16 ranks of expected_values: 3 EVAL 0340hj language 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.200 http://example.org/film/film/language #9162-018mm4 PRED entity: 018mm4 PRED relation: place_of_burial! PRED expected values: 01y8cr 01g42 => 88 concepts (37 used for prediction) PRED predicted values (max 10 best out of 291): 022p06 (0.43 #526, 0.43 #403, 0.40 #281), 081nh (0.40 #258, 0.29 #503, 0.29 #380), 0bkmf (0.40 #195, 0.29 #563, 0.29 #440), 01t94_1 (0.40 #191, 0.29 #559, 0.29 #436), 03bw6 (0.40 #177, 0.29 #545, 0.29 #422), 0cf2h (0.40 #170, 0.29 #538, 0.29 #415), 0hnp7 (0.40 #167, 0.29 #535, 0.29 #412), 03bdm4 (0.20 #323, 0.20 #200, 0.20 #78), 02sj1x (0.20 #265, 0.20 #142, 0.20 #20), 03n6r (0.20 #285, 0.20 #162, 0.20 #40) >> Best rule #526 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0lbp_; >> query: (?x3153, 022p06) <- place_of_burial(?x7414, ?x3153), place_of_burial(?x1300, ?x3153), nominated_for(?x1300, ?x8217), organizations_founded(?x7414, ?x5634), nationality(?x1300, ?x94), films(?x326, ?x8217) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #980 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 018mlg; *> query: (?x3153, ?x4597) <- place_of_burial(?x9709, ?x3153), place_of_burial(?x1300, ?x3153), nominated_for(?x1300, ?x8217), languages(?x9709, ?x90), film(?x4597, ?x8217), type_of_union(?x1300, ?x566) *> conf = 0.11 ranks of expected_values: 178 EVAL 018mm4 place_of_burial! 01g42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 37.000 0.429 http://example.org/people/deceased_person/place_of_burial EVAL 018mm4 place_of_burial! 01y8cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 88.000 37.000 0.429 http://example.org/people/deceased_person/place_of_burial #9161-0ddkf PRED entity: 0ddkf PRED relation: nationality PRED expected values: 09c7w0 => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 82): 09c7w0 (0.87 #6422, 0.87 #6322, 0.86 #5720), 02_286 (0.27 #14260, 0.26 #11749, 0.25 #9537), 059rby (0.27 #14260, 0.26 #11749, 0.25 #9537), 0n5gq (0.27 #14260, 0.01 #9336), 05fjf (0.27 #14260), 01nl79 (0.26 #11749, 0.25 #9537, 0.25 #12550), 0cr3d (0.26 #11749, 0.25 #9537, 0.25 #12550), 02jx1 (0.24 #1738, 0.23 #3443, 0.23 #1035), 07ssc (0.18 #516, 0.14 #3626, 0.14 #3425), 0d060g (0.09 #608, 0.08 #1009, 0.07 #2616) >> Best rule #6422 for best value: >> intensional similarity = 3 >> extensional distance = 393 >> proper extension: 015qq1; >> query: (?x6877, ?x94) <- student(?x11102, ?x6877), country(?x11102, ?x94), award(?x6877, ?x724) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ddkf nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.868 http://example.org/people/person/nationality #9160-020ngt PRED entity: 020ngt PRED relation: parent_genre PRED expected values: 018ysx => 45 concepts (31 used for prediction) PRED predicted values (max 10 best out of 215): 06by7 (0.88 #1488, 0.69 #2635, 0.55 #2962), 03lty (0.44 #2474, 0.43 #1327, 0.40 #1164), 01243b (0.33 #29, 0.26 #1989, 0.24 #1501), 0xhtw (0.33 #176, 0.25 #339, 0.24 #1485), 02yv6b (0.33 #228, 0.25 #391, 0.20 #554), 0pm85 (0.33 #261, 0.25 #424, 0.20 #587), 016clz (0.33 #4, 0.19 #2294, 0.18 #1476), 0dl5d (0.33 #15, 0.17 #667, 0.14 #1323), 0p9xd (0.33 #100, 0.17 #752, 0.14 #1408), 09jw2 (0.29 #1574, 0.21 #2062, 0.12 #2557) >> Best rule #1488 for best value: >> intensional similarity = 7 >> extensional distance = 15 >> proper extension: 028cl7; >> query: (?x2407, 06by7) <- parent_genre(?x2407, ?x11960), parent_genre(?x2407, ?x5934), ?x5934 = 05r6t, artists(?x11960, ?x3171), parent_genre(?x11759, ?x11960), ?x11759 = 02856r, place_of_death(?x3171, ?x6952) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3112 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 106 *> proper extension: 0133k0; *> query: (?x2407, ?x2491) <- parent_genre(?x2407, ?x5934), artists(?x5934, ?x8272), parent_genre(?x9881, ?x5934), parent_genre(?x2491, ?x5934), instrumentalists(?x227, ?x8272), ?x9881 = 01h0kx *> conf = 0.03 ranks of expected_values: 153 EVAL 020ngt parent_genre 018ysx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 45.000 31.000 0.882 http://example.org/music/genre/parent_genre #9159-018d6l PRED entity: 018d6l PRED relation: artists! PRED expected values: 0dl5d => 115 concepts (48 used for prediction) PRED predicted values (max 10 best out of 248): 06by7 (0.70 #5301, 0.66 #2505, 0.65 #4682), 03lty (0.50 #10894, 0.42 #12132, 0.38 #1888), 064t9 (0.48 #8401, 0.48 #7782, 0.46 #6849), 0155w (0.43 #4766, 0.41 #2589, 0.38 #1965), 016clz (0.41 #2177, 0.32 #2799, 0.31 #12730), 06j6l (0.36 #1286, 0.29 #3154, 0.28 #4709), 08jyyk (0.36 #1618, 0.18 #5348, 0.18 #6904), 0dl5d (0.33 #19, 0.27 #10885, 0.26 #12123), 0ggq0m (0.33 #630, 0.22 #3118, 0.18 #1250), 02w4v (0.33 #44, 0.15 #5015, 0.13 #3150) >> Best rule #5301 for best value: >> intensional similarity = 5 >> extensional distance = 59 >> proper extension: 07c0j; 01wv9xn; 02r1tx7; 03fbc; 0134s5; 0kr_t; 01q99h; 0178kd; 0ycp3; 048xh; ... >> query: (?x7193, 06by7) <- artists(?x505, ?x7193), artist(?x2149, ?x7193), ?x2149 = 011k1h, artists(?x505, ?x2170), ?x2170 = 0144l1 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #19 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 02lbrd; *> query: (?x7193, 0dl5d) <- artists(?x6210, ?x7193), artists(?x505, ?x7193), artist(?x2149, ?x7193), ?x2149 = 011k1h, ?x505 = 03_d0, ?x6210 = 01fh36 *> conf = 0.33 ranks of expected_values: 8 EVAL 018d6l artists! 0dl5d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 115.000 48.000 0.705 http://example.org/music/genre/artists #9158-04jpl PRED entity: 04jpl PRED relation: vacationer PRED expected values: 0161c2 07r1h => 182 concepts (166 used for prediction) PRED predicted values (max 10 best out of 273): 016fnb (0.25 #96, 0.20 #263, 0.14 #2264), 01vs_v8 (0.20 #205, 0.14 #1201, 0.11 #371), 0mm1q (0.20 #277, 0.14 #1273, 0.11 #443), 0f4vbz (0.20 #206, 0.11 #1370, 0.11 #372), 0151w_ (0.20 #186, 0.11 #1350, 0.11 #352), 04fzk (0.20 #250, 0.11 #416, 0.10 #582), 02d9k (0.20 #199, 0.11 #365, 0.10 #531), 01dw4q (0.20 #170, 0.11 #336, 0.10 #502), 011zd3 (0.20 #208, 0.11 #374, 0.10 #540), 016tbr (0.20 #654, 0.09 #1821, 0.08 #1988) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01llj3; >> query: (?x362, 016fnb) <- place_of_birth(?x361, ?x362), contains(?x362, ?x11821), ?x11821 = 015wy_ >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4291 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 42 *> proper extension: 04w58; 0n3g; 07fr_; 0f0sbl; *> query: (?x362, 07r1h) <- vacationer(?x362, ?x827), location_of_ceremony(?x2092, ?x362) *> conf = 0.07 ranks of expected_values: 47, 111 EVAL 04jpl vacationer 07r1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 182.000 166.000 0.250 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 04jpl vacationer 0161c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 182.000 166.000 0.250 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #9157-01cx_ PRED entity: 01cx_ PRED relation: contains PRED expected values: 01gr00 => 196 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2578): 02kj7g (0.66 #354847, 0.66 #357781, 0.48 #346048), 0gl5_ (0.66 #354847, 0.66 #357781, 0.48 #346048), 05k7sb (0.48 #129037, 0.46 #390039, 0.41 #363647), 01cx_ (0.48 #129037, 0.46 #390039, 0.41 #363647), 0k3l5 (0.48 #129037, 0.46 #390039, 0.41 #363647), 03kxzm (0.48 #129037, 0.46 #390039, 0.41 #363647), 09c7w0 (0.48 #129037, 0.46 #390039, 0.41 #363647), 05f7s1 (0.33 #151, 0.03 #47074, 0.03 #52938), 02cbvn (0.33 #545, 0.03 #47468, 0.03 #53332), 01zxx9 (0.33 #2878, 0.03 #49801, 0.03 #55665) >> Best rule #354847 for best value: >> intensional similarity = 2 >> extensional distance = 217 >> proper extension: 0hn4h; >> query: (?x3052, ?x6912) <- citytown(?x6912, ?x3052), colors(?x6912, ?x663) >> conf = 0.66 => this is the best rule for 2 predicted values *> Best rule #14573 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 03rt9; 03gj2; 0jdx; *> query: (?x3052, 01gr00) <- contains(?x3052, ?x1151), split_to(?x4132, ?x3052), film_release_region(?x204, ?x3052) *> conf = 0.11 ranks of expected_values: 547 EVAL 01cx_ contains 01gr00 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 196.000 162.000 0.662 http://example.org/location/location/contains #9156-023p29 PRED entity: 023p29 PRED relation: artists! PRED expected values: 06by7 => 127 concepts (100 used for prediction) PRED predicted values (max 10 best out of 274): 06by7 (0.67 #938, 0.48 #6140, 0.48 #14406), 016clz (0.50 #616, 0.44 #922, 0.27 #6124), 0m0jc (0.50 #620, 0.18 #3068, 0.11 #5516), 0xv2x (0.50 #762, 0.08 #6270, 0.06 #21742), 025sc50 (0.46 #3107, 0.38 #8309, 0.33 #2801), 05bt6j (0.46 #3100, 0.37 #2794, 0.33 #14426), 06j6l (0.39 #3105, 0.37 #2799, 0.33 #8307), 05w3f (0.33 #953, 0.23 #14080, 0.17 #18982), 02yv6b (0.33 #1014, 0.23 #6216, 0.14 #6522), 0y3_8 (0.33 #656, 0.14 #3104, 0.12 #14430) >> Best rule #938 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01518s; >> query: (?x10209, 06by7) <- artist(?x11715, ?x10209), ?x11715 = 015mlw, artists(?x284, ?x10209) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023p29 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 100.000 0.667 http://example.org/music/genre/artists #9155-044k8 PRED entity: 044k8 PRED relation: profession PRED expected values: 02hrh1q 0dgd_ => 195 concepts (182 used for prediction) PRED predicted values (max 10 best out of 107): 02hrh1q (0.90 #5529, 0.88 #7319, 0.87 #6125), 09jwl (0.70 #15079, 0.70 #6280, 0.68 #318), 01c72t (0.66 #2409, 0.54 #2111, 0.52 #1515), 016z4k (0.57 #5816, 0.45 #6264, 0.43 #600), 01d_h8 (0.57 #6, 0.53 #304, 0.44 #2689), 0dxtg (0.57 #14, 0.35 #1653, 0.34 #4038), 0cbd2 (0.52 #752, 0.47 #3733, 0.47 #2541), 0dz3r (0.47 #2834, 0.43 #598, 0.42 #6710), 02jknp (0.43 #8, 0.33 #2691, 0.31 #157), 0n1h (0.37 #310, 0.31 #6260, 0.31 #161) >> Best rule #5529 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 05m63c; 0d_84; 02qjj7; 0456xp; 04shbh; 0n6f8; 0f2df; 0tc7; 012_53; 01kj0p; ... >> query: (?x4608, 02hrh1q) <- student(?x1011, ?x4608), participant(?x4608, ?x3403), location(?x4608, ?x3269) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 19 EVAL 044k8 profession 0dgd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 195.000 182.000 0.900 http://example.org/people/person/profession EVAL 044k8 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 182.000 0.900 http://example.org/people/person/profession #9154-01p0w_ PRED entity: 01p0w_ PRED relation: nationality PRED expected values: 02jx1 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 32): 09c7w0 (0.82 #2302, 0.77 #6417, 0.76 #6116), 07ssc (0.75 #13332, 0.25 #7319, 0.18 #415), 02jx1 (0.64 #433, 0.35 #933, 0.30 #2134), 0d060g (0.12 #907, 0.09 #307, 0.08 #1207), 03_3d (0.08 #1306, 0.04 #3111, 0.02 #5319), 0ctw_b (0.07 #627, 0.04 #1427, 0.04 #3432), 035qy (0.07 #634, 0.03 #2135, 0.03 #2235), 0k6nt (0.07 #625, 0.03 #2126, 0.02 #3230), 0f8l9c (0.06 #2123, 0.06 #922, 0.06 #1122), 03rk0 (0.06 #14184, 0.06 #14284, 0.06 #14584) >> Best rule #2302 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0dxmyh; 022q32; >> query: (?x12422, 09c7w0) <- friend(?x12422, ?x7571), participant(?x7053, ?x12422), location(?x12422, ?x2235), artists(?x302, ?x7571) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #433 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01vvyfh; *> query: (?x12422, 02jx1) <- type_of_union(?x12422, ?x1873), ?x1873 = 01g63y, role(?x12422, ?x227), currency(?x12422, ?x170) *> conf = 0.64 ranks of expected_values: 3 EVAL 01p0w_ nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 160.000 160.000 0.816 http://example.org/people/person/nationality #9153-01nkxvx PRED entity: 01nkxvx PRED relation: artists! PRED expected values: 06by7 05bt6j => 94 concepts (48 used for prediction) PRED predicted values (max 10 best out of 231): 06by7 (0.54 #642, 0.51 #2510, 0.49 #5936), 01lyv (0.32 #654, 0.21 #4397, 0.20 #5017), 06j6l (0.31 #980, 0.28 #2226, 0.27 #4412), 0glt670 (0.28 #2218, 0.23 #5334, 0.23 #9071), 015wd7 (0.27 #173, 0.02 #1730, 0.02 #794), 0xhtw (0.25 #2505, 0.21 #5931, 0.20 #637), 025sc50 (0.24 #2228, 0.23 #4414, 0.21 #5034), 05bt6j (0.24 #5958, 0.22 #1600, 0.21 #13739), 0gywn (0.23 #9089, 0.21 #1615, 0.21 #4422), 03_d0 (0.22 #2811, 0.21 #4062, 0.20 #3437) >> Best rule #642 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 01vsyg9; >> query: (?x8599, 06by7) <- award_winner(?x2054, ?x8599), role(?x8599, ?x432), ?x432 = 042v_gx, artists(?x302, ?x8599) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 01nkxvx artists! 05bt6j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 94.000 48.000 0.540 http://example.org/music/genre/artists EVAL 01nkxvx artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 48.000 0.540 http://example.org/music/genre/artists #9152-07zlqp PRED entity: 07zlqp PRED relation: organization! PRED expected values: 0dq_5 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 19): 0dq_5 (0.74 #554, 0.73 #568, 0.72 #540), 014l7h (0.31 #674, 0.27 #734, 0.27 #764), 060c4 (0.29 #722, 0.27 #752, 0.26 #809), 0krdk (0.12 #765, 0.07 #675, 0.07 #719), 0dq3c (0.12 #765, 0.07 #675, 0.07 #719), 02k13d (0.12 #765, 0.07 #675, 0.07 #719), 01rk91 (0.12 #765, 0.06 #264, 0.04 #897), 07xl34 (0.12 #893, 0.12 #908, 0.11 #878), 09d6p2 (0.07 #719, 0.07 #644, 0.06 #643), 01yc02 (0.07 #719, 0.07 #644, 0.06 #643) >> Best rule #554 for best value: >> intensional similarity = 8 >> extensional distance = 78 >> proper extension: 0c_j5d; 0gztl; 02bh8z; 056ws9; 077w0b; 018_q8; 02bm1v; 01dfb6; 05th69; 0k9ts; ... >> query: (?x8315, 0dq_5) <- industry(?x8315, ?x2271), company(?x8314, ?x8315), company(?x8314, ?x13568), company(?x8314, ?x1762), organization(?x13568, ?x14299), nominated_for(?x1762, ?x782), state_province_region(?x1762, ?x335), program(?x1762, ?x623) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07zlqp organization! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.738 http://example.org/organization/role/leaders./organization/leadership/organization #9151-09rvwmy PRED entity: 09rvwmy PRED relation: film_crew_role PRED expected values: 02_n3z 0263ycg 0215hd => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 25): 09vw2b7 (0.67 #38, 0.65 #812, 0.65 #456), 0215hd (0.43 #81, 0.20 #418, 0.20 #177), 01pvkk (0.40 #107, 0.27 #461, 0.27 #881), 0dxtw (0.37 #459, 0.35 #815, 0.35 #879), 01vx2h (0.34 #460, 0.33 #42, 0.32 #816), 02_n3z (0.20 #418, 0.20 #97, 0.14 #65), 02ynfr (0.20 #418, 0.17 #820, 0.17 #46), 0d2b38 (0.20 #418, 0.17 #54, 0.14 #86), 0263ycg (0.20 #418, 0.14 #80, 0.05 #176), 0ckd1 (0.20 #418, 0.14 #67, 0.03 #453) >> Best rule #38 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0gtvpkw; >> query: (?x10918, 09vw2b7) <- film(?x6979, ?x10918), film(?x3154, ?x10918), award_winner(?x369, ?x3154), ?x6979 = 057176, genre(?x10918, ?x53) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #81 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02z3r8t; 02qmsr; 05c5z8j; 0gj96ln; 047vp1n; *> query: (?x10918, 0215hd) <- film(?x3154, ?x10918), film_festivals(?x10918, ?x11147), award_nominee(?x6920, ?x3154), ?x6920 = 02lgfh *> conf = 0.43 ranks of expected_values: 2, 6, 9 EVAL 09rvwmy film_crew_role 0215hd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09rvwmy film_crew_role 0263ycg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 71.000 71.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09rvwmy film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 71.000 71.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9150-014z8v PRED entity: 014z8v PRED relation: location PRED expected values: 01n7q => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 296): 030qb3t (0.22 #71486, 0.17 #77904, 0.16 #49822), 06_kh (0.21 #29682, 0.19 #28879, 0.18 #24066), 06yxd (0.14 #1047, 0.04 #11477, 0.04 #12279), 05k7sb (0.13 #1711, 0.09 #4920, 0.09 #909), 0cr3d (0.12 #6560, 0.12 #4154, 0.10 #29825), 04jpl (0.10 #71422, 0.08 #77840, 0.07 #78642), 059rby (0.09 #818, 0.07 #4829, 0.05 #71421), 0d6lp (0.09 #968, 0.05 #13804, 0.04 #21023), 0r0m6 (0.08 #48353, 0.05 #216, 0.05 #1018), 01n7q (0.08 #13699, 0.06 #20918, 0.06 #16908) >> Best rule #71486 for best value: >> intensional similarity = 2 >> extensional distance = 1239 >> proper extension: 0187y5; 04l3_z; 05k2s_; 07s8r0; 02yxwd; 015wfg; 01k70_; 0175wg; 06mt91; 020fgy; ... >> query: (?x4112, 030qb3t) <- location(?x4112, ?x739), location_of_ceremony(?x548, ?x739) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #13699 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 91 *> proper extension: 06jzh; *> query: (?x4112, 01n7q) <- location(?x4112, ?x739), person(?x424, ?x4112), film(?x4112, ?x994) *> conf = 0.08 ranks of expected_values: 10 EVAL 014z8v location 01n7q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 120.000 120.000 0.219 http://example.org/people/person/places_lived./people/place_lived/location #9149-084n_ PRED entity: 084n_ PRED relation: official_language PRED expected values: 04306rv => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 45): 04306rv (0.50 #49, 0.50 #5, 0.43 #93), 02h40lc (0.47 #2028, 0.42 #354, 0.32 #1148), 06nm1 (0.29 #1726, 0.24 #1022, 0.24 #668), 064_8sq (0.15 #2659, 0.14 #2703, 0.13 #2879), 0653m (0.14 #185, 0.11 #273, 0.09 #845), 06b_j (0.12 #149, 0.06 #281, 0.05 #2395), 0cjk9 (0.12 #3658, 0.12 #3304, 0.12 #3659), 05qqm (0.12 #3658, 0.12 #3304, 0.12 #3659), 0880p (0.12 #3658, 0.12 #3304, 0.12 #3659), 0jzc (0.12 #2789, 0.12 #2701, 0.11 #322) >> Best rule #49 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 03b79; >> query: (?x10003, 04306rv) <- nationality(?x11479, ?x10003), nationality(?x9178, ?x10003), nationality(?x3335, ?x10003), ?x9178 = 01kx1j, ?x11479 = 01llxp, peers(?x3335, ?x9836) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 084n_ official_language 04306rv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.500 http://example.org/location/country/official_language #9148-02xcb6n PRED entity: 02xcb6n PRED relation: award! PRED expected values: 0300ml => 50 concepts (24 used for prediction) PRED predicted values (max 10 best out of 715): 0180mw (0.67 #1689, 0.25 #670, 0.15 #3727), 015ppk (0.58 #1734, 0.25 #715, 0.15 #3772), 0g60z (0.50 #1047, 0.25 #28, 0.11 #3085), 030p35 (0.50 #1482, 0.13 #3520, 0.13 #2500), 039c26 (0.50 #323, 0.08 #1342, 0.04 #3380), 0828jw (0.44 #4076, 0.39 #7135, 0.39 #2037), 063ykwt (0.44 #4076, 0.39 #7135, 0.39 #2037), 01g03q (0.44 #4076, 0.39 #7135, 0.39 #2037), 02rcwq0 (0.44 #4076, 0.39 #7135, 0.39 #2037), 0464pz (0.44 #4076, 0.39 #7135, 0.39 #2037) >> Best rule #1689 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0bp_b2; 0bdw1g; 0fbvqf; 0cqh6z; 0ck27z; 0bdx29; 0gkts9; 0fbtbt; 0cqhb3; 0gkr9q; >> query: (?x8660, 0180mw) <- nominated_for(?x8660, ?x1849), award(?x687, ?x8660), award_winner(?x8660, ?x3260), award(?x3058, ?x8660), ?x1849 = 0kfv9 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2024 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0bp_b2; 0bdw1g; 0fbvqf; 0cqh6z; 0ck27z; 0bdx29; 0gkts9; 0fbtbt; 0cqhb3; 0gkr9q; *> query: (?x8660, 0300ml) <- nominated_for(?x8660, ?x1849), award(?x687, ?x8660), award_winner(?x8660, ?x3260), award(?x3058, ?x8660), ?x1849 = 0kfv9 *> conf = 0.17 ranks of expected_values: 33 EVAL 02xcb6n award! 0300ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 50.000 24.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #9147-09l3p PRED entity: 09l3p PRED relation: film PRED expected values: 062zjtt 03h0byn => 124 concepts (99 used for prediction) PRED predicted values (max 10 best out of 896): 040_lv (0.06 #1038, 0.04 #4590, 0.03 #6366), 03tbg6 (0.06 #1642, 0.03 #5194, 0.02 #12298), 083shs (0.06 #19, 0.03 #3571, 0.02 #7123), 02ryz24 (0.06 #464, 0.02 #34208, 0.02 #21776), 0kvgtf (0.06 #613, 0.02 #5941, 0.02 #7717), 0fphf3v (0.06 #6678, 0.04 #35094, 0.04 #36870), 0b3n61 (0.05 #3123, 0.03 #17331, 0.02 #10227), 02qydsh (0.05 #3262, 0.02 #10366, 0.02 #6814), 05sns6 (0.05 #2477, 0.02 #9581, 0.02 #6029), 01gkp1 (0.05 #2583, 0.02 #9687, 0.02 #6135) >> Best rule #1038 for best value: >> intensional similarity = 2 >> extensional distance = 30 >> proper extension: 01rrwf6; 01w524f; 03n52j; 0j5q3; 01x0yrt; 012ycy; >> query: (?x4295, 040_lv) <- diet(?x4295, ?x11141), ?x11141 = 07_hy >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #1688 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 30 *> proper extension: 01rrwf6; 01w524f; 03n52j; 0j5q3; 01x0yrt; 012ycy; *> query: (?x4295, 03h0byn) <- diet(?x4295, ?x11141), ?x11141 = 07_hy *> conf = 0.03 ranks of expected_values: 153, 639 EVAL 09l3p film 03h0byn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 124.000 99.000 0.062 http://example.org/film/actor/film./film/performance/film EVAL 09l3p film 062zjtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 124.000 99.000 0.062 http://example.org/film/actor/film./film/performance/film #9146-063k3h PRED entity: 063k3h PRED relation: people PRED expected values: 016qtt 083q7 0jt90f5 01k70_ 02n9k 07hyk => 28 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2212): 0f7fy (0.33 #2625, 0.33 #920, 0.21 #4328), 07hyk (0.33 #3184, 0.33 #1479, 0.18 #6592), 081t6 (0.33 #3363, 0.33 #1658, 0.14 #5066), 06c0j (0.33 #3344, 0.33 #1639, 0.14 #5047), 042d1 (0.33 #3147, 0.33 #1442, 0.14 #4850), 0g824 (0.33 #2590, 0.29 #4293, 0.24 #5998), 0311wg (0.33 #1994, 0.23 #8812, 0.22 #7107), 046zh (0.33 #2442, 0.21 #4145, 0.18 #9260), 0k9j_ (0.33 #2961, 0.21 #4664, 0.18 #6369), 08vr94 (0.33 #2242, 0.21 #3945, 0.18 #5650) >> Best rule #2625 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 07bch9; >> query: (?x7185, 0f7fy) <- people(?x7185, ?x5742), people(?x7185, ?x5442), people(?x7185, ?x5240), people(?x7185, ?x4196), people(?x7185, ?x3969), celebrities_impersonated(?x2101, ?x4196), ?x5742 = 0rlz, ?x5442 = 02jq1, location(?x3969, ?x1025), student(?x620, ?x4196), participant(?x5240, ?x2927) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3184 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 07bch9; *> query: (?x7185, 07hyk) <- people(?x7185, ?x5742), people(?x7185, ?x5442), people(?x7185, ?x5240), people(?x7185, ?x4196), people(?x7185, ?x3969), celebrities_impersonated(?x2101, ?x4196), ?x5742 = 0rlz, ?x5442 = 02jq1, location(?x3969, ?x1025), student(?x620, ?x4196), participant(?x5240, ?x2927) *> conf = 0.33 ranks of expected_values: 2, 18, 80, 191, 1383 EVAL 063k3h people 07hyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 28.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 063k3h people 02n9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 063k3h people 01k70_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 28.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 063k3h people 0jt90f5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 28.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 063k3h people 083q7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 28.000 23.000 0.333 http://example.org/people/ethnicity/people EVAL 063k3h people 016qtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 28.000 23.000 0.333 http://example.org/people/ethnicity/people #9145-01w1ywm PRED entity: 01w1ywm PRED relation: people! PRED expected values: 0d19y2 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 44): 0dq9p (0.33 #17, 0.20 #83, 0.17 #481), 0qcr0 (0.26 #133, 0.10 #465, 0.10 #531), 03p41 (0.24 #265, 0.07 #795, 0.05 #464), 0m32h (0.20 #89, 0.08 #221, 0.04 #619), 0gk4g (0.19 #1135, 0.16 #540, 0.15 #275), 0d19y2 (0.17 #253, 0.05 #783, 0.05 #452), 02k6hp (0.13 #368, 0.10 #501, 0.10 #567), 01_qc_ (0.12 #226, 0.11 #160, 0.07 #624), 04p3w (0.12 #541, 0.10 #475, 0.10 #1136), 02knxx (0.10 #496, 0.10 #562, 0.10 #363) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 044f7; >> query: (?x7997, 0dq9p) <- place_of_death(?x7997, ?x739), award_winner(?x4242, ?x7997), ?x4242 = 01vy_v8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 0ct9_; *> query: (?x7997, 0d19y2) <- place_of_death(?x7997, ?x739), gender(?x7997, ?x231), notable_people_with_this_condition(?x6656, ?x7997) *> conf = 0.17 ranks of expected_values: 6 EVAL 01w1ywm people! 0d19y2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 106.000 106.000 0.333 http://example.org/people/cause_of_death/people #9144-04vt98 PRED entity: 04vt98 PRED relation: award PRED expected values: 05f4m9q => 108 concepts (66 used for prediction) PRED predicted values (max 10 best out of 248): 05f4m9q (0.70 #22705, 0.69 #26356, 0.69 #24733), 0gs9p (0.53 #890, 0.37 #4132, 0.15 #5348), 019f4v (0.43 #877, 0.32 #4119, 0.14 #5335), 040njc (0.41 #818, 0.32 #4060, 0.16 #2033), 0gq9h (0.39 #888, 0.25 #4130, 0.16 #5346), 09sb52 (0.32 #3688, 0.28 #11385, 0.26 #9359), 02pqp12 (0.21 #4123, 0.12 #881, 0.09 #5339), 0gr4k (0.19 #4085, 0.17 #5301, 0.16 #6516), 02rdyk7 (0.18 #4144, 0.07 #7790, 0.07 #5360), 0gqy2 (0.18 #3002, 0.17 #2190, 0.12 #9483) >> Best rule #22705 for best value: >> intensional similarity = 3 >> extensional distance = 1547 >> proper extension: 028qdb; >> query: (?x9296, ?x350) <- award_winner(?x350, ?x9296), type_of_union(?x9296, ?x566), award(?x71, ?x350) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04vt98 award 05f4m9q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 66.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9143-02rl201 PRED entity: 02rl201 PRED relation: draft! PRED expected values: 051vz 051wf => 16 concepts (16 used for prediction) PRED predicted values (max 10 best out of 353): 01yjl (0.85 #76, 0.73 #600, 0.73 #374), 01d5z (0.85 #76, 0.73 #600, 0.73 #374), 051wf (0.85 #76, 0.73 #600, 0.73 #374), 06wpc (0.85 #76, 0.73 #600, 0.65 #448), 0713r (0.85 #76, 0.73 #600, 0.65 #448), 03m1n (0.85 #76, 0.73 #600, 0.65 #448), 051vz (0.85 #76, 0.73 #600, 0.65 #448), 03lpp_ (0.85 #76, 0.73 #600, 0.65 #448), 0x2p (0.85 #76, 0.73 #600, 0.60 #696), 0jmj7 (0.73 #374, 0.65 #448, 0.57 #298) >> Best rule #76 for best value: >> intensional similarity = 52 >> extensional distance = 1 >> proper extension: 09l0x9; >> query: (?x1633, ?x3333) <- draft(?x7060, ?x1633), draft(?x6074, ?x1633), draft(?x1632, ?x1633), draft(?x1438, ?x1633), draft(?x1160, ?x1633), draft(?x1438, ?x11905), school(?x7060, ?x4161), teams(?x1860, ?x7060), school(?x11905, ?x3777), team(?x2010, ?x7060), colors(?x7060, ?x4557), school(?x6074, ?x2948), category(?x6074, ?x134), team(?x261, ?x1438), place_of_birth(?x193, ?x1860), school(?x1633, ?x3948), jurisdiction_of_office(?x1195, ?x1860), location(?x827, ?x1860), source(?x1860, ?x958), featured_film_locations(?x195, ?x1860), month(?x1860, ?x9905), month(?x1860, ?x4925), month(?x1860, ?x4827), month(?x1860, ?x2140), teams(?x739, ?x1632), origin(?x1945, ?x1860), ?x3948 = 025v3k, ?x2140 = 040fb, citytown(?x1924, ?x1860), location_of_ceremony(?x566, ?x1860), school(?x1438, ?x8706), draft(?x3333, ?x11905), dog_breed(?x1860, ?x5194), ?x4827 = 03_ly, ?x5194 = 01t032, colors(?x1609, ?x4557), ?x8706 = 0trv, ?x4925 = 0ll3, ?x134 = 08mbj5d, school(?x1632, ?x2959), place_founded(?x6156, ?x1860), teams(?x2017, ?x1160), team(?x5412, ?x1632), ?x1609 = 02kth6, team(?x13623, ?x6074), contains(?x3818, ?x1860), sport(?x1632, ?x5063), ?x3777 = 012vwb, ?x2017 = 04f_d, contains(?x94, ?x4161), school(?x1160, ?x1428), ?x9905 = 028kb >> conf = 0.85 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 7 EVAL 02rl201 draft! 051wf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 16.000 16.000 0.848 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02rl201 draft! 051vz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 16.000 16.000 0.848 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #9142-095b70 PRED entity: 095b70 PRED relation: people! PRED expected values: 0x67 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 42): 041rx (0.14 #466, 0.14 #543, 0.13 #1005), 033tf_ (0.13 #161, 0.12 #392, 0.10 #469), 0x67 (0.10 #626, 0.10 #87, 0.10 #703), 02w7gg (0.10 #2, 0.06 #772, 0.06 #1080), 02ctzb (0.08 #169, 0.06 #323, 0.03 #92), 07hwkr (0.07 #243, 0.07 #782, 0.06 #1090), 07bch9 (0.07 #177, 0.06 #331, 0.06 #100), 0xnvg (0.07 #475, 0.07 #398, 0.07 #552), 065b6q (0.06 #157, 0.04 #3, 0.02 #388), 09vc4s (0.05 #163, 0.04 #394, 0.04 #9) >> Best rule #466 for best value: >> intensional similarity = 2 >> extensional distance = 394 >> proper extension: 017f4y; >> query: (?x5996, 041rx) <- place_of_birth(?x5996, ?x4733), participant(?x5996, ?x1909) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #626 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 410 *> proper extension: 07c0j; 058s57; 015pxr; 04qmr; 03xb2w; 023v4_; 028r4y; 0d608; 03q45x; 0l5yl; ... *> query: (?x5996, 0x67) <- award_nominee(?x848, ?x5996), award(?x5996, ?x1670), participant(?x1660, ?x5996) *> conf = 0.10 ranks of expected_values: 3 EVAL 095b70 people! 0x67 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.144 http://example.org/people/ethnicity/people #9141-0296rz PRED entity: 0296rz PRED relation: nominated_for! PRED expected values: 05f4m9q => 77 concepts (67 used for prediction) PRED predicted values (max 10 best out of 260): 0gq9h (0.36 #2683, 0.32 #2206, 0.26 #539), 019f4v (0.33 #55, 0.30 #2675, 0.28 #2198), 0f4x7 (0.33 #26, 0.25 #264, 0.25 #2646), 04dn09n (0.33 #36, 0.24 #2656, 0.22 #512), 027dtxw (0.33 #4, 0.20 #242, 0.18 #11907), 0k611 (0.31 #2693, 0.30 #549, 0.28 #2216), 0gs9p (0.31 #2684, 0.28 #2207, 0.26 #540), 054krc (0.30 #307, 0.22 #545, 0.22 #69), 0gq_v (0.27 #1687, 0.22 #2640, 0.20 #2163), 0gqy2 (0.25 #2743, 0.22 #2266, 0.22 #123) >> Best rule #2683 for best value: >> intensional similarity = 4 >> extensional distance = 307 >> proper extension: 06zn1c; >> query: (?x10300, 0gq9h) <- genre(?x10300, ?x53), ?x53 = 07s9rl0, films(?x5673, ?x10300), nominated_for(?x1243, ?x10300) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #4060 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 559 *> proper extension: 06mr2s; *> query: (?x10300, 05f4m9q) <- nominated_for(?x406, ?x10300), award_nominee(?x406, ?x1613), gender(?x406, ?x231), currency(?x406, ?x170) *> conf = 0.09 ranks of expected_values: 65 EVAL 0296rz nominated_for! 05f4m9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 77.000 67.000 0.356 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9140-01rh0w PRED entity: 01rh0w PRED relation: location PRED expected values: 030qb3t => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 175): 030qb3t (0.37 #882, 0.28 #6490, 0.27 #2484), 04jpl (0.14 #17, 0.07 #54491, 0.06 #5624), 0162v (0.10 #6409, 0.07 #9615, 0.02 #903), 06y57 (0.10 #254, 0.02 #1055, 0.01 #2657), 0cr3d (0.07 #54617, 0.07 #3347, 0.07 #29784), 01n7q (0.06 #862, 0.06 #6470, 0.05 #5668), 0k049 (0.06 #809, 0.03 #8019, 0.03 #7218), 059rby (0.06 #6425, 0.05 #9631, 0.05 #14437), 02jx1 (0.05 #1671, 0.03 #3273, 0.03 #2472), 06_kh (0.05 #11, 0.04 #812, 0.03 #3215) >> Best rule #882 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 06_bq1; >> query: (?x1424, 030qb3t) <- award_winner(?x1424, ?x628), participant(?x793, ?x1424), nominated_for(?x1424, ?x508) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rh0w location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.373 http://example.org/people/person/places_lived./people/place_lived/location #9139-05r6t PRED entity: 05r6t PRED relation: parent_genre! PRED expected values: 0m0fw 01_sz1 01hcvm 01nd9f => 74 concepts (58 used for prediction) PRED predicted values (max 10 best out of 246): 0y3_8 (0.50 #3869, 0.50 #2515, 0.40 #1387), 03fpx (0.43 #2869, 0.33 #2418, 0.25 #1065), 01243b (0.40 #1610, 0.40 #1384, 0.33 #2512), 01skxk (0.40 #1668, 0.38 #3247, 0.33 #540), 0g_bh (0.40 #1671, 0.33 #2347, 0.33 #543), 01gbcf (0.40 #1582, 0.33 #2258, 0.33 #454), 034487 (0.40 #1621, 0.33 #493, 0.25 #3200), 016y3j (0.40 #1690, 0.33 #562, 0.25 #3269), 016clz (0.40 #1357, 0.22 #3387, 0.20 #3839), 07v64s (0.33 #2292, 0.33 #488, 0.29 #2743) >> Best rule #3869 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 011j5x; 0y3_8; 059kh; 07gxw; 01_sz1; 02qm5j; 05c6073; >> query: (?x5934, 0y3_8) <- artists(?x5934, ?x10209), artists(?x5934, ?x8131), artists(?x5934, ?x6609), parent_genre(?x2407, ?x5934), ?x8131 = 02hzz, artist(?x3874, ?x6609), award(?x10209, ?x1361), award_winner(?x2054, ?x10209) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #661 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 01243b; *> query: (?x5934, 01nd9f) <- artists(?x5934, ?x6609), artists(?x5934, ?x6234), artists(?x5934, ?x4237), parent_genre(?x3642, ?x5934), ?x3642 = 0dls3, ?x6234 = 0l8g0, profession(?x6609, ?x131), ?x4237 = 01w524f *> conf = 0.33 ranks of expected_values: 18, 27, 73, 147 EVAL 05r6t parent_genre! 01nd9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 74.000 58.000 0.500 http://example.org/music/genre/parent_genre EVAL 05r6t parent_genre! 01hcvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 74.000 58.000 0.500 http://example.org/music/genre/parent_genre EVAL 05r6t parent_genre! 01_sz1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 74.000 58.000 0.500 http://example.org/music/genre/parent_genre EVAL 05r6t parent_genre! 0m0fw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 74.000 58.000 0.500 http://example.org/music/genre/parent_genre #9138-07g1sm PRED entity: 07g1sm PRED relation: list PRED expected values: 05glt => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.22 #9, 0.14 #93, 0.13 #310) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0jqp3; 011ycb; 0b44shh; >> query: (?x7016, 05glt) <- film_release_region(?x7016, ?x1453), nominated_for(?x1033, ?x7016), ?x1033 = 02x73k6, ?x1453 = 06qd3 >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07g1sm list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.222 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #9137-0n6f8 PRED entity: 0n6f8 PRED relation: award_winner! PRED expected values: 02y_rq5 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 301): 0gqwc (0.42 #855, 0.37 #45722, 0.36 #60683), 02y_rq5 (0.42 #855, 0.37 #45722, 0.36 #60683), 02x4x18 (0.42 #855, 0.37 #45722, 0.36 #60683), 0gq9h (0.33 #76, 0.16 #17174, 0.11 #26143), 05p1dby (0.33 #104, 0.07 #17202, 0.06 #26171), 07bdd_ (0.33 #64, 0.07 #17162, 0.05 #26131), 09sb52 (0.20 #467, 0.13 #4312, 0.13 #38074), 027dtxw (0.20 #431, 0.07 #859, 0.04 #16248), 099tbz (0.20 #483, 0.07 #37235, 0.07 #38090), 0gqy2 (0.20 #586, 0.05 #37338, 0.04 #35630) >> Best rule #855 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 03k7bd; >> query: (?x1299, ?x618) <- award(?x1299, ?x618), film(?x1299, ?x4729), ?x4729 = 0c1sgd3 >> conf = 0.42 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0n6f8 award_winner! 02y_rq5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 158.000 158.000 0.421 http://example.org/award/award_category/winners./award/award_honor/award_winner #9136-0rxyk PRED entity: 0rxyk PRED relation: contains! PRED expected values: 09c7w0 => 191 concepts (77 used for prediction) PRED predicted values (max 10 best out of 272): 09c7w0 (0.86 #898, 0.76 #16119, 0.75 #66274), 01n7q (0.25 #14404, 0.24 #16194, 0.22 #19776), 0kpys (0.25 #181, 0.11 #18983, 0.10 #16297), 0nzw2 (0.25 #745, 0.06 #2535, 0.04 #3430), 0gx1l (0.25 #604, 0.02 #6870, 0.02 #11348), 04_1l0v (0.19 #17461, 0.16 #27314, 0.16 #30898), 059rby (0.18 #8972, 0.13 #51958, 0.12 #17031), 05k7sb (0.17 #4608, 0.14 #9085, 0.11 #59237), 013yq (0.14 #1041, 0.13 #2831, 0.03 #58209), 07ssc (0.14 #20625, 0.10 #50178, 0.09 #45698) >> Best rule #898 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 02183k; 01_s9q; 01_r9k; 03818y; 03wv2g; >> query: (?x11376, 09c7w0) <- category(?x11376, ?x134), ?x134 = 08mbj5d, contains(?x3038, ?x11376), ?x3038 = 0d0x8 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rxyk contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 77.000 0.857 http://example.org/location/location/contains #9135-06vlk0 PRED entity: 06vlk0 PRED relation: current_club! PRED expected values: 03xh50 => 55 concepts (43 used for prediction) PRED predicted values (max 10 best out of 30): 03ylxn (0.05 #115, 0.05 #415, 0.05 #175), 03y_f8 (0.05 #723, 0.05 #603, 0.05 #213), 032jlh (0.04 #387, 0.04 #87, 0.04 #237), 033nzk (0.04 #362, 0.04 #692, 0.04 #872), 03yl2t (0.04 #124, 0.04 #214, 0.04 #4), 03d8m4 (0.03 #730, 0.03 #430, 0.03 #910), 03_44z (0.03 #809, 0.03 #419, 0.03 #479), 03xh50 (0.03 #582, 0.03 #72, 0.03 #192), 02ltg3 (0.03 #1030, 0.03 #1063, 0.03 #217), 03ys48 (0.03 #168, 0.03 #348, 0.03 #318) >> Best rule #115 for best value: >> intensional similarity = 13 >> extensional distance = 96 >> proper extension: 02gys2; 02q3n9c; 03j722; 01kj5h; 0303jw; 01tqfs; 03fmw_; 03zbg0; 0dy6c9; 03fn34; ... >> query: (?x7662, 03ylxn) <- position(?x7662, ?x530), position(?x7662, ?x63), position(?x7662, ?x60), team(?x203, ?x7662), ?x60 = 02nzb8, ?x203 = 0dgrmp, ?x63 = 02sdk9v, ?x530 = 02_j1w, position(?x7662, ?x60), team(?x63, ?x7662), position(?x7662, ?x63), team(?x530, ?x7662), position(?x7662, ?x530) >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #582 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 123 *> proper extension: 056xx8; 07sqm1; 0329nn; 02rxrh; 056jrs; 044lbv; 03w7kx; 06lckm; *> query: (?x7662, 03xh50) <- position(?x7662, ?x530), position(?x7662, ?x63), position(?x7662, ?x60), team(?x203, ?x7662), ?x60 = 02nzb8, ?x203 = 0dgrmp, ?x63 = 02sdk9v, ?x530 = 02_j1w, position(?x7662, ?x203), team(?x530, ?x7662), team(?x63, ?x7662), position(?x7662, ?x60) *> conf = 0.03 ranks of expected_values: 8 EVAL 06vlk0 current_club! 03xh50 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 55.000 43.000 0.051 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #9134-01wmgrf PRED entity: 01wmgrf PRED relation: nationality PRED expected values: 09c7w0 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.86 #3718, 0.82 #4928, 0.81 #4521), 04tgp (0.33 #12171, 0.33 #12777, 0.32 #10663), 04_1l0v (0.27 #12573, 0.26 #9758), 02jx1 (0.15 #5261, 0.14 #1036, 0.13 #5060), 07ssc (0.09 #1018, 0.09 #5343, 0.09 #1318), 0d060g (0.07 #809, 0.05 #208, 0.05 #2819), 03rk0 (0.05 #12923, 0.05 #12823, 0.05 #3863), 0f8l9c (0.04 #824, 0.03 #122, 0.03 #1728), 0345h (0.04 #332, 0.03 #1234, 0.03 #432), 03rjj (0.03 #807, 0.02 #9058, 0.02 #2112) >> Best rule #3718 for best value: >> intensional similarity = 3 >> extensional distance = 428 >> proper extension: 0bn9sc; 028rk; 01h320; 09bg4l; 03kdl; 014635; 0dq2k; 06bss; 05n19y; 03y2kr; ... >> query: (?x3122, 09c7w0) <- location(?x3122, ?x4622), district_represented(?x176, ?x4622), contains(?x4622, ?x1505) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wmgrf nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.858 http://example.org/people/person/nationality #9133-019c57 PRED entity: 019c57 PRED relation: student PRED expected values: 0d6d2 => 155 concepts (141 used for prediction) PRED predicted values (max 10 best out of 1377): 04wg38 (0.11 #7607, 0.01 #62002, 0.01 #64094), 05_pkf (0.10 #616, 0.09 #2708, 0.07 #4800), 014zcr (0.10 #27, 0.09 #2119, 0.07 #4211), 01h1b (0.10 #1189, 0.06 #15833, 0.05 #9557), 0cbgl (0.10 #2086, 0.06 #8362, 0.05 #10454), 06bss (0.10 #1166, 0.05 #9534, 0.04 #11626), 0fwy0h (0.10 #840, 0.05 #9208, 0.04 #11300), 033w9g (0.10 #771, 0.05 #9139, 0.03 #38430), 0147jt (0.10 #1571, 0.05 #9939, 0.03 #16215), 02bfmn (0.10 #21, 0.05 #8389, 0.03 #14665) >> Best rule #7607 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 02kj7g; >> query: (?x12874, 04wg38) <- colors(?x12874, ?x9778), student(?x12874, ?x1871), ?x9778 = 09ggk >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #20244 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 07w0v; 03ksy; 0h6rm; 08qnnv; 0ym17; *> query: (?x12874, 0d6d2) <- company(?x4486, ?x12874), major_field_of_study(?x12874, ?x2605), institution(?x1368, ?x12874), ?x2605 = 03g3w *> conf = 0.02 ranks of expected_values: 641 EVAL 019c57 student 0d6d2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 155.000 141.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #9132-01nr63 PRED entity: 01nr63 PRED relation: film PRED expected values: 05fm6m => 71 concepts (55 used for prediction) PRED predicted values (max 10 best out of 410): 04ghz4m (0.22 #1243, 0.06 #6616), 09dv8h (0.17 #2961, 0.14 #4752), 01cmp9 (0.17 #2840, 0.14 #4631), 02qr3k8 (0.11 #1290, 0.06 #6663, 0.04 #8454), 0jzw (0.11 #119, 0.03 #5492, 0.01 #7283), 05fm6m (0.11 #1321, 0.03 #6694, 0.01 #8485), 0992d9 (0.11 #992, 0.03 #6365, 0.01 #8156), 017d93 (0.11 #1113, 0.03 #6486, 0.01 #8277), 027fwmt (0.11 #1594, 0.03 #6967), 0f7hw (0.11 #1560, 0.03 #6933) >> Best rule #1243 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01vw917; >> query: (?x12412, 04ghz4m) <- place_of_birth(?x12412, ?x479), ?x479 = 02dtg, gender(?x12412, ?x231), people(?x3584, ?x12412) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1321 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01vw917; *> query: (?x12412, 05fm6m) <- place_of_birth(?x12412, ?x479), ?x479 = 02dtg, gender(?x12412, ?x231), people(?x3584, ?x12412) *> conf = 0.11 ranks of expected_values: 6 EVAL 01nr63 film 05fm6m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 71.000 55.000 0.222 http://example.org/film/actor/film./film/performance/film #9131-09f2j PRED entity: 09f2j PRED relation: school! PRED expected values: 0jmfv => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 81): 05m_8 (0.25 #83, 0.25 #3, 0.17 #1283), 049n7 (0.25 #92, 0.25 #12, 0.15 #812), 051vz (0.25 #101, 0.25 #21, 0.15 #821), 01d6g (0.25 #143, 0.25 #63, 0.11 #1103), 01slc (0.25 #52, 0.20 #612, 0.16 #1412), 02d02 (0.25 #141, 0.15 #1101, 0.14 #1341), 0512p (0.25 #15, 0.15 #1055, 0.13 #1375), 06wpc (0.25 #138, 0.14 #538, 0.11 #1418), 01yhm (0.25 #19, 0.13 #579, 0.13 #1699), 05tfm (0.25 #96, 0.13 #576, 0.11 #1056) >> Best rule #83 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 017rbx; >> query: (?x4955, 05m_8) <- student(?x4955, ?x4662), student(?x4955, ?x2214), ?x2214 = 02cyfz, award_nominee(?x157, ?x4662) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3442 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 112 *> proper extension: 05kj_; 06mkj; 0d05w3; *> query: (?x4955, ?x799) <- school(?x8542, ?x4955), draft(?x799, ?x8542) *> conf = 0.11 ranks of expected_values: 40 EVAL 09f2j school! 0jmfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 114.000 114.000 0.250 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #9130-093dqjy PRED entity: 093dqjy PRED relation: genre PRED expected values: 02n4kr => 73 concepts (71 used for prediction) PRED predicted values (max 10 best out of 90): 07s9rl0 (0.76 #1201, 0.75 #721, 0.69 #961), 05p553 (0.45 #6145, 0.40 #605, 0.34 #1085), 02kdv5l (0.36 #6143, 0.29 #1083, 0.28 #2528), 02l7c8 (0.35 #1217, 0.33 #617, 0.33 #257), 03k9fj (0.33 #373, 0.29 #493, 0.29 #6153), 04xvlr (0.31 #1202, 0.20 #722, 0.19 #602), 0lsxr (0.24 #730, 0.22 #370, 0.18 #3499), 01hmnh (0.17 #6158, 0.17 #258, 0.16 #1098), 01j1n2 (0.17 #300, 0.11 #420, 0.10 #540), 01t_vv (0.17 #654, 0.10 #774, 0.09 #894) >> Best rule #1201 for best value: >> intensional similarity = 4 >> extensional distance = 190 >> proper extension: 08cfr1; >> query: (?x3714, 07s9rl0) <- language(?x3714, ?x254), film(?x818, ?x3714), genre(?x3714, ?x1509), ?x1509 = 060__y >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #969 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 0fq27fp; *> query: (?x3714, 02n4kr) <- film_release_region(?x3714, ?x94), film_festivals(?x3714, ?x9080), currency(?x3714, ?x170) *> conf = 0.14 ranks of expected_values: 13 EVAL 093dqjy genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 73.000 71.000 0.755 http://example.org/film/film/genre #9129-06npd PRED entity: 06npd PRED relation: administrative_parent PRED expected values: 02j71 => 188 concepts (82 used for prediction) PRED predicted values (max 10 best out of 42): 02j71 (0.83 #9921, 0.82 #7712, 0.81 #11026), 09c7w0 (0.58 #2616, 0.54 #3163, 0.41 #831), 0345h (0.25 #162, 0.09 #6213, 0.06 #1131), 07ssc (0.10 #1940, 0.06 #6748, 0.06 #1531), 0f8l9c (0.09 #6205), 049nq (0.07 #787, 0.07 #648, 0.06 #1477), 03rjj (0.06 #8806, 0.05 #10189, 0.04 #2344), 0d060g (0.06 #8946, 0.02 #5366, 0.02 #6743), 0d05w3 (0.06 #1015, 0.05 #1703, 0.05 #4718), 03l5m1 (0.05 #8940, 0.02 #10185, 0.01 #6874) >> Best rule #9921 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 02lx0; >> query: (?x756, 02j71) <- country(?x150, ?x756), adjoins(?x756, ?x1558), film_release_region(?x1071, ?x1558), ?x1071 = 02d44q >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06npd administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 82.000 0.827 http://example.org/base/aareas/schema/administrative_area/administrative_parent #9128-03r8v_ PRED entity: 03r8v_ PRED relation: nominated_for PRED expected values: 0f42nz => 37 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1446): 0f42nz (0.60 #827, 0.10 #6385, 0.09 #14367), 052_mn (0.60 #1235, 0.10 #6385, 0.09 #12769), 09yxcz (0.60 #1494, 0.02 #7879, 0.02 #4684), 0gmgwnv (0.25 #2559, 0.21 #7350, 0.20 #5753), 0h95927 (0.25 #2760, 0.17 #7551, 0.16 #5954), 02c638 (0.25 #1902, 0.17 #6693, 0.15 #3498), 07w8fz (0.25 #2054, 0.16 #6845, 0.15 #5248), 05c46y6 (0.25 #1988, 0.16 #3584, 0.16 #5182), 04vr_f (0.25 #1750, 0.14 #6541, 0.14 #4944), 0bmhvpr (0.25 #2162, 0.13 #6953, 0.13 #5356) >> Best rule #827 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0b6jkkg; >> query: (?x10156, 0f42nz) <- award(?x3129, ?x10156), nominated_for(?x10156, ?x4444), award_winner(?x10156, ?x656), ?x4444 = 09fn1w >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03r8v_ nominated_for 0f42nz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 10.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9127-01bgqh PRED entity: 01bgqh PRED relation: ceremony PRED expected values: 02rjjll => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 125): 02rjjll (0.83 #1615, 0.70 #1119, 0.64 #1243), 09qftb (0.27 #4467, 0.25 #222, 0.21 #4342), 09pnw5 (0.27 #4467, 0.25 #212, 0.21 #4342), 026kqs9 (0.27 #4467, 0.25 #201, 0.21 #4342), 09p30_ (0.27 #4467, 0.25 #197, 0.21 #4342), 026kq4q (0.27 #4467, 0.25 #161, 0.21 #4342), 0bzm81 (0.27 #4467, 0.25 #141, 0.21 #4342), 0c53zb (0.27 #4467, 0.25 #175, 0.21 #4342), 05zksls (0.27 #4467, 0.25 #152, 0.20 #276), 02yw5r (0.27 #4467, 0.25 #133, 0.15 #1745) >> Best rule #1615 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 02wh75; 01d38g; 02grdc; 02g8mp; 01ckbq; 01c4_6; 02gx2k; 02nbqh; 01ck6h; 02581c; ... >> query: (?x724, 02rjjll) <- award(?x5536, ?x724), award_winner(?x724, ?x1238), ceremony(?x724, ?x6869), ?x6869 = 01xqqp, award_winner(?x1323, ?x5536) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bgqh ceremony 02rjjll CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.829 http://example.org/award/award_category/winners./award/award_honor/ceremony #9126-0b85mm PRED entity: 0b85mm PRED relation: film_release_region PRED expected values: 03f2w => 112 concepts (97 used for prediction) PRED predicted values (max 10 best out of 230): 015fr (0.89 #4623, 0.87 #2875, 0.86 #5579), 05qhw (0.86 #4620, 0.85 #5576, 0.81 #2872), 03gj2 (0.85 #5588, 0.83 #2884, 0.81 #4632), 0154j (0.85 #2861, 0.83 #3338, 0.82 #4609), 05b4w (0.85 #1177, 0.81 #5627, 0.79 #4671), 0jgd (0.84 #2065, 0.83 #5563, 0.81 #2859), 0b90_r (0.82 #4608, 0.81 #2860, 0.80 #5564), 0d060g (0.81 #3340, 0.79 #3499, 0.78 #4611), 06t2t (0.80 #4668, 0.79 #5624, 0.79 #2920), 03rt9 (0.79 #4619, 0.77 #2871, 0.75 #5575) >> Best rule #4623 for best value: >> intensional similarity = 11 >> extensional distance = 78 >> proper extension: 087wc7n; 0407yfx; 0gtsxr4; 0gmgwnv; 0gwjw0c; 0m63c; 0fpgp26; >> query: (?x11809, 015fr) <- film_release_region(?x11809, ?x2152), film_release_region(?x11809, ?x1558), film_release_region(?x11809, ?x1499), film_release_region(?x11809, ?x390), film_release_region(?x11809, ?x205), ?x205 = 03rjj, ?x1499 = 01znc_, ?x1558 = 01mjq, genre(?x11809, ?x53), ?x2152 = 06mkj, ?x390 = 0chghy >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #622 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 0b2qtl; 02754c9; *> query: (?x11809, 03f2w) <- country(?x11809, ?x205), language(?x11809, ?x90), ?x90 = 02bjrlw, film(?x9665, ?x11809), ?x205 = 03rjj, nominated_for(?x198, ?x11809) *> conf = 0.12 ranks of expected_values: 105 EVAL 0b85mm film_release_region 03f2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 112.000 97.000 0.887 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9125-02lyr4 PRED entity: 02lyr4 PRED relation: position! PRED expected values: 04b5l3 => 31 concepts (20 used for prediction) PRED predicted values (max 10 best out of 69): 02d02 (0.89 #89, 0.85 #90, 0.85 #40), 01ypc (0.89 #89, 0.84 #100, 0.83 #57), 07147 (0.85 #90, 0.83 #78, 0.83 #87), 01v3x8 (0.84 #100, 0.83 #87, 0.82 #98), 04b5l3 (0.84 #100, 0.83 #87, 0.82 #98), 04c9bn (0.84 #100, 0.83 #87, 0.82 #98), 0jmnl (0.34 #91, 0.28 #103, 0.22 #80), 0jmk7 (0.34 #91, 0.28 #103, 0.22 #80), 01k8vh (0.34 #91, 0.28 #103, 0.22 #80), 0jm3b (0.34 #91, 0.28 #103, 0.22 #80) >> Best rule #89 for best value: >> intensional similarity = 28 >> extensional distance = 4 >> proper extension: 02sddg; >> query: (?x2010, ?x700) <- team(?x2010, ?x8111), team(?x2010, ?x7725), team(?x2010, ?x4243), team(?x2010, ?x1160), team(?x2010, ?x700), ?x1160 = 049n7, position(?x662, ?x2010), school(?x8111, ?x4955), draft(?x8111, ?x11905), season(?x700, ?x701), position(?x7725, ?x261), school_type(?x4955, ?x3092), student(?x4955, ?x123), school(?x7725, ?x122), sport(?x8111, ?x5063), ?x701 = 05kcgsf, colors(?x7725, ?x663), organization(?x4955, ?x5487), institution(?x620, ?x4955), ?x261 = 02dwn9, ?x11905 = 047dpm0, major_field_of_study(?x4955, ?x3995), major_field_of_study(?x4955, ?x2606), ?x3995 = 0fdys, ?x2606 = 062z7, colors(?x4955, ?x332), ?x4243 = 0713r, school(?x4979, ?x4955) >> conf = 0.89 => this is the best rule for 2 predicted values *> Best rule #100 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 5 *> proper extension: 02rsl1; 02dwpf; *> query: (?x2010, ?x11919) <- team(?x2010, ?x11919), team(?x2010, ?x7357), team(?x2010, ?x1632), team(?x2010, ?x1160), team(?x2010, ?x580), position(?x1160, ?x13623), position(?x1160, ?x2066), school(?x1160, ?x10297), school(?x1160, ?x1884), position(?x1010, ?x2010), ?x10297 = 02rv1w, ?x2066 = 02s7tr, ?x13623 = 02sddg, season(?x7357, ?x701), draft(?x1160, ?x1161), company(?x2998, ?x1884), school(?x1883, ?x1884), major_field_of_study(?x1884, ?x1668), colors(?x1632, ?x3364), colors(?x11919, ?x332), school(?x7357, ?x9131), ?x9131 = 02pptm, fraternities_and_sororities(?x1884, ?x3697), team(?x5412, ?x1632), ?x580 = 05m_8, season(?x1010, ?x8923), institution(?x620, ?x1884), colors(?x388, ?x3364) *> conf = 0.84 ranks of expected_values: 5 EVAL 02lyr4 position! 04b5l3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 31.000 20.000 0.889 http://example.org/sports/sports_team/roster./baseball/baseball_roster_position/position #9124-021s9n PRED entity: 021s9n PRED relation: school_type PRED expected values: 05pcjw => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.59 #73, 0.51 #1042, 0.50 #947), 05pcjw (0.44 #139, 0.39 #93, 0.38 #24), 07tf8 (0.24 #54, 0.22 #169, 0.21 #261), 01_9fk (0.19 #163, 0.14 #255, 0.13 #485), 02p0qmm (0.12 #55, 0.08 #1501, 0.04 #699), 01_srz (0.08 #486, 0.08 #601, 0.07 #210), 0bwd5 (0.08 #110, 0.08 #1501, 0.07 #41), 01y64 (0.08 #1501, 0.04 #218, 0.04 #241), 04qbv (0.07 #245, 0.04 #222, 0.03 #61), 0bpgx (0.03 #204, 0.02 #388, 0.02 #457) >> Best rule #73 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 07tds; 02_jjm; 0ym4t; >> query: (?x6019, 05jxkf) <- institution(?x1305, ?x6019), school_type(?x6019, ?x3205), category(?x6019, ?x134), ?x1305 = 02mjs7 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #139 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 06rjp; *> query: (?x6019, 05pcjw) <- institution(?x1519, ?x6019), school_type(?x6019, ?x3205), contains(?x94, ?x6019), ?x1519 = 013zdg *> conf = 0.44 ranks of expected_values: 2 EVAL 021s9n school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.588 http://example.org/education/educational_institution/school_type #9123-01rnly PRED entity: 01rnly PRED relation: film! PRED expected values: 01438g => 116 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1071): 02hy9p (0.73 #78980, 0.72 #6234, 0.69 #49884), 02vyw (0.54 #6233, 0.47 #78979, 0.43 #60276), 02pqgt8 (0.41 #58198, 0.36 #35332, 0.32 #45727), 016ypb (0.30 #500, 0.06 #2578, 0.03 #6735), 0hvb2 (0.20 #300, 0.06 #62356, 0.03 #2378), 01j7z7 (0.20 #1320, 0.06 #62356, 0.02 #5475), 01z7s_ (0.20 #1035, 0.03 #78981, 0.01 #5190), 0479b (0.20 #1207, 0.02 #9520, 0.01 #38617), 03_wtr (0.20 #1330), 01kj0p (0.20 #482) >> Best rule #78980 for best value: >> intensional similarity = 4 >> extensional distance = 652 >> proper extension: 0gcrg; >> query: (?x9527, ?x6702) <- film(?x788, ?x9527), nominated_for(?x6702, ?x9527), genre(?x9527, ?x53), participant(?x6702, ?x300) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #19227 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 213 *> proper extension: 02vxq9m; 0dnvn3; 016fyc; 03h_yy; 01sxly; 03s6l2; 0gx9rvq; 026p_bs; 01r97z; 08gsvw; ... *> query: (?x9527, 01438g) <- film(?x788, ?x9527), country(?x9527, ?x94), genre(?x9527, ?x604), ?x604 = 0lsxr *> conf = 0.01 ranks of expected_values: 743 EVAL 01rnly film! 01438g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 49.000 0.731 http://example.org/film/actor/film./film/performance/film #9122-01hr11 PRED entity: 01hr11 PRED relation: institution! PRED expected values: 02h4rq6 => 130 concepts (106 used for prediction) PRED predicted values (max 10 best out of 20): 02h4rq6 (0.75 #2, 0.73 #214, 0.73 #129), 014mlp (0.73 #555, 0.66 #216, 0.65 #597), 019v9k (0.67 #8, 0.59 #135, 0.59 #559), 0bkj86 (0.50 #7, 0.47 #219, 0.44 #134), 013zdg (0.44 #112, 0.29 #6, 0.29 #218), 027f2w (0.38 #9, 0.29 #286, 0.25 #958), 04zx3q1 (0.30 #278, 0.29 #1374, 0.28 #1424), 022h5x (0.29 #1374, 0.28 #1424, 0.28 #1423), 0bjrnt (0.29 #1374, 0.28 #1424, 0.28 #1423), 071tyz (0.29 #1374, 0.28 #1424, 0.28 #1423) >> Best rule #2 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 017j69; >> query: (?x8643, 02h4rq6) <- major_field_of_study(?x8643, ?x7134), major_field_of_study(?x8643, ?x1154), ?x7134 = 02_7t, ?x1154 = 02lp1, organization(?x3484, ?x8643) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hr11 institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 106.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9121-01mkn_d PRED entity: 01mkn_d PRED relation: music! PRED expected values: 05qbckf 07p62k 03t95n 01jft4 => 94 concepts (77 used for prediction) PRED predicted values (max 10 best out of 344): 03t95n (0.46 #5029, 0.41 #4023, 0.28 #3016), 084qpk (0.33 #1075, 0.04 #3017, 0.02 #7040), 025s1wg (0.33 #1969, 0.04 #3017, 0.02 #7040), 0gyv0b4 (0.33 #1939, 0.04 #3017, 0.02 #7040), 0d6_s (0.33 #933, 0.04 #3017, 0.02 #7040), 0353tm (0.33 #869, 0.04 #3017, 0.02 #7040), 0n_hp (0.33 #863, 0.04 #3017, 0.02 #7040), 048vhl (0.33 #843, 0.04 #3017, 0.02 #7040), 02r2j8 (0.33 #782, 0.04 #3017, 0.02 #7040), 02825nf (0.33 #747, 0.04 #3017, 0.02 #7040) >> Best rule #5029 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 01vyp_; 01vsy9_; >> query: (?x6664, ?x3344) <- award_winner(?x3344, ?x6664), artists(?x4910, ?x6664), nationality(?x6664, ?x94) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mkn_d music! 01jft4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 77.000 0.455 http://example.org/film/film/music EVAL 01mkn_d music! 03t95n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 77.000 0.455 http://example.org/film/film/music EVAL 01mkn_d music! 07p62k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 77.000 0.455 http://example.org/film/film/music EVAL 01mkn_d music! 05qbckf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 77.000 0.455 http://example.org/film/film/music #9120-01b66t PRED entity: 01b66t PRED relation: nominated_for! PRED expected values: 02_3zj => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 185): 02p_04b (0.79 #6560, 0.79 #6324, 0.71 #10308), 0gq9h (0.41 #12712, 0.37 #13884, 0.36 #14119), 019f4v (0.35 #12703, 0.32 #13875, 0.32 #14110), 0gs9p (0.35 #12714, 0.33 #13886, 0.33 #14121), 02z0dfh (0.35 #235, 0.25 #11011, 0.25 #19699), 0bdwft (0.35 #235, 0.25 #11011, 0.25 #19699), 099cng (0.35 #235, 0.25 #11011, 0.25 #19699), 02x4x18 (0.35 #235, 0.25 #11011, 0.25 #19699), 027b9k6 (0.35 #235, 0.25 #11011, 0.25 #19699), 027571b (0.35 #235, 0.25 #11011, 0.25 #19699) >> Best rule #6560 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 0147w8; >> query: (?x4721, ?x2872) <- award(?x4721, ?x2872), nominated_for(?x2872, ?x416), actor(?x4721, ?x5097) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #182 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 07s8z_l; 0275kr; *> query: (?x4721, 02_3zj) <- award_winner(?x4721, ?x5097), award_winner(?x1132, ?x5097), category(?x4721, ?x134), producer_type(?x4721, ?x632) *> conf = 0.14 ranks of expected_values: 67 EVAL 01b66t nominated_for! 02_3zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 96.000 96.000 0.788 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9119-05_5_22 PRED entity: 05_5_22 PRED relation: film! PRED expected values: 016tw3 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 60): 054lpb6 (0.51 #679, 0.49 #830, 0.48 #452), 05qd_ (0.33 #310, 0.30 #235, 0.26 #688), 03xq0f (0.25 #81, 0.20 #5, 0.19 #1512), 016tw3 (0.20 #387, 0.19 #614, 0.16 #1216), 086k8 (0.20 #228, 0.17 #907, 0.17 #303), 01795t (0.20 #18, 0.13 #1223, 0.12 #94), 0g1rw (0.20 #8, 0.10 #1666, 0.10 #1743), 054g1r (0.20 #35, 0.10 #638, 0.09 #1391), 04mkft (0.20 #36, 0.08 #412, 0.07 #488), 017s11 (0.19 #1208, 0.15 #983, 0.14 #1058) >> Best rule #679 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 03lrqw; 01bn3l; 0f61tk; 0kbwb; 0gyv0b4; >> query: (?x5201, ?x1478) <- film(?x585, ?x5201), person(?x5201, ?x1206), production_companies(?x5201, ?x1478), nominated_for(?x1691, ?x5201) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #387 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0df2zx; *> query: (?x5201, 016tw3) <- film(?x585, ?x5201), person(?x5201, ?x1206), production_companies(?x5201, ?x1478), film_release_region(?x5201, ?x94) *> conf = 0.20 ranks of expected_values: 4 EVAL 05_5_22 film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 80.000 80.000 0.512 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #9118-011_6p PRED entity: 011_6p PRED relation: role! PRED expected values: 0dwtp => 55 concepts (46 used for prediction) PRED predicted values (max 10 best out of 106): 0l14qv (0.90 #1177, 0.79 #747, 0.73 #217), 05148p4 (0.86 #2537, 0.85 #2459, 0.83 #1730), 0l14md (0.86 #2537, 0.83 #2536, 0.82 #4257), 0l14j_ (0.86 #2537, 0.83 #2536, 0.82 #4257), 018vs (0.86 #2537, 0.83 #2536, 0.82 #4257), 0dwtp (0.86 #2537, 0.83 #2536, 0.82 #4257), 04rzd (0.83 #2536, 0.82 #4257, 0.82 #4583), 01vj9c (0.79 #747, 0.75 #1404, 0.71 #986), 0l15bq (0.79 #747, 0.71 #98, 0.65 #968), 042v_gx (0.75 #1811, 0.75 #1715, 0.73 #4165) >> Best rule #1177 for best value: >> intensional similarity = 19 >> extensional distance = 6 >> proper extension: 03qlv7; >> query: (?x2157, ?x228) <- role(?x1212, ?x2157), role(?x228, ?x2157), role(?x212, ?x2157), role(?x2460, ?x2157), ?x1212 = 07xzm, instrumentalists(?x2157, ?x10239), ?x2460 = 01wy6, ?x212 = 026t6, role(?x3967, ?x228), role(?x228, ?x645), instrumentalists(?x228, ?x7088), ?x3967 = 01p970, group(?x228, ?x6986), group(?x228, ?x2901), group(?x228, ?x1945), ?x2901 = 01vrwfv, ?x1945 = 02_5x9, award_winner(?x1974, ?x7088), ?x6986 = 02vgh >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2537 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 18 *> proper extension: 0j210; *> query: (?x2157, ?x885) <- role(?x2157, ?x8172), role(?x2157, ?x1267), role(?x2157, ?x885), role(?x2157, ?x316), ?x1267 = 07brj, group(?x2157, ?x5838), role(?x3967, ?x8172), role(?x2059, ?x8172), role(?x1750, ?x8172), ?x3967 = 01p970, ?x1750 = 02hnl, ?x2059 = 0dwr4, performance_role(?x6052, ?x8172), role(?x2764, ?x885), role(?x885, ?x868), ?x2764 = 01s0ps, role(?x885, ?x2205), group(?x316, ?x6876), group(?x316, ?x3516), instrumentalists(?x316, ?x4693), ?x4693 = 01vwbts, ?x3516 = 05563d, ?x6876 = 0ycp3 *> conf = 0.86 ranks of expected_values: 6 EVAL 011_6p role! 0dwtp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 55.000 46.000 0.905 http://example.org/music/performance_role/regular_performances./music/group_membership/role #9117-0h7x PRED entity: 0h7x PRED relation: olympics PRED expected values: 0124ld => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 30): 0kbvv (0.62 #268, 0.58 #212, 0.55 #156), 0l6vl (0.53 #86, 0.43 #647, 0.39 #1802), 0lgxj (0.49 #365, 0.45 #478, 0.43 #647), 09x3r (0.49 #365, 0.45 #478, 0.43 #647), 0sx8l (0.49 #365, 0.45 #478, 0.43 #647), 0l6mp (0.43 #647, 0.40 #96, 0.39 #1802), 0l98s (0.43 #647, 0.39 #1802, 0.39 #1660), 0l998 (0.43 #647, 0.39 #1802, 0.39 #1660), 0lv1x (0.43 #647, 0.39 #1802, 0.39 #1660), 0sx7r (0.43 #647, 0.39 #1802, 0.39 #1660) >> Best rule #268 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 05r4w; >> query: (?x1355, 0kbvv) <- film_release_region(?x1535, ?x1355), contains(?x1355, ?x863), ?x1535 = 02r1c18 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #107 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 09c7w0; 03rjj; 0d060g; 05qhw; 07ssc; 06mzp; 059j2; 0345h; 035qy; 06c1y; ... *> query: (?x1355, 0124ld) <- contains(?x455, ?x1355), film_release_region(?x66, ?x1355), first_level_division_of(?x863, ?x1355) *> conf = 0.20 ranks of expected_values: 30 EVAL 0h7x olympics 0124ld CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 164.000 164.000 0.621 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #9116-0jqzt PRED entity: 0jqzt PRED relation: film_release_distribution_medium PRED expected values: 07c52 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 3): 02nxhr (0.17 #5, 0.16 #9, 0.14 #1), 07c52 (0.10 #126, 0.10 #122, 0.10 #118), 07z4p (0.08 #8, 0.08 #12, 0.07 #100) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0cqr0q; >> query: (?x11074, 02nxhr) <- film_format(?x11074, ?x6392), genre(?x11074, ?x571), ?x571 = 03npn, film_release_distribution_medium(?x11074, ?x81) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #126 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 483 *> proper extension: 047q2k1; 063zky; *> query: (?x11074, 07c52) <- film_release_region(?x11074, ?x205), film_release_region(?x7204, ?x205), film_release_region(?x3498, ?x205), ?x3498 = 02fqrf, ?x7204 = 0280061 *> conf = 0.10 ranks of expected_values: 2 EVAL 0jqzt film_release_distribution_medium 07c52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.167 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #9115-01q2nx PRED entity: 01q2nx PRED relation: film_crew_role PRED expected values: 02r96rf => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 34): 09zzb8 (0.86 #37, 0.84 #932, 0.81 #825), 0ch6mp2 (0.81 #292, 0.79 #328, 0.77 #832), 02r96rf (0.78 #75, 0.75 #110, 0.72 #613), 01vx2h (0.42 #405, 0.41 #693, 0.41 #835), 02ynfr (0.21 #51, 0.21 #946, 0.20 #192), 01xy5l_ (0.17 #84, 0.17 #1148, 0.16 #1657), 0d2b38 (0.17 #96, 0.17 #1148, 0.16 #1657), 0215hd (0.17 #1148, 0.16 #1657, 0.16 #358), 089g0h (0.17 #1148, 0.16 #1657, 0.16 #358), 015h31 (0.17 #1148, 0.16 #1657, 0.16 #358) >> Best rule #37 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 05r3qc; >> query: (?x5275, 09zzb8) <- genre(?x5275, ?x53), film_crew_role(?x5275, ?x1171), film(?x6791, ?x5275), film(?x4681, ?x5275), nationality(?x6791, ?x94), ?x4681 = 024bbl >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 0kv2hv; *> query: (?x5275, 02r96rf) <- genre(?x5275, ?x53), production_companies(?x5275, ?x4564), ?x4564 = 01gb54, executive_produced_by(?x5275, ?x1533), film(?x820, ?x5275) *> conf = 0.78 ranks of expected_values: 3 EVAL 01q2nx film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9114-01lz4tf PRED entity: 01lz4tf PRED relation: profession PRED expected values: 01d_h8 => 144 concepts (73 used for prediction) PRED predicted values (max 10 best out of 72): 0nbcg (0.73 #322, 0.65 #906, 0.64 #468), 016z4k (0.64 #296, 0.58 #2342, 0.55 #1904), 0dz3r (0.60 #2486, 0.59 #1755, 0.50 #5118), 01d_h8 (0.44 #6, 0.41 #2782, 0.41 #3367), 0n1h (0.38 #1473, 0.35 #2350, 0.35 #888), 0kyk (0.36 #320, 0.27 #466, 0.17 #1051), 03gjzk (0.29 #1622, 0.25 #8515, 0.22 #3668), 0dxtg (0.29 #8514, 0.29 #3375, 0.27 #4837), 02jknp (0.28 #3369, 0.27 #4831, 0.24 #5272), 0d1pc (0.24 #1655, 0.21 #5458, 0.17 #3555) >> Best rule #322 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 01271h; 07g2v; 0fpj4lx; 01vtqml; 03lgg; 03h502k; 01386_; 019389; 0dw3l; >> query: (?x7233, 0nbcg) <- role(?x7233, ?x227), profession(?x7233, ?x1183), artists(?x3753, ?x7233), ?x1183 = 09jwl, ?x3753 = 01_bkd >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 07mvp; 0p8h0; *> query: (?x7233, 01d_h8) <- category(?x7233, ?x134), artists(?x2809, ?x7233), ?x2809 = 05w3f, music(?x9154, ?x7233) *> conf = 0.44 ranks of expected_values: 4 EVAL 01lz4tf profession 01d_h8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 144.000 73.000 0.727 http://example.org/people/person/profession #9113-07_l6 PRED entity: 07_l6 PRED relation: role! PRED expected values: 0565cz 03ryks 016wvy => 78 concepts (45 used for prediction) PRED predicted values (max 10 best out of 939): 050z2 (0.73 #12060, 0.73 #10163, 0.67 #7312), 023l9y (0.67 #7336, 0.67 #3533, 0.64 #12084), 01wxdn3 (0.67 #3735, 0.62 #6584, 0.56 #7538), 082brv (0.67 #3590, 0.56 #7393, 0.55 #12141), 0lzkm (0.67 #3493, 0.56 #7296, 0.50 #8716), 0l12d (0.67 #4336, 0.55 #11938, 0.50 #6236), 02s6sh (0.67 #3761, 0.50 #6610, 0.50 #4710), 0m_v0 (0.67 #4434, 0.50 #6334, 0.50 #3962), 0326tc (0.67 #4620, 0.50 #4148, 0.50 #3671), 03h502k (0.67 #3557, 0.50 #4034, 0.44 #7360) >> Best rule #12060 for best value: >> intensional similarity = 20 >> extensional distance = 9 >> proper extension: 01vj9c; 05842k; >> query: (?x3296, 050z2) <- role(?x3296, ?x960), role(?x3296, ?x645), role(?x3409, ?x3296), role(?x2253, ?x3296), role(?x1473, ?x3296), role(?x1437, ?x3296), role(?x4343, ?x3296), ?x645 = 028tv0, ?x3409 = 0680x0, ?x2253 = 01679d, ?x1473 = 0g2dz, award_nominee(?x4343, ?x248), award_winner(?x139, ?x4343), award_nominee(?x367, ?x4343), ?x1437 = 01vdm0, performance_role(?x4343, ?x10811), award_winner(?x247, ?x248), group(?x960, ?x5303), nationality(?x248, ?x512), role(?x960, ?x212) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #8206 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 7 *> proper extension: 0214km; *> query: (?x3296, 0565cz) <- role(?x3296, ?x11978), role(?x3296, ?x5417), role(?x3296, ?x745), role(?x3716, ?x745), role(?x2725, ?x745), role(?x2158, ?x745), role(?x2059, ?x745), role(?x569, ?x745), role(?x645, ?x745), role(?x4343, ?x3296), ?x2725 = 0l1589, instrumentalists(?x5417, ?x838), ?x2059 = 0dwr4, ?x2158 = 01dnws, role(?x5417, ?x5921), ?x838 = 025xt8y, ?x3716 = 03gvt, role(?x5417, ?x1332), ?x11978 = 02hrlh, ?x5921 = 0g2ff, ?x569 = 07c6l, role(?x745, ?x2620) *> conf = 0.44 ranks of expected_values: 57, 64, 387 EVAL 07_l6 role! 016wvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 78.000 45.000 0.727 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 07_l6 role! 03ryks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 78.000 45.000 0.727 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 07_l6 role! 0565cz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 78.000 45.000 0.727 http://example.org/music/artist/track_contributions./music/track_contribution/role #9112-0315q3 PRED entity: 0315q3 PRED relation: people! PRED expected values: 033tf_ => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 35): 041rx (0.24 #1302, 0.22 #1447, 0.21 #3178), 0x67 (0.20 #295, 0.17 #2173, 0.16 #3109), 033tf_ (0.16 #365, 0.15 #293, 0.14 #149), 07hwkr (0.15 #9, 0.07 #369, 0.06 #2175), 013xrm (0.15 #16, 0.04 #1314, 0.04 #881), 063k3h (0.15 #26, 0.03 #1324, 0.02 #386), 02w7gg (0.13 #1445, 0.10 #290, 0.08 #3176), 07bch9 (0.08 #18, 0.07 #378, 0.06 #90), 03bkbh (0.08 #27, 0.04 #387, 0.04 #315), 013b6_ (0.08 #48, 0.03 #1346, 0.02 #913) >> Best rule #1302 for best value: >> intensional similarity = 2 >> extensional distance = 611 >> proper extension: 0203v; 01wz3cx; 0137g1; 03kdl; 0kh6b; 02t_v1; 039crh; 07cbs; 094xh; 06c97; ... >> query: (?x4631, 041rx) <- religion(?x4631, ?x962), people(?x1176, ?x4631) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #365 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 271 *> proper extension: 019n7x; *> query: (?x4631, 033tf_) <- award_nominee(?x4631, ?x91), participant(?x793, ?x4631), people(?x1176, ?x4631) *> conf = 0.16 ranks of expected_values: 3 EVAL 0315q3 people! 033tf_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.238 http://example.org/people/ethnicity/people #9111-0kbvb PRED entity: 0kbvb PRED relation: olympics! PRED expected values: 0h3y 07t21 => 26 concepts (26 used for prediction) PRED predicted values (max 10 best out of 352): 0f8l9c (0.83 #970, 0.78 #2630, 0.78 #1640), 0d0vqn (0.83 #970, 0.78 #1640, 0.75 #2951), 05qhw (0.83 #970, 0.78 #1640, 0.75 #1130), 0k6nt (0.83 #970, 0.78 #1640, 0.75 #1130), 03rk0 (0.83 #970, 0.78 #1640, 0.75 #1130), 07f1x (0.83 #970, 0.78 #1640, 0.75 #1130), 02vzc (0.83 #970, 0.78 #1640, 0.75 #1130), 03rt9 (0.83 #970, 0.78 #1640, 0.75 #1130), 06vbd (0.83 #970, 0.78 #1640, 0.75 #1130), 01ls2 (0.83 #970, 0.78 #1640, 0.75 #1130) >> Best rule #970 for best value: >> intensional similarity = 58 >> extensional distance = 1 >> proper extension: 09n48; >> query: (?x778, ?x126) <- olympics(?x5147, ?x778), olympics(?x3277, ?x778), olympics(?x2513, ?x778), olympics(?x2346, ?x778), olympics(?x2188, ?x778), olympics(?x774, ?x778), olympics(?x583, ?x778), olympics(?x252, ?x778), olympics(?x94, ?x778), olympics(?x359, ?x778), sports(?x778, ?x3015), country(?x359, ?x789), sports(?x2369, ?x359), ?x2513 = 05b4w, ?x2346 = 0d05w3, country(?x3015, ?x8449), country(?x3015, ?x3227), country(?x3015, ?x2267), ?x2188 = 0163v, ?x252 = 03_3d, olympics(?x3015, ?x1931), ?x789 = 0f8l9c, film_release_region(?x2163, ?x5147), ?x3227 = 0bjv6, administrative_parent(?x5147, ?x551), film_release_region(?x11313, ?x583), film_release_region(?x9432, ?x583), film_release_region(?x8176, ?x583), film_release_region(?x6751, ?x583), film_release_region(?x5713, ?x583), film_release_region(?x3830, ?x583), film_release_region(?x2933, ?x583), film_release_region(?x559, ?x583), administrative_parent(?x12465, ?x583), organization(?x5147, ?x312), ?x774 = 06mzp, film_release_region(?x781, ?x8449), ?x9432 = 0gvt53w, ?x11313 = 0by17xn, sports(?x2369, ?x4045), ?x5713 = 0cc97st, form_of_government(?x8449, ?x6377), film_release_region(?x1421, ?x2267), film_release_region(?x1315, ?x2267), ?x8176 = 0gvvm6l, ?x2933 = 0407yj_, ?x94 = 09c7w0, ?x3830 = 0gjcrrw, olympics(?x126, ?x778), ?x2163 = 0j6b5, form_of_government(?x5147, ?x48), ?x559 = 05p1tzf, ?x6751 = 0372j5, olympics(?x2051, ?x2369), ?x1315 = 053tj7, ?x1421 = 07qg8v, ?x3277 = 06t8v, ?x4045 = 06z6r >> conf = 0.83 => this is the best rule for 30 predicted values *> Best rule #1130 for first EXPECTED value: *> intensional similarity = 55 *> extensional distance = 1 *> proper extension: 0kbvv; *> query: (?x778, ?x4521) <- olympics(?x5147, ?x778), olympics(?x2513, ?x778), olympics(?x2346, ?x778), olympics(?x2188, ?x778), olympics(?x2152, ?x778), olympics(?x583, ?x778), olympics(?x252, ?x778), olympics(?x205, ?x778), olympics(?x359, ?x778), sports(?x778, ?x3015), sports(?x778, ?x766), country(?x359, ?x789), country(?x359, ?x304), sports(?x584, ?x359), ?x2513 = 05b4w, ?x2346 = 0d05w3, country(?x3015, ?x6305), country(?x3015, ?x3227), country(?x3015, ?x2267), country(?x3015, ?x1781), ?x2188 = 0163v, ?x252 = 03_3d, olympics(?x3015, ?x1931), ?x789 = 0f8l9c, film_release_region(?x2163, ?x5147), ?x3227 = 0bjv6, administrative_parent(?x5147, ?x551), ?x583 = 015fr, ?x304 = 0d0vqn, medal(?x5147, ?x422), country(?x766, ?x4521), nationality(?x1524, ?x5147), entity_involved(?x7455, ?x5147), form_of_government(?x6305, ?x48), currency(?x4521, ?x170), sports(?x584, ?x2885), taxonomy(?x1781, ?x939), countries_spoken_in(?x13310, ?x6305), geographic_distribution(?x13008, ?x1781), jurisdiction_of_office(?x182, ?x4521), exported_to(?x1781, ?x5457), ?x422 = 02lq67, film_release_region(?x7692, ?x2267), film_release_region(?x5067, ?x2267), film_release_region(?x2889, ?x2267), film_release_region(?x1915, ?x2267), ?x2889 = 040b5k, ?x2152 = 06mkj, organization(?x2267, ?x7416), ?x7416 = 018cqq, ?x1915 = 0fq7dv_, ?x7692 = 0bt4g, adjoins(?x1780, ?x1781), ?x5067 = 01rwpj, ?x205 = 03rjj *> conf = 0.75 ranks of expected_values: 37, 55 EVAL 0kbvb olympics! 07t21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 26.000 26.000 0.826 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 0kbvb olympics! 0h3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 26.000 26.000 0.826 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #9110-03h_yfh PRED entity: 03h_yfh PRED relation: artists! PRED expected values: 01750n => 117 concepts (86 used for prediction) PRED predicted values (max 10 best out of 209): 03_d0 (0.93 #11849, 0.92 #11537, 0.89 #10602), 06by7 (0.66 #22130, 0.55 #6562, 0.52 #8121), 064t9 (0.66 #19630, 0.63 #6553, 0.62 #8112), 0h08p (0.43 #839, 0.28 #2084, 0.23 #3018), 05bt6j (0.35 #6585, 0.33 #19662, 0.31 #7521), 0gywn (0.33 #1306, 0.33 #61, 0.27 #8782), 0155w (0.33 #110, 0.29 #732, 0.27 #7585), 02w4v (0.33 #47, 0.23 #6586, 0.17 #8768), 07ym47 (0.33 #73, 0.08 #1318, 0.06 #7235), 01ydtg (0.33 #180, 0.06 #6096, 0.05 #7031) >> Best rule #11849 for best value: >> intensional similarity = 5 >> extensional distance = 147 >> proper extension: 05563d; 0h08p; >> query: (?x7803, 03_d0) <- artists(?x9427, ?x7803), artists(?x9427, ?x7331), artists(?x9427, ?x506), ?x506 = 026ps1, gender(?x7331, ?x514) >> conf = 0.93 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03h_yfh artists! 01750n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 86.000 0.926 http://example.org/music/genre/artists #9109-0jmj7 PRED entity: 0jmj7 PRED relation: teams! PRED expected values: 0dc95 => 90 concepts (82 used for prediction) PRED predicted values (max 10 best out of 200): 01_d4 (0.33 #870, 0.33 #330, 0.25 #1683), 01cx_ (0.33 #634, 0.25 #1988, 0.07 #5511), 02_286 (0.33 #22, 0.20 #2729, 0.20 #2459), 0ftxw (0.25 #2251, 0.20 #3064, 0.20 #2792), 0ply0 (0.25 #1724, 0.08 #4433, 0.08 #4978), 0c_m3 (0.25 #2028, 0.04 #2165, 0.03 #8532), 03l2n (0.20 #2566, 0.17 #3379, 0.06 #6627), 02h6_6p (0.20 #3058, 0.12 #3869, 0.08 #4681), 030qb3t (0.17 #4382, 0.15 #4927, 0.13 #5467), 01sn3 (0.17 #3365, 0.08 #4447, 0.08 #4992) >> Best rule #870 for best value: >> intensional similarity = 13 >> extensional distance = 1 >> proper extension: 01y3v; >> query: (?x2820, 01_d4) <- school(?x2820, ?x12485), school(?x2820, ?x7596), school(?x2820, ?x6763), school(?x2820, ?x5288), school(?x2820, ?x4599), institution(?x620, ?x5288), organization(?x346, ?x7596), student(?x7596, ?x5222), team(?x1579, ?x2820), organization(?x5288, ?x5487), contains(?x94, ?x4599), ?x12485 = 0225bv, currency(?x6763, ?x170) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6846 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 16 *> proper extension: 02b190; 01fwqn; *> query: (?x2820, 0dc95) <- colors(?x2820, ?x332), team(?x13926, ?x2820), team(?x4570, ?x2820), team(?x4570, ?x10409), team(?x4570, ?x799), ?x332 = 01l849, sport(?x10409, ?x4833), teams(?x739, ?x799) *> conf = 0.06 ranks of expected_values: 32 EVAL 0jmj7 teams! 0dc95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 90.000 82.000 0.333 http://example.org/sports/sports_team_location/teams #9108-07sc6nw PRED entity: 07sc6nw PRED relation: film! PRED expected values: 01nms7 04gc65 => 125 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1174): 03xnq9_ (0.25 #1012, 0.10 #2083), 01q_ph (0.25 #56, 0.05 #10467, 0.04 #79178), 0mdqp (0.25 #118, 0.05 #10529, 0.03 #31349), 01nfys (0.25 #1573, 0.05 #11984, 0.02 #55707), 029k55 (0.25 #1824, 0.05 #12235, 0.02 #16399), 022q4l9 (0.25 #1198, 0.05 #11609, 0.02 #19937), 015pxr (0.25 #348, 0.05 #10759, 0.02 #17005), 01rcmg (0.25 #1472, 0.05 #11883, 0.02 #20211), 0436kgz (0.25 #1166, 0.05 #11577, 0.01 #74040), 0m0nq (0.25 #1621, 0.05 #12032) >> Best rule #1012 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 0bmssv; >> query: (?x1192, 03xnq9_) <- film(?x8018, ?x1192), film(?x101, ?x1192), genre(?x1192, ?x258), genre(?x1192, ?x225), ?x101 = 06qgvf, ?x258 = 05p553, participant(?x8018, ?x5657), ?x225 = 02kdv5l >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #13907 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 31 *> proper extension: 05znbh7; *> query: (?x1192, 01nms7) <- film(?x3789, ?x1192), genre(?x1192, ?x812), genre(?x1192, ?x162), ?x812 = 01jfsb, ?x162 = 04xvlr, location(?x3789, ?x1523) *> conf = 0.03 ranks of expected_values: 377, 854 EVAL 07sc6nw film! 04gc65 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 125.000 59.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 07sc6nw film! 01nms7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 125.000 59.000 0.250 http://example.org/film/actor/film./film/performance/film #9107-0bv8h2 PRED entity: 0bv8h2 PRED relation: films! PRED expected values: 0fx2s => 90 concepts (36 used for prediction) PRED predicted values (max 10 best out of 61): 06796 (0.11 #130), 0fzyg (0.06 #524, 0.04 #210, 0.03 #1000), 081pw (0.06 #473, 0.04 #1109, 0.04 #631), 0fx2s (0.05 #1179, 0.03 #2131, 0.03 #543), 01vq3 (0.04 #1782, 0.04 #197, 0.04 #353), 07yjb (0.04 #221, 0.01 #1806, 0.01 #853), 0hkt6 (0.04 #276), 034p8 (0.04 #270), 05g7q (0.04 #245), 0d06vc (0.04 #180) >> Best rule #130 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 024hbv; >> query: (?x3595, 06796) <- nominated_for(?x4563, ?x3595), ?x4563 = 0dzf_, nominated_for(?x3458, ?x3595) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #1179 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 169 *> proper extension: 03cffvv; *> query: (?x3595, 0fx2s) <- film(?x4563, ?x3595), genre(?x3595, ?x1509), film_release_distribution_medium(?x3595, ?x81), ?x1509 = 060__y *> conf = 0.05 ranks of expected_values: 4 EVAL 0bv8h2 films! 0fx2s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 36.000 0.111 http://example.org/film/film_subject/films #9106-01rxw PRED entity: 01rxw PRED relation: organization PRED expected values: 0b6css => 90 concepts (88 used for prediction) PRED predicted values (max 10 best out of 49): 041288 (0.69 #36, 0.61 #78, 0.60 #99), 0b6css (0.67 #30, 0.56 #72, 0.56 #821), 0j7v_ (0.56 #821, 0.56 #820, 0.39 #131), 0_2v (0.32 #1204, 0.28 #339, 0.28 #633), 04k4l (0.32 #1204, 0.26 #319, 0.25 #676), 01rz1 (0.32 #1204, 0.25 #253, 0.25 #568), 018cqq (0.32 #1204, 0.17 #178, 0.17 #325), 085h1 (0.32 #1204, 0.16 #1675, 0.04 #116), 02jxk (0.32 #1204, 0.14 #317, 0.13 #569), 059dn (0.32 #1204, 0.04 #392, 0.04 #539) >> Best rule #36 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 07p7g; >> query: (?x6863, 041288) <- contains(?x2467, ?x6863), ?x2467 = 0dg3n1, administrative_parent(?x6863, ?x551) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 07p7g; *> query: (?x6863, 0b6css) <- contains(?x2467, ?x6863), ?x2467 = 0dg3n1, administrative_parent(?x6863, ?x551) *> conf = 0.67 ranks of expected_values: 2 EVAL 01rxw organization 0b6css CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 88.000 0.689 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #9105-0fz2y7 PRED entity: 0fz2y7 PRED relation: ceremony! PRED expected values: 0gqyl 0gr42 0gq_d => 38 concepts (37 used for prediction) PRED predicted values (max 10 best out of 372): 0gq_d (0.91 #4780, 0.91 #4536, 0.90 #4291), 0gqyl (0.89 #1773, 0.88 #4702, 0.88 #4458), 0gr4k (0.88 #3190, 0.87 #3434, 0.87 #2702), 0l8z1 (0.80 #1992, 0.79 #2236, 0.78 #1748), 0gr42 (0.80 #4466, 0.79 #4221, 0.78 #4710), 018wdw (0.75 #9056, 0.75 #9055, 0.75 #8808), 0gqxm (0.75 #9056, 0.75 #9055, 0.75 #8808), 0czp_ (0.75 #9056, 0.75 #9055, 0.75 #8808), 02x201b (0.75 #9056, 0.75 #9055, 0.75 #8808), 0gqzz (0.75 #9056, 0.75 #9055, 0.75 #8808) >> Best rule #4780 for best value: >> intensional similarity = 16 >> extensional distance = 63 >> proper extension: 0bzk8w; >> query: (?x4388, 0gq_d) <- ceremony(?x1307, ?x4388), award_winner(?x4388, ?x2801), award(?x6048, ?x1307), ?x6048 = 01cmp9, nominated_for(?x1307, ?x6137), nominated_for(?x1307, ?x4047), ceremony(?x1307, ?x7589), ceremony(?x1307, ?x7144), ceremony(?x1307, ?x7038), award_winner(?x1307, ?x163), ?x7144 = 02yxh9, ?x6137 = 06cm5, ?x7589 = 0fz0c2, award(?x71, ?x1307), ?x7038 = 073hgx, film_release_region(?x4047, ?x87) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5 EVAL 0fz2y7 ceremony! 0gq_d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 37.000 0.908 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0fz2y7 ceremony! 0gr42 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 38.000 37.000 0.908 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0fz2y7 ceremony! 0gqyl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 37.000 0.908 http://example.org/award/award_category/winners./award/award_honor/ceremony #9104-0gy0l_ PRED entity: 0gy0l_ PRED relation: film_crew_role PRED expected values: 09zzb8 09vw2b7 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 35): 09zzb8 (0.79 #1501, 0.77 #1242, 0.77 #695), 09vw2b7 (0.73 #1507, 0.73 #701, 0.72 #1835), 0dxtw (0.52 #705, 0.50 #741, 0.40 #1511), 01pvkk (0.31 #742, 0.31 #706, 0.30 #449), 02ynfr (0.23 #453, 0.22 #710, 0.21 #746), 0d2b38 (0.21 #756, 0.15 #720, 0.14 #463), 02rh1dz (0.20 #447, 0.20 #704, 0.17 #740), 015h31 (0.20 #703, 0.18 #739, 0.12 #3292), 0215hd (0.19 #749, 0.17 #713, 0.14 #2395), 02_n3z (0.18 #38, 0.13 #184, 0.13 #74) >> Best rule #1501 for best value: >> intensional similarity = 4 >> extensional distance = 443 >> proper extension: 02vqhv0; 0pvms; 04grkmd; 09v71cj; 0bmch_x; 02prwdh; 0dll_t2; 0bq6ntw; 05pdd86; 0gg5kmg; ... >> query: (?x9133, 09zzb8) <- genre(?x9133, ?x53), film_crew_role(?x9133, ?x468), ?x468 = 02r96rf, produced_by(?x9133, ?x1365) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0gy0l_ film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.789 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0gy0l_ film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.789 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9103-0260bz PRED entity: 0260bz PRED relation: film! PRED expected values: 02mjf2 => 73 concepts (37 used for prediction) PRED predicted values (max 10 best out of 884): 06pj8 (0.45 #37410, 0.45 #58188, 0.44 #62347), 016dmx (0.45 #37410, 0.45 #58188, 0.43 #12469), 0146pg (0.45 #58188, 0.44 #62347, 0.43 #12469), 046qq (0.14 #738, 0.02 #25678, 0.02 #13207), 05prs8 (0.11 #4157, 0.10 #31174), 0bxtg (0.10 #75, 0.06 #60268, 0.04 #2154), 0f0kz (0.10 #512, 0.04 #12981, 0.04 #15060), 01fh9 (0.10 #315, 0.03 #6550, 0.02 #14863), 0dvmd (0.10 #524, 0.03 #12993, 0.02 #17151), 0d608 (0.10 #1302, 0.02 #30398, 0.02 #20007) >> Best rule #37410 for best value: >> intensional similarity = 4 >> extensional distance = 513 >> proper extension: 0170z3; 014_x2; 0ds35l9; 0d90m; 03qcfvw; 0m313; 02y_lrp; 0sxg4; 083shs; 028_yv; ... >> query: (?x2107, ?x2135) <- titles(?x162, ?x2107), genre(?x2107, ?x53), film_crew_role(?x2107, ?x137), award_winner(?x2107, ?x2135) >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #15319 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 167 *> proper extension: 0jzw; *> query: (?x2107, 02mjf2) <- nominated_for(?x669, ?x2107), production_companies(?x2107, ?x2246), nominated_for(?x451, ?x2107), crewmember(?x2107, ?x6546) *> conf = 0.01 ranks of expected_values: 796 EVAL 0260bz film! 02mjf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 37.000 0.449 http://example.org/film/actor/film./film/performance/film #9102-02vqhv0 PRED entity: 02vqhv0 PRED relation: genre PRED expected values: 03k9fj => 118 concepts (68 used for prediction) PRED predicted values (max 10 best out of 102): 04xvlr (0.76 #5385, 0.44 #1313, 0.42 #2269), 03k9fj (0.64 #4077, 0.61 #1922, 0.60 #1682), 01jfsb (0.62 #1206, 0.53 #2400, 0.50 #2758), 05p553 (0.60 #1914, 0.59 #1674, 0.50 #242), 02kdv5l (0.59 #1195, 0.58 #2747, 0.57 #2389), 02l7c8 (0.56 #6239, 0.49 #1329, 0.43 #2524), 06n90 (0.46 #1207, 0.30 #2401, 0.29 #2759), 01zhp (0.34 #1746, 0.33 #1986, 0.25 #314), 060__y (0.33 #2525, 0.33 #2286, 0.31 #2644), 06qln (0.31 #693, 0.14 #217, 0.12 #336) >> Best rule #5385 for best value: >> intensional similarity = 7 >> extensional distance = 286 >> proper extension: 0b76kw1; 03cv_gy; 05z43v; >> query: (?x2024, 04xvlr) <- genre(?x2024, ?x4088), genre(?x8555, ?x4088), genre(?x4678, ?x4088), genre(?x2026, ?x4088), ?x2026 = 04kzqz, ?x4678 = 0prhz, ?x8555 = 04qk12 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #4077 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 198 *> proper extension: 0gtv7pk; 020fcn; 0340hj; 01f8gz; 0cd2vh9; 05p3738; 0fdv3; 0by1wkq; 09k56b7; 064n1pz; ... *> query: (?x2024, 03k9fj) <- film_crew_role(?x2024, ?x2154), genre(?x2024, ?x2540), genre(?x8846, ?x2540), ?x8846 = 0170k0, ?x2154 = 01vx2h *> conf = 0.64 ranks of expected_values: 2 EVAL 02vqhv0 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 68.000 0.760 http://example.org/film/film/genre #9101-01rm8b PRED entity: 01rm8b PRED relation: origin PRED expected values: 052bw => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 88): 04jpl (0.27 #478, 0.25 #6, 0.18 #714), 0rng (0.25 #144, 0.09 #616, 0.06 #852), 01vx3m (0.14 #379, 0.03 #2739), 02_286 (0.12 #3320, 0.12 #960, 0.11 #3793), 0d6lp (0.12 #1009, 0.11 #1717, 0.09 #3842), 030qb3t (0.12 #978, 0.10 #5227, 0.09 #4991), 01n7q (0.09 #499, 0.06 #735, 0.06 #1207), 0d9jr (0.06 #2222, 0.06 #1042, 0.04 #5055), 01_d4 (0.06 #2164, 0.03 #7358, 0.03 #4997), 013yq (0.06 #753, 0.06 #1225, 0.04 #1933) >> Best rule #478 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 0197tq; 0259r0; 02w4fkq; 01q32bd; 01vvyfh; 025ldg; >> query: (?x3773, 04jpl) <- artists(?x3928, ?x3773), artists(?x3370, ?x3773), artists(?x2823, ?x3773), ?x2823 = 02qdgx, award(?x3773, ?x1389), ?x3928 = 0gywn, artists(?x3370, ?x8226), ?x8226 = 017lb_ >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #2270 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 29 *> proper extension: 016890; *> query: (?x3773, 052bw) <- artists(?x3061, ?x3773), group(?x745, ?x3773), artists(?x3061, ?x7924), ?x7924 = 03t852, ?x745 = 01vj9c *> conf = 0.03 ranks of expected_values: 39 EVAL 01rm8b origin 052bw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 79.000 79.000 0.273 http://example.org/music/artist/origin #9100-02qdgx PRED entity: 02qdgx PRED relation: artists PRED expected values: 01vrt_c 0j1yf 01vn35l 0bqsy 01q3_2 => 60 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1037): 0g824 (0.71 #5776, 0.67 #4732, 0.50 #1600), 0127s7 (0.71 #5740, 0.67 #4696, 0.50 #1564), 01vvycq (0.71 #5262, 0.67 #4218, 0.50 #1086), 019g40 (0.71 #5355, 0.67 #4311, 0.50 #1179), 01dwrc (0.71 #5724, 0.67 #4680, 0.40 #2592), 01wcp_g (0.71 #5308, 0.67 #4264, 0.40 #2176), 02l840 (0.71 #5265, 0.67 #4221, 0.40 #2133), 01vrt_c (0.71 #5293, 0.67 #4249, 0.40 #2161), 0x3n (0.71 #5769, 0.67 #4725, 0.40 #2637), 0gbwp (0.71 #5555, 0.67 #4511, 0.40 #2423) >> Best rule #5776 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0glt670; >> query: (?x2823, 0g824) <- artists(?x2823, ?x8156), artists(?x2823, ?x4200), artists(?x2823, ?x3737), ?x3737 = 01q32bd, group(?x227, ?x8156), award_winner(?x3121, ?x4200), person(?x1183, ?x4200) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5293 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 0glt670; *> query: (?x2823, 01vrt_c) <- artists(?x2823, ?x8156), artists(?x2823, ?x4200), artists(?x2823, ?x3737), ?x3737 = 01q32bd, group(?x227, ?x8156), award_winner(?x3121, ?x4200), person(?x1183, ?x4200) *> conf = 0.71 ranks of expected_values: 8, 48, 55, 164, 457 EVAL 02qdgx artists 01q3_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 60.000 31.000 0.714 http://example.org/music/genre/artists EVAL 02qdgx artists 0bqsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 60.000 31.000 0.714 http://example.org/music/genre/artists EVAL 02qdgx artists 01vn35l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 60.000 31.000 0.714 http://example.org/music/genre/artists EVAL 02qdgx artists 0j1yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 60.000 31.000 0.714 http://example.org/music/genre/artists EVAL 02qdgx artists 01vrt_c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 60.000 31.000 0.714 http://example.org/music/genre/artists #9099-024bbl PRED entity: 024bbl PRED relation: location PRED expected values: 0cc56 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 105): 030qb3t (0.47 #46648, 0.42 #49063, 0.42 #49868), 02_286 (0.17 #2450, 0.17 #3254, 0.17 #37), 0rh6k (0.11 #1613, 0.05 #808, 0.04 #4829), 01cx_ (0.08 #163, 0.06 #1772, 0.05 #967), 0ccvx (0.08 #222, 0.05 #1026, 0.05 #2635), 04ykg (0.08 #68, 0.05 #872, 0.02 #2481), 03s0w (0.08 #48, 0.05 #852, 0.02 #2461), 01_d4 (0.08 #1711, 0.05 #3319, 0.05 #2515), 0chgzm (0.08 #411), 0ftyc (0.08 #259) >> Best rule #46648 for best value: >> intensional similarity = 2 >> extensional distance = 2312 >> proper extension: 05fh2; >> query: (?x4681, ?x1523) <- place_of_birth(?x4681, ?x1523), location(?x71, ?x1523) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #2470 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 61 *> proper extension: 0d0l91; *> query: (?x4681, 0cc56) <- student(?x1368, ?x4681), actor(?x715, ?x4681) *> conf = 0.08 ranks of expected_values: 12 EVAL 024bbl location 0cc56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 74.000 74.000 0.468 http://example.org/people/person/places_lived./people/place_lived/location #9098-01y20v PRED entity: 01y20v PRED relation: registering_agency PRED expected values: 03z19 => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.85 #10, 0.85 #7, 0.84 #6) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 04sylm; >> query: (?x6846, 03z19) <- contains(?x94, ?x6846), currency(?x6846, ?x170), school_type(?x6846, ?x3205), institution(?x1200, ?x6846) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y20v registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.849 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #9097-05k17c PRED entity: 05k17c PRED relation: organization PRED expected values: 014zws 04gxp2 => 31 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2125): 04rwx (0.72 #736, 0.67 #3687, 0.60 #9596), 0bwfn (0.72 #736, 0.67 #3687, 0.60 #9596), 08815 (0.72 #736, 0.67 #3687, 0.60 #9596), 05x_5 (0.72 #736, 0.67 #3687, 0.60 #9596), 07vhb (0.72 #736, 0.67 #3687, 0.60 #9596), 065y4w7 (0.72 #736, 0.67 #3687, 0.60 #9596), 01bm_ (0.72 #736, 0.67 #3687, 0.60 #9596), 018sg9 (0.72 #736, 0.67 #3687, 0.60 #9596), 0kqj1 (0.72 #736, 0.67 #3687, 0.60 #9596), 014xf6 (0.72 #736, 0.67 #3687, 0.60 #9596) >> Best rule #736 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 060c4; >> query: (?x3484, ?x4278) <- organization(?x3484, ?x12742), organization(?x3484, ?x11648), company(?x3484, ?x4278), company(?x3484, ?x1665), student(?x12742, ?x2208), state_province_region(?x11648, ?x1274), institution(?x1200, ?x12742), ?x1665 = 04rwx, student(?x4278, ?x3291), contains(?x5771, ?x11648) >> conf = 0.72 => this is the best rule for 13 predicted values *> Best rule #2948 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 0dq3c; *> query: (?x3484, ?x1513) <- organization(?x3484, ?x5178), organization(?x3484, ?x1520), organization(?x3484, ?x1306), company(?x3484, ?x1665), category(?x1306, ?x134), company(?x1913, ?x5178), state_province_region(?x1520, ?x2020), state_province_region(?x1513, ?x2020), service_location(?x1665, ?x94), contains(?x2020, ?x1151), district_represented(?x176, ?x2020) *> conf = 0.28 ranks of expected_values: 506, 1394 EVAL 05k17c organization 04gxp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 24.000 0.717 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 05k17c organization 014zws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 31.000 24.000 0.717 http://example.org/organization/role/leaders./organization/leadership/organization #9096-03nl5k PRED entity: 03nl5k PRED relation: ceremony PRED expected values: 019bk0 => 50 concepts (44 used for prediction) PRED predicted values (max 10 best out of 123): 0466p0j (0.91 #575, 0.89 #448, 0.86 #702), 05pd94v (0.85 #510, 0.84 #1399, 0.84 #1272), 019bk0 (0.84 #521, 0.84 #1156, 0.83 #648), 0gx1673 (0.51 #1377, 0.50 #1504, 0.49 #1758), 0bzm81 (0.23 #18, 0.16 #2177, 0.15 #1542), 0n8_m93 (0.23 #105, 0.16 #2264, 0.14 #2391), 02yxh9 (0.23 #88, 0.15 #2247, 0.14 #1612), 0bc773 (0.23 #45, 0.15 #2204, 0.14 #1569), 02yw5r (0.23 #9, 0.15 #2168, 0.14 #1533), 0gmdkyy (0.23 #25, 0.15 #2184, 0.13 #2311) >> Best rule #575 for best value: >> intensional similarity = 8 >> extensional distance = 53 >> proper extension: 026mg3; 01c9f2; 02nhxf; 025m8y; 03qbh5; 024fz9; 03qbnj; 01c9dd; 03q27t; 024fxq; >> query: (?x11048, 0466p0j) <- ceremony(?x11048, ?x5766), ceremony(?x11048, ?x2704), ceremony(?x11048, ?x2054), ceremony(?x11048, ?x486), ?x2704 = 01mhwk, ?x5766 = 013b2h, ?x486 = 02rjjll, ?x2054 = 0gpjbt >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #521 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 53 *> proper extension: 026mg3; 01c9f2; 02nhxf; 025m8y; 03qbh5; 024fz9; 03qbnj; 01c9dd; 03q27t; 024fxq; *> query: (?x11048, 019bk0) <- ceremony(?x11048, ?x5766), ceremony(?x11048, ?x2704), ceremony(?x11048, ?x2054), ceremony(?x11048, ?x486), ?x2704 = 01mhwk, ?x5766 = 013b2h, ?x486 = 02rjjll, ?x2054 = 0gpjbt *> conf = 0.84 ranks of expected_values: 3 EVAL 03nl5k ceremony 019bk0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 50.000 44.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony #9095-0190yn PRED entity: 0190yn PRED relation: artists PRED expected values: 01x1cn2 => 64 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1071): 03d9d6 (0.67 #1592, 0.44 #5914, 0.14 #8078), 04mn81 (0.61 #7565, 0.50 #3384, 0.48 #12975), 04vrxh (0.61 #7565, 0.48 #12975, 0.47 #21635), 01dwrc (0.57 #2685, 0.50 #7007, 0.50 #3764), 011z3g (0.57 #2765, 0.38 #3844, 0.37 #12496), 024qwq (0.57 #3028, 0.36 #8432, 0.27 #12759), 0x3n (0.57 #2731, 0.32 #8135, 0.25 #3810), 019f9z (0.57 #2760, 0.27 #8164, 0.27 #12491), 0gbwp (0.57 #2511, 0.27 #7915, 0.25 #8645), 0407f (0.57 #2441, 0.27 #7845, 0.23 #12172) >> Best rule #1592 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 01cbwl; >> query: (?x12818, 03d9d6) <- artists(?x12818, ?x6835), artists(?x12818, ?x677), ?x677 = 06y9c2, participant(?x6835, ?x827), participant(?x6835, ?x4394), nationality(?x6835, ?x1957), award_nominee(?x140, ?x6835) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2357 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 02x8m; 0glt670; 06j6l; 0gywn; 026z9; *> query: (?x12818, 01x1cn2) <- parent_genre(?x5630, ?x12818), artists(?x12818, ?x677), parent_genre(?x12818, ?x283), ?x5630 = 016_nr, profession(?x677, ?x2659), ?x2659 = 039v1 *> conf = 0.43 ranks of expected_values: 81 EVAL 0190yn artists 01x1cn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 64.000 33.000 0.667 http://example.org/music/genre/artists #9094-01y06y PRED entity: 01y06y PRED relation: institution! PRED expected values: 03bwzr4 => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.84 #1666, 0.70 #294, 0.70 #1029), 019v9k (0.77 #75, 0.69 #97, 0.68 #298), 03bwzr4 (0.77 #80, 0.69 #102, 0.61 #303), 027f2w (0.77 #76, 0.56 #98, 0.50 #165), 02h4rq6 (0.75 #491, 0.72 #869, 0.70 #1731), 0bkj86 (0.63 #297, 0.62 #74, 0.57 #496), 04zx3q1 (0.62 #68, 0.56 #90, 0.45 #291), 013zdg (0.55 #162, 0.54 #73, 0.50 #95), 01rr_d (0.55 #172, 0.50 #105, 0.46 #83), 07s6fsf (0.45 #290, 0.38 #67, 0.35 #644) >> Best rule #1666 for best value: >> intensional similarity = 6 >> extensional distance = 340 >> proper extension: 015nl4; 0373qt; 07wkd; >> query: (?x12877, 014mlp) <- institution(?x3437, ?x12877), student(?x12877, ?x3994), institution(?x3437, ?x5085), institution(?x3437, ?x546), ?x546 = 01j_9c, ?x5085 = 02dj3 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #80 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 0pspl; 01pj48; 0lk0l; *> query: (?x12877, 03bwzr4) <- institution(?x7817, ?x12877), institution(?x3437, ?x12877), school_type(?x12877, ?x3092), ?x7817 = 02cq61, ?x3437 = 02_xgp2, student(?x12877, ?x3994) *> conf = 0.77 ranks of expected_values: 3 EVAL 01y06y institution! 03bwzr4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 189.000 189.000 0.836 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9093-01q0kg PRED entity: 01q0kg PRED relation: contains! PRED expected values: 09c7w0 => 190 concepts (130 used for prediction) PRED predicted values (max 10 best out of 351): 09c7w0 (0.85 #82299, 0.84 #40252, 0.83 #37568), 0l2rj (0.50 #1414, 0.04 #40769, 0.03 #30036), 059rby (0.44 #91259, 0.35 #29536, 0.25 #10751), 02_286 (0.44 #29559, 0.17 #72501, 0.17 #73395), 04jpl (0.28 #72480, 0.26 #73374, 0.19 #88579), 07b_l (0.25 #3797, 0.21 #7376, 0.12 #8270), 015zxh (0.25 #1001, 0.11 #67090, 0.02 #19778), 030qb3t (0.24 #29616, 0.19 #41243, 0.12 #9043), 02jx1 (0.19 #72544, 0.18 #60017, 0.18 #73438), 03rjj (0.16 #113606, 0.06 #24154, 0.05 #23259) >> Best rule #82299 for best value: >> intensional similarity = 4 >> extensional distance = 248 >> proper extension: 05cwl_; 0sxgh; 02htv6; 043q2z; >> query: (?x4257, 09c7w0) <- currency(?x4257, ?x170), contains(?x1227, ?x4257), student(?x4257, ?x846), ?x170 = 09nqf >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q0kg contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 190.000 130.000 0.852 http://example.org/location/location/contains #9092-0kb1g PRED entity: 0kb1g PRED relation: nominated_for! PRED expected values: 0gr51 => 66 concepts (63 used for prediction) PRED predicted values (max 10 best out of 209): 0k611 (0.66 #7228, 0.66 #6062, 0.66 #7695), 019f4v (0.53 #517, 0.52 #284, 0.50 #750), 0gr4k (0.44 #24, 0.34 #490, 0.32 #723), 040njc (0.43 #473, 0.42 #240, 0.40 #706), 0gr51 (0.40 #307, 0.31 #540, 0.29 #773), 04dn09n (0.31 #266, 0.30 #2829, 0.30 #499), 0gr0m (0.30 #2853, 0.28 #523, 0.27 #290), 0gs96 (0.27 #2881, 0.22 #85, 0.19 #1017), 0gqwc (0.27 #291, 0.24 #524, 0.23 #757), 02qyntr (0.26 #2971, 0.25 #408, 0.23 #641) >> Best rule #7228 for best value: >> intensional similarity = 4 >> extensional distance = 953 >> proper extension: 0g60z; 02_1q9; 080dwhx; 02_1rq; 03kq98; 072kp; 039fgy; 0kfpm; 02k_4g; 0358x_; ... >> query: (?x9993, ?x1703) <- nominated_for(?x500, ?x9993), nominated_for(?x382, ?x9993), award(?x9993, ?x1703), award(?x197, ?x500) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #307 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 0jqb8; 0m3gy; *> query: (?x9993, 0gr51) <- genre(?x9993, ?x53), country(?x9993, ?x94), list(?x9993, ?x3004), written_by(?x9993, ?x6239) *> conf = 0.40 ranks of expected_values: 5 EVAL 0kb1g nominated_for! 0gr51 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 66.000 63.000 0.664 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9091-01w565 PRED entity: 01w565 PRED relation: artist PRED expected values: 0411q 0fcsd 01kymm => 143 concepts (13 used for prediction) PRED predicted values (max 10 best out of 850): 06k02 (0.33 #132, 0.29 #971, 0.22 #1812), 0fcsd (0.33 #304, 0.29 #1143, 0.17 #1984), 01k23t (0.33 #2246, 0.26 #5602, 0.25 #6441), 016376 (0.33 #2434, 0.25 #6629, 0.24 #7468), 02x8z_ (0.33 #310, 0.17 #1990, 0.14 #1149), 01wg3q (0.33 #645, 0.14 #1484, 0.11 #2325), 04rcr (0.33 #30, 0.14 #869, 0.11 #1710), 01jfr3y (0.33 #423, 0.14 #1262, 0.06 #2103), 0ftqr (0.29 #1558, 0.07 #10797, 0.06 #2399), 0g824 (0.28 #2137, 0.22 #5493, 0.21 #6332) >> Best rule #132 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 07gqbk; >> query: (?x7763, 06k02) <- citytown(?x7763, ?x9559), artist(?x7763, ?x12753), artist(?x7763, ?x7764), ?x12753 = 0f8grf, category(?x7763, ?x134), ?x134 = 08mbj5d, artists(?x9401, ?x7764), ?x9401 = 025g__ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #304 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 07gqbk; *> query: (?x7763, 0fcsd) <- citytown(?x7763, ?x9559), artist(?x7763, ?x12753), artist(?x7763, ?x7764), ?x12753 = 0f8grf, category(?x7763, ?x134), ?x134 = 08mbj5d, artists(?x9401, ?x7764), ?x9401 = 025g__ *> conf = 0.33 ranks of expected_values: 2, 608 EVAL 01w565 artist 01kymm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 143.000 13.000 0.333 http://example.org/music/record_label/artist EVAL 01w565 artist 0fcsd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 13.000 0.333 http://example.org/music/record_label/artist EVAL 01w565 artist 0411q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 13.000 0.333 http://example.org/music/record_label/artist #9090-03vyw8 PRED entity: 03vyw8 PRED relation: nominated_for! PRED expected values: 02x4sn8 => 81 concepts (75 used for prediction) PRED predicted values (max 10 best out of 220): 019f4v (0.33 #766, 0.32 #1003, 0.26 #2899), 0f4x7 (0.33 #737, 0.29 #974, 0.18 #2870), 057xs89 (0.33 #118, 0.29 #592, 0.24 #8298), 0gq9h (0.32 #1012, 0.32 #775, 0.29 #2908), 0k611 (0.32 #786, 0.29 #1023, 0.23 #2919), 0gs9p (0.29 #1014, 0.27 #777, 0.25 #2910), 0gs96 (0.29 #564, 0.22 #801, 0.21 #1038), 05ztjjw (0.29 #484, 0.17 #10, 0.12 #247), 0gq_v (0.28 #731, 0.24 #968, 0.21 #494), 0gr0m (0.27 #772, 0.22 #1009, 0.18 #2905) >> Best rule #766 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 0p_sc; 06krf3; 09gdm7q; 0c9k8; 0pd6l; 01k60v; 0gmgwnv; 04lhc4; 0294mx; 0n_hp; ... >> query: (?x6058, 019f4v) <- genre(?x6058, ?x6887), music(?x6058, ?x925), film(?x722, ?x6058), ?x6887 = 03bxz7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #16123 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1470 *> proper extension: 04bp0l; *> query: (?x6058, ?x8364) <- nominated_for(?x3960, ?x6058), award_winner(?x8364, ?x3960), award(?x697, ?x8364) *> conf = 0.22 ranks of expected_values: 23 EVAL 03vyw8 nominated_for! 02x4sn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 81.000 75.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9089-016ckq PRED entity: 016ckq PRED relation: category PRED expected values: 08mbj5d => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #128, 0.82 #127, 0.81 #143) >> Best rule #128 for best value: >> intensional similarity = 3 >> extensional distance = 606 >> proper extension: 06klyh; >> query: (?x7448, ?x134) <- citytown(?x7448, ?x739), contains(?x739, ?x6434), category(?x6434, ?x134) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016ckq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.819 http://example.org/common/topic/webpage./common/webpage/category #9088-02lj6p PRED entity: 02lj6p PRED relation: profession PRED expected values: 03gjzk => 101 concepts (60 used for prediction) PRED predicted values (max 10 best out of 74): 03gjzk (0.53 #160, 0.50 #14, 0.41 #2058), 0cbd2 (0.53 #737, 0.52 #2489, 0.51 #2343), 01d_h8 (0.52 #4973, 0.50 #6, 0.49 #1174), 02jknp (0.44 #4975, 0.42 #5561, 0.28 #1176), 0kyk (0.35 #757, 0.35 #2509, 0.33 #2363), 09jwl (0.30 #4675, 0.27 #3067, 0.24 #455), 01c72t (0.30 #4675, 0.27 #3067, 0.13 #1773), 04j5jl (0.30 #4675, 0.27 #3067, 0.02 #433), 05z96 (0.21 #770, 0.18 #624, 0.17 #2522), 015cjr (0.20 #193, 0.10 #1507, 0.10 #923) >> Best rule #160 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 01svw8n; 02v0ff; 01w9wwg; 020ffd; 018ygt; 01j7z7; 0c33pl; 016tbr; 03k48_; >> query: (?x8619, 03gjzk) <- award_nominee(?x192, ?x8619), film(?x8619, ?x7806), ?x7806 = 0b3n61 >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lj6p profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 60.000 0.533 http://example.org/people/person/profession #9087-01vsgrn PRED entity: 01vsgrn PRED relation: award_winner! PRED expected values: 01bx35 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 126): 0gx1673 (0.50 #115, 0.25 #385, 0.18 #250), 0466p0j (0.38 #72, 0.35 #342, 0.18 #207), 02rjjll (0.26 #543, 0.25 #3, 0.24 #138), 09n4nb (0.25 #44, 0.20 #314, 0.15 #584), 056878 (0.25 #29, 0.15 #299, 0.13 #569), 0gpjbt (0.13 #566, 0.12 #26, 0.09 #3401), 01c6qp (0.12 #16, 0.11 #3391, 0.09 #1771), 09gkdln (0.12 #117, 0.05 #387, 0.04 #6057), 013b2h (0.12 #3451, 0.10 #616, 0.10 #346), 01bx35 (0.10 #3380, 0.08 #4595, 0.07 #3785) >> Best rule #115 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 04lgymt; 02l840; 0412f5y; 01svw8n; 01vw20h; 05mxw33; >> query: (?x5536, 0gx1673) <- award_winner(?x6835, ?x5536), award_winner(?x1323, ?x5536), ?x6835 = 06mt91 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3380 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 340 *> proper extension: 01hrqc; 0knjh; *> query: (?x5536, 01bx35) <- award_winner(?x140, ?x5536), award_nominee(?x5536, ?x527), artists(?x2937, ?x5536) *> conf = 0.10 ranks of expected_values: 10 EVAL 01vsgrn award_winner! 01bx35 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 111.000 111.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9086-09v71cj PRED entity: 09v71cj PRED relation: film_release_region PRED expected values: 015fr 07twz 07f1x => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 166): 015fr (0.85 #2161, 0.82 #3021, 0.81 #2591), 05qhw (0.82 #2159, 0.79 #2589, 0.76 #3019), 03rt9 (0.76 #2158, 0.72 #2588, 0.69 #3018), 01mjq (0.61 #2185, 0.58 #2615, 0.55 #3045), 04gzd (0.60 #2154, 0.56 #2584, 0.54 #3014), 015qh (0.54 #2182, 0.49 #2612, 0.46 #3042), 0j1z8 (0.52 #860, 0.49 #2578, 0.47 #10052), 01ls2 (0.50 #2156, 0.49 #2586, 0.48 #3016), 016wzw (0.50 #2205, 0.47 #2635, 0.47 #3065), 06f32 (0.50 #2204, 0.46 #2634, 0.45 #3064) >> Best rule #2161 for best value: >> intensional similarity = 8 >> extensional distance = 149 >> proper extension: 0g56t9t; 0gx1bnj; 0gkz15s; 087wc7n; 0dgst_d; 03twd6; 0fpkhkz; 0gxtknx; 04n52p6; 05q4y12; ... >> query: (?x4352, 015fr) <- film_release_region(?x4352, ?x1264), film_release_region(?x4352, ?x390), film_release_region(?x4352, ?x151), film_release_region(?x4352, ?x87), ?x390 = 0chghy, ?x151 = 0b90_r, ?x87 = 05r4w, ?x1264 = 0345h >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 17, 25 EVAL 09v71cj film_release_region 07f1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 88.000 88.000 0.854 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v71cj film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 88.000 88.000 0.854 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v71cj film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.854 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9085-04jn6y7 PRED entity: 04jn6y7 PRED relation: film! PRED expected values: 086k8 => 79 concepts (73 used for prediction) PRED predicted values (max 10 best out of 90): 0283xx2 (0.45 #301, 0.44 #3462, 0.42 #1130), 0fvppk (0.45 #301, 0.44 #3462, 0.42 #1130), 05qd_ (0.40 #84, 0.38 #234, 0.33 #9), 01gb54 (0.33 #29, 0.20 #104, 0.12 #254), 016tt2 (0.25 #229, 0.18 #906, 0.16 #1287), 086k8 (0.25 #753, 0.24 #979, 0.24 #1055), 016tw3 (0.21 #387, 0.15 #3397, 0.15 #838), 017s11 (0.17 #454, 0.14 #529, 0.14 #153), 03xq0f (0.14 #155, 0.14 #832, 0.12 #230), 0g1rw (0.12 #459, 0.08 #5198, 0.07 #1138) >> Best rule #301 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 024l2y; >> query: (?x12693, ?x9518) <- genre(?x12693, ?x812), ?x812 = 01jfsb, film_crew_role(?x12693, ?x2472), film_crew_role(?x12693, ?x137), ?x137 = 09zzb8, films(?x12333, ?x12693), ?x2472 = 01xy5l_, production_companies(?x12693, ?x9518) >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #753 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 63 *> proper extension: 014lc_; 015qsq; 01hr1; 0p3_y; 059rc; 0946bb; 051zy_b; 02ny6g; 016y_f; 029k4p; ... *> query: (?x12693, 086k8) <- genre(?x12693, ?x812), genre(?x12693, ?x604), ?x812 = 01jfsb, ?x604 = 0lsxr, film(?x1286, ?x12693), production_companies(?x12693, ?x9518), language(?x12693, ?x254), spouse(?x8346, ?x1286) *> conf = 0.25 ranks of expected_values: 6 EVAL 04jn6y7 film! 086k8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 73.000 0.455 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #9084-018js4 PRED entity: 018js4 PRED relation: films! PRED expected values: 0fzyg 01w1sx => 108 concepts (52 used for prediction) PRED predicted values (max 10 best out of 73): 07jdr (0.25 #192, 0.01 #664, 0.01 #979), 081pw (0.23 #790, 0.21 #1262, 0.20 #1738), 07jq_ (0.22 #553, 0.06 #1341, 0.05 #1817), 018h2 (0.14 #336, 0.02 #3506, 0.02 #3982), 018jz (0.14 #356), 0kbq (0.11 #576, 0.08 #892, 0.05 #1840), 0hkt6 (0.11 #591, 0.02 #1379, 0.01 #2809), 0jnh (0.11 #567, 0.01 #883, 0.01 #1197), 075fzd (0.11 #602), 07_nf (0.07 #1326, 0.06 #2756, 0.06 #1802) >> Best rule #192 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 076zy_g; >> query: (?x155, 07jdr) <- nominated_for(?x707, ?x155), country(?x155, ?x94), film(?x902, ?x155), ?x707 = 02qggqc, film_release_distribution_medium(?x155, ?x81) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1826 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 126 *> proper extension: 02qrv7; 08tq4x; 01jwxx; 064q5v; *> query: (?x155, 01w1sx) <- film(?x338, ?x155), genre(?x155, ?x3515), ?x3515 = 082gq, country(?x155, ?x94) *> conf = 0.05 ranks of expected_values: 13, 14 EVAL 018js4 films! 01w1sx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 108.000 52.000 0.250 http://example.org/film/film_subject/films EVAL 018js4 films! 0fzyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 108.000 52.000 0.250 http://example.org/film/film_subject/films #9083-0bx8pn PRED entity: 0bx8pn PRED relation: school! PRED expected values: 02pq_rp 06439y => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 17): 025tn92 (0.23 #112, 0.17 #282, 0.11 #78), 092j54 (0.21 #110, 0.17 #280, 0.13 #59), 09th87 (0.20 #12, 0.16 #114, 0.13 #284), 05vsb7 (0.18 #273, 0.16 #103, 0.12 #86), 02pq_x5 (0.16 #116, 0.16 #65, 0.16 #286), 09l0x9 (0.16 #111, 0.16 #281, 0.16 #128), 038c0q (0.16 #107, 0.13 #277, 0.09 #379), 03nt7j (0.15 #6, 0.14 #108, 0.14 #278), 06439y (0.14 #289, 0.12 #119, 0.10 #136), 0g3zpp (0.13 #274, 0.12 #104, 0.10 #2) >> Best rule #112 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 08qnnv; >> query: (?x1884, 025tn92) <- major_field_of_study(?x1884, ?x1668), company(?x2998, ?x1884), school(?x580, ?x1884) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #289 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 100 *> proper extension: 02jyr8; 02zkz7; 016sd3; *> query: (?x1884, 06439y) <- colors(?x1884, ?x663), school(?x1883, ?x1884), school(?x580, ?x1884) *> conf = 0.14 ranks of expected_values: 9, 12 EVAL 0bx8pn school! 06439y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 107.000 107.000 0.233 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 0bx8pn school! 02pq_rp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 107.000 107.000 0.233 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #9082-034m8 PRED entity: 034m8 PRED relation: country! PRED expected values: 03_8r => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 55): 071t0 (0.80 #185, 0.79 #347, 0.75 #563), 03_8r (0.78 #184, 0.76 #346, 0.75 #562), 06f41 (0.76 #176, 0.56 #446, 0.56 #284), 03hr1p (0.71 #186, 0.55 #456, 0.55 #510), 06wrt (0.66 #178, 0.53 #232, 0.52 #502), 0w0d (0.63 #174, 0.55 #498, 0.53 #228), 01lb14 (0.62 #501, 0.61 #177, 0.60 #339), 03fyrh (0.61 #191, 0.52 #569, 0.52 #353), 064vjs (0.61 #194, 0.52 #518, 0.48 #356), 02y8z (0.61 #181, 0.49 #289, 0.47 #505) >> Best rule #185 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 077qn; >> query: (?x9459, 071t0) <- teams(?x9459, ?x6892), country(?x4673, ?x9459), ?x4673 = 07jbh >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #184 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 077qn; *> query: (?x9459, 03_8r) <- teams(?x9459, ?x6892), country(?x4673, ?x9459), ?x4673 = 07jbh *> conf = 0.78 ranks of expected_values: 2 EVAL 034m8 country! 03_8r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.805 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #9081-07_grx PRED entity: 07_grx PRED relation: place_of_death PRED expected values: 06_kh => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 49): 030qb3t (0.16 #1965, 0.13 #2160, 0.13 #798), 02_286 (0.13 #1177, 0.12 #983, 0.10 #1372), 0f2wj (0.10 #1371, 0.08 #788, 0.08 #1760), 0k049 (0.07 #2141, 0.06 #779, 0.06 #1946), 06_kh (0.05 #2922, 0.04 #3313, 0.04 #3508), 04vmp (0.04 #1662, 0.04 #1856, 0.02 #3806), 04jpl (0.03 #1561, 0.03 #783, 0.03 #1755), 05qtj (0.03 #3176, 0.03 #3372, 0.03 #3567), 0r04p (0.03 #1426, 0.02 #1621, 0.02 #843), 056_y (0.03 #1424, 0.02 #841, 0.02 #1813) >> Best rule #1965 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 01c1px; >> query: (?x4323, 030qb3t) <- award_nominee(?x4323, ?x6519), people(?x4322, ?x4323), nationality(?x4323, ?x94), award_winner(?x6518, ?x6519) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #2922 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 332 *> proper extension: 02jxsq; *> query: (?x4323, 06_kh) <- people(?x4322, ?x4323), location(?x4323, ?x739), place_of_birth(?x65, ?x739) *> conf = 0.05 ranks of expected_values: 5 EVAL 07_grx place_of_death 06_kh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 78.000 0.157 http://example.org/people/deceased_person/place_of_death #9080-0kvgxk PRED entity: 0kvgxk PRED relation: film! PRED expected values: 059m45 => 113 concepts (54 used for prediction) PRED predicted values (max 10 best out of 902): 076_74 (0.46 #81057, 0.43 #108075, 0.39 #97685), 0h1p (0.46 #81057, 0.43 #108075, 0.39 #97685), 02jsgf (0.46 #81057, 0.43 #108075, 0.39 #97685), 0184jc (0.33 #5, 0.02 #6238, 0.02 #12472), 03_2td (0.33 #1591), 02dbn2 (0.33 #853), 01f873 (0.25 #3970, 0.02 #33066, 0.01 #28911), 0m9v7 (0.25 #3828), 01ypsj (0.25 #3751), 0jlv5 (0.25 #3254) >> Best rule #81057 for best value: >> intensional similarity = 4 >> extensional distance = 529 >> proper extension: 09fc83; >> query: (?x2085, ?x2086) <- nominated_for(?x2086, ?x2085), film(?x9526, ?x2085), religion(?x9526, ?x1985), languages(?x9526, ?x254) >> conf = 0.46 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0kvgxk film! 059m45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 54.000 0.456 http://example.org/film/actor/film./film/performance/film #9079-09gq0x5 PRED entity: 09gq0x5 PRED relation: award_winner PRED expected values: 0kszw => 85 concepts (34 used for prediction) PRED predicted values (max 10 best out of 486): 05kwx2 (0.51 #49203, 0.45 #55767, 0.45 #44280), 061dn_ (0.51 #49203, 0.45 #55767, 0.45 #44280), 0csdzz (0.45 #44280, 0.45 #41001, 0.45 #49202), 0kszw (0.14 #54125, 0.11 #52484, 0.11 #49204), 06bzwt (0.14 #54125, 0.11 #52484, 0.11 #49204), 0c35b1 (0.14 #54125, 0.11 #52484, 0.11 #49204), 0278x6s (0.14 #54125, 0.11 #52484, 0.11 #49204), 0170qf (0.14 #54125, 0.11 #52484, 0.11 #49204), 0blq0z (0.14 #54125, 0.11 #52484, 0.11 #49204), 02tr7d (0.14 #54125, 0.11 #52484, 0.11 #49204) >> Best rule #49203 for best value: >> intensional similarity = 3 >> extensional distance = 480 >> proper extension: 01b7h8; >> query: (?x1813, ?x3462) <- nominated_for(?x3462, ?x1813), honored_for(?x1553, ?x1813), award_winner(?x163, ?x3462) >> conf = 0.51 => this is the best rule for 2 predicted values *> Best rule #54125 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 512 *> proper extension: 09xbpt; 03h_yy; 03s6l2; 04gknr; 03s5lz; 04kzqz; 01b195; 0pdp8; 01hvjx; 01h72l; ... *> query: (?x1813, ?x2280) <- award_winner(?x1813, ?x72), titles(?x162, ?x1813), award_winner(?x2280, ?x72), award_winner(?x2213, ?x72) *> conf = 0.14 ranks of expected_values: 4 EVAL 09gq0x5 award_winner 0kszw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 34.000 0.515 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #9078-0qzhw PRED entity: 0qzhw PRED relation: time_zones PRED expected values: 02lcqs => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 10): 02lcqs (0.89 #70, 0.83 #644, 0.83 #251), 02hcv8 (0.53 #185, 0.49 #159, 0.48 #371), 02fqwt (0.29 #14, 0.23 #170, 0.23 #157), 02hczc (0.09 #253, 0.09 #540, 0.09 #619), 02llzg (0.08 #936, 0.08 #989, 0.07 #963), 03bdv (0.07 #270, 0.06 #518, 0.05 #570), 03plfd (0.03 #929, 0.03 #969, 0.03 #995), 0gsrz4 (0.03 #927, 0.03 #940, 0.02 #967), 042g7t (0.02 #628, 0.02 #930, 0.02 #943), 02lcrv (0.01 #467) >> Best rule #70 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 02hyt; >> query: (?x9300, 02lcqs) <- jurisdiction_of_office(?x10525, ?x9300), jurisdiction_of_office(?x1195, ?x9300), contains(?x1227, ?x9300), ?x10525 = 01q24l, ?x1195 = 0pqc5, ?x1227 = 01n7q >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qzhw time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.889 http://example.org/location/location/time_zones #9077-01pvkk PRED entity: 01pvkk PRED relation: film_crew_role! PRED expected values: 0dq626 011yph 06z8s_ 0c8tkt 028cg00 0pdp8 0ct5zc 02q6gfp 021y7yw 0j_t1 078sj4 040b5k 07jxpf 049mql 04y5j64 057lbk 01k60v 01pj_5 02d49z 08ct6 0315w4 0194zl 067ghz 04h41v 0hv81 03_wm6 07jnt 09v3jyg 0dpl44 02dr9j 031hcx 07bx6 07f_t4 026wlxw 02bqvs 0hz6mv2 028kj0 05sxr_ 0gy0n 0ptdz 03s9kp => 55 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1006): 0661ql3 (0.71 #6700, 0.67 #5772, 0.60 #4844), 06znpjr (0.71 #7223, 0.67 #6295, 0.50 #11870), 047csmy (0.71 #6990, 0.67 #6062, 0.50 #11637), 016dj8 (0.71 #7089, 0.67 #6161, 0.50 #4305), 05qbbfb (0.71 #7062, 0.67 #6134, 0.50 #4278), 0dzlbx (0.71 #6957, 0.67 #6029, 0.50 #4173), 03whyr (0.71 #7325, 0.67 #6397, 0.50 #4541), 02mmwk (0.71 #7160, 0.67 #6232, 0.50 #4376), 062zjtt (0.71 #6878, 0.67 #5950, 0.50 #4094), 09rsjpv (0.71 #6802, 0.67 #5874, 0.50 #4018) >> Best rule #6700 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 01vx2h; >> query: (?x2178, 0661ql3) <- film_crew_role(?x7248, ?x2178), film_crew_role(?x6620, ?x2178), film_crew_role(?x4375, ?x2178), film_crew_role(?x3990, ?x2178), film_crew_role(?x1744, ?x2178), film_release_region(?x1744, ?x87), written_by(?x4375, ?x3117), genre(?x6620, ?x258), ?x7248 = 01jft4, ?x3990 = 033srr >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #7168 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 5 *> proper extension: 01vx2h; *> query: (?x2178, 031hcx) <- film_crew_role(?x7248, ?x2178), film_crew_role(?x6620, ?x2178), film_crew_role(?x4375, ?x2178), film_crew_role(?x3990, ?x2178), film_crew_role(?x1744, ?x2178), film_release_region(?x1744, ?x87), written_by(?x4375, ?x3117), genre(?x6620, ?x258), ?x7248 = 01jft4, ?x3990 = 033srr *> conf = 0.71 ranks of expected_values: 15, 17, 28, 56, 68, 69, 85, 106, 108, 125, 141, 148, 172, 180, 207, 216, 221, 240, 275, 353, 377, 401, 441, 453, 485, 544, 596, 684, 687, 692, 698, 730, 799, 868, 869, 878, 881, 889, 928, 1000 EVAL 01pvkk film_crew_role! 03s9kp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0ptdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0gy0n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 05sxr_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 028kj0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0hz6mv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 02bqvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 026wlxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 07f_t4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 07bx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 031hcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 02dr9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0dpl44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 09v3jyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 07jnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 03_wm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0hv81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 04h41v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 067ghz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0194zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0315w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 08ct6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 02d49z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 01pj_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 01k60v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 057lbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 04y5j64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 049mql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 07jxpf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 040b5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 078sj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0j_t1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 021y7yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 02q6gfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0ct5zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0pdp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 028cg00 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0c8tkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 06z8s_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 011yph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01pvkk film_crew_role! 0dq626 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 55.000 20.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9076-053xw6 PRED entity: 053xw6 PRED relation: film PRED expected values: 01gc7 0g5879y 031786 => 123 concepts (74 used for prediction) PRED predicted values (max 10 best out of 574): 03177r (0.50 #2239, 0.03 #7567, 0.03 #4015), 031778 (0.50 #2091, 0.02 #7419, 0.02 #10971), 031786 (0.40 #3041, 0.03 #4817, 0.02 #8369), 03176f (0.40 #2480, 0.02 #11360, 0.02 #14912), 03hxsv (0.40 #2887, 0.02 #11767, 0.02 #8215), 0dl6fv (0.20 #3250, 0.01 #12130, 0.01 #19234), 0dgst_d (0.20 #1971, 0.01 #7299), 03cfkrw (0.20 #2523), 03bx2lk (0.17 #185, 0.04 #7289, 0.03 #16169), 0340hj (0.17 #236, 0.02 #7340, 0.01 #73052) >> Best rule #2239 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 025t9b; >> query: (?x7147, 03177r) <- film(?x7147, ?x7304), place_of_birth(?x7147, ?x6357), ?x7304 = 031hcx >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3041 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 025t9b; *> query: (?x7147, 031786) <- film(?x7147, ?x7304), place_of_birth(?x7147, ?x6357), ?x7304 = 031hcx *> conf = 0.40 ranks of expected_values: 3, 178 EVAL 053xw6 film 031786 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 74.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 053xw6 film 0g5879y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 123.000 74.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 053xw6 film 01gc7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 74.000 0.500 http://example.org/film/actor/film./film/performance/film #9075-01rxw PRED entity: 01rxw PRED relation: adjoins PRED expected values: 0j4b => 65 concepts (59 used for prediction) PRED predicted values (max 10 best out of 335): 01nln (0.83 #31568, 0.80 #37739, 0.80 #37736), 07tp2 (0.40 #416, 0.22 #38510, 0.22 #37740), 05rznz (0.40 #745, 0.22 #38510, 0.22 #37740), 01rxw (0.40 #296, 0.22 #37740, 0.22 #22323), 06tw8 (0.22 #38510, 0.22 #37740, 0.22 #22323), 07dzf (0.22 #38510, 0.22 #37740, 0.22 #22323), 088vb (0.22 #37740, 0.22 #22323, 0.22 #32338), 06dfg (0.22 #37740, 0.22 #22323, 0.22 #32338), 01p1b (0.22 #37740, 0.22 #22323, 0.22 #32338), 0j4b (0.22 #37740, 0.22 #22323, 0.22 #32338) >> Best rule #31568 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 0rh6k; 05kkh; 01914; 0f4y_; 059rby; 04gzd; 03v1s; 05kj_; 02_286; 06mzp; ... >> query: (?x6863, ?x6974) <- adjoins(?x6863, ?x2804), administrative_parent(?x6863, ?x551), adjoins(?x6974, ?x6863) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #37740 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 594 *> proper extension: 0mrf1; *> query: (?x6863, ?x4121) <- adjoins(?x7871, ?x6863), adjoins(?x6974, ?x6863), adjoins(?x7871, ?x4121), contains(?x6974, ?x14027) *> conf = 0.22 ranks of expected_values: 10 EVAL 01rxw adjoins 0j4b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 65.000 59.000 0.825 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #9074-0130sy PRED entity: 0130sy PRED relation: role PRED expected values: 011k_j => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 94): 02hnl (0.42 #148, 0.17 #946, 0.16 #762), 05148p4 (0.32 #138, 0.27 #1043, 0.27 #936), 05r5c (0.21 #129, 0.19 #988, 0.18 #1420), 018vs (0.21 #135, 0.19 #933, 0.17 #994), 01vj9c (0.16 #136, 0.09 #566, 0.08 #995), 028tv0 (0.15 #993, 0.14 #748, 0.14 #932), 042v_gx (0.11 #130, 0.07 #798, 0.07 #989), 02sgy (0.11 #128, 0.07 #798, 0.06 #926), 026t6 (0.11 #125, 0.03 #923, 0.03 #616), 03qjg (0.10 #898, 0.09 #591, 0.09 #1020) >> Best rule #148 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 04954; >> query: (?x6838, 02hnl) <- nationality(?x6838, ?x94), ?x94 = 09c7w0, role(?x6838, ?x315), ?x315 = 0l14md >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #798 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 165 *> proper extension: 03cs_z7; 01wwvd2; 05683p; 02ldv0; 01r4zfk; 03cs_xw; *> query: (?x6838, ?x228) <- nationality(?x6838, ?x94), ?x94 = 09c7w0, role(?x6838, ?x315), performance_role(?x228, ?x315) *> conf = 0.07 ranks of expected_values: 27 EVAL 0130sy role 011k_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 95.000 95.000 0.421 http://example.org/music/group_member/membership./music/group_membership/role #9073-0m0fw PRED entity: 0m0fw PRED relation: artists PRED expected values: 07yg2 => 90 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1038): 01w5n51 (0.69 #6085, 0.56 #10404, 0.38 #11483), 01w8n89 (0.64 #7869, 0.53 #12188, 0.50 #11110), 07yg2 (0.60 #2526, 0.44 #24832, 0.25 #11161), 012zng (0.60 #2291, 0.38 #10926, 0.33 #134), 03fbc (0.56 #4519, 0.42 #12075, 0.38 #5599), 016lmg (0.56 #5069, 0.38 #5394, 0.33 #3990), 01y_rz (0.50 #8501, 0.25 #11742, 0.25 #8631), 0fpj4lx (0.50 #7877, 0.23 #19760, 0.22 #4640), 017j6 (0.50 #7844, 0.22 #4607, 0.19 #11085), 02ndj5 (0.46 #6291, 0.44 #24832, 0.44 #11689) >> Best rule #6085 for best value: >> intensional similarity = 9 >> extensional distance = 11 >> proper extension: 016jhr; 03xnwz; 0grjmv; 0b_6yv; >> query: (?x4711, 01w5n51) <- parent_genre(?x4711, ?x7220), artists(?x4711, ?x9757), artists(?x4711, ?x7221), artists(?x4711, ?x3399), parent_genre(?x13102, ?x4711), award_nominee(?x2392, ?x7221), nationality(?x7221, ?x94), place_of_birth(?x3399, ?x9300), ?x9757 = 06br6t >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2526 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 08jyyk; 0cx7f; 02qm5j; *> query: (?x4711, 07yg2) <- parent_genre(?x4711, ?x7220), artists(?x4711, ?x10144), artists(?x4711, ?x7221), artists(?x4711, ?x5751), parent_genre(?x13102, ?x4711), ?x7221 = 0191h5, ?x10144 = 016wvy, group(?x227, ?x5751), parent_genre(?x7220, ?x3916) *> conf = 0.60 ranks of expected_values: 3 EVAL 0m0fw artists 07yg2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 28.000 0.692 http://example.org/music/genre/artists #9072-030155 PRED entity: 030155 PRED relation: artist! PRED expected values: 0g768 => 124 concepts (66 used for prediction) PRED predicted values (max 10 best out of 107): 015_1q (0.23 #20, 0.22 #298, 0.21 #2663), 03mp8k (0.23 #66, 0.16 #344, 0.15 #483), 043g7l (0.19 #32, 0.13 #449, 0.12 #310), 03rhqg (0.19 #294, 0.16 #850, 0.15 #4193), 0181dw (0.17 #321, 0.14 #877, 0.12 #599), 0g768 (0.16 #316, 0.15 #594, 0.15 #872), 01cszh (0.15 #11, 0.11 #845, 0.11 #567), 073tm9 (0.15 #37, 0.06 #1288, 0.06 #315), 033hn8 (0.14 #292, 0.14 #848, 0.13 #2797), 0fb0v (0.13 #146, 0.10 #980, 0.07 #3209) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 07s3vqk; 01vvycq; 02l840; 01wcp_g; 04xrx; 01wwvc5; 01vx5w7; 015mrk; 0407f; 01w272y; ... >> query: (?x3320, 015_1q) <- artists(?x3319, ?x3320), gender(?x3320, ?x514), award_winner(?x3835, ?x3320), ?x3835 = 01cky2 >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #316 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 67 *> proper extension: 03f3_p3; 01wg25j; 020_4z; 0ql36; *> query: (?x3320, 0g768) <- artists(?x3928, ?x3320), artists(?x3319, ?x3320), gender(?x3320, ?x514), ?x3928 = 0gywn, ?x3319 = 06j6l *> conf = 0.16 ranks of expected_values: 6 EVAL 030155 artist! 0g768 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 124.000 66.000 0.231 http://example.org/music/record_label/artist #9071-069q4f PRED entity: 069q4f PRED relation: honored_for! PRED expected values: 01771z => 122 concepts (63 used for prediction) PRED predicted values (max 10 best out of 150): 01771z (0.84 #1081, 0.84 #1082, 0.83 #2324), 074rg9 (0.84 #1081, 0.84 #1082, 0.83 #2324), 0q9sg (0.84 #1081, 0.84 #1082, 0.83 #2324), 07sgdw (0.84 #1081, 0.84 #1082, 0.83 #2324), 069q4f (0.80 #331, 0.59 #1393, 0.56 #2012), 0cf08 (0.09 #2325, 0.05 #3098, 0.04 #3253), 053rxgm (0.09 #2325, 0.05 #3098, 0.04 #3253), 025s1wg (0.09 #2325, 0.05 #3098, 0.02 #9580), 05nyqk (0.09 #2325, 0.05 #3098, 0.02 #9580), 0dr_4 (0.09 #2325, 0.01 #496) >> Best rule #1081 for best value: >> intensional similarity = 5 >> extensional distance = 100 >> proper extension: 0g5qs2k; 0jzw; 0ddjy; 01hv3t; 09v8clw; >> query: (?x1311, ?x188) <- honored_for(?x1311, ?x5667), honored_for(?x1311, ?x188), nominated_for(?x382, ?x5667), music(?x1311, ?x7701), genre(?x5667, ?x225) >> conf = 0.84 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 069q4f honored_for! 01771z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 63.000 0.844 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #9070-0hr3c8y PRED entity: 0hr3c8y PRED relation: honored_for PRED expected values: 0gmcwlb 0h3mh3q => 20 concepts (14 used for prediction) PRED predicted values (max 10 best out of 667): 0kfv9 (0.75 #4264, 0.71 #3670, 0.25 #7132), 011ywj (0.50 #2261, 0.33 #480, 0.20 #5829), 09m6kg (0.50 #1792, 0.33 #11, 0.20 #4765), 011yxg (0.50 #1795, 0.33 #14, 0.20 #4768), 0bnzd (0.50 #2201, 0.33 #420, 0.20 #5174), 047d21r (0.50 #2592, 0.33 #1404, 0.08 #6754), 0b76kw1 (0.50 #2492, 0.33 #1304, 0.08 #6654), 09gq0x5 (0.50 #2477, 0.33 #1289, 0.08 #6639), 09k56b7 (0.50 #2491, 0.33 #1303, 0.07 #5463), 02rcwq0 (0.40 #5055, 0.40 #3271, 0.27 #5650) >> Best rule #4264 for best value: >> intensional similarity = 22 >> extensional distance = 6 >> proper extension: 09p30_; >> query: (?x873, 0kfv9) <- award_winner(?x873, ?x10814), award_winner(?x873, ?x1244), award_winner(?x873, ?x1169), award_winner(?x873, ?x874), award_winner(?x873, ?x368), ceremony(?x618, ?x873), award_nominee(?x539, ?x1244), award_nominee(?x1244, ?x2615), award_winner(?x2252, ?x368), award_nominee(?x1169, ?x10469), award_nominee(?x1169, ?x3688), award_nominee(?x1169, ?x1651), award_nominee(?x1169, ?x369), ?x10469 = 0c1ps1, film(?x874, ?x2852), ?x1651 = 02lg9w, location(?x1244, ?x739), actor(?x493, ?x874), ?x369 = 01r42_g, nationality(?x10814, ?x94), gender(?x1169, ?x231), ?x3688 = 03zyvw >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #7132 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 35 *> proper extension: 0gpjbt; 0gvstc3; 0fqpc7d; 0hndn2q; 02hn5v; 027hjff; 0275n3y; 02ywhz; 05q7cj; 073hgx; ... *> query: (?x873, ?x8533) <- award_winner(?x873, ?x10814), award_winner(?x873, ?x1244), award_winner(?x873, ?x1169), award_winner(?x873, ?x368), ceremony(?x618, ?x873), award_nominee(?x7337, ?x1244), award_nominee(?x1244, ?x2615), award_winner(?x2252, ?x368), award_nominee(?x1169, ?x369), gender(?x1244, ?x514), award_nominee(?x6920, ?x1169), film(?x368, ?x508), gender(?x1169, ?x231), nationality(?x1169, ?x94), award_winner(?x7337, ?x2790), actor(?x8533, ?x10814), ?x2790 = 0c9c0 *> conf = 0.25 ranks of expected_values: 57, 327 EVAL 0hr3c8y honored_for 0h3mh3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 20.000 14.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0hr3c8y honored_for 0gmcwlb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 14.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #9069-02qpt1w PRED entity: 02qpt1w PRED relation: film_crew_role PRED expected values: 02ynfr => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 24): 02r96rf (0.75 #140, 0.68 #209, 0.67 #2097), 09vw2b7 (0.67 #144, 0.66 #110, 0.66 #2101), 0dxtw (0.43 #2279, 0.37 #1933, 0.37 #2105), 01vx2h (0.33 #149, 0.32 #1934, 0.31 #2106), 02ynfr (0.21 #119, 0.21 #153, 0.18 #2284), 0215hd (0.17 #258, 0.14 #1321, 0.14 #705), 089g0h (0.13 #1322, 0.13 #706, 0.12 #1666), 0d2b38 (0.12 #195, 0.12 #1327, 0.11 #1671), 01xy5l_ (0.12 #254, 0.12 #701, 0.12 #220), 02_n3z (0.11 #207, 0.11 #241, 0.09 #343) >> Best rule #140 for best value: >> intensional similarity = 4 >> extensional distance = 181 >> proper extension: 02825kb; 03xj05; >> query: (?x5736, 02r96rf) <- titles(?x2480, ?x5736), film_crew_role(?x5736, ?x137), category(?x5736, ?x134), ?x137 = 09zzb8 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #119 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 179 *> proper extension: 07tw_b; 01hq1; 03m5y9p; 03hp2y1; *> query: (?x5736, 02ynfr) <- titles(?x2480, ?x5736), film_crew_role(?x5736, ?x137), category(?x5736, ?x134), production_companies(?x5736, ?x788) *> conf = 0.21 ranks of expected_values: 5 EVAL 02qpt1w film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 106.000 106.000 0.749 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9068-0kbws PRED entity: 0kbws PRED relation: olympics! PRED expected values: 05qx1 09lxtg => 64 concepts (59 used for prediction) PRED predicted values (max 10 best out of 183): 0f8l9c (0.88 #2661, 0.88 #3689, 0.85 #3620), 0h7x (0.83 #1034, 0.80 #616, 0.79 #2463), 02vzc (0.81 #2671, 0.80 #616, 0.79 #3214), 059j2 (0.80 #616, 0.77 #2320, 0.74 #3692), 06mkj (0.80 #616, 0.77 #2320, 0.67 #568), 03h64 (0.80 #616, 0.77 #2320, 0.63 #209), 035dk (0.80 #616, 0.76 #2726, 0.65 #3753), 01p1v (0.80 #616, 0.76 #2726, 0.63 #209), 07twz (0.80 #616, 0.76 #2726, 0.63 #209), 04g5k (0.80 #616, 0.67 #592, 0.63 #209) >> Best rule #2661 for best value: >> intensional similarity = 12 >> extensional distance = 24 >> proper extension: 0lv1x; >> query: (?x1931, 0f8l9c) <- olympics(?x4302, ?x1931), olympics(?x4073, ?x1931), olympics(?x5063, ?x1931), olympics(?x47, ?x1931), medal(?x4302, ?x422), currency(?x4302, ?x170), countries_spoken_in(?x5359, ?x4302), administrative_parent(?x4302, ?x551), ?x422 = 02lq67, titles(?x5063, ?x3009), athlete(?x5063, ?x5412), official_language(?x4073, ?x5671) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #2726 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 24 *> proper extension: 0lv1x; *> query: (?x1931, ?x4302) <- olympics(?x4302, ?x1931), olympics(?x4073, ?x1931), olympics(?x5063, ?x1931), olympics(?x47, ?x1931), medal(?x4302, ?x422), currency(?x4302, ?x170), countries_spoken_in(?x5359, ?x4302), administrative_parent(?x4302, ?x551), ?x422 = 02lq67, titles(?x5063, ?x3009), athlete(?x5063, ?x5412), official_language(?x4073, ?x5671) *> conf = 0.76 ranks of expected_values: 102, 107 EVAL 0kbws olympics! 09lxtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 64.000 59.000 0.885 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0kbws olympics! 05qx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 64.000 59.000 0.885 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #9067-09xvf7 PRED entity: 09xvf7 PRED relation: profession PRED expected values: 0dxtg => 120 concepts (89 used for prediction) PRED predicted values (max 10 best out of 77): 0dxtg (0.66 #6186, 0.65 #5745, 0.61 #7069), 0fj9f (0.50 #347, 0.50 #200, 0.12 #4757), 04gc2 (0.50 #187, 0.38 #334, 0.05 #4450), 03gjzk (0.41 #1630, 0.36 #11630, 0.33 #895), 0kyk (0.33 #175, 0.25 #322, 0.18 #4438), 01c72t (0.23 #1786, 0.18 #3550, 0.18 #3109), 0cbd2 (0.23 #5445, 0.21 #5151, 0.20 #4269), 02krf9 (0.23 #5758, 0.21 #7082, 0.20 #6199), 012t_z (0.19 #746, 0.16 #1628, 0.15 #2951), 09jwl (0.17 #7369, 0.17 #7810, 0.17 #10310) >> Best rule #6186 for best value: >> intensional similarity = 4 >> extensional distance = 440 >> proper extension: 022_lg; 06w33f8; 04b19t; 02_4fn; 0gv5c; 0dfjb8; 01d5vk; 0522wp; 04v048; 03mv0b; ... >> query: (?x13011, 0dxtg) <- profession(?x13011, ?x524), profession(?x13011, ?x319), ?x524 = 02jknp, ?x319 = 01d_h8 >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09xvf7 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 89.000 0.663 http://example.org/people/person/profession #9066-0g4gr PRED entity: 0g4gr PRED relation: student PRED expected values: 03_x5t => 77 concepts (46 used for prediction) PRED predicted values (max 10 best out of 292): 09b6zr (0.33 #331, 0.25 #1761, 0.17 #3667), 08f3b1 (0.33 #249, 0.25 #1679, 0.17 #3585), 0tc7 (0.33 #277, 0.25 #1707, 0.17 #3613), 0djywgn (0.33 #650, 0.20 #3033, 0.20 #2795), 04z0g (0.25 #1796, 0.25 #1081, 0.20 #2987), 06c0j (0.25 #1187, 0.20 #3093, 0.20 #2616), 071xj (0.25 #1149, 0.20 #3055, 0.20 #2578), 01zh29 (0.25 #1110, 0.20 #3016, 0.20 #2539), 036jb (0.25 #1057, 0.20 #2963, 0.20 #2486), 01k165 (0.25 #1011, 0.20 #2917, 0.20 #2440) >> Best rule #331 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 0g26h; >> query: (?x3213, 09b6zr) <- major_field_of_study(?x9911, ?x3213), major_field_of_study(?x4780, ?x3213), major_field_of_study(?x4296, ?x3213), major_field_of_study(?x2166, ?x3213), ?x2166 = 01jtp7, major_field_of_study(?x620, ?x3213), ?x4296 = 07vyf, major_field_of_study(?x3213, ?x4321), category(?x9911, ?x134), organization(?x346, ?x4780), colors(?x9911, ?x663) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6677 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 13 *> proper extension: 0fdys; 04gb7; 0w7c; 01400v; *> query: (?x3213, ?x10540) <- major_field_of_study(?x9200, ?x3213), major_field_of_study(?x6953, ?x3213), major_field_of_study(?x6912, ?x3213), major_field_of_study(?x620, ?x3213), contains(?x94, ?x6953), student(?x6912, ?x10540), ?x9200 = 0dzst, colors(?x6912, ?x663), school(?x580, ?x6953), participant(?x10540, ?x5246) *> conf = 0.01 ranks of expected_values: 241 EVAL 0g4gr student 03_x5t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 46.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #9065-08815 PRED entity: 08815 PRED relation: contains! PRED expected values: 01x73 => 89 concepts (79 used for prediction) PRED predicted values (max 10 best out of 216): 0f2nf (0.76 #57320, 0.69 #39409, 0.68 #40305), 01x73 (0.72 #47471, 0.71 #56424, 0.56 #66279), 02jx1 (0.42 #63591, 0.40 #50157, 0.19 #20679), 07ssc (0.42 #63591, 0.40 #50157, 0.14 #17938), 05kyr (0.42 #63591, 0.40 #50157), 059rby (0.33 #19, 0.09 #60028, 0.09 #64506), 02qkt (0.25 #41546, 0.24 #4822, 0.05 #19148), 0345h (0.18 #31342, 0.04 #9929, 0.04 #11719), 0dg3n1 (0.15 #41354, 0.05 #3735), 01n7q (0.14 #8135, 0.13 #19775, 0.13 #7239) >> Best rule #57320 for best value: >> intensional similarity = 2 >> extensional distance = 428 >> proper extension: 06klyh; >> query: (?x122, ?x9336) <- contains(?x94, ?x122), citytown(?x122, ?x9336) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #47471 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 343 *> proper extension: 06mvyf; *> query: (?x122, ?x1755) <- currency(?x122, ?x170), state_province_region(?x122, ?x1755) *> conf = 0.72 ranks of expected_values: 2 EVAL 08815 contains! 01x73 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 79.000 0.761 http://example.org/location/location/contains #9064-01w5gp PRED entity: 01w5gp PRED relation: organizations_founded! PRED expected values: 01s7qqw => 121 concepts (63 used for prediction) PRED predicted values (max 10 best out of 108): 0n839 (0.27 #1558, 0.25 #212, 0.17 #2564), 081nh (0.25 #1372, 0.17 #2154, 0.17 #2042), 06q8hf (0.25 #175, 0.14 #624, 0.14 #510), 04pg29 (0.25 #287, 0.04 #2414, 0.04 #2302), 05qd_ (0.25 #235, 0.03 #3144, 0.03 #3817), 0343h (0.20 #1477, 0.19 #1699, 0.17 #1365), 01vhrz (0.17 #1419, 0.16 #3319, 0.14 #634), 06pj8 (0.17 #1369, 0.13 #2151, 0.13 #2039), 07cbs (0.16 #4078, 0.14 #609, 0.14 #495), 0m593 (0.14 #508, 0.12 #734, 0.10 #1853) >> Best rule #1558 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0gh4g0; 0n85g; 03vtfp; >> query: (?x7169, 0n839) <- organizations_founded(?x1986, ?x7169), category(?x7169, ?x134), film(?x1986, ?x408), profession(?x1986, ?x1032) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w5gp organizations_founded! 01s7qqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 63.000 0.267 http://example.org/organization/organization_founder/organizations_founded #9063-0h0yt PRED entity: 0h0yt PRED relation: film PRED expected values: 04jpg2p => 122 concepts (83 used for prediction) PRED predicted values (max 10 best out of 940): 09gq0x5 (0.22 #2064, 0.06 #92617, 0.03 #106867), 03177r (0.13 #2245, 0.03 #85491, 0.03 #87273), 03_gz8 (0.13 #2899, 0.03 #85491, 0.03 #87273), 02ctc6 (0.09 #521, 0.04 #2302, 0.03 #20112), 02cbhg (0.09 #1399, 0.04 #3180, 0.03 #85491), 072zl1 (0.09 #1275, 0.04 #3056, 0.03 #85491), 01gkp1 (0.09 #812, 0.04 #4374, 0.03 #15060), 0b1y_2 (0.09 #478, 0.04 #4040, 0.03 #23631), 02qhqz4 (0.09 #343, 0.04 #5686, 0.03 #14591), 0661m4p (0.09 #375, 0.04 #5718, 0.02 #11061) >> Best rule #2064 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0184jc; 05cj4r; 09fqtq; 01sp81; 03f1zdw; 02tr7d; 0170pk; 015rkw; 06t61y; 02k6rq; ... >> query: (?x7746, 09gq0x5) <- award_winner(?x1223, ?x7746), ?x1223 = 016gr2, award_nominee(?x7746, ?x2045) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #3237 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0184jc; 05cj4r; 09fqtq; 01sp81; 03f1zdw; 02tr7d; 0170pk; 015rkw; 06t61y; 02k6rq; ... *> query: (?x7746, 04jpg2p) <- award_winner(?x1223, ?x7746), ?x1223 = 016gr2, award_nominee(?x7746, ?x2045) *> conf = 0.04 ranks of expected_values: 163 EVAL 0h0yt film 04jpg2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 122.000 83.000 0.217 http://example.org/film/actor/film./film/performance/film #9062-0gnbw PRED entity: 0gnbw PRED relation: award PRED expected values: 07cbcy => 89 concepts (82 used for prediction) PRED predicted values (max 10 best out of 282): 0gqy2 (0.77 #396, 0.71 #25303, 0.71 #10276), 05p09zm (0.22 #911, 0.11 #1306, 0.10 #4742), 05pcn59 (0.22 #78, 0.18 #1264, 0.16 #19370), 0gqwc (0.22 #71, 0.16 #19370, 0.13 #28863), 031b3h (0.22 #194, 0.16 #19370, 0.03 #3355), 0gqyl (0.22 #102, 0.14 #19767, 0.13 #28863), 01cky2 (0.22 #187, 0.13 #28863, 0.13 #25699), 0bfvw2 (0.22 #13, 0.13 #28863, 0.13 #25699), 0bb57s (0.22 #235, 0.13 #28863, 0.13 #25699), 03r00m (0.22 #377, 0.13 #28863, 0.13 #25699) >> Best rule #396 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01f2q5; >> query: (?x7269, ?x2375) <- award_nominee(?x7269, ?x3176), ?x3176 = 01w7nww, award_winner(?x2375, ?x7269) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #866 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 03pvt; 0bx_q; *> query: (?x7269, 07cbcy) <- profession(?x7269, ?x319), award(?x7269, ?x102), ?x102 = 04ljl_l *> conf = 0.21 ranks of expected_values: 11 EVAL 0gnbw award 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 89.000 82.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9061-046b0s PRED entity: 046b0s PRED relation: citytown PRED expected values: 0chgzm => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 126): 02_286 (0.38 #6988, 0.38 #6621, 0.38 #10659), 0r00l (0.33 #280, 0.12 #7620, 0.12 #8354), 04jpl (0.19 #1842, 0.19 #1475, 0.12 #12119), 0r04p (0.12 #2306, 0.12 #1939, 0.12 #1572), 07dfk (0.11 #13793, 0.10 #22237, 0.07 #18199), 0k049 (0.11 #3672, 0.08 #1103, 0.07 #6241), 0d6lp (0.09 #7775, 0.07 #11079, 0.07 #6307), 06_kh (0.08 #1105, 0.06 #8445, 0.05 #11015), 024bqj (0.08 #13779, 0.05 #18185, 0.04 #11576), 081yw (0.08 #5606, 0.03 #7808, 0.03 #11112) >> Best rule #6988 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 015_1q; 01q0kg; 03d96s; 01q940; 02975m; 01fb6d; >> query: (?x2548, 02_286) <- company(?x8503, ?x2548), child(?x382, ?x2548), category(?x2548, ?x134) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #11565 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 44 *> proper extension: 026s90; 06x2ww; 03qx_f; *> query: (?x2548, 0chgzm) <- child(?x382, ?x2548), artist(?x382, ?x547) *> conf = 0.04 ranks of expected_values: 20 EVAL 046b0s citytown 0chgzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 118.000 118.000 0.379 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #9060-017z49 PRED entity: 017z49 PRED relation: film! PRED expected values: 03s2dj => 81 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1117): 0184dt (0.45 #74866, 0.43 #39508, 0.43 #43667), 02p65p (0.13 #20, 0.02 #2099, 0.01 #6258), 0f0kz (0.09 #2594, 0.05 #6753, 0.05 #15069), 0j_c (0.08 #10806, 0.05 #27441, 0.03 #8727), 081lh (0.07 #20953, 0.03 #45910, 0.02 #2240), 0jfx1 (0.07 #406, 0.07 #85269, 0.07 #83187), 09l3p (0.07 #749, 0.05 #4908, 0.04 #2828), 0c6qh (0.07 #414, 0.04 #42001, 0.03 #8731), 09fb5 (0.07 #57, 0.04 #18769, 0.03 #29167), 0169dl (0.07 #401, 0.04 #10797, 0.03 #21193) >> Best rule #74866 for best value: >> intensional similarity = 3 >> extensional distance = 687 >> proper extension: 07s8z_l; 01j95; >> query: (?x3482, ?x2818) <- award_winner(?x3482, ?x2818), titles(?x812, ?x3482), location(?x2818, ?x1036) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #6112 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 73 *> proper extension: 08hmch; 0jjy0; 0gj8t_b; 03twd6; 0c8tkt; 0m491; 0j_tw; 08052t3; 07x4qr; 07f_7h; ... *> query: (?x3482, 03s2dj) <- featured_film_locations(?x3482, ?x2495), film_release_region(?x3482, ?x2645), film_release_region(?x3482, ?x390), ?x2645 = 03h64, ?x390 = 0chghy *> conf = 0.01 ranks of expected_values: 709 EVAL 017z49 film! 03s2dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 81.000 45.000 0.451 http://example.org/film/actor/film./film/performance/film #9059-06lj1m PRED entity: 06lj1m PRED relation: student! PRED expected values: 078bz => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 65): 065y4w7 (0.17 #14, 0.04 #1595, 0.04 #2122), 0bwfn (0.16 #1329, 0.06 #2383, 0.05 #5018), 02y9bj (0.14 #782, 0.01 #1836), 025v3k (0.14 #647, 0.01 #1701), 02fgdx (0.14 #629, 0.01 #1683), 09f2j (0.06 #1213, 0.02 #3321, 0.02 #5956), 04b_46 (0.06 #1281, 0.02 #4970, 0.02 #6024), 02xwzh (0.06 #1442), 017z88 (0.05 #2190, 0.03 #3771, 0.03 #5879), 015nl4 (0.04 #1648, 0.04 #3229, 0.03 #1121) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 045931; >> query: (?x2089, 065y4w7) <- film(?x2089, ?x11685), ?x11685 = 017n9, award(?x2089, ?x757) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #17469 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1860 *> proper extension: 03gkn5; 034rd; 0130sy; 067xw; 02n9k; 0k57l; 030dx5; 06f5j; 045g4l; 0bqch; ... *> query: (?x2089, 078bz) <- type_of_union(?x2089, ?x566), nationality(?x2089, ?x94), ?x94 = 09c7w0 *> conf = 0.01 ranks of expected_values: 63 EVAL 06lj1m student! 078bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 74.000 74.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #9058-02mc5v PRED entity: 02mc5v PRED relation: film! PRED expected values: 0738b8 => 110 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1375): 02_l96 (0.48 #45736, 0.47 #47817, 0.44 #45735), 01nglk (0.29 #4006, 0.06 #12320, 0.05 #22715), 0151ns (0.25 #94, 0.14 #4252, 0.03 #12566), 0z4s (0.25 #68, 0.05 #41646, 0.04 #22936), 0d608 (0.25 #1306, 0.04 #24174, 0.04 #53283), 015wnl (0.25 #651, 0.04 #67182, 0.04 #27675), 01ggc9 (0.25 #1729, 0.04 #28753, 0.04 #7965), 0chw_ (0.25 #1555, 0.03 #9870, 0.02 #26501), 01skmp (0.25 #1178, 0.03 #51074, 0.03 #13650), 02mjf2 (0.25 #777, 0.03 #13249, 0.03 #17407) >> Best rule #45736 for best value: >> intensional similarity = 5 >> extensional distance = 97 >> proper extension: 01771z; >> query: (?x8072, ?x9195) <- genre(?x8072, ?x258), written_by(?x8072, ?x9195), award(?x9195, ?x688), participant(?x3421, ?x9195), type_of_union(?x9195, ?x566) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #6641 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 0h1cdwq; 031t2d; 0661m4p; 06ztvyx; 0gvvf4j; 01bn3l; 0cmf0m0; 01f7jt; *> query: (?x8072, 0738b8) <- genre(?x8072, ?x258), executive_produced_by(?x8072, ?x4060), prequel(?x8072, ?x4500), ?x258 = 05p553 *> conf = 0.11 ranks of expected_values: 94 EVAL 02mc5v film! 0738b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 110.000 84.000 0.477 http://example.org/film/actor/film./film/performance/film #9057-03j24kf PRED entity: 03j24kf PRED relation: award PRED expected values: 0gqz2 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 294): 01c9jp (0.72 #37166, 0.70 #30193, 0.70 #31742), 0gqz2 (0.49 #6659, 0.21 #3949, 0.20 #23691), 01ckcd (0.35 #706, 0.23 #2254, 0.19 #5351), 02f716 (0.35 #555, 0.19 #5420, 0.16 #3484), 09sb52 (0.34 #29071, 0.24 #14751, 0.20 #29845), 02f73p (0.29 #566, 0.23 #2114, 0.20 #1340), 02f5qb (0.29 #534, 0.23 #2082, 0.19 #5954), 02v1m7 (0.29 #494, 0.16 #8236, 0.15 #1268), 02f6yz (0.29 #689, 0.08 #5334, 0.07 #6109), 0f4x7 (0.26 #10868, 0.26 #9707, 0.25 #11255) >> Best rule #37166 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 099ks0; >> query: (?x4701, ?x4416) <- award_winner(?x4416, ?x4701), award(?x248, ?x4416) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #6659 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 67 *> proper extension: 0lgsq; 01bpc9; 03rl84; 01vvpjj; 01817f; 01wqflx; *> query: (?x4701, 0gqz2) <- award(?x4701, ?x2238), profession(?x4701, ?x220), ?x2238 = 025m8l *> conf = 0.49 ranks of expected_values: 2 EVAL 03j24kf award 0gqz2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9056-0n5yv PRED entity: 0n5yv PRED relation: time_zones PRED expected values: 02hcv8 => 198 concepts (198 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.83 #1089, 0.81 #201, 0.81 #175), 02lcqs (0.32 #1474, 0.27 #295, 0.27 #715), 02fqwt (0.24 #186, 0.22 #304, 0.21 #119), 02hczc (0.23 #80, 0.21 #1795, 0.20 #28), 02lcrv (0.21 #1795, 0.20 #33, 0.04 #85), 042g7t (0.21 #1795, 0.20 #37, 0.03 #143), 02llzg (0.13 #439, 0.10 #1052, 0.09 #1026), 03bdv (0.05 #1358, 0.05 #676, 0.04 #937), 03plfd (0.05 #445, 0.04 #1058, 0.03 #1032), 0gsrz4 (0.03 #1056, 0.03 #1030, 0.02 #1659) >> Best rule #1089 for best value: >> intensional similarity = 3 >> extensional distance = 324 >> proper extension: 0f4y_; 0mlyw; 0nj1c; 0mmr1; 0mm0p; 0n5_g; 0ntwb; 0nm8n; 0drr3; 09dfcj; ... >> query: (?x7565, ?x2674) <- source(?x7565, ?x958), adjoins(?x7565, ?x10162), time_zones(?x10162, ?x2674) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n5yv time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 198.000 0.829 http://example.org/location/location/time_zones #9055-03c7twt PRED entity: 03c7twt PRED relation: genre PRED expected values: 01j1n2 => 81 concepts (64 used for prediction) PRED predicted values (max 10 best out of 97): 01z4y (0.72 #2324, 0.62 #6633, 0.62 #6982), 01jfsb (0.68 #3265, 0.66 #3615, 0.37 #3499), 02kdv5l (0.65 #3490, 0.55 #4073, 0.37 #3256), 017fp (0.39 #2571, 0.20 #2220, 0.20 #2337), 03k9fj (0.29 #3498, 0.26 #4081, 0.25 #241), 060__y (0.29 #2221, 0.28 #2338, 0.24 #2572), 0lsxr (0.28 #3261, 0.28 #3611, 0.21 #704), 0219x_ (0.27 #1534, 0.24 #1186, 0.13 #722), 03bxz7 (0.25 #283, 0.21 #2609, 0.19 #2258), 03g3w (0.25 #254, 0.16 #2580, 0.14 #2346) >> Best rule #2324 for best value: >> intensional similarity = 4 >> extensional distance = 205 >> proper extension: 0c0yh4; 016z5x; 05p3738; 0b76kw1; 0pvms; 07w8fz; 01wb95; 0kv9d3; 0bs5k8r; 034r25; ... >> query: (?x10697, ?x2480) <- genre(?x10697, ?x162), ?x162 = 04xvlr, film(?x4046, ?x10697), titles(?x2480, ?x10697) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #522 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0d68qy; *> query: (?x10697, 01j1n2) <- nominated_for(?x3927, ?x10697), people(?x1446, ?x3927), cast_members(?x3927, ?x905), film(?x3927, ?x86) *> conf = 0.11 ranks of expected_values: 24 EVAL 03c7twt genre 01j1n2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 81.000 64.000 0.725 http://example.org/film/film/genre #9054-025v3k PRED entity: 025v3k PRED relation: company! PRED expected values: 021q1c => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 36): 0dq_5 (0.55 #207, 0.47 #490, 0.42 #1434), 0krdk (0.47 #479, 0.45 #196, 0.44 #903), 060c4 (0.42 #192, 0.41 #945, 0.39 #475), 05_wyz (0.39 #208, 0.26 #491, 0.23 #1105), 0dq3c (0.32 #191, 0.31 #474, 0.26 #1844), 01yc02 (0.26 #198, 0.22 #1851, 0.21 #481), 021q1c (0.25 #58, 0.17 #389, 0.16 #672), 09d6p2 (0.19 #492, 0.15 #822, 0.14 #1106), 02y6fz (0.16 #214, 0.10 #497, 0.09 #921), 02211by (0.16 #193, 0.09 #1846, 0.09 #1562) >> Best rule #207 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 0vlf; >> query: (?x3948, 0dq_5) <- contact_category(?x3948, ?x6046), ?x6046 = 02zdwq, service_location(?x3948, ?x94) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 045c7b; 03_c8p; 0cv_2; *> query: (?x3948, 021q1c) <- contact_category(?x3948, ?x6046), service_language(?x3948, ?x254), organization(?x3948, ?x5487) *> conf = 0.25 ranks of expected_values: 7 EVAL 025v3k company! 021q1c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 122.000 122.000 0.548 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #9053-0lgsq PRED entity: 0lgsq PRED relation: award PRED expected values: 099vwn => 145 concepts (126 used for prediction) PRED predicted values (max 10 best out of 304): 054ks3 (0.65 #1739, 0.27 #3739, 0.27 #1339), 01by1l (0.53 #1310, 0.43 #1710, 0.34 #6110), 01bgqh (0.53 #1242, 0.33 #842, 0.33 #6042), 01ckcd (0.47 #1532, 0.42 #1132, 0.38 #732), 02x17c2 (0.40 #1416, 0.35 #1816, 0.33 #1016), 01ck6h (0.38 #519, 0.33 #1319, 0.25 #919), 02f6xy (0.38 #597, 0.25 #997, 0.23 #2197), 0c4z8 (0.35 #1671, 0.33 #1271, 0.25 #871), 0l8z1 (0.35 #1663, 0.27 #2863, 0.24 #3663), 02qvyrt (0.35 #1724, 0.25 #3724, 0.24 #6524) >> Best rule #1739 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 01k98nm; 01817f; 07hgkd; 01pbs9w; 08n__5; 016vqk; >> query: (?x1152, 054ks3) <- profession(?x1152, ?x220), award_winner(?x1323, ?x1152), instrumentalists(?x227, ?x1152), ?x1323 = 0gqz2 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #1813 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 01k98nm; 01817f; 07hgkd; 01pbs9w; 08n__5; 016vqk; *> query: (?x1152, 099vwn) <- profession(?x1152, ?x220), award_winner(?x1323, ?x1152), instrumentalists(?x227, ?x1152), ?x1323 = 0gqz2 *> conf = 0.17 ranks of expected_values: 28 EVAL 0lgsq award 099vwn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 145.000 126.000 0.652 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9052-01w9wwg PRED entity: 01w9wwg PRED relation: role PRED expected values: 01v1d8 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 115): 013y1f (0.50 #32, 0.21 #803, 0.21 #899), 0342h (0.40 #775, 0.40 #871, 0.39 #2706), 05148p4 (0.40 #886, 0.38 #790, 0.33 #2799), 018vs (0.33 #2799, 0.33 #1446, 0.32 #2896), 02hnl (0.33 #2799, 0.33 #1446, 0.32 #2896), 042v_gx (0.25 #6, 0.21 #1355, 0.21 #3674), 03gvt (0.25 #71, 0.10 #842, 0.10 #938), 03q5t (0.25 #1, 0.08 #193, 0.06 #964), 01v1d8 (0.25 #64, 0.06 #964, 0.04 #3766), 02sgy (0.25 #2707, 0.24 #1837, 0.24 #2804) >> Best rule #32 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01gx5f; >> query: (?x6162, 013y1f) <- role(?x6162, ?x1437), role(?x6162, ?x433), ?x1437 = 01vdm0, ?x433 = 025cbm >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #64 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01gx5f; *> query: (?x6162, 01v1d8) <- role(?x6162, ?x1437), role(?x6162, ?x433), ?x1437 = 01vdm0, ?x433 = 025cbm *> conf = 0.25 ranks of expected_values: 9 EVAL 01w9wwg role 01v1d8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 107.000 107.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #9051-02lf1j PRED entity: 02lf1j PRED relation: nominated_for PRED expected values: 02kk_c => 89 concepts (18 used for prediction) PRED predicted values (max 10 best out of 504): 035gnh (0.35 #3245, 0.25 #14610, 0.24 #17857), 085wqm (0.35 #3245, 0.25 #14610, 0.24 #17857), 02bj22 (0.35 #3245, 0.25 #14610, 0.24 #17857), 0sxgv (0.35 #3245, 0.25 #14610, 0.24 #17857), 01hw5kk (0.35 #3245, 0.25 #14610, 0.24 #17857), 016fyc (0.35 #3245, 0.25 #14610, 0.24 #17857), 091rc5 (0.35 #3245, 0.25 #14610, 0.24 #17857), 051zy_b (0.35 #3245, 0.25 #14610, 0.24 #17857), 0pvms (0.35 #3245, 0.25 #14610, 0.24 #17857), 0dtw1x (0.15 #3246, 0.08 #14611, 0.06 #21105) >> Best rule #3245 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 06jzh; >> query: (?x2564, ?x394) <- film(?x2564, ?x394), person(?x424, ?x2564), currency(?x2564, ?x170), participant(?x5884, ?x2564) >> conf = 0.35 => this is the best rule for 9 predicted values No rule for expected values ranks of expected_values: EVAL 02lf1j nominated_for 02kk_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 18.000 0.350 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9050-015q43 PRED entity: 015q43 PRED relation: nationality PRED expected values: 07ssc => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 28): 03rk0 (0.77 #6416, 0.77 #4011, 0.41 #3709), 09c7w0 (0.74 #2607, 0.74 #2006, 0.74 #1406), 03rjj (0.37 #5813, 0.34 #5, 0.30 #7819), 07ssc (0.37 #5813, 0.30 #7819, 0.12 #919), 02jx1 (0.30 #7819, 0.14 #937, 0.12 #535), 04v3q (0.30 #7819), 0f8l9c (0.14 #22, 0.05 #323, 0.04 #524), 0chghy (0.07 #10, 0.03 #2316, 0.03 #2516), 0d060g (0.05 #6321, 0.05 #1612, 0.04 #1412), 05bcl (0.03 #60, 0.02 #160) >> Best rule #6416 for best value: >> intensional similarity = 2 >> extensional distance = 1716 >> proper extension: 0f0y8; 0274ck; 0bn9sc; 0487c3; 01p45_v; 080dyk; 064p92m; 012zng; 014dq7; 02jg92; ... >> query: (?x5043, ?x2146) <- location(?x5043, ?x10242), country(?x10242, ?x2146) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #5813 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1482 *> proper extension: 02rgz4; 03g62; 01b0k1; 07z4fy; 02x20c9; *> query: (?x5043, ?x94) <- nominated_for(?x5043, ?x2812), gender(?x5043, ?x514), country(?x2812, ?x94) *> conf = 0.37 ranks of expected_values: 4 EVAL 015q43 nationality 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.770 http://example.org/people/person/nationality #9049-0557yqh PRED entity: 0557yqh PRED relation: languages PRED expected values: 02h40lc => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.90 #255, 0.90 #354, 0.89 #365), 0t_2 (0.07 #6, 0.06 #138, 0.06 #72), 06nm1 (0.06 #27, 0.04 #181, 0.04 #203), 03_9r (0.05 #400, 0.05 #433, 0.04 #488), 064_8sq (0.02 #293, 0.02 #337, 0.02 #436), 02bv9 (0.01 #185, 0.01 #174, 0.01 #207), 04306rv (0.01 #179, 0.01 #168, 0.01 #201), 02bjrlw (0.01 #177, 0.01 #166, 0.01 #199), 05zjd (0.01 #173, 0.01 #250), 07qv_ (0.01 #252, 0.01 #285) >> Best rule #255 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 0q9nj; 07s8z_l; 05b6s5j; >> query: (?x3630, 02h40lc) <- genre(?x3630, ?x258), program(?x6678, ?x3630), program_creator(?x3630, ?x3895), program(?x2819, ?x3630) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0557yqh languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.898 http://example.org/tv/tv_program/languages #9048-014mlp PRED entity: 014mlp PRED relation: student PRED expected values: 02qjj7 01n5309 01v_pj6 01d494 02k21g 04l19_ 01gbb4 06gn7r 051cc 070yzk 03_nq 071xj 0p_jc 0pqzh => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2159): 0tj9 (0.50 #1082, 0.33 #237, 0.22 #1931), 0frmb1 (0.50 #1048, 0.33 #203, 0.22 #1897), 02r6c_ (0.50 #1060, 0.33 #215, 0.22 #1909), 024jwt (0.38 #1676, 0.33 #585, 0.30 #2164), 059y0 (0.33 #1557, 0.33 #587, 0.33 #347), 01d494 (0.33 #1469, 0.33 #140, 0.25 #985), 05fg2 (0.33 #493, 0.33 #373, 0.25 #1584), 0203v (0.33 #618, 0.33 #497, 0.25 #860), 013pp3 (0.33 #541, 0.33 #182, 0.25 #1027), 03gkn5 (0.33 #394, 0.25 #1729, 0.25 #1605) >> Best rule #1082 for best value: >> intensional similarity = 16 >> extensional distance = 2 >> proper extension: 02h4rq6; >> query: (?x1368, 0tj9) <- student(?x1368, ?x10445), student(?x1368, ?x3789), student(?x1368, ?x3079), award_nominee(?x1342, ?x3789), institution(?x1368, ?x8016), institution(?x1368, ?x2013), institution(?x1368, ?x1884), location(?x10445, ?x682), award_nominee(?x2307, ?x3079), student(?x13049, ?x3079), film(?x10445, ?x4375), ?x1884 = 0bx8pn, ?x8016 = 02yxjs, ?x2013 = 07vk2, place_of_birth(?x3789, ?x4356), major_field_of_study(?x1368, ?x254) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1469 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 4 *> proper extension: 071tyz; *> query: (?x1368, 01d494) <- student(?x1368, ?x3789), student(?x1368, ?x3079), student(?x1368, ?x2669), institution(?x1368, ?x13424), institution(?x1368, ?x10869), institution(?x1368, ?x8016), institution(?x1368, ?x7545), institution(?x1368, ?x4220), gender(?x3789, ?x231), ?x13424 = 0yldt, type_of_union(?x2669, ?x566), contains(?x94, ?x8016), major_field_of_study(?x1368, ?x254), state_province_region(?x10869, ?x335), location(?x3079, ?x7213), student(?x7545, ?x7487), currency(?x4220, ?x1099), film(?x7487, ?x2085) *> conf = 0.33 ranks of expected_values: 6, 21, 318, 726, 1132, 1426, 1497, 1743, 1786, 1860, 1910, 2155 EVAL 014mlp student 0pqzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 0p_jc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 071xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 03_nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 070yzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 051cc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 06gn7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 01gbb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 04l19_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 02k21g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 01d494 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 01v_pj6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 01n5309 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student EVAL 014mlp student 02qjj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 24.000 24.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #9047-01c9f2 PRED entity: 01c9f2 PRED relation: award! PRED expected values: 016sp_ => 39 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2581): 01vsnff (0.77 #47112, 0.73 #47110, 0.72 #26919), 02fn5r (0.77 #47112, 0.73 #47110, 0.71 #47109), 03h_fk5 (0.77 #47112, 0.73 #47110, 0.71 #47109), 06rgq (0.57 #5813, 0.33 #2449, 0.19 #47113), 01hgwkr (0.57 #6060, 0.33 #2696, 0.12 #12790), 0197tq (0.43 #3399, 0.33 #35, 0.17 #43744), 058s57 (0.43 #3820, 0.33 #456, 0.17 #43744), 05sq20 (0.43 #5264, 0.33 #1900, 0.17 #43744), 01l47f5 (0.33 #1876, 0.29 #5240, 0.19 #47113), 01k_r5b (0.33 #1532, 0.29 #4896, 0.19 #47113) >> Best rule #47112 for best value: >> intensional similarity = 3 >> extensional distance = 191 >> proper extension: 02kgb7; >> query: (?x1361, ?x10209) <- award_winner(?x1361, ?x10209), award_nominee(?x10209, ?x1826), artists(?x284, ?x10209) >> conf = 0.77 => this is the best rule for 3 predicted values *> Best rule #666 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 026mfs; *> query: (?x1361, 016sp_) <- ceremony(?x1361, ?x9431), award(?x6592, ?x1361), ?x9431 = 02cg41, ?x6592 = 016sqs *> conf = 0.33 ranks of expected_values: 14 EVAL 01c9f2 award! 016sp_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 39.000 19.000 0.774 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9046-035wq7 PRED entity: 035wq7 PRED relation: languages PRED expected values: 02h40lc => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 14): 02h40lc (0.37 #626, 0.35 #1211, 0.29 #1955), 0t_2 (0.09 #48, 0.05 #243, 0.05 #126), 03hkp (0.04 #1601, 0.02 #166, 0.02 #244), 032f6 (0.04 #1601), 0880p (0.04 #1601), 06b_j (0.04 #1601), 064_8sq (0.04 #639, 0.03 #1224, 0.03 #1928), 02ztjwg (0.02 #181, 0.02 #259), 03_9r (0.02 #161, 0.02 #239), 02bjrlw (0.02 #625, 0.02 #547, 0.02 #742) >> Best rule #626 for best value: >> intensional similarity = 4 >> extensional distance = 283 >> proper extension: 04bbv7; 047jhq; >> query: (?x11885, 02h40lc) <- people(?x9428, ?x11885), actor(?x12739, ?x11885), profession(?x11885, ?x319), location(?x11885, ?x2850) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035wq7 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.368 http://example.org/people/person/languages #9045-09hy79 PRED entity: 09hy79 PRED relation: music PRED expected values: 018x3 => 75 concepts (55 used for prediction) PRED predicted values (max 10 best out of 78): 0150t6 (0.17 #46, 0.12 #256, 0.04 #1522), 016wvy (0.17 #179), 01m3b1t (0.17 #136), 0csdzz (0.12 #397, 0.02 #1663, 0.02 #607), 04ls53 (0.12 #289, 0.02 #1555, 0.02 #3673), 02jxmr (0.06 #704, 0.06 #1550, 0.04 #4301), 02bh9 (0.05 #1317, 0.05 #7455, 0.04 #8931), 03h610 (0.05 #1553, 0.05 #918, 0.05 #707), 0146pg (0.05 #3604, 0.05 #1064, 0.05 #3180), 02jxkw (0.05 #983, 0.03 #1408, 0.03 #1196) >> Best rule #46 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01xbxn; >> query: (?x7012, 0150t6) <- film(?x447, ?x7012), ?x447 = 02lfcm, nominated_for(?x112, ?x7012) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09hy79 music 018x3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 55.000 0.167 http://example.org/film/film/music #9044-025n07 PRED entity: 025n07 PRED relation: film! PRED expected values: 017s11 => 90 concepts (85 used for prediction) PRED predicted values (max 10 best out of 56): 025hwq (0.58 #2956, 0.48 #2424, 0.48 #1585), 086k8 (0.25 #2, 0.23 #229, 0.21 #380), 061dn_ (0.25 #24, 0.06 #779, 0.03 #1008), 032j_n (0.25 #58, 0.04 #813, 0.04 #134), 05qd_ (0.19 #85, 0.17 #160, 0.17 #312), 016tw3 (0.17 #1904, 0.17 #2056, 0.16 #690), 016tt2 (0.17 #608, 0.16 #912, 0.16 #307), 03xq0f (0.14 #534, 0.14 #1139, 0.13 #459), 017s11 (0.14 #835, 0.13 #682, 0.12 #2048), 01gb54 (0.10 #937, 0.09 #105, 0.08 #558) >> Best rule #2956 for best value: >> intensional similarity = 3 >> extensional distance = 893 >> proper extension: 016ztl; 0564x; 02pcq92; >> query: (?x2968, ?x7935) <- production_companies(?x2968, ?x7935), language(?x2968, ?x254), award_nominee(?x7935, ?x541) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #835 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 224 *> proper extension: 083skw; 027rpym; 014kkm; 0llcx; 029jt9; 0jqb8; 025scjj; 05sbv3; *> query: (?x2968, 017s11) <- production_companies(?x2968, ?x7935), film(?x2858, ?x2968), cinematography(?x2968, ?x7249) *> conf = 0.14 ranks of expected_values: 9 EVAL 025n07 film! 017s11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 90.000 85.000 0.576 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #9043-0bzkvd PRED entity: 0bzkvd PRED relation: ceremony! PRED expected values: 0f4x7 0gr4k => 30 concepts (29 used for prediction) PRED predicted values (max 10 best out of 341): 0f4x7 (0.88 #4905, 0.88 #4417, 0.87 #4173), 0gr4k (0.88 #1732, 0.86 #2222, 0.83 #3199), 0gqz2 (0.85 #2008, 0.84 #4450, 0.84 #1518), 018wdw (0.82 #1391, 0.81 #901, 0.81 #1146), 0gq_v (0.79 #4412, 0.79 #2948, 0.79 #2215), 0gqxm (0.75 #4887, 0.75 #7085, 0.73 #6354), 0czp_ (0.75 #4887, 0.75 #7085, 0.73 #6354), 0gqzz (0.75 #4887, 0.75 #7085, 0.73 #6354), 02x201b (0.75 #4887, 0.75 #7085, 0.73 #6354), 054krc (0.25 #488, 0.23 #2747, 0.21 #976) >> Best rule #4905 for best value: >> intensional similarity = 19 >> extensional distance = 57 >> proper extension: 073h1t; >> query: (?x8150, 0f4x7) <- ceremony(?x3617, ?x8150), ceremony(?x2209, ?x8150), honored_for(?x8150, ?x1903), ceremony(?x2209, ?x5703), ceremony(?x2209, ?x1084), nominated_for(?x2209, ?x10614), nominated_for(?x2209, ?x5767), nominated_for(?x2209, ?x5731), nominated_for(?x2209, ?x327), ?x10614 = 03bdkd, award_winner(?x8150, ?x538), award_winner(?x2209, ?x788), ?x3617 = 0gvx_, award(?x1392, ?x2209), written_by(?x5767, ?x3434), ?x1084 = 02yw5r, film_release_region(?x5731, ?x94), ?x5703 = 02yvhx, ?x327 = 0gzy02 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0bzkvd ceremony! 0gr4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 29.000 0.881 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzkvd ceremony! 0f4x7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 29.000 0.881 http://example.org/award/award_category/winners./award/award_honor/ceremony #9042-0h1fktn PRED entity: 0h1fktn PRED relation: person PRED expected values: 02qw2xb => 66 concepts (44 used for prediction) PRED predicted values (max 10 best out of 62): 0bq2g (0.30 #544, 0.08 #543, 0.05 #1091), 07m77x (0.08 #543, 0.05 #1274, 0.04 #1273), 0806vbn (0.08 #543, 0.05 #1274, 0.04 #1273), 0157m (0.08 #392, 0.08 #212, 0.04 #756), 04sry (0.08 #491, 0.08 #311, 0.04 #855), 02qsjt (0.08 #487, 0.08 #307, 0.04 #851), 0gs6vr (0.08 #478, 0.08 #298, 0.04 #842), 07mvp (0.08 #477, 0.08 #297, 0.04 #841), 0127s7 (0.08 #466, 0.08 #286, 0.04 #830), 01mwsnc (0.08 #449, 0.08 #269, 0.04 #813) >> Best rule #544 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 0jnwx; 03mnn0; 0dtzkt; >> query: (?x5639, ?x3553) <- genre(?x5639, ?x8681), film(?x3553, ?x5639), language(?x5639, ?x254), ?x8681 = 04rlf, person(?x5929, ?x3553) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0h1fktn person 02qw2xb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 44.000 0.304 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #9041-0ds5_72 PRED entity: 0ds5_72 PRED relation: film_crew_role PRED expected values: 0dxtw => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 25): 0dxtw (0.38 #222, 0.36 #759, 0.35 #543), 01vx2h (0.36 #223, 0.34 #544, 0.32 #760), 01pvkk (0.33 #47, 0.29 #545, 0.27 #976), 0215hd (0.20 #231, 0.16 #552, 0.13 #588), 02ynfr (0.18 #549, 0.17 #86, 0.16 #765), 01xy5l_ (0.17 #84, 0.14 #226, 0.10 #547), 0d2b38 (0.14 #238, 0.11 #96, 0.11 #559), 02rh1dz (0.13 #221, 0.11 #542, 0.11 #79), 089g0h (0.13 #232, 0.11 #553, 0.10 #125), 02_n3z (0.13 #214, 0.10 #107, 0.10 #535) >> Best rule #222 for best value: >> intensional similarity = 4 >> extensional distance = 173 >> proper extension: 0c0nhgv; 02c6d; 03twd6; 04n52p6; 01dyvs; 07p62k; 02qmsr; 0j_t1; 0cw3yd; 03z20c; ... >> query: (?x8495, 0dxtw) <- film_release_region(?x8495, ?x94), genre(?x8495, ?x258), featured_film_locations(?x8495, ?x1523), executive_produced_by(?x8495, ?x6985) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ds5_72 film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.377 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #9040-0147w8 PRED entity: 0147w8 PRED relation: nominated_for! PRED expected values: 027qq9b => 61 concepts (54 used for prediction) PRED predicted values (max 10 best out of 161): 02q1tc5 (0.77 #11624, 0.77 #11386, 0.72 #12100), 02pzz3p (0.73 #827, 0.70 #590, 0.60 #115), 027qq9b (0.73 #857, 0.70 #620, 0.60 #145), 02p_04b (0.55 #890, 0.50 #653, 0.22 #11862), 0gq9h (0.36 #11211, 0.35 #11449, 0.33 #11687), 0gs9p (0.33 #11213, 0.32 #11451, 0.32 #11689), 0fbtbt (0.30 #397, 0.27 #2295, 0.27 #3482), 0fbvqf (0.30 #275, 0.22 #2173, 0.21 #2648), 0bdw6t (0.30 #322, 0.22 #2220, 0.20 #2695), 019f4v (0.29 #11678, 0.29 #11202, 0.29 #11440) >> Best rule #11624 for best value: >> intensional similarity = 4 >> extensional distance = 679 >> proper extension: 07bz5; >> query: (?x11734, ?x2720) <- award(?x11734, ?x2720), award(?x439, ?x2720), ceremony(?x2720, ?x2751), award_winner(?x5469, ?x439) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #857 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 02_1q9; 02_1rq; 0358x_; 0phrl; 0gj50; 01y6dz; 02_1kl; *> query: (?x11734, 027qq9b) <- languages(?x11734, ?x254), nominated_for(?x3545, ?x11734), program(?x2062, ?x11734), ?x3545 = 02pzxlw, actor(?x11734, ?x1870) *> conf = 0.73 ranks of expected_values: 3 EVAL 0147w8 nominated_for! 027qq9b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 61.000 54.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9039-098n5 PRED entity: 098n5 PRED relation: award PRED expected values: 01l29r 027gs1_ => 110 concepts (103 used for prediction) PRED predicted values (max 10 best out of 300): 04mqgr (0.77 #6404, 0.72 #21616, 0.71 #6403), 01l29r (0.77 #6404, 0.72 #21616, 0.71 #6403), 03hkv_r (0.47 #3218, 0.43 #4018, 0.29 #16), 0fbtbt (0.40 #1431, 0.32 #2231, 0.28 #3031), 03hl6lc (0.37 #4177, 0.29 #175, 0.25 #3377), 0gq9h (0.35 #4077, 0.33 #3277, 0.22 #8081), 019f4v (0.34 #4067, 0.27 #3267, 0.23 #10071), 01l78d (0.33 #286, 0.12 #3488, 0.10 #4288), 02x17s4 (0.33 #4123, 0.32 #3323, 0.29 #121), 0gs9p (0.32 #3279, 0.31 #4079, 0.26 #10083) >> Best rule #6404 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 06lxn; >> query: (?x3555, ?x3105) <- award_winner(?x3105, ?x3555), inductee(?x9953, ?x3555), award(?x105, ?x3105) >> conf = 0.77 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 36 EVAL 098n5 award 027gs1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 110.000 103.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 098n5 award 01l29r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 103.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9038-060v34 PRED entity: 060v34 PRED relation: music PRED expected values: 0bs1yy => 77 concepts (55 used for prediction) PRED predicted values (max 10 best out of 61): 0b6yp2 (0.10 #893, 0.08 #683, 0.07 #52), 01tc9r (0.08 #485, 0.04 #2172, 0.04 #2384), 03c_8t (0.08 #841, 0.07 #1051, 0.04 #420), 02bh9 (0.07 #51, 0.04 #1523, 0.04 #4488), 020fgy (0.07 #164, 0.04 #374, 0.03 #1005), 089kpp (0.07 #204, 0.03 #1045, 0.01 #2311), 0dr5y (0.07 #171, 0.03 #1012), 015wc0 (0.07 #176, 0.01 #1227, 0.01 #2919), 016wvy (0.07 #179), 046b0s (0.07 #9292, 0.07 #5913, 0.06 #5279) >> Best rule #893 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 02x3lt7; 07h9gp; 0m491; 0fvr1; 0900j5; 0fz3b1; 01pj_5; 02d49z; 02v5_g; 03nm_fh; ... >> query: (?x570, 0b6yp2) <- film_release_region(?x570, ?x94), genre(?x570, ?x6452), ?x6452 = 02b5_l, currency(?x570, ?x170) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #3847 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 620 *> proper extension: 0gtsx8c; 0gtvrv3; 0gh8zks; *> query: (?x570, 0bs1yy) <- film_release_region(?x570, ?x94), film_crew_role(?x570, ?x1284), ?x1284 = 0ch6mp2, language(?x570, ?x254) *> conf = 0.01 ranks of expected_values: 45 EVAL 060v34 music 0bs1yy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 77.000 55.000 0.100 http://example.org/film/film/music #9037-01ls2 PRED entity: 01ls2 PRED relation: member_states! PRED expected values: 085h1 => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 13): 085h1 (0.86 #7, 0.81 #40, 0.81 #35), 018cqq (0.47 #2, 0.46 #6, 0.40 #39), 02jxk (0.41 #5, 0.28 #79, 0.27 #21), 059dn (0.37 #4, 0.35 #8, 0.30 #65), 07t65 (0.15 #37, 0.14 #78, 0.07 #578), 04k4l (0.15 #37, 0.14 #78, 0.07 #578), 02vk52z (0.07 #578, 0.06 #372, 0.05 #433), 041288 (0.07 #578), 0b6css (0.07 #578), 0gkjy (0.07 #578) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 05qx1; 06t2t; >> query: (?x410, 085h1) <- film_release_region(?x5070, ?x410), ?x5070 = 0dt8xq, olympics(?x410, ?x584) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ls2 member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.865 http://example.org/user/ktrueman/default_domain/international_organization/member_states #9036-0g9yrw PRED entity: 0g9yrw PRED relation: genre PRED expected values: 05p553 01jfsb => 68 concepts (40 used for prediction) PRED predicted values (max 10 best out of 82): 07s9rl0 (0.86 #3686, 0.85 #3801, 0.67 #116), 05p553 (0.67 #2077, 0.45 #349, 0.44 #1154), 01jfsb (0.64 #2314, 0.54 #701, 0.51 #1046), 01hmnh (0.47 #705, 0.34 #1280, 0.33 #935), 0btmb (0.42 #774, 0.09 #3455, 0.07 #889), 0gf28 (0.36 #405, 0.30 #290, 0.22 #175), 060__y (0.33 #129, 0.30 #244, 0.27 #359), 04xvlr (0.29 #2, 0.20 #3802, 0.17 #3687), 01t_vv (0.29 #51, 0.12 #2124, 0.09 #3851), 04xvh5 (0.29 #31, 0.08 #3831, 0.08 #3716) >> Best rule #3686 for best value: >> intensional similarity = 4 >> extensional distance = 958 >> proper extension: 02qjv1p; >> query: (?x4032, 07s9rl0) <- genre(?x4032, ?x1403), nominated_for(?x102, ?x4032), genre(?x5169, ?x1403), ?x5169 = 04jm_hq >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2077 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 625 *> proper extension: 0fq27fp; *> query: (?x4032, 05p553) <- currency(?x4032, ?x170), genre(?x4032, ?x271), genre(?x2102, ?x271), ?x2102 = 034qzw *> conf = 0.67 ranks of expected_values: 2, 3 EVAL 0g9yrw genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 40.000 0.859 http://example.org/film/film/genre EVAL 0g9yrw genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 40.000 0.859 http://example.org/film/film/genre #9035-013b6_ PRED entity: 013b6_ PRED relation: people PRED expected values: 014x77 01mqz0 0jcx 01t94_1 0hwqg 06myp => 52 concepts (33 used for prediction) PRED predicted values (max 10 best out of 2040): 0hskw (0.50 #7197, 0.40 #1710, 0.33 #359), 01h2_6 (0.50 #8490, 0.33 #1652, 0.14 #11967), 07h1q (0.50 #8239, 0.33 #1401, 0.14 #11967), 0nk72 (0.50 #8013, 0.33 #1175, 0.14 #11967), 026fd (0.50 #7670, 0.33 #832, 0.11 #11089), 0l9k1 (0.50 #8371, 0.33 #1533, 0.11 #11790), 02ln1 (0.50 #8016, 0.33 #1178, 0.11 #11435), 02wb6d (0.50 #7787, 0.33 #949, 0.11 #11206), 02h761 (0.50 #7380, 0.33 #542, 0.11 #10799), 04kj2v (0.50 #7157, 0.33 #319, 0.11 #10576) >> Best rule #7197 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 013xrm; >> query: (?x11490, 0hskw) <- people(?x11490, ?x9851), people(?x11490, ?x6342), people(?x11490, ?x5790), people(?x11490, ?x1211), politician(?x14092, ?x9851), ?x1211 = 0k4gf, people(?x268, ?x6342), nationality(?x6342, ?x94), student(?x3424, ?x5790) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1710 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 041rx; *> query: (?x11490, ?x118) <- people(?x11490, ?x9851), people(?x11490, ?x8375), people(?x11490, ?x4308), ?x9851 = 04jvt, ?x8375 = 0q9zc, languages_spoken(?x11490, ?x254), profession(?x4308, ?x353), language(?x54, ?x254), languages(?x118, ?x254) *> conf = 0.40 ranks of expected_values: 35, 583, 609, 611, 630, 693 EVAL 013b6_ people 06myp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 52.000 33.000 0.500 http://example.org/people/ethnicity/people EVAL 013b6_ people 0hwqg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 33.000 0.500 http://example.org/people/ethnicity/people EVAL 013b6_ people 01t94_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 33.000 0.500 http://example.org/people/ethnicity/people EVAL 013b6_ people 0jcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 33.000 0.500 http://example.org/people/ethnicity/people EVAL 013b6_ people 01mqz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 33.000 0.500 http://example.org/people/ethnicity/people EVAL 013b6_ people 014x77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 33.000 0.500 http://example.org/people/ethnicity/people #9034-02y0dd PRED entity: 02y0dd PRED relation: athlete! PRED expected values: 02vx4 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 5): 02vx4 (0.90 #302, 0.89 #262, 0.89 #312), 0jm_ (0.44 #93, 0.27 #193, 0.18 #243), 018w8 (0.33 #96, 0.26 #236, 0.25 #126), 018jz (0.11 #237, 0.11 #247, 0.08 #127), 03tmr (0.02 #331, 0.02 #341) >> Best rule #302 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 05_6_y; 08jbxf; 02vl_pz; 09l9xt; 02y9ln; 02v_4xv; 026n047; 08b0cj; 06sy4c; 0dhrqx; ... >> query: (?x11781, 02vx4) <- team(?x11781, ?x8585), team(?x11781, ?x3216), current_club(?x3587, ?x8585), team(?x60, ?x3216) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y0dd athlete! 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.903 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #9033-029h7y PRED entity: 029h7y PRED relation: artists PRED expected values: 06k02 => 55 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1000): 03f5spx (0.75 #3270, 0.72 #5412, 0.67 #2199), 01vtj38 (0.75 #4939, 0.67 #2797, 0.60 #1726), 0gbwp (0.75 #4629, 0.67 #2487, 0.60 #1416), 02yygk (0.75 #5181, 0.67 #3039, 0.60 #1968), 01vvycq (0.67 #2188, 0.62 #4330, 0.60 #1117), 0415mzy (0.67 #2642, 0.62 #4784, 0.60 #1571), 0gps0z (0.67 #3017, 0.62 #5159, 0.60 #1946), 01vrt_c (0.67 #2219, 0.62 #3290, 0.60 #1148), 01svw8n (0.67 #2481, 0.62 #4623, 0.60 #1410), 0x3n (0.67 #2705, 0.62 #4847, 0.60 #1634) >> Best rule #3270 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0k0r0n7; 012yc; >> query: (?x2936, 03f5spx) <- artists(?x2936, ?x6162), artists(?x2936, ?x4062), ?x6162 = 01w9wwg, award(?x4062, ?x1232), parent_genre(?x2936, ?x1127), instrumentalists(?x227, ?x4062), profession(?x4062, ?x131) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #7671 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 22 *> proper extension: 0fd3y; 03_d0; 02x8m; 011j5x; 01ym9b; 059kh; 07gxw; 01n4bh; 037n97; *> query: (?x2936, 06k02) <- artists(?x2936, ?x6162), artists(?x2936, ?x1732), award_nominee(?x399, ?x6162), artists(?x8847, ?x6162), artist(?x1954, ?x6162), ?x1732 = 03t9sp, parent_genre(?x8847, ?x7267), role(?x6162, ?x212) *> conf = 0.21 ranks of expected_values: 332 EVAL 029h7y artists 06k02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 21.000 0.750 http://example.org/music/genre/artists #9032-0gwlfnb PRED entity: 0gwlfnb PRED relation: film_release_region PRED expected values: 03_3d 07ssc 0f8l9c 0345h 05qx1 01znc_ => 75 concepts (64 used for prediction) PRED predicted values (max 10 best out of 146): 0345h (0.96 #171, 0.85 #898, 0.84 #1333), 0f8l9c (0.94 #453, 0.92 #890, 0.92 #308), 035qy (0.93 #28, 0.89 #900, 0.88 #609), 03_3d (0.90 #4, 0.83 #149, 0.82 #585), 07ssc (0.88 #592, 0.86 #1318, 0.85 #301), 03spz (0.85 #230, 0.79 #666, 0.78 #1392), 01znc_ (0.82 #616, 0.81 #470, 0.79 #325), 05v8c (0.81 #302, 0.76 #447, 0.72 #157), 03rk0 (0.79 #337, 0.71 #482, 0.70 #192), 016wzw (0.71 #491, 0.71 #346, 0.66 #56) >> Best rule #171 for best value: >> intensional similarity = 8 >> extensional distance = 45 >> proper extension: 0g5qs2k; 0gtvrv3; 0gd0c7x; 0bc1yhb; 09v9mks; >> query: (?x8891, 0345h) <- film_release_region(?x8891, ?x1471), film_release_region(?x8891, ?x151), film_release_region(?x8891, ?x87), film_crew_role(?x8891, ?x137), ?x87 = 05r4w, film_release_region(?x4514, ?x151), ?x4514 = 06tpmy, ?x1471 = 07t21 >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5, 7, 12 EVAL 0gwlfnb film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 75.000 64.000 0.957 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gwlfnb film_release_region 05qx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 75.000 64.000 0.957 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gwlfnb film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 64.000 0.957 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gwlfnb film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 64.000 0.957 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gwlfnb film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 64.000 0.957 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gwlfnb film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 64.000 0.957 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9031-06mt91 PRED entity: 06mt91 PRED relation: award_nominee PRED expected values: 05mt_q => 102 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1084): 01vsgrn (0.86 #6983, 0.83 #4655, 0.81 #27933), 0412f5y (0.86 #6983, 0.83 #4655, 0.81 #27933), 026yqrr (0.86 #6983, 0.83 #4655, 0.81 #27933), 05mt_q (0.86 #6983, 0.83 #4655, 0.81 #27933), 02cx90 (0.16 #12645, 0.08 #5662, 0.07 #54550), 05vzw3 (0.15 #3409, 0.09 #12720, 0.07 #17376), 03y82t6 (0.14 #67513, 0.14 #27932, 0.10 #46558), 049qx (0.14 #67513, 0.14 #27932, 0.10 #46558), 028q6 (0.14 #11658, 0.05 #4675, 0.04 #9331), 011zf2 (0.14 #11929, 0.03 #53834, 0.02 #58491) >> Best rule #6983 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 02r3zy; 07c0j; 011zf2; 03g5jw; 03fbc; 014hr0; 0kr_t; 07bzp; 0gr69; 01jkqfz; ... >> query: (?x6835, ?x140) <- award(?x6835, ?x3045), award_nominee(?x140, ?x6835), ?x3045 = 02sp_v >> conf = 0.86 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 06mt91 award_nominee 05mt_q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 33.000 0.864 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #9030-0gmdkyy PRED entity: 0gmdkyy PRED relation: award_winner PRED expected values: 06r_by => 33 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1356): 04ktcgn (0.50 #1815, 0.33 #275, 0.15 #7972), 0cw67g (0.33 #1408, 0.25 #2948, 0.24 #12182), 0b6mgp_ (0.33 #675, 0.25 #2215, 0.22 #5295), 0c94fn (0.33 #268, 0.25 #1808, 0.22 #4888), 0151w_ (0.33 #132, 0.25 #1672, 0.15 #7829), 09fb5 (0.33 #43, 0.25 #1583, 0.08 #12359), 092ys_y (0.33 #574, 0.25 #2114, 0.08 #8271), 0169dl (0.33 #339, 0.25 #1879, 0.08 #8036), 03_gd (0.33 #97, 0.25 #1637, 0.08 #7794), 04cy8rb (0.33 #30, 0.25 #1570, 0.08 #7727) >> Best rule #1815 for best value: >> intensional similarity = 18 >> extensional distance = 2 >> proper extension: 0bvfqq; >> query: (?x2082, 04ktcgn) <- award_winner(?x2082, ?x986), ceremony(?x6860, ?x2082), ceremony(?x4573, ?x2082), ceremony(?x2222, ?x2082), ceremony(?x77, ?x2082), ?x2222 = 0gs96, ?x77 = 0gqng, honored_for(?x2082, ?x6007), honored_for(?x2082, ?x5109), honored_for(?x2082, ?x224), ?x6860 = 018wdw, ?x4573 = 0gq_d, nominated_for(?x1723, ?x224), films(?x3530, ?x6007), film_regional_debut_venue(?x5109, ?x739), film_release_region(?x5109, ?x87), nominated_for(?x163, ?x6007), vacationer(?x739, ?x444) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4620 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 5 *> proper extension: 0hhtgcw; *> query: (?x2082, ?x617) <- award_winner(?x2082, ?x2551), honored_for(?x2082, ?x10241), honored_for(?x2082, ?x3784), honored_for(?x2082, ?x385), film_release_region(?x3784, ?x1917), nominated_for(?x198, ?x3784), ?x1917 = 01p1v, film_crew_role(?x3784, ?x468), award_winner(?x414, ?x2551), gender(?x2551, ?x514), ?x385 = 0ds3t5x, award_winner(?x618, ?x2551), award_winner(?x3784, ?x617), film(?x2551, ?x2846), film(?x400, ?x10241) *> conf = 0.28 ranks of expected_values: 26 EVAL 0gmdkyy award_winner 06r_by CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 33.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9029-027xq5 PRED entity: 027xq5 PRED relation: currency PRED expected values: 01nv4h => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 5): 01nv4h (0.75 #75, 0.73 #99, 0.72 #91), 09nqf (0.69 #61, 0.65 #100, 0.60 #170), 0ptk_ (0.17 #55, 0.16 #78, 0.12 #94), 02l6h (0.12 #56, 0.12 #79, 0.11 #87), 0kz1h (0.06 #72, 0.06 #49, 0.05 #57) >> Best rule #75 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 01v2xl; >> query: (?x13781, ?x1099) <- student(?x13781, ?x5332), contains(?x512, ?x13781), major_field_of_study(?x13781, ?x373), currency(?x13781, ?x1099) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027xq5 currency 01nv4h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.746 http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency #9028-012yc PRED entity: 012yc PRED relation: artists PRED expected values: 03f1d47 067nsm 01w5jwb 03f7jfh => 64 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1048): 01vvycq (0.60 #3206, 0.57 #6367, 0.56 #10582), 020_4z (0.60 #4070, 0.57 #7231, 0.50 #5123), 0892sx (0.60 #3365, 0.57 #6526, 0.50 #4418), 0259r0 (0.60 #3376, 0.50 #4429, 0.43 #6537), 01ydzx (0.60 #3746, 0.50 #4799, 0.43 #6907), 01wvxw1 (0.60 #3883, 0.50 #4936, 0.43 #7044), 01s21dg (0.60 #3566, 0.50 #4619, 0.43 #6727), 0qf3p (0.60 #3358, 0.50 #4411, 0.43 #6519), 0191h5 (0.60 #2735, 0.40 #3788, 0.38 #8003), 01p0w_ (0.60 #3117, 0.38 #8385, 0.33 #12599) >> Best rule #3206 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 06by7; 0gywn; >> query: (?x9630, 01vvycq) <- artists(?x9630, ?x11897), artists(?x9630, ?x6035), artists(?x9630, ?x3894), ?x11897 = 01f2q5, profession(?x6035, ?x1183), ?x3894 = 01vxlbm, participant(?x6035, ?x1093), role(?x6035, ?x1466) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #11313 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 03_d0; *> query: (?x9630, 01w5jwb) <- artists(?x9630, ?x11897), artists(?x9630, ?x6035), artists(?x9630, ?x3894), ?x11897 = 01f2q5, profession(?x6035, ?x1183), artists(?x3996, ?x3894), ?x3996 = 02lnbg *> conf = 0.44 ranks of expected_values: 43, 188, 447, 484 EVAL 012yc artists 03f7jfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 64.000 25.000 0.600 http://example.org/music/genre/artists EVAL 012yc artists 01w5jwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 64.000 25.000 0.600 http://example.org/music/genre/artists EVAL 012yc artists 067nsm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 64.000 25.000 0.600 http://example.org/music/genre/artists EVAL 012yc artists 03f1d47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 64.000 25.000 0.600 http://example.org/music/genre/artists #9027-02kcv4x PRED entity: 02kcv4x PRED relation: nutrient! PRED expected values: 0fbdb 07j87 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 10): 0fbdb (0.92 #440, 0.91 #424, 0.91 #420), 0dcfv (0.90 #242, 0.89 #187, 0.89 #10), 07j87 (0.89 #187, 0.89 #10, 0.88 #64), 06x4c (0.89 #187, 0.89 #10, 0.88 #64), 025rw19 (0.01 #101), 025tkqy (0.01 #101), 014d7f (0.01 #101), 06jry (0.01 #101), 025s7j4 (0.01 #101), 01sh2 (0.01 #101) >> Best rule #440 for best value: >> intensional similarity = 121 >> extensional distance = 22 >> proper extension: 08lb68; >> query: (?x8442, 0fbdb) <- nutrient(?x10612, ?x8442), nutrient(?x9732, ?x8442), nutrient(?x7719, ?x8442), nutrient(?x6285, ?x8442), nutrient(?x6191, ?x8442), nutrient(?x6159, ?x8442), nutrient(?x5009, ?x8442), nutrient(?x3900, ?x8442), nutrient(?x2701, ?x8442), nutrient(?x1959, ?x8442), nutrient(?x1303, ?x8442), nutrient(?x1257, ?x8442), nutrient(?x7719, ?x13944), nutrient(?x7719, ?x13498), nutrient(?x7719, ?x12902), nutrient(?x7719, ?x12868), nutrient(?x7719, ?x12454), nutrient(?x7719, ?x11784), nutrient(?x7719, ?x11758), nutrient(?x7719, ?x11592), nutrient(?x7719, ?x10709), nutrient(?x7719, ?x9915), nutrient(?x7719, ?x9855), nutrient(?x7719, ?x9840), nutrient(?x7719, ?x9733), nutrient(?x7719, ?x9708), nutrient(?x7719, ?x9436), nutrient(?x7719, ?x9426), nutrient(?x7719, ?x9365), nutrient(?x7719, ?x8413), nutrient(?x7719, ?x8243), nutrient(?x7719, ?x7894), nutrient(?x7719, ?x7720), nutrient(?x7719, ?x7652), nutrient(?x7719, ?x7431), nutrient(?x7719, ?x7135), nutrient(?x7719, ?x6586), nutrient(?x7719, ?x6286), nutrient(?x7719, ?x6192), nutrient(?x7719, ?x6026), nutrient(?x7719, ?x5374), nutrient(?x7719, ?x5337), nutrient(?x7719, ?x5010), nutrient(?x7719, ?x4069), nutrient(?x7719, ?x3469), nutrient(?x7719, ?x3203), nutrient(?x7719, ?x2702), nutrient(?x7719, ?x2018), nutrient(?x7719, ?x1960), nutrient(?x7719, ?x1258), ?x6586 = 05gh50, ?x4069 = 0hqw8p_, ?x7135 = 025rsfk, ?x1960 = 07hnp, ?x9915 = 025tkqy, ?x11758 = 0q01m, ?x12454 = 025rw19, ?x5337 = 06x4c, ?x7652 = 025s0s0, ?x3900 = 061_f, ?x5374 = 025s0zp, nutrient(?x1257, ?x14698), nutrient(?x1257, ?x12083), nutrient(?x1257, ?x11409), nutrient(?x1257, ?x10891), nutrient(?x1257, ?x10098), nutrient(?x1257, ?x6033), nutrient(?x1257, ?x5549), nutrient(?x1257, ?x5451), ?x9733 = 0h1tz, ?x5009 = 0fjfh, ?x1959 = 0f25w9, ?x6285 = 01645p, ?x10891 = 0g5gq, ?x9732 = 05z55, ?x12868 = 03d49, ?x9426 = 0h1yy, ?x7720 = 025s7x6, ?x2702 = 0838f, ?x9365 = 04k8n, nutrient(?x6191, ?x12481), nutrient(?x6191, ?x9949), nutrient(?x6191, ?x6160), ?x8413 = 02kc4sf, ?x6160 = 041r51, ?x1303 = 0fj52s, ?x9708 = 061xhr, ?x5010 = 0h1vz, ?x7431 = 09gwd, ?x6159 = 033cnk, ?x10612 = 0frq6, ?x5451 = 05wvs, ?x6026 = 025sf8g, ?x12902 = 0fzjh, ?x9436 = 025sqz8, ?x12481 = 027g6p7, ?x6033 = 04zjxcz, ?x10098 = 0h1_c, ?x9949 = 02kd0rh, ?x9840 = 02p0tjr, ?x6192 = 06jry, ?x3203 = 04kl74p, ?x11592 = 025sf0_, ?x7894 = 0f4hc, ?x2018 = 01sh2, nutrient(?x9489, ?x13498), ?x3469 = 0h1zw, nutrient(?x2701, ?x3901), ?x3901 = 0466p20, ?x11784 = 07zqy, ?x14698 = 02kb_jm, ?x13944 = 0f4kp, ?x8243 = 014d7f, ?x5549 = 025s7j4, ?x12083 = 01n78x, ?x9489 = 07j87, ?x9855 = 0d9t0, ?x1258 = 0h1wg, ?x11409 = 0h1yf, ?x6286 = 02y_3rf, ?x10709 = 0h1sz >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 02kcv4x nutrient! 07j87 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 58.000 0.917 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 02kcv4x nutrient! 0fbdb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.917 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #9026-02qlp4 PRED entity: 02qlp4 PRED relation: currency PRED expected values: 09nqf => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.80 #120, 0.80 #155, 0.79 #148), 02l6h (0.08 #4, 0.02 #74, 0.01 #32), 01nv4h (0.03 #79, 0.03 #65, 0.02 #100) >> Best rule #120 for best value: >> intensional similarity = 4 >> extensional distance = 499 >> proper extension: 02d44q; 0hgnl3t; >> query: (?x10902, 09nqf) <- film_crew_role(?x10902, ?x1171), country(?x10902, ?x94), nominated_for(?x2549, ?x10902), ?x1171 = 09vw2b7 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qlp4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.804 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #9025-02_jkc PRED entity: 02_jkc PRED relation: award_winner! PRED expected values: 0gpjbt => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 119): 01mh_q (0.33 #86, 0.21 #638, 0.17 #7315), 0jzphpx (0.33 #37, 0.18 #589, 0.17 #7315), 01s695 (0.33 #3, 0.17 #7315, 0.15 #555), 01xqqp (0.33 #93, 0.17 #7315, 0.12 #9249), 0gx1673 (0.33 #117, 0.17 #7315, 0.12 #9249), 02q690_ (0.33 #62, 0.17 #7315, 0.12 #9249), 0bzm81 (0.29 #297, 0.17 #7315, 0.12 #9249), 0bzm__ (0.20 #499, 0.17 #7315, 0.12 #7868), 0466p0j (0.18 #625, 0.17 #7315, 0.12 #7868), 02rjjll (0.18 #557, 0.11 #1385, 0.09 #1523) >> Best rule #86 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01htxr; >> query: (?x5298, 01mh_q) <- award_nominee(?x5298, ?x7088), award_nominee(?x5298, ?x6626), ?x6626 = 0b_j2, award_nominee(?x7088, ?x1974) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7315 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1364 *> proper extension: 01nrgq; 05b2f_k; *> query: (?x5298, ?x3579) <- award_winner(?x5298, ?x7088), award_winner(?x3579, ?x7088) *> conf = 0.17 ranks of expected_values: 14 EVAL 02_jkc award_winner! 0gpjbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 102.000 102.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9024-05l4yg PRED entity: 05l4yg PRED relation: nominated_for PRED expected values: 0g60z => 90 concepts (31 used for prediction) PRED predicted values (max 10 best out of 281): 0g60z (0.69 #3286, 0.60 #1664, 0.08 #4909), 01q2nx (0.25 #24343, 0.25 #25967, 0.25 #30835), 0888c3 (0.25 #24343, 0.25 #25967, 0.25 #30835), 03ln8b (0.15 #5171, 0.14 #303, 0.13 #6794), 0180mw (0.15 #5908, 0.13 #7531, 0.08 #4285), 015qqg (0.15 #5634, 0.13 #7257), 05f4vxd (0.14 #799, 0.10 #2422, 0.08 #5667), 02qkq0 (0.14 #1072, 0.08 #5940, 0.08 #4317), 07sp4l (0.14 #462, 0.08 #5330, 0.07 #6953), 0431v3 (0.10 #2499, 0.08 #4121, 0.02 #8990) >> Best rule #3286 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 03x3qv; 04bd8y; 02mqc4; 0jmj; 04yqlk; 04bcb1; 047c9l; 0335fp; 022yb4; 01ggc9; ... >> query: (?x6791, 0g60z) <- film(?x6791, ?x5275), award_nominee(?x3452, ?x6791), ?x3452 = 040t74 >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05l4yg nominated_for 0g60z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 31.000 0.692 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #9023-0dhd5 PRED entity: 0dhd5 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.35 #17, 0.35 #13, 0.34 #9) >> Best rule #17 for best value: >> intensional similarity = 2 >> extensional distance = 571 >> proper extension: 05kr_; 0pbhz; 05fly; 03902; 0df4y; 037n3; >> query: (?x13546, 04ztj) <- country(?x13546, ?x2236), film_release_region(?x66, ?x2236) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dhd5 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 19.000 19.000 0.351 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #9022-01ddth PRED entity: 01ddth PRED relation: people PRED expected values: 044qx 01k47c => 43 concepts (20 used for prediction) PRED predicted values (max 10 best out of 656): 07pzc (0.38 #2489, 0.33 #424, 0.18 #6620), 053yx (0.33 #786, 0.33 #98, 0.20 #3541), 04__f (0.33 #3101, 0.33 #347, 0.17 #1035), 04wqr (0.33 #700, 0.22 #2766, 0.20 #4143), 02cj_f (0.33 #1131, 0.22 #3197, 0.20 #4574), 0pj8m (0.33 #349, 0.20 #4480, 0.17 #1037), 013qvn (0.33 #325, 0.20 #4456, 0.17 #1013), 018qql (0.33 #669, 0.17 #1357, 0.12 #2734), 042fk (0.33 #650, 0.17 #1338, 0.12 #2715), 0klw (0.33 #186, 0.17 #874, 0.12 #2251) >> Best rule #2489 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0148xv; >> query: (?x13552, 07pzc) <- people(?x13552, ?x13125), people(?x13552, ?x8355), category(?x8355, ?x134), profession(?x8355, ?x1032), ?x1032 = 02hrh1q, film(?x8355, ?x11395), spouse(?x7342, ?x8355), influenced_by(?x13125, ?x3542) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #3862 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 01_qc_; *> query: (?x13552, 01k47c) <- people(?x13552, ?x8355), award_winner(?x8355, ?x1357), type_of_union(?x8355, ?x566), nationality(?x8355, ?x94), gender(?x8355, ?x514), symptom_of(?x13487, ?x13552), religion(?x8355, ?x1985) *> conf = 0.10 ranks of expected_values: 485, 610 EVAL 01ddth people 01k47c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 20.000 0.375 http://example.org/people/cause_of_death/people EVAL 01ddth people 044qx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 20.000 0.375 http://example.org/people/cause_of_death/people #9021-0pksh PRED entity: 0pksh PRED relation: gender PRED expected values: 05zppz => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #11, 0.86 #9, 0.86 #7), 02zsn (0.46 #215, 0.33 #44, 0.32 #60) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 138 >> proper extension: 092kgw; >> query: (?x12529, 05zppz) <- award_winner(?x12529, ?x10186), produced_by(?x5230, ?x12529), location(?x10186, ?x94) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pksh gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.864 http://example.org/people/person/gender #9020-046rfv PRED entity: 046rfv PRED relation: student! PRED expected values: 03g3w => 145 concepts (105 used for prediction) PRED predicted values (max 10 best out of 40): 04g51 (0.33 #39, 0.05 #914, 0.05 #1104), 02822 (0.16 #906, 0.15 #1096, 0.15 #1032), 0jjw (0.14 #150, 0.11 #275, 0.06 #526), 03qsdpk (0.10 #911, 0.10 #1101, 0.10 #1037), 01zc2w (0.09 #923, 0.08 #1113, 0.08 #1049), 0fdys (0.08 #403, 0.07 #2287, 0.07 #1094), 0w7c (0.07 #1360, 0.05 #1546, 0.04 #2110), 02vxn (0.07 #879, 0.07 #1069, 0.07 #1005), 040p_q (0.06 #611, 0.02 #924, 0.02 #1114), 0mg1w (0.06 #607, 0.02 #920, 0.02 #1110) >> Best rule #39 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0cbdf1; >> query: (?x8097, 04g51) <- student(?x9399, ?x8097), gender(?x8097, ?x514), ?x9399 = 02z6fs, profession(?x8097, ?x1383) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2215 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 173 *> proper extension: 023s8; 06w38l; *> query: (?x8097, 03g3w) <- profession(?x8097, ?x1383), type_of_union(?x8097, ?x566), student(?x1526, ?x8097), ?x566 = 04ztj *> conf = 0.06 ranks of expected_values: 12 EVAL 046rfv student! 03g3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 145.000 105.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #9019-05mph PRED entity: 05mph PRED relation: location! PRED expected values: 0c6qh 01dhmw => 221 concepts (142 used for prediction) PRED predicted values (max 10 best out of 2064): 02fn5r (0.52 #10037, 0.47 #100372, 0.45 #316166), 01dhmw (0.33 #644, 0.08 #185687, 0.07 #72770), 0gs5q (0.25 #4278, 0.17 #6787, 0.07 #24352), 0c6qh (0.25 #2968, 0.17 #5477, 0.06 #153525), 0ffgh (0.25 #3948, 0.17 #6457, 0.05 #19003), 01364q (0.25 #2907, 0.17 #5416, 0.05 #17962), 03h_fk5 (0.25 #3042, 0.17 #5551, 0.05 #18097), 025b3k (0.25 #4452, 0.17 #6961, 0.05 #24526), 017f4y (0.25 #4656, 0.17 #7165, 0.04 #207907), 014dq7 (0.25 #2854, 0.17 #5363, 0.04 #7872) >> Best rule #10037 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 0n96z; >> query: (?x6521, ?x2638) <- country(?x6521, ?x94), adjoins(?x1025, ?x6521), place_of_birth(?x2638, ?x6521) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #644 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 0fvzg; *> query: (?x6521, 01dhmw) <- country(?x6521, ?x94), contains(?x6521, ?x4145), ?x4145 = 035wtd *> conf = 0.33 ranks of expected_values: 2, 4 EVAL 05mph location! 01dhmw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 221.000 142.000 0.515 http://example.org/people/person/places_lived./people/place_lived/location EVAL 05mph location! 0c6qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 221.000 142.000 0.515 http://example.org/people/person/places_lived./people/place_lived/location #9018-0frq6 PRED entity: 0frq6 PRED relation: nutrient PRED expected values: 04zjxcz 075pwf 0h1sz 0h1yf => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 39): 06x4c (0.93 #21, 0.78 #50, 0.70 #419), 025s0zp (0.93 #21, 0.78 #50, 0.70 #420), 01sh2 (0.93 #21, 0.78 #50, 0.70 #416), 0h1yf (0.93 #21, 0.78 #50, 0.70 #429), 04zjxcz (0.93 #21, 0.78 #50, 0.70 #421), 0hqw8p_ (0.93 #21, 0.78 #50, 0.70 #418), 014d7f (0.93 #21, 0.78 #50, 0.62 #442), 0d9t0 (0.93 #21, 0.78 #50, 0.60 #425), 03d49 (0.93 #21, 0.78 #50, 0.60 #432), 0dcfv (0.93 #21, 0.78 #50, 0.60 #417) >> Best rule #21 for best value: >> intensional similarity = 112 >> extensional distance = 1 >> proper extension: 061_f; >> query: (?x10612, ?x2018) <- nutrient(?x10612, ?x13498), nutrient(?x10612, ?x12454), nutrient(?x10612, ?x11758), nutrient(?x10612, ?x11592), nutrient(?x10612, ?x11270), nutrient(?x10612, ?x10891), nutrient(?x10612, ?x9949), nutrient(?x10612, ?x9915), nutrient(?x10612, ?x9840), nutrient(?x10612, ?x9795), nutrient(?x10612, ?x9733), nutrient(?x10612, ?x9490), nutrient(?x10612, ?x9426), nutrient(?x10612, ?x9365), nutrient(?x10612, ?x8487), nutrient(?x10612, ?x8442), nutrient(?x10612, ?x7894), nutrient(?x10612, ?x7720), nutrient(?x10612, ?x7652), nutrient(?x10612, ?x7431), nutrient(?x10612, ?x7364), nutrient(?x10612, ?x7362), nutrient(?x10612, ?x7219), nutrient(?x10612, ?x6586), nutrient(?x10612, ?x6517), nutrient(?x10612, ?x6160), nutrient(?x10612, ?x5549), nutrient(?x10612, ?x5526), nutrient(?x10612, ?x5010), nutrient(?x10612, ?x3901), nutrient(?x10612, ?x3203), nutrient(?x10612, ?x2702), nutrient(?x10612, ?x1960), nutrient(?x10612, ?x1304), nutrient(?x10612, ?x1258), ?x8442 = 02kcv4x, ?x7364 = 09gvd, ?x7431 = 09gwd, ?x11758 = 0q01m, ?x6586 = 05gh50, ?x1960 = 07hnp, ?x1258 = 0h1wg, ?x12454 = 025rw19, nutrient(?x9732, ?x6517), nutrient(?x6159, ?x6517), nutrient(?x5373, ?x6517), nutrient(?x1959, ?x6517), ?x9915 = 025tkqy, nutrient(?x9489, ?x2702), nutrient(?x9005, ?x2702), nutrient(?x8298, ?x2702), nutrient(?x7719, ?x2702), nutrient(?x6285, ?x2702), nutrient(?x6191, ?x2702), nutrient(?x6032, ?x2702), nutrient(?x5337, ?x2702), nutrient(?x4068, ?x2702), nutrient(?x3468, ?x2702), nutrient(?x2701, ?x2702), nutrient(?x1257, ?x2702), ?x1257 = 09728, ?x7894 = 0f4hc, ?x9490 = 0h1sg, ?x3203 = 04kl74p, ?x8487 = 014yzm, ?x6160 = 041r51, ?x9840 = 02p0tjr, ?x5337 = 06x4c, ?x9365 = 04k8n, ?x6032 = 01nkt, ?x3468 = 0cxn2, ?x9733 = 0h1tz, ?x6285 = 01645p, ?x1304 = 08lb68, ?x5010 = 0h1vz, ?x11270 = 02kc008, ?x5526 = 09pbb, ?x2701 = 0hkxq, ?x7219 = 0h1vg, ?x9795 = 05v_8y, ?x9949 = 02kd0rh, ?x9489 = 07j87, ?x3901 = 0466p20, ?x7362 = 02kc5rj, ?x7720 = 025s7x6, ?x8298 = 037ls6, ?x13498 = 07q0m, ?x6159 = 033cnk, ?x10891 = 0g5gq, ?x9005 = 04zpv, ?x4068 = 0fbw6, ?x7652 = 025s0s0, ?x1959 = 0f25w9, ?x9426 = 0h1yy, ?x9732 = 05z55, ?x7719 = 0dj75, nutrient(?x6191, ?x12868), nutrient(?x6191, ?x12481), nutrient(?x6191, ?x11784), nutrient(?x6191, ?x10195), nutrient(?x6191, ?x8243), nutrient(?x6191, ?x5374), nutrient(?x6191, ?x2018), ?x11784 = 07zqy, ?x5373 = 0971v, ?x12481 = 027g6p7, ?x8243 = 014d7f, ?x5549 = 025s7j4, ?x12868 = 03d49, ?x5374 = 025s0zp, ?x10195 = 0hkwr, ?x11592 = 025sf0_ >> conf = 0.93 => this is the best rule for 16 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 5, 11, 17 EVAL 0frq6 nutrient 0h1yf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 22.000 0.927 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0frq6 nutrient 0h1sz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 22.000 22.000 0.927 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0frq6 nutrient 075pwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 22.000 22.000 0.927 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0frq6 nutrient 04zjxcz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 22.000 0.927 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #9017-01q7cb_ PRED entity: 01q7cb_ PRED relation: location PRED expected values: 0ggh3 => 144 concepts (114 used for prediction) PRED predicted values (max 10 best out of 257): 02_286 (0.50 #2446, 0.25 #4052, 0.24 #6461), 030qb3t (0.36 #7309, 0.34 #52289, 0.33 #4097), 0qpqn (0.25 #1255, 0.05 #91577, 0.05 #8482), 0cr3d (0.22 #3356, 0.12 #13796, 0.09 #15403), 0f2rq (0.20 #1886, 0.05 #91577, 0.05 #8310), 02xry (0.20 #1738, 0.03 #37076, 0.02 #29848), 02jx1 (0.17 #4085, 0.11 #5691, 0.11 #4888), 0r0m6 (0.17 #2626, 0.08 #16280, 0.08 #4232), 0h1k6 (0.17 #2970, 0.08 #4576, 0.05 #7788), 04lh6 (0.17 #2844, 0.07 #13284, 0.03 #11678) >> Best rule #2446 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0l12d; >> query: (?x970, 02_286) <- group(?x970, ?x10427), participant(?x970, ?x971), profession(?x970, ?x524), ?x524 = 02jknp >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #11619 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 30 *> proper extension: 06pwf6; 03pvt; 04g3p5; 024t0y; *> query: (?x970, 0ggh3) <- profession(?x970, ?x1032), profession(?x970, ?x967), type_of_union(?x970, ?x566), ?x967 = 012t_z, ?x1032 = 02hrh1q *> conf = 0.03 ranks of expected_values: 127 EVAL 01q7cb_ location 0ggh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 144.000 114.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #9016-01l_yg PRED entity: 01l_yg PRED relation: film PRED expected values: 0k2sk => 112 concepts (61 used for prediction) PRED predicted values (max 10 best out of 671): 0q9jk (0.58 #41012, 0.51 #5350, 0.38 #51711), 013q07 (0.14 #356, 0.03 #3922, 0.02 #5706), 016dj8 (0.14 #1111, 0.02 #6461, 0.02 #4677), 016017 (0.14 #1706, 0.02 #3489, 0.02 #8839), 0ds2n (0.14 #521, 0.02 #2304, 0.02 #4087), 011yqc (0.14 #233, 0.02 #2016), 09m6kg (0.14 #31, 0.02 #1814), 027pfg (0.14 #1219, 0.02 #6569, 0.01 #36881), 056xkh (0.14 #1594, 0.02 #5160, 0.01 #8727), 087wc7n (0.14 #114, 0.02 #3680, 0.01 #10813) >> Best rule #41012 for best value: >> intensional similarity = 4 >> extensional distance = 685 >> proper extension: 044mz_; 0q9kd; 0184jc; 04bdxl; 02s2ft; 0grwj; 05bnp0; 016qtt; 012d40; 07fq1y; ... >> query: (?x9700, ?x8132) <- type_of_union(?x9700, ?x566), profession(?x9700, ?x1032), award_winner(?x8132, ?x9700), film(?x9700, ?x407) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #7295 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 236 *> proper extension: 079vf; 05bp8g; 02g8h; 01rrwf6; 044rvb; 01vlj1g; 041ly3; 04yj5z; 03qd_; 0521rl1; ... *> query: (?x9700, 0k2sk) <- type_of_union(?x9700, ?x566), profession(?x9700, ?x1383), ?x1383 = 0np9r, ?x566 = 04ztj *> conf = 0.02 ranks of expected_values: 447 EVAL 01l_yg film 0k2sk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 61.000 0.577 http://example.org/film/actor/film./film/performance/film #9015-06101p PRED entity: 06101p PRED relation: award PRED expected values: 09v51c2 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 287): 09v8db5 (0.40 #254, 0.05 #660, 0.04 #3096), 09v51c2 (0.40 #326, 0.05 #732, 0.04 #3168), 09v92_x (0.40 #280, 0.05 #686, 0.04 #3122), 05ztrmj (0.20 #186, 0.12 #1810, 0.11 #592), 05q5t0b (0.20 #165, 0.04 #571, 0.03 #977), 09v1lrz (0.20 #376, 0.04 #782, 0.03 #1188), 0dgr5xp (0.20 #303, 0.03 #2333, 0.02 #2739), 09v0wy2 (0.20 #237, 0.02 #643, 0.02 #8527), 0f4x7 (0.18 #2467, 0.15 #2061, 0.07 #4091), 09sb52 (0.17 #2071, 0.16 #5725, 0.16 #1259) >> Best rule #254 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0pksh; >> query: (?x13074, 09v8db5) <- location(?x13074, ?x2645), ?x2645 = 03h64, profession(?x13074, ?x319), ?x319 = 01d_h8 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #326 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0pksh; *> query: (?x13074, 09v51c2) <- location(?x13074, ?x2645), ?x2645 = 03h64, profession(?x13074, ?x319), ?x319 = 01d_h8 *> conf = 0.40 ranks of expected_values: 2 EVAL 06101p award 09v51c2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9014-05ws7 PRED entity: 05ws7 PRED relation: split_to PRED expected values: 02fgmn => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 270): 0frsw (0.57 #1134, 0.40 #1229, 0.33 #287), 0c12h (0.33 #255, 0.11 #986, 0.01 #1197), 065y4w7 (0.14 #841, 0.04 #1055, 0.01 #1150), 09xzd (0.04 #1127, 0.01 #1222), 04m_kpx (0.04 #1122, 0.01 #1217), 07vfz (0.04 #1113, 0.01 #1208), 07vjm (0.04 #1105, 0.01 #1200), 04jr87 (0.04 #1100, 0.01 #1195), 02zd460 (0.04 #1096, 0.01 #1191), 07vhb (0.04 #1095, 0.01 #1190) >> Best rule #1134 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 01hhvg; 07vht; 01cx_; 08z129; 01f1r4; 057xkj_; 09f2j; 07vhb; 02zd460; 06rny; ... >> query: (?x14582, ?x2521) <- split_to(?x2521, ?x14582), category(?x2521, ?x134), ?x134 = 08mbj5d >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05ws7 split_to 02fgmn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 13.000 13.000 0.565 http://example.org/dataworld/gardening_hint/split_to #9013-01b39j PRED entity: 01b39j PRED relation: company! PRED expected values: 0dq3c => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 38): 0dq3c (0.59 #179, 0.56 #1464, 0.54 #1596), 09d6p2 (0.58 #326, 0.47 #547, 0.47 #414), 05_wyz (0.57 #413, 0.55 #193, 0.54 #325), 01yc02 (0.54 #316, 0.53 #537, 0.50 #1557), 01kr6k (0.35 #334, 0.33 #422, 0.31 #378), 02y6fz (0.28 #3008, 0.28 #3408, 0.25 #2211), 02211by (0.28 #3008, 0.28 #3408, 0.25 #2211), 09lq2c (0.28 #3008, 0.25 #2211, 0.21 #3541), 0142rn (0.21 #3541, 0.16 #509, 0.14 #2831), 04192r (0.21 #3541, 0.14 #2831, 0.13 #260) >> Best rule #179 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 02zs4; 087c7; 0cv9b; 0300cp; 0gvbw; 08z129; 0178g; 01yfp7; 01ym8l; 019rl6; ... >> query: (?x8934, 0dq3c) <- industry(?x8934, ?x14344), company(?x346, ?x8934), ?x346 = 060c4, citytown(?x8934, ?x5771), currency(?x8934, ?x170) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01b39j company! 0dq3c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.591 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #9012-0h8d PRED entity: 0h8d PRED relation: country! PRED expected values: 06z6r => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 56): 071t0 (0.83 #360, 0.66 #809, 0.57 #753), 06z6r (0.82 #762, 0.75 #1826, 0.75 #2162), 06wrt (0.78 #353, 0.59 #802, 0.50 #746), 019tzd (0.74 #378, 0.44 #827, 0.35 #771), 03_8r (0.70 #752, 0.62 #1984, 0.60 #1816), 01lb14 (0.70 #352, 0.56 #801, 0.50 #745), 03hr1p (0.70 #361, 0.49 #810, 0.42 #754), 01cgz (0.68 #743, 0.57 #350, 0.56 #799), 0w0d (0.65 #348, 0.51 #797, 0.47 #741), 06f41 (0.65 #351, 0.50 #744, 0.49 #800) >> Best rule #360 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 01k6y1; >> query: (?x3738, 071t0) <- location(?x4438, ?x3738), official_language(?x3738, ?x254), nationality(?x4438, ?x94), student(?x735, ?x4438) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #762 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 0f8l9c; 06srk; 06s_2; *> query: (?x3738, 06z6r) <- currency(?x3738, ?x170), country(?x5396, ?x3738), official_language(?x3738, ?x254), ?x5396 = 0486tv *> conf = 0.82 ranks of expected_values: 2 EVAL 0h8d country! 06z6r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 128.000 0.826 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #9011-01nm8w PRED entity: 01nm8w PRED relation: contains! PRED expected values: 0154j => 144 concepts (56 used for prediction) PRED predicted values (max 10 best out of 359): 0154j (0.84 #13448, 0.77 #27796, 0.73 #21520), 09c7w0 (0.69 #17040, 0.68 #18831, 0.64 #25107), 0d060g (0.67 #26911, 0.09 #27809, 0.08 #17946), 02jx1 (0.50 #3671, 0.43 #5467, 0.40 #1879), 0345h (0.42 #12632, 0.38 #9046, 0.30 #10840), 05kr_ (0.40 #27024, 0.05 #27922, 0.03 #37779), 04jpl (0.33 #3606, 0.25 #22, 0.24 #48435), 02j71 (0.33 #47518), 0chghy (0.25 #20647, 0.21 #12573, 0.04 #19748), 07ssc (0.25 #32, 0.20 #1824, 0.17 #3616) >> Best rule #13448 for best value: >> intensional similarity = 9 >> extensional distance = 17 >> proper extension: 02hrb2; >> query: (?x9658, ?x172) <- contains(?x14475, ?x9658), institution(?x734, ?x9658), country(?x14475, ?x172), combatants(?x172, ?x94), administrative_parent(?x172, ?x551), film_release_region(?x6446, ?x172), film_release_region(?x3151, ?x172), ?x3151 = 0gtsxr4, ?x6446 = 089j8p >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nm8w contains! 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 56.000 0.842 http://example.org/location/location/contains #9010-05qqm PRED entity: 05qqm PRED relation: languages_spoken! PRED expected values: 07hwkr => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 70): 07hwkr (0.70 #362, 0.49 #1622, 0.48 #1202), 0bbz66j (0.33 #45, 0.15 #745, 0.15 #675), 038723 (0.33 #62, 0.06 #1112, 0.05 #342), 03w9bjf (0.24 #958, 0.21 #328, 0.21 #538), 013b6_ (0.24 #2241, 0.18 #2453, 0.15 #117), 03ts0c (0.24 #2241, 0.18 #2453, 0.10 #2452), 041rx (0.24 #2241, 0.18 #2453, 0.06 #1054), 04czx7 (0.24 #487, 0.22 #277, 0.18 #837), 0d2by (0.24 #450, 0.22 #240, 0.16 #1080), 071x0k (0.23 #78, 0.21 #148, 0.17 #498) >> Best rule #362 for best value: >> intensional similarity = 15 >> extensional distance = 18 >> proper extension: 01gp_d; >> query: (?x10486, 07hwkr) <- countries_spoken_in(?x10486, ?x1558), film_release_region(?x9349, ?x1558), film_release_region(?x3603, ?x1558), film_release_region(?x1724, ?x1558), film_release_region(?x1022, ?x1558), film_release_region(?x972, ?x1558), country(?x453, ?x1558), ?x972 = 017gl1, medal(?x1558, ?x422), ?x3603 = 09gkx35, ?x9349 = 0jdr0, ?x1724 = 02r8hh_, olympics(?x1558, ?x418), country(?x1022, ?x94), category(?x1022, ?x134) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qqm languages_spoken! 07hwkr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.700 http://example.org/people/ethnicity/languages_spoken #9009-01wwvd2 PRED entity: 01wwvd2 PRED relation: award_winner PRED expected values: 01mxqyk => 110 concepts (58 used for prediction) PRED predicted values (max 10 best out of 747): 01vw20h (0.11 #766, 0.05 #82298, 0.02 #18513), 05crg7 (0.11 #271, 0.05 #82298, 0.02 #6724), 01s1zk (0.11 #1224, 0.05 #82298, 0.02 #80684), 02pt7h_ (0.11 #1118, 0.05 #82298, 0.02 #80684), 067nsm (0.11 #1087, 0.05 #82298, 0.02 #80684), 015mrk (0.11 #504, 0.05 #82298), 03m9c8 (0.11 #1122, 0.03 #4348, 0.02 #12415), 066yfh (0.11 #1589), 02ktrs (0.11 #1568), 062cg6 (0.11 #887) >> Best rule #766 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01g04k; >> query: (?x4467, 01vw20h) <- gender(?x4467, ?x231), profession(?x4467, ?x12718), ?x12718 = 047rgpy >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #82298 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1294 *> proper extension: 01jq34; 01_8w2; 01p5yn; 0khth; 014l4w; 07mvp; 03yxwq; 018_q8; 0gsgr; 04k05; ... *> query: (?x4467, ?x215) <- award_winner(?x3835, ?x4467), award_winner(?x4467, ?x2138), award_winner(?x3835, ?x215) *> conf = 0.05 ranks of expected_values: 109 EVAL 01wwvd2 award_winner 01mxqyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 110.000 58.000 0.111 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #9008-01d2v1 PRED entity: 01d2v1 PRED relation: costume_design_by PRED expected values: 026lyl4 => 74 concepts (67 used for prediction) PRED predicted values (max 10 best out of 17): 03mfqm (0.08 #46, 0.04 #158, 0.02 #300), 03y1mlp (0.05 #172, 0.03 #114, 0.02 #228), 02cqbx (0.04 #156, 0.02 #466, 0.02 #1030), 02mxbd (0.04 #157, 0.02 #523, 0.01 #947), 0gl88b (0.04 #145), 09pjnd (0.03 #170, 0.02 #1161, 0.02 #169), 0cbxl0 (0.03 #81), 02h1rt (0.02 #126, 0.01 #268, 0.01 #296), 0bytfv (0.02 #941, 0.02 #1055, 0.02 #1086), 0p_pd (0.02 #169, 0.01 #1626, 0.01 #790) >> Best rule #46 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0ds11z; 0fg04; 03t97y; 0cz8mkh; 045j3w; 07j94; 09jcj6; 04nnpw; 029k4p; 03t79f; ... >> query: (?x11174, 03mfqm) <- film(?x397, ?x11174), genre(?x11174, ?x6277), titles(?x2480, ?x11174), ?x6277 = 0fdjb >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01d2v1 costume_design_by 026lyl4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 67.000 0.077 http://example.org/film/film/costume_design_by #9007-0dr_9t7 PRED entity: 0dr_9t7 PRED relation: film_release_region PRED expected values: 07ssc 015fr 03rj0 03h64 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 166): 02vzc (0.86 #578, 0.83 #750, 0.82 #3493), 06mkj (0.84 #756, 0.82 #584, 0.82 #3499), 059j2 (0.84 #725, 0.82 #3468, 0.79 #553), 03_3d (0.81 #523, 0.76 #695, 0.75 #3438), 03rjj (0.81 #693, 0.77 #3436, 0.76 #3265), 07ssc (0.77 #707, 0.73 #3450, 0.72 #3279), 05b4w (0.75 #764, 0.67 #3507, 0.66 #592), 0jgd (0.75 #3433, 0.73 #3262, 0.71 #518), 0345h (0.74 #727, 0.71 #3470, 0.71 #3299), 03h64 (0.73 #767, 0.71 #3510, 0.70 #3339) >> Best rule #578 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 02d44q; 047svrl; 07k2mq; 0372j5; >> query: (?x4453, 02vzc) <- featured_film_locations(?x4453, ?x3634), film_release_region(?x4453, ?x985), nominated_for(?x4400, ?x4453), ?x985 = 0k6nt >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #707 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 101 *> proper extension: 0gtv7pk; 0jjy0; 0gj8t_b; 03twd6; 03qnvdl; 0c8tkt; 0j_tw; 06v9_x; 0661m4p; 085ccd; ... *> query: (?x4453, 07ssc) <- featured_film_locations(?x4453, ?x3634), film_release_region(?x4453, ?x87), ?x87 = 05r4w, genre(?x4453, ?x53) *> conf = 0.77 ranks of expected_values: 6, 10, 14, 24 EVAL 0dr_9t7 film_release_region 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 104.000 104.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dr_9t7 film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 104.000 104.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dr_9t7 film_release_region 015fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 104.000 104.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dr_9t7 film_release_region 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 104.000 104.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #9006-05m7zg PRED entity: 05m7zg PRED relation: nationality PRED expected values: 06q1r => 121 concepts (116 used for prediction) PRED predicted values (max 10 best out of 68): 09c7w0 (0.78 #1206, 0.77 #602, 0.76 #1509), 02jx1 (0.58 #533, 0.57 #735, 0.53 #836), 07ssc (0.38 #717, 0.38 #818, 0.38 #215), 06q1r (0.33 #377, 0.02 #7816, 0.02 #7212), 04jpl (0.23 #4726, 0.01 #7236, 0.01 #8543), 0chghy (0.12 #210, 0.04 #1316, 0.03 #2414), 0162v (0.12 #245), 03_3d (0.11 #1011, 0.07 #2520, 0.06 #2420), 0d060g (0.08 #1917, 0.08 #1816, 0.07 #1515), 03rk0 (0.06 #11110, 0.05 #10605, 0.05 #11009) >> Best rule #1206 for best value: >> intensional similarity = 4 >> extensional distance = 160 >> proper extension: 0c8hct; 06jrhz; 06sn8m; >> query: (?x13200, 09c7w0) <- profession(?x13200, ?x1383), location(?x13200, ?x362), ?x1383 = 0np9r, type_of_union(?x13200, ?x566) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #377 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0clvcx; *> query: (?x13200, 06q1r) <- type_of_union(?x13200, ?x566), student(?x13639, ?x13200), ?x13639 = 031ns1, ?x566 = 04ztj *> conf = 0.33 ranks of expected_values: 4 EVAL 05m7zg nationality 06q1r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 121.000 116.000 0.778 http://example.org/people/person/nationality #9005-0lm0n PRED entity: 0lm0n PRED relation: contains! PRED expected values: 059g4 => 34 concepts (17 used for prediction) PRED predicted values (max 10 best out of 54): 09c7w0 (0.50 #6305, 0.50 #1802, 0.48 #14414), 07c5l (0.50 #1295, 0.40 #3093, 0.27 #8498), 0lm0n (0.33 #624, 0.11 #9001, 0.10 #2423), 059g4 (0.33 #9903, 0.30 #2262, 0.30 #9905), 04_1l0v (0.33 #9903, 0.30 #9905, 0.27 #12607), 0d060g (0.33 #9903, 0.30 #9905, 0.27 #12607), 029jpy (0.33 #9903, 0.30 #9905, 0.27 #12607), 0jcpw (0.30 #9905, 0.27 #12607, 0.25 #7201), 02qkt (0.15 #12958, 0.12 #10252, 0.10 #12055), 0j0k (0.09 #4497, 0.08 #10283, 0.07 #4877) >> Best rule #6305 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01smm; >> query: (?x10954, 09c7w0) <- partially_contains(?x11542, ?x10954), partially_contains(?x1426, ?x10954), administrative_division(?x9846, ?x1426), contains(?x279, ?x11542) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9903 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 0fv_t; *> query: (?x10954, ?x94) <- partially_contains(?x1426, ?x10954), time_zones(?x1426, ?x2674), contains(?x94, ?x1426) *> conf = 0.33 ranks of expected_values: 4 EVAL 0lm0n contains! 059g4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 34.000 17.000 0.500 http://example.org/location/location/contains #9004-085wqm PRED entity: 085wqm PRED relation: nominated_for! PRED expected values: 07cbcy => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 182): 0gq9h (0.45 #1018, 0.40 #1257, 0.28 #2930), 0gr0m (0.44 #1015, 0.42 #1254, 0.22 #2927), 019f4v (0.39 #1009, 0.33 #1248, 0.22 #2921), 0k611 (0.39 #1029, 0.33 #1268, 0.20 #2941), 0gq_v (0.36 #974, 0.34 #1213, 0.21 #2886), 05b1610 (0.33 #31, 0.12 #12430, 0.10 #509), 07bdd_ (0.33 #52, 0.12 #12430, 0.09 #2681), 05p1dby (0.33 #82, 0.12 #12430, 0.05 #2711), 0p9sw (0.33 #975, 0.28 #1214, 0.19 #1453), 04dn09n (0.31 #990, 0.23 #1229, 0.20 #2902) >> Best rule #1018 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 01jc6q; 050r1z; 0jzw; 0147sh; 070fnm; 0jym0; 0m9p3; 083skw; 0k4f3; 0qmd5; ... >> query: (?x10397, 0gq9h) <- genre(?x10397, ?x571), nominated_for(?x2761, ?x10397), currency(?x10397, ?x170), cinematography(?x7947, ?x2761) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #13388 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1584 *> proper extension: 06g60w; *> query: (?x10397, ?x102) <- nominated_for(?x2387, ?x10397), award(?x2387, ?x102), nominated_for(?x102, ?x103) *> conf = 0.22 ranks of expected_values: 22 EVAL 085wqm nominated_for! 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 65.000 65.000 0.445 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #9003-01h4rj PRED entity: 01h4rj PRED relation: profession PRED expected values: 02hrh1q => 137 concepts (129 used for prediction) PRED predicted values (max 10 best out of 91): 02hrh1q (0.90 #8716, 0.88 #15921, 0.88 #15020), 0np9r (0.62 #172, 0.50 #772, 0.50 #322), 01d_h8 (0.34 #4056, 0.33 #6757, 0.33 #11260), 09jwl (0.33 #620, 0.33 #20, 0.25 #470), 016z4k (0.33 #4, 0.17 #18009, 0.13 #18160), 02jknp (0.31 #1958, 0.24 #11262, 0.24 #2108), 0dxtg (0.30 #3314, 0.29 #13968, 0.29 #7965), 03gjzk (0.30 #766, 0.29 #1216, 0.25 #1516), 018gz8 (0.25 #918, 0.25 #318, 0.24 #1068), 0d1pc (0.25 #3802, 0.23 #1852, 0.22 #2602) >> Best rule #8716 for best value: >> intensional similarity = 3 >> extensional distance = 428 >> proper extension: 01n8_g; 02byfd; 03crmd; 03fwln; >> query: (?x9709, 02hrh1q) <- film(?x9709, ?x4766), titles(?x3920, ?x4766), languages(?x9709, ?x90) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01h4rj profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 129.000 0.902 http://example.org/people/person/profession #9002-0237fw PRED entity: 0237fw PRED relation: award PRED expected values: 09qv_s => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 269): 09cm54 (0.70 #30194, 0.70 #36864, 0.70 #35685), 0gqyl (0.40 #100, 0.17 #2452, 0.17 #14901), 099t8j (0.40 #134, 0.17 #14901, 0.16 #47845), 02z0dfh (0.40 #71, 0.17 #14901, 0.16 #47845), 02x4x18 (0.40 #126, 0.16 #47845, 0.15 #17255), 05p09zm (0.30 #1294, 0.20 #1686, 0.18 #3646), 0gqwc (0.20 #70, 0.18 #462, 0.18 #7519), 09td7p (0.20 #115, 0.17 #14901, 0.16 #47845), 099tbz (0.20 #54, 0.17 #14901, 0.16 #47845), 099cng (0.20 #81, 0.16 #47845, 0.15 #17255) >> Best rule #30194 for best value: >> intensional similarity = 3 >> extensional distance = 1026 >> proper extension: 08xz51; >> query: (?x2443, ?x112) <- award_nominee(?x2626, ?x2443), award_winner(?x3609, ?x2443), award_winner(?x112, ?x2443) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #14901 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 405 *> proper extension: 0c01c; 02wb6yq; *> query: (?x2443, ?x112) <- nominated_for(?x2443, ?x7493), participant(?x262, ?x2443), nominated_for(?x112, ?x7493) *> conf = 0.17 ranks of expected_values: 19 EVAL 0237fw award 09qv_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 146.000 146.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #9001-02pv_d PRED entity: 02pv_d PRED relation: award_winner! PRED expected values: 09p30_ => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 126): 09p30_ (0.17 #9730, 0.03 #5562, 0.03 #3507), 0bzjgq (0.12 #115, 0.05 #252, 0.03 #800), 09qvms (0.12 #13, 0.05 #6589, 0.04 #7137), 0c53vt (0.12 #108, 0.04 #382, 0.02 #1478), 0bzkgg (0.12 #41, 0.01 #1959, 0.01 #2370), 09pnw5 (0.11 #236, 0.06 #2839, 0.06 #2976), 05zksls (0.11 #171, 0.05 #719, 0.04 #993), 0g5b0q5 (0.11 #157, 0.05 #705, 0.04 #842), 03nnm4t (0.09 #345, 0.08 #482, 0.07 #619), 05c1t6z (0.09 #289, 0.06 #426, 0.05 #563) >> Best rule #9730 for best value: >> intensional similarity = 2 >> extensional distance = 1364 >> proper extension: 01j53q; >> query: (?x8070, ?x2294) <- award_winner(?x8070, ?x1179), award_winner(?x2294, ?x1179) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pv_d award_winner! 09p30_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.172 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #9000-01797x PRED entity: 01797x PRED relation: group PRED expected values: 02r1tx7 => 153 concepts (64 used for prediction) PRED predicted values (max 10 best out of 74): 0394y (0.12 #244, 0.11 #352, 0.04 #568), 01v0sx2 (0.12 #1302, 0.08 #2166, 0.08 #1193), 07mvp (0.11 #370, 0.08 #802, 0.03 #2207), 0mjn2 (0.11 #410, 0.02 #1491, 0.01 #3656), 01dwrc (0.11 #472, 0.04 #1120, 0.03 #1877), 06mj4 (0.10 #929, 0.02 #1037, 0.02 #2334), 0123r4 (0.08 #800, 0.06 #1557, 0.05 #2205), 01qqwp9 (0.07 #2182, 0.06 #1534, 0.05 #777), 081wh1 (0.06 #1457, 0.03 #700, 0.01 #2971), 0frsw (0.05 #447, 0.03 #771, 0.01 #1636) >> Best rule #244 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 039crh; >> query: (?x10396, 0394y) <- location(?x10396, ?x4789), location(?x10396, ?x1523), gender(?x10396, ?x231), county(?x1110, ?x4789), ?x1523 = 030qb3t >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #2177 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 85 *> proper extension: 01k5t_3; 018gqj; 04bgy; 017l4; 04n32; *> query: (?x10396, 02r1tx7) <- role(?x10396, ?x227), artists(?x671, ?x10396), film(?x10396, ?x7141), profession(?x10396, ?x220) *> conf = 0.05 ranks of expected_values: 13 EVAL 01797x group 02r1tx7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 153.000 64.000 0.125 http://example.org/music/group_member/membership./music/group_membership/group #8999-02ktrs PRED entity: 02ktrs PRED relation: artist! PRED expected values: 01cszh => 105 concepts (74 used for prediction) PRED predicted values (max 10 best out of 88): 015_1q (0.21 #2246, 0.20 #1550, 0.18 #3497), 03rhqg (0.16 #2242, 0.14 #5999, 0.13 #3493), 01trtc (0.13 #905, 0.09 #1601, 0.08 #2297), 0n85g (0.12 #1592, 0.10 #1731, 0.08 #6045), 0g768 (0.12 #6020, 0.12 #871, 0.12 #1567), 033hn8 (0.11 #5997, 0.10 #1405, 0.10 #2240), 011k1h (0.10 #5993, 0.10 #1540, 0.09 #1401), 017l96 (0.10 #2245, 0.09 #6002, 0.09 #1549), 0fb0v (0.10 #1537, 0.08 #1676, 0.08 #1398), 0mzkr (0.09 #1138, 0.08 #860, 0.07 #1556) >> Best rule #2246 for best value: >> intensional similarity = 3 >> extensional distance = 403 >> proper extension: 01vvydl; 0lbj1; 01vrx3g; 089tm; 01pfr3; 04rcr; 02r3zy; 01wbgdv; 07c0j; 01v0sx2; ... >> query: (?x11519, 015_1q) <- award_winner(?x1972, ?x11519), category(?x11519, ?x134), artist(?x5836, ?x11519) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #1402 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 169 *> proper extension: 01l_vgt; *> query: (?x11519, 01cszh) <- gender(?x11519, ?x514), artists(?x671, ?x11519), ?x514 = 02zsn *> conf = 0.06 ranks of expected_values: 22 EVAL 02ktrs artist! 01cszh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 105.000 74.000 0.210 http://example.org/music/record_label/artist #8998-06zn1c PRED entity: 06zn1c PRED relation: nominated_for! PRED expected values: 04ljl_l 07bdd_ => 87 concepts (71 used for prediction) PRED predicted values (max 10 best out of 192): 05q8pss (0.67 #2134, 0.67 #8536, 0.66 #8535), 07bdd_ (0.50 #1236, 0.12 #12102, 0.12 #12101), 04ljl_l (0.46 #1188, 0.12 #12102, 0.12 #12101), 05b4l5x (0.45 #1191, 0.12 #243, 0.12 #12102), 0gq9h (0.44 #2432, 0.31 #1957, 0.30 #535), 0gs9p (0.37 #2433, 0.27 #536, 0.26 #10259), 0gq_v (0.35 #2390, 0.26 #1915, 0.25 #3338), 019f4v (0.33 #2423, 0.32 #526, 0.26 #1948), 0gr4k (0.30 #2397, 0.21 #1922, 0.19 #10223), 05p09zm (0.29 #1277, 0.12 #12102, 0.12 #12101) >> Best rule #2134 for best value: >> intensional similarity = 5 >> extensional distance = 217 >> proper extension: 016ks5; 0h95927; >> query: (?x10749, ?x350) <- nominated_for(?x9296, ?x10749), genre(?x10749, ?x1403), film(?x574, ?x10749), ?x1403 = 02l7c8, award(?x10749, ?x350) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1236 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 161 *> proper extension: 07kb7vh; *> query: (?x10749, 07bdd_) <- nominated_for(?x350, ?x10749), nominated_for(?x350, ?x3820), nominated_for(?x350, ?x103), ?x103 = 03qcfvw, ?x3820 = 02qzmz6 *> conf = 0.50 ranks of expected_values: 2, 3 EVAL 06zn1c nominated_for! 07bdd_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 71.000 0.673 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 06zn1c nominated_for! 04ljl_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 71.000 0.673 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8997-047d21r PRED entity: 047d21r PRED relation: nominated_for! PRED expected values: 02qyp19 02ppm4q 03hl6lc 05ztrmj => 162 concepts (148 used for prediction) PRED predicted values (max 10 best out of 211): 0gqyl (0.77 #21321, 0.74 #675, 0.71 #7416), 02ppm4q (0.62 #329, 0.58 #104, 0.38 #554), 03hkv_r (0.50 #13, 0.46 #238, 0.38 #463), 04dn09n (0.46 #482, 0.33 #32, 0.31 #257), 02pqp12 (0.46 #502, 0.26 #3645, 0.25 #52), 040njc (0.42 #6, 0.38 #231, 0.33 #11903), 02n9nmz (0.42 #51, 0.38 #276, 0.31 #501), 02r0csl (0.38 #454, 0.19 #3372, 0.16 #11901), 0gq_v (0.35 #18647, 0.31 #10345, 0.31 #11914), 02x17s4 (0.33 #83, 0.31 #533, 0.31 #308) >> Best rule #21321 for best value: >> intensional similarity = 4 >> extensional distance = 645 >> proper extension: 07bz5; >> query: (?x3743, ?x995) <- award(?x3743, ?x995), nominated_for(?x3961, ?x3743), award_winner(?x995, ?x192), ceremony(?x995, ?x472) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #329 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 04t9c0; *> query: (?x3743, 02ppm4q) <- award(?x3743, ?x2577), produced_by(?x3743, ?x3293), ?x2577 = 099t8j, nominated_for(?x112, ?x3743) *> conf = 0.62 ranks of expected_values: 2, 19, 20, 61 EVAL 047d21r nominated_for! 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 162.000 148.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 047d21r nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 162.000 148.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 047d21r nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 162.000 148.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 047d21r nominated_for! 02qyp19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 162.000 148.000 0.774 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8996-041c4 PRED entity: 041c4 PRED relation: profession PRED expected values: 02hrh1q => 128 concepts (100 used for prediction) PRED predicted values (max 10 best out of 86): 02hrh1q (0.91 #2494, 0.89 #9358, 0.89 #11257), 0cbd2 (0.59 #4240, 0.52 #5554, 0.52 #3364), 03gjzk (0.53 #597, 0.48 #2203, 0.48 #2787), 0kyk (0.43 #903, 0.39 #4261, 0.38 #3385), 09jwl (0.37 #10384, 0.37 #8340, 0.37 #9800), 02hv44_ (0.29 #931, 0.15 #1953, 0.15 #2099), 0nbcg (0.27 #10397, 0.26 #8353, 0.26 #9959), 0np9r (0.24 #4836, 0.21 #9364, 0.18 #1770), 016z4k (0.23 #8328, 0.23 #8620, 0.23 #10372), 02krf9 (0.23 #2214, 0.20 #4404, 0.19 #6594) >> Best rule #2494 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 05bnp0; 03zqc1; 05gml8; 058s57; 011zd3; 02rmfm; 06t74h; 02jsgf; 036jb; 024bbl; ... >> query: (?x4988, 02hrh1q) <- film(?x4988, ?x148), award(?x4988, ?x68), student(?x1305, ?x4988) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 041c4 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 100.000 0.907 http://example.org/people/person/profession #8995-02vnpv PRED entity: 02vnpv PRED relation: artists! PRED expected values: 016jny => 90 concepts (42 used for prediction) PRED predicted values (max 10 best out of 277): 016clz (0.96 #11759, 0.94 #7104, 0.92 #3399), 05r6t (0.71 #6561, 0.53 #8415, 0.29 #1622), 064t9 (0.56 #3100, 0.52 #4643, 0.50 #1555), 0xhtw (0.52 #4338, 0.44 #6186, 0.41 #9590), 0m0jc (0.50 #935, 0.25 #2476, 0.22 #3403), 05bt6j (0.40 #4362, 0.38 #3747, 0.34 #4671), 03lty (0.34 #6196, 0.27 #9600, 0.26 #4348), 06j6l (0.33 #357, 0.30 #4984, 0.28 #5291), 059kh (0.33 #974, 0.29 #1589, 0.22 #6528), 0glt670 (0.33 #966, 0.25 #8686, 0.23 #8993) >> Best rule #11759 for best value: >> intensional similarity = 10 >> extensional distance = 216 >> proper extension: 06x4l_; 02bh9; 037hgm; 01kd57; 01hrqc; 0326tc; 02mx98; 04bbv7; >> query: (?x11425, 016clz) <- artists(?x2996, ?x11425), artists(?x2996, ?x9757), artists(?x2996, ?x9116), artists(?x2996, ?x498), parent_genre(?x7960, ?x2996), parent_genre(?x7960, ?x5934), ?x9757 = 06br6t, ?x5934 = 05r6t, ?x498 = 0m19t, ?x9116 = 03vhvp >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #1338 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 0lzkm; 082brv; *> query: (?x11425, 016jny) <- artists(?x2996, ?x11425), ?x2996 = 01243b, origin(?x11425, ?x9605), source(?x9605, ?x958), location(?x8101, ?x9605), country(?x9605, ?x94), award_nominee(?x8101, ?x2285) *> conf = 0.20 ranks of expected_values: 36 EVAL 02vnpv artists! 016jny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 90.000 42.000 0.959 http://example.org/music/genre/artists #8994-02qsqmq PRED entity: 02qsqmq PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.89 #944, 0.88 #38, 0.83 #102), 0215hd (0.61 #48, 0.16 #80, 0.16 #369), 089g0h (0.57 #49, 0.13 #370, 0.13 #1519), 01vx2h (0.47 #43, 0.41 #107, 0.38 #171), 0dxtw (0.43 #948, 0.42 #202, 0.42 #106), 02_n3z (0.33 #33, 0.15 #65, 0.13 #1519), 02ynfr (0.21 #110, 0.19 #952, 0.19 #46), 015h31 (0.21 #40, 0.13 #1519, 0.08 #557), 02rh1dz (0.21 #105, 0.18 #41, 0.14 #169), 020xn5 (0.19 #39, 0.13 #1519, 0.04 #360) >> Best rule #944 for best value: >> intensional similarity = 6 >> extensional distance = 687 >> proper extension: 0cnztc4; 02phtzk; 0h95zbp; 0hv81; 0gh6j94; 0gpx6; 0j8f09z; 09rfpk; >> query: (?x5746, 0ch6mp2) <- language(?x5746, ?x254), film_crew_role(?x5746, ?x2178), film_crew_role(?x5746, ?x137), ?x137 = 09zzb8, film_crew_role(?x9961, ?x2178), ?x9961 = 0bx_hnp >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qsqmq film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.893 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8993-041c4 PRED entity: 041c4 PRED relation: film PRED expected values: 0164qt 0dc7hc => 102 concepts (83 used for prediction) PRED predicted values (max 10 best out of 601): 01s81 (0.60 #65725, 0.50 #51513, 0.49 #62172), 01q_y0 (0.60 #65725, 0.50 #51513, 0.49 #62172), 01lv85 (0.60 #65725, 0.50 #51513, 0.49 #62172), 0dr3sl (0.39 #46184, 0.02 #7563, 0.02 #2234), 0164qt (0.39 #46184), 03q0r1 (0.14 #632, 0.04 #7737, 0.03 #4185), 07kb7vh (0.14 #681, 0.03 #4234, 0.03 #2457), 0f2sx4 (0.14 #1375, 0.03 #3151, 0.02 #4928), 02f6g5 (0.14 #278, 0.03 #2054, 0.02 #3831), 0640m69 (0.14 #1748, 0.03 #3524, 0.02 #5301) >> Best rule #65725 for best value: >> intensional similarity = 3 >> extensional distance = 968 >> proper extension: 049tjg; 01nrq5; 06_bq1; 033071; >> query: (?x4988, ?x2293) <- film(?x4988, ?x148), location(?x4988, ?x957), nominated_for(?x4988, ?x2293) >> conf = 0.60 => this is the best rule for 3 predicted values *> Best rule #46184 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 615 *> proper extension: 01l1b90; 014x77; 0lzb8; 0kr5_; 012c6x; 03gm48; 05sq84; 01t2h2; 05r5w; 03dpqd; ... *> query: (?x4988, ?x2868) <- film(?x4988, ?x8075), award(?x4988, ?x68), prequel(?x8075, ?x2868) *> conf = 0.39 ranks of expected_values: 5, 69 EVAL 041c4 film 0dc7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 102.000 83.000 0.597 http://example.org/film/actor/film./film/performance/film EVAL 041c4 film 0164qt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 102.000 83.000 0.597 http://example.org/film/actor/film./film/performance/film #8992-04954 PRED entity: 04954 PRED relation: film PRED expected values: 0ddf2bm => 112 concepts (68 used for prediction) PRED predicted values (max 10 best out of 807): 011yd2 (0.55 #12482, 0.37 #62420, 0.36 #82038), 011ykb (0.15 #1137, 0.04 #4703, 0.01 #11835), 011yn5 (0.15 #922, 0.02 #4488, 0.01 #117709), 0296rz (0.15 #1636, 0.02 #5202), 03b1sb (0.15 #1499, 0.01 #12197), 07bzz7 (0.09 #2668, 0.03 #9800, 0.03 #6234), 01jnc_ (0.09 #3346, 0.03 #10478, 0.03 #14045), 01738w (0.08 #1125, 0.07 #2908, 0.02 #10040), 0888c3 (0.08 #1410, 0.07 #3193, 0.02 #10325), 02ht1k (0.08 #625, 0.07 #2408, 0.02 #9540) >> Best rule #12482 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 08h79x; 02vkvcz; >> query: (?x7530, ?x2215) <- award(?x7530, ?x704), award_winner(?x2215, ?x7530), spouse(?x7530, ?x8113) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #3456 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 0lbj1; 01vrx3g; 01vrncs; 018y2s; 0137n0; 01kx_81; 0pgjm; 01vsnff; 01vs_v8; 021bk; ... *> query: (?x7530, 0ddf2bm) <- film(?x7530, ?x796), award_nominee(?x7530, ?x496), role(?x7530, ?x227) *> conf = 0.02 ranks of expected_values: 236 EVAL 04954 film 0ddf2bm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 112.000 68.000 0.552 http://example.org/film/actor/film./film/performance/film #8991-0fz3b1 PRED entity: 0fz3b1 PRED relation: language PRED expected values: 02h40lc => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 49): 02h40lc (0.91 #298, 0.89 #594, 0.89 #3882), 064_8sq (0.14 #2409, 0.14 #259, 0.13 #2949), 06nm1 (0.12 #603, 0.12 #366, 0.11 #2639), 04306rv (0.10 #538, 0.10 #597, 0.09 #1666), 06b_j (0.08 #556, 0.07 #2651, 0.07 #674), 03_9r (0.08 #247, 0.06 #365, 0.06 #1136), 02bjrlw (0.07 #534, 0.06 #2629, 0.06 #416), 0jzc (0.05 #553, 0.04 #2648, 0.04 #671), 07zrf (0.05 #181, 0.03 #6028, 0.02 #299), 0653m (0.04 #367, 0.04 #1020, 0.04 #781) >> Best rule #298 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 0fq7dv_; 0g68zt; 0dqcs3; >> query: (?x4326, 02h40lc) <- genre(?x4326, ?x6452), genre(?x9213, ?x6452), nominated_for(?x4325, ?x4326), ?x9213 = 0353tm >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fz3b1 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.905 http://example.org/film/film/language #8990-06v9_x PRED entity: 06v9_x PRED relation: film_release_region PRED expected values: 0k6nt 01znc_ => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 148): 03h64 (0.90 #56, 0.86 #934, 0.86 #787), 015fr (0.88 #889, 0.86 #1327, 0.86 #742), 05qhw (0.87 #9, 0.87 #887, 0.86 #1325), 01znc_ (0.82 #910, 0.81 #32, 0.78 #1056), 0k6nt (0.82 #896, 0.80 #1334, 0.79 #1480), 06t2t (0.81 #929, 0.77 #782, 0.77 #1367), 01p1v (0.65 #42, 0.61 #920, 0.59 #1358), 015qh (0.64 #762, 0.64 #909, 0.62 #1347), 016wzw (0.64 #788, 0.61 #57, 0.59 #935), 06f32 (0.63 #1371, 0.62 #933, 0.61 #55) >> Best rule #56 for best value: >> intensional similarity = 7 >> extensional distance = 29 >> proper extension: 02vxq9m; 0c3ybss; 0gtv7pk; 0401sg; 08hmch; 0jjy0; 03twd6; 04n52p6; 035yn8; 0cc7hmk; ... >> query: (?x2318, 03h64) <- film_release_region(?x2318, ?x1023), film_release_region(?x2318, ?x429), genre(?x2318, ?x812), ?x429 = 03rt9, ?x1023 = 0ctw_b, film(?x2317, ?x2318), ?x812 = 01jfsb >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #910 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 83 *> proper extension: 026njb5; 07l50vn; 07s3m4g; 0j8f09z; *> query: (?x2318, 01znc_) <- film_release_region(?x2318, ?x1353), film_release_region(?x2318, ?x1023), film_release_region(?x2318, ?x512), film_release_region(?x2318, ?x429), genre(?x2318, ?x225), ?x429 = 03rt9, ?x1023 = 0ctw_b, ?x512 = 07ssc, ?x1353 = 035qy *> conf = 0.82 ranks of expected_values: 4, 5 EVAL 06v9_x film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 65.000 65.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06v9_x film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 65.000 65.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8989-02lv2v PRED entity: 02lv2v PRED relation: organization! PRED expected values: 060c4 => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 16): 060c4 (0.84 #483, 0.83 #457, 0.83 #379), 0dq_5 (0.70 #594, 0.67 #425, 0.67 #412), 07xl34 (0.25 #817, 0.25 #570, 0.23 #739), 05k17c (0.20 #46, 0.15 #1704, 0.15 #33), 09d6p2 (0.15 #1704, 0.01 #309, 0.01 #465), 0hm4q (0.05 #1386, 0.05 #1751, 0.05 #1855), 05c0jwl (0.04 #1552, 0.04 #1474, 0.04 #1266), 08jcfy (0.03 #818, 0.02 #1026, 0.02 #1559), 0dq3c (0.03 #2082, 0.03 #105, 0.03 #92), 01t7n9 (0.03 #2082) >> Best rule #483 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 02hft3; 07wrz; 01tx9m; 012mzw; 04bbpm; 02rky4; 01p7x7; 03bnd9; 02c9dj; >> query: (?x8434, 060c4) <- currency(?x8434, ?x170), ?x170 = 09nqf, major_field_of_study(?x8434, ?x5614), institution(?x620, ?x8434) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lv2v organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.837 http://example.org/organization/role/leaders./organization/leadership/organization #8988-01jq4b PRED entity: 01jq4b PRED relation: school_type PRED expected values: 05pcjw => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 21): 05jxkf (0.57 #556, 0.55 #748, 0.54 #628), 05pcjw (0.37 #337, 0.33 #73, 0.30 #169), 07tf8 (0.37 #129, 0.33 #9, 0.28 #321), 01_srz (0.33 #3, 0.09 #171, 0.08 #195), 02p0qmm (0.33 #34, 0.06 #250, 0.05 #178), 01_9fk (0.30 #626, 0.29 #554, 0.29 #674), 01rs41 (0.28 #173, 0.28 #1158, 0.24 #1447), 04qbv (0.20 #64, 0.07 #112, 0.02 #1530), 01y64 (0.08 #84, 0.05 #300, 0.04 #396), 04399 (0.04 #998, 0.03 #350, 0.03 #830) >> Best rule #556 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 02jyr8; >> query: (?x5907, 05jxkf) <- institution(?x620, ?x5907), contains(?x94, ?x5907), school(?x1161, ?x5907) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #337 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 017y26; 02897w; 037njl; 05njyy; 027mdh; 03tw2s; 09b_0m; 02jmst; 013719; 09k9d0; ... *> query: (?x5907, 05pcjw) <- institution(?x1519, ?x5907), major_field_of_study(?x5907, ?x2172), ?x1519 = 013zdg *> conf = 0.37 ranks of expected_values: 2 EVAL 01jq4b school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.566 http://example.org/education/educational_institution/school_type #8987-03ylxn PRED entity: 03ylxn PRED relation: teams! PRED expected values: 05cgv => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 117): 0f8l9c (0.25 #24, 0.07 #1106, 0.05 #1376), 0jgd (0.25 #3, 0.05 #1355, 0.05 #1625), 0h3y (0.17 #547, 0.17 #277, 0.12 #819), 06mzp (0.17 #563, 0.17 #293, 0.12 #835), 05r4w (0.17 #541, 0.17 #271, 0.07 #1083), 03_3d (0.17 #276, 0.05 #1358, 0.05 #1628), 05v10 (0.17 #592, 0.05 #1674, 0.04 #1944), 01nln (0.12 #969, 0.07 #1239, 0.05 #1509), 077qn (0.12 #925, 0.07 #1195, 0.05 #1465), 0hzlz (0.12 #837, 0.07 #1107, 0.04 #8650) >> Best rule #24 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 02bh_v; >> query: (?x11225, 0f8l9c) <- current_club(?x11225, ?x12301), current_club(?x11225, ?x11421), current_club(?x11225, ?x9434), current_club(?x11225, ?x1085), position(?x11225, ?x530), position(?x11225, ?x63), sport(?x12301, ?x471), ?x1085 = 02gys2, ?x530 = 02_j1w, ?x63 = 02sdk9v, team(?x982, ?x9434), colors(?x11421, ?x663) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03ylxn teams! 05cgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 68.000 0.250 http://example.org/sports/sports_team_location/teams #8986-0kv9d3 PRED entity: 0kv9d3 PRED relation: language PRED expected values: 064_8sq => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 47): 064_8sq (0.26 #193, 0.19 #2213, 0.17 #2153), 02bjrlw (0.21 #174, 0.12 #6606, 0.11 #117), 03_9r (0.17 #9, 0.08 #182, 0.06 #527), 04306rv (0.17 #120, 0.13 #177, 0.12 #349), 06nm1 (0.14 #298, 0.13 #241, 0.12 #815), 04h9h (0.13 #214, 0.06 #1712, 0.06 #1478), 06b_j (0.12 #6606, 0.09 #423, 0.08 #596), 0jzc (0.12 #6606, 0.07 #249, 0.05 #420), 02hxcvy (0.12 #6606, 0.06 #90, 0.03 #664), 012w70 (0.12 #6606, 0.06 #127, 0.04 #1915) >> Best rule #193 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 03ckwzc; 0pv3x; 020fcn; 05p09dd; 0c1sgd3; 0fjyzt; 01_0f7; 027pfg; 02jxrw; 0dw4b0; >> query: (?x4050, 064_8sq) <- titles(?x162, ?x4050), country(?x4050, ?x94), crewmember(?x4050, ?x7675), ?x162 = 04xvlr >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kv9d3 language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.256 http://example.org/film/film/language #8985-05ldnp PRED entity: 05ldnp PRED relation: profession PRED expected values: 03gjzk 02krf9 => 117 concepts (116 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.86 #453, 0.80 #3099, 0.77 #5306), 03gjzk (0.84 #1042, 0.83 #1483, 0.83 #1336), 0cbd2 (0.54 #594, 0.31 #2064, 0.29 #888), 02krf9 (0.44 #319, 0.35 #1495, 0.33 #5000), 0kyk (0.39 #616, 0.26 #8825, 0.25 #8089), 0dz3r (0.33 #5000, 0.26 #8825, 0.25 #8089), 012t_z (0.33 #5000, 0.26 #8825, 0.25 #8089), 0np9r (0.26 #313, 0.13 #754, 0.12 #460), 09jwl (0.21 #4134, 0.20 #4575, 0.19 #6782), 018gz8 (0.19 #750, 0.18 #1191, 0.16 #2514) >> Best rule #453 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0bqdvt; 029q_y; 026gb3v; >> query: (?x3260, 02hrh1q) <- award_nominee(?x3260, ?x192), gender(?x3260, ?x231), ?x192 = 02p65p >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1042 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 117 *> proper extension: 0f721s; 09pl3f; 08xz51; *> query: (?x3260, 03gjzk) <- award_winner(?x68, ?x3260), program(?x3260, ?x687), award_winner(?x4634, ?x3260) *> conf = 0.84 ranks of expected_values: 2, 4 EVAL 05ldnp profession 02krf9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 116.000 0.857 http://example.org/people/person/profession EVAL 05ldnp profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 116.000 0.857 http://example.org/people/person/profession #8984-07zhd7 PRED entity: 07zhd7 PRED relation: place_of_death PRED expected values: 0r3tb => 131 concepts (121 used for prediction) PRED predicted values (max 10 best out of 56): 02_286 (0.33 #13, 0.25 #208, 0.14 #596), 04jpl (0.25 #202, 0.17 #2537, 0.14 #2148), 030qb3t (0.19 #3528, 0.17 #5476, 0.17 #411), 0r3tq (0.17 #538, 0.15 #1123, 0.01 #3460), 0k_p5 (0.15 #866, 0.13 #1256, 0.02 #9241), 0f2wj (0.15 #986, 0.08 #790, 0.07 #1959), 0k049 (0.14 #586, 0.11 #4872, 0.11 #5457), 0r00l (0.14 #745, 0.08 #1136, 0.08 #940), 015zxh (0.08 #999), 04swd (0.05 #2455) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01r6jt2; >> query: (?x12188, 02_286) <- award_winner(?x8015, ?x12188), ?x8015 = 0c53vt, music(?x2779, ?x12188), place_of_birth(?x12188, ?x362) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3035 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 012gbb; 0jvtp; 01bh6y; *> query: (?x12188, 0r3tb) <- award_winner(?x4865, ?x12188), people(?x5855, ?x12188), location(?x12188, ?x362), country(?x4865, ?x94) *> conf = 0.01 ranks of expected_values: 49 EVAL 07zhd7 place_of_death 0r3tb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 131.000 121.000 0.333 http://example.org/people/deceased_person/place_of_death #8983-0cf08 PRED entity: 0cf08 PRED relation: genre PRED expected values: 02l7c8 => 58 concepts (57 used for prediction) PRED predicted values (max 10 best out of 87): 01cgz (0.63 #1334, 0.59 #3150, 0.54 #3635), 024qqx (0.63 #1334, 0.59 #3150, 0.54 #3635), 01jfsb (0.48 #1103, 0.32 #1709, 0.31 #861), 03k9fj (0.41 #1102, 0.27 #618, 0.26 #860), 03bxz7 (0.38 #56, 0.17 #1268, 0.10 #1390), 05p553 (0.33 #3760, 0.32 #5335, 0.32 #3516), 02l7c8 (0.30 #2439, 0.29 #2318, 0.29 #1834), 0lsxr (0.30 #129, 0.22 #372, 0.21 #1099), 04xvlr (0.29 #1213, 0.23 #1, 0.18 #3029), 06n90 (0.27 #1104, 0.14 #741, 0.14 #377) >> Best rule #1334 for best value: >> intensional similarity = 4 >> extensional distance = 345 >> proper extension: 03kq98; >> query: (?x7370, ?x1967) <- titles(?x1967, ?x7370), titles(?x53, ?x7370), ?x53 = 07s9rl0, nominated_for(?x198, ?x7370) >> conf = 0.63 => this is the best rule for 2 predicted values *> Best rule #2439 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 912 *> proper extension: 0cnztc4; 0crh5_f; 0413cff; 0h95zbp; 0g5q34q; 0gh6j94; 0bs8hvm; 015qy1; 0d8w2n; *> query: (?x7370, 02l7c8) <- genre(?x7370, ?x53), country(?x7370, ?x94), ?x53 = 07s9rl0 *> conf = 0.30 ranks of expected_values: 7 EVAL 0cf08 genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 58.000 57.000 0.627 http://example.org/film/film/genre #8982-03rk0 PRED entity: 03rk0 PRED relation: jurisdiction_of_office! PRED expected values: 0377k9 => 246 concepts (246 used for prediction) PRED predicted values (max 10 best out of 21): 0f6c3 (0.69 #1667, 0.65 #1747, 0.59 #2187), 09n5b9 (0.61 #1671, 0.59 #1751, 0.53 #2191), 0fkvn (0.55 #1663, 0.55 #1743, 0.49 #2183), 0pqc5 (0.47 #3544, 0.46 #2344, 0.39 #1064), 04syw (0.40 #1226, 0.36 #4263, 0.36 #4022), 0p5vf (0.36 #351, 0.35 #231, 0.33 #271), 0dq3c (0.36 #4263, 0.36 #4022, 0.23 #902), 09d6p2 (0.36 #4263, 0.36 #4022, 0.07 #447), 0789n (0.23 #188, 0.22 #248, 0.18 #228), 01zq91 (0.20 #1054, 0.19 #594, 0.19 #273) >> Best rule #1667 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 019fv4; >> query: (?x2146, 0f6c3) <- contains(?x2146, ?x11607), religion(?x2146, ?x109), student(?x11607, ?x656) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #274 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 07z5n; 03188; *> query: (?x2146, 0377k9) <- country(?x3411, ?x2146), adjoins(?x2236, ?x3411), country(?x1352, ?x2146) *> conf = 0.19 ranks of expected_values: 11 EVAL 03rk0 jurisdiction_of_office! 0377k9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 246.000 246.000 0.694 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #8981-01w23w PRED entity: 01w23w PRED relation: profession PRED expected values: 02hrh1q => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 65): 02hrh1q (0.90 #3617, 0.89 #3917, 0.88 #9318), 01d_h8 (0.43 #751, 0.37 #907, 0.36 #606), 03gjzk (0.43 #751, 0.25 #3018, 0.25 #3168), 02krf9 (0.43 #751, 0.11 #4530, 0.10 #6930), 01xr66 (0.43 #751, 0.04 #66, 0.03 #216), 0dxtg (0.33 #765, 0.32 #464, 0.30 #6916), 02jknp (0.28 #759, 0.27 #1959, 0.27 #458), 0np9r (0.27 #12454, 0.27 #12605, 0.15 #4074), 0n1h (0.27 #12454, 0.27 #12605, 0.07 #3014), 09jwl (0.24 #3322, 0.21 #620, 0.20 #3772) >> Best rule #3617 for best value: >> intensional similarity = 3 >> extensional distance = 164 >> proper extension: 01xcqc; 028lc8; 0b_fw; 0gr36; 02t_v1; 02wrrm; 03bpn6; 016k6x; 02tv80; 02f6s3; ... >> query: (?x6650, 02hrh1q) <- film(?x6650, ?x776), award(?x6650, ?x3066), ?x3066 = 0gqy2 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w23w profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.898 http://example.org/people/person/profession #8980-02624g PRED entity: 02624g PRED relation: award PRED expected values: 0ck27z => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 233): 0ck27z (0.67 #91, 0.41 #493, 0.21 #3709), 0bfvd4 (0.20 #515, 0.08 #1721, 0.07 #2927), 0bp_b2 (0.19 #420, 0.05 #3636, 0.04 #4440), 05pcn59 (0.14 #1286, 0.10 #6914, 0.10 #4100), 0bdwqv (0.14 #572, 0.09 #1778, 0.09 #2984), 0bdw6t (0.14 #511, 0.04 #4531, 0.04 #3727), 0f4x7 (0.13 #1639, 0.12 #2845, 0.12 #2443), 027dtxw (0.13 #23724, 0.12 #19300, 0.12 #20105), 063y_ky (0.13 #23724, 0.12 #19300, 0.12 #20105), 0hnf5vm (0.13 #23724, 0.12 #19300, 0.12 #20105) >> Best rule #91 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02gvwz; 07fpm3; 06gh0t; 06mnbn; 059xnf; 0356dp; 03rgvr; >> query: (?x7048, 0ck27z) <- award_nominee(?x2708, ?x7048), ?x2708 = 059t6d, profession(?x7048, ?x1032) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02624g award 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8979-086k8 PRED entity: 086k8 PRED relation: award_nominee! PRED expected values: 05hjmd => 134 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1276): 027kmrb (0.81 #159386, 0.81 #219454, 0.80 #212520), 0gn30 (0.81 #159386, 0.81 #219454, 0.80 #212520), 0dqmt0 (0.81 #159386, 0.81 #219454, 0.80 #212520), 0hqly (0.81 #159386, 0.81 #219454, 0.80 #212520), 01c6l (0.81 #159386, 0.81 #219454, 0.80 #212520), 07b3r9 (0.81 #159386, 0.81 #219454, 0.80 #212520), 056wb (0.79 #69289, 0.77 #200967, 0.77 #210210), 02qx1m2 (0.79 #69289, 0.77 #200967, 0.77 #210210), 017s11 (0.33 #4724, 0.33 #106, 0.28 #67085), 0794g (0.33 #740, 0.28 #203280, 0.08 #21526) >> Best rule #159386 for best value: >> intensional similarity = 3 >> extensional distance = 406 >> proper extension: 0knjh; >> query: (?x382, ?x847) <- award_winner(?x1300, ?x382), category(?x382, ?x134), award_nominee(?x382, ?x847) >> conf = 0.81 => this is the best rule for 6 predicted values *> Best rule #203280 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1164 *> proper extension: 018swb; 0c01c; 038rzr; 06_bq1; 079ws; 0gdhhy; 016z1c; *> query: (?x382, ?x5165) <- award_winner(?x1300, ?x382), nominated_for(?x382, ?x8119), nominated_for(?x5165, ?x8119) *> conf = 0.28 ranks of expected_values: 76 EVAL 086k8 award_nominee! 05hjmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 134.000 95.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8978-0k5g9 PRED entity: 0k5g9 PRED relation: film_release_region PRED expected values: 0f8l9c 059j2 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 140): 0f8l9c (0.89 #4736, 0.86 #9292, 0.86 #8664), 059j2 (0.84 #9303, 0.83 #4747, 0.80 #8675), 03gj2 (0.81 #9296, 0.78 #4740, 0.77 #8354), 0jgd (0.77 #9272, 0.76 #8644, 0.75 #8330), 05qhw (0.76 #4728, 0.72 #9284, 0.70 #10070), 0d060g (0.71 #4719, 0.70 #8490, 0.69 #8333), 06bnz (0.68 #4761, 0.63 #9317, 0.61 #10103), 03spz (0.68 #4811, 0.58 #9367, 0.57 #8582), 05v8c (0.68 #4730, 0.54 #9286, 0.50 #802), 0b90_r (0.67 #9273, 0.64 #4717, 0.62 #8331) >> Best rule #4736 for best value: >> intensional similarity = 4 >> extensional distance = 85 >> proper extension: 0c40vxk; 0401sg; 0jjy0; 026q3s3; 0cz8mkh; 03twd6; 06v9_x; 0crh5_f; 0gffmn8; 0gj8nq2; ... >> query: (?x2717, 0f8l9c) <- film_release_region(?x2717, ?x1264), ?x1264 = 0345h, genre(?x2717, ?x812), ?x812 = 01jfsb >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0k5g9 film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0k5g9 film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8977-012s1d PRED entity: 012s1d PRED relation: nominated_for! PRED expected values: 02hsq3m => 80 concepts (72 used for prediction) PRED predicted values (max 10 best out of 205): 0gq9h (0.30 #7851, 0.27 #7143, 0.27 #8087), 0gs9p (0.26 #7853, 0.24 #7617, 0.24 #7145), 019f4v (0.24 #1469, 0.24 #7842, 0.24 #7134), 099c8n (0.23 #764, 0.22 #1472, 0.21 #6429), 0k611 (0.22 #2432, 0.22 #1488, 0.22 #7861), 02r22gf (0.22 #27, 0.22 #16760, 0.19 #16761), 02hsq3m (0.22 #28, 0.16 #972, 0.16 #500), 02x258x (0.22 #96, 0.09 #6469, 0.08 #2220), 0gqy2 (0.22 #16760, 0.20 #2480, 0.19 #16761), 09sb52 (0.22 #16760, 0.19 #16761, 0.13 #741) >> Best rule #7851 for best value: >> intensional similarity = 3 >> extensional distance = 849 >> proper extension: 0bhwhj; 02zk08; 0c5qvw; >> query: (?x5305, 0gq9h) <- nominated_for(?x666, ?x5305), genre(?x5305, ?x225), award(?x5305, ?x1336) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #28 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0btyf5z; 09gb_4p; 0g7pm1; 07kdkfj; *> query: (?x5305, 02hsq3m) <- film(?x123, ?x5305), film_crew_role(?x5305, ?x2095), ?x2095 = 0dxtw, ?x123 = 05bnp0 *> conf = 0.22 ranks of expected_values: 7 EVAL 012s1d nominated_for! 02hsq3m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 80.000 72.000 0.298 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8976-03h_yy PRED entity: 03h_yy PRED relation: film! PRED expected values: 06qgvf => 80 concepts (62 used for prediction) PRED predicted values (max 10 best out of 963): 055c8 (0.38 #8842, 0.04 #4690, 0.03 #6766), 010xjr (0.32 #5822, 0.21 #9974), 06m6p7 (0.25 #3440, 0.04 #5516, 0.03 #9668), 03q5dr (0.20 #5828, 0.13 #9980, 0.01 #26591), 0bwh6 (0.20 #4367, 0.13 #8519), 0k525 (0.20 #1839, 0.04 #5991, 0.03 #10143), 07r1h (0.20 #1085, 0.03 #7313, 0.03 #15617), 0bksh (0.20 #850, 0.03 #7078, 0.02 #17458), 03w4sh (0.20 #1142, 0.03 #7370, 0.01 #15674), 014zn0 (0.20 #1925, 0.03 #8153) >> Best rule #8842 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 02fqrf; >> query: (?x557, 055c8) <- film(?x11069, ?x557), film(?x11069, ?x7470), film(?x11069, ?x1118), ?x1118 = 0_92w, nominated_for(?x618, ?x7470) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #6238 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 02yy9r; *> query: (?x557, 06qgvf) <- country(?x557, ?x94), titles(?x600, ?x557), award_winner(?x557, ?x2373), ?x600 = 02n4kr *> conf = 0.03 ranks of expected_values: 395 EVAL 03h_yy film! 06qgvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 80.000 62.000 0.385 http://example.org/film/actor/film./film/performance/film #8975-01l1b90 PRED entity: 01l1b90 PRED relation: artists! PRED expected values: 0gywn => 158 concepts (107 used for prediction) PRED predicted values (max 10 best out of 224): 0gywn (0.85 #57, 0.50 #12497, 0.41 #368), 06by7 (0.66 #30820, 0.60 #32692, 0.55 #29886), 02lnbg (0.53 #369, 0.53 #680, 0.51 #991), 0ggx5q (0.50 #389, 0.44 #700, 0.41 #1011), 016clz (0.43 #27689, 0.37 #1560, 0.32 #1249), 02x8m (0.37 #19, 0.27 #12459, 0.16 #17748), 05bt6j (0.35 #16840, 0.35 #1599, 0.34 #1288), 03_d0 (0.26 #12, 0.23 #12452, 0.19 #29876), 01lyv (0.25 #25853, 0.20 #2212, 0.19 #18386), 02b71x (0.22 #153, 0.06 #12593, 0.05 #11971) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 01364q; 01zmpg; 01wwvc5; 012z8_; 015x1f; 03q2t9; 0flpy; 01f9zw; 016vqk; 01jgkj2; ... >> query: (?x250, 0gywn) <- award(?x250, ?x2563), profession(?x250, ?x319), ?x2563 = 01cw51 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l1b90 artists! 0gywn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 107.000 0.852 http://example.org/music/genre/artists #8974-0d99k_ PRED entity: 0d99k_ PRED relation: film! PRED expected values: 04hpck => 90 concepts (36 used for prediction) PRED predicted values (max 10 best out of 896): 079vf (0.20 #6248, 0.12 #4168, 0.04 #22893), 0m6x4 (0.20 #3682), 03h_9lg (0.19 #4293, 0.14 #6373, 0.02 #27179), 02dztn (0.19 #5501, 0.09 #7581, 0.02 #15903), 06k02 (0.13 #2454, 0.11 #374), 01v42g (0.12 #4364, 0.09 #6444, 0.03 #21009), 01k53x (0.12 #5796, 0.09 #7876, 0.01 #26601), 02ck7w (0.12 #5100, 0.06 #7180, 0.03 #17583), 015wnl (0.12 #4810, 0.06 #6890, 0.03 #25615), 01twdk (0.12 #5004, 0.06 #7084, 0.03 #15406) >> Best rule #6248 for best value: >> intensional similarity = 6 >> extensional distance = 33 >> proper extension: 02qm_f; 0340hj; 048yqf; >> query: (?x11672, 079vf) <- film_crew_role(?x11672, ?x137), production_companies(?x11672, ?x2548), film(?x4366, ?x11672), genre(?x11672, ?x6888), ?x6888 = 04pbhw, award(?x4366, ?x704) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #8491 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 43 *> proper extension: 02d44q; *> query: (?x11672, 04hpck) <- film_crew_role(?x11672, ?x1078), film_crew_role(?x11672, ?x137), produced_by(?x11672, ?x8159), language(?x11672, ?x254), ?x1078 = 089fss, film_crew_role(?x1218, ?x137), ?x1218 = 02prw4h *> conf = 0.02 ranks of expected_values: 527 EVAL 0d99k_ film! 04hpck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 36.000 0.200 http://example.org/film/actor/film./film/performance/film #8973-0kq9l PRED entity: 0kq9l PRED relation: current_club! PRED expected values: 03ys48 => 93 concepts (84 used for prediction) PRED predicted values (max 10 best out of 29): 02bh_v (0.50 #167, 0.43 #314, 0.35 #813), 032jlh (0.44 #596, 0.36 #690, 0.34 #906), 02ltg3 (0.43 #301, 0.33 #517, 0.33 #154), 03ys48 (0.39 #557, 0.23 #924, 0.17 #712), 01l3vx (0.36 #423, 0.35 #813, 0.33 #5), 03zrhb (0.36 #403, 0.34 #906, 0.22 #372), 03xh50 (0.35 #385, 0.34 #417, 0.34 #906), 02s9vc (0.35 #385, 0.34 #417, 0.22 #448), 02rqxc (0.34 #906, 0.30 #177, 0.25 #845), 03_qj1 (0.34 #906, 0.30 #177, 0.25 #845) >> Best rule #167 for best value: >> intensional similarity = 18 >> extensional distance = 4 >> proper extension: 0y9j; 04ltf; >> query: (?x9177, 02bh_v) <- position(?x9177, ?x530), position(?x9177, ?x203), position(?x9177, ?x63), position(?x9177, ?x60), current_club(?x11225, ?x9177), ?x203 = 0dgrmp, ?x60 = 02nzb8, ?x530 = 02_j1w, category(?x9177, ?x134), current_club(?x11225, ?x11421), current_club(?x11225, ?x11337), current_club(?x4972, ?x11421), ?x63 = 02sdk9v, colors(?x11421, ?x663), ?x4972 = 03d8m4, team(?x5685, ?x11337), sport(?x11225, ?x471), team(?x7234, ?x11337) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #557 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 16 *> proper extension: 085v7; 03j70d; 070tng; 057pq5; 082237; *> query: (?x9177, 03ys48) <- position(?x9177, ?x530), position(?x9177, ?x203), position(?x9177, ?x60), current_club(?x11225, ?x9177), ?x203 = 0dgrmp, ?x60 = 02nzb8, ?x530 = 02_j1w, current_club(?x11225, ?x11337), team(?x5685, ?x11337), colors(?x11337, ?x663), team(?x7234, ?x11337), team(?x1142, ?x11225), sport(?x11337, ?x471), ?x5685 = 0f1pyf *> conf = 0.39 ranks of expected_values: 4 EVAL 0kq9l current_club! 03ys48 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 84.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #8972-0ggyr PRED entity: 0ggyr PRED relation: contains! PRED expected values: 0bzty => 157 concepts (95 used for prediction) PRED predicted values (max 10 best out of 293): 0bzty (0.89 #23254, 0.86 #29515, 0.83 #6259), 07ssc (0.64 #45644, 0.38 #16127, 0.22 #40279), 09c7w0 (0.62 #60838, 0.60 #21467, 0.59 #61733), 02j71 (0.59 #16991, 0.54 #59045, 0.47 #78742), 0947l (0.51 #59044, 0.50 #70682, 0.38 #65312), 0ggyr (0.51 #59044, 0.50 #70682, 0.38 #65312), 02jx1 (0.39 #45699, 0.38 #16182, 0.16 #40334), 02qkt (0.34 #27177, 0.17 #54017, 0.16 #56705), 02j9z (0.32 #26858, 0.18 #53698, 0.17 #56386), 052fbt (0.25 #540, 0.14 #4116, 0.12 #7693) >> Best rule #23254 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 04_xrs; 05314s; 03tm68; 098phg; 04_x4s; >> query: (?x10582, ?x10706) <- contains(?x10581, ?x10582), contains(?x205, ?x10582), ?x205 = 03rjj, administrative_parent(?x10581, ?x10706), contains(?x10706, ?x1356) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ggyr contains! 0bzty CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 95.000 0.889 http://example.org/location/location/contains #8971-0p9z5 PRED entity: 0p9z5 PRED relation: time_zones PRED expected values: 02hcv8 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.86 #55, 0.84 #133, 0.84 #146), 02lcqs (0.31 #109, 0.25 #174, 0.20 #161), 02fqwt (0.19 #288, 0.16 #261, 0.16 #1032), 02hczc (0.16 #261, 0.16 #1032, 0.12 #640), 042g7t (0.16 #261, 0.16 #1032, 0.12 #640), 05jphn (0.16 #261), 02llzg (0.16 #121, 0.08 #251, 0.08 #278), 02lcrv (0.16 #1032, 0.12 #640, 0.10 #613), 03bdv (0.04 #423, 0.04 #527, 0.04 #449), 03plfd (0.02 #271, 0.01 #310, 0.01 #663) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0tz41; >> query: (?x9863, 02hcv8) <- currency(?x9863, ?x170), ?x170 = 09nqf, contains(?x2020, ?x9863), ?x2020 = 05k7sb >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p9z5 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.862 http://example.org/location/location/time_zones #8970-01kwsg PRED entity: 01kwsg PRED relation: film PRED expected values: 0661m4p 05r3qc => 85 concepts (53 used for prediction) PRED predicted values (max 10 best out of 593): 0bc1yhb (0.25 #901, 0.09 #2676, 0.01 #11553), 03cffvv (0.21 #7054, 0.03 #65687, 0.03 #8829), 03h_yy (0.18 #1848, 0.04 #5398), 0dlngsd (0.18 #2547, 0.01 #7872, 0.01 #9648), 01q2nx (0.12 #903, 0.12 #4453, 0.03 #9779), 07vn_9 (0.12 #1669, 0.09 #3444, 0.02 #8769), 05qbckf (0.12 #305, 0.09 #2080, 0.01 #7405), 011xg5 (0.12 #1421, 0.07 #6746, 0.03 #65687), 02b6n9 (0.12 #1559, 0.06 #5109, 0.04 #6884), 0bscw (0.12 #215, 0.06 #3765, 0.04 #5540) >> Best rule #901 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 016k6x; >> query: (?x4702, 0bc1yhb) <- nominated_for(?x4702, ?x339), film(?x4702, ?x924), ?x924 = 04gknr >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #65687 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1310 *> proper extension: 05f7snc; 08_83x; *> query: (?x4702, ?x414) <- award_nominee(?x2353, ?x4702), nominated_for(?x4702, ?x339), film(?x2353, ?x414) *> conf = 0.03 ranks of expected_values: 333, 494 EVAL 01kwsg film 05r3qc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 85.000 53.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 01kwsg film 0661m4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 85.000 53.000 0.250 http://example.org/film/actor/film./film/performance/film #8969-04q24zv PRED entity: 04q24zv PRED relation: film_release_region PRED expected values: 0f8l9c => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.08 #206, 0.06 #714, 0.06 #845), 0345h (0.07 #12, 0.03 #216, 0.02 #63), 0jgd (0.07 #3, 0.02 #106, 0.02 #257), 0d0vqn (0.07 #7), 02vzc (0.06 #42, 0.01 #270, 0.01 #119), 06mkj (0.03 #1326, 0.03 #1560, 0.03 #1874), 0d060g (0.02 #57, 0.02 #210, 0.02 #667), 07ssc (0.02 #1480, 0.02 #1300, 0.01 #466), 01znc_ (0.01 #344, 0.01 #370, 0.01 #143) >> Best rule #206 for best value: >> intensional similarity = 5 >> extensional distance = 236 >> proper extension: 0h3y; 042rnl; 02z13jg; 03kwtb; 01_vfy; 05whq_9; 01q4qv; 01ycck; 01f7v_; 01c6l; ... >> query: (?x2797, 09c7w0) <- film_festivals(?x2797, ?x11147), film_festivals(?x4498, ?x11147), film_festivals(?x1481, ?x11147), film_crew_role(?x1481, ?x137), genre(?x4498, ?x53) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04q24zv film_release_region 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.076 http://example.org/film/film/runtime./film/film_cut/film_release_region #8968-05c46y6 PRED entity: 05c46y6 PRED relation: film_crew_role PRED expected values: 02_n3z 089g0h => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 23): 09zzb8 (0.79 #225, 0.75 #1028, 0.75 #836), 09vw2b7 (0.77 #231, 0.67 #1034, 0.64 #842), 089g0h (0.55 #241, 0.14 #209, 0.12 #1044), 0dxtw (0.38 #1038, 0.37 #846, 0.36 #235), 01pvkk (0.30 #12, 0.28 #847, 0.28 #1136), 02_n3z (0.28 #226, 0.09 #837, 0.08 #98), 02ynfr (0.21 #239, 0.19 #850, 0.17 #1042), 02rh1dz (0.17 #234, 0.12 #299, 0.11 #845), 015h31 (0.17 #233, 0.11 #298, 0.08 #522), 033smt (0.15 #248, 0.05 #859, 0.05 #441) >> Best rule #225 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 04jn6y7; >> query: (?x2742, 09zzb8) <- film(?x2614, ?x2742), film_crew_role(?x2742, ?x2472), ?x2472 = 01xy5l_ >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 04jn6y7; *> query: (?x2742, 089g0h) <- film(?x2614, ?x2742), film_crew_role(?x2742, ?x2472), ?x2472 = 01xy5l_ *> conf = 0.55 ranks of expected_values: 3, 6 EVAL 05c46y6 film_crew_role 089g0h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 84.000 0.789 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05c46y6 film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 84.000 84.000 0.789 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8967-026bk PRED entity: 026bk PRED relation: major_field_of_study! PRED expected values: 01jq0j => 114 concepts (82 used for prediction) PRED predicted values (max 10 best out of 609): 06pwq (0.70 #2981, 0.66 #16035, 0.64 #4171), 01w3v (0.70 #2984, 0.64 #4174, 0.43 #16038), 01jssp (0.70 #2974, 0.64 #4164, 0.19 #16028), 02zd460 (0.60 #3164, 0.55 #4354, 0.53 #16218), 07w0v (0.60 #2990, 0.55 #4180, 0.32 #16044), 03ksy (0.57 #16142, 0.50 #19704, 0.50 #3088), 01w5m (0.51 #16141, 0.50 #3087, 0.45 #4277), 08815 (0.50 #2376, 0.45 #3564, 0.43 #16024), 07szy (0.50 #3012, 0.45 #4202, 0.40 #16066), 0bwfn (0.50 #3271, 0.45 #4461, 0.36 #3865) >> Best rule #2981 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 02lp1; 0g26h; >> query: (?x12637, 06pwq) <- major_field_of_study(?x10332, ?x12637), major_field_of_study(?x5739, ?x12637), major_field_of_study(?x3437, ?x12637), ?x3437 = 02_xgp2, student(?x10332, ?x5105), ?x5739 = 01gkg3 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4158 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 02_7t; *> query: (?x12637, ?x99) <- films(?x12637, ?x308), major_field_of_study(?x8398, ?x12637), major_field_of_study(?x10332, ?x12637), student(?x8398, ?x516), institution(?x8398, ?x99) *> conf = 0.08 ranks of expected_values: 430 EVAL 026bk major_field_of_study! 01jq0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 114.000 82.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8966-03wh8kl PRED entity: 03wh8kl PRED relation: profession PRED expected values: 03gjzk => 82 concepts (81 used for prediction) PRED predicted values (max 10 best out of 42): 03gjzk (0.85 #1356, 0.84 #760, 0.84 #611), 02hrh1q (0.70 #2399, 0.68 #8960, 0.66 #5230), 01d_h8 (0.49 #2689, 0.49 #1198, 0.48 #6), 02jknp (0.42 #2691, 0.27 #8946, 0.26 #1200), 02krf9 (0.39 #1938, 0.35 #27, 0.33 #176), 018gz8 (0.27 #8946, 0.25 #8051, 0.18 #166), 0np9r (0.27 #8946, 0.25 #8051, 0.15 #21), 0cbd2 (0.24 #7, 0.22 #2690, 0.20 #1348), 09jwl (0.17 #4788, 0.17 #4639, 0.16 #5384), 0nbcg (0.12 #4801, 0.12 #5397, 0.11 #5547) >> Best rule #1356 for best value: >> intensional similarity = 2 >> extensional distance = 249 >> proper extension: 0f1vrl; 02k76g; >> query: (?x7095, 03gjzk) <- program(?x7095, ?x4517), profession(?x7095, ?x987) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03wh8kl profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 81.000 0.849 http://example.org/people/person/profession #8965-0jqp3 PRED entity: 0jqp3 PRED relation: featured_film_locations PRED expected values: 0fsv2 => 68 concepts (54 used for prediction) PRED predicted values (max 10 best out of 60): 02_286 (0.30 #2397, 0.27 #2634, 0.21 #496), 030qb3t (0.13 #2653, 0.12 #2416, 0.08 #1704), 04jpl (0.12 #2623, 0.09 #2386, 0.06 #3573), 0rh6k (0.06 #2615, 0.06 #2378, 0.04 #714), 02nd_ (0.06 #351, 0.02 #590, 0.02 #2491), 080h2 (0.04 #2401, 0.04 #2638, 0.03 #737), 01_d4 (0.04 #2424, 0.03 #284, 0.03 #2661), 0h7h6 (0.03 #2657, 0.03 #2420, 0.02 #1708), 03gh4 (0.03 #113, 0.02 #2727, 0.02 #2490), 0dclg (0.03 #529, 0.02 #2430, 0.01 #2667) >> Best rule #2397 for best value: >> intensional similarity = 3 >> extensional distance = 409 >> proper extension: 02sg5v; 0g5pv3; 05css_; 042fgh; 01_1hw; 058kh7; 03wy8t; >> query: (?x1069, 02_286) <- film(?x406, ?x1069), music(?x1069, ?x7544), featured_film_locations(?x1069, ?x3832) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #223 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 099bhp; *> query: (?x1069, 0fsv2) <- film(?x1930, ?x1069), company(?x1930, ?x8489), influenced_by(?x654, ?x1930) *> conf = 0.02 ranks of expected_values: 31 EVAL 0jqp3 featured_film_locations 0fsv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 68.000 54.000 0.299 http://example.org/film/film/featured_film_locations #8964-06hx2 PRED entity: 06hx2 PRED relation: profession PRED expected values: 0cbd2 => 132 concepts (113 used for prediction) PRED predicted values (max 10 best out of 106): 02hrh1q (0.96 #16450, 0.89 #10379, 0.89 #8899), 0cbd2 (0.53 #6218, 0.35 #1043, 0.27 #1783), 0dxtg (0.53 #6218, 0.35 #4159, 0.34 #3122), 0kyk (0.53 #6218, 0.27 #1807, 0.25 #2843), 01d_h8 (0.37 #4151, 0.35 #3558, 0.34 #2966), 0dz3r (0.33 #742, 0.33 #298, 0.30 #446), 0n1h (0.33 #308, 0.30 #456, 0.27 #752), 0nbcg (0.33 #329, 0.30 #477, 0.27 #773), 02jknp (0.29 #2080, 0.24 #3560, 0.21 #5929), 09jwl (0.27 #760, 0.26 #7422, 0.26 #7570) >> Best rule #16450 for best value: >> intensional similarity = 3 >> extensional distance = 2599 >> proper extension: 016qtt; 01vrx3g; 0d4fqn; 01mvth; 03qd_; 0f0p0; 0gcdzz; 05tk7y; 05fnl9; 06w33f8; ... >> query: (?x6138, 02hrh1q) <- profession(?x6138, ?x5805), profession(?x10172, ?x5805), ?x10172 = 05_zc7 >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #6218 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 100 *> proper extension: 0c7ct; 0170vn; 07vc_9; 02zyy4; 018phr; 02tf1y; *> query: (?x6138, ?x353) <- nationality(?x6138, ?x94), sibling(?x6138, ?x11088), profession(?x11088, ?x353), profession(?x6138, ?x3342) *> conf = 0.53 ranks of expected_values: 2 EVAL 06hx2 profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 113.000 0.960 http://example.org/people/person/profession #8963-01k5t_3 PRED entity: 01k5t_3 PRED relation: gender PRED expected values: 05zppz => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #37, 0.81 #23, 0.80 #25), 02zsn (0.54 #95, 0.32 #22, 0.30 #48) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 401 >> proper extension: 01m7f5r; >> query: (?x1247, 05zppz) <- nationality(?x1247, ?x94), role(?x1247, ?x227), profession(?x1247, ?x1032) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k5t_3 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.814 http://example.org/people/person/gender #8962-0fpv_3_ PRED entity: 0fpv_3_ PRED relation: nominated_for! PRED expected values: 03hkv_r 0p9sw 02pqp12 0gq9h => 98 concepts (89 used for prediction) PRED predicted values (max 10 best out of 197): 0l8z1 (0.78 #4801, 0.67 #1746, 0.67 #4800), 02pqp12 (0.75 #488, 0.44 #1578, 0.44 #1797), 0gq9h (0.70 #491, 0.55 #5293, 0.50 #54), 0p9sw (0.68 #455, 0.56 #673, 0.53 #1545), 03hkv_r (0.50 #13, 0.33 #450, 0.21 #1540), 0f4x7 (0.50 #23, 0.30 #5262, 0.30 #460), 09sb52 (0.50 #29, 0.30 #466, 0.20 #1556), 09qv_s (0.50 #98, 0.20 #535, 0.19 #5239), 099ck7 (0.50 #160, 0.12 #597, 0.12 #15072), 04dn09n (0.45 #467, 0.38 #30, 0.37 #5269) >> Best rule #4801 for best value: >> intensional similarity = 4 >> extensional distance = 343 >> proper extension: 0bmpm; 0yx7h; 01zfzb; 01k5y0; >> query: (?x2340, ?x1079) <- production_companies(?x2340, ?x574), award(?x2340, ?x1079), nominated_for(?x1079, ?x167), ceremony(?x1079, ?x78) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #488 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 0yyg4; *> query: (?x2340, 02pqp12) <- nominated_for(?x1107, ?x2340), nominated_for(?x637, ?x2340), ?x637 = 02r22gf, award_winner(?x2340, ?x2086), ?x1107 = 019f4v *> conf = 0.75 ranks of expected_values: 2, 3, 4, 5 EVAL 0fpv_3_ nominated_for! 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 89.000 0.784 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fpv_3_ nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 89.000 0.784 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fpv_3_ nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 89.000 0.784 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fpv_3_ nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 89.000 0.784 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8961-078g3l PRED entity: 078g3l PRED relation: gender PRED expected values: 05zppz => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #49, 0.72 #33, 0.72 #93), 02zsn (0.46 #16, 0.46 #125, 0.46 #14) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 1550 >> proper extension: 01xyt7; >> query: (?x6299, 05zppz) <- student(?x5842, ?x6299), school_type(?x5842, ?x3205), institution(?x620, ?x5842) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 078g3l gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.738 http://example.org/people/person/gender #8960-089pg7 PRED entity: 089pg7 PRED relation: artists! PRED expected values: 041738 => 84 concepts (38 used for prediction) PRED predicted values (max 10 best out of 263): 064t9 (0.91 #3393, 0.88 #2163, 0.86 #3700), 016jny (0.87 #2868, 0.46 #4405, 0.27 #1637), 0m0jc (0.78 #2466, 0.25 #8, 0.20 #3388), 06j6l (0.77 #6500, 0.54 #2197, 0.52 #8035), 02lnbg (0.65 #2207, 0.54 #3437, 0.53 #3744), 05r6t (0.64 #1923, 0.29 #999, 0.23 #4996), 025sc50 (0.60 #3429, 0.59 #3736, 0.50 #2199), 0cx7f (0.50 #1362, 0.50 #136, 0.36 #1670), 016ybr (0.50 #738, 0.33 #432, 0.29 #1044), 0xhtw (0.49 #3089, 0.41 #4933, 0.39 #6161) >> Best rule #3393 for best value: >> intensional similarity = 7 >> extensional distance = 85 >> proper extension: 013v5j; 01l_vgt; 02bwjv; 02jyhv; 017b2p; 09nhvw; 0gps0z; >> query: (?x7781, 064t9) <- artists(?x5876, ?x7781), artists(?x3734, ?x7781), artists(?x3734, ?x7683), artists(?x3734, ?x4484), ?x5876 = 0ggx5q, ?x4484 = 03xhj6, role(?x7683, ?x212) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #2536 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 39 *> proper extension: 0phx4; 01sxd1; *> query: (?x7781, 041738) <- artists(?x3734, ?x7781), artists(?x3734, ?x7683), artists(?x3734, ?x4484), ?x7683 = 043c4j, instrumentalists(?x227, ?x7781), ?x4484 = 03xhj6 *> conf = 0.05 ranks of expected_values: 164 EVAL 089pg7 artists! 041738 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 84.000 38.000 0.908 http://example.org/music/genre/artists #8959-02pqs8l PRED entity: 02pqs8l PRED relation: honored_for! PRED expected values: 09gkdln => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 83): 02q690_ (0.26 #406, 0.25 #288, 0.25 #524), 0gx_st (0.25 #29, 0.14 #265, 0.12 #501), 0bxs_d (0.25 #97, 0.09 #333, 0.09 #569), 09p3h7 (0.25 #58, 0.09 #4957, 0.06 #294), 02yw5r (0.25 #8, 0.09 #4957, 0.01 #3312), 092c5f (0.25 #10, 0.06 #246, 0.06 #482), 092_25 (0.25 #59, 0.04 #295, 0.03 #413), 09pnw5 (0.25 #86, 0.03 #322, 0.02 #558), 0gvstc3 (0.23 #380, 0.23 #262, 0.22 #498), 03nnm4t (0.21 #415, 0.20 #297, 0.20 #533) >> Best rule #406 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 03_8kz; >> query: (?x3822, 02q690_) <- actor(?x3822, ?x1116), honored_for(?x1265, ?x3822), languages(?x3822, ?x254) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #4957 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1315 *> proper extension: 04ddm4; 0cp08zg; *> query: (?x3822, ?x1112) <- nominated_for(?x1116, ?x3822), award_winner(?x1112, ?x1116) *> conf = 0.09 ranks of expected_values: 18 EVAL 02pqs8l honored_for! 09gkdln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 55.000 55.000 0.260 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #8958-02mt4k PRED entity: 02mt4k PRED relation: student! PRED expected values: 0bwfn => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 86): 0bwfn (0.14 #274, 0.09 #1326, 0.09 #2904), 033gn8 (0.14 #377, 0.02 #23153, 0.01 #22477), 0gk7z (0.14 #362, 0.02 #23153), 065y4w7 (0.08 #540, 0.06 #3170, 0.06 #2644), 03ksy (0.05 #632, 0.05 #5366, 0.04 #5892), 01w5m (0.04 #4313, 0.04 #5891, 0.04 #26416), 015nl4 (0.04 #14795, 0.04 #14269, 0.04 #20062), 09f2j (0.04 #3841, 0.04 #14887, 0.04 #20154), 0fr9jp (0.04 #4026, 0.02 #14546, 0.02 #15072), 04b_46 (0.04 #1279, 0.03 #1805, 0.03 #2331) >> Best rule #274 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 04bdxl; 02qgqt; 05k2s_; 02l4pj; 03mp9s; >> query: (?x4871, 0bwfn) <- award_winner(?x4872, ?x4871), location(?x4871, ?x12297), ?x4872 = 02d42t >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02mt4k student! 0bwfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #8957-05rfst PRED entity: 05rfst PRED relation: genre PRED expected values: 07s9rl0 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 81): 07s9rl0 (0.66 #365, 0.66 #122, 0.64 #1), 02kdv5l (0.59 #1702, 0.30 #246, 0.29 #852), 05p553 (0.37 #1826, 0.36 #3160, 0.35 #4735), 03k9fj (0.29 #1712, 0.26 #862, 0.25 #983), 02l7c8 (0.28 #1109, 0.28 #2686, 0.27 #6562), 06n90 (0.27 #1713, 0.15 #257, 0.14 #1227), 0lsxr (0.25 #1709, 0.21 #10, 0.19 #131), 04xvlr (0.21 #123, 0.19 #366, 0.16 #1094), 082gq (0.19 #396, 0.16 #153, 0.11 #639), 01hmnh (0.19 #868, 0.18 #989, 0.17 #262) >> Best rule #365 for best value: >> intensional similarity = 3 >> extensional distance = 518 >> proper extension: 01cgz; >> query: (?x5674, 07s9rl0) <- films(?x5673, ?x5674), films(?x5673, ?x7738), nominated_for(?x496, ?x7738) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05rfst genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.660 http://example.org/film/film/genre #8956-02w6s3 PRED entity: 02w6s3 PRED relation: parent_genre PRED expected values: 0dl5d 012x7b => 66 concepts (57 used for prediction) PRED predicted values (max 10 best out of 165): 0glt670 (0.44 #1812, 0.16 #4382, 0.15 #4083), 06by7 (0.40 #1478, 0.39 #1155, 0.38 #2773), 08cyft (0.34 #853, 0.20 #1824, 0.16 #4382), 05w3f (0.33 #25, 0.22 #677, 0.20 #352), 0mmp3 (0.33 #67, 0.22 #719, 0.16 #4382), 01g_bs (0.33 #153, 0.11 #805, 0.04 #4867), 0126t5 (0.33 #56, 0.11 #708, 0.03 #870), 0y3_8 (0.31 #846, 0.18 #1817, 0.16 #4382), 064t9 (0.31 #825, 0.18 #1796, 0.11 #663), 0fd3y (0.29 #498, 0.20 #335, 0.16 #4382) >> Best rule #1812 for best value: >> intensional similarity = 8 >> extensional distance = 53 >> proper extension: 025tjk_; >> query: (?x13782, 0glt670) <- parent_genre(?x13782, ?x2439), artists(?x2439, ?x3187), artists(?x2439, ?x1732), ?x3187 = 0840vq, artists(?x11723, ?x1732), artists(?x5876, ?x1732), ?x11723 = 07s72n, ?x5876 = 0ggx5q >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2933 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 93 *> proper extension: 01gbcf; *> query: (?x13782, 0dl5d) <- parent_genre(?x8847, ?x13782), artists(?x8847, ?x6162), parent_genre(?x13782, ?x2439), artists(?x2937, ?x6162), artist(?x1954, ?x6162), ?x2937 = 0glt670 *> conf = 0.06 ranks of expected_values: 69, 100 EVAL 02w6s3 parent_genre 012x7b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 66.000 57.000 0.436 http://example.org/music/genre/parent_genre EVAL 02w6s3 parent_genre 0dl5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 66.000 57.000 0.436 http://example.org/music/genre/parent_genre #8955-08mhyd PRED entity: 08mhyd PRED relation: profession PRED expected values: 0dgd_ => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 50): 0dgd_ (0.90 #483, 0.89 #635, 0.87 #785), 0dxtg (0.82 #917, 0.31 #1218, 0.29 #1067), 02hrh1q (0.70 #5424, 0.69 #6174, 0.69 #4073), 01d_h8 (0.57 #909, 0.39 #1059, 0.35 #3462), 02jknp (0.54 #911, 0.39 #1212, 0.31 #1061), 0cbd2 (0.26 #910, 0.12 #9618, 0.12 #5116), 03gjzk (0.24 #3757, 0.23 #1971, 0.23 #3622), 09jwl (0.24 #3757, 0.19 #3777, 0.18 #1525), 01c72t (0.24 #3757, 0.15 #1078, 0.10 #5134), 02krf9 (0.24 #3757, 0.14 #8560, 0.11 #931) >> Best rule #483 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 06cv1; 0f3zf_; 0gp9mp; 079hvk; 05dppk; 0dqzkv; 02rgz97; 06nz46; 06g60w; 0280mv7; ... >> query: (?x7327, 0dgd_) <- cinematography(?x8574, ?x7327), film_crew_role(?x8574, ?x137), nominated_for(?x112, ?x8574) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08mhyd profession 0dgd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.900 http://example.org/people/person/profession #8954-02fn5r PRED entity: 02fn5r PRED relation: role PRED expected values: 02w3w => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 96): 05148p4 (0.29 #17, 0.26 #639, 0.13 #1387), 05r5c (0.18 #629, 0.09 #1377, 0.09 #1189), 018vs (0.16 #635, 0.09 #1383, 0.08 #1195), 0gghm (0.16 #1245, 0.14 #63, 0.12 #126), 028tv0 (0.14 #634, 0.07 #1382, 0.06 #1570), 02hnl (0.13 #650, 0.09 #340, 0.08 #1398), 0l14md (0.11 #628, 0.06 #1376, 0.06 #1564), 042v_gx (0.10 #1433, 0.10 #1622, 0.10 #1621), 04rzd (0.10 #1433, 0.10 #1622, 0.10 #1621), 03qjg (0.09 #662, 0.05 #1222, 0.04 #1410) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01kv4mb; 0bhvtc; 03cfjg; 0p_47; 0pmw9; >> query: (?x2638, 05148p4) <- nominated_for(?x3146, ?x2638), instrumentalists(?x227, ?x2638), ?x3146 = 0ggjt >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1623 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 467 *> proper extension: 02fybl; 01m7f5r; 09g0h; *> query: (?x2638, ?x75) <- role(?x2638, ?x314), role(?x75, ?x314) *> conf = 0.01 ranks of expected_values: 44 EVAL 02fn5r role 02w3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 78.000 78.000 0.286 http://example.org/music/group_member/membership./music/group_membership/role #8953-0k9ctht PRED entity: 0k9ctht PRED relation: film PRED expected values: 027rpym => 96 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1588): 035s95 (0.29 #5066, 0.28 #9830, 0.28 #8242), 02rb84n (0.29 #5018, 0.28 #9782, 0.28 #8194), 0ndwt2w (0.29 #5649, 0.23 #4061, 0.22 #10413), 0cq7kw (0.29 #5442, 0.23 #3854, 0.22 #10206), 0dgq_kn (0.29 #5687, 0.22 #10451, 0.22 #8863), 014kq6 (0.29 #5071, 0.22 #9835, 0.22 #8247), 03mh_tp (0.28 #8387, 0.24 #22679, 0.24 #13151), 01dvbd (0.23 #30613, 0.17 #17909, 0.15 #24261), 047gpsd (0.22 #10586, 0.22 #8998, 0.21 #5822), 0k0rf (0.22 #10321, 0.22 #8733, 0.21 #5557) >> Best rule #5066 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 05gnf; >> query: (?x5537, 035s95) <- film(?x5537, ?x721), award_winner(?x5537, ?x11962), state_province_region(?x5537, ?x1227) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #29329 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 06rq1k; 02jd_7; 02b07b; *> query: (?x5537, 027rpym) <- citytown(?x5537, ?x1523), industry(?x5537, ?x373), ?x373 = 02vxn *> conf = 0.03 ranks of expected_values: 1471 EVAL 0k9ctht film 027rpym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 96.000 80.000 0.286 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #8952-04rjg PRED entity: 04rjg PRED relation: major_field_of_study! PRED expected values: 01gkg3 => 94 concepts (60 used for prediction) PRED predicted values (max 10 best out of 15): 0bjrnt (0.58 #294, 0.55 #202, 0.50 #125), 071tyz (0.58 #294, 0.50 #526, 0.35 #856), 01ysy9 (0.58 #294, 0.38 #246, 0.36 #214), 02m4yg (0.58 #294, 0.35 #856, 0.33 #161), 022h5x (0.40 #182, 0.35 #856, 0.33 #819), 028dcg (0.35 #856, 0.33 #819, 0.32 #839), 07s6fsf (0.35 #856, 0.33 #819, 0.32 #839), 03mkk4 (0.35 #856, 0.33 #819, 0.32 #839), 01rr_d (0.35 #856, 0.33 #819, 0.32 #839), 013zdg (0.35 #856, 0.33 #819, 0.32 #839) >> Best rule #294 for best value: >> intensional similarity = 8 >> extensional distance = 14 >> proper extension: 06ntj; >> query: (?x2014, ?x734) <- major_field_of_study(?x5864, ?x2014), major_field_of_study(?x3490, ?x2014), major_field_of_study(?x2014, ?x2605), ?x3490 = 05qfh, major_field_of_study(?x741, ?x5864), student(?x5864, ?x879), ?x741 = 01w3v, major_field_of_study(?x734, ?x5864) >> conf = 0.58 => this is the best rule for 4 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 01mkq; *> query: (?x2014, 01gkg3) <- major_field_of_study(?x732, ?x2014), major_field_of_study(?x8930, ?x2014), major_field_of_study(?x4338, ?x2014), major_field_of_study(?x3044, ?x2014), major_field_of_study(?x1011, ?x2014), major_field_of_study(?x388, ?x2014), major_field_of_study(?x734, ?x2014), ?x3044 = 01c333, ?x4338 = 0bqxw, ?x1011 = 07w0v, student(?x388, ?x643), ?x8930 = 0373qt *> conf = 0.33 ranks of expected_values: 14 EVAL 04rjg major_field_of_study! 01gkg3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 94.000 60.000 0.578 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #8951-05gml8 PRED entity: 05gml8 PRED relation: film PRED expected values: 06rmdr => 116 concepts (105 used for prediction) PRED predicted values (max 10 best out of 845): 030p35 (0.68 #46519, 0.59 #105565, 0.58 #128830), 0gj50 (0.68 #46519, 0.59 #105565, 0.58 #128830), 02cbhg (0.14 #1404, 0.06 #4982, 0.04 #6771), 09cr8 (0.14 #285, 0.05 #162832, 0.03 #146727), 046488 (0.14 #851, 0.05 #162832, 0.03 #146727), 0cfhfz (0.14 #492, 0.04 #2281, 0.04 #7648), 0bc1yhb (0.14 #911, 0.04 #2700, 0.03 #4489), 06z8s_ (0.14 #130, 0.04 #1919, 0.03 #146727), 09xbpt (0.14 #47, 0.04 #1836, 0.03 #146727), 02vzpb (0.14 #1613, 0.04 #3402, 0.03 #152095) >> Best rule #46519 for best value: >> intensional similarity = 3 >> extensional distance = 242 >> proper extension: 02wb6yq; >> query: (?x709, ?x4011) <- participant(?x709, ?x3101), participant(?x709, ?x710), nominated_for(?x709, ?x4011) >> conf = 0.68 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 05gml8 film 06rmdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 105.000 0.684 http://example.org/film/actor/film./film/performance/film #8950-03r8v_ PRED entity: 03r8v_ PRED relation: award! PRED expected values: 01p3ty => 29 concepts (21 used for prediction) PRED predicted values (max 10 best out of 655): 021pqy (0.50 #461, 0.05 #12307, 0.02 #4561), 01p3ty (0.33 #1026, 0.33 #254, 0.30 #1025), 047q2k1 (0.33 #17, 0.30 #1025, 0.22 #11282), 09yxcz (0.33 #968, 0.05 #12307, 0.01 #5068), 05hjnw (0.31 #1528, 0.09 #2552, 0.09 #4602), 04jwjq (0.30 #1025, 0.22 #11282, 0.22 #10256), 09fn1w (0.30 #1025, 0.22 #11282, 0.22 #10256), 0hfzr (0.25 #1444, 0.11 #3493, 0.10 #4518), 09cr8 (0.25 #1202, 0.08 #2226, 0.08 #3251), 0b6tzs (0.25 #1115, 0.07 #4189, 0.07 #3164) >> Best rule #461 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0b6jkkg; >> query: (?x10156, 021pqy) <- award(?x3129, ?x10156), nominated_for(?x10156, ?x2617), award_winner(?x10156, ?x656), ?x2617 = 01p3ty >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1026 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0b6jkkg; *> query: (?x10156, ?x2617) <- award(?x3129, ?x10156), nominated_for(?x10156, ?x2617), award_winner(?x10156, ?x656), ?x2617 = 01p3ty *> conf = 0.33 ranks of expected_values: 2 EVAL 03r8v_ award! 01p3ty CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 29.000 21.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8949-01h4rj PRED entity: 01h4rj PRED relation: film PRED expected values: 04t6fk => 178 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1006): 0ft18 (0.33 #1407, 0.14 #4983, 0.11 #24652), 070fnm (0.33 #312, 0.07 #16404, 0.06 #23557), 04vq33 (0.33 #1776, 0.07 #17868, 0.06 #25021), 0c_j9x (0.33 #374, 0.07 #16466, 0.06 #23619), 02k1pr (0.25 #3235, 0.07 #17539, 0.06 #26480), 07h9gp (0.25 #2054, 0.02 #82516), 03l6q0 (0.25 #2332), 02dwj (0.18 #9845, 0.12 #18785, 0.12 #6269), 03rg2b (0.17 #26125, 0.14 #4668, 0.12 #6456), 0k5fg (0.14 #4665, 0.12 #20757, 0.12 #6453) >> Best rule #1407 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0bdt8; >> query: (?x9709, 0ft18) <- languages(?x9709, ?x5607), place_of_death(?x9709, ?x191), film(?x9709, ?x3859), ?x5607 = 064_8sq >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7583 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0k8y7; 0mfj2; *> query: (?x9709, 04t6fk) <- place_of_burial(?x9709, ?x3153), film(?x9709, ?x3859), languages(?x9709, ?x90), award(?x9709, ?x2071) *> conf = 0.09 ranks of expected_values: 46 EVAL 01h4rj film 04t6fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 178.000 86.000 0.333 http://example.org/film/actor/film./film/performance/film #8948-01f2q5 PRED entity: 01f2q5 PRED relation: award_winner! PRED expected values: 01s695 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 83): 019bk0 (0.23 #157, 0.22 #298, 0.20 #16), 02rjjll (0.22 #287, 0.12 #1133, 0.12 #992), 0gx1673 (0.20 #120, 0.06 #1248, 0.06 #1812), 01bx35 (0.17 #430, 0.11 #1558, 0.10 #2122), 0jzphpx (0.15 #321, 0.09 #1167, 0.08 #1590), 01mhwk (0.15 #323, 0.07 #1592, 0.07 #3284), 01s695 (0.14 #426, 0.11 #1131, 0.11 #1554), 0466p0j (0.14 #499, 0.11 #358, 0.10 #76), 013b2h (0.13 #1631, 0.13 #2054, 0.13 #1208), 01c6qp (0.12 #1570, 0.11 #1711, 0.11 #1147) >> Best rule #157 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 032w8h; 029_l; >> query: (?x11897, 019bk0) <- award(?x11897, ?x3937), award_nominee(?x11897, ?x3176), ?x3176 = 01w7nww >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #426 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0kr_t; 09z1lg; 0c9l1; *> query: (?x11897, 01s695) <- award_winner(?x9295, ?x11897), group(?x227, ?x11897), award_nominee(?x1378, ?x11897), award(?x11897, ?x3937) *> conf = 0.14 ranks of expected_values: 7 EVAL 01f2q5 award_winner! 01s695 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 81.000 81.000 0.231 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8947-0hkqn PRED entity: 0hkqn PRED relation: citytown PRED expected values: 0bxbr => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 148): 02_286 (0.26 #23240, 0.25 #18075, 0.23 #4064), 01_d4 (0.25 #38, 0.06 #1142, 0.05 #1511), 094jv (0.23 #402, 0.20 #770, 0.03 #39818), 0bxbr (0.15 #502, 0.13 #870, 0.05 #1607), 0fvwg (0.15 #18060, 0.08 #531, 0.07 #899), 07dfk (0.15 #28969, 0.13 #30442, 0.13 #30811), 030qb3t (0.14 #8870, 0.08 #11082, 0.06 #13663), 0bxc4 (0.13 #1085), 01m1zk (0.10 #1932, 0.08 #3404, 0.06 #4878), 0f2rq (0.09 #5649, 0.09 #6386, 0.09 #6017) >> Best rule #23240 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 0974y; 02mdty; 021gk7; 01scmq; >> query: (?x12373, 02_286) <- industry(?x12373, ?x11820), state_province_region(?x12373, ?x1767), district_represented(?x176, ?x1767) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #502 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0352gk; 0g8fs; 0nzm; *> query: (?x12373, 0bxbr) <- category(?x12373, ?x134), ?x134 = 08mbj5d, state_province_region(?x12373, ?x1767), ?x1767 = 04rrd *> conf = 0.15 ranks of expected_values: 4 EVAL 0hkqn citytown 0bxbr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 110.000 0.265 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #8946-01rwpj PRED entity: 01rwpj PRED relation: film_format PRED expected values: 0cj16 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.25 #6, 0.20 #59, 0.19 #179), 0cj16 (0.22 #3, 0.19 #8, 0.19 #51), 017fx5 (0.06 #29, 0.06 #34, 0.06 #40) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 050gkf; 011ykb; >> query: (?x5067, 07fb8_) <- film_crew_role(?x5067, ?x468), award(?x5067, ?x451), ?x451 = 099jhq, titles(?x512, ?x5067) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 047gn4y; 04gknr; 0ch26b_; 0bc1yhb; 03cp4cn; 02cbg0; 07jqjx; *> query: (?x5067, 0cj16) <- film_crew_role(?x5067, ?x468), film_release_region(?x5067, ?x94), film(?x4969, ?x5067), ?x4969 = 016k6x *> conf = 0.22 ranks of expected_values: 2 EVAL 01rwpj film_format 0cj16 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.250 http://example.org/film/film/film_format #8945-0179q0 PRED entity: 0179q0 PRED relation: category PRED expected values: 08mbj5d => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #20, 0.80 #19, 0.77 #18) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 02qwgk; >> query: (?x13712, 08mbj5d) <- contains(?x7468, ?x13712), contains(?x279, ?x13712), ?x279 = 0d060g, administrative_division(?x8916, ?x7468) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0179q0 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.796 http://example.org/common/topic/webpage./common/webpage/category #8944-02pqp12 PRED entity: 02pqp12 PRED relation: award_winner PRED expected values: 013tcv => 57 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2450): 0c921 (0.62 #11784, 0.60 #4419, 0.60 #1964), 0p51w (0.60 #3046, 0.60 #591, 0.50 #10411), 0bwh6 (0.60 #2716, 0.40 #12537, 0.40 #261), 03_gd (0.60 #2590, 0.40 #12411, 0.40 #135), 0c1pj (0.60 #2552, 0.40 #97, 0.38 #9917), 02l5rm (0.60 #3094, 0.40 #639, 0.38 #10459), 0jgwf (0.40 #4287, 0.40 #1832, 0.38 #11652), 03bw6 (0.40 #4048, 0.40 #1593, 0.38 #11413), 0gv40 (0.40 #3497, 0.40 #1042, 0.38 #10862), 0g2lq (0.40 #4144, 0.40 #1689, 0.35 #54006) >> Best rule #11784 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 03hkv_r; >> query: (?x1198, 0c921) <- nominated_for(?x1198, ?x972), ?x972 = 017gl1, award(?x2451, ?x1198), award(?x826, ?x1198), participant(?x5413, ?x2451), ?x826 = 02kxbwx >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #54006 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 183 *> proper extension: 02hdky; *> query: (?x1198, ?x9754) <- award(?x9754, ?x1198), award(?x8041, ?x1198), award(?x3572, ?x1198), film(?x8041, ?x964), award_winner(?x1587, ?x9754), student(?x1368, ?x3572) *> conf = 0.35 ranks of expected_values: 51 EVAL 02pqp12 award_winner 013tcv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 57.000 26.000 0.625 http://example.org/award/award_category/winners./award/award_honor/award_winner #8943-0cz8mkh PRED entity: 0cz8mkh PRED relation: film_release_region PRED expected values: 059j2 01ly5m => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 186): 059j2 (0.90 #298, 0.87 #436, 0.85 #712), 05b4w (0.86 #740, 0.83 #188, 0.80 #464), 0k6nt (0.80 #707, 0.78 #983, 0.77 #431), 03rk0 (0.79 #181, 0.71 #457, 0.67 #595), 06qd3 (0.79 #166, 0.60 #580, 0.53 #442), 03rt9 (0.79 #422, 0.76 #284, 0.76 #698), 016wzw (0.75 #191, 0.73 #467, 0.64 #329), 06c1y (0.75 #171, 0.52 #309, 0.52 #723), 05qx1 (0.71 #169, 0.60 #445, 0.55 #583), 01pj7 (0.67 #176, 0.52 #590, 0.37 #1004) >> Best rule #298 for best value: >> intensional similarity = 6 >> extensional distance = 65 >> proper extension: 02vxq9m; 0g5qs2k; 02x3lt7; 01vksx; 08hmch; 0872p_c; 053rxgm; 0jqn5; 04w7rn; 0gj9qxr; ... >> query: (?x1456, 059j2) <- film_release_region(?x1456, ?x2267), film_release_region(?x1456, ?x1174), ?x1174 = 047yc, film_crew_role(?x1456, ?x468), ?x468 = 02r96rf, ?x2267 = 03rj0 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 120 EVAL 0cz8mkh film_release_region 01ly5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 57.000 57.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cz8mkh film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8942-0727h PRED entity: 0727h PRED relation: entity_involved PRED expected values: 0bk25 => 85 concepts (73 used for prediction) PRED predicted values (max 10 best out of 522): 017cw (0.60 #1686, 0.50 #2477, 0.25 #1053), 026pz9s (0.60 #1709, 0.50 #2500, 0.25 #1076), 01m41_ (0.50 #5670, 0.43 #3125, 0.40 #2020), 06f32 (0.50 #661, 0.25 #820, 0.25 #500), 01h3dj (0.47 #7209, 0.30 #4987, 0.29 #6892), 0285m87 (0.44 #4675, 0.43 #3091, 0.40 #1986), 09c7w0 (0.44 #4280, 0.25 #3485, 0.24 #8097), 0j5b8 (0.43 #3070, 0.40 #5615, 0.40 #1965), 03l5m1 (0.40 #1669, 0.33 #2460, 0.30 #5325), 024pcx (0.40 #1664, 0.33 #2455, 0.27 #7380) >> Best rule #1686 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 0ql7q; >> query: (?x13053, 017cw) <- locations(?x13053, ?x13353), entity_involved(?x13053, ?x8845), entity_involved(?x13053, ?x1353), taxonomy(?x13353, ?x939), official_language(?x8845, ?x11038), ?x11038 = 04h9h, contains(?x5903, ?x13353), combatants(?x1353, ?x172), nationality(?x1068, ?x1353), countries_spoken_in(?x732, ?x172) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0727h entity_involved 0bk25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 73.000 0.600 http://example.org/base/culturalevent/event/entity_involved #8941-02gjrc PRED entity: 02gjrc PRED relation: country_of_origin PRED expected values: 07ssc => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.87 #127, 0.86 #243, 0.86 #24), 02jx1 (0.60 #207, 0.55 #265, 0.46 #277), 07ssc (0.55 #265, 0.46 #277, 0.43 #427), 0d060g (0.55 #265, 0.46 #277, 0.43 #427), 06q1r (0.46 #277, 0.43 #427, 0.43 #348), 0b90_r (0.46 #277, 0.43 #427, 0.43 #348), 03rjj (0.43 #427, 0.42 #336, 0.32 #47), 07fj_ (0.43 #427, 0.42 #336, 0.32 #47), 0b1t1 (0.14 #312, 0.14 #324), 0kpzy (0.14 #312, 0.14 #324) >> Best rule #127 for best value: >> intensional similarity = 8 >> extensional distance = 131 >> proper extension: 0n2bh; 0gfzgl; 03y3bp7; 02648p; 0431v3; 03nt59; 01dvry; 097h2; 053x8hr; 02xhwm; >> query: (?x11482, 09c7w0) <- actor(?x11482, ?x4735), actor(?x11482, ?x2167), student(?x122, ?x4735), award(?x4735, ?x458), profession(?x4735, ?x1032), place_of_birth(?x4735, ?x5211), people(?x11321, ?x2167), award(?x2167, ?x102) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #265 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 171 *> proper extension: 01j67j; 0cpz4k; 0b6m5fy; 02wyzmv; 09v38qj; *> query: (?x11482, ?x94) <- actor(?x11482, ?x9578), actor(?x11482, ?x4735), actor(?x11482, ?x3651), student(?x122, ?x4735), award(?x4735, ?x458), profession(?x4735, ?x1032), gender(?x9578, ?x231), nationality(?x4735, ?x94), film(?x3651, ?x463) *> conf = 0.55 ranks of expected_values: 3 EVAL 02gjrc country_of_origin 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 55.000 55.000 0.872 http://example.org/tv/tv_program/country_of_origin #8940-0bjrnt PRED entity: 0bjrnt PRED relation: major_field_of_study PRED expected values: 06ms6 062z7 03lrls => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 144): 01mkq (0.70 #1155, 0.69 #723, 0.67 #102), 04rjg (0.70 #1160, 0.69 #723, 0.67 #102), 062z7 (0.70 #1167, 0.69 #723, 0.67 #102), 036hv (0.70 #1151, 0.69 #723, 0.67 #102), 03nfmq (0.69 #723, 0.67 #102, 0.66 #1039), 01zc2w (0.69 #723, 0.67 #102, 0.66 #1039), 02lp1 (0.69 #723, 0.67 #102, 0.66 #1039), 0h5k (0.69 #723, 0.67 #102, 0.66 #1039), 04sh3 (0.69 #723, 0.67 #102, 0.66 #1039), 04g51 (0.69 #723, 0.67 #102, 0.66 #1039) >> Best rule #1155 for best value: >> intensional similarity = 26 >> extensional distance = 8 >> proper extension: 03mkk4; >> query: (?x1390, 01mkq) <- institution(?x1390, ?x10104), institution(?x1390, ?x6132), institution(?x1390, ?x1768), institution(?x1390, ?x892), institution(?x1390, ?x122), ?x1768 = 09kvv, major_field_of_study(?x1390, ?x11378), major_field_of_study(?x1390, ?x254), major_field_of_study(?x10178, ?x11378), major_field_of_study(?x8095, ?x11378), major_field_of_study(?x1771, ?x11378), student(?x892, ?x10224), contains(?x1310, ?x892), ?x8095 = 02mp0g, category(?x6132, ?x134), contains(?x94, ?x10104), ?x122 = 08815, school_type(?x892, ?x3092), institution(?x1771, ?x5638), type_of_union(?x10224, ?x566), list(?x892, ?x2197), major_field_of_study(?x254, ?x2314), organization(?x346, ?x10104), contains(?x362, ?x6132), state_province_region(?x10178, ?x1227), ?x5638 = 02bqy >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1167 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 8 *> proper extension: 03mkk4; *> query: (?x1390, 062z7) <- institution(?x1390, ?x10104), institution(?x1390, ?x6132), institution(?x1390, ?x1768), institution(?x1390, ?x892), institution(?x1390, ?x122), ?x1768 = 09kvv, major_field_of_study(?x1390, ?x11378), major_field_of_study(?x1390, ?x254), major_field_of_study(?x10178, ?x11378), major_field_of_study(?x8095, ?x11378), major_field_of_study(?x1771, ?x11378), student(?x892, ?x10224), contains(?x1310, ?x892), ?x8095 = 02mp0g, category(?x6132, ?x134), contains(?x94, ?x10104), ?x122 = 08815, school_type(?x892, ?x3092), institution(?x1771, ?x5638), type_of_union(?x10224, ?x566), list(?x892, ?x2197), major_field_of_study(?x254, ?x2314), organization(?x346, ?x10104), contains(?x362, ?x6132), state_province_region(?x10178, ?x1227), ?x5638 = 02bqy *> conf = 0.70 ranks of expected_values: 3, 36, 55 EVAL 0bjrnt major_field_of_study 03lrls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 22.000 22.000 0.700 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 0bjrnt major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 22.000 0.700 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 0bjrnt major_field_of_study 06ms6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 22.000 22.000 0.700 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #8939-09lbv PRED entity: 09lbv PRED relation: profession! PRED expected values: 01vs14j 01vsnff 01w8n89 => 54 concepts (27 used for prediction) PRED predicted values (max 10 best out of 4305): 052hl (0.75 #31655, 0.67 #23235, 0.27 #33685), 08hsww (0.75 #30981, 0.67 #22561, 0.25 #35192), 0f87jy (0.75 #32937, 0.67 #24517, 0.25 #37148), 014z8v (0.75 #30742, 0.67 #22322, 0.25 #34953), 0c9c0 (0.75 #30289, 0.67 #21869, 0.25 #34500), 0pz7h (0.75 #29699, 0.67 #21279, 0.25 #33910), 0473q (0.71 #27606, 0.50 #14974, 0.50 #10764), 0g824 (0.71 #27329, 0.50 #14697, 0.50 #10487), 0ffgh (0.71 #27580, 0.50 #14948, 0.50 #10738), 0127xk (0.67 #24719, 0.62 #33139, 0.50 #12087) >> Best rule #31655 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 018gz8; 0np9r; >> query: (?x1359, 052hl) <- profession(?x6550, ?x1359), profession(?x5285, ?x1359), role(?x5285, ?x228), award(?x5285, ?x724), ?x6550 = 03nb5v >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #25875 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 0dz3r; 04f2zj; *> query: (?x1359, 01vsnff) <- profession(?x5285, ?x1359), profession(?x1282, ?x1359), role(?x5285, ?x228), award(?x5285, ?x1801), ?x1282 = 01wdqrx, ?x1801 = 01c92g *> conf = 0.57 ranks of expected_values: 75, 556, 623 EVAL 09lbv profession! 01w8n89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 27.000 0.750 http://example.org/people/person/profession EVAL 09lbv profession! 01vsnff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 54.000 27.000 0.750 http://example.org/people/person/profession EVAL 09lbv profession! 01vs14j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 27.000 0.750 http://example.org/people/person/profession #8938-03gyl PRED entity: 03gyl PRED relation: entity_involved! PRED expected values: 01hwkn => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 41): 07_nf (0.10 #17, 0.08 #611, 0.08 #1205), 03jqfx (0.10 #28, 0.07 #3328, 0.07 #226), 048n7 (0.06 #1210, 0.06 #748, 0.05 #1804), 0dl4z (0.06 #2051, 0.06 #665, 0.06 #731), 03jv8d (0.06 #711, 0.06 #777, 0.05 #51), 02cnqk (0.06 #779, 0.05 #1109, 0.04 #119), 05nqz (0.05 #1000, 0.05 #10, 0.04 #1792), 0727h (0.05 #57, 0.04 #189, 0.03 #255), 01fc7p (0.05 #2047, 0.04 #1651, 0.04 #661), 031x2 (0.04 #75, 0.04 #603, 0.04 #669) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 019rg5; 02vzc; >> query: (?x4714, 07_nf) <- teams(?x4714, ?x10006), country(?x11656, ?x4714), vacationer(?x4714, ?x3503) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #116 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 04pnx; 059g4; 06n3y; *> query: (?x4714, 01hwkn) <- contains(?x7273, ?x4714), contains(?x4714, ?x11656), ?x7273 = 07c5l *> conf = 0.04 ranks of expected_values: 12 EVAL 03gyl entity_involved! 01hwkn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 93.000 93.000 0.100 http://example.org/base/culturalevent/event/entity_involved #8937-06c62 PRED entity: 06c62 PRED relation: month PRED expected values: 040fb => 279 concepts (279 used for prediction) PRED predicted values (max 10 best out of 1): 040fb (0.89 #38, 0.83 #64, 0.83 #44) >> Best rule #38 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 03khn; >> query: (?x6959, 040fb) <- category(?x6959, ?x134), citytown(?x5994, ?x6959), month(?x6959, ?x1459) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06c62 month 040fb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 279.000 279.000 0.889 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #8936-09fqtq PRED entity: 09fqtq PRED relation: gender PRED expected values: 05zppz => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #135, 0.72 #125, 0.72 #133), 02zsn (0.31 #4, 0.31 #6, 0.30 #40) >> Best rule #135 for best value: >> intensional similarity = 2 >> extensional distance = 1874 >> proper extension: 01d494; 0cm03; 0frmb1; 0xnc3; 0cl_m; 02x8mt; 02vptk_; 09jrf; 03c_8t; >> query: (?x473, 05zppz) <- student(?x11740, ?x473), major_field_of_study(?x11740, ?x2014) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09fqtq gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.727 http://example.org/people/person/gender #8935-016ypb PRED entity: 016ypb PRED relation: diet PRED expected values: 07_jd => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 07_jd (0.07 #5, 0.06 #9, 0.06 #13), 07_hy (0.02 #54, 0.02 #10, 0.01 #114) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 09wj5; 01rh0w; 024n3z; 01v9l67; >> query: (?x2922, 07_jd) <- award_winner(?x4923, ?x2922), ?x4923 = 0svqs, award_winner(?x2922, ?x2727) >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016ypb diet 07_jd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.067 http://example.org/base/eating/practicer_of_diet/diet #8934-056ws9 PRED entity: 056ws9 PRED relation: production_companies! PRED expected values: 06ztvyx 0cmf0m0 => 155 concepts (131 used for prediction) PRED predicted values (max 10 best out of 1145): 05zlld0 (0.33 #410, 0.30 #17118, 0.25 #7256), 0872p_c (0.33 #125, 0.30 #17118, 0.18 #9253), 03n0cd (0.33 #959, 0.30 #17118, 0.17 #5523), 0cc5mcj (0.33 #263, 0.30 #17118, 0.17 #4827), 01q2nx (0.33 #590, 0.30 #17118, 0.17 #5154), 0c3zjn7 (0.33 #619, 0.30 #17118, 0.17 #5183), 09hy79 (0.33 #779, 0.30 #17118, 0.17 #5343), 03ct7jd (0.33 #546, 0.30 #17118, 0.17 #5110), 09g8vhw (0.33 #221, 0.17 #4785, 0.15 #11631), 07024 (0.33 #321, 0.17 #4885, 0.15 #11731) >> Best rule #410 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01gb54; >> query: (?x5970, 05zlld0) <- production_companies(?x3008, ?x5970), production_companies(?x1904, ?x5970), ?x3008 = 05wp1p, ?x1904 = 09146g >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #51350 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 0l8v5; 01nqfh_; 01wxyx1; 02cllz; 04pf4r; 01vswwx; 01mkn_d; 0n8bn; 01j7z7; 01r4hry; ... *> query: (?x5970, ?x7806) <- nominated_for(?x5970, ?x10192), category(?x5970, ?x134), prequel(?x7806, ?x10192) *> conf = 0.06 ranks of expected_values: 640 EVAL 056ws9 production_companies! 0cmf0m0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 131.000 0.333 http://example.org/film/film/production_companies EVAL 056ws9 production_companies! 06ztvyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 155.000 131.000 0.333 http://example.org/film/film/production_companies #8933-09cdxn PRED entity: 09cdxn PRED relation: nationality PRED expected values: 09c7w0 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.82 #1204, 0.80 #1504, 0.80 #2004), 01n7q (0.31 #3510), 02jx1 (0.19 #3308, 0.13 #1636, 0.12 #3038), 07ssc (0.19 #3308, 0.11 #1618, 0.11 #1718), 0d060g (0.19 #3308, 0.06 #207, 0.04 #1010), 0f8l9c (0.19 #3308, 0.04 #1025, 0.04 #323), 0b90_r (0.19 #3308), 0chghy (0.12 #210, 0.08 #110, 0.08 #611), 0345h (0.12 #231, 0.03 #3237, 0.03 #3439), 0d05w3 (0.08 #150, 0.06 #250, 0.04 #351) >> Best rule #1204 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 0207wx; 0gv2r; 01v5h; >> query: (?x6115, 09c7w0) <- award_winner(?x1243, ?x6115), award_nominee(?x6115, ?x6116), place_of_death(?x6115, ?x242), nominated_for(?x6116, ?x3294) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09cdxn nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.821 http://example.org/people/person/nationality #8932-013zs9 PRED entity: 013zs9 PRED relation: type_of_union PRED expected values: 01g63y => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #53, 0.71 #97, 0.71 #129), 01g63y (0.23 #10, 0.19 #6, 0.15 #2) >> Best rule #53 for best value: >> intensional similarity = 3 >> extensional distance = 1066 >> proper extension: 033071; >> query: (?x8702, 04ztj) <- film(?x8702, ?x1022), profession(?x8702, ?x1032), student(?x2486, ?x8702) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 04bdxl; 01j5ts; 0h1nt; 0l6px; 019f2f; 02s5v5; 02g0mx; 011_3s; 014g22; 01y64_; ... *> query: (?x8702, 01g63y) <- award_nominee(?x2938, ?x8702), profession(?x8702, ?x1032), award(?x8702, ?x1254), ?x1254 = 02z0dfh *> conf = 0.23 ranks of expected_values: 2 EVAL 013zs9 type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.718 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8931-02754c9 PRED entity: 02754c9 PRED relation: currency PRED expected values: 09nqf => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.85 #8, 0.85 #64, 0.77 #106), 01nv4h (0.06 #23, 0.03 #107, 0.02 #93), 02l6h (0.06 #25, 0.01 #95), 02gsvk (0.02 #62, 0.01 #76, 0.01 #83) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 03qcfvw; 02y_lrp; 0140g4; 08lr6s; 0ds33; 060v34; 04ddm4; 0pc62; 0dj0m5; 04fzfj; ... >> query: (?x6425, 09nqf) <- nominated_for(?x1105, ?x6425), genre(?x6425, ?x258), film(?x4935, ?x6425), ?x1105 = 07bdd_ >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02754c9 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.854 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8930-03cyslc PRED entity: 03cyslc PRED relation: film! PRED expected values: 03q3sy => 131 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1302): 017s11 (0.60 #114473, 0.46 #99901, 0.43 #104065), 03q3sy (0.52 #116559, 0.46 #99901, 0.44 #118642), 03n08b (0.29 #6478, 0.06 #12721, 0.04 #100136), 0p_pd (0.25 #54, 0.05 #20864, 0.05 #18783), 0h1nt (0.25 #197, 0.05 #21007, 0.05 #18926), 015c4g (0.25 #779, 0.05 #21589, 0.05 #19508), 0169dl (0.25 #401, 0.05 #21211, 0.05 #19130), 016gr2 (0.25 #195, 0.05 #21005, 0.05 #18924), 03kbb8 (0.25 #1245, 0.05 #22055, 0.05 #19974), 06hhrs (0.25 #275, 0.05 #21085, 0.05 #19004) >> Best rule #114473 for best value: >> intensional similarity = 4 >> extensional distance = 295 >> proper extension: 06mr2s; >> query: (?x6832, ?x541) <- nominated_for(?x541, ?x6832), award_nominee(?x541, ?x2221), category(?x2221, ?x134), participant(?x2221, ?x3536) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #116559 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 313 *> proper extension: 0cwrr; 01b66d; 01j7mr; 0gj50; 030cx; 01b66t; 05_z42; 05gnf; 04mx8h4; 03ctqqf; *> query: (?x6832, ?x5944) <- nominated_for(?x5944, ?x6832), category(?x6832, ?x134), ?x134 = 08mbj5d, film(?x5944, ?x603) *> conf = 0.52 ranks of expected_values: 2 EVAL 03cyslc film! 03q3sy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 58.000 0.602 http://example.org/film/actor/film./film/performance/film #8929-0bmch_x PRED entity: 0bmch_x PRED relation: film! PRED expected values: 0336mc => 126 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1182): 0171cm (0.29 #2504, 0.05 #12911, 0.02 #87836), 07r1h (0.29 #3169, 0.04 #19819, 0.04 #17738), 053xw6 (0.18 #13740, 0.04 #17902, 0.04 #22064), 0prjs (0.17 #218, 0.14 #4380, 0.14 #2299), 06cgy (0.17 #249, 0.14 #4411, 0.08 #18980), 01fh9 (0.17 #315, 0.14 #4477, 0.06 #10722), 0kszw (0.17 #417, 0.14 #4579, 0.06 #10824), 041c4 (0.17 #894, 0.14 #5056, 0.06 #11301), 01s7zw (0.17 #424, 0.14 #4586, 0.06 #10831), 05y5kf (0.17 #861, 0.14 #5023, 0.06 #11268) >> Best rule #2504 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0dmn0x; >> query: (?x4860, 0171cm) <- film_crew_role(?x4860, ?x137), country(?x4860, ?x279), film(?x382, ?x4860), featured_film_locations(?x4860, ?x1646), ?x1646 = 0156q, film_release_region(?x66, ?x279) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3599 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 0dmn0x; *> query: (?x4860, 0336mc) <- film_crew_role(?x4860, ?x137), country(?x4860, ?x279), film(?x382, ?x4860), featured_film_locations(?x4860, ?x1646), ?x1646 = 0156q, film_release_region(?x66, ?x279) *> conf = 0.14 ranks of expected_values: 38 EVAL 0bmch_x film! 0336mc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 126.000 47.000 0.286 http://example.org/film/actor/film./film/performance/film #8928-01gbn6 PRED entity: 01gbn6 PRED relation: award_nominee! PRED expected values: 01pgzn_ => 115 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1370): 0147dk (0.81 #25604, 0.81 #121036, 0.81 #118708), 0q9kd (0.20 #4, 0.05 #6985, 0.02 #16295), 041c4 (0.20 #1183, 0.01 #130346), 0dn44 (0.20 #2257, 0.01 #9238), 03dq9 (0.20 #2135, 0.01 #9116), 07h5d (0.20 #1653, 0.01 #8634), 0dpqk (0.20 #1182, 0.01 #8163), 01mqc_ (0.09 #4001, 0.05 #10982, 0.04 #8655), 030znt (0.09 #2604, 0.02 #65451, 0.02 #51486), 02__7n (0.08 #6293, 0.02 #15602, 0.02 #34228) >> Best rule #25604 for best value: >> intensional similarity = 3 >> extensional distance = 394 >> proper extension: 06w2sn5; 01wgxtl; 01vw20_; 01v40wd; 0bqsy; 01vz0g4; >> query: (?x9526, ?x521) <- award(?x9526, ?x154), award_nominee(?x9526, ?x521), participant(?x9526, ?x5408) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #9804 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 126 *> proper extension: 01yhvv; 03jjzf; 0fb7c; 09h4b5; 01xg_w; *> query: (?x9526, 01pgzn_) <- award(?x9526, ?x1336), film(?x9526, ?x518), ?x1336 = 05pcn59 *> conf = 0.05 ranks of expected_values: 80 EVAL 01gbn6 award_nominee! 01pgzn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 115.000 60.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8927-0flpy PRED entity: 0flpy PRED relation: award_nominee PRED expected values: 01jgkj2 => 154 concepts (57 used for prediction) PRED predicted values (max 10 best out of 772): 019x62 (0.33 #6300, 0.14 #8646, 0.14 #13334), 02qwg (0.25 #768, 0.20 #3111, 0.11 #10146), 01vrnsk (0.25 #1587, 0.20 #3930, 0.11 #10965), 01vrncs (0.25 #223, 0.20 #2566, 0.11 #9601), 01kv4mb (0.25 #452, 0.20 #2795, 0.11 #9830), 0gt_k (0.25 #414, 0.20 #2757, 0.11 #9792), 0pj8m (0.25 #1784, 0.20 #4127, 0.11 #11162), 03bnv (0.25 #751, 0.20 #3094, 0.11 #10129), 07hgkd (0.18 #12876, 0.02 #76150, 0.02 #78495), 01cbt3 (0.17 #5932, 0.14 #8278, 0.05 #12966) >> Best rule #6300 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0136p1; 01pgzn_; 01v0fn1; 0xsk8; >> query: (?x6290, 019x62) <- artist(?x12090, ?x6290), ?x12090 = 03q58q, place_of_birth(?x6290, ?x11968) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #77340 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 178 *> proper extension: 015cxv; *> query: (?x6290, ?x133) <- award_winner(?x342, ?x6290), award(?x6290, ?x1389), award(?x9176, ?x1389), award(?x133, ?x1389), ?x9176 = 01jgkj2 *> conf = 0.01 ranks of expected_values: 431 EVAL 0flpy award_nominee 01jgkj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 154.000 57.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8926-08yx9q PRED entity: 08yx9q PRED relation: type_of_union PRED expected values: 04ztj => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.70 #9, 0.70 #154, 0.70 #121), 01g63y (0.45 #133, 0.20 #14, 0.20 #26) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 08m4c8; 06s6hs; >> query: (?x4391, 04ztj) <- award_nominee(?x4391, ?x5996), award_nominee(?x2061, ?x4391), ?x5996 = 095b70 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08yx9q type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.700 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8925-03rk0 PRED entity: 03rk0 PRED relation: country! PRED expected values: 0bynt 01lb14 => 245 concepts (245 used for prediction) PRED predicted values (max 10 best out of 45): 0bynt (0.87 #2801, 0.86 #2126, 0.86 #6899), 06wrt (0.82 #1274, 0.81 #1949, 0.78 #554), 01lb14 (0.82 #1273, 0.79 #2083, 0.79 #1228), 03hr1p (0.79 #1280, 0.79 #1955, 0.77 #2180), 07jjt (0.74 #559, 0.64 #1954, 0.63 #1684), 064vjs (0.73 #1287, 0.70 #2097, 0.69 #1962), 02y8z (0.71 #1952, 0.68 #1682, 0.67 #1997), 02_5h (0.70 #282, 0.49 #2082, 0.48 #1272), 019tzd (0.70 #1294, 0.64 #2014, 0.64 #1969), 07gyv (0.69 #1807, 0.65 #547, 0.65 #412) >> Best rule #2801 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 09lxtg; >> query: (?x2146, 0bynt) <- country(?x3411, ?x2146), film_release_region(?x80, ?x2146), country(?x1352, ?x2146) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 03rk0 country! 01lb14 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 245.000 245.000 0.872 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03rk0 country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 245.000 245.000 0.872 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #8924-01csvq PRED entity: 01csvq PRED relation: film PRED expected values: 0jsf6 => 100 concepts (69 used for prediction) PRED predicted values (max 10 best out of 961): 0pv2t (0.57 #57127, 0.41 #80338, 0.39 #80337), 0gxsh4 (0.50 #51771, 0.48 #83909, 0.38 #89269), 05h43ls (0.08 #2198, 0.02 #3984, 0.02 #12909), 03cvvlg (0.08 #1441, 0.03 #107127, 0.01 #3571), 0cbv4g (0.08 #916, 0.03 #107127, 0.01 #13412), 04jpg2p (0.08 #1459, 0.02 #5030, 0.02 #6815), 027m5wv (0.08 #1055, 0.01 #13551), 01shy7 (0.07 #3993, 0.07 #5778, 0.05 #9348), 03kx49 (0.06 #19189, 0.01 #122754), 01l_pn (0.05 #2750, 0.03 #4536, 0.03 #20601) >> Best rule #57127 for best value: >> intensional similarity = 3 >> extensional distance = 902 >> proper extension: 03bdm4; 0gqrb; >> query: (?x719, ?x833) <- film(?x719, ?x718), profession(?x719, ?x987), award_winner(?x833, ?x719) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #26777 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 389 *> proper extension: 0162c8; 03n93; 01hrqc; 0knjh; 019n7x; *> query: (?x719, ?x240) <- award_nominee(?x719, ?x2551), participant(?x719, ?x4397), film(?x4397, ?x240) *> conf = 0.03 ranks of expected_values: 120 EVAL 01csvq film 0jsf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 100.000 69.000 0.572 http://example.org/film/actor/film./film/performance/film #8923-07j8kh PRED entity: 07j8kh PRED relation: music! PRED expected values: 01hr1 => 116 concepts (16 used for prediction) PRED predicted values (max 10 best out of 915): 07bzz7 (0.08 #1530, 0.07 #8572, 0.06 #4548), 0gvvm6l (0.08 #5831, 0.02 #6837), 0dgq_kn (0.07 #2614, 0.07 #3620, 0.04 #7644), 0jqp3 (0.07 #2109, 0.03 #8145), 09d3b7 (0.07 #7878, 0.07 #8884, 0.03 #10896), 01s7w3 (0.05 #10925, 0.05 #7907, 0.05 #8913), 08l0x2 (0.05 #7789), 0bmhn (0.05 #916, 0.04 #2928, 0.03 #3934), 0ccd3x (0.05 #452, 0.04 #2464, 0.03 #3470), 0140g4 (0.05 #14, 0.03 #9068, 0.02 #10074) >> Best rule #1530 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0m19t; 01_wfj; >> query: (?x5556, 07bzz7) <- artists(?x10290, ?x5556), ?x10290 = 03ckfl9, category(?x5556, ?x134) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07j8kh music! 01hr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 16.000 0.083 http://example.org/film/film/music #8922-01kp66 PRED entity: 01kp66 PRED relation: award_winner! PRED expected values: 073h1t => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 139): 0hndn2q (0.07 #40, 0.05 #181, 0.05 #463), 0275n3y (0.05 #1063, 0.03 #2332, 0.03 #1204), 09qvms (0.05 #1283, 0.04 #1847, 0.04 #2270), 0gx_st (0.05 #37, 0.04 #7053, 0.04 #7196), 092t4b (0.04 #1322, 0.03 #2027, 0.03 #1886), 05c1t6z (0.04 #7053, 0.04 #7196, 0.04 #7195), 03nnm4t (0.04 #7053, 0.04 #7196, 0.04 #7195), 02q690_ (0.04 #7053, 0.04 #7196, 0.04 #7195), 0hn821n (0.04 #7053, 0.04 #7196, 0.04 #7195), 059x66 (0.04 #7053, 0.04 #7196, 0.04 #7195) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 09pl3f; >> query: (?x4234, 0hndn2q) <- award_nominee(?x539, ?x4234), executive_produced_by(?x4596, ?x4234), award_winner(?x749, ?x4234) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #7053 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1897 *> proper extension: 01wz_ml; 0f6lx; 06lxn; *> query: (?x4234, ?x78) <- award_winner(?x1245, ?x4234), ceremony(?x1245, ?x78), award(?x241, ?x1245) *> conf = 0.04 ranks of expected_values: 55 EVAL 01kp66 award_winner! 073h1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 94.000 94.000 0.068 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8921-0b78hw PRED entity: 0b78hw PRED relation: religion PRED expected values: 0kpl => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 37): 0kpl (0.53 #230, 0.34 #495, 0.31 #142), 0c8wxp (0.36 #3368, 0.35 #4032, 0.34 #3855), 0kq2 (0.23 #149, 0.19 #325, 0.15 #681), 092bf5 (0.17 #59, 0.10 #279, 0.09 #679), 05sfs (0.12 #2964, 0.11 #1506, 0.08 #135), 0n2g (0.12 #2964, 0.10 #101, 0.08 #321), 0g5llry (0.10 #115, 0.01 #2638), 058x5 (0.10 #92), 03j6c (0.07 #3913, 0.07 #3869, 0.07 #4046), 051kv (0.07 #669, 0.05 #269, 0.03 #1377) >> Best rule #230 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0399p; >> query: (?x4308, 0kpl) <- influenced_by(?x4308, ?x7509), ?x7509 = 048cl, gender(?x4308, ?x231), ?x231 = 05zppz >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b78hw religion 0kpl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.526 http://example.org/people/person/religion #8920-02y9bj PRED entity: 02y9bj PRED relation: student PRED expected values: 02p8v8 => 132 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1591): 03ft8 (0.20 #2349, 0.12 #6533, 0.06 #8625), 01hbq0 (0.12 #8334, 0.10 #4150, 0.06 #10426), 02779r4 (0.12 #7440, 0.10 #3256, 0.04 #28362), 03swmf (0.10 #24593, 0.03 #37145, 0.02 #26685), 01l1hr (0.10 #23587, 0.02 #27771, 0.02 #109367), 010hn (0.10 #2460, 0.09 #4552, 0.06 #6644), 01h5f8 (0.10 #4005, 0.09 #6097, 0.06 #8189), 02p8v8 (0.10 #3753, 0.09 #5845, 0.06 #7937), 0ff3y (0.10 #4161, 0.08 #25083, 0.06 #10437), 02cyfz (0.10 #2426, 0.06 #8702, 0.06 #6610) >> Best rule #2349 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01jsk6; >> query: (?x7071, 03ft8) <- school(?x6462, ?x7071), major_field_of_study(?x7071, ?x4321), ?x6462 = 09l0x9, ?x4321 = 0g26h >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3753 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01jsk6; *> query: (?x7071, 02p8v8) <- school(?x6462, ?x7071), major_field_of_study(?x7071, ?x4321), ?x6462 = 09l0x9, ?x4321 = 0g26h *> conf = 0.10 ranks of expected_values: 8 EVAL 02y9bj student 02p8v8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 132.000 94.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #8919-022_q8 PRED entity: 022_q8 PRED relation: award PRED expected values: 02x1dht => 134 concepts (80 used for prediction) PRED predicted values (max 10 best out of 276): 02wkmx (0.70 #30281, 0.70 #27091, 0.69 #23100), 02w_6xj (0.70 #30281, 0.70 #27091, 0.69 #23100), 02wypbh (0.70 #30281, 0.70 #27091, 0.69 #23100), 019f4v (0.67 #862, 0.61 #463, 0.44 #65), 04dn09n (0.43 #440, 0.30 #839, 0.25 #2033), 0gr4k (0.42 #828, 0.35 #429, 0.28 #4410), 09sb52 (0.34 #5612, 0.28 #13574, 0.28 #18357), 0gq9h (0.33 #872, 0.30 #3658, 0.28 #10823), 02x4wr9 (0.26 #529, 0.26 #928, 0.16 #2122), 04g2jz2 (0.22 #223, 0.05 #2214, 0.04 #2612) >> Best rule #30281 for best value: >> intensional similarity = 3 >> extensional distance = 1547 >> proper extension: 02pp_q_; 067jsf; 0l56b; 034bs; 01t265; 015zql; 051m56; 023t0q; 0f6lx; 0f1jhc; ... >> query: (?x5591, ?x13075) <- type_of_union(?x5591, ?x566), award_winner(?x13075, ?x5591), award(?x286, ?x13075) >> conf = 0.70 => this is the best rule for 3 predicted values *> Best rule #451 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 081lh; 0bwh6; 01t07j; 06pj8; 04k25; 01f8ld; 0693l; 0bzyh; 0kvqv; 026dx; ... *> query: (?x5591, 02x1dht) <- nominated_for(?x5591, ?x1863), award_winner(?x1587, ?x5591), profession(?x5591, ?x524), ?x1587 = 02rdyk7 *> conf = 0.13 ranks of expected_values: 49 EVAL 022_q8 award 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 134.000 80.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8918-01cblr PRED entity: 01cblr PRED relation: artists! PRED expected values: 064t9 06by7 => 57 concepts (35 used for prediction) PRED predicted values (max 10 best out of 258): 06by7 (0.96 #4643, 0.80 #3415, 0.79 #5567), 064t9 (0.79 #9257, 0.70 #9564, 0.51 #6793), 0xhtw (0.64 #3101, 0.56 #1247, 0.44 #4638), 02yv6b (0.56 #1329, 0.50 #715, 0.45 #1636), 07sbbz2 (0.50 #623, 0.33 #1237, 0.33 #7), 02w4v (0.50 #659, 0.33 #43, 0.32 #7437), 03_d0 (0.48 #8637, 0.19 #8022, 0.18 #6791), 06924p (0.40 #1096, 0.25 #480, 0.24 #3873), 0155w (0.34 #8732, 0.25 #722, 0.22 #1336), 09n5t_ (0.33 #1441, 0.33 #211, 0.25 #827) >> Best rule #4643 for best value: >> intensional similarity = 8 >> extensional distance = 108 >> proper extension: 01t_xp_; 01pfr3; 0150jk; 02r3zy; 07c0j; 067mj; 01vsxdm; 03t9sp; 0dtd6; 0frsw; ... >> query: (?x4909, 06by7) <- artists(?x1928, ?x4909), artists(?x1928, ?x8344), artists(?x1928, ?x6456), artists(?x1928, ?x5385), group(?x227, ?x4909), ?x6456 = 0k1bs, ?x8344 = 01jfnvd, ?x5385 = 0134tg >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01cblr artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 35.000 0.964 http://example.org/music/genre/artists EVAL 01cblr artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 35.000 0.964 http://example.org/music/genre/artists #8917-01d38t PRED entity: 01d38t PRED relation: ceremony PRED expected values: 09n4nb => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 121): 09n4nb (0.89 #1539, 0.88 #914, 0.75 #1039), 0gx1673 (0.52 #1606, 0.50 #981, 0.50 #355), 0bzm81 (0.16 #2142, 0.10 #3142, 0.10 #3267), 0n8_m93 (0.16 #2229, 0.10 #3229, 0.10 #3354), 02yvhx (0.15 #2192, 0.10 #3192, 0.09 #3317), 02hn5v (0.15 #2158, 0.10 #3158, 0.09 #3283), 02yxh9 (0.15 #2212, 0.10 #3212, 0.09 #3337), 0bc773 (0.15 #2170, 0.10 #3170, 0.09 #3295), 02yw5r (0.15 #2134, 0.10 #3134, 0.09 #3259), 0bvfqq (0.15 #2151, 0.10 #3151, 0.09 #3276) >> Best rule #1539 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 024fz9; >> query: (?x9462, 09n4nb) <- award(?x2395, ?x9462), ceremony(?x9462, ?x2054), ?x2054 = 0gpjbt, award_nominee(?x2395, ?x1060) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d38t ceremony 09n4nb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.890 http://example.org/award/award_category/winners./award/award_honor/ceremony #8916-0b1hw PRED entity: 0b1hw PRED relation: artist! PRED expected values: 01cl0d => 124 concepts (99 used for prediction) PRED predicted values (max 10 best out of 126): 033hn8 (0.43 #294, 0.33 #854, 0.25 #2535), 041n43 (0.33 #253, 0.05 #3475, 0.04 #4736), 01qy6m (0.33 #229, 0.04 #6309, 0.02 #1909), 03rhqg (0.31 #1836, 0.22 #2257, 0.21 #716), 017l96 (0.29 #439, 0.29 #299, 0.22 #579), 015_1q (0.29 #300, 0.28 #2121, 0.25 #2541), 01w40h (0.29 #449, 0.25 #29, 0.19 #1569), 0181dw (0.29 #463, 0.17 #183, 0.16 #2704), 01dtcb (0.29 #328, 0.17 #188, 0.16 #1588), 0k_kr (0.29 #465, 0.12 #1305, 0.12 #2006) >> Best rule #294 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01gf5h; 01d1st; >> query: (?x10737, 033hn8) <- origin(?x10737, ?x5267), award(?x10737, ?x3103), ?x5267 = 0d9jr >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #196 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 02y7sr; *> query: (?x10737, 01cl0d) <- origin(?x10737, ?x5267), artist(?x12061, ?x10737), artists(?x302, ?x10737), ?x12061 = 012b30 *> conf = 0.17 ranks of expected_values: 25 EVAL 0b1hw artist! 01cl0d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 124.000 99.000 0.429 http://example.org/music/record_label/artist #8915-09v8db5 PRED entity: 09v8db5 PRED relation: award_winner PRED expected values: 065d1h => 39 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1374): 02mz_6 (0.42 #17314, 0.31 #17313, 0.29 #42058), 0pksh (0.33 #2402, 0.31 #17313, 0.29 #42058), 069_0y (0.33 #1685, 0.17 #4157, 0.13 #4945), 03_2y (0.33 #2133, 0.17 #4605, 0.13 #4945), 012d40 (0.33 #15, 0.17 #2487, 0.06 #12367), 07ftc0 (0.31 #17313, 0.29 #42058, 0.28 #49482), 054k_8 (0.31 #17313, 0.29 #42058, 0.28 #49482), 02nfjp (0.31 #17313, 0.29 #42058, 0.28 #49482), 0m9v7 (0.31 #17313, 0.29 #42058, 0.28 #49482), 04dz_y7 (0.17 #4861, 0.11 #24740, 0.11 #27215) >> Best rule #17314 for best value: >> intensional similarity = 4 >> extensional distance = 152 >> proper extension: 02f6yz; 03nl5k; >> query: (?x5923, ?x7216) <- award(?x7216, ?x5923), award_winner(?x7215, ?x7216), award_winner(?x5923, ?x11657), music(?x3376, ?x7216) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #24740 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 171 *> proper extension: 0m7yy; 02wwsh8; 03ybrwc; 02vl9ln; *> query: (?x5923, ?x541) <- award(?x7502, ?x5923), award(?x3376, ?x5923), nominated_for(?x541, ?x3376), film_release_region(?x7502, ?x87), film_release_distribution_medium(?x7502, ?x81) *> conf = 0.11 ranks of expected_values: 19 EVAL 09v8db5 award_winner 065d1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 39.000 20.000 0.422 http://example.org/award/award_category/winners./award/award_honor/award_winner #8914-0jwvf PRED entity: 0jwvf PRED relation: film_art_direction_by PRED expected values: 07hhnl => 121 concepts (84 used for prediction) PRED predicted values (max 10 best out of 22): 05v1sb (0.18 #88, 0.11 #61, 0.07 #632), 0fqjks (0.18 #97, 0.11 #70, 0.02 #152), 07hhnl (0.17 #9, 0.11 #63, 0.09 #90), 0dh73w (0.17 #6, 0.11 #60, 0.09 #87), 071jv5 (0.05 #514, 0.02 #650, 0.01 #405), 05b2f_k (0.04 #263, 0.03 #806, 0.03 #317), 05683cn (0.03 #403, 0.02 #648, 0.02 #757), 072twv (0.02 #220, 0.02 #628, 0.01 #1338), 07h5d (0.02 #232, 0.01 #558), 0523v5y (0.02 #642, 0.01 #996, 0.01 #669) >> Best rule #88 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0p9tm; >> query: (?x5856, 05v1sb) <- award(?x5856, ?x3902), nominated_for(?x1708, ?x5856), film_release_region(?x5856, ?x94), film_sets_designed(?x2716, ?x5856) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 05z7c; 0k5g9; 02r_pp; *> query: (?x5856, 07hhnl) <- genre(?x5856, ?x600), nominated_for(?x601, ?x5856), nominated_for(?x4136, ?x5856), ?x4136 = 02jr6k *> conf = 0.17 ranks of expected_values: 3 EVAL 0jwvf film_art_direction_by 07hhnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 121.000 84.000 0.182 http://example.org/film/film/film_art_direction_by #8913-072192 PRED entity: 072192 PRED relation: film! PRED expected values: 019l68 => 72 concepts (34 used for prediction) PRED predicted values (max 10 best out of 677): 02cqbx (0.62 #24999, 0.51 #33337, 0.51 #37505), 012vct (0.49 #24998, 0.46 #62508, 0.43 #39589), 02wb6d (0.49 #24998, 0.43 #39589, 0.43 #16665), 019l68 (0.42 #29166, 0.42 #20831, 0.31 #33339), 013sg6 (0.25 #1639, 0.04 #7888, 0.03 #9971), 0gnbw (0.25 #1272, 0.02 #20019, 0.02 #30438), 014zn0 (0.25 #1932, 0.01 #8181, 0.01 #10264), 015vq_ (0.25 #715, 0.01 #17380, 0.01 #27798), 06ltr (0.25 #948, 0.01 #17613), 06cgy (0.10 #2334, 0.05 #16916, 0.04 #10666) >> Best rule #24999 for best value: >> intensional similarity = 4 >> extensional distance = 324 >> proper extension: 07k2mq; >> query: (?x9100, ?x5611) <- award_winner(?x9100, ?x5611), award_nominee(?x5611, ?x2109), religion(?x5611, ?x1985), country(?x9100, ?x94) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #29166 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 374 *> proper extension: 0b60sq; 09rfpk; *> query: (?x9100, ?x5611) <- genre(?x9100, ?x1403), nominated_for(?x5611, ?x9100), ?x1403 = 02l7c8 *> conf = 0.42 ranks of expected_values: 4 EVAL 072192 film! 019l68 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 34.000 0.618 http://example.org/film/actor/film./film/performance/film #8912-043mk4y PRED entity: 043mk4y PRED relation: titles! PRED expected values: 03mdt => 92 concepts (67 used for prediction) PRED predicted values (max 10 best out of 62): 01z77k (0.35 #58, 0.01 #4378, 0.01 #5087), 03mdt (0.29 #44, 0.03 #2446, 0.03 #2849), 01f9r0 (0.25 #199, 0.23 #3106, 0.23 #5432), 01z4y (0.24 #335, 0.22 #734, 0.21 #234), 04xvlr (0.23 #3311, 0.23 #3816, 0.23 #4624), 017fp (0.23 #122, 0.12 #23, 0.11 #1922), 07c52 (0.18 #29, 0.12 #2331, 0.11 #2431), 01hmnh (0.17 #725, 0.14 #2731, 0.13 #526), 0jtdp (0.14 #220, 0.07 #321, 0.02 #1719), 01jfsb (0.13 #2724, 0.13 #718, 0.13 #3428) >> Best rule #58 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 03j63k; >> query: (?x7768, 01z77k) <- nominated_for(?x375, ?x7768), titles(?x53, ?x7768), ?x375 = 0bfvw2 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #44 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 03j63k; *> query: (?x7768, 03mdt) <- nominated_for(?x375, ?x7768), titles(?x53, ?x7768), ?x375 = 0bfvw2 *> conf = 0.29 ranks of expected_values: 2 EVAL 043mk4y titles! 03mdt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 67.000 0.353 http://example.org/media_common/netflix_genre/titles #8911-09qycb PRED entity: 09qycb PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.86 #56, 0.86 #31, 0.84 #46), 07z4p (0.08 #66, 0.06 #35, 0.06 #45), 07c52 (0.08 #33, 0.07 #43, 0.07 #48), 02nxhr (0.04 #168, 0.04 #183, 0.04 #193), 0735l (0.01 #65) >> Best rule #56 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 0cq8nx; >> query: (?x10349, 029j_) <- film(?x2733, ?x10349), films(?x326, ?x10349), cinematography(?x10349, ?x10078), nominated_for(?x968, ?x10349) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09qycb film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.863 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #8910-01l1rw PRED entity: 01l1rw PRED relation: music! PRED expected values: 01b195 07sgdw 014bpd 01d2v1 015gm8 => 120 concepts (102 used for prediction) PRED predicted values (max 10 best out of 819): 034xyf (0.12 #30185, 0.11 #31192, 0.10 #12073), 07bzz7 (0.11 #523, 0.04 #12596, 0.04 #3541), 03h3x5 (0.05 #255, 0.03 #4279, 0.03 #12328), 09cxm4 (0.05 #809, 0.03 #4833, 0.02 #8857), 0_7w6 (0.05 #183, 0.03 #4207, 0.02 #8231), 078mm1 (0.05 #822, 0.03 #4846, 0.02 #8870), 0y_pg (0.05 #782, 0.03 #4806, 0.02 #8830), 0h3k3f (0.05 #841, 0.03 #6877, 0.02 #19957), 05qm9f (0.05 #673, 0.03 #6709, 0.02 #12746), 015x74 (0.05 #174, 0.02 #8222, 0.02 #9228) >> Best rule #30185 for best value: >> intensional similarity = 3 >> extensional distance = 379 >> proper extension: 0kx4m; 0hpt3; 01795t; 046b0s; 03mdt; 0ccd3x; 056ws9; 020h2v; 02x2097; 032j_n; ... >> query: (?x5720, ?x2826) <- award(?x5720, ?x1079), nominated_for(?x5720, ?x2826), category(?x5720, ?x134) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01l1rw music! 015gm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 102.000 0.121 http://example.org/film/film/music EVAL 01l1rw music! 01d2v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 102.000 0.121 http://example.org/film/film/music EVAL 01l1rw music! 014bpd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 102.000 0.121 http://example.org/film/film/music EVAL 01l1rw music! 07sgdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 102.000 0.121 http://example.org/film/film/music EVAL 01l1rw music! 01b195 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 102.000 0.121 http://example.org/film/film/music #8909-07147 PRED entity: 07147 PRED relation: school PRED expected values: 01n6r0 => 74 concepts (65 used for prediction) PRED predicted values (max 10 best out of 604): 065y4w7 (0.88 #6449, 0.50 #1080, 0.48 #8605), 07szy (0.62 #2341, 0.45 #3054, 0.40 #1626), 0f1nl (0.60 #1458, 0.33 #566, 0.33 #209), 01jq0j (0.54 #4044, 0.46 #3863, 0.40 #1537), 0trv (0.50 #1382, 0.50 #1203, 0.38 #4067), 06pwq (0.50 #1078, 0.44 #2507, 0.44 #4478), 02pptm (0.50 #1209, 0.40 #1745, 0.38 #4251), 01dzg0 (0.50 #1226, 0.33 #2833, 0.33 #2655), 0bx8pn (0.50 #1094, 0.33 #2523, 0.33 #23), 03tw2s (0.50 #1174, 0.33 #282, 0.33 #103) >> Best rule #6449 for best value: >> intensional similarity = 18 >> extensional distance = 22 >> proper extension: 05g3v; >> query: (?x8111, 065y4w7) <- team(?x2010, ?x8111), school(?x8111, ?x6602), school(?x8111, ?x5288), sport(?x8111, ?x5063), draft(?x8111, ?x1161), fraternities_and_sororities(?x5288, ?x3697), major_field_of_study(?x5288, ?x8962), major_field_of_study(?x5288, ?x2606), major_field_of_study(?x5288, ?x254), ?x254 = 02h40lc, major_field_of_study(?x7546, ?x2606), major_field_of_study(?x5750, ?x2606), school_type(?x6602, ?x3092), contains(?x94, ?x5288), ?x8962 = 04g7x, student(?x5288, ?x460), ?x7546 = 01_qgp, ?x5750 = 01nnsv >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #73 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 1 *> proper extension: 0jmj7; *> query: (?x8111, 01n6r0) <- team(?x2010, ?x8111), school(?x8111, ?x10838), school(?x8111, ?x6602), school(?x8111, ?x3779), school(?x8111, ?x1011), student(?x6602, ?x3025), institution(?x620, ?x6602), ?x10838 = 016sd3, colors(?x6602, ?x332), ?x332 = 01l849, contains(?x177, ?x6602), ?x3779 = 01pq4w, major_field_of_study(?x1011, ?x254), institution(?x734, ?x1011), student(?x1011, ?x400), state_province_region(?x388, ?x177) *> conf = 0.33 ranks of expected_values: 81 EVAL 07147 school 01n6r0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 74.000 65.000 0.875 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #8908-0bv7t PRED entity: 0bv7t PRED relation: award_winner! PRED expected values: 0c_dx => 150 concepts (124 used for prediction) PRED predicted values (max 10 best out of 347): 04hddx (0.44 #430, 0.37 #30911, 0.35 #42499), 0g9wd99 (0.44 #430, 0.37 #30911, 0.35 #42499), 0grw_ (0.44 #430, 0.37 #30911, 0.35 #42499), 01by1l (0.15 #32740, 0.09 #35745, 0.07 #1829), 0c_n9 (0.14 #102, 0.07 #1819, 0.05 #27477), 01yz0x (0.14 #173, 0.07 #6612, 0.06 #18200), 06196 (0.14 #342, 0.05 #6781, 0.05 #4206), 040_9s0 (0.14 #313, 0.05 #6752, 0.05 #10184), 0265vt (0.14 #320, 0.05 #15771, 0.05 #18347), 047xyn (0.14 #225, 0.05 #15676, 0.05 #18252) >> Best rule #430 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0mb0; >> query: (?x5261, ?x8842) <- award(?x5261, ?x8842), influenced_by(?x5261, ?x5262), ?x5262 = 080r3, location(?x5261, ?x177) >> conf = 0.44 => this is the best rule for 3 predicted values *> Best rule #4566 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 0fx02; *> query: (?x5261, 0c_dx) <- story_by(?x11416, ?x5261), profession(?x5261, ?x353), nationality(?x5261, ?x94), cinematography(?x11416, ?x10542) *> conf = 0.05 ranks of expected_values: 84 EVAL 0bv7t award_winner! 0c_dx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 150.000 124.000 0.444 http://example.org/award/award_category/winners./award/award_honor/award_winner #8907-026f__m PRED entity: 026f__m PRED relation: gender PRED expected values: 05zppz => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.22 #235, 0.20 #233, 0.04 #85), 02zsn (0.07 #234, 0.06 #236) >> Best rule #235 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x7728, 05zppz) <- >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026f__m gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.217 http://example.org/people/person/gender #8906-087wc7n PRED entity: 087wc7n PRED relation: film_release_region PRED expected values: 09c7w0 06npd 05sb1 016wzw 01crd5 => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 124): 09c7w0 (0.92 #4832, 0.92 #4963, 0.92 #5225), 03rj0 (0.74 #171, 0.65 #692, 0.64 #1342), 06t8v (0.66 #187, 0.51 #578, 0.49 #1358), 016wzw (0.63 #698, 0.59 #568, 0.53 #177), 06f32 (0.57 #567, 0.54 #697, 0.53 #176), 06qd3 (0.54 #938, 0.53 #1198, 0.53 #1458), 06mzp (0.49 #145, 0.46 #926, 0.46 #536), 0h7x (0.44 #154, 0.41 #675, 0.41 #545), 047lj (0.44 #529, 0.43 #138, 0.38 #659), 07ylj (0.41 #541, 0.37 #671, 0.34 #150) >> Best rule #4832 for best value: >> intensional similarity = 7 >> extensional distance = 1323 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; 018js4; ... >> query: (?x791, 09c7w0) <- film_release_region(?x791, ?x304), olympics(?x304, ?x391), contains(?x304, ?x5168), film_release_region(?x8162, ?x304), film_release_region(?x5139, ?x304), ?x8162 = 0bs8ndx, ?x5139 = 07bzz7 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 11, 14, 16 EVAL 087wc7n film_release_region 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 60.000 60.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 087wc7n film_release_region 016wzw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 60.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 087wc7n film_release_region 05sb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 60.000 60.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 087wc7n film_release_region 06npd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 60.000 60.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 087wc7n film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8905-0h95927 PRED entity: 0h95927 PRED relation: nominated_for! PRED expected values: 0f4x7 094qd5 0gs9p 02x4sn8 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 176): 0gqwc (0.78 #4502, 0.73 #429, 0.68 #3214), 02x4x18 (0.73 #429, 0.68 #3214, 0.68 #2785), 02x4sn8 (0.73 #429, 0.68 #3214, 0.68 #2785), 027c95y (0.73 #429, 0.68 #3214, 0.68 #2785), 0gs9p (0.65 #267, 0.58 #482, 0.54 #910), 027dtxw (0.63 #433, 0.59 #218, 0.38 #861), 02x73k6 (0.53 #254, 0.53 #469, 0.27 #897), 019f4v (0.53 #259, 0.47 #474, 0.46 #902), 02ppm4q (0.53 #310, 0.47 #525, 0.35 #953), 099t8j (0.47 #299, 0.43 #942, 0.42 #514) >> Best rule #4502 for best value: >> intensional similarity = 4 >> extensional distance = 587 >> proper extension: 06w7mlh; 07bz5; >> query: (?x7651, ?x618) <- nominated_for(?x1445, ?x7651), award_winner(?x7651, ?x3462), award(?x7651, ?x618), ceremony(?x618, ?x873) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #429 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 03cvvlg; 0h1x5f; *> query: (?x7651, ?x1245) <- nominated_for(?x3066, ?x7651), nominated_for(?x995, ?x7651), ?x995 = 099tbz, ?x3066 = 0gqy2, award(?x7651, ?x1245) *> conf = 0.73 ranks of expected_values: 3, 5, 21, 46 EVAL 0h95927 nominated_for! 02x4sn8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 64.000 64.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0h95927 nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 64.000 64.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0h95927 nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 64.000 64.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0h95927 nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 64.000 64.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8904-0crlz PRED entity: 0crlz PRED relation: olympics PRED expected values: 018wrk => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 34): 06sks6 (0.84 #1063, 0.81 #1027, 0.80 #914), 0jhn7 (0.69 #33, 0.67 #1154, 0.62 #583), 0l6mp (0.69 #33, 0.67 #466, 0.58 #229), 0sxrz (0.69 #33, 0.62 #541, 0.55 #228), 0l6m5 (0.69 #33, 0.58 #229, 0.55 #977), 0jkvj (0.69 #33, 0.58 #229, 0.55 #523), 0l6ny (0.69 #33, 0.58 #229, 0.55 #228), 016r9z (0.69 #33, 0.58 #229, 0.55 #228), 0lv1x (0.69 #33, 0.58 #229, 0.55 #228), 09x3r (0.69 #33, 0.58 #229, 0.55 #228) >> Best rule #1063 for best value: >> intensional similarity = 51 >> extensional distance = 30 >> proper extension: 07rlg; 07gyv; 0w0d; 03_8r; 019w9j; 06z68; 07jbh; 019tzd; 07_53; 0194d; >> query: (?x5182, 06sks6) <- sports(?x4255, ?x5182), sports(?x2553, ?x5182), sports(?x1608, ?x5182), sports(?x4255, ?x2885), sports(?x4255, ?x1121), participating_countries(?x4255, ?x7430), participating_countries(?x4255, ?x2629), medal(?x2553, ?x422), olympics(?x5182, ?x778), participating_countries(?x2553, ?x172), film_release_region(?x9941, ?x172), film_release_region(?x8292, ?x172), film_release_region(?x8176, ?x172), film_release_region(?x5877, ?x172), film_release_region(?x5564, ?x172), film_release_region(?x4422, ?x172), film_release_region(?x3784, ?x172), film_release_region(?x3514, ?x172), film_release_region(?x3482, ?x172), film_release_region(?x2655, ?x172), film_release_region(?x1252, ?x172), film_release_region(?x972, ?x172), ?x1121 = 0bynt, country(?x5182, ?x1603), ?x1252 = 02c6d, olympics(?x1310, ?x1608), ?x1603 = 06bnz, locations(?x1608, ?x1646), ?x3482 = 017z49, nationality(?x2691, ?x172), ?x972 = 017gl1, ?x8292 = 0cmf0m0, ?x3784 = 0bmhvpr, combatants(?x456, ?x172), sports(?x2553, ?x1352), country(?x4826, ?x172), participating_countries(?x1608, ?x410), ?x5877 = 02qyv3h, ?x2885 = 07jjt, official_language(?x172, ?x5607), ?x5564 = 03yvf2, ?x9941 = 024lt6, jurisdiction_of_office(?x182, ?x172), ?x3514 = 04vh83, ?x8176 = 0gvvm6l, adjoins(?x2517, ?x7430), ?x2655 = 0fpmrm3, olympics(?x7430, ?x1617), contains(?x2629, ?x10324), ?x4422 = 06zn2v2, country(?x668, ?x2629) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #157 for first EXPECTED value: *> intensional similarity = 59 *> extensional distance = 2 *> proper extension: 02bkg; *> query: (?x5182, 018wrk) <- sports(?x7688, ?x5182), sports(?x6464, ?x5182), sports(?x5395, ?x5182), sports(?x4255, ?x5182), sports(?x3729, ?x5182), sports(?x2553, ?x5182), sports(?x2369, ?x5182), sports(?x1081, ?x5182), ?x4255 = 0lgxj, country(?x5182, ?x2979), country(?x5182, ?x205), olympics(?x1121, ?x2553), participating_countries(?x2553, ?x2146), participating_countries(?x2553, ?x1229), participating_countries(?x2553, ?x279), sports(?x2553, ?x2044), sports(?x2553, ?x779), ?x7688 = 0jkvj, ?x279 = 0d060g, ?x779 = 096f8, ?x205 = 03rjj, ?x2044 = 06f41, ?x1081 = 0l6m5, ?x3729 = 0jdk_, olympics(?x5182, ?x7441), olympics(?x5182, ?x1931), ?x2146 = 03rk0, ?x1931 = 0kbws, ?x5395 = 018qb4, adjoins(?x6435, ?x2979), ?x7441 = 0ldqf, locations(?x6464, ?x6959), olympics(?x252, ?x6464), film_release_region(?x9565, ?x1229), film_release_region(?x9349, ?x1229), film_release_region(?x5644, ?x1229), film_release_region(?x5400, ?x1229), film_release_region(?x3619, ?x1229), film_release_region(?x2954, ?x1229), film_release_region(?x2598, ?x1229), film_release_region(?x1724, ?x1229), film_release_region(?x1496, ?x1229), film_release_region(?x1022, ?x1229), ?x3619 = 0fphgb, second_level_divisions(?x1229, ?x3408), ?x9349 = 0jdr0, country(?x3407, ?x1229), ?x1022 = 0crfwmx, nationality(?x731, ?x1229), ?x5400 = 0bhwhj, ?x5644 = 0dll_t2, ?x2598 = 07f_7h, ?x9565 = 0hz6mv2, ?x1496 = 011yqc, olympics(?x2979, ?x2966), ?x1724 = 02r8hh_, ?x2369 = 0lbbj, ?x2954 = 0crh5_f, country(?x1009, ?x1229) *> conf = 0.50 ranks of expected_values: 17 EVAL 0crlz olympics 018wrk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 36.000 36.000 0.844 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #8903-0dq630k PRED entity: 0dq630k PRED relation: role PRED expected values: 05148p4 02k856 => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 108): 01vdm0 (0.86 #3881, 0.84 #2670, 0.84 #3343), 013y1f (0.85 #1632, 0.83 #2138, 0.83 #2060), 0g2dz (0.83 #1413, 0.80 #1630, 0.73 #1844), 05148p4 (0.82 #1919, 0.81 #1380, 0.81 #4176), 05r5c (0.82 #1919, 0.81 #1380, 0.81 #4176), 0319l (0.82 #1919, 0.81 #1380, 0.81 #4176), 02k856 (0.82 #1919, 0.81 #1380, 0.81 #4176), 018j2 (0.82 #1919, 0.81 #1380, 0.81 #4176), 02qjv (0.82 #1919, 0.81 #1380, 0.81 #4176), 01s0ps (0.82 #1010, 0.78 #1439, 0.77 #1870) >> Best rule #3881 for best value: >> intensional similarity = 21 >> extensional distance = 57 >> proper extension: 0bmnm; >> query: (?x2205, 01vdm0) <- role(?x314, ?x2205), role(?x212, ?x2205), ?x314 = 02sgy, role(?x4239, ?x2205), award_nominee(?x366, ?x4239), role(?x2205, ?x885), role(?x8048, ?x212), role(?x212, ?x5990), role(?x212, ?x4769), role(?x212, ?x3409), ?x5990 = 0192l, instrumentalists(?x212, ?x6577), instrumentalists(?x212, ?x642), ?x3409 = 0680x0, ?x4769 = 0dwt5, ?x6577 = 0gs6vr, award_winner(?x4239, ?x4343), role(?x74, ?x885), artists(?x302, ?x8048), performance_role(?x3399, ?x212), ?x642 = 032t2z >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1919 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 20 *> proper extension: 0jtg0; *> query: (?x2205, ?x212) <- role(?x1574, ?x2205), role(?x314, ?x2205), role(?x212, ?x2205), ?x314 = 02sgy, role(?x4239, ?x2205), award_nominee(?x366, ?x4239), category(?x4239, ?x134), award(?x4239, ?x4481), profession(?x4239, ?x220), role(?x2205, ?x227), award_winner(?x4481, ?x4940), ?x4940 = 09swkk, ?x1574 = 0l15bq, ceremony(?x4481, ?x762), location(?x4239, ?x335), ?x134 = 08mbj5d *> conf = 0.82 ranks of expected_values: 4, 7 EVAL 0dq630k role 02k856 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 50.000 50.000 0.864 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 0dq630k role 05148p4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 50.000 50.000 0.864 http://example.org/music/performance_role/track_performances./music/track_contribution/role #8902-03xnwz PRED entity: 03xnwz PRED relation: artists PRED expected values: 03fbc => 58 concepts (28 used for prediction) PRED predicted values (max 10 best out of 959): 01vvycq (0.71 #2194, 0.50 #5416, 0.31 #9715), 03f5spx (0.71 #2205, 0.50 #5427, 0.29 #9726), 06mt91 (0.57 #2754, 0.50 #5976, 0.40 #607), 01gf5h (0.57 #2210, 0.40 #9731, 0.40 #5432), 016vn3 (0.57 #3082, 0.40 #8455, 0.40 #6304), 01vrt_c (0.57 #2224, 0.40 #9745, 0.40 #5446), 03t9sp (0.57 #2270, 0.40 #5492, 0.30 #7643), 011z3g (0.57 #2746, 0.40 #5968, 0.29 #10267), 01dwrc (0.57 #2666, 0.40 #5888, 0.28 #3221), 01vvyfh (0.57 #2487, 0.40 #5709, 0.28 #3221) >> Best rule #2194 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 016clz; 064t9; 05bt6j; 0y3_8; 06j6l; >> query: (?x2542, 01vvycq) <- artists(?x2542, ?x4713), parent_genre(?x2542, ?x302), ?x4713 = 0czkbt, parent_genre(?x8385, ?x2542), artists(?x302, ?x8156), ?x8156 = 046p9 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2349 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 016clz; 064t9; 05bt6j; 0y3_8; 06j6l; *> query: (?x2542, 03fbc) <- artists(?x2542, ?x4713), parent_genre(?x2542, ?x302), ?x4713 = 0czkbt, parent_genre(?x8385, ?x2542), artists(?x302, ?x8156), ?x8156 = 046p9 *> conf = 0.43 ranks of expected_values: 24 EVAL 03xnwz artists 03fbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 58.000 28.000 0.714 http://example.org/music/genre/artists #8901-027tbrc PRED entity: 027tbrc PRED relation: country_of_origin PRED expected values: 09c7w0 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.95 #122, 0.94 #146, 0.94 #114), 03_3d (0.11 #27, 0.11 #180, 0.10 #83), 0ctw_b (0.07 #465, 0.06 #583, 0.06 #383), 03rjj (0.03 #18, 0.02 #42, 0.02 #34), 02jx1 (0.03 #32, 0.02 #80, 0.01 #177), 04jpl (0.03 #29), 0kctd (0.03 #97), 07c52 (0.03 #97), 05v8c (0.01 #184, 0.01 #232, 0.01 #249) >> Best rule #122 for best value: >> intensional similarity = 3 >> extensional distance = 150 >> proper extension: 017dcd; 05sy2k_; 0cpz4k; 01p4wv; 099pks; 05f7w84; 03gvm3t; 01yb1y; 03y317; 097h2; ... >> query: (?x2447, 09c7w0) <- country_of_origin(?x2447, ?x279), genre(?x2447, ?x53), program(?x1630, ?x2447) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027tbrc country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.954 http://example.org/tv/tv_program/country_of_origin #8900-03lvwp PRED entity: 03lvwp PRED relation: country PRED expected values: 07ssc => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 108): 07ssc (0.47 #256, 0.43 #498, 0.37 #316), 0f8l9c (0.25 #482, 0.13 #501, 0.10 #439), 0chghy (0.25 #482, 0.06 #252, 0.04 #1280), 03spz (0.25 #114, 0.14 #234, 0.01 #3676), 04xvlr (0.25 #544, 0.12 #543, 0.06 #967), 0345h (0.14 #327, 0.12 #2559, 0.11 #2139), 07s9rl0 (0.12 #543, 0.06 #967, 0.06 #2774), 0d060g (0.06 #248, 0.05 #368, 0.04 #2540), 0ctw_b (0.06 #263, 0.02 #383, 0.02 #688), 03rk0 (0.06 #279, 0.01 #945, 0.01 #3676) >> Best rule #256 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 04sskp; >> query: (?x6020, 07ssc) <- film(?x11364, ?x6020), film(?x9055, ?x6020), actor(?x5684, ?x9055), ?x11364 = 016ggh, award(?x9055, ?x1245) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03lvwp country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.471 http://example.org/film/film/country #8899-0gl5_ PRED entity: 0gl5_ PRED relation: major_field_of_study PRED expected values: 0g26h => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 90): 06ms6 (0.55 #1761, 0.23 #126, 0.21 #236), 04rjg (0.43 #239, 0.38 #129, 0.31 #459), 01lj9 (0.42 #144, 0.38 #254, 0.26 #474), 037mh8 (0.42 #170, 0.32 #280, 0.19 #720), 04sh3 (0.38 #288, 0.30 #178, 0.15 #728), 05qjt (0.36 #228, 0.33 #118, 0.25 #1658), 0g26h (0.35 #1357, 0.35 #1577, 0.34 #477), 01tbp (0.34 #274, 0.23 #494, 0.20 #164), 0fdys (0.33 #143, 0.32 #253, 0.21 #1683), 02h40lc (0.29 #224, 0.17 #114, 0.16 #1654) >> Best rule #1761 for best value: >> intensional similarity = 2 >> extensional distance = 191 >> proper extension: 0194_r; >> query: (?x6912, ?x10518) <- student(?x6912, ?x10578), student(?x10518, ?x10578) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1357 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 173 *> proper extension: 0frm7n; *> query: (?x6912, 0g26h) <- category(?x6912, ?x134), school(?x2820, ?x6912) *> conf = 0.35 ranks of expected_values: 7 EVAL 0gl5_ major_field_of_study 0g26h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 52.000 52.000 0.545 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8898-04sylm PRED entity: 04sylm PRED relation: educational_institution PRED expected values: 04sylm => 154 concepts (142 used for prediction) PRED predicted values (max 10 best out of 308): 017z88 (0.16 #47998, 0.10 #34515, 0.07 #64190), 03qdm (0.16 #47998, 0.10 #34515, 0.07 #64190), 04sylm (0.16 #47998, 0.10 #34515, 0.07 #64190), 03fgm (0.16 #47998, 0.10 #34515, 0.04 #1992), 031n5b (0.16 #47998, 0.10 #34515), 05njyy (0.07 #64190, 0.06 #153, 0.05 #692), 09k9d0 (0.07 #64190, 0.06 #457, 0.05 #996), 01n951 (0.07 #64190, 0.06 #265, 0.05 #804), 02lv2v (0.07 #64190, 0.06 #293, 0.05 #832), 01p7x7 (0.07 #64190, 0.05 #960, 0.05 #58790) >> Best rule #47998 for best value: >> intensional similarity = 4 >> extensional distance = 349 >> proper extension: 027xx3; 0pmcz; 0ny75; 01w_sh; 01tpvt; 08qs09; 0lvng; 0m7yh; 01hx2t; 01wqg8; ... >> query: (?x2767, ?x9612) <- student(?x2767, ?x9891), institution(?x1526, ?x2767), school_type(?x2767, ?x1044), student(?x9612, ?x9891) >> conf = 0.16 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 04sylm educational_institution 04sylm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 154.000 142.000 0.163 http://example.org/education/educational_institution_campus/educational_institution #8897-02mg7n PRED entity: 02mg7n PRED relation: student PRED expected values: 0170qf => 172 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1845): 015wnl (0.33 #613, 0.09 #8982, 0.04 #19443), 022_q8 (0.33 #979, 0.05 #15625, 0.02 #38640), 0171cm (0.33 #393, 0.03 #21315, 0.02 #31776), 01qn8k (0.33 #1606, 0.03 #75327, 0.02 #87890), 04rsd2 (0.33 #382, 0.03 #75327, 0.02 #87890), 01fwf1 (0.33 #868, 0.03 #75327, 0.02 #87890), 03f4w4 (0.33 #2034, 0.03 #75327, 0.02 #87890), 0884fm (0.33 #722, 0.03 #75327, 0.02 #87890), 06z8gn (0.33 #1520, 0.03 #24534, 0.01 #93595), 0prfz (0.33 #43, 0.02 #92118, 0.02 #94212) >> Best rule #613 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 015nl4; >> query: (?x11306, 015wnl) <- student(?x11306, ?x11054), student(?x11306, ?x1549), nationality(?x11054, ?x512), participant(?x9604, ?x11054), ?x1549 = 09y20 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #343 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 015nl4; *> query: (?x11306, 0170qf) <- student(?x11306, ?x11054), student(?x11306, ?x1549), nationality(?x11054, ?x512), participant(?x9604, ?x11054), ?x1549 = 09y20 *> conf = 0.33 ranks of expected_values: 49 EVAL 02mg7n student 0170qf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 172.000 57.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #8896-02hnl PRED entity: 02hnl PRED relation: group PRED expected values: 0m19t 03g5jw 02_5x9 0134s5 04qmr 01j59b0 02mq_y 0dw4g 03d9d6 013w2r 0123r4 01q99h 015cxv 0ycp3 02vgh 07r1_ 01jcxwp 048xh 089pg7 046p9 0jn38 07rnh 011_vz 01shhf 017959 01fchy 01_wfj 0fb2l 01dpts 03q_w5 => 79 concepts (61 used for prediction) PRED predicted values (max 10 best out of 868): 01q99h (0.67 #670, 0.62 #574, 0.50 #435), 013w2r (0.67 #433, 0.60 #341, 0.57 #480), 01fchy (0.62 #641, 0.62 #592, 0.56 #688), 048xh (0.60 #352, 0.60 #305, 0.50 #213), 0123r4 (0.60 #342, 0.56 #669, 0.50 #622), 02vgh (0.60 #348, 0.56 #675, 0.50 #209), 04qmr (0.60 #334, 0.55 #898, 0.44 #661), 03d9d6 (0.60 #339, 0.55 #903, 0.44 #666), 017959 (0.60 #360, 0.50 #221, 0.45 #924), 0fb2l (0.60 #364, 0.45 #928, 0.44 #691) >> Best rule #670 for best value: >> intensional similarity = 9 >> extensional distance = 7 >> proper extension: 085jw; >> query: (?x1750, 01q99h) <- group(?x1750, ?x2901), instrumentalists(?x1750, ?x6609), instrumentalists(?x1750, ?x2170), role(?x212, ?x1750), profession(?x2170, ?x220), performance_role(?x432, ?x1750), role(?x1750, ?x74), ?x2901 = 01vrwfv, artists(?x283, ?x6609) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 28, 29, 32, 34, 35, 37, 39 EVAL 02hnl group 03q_w5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 01dpts CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 0fb2l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 01_wfj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 01fchy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 017959 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 01shhf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 011_vz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 07rnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 0jn38 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 046p9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 089pg7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 048xh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 01jcxwp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 07r1_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 02vgh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 0ycp3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 015cxv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 01q99h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 0123r4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 013w2r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 03d9d6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 0dw4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 02mq_y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 01j59b0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 04qmr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 0134s5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 02_5x9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 03g5jw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02hnl group 0m19t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 79.000 61.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group #8895-01nd6v PRED entity: 01nd6v PRED relation: profession PRED expected values: 02hrh1q => 107 concepts (45 used for prediction) PRED predicted values (max 10 best out of 57): 02hrh1q (0.94 #3568, 0.94 #5790, 0.92 #4308), 01d_h8 (0.62 #598, 0.60 #450, 0.47 #894), 0dxtg (0.59 #902, 0.56 #1642, 0.53 #2087), 03gjzk (0.53 #904, 0.52 #1644, 0.46 #2533), 09jwl (0.42 #6090, 0.36 #5350, 0.28 #4164), 0d1pc (0.33 #50, 0.13 #494, 0.12 #642), 0nbcg (0.27 #6102, 0.25 #5362, 0.17 #3436), 02jknp (0.27 #452, 0.25 #600, 0.23 #748), 016z4k (0.24 #5335, 0.20 #6075, 0.16 #4149), 0dz3r (0.23 #6073, 0.22 #5333, 0.13 #3259) >> Best rule #3568 for best value: >> intensional similarity = 5 >> extensional distance = 279 >> proper extension: 0184jc; 04bdxl; 05d7rk; 05bnp0; 02qgqt; 04yywz; 02bfmn; 01j5ts; 01p7yb; 0bl2g; ... >> query: (?x14135, 02hrh1q) <- profession(?x14135, ?x1146), film(?x14135, ?x2177), award(?x2177, ?x68), nominated_for(?x3209, ?x2177), ?x3209 = 02w9sd7 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nd6v profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 45.000 0.940 http://example.org/people/person/profession #8894-01vttb9 PRED entity: 01vttb9 PRED relation: award_winner! PRED expected values: 01bgqh => 112 concepts (105 used for prediction) PRED predicted values (max 10 best out of 276): 054krc (0.42 #5073, 0.39 #847, 0.39 #5497), 05q8pss (0.42 #5073, 0.39 #847, 0.39 #5497), 054ks3 (0.42 #5073, 0.39 #847, 0.39 #5497), 02gdjb (0.42 #5073, 0.39 #847, 0.39 #5497), 02qvyrt (0.20 #2240, 0.10 #4350, 0.10 #1818), 01bgqh (0.17 #467, 0.08 #10608, 0.07 #15257), 025m8l (0.15 #2232, 0.13 #3076, 0.09 #9299), 09sb52 (0.14 #19899, 0.12 #22010, 0.11 #18631), 0fhpv4 (0.12 #2306, 0.06 #1884, 0.05 #3150), 01cky2 (0.11 #1033, 0.10 #1457, 0.06 #8639) >> Best rule #5073 for best value: >> intensional similarity = 3 >> extensional distance = 164 >> proper extension: 01r_t_; 03fw4y; 015np0; 02vkvcz; >> query: (?x7556, ?x1079) <- award(?x7556, ?x1079), award_winner(?x1452, ?x7556), place_of_death(?x7556, ?x1523) >> conf = 0.42 => this is the best rule for 4 predicted values *> Best rule #467 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 02lvtb; 013423; 03jg5t; 01vsy9_; 02bc74; *> query: (?x7556, 01bgqh) <- artists(?x8138, ?x7556), award(?x7556, ?x1079), ?x8138 = 0161rf *> conf = 0.17 ranks of expected_values: 6 EVAL 01vttb9 award_winner! 01bgqh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 112.000 105.000 0.418 http://example.org/award/award_category/winners./award/award_honor/award_winner #8893-08959 PRED entity: 08959 PRED relation: people! PRED expected values: 03tp4 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 38): 051_y (0.33 #114, 0.09 #510, 0.07 #576), 06z5s (0.33 #25, 0.05 #949, 0.03 #2269), 0gk4g (0.22 #274, 0.21 #604, 0.16 #1330), 0dq9p (0.21 #611, 0.20 #347, 0.18 #1205), 012hw (0.18 #514, 0.13 #712, 0.12 #778), 02k6hp (0.14 #235, 0.14 #1225, 0.12 #1357), 07jwr (0.14 #207, 0.11 #273, 0.10 #339), 02y0js (0.11 #266, 0.08 #1322, 0.07 #596), 014w_8 (0.10 #369, 0.09 #1227, 0.07 #633), 01l2m3 (0.10 #346, 0.09 #478, 0.07 #676) >> Best rule #114 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0gzh; >> query: (?x13592, 051_y) <- location(?x13592, ?x4061), basic_title(?x13592, ?x346), profession(?x13592, ?x1359), ?x4061 = 0498y, gender(?x13592, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08959 people! 03tp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 132.000 0.333 http://example.org/people/cause_of_death/people #8892-01fyzy PRED entity: 01fyzy PRED relation: film PRED expected values: 02qdrjx => 103 concepts (71 used for prediction) PRED predicted values (max 10 best out of 612): 03z20c (0.60 #78416, 0.58 #110501, 0.48 #92677), 0pdp8 (0.11 #365, 0.01 #7494, 0.01 #23535), 01mszz (0.08 #30299, 0.02 #29598, 0.01 #17124), 07sgdw (0.08 #30299, 0.01 #29323), 0q9sg (0.08 #30299, 0.01 #29277), 0946bb (0.08 #30299, 0.01 #14803), 03phtz (0.08 #30299), 06c0ns (0.08 #30299), 02scbv (0.08 #30299), 059lwy (0.08 #30299) >> Best rule #78416 for best value: >> intensional similarity = 3 >> extensional distance = 968 >> proper extension: 049tjg; 06jzh; 0785v8; 04shbh; 019_1h; 03f1zdw; 030znt; 02wrhj; 02k6rq; 01hkhq; ... >> query: (?x5975, ?x821) <- location(?x5975, ?x3125), nominated_for(?x5975, ?x821), film(?x5975, ?x148) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1555 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 016tt2; 05qd_; 030pr; 043q6n_; 024rgt; 031rq5; 03rwz3; 025hwq; *> query: (?x5975, 02qdrjx) <- nominated_for(?x5975, ?x821), award_nominee(?x5975, ?x541), ?x541 = 017s11 *> conf = 0.06 ranks of expected_values: 28 EVAL 01fyzy film 02qdrjx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 103.000 71.000 0.597 http://example.org/film/actor/film./film/performance/film #8891-02yxh9 PRED entity: 02yxh9 PRED relation: award_winner PRED expected values: 0d5wn3 => 33 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1504): 02mxbd (0.30 #871, 0.27 #3933, 0.20 #5463), 021yc7p (0.27 #1746, 0.20 #214, 0.18 #3276), 08hp53 (0.22 #16841, 0.21 #13776, 0.21 #15310), 07r1h (0.22 #16841, 0.21 #21447, 0.21 #15310), 01vb6z (0.22 #16841, 0.21 #21447, 0.21 #15310), 0d6484 (0.22 #16841, 0.21 #21447, 0.21 #15310), 03xp8d5 (0.22 #16841, 0.21 #21447, 0.21 #15310), 016ggh (0.22 #16841, 0.21 #15310, 0.12 #21444), 0bq2g (0.22 #16841, 0.21 #15310, 0.12 #21444), 03f1zdw (0.22 #16841, 0.21 #15310, 0.12 #21444) >> Best rule #871 for best value: >> intensional similarity = 21 >> extensional distance = 8 >> proper extension: 02yw5r; 0bzm81; 0bvfqq; 050yyb; 05qb8vx; 02yvhx; 02ywhz; 0bvhz9; >> query: (?x7144, 02mxbd) <- honored_for(?x7144, ?x1199), ceremony(?x6860, ?x7144), ceremony(?x2209, ?x7144), ceremony(?x1972, ?x7144), ceremony(?x1323, ?x7144), ceremony(?x1243, ?x7144), ceremony(?x591, ?x7144), ?x591 = 0f4x7, ?x1972 = 0gqyl, award_winner(?x7144, ?x5461), award_winner(?x7144, ?x3685), ?x1323 = 0gqz2, ?x6860 = 018wdw, ?x2209 = 0gr42, ?x1243 = 0gr0m, costume_design_by(?x4448, ?x3685), award_nominee(?x5461, ?x100), profession(?x3685, ?x7630), film(?x2551, ?x4448), type_of_union(?x5461, ?x566), nominated_for(?x1774, ?x4448) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #16841 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 40 *> proper extension: 0bzk8w; 0dthsy; *> query: (?x7144, ?x4385) <- honored_for(?x7144, ?x6472), ceremony(?x6860, ?x7144), ceremony(?x1972, ?x7144), ceremony(?x1323, ?x7144), ceremony(?x591, ?x7144), ?x591 = 0f4x7, ?x1972 = 0gqyl, award_winner(?x7144, ?x84), ?x1323 = 0gqz2, ceremony(?x6860, ?x7105), nominated_for(?x6860, ?x4441), nominated_for(?x6860, ?x1173), award_winner(?x6860, ?x1933), nominated_for(?x4385, ?x6472), ?x7105 = 073hd1, genre(?x6472, ?x53), film_release_region(?x4441, ?x87), person(?x1173, ?x5572), nominated_for(?x298, ?x6472) *> conf = 0.22 ranks of expected_values: 21 EVAL 02yxh9 award_winner 0d5wn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 33.000 20.000 0.300 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8890-07d370 PRED entity: 07d370 PRED relation: profession PRED expected values: 015h31 => 72 concepts (48 used for prediction) PRED predicted values (max 10 best out of 56): 02jknp (0.53 #436, 0.52 #865, 0.30 #1718), 01d_h8 (0.50 #435, 0.49 #1007, 0.46 #864), 0np9r (0.30 #1718, 0.28 #4438, 0.26 #6586), 0nbcg (0.30 #1718, 0.28 #4438, 0.26 #6586), 02hv44_ (0.30 #1718, 0.28 #4438, 0.26 #6586), 025352 (0.30 #1718, 0.28 #4438, 0.26 #6586), 018gz8 (0.20 #443, 0.18 #1015, 0.16 #872), 09jwl (0.18 #3022, 0.17 #2879, 0.17 #3880), 01c72t (0.11 #736, 0.09 #1451, 0.09 #3027), 0fj9f (0.11 #621, 0.05 #49, 0.05 #335) >> Best rule #436 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 0lzb8; 0htlr; 04n7njg; 031zkw; 01wyzyl; 01_vfy; 012_53; 05whq_9; 0m32_; 01f8ld; ... >> query: (?x3675, 02jknp) <- student(?x6611, ?x3675), profession(?x3675, ?x1943), ?x1943 = 02krf9 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #881 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 262 *> proper extension: 0c8hct; 02hhtj; *> query: (?x3675, 015h31) <- profession(?x3675, ?x1943), ?x1943 = 02krf9 *> conf = 0.04 ranks of expected_values: 22 EVAL 07d370 profession 015h31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 72.000 48.000 0.531 http://example.org/people/person/profession #8889-014_x2 PRED entity: 014_x2 PRED relation: nominated_for! PRED expected values: 02g2yr => 111 concepts (84 used for prediction) PRED predicted values (max 10 best out of 207): 0gq9h (0.37 #780, 0.36 #4604, 0.30 #1975), 019f4v (0.36 #771, 0.30 #4595, 0.28 #532), 099c8n (0.33 #57, 0.27 #1491, 0.19 #14828), 0gs9p (0.32 #4606, 0.30 #782, 0.26 #3172), 04dn09n (0.32 #752, 0.25 #1947, 0.24 #4576), 0k611 (0.31 #791, 0.27 #4615, 0.24 #1986), 0gq_v (0.30 #736, 0.24 #1931, 0.23 #4560), 0gr0m (0.28 #777, 0.24 #1972, 0.23 #2689), 0p9sw (0.27 #737, 0.24 #1932, 0.22 #20), 0gqy2 (0.27 #840, 0.24 #4664, 0.20 #2035) >> Best rule #780 for best value: >> intensional similarity = 4 >> extensional distance = 164 >> proper extension: 03l6q0; 02x8fs; 0ckt6; >> query: (?x83, 0gq9h) <- genre(?x83, ?x53), currency(?x83, ?x170), cinematography(?x83, ?x7327), award_winner(?x83, ?x84) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #9326 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 831 *> proper extension: 0c3xpwy; 07bz5; 03d17dg; *> query: (?x83, ?x783) <- award_winner(?x83, ?x3056), nominated_for(?x965, ?x83), award_winner(?x6861, ?x3056), award(?x965, ?x783) *> conf = 0.21 ranks of expected_values: 62 EVAL 014_x2 nominated_for! 02g2yr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 111.000 84.000 0.367 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8888-01v1d8 PRED entity: 01v1d8 PRED relation: role! PRED expected values: 07kc_ => 90 concepts (59 used for prediction) PRED predicted values (max 10 best out of 85): 0342h (0.87 #2541, 0.86 #2293, 0.85 #3284), 05148p4 (0.86 #2209, 0.84 #982, 0.83 #1967), 05r5c (0.85 #3531, 0.85 #3524, 0.85 #3451), 0l14md (0.84 #982, 0.83 #1967, 0.82 #899), 0dwtp (0.84 #982, 0.83 #1967, 0.82 #899), 051hrr (0.84 #982, 0.83 #1967, 0.82 #899), 03gvt (0.84 #982, 0.83 #1967, 0.82 #899), 05842k (0.84 #982, 0.83 #1967, 0.82 #899), 0680x0 (0.84 #982, 0.83 #1967, 0.82 #899), 0dwr4 (0.84 #982, 0.83 #1967, 0.82 #899) >> Best rule #2541 for best value: >> intensional similarity = 16 >> extensional distance = 13 >> proper extension: 018vs; >> query: (?x3161, 0342h) <- role(?x1969, ?x3161), performance_role(?x764, ?x3161), role(?x3161, ?x1466), role(?x3161, ?x1166), ?x1969 = 04rzd, group(?x1466, ?x10427), group(?x1466, ?x6699), ?x10427 = 04qzm, role(?x11443, ?x1466), role(?x806, ?x1466), ?x6699 = 09lwrt, performance_role(?x1466, ?x2944), role(?x3161, ?x1147), ?x1166 = 05148p4, film(?x806, ?x590), artists(?x1000, ?x11443) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #982 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 3 *> proper extension: 0979zs; *> query: (?x3161, ?x2059) <- role(?x7033, ?x3161), role(?x2764, ?x3161), role(?x2675, ?x3161), role(?x1268, ?x3161), role(?x894, ?x3161), role(?x228, ?x3161), role(?x74, ?x3161), role(?x2306, ?x3161), ?x2764 = 01s0ps, ?x7033 = 0gkd1, role(?x227, ?x2675), role(?x75, ?x2675), ?x894 = 03m5k, ?x228 = 0l14qv, role(?x1268, ?x5480), role(?x3161, ?x3716), role(?x3161, ?x2059), ?x3716 = 03gvt, ?x5480 = 01w4c9, ?x74 = 03q5t *> conf = 0.84 ranks of expected_values: 11 EVAL 01v1d8 role! 07kc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 90.000 59.000 0.867 http://example.org/music/performance_role/track_performances./music/track_contribution/role #8887-06_bq1 PRED entity: 06_bq1 PRED relation: profession PRED expected values: 02hrh1q => 127 concepts (109 used for prediction) PRED predicted values (max 10 best out of 64): 02hrh1q (0.92 #3740, 0.91 #1505, 0.91 #4932), 01d_h8 (0.44 #3135, 0.43 #1198, 0.39 #7906), 03gjzk (0.35 #8513, 0.33 #16, 0.33 #8811), 0dxtg (0.33 #14, 0.31 #8511, 0.31 #8809), 02jknp (0.33 #8, 0.28 #14162, 0.27 #9548), 01xr66 (0.29 #7303, 0.03 #1555, 0.03 #2300), 09jwl (0.28 #14162, 0.22 #1957, 0.21 #2106), 016z4k (0.28 #14162, 0.14 #1196, 0.13 #2239), 021wpb (0.28 #14162, 0.08 #351, 0.08 #500), 0np9r (0.21 #9413, 0.19 #7773, 0.19 #8072) >> Best rule #3740 for best value: >> intensional similarity = 3 >> extensional distance = 237 >> proper extension: 06cv1; 02wb6yq; >> query: (?x7046, 02hrh1q) <- participant(?x7046, ?x10053), participant(?x10139, ?x7046), nominated_for(?x7046, ?x2191) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06_bq1 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 109.000 0.921 http://example.org/people/person/profession #8886-06p0s1 PRED entity: 06p0s1 PRED relation: nationality PRED expected values: 07ssc => 81 concepts (56 used for prediction) PRED predicted values (max 10 best out of 32): 09c7w0 (0.77 #3492, 0.76 #5091, 0.74 #5491), 07ssc (0.38 #610, 0.34 #1007, 0.33 #1305), 0h7x (0.25 #3893, 0.01 #629, 0.01 #2427), 0chghy (0.09 #208, 0.08 #307, 0.07 #505), 0d0vqn (0.06 #4292, 0.05 #2891, 0.05 #3290), 0k6nt (0.06 #4292, 0.05 #2891, 0.05 #3290), 059j2 (0.06 #4292, 0.05 #2891, 0.05 #3290), 06bnz (0.06 #4292, 0.05 #2891, 0.05 #3290), 03_3d (0.06 #4292, 0.05 #2891, 0.05 #3290), 05v8c (0.06 #4292, 0.05 #2891, 0.05 #3290) >> Best rule #3492 for best value: >> intensional similarity = 3 >> extensional distance = 1165 >> proper extension: 08wq0g; 02wrhj; 0q1lp; >> query: (?x11915, 09c7w0) <- nominated_for(?x11915, ?x3535), location(?x11915, ?x362), nationality(?x11915, ?x1310) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #610 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 0487c3; 0f2df; 04rsd2; 0892sx; 0phx4; 0kh6b; 0f0qfz; 01vw20h; 02gyl0; 01vsy3q; ... *> query: (?x11915, 07ssc) <- gender(?x11915, ?x231), location(?x11915, ?x362), ?x231 = 05zppz, ?x362 = 04jpl *> conf = 0.38 ranks of expected_values: 2 EVAL 06p0s1 nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 56.000 0.769 http://example.org/people/person/nationality #8885-02508x PRED entity: 02508x PRED relation: profession PRED expected values: 08z956 => 119 concepts (68 used for prediction) PRED predicted values (max 10 best out of 109): 02hrh1q (0.95 #9007, 0.93 #8427, 0.91 #6975), 03gjzk (0.91 #6251, 0.85 #5671, 0.55 #885), 0dxtg (0.77 #6539, 0.64 #738, 0.62 #593), 018gz8 (0.50 #17, 0.46 #597, 0.36 #742), 09jwl (0.50 #164, 0.44 #454, 0.42 #7125), 01d_h8 (0.49 #5662, 0.47 #6242, 0.45 #6532), 0nbcg (0.38 #175, 0.33 #465, 0.27 #7136), 016z4k (0.38 #149, 0.33 #439, 0.25 #5805), 02jknp (0.36 #732, 0.35 #6533, 0.27 #877), 02krf9 (0.25 #26, 0.24 #6262, 0.22 #5682) >> Best rule #9007 for best value: >> intensional similarity = 6 >> extensional distance = 1673 >> proper extension: 04f62k; 0b9f7t; >> query: (?x5423, 02hrh1q) <- profession(?x5423, ?x9081), location(?x5423, ?x6511), profession(?x10398, ?x9081), profession(?x7512, ?x9081), ?x10398 = 0jbp0, award_nominee(?x1343, ?x7512) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 0dn44; *> query: (?x5423, 08z956) <- nationality(?x5423, ?x512), profession(?x5423, ?x4725), profession(?x5423, ?x353), ?x4725 = 015cjr, ?x512 = 07ssc, ?x353 = 0cbd2 *> conf = 0.25 ranks of expected_values: 16 EVAL 02508x profession 08z956 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 119.000 68.000 0.954 http://example.org/people/person/profession #8884-07fzq3 PRED entity: 07fzq3 PRED relation: type_of_union PRED expected values: 04ztj => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #13, 0.69 #57, 0.69 #69), 01g63y (0.11 #186, 0.11 #178, 0.11 #242) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 16 >> proper extension: 063tn; >> query: (?x6766, 04ztj) <- nationality(?x6766, ?x1603), ?x1603 = 06bnz >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07fzq3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.722 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8883-0djb3vw PRED entity: 0djb3vw PRED relation: film_release_region PRED expected values: 0jgd 05qhw => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 165): 03spz (0.90 #3350, 0.89 #2885, 0.87 #2420), 05qhw (0.89 #3269, 0.87 #2804, 0.87 #2339), 0b90_r (0.86 #2174, 0.83 #2329, 0.82 #3259), 03rt9 (0.85 #3268, 0.85 #3578, 0.85 #3888), 0jgd (0.82 #3258, 0.82 #2173, 0.80 #2793), 01znc_ (0.82 #2209, 0.80 #2364, 0.79 #3294), 0k6nt (0.80 #2348, 0.80 #2193, 0.79 #3278), 03rj0 (0.80 #2848, 0.79 #3623, 0.78 #2383), 05v8c (0.79 #3271, 0.78 #2806, 0.77 #3581), 05b4w (0.79 #3317, 0.78 #2852, 0.76 #2387) >> Best rule #3350 for best value: >> intensional similarity = 6 >> extensional distance = 60 >> proper extension: 03bx2lk; 03mgx6z; 02qk3fk; >> query: (?x542, 03spz) <- film_release_region(?x542, ?x2645), film_release_region(?x542, ?x789), film_release_region(?x542, ?x608), ?x608 = 02k54, ?x2645 = 03h64, country(?x251, ?x789) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #3269 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 60 *> proper extension: 03bx2lk; 03mgx6z; 02qk3fk; *> query: (?x542, 05qhw) <- film_release_region(?x542, ?x2645), film_release_region(?x542, ?x789), film_release_region(?x542, ?x608), ?x608 = 02k54, ?x2645 = 03h64, country(?x251, ?x789) *> conf = 0.89 ranks of expected_values: 2, 5 EVAL 0djb3vw film_release_region 05qhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0djb3vw film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 103.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8882-03_dj PRED entity: 03_dj PRED relation: profession PRED expected values: 05z96 => 163 concepts (110 used for prediction) PRED predicted values (max 10 best out of 144): 02hrh1q (0.81 #12081, 0.75 #12522, 0.75 #2219), 0dxtg (0.72 #13550, 0.67 #2806, 0.65 #1777), 01d_h8 (0.59 #9423, 0.55 #14279, 0.42 #13543), 02hv44_ (0.40 #203, 0.31 #644, 0.25 #8682), 05z96 (0.40 #188, 0.25 #8682, 0.25 #8681), 02jknp (0.35 #13544, 0.34 #14280, 0.34 #2359), 09jwl (0.33 #313, 0.31 #607, 0.31 #460), 03gjzk (0.31 #2808, 0.29 #13552, 0.28 #1779), 0d8qb (0.25 #8682, 0.25 #8681, 0.20 #225), 0q04f (0.25 #8682, 0.25 #8681, 0.20 #245) >> Best rule #12081 for best value: >> intensional similarity = 4 >> extensional distance = 792 >> proper extension: 016ywr; 08b8vd; 03fbb6; 0n839; >> query: (?x12345, 02hrh1q) <- religion(?x12345, ?x4641), profession(?x12345, ?x2225), profession(?x7269, ?x2225), ?x7269 = 0gnbw >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #188 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0686zv; *> query: (?x12345, 05z96) <- gender(?x12345, ?x231), location(?x12345, ?x1591), student(?x8694, ?x12345), ?x8694 = 011xy1, ?x231 = 05zppz *> conf = 0.40 ranks of expected_values: 5 EVAL 03_dj profession 05z96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 163.000 110.000 0.809 http://example.org/people/person/profession #8881-03k9fj PRED entity: 03k9fj PRED relation: genre! PRED expected values: 05q96q6 09q5w2 0jqn5 02r79_h 04n52p6 0k4kk 0jnwx 0btyf5z 02vqhv0 03h3x5 047p7fr 0gffmn8 014nq4 09rsjpv 0cn_b8 023p7l 05c26ss 0mcl0 02dpl9 0125xq 09fn1w 02sfnv 0bc1yhb 05szq8z 0295sy 01l_pn 05pyrb 016ztl 026hxwx 0dc_ms 01_0f7 042fgh 0fxmbn 03kx49 04gcyg 01718w 0cp0t91 04jpg2p 0f61tk 03whyr 0fh2v5 029v40 03vfr_ 034b6k => 58 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1548): 02yvct (0.79 #12064, 0.72 #21109, 0.71 #10556), 0y_9q (0.79 #12064, 0.72 #21109, 0.71 #10556), 01_0f7 (0.79 #12064, 0.72 #21109, 0.71 #10556), 015x74 (0.79 #12064, 0.72 #21109, 0.71 #10556), 034b6k (0.79 #12064, 0.72 #21109, 0.71 #10556), 03q5db (0.79 #12064, 0.72 #21109, 0.71 #10556), 034r25 (0.79 #12064, 0.72 #21109, 0.71 #10556), 01vksx (0.79 #12064, 0.72 #21109, 0.71 #10556), 04fzfj (0.67 #21192, 0.60 #12147, 0.50 #6114), 03cwwl (0.67 #22452, 0.60 #13407, 0.50 #7374) >> Best rule #12064 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0hn10; >> query: (?x811, ?x908) <- genre(?x5028, ?x811), genre(?x2475, ?x811), genre(?x1999, ?x811), award(?x5028, ?x112), film_release_region(?x1999, ?x87), titles(?x811, ?x908), ?x2475 = 0jdgr >> conf = 0.79 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 5, 27, 36, 38, 53, 62, 92, 93, 129, 174, 197, 275, 314, 367, 368, 390, 399, 424, 426, 470, 472, 491, 525, 606, 648, 653, 656, 753, 827, 830, 880, 901, 951, 1101, 1127, 1148, 1150, 1166, 1252, 1259, 1272, 1391, 1438 EVAL 03k9fj genre! 034b6k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 03vfr_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 029v40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0fh2v5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 03whyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0f61tk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 04jpg2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0cp0t91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 01718w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 04gcyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 03kx49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0fxmbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 042fgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 01_0f7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 026hxwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 016ztl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 05pyrb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 01l_pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0295sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 05szq8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0bc1yhb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 02sfnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 09fn1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0125xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 02dpl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0mcl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 023p7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0cn_b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 09rsjpv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 014nq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0gffmn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 047p7fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 02vqhv0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0btyf5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0jnwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0k4kk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 04n52p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 02r79_h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 0jqn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 09q5w2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 45.000 0.786 http://example.org/film/film/genre EVAL 03k9fj genre! 05q96q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 45.000 0.786 http://example.org/film/film/genre #8880-05p8bf9 PRED entity: 05p8bf9 PRED relation: team! PRED expected values: 02_j1w => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 34): 02sdk9v (0.87 #476, 0.86 #264, 0.86 #213), 0dgrmp (0.87 #476, 0.83 #639, 0.83 #158), 02_j1w (0.83 #5, 0.83 #482, 0.82 #111), 03f0fp (0.52 #851, 0.50 #959, 0.48 #1065), 02md_2 (0.52 #851, 0.50 #959, 0.48 #1065), 02qvgy (0.43 #958, 0.43 #956, 0.01 #928), 02g_6x (0.07 #972, 0.06 #1025), 06b1q (0.07 #966, 0.06 #1019), 02g_7z (0.07 #984, 0.06 #1037), 01r3hr (0.07 #961, 0.06 #1014) >> Best rule #476 for best value: >> intensional similarity = 32 >> extensional distance = 550 >> proper extension: 09c8bc; 05cwnc; >> query: (?x13530, ?x63) <- team(?x60, ?x13530), ?x60 = 02nzb8, position(?x13530, ?x203), position(?x13530, ?x63), team(?x203, ?x13520), team(?x203, ?x13434), team(?x203, ?x12952), team(?x203, ?x12612), team(?x203, ?x12325), team(?x203, ?x6542), team(?x203, ?x5209), team(?x203, ?x4802), team(?x203, ?x4523), team(?x203, ?x1599), team(?x203, ?x978), team(?x203, ?x928), ?x13520 = 019mcm, position(?x10463, ?x203), position(?x7773, ?x203), ?x12612 = 04j689, ?x4523 = 0f5hyg, ?x7773 = 0jv5x, ?x5209 = 0gd70t, ?x4802 = 019lty, ?x12952 = 04_bfq, ?x928 = 02279c, ?x1599 = 025txtg, ?x10463 = 032498, ?x12325 = 071rlr, ?x13434 = 0d3fdn, ?x978 = 03y_f8, ?x6542 = 04knkd >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #5 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 216 *> proper extension: 0cnk2q; 04nrcg; 0223bl; 0lhp1; 019lwb; 02279c; 075q_; 0dy68h; 05nmg_; 04b4yg; ... *> query: (?x13530, 02_j1w) <- position(?x13530, ?x63), position(?x13530, ?x60), ?x63 = 02sdk9v, position(?x13530, ?x203), ?x203 = 0dgrmp, ?x60 = 02nzb8, position(?x13530, ?x63), team(?x60, ?x13530) *> conf = 0.83 ranks of expected_values: 3 EVAL 05p8bf9 team! 02_j1w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 21.000 21.000 0.869 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #8879-01qh7 PRED entity: 01qh7 PRED relation: citytown! PRED expected values: 05bnq8 => 132 concepts (74 used for prediction) PRED predicted values (max 10 best out of 673): 01mpwj (0.72 #32928, 0.70 #34534, 0.69 #31322), 0hmyfsv (0.41 #5626, 0.36 #5625, 0.33 #2413), 043ljr (0.36 #5625, 0.33 #2413, 0.28 #804), 064f29 (0.11 #1115, 0.09 #1919, 0.07 #5133), 027lf1 (0.11 #569, 0.07 #1373, 0.06 #2177), 0dwcl (0.11 #723, 0.07 #1527, 0.06 #2331), 01dtcb (0.11 #382, 0.07 #5204, 0.06 #1990), 0146mv (0.11 #581, 0.06 #2189, 0.04 #5403), 06182p (0.11 #392, 0.06 #2000, 0.04 #5214), 059wk (0.09 #9290, 0.04 #5275, 0.04 #6079) >> Best rule #32928 for best value: >> intensional similarity = 3 >> extensional distance = 210 >> proper extension: 0fngy; >> query: (?x3007, ?x1665) <- contains(?x3007, ?x1665), citytown(?x8643, ?x3007), contains(?x94, ?x8643) >> conf = 0.72 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01qh7 citytown! 05bnq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 74.000 0.723 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #8878-0226cw PRED entity: 0226cw PRED relation: place_of_birth PRED expected values: 01vsl => 118 concepts (106 used for prediction) PRED predicted values (max 10 best out of 59): 0vrmb (0.37 #38784, 0.37 #60646, 0.35 #50777), 01vsl (0.33 #50070, 0.31 #26794, 0.31 #27501), 02hrh0_ (0.25 #190, 0.14 #3010, 0.12 #895), 02_286 (0.14 #6363, 0.12 #724, 0.12 #39508), 094jv (0.12 #766, 0.09 #2175, 0.04 #4289), 0p9z5 (0.10 #1781, 0.09 #2485, 0.07 #3191), 01_d4 (0.10 #1476, 0.07 #2886, 0.04 #3590), 0rh6k (0.10 #1412, 0.04 #3526, 0.04 #4230), 0hptm (0.09 #2339, 0.07 #3045, 0.04 #3749), 0cr3d (0.07 #45222, 0.06 #46634, 0.06 #48750) >> Best rule #38784 for best value: >> intensional similarity = 4 >> extensional distance = 949 >> proper extension: 0f1pyf; 031x_3; 02lyx4; 014g91; 06p0s1; 02vkvcz; 011zwl; >> query: (?x8607, ?x12794) <- location(?x8607, ?x12794), place_of_birth(?x115, ?x12794), category(?x12794, ?x134), artists(?x114, ?x115) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #50070 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1282 *> proper extension: 0879xc; *> query: (?x8607, ?x7770) <- profession(?x8607, ?x5805), location(?x8607, ?x7770), state(?x7770, ?x2982) *> conf = 0.33 ranks of expected_values: 2 EVAL 0226cw place_of_birth 01vsl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 106.000 0.372 http://example.org/people/person/place_of_birth #8877-035xwd PRED entity: 035xwd PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.87 #76, 0.87 #31, 0.86 #116), 02nxhr (0.05 #42, 0.05 #52, 0.05 #17), 07c52 (0.04 #58, 0.03 #43, 0.03 #33), 07z4p (0.03 #70, 0.03 #40, 0.03 #162) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 227 >> proper extension: 047svrl; >> query: (?x796, 029j_) <- film_crew_role(?x796, ?x137), music(?x796, ?x669), film(?x398, ?x796), featured_film_locations(?x796, ?x739), titles(?x53, ?x796) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035xwd film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #8876-0c38gj PRED entity: 0c38gj PRED relation: nominated_for! PRED expected values: 0gqy2 018wdw => 63 concepts (60 used for prediction) PRED predicted values (max 10 best out of 165): 0gq9h (0.64 #293, 0.25 #2158, 0.23 #8219), 0gs9p (0.50 #295, 0.33 #62, 0.22 #2160), 019f4v (0.48 #285, 0.33 #52, 0.23 #2150), 0gr4k (0.41 #257, 0.17 #2122, 0.16 #8183), 04dn09n (0.38 #266, 0.22 #33, 0.18 #2131), 0gqy2 (0.37 #352, 0.22 #12358, 0.20 #11424), 040njc (0.36 #240, 0.33 #7, 0.18 #2105), 04kxsb (0.34 #326, 0.22 #12358, 0.20 #11424), 054krc (0.33 #67, 0.20 #300, 0.13 #2631), 02qvyrt (0.33 #94, 0.12 #10724, 0.12 #327) >> Best rule #293 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 0c5qvw; >> query: (?x4633, 0gq9h) <- genre(?x4633, ?x53), production_companies(?x4633, ?x382), nominated_for(?x591, ?x4633), ?x591 = 0f4x7 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #352 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 105 *> proper extension: 0c5qvw; *> query: (?x4633, 0gqy2) <- genre(?x4633, ?x53), production_companies(?x4633, ?x382), nominated_for(?x591, ?x4633), ?x591 = 0f4x7 *> conf = 0.37 ranks of expected_values: 6, 45 EVAL 0c38gj nominated_for! 018wdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 63.000 60.000 0.645 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0c38gj nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 63.000 60.000 0.645 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8875-03gkn5 PRED entity: 03gkn5 PRED relation: student! PRED expected values: 019v9k => 167 concepts (120 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.64 #221, 0.63 #782, 0.62 #402), 04zx3q1 (0.38 #399, 0.29 #308, 0.27 #326), 019v9k (0.30 #189, 0.18 #1330, 0.17 #243), 0bkj86 (0.25 #405, 0.21 #314, 0.20 #332), 07s6fsf (0.25 #19, 0.17 #235, 0.17 #109), 028dcg (0.23 #756, 0.12 #793, 0.11 #1319), 02h4rq6 (0.20 #39, 0.15 #343, 0.13 #327), 0bjrnt (0.17 #114, 0.15 #343, 0.13 #924), 013zdg (0.15 #259, 0.15 #343, 0.13 #924), 01rr_d (0.15 #343, 0.13 #924, 0.10 #195) >> Best rule #221 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 04m_kpx; >> query: (?x3520, 014mlp) <- student(?x2605, ?x3520), type_of_union(?x3520, ?x566), ?x2605 = 03g3w, student(?x1200, ?x3520) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #189 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0d05fv; 07hyk; *> query: (?x3520, 019v9k) <- company(?x3520, ?x122), place_of_birth(?x3520, ?x3521), politician(?x1912, ?x3520), organization(?x122, ?x5487) *> conf = 0.30 ranks of expected_values: 3 EVAL 03gkn5 student! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 167.000 120.000 0.636 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #8874-01w7nww PRED entity: 01w7nww PRED relation: type_of_union PRED expected values: 01g63y => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.77 #17, 0.73 #33, 0.73 #29), 01g63y (0.38 #6, 0.30 #58, 0.27 #62) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 0n6f8; 03bnv; 02vntj; >> query: (?x3176, 04ztj) <- diet(?x3176, ?x11141), award(?x3176, ?x704), award_winner(?x342, ?x3176) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 01vvycq; *> query: (?x3176, 01g63y) <- diet(?x3176, ?x11141), award(?x3176, ?x2238), ?x2238 = 025m8l *> conf = 0.38 ranks of expected_values: 2 EVAL 01w7nww type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 105.000 0.772 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8873-06j8wx PRED entity: 06j8wx PRED relation: award_winner! PRED expected values: 092t4b => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 90): 092t4b (0.75 #331, 0.18 #4761, 0.17 #5463), 0g55tzk (0.20 #416, 0.18 #4761, 0.17 #5463), 0gx_st (0.18 #4761, 0.17 #5463, 0.17 #5322), 027hjff (0.18 #4761, 0.17 #5463, 0.17 #5322), 09qvms (0.18 #4761, 0.17 #5463, 0.17 #5322), 0275n3y (0.18 #4761, 0.17 #5463, 0.17 #5322), 03gyp30 (0.18 #4761, 0.17 #5463, 0.17 #5322), 0hr3c8y (0.18 #4761, 0.17 #5463, 0.17 #5322), 02q690_ (0.18 #4761, 0.17 #5463, 0.17 #5322), 0gvstc3 (0.18 #4761, 0.17 #5463, 0.17 #5322) >> Best rule #331 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 05cj4r; 01sp81; 016gr2; 02tr7d; 015rkw; 065jlv; 02k6rq; 015gw6; 0l6px; 01hkhq; ... >> query: (?x5422, 092t4b) <- award_winner(?x3604, ?x5422), ?x3604 = 03v3xp, award_nominee(?x374, ?x5422) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06j8wx award_winner! 092t4b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8872-013y1f PRED entity: 013y1f PRED relation: role! PRED expected values: 01v_pj6 01kv4mb 0lzkm 0fhxv 03ryks => 96 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1088): 01wxdn3 (0.67 #6971, 0.60 #5731, 0.60 #5319), 02s6sh (0.64 #9912, 0.62 #9888, 0.62 #9476), 023l9y (0.62 #9689, 0.60 #5555, 0.60 #5143), 01vsnff (0.62 #9586, 0.46 #12473, 0.43 #7932), 082brv (0.62 #12626, 0.60 #5605, 0.57 #7672), 0lzkm (0.60 #5518, 0.54 #12539, 0.50 #9652), 0140t7 (0.60 #5310, 0.50 #6962, 0.50 #6135), 01l4g5 (0.60 #5154, 0.50 #4330, 0.50 #3918), 0892sx (0.60 #5056, 0.50 #4232, 0.40 #4644), 01mxt_ (0.60 #5594, 0.43 #8074, 0.43 #7248) >> Best rule #6971 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0l14md; 05148p4; >> query: (?x1495, 01wxdn3) <- performance_role(?x130, ?x1495), role(?x3442, ?x1495), performance_role(?x3161, ?x1495), ?x3161 = 01v1d8, role(?x1495, ?x74), role(?x1473, ?x1495), award_winner(?x84, ?x3442), ?x1473 = 0g2dz, profession(?x130, ?x131), ?x131 = 0dz3r, role(?x642, ?x1495) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5518 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 02sgy; 018vs; *> query: (?x1495, 0lzkm) <- performance_role(?x130, ?x1495), role(?x3442, ?x1495), performance_role(?x3161, ?x1495), ?x3161 = 01v1d8, role(?x1495, ?x745), role(?x645, ?x1495), ?x3442 = 0m_v0, ?x745 = 01vj9c, category(?x130, ?x134), performance_role(?x1495, ?x1433), group(?x1495, ?x997) *> conf = 0.60 ranks of expected_values: 6, 31, 47, 122, 140 EVAL 013y1f role! 03ryks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 96.000 62.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 013y1f role! 0fhxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 96.000 62.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 013y1f role! 0lzkm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 62.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 013y1f role! 01kv4mb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 96.000 62.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 013y1f role! 01v_pj6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 96.000 62.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #8871-0g5lhl7 PRED entity: 0g5lhl7 PRED relation: award_winner PRED expected values: 0kc8y => 178 concepts (125 used for prediction) PRED predicted values (max 10 best out of 698): 05gnf (0.82 #62869, 0.81 #191859, 0.81 #191860), 0g5lhl7 (0.33 #2063, 0.29 #196697, 0.28 #198310), 01w92 (0.33 #2184, 0.29 #196697, 0.28 #198310), 07k2d (0.33 #3201, 0.29 #196697, 0.28 #198310), 0hm0k (0.33 #2641, 0.29 #196697, 0.28 #198310), 07ymr5 (0.33 #35757, 0.17 #16417, 0.14 #61559), 030_1_ (0.33 #13150, 0.12 #51837, 0.12 #29265), 05qd_ (0.32 #80733, 0.29 #196697, 0.22 #33971), 01b9ck (0.30 #37262, 0.17 #21144, 0.14 #59838), 0dbpwb (0.29 #196697, 0.28 #198310, 0.17 #193473) >> Best rule #62869 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0jrqq; 030_3z; 04glx0; >> query: (?x2776, ?x2246) <- award_nominee(?x5007, ?x2776), award_winner(?x6678, ?x2776), award_winner(?x2246, ?x2776), program(?x6678, ?x293) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #196697 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1012 *> proper extension: 0lzkm; 0khth; 01933d; 07sbk; 0gyy0; *> query: (?x2776, ?x902) <- award_winner(?x2776, ?x5007), award_winner(?x762, ?x2776), award_winner(?x902, ?x5007) *> conf = 0.29 ranks of expected_values: 12 EVAL 0g5lhl7 award_winner 0kc8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 178.000 125.000 0.817 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #8870-02p8v8 PRED entity: 02p8v8 PRED relation: award_nominee! PRED expected values: 016kft => 91 concepts (52 used for prediction) PRED predicted values (max 10 best out of 578): 016kft (0.27 #4337, 0.25 #60661, 0.16 #118995), 04cf09 (0.27 #2574, 0.16 #118995, 0.15 #83996), 01tfck (0.25 #60661, 0.18 #2797, 0.16 #118995), 02x7vq (0.25 #60661, 0.16 #118995, 0.15 #83996), 02p8v8 (0.25 #60661, 0.16 #118995, 0.15 #83996), 07qcbw (0.25 #60661, 0.09 #4463), 0438pz (0.25 #60661, 0.09 #4261), 046zh (0.25 #60661, 0.01 #57233, 0.01 #54900), 06r3p2 (0.25 #60661), 05gnf (0.25 #60661) >> Best rule #4337 for best value: >> intensional similarity = 2 >> extensional distance = 9 >> proper extension: 05gnf; >> query: (?x9686, 016kft) <- nominated_for(?x9686, ?x4898), ?x4898 = 017f3m >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02p8v8 award_nominee! 016kft CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 52.000 0.273 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8869-03ybrwc PRED entity: 03ybrwc PRED relation: award! PRED expected values: 03b1l8 => 42 concepts (11 used for prediction) PRED predicted values (max 10 best out of 1335): 05hjnw (0.33 #500, 0.23 #3569, 0.21 #2546), 04qw17 (0.33 #180, 0.20 #1203, 0.09 #2226), 0qmfz (0.33 #846, 0.20 #1869, 0.06 #2892), 0320fn (0.33 #395, 0.20 #1418, 0.06 #2441), 03hj5lq (0.33 #621, 0.20 #1644, 0.06 #2667), 0pv54 (0.33 #563, 0.20 #1586, 0.03 #2609), 0dx8gj (0.33 #380, 0.20 #1403, 0.03 #2426), 011yg9 (0.20 #1625, 0.12 #3671, 0.09 #2648), 01hqhm (0.20 #1227, 0.03 #3273, 0.01 #4296), 0_b3d (0.20 #1117, 0.03 #3163, 0.01 #4186) >> Best rule #500 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0789r6; >> query: (?x11779, 05hjnw) <- award_winner(?x11779, ?x10354), award_winner(?x11779, ?x10117), ?x10117 = 071xj, ?x10354 = 0gdqy, award(?x3287, ?x11779), film_release_region(?x3287, ?x1023), currency(?x1023, ?x170) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2848 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 32 *> proper extension: 09v4bym; *> query: (?x11779, 03b1l8) <- award_winner(?x11779, ?x10354), award_winner(?x11779, ?x10117), award(?x10117, ?x198), place_of_birth(?x10354, ?x4627), film_festivals(?x10354, ?x9932), place_of_birth(?x10117, ?x8297) *> conf = 0.06 ranks of expected_values: 179 EVAL 03ybrwc award! 03b1l8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 42.000 11.000 0.333 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8868-0jrny PRED entity: 0jrny PRED relation: category PRED expected values: 08mbj5d => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.76 #8, 0.53 #4, 0.50 #12) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 09hnb; 019f9z; >> query: (?x3194, 08mbj5d) <- profession(?x3194, ?x1032), award_winner(?x1480, ?x3194), gender(?x3194, ?x231), ?x1480 = 01c6qp >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jrny category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.762 http://example.org/common/topic/webpage./common/webpage/category #8867-02fjzt PRED entity: 02fjzt PRED relation: institution! PRED expected values: 02_xgp2 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 20): 0bkj86 (0.64 #48, 0.58 #69, 0.52 #90), 03bwzr4 (0.57 #162, 0.55 #96, 0.55 #54), 02_xgp2 (0.57 #94, 0.55 #52, 0.53 #73), 07s6fsf (0.57 #85, 0.50 #64, 0.50 #43), 016t_3 (0.55 #86, 0.50 #44, 0.49 #152), 013zdg (0.45 #47, 0.36 #68, 0.32 #89), 028dcg (0.32 #59, 0.30 #38, 0.28 #80), 03mkk4 (0.30 #30, 0.29 #9, 0.27 #51), 027f2w (0.29 #1475, 0.28 #70, 0.27 #49), 022h5x (0.29 #1475, 0.26 #302, 0.23 #102) >> Best rule #48 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 01pl14; >> query: (?x4341, 0bkj86) <- colors(?x4341, ?x663), student(?x4341, ?x12607), team(?x12607, ?x8228), school(?x2820, ?x4341) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #94 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 42 *> proper extension: 022r38; *> query: (?x4341, 02_xgp2) <- institution(?x734, ?x4341), major_field_of_study(?x4341, ?x10046), major_field_of_study(?x4341, ?x6756), ?x10046 = 041y2, major_field_of_study(?x6756, ?x2606) *> conf = 0.57 ranks of expected_values: 3 EVAL 02fjzt institution! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 131.000 131.000 0.636 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #8866-01bb9r PRED entity: 01bb9r PRED relation: produced_by PRED expected values: 0bkf72 => 98 concepts (65 used for prediction) PRED predicted values (max 10 best out of 255): 06m6z6 (0.38 #9704, 0.37 #8927, 0.36 #10093), 0bsb4j (0.30 #84, 0.02 #13595, 0.02 #5515), 014zcr (0.21 #5820, 0.21 #388, 0.20 #389), 0gy6z9 (0.21 #5820, 0.21 #388, 0.13 #13594), 01vw26l (0.21 #388, 0.15 #15927, 0.13 #13594), 06pk8 (0.21 #388, 0.13 #13594, 0.12 #16316), 0fvf9q (0.10 #6, 0.05 #395, 0.03 #1171), 02lf0c (0.10 #23, 0.05 #412, 0.02 #3901), 0c00lh (0.10 #190, 0.05 #579, 0.02 #4068), 02ld6x (0.10 #91, 0.03 #2032) >> Best rule #9704 for best value: >> intensional similarity = 4 >> extensional distance = 260 >> proper extension: 0g22z; 01br2w; 0b2v79; 01jc6q; 09m6kg; 0yyg4; 011yrp; 011yxg; 01hr1; 01k1k4; ... >> query: (?x2955, ?x3961) <- genre(?x2955, ?x53), films(?x11047, ?x2955), film(?x3961, ?x2955), language(?x2955, ?x5359) >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01bb9r produced_by 0bkf72 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 65.000 0.379 http://example.org/film/film/produced_by #8865-0371rb PRED entity: 0371rb PRED relation: company! PRED expected values: 02md_2 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 28): 060c4 (0.18 #2277, 0.16 #3888, 0.16 #2467), 01yc02 (0.17 #197, 0.07 #2283, 0.06 #479), 0dq_5 (0.14 #2292, 0.13 #3855, 0.12 #3903), 0krdk (0.12 #2281, 0.12 #3844, 0.11 #3892), 05_wyz (0.09 #2293, 0.08 #3856, 0.08 #3904), 021q1c (0.09 #434, 0.03 #3141, 0.02 #5695), 09d6p2 (0.08 #2294, 0.07 #2484, 0.06 #3857), 0dq3c (0.08 #3839, 0.07 #4031, 0.07 #3887), 01kr6k (0.07 #2302, 0.06 #2492, 0.04 #3865), 02md_2 (0.06 #534, 0.05 #1529, 0.04 #1911) >> Best rule #2277 for best value: >> intensional similarity = 5 >> extensional distance = 159 >> proper extension: 024y8p; 02zccd; 0k8z; 033x5p; 0269kx; 01zpmq; 03hdz8; 01n_g9; 04cnp4; 019n9w; ... >> query: (?x2096, 060c4) <- citytown(?x2096, ?x7475), jurisdiction_of_office(?x1195, ?x7475), contains(?x1264, ?x7475), category(?x7475, ?x134), ?x1195 = 0pqc5 >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #534 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 01lpx8; *> query: (?x2096, 02md_2) <- team(?x7703, ?x2096), colors(?x2096, ?x1101), teams(?x7475, ?x2096), ?x1101 = 06fvc, location_of_ceremony(?x566, ?x7475) *> conf = 0.06 ranks of expected_values: 10 EVAL 0371rb company! 02md_2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 143.000 143.000 0.180 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #8864-0r3tq PRED entity: 0r3tq PRED relation: place_of_death! PRED expected values: 0b80__ 012dr7 => 186 concepts (72 used for prediction) PRED predicted values (max 10 best out of 798): 04wqr (0.13 #8235, 0.07 #11228, 0.04 #26956), 02v2jy (0.13 #8235, 0.07 #1456, 0.06 #2205), 022p06 (0.13 #8235, 0.07 #966, 0.06 #1715), 02whj (0.13 #8235, 0.07 #780, 0.06 #1529), 02rf51g (0.13 #8235, 0.07 #1470, 0.06 #2219), 08gyz_ (0.13 #8235, 0.07 #1440, 0.06 #2189), 01j851 (0.13 #8235, 0.07 #1236, 0.06 #1985), 0bkmf (0.13 #8235, 0.07 #1226, 0.06 #1975), 040z9 (0.13 #8235, 0.07 #1093, 0.06 #1842), 03bw6 (0.13 #8235, 0.07 #1084, 0.06 #1833) >> Best rule #8235 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0chgsm; >> query: (?x11000, ?x1856) <- place_of_death(?x1357, ?x11000), place_of_burial(?x1357, ?x3691), gender(?x1357, ?x231), place_of_burial(?x1856, ?x3691) >> conf = 0.13 => this is the best rule for 61 predicted values *> Best rule #26956 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 77 *> proper extension: 01c40n; 0c8tk; 01t21q; 04swd; 02z0j; 01b8w_; 0d58_; 0d6yv; 03902; 079yb; ... *> query: (?x11000, ?x1159) <- place_of_death(?x11180, ?x11000), people(?x1158, ?x11180), location_of_ceremony(?x566, ?x11000), people(?x1158, ?x1159) *> conf = 0.04 ranks of expected_values: 604 EVAL 0r3tq place_of_death! 012dr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 186.000 72.000 0.135 http://example.org/people/deceased_person/place_of_death EVAL 0r3tq place_of_death! 0b80__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 72.000 0.135 http://example.org/people/deceased_person/place_of_death #8863-01xqw PRED entity: 01xqw PRED relation: role PRED expected values: 0l14qv => 53 concepts (45 used for prediction) PRED predicted values (max 10 best out of 97): 0l14qv (0.89 #1048, 0.87 #762, 0.85 #3243), 0myk8 (0.85 #3243, 0.81 #2192, 0.81 #3242), 01w4dy (0.85 #3243, 0.81 #2192, 0.81 #3242), 0l14md (0.83 #1232, 0.83 #1143, 0.80 #763), 05148p4 (0.81 #2192, 0.81 #3242, 0.80 #2864), 07_l6 (0.81 #2192, 0.81 #3242, 0.80 #2864), 0680x0 (0.81 #2192, 0.81 #3242, 0.80 #2864), 01679d (0.81 #2192, 0.81 #3242, 0.80 #2864), 06ncr (0.75 #415, 0.73 #468, 0.70 #606), 0jtg0 (0.75 #519, 0.73 #468, 0.69 #565) >> Best rule #1048 for best value: >> intensional similarity = 9 >> extensional distance = 16 >> proper extension: 05r5c; 0dwvl; 02g9p4; >> query: (?x4311, 0l14qv) <- role(?x2459, ?x4311), role(?x1495, ?x4311), ?x2459 = 021bmf, instrumentalists(?x4311, ?x3168), role(?x314, ?x4311), ?x314 = 02sgy, role(?x3735, ?x4311), artist(?x4483, ?x3168), instrumentalists(?x1495, ?x211) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xqw role 0l14qv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 45.000 0.889 http://example.org/music/performance_role/track_performances./music/track_contribution/role #8862-039v1 PRED entity: 039v1 PRED relation: profession! PRED expected values: 03c7ln 01vrncs 0137n0 0565cz 06gd4 01vtqml 037hgm 06m61 01vswx5 01bpnd 01p0vf 0fpj9pm 0j6cj 01d4cb 01797x 015196 01nn3m => 51 concepts (30 used for prediction) PRED predicted values (max 10 best out of 4028): 01271h (0.67 #37476, 0.60 #29332, 0.60 #25260), 0phx4 (0.67 #37696, 0.60 #29552, 0.60 #25480), 0g824 (0.67 #38628, 0.60 #26412, 0.57 #46773), 04mn81 (0.67 #37180, 0.60 #24964, 0.57 #45325), 045zr (0.67 #37386, 0.60 #25170, 0.57 #45531), 01w3lzq (0.67 #38196, 0.60 #25980, 0.50 #9694), 01w724 (0.67 #37420, 0.51 #44790, 0.50 #41493), 016szr (0.67 #38122, 0.51 #44790, 0.50 #9620), 01kv4mb (0.67 #37216, 0.51 #44790, 0.50 #8714), 0163r3 (0.67 #38724, 0.50 #42797, 0.50 #10222) >> Best rule #37476 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0fnpj; >> query: (?x2659, 01271h) <- profession(?x5405, ?x2659), profession(?x2876, ?x2659), profession(?x1652, ?x2659), award(?x5405, ?x567), ?x2876 = 01vn35l, award_winner(?x2698, ?x5405), award_nominee(?x1652, ?x368) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #24703 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 0dz3r; 0nbcg; *> query: (?x2659, 01vrncs) <- profession(?x5405, ?x2659), profession(?x4537, ?x2659), profession(?x2731, ?x2659), ?x5405 = 01vvlyt, ?x2731 = 01wwvc5, award(?x4537, ?x724), participant(?x4537, ?x1291) *> conf = 0.60 ranks of expected_values: 21, 35, 52, 69, 89, 184, 187, 208, 260, 293, 326, 345, 353, 521, 698, 774, 801 EVAL 039v1 profession! 01nn3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 015196 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 01797x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 01d4cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 0j6cj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 0fpj9pm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 01p0vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 01bpnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 01vswx5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 06m61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 037hgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 01vtqml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 06gd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 0565cz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 0137n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 01vrncs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 51.000 30.000 0.667 http://example.org/people/person/profession EVAL 039v1 profession! 03c7ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 51.000 30.000 0.667 http://example.org/people/person/profession #8861-04rrd PRED entity: 04rrd PRED relation: contains PRED expected values: 0tt6k 0cy8v 0cc1v => 189 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2760): 094jv (0.81 #96379, 0.80 #70095, 0.68 #58412), 0g8fs (0.72 #192772, 0.51 #146036, 0.50 #166484), 02p8454 (0.72 #192772, 0.51 #146036, 0.50 #166484), 0xszy (0.68 #58412, 0.08 #16110, 0.04 #21950), 0cc1v (0.63 #125590, 0.59 #245345, 0.52 #93458), 0mwq7 (0.63 #125590, 0.59 #245345, 0.08 #17026), 0mwcz (0.63 #125590, 0.59 #245345, 0.08 #16216), 0mwvq (0.63 #125590, 0.59 #245345, 0.08 #16000), 0mnz0 (0.63 #125590, 0.59 #245345, 0.04 #22664), 0mnlq (0.63 #125590, 0.59 #245345, 0.04 #22606) >> Best rule #96379 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 09hzw; >> query: (?x1767, ?x1705) <- adjoins(?x1767, ?x108), state(?x1705, ?x1767), contains(?x94, ?x1767) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #125590 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 0261m; *> query: (?x1767, ?x12515) <- contains(?x1767, ?x12233), contains(?x1767, ?x5390), school_type(?x5390, ?x13171), adjoins(?x12515, ?x12233) *> conf = 0.63 ranks of expected_values: 5, 853, 854 EVAL 04rrd contains 0cc1v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 189.000 86.000 0.807 http://example.org/location/location/contains EVAL 04rrd contains 0cy8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 189.000 86.000 0.807 http://example.org/location/location/contains EVAL 04rrd contains 0tt6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 189.000 86.000 0.807 http://example.org/location/location/contains #8860-09lxv9 PRED entity: 09lxv9 PRED relation: film! PRED expected values: 01hcj2 => 81 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1116): 02bh9 (0.65 #62132, 0.64 #68350, 0.64 #64205), 07rd7 (0.52 #78707, 0.49 #53845, 0.49 #97353), 027rwmr (0.49 #53845, 0.45 #78706, 0.44 #76634), 0f7hc (0.33 #828, 0.03 #15323, 0.02 #23610), 041c4 (0.33 #892, 0.03 #15387, 0.02 #17459), 04fhn_ (0.33 #681, 0.02 #29676, 0.02 #23463), 01b9z4 (0.33 #1639, 0.02 #24421, 0.01 #43061), 02_hj4 (0.33 #267, 0.02 #23049, 0.01 #25120), 01w23w (0.33 #1159, 0.02 #23941, 0.01 #28084), 02dth1 (0.33 #723, 0.02 #23505, 0.01 #27648) >> Best rule #62132 for best value: >> intensional similarity = 4 >> extensional distance = 710 >> proper extension: 0g60z; 080dwhx; 06cs95; 02k_4g; 019nnl; 0124k9; 08jgk1; 0584r4; 01xr2s; 03ln8b; ... >> query: (?x8906, ?x406) <- nominated_for(?x406, ?x8906), nominated_for(?x507, ?x8906), participant(?x241, ?x406), award_winner(?x670, ?x406) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09lxv9 film! 01hcj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 56.000 0.648 http://example.org/film/actor/film./film/performance/film #8859-04jjy PRED entity: 04jjy PRED relation: films PRED expected values: 0645k5 0571m 01k0vq => 59 concepts (18 used for prediction) PRED predicted values (max 10 best out of 503): 095zlp (0.36 #518, 0.10 #3637, 0.10 #3118), 02nczh (0.36 #518, 0.10 #3637, 0.10 #3118), 02fqxm (0.33 #514, 0.10 #3113, 0.09 #3632), 0d8w2n (0.33 #508, 0.10 #3107, 0.09 #3626), 06cgf (0.33 #486, 0.10 #3085, 0.09 #3604), 09v42sf (0.33 #477, 0.10 #3076, 0.09 #3595), 01fx4k (0.33 #464, 0.10 #3063, 0.09 #3582), 03hfmm (0.33 #427, 0.10 #3026, 0.09 #3545), 046f3p (0.33 #379, 0.10 #2978, 0.09 #3497), 0ptx_ (0.33 #303, 0.10 #2902, 0.09 #3421) >> Best rule #518 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 018h2; >> query: (?x942, ?x414) <- films(?x942, ?x8302), films(?x942, ?x943), ?x8302 = 04j14qc, genre(?x943, ?x53), titles(?x942, ?x414), film(?x100, ?x943), film_crew_role(?x943, ?x137) >> conf = 0.36 => this is the best rule for 2 predicted values *> Best rule #5352 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 28 *> proper extension: 0d06vc; 03n93; 081k8; 018jz; 06c97; 0jrg; 06796; 01lb5; *> query: (?x942, 0571m) <- films(?x942, ?x8302), produced_by(?x8302, ?x1554), film_crew_role(?x8302, ?x137), nominated_for(?x541, ?x8302), film(?x545, ?x8302), production_companies(?x80, ?x541), award(?x541, ?x1105) *> conf = 0.03 ranks of expected_values: 371 EVAL 04jjy films 01k0vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 18.000 0.364 http://example.org/film/film_subject/films EVAL 04jjy films 0571m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 59.000 18.000 0.364 http://example.org/film/film_subject/films EVAL 04jjy films 0645k5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 18.000 0.364 http://example.org/film/film_subject/films #8858-05_5_22 PRED entity: 05_5_22 PRED relation: currency PRED expected values: 09nqf => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.87 #169, 0.86 #211, 0.86 #218), 01nv4h (0.12 #16, 0.10 #149, 0.10 #30), 02gsvk (0.03 #118, 0.03 #132, 0.02 #153), 02l6h (0.02 #151, 0.01 #445, 0.01 #501) >> Best rule #169 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 03q8xj; >> query: (?x5201, 09nqf) <- featured_film_locations(?x5201, ?x362), film_crew_role(?x5201, ?x2472), ?x2472 = 01xy5l_ >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05_5_22 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.870 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8857-02cbs0 PRED entity: 02cbs0 PRED relation: film PRED expected values: 03qcfvw => 100 concepts (73 used for prediction) PRED predicted values (max 10 best out of 606): 0m313 (0.29 #13, 0.09 #28593, 0.04 #3587), 0prh7 (0.29 #834, 0.03 #4408, 0.01 #15130), 011yl_ (0.29 #587), 072x7s (0.29 #255), 03y0pn (0.14 #1255, 0.04 #3042, 0.04 #4829), 0cc5mcj (0.14 #390, 0.04 #2177), 03_gz8 (0.14 #1121, 0.04 #4695, 0.01 #8269), 01qz5 (0.14 #1413, 0.04 #4987, 0.01 #15709), 017kct (0.14 #582, 0.03 #4156, 0.03 #7730), 020y73 (0.14 #366, 0.03 #3940, 0.01 #7514) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0m31m; >> query: (?x5942, 0m313) <- gender(?x5942, ?x231), film(?x5942, ?x4159), ?x231 = 05zppz, ?x4159 = 011yr9 >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02cbs0 film 03qcfvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 73.000 0.286 http://example.org/film/actor/film./film/performance/film #8856-02zcnq PRED entity: 02zcnq PRED relation: school_type PRED expected values: 05jxkf => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.63 #348, 0.58 #26, 0.54 #486), 01rs41 (0.29 #901, 0.29 #1407, 0.28 #1591), 05pcjw (0.28 #760, 0.28 #898, 0.25 #1404), 07tf8 (0.22 #146, 0.19 #491, 0.17 #399), 01_srz (0.16 #25, 0.15 #71, 0.07 #1083), 02p0qmm (0.05 #9, 0.04 #1596, 0.04 #1527), 06cs1 (0.05 #28, 0.03 #166, 0.03 #189), 0bwd5 (0.05 #18, 0.02 #777, 0.01 #1720), 047951 (0.05 #30, 0.02 #1640, 0.01 #766), 04399 (0.05 #82, 0.04 #703, 0.04 #772) >> Best rule #348 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 0283sdr; 01zh3_; >> query: (?x4555, 05jxkf) <- colors(?x4555, ?x332), ?x332 = 01l849, school_type(?x4555, ?x1507), organization(?x346, ?x4555) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zcnq school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.626 http://example.org/education/educational_institution/school_type #8855-08qnnv PRED entity: 08qnnv PRED relation: major_field_of_study PRED expected values: 029g_vk => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 138): 02j62 (0.72 #5634, 0.68 #2200, 0.64 #3457), 01lj9 (0.66 #3812, 0.54 #5529, 0.53 #2210), 0_jm (0.61 #5088, 0.47 #4974, 0.33 #5317), 02lp1 (0.60 #1040, 0.53 #2183, 0.52 #4015), 0fdys (0.60 #1066, 0.53 #2209, 0.49 #5528), 04sh3 (0.60 #1098, 0.33 #984, 0.33 #527), 062z7 (0.58 #2197, 0.57 #3454, 0.50 #483), 05qfh (0.54 #3463, 0.53 #2206, 0.51 #5525), 0g4gr (0.52 #4376, 0.31 #4948, 0.30 #8349), 01tbp (0.50 #1086, 0.34 #3831, 0.33 #5663) >> Best rule #5634 for best value: >> intensional similarity = 7 >> extensional distance = 38 >> proper extension: 0bx8pn; 02fy0z; 03tw2s; 0cwx_; 0bwfn; 0trv; 0qlnr; 02l424; 0g2jl; 050xpd; ... >> query: (?x6315, 02j62) <- major_field_of_study(?x6315, ?x4321), major_field_of_study(?x6315, ?x742), major_field_of_study(?x3182, ?x742), major_field_of_study(?x3136, ?x742), ?x4321 = 0g26h, ?x3136 = 086xm, ?x3182 = 02ccqg >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #9606 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 85 *> proper extension: 03t4nx; *> query: (?x6315, ?x254) <- institution(?x3437, ?x6315), institution(?x1526, ?x6315), institution(?x865, ?x6315), ?x1526 = 0bkj86, ?x3437 = 02_xgp2, student(?x865, ?x1117), major_field_of_study(?x865, ?x254) *> conf = 0.11 ranks of expected_values: 96 EVAL 08qnnv major_field_of_study 029g_vk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 167.000 167.000 0.725 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8854-029v40 PRED entity: 029v40 PRED relation: genre PRED expected values: 03k9fj => 78 concepts (72 used for prediction) PRED predicted values (max 10 best out of 103): 07s9rl0 (0.90 #5893, 0.74 #3005, 0.62 #1322), 07ssc (0.62 #2644, 0.55 #4448, 0.55 #4689), 03k9fj (0.45 #131, 0.39 #1933, 0.39 #3616), 04xvlr (0.45 #3006, 0.21 #1321, 0.20 #2), 05p553 (0.41 #3489, 0.40 #4, 0.39 #2768), 0bkbm (0.36 #159, 0.30 #399, 0.21 #1321), 02l7c8 (0.35 #1336, 0.34 #3019, 0.28 #5666), 01hmnh (0.30 #17, 0.27 #1939, 0.25 #2420), 02n4kr (0.29 #2531, 0.23 #3253, 0.21 #488), 06n90 (0.28 #3377, 0.28 #3617, 0.25 #1934) >> Best rule #5893 for best value: >> intensional similarity = 5 >> extensional distance = 1119 >> proper extension: 0fq27fp; >> query: (?x10088, 07s9rl0) <- genre(?x10088, ?x604), genre(?x6009, ?x604), genre(?x5318, ?x604), ?x5318 = 0353xq, ?x6009 = 0b7l4x >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #131 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 08gsvw; *> query: (?x10088, 03k9fj) <- film(?x3692, ?x10088), currency(?x10088, ?x170), ?x3692 = 03kpvp, country(?x10088, ?x94) *> conf = 0.45 ranks of expected_values: 3 EVAL 029v40 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 72.000 0.897 http://example.org/film/film/genre #8853-0gslw PRED entity: 0gslw PRED relation: contains! PRED expected values: 0ctw_b => 85 concepts (29 used for prediction) PRED predicted values (max 10 best out of 100): 0ctw_b (0.72 #14390, 0.63 #17091, 0.58 #11689), 07ssc (0.69 #13522, 0.34 #11722, 0.34 #10822), 02jx1 (0.53 #13577, 0.27 #10877, 0.26 #11777), 09c7w0 (0.36 #18896, 0.34 #22493, 0.34 #19796), 05nrg (0.33 #567, 0.29 #1464, 0.25 #2363), 02qkt (0.32 #7539, 0.31 #8438, 0.30 #9338), 04_1l0v (0.24 #15741, 0.21 #22941, 0.21 #19344), 01n7q (0.23 #9969, 0.13 #12668, 0.10 #22568), 03rjj (0.19 #16200, 0.10 #10800, 0.10 #11700), 0d060g (0.18 #16203, 0.08 #10803, 0.08 #11703) >> Best rule #14390 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 07tgn; 0dhdp; 0fm2_; 022_6; 09tlh; 06y9v; 0978r; 0hyxv; 04p3c; 0fgj2; ... >> query: (?x14790, ?x1023) <- contains(?x14790, ?x13715), contains(?x1023, ?x13715), nationality(?x12733, ?x1023), ?x12733 = 01ckhj >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gslw contains! 0ctw_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 29.000 0.723 http://example.org/location/location/contains #8852-0cb77r PRED entity: 0cb77r PRED relation: nationality PRED expected values: 09c7w0 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 45): 09c7w0 (0.89 #6114, 0.80 #801, 0.79 #901), 0d060g (0.34 #9621, 0.33 #9520, 0.32 #4812), 06c1y (0.34 #9621, 0.03 #539), 03gj2 (0.33 #9520, 0.32 #4812, 0.03 #226), 07ssc (0.11 #515, 0.08 #2020, 0.08 #4225), 02jx1 (0.10 #5445, 0.10 #8951, 0.10 #3342), 03rk0 (0.05 #5358, 0.05 #10070, 0.05 #10271), 0345h (0.05 #531, 0.04 #4311, 0.04 #1935), 03_r3 (0.05 #112, 0.03 #212), 03rjj (0.05 #105, 0.02 #605, 0.02 #1407) >> Best rule #6114 for best value: >> intensional similarity = 2 >> extensional distance = 1420 >> proper extension: 07m69t; >> query: (?x200, 09c7w0) <- place_of_birth(?x200, ?x11669), source(?x11669, ?x958) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cb77r nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.891 http://example.org/people/person/nationality #8851-04q01mn PRED entity: 04q01mn PRED relation: currency PRED expected values: 09nqf => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.87 #64, 0.85 #85, 0.84 #197), 01nv4h (0.14 #9, 0.07 #23, 0.06 #44), 02l6h (0.07 #32, 0.06 #46, 0.06 #39), 02gsvk (0.02 #167, 0.02 #510, 0.01 #188), 0ptk_ (0.02 #73, 0.01 #94) >> Best rule #64 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 0bw20; >> query: (?x13884, 09nqf) <- executive_produced_by(?x13884, ?x8503), film(?x1365, ?x13884), award_winner(?x2060, ?x1365), ?x2060 = 054ky1 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04q01mn currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.869 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8850-027571b PRED entity: 027571b PRED relation: award! PRED expected values: 04qw17 => 42 concepts (19 used for prediction) PRED predicted values (max 10 best out of 912): 0p_th (0.50 #2152, 0.23 #1151, 0.11 #4155), 09cr8 (0.46 #1174, 0.29 #2175, 0.14 #4178), 03hkch7 (0.43 #2304, 0.10 #5308, 0.09 #4307), 07l450 (0.43 #2897, 0.09 #5901, 0.06 #6903), 0yzvw (0.36 #2212, 0.09 #5216, 0.08 #1211), 08zrbl (0.36 #2785, 0.08 #1784, 0.06 #6791), 092vkg (0.36 #2098, 0.07 #5102, 0.05 #6104), 011yhm (0.33 #663, 0.31 #1664, 0.21 #2665), 07cyl (0.33 #336, 0.31 #1337, 0.21 #2338), 0hmr4 (0.33 #64, 0.31 #1065, 0.14 #2066) >> Best rule #2152 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0f4x7; 027986c; 02rdyk7; 02y_rq5; 09cm54; 02x4x18; 02x4wr9; 09qv_s; 027c95y; 02w9sd7; ... >> query: (?x7192, 0p_th) <- award(?x2840, ?x7192), ?x2840 = 0f4vx, award_winner(?x7192, ?x2531), award_nominee(?x2531, ?x844) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3181 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 0789_m; 03nqnk3; 0bb57s; *> query: (?x7192, 04qw17) <- award_winner(?x7192, ?x5246), award_nominee(?x5246, ?x3604), ?x3604 = 03v3xp, nominated_for(?x5246, ?x857) *> conf = 0.12 ranks of expected_values: 211 EVAL 027571b award! 04qw17 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 19.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8849-017f4y PRED entity: 017f4y PRED relation: artists! PRED expected values: 02yv6b => 186 concepts (95 used for prediction) PRED predicted values (max 10 best out of 257): 064t9 (0.62 #10645, 0.61 #10020, 0.61 #8145), 01lyv (0.52 #2534, 0.40 #11915, 0.25 #2221), 0mhfr (0.48 #2524, 0.40 #11905, 0.19 #3776), 07sbbz2 (0.43 #11888, 0.14 #1881, 0.14 #7198), 0glt670 (0.37 #8173, 0.34 #10048, 0.33 #10673), 016clz (0.37 #5319, 0.36 #1878, 0.35 #12509), 025sc50 (0.34 #8183, 0.34 #10058, 0.33 #10683), 02lnbg (0.33 #8192, 0.31 #10067, 0.31 #10692), 01_bkd (0.33 #57, 0.12 #993, 0.11 #1618), 06cp5 (0.33 #95, 0.12 #1031, 0.11 #1656) >> Best rule #10645 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 03xnq9_; >> query: (?x10738, 064t9) <- profession(?x10738, ?x131), category(?x10738, ?x134), artists(?x1572, ?x10738), participant(?x10738, ?x7459) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1975 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 04bpm6; *> query: (?x10738, 02yv6b) <- role(?x10738, ?x1495), role(?x10738, ?x745), profession(?x10738, ?x131), ?x745 = 01vj9c, ?x1495 = 013y1f *> conf = 0.29 ranks of expected_values: 17 EVAL 017f4y artists! 02yv6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 186.000 95.000 0.618 http://example.org/music/genre/artists #8848-0sxlb PRED entity: 0sxlb PRED relation: nominated_for! PRED expected values: 02x73k6 02rdxsh 0gs96 => 91 concepts (81 used for prediction) PRED predicted values (max 10 best out of 200): 09cm54 (0.68 #5883, 0.68 #13129, 0.67 #13128), 099c8n (0.56 #279, 0.35 #1409, 0.25 #1635), 04kxsb (0.54 #312, 0.37 #1442, 0.28 #1668), 040njc (0.49 #233, 0.42 #1363, 0.39 #1589), 02pqp12 (0.49 #280, 0.41 #1410, 0.30 #1636), 02x17s4 (0.49 #311, 0.21 #537, 0.14 #1667), 054krc (0.44 #288, 0.29 #1418, 0.24 #1644), 02qyntr (0.43 #1524, 0.36 #394, 0.31 #1750), 0l8z1 (0.41 #275, 0.27 #1405, 0.24 #1631), 0gr0m (0.38 #281, 0.36 #1637, 0.35 #1411) >> Best rule #5883 for best value: >> intensional similarity = 3 >> extensional distance = 508 >> proper extension: 07bz5; >> query: (?x9761, ?x3209) <- award(?x9761, ?x3209), award(?x157, ?x3209), honored_for(?x7884, ?x9761) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2340 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 294 *> proper extension: 02wwmhc; *> query: (?x9761, 0gs96) <- genre(?x9761, ?x53), award_winner(?x9761, ?x241), honored_for(?x7884, ?x9761), ?x53 = 07s9rl0 *> conf = 0.26 ranks of expected_values: 21, 25, 52 EVAL 0sxlb nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 91.000 81.000 0.685 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0sxlb nominated_for! 02rdxsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 91.000 81.000 0.685 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0sxlb nominated_for! 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 91.000 81.000 0.685 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8847-01fm07 PRED entity: 01fm07 PRED relation: artists PRED expected values: 01w7nwm 044gyq => 45 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1509): 01dwrc (0.67 #2641, 0.50 #1578, 0.33 #515), 07ss8_ (0.67 #2291, 0.50 #1228, 0.33 #165), 0407f (0.64 #3467, 0.50 #1339, 0.44 #2402), 0127s7 (0.56 #2657, 0.55 #3722, 0.50 #1594), 01vvycq (0.56 #2173, 0.55 #3238, 0.50 #1110), 02l840 (0.56 #2176, 0.50 #1113, 0.38 #3191), 01trhmt (0.56 #2322, 0.50 #1259, 0.38 #3191), 01vvyvk (0.56 #2507, 0.50 #1444, 0.38 #3191), 01vrt_c (0.56 #2204, 0.50 #1141, 0.36 #3269), 0gbwp (0.56 #2469, 0.50 #1406, 0.36 #3534) >> Best rule #2641 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 064t9; 0glt670; 025sc50; 01fh36; 05lwjc; >> query: (?x8327, 01dwrc) <- artists(?x8327, ?x2908), artists(?x8327, ?x1378), ?x1378 = 01wcp_g, role(?x2908, ?x212), award_nominee(?x2908, ?x2415), artists(?x3319, ?x2908), ?x3319 = 06j6l >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1363 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 06j6l; *> query: (?x8327, 044gyq) <- artists(?x8327, ?x5405), artists(?x8327, ?x5225), artists(?x8327, ?x2908), artists(?x8327, ?x1378), ?x1378 = 01wcp_g, ?x2908 = 0161sp, ?x5225 = 01pq5j7, award(?x5405, ?x567) *> conf = 0.50 ranks of expected_values: 65, 104 EVAL 01fm07 artists 044gyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 45.000 19.000 0.667 http://example.org/music/genre/artists EVAL 01fm07 artists 01w7nwm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 45.000 19.000 0.667 http://example.org/music/genre/artists #8846-017gl1 PRED entity: 017gl1 PRED relation: film_release_region PRED expected values: 0hzlz 0345h 01pj7 01p1v 03rj0 06t2t 06t8v 0165v => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 74): 05r4w (0.87 #260, 0.86 #389, 0.84 #518), 0345h (0.86 #277, 0.84 #535, 0.83 #406), 06t2t (0.86 #429, 0.83 #558, 0.81 #300), 03rj0 (0.71 #298, 0.66 #427, 0.65 #556), 01p1v (0.62 #291, 0.60 #420, 0.56 #549), 06t8v (0.58 #314, 0.50 #443, 0.47 #572), 01pj7 (0.40 #288, 0.34 #546, 0.34 #417), 077qn (0.38 #452, 0.36 #581, 0.34 #323), 07t21 (0.36 #282, 0.34 #411, 0.33 #540), 06q1r (0.33 #5683, 0.30 #6716) >> Best rule #260 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 0fq7dv_; 01fmys; 072hx4; >> query: (?x972, 05r4w) <- film_release_region(?x972, ?x1497), film_release_region(?x972, ?x583), ?x583 = 015fr, ?x1497 = 015qh >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #277 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 121 *> proper extension: 0fq7dv_; 01fmys; 072hx4; *> query: (?x972, 0345h) <- film_release_region(?x972, ?x1497), film_release_region(?x972, ?x583), ?x583 = 015fr, ?x1497 = 015qh *> conf = 0.86 ranks of expected_values: 2, 3, 4, 5, 6, 7, 14, 34 EVAL 017gl1 film_release_region 0165v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 63.000 63.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gl1 film_release_region 06t8v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gl1 film_release_region 06t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gl1 film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gl1 film_release_region 01p1v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gl1 film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gl1 film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017gl1 film_release_region 0hzlz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 63.000 63.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8845-026t6 PRED entity: 026t6 PRED relation: instrumentalists PRED expected values: 01vvycq 01v3s2_ 09qr6 0161c2 015x1f 01wd9lv 095x_ => 76 concepts (46 used for prediction) PRED predicted values (max 10 best out of 3999): 03j24kf (0.76 #1105, 0.72 #2209, 0.71 #1104), 01w9wwg (0.76 #1105, 0.72 #2209, 0.71 #1104), 0144l1 (0.76 #1105, 0.72 #2209, 0.71 #1104), 0473q (0.76 #1105, 0.71 #5335, 0.71 #1104), 0161sp (0.76 #1105, 0.71 #1104, 0.70 #1657), 01vng3b (0.76 #1105, 0.71 #1104, 0.70 #1657), 0484q (0.76 #1105, 0.71 #1104, 0.70 #1657), 0fpj9pm (0.76 #1105, 0.71 #1104, 0.70 #1657), 07g2v (0.76 #1105, 0.71 #1104, 0.70 #1657), 03xl77 (0.76 #1105, 0.71 #1104, 0.70 #1657) >> Best rule #1105 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 0342h; >> query: (?x212, ?x8874) <- role(?x2620, ?x212), role(?x2460, ?x212), role(?x8874, ?x212), ?x2620 = 01kcd, instrumentalists(?x212, ?x7753), ?x2460 = 01wy6, ?x7753 = 03mszl, performance_role(?x212, ?x228), artist(?x1124, ?x8874) >> conf = 0.76 => this is the best rule for 36 predicted values *> Best rule #10528 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 9 *> proper extension: 042v_gx; 011k_j; *> query: (?x212, 01vvycq) <- role(?x3161, ?x212), role(?x2620, ?x212), role(?x10989, ?x212), instrumentalists(?x212, ?x226), performance_role(?x212, ?x228), role(?x212, ?x315), performance_role(?x6104, ?x2620), ?x10989 = 02s6sh, instrumentalists(?x3161, ?x140) *> conf = 0.64 ranks of expected_values: 49, 59, 90, 116, 223, 504, 867 EVAL 026t6 instrumentalists 095x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 76.000 46.000 0.761 http://example.org/music/instrument/instrumentalists EVAL 026t6 instrumentalists 01wd9lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 46.000 0.761 http://example.org/music/instrument/instrumentalists EVAL 026t6 instrumentalists 015x1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 76.000 46.000 0.761 http://example.org/music/instrument/instrumentalists EVAL 026t6 instrumentalists 0161c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 46.000 0.761 http://example.org/music/instrument/instrumentalists EVAL 026t6 instrumentalists 09qr6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 76.000 46.000 0.761 http://example.org/music/instrument/instrumentalists EVAL 026t6 instrumentalists 01v3s2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 46.000 0.761 http://example.org/music/instrument/instrumentalists EVAL 026t6 instrumentalists 01vvycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 76.000 46.000 0.761 http://example.org/music/instrument/instrumentalists #8844-014zcr PRED entity: 014zcr PRED relation: film PRED expected values: 02jkkv 025s1wg => 117 concepts (108 used for prediction) PRED predicted values (max 10 best out of 908): 0h03fhx (0.73 #12412, 0.63 #44333, 0.63 #42559), 0gg5qcw (0.73 #12412, 0.63 #44333, 0.63 #42559), 02q7yfq (0.25 #1188, 0.04 #79798, 0.04 #28372), 025s1wg (0.25 #1689, 0.03 #14101, 0.03 #10554), 084qpk (0.25 #119, 0.03 #10757, 0.02 #37357), 05ch98 (0.13 #23051, 0.12 #37238, 0.11 #54973), 03q0r1 (0.12 #625, 0.07 #2398, 0.02 #4171), 09cr8 (0.12 #281, 0.05 #148972, 0.03 #136555), 0ds5_72 (0.12 #1440, 0.04 #10305, 0.03 #17398), 0bxsk (0.12 #1193, 0.04 #79798, 0.04 #28372) >> Best rule #12412 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 035gjq; 06mt91; >> query: (?x286, ?x349) <- participant(?x286, ?x1554), nominated_for(?x286, ?x349), participant(?x286, ?x1735) >> conf = 0.73 => this is the best rule for 2 predicted values *> Best rule #1689 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 6 *> proper extension: 01xllf; 029k55; *> query: (?x286, 025s1wg) <- film(?x286, ?x4880), ?x4880 = 029k4p *> conf = 0.25 ranks of expected_values: 4, 294 EVAL 014zcr film 025s1wg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 108.000 0.729 http://example.org/film/actor/film./film/performance/film EVAL 014zcr film 02jkkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 117.000 108.000 0.729 http://example.org/film/actor/film./film/performance/film #8843-01f1r4 PRED entity: 01f1r4 PRED relation: institution! PRED expected values: 016t_3 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 16): 016t_3 (0.73 #39, 0.60 #57, 0.55 #76), 07s6fsf (0.52 #38, 0.47 #56, 0.39 #219), 027f2w (0.48 #43, 0.43 #61, 0.36 #80), 013zdg (0.35 #42, 0.29 #60, 0.28 #979), 03mkk4 (0.30 #45, 0.28 #979, 0.23 #82), 01rr_d (0.28 #979, 0.25 #67, 0.17 #49), 0bjrnt (0.28 #979, 0.23 #41, 0.19 #59), 022h5x (0.28 #979, 0.18 #52, 0.18 #143), 028dcg (0.28 #979, 0.15 #51, 0.15 #160), 071tyz (0.28 #979, 0.13 #62, 0.07 #44) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 01prf3; >> query: (?x4099, 016t_3) <- organization(?x4099, ?x5487), organization(?x6894, ?x5487), registering_agency(?x6894, ?x1982) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f1r4 institution! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.733 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #8842-0l14qv PRED entity: 0l14qv PRED relation: role! PRED expected values: 01vsxdm 01tp5bj 0fhxv 01vswwx 03ryks 02fybl 01w5jwb 02g40r => 97 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1082): 04bpm6 (0.75 #8972, 0.75 #8163, 0.70 #13429), 06x4l_ (0.75 #8212, 0.62 #9021, 0.60 #13478), 03c7ln (0.75 #8101, 0.55 #14179, 0.50 #13367), 0161sp (0.67 #5382, 0.64 #14293, 0.62 #8215), 01vsl3_ (0.67 #5780, 0.50 #9421, 0.50 #9016), 03j24kf (0.62 #9503, 0.62 #9098, 0.50 #13555), 01vs4ff (0.62 #8768, 0.60 #2695, 0.50 #9172), 01vsnff (0.62 #8991, 0.50 #13448, 0.50 #12231), 01bczm (0.62 #9128, 0.50 #13585, 0.50 #8319), 0j6cj (0.60 #3946, 0.60 #2732, 0.50 #9209) >> Best rule #8972 for best value: >> intensional similarity = 13 >> extensional distance = 6 >> proper extension: 026t6; >> query: (?x228, 04bpm6) <- role(?x2377, ?x228), role(?x1969, ?x228), role(?x1225, ?x228), instrumentalists(?x228, ?x6067), role(?x4186, ?x228), ?x1969 = 04rzd, performance_role(?x1267, ?x228), ?x2377 = 01bns_, ?x1225 = 01qbl, group(?x228, ?x1573), role(?x228, ?x645), ?x6067 = 018y81, category(?x4186, ?x134) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #11598 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 8 *> proper extension: 05842k; *> query: (?x228, 03ryks) <- role(?x3161, ?x228), role(?x2048, ?x228), role(?x1432, ?x228), role(?x1166, ?x228), role(?x316, ?x228), role(?x645, ?x228), ?x316 = 05r5c, ?x2048 = 018j2, ?x3161 = 01v1d8, ?x1432 = 0395lw, group(?x228, ?x1573), instrumentalists(?x1166, ?x5405), group(?x1166, ?x442), ?x5405 = 01vvlyt *> conf = 0.60 ranks of expected_values: 18, 38, 82, 176, 269, 293, 435, 438 EVAL 0l14qv role! 02g40r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 97.000 71.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0l14qv role! 01w5jwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 97.000 71.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0l14qv role! 02fybl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 97.000 71.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0l14qv role! 03ryks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 97.000 71.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0l14qv role! 01vswwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 97.000 71.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0l14qv role! 0fhxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 97.000 71.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0l14qv role! 01tp5bj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 97.000 71.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0l14qv role! 01vsxdm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 97.000 71.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role #8841-0hwqz PRED entity: 0hwqz PRED relation: student! PRED expected values: 033x5p => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 141): 01w5m (0.27 #631, 0.07 #3787, 0.05 #2209), 026gvfj (0.11 #111, 0.05 #637, 0.03 #1689), 02bq1j (0.11 #167, 0.01 #3849), 01stzp (0.09 #1036, 0.02 #2614), 0bwfn (0.08 #29733, 0.08 #29207, 0.08 #23420), 02l9wl (0.07 #1304, 0.05 #778, 0.02 #26554), 03ksy (0.07 #3788, 0.05 #19042, 0.04 #2736), 015nl4 (0.06 #3223, 0.05 #26369, 0.05 #7957), 07tg4 (0.06 #3768, 0.05 #2716, 0.04 #4294), 07tgn (0.06 #3699, 0.04 #14219, 0.04 #2647) >> Best rule #631 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 05zbm4; 01vrncs; 0k4gf; 0lccn; 0lrh; 02114t; 0b78hw; 01s3kv; 03f0324; 06wm0z; ... >> query: (?x5884, 01w5m) <- people(?x11490, ?x5884), gender(?x5884, ?x514), ?x11490 = 013b6_ >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hwqz student! 033x5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 111.000 0.273 http://example.org/education/educational_institution/students_graduates./education/education/student #8840-08720 PRED entity: 08720 PRED relation: film_release_region PRED expected values: 03rjj 082fr => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 158): 059j2 (0.86 #365, 0.84 #1522, 0.83 #2846), 03gj2 (0.85 #357, 0.79 #1514, 0.76 #2838), 0345h (0.81 #367, 0.76 #2848, 0.76 #1524), 015fr (0.79 #348, 0.69 #2498, 0.69 #2829), 03rjj (0.79 #2815, 0.79 #1491, 0.78 #2484), 05qhw (0.74 #345, 0.70 #1502, 0.69 #2826), 01znc_ (0.71 #378, 0.68 #1535, 0.66 #2859), 035qy (0.69 #369, 0.68 #1526, 0.67 #2850), 0154j (0.67 #2814, 0.67 #1490, 0.67 #2483), 03rt9 (0.67 #344, 0.61 #1501, 0.60 #2494) >> Best rule #365 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 0g56t9t; 0gtv7pk; 02x3lt7; 0crfwmx; 01c22t; 0872p_c; 0gj8t_b; 053tj7; 0dtfn; 03twd6; ... >> query: (?x641, 059j2) <- genre(?x641, ?x1013), film_release_region(?x641, ?x142), ?x142 = 0jgd, category(?x641, ?x134) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2815 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 307 *> proper extension: 0fpmrm3; *> query: (?x641, 03rjj) <- genre(?x641, ?x1013), film_release_region(?x641, ?x142), ?x142 = 0jgd *> conf = 0.79 ranks of expected_values: 5, 45 EVAL 08720 film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 77.000 77.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08720 film_release_region 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 77.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8839-011x_4 PRED entity: 011x_4 PRED relation: currency PRED expected values: 09nqf => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.82 #36, 0.81 #106, 0.78 #57), 01nv4h (0.03 #79, 0.03 #58, 0.03 #100), 02gsvk (0.01 #153), 088n7 (0.01 #63, 0.01 #77, 0.01 #84), 02l6h (0.01 #123) >> Best rule #36 for best value: >> intensional similarity = 3 >> extensional distance = 151 >> proper extension: 0gs973; 023cjg; >> query: (?x7656, 09nqf) <- genre(?x7656, ?x11523), films(?x11523, ?x408), featured_film_locations(?x7656, ?x1860) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011x_4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.824 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8838-02y7sr PRED entity: 02y7sr PRED relation: student! PRED expected values: 0778p => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 134): 0bwfn (0.40 #275, 0.25 #1327, 0.10 #51827), 02xwzh (0.20 #387, 0.12 #1439, 0.04 #4595), 04b_46 (0.20 #227, 0.12 #1279, 0.04 #45991), 0lfgr (0.20 #43, 0.12 #1095, 0.01 #45807), 07t90 (0.20 #147, 0.02 #7511, 0.02 #9089), 0fr9jp (0.12 #871, 0.06 #6657, 0.05 #3501), 0gl5_ (0.12 #770, 0.02 #51796, 0.01 #58109), 05krk (0.12 #533), 02g839 (0.11 #6337, 0.10 #2655, 0.10 #3181), 09f2j (0.10 #2789, 0.09 #5419, 0.09 #1737) >> Best rule #275 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 083chw; 05m883; 03rwng; >> query: (?x8560, 0bwfn) <- type_of_union(?x8560, ?x566), place_of_birth(?x8560, ?x5267), nationality(?x8560, ?x94), student(?x9865, ?x8560), ?x5267 = 0d9jr >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3266 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 0k60; *> query: (?x8560, 0778p) <- instrumentalists(?x228, ?x8560), place_of_birth(?x8560, ?x5267), nationality(?x8560, ?x94), ?x228 = 0l14qv *> conf = 0.05 ranks of expected_values: 29 EVAL 02y7sr student! 0778p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 150.000 150.000 0.400 http://example.org/education/educational_institution/students_graduates./education/education/student #8837-059j2 PRED entity: 059j2 PRED relation: form_of_government PRED expected values: 018wl5 => 247 concepts (247 used for prediction) PRED predicted values (max 10 best out of 4): 018wl5 (0.55 #42, 0.39 #162, 0.38 #106), 06cx9 (0.42 #561, 0.39 #731, 0.34 #333), 01d9r3 (0.34 #563, 0.33 #35, 0.32 #83), 026wp (0.17 #24, 0.16 #84, 0.15 #96) >> Best rule #42 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 09gnn; >> query: (?x1229, 018wl5) <- organizations_founded(?x1229, ?x1062), organization(?x1536, ?x1062), ?x1536 = 06c1y >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059j2 form_of_government 018wl5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 247.000 247.000 0.545 http://example.org/location/country/form_of_government #8836-01slc PRED entity: 01slc PRED relation: sport PRED expected values: 018jz => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 8): 018jz (0.86 #353, 0.85 #325, 0.85 #257), 02vx4 (0.69 #438, 0.67 #531, 0.65 #541), 0jm_ (0.42 #246, 0.39 #219, 0.38 #318), 09xp_ (0.33 #6, 0.10 #276, 0.03 #340), 018w8 (0.27 #404, 0.26 #347, 0.26 #376), 03tmr (0.19 #511, 0.18 #446, 0.15 #298), 039yzs (0.13 #360, 0.09 #425, 0.07 #295), 0z74 (0.02 #435, 0.01 #471, 0.01 #480) >> Best rule #353 for best value: >> intensional similarity = 12 >> extensional distance = 67 >> proper extension: 0jmfv; 0jm2v; 03lsq; 043vc; 0jmbv; 0jm4v; 0jmm4; 0jmjr; 0jm7n; 01k8vh; ... >> query: (?x7060, ?x5063) <- team(?x4244, ?x7060), draft(?x7060, ?x1161), school(?x7060, ?x581), team(?x4244, ?x6074), team(?x4244, ?x700), school(?x1161, ?x466), teams(?x1860, ?x7060), school(?x6074, ?x4904), ?x466 = 01pl14, school(?x700, ?x1428), sport(?x700, ?x5063), ?x4904 = 0lyjf >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01slc sport 018jz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.855 http://example.org/sports/sports_team/sport #8835-01ct6 PRED entity: 01ct6 PRED relation: school PRED expected values: 05kj_ => 99 concepts (70 used for prediction) PRED predicted values (max 10 best out of 406): 065y4w7 (0.62 #2465, 0.54 #10815, 0.50 #4740), 07szy (0.50 #585, 0.25 #773, 0.25 #395), 0lyjf (0.48 #9359, 0.44 #9169, 0.42 #7838), 07w0v (0.45 #4171, 0.44 #6446, 0.42 #4743), 01jq0j (0.35 #7307, 0.33 #5036, 0.33 #3327), 05krk (0.31 #5873, 0.28 #9290, 0.26 #7769), 09f2j (0.28 #10881, 0.22 #11068, 0.20 #1015), 012vwb (0.27 #4210, 0.20 #8197, 0.20 #3831), 0g8rj (0.25 #4624, 0.25 #838, 0.25 #650), 08qnnv (0.25 #855, 0.25 #667, 0.25 #477) >> Best rule #2465 for best value: >> intensional similarity = 17 >> extensional distance = 6 >> proper extension: 05tg3; >> query: (?x684, 065y4w7) <- team(?x1717, ?x684), team(?x935, ?x684), colors(?x684, ?x332), school(?x684, ?x2830), position_s(?x684, ?x3346), position_s(?x684, ?x2573), team(?x5412, ?x684), ?x3346 = 02g_7z, position(?x7312, ?x2573), position(?x4222, ?x2573), position(?x4189, ?x2573), ?x7312 = 0487_, ?x1717 = 02g_6x, ?x935 = 06b1q, team(?x2573, ?x387), ?x4222 = 051q5, ?x4189 = 026lg0s >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #13105 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 61 *> proper extension: 0jmdb; 0jmfv; 0jm2v; 0jml5; 0jmbv; 0jm64; 0jm4v; 07147; 0jmjr; 0jm7n; ... *> query: (?x684, ?x388) <- team(?x2312, ?x684), school(?x684, ?x2830), draft(?x684, ?x6462), team(?x2312, ?x5773), school(?x5773, ?x2171), team(?x5412, ?x684), school(?x6462, ?x388), ?x2171 = 01jq34 *> conf = 0.11 ranks of expected_values: 83 EVAL 01ct6 school 05kj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 99.000 70.000 0.625 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #8834-012v9y PRED entity: 012v9y PRED relation: type_of_union PRED expected values: 04ztj => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.88 #13, 0.88 #9, 0.87 #17), 01g63y (0.25 #413, 0.15 #46, 0.13 #170), 01bl8s (0.25 #413, 0.03 #7, 0.01 #19) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 036jp8; 03h_yfh; >> query: (?x7091, 04ztj) <- location(?x7091, ?x6987), award(?x7091, ?x2375), celebrities_impersonated(?x3649, ?x7091) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012v9y type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.882 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8833-01fxfk PRED entity: 01fxfk PRED relation: profession PRED expected values: 0dxtg => 111 concepts (72 used for prediction) PRED predicted values (max 10 best out of 73): 01d_h8 (0.71 #1049, 0.46 #6, 0.39 #304), 02hrh1q (0.70 #6725, 0.70 #5083, 0.69 #7321), 0dxtg (0.67 #908, 0.65 #461, 0.61 #1206), 02jknp (0.55 #1051, 0.26 #2392, 0.24 #2541), 0cbd2 (0.47 #1199, 0.46 #901, 0.35 #8953), 09jwl (0.46 #8966, 0.44 #3000, 0.33 #1361), 01c72t (0.44 #1366, 0.33 #3005, 0.22 #8971), 0nbcg (0.42 #3013, 0.34 #1374, 0.25 #8979), 03gjzk (0.35 #1059, 0.31 #16, 0.30 #314), 0kyk (0.35 #1223, 0.33 #925, 0.20 #478) >> Best rule #1049 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 05drq5; 0127m7; 02bfxb; 0hw1j; 03xp8d5; 01pp3p; 0gs1_; 01vb6z; 0bkf72; 06l6nj; >> query: (?x12872, 01d_h8) <- award_winner(?x3105, ?x12872), award_nominee(?x6237, ?x12872), award(?x5100, ?x3105), ?x5100 = 03flwk >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #908 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 76 *> proper extension: 0652ty; *> query: (?x12872, 0dxtg) <- profession(?x12872, ?x6421), ?x6421 = 02hv44_, type_of_union(?x12872, ?x566) *> conf = 0.67 ranks of expected_values: 3 EVAL 01fxfk profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 72.000 0.706 http://example.org/people/person/profession #8832-05q78ky PRED entity: 05q78ky PRED relation: child PRED expected values: 01yf92 => 192 concepts (169 used for prediction) PRED predicted values (max 10 best out of 232): 01qszl (0.33 #1360, 0.25 #508, 0.20 #848), 01swdw (0.33 #120, 0.25 #290, 0.20 #1144), 0225z1 (0.25 #341, 0.25 #287, 0.10 #1994), 01scmq (0.25 #495, 0.20 #1177, 0.20 #835), 03_c8p (0.25 #628, 0.20 #798, 0.17 #1310), 02b07b (0.25 #663, 0.06 #3404, 0.05 #3745), 032j_n (0.20 #2151, 0.17 #2322, 0.11 #3696), 01jx9 (0.20 #900, 0.15 #3811, 0.14 #5014), 0dwcl (0.20 #996, 0.15 #3907, 0.14 #5110), 025txrl (0.20 #977, 0.14 #1488, 0.10 #3888) >> Best rule #1360 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 09k0h5; >> query: (?x14458, 01qszl) <- industry(?x14458, ?x10022), citytown(?x14458, ?x9559), category(?x14458, ?x134), child(?x14458, ?x14246), ?x134 = 08mbj5d, contains(?x9559, ?x8951), citytown(?x8125, ?x9559), ?x8125 = 06q07 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4623 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 19 *> proper extension: 0g768; *> query: (?x14458, ?x244) <- category(?x14458, ?x134), child(?x14458, ?x14246), ?x134 = 08mbj5d, industry(?x14246, ?x245), industry(?x13890, ?x245), industry(?x244, ?x245), ?x13890 = 02b07b, category(?x14246, ?x134) *> conf = 0.04 ranks of expected_values: 161 EVAL 05q78ky child 01yf92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 192.000 169.000 0.333 http://example.org/organization/organization/child./organization/organization_relationship/child #8831-015fr PRED entity: 015fr PRED relation: adjoins! PRED expected values: 06nnj => 212 concepts (117 used for prediction) PRED predicted values (max 10 best out of 519): 0f8l9c (0.40 #814, 0.21 #38889, 0.19 #1592), 05rgl (0.25 #99, 0.13 #2429, 0.11 #10198), 0d060g (0.25 #10, 0.09 #72276, 0.08 #73053), 0b90_r (0.25 #4, 0.06 #27197, 0.06 #10103), 0hg5 (0.20 #909, 0.12 #1687, 0.08 #3241), 0154j (0.20 #781, 0.10 #5442, 0.10 #8550), 0jdd (0.20 #941, 0.09 #25031, 0.08 #3273), 05sb1 (0.20 #890, 0.09 #24980, 0.08 #3222), 04g61 (0.20 #1019, 0.09 #2573, 0.07 #4127), 04xn_ (0.20 #1052, 0.08 #3384, 0.07 #7267) >> Best rule #814 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06mx8; >> query: (?x583, 0f8l9c) <- contains(?x583, ?x1167), featured_film_locations(?x10515, ?x1167), titles(?x583, ?x7081) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #42737 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 080h2; 0n3g; 0f0sbl; 03rz4; 04kcn; 0ckhc; *> query: (?x583, ?x1144) <- vacationer(?x583, ?x1735), adjoins(?x9459, ?x583), adjoins(?x9459, ?x1144) *> conf = 0.18 ranks of expected_values: 18 EVAL 015fr adjoins! 06nnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 212.000 117.000 0.400 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #8830-056jm_ PRED entity: 056jm_ PRED relation: ceremony PRED expected values: 09n4nb => 37 concepts (33 used for prediction) PRED predicted values (max 10 best out of 135): 09n4nb (0.80 #714, 0.60 #178, 0.60 #1116), 02cg41 (0.77 #789, 0.67 #387, 0.60 #253), 01c6qp (0.77 #687, 0.60 #151, 0.57 #1089), 019bk0 (0.73 #684, 0.67 #282, 0.60 #148), 01bx35 (0.73 #675, 0.67 #273, 0.60 #139), 01mh_q (0.73 #753, 0.54 #1155, 0.54 #1021), 01s695 (0.71 #672, 0.54 #1074, 0.51 #940), 013b2h (0.71 #745, 0.53 #1147, 0.51 #1013), 01mhwk (0.70 #707, 0.52 #1109, 0.52 #975), 01xqqp (0.66 #760, 0.49 #1162, 0.47 #1028) >> Best rule #714 for best value: >> intensional similarity = 5 >> extensional distance = 102 >> proper extension: 054knh; >> query: (?x8331, 09n4nb) <- ceremony(?x8331, ?x2186), award_winner(?x2186, ?x5547), award_winner(?x2186, ?x4741), ?x5547 = 0dw4g, award(?x4741, ?x1232) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 056jm_ ceremony 09n4nb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 33.000 0.798 http://example.org/award/award_category/winners./award/award_honor/ceremony #8829-01b3bp PRED entity: 01b3bp PRED relation: type_of_union PRED expected values: 04ztj => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.73 #169, 0.71 #241, 0.71 #57), 01g63y (0.15 #14, 0.13 #242, 0.13 #226) >> Best rule #169 for best value: >> intensional similarity = 4 >> extensional distance = 870 >> proper extension: 0184jc; 02qgqt; 04yywz; 02bfmn; 06dv3; 0d_84; 01q_ph; 09fb5; 054_mz; 0bxtg; ... >> query: (?x14101, 04ztj) <- location(?x14101, ?x8263), film(?x14101, ?x2163), gender(?x14101, ?x231), ?x231 = 05zppz >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01b3bp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.726 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8828-08nvyr PRED entity: 08nvyr PRED relation: film! PRED expected values: 0dlglj => 104 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1159): 09swkk (0.56 #4170, 0.50 #14593, 0.45 #12508), 0c6qh (0.56 #4170, 0.45 #12508, 0.44 #18762), 02z2xdf (0.56 #4170, 0.45 #12508, 0.44 #18762), 02pq9yv (0.56 #4170, 0.45 #12508, 0.44 #18762), 03qmx_f (0.56 #4170, 0.45 #12508, 0.44 #18762), 092kgw (0.50 #14593, 0.40 #62546, 0.34 #56287), 014zcr (0.11 #37, 0.10 #2122, 0.05 #10460), 044rvb (0.08 #102, 0.05 #2187, 0.05 #10525), 03kpvp (0.08 #4803, 0.07 #25648, 0.07 #36070), 0lpjn (0.08 #4650, 0.06 #6734, 0.04 #25495) >> Best rule #4170 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 027qgy; 0gmgwnv; >> query: (?x4541, ?x2499) <- nominated_for(?x995, ?x4541), nominated_for(?x2499, ?x4541), film_release_region(?x4541, ?x94), ?x995 = 099tbz >> conf = 0.56 => this is the best rule for 5 predicted values *> Best rule #8598 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 075cph; 0fsw_7; 042g97; *> query: (?x4541, 0dlglj) <- nominated_for(?x4541, ?x1135), titles(?x53, ?x4541), honored_for(?x5592, ?x4541), country(?x4541, ?x94) *> conf = 0.03 ranks of expected_values: 198 EVAL 08nvyr film! 0dlglj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 104.000 33.000 0.556 http://example.org/film/actor/film./film/performance/film #8827-0n04r PRED entity: 0n04r PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #121, 0.83 #141, 0.82 #321), 07c52 (0.12 #3, 0.12 #8, 0.10 #63), 02nxhr (0.09 #112, 0.06 #173, 0.05 #162), 07z4p (0.08 #176, 0.07 #244, 0.07 #115) >> Best rule #121 for best value: >> intensional similarity = 4 >> extensional distance = 135 >> proper extension: 0gx9rvq; 03s5lz; 03twd6; 03sxd2; 01b195; 04g9gd; 03kg2v; 08k40m; 025n07; 033srr; ... >> query: (?x4024, 029j_) <- production_companies(?x4024, ?x541), film_release_region(?x4024, ?x87), genre(?x4024, ?x604), ?x604 = 0lsxr >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n04r film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.847 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #8826-016z7s PRED entity: 016z7s PRED relation: film! PRED expected values: 0dgskx => 79 concepts (31 used for prediction) PRED predicted values (max 10 best out of 838): 04kj2v (0.43 #52069, 0.43 #56235, 0.42 #47904), 016zp5 (0.41 #39573, 0.31 #8330, 0.31 #20828), 0l6qt (0.41 #39573, 0.31 #8330, 0.31 #20828), 0170pk (0.08 #4446, 0.06 #2364, 0.04 #282), 0z4s (0.07 #2150, 0.06 #68, 0.02 #33394), 0154qm (0.06 #561, 0.05 #8331, 0.03 #4725), 09fb5 (0.06 #58, 0.03 #2140, 0.02 #4222), 0pmhf (0.06 #441, 0.02 #4605, 0.01 #2523), 01yfm8 (0.06 #1294, 0.01 #15874, 0.01 #7540), 0gpprt (0.06 #1525, 0.01 #3607, 0.01 #34851) >> Best rule #52069 for best value: >> intensional similarity = 3 >> extensional distance = 681 >> proper extension: 028k2x; 07s8z_l; 06r1k; 025x1t; 03czz87; >> query: (?x2111, ?x2507) <- titles(?x53, ?x2111), award_winner(?x2111, ?x2507), genre(?x273, ?x53) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016z7s film! 0dgskx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 31.000 0.433 http://example.org/film/actor/film./film/performance/film #8825-01vw26l PRED entity: 01vw26l PRED relation: profession PRED expected values: 02hrh1q => 104 concepts (96 used for prediction) PRED predicted values (max 10 best out of 69): 02hrh1q (0.90 #1889, 0.89 #10689, 0.88 #6078), 09jwl (0.69 #1749, 0.67 #2326, 0.64 #3771), 016z4k (0.46 #2747, 0.42 #723, 0.42 #3759), 01c72t (0.41 #885, 0.34 #1464, 0.34 #1030), 018gz8 (0.40 #13, 0.26 #10967, 0.16 #1313), 02krf9 (0.28 #7942, 0.26 #10967, 0.21 #3345), 0d1pc (0.28 #7942, 0.26 #10967, 0.13 #766), 039v1 (0.27 #2343, 0.27 #1766, 0.23 #3788), 0cbd2 (0.26 #10967, 0.20 #5, 0.16 #5784), 0np9r (0.26 #10967, 0.20 #17, 0.15 #10695) >> Best rule #1889 for best value: >> intensional similarity = 3 >> extensional distance = 347 >> proper extension: 025p38; 05wjnt; 04b19t; 039crh; 01lqnff; 0418ft; 012x2b; 0q1lp; 01p47r; 050llt; ... >> query: (?x3494, 02hrh1q) <- nominated_for(?x3494, ?x1642), languages(?x3494, ?x254), profession(?x3494, ?x131) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vw26l profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 96.000 0.897 http://example.org/people/person/profession #8824-0340hj PRED entity: 0340hj PRED relation: music PRED expected values: 0fpjyd => 88 concepts (63 used for prediction) PRED predicted values (max 10 best out of 83): 02bh9 (0.29 #51, 0.09 #473, 0.08 #684), 023361 (0.21 #361, 0.08 #783, 0.05 #993), 01x6v6 (0.14 #334, 0.02 #1388, 0.02 #1177), 02jxmr (0.14 #496, 0.07 #285, 0.04 #5775), 04fzk (0.08 #2108, 0.07 #10781, 0.06 #12054), 015pkc (0.08 #2108, 0.07 #10781, 0.06 #12054), 028r4y (0.08 #2108, 0.05 #12687, 0.05 #12902), 0146pg (0.08 #643, 0.06 #3173, 0.06 #3385), 0150t6 (0.08 #679, 0.06 #2576, 0.06 #2787), 02g1jh (0.08 #761, 0.05 #971, 0.04 #2658) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 06ys2; >> query: (?x1511, 02bh9) <- nominated_for(?x5467, ?x1511), nominated_for(?x4106, ?x1511), ?x4106 = 04fzk, award_winner(?x395, ?x5467) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #5195 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 361 *> proper extension: 0522wp; *> query: (?x1511, 0fpjyd) <- film(?x541, ?x1511), category(?x1511, ?x134), ?x134 = 08mbj5d *> conf = 0.01 ranks of expected_values: 79 EVAL 0340hj music 0fpjyd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 88.000 63.000 0.286 http://example.org/film/film/music #8823-0164qt PRED entity: 0164qt PRED relation: film_production_design_by PRED expected values: 04_1nk => 87 concepts (80 used for prediction) PRED predicted values (max 10 best out of 18): 04_1nk (0.25 #76, 0.13 #170, 0.05 #107), 0d5wn3 (0.19 #103, 0.04 #292, 0.04 #197), 04kj2v (0.12 #65, 0.10 #159, 0.02 #472), 0dh73w (0.06 #164, 0.01 #477, 0.01 #987), 0cdf37 (0.03 #140, 0.03 #234, 0.02 #516), 0bytkq (0.03 #130, 0.01 #856, 0.01 #952), 05b5_tj (0.03 #154), 03gyh_z (0.03 #163, 0.01 #476), 018p4y (0.02 #345, 0.02 #629, 0.02 #1172), 02g1jh (0.02 #345, 0.02 #629, 0.02 #1172) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 02qrv7; 01kf4tt; 02n72k; 0fztbq; 025twgt; >> query: (?x835, 04_1nk) <- nominated_for(?x835, ?x1262), nominated_for(?x835, ?x836), ?x1262 = 0g5pv3, ?x836 = 02sg5v, country(?x835, ?x94) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0164qt film_production_design_by 04_1nk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 80.000 0.250 http://example.org/film/film/film_production_design_by #8822-02cg7g PRED entity: 02cg7g PRED relation: legislative_sessions! PRED expected values: 03tcbx => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 41): 03ww_x (0.87 #133, 0.86 #624, 0.86 #623), 032ft5 (0.87 #133, 0.86 #624, 0.86 #623), 024tkd (0.87 #133, 0.86 #624, 0.86 #623), 02bqmq (0.87 #133, 0.86 #624, 0.86 #623), 04gp1d (0.87 #133, 0.86 #624, 0.86 #623), 03tcbx (0.86 #624, 0.86 #623, 0.86 #1029), 060ny2 (0.86 #624, 0.86 #623, 0.86 #1029), 02cg7g (0.84 #449, 0.83 #1089, 0.80 #267), 043djx (0.62 #900, 0.49 #671, 0.48 #943), 01h7xx (0.62 #900, 0.49 #671, 0.48 #943) >> Best rule #133 for best value: >> intensional similarity = 47 >> extensional distance = 1 >> proper extension: 02bqmq; >> query: (?x4730, ?x6743) <- legislative_sessions(?x4730, ?x6933), legislative_sessions(?x4730, ?x6743), legislative_sessions(?x4730, ?x5339), legislative_sessions(?x4730, ?x4821), legislative_sessions(?x4730, ?x2976), legislative_sessions(?x4730, ?x2861), legislative_sessions(?x4730, ?x952), legislative_sessions(?x4730, ?x653), legislative_sessions(?x4730, ?x606), legislative_sessions(?x4730, ?x605), legislative_sessions(?x4730, ?x356), ?x2976 = 03rtmz, district_represented(?x4730, ?x6226), district_represented(?x4730, ?x4198), district_represented(?x4730, ?x2256), district_represented(?x4730, ?x1767), district_represented(?x4730, ?x1755), district_represented(?x4730, ?x1138), district_represented(?x4730, ?x1025), district_represented(?x4730, ?x961), district_represented(?x4730, ?x335), ?x335 = 059rby, ?x356 = 05l2z4, legislative_sessions(?x1027, ?x4730), ?x952 = 06f0dc, ?x1138 = 059_c, legislative_sessions(?x9334, ?x6743), legislative_sessions(?x652, ?x6743), ?x9334 = 02hy5d, ?x1767 = 04rrd, ?x1755 = 01x73, ?x4198 = 05fky, ?x606 = 03ww_x, legislative_sessions(?x4665, ?x6933), ?x6226 = 03gh4, ?x961 = 03s0w, ?x4821 = 02bqm0, ?x2256 = 07srw, legislative_sessions(?x2860, ?x4730), ?x5339 = 02glc4, legislative_sessions(?x2357, ?x4730), ?x2861 = 03tcbx, ?x653 = 070m6c, ?x605 = 077g7n, ?x652 = 021sv1, ?x1025 = 04ych, district_represented(?x6933, ?x177) >> conf = 0.87 => this is the best rule for 5 predicted values *> Best rule #624 for first EXPECTED value: *> intensional similarity = 39 *> extensional distance = 4 *> proper extension: 0495ys; *> query: (?x4730, ?x355) <- legislative_sessions(?x4730, ?x5339), legislative_sessions(?x4730, ?x4821), legislative_sessions(?x4730, ?x2976), legislative_sessions(?x4730, ?x1028), legislative_sessions(?x4730, ?x952), legislative_sessions(?x4730, ?x356), legislative_sessions(?x4730, ?x355), ?x2976 = 03rtmz, district_represented(?x4730, ?x2977), district_represented(?x4730, ?x2049), district_represented(?x4730, ?x1138), district_represented(?x4730, ?x1025), district_represented(?x4730, ?x448), district_represented(?x4730, ?x335), ?x335 = 059rby, ?x356 = 05l2z4, legislative_sessions(?x1829, ?x4730), ?x952 = 06f0dc, ?x4821 = 02bqm0, ?x5339 = 02glc4, first_level_division_of(?x1138, ?x94), district_represented(?x2712, ?x1025), contains(?x1025, ?x4356), ?x1028 = 032ft5, ?x1829 = 02bp37, capital(?x2049, ?x12644), ?x2712 = 01gst_, ?x2977 = 081mh, legislative_sessions(?x2860, ?x355), location(?x2295, ?x2049), jurisdiction_of_office(?x900, ?x1025), location(?x587, ?x1025), category(?x1025, ?x134), adjoins(?x2049, ?x1351), religion(?x448, ?x109), state_province_region(?x8287, ?x2049), place_of_birth(?x543, ?x4356), state_province_region(?x3367, ?x1138), source(?x4356, ?x958) *> conf = 0.86 ranks of expected_values: 6 EVAL 02cg7g legislative_sessions! 03tcbx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 38.000 38.000 0.867 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #8821-05pdd86 PRED entity: 05pdd86 PRED relation: film! PRED expected values: 02wgln => 82 concepts (35 used for prediction) PRED predicted values (max 10 best out of 979): 0169dl (0.33 #402, 0.14 #12903, 0.10 #14987), 02661h (0.27 #7650, 0.23 #11817, 0.15 #20151), 01wbg84 (0.25 #2131, 0.10 #54172, 0.09 #6298), 02_0d2 (0.25 #3261, 0.10 #54172, 0.05 #15762), 0184jc (0.25 #2089, 0.10 #54172, 0.05 #14590), 015c4g (0.25 #2865, 0.10 #54172, 0.05 #15366), 07cjqy (0.25 #2687, 0.09 #6854, 0.08 #11021), 045931 (0.25 #3987, 0.09 #8154, 0.08 #12321), 01pcbg (0.25 #2667, 0.08 #8918, 0.08 #11001), 0fx0mw (0.25 #2633, 0.05 #15134, 0.05 #27634) >> Best rule #402 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 07yk1xz; >> query: (?x6110, 0169dl) <- film_crew_role(?x6110, ?x7591), film_crew_role(?x6110, ?x5136), film_crew_role(?x6110, ?x468), ?x468 = 02r96rf, ?x5136 = 089g0h, titles(?x1510, ?x6110), executive_produced_by(?x6110, ?x3056), ?x7591 = 0d2b38, actor(?x1766, ?x3056) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #54172 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 181 *> proper extension: 05f67hw; *> query: (?x6110, ?x989) <- language(?x6110, ?x254), country(?x6110, ?x94), produced_by(?x6110, ?x1285), produced_by(?x667, ?x1285), program(?x1285, ?x10447), film(?x989, ?x667) *> conf = 0.10 ranks of expected_values: 81 EVAL 05pdd86 film! 02wgln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 82.000 35.000 0.333 http://example.org/film/actor/film./film/performance/film #8820-03r8gp PRED entity: 03r8gp PRED relation: industry! PRED expected values: 01qszl => 100 concepts (98 used for prediction) PRED predicted values (max 10 best out of 279): 02b07b (0.43 #1694, 0.40 #956, 0.33 #2432), 0dwl2 (0.43 #1478, 0.12 #5173, 0.11 #5914), 09glbnt (0.40 #319, 0.33 #2287, 0.30 #2533), 07k2x (0.40 #825, 0.33 #1317, 0.25 #2055), 0g1rw (0.40 #748, 0.33 #1240, 0.25 #1978), 03rwz3 (0.40 #834, 0.25 #2064, 0.22 #2310), 025hwq (0.40 #345, 0.25 #1821, 0.22 #2313), 01t9_0 (0.40 #886, 0.25 #2116, 0.22 #2362), 039cpd (0.40 #899, 0.25 #2129, 0.22 #2375), 081g_l (0.40 #789, 0.25 #2019, 0.22 #2265) >> Best rule #1694 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 01mf0; >> query: (?x10705, 02b07b) <- industry(?x9997, ?x10705), citytown(?x9997, ?x2611), category(?x9997, ?x134), film_release_region(?x903, ?x2611), ?x134 = 08mbj5d, place_of_death(?x7572, ?x2611), capital(?x1679, ?x2611) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #981 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 02jjt; *> query: (?x10705, 01qszl) <- industry(?x10773, ?x10705), industry(?x9997, ?x10705), production_companies(?x8234, ?x9997), production_companies(?x2676, ?x9997), category(?x10773, ?x134), film(?x9997, ?x1009), film_crew_role(?x2676, ?x137), genre(?x2676, ?x600), country(?x8234, ?x94) *> conf = 0.20 ranks of expected_values: 116 EVAL 03r8gp industry! 01qszl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 100.000 98.000 0.429 http://example.org/business/business_operation/industry #8819-019g40 PRED entity: 019g40 PRED relation: profession PRED expected values: 0dz3r => 121 concepts (60 used for prediction) PRED predicted values (max 10 best out of 59): 09jwl (0.74 #4431, 0.70 #5463, 0.70 #3401), 0dz3r (0.69 #1178, 0.65 #737, 0.55 #1325), 01d_h8 (0.45 #299, 0.38 #7963, 0.35 #446), 0dxtg (0.45 #307, 0.29 #454, 0.29 #7971), 0n1h (0.35 #746, 0.30 #2069, 0.25 #599), 01c72t (0.32 #2523, 0.29 #5468, 0.28 #5912), 039v1 (0.31 #3418, 0.31 #4448, 0.28 #3124), 02jknp (0.28 #7965, 0.18 #8112, 0.15 #5158), 03gjzk (0.28 #1043, 0.26 #3691, 0.25 #5165), 0d1pc (0.25 #784, 0.21 #1666, 0.20 #1372) >> Best rule #4431 for best value: >> intensional similarity = 4 >> extensional distance = 415 >> proper extension: 02mslq; 07_3qd; 011zf2; 01l03w2; 094xh; 01yzl2; 04mx7s; 014g91; >> query: (?x1953, 09jwl) <- nationality(?x1953, ?x94), artist(?x2149, ?x1953), artists(?x505, ?x1953), instrumentalists(?x316, ?x1953) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1178 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 56 *> proper extension: 0412f5y; 0415mzy; 067nsm; 01vs73g; 01wwnh2; *> query: (?x1953, 0dz3r) <- nationality(?x1953, ?x94), artists(?x3562, ?x1953), artists(?x2937, ?x1953), ?x3562 = 025sc50, ?x2937 = 0glt670 *> conf = 0.69 ranks of expected_values: 2 EVAL 019g40 profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 60.000 0.739 http://example.org/people/person/profession #8818-01vb6z PRED entity: 01vb6z PRED relation: award PRED expected values: 0gq9h => 126 concepts (105 used for prediction) PRED predicted values (max 10 best out of 318): 0gq9h (0.54 #2462, 0.35 #12015, 0.34 #11219), 0gr4k (0.52 #1225, 0.35 #4807, 0.28 #5205), 019f4v (0.49 #2451, 0.30 #1257, 0.22 #4839), 0gs9p (0.48 #2464, 0.30 #1270, 0.27 #76), 03hkv_r (0.45 #1208, 0.26 #4790, 0.23 #5188), 02x17s4 (0.39 #1315, 0.18 #4897, 0.18 #5295), 0cjcbg (0.38 #1155, 0.13 #39010, 0.13 #41798), 02n9nmz (0.33 #1260, 0.22 #4842, 0.19 #5240), 09sb52 (0.32 #16358, 0.25 #14766, 0.24 #21532), 0cjyzs (0.31 #898, 0.14 #37018, 0.14 #6369) >> Best rule #2462 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 03flwk; >> query: (?x6698, 0gq9h) <- award(?x6698, ?x198), type_of_union(?x6698, ?x566), ?x198 = 040njc >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vb6z award 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 105.000 0.540 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8817-02z_b PRED entity: 02z_b PRED relation: company! PRED expected values: 02k13d => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 37): 060c4 (0.67 #3714, 0.62 #3807, 0.60 #3853), 0dq_5 (0.57 #3804, 0.55 #3156, 0.45 #994), 0krdk (0.45 #983, 0.43 #7, 0.40 #194), 0dq3c (0.39 #746, 0.30 #189, 0.27 #3713), 02k13d (0.38 #61, 0.25 #107, 0.24 #2694), 021q1c (0.38 #151, 0.24 #2694, 0.20 #1595), 07t3gd (0.25 #163, 0.24 #2694, 0.09 #1746), 05_wyz (0.24 #2694, 0.24 #2666, 0.22 #2066), 09d6p2 (0.24 #2694, 0.21 #996, 0.21 #2667), 01yc02 (0.24 #2694, 0.19 #3859, 0.19 #3905) >> Best rule #3714 for best value: >> intensional similarity = 4 >> extensional distance = 195 >> proper extension: 09c7w0; 0f8l9c; 0l8sx; 03rj0; 0f1nl; 01_8w2; 02hcxm; 04htfd; 061v5m; 0537b; ... >> query: (?x13568, 060c4) <- company(?x8314, ?x13568), company(?x8314, ?x2554), program(?x2554, ?x50), contact_category(?x2554, ?x897) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #61 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01gl9g; *> query: (?x13568, 02k13d) <- company(?x8314, ?x13568), ?x8314 = 014l7h, state_province_region(?x13568, ?x335), child(?x9077, ?x13568) *> conf = 0.38 ranks of expected_values: 5 EVAL 02z_b company! 02k13d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 175.000 175.000 0.670 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #8816-019nnl PRED entity: 019nnl PRED relation: actor PRED expected values: 0gz5hs => 59 concepts (51 used for prediction) PRED predicted values (max 10 best out of 620): 027j79k (0.50 #1854, 0.38 #12035, 0.38 #12034), 0d9_96 (0.50 #1854, 0.38 #12035, 0.38 #12034), 05_swj (0.50 #1854, 0.38 #12035, 0.38 #12034), 0582cf (0.25 #1623, 0.25 #695, 0.07 #5326), 0f87jy (0.25 #1714, 0.10 #9258, 0.09 #16663), 0725ny (0.25 #1565, 0.06 #6193, 0.05 #5268), 02pzck (0.25 #1693, 0.02 #2619, 0.02 #5396), 02q6cv4 (0.11 #3705, 0.10 #9258, 0.09 #16663), 0f721s (0.10 #9258, 0.09 #16663, 0.09 #13886), 02wcx8c (0.07 #20367, 0.04 #1976, 0.04 #4753) >> Best rule #1854 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03y3bp7; 03nymk; >> query: (?x1395, ?x1537) <- nominated_for(?x3673, ?x1395), nominated_for(?x1537, ?x1395), ?x3673 = 021yw7, titles(?x2008, ?x1395), country_of_origin(?x1395, ?x94) >> conf = 0.50 => this is the best rule for 3 predicted values *> Best rule #6631 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 0d_rw; *> query: (?x1395, 0gz5hs) <- tv_program(?x1537, ?x1395), program(?x2554, ?x1395), genre(?x1395, ?x258) *> conf = 0.04 ranks of expected_values: 54 EVAL 019nnl actor 0gz5hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 59.000 51.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #8815-0q96 PRED entity: 0q96 PRED relation: category PRED expected values: 08mbj5d => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14796, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q96 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #8814-01_6dw PRED entity: 01_6dw PRED relation: award_winner! PRED expected values: 0d085 => 104 concepts (102 used for prediction) PRED predicted values (max 10 best out of 303): 04mqgr (0.40 #19310, 0.38 #5151, 0.37 #24031), 04njml (0.40 #19310, 0.38 #5151, 0.37 #24031), 04dn09n (0.40 #19310, 0.37 #24031, 0.37 #33481), 03hkv_r (0.40 #19310, 0.37 #24031, 0.37 #33481), 02n9nmz (0.40 #19310, 0.37 #24031, 0.37 #33481), 02x17s4 (0.40 #19310, 0.37 #24031, 0.37 #33481), 01lk0l (0.33 #707, 0.17 #20169, 0.15 #24890), 0m7yy (0.33 #609, 0.07 #2326, 0.07 #3613), 01lj_c (0.22 #723, 0.17 #20169, 0.15 #27037), 05p1dby (0.21 #3541, 0.07 #2254, 0.06 #966) >> Best rule #19310 for best value: >> intensional similarity = 3 >> extensional distance = 1219 >> proper extension: 012ljv; 0411q; 015rmq; 0244r8; 01sbf2; 030_1_; 01dw9z; 027l0b; 094wz7q; 0khth; ... >> query: (?x6534, ?x384) <- award_winner(?x7606, ?x6534), award_winner(?x6534, ?x5714), award(?x6534, ?x384) >> conf = 0.40 => this is the best rule for 6 predicted values *> Best rule #1537 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 0gthm; *> query: (?x6534, 0d085) <- award(?x6534, ?x7606), profession(?x6534, ?x353), ?x7606 = 01l78d *> conf = 0.15 ranks of expected_values: 20 EVAL 01_6dw award_winner! 0d085 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 104.000 102.000 0.398 http://example.org/award/award_category/winners./award/award_honor/award_winner #8813-02f6xy PRED entity: 02f6xy PRED relation: award! PRED expected values: 0lbj1 01q32bd 03q2t9 01htxr 0b_j2 02vr7 => 42 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2484): 0j1yf (0.85 #16650, 0.84 #19980, 0.81 #26641), 01vsgrn (0.85 #16650, 0.84 #19980, 0.81 #23310), 0gcs9 (0.85 #16650, 0.84 #19980, 0.81 #23310), 0147dk (0.85 #16650, 0.84 #19980, 0.81 #23310), 01j4ls (0.85 #16650, 0.84 #19980, 0.81 #23310), 063t3j (0.85 #16650, 0.84 #19980, 0.81 #23310), 01vv7sc (0.85 #16650, 0.84 #19980, 0.81 #23310), 06mt91 (0.60 #11940, 0.50 #5280, 0.33 #15270), 0lbj1 (0.54 #16694, 0.52 #20024, 0.50 #13364), 02z4b_8 (0.54 #18688, 0.50 #15358, 0.43 #22018) >> Best rule #16650 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 02f5qb; >> query: (?x3926, ?x521) <- award_winner(?x3926, ?x521), award(?x8166, ?x3926), award(?x6877, ?x3926), award(?x1231, ?x3926), ?x6877 = 0ddkf, profession(?x1231, ?x131), ?x8166 = 0bs1g5r >> conf = 0.85 => this is the best rule for 7 predicted values *> Best rule #16694 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 025m8y; 02f72n; 099vwn; *> query: (?x3926, 0lbj1) <- award_winner(?x3926, ?x521), award(?x6877, ?x3926), award(?x1231, ?x3926), ?x6877 = 0ddkf, profession(?x1231, ?x131) *> conf = 0.54 ranks of expected_values: 9, 14, 85, 131, 208, 209 EVAL 02f6xy award! 02vr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 23.000 0.847 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6xy award! 0b_j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 42.000 23.000 0.847 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6xy award! 01htxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 42.000 23.000 0.847 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6xy award! 03q2t9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 23.000 0.847 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6xy award! 01q32bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 42.000 23.000 0.847 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6xy award! 0lbj1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 42.000 23.000 0.847 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8812-02rq7nd PRED entity: 02rq7nd PRED relation: genre PRED expected values: 0lsxr => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 116): 05p553 (0.57 #1334, 0.54 #1501, 0.52 #3250), 0lsxr (0.56 #175, 0.18 #1172, 0.18 #1256), 01z4y (0.42 #682, 0.41 #1348, 0.40 #848), 0c4xc (0.38 #1540, 0.29 #3289, 0.26 #2537), 01t_vv (0.33 #1114, 0.27 #2030, 0.26 #1614), 0c031k6 (0.33 #221, 0.10 #719, 0.09 #304), 03k9fj (0.27 #4511, 0.25 #11, 0.24 #5255), 0hcr (0.25 #19, 0.22 #5274, 0.20 #1349), 06q7n (0.25 #45, 0.21 #1125, 0.19 #2456), 0jxy (0.25 #32, 0.10 #4532, 0.10 #5287) >> Best rule #1334 for best value: >> intensional similarity = 6 >> extensional distance = 49 >> proper extension: 09rfpk; >> query: (?x14197, 05p553) <- program(?x10016, ?x14197), genre(?x14197, ?x12176), service_language(?x10016, ?x254), ?x254 = 02h40lc, genre(?x9562, ?x12176), ?x9562 = 07wqr6 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #175 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 02sqkh; *> query: (?x14197, 0lsxr) <- program(?x10016, ?x14197), category(?x10016, ?x134), genre(?x14197, ?x12176), genre(?x14197, ?x53), ?x12176 = 02fgmn, languages(?x14197, ?x254), ?x53 = 07s9rl0 *> conf = 0.56 ranks of expected_values: 2 EVAL 02rq7nd genre 0lsxr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 108.000 0.569 http://example.org/tv/tv_program/genre #8811-0lgm5 PRED entity: 0lgm5 PRED relation: location PRED expected values: 0cc56 => 208 concepts (190 used for prediction) PRED predicted values (max 10 best out of 318): 02_286 (0.36 #6463, 0.33 #840, 0.24 #11288), 0xkq4 (0.31 #24112, 0.30 #16876, 0.29 #19287), 05fkf (0.25 #4054, 0.20 #38, 0.18 #5661), 030qb3t (0.22 #77256, 0.22 #82081, 0.19 #95750), 0dclg (0.20 #117, 0.14 #3330, 0.14 #2527), 0179qv (0.20 #770, 0.14 #3983, 0.14 #3180), 0vmt (0.20 #45, 0.14 #3258, 0.14 #2455), 0sgxg (0.20 #746, 0.14 #2353, 0.12 #4762), 0fhp9 (0.15 #17722, 0.12 #12097, 0.09 #18526), 0c_m3 (0.14 #8304, 0.09 #9912, 0.05 #21166) >> Best rule #6463 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0343h; 06pj8; 06kb_; 06g4_; >> query: (?x2836, 02_286) <- peers(?x2835, ?x2836), gender(?x2836, ?x231), place_of_birth(?x2836, ?x10564), languages(?x2836, ?x254) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #20952 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 021sv1; 0mb5x; 01ynzf; 02v2jy; *> query: (?x2836, 0cc56) <- place_of_death(?x2836, ?x1189), category(?x2836, ?x134), people(?x2510, ?x2836) *> conf = 0.10 ranks of expected_values: 20 EVAL 0lgm5 location 0cc56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 208.000 190.000 0.364 http://example.org/people/person/places_lived./people/place_lived/location #8810-044zvm PRED entity: 044zvm PRED relation: film PRED expected values: 03nx8mj => 81 concepts (45 used for prediction) PRED predicted values (max 10 best out of 623): 027qgy (0.67 #35783, 0.59 #55461, 0.59 #64406), 02kk_c (0.67 #35783, 0.59 #55461, 0.59 #64406), 07024 (0.12 #2269, 0.12 #479, 0.11 #4058), 01y9r2 (0.12 #3134, 0.12 #1344, 0.04 #6712), 0gvs1kt (0.12 #2326, 0.12 #536, 0.04 #5904), 09q23x (0.12 #2641, 0.12 #851, 0.03 #44728), 09w6br (0.12 #3463, 0.12 #1673, 0.03 #44728), 02d413 (0.12 #1793, 0.12 #3, 0.03 #23260), 02ndy4 (0.12 #3487, 0.12 #1697, 0.03 #23260), 01xdxy (0.12 #3355, 0.12 #1565, 0.03 #23260) >> Best rule #35783 for best value: >> intensional similarity = 3 >> extensional distance = 423 >> proper extension: 02dlfh; >> query: (?x12041, ?x238) <- film(?x12041, ?x1702), nominated_for(?x12041, ?x238), participant(?x496, ?x12041) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #6064 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 0gv40; 01c6l; *> query: (?x12041, 03nx8mj) <- produced_by(?x238, ?x12041), award_nominee(?x798, ?x12041), participant(?x496, ?x12041) *> conf = 0.02 ranks of expected_values: 227 EVAL 044zvm film 03nx8mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 81.000 45.000 0.666 http://example.org/film/actor/film./film/performance/film #8809-02_286 PRED entity: 02_286 PRED relation: place_of_birth! PRED expected values: 03x3qv 01cv3n 0pgjm 02fgpf 015c2f 025n3p 01kstn9 03f19q4 04crrxr 014ps4 05yzt_ 0bkf72 01ycfv 0829rj => 128 concepts (118 used for prediction) PRED predicted values (max 10 best out of 2593): 0jsg0m (0.34 #98101, 0.33 #1412, 0.33 #198599), 0146pg (0.34 #98101, 0.33 #198599, 0.33 #256021), 01vs_v8 (0.34 #98101, 0.33 #198599, 0.33 #256021), 01vsl3_ (0.34 #98101, 0.33 #198599, 0.33 #256021), 06449 (0.34 #98101, 0.33 #198599, 0.33 #256021), 0pyg6 (0.34 #98101, 0.33 #198599, 0.33 #256021), 0l12d (0.34 #98101, 0.33 #198599, 0.33 #256021), 06mn7 (0.34 #98101, 0.33 #198599, 0.33 #256021), 05bpg3 (0.34 #98101, 0.33 #198599, 0.33 #256021), 01ps2h8 (0.34 #98101, 0.33 #198599, 0.33 #256021) >> Best rule #98101 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 07ssc; 0hzlz; 0wh3; 04ykg; 0f2w0; 03rk0; 02h6_6p; 0ftxw; 0fvzg; 09tlh; ... >> query: (?x739, ?x305) <- place_of_birth(?x65, ?x739), location(?x305, ?x739), teams(?x739, ?x799) >> conf = 0.34 => this is the best rule for 233 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 30, 31, 466, 485, 526, 2003, 2022, 2132, 2158, 2177, 2203 EVAL 02_286 place_of_birth! 0829rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 01ycfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 0bkf72 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 05yzt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 014ps4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 04crrxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 03f19q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 01kstn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 025n3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 015c2f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 02fgpf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 0pgjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 01cv3n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 118.000 0.335 http://example.org/people/person/place_of_birth EVAL 02_286 place_of_birth! 03x3qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 118.000 0.335 http://example.org/people/person/place_of_birth #8808-07h34 PRED entity: 07h34 PRED relation: jurisdiction_of_office! PRED expected values: 0f6c3 => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 22): 0f6c3 (0.89 #366, 0.87 #176, 0.84 #91), 0pqc5 (0.69 #1771, 0.60 #1098, 0.57 #1498), 060c4 (0.68 #1455, 0.50 #1791, 0.50 #1581), 060bp (0.59 #1453, 0.43 #1789, 0.42 #2147), 0fkzq (0.36 #2693, 0.36 #99, 0.29 #184), 01t7n9 (0.36 #2693, 0.25 #59, 0.25 #17), 04syw (0.30 #259, 0.20 #111, 0.17 #196), 0fj45 (0.28 #271, 0.17 #123, 0.14 #81), 0789n (0.25 #9, 0.20 #114, 0.20 #30), 0dq3c (0.25 #2, 0.12 #1454, 0.11 #1811) >> Best rule #366 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 0g0syc; >> query: (?x3778, 0f6c3) <- district_represented(?x9702, ?x3778), district_represented(?x9702, ?x2020), ?x2020 = 05k7sb >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07h34 jurisdiction_of_office! 0f6c3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 187.000 0.889 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #8807-01l1b90 PRED entity: 01l1b90 PRED relation: film PRED expected values: 0872p_c => 133 concepts (97 used for prediction) PRED predicted values (max 10 best out of 672): 0872p_c (0.50 #175, 0.01 #157523), 040_lv (0.25 #1046, 0.04 #2834, 0.03 #4622), 05650n (0.25 #1011, 0.02 #31407, 0.02 #15315), 0gh65c5 (0.25 #597, 0.02 #32781, 0.02 #48873), 017jd9 (0.25 #779, 0.02 #147398, 0.02 #47267), 0ndwt2w (0.25 #999, 0.02 #76095, 0.01 #147618), 07z6xs (0.25 #883, 0.02 #29491, 0.01 #36643), 01dyvs (0.25 #280, 0.02 #28888, 0.01 #36040), 01738w (0.25 #1127, 0.01 #44039, 0.01 #47615), 0x25q (0.25 #503, 0.01 #61295, 0.01 #72023) >> Best rule #175 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 016ypb; 057_yx; >> query: (?x250, 0872p_c) <- award(?x250, ?x2563), film(?x250, ?x5277), type_of_union(?x250, ?x566), ?x5277 = 047csmy >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l1b90 film 0872p_c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 97.000 0.500 http://example.org/film/actor/film./film/performance/film #8806-0fq7dv_ PRED entity: 0fq7dv_ PRED relation: film_release_region PRED expected values: 0d060g 02k54 02vzc 06mkj => 77 concepts (74 used for prediction) PRED predicted values (max 10 best out of 158): 06mkj (0.92 #178, 0.89 #43, 0.88 #588), 03_3d (0.87 #5, 0.85 #140, 0.84 #550), 02vzc (0.87 #38, 0.80 #1128, 0.79 #1263), 0d060g (0.85 #1096, 0.80 #6, 0.79 #1636), 0b90_r (0.80 #1093, 0.80 #138, 0.78 #3), 047yc (0.64 #21, 0.57 #1111, 0.54 #1246), 04gzd (0.64 #1098, 0.62 #8, 0.58 #1233), 016wzw (0.62 #51, 0.52 #1276, 0.52 #1141), 03rk0 (0.58 #42, 0.55 #1132, 0.52 #177), 09pmkv (0.58 #20, 0.46 #155, 0.41 #1245) >> Best rule #178 for best value: >> intensional similarity = 7 >> extensional distance = 59 >> proper extension: 02prwdh; 0ndsl1x; >> query: (?x1915, 06mkj) <- film_release_region(?x1915, ?x1603), film_release_region(?x1915, ?x550), film_release_region(?x1915, ?x304), ?x304 = 0d0vqn, ?x1603 = 06bnz, titles(?x571, ?x1915), ?x550 = 05v8c >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 11 EVAL 0fq7dv_ film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 74.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fq7dv_ film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 74.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fq7dv_ film_release_region 02k54 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 77.000 74.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fq7dv_ film_release_region 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 74.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8805-02ht0ln PRED entity: 02ht0ln PRED relation: group! PRED expected values: 05148p4 => 91 concepts (70 used for prediction) PRED predicted values (max 10 best out of 119): 05148p4 (0.73 #1871, 0.72 #1518, 0.72 #2049), 03bx0bm (0.71 #291, 0.67 #201, 0.65 #1524), 018vs (0.71 #279, 0.65 #2043, 0.64 #1512), 028tv0 (0.62 #455, 0.43 #1511, 0.42 #2042), 05r5c (0.38 #450, 0.28 #1506, 0.25 #1948), 03qjg (0.36 #1635, 0.35 #1459, 0.33 #1547), 0l14qv (0.31 #1857, 0.25 #2476, 0.24 #1592), 07gql (0.29 #303, 0.19 #1096, 0.17 #213), 06ncr (0.24 #1098, 0.20 #127, 0.18 #1891), 026t6 (0.20 #91, 0.17 #179, 0.15 #2738) >> Best rule #1871 for best value: >> intensional similarity = 7 >> extensional distance = 97 >> proper extension: 0167_s; 03xhj6; 018gm9; 0123r4; 02vgh; 08w4pm; 02hzz; 0jn38; 0qmny; 0qmpd; ... >> query: (?x14291, 05148p4) <- group(?x227, ?x14291), artists(?x283, ?x14291), artists(?x283, ?x4918), artists(?x283, ?x460), ?x460 = 0fp_v1x, instrumentalists(?x716, ?x4918), group(?x4918, ?x6475) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ht0ln group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 70.000 0.727 http://example.org/music/performance_role/regular_performances./music/group_membership/group #8804-018pj3 PRED entity: 018pj3 PRED relation: profession PRED expected values: 0dz3r => 120 concepts (80 used for prediction) PRED predicted values (max 10 best out of 56): 016z4k (0.67 #296, 0.59 #150, 0.57 #3370), 0dz3r (0.52 #1171, 0.51 #1317, 0.50 #1463), 039v1 (0.42 #2230, 0.40 #3990, 0.38 #766), 01c72t (0.30 #2656, 0.29 #1484, 0.29 #1632), 01d_h8 (0.28 #9527, 0.27 #8943, 0.27 #11136), 0fnpj (0.26 #1374, 0.19 #1520, 0.18 #2254), 0n1h (0.26 #304, 0.24 #3378, 0.24 #2499), 0dxtg (0.25 #11144, 0.24 #10268, 0.24 #10706), 03gjzk (0.22 #10269, 0.22 #9831, 0.22 #11145), 02jknp (0.18 #10700, 0.18 #10992, 0.18 #9529) >> Best rule #296 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 095x_; >> query: (?x2575, 016z4k) <- artists(?x3108, ?x2575), artists(?x2823, ?x2575), ?x2823 = 02qdgx, artists(?x3108, ?x4239), ?x4239 = 0x3b7 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1171 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 01pfkw; *> query: (?x2575, 0dz3r) <- artist(?x9492, ?x2575), artist(?x9492, ?x6715), award_nominee(?x2575, ?x2862), ?x6715 = 011z3g *> conf = 0.52 ranks of expected_values: 2 EVAL 018pj3 profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 80.000 0.667 http://example.org/people/person/profession #8803-02l840 PRED entity: 02l840 PRED relation: award PRED expected values: 01bgqh 02f705 => 134 concepts (114 used for prediction) PRED predicted values (max 10 best out of 283): 023vrq (0.77 #16035, 0.71 #41856, 0.70 #39507), 03t5b6 (0.77 #16035, 0.71 #41856, 0.70 #39507), 01bgqh (0.45 #6299, 0.42 #825, 0.28 #2780), 02f73p (0.45 #963, 0.21 #17992, 0.17 #572), 03qbh5 (0.34 #979, 0.28 #6844, 0.25 #588), 02f71y (0.34 #958, 0.17 #567, 0.16 #2522), 09sb52 (0.32 #32113, 0.31 #1996, 0.28 #9034), 05pcn59 (0.31 #2036, 0.21 #8292, 0.20 #10247), 05p09zm (0.31 #2076, 0.20 #5986, 0.18 #8332), 0fbtbt (0.30 #4523, 0.24 #8042, 0.05 #25643) >> Best rule #16035 for best value: >> intensional similarity = 3 >> extensional distance = 405 >> proper extension: 03fbc; >> query: (?x827, ?x3835) <- award_nominee(?x827, ?x527), artists(?x671, ?x827), award_winner(?x3835, ?x827) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #6299 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 160 *> proper extension: 014hr0; 013423; *> query: (?x827, 01bgqh) <- award_nominee(?x527, ?x827), award(?x827, ?x2139), ?x2139 = 01by1l *> conf = 0.45 ranks of expected_values: 3, 20 EVAL 02l840 award 02f705 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 134.000 114.000 0.774 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02l840 award 01bgqh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 114.000 0.774 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8802-0bq8tmw PRED entity: 0bq8tmw PRED relation: film_release_region PRED expected values: 05v8c 03rk0 016wzw => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 133): 03rjj (0.88 #563, 0.88 #423, 0.84 #1123), 02vzc (0.80 #602, 0.79 #462, 0.79 #2002), 03_3d (0.79 #425, 0.79 #2245, 0.77 #565), 05v8c (0.71 #432, 0.70 #572, 0.66 #1272), 01mjq (0.68 #455, 0.66 #595, 0.59 #1995), 06qd3 (0.64 #312, 0.52 #1992, 0.51 #1292), 047yc (0.61 #583, 0.60 #443, 0.55 #1283), 03rk0 (0.61 #466, 0.60 #606, 0.52 #1166), 016wzw (0.57 #614, 0.57 #474, 0.55 #1314), 06mzp (0.54 #577, 0.53 #437, 0.48 #1137) >> Best rule #563 for best value: >> intensional similarity = 3 >> extensional distance = 120 >> proper extension: 02x3lt7; 017gl1; 0cnztc4; 0gvrws1; 08052t3; 0dr3sl; 040b5k; 023gxx; 06w839_; 0gh65c5; ... >> query: (?x1642, 03rjj) <- film(?x541, ?x1642), film_release_region(?x1642, ?x1497), ?x1497 = 015qh >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #432 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 103 *> proper extension: 0gtsx8c; 02vxq9m; 0ds3t5x; 0dscrwf; 05p1tzf; 0c40vxk; 0gx9rvq; 01vksx; 0crfwmx; 08hmch; ... *> query: (?x1642, 05v8c) <- film(?x541, ?x1642), film_release_region(?x1642, ?x1497), film_release_region(?x1642, ?x1264), ?x1497 = 015qh, ?x1264 = 0345h *> conf = 0.71 ranks of expected_values: 4, 8, 9 EVAL 0bq8tmw film_release_region 016wzw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 108.000 108.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bq8tmw film_release_region 03rk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 108.000 108.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bq8tmw film_release_region 05v8c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 108.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8801-03kts PRED entity: 03kts PRED relation: artists! PRED expected values: 0m40d => 126 concepts (120 used for prediction) PRED predicted values (max 10 best out of 209): 017_qw (0.67 #2563, 0.53 #3187, 0.45 #1626), 064t9 (0.55 #4695, 0.50 #638, 0.46 #4383), 06by7 (0.45 #11576, 0.43 #10639, 0.43 #8139), 06j6l (0.34 #2860, 0.27 #4732, 0.25 #8167), 05bt6j (0.31 #4727, 0.23 #13471, 0.22 #11599), 0glt670 (0.28 #4724, 0.19 #11596, 0.18 #13468), 03_d0 (0.27 #12, 0.22 #5317, 0.21 #19043), 02lnbg (0.27 #4743, 0.16 #4431, 0.14 #2871), 025sc50 (0.26 #4734, 0.19 #4422, 0.18 #13478), 0ggx5q (0.26 #4762, 0.14 #4450, 0.13 #11634) >> Best rule #2563 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 015rmq; 0bxtyq; >> query: (?x7906, 017_qw) <- profession(?x7906, ?x563), award(?x7906, ?x537), ?x563 = 01c8w0, artists(?x5424, ?x7906) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #151 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 06c44; 05n19y; *> query: (?x7906, 0m40d) <- profession(?x7906, ?x563), ?x563 = 01c8w0, artist(?x3265, ?x7906), artists(?x5424, ?x7906) *> conf = 0.18 ranks of expected_values: 21 EVAL 03kts artists! 0m40d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 126.000 120.000 0.667 http://example.org/music/genre/artists #8800-012vd6 PRED entity: 012vd6 PRED relation: artists! PRED expected values: 0ggq0m => 129 concepts (70 used for prediction) PRED predicted values (max 10 best out of 227): 0glt670 (0.46 #4940, 0.35 #8308, 0.30 #5858), 025sc50 (0.43 #5866, 0.33 #4948, 0.30 #13216), 01lyv (0.41 #645, 0.23 #1563, 0.21 #12282), 0xhtw (0.36 #323, 0.30 #935, 0.29 #1853), 05lls (0.31 #14, 0.16 #7670, 0.13 #1239), 02w4v (0.29 #1573, 0.20 #655, 0.14 #6473), 0ggq0m (0.28 #12, 0.20 #7668, 0.18 #2155), 02yv6b (0.27 #1626, 0.26 #1014, 0.24 #95), 02lnbg (0.25 #5874, 0.22 #13224, 0.18 #4901), 08jyyk (0.24 #64, 0.18 #371, 0.16 #1901) >> Best rule #4940 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 01jqr_5; 0lgm5; 0p3sf; 017yfz; 024zq; 01vtg4q; 01tpl1p; 0ql36; >> query: (?x5310, 0glt670) <- people(?x2510, ?x5310), artists(?x505, ?x5310), ?x2510 = 0x67 >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 01w524f; 0hgqq; 03h_fqv; 01vs4f3; 01vsqvs; 0hr3g; 0hqgp; 0459z; 0c73g; *> query: (?x5310, 0ggq0m) <- instrumentalists(?x316, ?x5310), ?x316 = 05r5c, influenced_by(?x5310, ?x7902) *> conf = 0.28 ranks of expected_values: 7 EVAL 012vd6 artists! 0ggq0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 129.000 70.000 0.459 http://example.org/music/genre/artists #8799-0dryh9k PRED entity: 0dryh9k PRED relation: people PRED expected values: 02vmzp 02ctyy 087z12 087_wh 046rfv 05yvfd 0738y5 06zmg7m 0bkg87 081hvm 05b1062 03cprft 04328m 027lfrs => 22 concepts (7 used for prediction) PRED predicted values (max 10 best out of 4200): 02ply6j (0.25 #2619, 0.04 #7558, 0.03 #6585), 04cbtrw (0.25 #2034, 0.04 #6973, 0.03 #10267), 044mz_ (0.25 #1648, 0.04 #6587, 0.03 #9881), 01vn0t_ (0.25 #2853, 0.04 #7792, 0.03 #11086), 0311wg (0.24 #3579, 0.18 #5225, 0.16 #8520), 06cgy (0.24 #3485, 0.16 #10072, 0.16 #8426), 016z2j (0.19 #3592, 0.18 #5238, 0.13 #8533), 01vrt_c (0.19 #3443, 0.18 #5089, 0.13 #8384), 052hl (0.19 #4213, 0.18 #5859, 0.13 #9154), 0g824 (0.19 #4178, 0.14 #5824, 0.13 #9119) >> Best rule #2619 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 078vc; >> query: (?x5025, 02ply6j) <- languages_spoken(?x5025, ?x9113), languages_spoken(?x5025, ?x1882), ?x9113 = 02hxcvy, ?x1882 = 03k50 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4607 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 19 *> proper extension: 02g7sp; 013xrm; 022dp5; *> query: (?x5025, 06zmg7m) <- people(?x5025, ?x13250), people(?x5025, ?x12595), people(?x5025, ?x12256), people(?x5025, ?x10061), people(?x5025, ?x5120), languages(?x12595, ?x5121), location_of_ceremony(?x12595, ?x4335), profession(?x13250, ?x1032), religion(?x5120, ?x492), nationality(?x12256, ?x2146), film(?x10061, ?x755), diet(?x12256, ?x3130) *> conf = 0.05 ranks of expected_values: 893, 1370, 1371, 1514, 1519, 1528, 1553, 1690, 1937, 1941, 1971, 1998, 2229, 2605 EVAL 0dryh9k people 027lfrs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 04328m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 03cprft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 05b1062 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 081hvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 0bkg87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 06zmg7m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 0738y5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 05yvfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 046rfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 087_wh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 087z12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 02ctyy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people EVAL 0dryh9k people 02vmzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 22.000 7.000 0.250 http://example.org/people/ethnicity/people #8798-057xs89 PRED entity: 057xs89 PRED relation: award! PRED expected values: 048lv 06w6_ 01vxxb 017r13 0j5q3 => 42 concepts (9 used for prediction) PRED predicted values (max 10 best out of 1829): 02fn5 (0.83 #16603, 0.12 #4500, 0.04 #24423), 0f276 (0.83 #16603, 0.11 #9365, 0.07 #26564), 0dvld (0.67 #11671, 0.31 #14992, 0.29 #18313), 0154qm (0.58 #10835, 0.46 #14156, 0.43 #17477), 0lpjn (0.58 #10700, 0.46 #14021, 0.43 #17342), 043kzcr (0.58 #10608, 0.46 #13929, 0.43 #17250), 01hkhq (0.58 #10604, 0.38 #13925, 0.36 #17246), 01tspc6 (0.58 #10189, 0.31 #13510, 0.29 #16831), 01p7yb (0.50 #10030, 0.46 #13351, 0.43 #16672), 028knk (0.50 #10474, 0.46 #13795, 0.43 #17116) >> Best rule #16603 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 02r0csl; 09qwmm; 094qd5; 02n9nmz; 0gqwc; 02z0dfh; 099cng; 05zvj3m; 0gs96; 063y_ky; ... >> query: (?x3019, ?x406) <- nominated_for(?x3019, ?x7493), nominated_for(?x3019, ?x5293), ?x5293 = 0cbv4g, award_winner(?x3019, ?x406), film_release_region(?x7493, ?x87) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #13282 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 027571b; *> query: (?x3019, ?x157) <- nominated_for(?x3019, ?x86), award(?x1958, ?x3019), award(?x748, ?x3019), ?x748 = 07lt7b, award_nominee(?x1958, ?x157) *> conf = 0.18 ranks of expected_values: 239, 259, 483, 513, 903 EVAL 057xs89 award! 0j5q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 9.000 0.834 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 057xs89 award! 017r13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 9.000 0.834 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 057xs89 award! 01vxxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 9.000 0.834 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 057xs89 award! 06w6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 9.000 0.834 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 057xs89 award! 048lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 9.000 0.834 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8797-0l35f PRED entity: 0l35f PRED relation: location_of_ceremony! PRED expected values: 04ztj => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.80 #21, 0.74 #45, 0.69 #25), 0jgjn (0.06 #28), 01g63y (0.04 #98, 0.03 #26, 0.03 #82) >> Best rule #21 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 03902; >> query: (?x7369, 04ztj) <- mode_of_transportation(?x7369, ?x8731), mode_of_transportation(?x7369, ?x6665), ?x8731 = 01bjv, contains(?x7369, ?x10937), ?x6665 = 025t3bg >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l35f location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.800 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #8796-03bzyn4 PRED entity: 03bzyn4 PRED relation: executive_produced_by PRED expected values: 03c9pqt => 138 concepts (104 used for prediction) PRED predicted values (max 10 best out of 122): 06pj8 (0.13 #1836, 0.12 #309, 0.10 #5894), 05hj_k (0.12 #98, 0.12 #352, 0.07 #3149), 06q8hf (0.12 #421, 0.06 #167, 0.05 #10813), 0glyyw (0.11 #697, 0.10 #951, 0.08 #443), 0343h (0.09 #1823, 0.07 #5374, 0.06 #5627), 030_3z (0.09 #1889, 0.04 #6704, 0.03 #6199), 02qzjj (0.08 #490, 0.07 #744, 0.04 #2017), 079vf (0.08 #256, 0.07 #764, 0.06 #2291), 070j61 (0.08 #9129, 0.08 #10394, 0.07 #12671), 03fg0r (0.08 #9129, 0.08 #10394, 0.07 #12671) >> Best rule #1836 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 0ddjy; 01f7kl; 0ddt_; 0dnqr; 05zlld0; 0184tc; 05c5z8j; 0mbql; 07g1sm; 0bt4g; ... >> query: (?x9496, 06pj8) <- production_companies(?x9496, ?x2246), film_distribution_medium(?x9496, ?x81), film(?x1205, ?x9496), written_by(?x9496, ?x4589) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #3553 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 0cnztc4; 04lqvlr; 07l50vn; *> query: (?x9496, 03c9pqt) <- titles(?x2480, ?x9496), film_crew_role(?x9496, ?x137), film_format(?x9496, ?x909), category(?x9496, ?x134) *> conf = 0.04 ranks of expected_values: 16 EVAL 03bzyn4 executive_produced_by 03c9pqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 138.000 104.000 0.130 http://example.org/film/film/executive_produced_by #8795-01mxnvc PRED entity: 01mxnvc PRED relation: role PRED expected values: 01wy6 0l14j_ => 108 concepts (74 used for prediction) PRED predicted values (max 10 best out of 120): 05r5c (0.43 #236, 0.36 #688, 0.36 #747), 0l14md (0.43 #7, 0.18 #523, 0.17 #235), 0l14qv (0.36 #688, 0.36 #747, 0.28 #1787), 0192l (0.36 #688, 0.36 #747, 0.28 #1787), 085jw (0.36 #688, 0.36 #747, 0.28 #1787), 03qjg (0.29 #38, 0.17 #266, 0.15 #727), 04rzd (0.21 #28, 0.07 #889, 0.07 #544), 028tv0 (0.17 #241, 0.16 #702, 0.16 #990), 013y1f (0.15 #195, 0.07 #24, 0.07 #369), 042v_gx (0.14 #9, 0.11 #756, 0.09 #813) >> Best rule #236 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 02fybl; >> query: (?x10802, 05r5c) <- role(?x10802, ?x1166), ?x1166 = 05148p4, profession(?x10802, ?x220), ?x220 = 016z4k, gender(?x10802, ?x231) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #270 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 02fybl; *> query: (?x10802, 0l14j_) <- role(?x10802, ?x1166), ?x1166 = 05148p4, profession(?x10802, ?x220), ?x220 = 016z4k, gender(?x10802, ?x231) *> conf = 0.04 ranks of expected_values: 28, 50 EVAL 01mxnvc role 0l14j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 108.000 74.000 0.435 http://example.org/music/group_member/membership./music/group_membership/role EVAL 01mxnvc role 01wy6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 108.000 74.000 0.435 http://example.org/music/group_member/membership./music/group_membership/role #8794-03h4fq7 PRED entity: 03h4fq7 PRED relation: genre PRED expected values: 05p553 => 84 concepts (83 used for prediction) PRED predicted values (max 10 best out of 106): 07s9rl0 (0.86 #1711, 0.67 #6247, 0.61 #3305), 05p553 (0.68 #4779, 0.56 #5, 0.41 #6251), 01z4y (0.61 #6982, 0.53 #4406, 0.53 #3427), 03k9fj (0.57 #135, 0.28 #2090, 0.26 #3441), 02l7c8 (0.53 #4792, 0.42 #1728, 0.32 #6264), 02kdv5l (0.52 #125, 0.33 #614, 0.31 #858), 01jfsb (0.37 #2457, 0.36 #2580, 0.36 #2827), 06n90 (0.30 #137, 0.16 #2581, 0.16 #2828), 01hmnh (0.26 #142, 0.20 #2097, 0.19 #3692), 060__y (0.22 #19, 0.17 #1729, 0.15 #3323) >> Best rule #1711 for best value: >> intensional similarity = 4 >> extensional distance = 461 >> proper extension: 0413cff; >> query: (?x5113, 07s9rl0) <- genre(?x5113, ?x11464), featured_film_locations(?x5113, ?x1523), genre(?x7062, ?x11464), ?x7062 = 0j90s >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #4779 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 779 *> proper extension: 04svwx; *> query: (?x5113, 05p553) <- genre(?x5113, ?x11464), country(?x5113, ?x94), genre(?x8039, ?x11464), ?x8039 = 0296vv *> conf = 0.68 ranks of expected_values: 2 EVAL 03h4fq7 genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 83.000 0.855 http://example.org/film/film/genre #8793-02w4fkq PRED entity: 02w4fkq PRED relation: award PRED expected values: 03qbnj => 107 concepts (77 used for prediction) PRED predicted values (max 10 best out of 262): 01bgqh (0.38 #43, 0.35 #1255, 0.31 #1659), 09sb52 (0.33 #12165, 0.27 #14186, 0.27 #15398), 0c4z8 (0.31 #1284, 0.25 #72, 0.22 #3708), 054ks3 (0.29 #1354, 0.22 #1758, 0.22 #2162), 02nhxf (0.27 #503, 0.25 #99, 0.13 #1715), 03qbnj (0.25 #233, 0.19 #1041, 0.18 #637), 02v1m7 (0.25 #113, 0.18 #517, 0.11 #3345), 01c9f2 (0.20 #4041, 0.20 #5254, 0.19 #25866), 01dk00 (0.20 #4041, 0.20 #5254, 0.19 #25866), 026m9w (0.20 #4041, 0.20 #5254, 0.19 #25866) >> Best rule #43 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0ggl02; >> query: (?x2824, 01bgqh) <- award_winner(?x2824, ?x2786), artists(?x2996, ?x2786), artists(?x671, ?x2786), ?x2996 = 01243b, ?x671 = 064t9 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 0ggl02; *> query: (?x2824, 03qbnj) <- award_winner(?x2824, ?x2786), artists(?x2996, ?x2786), artists(?x671, ?x2786), ?x2996 = 01243b, ?x671 = 064t9 *> conf = 0.25 ranks of expected_values: 6 EVAL 02w4fkq award 03qbnj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 107.000 77.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8792-06wvfq PRED entity: 06wvfq PRED relation: gender PRED expected values: 02zsn => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #33, 0.74 #39, 0.74 #43), 02zsn (0.71 #12, 0.71 #10, 0.62 #8) >> Best rule #33 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 02r99xw; >> query: (?x9608, 05zppz) <- people(?x5025, ?x9608), profession(?x9608, ?x1032), ?x5025 = 0dryh9k, ?x1032 = 02hrh1q >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 067jsf; 02ctyy; 05zdk2; 02c_wc; 07f0tw; 049468; *> query: (?x9608, 02zsn) <- award_winner(?x10156, ?x9608), ?x10156 = 03r8v_, profession(?x9608, ?x1032), type_of_union(?x9608, ?x566) *> conf = 0.71 ranks of expected_values: 2 EVAL 06wvfq gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.743 http://example.org/people/person/gender #8791-0g4vmj8 PRED entity: 0g4vmj8 PRED relation: film_release_region PRED expected values: 015qh 01pj7 02vzc => 74 concepts (57 used for prediction) PRED predicted values (max 10 best out of 144): 02vzc (0.86 #608, 0.84 #749, 0.81 #467), 0b90_r (0.84 #285, 0.80 #144, 0.79 #426), 05b4w (0.80 #337, 0.80 #196, 0.79 #478), 03rt9 (0.71 #434, 0.67 #293, 0.67 #152), 01ls2 (0.67 #291, 0.67 #150, 0.60 #432), 0ctw_b (0.67 #302, 0.63 #443, 0.62 #161), 015qh (0.67 #174, 0.60 #456, 0.59 #315), 03rj0 (0.65 #474, 0.62 #192, 0.61 #333), 06t8v (0.64 #209, 0.56 #491, 0.55 #350), 047lj (0.60 #149, 0.57 #290, 0.53 #431) >> Best rule #608 for best value: >> intensional similarity = 5 >> extensional distance = 63 >> proper extension: 011yrp; 0g5qs2k; 05p1tzf; 02x3lt7; 03hjv97; 01vksx; 0m_mm; 02d44q; 04hwbq; 047msdk; ... >> query: (?x7275, 02vzc) <- film_release_region(?x7275, ?x1355), film_release_region(?x7275, ?x87), award_winner(?x7275, ?x1401), ?x1355 = 0h7x, ?x87 = 05r4w >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 17 EVAL 0g4vmj8 film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 57.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g4vmj8 film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 74.000 57.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g4vmj8 film_release_region 015qh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 74.000 57.000 0.862 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8790-02dlfh PRED entity: 02dlfh PRED relation: film PRED expected values: 0640m69 => 137 concepts (109 used for prediction) PRED predicted values (max 10 best out of 1186): 0ch3qr1 (0.64 #116374, 0.59 #143228, 0.58 #155765), 0bshwmp (0.28 #16114, 0.04 #7319, 0.04 #5529), 0f42nz (0.16 #9861, 0.10 #22395, 0.09 #27766), 03n0cd (0.14 #5077, 0.08 #6867, 0.02 #85645), 0gfzfj (0.14 #5277, 0.04 #7067, 0.03 #21391), 0b3n61 (0.08 #21055, 0.06 #13891, 0.04 #22845), 0bvn25 (0.08 #19745, 0.06 #25116, 0.04 #7211), 05fm6m (0.08 #3112, 0.08 #6692, 0.07 #4902), 02hxhz (0.08 #1913, 0.08 #5493, 0.03 #9073), 0prrm (0.08 #2653, 0.05 #42044, 0.04 #8023) >> Best rule #116374 for best value: >> intensional similarity = 3 >> extensional distance = 431 >> proper extension: 01j5ts; 02g8h; 023tp8; 0l8v5; 03w1v2; 04wqr; 05hj0n; 03f2_rc; 01kwld; 04yj5z; ... >> query: (?x8160, ?x5672) <- profession(?x8160, ?x319), nominated_for(?x8160, ?x5672), participant(?x8160, ?x287) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #7133 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 01xdf5; 04bs3j; 0pz7h; 0pz91; 013cr; 0gz5hs; 01_x6v; 01jbx1; 029_3; 01trf3; ... *> query: (?x8160, 0640m69) <- profession(?x8160, ?x319), tv_program(?x8160, ?x6884), participant(?x1335, ?x8160) *> conf = 0.08 ranks of expected_values: 22 EVAL 02dlfh film 0640m69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 137.000 109.000 0.638 http://example.org/film/actor/film./film/performance/film #8789-0gr4k PRED entity: 0gr4k PRED relation: nominated_for PRED expected values: 0m_mm 092vkg 0pv3x 032_wv 0sxfd 072x7s 0jym0 021y7yw 0kcn7 0ywrc 03cw411 0pd57 097zcz 05c5z8j 0j80w 05hjnw 0dt8xq 0bcp9b 08zrbl 0bj25 0p7pw 05sbv3 => 71 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1264): 05hjnw (0.77 #5593, 0.77 #34968, 0.71 #11860), 0cq8nx (0.77 #5593, 0.77 #34968, 0.67 #9622), 09tkzy (0.77 #5593, 0.77 #34968, 0.64 #41965), 07s846j (0.77 #5593, 0.77 #34968, 0.64 #41965), 07xtqq (0.77 #5593, 0.77 #34968, 0.64 #41965), 0glbqt (0.77 #5593, 0.77 #34968, 0.64 #41965), 0h03fhx (0.77 #5593, 0.77 #34968, 0.63 #41964), 083shs (0.77 #5593, 0.77 #34968, 0.63 #41964), 09q5w2 (0.75 #12724, 0.71 #9928, 0.67 #8530), 0dr_4 (0.75 #12786, 0.71 #9990, 0.59 #26772) >> Best rule #5593 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 02rdxsh; >> query: (?x601, ?x167) <- nominated_for(?x601, ?x7231), nominated_for(?x601, ?x6100), nominated_for(?x601, ?x5674), ?x5674 = 05rfst, film_sets_designed(?x7876, ?x7231), genre(?x6100, ?x53), award(?x167, ?x601) >> conf = 0.77 => this is the best rule for 8 predicted values ranks of expected_values: 1, 15, 22, 26, 35, 40, 41, 49, 50, 72, 78, 103, 115, 118, 191, 206, 209, 279, 289, 390, 418, 523 EVAL 0gr4k nominated_for 05sbv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0p7pw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0bj25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 08zrbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0bcp9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0dt8xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 05hjnw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0j80w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 05c5z8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 097zcz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0pd57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 03cw411 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0ywrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0kcn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 021y7yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0jym0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 072x7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0sxfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 032_wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0pv3x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 092vkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr4k nominated_for 0m_mm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 71.000 34.000 0.770 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8788-01kt17 PRED entity: 01kt17 PRED relation: profession PRED expected values: 03gjzk => 109 concepts (105 used for prediction) PRED predicted values (max 10 best out of 69): 02hv44_ (0.67 #57, 0.57 #205, 0.51 #1389), 01d_h8 (0.56 #302, 0.53 #5630, 0.53 #598), 02jknp (0.46 #5632, 0.42 #6816, 0.33 #1784), 03gjzk (0.44 #310, 0.40 #606, 0.37 #6822), 05sxg2 (0.44 #297, 0.40 #593, 0.36 #445), 0cbd2 (0.40 #1783, 0.38 #895, 0.38 #1635), 0kyk (0.33 #29, 0.29 #917, 0.29 #177), 018gz8 (0.27 #5640, 0.18 #460, 0.17 #6824), 012t_z (0.22 #309, 0.18 #457, 0.13 #605), 02krf9 (0.22 #322, 0.17 #26, 0.16 #5650) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 04r7jc; 0yfp; 07h07; 02b29; >> query: (?x9256, 02hv44_) <- award_nominee(?x9256, ?x6866), ?x6866 = 03m9c8, written_by(?x5134, ?x9256), type_of_union(?x9256, ?x566) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #310 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 02rchht; 0hskw; 04cw0j; 05hj_k; 01vhrz; *> query: (?x9256, 03gjzk) <- award_nominee(?x9256, ?x6866), ?x6866 = 03m9c8, student(?x481, ?x9256), award(?x9256, ?x458) *> conf = 0.44 ranks of expected_values: 4 EVAL 01kt17 profession 03gjzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 105.000 0.667 http://example.org/people/person/profession #8787-032r1 PRED entity: 032r1 PRED relation: influenced_by PRED expected values: 0cpvcd => 186 concepts (89 used for prediction) PRED predicted values (max 10 best out of 329): 05qmj (0.56 #7499, 0.50 #12223, 0.41 #7928), 0gz_ (0.53 #12135, 0.53 #7840, 0.50 #7411), 0420y (0.40 #3839, 0.38 #7708, 0.23 #12432), 048cl (0.40 #660, 0.35 #7968, 0.25 #7539), 015n8 (0.40 #3845, 0.33 #12438, 0.30 #10719), 039n1 (0.38 #7631, 0.33 #5911, 0.29 #8060), 0j3v (0.35 #10373, 0.33 #5648, 0.29 #8226), 02wh0 (0.33 #1668, 0.30 #10692, 0.29 #8545), 07c37 (0.33 #1043, 0.24 #7922, 0.20 #3624), 081k8 (0.33 #1012, 0.22 #10467, 0.20 #7031) >> Best rule #7499 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 039n1; >> query: (?x11837, 05qmj) <- influenced_by(?x11837, ?x1857), interests(?x11837, ?x1858), nationality(?x11837, ?x512), company(?x11837, ?x2313) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #16760 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 47 *> proper extension: 07kb5; 05wh0sh; 03hnd; 099bk; 0372p; 0lcx; 01wd02c; 030dr; 0w6w; *> query: (?x11837, ?x587) <- religion(?x11837, ?x1985), influenced_by(?x11837, ?x7250), influenced_by(?x920, ?x7250), influenced_by(?x587, ?x7250), ?x920 = 04411 *> conf = 0.10 ranks of expected_values: 100 EVAL 032r1 influenced_by 0cpvcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 186.000 89.000 0.562 http://example.org/influence/influence_node/influenced_by #8786-09pnw5 PRED entity: 09pnw5 PRED relation: award_winner PRED expected values: 03m_k0 01j2xj => 41 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1999): 0h0wc (0.33 #3423, 0.33 #362, 0.31 #14127), 04sry (0.33 #4135, 0.33 #1074, 0.25 #14839), 01z_g6 (0.33 #3848, 0.33 #2317, 0.11 #20674), 07g7h2 (0.33 #4036, 0.30 #10157, 0.25 #13212), 0cp9f9 (0.33 #4240, 0.28 #16476, 0.26 #18006), 04ns3gy (0.33 #2846, 0.28 #16613, 0.26 #18143), 01j7rd (0.33 #1827, 0.28 #15594, 0.26 #17124), 02s2ft (0.33 #5, 0.28 #10709, 0.23 #38257), 018ygt (0.33 #4016, 0.26 #20842, 0.22 #16252), 01vttb9 (0.33 #7222, 0.25 #5691, 0.22 #8754) >> Best rule #3423 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 05zksls; >> query: (?x7452, 0h0wc) <- ceremony(?x2060, ?x7452), award_winner(?x7452, ?x9321), award_winner(?x7452, ?x2965), ?x2060 = 054ky1, honored_for(?x7452, ?x9452), role(?x9321, ?x228), award_winner(?x686, ?x2965), nominated_for(?x1587, ?x9452), award_nominee(?x368, ?x2965), profession(?x2965, ?x1032), ?x228 = 0l14qv, student(?x6760, ?x2965), film(?x92, ?x9452), award(?x276, ?x1587), award_winner(?x1587, ?x2135) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10709 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 8 *> proper extension: 09q_6t; 02wzl1d; 0g5b0q5; 0drtv8; 09p3h7; 09p2r9; 09pj68; *> query: (?x7452, ?x92) <- ceremony(?x2060, ?x7452), award_winner(?x7452, ?x9321), award_winner(?x7452, ?x2965), ?x2060 = 054ky1, honored_for(?x7452, ?x9452), role(?x9321, ?x228), award_winner(?x686, ?x2965), nominated_for(?x618, ?x9452), award_nominee(?x368, ?x2965), profession(?x2965, ?x1032), performance_role(?x228, ?x212), nominated_for(?x92, ?x9452), instrumentalists(?x228, ?x140), award_winner(?x724, ?x9321), ?x618 = 09qwmm, role(?x75, ?x228) *> conf = 0.28 ranks of expected_values: 85, 87 EVAL 09pnw5 award_winner 01j2xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 41.000 26.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09pnw5 award_winner 03m_k0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 41.000 26.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8785-04cf09 PRED entity: 04cf09 PRED relation: award PRED expected values: 0ck27z => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 254): 09sb52 (0.47 #1665, 0.45 #5319, 0.40 #6131), 0ck27z (0.33 #16333, 0.31 #1717, 0.25 #16739), 05pcn59 (0.30 #5360, 0.30 #2924, 0.30 #488), 05ztrmj (0.30 #592, 0.29 #186, 0.16 #3028), 01ck6h (0.30 #529, 0.14 #123, 0.04 #7431), 03tcnt (0.30 #574, 0.14 #168, 0.04 #980), 05zr6wv (0.29 #17, 0.22 #2859, 0.22 #3265), 057xs89 (0.29 #162, 0.20 #568, 0.18 #3004), 0gqy2 (0.29 #166, 0.20 #572, 0.11 #22903), 09qvc0 (0.29 #40, 0.10 #446, 0.09 #33295) >> Best rule #1665 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0159h6; 01kwld; 016khd; 03pmty; 01gq0b; 0j1yf; 03_wj_; 01tfck; 05dbf; 01pgzn_; ... >> query: (?x1205, 09sb52) <- celebrity(?x4126, ?x1205), actor(?x3787, ?x1205), award_nominee(?x1204, ?x1205) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #16333 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 525 *> proper extension: 064nh4k; 02gvwz; 01yb09; 04y79_n; 0clvcx; 07s8r0; 016ywr; 02k6rq; 06lgq8; 0pyg6; ... *> query: (?x1205, 0ck27z) <- nominated_for(?x1205, ?x3787), actor(?x4588, ?x1205), award_nominee(?x1205, ?x1204) *> conf = 0.33 ranks of expected_values: 2 EVAL 04cf09 award 0ck27z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.469 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8784-01vn0t_ PRED entity: 01vn0t_ PRED relation: gender PRED expected values: 05zppz => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 5): 05zppz (0.86 #23, 0.86 #55, 0.86 #89), 02zsn (0.42 #14, 0.36 #74, 0.33 #54), 012jc (0.12 #109), 01hbgs (0.12 #109), 059_w (0.12 #109) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 02w670; >> query: (?x8708, 05zppz) <- people(?x6260, ?x8708), award(?x8708, ?x3045), role(?x8708, ?x316) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vn0t_ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.865 http://example.org/people/person/gender #8783-06kbb6 PRED entity: 06kbb6 PRED relation: award_winner! PRED expected values: 05qm9f => 138 concepts (72 used for prediction) PRED predicted values (max 10 best out of 334): 05qm9f (0.44 #7957, 0.44 #64773, 0.44 #64772), 0ptxj (0.44 #7957, 0.44 #64773, 0.44 #64772), 0h03fhx (0.05 #507, 0.03 #9600, 0.02 #1643), 0d68qy (0.03 #9367, 0.02 #38912, 0.02 #23008), 0hv4t (0.03 #760, 0.02 #1896, 0.01 #67048), 0_9l_ (0.03 #1097, 0.02 #2233, 0.01 #67048), 01h18v (0.03 #797, 0.02 #1933, 0.01 #67048), 0bmhvpr (0.03 #416, 0.02 #1552, 0.01 #67048), 0gg5qcw (0.03 #576, 0.02 #1712, 0.01 #9669), 064lsn (0.03 #695, 0.02 #1831) >> Best rule #7957 for best value: >> intensional similarity = 4 >> extensional distance = 147 >> proper extension: 03wd5tk; 0gm34; 03f68r6; >> query: (?x11772, ?x6607) <- people(?x6720, ?x11772), nominated_for(?x11772, ?x6607), award(?x11772, ?x601), honored_for(?x6606, ?x6607) >> conf = 0.44 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 06kbb6 award_winner! 05qm9f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 72.000 0.444 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #8782-0kv238 PRED entity: 0kv238 PRED relation: film_release_region PRED expected values: 03rt9 03gj2 047yc 07ylj 015qh 05b4w => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 135): 03gj2 (0.87 #2296, 0.85 #553, 0.85 #2027), 05b4w (0.81 #2060, 0.79 #2463, 0.79 #1792), 03rt9 (0.75 #143, 0.72 #1215, 0.72 #2422), 06mzp (0.75 #147, 0.69 #549, 0.67 #1219), 03rj0 (0.74 #1252, 0.67 #1788, 0.67 #2056), 047yc (0.68 #1761, 0.65 #2029, 0.64 #2432), 015qh (0.65 #1771, 0.64 #2442, 0.64 #2039), 01p1v (0.63 #2049, 0.62 #2452, 0.60 #1781), 06f32 (0.62 #186, 0.53 #1258, 0.51 #2062), 02_286 (0.56 #280, 0.14 #12, 0.10 #1486) >> Best rule #2296 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 0b76d_m; 02vxq9m; 0dscrwf; 04969y; 02d44q; 0872p_c; 05z_kps; 0dgst_d; 0gmcwlb; 07qg8v; ... >> query: (?x2714, 03gj2) <- film_release_region(?x2714, ?x4743), nominated_for(?x3458, ?x2714), ?x4743 = 03spz >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 6, 7, 15 EVAL 0kv238 film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0kv238 film_release_region 015qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 104.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0kv238 film_release_region 07ylj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 104.000 104.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0kv238 film_release_region 047yc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 104.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0kv238 film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0kv238 film_release_region 03rt9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8781-06zn2v2 PRED entity: 06zn2v2 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 25): 0ch6mp2 (0.83 #532, 0.82 #1409, 0.80 #777), 09vw2b7 (0.79 #531, 0.72 #776, 0.71 #811), 01vx2h (0.52 #536, 0.43 #781, 0.42 #816), 02rh1dz (0.23 #535, 0.17 #780, 0.17 #815), 02ynfr (0.22 #540, 0.18 #820, 0.18 #785), 015h31 (0.14 #79, 0.12 #534, 0.11 #814), 0215hd (0.14 #543, 0.14 #823, 0.13 #788), 0d2b38 (0.13 #550, 0.13 #2212, 0.12 #830), 01xy5l_ (0.13 #2212, 0.12 #538, 0.10 #258), 089g0h (0.13 #2212, 0.12 #544, 0.10 #1421) >> Best rule #532 for best value: >> intensional similarity = 6 >> extensional distance = 263 >> proper extension: 03bzyn4; >> query: (?x4422, 0ch6mp2) <- film_crew_role(?x4422, ?x2095), film_crew_role(?x4422, ?x468), film(?x72, ?x4422), ?x2095 = 0dxtw, ?x468 = 02r96rf, language(?x4422, ?x254) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #531 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 263 *> proper extension: 03bzyn4; *> query: (?x4422, 09vw2b7) <- film_crew_role(?x4422, ?x2095), film_crew_role(?x4422, ?x468), film(?x72, ?x4422), ?x2095 = 0dxtw, ?x468 = 02r96rf, language(?x4422, ?x254) *> conf = 0.79 ranks of expected_values: 2 EVAL 06zn2v2 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.826 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8780-0139q5 PRED entity: 0139q5 PRED relation: nationality PRED expected values: 0d05w3 => 117 concepts (80 used for prediction) PRED predicted values (max 10 best out of 182): 09c7w0 (0.80 #2987, 0.78 #4589, 0.78 #7579), 0345h (0.38 #3787, 0.32 #7978, 0.27 #31), 0f8l9c (0.38 #3787, 0.32 #7978, 0.05 #3586), 07ssc (0.34 #1010, 0.31 #114, 0.31 #214), 03h64 (0.32 #7978, 0.05 #3586, 0.05 #3585), 03rjj (0.32 #7978, 0.05 #3586, 0.05 #3585), 0d05w3 (0.32 #7978, 0.02 #1242, 0.02 #1341), 03rk0 (0.12 #1535, 0.11 #1436, 0.11 #2235), 0d060g (0.09 #7, 0.07 #2096, 0.06 #2395), 03rt9 (0.09 #13, 0.04 #7578, 0.03 #7478) >> Best rule #2987 for best value: >> intensional similarity = 4 >> extensional distance = 857 >> proper extension: 014zn0; 04f62k; >> query: (?x9809, 09c7w0) <- film(?x9809, ?x2189), location(?x9809, ?x2645), gender(?x9809, ?x514), jurisdiction_of_office(?x900, ?x2645) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #7978 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1816 *> proper extension: 0f2c8g; 042gr4; *> query: (?x9809, ?x789) <- film(?x9809, ?x2189), country(?x2189, ?x789), film_release_region(?x2189, ?x87) *> conf = 0.32 ranks of expected_values: 7 EVAL 0139q5 nationality 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 117.000 80.000 0.802 http://example.org/people/person/nationality #8779-07g_0c PRED entity: 07g_0c PRED relation: language PRED expected values: 02h40lc => 84 concepts (78 used for prediction) PRED predicted values (max 10 best out of 42): 02h40lc (0.92 #606, 0.92 #547, 0.91 #1208), 04306rv (0.15 #429, 0.10 #1633, 0.09 #1875), 064_8sq (0.14 #1228, 0.14 #1650, 0.13 #804), 06nm1 (0.11 #556, 0.11 #615, 0.10 #734), 03_9r (0.11 #10, 0.08 #71, 0.06 #192), 06b_j (0.10 #447, 0.08 #568, 0.08 #627), 0jzc (0.10 #444, 0.05 #683, 0.04 #565), 02bjrlw (0.09 #304, 0.09 #1207, 0.09 #1629), 05zjd (0.06 #87, 0.05 #148, 0.05 #208), 02hxc3j (0.05 #7, 0.04 #68, 0.03 #129) >> Best rule #606 for best value: >> intensional similarity = 5 >> extensional distance = 169 >> proper extension: 0dnvn3; 0ds33; 03h_yy; 04fzfj; 03ckwzc; 0b73_1d; 02qm_f; 048scx; 03t97y; 020fcn; ... >> query: (?x1293, 02h40lc) <- film_crew_role(?x1293, ?x1171), genre(?x1293, ?x258), ?x1171 = 09vw2b7, crewmember(?x1293, ?x1983), currency(?x1293, ?x170) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07g_0c language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 78.000 0.924 http://example.org/film/film/language #8778-017r2 PRED entity: 017r2 PRED relation: influenced_by! PRED expected values: 082_p 0gdqy => 152 concepts (43 used for prediction) PRED predicted values (max 10 best out of 439): 01h2_6 (0.33 #2562, 0.25 #5637, 0.20 #1025), 040db (0.19 #4687, 0.19 #2638, 0.17 #9304), 0683n (0.19 #2901, 0.14 #851, 0.12 #9567), 047g6 (0.17 #2527, 0.10 #5602, 0.10 #6627), 07lp1 (0.15 #2977, 0.14 #5026, 0.13 #6564), 01hb6v (0.14 #12403, 0.11 #19577, 0.11 #7783), 0lcx (0.14 #665, 0.06 #6302, 0.06 #11281), 02yl42 (0.12 #2184, 0.11 #7824, 0.09 #18591), 0pqzh (0.12 #2504, 0.08 #5579, 0.08 #3017), 03_hd (0.12 #2230, 0.08 #5305, 0.07 #693) >> Best rule #2562 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 07h1q; 047g6; 01h2_6; >> query: (?x1645, ?x12592) <- place_of_birth(?x1645, ?x12866), peers(?x12592, ?x1645), influenced_by(?x6534, ?x1645), religion(?x1645, ?x2694) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4977 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 058w5; *> query: (?x1645, 082_p) <- profession(?x1645, ?x3746), ?x3746 = 05z96, religion(?x1645, ?x2694) *> conf = 0.06 ranks of expected_values: 136 EVAL 017r2 influenced_by! 0gdqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 152.000 43.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 017r2 influenced_by! 082_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 152.000 43.000 0.333 http://example.org/influence/influence_node/influenced_by #8777-0jc_p PRED entity: 0jc_p PRED relation: colors! PRED expected values: 01y20v 0sxgh 06thjt 0234_c 08htt0 => 21 concepts (20 used for prediction) PRED predicted values (max 10 best out of 675): 01jq34 (0.50 #6974, 0.50 #2357, 0.50 #1896), 02vnp2 (0.50 #7246, 0.50 #2629, 0.50 #2168), 0bsnm (0.50 #2573, 0.50 #2112, 0.40 #3958), 07lx1s (0.50 #2799, 0.50 #2338, 0.40 #4184), 021996 (0.50 #2120, 0.43 #6276, 0.40 #3966), 02607j (0.50 #1932, 0.43 #6088, 0.40 #3778), 0gl6x (0.50 #2644, 0.40 #4490, 0.38 #7261), 0gl6f (0.50 #2565, 0.40 #4411, 0.38 #7182), 0f1nl (0.50 #1907, 0.40 #3753, 0.33 #1447), 036hnm (0.50 #2255, 0.40 #4101, 0.33 #1795) >> Best rule #6974 for best value: >> intensional similarity = 38 >> extensional distance = 6 >> proper extension: 04d18d; >> query: (?x3315, 01jq34) <- colors(?x11502, ?x3315), colors(?x5486, ?x3315), colors(?x3777, ?x3315), colors(?x2948, ?x3315), institution(?x1368, ?x5486), major_field_of_study(?x5486, ?x10391), major_field_of_study(?x5486, ?x5031), major_field_of_study(?x5486, ?x2981), major_field_of_study(?x5486, ?x2605), school(?x799, ?x2948), currency(?x11502, ?x170), institution(?x3386, ?x11502), student(?x5486, ?x6718), student(?x5486, ?x118), colors(?x733, ?x3315), award_nominee(?x1676, ?x6718), student(?x2948, ?x129), team(?x60, ?x733), position(?x733, ?x530), major_field_of_study(?x4599, ?x5031), major_field_of_study(?x3439, ?x5031), ?x2605 = 03g3w, ?x3439 = 03ksy, program(?x6718, ?x2009), ?x1368 = 014mlp, influenced_by(?x117, ?x118), fraternities_and_sororities(?x2948, ?x3697), major_field_of_study(?x2948, ?x1668), ?x1668 = 01mkq, ?x4599 = 07t90, major_field_of_study(?x9522, ?x10391), major_field_of_study(?x1681, ?x10391), ?x2981 = 02j62, influenced_by(?x118, ?x587), ?x1681 = 07szy, ?x9522 = 01yqqv, school(?x685, ?x3777), school(?x260, ?x3777) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #670 for first EXPECTED value: *> intensional similarity = 40 *> extensional distance = 1 *> proper extension: 01g5v; *> query: (?x3315, 01y20v) <- colors(?x13181, ?x3315), colors(?x11502, ?x3315), colors(?x10421, ?x3315), colors(?x6455, ?x3315), colors(?x5486, ?x3315), colors(?x2948, ?x3315), institution(?x1368, ?x5486), institution(?x734, ?x5486), ?x2948 = 0j_sncb, school(?x465, ?x5486), ?x6455 = 026vcc, school(?x662, ?x5486), ?x734 = 04zx3q1, colors(?x733, ?x3315), currency(?x11502, ?x170), citytown(?x5486, ?x2298), major_field_of_study(?x5486, ?x2981), major_field_of_study(?x5486, ?x2601), major_field_of_study(?x5486, ?x2314), major_field_of_study(?x5486, ?x2014), major_field_of_study(?x732, ?x2014), organization(?x346, ?x11502), contains(?x335, ?x11502), major_field_of_study(?x7716, ?x2014), major_field_of_study(?x4296, ?x2014), ?x1368 = 014mlp, ?x2981 = 02j62, contact_category(?x11502, ?x897), ?x7716 = 01n_g9, student(?x5486, ?x118), ?x13181 = 016w7b, ?x4296 = 07vyf, student(?x2314, ?x5346), major_field_of_study(?x6271, ?x2601), major_field_of_study(?x3948, ?x2601), ?x6271 = 015q1n, district_represented(?x1830, ?x335), ?x3948 = 025v3k, ?x1830 = 03z5xd, registering_agency(?x10421, ?x1982) *> conf = 0.33 ranks of expected_values: 293, 550, 551, 661, 663 EVAL 0jc_p colors! 08htt0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 21.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 0jc_p colors! 0234_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 21.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 0jc_p colors! 06thjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 21.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 0jc_p colors! 0sxgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 21.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 0jc_p colors! 01y20v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 21.000 20.000 0.500 http://example.org/education/educational_institution/colors #8776-016dsy PRED entity: 016dsy PRED relation: category PRED expected values: 08mbj5d => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.87 #34, 0.85 #15, 0.85 #41) >> Best rule #34 for best value: >> intensional similarity = 5 >> extensional distance = 109 >> proper extension: 01wn718; 05szp; 0bqvs2; 05w6cw; >> query: (?x4082, 08mbj5d) <- gender(?x4082, ?x514), artist(?x3265, ?x4082), award(?x4082, ?x1008), artists(?x505, ?x4082), participant(?x4082, ?x5665) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016dsy category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.874 http://example.org/common/topic/webpage./common/webpage/category #8775-028v3 PRED entity: 028v3 PRED relation: genre! PRED expected values: 01j5ql => 71 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1896): 07phbc (0.73 #11197, 0.40 #9167, 0.27 #27997), 01j5ql (0.67 #12435, 0.50 #4970, 0.40 #8702), 09p0ct (0.67 #11417, 0.40 #7684, 0.33 #18880), 0k20s (0.67 #12974, 0.40 #9241, 0.33 #20437), 0296rz (0.67 #12903, 0.40 #9170, 0.33 #20366), 011ywj (0.60 #8944, 0.50 #20140, 0.50 #14545), 0dlngsd (0.60 #8267, 0.50 #19463, 0.50 #12000), 05_5rjx (0.60 #8129, 0.50 #11862, 0.50 #9995), 063_j5 (0.60 #9016, 0.50 #7151, 0.50 #5284), 0y_pg (0.60 #8886, 0.50 #10752, 0.43 #23814) >> Best rule #11197 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 01hmnh; >> query: (?x9549, ?x10268) <- genre(?x6726, ?x9549), genre(?x9774, ?x9549), genre(?x7864, ?x9549), genre(?x813, ?x9549), ?x6726 = 02r1ysd, film(?x541, ?x9774), country(?x9774, ?x94), film(?x525, ?x813), prequel(?x9774, ?x10268), film_release_region(?x7864, ?x87), film_release_distribution_medium(?x813, ?x81) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #12435 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 03mqtr; *> query: (?x9549, 01j5ql) <- genre(?x6726, ?x9549), genre(?x1769, ?x9549), genre(?x1708, ?x9549), genre(?x288, ?x9549), genre(?x6726, ?x1510), genre(?x6726, ?x53), ?x1510 = 01hmnh, ?x53 = 07s9rl0, film(?x1784, ?x1769), titles(?x600, ?x1769), ?x288 = 0yyg4, nominated_for(?x1708, ?x2094), award_nominee(?x3709, ?x1784) *> conf = 0.67 ranks of expected_values: 2 EVAL 028v3 genre! 01j5ql CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 24.000 0.728 http://example.org/film/film/genre #8774-0gyh PRED entity: 0gyh PRED relation: district_represented! PRED expected values: 02cg7g 01gsvp => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 34): 01gsvp (0.60 #87, 0.55 #1259, 0.45 #257), 02cg7g (0.55 #1259, 0.49 #387, 0.44 #489), 01gst9 (0.55 #1259, 0.40 #89, 0.39 #259), 01gssm (0.55 #1259, 0.40 #79, 0.39 #249), 01gsrl (0.55 #1259, 0.40 #80, 0.36 #250), 01grr2 (0.55 #1259, 0.40 #91, 0.33 #499), 01gsry (0.55 #1259, 0.40 #95, 0.31 #503), 01gssz (0.55 #1259, 0.39 #266, 0.38 #504), 03rtmz (0.55 #1259, 0.31 #484, 0.31 #382), 01grrf (0.55 #1259, 0.31 #501, 0.30 #569) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0f8x_r; >> query: (?x2831, 01gsvp) <- adjoins(?x3778, ?x2831), adjoins(?x2831, ?x3038), ?x3778 = 07h34 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0gyh district_represented! 01gsvp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.600 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 0gyh district_represented! 02cg7g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.600 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #8773-07k8rt4 PRED entity: 07k8rt4 PRED relation: currency PRED expected values: 09nqf => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.89 #8, 0.85 #36, 0.80 #15), 01nv4h (0.02 #79, 0.02 #23, 0.02 #226), 02l6h (0.02 #25, 0.01 #46, 0.01 #389) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 04jn6y7; >> query: (?x4427, 09nqf) <- language(?x4427, ?x254), genre(?x4427, ?x258), film_crew_role(?x4427, ?x5136), ?x5136 = 089g0h >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07k8rt4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.887 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8772-040njc PRED entity: 040njc PRED relation: nominated_for PRED expected values: 0sxg4 011yxg 0p_sc 092vkg 026390q 09p0ct 0m9p3 0dr3sl 07cdz 017jd9 09p3_s 0jwvf 01cmp9 0yxf4 0g4vmj8 01hv3t 0hvvf 02mpyh 0jdr0 0h1x5f 0k20s 05sbv3 0286hyp => 60 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1275): 06x77g (0.78 #22344, 0.67 #18109, 0.54 #23756), 0_9l_ (0.77 #29632, 0.76 #38112, 0.75 #18338), 017jd9 (0.77 #29632, 0.76 #38112, 0.75 #18338), 04j4tx (0.77 #29632, 0.76 #38112, 0.75 #18338), 0h6r5 (0.77 #29632, 0.76 #38112, 0.75 #18338), 0p9tm (0.77 #29632, 0.76 #38112, 0.75 #18338), 0bl5c (0.77 #29632, 0.76 #38112, 0.75 #18338), 05jf85 (0.77 #29632, 0.76 #38112, 0.75 #18338), 01cmp9 (0.67 #21975, 0.67 #17740, 0.62 #23387), 0mcl0 (0.67 #21660, 0.67 #17425, 0.57 #27308) >> Best rule #22344 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0p9sw; >> query: (?x198, 06x77g) <- award(?x6957, ?x198), nominated_for(?x198, ?x2958), nominated_for(?x198, ?x1077), ?x1077 = 09q5w2, ?x2958 = 0b_5d, type_of_union(?x6957, ?x566) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #29632 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 02rdxsh; *> query: (?x198, ?x144) <- award(?x144, ?x198), nominated_for(?x198, ?x7141), nominated_for(?x198, ?x5712), film_release_region(?x7141, ?x1790), ?x5712 = 0k4p0 *> conf = 0.77 ranks of expected_values: 3, 9, 14, 16, 29, 44, 45, 63, 65, 67, 68, 83, 91, 121, 137, 147, 149, 155, 199, 225, 254, 304, 501 EVAL 040njc nominated_for 0286hyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 05sbv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0k20s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0h1x5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0jdr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 02mpyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0hvvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 01hv3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0g4vmj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0yxf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 01cmp9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0jwvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 09p3_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 017jd9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 07cdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0dr3sl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0m9p3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 09p0ct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 026390q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 092vkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0p_sc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 011yxg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 040njc nominated_for 0sxg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 60.000 36.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8771-07xyn1 PRED entity: 07xyn1 PRED relation: service_location PRED expected values: 02j71 => 143 concepts (137 used for prediction) PRED predicted values (max 10 best out of 114): 09c7w0 (0.85 #2576, 0.84 #1786, 0.84 #3171), 0d060g (0.38 #2189, 0.30 #3079, 0.29 #3868), 0nt4s (0.26 #2675, 0.10 #6931, 0.07 #10512), 02j71 (0.26 #3384, 0.25 #3581, 0.25 #5262), 0chghy (0.20 #508, 0.15 #2193, 0.14 #3378), 07ssc (0.19 #1008, 0.16 #4767, 0.16 #1701), 06bnz (0.13 #528, 0.11 #1024, 0.07 #1122), 0345h (0.13 #3099, 0.12 #2209, 0.11 #1713), 03rjj (0.09 #1691, 0.08 #2187, 0.08 #3077), 03h64 (0.09 #1731, 0.08 #2227, 0.07 #1830) >> Best rule #2576 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 03rwz3; >> query: (?x8237, 09c7w0) <- contact_category(?x8237, ?x897), state_province_region(?x8237, ?x448), citytown(?x8237, ?x2879), contains(?x94, ?x2879) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #3384 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 0kc6x; 0cjdk; 05xbx; 0gsgr; 013fn; 0cv_2; *> query: (?x8237, 02j71) <- company(?x5161, ?x8237), contact_category(?x8237, ?x897), company(?x5161, ?x5956), industry(?x5956, ?x14344) *> conf = 0.26 ranks of expected_values: 4 EVAL 07xyn1 service_location 02j71 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 143.000 137.000 0.855 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #8770-0gcdzz PRED entity: 0gcdzz PRED relation: nationality PRED expected values: 09c7w0 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 14): 09c7w0 (0.84 #201, 0.76 #601, 0.76 #801), 03rk0 (0.84 #346, 0.06 #7050, 0.06 #4549), 02jx1 (0.11 #1133, 0.10 #533, 0.10 #1433), 07ssc (0.09 #1415, 0.08 #1115, 0.08 #4518), 0d060g (0.05 #1207, 0.05 #1507, 0.04 #407), 03_3d (0.03 #1506, 0.02 #1206, 0.01 #7611), 0345h (0.02 #4534, 0.02 #1431, 0.02 #7335), 03rjj (0.02 #4508, 0.02 #1105, 0.02 #1405), 03rt9 (0.02 #413, 0.02 #913, 0.01 #1213), 0f8l9c (0.02 #4525, 0.02 #4425, 0.02 #5825) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 01nrgq; 05gnf; 02js_6; >> query: (?x1379, 09c7w0) <- award_winner(?x1379, ?x2839), nominated_for(?x1379, ?x2528), ?x2528 = 0d68qy >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gcdzz nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.840 http://example.org/people/person/nationality #8769-011ypx PRED entity: 011ypx PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.87 #46, 0.85 #66, 0.85 #41), 02nxhr (0.07 #27, 0.05 #37, 0.04 #12), 07z4p (0.05 #25, 0.03 #96, 0.02 #350), 07c52 (0.03 #256, 0.03 #94, 0.03 #297) >> Best rule #46 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 026p_bs; 03h4fq7; 025twgf; >> query: (?x5927, 029j_) <- nominated_for(?x2262, ?x5927), country(?x5927, ?x94), film_crew_role(?x2262, ?x137) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011ypx film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #8768-081lh PRED entity: 081lh PRED relation: location PRED expected values: 0cr3d => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 290): 030qb3t (0.25 #886, 0.25 #83, 0.20 #56305), 05ksh (0.25 #863, 0.25 #60, 0.03 #4879), 0cr3d (0.25 #145, 0.22 #2555, 0.13 #7373), 0tj4y (0.25 #1079, 0.02 #5898, 0.01 #14731), 02_286 (0.21 #4053, 0.20 #1644, 0.19 #23326), 0ccvx (0.20 #1828, 0.04 #18691, 0.03 #33146), 0jbrr (0.20 #2341, 0.02 #7962), 04n3l (0.11 #2589, 0.07 #4195, 0.02 #8210), 094jv (0.11 #2503, 0.03 #11336, 0.03 #15351), 06y9v (0.11 #2566, 0.02 #9793, 0.01 #11399) >> Best rule #886 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0dr5y; >> query: (?x986, 030qb3t) <- artists(?x2480, ?x986), film(?x986, ?x306), nationality(?x986, ?x94) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #145 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 03f2_rc; 017yxq; *> query: (?x986, 0cr3d) <- artists(?x2480, ?x986), film(?x986, ?x306), award(?x986, ?x68) *> conf = 0.25 ranks of expected_values: 3 EVAL 081lh location 0cr3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 145.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #8767-02_j8x PRED entity: 02_j8x PRED relation: film PRED expected values: 02qhqz4 => 95 concepts (91 used for prediction) PRED predicted values (max 10 best out of 485): 0prrm (0.06 #861, 0.03 #8021, 0.02 #4441), 01shy7 (0.05 #423, 0.04 #5793, 0.04 #7583), 0gjcrrw (0.05 #629, 0.01 #2419), 0fpgp26 (0.05 #1537, 0.01 #5117), 06q8qh (0.04 #105613, 0.03 #121724, 0.03 #80551), 039cq4 (0.04 #105613, 0.03 #121724), 03c7twt (0.04 #105613), 0bq8tmw (0.04 #105613), 08jgk1 (0.04 #105613), 02rcwq0 (0.03 #121724) >> Best rule #861 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 0126rp; >> query: (?x8348, 0prrm) <- film(?x8348, ?x86), student(?x9823, ?x8348), producer_type(?x8348, ?x632) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #18243 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 410 *> proper extension: 0411q; 07c0j; 045zr; 01wgxtl; 04qmr; 025ldg; 018n6m; 03f19q4; 06mt91; 02tf1y; ... *> query: (?x8348, 02qhqz4) <- award_nominee(?x3927, ?x8348), award(?x8348, ?x704), participant(?x6187, ?x8348) *> conf = 0.01 ranks of expected_values: 379 EVAL 02_j8x film 02qhqz4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 95.000 91.000 0.062 http://example.org/film/actor/film./film/performance/film #8766-016kb7 PRED entity: 016kb7 PRED relation: award_winner! PRED expected values: 027986c => 116 concepts (99 used for prediction) PRED predicted values (max 10 best out of 184): 04kxsb (0.38 #25440, 0.37 #37091, 0.37 #18541), 0gqyl (0.22 #105, 0.05 #1829, 0.05 #2691), 09sb52 (0.14 #2626, 0.14 #902, 0.13 #1764), 0gq9h (0.13 #11210, 0.11 #31047, 0.11 #33204), 0gr4k (0.13 #11210, 0.11 #31047, 0.11 #33204), 0p9sw (0.13 #11210, 0.11 #31047, 0.11 #33204), 04dn09n (0.13 #11210, 0.11 #31047, 0.11 #33204), 02qyntr (0.13 #11210, 0.11 #31047, 0.11 #33204), 0gqxm (0.13 #11210, 0.11 #31047, 0.11 #33204), 02r0csl (0.13 #11210, 0.11 #31047, 0.11 #33204) >> Best rule #25440 for best value: >> intensional similarity = 3 >> extensional distance = 1625 >> proper extension: 030_1_; 03mdt; >> query: (?x7866, ?x2375) <- award(?x7866, ?x2375), award_winner(?x591, ?x7866), award(?x69, ?x591) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #11210 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1015 *> proper extension: 0gsg7; 03jvmp; 0cjdk; 03czrpj; 0kk9v; 031rx9; 027_tg; 05xbx; 05gnf; 025vwmy; *> query: (?x7866, ?x143) <- nominated_for(?x7866, ?x2376), award_winner(?x3618, ?x7866), award(?x2376, ?x143) *> conf = 0.13 ranks of expected_values: 11 EVAL 016kb7 award_winner! 027986c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 116.000 99.000 0.379 http://example.org/award/award_category/winners./award/award_honor/award_winner #8765-0164v PRED entity: 0164v PRED relation: organization PRED expected values: 07t65 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 48): 07t65 (0.91 #361, 0.90 #562, 0.90 #442), 0j7v_ (0.56 #946, 0.49 #146, 0.31 #66), 0_2v (0.41 #164, 0.40 #4, 0.39 #184), 04k4l (0.38 #165, 0.36 #185, 0.35 #245), 01rz1 (0.35 #162, 0.33 #22, 0.31 #182), 018cqq (0.32 #170, 0.30 #250, 0.29 #190), 02jxk (0.20 #163, 0.18 #243, 0.17 #183), 034h1h (0.18 #1139, 0.02 #730, 0.02 #792), 059dn (0.12 #14, 0.12 #34, 0.10 #54), 085h1 (0.07 #11, 0.07 #51, 0.07 #31) >> Best rule #361 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 020d5; 05c17; 05rznz; >> query: (?x8857, 07t65) <- jurisdiction_of_office(?x346, ?x8857), form_of_government(?x8857, ?x48), ?x346 = 060c4 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0164v organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.906 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #8764-0dzz6g PRED entity: 0dzz6g PRED relation: film! PRED expected values: 08wjf4 => 88 concepts (41 used for prediction) PRED predicted values (max 10 best out of 899): 031rx9 (0.46 #16643, 0.46 #27046, 0.45 #22885), 0170pk (0.08 #6522, 0.07 #282, 0.03 #2362), 05nzw6 (0.07 #1191, 0.07 #7431, 0.01 #51122), 02qgqt (0.06 #39530, 0.05 #47851, 0.03 #4178), 02l4pj (0.06 #39530, 0.05 #47851, 0.03 #2671), 02x7vq (0.06 #39530, 0.05 #47851, 0.02 #5139), 04bdxl (0.06 #39530, 0.05 #47851, 0.01 #6), 02d42t (0.06 #39530, 0.05 #47851, 0.01 #862), 05k2s_ (0.06 #39530, 0.05 #47851, 0.01 #2289), 02mt4k (0.06 #39530, 0.05 #47851) >> Best rule #16643 for best value: >> intensional similarity = 4 >> extensional distance = 252 >> proper extension: 0dnvn3; 03s6l2; 09gdm7q; 0m491; 020y73; 0gyy53; 05c26ss; 06tpmy; 0bxsk; 032clf; ... >> query: (?x3761, ?x3760) <- genre(?x3761, ?x53), nominated_for(?x3760, ?x3761), film_format(?x3761, ?x6392), film(?x2246, ?x3761) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #3452 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 77 *> proper extension: 0fvr1; 05r3qc; *> query: (?x3761, 08wjf4) <- genre(?x3761, ?x6674), film(?x2246, ?x3761), nominated_for(?x601, ?x3761), ?x6674 = 01t_vv *> conf = 0.01 ranks of expected_values: 720 EVAL 0dzz6g film! 08wjf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 88.000 41.000 0.456 http://example.org/film/actor/film./film/performance/film #8763-0785v8 PRED entity: 0785v8 PRED relation: award_nominee! PRED expected values: 0pmhf => 84 concepts (44 used for prediction) PRED predicted values (max 10 best out of 669): 03cglm (0.84 #6938, 0.84 #6937, 0.81 #48561), 0785v8 (0.50 #147, 0.33 #4771, 0.31 #85565), 06b0d2 (0.48 #4841, 0.18 #101756, 0.10 #2529), 04myfb7 (0.43 #5030, 0.18 #101756, 0.16 #6939), 0kryqm (0.43 #6175, 0.18 #101756, 0.16 #6939), 04mz10g (0.43 #4911, 0.18 #101756, 0.16 #6939), 04y79_n (0.43 #4912, 0.18 #101756, 0.16 #6939), 08s_lw (0.43 #5939, 0.18 #101756, 0.16 #6939), 065ydwb (0.38 #5938, 0.18 #101756, 0.16 #6939), 06lgq8 (0.38 #5056, 0.18 #101756, 0.16 #6939) >> Best rule #6938 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 02665kn; >> query: (?x818, ?x8739) <- award_nominee(?x818, ?x8739), award_nominee(?x818, ?x7992), ?x7992 = 05wqr1 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #94818 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1558 *> proper extension: 0kc9f; *> query: (?x818, ?x230) <- nominated_for(?x818, ?x3303), nominated_for(?x230, ?x3303), award_winner(?x2252, ?x818) *> conf = 0.23 ranks of expected_values: 31 EVAL 0785v8 award_nominee! 0pmhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 84.000 44.000 0.840 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8762-05cx7x PRED entity: 05cx7x PRED relation: type_of_union PRED expected values: 04ztj => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.87 #37, 0.86 #33, 0.72 #62), 01g63y (0.46 #310, 0.19 #30, 0.18 #18) >> Best rule #37 for best value: >> intensional similarity = 2 >> extensional distance = 241 >> proper extension: 02m30v; >> query: (?x7487, 04ztj) <- location_of_ceremony(?x7487, ?x739), profession(?x7487, ?x1032) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05cx7x type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.868 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8761-056k77g PRED entity: 056k77g PRED relation: film! PRED expected values: 01kyvx => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 4): 01kyvx (0.78 #32, 0.76 #27, 0.69 #22), 01pb34 (0.07 #54, 0.07 #39, 0.06 #90), 09_gdc (0.07 #38, 0.05 #53, 0.03 #68), 02t8yb (0.03 #50) >> Best rule #32 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 02pb2bp; >> query: (?x9201, 01kyvx) <- film(?x13180, ?x9201), actor(?x9201, ?x7742), actor(?x12518, ?x13180), genre(?x9201, ?x225), gender(?x13180, ?x231) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 056k77g film! 01kyvx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.778 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #8760-03ksy PRED entity: 03ksy PRED relation: major_field_of_study PRED expected values: 06ms6 04x_3 01lj9 02_7t 03lrls => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 93): 04x_3 (0.64 #461, 0.50 #550, 0.47 #728), 01lj9 (0.55 #467, 0.50 #556, 0.41 #734), 01r4k (0.55 #500, 0.42 #589, 0.35 #767), 06ms6 (0.55 #455, 0.37 #1702, 0.35 #722), 0db86 (0.45 #477, 0.33 #566, 0.29 #744), 01tbp (0.40 #2356, 0.36 #485, 0.36 #2624), 04gb7 (0.36 #470, 0.33 #559, 0.28 #3322), 01bt59 (0.36 #497, 0.33 #586, 0.24 #764), 02_7t (0.36 #2537, 0.32 #1735, 0.28 #2359), 0_jm (0.34 #2532, 0.28 #6461, 0.26 #6728) >> Best rule #461 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 05qd_; >> query: (?x3439, 04x_3) <- company(?x5796, ?x3439), company(?x346, ?x3439), organizations_founded(?x3439, ?x5487) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 9, 23 EVAL 03ksy major_field_of_study 03lrls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 135.000 135.000 0.636 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03ksy major_field_of_study 02_7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 135.000 135.000 0.636 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03ksy major_field_of_study 01lj9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.636 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03ksy major_field_of_study 04x_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.636 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03ksy major_field_of_study 06ms6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 135.000 0.636 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8759-0p9tm PRED entity: 0p9tm PRED relation: nominated_for PRED expected values: 0ptxj => 88 concepts (47 used for prediction) PRED predicted values (max 10 best out of 153): 0qmjd (0.83 #1003, 0.82 #6518, 0.81 #6520), 01s9vc (0.29 #991, 0.06 #2746, 0.05 #6506), 05css_ (0.24 #911, 0.06 #2666, 0.04 #6426), 05cj_j (0.24 #798, 0.06 #2553, 0.04 #6313), 01jr4j (0.19 #955, 0.06 #2710, 0.04 #6470), 0k0rf (0.19 #898, 0.06 #2653, 0.03 #6413), 02r_pp (0.19 #896, 0.04 #2651, 0.04 #6411), 075cph (0.19 #824, 0.04 #2579, 0.04 #6339), 02jr6k (0.19 #872, 0.04 #2627, 0.03 #6387), 01kf3_9 (0.14 #805, 0.07 #2309, 0.06 #1307) >> Best rule #1003 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 026p_bs; 0k0rf; 0fztbq; >> query: (?x7846, ?x1822) <- film(?x6558, ?x7846), film_sets_designed(?x4423, ?x7846), nominated_for(?x1822, ?x7846) >> conf = 0.83 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0p9tm nominated_for 0ptxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 47.000 0.828 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #8758-0b_6h7 PRED entity: 0b_6h7 PRED relation: locations PRED expected values: 0dclg 0c1d0 => 59 concepts (52 used for prediction) PRED predicted values (max 10 best out of 423): 013yq (0.56 #1281, 0.53 #1807, 0.50 #2158), 04f_d (0.50 #1098, 0.44 #1275, 0.42 #1450), 0f2r6 (0.50 #194, 0.42 #1422, 0.40 #1773), 0lphb (0.50 #290, 0.42 #1518, 0.33 #1343), 0ftxw (0.50 #415, 0.40 #766, 0.33 #1643), 029cr (0.50 #1460, 0.35 #2162, 0.33 #1285), 030qb3t (0.50 #209, 0.32 #2314, 0.31 #2664), 0fsb8 (0.38 #2064, 0.35 #2240, 0.33 #1713), 071cn (0.33 #1655, 0.33 #1480, 0.33 #1305), 0f2rq (0.33 #1504, 0.33 #1329, 0.30 #2206) >> Best rule #1281 for best value: >> intensional similarity = 12 >> extensional distance = 7 >> proper extension: 0b_6rk; >> query: (?x5258, 013yq) <- locations(?x5258, ?x3983), team(?x5258, ?x6803), team(?x5258, ?x5551), team(?x5258, ?x2303), ?x5551 = 02pjzvh, team(?x11210, ?x6803), team(?x10736, ?x6803), team(?x4368, ?x6803), ?x10736 = 0f9rw9, ?x2303 = 02plv57, ?x4368 = 0b_6x2, ?x11210 = 0b_6q5 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #52 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 0b_6v_; *> query: (?x5258, 0dclg) <- locations(?x5258, ?x12358), locations(?x5258, ?x11669), locations(?x5258, ?x5525), team(?x5258, ?x10846), team(?x5258, ?x6803), ?x6803 = 03by7wc, ?x12358 = 0qpsn, ?x10846 = 02pzy52, instance_of_recurring_event(?x5258, ?x10863), featured_film_locations(?x5116, ?x11669), category(?x5525, ?x134) *> conf = 0.33 ranks of expected_values: 14, 23 EVAL 0b_6h7 locations 0c1d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 59.000 52.000 0.556 http://example.org/time/event/locations EVAL 0b_6h7 locations 0dclg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 59.000 52.000 0.556 http://example.org/time/event/locations #8757-0180w8 PRED entity: 0180w8 PRED relation: award PRED expected values: 02ddq4 => 134 concepts (123 used for prediction) PRED predicted values (max 10 best out of 313): 01by1l (0.69 #10642, 0.40 #922, 0.33 #3757), 01bgqh (0.55 #10573, 0.40 #853, 0.34 #4093), 01c427 (0.40 #895, 0.29 #490, 0.17 #4135), 01c99j (0.40 #1037, 0.23 #4277, 0.22 #3872), 03qbnj (0.33 #1044, 0.17 #10764, 0.17 #4284), 02f6ym (0.33 #1069, 0.14 #3904, 0.13 #4309), 03qbh5 (0.29 #611, 0.27 #1016, 0.22 #10736), 0c4z8 (0.28 #10602, 0.20 #14247, 0.20 #1287), 01d38g (0.27 #28, 0.11 #2458, 0.11 #10558), 054ks3 (0.24 #1357, 0.23 #2167, 0.21 #3382) >> Best rule #10642 for best value: >> intensional similarity = 4 >> extensional distance = 210 >> proper extension: 04lgymt; 01x15dc; >> query: (?x4550, 01by1l) <- gender(?x4550, ?x231), award(?x4550, ?x1565), award(?x1566, ?x1565), ?x1566 = 0ggl02 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2371 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 42 *> proper extension: 01m15br; 0x3b7; 02pt7h_; 02z4b_8; *> query: (?x4550, 02ddq4) <- artists(?x3108, ?x4550), role(?x4550, ?x614), ?x3108 = 02w4v, role(?x614, ?x74), role(?x569, ?x614) *> conf = 0.14 ranks of expected_values: 27 EVAL 0180w8 award 02ddq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 134.000 123.000 0.689 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8756-0r771 PRED entity: 0r771 PRED relation: contains! PRED expected values: 06pvr => 106 concepts (89 used for prediction) PRED predicted values (max 10 best out of 260): 01n7q (0.84 #51909, 0.83 #19685, 0.83 #51012), 04_1l0v (0.33 #449, 0.14 #1343, 0.12 #2238), 07c5l (0.32 #74271, 0.03 #24553, 0.03 #26341), 0kpys (0.29 #6443, 0.22 #10022, 0.19 #3759), 02jx1 (0.24 #78830, 0.23 #37673, 0.19 #25139), 04jpl (0.24 #37608, 0.12 #25074, 0.06 #78765), 06pvr (0.22 #10007, 0.19 #7323, 0.16 #3744), 07ssc (0.21 #78775, 0.12 #37618, 0.11 #70724), 02_286 (0.15 #25095, 0.15 #37629, 0.12 #5410), 059rby (0.13 #25072, 0.13 #54613, 0.12 #37606) >> Best rule #51909 for best value: >> intensional similarity = 4 >> extensional distance = 291 >> proper extension: 0fvvg; >> query: (?x10937, ?x1227) <- state(?x10937, ?x1227), contains(?x94, ?x10937), contains(?x94, ?x1011), major_field_of_study(?x1011, ?x254) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #10007 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 0l1pj; *> query: (?x10937, 06pvr) <- state(?x10937, ?x1227), contains(?x94, ?x10937), ?x94 = 09c7w0, ?x1227 = 01n7q *> conf = 0.22 ranks of expected_values: 7 EVAL 0r771 contains! 06pvr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 106.000 89.000 0.840 http://example.org/location/location/contains #8755-03nkts PRED entity: 03nkts PRED relation: award_nominee PRED expected values: 02t_tp => 86 concepts (30 used for prediction) PRED predicted values (max 10 best out of 642): 01m4yn (0.06 #23416, 0.05 #18732, 0.04 #25758), 0hvb2 (0.05 #21474, 0.05 #16790, 0.04 #37867), 02qgqt (0.05 #2361, 0.04 #20, 0.04 #4702), 03hzl42 (0.05 #3393, 0.04 #1052, 0.04 #5734), 01g23m (0.05 #3257, 0.03 #7940, 0.03 #916), 02p65p (0.05 #37494, 0.04 #27, 0.04 #21101), 019pm_ (0.05 #21686, 0.04 #17002, 0.03 #19344), 0gy6z9 (0.04 #744, 0.04 #3085, 0.03 #5426), 01pgzn_ (0.04 #21574, 0.04 #16890, 0.03 #14547), 023kzp (0.04 #22468, 0.04 #17784, 0.04 #15441) >> Best rule #23416 for best value: >> intensional similarity = 3 >> extensional distance = 395 >> proper extension: 01sl1q; 01j5ts; 01qscs; 03w1v2; 06jzh; 01n5309; 01csvq; 018db8; 02l840; 01wmxfs; ... >> query: (?x6397, ?x6844) <- participant(?x6844, ?x6397), award_nominee(?x6397, ?x286), award(?x6397, ?x704) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03nkts award_nominee 02t_tp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 30.000 0.056 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8754-0bdt8 PRED entity: 0bdt8 PRED relation: film PRED expected values: 0ktpx => 146 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1230): 014kkm (0.25 #872, 0.07 #8024, 0.05 #13388), 0h1v19 (0.25 #437, 0.05 #16529), 03shpq (0.25 #1446, 0.02 #56880), 0jvt9 (0.24 #16630, 0.12 #21994, 0.10 #11266), 04954r (0.17 #18495, 0.13 #7767, 0.10 #13131), 03nqnnk (0.17 #9963, 0.04 #56457, 0.02 #120828), 05zpghd (0.14 #2742, 0.12 #4530, 0.08 #20622), 013q07 (0.14 #2144, 0.12 #3932, 0.07 #34329), 027fwmt (0.14 #3380, 0.12 #5168, 0.07 #8744), 016dj8 (0.14 #2902, 0.12 #4690, 0.06 #35087) >> Best rule #872 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01d5vk; >> query: (?x6440, 014kkm) <- people(?x3538, ?x6440), film(?x6440, ?x1973), ?x3538 = 0jdk0 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #17097 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 012dr7; *> query: (?x6440, 0ktpx) <- people(?x3538, ?x6440), nominated_for(?x6440, ?x1973), participant(?x6440, ?x1607) *> conf = 0.10 ranks of expected_values: 34 EVAL 0bdt8 film 0ktpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 146.000 124.000 0.250 http://example.org/film/actor/film./film/performance/film #8753-05sq0m PRED entity: 05sq0m PRED relation: artists! PRED expected values: 0gg8l => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 182): 064t9 (0.46 #5943, 0.46 #6880, 0.46 #6568), 06by7 (0.45 #1271, 0.43 #1583, 0.42 #10634), 016cjb (0.42 #389, 0.31 #701, 0.28 #1013), 0gg8l (0.33 #1070, 0.25 #446, 0.22 #6242), 06j6l (0.30 #4418, 0.29 #5354, 0.29 #6291), 02w4v (0.28 #981, 0.22 #45, 0.22 #6242), 0glt670 (0.27 #4410, 0.27 #4722, 0.26 #6907), 025sc50 (0.26 #4420, 0.25 #2236, 0.24 #4732), 05bt6j (0.24 #4101, 0.23 #10655, 0.22 #2541), 0gywn (0.24 #2244, 0.22 #4428, 0.22 #6301) >> Best rule #5943 for best value: >> intensional similarity = 3 >> extensional distance = 384 >> proper extension: 033wx9; 0565cz; 01w02sy; 01w806h; 039bpc; 0phx4; 016ksk; 03d9d6; 0473q; 019389; ... >> query: (?x7258, 064t9) <- artist(?x2241, ?x7258), award_nominee(?x7258, ?x2638), artists(?x2664, ?x2638) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1070 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 015882; 01ww2fs; 0m_v0; 01kstn9; 01m1dzc; 02cx90; 01l03w2; 01lvzbl; 02f1c; *> query: (?x7258, 0gg8l) <- artist(?x2241, ?x7258), award_winner(?x7258, ?x4239), ?x4239 = 0x3b7 *> conf = 0.33 ranks of expected_values: 4 EVAL 05sq0m artists! 0gg8l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 110.000 0.464 http://example.org/music/genre/artists #8752-01t265 PRED entity: 01t265 PRED relation: profession PRED expected values: 01d_h8 => 105 concepts (41 used for prediction) PRED predicted values (max 10 best out of 87): 01d_h8 (0.79 #6, 0.76 #1770, 0.75 #1182), 02hrh1q (0.78 #2218, 0.75 #307, 0.73 #1777), 02krf9 (0.26 #3702, 0.23 #5172, 0.23 #5319), 09jwl (0.25 #1634, 0.24 #2369, 0.24 #2075), 0cbd2 (0.22 #889, 0.21 #1918, 0.21 #5889), 0nbcg (0.17 #2382, 0.17 #2971, 0.16 #2088), 018gz8 (0.17 #5897, 0.14 #1191, 0.11 #309), 0kyk (0.16 #910, 0.12 #1939, 0.12 #5910), 01c72t (0.13 #463, 0.11 #610, 0.11 #3405), 0n1h (0.12 #11, 0.09 #1334, 0.08 #305) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 045n3p; >> query: (?x6662, 01d_h8) <- profession(?x6662, ?x524), ?x524 = 02jknp, people(?x6260, ?x6662), religion(?x6662, ?x1985) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01t265 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 41.000 0.788 http://example.org/people/person/profession #8751-05ztrmj PRED entity: 05ztrmj PRED relation: award! PRED expected values: 0prfz 01wgcvn 02wycg2 02vntj 03h2d4 05txrz 01pk3z 02pk6x 03hhd3 0c0k1 0b25vg => 43 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2234): 0kjrx (0.72 #16554, 0.69 #52988, 0.68 #16553), 02x0dzw (0.69 #52988, 0.68 #16553, 0.67 #56299), 0170s4 (0.50 #7239, 0.40 #619, 0.18 #3929), 0z4s (0.50 #6706, 0.40 #86, 0.12 #49674), 0794g (0.45 #4193, 0.20 #883, 0.12 #49674), 01kwsg (0.42 #7948, 0.40 #1328, 0.12 #49674), 014zcr (0.42 #6669, 0.21 #9980, 0.20 #49), 016k6x (0.42 #8035, 0.20 #1415, 0.13 #11346), 018db8 (0.42 #6780, 0.20 #160, 0.13 #16555), 01713c (0.42 #7004, 0.20 #384, 0.12 #49674) >> Best rule #16554 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 02r9qt; >> query: (?x3508, ?x2499) <- award_winner(?x3508, ?x2499), celebrity(?x2499, ?x2258), participant(?x286, ?x2499) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #2447 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 05zr6wv; 0f4x7; 02g2wv; *> query: (?x3508, 0c0k1) <- award(?x2373, ?x3508), award(?x521, ?x3508), ?x2373 = 016z2j, ?x521 = 0147dk, nominated_for(?x3508, ?x186) *> conf = 0.40 ranks of expected_values: 20, 145, 243, 420, 443, 479, 579, 591, 592, 659 EVAL 05ztrmj award! 0b25vg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05ztrmj award! 0c0k1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05ztrmj award! 03hhd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05ztrmj award! 02pk6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05ztrmj award! 01pk3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05ztrmj award! 05txrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05ztrmj award! 03h2d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05ztrmj award! 02vntj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05ztrmj award! 02wycg2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05ztrmj award! 01wgcvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05ztrmj award! 0prfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 19.000 0.719 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8750-07hyk PRED entity: 07hyk PRED relation: organizations_founded PRED expected values: 0bqc_ => 161 concepts (154 used for prediction) PRED predicted values (max 10 best out of 106): 01rz1 (0.70 #924, 0.25 #3264, 0.06 #6724), 034h1h (0.43 #3288, 0.14 #6236, 0.04 #9488), 09c7w0 (0.38 #3764, 0.11 #3967, 0.08 #4778), 03bnb (0.33 #72, 0.04 #3224, 0.03 #4140), 06dr9 (0.31 #1710, 0.18 #2527, 0.18 #2015), 03lb_v (0.24 #3357, 0.24 #3356, 0.14 #6304), 02_l9 (0.24 #3357, 0.24 #3356, 0.14 #6303), 07wbk (0.23 #1542, 0.18 #1035, 0.18 #1948), 02jd_7 (0.18 #1385, 0.18 #1181, 0.15 #1789), 05f4p (0.18 #1090, 0.15 #1597, 0.12 #2209) >> Best rule #924 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0154j; 0d0vqn; 03rt9; 07ssc; 0k6nt; 059j2; 04g61; >> query: (?x10888, 01rz1) <- organizations_founded(?x10888, ?x13997), organization(?x10888, ?x13242), company(?x346, ?x13242) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07hyk organizations_founded 0bqc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 161.000 154.000 0.700 http://example.org/organization/organization_founder/organizations_founded #8749-0p5wz PRED entity: 0p5wz PRED relation: institution! PRED expected values: 02h4rq6 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.78 #3, 0.75 #94, 0.74 #26), 03bwzr4 (0.75 #14, 0.71 #37, 0.71 #105), 014mlp (0.63 #75, 0.63 #97, 0.63 #6), 0bkj86 (0.61 #9, 0.58 #32, 0.57 #100), 016t_3 (0.59 #95, 0.59 #27, 0.59 #73), 04zx3q1 (0.52 #25, 0.50 #71, 0.49 #2), 027f2w (0.46 #10, 0.39 #101, 0.39 #33), 07s6fsf (0.45 #92, 0.42 #1, 0.41 #70), 013zdg (0.32 #8, 0.28 #99, 0.26 #31), 0bjrnt (0.21 #231, 0.20 #277, 0.20 #30) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 0277jc; 01pq4w; 017j69; 017cy9; 02bqy; 01jq4b; 01qd_r; >> query: (?x3362, 02h4rq6) <- list(?x3362, ?x2197), major_field_of_study(?x3362, ?x1668), citytown(?x3362, ?x9976) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p5wz institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.780 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #8748-02rv_dz PRED entity: 02rv_dz PRED relation: country PRED expected values: 0d060g => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 25): 0d060g (0.42 #3914, 0.08 #310, 0.07 #128), 07ssc (0.29 #923, 0.27 #76, 0.24 #2008), 01z4y (0.24 #968, 0.06 #1812, 0.06 #4635), 0345h (0.20 #87, 0.12 #693, 0.11 #813), 0f8l9c (0.13 #79, 0.10 #1410, 0.09 #2011), 0chghy (0.07 #192, 0.05 #618, 0.05 #798), 06mkj (0.07 #100, 0.02 #706, 0.02 #826), 03rjj (0.05 #186, 0.04 #1397, 0.04 #247), 0d0vqn (0.05 #190, 0.04 #251, 0.02 #312), 03_3d (0.04 #3079, 0.04 #5062, 0.04 #5182) >> Best rule #3914 for best value: >> intensional similarity = 3 >> extensional distance = 1441 >> proper extension: 02sqkh; 0c3xpwy; 0cskb; 07bz5; 0275kr; 0clpml; >> query: (?x1531, ?x279) <- nominated_for(?x1530, ?x1531), award_winner(?x2394, ?x1530), nationality(?x1530, ?x279) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rv_dz country 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.423 http://example.org/film/film/country #8747-02hrb2 PRED entity: 02hrb2 PRED relation: category PRED expected values: 08mbj5d => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #28, 0.91 #23, 0.90 #34) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 181 >> proper extension: 01ngz1; 03v6t; 049dk; 01bvw5; 02bjhv; 01y17m; 02897w; 02zd2b; 01xrlm; 027ydt; ... >> query: (?x9078, 08mbj5d) <- organization(?x346, ?x9078), institution(?x1771, ?x9078), ?x346 = 060c4, ?x1771 = 019v9k >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hrb2 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.913 http://example.org/common/topic/webpage./common/webpage/category #8746-06pj8 PRED entity: 06pj8 PRED relation: award_winner! PRED expected values: 0gs9p 054ky1 027b9ly => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 347): 019f4v (0.37 #42639, 0.37 #38457, 0.36 #57696), 04dn09n (0.37 #42639, 0.37 #38457, 0.36 #57696), 0drtkx (0.37 #42639, 0.37 #38457, 0.36 #57696), 07z2lx (0.37 #42639, 0.37 #38457, 0.36 #57696), 02qyxs5 (0.37 #42639, 0.37 #38457, 0.36 #57696), 0f_nbyh (0.37 #42639, 0.37 #38457, 0.36 #57696), 0gs9p (0.31 #8018, 0.21 #13452, 0.21 #14288), 02qyntr (0.29 #1514, 0.05 #57277), 0k611 (0.29 #1343, 0.05 #57277), 01by1l (0.27 #943, 0.23 #525, 0.12 #2197) >> Best rule #42639 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 01czx; 013w8y; >> query: (?x2135, ?x198) <- award_winner(?x747, ?x2135), award(?x2135, ?x198) >> conf = 0.37 => this is the best rule for 6 predicted values *> Best rule #8018 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 121 *> proper extension: 023w9s; *> query: (?x2135, 0gs9p) <- profession(?x2135, ?x319), award_winner(?x747, ?x2135), film(?x2135, ?x1452) *> conf = 0.31 ranks of expected_values: 7, 22, 52 EVAL 06pj8 award_winner! 027b9ly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 161.000 161.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 06pj8 award_winner! 054ky1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 161.000 161.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 06pj8 award_winner! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 161.000 161.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner #8745-02q42j_ PRED entity: 02q42j_ PRED relation: award_nominee PRED expected values: 0136g9 => 119 concepts (76 used for prediction) PRED predicted values (max 10 best out of 997): 0136g9 (0.81 #147197, 0.81 #86444, 0.81 #112147), 0bt4r4 (0.62 #7662, 0.02 #89434, 0.02 #77750), 0cj2t3 (0.56 #7663, 0.02 #63732, 0.01 #21680), 048wrb (0.56 #8696, 0.02 #64765, 0.01 #90468), 04t2l2 (0.50 #7048, 0.02 #63117, 0.02 #98166), 08hsww (0.50 #8127, 0.02 #89899, 0.02 #78215), 062ftr (0.50 #7905, 0.02 #63974, 0.01 #21922), 0cj2nl (0.50 #7893, 0.02 #63962, 0.01 #21910), 0h3mrc (0.44 #7898, 0.02 #89670, 0.02 #99016), 0cj2k3 (0.44 #8937, 0.02 #65006, 0.01 #22954) >> Best rule #147197 for best value: >> intensional similarity = 3 >> extensional distance = 1416 >> proper extension: 01wbsdz; >> query: (?x5973, ?x1039) <- award_nominee(?x6554, ?x5973), award_nominee(?x1039, ?x5973), category(?x6554, ?x134) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q42j_ award_nominee 0136g9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 76.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8744-04n1q6 PRED entity: 04n1q6 PRED relation: company PRED expected values: 04rwx 06fq2 => 35 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1690): 019rl6 (0.67 #4567, 0.50 #3552, 0.40 #1512), 0300cp (0.67 #4457, 0.50 #3442, 0.40 #1402), 060ppp (0.67 #4653, 0.50 #3638, 0.40 #1598), 011xy1 (0.56 #1352, 0.21 #4747, 0.21 #4746), 0lwkh (0.56 #4693, 0.50 #3678, 0.40 #1638), 02r5dz (0.56 #4479, 0.40 #1424, 0.38 #3803), 07xyn1 (0.56 #4593, 0.40 #1538, 0.38 #3917), 0sxdg (0.56 #4611, 0.40 #1556, 0.38 #3935), 01yfp7 (0.56 #4531, 0.40 #1476, 0.38 #3855), 09b3v (0.56 #4496, 0.40 #1441, 0.38 #3820) >> Best rule #4567 for best value: >> intensional similarity = 14 >> extensional distance = 7 >> proper extension: 01yc02; >> query: (?x3464, 019rl6) <- organization(?x3464, ?x4887), organization(?x3464, ?x1153), company(?x3464, ?x9386), company(?x3464, ?x3424), company(?x3464, ?x581), company(?x3464, ?x122), state_province_region(?x4887, ?x2235), citytown(?x122, ?x9336), category(?x3424, ?x134), company(?x920, ?x3424), list(?x122, ?x2197), ?x134 = 08mbj5d, service_location(?x581, ?x94), currency(?x9386, ?x170) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #706 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 060c4; 05k17c; *> query: (?x3464, 04rwx) <- organization(?x3464, ?x10197), company(?x3464, ?x5288), company(?x3464, ?x122), contains(?x1310, ?x10197), ?x122 = 08815, school_type(?x5288, ?x3092), major_field_of_study(?x5288, ?x254), student(?x5288, ?x9245), school(?x2820, ?x5288), currency(?x10197, ?x1099), producer_type(?x9245, ?x632), place_of_birth(?x9245, ?x10350) *> conf = 0.50 ranks of expected_values: 22, 135 EVAL 04n1q6 company 06fq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 35.000 25.000 0.667 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 04n1q6 company 04rwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 35.000 25.000 0.667 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #8743-05myd2 PRED entity: 05myd2 PRED relation: profession PRED expected values: 018gz8 => 87 concepts (78 used for prediction) PRED predicted values (max 10 best out of 121): 0dxtg (0.79 #6146, 0.62 #2640, 0.62 #5269), 03gjzk (0.56 #5855, 0.42 #6877, 0.39 #159), 09jwl (0.42 #1331, 0.28 #17, 0.19 #7757), 0dz3r (0.38 #1316, 0.16 #2, 0.12 #7742), 0nbcg (0.37 #1343, 0.19 #29, 0.13 #7769), 016z4k (0.36 #1318, 0.26 #4, 0.11 #7744), 02krf9 (0.31 #462, 0.24 #3675, 0.23 #3090), 018gz8 (0.30 #161, 0.27 #1621, 0.27 #1913), 0cbd2 (0.19 #6140, 0.16 #444, 0.15 #2634), 0n1h (0.18 #1324, 0.12 #10, 0.07 #886) >> Best rule #6146 for best value: >> intensional similarity = 4 >> extensional distance = 1094 >> proper extension: 0q9kd; 079vf; 0dbpyd; 06j0md; 01xdf5; 04t2l2; 05g8ky; 0h5f5n; 03rs8y; 050023; ... >> query: (?x9512, 0dxtg) <- nationality(?x9512, ?x94), profession(?x9512, ?x524), profession(?x11239, ?x524), ?x11239 = 0b_dh >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 04gycf; 01n7qlf; 02vntj; 01z5tr; 02dlfh; *> query: (?x9512, 018gz8) <- profession(?x9512, ?x1383), ?x1383 = 0np9r, participant(?x9512, ?x5662) *> conf = 0.30 ranks of expected_values: 8 EVAL 05myd2 profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 87.000 78.000 0.786 http://example.org/people/person/profession #8742-01b9ck PRED entity: 01b9ck PRED relation: nominated_for PRED expected values: 03bdkd => 99 concepts (57 used for prediction) PRED predicted values (max 10 best out of 870): 0cwy47 (0.52 #35572, 0.03 #17918, 0.03 #21151), 0kbf1 (0.29 #830, 0.09 #71152, 0.02 #5678), 03bdkd (0.29 #1498, 0.09 #71152, 0.01 #12813), 0cq7kw (0.22 #50125, 0.09 #71152, 0.03 #3921), 01y9r2 (0.14 #1200, 0.09 #71152, 0.04 #7664), 0dfw0 (0.14 #773, 0.09 #71152, 0.03 #13704), 0419kt (0.14 #1553, 0.09 #71152, 0.02 #16103), 0ds2n (0.14 #481, 0.09 #71152, 0.02 #15031), 0p_rk (0.14 #1210, 0.09 #71152, 0.02 #14141), 0ktx_ (0.14 #1571, 0.09 #71152, 0.02 #6419) >> Best rule #35572 for best value: >> intensional similarity = 2 >> extensional distance = 270 >> proper extension: 0454s1; >> query: (?x1300, ?x951) <- profession(?x1300, ?x319), film(?x1300, ?x951) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1498 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 016tt2; 02sj1x; *> query: (?x1300, 03bdkd) <- award_nominee(?x1300, ?x382), nominated_for(?x1300, ?x5183), ?x5183 = 0cq8qq *> conf = 0.29 ranks of expected_values: 3 EVAL 01b9ck nominated_for 03bdkd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 57.000 0.518 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #8741-0phx4 PRED entity: 0phx4 PRED relation: artists! PRED expected values: 016clz 064t9 => 138 concepts (84 used for prediction) PRED predicted values (max 10 best out of 243): 016clz (0.77 #617, 0.38 #1842, 0.30 #310), 064t9 (0.69 #625, 0.55 #7359, 0.53 #5831), 06by7 (0.67 #1859, 0.56 #4613, 0.49 #13489), 08cyft (0.38 #669, 0.10 #1282, 0.08 #2508), 06j6l (0.35 #7394, 0.35 #6171, 0.34 #1580), 05bt6j (0.33 #42, 0.31 #655, 0.30 #348), 0y3_8 (0.33 #46, 0.31 #659, 0.30 #352), 0xhtw (0.33 #1854, 0.31 #934, 0.28 #11640), 059kh (0.33 #48, 0.20 #354, 0.12 #1886), 02x8m (0.33 #18, 0.16 #1551, 0.15 #631) >> Best rule #617 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01pfr3; 0m19t; 03t9sp; 011z3g; >> query: (?x3667, 016clz) <- artists(?x6714, ?x3667), category(?x3667, ?x134), ?x6714 = 07d2d >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0phx4 artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 84.000 0.769 http://example.org/music/genre/artists EVAL 0phx4 artists! 016clz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 84.000 0.769 http://example.org/music/genre/artists #8740-02pg45 PRED entity: 02pg45 PRED relation: titles! PRED expected values: 01z4y => 92 concepts (65 used for prediction) PRED predicted values (max 10 best out of 54): 04xvlr (0.50 #4, 0.27 #823, 0.26 #618), 01z4y (0.42 #1888, 0.39 #3438, 0.38 #5195), 07s9rl0 (0.32 #1957, 0.32 #2370, 0.30 #2682), 017fp (0.25 #24, 0.07 #3221, 0.07 #5699), 04btyz (0.25 #82, 0.02 #1004, 0.02 #2141), 05p553 (0.19 #5779, 0.19 #717, 0.18 #4434), 011ys5 (0.19 #5779, 0.19 #717, 0.18 #5469), 01jfsb (0.17 #224, 0.15 #430, 0.12 #634), 03k9fj (0.17 #223, 0.10 #1045, 0.08 #838), 09q17 (0.17 #280, 0.06 #1928, 0.05 #3478) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 035s95; >> query: (?x5358, 04xvlr) <- written_by(?x5358, ?x523), film(?x1564, ?x5358), currency(?x5358, ?x170), ?x1564 = 01g257 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1888 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 185 *> proper extension: 02wk7b; *> query: (?x5358, 01z4y) <- written_by(?x5358, ?x523), genre(?x5358, ?x258), film_release_distribution_medium(?x5358, ?x81), ?x258 = 05p553 *> conf = 0.42 ranks of expected_values: 2 EVAL 02pg45 titles! 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 65.000 0.500 http://example.org/media_common/netflix_genre/titles #8739-04lhc4 PRED entity: 04lhc4 PRED relation: film_crew_role PRED expected values: 0dxtw => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 30): 09vw2b7 (0.71 #366, 0.66 #2467, 0.65 #1489), 0dxtw (0.46 #370, 0.41 #1493, 0.40 #82), 01pvkk (0.38 #372, 0.29 #2004, 0.28 #1968), 01vx2h (0.32 #2546, 0.31 #2472, 0.31 #1458), 02ynfr (0.19 #376, 0.18 #772, 0.18 #1645), 089g0h (0.17 #380, 0.11 #668, 0.11 #1467), 02rh1dz (0.14 #369, 0.11 #2470, 0.10 #729), 0215hd (0.13 #2480, 0.12 #1466, 0.12 #2263), 0d2b38 (0.12 #386, 0.10 #782, 0.10 #746), 01xy5l_ (0.12 #230, 0.10 #770, 0.10 #2475) >> Best rule #366 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 0pc62; 0dsvzh; 0164qt; 06_wqk4; 0p9lw; 01_mdl; 0872p_c; 0416y94; 01kff7; 044g_k; ... >> query: (?x6899, 09vw2b7) <- nominated_for(?x68, ?x6899), film(?x7903, ?x6899), nominated_for(?x6899, ?x1820), film_crew_role(?x6899, ?x137) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #370 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 0pc62; 0dsvzh; 0164qt; 06_wqk4; 0p9lw; 01_mdl; 0872p_c; 0416y94; 01kff7; 044g_k; ... *> query: (?x6899, 0dxtw) <- nominated_for(?x68, ?x6899), film(?x7903, ?x6899), nominated_for(?x6899, ?x1820), film_crew_role(?x6899, ?x137) *> conf = 0.46 ranks of expected_values: 2 EVAL 04lhc4 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.708 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8738-01v1ln PRED entity: 01v1ln PRED relation: nominated_for! PRED expected values: 05ztrmj => 80 concepts (70 used for prediction) PRED predicted values (max 10 best out of 320): 05b1610 (0.35 #1946, 0.13 #2902, 0.09 #4577), 05b4l5x (0.33 #6, 0.24 #1919, 0.17 #245), 05p09zm (0.33 #96, 0.20 #2009, 0.20 #11957), 0gq9h (0.33 #3889, 0.32 #3650, 0.32 #4368), 07bdd_ (0.32 #1967, 0.12 #2923, 0.08 #6271), 0l8z1 (0.31 #3878, 0.30 #3639, 0.30 #4117), 05f4m9q (0.30 #1925, 0.11 #2881, 0.08 #6229), 02hsq3m (0.30 #747, 0.29 #508, 0.24 #1704), 02r22gf (0.30 #746, 0.29 #507, 0.23 #985), 0gq_v (0.30 #3845, 0.29 #4084, 0.29 #4324) >> Best rule #1946 for best value: >> intensional similarity = 2 >> extensional distance = 174 >> proper extension: 05h95s; >> query: (?x6994, 05b1610) <- award(?x6994, ?x5734), category(?x5734, ?x134) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #11957 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1226 *> proper extension: 0c5dd; 02s4l6; 01dvbd; 02nczh; 0286vp; 01fs__; 04f6df0; 0qmfk; *> query: (?x6994, ?x1854) <- nominated_for(?x11879, ?x6994), nominated_for(?x7027, ?x6994), nominated_for(?x2585, ?x6994), location(?x11879, ?x1310), award(?x7027, ?x1854) *> conf = 0.20 ranks of expected_values: 23 EVAL 01v1ln nominated_for! 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 80.000 70.000 0.352 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8737-01yhm PRED entity: 01yhm PRED relation: draft PRED expected values: 04f4z1k => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 14): 02pq_rp (0.88 #347, 0.86 #191, 0.83 #261), 04f4z1k (0.83 #184, 0.78 #324, 0.78 #549), 0g3zpp (0.38 #15, 0.38 #490, 0.36 #506), 092j54 (0.38 #15, 0.36 #511, 0.36 #495), 09l0x9 (0.38 #15, 0.36 #513, 0.36 #497), 05vsb7 (0.38 #15, 0.36 #489, 0.35 #505), 03nt7j (0.38 #15, 0.34 #356, 0.34 #625), 025tn92 (0.38 #15, 0.34 #356, 0.34 #625), 02qw1zx (0.38 #15, 0.34 #356, 0.34 #625), 0f4vx0 (0.38 #15, 0.34 #356, 0.34 #625) >> Best rule #347 for best value: >> intensional similarity = 16 >> extensional distance = 22 >> proper extension: 0x2p; >> query: (?x1823, 02pq_rp) <- season(?x1823, ?x8529), school(?x1823, ?x10572), school(?x1823, ?x546), ?x8529 = 025ygws, draft(?x1823, ?x1161), institution(?x1368, ?x10572), institution(?x1305, ?x10572), ?x1368 = 014mlp, major_field_of_study(?x10572, ?x2014), institution(?x1305, ?x8930), institution(?x1305, ?x5306), ?x5306 = 0217m9, team(?x2010, ?x1823), school(?x4171, ?x546), ?x2010 = 02lyr4, ?x8930 = 0373qt >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #184 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 10 *> proper extension: 0713r; *> query: (?x1823, 04f4z1k) <- season(?x1823, ?x11501), season(?x1823, ?x10017), season(?x1823, ?x9267), season(?x1823, ?x8529), season(?x1823, ?x8517), season(?x1823, ?x3431), school(?x1823, ?x10572), ?x8529 = 025ygws, draft(?x1823, ?x8499), institution(?x865, ?x10572), ?x11501 = 027mvrc, ?x9267 = 0dx84s, ?x8499 = 02r6gw6, ?x8517 = 0285r5d, season(?x7399, ?x3431), season(?x1438, ?x3431), ?x7399 = 06wpc, school_type(?x10572, ?x1044), ?x10017 = 026fmqm, ?x865 = 02h4rq6, ?x1438 = 0512p *> conf = 0.83 ranks of expected_values: 2 EVAL 01yhm draft 04f4z1k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 61.000 61.000 0.875 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #8736-05k7sb PRED entity: 05k7sb PRED relation: adjoins PRED expected values: 01x73 => 181 concepts (128 used for prediction) PRED predicted values (max 10 best out of 546): 06btq (0.83 #78623, 0.82 #13094, 0.81 #93271), 0j3b (0.25 #2371, 0.25 #1600, 0.25 #829), 05rgl (0.25 #2413, 0.25 #1642, 0.25 #871), 0d060g (0.25 #2323, 0.25 #1552, 0.25 #781), 0k3kg (0.25 #1775, 0.25 #1004, 0.07 #49540), 0k3l5 (0.25 #2629, 0.25 #1087, 0.04 #49623), 03v0t (0.25 #4037, 0.18 #7115, 0.10 #17897), 05tbn (0.25 #3260, 0.14 #7107, 0.12 #17889), 05kkh (0.25 #3861, 0.14 #6939, 0.08 #17721), 0694j (0.25 #3379, 0.13 #21089, 0.12 #30332) >> Best rule #78623 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 0169t; 05v10; 02khs; 056vv; 05qkp; 0bjv6; 07bxhl; 07dvs; 01p1b; 0166v; ... >> query: (?x2020, ?x2713) <- jurisdiction_of_office(?x900, ?x2020), adjoins(?x2713, ?x2020), religion(?x2713, ?x109) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #9334 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 0wq36; *> query: (?x2020, 01x73) <- location(?x11088, ?x2020), sibling(?x6138, ?x11088), student(?x3178, ?x6138) *> conf = 0.04 ranks of expected_values: 170 EVAL 05k7sb adjoins 01x73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 181.000 128.000 0.825 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #8735-04xrx PRED entity: 04xrx PRED relation: participant! PRED expected values: 013w7j => 133 concepts (63 used for prediction) PRED predicted values (max 10 best out of 411): 0147dk (0.82 #36764, 0.80 #33591, 0.80 #23452), 013w7j (0.82 #36764, 0.80 #33591, 0.80 #23452), 01g0jn (0.15 #3170, 0.12 #5072, 0.08 #10777), 020hyj (0.15 #3804, 0.12 #5072, 0.08 #10777), 015f7 (0.12 #874, 0.06 #2142, 0.04 #5946), 0c9c0 (0.12 #828, 0.04 #2730, 0.03 #3365), 01pcvn (0.12 #1014, 0.04 #5453, 0.03 #3551), 01vvb4m (0.12 #850, 0.03 #3387, 0.02 #2752), 01q_ph (0.12 #659, 0.03 #6364, 0.02 #3196), 01d1st (0.12 #5072, 0.08 #10777, 0.07 #20282) >> Best rule #36764 for best value: >> intensional similarity = 3 >> extensional distance = 532 >> proper extension: 03qcq; 01pcrw; 01v3bn; 06s6hs; 012vf6; 06_bq1; 09k0f; 0dxmyh; 01gc7h; 025hzx; ... >> query: (?x2614, ?x521) <- gender(?x2614, ?x514), participant(?x2562, ?x2614), participant(?x2614, ?x521) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 04xrx participant! 013w7j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 63.000 0.823 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #8734-0394y PRED entity: 0394y PRED relation: award PRED expected values: 02f72n => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 224): 01bgqh (0.38 #7352, 0.33 #8570, 0.31 #10195), 01ckcd (0.38 #6429, 0.37 #5211, 0.36 #8053), 01c9jp (0.37 #5064, 0.28 #13186, 0.27 #12780), 01by1l (0.37 #7422, 0.36 #4580, 0.33 #4174), 03qbh5 (0.29 #7516, 0.25 #8734, 0.23 #9141), 01c92g (0.29 #4565, 0.25 #4159, 0.21 #7407), 02f72n (0.25 #6644, 0.23 #11518, 0.23 #7050), 01ck6h (0.25 #4184, 0.22 #7432, 0.21 #4590), 0c4z8 (0.25 #4133, 0.22 #7381, 0.21 #4539), 054ks3 (0.25 #4204, 0.21 #4610, 0.19 #7452) >> Best rule #7352 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 01zlh5; >> query: (?x4642, 01bgqh) <- category(?x4642, ?x134), inductee(?x1091, ?x4642), ?x1091 = 0g2c8, ?x134 = 08mbj5d >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #6644 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 58 *> proper extension: 089tm; 01pfr3; 0150jk; 04r1t; 01vrwfv; 0134s5; 0163m1; 018gm9; 01j59b0; 0178kd; ... *> query: (?x4642, 02f72n) <- group(?x227, ?x4642), ?x227 = 0342h, artists(?x7436, ?x4642), artists(?x1572, ?x4642), ?x1572 = 06by7, artists(?x7436, ?x7193), ?x7193 = 018d6l *> conf = 0.25 ranks of expected_values: 7 EVAL 0394y award 02f72n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 86.000 86.000 0.381 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8733-018y2s PRED entity: 018y2s PRED relation: location PRED expected values: 07z1m => 112 concepts (110 used for prediction) PRED predicted values (max 10 best out of 188): 0g284 (0.47 #55359, 0.47 #19252, 0.46 #30485), 02_286 (0.21 #51383, 0.17 #34534, 0.16 #58605), 030qb3t (0.15 #51429, 0.14 #34580, 0.14 #58651), 0cc56 (0.11 #57, 0.05 #60229, 0.04 #51403), 059rby (0.11 #16, 0.05 #818, 0.05 #60188), 01_d4 (0.11 #102, 0.05 #904, 0.04 #3310), 04ly1 (0.11 #202, 0.02 #1806, 0.02 #12233), 02xry (0.11 #132, 0.02 #3340, 0.02 #12163), 04jpl (0.10 #60189, 0.07 #51363, 0.06 #34514), 0cr3d (0.08 #51490, 0.07 #34641, 0.06 #12175) >> Best rule #55359 for best value: >> intensional similarity = 3 >> extensional distance = 1139 >> proper extension: 0bl60p; 012g92; >> query: (?x1165, ?x2204) <- film(?x1165, ?x1066), place_of_birth(?x1165, ?x2204), nationality(?x1165, ?x94) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1683 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 089tm; 03xhj6; 0knhk; 08w4pm; 01l_w0; 013w8y; 02cw1m; 016376; 0jltp; 016m5c; *> query: (?x1165, 07z1m) <- artists(?x302, ?x1165), artist(?x7089, ?x1165), ?x7089 = 0181dw *> conf = 0.02 ranks of expected_values: 33 EVAL 018y2s location 07z1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 112.000 110.000 0.473 http://example.org/people/person/places_lived./people/place_lived/location #8732-0fdtd7 PRED entity: 0fdtd7 PRED relation: nominated_for PRED expected values: 05dl1s => 59 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1474): 080dfr7 (0.70 #33593, 0.68 #6392, 0.67 #23988), 05dl1s (0.70 #33593, 0.68 #6392, 0.67 #23988), 03f7xg (0.50 #5286, 0.33 #492, 0.28 #38393), 0cmc26r (0.50 #5415, 0.33 #621, 0.22 #30391), 02r858_ (0.50 #6047, 0.33 #1253, 0.11 #7645), 01ffx4 (0.50 #5267, 0.33 #473, 0.05 #22863), 02vp1f_ (0.50 #4823, 0.33 #29, 0.03 #22419), 027r7k (0.50 #6323, 0.33 #1529, 0.02 #23919), 01srq2 (0.33 #1114, 0.28 #38393, 0.25 #5908), 0c0yh4 (0.33 #33, 0.28 #38393, 0.25 #4827) >> Best rule #33593 for best value: >> intensional similarity = 4 >> extensional distance = 188 >> proper extension: 0fqnzts; >> query: (?x11230, ?x7927) <- award(?x8652, ?x11230), award(?x7927, ?x11230), location(?x8652, ?x362), type_of_union(?x8652, ?x566) >> conf = 0.70 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0fdtd7 nominated_for 05dl1s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 59.000 26.000 0.695 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8731-04cr6qv PRED entity: 04cr6qv PRED relation: artists! PRED expected values: 05bt6j => 130 concepts (79 used for prediction) PRED predicted values (max 10 best out of 258): 05bt6j (0.75 #2527, 0.52 #14006, 0.41 #2482), 06by7 (0.62 #13983, 0.50 #1572, 0.50 #22), 0ggx5q (0.61 #3490, 0.58 #11249, 0.55 #2560), 06j6l (0.61 #3461, 0.55 #13390, 0.54 #6562), 016clz (0.57 #1555, 0.38 #4348, 0.33 #5), 0glt670 (0.57 #3454, 0.52 #6555, 0.52 #13383), 0gywn (0.44 #6571, 0.42 #13399, 0.34 #11229), 02yv6b (0.41 #2482, 0.21 #4131, 0.16 #7853), 016ybr (0.41 #2482, 0.10 #2610, 0.09 #3540), 03xnwz (0.41 #2482, 0.05 #2516, 0.05 #2205) >> Best rule #2527 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 0hvbj; 02twdq; >> query: (?x5514, 05bt6j) <- artists(?x8878, ?x5514), artists(?x3996, ?x5514), ?x3996 = 02lnbg, ?x8878 = 02ny8t, artist(?x3265, ?x5514) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cr6qv artists! 05bt6j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 79.000 0.750 http://example.org/music/genre/artists #8730-0c3p7 PRED entity: 0c3p7 PRED relation: award PRED expected values: 086vfb 02z0dfh => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 250): 0cqgl9 (0.77 #27197, 0.71 #27196, 0.70 #31533), 0bdw1g (0.77 #27197, 0.71 #27196, 0.70 #31533), 09sb52 (0.36 #14225, 0.36 #12255, 0.34 #20531), 0ck27z (0.34 #8362, 0.23 #7574, 0.23 #8756), 0gq9h (0.33 #5195, 0.12 #40995, 0.08 #35551), 040njc (0.26 #5130, 0.08 #11435, 0.07 #25229), 05pcn59 (0.22 #4017, 0.21 #5593, 0.21 #6775), 0gkts9 (0.20 #162, 0.13 #41785, 0.12 #40995), 0bfvd4 (0.20 #110, 0.13 #41785, 0.09 #1292), 0cqhk0 (0.19 #8310, 0.15 #8704, 0.14 #7522) >> Best rule #27197 for best value: >> intensional similarity = 3 >> extensional distance = 1371 >> proper extension: 01r42_g; 0kk9v; 04gtdnh; 025vwmy; >> query: (?x6314, ?x1132) <- award_nominee(?x6314, ?x539), award_winner(?x1132, ?x6314), ceremony(?x1132, ?x1265) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #24433 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1265 *> proper extension: 05cv94; 0l56b; 06rnl9; 07qy0b; 03kpvp; 0bc71w; 05zrx3v; 05hjmd; 06zd1c; *> query: (?x6314, ?x749) <- place_of_birth(?x6314, ?x10726), award_nominee(?x6314, ?x4234), award_winner(?x749, ?x4234) *> conf = 0.14 ranks of expected_values: 20, 244 EVAL 0c3p7 award 02z0dfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 122.000 122.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c3p7 award 086vfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 122.000 122.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8729-0gy4k PRED entity: 0gy4k PRED relation: film_release_region PRED expected values: 0jgd 03_3d 0chghy => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 153): 059j2 (0.85 #1391, 0.84 #1560, 0.83 #1898), 0chghy (0.82 #1704, 0.81 #1535, 0.81 #1366), 0345h (0.80 #1562, 0.79 #1393, 0.79 #1900), 03rjj (0.79 #1527, 0.79 #1358, 0.78 #1696), 0jgd (0.79 #1524, 0.78 #1355, 0.77 #1862), 03gj2 (0.78 #1383, 0.77 #1552, 0.75 #1890), 03h64 (0.78 #1600, 0.78 #1431, 0.76 #1938), 03_3d (0.76 #1529, 0.75 #1867, 0.75 #1022), 015fr (0.75 #1543, 0.73 #1374, 0.73 #1712), 01znc_ (0.74 #1403, 0.72 #1910, 0.72 #1572) >> Best rule #1391 for best value: >> intensional similarity = 7 >> extensional distance = 218 >> proper extension: 0ddfwj1; 0gkz15s; 0fpmrm3; 05q4y12; >> query: (?x11125, 059j2) <- genre(?x11125, ?x812), film_release_region(?x11125, ?x789), film_release_region(?x11125, ?x512), film_release_region(?x11125, ?x87), ?x512 = 07ssc, ?x87 = 05r4w, ?x789 = 0f8l9c >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1704 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 228 *> proper extension: 0gx1bnj; 087wc7n; 0fpkhkz; 02r1c18; 04n52p6; 0fpv_3_; 0192hw; 026njb5; 04vh83; 08tq4x; ... *> query: (?x11125, 0chghy) <- genre(?x11125, ?x812), film_release_region(?x11125, ?x512), film_release_region(?x11125, ?x87), ?x512 = 07ssc, ?x87 = 05r4w, language(?x11125, ?x7658) *> conf = 0.82 ranks of expected_values: 2, 5, 8 EVAL 0gy4k film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 77.000 0.845 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gy4k film_release_region 03_3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 77.000 0.845 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gy4k film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 77.000 0.845 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8728-09p3_s PRED entity: 09p3_s PRED relation: award PRED expected values: 09cn0c => 96 concepts (88 used for prediction) PRED predicted values (max 10 best out of 201): 0p9sw (0.35 #1617, 0.26 #7974, 0.26 #7519), 0gr51 (0.28 #1673, 0.16 #531, 0.09 #3495), 0gq_v (0.27 #686, 0.27 #474, 0.26 #7974), 0gq9h (0.26 #7974, 0.26 #7519, 0.26 #8429), 0gs96 (0.26 #7974, 0.26 #7519, 0.26 #8429), 0gs9p (0.26 #7974, 0.26 #7519, 0.26 #8429), 019f4v (0.26 #7974, 0.26 #7519, 0.26 #8429), 0gr0m (0.26 #7974, 0.26 #7519, 0.26 #8429), 0gr4k (0.26 #7974, 0.26 #7519, 0.26 #8429), 0k611 (0.26 #7974, 0.26 #7519, 0.26 #8429) >> Best rule #1617 for best value: >> intensional similarity = 4 >> extensional distance = 158 >> proper extension: 06mmr; >> query: (?x5519, 0p9sw) <- award(?x5519, ?x1079), nominated_for(?x1079, ?x3457), award_winner(?x1079, ?x84), ?x3457 = 03x7hd >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #13211 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x5519, ?x198) <- award(?x5519, ?x1079), award(?x4610, ?x1079), award(?x4610, ?x198) *> conf = 0.05 ranks of expected_values: 95 EVAL 09p3_s award 09cn0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 96.000 88.000 0.350 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8727-028k57 PRED entity: 028k57 PRED relation: film PRED expected values: 0gtsx8c 01xvjb => 114 concepts (91 used for prediction) PRED predicted values (max 10 best out of 751): 0f42nz (0.08 #4464, 0.07 #9804, 0.06 #13364), 07w8fz (0.07 #513, 0.02 #23653), 013q07 (0.07 #3916, 0.05 #2136, 0.05 #5696), 016dj8 (0.07 #4670, 0.05 #2890, 0.05 #6450), 0ds5_72 (0.06 #6787, 0.05 #3227, 0.05 #13907), 06ztvyx (0.06 #5770, 0.05 #2210, 0.05 #12890), 0pdp8 (0.06 #83664, 0.05 #142413, 0.04 #133511), 03nfnx (0.05 #3174, 0.05 #6734, 0.04 #13854), 03q0r1 (0.05 #2414, 0.05 #5974, 0.04 #13094), 01mszz (0.05 #2862, 0.05 #6422, 0.04 #13542) >> Best rule #4464 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 08p1gp; >> query: (?x4478, 0f42nz) <- gender(?x4478, ?x231), special_performance_type(?x4478, ?x3558), religion(?x4478, ?x7131) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #115709 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1663 *> proper extension: 02pp_q_; 07sgfsl; 0565cz; 02cm2m; 02x0bdb; 0191h5; 076df9; 04g_wd; 013ybx; *> query: (?x4478, ?x124) <- award_nominee(?x2383, ?x4478), film(?x2383, ?x124), award_nominee(?x806, ?x2383) *> conf = 0.03 ranks of expected_values: 97, 432 EVAL 028k57 film 01xvjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 114.000 91.000 0.082 http://example.org/film/actor/film./film/performance/film EVAL 028k57 film 0gtsx8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 114.000 91.000 0.082 http://example.org/film/actor/film./film/performance/film #8726-0830vk PRED entity: 0830vk PRED relation: genre PRED expected values: 01t_vv => 88 concepts (70 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.86 #4955, 0.71 #241, 0.71 #362), 01z4y (0.61 #6886, 0.52 #5316, 0.52 #6524), 02kdv5l (0.58 #123, 0.54 #3, 0.46 #485), 03k9fj (0.47 #131, 0.36 #493, 0.36 #11), 01jfsb (0.36 #2669, 0.35 #2790, 0.33 #132), 06n90 (0.33 #133, 0.31 #495, 0.16 #374), 082gq (0.25 #270, 0.19 #3293, 0.19 #632), 060__y (0.23 #256, 0.20 #1223, 0.17 #377), 0lsxr (0.22 #128, 0.21 #1336, 0.20 #1458), 04xvlr (0.21 #242, 0.20 #1815, 0.20 #1572) >> Best rule #4955 for best value: >> intensional similarity = 4 >> extensional distance = 798 >> proper extension: 0dckvs; 026njb5; 04lqvlr; 02wk7b; 08j7lh; >> query: (?x3601, 07s9rl0) <- film_crew_role(?x3601, ?x137), genre(?x3601, ?x1403), genre(?x6669, ?x1403), ?x6669 = 01fx6y >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1990 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 350 *> proper extension: 0ds35l9; 09xbpt; 0dtw1x; 03h_yy; 03s6l2; 011yph; 09p35z; 02hxhz; 0bshwmp; 07sc6nw; ... *> query: (?x3601, 01t_vv) <- film_crew_role(?x3601, ?x137), film(?x541, ?x3601), genre(?x3601, ?x258), ?x258 = 05p553 *> conf = 0.17 ranks of expected_values: 14 EVAL 0830vk genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 88.000 70.000 0.861 http://example.org/film/film/genre #8725-0cbn7c PRED entity: 0cbn7c PRED relation: featured_film_locations PRED expected values: 0d6lp => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 24): 02_286 (0.19 #20, 0.17 #260, 0.17 #500), 030qb3t (0.08 #2204, 0.08 #1240, 0.06 #1962), 04jpl (0.06 #969, 0.06 #2414, 0.06 #2657), 02nd_ (0.06 #116, 0.04 #356, 0.04 #596), 0rh6k (0.03 #1202, 0.03 #3858, 0.03 #2406), 01_d4 (0.03 #1007, 0.03 #287, 0.03 #527), 080h2 (0.03 #2189, 0.02 #3881, 0.02 #5575), 03rjj (0.03 #966, 0.01 #1207), 0d6lp (0.02 #312, 0.02 #552, 0.01 #792), 0fr0t (0.02 #324, 0.02 #564, 0.01 #804) >> Best rule #20 for best value: >> intensional similarity = 2 >> extensional distance = 51 >> proper extension: 03m8y5; 0cq8nx; >> query: (?x7864, 02_286) <- genre(?x7864, ?x1805), ?x1805 = 01g6gs >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #312 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 0jyx6; 012mrr; 0gcrg; 014kkm; 0jqj5; 0bl5c; 01bjbk; 0k419; *> query: (?x7864, 0d6lp) <- list(?x7864, ?x3004), nominated_for(?x382, ?x7864), nominated_for(?x601, ?x7864) *> conf = 0.02 ranks of expected_values: 9 EVAL 0cbn7c featured_film_locations 0d6lp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 39.000 39.000 0.189 http://example.org/film/film/featured_film_locations #8724-09p2r9 PRED entity: 09p2r9 PRED relation: honored_for PRED expected values: 030k94 => 35 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1120): 0d68qy (0.80 #4266, 0.39 #6617, 0.37 #7204), 0828jw (0.50 #2696, 0.33 #342, 0.25 #2109), 05lfwd (0.50 #2108, 0.33 #2695, 0.22 #6809), 04p5cr (0.50 #2155, 0.33 #2742, 0.17 #6856), 0kfv9 (0.46 #3635, 0.33 #696, 0.25 #1282), 030k94 (0.33 #2540, 0.25 #1953, 0.18 #7644), 02qm_f (0.33 #55, 0.25 #1232, 0.12 #4758), 03f7nt (0.33 #285, 0.25 #1462, 0.12 #3226), 02psgq (0.33 #325, 0.25 #1502, 0.12 #3266), 03lfd_ (0.33 #494, 0.25 #1671, 0.12 #3435) >> Best rule #4266 for best value: >> intensional similarity = 15 >> extensional distance = 13 >> proper extension: 02q690_; >> query: (?x6631, 0d68qy) <- honored_for(?x6631, ?x9364), honored_for(?x6631, ?x7858), honored_for(?x6631, ?x2436), languages(?x2436, ?x254), nominated_for(?x2016, ?x2436), nominated_for(?x1483, ?x2436), film_crew_role(?x7858, ?x1171), award_winner(?x6631, ?x241), ?x2016 = 0cjyzs, country_of_origin(?x2436, ?x94), ?x1483 = 0284gcb, titles(?x53, ?x9364), film(?x6804, ?x7858), nominated_for(?x384, ?x7858), country(?x9364, ?x279) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2540 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 07y9ts; *> query: (?x6631, 030k94) <- honored_for(?x6631, ?x7858), honored_for(?x6631, ?x2436), honored_for(?x6631, ?x2078), languages(?x2436, ?x254), nominated_for(?x678, ?x2436), nominated_for(?x436, ?x2436), award_winner(?x6631, ?x4295), award_winner(?x6631, ?x1365), award(?x7858, ?x384), film(?x6804, ?x7858), ?x2078 = 03ln8b, profession(?x4295, ?x1032), spouse(?x9817, ?x1365), film(?x4295, ?x755) *> conf = 0.33 ranks of expected_values: 6 EVAL 09p2r9 honored_for 030k94 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 35.000 23.000 0.800 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #8723-02kxwk PRED entity: 02kxwk PRED relation: award PRED expected values: 0bdwft => 94 concepts (72 used for prediction) PRED predicted values (max 10 best out of 243): 0ck27z (0.58 #86, 0.25 #2041, 0.16 #7125), 094qd5 (0.39 #432, 0.14 #2779, 0.10 #3170), 03qgjwc (0.30 #564, 0.07 #2911, 0.06 #2520), 09qwmm (0.29 #423, 0.13 #9386, 0.09 #2770), 02x4x18 (0.27 #516, 0.09 #3254, 0.09 #2863), 0bdwft (0.24 #455, 0.13 #2802, 0.10 #2411), 02y_rq5 (0.24 #480, 0.10 #2827, 0.07 #3218), 099cng (0.21 #471, 0.07 #21512, 0.07 #21120), 0cqhk0 (0.17 #35, 0.16 #1990, 0.10 #817), 0fbtbt (0.17 #220, 0.13 #9386, 0.10 #2175) >> Best rule #86 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01pg1d; >> query: (?x4367, 0ck27z) <- nominated_for(?x4367, ?x9350), place_of_birth(?x4367, ?x5381), ?x9350 = 01g03q >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #455 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 04jlgp; 013zs9; 02c7lt; 01wmcbg; *> query: (?x4367, 0bdwft) <- award(?x4367, ?x1254), profession(?x4367, ?x1032), ?x1254 = 02z0dfh *> conf = 0.24 ranks of expected_values: 6 EVAL 02kxwk award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 94.000 72.000 0.583 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8722-02qkk9_ PRED entity: 02qkk9_ PRED relation: award_winner PRED expected values: 0l6qt 0drc1 0g476 0g10g => 33 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1646): 06rgq (0.60 #4257, 0.08 #9142, 0.08 #16469), 02z4b_8 (0.42 #8891, 0.40 #4006, 0.17 #16218), 0d6d2 (0.40 #6646, 0.25 #1761, 0.17 #9089), 0bj9k (0.40 #5299, 0.25 #414, 0.11 #10185), 086qd (0.40 #2875, 0.25 #7760, 0.10 #15087), 09fb5 (0.40 #4948, 0.25 #63, 0.08 #7391), 0z4s (0.40 #4956, 0.25 #71, 0.08 #7399), 039bp (0.40 #5094, 0.25 #209, 0.08 #7537), 040z9 (0.40 #6506, 0.25 #1621, 0.08 #8949), 06cgy (0.40 #5191, 0.25 #306, 0.08 #7634) >> Best rule #4257 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 01bgqh; 01c427; 01c99j; >> query: (?x5180, 06rgq) <- award_winner(?x5180, ?x10914), award_winner(?x5180, ?x9583), award_winner(?x5180, ?x9095), award_winner(?x5180, ?x2683), ?x2683 = 01dw9z, languages(?x9095, ?x90), profession(?x10914, ?x1032), nominated_for(?x9583, ?x1542) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4668 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 01bgqh; 01c427; 01c99j; *> query: (?x5180, 0g10g) <- award_winner(?x5180, ?x10914), award_winner(?x5180, ?x9583), award_winner(?x5180, ?x9095), award_winner(?x5180, ?x2683), ?x2683 = 01dw9z, languages(?x9095, ?x90), profession(?x10914, ?x1032), nominated_for(?x9583, ?x1542) *> conf = 0.20 ranks of expected_values: 108, 226, 266, 533 EVAL 02qkk9_ award_winner 0g10g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 33.000 13.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02qkk9_ award_winner 0g476 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 33.000 13.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02qkk9_ award_winner 0drc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 13.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02qkk9_ award_winner 0l6qt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 13.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #8721-04m2zj PRED entity: 04m2zj PRED relation: role PRED expected values: 011k_j => 139 concepts (65 used for prediction) PRED predicted values (max 10 best out of 124): 01vdm0 (0.57 #838, 0.46 #1247, 0.38 #636), 0l14qv (0.54 #708, 0.54 #610, 0.50 #812), 05r5c (0.53 #3679, 0.50 #814, 0.47 #1018), 05148p4 (0.50 #829, 0.31 #627, 0.23 #1238), 03gvt (0.47 #707, 0.44 #2439, 0.44 #2648), 013y1f (0.46 #641, 0.43 #843, 0.29 #437), 01vj9c (0.43 #415, 0.31 #619, 0.30 #2349), 0cfdd (0.36 #897, 0.19 #1306, 0.15 #695), 018vs (0.35 #1126, 0.34 #503, 0.33 #1115), 0l14j_ (0.34 #503, 0.33 #5716, 0.33 #5820) >> Best rule #838 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 06449; >> query: (?x8152, 01vdm0) <- artists(?x1380, ?x8152), location(?x8152, ?x13437), performance_role(?x8152, ?x228), role(?x8152, ?x212), artists(?x1380, ?x535), ?x535 = 02rgz4 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2755 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 85 *> proper extension: 0163kf; *> query: (?x8152, ?x74) <- artists(?x1380, ?x8152), performance_role(?x8152, ?x228), role(?x2798, ?x228), role(?x74, ?x228), role(?x228, ?x214), ?x2798 = 03qjg *> conf = 0.08 ranks of expected_values: 44 EVAL 04m2zj role 011k_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 139.000 65.000 0.571 http://example.org/music/artist/track_contributions./music/track_contribution/role #8720-06br6t PRED entity: 06br6t PRED relation: category PRED expected values: 08mbj5d => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #34, 0.88 #23, 0.88 #22) >> Best rule #34 for best value: >> intensional similarity = 11 >> extensional distance = 40 >> proper extension: 0c7ct; 01dwrc; 0127s7; 06mt91; 01nz1q6; >> query: (?x9757, 08mbj5d) <- artists(?x4711, ?x9757), artists(?x2809, ?x9757), artists(?x474, ?x9757), artists(?x4711, ?x10144), artists(?x4711, ?x5751), artists(?x2809, ?x8328), ?x5751 = 0bpk2, profession(?x10144, ?x220), ?x474 = 0m0jc, ?x220 = 016z4k, ?x8328 = 02rn_bj >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06br6t category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.881 http://example.org/common/topic/webpage./common/webpage/category #8719-062zm5h PRED entity: 062zm5h PRED relation: film! PRED expected values: 01pcql 0f5xn => 70 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1019): 02lkcc (0.20 #241, 0.07 #14752, 0.06 #12679), 04__f (0.20 #1373, 0.03 #15884, 0.03 #17957), 01kwld (0.18 #2171, 0.15 #4244, 0.14 #6317), 03ym1 (0.18 #3078, 0.15 #5151, 0.14 #7224), 02ck7w (0.18 #3006, 0.15 #5079, 0.14 #7152), 02gvwz (0.18 #2259, 0.15 #4332, 0.14 #6405), 0js9s (0.18 #3219, 0.15 #5292, 0.14 #7365), 0svqs (0.18 #2941, 0.15 #5014, 0.14 #7087), 0241jw (0.18 #2367, 0.15 #4440, 0.14 #6513), 01v9l67 (0.18 #2535, 0.15 #4608, 0.14 #6681) >> Best rule #241 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 031778; 0cq7tx; 01mszz; 027r9t; >> query: (?x5016, 02lkcc) <- film(?x96, ?x5016), film(?x4832, ?x5016), honored_for(?x5016, ?x504), currency(?x5016, ?x170) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #5109 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 08052t3; 0bq6ntw; 02wtp6; *> query: (?x5016, 0f5xn) <- film_release_region(?x5016, ?x2000), film_format(?x5016, ?x10390), ?x2000 = 0d0kn *> conf = 0.08 ranks of expected_values: 153 EVAL 062zm5h film! 0f5xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 70.000 48.000 0.200 http://example.org/film/actor/film./film/performance/film EVAL 062zm5h film! 01pcql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 48.000 0.200 http://example.org/film/actor/film./film/performance/film #8718-0_b9f PRED entity: 0_b9f PRED relation: award PRED expected values: 02y_rq5 02y_j8g 09cn0c => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 161): 0gr0m (0.27 #13633, 0.27 #13408, 0.27 #13632), 0k611 (0.27 #13633, 0.27 #13408, 0.27 #13632), 040njc (0.27 #13633, 0.27 #13408, 0.27 #13632), 02qyntr (0.27 #13633, 0.27 #13408, 0.27 #13632), 0gq9h (0.27 #13633, 0.27 #13408, 0.27 #13632), 0gs9p (0.27 #13633, 0.27 #13408, 0.27 #13632), 02qvyrt (0.27 #13633, 0.27 #13408, 0.27 #13632), 04dn09n (0.27 #13633, 0.27 #13408, 0.27 #13632), 02r22gf (0.27 #13633, 0.27 #13408, 0.27 #13632), 054krc (0.27 #13633, 0.27 #13408, 0.27 #13632) >> Best rule #13633 for best value: >> intensional similarity = 4 >> extensional distance = 1000 >> proper extension: 02_1ky; >> query: (?x4742, ?x198) <- nominated_for(?x198, ?x4742), award(?x4742, ?x372), award(?x71, ?x198), award(?x144, ?x198) >> conf = 0.27 => this is the best rule for 12 predicted values *> Best rule #14528 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1063 *> proper extension: 01j95; *> query: (?x4742, ?x375) <- award_winner(?x4742, ?x843), profession(?x843, ?x1032), award(?x843, ?x375) *> conf = 0.10 ranks of expected_values: 46, 62, 113 EVAL 0_b9f award 09cn0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 94.000 94.000 0.272 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0_b9f award 02y_j8g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 94.000 94.000 0.272 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0_b9f award 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 94.000 94.000 0.272 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8717-06jrhz PRED entity: 06jrhz PRED relation: profession PRED expected values: 0cbd2 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 50): 02hrh1q (0.81 #2073, 0.76 #3102, 0.75 #1632), 01d_h8 (0.47 #153, 0.42 #300, 0.38 #736), 02krf9 (0.38 #736, 0.29 #172, 0.28 #5884), 02jknp (0.38 #736, 0.28 #5884, 0.28 #5589), 0cbd2 (0.38 #736, 0.28 #5884, 0.28 #5589), 01c72t (0.38 #736, 0.28 #5884, 0.28 #5589), 015h31 (0.38 #736, 0.28 #5884, 0.28 #5589), 0196pc (0.38 #736, 0.28 #5884, 0.28 #5589), 018gz8 (0.23 #1193, 0.16 #604, 0.14 #457), 01c8w0 (0.20 #9, 0.02 #3832, 0.02 #6628) >> Best rule #2073 for best value: >> intensional similarity = 3 >> extensional distance = 1046 >> proper extension: 076df9; >> query: (?x5832, 02hrh1q) <- award_nominee(?x10152, ?x5832), location(?x5832, ?x739), film(?x10152, ?x9169) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #736 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 152 *> proper extension: 08nz99; *> query: (?x5832, ?x319) <- award_nominee(?x3145, ?x5832), profession(?x3145, ?x319), tv_program(?x5832, ?x3144) *> conf = 0.38 ranks of expected_values: 5 EVAL 06jrhz profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 80.000 80.000 0.808 http://example.org/people/person/profession #8716-06f5j PRED entity: 06f5j PRED relation: gender PRED expected values: 05zppz => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 5): 05zppz (0.90 #46, 0.88 #124, 0.87 #108), 02zsn (0.50 #226, 0.48 #196, 0.46 #304), 012jc (0.12 #203), 01hbgs (0.12 #203), 059_w (0.12 #203) >> Best rule #46 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 075wq; >> query: (?x10013, 05zppz) <- place_of_death(?x10013, ?x7548), contains(?x1426, ?x7548), contains(?x7548, ?x3949), second_level_divisions(?x94, ?x7548) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06f5j gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.896 http://example.org/people/person/gender #8715-015fr PRED entity: 015fr PRED relation: vacationer PRED expected values: 06wm0z => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 194): 016fnb (0.15 #5327, 0.09 #9687, 0.09 #4630), 0261x8t (0.13 #5365, 0.10 #140, 0.10 #10769), 05r5w (0.13 #5300, 0.08 #9660, 0.07 #12969), 03lt8g (0.12 #1244, 0.10 #23, 0.09 #2636), 01dw4q (0.12 #1224, 0.08 #1572, 0.06 #5228), 01cwhp (0.11 #398, 0.10 #573, 0.08 #923), 0bksh (0.11 #5331, 0.09 #6551, 0.07 #11605), 0320jz (0.11 #5258, 0.08 #1254, 0.07 #4561), 01yf85 (0.10 #154, 0.09 #4682, 0.09 #5379), 0151w_ (0.10 #20, 0.08 #1241, 0.08 #1589) >> Best rule #5327 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 05ywg; 0cv3w; 0r0m6; 0fgj2; 06mxs; >> query: (?x583, 016fnb) <- contains(?x583, ?x1167), vacationer(?x583, ?x3503), category(?x3503, ?x134) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #13941 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 99 *> proper extension: 01bkb; *> query: (?x583, ?x1017) <- vacationer(?x583, ?x4294), participant(?x4294, ?x1017), participant(?x4294, ?x2443) *> conf = 0.05 ranks of expected_values: 73 EVAL 015fr vacationer 06wm0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 181.000 181.000 0.149 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #8714-03bx0bm PRED entity: 03bx0bm PRED relation: role! PRED expected values: 01q7cb_ 0285c 09889g 095x_ 01vsn38 01p0w_ => 73 concepts (64 used for prediction) PRED predicted values (max 10 best out of 2229): 016h9b (0.67 #2305, 0.60 #2118, 0.60 #1930), 04mx7s (0.60 #2213, 0.60 #2025, 0.55 #4481), 01vn35l (0.60 #2119, 0.55 #1131, 0.50 #1182), 05qhnq (0.60 #2192, 0.50 #5213, 0.50 #2379), 01v_pj6 (0.60 #1908, 0.50 #2661, 0.50 #2283), 01s7qqw (0.60 #1976, 0.50 #2729, 0.50 #2351), 01sb5r (0.60 #2141, 0.50 #1013, 0.40 #1953), 01p0w_ (0.60 #2252, 0.40 #2064, 0.34 #2633), 01wl38s (0.55 #1131, 0.50 #1327, 0.50 #752), 02qwg (0.55 #1131, 0.50 #1000, 0.50 #752) >> Best rule #2305 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0l14md; >> query: (?x1466, 016h9b) <- role(?x8957, ?x1466), group(?x1466, ?x9206), role(?x5589, ?x1466), role(?x3024, ?x1466), ?x8957 = 03f5mt, ?x9206 = 017mbb, artist(?x8489, ?x5589), gender(?x3024, ?x231), ?x8489 = 01cl0d, performance_role(?x248, ?x1466), artists(?x1000, ?x3024) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2252 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 3 *> proper extension: 05r5c; *> query: (?x1466, 01p0w_) <- role(?x8957, ?x1466), role(?x4917, ?x1466), role(?x780, ?x1466), group(?x1466, ?x1060), role(?x6774, ?x1466), role(?x2187, ?x1466), ?x8957 = 03f5mt, ?x780 = 01qzyz, ?x4917 = 06w7v, ?x2187 = 01vsnff, performance_role(?x1466, ?x716), ?x1060 = 02r3zy, artists(?x302, ?x6774) *> conf = 0.60 ranks of expected_values: 8, 55, 61, 128, 131, 204 EVAL 03bx0bm role! 01p0w_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 73.000 64.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role EVAL 03bx0bm role! 01vsn38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 73.000 64.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role EVAL 03bx0bm role! 095x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 73.000 64.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role EVAL 03bx0bm role! 09889g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 73.000 64.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role EVAL 03bx0bm role! 0285c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 73.000 64.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role EVAL 03bx0bm role! 01q7cb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 73.000 64.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role #8713-0knhk PRED entity: 0knhk PRED relation: group! PRED expected values: 03bx0bm => 122 concepts (93 used for prediction) PRED predicted values (max 10 best out of 125): 05148p4 (0.77 #2023, 0.76 #2110, 0.76 #3683), 03bx0bm (0.71 #2029, 0.68 #3602, 0.65 #3689), 03qjg (0.39 #2052, 0.31 #1878, 0.30 #3712), 0l14qv (0.35 #2010, 0.33 #876, 0.30 #2708), 05r5c (0.31 #1838, 0.30 #1925, 0.29 #2099), 01vj9c (0.29 #4728, 0.29 #5343, 0.29 #3677), 042v_gx (0.22 #2188, 0.21 #1664, 0.19 #2799), 04rzd (0.21 #2734, 0.20 #2822, 0.20 #3258), 06ncr (0.20 #2393, 0.17 #3703, 0.16 #2043), 02fsn (0.20 #222, 0.14 #396, 0.12 #832) >> Best rule #2023 for best value: >> intensional similarity = 10 >> extensional distance = 29 >> proper extension: 01wv9xn; 05563d; 014pg1; 01v0sxx; >> query: (?x7868, 05148p4) <- artists(?x1000, ?x7868), artist(?x2149, ?x7868), group(?x1750, ?x7868), group(?x645, ?x7868), group(?x315, ?x7868), group(?x227, ?x7868), ?x1750 = 02hnl, ?x315 = 0l14md, ?x645 = 028tv0, ?x227 = 0342h >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2029 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 29 *> proper extension: 01wv9xn; 05563d; 014pg1; 01v0sxx; *> query: (?x7868, 03bx0bm) <- artists(?x1000, ?x7868), artist(?x2149, ?x7868), group(?x1750, ?x7868), group(?x645, ?x7868), group(?x315, ?x7868), group(?x227, ?x7868), ?x1750 = 02hnl, ?x315 = 0l14md, ?x645 = 028tv0, ?x227 = 0342h *> conf = 0.71 ranks of expected_values: 2 EVAL 0knhk group! 03bx0bm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 93.000 0.774 http://example.org/music/performance_role/regular_performances./music/group_membership/group #8712-03c_cxn PRED entity: 03c_cxn PRED relation: genre PRED expected values: 05p553 0219x_ => 85 concepts (78 used for prediction) PRED predicted values (max 10 best out of 129): 05p553 (0.88 #2410, 0.86 #1207, 0.65 #3011), 01z4y (0.62 #7472, 0.55 #7471, 0.53 #6266), 07ssc (0.55 #7471, 0.53 #6266, 0.52 #7109), 03k9fj (0.42 #5312, 0.24 #4105, 0.22 #2659), 04xvlr (0.38 #1, 0.24 #1565, 0.21 #722), 06cvj (0.34 #1206, 0.28 #2409, 0.21 #1567), 01jfsb (0.32 #4227, 0.32 #3623, 0.31 #2660), 02kdv5l (0.30 #4095, 0.27 #5302, 0.26 #4216), 0lsxr (0.25 #730, 0.25 #851, 0.25 #1333), 060__y (0.25 #17, 0.22 #138, 0.20 #1581) >> Best rule #2410 for best value: >> intensional similarity = 4 >> extensional distance = 222 >> proper extension: 0c00zd0; 03n0cd; 02qdrjx; 0fzm0g; >> query: (?x5107, 05p553) <- genre(?x5107, ?x53), language(?x5107, ?x254), titles(?x2480, ?x5107), ?x2480 = 01z4y >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 13 EVAL 03c_cxn genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 85.000 78.000 0.879 http://example.org/film/film/genre EVAL 03c_cxn genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 78.000 0.879 http://example.org/film/film/genre #8711-0bq6ntw PRED entity: 0bq6ntw PRED relation: music PRED expected values: 01nqfh_ => 67 concepts (42 used for prediction) PRED predicted values (max 10 best out of 57): 02jxkw (0.20 #142, 0.04 #3509, 0.04 #3722), 01hw6wq (0.20 #38, 0.03 #1088, 0.03 #2349), 04ls53 (0.12 #499, 0.12 #709, 0.10 #919), 06fxnf (0.12 #699, 0.10 #909, 0.09 #279), 01tc9r (0.09 #275, 0.07 #1325, 0.06 #485), 02jxmr (0.09 #284, 0.06 #494, 0.06 #704), 0b6yp2 (0.09 #262, 0.06 #472, 0.06 #682), 0jn5l (0.09 #306, 0.06 #516, 0.06 #726), 01x6v6 (0.07 #1383, 0.05 #1593, 0.04 #1804), 0150t6 (0.07 #3413, 0.06 #3626, 0.06 #1096) >> Best rule #142 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 03t97y; >> query: (?x6095, 02jxkw) <- film(?x10780, ?x6095), film(?x6264, ?x6095), genre(?x6095, ?x225), ?x225 = 02kdv5l, ?x10780 = 014g_s, film_crew_role(?x6095, ?x468), award_winner(?x704, ?x6264), profession(?x6264, ?x131) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2741 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 114 *> proper extension: 04nlb94; *> query: (?x6095, 01nqfh_) <- film_format(?x6095, ?x10390), film_crew_role(?x6095, ?x2095), film_crew_role(?x6095, ?x1284), ?x2095 = 0dxtw, film_crew_role(?x4688, ?x1284), film_crew_role(?x430, ?x1284), ?x4688 = 09jcj6, ?x430 = 0m2kd *> conf = 0.02 ranks of expected_values: 44 EVAL 0bq6ntw music 01nqfh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 67.000 42.000 0.200 http://example.org/film/film/music #8710-0fn2g PRED entity: 0fn2g PRED relation: featured_film_locations! PRED expected values: 0dln8jk => 206 concepts (96 used for prediction) PRED predicted values (max 10 best out of 747): 0413cff (0.33 #3315, 0.15 #14365, 0.14 #20258), 07kdkfj (0.33 #566, 0.11 #2774, 0.07 #35182), 02q_4ph (0.33 #309, 0.03 #34925, 0.02 #56281), 02yvct (0.25 #1627, 0.15 #6779, 0.15 #5307), 05q4y12 (0.25 #940, 0.06 #42186, 0.05 #47342), 0c0nhgv (0.25 #813, 0.05 #15546, 0.05 #56049), 0k2sk (0.25 #810, 0.05 #15543, 0.05 #56046), 02_nsc (0.25 #1389, 0.05 #16122, 0.04 #27902), 05z7c (0.25 #884, 0.05 #15617, 0.04 #27397), 02fqxm (0.25 #1469, 0.05 #18411, 0.05 #56705) >> Best rule #3315 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0fngf; >> query: (?x6054, 0413cff) <- capital(?x7747, ?x6054), featured_film_locations(?x3257, ?x6054), form_of_government(?x7747, ?x1926), ?x1926 = 018wl5, category(?x6054, ?x134) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fn2g featured_film_locations! 0dln8jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 206.000 96.000 0.333 http://example.org/film/film/featured_film_locations #8709-06y7d PRED entity: 06y7d PRED relation: profession PRED expected values: 016fly 07lqg0 => 137 concepts (130 used for prediction) PRED predicted values (max 10 best out of 136): 0dxtg (0.78 #13107, 0.71 #13695, 0.47 #7957), 02hrh1q (0.77 #11785, 0.77 #9282, 0.75 #18699), 09jwl (0.52 #2224, 0.33 #2077, 0.33 #19), 0nbcg (0.45 #2236, 0.33 #31, 0.30 #2089), 01d_h8 (0.41 #5890, 0.41 #13100, 0.40 #2064), 02jknp (0.34 #13101, 0.33 #7, 0.32 #13689), 05z96 (0.33 #42, 0.21 #336, 0.20 #189), 03gjzk (0.31 #13109, 0.30 #5899, 0.30 #13697), 0fj9f (0.26 #2552, 0.23 #3583, 0.22 #4024), 01c72t (0.26 #2229, 0.23 #3113, 0.20 #5025) >> Best rule #13107 for best value: >> intensional similarity = 5 >> extensional distance = 1227 >> proper extension: 01vw87c; 0p_pd; 09fb5; 03ckxdg; 050023; 026dcvf; 0147dk; 02nb2s; 01wl38s; 02pp_q_; ... >> query: (?x12147, 0dxtg) <- profession(?x12147, ?x353), profession(?x2799, ?x353), profession(?x1946, ?x353), ?x1946 = 014dq7, ?x2799 = 01vsl3_ >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3603 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 0frmb1; *> query: (?x12147, 016fly) <- student(?x3424, ?x12147), student(?x1368, ?x12147), company(?x12147, ?x3439) *> conf = 0.19 ranks of expected_values: 16, 53 EVAL 06y7d profession 07lqg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 137.000 130.000 0.778 http://example.org/people/person/profession EVAL 06y7d profession 016fly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 137.000 130.000 0.778 http://example.org/people/person/profession #8708-01y_px PRED entity: 01y_px PRED relation: award PRED expected values: 0gr07 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 245): 09sb52 (0.39 #5266, 0.36 #24563, 0.35 #25367), 0ck27z (0.38 #14564, 0.34 #16574, 0.31 #18182), 0cqhk0 (0.29 #439, 0.21 #14510, 0.19 #16520), 05pcn59 (0.23 #11336, 0.21 #9326, 0.19 #10532), 0gqyl (0.18 #908, 0.14 #5732, 0.14 #27740), 05zr6wv (0.18 #1625, 0.15 #2831, 0.15 #2429), 02x4x18 (0.16 #935, 0.14 #131, 0.14 #27740), 03c7tr1 (0.16 #861, 0.14 #1263, 0.13 #6087), 05b4l5x (0.16 #810, 0.14 #4026, 0.13 #6036), 0gqwc (0.15 #6103, 0.14 #4093, 0.14 #877) >> Best rule #5266 for best value: >> intensional similarity = 3 >> extensional distance = 187 >> proper extension: 0dzc16; >> query: (?x2263, 09sb52) <- film(?x2263, ?x718), award_nominee(?x2263, ?x241), spouse(?x2263, ?x2715) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #7477 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 232 *> proper extension: 0d06m5; 01lz4tf; 0484q; 02n9k; 037s5h; 09zw90; 01hdht; 04rfq; *> query: (?x2263, 0gr07) <- location(?x2263, ?x739), spouse(?x2715, ?x2263), citytown(?x166, ?x739) *> conf = 0.01 ranks of expected_values: 224 EVAL 01y_px award 0gr07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 124.000 124.000 0.392 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8707-020x5r PRED entity: 020x5r PRED relation: award PRED expected values: 03hkv_r => 80 concepts (69 used for prediction) PRED predicted values (max 10 best out of 321): 0gs9p (0.73 #2065, 0.71 #1667, 0.71 #1269), 040njc (0.71 #1600, 0.69 #1998, 0.67 #1202), 02pqp12 (0.56 #1261, 0.54 #1659, 0.48 #863), 0gqy2 (0.37 #159, 0.31 #557, 0.09 #9712), 09sb52 (0.35 #438, 0.32 #40, 0.21 #9991), 02qyp19 (0.32 #797, 0.23 #1195, 0.21 #1593), 057xs89 (0.32 #155, 0.31 #553, 0.04 #10106), 02v1m7 (0.31 #2893, 0.04 #7165, 0.03 #27465), 02x4wr9 (0.27 #1324, 0.27 #1722, 0.23 #926), 04kxsb (0.27 #518, 0.26 #120, 0.06 #916) >> Best rule #2065 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 042l3v; 0kr5_; 06pk8; 022_lg; 0j_c; 03tf_h; 09p06; 01n9d9; 07rd7; 04g3p5; ... >> query: (?x8161, 0gs9p) <- award(?x8161, ?x1862), award(?x8161, ?x1107), ?x1107 = 019f4v, nominated_for(?x1862, ?x69) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1608 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 54 *> proper extension: 02r5w9; 0hskw; 02645b; 0bzyh; 01ycck; 0kvqv; 096hm; 0jgwf; 03fqv5; *> query: (?x8161, 03hkv_r) <- award(?x8161, ?x1862), award(?x8161, ?x1107), ?x1107 = 019f4v, award(?x361, ?x1862), ?x361 = 0h5f5n *> conf = 0.25 ranks of expected_values: 15 EVAL 020x5r award 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 80.000 69.000 0.732 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8706-01s21dg PRED entity: 01s21dg PRED relation: artist! PRED expected values: 015_1q => 162 concepts (145 used for prediction) PRED predicted values (max 10 best out of 108): 01trtc (0.50 #71, 0.14 #1601, 0.14 #1741), 0g768 (0.30 #36, 0.16 #731, 0.15 #2124), 015_1q (0.21 #5864, 0.20 #3776, 0.20 #2107), 073tm9 (0.20 #35, 0.06 #1705, 0.05 #4766), 03rhqg (0.17 #571, 0.17 #432, 0.17 #2103), 0fb0v (0.16 #146, 0.11 #424, 0.10 #7), 033hn8 (0.15 #2101, 0.11 #9760, 0.11 #12822), 016ckq (0.15 #2130, 0.07 #4773, 0.06 #1015), 0181dw (0.14 #2129, 0.12 #1851, 0.11 #5607), 0n85g (0.14 #757, 0.12 #201, 0.11 #618) >> Best rule #71 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01wgxtl; 01vw20h; 05vzw3; 01vw8mh; 01yzl2; 013w7j; 01vw37m; 04vrxh; >> query: (?x4741, 01trtc) <- artists(?x302, ?x4741), award_nominee(?x4741, ?x1989), ?x1989 = 04mn81 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5864 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 334 *> proper extension: 01czx; 010hn; 01dw9z; 016fmf; 0134s5; 024dgj; 02lbrd; 01vtqml; 0d193h; 049qx; ... *> query: (?x4741, 015_1q) <- artists(?x302, ?x4741), artist(?x2190, ?x4741), award_winner(?x1362, ?x4741) *> conf = 0.21 ranks of expected_values: 3 EVAL 01s21dg artist! 015_1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 162.000 145.000 0.500 http://example.org/music/record_label/artist #8705-082wbh PRED entity: 082wbh PRED relation: sport PRED expected values: 018w8 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 11): 02vx4 (0.60 #11, 0.56 #107, 0.56 #99), 03tmr (0.04 #654), 0jm_ (0.04 #656), 018jz (0.03 #658), 018w8 (0.03 #657), 07jbh (0.01 #127), 03_8r (0.01 #127), 06f41 (0.01 #127), 07bs0 (0.01 #127), 0bynt (0.01 #127) >> Best rule #11 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 01w_d6; >> query: (?x4973, 02vx4) <- position(?x4973, ?x530), position(?x4973, ?x203), position(?x4973, ?x63), position(?x4973, ?x60), ?x60 = 02nzb8, ?x203 = 0dgrmp, current_club(?x4972, ?x4973), ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x4972 = 03d8m4, position(?x4973, ?x203), team(?x203, ?x4973), team(?x530, ?x4973), team(?x60, ?x4973), position(?x4973, ?x60) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #657 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 968 *> proper extension: 0fbq2n; 01ypc; 02896; 05m_8; 0jmdb; 03lpp_; 01ct6; 06x68; 07k53y; 05g3b; ... *> query: (?x4973, 018w8) <- team(?x60, ?x4973), team(?x60, ?x11496), team(?x60, ?x3032), team(?x60, ?x676), sport(?x11496, ?x471), team(?x2201, ?x676), teams(?x1917, ?x676), colors(?x3032, ?x663) *> conf = 0.03 ranks of expected_values: 5 EVAL 082wbh sport 018w8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 73.000 0.600 http://example.org/sports/sports_team/sport #8704-059f4 PRED entity: 059f4 PRED relation: district_represented! PRED expected values: 06f0dc => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 21): 06f0dc (0.87 #655, 0.86 #571, 0.85 #109), 02bqn1 (0.46 #111, 0.44 #657, 0.44 #300), 02cg7g (0.46 #118, 0.44 #307, 0.43 #1303), 02gkzs (0.43 #1303, 0.41 #663, 0.39 #642), 03rtmz (0.43 #1303, 0.38 #114, 0.30 #660), 02glc4 (0.43 #1303, 0.38 #119, 0.28 #665), 03tcbx (0.43 #1303, 0.38 #113, 0.26 #302), 03ww_x (0.43 #1303, 0.20 #45, 0.19 #654), 03z5xd (0.43 #1303, 0.19 #658, 0.18 #574), 032ft5 (0.43 #1303, 0.15 #110, 0.11 #656) >> Best rule #655 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 0g0syc; >> query: (?x728, 06f0dc) <- district_represented(?x7714, ?x728), district_represented(?x7714, ?x177), ?x177 = 05kkh >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059f4 district_represented! 06f0dc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.870 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #8703-04z542 PRED entity: 04z542 PRED relation: student! PRED expected values: 09f2j => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 152): 065y4w7 (0.33 #14, 0.04 #11066, 0.04 #14222), 07tg4 (0.14 #612, 0.05 #5346, 0.04 #2716), 0bwfn (0.14 #2379, 0.09 #14483, 0.07 #11327), 08815 (0.10 #1580, 0.07 #4736, 0.07 #528), 0ks67 (0.10 #1767, 0.07 #715, 0.03 #2819), 07wrz (0.10 #1640, 0.07 #588, 0.03 #2692), 01w5m (0.10 #1683, 0.07 #2735, 0.06 #5365), 015nl4 (0.08 #5854, 0.06 #1119, 0.04 #10593), 02bqy (0.07 #708, 0.05 #1760, 0.01 #4916), 0qlnr (0.07 #852, 0.05 #1904, 0.01 #8948) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02f8lw; >> query: (?x5489, 065y4w7) <- award_nominee(?x3224, ?x5489), award_nominee(?x1871, ?x5489), ?x3224 = 0fx0mw, ?x1871 = 02bkdn >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1737 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 18 *> proper extension: 01d494; 0frmb1; 046rfv; *> query: (?x5489, 09f2j) <- student(?x1526, ?x5489), ?x1526 = 0bkj86 *> conf = 0.05 ranks of expected_values: 30 EVAL 04z542 student! 09f2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 98.000 98.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #8702-07mvp PRED entity: 07mvp PRED relation: influenced_by! PRED expected values: 0m2l9 03g5jw => 121 concepts (57 used for prediction) PRED predicted values (max 10 best out of 420): 03g5jw (0.33 #559, 0.24 #3657, 0.15 #13461), 0167xy (0.22 #949, 0.16 #4047, 0.13 #10754), 014_lq (0.22 #735, 0.16 #3833, 0.12 #1250), 0ph2w (0.17 #5319, 0.14 #6351, 0.08 #4286), 01vsy7t (0.17 #184, 0.10 #5862, 0.09 #13414), 0282x (0.17 #225, 0.09 #2804, 0.09 #13414), 05xq9 (0.17 #200, 0.09 #7426, 0.09 #3297), 0m2l9 (0.17 #13, 0.09 #7239, 0.09 #13414), 01r0t_j (0.17 #331, 0.09 #3428, 0.09 #13414), 01vs4f3 (0.17 #351, 0.09 #13414, 0.07 #26835) >> Best rule #559 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0m2l9; 0gcs9; 0qdyf; 02vr7; >> query: (?x6475, 03g5jw) <- influenced_by(?x7227, ?x6475), artist(?x3265, ?x6475), award(?x6475, ?x3103), ?x3103 = 03tcnt >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 07mvp influenced_by! 03g5jw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 57.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 07mvp influenced_by! 0m2l9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 121.000 57.000 0.333 http://example.org/influence/influence_node/influenced_by #8701-07t21 PRED entity: 07t21 PRED relation: film_release_region! PRED expected values: 0b76d_m 0gkz15s 04n52p6 0gvrws1 0407yfx 0661ql3 03qnc6q 0gh8zks => 152 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1265): 03nm_fh (0.81 #11913, 0.78 #18219, 0.77 #27046), 05zlld0 (0.81 #11784, 0.78 #18090, 0.69 #26917), 03qnc6q (0.81 #11639, 0.69 #17945, 0.65 #26772), 017gm7 (0.80 #17801, 0.75 #11495, 0.73 #26628), 07s846j (0.78 #18126, 0.72 #11820, 0.65 #26953), 04n52p6 (0.78 #11530, 0.69 #17836, 0.60 #26663), 0645k5 (0.78 #11676, 0.67 #17982, 0.64 #1588), 0fpv_3_ (0.77 #26736, 0.75 #11603, 0.73 #17909), 017gl1 (0.76 #17756, 0.75 #11450, 0.65 #26583), 0dtfn (0.76 #17800, 0.72 #11494, 0.71 #26627) >> Best rule #11913 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 0fq8f; 012wgb; >> query: (?x1471, 03nm_fh) <- film_release_region(?x1915, ?x1471), film_release_region(?x1463, ?x1471), ?x1463 = 0gtvrv3, genre(?x1915, ?x571) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #11639 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0fq8f; 012wgb; *> query: (?x1471, 03qnc6q) <- film_release_region(?x1915, ?x1471), film_release_region(?x1463, ?x1471), ?x1463 = 0gtvrv3, genre(?x1915, ?x571) *> conf = 0.81 ranks of expected_values: 3, 6, 15, 16, 24, 90, 96, 163 EVAL 07t21 film_release_region! 0gh8zks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 152.000 93.000 0.806 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07t21 film_release_region! 03qnc6q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 152.000 93.000 0.806 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07t21 film_release_region! 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 152.000 93.000 0.806 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07t21 film_release_region! 0407yfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 152.000 93.000 0.806 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07t21 film_release_region! 0gvrws1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 152.000 93.000 0.806 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07t21 film_release_region! 04n52p6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 152.000 93.000 0.806 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07t21 film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 152.000 93.000 0.806 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07t21 film_release_region! 0b76d_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 152.000 93.000 0.806 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8700-04xjp PRED entity: 04xjp PRED relation: profession PRED expected values: 0cbd2 => 113 concepts (96 used for prediction) PRED predicted values (max 10 best out of 103): 0dxtg (0.96 #12438, 0.65 #3520, 0.64 #4398), 0cbd2 (0.95 #3659, 0.89 #8921, 0.78 #2636), 02hrh1q (0.94 #3521, 0.82 #8783, 0.81 #10538), 0fj9f (0.88 #3121, 0.67 #1223, 0.50 #1077), 0dgd_ (0.80 #2806, 0.17 #9792, 0.05 #3214), 09jwl (0.73 #11422, 0.25 #13904, 0.25 #2211), 01d_h8 (0.47 #12430, 0.33 #3512, 0.30 #4384), 02jknp (0.41 #12432, 0.30 #4384, 0.27 #8182), 0jq47 (0.38 #3799), 0ck7l (0.38 #3799) >> Best rule #12438 for best value: >> intensional similarity = 7 >> extensional distance = 998 >> proper extension: 07nznf; 0q9kd; 079vf; 0dbpyd; 06j0md; 04t2l2; 02rchht; 083chw; 014zcr; 01vw87c; ... >> query: (?x2162, 0dxtg) <- profession(?x2162, ?x6421), profession(?x11529, ?x6421), profession(?x8441, ?x6421), profession(?x4008, ?x6421), ?x8441 = 0c1fs, ?x4008 = 07h07, ?x11529 = 05w1vf >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #3659 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 55 *> proper extension: 0hcvy; *> query: (?x2162, 0cbd2) <- profession(?x2162, ?x8290), profession(?x2162, ?x6421), ?x6421 = 02hv44_, profession(?x4736, ?x8290), ?x4736 = 07_m9_, specialization_of(?x14341, ?x8290) *> conf = 0.95 ranks of expected_values: 2 EVAL 04xjp profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 96.000 0.956 http://example.org/people/person/profession #8699-0345gh PRED entity: 0345gh PRED relation: company! PRED expected values: 0kn3g => 168 concepts (97 used for prediction) PRED predicted values (max 10 best out of 98): 047g6 (0.25 #233, 0.09 #14201, 0.09 #721), 06g4_ (0.11 #456, 0.04 #944, 0.03 #1433), 05m0h (0.11 #449, 0.04 #937, 0.03 #1426), 0nk72 (0.11 #408, 0.04 #3339, 0.04 #8236), 0x3r3 (0.11 #360, 0.04 #3291, 0.03 #5491), 07n39 (0.11 #433, 0.02 #5564, 0.01 #9243), 0d4jl (0.11 #302, 0.02 #3233, 0.01 #8130), 01hc9_ (0.11 #416, 0.01 #8244, 0.01 #9226), 083q7 (0.11 #263, 0.01 #8091, 0.01 #4904), 0ct9_ (0.11 #409, 0.01 #5540) >> Best rule #233 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0gl6x; >> query: (?x4692, 047g6) <- currency(?x4692, ?x1099), contains(?x362, ?x4692), ?x362 = 04jpl, institution(?x3437, ?x4692), ?x3437 = 02_xgp2 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0345gh company! 0kn3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 168.000 97.000 0.250 http://example.org/people/person/employment_history./business/employment_tenure/company #8698-01vs5c PRED entity: 01vs5c PRED relation: major_field_of_study PRED expected values: 041y2 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 107): 01mkq (0.60 #132, 0.46 #251, 0.42 #1442), 0g26h (0.50 #515, 0.43 #1111, 0.40 #1468), 04rjg (0.42 #851, 0.40 #137, 0.36 #732), 01tbp (0.40 #175, 0.39 #532, 0.32 #1128), 04x_3 (0.40 #143, 0.33 #500, 0.31 #262), 0_jm (0.39 #530, 0.33 #1364, 0.32 #1126), 02_7t (0.39 #537, 0.30 #1371, 0.30 #1133), 05qjt (0.37 #840, 0.36 #364, 0.33 #721), 062z7 (0.37 #858, 0.34 #1335, 0.33 #1097), 037mh8 (0.36 #778, 0.34 #897, 0.21 #1017) >> Best rule #132 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01bm_; >> query: (?x5621, 01mkq) <- school(?x12042, ?x5621), ?x12042 = 05xvj, student(?x5621, ?x525) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #551 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0frm7n; 03tw2s; *> query: (?x5621, 041y2) <- school(?x6462, ?x5621), school(?x260, ?x5621), ?x6462 = 09l0x9 *> conf = 0.28 ranks of expected_values: 17 EVAL 01vs5c major_field_of_study 041y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 94.000 94.000 0.600 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8697-07gp9 PRED entity: 07gp9 PRED relation: genre PRED expected values: 06n90 07s2s => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 104): 07s9rl0 (0.69 #727, 0.66 #3874, 0.64 #364), 05p553 (0.58 #12243, 0.37 #3029, 0.37 #1335), 024qqx (0.51 #13573, 0.50 #14664, 0.50 #8966), 03k9fj (0.44 #2069, 0.42 #5099, 0.40 #2916), 02l7c8 (0.40 #16, 0.32 #379, 0.30 #8860), 01hmnh (0.29 #2922, 0.29 #2075, 0.25 #1107), 06n90 (0.28 #5100, 0.24 #2917, 0.21 #12252), 0lsxr (0.25 #1945, 0.25 #372, 0.24 #856), 03npn (0.22 #249, 0.20 #7, 0.13 #128), 082gq (0.21 #757, 0.17 #273, 0.15 #2572) >> Best rule #727 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 0btyf5z; >> query: (?x324, 07s9rl0) <- production_companies(?x324, ?x1561), film(?x2387, ?x324), edited_by(?x324, ?x323), honored_for(?x3254, ?x324) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #5100 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 416 *> proper extension: 04svwx; *> query: (?x324, 06n90) <- genre(?x324, ?x812), genre(?x324, ?x225), ?x225 = 02kdv5l, titles(?x812, ?x80) *> conf = 0.28 ranks of expected_values: 7, 70 EVAL 07gp9 genre 07s2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 137.000 137.000 0.692 http://example.org/film/film/genre EVAL 07gp9 genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 137.000 137.000 0.692 http://example.org/film/film/genre #8696-05mc7y PRED entity: 05mc7y PRED relation: place_of_death PRED expected values: 030qb3t => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 15): 0f2wj (0.20 #12, 0.18 #207, 0.17 #401), 0r3tq (0.20 #149, 0.18 #344, 0.17 #538), 030qb3t (0.18 #217, 0.17 #411, 0.12 #605), 0r00l (0.12 #745, 0.10 #162, 0.09 #357), 0k049 (0.09 #198, 0.08 #392, 0.04 #781), 015zxh (0.08 #414), 0284jb (0.06 #604, 0.01 #799), 02_286 (0.06 #986, 0.05 #1557, 0.05 #1374), 06_kh (0.02 #783, 0.02 #2146, 0.02 #1366), 0k_p5 (0.02 #2229, 0.02 #1449, 0.01 #1645) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 07zhd7; >> query: (?x12893, 0f2wj) <- award_winner(?x9058, ?x12893), nationality(?x12893, ?x94), type_of_union(?x12893, ?x566), ?x9058 = 0fv89q >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #217 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 07hhnl; 025cn2; *> query: (?x12893, 030qb3t) <- award_winner(?x9058, ?x12893), award(?x12893, ?x720), ?x9058 = 0fv89q, gender(?x12893, ?x231) *> conf = 0.18 ranks of expected_values: 3 EVAL 05mc7y place_of_death 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 109.000 0.200 http://example.org/people/deceased_person/place_of_death #8695-01f8gz PRED entity: 01f8gz PRED relation: genre PRED expected values: 02n4lw => 102 concepts (90 used for prediction) PRED predicted values (max 10 best out of 124): 012w70 (0.57 #2503, 0.53 #5251, 0.52 #8972), 03h64 (0.57 #2503, 0.53 #5251, 0.52 #8972), 02kdv5l (0.54 #6335, 0.50 #359, 0.45 #1432), 03k9fj (0.43 #6344, 0.39 #1917, 0.38 #2636), 01jfsb (0.42 #726, 0.37 #1442, 0.37 #4186), 05p553 (0.36 #5015, 0.33 #4, 0.33 #7060), 01chg (0.33 #32, 0.23 #270, 0.10 #1581), 04t36 (0.33 #6, 0.19 #1077, 0.14 #1555), 0lsxr (0.31 #365, 0.29 #722, 0.26 #960), 04xvlr (0.31 #358, 0.28 #1072, 0.24 #834) >> Best rule #2503 for best value: >> intensional similarity = 4 >> extensional distance = 112 >> proper extension: 04sh80; >> query: (?x1625, ?x2645) <- language(?x1625, ?x254), edited_by(?x1625, ?x9086), ?x254 = 02h40lc, titles(?x2645, ?x1625) >> conf = 0.57 => this is the best rule for 2 predicted values *> Best rule #329 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 0dx8gj; 03hmt9b; 08g_jw; *> query: (?x1625, 02n4lw) <- nominated_for(?x4169, ?x1625), nominated_for(?x7215, ?x1625), language(?x1625, ?x1882), film_crew_role(?x1625, ?x137), ?x1882 = 03k50 *> conf = 0.23 ranks of expected_values: 19 EVAL 01f8gz genre 02n4lw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 102.000 90.000 0.566 http://example.org/film/film/genre #8694-023rwm PRED entity: 023rwm PRED relation: artist PRED expected values: 0p76z => 47 concepts (15 used for prediction) PRED predicted values (max 10 best out of 3775): 015xp4 (0.56 #8573, 0.45 #9393, 0.33 #2005), 0pkyh (0.50 #7575, 0.50 #5932, 0.50 #3468), 01323p (0.50 #7121, 0.50 #5480, 0.50 #3015), 01wg25j (0.50 #6361, 0.50 #4720, 0.44 #8824), 0178kd (0.50 #6193, 0.50 #2908, 0.33 #7014), 017lb_ (0.50 #7166, 0.50 #3060, 0.33 #596), 019g40 (0.50 #6670, 0.50 #2564, 0.25 #5029), 01q99h (0.50 #7004, 0.50 #2898, 0.25 #5363), 0565cz (0.50 #5117, 0.44 #8400, 0.36 #9220), 013rds (0.50 #4919, 0.44 #9023, 0.36 #9843) >> Best rule #8573 for best value: >> intensional similarity = 13 >> extensional distance = 7 >> proper extension: 03rhqg; 015_1q; 0mzkr; 01cf93; >> query: (?x441, 015xp4) <- artist(?x441, ?x10565), artist(?x441, ?x4484), artist(?x441, ?x3472), artist(?x441, ?x442), artists(?x2249, ?x442), award(?x10565, ?x2180), group(?x227, ?x10565), award(?x442, ?x3045), origin(?x442, ?x1658), ?x4484 = 03xhj6, place_of_birth(?x3472, ?x6764), ?x2249 = 03lty, artists(?x6210, ?x3472) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #3173 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 011k1h; *> query: (?x441, 0p76z) <- artist(?x441, ?x10565), artist(?x441, ?x9999), artist(?x441, ?x6225), ?x10565 = 0c9l1, category(?x9999, ?x134), artists(?x302, ?x9999), origin(?x9999, ?x362), artists(?x1572, ?x6225), group(?x228, ?x9999), profession(?x6225, ?x1359), ?x228 = 0l14qv, ?x1359 = 09lbv *> conf = 0.25 ranks of expected_values: 311 EVAL 023rwm artist 0p76z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 15.000 0.556 http://example.org/music/record_label/artist #8693-01q99h PRED entity: 01q99h PRED relation: group! PRED expected values: 01hww_ 02hnl => 107 concepts (75 used for prediction) PRED predicted values (max 10 best out of 117): 02hnl (0.79 #2334, 0.79 #2417, 0.79 #2252), 03bx0bm (0.79 #185, 0.74 #350, 0.73 #432), 05r5c (0.45 #252, 0.25 #2479, 0.22 #2313), 013y1f (0.36 #271, 0.17 #1094, 0.15 #2250), 0mkg (0.36 #255, 0.11 #2482, 0.09 #419), 018j2 (0.32 #276, 0.12 #440, 0.11 #358), 01vj9c (0.31 #670, 0.30 #2484, 0.30 #1080), 042v_gx (0.27 #253, 0.15 #335, 0.13 #1076), 06ncr (0.23 #282, 0.17 #695, 0.16 #2509), 02fsn (0.14 #208, 0.14 #291, 0.12 #455) >> Best rule #2334 for best value: >> intensional similarity = 6 >> extensional distance = 156 >> proper extension: 0dvqq; 0b1hw; 09jvl; >> query: (?x6228, 02hnl) <- group(?x227, ?x6228), artist(?x2299, ?x6228), artists(?x671, ?x6228), artist(?x2299, ?x3929), ?x227 = 0342h, profession(?x3929, ?x131) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 17 EVAL 01q99h group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 75.000 0.791 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01q99h group! 01hww_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 107.000 75.000 0.791 http://example.org/music/performance_role/regular_performances./music/group_membership/group #8692-01cf5 PRED entity: 01cf5 PRED relation: registering_agency PRED expected values: 03z19 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.92 #13, 0.84 #24, 0.83 #19) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 02d9nr; >> query: (?x12302, 03z19) <- student(?x12302, ?x1594), currency(?x12302, ?x170), ?x170 = 09nqf, citytown(?x12302, ?x739) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cf5 registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.920 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #8691-023cjg PRED entity: 023cjg PRED relation: language PRED expected values: 02h40lc => 92 concepts (72 used for prediction) PRED predicted values (max 10 best out of 54): 02h40lc (0.92 #954, 0.91 #1251, 0.91 #1310), 064_8sq (0.20 #22, 0.14 #81, 0.14 #974), 03_9r (0.18 #723, 0.18 #842, 0.16 #603), 06nm1 (0.14 #129, 0.12 #1260, 0.11 #1438), 0y1mh (0.14 #80, 0.05 #3770, 0.05 #892), 06b_j (0.09 #1272, 0.09 #1153, 0.09 #1331), 04306rv (0.09 #1969, 0.08 #2871, 0.08 #2931), 012w70 (0.07 #131, 0.05 #3770, 0.05 #892), 0880p (0.07 #164, 0.05 #3770, 0.05 #892), 0121sr (0.07 #166, 0.05 #892, 0.01 #701) >> Best rule #954 for best value: >> intensional similarity = 8 >> extensional distance = 147 >> proper extension: 0g22z; 028_yv; 09xbpt; 04v8x9; 01h7bb; 05p1tzf; 060v34; 050r1z; 06_wqk4; 04tc1g; ... >> query: (?x10962, 02h40lc) <- genre(?x10962, ?x53), film_release_distribution_medium(?x10962, ?x81), currency(?x10962, ?x170), film(?x1850, ?x10962), film(?x382, ?x10962), ?x81 = 029j_, ?x382 = 086k8, award_nominee(?x1850, ?x269) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023cjg language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 72.000 0.919 http://example.org/film/film/language #8690-0dckvs PRED entity: 0dckvs PRED relation: film_release_region PRED expected values: 02vzc 06mkj => 92 concepts (69 used for prediction) PRED predicted values (max 10 best out of 164): 03rjj (0.89 #3283, 0.86 #1970, 0.80 #1643), 06mkj (0.89 #1536, 0.87 #3340, 0.87 #1372), 0345h (0.87 #1511, 0.85 #1347, 0.82 #2002), 05r4w (0.86 #3280, 0.85 #1967, 0.85 #1312), 07ssc (0.85 #1491, 0.83 #1327, 0.81 #1982), 02vzc (0.85 #1694, 0.83 #2842, 0.82 #4153), 015fr (0.82 #1984, 0.79 #3297, 0.79 #1329), 0k6nt (0.81 #3305, 0.81 #4124, 0.80 #2813), 03gj2 (0.81 #1666, 0.81 #1993, 0.80 #1502), 05b4w (0.80 #2035, 0.71 #3348, 0.70 #1544) >> Best rule #3283 for best value: >> intensional similarity = 8 >> extensional distance = 138 >> proper extension: 0879bpq; 0gh65c5; 0g5qmbz; 0j8f09z; >> query: (?x467, 03rjj) <- nominated_for(?x5923, ?x467), language(?x467, ?x2890), film_release_region(?x467, ?x1353), film_release_region(?x467, ?x1229), film_release_region(?x467, ?x789), ?x1353 = 035qy, ?x789 = 0f8l9c, ?x1229 = 059j2 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1536 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 59 *> proper extension: 0fq27fp; *> query: (?x467, 06mkj) <- film_release_region(?x467, ?x1603), film_release_region(?x467, ?x1353), film_release_region(?x467, ?x94), ?x94 = 09c7w0, film_regional_debut_venue(?x467, ?x6601), ?x1603 = 06bnz, ?x1353 = 035qy *> conf = 0.89 ranks of expected_values: 2, 6 EVAL 0dckvs film_release_region 06mkj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 69.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dckvs film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 69.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8689-02glmx PRED entity: 02glmx PRED relation: award_winner PRED expected values: 0sz28 => 42 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1546): 0cw67g (0.41 #19888, 0.25 #13724, 0.20 #7562), 0151w_ (0.38 #12451, 0.25 #1671, 0.20 #4747), 0150t6 (0.38 #11231, 0.20 #6611, 0.17 #8149), 0gcs9 (0.38 #14303, 0.14 #9678, 0.12 #11217), 0g2lq (0.33 #1139, 0.25 #15001, 0.25 #4215), 016szr (0.33 #760, 0.25 #14622, 0.25 #3836), 0fgg4 (0.33 #776, 0.25 #14638, 0.25 #3852), 0bn3jg (0.33 #1463, 0.25 #4539, 0.25 #3001), 021yc7p (0.33 #216, 0.25 #3292, 0.25 #1754), 05mcjs (0.33 #1002, 0.25 #4078, 0.25 #2540) >> Best rule #19888 for best value: >> intensional similarity = 15 >> extensional distance = 15 >> proper extension: 0bxs_d; >> query: (?x5902, 0cw67g) <- award_winner(?x5902, ?x4691), award_winner(?x5902, ?x1983), ceremony(?x500, ?x5902), nominated_for(?x1983, ?x667), award_nominee(?x4393, ?x1983), country(?x667, ?x94), film(?x989, ?x667), produced_by(?x667, ?x1285), profession(?x4691, ?x7630), nominated_for(?x350, ?x667), award(?x4691, ?x640), genre(?x667, ?x53), honored_for(?x5902, ?x696), ?x640 = 02hsq3m, award_winner(?x972, ?x1983) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #4614 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 2 *> proper extension: 0bvfqq; *> query: (?x5902, ?x1208) <- award_winner(?x5902, ?x7794), award_winner(?x5902, ?x1983), ?x1983 = 04ktcgn, ceremony(?x3066, ?x5902), ceremony(?x2222, ?x5902), ceremony(?x1862, ?x5902), ceremony(?x1243, ?x5902), ceremony(?x591, ?x5902), ?x2222 = 0gs96, ?x591 = 0f4x7, ?x1243 = 0gr0m, award_winner(?x2238, ?x7794), honored_for(?x5902, ?x2116), role(?x7794, ?x1148), award_nominee(?x7794, ?x2219), artists(?x474, ?x7794), profession(?x7794, ?x220), ?x3066 = 0gqy2, ?x1862 = 0gr51, nominated_for(?x1208, ?x2116) *> conf = 0.12 ranks of expected_values: 430 EVAL 02glmx award_winner 0sz28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 14.000 0.412 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8688-013w7j PRED entity: 013w7j PRED relation: participant PRED expected values: 04xrx => 132 concepts (68 used for prediction) PRED predicted values (max 10 best out of 383): 04xrx (0.81 #24918, 0.81 #17255, 0.81 #27474), 0bksh (0.81 #24918, 0.81 #17255, 0.81 #27474), 01vw20h (0.44 #4472, 0.09 #1588, 0.06 #2866), 01vz0g4 (0.44 #4472, 0.05 #1916, 0.05 #31948), 043zg (0.16 #3194, 0.16 #1917, 0.11 #7026), 0bbf1f (0.10 #4028, 0.05 #7859, 0.04 #14889), 07r1h (0.10 #4244, 0.04 #25967, 0.03 #15105), 015f7 (0.09 #1509, 0.08 #2787, 0.07 #3426), 0227vl (0.09 #1814, 0.08 #3092, 0.05 #4370), 0c9c0 (0.09 #826, 0.03 #6573, 0.03 #1464) >> Best rule #24918 for best value: >> intensional similarity = 3 >> extensional distance = 308 >> proper extension: 01l1b90; 0d_84; 04bs3j; 03ds3; 013cr; 031zkw; 01pw2f1; 01mqz0; 0285c; 03rl84; ... >> query: (?x6151, ?x1125) <- people(?x2510, ?x6151), participant(?x1125, ?x6151), film(?x6151, ?x5201) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 013w7j participant 04xrx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 68.000 0.813 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #8687-07ymr5 PRED entity: 07ymr5 PRED relation: influenced_by PRED expected values: 0p_47 => 103 concepts (64 used for prediction) PRED predicted values (max 10 best out of 302): 09889g (0.20 #154, 0.17 #590, 0.06 #1897), 0p_47 (0.14 #1415, 0.14 #979, 0.10 #6211), 01svq8 (0.14 #1733, 0.14 #1297, 0.03 #3040), 014z8v (0.10 #6225, 0.10 #5355, 0.08 #2736), 01hmk9 (0.10 #6324, 0.08 #2835, 0.08 #5454), 014zfs (0.09 #6129, 0.08 #2640, 0.07 #3077), 032l1 (0.09 #7502, 0.09 #9679, 0.08 #12732), 03_87 (0.09 #7616, 0.09 #9793, 0.07 #12846), 081lh (0.09 #6124, 0.07 #2635, 0.06 #2199), 081k8 (0.09 #7569, 0.08 #10181, 0.08 #12799) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 091yn0; 05gnf; >> query: (?x1942, 09889g) <- award_winner(?x8139, ?x1942), award_winner(?x4760, ?x1942), ?x8139 = 09px1w >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1415 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 06cddt; *> query: (?x1942, 0p_47) <- cast_members(?x905, ?x1942), people(?x1050, ?x1942) *> conf = 0.14 ranks of expected_values: 2 EVAL 07ymr5 influenced_by 0p_47 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 64.000 0.200 http://example.org/influence/influence_node/influenced_by #8686-0gy4k PRED entity: 0gy4k PRED relation: language PRED expected values: 02h40lc => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 49): 02h40lc (0.97 #355, 0.97 #472, 0.96 #709), 064_8sq (0.14 #550, 0.14 #492, 0.14 #729), 04306rv (0.13 #121, 0.11 #1302, 0.11 #1361), 06nm1 (0.12 #1071, 0.11 #1250, 0.11 #951), 06b_j (0.08 #1083, 0.08 #1262, 0.08 #1379), 02bjrlw (0.08 #1298, 0.08 #766, 0.07 #1061), 03_9r (0.07 #1482, 0.07 #1540, 0.06 #950), 0jzc (0.05 #1376, 0.05 #136, 0.05 #785), 04h9h (0.05 #158, 0.05 #690, 0.05 #453), 012w70 (0.05 #129, 0.04 #1073, 0.03 #601) >> Best rule #355 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 070fnm; 01wb95; 0gcrg; 027rpym; 0bl06; 0p_tz; 0gndh; 0kvb6p; 025scjj; 0cq8nx; ... >> query: (?x11125, 02h40lc) <- genre(?x11125, ?x812), language(?x11125, ?x7658), nominated_for(?x484, ?x11125), film_art_direction_by(?x11125, ?x6766) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gy4k language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.975 http://example.org/film/film/language #8685-0ftns PRED entity: 0ftns PRED relation: category PRED expected values: 08mbj5d => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.70 #69, 0.69 #28, 0.67 #44) >> Best rule #69 for best value: >> intensional similarity = 3 >> extensional distance = 994 >> proper extension: 0fnff; >> query: (?x13472, 08mbj5d) <- contains(?x455, ?x13472), locations(?x10849, ?x455), ?x10849 = 01w1sx >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ftns category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.705 http://example.org/common/topic/webpage./common/webpage/category #8684-0137g1 PRED entity: 0137g1 PRED relation: artists! PRED expected values: 01243b 09nwwf => 184 concepts (91 used for prediction) PRED predicted values (max 10 best out of 280): 06by7 (0.79 #17797, 0.61 #12279, 0.56 #2781), 064t9 (0.64 #4302, 0.54 #7063, 0.51 #6756), 0glt670 (0.62 #1267, 0.37 #4330, 0.37 #2493), 0cx7f (0.46 #1978, 0.33 #751, 0.19 #2898), 025sc50 (0.38 #1277, 0.36 #4340, 0.30 #3116), 06j6l (0.36 #4338, 0.31 #16294, 0.31 #6792), 0xhtw (0.35 #17792, 0.33 #1856, 0.33 #3694), 05w3f (0.33 #1877, 0.28 #2797, 0.17 #650), 01243b (0.33 #43, 0.17 #655, 0.12 #5560), 0ggq0m (0.33 #624, 0.13 #15337, 0.12 #6755) >> Best rule #17797 for best value: >> intensional similarity = 4 >> extensional distance = 273 >> proper extension: 02t3ln; 02mq_y; >> query: (?x2784, 06by7) <- artists(?x9750, ?x2784), origin(?x2784, ?x1523), artists(?x9750, ?x9638), ?x9638 = 017959 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 01k_yf; *> query: (?x2784, 01243b) <- artists(?x10255, ?x2784), award(?x2784, ?x1565), ?x10255 = 096jwc *> conf = 0.33 ranks of expected_values: 9, 28 EVAL 0137g1 artists! 09nwwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 184.000 91.000 0.785 http://example.org/music/genre/artists EVAL 0137g1 artists! 01243b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 184.000 91.000 0.785 http://example.org/music/genre/artists #8683-09969 PRED entity: 09969 PRED relation: people PRED expected values: 03f4k => 69 concepts (45 used for prediction) PRED predicted values (max 10 best out of 947): 053yx (0.50 #2152, 0.40 #4890, 0.33 #7628), 0b22w (0.40 #6656, 0.33 #494, 0.29 #12134), 02hg53 (0.33 #604, 0.29 #12244, 0.29 #11560), 03lpd0 (0.33 #588, 0.29 #12228, 0.29 #11544), 01rw116 (0.33 #523, 0.29 #12163, 0.29 #11479), 0bdlj (0.33 #312, 0.29 #11952, 0.29 #11268), 0168dy (0.33 #511, 0.25 #12835, 0.25 #3250), 015gy7 (0.33 #265, 0.25 #12589, 0.25 #3004), 0chsq (0.33 #13, 0.25 #2752, 0.25 #2067), 0407f (0.33 #112, 0.25 #2851, 0.25 #2166) >> Best rule #2152 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 02y0js; >> query: (?x11307, 053yx) <- symptom_of(?x11307, ?x7586), risk_factors(?x11307, ?x4195), people(?x11307, ?x10716), risk_factors(?x12870, ?x4195), risk_factors(?x3984, ?x4195), notable_people_with_this_condition(?x12870, ?x2046), nationality(?x10716, ?x512), place_of_death(?x10716, ?x14504), profession(?x10716, ?x353), ?x3984 = 0h9dj >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09969 people 03f4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 45.000 0.500 http://example.org/people/cause_of_death/people #8682-01fwj8 PRED entity: 01fwj8 PRED relation: language PRED expected values: 02h40lc => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 4): 02h40lc (0.07 #55, 0.06 #10, 0.06 #13), 06b_j (0.01 #275, 0.01 #270), 06nm1 (0.01 #275, 0.01 #270), 02bjrlw (0.01 #275, 0.01 #270) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 272 >> proper extension: 058j2; >> query: (?x1690, 02h40lc) <- gender(?x1690, ?x514), ?x514 = 02zsn, category(?x1690, ?x134) >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fwj8 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.073 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #8681-087vz PRED entity: 087vz PRED relation: country! PRED expected values: 03krj => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 56): 06z6r (0.91 #1268, 0.88 #1660, 0.85 #1940), 071t0 (0.90 #1147, 0.90 #1035, 0.88 #1203), 0bynt (0.86 #1245, 0.85 #741, 0.85 #4834), 03_8r (0.79 #642, 0.76 #1146, 0.74 #1202), 06f41 (0.79 #578, 0.73 #1138, 0.73 #746), 064vjs (0.76 #597, 0.71 #91, 0.68 #1045), 01lb14 (0.76 #579, 0.69 #803, 0.68 #1083), 01cgz (0.72 #1081, 0.72 #633, 0.72 #801), 0w0d (0.72 #1079, 0.72 #575, 0.72 #799), 03hr1p (0.72 #588, 0.71 #82, 0.68 #1148) >> Best rule #1268 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 01ls2; 019rg5; 088vb; >> query: (?x3728, 06z6r) <- olympics(?x3728, ?x584), olympics(?x3728, ?x867), medal(?x3728, ?x422), ?x867 = 0l6ny >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #169 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 0f8l9c; 059z0; *> query: (?x3728, ?x150) <- combatants(?x3728, ?x1603), combatants(?x3728, ?x1497), combatants(?x3728, ?x279), ?x279 = 0d060g, ?x1497 = 015qh, country(?x150, ?x1603) *> conf = 0.50 ranks of expected_values: 36 EVAL 087vz country! 03krj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 145.000 145.000 0.907 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #8680-04bdqk PRED entity: 04bdqk PRED relation: gender PRED expected values: 02zsn => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.91 #6, 0.50 #141, 0.50 #12), 05zppz (0.73 #81, 0.71 #119, 0.71 #99) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 01gvr1; 030znt; 014g22; 0c3jz; 046m59; 039x1k; 0739z6; 018417; >> query: (?x10521, 02zsn) <- location(?x10521, ?x739), award(?x10521, ?x1132), ?x1132 = 0bdwft >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bdqk gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.914 http://example.org/people/person/gender #8679-0mqs0 PRED entity: 0mqs0 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 146 concepts (70 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.88 #439, 0.87 #383, 0.85 #67), 07b_l (0.14 #711, 0.12 #723, 0.11 #736), 0mqs0 (0.09 #837, 0.07 #651, 0.07 #553), 02jx1 (0.07 #526, 0.07 #367, 0.06 #343), 0106dv (0.05 #237, 0.02 #490), 03rjj (0.03 #146, 0.03 #168, 0.03 #203), 0f8l9c (0.02 #718, 0.02 #731), 03rt9 (0.02 #715), 0d060g (0.01 #169) >> Best rule #439 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 02cl1; 0235l; 0mnyn; 0njpq; >> query: (?x10365, 09c7w0) <- contains(?x3634, ?x10365), county_seat(?x10365, ?x10364), adjoins(?x3634, ?x3908) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mqs0 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 70.000 0.876 http://example.org/location/country/second_level_divisions #8678-0d90m PRED entity: 0d90m PRED relation: music PRED expected values: 01ycfv => 83 concepts (43 used for prediction) PRED predicted values (max 10 best out of 56): 02bh9 (0.13 #261, 0.07 #1313, 0.06 #2156), 01tc9r (0.10 #65, 0.03 #3856, 0.03 #4697), 07q1v4 (0.10 #15, 0.02 #1277, 0.02 #1487), 02g1jh (0.09 #338, 0.03 #1390, 0.02 #4550), 0146pg (0.08 #641, 0.08 #1062, 0.08 #1692), 01pcq3 (0.07 #2316, 0.06 #4844, 0.06 #8011), 03h_9lg (0.07 #2316, 0.05 #631, 0.05 #8649), 03knl (0.07 #2316, 0.05 #631, 0.05 #8649), 0150t6 (0.07 #256, 0.05 #2151, 0.05 #466), 02jxmr (0.05 #494, 0.04 #2179, 0.04 #1336) >> Best rule #261 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 02qydsh; >> query: (?x97, 02bh9) <- film_crew_role(?x97, ?x2154), ?x2154 = 01vx2h, prequel(?x936, ?x97), film(?x574, ?x97) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #1009 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 206 *> proper extension: 0b60sq; 02q3fdr; 016ztl; 0564x; *> query: (?x97, 01ycfv) <- genre(?x97, ?x225), titles(?x8581, ?x97), titles(?x8581, ?x2869), ?x2869 = 03177r *> conf = 0.03 ranks of expected_values: 27 EVAL 0d90m music 01ycfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 83.000 43.000 0.130 http://example.org/film/film/music #8677-01kqq7 PRED entity: 01kqq7 PRED relation: film_release_region PRED expected values: 0f8l9c => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 147): 0d0vqn (0.80 #551, 0.73 #191, 0.22 #1089), 0f8l9c (0.79 #571, 0.76 #211, 0.23 #1109), 09c7w0 (0.78 #542, 0.73 #363, 0.73 #182), 059j2 (0.72 #584, 0.56 #224, 0.22 #1122), 06mkj (0.72 #615, 0.67 #255, 0.23 #1153), 07ssc (0.71 #563, 0.60 #203, 0.22 #743), 02vzc (0.70 #249, 0.63 #609, 0.21 #1147), 0345h (0.70 #586, 0.59 #226, 0.18 #1124), 03rjj (0.69 #547, 0.60 #187, 0.19 #1085), 0chghy (0.68 #556, 0.63 #196, 0.20 #1094) >> Best rule #551 for best value: >> intensional similarity = 4 >> extensional distance = 167 >> proper extension: 0192hw; >> query: (?x10173, 0d0vqn) <- genre(?x10173, ?x809), language(?x10173, ?x254), film_regional_debut_venue(?x10173, ?x12806), genre(?x808, ?x809) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #571 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 167 *> proper extension: 0192hw; *> query: (?x10173, 0f8l9c) <- genre(?x10173, ?x809), language(?x10173, ?x254), film_regional_debut_venue(?x10173, ?x12806), genre(?x808, ?x809) *> conf = 0.79 ranks of expected_values: 2 EVAL 01kqq7 film_release_region 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.799 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8676-07s846j PRED entity: 07s846j PRED relation: genre PRED expected values: 060__y => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 149): 07s9rl0 (0.97 #8736, 0.82 #2670, 0.75 #10804), 0hcr (0.59 #3300, 0.21 #1482, 0.16 #1725), 017fp (0.54 #9950, 0.53 #7157, 0.53 #6427), 03k9fj (0.51 #498, 0.40 #1712, 0.40 #13), 05p553 (0.46 #247, 0.42 #1948, 0.41 #854), 02kdv5l (0.45 #1094, 0.44 #367, 0.42 #1459), 01jfsb (0.39 #1105, 0.37 #741, 0.36 #1348), 02l7c8 (0.36 #139, 0.35 #2687, 0.31 #1961), 06n90 (0.30 #1106, 0.30 #15, 0.25 #500), 01hmnh (0.29 #3294, 0.29 #747, 0.23 #505) >> Best rule #8736 for best value: >> intensional similarity = 3 >> extensional distance = 1039 >> proper extension: 032xky; >> query: (?x4047, 07s9rl0) <- genre(?x4047, ?x2605), genre(?x8723, ?x2605), ?x8723 = 09rvcvl >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #140 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 0416y94; 07yk1xz; 026p4q7; 03hmt9b; 049xgc; 043mk4y; *> query: (?x4047, 060__y) <- film_crew_role(?x4047, ?x2472), ?x2472 = 01xy5l_, award(?x4047, ?x289), film(?x1289, ?x4047) *> conf = 0.28 ranks of expected_values: 11 EVAL 07s846j genre 060__y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 107.000 107.000 0.966 http://example.org/film/film/genre #8675-0jvt9 PRED entity: 0jvt9 PRED relation: genre PRED expected values: 02kdv5l => 90 concepts (74 used for prediction) PRED predicted values (max 10 best out of 92): 06l3bl (0.52 #6438, 0.51 #4528, 0.51 #8828), 02kdv5l (0.48 #1788, 0.42 #2, 0.37 #8113), 05p553 (0.46 #8115, 0.42 #242, 0.40 #1314), 01hmnh (0.34 #1803, 0.17 #8128, 0.16 #4306), 01jfsb (0.32 #3229, 0.31 #846, 0.31 #369), 01g6gs (0.26 #258, 0.22 #139, 0.12 #1925), 06n90 (0.25 #1799, 0.17 #13, 0.14 #3230), 0lsxr (0.22 #366, 0.19 #962, 0.19 #723), 082gq (0.22 #744, 0.21 #506, 0.19 #3366), 06cvj (0.22 #1313, 0.19 #122, 0.16 #241) >> Best rule #6438 for best value: >> intensional similarity = 3 >> extensional distance = 954 >> proper extension: 02y_lrp; 0sxg4; 083shs; 04v8x9; 0ds33; 01ln5z; 01sxly; 04fzfj; 0dsvzh; 02hxhz; ... >> query: (?x3294, ?x4757) <- film(?x788, ?x3294), nominated_for(?x484, ?x3294), titles(?x4757, ?x3294) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1788 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 320 *> proper extension: 03_wm6; *> query: (?x3294, 02kdv5l) <- film(?x788, ?x3294), genre(?x3294, ?x811), ?x811 = 03k9fj *> conf = 0.48 ranks of expected_values: 2 EVAL 0jvt9 genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 74.000 0.519 http://example.org/film/film/genre #8674-041y2 PRED entity: 041y2 PRED relation: major_field_of_study! PRED expected values: 01vs5c 02h7qr 021996 02f4s3 01c57n => 46 concepts (30 used for prediction) PRED predicted values (max 10 best out of 724): 07szy (0.67 #3342, 0.60 #2791, 0.60 #2241), 08815 (0.67 #3856, 0.60 #2754, 0.60 #2204), 04rwx (0.67 #3340, 0.60 #2789, 0.60 #2239), 02bqy (0.67 #3485, 0.60 #2934, 0.60 #2384), 01q7q2 (0.67 #3599, 0.60 #2498, 0.50 #4150), 0g2jl (0.67 #3718, 0.60 #2617, 0.42 #5921), 0k9wp (0.67 #3504, 0.60 #2403, 0.42 #5707), 06fq2 (0.67 #3608, 0.60 #2507, 0.40 #3057), 06pwq (0.65 #7720, 0.56 #4965, 0.50 #5517), 0jkhr (0.60 #2997, 0.60 #2447, 0.56 #5199) >> Best rule #3342 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 02lp1; >> query: (?x10046, 07szy) <- major_field_of_study(?x13141, ?x10046), major_field_of_study(?x3439, ?x10046), major_field_of_study(?x2775, ?x10046), major_field_of_study(?x10046, ?x2605), school(?x2820, ?x13141), ?x2775 = 078bz, student(?x13141, ?x230), ?x3439 = 03ksy, major_field_of_study(?x2605, ?x732), major_field_of_study(?x122, ?x2605) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5820 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 10 *> proper extension: 01tbp; *> query: (?x10046, 021996) <- major_field_of_study(?x10045, ?x10046), major_field_of_study(?x9847, ?x10046), major_field_of_study(?x4955, ?x10046), major_field_of_study(?x2682, ?x10046), major_field_of_study(?x1675, ?x10046), ?x4955 = 09f2j, fraternities_and_sororities(?x2682, ?x10424), student(?x2682, ?x744), category(?x10045, ?x134), student(?x9847, ?x92), organization(?x5510, ?x9847), state_province_region(?x9847, ?x1025), ?x1675 = 01j_cy *> conf = 0.33 ranks of expected_values: 105, 163, 234, 296 EVAL 041y2 major_field_of_study! 01c57n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 30.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 041y2 major_field_of_study! 02f4s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 30.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 041y2 major_field_of_study! 021996 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 46.000 30.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 041y2 major_field_of_study! 02h7qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 30.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 041y2 major_field_of_study! 01vs5c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 30.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8673-02py7pj PRED entity: 02py7pj PRED relation: ceremony PRED expected values: 058m5m4 => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 131): 058m5m4 (0.73 #969, 0.37 #2623, 0.34 #525), 07y9ts (0.40 #325, 0.37 #2623, 0.34 #525), 03nnm4t (0.40 #330, 0.33 #68, 0.14 #724), 05c1t6z (0.40 #274, 0.33 #12, 0.14 #668), 0gx_st (0.40 #296, 0.33 #34, 0.14 #690), 0gvstc3 (0.40 #293, 0.33 #31, 0.14 #687), 0hn821n (0.40 #384, 0.33 #122, 0.14 #778), 0bxs_d (0.40 #370, 0.33 #108, 0.14 #764), 07y_p6 (0.40 #353, 0.33 #91, 0.14 #747), 07z31v (0.40 #290, 0.33 #28, 0.14 #684) >> Best rule #969 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 03m73lj; >> query: (?x8459, 058m5m4) <- ceremony(?x8459, ?x8347), ceremony(?x8459, ?x1193), ?x8347 = 03gyp30, award_winner(?x1193, ?x2728), award(?x2728, ?x704), award_nominee(?x230, ?x2728) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02py7pj ceremony 058m5m4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.733 http://example.org/award/award_category/winners./award/award_honor/ceremony #8672-01dys PRED entity: 01dys PRED relation: country PRED expected values: 04gzd 015qh 06t8v => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 380): 07ssc (0.90 #2645, 0.85 #6069, 0.82 #6443), 04hqz (0.90 #2645, 0.78 #2648, 0.77 #3789), 03_3d (0.82 #4937, 0.80 #6250, 0.79 #5876), 06t8v (0.80 #3286, 0.78 #2648, 0.77 #2649), 03h64 (0.78 #2066, 0.78 #2515, 0.73 #2262), 0b90_r (0.78 #2066, 0.78 #2648, 0.77 #2649), 03rt9 (0.78 #2066, 0.73 #2262, 0.70 #2644), 03rk0 (0.78 #2066, 0.66 #567, 0.65 #566), 059j2 (0.78 #2646, 0.78 #2487, 0.78 #2648), 06qd3 (0.78 #2493, 0.78 #2301, 0.78 #2648) >> Best rule #2645 for best value: >> intensional similarity = 55 >> extensional distance = 7 >> proper extension: 03hr1p; >> query: (?x520, ?x7413) <- country(?x520, ?x2346), country(?x520, ?x2188), country(?x520, ?x1536), country(?x520, ?x789), country(?x520, ?x756), country(?x520, ?x456), country(?x520, ?x304), country(?x520, ?x205), country(?x520, ?x94), sports(?x784, ?x520), olympics(?x453, ?x784), olympics(?x7413, ?x784), olympics(?x5186, ?x784), olympics(?x1229, ?x784), olympics(?x792, ?x784), ?x756 = 06npd, sports(?x1277, ?x520), ?x2188 = 0163v, capital(?x7413, ?x461), participating_countries(?x784, ?x126), film_release_region(?x8646, ?x7413), film_release_region(?x6932, ?x7413), film_release_region(?x4684, ?x7413), film_release_region(?x2394, ?x7413), film_release_region(?x1219, ?x7413), country(?x3015, ?x7413), country(?x2978, ?x7413), ?x3015 = 071t0, ?x94 = 09c7w0, ?x304 = 0d0vqn, ?x205 = 03rjj, ?x1229 = 059j2, ?x8646 = 05zvzf3, ?x2346 = 0d05w3, currency(?x7413, ?x170), ?x1219 = 03bx2lk, ?x2394 = 0661ql3, ?x792 = 0hzlz, ?x456 = 05qhw, ?x4684 = 03nm_fh, contains(?x455, ?x5186), ?x2978 = 03_8r, ?x1536 = 06c1y, film_release_region(?x6121, ?x789), film_release_region(?x5496, ?x789), film_release_region(?x4828, ?x789), film_release_region(?x3757, ?x789), film_release_region(?x1463, ?x789), ?x6932 = 027pfg, ?x4828 = 02fttd, ?x6121 = 064lsn, ?x1463 = 0gtvrv3, locations(?x3278, ?x789), ?x3757 = 02vr3gz, ?x5496 = 07l50vn >> conf = 0.90 => this is the best rule for 2 predicted values *> Best rule #3286 for first EXPECTED value: *> intensional similarity = 45 *> extensional distance = 8 *> proper extension: 07rlg; 035d1m; 019tzd; *> query: (?x520, 06t8v) <- country(?x520, ?x2346), country(?x520, ?x1536), country(?x520, ?x789), country(?x520, ?x756), country(?x520, ?x279), sports(?x784, ?x520), olympics(?x453, ?x784), olympics(?x8958, ?x784), olympics(?x7413, ?x784), olympics(?x3635, ?x784), olympics(?x151, ?x784), ?x756 = 06npd, ?x7413 = 04hqz, olympics(?x252, ?x784), partially_contains(?x1536, ?x10517), featured_film_locations(?x8084, ?x1536), country(?x5396, ?x1536), ?x5396 = 0486tv, olympics(?x1536, ?x391), jurisdiction_of_office(?x182, ?x1536), film_release_region(?x8176, ?x1536), film_release_region(?x7502, ?x1536), film_release_region(?x6321, ?x1536), film_release_region(?x4336, ?x1536), countries_within(?x455, ?x1536), contains(?x1536, ?x4962), ?x279 = 0d060g, combatants(?x1536, ?x1003), ?x7502 = 0233bn, contains(?x6304, ?x1536), ?x151 = 0b90_r, religion(?x1536, ?x962), organization(?x1536, ?x127), administrative_area_type(?x1536, ?x2792), ?x8958 = 01ppq, language(?x8176, ?x254), currency(?x1536, ?x170), ?x4336 = 0bpm4yw, ?x789 = 0f8l9c, ?x3635 = 019pcs, participating_countries(?x784, ?x126), ?x6321 = 0gg8z1f, film_festivals(?x8176, ?x4903), ?x2346 = 0d05w3, award_winner(?x8176, ?x8767) *> conf = 0.80 ranks of expected_values: 4, 18, 39 EVAL 01dys country 06t8v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 41.000 41.000 0.902 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01dys country 015qh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 41.000 41.000 0.902 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01dys country 04gzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 41.000 41.000 0.902 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #8671-031n5b PRED entity: 031n5b PRED relation: student PRED expected values: 023slg => 161 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1757): 02bn75 (0.40 #5542, 0.29 #7633, 0.20 #3451), 037lyl (0.29 #6935, 0.20 #4844, 0.20 #2753), 015wc0 (0.20 #5876, 0.20 #3785, 0.14 #7967), 0306ds (0.20 #4590, 0.20 #2499, 0.14 #6681), 0drc1 (0.20 #5621, 0.20 #3530, 0.14 #7712), 01l1rw (0.20 #5181, 0.20 #3090, 0.14 #7272), 011zf2 (0.20 #4393, 0.20 #2302, 0.14 #6484), 03rs8y (0.20 #4228, 0.20 #2137, 0.14 #6319), 04ls53 (0.20 #5001, 0.20 #2910, 0.14 #7092), 01l3mk3 (0.20 #5555, 0.20 #3464, 0.14 #7646) >> Best rule #5542 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 031n8c; >> query: (?x9612, 02bn75) <- category(?x9612, ?x134), ?x134 = 08mbj5d, school_type(?x9612, ?x9240), school_type(?x9612, ?x3205), ?x3205 = 01rs41, ?x9240 = 01y64, organization(?x3484, ?x9612) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 031n5b student 023slg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 161.000 77.000 0.400 http://example.org/education/educational_institution/students_graduates./education/education/student #8670-01zwy PRED entity: 01zwy PRED relation: people! PRED expected values: 0qcr0 0dq9p => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 40): 0gk4g (0.22 #736, 0.19 #1726, 0.19 #1264), 01psyx (0.18 #243, 0.14 #111, 0.09 #639), 0qcr0 (0.17 #397, 0.09 #2509, 0.08 #265), 01l2m3 (0.14 #82, 0.09 #214, 0.08 #280), 0d19y2 (0.14 #121, 0.09 #253, 0.05 #649), 07jwr (0.12 #339, 0.09 #207, 0.08 #273), 0dq9p (0.11 #1337, 0.11 #1271, 0.10 #1139), 04p3w (0.11 #737, 0.10 #473, 0.08 #1727), 02k6hp (0.09 #1027, 0.08 #301, 0.08 #697), 019dmc (0.09 #248, 0.08 #314, 0.06 #380) >> Best rule #736 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 01nrq5; 0bkmf; 02rf51g; >> query: (?x8508, 0gk4g) <- place_of_death(?x8508, ?x5267), award_winner(?x5418, ?x8508), place_of_burial(?x8508, ?x1730) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #397 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 02m7r; 0dx97; 02sdx; *> query: (?x8508, 0qcr0) <- student(?x2313, ?x8508), profession(?x8508, ?x3802), ?x3802 = 06q2q *> conf = 0.17 ranks of expected_values: 3, 7 EVAL 01zwy people! 0dq9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 139.000 139.000 0.222 http://example.org/people/cause_of_death/people EVAL 01zwy people! 0qcr0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 139.000 139.000 0.222 http://example.org/people/cause_of_death/people #8669-05fm6m PRED entity: 05fm6m PRED relation: film_crew_role PRED expected values: 02r96rf => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 24): 09zzb8 (0.72 #70, 0.72 #173, 0.71 #445), 02r96rf (0.64 #721, 0.64 #619, 0.63 #176), 01pvkk (0.30 #454, 0.27 #1408, 0.27 #1306), 02ynfr (0.16 #48, 0.15 #731, 0.15 #629), 0215hd (0.13 #86, 0.13 #17, 0.12 #1313), 02rh1dz (0.11 #181, 0.10 #43, 0.10 #624), 089g0h (0.11 #18, 0.11 #87, 0.10 #1314), 0d2b38 (0.11 #24, 0.09 #1320, 0.09 #912), 01xy5l_ (0.11 #184, 0.09 #1308, 0.09 #456), 02_n3z (0.09 #71, 0.08 #890, 0.08 #1026) >> Best rule #70 for best value: >> intensional similarity = 3 >> extensional distance = 314 >> proper extension: 0fq27fp; >> query: (?x7626, 09zzb8) <- genre(?x7626, ?x1403), ?x1403 = 02l7c8, film_crew_role(?x7626, ?x1171) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #721 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 826 *> proper extension: 0dtw1x; 0gj9qxr; 0crh5_f; 0h95zbp; 03_wm6; *> query: (?x7626, 02r96rf) <- production_companies(?x7626, ?x541), film_crew_role(?x7626, ?x1171), genre(?x7626, ?x239) *> conf = 0.64 ranks of expected_values: 2 EVAL 05fm6m film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.725 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8668-02p3cr5 PRED entity: 02p3cr5 PRED relation: artist PRED expected values: 01vv7sc 01wv9xn 01vsy7t => 72 concepts (24 used for prediction) PRED predicted values (max 10 best out of 3143): 01vw8mh (0.50 #11353, 0.43 #8916, 0.41 #8104), 0knhk (0.50 #4604, 0.40 #2173, 0.38 #6224), 01x1cn2 (0.50 #954, 0.20 #2574, 0.20 #1764), 014488 (0.50 #1030, 0.20 #1840, 0.17 #4271), 01k3qj (0.50 #1342, 0.20 #2152, 0.17 #4583), 020_4z (0.43 #5576, 0.38 #6386, 0.33 #4766), 01vxlbm (0.43 #5122, 0.31 #7553, 0.28 #12429), 014_xj (0.43 #5647, 0.25 #6457, 0.25 #1596), 01q99h (0.43 #5288, 0.25 #1237, 0.20 #2857), 019g40 (0.43 #4963, 0.25 #912, 0.20 #1722) >> Best rule #11353 for best value: >> intensional similarity = 6 >> extensional distance = 20 >> proper extension: 05p7tx; >> query: (?x4797, ?x4851) <- company(?x4851, ?x4797), profession(?x4851, ?x131), artists(?x283, ?x4851), profession(?x4620, ?x131), ?x4620 = 01vsy7t, artist(?x1954, ?x4851) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #900 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 0mzkr; *> query: (?x4797, 01wv9xn) <- artist(?x4797, ?x10043), artist(?x4797, ?x3767), artist(?x4797, ?x2854), ?x3767 = 01wbz9, origin(?x2854, ?x362), artists(?x671, ?x2854), group(?x227, ?x10043), role(?x2854, ?x1437) *> conf = 0.25 ranks of expected_values: 164, 336, 650 EVAL 02p3cr5 artist 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 72.000 24.000 0.500 http://example.org/music/record_label/artist EVAL 02p3cr5 artist 01wv9xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 72.000 24.000 0.500 http://example.org/music/record_label/artist EVAL 02p3cr5 artist 01vv7sc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 24.000 0.500 http://example.org/music/record_label/artist #8667-045gzq PRED entity: 045gzq PRED relation: film PRED expected values: 0sxmx => 99 concepts (27 used for prediction) PRED predicted values (max 10 best out of 712): 0872p_c (0.62 #3749, 0.03 #25195, 0.03 #30556), 05zlld0 (0.38 #4188, 0.02 #25634, 0.02 #30995), 014_x2 (0.33 #5, 0.10 #5366, 0.01 #10727), 014zwb (0.33 #505, 0.03 #11227, 0.02 #7653), 034qzw (0.33 #2120, 0.03 #30714, 0.03 #25353), 065_cjc (0.33 #2983, 0.02 #11918, 0.02 #26216), 01j5ql (0.33 #1200, 0.02 #11922, 0.01 #29794), 01k1k4 (0.33 #1845, 0.02 #25078, 0.02 #30439), 01pvxl (0.33 #908, 0.01 #31289, 0.01 #47372), 08952r (0.33 #2504, 0.01 #25737, 0.01 #34672) >> Best rule #3749 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 01lly5; 02gf_l; 057_yx; 083wr9; >> query: (?x13928, 0872p_c) <- film(?x13928, ?x5277), film(?x13928, ?x1456), place_of_birth(?x13928, ?x108), ?x5277 = 047csmy, genre(?x1456, ?x812) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #6170 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 02t1cp; 01x72k; 06mr6; 06bzwt; *> query: (?x13928, 0sxmx) <- film(?x13928, ?x7482), film(?x13928, ?x1456), ?x7482 = 07bx6, film(?x382, ?x1456), film_release_region(?x1456, ?x87) *> conf = 0.10 ranks of expected_values: 63 EVAL 045gzq film 0sxmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 99.000 27.000 0.625 http://example.org/film/actor/film./film/performance/film #8666-01y9jr PRED entity: 01y9jr PRED relation: country PRED expected values: 0345h => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 33): 07ssc (0.23 #1338, 0.21 #2419, 0.21 #2359), 0345h (0.17 #27, 0.12 #1349, 0.11 #207), 0d060g (0.17 #8, 0.04 #1330, 0.04 #128), 0f8l9c (0.10 #199, 0.10 #1341, 0.09 #139), 03h64 (0.10 #106, 0.03 #346, 0.03 #226), 0chghy (0.05 #192, 0.04 #1334, 0.04 #432), 03_3d (0.04 #2410, 0.04 #2350, 0.04 #1449), 0d05w3 (0.04 #343, 0.02 #103, 0.02 #1365), 03rjj (0.03 #1088, 0.03 #1328, 0.03 #547), 01mjq (0.02 #95, 0.02 #155, 0.02 #335) >> Best rule #1338 for best value: >> intensional similarity = 3 >> extensional distance = 1153 >> proper extension: 0170z3; 0b76d_m; 014_x2; 0ds35l9; 0d90m; 03qcfvw; 0g56t9t; 09sh8k; 0m313; 02y_lrp; ... >> query: (?x6578, 07ssc) <- genre(?x6578, ?x225), country(?x6578, ?x94), film_crew_role(?x6578, ?x468) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0vjr; *> query: (?x6578, 0345h) <- nominated_for(?x4782, ?x6578), nominated_for(?x2307, ?x6578), ?x2307 = 011zd3, award_winner(?x1811, ?x4782) *> conf = 0.17 ranks of expected_values: 2 EVAL 01y9jr country 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 45.000 45.000 0.227 http://example.org/film/film/country #8665-05zr0xl PRED entity: 05zr0xl PRED relation: nominated_for! PRED expected values: 0cjyzs 09qs08 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 185): 09qs08 (0.80 #5647, 0.72 #2117, 0.72 #2353), 0cjyzs (0.71 #315, 0.53 #550, 0.50 #1491), 09qrn4 (0.53 #633, 0.46 #398, 0.36 #1574), 09qj50 (0.50 #271, 0.31 #1447, 0.27 #1212), 09qvf4 (0.46 #381, 0.33 #146, 0.30 #1557), 0gq9h (0.37 #14882, 0.37 #14646, 0.37 #14175), 0gs9p (0.34 #14884, 0.33 #14648, 0.33 #14177), 019f4v (0.32 #14873, 0.32 #14166, 0.32 #14637), 0fbtbt (0.30 #2747, 0.29 #4863, 0.28 #6041), 0k611 (0.28 #14657, 0.28 #14186, 0.28 #14893) >> Best rule #5647 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 0fpxp; 023ny6; >> query: (?x8533, ?x870) <- genre(?x8533, ?x258), award(?x8533, ?x870), nominated_for(?x1541, ?x8533), award(?x71, ?x870) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05zr0xl nominated_for! 09qs08 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05zr0xl nominated_for! 0cjyzs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8664-0421v9q PRED entity: 0421v9q PRED relation: titles! PRED expected values: 01z4y => 108 concepts (88 used for prediction) PRED predicted values (max 10 best out of 66): 07s9rl0 (0.34 #5718, 0.34 #5613, 0.34 #5507), 01jfsb (0.30 #122, 0.15 #535, 0.14 #2507), 01z4y (0.24 #7527, 0.22 #1587, 0.22 #1484), 04xvlr (0.22 #5616, 0.22 #5406, 0.18 #4886), 05p553 (0.21 #5506, 0.20 #5717, 0.18 #3735), 06cvj (0.21 #5506, 0.20 #5717, 0.18 #3735), 07c52 (0.20 #30, 0.09 #3350, 0.09 #6474), 024qqx (0.19 #387, 0.18 #183, 0.18 #1010), 02n4kr (0.18 #116, 0.07 #529, 0.06 #2501), 01hmnh (0.13 #333, 0.13 #1996, 0.13 #749) >> Best rule #5718 for best value: >> intensional similarity = 4 >> extensional distance = 666 >> proper extension: 016ztl; >> query: (?x6543, ?x53) <- film_release_distribution_medium(?x6543, ?x81), film(?x7980, ?x6543), genre(?x6543, ?x53), ?x53 = 07s9rl0 >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #7527 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 993 *> proper extension: 04svwx; *> query: (?x6543, 01z4y) <- genre(?x6543, ?x239), genre(?x5534, ?x239), ?x5534 = 05zpghd *> conf = 0.24 ranks of expected_values: 3 EVAL 0421v9q titles! 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 88.000 0.340 http://example.org/media_common/netflix_genre/titles #8663-01w02sy PRED entity: 01w02sy PRED relation: currency PRED expected values: 09nqf => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.55 #31, 0.50 #1, 0.48 #4), 01nv4h (0.11 #11, 0.07 #41, 0.06 #59) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 01yg9y; 0g476; >> query: (?x3118, 09nqf) <- profession(?x3118, ?x131), participant(?x989, ?x3118), artists(?x302, ?x3118) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w02sy currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.552 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #8662-09kn9 PRED entity: 09kn9 PRED relation: category PRED expected values: 08mbj5d => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.44 #8, 0.42 #11, 0.40 #13) >> Best rule #8 for best value: >> intensional similarity = 7 >> extensional distance = 39 >> proper extension: 0584r4; 0557yqh; 06qwh; 07g9f; 06qxh; 06qw_; >> query: (?x1843, 08mbj5d) <- country_of_origin(?x1843, ?x94), titles(?x2008, ?x1843), genre(?x1843, ?x811), ?x2008 = 07c52, tv_program(?x1799, ?x1843), place_of_birth(?x1799, ?x739), gender(?x1799, ?x231) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09kn9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.439 http://example.org/common/topic/webpage./common/webpage/category #8661-05r7t PRED entity: 05r7t PRED relation: exported_to! PRED expected values: 0n3g => 168 concepts (88 used for prediction) PRED predicted values (max 10 best out of 64): 09c7w0 (0.29 #612, 0.29 #551, 0.29 #185), 06q1r (0.28 #474, 0.25 #46, 0.23 #290), 04sj3 (0.23 #301, 0.19 #240, 0.18 #606), 047t_ (0.17 #954, 0.16 #467, 0.15 #1680), 05r4w (0.16 #1096, 0.15 #489, 0.11 #2431), 0h3y (0.16 #435, 0.14 #556, 0.14 #190), 0ctw_b (0.16 #138, 0.14 #565, 0.14 #199), 05r7t (0.15 #306, 0.09 #3158, 0.07 #3589), 0b90_r (0.15 #306, 0.07 #3589, 0.07 #3096), 0j4b (0.14 #230, 0.14 #291, 0.12 #415) >> Best rule #612 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 0j5g9; >> query: (?x6559, 09c7w0) <- nationality(?x395, ?x6559), countries_spoken_in(?x254, ?x6559), administrative_parent(?x8428, ?x6559), teams(?x6559, ?x9821) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #224 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 0h44w; *> query: (?x6559, 0n3g) <- countries_spoken_in(?x254, ?x6559), capital(?x6559, ?x8428), location(?x56, ?x6559), place_of_birth(?x883, ?x8428) *> conf = 0.10 ranks of expected_values: 19 EVAL 05r7t exported_to! 0n3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 168.000 88.000 0.286 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #8660-02n9k PRED entity: 02n9k PRED relation: people! PRED expected values: 063k3h => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 53): 041rx (0.43 #386, 0.33 #309, 0.24 #9368), 02ctzb (0.38 #550, 0.31 #930, 0.29 #3744), 02w7gg (0.29 #1145, 0.25 #1373, 0.15 #3275), 07bch9 (0.27 #3751, 0.25 #3979, 0.25 #98), 0x67 (0.27 #1990, 0.22 #2675, 0.20 #2827), 033tf_ (0.25 #542, 0.19 #2139, 0.17 #382), 07hwkr (0.25 #241, 0.12 #547, 0.11 #2144), 063k3h (0.21 #3759, 0.15 #3987, 0.14 #3531), 0g5y6 (0.17 #341, 0.14 #418, 0.07 #1179), 013xrm (0.15 #1618, 0.10 #4357, 0.08 #1770) >> Best rule #386 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 016bx2; 01cspq; >> query: (?x7893, 041rx) <- influenced_by(?x11088, ?x7893), people(?x1446, ?x11088), sibling(?x6138, ?x11088), type_of_union(?x7893, ?x566) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3759 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 06c97; 09py7; *> query: (?x7893, 063k3h) <- people(?x5269, ?x7893), profession(?x7893, ?x9682), politician(?x8714, ?x7893) *> conf = 0.21 ranks of expected_values: 8 EVAL 02n9k people! 063k3h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 173.000 173.000 0.429 http://example.org/people/ethnicity/people #8659-0167km PRED entity: 0167km PRED relation: type_of_union PRED expected values: 04ztj => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.73 #85, 0.71 #29, 0.69 #105), 01g63y (0.20 #449, 0.18 #10, 0.17 #42), 0jgjn (0.20 #449), 01bl8s (0.20 #449) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 156 >> proper extension: 028qdb; >> query: (?x5879, 04ztj) <- instrumentalists(?x1166, ?x5879), award(?x5879, ?x1801), role(?x5879, ?x1466), role(?x1166, ?x74) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0167km type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.728 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8658-03hkch7 PRED entity: 03hkch7 PRED relation: crewmember PRED expected values: 02xc1w4 => 72 concepts (57 used for prediction) PRED predicted values (max 10 best out of 36): 0b79gfg (0.07 #65, 0.03 #401, 0.03 #258), 094wz7q (0.07 #66, 0.03 #402, 0.03 #306), 027rwmr (0.07 #53, 0.02 #918, 0.01 #1886), 095zvfg (0.05 #181, 0.03 #468, 0.03 #228), 04wp63 (0.05 #472, 0.03 #521, 0.02 #760), 03m49ly (0.03 #418, 0.03 #178, 0.02 #225), 0284n42 (0.03 #916, 0.03 #483, 0.02 #722), 02q9kqf (0.03 #557, 0.03 #605, 0.02 #990), 09rp4r_ (0.03 #392, 0.03 #249, 0.03 #439), 01g1lp (0.03 #479, 0.03 #960, 0.02 #2273) >> Best rule #65 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 0209xj; 0_92w; 07j8r; 011yl_; 03f7nt; 041td_; 01dc0c; 0gvt53w; 0yx_w; >> query: (?x3124, 0b79gfg) <- nominated_for(?x3209, ?x3124), nominated_for(?x68, ?x3124), nominated_for(?x123, ?x3124), ?x68 = 02qyp19, ?x3209 = 02w9sd7, award(?x3124, ?x834) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #1224 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 454 *> proper extension: 047svrl; *> query: (?x3124, 02xc1w4) <- film_release_region(?x3124, ?x94), film(?x1104, ?x3124), film(?x123, ?x3124), featured_film_locations(?x3124, ?x739) *> conf = 0.02 ranks of expected_values: 27 EVAL 03hkch7 crewmember 02xc1w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 72.000 57.000 0.071 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #8657-0dc7hc PRED entity: 0dc7hc PRED relation: film_crew_role PRED expected values: 01vx2h => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 27): 01vx2h (0.45 #180, 0.37 #523, 0.35 #662), 0dxtw (0.43 #522, 0.43 #179, 0.41 #661), 02rh1dz (0.23 #178, 0.15 #521, 0.14 #660), 02ynfr (0.21 #184, 0.21 #527, 0.19 #492), 0d2b38 (0.15 #194, 0.12 #537, 0.12 #676), 0215hd (0.15 #221, 0.15 #669, 0.14 #530), 01xy5l_ (0.13 #182, 0.12 #525, 0.12 #664), 089g0h (0.12 #670, 0.12 #531, 0.11 #222), 015h31 (0.10 #177, 0.08 #281, 0.08 #520), 02_n3z (0.09 #514, 0.09 #653, 0.08 #479) >> Best rule #180 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 0gtsx8c; 0cz8mkh; 014nq4; 0bc1yhb; 033qdy; 0642ykh; 0bwhdbl; 0hhggmy; 08c6k9; 056xkh; >> query: (?x9774, 01vx2h) <- film(?x1867, ?x9774), film_crew_role(?x9774, ?x1171), ?x1171 = 09vw2b7, prequel(?x9774, ?x10268) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dc7hc film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.452 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8656-01rzqj PRED entity: 01rzqj PRED relation: place_of_birth PRED expected values: 0nbrp => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 73): 0h7h6 (0.28 #13379, 0.28 #16902, 0.27 #64795), 04jpl (0.28 #13379, 0.28 #16902, 0.27 #64795), 02_286 (0.11 #19, 0.09 #4245, 0.07 #9877), 030qb3t (0.11 #54, 0.06 #4280, 0.05 #4984), 0x1y7 (0.11 #698), 01531 (0.07 #2217, 0.07 #809, 0.06 #1513), 0cr3d (0.07 #798, 0.04 #12768, 0.04 #9248), 052p7 (0.05 #2899, 0.01 #52116), 0b1t1 (0.05 #3183), 05ksh (0.04 #2854) >> Best rule #13379 for best value: >> intensional similarity = 3 >> extensional distance = 454 >> proper extension: 0dszr0; >> query: (?x3366, ?x362) <- actor(?x4932, ?x3366), location(?x3366, ?x362), nominated_for(?x435, ?x4932) >> conf = 0.28 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01rzqj place_of_birth 0nbrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 106.000 0.281 http://example.org/people/person/place_of_birth #8655-02jxsq PRED entity: 02jxsq PRED relation: split_to PRED expected values: 02jxsq => 137 concepts (53 used for prediction) No prediction ranks of expected_values: EVAL 02jxsq split_to 02jxsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 53.000 0.000 http://example.org/dataworld/gardening_hint/split_to #8654-0194xc PRED entity: 0194xc PRED relation: religion PRED expected values: 0c8wxp => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 37): 019cr (0.33 #371, 0.33 #236, 0.25 #11), 0c8wxp (0.33 #591, 0.30 #276, 0.25 #6), 05sfs (0.29 #183, 0.25 #3, 0.17 #363), 0631_ (0.29 #188, 0.17 #683, 0.17 #413), 0v53x (0.25 #389, 0.25 #29, 0.14 #209), 051kv (0.25 #50, 0.22 #230, 0.14 #185), 03_gx (0.20 #104, 0.14 #149, 0.11 #554), 01spm (0.18 #352, 0.10 #667, 0.06 #1343), 03j6c (0.17 #426, 0.11 #561, 0.09 #1597), 07y1z (0.14 #223, 0.11 #268, 0.10 #673) >> Best rule #371 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0157m; 042kg; >> query: (?x9569, 019cr) <- politician(?x8714, ?x9569), award_winner(?x3846, ?x9569), celebrities_impersonated(?x2101, ?x9569) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #591 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 02whj; 02knnd; 014dq7; 0ly5n; 044qx; 040z9; 013qvn; 0l5yl; 063_t; 0gyy0; ... *> query: (?x9569, 0c8wxp) <- celebrities_impersonated(?x2101, ?x9569), place_of_burial(?x9569, ?x7496), people(?x268, ?x9569) *> conf = 0.33 ranks of expected_values: 2 EVAL 0194xc religion 0c8wxp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.333 http://example.org/people/person/religion #8653-01bb9r PRED entity: 01bb9r PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 24): 0ch6mp2 (0.85 #569, 0.83 #72, 0.83 #1270), 01vx2h (0.38 #1440, 0.36 #1541, 0.36 #1407), 02ynfr (0.33 #15, 0.25 #81, 0.23 #644), 01pvkk (0.30 #2205, 0.30 #706, 0.30 #640), 0215hd (0.17 #1447, 0.17 #1548, 0.15 #1414), 02rh1dz (0.15 #1273, 0.14 #1439, 0.14 #1771), 089g0h (0.14 #1448, 0.14 #1549, 0.13 #1415), 0d2b38 (0.14 #619, 0.13 #1453, 0.13 #1554), 01xy5l_ (0.14 #1443, 0.13 #1544, 0.12 #1841), 015h31 (0.13 #971, 0.09 #1704, 0.09 #1438) >> Best rule #569 for best value: >> intensional similarity = 4 >> extensional distance = 193 >> proper extension: 0dq626; 0963mq; 09gdm7q; 09lcsj; 04z257; 0gh6j94; 0cf8qb; >> query: (?x2955, 0ch6mp2) <- film_crew_role(?x2955, ?x1171), ?x1171 = 09vw2b7, language(?x2955, ?x5359), films(?x11047, ?x2955) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bb9r film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.851 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8652-0kj34 PRED entity: 0kj34 PRED relation: nationality PRED expected values: 02jx1 => 167 concepts (158 used for prediction) PRED predicted values (max 10 best out of 77): 09c7w0 (0.93 #6685, 0.92 #6489, 0.87 #7078), 02jx1 (0.83 #6783, 0.79 #815, 0.76 #7176), 014tss (0.83 #6783, 0.76 #7176, 0.38 #1863), 01j_x (0.83 #6783, 0.76 #7176), 0345h (0.33 #13769, 0.12 #421, 0.08 #1793), 06q1r (0.33 #13769, 0.05 #4596, 0.03 #15443), 0d060g (0.25 #7, 0.09 #1477, 0.07 #2854), 05bcl (0.25 #58, 0.03 #15443, 0.01 #4579), 0f8l9c (0.22 #1784, 0.07 #314, 0.06 #412), 03rk0 (0.15 #6236, 0.10 #9091, 0.09 #10957) >> Best rule #6685 for best value: >> intensional similarity = 5 >> extensional distance = 467 >> proper extension: 06jzh; 022769; 01438g; 04n_g; 02nwxc; 02lyx4; 02cg2v; >> query: (?x9087, 09c7w0) <- gender(?x9087, ?x231), participant(?x4537, ?x9087), nationality(?x9087, ?x512), split_to(?x1310, ?x512), region(?x54, ?x512) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #6783 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 467 *> proper extension: 06jzh; 022769; 01438g; 04n_g; 02nwxc; 02lyx4; 02cg2v; *> query: (?x9087, ?x1310) <- gender(?x9087, ?x231), participant(?x4537, ?x9087), nationality(?x9087, ?x512), split_to(?x1310, ?x512), region(?x54, ?x512) *> conf = 0.83 ranks of expected_values: 2 EVAL 0kj34 nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 167.000 158.000 0.925 http://example.org/people/person/nationality #8651-0f6c3 PRED entity: 0f6c3 PRED relation: jurisdiction_of_office PRED expected values: 07h34 04ly1 => 20 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1340): 04ly1 (0.65 #1241, 0.50 #1387, 0.45 #3319), 0694j (0.65 #1241, 0.45 #3319, 0.41 #6260), 05kr_ (0.65 #1241, 0.45 #3319, 0.41 #6260), 04kbn (0.65 #1241, 0.45 #3319, 0.41 #6260), 0183z2 (0.65 #1241, 0.45 #3319, 0.41 #6260), 02_286 (0.65 #1241, 0.45 #3319, 0.41 #6260), 02gt5s (0.65 #1241, 0.45 #3319, 0.41 #6260), 0dclg (0.65 #1241, 0.45 #3319, 0.41 #4998), 0b90_r (0.65 #1241, 0.33 #5415, 0.32 #4156), 0ctw_b (0.62 #2939, 0.41 #4998, 0.41 #5839) >> Best rule #1241 for best value: >> intensional similarity = 27 >> extensional distance = 1 >> proper extension: 09n5b9; >> query: (?x3959, ?x1905) <- jurisdiction_of_office(?x3959, ?x7518), jurisdiction_of_office(?x3959, ?x3634), jurisdiction_of_office(?x3959, ?x1767), jurisdiction_of_office(?x3959, ?x1755), jurisdiction_of_office(?x3959, ?x1351), jurisdiction_of_office(?x3959, ?x1274), jurisdiction_of_office(?x3959, ?x1138), jurisdiction_of_office(?x3959, ?x1024), jurisdiction_of_office(?x3959, ?x760), jurisdiction_of_office(?x3959, ?x177), ?x7518 = 026mj, ?x1024 = 05fhy, ?x1351 = 06mz5, ?x1274 = 04ykg, ?x1755 = 01x73, ?x760 = 05fkf, ?x1138 = 059_c, ?x1767 = 04rrd, currency(?x177, ?x170), jurisdiction_of_office(?x1157, ?x177), contains(?x177, ?x388), taxonomy(?x177, ?x939), district_represented(?x1027, ?x177), ?x3634 = 07b_l, adjoins(?x1905, ?x177), location(?x932, ?x177), ?x1027 = 02bn_p >> conf = 0.65 => this is the best rule for 9 predicted values ranks of expected_values: 1, 11 EVAL 0f6c3 jurisdiction_of_office 04ly1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 20.000 16.000 0.654 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0f6c3 jurisdiction_of_office 07h34 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 20.000 16.000 0.654 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #8650-0168cl PRED entity: 0168cl PRED relation: profession PRED expected values: 0kyk => 116 concepts (84 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.84 #4886, 0.84 #5317, 0.81 #7614), 0dz3r (0.57 #1148, 0.55 #1578, 0.46 #2296), 0kyk (0.49 #8918, 0.36 #3899, 0.31 #7486), 0dxtg (0.43 #7038, 0.43 #7470, 0.42 #3883), 03gjzk (0.40 #1445, 0.34 #1875, 0.30 #4315), 0n1h (0.36 #9, 0.31 #439, 0.29 #1012), 01c72t (0.35 #2026, 0.31 #6331, 0.30 #6474), 02jknp (0.27 #1438, 0.24 #1868, 0.19 #7033), 04f2zj (0.26 #9609, 0.26 #5019, 0.21 #234), 025352 (0.26 #9609, 0.26 #5019, 0.11 #3496) >> Best rule #4886 for best value: >> intensional similarity = 3 >> extensional distance = 348 >> proper extension: 05bnp0; 05gml8; 03m8lq; 02g87m; 07f3xb; 01713c; 0806vbn; 03wpmd; 06w6_; 01fwk3; ... >> query: (?x672, 02hrh1q) <- award_nominee(?x672, ?x6592), languages(?x672, ?x254), profession(?x6592, ?x131) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #8918 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 661 *> proper extension: 063vn; 0d4jl; 06hmd; 06c97; 042kg; *> query: (?x672, 0kyk) <- profession(?x672, ?x353), profession(?x6504, ?x353), ?x6504 = 03f47xl *> conf = 0.49 ranks of expected_values: 3 EVAL 0168cl profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 84.000 0.837 http://example.org/people/person/profession #8649-0qm8b PRED entity: 0qm8b PRED relation: language PRED expected values: 02h40lc => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 36): 02h40lc (0.89 #1126, 0.89 #710, 0.88 #1245), 064_8sq (0.18 #553, 0.17 #22, 0.16 #81), 04306rv (0.17 #536, 0.13 #123, 0.13 #64), 06nm1 (0.13 #188, 0.12 #660, 0.11 #247), 0jzc (0.10 #551, 0.09 #138, 0.06 #256), 06b_j (0.10 #82, 0.09 #141, 0.09 #554), 02bjrlw (0.10 #60, 0.08 #473, 0.08 #119), 03_9r (0.06 #305, 0.06 #541, 0.06 #128), 07zrf (0.06 #121, 0.04 #534, 0.04 #180), 04h9h (0.06 #43, 0.05 #220, 0.05 #338) >> Best rule #1126 for best value: >> intensional similarity = 4 >> extensional distance = 479 >> proper extension: 0cbl95; >> query: (?x1586, 02h40lc) <- nominated_for(?x298, ?x1586), film(?x2803, ?x1586), genre(?x1586, ?x53), award_nominee(?x2803, ?x1039) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qm8b language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.894 http://example.org/film/film/language #8648-03qsdpk PRED entity: 03qsdpk PRED relation: major_field_of_study! PRED expected values: 028dcg => 81 concepts (66 used for prediction) PRED predicted values (max 10 best out of 17): 02_xgp2 (0.76 #358, 0.75 #181, 0.74 #323), 03bwzr4 (0.71 #147, 0.67 #251, 0.62 #164), 04zx3q1 (0.62 #175, 0.57 #317, 0.55 #209), 01gkg3 (0.50 #79, 0.40 #114, 0.38 #165), 02m4yg (0.50 #80, 0.40 #115, 0.34 #315), 01ysy9 (0.50 #86, 0.33 #51, 0.32 #173), 0bjrnt (0.45 #211, 0.45 #194, 0.38 #177), 022h5x (0.40 #136, 0.34 #315, 0.33 #49), 071tyz (0.38 #180, 0.34 #315, 0.32 #882), 01rr_d (0.34 #315, 0.33 #46, 0.32 #882) >> Best rule #358 for best value: >> intensional similarity = 9 >> extensional distance = 27 >> proper extension: 01tbp; >> query: (?x5614, 02_xgp2) <- major_field_of_study(?x9066, ?x5614), major_field_of_study(?x6912, ?x5614), major_field_of_study(?x3314, ?x5614), student(?x5614, ?x396), category(?x3314, ?x134), major_field_of_study(?x6912, ?x10518), currency(?x9066, ?x170), ?x134 = 08mbj5d, ?x10518 = 034ns >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 21 *> proper extension: 097df; *> query: (?x5614, ?x734) <- major_field_of_study(?x6925, ?x5614), major_field_of_study(?x8681, ?x5614), institution(?x734, ?x6925), major_field_of_study(?x581, ?x8681), student(?x6925, ?x981), company(?x3131, ?x6925), genre(?x9188, ?x8681) *> conf = 0.34 ranks of expected_values: 13 EVAL 03qsdpk major_field_of_study! 028dcg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 81.000 66.000 0.759 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #8647-0g55tzk PRED entity: 0g55tzk PRED relation: honored_for PRED expected values: 080dwhx => 28 concepts (21 used for prediction) PRED predicted values (max 10 best out of 976): 0524b41 (0.67 #3371, 0.50 #1599, 0.38 #4554), 080dwhx (0.50 #2977, 0.50 #1205, 0.38 #4160), 0ddd0gc (0.50 #3034, 0.50 #1262, 0.25 #4217), 01j7mr (0.50 #1393, 0.37 #7300, 0.33 #3165), 039cq4 (0.50 #1589, 0.33 #3361, 0.29 #3954), 07zhjj (0.50 #1674, 0.33 #3446, 0.29 #4039), 06mr2s (0.50 #1461, 0.33 #3233, 0.29 #3826), 06hwzy (0.50 #1332, 0.33 #3104, 0.29 #3697), 01vnbh (0.50 #1495, 0.33 #3267, 0.26 #7402), 0c3xpwy (0.50 #1515, 0.33 #3287, 0.25 #4470) >> Best rule #3371 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 0hndn2q; >> query: (?x11738, 0524b41) <- award_winner(?x11738, ?x3594), honored_for(?x11738, ?x8533), honored_for(?x11738, ?x3787), honored_for(?x11738, ?x1813), ?x8533 = 05zr0xl, award_winner(?x3594, ?x560), place_of_birth(?x3594, ?x2624), film(?x3594, ?x2655), award_winner(?x3787, ?x12148), award_nominee(?x1641, ?x3594), nominated_for(?x1205, ?x3787), award(?x1813, ?x68) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2977 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 0hndn2q; *> query: (?x11738, 080dwhx) <- award_winner(?x11738, ?x3594), honored_for(?x11738, ?x8533), honored_for(?x11738, ?x3787), honored_for(?x11738, ?x1813), ?x8533 = 05zr0xl, award_winner(?x3594, ?x560), place_of_birth(?x3594, ?x2624), film(?x3594, ?x2655), award_winner(?x3787, ?x12148), award_nominee(?x1641, ?x3594), nominated_for(?x1205, ?x3787), award(?x1813, ?x68) *> conf = 0.50 ranks of expected_values: 2 EVAL 0g55tzk honored_for 080dwhx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 28.000 21.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #8646-02qvyrt PRED entity: 02qvyrt PRED relation: award! PRED expected values: 0146pg 0244r8 0l12d 02cyfz 02bh9 01gg59 06fxnf 03h610 01pbs9w 01vrx35 0134wr 01yndb 01c7qd => 57 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2626): 020fgy (0.86 #13287, 0.82 #26582, 0.81 #29906), 01cbt3 (0.86 #13287, 0.82 #26582, 0.81 #29906), 0m_v0 (0.86 #13287, 0.82 #26582, 0.81 #29906), 05ldnp (0.57 #14177, 0.40 #7531, 0.38 #27472), 01ts_3 (0.57 #15313, 0.40 #8667, 0.31 #28608), 014zcr (0.54 #23312, 0.43 #13341, 0.40 #3372), 02kxbwx (0.46 #23439, 0.43 #13468, 0.40 #20115), 02hfp_ (0.46 #25554, 0.43 #15583, 0.40 #8937), 0184jw (0.46 #25493, 0.40 #8876, 0.31 #28817), 06b_0 (0.46 #25454, 0.40 #8837, 0.31 #28778) >> Best rule #13287 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 02n9nmz; 0274v0r; >> query: (?x2379, ?x84) <- award_winner(?x2379, ?x84), nominated_for(?x2379, ?x4359), nominated_for(?x2379, ?x3398), award_winner(?x4359, ?x2493), ?x3398 = 02n9bh >> conf = 0.86 => this is the best rule for 3 predicted values *> Best rule #37626 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 37 *> proper extension: 026mg3; 01c9dd; *> query: (?x2379, 01gg59) <- award_winner(?x2379, ?x4139), nominated_for(?x2379, ?x89), award(?x538, ?x2379), award_winner(?x4139, ?x2835), music(?x8575, ?x4139) *> conf = 0.21 ranks of expected_values: 223, 225, 258, 283, 293, 379, 425, 447, 575, 725, 904, 1840, 2106 EVAL 02qvyrt award! 01c7qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 01yndb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 0134wr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 01vrx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 01pbs9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 03h610 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 06fxnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 01gg59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 02bh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 02cyfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 0l12d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 0244r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qvyrt award! 0146pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 24.000 0.856 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8645-03f1zdw PRED entity: 03f1zdw PRED relation: award_nominee PRED expected values: 05vsxz => 98 concepts (46 used for prediction) PRED predicted values (max 10 best out of 783): 03t0k1 (0.87 #6933, 0.84 #9243, 0.82 #2310), 0bq2g (0.87 #6933, 0.84 #9243, 0.82 #2310), 0151w_ (0.87 #6933, 0.84 #9243, 0.82 #2310), 03yk8z (0.87 #6933, 0.84 #9243, 0.82 #2310), 02cllz (0.87 #6933, 0.84 #9243, 0.82 #4623), 04rsd2 (0.87 #6933, 0.84 #9243, 0.82 #4623), 07hbxm (0.87 #6933, 0.84 #9243, 0.82 #4623), 05vsxz (0.75 #6940, 0.73 #4630, 0.04 #11559), 03f1zdw (0.65 #7177, 0.62 #2555, 0.60 #4867), 0l6px (0.28 #106271, 0.27 #57753, 0.26 #90101) >> Best rule #6933 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0m2wm; 04rsd2; 0175wg; 01qrbf; >> query: (?x1222, ?x57) <- award_nominee(?x1549, ?x1222), award_nominee(?x57, ?x1222), type_of_union(?x1222, ?x566), ?x1549 = 09y20 >> conf = 0.87 => this is the best rule for 7 predicted values *> Best rule #6940 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 02zq43; 01yhvv; 0djywgn; *> query: (?x1222, 05vsxz) <- award_nominee(?x7186, ?x1222), award_nominee(?x1222, ?x380), ?x7186 = 01qrbf *> conf = 0.75 ranks of expected_values: 8 EVAL 03f1zdw award_nominee 05vsxz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 98.000 46.000 0.869 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8644-05j0wc PRED entity: 05j0wc PRED relation: location_of_ceremony PRED expected values: 03gh4 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 34): 0fr0t (0.06 #405, 0.02 #1128, 0.02 #2207), 0cv3w (0.04 #514, 0.04 #634, 0.02 #1117), 0r0m6 (0.03 #769, 0.02 #1253, 0.02 #1373), 0k049 (0.03 #3123, 0.02 #3720, 0.02 #3363), 03gh4 (0.02 #1266), 01n7q (0.02 #1341), 0qxhc (0.02 #1560, 0.02 #1681, 0.02 #1921), 013n2h (0.02 #1515, 0.02 #1636, 0.02 #1876), 06mxs (0.02 #1501, 0.02 #1622, 0.02 #1862), 0ggyr (0.02 #1535, 0.02 #2134, 0.01 #2853) >> Best rule #405 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01wy5m; 01nms7; 01bh6y; >> query: (?x10363, 0fr0t) <- nationality(?x10363, ?x94), actor(?x5936, ?x10363), nominated_for(?x535, ?x5936), genre(?x5936, ?x53) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #1266 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 030hcs; 0f6_x; 01gn36; 0143wl; 03q43g; 022s1m; *> query: (?x10363, 03gh4) <- nationality(?x10363, ?x94), student(?x3136, ?x10363), profession(?x10363, ?x987), language(?x10363, ?x254) *> conf = 0.02 ranks of expected_values: 5 EVAL 05j0wc location_of_ceremony 03gh4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 121.000 121.000 0.056 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #8643-02zd460 PRED entity: 02zd460 PRED relation: contains! PRED expected values: 01jr6 => 134 concepts (114 used for prediction) PRED predicted values (max 10 best out of 341): 01jr6 (0.68 #71397, 0.58 #29447, 0.47 #99073), 02qkt (0.35 #11052, 0.34 #66380, 0.31 #69060), 059rby (0.27 #8942, 0.13 #37498, 0.12 #26789), 02jx1 (0.25 #1870, 0.17 #51844, 0.13 #8115), 0dg3n1 (0.21 #66189, 0.19 #68869, 0.19 #69763), 02_286 (0.20 #8965, 0.07 #67864, 0.07 #8072), 02j9z (0.18 #10735, 0.14 #66063, 0.13 #69637), 0j0k (0.18 #66410, 0.16 #69984, 0.16 #69090), 02dtg (0.17 #3599, 0.03 #37509, 0.02 #24122), 05k7sb (0.14 #6377, 0.13 #8161, 0.12 #9946) >> Best rule #71397 for best value: >> intensional similarity = 3 >> extensional distance = 269 >> proper extension: 02jx_v; >> query: (?x5288, ?x3976) <- category(?x5288, ?x134), citytown(?x5288, ?x3976), currency(?x5288, ?x170) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zd460 contains! 01jr6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 114.000 0.681 http://example.org/location/location/contains #8642-02fqxm PRED entity: 02fqxm PRED relation: production_companies PRED expected values: 03sb38 => 72 concepts (53 used for prediction) PRED predicted values (max 10 best out of 64): 024rgt (0.25 #24, 0.06 #271, 0.05 #601), 0kx4m (0.25 #9, 0.04 #914, 0.04 #1079), 02jd_7 (0.25 #69, 0.03 #151, 0.03 #563), 05qd_ (0.15 #587, 0.13 #174, 0.12 #421), 086k8 (0.15 #413, 0.13 #579, 0.10 #743), 016tw3 (0.11 #589, 0.09 #671, 0.09 #94), 0g1rw (0.10 #255, 0.08 #1244, 0.07 #337), 016tt2 (0.08 #1240, 0.08 #415, 0.07 #909), 054lpb6 (0.08 #592, 0.06 #1665, 0.06 #1995), 017s11 (0.08 #662, 0.08 #1239, 0.07 #2479) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0hv8w; >> query: (?x12720, 024rgt) <- nominated_for(?x154, ?x12720), genre(?x12720, ?x53), featured_film_locations(?x12720, ?x10937), ?x10937 = 0r771 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1373 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 296 *> proper extension: 0gj9qxr; 01cjhz; 08cx5g; 0jq2r; 06f0k; *> query: (?x12720, 03sb38) <- titles(?x812, ?x12720), titles(?x812, ?x10799), titles(?x812, ?x5067), film_crew_role(?x10799, ?x468), ?x5067 = 01rwpj *> conf = 0.05 ranks of expected_values: 18 EVAL 02fqxm production_companies 03sb38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 72.000 53.000 0.250 http://example.org/film/film/production_companies #8641-06rgq PRED entity: 06rgq PRED relation: instrumentalists! PRED expected values: 018vs => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 115): 018vs (0.34 #2589, 0.30 #3922, 0.30 #3673), 03gvt (0.31 #2244, 0.26 #3997, 0.26 #4166), 0979zs (0.31 #2244, 0.26 #3997, 0.26 #4166), 05842k (0.31 #2244, 0.26 #3997, 0.26 #4166), 01vj9c (0.31 #2244, 0.26 #3997, 0.26 #4166), 02hnl (0.18 #2609, 0.17 #3693, 0.16 #3942), 018j2 (0.16 #533, 0.13 #865, 0.09 #2613), 0l14md (0.15 #505, 0.13 #1253, 0.13 #173), 0l14qv (0.14 #669, 0.12 #1417, 0.11 #1251), 026t6 (0.13 #169, 0.12 #335, 0.11 #4672) >> Best rule #2589 for best value: >> intensional similarity = 3 >> extensional distance = 295 >> proper extension: 07_3qd; 04mx7s; >> query: (?x8490, 018vs) <- category(?x8490, ?x134), instrumentalists(?x227, ?x8490), ?x227 = 0342h >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06rgq instrumentalists! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.340 http://example.org/music/instrument/instrumentalists #8640-0gfh84d PRED entity: 0gfh84d PRED relation: film_release_region PRED expected values: 03rt9 0f8l9c 02vzc 03spz => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 103): 0f8l9c (0.93 #1071, 0.91 #1371, 0.91 #1521), 0345h (0.86 #931, 0.86 #630, 0.85 #480), 02vzc (0.84 #1248, 0.84 #948, 0.82 #1398), 0b90_r (0.82 #603, 0.79 #453, 0.77 #904), 03_3d (0.79 #1506, 0.79 #906, 0.79 #1356), 06t2t (0.78 #657, 0.75 #808, 0.74 #507), 03spz (0.76 #690, 0.71 #540, 0.71 #841), 03rt9 (0.74 #763, 0.74 #462, 0.73 #913), 05v8c (0.69 #614, 0.62 #464, 0.62 #915), 03rk0 (0.62 #652, 0.49 #502, 0.49 #953) >> Best rule #1071 for best value: >> intensional similarity = 6 >> extensional distance = 176 >> proper extension: 0gkz15s; 0c0nhgv; 0dgst_d; 07g_0c; 02r1c18; 04n52p6; 0fpv_3_; 06wbm8q; 01shy7; 0fpmrm3; ... >> query: (?x6527, 0f8l9c) <- film(?x72, ?x6527), film_crew_role(?x6527, ?x137), film_release_region(?x6527, ?x1499), film_release_region(?x6527, ?x142), ?x142 = 0jgd, ?x1499 = 01znc_ >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 7, 8 EVAL 0gfh84d film_release_region 03spz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 72.000 72.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gfh84d film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gfh84d film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gfh84d film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 72.000 72.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8639-0cp0790 PRED entity: 0cp0790 PRED relation: film_crew_role PRED expected values: 0dxtw => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 28): 01vx2h (0.42 #78, 0.39 #146, 0.31 #725), 0dxtw (0.41 #77, 0.39 #145, 0.37 #724), 01pvkk (0.34 #113, 0.28 #726, 0.28 #1474), 02_n3z (0.29 #35, 0.13 #2385, 0.12 #2044), 0215hd (0.25 #119, 0.24 #273, 0.21 #51), 089fss (0.24 #273, 0.22 #141, 0.19 #73), 089g0h (0.24 #273, 0.21 #120, 0.14 #52), 01xy5l_ (0.24 #273, 0.16 #115, 0.14 #47), 0d2b38 (0.24 #273, 0.14 #58, 0.13 #126), 02vs3x5 (0.24 #273, 0.13 #2385, 0.12 #2044) >> Best rule #78 for best value: >> intensional similarity = 5 >> extensional distance = 97 >> proper extension: 0bvn25; 095zlp; 01h7bb; 01ln5z; 060v34; 01cssf; 0pc62; 04fzfj; 0_b3d; 09q5w2; ... >> query: (?x6981, 01vx2h) <- nominated_for(?x398, ?x6981), film_crew_role(?x6981, ?x3197), film_crew_role(?x6981, ?x1171), ?x3197 = 02ynfr, ?x1171 = 09vw2b7 >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #77 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 97 *> proper extension: 0bvn25; 095zlp; 01h7bb; 01ln5z; 060v34; 01cssf; 0pc62; 04fzfj; 0_b3d; 09q5w2; ... *> query: (?x6981, 0dxtw) <- nominated_for(?x398, ?x6981), film_crew_role(?x6981, ?x3197), film_crew_role(?x6981, ?x1171), ?x3197 = 02ynfr, ?x1171 = 09vw2b7 *> conf = 0.41 ranks of expected_values: 2 EVAL 0cp0790 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 79.000 0.424 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8638-023kzp PRED entity: 023kzp PRED relation: nominated_for PRED expected values: 0180mw => 115 concepts (59 used for prediction) PRED predicted values (max 10 best out of 468): 08r4x3 (0.65 #144, 0.29 #46947, 0.29 #25904), 029zqn (0.29 #46947, 0.29 #25904, 0.29 #1620), 06__m6 (0.29 #46947, 0.29 #25904, 0.29 #1620), 01jmyj (0.29 #46947, 0.29 #25904, 0.29 #1620), 0n1s0 (0.29 #46947, 0.29 #25904, 0.29 #1620), 07_fj54 (0.29 #46947, 0.29 #25904, 0.29 #1620), 04x4vj (0.29 #46947, 0.29 #25904, 0.29 #1620), 0gyfp9c (0.29 #46947, 0.29 #25904, 0.29 #1620), 0h7t36 (0.15 #84187, 0.02 #4753, 0.02 #6372), 095zlp (0.15 #84187, 0.02 #66374, 0.01 #8148) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 03_6y; >> query: (?x5925, 08r4x3) <- award_nominee(?x5834, ?x5925), film(?x5925, ?x1045), ?x5834 = 01z7s_ >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #9134 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 270 *> proper extension: 0hskw; *> query: (?x5925, 0180mw) <- nominated_for(?x5925, ?x2090), award_nominee(?x92, ?x5925), languages(?x5925, ?x254) *> conf = 0.02 ranks of expected_values: 256 EVAL 023kzp nominated_for 0180mw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 115.000 59.000 0.650 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #8637-011ypx PRED entity: 011ypx PRED relation: currency PRED expected values: 09nqf => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.86 #36, 0.85 #106, 0.85 #43), 01nv4h (0.15 #2, 0.04 #100, 0.04 #114), 02l6h (0.02 #214, 0.02 #179, 0.02 #200), 02gsvk (0.01 #300, 0.01 #426), 0ptk_ (0.01 #101) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0gjk1d; 0170xl; >> query: (?x5927, 09nqf) <- executive_produced_by(?x5927, ?x4060), nominated_for(?x2853, ?x5927), film(?x166, ?x5927), ?x2853 = 09qv_s >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011ypx currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.862 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8636-083q7 PRED entity: 083q7 PRED relation: people! PRED expected values: 02ctzb 063k3h => 193 concepts (189 used for prediction) PRED predicted values (max 10 best out of 55): 02ctzb (0.50 #320, 0.44 #2220, 0.42 #1156), 063k3h (0.42 #1171, 0.29 #2159, 0.25 #2387), 041rx (0.33 #689, 0.29 #5255, 0.29 #7937), 013b6_ (0.33 #737, 0.18 #965, 0.17 #1117), 03lmx1 (0.33 #167, 0.17 #395, 0.14 #547), 033tf_ (0.29 #464, 0.24 #1376, 0.20 #1224), 01qhm_ (0.25 #1451, 0.19 #2287, 0.18 #919), 0x67 (0.23 #10534, 0.19 #9162, 0.18 #9697), 013xrm (0.22 #705, 0.15 #2833, 0.11 #3061), 09vc4s (0.20 #770, 0.18 #846, 0.14 #466) >> Best rule #320 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03f5vvx; >> query: (?x1159, 02ctzb) <- basic_title(?x1159, ?x346), award_winner(?x8493, ?x1159), student(?x734, ?x1159), films(?x1159, ?x10614) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 083q7 people! 063k3h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 189.000 0.500 http://example.org/people/ethnicity/people EVAL 083q7 people! 02ctzb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 189.000 0.500 http://example.org/people/ethnicity/people #8635-0gxb2 PRED entity: 0gxb2 PRED relation: symptom_of PRED expected values: 025hl8 02psvcf 01_qc_ 09jg8 09969 0d19y2 0h1wz 024c2 => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 77): 0d19y2 (0.78 #370, 0.70 #535, 0.67 #288), 09jg8 (0.70 #442, 0.67 #361, 0.67 #279), 01gkcc (0.60 #237, 0.50 #278, 0.39 #611), 09d11 (0.60 #228, 0.50 #269, 0.39 #611), 02k6hp (0.50 #486, 0.50 #193, 0.40 #240), 01dcqj (0.50 #176, 0.50 #136, 0.33 #381), 025hl8 (0.50 #172, 0.50 #132, 0.33 #342), 074m2 (0.50 #276, 0.44 #358, 0.40 #523), 07s4l (0.50 #290, 0.44 #372, 0.40 #537), 07jwr (0.50 #173, 0.40 #424, 0.40 #220) >> Best rule #370 for best value: >> intensional similarity = 19 >> extensional distance = 7 >> proper extension: 0brgy; >> query: (?x9509, 0d19y2) <- symptom_of(?x9509, ?x10480), symptom_of(?x9509, ?x4322), people(?x4322, ?x11030), people(?x4322, ?x10562), people(?x4322, ?x8473), people(?x4322, ?x6369), people(?x4322, ?x5438), people(?x4322, ?x1645), ?x10480 = 0h1n9, location(?x5438, ?x3125), profession(?x10562, ?x353), award_nominee(?x382, ?x6369), religion(?x1645, ?x2694), award(?x5438, ?x1307), produced_by(?x9993, ?x11030), ?x353 = 0cbd2, award_winner(?x458, ?x8473), location(?x1645, ?x12866), nominated_for(?x6369, ?x2370) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 7, 16, 21, 22, 23, 26 EVAL 0gxb2 symptom_of 024c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 20.000 20.000 0.778 http://example.org/medicine/symptom/symptom_of EVAL 0gxb2 symptom_of 0h1wz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 20.000 20.000 0.778 http://example.org/medicine/symptom/symptom_of EVAL 0gxb2 symptom_of 0d19y2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 20.000 20.000 0.778 http://example.org/medicine/symptom/symptom_of EVAL 0gxb2 symptom_of 09969 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 20.000 20.000 0.778 http://example.org/medicine/symptom/symptom_of EVAL 0gxb2 symptom_of 09jg8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 20.000 20.000 0.778 http://example.org/medicine/symptom/symptom_of EVAL 0gxb2 symptom_of 01_qc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 20.000 20.000 0.778 http://example.org/medicine/symptom/symptom_of EVAL 0gxb2 symptom_of 02psvcf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 20.000 20.000 0.778 http://example.org/medicine/symptom/symptom_of EVAL 0gxb2 symptom_of 025hl8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 20.000 20.000 0.778 http://example.org/medicine/symptom/symptom_of #8634-02x17s4 PRED entity: 02x17s4 PRED relation: ceremony PRED expected values: 0fqpc7d 09gkdln => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 136): 02yxh9 (0.50 #777, 0.36 #913, 0.33 #369), 050yyb (0.50 #717, 0.36 #853, 0.33 #309), 02jp5r (0.50 #747, 0.36 #883, 0.33 #339), 0n8_m93 (0.50 #794, 0.36 #930, 0.33 #386), 02yvhx (0.50 #754, 0.36 #890, 0.33 #346), 0bzm81 (0.50 #701, 0.36 #837, 0.33 #293), 0bvfqq (0.50 #712, 0.36 #848, 0.33 #304), 02hn5v (0.50 #721, 0.36 #857, 0.33 #313), 02yw5r (0.50 #691, 0.36 #827, 0.33 #283), 04110lv (0.50 #786, 0.36 #922, 0.33 #378) >> Best rule #777 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 027dtxw; 09sb52; 04dn09n; 0gs9p; 0k611; 0gqy2; >> query: (?x2341, 02yxh9) <- award(?x1199, ?x2341), nominated_for(?x2341, ?x5991), nominated_for(?x2341, ?x167), award_winner(?x5991, ?x5351), ?x167 = 083shs, ceremony(?x2341, ?x762) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4494 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 339 *> proper extension: 02q3s; *> query: (?x2341, ?x1442) <- award_winner(?x2341, ?x8087), award_winner(?x2341, ?x3751), profession(?x3751, ?x319), award_winner(?x1442, ?x3751), award_nominee(?x844, ?x8087) *> conf = 0.27 ranks of expected_values: 85, 99 EVAL 02x17s4 ceremony 09gkdln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 48.000 48.000 0.500 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02x17s4 ceremony 0fqpc7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 48.000 48.000 0.500 http://example.org/award/award_category/winners./award/award_honor/ceremony #8633-016tvq PRED entity: 016tvq PRED relation: genre PRED expected values: 01z4y => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 88): 01z4y (0.82 #434, 0.81 #351, 0.80 #268), 07s9rl0 (0.63 #3161, 0.59 #1995, 0.53 #1331), 01t_vv (0.36 #201, 0.31 #1198, 0.29 #1032), 06n90 (0.29 #1509, 0.20 #3257, 0.18 #3173), 06nbt (0.25 #354, 0.23 #271, 0.21 #437), 0hcr (0.23 #3263, 0.22 #1515, 0.22 #1183), 01z77k (0.20 #112, 0.12 #3023, 0.11 #2607), 0pr6f (0.20 #50, 0.12 #2462, 0.11 #2878), 0vgkd (0.19 #343, 0.19 #1174, 0.16 #925), 01hmnh (0.19 #1512, 0.18 #3176, 0.15 #3260) >> Best rule #434 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x8536, 01z4y) <- genre(?x8536, ?x8534), genre(?x8536, ?x258), program(?x6678, ?x8536), ?x8534 = 0c4xc, ?x258 = 05p553 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016tvq genre 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.821 http://example.org/tv/tv_program/genre #8632-043tvp3 PRED entity: 043tvp3 PRED relation: film_crew_role PRED expected values: 0dxtw => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 27): 0dxtw (0.59 #241, 0.51 #444, 0.41 #946), 0d2b38 (0.36 #77, 0.19 #252, 0.17 #106), 015h31 (0.27 #65, 0.19 #240, 0.11 #443), 0215hd (0.27 #71, 0.16 #246, 0.15 #449), 089g0h (0.27 #72, 0.14 #450, 0.13 #334), 01xy5l_ (0.25 #97, 0.19 #243, 0.18 #68), 089fss (0.22 #34, 0.17 #121, 0.11 #209), 033smt (0.18 #79, 0.09 #2173, 0.09 #137), 04pyp5 (0.11 #40, 0.11 #11, 0.10 #331), 02_n3z (0.11 #1, 0.10 #437, 0.09 #59) >> Best rule #241 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 02qyv3h; >> query: (?x6882, 0dxtw) <- film(?x2531, ?x6882), country(?x6882, ?x205), film_crew_role(?x6882, ?x2091), film_release_region(?x6882, ?x87), ?x2091 = 02rh1dz >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043tvp3 film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.589 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8631-0bymv PRED entity: 0bymv PRED relation: legislative_sessions PRED expected values: 024tcq => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 35): 024tcq (0.80 #222, 0.58 #257, 0.46 #292), 032ft5 (0.60 #216, 0.42 #666, 0.33 #251), 03ww_x (0.60 #214, 0.42 #666, 0.33 #249), 04h1rz (0.50 #234, 0.42 #666, 0.25 #59), 05l2z4 (0.50 #213, 0.42 #666, 0.25 #38), 0495ys (0.50 #212, 0.42 #666, 0.25 #37), 03tcbx (0.42 #666, 0.40 #220, 0.25 #255), 04gp1d (0.42 #666, 0.40 #224, 0.25 #49), 060ny2 (0.42 #666, 0.40 #233, 0.25 #58), 03z5xd (0.42 #666, 0.40 #218, 0.20 #78) >> Best rule #222 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 06bss; 0226cw; 02hy5d; >> query: (?x2357, 024tcq) <- legislative_sessions(?x2357, ?x4821), nationality(?x2357, ?x94), ?x4821 = 02bqm0, profession(?x2357, ?x353) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bymv legislative_sessions 024tcq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.800 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #8630-01nczg PRED entity: 01nczg PRED relation: type_of_union PRED expected values: 04ztj => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.79 #13, 0.78 #41, 0.78 #21), 01g63y (0.26 #6, 0.25 #421, 0.19 #14), 01bl8s (0.25 #421, 0.02 #11, 0.02 #43) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 05slvm; 01l1ls; 01rnpy; 06r3p2; >> query: (?x1594, 04ztj) <- award(?x1594, ?x5455), ?x5455 = 0bb57s, profession(?x1594, ?x524) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nczg type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.788 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8629-08cfr1 PRED entity: 08cfr1 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.81 #202, 0.81 #104, 0.80 #141), 07c52 (0.22 #279, 0.20 #58, 0.05 #39), 07z4p (0.22 #279, 0.20 #58, 0.04 #103), 02nxhr (0.22 #279, 0.20 #58, 0.03 #105) >> Best rule #202 for best value: >> intensional similarity = 7 >> extensional distance = 910 >> proper extension: 0gtsx8c; 0gh8zks; 07kb7vh; 0hgnl3t; >> query: (?x6924, 029j_) <- production_companies(?x6924, ?x1104), film(?x4168, ?x6924), language(?x6924, ?x254), ?x254 = 02h40lc, nominated_for(?x4168, ?x6004), award_nominee(?x2716, ?x4168), genre(?x6004, ?x53) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08cfr1 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.810 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #8628-048htn PRED entity: 048htn PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 6): 029j_ (0.85 #16, 0.84 #51, 0.84 #56), 02nxhr (0.15 #439, 0.04 #92, 0.04 #57), 07c52 (0.15 #439, 0.03 #78, 0.03 #150), 0735l (0.15 #439), 0dq6p (0.15 #439), 07z4p (0.03 #80, 0.03 #85, 0.03 #95) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 01hqhm; 0ddjy; 01gkp1; 011yn5; 0yx_w; 0d87hc; >> query: (?x2571, 029j_) <- award_winner(?x2571, ?x6702), titles(?x812, ?x2571), executive_produced_by(?x2571, ?x4854), featured_film_locations(?x2571, ?x739) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 048htn film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.848 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #8627-07ssc PRED entity: 07ssc PRED relation: region! PRED expected values: 0dsvzh 0k2sk 050f0s 03nm_fh 0d4htf 06fqlk 08zrbl 0315rp 09p5mwg 01sbv9 => 167 concepts (70 used for prediction) PRED predicted values (max 10 best out of 136): 03g90h (0.33 #1, 0.25 #89, 0.21 #114), 0fqt1ns (0.33 #4, 0.20 #49, 0.17 #92), 07jnt (0.33 #6, 0.20 #51, 0.17 #57), 0dtfn (0.33 #3, 0.20 #48, 0.17 #54), 0f4_2k (0.07 #118), 011yxg (0.07 #115), 0286hyp (0.03 #70), 02yy9r (0.03 #70), 02rtqvb (0.03 #70), 06f0k (0.03 #70) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09c7w0; >> query: (?x512, 03g90h) <- country(?x8377, ?x512), ?x8377 = 0ds2l81, region(?x54, ?x512) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07ssc region! 01sbv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 70.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region EVAL 07ssc region! 09p5mwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 70.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region EVAL 07ssc region! 0315rp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 70.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region EVAL 07ssc region! 08zrbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 70.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region EVAL 07ssc region! 06fqlk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 70.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region EVAL 07ssc region! 0d4htf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 70.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region EVAL 07ssc region! 03nm_fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 70.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region EVAL 07ssc region! 050f0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 70.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region EVAL 07ssc region! 0k2sk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 70.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region EVAL 07ssc region! 0dsvzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 70.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #8626-04093 PRED entity: 04093 PRED relation: people! PRED expected values: 0c58k => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 36): 02y0js (0.09 #2, 0.07 #134, 0.06 #596), 0kh3 (0.09 #18, 0.05 #84), 0gk4g (0.09 #604, 0.08 #274, 0.08 #935), 0dq9p (0.06 #1339, 0.06 #1735, 0.05 #83), 02k6hp (0.05 #301, 0.04 #169, 0.04 #631), 01mtqf (0.05 #70, 0.01 #863, 0.01 #334), 01dcqj (0.05 #78), 07jwr (0.05 #603, 0.05 #934, 0.03 #1133), 06z5s (0.04 #157, 0.04 #421, 0.04 #619), 01psyx (0.04 #177, 0.04 #309, 0.03 #838) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0dj5q; >> query: (?x8699, 02y0js) <- place_of_birth(?x8699, ?x14322), people(?x6734, ?x8699), ?x6734 = 03ts0c, gender(?x8699, ?x231) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04093 people! 0c58k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 96.000 0.091 http://example.org/people/cause_of_death/people #8625-087_wh PRED entity: 087_wh PRED relation: people! PRED expected values: 0dryh9k => 139 concepts (121 used for prediction) PRED predicted values (max 10 best out of 47): 0dryh9k (0.54 #478, 0.48 #324, 0.46 #1172), 041rx (0.19 #698, 0.17 #1083, 0.17 #2934), 01rv7x (0.13 #964, 0.12 #347, 0.11 #810), 07bch9 (0.13 #1410, 0.10 #1256, 0.10 #1025), 02sch9 (0.12 #652, 0.11 #497, 0.09 #1191), 033tf_ (0.11 #4247, 0.10 #4170, 0.10 #2088), 0x67 (0.11 #1397, 0.10 #1012, 0.09 #7562), 02w7gg (0.10 #7708, 0.10 #7785, 0.09 #2083), 07hwkr (0.09 #2093, 0.09 #2711, 0.07 #4175), 04mvp8 (0.09 #67, 0.08 #144, 0.07 #1223) >> Best rule #478 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 06gn7r; >> query: (?x7867, 0dryh9k) <- location(?x7867, ?x3411), profession(?x7867, ?x1032), languages(?x7867, ?x1882), ?x1882 = 03k50 >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087_wh people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 121.000 0.536 http://example.org/people/ethnicity/people #8624-06mz5 PRED entity: 06mz5 PRED relation: religion PRED expected values: 04pk9 05w5d => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 25): 04pk9 (0.79 #120, 0.78 #93, 0.71 #228), 05w5d (0.76 #124, 0.71 #232, 0.68 #259), 021_0p (0.60 #271, 0.60 #119, 0.59 #92), 03_gx (0.60 #271, 0.47 #61, 0.44 #223), 0flw86 (0.60 #271, 0.43 #1137, 0.40 #1462), 058x5 (0.60 #271, 0.43 #1137, 0.40 #1462), 02t7t (0.60 #271, 0.43 #1137, 0.40 #1462), 092bf5 (0.60 #271, 0.43 #1137, 0.40 #1462), 03j6c (0.10 #67, 0.09 #798, 0.09 #852), 0kpl (0.05 #58, 0.04 #4, 0.03 #572) >> Best rule #120 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 03gh4; >> query: (?x1351, 04pk9) <- district_represented(?x3540, ?x1351), ?x3540 = 024tcq, state(?x11163, ?x1351) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 06mz5 religion 05w5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.786 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 06mz5 religion 04pk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.786 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #8623-013nky PRED entity: 013nky PRED relation: colors PRED expected values: 083jv => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 21): 083jv (0.35 #1137, 0.35 #170, 0.33 #233), 01g5v (0.26 #382, 0.25 #508, 0.24 #592), 019sc (0.25 #29, 0.20 #71, 0.16 #596), 0jc_p (0.25 #26, 0.20 #68, 0.13 #194), 01l849 (0.22 #1451, 0.21 #863, 0.21 #989), 06fvc (0.14 #591, 0.14 #907, 0.14 #213), 036k5h (0.12 #111, 0.11 #195, 0.09 #489), 03wkwg (0.12 #121, 0.10 #268, 0.09 #289), 04mkbj (0.11 #95, 0.09 #662, 0.09 #347), 01jnf1 (0.11 #96, 0.06 #117, 0.05 #138) >> Best rule #1137 for best value: >> intensional similarity = 6 >> extensional distance = 215 >> proper extension: 02htv6; >> query: (?x10197, 083jv) <- student(?x10197, ?x7583), student(?x10197, ?x3476), gender(?x3476, ?x231), location(?x3476, ?x1310), currency(?x10197, ?x1099), award_nominee(?x236, ?x7583) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013nky colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.350 http://example.org/education/educational_institution/colors #8622-07bxhl PRED entity: 07bxhl PRED relation: country! PRED expected values: 0w0d => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 55): 0bynt (0.92 #285, 0.86 #2980, 0.85 #1825), 06z6r (0.83 #196, 0.79 #306, 0.78 #581), 03fyrh (0.71 #303, 0.50 #28, 0.42 #468), 0486tv (0.70 #40, 0.62 #315, 0.50 #205), 03_8r (0.67 #1837, 0.66 #2882, 0.66 #1507), 01z27 (0.67 #292, 0.42 #182, 0.39 #457), 064vjs (0.62 #307, 0.58 #472, 0.54 #582), 03hr1p (0.62 #298, 0.51 #573, 0.48 #463), 07jbh (0.60 #34, 0.58 #309, 0.58 #199), 0w0d (0.60 #12, 0.54 #177, 0.46 #287) >> Best rule #285 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 09c7w0; 0jgd; 0d060g; 06npd; 06mzp; 0f8l9c; 03gj2; 0345h; 0h7x; 07t21; ... >> query: (?x3550, 0bynt) <- adjoins(?x2146, ?x3550), currency(?x3550, ?x170), partially_contains(?x3550, ?x8666) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 015zxh; *> query: (?x3550, 0w0d) <- adjoins(?x2346, ?x3550), location_of_ceremony(?x566, ?x3550), split_to(?x2346, ?x4271) *> conf = 0.60 ranks of expected_values: 10 EVAL 07bxhl country! 0w0d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 107.000 107.000 0.917 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #8621-0g5ff PRED entity: 0g5ff PRED relation: influenced_by! PRED expected values: 04x56 => 126 concepts (48 used for prediction) PRED predicted values (max 10 best out of 494): 0j0pf (0.50 #1209, 0.33 #2217, 0.30 #2722), 034bs (0.33 #149, 0.25 #653, 0.08 #18680), 06hmd (0.33 #215, 0.25 #719, 0.08 #18680), 041mt (0.33 #74, 0.25 #578, 0.05 #5118), 0gd5z (0.33 #84, 0.25 #588, 0.04 #17249), 01xwv7 (0.25 #921, 0.07 #12025, 0.06 #7990), 03hpr (0.17 #1416, 0.14 #1920, 0.13 #21208), 0gd_s (0.17 #1380, 0.14 #1884, 0.13 #21208), 01dhmw (0.17 #1126, 0.14 #1630, 0.13 #21208), 01zkxv (0.17 #1024, 0.14 #1528, 0.12 #4050) >> Best rule #1209 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 09dt7; 014ps4; 0gd_s; 05qzv; >> query: (?x6055, 0j0pf) <- award_winner(?x4879, ?x6055), influenced_by(?x1752, ?x6055), award(?x6055, ?x575), ?x1752 = 01dzz7 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #18680 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 259 *> proper extension: 07yg2; 05xq9; 0k57l; 0qmny; *> query: (?x6055, ?x3542) <- influenced_by(?x13125, ?x6055), influenced_by(?x476, ?x6055), award_winner(?x575, ?x476), influenced_by(?x2934, ?x476), influenced_by(?x13125, ?x3542) *> conf = 0.08 ranks of expected_values: 37 EVAL 0g5ff influenced_by! 04x56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 126.000 48.000 0.500 http://example.org/influence/influence_node/influenced_by #8620-01wz_ml PRED entity: 01wz_ml PRED relation: nationality PRED expected values: 09c7w0 => 128 concepts (95 used for prediction) PRED predicted values (max 10 best out of 31): 09c7w0 (0.78 #1003, 0.76 #1203, 0.75 #4613), 0ndh6 (0.39 #8933, 0.36 #5716, 0.34 #9542), 04ych (0.39 #8933, 0.36 #5716, 0.34 #9542), 02jx1 (0.21 #1335, 0.16 #2939, 0.15 #3341), 07ssc (0.16 #1117, 0.15 #515, 0.13 #4027), 0345h (0.10 #631, 0.06 #4043, 0.06 #4143), 03rk0 (0.09 #2351, 0.08 #6566, 0.08 #1649), 0d060g (0.05 #1610, 0.05 #2112, 0.05 #909), 0f8l9c (0.05 #4134, 0.04 #2828, 0.04 #4034), 0h7x (0.03 #4147, 0.03 #4047, 0.03 #4748) >> Best rule #1003 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 0f2zc; >> query: (?x3401, 09c7w0) <- gender(?x3401, ?x231), location(?x3401, ?x4356), ?x231 = 05zppz, inductee(?x1091, ?x3401) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wz_ml nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 95.000 0.785 http://example.org/people/person/nationality #8619-0170pk PRED entity: 0170pk PRED relation: award_winner! PRED expected values: 09cm54 => 93 concepts (72 used for prediction) PRED predicted values (max 10 best out of 184): 0bdwqv (0.40 #7604, 0.37 #10139, 0.37 #10562), 0gqy2 (0.40 #7604, 0.37 #10139, 0.37 #10562), 02w9sd7 (0.40 #7604, 0.37 #10139, 0.37 #10562), 09sdmz (0.40 #7604, 0.37 #10139, 0.37 #10562), 099jhq (0.40 #7604, 0.37 #10139, 0.37 #10562), 0279c15 (0.40 #7604, 0.37 #10139, 0.37 #10562), 057xs89 (0.40 #7604, 0.37 #10139, 0.37 #10562), 0789_m (0.40 #7604, 0.37 #10139, 0.37 #10562), 02y_rq5 (0.33 #90, 0.05 #30431, 0.05 #29586), 02x4x18 (0.33 #127, 0.05 #30431, 0.05 #29586) >> Best rule #7604 for best value: >> intensional similarity = 3 >> extensional distance = 1229 >> proper extension: 0khth; 014l4w; 07mvp; 04k05; 014g91; 07k2d; >> query: (?x1738, ?x112) <- award(?x1738, ?x112), award_winner(?x834, ?x1738), award_winner(?x989, ?x1738) >> conf = 0.40 => this is the best rule for 8 predicted values *> Best rule #514 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 118 *> proper extension: 0h1_w; 03ds3; 0f0p0; 0b_fw; 0gr36; 09qh1; 04n_g; 044qx; 015qt5; 015wfg; ... *> query: (?x1738, 09cm54) <- award(?x1738, ?x591), type_of_union(?x1738, ?x566), ?x591 = 0f4x7 *> conf = 0.13 ranks of expected_values: 13 EVAL 0170pk award_winner! 09cm54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 93.000 72.000 0.397 http://example.org/award/award_category/winners./award/award_honor/award_winner #8618-01wv9xn PRED entity: 01wv9xn PRED relation: group! PRED expected values: 032t2z => 117 concepts (50 used for prediction) PRED predicted values (max 10 best out of 197): 01gx5f (0.25 #61, 0.17 #654, 0.08 #1840), 01w02sy (0.25 #53, 0.17 #646, 0.08 #1832), 0k1bs (0.25 #1104, 0.11 #2688, 0.06 #3477), 016wvy (0.20 #572, 0.17 #769, 0.08 #1955), 03f0fnk (0.20 #484, 0.17 #681, 0.08 #1867), 025xt8y (0.20 #409, 0.11 #1201, 0.08 #1792), 023322 (0.17 #770, 0.11 #1365, 0.08 #1956), 06gd4 (0.17 #665, 0.08 #1851, 0.03 #3831), 023slg (0.17 #785, 0.08 #1971, 0.03 #3951), 01vrnsk (0.16 #2696, 0.03 #7458, 0.03 #8253) >> Best rule #61 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 01vsqvs; >> query: (?x1684, 01gx5f) <- origin(?x1684, ?x3301), artists(?x11746, ?x1684), artists(?x1000, ?x1684), ?x11746 = 03w94xt, artists(?x1000, ?x3930), ?x3930 = 01svw8n >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wv9xn group! 032t2z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 50.000 0.250 http://example.org/music/group_member/membership./music/group_membership/group #8617-01fszq PRED entity: 01fszq PRED relation: country_of_origin PRED expected values: 09c7w0 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 7): 09c7w0 (0.93 #45, 0.92 #89, 0.91 #23), 07ssc (0.11 #446, 0.10 #424, 0.10 #308), 03_3d (0.09 #359, 0.08 #418, 0.08 #473), 0d060g (0.04 #349, 0.03 #37, 0.03 #248), 02jx1 (0.02 #44, 0.02 #310, 0.02 #356), 05v8c (0.02 #43, 0.01 #355, 0.01 #65), 03rjj (0.01 #46) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 0cpz4k; 099pks; 02xhwm; >> query: (?x10618, 09c7w0) <- actor(?x10618, ?x3868), languages(?x10618, ?x254), program(?x8590, ?x10618), producer_type(?x10618, ?x632) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fszq country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.928 http://example.org/tv/tv_program/country_of_origin #8616-013tcv PRED entity: 013tcv PRED relation: award_winner! PRED expected values: 02pqp12 => 141 concepts (119 used for prediction) PRED predicted values (max 10 best out of 300): 040njc (0.37 #35374, 0.37 #35373, 0.37 #30629), 02pqp12 (0.37 #35374, 0.37 #35373, 0.37 #30629), 027b9ly (0.37 #35374, 0.37 #35373, 0.37 #30629), 02x1dht (0.37 #35374, 0.37 #35373, 0.37 #30629), 03hl6lc (0.37 #35374, 0.37 #35373, 0.37 #30629), 02qyp19 (0.37 #35374, 0.37 #35373, 0.37 #30629), 02x17c2 (0.37 #35374, 0.37 #35373, 0.37 #30629), 0bm70b (0.37 #35374, 0.37 #35373, 0.37 #30629), 0gs9p (0.20 #5258, 0.18 #3531, 0.18 #12590), 019f4v (0.16 #5246, 0.15 #12578, 0.15 #3519) >> Best rule #35374 for best value: >> intensional similarity = 3 >> extensional distance = 1452 >> proper extension: 015qt5; 0h005; 01n44c; 087yty; 014l4w; 0250f; 0gm8_p; 015gsv; 0ckcvk; 016l09; ... >> query: (?x9281, ?x9766) <- award(?x9281, ?x9766), award_winner(?x9766, ?x767), award_winner(?x472, ?x9281) >> conf = 0.37 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 013tcv award_winner! 02pqp12 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 119.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #8615-03c9pqt PRED entity: 03c9pqt PRED relation: executive_produced_by! PRED expected values: 03bzyn4 => 85 concepts (17 used for prediction) PRED predicted values (max 10 best out of 239): 0fvr1 (0.33 #117), 01pj_5 (0.09 #2821, 0.03 #3338, 0.02 #4371), 0bt4g (0.09 #2984, 0.03 #4016, 0.02 #4534), 0mbql (0.09 #2944, 0.03 #3976, 0.02 #4494), 01f7kl (0.09 #2708, 0.03 #3740, 0.02 #4258), 0bt3j9 (0.07 #3092), 0gjcrrw (0.06 #2778, 0.04 #4126, 0.02 #3295), 01bn3l (0.06 #2991, 0.03 #8147, 0.02 #8662), 0gwjw0c (0.06 #2952, 0.02 #4502, 0.02 #5017), 0fsd9t (0.06 #3029, 0.02 #4579, 0.02 #5094) >> Best rule #117 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 026c1; >> query: (?x12790, 0fvr1) <- executive_produced_by(?x7563, ?x12790), executive_produced_by(?x7514, ?x12790), film_release_region(?x7514, ?x94), film(?x147, ?x7514), ?x7563 = 03bzjpm >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03c9pqt executive_produced_by! 03bzyn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 17.000 0.333 http://example.org/film/film/executive_produced_by #8614-01xllf PRED entity: 01xllf PRED relation: type_of_union PRED expected values: 04ztj => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.79 #29, 0.78 #25, 0.76 #81), 01g63y (0.57 #2, 0.25 #266, 0.25 #261), 01bl8s (0.25 #266, 0.25 #261) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 04t7ts; 0bwh6; 01vvb4m; 0dpqk; 02k4b2; 01qklj; 020jqv; >> query: (?x10126, 04ztj) <- profession(?x10126, ?x319), film(?x10126, ?x2102), film_distribution_medium(?x2102, ?x2099), special_performance_type(?x10126, ?x3558) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xllf type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.787 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8613-031n8c PRED entity: 031n8c PRED relation: student PRED expected values: 01p7b6b => 191 concepts (139 used for prediction) PRED predicted values (max 10 best out of 1440): 04kr63w (0.33 #956, 0.04 #17708, 0.01 #65873), 05m7zg (0.33 #2069, 0.04 #18821, 0.01 #81645), 01wk3c (0.33 #1831, 0.04 #18583, 0.01 #81407), 01vzxld (0.33 #1736, 0.04 #18488, 0.01 #81312), 018d6l (0.33 #1249, 0.04 #18001, 0.01 #80825), 08c9b0 (0.33 #852, 0.04 #17604, 0.01 #80428), 06mnps (0.33 #524, 0.04 #17276, 0.01 #80100), 01v9l67 (0.33 #434, 0.04 #17186, 0.01 #80010), 0clvcx (0.33 #221, 0.04 #16973, 0.01 #79797), 01my4f (0.22 #7479, 0.20 #9573, 0.10 #11667) >> Best rule #956 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 031ns1; >> query: (?x2038, 04kr63w) <- school_type(?x2038, ?x9240), ?x9240 = 01y64, company(?x346, ?x2038), ?x346 = 060c4 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 031n8c student 01p7b6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 191.000 139.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #8612-09y6pb PRED entity: 09y6pb PRED relation: genre PRED expected values: 01lrrt => 86 concepts (85 used for prediction) PRED predicted values (max 10 best out of 86): 02kdv5l (0.50 #2, 0.40 #120, 0.32 #1065), 04xvlr (0.35 #2362, 0.32 #3070, 0.18 #3424), 01jfsb (0.35 #1074, 0.33 #956, 0.33 #1310), 03k9fj (0.25 #10, 0.23 #482, 0.23 #5206), 0gf28 (0.25 #62, 0.20 #180, 0.10 #2541), 01hwc6 (0.25 #18, 0.20 #136, 0.02 #2143), 07s2s (0.25 #89, 0.20 #207, 0.01 #3394), 0lsxr (0.23 #1071, 0.22 #1307, 0.22 #1779), 060__y (0.22 #2376, 0.22 #3084, 0.16 #3438), 06cvj (0.22 #2128, 0.21 #2482, 0.20 #121) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01k1k4; 014kq6; >> query: (?x9379, 02kdv5l) <- nominated_for(?x2237, ?x9379), ?x2237 = 01vs_v8, film_crew_role(?x9379, ?x137), currency(?x9379, ?x170) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2411 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 518 *> proper extension: 01qn7n; 03y317; *> query: (?x9379, 01lrrt) <- titles(?x53, ?x9379), titles(?x53, ?x7243), ?x7243 = 0yzbg *> conf = 0.02 ranks of expected_values: 62 EVAL 09y6pb genre 01lrrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 86.000 85.000 0.500 http://example.org/film/film/genre #8611-02mc79 PRED entity: 02mc79 PRED relation: award PRED expected values: 05f4m9q => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 295): 040njc (0.50 #8, 0.34 #6440, 0.31 #6038), 05p1dby (0.44 #2115, 0.42 #507, 0.39 #2919), 0gq9h (0.44 #477, 0.32 #2889, 0.32 #3291), 0gs9p (0.39 #6509, 0.38 #77, 0.37 #6107), 019f4v (0.38 #64, 0.35 #6496, 0.33 #4888), 02pqp12 (0.38 #68, 0.22 #6500, 0.22 #4892), 0fbtbt (0.27 #3848, 0.17 #4250, 0.12 #230), 09sb52 (0.25 #9687, 0.25 #10089, 0.23 #18934), 0gr51 (0.25 #98, 0.25 #4922, 0.22 #6530), 03hkv_r (0.25 #16, 0.12 #4840, 0.11 #4438) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0kr5_; >> query: (?x8071, 040njc) <- profession(?x8071, ?x319), produced_by(?x4500, ?x8071), sibling(?x8897, ?x8071) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4837 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 169 *> proper extension: 03wpmd; 02645b; 07g7h2; 0gv2r; *> query: (?x8071, 05f4m9q) <- profession(?x8071, ?x319), film(?x8071, ?x8072), award_nominee(?x8071, ?x541) *> conf = 0.17 ranks of expected_values: 25 EVAL 02mc79 award 05f4m9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 95.000 95.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8610-015ppk PRED entity: 015ppk PRED relation: genre PRED expected values: 0c031k6 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 76): 05p553 (0.52 #414, 0.51 #496, 0.51 #1152), 01z4y (0.36 #509, 0.35 #2068, 0.35 #591), 0djd22 (0.33 #21, 0.25 #103, 0.13 #185), 04gm78f (0.33 #46, 0.25 #128, 0.13 #210), 0c4xc (0.29 #534, 0.28 #452, 0.27 #1190), 01htzx (0.25 #98, 0.20 #918, 0.19 #2067), 03k9fj (0.25 #92, 0.18 #3048, 0.17 #1978), 01jfsb (0.25 #93, 0.13 #175, 0.12 #257), 02n4kr (0.25 #90, 0.13 #172, 0.12 #254), 0hcr (0.22 #3630, 0.19 #3795, 0.19 #3056) >> Best rule #414 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 019nnl; 0n2bh; 03y3bp7; 01f3p_; 0557yqh; 02pqs8l; 09fc83; 028k2x; 08bytj; 03nymk; ... >> query: (?x7116, 05p553) <- nominated_for(?x8663, ?x7116), producer_type(?x7116, ?x632), film(?x8663, ?x394), program(?x1394, ?x7116) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #382 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 79 *> proper extension: 064q5v; *> query: (?x7116, 0c031k6) <- award(?x7116, ?x9640), award_winner(?x9640, ?x4035), award(?x6482, ?x9640), ?x6482 = 0180mw *> conf = 0.05 ranks of expected_values: 38 EVAL 015ppk genre 0c031k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 86.000 86.000 0.520 http://example.org/tv/tv_program/genre #8609-01n7q PRED entity: 01n7q PRED relation: place_of_birth! PRED expected values: 02f9wb => 182 concepts (151 used for prediction) PRED predicted values (max 10 best out of 1928): 02k21g (0.38 #5224, 0.34 #248138, 0.29 #49628), 02t__3 (0.38 #5224, 0.34 #248138, 0.29 #49628), 0c01c (0.38 #5224, 0.34 #248138, 0.29 #49628), 016z2j (0.38 #5224, 0.34 #248138, 0.29 #49628), 09889g (0.38 #5224, 0.34 #248138, 0.29 #49628), 02ts3h (0.38 #5224, 0.34 #248138, 0.29 #49628), 01_f_5 (0.38 #5224, 0.34 #248138, 0.29 #49628), 0164nb (0.38 #5224, 0.34 #248138, 0.29 #49628), 0mb0 (0.38 #5224, 0.34 #248138, 0.29 #49628), 0b0pf (0.38 #5224, 0.34 #248138, 0.29 #49628) >> Best rule #5224 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0cr3d; >> query: (?x1227, ?x397) <- location(?x3663, ?x1227), location(?x397, ?x1227), ?x3663 = 02yl42, contains(?x1227, ?x191) >> conf = 0.38 => this is the best rule for 54 predicted values *> Best rule #355248 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 291 *> proper extension: 02k54; 07tgn; 01mc11; 0fm2_; 03shp; 0c5_3; 04kf4; 013hxv; 0nvt9; 0fvwg; ... *> query: (?x1227, ?x460) <- contains(?x1227, ?x5288), major_field_of_study(?x5288, ?x254), student(?x5288, ?x460) *> conf = 0.03 ranks of expected_values: 993 EVAL 01n7q place_of_birth! 02f9wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 182.000 151.000 0.379 http://example.org/people/person/place_of_birth #8608-0978r PRED entity: 0978r PRED relation: contains! PRED expected values: 02jx1 => 196 concepts (127 used for prediction) PRED predicted values (max 10 best out of 317): 02jx1 (0.83 #1875, 0.70 #101978, 0.67 #981), 09c7w0 (0.65 #82300, 0.64 #16997, 0.63 #103769), 0chghy (0.64 #90347, 0.51 #71559, 0.06 #28644), 0978r (0.58 #1995, 0.52 #43830, 0.49 #74244), 02j9z (0.52 #43830, 0.49 #74244, 0.24 #96608), 04_1l0v (0.51 #40700, 0.40 #59484, 0.26 #19231), 01n7q (0.28 #71636, 0.22 #60903, 0.21 #42118), 03rjj (0.23 #63519, 0.06 #21475, 0.06 #11635), 02qkt (0.21 #97848, 0.20 #96056, 0.20 #110377), 04jpl (0.17 #27749, 0.09 #17910, 0.05 #71581) >> Best rule #1875 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 07tl0; 018m5q; 02hmw9; 013nky; 07tlg; 029rmn; >> query: (?x3301, 02jx1) <- contains(?x3302, ?x3301), contains(?x512, ?x3301), ?x3302 = 01w0v, country(?x124, ?x512) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0978r contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 127.000 0.833 http://example.org/location/location/contains #8607-0cskb PRED entity: 0cskb PRED relation: actor PRED expected values: 01g5kv => 93 concepts (33 used for prediction) PRED predicted values (max 10 best out of 651): 0c01c (0.50 #22214, 0.38 #18511, 0.36 #22213), 01f9mq (0.50 #22214, 0.38 #18511, 0.36 #22213), 04qt29 (0.25 #1615, 0.06 #4391, 0.05 #6241), 04x1_w (0.25 #577, 0.03 #3353, 0.02 #7054), 03zz8b (0.25 #1498, 0.03 #4274, 0.02 #6124), 035wq7 (0.25 #1767, 0.03 #4543, 0.02 #6393), 01jbx1 (0.25 #1189, 0.02 #7665, 0.02 #9515), 05w6cw (0.25 #644, 0.02 #8046, 0.02 #9896), 045w_4 (0.25 #375, 0.02 #7777, 0.02 #9627), 0315q3 (0.25 #374) >> Best rule #22214 for best value: >> intensional similarity = 4 >> extensional distance = 146 >> proper extension: 0bx_hnp; >> query: (?x9843, ?x11865) <- languages(?x9843, ?x254), nominated_for(?x11865, ?x9843), film(?x11865, ?x6422), film_release_region(?x6422, ?x94) >> conf = 0.50 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0cskb actor 01g5kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 33.000 0.501 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #8606-09nz_c PRED entity: 09nz_c PRED relation: nationality PRED expected values: 09c7w0 => 125 concepts (91 used for prediction) PRED predicted values (max 10 best out of 33): 09c7w0 (0.89 #601, 0.88 #801, 0.86 #1305), 02jx1 (0.16 #1938, 0.14 #3046, 0.12 #333), 03rjj (0.12 #205, 0.10 #405, 0.05 #5933), 0k6nt (0.12 #225, 0.10 #425, 0.05 #5933), 03rt9 (0.12 #313, 0.02 #3026, 0.02 #1617), 07ssc (0.12 #1920, 0.11 #915, 0.10 #3028), 0d060g (0.08 #907, 0.06 #7951, 0.05 #3221), 020d5 (0.08 #589), 0f8l9c (0.07 #922, 0.05 #5933, 0.04 #6337), 03rk0 (0.06 #4167, 0.06 #4067, 0.06 #3967) >> Best rule #601 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 012zng; 02lt8; 0gs5q; 018zvb; >> query: (?x9934, 09c7w0) <- location(?x9934, ?x1426), ?x1426 = 07z1m, gender(?x9934, ?x231) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09nz_c nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 91.000 0.889 http://example.org/people/person/nationality #8605-02h40lc PRED entity: 02h40lc PRED relation: languages_spoken! PRED expected values: 0dryh9k 06gbnc 022fdt 0g96wd => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 47): 07hwkr (0.75 #618, 0.71 #571, 0.71 #524), 02vsw1 (0.60 #408, 0.57 #549, 0.50 #643), 03295l (0.33 #107, 0.33 #13, 0.25 #953), 0fk1z (0.33 #46, 0.30 #845, 0.25 #986), 04gfy7 (0.33 #87, 0.29 #1733, 0.25 #369), 022fdt (0.33 #693, 0.29 #599, 0.25 #223), 01g7zj (0.33 #33, 0.20 #832, 0.17 #973), 04dbw3 (0.33 #17, 0.20 #816, 0.17 #957), 03ttfc (0.33 #6, 0.14 #570, 0.14 #523), 0dryh9k (0.33 #56, 0.11 #1138, 0.10 #1326) >> Best rule #618 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 05zjd; >> query: (?x254, 07hwkr) <- language(?x5020, ?x254), languages(?x50, ?x254), languages(?x118, ?x254), countries_spoken_in(?x254, ?x126), service_language(?x127, ?x254), film(?x4051, ?x5020) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #693 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 0jzc; *> query: (?x254, 022fdt) <- language(?x54, ?x254), major_field_of_study(?x1695, ?x254), service_language(?x127, ?x254), languages(?x12888, ?x254), official_language(?x183, ?x254), influenced_by(?x12888, ?x4072) *> conf = 0.33 ranks of expected_values: 6, 10, 38, 44 EVAL 02h40lc languages_spoken! 0g96wd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 72.000 72.000 0.750 http://example.org/people/ethnicity/languages_spoken EVAL 02h40lc languages_spoken! 022fdt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 72.000 72.000 0.750 http://example.org/people/ethnicity/languages_spoken EVAL 02h40lc languages_spoken! 06gbnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 72.000 72.000 0.750 http://example.org/people/ethnicity/languages_spoken EVAL 02h40lc languages_spoken! 0dryh9k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 72.000 72.000 0.750 http://example.org/people/ethnicity/languages_spoken #8604-013nky PRED entity: 013nky PRED relation: student PRED expected values: 09gnn => 158 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1627): 0h0wc (0.33 #393, 0.17 #6663, 0.12 #10843), 023jq1 (0.33 #3765, 0.17 #7945, 0.12 #12125), 01l79yc (0.33 #3175, 0.17 #7355, 0.12 #11535), 0134w7 (0.33 #2222, 0.17 #6402, 0.12 #10582), 02g3w (0.33 #1898, 0.07 #148444, 0.07 #146353), 0453t (0.33 #338, 0.07 #148444, 0.07 #146353), 05y5fw (0.33 #871, 0.07 #148444, 0.07 #146353), 02q4mt (0.33 #1948, 0.07 #148444, 0.07 #146353), 08ff1k (0.33 #941, 0.07 #148444, 0.07 #146353), 03pp73 (0.33 #887, 0.07 #148444, 0.07 #146353) >> Best rule #393 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02bqy; >> query: (?x10197, 0h0wc) <- contains(?x1310, ?x10197), student(?x10197, ?x7583), ?x7583 = 087qxp, category(?x10197, ?x134), institution(?x1368, ?x10197) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #14310 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 0ylsr; *> query: (?x10197, 09gnn) <- company(?x2998, ?x10197), ?x2998 = 021q0l, contains(?x1310, ?x10197), institution(?x1368, ?x10197), ?x1368 = 014mlp *> conf = 0.09 ranks of expected_values: 282 EVAL 013nky student 09gnn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 158.000 73.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #8603-0gffmn8 PRED entity: 0gffmn8 PRED relation: film_regional_debut_venue PRED expected values: 05qtj => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 12): 018cvf (0.10 #309, 0.09 #162, 0.08 #131), 0prpt (0.08 #320, 0.06 #142, 0.06 #173), 015hr (0.07 #307, 0.05 #99, 0.05 #129), 0kfhjq0 (0.05 #41, 0.04 #161, 0.04 #130), 0j63cyr (0.05 #39, 0.04 #98, 0.04 #128), 07751 (0.03 #95, 0.03 #303, 0.03 #125), 0gg7gsl (0.03 #155, 0.02 #94, 0.02 #124), 07zmj (0.03 #323, 0.02 #145, 0.01 #207), 02_286 (0.02 #298, 0.02 #31, 0.01 #61), 0h7h6 (0.02 #92, 0.02 #122, 0.01 #153) >> Best rule #309 for best value: >> intensional similarity = 3 >> extensional distance = 495 >> proper extension: 0bh72t; 015qy1; >> query: (?x3217, 018cvf) <- film_release_region(?x3217, ?x1497), administrative_parent(?x1497, ?x551), olympics(?x1497, ?x418) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gffmn8 film_regional_debut_venue 05qtj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 42.000 0.103 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #8602-0ds11z PRED entity: 0ds11z PRED relation: featured_film_locations PRED expected values: 04jpl => 122 concepts (98 used for prediction) PRED predicted values (max 10 best out of 91): 02_286 (0.50 #20, 0.20 #2188, 0.20 #1707), 052p7 (0.25 #58, 0.08 #298, 0.04 #780), 04jpl (0.17 #3139, 0.17 #3381, 0.07 #7728), 030qb3t (0.17 #279, 0.12 #39, 0.08 #6069), 080h2 (0.17 #264, 0.07 #1228, 0.04 #746), 03gh4 (0.12 #355, 0.02 #2283, 0.02 #837), 0rh6k (0.08 #241, 0.05 #723, 0.05 #964), 06y57 (0.04 #343, 0.03 #1066, 0.02 #2031), 01_d4 (0.04 #287, 0.03 #3661, 0.03 #2936), 035p3 (0.04 #473, 0.02 #955, 0.02 #7710) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0435vm; >> query: (?x485, 02_286) <- produced_by(?x485, ?x1533), film(?x4314, ?x485), genre(?x485, ?x53), ?x1533 = 05prs8 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3139 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 235 *> proper extension: 01kf3_9; 01kf4tt; *> query: (?x485, 04jpl) <- country(?x485, ?x512), nominated_for(?x143, ?x485), film(?x609, ?x485), ?x512 = 07ssc *> conf = 0.17 ranks of expected_values: 3 EVAL 0ds11z featured_film_locations 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 122.000 98.000 0.500 http://example.org/film/film/featured_film_locations #8601-050f0s PRED entity: 050f0s PRED relation: genre PRED expected values: 0hcr 0gf28 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 86): 07s9rl0 (0.65 #7091, 0.60 #4326, 0.59 #2643), 01z4y (0.54 #2642, 0.53 #4446, 0.52 #5287), 02kdv5l (0.33 #123, 0.30 #363, 0.30 #483), 01jfsb (0.32 #2173, 0.31 #2053, 0.30 #3255), 02l7c8 (0.29 #4341, 0.28 #2658, 0.28 #5182), 04xvlr (0.22 #2, 0.20 #242, 0.17 #4327), 0gf28 (0.22 #64, 0.07 #544, 0.07 #664), 0l4h_ (0.22 #73), 01hmnh (0.22 #378, 0.21 #138, 0.18 #2299), 06n90 (0.19 #133, 0.18 #373, 0.15 #493) >> Best rule #7091 for best value: >> intensional similarity = 3 >> extensional distance = 1544 >> proper extension: 0c0wvx; >> query: (?x1965, 07s9rl0) <- genre(?x1965, ?x258), genre(?x6924, ?x258), ?x6924 = 08cfr1 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #64 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 016z43; *> query: (?x1965, 0gf28) <- nominated_for(?x3629, ?x1965), film(?x806, ?x1965), location(?x3629, ?x3764), ?x806 = 03qd_ *> conf = 0.22 ranks of expected_values: 7, 30 EVAL 050f0s genre 0gf28 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 74.000 74.000 0.651 http://example.org/film/film/genre EVAL 050f0s genre 0hcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 74.000 74.000 0.651 http://example.org/film/film/genre #8600-014zcr PRED entity: 014zcr PRED relation: nominated_for PRED expected values: 02jkkv => 116 concepts (92 used for prediction) PRED predicted values (max 10 best out of 867): 078sj4 (0.79 #9667, 0.79 #40301, 0.78 #87074), 08s6mr (0.79 #9667, 0.79 #40301, 0.78 #87074), 0180mw (0.49 #77398, 0.05 #25209, 0.03 #76815), 05ch98 (0.37 #46754), 0418wg (0.34 #17729, 0.32 #11280, 0.30 #58044), 02704ff (0.34 #17729, 0.32 #11280, 0.30 #58044), 0bmhvpr (0.34 #17729, 0.32 #11280, 0.30 #58044), 011ysn (0.34 #17729, 0.32 #11280, 0.30 #58044), 01hvjx (0.34 #17729, 0.32 #11280, 0.30 #58044), 02rrfzf (0.34 #17729, 0.32 #11280, 0.30 #58044) >> Best rule #9667 for best value: >> intensional similarity = 2 >> extensional distance = 89 >> proper extension: 041h0; 0jrny; >> query: (?x286, ?x349) <- award_winner(?x349, ?x286), friend(?x2499, ?x286) >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #48368 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 391 *> proper extension: 01r42_g; 01my_c; *> query: (?x286, ?x3133) <- award_nominee(?x1537, ?x286), participant(?x1554, ?x286), film(?x1537, ?x3133) *> conf = 0.03 ranks of expected_values: 192 EVAL 014zcr nominated_for 02jkkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 116.000 92.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #8599-01mkq PRED entity: 01mkq PRED relation: major_field_of_study PRED expected values: 01540 => 54 concepts (46 used for prediction) PRED predicted values (max 10 best out of 123): 037mh8 (0.71 #964, 0.47 #1726, 0.41 #1809), 0g26h (0.50 #1028, 0.24 #1622, 0.18 #1506), 03g3w (0.43 #1442, 0.40 #767, 0.36 #1274), 01mkq (0.43 #925, 0.40 #758, 0.33 #258), 01lj9 (0.43 #943, 0.33 #276, 0.33 #194), 062z7 (0.43 #935, 0.33 #186, 0.33 #103), 0_jm (0.38 #1038, 0.33 #40, 0.29 #1632), 0fdys (0.33 #1196, 0.33 #110, 0.29 #1619), 0jjw (0.33 #272, 0.33 #107, 0.25 #438), 04306rv (0.33 #252, 0.29 #919, 0.25 #418) >> Best rule #964 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 05qjt; 03g3w; 02j62; >> query: (?x1668, 037mh8) <- major_field_of_study(?x10355, ?x1668), major_field_of_study(?x6925, ?x1668), major_field_of_study(?x3779, ?x1668), major_field_of_study(?x546, ?x1668), school_type(?x10355, ?x5931), institution(?x734, ?x10355), ?x6925 = 01bm_, currency(?x3779, ?x170), citytown(?x10355, ?x3301), ?x5931 = 02p0qmm, ?x546 = 01j_9c >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 05qfh; *> query: (?x1668, ?x1154) <- major_field_of_study(?x13080, ?x1668), major_field_of_study(?x10355, ?x1668), major_field_of_study(?x5280, ?x1668), major_field_of_study(?x5167, ?x1668), major_field_of_study(?x3948, ?x1668), major_field_of_study(?x1768, ?x1668), school_type(?x10355, ?x5931), ?x13080 = 01trxd, ?x5280 = 07vhb, colors(?x10355, ?x3621), organization(?x2361, ?x10355), institution(?x620, ?x5167), ?x3948 = 025v3k, major_field_of_study(?x1768, ?x1154) *> conf = 0.15 ranks of expected_values: 69 EVAL 01mkq major_field_of_study 01540 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 54.000 46.000 0.714 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #8598-0kc6x PRED entity: 0kc6x PRED relation: citytown PRED expected values: 0d6lp => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 271): 02_286 (0.75 #23149, 0.74 #12500, 0.65 #20577), 013yq (0.33 #42, 0.25 #409, 0.18 #40760), 059rby (0.27 #48842, 0.06 #47372, 0.05 #12491), 01n7q (0.27 #48842, 0.06 #47372, 0.01 #38556), 09c7w0 (0.27 #48842, 0.01 #38556), 0gx1l (0.27 #48842), 04jpl (0.18 #40760, 0.14 #31584, 0.13 #32318), 0k049 (0.18 #40760, 0.12 #2572, 0.10 #3675), 06_kh (0.18 #40760, 0.12 #2574, 0.03 #10286), 07dfk (0.18 #40760, 0.11 #31789, 0.11 #32523) >> Best rule #23149 for best value: >> intensional similarity = 5 >> extensional distance = 123 >> proper extension: 02301; 027kmrb; 024rdh; 04b_46; 03m9c8; 0dbpwb; 02vptk_; 01p7x7; 049ql1; 04_j5s; ... >> query: (?x234, 02_286) <- citytown(?x234, ?x1523), place_of_death(?x457, ?x1523), location(?x9207, ?x1523), ?x9207 = 023mdt, place_of_birth(?x338, ?x1523) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #4843 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 09d5h; 0g5lhl7; 03mnk; 045c7b; 01yfp7; 07ccs; 05njw; 0gy1_; *> query: (?x234, 0d6lp) <- company(?x233, ?x234), citytown(?x234, ?x1523), contact_category(?x234, ?x897), company(?x4451, ?x234) *> conf = 0.08 ranks of expected_values: 37 EVAL 0kc6x citytown 0d6lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 151.000 151.000 0.752 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #8597-03f7xg PRED entity: 03f7xg PRED relation: award PRED expected values: 02qrbbx => 107 concepts (81 used for prediction) PRED predicted values (max 10 best out of 191): 02qwdhq (0.58 #695, 0.58 #568, 0.34 #694), 02qsfzv (0.34 #694, 0.27 #12718, 0.25 #12486), 02g3v6 (0.34 #694, 0.27 #12718, 0.25 #12486), 0p9sw (0.33 #20, 0.15 #2333, 0.14 #715), 02rdyk7 (0.33 #302, 0.12 #12254, 0.10 #16655), 02r0csl (0.33 #5, 0.08 #700, 0.08 #467), 0gq_v (0.22 #1408, 0.21 #1870, 0.17 #714), 0l8z1 (0.21 #2365, 0.12 #12254, 0.10 #16655), 02qvyrt (0.17 #2409, 0.12 #12254, 0.10 #16655), 02qwzkm (0.17 #678, 0.17 #447, 0.05 #16192) >> Best rule #695 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 027r7k; >> query: (?x3306, ?x2599) <- nominated_for(?x1867, ?x3306), film(?x382, ?x3306), nominated_for(?x2599, ?x3306), ?x2599 = 02qwdhq >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #684 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 027r7k; *> query: (?x3306, 02qrbbx) <- nominated_for(?x1867, ?x3306), film(?x382, ?x3306), nominated_for(?x2599, ?x3306), ?x2599 = 02qwdhq *> conf = 0.17 ranks of expected_values: 11 EVAL 03f7xg award 02qrbbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 107.000 81.000 0.583 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8596-0krdk PRED entity: 0krdk PRED relation: company PRED expected values: 04qhdf 04htfd 018p5f 01pf21 0z07 03s7h => 35 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1362): 03s7h (0.77 #5600, 0.71 #5870, 0.71 #2903), 06py2 (0.67 #2672, 0.57 #2941, 0.50 #1325), 01w5m (0.67 #2213, 0.50 #866, 0.44 #3291), 01pf21 (0.50 #3133, 0.50 #2055, 0.50 #1787), 08z129 (0.50 #3027, 0.50 #2489, 0.50 #1681), 03y7ml (0.50 #2039, 0.50 #1771, 0.43 #2848), 03bnb (0.50 #2617, 0.50 #2077, 0.43 #2886), 018c_r (0.50 #1847, 0.38 #3193, 0.36 #3770), 0k8z (0.50 #2493, 0.36 #3770, 0.33 #606), 01xdn1 (0.50 #1931, 0.36 #3770, 0.33 #584) >> Best rule #5600 for best value: >> intensional similarity = 11 >> extensional distance = 11 >> proper extension: 02y6fz; 09lq2c; >> query: (?x1491, 03s7h) <- company(?x1491, ?x8931), company(?x1491, ?x7471), company(?x1491, ?x3379), company(?x1491, ?x1908), industry(?x1908, ?x2271), currency(?x7471, ?x170), child(?x1908, ?x382), list(?x7471, ?x5997), ?x5997 = 04k4rt, state_province_region(?x3379, ?x1767), service_location(?x8931, ?x94) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 14, 17, 19, 21 EVAL 0krdk company 03s7h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 32.000 0.769 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0krdk company 0z07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 35.000 32.000 0.769 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0krdk company 01pf21 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 32.000 0.769 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0krdk company 018p5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 35.000 32.000 0.769 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0krdk company 04htfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 35.000 32.000 0.769 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 0krdk company 04qhdf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 35.000 32.000 0.769 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #8595-01ypsj PRED entity: 01ypsj PRED relation: student! PRED expected values: 0k__z => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 87): 07t90 (0.25 #147, 0.20 #674), 01bcwk (0.20 #688), 0bwfn (0.07 #1329, 0.07 #5019, 0.06 #3965), 01w5m (0.05 #1686, 0.04 #4849, 0.03 #12754), 065y4w7 (0.05 #3704, 0.04 #7920, 0.04 #6866), 03ksy (0.04 #4850, 0.03 #3796, 0.03 #20133), 015nl4 (0.04 #5338, 0.04 #9554, 0.04 #16405), 09f2j (0.04 #1213, 0.04 #4903, 0.03 #7538), 017z88 (0.04 #1136, 0.03 #7461, 0.03 #4826), 08815 (0.04 #4746, 0.03 #1056, 0.03 #6854) >> Best rule #147 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01yfm8; 048hf; >> query: (?x9813, 07t90) <- profession(?x9813, ?x319), film(?x9813, ?x4276), ?x4276 = 0bs5k8r, nationality(?x9813, ?x94) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01ypsj student! 0k__z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 100.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #8594-0d060g PRED entity: 0d060g PRED relation: service_location! PRED expected values: 087c7 077w0b 06q07 01bvx1 => 214 concepts (214 used for prediction) PRED predicted values (max 10 best out of 190): 01c6k4 (0.53 #1506, 0.52 #1615, 0.50 #756), 018mxj (0.40 #759, 0.34 #3973, 0.33 #1725), 077w0b (0.40 #803, 0.29 #1769, 0.29 #696), 06_9lg (0.30 #13490, 0.30 #8768, 0.26 #2009), 0cv9b (0.30 #760, 0.29 #1619, 0.24 #1726), 06p8m (0.30 #835, 0.29 #728, 0.19 #1156), 01m_zd (0.29 #734, 0.20 #841, 0.16 #1591), 03s7h (0.20 #1053, 0.19 #1698, 0.19 #1160), 011k1h (0.20 #764, 0.14 #657, 0.12 #1085), 01xdn1 (0.20 #765, 0.14 #658, 0.12 #1086) >> Best rule #1506 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 07c5l; >> query: (?x279, 01c6k4) <- contains(?x279, ?x2243), service_location(?x610, ?x279), company(?x5510, ?x2243) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #803 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 03b79; *> query: (?x279, 077w0b) <- combatants(?x613, ?x279), nationality(?x199, ?x279), ?x613 = 0bq0p9 *> conf = 0.40 ranks of expected_values: 3, 16, 45, 88 EVAL 0d060g service_location! 01bvx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 214.000 214.000 0.526 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 0d060g service_location! 06q07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 214.000 214.000 0.526 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 0d060g service_location! 077w0b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 214.000 214.000 0.526 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 0d060g service_location! 087c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 214.000 214.000 0.526 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #8593-01y8cr PRED entity: 01y8cr PRED relation: actor! PRED expected values: 01f39b => 131 concepts (39 used for prediction) PRED predicted values (max 10 best out of 66): 0kfpm (0.17 #13), 097zcz (0.16 #2657, 0.14 #6646, 0.14 #5580), 02gjrc (0.09 #493, 0.07 #1556, 0.05 #1289), 02bg8v (0.09 #290, 0.03 #1353, 0.01 #820), 026bfsh (0.08 #97, 0.07 #1159, 0.03 #1957), 01fszq (0.08 #210, 0.01 #1272), 026y3cf (0.05 #516, 0.04 #1579, 0.03 #1312), 090s_0 (0.05 #269, 0.03 #1332, 0.02 #2129), 02py4c8 (0.05 #278, 0.03 #1341, 0.01 #2138), 03cv_gy (0.05 #360, 0.03 #1423, 0.01 #2220) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0bw87; 040z9; >> query: (?x4279, 0kfpm) <- award_winner(?x4280, ?x4279), location(?x4279, ?x10584), award_winner(?x8459, ?x4279), ?x8459 = 02py7pj >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #3290 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 319 *> proper extension: 05dxl_; 0gry51; *> query: (?x4279, 01f39b) <- profession(?x4279, ?x1032), ?x1032 = 02hrh1q, people(?x9771, ?x4279) *> conf = 0.02 ranks of expected_values: 31 EVAL 01y8cr actor! 01f39b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 131.000 39.000 0.167 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #8592-01b9w3 PRED entity: 01b9w3 PRED relation: program! PRED expected values: 0gsg7 => 70 concepts (51 used for prediction) PRED predicted values (max 10 best out of 50): 0gsg7 (0.37 #59, 0.25 #174, 0.24 #290), 05gnf (0.31 #128, 0.30 #359, 0.28 #186), 09d5h (0.19 #60, 0.15 #811, 0.15 #751), 0cjdk (0.16 #407, 0.16 #578, 0.15 #696), 03mdt (0.13 #637, 0.13 #876, 0.12 #755), 07c52 (0.10 #1509, 0.01 #2607), 0ljc_ (0.07 #488, 0.05 #431, 0.04 #29), 0146mv (0.07 #486, 0.05 #429, 0.03 #2255), 03lpbx (0.07 #492, 0.05 #606, 0.04 #724), 0g5lhl7 (0.06 #1457, 0.06 #1282, 0.06 #1515) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0n2bh; 01f3p_; 0ckh4k; 06dfz1; 02qfh; 01_2n; 02vjhf; >> query: (?x4384, 0gsg7) <- actor(?x4384, ?x4432), genre(?x4384, ?x8805), ?x8805 = 06q7n, country_of_origin(?x4384, ?x94) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01b9w3 program! 0gsg7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 51.000 0.370 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #8591-0cqhb3 PRED entity: 0cqhb3 PRED relation: ceremony PRED expected values: 092_25 => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 131): 05c1t6z (0.52 #274, 0.47 #405, 0.33 #12), 0gvstc3 (0.44 #293, 0.38 #424, 0.33 #31), 02q690_ (0.43 #452, 0.43 #321, 0.33 #59), 0gx_st (0.41 #296, 0.37 #427, 0.33 #34), 03nnm4t (0.40 #461, 0.39 #330, 0.33 #68), 0gpjbt (0.34 #2388, 0.33 #2126, 0.32 #2651), 0hn821n (0.33 #122, 0.28 #384, 0.27 #2494), 0bx6zs (0.33 #118, 0.27 #2494, 0.24 #787), 07y_p6 (0.33 #91, 0.27 #2494, 0.24 #787), 07y9ts (0.33 #62, 0.27 #2494, 0.24 #787) >> Best rule #274 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 02rdxsh; 09v7wsg; 054knh; 02py_sj; >> query: (?x8250, 05c1t6z) <- nominated_for(?x8250, ?x2042), genre(?x2042, ?x53), ?x53 = 07s9rl0, nominated_for(?x4155, ?x2042) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #197 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0m7yy; *> query: (?x8250, 092_25) <- award(?x6482, ?x8250), award(?x337, ?x8250), ?x337 = 0g60z, award_winner(?x8250, ?x368), ?x6482 = 0180mw *> conf = 0.33 ranks of expected_values: 12 EVAL 0cqhb3 ceremony 092_25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 44.000 44.000 0.519 http://example.org/award/award_category/winners./award/award_honor/ceremony #8590-01r93l PRED entity: 01r93l PRED relation: film PRED expected values: 0ch26b_ 0g83dv 04k9y6 => 113 concepts (78 used for prediction) PRED predicted values (max 10 best out of 910): 02vjp3 (0.66 #55030, 0.63 #62131, 0.54 #30178), 0830vk (0.66 #55030, 0.63 #62131, 0.54 #30178), 02825cv (0.18 #6458, 0.09 #4683, 0.05 #8233), 0pc62 (0.18 #3644, 0.04 #7194, 0.03 #8969), 0ndwt2w (0.18 #4542, 0.03 #15193, 0.01 #47145), 017gl1 (0.18 #3693, 0.01 #14344, 0.01 #90675), 06q8qh (0.17 #601, 0.11 #2376, 0.09 #4151), 015x74 (0.17 #284, 0.11 #2059, 0.01 #14485), 0640m69 (0.17 #1747, 0.11 #3522, 0.01 #40800), 0yzbg (0.17 #1255, 0.11 #3030, 0.01 #40308) >> Best rule #55030 for best value: >> intensional similarity = 3 >> extensional distance = 378 >> proper extension: 04shbh; >> query: (?x4294, ?x3601) <- film(?x4294, ?x1444), participant(?x2275, ?x4294), nominated_for(?x4294, ?x3601) >> conf = 0.66 => this is the best rule for 2 predicted values *> Best rule #1034 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 06fc0b; *> query: (?x4294, 04k9y6) <- film(?x4294, ?x8349), award_nominee(?x4294, ?x2353), ?x8349 = 011xg5 *> conf = 0.17 ranks of expected_values: 32, 283, 595 EVAL 01r93l film 04k9y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 113.000 78.000 0.664 http://example.org/film/actor/film./film/performance/film EVAL 01r93l film 0g83dv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 113.000 78.000 0.664 http://example.org/film/actor/film./film/performance/film EVAL 01r93l film 0ch26b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 113.000 78.000 0.664 http://example.org/film/actor/film./film/performance/film #8589-05sb1 PRED entity: 05sb1 PRED relation: film_release_region! PRED expected values: 087wc7n 0gvrws1 0crc2cp 02w86hz 05c26ss 0bh8tgs => 169 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1800): 01fmys (0.90 #10474, 0.76 #24554, 0.74 #16874), 017jd9 (0.87 #24885, 0.87 #10805, 0.84 #17205), 08hmch (0.87 #24434, 0.83 #10354, 0.81 #16754), 03nm_fh (0.87 #10818, 0.84 #17218, 0.83 #24898), 087wc7n (0.87 #10326, 0.83 #24406, 0.77 #16726), 04w7rn (0.87 #10414, 0.79 #16814, 0.74 #24494), 017gl1 (0.87 #10344, 0.78 #24424, 0.77 #16744), 03qnc6q (0.87 #10541, 0.74 #16941, 0.74 #24621), 043tvp3 (0.84 #17527, 0.83 #11127, 0.83 #25207), 017gm7 (0.83 #10394, 0.83 #24474, 0.79 #16794) >> Best rule #10474 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 01ls2; 07f1x; >> query: (?x2236, 01fmys) <- combatants(?x12844, ?x2236), film_release_region(?x2512, ?x2236), ?x2512 = 07x4qr >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #10326 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 01ls2; 07f1x; *> query: (?x2236, 087wc7n) <- combatants(?x12844, ?x2236), film_release_region(?x2512, ?x2236), ?x2512 = 07x4qr *> conf = 0.87 ranks of expected_values: 5, 24, 41, 52, 118, 347 EVAL 05sb1 film_release_region! 0bh8tgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 169.000 112.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05sb1 film_release_region! 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 169.000 112.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05sb1 film_release_region! 02w86hz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 169.000 112.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05sb1 film_release_region! 0crc2cp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 169.000 112.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05sb1 film_release_region! 0gvrws1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 169.000 112.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05sb1 film_release_region! 087wc7n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 169.000 112.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8588-047tsx3 PRED entity: 047tsx3 PRED relation: titles! PRED expected values: 04xvlr => 116 concepts (83 used for prediction) PRED predicted values (max 10 best out of 54): 01hmnh (0.38 #26, 0.29 #332, 0.29 #229), 07s9rl0 (0.37 #720, 0.33 #3180, 0.33 #2256), 07c52 (0.26 #1055, 0.21 #1463, 0.15 #4132), 01z4y (0.25 #35, 0.24 #548, 0.21 #5581), 024qqx (0.25 #80, 0.11 #2026, 0.10 #2232), 04xvlr (0.24 #1234, 0.24 #1336, 0.23 #2775), 06nm1 (0.18 #137, 0.05 #549, 0.02 #755), 06mkj (0.18 #132, 0.05 #544, 0.02 #646), 01jfsb (0.13 #4225, 0.12 #5566, 0.12 #635), 09q17 (0.12 #75, 0.08 #588, 0.03 #5621) >> Best rule #26 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0d4htf; >> query: (?x3981, 01hmnh) <- film_crew_role(?x3981, ?x137), nominated_for(?x1336, ?x3981), film(?x7255, ?x3981), ?x7255 = 042ly5 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1234 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 133 *> proper extension: 0b76kw1; 02rlj20; *> query: (?x3981, 04xvlr) <- genre(?x3981, ?x53), executive_produced_by(?x3981, ?x12790), honored_for(?x8762, ?x3981), film(?x3980, ?x3981) *> conf = 0.24 ranks of expected_values: 6 EVAL 047tsx3 titles! 04xvlr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 116.000 83.000 0.375 http://example.org/media_common/netflix_genre/titles #8587-01w8g3 PRED entity: 01w8g3 PRED relation: film_crew_role PRED expected values: 09zzb8 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.85 #1236, 0.83 #1271, 0.81 #1588), 09zzb8 (0.79 #1266, 0.79 #1231, 0.78 #176), 0dxtw (0.41 #325, 0.40 #1770, 0.40 #1592), 01pvkk (0.38 #12, 0.36 #82, 0.34 #642), 01vx2h (0.35 #1593, 0.35 #1771, 0.32 #888), 02ynfr (0.22 #191, 0.19 #1598, 0.19 #646), 01xy5l_ (0.21 #364, 0.18 #49, 0.16 #294), 0d2b38 (0.16 #305, 0.15 #235, 0.15 #375), 02rh1dz (0.16 #324, 0.15 #359, 0.13 #1769), 089g0h (0.15 #1284, 0.14 #89, 0.12 #19) >> Best rule #1236 for best value: >> intensional similarity = 4 >> extensional distance = 391 >> proper extension: 0gj9qxr; 091z_p; 02phtzk; 02hfk5; 02h22; 064lsn; 072r5v; >> query: (?x4027, 0ch6mp2) <- production_companies(?x4027, ?x7980), film_crew_role(?x4027, ?x1171), titles(?x512, ?x4027), ?x1171 = 09vw2b7 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1266 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 473 *> proper extension: 0cnztc4; *> query: (?x4027, 09zzb8) <- film_crew_role(?x4027, ?x1171), titles(?x512, ?x4027), ?x1171 = 09vw2b7, film_release_distribution_medium(?x4027, ?x81) *> conf = 0.79 ranks of expected_values: 2 EVAL 01w8g3 film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 108.000 0.847 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8586-03s9b PRED entity: 03s9b PRED relation: award_winner! PRED expected values: 02wypbh => 167 concepts (165 used for prediction) PRED predicted values (max 10 best out of 315): 02rdyk7 (0.40 #515, 0.39 #48175, 0.37 #46042), 02w_6xj (0.40 #662, 0.20 #1515, 0.14 #8335), 0gs9p (0.39 #48175, 0.37 #46042, 0.37 #52014), 019f4v (0.39 #48175, 0.37 #46042, 0.37 #52014), 040njc (0.39 #48175, 0.37 #46042, 0.37 #52014), 0gq9h (0.39 #48175, 0.37 #46042, 0.37 #52014), 02z1nbg (0.38 #1043, 0.18 #2749, 0.09 #14261), 027b9ly (0.30 #1519, 0.14 #8339, 0.13 #2371), 0789r6 (0.27 #2524, 0.07 #6786, 0.07 #7638), 054ky1 (0.25 #107, 0.15 #1812, 0.08 #4369) >> Best rule #515 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 023t0q; >> query: (?x6957, 02rdyk7) <- award_winner(?x372, ?x6957), influenced_by(?x986, ?x6957), ?x372 = 02wkmx >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #770 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 023t0q; *> query: (?x6957, 02wypbh) <- award_winner(?x372, ?x6957), influenced_by(?x986, ?x6957), ?x372 = 02wkmx *> conf = 0.20 ranks of expected_values: 19 EVAL 03s9b award_winner! 02wypbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 167.000 165.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #8585-0ljsz PRED entity: 0ljsz PRED relation: contains PRED expected values: 05zl0 => 145 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1710): 0194_r (0.80 #47129, 0.77 #50075, 0.77 #164923), 0ljsz (0.27 #291580, 0.25 #276857, 0.03 #73635), 05fjf (0.27 #291580, 0.25 #276857, 0.02 #270963), 09c7w0 (0.27 #291580, 0.25 #276857, 0.02 #270963), 02m0sc (0.14 #61856, 0.10 #55966, 0.09 #106032), 01q7q2 (0.14 #61856, 0.10 #55966, 0.09 #106032), 01n951 (0.14 #1086, 0.13 #41238, 0.13 #8835), 02lwv5 (0.14 #1744, 0.11 #7634, 0.09 #4689), 021q2j (0.14 #1262, 0.11 #7152, 0.09 #4207), 03bmmc (0.14 #778, 0.11 #6668, 0.09 #3723) >> Best rule #47129 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 01yj2; 081m_; >> query: (?x10988, ?x12284) <- featured_film_locations(?x253, ?x10988), citytown(?x12284, ?x10988), contains(?x12221, ?x12284), currency(?x12284, ?x170) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #250341 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 287 *> proper extension: 06pwq; 01mc11; 01ykl0; 029spt; 01t3h6; *> query: (?x10988, ?x4793) <- state(?x10988, ?x6895), contains(?x6895, ?x1214), state_province_region(?x4793, ?x6895) *> conf = 0.02 ranks of expected_values: 925 EVAL 0ljsz contains 05zl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 145.000 114.000 0.798 http://example.org/location/location/contains #8584-01kt_j PRED entity: 01kt_j PRED relation: tv_program! PRED expected values: 0693l => 72 concepts (63 used for prediction) PRED predicted values (max 10 best out of 193): 05mcjs (0.33 #117, 0.11 #308, 0.01 #1269), 01t6b4 (0.29 #1537, 0.27 #1344, 0.26 #2883), 038bht (0.29 #1537, 0.27 #1344, 0.26 #2883), 0311wg (0.16 #2305, 0.14 #2691, 0.12 #4231), 02q3bb (0.16 #2305, 0.14 #2691, 0.12 #4231), 04vq3h (0.16 #2305, 0.14 #2691, 0.12 #4231), 02rmfm (0.16 #2305, 0.14 #2691, 0.12 #4231), 0693l (0.12 #4231, 0.12 #2498, 0.12 #4037), 02773nt (0.11 #202, 0.06 #585, 0.03 #776), 09_99w (0.11 #342, 0.06 #1303, 0.06 #534) >> Best rule #117 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0ddd0gc; >> query: (?x10595, 05mcjs) <- nominated_for(?x4728, ?x10595), nominated_for(?x686, ?x10595), ?x4728 = 04ldyx1, ?x686 = 0bdw1g >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4231 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 187 *> proper extension: 04glx0; 06w7mlh; 01b7h8; 03cf9ly; *> query: (?x10595, ?x3117) <- nominated_for(?x3117, ?x10595), genre(?x10595, ?x53), profession(?x3117, ?x319) *> conf = 0.12 ranks of expected_values: 8 EVAL 01kt_j tv_program! 0693l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 72.000 63.000 0.333 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #8583-09ctj PRED entity: 09ctj PRED relation: location! PRED expected values: 0h0yt => 123 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1918): 018y81 (0.51 #75520, 0.46 #47829, 0.41 #15104), 044mvs (0.50 #2065, 0.05 #14651, 0.04 #42341), 0dx97 (0.33 #1065, 0.08 #3582, 0.05 #13651), 03d_w3h (0.33 #150, 0.05 #40426, 0.05 #12736), 0219q (0.33 #823, 0.05 #13409, 0.04 #3340), 03h8_g (0.33 #2223, 0.04 #42499, 0.04 #4740), 01vw20h (0.33 #902, 0.04 #41178, 0.04 #3419), 07ym0 (0.33 #1703, 0.04 #4220, 0.04 #6737), 01w8sf (0.17 #7552), 023s8 (0.17 #2111, 0.10 #14697, 0.08 #24766) >> Best rule #75520 for best value: >> intensional similarity = 3 >> extensional distance = 169 >> proper extension: 016v46; 0jp26; 0727_; 01t8gz; 0mb2b; 0xt3t; 0qxzd; 0ncy4; 01t3h6; >> query: (?x13763, ?x6067) <- place_of_birth(?x6067, ?x13763), contains(?x512, ?x13763), currency(?x6067, ?x1099) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #1551 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 059rby; 04jpl; 0f2v0; 05qtj; *> query: (?x13763, 0h0yt) <- location(?x11985, ?x13763), location(?x2045, ?x13763), ?x11985 = 01vh3r, contains(?x512, ?x13763), award_winner(?x704, ?x2045) *> conf = 0.17 ranks of expected_values: 72 EVAL 09ctj location! 0h0yt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 123.000 33.000 0.506 http://example.org/people/person/places_lived./people/place_lived/location #8582-07s3m4g PRED entity: 07s3m4g PRED relation: nominated_for! PRED expected values: 05zrvfd => 104 concepts (91 used for prediction) PRED predicted values (max 10 best out of 187): 0gr42 (0.29 #332, 0.12 #4671, 0.12 #3947), 05ztrmj (0.28 #860, 0.25 #1583, 0.14 #3752), 02x4sn8 (0.25 #2771, 0.18 #4941, 0.06 #16153), 02g3v6 (0.24 #263, 0.17 #7737, 0.12 #2432), 05ztjjw (0.24 #251, 0.17 #10, 0.15 #3866), 0gq9h (0.22 #13322, 0.22 #18147, 0.21 #14286), 03hl6lc (0.22 #4954, 0.21 #2784, 0.12 #374), 02x4w6g (0.21 #2740, 0.14 #4910, 0.06 #12865), 099c8n (0.21 #3432, 0.19 #8737, 0.18 #299), 0gs9p (0.20 #12842, 0.20 #18149, 0.19 #14288) >> Best rule #332 for best value: >> intensional similarity = 12 >> extensional distance = 15 >> proper extension: 02d44q; >> query: (?x6587, 0gr42) <- film_release_region(?x6587, ?x2629), film_release_region(?x6587, ?x1355), film_release_region(?x6587, ?x1353), film_release_region(?x6587, ?x456), film_release_region(?x6587, ?x304), film_release_distribution_medium(?x6587, ?x81), ?x2629 = 06f32, titles(?x571, ?x6587), ?x1355 = 0h7x, ?x304 = 0d0vqn, ?x1353 = 035qy, ?x456 = 05qhw >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #16153 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 978 *> proper extension: 07kb7vh; *> query: (?x6587, ?x618) <- currency(?x6587, ?x170), film(?x10629, ?x6587), ?x170 = 09nqf, country(?x6587, ?x94), production_companies(?x7470, ?x10629), country(?x7470, ?x512), nominated_for(?x618, ?x7470) *> conf = 0.06 ranks of expected_values: 114 EVAL 07s3m4g nominated_for! 05zrvfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 104.000 91.000 0.294 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8581-04t9c0 PRED entity: 04t9c0 PRED relation: titles! PRED expected values: 01z4y => 51 concepts (33 used for prediction) PRED predicted values (max 10 best out of 49): 01z4y (0.60 #36, 0.38 #1584, 0.19 #554), 07s9rl0 (0.56 #208, 0.51 #311, 0.30 #829), 05p553 (0.27 #310, 0.20 #3211, 0.19 #2276), 02l7c8 (0.27 #310, 0.19 #2276, 0.19 #2380), 0219x_ (0.27 #310, 0.19 #2276, 0.19 #2380), 0gsy3b (0.27 #310, 0.19 #2276, 0.19 #2380), 06cvj (0.27 #310, 0.19 #2276, 0.19 #2380), 04xvlr (0.24 #107, 0.22 #728, 0.20 #625), 01hmnh (0.24 #130, 0.10 #545, 0.10 #1575), 024qqx (0.14 #184, 0.07 #909, 0.07 #2045) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 047vp1n; 0456zg; 06cgf; >> query: (?x5353, 01z4y) <- genre(?x5353, ?x12008), film(?x2531, ?x5353), ?x12008 = 0gsy3b, type_of_union(?x2531, ?x1873) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04t9c0 titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 33.000 0.600 http://example.org/media_common/netflix_genre/titles #8580-01jgkj2 PRED entity: 01jgkj2 PRED relation: award_nominee! PRED expected values: 015882 0flpy => 119 concepts (56 used for prediction) PRED predicted values (max 10 best out of 953): 02jxmr (0.81 #109626, 0.81 #25655, 0.81 #111959), 015882 (0.81 #109626, 0.81 #25655, 0.81 #111959), 06cc_1 (0.71 #76968, 0.71 #55976, 0.71 #32652), 018gqj (0.27 #83965, 0.22 #1403, 0.14 #88632), 01n8gr (0.27 #83965, 0.14 #88632, 0.11 #757), 01k98nm (0.27 #83965, 0.14 #88632, 0.11 #729), 01l3mk3 (0.27 #83965, 0.14 #88632, 0.05 #83966), 02dbp7 (0.27 #83965, 0.14 #88632, 0.02 #43066), 01jgkj2 (0.27 #83965, 0.14 #88632, 0.01 #43955), 013423 (0.27 #83965, 0.14 #88632) >> Best rule #109626 for best value: >> intensional similarity = 3 >> extensional distance = 610 >> proper extension: 09d5h; 05xbx; >> query: (?x9176, ?x1817) <- category(?x9176, ?x134), award_nominee(?x9176, ?x1817), award_winner(?x537, ?x1817) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01jgkj2 award_nominee! 0flpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 56.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 01jgkj2 award_nominee! 015882 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 56.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8579-02hnl PRED entity: 02hnl PRED relation: role! PRED expected values: 07kc_ => 64 concepts (49 used for prediction) PRED predicted values (max 10 best out of 60): 042v_gx (0.87 #1019, 0.85 #200, 0.85 #1915), 0395lw (0.87 #1019, 0.85 #200, 0.84 #914), 0l14j_ (0.85 #200, 0.84 #914, 0.84 #916), 03qjg (0.85 #200, 0.84 #914, 0.84 #916), 0mkg (0.85 #200, 0.84 #914, 0.84 #916), 01dnws (0.85 #200, 0.84 #914, 0.84 #916), 0151b0 (0.85 #200, 0.84 #914, 0.84 #916), 01p970 (0.85 #200, 0.84 #914, 0.84 #250), 02snj9 (0.85 #200, 0.84 #914, 0.84 #250), 02hnl (0.75 #1084, 0.74 #1138, 0.73 #983) >> Best rule #1019 for best value: >> intensional similarity = 12 >> extensional distance = 9 >> proper extension: 01dnws; >> query: (?x1750, ?x2059) <- role(?x1750, ?x5480), role(?x1750, ?x2059), group(?x1750, ?x442), ?x5480 = 01w4c9, instrumentalists(?x1750, ?x3890), instrumentalists(?x1750, ?x3422), instrumentalists(?x1750, ?x460), role(?x2059, ?x2253), category(?x460, ?x134), people(?x13213, ?x3422), ?x2253 = 01679d, gender(?x3890, ?x231) >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 0l14md; *> query: (?x1750, ?x212) <- role(?x1750, ?x5480), role(?x1750, ?x1433), group(?x1750, ?x11704), group(?x1750, ?x9206), ?x5480 = 01w4c9, ?x11704 = 0560w, ?x9206 = 017mbb, instrumentalists(?x1750, ?x300), role(?x3703, ?x1433), role(?x212, ?x1433), ?x3703 = 02dlh2, performance_role(?x1260, ?x1433) *> conf = 0.65 ranks of expected_values: 27 EVAL 02hnl role! 07kc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 64.000 49.000 0.871 http://example.org/music/performance_role/regular_performances./music/group_membership/role #8578-02lf_x PRED entity: 02lf_x PRED relation: contains! PRED expected values: 0g3bw => 181 concepts (123 used for prediction) PRED predicted values (max 10 best out of 219): 09c7w0 (0.80 #98570, 0.61 #82440, 0.59 #25980), 0g3bw (0.64 #8229, 0.55 #7334, 0.53 #11812), 04_1l0v (0.59 #26427, 0.56 #25531, 0.51 #40764), 02lf_x (0.57 #98567, 0.24 #7167, 0.15 #39418), 07ssc (0.54 #21528, 0.53 #23320, 0.41 #77088), 02qkt (0.37 #14677, 0.27 #41555, 0.26 #29011), 049yf (0.33 #1856, 0.19 #12604, 0.18 #7231), 02jx1 (0.32 #21583, 0.29 #23375, 0.25 #77143), 02j9z (0.32 #22420, 0.19 #41236, 0.17 #9880), 02j71 (0.31 #51066) >> Best rule #98570 for best value: >> intensional similarity = 4 >> extensional distance = 280 >> proper extension: 0ydpd; 0pmq2; 0ftxw; 0fvzg; 0d1qn; 01qh7; 019k6n; 0pc7r; 0n6bs; 0rp46; ... >> query: (?x13415, 09c7w0) <- location(?x4944, ?x13415), contains(?x252, ?x13415), category(?x13415, ?x134), country_of_origin(?x419, ?x252) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #8229 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0193fp; *> query: (?x13415, 0g3bw) <- category(?x13415, ?x134), ?x134 = 08mbj5d, country(?x13415, ?x252), ?x252 = 03_3d *> conf = 0.64 ranks of expected_values: 2 EVAL 02lf_x contains! 0g3bw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 181.000 123.000 0.805 http://example.org/location/location/contains #8577-01mxnvc PRED entity: 01mxnvc PRED relation: role PRED expected values: 01vj9c => 117 concepts (80 used for prediction) PRED predicted values (max 10 best out of 126): 05r5c (0.59 #432, 0.44 #751, 0.43 #113), 026t6 (0.54 #851, 0.50 #532, 0.28 #427), 02sgy (0.45 #430, 0.30 #3407, 0.29 #961), 05842k (0.41 #928, 0.31 #609, 0.24 #504), 018vs (0.34 #2224, 0.32 #4576, 0.32 #2653), 05148p4 (0.34 #2224, 0.32 #2653, 0.30 #3188), 02hnl (0.34 #2224, 0.32 #2653, 0.30 #3188), 01xqw (0.32 #4576, 0.32 #2653, 0.31 #3080), 0l14qv (0.32 #4576, 0.31 #3080, 0.29 #217), 0192l (0.32 #4576, 0.31 #3080, 0.27 #1376) >> Best rule #432 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 0m_v0; >> query: (?x10802, 05r5c) <- profession(?x10802, ?x1614), profession(?x10802, ?x220), artists(?x671, ?x10802), ?x1614 = 01c72t, ?x220 = 016z4k, role(?x10802, ?x227) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #864 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 02lvtb; 05qhnq; *> query: (?x10802, 01vj9c) <- profession(?x10802, ?x131), artists(?x671, ?x10802), role(?x10802, ?x1750), ?x1750 = 02hnl *> conf = 0.17 ranks of expected_values: 23 EVAL 01mxnvc role 01vj9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 117.000 80.000 0.586 http://example.org/music/artist/track_contributions./music/track_contribution/role #8576-02y_2y PRED entity: 02y_2y PRED relation: student! PRED expected values: 0bwfn => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 96): 0cwx_ (0.20 #240, 0.02 #1290, 0.02 #5490), 021w0_ (0.20 #322, 0.02 #1372, 0.01 #1897), 0bwfn (0.17 #799, 0.09 #9724, 0.08 #1849), 04b_46 (0.08 #751, 0.06 #1801, 0.04 #3901), 02g839 (0.08 #549, 0.03 #4224, 0.02 #1074), 01w3v (0.08 #539, 0.01 #40445, 0.01 #31518), 012lzr (0.08 #859), 03ksy (0.07 #1680, 0.06 #4830, 0.04 #26357), 01t0dy (0.04 #1266, 0.01 #5466, 0.01 #3891), 01jt2w (0.04 #1332) >> Best rule #240 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 03wbzp; >> query: (?x4470, 0cwx_) <- nominated_for(?x4470, ?x6482), participant(?x4360, ?x4470), ?x6482 = 0180mw >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #799 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0lgsq; 021yc7p; 02pq9yv; 02pqgt8; 09swkk; 013t9y; 08h79x; 027y151; *> query: (?x4470, 0bwfn) <- nominated_for(?x4470, ?x2042), award_winner(?x9921, ?x4470), ?x9921 = 0bvhz9 *> conf = 0.17 ranks of expected_values: 3 EVAL 02y_2y student! 0bwfn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 114.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #8575-044lyq PRED entity: 044lyq PRED relation: film PRED expected values: 0286vp 02tgz4 02qlp4 => 82 concepts (44 used for prediction) PRED predicted values (max 10 best out of 243): 02rzdcp (0.59 #35692, 0.59 #23199, 0.59 #37477), 026p4q7 (0.59 #35692, 0.59 #23199, 0.59 #37477), 09cr8 (0.07 #284, 0.03 #5636, 0.03 #21414), 020bv3 (0.07 #318, 0.03 #67818, 0.03 #21414), 01k0xy (0.07 #1279, 0.03 #67818, 0.03 #21414), 03ydlnj (0.07 #1394, 0.03 #67818, 0.03 #21414), 02d003 (0.07 #1234, 0.03 #67818, 0.03 #21414), 0bmch_x (0.07 #829, 0.03 #67818, 0.03 #21414), 06w99h3 (0.07 #26, 0.03 #67818, 0.03 #21414), 0cp0t91 (0.07 #1449, 0.03 #67818, 0.03 #21414) >> Best rule #35692 for best value: >> intensional similarity = 3 >> extensional distance = 1270 >> proper extension: 049tjg; 03gm48; 0f0p0; 0n6f8; 03xmy1; 0m32_; 01nrq5; 01v3vp; 02dbn2; 01pqy_; ... >> query: (?x7242, ?x2490) <- nominated_for(?x7242, ?x2490), gender(?x7242, ?x231), film(?x7242, ?x586) >> conf = 0.59 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 044lyq film 02qlp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 44.000 0.594 http://example.org/film/actor/film./film/performance/film EVAL 044lyq film 02tgz4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 44.000 0.594 http://example.org/film/actor/film./film/performance/film EVAL 044lyq film 0286vp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 44.000 0.594 http://example.org/film/actor/film./film/performance/film #8574-03lty PRED entity: 03lty PRED relation: parent_genre! PRED expected values: 0dls3 07bbw 02yy88 01f1p9 => 62 concepts (37 used for prediction) PRED predicted values (max 10 best out of 239): 01h0kx (0.50 #2410, 0.50 #2180, 0.50 #1030), 0dl5d (0.50 #935, 0.40 #1627, 0.40 #1165), 03lty (0.50 #942, 0.40 #1634, 0.40 #1172), 0xv2x (0.50 #1028, 0.40 #1720, 0.40 #1258), 0dls3 (0.50 #963, 0.40 #1655, 0.40 #1193), 05r6t (0.50 #982, 0.40 #1674, 0.40 #1212), 059kh (0.40 #1191, 0.38 #2341, 0.38 #2111), 0y3_8 (0.40 #1189, 0.38 #2339, 0.38 #2109), 0pm85 (0.40 #1263, 0.25 #2413, 0.25 #2183), 09jw2 (0.40 #1266, 0.25 #2416, 0.25 #2186) >> Best rule #2410 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 02k_kn; >> query: (?x2249, 01h0kx) <- artists(?x2249, ?x9589), artists(?x2249, ?x3867), artists(?x2249, ?x1970), ?x9589 = 02cw1m, parent_genre(?x2249, ?x7083), diet(?x3867, ?x3130), instrumentalists(?x227, ?x1970) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #963 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 06by7; *> query: (?x2249, 0dls3) <- artists(?x2249, ?x12880), artists(?x2249, ?x12506), artists(?x2249, ?x10091), artists(?x2249, ?x5208), ?x12506 = 01518s, parent_genre(?x302, ?x2249), ?x10091 = 048tgl, ?x12880 = 011xhx, influenced_by(?x5208, ?x3917) *> conf = 0.50 ranks of expected_values: 5, 62, 85, 100 EVAL 03lty parent_genre! 01f1p9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 62.000 37.000 0.500 http://example.org/music/genre/parent_genre EVAL 03lty parent_genre! 02yy88 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 62.000 37.000 0.500 http://example.org/music/genre/parent_genre EVAL 03lty parent_genre! 07bbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 62.000 37.000 0.500 http://example.org/music/genre/parent_genre EVAL 03lty parent_genre! 0dls3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 37.000 0.500 http://example.org/music/genre/parent_genre #8573-01vsqvs PRED entity: 01vsqvs PRED relation: group PRED expected values: 07yg2 => 139 concepts (41 used for prediction) PRED predicted values (max 10 best out of 65): 01qqwp9 (0.14 #129, 0.10 #777, 0.08 #1211), 0123r4 (0.10 #800, 0.08 #260, 0.05 #1234), 01v0sx2 (0.08 #1195, 0.07 #1413, 0.07 #1522), 0hvbj (0.08 #355, 0.06 #463, 0.06 #571), 07yg2 (0.05 #1324, 0.04 #1869, 0.04 #1978), 04k05 (0.05 #1389, 0.04 #1934, 0.03 #3025), 015srx (0.05 #797, 0.03 #2651, 0.03 #1231), 06gcn (0.05 #815, 0.03 #924, 0.03 #1249), 01fl3 (0.05 #765, 0.03 #1199, 0.03 #1308), 016376 (0.05 #847, 0.03 #1281, 0.03 #1499) >> Best rule #129 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 05k2s_; >> query: (?x9179, 01qqwp9) <- profession(?x9179, ?x1614), nationality(?x9179, ?x1264), participant(?x9179, ?x1089), ?x1614 = 01c72t >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1324 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 01vwyqp; 0psss; *> query: (?x9179, 07yg2) <- artists(?x7329, ?x9179), artists(?x2491, ?x9179), ?x7329 = 016jny, parent_genre(?x2491, ?x283), type_of_union(?x9179, ?x1873) *> conf = 0.05 ranks of expected_values: 5 EVAL 01vsqvs group 07yg2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 139.000 41.000 0.143 http://example.org/music/group_member/membership./music/group_membership/group #8572-040p_q PRED entity: 040p_q PRED relation: major_field_of_study! PRED expected values: 016t_3 => 50 concepts (45 used for prediction) PRED predicted values (max 10 best out of 21): 016t_3 (0.83 #152, 0.82 #131, 0.80 #110), 019v9k (0.83 #326, 0.80 #304, 0.80 #263), 02h4rq6 (0.78 #321, 0.75 #151, 0.73 #130), 02_xgp2 (0.77 #307, 0.77 #373, 0.73 #458), 03bwzr4 (0.68 #255, 0.67 #97, 0.64 #139), 04zx3q1 (0.68 #255, 0.60 #298, 0.55 #129), 028dcg (0.47 #708, 0.42 #192, 0.40 #230), 01gkg3 (0.47 #708, 0.33 #13, 0.23 #205), 0bjrnt (0.42 #192, 0.37 #318, 0.37 #685), 07s6fsf (0.42 #192, 0.37 #318, 0.37 #685) >> Best rule #152 for best value: >> intensional similarity = 11 >> extensional distance = 10 >> proper extension: 011s0; >> query: (?x9093, 016t_3) <- major_field_of_study(?x1368, ?x9093), major_field_of_study(?x2775, ?x9093), major_field_of_study(?x735, ?x9093), ?x1368 = 014mlp, ?x2775 = 078bz, student(?x735, ?x7837), school(?x580, ?x735), award_nominee(?x7837, ?x2015), ?x580 = 05m_8, produced_by(?x253, ?x7837), contains(?x94, ?x735) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 040p_q major_field_of_study! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 45.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #8571-03s5t PRED entity: 03s5t PRED relation: religion PRED expected values: 01lp8 051kv => 226 concepts (226 used for prediction) PRED predicted values (max 10 best out of 22): 051kv (0.91 #627, 0.82 #243, 0.82 #363), 01lp8 (0.89 #625, 0.82 #241, 0.82 #361), 03_gx (0.60 #985, 0.45 #632, 0.45 #32), 092bf5 (0.60 #985, 0.37 #3344, 0.37 #3319), 0flw86 (0.39 #1709, 0.39 #2022, 0.38 #1613), 03j6c (0.23 #2333, 0.09 #2248, 0.09 #2320), 0n2g (0.23 #2333, 0.04 #1713, 0.03 #2242), 01spm (0.23 #2333, 0.03 #333, 0.03 #357), 01gr6h (0.23 #2333, 0.01 #2039), 01fgks (0.23 #2333, 0.01 #2030) >> Best rule #627 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 059rby; 03s0w; 04ykg; 04rrd; 04tgp; 06yxd; 05mph; >> query: (?x2768, 051kv) <- location(?x744, ?x2768), religion(?x2768, ?x962), district_represented(?x605, ?x2768), contains(?x2768, ?x2087) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03s5t religion 051kv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 226.000 0.909 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 03s5t religion 01lp8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 226.000 226.000 0.909 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #8570-0300ml PRED entity: 0300ml PRED relation: nominated_for! PRED expected values: 0fbtbt => 65 concepts (57 used for prediction) PRED predicted values (max 10 best out of 215): 0f4x7 (0.80 #8328, 0.22 #10700, 0.20 #11655), 0fbtbt (0.78 #10914, 0.75 #870, 0.70 #12108), 09sb52 (0.57 #4542, 0.11 #6676, 0.09 #10708), 0bdx29 (0.55 #793, 0.40 #82, 0.27 #319), 0gkr9q (0.55 #919, 0.36 #445, 0.30 #208), 0ck27z (0.55 #781, 0.24 #11630, 0.19 #1492), 0gq9h (0.50 #8364, 0.34 #4570, 0.33 #10736), 0gs9p (0.45 #8366, 0.31 #10738, 0.31 #11455), 0bdw6t (0.45 #794, 0.30 #83, 0.27 #320), 0cqhb3 (0.40 #909, 0.30 #198, 0.27 #435) >> Best rule #8328 for best value: >> intensional similarity = 7 >> extensional distance = 225 >> proper extension: 0170z3; 02d413; 015qsq; 0g22z; 0sxg4; 0yyg4; 07xtqq; 0ds11z; 04v8x9; 0n0bp; ... >> query: (?x12324, 0f4x7) <- nominated_for(?x783, ?x12324), award(?x9359, ?x783), award(?x5545, ?x783), award(?x2841, ?x783), type_of_union(?x2841, ?x1873), ?x9359 = 016kft, film(?x5545, ?x1597) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #10914 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 549 *> proper extension: 05m_jsg; *> query: (?x12324, ?x4921) <- award(?x12324, ?x4921), award(?x10215, ?x4921), award(?x4238, ?x4921), tv_program(?x4238, ?x9649), nominated_for(?x4921, ?x337), program(?x10215, ?x10089) *> conf = 0.78 ranks of expected_values: 2 EVAL 0300ml nominated_for! 0fbtbt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 57.000 0.802 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8569-0qkj7 PRED entity: 0qkj7 PRED relation: profession PRED expected values: 01d_h8 02hrh1q 018gz8 => 100 concepts (82 used for prediction) PRED predicted values (max 10 best out of 85): 02hrh1q (0.72 #8124, 0.71 #2416, 0.71 #2866), 01d_h8 (0.36 #3308, 0.36 #1957, 0.33 #6), 0dxtg (0.33 #14, 0.32 #3316, 0.31 #2115), 03gjzk (0.33 #16, 0.25 #466, 0.23 #2117), 0d8qb (0.33 #81, 0.25 #531, 0.11 #9312), 0196pc (0.33 #75, 0.25 #525, 0.11 #9312), 0fj9f (0.31 #806, 0.20 #656, 0.15 #957), 09jwl (0.29 #3322, 0.26 #3472, 0.25 #3021), 0cbd2 (0.28 #757, 0.25 #607, 0.25 #307), 0kyk (0.25 #331, 0.16 #1232, 0.15 #1082) >> Best rule #8124 for best value: >> intensional similarity = 5 >> extensional distance = 659 >> proper extension: 09fqd3; 01vh3r; >> query: (?x13834, 02hrh1q) <- location(?x13834, ?x3807), contains(?x7492, ?x3807), place_of_birth(?x2969, ?x3807), adjoins(?x321, ?x7492), currency(?x7492, ?x170) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 14 EVAL 0qkj7 profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 100.000 82.000 0.716 http://example.org/people/person/profession EVAL 0qkj7 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 82.000 0.716 http://example.org/people/person/profession EVAL 0qkj7 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 82.000 0.716 http://example.org/people/person/profession #8568-01fx1l PRED entity: 01fx1l PRED relation: languages PRED expected values: 02h40lc => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.89 #178, 0.89 #200, 0.88 #79), 06nm1 (0.05 #27, 0.05 #16, 0.05 #60), 064_8sq (0.05 #18, 0.02 #62, 0.02 #139), 0t_2 (0.04 #171, 0.03 #226, 0.03 #138), 03_9r (0.04 #345, 0.03 #334, 0.03 #312), 02bv9 (0.01 #64, 0.01 #97), 04306rv (0.01 #58, 0.01 #91), 02bjrlw (0.01 #56, 0.01 #89) >> Best rule #178 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 0h95b81; 04bp0l; >> query: (?x5594, 02h40lc) <- nominated_for(?x3815, ?x5594), genre(?x5594, ?x53), award_winner(?x2307, ?x3815) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fx1l languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.892 http://example.org/tv/tv_program/languages #8567-0d1xh PRED entity: 0d1xh PRED relation: currency PRED expected values: 09nqf => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.86 #8, 0.86 #11, 0.86 #10) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 03fb3t; >> query: (?x7949, 09nqf) <- second_level_divisions(?x94, ?x7949), contains(?x3634, ?x7949), source(?x7949, ?x958), featured_film_locations(?x945, ?x3634) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d1xh currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.862 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #8566-0m9p3 PRED entity: 0m9p3 PRED relation: nominated_for! PRED expected values: 040njc => 97 concepts (89 used for prediction) PRED predicted values (max 10 best out of 208): 040njc (0.60 #2602, 0.45 #1658, 0.29 #3074), 0gs9p (0.53 #2659, 0.40 #9503, 0.36 #1715), 019f4v (0.52 #2649, 0.50 #289, 0.41 #1233), 0gq9h (0.52 #2657, 0.46 #9501, 0.36 #1713), 0k611 (0.52 #2668, 0.33 #9512, 0.32 #1252), 04dn09n (0.49 #2631, 0.41 #1215, 0.33 #271), 0gqy2 (0.49 #1301, 0.30 #2717, 0.28 #9561), 02r22gf (0.43 #2624, 0.16 #9468, 0.15 #1680), 0gq_v (0.42 #255, 0.37 #9459, 0.33 #2615), 0gr4k (0.42 #262, 0.30 #9466, 0.30 #1678) >> Best rule #2602 for best value: >> intensional similarity = 4 >> extensional distance = 126 >> proper extension: 027ct7c; 0g4pl7z; >> query: (?x2423, 040njc) <- nominated_for(?x6909, ?x2423), ?x6909 = 02qyntr, film_release_distribution_medium(?x2423, ?x81), genre(?x2423, ?x162) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m9p3 nominated_for! 040njc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 89.000 0.602 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8565-02md_2 PRED entity: 02md_2 PRED relation: company PRED expected values: 06pwq 0371rb 05l71 0175rc => 36 concepts (18 used for prediction) PRED predicted values (max 10 best out of 287): 06pwq (0.80 #4884, 0.43 #2931, 0.38 #4232), 04sv4 (0.60 #2151, 0.50 #2475, 0.50 #1501), 03s7h (0.60 #2203, 0.50 #2527, 0.50 #1229), 0z90c (0.60 #2113, 0.50 #2437, 0.50 #1139), 0841v (0.60 #2243, 0.50 #2567, 0.50 #1269), 019rl6 (0.50 #1452, 0.50 #1128, 0.40 #2102), 0537b (0.50 #1438, 0.50 #1114, 0.40 #2088), 01qygl (0.50 #1489, 0.50 #1165, 0.40 #2139), 01dfb6 (0.50 #1509, 0.50 #1185, 0.40 #2159), 060ppp (0.50 #1212, 0.40 #2186, 0.33 #4786) >> Best rule #4884 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 01rk91; 060c4; 0krdk; 021q1c; 04n1q6; 01___w; 05smlt; >> query: (?x4486, 06pwq) <- company(?x4486, ?x4296), school(?x8786, ?x4296), school(?x1161, ?x4296), major_field_of_study(?x4296, ?x1154), school(?x700, ?x4296), ?x8786 = 02pq_x5, ?x1161 = 02x2khw >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 161 EVAL 02md_2 company 0175rc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 36.000 18.000 0.800 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 02md_2 company 05l71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 36.000 18.000 0.800 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 02md_2 company 0371rb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 36.000 18.000 0.800 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 02md_2 company 06pwq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 18.000 0.800 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #8564-0p__8 PRED entity: 0p__8 PRED relation: type_of_union PRED expected values: 04ztj => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.76 #49, 0.75 #81, 0.75 #53), 01g63y (0.19 #22, 0.17 #26, 0.16 #126), 0jgjn (0.02 #16, 0.01 #24) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 0h5f5n; 06cv1; 0jf1b; 012t1; 0b_c7; 01gzm2; 01q415; 0184dt; 02l5rm; 02bfxb; ... >> query: (?x5940, 04ztj) <- award_winner(?x401, ?x5940), award_nominee(?x815, ?x5940), written_by(?x146, ?x5940) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p__8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.765 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8563-031hcx PRED entity: 031hcx PRED relation: film_crew_role PRED expected values: 01pvkk => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 26): 01pvkk (0.31 #200, 0.29 #264, 0.29 #521), 0d2b38 (0.29 #54, 0.15 #182, 0.14 #214), 0215hd (0.29 #47, 0.14 #625, 0.13 #175), 015h31 (0.29 #39, 0.13 #423, 0.12 #71), 02zdwq (0.29 #51, 0.03 #211, 0.03 #179), 04pyp5 (0.25 #13, 0.14 #45, 0.11 #141), 02ynfr (0.22 #204, 0.22 #172, 0.18 #140), 01xy5l_ (0.14 #170, 0.14 #42, 0.12 #234), 089g0h (0.14 #176, 0.13 #272, 0.11 #561), 033smt (0.14 #56, 0.09 #216, 0.08 #184) >> Best rule #200 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 0ckrgs; 014nq4; 0432_5; >> query: (?x7304, 01pvkk) <- film(?x488, ?x7304), film_crew_role(?x7304, ?x137), language(?x7304, ?x254), prequel(?x7304, ?x7305) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031hcx film_crew_role 01pvkk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.305 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8562-086qd PRED entity: 086qd PRED relation: award PRED expected values: 02f705 05q8pss 02f764 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 309): 01c99j (0.78 #17256, 0.77 #11374, 0.77 #18041), 01cw7s (0.78 #17256, 0.77 #11374, 0.77 #18041), 02f73p (0.60 #178, 0.14 #2139, 0.12 #7059), 02f71y (0.40 #173, 0.22 #2134, 0.15 #566), 02f777 (0.40 #298, 0.21 #2259, 0.15 #691), 02f5qb (0.40 #147, 0.16 #2108, 0.12 #17010), 02v1m7 (0.40 #107, 0.13 #2068, 0.12 #7059), 02f764 (0.40 #210, 0.10 #603, 0.09 #2171), 09sb52 (0.35 #430, 0.25 #20822, 0.24 #13371), 05pcn59 (0.35 #469, 0.20 #76, 0.10 #16155) >> Best rule #17256 for best value: >> intensional similarity = 3 >> extensional distance = 575 >> proper extension: 016qtt; 012d40; 01vw87c; 089tm; 01pfr3; 0152cw; 07q1v4; 01v0sx2; 09qr6; 05mt_q; ... >> query: (?x2138, ?x462) <- award_winner(?x462, ?x2138), award(?x2138, ?x401), artists(?x671, ?x2138) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #210 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 03f3yfj; *> query: (?x2138, 02f764) <- award_winner(?x2431, ?x2138), award_winner(?x462, ?x2138), ?x462 = 05zkcn5 *> conf = 0.40 ranks of expected_values: 8, 28, 51 EVAL 086qd award 02f764 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 128.000 128.000 0.780 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 086qd award 05q8pss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 128.000 128.000 0.780 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 086qd award 02f705 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 128.000 128.000 0.780 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8561-0154j PRED entity: 0154j PRED relation: country! PRED expected values: 01hp22 0bynt => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 32): 0bynt (0.90 #1223, 0.88 #1799, 0.87 #2055), 01lb14 (0.86 #137, 0.84 #233, 0.81 #585), 07gyv (0.74 #229, 0.71 #133, 0.62 #1061), 01cgz (0.73 #1160, 0.71 #552, 0.70 #808), 01hp22 (0.71 #134, 0.68 #230, 0.65 #166), 01z27 (0.68 #234, 0.58 #586, 0.57 #138), 03rbzn (0.63 #237, 0.58 #589, 0.57 #141), 09qgm (0.58 #236, 0.57 #140, 0.53 #172), 03fyrh (0.58 #238, 0.56 #558, 0.56 #590), 02bkg (0.57 #1, 0.53 #225, 0.50 #577) >> Best rule #1223 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 0b90_r; 047lj; 047yc; 05qx1; 015qh; 0d0kn; 06f32; 0697s; 0161c; 0d05q4; ... >> query: (?x172, 0bynt) <- film_release_region(?x10535, ?x172), film_release_region(?x1178, ?x172), ?x1178 = 053rxgm, genre(?x10535, ?x258) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 0154j country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0154j country! 01hp22 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 164.000 164.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #8560-01nz1q6 PRED entity: 01nz1q6 PRED relation: actor! PRED expected values: 026bfsh => 175 concepts (133 used for prediction) PRED predicted values (max 10 best out of 55): 026bfsh (0.09 #4073, 0.07 #892, 0.07 #5400), 0dl6fv (0.08 #4943, 0.03 #7859, 0.03 #8124), 03bww6 (0.05 #1723), 080dwhx (0.05 #4778, 0.02 #7694, 0.02 #7959), 03ffcz (0.05 #4894, 0.02 #7810, 0.02 #8075), 03ctqqf (0.05 #5011, 0.02 #8192, 0.02 #8457), 01hn_t (0.05 #1927, 0.05 #2192, 0.04 #2723), 05631 (0.04 #2642, 0.04 #3703, 0.03 #4233), 026y3cf (0.04 #2901, 0.01 #11913), 06r1k (0.04 #2868, 0.01 #10025) >> Best rule #4073 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 01vrncs; 02whj; 01kx_81; 0l12d; 0j1yf; 0gt_k; 01vsl3_; 01vx5w7; 01w02sy; 01w806h; ... >> query: (?x10924, 026bfsh) <- location(?x10924, ?x362), featured_film_locations(?x136, ?x362), month(?x362, ?x1459), group(?x10924, ?x3207) >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nz1q6 actor! 026bfsh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 133.000 0.094 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #8559-014nq4 PRED entity: 014nq4 PRED relation: nominated_for! PRED expected values: 0262s1 => 123 concepts (113 used for prediction) PRED predicted values (max 10 best out of 205): 0gq9h (0.40 #3197, 0.30 #14287, 0.30 #17421), 019f4v (0.31 #3188, 0.28 #17412, 0.26 #14278), 099c8n (0.29 #4155, 0.26 #11388, 0.26 #12353), 0gs9p (0.29 #14289, 0.27 #17423, 0.26 #15012), 040njc (0.29 #3140, 0.26 #5068, 0.25 #7), 0gs96 (0.29 #3225, 0.25 #5153, 0.20 #4189), 0p9sw (0.28 #1226, 0.26 #3154, 0.21 #1708), 02hsq3m (0.27 #4609, 0.27 #1476, 0.26 #2440), 02g3v6 (0.26 #2673, 0.25 #4601, 0.22 #5565), 0gr42 (0.26 #2501, 0.24 #2019, 0.24 #4670) >> Best rule #3197 for best value: >> intensional similarity = 6 >> extensional distance = 33 >> proper extension: 0jzw; 0jym0; 051zy_b; 0bykpk; 04gcyg; 0gyv0b4; >> query: (?x3221, 0gq9h) <- genre(?x3221, ?x53), written_by(?x3221, ?x11373), ?x53 = 07s9rl0, story_by(?x3221, ?x1683), language(?x3221, ?x254), production_companies(?x3221, ?x902) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #18322 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 451 *> proper extension: 0gxtknx; 05c5z8j; *> query: (?x3221, ?x68) <- genre(?x3221, ?x53), written_by(?x3221, ?x11373), genre(?x5648, ?x53), genre(?x2189, ?x53), nominated_for(?x166, ?x5648), ?x2189 = 02yvct, nominated_for(?x68, ?x5648) *> conf = 0.04 ranks of expected_values: 173 EVAL 014nq4 nominated_for! 0262s1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 123.000 113.000 0.400 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8558-02wr2r PRED entity: 02wr2r PRED relation: location PRED expected values: 04rrd => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 147): 0mn8t (0.51 #23295, 0.51 #20081, 0.50 #28118), 030qb3t (0.29 #885, 0.17 #45874, 0.16 #3294), 01cx_ (0.25 #162, 0.12 #965, 0.06 #1768), 02cl1 (0.25 #31, 0.12 #834, 0.02 #3243), 02_286 (0.22 #45828, 0.18 #839, 0.17 #48239), 0cr3d (0.12 #144, 0.08 #45936, 0.07 #48347), 05k7sb (0.12 #108, 0.06 #911, 0.03 #30528), 06yxd (0.12 #246, 0.06 #1049, 0.03 #30528), 013kcv (0.12 #41, 0.06 #844, 0.01 #8875), 07_fl (0.12 #566, 0.06 #1369, 0.01 #3778) >> Best rule #23295 for best value: >> intensional similarity = 3 >> extensional distance = 748 >> proper extension: 01xyt7; >> query: (?x4558, ?x7689) <- people(?x2510, ?x4558), place_of_birth(?x4558, ?x7689), type_of_union(?x4558, ?x566) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #2506 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 086k8; 0kx4m; 07c0j; 05crg7; 0dw4g; 07mvp; 05gnf; 01f2q5; *> query: (?x4558, 04rrd) <- category(?x4558, ?x134), award_winner(?x6693, ?x4558), influenced_by(?x6693, ?x986) *> conf = 0.01 ranks of expected_values: 98 EVAL 02wr2r location 04rrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 84.000 84.000 0.513 http://example.org/people/person/places_lived./people/place_lived/location #8557-0gqzz PRED entity: 0gqzz PRED relation: nominated_for PRED expected values: 07y9w5 => 56 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1412): 027s39y (0.88 #1571, 0.79 #3142, 0.78 #22014), 0407yfx (0.88 #1571, 0.79 #3142, 0.78 #22014), 0gmgwnv (0.33 #945, 0.30 #15100, 0.27 #22960), 026p4q7 (0.33 #349, 0.29 #14504, 0.28 #16075), 017gl1 (0.33 #129, 0.28 #14284, 0.25 #1700), 03hmt9b (0.33 #579, 0.25 #14734, 0.25 #2150), 05hjnw (0.33 #750, 0.25 #14905, 0.25 #2321), 0pv3x (0.33 #158, 0.25 #1729, 0.23 #14313), 07s846j (0.33 #588, 0.25 #2159, 0.23 #14743), 011yqc (0.33 #203, 0.25 #1774, 0.23 #14358) >> Best rule #1571 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02n9nmz; >> query: (?x1053, ?x224) <- award(?x2868, ?x1053), award(?x224, ?x1053), ?x2868 = 0dr3sl, nominated_for(?x1053, ?x1259), ?x1259 = 04hwbq >> conf = 0.88 => this is the best rule for 2 predicted values *> Best rule #1768 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09tqxt; *> query: (?x1053, 07y9w5) <- award(?x2868, ?x1053), ?x2868 = 0dr3sl, nominated_for(?x1053, ?x1259), film(?x2718, ?x1259) *> conf = 0.25 ranks of expected_values: 123 EVAL 0gqzz nominated_for 07y9w5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 56.000 19.000 0.875 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8556-061xq PRED entity: 061xq PRED relation: draft PRED expected values: 02z6872 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 13): 02z6872 (0.81 #230, 0.77 #387, 0.75 #401), 025tn92 (0.40 #351, 0.34 #747, 0.34 #394), 092j54 (0.38 #177, 0.38 #111, 0.36 #543), 09l0x9 (0.38 #180, 0.38 #114, 0.34 #747), 0g3zpp (0.38 #173, 0.38 #107, 0.34 #747), 05vsb7 (0.38 #106, 0.34 #747, 0.34 #538), 0f4vx0 (0.37 #349, 0.34 #747, 0.34 #394), 09th87 (0.34 #747, 0.34 #394, 0.33 #263), 038c0q (0.34 #747, 0.34 #394, 0.33 #263), 03nt7j (0.34 #747, 0.34 #394, 0.33 #263) >> Best rule #230 for best value: >> intensional similarity = 10 >> extensional distance = 14 >> proper extension: 05m_8; 01yhm; 02d02; >> query: (?x4208, 02z6872) <- draft(?x4208, ?x8786), draft(?x4208, ?x3334), draft(?x4208, ?x1161), season(?x4208, ?x9498), school(?x4208, ?x2522), ?x9498 = 027pwzc, position(?x4208, ?x2010), ?x8786 = 02pq_x5, school(?x3334, ?x581), ?x1161 = 02x2khw >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 061xq draft 02z6872 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.812 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #8555-02l6h PRED entity: 02l6h PRED relation: currency! PRED expected values: 0m7yh => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 2074): 0q19t (0.82 #1389, 0.78 #1385, 0.77 #1382), 09b_0m (0.82 #1389, 0.22 #2314, 0.07 #2322), 01y06y (0.79 #1847, 0.78 #1384, 0.78 #1385), 01z3bz (0.79 #1847, 0.78 #1384, 0.77 #1382), 017ztv (0.79 #1847, 0.78 #1384, 0.77 #1382), 01c0cc (0.78 #1384, 0.77 #1382, 0.76 #1848), 02m_41 (0.78 #1384, 0.77 #1382, 0.76 #1848), 0373qg (0.67 #2323, 0.67 #2783, 0.66 #460), 0f11p (0.67 #2323, 0.67 #2783, 0.66 #460), 04jr87 (0.46 #1381, 0.33 #455, 0.22 #2314) >> Best rule #1389 for best value: >> intensional similarity = 96 >> extensional distance = 1 >> proper extension: 09nqf; >> query: (?x5696, ?x7575) <- currency(?x7190, ?x5696), currency(?x2985, ?x5696), currency(?x2856, ?x5696), currency(?x13080, ?x5696), currency(?x12944, ?x5696), currency(?x11093, ?x5696), currency(?x7154, ?x5696), currency(?x5695, ?x5696), currency(?x2637, ?x5696), currency(?x12877, ?x5696), currency(?x11717, ?x5696), currency(?x2351, ?x5696), currency(?x1646, ?x5696), contains(?x10582, ?x5695), currency(?x10619, ?x5696), currency(?x2685, ?x5696), currency(?x1421, ?x5696), currency(?x1370, ?x5696), institution(?x4981, ?x13080), state_province_region(?x2351, ?x9186), ?x4981 = 03bwzr4, category(?x12944, ?x134), contains(?x1264, ?x11717), institution(?x620, ?x11717), genre(?x10619, ?x1626), ?x1626 = 03q4nz, organization(?x346, ?x12944), contains(?x6959, ?x11093), school_type(?x11093, ?x11041), film_crew_role(?x10619, ?x1171), major_field_of_study(?x13080, ?x4321), award(?x10619, ?x4894), citytown(?x12877, ?x5577), currency(?x3199, ?x5696), major_field_of_study(?x7154, ?x742), language(?x10619, ?x5671), language(?x10619, ?x732), company(?x3131, ?x5695), contains(?x1679, ?x7154), contains(?x10581, ?x10582), month(?x2985, ?x4827), ?x4321 = 0g26h, company(?x5652, ?x13080), colors(?x7154, ?x663), vacationer(?x2856, ?x513), adjoins(?x2856, ?x4521), language(?x11385, ?x732), language(?x11148, ?x732), language(?x4431, ?x732), language(?x4216, ?x732), language(?x3201, ?x732), language(?x2090, ?x732), language(?x1685, ?x732), language(?x1261, ?x732), language(?x308, ?x732), ?x1261 = 02qrv7, ?x4431 = 0pd4f, ?x308 = 011yxg, administrative_parent(?x8502, ?x2856), contains(?x7190, ?x7191), ?x11385 = 01c9d, student(?x7154, ?x2595), combatants(?x1679, ?x6371), place_of_death(?x4732, ?x6959), countries_spoken_in(?x732, ?x172), film_release_region(?x2685, ?x1353), major_field_of_study(?x1368, ?x732), currency(?x7575, ?x5696), ?x1685 = 072x7s, location(?x914, ?x6959), languages(?x11354, ?x732), ?x11354 = 01q8fxx, major_field_of_study(?x2014, ?x732), ?x4216 = 0hfzr, vacationer(?x6959, ?x444), school_type(?x2637, ?x3092), ?x1353 = 035qy, ?x2090 = 01hqhm, student(?x2637, ?x2800), company(?x10111, ?x7154), contains(?x455, ?x12944), nominated_for(?x618, ?x2685), ?x11148 = 01qdmh, ?x1171 = 09vw2b7, service_language(?x555, ?x5671), award(?x2595, ?x3617), film(?x4004, ?x1370), genre(?x1370, ?x258), featured_film_locations(?x1370, ?x1523), nominated_for(?x68, ?x1370), ?x11041 = 04399, ?x3201 = 01ffx4, teams(?x2985, ?x3791), ?x4827 = 03_ly, languages_spoken(?x1423, ?x732), titles(?x812, ?x1421) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #2314 for first EXPECTED value: *> intensional similarity = 113 *> extensional distance = 3 *> proper extension: 0kz1h; *> query: (?x5696, ?x122) <- currency(?x12257, ?x5696), currency(?x11717, ?x5696), currency(?x9344, ?x5696), currency(?x196, ?x5696), currency(?x2637, ?x5696), currency(?x639, ?x5696), currency(?x2685, ?x5696), currency(?x534, ?x5696), institution(?x3437, ?x9344), major_field_of_study(?x11717, ?x5954), citytown(?x196, ?x1646), category(?x196, ?x134), institution(?x2636, ?x2637), school_type(?x9344, ?x4994), colors(?x9344, ?x663), student(?x11717, ?x8768), citytown(?x11717, ?x2611), featured_film_locations(?x2189, ?x1646), currency(?x3199, ?x5696), location(?x1211, ?x1646), month(?x1646, ?x1459), place_of_birth(?x628, ?x1646), school_type(?x7546, ?x4994), school_type(?x5968, ?x4994), citytown(?x12257, ?x4627), contains(?x1264, ?x1646), currency(?x639, ?x1099), film_crew_role(?x534, ?x2095), award_winner(?x534, ?x1582), mode_of_transportation(?x1646, ?x4272), major_field_of_study(?x3437, ?x9079), major_field_of_study(?x3437, ?x2921), nominated_for(?x77, ?x534), institution(?x3437, ?x13543), institution(?x3437, ?x11555), institution(?x3437, ?x8822), institution(?x3437, ?x8706), institution(?x3437, ?x2948), institution(?x3437, ?x2497), institution(?x3437, ?x741), institution(?x3437, ?x546), institution(?x3437, ?x122), student(?x3437, ?x5105), student(?x3437, ?x3520), ?x5105 = 047c9l, ?x8822 = 020ddc, ?x7546 = 01_qgp, institution(?x1368, ?x12257), teams(?x1646, ?x2905), film_release_region(?x2685, ?x1229), film_release_region(?x2685, ?x252), film_release_region(?x2685, ?x172), student(?x1368, ?x1159), film_release_distribution_medium(?x534, ?x81), ?x9079 = 0l5mz, ?x2948 = 0j_sncb, ?x5968 = 05xb7q, ?x13543 = 01gpkz, award(?x534, ?x7965), ?x1159 = 083q7, institution(?x1368, ?x6814), institution(?x1368, ?x4889), institution(?x1368, ?x2396), student(?x2637, ?x2800), ?x2396 = 07xpm, ?x6814 = 03tw2s, film_crew_role(?x11372, ?x2095), film_crew_role(?x8886, ?x2095), film_crew_role(?x7755, ?x2095), film_crew_role(?x7304, ?x2095), film_crew_role(?x6798, ?x2095), film_crew_role(?x6445, ?x2095), film_crew_role(?x5730, ?x2095), film_crew_role(?x5388, ?x2095), film_crew_role(?x5313, ?x2095), film_crew_role(?x3596, ?x2095), film_crew_role(?x3517, ?x2095), film_crew_role(?x1812, ?x2095), film_crew_role(?x1701, ?x2095), film_crew_role(?x1511, ?x2095), service_location(?x610, ?x1646), ?x6445 = 05v38p, ?x5388 = 03cd0x, ?x11372 = 0419kt, locations(?x1608, ?x1646), ?x11555 = 06rjp, ?x6798 = 0g7pm1, major_field_of_study(?x1368, ?x373), ?x1812 = 0fdv3, ?x5313 = 01f6x7, ?x1511 = 0340hj, ?x546 = 01j_9c, genre(?x2685, ?x53), ?x3517 = 09rsjpv, ?x7755 = 0298n7, ?x8706 = 0trv, ?x2636 = 027f2w, ?x3520 = 03gkn5, ?x1701 = 0bh8yn3, ?x5730 = 0992d9, ?x2497 = 0f1nl, ?x7304 = 031hcx, ?x3596 = 0cc5qkt, ?x8886 = 076xkps, ?x1229 = 059j2, film_release_region(?x186, ?x172), ?x741 = 01w3v, ?x252 = 03_3d, ?x4889 = 02dq8f, contains(?x172, ?x4826), organization(?x172, ?x127), ?x2921 = 06n6p, ?x186 = 02vxq9m *> conf = 0.22 ranks of expected_values: 651 EVAL 02l6h currency! 0m7yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 8.000 8.000 0.819 http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency #8554-0cc97st PRED entity: 0cc97st PRED relation: film_release_region PRED expected values: 03rjj 06npd 0ctw_b 015qh 06c1y 05v10 02vzc 06mkj 06t2t 01crd5 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 150): 06mkj (0.93 #602, 0.88 #741, 0.88 #1158), 03rjj (0.90 #559, 0.86 #1115, 0.86 #698), 06t2t (0.85 #607, 0.82 #1163, 0.81 #746), 02vzc (0.80 #320, 0.79 #42, 0.77 #737), 015qh (0.67 #589, 0.66 #1145, 0.65 #728), 0ctw_b (0.61 #575, 0.61 #19, 0.60 #714), 02k54 (0.61 #11, 0.45 #289, 0.41 #428), 06t8v (0.60 #619, 0.56 #1175, 0.54 #63), 047lj (0.57 #8, 0.57 #286, 0.51 #425), 06qd3 (0.57 #29, 0.57 #307, 0.51 #446) >> Best rule #602 for best value: >> intensional similarity = 6 >> extensional distance = 70 >> proper extension: 0b76d_m; 02vxq9m; 0g5qs2k; 0dscrwf; 05p1tzf; 0gkz15s; 01vksx; 017gl1; 0bwfwpj; 0c0nhgv; ... >> query: (?x5713, 06mkj) <- film_release_region(?x5713, ?x2146), film_release_region(?x5713, ?x172), nominated_for(?x1053, ?x5713), ?x2146 = 03rk0, film(?x545, ?x5713), ?x172 = 0154j >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 14, 20, 26, 29 EVAL 0cc97st film_release_region 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 64.000 64.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc97st film_release_region 06t2t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc97st film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc97st film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc97st film_release_region 05v10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 64.000 64.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc97st film_release_region 06c1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 64.000 64.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc97st film_release_region 015qh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc97st film_release_region 0ctw_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc97st film_release_region 06npd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 64.000 64.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cc97st film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8553-0k3g3 PRED entity: 0k3g3 PRED relation: adjoins! PRED expected values: 0k3kv => 123 concepts (42 used for prediction) PRED predicted values (max 10 best out of 329): 0k3kv (0.82 #18892, 0.82 #22830, 0.81 #1570), 0k3kg (0.40 #244, 0.31 #1029, 0.29 #1571), 0k3k1 (0.40 #419, 0.28 #8649, 0.27 #22043), 0mw5x (0.40 #486, 0.28 #8649, 0.27 #22043), 0k3l5 (0.31 #1110, 0.29 #1571, 0.28 #8649), 0k3gw (0.29 #1571, 0.28 #8649, 0.27 #22043), 0k3g3 (0.29 #1571, 0.25 #11801, 0.25 #5499), 0k3ll (0.28 #8649, 0.27 #22043, 0.24 #18104), 0k3hn (0.28 #8649, 0.27 #22043, 0.24 #18104), 0m2gz (0.15 #961, 0.07 #1748, 0.06 #2533) >> Best rule #18892 for best value: >> intensional similarity = 4 >> extensional distance = 299 >> proper extension: 02_286; 0mwh1; 0mk7z; 0m7d0; 0rj0z; 0lhql; 0s5cg; 0rqyx; 0psxp; 0nht0; ... >> query: (?x13448, ?x5874) <- adjoins(?x13448, ?x5874), source(?x13448, ?x958), contains(?x2020, ?x13448), time_zones(?x5874, ?x2674) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k3g3 adjoins! 0k3kv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 42.000 0.820 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #8552-03nm_fh PRED entity: 03nm_fh PRED relation: film_crew_role PRED expected values: 089fss => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 24): 01vx2h (0.56 #9, 0.53 #75, 0.50 #42), 0dxtw (0.40 #1308, 0.40 #1342, 0.36 #274), 01pvkk (0.29 #1344, 0.28 #1041, 0.28 #1745), 0d2b38 (0.26 #89, 0.18 #156, 0.17 #56), 0215hd (0.22 #16, 0.21 #82, 0.17 #49), 015h31 (0.21 #73, 0.18 #339, 0.15 #140), 033smt (0.21 #91, 0.17 #58, 0.10 #357), 01xy5l_ (0.17 #145, 0.16 #78, 0.12 #1312), 089g0h (0.16 #83, 0.12 #150, 0.12 #1317), 02_n3z (0.11 #1, 0.11 #67, 0.09 #1301) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0gtvrv3; 0kv238; 05zlld0; 05c26ss; 062zm5h; 0421v9q; 027pfg; >> query: (?x4684, 01vx2h) <- nominated_for(?x3860, ?x4684), film_release_region(?x4684, ?x5482), ?x5482 = 04g5k, category(?x4684, ?x134) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #104 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 0gffmn8; 0fpgp26; *> query: (?x4684, 089fss) <- genre(?x4684, ?x53), film_release_region(?x4684, ?x8593), ?x8593 = 01crd5 *> conf = 0.09 ranks of expected_values: 12 EVAL 03nm_fh film_crew_role 089fss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 93.000 93.000 0.556 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8551-01jw67 PRED entity: 01jw67 PRED relation: written_by PRED expected values: 02l5rm => 116 concepts (65 used for prediction) PRED predicted values (max 10 best out of 109): 01dw9z (0.11 #15505, 0.09 #3031, 0.09 #4714), 012wg (0.10 #146, 0.01 #7218, 0.01 #7555), 0kb3n (0.09 #1940, 0.09 #929, 0.08 #1266), 02vyw (0.09 #776, 0.08 #1113, 0.08 #2462), 02kxbwx (0.09 #695, 0.07 #359, 0.05 #1032), 02kxbx3 (0.09 #774, 0.07 #438, 0.05 #1111), 0343h (0.07 #380, 0.03 #3075, 0.03 #4085), 02fcs2 (0.07 #404, 0.03 #6130, 0.02 #6803), 0p50v (0.06 #926, 0.05 #1263, 0.03 #2612), 03thw4 (0.06 #3172, 0.05 #1150, 0.05 #1824) >> Best rule #15505 for best value: >> intensional similarity = 4 >> extensional distance = 431 >> proper extension: 0564x; >> query: (?x6222, ?x2683) <- film_release_distribution_medium(?x6222, ?x81), film_release_region(?x6222, ?x94), titles(?x53, ?x6222), award_winner(?x6222, ?x2683) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jw67 written_by 02l5rm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 65.000 0.112 http://example.org/film/film/written_by #8550-059xvg PRED entity: 059xvg PRED relation: place_of_birth PRED expected values: 0grd7 => 63 concepts (58 used for prediction) PRED predicted values (max 10 best out of 49): 04jpl (0.25 #8, 0.12 #715, 0.10 #1423), 0619_ (0.12 #1320, 0.10 #2028), 06wxw (0.12 #864, 0.10 #1572), 0r7fy (0.10 #1464), 0nbrp (0.08 #2655, 0.05 #4068, 0.04 #3361), 05l5n (0.08 #2894, 0.05 #3601), 09bkv (0.08 #2501, 0.03 #3914, 0.01 #13072), 0d2lt (0.08 #2772, 0.03 #4185), 0f3ys2 (0.08 #2601, 0.03 #4014), 02_286 (0.08 #21180, 0.07 #7078, 0.07 #25406) >> Best rule #8 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0935jw; >> query: (?x3664, 04jpl) <- profession(?x3664, ?x1383), ?x1383 = 0np9r, nationality(?x3664, ?x1310), nationality(?x3664, ?x94), ?x1310 = 02jx1, ?x94 = 09c7w0 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 059xvg place_of_birth 0grd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 58.000 0.250 http://example.org/people/person/place_of_birth #8549-01tz6vs PRED entity: 01tz6vs PRED relation: story_by! PRED expected values: 0gy7bj4 => 152 concepts (53 used for prediction) PRED predicted values (max 10 best out of 268): 04ltlj (0.09 #4440, 0.07 #5808, 0.05 #6492), 0291ck (0.09 #4406, 0.07 #5774, 0.05 #6458), 0jzw (0.08 #4479, 0.02 #10293, 0.02 #11320), 06t2t2 (0.07 #5794, 0.02 #10582, 0.02 #12293), 08ct6 (0.06 #9067, 0.05 #10435, 0.04 #11804), 01_mdl (0.04 #13726, 0.04 #6883, 0.03 #8935), 063y9fp (0.04 #13976, 0.02 #11922, 0.02 #12949), 0bpm4yw (0.04 #13837, 0.02 #11783, 0.02 #12810), 0gjc4d3 (0.04 #13795), 044g_k (0.04 #13731) >> Best rule #4440 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01dvtx; >> query: (?x5434, 04ltlj) <- influenced_by(?x118, ?x5434), influenced_by(?x5434, ?x11830), ?x11830 = 0420y, profession(?x5434, ?x353) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01tz6vs story_by! 0gy7bj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 152.000 53.000 0.091 http://example.org/film/film/story_by #8548-0kxbc PRED entity: 0kxbc PRED relation: performance_role PRED expected values: 03bx0bm => 76 concepts (45 used for prediction) PRED predicted values (max 10 best out of 49): 026t6 (0.16 #181, 0.16 #135, 0.07 #497), 03bx0bm (0.12 #106, 0.12 #421, 0.12 #513), 0l14md (0.07 #409, 0.07 #364, 0.05 #320), 0342h (0.05 #406, 0.04 #448, 0.04 #317), 013y1f (0.05 #422, 0.03 #1190, 0.03 #605), 0l14qv (0.04 #92, 0.04 #362, 0.04 #1175), 0l15bq (0.04 #108, 0.03 #153, 0.02 #423), 0l14jd (0.04 #131, 0.01 #357, 0.01 #401), 01qbl (0.04 #102, 0.01 #372, 0.01 #417), 0j210 (0.04 #124) >> Best rule #181 for best value: >> intensional similarity = 6 >> extensional distance = 29 >> proper extension: 0bg539; >> query: (?x5635, ?x212) <- instrumentalists(?x1969, ?x5635), instrumentalists(?x716, ?x5635), instrumentalists(?x212, ?x5635), ?x716 = 018vs, ?x212 = 026t6, role(?x74, ?x1969) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #106 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 01l87db; 01ydzx; 01w5gg6; *> query: (?x5635, 03bx0bm) <- artists(?x3370, ?x5635), artists(?x1572, ?x5635), ?x1572 = 06by7, instrumentalists(?x212, ?x5635), ?x3370 = 059kh *> conf = 0.12 ranks of expected_values: 2 EVAL 0kxbc performance_role 03bx0bm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 45.000 0.161 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #8547-07l8f PRED entity: 07l8f PRED relation: colors PRED expected values: 01l849 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 18): 06fvc (0.89 #343, 0.88 #326, 0.86 #223), 01l849 (0.63 #809, 0.61 #775, 0.31 #653), 01g5v (0.60 #54, 0.50 #917, 0.34 #1045), 019sc (0.43 #280, 0.42 #555, 0.36 #642), 03vtbc (0.29 #281, 0.27 #860, 0.27 #1129), 0jc_p (0.26 #514, 0.21 #277, 0.21 #208), 038hg (0.25 #12, 0.20 #46, 0.18 #566), 036k5h (0.20 #56, 0.18 #566, 0.18 #256), 06kqt3 (0.18 #566, 0.18 #256, 0.17 #496), 088fh (0.18 #566, 0.18 #256, 0.16 #1041) >> Best rule #343 for best value: >> intensional similarity = 10 >> extensional distance = 16 >> proper extension: 0487_; >> query: (?x6823, 06fvc) <- colors(?x6823, ?x12170), colors(?x6823, ?x663), draft(?x6823, ?x1161), sport(?x6823, ?x5063), team(?x11883, ?x6823), school(?x6823, ?x3779), colors(?x2388, ?x12170), ?x2388 = 02bjhv, colors(?x1010, ?x663), ?x1010 = 01d5z >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #809 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 65 *> proper extension: 03d555l; 020wyp; 03k2hn; 01k6zy; 038_0z; *> query: (?x6823, 01l849) <- colors(?x6823, ?x8271), colors(?x11919, ?x8271), colors(?x8270, ?x8271), colors(?x6348, ?x8271), colors(?x3216, ?x8271), colors(?x2405, ?x8271), colors(?x662, ?x8271), colors(?x6637, ?x8271), ?x6637 = 07vjm, draft(?x2405, ?x3334), position(?x8270, ?x2918), ?x662 = 03lpp_, season(?x2405, ?x2406), ?x11919 = 04b5l3, ?x2406 = 03c6sl9, position(?x6348, ?x2010), position(?x3216, ?x63) *> conf = 0.63 ranks of expected_values: 2 EVAL 07l8f colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.889 http://example.org/sports/sports_team/colors #8546-0448r PRED entity: 0448r PRED relation: influenced_by! PRED expected values: 0c5tl => 125 concepts (40 used for prediction) PRED predicted values (max 10 best out of 413): 0lrh (0.55 #3120, 0.19 #6144, 0.18 #6649), 07lp1 (0.46 #4434, 0.43 #4939, 0.39 #5946), 032l1 (0.43 #1122, 0.11 #1627, 0.10 #5654), 040db (0.40 #3092, 0.35 #5612, 0.33 #74), 08433 (0.35 #3046, 0.27 #4054, 0.23 #5566), 01hb6v (0.33 #1603, 0.19 #4118, 0.18 #4623), 0dzkq (0.33 #1633, 0.15 #4148, 0.14 #19154), 0dfrq (0.33 #369, 0.15 #3387, 0.14 #1375), 016hvl (0.33 #36, 0.15 #3054, 0.14 #1042), 03f47xl (0.33 #1766, 0.14 #4786, 0.14 #1261) >> Best rule #3120 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 03f0324; 0465_; 015k7; 085gk; >> query: (?x8085, 0lrh) <- influenced_by(?x2625, ?x8085), influenced_by(?x8753, ?x2625), influenced_by(?x2120, ?x2625), influenced_by(?x3858, ?x8753), profession(?x8753, ?x353), ?x2120 = 05qw5 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1210 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 04xjp; 081k8; 0d0mbj; 0dw6b; 084nh; *> query: (?x8085, 0c5tl) <- influenced_by(?x2625, ?x8085), influenced_by(?x8753, ?x2625), influenced_by(?x2845, ?x2625), languages(?x8085, ?x90), ?x2845 = 0lrh, story_by(?x383, ?x8753) *> conf = 0.14 ranks of expected_values: 75 EVAL 0448r influenced_by! 0c5tl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 125.000 40.000 0.550 http://example.org/influence/influence_node/influenced_by #8545-0hdf8 PRED entity: 0hdf8 PRED relation: artists PRED expected values: 0274ck 01gx5f 01mxt_ 01386_ 01_wfj => 76 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1034): 09lwrt (0.62 #4884, 0.57 #3812, 0.56 #5955), 0kxbc (0.57 #3724, 0.50 #4796, 0.50 #1583), 03d9d6 (0.57 #3722, 0.50 #4794, 0.50 #1581), 0161c2 (0.57 #3467, 0.50 #4539, 0.50 #1326), 01wg982 (0.54 #7681, 0.33 #184, 0.26 #8751), 089pg7 (0.50 #4985, 0.50 #1772, 0.44 #6056), 0191h5 (0.50 #2787, 0.50 #1717, 0.43 #3858), 01gx5f (0.50 #2432, 0.50 #1362, 0.33 #6719), 01j59b0 (0.50 #2610, 0.50 #1540, 0.33 #470), 020_4z (0.50 #3070, 0.50 #2000, 0.33 #930) >> Best rule #4884 for best value: >> intensional similarity = 9 >> extensional distance = 6 >> proper extension: 0pm85; >> query: (?x5436, 09lwrt) <- artists(?x5436, ?x8614), artists(?x5436, ?x7810), artists(?x5436, ?x680), ?x680 = 01cv3n, artists(?x3061, ?x7810), artist(?x648, ?x7810), group(?x227, ?x8614), ?x3061 = 05bt6j, artist(?x382, ?x8614) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2432 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 0781g; *> query: (?x5436, 01gx5f) <- artists(?x5436, ?x7121), artists(?x5436, ?x680), profession(?x680, ?x1183), award(?x680, ?x247), origin(?x680, ?x1755), ?x1183 = 09jwl, ?x7121 = 04kjrv *> conf = 0.50 ranks of expected_values: 8, 124, 201, 227, 266 EVAL 0hdf8 artists 01_wfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 76.000 23.000 0.625 http://example.org/music/genre/artists EVAL 0hdf8 artists 01386_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 76.000 23.000 0.625 http://example.org/music/genre/artists EVAL 0hdf8 artists 01mxt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 23.000 0.625 http://example.org/music/genre/artists EVAL 0hdf8 artists 01gx5f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 76.000 23.000 0.625 http://example.org/music/genre/artists EVAL 0hdf8 artists 0274ck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 23.000 0.625 http://example.org/music/genre/artists #8544-04q24zv PRED entity: 04q24zv PRED relation: currency PRED expected values: 09nqf => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.79 #71, 0.78 #85, 0.77 #99), 02gsvk (0.12 #20, 0.02 #111, 0.02 #139), 01nv4h (0.10 #2, 0.10 #9, 0.05 #23), 02l6h (0.09 #18, 0.03 #60, 0.03 #4), 0kz1h (0.02 #19, 0.02 #26), 088n7 (0.02 #21, 0.01 #35) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 233 >> proper extension: 02d44q; >> query: (?x2797, 09nqf) <- film(?x1414, ?x2797), nominated_for(?x533, ?x2797), category(?x2797, ?x134), film_crew_role(?x2797, ?x137) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04q24zv currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.787 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8543-0g1rw PRED entity: 0g1rw PRED relation: award_winner! PRED expected values: 0fz0c2 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 114): 0275n3y (0.35 #2035, 0.33 #775, 0.33 #495), 09bymc (0.33 #120, 0.15 #2080, 0.14 #400), 0fz0c2 (0.20 #245, 0.10 #2065, 0.08 #805), 0dznvw (0.20 #274, 0.08 #834, 0.08 #974), 0fqpc7d (0.18 #1436, 0.18 #1296, 0.16 #1716), 09gkdln (0.17 #821, 0.15 #2081, 0.14 #4321), 04n2r9h (0.17 #745, 0.15 #2005, 0.12 #4245), 09p2r9 (0.14 #372, 0.02 #9472, 0.02 #8492), 0d__c3 (0.11 #544, 0.08 #824, 0.08 #964), 0c53zb (0.11 #481, 0.08 #761, 0.08 #901) >> Best rule #2035 for best value: >> intensional similarity = 2 >> extensional distance = 18 >> proper extension: 01fsyp; >> query: (?x788, 0275n3y) <- award_winner(?x6323, ?x788), state_province_region(?x788, ?x1227) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #245 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 03xq0f; 01795t; *> query: (?x788, 0fz0c2) <- film(?x788, ?x4175), film(?x788, ?x3845), ?x3845 = 0639bg, film(?x1867, ?x4175) *> conf = 0.20 ranks of expected_values: 3 EVAL 0g1rw award_winner! 0fz0c2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 124.000 124.000 0.350 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8542-047yc PRED entity: 047yc PRED relation: film_release_region! PRED expected values: 0ds3t5x 0401sg 03twd6 0407yfx 0kv238 0dzlbx 026lgs 0bq6ntw 0gg8z1f 01mgw 0gy7bj4 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1183): 01fmys (0.90 #9669, 0.83 #8486, 0.83 #6120), 09k56b7 (0.87 #9665, 0.83 #6116, 0.83 #4933), 0gj8t_b (0.87 #9581, 0.80 #8398, 0.79 #6032), 0gj9tn5 (0.87 #9641, 0.79 #6092, 0.77 #8458), 0gtsx8c (0.87 #9473, 0.79 #5924, 0.73 #8290), 0h03fhx (0.86 #6418, 0.83 #9967, 0.80 #17065), 0407yfx (0.86 #6135, 0.80 #9684, 0.77 #19148), 0dt8xq (0.86 #6487, 0.80 #10036, 0.71 #17134), 03qnc6q (0.83 #9731, 0.83 #6182, 0.80 #8548), 0dzlbx (0.83 #10021, 0.83 #6472, 0.79 #19485) >> Best rule #9669 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 06f32; >> query: (?x1174, 01fmys) <- film_release_region(?x3035, ?x1174), film_release_region(?x1163, ?x1174), ?x1163 = 0c0nhgv, ?x3035 = 0j43swk >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #6135 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 0jgd; 03rjj; 015fr; 0ctw_b; 05v10; 06mkj; 03h64; 03spz; *> query: (?x1174, 0407yfx) <- film_release_region(?x3191, ?x1174), film_release_region(?x1163, ?x1174), ?x1163 = 0c0nhgv, ?x3191 = 0crc2cp *> conf = 0.86 ranks of expected_values: 7, 10, 22, 24, 26, 34, 41, 53, 55, 65, 82 EVAL 047yc film_release_region! 0gy7bj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047yc film_release_region! 01mgw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047yc film_release_region! 0gg8z1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047yc film_release_region! 0bq6ntw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047yc film_release_region! 026lgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047yc film_release_region! 0dzlbx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047yc film_release_region! 0kv238 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047yc film_release_region! 0407yfx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047yc film_release_region! 03twd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047yc film_release_region! 0401sg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047yc film_release_region! 0ds3t5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 87.000 87.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8541-01k98nm PRED entity: 01k98nm PRED relation: award PRED expected values: 025mb9 => 134 concepts (117 used for prediction) PRED predicted values (max 10 best out of 270): 054ks3 (0.80 #1207, 0.78 #5229, 0.78 #30971), 0gqz2 (0.80 #1207, 0.78 #5229, 0.78 #30971), 025mb9 (0.80 #1207, 0.78 #5229, 0.78 #30971), 01c427 (0.80 #1207, 0.78 #5229, 0.78 #30971), 09sb52 (0.25 #26583, 0.24 #27789, 0.24 #11303), 03qbh5 (0.25 #1007, 0.25 #605, 0.24 #3018), 04njml (0.23 #903, 0.06 #16991, 0.06 #2512), 025m8l (0.20 #920, 0.15 #32180, 0.14 #2529), 026mfs (0.20 #528, 0.15 #7768, 0.14 #2137), 02gdjb (0.20 #620, 0.12 #5044, 0.11 #2229) >> Best rule #1207 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 03f2_rc; 018pj3; 02v3yy; 0412f5y; 037lyl; 02lfp4; 077rj; 01k_mc; 02zft0; 018gqj; ... >> query: (?x3234, ?x724) <- profession(?x3234, ?x131), award_nominee(?x3235, ?x3234), award_winner(?x1232, ?x3234), award_winner(?x724, ?x3234), ?x1232 = 0c4z8 >> conf = 0.80 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 01k98nm award 025mb9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 117.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8540-06j0md PRED entity: 06j0md PRED relation: inductee! PRED expected values: 06szd3 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2): 06szd3 (0.09 #11, 0.07 #29, 0.07 #20), 0g2c8 (0.05 #172, 0.03 #388, 0.03 #370) >> Best rule #11 for best value: >> intensional similarity = 2 >> extensional distance = 91 >> proper extension: 03jl0_; >> query: (?x201, 06szd3) <- program_creator(?x4517, ?x201), program(?x6678, ?x4517) >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06j0md inductee! 06szd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.086 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #8539-09c7w0 PRED entity: 09c7w0 PRED relation: contains PRED expected values: 03v6t 01wdj_ 02qvvv 0sb1r 0_75d 01q0kg 0kqj1 02zcnq 01lnyf 027kp3 0lyjf 01h8rk 01jszm 02zkz7 0rjg8 02s8qk 027ydt 07vjm 0jkhr 01bm_ 0r80l 010bxh 01qgr3 0gy3w 01rc6f 0qkcb 06fq2 07vfz 0r3tb 01hx2t 021w0_ 0xqf3 0qzhw 013nws 0v0d9 031n5b 010t4v 0r3wm 03b12 02d6c 023zl 0t_07 0rqf1 0rxyk 02j416 0ghvb 01r47h 01xysf 0smfm 05qgd9 02htv6 027b43 01mb87 0vrmb 0yx74 0r3w7 0th3k 01nhgd 0xgpv 01m8dg 0rrhp 0rydq => 183 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2284): 01n4w (0.85 #104728, 0.83 #158124, 0.83 #217679), 0d0x8 (0.85 #104728, 0.83 #158124, 0.83 #217679), 04ykg (0.85 #104728, 0.83 #158124, 0.83 #217679), 01cx_ (0.80 #102674, 0.79 #36956, 0.55 #170448), 034tl (0.80 #102674, 0.79 #36956, 0.55 #170448), 0vzm (0.80 #102674, 0.79 #36956, 0.55 #170448), 0f2tj (0.80 #102674, 0.79 #36956, 0.55 #170448), 07b_l (0.80 #102674, 0.79 #36956, 0.23 #295712), 03s5t (0.80 #102674, 0.79 #36956, 0.23 #295712), 01c40n (0.80 #102674, 0.60 #18616, 0.55 #170448) >> Best rule #104728 for best value: >> intensional similarity = 2 >> extensional distance = 43 >> proper extension: 04w58; >> query: (?x94, ?x108) <- administrative_parent(?x108, ?x94), olympics(?x94, ?x358) >> conf = 0.85 => this is the best rule for 3 predicted values *> Best rule #170448 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 78 *> proper extension: 03gk2; 03b79; 01k6y1; 05kyr; 06sw9; 06cmp; 024pcx; *> query: (?x94, ?x3689) <- nationality(?x9407, ?x94), place_of_birth(?x9407, ?x3689) *> conf = 0.55 ranks of expected_values: 96, 100, 101, 102, 103, 106, 107, 114, 119, 133, 135, 136, 137, 143, 232, 236, 239, 241, 242, 244, 250, 251, 255, 256, 257, 258, 259, 260, 395, 429, 486, 536, 545, 563, 564, 565, 567, 599, 605, 614, 660, 661, 662, 663, 689, 706, 735, 743, 759, 777, 779, 799, 810, 828, 829, 834, 851, 867, 868, 895, 2255 EVAL 09c7w0 contains 0rydq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0rrhp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01m8dg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0xgpv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01nhgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0th3k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0r3w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0yx74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0vrmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01mb87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 027b43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 02htv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 05qgd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0smfm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01xysf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01r47h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0ghvb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 02j416 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0rxyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0rqf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0t_07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 023zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 02d6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 03b12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0r3wm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 010t4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 031n5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0v0d9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 013nws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0qzhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0xqf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 021w0_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01hx2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0r3tb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 07vfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 06fq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0qkcb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01rc6f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0gy3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01qgr3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 010bxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0r80l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01bm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0jkhr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 07vjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 027ydt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 02s8qk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0rjg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 02zkz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01jszm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01h8rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0lyjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 027kp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01lnyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 02zcnq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0kqj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01q0kg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0_75d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 0sb1r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 02qvvv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 01wdj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 183.000 162.000 0.849 http://example.org/location/location/contains EVAL 09c7w0 contains 03v6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 183.000 162.000 0.849 http://example.org/location/location/contains #8538-0bqs56 PRED entity: 0bqs56 PRED relation: place_of_birth PRED expected values: 0p9z5 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 150): 0cr3d (0.33 #94, 0.20 #798, 0.12 #2207), 030qb3t (0.28 #61274, 0.28 #77471, 0.27 #72542), 05k7sb (0.28 #61274, 0.28 #77471, 0.27 #72542), 01cx_ (0.28 #61274, 0.28 #77471, 0.27 #72542), 0s5cg (0.20 #885, 0.05 #5814, 0.03 #10744), 0v1xg (0.17 #1727, 0.12 #3136, 0.07 #4544), 0hptm (0.17 #1633, 0.03 #10788, 0.03 #18534), 02_286 (0.16 #4948, 0.12 #2836, 0.12 #2132), 0fpzwf (0.12 #3023, 0.08 #3727, 0.07 #4431), 0yc84 (0.12 #2145, 0.03 #29576, 0.03 #4961) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01n5309; >> query: (?x6008, 0cr3d) <- influenced_by(?x6008, ?x2942), participant(?x6008, ?x3694), ?x2942 = 046lt >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bqs56 place_of_birth 0p9z5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 123.000 0.333 http://example.org/people/person/place_of_birth #8537-0chgzm PRED entity: 0chgzm PRED relation: contains! PRED expected values: 05nrg => 237 concepts (229 used for prediction) PRED predicted values (max 10 best out of 439): 0chgr2 (0.95 #149546, 0.92 #127154, 0.86 #157611), 09c7w0 (0.77 #152237, 0.77 #154030, 0.71 #159405), 02j71 (0.67 #149547, 0.46 #204175), 03rk0 (0.64 #9984, 0.58 #51174, 0.56 #12671), 07ssc (0.46 #43011, 0.44 #39430, 0.35 #86889), 05nrg (0.46 #61454, 0.38 #1461, 0.22 #2357), 02qkt (0.38 #135563, 0.38 #127500, 0.36 #128396), 04_1l0v (0.37 #72981, 0.36 #89098, 0.36 #88203), 02jx1 (0.36 #187245, 0.33 #188140, 0.29 #43066), 02j9z (0.29 #8085, 0.25 #6294, 0.20 #127181) >> Best rule #149546 for best value: >> intensional similarity = 3 >> extensional distance = 208 >> proper extension: 0yshw; 0bx9y; 0nvg4; 0rmwd; 0_kfv; >> query: (?x8602, ?x9494) <- state(?x8602, ?x9494), contains(?x390, ?x8602), administrative_parent(?x390, ?x551) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #61454 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 07z5n; 05qkp; 07fsv; 07fb6; 06s9y; 01n8qg; 03188; *> query: (?x8602, 05nrg) <- contains(?x390, ?x8602), contains(?x390, ?x5036), ?x5036 = 06y57 *> conf = 0.46 ranks of expected_values: 6 EVAL 0chgzm contains! 05nrg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 237.000 229.000 0.952 http://example.org/location/location/contains #8536-0b1xl PRED entity: 0b1xl PRED relation: colors PRED expected values: 04mkbj => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.47 #649, 0.30 #212, 0.28 #915), 083jv (0.42 #324, 0.42 #305, 0.40 #913), 06fvc (0.23 #230, 0.21 #97, 0.19 #116), 04mkbj (0.18 #66, 0.13 #218, 0.13 #446), 038hg (0.15 #334, 0.13 #315, 0.10 #790), 067z2v (0.15 #236, 0.10 #426, 0.06 #331), 04d18d (0.14 #113, 0.12 #132, 0.10 #56), 03wkwg (0.14 #185, 0.12 #413, 0.10 #280), 036k5h (0.12 #119, 0.11 #765, 0.10 #594), 09ggk (0.12 #129, 0.11 #148, 0.10 #452) >> Best rule #649 for best value: >> intensional similarity = 6 >> extensional distance = 98 >> proper extension: 02xwzh; >> query: (?x5145, 01g5v) <- state_province_region(?x5145, ?x3818), student(?x5145, ?x3504), colors(?x5145, ?x332), school_type(?x5145, ?x1044), colors(?x2621, ?x332), ?x2621 = 07vht >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #66 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 017zq0; 02yxjs; 02f4s3; 02zkdz; *> query: (?x5145, 04mkbj) <- state_province_region(?x5145, ?x3818), student(?x5145, ?x4333), major_field_of_study(?x5145, ?x742), institution(?x620, ?x5145), ?x3818 = 03v0t, award_nominee(?x237, ?x4333) *> conf = 0.18 ranks of expected_values: 4 EVAL 0b1xl colors 04mkbj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 186.000 186.000 0.470 http://example.org/education/educational_institution/colors #8535-0q8p8 PRED entity: 0q8p8 PRED relation: place! PRED expected values: 0q8p8 => 108 concepts (48 used for prediction) PRED predicted values (max 10 best out of 78): 0q8p8 (0.14 #19603, 0.11 #17539, 0.09 #21151), 0gyh (0.14 #19603, 0.11 #17539, 0.09 #21151), 0fttg (0.09 #378, 0.08 #1409, 0.08 #893), 0q6lr (0.09 #361, 0.08 #1392, 0.08 #876), 0q48z (0.09 #316, 0.08 #1347, 0.08 #831), 0q8jl (0.09 #292, 0.08 #1323, 0.08 #807), 0q8s4 (0.09 #110, 0.08 #1141, 0.08 #625), 0qc7l (0.08 #993, 0.04 #2539, 0.04 #2024), 0lphb (0.08 #1206, 0.01 #6873), 06wxw (0.04 #2161, 0.04 #1646, 0.03 #2676) >> Best rule #19603 for best value: >> intensional similarity = 4 >> extensional distance = 321 >> proper extension: 0m2gk; >> query: (?x11901, ?x2831) <- source(?x11901, ?x958), contains(?x11901, ?x2830), ?x958 = 0jbk9, contains(?x2831, ?x2830) >> conf = 0.14 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0q8p8 place! 0q8p8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 48.000 0.140 http://example.org/location/hud_county_place/place #8534-0bp_7 PRED entity: 0bp_7 PRED relation: location! PRED expected values: 0n6f8 => 119 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1280): 042q3 (0.25 #2145, 0.17 #7183, 0.15 #25191), 01p85y (0.25 #1775, 0.17 #6813, 0.12 #9332), 01kb2j (0.25 #1036, 0.17 #6074, 0.12 #8593), 0j3v (0.25 #404, 0.17 #5442, 0.12 #7961), 03pm9 (0.07 #10565, 0.06 #13084, 0.03 #25680), 01qq_lp (0.07 #10838, 0.03 #13357, 0.03 #15876), 03h_0_z (0.07 #11325, 0.03 #23920, 0.01 #51631), 01_x6v (0.07 #10510, 0.03 #23105, 0.01 #50816), 01vqrm (0.07 #10815, 0.01 #23410, 0.01 #28449), 0g7k2g (0.06 #14340, 0.03 #11821, 0.02 #29455) >> Best rule #2145 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 02z0j; >> query: (?x12705, 042q3) <- contains(?x8264, ?x12705), contains(?x1264, ?x12705), ?x8264 = 09krp, location_of_ceremony(?x566, ?x12705), ?x566 = 04ztj, ?x1264 = 0345h >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #30452 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 94 *> proper extension: 0ldff; 017wh; 0fw4v; 086g2; 026mx4; 01zlx; 018jmn; *> query: (?x12705, 0n6f8) <- administrative_parent(?x12705, ?x8264), category(?x12705, ?x134), location(?x10654, ?x8264) *> conf = 0.01 ranks of expected_values: 1271 EVAL 0bp_7 location! 0n6f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 119.000 63.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #8533-03tk6z PRED entity: 03tk6z PRED relation: ceremony PRED expected values: 019bk0 01c6qp 09n4nb => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 126): 09n4nb (0.89 #544, 0.76 #1175, 0.76 #922), 01c6qp (0.83 #519, 0.73 #1150, 0.73 #1023), 019bk0 (0.77 #516, 0.70 #1147, 0.69 #894), 07y_p6 (0.53 #1135, 0.49 #2145, 0.42 #2524), 0bz6sb (0.53 #1135, 0.49 #2145, 0.42 #2524), 0clfdj (0.53 #1135, 0.49 #2145, 0.42 #2524), 0c4hx0 (0.53 #1135, 0.42 #2524, 0.42 #2904), 027hjff (0.53 #1135, 0.42 #2524, 0.42 #2904), 08pc1x (0.53 #1135, 0.42 #2524, 0.42 #2904), 0c53zb (0.49 #2145, 0.42 #2524, 0.42 #2904) >> Best rule #544 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 02gx2k; 025mb9; 0248jb; 02v703; 02fm4d; 024_dt; >> query: (?x4382, 09n4nb) <- award(?x3673, ?x4382), ceremony(?x4382, ?x486), ?x486 = 02rjjll, nominated_for(?x3673, ?x1395) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 03tk6z ceremony 09n4nb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.894 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03tk6z ceremony 01c6qp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.894 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03tk6z ceremony 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.894 http://example.org/award/award_category/winners./award/award_honor/ceremony #8532-04b2qn PRED entity: 04b2qn PRED relation: nominated_for! PRED expected values: 09sb52 02rdyk7 099ck7 => 112 concepts (104 used for prediction) PRED predicted values (max 10 best out of 183): 02x73k6 (0.68 #13627, 0.67 #3026, 0.67 #8001), 02x8n1n (0.68 #13627, 0.67 #3026, 0.67 #8001), 02x4w6g (0.68 #13627, 0.67 #3026, 0.67 #8001), 09cm54 (0.68 #13627, 0.67 #3026, 0.67 #8001), 09d28z (0.68 #13627, 0.67 #3026, 0.67 #8001), 02w_6xj (0.68 #13627, 0.67 #3026, 0.67 #8001), 0k611 (0.59 #926, 0.38 #710, 0.33 #62), 040njc (0.53 #871, 0.32 #4978, 0.31 #2816), 02qyntr (0.50 #1022, 0.28 #806, 0.27 #158), 02pqp12 (0.48 #915, 0.25 #51, 0.24 #699) >> Best rule #13627 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 06w7mlh; 07bz5; 06mmr; >> query: (?x7858, ?x2577) <- award_winner(?x7858, ?x1179), award(?x7858, ?x2577), award(?x91, ?x2577) >> conf = 0.68 => this is the best rule for 6 predicted values *> Best rule #680 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 01242_; 047myg9; 04165w; *> query: (?x7858, 09sb52) <- film_crew_role(?x7858, ?x1171), nominated_for(?x4091, ?x7858), ?x4091 = 09sdmz *> conf = 0.32 ranks of expected_values: 20, 22, 30 EVAL 04b2qn nominated_for! 099ck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 112.000 104.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04b2qn nominated_for! 02rdyk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 112.000 104.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04b2qn nominated_for! 09sb52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 112.000 104.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8531-01jmv8 PRED entity: 01jmv8 PRED relation: place_of_birth PRED expected values: 062qg => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 51): 0cr3d (0.33 #798, 0.04 #12766, 0.04 #12062), 04jpl (0.25 #8, 0.02 #4936, 0.02 #4232), 06c62 (0.20 #1665, 0.06 #3073), 01_d4 (0.17 #770, 0.03 #4290, 0.03 #6402), 02_286 (0.12 #2131, 0.08 #3539, 0.07 #4243), 030qb3t (0.10 #1462, 0.06 #2870, 0.04 #7798), 01b8w_ (0.10 #1742), 0n9r8 (0.10 #1655), 02dtg (0.06 #2122, 0.06 #2826, 0.02 #3530), 01531 (0.06 #2217, 0.02 #8553, 0.02 #19819) >> Best rule #798 for best value: >> intensional similarity = 2 >> extensional distance = 4 >> proper extension: 0kcdl; >> query: (?x8674, 0cr3d) <- nominated_for(?x8674, ?x4581), ?x4581 = 02ppg1r >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jmv8 place_of_birth 062qg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 77.000 0.333 http://example.org/people/person/place_of_birth #8530-015rkw PRED entity: 015rkw PRED relation: award_winner! PRED expected values: 0clfdj => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 108): 0clfdj (0.43 #4, 0.17 #2503, 0.03 #282), 0275n3y (0.17 #2503, 0.07 #74, 0.04 #2994), 0gx_st (0.17 #2503, 0.07 #37, 0.03 #454), 0drtv8 (0.17 #2503, 0.07 #65, 0.02 #482), 09qvms (0.17 #2503, 0.05 #430, 0.05 #1125), 0418154 (0.17 #2503, 0.05 #246, 0.03 #107), 09g90vz (0.17 #2503, 0.04 #3460, 0.04 #2626), 03gyp30 (0.17 #2503, 0.04 #533, 0.04 #2479), 05c1t6z (0.17 #2503, 0.03 #2935, 0.03 #3352), 027hjff (0.17 #2503, 0.03 #473, 0.03 #56) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 02cllz; >> query: (?x1739, 0clfdj) <- award_nominee(?x1739, ?x2531), award_nominee(?x1739, ?x2372), ?x2372 = 0l6px, participant(?x2531, ?x1371) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015rkw award_winner! 0clfdj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.433 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8529-04j14qc PRED entity: 04j14qc PRED relation: film_crew_role PRED expected values: 02r96rf => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.69 #112, 0.68 #1599, 0.67 #76), 0dxtw (0.44 #83, 0.42 #1606, 0.41 #119), 01vx2h (0.33 #84, 0.31 #47, 0.31 #1607), 01pvkk (0.29 #2554, 0.28 #1754, 0.28 #951), 02ynfr (0.21 #125, 0.18 #1612, 0.18 #955), 02rh1dz (0.16 #82, 0.12 #1605, 0.11 #2006), 0215hd (0.13 #200, 0.12 #1250, 0.12 #1214), 015h31 (0.12 #81, 0.09 #44, 0.09 #2005), 089g0h (0.10 #201, 0.10 #129, 0.10 #56), 01xy5l_ (0.10 #50, 0.10 #411, 0.10 #1610) >> Best rule #112 for best value: >> intensional similarity = 4 >> extensional distance = 175 >> proper extension: 05q7874; >> query: (?x8302, 02r96rf) <- films(?x942, ?x8302), nominated_for(?x541, ?x8302), film_crew_role(?x8302, ?x1171), ?x1171 = 09vw2b7 >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04j14qc film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.695 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8528-04zl8 PRED entity: 04zl8 PRED relation: film_release_region PRED expected values: 035qy => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 125): 0chghy (0.82 #330, 0.79 #1132, 0.77 #1292), 07ssc (0.78 #337, 0.74 #1139, 0.74 #978), 0jgd (0.78 #323, 0.74 #1125, 0.74 #1285), 01znc_ (0.76 #367, 0.69 #1169, 0.66 #848), 0345h (0.75 #356, 0.75 #1158, 0.71 #997), 035qy (0.74 #358, 0.70 #1160, 0.66 #999), 05qhw (0.72 #335, 0.68 #1137, 0.63 #1297), 05b4w (0.72 #390, 0.67 #1192, 0.67 #1352), 015fr (0.72 #339, 0.66 #1141, 0.64 #1301), 0154j (0.71 #325, 0.68 #1127, 0.62 #1287) >> Best rule #330 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 01gc7; 0m2kd; 05p1tzf; 0gkz15s; 01vksx; 017gl1; 08hmch; 01c22t; 0_92w; 0872p_c; ... >> query: (?x5317, 0chghy) <- film_release_region(?x5317, ?x1558), currency(?x5317, ?x1099), nominated_for(?x4297, ?x5317), ?x1558 = 01mjq >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #358 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 01gc7; 0m2kd; 05p1tzf; 0gkz15s; 01vksx; 017gl1; 08hmch; 01c22t; 0_92w; 0872p_c; ... *> query: (?x5317, 035qy) <- film_release_region(?x5317, ?x1558), currency(?x5317, ?x1099), nominated_for(?x4297, ?x5317), ?x1558 = 01mjq *> conf = 0.74 ranks of expected_values: 6 EVAL 04zl8 film_release_region 035qy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 79.000 0.824 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8527-024tkd PRED entity: 024tkd PRED relation: district_represented PRED expected values: 05k7sb 02xry 03s5t 081mh 07b_l 05tbn => 42 concepts (38 used for prediction) PRED predicted values (max 10 best out of 996): 05tbn (0.91 #986, 0.91 #968, 0.90 #921), 05fjf (0.89 #236, 0.89 #1130, 0.88 #1006), 05k7sb (0.89 #236, 0.88 #1050, 0.88 #1023), 0d0x8 (0.89 #236, 0.88 #1062, 0.86 #539), 06yxd (0.89 #236, 0.88 #1068, 0.86 #539), 081mh (0.89 #236, 0.86 #539, 0.86 #477), 05fkf (0.89 #236, 0.86 #539, 0.86 #477), 07b_l (0.89 #236, 0.86 #539, 0.86 #477), 04ykg (0.89 #236, 0.86 #539, 0.86 #477), 02xry (0.89 #236, 0.86 #539, 0.86 #477) >> Best rule #986 for best value: >> intensional similarity = 31 >> extensional distance = 21 >> proper extension: 01gt99; >> query: (?x6933, ?x3670) <- district_represented(?x6933, ?x7405), district_represented(?x6933, ?x4622), district_represented(?x6933, ?x1906), district_represented(?x6933, ?x1755), district_represented(?x6933, ?x448), legislative_sessions(?x8607, ?x6933), legislative_sessions(?x3445, ?x6933), legislative_sessions(?x952, ?x6933), legislative_sessions(?x605, ?x6933), ?x7405 = 07_f2, ?x448 = 03v1s, contains(?x1906, ?x4025), currency(?x1906, ?x170), country(?x1906, ?x94), religion(?x1906, ?x10107), ?x4622 = 04tgp, location(?x5574, ?x1906), legislative_sessions(?x2860, ?x605), adjoins(?x279, ?x1906), ?x10107 = 05w5d, profession(?x8607, ?x3342), district_represented(?x952, ?x6895), district_represented(?x952, ?x3670), location(?x940, ?x4025), jurisdiction_of_office(?x900, ?x1906), ?x3670 = 05tbn, ?x1755 = 01x73, ?x6895 = 05fjf, district_represented(?x605, ?x2982), basic_title(?x3445, ?x5402), state_province_region(?x266, ?x1906) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 6, 8, 10, 14 EVAL 024tkd district_represented 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 38.000 0.913 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 024tkd district_represented 07b_l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 42.000 38.000 0.913 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 024tkd district_represented 081mh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 42.000 38.000 0.913 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 024tkd district_represented 03s5t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 42.000 38.000 0.913 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 024tkd district_represented 02xry CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 42.000 38.000 0.913 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 024tkd district_represented 05k7sb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 42.000 38.000 0.913 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #8526-01p8s PRED entity: 01p8s PRED relation: film_release_region! PRED expected values: 0h3xztt 0gj8nq2 => 112 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1364): 067ghz (0.77 #10019, 0.72 #13985, 0.66 #16629), 027pfg (0.77 #10182, 0.69 #14148, 0.65 #11504), 01fmys (0.77 #9501, 0.69 #13467, 0.62 #16111), 017gm7 (0.73 #9415, 0.69 #13381, 0.65 #10737), 02vxq9m (0.69 #13237, 0.68 #9271, 0.65 #10593), 04f52jw (0.68 #9585, 0.66 #13551, 0.65 #10907), 05pdh86 (0.68 #9823, 0.66 #13789, 0.65 #11145), 017jd9 (0.68 #9847, 0.66 #13813, 0.62 #11169), 0dzlbx (0.68 #9905, 0.65 #11227, 0.62 #13871), 087wc7n (0.68 #9344, 0.62 #13310, 0.62 #10666) >> Best rule #10019 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0b90_r; 03rjj; 0h3y; 0d0vqn; 0chghy; 03_r3; 015fr; 0f8l9c; 0hzlz; 019rg5; ... >> query: (?x9730, 067ghz) <- olympics(?x9730, ?x3971), vacationer(?x9730, ?x2697), ?x3971 = 0jhn7 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #5706 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0j11; *> query: (?x9730, 0gj8nq2) <- contains(?x8483, ?x9730), film_release_region(?x280, ?x9730), ?x8483 = 059g4 *> conf = 0.67 ranks of expected_values: 13, 43 EVAL 01p8s film_release_region! 0gj8nq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 112.000 106.000 0.773 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p8s film_release_region! 0h3xztt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 112.000 106.000 0.773 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8525-01x2tm8 PRED entity: 01x2tm8 PRED relation: category PRED expected values: 08mbj5d => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.38 #6, 0.38 #2, 0.37 #31) >> Best rule #6 for best value: >> intensional similarity = 6 >> extensional distance = 22 >> proper extension: 03q3sy; 0479b; 013zs9; >> query: (?x9253, 08mbj5d) <- profession(?x9253, ?x6476), profession(?x9253, ?x1383), ?x1383 = 0np9r, award(?x9253, ?x1937), profession(?x10094, ?x6476), ?x10094 = 01wdcxk >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x2tm8 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.375 http://example.org/common/topic/webpage./common/webpage/category #8524-04rqd PRED entity: 04rqd PRED relation: production_companies! PRED expected values: 09rx7tx => 139 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1891): 09g8vhw (0.33 #1367, 0.20 #5952, 0.15 #19714), 047d21r (0.33 #1558, 0.20 #6143, 0.10 #19905), 03clwtw (0.33 #1940, 0.15 #20287, 0.12 #40925), 06_wqk4 (0.33 #1238, 0.15 #19585, 0.12 #25320), 03mh_tp (0.33 #1492, 0.15 #19839, 0.12 #25574), 0glqh5_ (0.33 #1747, 0.10 #20094, 0.10 #40732), 07y9w5 (0.33 #1302, 0.10 #19649, 0.08 #25384), 03wbqc4 (0.33 #1631, 0.10 #19978, 0.08 #25713), 0bq8tmw (0.33 #1318, 0.10 #19665, 0.08 #25400), 0cz_ym (0.33 #1351, 0.10 #19698, 0.08 #25433) >> Best rule #1367 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 054lpb6; >> query: (?x12476, 09g8vhw) <- production_companies(?x4788, ?x12476), film_regional_debut_venue(?x4788, ?x3288), nominated_for(?x2456, ?x4788), ?x2456 = 063y_ky >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6819 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0jrqq; 030_3z; *> query: (?x12476, 09rx7tx) <- award_winner(?x3486, ?x12476), award_winner(?x12476, ?x11453), ?x11453 = 0146mv, award_winner(?x7326, ?x12476) *> conf = 0.20 ranks of expected_values: 115 EVAL 04rqd production_companies! 09rx7tx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 139.000 54.000 0.333 http://example.org/film/film/production_companies #8523-02r_d4 PRED entity: 02r_d4 PRED relation: student! PRED expected values: 01s0_f => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 149): 0bwfn (0.20 #275, 0.12 #1327, 0.08 #19213), 026gvfj (0.20 #111, 0.12 #1163, 0.02 #11683), 04b_46 (0.20 #227, 0.06 #2857, 0.05 #3909), 06pwq (0.20 #12, 0.02 #3694, 0.01 #4746), 02q253 (0.20 #504), 0bx8pn (0.20 #45), 015nl4 (0.14 #593, 0.04 #11639, 0.04 #7431), 02s62q (0.14 #578, 0.02 #2682, 0.01 #2156), 025v3k (0.14 #646, 0.01 #4854, 0.01 #2224), 02237m (0.14 #923, 0.01 #3027) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02t_w8; >> query: (?x665, 0bwfn) <- nationality(?x665, ?x94), film(?x665, ?x5320), ?x5320 = 01zfzb >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3216 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 161 *> proper extension: 0n6f8; *> query: (?x665, 01s0_f) <- award_winner(?x2436, ?x665), film(?x665, ?x559), currency(?x665, ?x170) *> conf = 0.01 ranks of expected_values: 102 EVAL 02r_d4 student! 01s0_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 92.000 92.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #8522-0fzm0g PRED entity: 0fzm0g PRED relation: genre PRED expected values: 02l7c8 => 131 concepts (89 used for prediction) PRED predicted values (max 10 best out of 99): 02l7c8 (0.83 #1715, 0.54 #7558, 0.45 #9136), 01hmnh (0.80 #6206, 0.78 #2188, 0.78 #2082), 04xvlr (0.75 #2552, 0.74 #8635, 0.74 #4138), 01z4y (0.65 #2796, 0.64 #2551, 0.62 #1455), 03k9fj (0.58 #1587, 0.53 #2075, 0.52 #979), 01jfsb (0.39 #3661, 0.36 #2928, 0.35 #4999), 02kdv5l (0.36 #1579, 0.35 #8030, 0.35 #3652), 01t_vv (0.33 #538, 0.22 #1754, 0.16 #5406), 0gf28 (0.32 #427, 0.25 #790, 0.16 #3224), 060__y (0.30 #1349, 0.28 #3298, 0.27 #1472) >> Best rule #1715 for best value: >> intensional similarity = 5 >> extensional distance = 91 >> proper extension: 0hmr4; 0gxfz; 0k4f3; 0p4v_; 0kvgtf; 02q8ms8; 034xyf; 0f7hw; 01xlqd; >> query: (?x12934, 02l7c8) <- film(?x3079, ?x12934), titles(?x162, ?x12934), film_release_distribution_medium(?x12934, ?x81), genre(?x12934, ?x239), ?x239 = 06cvj >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fzm0g genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 89.000 0.828 http://example.org/film/film/genre #8521-02404v PRED entity: 02404v PRED relation: nominated_for PRED expected values: 01f8gz 0198b6 => 117 concepts (35 used for prediction) PRED predicted values (max 10 best out of 465): 0fjyzt (0.52 #24291, 0.51 #9710, 0.44 #14568), 0c9t0y (0.52 #24291, 0.44 #21047, 0.42 #17807), 05k4my (0.52 #24291, 0.44 #21047, 0.42 #17807), 0bz3jx (0.51 #29153, 0.51 #35632, 0.50 #42110), 01f8gz (0.44 #14568, 0.44 #21047, 0.42 #17807), 02r1c18 (0.40 #1838, 0.14 #6692, 0.04 #8310), 01jzyf (0.40 #2178, 0.14 #7032, 0.04 #8650), 02704ff (0.40 #2515, 0.09 #7369, 0.02 #25188), 0dkv90 (0.33 #1210, 0.14 #4446), 0dx8gj (0.33 #584, 0.14 #3820) >> Best rule #24291 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 04cw0n4; >> query: (?x7740, ?x10422) <- cinematography(?x10422, ?x7740), titles(?x812, ?x10422), genre(?x10422, ?x258), award(?x10422, ?x102) >> conf = 0.52 => this is the best rule for 3 predicted values *> Best rule #14568 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 0gp9mp; *> query: (?x7740, ?x6788) <- cinematography(?x6788, ?x7740), profession(?x7740, ?x524), written_by(?x6788, ?x4169), nominated_for(?x7740, ?x7502) *> conf = 0.44 ranks of expected_values: 5, 31 EVAL 02404v nominated_for 0198b6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 117.000 35.000 0.518 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 02404v nominated_for 01f8gz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 117.000 35.000 0.518 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #8520-01vng3b PRED entity: 01vng3b PRED relation: artists! PRED expected values: 01_bkd => 158 concepts (75 used for prediction) PRED predicted values (max 10 best out of 266): 064t9 (0.56 #3962, 0.51 #6395, 0.49 #7002), 0dl5d (0.50 #4575, 0.26 #10045, 0.25 #7616), 0glt670 (0.42 #3986, 0.31 #7026, 0.30 #6419), 06j6l (0.41 #22234, 0.40 #3994, 0.30 #7034), 05jg58 (0.40 #722, 0.38 #419, 0.32 #1938), 0155w (0.38 #7394, 0.26 #10128, 0.23 #3137), 05r6t (0.36 #1900, 0.33 #684, 0.19 #4938), 025sc50 (0.33 #3996, 0.25 #6429, 0.25 #956), 05bt6j (0.32 #4596, 0.29 #3989, 0.27 #4900), 01_bkd (0.31 #960, 0.23 #354, 0.20 #657) >> Best rule #3962 for best value: >> intensional similarity = 5 >> extensional distance = 43 >> proper extension: 05cljf; 01l1b90; 01vrt_c; 033wx9; 0gy6z9; 039bpc; 01q32bd; 01wv9p; 01vw20h; 03y82t6; ... >> query: (?x6225, 064t9) <- type_of_union(?x6225, ?x566), profession(?x6225, ?x131), participant(?x1955, ?x6225), artist(?x441, ?x6225), origin(?x6225, ?x8448) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #960 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 01vsxdm; *> query: (?x6225, 01_bkd) <- artist(?x10426, ?x6225), role(?x6225, ?x212), artists(?x302, ?x6225), ?x10426 = 01trtc *> conf = 0.31 ranks of expected_values: 10 EVAL 01vng3b artists! 01_bkd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 158.000 75.000 0.556 http://example.org/music/genre/artists #8519-01p7yb PRED entity: 01p7yb PRED relation: film PRED expected values: 0320fn => 107 concepts (65 used for prediction) PRED predicted values (max 10 best out of 736): 011yhm (0.59 #81859, 0.59 #49824, 0.58 #1780), 0b3n61 (0.54 #3129, 0.07 #4908, 0.06 #6687), 01sbv9 (0.31 #3404), 06w99h3 (0.29 #26, 0.01 #21377, 0.01 #23157), 043t8t (0.27 #4342, 0.25 #6121, 0.04 #115679), 01gkp1 (0.20 #4368, 0.19 #6147, 0.04 #115679), 08s6mr (0.19 #6647, 0.13 #4868, 0.03 #97877), 08952r (0.15 #2491, 0.02 #32737), 0g56t9t (0.15 #1790), 0fpmrm3 (0.14 #422, 0.08 #2202) >> Best rule #81859 for best value: >> intensional similarity = 3 >> extensional distance = 1315 >> proper extension: 04bs3j; 0241wg; 01gw4f; 078g3l; 04kwbt; >> query: (?x396, ?x288) <- nominated_for(?x396, ?x288), film(?x396, ?x1173), award(?x396, ?x375) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01p7yb film 0320fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 65.000 0.588 http://example.org/film/actor/film./film/performance/film #8518-0653m PRED entity: 0653m PRED relation: languages_spoken! PRED expected values: 04l_pt => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 70): 07hwkr (0.68 #502, 0.50 #572, 0.44 #1483), 03w9bjf (0.35 #468, 0.33 #188, 0.20 #328), 059_w (0.33 #167, 0.33 #97, 0.20 #307), 0x67 (0.33 #150, 0.33 #80, 0.20 #290), 071x0k (0.33 #148, 0.33 #78, 0.20 #288), 04l_pt (0.33 #40, 0.25 #250, 0.20 #320), 078vc (0.33 #182, 0.24 #462, 0.20 #322), 033tf_ (0.33 #147, 0.20 #287, 0.17 #2032), 0bbz66j (0.33 #184, 0.20 #324, 0.14 #604), 09zyn5 (0.33 #206, 0.20 #346, 0.12 #486) >> Best rule #502 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 0swlx; >> query: (?x2890, 07hwkr) <- languages_spoken(?x7562, ?x2890), official_language(?x2346, ?x2890), olympics(?x2346, ?x418), locations(?x3654, ?x2346), adjoins(?x2346, ?x404), country(?x150, ?x2346) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 012w70; *> query: (?x2890, 04l_pt) <- language(?x10446, ?x2890), language(?x10080, ?x2890), language(?x6788, ?x2890), film_release_region(?x10080, ?x94), ?x10446 = 0gyv0b4, film_regional_debut_venue(?x10080, ?x13344), ?x6788 = 01f8f7, film(?x1864, ?x10080), languages_spoken(?x7562, ?x2890) *> conf = 0.33 ranks of expected_values: 6 EVAL 0653m languages_spoken! 04l_pt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 45.000 45.000 0.684 http://example.org/people/ethnicity/languages_spoken #8517-04wddl PRED entity: 04wddl PRED relation: nominated_for! PRED expected values: 0gs9p => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 201): 0gq9h (0.69 #1234, 0.64 #4289, 0.62 #1939), 0gs9p (0.58 #1941, 0.55 #4291, 0.53 #3351), 019f4v (0.58 #1932, 0.53 #3342, 0.46 #4282), 0k611 (0.54 #3360, 0.52 #1950, 0.43 #4300), 040njc (0.49 #1887, 0.39 #3297, 0.37 #4237), 0p9sw (0.45 #3310, 0.36 #1900, 0.29 #1665), 0f4x7 (0.43 #965, 0.42 #1905, 0.41 #730), 0l8z1 (0.37 #3340, 0.24 #6395, 0.23 #7100), 0gqy2 (0.35 #4349, 0.35 #1294, 0.34 #1999), 02qyntr (0.35 #3467, 0.26 #2057, 0.25 #6522) >> Best rule #1234 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 0h1v19; 0kb57; 04tqtl; 0kb07; 0gt1k; 0ktpx; 0gnjh; 0gndh; 0cbn7c; 0jdr0; ... >> query: (?x9183, 0gq9h) <- genre(?x9183, ?x1805), nominated_for(?x484, ?x9183), nominated_for(?x2716, ?x9183), ?x1805 = 01g6gs >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1941 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 019kyn; *> query: (?x9183, 0gs9p) <- award_winner(?x9183, ?x4405), list(?x9183, ?x3004), film(?x5348, ?x9183), genre(?x9183, ?x53) *> conf = 0.58 ranks of expected_values: 2 EVAL 04wddl nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.694 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8516-0dscrwf PRED entity: 0dscrwf PRED relation: film_release_region PRED expected values: 03_3d => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 145): 02vzc (0.82 #317, 0.81 #455, 0.81 #1560), 03_3d (0.81 #418, 0.78 #280, 0.76 #142), 03rj0 (0.65 #48, 0.65 #186, 0.65 #1567), 06qd3 (0.62 #442, 0.60 #304, 0.52 #1547), 01mjq (0.55 #447, 0.55 #1552, 0.53 #33), 05qx1 (0.53 #307, 0.53 #31, 0.51 #169), 047lj (0.53 #284, 0.46 #422, 0.42 #8), 06mzp (0.52 #429, 0.51 #1534, 0.49 #291), 01pj7 (0.47 #38, 0.37 #314, 0.33 #176), 0h7x (0.43 #1130, 0.42 #439, 0.41 #716) >> Best rule #317 for best value: >> intensional similarity = 6 >> extensional distance = 90 >> proper extension: 0407yj_; 01f85k; 07s3m4g; 0fpgp26; >> query: (?x511, 02vzc) <- film_release_region(?x511, ?x2843), film_release_region(?x511, ?x390), film_release_region(?x511, ?x279), ?x390 = 0chghy, ?x2843 = 016wzw, ?x279 = 0d060g >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #418 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 116 *> proper extension: 0h3xztt; 0m63c; *> query: (?x511, 03_3d) <- film_release_region(?x511, ?x2843), film_release_region(?x511, ?x390), film_release_region(?x511, ?x279), ?x390 = 0chghy, ?x2843 = 016wzw, contains(?x279, ?x481) *> conf = 0.81 ranks of expected_values: 2 EVAL 0dscrwf film_release_region 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 75.000 0.815 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8515-02f716 PRED entity: 02f716 PRED relation: award! PRED expected values: 01j4ls 03h_fk5 01wj18h 018n6m 0127s7 01323p => 41 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2124): 06mj4 (0.84 #13283, 0.83 #9961, 0.67 #18893), 0fhxv (0.84 #13283, 0.83 #9961, 0.67 #17928), 01w7nww (0.84 #13283, 0.83 #9961, 0.60 #14138), 01v_pj6 (0.84 #13283, 0.83 #9961, 0.50 #17023), 018n6m (0.67 #17925, 0.60 #11282, 0.50 #7961), 01vvycq (0.64 #30031, 0.44 #23391, 0.40 #13430), 0gcs9 (0.60 #10764, 0.55 #30687, 0.50 #7443), 02qwg (0.55 #30800, 0.50 #27480, 0.43 #34121), 0bs1g5r (0.55 #32212, 0.33 #18932, 0.33 #2328), 03h_fk5 (0.50 #17357, 0.50 #7393, 0.45 #30637) >> Best rule #13283 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 02f79n; >> query: (?x3365, ?x1674) <- award(?x9868, ?x3365), award(?x7407, ?x3365), award(?x1896, ?x3365), award(?x1206, ?x3365), ?x9868 = 0134pk, ?x1206 = 01vrt_c, award_winner(?x3365, ?x1674), origin(?x7407, ?x362), award_nominee(?x959, ?x1896) >> conf = 0.84 => this is the best rule for 4 predicted values *> Best rule #17925 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 01by1l; *> query: (?x3365, 018n6m) <- award(?x2876, ?x3365), award(?x1060, ?x3365), award(?x827, ?x3365), ?x1060 = 02r3zy, profession(?x2876, ?x220), ?x827 = 02l840 *> conf = 0.67 ranks of expected_values: 5, 10, 13, 25, 30, 91 EVAL 02f716 award! 01323p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 41.000 18.000 0.840 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f716 award! 0127s7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 41.000 18.000 0.840 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f716 award! 018n6m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 41.000 18.000 0.840 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f716 award! 01wj18h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 41.000 18.000 0.840 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f716 award! 03h_fk5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 41.000 18.000 0.840 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f716 award! 01j4ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 41.000 18.000 0.840 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8514-077q8x PRED entity: 077q8x PRED relation: films! PRED expected values: 0f6lx => 94 concepts (32 used for prediction) PRED predicted values (max 10 best out of 56): 081pw (0.12 #3, 0.05 #1731, 0.05 #2050), 0fx2s (0.12 #73, 0.04 #700, 0.03 #1801), 0g1x2_ (0.12 #27, 0.02 #183, 0.02 #810), 05489 (0.04 #208, 0.03 #1780, 0.03 #522), 0jm_ (0.04 #164, 0.01 #478), 06d4h (0.03 #3356, 0.03 #4778, 0.03 #513), 03r8gp (0.03 #873, 0.02 #560, 0.02 #1660), 0d1w9 (0.03 #976, 0.02 #1133, 0.02 #1448), 07_nf (0.03 #380, 0.02 #537, 0.02 #1795), 0fzyg (0.02 #681, 0.02 #837, 0.02 #210) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 09gdh6k; >> query: (?x6169, 081pw) <- film(?x382, ?x6169), nominated_for(?x1365, ?x6169), titles(?x53, ?x6169), ?x1365 = 0bwh6 >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 077q8x films! 0f6lx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 32.000 0.125 http://example.org/film/film_subject/films #8513-05c9zr PRED entity: 05c9zr PRED relation: edited_by PRED expected values: 02qggqc => 141 concepts (93 used for prediction) PRED predicted values (max 10 best out of 23): 04wp63 (0.14 #606, 0.07 #752, 0.03 #841), 03q8ch (0.08 #652, 0.08 #798, 0.07 #1003), 02qggqc (0.07 #729, 0.07 #1051, 0.04 #642), 0343h (0.05 #587, 0.03 #997, 0.02 #1234), 027pdrh (0.05 #590, 0.02 #736, 0.02 #1237), 03_gd (0.05 #584, 0.02 #760, 0.01 #848), 04cy8rb (0.04 #1049, 0.03 #1439, 0.02 #1348), 02kxbx3 (0.03 #1060, 0.02 #1450, 0.02 #1687), 0bn3jg (0.03 #813, 0.02 #1076, 0.02 #959), 03crcpt (0.03 #977, 0.01 #1932, 0.01 #1662) >> Best rule #606 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 0d90m; 07sc6nw; 0j6b5; 02w86hz; 02wgk1; 07bzz7; 012s1d; >> query: (?x4132, 04wp63) <- film_crew_role(?x4132, ?x2848), genre(?x4132, ?x1510), country(?x4132, ?x94), film(?x548, ?x4132), ?x1510 = 01hmnh, ?x2848 = 094hwz >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #729 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 42 *> proper extension: 07nt8p; 0243cq; 0642xf3; 04k9y6; 03hxsv; 03t95n; 027pfg; 02q5bx2; *> query: (?x4132, 02qggqc) <- film_crew_role(?x4132, ?x468), genre(?x4132, ?x1510), country(?x4132, ?x94), film(?x548, ?x4132), ?x1510 = 01hmnh, category(?x4132, ?x134) *> conf = 0.07 ranks of expected_values: 3 EVAL 05c9zr edited_by 02qggqc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 141.000 93.000 0.143 http://example.org/film/film/edited_by #8512-0nt6b PRED entity: 0nt6b PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 179 concepts (89 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.90 #376, 0.87 #437, 0.87 #499), 03v1s (0.20 #498, 0.13 #790, 0.12 #388), 0nt6b (0.20 #498, 0.13 #790, 0.09 #322), 03v0t (0.12 #388, 0.11 #725, 0.10 #831), 02jx1 (0.08 #469, 0.07 #318, 0.07 #867), 03rt9 (0.03 #515, 0.03 #591, 0.02 #835), 0f8l9c (0.02 #228), 03rjj (0.01 #1074, 0.01 #285, 0.01 #527), 07ssc (0.01 #289) >> Best rule #376 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 0mxcf; 0p0cw; 0fc2c; 0p01x; 0l30v; 0mvxt; >> query: (?x11129, 09c7w0) <- adjoins(?x11130, ?x11129), currency(?x11130, ?x170), contains(?x448, ?x11130), county(?x7826, ?x11129) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nt6b second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 89.000 0.901 http://example.org/location/country/second_level_divisions #8511-0sw6g PRED entity: 0sw6g PRED relation: film PRED expected values: 050f0s 01pvxl => 102 concepts (85 used for prediction) PRED predicted values (max 10 best out of 994): 07c72 (0.65 #3564, 0.64 #7128, 0.59 #87300), 030cx (0.65 #3564, 0.64 #7128, 0.59 #87300), 02czd5 (0.65 #3564, 0.64 #7128, 0.59 #87300), 07tw_b (0.22 #676, 0.03 #115804, 0.01 #14929), 0dtw1x (0.19 #5346, 0.13 #10691), 03mh94 (0.11 #63, 0.05 #1845, 0.03 #115804), 0m63c (0.11 #1327, 0.03 #3109, 0.03 #4891), 02ctc6 (0.11 #519, 0.03 #2301, 0.02 #5865), 02qhqz4 (0.11 #342, 0.03 #115804, 0.02 #5688), 02c7k4 (0.11 #1095, 0.02 #22474, 0.01 #2877) >> Best rule #3564 for best value: >> intensional similarity = 3 >> extensional distance = 86 >> proper extension: 015qq1; >> query: (?x8061, ?x3180) <- nominated_for(?x8061, ?x3180), award(?x8061, ?x2192), ?x2192 = 0bfvd4 >> conf = 0.65 => this is the best rule for 3 predicted values *> Best rule #21687 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 375 *> proper extension: 06v8s0; 02qjj7; 01g4zr; 01c58j; 01gx5f; 059xvg; 08n9ng; 0chrwb; 01s7qqw; 081jbk; ... *> query: (?x8061, 050f0s) <- profession(?x8061, ?x1383), ?x1383 = 0np9r *> conf = 0.02 ranks of expected_values: 263, 492 EVAL 0sw6g film 01pvxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 102.000 85.000 0.649 http://example.org/film/actor/film./film/performance/film EVAL 0sw6g film 050f0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 102.000 85.000 0.649 http://example.org/film/actor/film./film/performance/film #8510-01x73 PRED entity: 01x73 PRED relation: adjoins! PRED expected values: 05k7sb => 168 concepts (109 used for prediction) PRED predicted values (max 10 best out of 1298): 059rby (0.86 #50234, 0.83 #50233, 0.82 #28241), 07h34 (0.35 #9595, 0.32 #7244, 0.30 #8810), 0d060g (0.33 #11, 0.25 #3146, 0.25 #2361), 05rgl (0.33 #103, 0.25 #3238, 0.25 #2453), 0b90_r (0.33 #5, 0.25 #3140, 0.25 #2355), 07_f2 (0.29 #5818, 0.25 #1899, 0.20 #6603), 0694j (0.29 #5789, 0.25 #1870, 0.16 #7358), 059f4 (0.29 #5521, 0.11 #7090, 0.09 #21996), 0j3b (0.29 #5546, 0.07 #25946, 0.07 #6331), 081mh (0.27 #6418, 0.25 #1714, 0.22 #8768) >> Best rule #50234 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 04hvw; >> query: (?x1755, ?x335) <- adjoins(?x1755, ?x335), currency(?x1755, ?x170), administrative_parent(?x334, ?x335) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1679 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 059rby; 0498y; *> query: (?x1755, 05k7sb) <- district_represented(?x11142, ?x1755), district_represented(?x2019, ?x1755), ?x2019 = 01gtbb, ?x11142 = 01grq1, location_of_ceremony(?x1545, ?x1755) *> conf = 0.25 ranks of expected_values: 17 EVAL 01x73 adjoins! 05k7sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 168.000 109.000 0.856 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #8509-011ywj PRED entity: 011ywj PRED relation: country PRED expected values: 03rjj 07ssc => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 23): 07ssc (0.50 #16, 0.37 #2888, 0.22 #196), 0f8l9c (0.37 #2888, 0.17 #79, 0.09 #1401), 03rt9 (0.37 #2888, 0.10 #14, 0.02 #74), 02jx1 (0.37 #2888, 0.01 #88), 04xn_ (0.37 #2888), 0345h (0.13 #207, 0.11 #1409, 0.10 #27), 0ctw_b (0.10 #23, 0.02 #143, 0.02 #743), 01z4y (0.06 #2347, 0.06 #2346, 0.06 #1683), 01jfsb (0.06 #2347, 0.06 #2346, 0.06 #1683), 02n4kr (0.06 #2347, 0.06 #2346, 0.06 #1683) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0g5pv3; >> query: (?x8367, 07ssc) <- film(?x5422, ?x8367), genre(?x8367, ?x53), ?x5422 = 06j8wx >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 13 EVAL 011ywj country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.500 http://example.org/film/film/country EVAL 011ywj country 03rjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 50.000 50.000 0.500 http://example.org/film/film/country #8508-03bxp5 PRED entity: 03bxp5 PRED relation: film_sets_designed! PRED expected values: 057bc6m => 78 concepts (55 used for prediction) PRED predicted values (max 10 best out of 15): 076lxv (0.04 #125, 0.04 #51, 0.04 #175), 07h1tr (0.04 #53, 0.04 #177, 0.04 #102), 057bc6m (0.03 #109, 0.03 #210, 0.03 #60), 01x6v6 (0.03 #149, 0.02 #199, 0.02 #1145), 072twv (0.03 #149, 0.02 #199, 0.02 #1145), 086sj (0.03 #149, 0.02 #199, 0.02 #1145), 0c0tzp (0.02 #142, 0.02 #192, 0.02 #218), 051ysmf (0.02 #46, 0.02 #71, 0.02 #120), 076psv (0.02 #205, 0.02 #281, 0.02 #55), 0cb77r (0.02 #99, 0.02 #200, 0.01 #50) >> Best rule #125 for best value: >> intensional similarity = 4 >> extensional distance = 176 >> proper extension: 02d413; 0b2v79; 02v8kmz; 047q2k1; 090s_0; 011yrp; 095zlp; 0ds11z; 0g5qs2k; 04jwjq; ... >> query: (?x6199, 076lxv) <- costume_design_by(?x6199, ?x6327), nominated_for(?x786, ?x6199), genre(?x6199, ?x53), nominated_for(?x384, ?x6199) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #109 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 162 *> proper extension: 048rn; 0jqb8; *> query: (?x6199, 057bc6m) <- costume_design_by(?x6199, ?x6327), film(?x4106, ?x6199), film_release_distribution_medium(?x6199, ?x81), award(?x4106, ?x1008) *> conf = 0.03 ranks of expected_values: 3 EVAL 03bxp5 film_sets_designed! 057bc6m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 55.000 0.039 http://example.org/film/film_set_designer/film_sets_designed #8507-0dl9_4 PRED entity: 0dl9_4 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.81 #169, 0.81 #82, 0.81 #87), 07c52 (0.30 #66, 0.05 #23, 0.05 #63), 07z4p (0.05 #45, 0.05 #40, 0.04 #65), 02nxhr (0.04 #78, 0.03 #37, 0.03 #220), 0735l (0.02 #24) >> Best rule #169 for best value: >> intensional similarity = 3 >> extensional distance = 724 >> proper extension: 04lqvlr; >> query: (?x5185, 029j_) <- nominated_for(?x591, ?x5185), film_crew_role(?x5185, ?x137), currency(?x5185, ?x1099) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dl9_4 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.814 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #8506-0tj4y PRED entity: 0tj4y PRED relation: source PRED expected values: 0jbk9 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #77, 0.78 #39, 0.77 #50) >> Best rule #77 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 0_rwf; 010bnr; >> query: (?x5525, 0jbk9) <- category(?x5525, ?x134), ?x134 = 08mbj5d, place(?x5525, ?x5525) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tj4y source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #8505-0133x7 PRED entity: 0133x7 PRED relation: award PRED expected values: 054ks3 => 129 concepts (107 used for prediction) PRED predicted values (max 10 best out of 286): 02gdjb (0.86 #2395, 0.80 #7184, 0.79 #7584), 0c4z8 (0.68 #72, 0.33 #870, 0.33 #2067), 01c92g (0.64 #96, 0.27 #894, 0.27 #2091), 01by1l (0.59 #111, 0.52 #909, 0.50 #21268), 01bgqh (0.50 #43, 0.44 #841, 0.42 #2038), 02f73p (0.36 #187, 0.31 #2182, 0.26 #2582), 02f6xy (0.36 #200, 0.17 #3393, 0.16 #6984), 01ckcd (0.34 #1927, 0.27 #2726, 0.26 #3125), 02f5qb (0.33 #2150, 0.30 #1751, 0.27 #953), 054ks3 (0.32 #21298, 0.32 #141, 0.25 #939) >> Best rule #2395 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 0152cw; 01vrwfv; 0161c2; 03f0vvr; 081wh1; 07r1_; 01wf86y; 07hgm; 04qzm; 016vn3; ... >> query: (?x7112, ?x4488) <- artist(?x6672, ?x7112), award(?x7112, ?x1479), award_winner(?x4488, ?x7112) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #21298 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 374 *> proper extension: 012wg; 01jrvr6; 01pbs9w; 01v6480; 08c7cz; 02bn75; 023361; 0pkgt; 09xx0m; 0164y7; *> query: (?x7112, 054ks3) <- award(?x7112, ?x4416), award_winner(?x4416, ?x1826), ?x1826 = 09mq4m *> conf = 0.32 ranks of expected_values: 10 EVAL 0133x7 award 054ks3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 129.000 107.000 0.861 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8504-01d2v1 PRED entity: 01d2v1 PRED relation: crewmember PRED expected values: 09pjnd => 76 concepts (61 used for prediction) PRED predicted values (max 10 best out of 30): 092ys_y (0.16 #209, 0.15 #256, 0.12 #162), 051z6rz (0.08 #171, 0.08 #218, 0.08 #265), 0cw67g (0.08 #186, 0.08 #280, 0.06 #139), 0bbxx9b (0.08 #210, 0.08 #257, 0.05 #447), 03r1pr (0.08 #65, 0.07 #395, 0.06 #112), 03m49ly (0.08 #83, 0.06 #130, 0.04 #177), 021yc7p (0.08 #56, 0.06 #103, 0.04 #150), 04ktcgn (0.07 #343, 0.07 #438, 0.07 #535), 09pjnd (0.07 #572, 0.07 #571, 0.07 #474), 0p_pd (0.07 #572, 0.07 #571, 0.07 #474) >> Best rule #209 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 04nnpw; 03t79f; 05ch98; >> query: (?x11174, 092ys_y) <- genre(?x11174, ?x6277), ?x6277 = 0fdjb, film_release_distribution_medium(?x11174, ?x81), country(?x11174, ?x94) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #572 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 99 *> proper extension: 01ln5z; 0639bg; 057lbk; 06gb1w; 0432_5; 031hcx; 0233bn; 01k0vq; 02bj22; 03cwwl; ... *> query: (?x11174, ?x397) <- prequel(?x5045, ?x11174), nominated_for(?x397, ?x11174), film_crew_role(?x11174, ?x2154), award(?x397, ?x591) *> conf = 0.07 ranks of expected_values: 9 EVAL 01d2v1 crewmember 09pjnd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 76.000 61.000 0.160 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #8503-027fwmt PRED entity: 027fwmt PRED relation: nominated_for! PRED expected values: 02g3v6 0gs96 => 70 concepts (58 used for prediction) PRED predicted values (max 10 best out of 219): 054krc (0.33 #69, 0.20 #12679, 0.20 #13397), 0gr51 (0.33 #78, 0.14 #4145, 0.12 #7973), 0gs96 (0.33 #90, 0.12 #1763, 0.12 #4157), 094qd5 (0.33 #36, 0.10 #1709, 0.09 #11004), 02g3v6 (0.29 #260, 0.18 #499, 0.17 #738), 0gq9h (0.24 #4129, 0.23 #7957, 0.22 #7240), 0gs9p (0.21 #4131, 0.20 #7959, 0.19 #7242), 02hsq3m (0.21 #268, 0.21 #507, 0.20 #746), 019f4v (0.21 #4121, 0.18 #7949, 0.18 #6513), 024dzn (0.20 #11483, 0.20 #12679, 0.20 #13397) >> Best rule #69 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01gvpz; >> query: (?x9800, 054krc) <- film(?x6042, ?x9800), titles(?x1510, ?x9800), genre(?x97, ?x1510), ?x6042 = 01wrcxr >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #90 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 01gvpz; *> query: (?x9800, 0gs96) <- film(?x6042, ?x9800), titles(?x1510, ?x9800), genre(?x97, ?x1510), ?x6042 = 01wrcxr *> conf = 0.33 ranks of expected_values: 3, 5 EVAL 027fwmt nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 70.000 58.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027fwmt nominated_for! 02g3v6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 70.000 58.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8502-013rds PRED entity: 013rds PRED relation: profession PRED expected values: 0np9r => 132 concepts (91 used for prediction) PRED predicted values (max 10 best out of 92): 02hrh1q (0.96 #6293, 0.88 #8924, 0.87 #3810), 01d_h8 (0.82 #4825, 0.82 #7747, 0.80 #6431), 03gjzk (0.71 #1036, 0.67 #744, 0.57 #890), 0nbcg (0.67 #468, 0.53 #9822, 0.50 #1928), 0dxtg (0.52 #4978, 0.51 #5708, 0.50 #5854), 0dz3r (0.50 #1608, 0.50 #2, 0.49 #3507), 02jknp (0.48 #4243, 0.46 #4097, 0.44 #5557), 01c72t (0.42 #1628, 0.33 #460, 0.32 #11422), 0cbd2 (0.33 #590, 0.29 #882, 0.25 #1320), 012t_z (0.33 #741, 0.29 #1033, 0.14 #1179) >> Best rule #6293 for best value: >> intensional similarity = 5 >> extensional distance = 92 >> proper extension: 02hy9p; 01vh3r; 044zvm; >> query: (?x12825, 02hrh1q) <- executive_produced_by(?x9565, ?x12825), profession(?x12825, ?x1183), film(?x12825, ?x8028), profession(?x1660, ?x1183), ?x1660 = 012x4t >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #6884 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 115 *> proper extension: 06jzh; *> query: (?x12825, 0np9r) <- person(?x9565, ?x12825), film(?x12825, ?x8028), gender(?x12825, ?x231) *> conf = 0.17 ranks of expected_values: 19 EVAL 013rds profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 132.000 91.000 0.957 http://example.org/people/person/profession #8501-03f7m4h PRED entity: 03f7m4h PRED relation: instrumentalists! PRED expected values: 0mkg => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 97): 0342h (0.70 #354, 0.63 #1311, 0.62 #1486), 018vs (0.33 #1319, 0.33 #2103, 0.32 #1407), 02hnl (0.21 #1340, 0.20 #383, 0.20 #1428), 0l14qv (0.15 #355, 0.10 #2096, 0.09 #616), 03qjg (0.15 #1532, 0.14 #400, 0.14 #1880), 0l14md (0.14 #357, 0.11 #879, 0.11 #2708), 026t6 (0.14 #352, 0.11 #1309, 0.11 #2703), 018j2 (0.09 #2738, 0.08 #1519, 0.08 #1867), 04rzd (0.08 #386, 0.08 #2737, 0.08 #1866), 06ncr (0.08 #1525, 0.07 #1873, 0.07 #2744) >> Best rule #354 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 0cbm64; >> query: (?x8352, 0342h) <- participant(?x10754, ?x8352), artists(?x671, ?x8352), instrumentalists(?x316, ?x8352), artist(?x2190, ?x8352) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1840 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 324 *> proper extension: 03_0p; *> query: (?x8352, 0mkg) <- award(?x8352, ?x1801), instrumentalists(?x316, ?x8352), award_winner(?x4317, ?x8352), ceremony(?x1801, ?x139) *> conf = 0.04 ranks of expected_values: 27 EVAL 03f7m4h instrumentalists! 0mkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 109.000 109.000 0.702 http://example.org/music/instrument/instrumentalists #8500-0kbws PRED entity: 0kbws PRED relation: participating_countries PRED expected values: 056vv 09lxtg 027jk 05bmq 04vjh 04hvw => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 108): 03gj2 (0.86 #658, 0.82 #657, 0.80 #41), 0k6nt (0.86 #658, 0.82 #657, 0.80 #41), 0154j (0.86 #658, 0.82 #657, 0.80 #41), 01znc_ (0.86 #658, 0.82 #657, 0.80 #41), 03rt9 (0.86 #658, 0.82 #657, 0.80 #41), 01mjq (0.86 #658, 0.82 #657, 0.80 #41), 04gzd (0.86 #658, 0.82 #657, 0.80 #41), 03rk0 (0.82 #657, 0.80 #41, 0.72 #963), 01crd5 (0.82 #657, 0.80 #41, 0.72 #963), 02k54 (0.82 #657, 0.80 #41, 0.72 #963) >> Best rule #658 for best value: >> intensional similarity = 22 >> extensional distance = 5 >> proper extension: 018ctl; 09x3r; 0sx8l; 0blfl; >> query: (?x1931, ?x344) <- participating_countries(?x1931, ?x6307), participating_countries(?x1931, ?x4954), participating_countries(?x1931, ?x2513), participating_countries(?x1931, ?x2267), participating_countries(?x1931, ?x1497), participating_countries(?x1931, ?x304), sports(?x1931, ?x668), ?x2267 = 03rj0, ?x1497 = 015qh, ?x304 = 0d0vqn, olympics(?x344, ?x1931), ?x2513 = 05b4w, olympics(?x150, ?x1931), film_release_region(?x8292, ?x344), film_release_region(?x1927, ?x344), film_release_region(?x1386, ?x344), organization(?x4954, ?x127), ?x1927 = 0by1wkq, ?x8292 = 0cmf0m0, ?x1386 = 0dtfn, contains(?x6304, ?x6307), olympics(?x1122, ?x1931) >> conf = 0.86 => this is the best rule for 7 predicted values *> Best rule #770 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 6 *> proper extension: 09n48; *> query: (?x1931, ?x172) <- participating_countries(?x1931, ?x6307), participating_countries(?x1931, ?x2267), participating_countries(?x1931, ?x1497), participating_countries(?x1931, ?x304), sports(?x1931, ?x1352), ?x2267 = 03rj0, ?x1497 = 015qh, country(?x150, ?x304), contains(?x455, ?x304), film_release_region(?x7009, ?x304), film_release_region(?x4841, ?x304), film_release_region(?x2168, ?x304), film_release_region(?x1859, ?x304), film_release_region(?x324, ?x304), countries_spoken_in(?x4442, ?x304), ?x1859 = 0m491, jurisdiction_of_office(?x346, ?x6307), ?x2168 = 0bx0l, country(?x1352, ?x172), ?x324 = 07gp9, ?x4841 = 0k4fz, ?x7009 = 0bs8s1p *> conf = 0.34 ranks of expected_values: 42, 48, 50, 51, 53, 63 EVAL 0kbws participating_countries 04hvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 67.000 67.000 0.860 http://example.org/olympics/olympic_games/participating_countries EVAL 0kbws participating_countries 04vjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 67.000 67.000 0.860 http://example.org/olympics/olympic_games/participating_countries EVAL 0kbws participating_countries 05bmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 67.000 67.000 0.860 http://example.org/olympics/olympic_games/participating_countries EVAL 0kbws participating_countries 027jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 67.000 67.000 0.860 http://example.org/olympics/olympic_games/participating_countries EVAL 0kbws participating_countries 09lxtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 67.000 67.000 0.860 http://example.org/olympics/olympic_games/participating_countries EVAL 0kbws participating_countries 056vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 67.000 67.000 0.860 http://example.org/olympics/olympic_games/participating_countries #8499-03g5_y PRED entity: 03g5_y PRED relation: category PRED expected values: 08mbj5d => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.61 #2, 0.60 #3, 0.52 #5) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 01q_ph; 01vrncs; 0343h; 01n4f8; 016_mj; 0j1yf; 0bj9k; 01vs_v8; 01pgzn_; 01trhmt; ... >> query: (?x7872, 08mbj5d) <- influenced_by(?x7872, ?x1145), participant(?x1735, ?x7872), award_nominee(?x7872, ?x8716) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03g5_y category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.609 http://example.org/common/topic/webpage./common/webpage/category #8498-0cr3d PRED entity: 0cr3d PRED relation: origin! PRED expected values: 0677ng => 105 concepts (84 used for prediction) PRED predicted values (max 10 best out of 580): 03f2_rc (0.37 #2510, 0.23 #28123, 0.17 #2007), 01lvcs1 (0.37 #2510, 0.17 #2007, 0.13 #9546), 0h7pj (0.33 #383, 0.25 #1887, 0.02 #3896), 0g_g2 (0.33 #205, 0.25 #1709, 0.02 #3718), 0gcs9 (0.33 #115, 0.25 #1619, 0.02 #3628), 01vrt_c (0.33 #533, 0.09 #2039, 0.04 #2542), 0gr69 (0.33 #805, 0.04 #2311, 0.04 #4318), 06s7rd (0.33 #853, 0.04 #2359, 0.04 #2862), 019g40 (0.33 #558, 0.04 #2064, 0.04 #2567), 0136pk (0.33 #580, 0.04 #2086, 0.04 #2589) >> Best rule #2510 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 01c40n; >> query: (?x2850, ?x538) <- place_of_birth(?x538, ?x2850), featured_film_locations(?x204, ?x2850), performance_role(?x538, ?x1466) >> conf = 0.37 => this is the best rule for 2 predicted values *> Best rule #3321 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 07tgn; 01mc11; 024bqj; 0fb18; 0ttxp; 0chgsm; 0yb_4; *> query: (?x2850, 0677ng) <- contains(?x739, ?x2850), contains(?x2850, ?x5981), citytown(?x166, ?x739) *> conf = 0.02 ranks of expected_values: 371 EVAL 0cr3d origin! 0677ng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 105.000 84.000 0.370 http://example.org/music/artist/origin #8497-076689 PRED entity: 076689 PRED relation: people! PRED expected values: 0m32h => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 31): 0gk4g (0.20 #76, 0.20 #10, 0.19 #736), 0dq9p (0.20 #83, 0.12 #743, 0.08 #809), 02y0js (0.19 #134, 0.05 #1192, 0.05 #1391), 02k6hp (0.12 #169, 0.06 #235, 0.05 #963), 04p3w (0.08 #737, 0.06 #143, 0.04 #1400), 0qcr0 (0.07 #860, 0.07 #1059, 0.06 #927), 0gg4h (0.06 #234, 0.02 #762, 0.02 #432), 014w_8 (0.06 #237), 09jg8 (0.06 #232), 0m32h (0.05 #749, 0.05 #815, 0.04 #1081) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 05v1sb; >> query: (?x11606, 0gk4g) <- student(?x4889, ?x11606), place_of_death(?x11606, ?x5895), ?x5895 = 0k_p5, state_province_region(?x4889, ?x335) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #749 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 151 *> proper extension: 01k7d9; 07s3vqk; 0h1_w; 0h1m9; 02knnd; 02lkcc; 04nw9; 01xcqc; 028lc8; 018swb; ... *> query: (?x11606, 0m32h) <- gender(?x11606, ?x231), nationality(?x11606, ?x94), place_of_death(?x11606, ?x5895), film(?x11606, ?x974) *> conf = 0.05 ranks of expected_values: 10 EVAL 076689 people! 0m32h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 105.000 105.000 0.200 http://example.org/people/cause_of_death/people #8496-0fb1q PRED entity: 0fb1q PRED relation: film PRED expected values: 085bd1 => 103 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1211): 08xvpn (0.65 #19625, 0.58 #108839, 0.58 #82076), 0304nh (0.65 #19625, 0.58 #108839, 0.58 #82076), 0gxsh4 (0.58 #108839, 0.58 #82076, 0.48 #66016), 0ds5_72 (0.14 #32114, 0.05 #8587, 0.05 #10371), 0dtw1x (0.14 #32114), 06qv_ (0.08 #57091, 0.07 #62445), 0fpxp (0.08 #57091, 0.07 #62445), 013q07 (0.07 #7491, 0.07 #9275, 0.05 #16411), 03q0r1 (0.07 #636, 0.05 #7772, 0.05 #9556), 03nfnx (0.07 #1397, 0.05 #8533, 0.04 #17453) >> Best rule #19625 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 0htlr; 0456xp; 04shbh; 0prjs; 01mqz0; 03xmy1; 05hdf; 01gbbz; 01ft2l; 0h32q; ... >> query: (?x3183, ?x4891) <- spouse(?x1922, ?x3183), participant(?x3183, ?x6028), award_winner(?x4891, ?x3183) >> conf = 0.65 => this is the best rule for 2 predicted values *> Best rule #450 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 12 *> proper extension: 01nczg; 01jbx1; *> query: (?x3183, 085bd1) <- award(?x3183, ?x7644), ?x7644 = 04fgkf_ *> conf = 0.07 ranks of expected_values: 43 EVAL 0fb1q film 085bd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 103.000 98.000 0.653 http://example.org/film/actor/film./film/performance/film #8495-0kz1h PRED entity: 0kz1h PRED relation: currency! PRED expected values: 0chghy => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 202): 09c7w0 (0.85 #799, 0.76 #806, 0.43 #1198), 0b90_r (0.85 #799, 0.76 #806, 0.43 #1198), 035qy (0.85 #799, 0.76 #806, 0.43 #1198), 0k6nt (0.85 #799, 0.76 #806, 0.43 #1198), 05r4w (0.85 #799, 0.76 #806, 0.43 #1198), 0chghy (0.85 #799, 0.76 #806, 0.43 #1198), 0d0vqn (0.85 #799, 0.76 #806, 0.43 #1198), 02vzc (0.85 #799, 0.76 #806, 0.43 #1198), 0345h (0.85 #799, 0.76 #806, 0.43 #1198), 015fr (0.85 #799, 0.76 #806, 0.43 #1198) >> Best rule #799 for best value: >> intensional similarity = 126 >> extensional distance = 1 >> proper extension: 09nqf; >> query: (?x7888, ?x456) <- currency(?x12293, ?x7888), currency(?x11640, ?x7888), currency(?x10889, ?x7888), currency(?x9181, ?x7888), currency(?x7950, ?x7888), currency(?x7887, ?x7888), currency(?x5835, ?x7888), currency(?x4545, ?x7888), school_type(?x7950, ?x3092), institution(?x1305, ?x7950), institution(?x734, ?x7950), state_province_region(?x7950, ?x12854), currency(?x8507, ?x7888), major_field_of_study(?x9181, ?x10417), major_field_of_study(?x12620, ?x10417), major_field_of_study(?x5754, ?x10417), major_field_of_study(?x5288, ?x10417), major_field_of_study(?x4599, ?x10417), major_field_of_study(?x3779, ?x10417), major_field_of_study(?x741, ?x10417), currency(?x12051, ?x7888), ?x1305 = 02mjs7, organization(?x4095, ?x7950), colors(?x9181, ?x3621), ?x4599 = 07t90, contains(?x390, ?x9181), citytown(?x7950, ?x8963), ?x12620 = 012gyf, category(?x12293, ?x134), film_release_region(?x4545, ?x2267), film_release_region(?x4545, ?x1353), film_release_region(?x4545, ?x985), film_release_region(?x4545, ?x151), film_release_region(?x4545, ?x94), film_release_region(?x4545, ?x87), major_field_of_study(?x12051, ?x4100), student(?x12051, ?x11399), ?x5754 = 02ln0f, institution(?x1519, ?x11640), institution(?x865, ?x11640), major_field_of_study(?x7950, ?x9111), major_field_of_study(?x11640, ?x3400), ?x1519 = 013zdg, nominated_for(?x3281, ?x4545), major_field_of_study(?x13316, ?x9111), major_field_of_study(?x2327, ?x9111), major_field_of_study(?x2142, ?x9111), contains(?x1023, ?x12293), titles(?x2480, ?x7887), institution(?x1526, ?x9181), institution(?x1200, ?x9181), organization(?x5510, ?x12293), film_release_region(?x7887, ?x2316), film_release_region(?x7887, ?x1603), film_release_region(?x7887, ?x1453), film_release_region(?x7887, ?x1003), film_release_region(?x7887, ?x789), film_release_region(?x7887, ?x774), film_release_region(?x7887, ?x550), film_release_region(?x7887, ?x456), film_release_region(?x7887, ?x279), ?x87 = 05r4w, state_province_region(?x11640, ?x9494), ?x1526 = 0bkj86, ?x279 = 0d060g, ?x550 = 05v8c, ?x2142 = 0dplh, institution(?x865, ?x6912), institution(?x865, ?x6132), institution(?x865, ?x5750), institution(?x865, ?x5306), institution(?x865, ?x5149), institution(?x865, ?x2775), institution(?x865, ?x1884), major_field_of_study(?x865, ?x254), ?x5288 = 02zd460, genre(?x7887, ?x2540), genre(?x7887, ?x258), ?x2267 = 03rj0, ?x985 = 0k6nt, nominated_for(?x2222, ?x4545), ?x2327 = 07wjk, ?x1353 = 035qy, ?x2316 = 06t2t, ?x1003 = 03gj2, film(?x4318, ?x7887), film(?x157, ?x7887), film_release_distribution_medium(?x7887, ?x81), ?x151 = 0b90_r, film(?x10629, ?x7887), company(?x5652, ?x10889), school_type(?x12051, ?x4722), ?x5750 = 01nnsv, ?x5149 = 02mj7c, ?x94 = 09c7w0, language(?x7887, ?x12272), taxonomy(?x3400, ?x939), ?x2540 = 0hcr, major_field_of_study(?x12035, ?x9111), award_nominee(?x92, ?x157), ?x134 = 08mbj5d, ?x1200 = 016t_3, ?x789 = 0f8l9c, ?x1884 = 0bx8pn, award(?x157, ?x112), ?x6132 = 0hsb3, institution(?x2636, ?x12293), student(?x865, ?x1117), ?x1603 = 06bnz, ?x2775 = 078bz, student(?x9181, ?x6629), ?x258 = 05p553, colors(?x179, ?x3621), major_field_of_study(?x734, ?x5864), ?x774 = 06mzp, ?x741 = 01w3v, ?x3779 = 01pq4w, ?x6912 = 0gl5_, ?x13316 = 01stzp, ?x5306 = 0217m9, jurisdiction_of_office(?x900, ?x9494), award_winner(?x4318, ?x2455), ?x1453 = 06qd3, written_by(?x5835, ?x2179), titles(?x162, ?x4545), student(?x734, ?x920) >> conf = 0.85 => this is the best rule for 28 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 0kz1h currency! 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 8.000 8.000 0.847 http://example.org/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency #8494-08jyyk PRED entity: 08jyyk PRED relation: parent_genre! PRED expected values: 01gbcf => 72 concepts (32 used for prediction) PRED predicted values (max 10 best out of 318): 01gbcf (0.40 #799, 0.40 #533, 0.38 #1064), 0781g (0.40 #950, 0.38 #1481, 0.33 #155), 01_bkd (0.40 #842, 0.33 #1908, 0.33 #47), 0190_q (0.40 #826, 0.33 #31, 0.25 #1357), 0cx7f (0.40 #910, 0.33 #115, 0.25 #1441), 06cp5 (0.38 #1402, 0.33 #1670, 0.25 #2470), 0dl5d (0.38 #1342, 0.33 #2410, 0.25 #280), 01h0kx (0.38 #1455, 0.25 #2523, 0.25 #393), 01pfpt (0.33 #75, 0.25 #1401, 0.20 #870), 0hdf8 (0.33 #59, 0.20 #854, 0.12 #1385) >> Best rule #799 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 016clz; 05w3f; >> query: (?x5379, 01gbcf) <- artists(?x5379, ?x6234), artists(?x5379, ?x3516), artists(?x5379, ?x764), ?x3516 = 05563d, profession(?x764, ?x131), type_of_union(?x764, ?x566), performance_role(?x764, ?x228), ?x6234 = 0l8g0 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08jyyk parent_genre! 01gbcf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 32.000 0.400 http://example.org/music/genre/parent_genre #8493-03y_46 PRED entity: 03y_46 PRED relation: film PRED expected values: 03hxsv => 82 concepts (55 used for prediction) PRED predicted values (max 10 best out of 532): 03176f (0.38 #2489, 0.02 #71411, 0.02 #11416), 03hxsv (0.38 #2900, 0.02 #71411, 0.02 #11827), 0dl6fv (0.23 #3269, 0.10 #1484, 0.03 #23209), 09gq0x5 (0.20 #283, 0.15 #2068, 0.05 #85692), 0dgst_d (0.20 #194, 0.15 #1979, 0.03 #94618), 04jpg2p (0.20 #1459, 0.15 #3244, 0.03 #23209), 01242_ (0.20 #700, 0.03 #94618, 0.03 #98190), 0prh7 (0.15 #2618, 0.10 #833, 0.03 #23209), 011yg9 (0.15 #2811, 0.10 #1026, 0.03 #23209), 020bv3 (0.15 #2102, 0.03 #94618, 0.03 #98190) >> Best rule #2489 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 065jlv; 0l6px; 013_vh; 01f6zc; 05kwx2; >> query: (?x5699, 03176f) <- award_nominee(?x988, ?x5699), film(?x5699, ?x2869), ?x2869 = 03177r >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2900 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 065jlv; 0l6px; 013_vh; 01f6zc; 05kwx2; *> query: (?x5699, 03hxsv) <- award_nominee(?x988, ?x5699), film(?x5699, ?x2869), ?x2869 = 03177r *> conf = 0.38 ranks of expected_values: 2 EVAL 03y_46 film 03hxsv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 55.000 0.385 http://example.org/film/actor/film./film/performance/film #8492-0f2r6 PRED entity: 0f2r6 PRED relation: featured_film_locations! PRED expected values: 09gb_4p => 243 concepts (243 used for prediction) PRED predicted values (max 10 best out of 719): 04dsnp (0.14 #19204, 0.12 #79493, 0.12 #16996), 061681 (0.14 #19185, 0.11 #28018, 0.09 #16977), 0192hw (0.12 #969, 0.12 #79493, 0.12 #6123), 092vkg (0.12 #805, 0.12 #79493, 0.11 #2277), 042zrm (0.12 #1330, 0.12 #79493, 0.11 #2802), 01rxyb (0.12 #1050, 0.12 #79493, 0.11 #2522), 01svry (0.12 #1235, 0.12 #79493, 0.11 #2707), 04cppj (0.12 #1223, 0.12 #79493, 0.11 #2695), 02nczh (0.12 #1214, 0.12 #79493, 0.11 #2686), 02j69w (0.12 #1078, 0.12 #79493, 0.11 #2550) >> Best rule #19204 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0jgd; 04wgh; >> query: (?x674, 04dsnp) <- locations(?x418, ?x674), contains(?x673, ?x674), teams(?x674, ?x11420), featured_film_locations(?x5116, ?x674) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #5890 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 017_4z; *> query: (?x674, ?x4602) <- capital(?x2256, ?x674), administrative_division(?x674, ?x673), contains(?x2256, ?x1698), featured_film_locations(?x4602, ?x2256) *> conf = 0.09 ranks of expected_values: 162 EVAL 0f2r6 featured_film_locations! 09gb_4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 243.000 243.000 0.143 http://example.org/film/film/featured_film_locations #8491-0qmhk PRED entity: 0qmhk PRED relation: award PRED expected values: 02wkmx => 84 concepts (66 used for prediction) PRED predicted values (max 10 best out of 189): 0gq9h (0.36 #757, 0.28 #928, 0.27 #11820), 0gs9p (0.30 #759, 0.23 #1922, 0.21 #2619), 040njc (0.29 #929, 0.29 #703, 0.28 #928), 094qd5 (0.28 #928, 0.27 #11820, 0.27 #2091), 04kxsb (0.28 #928, 0.27 #11820, 0.27 #2091), 0f4x7 (0.28 #928, 0.27 #11820, 0.27 #2091), 02qvyrt (0.28 #928, 0.27 #11820, 0.27 #2091), 02pqp12 (0.28 #928, 0.27 #11820, 0.27 #2091), 019f4v (0.28 #928, 0.27 #11820, 0.27 #2091), 04dn09n (0.28 #928, 0.27 #11820, 0.27 #2091) >> Best rule #757 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 0gmgwnv; 0286gm1; 0kt_4; >> query: (?x5515, 0gq9h) <- award(?x5515, ?x601), nominated_for(?x591, ?x5515), nominated_for(?x198, ?x5515), ?x591 = 0f4x7, ?x198 = 040njc >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #12287 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x5515, ?x143) <- award(?x5515, ?x6909), award(?x2376, ?x6909), award(?x2376, ?x143) *> conf = 0.05 ranks of expected_values: 80 EVAL 0qmhk award 02wkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 84.000 66.000 0.356 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8490-03w94xt PRED entity: 03w94xt PRED relation: artists PRED expected values: 06k02 0191h5 01vt5c_ => 45 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1077): 01vsy3q (0.57 #4720, 0.40 #6862, 0.33 #435), 05563d (0.53 #6734, 0.50 #3521, 0.46 #7804), 0p76z (0.53 #7334, 0.43 #5192, 0.42 #8404), 02ndj5 (0.53 #7316, 0.38 #8386, 0.33 #889), 07r4c (0.50 #5912, 0.50 #3770, 0.40 #2698), 0191h5 (0.50 #1715, 0.47 #7072, 0.40 #2787), 070b4 (0.50 #1883, 0.46 #2141, 0.40 #2955), 0dw3l (0.50 #1799, 0.40 #2871, 0.33 #3943), 01vt5c_ (0.50 #1786, 0.40 #2858, 0.33 #3930), 07rnh (0.50 #1900, 0.40 #2972, 0.33 #4044) >> Best rule #4720 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 05w3f; 02yv6b; 0155w; 04z1v0; >> query: (?x11746, 01vsy3q) <- artists(?x11746, ?x8579), artists(?x11746, ?x8539), artists(?x11746, ?x6949), ?x8579 = 01vs4f3, role(?x6949, ?x74), nationality(?x8539, ?x512), profession(?x6949, ?x1183), role(?x8539, ?x227) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1715 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 016clz; 0k345; *> query: (?x11746, 0191h5) <- artists(?x11746, ?x8539), artists(?x11746, ?x7476), artists(?x11746, ?x3657), ?x8539 = 01w9mnm, ?x3657 = 01w8n89, group(?x75, ?x7476), influenced_by(?x4620, ?x7476), artist(?x382, ?x7476) *> conf = 0.50 ranks of expected_values: 6, 9, 125 EVAL 03w94xt artists 01vt5c_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 45.000 16.000 0.571 http://example.org/music/genre/artists EVAL 03w94xt artists 0191h5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 45.000 16.000 0.571 http://example.org/music/genre/artists EVAL 03w94xt artists 06k02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 45.000 16.000 0.571 http://example.org/music/genre/artists #8489-02822 PRED entity: 02822 PRED relation: student PRED expected values: 01pkhw 02yplc 07rzf => 63 concepts (42 used for prediction) PRED predicted values (max 10 best out of 450): 0d608 (0.33 #749, 0.10 #2589, 0.08 #2998), 06y7d (0.33 #1223, 0.10 #2654, 0.08 #3063), 03kdl (0.33 #1092, 0.10 #2523, 0.08 #2932), 0203v (0.33 #1052, 0.10 #2483, 0.08 #2892), 0d06m5 (0.17 #1697, 0.12 #2310, 0.12 #2105), 01pkhw (0.17 #1717, 0.12 #2125, 0.10 #2534), 049dyj (0.17 #1656, 0.12 #2064, 0.10 #2473), 042q3 (0.17 #1820, 0.12 #2228, 0.06 #3250), 09k0f (0.17 #1801, 0.12 #2209, 0.06 #3231), 04xfb (0.17 #1785, 0.12 #2193, 0.06 #3215) >> Best rule #749 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 01x3g; >> query: (?x4268, 0d608) <- major_field_of_study(?x865, ?x4268), student(?x4268, ?x12100), major_field_of_study(?x865, ?x4100), institution(?x865, ?x8202), institution(?x865, ?x6584), ?x6584 = 027ydt, ?x4100 = 01lj9, ?x8202 = 06fq2, ?x12100 = 01mylz >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1717 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 01z4y; *> query: (?x4268, 01pkhw) <- major_field_of_study(?x1526, ?x4268), major_field_of_study(?x1368, ?x4268), ?x1368 = 014mlp, split_to(?x4268, ?x53), institution(?x1526, ?x12293), institution(?x1526, ?x6919), ?x12293 = 01pj48, major_field_of_study(?x1526, ?x1527), ?x1527 = 04_tv, ?x6919 = 017v3q *> conf = 0.17 ranks of expected_values: 6, 17 EVAL 02822 student 07rzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 63.000 42.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 02822 student 02yplc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 42.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 02822 student 01pkhw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 63.000 42.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #8488-03whyr PRED entity: 03whyr PRED relation: film_crew_role PRED expected values: 04pyp5 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 21): 0215hd (0.62 #13, 0.60 #43, 0.18 #165), 089g0h (0.62 #14, 0.47 #44, 0.13 #166), 0d2b38 (0.50 #20, 0.33 #50, 0.16 #172), 01pvkk (0.50 #8, 0.30 #68, 0.29 #160), 015h31 (0.50 #7, 0.27 #37, 0.18 #221), 05smlt (0.50 #15, 0.13 #45, 0.10 #75), 02_n3z (0.27 #31, 0.25 #1, 0.12 #153), 033smt (0.27 #52, 0.12 #22, 0.10 #236), 0263ycg (0.27 #42, 0.06 #133, 0.05 #350), 020xn5 (0.25 #6, 0.20 #36, 0.04 #282) >> Best rule #13 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 01gglm; >> query: (?x9524, 0215hd) <- film(?x1676, ?x9524), story_by(?x9524, ?x8210), film_crew_role(?x9524, ?x2472), ?x2472 = 01xy5l_, written_by(?x9524, ?x1052) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #318 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 235 *> proper extension: 02rb607; 02n9bh; 0k2m6; *> query: (?x9524, 04pyp5) <- genre(?x9524, ?x225), story_by(?x9524, ?x8210), film_crew_role(?x9524, ?x137) *> conf = 0.07 ranks of expected_values: 18 EVAL 03whyr film_crew_role 04pyp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 57.000 57.000 0.625 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8487-01w60_p PRED entity: 01w60_p PRED relation: profession PRED expected values: 02hrh1q => 104 concepts (103 used for prediction) PRED predicted values (max 10 best out of 82): 02hrh1q (0.89 #11932, 0.87 #8161, 0.86 #12512), 0dxtg (0.47 #1463, 0.39 #1898, 0.38 #2626), 01d_h8 (0.46 #1455, 0.43 #1019, 0.35 #1600), 01c72t (0.43 #892, 0.31 #4384, 0.30 #2781), 0cbd2 (0.41 #3346, 0.41 #4221, 0.40 #3929), 03gjzk (0.33 #1029, 0.32 #1465, 0.28 #10469), 02jknp (0.28 #1457, 0.26 #1021, 0.20 #1602), 0n1h (0.28 #10469, 0.28 #1170, 0.25 #4362), 0fnpj (0.28 #10469, 0.17 #637, 0.15 #347), 05vyk (0.28 #10469, 0.10 #961, 0.08 #381) >> Best rule #11932 for best value: >> intensional similarity = 2 >> extensional distance = 2012 >> proper extension: 05bp8g; 05m63c; 01ty7ll; 033hqf; 018dnt; 041ly3; 01wjrn; 045bs6; 01wyzyl; 05wjnt; ... >> query: (?x2169, 02hrh1q) <- film(?x2169, ?x6864), profession(?x2169, ?x131) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w60_p profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 103.000 0.892 http://example.org/people/person/profession #8486-01wk3c PRED entity: 01wk3c PRED relation: profession PRED expected values: 02hrh1q => 87 concepts (86 used for prediction) PRED predicted values (max 10 best out of 55): 02hrh1q (0.89 #315, 0.88 #2115, 0.88 #3015), 01d_h8 (0.49 #306, 0.36 #2406, 0.33 #1356), 0dxtg (0.33 #2414, 0.32 #1364, 0.32 #914), 09jwl (0.29 #170, 0.19 #1520, 0.18 #1220), 02jknp (0.28 #908, 0.28 #308, 0.26 #2408), 03gjzk (0.25 #1366, 0.25 #1816, 0.25 #2416), 018gz8 (0.24 #168, 0.14 #5568, 0.14 #3018), 015cjr (0.18 #201, 0.04 #2601, 0.04 #501), 0d1pc (0.17 #52, 0.09 #2152, 0.07 #1102), 0np9r (0.16 #5572, 0.14 #9625, 0.14 #8724) >> Best rule #315 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 016khd; 01713c; 016ywr; 0gyx4; 0gnbw; >> query: (?x10886, 02hrh1q) <- award_winner(?x2556, ?x10886), award(?x10886, ?x2375), ?x2375 = 04kxsb >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wk3c profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 86.000 0.885 http://example.org/people/person/profession #8485-06cc_1 PRED entity: 06cc_1 PRED relation: nationality PRED expected values: 09c7w0 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 29): 09c7w0 (0.76 #7203, 0.70 #1701, 0.70 #7805), 02jx1 (0.26 #633, 0.25 #933, 0.19 #1033), 07ssc (0.18 #615, 0.14 #715, 0.13 #515), 03rk0 (0.14 #746, 0.06 #8651, 0.05 #8551), 0d060g (0.08 #507, 0.07 #807, 0.06 #1107), 0345h (0.04 #131, 0.02 #2332, 0.02 #2432), 03rjj (0.04 #105, 0.02 #305, 0.02 #6306), 03ryn (0.04 #157, 0.02 #457), 0f8l9c (0.03 #2323, 0.02 #3623, 0.02 #4323), 06q1r (0.02 #877, 0.02 #1177, 0.02 #977) >> Best rule #7203 for best value: >> intensional similarity = 2 >> extensional distance = 1736 >> proper extension: 07c37; 0399p; 07m69t; 02wh0; 047g6; 015n8; 01h2_6; 069d71; >> query: (?x568, 09c7w0) <- location(?x568, ?x4356), time_zones(?x4356, ?x1638) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06cc_1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.758 http://example.org/people/person/nationality #8484-0r4h3 PRED entity: 0r4h3 PRED relation: place PRED expected values: 0r4h3 => 142 concepts (96 used for prediction) PRED predicted values (max 10 best out of 249): 0r3wm (0.22 #23717, 0.16 #13916, 0.16 #18555), 0r4qq (0.15 #26297, 0.15 #28882, 0.02 #4807), 030qb3t (0.09 #30, 0.07 #545, 0.05 #1060), 02_286 (0.09 #14, 0.07 #529, 0.05 #1044), 0n6dc (0.09 #342, 0.07 #857, 0.05 #1372), 013yq (0.09 #45, 0.04 #1590, 0.04 #2105), 0fpzwf (0.09 #140, 0.03 #3230, 0.03 #2715), 0r03f (0.07 #875, 0.05 #1390, 0.04 #1905), 0z2gq (0.07 #759, 0.05 #1274, 0.04 #1789), 03b12 (0.07 #806, 0.05 #1321, 0.04 #1836) >> Best rule #23717 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 0b_cr; >> query: (?x12025, ?x10400) <- place_of_birth(?x2946, ?x12025), category(?x12025, ?x134), location(?x2946, ?x10400), county(?x12025, ?x9887) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r4h3 place 0r4h3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 96.000 0.222 http://example.org/location/hud_county_place/place #8483-09bw4_ PRED entity: 09bw4_ PRED relation: category PRED expected values: 08mbj5d => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.50 #1, 0.33 #2, 0.32 #10) >> Best rule #1 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0fdv3; >> query: (?x8658, 08mbj5d) <- film(?x574, ?x8658), film(?x9781, ?x8658), nominated_for(?x3508, ?x8658), ?x9781 = 0f276, film_crew_role(?x8658, ?x468) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09bw4_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.500 http://example.org/common/topic/webpage./common/webpage/category #8482-05t2fh4 PRED entity: 05t2fh4 PRED relation: locations PRED expected values: 02j9z => 44 concepts (34 used for prediction) PRED predicted values (max 10 best out of 693): 02j9z (0.67 #558, 0.42 #1292, 0.38 #2211), 059g4 (0.38 #862, 0.25 #1412, 0.23 #2331), 013yq (0.33 #1518, 0.25 #5387, 0.24 #5570), 048fz (0.33 #150, 0.20 #332, 0.13 #697), 05vz3zq (0.33 #94, 0.20 #276, 0.12 #1192), 035qy (0.33 #29, 0.20 #211, 0.09 #943), 01crd5 (0.33 #133, 0.20 #315, 0.07 #680), 0d04z6 (0.33 #96, 0.20 #278, 0.07 #643), 04jpl (0.33 #374, 0.09 #5335, 0.08 #4428), 0f2rq (0.25 #1570, 0.14 #5439, 0.13 #5622) >> Best rule #558 for best value: >> intensional similarity = 9 >> extensional distance = 13 >> proper extension: 048n7; >> query: (?x13643, 02j9z) <- locations(?x13643, ?x12315), locations(?x13643, ?x279), contains(?x12315, ?x410), location_of_ceremony(?x566, ?x279), contains(?x279, ?x481), locations(?x10849, ?x12315), film_release_region(?x1219, ?x410), ?x1219 = 03bx2lk, ?x10849 = 01w1sx >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05t2fh4 locations 02j9z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 34.000 0.667 http://example.org/time/event/locations #8481-0gr4k PRED entity: 0gr4k PRED relation: ceremony PRED expected values: 059x66 02yv_b 0bzmt8 0fz0c2 09306z 0bzkvd 0fy59t => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 89): 0gpjbt (0.61 #1696, 0.36 #3281, 0.34 #3810), 09n4nb (0.60 #1709, 0.35 #3294, 0.33 #3823), 0466p0j (0.59 #1726, 0.35 #3311, 0.33 #3752), 05pd94v (0.59 #1675, 0.33 #3260, 0.32 #3525), 02cg41 (0.58 #1752, 0.35 #3337, 0.32 #3866), 02rjjll (0.58 #1678, 0.34 #3263, 0.33 #3792), 056878 (0.58 #1698, 0.34 #3283, 0.32 #3812), 01c6qp (0.57 #1690, 0.33 #3275, 0.32 #3804), 01mh_q (0.54 #1731, 0.32 #3316, 0.31 #3845), 019bk0 (0.54 #1687, 0.32 #3272, 0.31 #3537) >> Best rule #1696 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 07n52; 02xzd9; >> query: (?x601, 0gpjbt) <- category_of(?x601, ?x3459), category_of(?x720, ?x3459), disciplines_or_subjects(?x720, ?x373) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #326 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0gq_v; 0l8z1; 0gs96; 02x258x; *> query: (?x601, 0bzmt8) <- nominated_for(?x601, ?x4874), award_winner(?x601, ?x488), ?x4874 = 0prh7, award(?x164, ?x601) *> conf = 0.50 ranks of expected_values: 15, 21, 22, 23, 24, 25, 49 EVAL 0gr4k ceremony 0fy59t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 62.000 62.000 0.614 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr4k ceremony 0bzkvd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 62.000 62.000 0.614 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr4k ceremony 09306z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 62.000 62.000 0.614 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr4k ceremony 0fz0c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 62.000 62.000 0.614 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr4k ceremony 0bzmt8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 62.000 62.000 0.614 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr4k ceremony 02yv_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 62.000 62.000 0.614 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr4k ceremony 059x66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 62.000 62.000 0.614 http://example.org/award/award_category/winners./award/award_honor/ceremony #8480-0hv81 PRED entity: 0hv81 PRED relation: film_release_region PRED expected values: 0jgd 082fr => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 159): 0f8l9c (0.88 #2634, 0.88 #7487, 0.86 #6448), 059j2 (0.85 #4380, 0.82 #7499, 0.81 #2646), 05r4w (0.82 #6420, 0.81 #7459, 0.78 #4340), 0jgd (0.82 #177, 0.78 #873, 0.76 #4342), 0345h (0.78 #4382, 0.75 #5595, 0.74 #3514), 03_3d (0.78 #878, 0.77 #6427, 0.75 #2613), 07ssc (0.78 #4360, 0.75 #4880, 0.75 #5573), 03rjj (0.77 #7464, 0.77 #6425, 0.76 #2611), 0chghy (0.77 #7472, 0.77 #4353, 0.77 #6433), 0d060g (0.73 #4348, 0.68 #3480, 0.68 #5561) >> Best rule #2634 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 02d44q; 047svrl; 07k2mq; 0372j5; >> query: (?x5980, 0f8l9c) <- film_release_region(?x5980, ?x985), film_crew_role(?x5980, ?x137), featured_film_locations(?x5980, ?x739), ?x985 = 0k6nt >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #177 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0dtfn; 04jkpgv; 0fpmrm3; *> query: (?x5980, 0jgd) <- genre(?x5980, ?x53), film_crew_role(?x5980, ?x137), film_regional_debut_venue(?x5980, ?x739), nominated_for(?x5980, ?x2898) *> conf = 0.82 ranks of expected_values: 4, 34 EVAL 0hv81 film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 117.000 117.000 0.881 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hv81 film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 117.000 0.881 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8479-01p1b PRED entity: 01p1b PRED relation: organization PRED expected values: 0b6css => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 48): 0b6css (0.65 #262, 0.58 #804, 0.57 #9), 0j7v_ (0.65 #262, 0.56 #1148, 0.56 #1147), 0_2v (0.32 #163, 0.32 #1432, 0.31 #305), 04k4l (0.32 #185, 0.32 #1432, 0.30 #164), 01rz1 (0.32 #1432, 0.28 #161, 0.27 #303), 085h1 (0.32 #1432, 0.22 #181, 0.21 #323), 018cqq (0.32 #1432, 0.22 #170, 0.21 #312), 02jxk (0.32 #1432, 0.17 #162, 0.15 #304), 059dn (0.32 #1432, 0.05 #174, 0.05 #276), 034h1h (0.22 #1338, 0.22 #1379, 0.18 #1419) >> Best rule #262 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 084n_; >> query: (?x4121, ?x127) <- adjoins(?x4121, ?x7871), form_of_government(?x4121, ?x6377), organization(?x7871, ?x127) >> conf = 0.65 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01p1b organization 0b6css CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.645 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #8478-013pk3 PRED entity: 013pk3 PRED relation: award_winner! PRED expected values: 0drtv8 0275n3y => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 123): 03gyp30 (0.31 #804, 0.07 #5082, 0.06 #5634), 0275n3y (0.31 #763, 0.05 #5041, 0.04 #1591), 07y9ts (0.25 #66, 0.20 #342, 0.08 #3516), 0bq_mx (0.25 #130, 0.20 #406, 0.07 #3028), 09v0p2c (0.25 #81, 0.06 #2151, 0.05 #3531), 01c6qp (0.20 #847, 0.13 #1537, 0.11 #4711), 0gx_st (0.20 #175, 0.13 #2107, 0.11 #3487), 013b2h (0.20 #906, 0.12 #1596, 0.11 #4080), 09p2r9 (0.20 #229, 0.03 #1609, 0.03 #1195), 073hd1 (0.20 #236, 0.03 #788, 0.02 #1340) >> Best rule #804 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0bt4r4; 0cnl09; >> query: (?x7638, 03gyp30) <- award_winner(?x8250, ?x7638), award_winner(?x3624, ?x7638), ?x3624 = 027hjff >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #763 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 0bt4r4; 0cnl09; *> query: (?x7638, 0275n3y) <- award_winner(?x8250, ?x7638), award_winner(?x3624, ?x7638), ?x3624 = 027hjff *> conf = 0.31 ranks of expected_values: 2, 12 EVAL 013pk3 award_winner! 0275n3y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.314 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 013pk3 award_winner! 0drtv8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 133.000 133.000 0.314 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8477-047csmy PRED entity: 047csmy PRED relation: featured_film_locations PRED expected values: 013ksx => 107 concepts (64 used for prediction) PRED predicted values (max 10 best out of 58): 04jpl (0.15 #7986, 0.13 #7282, 0.12 #9162), 0rh6k (0.08 #1174, 0.08 #7978, 0.06 #7274), 080h2 (0.08 #727, 0.05 #7296, 0.05 #8000), 01_d4 (0.06 #984, 0.04 #9198, 0.04 #8022), 03gh4 (0.06 #110, 0.04 #1283, 0.04 #1049), 06y57 (0.06 #1037, 0.04 #1271, 0.04 #1975), 035p3 (0.04 #1166, 0.04 #1400, 0.02 #1634), 0h7h6 (0.04 #8018, 0.04 #2856, 0.03 #7314), 052p7 (0.04 #7328, 0.03 #8032, 0.03 #994), 0d6lp (0.04 #1242, 0.02 #8046, 0.02 #3118) >> Best rule #7986 for best value: >> intensional similarity = 4 >> extensional distance = 435 >> proper extension: 0192hw; 0g5q34q; 0gh6j94; >> query: (?x5277, 04jpl) <- genre(?x5277, ?x225), film_crew_role(?x5277, ?x137), featured_film_locations(?x5277, ?x2254), citytown(?x2166, ?x2254) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 047csmy featured_film_locations 013ksx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 64.000 0.151 http://example.org/film/film/featured_film_locations #8476-09nqf PRED entity: 09nqf PRED relation: currency! PRED expected values: 04sv4 => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 47): 03lb_v (0.33 #4, 0.33 #3), 02c9dj (0.33 #4, 0.33 #3), 037q2p (0.33 #4, 0.33 #3), 06thjt (0.33 #4, 0.33 #3), 01pcj4 (0.33 #4, 0.33 #3), 0hpv3 (0.33 #4, 0.33 #3), 04sv4 (0.33 #4, 0.33 #3), 08qs09 (0.33 #4, 0.33 #3), 017y6l (0.33 #4, 0.33 #3), 0537b (0.33 #4, 0.33 #3) >> Best rule #4 for best value: >> intensional similarity = 26 >> extensional distance = 1 >> proper extension: 02l6h; >> query: (?x170, ?x1908) <- currency(?x99, ?x170), currency(?x47, ?x170), currency(?x8000, ?x170), currency(?x6429, ?x170), currency(?x4810, ?x170), currency(?x4179, ?x170), currency(?x3755, ?x170), currency(?x1597, ?x170), currency(?x1470, ?x170), currency(?x122, ?x170), currency(?x918, ?x170), currency(?x4325, ?x170), film(?x4192, ?x4179), currency(?x1908, ?x170), award(?x4325, ?x112), nominated_for(?x507, ?x4810), nominated_for(?x3519, ?x4179), film_release_distribution_medium(?x6429, ?x81), currency(?x1675, ?x170), titles(?x811, ?x3755), film_release_region(?x1470, ?x456), currency(?x108, ?x170), award(?x1597, ?x298), film(?x544, ?x3755), genre(?x4179, ?x225), honored_for(?x8478, ?x8000) >> conf = 0.33 => this is the best rule for 36 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7 EVAL 09nqf currency! 04sv4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 8.000 8.000 0.333 http://example.org/business/business_operation/revenue./measurement_unit/dated_money_value/currency #8475-06q1r PRED entity: 06q1r PRED relation: featured_film_locations! PRED expected values: 0d_2fb => 303 concepts (275 used for prediction) PRED predicted values (max 10 best out of 711): 0872p_c (0.23 #14820, 0.13 #16295, 0.12 #20718), 061681 (0.22 #5943, 0.20 #15527, 0.18 #11103), 047csmy (0.22 #4081, 0.18 #11452, 0.18 #21773), 072x7s (0.22 #3798, 0.18 #11169, 0.18 #21490), 0ds2n (0.22 #6127, 0.18 #11287, 0.15 #14973), 033srr (0.22 #6176, 0.18 #11336, 0.13 #15760), 024l2y (0.22 #6010, 0.18 #11170, 0.13 #15594), 01q2nx (0.22 #6291, 0.18 #11451, 0.13 #15875), 035yn8 (0.22 #6016, 0.18 #11176, 0.13 #15600), 05pbl56 (0.22 #6005, 0.18 #11165, 0.13 #15589) >> Best rule #14820 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 02kx3; >> query: (?x6401, 0872p_c) <- place_founded(?x13872, ?x6401), time_zones(?x6401, ?x5327), film_release_region(?x2155, ?x6401) >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06q1r featured_film_locations! 0d_2fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 303.000 275.000 0.231 http://example.org/film/film/featured_film_locations #8474-04mn81 PRED entity: 04mn81 PRED relation: nationality PRED expected values: 09c7w0 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 47): 09c7w0 (0.88 #802, 0.85 #1704, 0.78 #9514), 02jx1 (0.22 #233, 0.21 #333, 0.20 #1836), 07ssc (0.16 #215, 0.12 #415, 0.11 #1818), 03rk0 (0.06 #11166, 0.06 #11566, 0.06 #9059), 0d060g (0.05 #3413, 0.05 #1309, 0.05 #1509), 0mn0v (0.04 #1102, 0.03 #4407, 0.01 #2905), 03rt9 (0.04 #113, 0.02 #313, 0.02 #413), 0j5g9 (0.04 #162, 0.02 #462, 0.02 #12222), 035qy (0.04 #134, 0.02 #12222, 0.02 #935), 0f8l9c (0.03 #2826, 0.02 #1124, 0.02 #3328) >> Best rule #802 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 0grwj; 02p65p; 0337vz; 03rs8y; 01gvr1; 01wmxfs; 03knl; 06b0d2; 01ztgm; 0gjvqm; ... >> query: (?x1989, 09c7w0) <- people(?x2510, ?x1989), award_nominee(?x2732, ?x1989), ?x2510 = 0x67 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mn81 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.876 http://example.org/people/person/nationality #8473-0mvxt PRED entity: 0mvxt PRED relation: contains! PRED expected values: 06yxd => 130 concepts (71 used for prediction) PRED predicted values (max 10 best out of 99): 06yxd (0.73 #25168, 0.71 #14380, 0.71 #37748), 09c7w0 (0.56 #63807, 0.55 #36849, 0.54 #58415), 0_lr1 (0.30 #12582, 0.29 #15279, 0.01 #4491), 04_1l0v (0.30 #1349, 0.28 #6740, 0.27 #22925), 059rby (0.23 #31479, 0.22 #1817, 0.22 #26987), 01n7q (0.20 #13559, 0.20 #976, 0.16 #15359), 05tbn (0.15 #45161, 0.14 #27191, 0.14 #39770), 05kkh (0.14 #1806, 0.13 #8097, 0.13 #20686), 05fjf (0.14 #39920, 0.13 #40819, 0.13 #3069), 07b_l (0.12 #4714, 0.11 #7411, 0.08 #12804) >> Best rule #25168 for best value: >> intensional similarity = 5 >> extensional distance = 123 >> proper extension: 05rh2; >> query: (?x12717, ?x4776) <- adjoins(?x12716, ?x12717), administrative_division(?x8853, ?x12717), administrative_division(?x14486, ?x12716), time_zones(?x12716, ?x2674), contains(?x4776, ?x12716) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mvxt contains! 06yxd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 71.000 0.729 http://example.org/location/location/contains #8472-0qmd5 PRED entity: 0qmd5 PRED relation: nominated_for! PRED expected values: 0gs96 => 73 concepts (62 used for prediction) PRED predicted values (max 10 best out of 203): 02qvyrt (0.47 #1000, 0.47 #544, 0.47 #88), 02n9nmz (0.46 #737, 0.38 #2333, 0.35 #53), 0l8z1 (0.44 #732, 0.44 #960, 0.33 #504), 0gqyl (0.44 #756, 0.37 #984, 0.35 #72), 03hkv_r (0.44 #698, 0.36 #1610, 0.35 #14), 0gr0m (0.42 #966, 0.39 #510, 0.36 #1422), 027dtxw (0.42 #460, 0.33 #916, 0.29 #1372), 0gqy2 (0.41 #114, 0.37 #1710, 0.36 #2166), 054krc (0.41 #62, 0.35 #746, 0.35 #974), 0gs96 (0.36 #538, 0.27 #766, 0.27 #3730) >> Best rule #1000 for best value: >> intensional similarity = 5 >> extensional distance = 76 >> proper extension: 0gmcwlb; 03pc89; >> query: (?x3116, 02qvyrt) <- nominated_for(?x6909, ?x3116), nominated_for(?x1307, ?x3116), titles(?x53, ?x3116), ?x1307 = 0gq9h, ?x6909 = 02qyntr >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #538 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 011yfd; 0k20s; *> query: (?x3116, 0gs96) <- nominated_for(?x1307, ?x3116), titles(?x512, ?x3116), ?x1307 = 0gq9h, film_release_region(?x66, ?x512) *> conf = 0.36 ranks of expected_values: 10 EVAL 0qmd5 nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 73.000 62.000 0.474 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8471-039c26 PRED entity: 039c26 PRED relation: actor PRED expected values: 05w1vf => 72 concepts (53 used for prediction) PRED predicted values (max 10 best out of 784): 0cv9fc (0.51 #2756, 0.39 #10109, 0.37 #12864), 03mdt (0.51 #2756, 0.39 #10109, 0.37 #12864), 02bfmn (0.51 #20220, 0.35 #20219, 0.34 #12865), 0h584v (0.35 #20219, 0.34 #12865, 0.34 #14703), 0g69lg (0.35 #20219, 0.34 #12865, 0.34 #14703), 05cqhl (0.35 #20219, 0.34 #12865, 0.34 #14703), 02fz3w (0.25 #689, 0.01 #6203, 0.01 #8041), 02j8nx (0.25 #256, 0.01 #5770, 0.01 #7608), 03q5dr (0.16 #1651, 0.10 #2569, 0.07 #8084), 02x0dzw (0.15 #4595, 0.10 #19299, 0.10 #21140) >> Best rule #2756 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 0m123; >> query: (?x3303, ?x3381) <- award_winner(?x3303, ?x3381), nominated_for(?x2041, ?x3303), ?x2041 = 0bdx29 >> conf = 0.51 => this is the best rule for 2 predicted values *> Best rule #1741 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 0524b41; 08bytj; 0d7vtk; *> query: (?x3303, 05w1vf) <- actor(?x3303, ?x818), nominated_for(?x9640, ?x3303), ?x9640 = 0gkr9q *> conf = 0.04 ranks of expected_values: 273 EVAL 039c26 actor 05w1vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 72.000 53.000 0.515 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #8470-01wgjj5 PRED entity: 01wgjj5 PRED relation: origin PRED expected values: 04jpl => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 95): 04jpl (0.33 #1419, 0.10 #947, 0.10 #477), 030qb3t (0.22 #2391, 0.20 #4043, 0.20 #975), 01hvzr (0.14 #468, 0.09 #938, 0.03 #1645), 0sbv7 (0.14 #454, 0.09 #924), 02_286 (0.10 #957, 0.08 #4025, 0.08 #7562), 03dm7 (0.10 #1127, 0.06 #1835, 0.05 #2307), 0n90z (0.10 #702, 0.05 #1172, 0.03 #1644), 0hyxv (0.10 #547, 0.03 #1489, 0.02 #4557), 05l64 (0.10 #654), 01_d4 (0.09 #746, 0.05 #981, 0.03 #7586) >> Best rule #1419 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 04pf4r; >> query: (?x5883, 04jpl) <- nationality(?x5883, ?x1310), artists(?x302, ?x5883), ?x1310 = 02jx1, origin(?x5883, ?x10852) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wgjj5 origin 04jpl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.333 http://example.org/music/artist/origin #8469-01xl5 PRED entity: 01xl5 PRED relation: citytown PRED expected values: 0tbql => 89 concepts (43 used for prediction) PRED predicted values (max 10 best out of 95): 04f_d (0.33 #39, 0.25 #407, 0.20 #775), 02_286 (0.31 #3332, 0.29 #4072, 0.28 #1857), 013cz2 (0.25 #429, 0.20 #797, 0.17 #1165), 0tbql (0.20 #815, 0.17 #1183, 0.09 #1841), 0t6hk (0.17 #1377, 0.08 #1745, 0.05 #1842), 07dfk (0.14 #5379, 0.14 #5749, 0.14 #6119), 01_d4 (0.13 #5536, 0.07 #5166, 0.07 #4796), 0ftyc (0.09 #1841, 0.06 #2580, 0.05 #1842), 01z1c (0.08 #1794), 0djd3 (0.08 #1623) >> Best rule #39 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 077w0b; >> query: (?x12229, 04f_d) <- state_province_region(?x12229, ?x1782), ?x1782 = 0488g, industry(?x12229, ?x14371), organization(?x4682, ?x12229), industry(?x3795, ?x14371), ?x4682 = 0dq_5, company(?x265, ?x3795), category(?x3795, ?x134) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #815 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 01lnyf; *> query: (?x12229, 0tbql) <- state_province_region(?x12229, ?x1782), ?x1782 = 0488g, organization(?x4682, ?x12229), organization(?x4682, ?x6945), organization(?x4682, ?x574), production_companies(?x136, ?x574), film(?x574, ?x97), list(?x6945, ?x5997), award(?x574, ?x720) *> conf = 0.20 ranks of expected_values: 4 EVAL 01xl5 citytown 0tbql CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 89.000 43.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #8468-02vk52z PRED entity: 02vk52z PRED relation: organization! PRED expected values: 01ls2 0ctw_b 04v3q 07t21 06s6l 03rj0 06t2t 016wzw 04w4s 077qn 07fj_ 09lxtg 088vb 07f1x 04hhv 035yg 01ppq 01p8s => 122 concepts (75 used for prediction) PRED predicted values (max 10 best out of 178): 09gnn (0.69 #2042, 0.24 #7257, 0.23 #7414), 05br2 (0.60 #1067, 0.50 #599, 0.33 #287), 07fsv (0.60 #1038, 0.50 #570, 0.33 #258), 0ctw_b (0.50 #486, 0.43 #1423, 0.40 #954), 07f1x (0.50 #583, 0.43 #1520, 0.40 #1051), 06f32 (0.50 #507, 0.43 #1444, 0.40 #975), 088vb (0.50 #710, 0.40 #1022, 0.38 #3391), 04v3q (0.50 #800, 0.40 #956, 0.33 #176), 04hhv (0.50 #585, 0.40 #1053, 0.33 #273), 03rj0 (0.50 #812, 0.33 #188, 0.29 #1437) >> Best rule #2042 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 02hcxm; 01prf3; >> query: (?x127, ?x10499) <- organization(?x47, ?x127), organizations_founded(?x10499, ?x127) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #486 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0_2v; *> query: (?x127, 0ctw_b) <- organization(?x8593, ?x127), organization(?x1790, ?x127), organization(?x774, ?x127), ?x774 = 06mzp, ?x8593 = 01crd5, film_release_region(?x6175, ?x1790), ?x6175 = 0gg5kmg *> conf = 0.50 ranks of expected_values: 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 20, 22, 24, 27, 30, 32 EVAL 02vk52z organization! 01p8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 01ppq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 035yg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 04hhv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 07f1x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 088vb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 09lxtg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 07fj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 04w4s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 016wzw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 06t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 03rj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 06s6l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 07t21 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 04v3q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02vk52z organization! 01ls2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 122.000 75.000 0.692 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #8467-07gql PRED entity: 07gql PRED relation: group PRED expected values: 01vrwfv => 72 concepts (42 used for prediction) PRED predicted values (max 10 best out of 429): 02vnpv (0.80 #2828, 0.75 #2293, 0.71 #3544), 02dw1_ (0.75 #2206, 0.72 #5815, 0.62 #1427), 02r3zy (0.71 #1612, 0.67 #1435, 0.67 #1258), 0b_xm (0.71 #1695, 0.67 #1518, 0.62 #1427), 01fchy (0.67 #2447, 0.62 #1427, 0.60 #1020), 01qqwp9 (0.67 #1280, 0.62 #1427, 0.60 #924), 03qkcn9 (0.67 #1420, 0.62 #1427, 0.60 #707), 0khth (0.67 #2363, 0.62 #1427, 0.60 #757), 01j59b0 (0.67 #1299, 0.62 #1427, 0.57 #1653), 0bk1p (0.67 #1363, 0.62 #1427, 0.57 #1717) >> Best rule #2828 for best value: >> intensional similarity = 21 >> extensional distance = 8 >> proper extension: 0g2dz; 02fsn; >> query: (?x2206, 02vnpv) <- role(?x2206, ?x4769), role(?x2206, ?x3703), role(?x2206, ?x2309), role(?x2206, ?x1750), role(?x2206, ?x1212), role(?x2206, ?x716), ?x2309 = 06ncr, ?x1750 = 02hnl, group(?x2206, ?x7597), group(?x2206, ?x2395), role(?x75, ?x2206), ?x716 = 018vs, ?x1212 = 07xzm, instrumentalists(?x2206, ?x669), artists(?x302, ?x2395), role(?x315, ?x3703), award(?x2395, ?x462), artist(?x2931, ?x7597), role(?x4769, ?x74), artists(?x378, ?x7597), origin(?x2395, ?x3976) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1427 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 4 *> proper extension: 028tv0; *> query: (?x2206, ?x498) <- role(?x2206, ?x2309), role(?x2206, ?x1750), role(?x2206, ?x1212), role(?x2206, ?x716), role(?x2206, ?x314), role(?x2206, ?x228), ?x2309 = 06ncr, ?x1750 = 02hnl, group(?x2206, ?x4010), role(?x745, ?x2206), ?x716 = 018vs, role(?x1212, ?x894), role(?x1212, ?x615), family(?x1212, ?x7256), ?x615 = 0dwsp, ?x228 = 0l14qv, ?x894 = 03m5k, ?x4010 = 0163m1, ?x314 = 02sgy, group(?x745, ?x498) *> conf = 0.62 ranks of expected_values: 119 EVAL 07gql group 01vrwfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 72.000 42.000 0.800 http://example.org/music/performance_role/regular_performances./music/group_membership/group #8466-04v7kt PRED entity: 04v7kt PRED relation: film PRED expected values: 062zjtt => 88 concepts (48 used for prediction) PRED predicted values (max 10 best out of 618): 0b6tzs (0.30 #1928, 0.12 #140, 0.03 #5505), 03shpq (0.25 #1446, 0.10 #3234, 0.07 #5023), 01719t (0.25 #231, 0.10 #2019, 0.07 #3808), 093dqjy (0.20 #2397, 0.12 #609, 0.07 #4186), 07p12s (0.14 #5251, 0.12 #1674, 0.10 #3462), 01718w (0.12 #1398, 0.10 #3186, 0.07 #4975), 0gmgwnv (0.12 #1079, 0.10 #2867, 0.07 #4656), 01rwyq (0.12 #549, 0.10 #2337, 0.07 #4126), 06sfk6 (0.12 #762, 0.10 #2550, 0.07 #4339), 0170_p (0.12 #96, 0.10 #1884, 0.07 #3673) >> Best rule #1928 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06qgvf; 0785v8; 02tr7d; 0170s4; 0pmhf; 0f4dx2; 05mc99; 04qsdh; >> query: (?x12494, 0b6tzs) <- actor(?x1653, ?x12494), award_nominee(?x2414, ?x12494), ?x2414 = 03n_7k >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04v7kt film 062zjtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 48.000 0.300 http://example.org/film/actor/film./film/performance/film #8465-06bnz PRED entity: 06bnz PRED relation: combatants! PRED expected values: 0f8l9c => 173 concepts (121 used for prediction) PRED predicted values (max 10 best out of 295): 015fr (0.83 #5605, 0.83 #4264, 0.83 #4825), 015qh (0.83 #5605, 0.83 #4825, 0.83 #5607), 0f8l9c (0.69 #494, 0.59 #1747, 0.55 #1885), 05qhw (0.69 #488, 0.52 #1879, 0.48 #1741), 06f32 (0.69 #514, 0.52 #1905, 0.44 #1767), 05b4w (0.69 #513, 0.48 #1904, 0.48 #1766), 01mk6 (0.62 #535, 0.44 #1788, 0.38 #1926), 06bnz (0.54 #501, 0.34 #1892, 0.30 #1754), 0bq0p9 (0.46 #492, 0.40 #283, 0.39 #840), 059z0 (0.37 #3197, 0.34 #2069, 0.34 #3337) >> Best rule #5605 for best value: >> intensional similarity = 2 >> extensional distance = 79 >> proper extension: 02g1px; 0j06n; 0bxjv; >> query: (?x1603, ?x172) <- combatants(?x1603, ?x172), combatants(?x172, ?x789) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #494 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 0bq0p9; *> query: (?x1603, 0f8l9c) <- nationality(?x889, ?x1603), combatants(?x1264, ?x1603), ?x1264 = 0345h *> conf = 0.69 ranks of expected_values: 3 EVAL 06bnz combatants! 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 173.000 121.000 0.830 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #8464-01vsps PRED entity: 01vsps PRED relation: person! PRED expected values: 03lrqw => 119 concepts (75 used for prediction) PRED predicted values (max 10 best out of 30): 05t54s (0.10 #115, 0.09 #467, 0.08 #747), 064q5v (0.05 #460, 0.05 #108, 0.04 #37), 0bhwhj (0.05 #452, 0.05 #100, 0.04 #29), 04dsnp (0.05 #76, 0.04 #5, 0.04 #428), 02qr3k8 (0.05 #118, 0.04 #47, 0.04 #470), 06929s (0.05 #93, 0.04 #22, 0.04 #445), 05_61y (0.04 #746, 0.04 #254, 0.04 #606), 0872p_c (0.04 #217, 0.02 #77, 0.02 #709), 02v570 (0.04 #48, 0.02 #119, 0.02 #821), 043mk4y (0.04 #50, 0.02 #121, 0.02 #473) >> Best rule #115 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0157m; 012x4t; 0l5yl; 01vsy9_; >> query: (?x4379, 05t54s) <- type_of_union(?x4379, ?x566), celebrities_impersonated(?x3649, ?x4379), people(?x1050, ?x4379), location(?x4379, ?x362) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01vsps person! 03lrqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 75.000 0.095 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #8463-028rk PRED entity: 028rk PRED relation: location PRED expected values: 05tbn => 120 concepts (117 used for prediction) PRED predicted values (max 10 best out of 264): 0vzm (0.25 #173, 0.10 #6599, 0.07 #7403), 02_286 (0.23 #22542, 0.22 #57885, 0.20 #5660), 0rh6k (0.22 #57885, 0.20 #6430, 0.19 #19294), 013n2h (0.22 #57885, 0.20 #6832, 0.17 #2012), 0rd6b (0.22 #57885, 0.17 #2134, 0.10 #6954), 050ks (0.22 #57885, 0.17 #1944, 0.10 #6764), 03s0w (0.22 #57885, 0.12 #4867, 0.06 #8082), 09c7w0 (0.22 #57885, 0.11 #30549, 0.08 #40198), 0cr3d (0.22 #57885, 0.10 #21846, 0.10 #10592), 0chrx (0.22 #57885, 0.06 #22909, 0.06 #8438) >> Best rule #173 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 06c97; 0f7fy; >> query: (?x2663, 0vzm) <- profession(?x2663, ?x5805), entity_involved(?x12031, ?x2663), person(?x6767, ?x2663), ?x6767 = 05_61y >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #57885 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 202 *> proper extension: 0fvf9q; 02g8h; 02lk1s; 01p45_v; 03ft8; 016_mj; 03k7bd; 01wbl_r; 040db; 04gcd1; ... *> query: (?x2663, ?x8451) <- profession(?x2663, ?x5805), company(?x2663, ?x94), company(?x9684, ?x94), location(?x9684, ?x8451) *> conf = 0.22 ranks of expected_values: 20 EVAL 028rk location 05tbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 120.000 117.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #8462-0bby9p5 PRED entity: 0bby9p5 PRED relation: film! PRED expected values: 0c4f4 => 82 concepts (37 used for prediction) PRED predicted values (max 10 best out of 825): 0b6yp2 (0.46 #72852, 0.45 #39545, 0.44 #41627), 016zp5 (0.08 #978, 0.03 #3059, 0.03 #5140), 06lvlf (0.08 #1053, 0.02 #17702, 0.02 #7297), 04fhn_ (0.08 #683, 0.02 #19413, 0.01 #33983), 025j1t (0.08 #1077, 0.02 #28133, 0.01 #51029), 05nzw6 (0.08 #1193, 0.02 #38656, 0.02 #40738), 0bj9k (0.08 #328, 0.02 #54444, 0.02 #46117), 01wmxfs (0.08 #130, 0.02 #54246, 0.01 #37593), 0bksh (0.08 #856, 0.02 #57054, 0.02 #27912), 017149 (0.08 #83, 0.02 #33383, 0.02 #37546) >> Best rule #72852 for best value: >> intensional similarity = 4 >> extensional distance = 739 >> proper extension: 020fcn; 048htn; 04t6fk; 01dvbd; 05c5z8j; 0prh7; 02psgq; 05n6sq; 0286vp; 01k0vq; ... >> query: (?x2558, ?x3414) <- titles(?x53, ?x2558), nominated_for(?x3414, ?x2558), film(?x1794, ?x2558), film_crew_role(?x2558, ?x137) >> conf = 0.46 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bby9p5 film! 0c4f4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 37.000 0.458 http://example.org/film/actor/film./film/performance/film #8461-05wdgq PRED entity: 05wdgq PRED relation: place_of_birth PRED expected values: 04vmp => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 53): 04vmp (0.18 #1676, 0.18 #4495, 0.15 #3085), 0dlv0 (0.17 #354, 0.14 #2467, 0.12 #1058), 01_yvy (0.17 #390, 0.05 #1798, 0.03 #3207), 0xnt5 (0.17 #273, 0.02 #26067, 0.01 #4500), 0c8tk (0.15 #2972, 0.06 #5792, 0.06 #7201), 02p3my (0.12 #1360, 0.03 #2769, 0.02 #26067), 0hj6h (0.09 #1894, 0.07 #2599, 0.04 #5418), 029kpy (0.07 #2390, 0.04 #3799, 0.03 #5209), 02_286 (0.06 #36651, 0.06 #31722, 0.06 #40173), 0cvw9 (0.05 #3116, 0.05 #8050, 0.05 #1707) >> Best rule #1676 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 07jmnh; >> query: (?x12311, 04vmp) <- people(?x5025, ?x12311), nationality(?x12311, ?x2146), award(?x12311, ?x4687), ?x4687 = 03rbj2 >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05wdgq place_of_birth 04vmp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.182 http://example.org/people/person/place_of_birth #8460-026wlxw PRED entity: 026wlxw PRED relation: story_by PRED expected values: 052hl => 86 concepts (51 used for prediction) PRED predicted values (max 10 best out of 63): 02nygk (0.11 #1507, 0.04 #1939, 0.03 #2586), 01y8d4 (0.09 #1434, 0.03 #2513, 0.02 #2730), 011s9r (0.08 #1494, 0.02 #2573, 0.02 #2790), 042xh (0.06 #1511, 0.02 #1943, 0.02 #2590), 04hw4b (0.05 #1421, 0.02 #1853, 0.02 #2069), 0343h (0.04 #1099, 0.02 #2611, 0.02 #3258), 0fx02 (0.03 #2653, 0.03 #3515, 0.03 #3730), 0yxl (0.03 #1451, 0.01 #1883, 0.01 #2099), 0jt90f5 (0.03 #1329, 0.01 #1761, 0.01 #1977), 0ff2k (0.03 #1491, 0.01 #1923) >> Best rule #1507 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 023cjg; >> query: (?x8214, 02nygk) <- film(?x382, ?x8214), ?x382 = 086k8, film(?x237, ?x8214), story_by(?x8214, ?x11873) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026wlxw story_by 052hl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 51.000 0.109 http://example.org/film/film/story_by #8459-0ny57 PRED entity: 0ny57 PRED relation: country PRED expected values: 09c7w0 => 125 concepts (83 used for prediction) PRED predicted values (max 10 best out of 37): 09c7w0 (0.77 #91, 0.76 #2344, 0.76 #2259), 03ryn (0.35 #5576), 0m24v (0.33 #5138, 0.32 #3128, 0.32 #779), 0m2dk (0.33 #5138, 0.32 #3128, 0.32 #779), 0vmt (0.30 #6546, 0.12 #7261, 0.12 #6994), 0d060g (0.15 #183, 0.11 #269, 0.06 #1483), 07c5l (0.12 #7261, 0.12 #6994), 07ssc (0.10 #364, 0.08 #3058, 0.08 #3320), 0chghy (0.06 #705, 0.05 #533, 0.05 #619), 059j2 (0.05 #3160, 0.03 #5170, 0.02 #5081) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0f2tj; >> query: (?x13006, 09c7w0) <- origin(?x6461, ?x13006), state(?x13006, ?x938), location_of_ceremony(?x12123, ?x13006), time_zones(?x13006, ?x2088) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ny57 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 83.000 0.769 http://example.org/base/biblioness/bibs_location/country #8458-07cdz PRED entity: 07cdz PRED relation: film! PRED expected values: 03ryzs => 90 concepts (70 used for prediction) PRED predicted values (max 10 best out of 55): 05qd_ (0.22 #84, 0.17 #306, 0.13 #1643), 086k8 (0.18 #2, 0.17 #299, 0.17 #3433), 016tw3 (0.17 #86, 0.14 #3517, 0.14 #1495), 016tt2 (0.17 #79, 0.13 #301, 0.13 #1638), 017s11 (0.14 #226, 0.13 #745, 0.13 #1413), 03xq0f (0.14 #1415, 0.13 #80, 0.12 #1191), 0g1rw (0.14 #8, 0.09 #231, 0.08 #1046), 024rbz (0.12 #161, 0.05 #1496, 0.04 #457), 0jz9f (0.10 #1039, 0.10 #446, 0.09 #298), 0fqy4p (0.08 #176, 0.03 #250, 0.02 #1213) >> Best rule #84 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 09rvwmy; >> query: (?x3510, 05qd_) <- genre(?x3510, ?x53), featured_film_locations(?x3510, ?x5288), film(?x398, ?x3510), major_field_of_study(?x5288, ?x254) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #71 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 07xtqq; 0b73_1d; 0pv2t; 05jzt3; 0gmcwlb; 026gyn_; 03hj3b3; 016z7s; 0f4_l; 0yyts; ... *> query: (?x3510, 03ryzs) <- nominated_for(?x749, ?x3510), nominated_for(?x591, ?x3510), nominated_for(?x398, ?x3510), ?x749 = 094qd5, ?x591 = 0f4x7 *> conf = 0.05 ranks of expected_values: 26 EVAL 07cdz film! 03ryzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 90.000 70.000 0.217 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #8457-037jz PRED entity: 037jz PRED relation: influenced_by! PRED expected values: 01wd02c 02ghq => 132 concepts (24 used for prediction) PRED predicted values (max 10 best out of 704): 045bg (0.57 #2032, 0.50 #2532, 0.33 #34), 073v6 (0.50 #1613, 0.14 #2112, 0.12 #2612), 0lcx (0.43 #2146, 0.38 #2646, 0.12 #10503), 099bk (0.43 #2142, 0.38 #2642, 0.11 #3497), 0372p (0.43 #2143, 0.38 #2643, 0.05 #7001), 0683n (0.40 #1327, 0.29 #3326, 0.18 #5328), 051cc (0.40 #1335, 0.12 #2835, 0.12 #10503), 0w6w (0.38 #2994, 0.29 #2494, 0.11 #3497), 05qzv (0.33 #891, 0.33 #392, 0.17 #1891), 0j3v (0.33 #77, 0.29 #2075, 0.25 #2575) >> Best rule #2032 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0gz_; 03f0324; 05qmj; 0w6w; >> query: (?x6810, 045bg) <- influenced_by(?x8404, ?x6810), influenced_by(?x5334, ?x6810), profession(?x6810, ?x353), ?x8404 = 0nk72, people(?x6821, ?x5334) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #763 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 03hnd; *> query: (?x6810, 01wd02c) <- influenced_by(?x2161, ?x6810), influenced_by(?x477, ?x6810), student(?x2142, ?x6810), ?x2161 = 040db, influenced_by(?x477, ?x6512), ?x6512 = 08304 *> conf = 0.33 ranks of expected_values: 13, 180 EVAL 037jz influenced_by! 02ghq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 132.000 24.000 0.571 http://example.org/influence/influence_node/influenced_by EVAL 037jz influenced_by! 01wd02c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 132.000 24.000 0.571 http://example.org/influence/influence_node/influenced_by #8456-0j3v PRED entity: 0j3v PRED relation: student! PRED expected values: 01zzy3 => 211 concepts (161 used for prediction) PRED predicted values (max 10 best out of 278): 0dy04 (0.30 #1123, 0.25 #2175, 0.13 #7435), 01lhdt (0.27 #3416, 0.16 #6572, 0.14 #4994), 03ksy (0.25 #53771, 0.12 #16413, 0.12 #14835), 07tk7 (0.22 #442, 0.12 #4650, 0.09 #2020), 09f2j (0.19 #53824, 0.06 #50667, 0.06 #9101), 01y06y (0.18 #5229, 0.16 #6807, 0.12 #4703), 0h6rm (0.18 #1722, 0.11 #670, 0.10 #1196), 01w5m (0.15 #7995, 0.14 #9047, 0.14 #8521), 0bwfn (0.14 #50783, 0.10 #1327, 0.09 #1853), 07tg4 (0.13 #53751, 0.11 #612, 0.11 #86) >> Best rule #1123 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0jcx; 0b78hw; 03_hd; 0399p; 02ln1; 032r1; 01lwx; 047g6; >> query: (?x2240, 0dy04) <- influenced_by(?x2240, ?x1857), religion(?x2240, ?x7422), ?x1857 = 026lj, religion(?x94, ?x7422) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #4683 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 03sbs; 03jht; 05qzv; *> query: (?x2240, 01zzy3) <- influenced_by(?x2240, ?x12259), gender(?x2240, ?x231), ?x12259 = 015n8, influenced_by(?x1236, ?x2240) *> conf = 0.12 ranks of expected_values: 12 EVAL 0j3v student! 01zzy3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 211.000 161.000 0.300 http://example.org/education/educational_institution/students_graduates./education/education/student #8455-06crng PRED entity: 06crng PRED relation: film PRED expected values: 02qsqmq => 107 concepts (68 used for prediction) PRED predicted values (max 10 best out of 928): 0407yj_ (0.13 #4059, 0.11 #5847, 0.09 #9423), 03q0r1 (0.13 #4213, 0.09 #9577, 0.08 #13153), 0g7pm1 (0.13 #4779, 0.06 #10143, 0.06 #11931), 05fm6m (0.13 #4895, 0.06 #10259, 0.06 #12047), 01hvjx (0.13 #3950, 0.06 #9314, 0.06 #11102), 03n3gl (0.13 #4700, 0.06 #10064, 0.06 #11852), 020bv3 (0.12 #318, 0.08 #2106, 0.08 #34291), 04gv3db (0.12 #753, 0.08 #2541, 0.07 #16845), 03lrht (0.12 #257, 0.08 #2045, 0.07 #3833), 01jrbb (0.12 #471, 0.08 #2259, 0.07 #4047) >> Best rule #4059 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 04t2l2; 0pz7h; 02p21g; 0126rp; 01_x6v; 01gbbz; 019vgs; 01_x6d; 04h07s; 0bqs56; ... >> query: (?x7527, 0407yj_) <- profession(?x7527, ?x1383), ?x1383 = 0np9r, influenced_by(?x7527, ?x2125), currency(?x7527, ?x170) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #33181 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 162 *> proper extension: 0cm03; 0xnc3; *> query: (?x7527, 02qsqmq) <- student(?x12936, ?x7527), nationality(?x7527, ?x512), ?x512 = 07ssc *> conf = 0.01 ranks of expected_values: 803 EVAL 06crng film 02qsqmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 107.000 68.000 0.133 http://example.org/film/actor/film./film/performance/film #8454-0420y PRED entity: 0420y PRED relation: interests PRED expected values: 05r79 => 126 concepts (58 used for prediction) PRED predicted values (max 10 best out of 17): 02jcc (0.56 #188, 0.50 #52, 0.42 #275), 04s0m (0.40 #233, 0.40 #113, 0.38 #292), 02jhc (0.38 #292, 0.30 #231, 0.29 #162), 05r79 (0.38 #292, 0.29 #157, 0.25 #72), 0gt_hv (0.38 #292, 0.25 #85, 0.20 #119), 0x0w (0.25 #288, 0.25 #65, 0.25 #48), 05qfh (0.25 #91, 0.25 #40, 0.24 #240), 05qt0 (0.25 #59, 0.24 #240, 0.22 #205), 09xq9d (0.24 #240, 0.22 #205, 0.11 #194), 06ms6 (0.22 #205, 0.08 #277, 0.04 #380) >> Best rule #188 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 039n1; >> query: (?x11830, 02jcc) <- influenced_by(?x9600, ?x11830), influenced_by(?x7509, ?x11830), influenced_by(?x920, ?x11830), ?x920 = 04411, student(?x7508, ?x7509), interests(?x9600, ?x713), influenced_by(?x1236, ?x7509) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #292 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0399p; *> query: (?x11830, ?x713) <- influenced_by(?x11830, ?x3712), place_of_birth(?x11830, ?x10610), peers(?x1857, ?x11830), religion(?x11830, ?x1985), interests(?x1857, ?x713) *> conf = 0.38 ranks of expected_values: 4 EVAL 0420y interests 05r79 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 126.000 58.000 0.556 http://example.org/user/alexander/philosophy/philosopher/interests #8453-04gp1d PRED entity: 04gp1d PRED relation: legislative_sessions PRED expected values: 02bn_p => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 40): 03ww_x (0.91 #124, 0.89 #123, 0.88 #504), 02bqn1 (0.91 #124, 0.89 #123, 0.88 #504), 02glc4 (0.91 #124, 0.89 #123, 0.88 #504), 02cg7g (0.91 #124, 0.89 #123, 0.88 #504), 04gp1d (0.89 #123, 0.86 #297, 0.84 #381), 02bn_p (0.89 #123, 0.86 #297, 0.84 #381), 077g7n (0.89 #123, 0.86 #297, 0.84 #381), 01gsvb (0.40 #755, 0.33 #1056, 0.32 #1340), 01gsvp (0.40 #755, 0.33 #1056, 0.31 #1227), 01gst_ (0.40 #755, 0.33 #1056, 0.31 #1227) >> Best rule #124 for best value: >> intensional similarity = 46 >> extensional distance = 2 >> proper extension: 03rtmz; >> query: (?x3765, ?x606) <- legislative_sessions(?x11605, ?x3765), legislative_sessions(?x9334, ?x3765), legislative_sessions(?x6742, ?x3765), legislative_sessions(?x652, ?x3765), legislative_sessions(?x3765, ?x6933), legislative_sessions(?x3765, ?x6743), legislative_sessions(?x3765, ?x6728), legislative_sessions(?x3765, ?x6139), legislative_sessions(?x3765, ?x3766), legislative_sessions(?x3765, ?x3540), legislative_sessions(?x3765, ?x2976), legislative_sessions(?x3765, ?x1829), legislative_sessions(?x3765, ?x952), legislative_sessions(?x3765, ?x356), ?x6743 = 04h1rz, district_represented(?x3765, ?x2977), ?x652 = 021sv1, ?x6742 = 06bss, ?x3766 = 02gkzs, ?x1829 = 02bp37, ?x3540 = 024tcq, legislative_sessions(?x4730, ?x3765), legislative_sessions(?x1137, ?x3765), legislative_sessions(?x606, ?x3765), ?x6139 = 060ny2, legislative_sessions(?x2860, ?x3765), ?x2977 = 081mh, ?x6728 = 070mff, ?x9334 = 02hy5d, ?x6933 = 024tkd, ?x356 = 05l2z4, ?x2860 = 0b3wk, district_represented(?x2976, ?x5575), district_represented(?x2976, ?x3670), district_represented(?x2976, ?x2256), ?x11605 = 024_vw, district_represented(?x1137, ?x1227), ?x3670 = 05tbn, ?x4730 = 02cg7g, legislative_sessions(?x605, ?x2976), legislative_sessions(?x2357, ?x1137), ?x605 = 077g7n, ?x1227 = 01n7q, ?x5575 = 05fjy, ?x2256 = 07srw, ?x952 = 06f0dc >> conf = 0.91 => this is the best rule for 4 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 46 *> extensional distance = 2 *> proper extension: 03rtmz; *> query: (?x3765, ?x1027) <- legislative_sessions(?x11605, ?x3765), legislative_sessions(?x9334, ?x3765), legislative_sessions(?x6742, ?x3765), legislative_sessions(?x652, ?x3765), legislative_sessions(?x3765, ?x6933), legislative_sessions(?x3765, ?x6743), legislative_sessions(?x3765, ?x6728), legislative_sessions(?x3765, ?x6139), legislative_sessions(?x3765, ?x3766), legislative_sessions(?x3765, ?x3540), legislative_sessions(?x3765, ?x2976), legislative_sessions(?x3765, ?x1829), legislative_sessions(?x3765, ?x952), legislative_sessions(?x3765, ?x356), ?x6743 = 04h1rz, district_represented(?x3765, ?x2977), ?x652 = 021sv1, ?x6742 = 06bss, ?x3766 = 02gkzs, ?x1829 = 02bp37, ?x3540 = 024tcq, legislative_sessions(?x4730, ?x3765), legislative_sessions(?x1137, ?x3765), ?x6139 = 060ny2, legislative_sessions(?x2860, ?x3765), ?x2977 = 081mh, ?x6728 = 070mff, ?x9334 = 02hy5d, ?x6933 = 024tkd, ?x356 = 05l2z4, ?x2860 = 0b3wk, district_represented(?x2976, ?x5575), district_represented(?x2976, ?x3670), district_represented(?x2976, ?x2256), ?x11605 = 024_vw, district_represented(?x1137, ?x1227), ?x3670 = 05tbn, ?x4730 = 02cg7g, legislative_sessions(?x1027, ?x2976), legislative_sessions(?x605, ?x2976), legislative_sessions(?x2357, ?x1137), ?x605 = 077g7n, ?x1227 = 01n7q, ?x5575 = 05fjy, ?x2256 = 07srw, ?x952 = 06f0dc *> conf = 0.89 ranks of expected_values: 6 EVAL 04gp1d legislative_sessions 02bn_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 34.000 34.000 0.911 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #8452-0fpkhkz PRED entity: 0fpkhkz PRED relation: film_release_region PRED expected values: 04gzd 012wgb => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 226): 0d0vqn (0.91 #436, 0.90 #722, 0.89 #1008), 0154j (0.84 #434, 0.79 #720, 0.78 #1006), 0jgd (0.82 #719, 0.82 #433, 0.82 #1005), 03rt9 (0.70 #442, 0.66 #1014, 0.65 #1301), 03rj0 (0.65 #481, 0.61 #767, 0.60 #1340), 05v8c (0.65 #444, 0.60 #1016, 0.60 #730), 04gzd (0.63 #439, 0.55 #725, 0.53 #1011), 0ctw_b (0.59 #451, 0.54 #1023, 0.53 #737), 01p1v (0.56 #473, 0.47 #759, 0.47 #1045), 047yc (0.55 #454, 0.46 #740, 0.46 #1026) >> Best rule #436 for best value: >> intensional similarity = 6 >> extensional distance = 162 >> proper extension: 014lc_; 0401sg; 087wc7n; 0crfwmx; 0h3xztt; 03bx2lk; 053tj7; 0fq7dv_; 01fmys; 0407yj_; ... >> query: (?x1490, 0d0vqn) <- film_release_region(?x1490, ?x1603), film_release_region(?x1490, ?x1264), film_release_region(?x1490, ?x456), ?x456 = 05qhw, ?x1264 = 0345h, ?x1603 = 06bnz >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #439 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 162 *> proper extension: 014lc_; 0401sg; 087wc7n; 0crfwmx; 0h3xztt; 03bx2lk; 053tj7; 0fq7dv_; 01fmys; 0407yj_; ... *> query: (?x1490, 04gzd) <- film_release_region(?x1490, ?x1603), film_release_region(?x1490, ?x1264), film_release_region(?x1490, ?x456), ?x456 = 05qhw, ?x1264 = 0345h, ?x1603 = 06bnz *> conf = 0.63 ranks of expected_values: 7, 77 EVAL 0fpkhkz film_release_region 012wgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 74.000 74.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fpkhkz film_release_region 04gzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 74.000 74.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8451-02g2wv PRED entity: 02g2wv PRED relation: award_winner PRED expected values: 01mt1fy => 51 concepts (17 used for prediction) PRED predicted values (max 10 best out of 984): 016yvw (0.56 #4946, 0.56 #3688, 0.30 #2473), 09fb5 (0.56 #2536, 0.08 #17380, 0.08 #9955), 039bp (0.56 #2683, 0.05 #10102, 0.05 #12577), 0pmhf (0.44 #3021, 0.08 #27209, 0.03 #10440), 0170pk (0.44 #2828, 0.07 #10247, 0.06 #12722), 02qgqt (0.44 #2490, 0.07 #9909, 0.06 #12384), 0bl2g (0.44 #2533, 0.05 #9952, 0.05 #12427), 01vs_v8 (0.33 #460, 0.23 #5406, 0.18 #7878), 01vvycq (0.33 #119, 0.15 #5065, 0.12 #7537), 015vq_ (0.33 #3375, 0.08 #27209, 0.05 #29683) >> Best rule #4946 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0f4x7; 09sb52; 027986c; 09cm54; 04kxsb; 0279c15; 057xs89; >> query: (?x5734, ?x5363) <- award(?x5363, ?x5734), nominated_for(?x5734, ?x641), award(?x1474, ?x5734), ?x5363 = 016yvw >> conf = 0.56 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02g2wv award_winner 01mt1fy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 17.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner #8450-01yj2 PRED entity: 01yj2 PRED relation: place_of_birth! PRED expected values: 01x53m => 151 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1664): 041h0 (0.25 #2672, 0.07 #20949, 0.05 #112289), 03d9wk (0.17 #13044, 0.17 #10433, 0.08 #18266), 0kbg6 (0.17 #13028, 0.17 #10417, 0.08 #18250), 09jd9 (0.17 #13022, 0.17 #10411, 0.08 #18244), 026sb55 (0.17 #13005, 0.17 #10394, 0.08 #18227), 06lhbl (0.17 #12978, 0.17 #10367, 0.08 #18200), 03f4w4 (0.17 #12928, 0.17 #10317, 0.08 #18150), 01b0k1 (0.17 #12927, 0.17 #10316, 0.08 #18149), 0935jw (0.17 #12926, 0.17 #10315, 0.08 #18148), 02vkvcz (0.17 #12894, 0.17 #10283, 0.08 #18116) >> Best rule #2672 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 067z4; 0c499; >> query: (?x8751, 041h0) <- capital(?x5738, ?x8751), capital(?x792, ?x8751), ?x792 = 0hzlz, ?x5738 = 0c4b8 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #151456 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 138 *> proper extension: 0cy41; 0_wm_; 018_7x; *> query: (?x8751, ?x926) <- citytown(?x9861, ?x8751), category(?x8751, ?x134), student(?x9861, ?x926) *> conf = 0.06 ranks of expected_values: 111 EVAL 01yj2 place_of_birth! 01x53m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 151.000 126.000 0.250 http://example.org/people/person/place_of_birth #8449-04d18d PRED entity: 04d18d PRED relation: colors! PRED expected values: 02gr81 0ks67 => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1354): 01jq34 (0.67 #4819, 0.60 #3870, 0.57 #5294), 02vnp2 (0.67 #5101, 0.60 #4152, 0.57 #5576), 0bsnm (0.60 #4094, 0.50 #5043, 0.43 #5518), 0yls9 (0.60 #4502, 0.50 #3067, 0.43 #5932), 07lx1s (0.56 #2384, 0.54 #3820, 0.51 #1906), 01hjy5 (0.56 #2384, 0.54 #3820, 0.51 #1906), 065r8g (0.56 #2384, 0.54 #3820, 0.51 #1906), 02mw6c (0.56 #2384, 0.54 #3820, 0.51 #1906), 07x4c (0.56 #2384, 0.54 #3820, 0.51 #1906), 03b8c4 (0.56 #2384, 0.54 #3820, 0.51 #1906) >> Best rule #4819 for best value: >> intensional similarity = 43 >> extensional distance = 4 >> proper extension: 06fvc; >> query: (?x12676, 01jq34) <- colors(?x5773, ?x12676), colors(?x10659, ?x12676), colors(?x6912, ?x12676), colors(?x2980, ?x12676), colors(?x388, ?x12676), colors(?x10659, ?x3364), ?x3364 = 036k5h, position_s(?x5773, ?x3346), position_s(?x5773, ?x2573), position_s(?x5773, ?x1792), position_s(?x5773, ?x180), ?x180 = 01r3hr, contains(?x94, ?x10659), ?x3346 = 02g_7z, teams(?x3125, ?x5773), ?x2573 = 05b3ts, school(?x5773, ?x2760), organization(?x346, ?x2980), major_field_of_study(?x2980, ?x2981), institution(?x865, ?x2760), institution(?x620, ?x2760), currency(?x6912, ?x170), student(?x6912, ?x5097), school_type(?x2760, ?x3092), ?x620 = 07s6fsf, school(?x387, ?x388), ?x865 = 02h4rq6, nominated_for(?x5097, ?x414), team(?x11323, ?x5773), major_field_of_study(?x388, ?x1154), fraternities_and_sororities(?x6912, ?x3697), team(?x10287, ?x5773), award_nominee(?x192, ?x5097), school(?x465, ?x2760), ?x1154 = 02lp1, ?x10287 = 019g65, institution(?x1305, ?x388), contains(?x674, ?x2760), citytown(?x6912, ?x3052), ?x1792 = 05zm34, award(?x5097, ?x618), major_field_of_study(?x10659, ?x1668), organization(?x388, ?x5487) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1064 for first EXPECTED value: *> intensional similarity = 45 *> extensional distance = 1 *> proper extension: 083jv; *> query: (?x12676, 02gr81) <- colors(?x5773, ?x12676), colors(?x10659, ?x12676), colors(?x7716, ?x12676), colors(?x5842, ?x12676), colors(?x2980, ?x12676), colors(?x10659, ?x3364), ?x3364 = 036k5h, position_s(?x5773, ?x3346), position_s(?x5773, ?x2573), position_s(?x5773, ?x2312), position_s(?x5773, ?x1792), position_s(?x5773, ?x1517), position_s(?x5773, ?x180), ?x180 = 01r3hr, contains(?x94, ?x10659), ?x3346 = 02g_7z, teams(?x3125, ?x5773), ?x2573 = 05b3ts, school(?x5773, ?x2760), school(?x5773, ?x2171), organization(?x346, ?x2980), major_field_of_study(?x2980, ?x2981), organization(?x10659, ?x5487), major_field_of_study(?x10659, ?x8962), major_field_of_study(?x2760, ?x9111), major_field_of_study(?x2760, ?x3995), ?x9111 = 04sh3, category(?x2980, ?x134), draft(?x5773, ?x685), ?x1517 = 02g_6j, currency(?x2980, ?x170), sport(?x5773, ?x1083), institution(?x620, ?x2760), student(?x2760, ?x1934), school(?x6976, ?x7716), ?x3995 = 0fdys, team(?x5412, ?x5773), major_field_of_study(?x7716, ?x1682), institution(?x1305, ?x7716), ?x1792 = 05zm34, ?x2312 = 02qpbqj, interests(?x4308, ?x8962), ?x5842 = 01p5xy, school_type(?x2980, ?x1044), ?x2171 = 01jq34 *> conf = 0.33 ranks of expected_values: 312, 362 EVAL 04d18d colors! 0ks67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 21.000 21.000 0.667 http://example.org/education/educational_institution/colors EVAL 04d18d colors! 02gr81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 21.000 21.000 0.667 http://example.org/education/educational_institution/colors #8448-02px_23 PRED entity: 02px_23 PRED relation: position PRED expected values: 02vkdwz => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 13): 02g_6j (0.89 #465, 0.89 #458, 0.89 #452), 047g8h (0.89 #457, 0.89 #443, 0.87 #348), 06b1q (0.88 #418, 0.83 #427, 0.80 #347), 01r3hr (0.88 #418, 0.82 #497, 0.81 #360), 02qpbqj (0.80 #344, 0.77 #562, 0.77 #388), 02vkdwz (0.77 #562, 0.77 #389, 0.75 #374), 01_9c1 (0.76 #473, 0.75 #537, 0.75 #374), 05fyy5 (0.69 #373, 0.61 #325, 0.58 #276), 0bgv4g (0.65 #437, 0.62 #233, 0.62 #232), 01snvb (0.64 #565, 0.54 #294, 0.45 #608) >> Best rule #465 for best value: >> intensional similarity = 45 >> extensional distance = 17 >> proper extension: 06rny; >> query: (?x7539, ?x1517) <- position_s(?x7539, ?x1792), position_s(?x7539, ?x1717), position_s(?x7539, ?x1517), position_s(?x7539, ?x1240), ?x1517 = 02g_6j, team(?x7079, ?x7539), ?x1717 = 02g_6x, position(?x9748, ?x7079), position(?x8516, ?x7079), position(?x7312, ?x7079), position(?x6696, ?x7079), position(?x6645, ?x7079), position(?x4924, ?x7079), position(?x4723, ?x7079), position(?x4469, ?x7079), position(?x4256, ?x7079), position(?x4222, ?x7079), position(?x4170, ?x7079), position(?x2198, ?x7079), position(?x1576, ?x7079), position(?x1239, ?x7079), position(?x1115, ?x7079), position(?x729, ?x7079), ?x6645 = 0wsr, ?x2198 = 05g3v, ?x4256 = 03lsq, ?x4170 = 05l71, ?x4469 = 043vc, position(?x4189, ?x7079), position(?x1337, ?x7079), ?x4189 = 026lg0s, ?x1792 = 05zm34, ?x8516 = 0fbtm7, ?x1240 = 023wyl, ?x4723 = 043tz8m, ?x6696 = 0fjzsy, ?x1337 = 0ftf0f, ?x7312 = 0487_, ?x729 = 05g3b, ?x4222 = 051q5, ?x9748 = 0fsb_6, ?x1576 = 05tfm, colors(?x1115, ?x663), ?x1239 = 01xvb, ?x4924 = 025_64l >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #562 for first EXPECTED value: *> intensional similarity = 42 *> extensional distance = 24 *> proper extension: 01ct6; *> query: (?x7539, ?x706) <- position_s(?x7539, ?x1717), position_s(?x7539, ?x1517), ?x1517 = 02g_6j, team(?x7079, ?x7539), team(?x3346, ?x7539), team(?x706, ?x7539), ?x1717 = 02g_6x, ?x7079 = 08ns5s, position_s(?x9172, ?x3346), position_s(?x7450, ?x3346), position_s(?x6466, ?x3346), position_s(?x5773, ?x3346), position_s(?x5229, ?x3346), position_s(?x4661, ?x3346), position_s(?x4613, ?x3346), position_s(?x4469, ?x3346), position_s(?x4170, ?x3346), position_s(?x2574, ?x3346), position_s(?x1337, ?x3346), position_s(?x1115, ?x3346), position_s(?x387, ?x3346), position(?x2148, ?x3346), position(?x3347, ?x3346), ?x387 = 02896, ?x4613 = 03ttn0, ?x4170 = 05l71, position(?x7450, ?x2247), ?x1337 = 0ftf0f, ?x4661 = 0frm7n, ?x6466 = 057xlyq, draft(?x2574, ?x3089), ?x5773 = 06rny, ?x9172 = 06rpd, position(?x6022, ?x3346), ?x4469 = 043vc, ?x5229 = 07l2m, ?x1115 = 01y3c, position_s(?x6294, ?x706), ?x3089 = 03nt7j, school(?x2574, ?x1011), ?x2148 = 0fht9f, position(?x10168, ?x706) *> conf = 0.77 ranks of expected_values: 6 EVAL 02px_23 position 02vkdwz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 66.000 66.000 0.895 http://example.org/sports/sports_team/roster./american_football/football_roster_position/position #8447-071tyz PRED entity: 071tyz PRED relation: major_field_of_study PRED expected values: 06ms6 02j62 02cm61 => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 140): 02j62 (0.87 #2049, 0.86 #1824, 0.83 #1599), 01mkq (0.78 #1237, 0.78 #1250, 0.78 #1010), 03g3w (0.78 #1237, 0.78 #1010, 0.76 #223), 05qfh (0.78 #1237, 0.76 #223, 0.75 #1605), 02822 (0.78 #1237, 0.76 #223, 0.75 #675), 03nfmq (0.78 #1273, 0.71 #1049, 0.71 #334), 02jfc (0.75 #1647, 0.75 #1199, 0.71 #1089), 04rjg (0.75 #1141, 0.75 #675, 0.73 #1460), 01lj9 (0.75 #1160, 0.75 #675, 0.73 #1460), 0h5k (0.75 #1145, 0.72 #1009, 0.71 #1035) >> Best rule #2049 for best value: >> intensional similarity = 28 >> extensional distance = 13 >> proper extension: 022h5x; >> query: (?x2759, 02j62) <- institution(?x2759, ?x2760), institution(?x2759, ?x1220), institution(?x2759, ?x892), major_field_of_study(?x2759, ?x742), institution(?x2636, ?x1220), ?x2636 = 027f2w, citytown(?x1220, ?x5952), institution(?x865, ?x2760), category(?x1220, ?x134), contains(?x1310, ?x892), country(?x2760, ?x94), student(?x892, ?x164), major_field_of_study(?x2760, ?x254), ?x865 = 02h4rq6, state_province_region(?x2760, ?x2256), major_field_of_study(?x7178, ?x742), major_field_of_study(?x4187, ?x742), major_field_of_study(?x2313, ?x742), ?x2313 = 07wrz, major_field_of_study(?x892, ?x866), student(?x742, ?x8508), ?x4187 = 05mv4, school(?x1115, ?x2760), major_field_of_study(?x5031, ?x742), religion(?x8508, ?x2694), place_of_death(?x8508, ?x5267), ?x7178 = 03hdz8, ?x866 = 088tb >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 25, 60 EVAL 071tyz major_field_of_study 02cm61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 25.000 25.000 0.867 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 071tyz major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.867 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 071tyz major_field_of_study 06ms6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 25.000 25.000 0.867 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #8446-03ksy PRED entity: 03ksy PRED relation: institution! PRED expected values: 013zdg => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 10): 03mkk4 (0.62 #70, 0.50 #125, 0.50 #103), 013zdg (0.50 #123, 0.50 #101, 0.44 #134), 02mjs7 (0.33 #111, 0.30 #100, 0.29 #122), 02m4yg (0.30 #106, 0.29 #128, 0.28 #1346), 022h5x (0.28 #1346, 0.28 #206, 0.25 #74), 071tyz (0.28 #1346, 0.22 #91, 0.22 #860), 01ysy9 (0.28 #1346, 0.11 #98, 0.06 #495), 01gkg3 (0.28 #1346, 0.01 #877, 0.01 #1077), 0g26h (0.03 #335, 0.02 #501), 01kxxq (0.02 #1158, 0.02 #1203, 0.02 #1240) >> Best rule #70 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 059j2; >> query: (?x3439, 03mkk4) <- organizations_founded(?x3439, ?x5487), contains(?x2020, ?x3439), company(?x346, ?x3439) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 019fz; *> query: (?x3439, 013zdg) <- organizations_founded(?x3439, ?x5487), country(?x5487, ?x94), organization(?x122, ?x5487) *> conf = 0.50 ranks of expected_values: 2 EVAL 03ksy institution! 013zdg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.625 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #8445-01g23m PRED entity: 01g23m PRED relation: award_nominee! PRED expected values: 02qgqt => 154 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1347): 0171cm (0.46 #5193, 0.13 #92928, 0.09 #65048), 0dgskx (0.42 #6146, 0.13 #92928, 0.06 #17760), 0151w_ (0.33 #4845, 0.13 #92928, 0.12 #7167), 03y_46 (0.33 #5981, 0.13 #92928, 0.09 #65048), 0170pk (0.33 #5007, 0.13 #92928, 0.06 #16621), 016xk5 (0.29 #6237, 0.13 #92928, 0.09 #65048), 03f1zdw (0.29 #4888, 0.13 #92928, 0.09 #65048), 01tspc6 (0.29 #4844, 0.09 #65048, 0.04 #9489), 02cgb8 (0.25 #6003, 0.13 #92928, 0.09 #65048), 0bq2g (0.25 #5440, 0.13 #92928, 0.09 #65048) >> Best rule #5193 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 07lt7b; 01tspc6; 04y9dk; 0l6px; 02cllz; 0171cm; 03t0k1; 0m31m; 01kj0p; 0fbx6; ... >> query: (?x4005, 0171cm) <- award(?x4005, ?x704), award_nominee(?x2805, ?x4005), ?x2805 = 0lpjn >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #92928 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 128 *> proper extension: 032xhg; 01dw4q; 0mdqp; 02l840; 034x61; 03knl; 0134w7; 03lt8g; 01l2fn; 0l12d; ... *> query: (?x4005, ?x123) <- vacationer(?x279, ?x4005), award_nominee(?x4247, ?x4005), award_nominee(?x123, ?x4247) *> conf = 0.13 ranks of expected_values: 114 EVAL 01g23m award_nominee! 02qgqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 154.000 76.000 0.458 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8444-023l9y PRED entity: 023l9y PRED relation: gender PRED expected values: 05zppz => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #49, 0.83 #47, 0.82 #29), 02zsn (0.46 #189, 0.29 #28, 0.28 #32) >> Best rule #49 for best value: >> intensional similarity = 6 >> extensional distance = 160 >> proper extension: 0bg539; >> query: (?x4595, 05zppz) <- instrumentalists(?x1166, ?x4595), instrumentalists(?x716, ?x4595), profession(?x4595, ?x955), ?x716 = 018vs, role(?x74, ?x1166), role(?x248, ?x1166) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023l9y gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.840 http://example.org/people/person/gender #8443-02f73b PRED entity: 02f73b PRED relation: award_winner PRED expected values: 016t0h => 35 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1676): 07r1_ (0.38 #11426, 0.37 #7398, 0.33 #13892), 01xzb6 (0.38 #11057, 0.33 #13523, 0.33 #3659), 0frsw (0.37 #7398, 0.33 #2991, 0.32 #34526), 0134pk (0.37 #7398, 0.33 #7009, 0.32 #34526), 04rcr (0.37 #7398, 0.33 #5044, 0.32 #34526), 0kr_t (0.37 #7398, 0.33 #6168, 0.32 #34526), 06mj4 (0.37 #7398, 0.32 #34526, 0.32 #41919), 0dw4g (0.37 #7398, 0.32 #34526, 0.32 #41919), 0hvbj (0.37 #7398, 0.32 #34526, 0.32 #41919), 01dq9q (0.37 #7398, 0.32 #34526, 0.32 #41919) >> Best rule #11426 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 01ckbq; 02f716; 01c99j; 02f72_; >> query: (?x7535, 07r1_) <- award_winner(?x7535, ?x1674), award(?x5901, ?x7535), award(?x1544, ?x7535), ?x1544 = 01r9fv, profession(?x5901, ?x1032), currency(?x5901, ?x170) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #4782 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 02f73p; *> query: (?x7535, 016t0h) <- award_winner(?x7535, ?x1674), award(?x5901, ?x7535), award(?x1544, ?x7535), award(?x646, ?x7535), ?x1544 = 01r9fv, profession(?x5901, ?x1032), ?x646 = 04rcr *> conf = 0.33 ranks of expected_values: 32 EVAL 02f73b award_winner 016t0h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 35.000 17.000 0.375 http://example.org/award/award_category/winners./award/award_honor/award_winner #8442-02t_8z PRED entity: 02t_8z PRED relation: profession PRED expected values: 02hrh1q => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 40): 02hrh1q (0.70 #592, 0.66 #1027, 0.65 #3203), 02krf9 (0.28 #168, 0.17 #313, 0.17 #23), 0196pc (0.28 #1741, 0.26 #3772, 0.04 #215), 015h31 (0.28 #1741, 0.26 #3772, 0.04 #169), 02jknp (0.22 #151, 0.21 #296, 0.20 #2327), 09jwl (0.22 #741, 0.19 #451, 0.18 #596), 0nbcg (0.16 #753, 0.12 #463, 0.12 #1478), 018gz8 (0.16 #14, 0.11 #304, 0.10 #1029), 0dz3r (0.15 #727, 0.14 #437, 0.11 #2033), 016z4k (0.12 #729, 0.10 #584, 0.10 #439) >> Best rule #592 for best value: >> intensional similarity = 2 >> extensional distance = 835 >> proper extension: 0g51l1; 018swb; 0c01c; 01wz_ml; 01pcql; 02t_v1; 036px; 02wr2r; 0pmw9; 044k8; ... >> query: (?x11386, 02hrh1q) <- award_winner(?x11386, ?x4459), location(?x11386, ?x1767) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02t_8z profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.703 http://example.org/people/person/profession #8441-02vr7 PRED entity: 02vr7 PRED relation: award_winner! PRED expected values: 02f6xy => 129 concepts (127 used for prediction) PRED predicted values (max 10 best out of 287): 01ck6h (0.38 #11216, 0.37 #27173, 0.37 #32346), 01c427 (0.38 #11216, 0.37 #27173, 0.37 #32346), 01ckcd (0.38 #11216, 0.37 #27173, 0.37 #32346), 03tcnt (0.38 #11216, 0.37 #27173, 0.37 #32346), 01c4_6 (0.38 #11216, 0.37 #27173, 0.37 #32346), 03qbnj (0.38 #11216, 0.37 #27173, 0.37 #32346), 01c92g (0.38 #11216, 0.37 #32346, 0.36 #21133), 0gqz2 (0.38 #11216, 0.37 #32346, 0.36 #21133), 025m8l (0.38 #11216, 0.37 #32346, 0.36 #21133), 02x17c2 (0.38 #11216, 0.37 #32346, 0.36 #21133) >> Best rule #11216 for best value: >> intensional similarity = 3 >> extensional distance = 245 >> proper extension: 05crg7; >> query: (?x8311, ?x1323) <- award(?x8311, ?x1323), award_nominee(?x8311, ?x3442), role(?x8311, ?x227) >> conf = 0.38 => this is the best rule for 10 predicted values *> Best rule #2356 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 01pbxb; 0m2l9; 01gf5h; 02whj; 01wp8w7; 0137g1; 0pkyh; 0gcs9; 01wbz9; 0qf11; ... *> query: (?x8311, 02f6xy) <- award(?x8311, ?x2322), artist(?x382, ?x8311), ?x2322 = 01ck6h *> conf = 0.20 ranks of expected_values: 12 EVAL 02vr7 award_winner! 02f6xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 129.000 127.000 0.383 http://example.org/award/award_category/winners./award/award_honor/award_winner #8440-0gd92 PRED entity: 0gd92 PRED relation: featured_film_locations PRED expected values: 02_286 => 76 concepts (55 used for prediction) PRED predicted values (max 10 best out of 71): 02_286 (0.29 #20, 0.24 #500, 0.24 #740), 030qb3t (0.08 #519, 0.07 #759, 0.07 #3650), 04jpl (0.06 #5070, 0.06 #8452, 0.06 #2413), 02nd_ (0.06 #116, 0.02 #836, 0.01 #356), 0d6lp (0.04 #312, 0.01 #3440, 0.01 #1514), 0rh6k (0.04 #1, 0.03 #2405, 0.03 #6029), 01_d4 (0.03 #2451, 0.03 #3415, 0.02 #767), 0h7h6 (0.03 #2689, 0.03 #283, 0.03 #1003), 0cr3d (0.03 #306, 0.02 #66, 0.01 #3194), 05kj_ (0.03 #258, 0.01 #3386, 0.01 #978) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 0ddfwj1; 0888c3; 07bxqz; >> query: (?x7501, 02_286) <- film(?x7984, ?x7501), religion(?x7984, ?x1985), influenced_by(?x7984, ?x4112), film(?x7984, ?x994) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gd92 featured_film_locations 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 55.000 0.288 http://example.org/film/film/featured_film_locations #8439-067ghz PRED entity: 067ghz PRED relation: film_release_region PRED expected values: 03_3d 05v8c 03gj2 059j2 01znc_ 05vz3zq => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 123): 03gj2 (0.91 #1377, 0.90 #561, 0.89 #425), 059j2 (0.89 #1926, 0.88 #566, 0.88 #2199), 03_3d (0.85 #548, 0.81 #412, 0.77 #1908), 01znc_ (0.83 #1391, 0.81 #439, 0.77 #1935), 06bnz (0.81 #444, 0.80 #1396, 0.77 #1940), 05v8c (0.78 #418, 0.66 #1370, 0.62 #1914), 03rj0 (0.73 #593, 0.72 #457, 0.67 #1953), 06f32 (0.69 #462, 0.58 #598, 0.58 #1414), 04gzd (0.69 #1367, 0.67 #415, 0.55 #1911), 015qh (0.67 #438, 0.60 #1390, 0.54 #1934) >> Best rule #1377 for best value: >> intensional similarity = 5 >> extensional distance = 104 >> proper extension: 01vksx; 0gj8t_b; 03bx2lk; 0gmcwlb; 0cc7hmk; 0fq7dv_; 0ch26b_; 0gd0c7x; 0j6b5; 040rmy; ... >> query: (?x5825, 03gj2) <- film_release_region(?x5825, ?x1917), film_release_region(?x5825, ?x142), ?x142 = 0jgd, film(?x574, ?x5825), ?x1917 = 01p1v >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 53 EVAL 067ghz film_release_region 05vz3zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 87.000 87.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 067ghz film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 067ghz film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 067ghz film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 067ghz film_release_region 05v8c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 067ghz film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8438-039n1 PRED entity: 039n1 PRED relation: influenced_by! PRED expected values: 048cl 043tg 07h1q 01ty4 => 162 concepts (85 used for prediction) PRED predicted values (max 10 best out of 399): 040db (0.62 #3086, 0.33 #575, 0.21 #3013), 032r1 (0.47 #11565, 0.45 #3978, 0.21 #3013), 07h1q (0.47 #11565, 0.43 #2908, 0.23 #7434), 0j3v (0.47 #11565, 0.38 #3090, 0.33 #579), 07dnx (0.47 #11565, 0.36 #4877, 0.33 #2359), 01tz6vs (0.47 #11565, 0.33 #722, 0.33 #220), 04hcw (0.47 #11565, 0.33 #2288, 0.29 #4806), 0bk5r (0.47 #11565, 0.33 #2208, 0.29 #4726), 03cdg (0.47 #11565, 0.33 #2462, 0.29 #2964), 03f0324 (0.47 #11565, 0.33 #2196, 0.29 #4714) >> Best rule #3086 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 04xjp; 081k8; >> query: (?x9600, 040db) <- influenced_by(?x12402, ?x9600), influenced_by(?x8233, ?x9600), influenced_by(?x3336, ?x9600), location(?x12402, ?x335), ?x3336 = 032l1, interests(?x8233, ?x8405) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #11565 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 43 *> proper extension: 0gbwp; *> query: (?x9600, ?x4003) <- company(?x9600, ?x4096), category(?x4096, ?x134), company(?x10111, ?x4096), people(?x5540, ?x9600), influenced_by(?x4003, ?x10111) *> conf = 0.47 ranks of expected_values: 3, 29, 63, 110 EVAL 039n1 influenced_by! 01ty4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 162.000 85.000 0.625 http://example.org/influence/influence_node/influenced_by EVAL 039n1 influenced_by! 07h1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 162.000 85.000 0.625 http://example.org/influence/influence_node/influenced_by EVAL 039n1 influenced_by! 043tg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 162.000 85.000 0.625 http://example.org/influence/influence_node/influenced_by EVAL 039n1 influenced_by! 048cl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 162.000 85.000 0.625 http://example.org/influence/influence_node/influenced_by #8437-048lv PRED entity: 048lv PRED relation: award_winner! PRED expected values: 02yw5r => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 121): 09p2r9 (0.29 #92, 0.03 #1065, 0.03 #648), 09bymc (0.29 #119, 0.03 #1509, 0.02 #675), 0hr3c8y (0.14 #10, 0.05 #3068, 0.05 #3346), 05pd94v (0.14 #2, 0.04 #975, 0.04 #5006), 0g55tzk (0.14 #135, 0.04 #3193, 0.04 #3471), 0418154 (0.14 #106, 0.04 #940, 0.03 #384), 09p30_ (0.14 #84, 0.04 #640, 0.03 #779), 050yyb (0.14 #38, 0.03 #455, 0.02 #4069), 0hn821n (0.14 #129, 0.02 #407, 0.02 #824), 02q690_ (0.12 #204, 0.06 #1038, 0.06 #1455) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0ywqc; >> query: (?x1384, 09p2r9) <- award_winner(?x5459, ?x1384), film(?x1384, ?x10191), ?x10191 = 0crd8q6 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #429 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 142 *> proper extension: 03ft8; 03y2kr; 03p01x; 09zw90; 01s7z0; 01g04k; *> query: (?x1384, 02yw5r) <- executive_produced_by(?x4047, ?x1384), location(?x1384, ?x739) *> conf = 0.01 ranks of expected_values: 103 EVAL 048lv award_winner! 02yw5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 101.000 101.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8436-087vz PRED entity: 087vz PRED relation: contains! PRED expected values: 02j9z => 141 concepts (33 used for prediction) PRED predicted values (max 10 best out of 61): 02qkt (0.56 #28204, 0.55 #21909, 0.55 #3942), 02j9z (0.38 #2727, 0.35 #3623, 0.32 #10817), 09c7w0 (0.35 #6295, 0.24 #23360, 0.16 #28758), 0j0k (0.28 #21940, 0.26 #28235, 0.24 #16556), 07c5l (0.21 #16573, 0.20 #11184, 0.20 #2194), 07ssc (0.20 #6324), 06n3y (0.16 #11515, 0.12 #15108, 0.08 #12412), 05nrg (0.15 #3266, 0.10 #5059, 0.10 #4162), 04pnx (0.14 #16603, 0.14 #19294, 0.12 #11214), 04wsz (0.12 #22061, 0.12 #24758, 0.12 #26556) >> Best rule #28204 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 0165v; >> query: (?x3728, 02qkt) <- participating_countries(?x418, ?x3728), olympics(?x3728, ?x584), combatants(?x94, ?x3728), country(?x150, ?x94), film_release_region(?x54, ?x94) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2727 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 11 *> proper extension: 03_3d; 0d0vqn; 0h7x; *> query: (?x3728, 02j9z) <- participating_countries(?x418, ?x3728), olympics(?x3728, ?x6464), olympics(?x3728, ?x2233), olympics(?x3728, ?x2043), ?x2233 = 0l6mp, ?x2043 = 0lv1x, ?x6464 = 0lbd9 *> conf = 0.38 ranks of expected_values: 2 EVAL 087vz contains! 02j9z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 33.000 0.558 http://example.org/location/location/contains #8435-036k0s PRED entity: 036k0s PRED relation: place_of_birth! PRED expected values: 049g_xj => 143 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1791): 031v3p (0.25 #5071, 0.11 #10295, 0.08 #12907), 011s9r (0.25 #4993, 0.11 #10217, 0.08 #12829), 0d_w7 (0.25 #4946, 0.11 #10170, 0.08 #12782), 02184q (0.25 #4656, 0.11 #9880, 0.08 #12492), 01vrx35 (0.25 #4200, 0.11 #9424, 0.08 #12036), 05d1dy (0.25 #4020, 0.11 #9244, 0.08 #11856), 09fp45 (0.25 #3836, 0.11 #9060, 0.08 #11672), 026fd (0.25 #3829, 0.11 #9053, 0.08 #11665), 012rng (0.25 #3470, 0.11 #8694, 0.08 #11306), 01tc9r (0.25 #3382, 0.11 #8606, 0.08 #11218) >> Best rule #5071 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01yj2; >> query: (?x2541, 031v3p) <- place_of_birth(?x7112, ?x2541), contains(?x2541, ?x5085), featured_film_locations(?x5724, ?x2541), ?x5724 = 0415ggl >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 036k0s place_of_birth! 049g_xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 30.000 0.250 http://example.org/people/person/place_of_birth #8434-03t9sp PRED entity: 03t9sp PRED relation: artists! PRED expected values: 041738 02w6s3 => 59 concepts (37 used for prediction) PRED predicted values (max 10 best out of 276): 025sc50 (0.69 #2593, 0.68 #2308, 0.61 #2024), 06j6l (0.51 #2592, 0.48 #2307, 0.48 #604), 0glt670 (0.40 #2302, 0.39 #2587, 0.33 #32), 0gywn (0.34 #2599, 0.32 #2314, 0.30 #1746), 06cqb (0.33 #3, 0.26 #1702, 0.26 #2270), 02k_kn (0.33 #51, 0.20 #2890, 0.18 #3456), 02c8d7 (0.33 #20, 0.14 #303, 0.10 #2290), 035wcs (0.33 #199, 0.07 #1049, 0.07 #482), 0190yn (0.33 #213, 0.04 #2839, 0.04 #2555), 02ny8t (0.29 #398, 0.23 #2385, 0.22 #2670) >> Best rule #2593 for best value: >> intensional similarity = 7 >> extensional distance = 83 >> proper extension: 016890; 05szp; 09h4b5; >> query: (?x1732, 025sc50) <- artists(?x3996, ?x1732), artists(?x1572, ?x1732), ?x3996 = 02lnbg, artists(?x1572, ?x7924), artists(?x1572, ?x475), ?x7924 = 03t852, award(?x475, ?x247) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1702 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 33 *> proper extension: 0lk90; 01x1cn2; 01vwyqp; 01svw8n; 012z8_; 0gs6vr; *> query: (?x1732, ?x13636) <- artists(?x11723, ?x1732), artists(?x6714, ?x1732), artists(?x3996, ?x1732), artists(?x1572, ?x1732), ?x3996 = 02lnbg, ?x1572 = 06by7, artists(?x6714, ?x9262), ?x9262 = 04n2vgk, parent_genre(?x11723, ?x13636) *> conf = 0.26 ranks of expected_values: 29, 118 EVAL 03t9sp artists! 02w6s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 59.000 37.000 0.694 http://example.org/music/genre/artists EVAL 03t9sp artists! 041738 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 59.000 37.000 0.694 http://example.org/music/genre/artists #8433-03qlv7 PRED entity: 03qlv7 PRED relation: role! PRED expected values: 0l14qv => 72 concepts (48 used for prediction) PRED predicted values (max 10 best out of 91): 0342h (0.83 #1059, 0.83 #3704, 0.81 #1148), 07c6l (0.83 #1059, 0.81 #1148, 0.81 #2820), 05148p4 (0.83 #1059, 0.81 #1148, 0.80 #4057), 0214km (0.83 #1059, 0.81 #1148, 0.80 #4057), 07brj (0.83 #1059, 0.81 #1148, 0.80 #4057), 020w2 (0.83 #1059, 0.81 #1148, 0.80 #4057), 0dwtp (0.82 #2563, 0.79 #3714, 0.77 #2832), 02sgy (0.77 #2911, 0.77 #2824, 0.77 #3440), 0bxl5 (0.76 #2519, 0.75 #2085, 0.73 #1998), 0dwt5 (0.76 #2616, 0.71 #968, 0.69 #4055) >> Best rule #1059 for best value: >> intensional similarity = 18 >> extensional distance = 4 >> proper extension: 0214km; >> query: (?x1332, ?x569) <- role(?x7449, ?x1332), role(?x6938, ?x1332), role(?x6039, ?x1332), role(?x3239, ?x1332), role(?x2944, ?x1332), role(?x868, ?x1332), role(?x315, ?x1332), ?x6039 = 05kms, role(?x1466, ?x7449), role(?x1472, ?x6938), ?x3239 = 03qmg1, instrumentalists(?x6938, ?x120), ?x868 = 0dwvl, ?x1472 = 0319l, role(?x1332, ?x569), ?x2944 = 0l14j_, role(?x487, ?x1332), group(?x315, ?x379) >> conf = 0.83 => this is the best rule for 6 predicted values *> Best rule #2035 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 10 *> proper extension: 0l15bq; *> query: (?x1332, 0l14qv) <- role(?x1750, ?x1332), role(?x1437, ?x1332), role(?x316, ?x1332), role(?x75, ?x1332), role(?x2747, ?x1332), role(?x1332, ?x1147), type_of_union(?x2747, ?x566), ?x75 = 07y_7, ?x1437 = 01vdm0, location(?x2747, ?x6253), award(?x2747, ?x2561), award_winner(?x2747, ?x5356), profession(?x2747, ?x1614), ?x316 = 05r5c, ?x1750 = 02hnl *> conf = 0.75 ranks of expected_values: 13 EVAL 03qlv7 role! 0l14qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 72.000 48.000 0.831 http://example.org/music/performance_role/track_performances./music/track_contribution/role #8432-09gq0x5 PRED entity: 09gq0x5 PRED relation: film! PRED expected values: 017s11 04g2mkf => 89 concepts (50 used for prediction) PRED predicted values (max 10 best out of 57): 05mgj0 (0.42 #801), 0jz9f (0.32 #218, 0.11 #1681, 0.10 #364), 016tw3 (0.23 #82, 0.18 #301, 0.15 #737), 086k8 (0.19 #146, 0.18 #1979, 0.17 #365), 016tt2 (0.17 #367, 0.13 #1025, 0.12 #3084), 01795t (0.15 #1256, 0.11 #1681, 0.07 #965), 0170pk (0.14 #291, 0.06 #509, 0.06 #3226), 03f1zdw (0.14 #291, 0.06 #509, 0.06 #3226), 03xq0f (0.14 #1759, 0.14 #77, 0.14 #1687), 017s11 (0.14 #1097, 0.13 #1685, 0.13 #1757) >> Best rule #801 for best value: >> intensional similarity = 3 >> extensional distance = 183 >> proper extension: 03twd6; 02x8fs; 047gpsd; >> query: (?x1813, ?x3462) <- award_winner(?x1813, ?x72), executive_produced_by(?x1813, ?x4060), production_companies(?x1813, ?x3462) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1097 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 231 *> proper extension: 03kx49; *> query: (?x1813, 017s11) <- nominated_for(?x9924, ?x1813), category(?x1813, ?x134), film(?x902, ?x1813), film(?x9924, ?x3035) *> conf = 0.14 ranks of expected_values: 10, 44 EVAL 09gq0x5 film! 04g2mkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 89.000 50.000 0.417 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 09gq0x5 film! 017s11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 89.000 50.000 0.417 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #8431-0lk0l PRED entity: 0lk0l PRED relation: major_field_of_study PRED expected values: 05qjt => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 115): 01mkq (0.83 #2725, 0.63 #877, 0.59 #3340), 02j62 (0.56 #891, 0.52 #4464, 0.45 #1753), 02lp1 (0.54 #873, 0.39 #2721, 0.39 #2351), 03g3w (0.52 #888, 0.39 #1750, 0.39 #2366), 05qjt (0.37 #869, 0.31 #2347, 0.31 #2471), 037mh8 (0.37 #930, 0.24 #1792, 0.24 #2408), 01tbp (0.37 #922, 0.24 #2276, 0.23 #2400), 05qfh (0.33 #897, 0.26 #2745, 0.24 #3360), 0342h (0.33 #3, 0.20 #249, 0.02 #618), 01lj9 (0.31 #901, 0.30 #532, 0.29 #2379) >> Best rule #2725 for best value: >> intensional similarity = 5 >> extensional distance = 163 >> proper extension: 03bwzr4; >> query: (?x12823, 01mkq) <- major_field_of_study(?x12823, ?x9111), major_field_of_study(?x4889, ?x9111), major_field_of_study(?x2142, ?x9111), ?x2142 = 0dplh, ?x4889 = 02dq8f >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #869 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 52 *> proper extension: 08815; 07tgn; 0kz2w; 04rwx; 01j_cy; 07szy; 09kvv; 0bx8pn; 07wrz; 07wjk; ... *> query: (?x12823, 05qjt) <- student(?x12823, ?x916), major_field_of_study(?x12823, ?x2606), institution(?x3437, ?x12823), ?x3437 = 02_xgp2, ?x2606 = 062z7 *> conf = 0.37 ranks of expected_values: 5 EVAL 0lk0l major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 141.000 141.000 0.830 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8430-0c8tkt PRED entity: 0c8tkt PRED relation: film_release_region PRED expected values: 02vzc 06mkj => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 151): 06mkj (0.89 #1503, 0.88 #3425, 0.88 #2144), 03gj2 (0.87 #1948, 0.83 #2749, 0.83 #1467), 05qhw (0.85 #1937, 0.77 #3378, 0.77 #2738), 02vzc (0.85 #2458, 0.84 #3739, 0.83 #1497), 0345h (0.84 #2117, 0.84 #1476, 0.84 #1797), 015fr (0.84 #1940, 0.81 #1459, 0.79 #2741), 0jgd (0.82 #3367, 0.81 #2727, 0.81 #2406), 035qy (0.81 #1959, 0.81 #3400, 0.76 #1478), 0154j (0.78 #3369, 0.72 #4010, 0.72 #4490), 06bnz (0.77 #3413, 0.73 #1972, 0.68 #2773) >> Best rule #1503 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 0g56t9t; 02vxq9m; 07gp9; 053rxgm; 0gffmn8; 04zl8; 09v3jyg; 07ghq; >> query: (?x1743, 06mkj) <- film_release_region(?x1743, ?x1229), film(?x2135, ?x1743), ?x1229 = 059j2, executive_produced_by(?x5277, ?x2135), featured_film_locations(?x5277, ?x1523) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0c8tkt film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0c8tkt film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 90.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8429-04165w PRED entity: 04165w PRED relation: nominated_for! PRED expected values: 02r0csl 0f4x7 09sb52 0gq9h 0gs96 => 66 concepts (60 used for prediction) PRED predicted values (max 10 best out of 211): 0gq9h (0.62 #1386, 0.54 #2940, 0.50 #2718), 0gqwc (0.60 #718, 0.26 #3828, 0.25 #3331), 02qyntr (0.52 #1940, 0.52 #1718, 0.51 #830), 0gs9p (0.51 #1388, 0.47 #2942, 0.46 #722), 0gr0m (0.43 #717, 0.37 #1605, 0.37 #1827), 09sb52 (0.43 #1364, 0.32 #2030, 0.24 #920), 02r22gf (0.41 #1802, 0.41 #1580, 0.35 #1358), 0gs96 (0.40 #2744, 0.37 #746, 0.30 #1856), 0f_nbyh (0.38 #1339, 0.28 #2005, 0.19 #2893), 02x17s4 (0.34 #1417, 0.30 #2083, 0.17 #2971) >> Best rule #1386 for best value: >> intensional similarity = 5 >> extensional distance = 96 >> proper extension: 02phtzk; 0gy0l_; >> query: (?x7580, 0gq9h) <- nominated_for(?x2375, ?x7580), nominated_for(?x1162, ?x7580), honored_for(?x2294, ?x7580), ?x1162 = 099c8n, award_winner(?x2375, ?x157) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 8, 13, 24 EVAL 04165w nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 66.000 60.000 0.622 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04165w nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 60.000 0.622 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04165w nominated_for! 09sb52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 66.000 60.000 0.622 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04165w nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 66.000 60.000 0.622 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04165w nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 66.000 60.000 0.622 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8428-05gnf PRED entity: 05gnf PRED relation: program PRED expected values: 05jyb2 06r1k => 159 concepts (141 used for prediction) PRED predicted values (max 10 best out of 220): 024hbv (0.29 #4274, 0.28 #5090, 0.23 #7332), 01h1bf (0.29 #4274, 0.28 #5090, 0.23 #7332), 017dcd (0.25 #1422, 0.25 #204, 0.20 #407), 015w8_ (0.25 #1453, 0.20 #438, 0.13 #2879), 017dbx (0.25 #391, 0.20 #594, 0.12 #3442), 020qr4 (0.25 #205, 0.13 #2646, 0.12 #1423), 045qmr (0.25 #313, 0.12 #9475, 0.11 #9069), 0170k0 (0.25 #325, 0.12 #1543, 0.08 #6438), 0vhm (0.25 #264, 0.12 #6377, 0.11 #6580), 01hn_t (0.20 #454, 0.16 #8398, 0.13 #9007) >> Best rule #4274 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 025jfl; 0kk9v; 056ws9; 01s0l0; >> query: (?x6678, ?x1631) <- company(?x1491, ?x6678), nominated_for(?x6678, ?x337), award_winner(?x1631, ?x6678) >> conf = 0.29 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 05gnf program 06r1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 141.000 0.287 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program EVAL 05gnf program 05jyb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 141.000 0.287 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #8427-015h31 PRED entity: 015h31 PRED relation: film_crew_role! PRED expected values: 09sh8k 047gn4y 01qb5d 0bq8tmw 02lk60 047csmy 05pdd86 0241y7 0fs9vc 02fj8n => 56 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1612): 01gwk3 (0.77 #22146, 0.75 #19772, 0.75 #17398), 09sh8k (0.75 #19003, 0.75 #17816, 0.75 #16629), 076xkps (0.75 #20022, 0.75 #18835, 0.75 #17648), 047csmy (0.75 #19628, 0.75 #18441, 0.75 #17254), 03whyr (0.75 #20057, 0.75 #17683, 0.69 #22431), 0dzlbx (0.75 #19593, 0.75 #17219, 0.67 #10093), 03cp4cn (0.75 #19751, 0.75 #18564, 0.67 #10251), 076zy_g (0.75 #19617, 0.75 #18430, 0.67 #10117), 05qbbfb (0.75 #18533, 0.67 #10220, 0.67 #9033), 043t8t (0.75 #19548, 0.67 #10048, 0.67 #8861) >> Best rule #22146 for best value: >> intensional similarity = 8 >> extensional distance = 11 >> proper extension: 05smlt; >> query: (?x1966, 01gwk3) <- film_crew_role(?x10509, ?x1966), film_crew_role(?x2471, ?x1966), film_crew_role(?x1688, ?x1966), ?x1688 = 024l2y, executive_produced_by(?x2471, ?x4854), genre(?x10509, ?x53), film_release_region(?x2471, ?x87), currency(?x10509, ?x170) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #19003 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 01xy5l_; *> query: (?x1966, 09sh8k) <- film_crew_role(?x10509, ?x1966), film_crew_role(?x2203, ?x1966), film_crew_role(?x1688, ?x1966), ?x1688 = 024l2y, ?x2203 = 07yk1xz, currency(?x10509, ?x170), genre(?x10509, ?x811) *> conf = 0.75 ranks of expected_values: 2, 4, 82, 138, 168, 242, 725, 802, 865, 1091 EVAL 015h31 film_crew_role! 02fj8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 29.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 015h31 film_crew_role! 0fs9vc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 29.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 015h31 film_crew_role! 0241y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 29.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 015h31 film_crew_role! 05pdd86 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 56.000 29.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 015h31 film_crew_role! 047csmy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 56.000 29.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 015h31 film_crew_role! 02lk60 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 29.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 015h31 film_crew_role! 0bq8tmw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 29.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 015h31 film_crew_role! 01qb5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 56.000 29.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 015h31 film_crew_role! 047gn4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 56.000 29.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 015h31 film_crew_role! 09sh8k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 29.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8426-0465_ PRED entity: 0465_ PRED relation: films PRED expected values: 047n8xt => 149 concepts (123 used for prediction) PRED predicted values (max 10 best out of 9): 0m313 (0.12 #1596, 0.04 #2658, 0.02 #4252), 047myg9 (0.08 #2453), 080lkt7 (0.02 #4482, 0.01 #6077, 0.01 #7139), 0209hj (0.02 #4281, 0.01 #5876), 0djlxb (0.02 #4410), 0dj0m5 (0.02 #5343), 09sr0 (0.01 #6294, 0.01 #7356), 047bynf (0.01 #7249), 042y1c (0.01 #7553) >> Best rule #1596 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 08433; 041mt; 03f0324; 081k8; 03_87; 085gk; >> query: (?x6370, 0m313) <- influenced_by(?x11412, ?x6370), influenced_by(?x2845, ?x6370), nationality(?x6370, ?x1310), location(?x6370, ?x205), ?x2845 = 0lrh, influenced_by(?x587, ?x11412) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0465_ films 047n8xt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 149.000 123.000 0.125 http://example.org/film/film_subject/films #8425-08mg_b PRED entity: 08mg_b PRED relation: nominated_for PRED expected values: 0kvbl6 => 84 concepts (40 used for prediction) PRED predicted values (max 10 best out of 154): 01msrb (0.84 #1002, 0.77 #501, 0.20 #134), 0kvbl6 (0.84 #1002, 0.77 #501, 0.02 #3524), 08mg_b (0.20 #186, 0.03 #436, 0.03 #687), 01kf3_9 (0.06 #555, 0.05 #805, 0.03 #304), 0fsw_7 (0.06 #654, 0.05 #904, 0.03 #403), 014kq6 (0.06 #565, 0.04 #815, 0.04 #314), 0g5pvv (0.06 #670, 0.04 #920, 0.03 #419), 01s9vc (0.05 #990, 0.04 #740, 0.01 #489), 0fdv3 (0.05 #301, 0.04 #552, 0.02 #802), 01kf4tt (0.05 #576, 0.05 #826, 0.03 #325) >> Best rule #1002 for best value: >> intensional similarity = 3 >> extensional distance = 225 >> proper extension: 02fn5r; >> query: (?x6352, ?x4623) <- nominated_for(?x6352, ?x7354), nominated_for(?x4623, ?x6352), nominated_for(?x298, ?x7354) >> conf = 0.84 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 08mg_b nominated_for 0kvbl6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 40.000 0.844 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #8424-027pfg PRED entity: 027pfg PRED relation: featured_film_locations PRED expected values: 0fttg => 81 concepts (44 used for prediction) PRED predicted values (max 10 best out of 65): 02_286 (0.19 #1947, 0.16 #2428, 0.14 #5567), 0rh6k (0.17 #483, 0.11 #963, 0.11 #723), 0b90_r (0.17 #486, 0.11 #726, 0.03 #1447), 0cv3w (0.11 #1032, 0.02 #2720, 0.02 #2962), 030qb3t (0.10 #2931, 0.09 #3174, 0.09 #1723), 01_d4 (0.10 #1249, 0.03 #2214, 0.03 #6076), 06y57 (0.09 #1546, 0.02 #1787, 0.02 #3721), 04jpl (0.06 #6521, 0.06 #9414, 0.06 #9173), 052p7 (0.06 #1501, 0.02 #1985, 0.02 #6087), 0h7h6 (0.05 #1245, 0.03 #5590, 0.03 #4865) >> Best rule #1947 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 0963mq; 03m8y5; 0pvms; 058kh7; 03wy8t; 0ptdz; >> query: (?x6932, 02_286) <- film(?x4314, ?x6932), genre(?x6932, ?x6674), ?x6674 = 01t_vv >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027pfg featured_film_locations 0fttg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 44.000 0.185 http://example.org/film/film/featured_film_locations #8423-016nvh PRED entity: 016nvh PRED relation: place_of_birth PRED expected values: 04jpl => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 108): 0214m4 (0.33 #296, 0.25 #1705, 0.06 #3817), 0cc56 (0.33 #737, 0.02 #7779, 0.02 #7075), 0fp5z (0.25 #1798, 0.07 #3206), 0f94t (0.14 #2141, 0.07 #2845, 0.06 #3549), 030qb3t (0.12 #7096, 0.07 #10618, 0.06 #5687), 0cr3d (0.11 #3615, 0.04 #5727, 0.03 #19816), 02_286 (0.08 #7061, 0.07 #10583, 0.07 #9174), 04jpl (0.08 #11981, 0.07 #14096, 0.07 #13391), 05qtj (0.06 #3688, 0.05 #4392, 0.04 #5096), 013yq (0.06 #3600, 0.05 #4304) >> Best rule #296 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01v_pj6; >> query: (?x10624, 0214m4) <- profession(?x10624, ?x1032), artists(?x14374, ?x10624), ?x14374 = 01g_bs, award_nominee(?x10624, ?x2237) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #11981 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 231 *> proper extension: 0xnc3; 0cmpn; 02784z; 011zwl; *> query: (?x10624, 04jpl) <- nationality(?x10624, ?x512), ?x512 = 07ssc, gender(?x10624, ?x231), ?x231 = 05zppz *> conf = 0.08 ranks of expected_values: 8 EVAL 016nvh place_of_birth 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 81.000 81.000 0.333 http://example.org/people/person/place_of_birth #8422-0421v9q PRED entity: 0421v9q PRED relation: film_release_region PRED expected values: 0154j 05qhw 06mkj 016wzw => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 122): 05qhw (0.90 #1713, 0.86 #1582, 0.85 #402), 06mkj (0.89 #1479, 0.88 #693, 0.87 #1610), 0154j (0.88 #396, 0.84 #1445, 0.82 #1707), 016wzw (0.85 #177, 0.60 #1488, 0.58 #439), 01p1v (0.77 #164, 0.62 #1737, 0.58 #1475), 03rk0 (0.77 #167, 0.61 #1740, 0.57 #1609), 06f32 (0.69 #176, 0.52 #438, 0.52 #701), 07ylj (0.69 #149, 0.42 #411, 0.38 #1460), 02k1b (0.69 #232, 0.15 #494, 0.13 #1543), 06c1y (0.62 #157, 0.47 #1730, 0.44 #1468) >> Best rule #1713 for best value: >> intensional similarity = 4 >> extensional distance = 152 >> proper extension: 0407yfx; >> query: (?x6543, 05qhw) <- film_release_region(?x6543, ?x1174), film_release_region(?x6543, ?x344), ?x344 = 04gzd, combatants(?x1174, ?x4092) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0421v9q film_release_region 016wzw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0421v9q film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0421v9q film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0421v9q film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8421-06mkj PRED entity: 06mkj PRED relation: administrative_parent PRED expected values: 02j71 => 216 concepts (148 used for prediction) PRED predicted values (max 10 best out of 67): 02j71 (0.82 #15082, 0.82 #18922, 0.82 #19472), 07ssc (0.45 #19736, 0.04 #10564, 0.04 #8096), 09c7w0 (0.42 #10963, 0.40 #12062, 0.31 #14661), 02qkt (0.15 #11786, 0.05 #7676, 0.04 #12884), 0dg3n1 (0.15 #11786, 0.03 #1096, 0.01 #9046), 02j9z (0.15 #11786, 0.03 #1096, 0.01 #9046), 065ky (0.15 #11786), 03v9w (0.15 #11786), 0345h (0.11 #11673, 0.07 #6196, 0.07 #19623), 03_3d (0.09 #8090, 0.03 #5493, 0.01 #11928) >> Best rule #15082 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 05rznz; >> query: (?x2152, 02j71) <- jurisdiction_of_office(?x182, ?x2152), adjoins(?x87, ?x2152), form_of_government(?x2152, ?x1926) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mkj administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 216.000 148.000 0.824 http://example.org/base/aareas/schema/administrative_area/administrative_parent #8420-01c6k4 PRED entity: 01c6k4 PRED relation: service_location PRED expected values: 03_3d 03rk0 0d05w3 04pnx => 132 concepts (123 used for prediction) PRED predicted values (max 10 best out of 379): 0d060g (0.71 #2536, 0.64 #3167, 0.64 #954), 03h64 (0.50 #425, 0.40 #820, 0.33 #1055), 05v8c (0.38 #485, 0.38 #407, 0.33 #91), 03_3d (0.38 #401, 0.33 #85, 0.30 #796), 03rk0 (0.33 #104, 0.25 #420, 0.20 #894), 0ctw_b (0.33 #95, 0.12 #489, 0.12 #411), 03ryn (0.33 #112, 0.12 #506, 0.12 #428), 03rt9 (0.33 #9, 0.12 #483, 0.10 #879), 059j2 (0.29 #1840, 0.26 #2136, 0.25 #1505), 0b90_r (0.26 #2136, 0.25 #1505, 0.22 #790) >> Best rule #2536 for best value: >> intensional similarity = 12 >> extensional distance = 26 >> proper extension: 0k8z; 04sv4; 0z07; 03_c8p; 055z7; 0gy1_; >> query: (?x555, 0d060g) <- service_language(?x555, ?x90), service_location(?x555, ?x2152), company(?x265, ?x555), film_release_region(?x9565, ?x2152), film_release_region(?x5109, ?x2152), film_release_region(?x4950, ?x2152), film_release_region(?x2318, ?x2152), ?x2318 = 06v9_x, ?x5109 = 0b44shh, ?x4950 = 07k2mq, ?x9565 = 0hz6mv2, country(?x150, ?x2152) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #401 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 6 *> proper extension: 069b85; *> query: (?x555, 03_3d) <- service_language(?x555, ?x90), service_location(?x555, ?x2316), film_release_region(?x5827, ?x2316), film_release_region(?x5315, ?x2316), film_release_region(?x4041, ?x2316), film_release_region(?x1927, ?x2316), film_release_region(?x1219, ?x2316), month(?x2316, ?x1650), ?x4041 = 0gy2y8r, ?x1219 = 03bx2lk, ?x1927 = 0by1wkq, ?x5315 = 0glqh5_, ?x5827 = 0ggbfwf *> conf = 0.38 ranks of expected_values: 4, 5, 15 EVAL 01c6k4 service_location 04pnx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 123.000 0.714 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 01c6k4 service_location 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 132.000 123.000 0.714 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 01c6k4 service_location 03rk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 123.000 0.714 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 01c6k4 service_location 03_3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 123.000 0.714 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #8419-0gq9h PRED entity: 0gq9h PRED relation: award_winner PRED expected values: 0cjdk => 58 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1931): 081lh (0.55 #14788, 0.43 #19659, 0.38 #9920), 05kfs (0.50 #4995, 0.40 #2560, 0.37 #9741), 081nh (0.43 #7797, 0.37 #9741, 0.36 #90115), 0159h6 (0.40 #2510, 0.40 #75, 0.33 #4945), 03xp8d5 (0.40 #3385, 0.37 #9741, 0.36 #90115), 02pv_d (0.40 #4159, 0.37 #9741, 0.36 #90115), 05m883 (0.40 #2649, 0.37 #9741, 0.36 #90115), 05drq5 (0.40 #2681, 0.33 #5116, 0.29 #19726), 01d8yn (0.40 #3225, 0.33 #5660, 0.27 #15399), 01q415 (0.40 #2884, 0.33 #5319, 0.27 #15058) >> Best rule #14788 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 027c924; 02g3ft; >> query: (?x1307, 081lh) <- award(?x2332, ?x1307), nominated_for(?x1307, ?x161), award_winner(?x1307, ?x574), ?x2332 = 04y8r >> conf = 0.55 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gq9h award_winner 0cjdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 40.000 0.545 http://example.org/award/award_category/winners./award/award_honor/award_winner #8418-0dvld PRED entity: 0dvld PRED relation: award PRED expected values: 09qwmm => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 284): 0cqgl9 (0.77 #38556, 0.76 #42064, 0.72 #46735), 0f4x7 (0.50 #30, 0.15 #1199, 0.14 #1979), 027dtxw (0.50 #4, 0.14 #35050, 0.14 #39727), 02w9sd7 (0.50 #158, 0.14 #35050, 0.14 #39727), 09sdmz (0.50 #194, 0.12 #3506, 0.12 #973), 0gqy2 (0.33 #152, 0.15 #931, 0.14 #35050), 04kxsb (0.33 #115, 0.14 #35050, 0.14 #1284), 057xs89 (0.33 #148, 0.14 #35050, 0.14 #39727), 0bdwqv (0.33 #160, 0.14 #35050, 0.14 #39727), 099jhq (0.33 #18, 0.12 #3506, 0.11 #5843) >> Best rule #38556 for best value: >> intensional similarity = 3 >> extensional distance = 1733 >> proper extension: 01lcxbb; 034bs; 0f6lx; 03mv0b; 0f1jhc; 02rf51g; >> query: (?x5951, ?x4183) <- award_winner(?x4183, ?x5951), ceremony(?x4183, ?x139), profession(?x5951, ?x1032) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2760 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 110 *> proper extension: 02mhfy; 023mdt; 03tdlh; 041b4j; 06g4l; 04bdqk; 01hkck; 02qhm3; *> query: (?x5951, 09qwmm) <- film(?x5951, ?x1597), award(?x5951, ?x1972), ?x1972 = 0gqyl *> conf = 0.19 ranks of expected_values: 18 EVAL 0dvld award 09qwmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 138.000 138.000 0.766 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8417-065_cjc PRED entity: 065_cjc PRED relation: produced_by PRED expected values: 02q42j_ => 82 concepts (51 used for prediction) PRED predicted values (max 10 best out of 125): 0cv9fc (0.25 #749, 0.02 #3854, 0.02 #5411), 012x2b (0.23 #7371, 0.03 #9308, 0.03 #1938), 02r251z (0.21 #1792, 0.07 #2960, 0.02 #9162), 0jrqq (0.12 #520, 0.02 #2461, 0.01 #4791), 043q6n_ (0.12 #439, 0.01 #8972, 0.01 #5488), 03_gd (0.12 #417), 05ty4m (0.09 #787, 0.07 #1174, 0.05 #1953), 0d6484 (0.09 #1100, 0.07 #1487, 0.05 #2266), 01gzm2 (0.09 #837, 0.07 #1224, 0.05 #2003), 06pj8 (0.05 #3172, 0.05 #2008, 0.04 #3948) >> Best rule #749 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0dq626; >> query: (?x6752, 0cv9fc) <- film_crew_role(?x6752, ?x1284), film(?x4771, ?x6752), country(?x6752, ?x512), ?x1284 = 0ch6mp2, ?x4771 = 0h96g >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #9130 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 457 *> proper extension: 0ds35l9; 09sh8k; 0m313; 02y_lrp; 09m6kg; 03g90h; 09xbpt; 0ds3t5x; 0dnvn3; 01k1k4; ... *> query: (?x6752, 02q42j_) <- film_crew_role(?x6752, ?x1284), film(?x436, ?x6752), country(?x6752, ?x512), ?x1284 = 0ch6mp2, produced_by(?x6752, ?x3568) *> conf = 0.04 ranks of expected_values: 32 EVAL 065_cjc produced_by 02q42j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 82.000 51.000 0.250 http://example.org/film/film/produced_by #8416-07kg3 PRED entity: 07kg3 PRED relation: location_of_ceremony! PRED expected values: 0m31m => 196 concepts (121 used for prediction) PRED predicted values (max 10 best out of 242): 04vmqg (0.33 #219, 0.04 #9128, 0.04 #4527), 037s5h (0.33 #213, 0.04 #9128, 0.04 #4521), 01x72k (0.33 #101, 0.04 #9128, 0.04 #4409), 01f7j9 (0.33 #49, 0.04 #9128, 0.04 #4357), 03m2fg (0.12 #941, 0.07 #3222, 0.03 #4996), 0ngg (0.07 #3801, 0.04 #4560, 0.03 #5066), 01wqflx (0.07 #3741, 0.04 #4500, 0.03 #5006), 060j8b (0.07 #3701, 0.04 #4460, 0.02 #6741), 04205z (0.07 #3670, 0.04 #4429, 0.02 #6710), 01t2h2 (0.07 #3079, 0.03 #4853, 0.03 #5106) >> Best rule #219 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03rjj; >> query: (?x6408, 04vmqg) <- contains(?x6408, ?x9660), ?x9660 = 031y2, location_of_ceremony(?x1289, ?x6408), currency(?x6408, ?x5696) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07kg3 location_of_ceremony! 0m31m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 121.000 0.333 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #8415-02mjmr PRED entity: 02mjmr PRED relation: award_winner! PRED expected values: 09n4nb => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 130): 05pd94v (0.29 #6862, 0.09 #15962, 0.08 #12182), 02cg41 (0.27 #6985, 0.14 #825, 0.11 #4605), 01mh_q (0.25 #88, 0.20 #508, 0.20 #368), 09g90vz (0.25 #1523, 0.11 #8103, 0.11 #1663), 0drtv8 (0.25 #1465, 0.11 #3005, 0.10 #5105), 0466p0j (0.25 #6935, 0.10 #4975, 0.09 #16035), 056878 (0.23 #6891, 0.12 #2691, 0.09 #7171), 019bk0 (0.19 #6875, 0.08 #15975, 0.07 #14575), 0gx1673 (0.17 #6979, 0.09 #7259, 0.07 #8099), 09n4nb (0.15 #6907, 0.12 #1447, 0.09 #7187) >> Best rule #6862 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 09z1lg; 016l09; >> query: (?x2669, 05pd94v) <- award_winner(?x594, ?x2669), award_winner(?x486, ?x2669), ?x486 = 02rjjll >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #6907 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 09z1lg; 016l09; *> query: (?x2669, 09n4nb) <- award_winner(?x594, ?x2669), award_winner(?x486, ?x2669), ?x486 = 02rjjll *> conf = 0.15 ranks of expected_values: 10 EVAL 02mjmr award_winner! 09n4nb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 182.000 182.000 0.288 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8414-06cv1 PRED entity: 06cv1 PRED relation: participant PRED expected values: 0693l => 140 concepts (94 used for prediction) PRED predicted values (max 10 best out of 302): 06x58 (0.80 #38562), 014zcr (0.17 #2587, 0.14 #3230, 0.09 #1303), 0c6qh (0.14 #3378, 0.08 #2735, 0.05 #11087), 018db8 (0.14 #3260, 0.08 #2617, 0.05 #14184), 01m4yn (0.10 #20563, 0.10 #28277, 0.10 #23777), 0gx_p (0.09 #2351, 0.09 #1709, 0.08 #11345), 01vhrz (0.09 #2495, 0.09 #1853, 0.06 #5066), 0151w_ (0.09 #1990, 0.07 #3275, 0.05 #5203), 01pllx (0.08 #3118, 0.07 #3761, 0.06 #12756), 01q_ph (0.08 #2595, 0.07 #3238, 0.05 #10947) >> Best rule #38562 for best value: >> intensional similarity = 3 >> extensional distance = 394 >> proper extension: 02w5q6; 01npcy7; >> query: (?x523, ?x1880) <- place_of_birth(?x523, ?x1719), profession(?x523, ?x319), participant(?x1880, ?x523) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #8566 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 01vs_v8; 033wx9; 01817f; 02cpb7; 094xh; 026_dq6; *> query: (?x523, 0693l) <- celebrity(?x523, ?x6844), student(?x1011, ?x523), category(?x523, ?x134) *> conf = 0.04 ranks of expected_values: 75 EVAL 06cv1 participant 0693l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 140.000 94.000 0.800 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #8413-05b4w PRED entity: 05b4w PRED relation: form_of_government PRED expected values: 01q20 => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 4): 06cx9 (0.47 #317, 0.45 #377, 0.42 #385), 01q20 (0.38 #2, 0.36 #14, 0.32 #482), 01d9r3 (0.35 #319, 0.34 #283, 0.34 #423), 026wp (0.14 #8, 0.11 #44, 0.11 #20) >> Best rule #317 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 05rznz; >> query: (?x2513, 06cx9) <- countries_within(?x455, ?x2513), organization(?x2513, ?x127), form_of_government(?x2513, ?x1926) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 03bxbql; 02psqkz; *> query: (?x2513, 01q20) <- combatants(?x94, ?x2513), combatants(?x2513, ?x456), ?x456 = 05qhw *> conf = 0.38 ranks of expected_values: 2 EVAL 05b4w form_of_government 01q20 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 178.000 178.000 0.466 http://example.org/location/country/form_of_government #8412-0137hn PRED entity: 0137hn PRED relation: diet PRED expected values: 07_hy => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 2): 07_jd (0.18 #1, 0.08 #39, 0.07 #35), 07_hy (0.05 #36, 0.04 #10, 0.03 #30) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 03dq9; >> query: (?x6639, 07_jd) <- award_nominee(?x2865, ?x6639), people(?x4959, ?x6639), group(?x6639, ?x8078) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #36 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 0146pg; 01vs14j; 02fgpf; 02cyfz; 05dbf; 02qgyv; 03n0q5; 02v3yy; 01jpmpv; 0k269; ... *> query: (?x6639, 07_hy) <- award_nominee(?x2865, ?x6639), award(?x6639, ?x2585), ?x2585 = 054ks3 *> conf = 0.05 ranks of expected_values: 2 EVAL 0137hn diet 07_hy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 113.000 0.182 http://example.org/base/eating/practicer_of_diet/diet #8411-01730d PRED entity: 01730d PRED relation: artists PRED expected values: 01vtg4q => 56 concepts (21 used for prediction) PRED predicted values (max 10 best out of 3233): 015882 (0.83 #2303, 0.20 #4477, 0.20 #1215), 01w60_p (0.80 #1244, 0.50 #3260, 0.44 #3259), 020_4z (0.60 #2028, 0.35 #4204, 0.33 #943), 07s3vqk (0.60 #1096, 0.33 #2184, 0.33 #11), 01tw31 (0.60 #2062, 0.33 #977, 0.28 #3150), 01z9_x (0.50 #3260, 0.44 #3259, 0.43 #3261), 0pk41 (0.50 #3260, 0.44 #3259, 0.43 #3261), 02qwg (0.50 #3260, 0.44 #3259, 0.43 #3261), 01vvycq (0.50 #1132, 0.33 #2220, 0.33 #47), 011z3g (0.50 #1692, 0.33 #607, 0.31 #4954) >> Best rule #2303 for best value: >> intensional similarity = 8 >> extensional distance = 16 >> proper extension: 01kp8z; 05fw6t; >> query: (?x14776, 015882) <- artists(?x14776, ?x6939), artist(?x12993, ?x6939), award_winner(?x9246, ?x6939), award_winner(?x7882, ?x6939), award(?x6939, ?x6378), ?x12993 = 03vv61, artists(?x1572, ?x7882), role(?x9246, ?x227) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1853 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 03_d0; 06by7; 06j6l; 0gywn; 02yv6b; 017510; 0175yg; *> query: (?x14776, 01vtg4q) <- artists(?x14776, ?x6939), role(?x6939, ?x227), award_nominee(?x7882, ?x6939), artist(?x2241, ?x6939), award_winner(?x6939, ?x3403), person(?x1619, ?x6939), ?x7882 = 01z9_x *> conf = 0.40 ranks of expected_values: 34 EVAL 01730d artists 01vtg4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 56.000 21.000 0.833 http://example.org/music/genre/artists #8410-04j53 PRED entity: 04j53 PRED relation: medal PRED expected values: 02lpp7 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.76 #10, 0.74 #8, 0.62 #32) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 014tss; >> query: (?x3040, 02lpp7) <- country(?x6450, ?x3040), countries_spoken_in(?x732, ?x3040), film(?x368, ?x6450) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04j53 medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.755 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #8409-07kc_ PRED entity: 07kc_ PRED relation: role! PRED expected values: 02hnl => 66 concepts (54 used for prediction) PRED predicted values (max 10 best out of 109): 03bx0bm (0.88 #2739, 0.83 #1983, 0.80 #1762), 0l14md (0.85 #1090, 0.83 #1629, 0.82 #4768), 01dnws (0.85 #1090, 0.83 #1629, 0.82 #4768), 0l14j_ (0.79 #2226, 0.73 #2549, 0.70 #1791), 02hnl (0.75 #3497, 0.72 #2063, 0.70 #1412), 0bxl5 (0.73 #1952, 0.73 #1910, 0.72 #2169), 013y1f (0.71 #1335, 0.69 #759, 0.68 #3063), 01xqw (0.71 #862, 0.69 #1838, 0.68 #4653), 01s0ps (0.71 #1354, 0.67 #3240, 0.62 #859), 02k856 (0.71 #1467, 0.60 #708, 0.60 #4764) >> Best rule #2739 for best value: >> intensional similarity = 24 >> extensional distance = 14 >> proper extension: 02snj9; >> query: (?x1147, 03bx0bm) <- role(?x1886, ?x1147), role(?x885, ?x1147), role(?x432, ?x1147), role(?x228, ?x1147), performance_role(?x212, ?x1147), instrumentalists(?x1147, ?x2242), role(?x1212, ?x1886), role(?x780, ?x1886), ?x1212 = 07xzm, role(?x4918, ?x1886), ?x228 = 0l14qv, ?x885 = 0dwtp, role(?x4701, ?x432), role(?x1997, ?x432), role(?x8014, ?x432), role(?x3703, ?x432), role(?x2157, ?x432), ?x3703 = 02dlh2, ?x4701 = 03j24kf, ?x1997 = 01wsl7c, ?x8014 = 0214km, instrumentalists(?x432, ?x133), ?x2157 = 011_6p, group(?x432, ?x442) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3497 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 22 *> proper extension: 0l14jd; *> query: (?x1147, 02hnl) <- performance_role(?x1147, ?x1495), group(?x1495, ?x11551), group(?x1495, ?x7476), group(?x1495, ?x6475), role(?x6039, ?x1495), role(?x3703, ?x1495), role(?x885, ?x1495), role(?x10738, ?x1495), role(?x1089, ?x1495), ?x7476 = 048xh, role(?x8014, ?x1495), performance_role(?x1495, ?x1433), ?x3703 = 02dlh2, ?x6039 = 05kms, ?x885 = 0dwtp, ?x11551 = 0cfgd, artist(?x3265, ?x10738), category(?x10738, ?x134), role(?x487, ?x8014), artist(?x4483, ?x1089), artists(?x378, ?x1089), ?x6475 = 07mvp, influenced_by(?x1089, ?x587) *> conf = 0.75 ranks of expected_values: 5 EVAL 07kc_ role! 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 66.000 54.000 0.875 http://example.org/music/performance_role/regular_performances./music/group_membership/role #8408-019q50 PRED entity: 019q50 PRED relation: organization! PRED expected values: 060c4 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 8): 060c4 (0.62 #80, 0.61 #93, 0.61 #106), 07xl34 (0.37 #11, 0.37 #24, 0.28 #50), 0dq_5 (0.24 #35, 0.20 #74, 0.16 #256), 05k17c (0.09 #59, 0.08 #163, 0.08 #189), 0hm4q (0.08 #47, 0.07 #8, 0.06 #21), 05c0jwl (0.04 #57, 0.03 #174, 0.02 #161), 08jcfy (0.02 #12, 0.02 #25, 0.01 #116), 04n1q6 (0.02 #6, 0.02 #19, 0.01 #58) >> Best rule #80 for best value: >> intensional similarity = 2 >> extensional distance = 373 >> proper extension: 024y8p; 071_8; 0mbwf; >> query: (?x9822, 060c4) <- school_type(?x9822, ?x4722), currency(?x9822, ?x12281) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019q50 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.621 http://example.org/organization/role/leaders./organization/leadership/organization #8407-01hw6wq PRED entity: 01hw6wq PRED relation: category PRED expected values: 08mbj5d => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #37, 0.82 #49, 0.82 #29) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 398 >> proper extension: 04rcr; 02r3zy; 03g5jw; 0dvqq; 03fbc; 0249kn; 018ndc; 017j6; 04qmr; 0hvbj; ... >> query: (?x2363, 08mbj5d) <- artists(?x597, ?x2363), artist(?x1543, ?x2363), award_nominee(?x2363, ?x5550) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hw6wq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.840 http://example.org/common/topic/webpage./common/webpage/category #8406-02z9hqn PRED entity: 02z9hqn PRED relation: film! PRED expected values: 02t1dv 01kym3 => 75 concepts (39 used for prediction) PRED predicted values (max 10 best out of 977): 02t1dv (0.43 #4117, 0.31 #10363, 0.27 #8281), 0dt645q (0.36 #8011, 0.31 #10093, 0.30 #5929), 01kym3 (0.20 #6218, 0.18 #8300, 0.15 #10382), 03q64h (0.18 #8282, 0.15 #10364, 0.14 #4118), 079vf (0.16 #14581, 0.11 #22910, 0.09 #29154), 042ly5 (0.16 #15841, 0.11 #24170, 0.07 #32495), 0f0kz (0.16 #13009, 0.10 #17171, 0.09 #31744), 0p8r1 (0.16 #13079, 0.10 #17241, 0.05 #54706), 0jlv5 (0.16 #13674, 0.10 #17836, 0.04 #28247), 03h_9lg (0.16 #14706, 0.09 #31360, 0.08 #23035) >> Best rule #4117 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 0ckr7s; >> query: (?x869, 02t1dv) <- genre(?x869, ?x2540), genre(?x869, ?x1510), ?x1510 = 01hmnh, film(?x4944, ?x869), actor(?x869, ?x5779), film_release_distribution_medium(?x869, ?x81), genre(?x1876, ?x2540), ?x1876 = 0584r4 >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 02z9hqn film! 01kym3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 39.000 0.429 http://example.org/film/actor/film./film/performance/film EVAL 02z9hqn film! 02t1dv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 39.000 0.429 http://example.org/film/actor/film./film/performance/film #8405-04ljl_l PRED entity: 04ljl_l PRED relation: award_winner PRED expected values: 0jrqq => 44 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1521): 0pz91 (0.62 #10108, 0.50 #5182, 0.43 #7645), 0jrqq (0.50 #7387, 0.50 #5760, 0.43 #8223), 0p__8 (0.50 #6249, 0.38 #11175, 0.33 #3787), 014zfs (0.50 #5140, 0.38 #10066, 0.33 #2678), 02t_99 (0.50 #5969, 0.38 #10895, 0.29 #8432), 0gn30 (0.38 #11055, 0.33 #3667, 0.33 #1205), 01vs_v8 (0.38 #10307, 0.33 #457, 0.17 #15233), 0227vl (0.38 #11725, 0.33 #1875, 0.14 #9262), 0187y5 (0.33 #2581, 0.33 #119, 0.32 #12313), 02_l96 (0.33 #3606, 0.33 #1144, 0.30 #54189) >> Best rule #10108 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 05b4l5x; 03c7tr1; >> query: (?x102, 0pz91) <- nominated_for(?x102, ?x4709), award(?x4935, ?x102), ?x4709 = 01qvz8, category(?x4935, ?x134) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #7387 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 05b1610; 07bdd_; *> query: (?x102, ?x3873) <- nominated_for(?x102, ?x4709), award(?x3873, ?x102), ?x4709 = 01qvz8, ?x3873 = 0jrqq *> conf = 0.50 ranks of expected_values: 2 EVAL 04ljl_l award_winner 0jrqq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 24.000 0.625 http://example.org/award/award_category/winners./award/award_honor/award_winner #8404-022xml PRED entity: 022xml PRED relation: institution! PRED expected values: 02h4rq6 => 111 concepts (58 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.77 #130, 0.77 #105, 0.69 #155), 014mlp (0.73 #108, 0.72 #133, 0.70 #385), 019v9k (0.70 #162, 0.68 #112, 0.66 #137), 02_xgp2 (0.58 #166, 0.49 #116, 0.49 #393), 03bwzr4 (0.58 #92, 0.51 #143, 0.51 #168), 0bkj86 (0.52 #161, 0.39 #388, 0.38 #85), 016t_3 (0.48 #156, 0.42 #80, 0.42 #131), 07s6fsf (0.36 #128, 0.35 #103, 0.33 #153), 04zx3q1 (0.32 #154, 0.24 #381, 0.23 #104), 027f2w (0.25 #163, 0.23 #87, 0.21 #113) >> Best rule #130 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 07wrz; 02xpy5; >> query: (?x2034, 02h4rq6) <- student(?x2034, ?x2409), fraternities_and_sororities(?x2034, ?x3697), contains(?x94, ?x2034), organization(?x346, ?x2034) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022xml institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 58.000 0.771 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #8403-0jf1b PRED entity: 0jf1b PRED relation: gender PRED expected values: 05zppz => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #27, 0.87 #3, 0.86 #57), 02zsn (0.37 #64, 0.36 #94, 0.36 #68) >> Best rule #27 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 03s2y9; >> query: (?x767, 05zppz) <- profession(?x767, ?x524), award(?x767, ?x198), nominated_for(?x767, ?x197), story_by(?x6048, ?x767) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jf1b gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.904 http://example.org/people/person/gender #8402-01wc7p PRED entity: 01wc7p PRED relation: award PRED expected values: 0bfvw2 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 274): 0gqwc (0.46 #4103, 0.19 #23854, 0.16 #2088), 0cqgl9 (0.42 #4222, 0.09 #23973, 0.07 #23570), 05p09zm (0.39 #1332, 0.33 #2944, 0.31 #3750), 09sb52 (0.39 #2862, 0.34 #1250, 0.34 #32287), 0gqyl (0.38 #4134, 0.16 #23885, 0.15 #507), 0cqhk0 (0.33 #440, 0.16 #29059, 0.15 #31477), 02y_rq5 (0.33 #4124, 0.10 #23875, 0.09 #497), 05pcn59 (0.32 #3707, 0.32 #1289, 0.30 #4513), 02ppm4q (0.32 #4186, 0.15 #559, 0.13 #23937), 094qd5 (0.30 #4075, 0.16 #2060, 0.14 #2866) >> Best rule #4103 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 02rmxx; >> query: (?x5848, 0gqwc) <- award(?x5848, ?x1132), profession(?x5848, ?x319), ?x1132 = 0bdwft >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #4045 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 77 *> proper extension: 02rmxx; *> query: (?x5848, 0bfvw2) <- award(?x5848, ?x1132), profession(?x5848, ?x319), ?x1132 = 0bdwft *> conf = 0.28 ranks of expected_values: 11 EVAL 01wc7p award 0bfvw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 134.000 134.000 0.456 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8401-01g_bs PRED entity: 01g_bs PRED relation: artists PRED expected values: 0m19t => 79 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1031): 03t9sp (0.67 #3370, 0.60 #8782, 0.58 #10949), 01dwrc (0.67 #3773, 0.41 #17859, 0.33 #11352), 03xl77 (0.56 #13239, 0.23 #19743, 0.17 #4574), 07sbk (0.56 #8349, 0.50 #9432, 0.50 #5103), 06p03s (0.56 #8589, 0.50 #11839, 0.50 #9672), 03fbc (0.56 #7780, 0.50 #8863, 0.42 #11030), 01d_h (0.54 #12726, 0.50 #11640, 0.50 #9473), 01vxlbm (0.50 #9001, 0.44 #7918, 0.42 #11168), 0m19t (0.50 #4358, 0.44 #7604, 0.40 #8687), 01vv7sc (0.50 #3311, 0.40 #2229, 0.33 #7640) >> Best rule #3370 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0m0jc; 07lnk; 08cyft; >> query: (?x14374, 03t9sp) <- artists(?x14374, ?x10624), artists(?x14374, ?x8636), parent_genre(?x12831, ?x14374), student(?x1682, ?x8636), instrumentalists(?x228, ?x8636), ?x10624 = 016nvh >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4358 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 0193f; *> query: (?x14374, 0m19t) <- artists(?x14374, ?x10624), artists(?x14374, ?x8199), artists(?x14374, ?x1674), ?x1674 = 01v_pj6, ?x8199 = 016lmg, artists(?x2439, ?x10624), parent_genre(?x14374, ?x7267), ?x2439 = 07lnk, gender(?x10624, ?x231) *> conf = 0.50 ranks of expected_values: 9 EVAL 01g_bs artists 0m19t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 79.000 23.000 0.667 http://example.org/music/genre/artists #8400-012x2b PRED entity: 012x2b PRED relation: student! PRED expected values: 07c52 0fdys => 94 concepts (91 used for prediction) PRED predicted values (max 10 best out of 43): 03nfmq (0.14 #86, 0.03 #1097, 0.03 #740), 0w7c (0.12 #811, 0.10 #1170, 0.08 #1348), 0fdys (0.10 #1038, 0.10 #1098, 0.10 #859), 03g3w (0.09 #438, 0.08 #1090, 0.08 #1030), 01zc2w (0.08 #463, 0.07 #758, 0.06 #995), 05qfh (0.08 #444, 0.04 #976, 0.04 #1096), 02h40lc (0.06 #775, 0.05 #1134, 0.05 #1612), 062z7 (0.06 #852, 0.06 #1031, 0.05 #1091), 04rlf (0.06 #462, 0.04 #1175, 0.04 #816), 04gb7 (0.04 #1341, 0.04 #1521, 0.04 #1581) >> Best rule #86 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0q5hw; >> query: (?x9601, 03nfmq) <- student(?x5614, ?x9601), gender(?x9601, ?x231), participant(?x9601, ?x3503), ?x5614 = 03qsdpk >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1038 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 122 *> proper extension: 0168cl; 06n7h7; 03ldxq; 08f3b1; 02kxbwx; 0d0vj4; 083q7; 04jzj; 0kn4c; 0203v; ... *> query: (?x9601, 0fdys) <- student(?x373, ?x9601), gender(?x9601, ?x231), student(?x1368, ?x9601), profession(?x9601, ?x987) *> conf = 0.10 ranks of expected_values: 3, 26 EVAL 012x2b student! 0fdys CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 91.000 0.143 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 012x2b student! 07c52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 94.000 91.000 0.143 http://example.org/education/field_of_study/students_majoring./education/education/student #8399-03xsby PRED entity: 03xsby PRED relation: production_companies! PRED expected values: 024l2y => 144 concepts (128 used for prediction) PRED predicted values (max 10 best out of 1146): 01ffx4 (0.50 #16943, 0.46 #21462, 0.46 #21463), 05c46y6 (0.50 #16943, 0.46 #21462, 0.46 #21463), 0bcp9b (0.36 #39535, 0.35 #39534, 0.31 #38404), 06zn2v2 (0.36 #39535, 0.35 #39534, 0.31 #38404), 05b6rdt (0.36 #39535, 0.35 #39534, 0.31 #38404), 03h0byn (0.36 #39535, 0.35 #39534, 0.31 #38404), 0cmdwwg (0.36 #39535, 0.35 #39534, 0.31 #38404), 0404j37 (0.36 #39535, 0.35 #39534, 0.31 #38404), 0gtxj2q (0.36 #39535, 0.35 #39534, 0.31 #38404), 0c40vxk (0.36 #39535, 0.35 #39534, 0.31 #38404) >> Best rule #16943 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 0cjdk; >> query: (?x1914, ?x3201) <- organization(?x4682, ?x1914), nominated_for(?x1914, ?x3201), language(?x3201, ?x90) >> conf = 0.50 => this is the best rule for 2 predicted values *> Best rule #12595 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 07tgn; 07tg4; 0bwfn; 023zl; *> query: (?x1914, 024l2y) <- organization(?x4682, ?x1914), child(?x1914, ?x963), company(?x3960, ?x1914) *> conf = 0.06 ranks of expected_values: 733 EVAL 03xsby production_companies! 024l2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 144.000 128.000 0.497 http://example.org/film/film/production_companies #8398-02yvhx PRED entity: 02yvhx PRED relation: award_winner PRED expected values: 03knl => 44 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2753): 06pj8 (0.50 #1834, 0.33 #6436, 0.33 #299), 0bxtg (0.50 #1594, 0.33 #6196, 0.33 #59), 02bj6k (0.50 #2689, 0.33 #7291, 0.33 #1154), 0g9zcgx (0.38 #11726, 0.30 #14795, 0.29 #10192), 01rzqj (0.33 #6640, 0.33 #503, 0.25 #2038), 03h26tm (0.33 #7797, 0.29 #9332, 0.25 #12400), 03m_k0 (0.33 #450, 0.25 #1985, 0.17 #8123), 01jmv8 (0.33 #1235, 0.25 #2770, 0.17 #25792), 070j61 (0.33 #1114, 0.25 #2649, 0.17 #7251), 06dv3 (0.33 #25, 0.25 #1560, 0.17 #6162) >> Best rule #1834 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 09q_6t; >> query: (?x5703, 06pj8) <- award_winner(?x5703, ?x8692), award_winner(?x5703, ?x1039), honored_for(?x5703, ?x253), ceremony(?x484, ?x5703), ?x1039 = 04wvhz, award(?x4751, ?x484), written_by(?x339, ?x8692), nominated_for(?x484, ?x7880), nominated_for(?x484, ?x4841), titles(?x53, ?x4751), film(?x748, ?x7880), ?x4841 = 0k4fz, type_of_union(?x8692, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #24687 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 22 *> proper extension: 02wzl1d; 0h_cssd; 05zksls; 0hndn2q; 026kq4q; 0drtv8; 09p30_; 09pnw5; 09pj68; 0418154; ... *> query: (?x5703, 03knl) <- award_winner(?x5703, ?x8692), award_winner(?x5703, ?x1039), honored_for(?x5703, ?x253), ceremony(?x1703, ?x5703), award(?x8692, ?x384), program(?x1039, ?x2436), award(?x197, ?x1703), nominated_for(?x1703, ?x7755), nominated_for(?x1703, ?x1820), ?x1820 = 09cr8, produced_by(?x542, ?x1039), produced_by(?x1487, ?x8692), award_winner(?x496, ?x1039), film(?x1104, ?x7755), type_of_union(?x1039, ?x566) *> conf = 0.08 ranks of expected_values: 650 EVAL 02yvhx award_winner 03knl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 26.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8397-0d6d2 PRED entity: 0d6d2 PRED relation: people! PRED expected values: 04p3w => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 33): 0qcr0 (0.25 #1, 0.17 #199, 0.15 #265), 0dq9p (0.25 #17, 0.10 #149, 0.08 #347), 04p3w (0.14 #77, 0.08 #209, 0.08 #275), 0gg4h (0.14 #102, 0.08 #234, 0.08 #300), 0x2fg (0.14 #104, 0.08 #236, 0.08 #302), 01l2m3 (0.14 #82, 0.08 #214, 0.08 #280), 01tf_6 (0.14 #97, 0.08 #229, 0.08 #295), 0gk4g (0.13 #1462, 0.12 #2056, 0.10 #142), 09969 (0.08 #242, 0.08 #308, 0.04 #374), 04psf (0.08 #205, 0.08 #271, 0.02 #1459) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 017g2y; 04xbr4; >> query: (?x8151, 0qcr0) <- film(?x8151, ?x650), nationality(?x8151, ?x94), ?x650 = 026p_bs >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #77 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 07bty; *> query: (?x8151, 04p3w) <- diet(?x8151, ?x3130), place_of_death(?x8151, ?x12696), award_winner(?x591, ?x8151) *> conf = 0.14 ranks of expected_values: 3 EVAL 0d6d2 people! 04p3w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 116.000 0.250 http://example.org/people/cause_of_death/people #8396-0bzjgq PRED entity: 0bzjgq PRED relation: honored_for PRED expected values: 09d3b7 => 43 concepts (30 used for prediction) PRED predicted values (max 10 best out of 630): 05qm9f (0.36 #7771, 0.18 #7769, 0.18 #10163), 0hv81 (0.36 #7771, 0.15 #7770, 0.13 #10164), 07xtqq (0.36 #7771, 0.15 #7770, 0.13 #10164), 0hmm7 (0.36 #7771, 0.15 #7770, 0.13 #10164), 0hv8w (0.36 #7771, 0.15 #7770, 0.13 #10164), 02vnmc9 (0.33 #1651, 0.20 #2846, 0.18 #11358), 0p9rz (0.33 #1707, 0.20 #2902, 0.09 #8368), 026gyn_ (0.33 #1304, 0.20 #2499, 0.02 #12069), 08ct6 (0.33 #1475, 0.20 #2670, 0.02 #12240), 0b2qtl (0.33 #1503, 0.20 #2698, 0.02 #12268) >> Best rule #7771 for best value: >> intensional similarity = 19 >> extensional distance = 11 >> proper extension: 0dthsy; >> query: (?x8478, ?x2047) <- ceremony(?x3066, ?x8478), ceremony(?x2222, ?x8478), ceremony(?x1323, ?x8478), award_winner(?x8478, ?x12848), award_winner(?x8478, ?x3527), award_winner(?x8478, ?x2466), ?x2222 = 0gs96, ?x3066 = 0gqy2, honored_for(?x8478, ?x6013), cinematography(?x6607, ?x2466), cinematography(?x2047, ?x2466), instance_of_recurring_event(?x8478, ?x3459), nominated_for(?x198, ?x6607), titles(?x600, ?x6607), nationality(?x3527, ?x94), ?x1323 = 0gqz2, award_winner(?x7846, ?x3527), list(?x2047, ?x3004), award_nominee(?x2068, ?x12848) >> conf = 0.36 => this is the best rule for 5 predicted values *> Best rule #11358 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 23 *> proper extension: 0bzm81; 0fy6bh; 0bzn6_; 0fz2y7; 02pgky2; 0c4hnm; 0fzrhn; *> query: (?x8478, ?x303) <- ceremony(?x3066, ?x8478), ceremony(?x2222, ?x8478), award_winner(?x8478, ?x12848), award_winner(?x8478, ?x3527), award_winner(?x8478, ?x2466), ?x2222 = 0gs96, ?x3066 = 0gqy2, honored_for(?x8478, ?x8000), cinematography(?x2047, ?x2466), written_by(?x518, ?x3527), award_nominee(?x2068, ?x12848), genre(?x8000, ?x53), currency(?x2047, ?x170), film(?x398, ?x8000), nominated_for(?x3662, ?x2047), language(?x8000, ?x2502), award_winner(?x303, ?x12848) *> conf = 0.18 ranks of expected_values: 50 EVAL 0bzjgq honored_for 09d3b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 43.000 30.000 0.357 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #8395-05hmp6 PRED entity: 05hmp6 PRED relation: ceremony! PRED expected values: 0f4x7 0gq9h => 36 concepts (33 used for prediction) PRED predicted values (max 10 best out of 293): 0gq9h (0.91 #4688, 0.89 #3954, 0.89 #4931), 0f4x7 (0.88 #5385, 0.88 #5142, 0.87 #4898), 0l8z1 (0.80 #4678, 0.77 #2727, 0.76 #2241), 0gr07 (0.77 #4792, 0.77 #3570, 0.77 #2841), 0gqng (0.77 #3417, 0.75 #4639, 0.74 #4150), 018wdw (0.72 #2370, 0.72 #6591, 0.71 #3585), 0gqxm (0.72 #6591, 0.52 #2318, 0.51 #3533), 0gqzz (0.72 #6591, 0.24 #2482, 0.24 #2239), 02x201b (0.72 #6591, 0.19 #1642, 0.17 #2131), 019f4v (0.69 #244, 0.33 #733, 0.30 #1466) >> Best rule #4688 for best value: >> intensional similarity = 17 >> extensional distance = 42 >> proper extension: 0bzk2h; 0fy59t; >> query: (?x6323, 0gq9h) <- award_winner(?x6323, ?x7676), award_winner(?x6323, ?x6921), ceremony(?x3617, ?x6323), ceremony(?x1703, ?x6323), ?x1703 = 0k611, ceremony(?x3617, ?x8150), ceremony(?x3617, ?x3332), award_winner(?x3617, ?x10301), participant(?x5336, ?x7676), ?x3332 = 0bz6l9, nominated_for(?x3617, ?x1015), gender(?x6921, ?x231), award_nominee(?x10301, ?x3018), program(?x10301, ?x4637), film(?x7676, ?x3369), ?x8150 = 0bzkvd, award_nominee(?x6921, ?x2801) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05hmp6 ceremony! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 33.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 05hmp6 ceremony! 0f4x7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 33.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony #8394-03q2t9 PRED entity: 03q2t9 PRED relation: artist! PRED expected values: 016ckq => 103 concepts (80 used for prediction) PRED predicted values (max 10 best out of 100): 015_1q (0.31 #995, 0.24 #158, 0.24 #19), 016ckq (0.22 #321, 0.14 #1298, 0.12 #460), 0181dw (0.22 #320, 0.14 #1297, 0.13 #3669), 05s34b (0.19 #976, 0.08 #836, 0.05 #1535), 017l96 (0.19 #157, 0.18 #18, 0.15 #994), 0g768 (0.18 #36, 0.14 #1292, 0.14 #454), 02bh8z (0.18 #21, 0.14 #160, 0.14 #997), 01cl0d (0.18 #54, 0.14 #193, 0.10 #1030), 02p11jq (0.18 #13, 0.14 #152, 0.10 #4337), 01trtc (0.15 #908, 0.10 #2305, 0.09 #1886) >> Best rule #995 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 07c0j; 05crg7; 0dtd6; 016fmf; 01vrwfv; 01rm8b; 0163m1; 0hvbj; 0kr_t; 02jqjm; ... >> query: (?x5456, 015_1q) <- award(?x5456, ?x3647), award(?x5456, ?x2563), ?x3647 = 01c9jp, award_winner(?x2563, ?x215) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #321 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 01l1b90; 01364q; 07ss8_; 01trhmt; *> query: (?x5456, 016ckq) <- award(?x5456, ?x2563), ?x2563 = 01cw51, nationality(?x5456, ?x94) *> conf = 0.22 ranks of expected_values: 2 EVAL 03q2t9 artist! 016ckq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 80.000 0.305 http://example.org/music/record_label/artist #8393-01sbv9 PRED entity: 01sbv9 PRED relation: film! PRED expected values: 03xq0f => 69 concepts (64 used for prediction) PRED predicted values (max 10 best out of 63): 056ws9 (0.70 #890, 0.53 #445, 0.46 #594), 03xq0f (0.57 #228, 0.25 #78, 0.21 #154), 01795t (0.41 #166, 0.19 #462, 0.09 #611), 054g1r (0.26 #182, 0.15 #478, 0.08 #1219), 020h2v (0.20 #43, 0.06 #1007, 0.05 #1229), 086k8 (0.18 #1188, 0.18 #1481, 0.16 #1408), 016tt2 (0.17 #1190, 0.15 #523, 0.15 #1410), 016tw3 (0.15 #974, 0.15 #2379, 0.13 #3415), 017s11 (0.15 #522, 0.14 #597, 0.13 #1189), 0150t6 (0.13 #889, 0.13 #223, 0.13 #1775) >> Best rule #890 for best value: >> intensional similarity = 3 >> extensional distance = 226 >> proper extension: 080dwhx; 03ctqqf; >> query: (?x10192, ?x5970) <- award_winner(?x10192, ?x1835), nominated_for(?x5970, ?x10192), production_companies(?x2746, ?x5970) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #228 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 0522wp; *> query: (?x10192, 03xq0f) <- film_distribution_medium(?x10192, ?x2099), category(?x10192, ?x134), ?x134 = 08mbj5d, film(?x902, ?x10192) *> conf = 0.57 ranks of expected_values: 2 EVAL 01sbv9 film! 03xq0f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 64.000 0.700 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #8392-07k2x PRED entity: 07k2x PRED relation: film PRED expected values: 0b60sq => 114 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1833): 03mh_tp (0.43 #6800, 0.38 #8388, 0.33 #448), 02rb84n (0.43 #6606, 0.36 #9782, 0.33 #11370), 0dgq_kn (0.43 #7277, 0.33 #925, 0.25 #8865), 014kq6 (0.43 #6660, 0.33 #308, 0.25 #8248), 0g7pm1 (0.43 #7425, 0.33 #1073, 0.25 #9013), 02vw1w2 (0.38 #23825, 0.06 #14293), 0ckrgs (0.38 #8397, 0.29 #6809, 0.18 #9985), 035s95 (0.33 #303, 0.31 #13007, 0.29 #6655), 043tvp3 (0.33 #1080, 0.29 #7432, 0.27 #10608), 02rrfzf (0.33 #482, 0.29 #6834, 0.25 #8422) >> Best rule #6800 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 016tt2; >> query: (?x7030, 03mh_tp) <- film(?x7030, ?x10187), film(?x7030, ?x3135), actor(?x10187, ?x51), citytown(?x7030, ?x9559), film_release_region(?x3135, ?x4743), film_release_region(?x3135, ?x1892), ?x4743 = 03spz, industry(?x7030, ?x373), ?x1892 = 02vzc >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #8012 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 0g1rw; 0674l0; *> query: (?x7030, 0b60sq) <- film(?x7030, ?x10187), actor(?x10187, ?x51), language(?x10187, ?x2164), film_release_region(?x10187, ?x94), film(?x296, ?x10187), genre(?x10187, ?x5937), ?x94 = 09c7w0 *> conf = 0.25 ranks of expected_values: 204 EVAL 07k2x film 0b60sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 114.000 21.000 0.429 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #8391-0cqt41 PRED entity: 0cqt41 PRED relation: season PRED expected values: 0285r5d => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 4): 0285r5d (0.90 #93, 0.89 #81, 0.89 #77), 03c6s24 (0.39 #79, 0.33 #95, 0.33 #35), 03c74_8 (0.33 #78, 0.29 #94, 0.25 #22), 04n36qk (0.25 #24, 0.17 #28, 0.09 #40) >> Best rule #93 for best value: >> intensional similarity = 9 >> extensional distance = 19 >> proper extension: 01ync; >> query: (?x1632, 0285r5d) <- position(?x1632, ?x8520), position(?x1632, ?x5727), colors(?x1632, ?x663), ?x5727 = 02wszf, school(?x1632, ?x735), draft(?x1632, ?x8499), team(?x8520, ?x2405), ?x8499 = 02r6gw6, season(?x1632, ?x701) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cqt41 season 0285r5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.905 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #8390-06by7 PRED entity: 06by7 PRED relation: parent_genre! PRED expected values: 0dn16 041738 01skxk 016ybr 0cx7f 0133_p 05c6073 02lw8j => 70 concepts (56 used for prediction) PRED predicted values (max 10 best out of 254): 0193f (0.50 #2141, 0.14 #4635, 0.14 #7687), 03xnwz (0.43 #3957, 0.40 #3124, 0.33 #845), 07ym47 (0.43 #4185, 0.33 #34, 0.20 #2314), 016_nr (0.43 #4190, 0.33 #39, 0.14 #6060), 0173b0 (0.40 #2597, 0.33 #3633, 0.33 #1353), 0b_6yv (0.40 #2652, 0.33 #3688, 0.33 #1201), 0dn16 (0.40 #2907, 0.33 #1249, 0.33 #835), 01cbwl (0.40 #2924, 0.33 #1266, 0.33 #852), 04_sqm (0.40 #2627, 0.33 #3663, 0.33 #1176), 05jg58 (0.33 #1104, 0.33 #689, 0.25 #1725) >> Best rule #2141 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 08cyft; >> query: (?x1572, 0193f) <- artists(?x1572, ?x9262), artists(?x1572, ?x7221), artists(?x1572, ?x4873), artists(?x1572, ?x2237), ?x2237 = 01vs_v8, ?x7221 = 0191h5, origin(?x9262, ?x739), role(?x4873, ?x74) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2907 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 0dn16; *> query: (?x1572, 0dn16) <- artists(?x1572, ?x12194), artists(?x1572, ?x9262), artists(?x1572, ?x8693), artists(?x1572, ?x2237), ?x12194 = 01mbwlb, friend(?x3397, ?x2237), ?x8693 = 0bdxs5, award_nominee(?x827, ?x9262) *> conf = 0.40 ranks of expected_values: 7, 14, 19, 21, 59, 97, 151, 154 EVAL 06by7 parent_genre! 02lw8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 70.000 56.000 0.500 http://example.org/music/genre/parent_genre EVAL 06by7 parent_genre! 05c6073 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 70.000 56.000 0.500 http://example.org/music/genre/parent_genre EVAL 06by7 parent_genre! 0133_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 70.000 56.000 0.500 http://example.org/music/genre/parent_genre EVAL 06by7 parent_genre! 0cx7f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 70.000 56.000 0.500 http://example.org/music/genre/parent_genre EVAL 06by7 parent_genre! 016ybr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 70.000 56.000 0.500 http://example.org/music/genre/parent_genre EVAL 06by7 parent_genre! 01skxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 70.000 56.000 0.500 http://example.org/music/genre/parent_genre EVAL 06by7 parent_genre! 041738 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 70.000 56.000 0.500 http://example.org/music/genre/parent_genre EVAL 06by7 parent_genre! 0dn16 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 70.000 56.000 0.500 http://example.org/music/genre/parent_genre #8389-0m491 PRED entity: 0m491 PRED relation: featured_film_locations PRED expected values: 0sb1r => 116 concepts (109 used for prediction) PRED predicted values (max 10 best out of 136): 02_286 (0.35 #15881, 0.35 #16357, 0.32 #5223), 02jx1 (0.33 #35, 0.02 #5238, 0.01 #6655), 04jpl (0.30 #1426, 0.19 #5212, 0.15 #15870), 0r0m6 (0.25 #323, 0.04 #3868, 0.02 #22489), 0d6lp (0.25 #306, 0.02 #16407, 0.02 #7875), 04lyk (0.25 #414), 0r62v (0.25 #257), 0cv3w (0.11 #1012, 0.04 #7163, 0.03 #4324), 0cwx_ (0.11 #1067, 0.02 #6034, 0.02 #6507), 0rh6k (0.11 #3782, 0.10 #1182, 0.08 #8516) >> Best rule #15881 for best value: >> intensional similarity = 4 >> extensional distance = 408 >> proper extension: 02d44q; 047svrl; 07k2mq; >> query: (?x1859, 02_286) <- featured_film_locations(?x1859, ?x8468), film_release_region(?x1859, ?x87), place_of_birth(?x3056, ?x8468), contains(?x8468, ?x3360) >> conf = 0.35 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0m491 featured_film_locations 0sb1r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 109.000 0.349 http://example.org/film/film/featured_film_locations #8388-02hrh1q PRED entity: 02hrh1q PRED relation: profession! PRED expected values: 0q9kd 0f0y8 06151l 049tjg 02g8h 0h1_w 03zqc1 017149 06jzh 06n7h7 01n5309 03_gd 03qd_ 0785v8 04sx9_ 04l3_z 04shbh 019_1h 01vrncs 0lk90 01vrt_c 02lnhv 03f1zdw 07vc_9 01v42g 030znt 0pgjm 0clvcx 03jldb 027f7dj 02lg9w 01bpc9 03ft8 01n4f8 015pkc 03gr7w 064p92m 016_mj 01fh9 02k6rq 06lgq8 022769 0f6_dy 01w60_p 01tfck 02lq10 026c1 04gcd1 01vhb0 01pgzn_ 080knyg 02xb2bt 021lby 01hkhq 0hwd8 05hdf 02lf1j 0pmhf 03q1vd 019pm_ 0lpjn 01wj92r 02j9lm 03pmzt 01438g 0qdyf 01nrq5 03bnv 02f8lw 01l_vgt 044gyq 01vw26l 039bpc 0bq2g 01309x 01y0y6 01lly5 04n_g 01v40wd 050t68 01wb8bs 02v0ff 016yzz 04w391 038bht 03lq43 0g28b1 0308kx 01vy_v8 04yt7 01z7_f 015wfg 05txrz 01vw20h 03q95r 07m9cm 0dzf_ 044k8 01s3kv 05typm 0fhxv 02gyl0 01kwsg 019r_1 01ry0f 01mwsnc 01bcq 06lht1 041c4 069nzr 03h502k 046zh 0gn30 043zg 03n52j 046mxj 04crrxr 03q2t9 09zmys 017khj 07j8kh 01mxt_ 03ym1 02nwxc 0175wg 01l87db 01_p6t 01z7s_ 02hhtj 0d02km 025j1t 013w7j 01h910 04bdzg 02756j 018ygt 0807ml 01386_ 08d6bd 04bgy 01vw917 0js9s 016sqs 05lb30 08hhm6 01w23w 03m6pk 04l19_ 05dtsb 01d1st 06_bq1 0265z9l 02vq8xn 01yfm8 0gm34 02h0f3 03xx9l 013pk3 04zkj5 01vrx35 0739y 0bl60p 02nfhx 05l0j5 02qw2xb 01lqnff 0gd9k 081l_ 02661h 031k24 046rfv 04jb97 059fjj 03j149k 0d6d2 01f492 0cp9f9 012gbb 0jvtp 0725ny 0l5yl 06gn7r 0263tn1 027n4zv 063_t 0f13b 01nr36 02m92h 0djywgn 050_qx 09tqx3 026rm_y 01vxqyl 01lqf49 012xdf 036hf4 0147jt 05mlqj 02yy_j 01kp_1t 0h10vt 01bh6y 0q1lp 04tnqn 05w88j 01h4rj 01ypsj 0fxky3 01wdcxk 04y0yc 01ggc9 03p01x 02lyx4 02784z 01jz6x 017dpj 065d1h 022q32 01wk3c 045g4l 0652ty 044bn 0b5x23 02c7lt 02hkv5 015qq1 02t_8z 01mz9lt 07rzf 03k545 01hdht 0436zq 09g0h 05p606 02q4mt 04bdlg 0739z6 07gknc 081hvm 01dbgw 05g3ss 012g92 0m68w 01dpsv 047s_cr 01qnfc 01wmcbg 091n7z 0131kb 01svq8 027hq5f 0brddh 083wr9 018fwv 09ld6g 01l3j 03t8v3 0qkj7 033db3 0cfywh 01nd6v => 37 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1877): 06y9c2 (0.60 #17520, 0.57 #21015, 0.50 #19267), 01vsy3q (0.60 #18070, 0.57 #21565, 0.50 #11081), 016ntp (0.60 #17800, 0.57 #21295, 0.50 #10811), 01vrkdt (0.60 #17920, 0.57 #21415, 0.50 #10931), 02whj (0.60 #17558, 0.57 #21053, 0.50 #10569), 01wx756 (0.60 #19028, 0.57 #22523, 0.50 #12039), 01wg3q (0.60 #18633, 0.57 #22128, 0.50 #11644), 01r0t_j (0.60 #18558, 0.57 #22053, 0.50 #11569), 01bczm (0.60 #18168, 0.50 #19915, 0.50 #11179), 01mwsnc (0.60 #18076, 0.50 #19823, 0.50 #11087) >> Best rule #17520 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 039v1; >> query: (?x1032, 06y9c2) <- profession(?x6124, ?x1032), profession(?x4740, ?x1032), profession(?x680, ?x1032), category(?x6124, ?x134), ?x680 = 01cv3n, ?x4740 = 03y82t6 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #18076 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 039v1; *> query: (?x1032, 01mwsnc) <- profession(?x6124, ?x1032), profession(?x4740, ?x1032), profession(?x680, ?x1032), category(?x6124, ?x134), ?x680 = 01cv3n, ?x4740 = 03y82t6 *> conf = 0.60 ranks of expected_values: 10, 12, 19, 26, 65, 92, 98, 99, 100, 106, 111, 121, 124, 128, 129, 130, 139, 143, 146, 148, 155, 165, 177, 193, 196, 199, 229, 250, 251, 254, 255, 262, 263, 267, 279, 286, 289, 290, 291, 292, 294, 300, 316, 319, 321, 324, 330, 331, 335, 342, 345, 347, 350, 353, 370, 382, 397, 398, 418, 428, 464, 531, 545, 559, 574, 577, 578, 584, 585, 607, 615, 617, 618, 621, 622, 626, 635, 636, 637, 647, 654, 662, 664, 679, 688, 691, 700, 710, 725, 726, 727, 733, 742, 743, 744, 745, 746, 754, 756, 769, 778, 779, 783, 787, 793, 794, 799, 800, 801, 802, 804, 805, 806, 807, 808, 811, 812, 824, 827, 828, 831, 837, 841, 852, 861, 866, 899, 903, 904, 925, 933, 945, 953, 967, 972, 974, 976, 979, 986, 995, 1003, 1009, 1013, 1076, 1099, 1105, 1109, 1112, 1116, 1117, 1119, 1124, 1125, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1157, 1158, 1165, 1175, 1179, 1182, 1184, 1185, 1189, 1194, 1196, 1202, 1204, 1206, 1208, 1215, 1216, 1219, 1220, 1221, 1223, 1226, 1228, 1229, 1232, 1235, 1240, 1241, 1242, 1250, 1253, 1256, 1260, 1261, 1263, 1268, 1269, 1270, 1272, 1275, 1279, 1281, 1282, 1287, 1294, 1300, 1303, 1306, 1326, 1334, 1428, 1445, 1446, 1452, 1472, 1476, 1477, 1481, 1483, 1484, 1485, 1486, 1591, 1618, 1655, 1698, 1700, 1701, 1702, 1707, 1708, 1714, 1727 EVAL 02hrh1q profession! 01nd6v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0cfywh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 033db3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0qkj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03t8v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01l3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 09ld6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 018fwv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 083wr9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0brddh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 027hq5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01svq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0131kb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 091n7z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01wmcbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01qnfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 047s_cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01dpsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0m68w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 012g92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 05g3ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01dbgw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 081hvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 07gknc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0739z6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04bdlg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02q4mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 05p606 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 09g0h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0436zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01hdht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03k545 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 07rzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01mz9lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02t_8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 015qq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02hkv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02c7lt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0b5x23 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 044bn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0652ty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 045g4l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01wk3c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 022q32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 065d1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 017dpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01jz6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02784z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02lyx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03p01x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01ggc9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04y0yc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01wdcxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0fxky3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01ypsj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01h4rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 05w88j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04tnqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0q1lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01bh6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0h10vt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01kp_1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02yy_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 05mlqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0147jt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 036hf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 012xdf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01lqf49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01vxqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 026rm_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 09tqx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 050_qx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0djywgn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02m92h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01nr36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0f13b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 063_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 027n4zv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0263tn1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 06gn7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0l5yl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0725ny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0jvtp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 012gbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0cp9f9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01f492 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0d6d2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03j149k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 059fjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04jb97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 046rfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 031k24 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02661h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 081l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0gd9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01lqnff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02qw2xb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 05l0j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02nfhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0bl60p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0739y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01vrx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04zkj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 013pk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03xx9l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02h0f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0gm34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01yfm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02vq8xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0265z9l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 06_bq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01d1st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 05dtsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04l19_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03m6pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01w23w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 08hhm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 05lb30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 016sqs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0js9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01vw917 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04bgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 08d6bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01386_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0807ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 018ygt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02756j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04bdzg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01h910 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 013w7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 025j1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0d02km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02hhtj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01z7s_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01_p6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01l87db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0175wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02nwxc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03ym1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01mxt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 07j8kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 017khj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 09zmys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03q2t9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04crrxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 046mxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03n52j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 043zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0gn30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 046zh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03h502k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 069nzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 041c4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 06lht1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01bcq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01mwsnc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01ry0f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 019r_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01kwsg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02gyl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0fhxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 05typm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01s3kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 044k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0dzf_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 07m9cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03q95r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01vw20h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 05txrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 015wfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01z7_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04yt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01vy_v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0308kx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0g28b1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03lq43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 038bht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04w391 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 016yzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02v0ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01wb8bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 050t68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01v40wd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04n_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01lly5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01y0y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01309x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0bq2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 039bpc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01vw26l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 044gyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01l_vgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02f8lw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03bnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01nrq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0qdyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01438g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03pmzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02j9lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01wj92r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0lpjn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 019pm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03q1vd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0pmhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02lf1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 05hdf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0hwd8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01hkhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 021lby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02xb2bt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 080knyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01pgzn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01vhb0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04gcd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 026c1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02lq10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01tfck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01w60_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0f6_dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 022769 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 06lgq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02k6rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01fh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 016_mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 064p92m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03gr7w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 015pkc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01n4f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03ft8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01bpc9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02lg9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 027f7dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03jldb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0clvcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0pgjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 030znt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01v42g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 07vc_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03f1zdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02lnhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01vrt_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0lk90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01vrncs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 019_1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04shbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04l3_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 04sx9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0785v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03qd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03_gd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 01n5309 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 06n7h7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 06jzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 017149 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 03zqc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0h1_w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 02g8h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 049tjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 06151l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0f0y8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 37.000 30.000 0.600 http://example.org/people/person/profession EVAL 02hrh1q profession! 0q9kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 37.000 30.000 0.600 http://example.org/people/person/profession #8387-09xq9d PRED entity: 09xq9d PRED relation: interests! PRED expected values: 03sbs => 63 concepts (39 used for prediction) PRED predicted values (max 10 best out of 196): 045bg (0.67 #76, 0.44 #334, 0.43 #186), 026lj (0.62 #226, 0.57 #188, 0.56 #336), 03sbs (0.50 #243, 0.50 #95, 0.43 #205), 01dvtx (0.50 #87, 0.44 #345, 0.43 #197), 0nk72 (0.50 #98, 0.44 #356, 0.43 #208), 043s3 (0.50 #237, 0.44 #347, 0.43 #199), 015n8 (0.50 #107, 0.43 #217, 0.38 #291), 047g6 (0.44 #364, 0.38 #254, 0.29 #216), 0ct9_ (0.43 #210, 0.38 #284, 0.38 #248), 0j3v (0.38 #264, 0.38 #228, 0.33 #80) >> Best rule #76 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 05qt0; >> query: (?x3561, 045bg) <- interests(?x8232, ?x3561), interests(?x7341, ?x3561), profession(?x7341, ?x3801), religion(?x7341, ?x492), gender(?x7341, ?x231), place_of_birth(?x8232, ?x4627), influenced_by(?x7341, ?x3712), ?x4627 = 05qtj, profession(?x8232, ?x13995), peers(?x8233, ?x8232) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #243 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 6 *> proper extension: 0x0w; *> query: (?x3561, 03sbs) <- interests(?x7341, ?x3561), interests(?x7296, ?x3561), profession(?x7341, ?x3801), interests(?x7341, ?x742), major_field_of_study(?x3948, ?x742), gender(?x7341, ?x231), ?x3948 = 025v3k, influenced_by(?x1279, ?x7341), nationality(?x7341, ?x3730), major_field_of_study(?x734, ?x742), people(?x6720, ?x7296) *> conf = 0.50 ranks of expected_values: 3 EVAL 09xq9d interests! 03sbs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 63.000 39.000 0.667 http://example.org/user/alexander/philosophy/philosopher/interests #8386-01br2w PRED entity: 01br2w PRED relation: genre PRED expected values: 06l3bl => 114 concepts (87 used for prediction) PRED predicted values (max 10 best out of 206): 05p553 (0.50 #121, 0.44 #593, 0.41 #9129), 03k9fj (0.50 #129, 0.35 #3205, 0.33 #483), 01jfsb (0.44 #248, 0.36 #2497, 0.32 #3682), 082gq (0.41 #2395, 0.41 #1446, 0.36 #973), 0hcr (0.39 #3218, 0.11 #260, 0.08 #5004), 02l7c8 (0.38 #1433, 0.36 #2974, 0.35 #842), 02kdv5l (0.36 #355, 0.36 #2486, 0.33 #119), 017fp (0.36 #1196, 0.28 #1672, 0.26 #959), 0lsxr (0.33 #244, 0.25 #8, 0.22 #1901), 06n90 (0.33 #131, 0.25 #13, 0.19 #603) >> Best rule #121 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 04g73n; 03tbg6; >> query: (?x174, 05p553) <- film(?x10629, ?x174), nominated_for(?x6664, ?x174), ?x6664 = 01mkn_d, genre(?x174, ?x53), production_companies(?x299, ?x10629) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #980 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 034r25; *> query: (?x174, 06l3bl) <- film_crew_role(?x174, ?x137), genre(?x174, ?x2605), ?x2605 = 03g3w, currency(?x174, ?x170) *> conf = 0.13 ranks of expected_values: 27 EVAL 01br2w genre 06l3bl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 114.000 87.000 0.500 http://example.org/film/film/genre #8385-0738y5 PRED entity: 0738y5 PRED relation: profession PRED expected values: 01d_h8 => 86 concepts (32 used for prediction) PRED predicted values (max 10 best out of 61): 02hrh1q (0.86 #1777, 0.85 #2072, 0.85 #1483), 01d_h8 (0.76 #594, 0.75 #447, 0.73 #1035), 025352 (0.33 #205, 0.33 #58, 0.25 #352), 0cbd2 (0.33 #7, 0.25 #301, 0.24 #595), 0np9r (0.33 #20, 0.25 #314, 0.13 #2814), 09jwl (0.33 #165, 0.18 #3549, 0.18 #4137), 0lgw7 (0.33 #487, 0.17 #928, 0.14 #634), 028kk_ (0.33 #221, 0.11 #1691, 0.09 #2574), 0nbcg (0.33 #177, 0.11 #3561, 0.10 #4296), 02t8yb (0.33 #227, 0.08 #1256, 0.05 #1697) >> Best rule #1777 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 03f2_rc; 015grj; 0sz28; 01vs_v8; 0gv5c; 0dfjb8; 0gs1_; 06gn7r; 06pjs; 0py5b; >> query: (?x9506, 02hrh1q) <- profession(?x9506, ?x524), ?x524 = 02jknp, languages(?x9506, ?x1882), people(?x13008, ?x9506) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #594 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 19 *> proper extension: 06cv1; 079hvk; 02kxbx3; 01vqrm; 01xv77; 014dm6; 08t7nz; 023t0q; 02yy_j; 031bf1; ... *> query: (?x9506, 01d_h8) <- profession(?x9506, ?x2265), profession(?x9506, ?x524), ?x524 = 02jknp, nationality(?x9506, ?x2146), student(?x9536, ?x9506), ?x2265 = 0dgd_ *> conf = 0.76 ranks of expected_values: 2 EVAL 0738y5 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 32.000 0.855 http://example.org/people/person/profession #8384-095kp PRED entity: 095kp PRED relation: major_field_of_study PRED expected values: 0g4gr => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 119): 0g26h (0.56 #44, 0.41 #1780, 0.33 #4887), 01mkq (0.50 #16, 0.45 #4610, 0.41 #1752), 041y2 (0.50 #80, 0.18 #1816, 0.14 #4923), 04rjg (0.44 #21, 0.43 #3621, 0.37 #641), 03g3w (0.44 #28, 0.43 #3628, 0.40 #4125), 04x_3 (0.39 #27, 0.25 #1019, 0.22 #1763), 01lj9 (0.39 #41, 0.25 #2150, 0.24 #4884), 02h40lc (0.39 #4, 0.22 #996, 0.19 #1244), 02lp1 (0.38 #1748, 0.33 #12, 0.33 #1004), 062z7 (0.37 #5121, 0.31 #2138, 0.31 #2014) >> Best rule #44 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 01pl14; 065y4w7; 07w0v; 0gl5_; >> query: (?x6112, 0g26h) <- major_field_of_study(?x6112, ?x7134), major_field_of_study(?x6112, ?x2981), ?x2981 = 02j62, ?x7134 = 02_7t, citytown(?x6112, ?x739) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #32 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 01pl14; 065y4w7; 07w0v; 0gl5_; *> query: (?x6112, 0g4gr) <- major_field_of_study(?x6112, ?x7134), major_field_of_study(?x6112, ?x2981), ?x2981 = 02j62, ?x7134 = 02_7t, citytown(?x6112, ?x739) *> conf = 0.28 ranks of expected_values: 15 EVAL 095kp major_field_of_study 0g4gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 131.000 131.000 0.556 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8383-03f02ct PRED entity: 03f02ct PRED relation: religion PRED expected values: 03j6c => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 15): 03j6c (0.71 #21, 0.46 #156, 0.28 #201), 0flw86 (0.33 #47, 0.15 #92, 0.12 #137), 0c8wxp (0.17 #502, 0.16 #637, 0.16 #682), 06yyp (0.14 #22, 0.04 #157, 0.02 #202), 03_gx (0.12 #284, 0.08 #239, 0.08 #1142), 0kpl (0.06 #280, 0.06 #1229, 0.06 #235), 0kq2 (0.02 #1237, 0.02 #334, 0.02 #288), 01lp8 (0.02 #136, 0.02 #226, 0.02 #1038), 078vc (0.02 #161), 092bf5 (0.02 #241, 0.02 #782, 0.02 #1144) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 05yvfd; >> query: (?x10570, 03j6c) <- profession(?x10570, ?x524), location(?x10570, ?x7412), people(?x7838, ?x10570), ?x7838 = 02sch9 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f02ct religion 03j6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.714 http://example.org/people/person/religion #8382-01wv9xn PRED entity: 01wv9xn PRED relation: artist! PRED expected values: 02p3cr5 => 86 concepts (65 used for prediction) PRED predicted values (max 10 best out of 127): 03rhqg (0.40 #571, 0.30 #711, 0.29 #989), 033hn8 (0.33 #13, 0.25 #291, 0.23 #1404), 0181dw (0.33 #40, 0.20 #457, 0.13 #5047), 02bh8z (0.33 #158, 0.17 #1132, 0.12 #993), 015mlw (0.33 #224, 0.08 #920, 0.06 #1755), 043g7l (0.32 #5036, 0.27 #2783, 0.23 #1670), 041p3y (0.25 #351, 0.10 #1325, 0.10 #769), 06gst (0.25 #377, 0.08 #934, 0.04 #6261), 01cf93 (0.23 #891, 0.20 #473, 0.13 #1447), 01clyr (0.22 #1701, 0.19 #1979, 0.17 #2118) >> Best rule #571 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0qmpd; >> query: (?x1684, 03rhqg) <- artists(?x2808, ?x1684), group(?x7084, ?x1684), group(?x3266, ?x1684), artist(?x2149, ?x7084), instrumentalists(?x212, ?x3266), ?x2808 = 0190_q >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1695 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 30 *> proper extension: 032t2z; 01386_; 01mxnvc; *> query: (?x1684, 02p3cr5) <- artists(?x9063, ?x1684), artists(?x7436, ?x1684), artists(?x1380, ?x1684), artists(?x7436, ?x8637), ?x8637 = 0qmny, ?x1380 = 0dl5d, ?x9063 = 0cx7f *> conf = 0.19 ranks of expected_values: 18 EVAL 01wv9xn artist! 02p3cr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 86.000 65.000 0.400 http://example.org/music/record_label/artist #8381-011zd3 PRED entity: 011zd3 PRED relation: film PRED expected values: 09146g => 77 concepts (66 used for prediction) PRED predicted values (max 10 best out of 477): 0vjr (0.61 #10693, 0.51 #23171, 0.49 #17823), 019vhk (0.17 #460, 0.14 #2242, 0.04 #24954), 035s95 (0.17 #340, 0.04 #24954, 0.03 #3904), 0830vk (0.17 #591, 0.04 #24954, 0.03 #101624), 01pgp6 (0.17 #281, 0.04 #24954, 0.03 #101624), 05567m (0.17 #1541, 0.04 #24954, 0.03 #101624), 01z452 (0.17 #1537, 0.04 #24954, 0.03 #101624), 0f40w (0.17 #362, 0.04 #24954, 0.03 #101624), 02f6g5 (0.17 #280, 0.04 #24954, 0.03 #49919), 0ch3qr1 (0.17 #971, 0.04 #24954, 0.03 #26737) >> Best rule #10693 for best value: >> intensional similarity = 2 >> extensional distance = 311 >> proper extension: 05hdf; 02wb6yq; 0d05fv; 01twdk; 0bkmf; >> query: (?x2307, ?x4375) <- award_winner(?x4375, ?x2307), participant(?x2307, ?x2221) >> conf = 0.61 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 011zd3 film 09146g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 66.000 0.610 http://example.org/film/actor/film./film/performance/film #8380-01j7rd PRED entity: 01j7rd PRED relation: award_winner! PRED expected values: 0hn821n => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 118): 0418154 (0.25 #491, 0.04 #2980, 0.04 #3373), 05pd94v (0.17 #12187, 0.13 #11793, 0.09 #5765), 0hn821n (0.17 #12187, 0.13 #11793, 0.09 #5765), 0jt3qpk (0.15 #954, 0.09 #561, 0.03 #299), 0gkxgfq (0.15 #1014, 0.09 #621, 0.03 #359), 03gwpw2 (0.13 #11793, 0.09 #5765, 0.08 #9), 09v0p2c (0.12 #467, 0.05 #1384, 0.04 #2956), 02rjjll (0.10 #136, 0.09 #1184, 0.08 #1577), 09n4nb (0.09 #304, 0.08 #42, 0.06 #1614), 02wzl1d (0.09 #404, 0.07 #666, 0.06 #1059) >> Best rule #491 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0b79gfg; 030vmc; 0f3zsq; >> query: (?x2127, 0418154) <- nominated_for(?x2127, ?x3626), award_winner(?x5585, ?x2127), ?x5585 = 03nnm4t >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #12187 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1379 *> proper extension: 0f721s; 01p5yn; 05s34b; *> query: (?x2127, ?x1265) <- award_winner(?x5447, ?x2127), award_winner(?x1265, ?x5447) *> conf = 0.17 ranks of expected_values: 3 EVAL 01j7rd award_winner! 0hn821n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 141.000 141.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8379-03j1p2n PRED entity: 03j1p2n PRED relation: artist! PRED expected values: 011k1h 0n85g => 95 concepts (56 used for prediction) PRED predicted values (max 10 best out of 93): 01w40h (0.33 #28, 0.27 #165, 0.19 #302), 0mzkr (0.33 #25, 0.14 #299, 0.09 #162), 015_1q (0.27 #156, 0.22 #567, 0.20 #2623), 01cszh (0.27 #148, 0.05 #5362, 0.05 #5225), 0181dw (0.24 #315, 0.18 #178, 0.12 #589), 01clyr (0.19 #307, 0.18 #170, 0.08 #444), 01dtcb (0.19 #320, 0.09 #183, 0.09 #594), 01cl2y (0.19 #304, 0.09 #167, 0.07 #578), 01cl0d (0.19 #328, 0.07 #2933, 0.07 #602), 0g768 (0.18 #173, 0.14 #310, 0.12 #5250) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0m19t; >> query: (?x7859, 01w40h) <- artists(?x14419, ?x7859), artists(?x12959, ?x7859), ?x14419 = 03rlps, ?x12959 = 01n4bh >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #198 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 020_4z; *> query: (?x7859, 0n85g) <- instrumentalists(?x1166, ?x7859), artist(?x9286, ?x7859), ?x9286 = 01t04r *> conf = 0.18 ranks of expected_values: 12, 18 EVAL 03j1p2n artist! 0n85g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 95.000 56.000 0.333 http://example.org/music/record_label/artist EVAL 03j1p2n artist! 011k1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 95.000 56.000 0.333 http://example.org/music/record_label/artist #8378-016dj8 PRED entity: 016dj8 PRED relation: film! PRED expected values: 02661h => 105 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1090): 02bh9 (0.57 #122442, 0.57 #126595, 0.55 #95460), 03rwz3 (0.50 #10374, 0.46 #89234, 0.46 #16599), 01qg7c (0.46 #89234, 0.46 #16599, 0.45 #51880), 05prs8 (0.46 #89234, 0.46 #16599, 0.45 #51880), 01vsn38 (0.38 #1848, 0.02 #70329, 0.02 #8072), 05txrz (0.25 #762, 0.03 #69243, 0.03 #29813), 012d40 (0.25 #15, 0.03 #14539, 0.03 #8314), 0bl2g (0.25 #54, 0.03 #68535, 0.02 #51934), 0f4vbz (0.25 #359, 0.02 #12808, 0.02 #6583), 0p8r1 (0.23 #6806, 0.04 #27557, 0.04 #31708) >> Best rule #122442 for best value: >> intensional similarity = 4 >> extensional distance = 844 >> proper extension: 0g60z; 080dwhx; 03kq98; 072kp; 039fgy; 0kfpm; 02k_4g; 0cwrr; 019nnl; 0ddd0gc; ... >> query: (?x6306, ?x10050) <- award_winner(?x6306, ?x10050), film(?x10050, ?x2128), profession(?x10050, ?x319), nominated_for(?x1533, ?x6306) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1391 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 06ztvyx; *> query: (?x6306, 02661h) <- nominated_for(?x298, ?x6306), film(?x2437, ?x6306), ?x2437 = 0738b8, film_crew_role(?x6306, ?x137) *> conf = 0.12 ranks of expected_values: 15 EVAL 016dj8 film! 02661h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 105.000 61.000 0.575 http://example.org/film/actor/film./film/performance/film #8377-05mgj0 PRED entity: 05mgj0 PRED relation: production_companies! PRED expected values: 05z_kps => 32 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1827): 07y9w5 (0.38 #4698, 0.15 #6969, 0.11 #9238), 047d21r (0.33 #3811, 0.19 #6082, 0.12 #4945), 07vn_9 (0.33 #2212, 0.17 #4480, 0.12 #6751), 0gvs1kt (0.33 #1499, 0.17 #3767, 0.12 #6038), 011yph (0.33 #60, 0.17 #3468, 0.10 #6873), 09sr0 (0.33 #2108, 0.17 #4376, 0.10 #7781), 011yqc (0.33 #1298, 0.17 #3566, 0.10 #6971), 0bxsk (0.33 #1896, 0.17 #4164, 0.10 #7569), 059rc (0.33 #1442, 0.17 #3710, 0.10 #7115), 03mz5b (0.33 #557, 0.17 #3965, 0.10 #7370) >> Best rule #4698 for best value: >> intensional similarity = 20 >> extensional distance = 6 >> proper extension: 04mwxk3; >> query: (?x9041, 07y9w5) <- production_companies(?x3252, ?x9041), production_companies(?x1803, ?x9041), film_release_region(?x1803, ?x2843), film_release_region(?x1803, ?x2645), film_release_region(?x1803, ?x1003), film_release_region(?x1803, ?x252), film_release_region(?x1803, ?x151), nominated_for(?x749, ?x1803), ?x1003 = 03gj2, film_festivals(?x3252, ?x2686), film_crew_role(?x1803, ?x137), film_release_region(?x3252, ?x774), ?x252 = 03_3d, award(?x396, ?x749), ?x2645 = 03h64, ?x151 = 0b90_r, ?x2686 = 0gg7gsl, medal(?x2843, ?x1242), country(?x7108, ?x2843), ?x7108 = 0194d >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #4672 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 6 *> proper extension: 04mwxk3; *> query: (?x9041, 05z_kps) <- production_companies(?x3252, ?x9041), production_companies(?x1803, ?x9041), film_release_region(?x1803, ?x2843), film_release_region(?x1803, ?x2645), film_release_region(?x1803, ?x1003), film_release_region(?x1803, ?x252), film_release_region(?x1803, ?x151), nominated_for(?x749, ?x1803), ?x1003 = 03gj2, film_festivals(?x3252, ?x2686), film_crew_role(?x1803, ?x137), film_release_region(?x3252, ?x774), ?x252 = 03_3d, award(?x396, ?x749), ?x2645 = 03h64, ?x151 = 0b90_r, ?x2686 = 0gg7gsl, medal(?x2843, ?x1242), country(?x7108, ?x2843), ?x7108 = 0194d *> conf = 0.12 ranks of expected_values: 618 EVAL 05mgj0 production_companies! 05z_kps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 12.000 0.375 http://example.org/film/film/production_companies #8376-01kf5lf PRED entity: 01kf5lf PRED relation: genre PRED expected values: 02kdv5l => 75 concepts (74 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.65 #2179, 0.63 #7626, 0.63 #4115), 02kdv5l (0.52 #608, 0.50 #1092, 0.46 #729), 03k9fj (0.41 #1101, 0.40 #617, 0.36 #375), 05p553 (0.37 #7630, 0.34 #5936, 0.33 #7146), 02n4kr (0.33 #130, 0.26 #735, 0.18 #372), 04xvlr (0.33 #2, 0.25 #486, 0.21 #970), 06l3bl (0.33 #39, 0.17 #523, 0.14 #1007), 02p0szs (0.33 #29, 0.17 #513, 0.09 #271), 04xvh5 (0.33 #35, 0.16 #1003, 0.13 #882), 03mqtr (0.33 #30, 0.12 #7869, 0.08 #514) >> Best rule #2179 for best value: >> intensional similarity = 4 >> extensional distance = 292 >> proper extension: 02d413; 02vp1f_; 0ds3t5x; 09q5w2; 0gjk1d; 09gq0x5; 0bm2g; 0cw3yd; 01jrbb; 0ds2n; ... >> query: (?x5870, 07s9rl0) <- film_release_region(?x5870, ?x94), country(?x5870, ?x512), honored_for(?x9667, ?x5870), ?x94 = 09c7w0 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #608 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 02sg5v; 0g5pv3; 0340hj; 01kf3_9; 01kf4tt; 0d1qmz; 031786; 0g5ptf; *> query: (?x5870, 02kdv5l) <- film_production_design_by(?x5870, ?x2507), language(?x5870, ?x254), prequel(?x5870, ?x11362), genre(?x5870, ?x604) *> conf = 0.52 ranks of expected_values: 2 EVAL 01kf5lf genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 74.000 0.653 http://example.org/film/film/genre #8375-0ftxw PRED entity: 0ftxw PRED relation: locations! PRED expected values: 0b_6qj => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 113): 0bzrxn (0.30 #549, 0.18 #10056, 0.16 #3277), 0b_6jz (0.23 #3257, 0.20 #529, 0.18 #10056), 0b_6s7 (0.22 #1056, 0.18 #10056, 0.17 #2048), 0b_6lb (0.20 #572, 0.19 #2308, 0.18 #10056), 0bzrsh (0.20 #574, 0.18 #10056, 0.18 #3302), 0b_6q5 (0.20 #589, 0.18 #10056, 0.15 #3814), 0b_75k (0.19 #3271, 0.18 #10056, 0.17 #1039), 0b_6xf (0.19 #3327, 0.18 #10056, 0.16 #2583), 0b_6qj (0.18 #1802, 0.18 #10056, 0.17 #1058), 0b_6v_ (0.18 #10056, 0.16 #3287, 0.12 #3784) >> Best rule #549 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0lphb; >> query: (?x2879, 0bzrxn) <- place_of_birth(?x2580, ?x2879), locations(?x12162, ?x2879), ?x12162 = 0b_6_l >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #1802 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0dg3n1; *> query: (?x2879, 0b_6qj) <- location(?x3378, ?x2879), locations(?x3797, ?x2879), role(?x3378, ?x2206) *> conf = 0.18 ranks of expected_values: 9 EVAL 0ftxw locations! 0b_6qj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 166.000 166.000 0.300 http://example.org/time/event/locations #8374-057bc6m PRED entity: 057bc6m PRED relation: film_sets_designed PRED expected values: 01wb95 03bxp5 => 107 concepts (101 used for prediction) PRED predicted values (max 10 best out of 91): 0h0wd9 (0.15 #80, 0.10 #167, 0.09 #340), 0kvb6p (0.15 #72, 0.10 #159, 0.09 #332), 02q_4ph (0.15 #36, 0.10 #123, 0.09 #296), 0bcndz (0.15 #8, 0.10 #95, 0.09 #268), 0p7qm (0.15 #21, 0.09 #281, 0.09 #195), 025scjj (0.15 #79, 0.09 #339, 0.09 #253), 0gcrg (0.15 #31, 0.09 #291, 0.09 #205), 02r_pp (0.09 #303, 0.09 #217, 0.08 #43), 0bj25 (0.09 #334, 0.08 #74, 0.07 #174), 0bkq7 (0.09 #331, 0.08 #71, 0.07 #174) >> Best rule #80 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 076lxv; 07h1tr; 076psv; 05b49tt; 058vfp4; 053vcrp; 0fd6qb; 0c0tzp; 0579tg2; 03qhyn8; ... >> query: (?x8401, 0h0wd9) <- nationality(?x8401, ?x94), award_winner(?x8401, ?x2716), film_sets_designed(?x8401, ?x951) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 057bc6m film_sets_designed 03bxp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 101.000 0.154 http://example.org/film/film_set_designer/film_sets_designed EVAL 057bc6m film_sets_designed 01wb95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 101.000 0.154 http://example.org/film/film_set_designer/film_sets_designed #8373-01vsn38 PRED entity: 01vsn38 PRED relation: profession PRED expected values: 0nbcg => 87 concepts (82 used for prediction) PRED predicted values (max 10 best out of 54): 0nbcg (0.49 #3304, 0.47 #1877, 0.46 #3161), 02jknp (0.47 #1573, 0.38 #2287, 0.24 #4857), 016z4k (0.45 #1855, 0.42 #3282, 0.40 #3139), 01c72t (0.43 #444, 0.38 #1015, 0.29 #2440), 039v1 (0.26 #2453, 0.24 #1882, 0.24 #600), 0n1h (0.25 #722, 0.21 #865, 0.19 #3289), 015cjr (0.21 #185, 0.04 #1326, 0.03 #3037), 01c8w0 (0.20 #434, 0.11 #1005, 0.07 #3856), 02krf9 (0.20 #2301, 0.16 #162, 0.13 #1587), 0cbd2 (0.19 #2286, 0.16 #147, 0.14 #8834) >> Best rule #3304 for best value: >> intensional similarity = 3 >> extensional distance = 629 >> proper extension: 032t2z; 06y9c2; 025xt8y; 01p9hgt; 01r9fv; 0zjpz; 05d8vw; 05qw5; 086qd; 09prnq; ... >> query: (?x11233, 0nbcg) <- profession(?x11233, ?x131), artists(?x1000, ?x11233), artist(?x2299, ?x11233) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vsn38 profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 82.000 0.490 http://example.org/people/person/profession #8372-042v_gx PRED entity: 042v_gx PRED relation: group PRED expected values: 0kr_t 0pqp3 => 88 concepts (58 used for prediction) PRED predicted values (max 10 best out of 718): 02dw1_ (0.78 #4518, 0.77 #5903, 0.75 #7808), 0134wr (0.75 #4211, 0.71 #3347, 0.62 #4040), 0khth (0.71 #3294, 0.60 #4674, 0.54 #4976), 048xh (0.67 #2643, 0.50 #4196, 0.50 #1273), 05563d (0.62 #3976, 0.60 #4663, 0.59 #9511), 03qkcn9 (0.62 #4107, 0.60 #4794, 0.57 #3414), 0gr69 (0.62 #4193, 0.60 #2299, 0.57 #3329), 0134tg (0.62 #3995, 0.57 #3302, 0.50 #4682), 017_hq (0.62 #4261, 0.57 #3397, 0.50 #4777), 03c3yf (0.62 #3855, 0.57 #3334, 0.50 #4714) >> Best rule #4518 for best value: >> intensional similarity = 13 >> extensional distance = 7 >> proper extension: 0mkg; 07_l6; >> query: (?x432, 02dw1_) <- role(?x432, ?x5676), role(?x432, ?x2764), role(?x432, ?x2158), ?x2158 = 01dnws, role(?x74, ?x432), role(?x1291, ?x432), role(?x211, ?x432), ?x2764 = 01s0ps, family(?x432, ?x7256), role(?x75, ?x5676), group(?x432, ?x442), role(?x432, ?x1332), profession(?x1291, ?x131) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1677 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 02hnl; *> query: (?x432, 0pqp3) <- role(?x432, ?x7772), role(?x432, ?x1886), role(?x432, ?x922), role(?x432, ?x228), ?x922 = 050rj, ?x228 = 0l14qv, role(?x1291, ?x432), role(?x432, ?x75), ?x1886 = 02k84w, role(?x8311, ?x432), profession(?x8311, ?x131), performance_role(?x314, ?x432), ?x7772 = 0j862 *> conf = 0.50 ranks of expected_values: 81, 97 EVAL 042v_gx group 0pqp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 88.000 58.000 0.778 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 042v_gx group 0kr_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 88.000 58.000 0.778 http://example.org/music/performance_role/regular_performances./music/group_membership/group #8371-0kj34 PRED entity: 0kj34 PRED relation: profession PRED expected values: 016z4k 01c72t => 184 concepts (167 used for prediction) PRED predicted values (max 10 best out of 99): 02hrh1q (0.95 #14306, 0.92 #10903, 0.91 #14749), 01d_h8 (0.77 #20347, 0.60 #300, 0.41 #21820), 0nbcg (0.68 #2383, 0.61 #4735, 0.60 #3853), 016z4k (0.65 #3385, 0.62 #2356, 0.61 #2209), 0dxtg (0.61 #21827, 0.55 #23740, 0.38 #20354), 0dz3r (0.50 #2207, 0.49 #6177, 0.49 #2354), 05z96 (0.50 #923, 0.22 #21225, 0.21 #12517), 02jknp (0.42 #21822, 0.38 #23735, 0.37 #20349), 0cbd2 (0.40 #889, 0.33 #7, 0.31 #13111), 01c72t (0.38 #4874, 0.38 #1493, 0.33 #23) >> Best rule #14306 for best value: >> intensional similarity = 5 >> extensional distance = 582 >> proper extension: 05m63c; 033hqf; 031zkw; 01csrl; 02mjf2; 0210hf; 023v4_; 0m66w; 0bdt8; 023s8; ... >> query: (?x9087, 02hrh1q) <- participant(?x4537, ?x9087), profession(?x9087, ?x955), specialization_of(?x1776, ?x955), profession(?x9262, ?x955), ?x9262 = 04n2vgk >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #3385 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 016jfw; *> query: (?x9087, 016z4k) <- gender(?x9087, ?x231), artists(?x5300, ?x9087), ?x5300 = 02k_kn, category(?x9087, ?x134) *> conf = 0.65 ranks of expected_values: 4, 10 EVAL 0kj34 profession 01c72t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 184.000 167.000 0.949 http://example.org/people/person/profession EVAL 0kj34 profession 016z4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 184.000 167.000 0.949 http://example.org/people/person/profession #8370-03ckfl9 PRED entity: 03ckfl9 PRED relation: parent_genre PRED expected values: 0283d => 53 concepts (33 used for prediction) PRED predicted values (max 10 best out of 279): 06by7 (0.50 #840, 0.40 #1004, 0.36 #1168), 05r6t (0.45 #1207, 0.40 #1043, 0.38 #879), 09jw2 (0.38 #927, 0.36 #1255, 0.33 #762), 011j5x (0.38 #846, 0.33 #351, 0.30 #1010), 02x8m (0.33 #673, 0.25 #838, 0.20 #1002), 06cqb (0.33 #660, 0.25 #825, 0.20 #989), 03lty (0.33 #348, 0.17 #2335, 0.17 #678), 05w3f (0.33 #26, 0.14 #4967, 0.08 #3338), 0dl5d (0.33 #15, 0.11 #4969, 0.11 #4468), 05bt6j (0.25 #854, 0.20 #1018, 0.18 #1182) >> Best rule #840 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 03_d0; 0xjl2; 0y3_8; >> query: (?x10290, 06by7) <- artists(?x10290, ?x9631), artists(?x10290, ?x8058), artists(?x10290, ?x1656), ?x8058 = 014pg1, instrumentalists(?x227, ?x1656), music(?x3453, ?x1656), award(?x9631, ?x884), category(?x1656, ?x134) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2222 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 101 *> proper extension: 0kz10; 01hydr; *> query: (?x10290, 0283d) <- artists(?x10290, ?x7410), artists(?x10290, ?x1694), artists(?x10290, ?x1656), role(?x1656, ?x227), music(?x3453, ?x1656), student(?x817, ?x7410), artist(?x1693, ?x1694), profession(?x1656, ?x131) *> conf = 0.03 ranks of expected_values: 133 EVAL 03ckfl9 parent_genre 0283d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 53.000 33.000 0.500 http://example.org/music/genre/parent_genre #8369-03hj5vf PRED entity: 03hj5vf PRED relation: award! PRED expected values: 02kxbwx 021bk 02kxbx3 => 40 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2993): 02f93t (0.57 #2705, 0.11 #6085, 0.11 #9465), 01t07j (0.57 #479, 0.10 #7239, 0.10 #3859), 02kxbx3 (0.50 #987, 0.18 #7747, 0.16 #11128), 0693l (0.50 #855, 0.18 #7615, 0.16 #10996), 01ts_3 (0.50 #2056, 0.12 #8816, 0.11 #12197), 02r6c_ (0.50 #2545, 0.09 #9305, 0.08 #12686), 02kxbwx (0.43 #178, 0.16 #6938, 0.15 #13701), 0c12h (0.43 #1823, 0.13 #8583, 0.12 #5203), 04sry (0.43 #2126, 0.13 #8886, 0.12 #12267), 01_f_5 (0.43 #1837, 0.13 #8597, 0.12 #11978) >> Best rule #2705 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 02grdc; 03nqnk3; >> query: (?x3190, 02f93t) <- award(?x4744, ?x3190), award(?x986, ?x3190), ?x986 = 081lh, profession(?x4744, ?x319), nominated_for(?x4744, ?x4745), nationality(?x4744, ?x94) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #987 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 02grdc; 03nqnk3; *> query: (?x3190, 02kxbx3) <- award(?x4744, ?x3190), award(?x986, ?x3190), ?x986 = 081lh, profession(?x4744, ?x319), nominated_for(?x4744, ?x4745), nationality(?x4744, ?x94) *> conf = 0.50 ranks of expected_values: 3, 7, 313 EVAL 03hj5vf award! 02kxbx3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 40.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hj5vf award! 021bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 40.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hj5vf award! 02kxbwx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 40.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8368-0ncy4 PRED entity: 0ncy4 PRED relation: place_of_birth! PRED expected values: 02cgb8 => 94 concepts (19 used for prediction) PRED predicted values (max 10 best out of 263): 03jg5t (0.07 #1597, 0.06 #4208, 0.06 #6819), 01pcvn (0.07 #1171, 0.06 #3782, 0.06 #6393), 0bd2n4 (0.07 #715, 0.06 #3326, 0.06 #5937), 04bsx1 (0.07 #2109, 0.05 #9944, 0.04 #17780), 023jq1 (0.07 #2038, 0.05 #9873, 0.04 #17709), 013knm (0.07 #710, 0.05 #8545, 0.04 #16381), 0fvt2 (0.07 #2278, 0.04 #15338, 0.04 #17949), 02465 (0.07 #2273, 0.04 #15333, 0.04 #17944), 037q1z (0.07 #1984, 0.04 #15044, 0.04 #17655), 07rhpg (0.07 #1649, 0.04 #14709, 0.04 #17320) >> Best rule #1597 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 02jx1; >> query: (?x14413, 03jg5t) <- contains(?x512, ?x14413), ?x512 = 07ssc, place_of_birth(?x11208, ?x14413), student(?x4904, ?x11208), time_zones(?x14413, ?x5327) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ncy4 place_of_birth! 02cgb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 19.000 0.067 http://example.org/people/person/place_of_birth #8367-05nyqk PRED entity: 05nyqk PRED relation: currency PRED expected values: 09nqf => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.83 #36, 0.83 #29, 0.81 #50), 01nv4h (0.12 #9, 0.02 #247, 0.02 #226), 02l6h (0.02 #18, 0.01 #228, 0.01 #312) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 251 >> proper extension: 076xkdz; >> query: (?x9199, 09nqf) <- film_release_distribution_medium(?x9199, ?x81), genre(?x9199, ?x225), ?x225 = 02kdv5l, nominated_for(?x1312, ?x9199) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05nyqk currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.834 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8366-0d1y7 PRED entity: 0d1y7 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 191 concepts (132 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.93 #677, 0.89 #1165, 0.89 #714), 059_c (0.43 #992, 0.29 #253, 0.29 #761), 01n7q (0.18 #91, 0.18 #773, 0.12 #1176), 0k_s5 (0.18 #91, 0.12 #1176, 0.11 #182), 0vmt (0.18 #91, 0.12 #1176, 0.11 #182), 0kvt9 (0.18 #773), 03rjj (0.11 #105, 0.06 #230, 0.05 #487), 07srw (0.10 #1511, 0.09 #1544), 0d1y7 (0.09 #1430, 0.08 #1373, 0.07 #1104), 02jx1 (0.09 #1328, 0.06 #636, 0.06 #672) >> Best rule #677 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 0mmty; 0kv7k; >> query: (?x12569, 09c7w0) <- time_zones(?x12569, ?x2950), ?x2950 = 02lcqs, currency(?x12569, ?x170), ?x170 = 09nqf, contains(?x1138, ?x12569) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d1y7 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 132.000 0.927 http://example.org/location/country/second_level_divisions #8365-01nmgc PRED entity: 01nmgc PRED relation: organization! PRED expected values: 07xl34 => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 8): 060c4 (0.66 #119, 0.64 #171, 0.63 #223), 07xl34 (0.43 #89, 0.42 #24, 0.36 #50), 0dq_5 (0.37 #165, 0.30 #152, 0.20 #204), 0hm4q (0.15 #112, 0.07 #190, 0.05 #333), 05c0jwl (0.09 #109, 0.02 #330, 0.02 #252), 05k17c (0.09 #241, 0.08 #293, 0.07 #267), 08jcfy (0.03 #116, 0.01 #337, 0.01 #194), 04n1q6 (0.02 #84) >> Best rule #119 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 01jt2w; >> query: (?x9440, 060c4) <- institution(?x865, ?x9440), institution(?x620, ?x9440), ?x865 = 02h4rq6, ?x620 = 07s6fsf >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #89 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 017j69; 0373qt; *> query: (?x9440, 07xl34) <- institution(?x1368, ?x9440), list(?x9440, ?x2197), ?x1368 = 014mlp *> conf = 0.43 ranks of expected_values: 2 EVAL 01nmgc organization! 07xl34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 41.000 0.660 http://example.org/organization/role/leaders./organization/leadership/organization #8364-02897w PRED entity: 02897w PRED relation: colors PRED expected values: 01l849 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.42 #222, 0.40 #682, 0.39 #722), 01g5v (0.32 #284, 0.32 #824, 0.30 #804), 038hg (0.29 #13, 0.09 #733, 0.09 #1133), 01l849 (0.26 #1141, 0.26 #681, 0.26 #1121), 019sc (0.19 #408, 0.18 #388, 0.18 #28), 06fvc (0.18 #403, 0.18 #823, 0.18 #23), 04mkbj (0.16 #231, 0.16 #51, 0.12 #391), 036k5h (0.16 #226, 0.11 #366, 0.11 #126), 0jc_p (0.11 #25, 0.09 #45, 0.08 #265), 09ggk (0.11 #36, 0.07 #1842, 0.07 #496) >> Best rule #222 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 01xk7r; >> query: (?x4582, 083jv) <- school_type(?x4582, ?x1044), organization(?x346, ?x4582), colors(?x4582, ?x9464), ?x1044 = 05pcjw >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1141 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 342 *> proper extension: 021l5s; 04bfg; 02qwgk; 0283sdr; 02mw6c; 02rk23; 02p72j; 03p2m1; *> query: (?x4582, 01l849) <- school_type(?x4582, ?x1044), organization(?x346, ?x4582), colors(?x4582, ?x9464) *> conf = 0.26 ranks of expected_values: 4 EVAL 02897w colors 01l849 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 136.000 136.000 0.422 http://example.org/education/educational_institution/colors #8363-01b195 PRED entity: 01b195 PRED relation: music PRED expected values: 01l1rw => 82 concepts (70 used for prediction) PRED predicted values (max 10 best out of 73): 012201 (0.22 #151, 0.03 #784, 0.01 #994), 05yzt_ (0.22 #153, 0.01 #996), 02bh9 (0.20 #263, 0.07 #1105, 0.06 #1740), 02vyw (0.11 #211, 0.11 #57, 0.07 #4868), 023361 (0.11 #150, 0.10 #362, 0.05 #783), 01cbt3 (0.11 #91, 0.06 #513, 0.03 #1568), 07hgkd (0.11 #82), 0fpjyd (0.10 #337), 01pbs9w (0.10 #317), 0146pg (0.09 #1275, 0.08 #1487, 0.07 #1699) >> Best rule #151 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0pd4f; >> query: (?x2262, 012201) <- film_release_region(?x2262, ?x94), written_by(?x2262, ?x3662), film(?x71, ?x2262), ?x3662 = 02vyw >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #2429 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 262 *> proper extension: 03nqnnk; *> query: (?x2262, 01l1rw) <- film_release_region(?x2262, ?x94), film_release_distribution_medium(?x2262, ?x81), film(?x71, ?x2262), films(?x5179, ?x2262) *> conf = 0.02 ranks of expected_values: 53 EVAL 01b195 music 01l1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 82.000 70.000 0.222 http://example.org/film/film/music #8362-01wgfp6 PRED entity: 01wgfp6 PRED relation: award PRED expected values: 01by1l => 156 concepts (138 used for prediction) PRED predicted values (max 10 best out of 275): 02f72_ (0.56 #615, 0.36 #1794, 0.29 #1401), 02f73p (0.50 #577, 0.43 #1756, 0.38 #1363), 01by1l (0.46 #13080, 0.43 #2469, 0.42 #4434), 03qbh5 (0.44 #592, 0.42 #1378, 0.38 #985), 02f5qb (0.44 #547, 0.39 #2119, 0.29 #1333), 01d38g (0.43 #813, 0.21 #1992, 0.21 #1206), 054ks3 (0.42 #1320, 0.39 #1713, 0.33 #534), 0c4z8 (0.42 #1249, 0.36 #1642, 0.33 #13039), 02f716 (0.39 #567, 0.33 #2139, 0.32 #1746), 02v1m7 (0.39 #505, 0.25 #1684, 0.25 #1291) >> Best rule #615 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01v_pj6; 09hnb; 0b_j2; 0k60; 01wqpnm; >> query: (?x5901, 02f72_) <- award(?x5901, ?x2634), place_of_birth(?x5901, ?x2552), ?x2634 = 02f72n, artist(?x2149, ?x5901) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #13080 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 184 *> proper extension: 06w2sn5; 02zmh5; 01hw6wq; 02qlg7s; 01w724; 016pns; 01k98nm; 01wn718; 01m1dzc; 02dbp7; ... *> query: (?x5901, 01by1l) <- award(?x5901, ?x2634), place_of_birth(?x5901, ?x2552), award(?x827, ?x2634), ?x827 = 02l840 *> conf = 0.46 ranks of expected_values: 3 EVAL 01wgfp6 award 01by1l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 156.000 138.000 0.556 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8361-03nt7j PRED entity: 03nt7j PRED relation: school PRED expected values: 01jssp 01j_9c 0hd7j 0160nk => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 713): 04gd8j (0.61 #1837, 0.45 #911, 0.45 #1736), 012vwb (0.60 #1047, 0.52 #101, 0.50 #1359), 0g8rj (0.52 #607, 0.52 #101, 0.48 #1428), 02y9bj (0.52 #607, 0.52 #101, 0.48 #1428), 01ptt7 (0.52 #607, 0.52 #101, 0.35 #1533), 07wlf (0.52 #607, 0.50 #630, 0.35 #1533), 05x_5 (0.52 #607, 0.48 #1428, 0.43 #1498), 01n6r0 (0.52 #607, 0.48 #1428, 0.35 #1533), 01jssp (0.52 #607, 0.48 #1428, 0.35 #1533), 01pq4w (0.52 #101, 0.50 #536, 0.48 #1428) >> Best rule #1837 for best value: >> intensional similarity = 39 >> extensional distance = 15 >> proper extension: 038981; >> query: (?x3089, ?x9865) <- school(?x3089, ?x6814), school(?x3089, ?x4599), school(?x3089, ?x1011), school(?x3089, ?x735), school(?x3089, ?x388), draft(?x7643, ?x3089), colors(?x388, ?x3364), organization(?x346, ?x388), citytown(?x388, ?x6453), major_field_of_study(?x1011, ?x3213), major_field_of_study(?x1011, ?x2601), major_field_of_study(?x1011, ?x1154), team(?x180, ?x7643), school(?x580, ?x6814), ?x3213 = 0g4gr, student(?x735, ?x10139), student(?x735, ?x5351), fraternities_and_sororities(?x388, ?x3697), profession(?x10139, ?x1032), school(?x11168, ?x1011), contains(?x94, ?x735), ?x1154 = 02lp1, citytown(?x1011, ?x3269), participant(?x7046, ?x10139), major_field_of_study(?x6814, ?x1527), institution(?x620, ?x388), film(?x10139, ?x1868), major_field_of_study(?x388, ?x742), sport(?x11168, ?x4833), student(?x9865, ?x10139), award(?x5351, ?x198), major_field_of_study(?x4599, ?x3489), draft(?x11168, ?x4979), student(?x1011, ?x400), organization(?x4599, ?x5487), award_nominee(?x1384, ?x10139), major_field_of_study(?x6117, ?x2601), ?x6117 = 02m4yg, contains(?x4776, ?x6814) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #607 for first EXPECTED value: *> intensional similarity = 37 *> extensional distance = 2 *> proper extension: 0f4vx0; *> query: (?x3089, ?x2760) <- school(?x3089, ?x5621), school(?x3089, ?x4904), school(?x3089, ?x735), school(?x3089, ?x388), draft(?x4170, ?x3089), draft(?x3114, ?x3089), draft(?x1115, ?x3089), ?x388 = 05krk, team(?x180, ?x1115), institution(?x865, ?x5621), school(?x1115, ?x2760), school(?x1115, ?x331), colors(?x1115, ?x3189), contains(?x94, ?x5621), major_field_of_study(?x5621, ?x2981), school(?x6462, ?x5621), category(?x3114, ?x134), school(?x4170, ?x546), ?x94 = 09c7w0, ?x331 = 01jssp, ?x2981 = 02j62, contains(?x2623, ?x4904), ?x735 = 065y4w7, institution(?x620, ?x4904), student(?x4904, ?x1683), colors(?x7725, ?x3189), colors(?x12795, ?x3189), colors(?x12742, ?x3189), sport(?x4170, ?x1083), ?x6462 = 09l0x9, ?x7725 = 07l8x, currency(?x4904, ?x170), ?x12742 = 032r4n, fraternities_and_sororities(?x4904, ?x3697), student(?x5621, ?x525), ?x12795 = 03np_7, school(?x260, ?x5621) *> conf = 0.52 ranks of expected_values: 9, 26, 46, 70 EVAL 03nt7j school 0160nk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 20.000 20.000 0.610 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 03nt7j school 0hd7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 20.000 20.000 0.610 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 03nt7j school 01j_9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 20.000 20.000 0.610 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 03nt7j school 01jssp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 20.000 20.000 0.610 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #8360-03tcnt PRED entity: 03tcnt PRED relation: ceremony PRED expected values: 01mhwk => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 126): 01mhwk (0.78 #1923, 0.77 #1797, 0.76 #2049), 01xqqp (0.73 #1847, 0.72 #1973, 0.72 #2099), 0jzphpx (0.68 #1921, 0.67 #1795, 0.67 #2047), 05c1t6z (0.17 #2783, 0.15 #3539, 0.12 #4043), 02q690_ (0.16 #2828, 0.14 #3584, 0.11 #4088), 0gvstc3 (0.16 #2798, 0.13 #3554, 0.10 #4058), 0n8_m93 (0.16 #2373, 0.13 #2877, 0.12 #3633), 0bzm81 (0.16 #2284, 0.13 #2788, 0.11 #3544), 03nnm4t (0.15 #2837, 0.13 #3593, 0.10 #4223), 02yxh9 (0.15 #2356, 0.12 #2860, 0.11 #3616) >> Best rule #1923 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 02581q; 026mg3; 02grdc; 02g8mp; 01c9f2; 02581c; 026mfs; 01dpdh; 02hgm4; 01dk00; ... >> query: (?x3103, 01mhwk) <- award(?x3403, ?x3103), ceremony(?x3103, ?x2054), ?x2054 = 0gpjbt, award_winner(?x1089, ?x3403) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03tcnt ceremony 01mhwk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.775 http://example.org/award/award_category/winners./award/award_honor/ceremony #8359-0c40vxk PRED entity: 0c40vxk PRED relation: genre PRED expected values: 02kdv5l => 102 concepts (99 used for prediction) PRED predicted values (max 10 best out of 134): 07s9rl0 (0.79 #9839, 0.73 #3882, 0.71 #10326), 02kdv5l (0.72 #1092, 0.56 #850, 0.52 #7771), 02l7c8 (0.51 #7906, 0.40 #8881, 0.33 #3898), 05p553 (0.50 #9112, 0.48 #489, 0.40 #126), 03k9fj (0.48 #618, 0.44 #860, 0.43 #376), 01hmnh (0.43 #382, 0.35 #7056, 0.35 #624), 0lsxr (0.40 #736, 0.40 #131, 0.33 #5954), 06n90 (0.35 #6686, 0.35 #619, 0.33 #861), 082gq (0.29 #395, 0.14 #1121, 0.13 #4276), 04pbhw (0.29 #420, 0.12 #4059, 0.07 #6729) >> Best rule #9839 for best value: >> intensional similarity = 7 >> extensional distance = 1267 >> proper extension: 05jyb2; 0hr41p6; >> query: (?x633, 07s9rl0) <- genre(?x633, ?x812), titles(?x812, ?x80), genre(?x10173, ?x812), genre(?x776, ?x812), genre(?x2009, ?x812), ?x10173 = 01kqq7, ?x776 = 0p_sc >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1092 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 34 *> proper extension: 025twgt; *> query: (?x633, 02kdv5l) <- genre(?x633, ?x5104), genre(?x633, ?x812), film(?x5910, ?x633), film(?x3580, ?x633), ?x812 = 01jfsb, ?x5104 = 0bkbm, award_nominee(?x5910, ?x3494), profession(?x3580, ?x987) *> conf = 0.72 ranks of expected_values: 2 EVAL 0c40vxk genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 99.000 0.793 http://example.org/film/film/genre #8358-02sp_v PRED entity: 02sp_v PRED relation: award! PRED expected values: 01wv9xn 01s21dg 01k_r5b 01wd9lv 016l09 => 48 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2781): 0fhxv (0.85 #3332, 0.79 #69956, 0.78 #56632), 09889g (0.85 #3332, 0.79 #69956, 0.78 #49968), 0gcs9 (0.85 #3332, 0.79 #69956, 0.78 #49968), 0dw4g (0.85 #3332, 0.79 #69956, 0.78 #49968), 04sry (0.85 #3332, 0.78 #19986, 0.76 #39974), 0478__m (0.67 #11304, 0.56 #21296, 0.52 #24627), 02z4b_8 (0.67 #12035, 0.44 #22027, 0.41 #25358), 01vrt_c (0.67 #10277, 0.40 #20269, 0.37 #23600), 0136p1 (0.67 #10489, 0.38 #17150, 0.33 #3827), 01dw9z (0.67 #10702, 0.33 #4040, 0.17 #26648) >> Best rule #3332 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 01ck6h; >> query: (?x3045, ?x2963) <- award(?x4712, ?x3045), award(?x3997, ?x3045), ceremony(?x3045, ?x139), ?x4712 = 03f0fnk, award_winner(?x3045, ?x2963), award_nominee(?x3997, ?x2732) >> conf = 0.85 => this is the best rule for 5 predicted values *> Best rule #8503 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 01bgqh; 02nhxf; 02gdjb; 03qpp9; *> query: (?x3045, 01wd9lv) <- award(?x5543, ?x3045), award(?x4712, ?x3045), award(?x3997, ?x3045), ceremony(?x3045, ?x139), artists(?x1000, ?x4712), artist(?x4483, ?x3997), ?x5543 = 01kd57 *> conf = 0.50 ranks of expected_values: 16, 91, 103, 172, 308 EVAL 02sp_v award! 016l09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 48.000 27.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02sp_v award! 01wd9lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 48.000 27.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02sp_v award! 01k_r5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 27.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02sp_v award! 01s21dg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 48.000 27.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02sp_v award! 01wv9xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 48.000 27.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8357-013cr PRED entity: 013cr PRED relation: type_of_union PRED expected values: 04ztj => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.79 #13, 0.76 #77, 0.76 #65), 01g63y (0.22 #74, 0.21 #86, 0.21 #90) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 0pj9t; 01t94_1; 01kt17; 07y_r; 014v1q; >> query: (?x1401, 04ztj) <- award(?x1401, ?x3066), profession(?x1401, ?x987), ?x3066 = 0gqy2 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013cr type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.788 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8356-027qb1 PRED entity: 027qb1 PRED relation: company PRED expected values: 03z19 => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 027qb1 company 03z19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #8355-04zxrt PRED entity: 04zxrt PRED relation: colors PRED expected values: 083jv => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 16): 083jv (0.40 #1103, 0.40 #1163, 0.40 #1209), 06fvc (0.27 #383, 0.27 #963, 0.27 #203), 01g5v (0.25 #4, 0.25 #24, 0.25 #304), 019sc (0.18 #328, 0.17 #1169, 0.17 #1109), 038hg (0.13 #213, 0.12 #433, 0.12 #293), 088fh (0.11 #1228, 0.10 #107, 0.10 #1229), 01l849 (0.11 #1228, 0.10 #1229, 0.09 #1187), 0jc_p (0.11 #1228, 0.10 #1229, 0.09 #1187), 04mkbj (0.11 #1228, 0.10 #1229, 0.09 #1187), 036k5h (0.11 #1228, 0.10 #1229, 0.09 #1187) >> Best rule #1103 for best value: >> intensional similarity = 8 >> extensional distance = 414 >> proper extension: 01k6zy; >> query: (?x12544, 083jv) <- sport(?x12544, ?x471), athlete(?x471, ?x9106), sport(?x9473, ?x471), sport(?x5027, ?x471), team(?x60, ?x9473), nationality(?x9106, ?x1355), team(?x5471, ?x5027), team(?x9106, ?x202) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04zxrt colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.399 http://example.org/sports/sports_team/colors #8354-031k24 PRED entity: 031k24 PRED relation: award_nominee! PRED expected values: 02l4pj => 98 concepts (61 used for prediction) PRED predicted values (max 10 best out of 933): 016yvw (0.81 #55520, 0.81 #134183, 0.81 #134184), 016z2j (0.81 #55520, 0.81 #134183, 0.81 #134184), 02l4pj (0.81 #55520, 0.81 #134183, 0.81 #134184), 03jldb (0.81 #55520, 0.81 #134183, 0.81 #64775), 02p65p (0.40 #24, 0.17 #4650, 0.17 #141129), 0210hf (0.31 #50893, 0.26 #122614, 0.21 #27759), 031k24 (0.31 #50893, 0.26 #122614, 0.21 #80971), 01gq0b (0.31 #50893, 0.26 #122614, 0.21 #80971), 0509bl (0.31 #50893, 0.26 #122614, 0.21 #80971), 03jvmp (0.31 #50893, 0.05 #120300) >> Best rule #55520 for best value: >> intensional similarity = 3 >> extensional distance = 873 >> proper extension: 03b78r; >> query: (?x8066, ?x262) <- film(?x8066, ?x763), location(?x8066, ?x3125), award_nominee(?x8066, ?x262) >> conf = 0.81 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 031k24 award_nominee! 02l4pj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 61.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8353-01v9724 PRED entity: 01v9724 PRED relation: location PRED expected values: 04jpl => 126 concepts (105 used for prediction) PRED predicted values (max 10 best out of 258): 0b2lw (0.20 #1154, 0.03 #8393, 0.02 #10806), 04ykg (0.20 #872, 0.03 #8111, 0.02 #10524), 04jpl (0.18 #59550, 0.17 #45873, 0.16 #69210), 0bvqq (0.18 #3217, 0.10 #4022, 0.02 #21719), 01xd9 (0.15 #4911, 0.13 #5715, 0.10 #7323), 05l5n (0.14 #1709, 0.10 #4123, 0.09 #4927), 02m77 (0.14 #1939, 0.07 #4353, 0.05 #6765), 0125q1 (0.14 #1926, 0.03 #4340, 0.03 #5144), 0k33p (0.14 #2090, 0.02 #10938, 0.02 #45533), 05qtj (0.13 #7479, 0.11 #5871, 0.08 #18742) >> Best rule #1154 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 012cph; 01tz6vs; 02zjd; >> query: (?x5435, 0b2lw) <- influenced_by(?x8389, ?x5435), people(?x1158, ?x5435), story_by(?x2024, ?x5435), ?x8389 = 0683n >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #59550 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 394 *> proper extension: 0784v1; 07m69t; *> query: (?x5435, 04jpl) <- nationality(?x5435, ?x1310), ?x1310 = 02jx1 *> conf = 0.18 ranks of expected_values: 3 EVAL 01v9724 location 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 105.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location #8352-02s6sh PRED entity: 02s6sh PRED relation: artists! PRED expected values: 02k_kn => 125 concepts (83 used for prediction) PRED predicted values (max 10 best out of 254): 06by7 (0.78 #16669, 0.62 #7250, 0.62 #21371), 016clz (0.44 #16025, 0.39 #1260, 0.36 #3780), 06j6l (0.38 #13245, 0.32 #17009, 0.29 #676), 03_d0 (0.36 #12, 0.29 #2525, 0.27 #13208), 0155w (0.36 #109, 0.26 #1049, 0.24 #6708), 025sc50 (0.30 #5711, 0.26 #5398, 0.24 #678), 0xhtw (0.29 #644, 0.28 #2216, 0.26 #16664), 0gywn (0.29 #686, 0.27 #5719, 0.23 #13255), 0dl5d (0.29 #1589, 0.28 #1903, 0.22 #960), 02yv6b (0.27 #101, 0.24 #728, 0.21 #1356) >> Best rule #16669 for best value: >> intensional similarity = 5 >> extensional distance = 496 >> proper extension: 02mq_y; 015cxv; 0qmpd; >> query: (?x10989, 06by7) <- artists(?x3061, ?x10989), artists(?x3061, ?x8323), artists(?x3061, ?x2796), ?x2796 = 0gdh5, ?x8323 = 01r0t_j >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #5414 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 103 *> proper extension: 0134wr; *> query: (?x10989, 02k_kn) <- artists(?x3061, ?x10989), gender(?x10989, ?x231), ?x3061 = 05bt6j, category(?x10989, ?x134) *> conf = 0.27 ranks of expected_values: 12 EVAL 02s6sh artists! 02k_kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 125.000 83.000 0.781 http://example.org/music/genre/artists #8351-01l87db PRED entity: 01l87db PRED relation: profession PRED expected values: 02hrh1q => 169 concepts (103 used for prediction) PRED predicted values (max 10 best out of 92): 01d_h8 (0.79 #2049, 0.61 #3509, 0.44 #4532), 02hrh1q (0.77 #1474, 0.74 #4980, 0.71 #160), 0dz3r (0.71 #2, 0.60 #878, 0.51 #13179), 039v1 (0.47 #5877, 0.44 #4853, 0.42 #6463), 0dxtg (0.47 #1473, 0.38 #2933, 0.35 #7906), 02jknp (0.47 #1467, 0.36 #2051, 0.33 #3511), 0cbd2 (0.44 #7899, 0.33 #2926, 0.31 #5264), 047rgpy (0.43 #107, 0.25 #545, 0.25 #399), 01c72t (0.39 #1775, 0.31 #4842, 0.30 #11586), 03gjzk (0.34 #3519, 0.30 #1475, 0.28 #2059) >> Best rule #2049 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 02q_cc; 04wvhz; 05cv94; 0343h; 06pj8; 081nh; 0gg9_5q; 030_3z; 04pqqb; 03h304l; ... >> query: (?x5745, 01d_h8) <- organizations_founded(?x5745, ?x6202), type_of_union(?x5745, ?x566), profession(?x5745, ?x220), profession(?x2963, ?x220), ?x2963 = 0gcs9 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1474 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 0c1pj; 0gz5hs; 0b478; 07_m9_; 01v5h; 082xp; 0n839; 06c0j; *> query: (?x5745, 02hrh1q) <- organizations_founded(?x5745, ?x6202), type_of_union(?x5745, ?x566), profession(?x5745, ?x220), profession(?x4640, ?x220), ?x4640 = 018n6m *> conf = 0.77 ranks of expected_values: 2 EVAL 01l87db profession 02hrh1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 169.000 103.000 0.795 http://example.org/people/person/profession #8350-0k611 PRED entity: 0k611 PRED relation: award! PRED expected values: 0cf08 0llcx => 64 concepts (38 used for prediction) PRED predicted values (max 10 best out of 972): 0cq806 (0.62 #7640, 0.60 #4716, 0.50 #2765), 0c0zq (0.60 #5733, 0.50 #16455, 0.50 #7683), 042y1c (0.60 #4121, 0.50 #1196, 0.39 #15817), 0mcl0 (0.56 #8157, 0.50 #2308, 0.50 #1334), 0pv3x (0.56 #7899, 0.50 #1076, 0.46 #10825), 01gc7 (0.50 #1969, 0.50 #995, 0.40 #4894), 01dc0c (0.50 #3898, 0.50 #3714, 0.33 #6638), 0bm2g (0.50 #1172, 0.44 #7995, 0.40 #4873), 0cq7kw (0.50 #1395, 0.44 #8218, 0.40 #4320), 0pd4f (0.50 #1384, 0.44 #8207, 0.33 #15031) >> Best rule #7640 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0f4x7; >> query: (?x1703, 0cq806) <- nominated_for(?x1703, ?x4024), nominated_for(?x1703, ?x1077), award(?x763, ?x1703), award(?x1077, ?x451), ceremony(?x1703, ?x78), ?x4024 = 0n04r >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #36058 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 176 *> proper extension: 02x0gk1; *> query: (?x1703, ?x1077) <- nominated_for(?x1703, ?x1450), nominated_for(?x1703, ?x1077), award(?x763, ?x1703), award(?x1077, ?x451), film(?x820, ?x1450), film_release_region(?x1077, ?x94) *> conf = 0.26 ranks of expected_values: 118, 326 EVAL 0k611 award! 0llcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 38.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0k611 award! 0cf08 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 64.000 38.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8349-06qm3 PRED entity: 06qm3 PRED relation: titles PRED expected values: 0jz71 => 40 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1821): 03tps5 (0.46 #1551, 0.45 #1549, 0.44 #1550), 0gnjh (0.46 #1551, 0.45 #1549, 0.44 #1550), 05fgt1 (0.46 #1551, 0.45 #1549, 0.44 #1550), 0jqb8 (0.46 #1551, 0.45 #1549, 0.44 #1550), 0f2sx4 (0.46 #1551, 0.45 #1549, 0.40 #15504), 0glnm (0.45 #1549, 0.44 #1550, 0.40 #15504), 07bwr (0.45 #1549, 0.44 #1550, 0.40 #15504), 02nt3d (0.33 #4006, 0.33 #2457, 0.25 #7104), 011yxg (0.33 #1587, 0.33 #36, 0.25 #6234), 02_kd (0.33 #3586, 0.33 #2037, 0.25 #6684) >> Best rule #1551 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 04t36; >> query: (?x4150, ?x6603) <- genre(?x6603, ?x4150), genre(?x4920, ?x4150), titles(?x4150, ?x1743), ?x1743 = 0c8tkt, film(?x719, ?x6603), language(?x4920, ?x254), film_release_region(?x6603, ?x87) >> conf = 0.46 => this is the best rule for 5 predicted values *> Best rule #27897 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 68 *> proper extension: 06qd3; 0g5lhl7; 02hwhyv; *> query: (?x4150, ?x86) <- titles(?x4150, ?x6798), film(?x123, ?x6798), genre(?x6798, ?x258), genre(?x349, ?x258), genre(?x86, ?x258), ?x349 = 09xbpt, genre(?x419, ?x258) *> conf = 0.03 ranks of expected_values: 1474 EVAL 06qm3 titles 0jz71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 40.000 18.000 0.458 http://example.org/media_common/netflix_genre/titles #8348-0m31m PRED entity: 0m31m PRED relation: location_of_ceremony PRED expected values: 07kg3 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 17): 0cv3w (0.14 #35, 0.05 #273, 0.03 #392), 030qb3t (0.14 #19, 0.03 #376), 07fr_ (0.08 #192, 0.03 #430, 0.03 #549), 06y57 (0.05 #295, 0.03 #533, 0.02 #771), 027rn (0.05 #239, 0.03 #477, 0.02 #715), 059rby (0.03 #722, 0.03 #484, 0.03 #603), 0r0m6 (0.03 #407, 0.02 #764, 0.01 #1003), 0162v (0.03 #382, 0.01 #858), 01x73 (0.03 #499, 0.02 #737, 0.01 #976), 02h6_6p (0.02 #745, 0.01 #984, 0.01 #1104) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01kgv4; >> query: (?x2654, 0cv3w) <- award_nominee(?x989, ?x2654), ?x989 = 0151w_, religion(?x2654, ?x1985) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0m31m location_of_ceremony 07kg3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 91.000 0.143 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #8347-0l8sx PRED entity: 0l8sx PRED relation: company! PRED expected values: 09d6p2 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 42): 0dq3c (0.59 #2587, 0.56 #3623, 0.53 #3365), 09d6p2 (0.50 #833, 0.50 #489, 0.43 #575), 05_wyz (0.50 #1305, 0.48 #3636, 0.47 #3378), 02211by (0.38 #734, 0.25 #390, 0.22 #2588), 01kr6k (0.33 #110, 0.29 #583, 0.27 #3559), 014l7h (0.30 #971, 0.17 #1186, 0.15 #4789), 01rk91 (0.25 #1162, 0.25 #388, 0.17 #474), 02y6fz (0.25 #408, 0.17 #1569, 0.17 #1139), 04192r (0.25 #769, 0.17 #468, 0.15 #1242), 02k13d (0.20 #957, 0.20 #828, 0.17 #484) >> Best rule #2587 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0dmtp; 05njw; >> query: (?x1908, 0dq3c) <- company(?x1491, ?x1908), list(?x1908, ?x5997), ?x1491 = 0krdk, ?x5997 = 04k4rt >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #833 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 07zlqp; *> query: (?x1908, 09d6p2) <- company(?x346, ?x1908), industry(?x1908, ?x2271), ?x2271 = 03qh03g *> conf = 0.50 ranks of expected_values: 2 EVAL 0l8sx company! 09d6p2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 147.000 0.595 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #8346-07dvs PRED entity: 07dvs PRED relation: country! PRED expected values: 07gyv 071t0 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 52): 071t0 (0.73 #333, 0.72 #21, 0.69 #177), 07gyv (0.60 #7, 0.55 #163, 0.50 #215), 06f41 (0.55 #13, 0.55 #169, 0.53 #221), 01lb14 (0.53 #14, 0.52 #1002, 0.50 #482), 0194d (0.53 #45, 0.49 #201, 0.45 #253), 03hr1p (0.48 #1010, 0.47 #22, 0.47 #490), 06wrt (0.47 #15, 0.43 #223, 0.42 #1003), 07jbh (0.47 #240, 0.44 #1020, 0.44 #500), 064vjs (0.45 #30, 0.43 #186, 0.43 #238), 02y8z (0.43 #174, 0.42 #18, 0.41 #226) >> Best rule #333 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 07ytt; >> query: (?x4073, 071t0) <- contains(?x6304, ?x4073), ?x6304 = 02qkt, countries_spoken_in(?x13310, ?x4073) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07dvs country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.726 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07dvs country! 07gyv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.726 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #8345-02mpb PRED entity: 02mpb PRED relation: profession PRED expected values: 0kyk => 132 concepts (116 used for prediction) PRED predicted values (max 10 best out of 92): 02hrh1q (0.68 #13885, 0.66 #14630, 0.65 #14034), 0dxtg (0.63 #5380, 0.63 #5231, 0.61 #5828), 0kyk (0.56 #1222, 0.52 #2265, 0.50 #328), 01d_h8 (0.41 #5224, 0.41 #5373, 0.39 #5821), 0fj9f (0.39 #6115, 0.35 #11038, 0.31 #8502), 05z96 (0.39 #6115, 0.35 #11038, 0.31 #8502), 0d8qb (0.39 #6115, 0.35 #11038, 0.31 #8502), 05t4q (0.39 #6115, 0.35 #11038, 0.31 #8502), 03jgz (0.39 #6115, 0.31 #8502, 0.29 #11784), 02jknp (0.35 #5225, 0.34 #5374, 0.32 #5822) >> Best rule #13885 for best value: >> intensional similarity = 3 >> extensional distance = 1541 >> proper extension: 015882; 01l4g5; 01520h; >> query: (?x8210, 02hrh1q) <- student(?x13219, ?x8210), award(?x8210, ?x1375), profession(?x8210, ?x353) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1222 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 01963w; 05x8n; *> query: (?x8210, 0kyk) <- gender(?x8210, ?x231), award(?x8210, ?x1375), ?x1375 = 0262zm, nationality(?x8210, ?x94) *> conf = 0.56 ranks of expected_values: 3 EVAL 02mpb profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 132.000 116.000 0.677 http://example.org/people/person/profession #8344-02ynfr PRED entity: 02ynfr PRED relation: film_crew_role! PRED expected values: 03ckwzc 05pbl56 075wx7_ 0cz_ym 0661ql3 078sj4 03mh_tp 04tqtl 03cw411 07k8rt4 0fb7sd 02qpt1w 016dj8 034qbx 02nx2k 02dr9j 02fj8n 06x43v 02x3y41 02cbhg 07l450 02wwmhc 08g_jw 02qlp4 => 53 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1162): 03nx8mj (0.83 #19124, 0.71 #12533, 0.50 #18024), 0gj8nq2 (0.80 #17930, 0.71 #12439, 0.60 #16832), 0ct2tf5 (0.80 #18546, 0.70 #17448, 0.67 #19646), 0435vm (0.80 #17990, 0.70 #16892, 0.57 #13598), 076xkps (0.80 #18523, 0.67 #19623, 0.60 #17425), 09sh8k (0.80 #17582, 0.60 #16484, 0.58 #18682), 031t2d (0.80 #17739, 0.60 #16641, 0.57 #13347), 01kff7 (0.80 #17707, 0.60 #16609, 0.57 #13315), 0gh65c5 (0.80 #17958, 0.60 #16860, 0.57 #13566), 06r2h (0.80 #18526, 0.60 #17428, 0.57 #14134) >> Best rule #19124 for best value: >> intensional similarity = 10 >> extensional distance = 10 >> proper extension: 0263ycg; 0215hd; 089g0h; >> query: (?x3197, 03nx8mj) <- film_crew_role(?x8906, ?x3197), film_crew_role(?x8574, ?x3197), film_crew_role(?x5948, ?x3197), film_crew_role(?x1163, ?x3197), ?x8574 = 02mpyh, film_release_region(?x1163, ?x87), nominated_for(?x112, ?x1163), featured_film_locations(?x5948, ?x739), nominated_for(?x406, ?x8906), genre(?x8906, ?x225) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #19377 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 10 *> proper extension: 0263ycg; 0215hd; 089g0h; *> query: (?x3197, 016dj8) <- film_crew_role(?x8906, ?x3197), film_crew_role(?x8574, ?x3197), film_crew_role(?x5948, ?x3197), film_crew_role(?x1163, ?x3197), ?x8574 = 02mpyh, film_release_region(?x1163, ?x87), nominated_for(?x112, ?x1163), featured_film_locations(?x5948, ?x739), nominated_for(?x406, ?x8906), genre(?x8906, ?x225) *> conf = 0.75 ranks of expected_values: 13, 14, 15, 16, 23, 50, 84, 105, 144, 152, 163, 237, 240, 303, 320, 347, 369, 401, 403, 410, 510, 514, 680, 975 EVAL 02ynfr film_crew_role! 02qlp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 08g_jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 02wwmhc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 07l450 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 02cbhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 02x3y41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 06x43v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 02fj8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 02dr9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 02nx2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 034qbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 016dj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 02qpt1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 0fb7sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 07k8rt4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 03cw411 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 04tqtl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 03mh_tp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 078sj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 0cz_ym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 075wx7_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 05pbl56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02ynfr film_crew_role! 03ckwzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 53.000 31.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8343-02wh0 PRED entity: 02wh0 PRED relation: influenced_by PRED expected values: 015n8 => 173 concepts (101 used for prediction) PRED predicted values (max 10 best out of 421): 02wh0 (0.57 #3332, 0.33 #2065, 0.25 #14292), 043s3 (0.50 #2653, 0.38 #4337, 0.33 #9821), 07kb5 (0.50 #2132, 0.38 #4239, 0.28 #8879), 048cl (0.43 #3187, 0.33 #9935, 0.24 #11618), 099bk (0.43 #3069, 0.19 #9817, 0.17 #11500), 0ct9_ (0.43 #3232, 0.12 #11814, 0.11 #14345), 040db (0.43 #3016, 0.11 #14345, 0.11 #14344), 015n8 (0.38 #4196, 0.33 #10104, 0.33 #9260), 026lj (0.38 #4269, 0.33 #2162, 0.29 #9753), 04xjp (0.33 #482, 0.33 #57, 0.29 #3017) >> Best rule #3332 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 01d494; >> query: (?x11097, 02wh0) <- influenced_by(?x11097, ?x9600), influenced_by(?x11097, ?x9297), people(?x6260, ?x11097), student(?x7154, ?x9297), ?x9600 = 039n1 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4196 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 0hnlx; 0j3v; 0bk5r; 042q3; 06myp; *> query: (?x11097, 015n8) <- influenced_by(?x8768, ?x11097), influenced_by(?x6457, ?x11097), ?x8768 = 07dnx, influenced_by(?x1645, ?x6457), influenced_by(?x11097, ?x2240), religion(?x11097, ?x2694) *> conf = 0.38 ranks of expected_values: 8 EVAL 02wh0 influenced_by 015n8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 173.000 101.000 0.571 http://example.org/influence/influence_node/influenced_by #8342-0jqd3 PRED entity: 0jqd3 PRED relation: nominated_for! PRED expected values: 0gr51 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 204): 0gq9h (0.62 #2214, 0.61 #1497, 0.60 #2454), 0gs9p (0.52 #2456, 0.52 #2216, 0.43 #1499), 019f4v (0.51 #2445, 0.51 #2205, 0.46 #532), 04dn09n (0.46 #513, 0.30 #5295, 0.29 #3621), 040njc (0.43 #2158, 0.42 #2398, 0.38 #485), 0f4x7 (0.42 #2416, 0.41 #264, 0.35 #2176), 0gr4k (0.41 #265, 0.39 #2417, 0.36 #2177), 0gqy2 (0.41 #362, 0.34 #2514, 0.33 #2274), 0gqyl (0.38 #558, 0.37 #2471, 0.32 #2231), 02qyntr (0.38 #659, 0.27 #3767, 0.27 #5441) >> Best rule #2214 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 07bz5; >> query: (?x6309, 0gq9h) <- nominated_for(?x2465, ?x6309), list(?x6309, ?x3004), award(?x2465, ?x198) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #78 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0jvt9; *> query: (?x6309, 0gr51) <- production_companies(?x6309, ?x788), language(?x6309, ?x254), films(?x10849, ?x6309), ?x788 = 0g1rw *> conf = 0.33 ranks of expected_values: 14 EVAL 0jqd3 nominated_for! 0gr51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 105.000 105.000 0.620 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8341-01kkx2 PRED entity: 01kkx2 PRED relation: people! PRED expected values: 0c58k => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 42): 0qcr0 (0.25 #1, 0.20 #66, 0.12 #1041), 01psyx (0.25 #44, 0.20 #109, 0.03 #2124), 04p3w (0.20 #75, 0.14 #140, 0.09 #1050), 02knxx (0.20 #96, 0.05 #3281, 0.04 #2111), 01l2m3 (0.14 #145, 0.07 #210, 0.06 #470), 0x2fg (0.14 #167, 0.07 #232, 0.05 #362), 0gg4h (0.14 #165, 0.07 #230, 0.04 #620), 06z5s (0.13 #219, 0.07 #479, 0.05 #544), 0dq9p (0.13 #1251, 0.12 #731, 0.12 #1056), 02k6hp (0.10 #1076, 0.08 #1271, 0.08 #2116) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0byfz; >> query: (?x12037, 0qcr0) <- actor(?x12533, ?x12037), place_of_burial(?x12037, ?x3153), student(?x5907, ?x12037) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #354 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 01h4rj; *> query: (?x12037, 0c58k) <- place_of_death(?x12037, ?x4801), languages(?x12037, ?x90), award(?x12037, ?x2071) *> conf = 0.02 ranks of expected_values: 28 EVAL 01kkx2 people! 0c58k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 126.000 126.000 0.250 http://example.org/people/cause_of_death/people #8340-02_hj4 PRED entity: 02_hj4 PRED relation: nationality PRED expected values: 09c7w0 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 83): 09c7w0 (0.78 #101, 0.77 #401, 0.76 #501), 07ssc (0.30 #6919, 0.11 #1316, 0.09 #1216), 0d060g (0.30 #6919, 0.07 #407, 0.06 #307), 0345h (0.30 #6919, 0.03 #3408, 0.03 #1332), 0chghy (0.30 #6919, 0.03 #3408, 0.03 #2814), 0f8l9c (0.30 #6919, 0.03 #3408, 0.02 #1323), 03rjj (0.30 #6919, 0.03 #3408, 0.02 #6621), 059j2 (0.30 #6919, 0.03 #3408), 02jx1 (0.11 #1234, 0.11 #2637, 0.11 #633), 03rk0 (0.06 #6662, 0.06 #6562, 0.05 #7973) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0350l7; 03kcyd; >> query: (?x1672, 09c7w0) <- award_winner(?x1672, ?x2373), award_nominee(?x1538, ?x2373), ?x1538 = 02wcx8c >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_hj4 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.783 http://example.org/people/person/nationality #8339-02kzfw PRED entity: 02kzfw PRED relation: institution! PRED expected values: 0bkj86 => 148 concepts (92 used for prediction) PRED predicted values (max 10 best out of 20): 014mlp (0.83 #1390, 0.74 #1672, 0.71 #650), 02h4rq6 (0.80 #259, 0.76 #648, 0.75 #519), 019v9k (0.73 #50, 0.66 #701, 0.65 #265), 03bwzr4 (0.60 #269, 0.56 #33, 0.55 #705), 0bkj86 (0.60 #264, 0.47 #653, 0.46 #807), 013zdg (0.44 #27, 0.38 #263, 0.26 #652), 01rr_d (0.44 #36, 0.36 #57, 0.28 #122), 04zx3q1 (0.40 #108, 0.37 #129, 0.33 #22), 0bjrnt (0.30 #133, 0.29 #1779, 0.28 #112), 022h5x (0.29 #1779, 0.26 #452, 0.25 #275) >> Best rule #1390 for best value: >> intensional similarity = 5 >> extensional distance = 371 >> proper extension: 03gdf1; 06kknt; >> query: (?x6193, 014mlp) <- organization(?x5510, ?x6193), institution(?x1200, ?x6193), major_field_of_study(?x1200, ?x254), institution(?x1200, ?x11987), ?x11987 = 0159r9 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #264 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 027mdh; *> query: (?x6193, 0bkj86) <- major_field_of_study(?x6193, ?x7134), institution(?x620, ?x6193), ?x620 = 07s6fsf, category(?x6193, ?x134), ?x7134 = 02_7t *> conf = 0.60 ranks of expected_values: 5 EVAL 02kzfw institution! 0bkj86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 148.000 92.000 0.828 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #8338-0qdyf PRED entity: 0qdyf PRED relation: gender PRED expected values: 05zppz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #27, 0.86 #51, 0.84 #13), 02zsn (0.41 #38, 0.40 #42, 0.38 #30) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 0j3v; 015k7; >> query: (?x3166, 05zppz) <- influenced_by(?x10670, ?x3166), nationality(?x3166, ?x1310), artists(?x1000, ?x10670) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qdyf gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.888 http://example.org/people/person/gender #8337-0gq9h PRED entity: 0gq9h PRED relation: award! PRED expected values: 01qz5 0bkq7 => 52 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1311): 03hmt9b (0.50 #2302, 0.42 #10026, 0.42 #10993), 07s846j (0.50 #1343, 0.37 #10033, 0.25 #6171), 0hv4t (0.50 #1604, 0.23 #13522, 0.23 #17385), 05hjnw (0.38 #2399, 0.38 #1433, 0.33 #4330), 0p9tm (0.38 #1709, 0.33 #4606, 0.25 #2675), 0bx0l (0.38 #2133, 0.32 #9857, 0.23 #13522), 064lsn (0.38 #2519, 0.29 #7347, 0.23 #13522), 04j13sx (0.38 #1536, 0.23 #13522, 0.23 #17385), 0ptx_ (0.38 #1551, 0.23 #13522, 0.23 #17385), 0dr_4 (0.37 #9795, 0.29 #7864, 0.25 #5933) >> Best rule #2302 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 040njc; 0cjyzs; 0cc8l6d; >> query: (?x1307, 03hmt9b) <- nominated_for(?x1307, ?x161), award(?x7837, ?x1307), ceremony(?x1307, ?x1084), ?x7837 = 0g2lq >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13522 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 122 *> proper extension: 09tqxt; 03m73lj; 054knh; 02py_sj; *> query: (?x1307, ?x2370) <- nominated_for(?x1307, ?x2370), award(?x144, ?x1307), award_winner(?x2370, ?x1197), ceremony(?x1307, ?x1084) *> conf = 0.23 ranks of expected_values: 69, 318 EVAL 0gq9h award! 0bkq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 20.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0gq9h award! 01qz5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 52.000 20.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8336-057lbk PRED entity: 057lbk PRED relation: nominated_for! PRED expected values: 058s44 => 125 concepts (53 used for prediction) PRED predicted values (max 10 best out of 899): 02xnjd (0.55 #2339, 0.35 #121655, 0.34 #56146), 0bs1yy (0.40 #51468, 0.40 #49127, 0.38 #58487), 0dvmd (0.31 #3000, 0.02 #122316, 0.02 #14693), 046_v (0.27 #21050, 0.25 #51469, 0.25 #46787), 079vf (0.27 #21050, 0.25 #51469, 0.25 #46787), 0dvld (0.23 #3653, 0.02 #45761, 0.02 #15346), 01yf85 (0.22 #119315, 0.22 #112297, 0.20 #79546), 07myb2 (0.22 #119315, 0.22 #112297, 0.20 #79546), 02kxwk (0.18 #3290, 0.02 #14983, 0.01 #36040), 016tt2 (0.18 #46788, 0.14 #44447, 0.12 #79547) >> Best rule #2339 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0kvgtf; >> query: (?x4378, ?x7976) <- produced_by(?x4378, ?x7976), country(?x4378, ?x1264), nominated_for(?x1007, ?x4378), ?x1007 = 03c7tr1 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #109958 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 335 *> proper extension: 0cwrr; 01vrwfv; 03y3bp7; 08cx5g; 05gnf; 05fgr_; 03cf9ly; 03czz87; 03_b1g; *> query: (?x4378, ?x5788) <- category(?x4378, ?x134), ?x134 = 08mbj5d, nominated_for(?x9780, ?x4378), award_nominee(?x9780, ?x5788) *> conf = 0.09 ranks of expected_values: 20 EVAL 057lbk nominated_for! 058s44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 125.000 53.000 0.551 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #8335-01pr_j6 PRED entity: 01pr_j6 PRED relation: type_of_union PRED expected values: 04ztj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.79 #9, 0.74 #37, 0.72 #97), 01g63y (0.34 #361, 0.25 #390, 0.21 #34), 0jgjn (0.25 #390, 0.05 #20, 0.04 #24), 01bl8s (0.04 #35) >> Best rule #9 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 01l1b90; 086qd; 0gy6z9; 01vw20h; 043zg; 013w7j; 01d1st; 01mbwlb; >> query: (?x1073, 04ztj) <- origin(?x1073, ?x3411), profession(?x1073, ?x1041), people(?x5025, ?x1073), ?x1041 = 03gjzk, artists(?x671, ?x1073) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pr_j6 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.786 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8334-01l9p PRED entity: 01l9p PRED relation: film PRED expected values: 03m8y5 0992d9 057__d => 124 concepts (109 used for prediction) PRED predicted values (max 10 best out of 994): 0294mx (0.73 #28561, 0.72 #32132, 0.64 #80329), 083shs (0.73 #28561, 0.72 #32132, 0.64 #80329), 06nr2h (0.73 #28561, 0.72 #32132, 0.64 #80329), 0g5qs2k (0.58 #108891, 0.54 #71403, 0.36 #114248), 08r4x3 (0.09 #3723, 0.08 #1938, 0.08 #5508), 011wtv (0.08 #767, 0.04 #2552, 0.04 #4337), 02ryz24 (0.08 #464, 0.04 #2249, 0.04 #4034), 0gtt5fb (0.08 #959, 0.04 #2744, 0.04 #4529), 0340hj (0.08 #236, 0.04 #2021, 0.04 #3806), 02wgk1 (0.08 #754, 0.04 #2539, 0.04 #4324) >> Best rule #28561 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 032xhg; 01rr9f; 03zqc1; 0147dk; 01kwld; 018db8; 0mdqp; 03h_9lg; 034x61; 0pz7h; ... >> query: (?x1735, ?x167) <- vacationer(?x583, ?x1735), film(?x1735, ?x721), nominated_for(?x1735, ?x167) >> conf = 0.73 => this is the best rule for 3 predicted values *> Best rule #53956 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 169 *> proper extension: 0408np; 08swgx; 025n3p; 01v40wd; 02pk6x; 0436kgz; *> query: (?x1735, 03m8y5) <- gender(?x1735, ?x514), award_nominee(?x1735, ?x1733), participant(?x1735, ?x286) *> conf = 0.03 ranks of expected_values: 133, 626, 711 EVAL 01l9p film 057__d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 109.000 0.732 http://example.org/film/actor/film./film/performance/film EVAL 01l9p film 0992d9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 124.000 109.000 0.732 http://example.org/film/actor/film./film/performance/film EVAL 01l9p film 03m8y5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 124.000 109.000 0.732 http://example.org/film/actor/film./film/performance/film #8333-02l6h PRED entity: 02l6h PRED relation: currency! PRED expected values: 0gh6j94 => 8 concepts (3 used for prediction) PRED predicted values (max 10 best out of 1858): 03xf_m (0.76 #1310, 0.75 #2630, 0.71 #1308), 07024 (0.76 #1310, 0.75 #2630, 0.71 #1308), 0cc5qkt (0.76 #1310, 0.75 #2630, 0.71 #1308), 0c0zq (0.76 #1310, 0.75 #2630, 0.71 #1308), 01y9r2 (0.76 #1310, 0.75 #2630, 0.71 #1308), 027r9t (0.76 #1310, 0.75 #2630, 0.71 #1308), 011yxg (0.76 #1310, 0.75 #2630, 0.71 #1308), 0b1y_2 (0.76 #1310, 0.75 #2630, 0.71 #1308), 0bcp9b (0.76 #1310, 0.75 #2630, 0.71 #1308), 033dbw (0.76 #1310, 0.75 #2630, 0.71 #1308) >> Best rule #1310 for best value: >> intensional similarity = 77 >> extensional distance = 1 >> proper extension: 09nqf; >> query: (?x5696, ?x80) <- currency(?x8930, ?x5696), currency(?x4002, ?x5696), currency(?x1421, ?x5696), currency(?x2856, ?x5696), student(?x8930, ?x7068), student(?x8930, ?x6457), currency(?x11426, ?x5696), titles(?x789, ?x1421), film_release_region(?x1421, ?x4743), film_release_region(?x1421, ?x1499), film_release_region(?x1421, ?x1453), film_release_region(?x1421, ?x1355), film_release_region(?x1421, ?x1264), film_release_region(?x1421, ?x1174), film_release_region(?x1421, ?x774), film_release_region(?x1421, ?x172), film_release_region(?x1421, ?x142), ?x172 = 0154j, nominated_for(?x1008, ?x1421), nominated_for(?x8888, ?x1421), films(?x942, ?x1421), ?x774 = 06mzp, time_zones(?x2856, ?x2864), genre(?x1421, ?x53), contains(?x8264, ?x11426), currency(?x11892, ?x5696), ?x1355 = 0h7x, vacationer(?x2856, ?x2857), institution(?x1305, ?x8930), ?x1305 = 02mjs7, production_companies(?x1421, ?x3331), adjoins(?x4521, ?x2856), major_field_of_study(?x8930, ?x3995), influenced_by(?x6457, ?x11412), influenced_by(?x6457, ?x2994), ?x1453 = 06qd3, influenced_by(?x118, ?x6457), film_crew_role(?x1421, ?x137), film(?x1414, ?x1421), religion(?x7068, ?x8140), ?x1264 = 0345h, ?x11412 = 084nh, ?x142 = 0jgd, organization(?x346, ?x4002), ?x4743 = 03spz, spouse(?x8888, ?x7261), contains(?x2856, ?x8502), participant(?x521, ?x2857), titles(?x53, ?x3919), genre(?x8466, ?x53), genre(?x11735, ?x53), genre(?x7975, ?x53), genre(?x7947, ?x53), genre(?x7922, ?x53), genre(?x7208, ?x53), genre(?x5721, ?x53), genre(?x3904, ?x53), genre(?x80, ?x53), ?x5721 = 01d259, ?x11735 = 02x2jl_, major_field_of_study(?x4002, ?x8221), ?x2994 = 0379s, ?x1174 = 047yc, ?x7975 = 06yykb, ?x7922 = 0y_pg, participant(?x6730, ?x2857), profession(?x7068, ?x319), gender(?x6457, ?x231), ?x1499 = 01znc_, ?x3904 = 02rq8k8, ?x3995 = 0fdys, student(?x3424, ?x2857), ?x7208 = 0b6l1st, ?x3919 = 05_5rjx, ?x7947 = 04gcyg, ?x8466 = 04f6hhm, adjoins(?x8264, ?x1679) >> conf = 0.76 => this is the best rule for 1124 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 984 EVAL 02l6h currency! 0gh6j94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 3.000 0.763 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8332-0279c15 PRED entity: 0279c15 PRED relation: award! PRED expected values: 0147dk 0blq0z => 44 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2661): 0blq0z (0.81 #3373, 0.69 #47248, 0.69 #50625), 0z4s (0.81 #3373, 0.69 #50625, 0.69 #30374), 0f502 (0.57 #1234, 0.13 #37126, 0.13 #4607), 0147dk (0.50 #108, 0.14 #13499, 0.14 #23624), 07r1h (0.43 #1803, 0.15 #5176, 0.13 #37126), 0169dl (0.43 #637, 0.13 #37126, 0.12 #40500), 0mdqp (0.43 #165, 0.13 #37126, 0.12 #40500), 0dvmd (0.43 #848, 0.12 #40500, 0.11 #4221), 0170qf (0.36 #588, 0.19 #47250, 0.13 #37126), 0gy6z9 (0.36 #903, 0.13 #37126, 0.12 #4276) >> Best rule #3373 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 02f79n; >> query: (?x2535, ?x450) <- award(?x2534, ?x2535), award(?x157, ?x2535), award_winner(?x2535, ?x450), ?x2534 = 0lx2l, award_winner(?x451, ?x157) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1, 4 EVAL 0279c15 award! 0blq0z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 16.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0279c15 award! 0147dk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 44.000 16.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8331-025j1t PRED entity: 025j1t PRED relation: place_of_birth PRED expected values: 02dtg => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 60): 02_286 (0.50 #19, 0.07 #33813, 0.07 #12691), 030qb3t (0.10 #3574, 0.10 #758, 0.09 #2870), 04jpl (0.10 #712, 0.02 #15496, 0.02 #6344), 0cc56 (0.10 #737, 0.02 #2145, 0.02 #4961), 01qh7 (0.10 #808, 0.01 #49989, 0.01 #54919), 013n2h (0.10 #1011), 0cr3d (0.05 #8542, 0.04 #9246, 0.04 #1502), 01_d4 (0.04 #8514, 0.04 #9218, 0.04 #10626), 02dtg (0.03 #48580, 0.01 #36620, 0.01 #59860), 013d_f (0.03 #48580) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01wjrn; >> query: (?x6068, 02_286) <- film(?x6068, ?x9199), ?x9199 = 05nyqk, student(?x1675, ?x6068) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #48580 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1760 *> proper extension: 03wjb7; *> query: (?x6068, ?x94) <- student(?x1675, ?x6068), contains(?x94, ?x1675) *> conf = 0.03 ranks of expected_values: 9 EVAL 025j1t place_of_birth 02dtg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 103.000 103.000 0.500 http://example.org/people/person/place_of_birth #8330-0cv3w PRED entity: 0cv3w PRED relation: month PRED expected values: 0ll3 => 211 concepts (211 used for prediction) PRED predicted values (max 10 best out of 1): 0ll3 (0.92 #48, 0.91 #43, 0.89 #42) >> Best rule #48 for best value: >> intensional similarity = 2 >> extensional distance = 47 >> proper extension: 06t2t; 03hrz; >> query: (?x3026, 0ll3) <- month(?x3026, ?x3107), ?x3107 = 05lf_ >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cv3w month 0ll3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 211.000 211.000 0.918 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #8329-02q42j_ PRED entity: 02q42j_ PRED relation: type_of_union PRED expected values: 04ztj => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.95 #61, 0.94 #386, 0.94 #383) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 216 >> proper extension: 01f8ld; 0gv40; 01r_t_; 03hy3g; 0522wp; 081l_; 04353; 014hdb; 0hsmh; 015nvj; ... >> query: (?x5973, 04ztj) <- produced_by(?x2029, ?x5973), award_winner(?x3012, ?x5973), type_of_union(?x5973, ?x1873) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q42j_ type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.954 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8328-04hk0w PRED entity: 04hk0w PRED relation: crewmember PRED expected values: 03m49ly 027y151 => 97 concepts (71 used for prediction) PRED predicted values (max 10 best out of 31): 01nc3rh (0.13 #47, 0.03 #564, 0.02 #706), 0284n42 (0.09 #425, 0.09 #4, 0.04 #283), 03m49ly (0.09 #128, 0.06 #361, 0.05 #267), 092ys_y (0.09 #20, 0.06 #114, 0.04 #632), 051z6rz (0.09 #29, 0.05 #641, 0.04 #450), 0c94fn (0.06 #105, 0.05 #11, 0.03 #623), 0b79gfg (0.06 #297, 0.04 #439, 0.04 #961), 021yc7p (0.05 #194, 0.04 #148, 0.03 #667), 04wp63 (0.05 #227, 0.02 #842, 0.02 #937), 0bbxx9b (0.05 #633, 0.05 #869, 0.04 #727) >> Best rule #47 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0ds11z; 060v34; 0dqytn; 04fzfj; 03t97y; 04jkpgv; 035yn8; 05z7c; 0hx4y; 01qxc7; ... >> query: (?x12964, ?x10295) <- country(?x12964, ?x94), film_crew_role(?x12964, ?x137), award_winner(?x12964, ?x10295), genre(?x12964, ?x571), ?x571 = 03npn >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 31 *> proper extension: 019nnl; 0266s9; *> query: (?x12964, 03m49ly) <- nominated_for(?x10295, ?x12964), category(?x12964, ?x134), place_of_birth(?x10295, ?x8977), music(?x908, ?x10295), month(?x8977, ?x1459) *> conf = 0.09 ranks of expected_values: 3, 12 EVAL 04hk0w crewmember 027y151 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 97.000 71.000 0.132 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember EVAL 04hk0w crewmember 03m49ly CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 71.000 0.132 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #8327-07ssc PRED entity: 07ssc PRED relation: titles PRED expected values: 0194zl 0642ykh 0bh8drv 0fxmbn => 230 concepts (175 used for prediction) PRED predicted values (max 10 best out of 1535): 091z_p (0.33 #7360, 0.20 #18785, 0.12 #23071), 085ccd (0.33 #7451, 0.12 #21733, 0.11 #24590), 02q3fdr (0.33 #7928, 0.11 #25067, 0.07 #35066), 03hxsv (0.33 #7998, 0.11 #25708, 0.04 #95703), 05567m (0.33 #8349, 0.11 #25708, 0.03 #161186), 026p4q7 (0.33 #7459, 0.11 #25708, 0.03 #160296), 03176f (0.33 #7689, 0.11 #25708, 0.03 #49994), 05c9zr (0.33 #7676, 0.11 #25708, 0.03 #49994), 04z257 (0.33 #7601, 0.11 #25708, 0.03 #49994), 03y0pn (0.33 #8109, 0.11 #25708, 0.03 #49994) >> Best rule #7360 for best value: >> intensional similarity = 2 >> extensional distance = 1 >> proper extension: 01hmnh; >> query: (?x512, 091z_p) <- titles(?x512, ?x3845), ?x3845 = 0639bg >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8069 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1 *> proper extension: 01hmnh; *> query: (?x512, 0642ykh) <- titles(?x512, ?x3845), ?x3845 = 0639bg *> conf = 0.33 ranks of expected_values: 109, 332, 677, 980 EVAL 07ssc titles 0fxmbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 230.000 175.000 0.333 http://example.org/media_common/netflix_genre/titles EVAL 07ssc titles 0bh8drv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 230.000 175.000 0.333 http://example.org/media_common/netflix_genre/titles EVAL 07ssc titles 0642ykh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 230.000 175.000 0.333 http://example.org/media_common/netflix_genre/titles EVAL 07ssc titles 0194zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 230.000 175.000 0.333 http://example.org/media_common/netflix_genre/titles #8326-034qt_ PRED entity: 034qt_ PRED relation: profession PRED expected values: 089fss => 94 concepts (75 used for prediction) PRED predicted values (max 10 best out of 66): 02hrh1q (0.76 #3592, 0.73 #3443, 0.72 #2996), 0cbd2 (0.69 #2689, 0.21 #1348, 0.21 #1199), 02pjxr (0.53 #184, 0.35 #333, 0.25 #35), 0kyk (0.46 #2713, 0.13 #1372, 0.13 #1223), 089fss (0.46 #315, 0.25 #17, 0.20 #166), 0dxtg (0.39 #2696, 0.37 #9849, 0.30 #461), 01c979 (0.35 #9985, 0.01 #2765), 01d_h8 (0.31 #3285, 0.31 #4030, 0.31 #2987), 02jknp (0.26 #3287, 0.24 #753, 0.22 #5671), 09jwl (0.24 #9855, 0.18 #6130, 0.17 #5534) >> Best rule #3592 for best value: >> intensional similarity = 3 >> extensional distance = 1243 >> proper extension: 015882; 01520h; >> query: (?x14143, 02hrh1q) <- location(?x14143, ?x4025), award_nominee(?x14143, ?x12725), profession(?x14143, ?x8368) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 55 *> proper extension: 0ccd3x; *> query: (?x14143, 089fss) <- award(?x14143, ?x484), ?x484 = 0gq_v *> conf = 0.46 ranks of expected_values: 5 EVAL 034qt_ profession 089fss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 75.000 0.757 http://example.org/people/person/profession #8325-02cvp8 PRED entity: 02cvp8 PRED relation: location PRED expected values: 0cr3d => 91 concepts (84 used for prediction) PRED predicted values (max 10 best out of 99): 0cr3d (0.35 #4025, 0.31 #12076, 0.27 #11270), 02_286 (0.26 #8893, 0.22 #9698, 0.22 #6477), 030qb3t (0.17 #8939, 0.15 #2498, 0.15 #3303), 04jpl (0.15 #2432, 0.12 #6457, 0.11 #7262), 059rby (0.11 #3236, 0.09 #4043, 0.09 #12897), 01cx_ (0.11 #163, 0.10 #968, 0.09 #1774), 05k7sb (0.11 #109, 0.09 #1720, 0.06 #5744), 04ykg (0.10 #873, 0.07 #3288, 0.05 #68), 01b8jj (0.10 #1398, 0.06 #11058, 0.05 #593), 06btq (0.07 #3360, 0.06 #5775, 0.05 #6580) >> Best rule #4025 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 02x8mt; >> query: (?x11256, ?x2850) <- sibling(?x11256, ?x10901), student(?x10621, ?x11256), sibling(?x9015, ?x11256), nationality(?x11256, ?x94), location(?x10901, ?x2850) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cvp8 location 0cr3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 84.000 0.349 http://example.org/people/person/places_lived./people/place_lived/location #8324-07l450 PRED entity: 07l450 PRED relation: film_crew_role PRED expected values: 02ynfr => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 21): 089g0h (0.40 #83, 0.33 #116, 0.33 #50), 01pvkk (0.33 #109, 0.33 #43, 0.29 #1141), 01xy5l_ (0.33 #45, 0.21 #2203, 0.20 #78), 01vx2h (0.30 #1140, 0.29 #1875, 0.28 #1573), 0215hd (0.29 #148, 0.21 #2203, 0.20 #82), 0d2b38 (0.21 #2203, 0.20 #89, 0.17 #122), 089fss (0.21 #2203, 0.20 #71, 0.17 #104), 0263ycg (0.21 #2203, 0.20 #81, 0.17 #114), 026sdt1 (0.21 #2203, 0.20 #90, 0.17 #123), 02_n3z (0.21 #2203, 0.15 #1032, 0.14 #133) >> Best rule #83 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03ckwzc; 03ntbmw; >> query: (?x9599, 089g0h) <- film(?x10643, ?x9599), titles(?x53, ?x9599), ?x10643 = 07myb2, ?x53 = 07s9rl0 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #80 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 03ckwzc; 03ntbmw; *> query: (?x9599, 02ynfr) <- film(?x10643, ?x9599), titles(?x53, ?x9599), ?x10643 = 07myb2, ?x53 = 07s9rl0 *> conf = 0.20 ranks of expected_values: 18 EVAL 07l450 film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 84.000 84.000 0.400 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8323-01tz3c PRED entity: 01tz3c PRED relation: genre! PRED expected values: 0d_rw => 62 concepts (36 used for prediction) PRED predicted values (max 10 best out of 308): 0h3mh3q (0.60 #1087, 0.57 #1679, 0.44 #2273), 0828jw (0.57 #1591, 0.44 #2185, 0.40 #3073), 07gbf (0.57 #1686, 0.42 #5240, 0.40 #1094), 099pks (0.50 #1883, 0.33 #399, 0.29 #6321), 03ln8b (0.50 #1813, 0.33 #329, 0.24 #6251), 0gxr1c (0.44 #2352, 0.43 #1758, 0.40 #3240), 0ctzf1 (0.44 #2215, 0.43 #1621, 0.40 #3103), 03g9xj (0.43 #1688, 0.40 #1096, 0.37 #5242), 0d_rw (0.43 #1763, 0.40 #1171, 0.33 #2357), 0d7vtk (0.43 #1687, 0.40 #1095, 0.33 #2281) >> Best rule #1087 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 09n3wz; >> query: (?x9083, 0h3mh3q) <- genre(?x12434, ?x9083), genre(?x9082, ?x9083), genre(?x8759, ?x9083), ?x12434 = 04x4gj, program(?x8760, ?x8759), ?x9082 = 06w7mlh, genre(?x8759, ?x225), company(?x8314, ?x8760), genre(?x7975, ?x225), ?x7975 = 06yykb >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1763 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 0lsxr; *> query: (?x9083, 0d_rw) <- disciplines_or_subjects(?x13751, ?x9083), genre(?x12434, ?x9083), genre(?x6450, ?x9083), disciplines_or_subjects(?x13751, ?x6647), ?x6647 = 02xlf, program(?x2062, ?x12434) *> conf = 0.43 ranks of expected_values: 9 EVAL 01tz3c genre! 0d_rw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 62.000 36.000 0.600 http://example.org/tv/tv_program/genre #8322-03nc9d PRED entity: 03nc9d PRED relation: ceremony PRED expected values: 01s695 01bx35 056878 09n4nb => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 124): 09n4nb (0.91 #42, 0.91 #426, 0.88 #170), 056878 (0.87 #27, 0.86 #155, 0.85 #411), 01s695 (0.86 #130, 0.85 #2, 0.84 #386), 01bx35 (0.85 #389, 0.85 #5, 0.81 #261), 0jzphpx (0.85 #34, 0.80 #162, 0.79 #290), 0bzm81 (0.25 #530, 0.16 #1042, 0.14 #1170), 0n8_m93 (0.25 #619, 0.16 #1131, 0.14 #1259), 02yxh9 (0.25 #602, 0.15 #1114, 0.14 #1242), 0bc773 (0.25 #560, 0.15 #1072, 0.14 #1200), 02yw5r (0.25 #522, 0.15 #1034, 0.14 #1162) >> Best rule #42 for best value: >> intensional similarity = 7 >> extensional distance = 44 >> proper extension: 02gx2k; 025mb9; 0248jb; 02v703; 02fm4d; >> query: (?x9034, 09n4nb) <- ceremony(?x9034, ?x6869), ceremony(?x9034, ?x5766), ceremony(?x9034, ?x139), award_winner(?x9034, ?x1613), ?x6869 = 01xqqp, ?x139 = 05pd94v, ?x5766 = 013b2h >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 03nc9d ceremony 09n4nb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03nc9d ceremony 056878 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03nc9d ceremony 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03nc9d ceremony 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony #8321-0d90m PRED entity: 0d90m PRED relation: film_crew_role PRED expected values: 09zzb8 0dxtw => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 23): 09zzb8 (0.79 #240, 0.74 #514, 0.74 #378), 0dxtw (0.43 #283, 0.41 #249, 0.40 #44), 01pvkk (0.29 #250, 0.28 #524, 0.28 #45), 0215hd (0.20 #17, 0.19 #256, 0.15 #394), 01xy5l_ (0.20 #13, 0.14 #252, 0.13 #81), 02rh1dz (0.19 #43, 0.17 #9, 0.16 #282), 02ynfr (0.19 #768, 0.18 #287, 0.18 #48), 015h31 (0.16 #315, 0.13 #76, 0.13 #42), 0d2b38 (0.14 #263, 0.13 #58, 0.13 #401), 02_n3z (0.13 #241, 0.12 #36, 0.10 #379) >> Best rule #240 for best value: >> intensional similarity = 3 >> extensional distance = 208 >> proper extension: 026njb5; >> query: (?x97, 09zzb8) <- executive_produced_by(?x97, ?x96), featured_film_locations(?x97, ?x739), film_crew_role(?x97, ?x468) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0d90m film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.786 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0d90m film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.786 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8320-017g21 PRED entity: 017g21 PRED relation: type_of_union PRED expected values: 04ztj => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.78 #73, 0.77 #21, 0.75 #333), 01g63y (0.19 #514, 0.15 #6, 0.14 #26), 0jgjn (0.19 #514, 0.01 #120), 01bl8s (0.19 #514, 0.01 #67, 0.01 #75) >> Best rule #73 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 0dpqk; 07h5d; 03dq9; 0dn44; >> query: (?x7252, 04ztj) <- nationality(?x7252, ?x1310), profession(?x7252, ?x1032), group(?x7252, ?x1684), ?x1032 = 02hrh1q >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017g21 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.778 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8319-07ylj PRED entity: 07ylj PRED relation: organization PRED expected values: 04k4l => 104 concepts (101 used for prediction) PRED predicted values (max 10 best out of 50): 04k4l (0.58 #1103, 0.52 #70, 0.43 #180), 041288 (0.58 #1103, 0.38 #457, 0.35 #523), 0b6css (0.52 #10, 0.40 #54, 0.39 #120), 01rz1 (0.52 #199, 0.47 #133, 0.44 #111), 0_2v (0.48 #3, 0.45 #69, 0.44 #113), 018cqq (0.45 #77, 0.45 #209, 0.43 #11), 0gkjy (0.35 #448, 0.32 #1570, 0.28 #470), 02jxk (0.32 #1570, 0.29 #200, 0.29 #134), 0j7v_ (0.32 #1570, 0.28 #512, 0.24 #379), 059dn (0.32 #1570, 0.14 #15, 0.11 #147) >> Best rule #1103 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 015zxh; 0cwx_; 02hyt; >> query: (?x1203, ?x312) <- contains(?x7273, ?x1203), adjoins(?x410, ?x1203), organization(?x410, ?x312) >> conf = 0.58 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 07ylj organization 04k4l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 101.000 0.581 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #8318-0377k9 PRED entity: 0377k9 PRED relation: jurisdiction_of_office PRED expected values: 03rk0 => 17 concepts (15 used for prediction) PRED predicted values (max 10 best out of 732): 07f5x (0.67 #922, 0.60 #921, 0.48 #1845), 0fv4v (0.67 #922, 0.60 #921, 0.48 #1845), 035qy (0.67 #1449, 0.57 #1911, 0.56 #2830), 03shp (0.67 #1552, 0.57 #2014, 0.50 #1090), 09c7w0 (0.58 #3229, 0.39 #4614, 0.38 #4147), 019rg5 (0.50 #1424, 0.50 #962, 0.43 #1886), 03gj2 (0.50 #1429, 0.44 #2810, 0.44 #2349), 03spz (0.50 #1591, 0.44 #2972, 0.44 #2511), 05r4w (0.50 #1386, 0.44 #2767, 0.43 #1848), 03rt9 (0.50 #945, 0.33 #2788, 0.33 #1407) >> Best rule #922 for best value: >> intensional similarity = 20 >> extensional distance = 1 >> proper extension: 060bp; >> query: (?x11622, ?x7360) <- jurisdiction_of_office(?x11622, ?x2267), jurisdiction_of_office(?x11622, ?x2051), jurisdiction_of_office(?x11622, ?x1229), ?x2267 = 03rj0, contains(?x2051, ?x12330), form_of_government(?x2051, ?x4763), countries_spoken_in(?x254, ?x2051), olympics(?x2051, ?x2369), adjoins(?x8948, ?x2051), adjoins(?x7360, ?x2051), currency(?x2051, ?x170), taxonomy(?x2051, ?x939), featured_film_locations(?x5044, ?x2051), ?x1229 = 059j2, ?x4763 = 01fpfn, country(?x471, ?x2051), ?x2369 = 0lbbj, category(?x12330, ?x134), organization(?x8948, ?x127), participating_countries(?x784, ?x2051) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #2405 for first EXPECTED value: *> intensional similarity = 29 *> extensional distance = 7 *> proper extension: 0fj45; *> query: (?x11622, 03rk0) <- jurisdiction_of_office(?x11622, ?x2267), film_release_region(?x11209, ?x2267), film_release_region(?x9002, ?x2267), film_release_region(?x8867, ?x2267), film_release_region(?x6556, ?x2267), film_release_region(?x4690, ?x2267), film_release_region(?x4290, ?x2267), film_release_region(?x2868, ?x2267), film_release_region(?x1546, ?x2267), film_release_region(?x1456, ?x2267), film_release_region(?x1386, ?x2267), film_release_region(?x1259, ?x2267), film_release_region(?x634, ?x2267), film_release_region(?x504, ?x2267), ?x1386 = 0dtfn, ?x11209 = 04fjzv, ?x1546 = 0d6b7, ?x4690 = 0gkz3nz, ?x8867 = 03lfd_, ?x2868 = 0dr3sl, film_distribution_medium(?x504, ?x81), film_release_region(?x504, ?x1536), ?x9002 = 0ndsl1x, ?x1456 = 0cz8mkh, ?x6556 = 05dss7, ?x4290 = 0gtxj2q, ?x634 = 0gx9rvq, ?x1259 = 04hwbq, ?x1536 = 06c1y *> conf = 0.33 ranks of expected_values: 44 EVAL 0377k9 jurisdiction_of_office 03rk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 17.000 15.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #8317-0fr61 PRED entity: 0fr61 PRED relation: source PRED expected values: 0jbk9 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #14, 0.92 #13, 0.91 #12) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 220 >> proper extension: 0ntwb; >> query: (?x7420, 0jbk9) <- adjoins(?x7420, ?x12702), second_level_divisions(?x94, ?x12702), county(?x1427, ?x12702), contains(?x1426, ?x12702) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fr61 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.919 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #8316-0227tr PRED entity: 0227tr PRED relation: people! PRED expected values: 01qhm_ => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 37): 041rx (0.29 #150, 0.24 #4822, 0.24 #880), 0x67 (0.23 #2053, 0.23 #1980, 0.21 #2418), 02w7gg (0.13 #805, 0.12 #3068, 0.12 #3141), 09vc4s (0.13 #154, 0.10 #81, 0.10 #300), 07hwkr (0.13 #157, 0.09 #230, 0.08 #814), 013xrm (0.10 #92, 0.04 #3815, 0.04 #5786), 025rpb0 (0.10 #114, 0.02 #625, 0.02 #260), 09v5bdn (0.10 #83, 0.01 #229, 0.01 #521), 01g7zj (0.10 #121, 0.01 #413, 0.01 #851), 0xnvg (0.10 #1764, 0.10 #158, 0.09 #888) >> Best rule #150 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 085gk; >> query: (?x2580, 041rx) <- people(?x1446, ?x2580), location(?x2580, ?x335), ?x335 = 059rby >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #298 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 155 *> proper extension: 0chrwb; *> query: (?x2580, 01qhm_) <- people(?x1446, ?x2580), profession(?x2580, ?x319), ?x1446 = 033tf_ *> conf = 0.10 ranks of expected_values: 11 EVAL 0227tr people! 01qhm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 151.000 151.000 0.290 http://example.org/people/ethnicity/people #8315-0gs1_ PRED entity: 0gs1_ PRED relation: nationality PRED expected values: 09c7w0 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 60): 09c7w0 (0.76 #4411, 0.75 #1802, 0.74 #6413), 02jx1 (0.62 #1233, 0.17 #4943, 0.14 #3841), 07ssc (0.39 #1215, 0.30 #13937, 0.12 #3823), 0d060g (0.33 #7, 0.15 #207, 0.10 #307), 0f8l9c (0.30 #13937, 0.04 #11728, 0.04 #122), 03_3d (0.30 #13937, 0.04 #11728, 0.04 #106), 0345h (0.30 #13937, 0.04 #11728, 0.04 #931), 02_286 (0.26 #11427, 0.25 #11026, 0.24 #2203), 059rby (0.26 #11427, 0.25 #11026, 0.24 #2203), 0ncj8 (0.26 #11427, 0.25 #11026, 0.24 #2203) >> Best rule #4411 for best value: >> intensional similarity = 3 >> extensional distance = 306 >> proper extension: 02lnhv; 0721cy; 01w7nwm; 058s44; 03m6pk; 03zz8b; 01vvybv; >> query: (?x6558, 09c7w0) <- participant(?x6558, ?x1126), profession(?x1126, ?x353), student(?x5981, ?x6558) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gs1_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.760 http://example.org/people/person/nationality #8314-0h5f5n PRED entity: 0h5f5n PRED relation: profession PRED expected values: 01d_h8 => 134 concepts (119 used for prediction) PRED predicted values (max 10 best out of 74): 01d_h8 (0.82 #300, 0.81 #6, 0.81 #1476), 02hrh1q (0.75 #4720, 0.74 #5896, 0.70 #6779), 02jknp (0.68 #449, 0.68 #1184, 0.62 #8), 0cbd2 (0.48 #1624, 0.47 #1330, 0.31 #3831), 02krf9 (0.27 #15881, 0.26 #5614, 0.22 #320), 0kyk (0.27 #1646, 0.25 #1352, 0.16 #1058), 09jwl (0.20 #4577, 0.19 #7665, 0.18 #6047), 018gz8 (0.19 #5604, 0.17 #2516, 0.17 #3251), 0nbcg (0.14 #4590, 0.13 #7678, 0.13 #6207), 0np9r (0.13 #314, 0.13 #5608, 0.13 #608) >> Best rule #300 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 09pl3s; >> query: (?x361, 01d_h8) <- award_nominee(?x361, ?x846), executive_produced_by(?x2989, ?x361), written_by(?x2932, ?x361) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h5f5n profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 119.000 0.817 http://example.org/people/person/profession #8313-06mmb PRED entity: 06mmb PRED relation: award_nominee! PRED expected values: 05vsxz => 79 concepts (29 used for prediction) PRED predicted values (max 10 best out of 756): 01x_d8 (0.78 #27799, 0.77 #50971, 0.77 #48651), 03n08b (0.78 #27799, 0.77 #50971, 0.77 #48651), 032wdd (0.78 #27799, 0.77 #50971, 0.77 #60244), 0c01c (0.78 #27799, 0.77 #48651, 0.77 #60244), 05vsxz (0.64 #2325, 0.62 #4641, 0.16 #64877), 06mmb (0.62 #5177, 0.57 #2861, 0.27 #53292), 048lv (0.27 #53292, 0.25 #30118, 0.18 #50972), 0c3jz (0.27 #53292, 0.25 #30118, 0.18 #50972), 02g87m (0.27 #53292, 0.25 #30118, 0.18 #50972), 02bkdn (0.27 #53292, 0.25 #30118, 0.04 #37451) >> Best rule #27799 for best value: >> intensional similarity = 3 >> extensional distance = 797 >> proper extension: 033jkj; 024qwq; 0cbxl0; >> query: (?x2559, ?x1461) <- award_winner(?x1461, ?x2559), award_nominee(?x7186, ?x2559), actor(?x8686, ?x7186) >> conf = 0.78 => this is the best rule for 4 predicted values *> Best rule #2325 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0h5g_; 01yhvv; 09y20; 07hbxm; 02cllz; 04rsd2; 0993r; 016xh5; 0djywgn; 02fz3w; *> query: (?x2559, 05vsxz) <- award_nominee(?x2559, ?x5743), award_nominee(?x2559, ?x1550), ?x5743 = 0175wg, ?x1550 = 05tk7y *> conf = 0.64 ranks of expected_values: 5 EVAL 06mmb award_nominee! 05vsxz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 29.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8312-049n7 PRED entity: 049n7 PRED relation: season PRED expected values: 027mvrc => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 7): 027mvrc (0.89 #88, 0.79 #109, 0.73 #102), 05kcgsf (0.71 #43, 0.71 #36, 0.55 #106), 02h7s73 (0.43 #47, 0.43 #40, 0.34 #110), 04110b0 (0.43 #45, 0.35 #101, 0.34 #108), 03c6s24 (0.29 #48, 0.29 #41, 0.28 #111), 03c74_8 (0.29 #44, 0.29 #37, 0.24 #107), 04n36qk (0.09 #98, 0.08 #105, 0.07 #112) >> Best rule #88 for best value: >> intensional similarity = 9 >> extensional distance = 17 >> proper extension: 0512p; 01yjl; 061xq; 01ync; 02__x; 01slc; 06wpc; 07l8x; 0x0d; >> query: (?x1160, 027mvrc) <- position(?x1160, ?x2010), school(?x1160, ?x1884), school(?x1160, ?x1428), school(?x10600, ?x1428), category(?x1160, ?x134), contains(?x94, ?x1428), student(?x1884, ?x1815), school_type(?x1884, ?x4994), institution(?x865, ?x1428) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049n7 season 027mvrc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.895 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #8311-03bdv PRED entity: 03bdv PRED relation: time_zones! PRED expected values: 04jpl 035dk 0214m4 07f5x 01_c4 0fmyd 01xr6x => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1749): 0jgx (0.79 #4740, 0.78 #4741, 0.77 #5927), 035dk (0.79 #4740, 0.78 #4741, 0.77 #5927), 05r4w (0.79 #4740, 0.78 #4741, 0.77 #5927), 0d05q4 (0.79 #4740, 0.78 #4741, 0.77 #5927), 02j9z (0.79 #4740, 0.78 #4741, 0.77 #5927), 0dg3n1 (0.79 #4740, 0.78 #4741, 0.77 #5927), 02j7k (0.79 #4740, 0.78 #4741, 0.77 #5927), 017jq (0.79 #4740, 0.78 #4741, 0.77 #5927), 0d0kn (0.79 #4740, 0.78 #4741, 0.77 #5927), 012wgb (0.79 #4740, 0.78 #4741, 0.77 #5927) >> Best rule #4740 for best value: >> intensional similarity = 37 >> extensional distance = 2 >> proper extension: 02fqwt; 02hcv8; >> query: (?x5327, ?x9990) <- time_zones(?x14093, ?x5327), time_zones(?x13391, ?x5327), time_zones(?x9878, ?x5327), time_zones(?x9818, ?x5327), time_zones(?x9588, ?x5327), time_zones(?x7360, ?x5327), time_zones(?x7037, ?x5327), time_zones(?x5498, ?x5327), time_zones(?x4071, ?x5327), participating_countries(?x1931, ?x7360), adjoins(?x7360, ?x2051), category(?x9818, ?x134), place_of_birth(?x3708, ?x9818), location(?x2876, ?x9878), place_founded(?x11720, ?x9878), ?x134 = 08mbj5d, capital(?x3855, ?x13391), country(?x668, ?x7360), origin(?x10145, ?x9588), contains(?x9990, ?x5498), role(?x2876, ?x227), contains(?x7360, ?x10885), award(?x2876, ?x724), country(?x1121, ?x7037), artists(?x6107, ?x10145), organization(?x7037, ?x127), adjoins(?x9990, ?x789), countries_spoken_in(?x254, ?x7037), ?x254 = 02h40lc, location_of_ceremony(?x566, ?x14093), instrumentalists(?x75, ?x2876), country(?x12190, ?x4071), artist(?x3265, ?x2876), administrative_parent(?x7037, ?x551), administrative_area_type(?x7037, ?x2792), adjustment_currency(?x7037, ?x170), ?x6107 = 0126t5 >> conf = 0.79 => this is the best rule for 11 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 26, 57, 118, 252, 338, 340 EVAL 03bdv time_zones! 01xr6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 12.000 12.000 0.787 http://example.org/location/location/time_zones EVAL 03bdv time_zones! 0fmyd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 12.000 12.000 0.787 http://example.org/location/location/time_zones EVAL 03bdv time_zones! 01_c4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 12.000 12.000 0.787 http://example.org/location/location/time_zones EVAL 03bdv time_zones! 07f5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 12.000 12.000 0.787 http://example.org/location/location/time_zones EVAL 03bdv time_zones! 0214m4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 12.000 12.000 0.787 http://example.org/location/location/time_zones EVAL 03bdv time_zones! 035dk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 12.000 12.000 0.787 http://example.org/location/location/time_zones EVAL 03bdv time_zones! 04jpl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 12.000 12.000 0.787 http://example.org/location/location/time_zones #8310-03gm48 PRED entity: 03gm48 PRED relation: film PRED expected values: 05pbl56 => 119 concepts (97 used for prediction) PRED predicted values (max 10 best out of 766): 024hbv (0.62 #55451, 0.58 #59028, 0.54 #19677), 03mnn0 (0.22 #8945, 0.13 #7156, 0.11 #10734), 02_1sj (0.09 #78, 0.05 #7234, 0.04 #1867), 0prrm (0.06 #860, 0.04 #8016, 0.04 #11594), 013q07 (0.06 #355, 0.04 #5722, 0.03 #7511), 01qvz8 (0.06 #805, 0.03 #2594, 0.02 #6172), 051zy_b (0.06 #578, 0.02 #7734, 0.02 #9523), 07bzz7 (0.06 #889, 0.02 #8045, 0.02 #9834), 0df2zx (0.06 #1715, 0.02 #8871, 0.02 #7082), 05fm6m (0.06 #1319, 0.02 #4897, 0.02 #6686) >> Best rule #55451 for best value: >> intensional similarity = 4 >> extensional distance = 672 >> proper extension: 0lbj1; 023tp8; 01g257; 01713c; 01mqz0; 01l2fn; 02pb53; 06t61y; 015pxr; 04kj2v; ... >> query: (?x965, ?x83) <- people(?x2510, ?x965), profession(?x965, ?x319), nominated_for(?x965, ?x83), film(?x965, ?x136) >> conf = 0.62 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03gm48 film 05pbl56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 97.000 0.620 http://example.org/film/actor/film./film/performance/film #8309-0404j37 PRED entity: 0404j37 PRED relation: award PRED expected values: 02rdxsh => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 173): 04dn09n (0.28 #443, 0.26 #1109, 0.24 #1554), 0k611 (0.28 #443, 0.26 #1109, 0.24 #1554), 0gr0m (0.28 #443, 0.26 #1109, 0.24 #1554), 04kxsb (0.28 #443, 0.26 #1109, 0.24 #1554), 02pqp12 (0.28 #443, 0.26 #1109, 0.24 #1554), 03hl6lc (0.28 #443, 0.26 #1109, 0.24 #1554), 0f4x7 (0.28 #443, 0.26 #1109, 0.24 #1554), 018wdw (0.28 #443, 0.26 #1109, 0.24 #1554), 099ck7 (0.28 #443, 0.26 #1109, 0.24 #1554), 02w9sd7 (0.28 #443, 0.26 #1109, 0.24 #1554) >> Best rule #443 for best value: >> intensional similarity = 6 >> extensional distance = 61 >> proper extension: 0m313; 01jc6q; 0yyg4; 07xtqq; 0209hj; 0b6tzs; 0_92w; 0pv3x; 026390q; 0gmcwlb; ... >> query: (?x6448, ?x68) <- nominated_for(?x1243, ?x6448), nominated_for(?x746, ?x6448), nominated_for(?x68, ?x6448), ?x746 = 04dn09n, nominated_for(?x72, ?x6448), ?x1243 = 0gr0m >> conf = 0.28 => this is the best rule for 13 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 13 EVAL 0404j37 award 02rdxsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 84.000 84.000 0.281 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8308-07y_7 PRED entity: 07y_7 PRED relation: instrumentalists PRED expected values: 043d4 => 118 concepts (50 used for prediction) PRED predicted values (max 10 best out of 732): 01vw20_ (0.71 #7245, 0.67 #12564, 0.67 #4880), 01sb5r (0.71 #7316, 0.67 #4951, 0.64 #17955), 0zjpz (0.71 #6000, 0.56 #11913, 0.56 #11322), 02w670 (0.71 #1182, 0.65 #7085, 0.64 #7084), 01y0y6 (0.71 #1182, 0.65 #7085, 0.64 #7084), 01lvzbl (0.71 #1182, 0.64 #7084, 0.57 #3536), 01wgjj5 (0.71 #1182, 0.64 #7084, 0.57 #3536), 0p5mw (0.71 #1182, 0.64 #7084, 0.57 #3536), 014hr0 (0.71 #1182, 0.64 #7084, 0.57 #3536), 01vvycq (0.67 #4754, 0.62 #10075, 0.60 #14804) >> Best rule #7245 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 018vs; >> query: (?x75, 01vw20_) <- role(?x75, ?x1969), role(?x75, ?x1655), role(?x75, ?x922), group(?x75, ?x7476), instrumentalists(?x75, ?x535), role(?x75, ?x894), ?x1655 = 01hww_, role(?x3739, ?x75), award_nominee(?x3739, ?x495), ?x894 = 03m5k, role(?x366, ?x1969), role(?x868, ?x922), ?x7476 = 048xh >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #7081 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 5 *> proper extension: 02w3w; *> query: (?x75, ?x1001) <- role(?x75, ?x1969), role(?x75, ?x1655), group(?x75, ?x1751), instrumentalists(?x75, ?x535), role(?x75, ?x960), ?x1655 = 01hww_, role(?x3739, ?x75), award_nominee(?x3739, ?x495), role(?x1969, ?x615), ?x960 = 04q7r, role(?x366, ?x1969), instrumentalists(?x1969, ?x1001), performance_role(?x75, ?x736) *> conf = 0.16 ranks of expected_values: 686 EVAL 07y_7 instrumentalists 043d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 118.000 50.000 0.714 http://example.org/music/instrument/instrumentalists #8307-0b_6_l PRED entity: 0b_6_l PRED relation: team PRED expected values: 026xxv_ => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 13): 02q4ntp (0.87 #113, 0.83 #101, 0.82 #95), 026xxv_ (0.85 #105, 0.80 #135, 0.75 #123), 04088s0 (0.67 #50, 0.67 #38, 0.60 #140), 026dqjm (0.60 #115, 0.56 #121, 0.55 #139), 02pyyld (0.50 #66, 0.50 #24, 0.40 #144), 0263cyj (0.50 #22, 0.40 #112, 0.38 #124), 061xq (0.08 #152, 0.08 #195, 0.05 #313), 0hn6d (0.08 #152, 0.08 #195, 0.05 #313), 05tfm (0.08 #152, 0.08 #195, 0.05 #313), 04l5f2 (0.08 #152, 0.08 #195, 0.05 #313) >> Best rule #113 for best value: >> intensional similarity = 12 >> extensional distance = 13 >> proper extension: 0b_6mr; >> query: (?x12162, 02q4ntp) <- team(?x12162, ?x10846), team(?x12162, ?x10171), team(?x12162, ?x9833), ?x9833 = 03y9p40, locations(?x12162, ?x5381), team(?x4803, ?x10171), origin(?x2807, ?x5381), place_of_birth(?x5310, ?x5381), county_seat(?x11670, ?x5381), nationality(?x5310, ?x94), position(?x10846, ?x4747), ?x4803 = 0b_6jz >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #105 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 11 *> proper extension: 0b_6x2; 0b_6s7; *> query: (?x12162, 026xxv_) <- team(?x12162, ?x9833), team(?x12162, ?x6847), team(?x12162, ?x4369), team(?x12798, ?x9833), team(?x9908, ?x9833), team(?x8824, ?x9833), team(?x6802, ?x9833), team(?x3797, ?x9833), ?x6802 = 0br1x_, ?x8824 = 05g_nr, locations(?x12162, ?x1719), ?x9908 = 0b_6lb, ?x3797 = 0b_6zk, position(?x6847, ?x1579), ?x12798 = 0b_770, ?x4369 = 02pqcfz *> conf = 0.85 ranks of expected_values: 2 EVAL 0b_6_l team 026xxv_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.867 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #8306-0bthb PRED entity: 0bthb PRED relation: major_field_of_study PRED expected values: 01bt59 => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 118): 01mkq (0.58 #502, 0.50 #1844, 0.44 #2088), 062z7 (0.50 #148, 0.49 #514, 0.37 #1856), 02j62 (0.47 #517, 0.41 #4543, 0.39 #6252), 0g26h (0.40 #530, 0.37 #2116, 0.37 #652), 04rjg (0.40 #506, 0.37 #1848, 0.33 #628), 01tbp (0.38 #547, 0.37 #1889, 0.28 #2133), 01lj9 (0.36 #527, 0.30 #649, 0.28 #2113), 04x_3 (0.36 #512, 0.28 #2098, 0.28 #1854), 03g3w (0.36 #7102, 0.33 #513, 0.32 #1733), 05qfh (0.35 #523, 0.25 #7112, 0.25 #1621) >> Best rule #502 for best value: >> intensional similarity = 6 >> extensional distance = 53 >> proper extension: 01nnsv; 08qnnv; >> query: (?x1772, 01mkq) <- institution(?x1771, ?x1772), institution(?x865, ?x1772), ?x865 = 02h4rq6, ?x1771 = 019v9k, fraternities_and_sororities(?x1772, ?x4348), school_type(?x1772, ?x3205) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #567 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 53 *> proper extension: 01nnsv; 08qnnv; *> query: (?x1772, 01bt59) <- institution(?x1771, ?x1772), institution(?x865, ?x1772), ?x865 = 02h4rq6, ?x1771 = 019v9k, fraternities_and_sororities(?x1772, ?x4348), school_type(?x1772, ?x3205) *> conf = 0.24 ranks of expected_values: 16 EVAL 0bthb major_field_of_study 01bt59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 176.000 176.000 0.582 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8305-03x83_ PRED entity: 03x83_ PRED relation: major_field_of_study PRED expected values: 0fdys => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 105): 02j62 (0.35 #1017, 0.33 #770, 0.30 #646), 01mkq (0.34 #1002, 0.32 #755, 0.25 #1987), 02lp1 (0.31 #998, 0.31 #751, 0.25 #627), 062z7 (0.27 #767, 0.27 #1014, 0.25 #643), 03g3w (0.27 #642, 0.25 #1013, 0.25 #766), 05qjt (0.25 #747, 0.23 #994, 0.20 #623), 0g26h (0.25 #1030, 0.23 #783, 0.19 #1892), 01lj9 (0.20 #656, 0.19 #780, 0.18 #1027), 0_jm (0.19 #1045, 0.18 #798, 0.14 #1907), 01540 (0.19 #801, 0.19 #1048, 0.14 #677) >> Best rule #1017 for best value: >> intensional similarity = 3 >> extensional distance = 308 >> proper extension: 06pwq; 01w3v; 0kz2w; 01w5m; 02gr81; 017j69; 071_8; 09f2j; 027mdh; 01nnsv; ... >> query: (?x4344, 02j62) <- institution(?x865, ?x4344), ?x865 = 02h4rq6, major_field_of_study(?x4344, ?x254) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #655 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 173 *> proper extension: 02l9wl; 01dnnt; *> query: (?x4344, 0fdys) <- student(?x4344, ?x10074), people(?x4322, ?x10074), place_of_death(?x10074, ?x362) *> conf = 0.16 ranks of expected_values: 16 EVAL 03x83_ major_field_of_study 0fdys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 67.000 67.000 0.355 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8304-017z49 PRED entity: 017z49 PRED relation: film_regional_debut_venue PRED expected values: 018cvf 07zmj => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 17): 018cvf (0.11 #499, 0.11 #704, 0.10 #86), 0prpt (0.10 #98, 0.08 #511, 0.07 #716), 015hr (0.08 #702, 0.07 #84, 0.07 #497), 0gg7gsl (0.05 #77, 0.03 #181, 0.03 #42), 0kfhjq0 (0.05 #189, 0.05 #50, 0.03 #120), 0j63cyr (0.04 #496, 0.04 #118, 0.04 #701), 07751 (0.04 #79, 0.03 #697, 0.02 #458), 07zmj (0.04 #514, 0.04 #719, 0.03 #101), 04jpl (0.03 #105, 0.02 #313, 0.02 #35), 01ly5m (0.03 #113, 0.02 #43, 0.02 #182) >> Best rule #499 for best value: >> intensional similarity = 5 >> extensional distance = 177 >> proper extension: 02d44q; 0gh8zks; 0hgnl3t; 07k2mq; >> query: (?x3482, 018cvf) <- film_release_region(?x3482, ?x1229), film_release_region(?x3482, ?x390), nominated_for(?x746, ?x3482), ?x390 = 0chghy, ?x1229 = 059j2 >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 017z49 film_regional_debut_venue 07zmj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 116.000 116.000 0.112 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 017z49 film_regional_debut_venue 018cvf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.112 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #8303-02hnl PRED entity: 02hnl PRED relation: role PRED expected values: 07kc_ => 74 concepts (58 used for prediction) PRED predicted values (max 10 best out of 59): 0g2dz (0.92 #383, 0.90 #439, 0.87 #382), 026t6 (0.92 #383, 0.90 #439, 0.87 #382), 02dlh2 (0.92 #383, 0.90 #439, 0.87 #382), 06rvn (0.87 #382, 0.85 #438, 0.79 #53), 013y1f (0.83 #710, 0.82 #1366, 0.82 #1477), 05r5c (0.83 #710, 0.82 #1366, 0.82 #1477), 07xzm (0.83 #710, 0.82 #1366, 0.82 #1477), 02w3w (0.83 #710, 0.82 #1366, 0.82 #1477), 04rzd (0.83 #710, 0.82 #1366, 0.82 #1477), 0bxl5 (0.83 #710, 0.82 #1366, 0.82 #1477) >> Best rule #383 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 028tv0; >> query: (?x1750, ?x9219) <- performance_role(?x9219, ?x1750), group(?x9219, ?x5838), role(?x211, ?x1750), group(?x1750, ?x10737), group(?x1750, ?x4995), role(?x1750, ?x74), ?x10737 = 0b1hw, role(?x1969, ?x1750), ?x4995 = 01fmz6 >> conf = 0.92 => this is the best rule for 3 predicted values *> Best rule #720 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 0mkg; *> query: (?x1750, 07kc_) <- role(?x211, ?x1750), instrumentalists(?x1750, ?x6467), group(?x1750, ?x10938), role(?x1750, ?x4769), role(?x316, ?x1750), ?x10938 = 09jvl, award_winner(?x6467, ?x367), ?x4769 = 0dwt5 *> conf = 0.71 ranks of expected_values: 20 EVAL 02hnl role 07kc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 74.000 58.000 0.919 http://example.org/music/performance_role/regular_performances./music/group_membership/role #8302-0gqyl PRED entity: 0gqyl PRED relation: nominated_for PRED expected values: 0dsvzh 04mzf8 016z7s 021y7yw 0gyfp9c 0cq7kw 0j90s 0bnzd 09tkzy 01z452 => 49 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1443): 09q5w2 (0.82 #7338, 0.54 #11662, 0.29 #13103), 0_92w (0.82 #7340, 0.43 #11664, 0.20 #13105), 017gl1 (0.73 #7322, 0.49 #11646, 0.35 #13087), 011yl_ (0.64 #7680, 0.51 #12004, 0.30 #13445), 019vhk (0.64 #7573, 0.49 #11897, 0.28 #13338), 07w8fz (0.64 #7614, 0.49 #11938, 0.27 #13379), 0209hj (0.64 #7287, 0.49 #11611, 0.25 #4408), 0hv4t (0.64 #8138, 0.43 #12462, 0.33 #940), 0b6tzs (0.64 #7320, 0.43 #11644, 0.27 #13085), 02yvct (0.64 #7488, 0.43 #11812, 0.24 #13253) >> Best rule #7338 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 040njc; 0gq_v; 019f4v; 0gr0m; 0gq9h; 0gs9p; 0k611; 0gr51; 0gqy2; >> query: (?x1972, 09q5w2) <- nominated_for(?x1972, ?x10114), award(?x91, ?x1972), award_winner(?x1972, ?x1559), ?x10114 = 0bmhn >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #8178 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 040njc; 0gq_v; 019f4v; 0gr0m; 0gq9h; 0gs9p; 0k611; 0gr51; 0gqy2; *> query: (?x1972, 0j90s) <- nominated_for(?x1972, ?x10114), award(?x91, ?x1972), award_winner(?x1972, ?x1559), ?x10114 = 0bmhn *> conf = 0.55 ranks of expected_values: 43, 48, 58, 63, 140, 207, 303, 304, 424, 480 EVAL 0gqyl nominated_for 01z452 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 19.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqyl nominated_for 09tkzy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 49.000 19.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqyl nominated_for 0bnzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 49.000 19.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqyl nominated_for 0j90s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 49.000 19.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqyl nominated_for 0cq7kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 49.000 19.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqyl nominated_for 0gyfp9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 19.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqyl nominated_for 021y7yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 19.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqyl nominated_for 016z7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 19.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqyl nominated_for 04mzf8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 49.000 19.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqyl nominated_for 0dsvzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 19.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8301-0jrqq PRED entity: 0jrqq PRED relation: award_winner! PRED expected values: 04ljl_l => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 285): 0gs9p (0.47 #503, 0.38 #34609, 0.37 #45295), 019f4v (0.42 #490, 0.29 #1771, 0.15 #2200), 05p1dby (0.38 #34609, 0.37 #45295, 0.37 #37603), 02pqp12 (0.38 #34609, 0.37 #45295, 0.37 #37603), 04dn09n (0.38 #34609, 0.37 #45295, 0.37 #37603), 03hl6lc (0.38 #34609, 0.37 #45295, 0.37 #37603), 0gr51 (0.38 #34609, 0.37 #45295, 0.37 #37603), 04ljl_l (0.38 #34609, 0.37 #45295, 0.37 #37603), 02w_6xj (0.32 #662, 0.15 #1943, 0.11 #2372), 02rdyk7 (0.32 #515, 0.14 #1796, 0.07 #2225) >> Best rule #503 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 052gzr; >> query: (?x3873, 0gs9p) <- award_winner(?x3233, ?x3873), profession(?x3873, ?x319), ?x3233 = 02qt02v >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #34609 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1683 *> proper extension: 014l4w; *> query: (?x3873, ?x102) <- award_winner(?x899, ?x3873), award(?x3873, ?x102), nominated_for(?x899, ?x54) *> conf = 0.38 ranks of expected_values: 8 EVAL 0jrqq award_winner! 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 120.000 120.000 0.474 http://example.org/award/award_category/winners./award/award_honor/award_winner #8300-0ckt6 PRED entity: 0ckt6 PRED relation: genre PRED expected values: 01hwc6 => 124 concepts (49 used for prediction) PRED predicted values (max 10 best out of 101): 07s9rl0 (0.85 #4584, 0.67 #2116, 0.66 #1529), 01z4y (0.58 #4583, 0.56 #5169, 0.53 #1294), 03k9fj (0.55 #4125, 0.36 #835, 0.35 #1657), 01jfsb (0.50 #365, 0.45 #1893, 0.44 #483), 03npn (0.48 #3649, 0.33 #124, 0.31 #477), 02kdv5l (0.46 #2824, 0.44 #1884, 0.42 #3058), 0vgkd (0.42 #245, 0.33 #128, 0.31 #481), 06cvj (0.38 #1415, 0.33 #122, 0.33 #1063), 0hcr (0.33 #23, 0.28 #847, 0.24 #1669), 09q17 (0.33 #59, 0.09 #3702, 0.06 #4173) >> Best rule #4584 for best value: >> intensional similarity = 6 >> extensional distance = 242 >> proper extension: 0gcrg; 072r5v; 0bs8hvm; 0cq8nx; 0267wwv; 0c5qvw; 02yy9r; >> query: (?x12899, 07s9rl0) <- genre(?x12899, ?x1403), cinematography(?x12899, ?x5862), genre(?x3398, ?x1403), genre(?x622, ?x1403), ?x622 = 0fq27fp, ?x3398 = 02n9bh >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #254 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 06__m6; *> query: (?x12899, 01hwc6) <- genre(?x12899, ?x2700), genre(?x12899, ?x271), executive_produced_by(?x12899, ?x5394), ?x2700 = 06nbt, genre(?x7927, ?x271), ?x7927 = 014bpd *> conf = 0.08 ranks of expected_values: 43 EVAL 0ckt6 genre 01hwc6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 124.000 49.000 0.852 http://example.org/film/film/genre #8299-07z4p PRED entity: 07z4p PRED relation: film_release_distribution_medium! PRED expected values: 02bg55 01s9vc => 6 concepts (3 used for prediction) PRED predicted values (max 10 best out of 2082): 0f3m1 (0.77 #1449, 0.76 #1455, 0.70 #1459), 0dtfn (0.77 #1449, 0.76 #1455, 0.70 #1459), 0gt14 (0.77 #1449, 0.76 #1455, 0.70 #1459), 0gcpc (0.77 #1449, 0.76 #1455, 0.70 #1459), 05ypj5 (0.77 #1449, 0.76 #1455, 0.70 #1459), 0k_9j (0.77 #1449, 0.76 #1455, 0.70 #1459), 09sr0 (0.77 #1449, 0.76 #1455, 0.70 #1459), 0ktpx (0.77 #1449, 0.76 #1455, 0.70 #1459), 013q07 (0.77 #1449, 0.76 #1455, 0.70 #1459), 01d2v1 (0.77 #1449, 0.76 #1455, 0.70 #1459) >> Best rule #1449 for best value: >> intensional similarity = 90 >> extensional distance = 1 >> proper extension: 029j_; >> query: (?x10850, ?x54) <- film_release_distribution_medium(?x11351, ?x10850), film_release_distribution_medium(?x9941, ?x10850), film_release_distribution_medium(?x9194, ?x10850), film_release_distribution_medium(?x8867, ?x10850), film_release_distribution_medium(?x8657, ?x10850), film_release_distribution_medium(?x8236, ?x10850), film_release_distribution_medium(?x8162, ?x10850), film_release_distribution_medium(?x7897, ?x10850), film_release_distribution_medium(?x7678, ?x10850), film_release_distribution_medium(?x7502, ?x10850), film_release_distribution_medium(?x7494, ?x10850), film_release_distribution_medium(?x7170, ?x10850), film_release_distribution_medium(?x5578, ?x10850), film_release_distribution_medium(?x5230, ?x10850), film_release_distribution_medium(?x4811, ?x10850), film_release_distribution_medium(?x3201, ?x10850), film_release_distribution_medium(?x2717, ?x10850), film_release_distribution_medium(?x2318, ?x10850), film_release_distribution_medium(?x1743, ?x10850), film_release_distribution_medium(?x1071, ?x10850), film_release_distribution_medium(?x664, ?x10850), ?x7897 = 03np63f, ?x7494 = 0dgrwqr, ?x11351 = 02wtp6, ?x1071 = 02d44q, ?x8657 = 030z4z, film_distribution_medium(?x4500, ?x10850), ?x664 = 0401sg, ?x3201 = 01ffx4, ?x8867 = 03lfd_, ?x8236 = 042zrm, ?x7678 = 0gvvf4j, ?x4811 = 0f4k49, ?x9941 = 024lt6, film_release_region(?x5230, ?x1453), film_release_region(?x5230, ?x1353), film_release_region(?x5230, ?x94), titles(?x3271, ?x5230), ?x2717 = 0k5g9, ?x9194 = 0fpgp26, ?x1453 = 06qd3, award(?x5230, ?x9217), award_winner(?x5230, ?x10186), ?x3271 = 012w70, ?x2318 = 06v9_x, genre(?x5230, ?x225), nominated_for(?x4056, ?x5578), ?x1353 = 035qy, country(?x5230, ?x2346), ?x8162 = 0bs8ndx, nominated_for(?x11702, ?x5230), film_release_region(?x5578, ?x1264), ?x11702 = 09v1lrz, ?x1743 = 0c8tkt, ?x7502 = 0233bn, country(?x5578, ?x429), genre(?x8001, ?x225), genre(?x5098, ?x225), genre(?x2917, ?x225), genre(?x2441, ?x225), genre(?x1224, ?x225), genre(?x188, ?x225), genre(?x54, ?x225), ?x1224 = 020fcn, genre(?x3721, ?x225), ?x188 = 0140g4, ?x2441 = 0cc5mcj, ?x5098 = 05znxx, ?x8001 = 02qkwl, language(?x5230, ?x254), ?x1264 = 0345h, nationality(?x51, ?x94), country(?x10752, ?x94), country(?x6900, ?x94), contains(?x94, ?x95), olympics(?x94, ?x358), country_of_origin(?x50, ?x94), country(?x2768, ?x94), second_level_divisions(?x94, ?x322), film_release_region(?x7293, ?x94), time_zones(?x94, ?x1638), ?x7170 = 02pxst, service_location(?x127, ?x94), ?x7293 = 027m67, geographic_distribution(?x1423, ?x94), ?x6900 = 02z0f6l, ?x2917 = 03kg2v, ?x10752 = 01k5y0, country(?x310, ?x94), country(?x150, ?x94) >> conf = 0.77 => this is the best rule for 1289 predicted values *> Best rule #1455 for first EXPECTED value: *> intensional similarity = 90 *> extensional distance = 1 *> proper extension: 029j_; *> query: (?x10850, ?x66) <- film_release_distribution_medium(?x11351, ?x10850), film_release_distribution_medium(?x9941, ?x10850), film_release_distribution_medium(?x9194, ?x10850), film_release_distribution_medium(?x8867, ?x10850), film_release_distribution_medium(?x8657, ?x10850), film_release_distribution_medium(?x8236, ?x10850), film_release_distribution_medium(?x8162, ?x10850), film_release_distribution_medium(?x7897, ?x10850), film_release_distribution_medium(?x7678, ?x10850), film_release_distribution_medium(?x7502, ?x10850), film_release_distribution_medium(?x7494, ?x10850), film_release_distribution_medium(?x7170, ?x10850), film_release_distribution_medium(?x5578, ?x10850), film_release_distribution_medium(?x5230, ?x10850), film_release_distribution_medium(?x4811, ?x10850), film_release_distribution_medium(?x3201, ?x10850), film_release_distribution_medium(?x2717, ?x10850), film_release_distribution_medium(?x2318, ?x10850), film_release_distribution_medium(?x1743, ?x10850), film_release_distribution_medium(?x1071, ?x10850), film_release_distribution_medium(?x664, ?x10850), ?x7897 = 03np63f, ?x7494 = 0dgrwqr, ?x11351 = 02wtp6, ?x1071 = 02d44q, ?x8657 = 030z4z, film_distribution_medium(?x4500, ?x10850), ?x664 = 0401sg, ?x3201 = 01ffx4, ?x8867 = 03lfd_, ?x8236 = 042zrm, ?x7678 = 0gvvf4j, ?x4811 = 0f4k49, ?x9941 = 024lt6, film_release_region(?x5230, ?x1453), film_release_region(?x5230, ?x1353), film_release_region(?x5230, ?x94), titles(?x3271, ?x5230), ?x2717 = 0k5g9, ?x9194 = 0fpgp26, ?x1453 = 06qd3, award(?x5230, ?x9217), award_winner(?x5230, ?x10186), ?x3271 = 012w70, ?x2318 = 06v9_x, genre(?x5230, ?x225), nominated_for(?x4056, ?x5578), ?x1353 = 035qy, country(?x5230, ?x2346), ?x8162 = 0bs8ndx, nominated_for(?x11702, ?x5230), film_release_region(?x5578, ?x1264), ?x11702 = 09v1lrz, ?x1743 = 0c8tkt, ?x7502 = 0233bn, country(?x5578, ?x429), genre(?x8001, ?x225), genre(?x5098, ?x225), genre(?x2917, ?x225), genre(?x2441, ?x225), genre(?x1224, ?x225), genre(?x188, ?x225), ?x1224 = 020fcn, genre(?x3721, ?x225), ?x188 = 0140g4, ?x2441 = 0cc5mcj, ?x5098 = 05znxx, ?x8001 = 02qkwl, language(?x5230, ?x254), ?x1264 = 0345h, nationality(?x51, ?x94), country(?x10752, ?x94), country(?x6900, ?x94), country(?x66, ?x94), contains(?x94, ?x95), olympics(?x94, ?x358), country_of_origin(?x50, ?x94), country(?x2768, ?x94), second_level_divisions(?x94, ?x322), film_release_region(?x7293, ?x94), time_zones(?x94, ?x1638), ?x7170 = 02pxst, service_location(?x127, ?x94), ?x7293 = 027m67, geographic_distribution(?x1423, ?x94), ?x6900 = 02z0f6l, ?x2917 = 03kg2v, ?x10752 = 01k5y0, country(?x310, ?x94), country(?x150, ?x94) *> conf = 0.76 ranks of expected_values: 1301, 1412 EVAL 07z4p film_release_distribution_medium! 01s9vc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 6.000 3.000 0.771 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 07z4p film_release_distribution_medium! 02bg55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 6.000 3.000 0.771 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #8298-01v42g PRED entity: 01v42g PRED relation: award_nominee! PRED expected values: 073w14 => 78 concepts (32 used for prediction) PRED predicted values (max 10 best out of 722): 0c6qh (0.80 #44278, 0.80 #44279, 0.80 #37286), 01wgcvn (0.33 #848, 0.09 #7840, 0.09 #3178), 06wm0z (0.33 #1198, 0.09 #8190, 0.09 #3528), 03yrkt (0.33 #1829, 0.09 #8821, 0.09 #4159), 016fjj (0.33 #832, 0.09 #7824, 0.09 #3162), 07ddz9 (0.33 #2109, 0.09 #9101, 0.09 #4439), 01j5ws (0.33 #676, 0.09 #7668, 0.09 #3006), 01vvb4m (0.25 #55931, 0.10 #41947, 0.09 #20974), 073w14 (0.25 #55931, 0.10 #41947, 0.09 #20974), 01v42g (0.25 #55931, 0.10 #41947, 0.09 #20974) >> Best rule #44278 for best value: >> intensional similarity = 4 >> extensional distance = 1157 >> proper extension: 02q6cv4; >> query: (?x1289, ?x1290) <- award(?x1289, ?x704), award_nominee(?x1289, ?x2499), award_nominee(?x1289, ?x1290), participant(?x2715, ?x2499) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #55931 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1179 *> proper extension: 04f9r2; *> query: (?x1289, ?x396) <- award_nominee(?x2646, ?x1289), participant(?x2646, ?x3117), award_nominee(?x2646, ?x396) *> conf = 0.25 ranks of expected_values: 9 EVAL 01v42g award_nominee! 073w14 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 78.000 32.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8297-0dk0dj PRED entity: 0dk0dj PRED relation: genre PRED expected values: 07s9rl0 => 36 concepts (30 used for prediction) PRED predicted values (max 10 best out of 185): 07s9rl0 (0.96 #1279, 0.90 #1359, 0.66 #1930), 06n90 (0.84 #885, 0.50 #329, 0.46 #727), 0hcr (0.68 #412, 0.62 #652, 0.62 #333), 01z4y (0.65 #1053, 0.63 #1132, 0.55 #1211), 0jxy (0.62 #346, 0.41 #586, 0.36 #744), 01jfsb (0.57 #726, 0.25 #1520, 0.25 #328), 0lsxr (0.56 #802, 0.18 #723, 0.14 #2253), 0c4xc (0.44 #1078, 0.40 #1236, 0.39 #1157), 03k9fj (0.44 #883, 0.44 #327, 0.36 #567), 01htzx (0.40 #887, 0.27 #571, 0.27 #252) >> Best rule #1279 for best value: >> intensional similarity = 24 >> extensional distance = 104 >> proper extension: 02py4c8; 02k_4g; 08l0x2; 06w7mlh; 06r4f; 045r_9; 09rfpk; 053x8hr; 070ltt; 01kt_j; ... >> query: (?x8759, 07s9rl0) <- program(?x8760, ?x8759), genre(?x8759, ?x1403), genre(?x8941, ?x1403), genre(?x7757, ?x1403), genre(?x7354, ?x1403), genre(?x4653, ?x1403), genre(?x4581, ?x1403), genre(?x3071, ?x1403), genre(?x1702, ?x1403), genre(?x1493, ?x1403), genre(?x607, ?x1403), genre(?x499, ?x1403), genre(?x155, ?x1403), ?x499 = 04v8x9, ?x155 = 018js4, ?x1702 = 0c00zd0, ?x4581 = 02ppg1r, ?x7354 = 0258dh, ?x1493 = 05j82v, ?x7757 = 02ljhg, film_release_region(?x607, ?x87), ?x4653 = 0jsqk, film_crew_role(?x3071, ?x137), ?x8941 = 029jt9 >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dk0dj genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 30.000 0.962 http://example.org/tv/tv_program/genre #8296-090gpr PRED entity: 090gpr PRED relation: place_of_birth PRED expected values: 09c17 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 75): 04vmp (0.17 #268, 0.15 #4495, 0.14 #12948), 02_286 (0.10 #3541, 0.08 #6358, 0.08 #4950), 0cr3d (0.07 #799, 0.05 #5729, 0.05 #7138), 06c62 (0.07 #962, 0.05 #1666, 0.03 #2370), 0c9cw (0.07 #1376, 0.05 #2080, 0.03 #2784), 01qh7 (0.07 #809, 0.05 #1513, 0.02 #3626), 0c_m3 (0.07 #902, 0.05 #1606, 0.02 #6536), 03l2n (0.07 #874, 0.05 #1578, 0.01 #3691), 0vm39 (0.07 #1035, 0.05 #1739, 0.01 #3852), 0r04p (0.07 #877, 0.05 #1581, 0.01 #3694) >> Best rule #268 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 02756j; >> query: (?x13519, 04vmp) <- gender(?x13519, ?x514), nationality(?x13519, ?x2146), ?x514 = 02zsn, award_winner(?x10156, ?x13519), ?x10156 = 03r8v_ >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 090gpr place_of_birth 09c17 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 70.000 0.167 http://example.org/people/person/place_of_birth #8295-01_d4 PRED entity: 01_d4 PRED relation: place_of_birth! PRED expected values: 03d_w3h 01rnxn 085pr 098n5 01y8cr 0cnl09 02t_99 02b29 06kxk2 019389 02mpb 025jbj 05tjm3 => 228 concepts (113 used for prediction) PRED predicted values (max 10 best out of 2379): 081nh (0.36 #62817, 0.35 #138202, 0.34 #153285), 059x0w (0.36 #62817, 0.35 #138202, 0.34 #153285), 02l840 (0.36 #62817, 0.34 #153285, 0.34 #273903), 01vtg4q (0.36 #62817, 0.34 #153285, 0.34 #273903), 03h_yfh (0.36 #62817, 0.34 #153285, 0.34 #273903), 033jkj (0.36 #62817, 0.34 #153285, 0.34 #273903), 02qgyv (0.36 #62817, 0.34 #153285, 0.34 #273903), 0cjsxp (0.36 #62817, 0.34 #153285, 0.34 #273903), 01x6jd (0.36 #62817, 0.34 #153285, 0.34 #273903), 023kzp (0.36 #62817, 0.34 #153285, 0.34 #273903) >> Best rule #62817 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0clz7; 013m43; 0r6cx; 08809; 0xn7b; 0f485; >> query: (?x1860, ?x396) <- contains(?x3818, ?x1860), citytown(?x1924, ?x1860), adjoins(?x448, ?x1860), location(?x396, ?x1860) >> conf = 0.36 => this is the best rule for 18 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 18, 379, 389, 392, 976 EVAL 01_d4 place_of_birth! 05tjm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 025jbj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 02mpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 019389 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 06kxk2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 02b29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 02t_99 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 0cnl09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 01y8cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 098n5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 085pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 01rnxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 113.000 0.360 http://example.org/people/person/place_of_birth EVAL 01_d4 place_of_birth! 03d_w3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 228.000 113.000 0.360 http://example.org/people/person/place_of_birth #8294-01vsl3_ PRED entity: 01vsl3_ PRED relation: role PRED expected values: 02k856 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 111): 03gvt (0.37 #1251, 0.32 #1928, 0.32 #1927), 03qjg (0.37 #1251, 0.32 #1928, 0.32 #1927), 05148p4 (0.37 #1251, 0.32 #1928, 0.32 #1927), 018j2 (0.37 #1251, 0.32 #1928, 0.32 #1927), 06w7v (0.37 #1251, 0.32 #1928, 0.32 #1927), 0mkg (0.37 #1251, 0.32 #1928, 0.32 #1927), 04rzd (0.29 #328, 0.25 #39, 0.14 #232), 01vdm0 (0.28 #1471, 0.27 #8016, 0.26 #8113), 03bx0bm (0.27 #866, 0.24 #4619, 0.24 #5966), 03_vpw (0.27 #866, 0.24 #4619, 0.24 #5966) >> Best rule #1251 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0282x; >> query: (?x2799, ?x227) <- influenced_by(?x2799, ?x2845), award(?x2799, ?x724), instrumentalists(?x227, ?x2799) >> conf = 0.37 => this is the best rule for 6 predicted values *> Best rule #1929 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 0pcc0; 0hgqq; 01s7qqw; 021r7r; 043d4; 01r0t_j; 0hqgp; 0c73z; 0c73g; *> query: (?x2799, ?x74) <- influenced_by(?x2799, ?x2845), instrumentalists(?x614, ?x2799), role(?x614, ?x74) *> conf = 0.03 ranks of expected_values: 69 EVAL 01vsl3_ role 02k856 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 158.000 158.000 0.365 http://example.org/music/artist/track_contributions./music/track_contribution/role #8293-017kz7 PRED entity: 017kz7 PRED relation: film! PRED expected values: 086k8 => 118 concepts (109 used for prediction) PRED predicted values (max 10 best out of 64): 086k8 (0.95 #2792, 0.21 #661, 0.20 #587), 03xq0f (0.58 #2355, 0.56 #885, 0.54 #1841), 016tt2 (0.25 #4, 0.21 #224, 0.19 #151), 01795t (0.23 #164, 0.21 #90, 0.19 #1118), 016tw3 (0.17 #3242, 0.16 #816, 0.16 #3832), 024rgt (0.16 #385, 0.11 #92, 0.08 #678), 0g1rw (0.14 #301, 0.09 #1404, 0.09 #1697), 017s11 (0.13 #2719, 0.12 #4939, 0.12 #6354), 054g1r (0.13 #619, 0.12 #693, 0.12 #1796), 093h7p (0.11 #127, 0.08 #201, 0.06 #1155) >> Best rule #2792 for best value: >> intensional similarity = 5 >> extensional distance = 254 >> proper extension: 05r3qc; >> query: (?x7760, 086k8) <- film(?x5854, ?x7760), film(?x5854, ?x6999), film(?x5854, ?x6782), ?x6782 = 07jnt, ?x6999 = 02r9p0c >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017kz7 film! 086k8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 109.000 0.945 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #8292-02gf_l PRED entity: 02gf_l PRED relation: film PRED expected values: 0k54q => 101 concepts (66 used for prediction) PRED predicted values (max 10 best out of 755): 05zlld0 (0.36 #47962, 0.06 #117235, 0.04 #18371), 0b3n61 (0.36 #47962, 0.04 #17335, 0.04 #19111), 0k5fg (0.33 #1078, 0.02 #17066, 0.02 #18842), 01f39b (0.33 #966, 0.02 #34718, 0.02 #15177), 09ps01 (0.25 #4360, 0.22 #6136, 0.10 #7913), 02_qt (0.25 #4175, 0.22 #5951, 0.03 #20163), 056xkh (0.25 #5140, 0.22 #6916, 0.02 #30008), 0fs9vc (0.22 #6569, 0.12 #4793, 0.02 #11898), 01xdxy (0.17 #3331, 0.12 #5107, 0.11 #6883), 03n3gl (0.17 #2888, 0.10 #9993, 0.02 #13546) >> Best rule #47962 for best value: >> intensional similarity = 3 >> extensional distance = 439 >> proper extension: 06jzh; 04shbh; 019_1h; 02xb2bt; 01z7_f; 03q95r; 01ry0f; 03ym1; 05dtsb; 031k24; ... >> query: (?x7266, ?x3748) <- film(?x7266, ?x5277), featured_film_locations(?x5277, ?x739), prequel(?x5277, ?x3748) >> conf = 0.36 => this is the best rule for 2 predicted values *> Best rule #29343 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 161 *> proper extension: 030x48; 0205dx; 023v4_; 02zfdp; 05myd2; 0q1lp; 033jj1; 069z_5; 0534nr; 01r4bps; ... *> query: (?x7266, 0k54q) <- profession(?x7266, ?x1383), actor(?x2555, ?x7266), ?x1383 = 0np9r, film(?x7266, ?x124) *> conf = 0.03 ranks of expected_values: 184 EVAL 02gf_l film 0k54q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 101.000 66.000 0.359 http://example.org/film/actor/film./film/performance/film #8291-07s4l PRED entity: 07s4l PRED relation: symptom_of! PRED expected values: 02tfl8 => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 67): 012qjw (0.75 #320, 0.73 #515, 0.67 #129), 02tfl8 (0.70 #389, 0.59 #337, 0.56 #363), 0brgy (0.67 #528, 0.62 #332, 0.60 #394), 0hgxh (0.67 #528, 0.62 #332, 0.40 #530), 0gxb2 (0.62 #332, 0.56 #532, 0.40 #147), 01cdt5 (0.59 #337, 0.56 #532, 0.54 #308), 0dq9p (0.56 #363, 0.40 #530, 0.39 #334), 01l2m3 (0.56 #363, 0.40 #530, 0.39 #334), 08g5q7 (0.54 #308, 0.40 #147, 0.33 #310), 02y0js (0.40 #147, 0.40 #530, 0.39 #334) >> Best rule #320 for best value: >> intensional similarity = 23 >> extensional distance = 6 >> proper extension: 09d11; 01gkcc; 09jg8; >> query: (?x13485, 012qjw) <- symptom_of(?x13373, ?x13485), symptom_of(?x10717, ?x13485), symptom_of(?x4905, ?x13485), ?x4905 = 01j6t0, symptom_of(?x13373, ?x14430), symptom_of(?x13373, ?x14096), symptom_of(?x13373, ?x11739), symptom_of(?x13373, ?x10480), symptom_of(?x13373, ?x7006), symptom_of(?x13373, ?x4959), ?x14430 = 024c2, symptom_of(?x9510, ?x7006), ?x9510 = 0hgxh, risk_factors(?x14096, ?x8524), ?x8524 = 01hbgs, ?x10480 = 0h1n9, symptom_of(?x9509, ?x14096), ?x11739 = 0167bx, ?x10717 = 0cjf0, people(?x4959, ?x8473), ?x8473 = 0gyy0, ?x9509 = 0gxb2, risk_factors(?x4959, ?x8023) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #389 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 8 *> proper extension: 01bcp7; *> query: (?x13485, 02tfl8) <- symptom_of(?x13373, ?x13485), symptom_of(?x4905, ?x13485), symptom_of(?x4905, ?x13131), symptom_of(?x4905, ?x11064), symptom_of(?x4905, ?x7586), symptom_of(?x4905, ?x7006), symptom_of(?x4905, ?x6656), symptom_of(?x4905, ?x3680), people(?x13485, ?x11497), ?x11064 = 01n3bm, notable_people_with_this_condition(?x6656, ?x13842), notable_people_with_this_condition(?x6656, ?x8708), ?x13131 = 0d19y2, ?x7006 = 02psvcf, risk_factors(?x3680, ?x514), ?x514 = 02zsn, ?x7586 = 074m2, ?x13842 = 095nx, ?x8708 = 01vn0t_, symptom_of(?x13373, ?x12536), ?x12536 = 0dcp_ *> conf = 0.70 ranks of expected_values: 2 EVAL 07s4l symptom_of! 02tfl8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 52.000 0.750 http://example.org/medicine/symptom/symptom_of #8290-05pdh86 PRED entity: 05pdh86 PRED relation: nominated_for! PRED expected values: 04ljl_l => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 207): 04dn09n (0.36 #742, 0.24 #6642, 0.24 #10654), 0gq9h (0.36 #10682, 0.34 #7378, 0.31 #298), 02hsq3m (0.34 #265, 0.25 #1917, 0.25 #1445), 05ztrmj (0.33 #132, 0.26 #604, 0.25 #19121), 03c7tr1 (0.33 #46, 0.25 #19121, 0.24 #17231), 0gs9p (0.32 #10684, 0.27 #6908, 0.26 #16822), 099c8n (0.31 #292, 0.29 #528, 0.24 #6428), 02qyntr (0.31 #414, 0.28 #886, 0.23 #6550), 0l8z1 (0.31 #287, 0.25 #19121, 0.22 #759), 0gr42 (0.31 #323, 0.22 #1503, 0.21 #1975) >> Best rule #742 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 02rlj20; >> query: (?x4464, 04dn09n) <- award(?x4464, ?x298), film(?x9314, ?x4464), award_nominee(?x3289, ?x9314), ?x3289 = 0347xl >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #19121 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1021 *> proper extension: 090s_0; 0yx7h; *> query: (?x4464, ?x102) <- award_winner(?x4464, ?x4046), award_winner(?x4464, ?x3860), nominated_for(?x298, ?x4464), nominated_for(?x4046, ?x504), award(?x3860, ?x102) *> conf = 0.25 ranks of expected_values: 23 EVAL 05pdh86 nominated_for! 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 103.000 103.000 0.361 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8289-03ktjq PRED entity: 03ktjq PRED relation: program PRED expected values: 0l76z => 97 concepts (31 used for prediction) PRED predicted values (max 10 best out of 87): 0pk1p (0.33 #1573, 0.06 #4724, 0.04 #349), 06rmdr (0.33 #1573, 0.06 #4724, 0.04 #349), 060v34 (0.33 #1573, 0.06 #4724), 0l76z (0.08 #1106, 0.07 #1281, 0.07 #581), 0828jw (0.06 #3753, 0.05 #1476, 0.05 #3579), 02r1ysd (0.05 #264, 0.04 #439, 0.02 #1138), 01b9w3 (0.05 #229, 0.04 #404, 0.01 #2155), 01fszq (0.05 #323, 0.04 #498), 08jgk1 (0.05 #3518, 0.05 #3692, 0.04 #2644), 0d68qy (0.05 #3704, 0.04 #1427, 0.04 #4052) >> Best rule #1573 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 01l1ls; >> query: (?x5781, ?x570) <- producer_type(?x5781, ?x632), nominated_for(?x5781, ?x570), award_winner(?x574, ?x5781) >> conf = 0.33 => this is the best rule for 3 predicted values *> Best rule #1106 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 131 *> proper extension: 02vyw; 021yw7; 026dx; 03_80b; 06mr6; 0gs1_; 0272kv; *> query: (?x5781, 0l76z) <- produced_by(?x3055, ?x5781), film_format(?x3055, ?x909), award_nominee(?x382, ?x5781) *> conf = 0.08 ranks of expected_values: 4 EVAL 03ktjq program 0l76z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 31.000 0.335 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/program #8288-0418wg PRED entity: 0418wg PRED relation: genre PRED expected values: 0lsxr => 81 concepts (79 used for prediction) PRED predicted values (max 10 best out of 84): 07s9rl0 (0.71 #1299, 0.62 #473, 0.61 #237), 02l7c8 (0.62 #133, 0.36 #15, 0.33 #251), 03k9fj (0.39 #601, 0.38 #719, 0.38 #1782), 06cvj (0.36 #3, 0.31 #121, 0.16 #357), 06n90 (0.28 #1783, 0.24 #838, 0.23 #602), 01hmnh (0.28 #843, 0.27 #725, 0.27 #607), 0lsxr (0.23 #126, 0.22 #480, 0.21 #1779), 03bxz7 (0.23 #173, 0.18 #55, 0.11 #291), 060__y (0.19 #1314, 0.17 #252, 0.17 #1432), 01f9r0 (0.18 #74, 0.10 #9215, 0.08 #192) >> Best rule #1299 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 03j63k; >> query: (?x2500, 07s9rl0) <- nominated_for(?x995, ?x2500), award(?x3756, ?x995), ?x3756 = 01wgcvn >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #126 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 07kdkfj; *> query: (?x2500, 0lsxr) <- film(?x5246, ?x2500), ?x5246 = 046zh, film_crew_role(?x2500, ?x137) *> conf = 0.23 ranks of expected_values: 7 EVAL 0418wg genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 81.000 79.000 0.712 http://example.org/film/film/genre #8287-019_1h PRED entity: 019_1h PRED relation: profession PRED expected values: 02hrh1q => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 72): 02hrh1q (0.92 #1365, 0.91 #915, 0.90 #1065), 01d_h8 (0.37 #2106, 0.34 #3006, 0.33 #2406), 0dxtg (0.34 #1514, 0.30 #8565, 0.29 #7815), 03gjzk (0.33 #166, 0.25 #2116, 0.25 #1666), 0cbd2 (0.32 #1507, 0.18 #4357, 0.15 #7958), 0np9r (0.28 #11404, 0.26 #622, 0.26 #14706), 0kyk (0.26 #1531, 0.16 #4381, 0.12 #6782), 02jknp (0.22 #7509, 0.21 #10660, 0.21 #10359), 09jwl (0.18 #6471, 0.17 #4671, 0.17 #9171), 018gz8 (0.15 #1818, 0.15 #1968, 0.15 #1518) >> Best rule #1365 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 044mfr; 07bsj; >> query: (?x1020, 02hrh1q) <- actor(?x293, ?x1020), participant(?x538, ?x1020), nationality(?x1020, ?x608) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019_1h profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.920 http://example.org/people/person/profession #8286-0484q PRED entity: 0484q PRED relation: influenced_by PRED expected values: 073bb => 137 concepts (46 used for prediction) PRED predicted values (max 10 best out of 267): 084w8 (0.18 #1749, 0.07 #11362, 0.06 #13983), 0lrh (0.15 #74, 0.12 #1384, 0.10 #2692), 0m2l9 (0.15 #448, 0.12 #884, 0.09 #3065), 01lc5 (0.15 #387, 0.04 #6499, 0.03 #9557), 0l5yl (0.15 #270, 0.03 #9440, 0.02 #6382), 01s7qqw (0.15 #164, 0.03 #9334, 0.02 #6276), 01t9qj_ (0.15 #257, 0.03 #9427, 0.02 #6369), 032l1 (0.12 #11448, 0.12 #14069, 0.12 #1835), 02wh0 (0.12 #2130, 0.10 #11743, 0.09 #14364), 015n8 (0.12 #2158, 0.08 #11771, 0.07 #14392) >> Best rule #1749 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 01kx1j; >> query: (?x7375, 084w8) <- gender(?x7375, ?x231), people(?x6821, ?x7375), ?x6821 = 06z5s, ?x231 = 05zppz >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0484q influenced_by 073bb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 46.000 0.176 http://example.org/influence/influence_node/influenced_by #8285-01p7s6 PRED entity: 01p7s6 PRED relation: people PRED expected values: 022p06 => 41 concepts (31 used for prediction) PRED predicted values (max 10 best out of 2398): 06cgy (0.42 #17432, 0.29 #5368, 0.28 #32943), 016z2j (0.40 #3750, 0.36 #8922, 0.33 #305), 01vrt_c (0.40 #3599, 0.33 #154, 0.27 #8771), 0c0k1 (0.40 #4671, 0.33 #1226, 0.27 #9843), 01gbn6 (0.40 #4784, 0.33 #1339, 0.27 #9956), 048cl (0.40 #4482, 0.33 #1037, 0.25 #7930), 0k4gf (0.40 #3601, 0.33 #156, 0.25 #7049), 03rx9 (0.40 #4814, 0.33 #1369, 0.18 #9986), 0mb5x (0.40 #4626, 0.33 #1181, 0.18 #9798), 0lrh (0.40 #3831, 0.33 #386, 0.18 #9003) >> Best rule #17432 for best value: >> intensional similarity = 9 >> extensional distance = 17 >> proper extension: 05l3g_; >> query: (?x12168, 06cgy) <- people(?x12168, ?x7670), people(?x12168, ?x3705), student(?x3564, ?x3705), participant(?x3705, ?x844), film(?x7670, ?x5074), award(?x3705, ?x749), award_winner(?x2707, ?x7670), celebrity(?x395, ?x3705), award(?x7670, ?x198) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #34469 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 27 *> proper extension: 0dryh9k; *> query: (?x12168, ?x71) <- people(?x12168, ?x7670), people(?x12168, ?x3705), student(?x3564, ?x3705), participant(?x3705, ?x844), film(?x7670, ?x5074), award(?x3705, ?x749), type_of_union(?x3705, ?x566), award(?x7670, ?x1307), award(?x71, ?x1307), nominated_for(?x1307, ?x161) *> conf = 0.03 ranks of expected_values: 1957 EVAL 01p7s6 people 022p06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 31.000 0.421 http://example.org/people/ethnicity/people #8284-03ln8b PRED entity: 03ln8b PRED relation: nominated_for! PRED expected values: 09qvc0 0cjyzs => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 195): 0cqhk0 (0.77 #8935, 0.69 #1881, 0.68 #10350), 09qvf4 (0.77 #8935, 0.69 #1881, 0.68 #10350), 0fbtbt (0.42 #395, 0.27 #160, 0.25 #1805), 0ck27z (0.42 #307, 0.27 #72, 0.24 #11060), 0bp_b2 (0.42 #252, 0.27 #17, 0.20 #13883), 099c8n (0.36 #57, 0.17 #7814, 0.17 #8049), 0gq9h (0.35 #8761, 0.34 #8055, 0.34 #8290), 0gkts9 (0.33 #359, 0.27 #124, 0.24 #11060), 0gkr9q (0.33 #442, 0.27 #207, 0.21 #1147), 0gs9p (0.32 #8763, 0.31 #8057, 0.30 #8292) >> Best rule #8935 for best value: >> intensional similarity = 2 >> extensional distance = 691 >> proper extension: 085bd1; 03q5db; 011yfd; 032zq6; 02q_4ph; 07l50vn; 05_61y; 037q31; 09hy79; 0k2m6; ... >> query: (?x2078, ?x11179) <- award(?x2078, ?x11179), ceremony(?x11179, ?x873) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #1257 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 98 *> proper extension: 04mx8h4; 0hr41p6; *> query: (?x2078, 0cjyzs) <- genre(?x2078, ?x600), honored_for(?x1112, ?x2078), genre(?x280, ?x600) *> conf = 0.23 ranks of expected_values: 26, 95 EVAL 03ln8b nominated_for! 0cjyzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 66.000 66.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03ln8b nominated_for! 09qvc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 66.000 66.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8283-01grpc PRED entity: 01grpc PRED relation: legislative_sessions PRED expected values: 01gsrl => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 53): 01grp0 (0.87 #951, 0.87 #463, 0.87 #1909), 01gsry (0.87 #463, 0.86 #590, 0.86 #583), 01grq1 (0.87 #463, 0.86 #590, 0.86 #583), 024tkd (0.80 #1492, 0.80 #1475, 0.72 #1610), 032ft5 (0.80 #1446, 0.78 #1802, 0.78 #1564), 03ww_x (0.80 #1440, 0.78 #1558, 0.74 #1796), 01gsvb (0.79 #1491, 0.79 #1609, 0.78 #529), 01gsvp (0.79 #1491, 0.79 #1609, 0.78 #529), 01gsrl (0.79 #1491, 0.79 #1609, 0.78 #529), 01grpc (0.79 #1491, 0.79 #1609, 0.78 #529) >> Best rule #951 for best value: >> intensional similarity = 37 >> extensional distance = 6 >> proper extension: 01gsvp; >> query: (?x4812, ?x10638) <- legislative_sessions(?x10638, ?x4812), legislative_sessions(?x7714, ?x4812), legislative_sessions(?x2860, ?x4812), district_represented(?x4812, ?x6895), district_represented(?x4812, ?x3778), district_represented(?x4812, ?x3670), district_represented(?x4812, ?x3038), district_represented(?x4812, ?x2713), district_represented(?x4812, ?x1767), district_represented(?x4812, ?x1426), district_represented(?x4812, ?x335), ?x335 = 059rby, ?x7714 = 01grr2, ?x1767 = 04rrd, ?x1426 = 07z1m, ?x3670 = 05tbn, ?x3778 = 07h34, ?x3038 = 0d0x8, contains(?x6895, ?x12111), contains(?x6895, ?x11803), district_represented(?x7914, ?x6895), district_represented(?x5339, ?x6895), ?x2713 = 06btq, location(?x8535, ?x6895), administrative_parent(?x6895, ?x94), adjoins(?x739, ?x6895), taxonomy(?x6895, ?x939), adjoins(?x11803, ?x12023), state_province_region(?x4793, ?x6895), place_of_birth(?x4509, ?x6895), contains(?x8260, ?x6895), ?x5339 = 02glc4, currency(?x11803, ?x170), ?x7914 = 01grrf, source(?x12111, ?x958), legislative_sessions(?x5978, ?x10638), student(?x3439, ?x8535) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1491 for first EXPECTED value: *> intensional similarity = 33 *> extensional distance = 13 *> proper extension: 02bn_p; 02bqn1; 02gkzs; 02cg7g; 02bqm0; 02glc4; *> query: (?x4812, ?x2712) <- legislative_sessions(?x7714, ?x4812), legislative_sessions(?x4665, ?x4812), district_represented(?x4812, ?x7518), district_represented(?x4812, ?x6895), district_represented(?x4812, ?x4754), district_represented(?x4812, ?x4061), district_represented(?x4812, ?x2713), district_represented(?x4812, ?x1755), district_represented(?x4812, ?x335), ?x335 = 059rby, legislative_sessions(?x7891, ?x7714), ?x4754 = 0g0syc, legislative_sessions(?x4812, ?x4787), legislative_sessions(?x7714, ?x2712), legislative_sessions(?x5742, ?x4812), district_represented(?x6933, ?x7518), ?x4665 = 07t58, location(?x4806, ?x2713), ?x1755 = 01x73, religion(?x2713, ?x10107), religion(?x2713, ?x2769), contains(?x2713, ?x2056), state_province_region(?x2959, ?x7518), contains(?x94, ?x2713), ?x10107 = 05w5d, religion(?x7518, ?x492), jurisdiction_of_office(?x3959, ?x2713), contains(?x7518, ?x2832), ?x6895 = 05fjf, ?x6933 = 024tkd, ?x4061 = 0498y, state_province_region(?x2021, ?x2713), ?x2769 = 019cr *> conf = 0.79 ranks of expected_values: 9 EVAL 01grpc legislative_sessions 01gsrl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 39.000 39.000 0.867 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #8282-081mh PRED entity: 081mh PRED relation: religion PRED expected values: 0c8wxp => 198 concepts (198 used for prediction) PRED predicted values (max 10 best out of 25): 0c8wxp (0.90 #165, 0.86 #84, 0.72 #111), 03_gx (0.48 #170, 0.47 #35, 0.45 #89), 01s5nb (0.47 #43, 0.46 #178, 0.45 #97), 0flw86 (0.42 #704, 0.40 #731, 0.40 #758), 058x5 (0.40 #164, 0.38 #83, 0.31 #56), 092bf5 (0.29 #495, 0.26 #414, 0.26 #739), 02t7t (0.27 #176, 0.27 #41, 0.25 #149), 072w0 (0.25 #179, 0.23 #1217, 0.20 #152), 03j6c (0.25 #12, 0.23 #1217, 0.09 #498), 0kpl (0.25 #5, 0.23 #1217, 0.03 #491) >> Best rule #165 for best value: >> intensional similarity = 2 >> extensional distance = 46 >> proper extension: 05fkf; 05fhy; 059_c; 03s5t; >> query: (?x2977, 0c8wxp) <- religion(?x2977, ?x109), district_represented(?x355, ?x2977) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 081mh religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 198.000 0.896 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #8281-02vjzr PRED entity: 02vjzr PRED relation: artists PRED expected values: 018y2s 030155 0133x7 01qrbf => 49 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1024): 0140t7 (0.67 #7019, 0.67 #4953, 0.60 #3920), 011z3g (0.67 #4702, 0.64 #9871, 0.62 #8837), 01vvycq (0.67 #4176, 0.62 #8311, 0.60 #3143), 01vrt_c (0.67 #4205, 0.60 #3172, 0.60 #2138), 0136p1 (0.67 #5303, 0.60 #3236, 0.60 #2202), 019f9z (0.67 #5731, 0.60 #3664, 0.60 #2630), 016376 (0.67 #6080, 0.60 #4013, 0.60 #2979), 0ffgh (0.67 #5773, 0.60 #3706, 0.60 #2672), 086qd (0.67 #5316, 0.60 #3249, 0.60 #2215), 016ppr (0.67 #6083, 0.60 #4016, 0.60 #2982) >> Best rule #7019 for best value: >> intensional similarity = 10 >> extensional distance = 7 >> proper extension: 01lyv; 02qdgx; >> query: (?x9007, 0140t7) <- artists(?x9007, ?x7924), artists(?x9007, ?x7115), artists(?x9007, ?x6573), artists(?x9007, ?x4200), ?x7115 = 02z4b_8, profession(?x7924, ?x131), ?x4200 = 025ldg, origin(?x7924, ?x1860), award_nominee(?x6573, ?x527), instrumentalists(?x227, ?x7924) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1103 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 06by7; *> query: (?x9007, 018y2s) <- artists(?x9007, ?x7924), artists(?x9007, ?x7115), artists(?x9007, ?x6418), artists(?x9007, ?x538), ?x7115 = 02z4b_8, ?x7924 = 03t852, film(?x538, ?x2075), award_winner(?x537, ?x538), award_nominee(?x772, ?x538), ?x6418 = 013423 *> conf = 0.50 ranks of expected_values: 193, 240, 452, 460 EVAL 02vjzr artists 01qrbf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 18.000 0.667 http://example.org/music/genre/artists EVAL 02vjzr artists 0133x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 18.000 0.667 http://example.org/music/genre/artists EVAL 02vjzr artists 030155 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 18.000 0.667 http://example.org/music/genre/artists EVAL 02vjzr artists 018y2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 18.000 0.667 http://example.org/music/genre/artists #8280-016ybr PRED entity: 016ybr PRED relation: parent_genre PRED expected values: 06by7 05bt6j 06j6l 02856r => 78 concepts (49 used for prediction) PRED predicted values (max 10 best out of 304): 06by7 (0.91 #3744, 0.89 #4067, 0.62 #2933), 0glt670 (0.71 #3432, 0.20 #2297, 0.15 #3108), 03_d0 (0.70 #3253, 0.14 #1308, 0.13 #5199), 016clz (0.56 #2922, 0.29 #1464, 0.13 #3733), 05bt6j (0.44 #2108, 0.44 #1974, 0.27 #2623), 05r6t (0.43 #1513, 0.38 #2971, 0.33 #1999), 06j6l (0.33 #1006, 0.25 #194, 0.20 #844), 03lty (0.30 #2288, 0.22 #1639, 0.20 #667), 02qm5j (0.29 #1392, 0.09 #2109, 0.06 #3011), 02x8m (0.26 #3418, 0.20 #2283, 0.18 #2608) >> Best rule #3744 for best value: >> intensional similarity = 6 >> extensional distance = 44 >> proper extension: 01h0kx; 018ysx; >> query: (?x8386, 06by7) <- parent_genre(?x996, ?x8386), parent_genre(?x8386, ?x9935), artists(?x9935, ?x6818), artists(?x9935, ?x1573), award_winner(?x5656, ?x6818), ?x1573 = 03g5jw >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 7, 78 EVAL 016ybr parent_genre 02856r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 78.000 49.000 0.913 http://example.org/music/genre/parent_genre EVAL 016ybr parent_genre 06j6l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 49.000 0.913 http://example.org/music/genre/parent_genre EVAL 016ybr parent_genre 05bt6j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 78.000 49.000 0.913 http://example.org/music/genre/parent_genre EVAL 016ybr parent_genre 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 49.000 0.913 http://example.org/music/genre/parent_genre #8279-03pc89 PRED entity: 03pc89 PRED relation: language PRED expected values: 02h40lc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 50): 02h40lc (0.92 #533, 0.91 #832, 0.91 #1010), 064_8sq (0.21 #258, 0.20 #435, 0.19 #376), 06b_j (0.15 #23, 0.08 #318, 0.07 #377), 04306rv (0.15 #359, 0.14 #418, 0.12 #300), 02bjrlw (0.12 #591, 0.11 #414, 0.11 #355), 06nm1 (0.10 #483, 0.10 #1256, 0.10 #542), 0jzc (0.08 #20, 0.05 #1205, 0.05 #3327), 07zrf (0.08 #3, 0.05 #239, 0.05 #3327), 02hwyss (0.08 #42, 0.05 #3327, 0.05 #160), 04h9h (0.07 #397, 0.07 #456, 0.05 #633) >> Best rule #533 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 0jqp3; 0jyx6; 0c5dd; 018f8; 0hv1t; 0k4kk; 02q52q; 0283_zv; 05z7c; 083skw; ... >> query: (?x8551, 02h40lc) <- nominated_for(?x1107, ?x8551), list(?x8551, ?x3004), award(?x276, ?x1107) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03pc89 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.924 http://example.org/film/film/language #8278-04qbv PRED entity: 04qbv PRED relation: school_type! PRED expected values: 02rg_4 016wyn => 23 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1337): 02km0m (0.60 #3126, 0.40 #2552, 0.38 #6600), 012mzw (0.50 #4915, 0.50 #1453, 0.40 #3180), 02sjgpq (0.50 #4901, 0.50 #1439, 0.40 #3166), 02bhj4 (0.50 #4900, 0.50 #1438, 0.40 #3165), 04rwx (0.50 #6399, 0.40 #2925, 0.38 #5814), 020yvh (0.50 #1605, 0.40 #2758, 0.33 #5067), 05qgd9 (0.50 #2234, 0.40 #3964, 0.33 #500), 037njl (0.50 #4785, 0.38 #6524, 0.36 #8854), 01k2wn (0.50 #1180, 0.33 #4642, 0.33 #22), 01ljpm (0.50 #1397, 0.33 #4859, 0.33 #239) >> Best rule #3126 for best value: >> intensional similarity = 34 >> extensional distance = 3 >> proper extension: 07tf8; >> query: (?x11936, 02km0m) <- school_type(?x10175, ?x11936), school_type(?x5777, ?x11936), school_type(?x2970, ?x11936), major_field_of_study(?x5777, ?x3490), currency(?x5777, ?x170), student(?x5777, ?x9105), institution(?x1368, ?x5777), institution(?x1200, ?x5777), major_field_of_study(?x10175, ?x8221), major_field_of_study(?x10175, ?x3995), major_field_of_study(?x10175, ?x2981), ?x1200 = 016t_3, ?x3995 = 0fdys, colors(?x10175, ?x4557), ?x1368 = 014mlp, contains(?x94, ?x5777), category(?x5777, ?x134), contains(?x961, ?x10175), ?x8221 = 037mh8, fraternities_and_sororities(?x2970, ?x10424), ?x2981 = 02j62, organization(?x346, ?x2970), executive_produced_by(?x3053, ?x9105), company(?x9105, ?x3230), place_of_birth(?x9105, ?x3125), major_field_of_study(?x4980, ?x3490), major_field_of_study(?x3424, ?x3490), major_field_of_study(?x3416, ?x3490), major_field_of_study(?x388, ?x3490), ?x10424 = 04m8fy, ?x3424 = 01w5m, ?x4980 = 01n6r0, ?x388 = 05krk, ?x3416 = 02183k >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3030 for first EXPECTED value: *> intensional similarity = 34 *> extensional distance = 3 *> proper extension: 07tf8; *> query: (?x11936, 02rg_4) <- school_type(?x10175, ?x11936), school_type(?x5777, ?x11936), school_type(?x2970, ?x11936), major_field_of_study(?x5777, ?x3490), currency(?x5777, ?x170), student(?x5777, ?x9105), institution(?x1368, ?x5777), institution(?x1200, ?x5777), major_field_of_study(?x10175, ?x8221), major_field_of_study(?x10175, ?x3995), major_field_of_study(?x10175, ?x2981), ?x1200 = 016t_3, ?x3995 = 0fdys, colors(?x10175, ?x4557), ?x1368 = 014mlp, contains(?x94, ?x5777), category(?x5777, ?x134), contains(?x961, ?x10175), ?x8221 = 037mh8, fraternities_and_sororities(?x2970, ?x10424), ?x2981 = 02j62, organization(?x346, ?x2970), executive_produced_by(?x3053, ?x9105), company(?x9105, ?x3230), place_of_birth(?x9105, ?x3125), major_field_of_study(?x4980, ?x3490), major_field_of_study(?x3424, ?x3490), major_field_of_study(?x3416, ?x3490), major_field_of_study(?x388, ?x3490), ?x10424 = 04m8fy, ?x3424 = 01w5m, ?x4980 = 01n6r0, ?x388 = 05krk, ?x3416 = 02183k *> conf = 0.40 ranks of expected_values: 43, 157 EVAL 04qbv school_type! 016wyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 23.000 21.000 0.600 http://example.org/education/educational_institution/school_type EVAL 04qbv school_type! 02rg_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 23.000 21.000 0.600 http://example.org/education/educational_institution/school_type #8277-05p7tx PRED entity: 05p7tx PRED relation: time_zones PRED expected values: 02llzg => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 10): 02llzg (0.40 #459, 0.39 #472, 0.33 #82), 02hcv8 (0.25 #1904, 0.24 #1917, 0.24 #1930), 02lcqs (0.18 #1776, 0.14 #1893, 0.11 #1828), 02fqwt (0.10 #1850, 0.09 #1863, 0.08 #1902), 03bdv (0.09 #1439, 0.08 #344, 0.08 #1582), 02hczc (0.04 #1851, 0.04 #1864, 0.04 #1903), 03plfd (0.02 #1742, 0.02 #1794, 0.01 #1846), 052vwh (0.01 #1744, 0.01 #1796, 0.01 #1809), 042g7t (0.01 #544), 02lcrv (0.01 #540) >> Best rule #459 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 01lfvj; 078lk; 0dlw0; 0jfvs; 096g3; 07kg3; 0cht6; 06c62; 0c7hq; 04_xrs; ... >> query: (?x8475, 02llzg) <- contains(?x8956, ?x8475), contains(?x205, ?x8475), ?x205 = 03rjj, vacationer(?x8956, ?x1898) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05p7tx time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 193.000 0.400 http://example.org/location/location/time_zones #8276-06t2t PRED entity: 06t2t PRED relation: film_release_region! PRED expected values: 0ds35l9 05p1tzf 017gl1 053rxgm 0cc7hmk 01fmys 045j3w 0bmc4cm 01ffx4 0gjc4d3 047fjjr 0198b6 0cmc26r 02rmd_2 043sct5 0bc1yhb 026lgs 0cc97st 01d259 03mgx6z 05b6rdt 0gvvf4j 0gvvm6l 0bmfnjs 0gvt53w 0by17xn => 115 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1118): 01vksx (0.93 #15667, 0.91 #6761, 0.83 #21232), 0cc97st (0.86 #7274, 0.80 #16180, 0.71 #21745), 045j3w (0.86 #6966, 0.71 #20324, 0.68 #11419), 01fmys (0.83 #15771, 0.82 #6865, 0.80 #11318), 017gl1 (0.83 #21239, 0.82 #24578, 0.82 #6768), 053rxgm (0.82 #6788, 0.77 #21259, 0.71 #33503), 03twd6 (0.82 #6814, 0.68 #24624, 0.67 #15720), 0ds35l9 (0.80 #3345, 0.79 #20042, 0.77 #6684), 07l50vn (0.80 #3914, 0.77 #7253, 0.74 #20611), 02mt51 (0.80 #3730, 0.77 #7069, 0.68 #11522) >> Best rule #15667 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 01ls2; 01p1v; 077qn; >> query: (?x2316, 01vksx) <- film_release_region(?x4607, ?x2316), film_release_region(?x1150, ?x2316), ?x1150 = 0h3xztt, ?x4607 = 0h03fhx >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #7274 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 05r4w; *> query: (?x2316, 0cc97st) <- film_release_region(?x9657, ?x2316), film_release_region(?x1150, ?x2316), ?x1150 = 0h3xztt, ?x9657 = 07jqjx *> conf = 0.86 ranks of expected_values: 2, 3, 4, 5, 6, 8, 13, 14, 17, 18, 20, 21, 23, 34, 37, 39, 42, 46, 52, 56, 58, 75, 111, 116, 137, 165 EVAL 06t2t film_release_region! 0by17xn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0gvt53w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0bmfnjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0gvvm6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0gvvf4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 05b6rdt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 03mgx6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 01d259 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0cc97st CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 026lgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0bc1yhb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 043sct5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 02rmd_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0cmc26r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0198b6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 047fjjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0gjc4d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 01ffx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0bmc4cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 045j3w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 01fmys CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0cc7hmk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 053rxgm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 017gl1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 05p1tzf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06t2t film_release_region! 0ds35l9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 39.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8275-0h1x5f PRED entity: 0h1x5f PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.77 #1345, 0.70 #2265, 0.67 #503), 09zzb8 (0.73 #1338, 0.69 #2258, 0.66 #842), 09vw2b7 (0.68 #1344, 0.57 #2264, 0.52 #540), 01vx2h (0.38 #1350, 0.29 #2270, 0.28 #546), 0dxtw (0.37 #1349, 0.34 #2269, 0.32 #853), 04pyp5 (0.33 #19, 0.09 #133, 0.06 #860), 02vs3x5 (0.33 #26, 0.07 #407, 0.05 #867), 01pvkk (0.28 #2271, 0.28 #855, 0.27 #1351), 0d2b38 (0.25 #66, 0.25 #841, 0.11 #1365), 01xy5l_ (0.25 #54, 0.25 #841, 0.11 #1353) >> Best rule #1345 for best value: >> intensional similarity = 3 >> extensional distance = 769 >> proper extension: 0fq27fp; >> query: (?x9701, 0ch6mp2) <- film_crew_role(?x9701, ?x468), genre(?x9701, ?x258), ?x468 = 02r96rf >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h1x5f film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.769 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8274-01p1v PRED entity: 01p1v PRED relation: film_release_region! PRED expected values: 0gx9rvq 017gl1 0bwfwpj 0c0nhgv 09k56b7 0gvrws1 01jrbb 047p7fr 0gh65c5 047tsx3 0gwjw0c 0gvt53w => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 1231): 047vnkj (0.85 #609, 0.83 #1802, 0.78 #11346), 09k56b7 (0.85 #202, 0.77 #8553, 0.76 #10939), 03q0r1 (0.85 #417, 0.72 #1610, 0.70 #11154), 0125xq (0.85 #483, 0.69 #1676, 0.67 #8834), 0gwjw0c (0.85 #812, 0.69 #2005, 0.67 #11549), 0g9zljd (0.85 #740, 0.65 #9091, 0.63 #11477), 0dzlbx (0.83 #1758, 0.74 #8916, 0.74 #565), 04w7rn (0.81 #153, 0.76 #1346, 0.72 #8504), 024mpp (0.81 #424, 0.72 #1617, 0.72 #11161), 0gvrws1 (0.81 #205, 0.70 #8556, 0.66 #1398) >> Best rule #609 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 05r4w; 09c7w0; 0jgd; 0154j; 03rjj; 0d060g; 0d0vqn; 0chghy; 05qhw; 07ssc; ... >> query: (?x1917, 047vnkj) <- film_release_region(?x4041, ?x1917), film_release_region(?x124, ?x1917), ?x124 = 0g56t9t, ?x4041 = 0gy2y8r >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #202 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 05r4w; 09c7w0; 0jgd; 0154j; 03rjj; 0d060g; 0d0vqn; 0chghy; 05qhw; 07ssc; ... *> query: (?x1917, 09k56b7) <- film_release_region(?x4041, ?x1917), film_release_region(?x124, ?x1917), ?x124 = 0g56t9t, ?x4041 = 0gy2y8r *> conf = 0.85 ranks of expected_values: 2, 5, 10, 18, 21, 33, 40, 41, 57, 75, 97, 103 EVAL 01p1v film_release_region! 0gvt53w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 0gwjw0c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 047tsx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 0gh65c5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 047p7fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 01jrbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 0gvrws1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 09k56b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 0c0nhgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 0bwfwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 017gl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01p1v film_release_region! 0gx9rvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 108.000 108.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8273-01svry PRED entity: 01svry PRED relation: film! PRED expected values: 06z8gn => 89 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1144): 06cv1 (0.37 #16608, 0.17 #39454, 0.17 #24918), 03p01x (0.21 #26997, 0.17 #33226), 016zp5 (0.19 #9277, 0.14 #17583, 0.05 #4153), 03ym1 (0.18 #3083, 0.16 #9311, 0.12 #17617), 0f0kz (0.18 #2587, 0.14 #17121, 0.05 #4153), 01ps2h8 (0.15 #3012, 0.09 #17546, 0.05 #4153), 016ypb (0.15 #8798, 0.11 #17104, 0.05 #4153), 05hj_k (0.13 #45685, 0.11 #53987, 0.08 #26996), 02bfmn (0.12 #8329, 0.09 #16635, 0.05 #4153), 09wj5 (0.12 #2176, 0.11 #8404, 0.09 #16710) >> Best rule #16608 for best value: >> intensional similarity = 5 >> extensional distance = 124 >> proper extension: 0209xj; 0jsqk; 0cq8qq; >> query: (?x6731, ?x523) <- produced_by(?x6731, ?x523), film(?x12183, ?x6731), genre(?x6731, ?x571), actor(?x5529, ?x12183), participant(?x523, ?x6844) >> conf = 0.37 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01svry film! 06z8gn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 59.000 0.373 http://example.org/film/actor/film./film/performance/film #8272-07szy PRED entity: 07szy PRED relation: student PRED expected values: 0yfp 030b93 02t_vx 031x_3 => 128 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1431): 03swmf (0.33 #1572, 0.08 #11978, 0.08 #9896), 027vps (0.33 #1419, 0.08 #11825, 0.08 #9743), 0432b (0.33 #1054, 0.08 #11460, 0.08 #9378), 01nr36 (0.33 #1468, 0.05 #16039, 0.03 #30610), 0fz27v (0.33 #1745, 0.03 #24642, 0.03 #30887), 01mvjl0 (0.33 #1046, 0.01 #115528), 01y0y6 (0.33 #599), 0lgm5 (0.33 #449), 016_mj (0.33 #275), 033hqf (0.33 #69) >> Best rule #1572 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 026036; >> query: (?x1681, 03swmf) <- school_type(?x1681, ?x3092), student(?x1681, ?x2307), ?x2307 = 011zd3 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #59513 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 03wv2g; *> query: (?x1681, 030b93) <- school_type(?x1681, ?x3092), school(?x260, ?x1681), fraternities_and_sororities(?x1681, ?x3697) *> conf = 0.01 ranks of expected_values: 1280 EVAL 07szy student 031x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 107.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07szy student 02t_vx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 107.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07szy student 030b93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 107.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07szy student 0yfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 107.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #8271-0jm4v PRED entity: 0jm4v PRED relation: draft PRED expected values: 09th87 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 17): 038c0q (0.85 #213, 0.82 #414, 0.81 #870), 06439y (0.82 #414, 0.81 #396, 0.81 #870), 09th87 (0.82 #414, 0.81 #870, 0.80 #764), 02pq_rp (0.50 #111, 0.44 #146, 0.43 #896), 047dpm0 (0.50 #119, 0.44 #154, 0.39 #904), 02x2khw (0.50 #106, 0.44 #141, 0.34 #891), 092j54 (0.48 #755, 0.42 #791, 0.33 #949), 05vsb7 (0.46 #747, 0.42 #783, 0.33 #941), 0g3zpp (0.46 #748, 0.40 #784, 0.33 #1082), 09l0x9 (0.46 #757, 0.40 #793, 0.33 #327) >> Best rule #213 for best value: >> intensional similarity = 19 >> extensional distance = 11 >> proper extension: 0jmbv; 0jm3b; 0jm5b; >> query: (?x7158, 038c0q) <- draft(?x7158, ?x8133), draft(?x7158, ?x4979), team(?x6848, ?x7158), team(?x4747, ?x7158), team(?x1348, ?x7158), ?x4747 = 02sf_r, ?x8133 = 025tn92, ?x1348 = 01pv51, ?x4979 = 0f4vx0, team(?x8996, ?x7158), position(?x7158, ?x4570), position(?x9833, ?x4570), position(?x2820, ?x4570), position(?x1578, ?x4570), ?x9833 = 03y9p40, ?x6848 = 02_ssl, ?x2820 = 0jmj7, school(?x7158, ?x2497), ?x1578 = 0jm2v >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #414 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 20 *> proper extension: 0jmj7; 0jmjr; *> query: (?x7158, ?x8542) <- draft(?x7158, ?x8133), team(?x4747, ?x7158), team(?x1348, ?x7158), ?x4747 = 02sf_r, ?x8133 = 025tn92, team(?x1348, ?x12124), team(?x1348, ?x11168), team(?x1348, ?x10409), team(?x1348, ?x6003), team(?x1348, ?x5551), team(?x1348, ?x799), ?x6003 = 02py8_w, ?x12124 = 0jmgb, ?x799 = 0jm3v, position(?x2303, ?x1348), ?x10409 = 0jmh7, school(?x11168, ?x1011), draft(?x11168, ?x8542), team(?x8824, ?x5551), ?x8824 = 05g_nr *> conf = 0.82 ranks of expected_values: 3 EVAL 0jm4v draft 09th87 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.846 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #8270-0372kf PRED entity: 0372kf PRED relation: award_nominee! PRED expected values: 01_p6t => 80 concepts (34 used for prediction) PRED predicted values (max 10 best out of 634): 02bkdn (0.81 #60139, 0.81 #55511, 0.81 #37004), 01_p6t (0.81 #60139, 0.81 #55511, 0.81 #37004), 020_95 (0.81 #60139, 0.81 #55511, 0.81 #37004), 0372kf (0.54 #1204, 0.16 #48572, 0.03 #10454), 0151w_ (0.23 #199, 0.16 #48572, 0.03 #9449), 024bbl (0.16 #48572, 0.15 #1101, 0.02 #33477), 02p65p (0.16 #48572, 0.08 #26, 0.05 #6963), 06dv3 (0.16 #48572, 0.08 #41, 0.03 #6978), 0bxtg (0.16 #48572, 0.08 #89, 0.02 #32465), 043js (0.16 #48572, 0.08 #568, 0.02 #9818) >> Best rule #60139 for best value: >> intensional similarity = 3 >> extensional distance = 1444 >> proper extension: 01w92; 02vyh; 026v1z; >> query: (?x5156, ?x286) <- award_nominee(?x5156, ?x286), award_winner(?x8813, ?x5156), award_nominee(?x525, ?x5156) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0372kf award_nominee! 01_p6t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 34.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8269-05r5c PRED entity: 05r5c PRED relation: role! PRED expected values: 021bk 03m6_z => 92 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1015): 01mwsnc (0.50 #3746, 0.43 #3503, 0.40 #1078), 016s_5 (0.50 #851, 0.40 #1092, 0.33 #366), 017g21 (0.50 #648, 0.33 #3072, 0.33 #164), 019389 (0.50 #903, 0.33 #418, 0.33 #175), 01p0vf (0.50 #883, 0.33 #398, 0.28 #11163), 01cv3n (0.50 #739, 0.33 #254, 0.28 #11163), 01vs4ff (0.50 #884, 0.33 #399, 0.26 #5247), 021bk (0.50 #769, 0.33 #284, 0.26 #5132), 0lzkm (0.50 #560, 0.33 #76, 0.25 #2984), 01bpnd (0.50 #860, 0.33 #375, 0.25 #616) >> Best rule #3746 for best value: >> intensional similarity = 7 >> extensional distance = 12 >> proper extension: 0l14qv; 02sgy; 0l14md; 013y1f; 02hnl; 02k84w; 06w7v; >> query: (?x316, 01mwsnc) <- role(?x2187, ?x316), instrumentalists(?x316, ?x115), role(?x316, ?x74), role(?x316, ?x645), ?x645 = 028tv0, group(?x316, ?x997), person(?x1619, ?x2187) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #769 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 028tv0; *> query: (?x316, 021bk) <- role(?x3867, ?x316), role(?x316, ?x569), role(?x316, ?x74), group(?x316, ?x997), role(?x569, ?x716), ?x3867 = 0bkg4 *> conf = 0.50 ranks of expected_values: 8, 88 EVAL 05r5c role! 03m6_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 92.000 53.000 0.500 http://example.org/music/group_member/membership./music/group_membership/role EVAL 05r5c role! 021bk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 92.000 53.000 0.500 http://example.org/music/group_member/membership./music/group_membership/role #8268-07c5l PRED entity: 07c5l PRED relation: contains PRED expected values: 0160w 0162v 09lxtg 02k8k 034m8 => 87 concepts (48 used for prediction) PRED predicted values (max 10 best out of 2843): 02613 (0.64 #107331, 0.64 #121837, 0.63 #133442), 0j3b (0.64 #107331, 0.64 #121837, 0.63 #133442), 05rgl (0.64 #107331, 0.64 #121837, 0.63 #133442), 059f4 (0.64 #107331, 0.64 #121837, 0.63 #133442), 04_1l0v (0.64 #107331, 0.64 #121837, 0.63 #133442), 04rrx (0.64 #107331, 0.64 #121837, 0.63 #133442), 034m8 (0.64 #107331, 0.64 #121837, 0.63 #133442), 02k8k (0.64 #107331, 0.64 #121837, 0.63 #133442), 02gt5s (0.64 #107331, 0.64 #121837, 0.63 #133442), 02dtg (0.64 #107331, 0.64 #121837, 0.63 #133442) >> Best rule #107331 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 04q42; >> query: (?x7273, ?x1144) <- contains(?x7273, ?x5622), contains(?x7273, ?x5411), adjoins(?x1144, ?x5411), administrative_parent(?x5622, ?x551), time_zones(?x5622, ?x11506) >> conf = 0.64 => this is the best rule for 10 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7, 8, 262, 1195 EVAL 07c5l contains 034m8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 48.000 0.639 http://example.org/location/location/contains EVAL 07c5l contains 02k8k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 48.000 0.639 http://example.org/location/location/contains EVAL 07c5l contains 09lxtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 87.000 48.000 0.639 http://example.org/location/location/contains EVAL 07c5l contains 0162v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 87.000 48.000 0.639 http://example.org/location/location/contains EVAL 07c5l contains 0160w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 48.000 0.639 http://example.org/location/location/contains #8267-01qb5d PRED entity: 01qb5d PRED relation: film_crew_role PRED expected values: 015h31 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 26): 01pvkk (0.50 #9, 0.30 #1011, 0.30 #409), 02rh1dz (0.25 #8, 0.24 #342, 0.22 #173), 01c979 (0.25 #27), 0215hd (0.24 #148, 0.19 #416, 0.17 #784), 01xy5l_ (0.24 #143, 0.18 #378, 0.17 #411), 02ynfr (0.22 #112, 0.22 #1083, 0.21 #380), 0d2b38 (0.21 #423, 0.17 #390, 0.15 #188), 089g0h (0.18 #417, 0.16 #384, 0.14 #1087), 015h31 (0.16 #239, 0.16 #273, 0.15 #172), 02_n3z (0.15 #100, 0.14 #401, 0.14 #199) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0344gc; 0gh65c5; >> query: (?x936, 01pvkk) <- film(?x971, ?x936), film_crew_role(?x936, ?x137), film_format(?x936, ?x909), ?x971 = 03knl >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #239 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 71 *> proper extension: 0df2zx; *> query: (?x936, 015h31) <- film(?x971, ?x936), genre(?x936, ?x225), region(?x936, ?x512), vacationer(?x2474, ?x971) *> conf = 0.16 ranks of expected_values: 9 EVAL 01qb5d film_crew_role 015h31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 87.000 0.500 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8266-07s363 PRED entity: 07s363 PRED relation: artist PRED expected values: 01v27pl => 157 concepts (72 used for prediction) PRED predicted values (max 10 best out of 498): 01k23t (0.50 #4779, 0.50 #2253, 0.40 #3096), 06k02 (0.40 #3503, 0.33 #9394, 0.33 #6870), 020_4z (0.40 #4113, 0.33 #7480, 0.33 #4954), 01k3qj (0.40 #3921, 0.33 #550, 0.29 #5604), 0bk1p (0.40 #4038, 0.33 #667, 0.29 #5721), 0kr_t (0.40 #3767, 0.33 #396, 0.29 #5450), 09qr6 (0.40 #3438, 0.33 #67, 0.29 #5121), 01q99h (0.33 #7182, 0.33 #4656, 0.33 #444), 01vxlbm (0.33 #7006, 0.33 #4480, 0.33 #268), 0fcsd (0.33 #7042, 0.33 #4516, 0.29 #5358) >> Best rule #4779 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 02bh8z; 03mp8k; >> query: (?x12227, 01k23t) <- industry(?x12227, ?x8681), industry(?x12227, ?x3368), citytown(?x12227, ?x9310), ?x8681 = 04rlf, ?x3368 = 02jjt, administrative_parent(?x9310, ?x1453), teams(?x9310, ?x4006), contains(?x9954, ?x9310), origin(?x11667, ?x9310) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #843 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 011k1h; *> query: (?x12227, ?x11667) <- industry(?x12227, ?x8681), citytown(?x12227, ?x9310), organization(?x4682, ?x12227), ?x8681 = 04rlf, origin(?x11667, ?x9310), place_of_birth(?x823, ?x9310), contains(?x1453, ?x9310), artists(?x996, ?x11667), category(?x9310, ?x134) *> conf = 0.24 ranks of expected_values: 182 EVAL 07s363 artist 01v27pl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 157.000 72.000 0.500 http://example.org/music/record_label/artist #8265-03mv0b PRED entity: 03mv0b PRED relation: type_of_union PRED expected values: 04ztj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.98 #64, 0.97 #158, 0.97 #155), 01g63y (0.20 #5, 0.20 #251, 0.20 #56), 0jgjn (0.20 #251, 0.19 #264, 0.19 #277) >> Best rule #64 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 0bk4s; >> query: (?x9579, 04ztj) <- place_of_burial(?x9579, ?x8044), gender(?x9579, ?x231), place_of_death(?x9579, ?x242), type_of_union(?x9579, ?x11744) >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mv0b type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.978 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8264-01qgr3 PRED entity: 01qgr3 PRED relation: major_field_of_study PRED expected values: 02lp1 0g26h 04sh3 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 119): 01mkq (0.63 #1458, 0.57 #16, 0.53 #2058), 02j62 (0.61 #870, 0.56 #510, 0.53 #1472), 01lj9 (0.55 #879, 0.36 #1481, 0.34 #2081), 02lp1 (0.52 #1454, 0.50 #492, 0.49 #1093), 0g26h (0.52 #882, 0.50 #522, 0.49 #5214), 04rjg (0.48 #861, 0.48 #1463, 0.42 #2063), 02_7t (0.45 #1144, 0.44 #1023, 0.39 #903), 03g3w (0.42 #867, 0.42 #2069, 0.41 #1469), 05qjt (0.35 #2050, 0.31 #4580, 0.30 #3015), 01540 (0.35 #1019, 0.32 #1140, 0.32 #1501) >> Best rule #1458 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 01hhvg; 03v6t; 03ksy; 07tds; 02zd460; 01nnsv; 01n_g9; 01qrb2; 02tz9z; >> query: (?x7338, 01mkq) <- institution(?x3437, ?x7338), ?x3437 = 02_xgp2, school(?x729, ?x7338), major_field_of_study(?x7338, ?x2601) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #1454 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 71 *> proper extension: 01hhvg; 03v6t; 03ksy; 07tds; 02zd460; 01nnsv; 01n_g9; 01qrb2; 02tz9z; *> query: (?x7338, 02lp1) <- institution(?x3437, ?x7338), ?x3437 = 02_xgp2, school(?x729, ?x7338), major_field_of_study(?x7338, ?x2601) *> conf = 0.52 ranks of expected_values: 4, 5, 24 EVAL 01qgr3 major_field_of_study 04sh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 160.000 160.000 0.630 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01qgr3 major_field_of_study 0g26h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 160.000 160.000 0.630 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01qgr3 major_field_of_study 02lp1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 160.000 160.000 0.630 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8263-0226cw PRED entity: 0226cw PRED relation: religion PRED expected values: 0c8wxp => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 37): 019cr (0.40 #146, 0.38 #56, 0.36 #191), 0c8wxp (0.27 #186, 0.26 #276, 0.22 #96), 0v53x (0.25 #74, 0.22 #119, 0.20 #164), 0631_ (0.22 #323, 0.16 #413, 0.14 #458), 051kv (0.20 #5, 0.13 #635, 0.13 #680), 02rsw (0.14 #384, 0.12 #744, 0.11 #969), 05sfs (0.12 #48, 0.11 #93, 0.10 #138), 0kpl (0.11 #1135, 0.07 #1000, 0.06 #775), 07x21 (0.10 #578, 0.09 #443, 0.09 #623), 03j6c (0.08 #1011, 0.08 #1056, 0.05 #516) >> Best rule #146 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 03txms; >> query: (?x8607, 019cr) <- legislative_sessions(?x8607, ?x4821), legislative_sessions(?x8607, ?x3463), legislative_sessions(?x8607, ?x2976), ?x4821 = 02bqm0, profession(?x8607, ?x3342), ?x3463 = 02bqmq, district_represented(?x2976, ?x335) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #186 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 9 *> proper extension: 012v1t; *> query: (?x8607, 0c8wxp) <- legislative_sessions(?x8607, ?x6743), legislative_sessions(?x8607, ?x4821), legislative_sessions(?x8607, ?x1830), ?x4821 = 02bqm0, legislative_sessions(?x1027, ?x6743), district_represented(?x6743, ?x335), legislative_sessions(?x2860, ?x1830) *> conf = 0.27 ranks of expected_values: 2 EVAL 0226cw religion 0c8wxp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 115.000 0.400 http://example.org/people/person/religion #8262-030tjk PRED entity: 030tjk PRED relation: type_of_union PRED expected values: 04ztj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.76 #21, 0.75 #17, 0.73 #13), 01g63y (0.15 #2, 0.13 #62, 0.13 #38) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 302 >> proper extension: 01d5vk; >> query: (?x7943, 04ztj) <- profession(?x7943, ?x1041), profession(?x7943, ?x319), ?x1041 = 03gjzk, ?x319 = 01d_h8 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030tjk type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.763 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8261-0klw PRED entity: 0klw PRED relation: influenced_by! PRED expected values: 01zwy => 153 concepts (110 used for prediction) PRED predicted values (max 10 best out of 384): 05jm7 (0.20 #13484, 0.14 #4246, 0.10 #12457), 05rx__ (0.18 #1335, 0.15 #1848, 0.07 #12625), 05ty4m (0.17 #8218, 0.06 #10271, 0.04 #23107), 0c00lh (0.13 #8436, 0.06 #10489, 0.04 #2791), 034bs (0.12 #154, 0.05 #41077, 0.05 #1694), 09gnn (0.12 #419, 0.05 #1959, 0.04 #4011), 047g6 (0.12 #479, 0.04 #9716, 0.04 #4071), 03_hd (0.12 #180, 0.04 #9417, 0.04 #3772), 04z0g (0.12 #238, 0.04 #3830, 0.03 #5370), 016lh0 (0.12 #215, 0.04 #3807, 0.03 #5347) >> Best rule #13484 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 01t_xp_; 04cbtrw; 0d4jl; 02g75; 0yxl; 0gthm; 04x56; 098sx; 05cv8; 03v36; >> query: (?x4895, 05jm7) <- award(?x4895, ?x8880), award(?x12614, ?x8880), ?x12614 = 01k56k >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #41077 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 515 *> proper extension: 02pb2bp; 0lhn5; 02m4t; 01d5g; 0chnf; 0716b6; *> query: (?x4895, ?x2161) <- influenced_by(?x4895, ?x3542), influenced_by(?x2161, ?x3542) *> conf = 0.05 ranks of expected_values: 108 EVAL 0klw influenced_by! 01zwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 153.000 110.000 0.200 http://example.org/influence/influence_node/influenced_by #8260-0mnsf PRED entity: 0mnsf PRED relation: place! PRED expected values: 0mnsf => 111 concepts (106 used for prediction) PRED predicted values (max 10 best out of 228): 0mnsf (0.20 #15977, 0.18 #24222, 0.16 #36596), 0dclg (0.20 #15977, 0.10 #50010, 0.08 #52074), 013yq (0.20 #15977, 0.10 #50010, 0.08 #52074), 07z1m (0.18 #24222, 0.16 #36596, 0.14 #19071), 09c7w0 (0.18 #24222, 0.16 #36596, 0.14 #19071), 094jv (0.04 #551, 0.04 #1067, 0.03 #1582), 0dzt9 (0.04 #779, 0.04 #1295, 0.02 #2325), 0c1d0 (0.04 #732, 0.04 #1248, 0.02 #2278), 0pc7r (0.04 #578, 0.04 #1094, 0.02 #2124), 01cx_ (0.04 #579, 0.04 #1095, 0.02 #2640) >> Best rule #15977 for best value: >> intensional similarity = 4 >> extensional distance = 135 >> proper extension: 0f94t; 0281y0; 0281rp; 0jbs5; 027l4q; >> query: (?x7478, ?x2254) <- location(?x8996, ?x7478), people(?x2510, ?x8996), ?x2510 = 0x67, location(?x8996, ?x2254) >> conf = 0.20 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0mnsf place! 0mnsf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 106.000 0.199 http://example.org/location/hud_county_place/place #8259-014pg1 PRED entity: 014pg1 PRED relation: artist! PRED expected values: 01cl0d => 69 concepts (56 used for prediction) PRED predicted values (max 10 best out of 139): 0181dw (0.60 #2127, 0.12 #2266, 0.12 #3658), 03rhqg (0.40 #15, 0.33 #4188, 0.22 #2378), 01trtc (0.33 #349, 0.33 #210, 0.27 #488), 01clyr (0.33 #172, 0.20 #33, 0.17 #311), 01cl0d (0.33 #193, 0.18 #471, 0.17 #332), 01dtcb (0.33 #185, 0.17 #324, 0.14 #4219), 015_1q (0.24 #1548, 0.23 #853, 0.23 #1826), 017l96 (0.23 #1269, 0.16 #1825, 0.15 #2520), 03mp8k (0.20 #621, 0.20 #65, 0.18 #482), 0n85g (0.20 #62, 0.19 #4235, 0.18 #479) >> Best rule #2127 for best value: >> intensional similarity = 6 >> extensional distance = 99 >> proper extension: 01wmxfs; >> query: (?x8058, 0181dw) <- award(?x8058, ?x3045), artist(?x2241, ?x8058), artist(?x2241, ?x2638), artist(?x2241, ?x2300), ?x2638 = 02fn5r, award_nominee(?x2300, ?x367) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #193 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 016fnb; *> query: (?x8058, 01cl0d) <- origin(?x8058, ?x1523), artists(?x13882, ?x8058), artists(?x2722, ?x8058), ?x2722 = 01g888, parent_genre(?x13882, ?x5934), artist(?x2241, ?x8058), award(?x8058, ?x3045) *> conf = 0.33 ranks of expected_values: 5 EVAL 014pg1 artist! 01cl0d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 69.000 56.000 0.604 http://example.org/music/record_label/artist #8258-0by1wkq PRED entity: 0by1wkq PRED relation: film! PRED expected values: 0fsm8c => 69 concepts (27 used for prediction) PRED predicted values (max 10 best out of 942): 05zbm4 (0.64 #56210, 0.63 #31220, 0.45 #45796), 01l_yg (0.13 #1658, 0.03 #3739, 0.01 #9983), 0hz_1 (0.13 #1493, 0.03 #3574, 0.01 #9818), 0gz5hs (0.13 #319, 0.01 #29456), 02qgqt (0.10 #2099, 0.04 #8343, 0.04 #10424), 01kb2j (0.10 #2991, 0.04 #9235, 0.04 #11316), 02d4ct (0.10 #2472, 0.04 #8716, 0.04 #10797), 02qgyv (0.10 #2466, 0.03 #8710, 0.03 #10791), 01f6zc (0.09 #943, 0.04 #5106, 0.03 #7187), 016vg8 (0.09 #833, 0.03 #2914, 0.02 #9158) >> Best rule #56210 for best value: >> intensional similarity = 3 >> extensional distance = 847 >> proper extension: 0gfzgl; 01f3p_; 07wqr6; 0cskb; 0123qq; >> query: (?x1927, ?x949) <- nominated_for(?x949, ?x1927), profession(?x949, ?x106), participant(?x949, ?x950) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #4439 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 44 *> proper extension: 014lc_; 0g56t9t; 02vxq9m; 03g90h; 0g5qs2k; 0gkz15s; 0872p_c; 053rxgm; 04hwbq; 03qnvdl; ... *> query: (?x1927, 0fsm8c) <- film(?x3273, ?x1927), film_release_region(?x1927, ?x2236), film_release_region(?x1927, ?x1603), ?x1603 = 06bnz, ?x2236 = 05sb1 *> conf = 0.02 ranks of expected_values: 453 EVAL 0by1wkq film! 0fsm8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 69.000 27.000 0.638 http://example.org/film/actor/film./film/performance/film #8257-05cgv PRED entity: 05cgv PRED relation: form_of_government PRED expected values: 01d9r3 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 6): 06cx9 (0.46 #115, 0.41 #331, 0.36 #409), 018wl5 (0.42 #50, 0.40 #122, 0.38 #86), 01fpfn (0.42 #129, 0.38 #153, 0.38 #303), 01q20 (0.38 #58, 0.37 #22, 0.35 #124), 01d9r3 (0.34 #335, 0.34 #119, 0.33 #323), 026wp (0.12 #54, 0.12 #60, 0.10 #90) >> Best rule #115 for best value: >> intensional similarity = 2 >> extensional distance = 57 >> proper extension: 0lnfy; 0dbks; 01pxqx; >> query: (?x1241, 06cx9) <- contains(?x2467, ?x1241), ?x2467 = 0dg3n1 >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #335 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 149 *> proper extension: 04vg8; *> query: (?x1241, 01d9r3) <- jurisdiction_of_office(?x265, ?x1241), company(?x265, ?x6676), ?x6676 = 0537b *> conf = 0.34 ranks of expected_values: 5 EVAL 05cgv form_of_government 01d9r3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 143.000 143.000 0.458 http://example.org/location/country/form_of_government #8256-0xpp5 PRED entity: 0xpp5 PRED relation: place! PRED expected values: 0xpp5 => 153 concepts (83 used for prediction) PRED predicted values (max 10 best out of 249): 0xpq9 (0.47 #3095, 0.47 #8768, 0.39 #19086), 0xpp5 (0.47 #3095, 0.47 #8768, 0.39 #19086), 0hptm (0.08 #157, 0.06 #673, 0.04 #1190), 0xn7q (0.08 #346, 0.06 #862, 0.04 #1379), 0fvxz (0.08 #22, 0.06 #538, 0.04 #1055), 0h6l4 (0.08 #376, 0.06 #892, 0.04 #1409), 0xn5b (0.08 #134, 0.06 #650, 0.04 #1167), 0xt3t (0.08 #345, 0.06 #861, 0.02 #3440), 0n5df (0.07 #19602, 0.05 #9800, 0.05 #32505), 0xn7b (0.06 #888, 0.04 #1405, 0.02 #3467) >> Best rule #3095 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 01mc11; >> query: (?x6142, ?x7557) <- administrative_division(?x6142, ?x6143), state(?x6142, ?x6895), county(?x7557, ?x6143), adjoins(?x6143, ?x12221) >> conf = 0.47 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0xpp5 place! 0xpp5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 153.000 83.000 0.468 http://example.org/location/hud_county_place/place #8255-03_c8p PRED entity: 03_c8p PRED relation: service_location PRED expected values: 07f1x => 204 concepts (204 used for prediction) PRED predicted values (max 10 best out of 126): 0d060g (0.66 #5468, 0.66 #7644, 0.50 #194), 0chghy (0.66 #7644, 0.34 #10004, 0.34 #5472), 06qd3 (0.66 #7644, 0.34 #10004, 0.32 #9254), 05kr_ (0.42 #6030, 0.34 #10004, 0.34 #7930), 048fz (0.34 #10004, 0.32 #9254, 0.28 #1964), 07ssc (0.34 #5476, 0.30 #1601, 0.22 #3871), 07dfk (0.32 #9254, 0.28 #1964, 0.09 #7167), 02j71 (0.32 #4251, 0.30 #9548, 0.29 #3683), 0345h (0.27 #5486, 0.20 #1611, 0.13 #9089), 0f8l9c (0.22 #5481, 0.20 #1606, 0.15 #3403) >> Best rule #5468 for best value: >> intensional similarity = 6 >> extensional distance = 39 >> proper extension: 02slt7; >> query: (?x11303, 0d060g) <- contact_category(?x11303, ?x897), service_location(?x11303, ?x2316), service_language(?x11303, ?x2164), film_release_region(?x8580, ?x2316), geographic_distribution(?x9148, ?x2316), ?x8580 = 0hhggmy >> conf = 0.66 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_c8p service_location 07f1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 204.000 0.659 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #8254-03lt8g PRED entity: 03lt8g PRED relation: location_of_ceremony PRED expected values: 0cv3w => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 55): 0f2v0 (0.09 #40, 0.04 #158), 0cv3w (0.09 #744, 0.03 #862, 0.03 #271), 0k049 (0.07 #713, 0.03 #476, 0.02 #1425), 02_286 (0.06 #722, 0.03 #1434, 0.02 #2027), 0pswc (0.05 #455, 0.03 #337, 0.03 #810), 030qb3t (0.05 #728, 0.03 #373, 0.02 #846), 0b90_r (0.05 #712, 0.03 #357, 0.02 #1067), 0r0m6 (0.05 #759, 0.03 #522, 0.01 #640), 03rjj (0.04 #123, 0.01 #714), 0162v (0.03 #261, 0.03 #379, 0.01 #734) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 07z1_q; 048hf; >> query: (?x1117, 0f2v0) <- award_nominee(?x8256, ?x1117), award_nominee(?x1094, ?x1117), ?x1094 = 035gjq, ?x8256 = 02s_qz >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #744 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 02p21g; 0cqt90; 02kz_; 0bw6y; 0fpj9pm; 01npcy7; 01kgg9; *> query: (?x1117, 0cv3w) <- participant(?x444, ?x1117), location_of_ceremony(?x1117, ?x4627), participant(?x1117, ?x3708) *> conf = 0.09 ranks of expected_values: 2 EVAL 03lt8g location_of_ceremony 0cv3w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 117.000 0.091 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #8253-01nn3m PRED entity: 01nn3m PRED relation: artists! PRED expected values: 015y_n => 106 concepts (53 used for prediction) PRED predicted values (max 10 best out of 211): 06by7 (0.58 #647, 0.57 #2514, 0.54 #3447), 05bt6j (0.50 #669, 0.45 #4358, 0.31 #2536), 06j6l (0.50 #674, 0.32 #9692, 0.31 #4717), 0xhtw (0.45 #4358, 0.36 #4062, 0.35 #3130), 05w3f (0.45 #4358, 0.26 #3152, 0.25 #351), 03lty (0.45 #4358, 0.23 #3142, 0.23 #4074), 029fbr (0.45 #4358, 0.06 #1744, 0.04 #3297), 0p9xd (0.45 #4358, 0.04 #4828, 0.02 #7625), 061fhg (0.45 #4358, 0.02 #5620, 0.02 #6551), 01fh36 (0.42 #713, 0.21 #4756, 0.19 #1648) >> Best rule #647 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 011z3g; 013w8y; 014kyy; >> query: (?x12623, 06by7) <- artists(?x5300, ?x12623), artists(?x505, ?x12623), ?x505 = 03_d0, ?x5300 = 02k_kn >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #4890 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 137 *> proper extension: 067mj; 03t9sp; 017j6; 05563d; 02lbrd; 01rm8b; 016890; 014pg1; 01f2q5; 0h08p; *> query: (?x12623, 015y_n) <- artists(?x505, ?x12623), ?x505 = 03_d0 *> conf = 0.06 ranks of expected_values: 92 EVAL 01nn3m artists! 015y_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 106.000 53.000 0.583 http://example.org/music/genre/artists #8252-02m_41 PRED entity: 02m_41 PRED relation: school_type PRED expected values: 05jxkf => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 16): 05jxkf (0.43 #413, 0.42 #1304, 0.42 #1449), 01rs41 (0.28 #245, 0.25 #775, 0.25 #630), 05pcjw (0.24 #747, 0.24 #771, 0.24 #217), 01_9fk (0.14 #411, 0.09 #627, 0.09 #579), 07tf8 (0.13 #418, 0.12 #730, 0.12 #586), 02p0qmm (0.07 #299, 0.04 #563, 0.03 #707), 01_srz (0.05 #749, 0.05 #628, 0.05 #1110), 01y64 (0.03 #349, 0.03 #373, 0.03 #132), 047951 (0.03 #128, 0.03 #152, 0.01 #1308), 04399 (0.03 #230, 0.03 #254, 0.02 #639) >> Best rule #413 for best value: >> intensional similarity = 4 >> extensional distance = 218 >> proper extension: 08qnnv; 01p896; >> query: (?x11426, 05jxkf) <- category(?x11426, ?x134), institution(?x865, ?x11426), ?x865 = 02h4rq6, currency(?x11426, ?x5696) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02m_41 school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.427 http://example.org/education/educational_institution/school_type #8251-01pj5q PRED entity: 01pj5q PRED relation: award_winner! PRED expected values: 0h10vt => 94 concepts (41 used for prediction) PRED predicted values (max 10 best out of 520): 0169dl (0.81 #48065, 0.81 #48064, 0.81 #24023), 0h10vt (0.37 #30433, 0.30 #27229, 0.28 #51274), 01pj5q (0.37 #30433, 0.30 #27229, 0.28 #51274), 0l6px (0.37 #30433, 0.28 #51274, 0.19 #65700), 016gr2 (0.37 #30433, 0.28 #51274, 0.19 #65700), 0h0yt (0.37 #30433, 0.28 #51274, 0.19 #65700), 0755wz (0.37 #30433, 0.28 #51274, 0.19 #65700), 02k6rq (0.37 #30433, 0.28 #51274, 0.19 #65700), 06t61y (0.37 #30433, 0.28 #51274, 0.19 #65700), 02l4rh (0.37 #30433, 0.28 #51274, 0.19 #65700) >> Best rule #48065 for best value: >> intensional similarity = 3 >> extensional distance = 1004 >> proper extension: 0hm0k; >> query: (?x7733, ?x851) <- award_winner(?x7733, ?x851), award_winner(?x4093, ?x7733), award_nominee(?x91, ?x851) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #30433 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 699 *> proper extension: 039cq4; *> query: (?x7733, ?x1815) <- award_winner(?x7733, ?x851), actor(?x9188, ?x851), award_winner(?x1815, ?x851) *> conf = 0.37 ranks of expected_values: 2 EVAL 01pj5q award_winner! 0h10vt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 41.000 0.814 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #8250-03hpr PRED entity: 03hpr PRED relation: award PRED expected values: 02664f 06196 => 144 concepts (126 used for prediction) PRED predicted values (max 10 best out of 318): 040vk98 (0.76 #4431, 0.67 #830, 0.56 #2030), 047xyn (0.72 #10803, 0.71 #40417, 0.70 #39616), 0265vt (0.71 #4724, 0.64 #3524, 0.56 #2323), 01yz0x (0.67 #4577, 0.67 #2176, 0.64 #3377), 02664f (0.67 #4619, 0.56 #2218, 0.55 #3419), 040_9s0 (0.56 #2315, 0.38 #4716, 0.33 #1115), 0p9sw (0.46 #7225, 0.43 #7625, 0.37 #4025), 02r22gf (0.38 #7237, 0.35 #7637, 0.26 #5637), 02x4wr9 (0.33 #1336, 0.33 #135, 0.25 #1736), 0208wk (0.33 #2344, 0.27 #3545, 0.18 #3144) >> Best rule #4431 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01963w; 05x8n; 0jt86; >> query: (?x10275, 040vk98) <- profession(?x10275, ?x353), award(?x10275, ?x1375), ?x1375 = 0262zm >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #4619 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 01963w; 05x8n; 0jt86; *> query: (?x10275, 02664f) <- profession(?x10275, ?x353), award(?x10275, ?x1375), ?x1375 = 0262zm *> conf = 0.67 ranks of expected_values: 5, 59 EVAL 03hpr award 06196 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 144.000 126.000 0.762 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03hpr award 02664f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 144.000 126.000 0.762 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8249-0dwtp PRED entity: 0dwtp PRED relation: role! PRED expected values: 04bpm6 082brv => 89 concepts (44 used for prediction) PRED predicted values (max 10 best out of 713): 04bpm6 (0.69 #16383, 0.55 #14512, 0.50 #19182), 082brv (0.67 #9107, 0.55 #14707, 0.50 #12373), 0770cd (0.67 #8451, 0.43 #19652, 0.33 #15452), 037hgm (0.67 #9060, 0.40 #12326, 0.40 #11394), 023l9y (0.60 #4861, 0.56 #10450, 0.55 #14651), 0lzkm (0.60 #4822, 0.40 #7150, 0.40 #6219), 0l12d (0.50 #14973, 0.50 #8442, 0.40 #7046), 0j6cj (0.50 #9186, 0.45 #14786, 0.40 #12452), 03j24kf (0.50 #9056, 0.45 #14656, 0.40 #11390), 01vs4ff (0.50 #8677, 0.43 #9610, 0.42 #15208) >> Best rule #16383 for best value: >> intensional similarity = 21 >> extensional distance = 11 >> proper extension: 0dq630k; >> query: (?x885, 04bpm6) <- role(?x885, ?x1750), role(?x885, ?x615), role(?x885, ?x315), role(?x885, ?x227), role(?x885, ?x3296), role(?x885, ?x2888), role(?x885, ?x1574), role(?x885, ?x745), ?x315 = 0l14md, ?x227 = 0342h, role(?x2306, ?x615), ?x2306 = 06k02, ?x745 = 01vj9c, family(?x2888, ?x9885), role(?x1148, ?x2888), ?x1148 = 02qjv, group(?x2888, ?x2521), role(?x615, ?x894), ?x1750 = 02hnl, ?x1574 = 0l15bq, role(?x2157, ?x3296) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0dwtp role! 082brv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 44.000 0.692 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0dwtp role! 04bpm6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 44.000 0.692 http://example.org/music/artist/track_contributions./music/track_contribution/role #8248-0czr9_ PRED entity: 0czr9_ PRED relation: contains PRED expected values: 0n5_t => 106 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2146): 0n5yh (0.48 #67704, 0.46 #35327, 0.42 #41215), 0n5xb (0.48 #67704, 0.46 #35327, 0.42 #41215), 059f4 (0.48 #67704, 0.46 #35327, 0.34 #38271), 09c7w0 (0.48 #67704, 0.46 #35327, 0.04 #61817), 0czr9_ (0.48 #67704, 0.46 #35327, 0.02 #23042), 0xhmb (0.33 #7525, 0.33 #4581, 0.29 #10469), 021gk7 (0.30 #55930), 01cyd5 (0.30 #55930), 0n5yv (0.17 #6941, 0.17 #3997, 0.14 #9885), 01m94f (0.17 #3996, 0.14 #9884, 0.08 #12830) >> Best rule #67704 for best value: >> intensional similarity = 5 >> extensional distance = 187 >> proper extension: 05rgl; 02613; >> query: (?x13269, ?x13066) <- adjoins(?x13269, ?x12828), contains(?x13269, ?x14091), contains(?x13269, ?x7600), contains(?x13066, ?x7600), location_of_ceremony(?x566, ?x14091) >> conf = 0.48 => this is the best rule for 5 predicted values *> Best rule #22770 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 05kr_; *> query: (?x13269, 0n5_t) <- adjoins(?x13269, ?x12828), contains(?x13269, ?x7600), district_represented(?x605, ?x13269), legislative_sessions(?x2357, ?x605) *> conf = 0.02 ranks of expected_values: 1767 EVAL 0czr9_ contains 0n5_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 106.000 24.000 0.485 http://example.org/location/location/contains #8247-0320fn PRED entity: 0320fn PRED relation: nominated_for! PRED expected values: 04dn09n => 78 concepts (67 used for prediction) PRED predicted values (max 10 best out of 211): 0gq9h (0.76 #2177, 0.74 #2648, 0.55 #1237), 02x4wr9 (0.68 #10118, 0.67 #10117, 0.66 #9409), 0789r6 (0.68 #10118, 0.67 #10117, 0.66 #9409), 019f4v (0.65 #2169, 0.63 #2640, 0.43 #289), 0k611 (0.55 #2186, 0.54 #2657, 0.36 #1246), 0gq_v (0.50 #20, 0.36 #2135, 0.36 #2606), 040njc (0.49 #2122, 0.45 #2593, 0.34 #1182), 04dn09n (0.46 #2151, 0.44 #2622, 0.43 #271), 0gr4k (0.44 #2613, 0.44 #2142, 0.28 #1202), 0gqyl (0.43 #313, 0.33 #2664, 0.32 #2193) >> Best rule #2177 for best value: >> intensional similarity = 4 >> extensional distance = 195 >> proper extension: 011yfd; 0j8f09z; >> query: (?x4009, 0gq9h) <- nominated_for(?x1313, ?x4009), film(?x2549, ?x4009), ?x1313 = 0gs9p, award(?x4009, ?x2532) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #2151 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 195 *> proper extension: 011yfd; 0j8f09z; *> query: (?x4009, 04dn09n) <- nominated_for(?x1313, ?x4009), film(?x2549, ?x4009), ?x1313 = 0gs9p, award(?x4009, ?x2532) *> conf = 0.46 ranks of expected_values: 8 EVAL 0320fn nominated_for! 04dn09n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 78.000 67.000 0.761 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8246-02qnhk1 PRED entity: 02qnhk1 PRED relation: profession PRED expected values: 02jknp => 49 concepts (36 used for prediction) PRED predicted values (max 10 best out of 42): 02jknp (0.57 #1044, 0.49 #303, 0.48 #896), 0dxtg (0.53 #1198, 0.51 #309, 0.51 #902), 03gjzk (0.32 #903, 0.30 #458, 0.29 #310), 09jwl (0.21 #2831, 0.19 #3572, 0.18 #4904), 0cbd2 (0.17 #3264, 0.17 #2227, 0.15 #4596), 018gz8 (0.17 #1201, 0.14 #312, 0.13 #608), 02krf9 (0.16 #322, 0.15 #1063, 0.15 #470), 0nbcg (0.14 #2844, 0.12 #3585, 0.10 #2992), 0np9r (0.14 #1649, 0.14 #1797, 0.11 #1353), 016z4k (0.12 #2521, 0.12 #1781, 0.11 #3558) >> Best rule #1044 for best value: >> intensional similarity = 7 >> extensional distance = 750 >> proper extension: 0jf1b; 031zkw; 027l0b; 021yzs; 022g44; 06j8wx; 043hg; 04r7p; 02404v; 0k57l; ... >> query: (?x14496, 02jknp) <- profession(?x14496, ?x1032), profession(?x14496, ?x319), ?x1032 = 02hrh1q, profession(?x9014, ?x319), profession(?x2465, ?x319), ?x9014 = 05bht9, ?x2465 = 0j_c >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qnhk1 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 36.000 0.565 http://example.org/people/person/profession #8245-03k50 PRED entity: 03k50 PRED relation: language! PRED expected values: 034qmv 0h2zvzr => 46 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1813): 041td_ (0.50 #9601, 0.38 #18158, 0.33 #14735), 09yxcz (0.50 #15402, 0.38 #17004, 0.33 #5024), 01xq8v (0.50 #9834, 0.33 #14968, 0.33 #4700), 0209xj (0.50 #8644, 0.33 #3510, 0.31 #17201), 08nvyr (0.50 #9281, 0.33 #4147, 0.31 #17838), 024l2y (0.50 #8798, 0.33 #3664, 0.31 #17355), 04fzfj (0.50 #8649, 0.33 #3515, 0.31 #17206), 06x43v (0.50 #9796, 0.33 #4662, 0.27 #21775), 017n9 (0.50 #10224, 0.33 #5090, 0.27 #22203), 01y9jr (0.50 #9654, 0.33 #4520, 0.27 #21633) >> Best rule #9601 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 03_9r; >> query: (?x1882, 041td_) <- languages(?x14156, ?x1882), language(?x3863, ?x1882), languages_spoken(?x5025, ?x1882), people(?x5855, ?x14156), ?x3863 = 0dx8gj, profession(?x14156, ?x524) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13702 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 0f8l9c; *> query: (?x1882, 034qmv) <- titles(?x1882, ?x8074), language(?x2914, ?x1882), music(?x2914, ?x669), costume_design_by(?x8074, ?x12771), written_by(?x2914, ?x635), genre(?x8074, ?x53), production_companies(?x2914, ?x541) *> conf = 0.33 ranks of expected_values: 79, 83 EVAL 03k50 language! 0h2zvzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 46.000 28.000 0.500 http://example.org/film/film/language EVAL 03k50 language! 034qmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 46.000 28.000 0.500 http://example.org/film/film/language #8244-053j4w4 PRED entity: 053j4w4 PRED relation: profession PRED expected values: 089fss => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 125): 089fss (0.76 #167, 0.48 #317, 0.12 #767), 02hrh1q (0.66 #11867, 0.65 #5417, 0.64 #9767), 01d_h8 (0.37 #2856, 0.35 #3756, 0.33 #6), 0dxtg (0.33 #14, 0.27 #4366, 0.27 #1214), 02jknp (0.33 #8, 0.24 #3758, 0.22 #3908), 02pjxr (0.27 #4202, 0.10 #335, 0.07 #785), 03gjzk (0.24 #4368, 0.24 #4518, 0.23 #4818), 09jwl (0.20 #2420, 0.20 #3620, 0.19 #4071), 0cbd2 (0.17 #2257, 0.16 #1657, 0.16 #3157), 026sdt1 (0.14 #370, 0.12 #220, 0.09 #820) >> Best rule #167 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 0cb77r; >> query: (?x7438, 089fss) <- place_of_death(?x7438, ?x1523), film_sets_designed(?x7438, ?x810), award_nominee(?x6096, ?x7438) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 053j4w4 profession 089fss CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.765 http://example.org/people/person/profession #8243-04cnp4 PRED entity: 04cnp4 PRED relation: major_field_of_study PRED expected values: 05qt0 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 109): 01mkq (0.45 #2016, 0.40 #4891, 0.38 #4391), 02lp1 (0.42 #2012, 0.32 #4887, 0.31 #4387), 04rjg (0.36 #2021, 0.31 #4896, 0.30 #4396), 02j62 (0.36 #4907, 0.35 #2032, 0.34 #4407), 01lj9 (0.29 #2042, 0.23 #4417, 0.23 #4917), 05qjt (0.29 #258, 0.25 #2008, 0.25 #4883), 0g26h (0.27 #2045, 0.21 #545, 0.21 #4795), 01tbp (0.27 #2063, 0.20 #4938, 0.19 #4438), 02_7t (0.27 #2068, 0.19 #193, 0.18 #568), 062z7 (0.26 #4904, 0.25 #2029, 0.24 #529) >> Best rule #2016 for best value: >> intensional similarity = 4 >> extensional distance = 94 >> proper extension: 08815; 0f1nl; 02bb47; 01mpwj; 04d5v9; 0bqxw; 04hgpt; 095kp; 02y9bj; 0jpn8; ... >> query: (?x8463, 01mkq) <- contains(?x94, ?x8463), institution(?x3437, ?x8463), ?x3437 = 02_xgp2, ?x94 = 09c7w0 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #9383 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 580 *> proper extension: 05zjtn4; 01fpvz; 01b1mj; 01j_06; 02hft3; 02301; 0cchk3; 0l2tk; 0373qg; 01q460; ... *> query: (?x8463, ?x254) <- contains(?x94, ?x8463), institution(?x3437, ?x8463), major_field_of_study(?x3437, ?x254) *> conf = 0.05 ranks of expected_values: 56 EVAL 04cnp4 major_field_of_study 05qt0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 145.000 145.000 0.448 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8242-01sxq9 PRED entity: 01sxq9 PRED relation: award PRED expected values: 0bfvw2 => 104 concepts (65 used for prediction) PRED predicted values (max 10 best out of 248): 02lp0w (0.80 #1613, 0.77 #23411, 0.74 #2823), 0cqhk0 (0.47 #1246, 0.15 #2456, 0.14 #7261), 0gkts9 (0.41 #1377, 0.12 #17755, 0.09 #2991), 0cqhmg (0.41 #1570, 0.12 #17755, 0.05 #19373), 0gqy2 (0.40 #567, 0.13 #7425, 0.12 #970), 02x73k6 (0.40 #464, 0.07 #25025, 0.07 #7322), 09sb52 (0.31 #7302, 0.29 #15374, 0.28 #12951), 09qj50 (0.29 #1255, 0.14 #7261, 0.14 #14929), 0ck27z (0.23 #2511, 0.22 #6949, 0.19 #898), 0gqwc (0.20 #477, 0.19 #880, 0.18 #2897) >> Best rule #1613 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 015c2f; 03rwng; 07m77x; 05ggt_; 01rs5p; >> query: (?x1057, ?x1058) <- award_winner(?x2603, ?x1057), award_winner(?x1058, ?x1057), profession(?x1057, ?x1032), ?x2603 = 09qs08, film(?x1057, ?x1941) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #821 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 044rvb; 086sj; 04z542; 04wp3s; 02pk6x; 06fc0b; *> query: (?x1057, 0bfvw2) <- film(?x1057, ?x3255), film(?x1057, ?x2529), produced_by(?x3255, ?x4397), ?x2529 = 03m8y5 *> conf = 0.12 ranks of expected_values: 41 EVAL 01sxq9 award 0bfvw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 104.000 65.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8241-0bvn25 PRED entity: 0bvn25 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.66 #44, 0.63 #480, 0.57 #812), 09zzb8 (0.57 #38, 0.56 #474, 0.55 #806), 0dxtw (0.29 #484, 0.29 #816, 0.27 #1400), 01vx2h (0.28 #485, 0.25 #1180, 0.25 #49), 01pvkk (0.21 #818, 0.21 #1291, 0.21 #1000), 04pyp5 (0.13 #17, 0.07 #54, 0.06 #91), 0215hd (0.12 #824, 0.11 #382, 0.10 #166), 089g0h (0.10 #383, 0.09 #167, 0.09 #1298), 02rh1dz (0.09 #193, 0.09 #301, 0.09 #229), 01xy5l_ (0.09 #378, 0.09 #820, 0.09 #488) >> Best rule #44 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 0g5q34q; 076xkdz; >> query: (?x365, 0ch6mp2) <- genre(?x365, ?x1403), ?x1403 = 02l7c8, category(?x365, ?x134), film_release_distribution_medium(?x365, ?x81) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bvn25 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.662 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8240-02bn_p PRED entity: 02bn_p PRED relation: legislative_sessions! PRED expected values: 03txms 0226cw => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 47): 0226cw (0.82 #354, 0.78 #490, 0.75 #255), 0d06m5 (0.75 #255, 0.73 #343, 0.73 #342), 03txms (0.75 #255, 0.73 #343, 0.73 #342), 02mjmr (0.75 #255, 0.73 #343, 0.73 #342), 01lct6 (0.73 #343, 0.73 #342, 0.69 #137), 06hx2 (0.65 #546, 0.65 #138, 0.63 #522), 0dq2k (0.33 #438, 0.18 #415, 0.17 #616), 01mvpv (0.27 #429, 0.22 #338, 0.17 #452), 042fk (0.17 #453, 0.14 #541, 0.11 #586), 01k165 (0.11 #503, 0.05 #678, 0.02 #721) >> Best rule #354 for best value: >> intensional similarity = 37 >> extensional distance = 9 >> proper extension: 03ww_x; >> query: (?x1027, 0226cw) <- district_represented(?x1027, ?x6895), district_represented(?x1027, ?x4754), district_represented(?x1027, ?x2768), district_represented(?x1027, ?x1906), legislative_sessions(?x1027, ?x6933), legislative_sessions(?x1027, ?x4821), legislative_sessions(?x1027, ?x3463), legislative_sessions(?x1027, ?x952), legislative_sessions(?x1027, ?x845), ?x3463 = 02bqmq, ?x1906 = 04rrx, taxonomy(?x6895, ?x939), legislative_sessions(?x11605, ?x1027), ?x6933 = 024tkd, state_province_region(?x4793, ?x6895), ?x845 = 07p__7, administrative_division(?x1214, ?x6895), ?x11605 = 024_vw, district_represented(?x4821, ?x6226), location(?x7733, ?x6895), location(?x5059, ?x6895), legislative_sessions(?x2860, ?x4821), religion(?x6895, ?x2672), religion(?x6895, ?x1624), religion(?x6895, ?x492), ?x2672 = 01y0s9, contains(?x8260, ?x2768), location_of_ceremony(?x2426, ?x2768), ?x1624 = 051kv, ?x952 = 06f0dc, contains(?x6895, ?x1325), award_nominee(?x5059, ?x1231), ?x6226 = 03gh4, ?x4754 = 0g0syc, location(?x744, ?x2768), religion(?x111, ?x492), award(?x7733, ?x102) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 02bn_p legislative_sessions! 0226cw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.818 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 02bn_p legislative_sessions! 03txms CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 36.000 36.000 0.818 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #8239-01wg3q PRED entity: 01wg3q PRED relation: artist! PRED expected values: 01clyr => 105 concepts (83 used for prediction) PRED predicted values (max 10 best out of 118): 015_1q (0.26 #2105, 0.21 #1270, 0.21 #3773), 011k1h (0.20 #149, 0.18 #10, 0.16 #3486), 017l96 (0.20 #574, 0.16 #2382, 0.15 #713), 01w40h (0.18 #28, 0.17 #723, 0.14 #1557), 01clyr (0.18 #33, 0.16 #450, 0.12 #172), 0181dw (0.17 #320, 0.15 #737, 0.15 #2406), 033hn8 (0.14 #1265, 0.13 #4741, 0.13 #5575), 0fb0v (0.13 #285, 0.12 #563, 0.12 #1675), 0k_kr (0.13 #322, 0.12 #600, 0.12 #44), 0g768 (0.13 #5598, 0.13 #9073, 0.13 #4764) >> Best rule #2105 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 02qlg7s; 01gx5f; 016k62; >> query: (?x8754, 015_1q) <- profession(?x8754, ?x131), artists(?x505, ?x8754), role(?x8754, ?x227), nationality(?x8754, ?x1310), ?x505 = 03_d0 >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #33 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 01w923; 02rn_bj; *> query: (?x8754, 01clyr) <- profession(?x8754, ?x2659), profession(?x8754, ?x1614), instrumentalists(?x314, ?x8754), ?x2659 = 039v1, origin(?x8754, ?x8755), ?x1614 = 01c72t *> conf = 0.18 ranks of expected_values: 5 EVAL 01wg3q artist! 01clyr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 105.000 83.000 0.260 http://example.org/music/record_label/artist #8238-0n5_t PRED entity: 0n5_t PRED relation: time_zones PRED expected values: 02hcv8 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.86 #224, 0.79 #462, 0.78 #131), 02lcqs (0.31 #188, 0.29 #175, 0.26 #281), 02hczc (0.19 #119, 0.14 #133, 0.10 #265), 02fqwt (0.18 #922, 0.17 #619, 0.17 #1119), 03bdv (0.09 #598, 0.06 #97, 0.05 #110), 02llzg (0.09 #596, 0.07 #687, 0.06 #873), 042g7t (0.03 #115, 0.01 #694, 0.01 #958), 03plfd (0.02 #693, 0.02 #879, 0.02 #957), 0gsrz4 (0.02 #890, 0.02 #903, 0.02 #916) >> Best rule #224 for best value: >> intensional similarity = 5 >> extensional distance = 240 >> proper extension: 0mm0p; >> query: (?x12433, ?x2674) <- adjoins(?x12433, ?x7565), source(?x12433, ?x958), adjoins(?x7565, ?x10162), time_zones(?x10162, ?x2674), administrative_division(?x6188, ?x10162) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n5_t time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.856 http://example.org/location/location/time_zones #8237-09p4w8 PRED entity: 09p4w8 PRED relation: film! PRED expected values: 022qw7 045931 => 84 concepts (53 used for prediction) PRED predicted values (max 10 best out of 686): 0f5xn (0.44 #74774, 0.44 #31155, 0.43 #91388), 01rzqj (0.44 #74774, 0.44 #31155, 0.43 #91388), 06dkzt (0.44 #31155, 0.43 #91388, 0.42 #85156), 086k8 (0.44 #31155, 0.43 #91388, 0.42 #85156), 01l2fn (0.20 #261, 0.12 #4413, 0.10 #6489), 0bwgc_ (0.20 #1927, 0.12 #6079, 0.10 #8155), 03bxsw (0.20 #572, 0.12 #4724, 0.10 #6800), 0755wz (0.20 #1223, 0.12 #5375, 0.10 #7451), 017gxw (0.20 #916, 0.12 #5068, 0.10 #7144), 01vvb4m (0.20 #521, 0.12 #4673, 0.08 #8826) >> Best rule #74774 for best value: >> intensional similarity = 3 >> extensional distance = 828 >> proper extension: 07bz5; >> query: (?x4853, ?x3366) <- nominated_for(?x3366, ?x4853), award(?x4853, ?x2456), location(?x3366, ?x362) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #8124 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0cwfgz; *> query: (?x4853, 045931) <- language(?x4853, ?x254), film(?x10626, ?x4853), ?x10626 = 0ywqc, nominated_for(?x382, ?x4853) *> conf = 0.10 ranks of expected_values: 86 EVAL 09p4w8 film! 045931 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 84.000 53.000 0.440 http://example.org/film/actor/film./film/performance/film EVAL 09p4w8 film! 022qw7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 53.000 0.440 http://example.org/film/actor/film./film/performance/film #8236-0sgtz PRED entity: 0sgtz PRED relation: place PRED expected values: 0sgtz => 114 concepts (37 used for prediction) PRED predicted values (max 10 best out of 22): 0sgxg (0.35 #516, 0.12 #462, 0.07 #978), 0s9b_ (0.12 #436, 0.07 #952, 0.06 #1467), 0s9z_ (0.12 #332, 0.07 #848, 0.06 #1363), 0s3pw (0.12 #469, 0.07 #985, 0.04 #2015), 0sb1r (0.12 #88, 0.07 #604, 0.04 #1634), 0s69k (0.12 #38, 0.04 #1584, 0.03 #2102), 0s2z0 (0.07 #891, 0.06 #1406, 0.04 #1921), 0sjqm (0.07 #789, 0.06 #1304, 0.04 #1819), 0sf9_ (0.07 #603, 0.06 #1118, 0.04 #1633), 0s3y5 (0.07 #523, 0.06 #1038, 0.04 #1553) >> Best rule #516 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0s69k; >> query: (?x10876, ?x13584) <- contains(?x10877, ?x10876), contains(?x3818, ?x10876), ?x3818 = 03v0t, category(?x10876, ?x134), ?x134 = 08mbj5d, county(?x13584, ?x10877) >> conf = 0.35 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0sgtz place 0sgtz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 37.000 0.353 http://example.org/location/hud_county_place/place #8235-01nrnm PRED entity: 01nrnm PRED relation: organization! PRED expected values: 0hm4q => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 17): 060c4 (0.87 #974, 0.65 #1154, 0.64 #1070), 07xl34 (0.67 #82, 0.65 #106, 0.56 #226), 0dq_5 (0.50 #368, 0.38 #44, 0.36 #440), 05c0jwl (0.33 #173, 0.28 #125, 0.26 #233), 05k17c (0.13 #246, 0.11 #978, 0.11 #366), 08jcfy (0.11 #83, 0.11 #107, 0.09 #263), 0hm4q (0.08 #403, 0.06 #1817, 0.06 #595), 02wlwtm (0.06 #1817, 0.03 #108, 0.03 #1779), 07t3gd (0.05 #1381, 0.03 #1779, 0.03 #1754), 01___w (0.05 #1381, 0.03 #1779, 0.03 #1754) >> Best rule #974 for best value: >> intensional similarity = 3 >> extensional distance = 425 >> proper extension: 0ljl8; 02dqdp; >> query: (?x6120, 060c4) <- organization(?x3464, ?x6120), company(?x3464, ?x122), ?x122 = 08815 >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #403 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 206 *> proper extension: 02jx_v; *> query: (?x6120, 0hm4q) <- category(?x6120, ?x134), organization(?x3464, ?x6120), school_type(?x6120, ?x3092), ?x3092 = 05jxkf *> conf = 0.08 ranks of expected_values: 7 EVAL 01nrnm organization! 0hm4q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 157.000 157.000 0.871 http://example.org/organization/role/leaders./organization/leadership/organization #8234-04kzqz PRED entity: 04kzqz PRED relation: honored_for! PRED expected values: 0gvstc3 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 110): 0lp_cd3 (0.33 #139, 0.25 #261, 0.14 #627), 0gvstc3 (0.33 #149, 0.24 #1615, 0.23 #1737), 09g90vz (0.33 #230, 0.11 #962, 0.09 #1084), 0g55tzk (0.33 #242, 0.06 #1096, 0.06 #974), 02q690_ (0.23 #1030, 0.22 #1642, 0.21 #1764), 03nnm4t (0.23 #1039, 0.20 #1651, 0.20 #1773), 05c1t6z (0.22 #1599, 0.21 #1721, 0.15 #5137), 09pnw5 (0.21 #1099, 0.14 #1344, 0.06 #1064), 09p3h7 (0.14 #426, 0.11 #1036, 0.11 #1281), 0gx_st (0.14 #1251, 0.11 #1618, 0.11 #1740) >> Best rule #139 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 063ykwt; >> query: (?x2026, 0lp_cd3) <- nominated_for(?x9785, ?x2026), producer_type(?x2026, ?x632), ?x632 = 0ckd1, ?x9785 = 02tn0_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 063ykwt; *> query: (?x2026, 0gvstc3) <- nominated_for(?x9785, ?x2026), producer_type(?x2026, ?x632), ?x632 = 0ckd1, ?x9785 = 02tn0_ *> conf = 0.33 ranks of expected_values: 2 EVAL 04kzqz honored_for! 0gvstc3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #8233-0c38gj PRED entity: 0c38gj PRED relation: films! PRED expected values: 0fx2s => 67 concepts (36 used for prediction) PRED predicted values (max 10 best out of 42): 06c97 (0.20 #48, 0.01 #360), 0fx2s (0.10 #229, 0.03 #385, 0.03 #1808), 05489 (0.10 #208, 0.02 #1787, 0.02 #2895), 01w1sx (0.10 #247, 0.02 #403, 0.02 #1511), 07jq_ (0.10 #238, 0.02 #394, 0.02 #867), 0d1w9 (0.10 #192, 0.01 #4935, 0.01 #2879), 0d06vc (0.10 #180), 04gb7 (0.04 #357, 0.02 #1780, 0.02 #1622), 07c52 (0.04 #332, 0.02 #1912, 0.02 #1755), 01cgz (0.04 #331, 0.01 #1596, 0.01 #2227) >> Best rule #48 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0g9lm2; 04jpg2p; >> query: (?x4633, 06c97) <- film(?x4053, ?x4633), produced_by(?x4633, ?x3528), currency(?x4633, ?x170), ?x4053 = 01pkhw >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #229 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01cssf; 04vr_f; 03bx2lk; 0dr_4; 0661ql3; 01flv_; 03cp4cn; 02q0k7v; *> query: (?x4633, 0fx2s) <- film(?x3101, ?x4633), film_release_distribution_medium(?x4633, ?x81), ?x81 = 029j_, ?x3101 = 0dvmd *> conf = 0.10 ranks of expected_values: 2 EVAL 0c38gj films! 0fx2s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 36.000 0.200 http://example.org/film/film_subject/films #8232-04s2z PRED entity: 04s2z PRED relation: profession! PRED expected values: 0jcx => 65 concepts (34 used for prediction) PRED predicted values (max 10 best out of 4205): 01zwy (0.50 #36670, 0.50 #11261, 0.43 #32435), 02m7r (0.50 #9145, 0.50 #4913, 0.40 #13376), 099p5 (0.50 #11555, 0.50 #7323, 0.40 #15786), 052h3 (0.50 #43470, 0.43 #30762, 0.40 #47706), 05d1y (0.50 #11189, 0.40 #15420, 0.33 #28127), 02sdx (0.50 #12076, 0.40 #16307, 0.33 #29014), 06crk (0.50 #10528, 0.40 #14759, 0.33 #27466), 09gnn (0.43 #101638, 0.36 #105875, 0.24 #29635), 05wm88 (0.40 #71566, 0.29 #67330, 0.28 #101216), 0mb5x (0.40 #70513, 0.29 #66277, 0.25 #36630) >> Best rule #36670 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 025rxky; >> query: (?x7290, 01zwy) <- profession(?x11104, ?x7290), profession(?x7251, ?x7290), profession(?x3712, ?x7290), influenced_by(?x11097, ?x3712), influenced_by(?x5797, ?x3712), ?x5797 = 07c37, nationality(?x3712, ?x1353), influenced_by(?x118, ?x11097), gender(?x7251, ?x231), student(?x892, ?x11104) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #34869 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 6 *> proper extension: 025rxky; *> query: (?x7290, 0jcx) <- profession(?x11104, ?x7290), profession(?x7251, ?x7290), profession(?x3712, ?x7290), influenced_by(?x11097, ?x3712), influenced_by(?x5797, ?x3712), ?x5797 = 07c37, nationality(?x3712, ?x1353), influenced_by(?x118, ?x11097), gender(?x7251, ?x231), student(?x892, ?x11104) *> conf = 0.38 ranks of expected_values: 12 EVAL 04s2z profession! 0jcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 65.000 34.000 0.500 http://example.org/people/person/profession #8231-01c9d1 PRED entity: 01c9d1 PRED relation: ceremony PRED expected values: 02rjjll 01xqqp => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 125): 02rjjll (0.88 #253, 0.87 #128, 0.87 #3), 01xqqp (0.74 #332, 0.73 #82, 0.70 #207), 05c1t6z (0.19 #1137, 0.18 #1012, 0.17 #1637), 0gvstc3 (0.18 #1152, 0.16 #1027, 0.16 #1652), 02q690_ (0.17 #1180, 0.17 #1055, 0.16 #1680), 0n8_m93 (0.16 #1479, 0.16 #979, 0.14 #1104), 0bzm81 (0.16 #1392, 0.16 #892, 0.14 #1017), 03nnm4t (0.16 #1189, 0.16 #1064, 0.15 #1689), 02hn5v (0.16 #1408, 0.15 #908, 0.14 #1033), 02yxh9 (0.16 #1462, 0.15 #962, 0.14 #1087) >> Best rule #253 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 02grdc; 01c427; 01c92g; 01ck6h; 02581c; 02sp_v; 02flpc; 02h3d1; 024vjd; 03tk6z; ... >> query: (?x12813, 02rjjll) <- ceremony(?x12813, ?x9431), award_winner(?x12813, ?x12825), award(?x11953, ?x12813), ?x9431 = 02cg41 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01c9d1 ceremony 01xqqp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c9d1 ceremony 02rjjll CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.875 http://example.org/award/award_category/winners./award/award_honor/ceremony #8230-0mfc0 PRED entity: 0mfc0 PRED relation: award PRED expected values: 01yz0x => 158 concepts (132 used for prediction) PRED predicted values (max 10 best out of 267): 01yz0x (0.71 #571, 0.71 #1365, 0.68 #3350), 01tgwv (0.50 #358, 0.40 #3534, 0.35 #1549), 058bzgm (0.50 #367, 0.24 #3543, 0.24 #1558), 040_9s0 (0.48 #3488, 0.47 #1503, 0.40 #2297), 039yzf (0.40 #2330, 0.40 #1139, 0.38 #2727), 045xh (0.35 #1562, 0.32 #3547, 0.30 #2356), 0208wk (0.35 #2325, 0.30 #3119, 0.20 #1134), 09sb52 (0.19 #38949, 0.18 #41728, 0.17 #43714), 06196 (0.14 #735, 0.12 #3514, 0.12 #1529), 03mv9j (0.14 #786, 0.12 #1580, 0.11 #25807) >> Best rule #571 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 04mhl; 0gd_s; 01k56k; >> query: (?x9425, 01yz0x) <- award(?x9425, ?x9285), award(?x9425, ?x1288), ?x1288 = 02662b, student(?x1675, ?x9425), ?x9285 = 0265vt >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mfc0 award 01yz0x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 132.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8229-08hp53 PRED entity: 08hp53 PRED relation: profession PRED expected values: 02krf9 => 83 concepts (82 used for prediction) PRED predicted values (max 10 best out of 45): 02jknp (0.53 #732, 0.48 #1167, 0.47 #1022), 0cbd2 (0.42 #441, 0.19 #151, 0.15 #7111), 0kyk (0.23 #461, 0.11 #3796, 0.10 #2926), 02krf9 (0.19 #168, 0.14 #748, 0.13 #1183), 09jwl (0.17 #4075, 0.16 #2770, 0.16 #8135), 02hv44_ (0.15 #489, 0.04 #2954, 0.04 #7159), 0np9r (0.15 #4512, 0.15 #7412, 0.10 #162), 018gz8 (0.13 #4508, 0.13 #7408, 0.12 #1608), 0nbcg (0.11 #4088, 0.11 #2783, 0.11 #8148), 0dz3r (0.11 #4062, 0.10 #8122, 0.10 #6672) >> Best rule #732 for best value: >> intensional similarity = 3 >> extensional distance = 259 >> proper extension: 09gffmz; 03kpvp; 047q2wc; 01515w; >> query: (?x1799, 02jknp) <- award_winner(?x723, ?x1799), nominated_for(?x1799, ?x3218), produced_by(?x8736, ?x1799) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #168 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 111 *> proper extension: 087qxp; *> query: (?x1799, 02krf9) <- award_winner(?x723, ?x1799), profession(?x1799, ?x319), tv_program(?x1799, ?x1843) *> conf = 0.19 ranks of expected_values: 4 EVAL 08hp53 profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 82.000 0.529 http://example.org/people/person/profession #8228-02tr7d PRED entity: 02tr7d PRED relation: film PRED expected values: 0gy7bj4 => 88 concepts (60 used for prediction) PRED predicted values (max 10 best out of 349): 080dwhx (0.70 #1787, 0.46 #17865, 0.40 #14291), 03177r (0.13 #463, 0.12 #2250, 0.11 #4036), 04gcyg (0.13 #1381, 0.12 #3168, 0.11 #4954), 031786 (0.13 #1273, 0.12 #3060, 0.11 #4846), 02_kd (0.13 #585, 0.06 #2372, 0.06 #46453), 03_gz8 (0.13 #1121, 0.06 #2908, 0.06 #4694), 07tj4c (0.13 #1697, 0.06 #5270, 0.03 #8932), 09gq0x5 (0.12 #2070, 0.11 #3856, 0.07 #283), 03wjm2 (0.12 #3543, 0.11 #5329, 0.07 #1756), 0bz6sq (0.12 #3299, 0.11 #5085, 0.07 #1512) >> Best rule #1787 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 06t61y; 03v3xp; >> query: (?x1669, ?x493) <- award_winner(?x1669, ?x1951), nominated_for(?x1669, ?x493), ?x1951 = 065jlv >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02tr7d film 0gy7bj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 60.000 0.703 http://example.org/film/actor/film./film/performance/film #8227-034zc0 PRED entity: 034zc0 PRED relation: award_nominee! PRED expected values: 02t_vx => 68 concepts (23 used for prediction) PRED predicted values (max 10 best out of 659): 02t_vx (0.71 #4051, 0.55 #8680, 0.16 #43974), 034zc0 (0.50 #3660, 0.45 #8289, 0.16 #43974), 02s2ft (0.33 #7, 0.06 #4635, 0.01 #30092), 0716t2 (0.33 #2246), 019pm_ (0.17 #605, 0.16 #43974, 0.15 #53233), 07s8r0 (0.17 #338, 0.16 #43974, 0.15 #53233), 01kb2j (0.17 #1192, 0.15 #41658, 0.14 #46289), 02qgyv (0.17 #493, 0.15 #41658, 0.14 #46289), 023kzp (0.17 #1373, 0.15 #41658, 0.14 #46289), 0btpx (0.17 #1844, 0.15 #41658, 0.14 #46289) >> Best rule #4051 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0z4s; 017149; 021vwt; 03q1vd; 02p7_k; 050t68; 02jsgf; 0410cp; 014g22; 042z_g; ... >> query: (?x5806, 02t_vx) <- award_nominee(?x11100, ?x5806), film(?x5806, ?x1734), ?x11100 = 057_yx >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034zc0 award_nominee! 02t_vx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 23.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8226-0y_9q PRED entity: 0y_9q PRED relation: genre PRED expected values: 04cb4x => 71 concepts (70 used for prediction) PRED predicted values (max 10 best out of 83): 04xvlr (0.61 #4699, 0.56 #822, 0.55 #1176), 03k9fj (0.61 #4699, 0.56 #822, 0.55 #1176), 02kdv5l (0.38 #4465, 0.27 #2, 0.25 #6109), 02l7c8 (0.38 #4480, 0.35 #486, 0.33 #604), 05p553 (0.35 #356, 0.34 #5641, 0.33 #6111), 01jfsb (0.32 #3655, 0.28 #1540, 0.28 #365), 02p0szs (0.24 #29, 0.05 #263, 0.05 #1673), 03g3w (0.22 #25, 0.08 #1669, 0.08 #2493), 0lsxr (0.20 #243, 0.19 #1653, 0.19 #1067), 01g6gs (0.20 #138, 0.09 #1431, 0.08 #21) >> Best rule #4699 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 02xhwm; >> query: (?x5304, ?x53) <- titles(?x53, ?x5304), genre(?x273, ?x53) >> conf = 0.61 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0y_9q genre 04cb4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 70.000 0.612 http://example.org/film/film/genre #8225-0f2s6 PRED entity: 0f2s6 PRED relation: location! PRED expected values: 04vcdj => 109 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1931): 092ggq (0.46 #110365, 0.45 #137957, 0.44 #137956), 025xt8y (0.46 #110365, 0.44 #137956, 0.43 #60202), 073749 (0.14 #3306, 0.13 #8325, 0.13 #5815), 023kzp (0.14 #3718, 0.11 #11246, 0.10 #13754), 016h4r (0.12 #668, 0.06 #7526, 0.05 #15053), 06mmb (0.12 #470, 0.06 #30573, 0.05 #2978), 025_nbr (0.12 #2344, 0.05 #4852, 0.04 #32447), 01pgk0 (0.12 #2331, 0.05 #4839, 0.03 #9858), 0p_47 (0.12 #754, 0.05 #3262, 0.03 #8281), 053y0s (0.12 #11, 0.02 #25098, 0.01 #30114) >> Best rule #110365 for best value: >> intensional similarity = 3 >> extensional distance = 250 >> proper extension: 0dhdp; 0mnzd; 0r2l7; 0r5wt; 0r04p; 0177z; 0ptj2; 02m__; 0c5_3; 0bxbb; ... >> query: (?x9713, ?x838) <- contains(?x94, ?x9713), place_of_birth(?x838, ?x9713), citytown(?x4211, ?x9713) >> conf = 0.46 => this is the best rule for 2 predicted values *> Best rule #27557 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 49 *> proper extension: 05zjtn4; 07w0v; 02fgdx; 0221g_; 01tx9m; 07ccs; 02km0m; 01n_g9; 0mrs1; 0d1xh; ... *> query: (?x9713, 04vcdj) <- contains(?x3634, ?x9713), ?x3634 = 07b_l *> conf = 0.02 ranks of expected_values: 1217 EVAL 0f2s6 location! 04vcdj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 109.000 87.000 0.456 http://example.org/people/person/places_lived./people/place_lived/location #8224-09gq0x5 PRED entity: 09gq0x5 PRED relation: genre PRED expected values: 07s9rl0 => 94 concepts (89 used for prediction) PRED predicted values (max 10 best out of 101): 07s9rl0 (0.82 #2156, 0.82 #1678, 0.80 #2276), 05p553 (0.68 #2758, 0.64 #3362, 0.44 #484), 07ssc (0.60 #3600, 0.58 #3599, 0.58 #1797), 017fp (0.58 #3599, 0.58 #1797, 0.58 #2395), 02l7c8 (0.57 #376, 0.42 #616, 0.40 #2771), 060__y (0.40 #137, 0.26 #1215, 0.26 #1695), 01jfsb (0.35 #252, 0.31 #3971, 0.30 #2888), 02kdv5l (0.33 #2397, 0.28 #1319, 0.28 #5994), 03k9fj (0.32 #970, 0.28 #2407, 0.27 #1329), 04xvh5 (0.27 #393, 0.22 #633, 0.21 #1111) >> Best rule #2156 for best value: >> intensional similarity = 4 >> extensional distance = 175 >> proper extension: 0m313; 0sxg4; 01jc6q; 028_yv; 09m6kg; 0c0yh4; 0yyg4; 01gc7; 07xtqq; 095zlp; ... >> query: (?x1813, 07s9rl0) <- titles(?x162, ?x1813), nominated_for(?x1107, ?x1813), award_winner(?x1813, ?x72), ?x1107 = 019f4v >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09gq0x5 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 89.000 0.825 http://example.org/film/film/genre #8223-068g3p PRED entity: 068g3p PRED relation: category PRED expected values: 08mbj5d => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.30 #70, 0.30 #40, 0.30 #43) >> Best rule #70 for best value: >> intensional similarity = 5 >> extensional distance = 3281 >> proper extension: 03ckxdg; 05drq5; 01p9hgt; 09gffmz; 09mq4m; 02_4fn; 03bx_5q; 03ckvj9; 01l4g5; 01lqf49; ... >> query: (?x9291, 08mbj5d) <- profession(?x9291, ?x1032), profession(?x7252, ?x1032), profession(?x1126, ?x1032), ?x7252 = 017g21, people(?x1446, ?x1126) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 068g3p category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.303 http://example.org/common/topic/webpage./common/webpage/category #8222-06ms6 PRED entity: 06ms6 PRED relation: student PRED expected values: 03f1r6t => 73 concepts (17 used for prediction) PRED predicted values (max 10 best out of 409): 012x2b (0.50 #648, 0.33 #184, 0.17 #1347), 083q7 (0.33 #1416, 0.33 #251, 0.20 #950), 02z1yj (0.33 #188, 0.25 #652, 0.20 #1119), 02hsgn (0.33 #107, 0.25 #571, 0.15 #3142), 0c3p7 (0.33 #134, 0.25 #598, 0.08 #3169), 0ywqc (0.33 #203, 0.25 #667, 0.08 #3238), 099d4 (0.33 #225, 0.25 #689, 0.08 #3260), 02p5hf (0.33 #198, 0.25 #662, 0.08 #3233), 04qt29 (0.33 #176, 0.25 #640, 0.08 #3211), 02l3_5 (0.33 #155, 0.25 #619, 0.08 #3190) >> Best rule #648 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 03qsdpk; >> query: (?x1695, 012x2b) <- major_field_of_study(?x13670, ?x1695), major_field_of_study(?x11821, ?x1695), major_field_of_study(?x4410, ?x1695), student(?x1695, ?x12525), major_field_of_study(?x1695, ?x254), ?x11821 = 015wy_, people(?x4195, ?x12525), institution(?x865, ?x13670), ?x4410 = 017j69 >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06ms6 student 03f1r6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 17.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/student #8221-02pgky2 PRED entity: 02pgky2 PRED relation: award_winner PRED expected values: 01713c 02kxbx3 => 50 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2494): 04sry (0.43 #5707, 0.33 #7246, 0.23 #8786), 02kxbx3 (0.42 #18492, 0.40 #3619, 0.36 #18491), 026670 (0.42 #18492, 0.36 #18491, 0.05 #3082), 0c00lh (0.42 #18492, 0.15 #8535, 0.14 #5456), 025jfl (0.40 #3159, 0.36 #18491, 0.33 #74), 0g5lhl7 (0.40 #3481, 0.33 #396, 0.23 #8099), 0c3p7 (0.40 #4048, 0.08 #8666, 0.07 #19457), 026rm_y (0.38 #8949, 0.33 #7409, 0.29 #5870), 0h0wc (0.38 #8066, 0.29 #4987, 0.22 #6526), 018ygt (0.38 #8667, 0.29 #5588, 0.22 #7127) >> Best rule #5707 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 05zksls; 0drtv8; >> query: (?x6594, 04sry) <- honored_for(?x6594, ?x1531), ceremony(?x500, ?x6594), country(?x1531, ?x94), nominated_for(?x1441, ?x1531), nominated_for(?x277, ?x1531), genre(?x1531, ?x53), ?x1441 = 099cng, ?x277 = 0f_nbyh, nominated_for(?x500, ?x7214), nominated_for(?x500, ?x810), featured_film_locations(?x1531, ?x1036), ?x810 = 0jzw, ?x7214 = 02dr9j, award(?x382, ?x500) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #18492 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 27 *> proper extension: 0fk0xk; *> query: (?x6594, ?x5351) <- honored_for(?x6594, ?x1531), ceremony(?x3066, ?x6594), ceremony(?x1053, ?x6594), ceremony(?x500, ?x6594), ceremony(?x77, ?x6594), country(?x1531, ?x94), nominated_for(?x9343, ?x1531), ?x500 = 0p9sw, film_crew_role(?x1531, ?x137), film(?x5351, ?x1531), ?x77 = 0gqng, award_winner(?x1531, ?x617), ?x3066 = 0gqy2, award(?x1531, ?x3190), award(?x368, ?x9343), nominated_for(?x1053, ?x124) *> conf = 0.42 ranks of expected_values: 2, 15 EVAL 02pgky2 award_winner 02kxbx3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 50.000 16.000 0.429 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02pgky2 award_winner 01713c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 50.000 16.000 0.429 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8220-027r7k PRED entity: 027r7k PRED relation: genre PRED expected values: 03npn 01jfsb => 77 concepts (73 used for prediction) PRED predicted values (max 10 best out of 88): 01jfsb (0.64 #809, 0.60 #1152, 0.56 #238), 07ssc (0.53 #1028, 0.49 #4920, 0.49 #3775), 05p553 (0.43 #5494, 0.32 #8008, 0.31 #6522), 02l7c8 (0.39 #4132, 0.38 #356, 0.35 #1042), 02kdv5l (0.32 #5949, 0.32 #1713, 0.32 #5721), 03k9fj (0.30 #9, 0.26 #694, 0.24 #580), 01hmnh (0.30 #15, 0.21 #129, 0.14 #7335), 06n90 (0.22 #582, 0.21 #125, 0.20 #11), 09blyk (0.22 #828, 0.21 #1171, 0.14 #143), 0219x_ (0.21 #138, 0.20 #24, 0.14 #480) >> Best rule #809 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 05pbl56; 01f8gz; 09k56b7; 02qr69m; 04fv5b; 035bcl; 02mpyh; >> query: (?x11324, 01jfsb) <- genre(?x11324, ?x600), nominated_for(?x2599, ?x11324), ?x600 = 02n4kr, film_release_region(?x11324, ?x304) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14 EVAL 027r7k genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 73.000 0.636 http://example.org/film/film/genre EVAL 027r7k genre 03npn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 77.000 73.000 0.636 http://example.org/film/film/genre #8219-017z49 PRED entity: 017z49 PRED relation: nominated_for! PRED expected values: 02x1dht => 79 concepts (70 used for prediction) PRED predicted values (max 10 best out of 206): 0gq9h (0.69 #994, 0.33 #3557, 0.32 #8689), 0gs9p (0.59 #996, 0.31 #3559, 0.30 #2860), 019f4v (0.55 #986, 0.31 #3549, 0.28 #2850), 040njc (0.44 #939, 0.27 #10726, 0.27 #7695), 0p9sw (0.44 #953, 0.20 #3516, 0.20 #8648), 0gr0m (0.43 #991, 0.21 #8686, 0.21 #2855), 0gr4k (0.38 #959, 0.23 #27, 0.21 #5155), 02qyntr (0.38 #1108, 0.24 #2972, 0.22 #3671), 0gq_v (0.37 #952, 0.26 #8647, 0.23 #5148), 02pqp12 (0.36 #990, 0.24 #2854, 0.21 #58) >> Best rule #994 for best value: >> intensional similarity = 5 >> extensional distance = 194 >> proper extension: 0gcrg; 0cq8nx; >> query: (?x3482, 0gq9h) <- nominated_for(?x13311, ?x3482), nominated_for(?x1703, ?x3482), film(?x963, ?x3482), ?x1703 = 0k611, award(?x1616, ?x13311) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #10726 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1082 *> proper extension: 01h1bf; 0c3xpwy; 03d17dg; 0gxsh4; *> query: (?x3482, ?x3435) <- award_winner(?x3482, ?x2533), nominated_for(?x2533, ?x3196), award(?x2533, ?x3435), nominated_for(?x3435, ?x69) *> conf = 0.27 ranks of expected_values: 20 EVAL 017z49 nominated_for! 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 79.000 70.000 0.689 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8218-0ptxj PRED entity: 0ptxj PRED relation: film! PRED expected values: 016tt2 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 46): 016tt2 (0.15 #227, 0.12 #1272, 0.12 #523), 05qd_ (0.15 #157, 0.15 #232, 0.13 #306), 017s11 (0.15 #2, 0.12 #1420, 0.12 #226), 016tw3 (0.13 #234, 0.13 #5102, 0.13 #1428), 03xq0f (0.09 #1796, 0.09 #153, 0.09 #78), 054g1r (0.08 #34, 0.07 #2354, 0.07 #183), 0g1rw (0.08 #453, 0.08 #305, 0.08 #7), 017jv5 (0.08 #312, 0.06 #460, 0.06 #14), 0jz9f (0.07 #521, 0.06 #75, 0.06 #671), 01gb54 (0.07 #102, 0.07 #177, 0.06 #400) >> Best rule #227 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 01jc6q; 01_mdl; 0ddjy; 04t6fk; 0qm9n; 012s1d; 0jyb4; 07jnt; 07bx6; 0gndh; ... >> query: (?x5212, 016tt2) <- film(?x772, ?x5212), nominated_for(?x500, ?x5212), ?x500 = 0p9sw >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ptxj film! 016tt2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.152 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #8217-07m69t PRED entity: 07m69t PRED relation: gender PRED expected values: 05zppz => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #23, 0.91 #31, 0.91 #27), 02zsn (0.46 #129, 0.46 #126, 0.46 #108) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 0f2zc; >> query: (?x8598, 05zppz) <- team(?x8598, ?x6831), nationality(?x8598, ?x94), team(?x60, ?x6831), colors(?x6831, ?x3189) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07m69t gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.915 http://example.org/people/person/gender #8216-02qgqt PRED entity: 02qgqt PRED relation: student! PRED expected values: 04b_46 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 54): 02vnp2 (0.20 #357), 02zcnq (0.20 #146), 017z88 (0.13 #1134, 0.05 #4816, 0.05 #1660), 015nl4 (0.09 #1119, 0.06 #1645, 0.04 #8485), 0cwx_ (0.09 #1293, 0.04 #1819, 0.01 #14447), 017j69 (0.06 #671, 0.02 #4879, 0.02 #28034), 033gn8 (0.06 #903, 0.01 #14583, 0.01 #28266), 015fs3 (0.06 #948), 01jsk6 (0.06 #938), 02fjzt (0.06 #665) >> Best rule #357 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0bxtg; 0gd_b_; 0b25vg; >> query: (?x157, 02vnp2) <- award_nominee(?x92, ?x157), film(?x157, ?x5001), ?x5001 = 09q23x >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #4961 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 760 *> proper extension: 023jq1; *> query: (?x157, 04b_46) <- award_winner(?x156, ?x157), student(?x7545, ?x157) *> conf = 0.03 ranks of expected_values: 28 EVAL 02qgqt student! 04b_46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 95.000 95.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #8215-04ls53 PRED entity: 04ls53 PRED relation: award_winner! PRED expected values: 05qb8vx => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 134): 09g90vz (0.28 #10554, 0.05 #7109, 0.04 #8890), 09gkdln (0.28 #10554, 0.05 #7107, 0.05 #5599), 05qb8vx (0.28 #10554, 0.04 #14392, 0.04 #9867), 02cg41 (0.27 #1355, 0.15 #5329, 0.11 #1629), 0466p0j (0.25 #1306, 0.16 #5280, 0.12 #5691), 056878 (0.23 #1263, 0.11 #5374, 0.10 #5648), 019bk0 (0.21 #1247, 0.20 #288, 0.15 #14), 09n4nb (0.20 #319, 0.15 #1278, 0.14 #5252), 01s695 (0.19 #687, 0.15 #5209, 0.13 #2331), 0gpjbt (0.17 #1260, 0.14 #5234, 0.11 #712) >> Best rule #10554 for best value: >> intensional similarity = 3 >> extensional distance = 1111 >> proper extension: 01nzs7; >> query: (?x4727, ?x4224) <- award_winner(?x5128, ?x4727), honored_for(?x4224, ?x5128), nominated_for(?x4727, ?x1012) >> conf = 0.28 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 04ls53 award_winner! 05qb8vx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 134.000 0.284 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8214-0fvvz PRED entity: 0fvvz PRED relation: contains PRED expected values: 01pl14 => 244 concepts (173 used for prediction) PRED predicted values (max 10 best out of 2497): 01pl14 (0.71 #267997, 0.71 #156087, 0.71 #138415), 021q2j (0.09 #15989, 0.07 #4206, 0.07 #42493), 03bmmc (0.09 #15505, 0.07 #3722, 0.07 #42009), 03_fmr (0.09 #16493, 0.07 #4710, 0.04 #113676), 02sdwt (0.09 #16377, 0.07 #4594, 0.04 #113560), 0ch280 (0.08 #2633, 0.07 #43865, 0.06 #11469), 07w0v (0.08 #96, 0.07 #41328, 0.06 #8932), 0qpqn (0.08 #1370, 0.06 #10206, 0.04 #16098), 07wlf (0.08 #327, 0.06 #9163, 0.04 #17999), 02zr0z (0.08 #2377, 0.06 #11213, 0.04 #20049) >> Best rule #267997 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 01zk9d; >> query: (?x1248, ?x466) <- citytown(?x466, ?x1248), origin(?x10025, ?x1248), category(?x466, ?x134), school_type(?x466, ?x1507) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fvvz contains 01pl14 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 244.000 173.000 0.714 http://example.org/location/location/contains #8213-025ygqm PRED entity: 025ygqm PRED relation: season! PRED expected values: 05g76 0713r 01d6g 05xvj => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 283): 01d6g (0.83 #66, 0.81 #27, 0.71 #71), 05g76 (0.83 #66, 0.81 #27, 0.71 #69), 0713r (0.83 #66, 0.81 #27, 0.71 #70), 05xvj (0.83 #66, 0.81 #27, 0.71 #10), 04wmvz (0.83 #66, 0.81 #27, 0.71 #10), 03lpp_ (0.83 #66, 0.81 #27, 0.63 #67), 04b5l3 (0.63 #67, 0.57 #8, 0.49 #95), 021f30 (0.63 #67, 0.57 #8, 0.49 #95), 03qrh9 (0.63 #67, 0.57 #8, 0.49 #95), 02hfgl (0.63 #67, 0.57 #8, 0.49 #95) >> Best rule #66 for best value: >> intensional similarity = 105 >> extensional distance = 2 >> proper extension: 027mvrc; >> query: (?x3431, ?x662) <- season(?x12956, ?x3431), season(?x11361, ?x3431), season(?x10939, ?x3431), season(?x8901, ?x3431), season(?x8894, ?x3431), season(?x7357, ?x3431), season(?x7060, ?x3431), season(?x6074, ?x3431), season(?x4487, ?x3431), season(?x4208, ?x3431), season(?x2405, ?x3431), season(?x2174, ?x3431), season(?x1823, ?x3431), season(?x1438, ?x3431), season(?x1160, ?x3431), season(?x1010, ?x3431), season(?x700, ?x3431), season(?x580, ?x3431), season(?x260, ?x3431), ?x2174 = 051vz, ?x260 = 01ypc, ?x1823 = 01yhm, ?x10939 = 0x0d, ?x700 = 06x68, ?x4487 = 01ync, ?x580 = 05m_8, ?x8894 = 02d02, ?x11361 = 03m1n, ?x7060 = 01slc, ?x6074 = 02__x, ?x8901 = 07l4z, sport(?x1160, ?x5063), position(?x1160, ?x13623), position(?x1160, ?x2010), ?x1438 = 0512p, season(?x1160, ?x8529), season(?x1160, ?x2406), draft(?x1160, ?x11905), draft(?x1160, ?x10600), draft(?x1160, ?x8786), draft(?x1160, ?x8499), draft(?x1160, ?x4779), draft(?x1160, ?x1633), ?x2010 = 02lyr4, school(?x12956, ?x6919), school(?x12956, ?x4955), ?x10600 = 04f4z1k, company(?x4682, ?x1160), ?x8529 = 025ygws, school(?x1160, ?x4363), school(?x1160, ?x581), major_field_of_study(?x581, ?x1527), major_field_of_study(?x581, ?x742), ?x1527 = 04_tv, student(?x581, ?x1299), school(?x8499, ?x3779), colors(?x6919, ?x332), ?x11905 = 047dpm0, list(?x581, ?x2197), currency(?x581, ?x170), ?x3779 = 01pq4w, student(?x6919, ?x2127), ?x1633 = 02rl201, ?x4682 = 0dq_5, ?x2406 = 03c6sl9, major_field_of_study(?x6919, ?x4268), contains(?x1227, ?x4363), ?x742 = 05qjt, school(?x7357, ?x8202), school(?x7357, ?x4257), institution(?x1771, ?x4363), institution(?x1368, ?x4363), draft(?x662, ?x8786), team(?x7724, ?x2405), ?x8202 = 06fq2, team(?x8110, ?x7357), organization(?x346, ?x581), institution(?x620, ?x581), ?x4779 = 02z6872, ?x1771 = 019v9k, team(?x11883, ?x7357), team(?x12826, ?x12956), teams(?x1523, ?x7357), ?x4257 = 01q0kg, team(?x13623, ?x4243), season(?x7357, ?x701), ?x701 = 05kcgsf, ?x4208 = 061xq, school_type(?x581, ?x1044), teams(?x2277, ?x2405), country(?x581, ?x94), service_language(?x581, ?x254), team(?x5412, ?x2405), ?x4955 = 09f2j, school(?x4171, ?x581), ?x7724 = 02rsl1, school(?x1010, ?x6177), ?x170 = 09nqf, colors(?x581, ?x663), school(?x2405, ?x3416), ?x4268 = 02822, ?x332 = 01l849, ?x6177 = 01tx9m, ?x1368 = 014mlp, ?x254 = 02h40lc >> conf = 0.83 => this is the best rule for 6 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 025ygqm season! 05xvj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.825 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 025ygqm season! 01d6g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.825 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 025ygqm season! 0713r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.825 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 025ygqm season! 05g76 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.825 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #8212-027lfrs PRED entity: 027lfrs PRED relation: people! PRED expected values: 0dryh9k => 113 concepts (87 used for prediction) PRED predicted values (max 10 best out of 31): 041rx (0.50 #235, 0.44 #158, 0.43 #312), 013xrm (0.14 #328, 0.14 #97, 0.11 #174), 013b6_ (0.14 #130, 0.08 #284, 0.07 #361), 0x67 (0.11 #164, 0.10 #3092, 0.10 #934), 02w7gg (0.11 #156, 0.08 #233, 0.07 #310), 03ts0c (0.11 #411), 033tf_ (0.07 #4552, 0.07 #5092, 0.07 #4321), 0xnvg (0.05 #4327, 0.04 #4558, 0.04 #2787), 07bch9 (0.04 #1564, 0.04 #1872, 0.04 #870), 02ctzb (0.04 #1247, 0.04 #1479, 0.03 #3251) >> Best rule #235 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 05pq9; 03t0k1; 0f8pz; 01j2xj; 016tbr; 01r9c_; >> query: (?x14055, 041rx) <- place_of_birth(?x14055, ?x11134), gender(?x14055, ?x231), profession(?x14055, ?x11804), ?x11804 = 0q04f, type_of_union(?x14055, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5640 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1724 *> proper extension: 05d7rk; 016qtt; 0lbj1; 01l1b90; 0byfz; 0c9d9; 03x3qv; 01vrx3g; 0c1pj; 01wl38s; ... *> query: (?x14055, 0dryh9k) <- type_of_union(?x14055, ?x566), ?x566 = 04ztj, profession(?x14055, ?x1032), ?x1032 = 02hrh1q *> conf = 0.03 ranks of expected_values: 13 EVAL 027lfrs people! 0dryh9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 113.000 87.000 0.500 http://example.org/people/ethnicity/people #8211-02t_99 PRED entity: 02t_99 PRED relation: nationality PRED expected values: 09c7w0 => 113 concepts (87 used for prediction) PRED predicted values (max 10 best out of 57): 09c7w0 (0.85 #5327, 0.82 #2507, 0.81 #1103), 02jx1 (0.15 #133, 0.13 #1536, 0.13 #2439), 07ssc (0.14 #2624, 0.13 #515, 0.13 #2421), 03rk0 (0.13 #346, 0.11 #446, 0.10 #546), 0d060g (0.06 #207, 0.05 #5635, 0.05 #1109), 0f8l9c (0.04 #2328, 0.04 #823, 0.03 #122), 0345h (0.04 #2437, 0.03 #2640, 0.02 #2940), 03rjj (0.04 #706, 0.03 #906, 0.03 #1308), 0jdx (0.03 #82), 03h64 (0.03 #153, 0.02 #7640, 0.02 #353) >> Best rule #5327 for best value: >> intensional similarity = 3 >> extensional distance = 1314 >> proper extension: 03n69x; >> query: (?x4638, 09c7w0) <- gender(?x4638, ?x514), location(?x4638, ?x1860), source(?x1860, ?x958) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02t_99 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 87.000 0.847 http://example.org/people/person/nationality #8210-024mxd PRED entity: 024mxd PRED relation: currency PRED expected values: 09nqf => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.92 #120, 0.86 #323, 0.86 #190), 01nv4h (0.20 #23, 0.05 #51, 0.04 #597), 088n7 (0.02 #308, 0.02 #301, 0.01 #469), 02l6h (0.02 #599, 0.01 #277), 02gsvk (0.02 #601) >> Best rule #120 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 03qcfvw; 060v34; 06krf3; 026n4h6; 031t2d; 07h9gp; 0gvrws1; 082scv; 07sp4l; 02x6dqb; ... >> query: (?x3672, 09nqf) <- film_release_region(?x3672, ?x94), nominated_for(?x154, ?x3672), ?x94 = 09c7w0, genre(?x3672, ?x225), ?x154 = 05b4l5x >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024mxd currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.920 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8209-0vhm PRED entity: 0vhm PRED relation: actor PRED expected values: 02gf_l => 94 concepts (53 used for prediction) PRED predicted values (max 10 best out of 825): 09b0xs (0.41 #16684, 0.40 #24101, 0.39 #25029), 09gb9xh (0.41 #16684, 0.40 #24101, 0.39 #25029), 030_1_ (0.41 #16684, 0.40 #24101, 0.39 #25029), 03lpbx (0.41 #16684, 0.40 #24101, 0.38 #25957), 02tkzn (0.38 #453, 0.05 #47270, 0.04 #45416), 06pj8 (0.36 #33375, 0.36 #17612, 0.34 #30593), 02gf_l (0.18 #2423, 0.17 #3349, 0.12 #569), 01rw116 (0.12 #809, 0.05 #47270, 0.04 #45416), 0p8r1 (0.12 #272, 0.05 #47270, 0.04 #45416), 01tkgy (0.12 #906, 0.05 #47270, 0.04 #45416) >> Best rule #16684 for best value: >> intensional similarity = 4 >> extensional distance = 106 >> proper extension: 04glx0; 06w7mlh; >> query: (?x5219, ?x1686) <- genre(?x5219, ?x258), award_winner(?x5219, ?x1686), award(?x5219, ?x3486), genre(?x86, ?x258) >> conf = 0.41 => this is the best rule for 4 predicted values *> Best rule #2423 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 0283ph; *> query: (?x5219, 02gf_l) <- genre(?x5219, ?x2540), languages(?x5219, ?x254), ?x254 = 02h40lc, ?x2540 = 0hcr *> conf = 0.18 ranks of expected_values: 7 EVAL 0vhm actor 02gf_l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 94.000 53.000 0.414 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #8208-04ljl_l PRED entity: 04ljl_l PRED relation: award! PRED expected values: 0ch3qr1 => 63 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1385): 026n4h6 (0.75 #5194, 0.67 #2165, 0.50 #1155), 0ch3qr1 (0.71 #3592, 0.62 #5611, 0.50 #2582), 0n83s (0.67 #2539, 0.62 #5568, 0.50 #1529), 02krdz (0.57 #3366, 0.50 #5385, 0.50 #2356), 0hfzr (0.52 #10501, 0.23 #8486, 0.20 #11512), 01qvz8 (0.50 #5516, 0.50 #2487, 0.50 #1477), 01kff7 (0.50 #5169, 0.50 #2140, 0.50 #1130), 03cd0x (0.50 #5591, 0.50 #2562, 0.34 #3027), 082scv (0.50 #5318, 0.50 #2289, 0.34 #3027), 05b_gq (0.50 #5683, 0.33 #2654, 0.29 #3664) >> Best rule #5194 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 05p1dby; >> query: (?x102, 026n4h6) <- nominated_for(?x102, ?x8562), nominated_for(?x102, ?x351), award(?x7522, ?x102), ?x351 = 08lr6s, award(?x8562, ?x154), film(?x7522, ?x518) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3592 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 05b4l5x; 05f4m9q; 07bdd_; 05p09zm; *> query: (?x102, 0ch3qr1) <- nominated_for(?x102, ?x8562), nominated_for(?x102, ?x3748), award(?x4192, ?x102), ?x8562 = 0gzlb9, film_release_region(?x3748, ?x87), film(?x4192, ?x4179) *> conf = 0.71 ranks of expected_values: 2 EVAL 04ljl_l award! 0ch3qr1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 20.000 0.750 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8207-027l0b PRED entity: 027l0b PRED relation: nationality PRED expected values: 09c7w0 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 62): 09c7w0 (0.86 #3508, 0.84 #7520, 0.79 #4310), 07ssc (0.44 #201, 0.20 #817, 0.13 #2019), 0345h (0.44 #201, 0.09 #533, 0.05 #2035), 0824r (0.33 #9724, 0.33 #6214), 02jx1 (0.22 #835, 0.13 #2037, 0.12 #735), 0d060g (0.11 #509, 0.08 #308, 0.08 #2011), 0h7x (0.08 #537, 0.03 #2039, 0.02 #3842), 03rk0 (0.08 #4956, 0.08 #5056, 0.08 #3953), 03gj2 (0.05 #528, 0.02 #2030, 0.01 #3032), 03rt9 (0.05 #903, 0.03 #715, 0.02 #1116) >> Best rule #3508 for best value: >> intensional similarity = 3 >> extensional distance = 541 >> proper extension: 02vptk_; >> query: (?x2794, 09c7w0) <- student(?x546, ?x2794), colors(?x546, ?x4557), school(?x4171, ?x546) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027l0b nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.862 http://example.org/people/person/nationality #8206-04mwxk3 PRED entity: 04mwxk3 PRED relation: citytown PRED expected values: 05qtj => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 60): 02_286 (0.29 #3697, 0.27 #14395, 0.24 #14763), 0d6lp (0.25 #1173, 0.17 #1542, 0.13 #3383), 0r00l (0.20 #5439, 0.18 #8022, 0.14 #10971), 030qb3t (0.20 #4817, 0.14 #1869, 0.13 #2606), 0r04p (0.14 #11163, 0.13 #8214, 0.13 #11532), 04jpl (0.14 #6273, 0.13 #2585, 0.12 #1111), 07dfk (0.14 #31542, 0.11 #32280, 0.10 #31911), 0r5wt (0.12 #1205, 0.12 #837, 0.08 #1574), 0k_q_ (0.12 #1152, 0.12 #784, 0.08 #1521), 0rh6k (0.12 #737, 0.05 #26543, 0.05 #23224) >> Best rule #3697 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 03xq0f; >> query: (?x13152, 02_286) <- film(?x13152, ?x8137), genre(?x8137, ?x1626), film_release_region(?x8137, ?x142), ?x142 = 0jgd, ?x1626 = 03q4nz >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #6635 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 01f9wm; 04kqk; *> query: (?x13152, ?x362) <- category(?x13152, ?x134), industry(?x13152, ?x373), organizations_founded(?x4685, ?x13152), industry(?x6082, ?x373), citytown(?x6082, ?x362) *> conf = 0.06 ranks of expected_values: 19 EVAL 04mwxk3 citytown 05qtj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 91.000 91.000 0.294 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #8205-09889g PRED entity: 09889g PRED relation: award_winner! PRED expected values: 05pd94v => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 133): 02cg41 (0.22 #1245, 0.11 #6845, 0.10 #7125), 0jzphpx (0.22 #1159, 0.10 #39, 0.09 #6759), 01c6qp (0.20 #1139, 0.17 #159, 0.12 #6739), 01xqqp (0.20 #1215, 0.10 #95, 0.09 #655), 02rjjll (0.17 #145, 0.13 #6725, 0.13 #1125), 01s695 (0.17 #143, 0.12 #6723, 0.09 #7143), 0bz6sb (0.17 #203, 0.06 #623, 0.05 #903), 0hndn2q (0.17 #180, 0.04 #1300, 0.04 #1440), 019bk0 (0.15 #1136, 0.11 #6736, 0.09 #7016), 05pd94v (0.13 #6722, 0.10 #6582, 0.09 #7142) >> Best rule #1245 for best value: >> intensional similarity = 2 >> extensional distance = 44 >> proper extension: 01gn36; 015cxv; 01q9b9; >> query: (?x4960, 02cg41) <- award_winner(?x2704, ?x4960), ?x2704 = 01mhwk >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #6722 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 339 *> proper extension: 0197tq; 01lmj3q; 0m2l9; 026ps1; 03f2_rc; 02mslq; 04rcr; 01vvycq; 01w61th; 01kwlwp; ... *> query: (?x4960, 05pd94v) <- award_winner(?x2704, ?x4960), artist(?x2299, ?x4960) *> conf = 0.13 ranks of expected_values: 10 EVAL 09889g award_winner! 05pd94v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 123.000 123.000 0.217 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8204-02xv8m PRED entity: 02xv8m PRED relation: type_of_union PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.75 #9, 0.71 #225, 0.70 #45), 01g63y (0.21 #22, 0.20 #30, 0.20 #18), 0jgjn (0.03 #12) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 024jwt; >> query: (?x3876, 04ztj) <- award_nominee(?x3876, ?x710), award(?x3876, ?x435), ?x435 = 0bp_b2 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xv8m type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.750 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8203-017z49 PRED entity: 017z49 PRED relation: genre PRED expected values: 0lsxr => 96 concepts (82 used for prediction) PRED predicted values (max 10 best out of 89): 02kdv5l (0.47 #2903, 0.34 #350, 0.29 #582), 05p553 (0.47 #120, 0.45 #236, 0.39 #816), 0lsxr (0.37 #936, 0.32 #2909, 0.25 #1632), 02l7c8 (0.34 #7679, 0.33 #131, 0.32 #1175), 03k9fj (0.32 #475, 0.29 #591, 0.28 #707), 06n90 (0.27 #360, 0.25 #12, 0.20 #592), 060__y (0.20 #1640, 0.19 #1292, 0.18 #4661), 01t_vv (0.20 #167, 0.18 #283, 0.17 #51), 06cvj (0.20 #119, 0.09 #2556, 0.09 #3020), 04xvlr (0.19 #4646, 0.19 #5110, 0.18 #5923) >> Best rule #2903 for best value: >> intensional similarity = 4 >> extensional distance = 420 >> proper extension: 03t97y; 05p3738; 05ch98; 025twgt; 04jn6y7; >> query: (?x3482, 02kdv5l) <- film(?x72, ?x3482), genre(?x3482, ?x812), country(?x3482, ?x94), ?x812 = 01jfsb >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #936 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 145 *> proper extension: 05_5rjx; 05css_; 02tktw; 01j5ql; *> query: (?x3482, 0lsxr) <- film(?x72, ?x3482), film(?x963, ?x3482), genre(?x3482, ?x600), ?x600 = 02n4kr *> conf = 0.37 ranks of expected_values: 3 EVAL 017z49 genre 0lsxr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 82.000 0.474 http://example.org/film/film/genre #8202-0dn16 PRED entity: 0dn16 PRED relation: parent_genre PRED expected values: 06by7 => 55 concepts (33 used for prediction) PRED predicted values (max 10 best out of 184): 06by7 (0.60 #15, 0.57 #3247, 0.51 #3407), 0glt670 (0.49 #1635, 0.10 #4067, 0.09 #5044), 05r6t (0.39 #3284, 0.36 #695, 0.24 #1017), 03_d0 (0.35 #2430, 0.20 #3401, 0.11 #1457), 06j6l (0.30 #1479, 0.29 #1639, 0.21 #2452), 011j5x (0.27 #664, 0.20 #21, 0.17 #181), 01243b (0.27 #671, 0.17 #3260, 0.16 #993), 02x8m (0.27 #1621, 0.18 #656, 0.13 #1461), 05w3f (0.26 #1473, 0.18 #668, 0.08 #990), 0155w (0.26 #1518, 0.10 #3462, 0.09 #2976) >> Best rule #15 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 064t9; 05bt6j; 0y3_8; >> query: (?x996, 06by7) <- artists(?x996, ?x10320), artists(?x996, ?x1093), ?x1093 = 0lk90, parent_genre(?x996, ?x8386), artists(?x8386, ?x5589), ?x5589 = 044mfr, ?x10320 = 02twdq >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dn16 parent_genre 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 33.000 0.600 http://example.org/music/genre/parent_genre #8201-04ls53 PRED entity: 04ls53 PRED relation: people! PRED expected values: 0xnvg => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 35): 041rx (0.27 #81, 0.19 #312, 0.18 #158), 0x67 (0.20 #3090, 0.17 #3937, 0.16 #3167), 013xrm (0.11 #20, 0.05 #174, 0.04 #2176), 07hwkr (0.11 #12, 0.05 #4940, 0.04 #3939), 01rv7x (0.11 #39, 0.02 #193, 0.02 #270), 0xnvg (0.08 #2015, 0.07 #2554, 0.07 #90), 033tf_ (0.06 #3857, 0.06 #3934, 0.06 #4319), 02w7gg (0.06 #3467, 0.06 #5854, 0.06 #5931), 013b6_ (0.06 #361, 0.05 #515, 0.05 #1054), 0d7wh (0.05 #787, 0.04 #1249, 0.04 #2173) >> Best rule #81 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 01d_h; >> query: (?x4727, 041rx) <- music(?x1012, ?x4727), student(?x2909, ?x4727), nationality(?x4727, ?x94), currency(?x2909, ?x170) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #2015 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 128 *> proper extension: 01nzs7; *> query: (?x4727, 0xnvg) <- category(?x4727, ?x134), nominated_for(?x4727, ?x5810), actor(?x5810, ?x56) *> conf = 0.08 ranks of expected_values: 6 EVAL 04ls53 people! 0xnvg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 122.000 0.267 http://example.org/people/ethnicity/people #8200-07dfk PRED entity: 07dfk PRED relation: origin! PRED expected values: 02twdq => 265 concepts (205 used for prediction) PRED predicted values (max 10 best out of 418): 017b2p (0.33 #1549, 0.33 #1427, 0.25 #1548), 06nv27 (0.33 #735, 0.20 #11566, 0.14 #10019), 0d193h (0.33 #689, 0.12 #13065, 0.08 #19774), 015bwt (0.33 #991, 0.07 #23171, 0.07 #10275), 01wv9p (0.33 #685, 0.07 #9969, 0.07 #11516), 03j0br4 (0.33 #606, 0.07 #9890, 0.07 #11437), 0837ql (0.33 #719, 0.07 #10003, 0.07 #11550), 0cbm64 (0.33 #926, 0.07 #10210, 0.07 #11757), 0153nq (0.33 #1031, 0.07 #10315, 0.07 #11862), 01tpl1p (0.33 #972, 0.07 #10256, 0.07 #11803) >> Best rule #1549 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0gqkd; >> query: (?x9559, ?x8947) <- place_of_birth(?x256, ?x9559), location(?x8947, ?x9559), ?x8947 = 017b2p >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07dfk origin! 02twdq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 265.000 205.000 0.333 http://example.org/music/artist/origin #8199-0_b9f PRED entity: 0_b9f PRED relation: nominated_for! PRED expected values: 019f4v 02pqp12 02z0dfh 02y_rq5 0gs96 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 204): 0gr51 (0.67 #4240, 0.66 #4913, 0.66 #4688), 094qd5 (0.67 #4240, 0.66 #4913, 0.66 #4688), 02wkmx (0.67 #4240, 0.66 #4913, 0.66 #4688), 027571b (0.67 #4240, 0.66 #4913, 0.66 #4688), 027b9k6 (0.67 #4240, 0.66 #4913, 0.66 #4688), 02z1nbg (0.67 #4240, 0.66 #4913, 0.66 #4688), 02w_6xj (0.67 #4240, 0.66 #4913, 0.66 #4688), 019f4v (0.39 #2949, 0.38 #3395, 0.34 #3618), 0gq_v (0.39 #2917, 0.37 #3363, 0.34 #1579), 0l8z1 (0.32 #2055, 0.21 #2947, 0.21 #3393) >> Best rule #4240 for best value: >> intensional similarity = 4 >> extensional distance = 711 >> proper extension: 0cwrr; 05sy0cv; 06mmr; >> query: (?x4742, ?x372) <- award(?x4742, ?x372), award_winner(?x4742, ?x4234), profession(?x4234, ?x1032), award_nominee(?x4234, ?x539) >> conf = 0.67 => this is the best rule for 7 predicted values *> Best rule #2949 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 588 *> proper extension: 04z_x4v; *> query: (?x4742, 019f4v) <- nominated_for(?x1703, ?x4742), nominated_for(?x1703, ?x984), ?x984 = 0m_mm, ceremony(?x1703, ?x78) *> conf = 0.39 ranks of expected_values: 8, 14, 16, 19, 22 EVAL 0_b9f nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 64.000 64.000 0.669 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0_b9f nominated_for! 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 64.000 64.000 0.669 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0_b9f nominated_for! 02z0dfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 64.000 64.000 0.669 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0_b9f nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 64.000 64.000 0.669 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0_b9f nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 64.000 64.000 0.669 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8198-02x08c PRED entity: 02x08c PRED relation: gender PRED expected values: 05zppz => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #17, 0.87 #13, 0.74 #21), 02zsn (0.28 #54, 0.28 #78, 0.28 #84) >> Best rule #17 for best value: >> intensional similarity = 5 >> extensional distance = 156 >> proper extension: 01kt17; >> query: (?x9159, 05zppz) <- award(?x9159, ?x3247), award(?x3002, ?x3247), award(?x2033, ?x3247), ?x2033 = 01ycbq, ?x3002 = 0cj8x >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02x08c gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.911 http://example.org/people/person/gender #8197-03_c8p PRED entity: 03_c8p PRED relation: service_language PRED expected values: 02h40lc => 206 concepts (206 used for prediction) PRED predicted values (max 10 best out of 19): 02h40lc (0.96 #2190, 0.96 #2684, 0.96 #1791), 04306rv (0.42 #346, 0.33 #41, 0.33 #22), 01r2l (0.42 #353, 0.33 #48, 0.15 #1095), 064_8sq (0.33 #1093, 0.33 #351, 0.33 #46), 06nm1 (0.33 #43, 0.27 #1090, 0.27 #501), 06b_j (0.33 #47, 0.25 #352, 0.09 #1094), 05zjd (0.33 #49, 0.17 #354, 0.12 #1096), 02bjrlw (0.33 #39, 0.17 #344, 0.09 #1086), 02hwhyv (0.33 #52, 0.17 #357, 0.07 #510), 01gp_d (0.33 #53, 0.08 #358, 0.03 #1100) >> Best rule #2190 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 01fsv9; >> query: (?x11303, 02h40lc) <- service_language(?x11303, ?x2164), citytown(?x11303, ?x1658), languages(?x5314, ?x2164), language(?x136, ?x2164) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_c8p service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 206.000 206.000 0.962 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #8196-0gqy2 PRED entity: 0gqy2 PRED relation: award! PRED expected values: 04yywz 0c6qh 02t_tp 02_fj 0dvmd 016fjj 035rnz 0fby2t 023kzp 0gm34 03bdm4 0341n5 01xpxv 02qx5h => 43 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2485): 01kwsg (0.77 #61064, 0.76 #61063, 0.69 #48206), 055c8 (0.77 #61064, 0.76 #61063, 0.69 #48206), 0gnbw (0.77 #61064, 0.76 #61063, 0.69 #48206), 01nr36 (0.77 #61064, 0.76 #61063, 0.69 #48206), 026rm_y (0.77 #61064, 0.76 #61063, 0.69 #48206), 01qscs (0.77 #61064, 0.76 #61063, 0.69 #48206), 0dzf_ (0.77 #61064, 0.76 #61063, 0.69 #48206), 0141kz (0.76 #61063, 0.69 #48206, 0.69 #54636), 03bggl (0.76 #61063, 0.69 #48206, 0.69 #54636), 03bdm4 (0.76 #61063, 0.69 #48206, 0.69 #54636) >> Best rule #61064 for best value: >> intensional similarity = 3 >> extensional distance = 232 >> proper extension: 02qkk9_; 02py7pj; >> query: (?x3066, ?x8491) <- ceremony(?x3066, ?x78), award_winner(?x3066, ?x8491), award_nominee(?x1208, ?x8491) >> conf = 0.77 => this is the best rule for 7 predicted values *> Best rule #61063 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 232 *> proper extension: 02qkk9_; 02py7pj; *> query: (?x3066, ?x92) <- ceremony(?x3066, ?x78), award_winner(?x3066, ?x8491), award_winner(?x3066, ?x92), award_nominee(?x1208, ?x8491) *> conf = 0.76 ranks of expected_values: 10, 21, 22, 44, 45, 46, 233, 334, 384, 504, 516, 1037 EVAL 0gqy2 award! 02qx5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 01xpxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 0341n5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 03bdm4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 0gm34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 023kzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 0fby2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 035rnz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 016fjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 0dvmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 02_fj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 02t_tp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 0c6qh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqy2 award! 04yywz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 21.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8195-0l14j_ PRED entity: 0l14j_ PRED relation: role! PRED expected values: 01vdm0 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 83): 01vdm0 (0.89 #1336, 0.83 #862, 0.78 #626), 02sgy (0.87 #1164, 0.79 #1004, 0.77 #2479), 026g73 (0.86 #1162, 0.86 #1131, 0.83 #1157), 0bxl5 (0.85 #968, 0.80 #731, 0.79 #1045), 03gvt (0.83 #1157, 0.83 #1156, 0.83 #1705), 06ncr (0.83 #1157, 0.83 #1156, 0.83 #1705), 07c6l (0.83 #1157, 0.83 #1156, 0.83 #1705), 05kms (0.83 #1157, 0.83 #1156, 0.83 #1705), 01wy6 (0.83 #1157, 0.83 #1156, 0.83 #1705), 023r2x (0.83 #1157, 0.83 #1156, 0.83 #1705) >> Best rule #1336 for best value: >> intensional similarity = 10 >> extensional distance = 17 >> proper extension: 0319l; >> query: (?x2944, 01vdm0) <- instrumentalists(?x2944, ?x4288), role(?x2944, ?x2888), role(?x2944, ?x1466), role(?x212, ?x2944), profession(?x4288, ?x131), group(?x2944, ?x1751), role(?x2944, ?x569), ?x2888 = 02fsn, role(?x115, ?x1466), role(?x1466, ?x1750) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l14j_ role! 01vdm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.895 http://example.org/music/performance_role/track_performances./music/track_contribution/role #8194-05_5_22 PRED entity: 05_5_22 PRED relation: film! PRED expected values: 0fby2t => 85 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1231): 0gd9k (0.33 #1384, 0.06 #3463, 0.05 #7620), 0h7pj (0.33 #1541, 0.05 #7777, 0.04 #9856), 07cjqy (0.33 #603, 0.05 #6839, 0.04 #8918), 073x6y (0.33 #1189, 0.05 #7425, 0.04 #9504), 02l3_5 (0.25 #3487, 0.04 #32593, 0.02 #42984), 07rzf (0.25 #3957, 0.02 #43454, 0.01 #89177), 03y82t6 (0.23 #22872, 0.20 #20792, 0.19 #14553), 0127s7 (0.23 #22872, 0.20 #20792, 0.19 #14553), 020ffd (0.23 #22872, 0.20 #20792, 0.19 #14553), 01vrt_c (0.23 #22872, 0.20 #20792, 0.19 #14553) >> Best rule #1384 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 05t54s; >> query: (?x5201, 0gd9k) <- film(?x585, ?x5201), featured_film_locations(?x5201, ?x362), person(?x5201, ?x1206), prequel(?x5201, ?x6984) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #37420 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 119 *> proper extension: 0cz8mkh; 0642ykh; *> query: (?x5201, ?x3051) <- film(?x4507, ?x5201), film_crew_role(?x5201, ?x137), prequel(?x5201, ?x6984), award_nominee(?x3051, ?x4507) *> conf = 0.04 ranks of expected_values: 319 EVAL 05_5_22 film! 0fby2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 85.000 53.000 0.333 http://example.org/film/actor/film./film/performance/film #8193-01r4k PRED entity: 01r4k PRED relation: major_field_of_study! PRED expected values: 01bk1y 0gl6x => 71 concepts (41 used for prediction) PRED predicted values (max 10 best out of 635): 09f2j (0.80 #8014, 0.79 #9134, 0.71 #9694), 065y4w7 (0.75 #5055, 0.60 #3931, 0.60 #3371), 03ksy (0.74 #9077, 0.71 #9637, 0.67 #7957), 03v6t (0.67 #7324, 0.64 #6202, 0.60 #3956), 07wrz (0.63 #9030, 0.60 #7910, 0.50 #9590), 04rwx (0.62 #5079, 0.60 #3955, 0.60 #3395), 01jssp (0.62 #5048, 0.60 #3924, 0.50 #4484), 01ky7c (0.62 #5279, 0.60 #4155, 0.45 #6401), 01jswq (0.62 #5114, 0.60 #3990, 0.40 #3430), 0bwfn (0.62 #5322, 0.53 #8129, 0.53 #9249) >> Best rule #8014 for best value: >> intensional similarity = 16 >> extensional distance = 13 >> proper extension: 06ms6; 0h5k; 03g3w; 05qfh; 0fdys; 01lj9; >> query: (?x10417, 09f2j) <- major_field_of_study(?x865, ?x10417), major_field_of_study(?x734, ?x10417), major_field_of_study(?x10576, ?x10417), major_field_of_study(?x8850, ?x10417), major_field_of_study(?x7127, ?x10417), major_field_of_study(?x1681, ?x10417), major_field_of_study(?x741, ?x10417), ?x865 = 02h4rq6, ?x741 = 01w3v, ?x734 = 04zx3q1, citytown(?x10576, ?x4356), currency(?x8850, ?x170), school_type(?x10576, ?x1044), colors(?x10576, ?x1101), student(?x7127, ?x2663), ?x1681 = 07szy >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5329 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 6 *> proper extension: 0g26h; *> query: (?x10417, 01bk1y) <- major_field_of_study(?x11690, ?x10417), major_field_of_study(?x865, ?x10417), major_field_of_study(?x734, ?x10417), major_field_of_study(?x11963, ?x10417), major_field_of_study(?x10576, ?x10417), major_field_of_study(?x741, ?x10417), ?x865 = 02h4rq6, ?x741 = 01w3v, ?x734 = 04zx3q1, ?x10576 = 0g2jl, colors(?x11963, ?x1101), organization(?x5510, ?x11963), institution(?x11690, ?x12726), ?x12726 = 09vzz *> conf = 0.50 ranks of expected_values: 30, 44 EVAL 01r4k major_field_of_study! 0gl6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 71.000 41.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01r4k major_field_of_study! 01bk1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 71.000 41.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8192-0jtg0 PRED entity: 0jtg0 PRED relation: instrumentalists PRED expected values: 05cljf => 82 concepts (25 used for prediction) PRED predicted values (max 10 best out of 3182): 01vw20_ (0.70 #11181, 0.62 #8117, 0.60 #2610), 09prnq (0.64 #1832, 0.57 #6232, 0.55 #6727), 0137g1 (0.64 #1832, 0.55 #6727, 0.53 #10400), 0pj8m (0.64 #1832, 0.55 #6727, 0.53 #10400), 018y2s (0.64 #1832, 0.55 #6727, 0.53 #10400), 02qsjt (0.64 #1832, 0.55 #6727, 0.53 #10400), 01mxt_ (0.64 #1832, 0.55 #6727, 0.53 #10400), 01nhkxp (0.64 #1832, 0.55 #6727, 0.53 #10400), 01sb5r (0.62 #8189, 0.60 #11253, 0.60 #2682), 01vvycq (0.62 #7985, 0.60 #11049, 0.60 #2478) >> Best rule #11181 for best value: >> intensional similarity = 14 >> extensional distance = 8 >> proper extension: 057cc; >> query: (?x2785, 01vw20_) <- instrumentalists(?x2785, ?x10907), instrumentalists(?x2785, ?x7874), instrumentalists(?x2785, ?x2908), artists(?x3319, ?x10907), artist(?x1543, ?x10907), instrumentalists(?x716, ?x10907), gender(?x10907, ?x231), ?x231 = 05zppz, ?x716 = 018vs, ?x3319 = 06j6l, profession(?x7874, ?x131), role(?x7874, ?x645), ?x2908 = 0161sp, artists(?x302, ?x7874) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1844 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 2 *> proper extension: 042v_gx; *> query: (?x2785, 05cljf) <- role(?x1148, ?x2785), role(?x960, ?x2785), role(?x315, ?x2785), role(?x745, ?x2785), instrumentalists(?x2785, ?x7201), ?x960 = 04q7r, artists(?x302, ?x7201), ?x315 = 0l14md, award_winner(?x3390, ?x7201), profession(?x7201, ?x131), group(?x2785, ?x6475), group(?x2785, ?x1945), ?x1945 = 02_5x9, ?x1148 = 02qjv, ?x745 = 01vj9c, ?x6475 = 07mvp, category(?x7201, ?x134), location(?x7201, ?x1131) *> conf = 0.50 ranks of expected_values: 93 EVAL 0jtg0 instrumentalists 05cljf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 82.000 25.000 0.700 http://example.org/music/instrument/instrumentalists #8191-03c5f7l PRED entity: 03c5f7l PRED relation: profession PRED expected values: 02jknp => 73 concepts (41 used for prediction) PRED predicted values (max 10 best out of 41): 03gjzk (0.48 #160, 0.48 #454, 0.37 #307), 02jknp (0.46 #301, 0.42 #1478, 0.39 #448), 018gz8 (0.40 #162, 0.27 #309, 0.18 #456), 0np9r (0.25 #166, 0.21 #1342, 0.20 #1195), 02krf9 (0.23 #172, 0.20 #466, 0.16 #319), 0cbd2 (0.22 #1477, 0.19 #300, 0.19 #447), 09jwl (0.21 #2224, 0.17 #5458, 0.16 #5017), 0nbcg (0.15 #2237, 0.11 #5030, 0.11 #5471), 0dz3r (0.15 #2209, 0.10 #5002, 0.10 #5443), 016z4k (0.13 #2211, 0.10 #5445, 0.09 #5004) >> Best rule #160 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 01vw87c; 01nczg; 02_j7t; 0jt90f5; 0309jm; 0h3mrc; 014z8v; 04h07s; 01vw8mh; 03sww; ... >> query: (?x9796, 03gjzk) <- profession(?x9796, ?x987), actor(?x3303, ?x9796), ?x987 = 0dxtg >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #301 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 529 *> proper extension: 02qjj7; 04rs03; 042rnl; 01pr_j6; 01g4zr; 01p45_v; 0177s6; 025tdwc; 01wj9y9; 09ftwr; ... *> query: (?x9796, 02jknp) <- profession(?x9796, ?x1032), profession(?x9796, ?x987), ?x987 = 0dxtg, ?x1032 = 02hrh1q *> conf = 0.46 ranks of expected_values: 2 EVAL 03c5f7l profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 41.000 0.477 http://example.org/people/person/profession #8190-09glbnt PRED entity: 09glbnt PRED relation: state_province_region PRED expected values: 059rby => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 102): 05kr_ (0.72 #11710, 0.66 #5961, 0.66 #6706), 059rby (0.65 #7577, 0.34 #8956, 0.33 #3111), 07h34 (0.63 #3852, 0.61 #4969, 0.24 #11080), 02jx1 (0.59 #13971, 0.33 #145, 0.24 #11080), 01n7q (0.50 #1261, 0.48 #1756, 0.44 #3250), 081yw (0.33 #437, 0.03 #8382, 0.03 #2296), 0d060g (0.27 #9079, 0.27 #9080, 0.26 #14097), 07ssc (0.26 #14097, 0.25 #15484, 0.25 #15104), 04jpl (0.24 #11080, 0.21 #11332, 0.16 #15230), 036wy (0.24 #11080, 0.21 #11332, 0.11 #15610) >> Best rule #11710 for best value: >> intensional similarity = 4 >> extensional distance = 353 >> proper extension: 01j_cy; 049dk; 02hft3; 022lly; 0f102; 01ymvk; 02d_zc; 0269kx; 015q1n; 07ccs; ... >> query: (?x6082, ?x1905) <- citytown(?x6082, ?x1658), contains(?x1658, ?x1306), time_zones(?x1658, ?x2674), state_province_region(?x1306, ?x1905) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #7577 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 108 *> proper extension: 07t65; 0gsg7; 02301; 04sylm; 017z88; 01hb1t; 01w5m; 03p7gb; 0152x_; 05njyy; ... *> query: (?x6082, 059rby) <- citytown(?x6082, ?x1658), location(?x483, ?x1658), featured_film_locations(?x97, ?x1658), citytown(?x8559, ?x1658), ?x8559 = 05cl8y *> conf = 0.65 ranks of expected_values: 2 EVAL 09glbnt state_province_region 059rby CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.720 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #8189-012wgb PRED entity: 012wgb PRED relation: country! PRED expected values: 0c40vxk => 171 concepts (137 used for prediction) PRED predicted values (max 10 best out of 1844): 01m13b (0.50 #37733, 0.50 #18938, 0.38 #39444), 0dscrwf (0.43 #11957, 0.30 #18859, 0.29 #13666), 0gxtknx (0.43 #11957, 0.29 #13666, 0.29 #25628), 0cp08zg (0.43 #11957, 0.29 #13666, 0.29 #25628), 0401sg (0.43 #11957, 0.29 #13666, 0.29 #25628), 0by1wkq (0.43 #11957, 0.29 #13666, 0.29 #25628), 0bc1yhb (0.43 #11957, 0.29 #13666, 0.29 #25628), 09gkx35 (0.43 #11957, 0.29 #13666, 0.29 #25628), 0yzvw (0.43 #11957, 0.29 #13666, 0.29 #25628), 02d44q (0.43 #11957, 0.29 #13666, 0.29 #25628) >> Best rule #37733 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 06qd3; >> query: (?x3699, 01m13b) <- contains(?x3699, ?x429), film_release_region(?x6492, ?x3699), film_release_region(?x5255, ?x3699), ?x5255 = 01sby_, ?x6492 = 0ds6bmk >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #181104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 148 *> proper extension: 0s3y5; 0284jb; 01c40n; 0r540; 0fw2y; 0f04c; 0rd5k; 03pbf; 019fh; 01m1zk; ... *> query: (?x3699, ?x66) <- place_of_death(?x12345, ?x3699), contains(?x455, ?x3699), contains(?x455, ?x1264), film_release_region(?x66, ?x1264) *> conf = 0.12 ranks of expected_values: 1581 EVAL 012wgb country! 0c40vxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 137.000 0.500 http://example.org/film/film/country #8188-03hnd PRED entity: 03hnd PRED relation: profession PRED expected values: 0cbd2 01d30f => 108 concepts (93 used for prediction) PRED predicted values (max 10 best out of 95): 02hrh1q (0.98 #12597, 0.89 #13486, 0.81 #5491), 0cbd2 (0.96 #4891, 0.90 #7999, 0.70 #9924), 015cjr (0.65 #4195, 0.40 #1087, 0.31 #1531), 0dxtg (0.60 #5490, 0.51 #4750, 0.50 #458), 0kyk (0.56 #1215, 0.47 #9948, 0.44 #4915), 0fj9f (0.47 #5236, 0.39 #1980, 0.25 #204), 01d_h8 (0.44 #5482, 0.44 #4742, 0.34 #7850), 03gjzk (0.40 #5492, 0.27 #1052, 0.26 #4752), 018gz8 (0.36 #5494, 0.29 #4754, 0.27 #3126), 05z96 (0.33 #488, 0.29 #636, 0.28 #3448) >> Best rule #12597 for best value: >> intensional similarity = 5 >> extensional distance = 2543 >> proper extension: 06v8s0; 01sl1q; 07nznf; 05vsxz; 0197tq; 06gp3f; 01j5ts; 0cnl80; 01r42_g; 02zq43; ... >> query: (?x3542, 02hrh1q) <- profession(?x3542, ?x9081), profession(?x8045, ?x9081), profession(?x6171, ?x9081), ?x6171 = 020ffd, ?x8045 = 04qsdh >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #4891 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 169 *> proper extension: 084w8; 07w21; 041h0; 07g2b; 07kb5; 012cph; 01vrncs; 0m77m; 045bg; 028p0; ... *> query: (?x3542, 0cbd2) <- profession(?x3542, ?x9081), influenced_by(?x2161, ?x3542), profession(?x6810, ?x9081), ?x6810 = 037jz *> conf = 0.96 ranks of expected_values: 2, 57 EVAL 03hnd profession 01d30f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 108.000 93.000 0.982 http://example.org/people/person/profession EVAL 03hnd profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 93.000 0.982 http://example.org/people/person/profession #8187-0b_5d PRED entity: 0b_5d PRED relation: nominated_for! PRED expected values: 0gr0m => 83 concepts (74 used for prediction) PRED predicted values (max 10 best out of 210): 0gq9h (0.70 #2137, 0.68 #10176, 0.67 #10175), 04dn09n (0.44 #2111, 0.40 #263, 0.37 #1187), 0gqyl (0.42 #304, 0.33 #1228, 0.33 #1459), 0gr4k (0.42 #1178, 0.42 #1409, 0.39 #2102), 04kxsb (0.42 #1475, 0.42 #1244, 0.30 #320), 0gr0m (0.42 #2135, 0.33 #518, 0.31 #56), 02qyntr (0.38 #2252, 0.33 #404, 0.26 #1559), 02pqp12 (0.34 #2134, 0.33 #286, 0.26 #1441), 0l8z1 (0.32 #2128, 0.24 #6980, 0.24 #3976), 0gs96 (0.31 #4010, 0.25 #2162, 0.24 #7014) >> Best rule #2137 for best value: >> intensional similarity = 4 >> extensional distance = 206 >> proper extension: 0qm8b; 0bmpm; 0yx7h; 0y_9q; >> query: (?x2958, 0gq9h) <- nominated_for(?x1703, ?x2958), film(?x190, ?x2958), nominated_for(?x7537, ?x2958), ?x1703 = 0k611 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2135 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 206 *> proper extension: 0qm8b; 0bmpm; 0yx7h; 0y_9q; *> query: (?x2958, 0gr0m) <- nominated_for(?x1703, ?x2958), film(?x190, ?x2958), nominated_for(?x7537, ?x2958), ?x1703 = 0k611 *> conf = 0.42 ranks of expected_values: 6 EVAL 0b_5d nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 83.000 74.000 0.697 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8186-012ycy PRED entity: 012ycy PRED relation: profession PRED expected values: 0nbcg => 117 concepts (81 used for prediction) PRED predicted values (max 10 best out of 88): 02hrh1q (0.87 #3250, 0.78 #4135, 0.74 #1337), 0342h (0.80 #3384), 0nbcg (0.71 #178, 0.59 #1207, 0.58 #1060), 016z4k (0.60 #9585, 0.48 #4271, 0.46 #2355), 01d_h8 (0.53 #1769, 0.53 #2063, 0.46 #2652), 01c72t (0.43 #5324, 0.41 #5029, 0.41 #5176), 03gjzk (0.33 #6649, 0.31 #1779, 0.29 #1338), 0dxtg (0.30 #6647, 0.28 #7384, 0.28 #8711), 0fnpj (0.29 #4327, 0.29 #1970, 0.28 #2558), 047rgpy (0.29 #255, 0.07 #696, 0.05 #2166) >> Best rule #3250 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 0q9kd; 0bl2g; 01rrwf6; 09wj5; 018db8; 01pcq3; 032_jg; 03d_w3h; 049dyj; 01pw2f1; ... >> query: (?x9603, 02hrh1q) <- nationality(?x9603, ?x94), profession(?x9603, ?x2659), diet(?x9603, ?x11141), split_to(?x2659, ?x227) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #178 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0pz91; 01l87db; 028qyn; *> query: (?x9603, 0nbcg) <- nationality(?x9603, ?x94), instrumentalists(?x227, ?x9603), organizations_founded(?x9603, ?x9121), profession(?x9603, ?x131) *> conf = 0.71 ranks of expected_values: 3 EVAL 012ycy profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 81.000 0.872 http://example.org/people/person/profession #8185-02_1ky PRED entity: 02_1ky PRED relation: actor PRED expected values: 04yqlk => 62 concepts (44 used for prediction) PRED predicted values (max 10 best out of 801): 026n3rs (0.39 #12106, 0.37 #13037, 0.36 #9309), 025vl4m (0.39 #12106, 0.37 #13037, 0.36 #9309), 02_2v2 (0.19 #7447, 0.16 #4651, 0.16 #10243), 043js (0.17 #11174, 0.12 #12105, 0.10 #22341), 039g82 (0.17 #11174, 0.12 #12105, 0.10 #22341), 084m3 (0.17 #11174, 0.12 #12105, 0.10 #22341), 0404wqb (0.17 #11174, 0.12 #12105, 0.10 #22341), 01x6jd (0.17 #11174, 0.12 #12105, 0.10 #22341), 0blbxk (0.17 #11174, 0.12 #12105, 0.10 #22341), 096lf_ (0.17 #11174, 0.12 #12105, 0.10 #22341) >> Best rule #12106 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 0c3xpwy; >> query: (?x10911, ?x4147) <- actor(?x10911, ?x3709), location(?x3709, ?x1705), award_nominee(?x1065, ?x3709), award_winner(?x10911, ?x4147) >> conf = 0.39 => this is the best rule for 2 predicted values *> Best rule #8730 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 93 *> proper extension: 0bx_hnp; *> query: (?x10911, 04yqlk) <- program(?x1762, ?x10911), award_winner(?x10911, ?x7426), award_winner(?x10911, ?x4147), award_winner(?x438, ?x7426), award_nominee(?x4147, ?x415) *> conf = 0.01 ranks of expected_values: 792 EVAL 02_1ky actor 04yqlk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 44.000 0.394 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #8184-03b1sb PRED entity: 03b1sb PRED relation: film! PRED expected values: 05w1vf => 74 concepts (45 used for prediction) PRED predicted values (max 10 best out of 803): 03nk3t (0.51 #16624, 0.51 #10389, 0.44 #56107), 05cl2w (0.20 #1497, 0.05 #5653, 0.02 #26433), 01kgv4 (0.20 #1181), 09byk (0.20 #111), 01qscs (0.20 #51), 0c0k1 (0.11 #7738, 0.10 #1505, 0.09 #3583), 039bp (0.11 #6413, 0.04 #66498, 0.02 #35506), 0h32q (0.11 #4930, 0.03 #17398, 0.02 #19476), 0h96g (0.11 #5008, 0.02 #40335, 0.02 #13321), 01fwk3 (0.11 #4616) >> Best rule #16624 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 0ds3t5x; 016z9n; 0kvgnq; 04h41v; 011ywj; 03pc89; >> query: (?x8890, ?x241) <- nominated_for(?x1254, ?x8890), nominated_for(?x241, ?x8890), genre(?x8890, ?x53), ?x1254 = 02z0dfh >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #8114 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 0127ps; 02q0v8n; 032sl_; *> query: (?x8890, 05w1vf) <- award_winner(?x8890, ?x2028), genre(?x8890, ?x4205), film(?x241, ?x8890), ?x4205 = 0c3351 *> conf = 0.05 ranks of expected_values: 128 EVAL 03b1sb film! 05w1vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 74.000 45.000 0.515 http://example.org/film/actor/film./film/performance/film #8183-01hkck PRED entity: 01hkck PRED relation: gender PRED expected values: 02zsn => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #71, 0.86 #157, 0.86 #177), 02zsn (0.49 #24, 0.46 #104, 0.46 #108) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 186 >> proper extension: 03_87; 05np2; 03_dj; >> query: (?x11311, 05zppz) <- location(?x11311, ?x13692), place_of_death(?x11311, ?x1523), student(?x4363, ?x11311), profession(?x11311, ?x1032) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 01vsykc; 03f0fnk; 01nz1q6; *> query: (?x11311, 02zsn) <- location(?x11311, ?x13692), location_of_ceremony(?x11311, ?x12655), spouse(?x11311, ?x10795) *> conf = 0.49 ranks of expected_values: 2 EVAL 01hkck gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 166.000 166.000 0.856 http://example.org/people/person/gender #8182-0ccvx PRED entity: 0ccvx PRED relation: category PRED expected values: 08mbj5d => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #34, 0.75 #27, 0.73 #43) >> Best rule #34 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 01fq7; 0r4xt; 0rkkv; 01x96; 0zchj; 0r4wn; 0r4z7; 0mrhq; 0fw3f; 0rwgm; ... >> query: (?x4253, 08mbj5d) <- location_of_ceremony(?x566, ?x4253), source(?x4253, ?x958), ?x566 = 04ztj >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ccvx category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.780 http://example.org/common/topic/webpage./common/webpage/category #8181-0dvld PRED entity: 0dvld PRED relation: award_winner! PRED expected values: 0gvstc3 04110lv => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 133): 09q_6t (0.25 #7, 0.07 #143, 0.06 #279), 0gvstc3 (0.25 #32, 0.07 #168, 0.03 #6152), 02ywhz (0.25 #75, 0.07 #211, 0.02 #15641), 01mhwk (0.13 #174, 0.05 #582, 0.04 #1126), 02cg41 (0.13 #257, 0.04 #10185, 0.04 #5017), 01bx35 (0.13 #142, 0.03 #10070, 0.03 #10614), 013b2h (0.07 #212, 0.06 #756, 0.06 #892), 027hjff (0.07 #189, 0.06 #325, 0.06 #461), 092c5f (0.07 #148, 0.06 #3548, 0.04 #1236), 01c6qp (0.07 #153, 0.05 #1785, 0.05 #697) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0bq2g; 01mqh5; >> query: (?x5951, 09q_6t) <- award(?x5951, ?x4183), award(?x5951, ?x1336), ?x4183 = 024fz9, ?x1336 = 05pcn59 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #32 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0bq2g; 01mqh5; *> query: (?x5951, 0gvstc3) <- award(?x5951, ?x4183), award(?x5951, ?x1336), ?x4183 = 024fz9, ?x1336 = 05pcn59 *> conf = 0.25 ranks of expected_values: 2, 107 EVAL 0dvld award_winner! 04110lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 124.000 124.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0dvld award_winner! 0gvstc3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 124.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8180-02x9cv PRED entity: 02x9cv PRED relation: institution! PRED expected values: 0bkj86 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 20): 02h4rq6 (0.78 #25, 0.68 #135, 0.67 #157), 014mlp (0.70 #138, 0.69 #292, 0.69 #160), 019v9k (0.61 #141, 0.61 #31, 0.58 #163), 02_xgp2 (0.61 #34, 0.47 #78, 0.44 #56), 07s6fsf (0.59 #23, 0.41 #67, 0.39 #45), 016t_3 (0.57 #26, 0.45 #48, 0.42 #70), 0bkj86 (0.50 #30, 0.37 #74, 0.36 #206), 04zx3q1 (0.35 #24, 0.31 #46, 0.30 #68), 027f2w (0.28 #32, 0.21 #54, 0.19 #76), 028dcg (0.24 #40, 0.18 #62, 0.16 #84) >> Best rule #25 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 03b8c4; >> query: (?x8825, 02h4rq6) <- institution(?x1519, ?x8825), ?x1519 = 013zdg, organization(?x346, ?x8825), colors(?x8825, ?x5325) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 03b8c4; *> query: (?x8825, 0bkj86) <- institution(?x1519, ?x8825), ?x1519 = 013zdg, organization(?x346, ?x8825), colors(?x8825, ?x5325) *> conf = 0.50 ranks of expected_values: 7 EVAL 02x9cv institution! 0bkj86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 118.000 118.000 0.783 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #8179-0bh8tgs PRED entity: 0bh8tgs PRED relation: film_release_region PRED expected values: 03gj2 015qh 05v10 02vzc 05sb1 01ly5m => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 102): 09c7w0 (0.92 #5558, 0.92 #5038, 0.81 #1938), 03gj2 (0.91 #1308, 0.88 #16, 0.88 #1437), 02vzc (0.84 #37, 0.83 #1458, 0.82 #1329), 015qh (0.76 #28, 0.59 #1449, 0.56 #1191), 06t8v (0.68 #57, 0.51 #1478, 0.48 #1220), 06mzp (0.64 #12, 0.61 #1304, 0.52 #1433), 06qd3 (0.60 #26, 0.57 #1318, 0.56 #1447), 047lj (0.49 #1170, 0.44 #1428, 0.40 #7), 07ylj (0.48 #19, 0.38 #1440, 0.32 #1311), 077qn (0.48 #66, 0.36 #1229, 0.32 #1487) >> Best rule #5558 for best value: >> intensional similarity = 3 >> extensional distance = 1327 >> proper extension: 01br2w; 0dtw1x; 04969y; 04dsnp; 04m1bm; 091z_p; 064n1pz; 02rb607; 0crh5_f; 016kz1; ... >> query: (?x5089, 09c7w0) <- film_release_region(?x5089, ?x390), country(?x3598, ?x390), ?x3598 = 03rbzn >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1308 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 116 *> proper extension: 0fq27fp; *> query: (?x5089, 03gj2) <- film_release_region(?x5089, ?x4743), film_release_region(?x5089, ?x2267), film_release_region(?x5089, ?x390), ?x390 = 0chghy, ?x2267 = 03rj0, ?x4743 = 03spz *> conf = 0.91 ranks of expected_values: 2, 3, 4, 12, 23, 47 EVAL 0bh8tgs film_release_region 01ly5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 64.000 64.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bh8tgs film_release_region 05sb1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 64.000 64.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bh8tgs film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bh8tgs film_release_region 05v10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 64.000 64.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bh8tgs film_release_region 015qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bh8tgs film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8178-0cv3w PRED entity: 0cv3w PRED relation: vacationer PRED expected values: 0127s7 026_dq6 022q32 => 209 concepts (173 used for prediction) PRED predicted values (max 10 best out of 241): 04fzk (0.14 #1385, 0.14 #2522, 0.13 #897), 01xyt7 (0.14 #1419, 0.13 #769, 0.12 #2071), 016tbr (0.13 #966, 0.13 #640, 0.11 #314), 0lk90 (0.13 #835, 0.12 #1975, 0.11 #2298), 02d9k (0.13 #686, 0.12 #1988, 0.11 #2150), 01vs_v8 (0.13 #691, 0.11 #201, 0.09 #5577), 0mm1q (0.13 #760, 0.11 #270, 0.08 #2062), 02mjf2 (0.13 #905, 0.11 #5629, 0.09 #1556), 046zh (0.13 #755, 0.08 #3519, 0.07 #2542), 01z0rcq (0.13 #884, 0.06 #2999, 0.05 #3486) >> Best rule #1385 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 04jpl; 02_286; 030qb3t; 0h7h6; 01_d4; 0dclg; 02h6_6p; 03h64; 01cx_; 0d6lp; ... >> query: (?x3026, 04fzk) <- location(?x1773, ?x3026), location_of_ceremony(?x286, ?x3026), month(?x3026, ?x1459) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #930 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0r1yc; 0ckhc; *> query: (?x3026, 0127s7) <- vacationer(?x3026, ?x2258), source(?x3026, ?x958), award_nominee(?x3295, ?x2258) *> conf = 0.07 ranks of expected_values: 41, 119, 163 EVAL 0cv3w vacationer 022q32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 209.000 173.000 0.143 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 0cv3w vacationer 026_dq6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 209.000 173.000 0.143 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 0cv3w vacationer 0127s7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 209.000 173.000 0.143 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #8177-0gvrws1 PRED entity: 0gvrws1 PRED relation: film_release_region PRED expected values: 05v8c 03gj2 0345h 07t21 01mjq 01p1v 05sb1 03spz 07f1x => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 182): 03gj2 (0.86 #19, 0.77 #156, 0.74 #430), 0345h (0.83 #26, 0.81 #163, 0.76 #437), 03spz (0.70 #80, 0.68 #217, 0.60 #491), 01p1v (0.68 #42, 0.46 #179, 0.38 #453), 01mjq (0.63 #34, 0.53 #171, 0.49 #445), 05v8c (0.60 #147, 0.58 #10, 0.53 #421), 06qd3 (0.52 #167, 0.52 #441, 0.48 #30), 06f32 (0.51 #53, 0.48 #190, 0.39 #464), 06mzp (0.49 #152, 0.45 #426, 0.42 #15), 01pj7 (0.44 #39, 0.34 #176, 0.28 #1235) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 0gh6j94; >> query: (?x2037, 03gj2) <- film_release_region(?x2037, ?x4059), film_release_region(?x2037, ?x205), ?x205 = 03rjj, ?x4059 = 077qn >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 13, 15, 20 EVAL 0gvrws1 film_release_region 07f1x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 59.000 59.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvrws1 film_release_region 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvrws1 film_release_region 05sb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 59.000 59.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvrws1 film_release_region 01p1v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvrws1 film_release_region 01mjq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvrws1 film_release_region 07t21 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 59.000 59.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvrws1 film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvrws1 film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvrws1 film_release_region 05v8c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.859 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8176-0qf3p PRED entity: 0qf3p PRED relation: artists! PRED expected values: 0cx7f => 144 concepts (90 used for prediction) PRED predicted values (max 10 best out of 252): 0glt670 (0.41 #4909, 0.34 #7344, 0.32 #1560), 06j6l (0.39 #5523, 0.36 #20127, 0.33 #21346), 0xhtw (0.34 #12494, 0.33 #320, 0.31 #2145), 025sc50 (0.34 #7351, 0.32 #20128, 0.30 #5524), 0y3_8 (0.33 #957, 0.27 #2478, 0.25 #3086), 09nwwf (0.33 #1046, 0.27 #2567, 0.13 #13524), 0155w (0.33 #407, 0.25 #5277, 0.24 #11971), 0m0jc (0.33 #921, 0.23 #2442, 0.18 #9138), 05r6t (0.33 #3120, 0.23 #9208, 0.22 #17726), 02k_kn (0.30 #5539, 0.25 #365, 0.20 #15885) >> Best rule #4909 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 01vw87c; 0147dk; 03f2_rc; 081lh; 0lk90; 01vrt_c; 09qr6; 06w2sn5; 0285c; 03rl84; ... >> query: (?x2600, 0glt670) <- gender(?x2600, ?x231), currency(?x2600, ?x1099), participant(?x2600, ?x3382), artists(?x302, ?x2600) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1048 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 03fbc; 0kr_t; 02cpp; 0178kd; 0ycp3; 01323p; 0fb2l; *> query: (?x2600, 0cx7f) <- artist(?x2149, ?x2600), artists(?x302, ?x2600), ?x302 = 016clz, ?x2149 = 011k1h *> conf = 0.17 ranks of expected_values: 32 EVAL 0qf3p artists! 0cx7f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 144.000 90.000 0.407 http://example.org/music/genre/artists #8175-02js6_ PRED entity: 02js6_ PRED relation: film PRED expected values: 01zfzb => 156 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1322): 0h7t36 (0.36 #3464, 0.02 #64120), 0gtt5fb (0.29 #959, 0.02 #25935, 0.01 #33071), 092vkg (0.14 #1940, 0.14 #156, 0.02 #21564), 02ryz24 (0.14 #465, 0.04 #12953, 0.03 #32577), 02wgk1 (0.14 #754, 0.04 #13242, 0.03 #124881), 033qdy (0.14 #2955, 0.04 #4739, 0.03 #22579), 03tbg6 (0.14 #1652, 0.03 #7004, 0.03 #8788), 0462hhb (0.14 #810, 0.03 #7946, 0.03 #9730), 0340hj (0.14 #236, 0.03 #12724, 0.03 #124881), 012s1d (0.14 #916, 0.03 #13404, 0.03 #124881) >> Best rule #3464 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 04bdxl; 02qgqt; 05k2s_; 0237fw; 02l4pj; 02d42t; 02x7vq; 01pk3z; 02pjvc; 01w23w; ... >> query: (?x2626, 0h7t36) <- film(?x2626, ?x363), award_nominee(?x2626, ?x6977), ?x6977 = 03mp9s >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02js6_ film 01zfzb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 156.000 114.000 0.357 http://example.org/film/actor/film./film/performance/film #8174-01b7h8 PRED entity: 01b7h8 PRED relation: genre PRED expected values: 04rlf => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 74): 05p553 (0.57 #5, 0.56 #169, 0.56 #87), 01z4y (0.57 #19, 0.34 #1495, 0.33 #1003), 07s9rl0 (0.50 #1313, 0.49 #2133, 0.48 #2215), 06nbt (0.44 #186, 0.44 #104, 0.40 #350), 05jhg (0.31 #549, 0.22 #221, 0.22 #139), 01w613 (0.30 #295, 0.25 #459, 0.22 #213), 0c4xc (0.26 #1519, 0.24 #1191, 0.24 #945), 06n90 (0.23 #1080, 0.16 #3132, 0.14 #2393), 0dm00 (0.23 #563, 0.22 #235, 0.22 #153), 0m1xv (0.23 #569, 0.22 #159, 0.22 #897) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0gxsh4; >> query: (?x9788, 05p553) <- nominated_for(?x10025, ?x9788), genre(?x9788, ?x5728), group(?x10025, ?x8335) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #536 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 04xbq3; *> query: (?x9788, 04rlf) <- program(?x2614, ?x9788), honored_for(?x1265, ?x9788), participant(?x2562, ?x2614) *> conf = 0.08 ranks of expected_values: 39 EVAL 01b7h8 genre 04rlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 61.000 61.000 0.571 http://example.org/tv/tv_program/genre #8173-01gtcc PRED entity: 01gtcc PRED relation: district_represented PRED expected values: 06btq 0d0x8 04ly1 0498y 026mj => 38 concepts (34 used for prediction) PRED predicted values (max 10 best out of 152): 026mj (0.92 #1404, 0.89 #1209, 0.89 #630), 0d0x8 (0.92 #1389, 0.89 #1194, 0.89 #630), 06btq (0.90 #1241, 0.89 #630, 0.89 #628), 0498y (0.89 #630, 0.89 #628, 0.88 #1493), 07b_l (0.89 #630, 0.89 #628, 0.87 #294), 04ly1 (0.89 #630, 0.89 #628, 0.87 #294), 0824r (0.89 #630, 0.89 #628, 0.87 #294), 02xry (0.89 #630, 0.89 #628, 0.87 #294), 03s0w (0.89 #630, 0.89 #628, 0.87 #294), 01n7q (0.89 #630, 0.89 #628, 0.87 #294) >> Best rule #1404 for best value: >> intensional similarity = 38 >> extensional distance = 22 >> proper extension: 01grq1; >> query: (?x3669, 026mj) <- district_represented(?x3669, ?x4622), district_represented(?x3669, ?x760), district_represented(?x3669, ?x728), district_represented(?x3669, ?x335), legislative_sessions(?x6021, ?x3669), legislative_sessions(?x5006, ?x3669), legislative_sessions(?x759, ?x3669), ?x728 = 059f4, district_represented(?x5006, ?x2713), legislative_sessions(?x5401, ?x5006), legislative_sessions(?x2860, ?x5006), district_represented(?x759, ?x3908), district_represented(?x759, ?x2623), ?x335 = 059rby, ?x3908 = 04ly1, religion(?x4622, ?x8613), religion(?x4622, ?x1624), time_zones(?x4622, ?x1638), jurisdiction_of_office(?x900, ?x4622), state_province_region(?x2821, ?x4622), ?x8613 = 04pk9, ?x1624 = 051kv, contains(?x4622, ?x1505), location(?x11961, ?x4622), district_represented(?x4730, ?x4622), district_represented(?x3766, ?x4622), district_represented(?x1830, ?x4622), ?x1830 = 03z5xd, ?x760 = 05fkf, ?x4730 = 02cg7g, contains(?x2623, ?x95), ?x3766 = 02gkzs, country(?x2623, ?x94), legislative_sessions(?x3973, ?x6021), currency(?x4622, ?x170), location(?x91, ?x2623), contains(?x8260, ?x2623), film(?x11961, ?x3986) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6 EVAL 01gtcc district_represented 026mj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 34.000 0.917 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtcc district_represented 0498y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 34.000 0.917 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtcc district_represented 04ly1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 38.000 34.000 0.917 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtcc district_represented 0d0x8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 34.000 0.917 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtcc district_represented 06btq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 34.000 0.917 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #8172-04gd8j PRED entity: 04gd8j PRED relation: student PRED expected values: 0bl2g 01vhrz => 113 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1315): 01520h (0.33 #1167, 0.14 #3252, 0.03 #15764), 02t901 (0.33 #2033, 0.14 #4118, 0.03 #14545), 029k55 (0.33 #1828, 0.14 #3913, 0.03 #14340), 03_x5t (0.33 #1750, 0.14 #3835, 0.03 #14262), 05szp (0.33 #1151, 0.14 #3236, 0.03 #13663), 01386_ (0.33 #1117, 0.14 #3202, 0.03 #13629), 0c01c (0.33 #393, 0.14 #2478, 0.03 #12905), 02mhfy (0.33 #306, 0.14 #2391, 0.03 #12818), 01hbq0 (0.14 #4136, 0.07 #8307, 0.06 #12477), 03ft8 (0.14 #2340, 0.06 #8596, 0.06 #14852) >> Best rule #1167 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0k__z; >> query: (?x9865, 01520h) <- student(?x9865, ?x7831), student(?x9865, ?x6707), ?x6707 = 03d_zl4, gender(?x7831, ?x514), film(?x7831, ?x5724) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #12035 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 01wqg8; *> query: (?x9865, 01vhrz) <- student(?x9865, ?x1208), sibling(?x1208, ?x13442), contains(?x94, ?x9865), citytown(?x9865, ?x242) *> conf = 0.03 ranks of expected_values: 694, 996 EVAL 04gd8j student 01vhrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 113.000 72.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 04gd8j student 0bl2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 113.000 72.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #8171-0zcbl PRED entity: 0zcbl PRED relation: film PRED expected values: 07nt8p 06lpmt 04t9c0 05k4my => 113 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1058): 050gkf (0.64 #14218, 0.53 #88864, 0.50 #56874), 0btyf5z (0.40 #35545, 0.03 #10965, 0.02 #135078), 0gh65c5 (0.20 #593, 0.14 #4147, 0.07 #5924), 01xdxy (0.20 #1554, 0.13 #6885, 0.08 #8662), 04vr_f (0.20 #170, 0.11 #1947, 0.02 #135078), 03q0r1 (0.20 #633, 0.07 #4187, 0.07 #5964), 01y9r2 (0.20 #1334, 0.07 #4888, 0.07 #6665), 0322yj (0.20 #1761, 0.07 #5315, 0.07 #7092), 014zwb (0.20 #502, 0.07 #4056, 0.07 #5833), 02x3y41 (0.20 #1352, 0.07 #4906, 0.02 #135078) >> Best rule #14218 for best value: >> intensional similarity = 3 >> extensional distance = 86 >> proper extension: 02g8h; 04hpck; 01xcqc; 02mxw0; 012gq6; 01v3vp; 031y07; 02vg0; 0f13b; 0k525; ... >> query: (?x6980, ?x1968) <- award(?x6980, ?x2192), ?x2192 = 0bfvd4, nominated_for(?x6980, ?x1968) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #4235 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 073749; *> query: (?x6980, 06lpmt) <- film(?x6980, ?x4847), film(?x6980, ?x463), ?x463 = 03mh94, film_crew_role(?x4847, ?x137) *> conf = 0.14 ranks of expected_values: 48, 85, 382 EVAL 0zcbl film 05k4my CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 113.000 76.000 0.640 http://example.org/film/actor/film./film/performance/film EVAL 0zcbl film 04t9c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 113.000 76.000 0.640 http://example.org/film/actor/film./film/performance/film EVAL 0zcbl film 06lpmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 113.000 76.000 0.640 http://example.org/film/actor/film./film/performance/film EVAL 0zcbl film 07nt8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 76.000 0.640 http://example.org/film/actor/film./film/performance/film #8170-03s9v PRED entity: 03s9v PRED relation: nationality PRED expected values: 024pcx => 134 concepts (132 used for prediction) PRED predicted values (max 10 best out of 64): 09c7w0 (0.84 #10353, 0.82 #10550, 0.81 #11533), 07ssc (0.79 #8671, 0.78 #8971, 0.77 #5027), 036wy (0.36 #5407, 0.36 #5507, 0.34 #8972), 04jpl (0.36 #5407, 0.36 #5507, 0.34 #8972), 0345h (0.33 #913, 0.29 #1109, 0.26 #6896), 0h7x (0.26 #6896, 0.25 #8081, 0.24 #5012), 06mzp (0.26 #6896, 0.25 #8081, 0.24 #5012), 084n_ (0.26 #6896, 0.25 #8081, 0.24 #5012), 03rt9 (0.20 #1189, 0.19 #1679, 0.17 #797), 06bnz (0.20 #628, 0.06 #1804, 0.05 #1902) >> Best rule #10353 for best value: >> intensional similarity = 4 >> extensional distance = 1712 >> proper extension: 02mslq; 04sx9_; 019y64; 011zf2; 06lgq8; 022769; 0f6_dy; 05qsxy; 06rnl9; 0b6yp2; ... >> query: (?x7251, 09c7w0) <- nationality(?x7251, ?x6371), student(?x11614, ?x7251), combatants(?x1679, ?x6371), combatants(?x1777, ?x6371) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #10057 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1200 *> proper extension: 0ct_yc; 0ngg; *> query: (?x7251, ?x512) <- nationality(?x7251, ?x6371), nationality(?x7251, ?x1310), official_language(?x6371, ?x254), nationality(?x8042, ?x1310), nationality(?x8042, ?x512) *> conf = 0.07 ranks of expected_values: 41 EVAL 03s9v nationality 024pcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 134.000 132.000 0.837 http://example.org/people/person/nationality #8169-0hwpz PRED entity: 0hwpz PRED relation: language PRED expected values: 04306rv => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 39): 04306rv (0.62 #1370, 0.57 #2331, 0.57 #2445), 06nm1 (0.29 #67, 0.17 #410, 0.17 #10), 02bjrlw (0.17 #1, 0.14 #229, 0.14 #58), 06b_j (0.17 #20, 0.14 #248, 0.14 #77), 06mp7 (0.17 #15, 0.14 #72, 0.02 #700), 07zrf (0.17 #2, 0.14 #59, 0.01 #1372), 0349s (0.14 #270, 0.10 #213, 0.02 #1526), 09c7w0 (0.11 #57, 0.10 #114), 02ztjwg (0.10 #200, 0.07 #257, 0.02 #314), 01wgr (0.10 #208, 0.07 #265, 0.02 #380) >> Best rule #1370 for best value: >> intensional similarity = 4 >> extensional distance = 404 >> proper extension: 0clpml; >> query: (?x7444, ?x254) <- nominated_for(?x5884, ?x7444), participant(?x5884, ?x2564), languages(?x5884, ?x254), award(?x5884, ?x749) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hwpz language 04306rv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.616 http://example.org/film/film/language #8168-0294mx PRED entity: 0294mx PRED relation: film_format PRED expected values: 0cj16 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 3): 0cj16 (0.24 #8, 0.20 #18, 0.20 #23), 07fb8_ (0.18 #32, 0.15 #73, 0.15 #103), 017fx5 (0.03 #76, 0.03 #86, 0.03 #101) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 064n1pz; >> query: (?x7283, 0cj16) <- nominated_for(?x1441, ?x7283), language(?x7283, ?x254), ?x1441 = 099cng, films(?x942, ?x7283) >> conf = 0.24 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0294mx film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.235 http://example.org/film/film/film_format #8167-0181hw PRED entity: 0181hw PRED relation: artist PRED expected values: 01wxdn3 01vvybv => 67 concepts (46 used for prediction) PRED predicted values (max 10 best out of 885): 08w4pm (0.40 #2248, 0.06 #28933, 0.06 #29767), 01vzz1c (0.40 #1605, 0.03 #28290, 0.03 #29123), 02cw1m (0.40 #1528, 0.03 #28213, 0.02 #29046), 0fb2l (0.25 #715, 0.07 #29900, 0.07 #28233), 01t8399 (0.25 #753, 0.05 #29938, 0.05 #30771), 01vtj38 (0.20 #2192, 0.20 #1359, 0.08 #28044), 01k23t (0.20 #2227, 0.20 #1394, 0.08 #28079), 01vvybv (0.20 #2403, 0.20 #1570, 0.08 #28255), 01wg25j (0.20 #2285, 0.20 #1452, 0.07 #28137), 016dsy (0.20 #1945, 0.20 #1112, 0.06 #28630) >> Best rule #2248 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01cszh; 02p11jq; 03y5g8; >> query: (?x8170, 08w4pm) <- artist(?x8170, ?x11107), artist(?x8170, ?x2806), nationality(?x2806, ?x94), artists(?x378, ?x2806), ?x11107 = 0pqp3 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2403 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 01cszh; 02p11jq; 03y5g8; *> query: (?x8170, 01vvybv) <- artist(?x8170, ?x11107), artist(?x8170, ?x2806), nationality(?x2806, ?x94), artists(?x378, ?x2806), ?x11107 = 0pqp3 *> conf = 0.20 ranks of expected_values: 8 EVAL 0181hw artist 01vvybv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 67.000 46.000 0.400 http://example.org/music/record_label/artist EVAL 0181hw artist 01wxdn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 46.000 0.400 http://example.org/music/record_label/artist #8166-040696 PRED entity: 040696 PRED relation: profession PRED expected values: 0np9r => 108 concepts (93 used for prediction) PRED predicted values (max 10 best out of 55): 01d_h8 (0.53 #304, 0.50 #6, 0.46 #453), 03gjzk (0.36 #313, 0.31 #760, 0.31 #611), 0dxtg (0.33 #461, 0.31 #6112, 0.31 #1057), 09jwl (0.30 #9840, 0.30 #4322, 0.27 #10288), 0np9r (0.30 #9840, 0.30 #4322, 0.27 #10288), 02jknp (0.26 #306, 0.23 #604, 0.23 #753), 0dz3r (0.23 #598, 0.23 #747, 0.21 #1939), 0nbcg (0.20 #1969, 0.19 #628, 0.19 #777), 018gz8 (0.20 #464, 0.18 #315, 0.15 #2550), 0cbd2 (0.16 #1050, 0.15 #12682, 0.14 #12384) >> Best rule #304 for best value: >> intensional similarity = 3 >> extensional distance = 150 >> proper extension: 079vf; 02tkzn; >> query: (?x7245, 01d_h8) <- award_winner(?x1596, ?x7245), currency(?x7245, ?x170), film(?x7245, ?x755) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #9840 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1409 *> proper extension: 049gc; 02__94; 03ysmg; 095p3z; *> query: (?x7245, ?x319) <- award_winner(?x5999, ?x7245), profession(?x7245, ?x1032), profession(?x5999, ?x319) *> conf = 0.30 ranks of expected_values: 5 EVAL 040696 profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 93.000 0.533 http://example.org/people/person/profession #8165-0djc3s PRED entity: 0djc3s PRED relation: artists! PRED expected values: 064t9 => 135 concepts (58 used for prediction) PRED predicted values (max 10 best out of 269): 03_d0 (0.84 #5632, 0.77 #6258, 0.24 #3134), 064t9 (0.67 #9070, 0.66 #8134, 0.62 #15320), 0gywn (0.42 #8179, 0.41 #9115, 0.29 #5679), 025sc50 (0.40 #9107, 0.40 #8171, 0.30 #10981), 0glt670 (0.39 #11285, 0.39 #10972, 0.33 #12848), 0ggq0m (0.38 #1574, 0.25 #1886, 0.25 #637), 016clz (0.33 #16562, 0.28 #11561, 0.26 #6564), 0155w (0.33 #5728, 0.23 #5415, 0.22 #10414), 05bt6j (0.32 #6604, 0.31 #8477, 0.30 #10663), 02x8m (0.29 #8140, 0.28 #9076, 0.27 #5640) >> Best rule #5632 for best value: >> intensional similarity = 6 >> extensional distance = 49 >> proper extension: 0163m1; >> query: (?x11871, 03_d0) <- artists(?x4910, ?x11871), artists(?x1572, ?x11871), ?x1572 = 06by7, gender(?x11871, ?x231), artists(?x4910, ?x7240), ?x7240 = 01m3b1t >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #9070 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 0b68vs; 05mt_q; 01pgzn_; 01wz_ml; *> query: (?x11871, 064t9) <- profession(?x11871, ?x131), artists(?x3319, ?x11871), place_of_birth(?x11871, ?x8297), ?x3319 = 06j6l *> conf = 0.67 ranks of expected_values: 2 EVAL 0djc3s artists! 064t9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 58.000 0.843 http://example.org/music/genre/artists #8164-06m61 PRED entity: 06m61 PRED relation: artist! PRED expected values: 01w40h => 110 concepts (69 used for prediction) PRED predicted values (max 10 best out of 90): 033hn8 (0.29 #4130, 0.21 #151, 0.11 #7296), 011k1h (0.28 #4126, 0.10 #7292, 0.10 #7429), 015_1q (0.27 #20, 0.26 #157, 0.22 #433), 01w40h (0.27 #29, 0.11 #990, 0.08 #5767), 01cl2y (0.27 #31, 0.08 #5767, 0.08 #7145), 017l96 (0.26 #4135, 0.14 #432, 0.12 #843), 03rhqg (0.21 #153, 0.18 #16, 0.14 #7298), 043g7l (0.19 #4148, 0.07 #4971, 0.07 #7314), 0181dw (0.18 #42, 0.12 #866, 0.11 #1415), 0k_kr (0.18 #44, 0.09 #1005, 0.08 #457) >> Best rule #4130 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 01pfkw; >> query: (?x4840, 033hn8) <- artist(?x9224, ?x4840), artist(?x9224, ?x7682), ?x7682 = 01323p >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #29 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 01j4ls; 01w524f; 07h76; *> query: (?x4840, 01w40h) <- artists(?x10797, ?x4840), ?x10797 = 017371, artist(?x6946, ?x4840) *> conf = 0.27 ranks of expected_values: 4 EVAL 06m61 artist! 01w40h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 69.000 0.295 http://example.org/music/record_label/artist #8163-0bq2g PRED entity: 0bq2g PRED relation: participant! PRED expected values: 012x2b => 116 concepts (77 used for prediction) PRED predicted values (max 10 best out of 349): 01rh0w (0.41 #636, 0.38 #1909, 0.38 #3819), 02k21g (0.41 #636, 0.38 #1909, 0.38 #3819), 01vw20h (0.41 #636, 0.38 #1909, 0.38 #3819), 06pj8 (0.41 #636, 0.38 #1909, 0.38 #3819), 0151w_ (0.21 #637, 0.10 #1273, 0.08 #1910), 030vnj (0.21 #637, 0.08 #1910, 0.07 #8271), 0c6qh (0.10 #1273, 0.08 #1910, 0.07 #8271), 01rr9f (0.09 #34, 0.07 #1307, 0.06 #3217), 0gy6z9 (0.08 #1910, 0.07 #8271, 0.07 #5090), 02g0mx (0.08 #1910, 0.07 #8271, 0.07 #5090) >> Best rule #636 for best value: >> intensional similarity = 2 >> extensional distance = 41 >> proper extension: 02wb6yq; >> query: (?x3553, ?x1424) <- friend(?x3553, ?x1424), celebrity(?x3553, ?x989) >> conf = 0.41 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 0bq2g participant! 012x2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 77.000 0.411 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #8162-01l1b90 PRED entity: 01l1b90 PRED relation: profession PRED expected values: 0dxtg => 129 concepts (93 used for prediction) PRED predicted values (max 10 best out of 64): 09jwl (0.66 #5353, 0.65 #4918, 0.64 #1601), 0dz3r (0.65 #1010, 0.59 #1154, 0.57 #1442), 0np9r (0.57 #163, 0.38 #307, 0.17 #10710), 0nbcg (0.54 #1613, 0.54 #4930, 0.53 #605), 016z4k (0.51 #1732, 0.51 #1876, 0.50 #3607), 02jknp (0.47 #8816, 0.22 #12864, 0.19 #13297), 0dxtg (0.47 #8822, 0.42 #12870, 0.33 #733), 0cbd2 (0.34 #13002, 0.14 #12863, 0.13 #11418), 014ktf (0.34 #13002, 0.01 #1392, 0.01 #1680), 0n1h (0.33 #2315, 0.32 #1883, 0.31 #2171) >> Best rule #5353 for best value: >> intensional similarity = 4 >> extensional distance = 377 >> proper extension: 01l_vgt; 04mx7s; >> query: (?x250, 09jwl) <- type_of_union(?x250, ?x566), artists(?x671, ?x250), ?x566 = 04ztj, artist(?x7089, ?x250) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #8822 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 611 *> proper extension: 0fvf9q; 02rchht; 042l3v; 04rs03; 06cv1; 02kxbwx; 02q_cc; 019z7q; 04wvhz; 01g4zr; ... *> query: (?x250, 0dxtg) <- type_of_union(?x250, ?x566), profession(?x250, ?x319), award(?x250, ?x2563), ?x319 = 01d_h8 *> conf = 0.47 ranks of expected_values: 7 EVAL 01l1b90 profession 0dxtg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 129.000 93.000 0.665 http://example.org/people/person/profession #8161-01lhdt PRED entity: 01lhdt PRED relation: major_field_of_study PRED expected values: 062z7 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 106): 01mkq (0.66 #494, 0.55 #614, 0.39 #734), 02lp1 (0.64 #491, 0.45 #611, 0.42 #731), 02j62 (0.51 #509, 0.45 #629, 0.45 #749), 04rjg (0.47 #499, 0.40 #619, 0.34 #739), 03g3w (0.42 #505, 0.35 #745, 0.31 #145), 062z7 (0.42 #506, 0.31 #746, 0.30 #266), 01lj9 (0.39 #519, 0.39 #639, 0.33 #279), 05qfh (0.36 #515, 0.34 #755, 0.26 #635), 01tbp (0.34 #539, 0.31 #659, 0.23 #779), 0fdys (0.34 #518, 0.29 #638, 0.26 #278) >> Best rule #494 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 06pwq; 01w5m; 017j69; 09f2j; 01nnsv; 0gl5_; 0c5x_; 0g2jl; 0jksm; >> query: (?x7154, 01mkq) <- major_field_of_study(?x7154, ?x742), colors(?x7154, ?x663), list(?x7154, ?x2197) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #506 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 06pwq; 01w5m; 017j69; 09f2j; 01nnsv; 0gl5_; 0c5x_; 0g2jl; 0jksm; *> query: (?x7154, 062z7) <- major_field_of_study(?x7154, ?x742), colors(?x7154, ?x663), list(?x7154, ?x2197) *> conf = 0.42 ranks of expected_values: 6 EVAL 01lhdt major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 115.000 115.000 0.661 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8160-02xry PRED entity: 02xry PRED relation: district_represented! PRED expected values: 024tkd => 198 concepts (198 used for prediction) PRED predicted values (max 10 best out of 50): 024tkd (0.63 #1285, 0.63 #1235, 0.61 #385), 02bn_p (0.62 #1254, 0.61 #354, 0.59 #1204), 02bp37 (0.58 #1258, 0.57 #1208, 0.56 #2101), 02bqm0 (0.56 #1274, 0.56 #2101, 0.55 #1224), 02bqmq (0.56 #2101, 0.54 #1264, 0.53 #1214), 01gt99 (0.56 #2101, 0.50 #396, 0.49 #2452), 01gtdd (0.56 #2101, 0.49 #2452, 0.47 #393), 01gtcc (0.56 #2101, 0.49 #2452, 0.47 #366), 01gst_ (0.56 #2101, 0.49 #2452, 0.47 #361), 01gtbb (0.56 #2101, 0.49 #2452, 0.47 #360) >> Best rule #1285 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 03gh4; >> query: (?x2623, 024tkd) <- location(?x91, ?x2623), contains(?x2623, ?x95), district_represented(?x176, ?x2623) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xry district_represented! 024tkd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 198.000 0.635 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #8159-01q7q2 PRED entity: 01q7q2 PRED relation: institution! PRED expected values: 02h4rq6 019v9k => 128 concepts (83 used for prediction) PRED predicted values (max 10 best out of 21): 02h4rq6 (0.86 #214, 0.86 #73, 0.83 #423), 016t_3 (0.86 #74, 0.78 #94, 0.71 #215), 019v9k (0.81 #126, 0.77 #339, 0.77 #429), 0bkj86 (0.79 #78, 0.78 #55, 0.75 #125), 03bwzr4 (0.79 #83, 0.76 #200, 0.72 #155), 04zx3q1 (0.79 #72, 0.67 #189, 0.67 #49), 01gkg3 (0.78 #94, 0.58 #444, 0.50 #558), 028dcg (0.78 #94, 0.41 #766, 0.39 #606), 07s6fsf (0.64 #71, 0.62 #118, 0.62 #212), 013zdg (0.64 #77, 0.56 #149, 0.52 #194) >> Best rule #214 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 052nd; 07wjk; 07vht; 07wlf; >> query: (?x8008, 02h4rq6) <- major_field_of_study(?x8008, ?x3995), major_field_of_study(?x8008, ?x1154), ?x3995 = 0fdys, ?x1154 = 02lp1, organization(?x346, ?x8008), institution(?x1368, ?x8008) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01q7q2 institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 83.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01q7q2 institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 83.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #8158-04n3l PRED entity: 04n3l PRED relation: location! PRED expected values: 01r42_g 013w7j => 104 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1949): 023p29 (0.56 #5025, 0.53 #15071, 0.52 #15070), 0b6yp2 (0.56 #5025, 0.53 #15071, 0.52 #15070), 0284n42 (0.56 #5025, 0.53 #15071, 0.52 #15070), 0jmj (0.40 #864, 0.08 #3377, 0.08 #13422), 0sx5w (0.25 #4648, 0.23 #7160, 0.20 #2135), 02sjf5 (0.25 #2714, 0.23 #5226, 0.20 #201), 0pyww (0.20 #980, 0.17 #3493, 0.15 #6005), 01qn8k (0.20 #1882, 0.17 #4395, 0.15 #6907), 05myd2 (0.20 #1924, 0.17 #4437, 0.15 #6949), 0dszr0 (0.20 #2464, 0.17 #4977, 0.15 #7489) >> Best rule #5025 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0cyn3; 0d_kd; >> query: (?x3415, ?x666) <- contains(?x335, ?x3415), contains(?x3415, ?x3148), ?x335 = 059rby, place_of_birth(?x666, ?x3415) >> conf = 0.56 => this is the best rule for 3 predicted values *> Best rule #1248 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 01mb87; *> query: (?x3415, 013w7j) <- contains(?x335, ?x3415), place_of_birth(?x666, ?x3415), ?x335 = 059rby, location_of_ceremony(?x6231, ?x3415) *> conf = 0.20 ranks of expected_values: 54, 408 EVAL 04n3l location! 013w7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 104.000 65.000 0.557 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04n3l location! 01r42_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 104.000 65.000 0.557 http://example.org/people/person/places_lived./people/place_lived/location #8157-03bzyn4 PRED entity: 03bzyn4 PRED relation: film_release_region PRED expected values: 09c7w0 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 121): 09c7w0 (0.77 #719, 0.76 #898, 0.75 #2511), 06mkj (0.37 #971, 0.37 #792, 0.35 #613), 0f8l9c (0.37 #748, 0.35 #927, 0.33 #569), 0d0vqn (0.37 #728, 0.34 #907, 0.33 #549), 03gj2 (0.37 #753, 0.34 #932, 0.33 #574), 059j2 (0.35 #940, 0.35 #761, 0.33 #582), 03_3d (0.35 #726, 0.34 #905, 0.33 #547), 02vzc (0.35 #786, 0.33 #607, 0.32 #965), 015fr (0.35 #742, 0.32 #921, 0.30 #563), 07ssc (0.33 #740, 0.33 #561, 0.32 #919) >> Best rule #719 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 0372j5; >> query: (?x9496, 09c7w0) <- film_distribution_medium(?x9496, ?x81), film(?x1205, ?x9496), written_by(?x9496, ?x4589), language(?x9496, ?x254) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bzyn4 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.767 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8156-0m19t PRED entity: 0m19t PRED relation: group! PRED expected values: 02hnl => 71 concepts (65 used for prediction) PRED predicted values (max 10 best out of 118): 02hnl (0.76 #2149, 0.73 #2501, 0.68 #1797), 018vs (0.63 #1781, 0.62 #2133, 0.59 #2485), 03bx0bm (0.61 #1792, 0.60 #2144, 0.57 #2496), 0l14md (0.59 #2478, 0.58 #2126, 0.54 #1774), 028tv0 (0.37 #2132, 0.36 #1780, 0.35 #2484), 0l14qv (0.33 #5, 0.29 #1066, 0.26 #2124), 05r5c (0.33 #8, 0.28 #1775, 0.24 #2127), 03qjg (0.33 #313, 0.27 #2520, 0.25 #755), 01v1d8 (0.33 #57, 0.20 #939, 0.07 #1383), 02qjv (0.33 #19, 0.20 #901, 0.07 #618) >> Best rule #2149 for best value: >> intensional similarity = 9 >> extensional distance = 110 >> proper extension: 01wv9xn; >> query: (?x498, 02hnl) <- artists(?x6173, ?x498), artists(?x474, ?x498), artists(?x474, ?x10574), artists(?x474, ?x6208), parent_genre(?x2809, ?x6173), artist(?x3874, ?x10574), ?x6208 = 07r4c, music(?x755, ?x10574), group(?x227, ?x498) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m19t group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 65.000 0.759 http://example.org/music/performance_role/regular_performances./music/group_membership/group #8155-0jmnl PRED entity: 0jmnl PRED relation: draft PRED expected values: 025tn92 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 15): 025tn92 (0.83 #193, 0.83 #177, 0.81 #270), 0g3zpp (0.48 #456, 0.39 #396, 0.38 #531), 09l0x9 (0.46 #464, 0.41 #404, 0.38 #539), 092j54 (0.46 #462, 0.38 #537, 0.37 #402), 05vsb7 (0.46 #455, 0.36 #530, 0.35 #395), 02qw1zx (0.43 #399, 0.36 #459, 0.33 #681), 03nt7j (0.39 #460, 0.33 #681, 0.33 #182), 02z6872 (0.34 #568, 0.26 #674, 0.25 #720), 02pq_rp (0.33 #681, 0.33 #182, 0.29 #566), 047dpm0 (0.33 #681, 0.33 #182, 0.27 #574) >> Best rule #193 for best value: >> intensional similarity = 14 >> extensional distance = 21 >> proper extension: 0jmdb; >> query: (?x13777, 025tn92) <- draft(?x13777, ?x8586), draft(?x13777, ?x4979), school(?x4979, ?x6973), school(?x4979, ?x1884), draft(?x11168, ?x4979), draft(?x2820, ?x4979), institution(?x2636, ?x1884), ?x2820 = 0jmj7, category(?x1884, ?x134), ?x8586 = 038981, ?x11168 = 01k8vh, school_type(?x6973, ?x1507), ?x2636 = 027f2w, organization(?x6973, ?x5487) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jmnl draft 025tn92 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.826 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #8154-019rg5 PRED entity: 019rg5 PRED relation: contains PRED expected values: 0820xz => 116 concepts (113 used for prediction) PRED predicted values (max 10 best out of 2549): 05d49 (0.76 #100154, 0.72 #23564, 0.65 #120775), 05l5n (0.42 #70698, 0.05 #6114, 0.05 #9059), 06fz_ (0.14 #9869, 0.06 #27541, 0.05 #36378), 0bwfn (0.12 #12830, 0.07 #39340, 0.07 #15775), 0dttf (0.12 #1506, 0.06 #4453, 0.03 #22124), 02w6bq (0.12 #1805, 0.04 #13587, 0.03 #16532), 013g3 (0.12 #1579, 0.04 #13361, 0.03 #16306), 01w2v (0.12 #837, 0.04 #12619, 0.03 #15564), 07p7g (0.12 #2655, 0.02 #120777, 0.02 #61567), 0cf0s (0.12 #2775, 0.02 #67580, 0.01 #97037) >> Best rule #100154 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0285m87; >> query: (?x910, ?x13481) <- capital(?x910, ?x13481), contains(?x2467, ?x910), jurisdiction_of_office(?x182, ?x910) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #3335 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 0gclb; 05d49; *> query: (?x910, 0820xz) <- time_zones(?x910, ?x6582), ?x6582 = 0gsrz4 *> conf = 0.06 ranks of expected_values: 213 EVAL 019rg5 contains 0820xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 116.000 113.000 0.761 http://example.org/location/location/contains #8153-084nh PRED entity: 084nh PRED relation: influenced_by! PRED expected values: 06whf 0dfrq 01v_0b => 117 concepts (44 used for prediction) PRED predicted values (max 10 best out of 631): 040db (0.71 #1602, 0.60 #3133, 0.54 #4663), 06whf (0.50 #672, 0.43 #1691, 0.40 #3222), 0d4jl (0.44 #2664, 0.33 #115, 0.30 #3174), 040_t (0.44 #2804, 0.33 #255, 0.21 #4844), 073v6 (0.44 #2665, 0.33 #116, 0.20 #3175), 03qcq (0.36 #3569, 0.14 #1019, 0.11 #2550), 084w8 (0.33 #2551, 0.33 #2, 0.30 #3061), 0683n (0.33 #2884, 0.33 #335, 0.29 #4924), 01v_0b (0.33 #3028, 0.33 #479, 0.18 #4047), 0lrh (0.33 #2652, 0.33 #103, 0.17 #4692) >> Best rule #1602 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 028p0; 02wh0; >> query: (?x11412, 040db) <- location(?x11412, ?x1591), influenced_by(?x6457, ?x11412), ?x6457 = 03_87, religion(?x11412, ?x4641) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #672 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 05np2; 03_dj; *> query: (?x11412, 06whf) <- location(?x11412, ?x1591), influenced_by(?x6457, ?x11412), ?x6457 = 03_87, ?x1591 = 01xd9 *> conf = 0.50 ranks of expected_values: 2, 9, 15 EVAL 084nh influenced_by! 01v_0b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 117.000 44.000 0.714 http://example.org/influence/influence_node/influenced_by EVAL 084nh influenced_by! 0dfrq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 117.000 44.000 0.714 http://example.org/influence/influence_node/influenced_by EVAL 084nh influenced_by! 06whf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 44.000 0.714 http://example.org/influence/influence_node/influenced_by #8152-0qm98 PRED entity: 0qm98 PRED relation: nominated_for! PRED expected values: 0gqy2 => 91 concepts (80 used for prediction) PRED predicted values (max 10 best out of 207): 0k611 (0.72 #67, 0.71 #300, 0.56 #1463), 027c924 (0.71 #467, 0.70 #233, 0.70 #466), 09d28z (0.71 #467, 0.70 #233, 0.70 #466), 040njc (0.61 #7, 0.59 #240, 0.49 #1403), 0f4x7 (0.59 #26, 0.59 #259, 0.42 #1422), 0gq_v (0.56 #487, 0.48 #20, 0.46 #253), 0gqy2 (0.51 #116, 0.51 #349, 0.42 #1512), 0p9sw (0.49 #21, 0.48 #254, 0.35 #1417), 0gr0m (0.44 #289, 0.43 #56, 0.40 #1452), 02qyntr (0.41 #174, 0.40 #407, 0.34 #1570) >> Best rule #67 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 0m313; 09m6kg; 01gc7; 07xtqq; 04v8x9; 0209hj; 0hmr4; 0b6tzs; 09q5w2; 0_92w; ... >> query: (?x1454, 0k611) <- award(?x1454, ?x1307), nominated_for(?x746, ?x1454), film(?x434, ?x1454), ?x1307 = 0gq9h >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #116 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 0m313; 09m6kg; 01gc7; 07xtqq; 04v8x9; 0209hj; 0hmr4; 0b6tzs; 09q5w2; 0_92w; ... *> query: (?x1454, 0gqy2) <- award(?x1454, ?x1307), nominated_for(?x746, ?x1454), film(?x434, ?x1454), ?x1307 = 0gq9h *> conf = 0.51 ranks of expected_values: 7 EVAL 0qm98 nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 91.000 80.000 0.721 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8151-0cbgl PRED entity: 0cbgl PRED relation: location PRED expected values: 0r02m => 151 concepts (108 used for prediction) PRED predicted values (max 10 best out of 271): 02_286 (0.33 #37, 0.28 #24153, 0.25 #3250), 05k7sb (0.33 #109, 0.25 #3322, 0.06 #9748), 07_f2 (0.33 #351, 0.25 #3564, 0.06 #9990), 0f2wj (0.33 #34, 0.25 #3247, 0.06 #9673), 07_fl (0.33 #566, 0.25 #3779, 0.06 #10205), 030qb3t (0.29 #4903, 0.14 #39480, 0.14 #5707), 09c7w0 (0.29 #8839, 0.09 #7233, 0.09 #12855), 0hptm (0.25 #1908, 0.20 #4319, 0.05 #11547), 04jpl (0.21 #17695, 0.07 #33779, 0.07 #13672), 0cr3d (0.20 #4162, 0.14 #8981, 0.14 #24261) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02p5hf; >> query: (?x14008, 02_286) <- people(?x14284, ?x14008), award_winner(?x12729, ?x14008), ?x14284 = 0148xv, student(?x741, ?x14008) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #15977 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 084w8; 019z7q; 0yfp; 02whj; 016hvl; 09dt7; 01t07j; 073bb; 040db; 0j_c; ... *> query: (?x14008, 0r02m) <- people(?x6821, ?x14008), award_winner(?x12729, ?x14008), influenced_by(?x14008, ?x117), profession(?x14008, ?x353) *> conf = 0.02 ranks of expected_values: 169 EVAL 0cbgl location 0r02m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 151.000 108.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #8150-01r2c7 PRED entity: 01r2c7 PRED relation: award PRED expected values: 040njc => 138 concepts (108 used for prediction) PRED predicted values (max 10 best out of 284): 040njc (0.65 #5262, 0.63 #1624, 0.44 #7688), 019f4v (0.47 #11786, 0.45 #7746, 0.44 #9362), 0gs9p (0.46 #11798, 0.44 #13011, 0.44 #7758), 02pqp12 (0.36 #474, 0.33 #70, 0.31 #11790), 02g3ft (0.33 #84, 0.16 #488, 0.07 #2106), 07bdd_ (0.30 #3299, 0.26 #6128, 0.20 #17039), 0gr51 (0.29 #10203, 0.29 #6971, 0.27 #11819), 0gr4k (0.29 #10136, 0.28 #17410, 0.26 #5286), 04dn09n (0.29 #10147, 0.27 #1659, 0.26 #8935), 09sb52 (0.27 #4486, 0.27 #30760, 0.25 #29952) >> Best rule #5262 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 0b455l; >> query: (?x9354, 040njc) <- produced_by(?x385, ?x9354), award_winner(?x385, ?x624), nominated_for(?x1162, ?x385), ?x1162 = 099c8n >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r2c7 award 040njc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 108.000 0.649 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8149-013x0b PRED entity: 013x0b PRED relation: industry PRED expected values: 02jjt => 110 concepts (57 used for prediction) PRED predicted values (max 10 best out of 122): 02vxn (0.77 #2314, 0.71 #1321, 0.70 #2596), 02jjt (0.68 #1045, 0.67 #714, 0.60 #942), 01mw1 (0.52 #2360, 0.27 #1510, 0.25 #190), 03qh03g (0.50 #947, 0.28 #711, 0.24 #2175), 01mf0 (0.33 #924, 0.16 #2152, 0.09 #2389), 020mfr (0.32 #2375, 0.22 #1525, 0.20 #111), 029g_vk (0.23 #2133, 0.18 #1708, 0.16 #1851), 0191_7 (0.15 #933, 0.08 #2161), 01mfj (0.15 #930, 0.07 #2158, 0.06 #601), 0g4gr (0.12 #196, 0.08 #384, 0.06 #525) >> Best rule #2314 for best value: >> intensional similarity = 8 >> extensional distance = 64 >> proper extension: 05h4t7; 030_1_; 06rq1k; 05d6kv; 02vyh; 0278rq7; 034f0d; 0k9ctht; 04rtpt; 081bls; ... >> query: (?x648, 02vxn) <- industry(?x648, ?x8681), major_field_of_study(?x865, ?x8681), major_field_of_study(?x7545, ?x8681), major_field_of_study(?x2909, ?x8681), ?x7545 = 0bwfn, student(?x2909, ?x338), contains(?x94, ?x2909), institution(?x865, ?x216) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1045 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 29 *> proper extension: 03xsby; 04qhdf; 02rr_z4; *> query: (?x648, 02jjt) <- industry(?x648, ?x8681), industry(?x9492, ?x8681), industry(?x5666, ?x8681), industry(?x2149, ?x8681), ?x5666 = 043g7l, ?x9492 = 03mp8k, category(?x2149, ?x134), artist(?x2149, ?x10437), ?x10437 = 020_4z *> conf = 0.68 ranks of expected_values: 2 EVAL 013x0b industry 02jjt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 57.000 0.773 http://example.org/business/business_operation/industry #8148-063ykwt PRED entity: 063ykwt PRED relation: honored_for! PRED expected values: 05zksls => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 84): 05c1t6z (0.28 #1191, 0.27 #483, 0.24 #1309), 02q690_ (0.27 #1232, 0.25 #1350, 0.23 #878), 03nnm4t (0.23 #1241, 0.20 #1359, 0.14 #2775), 0bx6zs (0.18 #580, 0.16 #698, 0.09 #3069), 07z31v (0.18 #496, 0.12 #614, 0.09 #3069), 05zksls (0.18 #6965, 0.16 #7320, 0.09 #498), 0bxs_d (0.16 #688, 0.14 #98, 0.14 #570), 0275n3y (0.14 #416, 0.14 #534, 0.09 #1242), 03gyp30 (0.14 #100, 0.11 #218, 0.10 #336), 03gwpw2 (0.14 #5, 0.11 #123, 0.10 #241) >> Best rule #1191 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 07s8z_l; >> query: (?x3787, 05c1t6z) <- genre(?x3787, ?x53), program(?x2802, ?x3787), honored_for(?x1764, ?x3787) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #6965 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 623 *> proper extension: 04gknr; 047gpsd; 07ghq; *> query: (?x3787, ?x1764) <- award_winner(?x3787, ?x12148), award_winner(?x1764, ?x12148), film(?x12148, ?x6058) *> conf = 0.18 ranks of expected_values: 6 EVAL 063ykwt honored_for! 05zksls CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 90.000 0.278 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #8147-049dyj PRED entity: 049dyj PRED relation: diet PRED expected values: 07_hy => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 1): 07_hy (0.26 #7, 0.22 #19, 0.02 #25) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 01rrwf6; 0mdyn; 02jyhv; 01d_4t; >> query: (?x1065, 07_hy) <- profession(?x1065, ?x353), film(?x1065, ?x1066), diet(?x1065, ?x3130) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049dyj diet 07_hy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.258 http://example.org/base/eating/practicer_of_diet/diet #8146-05qgd9 PRED entity: 05qgd9 PRED relation: contains! PRED expected values: 09c7w0 => 193 concepts (134 used for prediction) PRED predicted values (max 10 best out of 442): 09c7w0 (0.83 #10742, 0.80 #8057, 0.76 #81410), 04jpl (0.61 #45652, 0.42 #49230, 0.14 #2705), 02jx1 (0.44 #45717, 0.28 #49295, 0.17 #60922), 02xry (0.38 #50264, 0.05 #79778, 0.04 #53841), 05fjf (0.38 #22373, 0.09 #54052, 0.06 #79989), 01n7q (0.32 #40340, 0.22 #79695, 0.22 #81485), 030qb3t (0.29 #40362, 0.15 #49308, 0.10 #21577), 07ssc (0.27 #45662, 0.17 #49240, 0.14 #2715), 02_286 (0.27 #49251, 0.17 #18835, 0.14 #2726), 0dzt9 (0.25 #11281, 0.17 #17545, 0.15 #22915) >> Best rule #10742 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01jswq; 01n4w_; 0438f; >> query: (?x12026, 09c7w0) <- state_province_region(?x12026, ?x1426), school_type(?x12026, ?x3092), organization(?x3484, ?x12026), ?x1426 = 07z1m >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qgd9 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 134.000 0.833 http://example.org/location/location/contains #8145-0g5879y PRED entity: 0g5879y PRED relation: nominated_for! PRED expected values: 09td7p => 88 concepts (77 used for prediction) PRED predicted values (max 10 best out of 208): 0p9sw (0.49 #2841, 0.23 #6839, 0.20 #3546), 0gq_v (0.46 #6838, 0.29 #1900, 0.22 #2840), 0gq9h (0.34 #532, 0.31 #1942, 0.28 #6880), 019f4v (0.31 #1934, 0.24 #4756, 0.23 #524), 02r22gf (0.30 #2848, 0.17 #6846, 0.14 #4495), 0k611 (0.29 #2893, 0.27 #543, 0.23 #6891), 02hsq3m (0.29 #2849, 0.21 #6847, 0.19 #1439), 0gs9p (0.28 #534, 0.25 #299, 0.24 #1944), 040njc (0.25 #477, 0.23 #712, 0.22 #1652), 0gr0m (0.25 #2880, 0.22 #6878, 0.20 #530) >> Best rule #2841 for best value: >> intensional similarity = 4 >> extensional distance = 162 >> proper extension: 0c3xpwy; >> query: (?x2685, 0p9sw) <- nominated_for(?x2887, ?x2685), crewmember(?x5608, ?x2887), film_crew_role(?x5608, ?x2095), ?x2095 = 0dxtw >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #327 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 0kvgtf; 03prz_; 01qbg5; 01k5y0; 0170xl; *> query: (?x2685, 09td7p) <- film(?x1914, ?x2685), genre(?x2685, ?x714), nominated_for(?x2887, ?x2685), ?x714 = 0hn10 *> conf = 0.14 ranks of expected_values: 53 EVAL 0g5879y nominated_for! 09td7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 88.000 77.000 0.488 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8144-01520h PRED entity: 01520h PRED relation: profession PRED expected values: 01d_h8 => 114 concepts (99 used for prediction) PRED predicted values (max 10 best out of 73): 0dxtg (0.56 #1938, 0.54 #2086, 0.53 #2234), 03gjzk (0.45 #1939, 0.39 #2235, 0.39 #2087), 0cbd2 (0.38 #303, 0.21 #599, 0.21 #747), 0kyk (0.38 #325, 0.21 #621, 0.19 #769), 01d_h8 (0.34 #1930, 0.33 #6, 0.33 #5632), 0np9r (0.33 #168, 0.28 #2240, 0.28 #2092), 02jknp (0.33 #8, 0.25 #748, 0.24 #600), 02hv44_ (0.25 #353, 0.04 #6720, 0.04 #9534), 0nbcg (0.24 #1363, 0.23 #1659, 0.23 #1511), 09jwl (0.21 #462, 0.21 #1350, 0.20 #1646) >> Best rule #1938 for best value: >> intensional similarity = 3 >> extensional distance = 231 >> proper extension: 0bz60q; 04rg6; 05b1062; >> query: (?x6755, 0dxtg) <- award(?x6755, ?x591), profession(?x6755, ?x1146), ?x1146 = 018gz8 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1930 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 231 *> proper extension: 0bz60q; 04rg6; 05b1062; *> query: (?x6755, 01d_h8) <- award(?x6755, ?x591), profession(?x6755, ?x1146), ?x1146 = 018gz8 *> conf = 0.34 ranks of expected_values: 5 EVAL 01520h profession 01d_h8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 114.000 99.000 0.558 http://example.org/people/person/profession #8143-02_ssl PRED entity: 02_ssl PRED relation: team PRED expected values: 02plv57 0jm9w => 24 concepts (16 used for prediction) PRED predicted values (max 10 best out of 944): 0jm9w (0.82 #2815, 0.81 #4682, 0.80 #5629), 0jm74 (0.82 #2815, 0.81 #4682, 0.80 #5629), 0jm8l (0.82 #2815, 0.81 #4682, 0.80 #5629), 01k8vh (0.81 #4682, 0.80 #5629, 0.80 #5621), 02pqcfz (0.81 #4682, 0.80 #2810, 0.80 #2809), 0jmm4 (0.81 #4682, 0.80 #2810, 0.80 #2809), 02q4ntp (0.81 #4682, 0.80 #2810, 0.80 #2809), 03d555l (0.81 #4682, 0.80 #2810, 0.80 #2809), 0jmnl (0.78 #13099, 0.27 #11226, 0.25 #14032), 0ws7 (0.67 #12474, 0.60 #9671, 0.46 #6872) >> Best rule #2815 for best value: >> intensional similarity = 35 >> extensional distance = 1 >> proper extension: 0ctt4z; >> query: (?x6848, ?x9931) <- position(?x12124, ?x6848), position(?x9995, ?x6848), position(?x9983, ?x6848), position(?x9931, ?x6848), position(?x8228, ?x6848), position(?x8079, ?x6848), position(?x6847, ?x6848), position(?x4571, ?x6848), position(?x4369, ?x6848), team(?x6848, ?x14256), team(?x6848, ?x8528), team(?x4570, ?x9931), draft(?x12124, ?x8542), draft(?x12124, ?x8133), ?x4369 = 02pqcfz, ?x6847 = 02r2qt7, company(?x6010, ?x9995), team(?x13926, ?x9931), ?x4570 = 03558l, ?x8542 = 09th87, sport(?x14256, ?x4833), ?x8079 = 04cxw5b, draft(?x9995, ?x8586), colors(?x14256, ?x3189), school(?x9995, ?x4296), team(?x13045, ?x8528), team(?x3797, ?x8528), ?x6010 = 02y6fz, ?x13045 = 0bqthy, teams(?x10519, ?x14256), ?x8133 = 025tn92, ?x3797 = 0b_6zk, ?x4571 = 0jm6n, colors(?x8228, ?x663), teams(?x3786, ?x9983) >> conf = 0.82 => this is the best rule for 3 predicted values ranks of expected_values: 1, 698 EVAL 02_ssl team 0jm9w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 16.000 0.818 http://example.org/sports/sports_position/players./sports/sports_team_roster/team EVAL 02_ssl team 02plv57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 24.000 16.000 0.818 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #8142-01pq4w PRED entity: 01pq4w PRED relation: major_field_of_study PRED expected values: 04x_3 04gb7 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 107): 02lp1 (0.71 #1101, 0.67 #12, 0.64 #1587), 04rjg (0.67 #20, 0.60 #141, 0.59 #1109), 02j62 (0.62 #1120, 0.58 #1606, 0.53 #2332), 062z7 (0.60 #149, 0.56 #28, 0.50 #1117), 01lj9 (0.56 #41, 0.53 #1130, 0.50 #162), 037mh8 (0.55 #310, 0.44 #68, 0.40 #1643), 03g3w (0.50 #1116, 0.48 #1602, 0.42 #1965), 0fdys (0.50 #1129, 0.44 #40, 0.40 #1615), 01540 (0.50 #1150, 0.40 #1636, 0.36 #1999), 05qjt (0.47 #1097, 0.45 #250, 0.40 #2793) >> Best rule #1101 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 08815; 01jssp; 05krk; 06pwq; 065y4w7; 07szy; 0bx8pn; 01jq34; 0f1nl; 07wlf; ... >> query: (?x3779, 02lp1) <- list(?x3779, ?x2197), school(?x1438, ?x3779), student(?x3779, ?x2409) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1115 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 08815; 01jssp; 05krk; 06pwq; 065y4w7; 07szy; 0bx8pn; 01jq34; 0f1nl; 07wlf; ... *> query: (?x3779, 04x_3) <- list(?x3779, ?x2197), school(?x1438, ?x3779), student(?x3779, ?x2409) *> conf = 0.38 ranks of expected_values: 13, 35 EVAL 01pq4w major_field_of_study 04gb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 79.000 79.000 0.706 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01pq4w major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 79.000 79.000 0.706 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8141-0cwtm PRED entity: 0cwtm PRED relation: award PRED expected values: 02y_rq5 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 308): 02ppm4q (0.52 #556, 0.41 #958, 0.13 #14224), 09td7p (0.43 #520, 0.35 #922, 0.08 #14188), 09sb52 (0.41 #844, 0.39 #442, 0.29 #16924), 0bdwft (0.39 #469, 0.35 #871, 0.14 #5293), 02z0dfh (0.38 #877, 0.35 #475, 0.11 #14143), 0cqgl9 (0.26 #994, 0.26 #592, 0.10 #14260), 099t8j (0.26 #942, 0.26 #540, 0.07 #1344), 0bfvw2 (0.26 #818, 0.22 #416, 0.12 #14084), 019f4v (0.25 #65, 0.10 #16949, 0.10 #1673), 03nqnk3 (0.25 #132, 0.06 #936, 0.05 #2544) >> Best rule #556 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 014g22; >> query: (?x9782, 02ppm4q) <- award_winner(?x1972, ?x9782), ?x1972 = 0gqyl, people(?x1050, ?x9782) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #897 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 019l3m; *> query: (?x9782, 02y_rq5) <- award_winner(?x1972, ?x9782), ?x1972 = 0gqyl, award(?x9782, ?x154) *> conf = 0.18 ranks of expected_values: 15 EVAL 0cwtm award 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 138.000 138.000 0.522 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8140-04r68 PRED entity: 04r68 PRED relation: award PRED expected values: 06196 => 111 concepts (93 used for prediction) PRED predicted values (max 10 best out of 364): 0262x6 (0.76 #30281, 0.73 #34269, 0.73 #34669), 0262yt (0.53 #1062, 0.48 #664, 0.42 #1460), 02662b (0.50 #1271, 0.50 #873, 0.48 #475), 02664f (0.47 #1014, 0.45 #1412, 0.43 #616), 0262zm (0.44 #880, 0.39 #482, 0.33 #1676), 0ck27z (0.33 #92, 0.08 #18814, 0.08 #25193), 0bdw6t (0.33 #110, 0.03 #7677, 0.03 #18433), 01tgwv (0.29 #1555, 0.28 #1157, 0.26 #759), 039yzf (0.26 #746, 0.25 #1144, 0.21 #1542), 06196 (0.19 #1934, 0.16 #1138, 0.13 #740) >> Best rule #30281 for best value: >> intensional similarity = 5 >> extensional distance = 1842 >> proper extension: 0kk9v; >> query: (?x5049, ?x575) <- award_winner(?x575, ?x5049), award(?x12575, ?x575), award(?x7332, ?x575), influenced_by(?x7332, ?x1029), profession(?x12575, ?x353) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1934 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 0210f1; *> query: (?x5049, 06196) <- profession(?x5049, ?x353), award(?x5049, ?x3337), award(?x10438, ?x3337), ?x10438 = 07zl1 *> conf = 0.19 ranks of expected_values: 10 EVAL 04r68 award 06196 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 111.000 93.000 0.762 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8139-05vz3zq PRED entity: 05vz3zq PRED relation: combatants PRED expected values: 0chghy 07ssc 015qh => 187 concepts (122 used for prediction) PRED predicted values (max 10 best out of 298): 0chghy (0.83 #1426, 0.83 #4954, 0.82 #4887), 05qhw (0.83 #4954, 0.82 #4887, 0.82 #4953), 015qh (0.83 #4954, 0.82 #4887, 0.82 #4953), 01mk6 (0.82 #4887, 0.82 #4953, 0.82 #6277), 02psqkz (0.80 #865, 0.60 #287, 0.57 #480), 05vz3zq (0.72 #1457, 0.70 #1782, 0.50 #1068), 04g61 (0.60 #874, 0.60 #360, 0.43 #489), 07ssc (0.50 #1428, 0.45 #1753, 0.43 #3265), 06c1y (0.43 #468, 0.40 #339, 0.40 #275), 07f1x (0.43 #498, 0.40 #369, 0.40 #305) >> Best rule #1426 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 015qh; 087vz; >> query: (?x5114, 0chghy) <- combatants(?x5114, ?x13069), combatants(?x5114, ?x583), combatants(?x5352, ?x13069), olympics(?x5114, ?x391), ?x583 = 015fr >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 8 EVAL 05vz3zq combatants 015qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 187.000 122.000 0.833 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants EVAL 05vz3zq combatants 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 187.000 122.000 0.833 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants EVAL 05vz3zq combatants 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 122.000 0.833 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #8138-09q5w2 PRED entity: 09q5w2 PRED relation: nominated_for! PRED expected values: 02qyp19 02r0csl 02x2gy0 09sdmz => 93 concepts (87 used for prediction) PRED predicted values (max 10 best out of 200): 099c8n (0.78 #4284, 0.77 #8572, 0.77 #5142), 02qyntr (0.77 #8572, 0.77 #5142, 0.68 #11362), 0f4x7 (0.77 #8572, 0.77 #5142, 0.68 #11362), 0fhpv4 (0.77 #8572, 0.68 #11362, 0.68 #11578), 02x258x (0.77 #8572, 0.68 #11362, 0.68 #11578), 027b9j5 (0.68 #11362, 0.68 #11578, 0.67 #5571), 09sdmz (0.68 #335, 0.24 #12656, 0.19 #13085), 04dn09n (0.54 #1097, 0.53 #1311, 0.52 #2809), 0gr4k (0.47 #1305, 0.41 #2803, 0.40 #2375), 099tbz (0.44 #252, 0.20 #17369, 0.19 #16511) >> Best rule #4284 for best value: >> intensional similarity = 4 >> extensional distance = 309 >> proper extension: 01kf5lf; >> query: (?x1077, ?x1162) <- film(?x1104, ?x1077), award(?x1077, ?x1162), nominated_for(?x1162, ?x945), ?x945 = 0b6tzs >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #335 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 02vxq9m; 0b6tzs; 0fh694; 020fcn; 0dgst_d; 09gq0x5; 0btyf5z; 02c638; 011yd2; 078sj4; ... *> query: (?x1077, 09sdmz) <- nominated_for(?x451, ?x1077), nominated_for(?x262, ?x1077), honored_for(?x1819, ?x1077), ?x451 = 099jhq *> conf = 0.68 ranks of expected_values: 7, 20, 47, 57 EVAL 09q5w2 nominated_for! 09sdmz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 93.000 87.000 0.784 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09q5w2 nominated_for! 02x2gy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 93.000 87.000 0.784 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09q5w2 nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 93.000 87.000 0.784 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09q5w2 nominated_for! 02qyp19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 93.000 87.000 0.784 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8137-0fh694 PRED entity: 0fh694 PRED relation: nominated_for! PRED expected values: 099t8j 03hl6lc => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 187): 027c95y (0.67 #6603, 0.67 #6604, 0.66 #4182), 03hl6lc (0.56 #775, 0.30 #335, 0.25 #995), 019f4v (0.55 #1368, 0.53 #2689, 0.53 #2249), 0gr0m (0.54 #5335, 0.32 #1373, 0.28 #273), 09sb52 (0.49 #250, 0.36 #910, 0.31 #690), 099t8j (0.47 #312, 0.25 #972, 0.19 #16730), 0k611 (0.45 #2265, 0.44 #2705, 0.42 #944), 0gr4k (0.42 #1342, 0.38 #2663, 0.36 #7946), 040njc (0.42 #225, 0.38 #885, 0.35 #1325), 0gq_v (0.40 #1336, 0.38 #9041, 0.37 #2217) >> Best rule #6603 for best value: >> intensional similarity = 5 >> extensional distance = 459 >> proper extension: 06mmr; >> query: (?x964, ?x2880) <- award_winner(?x964, ?x2646), honored_for(?x6594, ?x964), award(?x964, ?x2915), award(?x964, ?x2880), award_winner(?x2915, ?x157) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #775 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 62 *> proper extension: 047n8xt; *> query: (?x964, 03hl6lc) <- nominated_for(?x2257, ?x964), nominated_for(?x899, ?x964), film(?x286, ?x964), ?x899 = 02x1dht, award_winner(?x2257, ?x548) *> conf = 0.56 ranks of expected_values: 2, 6 EVAL 0fh694 nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.673 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fh694 nominated_for! 099t8j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 101.000 0.673 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8136-06c1y PRED entity: 06c1y PRED relation: participating_countries! PRED expected values: 0sx8l => 228 concepts (228 used for prediction) PRED predicted values (max 10 best out of 39): 018ctl (0.85 #583, 0.81 #620, 0.79 #800), 0lgxj (0.74 #421, 0.73 #277, 0.73 #241), 0sx8l (0.50 #625, 0.50 #372, 0.47 #336), 0c_tl (0.47 #236, 0.41 #344, 0.36 #200), 06sks6 (0.42 #634, 0.40 #237, 0.36 #201), 0jdk_ (0.29 #203, 0.29 #167, 0.28 #1483), 0l6ny (0.28 #1483, 0.28 #1700, 0.27 #3224), 0l98s (0.28 #1483, 0.28 #1700, 0.27 #3224), 0jkvj (0.28 #1483, 0.28 #1700, 0.27 #3224), 0lbbj (0.28 #1483, 0.28 #1700, 0.27 #3224) >> Best rule #583 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 06npd; 077qn; >> query: (?x1536, 018ctl) <- olympics(?x1536, ?x391), countries_spoken_in(?x403, ?x1536), film_release_region(?x5425, ?x1536), ?x5425 = 02prwdh >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #625 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 014tss; *> query: (?x1536, 0sx8l) <- country(?x4355, ?x1536), genre(?x4355, ?x53), combatants(?x756, ?x1536), nationality(?x4379, ?x1536) *> conf = 0.50 ranks of expected_values: 3 EVAL 06c1y participating_countries! 0sx8l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 228.000 228.000 0.846 http://example.org/olympics/olympic_games/participating_countries #8135-0gwf191 PRED entity: 0gwf191 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 46 concepts (43 used for prediction) PRED predicted values (max 10 best out of 27): 02r96rf (0.74 #42, 0.71 #120, 0.67 #80), 09zzb8 (0.70 #582, 0.69 #621, 0.68 #271), 09vw2b7 (0.67 #84, 0.67 #8, 0.59 #589), 01vx2h (0.41 #51, 0.33 #13, 0.30 #89), 0dxtw (0.34 #593, 0.34 #632, 0.33 #282), 01xy5l_ (0.33 #16, 0.20 #92, 0.09 #286), 01pvkk (0.28 #284, 0.27 #634, 0.27 #595), 02ynfr (0.20 #94, 0.15 #134, 0.15 #56), 02rh1dz (0.18 #49, 0.13 #127, 0.11 #87), 0215hd (0.17 #21, 0.15 #97, 0.12 #291) >> Best rule #42 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 0h95zbp; >> query: (?x9529, 02r96rf) <- film_release_region(?x9529, ?x1536), film_release_region(?x9529, ?x1471), ?x1536 = 06c1y, ?x1471 = 07t21, language(?x9529, ?x254) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #84 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 52 *> proper extension: 06w99h3; 0fh694; 017gl1; 05q96q6; 0bscw; 020bv3; 021y7yw; 08k40m; 0djlxb; 011ysn; ... *> query: (?x9529, 09vw2b7) <- film(?x1290, ?x9529), profession(?x1290, ?x1032), award_nominee(?x9930, ?x1290), award_nominee(?x7242, ?x1290), ?x7242 = 044lyq, profession(?x9930, ?x319) *> conf = 0.67 ranks of expected_values: 3 EVAL 0gwf191 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 46.000 43.000 0.735 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8134-043mk4y PRED entity: 043mk4y PRED relation: honored_for! PRED expected values: 03nnm4t => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 85): 0bxs_d (0.16 #946, 0.05 #583, 0.03 #1067), 09p3h7 (0.16 #907, 0.02 #1028, 0.02 #1875), 09q_6t (0.14 #851, 0.05 #488, 0.02 #1819), 0hn821n (0.14 #961, 0.03 #1082, 0.02 #1203), 059x66 (0.14 #860, 0.02 #1828, 0.02 #981), 05c1t6z (0.12 #858, 0.12 #374, 0.07 #979), 0gvstc3 (0.12 #874, 0.11 #995, 0.09 #1116), 09qvms (0.12 #130, 0.04 #977, 0.04 #1098), 09bymc (0.12 #225, 0.04 #1193, 0.03 #1919), 02q690_ (0.12 #417, 0.08 #1022, 0.08 #901) >> Best rule #946 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 06hwzy; >> query: (?x7768, 0bxs_d) <- honored_for(?x7767, ?x7768), award_winner(?x7767, ?x496), ?x496 = 0bxtg >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #910 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 06hwzy; *> query: (?x7768, 03nnm4t) <- honored_for(?x7767, ?x7768), award_winner(?x7767, ?x496), ?x496 = 0bxtg *> conf = 0.11 ranks of expected_values: 12 EVAL 043mk4y honored_for! 03nnm4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 72.000 72.000 0.156 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #8133-09fb5 PRED entity: 09fb5 PRED relation: people! PRED expected values: 03bkbh => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 42): 0x67 (0.30 #1711, 0.25 #83, 0.21 #3931), 041rx (0.22 #3185, 0.21 #5257, 0.21 #3925), 033tf_ (0.16 #2226, 0.15 #2152, 0.13 #1486), 03bkbh (0.14 #474, 0.12 #622, 0.07 #1066), 07bch9 (0.11 #1353, 0.11 #1427, 0.08 #1279), 065b6q (0.11 #742, 0.04 #1630, 0.04 #1186), 063k3h (0.10 #1139, 0.06 #1435, 0.06 #1361), 02ctzb (0.08 #901, 0.07 #1345, 0.06 #1419), 09vc4s (0.08 #896, 0.05 #2154, 0.05 #1636), 02rbdlq (0.08 #889) >> Best rule #1711 for best value: >> intensional similarity = 2 >> extensional distance = 297 >> proper extension: 032t2z; 016lh0; 021r7r; 06y3r; 024t0y; >> query: (?x406, 0x67) <- people(?x743, ?x406), currency(?x406, ?x170) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #474 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 03hy3g; *> query: (?x406, 03bkbh) <- nominated_for(?x398, ?x406), award_winner(?x112, ?x406), award_winner(?x670, ?x406) *> conf = 0.14 ranks of expected_values: 4 EVAL 09fb5 people! 03bkbh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 130.000 130.000 0.301 http://example.org/people/ethnicity/people #8132-06vbd PRED entity: 06vbd PRED relation: taxonomy PRED expected values: 04n6k => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.79 #58, 0.77 #20, 0.73 #38) >> Best rule #58 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 05kr_; >> query: (?x4302, 04n6k) <- currency(?x4302, ?x170), adjoins(?x4302, ?x1499), contains(?x4302, ?x13593) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06vbd taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.792 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #8131-0ply0 PRED entity: 0ply0 PRED relation: month PRED expected values: 06vkl 040fv 05cw8 0ll3 => 240 concepts (240 used for prediction) PRED predicted values (max 10 best out of 4): 0ll3 (0.93 #184, 0.91 #92, 0.90 #172), 05cw8 (0.91 #31, 0.90 #183, 0.89 #23), 06vkl (0.89 #21, 0.88 #181, 0.87 #29), 040fv (0.85 #170, 0.85 #158, 0.84 #126) >> Best rule #184 for best value: >> intensional similarity = 6 >> extensional distance = 40 >> proper extension: 06wjf; 0g6xq; >> query: (?x3373, 0ll3) <- month(?x3373, ?x6303), month(?x3373, ?x4827), month(?x3373, ?x3107), ?x4827 = 03_ly, ?x6303 = 0lkm, ?x3107 = 05lf_ >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0ply0 month 0ll3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 240.000 240.000 0.929 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0ply0 month 05cw8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 240.000 240.000 0.929 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0ply0 month 040fv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 240.000 240.000 0.929 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0ply0 month 06vkl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 240.000 240.000 0.929 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #8130-02_1q9 PRED entity: 02_1q9 PRED relation: actor PRED expected values: 026_w57 => 75 concepts (49 used for prediction) PRED predicted values (max 10 best out of 808): 057d89 (0.36 #20957, 0.36 #24601, 0.36 #26423), 050023 (0.36 #20957, 0.36 #24601, 0.36 #26423), 03ckxdg (0.36 #20957, 0.36 #24601, 0.36 #26423), 026n3rs (0.36 #20957, 0.36 #24601, 0.36 #26423), 070w7s (0.36 #20957, 0.36 #24601, 0.36 #26423), 026dcvf (0.36 #20957, 0.36 #24601, 0.36 #26423), 0277470 (0.36 #20957, 0.36 #24601, 0.36 #26423), 0g28b1 (0.36 #20957, 0.36 #24601, 0.36 #26423), 0pz7h (0.33 #74, 0.13 #10023, 0.11 #7289), 018ygt (0.33 #494, 0.13 #10023, 0.11 #7289) >> Best rule #20957 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 015w8_; 0170k0; 07vqnc; >> query: (?x416, ?x415) <- actor(?x416, ?x1594), nominated_for(?x415, ?x416), nominated_for(?x588, ?x416) >> conf = 0.36 => this is the best rule for 8 predicted values *> Best rule #6665 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 06mr2s; 03r0rq; *> query: (?x416, 026_w57) <- actor(?x416, ?x8113), award_nominee(?x643, ?x8113), program_creator(?x416, ?x1056) *> conf = 0.01 ranks of expected_values: 776 EVAL 02_1q9 actor 026_w57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 75.000 49.000 0.363 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #8129-05jf85 PRED entity: 05jf85 PRED relation: film! PRED expected values: 01f_mw => 86 concepts (43 used for prediction) PRED predicted values (max 10 best out of 49): 01f_mw (0.29 #49, 0.18 #124, 0.04 #199), 0jz9f (0.18 #76, 0.14 #1, 0.08 #151), 086k8 (0.18 #227, 0.17 #303, 0.15 #1667), 0g1rw (0.14 #8, 0.11 #233, 0.10 #309), 061dn_ (0.14 #24, 0.09 #99, 0.03 #174), 030_1m (0.14 #14, 0.09 #89, 0.03 #2136), 017s11 (0.14 #456, 0.12 #1213, 0.12 #1365), 05qd_ (0.14 #614, 0.13 #2435, 0.13 #386), 016tw3 (0.14 #1373, 0.13 #2209, 0.13 #539), 016tt2 (0.13 #229, 0.13 #305, 0.12 #1064) >> Best rule #49 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 03m8y5; >> query: (?x306, 01f_mw) <- genre(?x306, ?x53), film(?x986, ?x306), ?x986 = 081lh, ?x53 = 07s9rl0 >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05jf85 film! 01f_mw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 43.000 0.286 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #8128-02jx1 PRED entity: 02jx1 PRED relation: origin! PRED expected values: 01wv9xn => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 450): 02lfp4 (0.33 #715, 0.33 #204, 0.25 #2246), 0dm5l (0.33 #617, 0.33 #106, 0.25 #2148), 0892sx (0.33 #606, 0.04 #12351, 0.03 #34328), 04k05 (0.33 #965, 0.02 #40819, 0.02 #42859), 03fbc (0.33 #601, 0.02 #40455, 0.02 #23585), 079kr (0.33 #1006, 0.02 #40860, 0.02 #23990), 01_wfj (0.33 #940, 0.02 #40794, 0.02 #23924), 0phx4 (0.33 #658, 0.02 #23642, 0.02 #34380), 07n68 (0.33 #1019, 0.02 #24003, 0.02 #34741), 01vzz1c (0.33 #979, 0.02 #23963, 0.02 #34701) >> Best rule #715 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 04jpl; >> query: (?x1310, 02lfp4) <- contains(?x1310, ?x11732), ?x11732 = 0n95v, location(?x981, ?x1310) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2089 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2 *> proper extension: 0978r; *> query: (?x1310, 01wv9xn) <- contains(?x1310, ?x11614), ?x11614 = 07tk7 *> conf = 0.25 ranks of expected_values: 39 EVAL 02jx1 origin! 01wv9xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 180.000 180.000 0.333 http://example.org/music/artist/origin #8127-01r4k PRED entity: 01r4k PRED relation: major_field_of_study! PRED expected values: 0bkj86 02_xgp2 => 68 concepts (60 used for prediction) PRED predicted values (max 10 best out of 17): 014mlp (0.85 #610, 0.79 #805, 0.77 #444), 02_xgp2 (0.85 #348, 0.78 #470, 0.78 #415), 0bkj86 (0.67 #291, 0.67 #186, 0.64 #309), 022h5x (0.61 #56, 0.54 #19, 0.46 #784), 02mjs7 (0.61 #56, 0.54 #19, 0.46 #784), 013zdg (0.61 #56, 0.54 #19, 0.41 #108), 027f2w (0.61 #56, 0.54 #19, 0.41 #108), 07s6fsf (0.61 #56, 0.54 #19, 0.41 #108), 03mkk4 (0.61 #56, 0.54 #19, 0.41 #108), 0bjrnt (0.61 #56, 0.41 #108, 0.41 #163) >> Best rule #610 for best value: >> intensional similarity = 11 >> extensional distance = 57 >> proper extension: 06ntj; >> query: (?x10417, 014mlp) <- major_field_of_study(?x10417, ?x2601), major_field_of_study(?x2601, ?x1154), major_field_of_study(?x6925, ?x2601), major_field_of_study(?x4672, ?x2601), major_field_of_study(?x1681, ?x2601), ?x4672 = 07tds, student(?x6925, ?x981), colors(?x6925, ?x663), institution(?x1526, ?x6925), ?x1681 = 07szy, ?x1526 = 0bkj86 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #348 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 11 *> proper extension: 0dc_v; *> query: (?x10417, 02_xgp2) <- major_field_of_study(?x3354, ?x10417), major_field_of_study(?x2396, ?x10417), major_field_of_study(?x1011, ?x10417), major_field_of_study(?x741, ?x10417), ?x1011 = 07w0v, organization(?x346, ?x3354), institution(?x734, ?x2396), major_field_of_study(?x1771, ?x10417), institution(?x1771, ?x11452), institution(?x1771, ?x10333), institution(?x1771, ?x5638), ?x741 = 01w3v, ?x5638 = 02bqy, ?x10333 = 02gkxp, ?x11452 = 03k7dn *> conf = 0.85 ranks of expected_values: 2, 3 EVAL 01r4k major_field_of_study! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 60.000 0.847 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 01r4k major_field_of_study! 0bkj86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 60.000 0.847 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #8126-0642ykh PRED entity: 0642ykh PRED relation: titles! PRED expected values: 07ssc => 155 concepts (139 used for prediction) PRED predicted values (max 10 best out of 126): 01z4y (0.69 #7077, 0.25 #35, 0.24 #1358), 024qqx (0.43 #487, 0.36 #386, 0.24 #996), 07s9rl0 (0.38 #611, 0.37 #12481, 0.36 #12889), 04xvlr (0.33 #818, 0.31 #614, 0.25 #3055), 01jfsb (0.33 #834, 0.31 #630, 0.25 #122), 03k9fj (0.30 #916, 0.23 #6839, 0.23 #10225), 012w70 (0.25 #145, 0.09 #1163, 0.06 #551), 09blyk (0.22 #860, 0.19 #656, 0.11 #2790), 03h64 (0.19 #545, 0.18 #241, 0.14 #1157), 07ssc (0.17 #1232, 0.13 #1637, 0.12 #1333) >> Best rule #7077 for best value: >> intensional similarity = 4 >> extensional distance = 357 >> proper extension: 01sxly; 015whm; 02ppg1r; 091rc5; 0f7hw; 0f8j13; 0sxlb; >> query: (?x6826, 01z4y) <- film(?x489, ?x6826), titles(?x1510, ?x6826), titles(?x1510, ?x2207), ?x2207 = 07p62k >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1232 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 08gsvw; 0299hs; 05dmmc; 08984j; 0dc7hc; *> query: (?x6826, 07ssc) <- film_crew_role(?x6826, ?x137), music(?x6826, ?x7027), currency(?x6826, ?x170), film(?x788, ?x6826), ?x788 = 0g1rw *> conf = 0.17 ranks of expected_values: 10 EVAL 0642ykh titles! 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 155.000 139.000 0.691 http://example.org/media_common/netflix_genre/titles #8125-07brj PRED entity: 07brj PRED relation: instrumentalists PRED expected values: 01dw_f => 88 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1121): 01vrnsk (0.71 #1236, 0.70 #3707, 0.68 #4326), 03h_fqv (0.71 #1236, 0.70 #3707, 0.68 #4326), 0137g1 (0.71 #1236, 0.70 #3707, 0.68 #4326), 01wl38s (0.71 #1236, 0.70 #3707, 0.68 #4326), 0140t7 (0.71 #1236, 0.70 #3707, 0.68 #4326), 0pk41 (0.71 #1236, 0.70 #3707, 0.68 #4326), 02whj (0.71 #1236, 0.70 #3707, 0.68 #4326), 0161sp (0.71 #1236, 0.70 #3707, 0.68 #4326), 01qvgl (0.71 #1236, 0.70 #3707, 0.68 #4326), 050z2 (0.71 #1236, 0.70 #3707, 0.68 #4326) >> Best rule #1236 for best value: >> intensional similarity = 22 >> extensional distance = 1 >> proper extension: 0342h; >> query: (?x1267, ?x565) <- role(?x1267, ?x4078), role(?x1267, ?x3238), role(?x1267, ?x2157), role(?x1267, ?x645), ?x4078 = 011k_j, role(?x5921, ?x1267), role(?x3409, ?x1267), role(?x2785, ?x1267), role(?x2764, ?x1267), role(?x745, ?x1267), ?x2764 = 01s0ps, role(?x565, ?x1267), instrumentalists(?x1267, ?x1521), ?x3409 = 0680x0, ?x2785 = 0jtg0, ?x2157 = 011_6p, group(?x1267, ?x8999), ?x8999 = 0bk1p, ?x745 = 01vj9c, ?x645 = 028tv0, ?x3238 = 0j210, ?x5921 = 0g2ff >> conf = 0.71 => this is the best rule for 21 predicted values *> Best rule #3704 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 2 *> proper extension: 0395lw; *> query: (?x1267, ?x115) <- role(?x1267, ?x4078), role(?x1267, ?x1466), ?x4078 = 011k_j, role(?x3409, ?x1267), role(?x3328, ?x1267), role(?x2764, ?x1267), ?x2764 = 01s0ps, role(?x9321, ?x1267), instrumentalists(?x1267, ?x2930), ?x3409 = 0680x0, ?x3328 = 016622, performance_role(?x248, ?x1466), role(?x8143, ?x1466), role(?x115, ?x1466), ?x8143 = 01wvxw1, role(?x1466, ?x2460), award(?x9321, ?x1232), ?x2460 = 01wy6, artists(?x284, ?x2930) *> conf = 0.53 ranks of expected_values: 230 EVAL 07brj instrumentalists 01dw_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 88.000 38.000 0.711 http://example.org/music/instrument/instrumentalists #8124-017l4 PRED entity: 017l4 PRED relation: profession PRED expected values: 09jwl 0nbcg => 155 concepts (57 used for prediction) PRED predicted values (max 10 best out of 81): 02hrh1q (0.94 #5194, 0.87 #4010, 0.86 #1790), 09jwl (0.86 #2091, 0.85 #4755, 0.83 #2831), 01d_h8 (0.78 #7559, 0.77 #8301, 0.68 #5185), 0nbcg (0.70 #2547, 0.62 #1215, 0.60 #3731), 016z4k (0.67 #1187, 0.62 #1631, 0.57 #4739), 0dxtg (0.50 #309, 0.47 #7567, 0.46 #8309), 02jknp (0.50 #303, 0.44 #5187, 0.39 #7561), 039v1 (0.48 #1220, 0.47 #2108, 0.47 #4772), 03gjzk (0.45 #1347, 0.45 #7569, 0.45 #8311), 0fnpj (0.43 #208, 0.30 #2724, 0.25 #504) >> Best rule #5194 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 0gdhhy; 044zvm; 016lv3; 01s7z0; >> query: (?x7799, 02hrh1q) <- profession(?x7799, ?x131), executive_produced_by(?x4501, ?x7799), profession(?x8640, ?x131), ?x8640 = 020hh3 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #2091 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 0m2l9; 0lgsq; 01kv4mb; 0136pk; 0pkyh; 01309x; 0qf11; 016z1t; 01vsyg9; 013423; ... *> query: (?x7799, 09jwl) <- artists(?x7329, ?x7799), profession(?x7799, ?x131), ?x7329 = 016jny, type_of_union(?x7799, ?x566), instrumentalists(?x227, ?x7799) *> conf = 0.86 ranks of expected_values: 2, 4 EVAL 017l4 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 155.000 57.000 0.941 http://example.org/people/person/profession EVAL 017l4 profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 155.000 57.000 0.941 http://example.org/people/person/profession #8123-03bzyn4 PRED entity: 03bzyn4 PRED relation: country PRED expected values: 09c7w0 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.84 #1228, 0.83 #798, 0.83 #738), 03rk0 (0.33 #40, 0.20 #101, 0.02 #286), 07ssc (0.22 #2897, 0.22 #3449, 0.22 #3204), 0345h (0.16 #642, 0.13 #2478, 0.13 #3154), 0f8l9c (0.11 #1736, 0.10 #4555, 0.10 #4127), 0d060g (0.11 #623, 0.10 #1296, 0.10 #1174), 03_3d (0.08 #131, 0.06 #192, 0.04 #5829), 0chghy (0.07 #382, 0.07 #933, 0.06 #1361), 01z4y (0.06 #3494, 0.06 #246, 0.06 #5821), 03rjj (0.04 #376, 0.03 #3439, 0.03 #4542) >> Best rule #1228 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 0dll_t2; >> query: (?x9496, 09c7w0) <- genre(?x9496, ?x1403), produced_by(?x9496, ?x595), film_release_distribution_medium(?x9496, ?x81), ?x1403 = 02l7c8 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bzyn4 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.841 http://example.org/film/film/country #8122-02v8kmz PRED entity: 02v8kmz PRED relation: cinematography PRED expected values: 03_fk9 => 82 concepts (62 used for prediction) PRED predicted values (max 10 best out of 41): 02rybfn (0.12 #108, 0.04 #551, 0.03 #488), 0dqzkv (0.12 #72, 0.04 #515, 0.03 #452), 071jrc (0.12 #124, 0.03 #504, 0.02 #567), 03cx282 (0.09 #142, 0.06 #586, 0.05 #268), 0f3zf_ (0.06 #129, 0.04 #573, 0.03 #192), 04qvl7 (0.05 #635, 0.04 #761, 0.03 #824), 02404v (0.05 #481, 0.03 #989, 0.03 #672), 08mhyd (0.05 #475, 0.03 #666, 0.02 #792), 0f3zsq (0.04 #810, 0.04 #937, 0.03 #1066), 070bjw (0.04 #542, 0.03 #288, 0.03 #415) >> Best rule #108 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 06wzvr; 0bcndz; 02x6dqb; 05dmmc; 0k4fz; 063hp4; >> query: (?x240, 02rybfn) <- titles(?x2480, ?x240), costume_design_by(?x240, ?x4190), film_crew_role(?x240, ?x137), film_sets_designed(?x8814, ?x240) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #876 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 92 *> proper extension: 02d413; 0b2v79; 0p_sc; 0pv2t; 02q52q; 070fnm; 0bm2g; 02s4l6; 0c_j9x; 083skw; ... *> query: (?x240, 03_fk9) <- titles(?x2480, ?x240), costume_design_by(?x240, ?x4190), film(?x4397, ?x240), nominated_for(?x746, ?x240) *> conf = 0.02 ranks of expected_values: 28 EVAL 02v8kmz cinematography 03_fk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 82.000 62.000 0.125 http://example.org/film/film/cinematography #8121-071vr PRED entity: 071vr PRED relation: featured_film_locations! PRED expected values: 07s3m4g => 186 concepts (112 used for prediction) PRED predicted values (max 10 best out of 698): 04dsnp (0.25 #1536, 0.21 #5211, 0.19 #2271), 0pvms (0.25 #184, 0.04 #4594, 0.03 #6799), 0872p_c (0.19 #2283, 0.19 #1548, 0.14 #5223), 0ds2n (0.19 #2436, 0.19 #1701, 0.12 #3906), 047csmy (0.19 #1866, 0.17 #4071, 0.12 #2601), 033srr (0.19 #1750, 0.12 #3955, 0.12 #2485), 061681 (0.17 #3722, 0.12 #2252, 0.12 #1517), 0473rc (0.16 #3394, 0.12 #2659, 0.12 #1924), 017z49 (0.15 #981, 0.11 #3186, 0.08 #4656), 09fc83 (0.14 #5526, 0.12 #1851, 0.11 #13611) >> Best rule #1536 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0rh6k; 02cl1; 02_286; 030qb3t; 01_d4; 0dclg; 013yq; 0cv3w; 01cx_; 0d6lp; ... >> query: (?x6960, 04dsnp) <- contains(?x94, ?x6960), dog_breed(?x6960, ?x1706), featured_film_locations(?x8302, ?x6960), month(?x6960, ?x1459) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 071vr featured_film_locations! 07s3m4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 112.000 0.250 http://example.org/film/film/featured_film_locations #8120-0427y PRED entity: 0427y PRED relation: award_winner! PRED expected values: 09bymc => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 131): 027hjff (0.26 #617, 0.24 #897, 0.04 #757), 09p2r9 (0.17 #9664, 0.17 #9665, 0.12 #92), 02q690_ (0.17 #9664, 0.17 #9665, 0.09 #625), 05c1t6z (0.17 #9664, 0.17 #9665, 0.05 #155), 07y9ts (0.17 #9664, 0.17 #9665, 0.05 #208), 0gx_st (0.17 #9664, 0.17 #9665, 0.04 #737), 0466p0j (0.16 #495, 0.11 #1475, 0.10 #1055), 013b2h (0.14 #499, 0.12 #79, 0.12 #1339), 0drtv8 (0.14 #626, 0.12 #906, 0.08 #766), 019bk0 (0.14 #436, 0.10 #996, 0.09 #1416) >> Best rule #617 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 05qd_; 0g5lhl7; 024rdh; >> query: (?x9596, 027hjff) <- nominated_for(?x9596, ?x3326), award_winner(?x5592, ?x9596), ?x5592 = 0275n3y >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #680 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 05qd_; 0g5lhl7; 024rdh; *> query: (?x9596, 09bymc) <- nominated_for(?x9596, ?x3326), award_winner(?x5592, ?x9596), ?x5592 = 0275n3y *> conf = 0.05 ranks of expected_values: 46 EVAL 0427y award_winner! 09bymc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 109.000 109.000 0.256 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #8119-0295sy PRED entity: 0295sy PRED relation: genre PRED expected values: 03k9fj => 96 concepts (94 used for prediction) PRED predicted values (max 10 best out of 101): 07s9rl0 (0.62 #3722, 0.62 #6965, 0.62 #241), 03k9fj (0.60 #12, 0.45 #732, 0.43 #372), 02kdv5l (0.60 #3, 0.45 #723, 0.38 #1203), 06n90 (0.60 #14, 0.27 #974, 0.23 #2774), 02l7c8 (0.42 #137, 0.39 #977, 0.30 #1337), 070yc (0.40 #93, 0.10 #813, 0.03 #933), 06cvj (0.33 #124, 0.17 #2644, 0.14 #1564), 01jfsb (0.33 #1693, 0.33 #6137, 0.32 #6377), 0hcr (0.28 #1584, 0.12 #504, 0.12 #2664), 04xvlr (0.24 #962, 0.18 #1682, 0.16 #3723) >> Best rule #3722 for best value: >> intensional similarity = 4 >> extensional distance = 265 >> proper extension: 02q3fdr; >> query: (?x5570, 07s9rl0) <- produced_by(?x5570, ?x4552), film(?x2135, ?x5570), music(?x5570, ?x669), nominated_for(?x154, ?x5570) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0ddjy; *> query: (?x5570, 03k9fj) <- film_distribution_medium(?x5570, ?x81), nominated_for(?x154, ?x5570), film(?x6059, ?x5570), ?x6059 = 01tnbn *> conf = 0.60 ranks of expected_values: 2 EVAL 0295sy genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 94.000 0.622 http://example.org/film/film/genre #8118-01cm8w PRED entity: 01cm8w PRED relation: film_release_region PRED expected values: 0chghy 0k6nt => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 171): 05r4w (0.88 #3761, 0.86 #6706, 0.84 #4089), 0345h (0.87 #6739, 0.80 #7066, 0.79 #6412), 0k6nt (0.87 #1007, 0.86 #843, 0.83 #4113), 0chghy (0.81 #6715, 0.80 #7042, 0.79 #2626), 01znc_ (0.75 #1682, 0.75 #537, 0.71 #6750), 0154j (0.75 #6708, 0.72 #6381, 0.70 #7035), 035qy (0.75 #6741, 0.71 #7068, 0.71 #3796), 05qhw (0.74 #6720, 0.70 #7047, 0.70 #6393), 0d060g (0.73 #6710, 0.69 #1642, 0.67 #6383), 05b4w (0.72 #6775, 0.71 #6448, 0.69 #7102) >> Best rule #3761 for best value: >> intensional similarity = 9 >> extensional distance = 127 >> proper extension: 0ddfwj1; 0198b6; 0hv27; 05ft32; 0g4vmj8; 0jdr0; 09v42sf; 0b85mm; >> query: (?x7524, 05r4w) <- film_release_region(?x7524, ?x2645), film_release_region(?x7524, ?x789), film_release_region(?x7524, ?x304), ?x2645 = 03h64, titles(?x3920, ?x7524), genre(?x7524, ?x53), ?x789 = 0f8l9c, film(?x3504, ?x7524), ?x304 = 0d0vqn >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1007 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 21 *> proper extension: 02h22; *> query: (?x7524, 0k6nt) <- film_release_region(?x7524, ?x2984), film_release_region(?x7524, ?x2645), film_release_region(?x7524, ?x1892), ?x2645 = 03h64, titles(?x3920, ?x7524), production_companies(?x7524, ?x10685), ?x2984 = 082fr, film_release_region(?x10404, ?x1892), ?x10404 = 01s9vc *> conf = 0.87 ranks of expected_values: 3, 4 EVAL 01cm8w film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 103.000 0.876 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01cm8w film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 103.000 0.876 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8117-02dbp7 PRED entity: 02dbp7 PRED relation: type_of_union PRED expected values: 04ztj => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.77 #121, 0.75 #149, 0.71 #1), 01g63y (0.29 #2, 0.19 #509, 0.14 #6), 0jgjn (0.19 #509, 0.03 #12, 0.01 #32), 01bl8s (0.19 #509) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 344 >> proper extension: 02qjj7; 012_53; 02dh86; 01mt1fy; 01jb26; 03p01x; 01gc7h; 01s7z0; >> query: (?x4574, 04ztj) <- profession(?x4574, ?x1041), ?x1041 = 03gjzk, location(?x4574, ?x8916) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02dbp7 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.769 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8116-02v570 PRED entity: 02v570 PRED relation: award PRED expected values: 02wkmx => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 249): 07cbcy (0.22 #14173, 0.22 #14172, 0.22 #15105), 02qvyrt (0.20 #1022, 0.06 #1254, 0.05 #4041), 07bdd_ (0.20 #51, 0.07 #1211, 0.06 #3532), 05b1610 (0.20 #30, 0.07 #1190, 0.05 #494), 02xj3rw (0.20 #197, 0.04 #661, 0.02 #3678), 0l8z1 (0.15 #978, 0.05 #9102, 0.05 #9567), 0gvx_ (0.13 #365, 0.13 #13939, 0.12 #14407), 03y8cbv (0.13 #13939, 0.12 #14407, 0.12 #14406), 03hl6lc (0.13 #13939, 0.12 #14407, 0.12 #14406), 02wkmx (0.13 #13939, 0.12 #14407, 0.12 #14406) >> Best rule #14173 for best value: >> intensional similarity = 2 >> extensional distance = 1455 >> proper extension: 0g60z; 02_1q9; 080dwhx; 06cs95; 02_1rq; 03kq98; 072kp; 039fgy; 0kfpm; 02k_4g; ... >> query: (?x7462, ?x102) <- nominated_for(?x3593, ?x7462), nominated_for(?x102, ?x7462) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #13939 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1451 *> proper extension: 01vrwfv; 0c3xpwy; 0275kr; *> query: (?x7462, ?x372) <- nominated_for(?x3593, ?x7462), profession(?x3593, ?x319), award_winner(?x372, ?x3593) *> conf = 0.13 ranks of expected_values: 10 EVAL 02v570 award 02wkmx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 74.000 74.000 0.222 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8115-06f32 PRED entity: 06f32 PRED relation: combatants! PRED expected values: 06k75 => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 64): 0f6rc (0.64 #1269, 0.61 #3990, 0.61 #3926), 03gqgt3 (0.60 #754, 0.52 #1070, 0.50 #1007), 0cm2xh (0.53 #267, 0.30 #203, 0.29 #457), 048n7 (0.43 #1100, 0.42 #1227, 0.41 #468), 01h6pn (0.39 #585, 0.29 #458, 0.27 #268), 01gjd0 (0.35 #448, 0.33 #890, 0.27 #258), 01fc7p (0.33 #257, 0.24 #447, 0.20 #4436), 02h2z_ (0.30 #243, 0.29 #497, 0.28 #624), 06k75 (0.30 #207, 0.28 #2993, 0.27 #3877), 02kxg_ (0.30 #225, 0.20 #4436, 0.17 #606) >> Best rule #1269 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0285m87; >> query: (?x2629, ?x7455) <- combatants(?x2629, ?x94), entity_involved(?x7455, ?x2629), jurisdiction_of_office(?x346, ?x2629) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #207 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 02psqkz; 0g8bw; *> query: (?x2629, 06k75) <- combatants(?x2629, ?x7747), entity_involved(?x7455, ?x2629), ?x7747 = 07f1x *> conf = 0.30 ranks of expected_values: 9 EVAL 06f32 combatants! 06k75 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 190.000 190.000 0.638 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #8114-03bkbh PRED entity: 03bkbh PRED relation: languages_spoken PRED expected values: 0h407 => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 54): 0t_2 (0.48 #335, 0.41 #389, 0.41 #660), 06nm1 (0.33 #8, 0.27 #224, 0.25 #116), 0jzc (0.33 #16, 0.25 #124, 0.09 #232), 07qv_ (0.33 #31, 0.25 #139, 0.09 #247), 01jb8r (0.33 #46, 0.25 #154, 0.09 #262), 064_8sq (0.27 #234, 0.24 #504, 0.24 #595), 06mp7 (0.25 #67, 0.10 #553, 0.08 #824), 05f_3 (0.25 #77, 0.06 #563, 0.05 #834), 0295r (0.25 #79, 0.05 #457, 0.04 #565), 0h407 (0.20 #210, 0.10 #426, 0.08 #588) >> Best rule #335 for best value: >> intensional similarity = 9 >> extensional distance = 25 >> proper extension: 065b6q; 041rx; 01qhm_; 033tf_; 09vc4s; 0x67; 07hwkr; 0xnvg; 02ctzb; 0d7wh; ... >> query: (?x7322, 0t_2) <- people(?x7322, ?x11410), people(?x7322, ?x11396), people(?x7322, ?x875), location(?x11396, ?x6357), film(?x875, ?x349), award_nominee(?x192, ?x875), place_of_burial(?x11410, ?x14321), award_winner(?x762, ?x875), profession(?x875, ?x1032) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #210 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 02w7gg; 013xrm; 07bch9; *> query: (?x7322, 0h407) <- people(?x7322, ?x11396), people(?x7322, ?x6424), people(?x7322, ?x875), film(?x6424, ?x2287), location(?x11396, ?x6357), ?x875 = 032_jg, film(?x11396, ?x2896), religion(?x6424, ?x1985), profession(?x11396, ?x1032) *> conf = 0.20 ranks of expected_values: 10 EVAL 03bkbh languages_spoken 0h407 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 22.000 22.000 0.481 http://example.org/people/ethnicity/languages_spoken #8113-0dckvs PRED entity: 0dckvs PRED relation: prequel PRED expected values: 08j7lh => 110 concepts (21 used for prediction) PRED predicted values (max 10 best out of 13): 03nm_fh (0.11 #451, 0.10 #632, 0.06 #814), 0dzlbx (0.11 #458, 0.10 #639, 0.06 #821), 017kz7 (0.02 #1596, 0.01 #1777), 027m67 (0.02 #1581, 0.01 #1762), 0198b6 (0.02 #1520, 0.01 #1701), 014nq4 (0.02 #1507), 0dyb1 (0.02 #1503), 09146g (0.02 #1488), 04n52p6 (0.02 #1484), 017gm7 (0.02 #1477) >> Best rule #451 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 028_yv; 0gkz15s; 05qbckf; 0cmc26r; 05pdh86; 07k2mq; 035zr0; >> query: (?x467, 03nm_fh) <- film_release_region(?x467, ?x2645), film_release_region(?x467, ?x1523), film_release_region(?x467, ?x1229), film_release_distribution_medium(?x467, ?x81), ?x2645 = 03h64, ?x1229 = 059j2, ?x1523 = 030qb3t, ?x81 = 029j_ >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dckvs prequel 08j7lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 21.000 0.111 http://example.org/film/film/prequel #8112-04093 PRED entity: 04093 PRED relation: religion PRED expected values: 0c8wxp => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 34): 0c8wxp (0.26 #276, 0.17 #186, 0.15 #3668), 03_gx (0.24 #736, 0.24 #918, 0.22 #872), 0kpl (0.24 #370, 0.23 #732, 0.22 #461), 07w8f (0.12 #260, 0.05 #350, 0.02 #939), 0kq2 (0.10 #1196, 0.10 #153, 0.09 #424), 0n2g (0.10 #103, 0.10 #645, 0.10 #373), 01lp8 (0.10 #91, 0.08 #181, 0.05 #271), 02rxj (0.10 #97, 0.08 #187, 0.05 #322), 092bf5 (0.07 #513, 0.07 #693, 0.05 #558), 051kv (0.05 #365, 0.03 #411, 0.02 #502) >> Best rule #276 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0j5b8; 04pwg; >> query: (?x8699, 0c8wxp) <- gender(?x8699, ?x231), people(?x6734, ?x8699), type_of_union(?x8699, ?x566), ?x6734 = 03ts0c >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04093 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.263 http://example.org/people/person/religion #8111-03cvvlg PRED entity: 03cvvlg PRED relation: award PRED expected values: 027cyf7 => 78 concepts (69 used for prediction) PRED predicted values (max 10 best out of 218): 02z0dfh (0.33 #1647, 0.33 #1471, 0.31 #940), 0gqyl (0.26 #703, 0.25 #939, 0.24 #1646), 099jhq (0.26 #703, 0.25 #939, 0.24 #1646), 0gqy2 (0.26 #703, 0.25 #939, 0.24 #1646), 099t8j (0.26 #703, 0.25 #939, 0.24 #1646), 02ppm4q (0.26 #703, 0.25 #939, 0.24 #1646), 04dn09n (0.26 #703, 0.25 #939, 0.24 #1646), 0gqwc (0.26 #703, 0.25 #939, 0.24 #1646), 099cng (0.26 #703, 0.25 #939, 0.24 #1646), 0gr4k (0.26 #703, 0.25 #939, 0.24 #1646) >> Best rule #1647 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 03pc89; >> query: (?x8438, ?x1254) <- nominated_for(?x1254, ?x8438), film(?x157, ?x8438), ?x1254 = 02z0dfh >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 08sk8l; 03bzjpm; 04180vy; *> query: (?x8438, 027cyf7) <- film_crew_role(?x8438, ?x137), film(?x157, ?x8438), cinematography(?x8438, ?x185), category(?x8438, ?x134) *> conf = 0.06 ranks of expected_values: 85 EVAL 03cvvlg award 027cyf7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 78.000 69.000 0.329 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #8110-01gglm PRED entity: 01gglm PRED relation: film_crew_role PRED expected values: 02_n3z => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 21): 09vw2b7 (0.85 #68, 0.80 #99, 0.78 #223), 0d2b38 (0.65 #83, 0.19 #300, 0.19 #331), 01pvkk (0.40 #103, 0.33 #351, 0.33 #227), 02_n3z (0.35 #63, 0.12 #2049, 0.10 #280), 02rh1dz (0.29 #102, 0.24 #226, 0.21 #350), 015h31 (0.28 #70, 0.18 #411, 0.18 #380), 033smt (0.28 #85, 0.12 #116, 0.12 #2049), 02ynfr (0.27 #106, 0.26 #230, 0.23 #168), 089fss (0.17 #5, 0.12 #2049, 0.11 #67), 0263ycg (0.17 #15, 0.12 #2049, 0.11 #77) >> Best rule #68 for best value: >> intensional similarity = 6 >> extensional distance = 44 >> proper extension: 057lbk; >> query: (?x8089, 09vw2b7) <- film_crew_role(?x8089, ?x5136), film_crew_role(?x8089, ?x2154), film_crew_role(?x8089, ?x137), ?x2154 = 01vx2h, ?x137 = 09zzb8, ?x5136 = 089g0h >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #63 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 44 *> proper extension: 057lbk; *> query: (?x8089, 02_n3z) <- film_crew_role(?x8089, ?x5136), film_crew_role(?x8089, ?x2154), film_crew_role(?x8089, ?x137), ?x2154 = 01vx2h, ?x137 = 09zzb8, ?x5136 = 089g0h *> conf = 0.35 ranks of expected_values: 4 EVAL 01gglm film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 85.000 0.848 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8109-07cjqy PRED entity: 07cjqy PRED relation: nominated_for PRED expected values: 034qrh => 120 concepts (84 used for prediction) PRED predicted values (max 10 best out of 762): 048qrd (0.31 #76274, 0.30 #86015, 0.30 #86014), 03nx8mj (0.31 #76274, 0.30 #86015, 0.30 #86014), 034qrh (0.31 #76274, 0.30 #86015, 0.30 #86014), 02825cv (0.31 #76274, 0.30 #86015, 0.30 #86014), 0h1cdwq (0.31 #76274, 0.30 #86015, 0.30 #86014), 03fts (0.31 #76274, 0.30 #86015, 0.30 #86014), 03bzjpm (0.31 #76274, 0.30 #86015, 0.30 #86014), 02825nf (0.31 #76274, 0.30 #86015, 0.30 #86014), 047vp1n (0.31 #76274, 0.30 #86015, 0.30 #86014), 05t54s (0.31 #76274, 0.30 #86015, 0.30 #86014) >> Best rule #76274 for best value: >> intensional similarity = 3 >> extensional distance = 436 >> proper extension: 01sl1q; 07nznf; 0q9kd; 0184jc; 04bdxl; 0grwj; 05bnp0; 01vvydl; 0337vz; 01xdf5; ... >> query: (?x3536, ?x428) <- participant(?x4106, ?x3536), film(?x3536, ?x428), award(?x3536, ?x401) >> conf = 0.31 => this is the best rule for 12 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 07cjqy nominated_for 034qrh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 84.000 0.308 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #8108-01r3kd PRED entity: 01r3kd PRED relation: country PRED expected values: 09c7w0 => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 2): 09c7w0 (0.85 #91, 0.85 #85, 0.84 #76), 03h64 (0.01 #126, 0.01 #75) >> Best rule #91 for best value: >> intensional similarity = 3 >> extensional distance = 120 >> proper extension: 0l2tk; 01z_jj; >> query: (?x4542, 09c7w0) <- registering_agency(?x4542, ?x1982), ?x1982 = 03z19, state_province_region(?x4542, ?x335) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r3kd country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.852 http://example.org/organization/organization/headquarters./location/mailing_address/country #8107-02vkvcz PRED entity: 02vkvcz PRED relation: type_of_union PRED expected values: 04ztj => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.86 #29, 0.85 #141, 0.85 #97), 01g63y (0.41 #453, 0.33 #86, 0.31 #82) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 01pl9g; >> query: (?x12364, 04ztj) <- gender(?x12364, ?x514), place_of_death(?x12364, ?x362), nationality(?x12364, ?x1310), spouse(?x12364, ?x4813) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vkvcz type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.862 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8106-0l3kx PRED entity: 0l3kx PRED relation: currency PRED expected values: 09nqf => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.87 #52, 0.87 #51, 0.85 #35) >> Best rule #52 for best value: >> intensional similarity = 5 >> extensional distance = 238 >> proper extension: 0n6rv; >> query: (?x11658, ?x170) <- adjoins(?x10134, ?x11658), adjoins(?x1963, ?x11658), currency(?x10134, ?x170), second_level_divisions(?x94, ?x11658), time_zones(?x1963, ?x1638) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l3kx currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.867 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #8105-03_fk9 PRED entity: 03_fk9 PRED relation: people! PRED expected values: 0222qb => 130 concepts (128 used for prediction) PRED predicted values (max 10 best out of 29): 0xnvg (0.32 #321, 0.20 #13, 0.06 #1014), 0222qb (0.27 #352, 0.25 #198, 0.20 #44), 033tf_ (0.17 #84, 0.14 #315, 0.08 #1008), 041rx (0.14 #1313, 0.14 #1621, 0.13 #851), 065b6q (0.12 #157, 0.05 #311, 0.02 #1081), 0x67 (0.10 #1627, 0.10 #1242, 0.10 #1165), 09vc4s (0.09 #317, 0.03 #1010, 0.02 #1087), 07mqps (0.09 #327, 0.02 #712, 0.02 #1020), 02w7gg (0.08 #1003, 0.08 #926, 0.08 #1080), 07bch9 (0.05 #7472, 0.04 #1024, 0.04 #716) >> Best rule #321 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 01pw2f1; 01fxck; 028pzq; >> query: (?x10650, 0xnvg) <- film(?x10650, ?x810), nationality(?x10650, ?x205), gender(?x10650, ?x231), ?x205 = 03rjj >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #352 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 01pw2f1; 01fxck; 028pzq; *> query: (?x10650, 0222qb) <- film(?x10650, ?x810), nationality(?x10650, ?x205), gender(?x10650, ?x231), ?x205 = 03rjj *> conf = 0.27 ranks of expected_values: 2 EVAL 03_fk9 people! 0222qb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 128.000 0.318 http://example.org/people/ethnicity/people #8104-06hmd PRED entity: 06hmd PRED relation: location PRED expected values: 07b_l 013m_x => 100 concepts (78 used for prediction) PRED predicted values (max 10 best out of 204): 04ych (0.25 #857, 0.17 #2466, 0.04 #11316), 0cr3d (0.22 #4166, 0.10 #5775, 0.06 #8994), 02_286 (0.20 #4862, 0.18 #6472, 0.17 #7277), 05qtj (0.12 #11504, 0.12 #7481, 0.10 #12309), 05k7sb (0.12 #3326, 0.10 #4934, 0.09 #6544), 02m77 (0.12 #3548, 0.10 #5156, 0.09 #6766), 05l5n (0.12 #3318, 0.10 #4926, 0.09 #6536), 05tbn (0.12 #3405, 0.10 #5013, 0.09 #6623), 01qh7 (0.12 #3374, 0.10 #4982, 0.09 #6592), 0c1d0 (0.12 #3612, 0.10 #5220, 0.09 #6830) >> Best rule #857 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 08433; 041xl; >> query: (?x5334, 04ych) <- influenced_by(?x10232, ?x5334), influenced_by(?x5334, ?x5040), people(?x6821, ?x5334), ?x10232 = 04x56 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06hmd location 013m_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 78.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location EVAL 06hmd location 07b_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 78.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #8103-04jb97 PRED entity: 04jb97 PRED relation: film PRED expected values: 028cg00 0gl02yg => 85 concepts (35 used for prediction) PRED predicted values (max 10 best out of 351): 05znbh7 (0.25 #1093, 0.04 #2881, 0.02 #4669), 01bl7g (0.25 #947, 0.04 #2735), 034qmv (0.12 #15, 0.04 #1803, 0.01 #5379), 02sfnv (0.12 #898, 0.02 #2686, 0.02 #4474), 01mszz (0.12 #1085, 0.02 #2873, 0.02 #4661), 0df92l (0.12 #1001, 0.02 #2789, 0.02 #4577), 02gpkt (0.12 #1311, 0.02 #3099, 0.01 #12039), 065ym0c (0.12 #1619, 0.02 #3407), 01mgw (0.12 #1312, 0.02 #3100), 06x43v (0.12 #1306, 0.02 #3094) >> Best rule #1093 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 03cp7b3; >> query: (?x8104, 05znbh7) <- nationality(?x8104, ?x2645), ?x2645 = 03h64, gender(?x8104, ?x231), nominated_for(?x8104, ?x7502) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1008 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 03cp7b3; *> query: (?x8104, 0gl02yg) <- nationality(?x8104, ?x2645), ?x2645 = 03h64, gender(?x8104, ?x231), nominated_for(?x8104, ?x7502) *> conf = 0.12 ranks of expected_values: 14 EVAL 04jb97 film 0gl02yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 85.000 35.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 04jb97 film 028cg00 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 35.000 0.250 http://example.org/film/actor/film./film/performance/film #8102-01cf93 PRED entity: 01cf93 PRED relation: artist PRED expected values: 0kzy0 0259r0 01vsy95 01rm8b 0132k4 048xh 016s0m => 90 concepts (56 used for prediction) PRED predicted values (max 10 best out of 3505): 016376 (0.60 #6262, 0.36 #9434, 0.33 #3092), 0qf11 (0.60 #5832, 0.36 #9004, 0.33 #2662), 07zft (0.60 #6167, 0.33 #2997, 0.27 #9339), 020_4z (0.57 #11799, 0.33 #1495, 0.33 #703), 03d2k (0.50 #3815, 0.40 #4607, 0.33 #3022), 013rfk (0.40 #6100, 0.40 #5308, 0.38 #6893), 02f1c (0.40 #6160, 0.40 #4575, 0.33 #2990), 047cx (0.40 #5868, 0.36 #9040, 0.33 #2698), 0565cz (0.40 #5729, 0.36 #8901, 0.33 #974), 089tm (0.40 #5563, 0.33 #2393, 0.33 #808) >> Best rule #6262 for best value: >> intensional similarity = 13 >> extensional distance = 3 >> proper extension: 0g768; 0181dw; >> query: (?x8721, 016376) <- artist(?x8721, ?x11514), artist(?x8721, ?x8362), artist(?x8721, ?x8199), artist(?x8721, ?x7437), artist(?x8721, ?x4484), artists(?x378, ?x7437), profession(?x7437, ?x131), instrumentalists(?x316, ?x7437), place_of_birth(?x7437, ?x1860), performance_role(?x11514, ?x14165), ?x4484 = 03xhj6, award_nominee(?x8199, ?x5618), ?x8362 = 01wg25j >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2849 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 03rhqg; *> query: (?x8721, 0132k4) <- artist(?x8721, ?x8199), artist(?x8721, ?x7437), artist(?x8721, ?x6241), artist(?x8721, ?x4239), artists(?x378, ?x7437), profession(?x7437, ?x524), instrumentalists(?x316, ?x7437), place_of_birth(?x7437, ?x1860), ?x8199 = 016lmg, ?x4239 = 0x3b7, award_nominee(?x6241, ?x8831), ?x524 = 02jknp *> conf = 0.33 ranks of expected_values: 31, 84, 107, 367, 370, 448, 512 EVAL 01cf93 artist 016s0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 90.000 56.000 0.600 http://example.org/music/record_label/artist EVAL 01cf93 artist 048xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 56.000 0.600 http://example.org/music/record_label/artist EVAL 01cf93 artist 0132k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 90.000 56.000 0.600 http://example.org/music/record_label/artist EVAL 01cf93 artist 01rm8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 56.000 0.600 http://example.org/music/record_label/artist EVAL 01cf93 artist 01vsy95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 90.000 56.000 0.600 http://example.org/music/record_label/artist EVAL 01cf93 artist 0259r0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 90.000 56.000 0.600 http://example.org/music/record_label/artist EVAL 01cf93 artist 0kzy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 90.000 56.000 0.600 http://example.org/music/record_label/artist #8101-02_cx_ PRED entity: 02_cx_ PRED relation: major_field_of_study PRED expected values: 062z7 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 119): 01mkq (0.70 #631, 0.55 #508, 0.51 #2355), 04rjg (0.57 #636, 0.45 #513, 0.36 #882), 02lp1 (0.53 #627, 0.45 #504, 0.42 #873), 062z7 (0.53 #643, 0.41 #520, 0.34 #1136), 05qjt (0.47 #623, 0.41 #500, 0.25 #869), 02j62 (0.47 #646, 0.36 #523, 0.36 #892), 01lj9 (0.43 #656, 0.41 #533, 0.28 #1149), 05qfh (0.43 #652, 0.36 #529, 0.27 #1145), 0g26h (0.41 #1152, 0.40 #659, 0.37 #2383), 0fdys (0.37 #655, 0.32 #532, 0.20 #1025) >> Best rule #631 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 02bqy; 0ks67; 08qnnv; >> query: (?x6280, 01mkq) <- major_field_of_study(?x6280, ?x2605), fraternities_and_sororities(?x6280, ?x3697), ?x2605 = 03g3w, student(?x6280, ?x10696) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #643 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 02bqy; 0ks67; 08qnnv; *> query: (?x6280, 062z7) <- major_field_of_study(?x6280, ?x2605), fraternities_and_sororities(?x6280, ?x3697), ?x2605 = 03g3w, student(?x6280, ?x10696) *> conf = 0.53 ranks of expected_values: 4 EVAL 02_cx_ major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 111.000 0.700 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8100-0x3b7 PRED entity: 0x3b7 PRED relation: award PRED expected values: 026m9w => 117 concepts (92 used for prediction) PRED predicted values (max 10 best out of 247): 05b4l5x (0.50 #798, 0.08 #16639, 0.04 #5946), 01dk00 (0.45 #2117, 0.19 #26931, 0.18 #32084), 01dpdh (0.36 #2107, 0.20 #1711, 0.19 #26931), 01bgqh (0.30 #1626, 0.28 #4002, 0.27 #4398), 03qbh5 (0.30 #1785, 0.24 #4161, 0.23 #4557), 09sb52 (0.26 #5980, 0.24 #21425, 0.24 #20633), 02qvyrt (0.25 #125, 0.19 #26931, 0.18 #32084), 03tk6z (0.25 #606, 0.19 #26931, 0.18 #32084), 0gqwc (0.25 #470, 0.12 #16707, 0.06 #24628), 094qd5 (0.25 #836, 0.09 #16677, 0.05 #5984) >> Best rule #798 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01gvyp; >> query: (?x4239, 05b4l5x) <- award(?x4239, ?x341), location(?x4239, ?x6952), ?x6952 = 0lphb >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #26931 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1388 *> proper extension: 01nrgq; 0gv2r; 014hdb; 0hsmh; *> query: (?x4239, ?x1088) <- award_winner(?x4239, ?x8799), award(?x4239, ?x341), award_winner(?x1088, ?x8799) *> conf = 0.19 ranks of expected_values: 24 EVAL 0x3b7 award 026m9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 117.000 92.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8099-08x5c_ PRED entity: 08x5c_ PRED relation: film PRED expected values: 060__7 => 101 concepts (66 used for prediction) PRED predicted values (max 10 best out of 307): 01mgw (0.40 #3096), 031hcx (0.25 #1272, 0.02 #42306, 0.02 #40522), 031778 (0.25 #315, 0.02 #39565, 0.02 #41349), 0407yj_ (0.25 #483, 0.01 #5835, 0.01 #39733), 06_wqk4 (0.25 #126, 0.01 #39376, 0.01 #41160), 0qm8b (0.25 #243, 0.01 #39493, 0.01 #41277), 084qpk (0.25 #120, 0.01 #41154, 0.01 #39370), 06lpmt (0.25 #684, 0.01 #9604), 0cfhfz (0.25 #492, 0.01 #39742, 0.01 #41526), 01flv_ (0.25 #1065, 0.01 #6417) >> Best rule #3096 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0jlv5; >> query: (?x12137, 01mgw) <- profession(?x12137, ?x319), film(?x12137, ?x6679), ?x6679 = 0drnwh, location(?x12137, ?x9559) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08x5c_ film 060__7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 66.000 0.400 http://example.org/film/actor/film./film/performance/film #8098-09f2j PRED entity: 09f2j PRED relation: country PRED expected values: 09c7w0 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 2): 09c7w0 (0.30 #13, 0.29 #19, 0.29 #7), 03h64 (0.03 #27, 0.01 #51) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 052nd; 01k2wn; 02hft3; 07vk2; 0dplh; 07wrz; 01k7xz; 02fgdx; 01w5m; 03ksy; ... >> query: (?x4955, 09c7w0) <- major_field_of_study(?x4955, ?x2314), ?x2314 = 0h5k, student(?x4955, ?x123) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09f2j country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.300 http://example.org/organization/organization/headquarters./location/mailing_address/country #8097-02t1dv PRED entity: 02t1dv PRED relation: film PRED expected values: 02z9hqn 0ckrgs => 129 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1081): 026q3s3 (0.44 #3779, 0.40 #5567, 0.27 #14508), 02z9hqn (0.40 #128, 0.36 #67971, 0.35 #66181), 0dh8v4 (0.40 #6306, 0.33 #4518, 0.20 #942), 0ckrgs (0.36 #67971, 0.35 #66181, 0.20 #518), 0jdgr (0.29 #11124, 0.02 #73731, 0.01 #48686), 0645k5 (0.29 #11201, 0.02 #73808), 02vw1w2 (0.27 #14518, 0.20 #18096, 0.20 #12730), 056k77g (0.22 #5113, 0.20 #6901, 0.15 #14054), 07ng9k (0.20 #205, 0.18 #14510, 0.16 #18088), 05pyrb (0.20 #993, 0.14 #2781, 0.06 #11722) >> Best rule #3779 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 03ydry; >> query: (?x13175, 026q3s3) <- special_performance_type(?x13175, ?x296), nationality(?x13175, ?x252), ?x296 = 01kyvx, gender(?x13175, ?x514), place_of_birth(?x13175, ?x9559) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 01kym3; *> query: (?x13175, 02z9hqn) <- special_performance_type(?x13175, ?x296), film(?x13175, ?x6840), film(?x13175, ?x6610), ?x6840 = 02z5x7l, actor(?x6610, ?x6414), gender(?x13175, ?x514) *> conf = 0.40 ranks of expected_values: 2, 4 EVAL 02t1dv film 0ckrgs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 44.000 0.444 http://example.org/film/actor/film./film/performance/film EVAL 02t1dv film 02z9hqn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 44.000 0.444 http://example.org/film/actor/film./film/performance/film #8096-02l_7y PRED entity: 02l_7y PRED relation: instrumentalists! PRED expected values: 06w7v => 121 concepts (85 used for prediction) PRED predicted values (max 10 best out of 129): 018vs (0.54 #985, 0.54 #1074, 0.53 #1162), 02sgy (0.53 #3106, 0.49 #3379, 0.43 #3197), 042v_gx (0.53 #3106, 0.49 #3379, 0.43 #3197), 02k856 (0.53 #3106, 0.49 #3379, 0.43 #3197), 04rzd (0.53 #3106, 0.49 #3379, 0.43 #3197), 011k_j (0.53 #3106, 0.49 #3379, 0.43 #3197), 05148p4 (0.48 #461, 0.43 #1082, 0.43 #1346), 05r5c (0.45 #712, 0.44 #1333, 0.44 #3024), 02hnl (0.33 #35, 0.27 #1360, 0.27 #1096), 03qjg (0.33 #52, 0.24 #1113, 0.23 #1201) >> Best rule #985 for best value: >> intensional similarity = 5 >> extensional distance = 88 >> proper extension: 02fybl; >> query: (?x7172, 018vs) <- role(?x7172, ?x227), gender(?x7172, ?x231), role(?x7172, ?x314), profession(?x7172, ?x131), ?x227 = 0342h >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1045 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 88 *> proper extension: 02fybl; *> query: (?x7172, 06w7v) <- role(?x7172, ?x227), gender(?x7172, ?x231), role(?x7172, ?x314), profession(?x7172, ?x131), ?x227 = 0342h *> conf = 0.13 ranks of expected_values: 20 EVAL 02l_7y instrumentalists! 06w7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 121.000 85.000 0.544 http://example.org/music/instrument/instrumentalists #8095-02p21g PRED entity: 02p21g PRED relation: religion PRED expected values: 0c8wxp => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 22): 0c8wxp (0.49 #2163, 0.45 #578, 0.42 #2031), 03_gx (0.27 #57, 0.24 #585, 0.20 #13), 01lp8 (0.09 #89, 0.06 #1190, 0.06 #1234), 058x5 (0.09 #92), 03j6c (0.08 #3937, 0.07 #4113, 0.04 #2045), 04pk9 (0.07 #151, 0.05 #591, 0.05 #195), 019cr (0.06 #318, 0.05 #582, 0.04 #1199), 0kq2 (0.05 #545, 0.05 #2350, 0.05 #4110), 092bf5 (0.05 #2172, 0.05 #2040, 0.05 #2656), 0flw86 (0.05 #4095, 0.04 #2027, 0.04 #266) >> Best rule #2163 for best value: >> intensional similarity = 3 >> extensional distance = 237 >> proper extension: 02d9k; 012rng; 01pcvn; 0hnp7; 09k0f; 01j851; 01kgg9; >> query: (?x1593, 0c8wxp) <- participant(?x1593, ?x3694), profession(?x1593, ?x987), religion(?x1593, ?x2694) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02p21g religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.490 http://example.org/people/person/religion #8094-04mcw4 PRED entity: 04mcw4 PRED relation: film_distribution_medium PRED expected values: 029j_ => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.61 #66, 0.29 #14, 0.16 #19), 029j_ (0.50 #1, 0.37 #63, 0.15 #21), 07z4p (0.25 #5, 0.02 #30, 0.02 #40), 0dq6p (0.20 #64, 0.07 #22, 0.07 #32) >> Best rule #66 for best value: >> intensional similarity = 2 >> extensional distance = 207 >> proper extension: 0522wp; >> query: (?x4551, 0735l) <- film_distribution_medium(?x4551, ?x627), film(?x902, ?x4551) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0jzw; *> query: (?x4551, 029j_) <- nominated_for(?x846, ?x4551), film(?x8704, ?x4551), edited_by(?x4551, ?x4215), ?x8704 = 0c0k1 *> conf = 0.50 ranks of expected_values: 2 EVAL 04mcw4 film_distribution_medium 029j_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.612 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #8093-0hzlz PRED entity: 0hzlz PRED relation: country! PRED expected values: 07rlg 03rbzn 01gqfm => 232 concepts (232 used for prediction) PRED predicted values (max 10 best out of 33): 07gyv (0.71 #203, 0.70 #170, 0.69 #500), 07jbh (0.70 #183, 0.68 #645, 0.68 #216), 07rlg (0.64 #199, 0.61 #1, 0.58 #100), 03rbzn (0.62 #112, 0.59 #178, 0.59 #706), 09_bl (0.62 #107, 0.50 #8, 0.49 #470), 01hp22 (0.62 #699, 0.56 #171, 0.54 #105), 0486tv (0.61 #518, 0.58 #122, 0.57 #221), 096f8 (0.61 #7, 0.46 #205, 0.46 #106), 03fyrh (0.61 #641, 0.59 #707, 0.56 #14), 01gqfm (0.58 #128, 0.58 #656, 0.56 #194) >> Best rule #203 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 07t_x; >> query: (?x792, 07gyv) <- olympics(?x792, ?x778), country(?x841, ?x792), exported_to(?x792, ?x5360) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #199 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 07t_x; *> query: (?x792, 07rlg) <- olympics(?x792, ?x778), country(?x841, ?x792), exported_to(?x792, ?x5360) *> conf = 0.64 ranks of expected_values: 3, 4, 10 EVAL 0hzlz country! 01gqfm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 232.000 232.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0hzlz country! 03rbzn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 232.000 232.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0hzlz country! 07rlg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 232.000 232.000 0.714 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #8092-01y06y PRED entity: 01y06y PRED relation: student PRED expected values: 02wh0 => 190 concepts (118 used for prediction) PRED predicted values (max 10 best out of 1535): 01h2_6 (0.29 #8294, 0.20 #4114, 0.17 #14564), 04xm_ (0.29 #7989, 0.06 #14259, 0.05 #16349), 048cl (0.20 #3386, 0.17 #13836, 0.14 #7566), 07dnx (0.20 #3612, 0.14 #7792, 0.11 #14062), 0453t (0.20 #2428, 0.14 #6608, 0.06 #12878), 0hr3g (0.20 #3690, 0.14 #7870, 0.06 #14140), 05whq_9 (0.20 #2493, 0.14 #6673, 0.06 #12943), 017r2 (0.20 #2344, 0.14 #6524, 0.06 #12794), 03bxh (0.20 #3072, 0.14 #7252, 0.06 #13522), 0nk72 (0.14 #7730, 0.11 #14000, 0.03 #29262) >> Best rule #8294 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 035yzw; >> query: (?x12877, 01h2_6) <- school_type(?x12877, ?x3092), contains(?x1264, ?x12877), ?x1264 = 0345h, category(?x12877, ?x134), organization(?x4095, ?x12877) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #29262 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 101 *> proper extension: 0pz6q; 026m3y; *> query: (?x12877, ?x1236) <- organization(?x4095, ?x12877), student(?x12877, ?x8418), contains(?x1264, ?x12877), institution(?x1200, ?x12877), influenced_by(?x1236, ?x8418) *> conf = 0.03 ranks of expected_values: 103 EVAL 01y06y student 02wh0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 190.000 118.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #8091-02y_2y PRED entity: 02y_2y PRED relation: languages PRED expected values: 02h40lc => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 13): 02h40lc (0.29 #275, 0.28 #977, 0.28 #41), 064_8sq (0.07 #54, 0.07 #1289, 0.07 #2772), 04306rv (0.07 #1289, 0.07 #2772, 0.01 #42), 01wgr (0.07 #2772), 07zrf (0.07 #2772), 06nm1 (0.03 #45, 0.02 #357, 0.01 #513), 03k50 (0.02 #1293, 0.02 #1878, 0.02 #1136), 03_9r (0.02 #122, 0.01 #317), 02bjrlw (0.02 #352, 0.02 #508, 0.02 #859), 04h9h (0.01 #69) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 218 >> proper extension: 036hf4; >> query: (?x4470, 02h40lc) <- film(?x4470, ?x1644), award_winner(?x3624, ?x4470), participant(?x4470, ?x4360) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y_2y languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.291 http://example.org/people/person/languages #8090-0byfz PRED entity: 0byfz PRED relation: film PRED expected values: 0m9p3 => 110 concepts (96 used for prediction) PRED predicted values (max 10 best out of 974): 02qr46y (0.54 #24966, 0.50 #8916, 0.40 #128395), 0gy4k (0.50 #5270, 0.17 #8836, 0.06 #15970), 04t9c0 (0.33 #2709, 0.17 #8058, 0.06 #15192), 0gl3hr (0.33 #1094, 0.06 #11793, 0.03 #18926), 02_qt (0.33 #630, 0.03 #18462, 0.02 #25596), 04smdd (0.33 #2504, 0.02 #23902, 0.02 #27470), 0286hyp (0.33 #3566), 05z7c (0.25 #3896, 0.17 #7462, 0.06 #14596), 0ktpx (0.25 #4567, 0.17 #8133, 0.06 #15267), 0k0rf (0.25 #4448, 0.17 #8014, 0.06 #15148) >> Best rule #24966 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 01vq3nl; >> query: (?x269, ?x1547) <- people(?x268, ?x269), actor(?x11482, ?x269), nominated_for(?x269, ?x1547) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #20001 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 01p45_v; *> query: (?x269, 0m9p3) <- location_of_ceremony(?x269, ?x957), profession(?x269, ?x524), ?x524 = 02jknp *> conf = 0.03 ranks of expected_values: 209 EVAL 0byfz film 0m9p3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 110.000 96.000 0.536 http://example.org/film/actor/film./film/performance/film #8089-0gh65c5 PRED entity: 0gh65c5 PRED relation: nominated_for! PRED expected values: 02x2gy0 => 76 concepts (67 used for prediction) PRED predicted values (max 10 best out of 222): 019f4v (0.45 #4136, 0.29 #9902, 0.24 #1975), 0l8z1 (0.45 #4134, 0.19 #9900, 0.16 #2453), 0k611 (0.42 #4155, 0.27 #9921, 0.22 #2474), 0gq9h (0.38 #4145, 0.35 #9911, 0.31 #3664), 0gq_v (0.35 #4101, 0.29 #9867, 0.20 #11067), 04dn09n (0.35 #4117, 0.21 #9883, 0.20 #3636), 0p9sw (0.34 #4102, 0.25 #261, 0.21 #9868), 02hsq3m (0.33 #1470, 0.29 #1950, 0.25 #1710), 0gs9p (0.33 #4147, 0.31 #9913, 0.24 #11113), 0gr0m (0.31 #4142, 0.23 #9908, 0.17 #11108) >> Best rule #4136 for best value: >> intensional similarity = 6 >> extensional distance = 108 >> proper extension: 0c5qvw; >> query: (?x3606, 019f4v) <- country(?x3606, ?x2645), country(?x3606, ?x94), nominated_for(?x1443, ?x3606), ?x94 = 09c7w0, ?x1443 = 054krc, film_release_region(?x80, ?x2645) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1783 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 18 *> proper extension: 0407yfx; 0m63c; *> query: (?x3606, 02x2gy0) <- film_release_region(?x3606, ?x3227), film_release_region(?x3606, ?x2267), film_release_region(?x3606, ?x1592), olympics(?x3227, ?x784), ?x1592 = 05v10, ?x2267 = 03rj0 *> conf = 0.10 ranks of expected_values: 85 EVAL 0gh65c5 nominated_for! 02x2gy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 76.000 67.000 0.455 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8088-0785v8 PRED entity: 0785v8 PRED relation: profession PRED expected values: 02hrh1q => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 46): 02hrh1q (0.89 #3015, 0.89 #15, 0.88 #6917), 01d_h8 (0.41 #306, 0.33 #6158, 0.32 #4056), 0dxtg (0.31 #6166, 0.28 #2864, 0.27 #6016), 03gjzk (0.26 #9304, 0.25 #2866, 0.24 #3916), 0np9r (0.26 #9304, 0.21 #1522, 0.20 #1822), 09jwl (0.26 #9304, 0.19 #1970, 0.19 #320), 0d1pc (0.26 #9304, 0.08 #2752, 0.07 #2152), 02hv44_ (0.26 #9304, 0.06 #59, 0.05 #209), 0d8qb (0.26 #9304, 0.05 #231, 0.02 #531), 02dsz (0.26 #9304) >> Best rule #3015 for best value: >> intensional similarity = 3 >> extensional distance = 968 >> proper extension: 02wrhj; 02yplc; 01tnbn; 0q1lp; 04bdqk; 024jwt; 01hkck; 033071; 01c65z; >> query: (?x818, 02hrh1q) <- film(?x818, ?x1877), location(?x818, ?x3269), nominated_for(?x818, ?x3303) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0785v8 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.890 http://example.org/people/person/profession #8087-0gqz2 PRED entity: 0gqz2 PRED relation: ceremony PRED expected values: 02jp5r 02pgky2 0c53vt => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 87): 02jp5r (0.75 #2354, 0.28 #2179, 0.19 #2790), 0dth6b (0.75 #2354, 0.28 #2179, 0.19 #2790), 0fk0xk (0.75 #2354, 0.28 #2179, 0.19 #2790), 0c53vt (0.75 #2354, 0.28 #2179, 0.19 #2790), 0c4hnm (0.75 #2354, 0.28 #2179, 0.19 #2790), 0c4hx0 (0.75 #2354, 0.28 #2179, 0.19 #2790), 0bzkvd (0.75 #2354, 0.28 #2179, 0.19 #2790), 0c4hgj (0.75 #2354, 0.28 #2179, 0.19 #2790), 0ftlkg (0.75 #2354, 0.28 #2179, 0.19 #2790), 02pgky2 (0.75 #2354, 0.14 #579, 0.13 #753) >> Best rule #2354 for best value: >> intensional similarity = 4 >> extensional distance = 242 >> proper extension: 02qkk9_; 02py7pj; >> query: (?x1323, ?x1793) <- ceremony(?x1323, ?x5053), award_winner(?x1323, ?x538), ceremony(?x2209, ?x5053), ceremony(?x2209, ?x1793) >> conf = 0.75 => this is the best rule for 11 predicted values ranks of expected_values: 1, 4, 10 EVAL 0gqz2 ceremony 0c53vt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 47.000 47.000 0.749 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqz2 ceremony 02pgky2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 47.000 47.000 0.749 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqz2 ceremony 02jp5r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.749 http://example.org/award/award_category/winners./award/award_honor/ceremony #8086-014ps4 PRED entity: 014ps4 PRED relation: nationality PRED expected values: 09c7w0 => 112 concepts (107 used for prediction) PRED predicted values (max 10 best out of 106): 09c7w0 (0.83 #802, 0.82 #1103, 0.75 #402), 07ssc (0.24 #4213, 0.19 #1017, 0.15 #917), 02jx1 (0.24 #4213, 0.13 #1538, 0.13 #1840), 0hzlz (0.24 #4213, 0.11 #9144, 0.06 #5215), 0f8l9c (0.24 #4213, 0.11 #723, 0.06 #5215), 03rt9 (0.24 #4213, 0.06 #714, 0.06 #5215), 06bnz (0.24 #4213, 0.06 #742, 0.02 #2652), 06q1r (0.24 #4213, 0.06 #5215, 0.03 #1079), 03rjj (0.11 #9144, 0.11 #506, 0.06 #5215), 0d060g (0.11 #9144, 0.06 #708, 0.06 #5215) >> Best rule #802 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 045g4l; >> query: (?x7828, 09c7w0) <- location(?x7828, ?x2850), profession(?x7828, ?x353), ?x2850 = 0cr3d, religion(?x7828, ?x1985) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014ps4 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 107.000 0.833 http://example.org/people/person/nationality #8085-0fx02 PRED entity: 0fx02 PRED relation: student! PRED expected values: 0dzbl => 129 concepts (116 used for prediction) PRED predicted values (max 10 best out of 261): 07tgn (0.29 #543, 0.25 #1069, 0.23 #8433), 07tg4 (0.25 #3768, 0.18 #14814, 0.17 #4294), 01w5m (0.17 #3261, 0.17 #105, 0.15 #6943), 0dzbl (0.17 #501, 0.14 #1027, 0.12 #1553), 02mw6c (0.17 #430, 0.14 #956, 0.12 #1482), 0yjf0 (0.17 #48, 0.14 #574, 0.12 #1100), 011xy1 (0.17 #318, 0.14 #844, 0.12 #1370), 01jq34 (0.17 #57, 0.10 #2161, 0.10 #1635), 015nl4 (0.15 #3749, 0.13 #26367, 0.10 #7957), 0gjv_ (0.14 #732, 0.12 #1258, 0.10 #3888) >> Best rule #543 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 052h3; >> query: (?x3686, 07tgn) <- profession(?x3686, ?x9081), place_of_death(?x3686, ?x13829), ?x9081 = 0d8qb, influenced_by(?x7264, ?x3686), student(?x13297, ?x3686) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #501 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 02vqpx8; *> query: (?x3686, 0dzbl) <- profession(?x3686, ?x9081), profession(?x3686, ?x353), place_of_death(?x3686, ?x13829), ?x9081 = 0d8qb, ?x353 = 0cbd2, nationality(?x3686, ?x512) *> conf = 0.17 ranks of expected_values: 4 EVAL 0fx02 student! 0dzbl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 129.000 116.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #8084-06c0ns PRED entity: 06c0ns PRED relation: film! PRED expected values: 06qgjh => 90 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1177): 0341n5 (0.29 #3826, 0.20 #1747, 0.02 #20459), 07r1h (0.21 #19801, 0.20 #1089, 0.03 #44757), 0bksh (0.20 #856, 0.19 #19568, 0.03 #73645), 05kwx2 (0.20 #1095, 0.15 #19807, 0.02 #44763), 0d_84 (0.20 #44, 0.14 #2123, 0.05 #10439), 01vxxb (0.20 #764, 0.14 #2843, 0.05 #11159), 03x16f (0.20 #1515, 0.14 #3594, 0.03 #60305), 06m6p7 (0.20 #1368, 0.14 #3447, 0.03 #15922), 0chsq (0.20 #78, 0.14 #2157, 0.02 #18790), 03xpsrx (0.20 #486, 0.14 #2565, 0.01 #17119) >> Best rule #3826 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 070g7; 01k60v; 0bdjd; 035gnh; 02ljhg; >> query: (?x6963, 0341n5) <- film(?x9783, ?x6963), ?x9783 = 01g969, music(?x6963, ?x7222) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #16024 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 0fq27fp; *> query: (?x6963, 06qgjh) <- film_release_region(?x6963, ?x94), film_release_region(?x6963, ?x1264), currency(?x6963, ?x170) *> conf = 0.01 ranks of expected_values: 749 EVAL 06c0ns film! 06qgjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 90.000 55.000 0.286 http://example.org/film/actor/film./film/performance/film #8083-0m32_ PRED entity: 0m32_ PRED relation: nationality PRED expected values: 09c7w0 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 101): 09c7w0 (0.87 #2310, 0.83 #1605, 0.82 #401), 02jx1 (0.12 #233, 0.11 #3756, 0.10 #5572), 07ssc (0.11 #1017, 0.09 #5554, 0.09 #6959), 0d060g (0.09 #107, 0.07 #207, 0.06 #7), 03rk0 (0.06 #9411, 0.06 #9911, 0.06 #9711), 0345h (0.04 #1033, 0.03 #733, 0.02 #1534), 0d05w3 (0.03 #651, 0.03 #1553, 0.03 #1353), 03rt9 (0.03 #113, 0.03 #1015, 0.03 #1718), 03rjj (0.03 #105, 0.02 #807, 0.02 #907), 0f8l9c (0.03 #122, 0.02 #222, 0.02 #10769) >> Best rule #2310 for best value: >> intensional similarity = 4 >> extensional distance = 590 >> proper extension: 01d494; 01xyt7; 01gct2; 02vptk_; 03c_8t; 02cg2v; >> query: (?x2774, 09c7w0) <- gender(?x2774, ?x231), student(?x4750, ?x2774), ?x231 = 05zppz, fraternities_and_sororities(?x4750, ?x3697) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m32_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.870 http://example.org/people/person/nationality #8082-0bz60q PRED entity: 0bz60q PRED relation: place_of_birth PRED expected values: 0cr3d => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 77): 013kcv (0.12 #23, 0.06 #727, 0.02 #3543), 0rh6k (0.08 #1410, 0.03 #2818, 0.02 #4226), 02_286 (0.07 #9876, 0.07 #16214, 0.07 #6355), 03l2n (0.06 #873, 0.06 #169, 0.02 #2281), 0cr3d (0.06 #798, 0.04 #2910, 0.04 #16289), 030qb3t (0.06 #758, 0.04 #7094, 0.04 #38786), 02dtg (0.06 #714, 0.01 #38742, 0.01 #39447), 0r00l (0.06 #1191, 0.01 #2599), 0pmpl (0.06 #56, 0.01 #2168), 02frhbc (0.06 #1066) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 01x15dc; >> query: (?x7000, 013kcv) <- award_winner(?x2390, ?x7000), role(?x2390, ?x1166), student(?x2164, ?x2390) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #798 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 03nb5v; 0b7gr2; 03xpfzg; *> query: (?x7000, 0cr3d) <- profession(?x7000, ?x353), award(?x7000, ?x11272), ?x11272 = 0cjcbg *> conf = 0.06 ranks of expected_values: 5 EVAL 0bz60q place_of_birth 0cr3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 75.000 75.000 0.125 http://example.org/people/person/place_of_birth #8081-016dp0 PRED entity: 016dp0 PRED relation: nationality PRED expected values: 02jx1 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 28): 09c7w0 (0.79 #1685, 0.79 #5760, 0.79 #5361), 02jx1 (0.34 #824, 0.12 #329, 0.12 #428), 03rk0 (0.08 #4511, 0.08 #4610, 0.08 #4909), 06q1r (0.07 #868, 0.03 #1067, 0.02 #1562), 0f8l9c (0.06 #516, 0.05 #120, 0.04 #1111), 03rt9 (0.06 #508, 0.03 #112, 0.03 #1301), 0345h (0.06 #1318, 0.06 #1913, 0.06 #1516), 0h7x (0.05 #1322, 0.03 #1917, 0.02 #1520), 0d060g (0.05 #2782, 0.04 #4473, 0.04 #700), 03rjj (0.03 #599, 0.03 #1095, 0.03 #897) >> Best rule #1685 for best value: >> intensional similarity = 3 >> extensional distance = 325 >> proper extension: 03qcq; 01g4zr; 0k4gf; 04jzj; 04zd4m; 01pl9g; 03ft8; 01c59k; 01c58j; 083pr; ... >> query: (?x12849, 09c7w0) <- place_of_birth(?x12849, ?x12603), nationality(?x12849, ?x512), people(?x8523, ?x12849) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #824 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 161 *> proper extension: 02c4s; 01wsl7c; 01tp5bj; 01l_vgt; 081k8; 0135nb; 02508x; 026y23w; 08304; 04kjrv; ... *> query: (?x12849, 02jx1) <- place_of_birth(?x12849, ?x12603), nationality(?x12849, ?x512), ?x512 = 07ssc *> conf = 0.34 ranks of expected_values: 2 EVAL 016dp0 nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.789 http://example.org/people/person/nationality #8080-0g5y6 PRED entity: 0g5y6 PRED relation: people PRED expected values: 0fqyzz => 43 concepts (8 used for prediction) PRED predicted values (max 10 best out of 3418): 02qfhb (0.50 #5848, 0.33 #4131, 0.25 #12712), 01twdk (0.50 #5822, 0.33 #4105, 0.25 #12686), 047hpm (0.50 #5531, 0.33 #3814, 0.25 #12395), 05dbf (0.38 #12300, 0.02 #13734, 0.02 #13730), 0163t3 (0.33 #4704, 0.25 #13285, 0.25 #6421), 02wycg2 (0.33 #4000, 0.25 #12581, 0.25 #5717), 0l9k1 (0.33 #4975, 0.25 #6692, 0.14 #10124), 0nk72 (0.33 #4619, 0.25 #6336, 0.14 #9768), 026fd (0.33 #4273, 0.25 #5990, 0.14 #9422), 02h761 (0.33 #3978, 0.25 #5695, 0.14 #9127) >> Best rule #5848 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 0xnvg; >> query: (?x8649, 02qfhb) <- people(?x8649, ?x11330), people(?x8649, ?x11251), people(?x8649, ?x10795), people(?x8649, ?x4005), location(?x11330, ?x1374), profession(?x11330, ?x1032), ?x4005 = 01g23m, category(?x10795, ?x134), nationality(?x10795, ?x94), people(?x268, ?x11251), nominated_for(?x11251, ?x8119) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13734 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 03lmx1; 03bkbh; 0bbz66j; 0432mrk; *> query: (?x8649, ?x199) <- people(?x8649, ?x11330), people(?x8649, ?x4005), location(?x11330, ?x1374), profession(?x11330, ?x1078), award_nominee(?x4005, ?x5363), participant(?x8206, ?x4005), award_nominee(?x2275, ?x4005), ?x5363 = 016yvw, profession(?x199, ?x1078) *> conf = 0.02 ranks of expected_values: 2667 EVAL 0g5y6 people 0fqyzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 8.000 0.500 http://example.org/people/ethnicity/people #8079-0bmssv PRED entity: 0bmssv PRED relation: country PRED expected values: 07ssc => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 27): 07ssc (0.45 #317, 0.24 #2971, 0.23 #3395), 01hmnh (0.37 #362, 0.06 #423, 0.06 #3138), 0345h (0.14 #450, 0.12 #810, 0.12 #871), 03rjj (0.10 #6, 0.06 #3077, 0.03 #3565), 0f8l9c (0.09 #3398, 0.09 #3578, 0.08 #3759), 03_3d (0.08 #67, 0.06 #3077, 0.04 #308), 0chghy (0.07 #253, 0.06 #3077, 0.03 #1278), 0d060g (0.06 #852, 0.06 #791, 0.06 #3077), 01mjq (0.06 #3077, 0.04 #35, 0.03 #276), 0ctw_b (0.06 #3077, 0.03 #264, 0.03 #324) >> Best rule #317 for best value: >> intensional similarity = 5 >> extensional distance = 267 >> proper extension: 01cjhz; 08cx5g; 0jq2r; 06f0k; >> query: (?x4178, 07ssc) <- titles(?x1510, ?x4178), titles(?x1510, ?x6642), titles(?x1510, ?x6411), crewmember(?x6411, ?x2871), ?x6642 = 063fh9 >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bmssv country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.454 http://example.org/film/film/country #8078-02vntj PRED entity: 02vntj PRED relation: gender PRED expected values: 02zsn => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #147, 0.72 #49, 0.72 #157), 02zsn (0.49 #20, 0.48 #12, 0.47 #14) >> Best rule #147 for best value: >> intensional similarity = 2 >> extensional distance = 1515 >> proper extension: 01s7qqw; 080r3; 027dpx; 0c1fs; 01dhjz; >> query: (?x4247, 05zppz) <- student(?x6501, ?x4247), colors(?x6501, ?x3364) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 136 *> proper extension: 01vv126; 015z4j; 02r3cn; 0dxmyh; 0dq9wx; *> query: (?x4247, 02zsn) <- participant(?x4247, ?x5467), vacationer(?x205, ?x4247) *> conf = 0.49 ranks of expected_values: 2 EVAL 02vntj gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.724 http://example.org/people/person/gender #8077-013w7j PRED entity: 013w7j PRED relation: profession PRED expected values: 02hrh1q => 132 concepts (130 used for prediction) PRED predicted values (max 10 best out of 90): 02hrh1q (0.89 #14971, 0.89 #5815, 0.89 #3928), 016z4k (0.54 #873, 0.52 #2178, 0.50 #728), 0nbcg (0.54 #4380, 0.51 #1188, 0.50 #2784), 02jknp (0.39 #9011, 0.38 #151, 0.25 #586), 0n1h (0.35 #2185, 0.34 #2766, 0.27 #7839), 0d1pc (0.30 #917, 0.25 #1207, 0.24 #3383), 01c72t (0.29 #8442, 0.28 #7859, 0.26 #15831), 0fnpj (0.27 #7839, 0.26 #15831, 0.26 #16122), 064xm0 (0.27 #7839, 0.26 #15831, 0.26 #16122), 04f2zj (0.27 #7839, 0.26 #15831, 0.26 #16122) >> Best rule #14971 for best value: >> intensional similarity = 2 >> extensional distance = 2012 >> proper extension: 026c0p; 03d63lb; >> query: (?x6151, 02hrh1q) <- film(?x6151, ?x5201), profession(?x6151, ?x131) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013w7j profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 130.000 0.892 http://example.org/people/person/profession #8076-02vzc PRED entity: 02vzc PRED relation: nationality! PRED expected values: 034hck => 206 concepts (142 used for prediction) PRED predicted values (max 10 best out of 4216): 0jcx (0.23 #9084, 0.15 #13152, 0.14 #98585), 0p__8 (0.19 #26257, 0.16 #42530, 0.15 #46598), 059xvg (0.19 #25460, 0.16 #41733, 0.15 #45801), 0479b (0.19 #26546, 0.13 #59092, 0.12 #71297), 01llxp (0.17 #3573, 0.14 #7641, 0.08 #129687), 01kx1j (0.17 #2904, 0.14 #6972, 0.08 #129018), 0149xx (0.17 #1575, 0.14 #5643, 0.08 #13779), 04jvt (0.17 #3118, 0.14 #7186, 0.08 #11254), 05wh0sh (0.17 #953, 0.14 #5021, 0.07 #90453), 02ck1 (0.17 #755, 0.14 #4823, 0.07 #90255) >> Best rule #9084 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 06mx8; >> query: (?x1892, 0jcx) <- taxonomy(?x1892, ?x939), contains(?x1892, ?x7061), region(?x1315, ?x1892) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #15180 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 07ytt; *> query: (?x1892, 034hck) <- time_zones(?x1892, ?x10735), administrative_area_type(?x1892, ?x2792), location_of_ceremony(?x566, ?x1892), taxonomy(?x1892, ?x939) *> conf = 0.08 ranks of expected_values: 1629 EVAL 02vzc nationality! 034hck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 206.000 142.000 0.231 http://example.org/people/person/nationality #8075-05kkh PRED entity: 05kkh PRED relation: state_province_region! PRED expected values: 01qygl => 142 concepts (128 used for prediction) PRED predicted values (max 10 best out of 773): 05mv4 (0.44 #54975, 0.35 #20512, 0.29 #32978), 030w19 (0.44 #54975, 0.35 #20512, 0.29 #32978), 01p79b (0.35 #20512, 0.29 #32978, 0.27 #49112), 02fy0z (0.35 #20512, 0.29 #32978, 0.25 #65973), 0z1l8 (0.35 #20512, 0.29 #32978, 0.23 #8059), 0yz30 (0.35 #20512, 0.29 #32978, 0.23 #8059), 0yw93 (0.35 #20512, 0.29 #32978, 0.23 #8059), 0yvjx (0.35 #20512, 0.29 #32978, 0.23 #8059), 0z18v (0.35 #20512, 0.29 #32978, 0.23 #8059), 0yzyn (0.35 #20512, 0.29 #32978, 0.23 #8059) >> Best rule #54975 for best value: >> intensional similarity = 3 >> extensional distance = 222 >> proper extension: 0fngy; >> query: (?x177, ?x6602) <- contains(?x177, ?x6602), country(?x177, ?x94), school_type(?x6602, ?x3092) >> conf = 0.44 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 05kkh state_province_region! 01qygl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 128.000 0.436 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #8074-0d04z6 PRED entity: 0d04z6 PRED relation: location! PRED expected values: 018_lb => 140 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1906): 0cms7f (0.49 #42835, 0.44 #246911, 0.44 #168813), 03f0qd7 (0.47 #83150, 0.46 #168812, 0.37 #201563), 032r1 (0.20 #2316, 0.18 #4837, 0.15 #9876), 099d4 (0.20 #2366, 0.18 #4887, 0.15 #9926), 04z0g (0.20 #1180, 0.09 #26376, 0.09 #3701), 01797x (0.20 #2095, 0.09 #4616, 0.08 #60049), 0b78hw (0.20 #852, 0.09 #3373, 0.08 #8412), 01zwy (0.20 #1725, 0.09 #4246, 0.08 #9285), 03f1zdw (0.20 #208, 0.09 #2729, 0.08 #7768), 099p5 (0.20 #1901, 0.09 #4422, 0.08 #9461) >> Best rule #42835 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 06wjf; 09f8q; 0gqfy; >> query: (?x5147, ?x6263) <- film_release_region(?x2163, ?x5147), place_of_birth(?x6263, ?x5147), administrative_parent(?x5147, ?x551) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #19870 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 05fkf; 01x73; 0498y; 0vbk; *> query: (?x5147, 018_lb) <- origin(?x11709, ?x5147), contains(?x5147, ?x10708), currency(?x5147, ?x170) *> conf = 0.10 ranks of expected_values: 50 EVAL 0d04z6 location! 018_lb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 140.000 107.000 0.493 http://example.org/people/person/places_lived./people/place_lived/location #8073-029j_ PRED entity: 029j_ PRED relation: film_distribution_medium! PRED expected values: 0cpllql 06_wqk4 0fq7dv_ 04mcw4 01f7jt => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 1688): 0btpm6 (0.43 #582, 0.17 #685, 0.10 #286), 01qb5d (0.43 #582, 0.17 #596, 0.06 #300), 031hcx (0.43 #582, 0.17 #682, 0.04 #296), 016dj8 (0.43 #582, 0.10 #286, 0.07 #299), 031t2d (0.43 #582, 0.10 #286, 0.06 #300), 06bc59 (0.43 #582, 0.10 #286, 0.04 #296), 0gd92 (0.43 #582, 0.09 #291, 0.09 #287), 0fdv3 (0.43 #582, 0.07 #299, 0.06 #300), 01k0xy (0.43 #582, 0.04 #296, 0.04 #282), 012s1d (0.43 #582, 0.02 #583, 0.02 #584) >> Best rule #582 for best value: >> intensional similarity = 46 >> extensional distance = 1 >> proper extension: 0dq6p; >> query: (?x81, ?x1673) <- film_distribution_medium(?x9213, ?x81), film_distribution_medium(?x8072, ?x81), film_distribution_medium(?x7723, ?x81), film_distribution_medium(?x7628, ?x81), film_distribution_medium(?x5473, ?x81), film_distribution_medium(?x2350, ?x81), film_distribution_medium(?x1035, ?x81), film_distribution_medium(?x908, ?x81), ?x8072 = 02mc5v, film_release_region(?x7628, ?x1892), film_release_region(?x7628, ?x151), film_release_region(?x7628, ?x87), music(?x7628, ?x7955), film(?x2551, ?x7628), ?x9213 = 0353tm, film_release_region(?x5473, ?x252), film_release_region(?x1035, ?x2316), film_release_region(?x1035, ?x756), ?x7723 = 03kx49, executive_produced_by(?x1035, ?x2464), ?x87 = 05r4w, production_companies(?x5473, ?x847), titles(?x53, ?x7628), nominated_for(?x749, ?x7628), country(?x8601, ?x756), ?x151 = 0b90_r, film_release_region(?x4684, ?x756), film_release_region(?x1080, ?x756), film_release_region(?x2350, ?x1475), country(?x3598, ?x756), film(?x526, ?x5473), ?x847 = 0kx4m, genre(?x1035, ?x812), ?x1892 = 02vzc, ?x908 = 01vksx, featured_film_locations(?x1035, ?x1036), time_zones(?x756, ?x2864), combatants(?x6465, ?x756), ?x1080 = 01c22t, currency(?x7628, ?x170), nominated_for(?x1107, ?x5473), prequel(?x2350, ?x1673), genre(?x5473, ?x258), geographic_distribution(?x9148, ?x2316), ?x4684 = 03nm_fh, ?x3598 = 03rbzn >> conf = 0.43 => this is the best rule for 10 predicted values *> Best rule #574 for first EXPECTED value: *> intensional similarity = 46 *> extensional distance = 1 *> proper extension: 0dq6p; *> query: (?x81, 01f7jt) <- film_distribution_medium(?x9213, ?x81), film_distribution_medium(?x8072, ?x81), film_distribution_medium(?x7723, ?x81), film_distribution_medium(?x7628, ?x81), film_distribution_medium(?x5473, ?x81), film_distribution_medium(?x2350, ?x81), film_distribution_medium(?x1035, ?x81), film_distribution_medium(?x908, ?x81), ?x8072 = 02mc5v, film_release_region(?x7628, ?x1892), film_release_region(?x7628, ?x151), film_release_region(?x7628, ?x87), music(?x7628, ?x7955), film(?x2551, ?x7628), ?x9213 = 0353tm, film_release_region(?x5473, ?x252), film_release_region(?x1035, ?x2316), film_release_region(?x1035, ?x756), ?x7723 = 03kx49, executive_produced_by(?x1035, ?x2464), ?x87 = 05r4w, production_companies(?x5473, ?x847), titles(?x53, ?x7628), nominated_for(?x749, ?x7628), country(?x8601, ?x756), ?x151 = 0b90_r, film_release_region(?x4684, ?x756), film_release_region(?x1080, ?x756), film_release_region(?x2350, ?x1475), country(?x3598, ?x756), film(?x526, ?x5473), ?x847 = 0kx4m, genre(?x1035, ?x812), ?x1892 = 02vzc, ?x908 = 01vksx, featured_film_locations(?x1035, ?x1036), time_zones(?x756, ?x2864), combatants(?x6465, ?x756), ?x1080 = 01c22t, currency(?x7628, ?x170), nominated_for(?x1107, ?x5473), prequel(?x2350, ?x1673), genre(?x5473, ?x258), geographic_distribution(?x9148, ?x2316), ?x4684 = 03nm_fh, ?x3598 = 03rbzn *> conf = 0.33 ranks of expected_values: 12, 23, 24, 29, 537 EVAL 029j_ film_distribution_medium! 01f7jt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 6.000 6.000 0.429 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 029j_ film_distribution_medium! 04mcw4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 6.000 6.000 0.429 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 029j_ film_distribution_medium! 0fq7dv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 6.000 6.000 0.429 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 029j_ film_distribution_medium! 06_wqk4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 6.000 6.000 0.429 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 029j_ film_distribution_medium! 0cpllql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 6.000 6.000 0.429 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #8072-079vf PRED entity: 079vf PRED relation: executive_produced_by! PRED expected values: 01qb5d 08hmch 0cd2vh9 => 91 concepts (37 used for prediction) PRED predicted values (max 10 best out of 386): 0dzlbx (0.32 #1542, 0.19 #1028, 0.07 #7710), 062zm5h (0.32 #1542, 0.19 #1028, 0.07 #9253), 03t79f (0.05 #1325, 0.04 #297, 0.04 #2867), 01bn3l (0.05 #1445, 0.04 #417, 0.04 #931), 09gdh6k (0.05 #1426, 0.04 #398, 0.04 #912), 03x7hd (0.05 #1214, 0.03 #2756, 0.01 #7381), 02rn00y (0.05 #1213, 0.03 #2755, 0.01 #7380), 01c22t (0.05 #1077, 0.03 #2619, 0.01 #7244), 0407yfx (0.05 #1139, 0.03 #2681, 0.01 #7306), 0bt4g (0.04 #410, 0.04 #2980, 0.04 #924) >> Best rule #1542 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0h5f5n; 0mdqp; 04l3_z; 04wvhz; 05_k56; 0343h; 07s93v; 03ft8; 032v0v; 06pj8; ... >> query: (?x96, ?x4998) <- executive_produced_by(?x4273, ?x96), film(?x450, ?x4273), story_by(?x4998, ?x96) >> conf = 0.32 => this is the best rule for 2 predicted values *> Best rule #2610 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 01yznp; *> query: (?x96, 08hmch) <- executive_produced_by(?x4392, ?x96), profession(?x96, ?x319), crewmember(?x4392, ?x10416) *> conf = 0.01 ranks of expected_values: 360, 362 EVAL 079vf executive_produced_by! 0cd2vh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 37.000 0.321 http://example.org/film/film/executive_produced_by EVAL 079vf executive_produced_by! 08hmch CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 91.000 37.000 0.321 http://example.org/film/film/executive_produced_by EVAL 079vf executive_produced_by! 01qb5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 91.000 37.000 0.321 http://example.org/film/film/executive_produced_by #8071-0134wr PRED entity: 0134wr PRED relation: group! PRED expected values: 07y_7 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 112): 03bx0bm (0.79 #251, 0.75 #97, 0.74 #405), 01vj9c (0.29 #782, 0.28 #859, 0.27 #1167), 06ncr (0.15 #804, 0.15 #881, 0.14 #1189), 07gql (0.14 #29, 0.12 #107, 0.11 #184), 07c6l (0.14 #6, 0.12 #84, 0.07 #161), 05842k (0.14 #58, 0.07 #464, 0.07 #1236), 03_vpw (0.14 #41, 0.07 #464, 0.07 #1236), 07y_7 (0.12 #774, 0.12 #851, 0.11 #1159), 0l14j_ (0.11 #1201, 0.10 #429, 0.10 #816), 042v_gx (0.10 #778, 0.10 #855, 0.10 #1163) >> Best rule #251 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 02r3zy; 03g5jw; 01wv9xn; 0134s5; 04qmr; 0163m1; 0d193h; 0g_g2; 03d9d6; 07h76; ... >> query: (?x8078, 03bx0bm) <- group(?x645, ?x8078), ?x645 = 028tv0, artists(?x671, ?x8078), award_winner(?x1827, ?x8078) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #774 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 106 *> proper extension: 0167xy; 0ql36; *> query: (?x8078, 07y_7) <- group(?x6129, ?x8078), artists(?x5300, ?x8078), parent_genre(?x5300, ?x378) *> conf = 0.12 ranks of expected_values: 8 EVAL 0134wr group! 07y_7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 80.000 80.000 0.794 http://example.org/music/performance_role/regular_performances./music/group_membership/group #8070-03jm6c PRED entity: 03jm6c PRED relation: story_by! PRED expected values: 0g9yrw => 102 concepts (95 used for prediction) PRED predicted values (max 10 best out of 58): 063y9fp (0.13 #1322, 0.07 #2010), 04t6fk (0.09 #88, 0.08 #432, 0.07 #776), 0bpm4yw (0.09 #1182, 0.05 #1870), 02fqrf (0.09 #1148, 0.05 #1836), 0gjc4d3 (0.09 #1140, 0.05 #1828), 01srq2 (0.08 #589, 0.07 #933), 048tv9 (0.05 #1984, 0.04 #1296), 02fj8n (0.05 #1969, 0.04 #1281), 062zjtt (0.05 #1864, 0.04 #1176), 042g97 (0.04 #1373, 0.02 #2061) >> Best rule #1322 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 067xw; >> query: (?x2401, 063y9fp) <- profession(?x2401, ?x8310), profession(?x2401, ?x353), ?x8310 = 0196pc, ?x353 = 0cbd2 >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03jm6c story_by! 0g9yrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 95.000 0.130 http://example.org/film/film/story_by #8069-01j4ls PRED entity: 01j4ls PRED relation: award PRED expected values: 02f716 02f73b => 99 concepts (80 used for prediction) PRED predicted values (max 10 best out of 280): 02f6xy (0.78 #20804, 0.72 #28807, 0.68 #18402), 02f72_ (0.67 #11826, 0.56 #12226, 0.52 #9026), 054ks3 (0.62 #3742, 0.38 #6142, 0.36 #3342), 01ckcd (0.58 #6733, 0.25 #9133, 0.24 #12333), 02f716 (0.56 #12174, 0.49 #8974, 0.47 #11774), 01ck6v (0.54 #3870, 0.18 #3470, 0.12 #10270), 01by1l (0.48 #6513, 0.46 #3713, 0.45 #3313), 02x17c2 (0.46 #3816, 0.27 #3416, 0.21 #6616), 01bgqh (0.45 #16444, 0.42 #17244, 0.39 #6443), 02f73b (0.45 #9084, 0.41 #11884, 0.40 #12284) >> Best rule #20804 for best value: >> intensional similarity = 3 >> extensional distance = 463 >> proper extension: 06lxn; >> query: (?x1398, ?x3926) <- award_winner(?x3926, ?x1398), artist(?x2931, ?x1398), artists(?x378, ?x1398) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #12174 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 115 *> proper extension: 01wv9xn; 015mrk; 017j6; 04qmr; 0d193h; 02jqjm; 015cxv; 05szp; 011z3g; 0838y; ... *> query: (?x1398, 02f716) <- award(?x1398, ?x3631), award(?x3397, ?x3631), award(?x475, ?x3631), ?x3397 = 015f7, ?x475 = 01pfr3 *> conf = 0.56 ranks of expected_values: 5, 10 EVAL 01j4ls award 02f73b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 99.000 80.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01j4ls award 02f716 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 99.000 80.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8068-04tng0 PRED entity: 04tng0 PRED relation: country PRED expected values: 09c7w0 => 112 concepts (111 used for prediction) PRED predicted values (max 10 best out of 131): 09c7w0 (0.89 #1181, 0.84 #1057, 0.84 #935), 03h64 (0.42 #5704, 0.33 #62, 0.23 #681), 06y57 (0.42 #5704, 0.33 #62, 0.23 #681), 056_y (0.42 #5704, 0.33 #62, 0.23 #681), 030qb3t (0.42 #5704, 0.33 #62, 0.23 #681), 02_286 (0.42 #5704, 0.33 #62, 0.23 #681), 07ssc (0.38 #2299, 0.31 #950, 0.29 #574), 02jx1 (0.38 #2299, 0.02 #898, 0.01 #3008), 0k6nt (0.33 #62, 0.23 #681, 0.16 #1179), 059j2 (0.33 #62, 0.23 #681, 0.16 #1179) >> Best rule #1181 for best value: >> intensional similarity = 5 >> extensional distance = 90 >> proper extension: 09y6pb; >> query: (?x7265, 09c7w0) <- nominated_for(?x1822, ?x7265), film(?x8772, ?x7265), produced_by(?x7265, ?x3637), film_crew_role(?x7265, ?x137), nominated_for(?x8772, ?x4653) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04tng0 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 111.000 0.891 http://example.org/film/film/country #8067-01gx5f PRED entity: 01gx5f PRED relation: profession PRED expected values: 0dz3r 09jwl 01c72t => 104 concepts (56 used for prediction) PRED predicted values (max 10 best out of 67): 09jwl (0.90 #4887, 0.85 #5035, 0.82 #3999), 0dz3r (0.64 #1033, 0.64 #738, 0.56 #443), 01c72t (0.52 #1499, 0.50 #1350, 0.50 #170), 039v1 (0.50 #1066, 0.45 #771, 0.39 #4015), 018gz8 (0.50 #605, 0.11 #7691, 0.08 #3702), 016z4k (0.45 #5759, 0.44 #445, 0.44 #6053), 0fnpj (0.36 #1090, 0.33 #500, 0.30 #1535), 05vyk (0.33 #93, 0.25 #240, 0.22 #1569), 0cbd2 (0.33 #7, 0.25 #154, 0.12 #1334), 025352 (0.33 #499, 0.13 #3596, 0.11 #4631) >> Best rule #4887 for best value: >> intensional similarity = 6 >> extensional distance = 252 >> proper extension: 0pgjm; 03m6pk; >> query: (?x3399, 09jwl) <- profession(?x3399, ?x1383), role(?x3399, ?x228), profession(?x10527, ?x1383), profession(?x3025, ?x1383), ?x10527 = 020jqv, actor(?x5955, ?x3025) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 01gx5f profession 01c72t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 56.000 0.902 http://example.org/people/person/profession EVAL 01gx5f profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 56.000 0.902 http://example.org/people/person/profession EVAL 01gx5f profession 0dz3r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 56.000 0.902 http://example.org/people/person/profession #8066-05qw5 PRED entity: 05qw5 PRED relation: category PRED expected values: 08mbj5d => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #10, 0.85 #44, 0.85 #22) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 01fl3; 0dtd6; 0187x8; >> query: (?x2120, 08mbj5d) <- artist(?x2190, ?x2120), artists(?x9063, ?x2120), ?x9063 = 0cx7f >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qw5 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.855 http://example.org/common/topic/webpage./common/webpage/category #8065-042g97 PRED entity: 042g97 PRED relation: nominated_for! PRED expected values: 04ljl_l => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 211): 0gq9h (0.43 #4165, 0.42 #787, 0.33 #9234), 0p9sw (0.42 #985, 0.31 #262, 0.30 #3619), 0gq_v (0.39 #4121, 0.30 #984, 0.24 #9190), 0l8z1 (0.38 #294, 0.30 #3619, 0.29 #1017), 019f4v (0.37 #778, 0.32 #4156, 0.29 #1983), 0gs9p (0.36 #4167, 0.34 #789, 0.29 #9236), 0gkr9q (0.33 #212, 0.05 #1417, 0.04 #2381), 0bp_b2 (0.33 #17, 0.05 #1222, 0.04 #2186), 0fbtbt (0.33 #163, 0.05 #1368, 0.04 #9333), 0ck27z (0.33 #73, 0.05 #1278, 0.03 #9243) >> Best rule #4165 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 01b64v; 01vrwfv; 063ykwt; 014gjp; 07g9f; >> query: (?x12214, 0gq9h) <- nominated_for(?x7183, ?x12214), award(?x7183, ?x102), award_winner(?x7183, ?x2300), people(?x12870, ?x7183) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4343 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 153 *> proper extension: 01b64v; 01vrwfv; 063ykwt; 014gjp; 07g9f; *> query: (?x12214, ?x102) <- nominated_for(?x7183, ?x12214), award(?x7183, ?x102), award_winner(?x7183, ?x2300), people(?x12870, ?x7183) *> conf = 0.30 ranks of expected_values: 19 EVAL 042g97 nominated_for! 04ljl_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 88.000 88.000 0.432 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8064-02__ww PRED entity: 02__ww PRED relation: actor! PRED expected values: 06cs95 => 78 concepts (46 used for prediction) PRED predicted values (max 10 best out of 88): 06dfz1 (0.33 #166), 080dwhx (0.17 #6, 0.03 #533, 0.03 #1850), 0jwl2 (0.17 #73, 0.02 #4818, 0.02 #1126), 06zsk51 (0.17 #181, 0.01 #1761, 0.01 #2289), 02_1q9 (0.10 #269, 0.03 #795, 0.03 #4750), 05jyb2 (0.10 #322, 0.02 #848, 0.02 #1111), 0sw0q (0.10 #442), 0qmk5 (0.07 #8440, 0.03 #8441, 0.01 #747), 02k_4g (0.07 #8440, 0.03 #8441, 0.01 #804), 026bfsh (0.05 #624, 0.04 #2733, 0.04 #1941) >> Best rule #166 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 06t74h; 012q4n; >> query: (?x11925, 06dfz1) <- actor(?x7175, ?x11925), film(?x11925, ?x508), ?x508 = 0ds33 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #534 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 378 *> proper extension: 06n7h7; 016kjs; 01ztgm; 086qd; 044gyq; 0bqsy; 01wv9p; 01jgkj2; 02yygk; 0163kf; *> query: (?x11925, 06cs95) <- actor(?x7175, ?x11925), place_of_birth(?x11925, ?x9394), award_nominee(?x1051, ?x11925) *> conf = 0.02 ranks of expected_values: 48 EVAL 02__ww actor! 06cs95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 78.000 46.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #8063-01vng3b PRED entity: 01vng3b PRED relation: role PRED expected values: 02hnl => 118 concepts (116 used for prediction) PRED predicted values (max 10 best out of 121): 05r5c (0.28 #1464, 0.28 #1340, 0.27 #1711), 013y1f (0.28 #1464, 0.28 #1340, 0.27 #1711), 02k856 (0.28 #1464, 0.28 #1340, 0.27 #1711), 028tv0 (0.25 #194, 0.16 #1291, 0.15 #1477), 026t6 (0.20 #424, 0.20 #1341, 0.19 #1403), 02hnl (0.18 #1304, 0.17 #1367, 0.17 #1428), 03qjg (0.12 #1073, 0.11 #645, 0.11 #220), 06ncr (0.11 #395, 0.05 #1311, 0.04 #516), 0l14qv (0.09 #5, 0.08 #1347, 0.08 #1284), 02sgy (0.09 #369, 0.06 #1348, 0.06 #1285) >> Best rule #1464 for best value: >> intensional similarity = 4 >> extensional distance = 220 >> proper extension: 0bg539; 07s6prs; 021bk; 02qfhb; 08n__5; >> query: (?x6225, ?x316) <- instrumentalists(?x316, ?x6225), nationality(?x6225, ?x94), profession(?x6225, ?x131), role(?x6225, ?x227) >> conf = 0.28 => this is the best rule for 3 predicted values *> Best rule #1304 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 188 *> proper extension: 028qdb; 037hgm; 082brv; 02mx98; *> query: (?x6225, 02hnl) <- instrumentalists(?x316, ?x6225), role(?x6225, ?x212), artists(?x302, ?x6225), role(?x6225, ?x315) *> conf = 0.18 ranks of expected_values: 6 EVAL 01vng3b role 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 118.000 116.000 0.279 http://example.org/music/group_member/membership./music/group_membership/role #8062-0bwfn PRED entity: 0bwfn PRED relation: organization PRED expected values: 034h1h => 129 concepts (120 used for prediction) PRED predicted values (max 10 best out of 12): 034h1h (0.42 #275, 0.41 #203, 0.34 #179), 07t65 (0.05 #2852, 0.03 #2877, 0.02 #1635), 02vk52z (0.04 #2851, 0.03 #2876, 0.02 #1634), 03mbdx_ (0.04 #744, 0.03 #624, 0.03 #216), 0_2v (0.02 #2855), 0gkjy (0.02 #2884), 01rz1 (0.02 #2853, 0.01 #2878), 04k4l (0.02 #2856), 0b6css (0.02 #2887, 0.01 #1645, 0.01 #1958), 041288 (0.01 #2893) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 02bh8z; >> query: (?x7545, 034h1h) <- list(?x7545, ?x2197), citytown(?x7545, ?x739), company(?x12147, ?x7545) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bwfn organization 034h1h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 120.000 0.425 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #8061-054krc PRED entity: 054krc PRED relation: award! PRED expected values: 02ryx0 016jfw => 56 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2910): 018gqj (0.77 #62848, 0.75 #33068, 0.74 #49612), 019x62 (0.77 #62848, 0.75 #33068, 0.74 #62850), 02g40r (0.75 #33068, 0.74 #62850, 0.74 #62847), 04ls53 (0.75 #33068, 0.74 #62850, 0.74 #62847), 01vrz41 (0.71 #16825, 0.21 #62849, 0.17 #46300), 077rj (0.71 #18229, 0.05 #28150, 0.04 #47999), 0178rl (0.57 #18028, 0.21 #62849, 0.17 #59540), 024yxd (0.57 #19533, 0.21 #62849, 0.11 #59539), 01vvdm (0.57 #17560, 0.17 #46300, 0.17 #10945), 01c7p_ (0.57 #19212, 0.17 #15905, 0.10 #29133) >> Best rule #62848 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 058vy5; >> query: (?x1443, ?x669) <- award_winner(?x1443, ?x669), award_nominee(?x669, ?x2641), instrumentalists(?x316, ?x669) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #18288 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0c4z8; 04njml; 02h3d1; *> query: (?x1443, 016jfw) <- award(?x4505, ?x1443), award(?x1894, ?x1443), ?x4505 = 012wg, ceremony(?x1443, ?x747), ?x1894 = 02fgpf *> conf = 0.29 ranks of expected_values: 219, 360 EVAL 054krc award! 016jfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 56.000 26.000 0.774 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054krc award! 02ryx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 26.000 0.774 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8060-0713r PRED entity: 0713r PRED relation: season PRED expected values: 025ygqm 04n36qk => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 6): 025ygqm (0.77 #175, 0.76 #163, 0.75 #157), 02h7s73 (0.50 #40, 0.50 #34, 0.50 #28), 03c6s24 (0.50 #41, 0.50 #35, 0.47 #205), 03c74_8 (0.50 #38, 0.47 #205, 0.25 #116), 04110b0 (0.47 #205, 0.40 #51, 0.38 #165), 04n36qk (0.47 #205, 0.12 #120, 0.11 #150) >> Best rule #175 for best value: >> intensional similarity = 17 >> extensional distance = 20 >> proper extension: 0x2p; 01ync; 02__x; 07l8f; 0x0d; >> query: (?x4243, 025ygqm) <- team(?x10822, ?x4243), team(?x10434, ?x4243), team(?x4244, ?x4243), team(?x2010, ?x4243), season(?x4243, ?x10017), school(?x4243, ?x4599), ?x10017 = 026fmqm, ?x4244 = 028c_8, position(?x12042, ?x10822), position(?x8995, ?x10822), ?x12042 = 05xvj, team(?x10434, ?x7725), ?x2010 = 02lyr4, contains(?x94, ?x4599), major_field_of_study(?x4599, ?x742), ?x8995 = 01d6g, ?x7725 = 07l8x >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 0713r season 04n36qk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 69.000 69.000 0.773 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 0713r season 025ygqm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.773 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #8059-0424m PRED entity: 0424m PRED relation: legislative_sessions PRED expected values: 01grpc 01grp0 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 48): 06f0dc (0.52 #345, 0.52 #393, 0.50 #297), 024tkd (0.52 #374, 0.52 #422, 0.45 #326), 07p__7 (0.48 #392, 0.48 #344, 0.45 #296), 070m6c (0.48 #342, 0.43 #390, 0.40 #294), 024tcq (0.43 #404, 0.43 #356, 0.38 #500), 02bqm0 (0.43 #412, 0.43 #364, 0.35 #316), 02cg7g (0.43 #409, 0.43 #361, 0.35 #313), 02bqmq (0.43 #402, 0.43 #354, 0.35 #306), 02bn_p (0.43 #346, 0.39 #394, 0.35 #298), 02gkzs (0.43 #359, 0.39 #407, 0.35 #311) >> Best rule #345 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 06bss; 0194xc; >> query: (?x5978, 06f0dc) <- type_of_union(?x5978, ?x566), nationality(?x5978, ?x94), legislative_sessions(?x5978, ?x10638), legislative_sessions(?x2860, ?x10638) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 01hnp; *> query: (?x5978, 01grpc) <- entity_involved(?x8416, ?x5978), ?x8416 = 086m1, taxonomy(?x5978, ?x939) *> conf = 0.20 ranks of expected_values: 26, 46 EVAL 0424m legislative_sessions 01grp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 85.000 85.000 0.524 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 0424m legislative_sessions 01grpc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 85.000 85.000 0.524 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #8058-0cq806 PRED entity: 0cq806 PRED relation: language PRED expected values: 04h9h => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 46): 06b_j (0.27 #20, 0.24 #76, 0.18 #132), 04306rv (0.19 #60, 0.18 #229, 0.14 #116), 02bjrlw (0.19 #169, 0.15 #226, 0.15 #283), 04h9h (0.11 #830, 0.11 #322, 0.10 #265), 0jzc (0.08 #186, 0.07 #525, 0.07 #300), 0653m (0.07 #630, 0.05 #856, 0.04 #687), 012w70 (0.07 #518, 0.05 #631, 0.05 #67), 02ztjwg (0.07 #29, 0.05 #85, 0.05 #141), 03_9r (0.06 #347, 0.05 #234, 0.05 #3174), 02hxcvy (0.05 #199, 0.04 #313, 0.04 #425) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0fpkhkz; 0645k5; 0ggbhy7; 0qmd5; 04mcw4; 026lgs; 067ghz; 043mk4y; 023g6w; 0symg; ... >> query: (?x8773, 06b_j) <- film(?x3780, ?x8773), ?x3780 = 015wnl, genre(?x8773, ?x53), award_winner(?x8773, ?x6514) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #830 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 039zft; *> query: (?x8773, 04h9h) <- film(?x3780, ?x8773), genre(?x8773, ?x4088), ?x4088 = 04xvh5 *> conf = 0.11 ranks of expected_values: 4 EVAL 0cq806 language 04h9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 82.000 0.267 http://example.org/film/film/language #8057-01p0w_ PRED entity: 01p0w_ PRED relation: instrumentalists! PRED expected values: 05r5c => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 123): 05r5c (0.60 #6, 0.53 #2097, 0.52 #422), 02hnl (0.40 #31, 0.35 #614, 0.31 #2122), 026g73 (0.40 #65, 0.08 #481, 0.06 #314), 03qjg (0.35 #631, 0.24 #1385, 0.24 #464), 01vdm0 (0.33 #2176, 0.31 #3432, 0.31 #3932), 03gvt (0.30 #144, 0.20 #61, 0.16 #811), 06w7v (0.30 #151, 0.20 #400, 0.14 #1070), 0l14qv (0.22 #1341, 0.20 #336, 0.20 #87), 04rzd (0.20 #366, 0.19 #868, 0.17 #700), 06ncr (0.20 #124, 0.14 #959, 0.12 #3303) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03h_fqv; >> query: (?x12422, 05r5c) <- instrumentalists(?x212, ?x12422), award(?x12422, ?x1565), friend(?x7571, ?x12422), ?x212 = 026t6 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p0w_ instrumentalists! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.600 http://example.org/music/instrument/instrumentalists #8056-09889g PRED entity: 09889g PRED relation: artist! PRED expected values: 04rqd => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 3): 03gfvsz (0.15 #11, 0.09 #123, 0.09 #92), 04rqd (0.03 #49, 0.03 #70, 0.03 #266), 04y652m (0.03 #3, 0.02 #8, 0.02 #48) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 06cc_1; 03qd_; 04mn81; 01dw9z; 01vx5w7; 03bnv; 01svw8n; 0ddkf; >> query: (?x4960, 03gfvsz) <- profession(?x4960, ?x131), award_winner(?x4960, ?x2319), group(?x4960, ?x1271) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #49 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 116 *> proper extension: 085j0; *> query: (?x4960, 04rqd) <- category(?x4960, ?x134), influenced_by(?x1896, ?x4960) *> conf = 0.03 ranks of expected_values: 2 EVAL 09889g artist! 04rqd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.152 http://example.org/broadcast/content/artist #8055-08ct6 PRED entity: 08ct6 PRED relation: film_crew_role PRED expected values: 01pvkk => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 35): 0ch6mp2 (0.66 #477, 0.61 #712, 0.58 #831), 09zzb8 (0.61 #586, 0.59 #79, 0.58 #783), 02r96rf (0.57 #316, 0.56 #511, 0.56 #394), 09vw2b7 (0.51 #320, 0.51 #398, 0.51 #593), 0dxtw (0.36 #520, 0.31 #677, 0.31 #325), 01vx2h (0.33 #521, 0.31 #404, 0.30 #678), 01pvkk (0.26 #483, 0.25 #718, 0.23 #522), 0215hd (0.22 #490, 0.17 #725, 0.15 #844), 02ynfr (0.17 #565, 0.15 #604, 0.14 #331), 02rh1dz (0.16 #519, 0.16 #676, 0.16 #402) >> Best rule #477 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 0djb3vw; 04lqvlr; 04lqvly; 07l50vn; 0g9zljd; 05zvzf3; >> query: (?x4699, 0ch6mp2) <- nominated_for(?x1313, ?x4699), currency(?x4699, ?x170), film_festivals(?x4699, ?x3831), award(?x269, ?x1313) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #483 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 99 *> proper extension: 0djb3vw; 04lqvlr; 04lqvly; 07l50vn; 0g9zljd; 05zvzf3; *> query: (?x4699, 01pvkk) <- nominated_for(?x1313, ?x4699), currency(?x4699, ?x170), film_festivals(?x4699, ?x3831), award(?x269, ?x1313) *> conf = 0.26 ranks of expected_values: 7 EVAL 08ct6 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 97.000 0.663 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #8054-050kh5 PRED entity: 050kh5 PRED relation: category PRED expected values: 08mbj5d => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.45 #8, 0.40 #28, 0.35 #34) >> Best rule #8 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 02xhwm; >> query: (?x12165, 08mbj5d) <- actor(?x12165, ?x12616), actor(?x12165, ?x11799), gender(?x11799, ?x231), profession(?x12616, ?x319), location(?x11799, ?x7412), program_creator(?x12165, ?x6937), genre(?x12165, ?x5728), program(?x656, ?x12165) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050kh5 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.455 http://example.org/common/topic/webpage./common/webpage/category #8053-05_pkf PRED entity: 05_pkf PRED relation: gender PRED expected values: 05zppz => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #27, 0.92 #17, 0.91 #19), 02zsn (0.53 #16, 0.38 #48, 0.35 #98) >> Best rule #27 for best value: >> intensional similarity = 5 >> extensional distance = 86 >> proper extension: 03h502k; 01vtmw6; 0dr5y; >> query: (?x3805, 05zppz) <- location(?x3805, ?x3807), music(?x9303, ?x3805), music(?x5502, ?x3805), film(?x919, ?x9303), genre(?x5502, ?x258) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05_pkf gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.920 http://example.org/people/person/gender #8052-071g6 PRED entity: 071g6 PRED relation: second_level_divisions! PRED expected values: 05qhw => 21 concepts (19 used for prediction) PRED predicted values (max 10 best out of 114): 09c7w0 (0.28 #331, 0.27 #313, 0.25 #347), 03rjj (0.21 #325, 0.10 #365, 0.10 #327), 05qhw (0.21 #325, 0.10 #327, 0.06 #360), 05b4w (0.21 #325, 0.10 #327, 0.06 #360), 059j2 (0.10 #365, 0.10 #327, 0.10 #8), 03gj2 (0.10 #365, 0.10 #327, 0.06 #360), 0d0vqn (0.10 #365, 0.06 #360, 0.04 #150), 06mzp (0.10 #365, 0.06 #360, 0.04 #345), 01pj7 (0.10 #327, 0.06 #360, 0.04 #150), 0345h (0.10 #327, 0.06 #360, 0.04 #345) >> Best rule #331 for best value: >> intensional similarity = 34 >> extensional distance = 1109 >> proper extension: 0cb4j; 0mx4_; 01n7q; 030qb3t; 0mxcf; 0mx6c; 0r1jr; 0d22f; 0l2l_; 0l2hf; ... >> query: (?x13657, 09c7w0) <- time_zones(?x13657, ?x2864), time_zones(?x12379, ?x2864), time_zones(?x2513, ?x2864), time_zones(?x1790, ?x2864), film_release_region(?x8176, ?x2513), film_release_region(?x6270, ?x2513), film_release_region(?x5825, ?x2513), film_release_region(?x4041, ?x2513), film_release_region(?x3076, ?x2513), film_release_region(?x2340, ?x2513), film_release_region(?x1988, ?x2513), film_release_region(?x1785, ?x2513), film_release_region(?x1392, ?x2513), film_release_region(?x370, ?x2513), ?x6270 = 0g9zljd, combatants(?x1790, ?x1497), film_release_region(?x141, ?x1790), ?x3076 = 0g5838s, ?x370 = 0ddfwj1, ?x1785 = 0gj9tn5, olympics(?x2513, ?x778), country(?x2266, ?x1790), jurisdiction_of_office(?x182, ?x2513), ?x5825 = 067ghz, contains(?x455, ?x2513), ?x1988 = 09k56b7, ?x4041 = 0gy2y8r, ?x778 = 0kbvb, currency(?x1790, ?x170), administrative_parent(?x2144, ?x12379), ?x2266 = 01lb14, ?x8176 = 0gvvm6l, ?x1392 = 017gm7, ?x2340 = 0fpv_3_ >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #325 for first EXPECTED value: *> intensional similarity = 32 *> extensional distance = 924 *> proper extension: 0160w; 0njvn; 0t015; 0n5j_; 0f4y_; 0n4m5; 0jcgs; 0mwl2; 0vmt; 0mw93; ... *> query: (?x13657, ?x2513) <- time_zones(?x13657, ?x2864), time_zones(?x11045, ?x2864), time_zones(?x10537, ?x2864), time_zones(?x2513, ?x2864), time_zones(?x1790, ?x2864), film_release_region(?x6270, ?x2513), film_release_region(?x5067, ?x2513), film_release_region(?x3958, ?x2513), film_release_region(?x3745, ?x2513), film_release_region(?x3076, ?x2513), film_release_region(?x1315, ?x2513), film_release_region(?x1202, ?x2513), ?x6270 = 0g9zljd, combatants(?x1790, ?x1497), film_release_region(?x141, ?x1790), ?x3076 = 0g5838s, ?x3745 = 03cw411, administrative_parent(?x11045, ?x8264), country(?x520, ?x2513), contains(?x455, ?x1790), country(?x1009, ?x2513), combatants(?x94, ?x2513), medal(?x1790, ?x1242), ?x5067 = 01rwpj, olympics(?x2513, ?x784), ?x1202 = 0gj8t_b, ?x1315 = 053tj7, ?x784 = 018ctl, category(?x10537, ?x134), country(?x668, ?x1790), ?x3958 = 0gyh2wm, ?x520 = 01dys *> conf = 0.21 ranks of expected_values: 3 EVAL 071g6 second_level_divisions! 05qhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 21.000 19.000 0.284 http://example.org/location/country/second_level_divisions #8051-0c9t0y PRED entity: 0c9t0y PRED relation: film! PRED expected values: 01wwvt2 0315q3 023kzp => 103 concepts (42 used for prediction) PRED predicted values (max 10 best out of 638): 04wvhz (0.44 #74826, 0.42 #45729, 0.40 #74825), 01g1lp (0.44 #74826, 0.42 #45729, 0.40 #74825), 016tw3 (0.42 #45729, 0.40 #74825, 0.40 #83141), 04xhwn (0.30 #1986, 0.03 #18612, 0.02 #6142), 06dv3 (0.20 #33, 0.03 #6267, 0.03 #4189), 0bxtg (0.10 #77, 0.06 #10467, 0.06 #29102), 018ygt (0.10 #1115, 0.05 #7349, 0.04 #5271), 0c7lcx (0.10 #509, 0.03 #68590, 0.03 #76904), 017149 (0.10 #83, 0.03 #68590, 0.03 #8394), 04954 (0.10 #1305, 0.02 #5461, 0.02 #17931) >> Best rule #74826 for best value: >> intensional similarity = 4 >> extensional distance = 723 >> proper extension: 04m1bm; 02rb607; 03q8xj; 05zvzf3; 0dmn0x; >> query: (?x7187, ?x7855) <- film(?x1104, ?x7187), award(?x7187, ?x350), nominated_for(?x7855, ?x7187), profession(?x7855, ?x319) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #68590 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 669 *> proper extension: 02x8fs; 07p12s; *> query: (?x7187, ?x879) <- produced_by(?x7187, ?x1039), currency(?x7187, ?x170), film(?x9384, ?x7187), award_nominee(?x9384, ?x879) *> conf = 0.03 ranks of expected_values: 106, 537, 622 EVAL 0c9t0y film! 023kzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 103.000 42.000 0.440 http://example.org/film/actor/film./film/performance/film EVAL 0c9t0y film! 0315q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 103.000 42.000 0.440 http://example.org/film/actor/film./film/performance/film EVAL 0c9t0y film! 01wwvt2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 103.000 42.000 0.440 http://example.org/film/actor/film./film/performance/film #8050-0wsr PRED entity: 0wsr PRED relation: category PRED expected values: 08mbj5d => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.57 #72, 0.56 #70, 0.55 #62) >> Best rule #72 for best value: >> intensional similarity = 13 >> extensional distance = 79 >> proper extension: 025v1sx; >> query: (?x6645, 08mbj5d) <- position_s(?x6645, ?x2247), team(?x1717, ?x6645), position(?x8516, ?x2247), position(?x5773, ?x2247), position(?x2148, ?x2247), ?x5773 = 06rny, position_s(?x1239, ?x2247), position_s(?x684, ?x2247), ?x684 = 01ct6, ?x8516 = 0fbtm7, ?x2148 = 0fht9f, position_s(?x11424, ?x2247), ?x1239 = 01xvb >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0wsr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.568 http://example.org/common/topic/webpage./common/webpage/category #8049-012vd6 PRED entity: 012vd6 PRED relation: category PRED expected values: 08mbj5d => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #4, 0.83 #10, 0.77 #37) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 03x82v; >> query: (?x5310, 08mbj5d) <- award_winner(?x3929, ?x5310), artists(?x3061, ?x5310), ?x3061 = 05bt6j >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012vd6 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.910 http://example.org/common/topic/webpage./common/webpage/category #8048-0299ct PRED entity: 0299ct PRED relation: major_field_of_study! PRED expected values: 0885n => 58 concepts (37 used for prediction) PRED predicted values (max 10 best out of 629): 01w5m (0.75 #712, 0.73 #1906, 0.67 #4290), 03ksy (0.75 #713, 0.71 #4291, 0.70 #3694), 08815 (0.75 #594, 0.62 #4172, 0.60 #1788), 07wrz (0.75 #659, 0.60 #1853, 0.60 #67), 0bwfn (0.75 #894, 0.60 #302, 0.54 #1491), 07tds (0.75 #763, 0.60 #171, 0.53 #1957), 07wjk (0.75 #660, 0.60 #68, 0.47 #1854), 03fgm (0.75 #1023, 0.60 #431, 0.40 #2217), 06pwq (0.70 #3586, 0.68 #5374, 0.67 #7765), 017j69 (0.69 #1353, 0.50 #756, 0.48 #4929) >> Best rule #712 for best value: >> intensional similarity = 15 >> extensional distance = 6 >> proper extension: 01mkq; 062z7; 0fdys; >> query: (?x13318, 01w5m) <- major_field_of_study(?x3437, ?x13318), major_field_of_study(?x1368, ?x13318), major_field_of_study(?x6364, ?x13318), ?x3437 = 02_xgp2, ?x1368 = 014mlp, major_field_of_study(?x6364, ?x5359), major_field_of_study(?x13827, ?x6364), major_field_of_study(?x7097, ?x6364), major_field_of_study(?x6056, ?x6364), ?x7097 = 01jvxb, student(?x6056, ?x445), institution(?x734, ?x6056), language(?x136, ?x5359), contains(?x1310, ?x13827), countries_spoken_in(?x5359, ?x279) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #282 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 03g3w; 02j62; 037mh8; *> query: (?x13318, 0885n) <- major_field_of_study(?x3437, ?x13318), major_field_of_study(?x1368, ?x13318), major_field_of_study(?x6364, ?x13318), major_field_of_study(?x5864, ?x13318), ?x3437 = 02_xgp2, ?x1368 = 014mlp, ?x6364 = 05qt0, major_field_of_study(?x5280, ?x13318), disciplines_or_subjects(?x921, ?x5864), award_winner(?x921, ?x118), major_field_of_study(?x2014, ?x5864) *> conf = 0.40 ranks of expected_values: 62 EVAL 0299ct major_field_of_study! 0885n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 58.000 37.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #8047-0465_ PRED entity: 0465_ PRED relation: profession PRED expected values: 05z96 => 186 concepts (109 used for prediction) PRED predicted values (max 10 best out of 93): 0cbd2 (0.97 #5371, 0.69 #8353, 0.62 #1646), 02hrh1q (0.72 #2101, 0.71 #14328, 0.70 #15970), 05z96 (0.55 #1087, 0.50 #342, 0.46 #1534), 0kyk (0.54 #8377, 0.45 #1074, 0.43 #925), 0dxtg (0.54 #7615, 0.45 #4931, 0.45 #5975), 018gz8 (0.33 #18, 0.31 #5979, 0.31 #4935), 04gc2 (0.33 #639, 0.08 #1682, 0.07 #6302), 05snw (0.31 #1434, 0.20 #2030, 0.16 #3222), 06q2q (0.31 #1387, 0.16 #1983, 0.14 #3175), 01d_h8 (0.29 #5967, 0.27 #1198, 0.27 #7607) >> Best rule #5371 for best value: >> intensional similarity = 5 >> extensional distance = 124 >> proper extension: 06l6nj; >> query: (?x6370, 0cbd2) <- profession(?x6370, ?x6630), place_of_death(?x6370, ?x6959), profession(?x4292, ?x6630), specialization_of(?x13369, ?x6630), ?x4292 = 0zm1 >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #1087 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 03f0324; 0c1jh; *> query: (?x6370, 05z96) <- profession(?x6370, ?x6630), influenced_by(?x11412, ?x6370), influenced_by(?x2845, ?x6370), ?x2845 = 0lrh, influenced_by(?x9508, ?x11412), nationality(?x9508, ?x94) *> conf = 0.55 ranks of expected_values: 3 EVAL 0465_ profession 05z96 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 186.000 109.000 0.968 http://example.org/people/person/profession #8046-0dpl44 PRED entity: 0dpl44 PRED relation: genre PRED expected values: 06lbpz => 70 concepts (56 used for prediction) PRED predicted values (max 10 best out of 83): 02l7c8 (0.43 #486, 0.37 #369, 0.30 #3660), 01jfsb (0.41 #717, 0.35 #5886, 0.35 #1539), 02kdv5l (0.32 #708, 0.31 #1530, 0.30 #1295), 060__y (0.30 #15, 0.21 #1427, 0.16 #4481), 02b5_l (0.25 #47, 0.06 #519, 0.04 #402), 03k9fj (0.23 #4123, 0.23 #2128, 0.22 #1303), 06cvj (0.23 #357, 0.19 #474, 0.12 #2), 017fp (0.19 #4349, 0.14 #1425, 0.12 #6112), 04t36 (0.19 #4349, 0.12 #6112, 0.10 #476), 04xvh5 (0.19 #4349, 0.12 #6112, 0.09 #1445) >> Best rule #486 for best value: >> intensional similarity = 5 >> extensional distance = 294 >> proper extension: 011yfd; 02zk08; 06zn1c; 0d8w2n; 0hr41p6; >> query: (?x7103, 02l7c8) <- genre(?x7103, ?x7223), genre(?x7103, ?x53), ?x53 = 07s9rl0, genre(?x3559, ?x7223), ?x3559 = 02xtxw >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dpl44 genre 06lbpz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 56.000 0.426 http://example.org/film/film/genre #8045-026yqrr PRED entity: 026yqrr PRED relation: gender PRED expected values: 05zppz => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #1, 0.79 #47, 0.75 #7), 02zsn (0.54 #10, 0.33 #18, 0.32 #12) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02l840; 01wgxtl; 01vw20h; 0837ql; 03f19q4; 0677ng; 03j3pg9; 01wlt3k; >> query: (?x6268, 05zppz) <- award_nominee(?x6268, ?x4475), artists(?x671, ?x6268), ?x4475 = 01ws9n6 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026yqrr gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.800 http://example.org/people/person/gender #8044-0b1zz PRED entity: 0b1zz PRED relation: group! PRED expected values: 0342h => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 119): 0342h (0.92 #4376, 0.91 #2590, 0.91 #4109), 0l14md (0.83 #1159, 0.79 #446, 0.76 #4550), 028tv0 (0.83 #1159, 0.79 #446, 0.76 #4550), 05148p4 (0.81 #2606, 0.76 #4392, 0.72 #4481), 05r5c (0.50 #9, 0.43 #3301, 0.35 #3488), 026t6 (0.43 #3301, 0.29 #2584, 0.27 #2764), 02sgy (0.43 #3301, 0.26 #2854, 0.07 #4641), 042v_gx (0.43 #3301, 0.20 #188, 0.17 #2774), 03qjg (0.41 #1742, 0.40 #227, 0.37 #2098), 01vj9c (0.38 #282, 0.36 #639, 0.32 #3494) >> Best rule #4376 for best value: >> intensional similarity = 5 >> extensional distance = 89 >> proper extension: 01pfr3; 05k79; 0167_s; 03xhj6; 018gm9; 02t3ln; 01cblr; 01fmz6; 01k_yf; 02jqjm; ... >> query: (?x5935, 0342h) <- group(?x1750, ?x5935), group(?x716, ?x5935), artists(?x302, ?x5935), ?x1750 = 02hnl, ?x716 = 018vs >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b1zz group! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.923 http://example.org/music/performance_role/regular_performances./music/group_membership/group #8043-02jxk PRED entity: 02jxk PRED relation: organization! PRED expected values: 04v3q => 77 concepts (15 used for prediction) PRED predicted values (max 10 best out of 356): 05b4w (0.78 #1280, 0.75 #980, 0.67 #681), 06mkj (0.78 #1267, 0.75 #967, 0.67 #668), 0345h (0.67 #1240, 0.67 #641, 0.64 #1541), 06mzp (0.67 #1228, 0.62 #928, 0.57 #3006), 05qhw (0.67 #619, 0.57 #3006, 0.57 #3009), 0jdx (0.57 #3006, 0.57 #3009, 0.57 #3007), 04g5k (0.57 #3006, 0.57 #3009, 0.57 #3007), 06bnz (0.57 #3006, 0.57 #3009, 0.57 #3007), 01pj7 (0.57 #3006, 0.57 #3009, 0.57 #3007), 0bjv6 (0.57 #3006, 0.57 #3009, 0.57 #3007) >> Best rule #1280 for best value: >> intensional similarity = 9 >> extensional distance = 7 >> proper extension: 0_2v; >> query: (?x2106, 05b4w) <- organization(?x1229, ?x2106), organization(?x756, ?x2106), organization(?x205, ?x2106), combatants(?x756, ?x1790), ?x205 = 03rjj, film_release_region(?x303, ?x756), olympics(?x756, ?x418), olympics(?x1229, ?x391), contains(?x1229, ?x2351) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2704 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 11 *> proper extension: 02_l9; *> query: (?x2106, ?x94) <- organization(?x1892, ?x2106), organization(?x304, ?x2106), entity_involved(?x5352, ?x1892), taxonomy(?x304, ?x939), organization(?x1892, ?x5701), organization(?x94, ?x5701), locations(?x5352, ?x1471) *> conf = 0.35 ranks of expected_values: 188 EVAL 02jxk organization! 04v3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 77.000 15.000 0.778 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #8042-01cvtf PRED entity: 01cvtf PRED relation: titles! PRED expected values: 07c52 => 89 concepts (63 used for prediction) PRED predicted values (max 10 best out of 45): 07c52 (0.71 #30, 0.66 #1183, 0.66 #659), 07s9rl0 (0.34 #5747, 0.28 #6484, 0.25 #5955), 04xvlr (0.19 #5750, 0.18 #6487, 0.16 #6063), 01z4y (0.14 #5782, 0.11 #5885, 0.11 #5990), 05gnf (0.11 #3029, 0.10 #4172, 0.10 #4066), 03mdt (0.11 #1091, 0.10 #567, 0.10 #778), 01z77k (0.09 #2670, 0.09 #2147, 0.09 #1214), 0215n (0.09 #285, 0.06 #599, 0.06 #3938), 07ssc (0.09 #6493, 0.06 #5756, 0.06 #6175), 01jfsb (0.08 #5766, 0.08 #5974, 0.08 #6291) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0300ml; >> query: (?x11250, 07c52) <- award(?x11250, ?x4921), genre(?x11250, ?x53), ?x4921 = 0fbtbt, program(?x6678, ?x11250) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cvtf titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 63.000 0.714 http://example.org/media_common/netflix_genre/titles #8041-04qsdh PRED entity: 04qsdh PRED relation: award_nominee PRED expected values: 014v6f => 98 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1120): 01vh18t (0.83 #2335, 0.81 #81724, 0.81 #32685), 05vsxz (0.83 #2335, 0.81 #81724, 0.81 #32685), 014v6f (0.83 #2335, 0.81 #81724, 0.81 #32685), 04qsdh (0.73 #1787, 0.29 #51366, 0.28 #51367), 04__f (0.29 #51366, 0.28 #51367, 0.23 #70049), 0cj8x (0.29 #51366, 0.28 #51367, 0.03 #3004), 0m6x4 (0.29 #51366, 0.28 #51367, 0.02 #74719), 01m4kpp (0.29 #51366, 0.28 #51367), 015qq1 (0.29 #51366, 0.28 #51367), 02h0f3 (0.29 #51366, 0.28 #51367) >> Best rule #2335 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 05vsxz; 06qgvf; 06dv3; 0785v8; 03n_7k; 014v6f; 03cglm; 027bs_2; 04v7kt; >> query: (?x8045, ?x100) <- award_nominee(?x7595, ?x8045), award_nominee(?x100, ?x8045), award(?x8045, ?x375), ?x7595 = 05mc99 >> conf = 0.83 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 04qsdh award_nominee 014v6f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 39.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8040-04rjg PRED entity: 04rjg PRED relation: taxonomy PRED expected values: 04n6k => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.75 #30, 0.67 #24, 0.67 #14) >> Best rule #30 for best value: >> intensional similarity = 9 >> extensional distance = 22 >> proper extension: 02h40lc; 088tb; 0193x; 05qfh; 05qt0; 02_7t; 04g7x; 0l5mz; 02stgt; 01400v; >> query: (?x2014, 04n6k) <- major_field_of_study(?x5167, ?x2014), major_field_of_study(?x2775, ?x2014), school(?x4979, ?x2775), major_field_of_study(?x4981, ?x2014), major_field_of_study(?x1526, ?x2014), ?x1526 = 0bkj86, major_field_of_study(?x732, ?x2014), institution(?x620, ?x5167), ?x4981 = 03bwzr4 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04rjg taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.750 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #8039-03_gz8 PRED entity: 03_gz8 PRED relation: nominated_for! PRED expected values: 09qwmm => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 201): 0gq_v (0.57 #19, 0.56 #254, 0.47 #1194), 040njc (0.57 #6, 0.50 #241, 0.34 #3297), 0gq9h (0.46 #4057, 0.45 #3352, 0.44 #4292), 0k611 (0.44 #306, 0.43 #71, 0.36 #3362), 0l8z1 (0.44 #286, 0.36 #51, 0.35 #1226), 0gr0m (0.43 #1234, 0.38 #294, 0.29 #59), 019f4v (0.42 #3344, 0.40 #1228, 0.40 #3814), 0gs9p (0.41 #4059, 0.40 #3824, 0.40 #4294), 0f4x7 (0.38 #260, 0.36 #25, 0.29 #4021), 0p9sw (0.38 #255, 0.36 #20, 0.29 #3311) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0209hj; 0dgst_d; 0168ls; 0dr_4; 09gq0x5; 016z7s; 0bx0l; 019vhk; 0c8qq; 01242_; ... >> query: (?x6362, 0gq_v) <- genre(?x6362, ?x3312), honored_for(?x6594, ?x6362), film_crew_role(?x6362, ?x137), ?x3312 = 02p0szs >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 100 *> proper extension: 09xbpt; 08hmch; 03t97y; 02v63m; 0661m4p; 065zlr; 014nq4; 05c26ss; 04cv9m; 0bc1yhb; ... *> query: (?x6362, ?x68) <- genre(?x6362, ?x53), film_crew_role(?x6362, ?x137), prequel(?x6362, ?x4159), nominated_for(?x68, ?x4159) *> conf = 0.25 ranks of expected_values: 24 EVAL 03_gz8 nominated_for! 09qwmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 84.000 84.000 0.571 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8038-0h5g_ PRED entity: 0h5g_ PRED relation: award_nominee PRED expected values: 016xh5 => 121 concepts (69 used for prediction) PRED predicted values (max 10 best out of 853): 01yhvv (0.81 #148952, 0.81 #148953, 0.81 #123355), 016xh5 (0.81 #148952, 0.81 #148953, 0.81 #123355), 0k269 (0.81 #148952, 0.81 #148953, 0.81 #123355), 0h5g_ (0.72 #2410, 0.72 #84, 0.16 #134989), 0170qf (0.17 #74486, 0.14 #76814, 0.14 #93105), 01rr9f (0.17 #74486, 0.14 #76814, 0.14 #93105), 0dvld (0.17 #3712, 0.17 #1386, 0.06 #6038), 03x400 (0.16 #134989, 0.07 #114048, 0.06 #3832), 060j8b (0.16 #134989, 0.07 #114048, 0.02 #96876), 02xv8m (0.16 #134989, 0.07 #114048, 0.02 #40445) >> Best rule #148952 for best value: >> intensional similarity = 4 >> extensional distance = 1439 >> proper extension: 012ljv; 012t1; 0244r8; 01ycck; 09pl3f; 0d_skg; 01lct6; >> query: (?x489, ?x100) <- nominated_for(?x489, ?x2029), award_nominee(?x1410, ?x489), award_nominee(?x100, ?x489), location(?x1410, ?x1411) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0h5g_ award_nominee 016xh5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 69.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8037-01n7q PRED entity: 01n7q PRED relation: district_represented! PRED expected values: 043djx 02bp37 => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 47): 02bp37 (0.71 #1370, 0.58 #2122, 0.57 #1746), 043djx (0.62 #1414, 0.60 #1461, 0.56 #1226), 01h7xx (0.56 #1445, 0.55 #3056, 0.55 #1492), 01gt99 (0.56 #1453, 0.55 #1500, 0.47 #1265), 01gtdd (0.55 #3057, 0.55 #3056, 0.54 #1450), 01gtcc (0.55 #3057, 0.55 #3056, 0.51 #1424), 02cg7g (0.55 #3057, 0.55 #3056, 0.47 #1382), 02gkzs (0.55 #3057, 0.55 #3056, 0.45 #1379), 03rtmz (0.55 #3057, 0.55 #3056, 0.29 #1375), 02glc4 (0.55 #3057, 0.55 #3056, 0.27 #402) >> Best rule #1370 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0gj4fx; >> query: (?x1227, 02bp37) <- district_represented(?x6933, ?x1227), contains(?x1227, ?x191), ?x6933 = 024tkd >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01n7q district_represented! 02bp37 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.711 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01n7q district_represented! 043djx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.711 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #8036-040fv PRED entity: 040fv PRED relation: seasonal_months PRED expected values: 028kb => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 4): 05cw8 (0.89 #31, 0.89 #30, 0.85 #50), 02xx5 (0.89 #31, 0.89 #30, 0.85 #50), 040fv (0.81 #33, 0.79 #52, 0.76 #65), 028kb (0.81 #33, 0.79 #52, 0.76 #65) >> Best rule #31 for best value: >> intensional similarity = 90 >> extensional distance = 2 >> proper extension: 028kb; >> query: (?x2255, ?x7298) <- seasonal_months(?x7298, ?x2255), seasonal_months(?x3270, ?x2255), seasonal_months(?x1459, ?x2255), month(?x12674, ?x2255), month(?x11197, ?x2255), month(?x10610, ?x2255), month(?x10143, ?x2255), month(?x8602, ?x2255), month(?x6960, ?x2255), month(?x4698, ?x2255), month(?x4271, ?x2255), month(?x3501, ?x2255), month(?x3106, ?x2255), month(?x1860, ?x2255), month(?x1646, ?x2255), month(?x1458, ?x2255), month(?x1036, ?x2255), ?x1036 = 080h2, ?x12674 = 0g6xq, place_of_death(?x1645, ?x1646), country(?x1646, ?x1264), mode_of_transportation(?x1646, ?x8731), location(?x8240, ?x1646), location(?x8129, ?x1646), contains(?x1646, ?x196), religion(?x1646, ?x962), ?x3270 = 05cw8, ?x1458 = 05ywg, time_zones(?x1646, ?x2864), ?x11197 = 05l64, ?x4271 = 06wjf, citytown(?x4257, ?x6960), ?x1459 = 04w_7, featured_film_locations(?x6489, ?x1646), featured_film_locations(?x2189, ?x1646), locations(?x1608, ?x1646), teams(?x1646, ?x2905), adjoins(?x1646, ?x6325), film(?x157, ?x6489), ?x3501 = 0f2v0, location(?x194, ?x6960), ?x4698 = 056_y, ?x8731 = 01bjv, profession(?x8129, ?x563), contains(?x94, ?x6960), film(?x4035, ?x6489), ?x962 = 05sfs, ?x3106 = 049d1, honored_for(?x5592, ?x6489), award_winner(?x1362, ?x8129), ?x8602 = 0chgzm, major_field_of_study(?x196, ?x1527), ?x1860 = 01_d4, origin(?x5751, ?x1646), people(?x1423, ?x8240), award_winner(?x5151, ?x8129), award_winner(?x2189, ?x815), nationality(?x380, ?x1264), film_release_region(?x9652, ?x1264), film_release_region(?x6556, ?x1264), film_release_region(?x6175, ?x1264), film_release_region(?x4694, ?x1264), film_release_region(?x4610, ?x1264), film_release_region(?x4545, ?x1264), film_release_region(?x3226, ?x1264), film_release_region(?x634, ?x1264), ?x4610 = 017jd9, ?x6175 = 0gg5kmg, location_of_ceremony(?x566, ?x1264), administrative_parent(?x1679, ?x1264), organization(?x346, ?x196), country(?x7463, ?x1264), ?x10610 = 03902, genre(?x2189, ?x53), month(?x362, ?x7298), ?x6556 = 05dss7, ?x10143 = 0h3tv, film_release_region(?x2189, ?x1023), ?x4545 = 05p09dd, ?x3226 = 0gyfp9c, ?x7463 = 02fj8n, ?x9652 = 0ddbjy4, service_location(?x555, ?x1264), ?x634 = 0gx9rvq, film_crew_role(?x6489, ?x137), origin(?x5391, ?x6960), ?x1023 = 0ctw_b, ?x4694 = 02j69w, place_of_birth(?x1182, ?x6960), award(?x8129, ?x2324) >> conf = 0.89 => this is the best rule for 2 predicted values *> Best rule #33 for first EXPECTED value: *> intensional similarity = 91 *> extensional distance = 2 *> proper extension: 028kb; *> query: (?x2255, ?x2140) <- seasonal_months(?x7298, ?x2255), seasonal_months(?x3270, ?x2255), seasonal_months(?x1459, ?x2255), month(?x12674, ?x2255), month(?x11197, ?x2255), month(?x10610, ?x2255), month(?x10143, ?x2255), month(?x8602, ?x2255), month(?x6960, ?x2255), month(?x4698, ?x2255), month(?x4271, ?x2255), month(?x3501, ?x2255), month(?x3106, ?x2255), month(?x1860, ?x2255), month(?x1646, ?x2255), month(?x1458, ?x2255), month(?x1036, ?x2255), ?x1036 = 080h2, ?x12674 = 0g6xq, place_of_death(?x1645, ?x1646), country(?x1646, ?x1264), mode_of_transportation(?x1646, ?x8731), location(?x8240, ?x1646), location(?x8129, ?x1646), contains(?x1646, ?x196), religion(?x1646, ?x962), ?x3270 = 05cw8, ?x1458 = 05ywg, time_zones(?x1646, ?x2864), ?x11197 = 05l64, ?x4271 = 06wjf, citytown(?x4257, ?x6960), ?x1459 = 04w_7, featured_film_locations(?x6489, ?x1646), featured_film_locations(?x2189, ?x1646), locations(?x1608, ?x1646), month(?x6960, ?x2140), teams(?x1646, ?x2905), adjoins(?x1646, ?x6325), film(?x157, ?x6489), ?x3501 = 0f2v0, location(?x194, ?x6960), ?x4698 = 056_y, ?x8731 = 01bjv, profession(?x8129, ?x563), contains(?x94, ?x6960), film(?x4035, ?x6489), ?x962 = 05sfs, ?x3106 = 049d1, honored_for(?x5592, ?x6489), award_winner(?x1362, ?x8129), ?x8602 = 0chgzm, major_field_of_study(?x196, ?x1527), ?x1860 = 01_d4, origin(?x5751, ?x1646), people(?x1423, ?x8240), award_winner(?x5151, ?x8129), award_winner(?x2189, ?x815), nationality(?x380, ?x1264), film_release_region(?x9652, ?x1264), film_release_region(?x6556, ?x1264), film_release_region(?x6175, ?x1264), film_release_region(?x4694, ?x1264), film_release_region(?x4610, ?x1264), film_release_region(?x4545, ?x1264), film_release_region(?x3226, ?x1264), film_release_region(?x634, ?x1264), ?x4610 = 017jd9, ?x6175 = 0gg5kmg, location_of_ceremony(?x566, ?x1264), administrative_parent(?x1679, ?x1264), organization(?x346, ?x196), country(?x7463, ?x1264), ?x10610 = 03902, genre(?x2189, ?x53), month(?x362, ?x7298), ?x6556 = 05dss7, ?x10143 = 0h3tv, film_release_region(?x2189, ?x1023), ?x4545 = 05p09dd, ?x3226 = 0gyfp9c, ?x7463 = 02fj8n, ?x9652 = 0ddbjy4, service_location(?x555, ?x1264), ?x634 = 0gx9rvq, film_crew_role(?x6489, ?x137), origin(?x5391, ?x6960), ?x1023 = 0ctw_b, ?x4694 = 02j69w, place_of_birth(?x1182, ?x6960), award(?x8129, ?x2324) *> conf = 0.81 ranks of expected_values: 4 EVAL 040fv seasonal_months 028kb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 12.000 12.000 0.895 http://example.org/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months #8035-01385g PRED entity: 01385g PRED relation: profession PRED expected values: 0dxtg => 134 concepts (83 used for prediction) PRED predicted values (max 10 best out of 80): 01d_h8 (0.69 #4711, 0.68 #5005, 0.67 #5447), 0cbd2 (0.69 #4859, 0.57 #5595, 0.43 #2653), 0dxtg (0.62 #4718, 0.61 #5454, 0.60 #5012), 03gjzk (0.36 #11631, 0.33 #896, 0.27 #4719), 018gz8 (0.27 #898, 0.17 #16, 0.16 #1927), 0n1h (0.23 #5599, 0.07 #1922, 0.07 #3099), 09jwl (0.23 #4135, 0.22 #5606, 0.19 #10458), 02krf9 (0.23 #4731, 0.22 #5025, 0.21 #3849), 016z4k (0.17 #5592, 0.10 #4121, 0.10 #1915), 0np9r (0.15 #6344, 0.14 #902, 0.14 #10166) >> Best rule #4711 for best value: >> intensional similarity = 4 >> extensional distance = 358 >> proper extension: 03c_pqj; 02znwv; 05rnp1; 026xt5c; 0d8cr0; 06z9yh; 0brddh; 0g_rs_; 0b66qd; >> query: (?x12677, 01d_h8) <- gender(?x12677, ?x231), profession(?x12677, ?x524), ?x524 = 02jknp, place_of_birth(?x12677, ?x9929) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #4718 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 358 *> proper extension: 03c_pqj; 02znwv; 05rnp1; 026xt5c; 0d8cr0; 06z9yh; 0brddh; 0g_rs_; 0b66qd; *> query: (?x12677, 0dxtg) <- gender(?x12677, ?x231), profession(?x12677, ?x524), ?x524 = 02jknp, place_of_birth(?x12677, ?x9929) *> conf = 0.62 ranks of expected_values: 3 EVAL 01385g profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 83.000 0.694 http://example.org/people/person/profession #8034-0kt_4 PRED entity: 0kt_4 PRED relation: genre PRED expected values: 01g6gs => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 158): 05p553 (0.65 #2646, 0.33 #5408, 0.32 #4928), 02kdv5l (0.50 #2, 0.37 #242, 0.25 #5406), 03k9fj (0.50 #12, 0.26 #252, 0.24 #2654), 02l7c8 (0.42 #257, 0.42 #1097, 0.40 #137), 04xvlr (0.38 #121, 0.32 #241, 0.29 #481), 01jfsb (0.31 #733, 0.28 #4937, 0.27 #6017), 01hmnh (0.28 #2660, 0.25 #18, 0.18 #258), 082gq (0.28 #271, 0.25 #31, 0.23 #151), 06l3bl (0.25 #158, 0.25 #38, 0.14 #278), 03bxz7 (0.25 #55, 0.17 #175, 0.12 #1255) >> Best rule #2646 for best value: >> intensional similarity = 3 >> extensional distance = 897 >> proper extension: 0dtw1x; 0gj9qxr; 016kz1; 011yfd; 043sct5; 0g5q34q; 0gh6j94; 076xkdz; 0j8f09z; 0564x; ... >> query: (?x8984, 05p553) <- genre(?x8984, ?x4088), genre(?x5513, ?x4088), ?x5513 = 0d4htf >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #981 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 323 *> proper extension: 0n2bh; 01vrwfv; *> query: (?x8984, 01g6gs) <- nominated_for(?x269, ?x8984), award(?x269, ?x102), people(?x268, ?x269) *> conf = 0.12 ranks of expected_values: 23 EVAL 0kt_4 genre 01g6gs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 61.000 61.000 0.648 http://example.org/film/film/genre #8033-05z_kps PRED entity: 05z_kps PRED relation: currency PRED expected values: 09nqf => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.82 #43, 0.81 #148, 0.79 #246), 01nv4h (0.24 #9, 0.09 #16, 0.08 #170), 02l6h (0.05 #25, 0.04 #67, 0.03 #95), 088n7 (0.02 #28), 0kz1h (0.01 #96, 0.01 #103, 0.01 #117), 02gsvk (0.01 #293, 0.01 #272) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 0gy30w; >> query: (?x1228, 09nqf) <- country(?x1228, ?x512), film_crew_role(?x1228, ?x4305), titles(?x53, ?x1228), ?x4305 = 0215hd >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05z_kps currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.816 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #8032-09gkx35 PRED entity: 09gkx35 PRED relation: film_distribution_medium PRED expected values: 0735l => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.84 #61, 0.78 #48, 0.14 #17), 029j_ (0.13 #57, 0.11 #44, 0.10 #50), 02nxhr (0.12 #58, 0.08 #33, 0.08 #45), 0dq6p (0.08 #59, 0.06 #46, 0.04 #21), 07z4p (0.01 #49) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 0522wp; >> query: (?x3603, 0735l) <- film(?x609, ?x3603), region(?x3603, ?x512), ?x609 = 03xq0f >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09gkx35 film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.842 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #8031-01wbz9 PRED entity: 01wbz9 PRED relation: category PRED expected values: 08mbj5d => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #38, 0.90 #16, 0.86 #57) >> Best rule #38 for best value: >> intensional similarity = 4 >> extensional distance = 174 >> proper extension: 01gv_f; >> query: (?x3767, 08mbj5d) <- award(?x3767, ?x3926), award(?x2987, ?x3926), profession(?x3767, ?x131), ?x2987 = 01vw20_ >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wbz9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.898 http://example.org/common/topic/webpage./common/webpage/category #8030-04rlf PRED entity: 04rlf PRED relation: industry! PRED expected values: 073tm9 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 318): 0l8sx (0.43 #6839, 0.40 #4028, 0.33 #22), 016tw3 (0.43 #6839, 0.24 #10384, 0.24 #10383), 049ql1 (0.43 #6839, 0.20 #4161, 0.10 #6758), 0g768 (0.43 #6839, 0.08 #8492, 0.02 #4242), 03rhqg (0.43 #6839, 0.08 #8492, 0.02 #4242), 01dfb6 (0.43 #6839, 0.05 #8376, 0.03 #9796), 01bfjy (0.43 #6839), 086k8 (0.43 #6839), 07k2x (0.40 #3853, 0.33 #82, 0.25 #5741), 0g1rw (0.40 #3780, 0.33 #9, 0.25 #5668) >> Best rule #6839 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 020mfr; 0hz28; 019mlh; >> query: (?x8681, ?x14343) <- industry(?x13890, ?x8681), industry(?x12261, ?x8681), industry(?x8559, ?x8681), industry(?x5666, ?x8681), category(?x8559, ?x134), child(?x14343, ?x13890), place_founded(?x12261, ?x9559), citytown(?x5666, ?x739) >> conf = 0.43 => this is the best rule for 8 predicted values *> Best rule #5731 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 02jjt; 019z7b; 08mh3kd; *> query: (?x8681, 073tm9) <- industry(?x8559, ?x8681), industry(?x648, ?x8681), category(?x8559, ?x134), artist(?x648, ?x4062), type_of_union(?x4062, ?x566) *> conf = 0.25 ranks of expected_values: 36 EVAL 04rlf industry! 073tm9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 90.000 90.000 0.427 http://example.org/business/business_operation/industry #8029-086h6p PRED entity: 086h6p PRED relation: category PRED expected values: 08mbj5d => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.77 #41, 0.67 #45, 0.66 #59) >> Best rule #41 for best value: >> intensional similarity = 13 >> extensional distance = 29 >> proper extension: 013x0b; 03rhqg; 0229rs; 01gfq4; 0g768; 0181dw; 01q940; 01cf93; 03qx_f; 03x8cz; ... >> query: (?x13919, 08mbj5d) <- child(?x11303, ?x13919), child(?x11303, ?x6141), industry(?x11303, ?x245), industry(?x6230, ?x245), citytown(?x11303, ?x1658), ?x6230 = 073tm9, citytown(?x6141, ?x2474), place_of_birth(?x877, ?x1658), featured_film_locations(?x97, ?x1658), contains(?x1658, ?x14707), location(?x1657, ?x1658), mode_of_transportation(?x1658, ?x4272), ?x1657 = 07csf4 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 086h6p category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.774 http://example.org/common/topic/webpage./common/webpage/category #8028-02cl1 PRED entity: 02cl1 PRED relation: dog_breed PRED expected values: 0km5c 01_gx_ => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 2): 0km5c (0.87 #93, 0.52 #117, 0.50 #53), 01_gx_ (0.81 #94, 0.50 #118, 0.48 #28) >> Best rule #93 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0chrx; >> query: (?x659, 0km5c) <- location(?x2390, ?x659), place_of_birth(?x1775, ?x659), dog_breed(?x659, ?x5194), contains(?x94, ?x659) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02cl1 dog_breed 01_gx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.872 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 02cl1 dog_breed 0km5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.872 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #8027-02t7t PRED entity: 02t7t PRED relation: religion! PRED expected values: 03s0w 05tbn 05mph => 43 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1227): 050l8 (0.82 #1410, 0.77 #1606, 0.75 #1117), 059_c (0.82 #1393, 0.75 #1790, 0.75 #1100), 01x73 (0.82 #1403, 0.75 #1800, 0.75 #1110), 0gyh (0.77 #1612, 0.75 #1123, 0.75 #1026), 03s0w (0.75 #1097, 0.75 #1000, 0.73 #1390), 01n7q (0.75 #1791, 0.75 #1101, 0.73 #1394), 04rrx (0.75 #1114, 0.75 #1017, 0.71 #621), 050ks (0.75 #1156, 0.75 #1059, 0.71 #663), 07h34 (0.75 #1133, 0.73 #1426, 0.71 #738), 0498y (0.75 #1136, 0.73 #1429, 0.71 #741) >> Best rule #1410 for best value: >> intensional similarity = 14 >> extensional distance = 9 >> proper extension: 058x5; >> query: (?x9091, 050l8) <- religion(?x744, ?x9091), religion(?x2768, ?x9091), religion(?x335, ?x9091), ?x2768 = 03s5t, student(?x2682, ?x744), award(?x744, ?x1007), contains(?x335, ?x322), location(?x1521, ?x335), location(?x940, ?x335), ?x1521 = 01wp8w7, location_of_ceremony(?x1652, ?x335), state_province_region(?x6230, ?x335), film(?x940, ?x4048), artist(?x6230, ?x827) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1097 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 6 *> proper extension: 021_0p; *> query: (?x9091, 03s0w) <- religion(?x3099, ?x9091), religion(?x744, ?x9091), religion(?x2768, ?x9091), religion(?x2623, ?x9091), ?x2768 = 03s5t, student(?x2682, ?x744), people(?x743, ?x744), profession(?x744, ?x353), location(?x744, ?x953), category(?x744, ?x134), politician(?x3098, ?x3099), student(?x2327, ?x3099), ?x2623 = 02xry *> conf = 0.75 ranks of expected_values: 5, 11, 18 EVAL 02t7t religion! 05mph CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 43.000 34.000 0.818 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 02t7t religion! 05tbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 43.000 34.000 0.818 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 02t7t religion! 03s0w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 43.000 34.000 0.818 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #8026-07s2s PRED entity: 07s2s PRED relation: films PRED expected values: 07gp9 0661m4p 08phg9 => 67 concepts (13 used for prediction) PRED predicted values (max 10 best out of 59): 0fvr1 (0.33 #1024, 0.33 #1023, 0.33 #1022), 07ghq (0.33 #1023, 0.33 #1022, 0.12 #3580), 02bg55 (0.33 #1023, 0.33 #1022, 0.12 #3580), 0dmn0x (0.33 #969, 0.20 #3526, 0.17 #4038), 0jdr0 (0.33 #958, 0.20 #3515, 0.17 #4027), 0bw20 (0.33 #865, 0.20 #3422, 0.17 #3934), 02scbv (0.33 #855, 0.20 #3412, 0.17 #3924), 0jqd3 (0.33 #827, 0.20 #3384, 0.17 #3896), 03cp4cn (0.33 #824, 0.20 #3381, 0.17 #3893), 0fjyzt (0.33 #778, 0.20 #3335, 0.17 #3847) >> Best rule #1024 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 01w1sx; >> query: (?x11523, ?x2184) <- films(?x11523, ?x10943), films(?x11523, ?x10902), films(?x11523, ?x10029), genre(?x6520, ?x11523), genre(?x2184, ?x11523), nominated_for(?x2183, ?x2184), music(?x10902, ?x10079), featured_film_locations(?x10029, ?x739), country(?x2184, ?x94), crewmember(?x10943, ?x929), film_release_region(?x6520, ?x87) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07s2s films 08phg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 13.000 0.333 http://example.org/film/film_subject/films EVAL 07s2s films 0661m4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 13.000 0.333 http://example.org/film/film_subject/films EVAL 07s2s films 07gp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 13.000 0.333 http://example.org/film/film_subject/films #8025-0gvs1kt PRED entity: 0gvs1kt PRED relation: film_release_region PRED expected values: 0d060g 0f8l9c => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 141): 0f8l9c (0.90 #295, 0.89 #1129, 0.89 #1268), 0d060g (0.78 #421, 0.77 #560, 0.76 #1255), 06bnz (0.73 #1289, 0.72 #594, 0.70 #316), 03rj0 (0.71 #606, 0.64 #328, 0.62 #1301), 06qd3 (0.62 #308, 0.54 #586, 0.51 #1699), 06mzp (0.57 #572, 0.52 #294, 0.50 #16), 015qh (0.57 #312, 0.54 #590, 0.50 #34), 04gzd (0.54 #285, 0.53 #563, 0.52 #1258), 06npd (0.50 #15, 0.35 #1809, 0.32 #571), 0h7x (0.49 #305, 0.40 #583, 0.38 #1696) >> Best rule #295 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 07l50vn; >> query: (?x3292, 0f8l9c) <- film_release_region(?x3292, ?x456), ?x456 = 05qhw, honored_for(?x1442, ?x3292) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0gvs1kt film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvs1kt film_release_region 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8024-06gcn PRED entity: 06gcn PRED relation: artists! PRED expected values: 01b4p4 => 87 concepts (40 used for prediction) PRED predicted values (max 10 best out of 293): 06by7 (0.88 #7398, 0.82 #9548, 0.80 #4018), 0xhtw (0.70 #4014, 0.67 #4627, 0.65 #5551), 05r6t (0.65 #10527, 0.24 #3996, 0.23 #11987), 064t9 (0.53 #9232, 0.50 #5855, 0.50 #3083), 02yv6b (0.45 #4093, 0.38 #4706, 0.33 #404), 016clz (0.43 #10452, 0.43 #8915, 0.43 #1847), 0gywn (0.43 #1897, 0.38 #2204, 0.33 #55), 08jyyk (0.42 #4674, 0.40 #4061, 0.39 #8975), 01fh36 (0.40 #4081, 0.38 #4694, 0.38 #2540), 016jny (0.33 #717, 0.33 #410, 0.25 #2252) >> Best rule #7398 for best value: >> intensional similarity = 11 >> extensional distance = 41 >> proper extension: 01p9hgt; >> query: (?x7620, 06by7) <- artist(?x5744, ?x7620), ?x5744 = 01clyr, artists(?x3370, ?x7620), artists(?x3370, ?x9241), artists(?x3370, ?x7612), artists(?x3370, ?x3767), artists(?x3370, ?x2600), ?x3767 = 01wbz9, ?x2600 = 0qf3p, ?x9241 = 01w5gg6, award_nominee(?x9262, ?x7612) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #193 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 0qf3p; *> query: (?x7620, 01b4p4) <- artist(?x5744, ?x7620), artist(?x5634, ?x7620), ?x5744 = 01clyr, artists(?x9853, ?x7620), artists(?x3370, ?x7620), ?x3370 = 059kh, ?x5634 = 01cl2y, parent_genre(?x9043, ?x9853) *> conf = 0.33 ranks of expected_values: 15 EVAL 06gcn artists! 01b4p4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 87.000 40.000 0.884 http://example.org/music/genre/artists #8023-0jz71 PRED entity: 0jz71 PRED relation: titles! PRED expected values: 06qm3 => 67 concepts (39 used for prediction) PRED predicted values (max 10 best out of 69): 07s9rl0 (0.51 #518, 0.34 #1339, 0.32 #927), 01z4y (0.42 #1168, 0.23 #3246, 0.17 #2203), 04xvlr (0.28 #521, 0.23 #1342, 0.21 #315), 017fp (0.22 #541, 0.12 #950, 0.11 #335), 02l7c8 (0.18 #3733, 0.17 #3628, 0.11 #3313), 05p553 (0.18 #3733, 0.17 #3628, 0.11 #3313), 07ssc (0.14 #527, 0.14 #1348, 0.14 #217), 03mqtr (0.14 #563, 0.09 #1384, 0.06 #972), 024qqx (0.14 #1627, 0.12 #1523, 0.11 #1938), 04t36 (0.14 #215, 0.13 #730, 0.13 #627) >> Best rule #518 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 07w8fz; >> query: (?x10829, 07s9rl0) <- nominated_for(?x591, ?x10829), film_crew_role(?x10829, ?x137), ?x591 = 0f4x7 >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #1183 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 194 *> proper extension: 04svwx; *> query: (?x10829, 06qm3) <- genre(?x10829, ?x1403), genre(?x10829, ?x258), ?x1403 = 02l7c8, ?x258 = 05p553 *> conf = 0.05 ranks of expected_values: 23 EVAL 0jz71 titles! 06qm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 67.000 39.000 0.505 http://example.org/media_common/netflix_genre/titles #8022-026y23w PRED entity: 026y23w PRED relation: athlete! PRED expected values: 02vx4 => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 6): 02vx4 (0.89 #105, 0.88 #64, 0.88 #52), 0jm_ (0.15 #147, 0.14 #136, 0.02 #55), 018w8 (0.15 #139, 0.12 #150, 0.01 #119), 018jz (0.08 #140, 0.07 #151), 03tmr (0.02 #145), 09xp_ (0.02 #102, 0.01 #132, 0.01 #122) >> Best rule #105 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 0784v1; 0c11mj; 071pf2; 0fv6dr; 09lhln; 0f1pyf; 0bw7ly; 0457w0; 09r1j5; 0djvzd; ... >> query: (?x5763, 02vx4) <- team(?x5763, ?x2355), position(?x2355, ?x60), ?x60 = 02nzb8, nationality(?x5763, ?x512) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026y23w athlete! 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.886 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #8021-032md PRED entity: 032md PRED relation: gender PRED expected values: 05zppz => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #33, 0.90 #105, 0.90 #75), 02zsn (0.46 #297, 0.28 #150, 0.27 #172) >> Best rule #33 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 014zfs; 01j7rd; 023t0q; 0427y; 02465; >> query: (?x8043, 05zppz) <- influenced_by(?x4353, ?x8043), profession(?x8043, ?x319), ?x319 = 01d_h8, written_by(?x4699, ?x4353) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 032md gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.912 http://example.org/people/person/gender #8020-0b6tzs PRED entity: 0b6tzs PRED relation: nominated_for! PRED expected values: 02qyntr => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 195): 027dtxw (0.70 #1499, 0.70 #1714, 0.69 #3214), 09d28z (0.70 #1499, 0.70 #1714, 0.69 #3214), 027cyf7 (0.70 #1499, 0.70 #1714, 0.69 #3214), 0f4x7 (0.58 #1305, 0.57 #1520, 0.50 #3020), 0gq_v (0.57 #3015, 0.46 #1300, 0.44 #1515), 02qyntr (0.55 #4440, 0.41 #1440, 0.39 #1655), 0l8z1 (0.48 #683, 0.39 #1325, 0.38 #1540), 0gs96 (0.45 #3070, 0.39 #1355, 0.38 #1570), 09qv_s (0.44 #1165, 0.29 #10927, 0.22 #15641), 054krc (0.42 #6690, 0.42 #694, 0.35 #1122) >> Best rule #1499 for best value: >> intensional similarity = 5 >> extensional distance = 57 >> proper extension: 0c5dd; 0sxfd; 0qm98; 0bmpm; 0cq7kw; 0bm2x; 0cq86w; 0pd64; >> query: (?x945, ?x112) <- award_winner(?x945, ?x163), award(?x945, ?x1307), award(?x945, ?x112), ?x1307 = 0gq9h, film(?x2414, ?x945) >> conf = 0.70 => this is the best rule for 3 predicted values *> Best rule #4440 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 129 *> proper extension: 016fyc; 04v8x9; 01sxly; 0n0bp; 0209xj; 0hmr4; 0jzw; 0p_th; 0283_zv; 0jym0; ... *> query: (?x945, 02qyntr) <- honored_for(?x762, ?x945), award_winner(?x945, ?x163), nominated_for(?x198, ?x945), ?x198 = 040njc *> conf = 0.55 ranks of expected_values: 6 EVAL 0b6tzs nominated_for! 02qyntr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 120.000 120.000 0.700 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8019-0pj8m PRED entity: 0pj8m PRED relation: languages PRED expected values: 01c7y => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 22): 03k50 (0.22 #891, 0.07 #2408, 0.06 #1298), 07c9s (0.12 #900, 0.04 #2417, 0.01 #2972), 02bjrlw (0.06 #1296, 0.05 #1370, 0.05 #1629), 0999q (0.06 #909, 0.02 #2426), 09s02 (0.04 #922, 0.02 #2439), 09bnf (0.04 #925, 0.01 #2442), 04306rv (0.04 #1260, 0.04 #113, 0.03 #779), 06nm1 (0.04 #116, 0.03 #2410, 0.03 #1633), 06b_j (0.04 #125, 0.01 #828), 055qm (0.03 #910, 0.01 #2427) >> Best rule #891 for best value: >> intensional similarity = 2 >> extensional distance = 205 >> proper extension: 04rs03; 01pr_j6; 025tdwc; 06pwf6; 0288crq; 02wxvtv; 07yw6t; 0fr7nt; 0kvjrw; 0cc63l; ... >> query: (?x7995, 03k50) <- nationality(?x7995, ?x2146), ?x2146 = 03rk0 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #917 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 205 *> proper extension: 04rs03; 01pr_j6; 025tdwc; 06pwf6; 0288crq; 02wxvtv; 07yw6t; 0fr7nt; 0kvjrw; 0cc63l; ... *> query: (?x7995, 01c7y) <- nationality(?x7995, ?x2146), ?x2146 = 03rk0 *> conf = 0.02 ranks of expected_values: 12 EVAL 0pj8m languages 01c7y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 154.000 154.000 0.222 http://example.org/people/person/languages #8018-08k1lz PRED entity: 08k1lz PRED relation: languages PRED expected values: 02h40lc => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 21): 02h40lc (0.93 #688, 0.92 #611, 0.91 #764), 064_8sq (0.09 #966, 0.09 #852, 0.09 #1081), 03k50 (0.09 #117, 0.08 #1108, 0.08 #1146), 07c9s (0.07 #126, 0.04 #1117, 0.04 #1155), 0999q (0.07 #136, 0.03 #440, 0.02 #1127), 09s02 (0.06 #149, 0.02 #453, 0.02 #263), 06nm1 (0.05 #576, 0.04 #691, 0.04 #614), 02bjrlw (0.04 #915, 0.04 #1106, 0.04 #839), 09bnf (0.03 #152, 0.01 #609, 0.01 #1143), 0t_2 (0.02 #694, 0.02 #922, 0.02 #617) >> Best rule #688 for best value: >> intensional similarity = 4 >> extensional distance = 382 >> proper extension: 067xw; >> query: (?x10214, 02h40lc) <- nationality(?x10214, ?x94), profession(?x10214, ?x1032), ?x94 = 09c7w0, languages(?x10214, ?x732) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08k1lz languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.927 http://example.org/people/person/languages #8017-05y0cr PRED entity: 05y0cr PRED relation: nominated_for! PRED expected values: 05zvq6g 0gqxm => 91 concepts (55 used for prediction) PRED predicted values (max 10 best out of 226): 0gq9h (0.71 #1923, 0.54 #7736, 0.52 #758), 054knh (0.70 #3262, 0.68 #1398, 0.68 #6977), 02z13jg (0.69 #8143, 0.68 #1398, 0.68 #6977), 0gs9p (0.62 #1925, 0.46 #7738, 0.46 #5875), 04dn09n (0.59 #5846, 0.33 #266, 0.31 #1896), 019f4v (0.56 #1915, 0.47 #5865, 0.44 #4705), 0l8z1 (0.50 #283, 0.31 #4703, 0.31 #4935), 094qd5 (0.50 #267, 0.24 #5847, 0.23 #6544), 0k611 (0.48 #7747, 0.46 #1934, 0.41 #5884), 040njc (0.48 #1869, 0.38 #704, 0.36 #5819) >> Best rule #1923 for best value: >> intensional similarity = 7 >> extensional distance = 46 >> proper extension: 0k4f3; 027ct7c; 0286hyp; >> query: (?x9279, 0gq9h) <- nominated_for(?x6165, ?x9279), nominated_for(?x591, ?x9279), nominated_for(?x484, ?x9279), ?x484 = 0gq_v, award(?x1370, ?x6165), film(?x9001, ?x9279), ?x591 = 0f4x7 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #592 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 16 *> proper extension: 0m313; 050r1z; 0dr_4; 0bcndz; 0_7w6; 083skw; 0c8qq; 03q5db; 01242_; 0b2qtl; ... *> query: (?x9279, 0gqxm) <- award(?x9279, ?x143), genre(?x9279, ?x4088), genre(?x9279, ?x1403), ?x4088 = 04xvh5, film(?x9001, ?x9279), ?x1403 = 02l7c8, honored_for(?x7105, ?x9279) *> conf = 0.33 ranks of expected_values: 21, 86 EVAL 05y0cr nominated_for! 0gqxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 91.000 55.000 0.708 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05y0cr nominated_for! 05zvq6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 91.000 55.000 0.708 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8016-0jdd PRED entity: 0jdd PRED relation: locations! PRED expected values: 0c6cwg => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 102): 081pw (0.73 #1, 0.11 #1796, 0.06 #2823), 03jqfx (0.21 #336, 0.18 #464, 0.18 #1234), 06k75 (0.20 #1209, 0.19 #952, 0.17 #1594), 086m1 (0.18 #64, 0.04 #1859, 0.04 #2627), 0cm2xh (0.18 #44, 0.03 #1839, 0.01 #6337), 02h2z_ (0.12 #1010, 0.09 #1652, 0.09 #1267), 01w1sx (0.11 #1886, 0.09 #1245, 0.09 #347), 05nqz (0.11 #425, 0.09 #1195, 0.08 #1836), 0b_75k (0.10 #3515, 0.08 #6343, 0.07 #6600), 01gqg3 (0.10 #1880, 0.09 #85, 0.08 #2907) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 02qkt; 04wsz; >> query: (?x3352, 081pw) <- locations(?x13022, ?x3352), combatants(?x13022, ?x1536), ?x1536 = 06c1y >> conf = 0.73 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jdd locations! 0c6cwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 106.000 0.727 http://example.org/time/event/locations #8015-0kf9p PRED entity: 0kf9p PRED relation: contains! PRED expected values: 081yw => 129 concepts (83 used for prediction) PRED predicted values (max 10 best out of 256): 081yw (0.80 #15226, 0.78 #19705, 0.69 #53748), 09c7w0 (0.80 #8059, 0.76 #13433, 0.75 #29560), 01n7q (0.59 #19783, 0.25 #1868, 0.22 #47553), 07ssc (0.17 #53780, 0.16 #71689, 0.16 #59152), 05k7sb (0.17 #1028, 0.10 #133, 0.09 #36863), 0k3l5 (0.17 #1294, 0.10 #399, 0.08 #2189), 02xry (0.15 #68239, 0.14 #70029, 0.05 #35997), 06pvr (0.14 #19871, 0.08 #1956, 0.06 #36896), 04_1l0v (0.13 #38972, 0.13 #60466, 0.10 #67631), 05kj_ (0.13 #19746, 0.03 #18849, 0.03 #20641) >> Best rule #15226 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 0_7z2; 0qr4n; 0rqyx; 0r80l; 0d23k; 0ggh3; 0fsb8; 0rmby; 0sgtz; 0r8bh; ... >> query: (?x11367, ?x4600) <- administrative_division(?x11367, ?x11366), contains(?x4600, ?x11366), source(?x11366, ?x958) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kf9p contains! 081yw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 83.000 0.800 http://example.org/location/location/contains #8014-075npt PRED entity: 075npt PRED relation: type_of_union PRED expected values: 04ztj => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.79 #137, 0.77 #153, 0.77 #117), 01g63y (0.47 #357, 0.14 #18, 0.13 #70) >> Best rule #137 for best value: >> intensional similarity = 5 >> extensional distance = 153 >> proper extension: 05bnp0; 01p7yb; 02qjj7; 02r_d4; 0168cl; 06y9c2; 03ldxq; 05gml8; 08f3b1; 05ml_s; ... >> query: (?x12240, 04ztj) <- student(?x5807, ?x12240), student(?x6760, ?x12240), profession(?x12240, ?x1383), major_field_of_study(?x5807, ?x1154), state_province_region(?x5807, ?x6895) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 075npt type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.787 http://example.org/people/person/spouse_s./people/marriage/type_of_union #8013-03nfnx PRED entity: 03nfnx PRED relation: film! PRED expected values: 01vh3r => 69 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1254): 0l6px (0.40 #4531, 0.15 #10746, 0.12 #12817), 06ltr (0.40 #5084, 0.15 #11299, 0.12 #13370), 0134w7 (0.40 #4304, 0.15 #10519, 0.12 #12590), 065jlv (0.40 #4456, 0.15 #10671, 0.12 #12742), 09y20 (0.40 #4392, 0.15 #10607, 0.12 #12678), 013_vh (0.40 #4806, 0.12 #13092, 0.08 #11021), 05sq84 (0.40 #4379, 0.12 #12665, 0.08 #10594), 010xjr (0.40 #5807, 0.12 #14093, 0.08 #12022), 07m77x (0.33 #1533, 0.11 #9819, 0.06 #13962), 02p21g (0.33 #257, 0.11 #8543, 0.06 #12686) >> Best rule #4531 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 03176f; >> query: (?x8075, 0l6px) <- region(?x8075, ?x512), film(?x9152, ?x8075), film(?x4988, ?x8075), ?x4988 = 041c4, award_nominee(?x230, ?x9152) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #8138 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0dr3sl; 0cn_b8; *> query: (?x8075, 01vh3r) <- region(?x8075, ?x512), film(?x5940, ?x8075), film(?x4988, ?x8075), award(?x4988, ?x68), ?x5940 = 0p__8 *> conf = 0.20 ranks of expected_values: 27 EVAL 03nfnx film! 01vh3r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 69.000 49.000 0.400 http://example.org/film/actor/film./film/performance/film #8012-0pmhf PRED entity: 0pmhf PRED relation: participant! PRED expected values: 0c1pj => 126 concepts (73 used for prediction) PRED predicted values (max 10 best out of 206): 046zh (0.80 #16550, 0.80 #15912, 0.79 #21007), 0237fw (0.08 #3350, 0.07 #805, 0.06 #8443), 0c1pj (0.07 #676, 0.01 #9586, 0.01 #10858), 010xjr (0.07 #1213), 05cljf (0.07 #649), 014zcr (0.06 #9565, 0.05 #3200, 0.05 #10837), 01rr9f (0.04 #3216, 0.04 #9581, 0.03 #8309), 0c6qh (0.04 #3355, 0.03 #8448, 0.03 #16085), 029q_y (0.04 #3664, 0.03 #8757, 0.03 #16394), 05dbf (0.04 #3337, 0.03 #8430, 0.02 #10338) >> Best rule #16550 for best value: >> intensional similarity = 3 >> extensional distance = 397 >> proper extension: 012dr7; 0dzlk; >> query: (?x2596, ?x2908) <- award(?x2596, ?x401), nominated_for(?x2596, ?x69), participant(?x2596, ?x2908) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #676 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 046chh; *> query: (?x2596, 0c1pj) <- film(?x2596, ?x4361), film(?x2596, ?x1877), genre(?x4361, ?x225), ?x1877 = 0cz_ym *> conf = 0.07 ranks of expected_values: 3 EVAL 0pmhf participant! 0c1pj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 73.000 0.805 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #8011-0btpx PRED entity: 0btpx PRED relation: film PRED expected values: 03nqnnk => 110 concepts (86 used for prediction) PRED predicted values (max 10 best out of 771): 02k_4g (0.67 #12496, 0.62 #33919, 0.57 #83905), 04lhc4 (0.25 #1212, 0.02 #8352, 0.02 #10137), 0jqn5 (0.25 #221, 0.02 #7361, 0.02 #9146), 06929s (0.25 #716), 04kkz8 (0.25 #144), 01l_pn (0.12 #2749, 0.04 #8104, 0.04 #9889), 0jzw (0.12 #1904, 0.03 #146391, 0.03 #146390), 05dss7 (0.12 #2941), 0blpg (0.11 #7794, 0.08 #9579, 0.07 #11364), 0f40w (0.09 #3932, 0.06 #2147, 0.01 #12858) >> Best rule #12496 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 029ghl; >> query: (?x8445, ?x782) <- award(?x8445, ?x154), ?x154 = 05b4l5x, award_winner(?x782, ?x8445) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #9944 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 014vk4; *> query: (?x8445, 03nqnnk) <- award(?x8445, ?x154), ?x154 = 05b4l5x, award_nominee(?x221, ?x8445) *> conf = 0.04 ranks of expected_values: 173 EVAL 0btpx film 03nqnnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 110.000 86.000 0.667 http://example.org/film/actor/film./film/performance/film #8010-01mylz PRED entity: 01mylz PRED relation: film PRED expected values: 01dc0c => 87 concepts (32 used for prediction) PRED predicted values (max 10 best out of 685): 0d90m (0.47 #3568, 0.06 #1788, 0.02 #12468), 01qb5d (0.42 #3696, 0.01 #39296, 0.01 #23276), 0cd2vh9 (0.14 #248, 0.11 #3808), 043tvp3 (0.14 #1205, 0.03 #6545, 0.03 #8325), 03vfr_ (0.14 #1634, 0.03 #12314, 0.02 #14094), 07x4qr (0.14 #401, 0.02 #23541, 0.02 #25321), 020bv3 (0.14 #315, 0.02 #23455, 0.02 #25235), 02qr3k8 (0.14 #1282, 0.02 #10182, 0.02 #54682), 063y9fp (0.14 #1522, 0.02 #49582, 0.01 #47802), 043tz0c (0.14 #748, 0.02 #13208) >> Best rule #3568 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 03ym1; >> query: (?x12100, 0d90m) <- film(?x12100, ?x4392), film(?x12100, ?x3588), ?x4392 = 06gb1w, genre(?x3588, ?x812), ?x812 = 01jfsb >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #17465 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 132 *> proper extension: 05bnp0; 05m63c; 01p7yb; 02r_d4; 05gml8; 05ml_s; 01yk13; 049dyj; 02r34n; 0n6f8; ... *> query: (?x12100, 01dc0c) <- student(?x4268, ?x12100), film(?x12100, ?x288), major_field_of_study(?x4268, ?x254), major_field_of_study(?x122, ?x4268) *> conf = 0.01 ranks of expected_values: 631 EVAL 01mylz film 01dc0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 32.000 0.474 http://example.org/film/actor/film./film/performance/film #8009-049fgvm PRED entity: 049fgvm PRED relation: film PRED expected values: 05zpghd => 108 concepts (75 used for prediction) PRED predicted values (max 10 best out of 438): 0prrm (0.33 #861, 0.18 #4443, 0.18 #11607), 011xg5 (0.22 #1434, 0.18 #5016, 0.17 #6807), 0p9lw (0.22 #146, 0.12 #10892, 0.12 #9101), 0d87hc (0.18 #5224, 0.12 #12388, 0.12 #10597), 016z43 (0.17 #7144, 0.11 #1771, 0.09 #5353), 03kx49 (0.17 #6716, 0.04 #17462, 0.03 #21046), 0b3n61 (0.12 #10315, 0.11 #1360, 0.09 #4942), 0407yj_ (0.12 #7648, 0.11 #2275, 0.09 #4066), 063fh9 (0.12 #8344, 0.11 #2971, 0.09 #4762), 07p62k (0.11 #2145, 0.11 #354, 0.09 #3936) >> Best rule #861 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 014z8v; >> query: (?x6693, 0prrm) <- influenced_by(?x6693, ?x7183), ?x7183 = 01hmk9, award_winner(?x8500, ?x6693) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8119 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 02_j7t; 04h07s; 01s7qqw; *> query: (?x6693, 05zpghd) <- influenced_by(?x6693, ?x7183), influenced_by(?x6693, ?x3917), profession(?x7183, ?x319), ?x3917 = 0p_47 *> conf = 0.06 ranks of expected_values: 121 EVAL 049fgvm film 05zpghd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 108.000 75.000 0.333 http://example.org/film/actor/film./film/performance/film #8008-0j_tw PRED entity: 0j_tw PRED relation: film_release_region PRED expected values: 01znc_ => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 140): 0jgd (0.86 #651, 0.79 #3081, 0.77 #975), 03gj2 (0.86 #676, 0.77 #1810, 0.76 #3106), 0345h (0.84 #685, 0.83 #3115, 0.76 #1171), 07ssc (0.84 #665, 0.79 #3095, 0.79 #1151), 01znc_ (0.82 #695, 0.73 #3125, 0.70 #2315), 05qhw (0.79 #663, 0.73 #3093, 0.71 #1149), 035qy (0.76 #3117, 0.75 #687, 0.70 #2307), 015fr (0.76 #3097, 0.75 #667, 0.70 #3908), 0154j (0.75 #3083, 0.68 #1139, 0.68 #3894), 0d060g (0.73 #655, 0.72 #1141, 0.71 #3085) >> Best rule #651 for best value: >> intensional similarity = 6 >> extensional distance = 54 >> proper extension: 07l50vn; >> query: (?x2104, 0jgd) <- film_release_region(?x2104, ?x4737), film_release_region(?x2104, ?x390), film_release_region(?x2104, ?x252), ?x390 = 0chghy, ?x4737 = 07twz, ?x252 = 03_3d >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #695 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 54 *> proper extension: 07l50vn; *> query: (?x2104, 01znc_) <- film_release_region(?x2104, ?x4737), film_release_region(?x2104, ?x390), film_release_region(?x2104, ?x252), ?x390 = 0chghy, ?x4737 = 07twz, ?x252 = 03_3d *> conf = 0.82 ranks of expected_values: 5 EVAL 0j_tw film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 74.000 74.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #8007-01tlmw PRED entity: 01tlmw PRED relation: location! PRED expected values: 04hpck => 144 concepts (51 used for prediction) PRED predicted values (max 10 best out of 2020): 02gyl0 (0.40 #5982, 0.04 #51310, 0.03 #56348), 0151ns (0.30 #5120, 0.05 #32820, 0.05 #20229), 01s21dg (0.20 #6000, 0.20 #3482, 0.15 #13554), 0dn3n (0.20 #5624, 0.12 #588, 0.10 #3106), 0hnjt (0.20 #5998, 0.08 #8516, 0.08 #13552), 01vsy3q (0.20 #6027, 0.07 #38763, 0.06 #51355), 0pyww (0.20 #6017, 0.07 #21126, 0.06 #48826), 023kzp (0.20 #6252, 0.07 #21361, 0.06 #38988), 0jsg0m (0.20 #6532, 0.07 #21641, 0.05 #19122), 014g9y (0.20 #7182, 0.07 #22291, 0.05 #19772) >> Best rule #5982 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0h44w; >> query: (?x503, 02gyl0) <- location(?x3955, ?x503), award_winner(?x7788, ?x3955), ?x7788 = 09lvl1, nationality(?x3955, ?x94) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01tlmw location! 04hpck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 51.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #8006-054ks3 PRED entity: 054ks3 PRED relation: nominated_for PRED expected values: 07tw_b => 52 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1833): 09p3_s (0.60 #861, 0.29 #2448, 0.25 #4037), 026p4q7 (0.43 #1943, 0.40 #356, 0.38 #3532), 09gq0x5 (0.43 #1841, 0.40 #254, 0.38 #3430), 0gmgwnv (0.43 #2551, 0.40 #964, 0.38 #4140), 095zlp (0.43 #1638, 0.40 #51, 0.38 #3227), 0mcl0 (0.43 #2167, 0.40 #580, 0.38 #3756), 0jsf6 (0.43 #2560, 0.40 #973, 0.38 #4149), 0jyb4 (0.43 #2562, 0.40 #975, 0.38 #4151), 0p9tm (0.43 #2783, 0.40 #1196, 0.38 #4372), 01mgw (0.43 #2738, 0.40 #1151, 0.38 #4327) >> Best rule #861 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 054knh; >> query: (?x2585, 09p3_s) <- ceremony(?x2585, ?x6238), ceremony(?x2585, ?x5392), ?x5392 = 09p3h7, ?x6238 = 09p30_, nominated_for(?x2585, ?x83) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #5382 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0c4z8; 0gqz2; 01c9f2; 025m8l; 01dk00; 099vwn; 026m9w; *> query: (?x2585, 07tw_b) <- award_winner(?x2585, ?x248), award(?x9163, ?x2585), award(?x8799, ?x2585), artists(?x505, ?x9163), ?x8799 = 02f1c *> conf = 0.08 ranks of expected_values: 524 EVAL 054ks3 nominated_for 07tw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 14.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #8005-085wqm PRED entity: 085wqm PRED relation: production_companies PRED expected values: 016tw3 => 67 concepts (51 used for prediction) PRED predicted values (max 10 best out of 62): 054g1r (0.30 #3007, 0.29 #3424), 0gfmc_ (0.17 #50, 0.01 #133, 0.01 #469), 086k8 (0.13 #85, 0.11 #757, 0.11 #1173), 05qd_ (0.10 #177, 0.09 #345, 0.09 #2098), 01gb54 (0.09 #541, 0.07 #1209, 0.07 #1126), 016tw3 (0.09 #767, 0.09 #179, 0.08 #1183), 016tt2 (0.09 #171, 0.08 #759, 0.07 #842), 017s11 (0.09 #170, 0.07 #338, 0.07 #1758), 054lpb6 (0.08 #182, 0.07 #1519, 0.06 #1436), 024rgt (0.06 #276, 0.05 #444, 0.05 #1529) >> Best rule #3007 for best value: >> intensional similarity = 4 >> extensional distance = 1024 >> proper extension: 03_wm6; 03xj05; 04nlb94; >> query: (?x10397, ?x5636) <- film_crew_role(?x10397, ?x468), language(?x10397, ?x11038), film(?x5636, ?x10397), official_language(?x1778, ?x11038) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #767 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 251 *> proper extension: 0bmc4cm; 09rfh9; *> query: (?x10397, 016tw3) <- nominated_for(?x102, ?x10397), genre(?x10397, ?x812), ?x812 = 01jfsb, film_release_region(?x10397, ?x94) *> conf = 0.09 ranks of expected_values: 6 EVAL 085wqm production_companies 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 51.000 0.299 http://example.org/film/film/production_companies #8004-0d6_s PRED entity: 0d6_s PRED relation: country PRED expected values: 0345h => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 35): 02jx1 (0.37 #5424, 0.02 #1481, 0.01 #1423), 01xbgx (0.37 #5424), 0f8l9c (0.35 #17, 0.14 #1472, 0.13 #366), 0345h (0.23 #25, 0.22 #141, 0.18 #258), 0d060g (0.12 #7, 0.08 #415, 0.06 #1636), 01hmnh (0.08 #816, 0.07 #2272, 0.06 #4200), 03k9fj (0.08 #816, 0.07 #2272, 0.06 #4200), 03h64 (0.07 #277, 0.06 #626, 0.05 #918), 0ctw_b (0.06 #79, 0.04 #254, 0.04 #21), 0chghy (0.05 #186, 0.05 #1349, 0.04 #244) >> Best rule #5424 for best value: >> intensional similarity = 3 >> extensional distance = 1768 >> proper extension: 0dr1c2; >> query: (?x10405, ?x512) <- film(?x4468, ?x10405), genre(?x10405, ?x225), nationality(?x4468, ?x512) >> conf = 0.37 => this is the best rule for 2 predicted values *> Best rule #25 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 056k77g; *> query: (?x10405, 0345h) <- film_crew_role(?x10405, ?x468), genre(?x10405, ?x225), country(?x10405, ?x252), ?x252 = 03_3d *> conf = 0.23 ranks of expected_values: 4 EVAL 0d6_s country 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 95.000 95.000 0.370 http://example.org/film/film/country #8003-03bdm4 PRED entity: 03bdm4 PRED relation: award PRED expected values: 0gqy2 => 112 concepts (74 used for prediction) PRED predicted values (max 10 best out of 298): 0gqy2 (0.81 #13407, 0.79 #19908, 0.78 #20315), 02x73k6 (0.52 #2093, 0.50 #1686, 0.10 #13061), 09sb52 (0.46 #1666, 0.41 #2073, 0.29 #21577), 09sdmz (0.39 #1833, 0.34 #2240, 0.09 #13208), 0f4x7 (0.36 #1656, 0.34 #2063, 0.24 #844), 027dtxw (0.36 #1629, 0.31 #2036, 0.12 #13004), 0bdwqv (0.36 #1799, 0.31 #2206, 0.11 #13174), 0789_m (0.32 #1645, 0.31 #2052, 0.12 #20), 04kxsb (0.32 #1752, 0.28 #2159, 0.16 #13127), 099jhq (0.29 #1644, 0.24 #2051, 0.08 #13019) >> Best rule #13407 for best value: >> intensional similarity = 4 >> extensional distance = 328 >> proper extension: 086k8; 017s11; 016tt2; 0g1rw; 05qd_; 016tw3; 022_lg; 0244r8; 04ktcgn; 092ys_y; ... >> query: (?x9710, ?x3066) <- award_winner(?x3066, ?x9710), nominated_for(?x3066, ?x7016), ?x7016 = 07g1sm, ceremony(?x3066, ?x78) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bdm4 award 0gqy2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 74.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #8002-0165v PRED entity: 0165v PRED relation: member_states! PRED expected values: 085h1 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 13): 085h1 (0.85 #27, 0.79 #84, 0.79 #64), 018cqq (0.38 #26, 0.36 #6, 0.33 #18), 02jxk (0.32 #25, 0.25 #62, 0.25 #17), 059dn (0.28 #20, 0.26 #36, 0.26 #28), 07t65 (0.14 #37, 0.07 #433, 0.05 #382), 02vk52z (0.14 #37, 0.07 #433, 0.05 #382), 0b6css (0.07 #433, 0.05 #330), 04k4l (0.07 #433, 0.05 #330), 041288 (0.07 #433), 0gkjy (0.07 #433) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 03_r3; 05v8c; 03h64; >> query: (?x9816, 085h1) <- currency(?x9816, ?x170), film_release_region(?x2050, ?x9816), ?x2050 = 01fmys >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0165v member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.851 http://example.org/user/ktrueman/default_domain/international_organization/member_states #8001-03qjg PRED entity: 03qjg PRED relation: group PRED expected values: 015cqh => 84 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1039): 0b_xm (0.67 #950, 0.62 #1393, 0.58 #2877), 02t3ln (0.67 #916, 0.60 #768, 0.58 #2843), 0ycp3 (0.67 #941, 0.50 #2276, 0.50 #501), 047cx (0.62 #1506, 0.58 #2842, 0.56 #1950), 014_lq (0.62 #1514, 0.58 #2850, 0.50 #1366), 014pg1 (0.62 #1549, 0.58 #2885, 0.50 #2293), 01k_yf (0.62 #1515, 0.58 #2851, 0.50 #924), 0bk1p (0.62 #1563, 0.53 #4677, 0.50 #2899), 01qqwp9 (0.62 #1495, 0.50 #3128, 0.50 #2980), 017_hq (0.62 #1595, 0.50 #3080, 0.50 #2931) >> Best rule #950 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 042v_gx; >> query: (?x2798, 0b_xm) <- performance_role(?x227, ?x2798), role(?x212, ?x2798), instrumentalists(?x2798, ?x9144), role(?x8957, ?x2798), group(?x2798, ?x9868), ?x8957 = 03f5mt, role(?x2798, ?x2157), role(?x565, ?x2798), artists(?x283, ?x9144), ?x9868 = 0134pk >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1556 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 6 *> proper extension: 02dlh2; *> query: (?x2798, 015cqh) <- performance_role(?x432, ?x2798), role(?x3296, ?x2798), instrumentalists(?x2798, ?x211), role(?x5926, ?x2798), group(?x2798, ?x7597), role(?x3296, ?x75), ?x7597 = 03c3yf, role(?x217, ?x432), ?x5926 = 0cfdd, role(?x433, ?x432) *> conf = 0.62 ranks of expected_values: 22 EVAL 03qjg group 015cqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 84.000 56.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group #8000-0ctb4g PRED entity: 0ctb4g PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #16, 0.81 #249, 0.81 #131), 07c52 (0.03 #313, 0.03 #365, 0.03 #38), 02nxhr (0.03 #312, 0.03 #364, 0.03 #132), 07z4p (0.03 #40, 0.03 #50, 0.03 #35) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 0gtsx8c; 03mh_tp; 0b7l4x; 0gmd3k7; >> query: (?x3430, 029j_) <- film(?x1104, ?x3430), film_crew_role(?x3430, ?x137), production_companies(?x3430, ?x1478), ?x1478 = 054lpb6 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ctb4g film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.831 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #7999-017cy9 PRED entity: 017cy9 PRED relation: institution! PRED expected values: 01kxxq => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 14): 03bwzr4 (0.56 #113, 0.51 #207, 0.51 #147), 07s6fsf (0.56 #106, 0.41 #200, 0.41 #140), 0bkj86 (0.47 #110, 0.43 #80, 0.41 #189), 013zdg (0.36 #109, 0.28 #1151, 0.27 #127), 022h5x (0.28 #1151, 0.20 #151, 0.18 #135), 0bjrnt (0.28 #1151, 0.14 #78, 0.14 #126), 01ysy9 (0.28 #1151, 0.11 #119, 0.08 #258), 071tyz (0.28 #1151, 0.08 #190, 0.07 #81), 02m4yg (0.28 #1151, 0.07 #85, 0.07 #254), 01kxxq (0.28 #1151, 0.03 #257, 0.02 #993) >> Best rule #113 for best value: >> intensional similarity = 2 >> extensional distance = 34 >> proper extension: 02gr81; 0ks67; >> query: (?x4780, 03bwzr4) <- major_field_of_study(?x4780, ?x3213), ?x3213 = 0g4gr >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1151 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 590 *> proper extension: 06pwq; 01w3v; 0gkkf; 0ym8f; 0473m9; 024y8p; 0yjf0; 015nl4; 026gvfj; 0ymdn; ... *> query: (?x4780, ?x620) <- major_field_of_study(?x4780, ?x3213), major_field_of_study(?x620, ?x3213) *> conf = 0.28 ranks of expected_values: 10 EVAL 017cy9 institution! 01kxxq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 94.000 94.000 0.556 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #7998-02_286 PRED entity: 02_286 PRED relation: film_release_region! PRED expected values: 0jqp3 0n04r 014knw => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 1372): 08hmch (0.67 #11804, 0.65 #24788, 0.47 #113069), 0bpm4yw (0.67 #12223, 0.61 #25207, 0.49 #113488), 0gffmn8 (0.67 #12076, 0.61 #25060, 0.38 #113341), 017jd9 (0.67 #12270, 0.57 #25254, 0.47 #113535), 043tvp3 (0.67 #12594, 0.57 #25578, 0.42 #113859), 04hwbq (0.67 #11832, 0.52 #24816, 0.37 #113097), 04f52jw (0.67 #12013, 0.48 #24997, 0.42 #113278), 0407yfx (0.67 #11945, 0.48 #24929, 0.41 #113210), 03q0r1 (0.67 #12167, 0.40 #10869, 0.33 #113432), 0glqh5_ (0.67 #12379, 0.33 #11081, 0.31 #113644) >> Best rule #11804 for best value: >> intensional similarity = 2 >> extensional distance = 13 >> proper extension: 07ww5; >> query: (?x739, 08hmch) <- service_location(?x8434, ?x739), jurisdiction_of_office(?x7891, ?x739) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #11809 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 13 *> proper extension: 07ww5; *> query: (?x739, 0jqp3) <- service_location(?x8434, ?x739), jurisdiction_of_office(?x7891, ?x739) *> conf = 0.27 ranks of expected_values: 354, 364, 396 EVAL 02_286 film_release_region! 014knw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 196.000 196.000 0.667 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02_286 film_release_region! 0n04r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 196.000 196.000 0.667 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02_286 film_release_region! 0jqp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 196.000 196.000 0.667 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7997-073bb PRED entity: 073bb PRED relation: influenced_by! PRED expected values: 0484q => 170 concepts (47 used for prediction) PRED predicted values (max 10 best out of 391): 02yl42 (0.33 #1172, 0.33 #654, 0.10 #1690), 01w8sf (0.33 #1132, 0.33 #614, 0.05 #23263), 013pp3 (0.33 #742, 0.17 #1260, 0.08 #12632), 0cbgl (0.33 #1033, 0.17 #1551, 0.06 #2586), 03_87 (0.20 #1817, 0.08 #2072, 0.06 #9569), 07g2b (0.20 #1572, 0.05 #23263, 0.04 #22761), 0c1jh (0.20 #1944, 0.05 #23263, 0.04 #8663), 01wd02c (0.20 #1828, 0.05 #23263, 0.04 #7240), 07lp1 (0.17 #1456, 0.11 #2491, 0.10 #1974), 0c00lh (0.17 #1264, 0.02 #17804, 0.02 #12116) >> Best rule #1172 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02ld6x; 013pp3; 01hc9_; >> query: (?x1900, 02yl42) <- profession(?x1900, ?x353), influenced_by(?x1900, ?x7039), ?x7039 = 041_y, nationality(?x1900, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8053 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 03jm6c; 0p3sf; 01kws3; 03gvpk; 039xcr; 046_v; 0223g8; 041wm; 0blgl; 04rfq; *> query: (?x1900, 0484q) <- profession(?x1900, ?x353), people(?x6821, ?x1900), location(?x1900, ?x362), ?x353 = 0cbd2 *> conf = 0.01 ranks of expected_values: 323 EVAL 073bb influenced_by! 0484q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 170.000 47.000 0.333 http://example.org/influence/influence_node/influenced_by #7996-0g5qs2k PRED entity: 0g5qs2k PRED relation: language PRED expected values: 02h40lc => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 47): 02h40lc (0.90 #534, 0.89 #2024, 0.89 #3035), 064_8sq (0.21 #259, 0.14 #1264, 0.14 #495), 02bjrlw (0.15 #60, 0.09 #1006, 0.09 #1184), 04306rv (0.14 #1010, 0.13 #1188, 0.12 #242), 06nm1 (0.13 #602, 0.13 #661, 0.12 #129), 06b_j (0.10 #260, 0.07 #791, 0.06 #614), 03_9r (0.09 #542, 0.08 #424, 0.08 #69), 0653m (0.08 #130, 0.08 #71, 0.06 #1795), 05qqm (0.08 #100, 0.02 #455, 0.02 #278), 0jzc (0.06 #1724, 0.06 #197, 0.05 #434) >> Best rule #534 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 050r1z; 0209xj; 02hxhz; 0k2sk; 06rmdr; 0cz_ym; 050gkf; 0260bz; 02s4l6; 026p4q7; ... >> query: (?x504, 02h40lc) <- nominated_for(?x1007, ?x504), produced_by(?x504, ?x10540), executive_produced_by(?x2471, ?x10540), organizations_founded(?x10540, ?x11695) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g5qs2k language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.896 http://example.org/film/film/language #7995-054lpb6 PRED entity: 054lpb6 PRED relation: production_companies! PRED expected values: 076tq0z 0gyy53 0btbyn 02704ff 05t0_2v 047gpsd => 138 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1511): 0c40vxk (0.39 #11863, 0.38 #10782, 0.38 #11862), 09qljs (0.33 #3171, 0.22 #9640, 0.20 #10718), 02qpt1w (0.33 #2761, 0.22 #9230, 0.20 #10308), 04z257 (0.33 #2534, 0.22 #9003, 0.20 #10081), 02xs6_ (0.33 #2676, 0.20 #10223, 0.15 #11303), 08984j (0.33 #2894, 0.16 #20147, 0.15 #11521), 08gsvw (0.33 #2235, 0.14 #13020, 0.13 #14098), 07phbc (0.33 #3152, 0.14 #13937, 0.12 #8543), 0dgq_kn (0.33 #2786, 0.14 #13571, 0.12 #8177), 02zk08 (0.33 #3053, 0.13 #14916, 0.12 #7365) >> Best rule #11863 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 049ql1; >> query: (?x1478, ?x5315) <- organization(?x346, ?x1478), film(?x1478, ?x5315), film_crew_role(?x5315, ?x137), child(?x1478, ?x9481) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #8142 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0dbpwb; *> query: (?x1478, 02704ff) <- award(?x1478, ?x3105), ?x3105 = 01l29r, citytown(?x1478, ?x9405), award_nominee(?x1478, ?x7274) *> conf = 0.12 ranks of expected_values: 325, 691, 1125, 1343 EVAL 054lpb6 production_companies! 047gpsd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 22.000 0.390 http://example.org/film/film/production_companies EVAL 054lpb6 production_companies! 05t0_2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 138.000 22.000 0.390 http://example.org/film/film/production_companies EVAL 054lpb6 production_companies! 02704ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 138.000 22.000 0.390 http://example.org/film/film/production_companies EVAL 054lpb6 production_companies! 0btbyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 138.000 22.000 0.390 http://example.org/film/film/production_companies EVAL 054lpb6 production_companies! 0gyy53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 22.000 0.390 http://example.org/film/film/production_companies EVAL 054lpb6 production_companies! 076tq0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 138.000 22.000 0.390 http://example.org/film/film/production_companies #7994-018dyl PRED entity: 018dyl PRED relation: profession PRED expected values: 01xy5l_ => 104 concepts (103 used for prediction) PRED predicted values (max 10 best out of 67): 0nbcg (0.57 #1052, 0.55 #2808, 0.53 #613), 0dxtg (0.48 #7474, 0.30 #158, 0.28 #10825), 02jknp (0.45 #7468, 0.28 #10825, 0.24 #10239), 016z4k (0.45 #2198, 0.44 #3368, 0.44 #1026), 039v1 (0.36 #2813, 0.35 #1057, 0.35 #618), 01c72t (0.33 #1631, 0.31 #4410, 0.30 #3971), 03gjzk (0.31 #7475, 0.30 #2354, 0.28 #10825), 0n1h (0.28 #10825, 0.24 #10239, 0.23 #2205), 0fnpj (0.28 #10825, 0.24 #10239, 0.22 #642), 0cbd2 (0.28 #10825, 0.24 #10239, 0.16 #1469) >> Best rule #1052 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 01pbxb; 01vrx3g; 0m2l9; 025xt8y; 018y2s; 01hw6wq; 01wg982; 014q2g; 01vn35l; 01w02sy; ... >> query: (?x4288, 0nbcg) <- award_nominee(?x1089, ?x4288), role(?x4288, ?x745), artist(?x2149, ?x4288) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #616 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 02bh9; 01vswwx; 0bkf4; *> query: (?x4288, 01xy5l_) <- award_nominee(?x1089, ?x4288), role(?x4288, ?x745), role(?x4288, ?x432) *> conf = 0.01 ranks of expected_values: 65 EVAL 018dyl profession 01xy5l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 104.000 103.000 0.573 http://example.org/people/person/profession #7993-02j8nx PRED entity: 02j8nx PRED relation: student! PRED expected values: 01bzs9 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 113): 031ns1 (0.20 #518), 01sjz_ (0.20 #240), 015nl4 (0.14 #4283, 0.11 #5337, 0.09 #7973), 03ksy (0.11 #1687, 0.08 #5903, 0.06 #6430), 065y4w7 (0.09 #1068, 0.07 #2122, 0.05 #1595), 0bwfn (0.07 #1329, 0.07 #10289, 0.06 #1856), 07tg4 (0.06 #7992, 0.06 #4302, 0.06 #5356), 01w5m (0.06 #3794, 0.06 #1159, 0.05 #9065), 0m4yg (0.05 #4581, 0.05 #5635, 0.03 #8271), 02l9wl (0.05 #4468, 0.04 #5522, 0.03 #8685) >> Best rule #518 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 06mnps; 06j8wx; 0ksrf8; >> query: (?x3282, 031ns1) <- profession(?x3282, ?x353), film(?x3282, ?x797), ?x797 = 09p35z >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #8366 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 282 *> proper extension: 027pdrh; 01l79yc; 07zr66; *> query: (?x3282, 01bzs9) <- nationality(?x3282, ?x1310), type_of_union(?x3282, ?x566), ?x1310 = 02jx1 *> conf = 0.01 ranks of expected_values: 91 EVAL 02j8nx student! 01bzs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 75.000 75.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #7992-0frmb1 PRED entity: 0frmb1 PRED relation: company PRED expected values: 05gnf => 121 concepts (42 used for prediction) PRED predicted values (max 10 best out of 158): 0gsgr (0.33 #110, 0.05 #2006, 0.04 #2196), 07wh1 (0.30 #1314, 0.27 #1503, 0.10 #6264), 05gnf (0.25 #475, 0.17 #664, 0.12 #854), 0g5lhl7 (0.24 #1936, 0.20 #2126, 0.03 #6127), 05tg3 (0.23 #758, 0.05 #1896, 0.04 #2851), 0fbq2n (0.23 #758, 0.05 #1896, 0.04 #2851), 07wj1 (0.20 #1279, 0.18 #1468, 0.12 #1090), 05njw (0.17 #722, 0.12 #912, 0.06 #1860), 05g49 (0.17 #646, 0.12 #836, 0.06 #1784), 03b3j (0.17 #619, 0.12 #809, 0.06 #1757) >> Best rule #110 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01w_10; >> query: (?x7749, 0gsgr) <- company(?x7749, ?x2062), company(?x7749, ?x1762), inductee(?x14281, ?x7749), ?x2062 = 09d5h, ?x1762 = 0gsg7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #475 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01svq8; *> query: (?x7749, 05gnf) <- gender(?x7749, ?x231), person(?x8144, ?x7749), ?x8144 = 05ll37, ?x231 = 05zppz *> conf = 0.25 ranks of expected_values: 3 EVAL 0frmb1 company 05gnf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 121.000 42.000 0.333 http://example.org/people/person/employment_history./business/employment_tenure/company #7991-09j9h PRED entity: 09j9h PRED relation: specialization_of! PRED expected values: 0w7s 05xls => 54 concepts (45 used for prediction) PRED predicted values (max 10 best out of 112): 04s84y (0.33 #197, 0.25 #608, 0.20 #710), 028sdw (0.33 #196, 0.25 #607, 0.20 #709), 0fgsq2 (0.33 #192, 0.25 #603, 0.20 #705), 05798 (0.33 #182, 0.25 #593, 0.20 #695), 011s0 (0.33 #128, 0.25 #539, 0.20 #641), 0nbcg (0.33 #219, 0.08 #1245, 0.08 #1143), 016z4k (0.33 #207, 0.08 #1233, 0.08 #1131), 039v1 (0.33 #224, 0.08 #1250, 0.08 #1148), 025sd_y (0.33 #289, 0.08 #1315, 0.08 #1213), 03lgtv (0.33 #273, 0.08 #1299, 0.08 #1197) >> Best rule #197 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 012t_z; >> query: (?x8498, 04s84y) <- profession(?x9393, ?x8498), profession(?x4058, ?x8498), specialization_of(?x9444, ?x8498), ?x4058 = 03n93, peers(?x9393, ?x9392), major_field_of_study(?x3208, ?x9444) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09j9h specialization_of! 05xls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 45.000 0.333 http://example.org/people/profession/specialization_of EVAL 09j9h specialization_of! 0w7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 45.000 0.333 http://example.org/people/profession/specialization_of #7990-0rjg8 PRED entity: 0rjg8 PRED relation: place! PRED expected values: 0rjg8 => 75 concepts (23 used for prediction) PRED predicted values (max 10 best out of 139): 0rql_ (0.48 #6732, 0.43 #10362, 0.39 #7252), 0rj0z (0.48 #6732, 0.43 #10362, 0.39 #7252), 0rj4g (0.37 #6731, 0.33 #266, 0.31 #7250), 0rqf1 (0.37 #6731, 0.31 #7250, 0.31 #10361), 0rjg8 (0.37 #6731, 0.31 #7250, 0.31 #10361), 0c5v2 (0.37 #6731, 0.31 #10361, 0.30 #5174), 03xpx0 (0.37 #6731, 0.31 #10361, 0.28 #7251), 0jgk3 (0.07 #6213, 0.07 #8807, 0.03 #3621), 0rqyx (0.07 #8807, 0.06 #2199, 0.02 #3753), 0ply0 (0.07 #8807, 0.06 #2141, 0.02 #4212) >> Best rule #6732 for best value: >> intensional similarity = 5 >> extensional distance = 138 >> proper extension: 0vzm; >> query: (?x6194, ?x3892) <- contains(?x9290, ?x6194), contains(?x8219, ?x6194), county_seat(?x8219, ?x3892), time_zones(?x6194, ?x2674), source(?x9290, ?x958) >> conf = 0.48 => this is the best rule for 2 predicted values *> Best rule #6731 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 138 *> proper extension: 0vzm; *> query: (?x6194, ?x9938) <- contains(?x9290, ?x6194), contains(?x8219, ?x6194), county_seat(?x8219, ?x3892), contains(?x8219, ?x9938), time_zones(?x6194, ?x2674), source(?x9290, ?x958) *> conf = 0.37 ranks of expected_values: 5 EVAL 0rjg8 place! 0rjg8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 75.000 23.000 0.483 http://example.org/location/hud_county_place/place #7989-019l68 PRED entity: 019l68 PRED relation: film PRED expected values: 072192 => 163 concepts (84 used for prediction) PRED predicted values (max 10 best out of 946): 01k7b0 (0.72 #17865, 0.72 #7146, 0.67 #17864), 072192 (0.58 #82167, 0.46 #85740, 0.41 #3573), 0k5fg (0.10 #1089, 0.05 #4662, 0.03 #2875), 0bmhn (0.10 #1621, 0.03 #8767, 0.03 #3407), 0199wf (0.10 #1656, 0.03 #3442, 0.02 #26666), 0n04r (0.10 #663, 0.02 #4236, 0.02 #7809), 01xbxn (0.10 #1392, 0.02 #4965, 0.01 #87132), 0bz3jx (0.10 #1138, 0.02 #17215, 0.02 #33294), 023g6w (0.10 #1478, 0.02 #17555, 0.01 #24701), 097zcz (0.10 #713, 0.02 #7859) >> Best rule #17865 for best value: >> intensional similarity = 3 >> extensional distance = 231 >> proper extension: 01hrqc; >> query: (?x9055, ?x6680) <- award_winner(?x6680, ?x9055), participant(?x6073, ?x9055), film_release_distribution_medium(?x6680, ?x81) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #82167 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 591 *> proper extension: 0cnl80; 023tp8; 02lfcm; 04bs3j; 0lzb8; 01sp81; 0170vn; 01vrncs; 01vrz41; 02lgj6; ... *> query: (?x9055, ?x9100) <- film(?x9055, ?x3783), nominated_for(?x9055, ?x9100), award_winner(?x7226, ?x9055) *> conf = 0.58 ranks of expected_values: 2 EVAL 019l68 film 072192 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 163.000 84.000 0.725 http://example.org/film/actor/film./film/performance/film #7988-0dl6fv PRED entity: 0dl6fv PRED relation: program! PRED expected values: 01f2w0 => 65 concepts (52 used for prediction) PRED predicted values (max 10 best out of 43): 03mdt (0.25 #7, 0.14 #406, 0.13 #121), 05gnf (0.21 #755, 0.19 #926, 0.18 #1041), 0gsg7 (0.20 #743, 0.20 #116, 0.18 #914), 0g5lhl7 (0.19 #234, 0.14 #576, 0.14 #405), 01w92 (0.13 #122, 0.12 #293, 0.07 #464), 09d5h (0.13 #744, 0.10 #1600, 0.10 #1258), 01f2w0 (0.12 #251, 0.07 #479, 0.06 #536), 0cjdk (0.10 #917, 0.10 #1374, 0.09 #1317), 05xbx (0.07 #410, 0.06 #581, 0.04 #353), 01nzs7 (0.07 #115, 0.06 #229, 0.06 #286) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 080dwhx; 02rzdcp; >> query: (?x8733, 03mdt) <- genre(?x8733, ?x4088), languages(?x8733, ?x254), ?x4088 = 04xvh5 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #251 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 015g28; 03wh49y; 063zky; 03ffcz; 02wyzmv; 032xky; *> query: (?x8733, 01f2w0) <- country(?x8733, ?x94), actor(?x8733, ?x7353), film_release_distribution_medium(?x8733, ?x81), type_of_union(?x7353, ?x566) *> conf = 0.12 ranks of expected_values: 7 EVAL 0dl6fv program! 01f2w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 65.000 52.000 0.250 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #7987-0140t7 PRED entity: 0140t7 PRED relation: currency PRED expected values: 01nv4h => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.40 #7, 0.38 #1, 0.37 #43), 01nv4h (0.07 #17, 0.07 #29, 0.06 #20), 02l6h (0.02 #15, 0.01 #27) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 01vvydl; 01vvycq; 03f5spx; 02r4qs; 012x4t; 01wwvc5; 09889g; 018x3; 01kd57; 01w9wwg; ... >> query: (?x9321, 09nqf) <- instrumentalists(?x212, ?x9321), location(?x9321, ?x362), award(?x9321, ?x1827), ?x1827 = 02nhxf >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 016ggh; *> query: (?x9321, 01nv4h) <- performance_role(?x9321, ?x212), award_winner(?x342, ?x9321), award(?x9321, ?x1232) *> conf = 0.07 ranks of expected_values: 2 EVAL 0140t7 currency 01nv4h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 135.000 0.400 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #7986-08ct6 PRED entity: 08ct6 PRED relation: music PRED expected values: 0hr3g => 62 concepts (48 used for prediction) PRED predicted values (max 10 best out of 97): 0146pg (0.22 #10, 0.17 #640, 0.17 #851), 0150t6 (0.11 #46, 0.06 #1307, 0.06 #256), 01tc9r (0.11 #65, 0.06 #275, 0.05 #1326), 015wc0 (0.11 #176, 0.05 #596, 0.04 #2489), 02jxkw (0.09 #772, 0.06 #1823, 0.05 #562), 02cyfz (0.07 #875, 0.06 #1295, 0.04 #664), 02bh9 (0.07 #892, 0.04 #3417, 0.04 #681), 06449 (0.06 #252, 0.05 #462, 0.04 #672), 06fxnf (0.06 #279, 0.05 #489, 0.04 #4700), 01hw6wq (0.06 #248, 0.05 #458, 0.01 #1719) >> Best rule #10 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0gd0c7x; 031778; 0dlngsd; >> query: (?x4699, 0146pg) <- genre(?x4699, ?x811), genre(?x4699, ?x600), ?x811 = 03k9fj, ?x600 = 02n4kr, film(?x4353, ?x4699) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #795 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 06qwh; *> query: (?x4699, 0hr3g) <- award(?x4699, ?x10747), nominated_for(?x198, ?x4699), ?x10747 = 0262s1, award(?x71, ?x198) *> conf = 0.04 ranks of expected_values: 21 EVAL 08ct6 music 0hr3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 62.000 48.000 0.222 http://example.org/film/film/music #7985-0r00l PRED entity: 0r00l PRED relation: location! PRED expected values: 0g2lq 04xhwn => 127 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2074): 0534nr (0.52 #143083, 0.52 #92881, 0.51 #25103), 038g2x (0.51 #25103, 0.48 #140572, 0.47 #80324), 0cm89v (0.51 #25103, 0.48 #140572, 0.47 #80324), 01s9ftn (0.51 #25103, 0.48 #140572, 0.47 #80324), 0pk41 (0.48 #140572, 0.47 #97901, 0.46 #143082), 0b7gr2 (0.48 #140572, 0.46 #143082, 0.46 #165671), 01vsy3q (0.29 #6010, 0.15 #13539, 0.11 #28603), 023kzp (0.23 #3723, 0.15 #13762, 0.14 #6233), 05ry0p (0.23 #4665, 0.14 #7175, 0.14 #2154), 023mdt (0.23 #4370, 0.14 #6880, 0.14 #1859) >> Best rule #143083 for best value: >> intensional similarity = 3 >> extensional distance = 206 >> proper extension: 02c7tb; >> query: (?x11930, ?x4882) <- place_of_birth(?x4882, ?x11930), actor(?x4223, ?x4882), location(?x1208, ?x11930) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #9099 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 027l4q; 07_fl; 0r111; *> query: (?x11930, 0g2lq) <- contains(?x2949, ?x11930), location(?x1208, ?x11930), ?x2949 = 0kpys *> conf = 0.06 ranks of expected_values: 1043 EVAL 0r00l location! 04xhwn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 86.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0r00l location! 0g2lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 127.000 86.000 0.518 http://example.org/people/person/places_lived./people/place_lived/location #7984-0b_7k PRED entity: 0b_7k PRED relation: profession PRED expected values: 01d_h8 => 114 concepts (76 used for prediction) PRED predicted values (max 10 best out of 69): 01d_h8 (0.82 #1758, 0.81 #1466, 0.70 #2488), 09jwl (0.44 #2791, 0.42 #455, 0.39 #17), 03gjzk (0.38 #2203, 0.37 #5124, 0.36 #1035), 016z4k (0.35 #4, 0.32 #442, 0.28 #2778), 0nbcg (0.33 #2804, 0.27 #4849, 0.26 #30), 0cbd2 (0.33 #1029, 0.32 #591, 0.26 #2197), 0dz3r (0.32 #2776, 0.28 #4821, 0.19 #440), 0n1h (0.26 #11, 0.19 #449, 0.16 #887), 018gz8 (0.25 #2935, 0.17 #5126, 0.17 #1037), 0kyk (0.24 #9346, 0.24 #612, 0.17 #2948) >> Best rule #1758 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 0fvf9q; 0byfz; 054_mz; 0kr5_; 03_gd; 02kxbwx; 02q_cc; 02ndbd; 04wvhz; 05_k56; ... >> query: (?x2793, 01d_h8) <- award(?x2793, ?x198), ?x198 = 040njc, profession(?x2793, ?x524) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_7k profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 76.000 0.816 http://example.org/people/person/profession #7983-02x4x18 PRED entity: 02x4x18 PRED relation: award! PRED expected values: 0dgst_d => 48 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1371): 0m313 (0.50 #3055, 0.24 #4069, 0.20 #1022), 0gzlb9 (0.43 #2867, 0.33 #835, 0.05 #14031), 0ch3qr1 (0.43 #2600, 0.33 #568, 0.05 #13764), 06krf3 (0.43 #2127, 0.33 #95, 0.03 #13291), 069q4f (0.43 #2152, 0.33 #120, 0.02 #13316), 01qvz8 (0.43 #2505, 0.03 #13669, 0.03 #14684), 0ds3t5x (0.40 #1046, 0.38 #3079, 0.24 #4093), 01cmp9 (0.40 #1628, 0.25 #3661, 0.24 #4675), 0421ng (0.40 #1519, 0.14 #2535, 0.12 #3552), 0kv2hv (0.33 #84, 0.29 #2116, 0.02 #13280) >> Best rule #3055 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 024fz9; >> query: (?x2478, 0m313) <- award(?x3553, ?x2478), award(?x241, ?x2478), award_winner(?x6631, ?x241), ?x3553 = 0bq2g >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1015 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 05b4l5x; *> query: (?x2478, ?x1071) <- nominated_for(?x2478, ?x1071), nominated_for(?x2478, ?x915), ?x915 = 03cvwkr, award(?x2275, ?x2478), ?x2275 = 05dbf *> conf = 0.28 ranks of expected_values: 48 EVAL 02x4x18 award! 0dgst_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 48.000 30.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #7982-0bksh PRED entity: 0bksh PRED relation: notable_people_with_this_condition! PRED expected values: 0h99n => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 5): 0h99n (0.10 #54, 0.09 #76, 0.09 #10), 029sk (0.06 #111, 0.05 #353, 0.05 #155), 02vrr (0.01 #179, 0.01 #685, 0.01 #355), 068p_ (0.01 #284, 0.01 #306, 0.01 #372), 01g2q (0.01 #339) >> Best rule #54 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 02wb6yq; 01wrcxr; >> query: (?x4782, 0h99n) <- celebrity(?x1896, ?x4782), friend(?x2221, ?x4782), award_winner(?x1811, ?x4782) >> conf = 0.10 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bksh notable_people_with_this_condition! 0h99n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.100 http://example.org/medicine/disease/notable_people_with_this_condition #7981-089pg7 PRED entity: 089pg7 PRED relation: award PRED expected values: 02f6yz => 81 concepts (53 used for prediction) PRED predicted values (max 10 best out of 184): 02f5qb (0.84 #1601, 0.83 #7604, 0.79 #4002), 02v1m7 (0.63 #1314, 0.49 #3715, 0.34 #2115), 02f73p (0.59 #2188, 0.37 #1387, 0.34 #3788), 02f73b (0.51 #2285, 0.50 #1484, 0.39 #3885), 02f72n (0.51 #2148, 0.50 #1347, 0.36 #3748), 01c9jp (0.50 #589, 0.38 #1790, 0.35 #2990), 01by1l (0.48 #913, 0.45 #1313, 0.40 #7717), 01bgqh (0.47 #1243, 0.42 #6045, 0.41 #2044), 01ckcd (0.41 #3134, 0.37 #733, 0.29 #2334), 01c427 (0.37 #8489, 0.27 #485, 0.21 #2886) >> Best rule #1601 for best value: >> intensional similarity = 7 >> extensional distance = 36 >> proper extension: 0l56b; >> query: (?x7781, ?x2877) <- award_winner(?x2877, ?x7781), award(?x6368, ?x2877), award(?x2807, ?x2877), award(?x521, ?x2877), ?x2807 = 03h_fk5, ?x521 = 0147dk, ?x6368 = 0178kd >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2317 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 01r9fv; 02z4b_8; *> query: (?x7781, 02f6yz) <- artists(?x1572, ?x7781), award(?x7781, ?x4892), ?x1572 = 06by7, artist(?x6474, ?x7781), ?x4892 = 02f72_ *> conf = 0.29 ranks of expected_values: 17 EVAL 089pg7 award 02f6yz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 81.000 53.000 0.837 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7980-0209xj PRED entity: 0209xj PRED relation: film_crew_role PRED expected values: 09zzb8 0ch6mp2 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.80 #9, 0.79 #321, 0.69 #243), 09vw2b7 (0.70 #8, 0.66 #320, 0.60 #86), 09zzb8 (0.70 #1, 0.64 #313, 0.60 #823), 02r96rf (0.70 #4, 0.60 #43, 0.59 #826), 01pvkk (0.40 #54, 0.27 #132, 0.23 #210), 0dxtw (0.33 #325, 0.31 #247, 0.30 #835), 01vx2h (0.32 #836, 0.31 #875, 0.30 #14), 02rh1dz (0.30 #12, 0.10 #51, 0.10 #638), 015h31 (0.20 #50, 0.10 #11, 0.07 #637), 02ynfr (0.17 #645, 0.15 #841, 0.14 #880) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0glqh5_; >> query: (?x696, 0ch6mp2) <- film(?x5500, ?x696), genre(?x696, ?x53), produced_by(?x696, ?x3662), ?x5500 = 03fbb6 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0209xj film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0209xj film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7979-0jsqk PRED entity: 0jsqk PRED relation: nominated_for! PRED expected values: 0gr0m => 105 concepts (100 used for prediction) PRED predicted values (max 10 best out of 201): 0gs96 (0.71 #3056, 0.70 #3292, 0.66 #14827), 027c95y (0.71 #3056, 0.70 #3292, 0.66 #14827), 0k611 (0.50 #305, 0.47 #775, 0.45 #1010), 040njc (0.43 #1652, 0.42 #712, 0.40 #947), 0gqyl (0.38 #782, 0.36 #1017, 0.35 #547), 0p9sw (0.38 #255, 0.32 #725, 0.31 #1665), 0gr4k (0.36 #1671, 0.34 #731, 0.33 #966), 0f4x7 (0.35 #1670, 0.33 #260, 0.32 #730), 04dn09n (0.35 #270, 0.29 #1680, 0.29 #505), 07bdd_ (0.32 #53, 0.21 #3345, 0.20 #13886) >> Best rule #3056 for best value: >> intensional similarity = 4 >> extensional distance = 169 >> proper extension: 080dwhx; 0124k9; 0464pz; 0kfv9; 0l76z; 03nt59; 0524b41; 01g03q; 01ft14; >> query: (?x4653, ?x2222) <- titles(?x162, ?x4653), award(?x4653, ?x2222), nominated_for(?x902, ?x4653), production_companies(?x66, ?x902) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #529 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 018f8; 0k5g9; 0dyb1; 0fy66; 048rn; 0hv8w; 0jqd3; 063hp4; 09d38d; *> query: (?x4653, 0gr0m) <- titles(?x162, ?x4653), list(?x4653, ?x3004), film(?x4652, ?x4653), language(?x4653, ?x254) *> conf = 0.31 ranks of expected_values: 13 EVAL 0jsqk nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 105.000 100.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7978-02cvp8 PRED entity: 02cvp8 PRED relation: place_of_death PRED expected values: 030qb3t => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 20): 0f2wj (0.33 #12, 0.06 #401, 0.06 #597), 030qb3t (0.18 #411, 0.17 #607, 0.14 #3143), 04vmp (0.17 #693, 0.10 #888, 0.06 #497), 04jpl (0.12 #396, 0.10 #787, 0.06 #1567), 02_286 (0.10 #3523, 0.07 #4300, 0.07 #4495), 0k_p5 (0.10 #868, 0.06 #477, 0.06 #673), 0cr3d (0.07 #584), 0k049 (0.06 #3124, 0.05 #3707, 0.05 #3513), 0f2rq (0.06 #475, 0.06 #671, 0.05 #866), 06_kh (0.06 #394, 0.05 #785, 0.04 #3126) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 030dx5; >> query: (?x11256, 0f2wj) <- sibling(?x10901, ?x11256), nationality(?x11256, ?x94), people(?x268, ?x11256), ?x10901 = 045g4l >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #411 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 0l99s; 015076; *> query: (?x11256, 030qb3t) <- sibling(?x10901, ?x11256), nationality(?x11256, ?x94), people(?x268, ?x11256), location(?x10901, ?x2850) *> conf = 0.18 ranks of expected_values: 2 EVAL 02cvp8 place_of_death 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.333 http://example.org/people/deceased_person/place_of_death #7977-023fb PRED entity: 023fb PRED relation: colors PRED expected values: 01g5v => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.57 #142, 0.55 #263, 0.53 #631), 06fvc (0.50 #103, 0.40 #63, 0.33 #123), 01g5v (0.33 #124, 0.33 #44, 0.32 #633), 019sc (0.29 #553, 0.28 #637, 0.25 #471), 038hg (0.23 #334, 0.20 #690, 0.20 #629), 01l849 (0.20 #546, 0.19 #464, 0.17 #506), 04d18d (0.20 #79, 0.17 #711, 0.16 #568), 03vtbc (0.19 #472, 0.13 #554, 0.09 #577), 02rnmb (0.18 #559, 0.17 #711, 0.17 #134), 088fh (0.17 #711, 0.16 #568, 0.16 #567) >> Best rule #142 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 02_lt; 0j46b; >> query: (?x6670, 083jv) <- position(?x6670, ?x203), team(?x6523, ?x6670), current_club(?x978, ?x6670), teams(?x12603, ?x6670), team(?x6523, ?x6524), ?x6524 = 01cwq9, position(?x6670, ?x60) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 03w7kx; *> query: (?x6670, 01g5v) <- position(?x6670, ?x530), position(?x6670, ?x203), position(?x6670, ?x60), ?x530 = 02_j1w, team(?x6523, ?x6670), ?x203 = 0dgrmp, current_club(?x2427, ?x6670), ?x2427 = 01l3vx *> conf = 0.33 ranks of expected_values: 3 EVAL 023fb colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 103.000 0.571 http://example.org/sports/sports_team/colors #7976-024hbv PRED entity: 024hbv PRED relation: nominated_for! PRED expected values: 01wbg84 02zfg3 => 69 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1352): 02b29 (0.79 #105026, 0.79 #39673, 0.78 #49009), 04mx__ (0.68 #37339, 0.68 #35005, 0.65 #44341), 01541z (0.51 #70012, 0.48 #63010, 0.45 #65345), 04wx2v (0.51 #70012, 0.48 #63010, 0.45 #65345), 09fb5 (0.48 #11730, 0.32 #16397, 0.27 #18732), 02mxw0 (0.45 #65345, 0.44 #44340, 0.39 #63009), 03mdt (0.38 #14708, 0.22 #21711, 0.17 #3042), 052hl (0.25 #10798, 0.12 #100358, 0.04 #13131), 0gsg7 (0.24 #14349, 0.17 #42007, 0.15 #98025), 01l1ls (0.20 #11294, 0.12 #100358, 0.08 #4295) >> Best rule #105026 for best value: >> intensional similarity = 4 >> extensional distance = 720 >> proper extension: 05n6sq; >> query: (?x12105, ?x6914) <- nominated_for(?x8950, ?x12105), award_winner(?x12105, ?x6914), people(?x1050, ?x6914), award(?x8950, ?x350) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #14051 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 05h43ls; 01qbg5; *> query: (?x12105, 01wbg84) <- nominated_for(?x6678, ?x12105), program(?x6678, ?x11549), film_crew_role(?x11549, ?x137), company(?x1491, ?x6678) *> conf = 0.06 ranks of expected_values: 315, 680 EVAL 024hbv nominated_for! 02zfg3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 69.000 45.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 024hbv nominated_for! 01wbg84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 45.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #7975-01jssp PRED entity: 01jssp PRED relation: major_field_of_study PRED expected values: 02j62 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 111): 04rjg (0.61 #1148, 0.47 #1374, 0.38 #1713), 02j62 (0.52 #1158, 0.51 #1384, 0.40 #1723), 0fdys (0.48 #1167, 0.33 #1393, 0.29 #1506), 062z7 (0.46 #1381, 0.42 #1155, 0.40 #1494), 05qjt (0.45 #1138, 0.44 #1364, 0.33 #1703), 03g3w (0.45 #1154, 0.43 #1493, 0.42 #1380), 05qfh (0.45 #1164, 0.42 #1390, 0.31 #1729), 06ms6 (0.45 #1145, 0.33 #1371, 0.28 #806), 037mh8 (0.42 #1193, 0.35 #1419, 0.29 #1532), 01lj9 (0.42 #1168, 0.35 #1394, 0.28 #1733) >> Best rule #1148 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 045c7b; >> query: (?x331, 04rjg) <- organization(?x5510, ?x331), organization(?x331, ?x5487), list(?x331, ?x2197) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1158 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 045c7b; *> query: (?x331, 02j62) <- organization(?x5510, ?x331), organization(?x331, ?x5487), list(?x331, ?x2197) *> conf = 0.52 ranks of expected_values: 2 EVAL 01jssp major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.606 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7974-0262zm PRED entity: 0262zm PRED relation: disciplines_or_subjects PRED expected values: 01hmnh => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 118): 01hmnh (0.71 #184, 0.67 #149, 0.64 #289), 05hgj (0.50 #267, 0.50 #92, 0.42 #458), 014dfn (0.42 #458, 0.33 #95, 0.33 #1033), 0l67h (0.42 #458, 0.33 #1033, 0.29 #206), 02vxn (0.42 #887, 0.40 #352, 0.38 #923), 08_lx0 (0.33 #1033, 0.21 #1462, 0.20 #996), 0w7c (0.24 #731, 0.20 #909, 0.19 #945), 0dwly (0.21 #1069, 0.21 #1247, 0.20 #996), 0j7v_ (0.20 #996, 0.20 #995, 0.20 #994), 01tz3c (0.20 #996, 0.20 #995, 0.20 #994) >> Best rule #184 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0265wl; >> query: (?x1375, 01hmnh) <- award(?x11287, ?x1375), award(?x10275, ?x1375), award_winner(?x1375, ?x1727), student(?x7278, ?x11287), colors(?x7278, ?x663), ?x10275 = 03hpr, award_winner(?x575, ?x11287) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0262zm disciplines_or_subjects 01hmnh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.714 http://example.org/award/award_category/disciplines_or_subjects #7973-0b3wk PRED entity: 0b3wk PRED relation: legislative_sessions PRED expected values: 02bqmq 024tcq 01gstn 024tkd 01grrf 01gsry => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 12): 024tkd (0.86 #28, 0.86 #26, 0.85 #27), 024tcq (0.86 #28, 0.86 #26, 0.85 #27), 02bqmq (0.86 #28, 0.86 #26, 0.85 #27), 01grrf (0.86 #28, 0.86 #26, 0.85 #27), 01gsry (0.86 #28, 0.86 #26, 0.82 #53), 01gstn (0.86 #28, 0.86 #26, 0.82 #53), 05rrw9 (0.81 #55, 0.80 #41), 04fhps (0.57 #37, 0.50 #24, 0.43 #50), 034_7s (0.50 #25, 0.43 #51, 0.43 #38), 03h_f4 (0.43 #49, 0.33 #23, 0.29 #36) >> Best rule #28 for best value: >> intensional similarity = 45 >> extensional distance = 4 >> proper extension: 0x2sv; 0h6dy; 0l_j_; >> query: (?x2860, ?x5005) <- category(?x2860, ?x134), legislative_sessions(?x2860, ?x9702), legislative_sessions(?x2860, ?x1028), legislative_sessions(?x2860, ?x653), district_represented(?x9702, ?x7518), district_represented(?x9702, ?x2713), district_represented(?x9702, ?x177), legislative_sessions(?x8607, ?x1028), legislative_sessions(?x5266, ?x1028), ?x134 = 08mbj5d, politician(?x8714, ?x8607), legislative_sessions(?x1028, ?x3463), contains(?x7518, ?x2832), district_represented(?x1028, ?x2977), gender(?x8607, ?x231), administrative_parent(?x7518, ?x94), district_represented(?x653, ?x4758), district_represented(?x653, ?x3634), district_represented(?x653, ?x2982), district_represented(?x653, ?x2049), religion(?x7518, ?x7131), ?x7131 = 03_gx, featured_film_locations(?x945, ?x3634), adjoins(?x7468, ?x2049), contains(?x3634, ?x216), time_zones(?x3634, ?x1638), taxonomy(?x2982, ?x939), legislative_sessions(?x2357, ?x653), state_province_region(?x1492, ?x3634), location(?x56, ?x3634), contains(?x2982, ?x659), legislative_sessions(?x9702, ?x5005), religion(?x2982, ?x109), jurisdiction_of_office(?x900, ?x2049), location(?x4806, ?x2713), state(?x3987, ?x2982), partially_contains(?x4758, ?x4540), currency(?x2049, ?x170), contains(?x4758, ?x7812), student(?x331, ?x5266), location(?x932, ?x177), location(?x2219, ?x4758), state(?x8263, ?x2713), contains(?x177, ?x388), profession(?x8607, ?x3342) >> conf = 0.86 => this is the best rule for 6 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6 EVAL 0b3wk legislative_sessions 01gsry CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 6.000 6.000 0.857 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions EVAL 0b3wk legislative_sessions 01grrf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 6.000 6.000 0.857 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions EVAL 0b3wk legislative_sessions 024tkd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 6.000 6.000 0.857 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions EVAL 0b3wk legislative_sessions 01gstn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 6.000 6.000 0.857 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions EVAL 0b3wk legislative_sessions 024tcq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 6.000 6.000 0.857 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions EVAL 0b3wk legislative_sessions 02bqmq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 6.000 6.000 0.857 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #7972-03hxsv PRED entity: 03hxsv PRED relation: language PRED expected values: 02h40lc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.96 #990, 0.96 #4390, 0.96 #4449), 064_8sq (0.21 #312, 0.21 #80, 0.18 #254), 06nm1 (0.20 #185, 0.17 #69, 0.13 #766), 03_9r (0.15 #10, 0.10 #1929, 0.09 #765), 06b_j (0.15 #197, 0.09 #139, 0.08 #2059), 04306rv (0.14 #295, 0.14 #237, 0.12 #179), 02bjrlw (0.14 #59, 0.12 #175, 0.11 #291), 0653m (0.06 #2048, 0.06 #128, 0.06 #767), 0jzc (0.06 #136, 0.05 #310, 0.04 #2056), 012w70 (0.06 #129, 0.04 #2049, 0.04 #303) >> Best rule #990 for best value: >> intensional similarity = 4 >> extensional distance = 172 >> proper extension: 02d44q; >> query: (?x6332, 02h40lc) <- featured_film_locations(?x6332, ?x13688), film(?x981, ?x6332), category(?x6332, ?x134), language(?x6332, ?x5814) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hxsv language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.960 http://example.org/film/film/language #7971-06pwf6 PRED entity: 06pwf6 PRED relation: student! PRED expected values: 015fsv => 116 concepts (108 used for prediction) PRED predicted values (max 10 best out of 175): 03w1lf (0.26 #7699, 0.26 #4549, 0.20 #6649), 04b_46 (0.25 #226, 0.11 #751, 0.05 #3376), 01pcj4 (0.25 #367, 0.11 #892, 0.03 #4042), 01qd_r (0.25 #280, 0.11 #805, 0.03 #3955), 03bmmc (0.25 #195, 0.11 #720, 0.03 #3870), 08815 (0.20 #2102, 0.11 #1052, 0.10 #3677), 03ksy (0.13 #3780, 0.11 #630, 0.10 #2205), 015nl4 (0.13 #22116, 0.03 #34719, 0.03 #35245), 0bwfn (0.13 #24425, 0.11 #1849, 0.11 #1324), 02hwww (0.11 #6739, 0.11 #7789, 0.10 #4639) >> Best rule #7699 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 03d63lb; >> query: (?x2873, 03w1lf) <- nationality(?x2873, ?x2146), ?x2146 = 03rk0, profession(?x2873, ?x319), student(?x1681, ?x2873) >> conf = 0.26 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06pwf6 student! 015fsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 108.000 0.263 http://example.org/education/educational_institution/students_graduates./education/education/student #7970-058vp PRED entity: 058vp PRED relation: profession PRED expected values: 01l5t6 => 114 concepts (99 used for prediction) PRED predicted values (max 10 best out of 79): 0dxtg (0.93 #12760, 0.63 #13501, 0.54 #2978), 05z96 (0.71 #636, 0.67 #1524, 0.62 #932), 02hrh1q (0.67 #13650, 0.65 #12169, 0.64 #13353), 0kyk (0.57 #1956, 0.57 #771, 0.56 #2104), 01d_h8 (0.51 #2971, 0.46 #12753, 0.38 #4008), 02jknp (0.39 #12754, 0.34 #2972, 0.33 #1488), 02hv44_ (0.38 #1243, 0.37 #2965, 0.33 #6521), 0q04f (0.37 #2965, 0.27 #1630, 0.27 #2668), 03gjzk (0.36 #12762, 0.26 #13503, 0.24 #2980), 04cvn_ (0.33 #6521, 0.32 #6967, 0.32 #6670) >> Best rule #12760 for best value: >> intensional similarity = 6 >> extensional distance = 919 >> proper extension: 0lzb8; 0jf1b; 03mz9r; 0c3ns; 01q415; 01zfmm; 0cj2t3; 01q4qv; 02j8nx; 0347xl; ... >> query: (?x5612, 0dxtg) <- profession(?x5612, ?x11999), profession(?x3279, ?x11999), profession(?x2127, ?x11999), nationality(?x5612, ?x789), ?x3279 = 0d4jl, ?x2127 = 01j7rd >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #3521 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 116 *> proper extension: 0k4gf; 0gcs9; 018x3; 034ks; *> query: (?x5612, 01l5t6) <- influenced_by(?x5612, ?x2080), influenced_by(?x4265, ?x5612), religion(?x5612, ?x7131), profession(?x5612, ?x353), influenced_by(?x1029, ?x4265) *> conf = 0.03 ranks of expected_values: 59 EVAL 058vp profession 01l5t6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 114.000 99.000 0.935 http://example.org/people/person/profession #7969-06cm5 PRED entity: 06cm5 PRED relation: nominated_for! PRED expected values: 0gqy2 => 87 concepts (75 used for prediction) PRED predicted values (max 10 best out of 200): 0f4x7 (0.67 #7696, 0.66 #6790, 0.66 #7017), 09cm54 (0.67 #7696, 0.66 #6790, 0.66 #7017), 027986c (0.67 #7696, 0.66 #6790, 0.66 #7017), 0gr4k (0.53 #930, 0.45 #2064, 0.36 #1156), 040njc (0.52 #1137, 0.51 #1364, 0.48 #685), 0gqy2 (0.46 #1016, 0.46 #338, 0.37 #2150), 0gq_v (0.43 #698, 0.41 #1150, 0.40 #1377), 0gqwc (0.36 #960, 0.25 #2094, 0.25 #1186), 02pqp12 (0.35 #959, 0.34 #2093, 0.31 #1185), 0gr51 (0.33 #1199, 0.33 #747, 0.33 #1426) >> Best rule #7696 for best value: >> intensional similarity = 4 >> extensional distance = 856 >> proper extension: 02gl58; 02_1ky; >> query: (?x6137, ?x591) <- award_winner(?x6137, ?x1554), nominated_for(?x1307, ?x6137), award(?x6137, ?x591), award(?x71, ?x1307) >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #1016 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 78 *> proper extension: 0kt_4; 0jz71; 03mr85; *> query: (?x6137, 0gqy2) <- nominated_for(?x1972, ?x6137), nominated_for(?x1313, ?x6137), ?x1313 = 0gs9p, ?x1972 = 0gqyl, film(?x1554, ?x6137) *> conf = 0.46 ranks of expected_values: 6 EVAL 06cm5 nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 87.000 75.000 0.665 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7968-03nymk PRED entity: 03nymk PRED relation: genre PRED expected values: 0hcr 095bb => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 70): 07s9rl0 (0.56 #1368, 0.55 #1769, 0.53 #3134), 0hcr (0.54 #97, 0.38 #258, 0.29 #178), 01t_vv (0.38 #272, 0.30 #594, 0.26 #1238), 025s89p (0.25 #49, 0.23 #129, 0.19 #290), 01htzx (0.20 #980, 0.20 #1061, 0.19 #659), 06n90 (0.19 #3145, 0.17 #1057, 0.16 #3227), 01hmnh (0.19 #256, 0.15 #1222, 0.15 #3148), 0djd22 (0.19 #260, 0.09 #1467, 0.08 #1547), 04gm78f (0.19 #284, 0.08 #1491, 0.08 #123), 03k9fj (0.18 #1055, 0.18 #974, 0.17 #3143) >> Best rule #1368 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 090s_0; 06cs95; 03kq98; 02py4c8; 0124k9; 02bg8v; 0584r4; 01xr2s; 01q_y0; 027tbrc; ... >> query: (?x8396, 07s9rl0) <- nominated_for(?x3673, ?x8396), program(?x2554, ?x8396), titles(?x2008, ?x8396), genre(?x8396, ?x258) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 019nnl; 03y3bp7; 0557yqh; 099pks; *> query: (?x8396, 0hcr) <- program_creator(?x8396, ?x3673), genre(?x8396, ?x2700), ?x2700 = 06nbt, titles(?x2008, ?x8396) *> conf = 0.54 ranks of expected_values: 2, 12 EVAL 03nymk genre 095bb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 80.000 80.000 0.557 http://example.org/tv/tv_program/genre EVAL 03nymk genre 0hcr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.557 http://example.org/tv/tv_program/genre #7967-0n8_m93 PRED entity: 0n8_m93 PRED relation: honored_for PRED expected values: 0407yfx 0gwjw0c 0gvvm6l => 39 concepts (14 used for prediction) PRED predicted values (max 10 best out of 396): 02cbhg (0.33 #1063, 0.20 #1654, 0.17 #2245), 0294mx (0.33 #1022, 0.20 #1613, 0.17 #2204), 02hfk5 (0.33 #879, 0.20 #1470, 0.17 #2061), 017jd9 (0.33 #866, 0.20 #1457, 0.17 #2048), 01jrbb (0.33 #763, 0.20 #1354, 0.17 #1945), 020fcn (0.33 #657, 0.20 #1248, 0.17 #1839), 0209xj (0.33 #626, 0.20 #1217, 0.17 #1808), 02c638 (0.33 #720, 0.20 #1311, 0.17 #1902), 0bcp9b (0.33 #446, 0.17 #2220, 0.10 #2811), 0qmhk (0.33 #330, 0.17 #2104, 0.10 #2695) >> Best rule #1063 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 02glmx; >> query: (?x8407, 02cbhg) <- honored_for(?x8407, ?x6176), honored_for(?x8407, ?x4607), ceremony(?x1307, ?x8407), ceremony(?x601, ?x8407), ?x1307 = 0gq9h, film_release_region(?x4607, ?x1790), film_release_region(?x4607, ?x774), film_release_region(?x4607, ?x550), ?x774 = 06mzp, ?x550 = 05v8c, produced_by(?x4607, ?x286), film(?x1676, ?x4607), film(?x818, ?x6176), nominated_for(?x112, ?x4607), ?x1790 = 01pj7, ?x601 = 0gr4k, genre(?x6176, ?x53) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5920 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 11 *> proper extension: 02pgky2; *> query: (?x8407, ?x197) <- honored_for(?x8407, ?x4607), ceremony(?x1313, ?x8407), ceremony(?x1307, ?x8407), ceremony(?x601, ?x8407), ceremony(?x500, ?x8407), ?x1307 = 0gq9h, film_release_region(?x4607, ?x1475), film_release_region(?x4607, ?x774), award_winner(?x8407, ?x286), ?x1475 = 05qx1, olympics(?x774, ?x358), ?x500 = 0p9sw, ?x601 = 0gr4k, award(?x197, ?x1313), nominated_for(?x1313, ?x144) *> conf = 0.02 ranks of expected_values: 227, 353 EVAL 0n8_m93 honored_for 0gvvm6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 14.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0n8_m93 honored_for 0gwjw0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 39.000 14.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0n8_m93 honored_for 0407yfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 39.000 14.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #7966-02ppm4q PRED entity: 02ppm4q PRED relation: nominated_for PRED expected values: 028_yv 0fgpvf 09k56b7 08rr3p 047d21r 0yxm1 0hv8w 0gmgwnv 0bh8drv 02k1pr 03b1sb => 42 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1386): 04j13sx (0.75 #3871, 0.33 #2372, 0.27 #9875), 09tkzy (0.67 #24008, 0.67 #27013, 0.67 #27012), 04vr_f (0.65 #10647, 0.25 #3141, 0.24 #15008), 0gmcwlb (0.62 #3165, 0.60 #10671, 0.33 #1666), 0g9lm2 (0.62 #3615, 0.50 #11121, 0.46 #6617), 0f4_l (0.62 #3294, 0.45 #10800, 0.36 #4796), 011yhm (0.62 #3965, 0.45 #11471, 0.31 #6967), 0hv4t (0.62 #3976, 0.35 #11482, 0.19 #12982), 015qqg (0.62 #3711, 0.33 #2212, 0.30 #11217), 0hmr4 (0.62 #3085, 0.25 #10591, 0.23 #6087) >> Best rule #3871 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 040njc; 02pqp12; 0gr51; >> query: (?x2880, 04j13sx) <- nominated_for(?x2880, ?x7194), award(?x2805, ?x2880), ?x7194 = 01gvts, award_nominee(?x748, ?x2805) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #11406 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 099c8n; *> query: (?x2880, 0gmgwnv) <- nominated_for(?x2880, ?x6079), nominated_for(?x2880, ?x2029), film_crew_role(?x2029, ?x137), ?x6079 = 05sy_5, nominated_for(?x100, ?x2029) *> conf = 0.50 ranks of expected_values: 25, 28, 45, 135, 137, 166, 171, 173, 349, 457, 460 EVAL 02ppm4q nominated_for 03b1sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ppm4q nominated_for 02k1pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ppm4q nominated_for 0bh8drv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ppm4q nominated_for 0gmgwnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ppm4q nominated_for 0hv8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ppm4q nominated_for 0yxm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ppm4q nominated_for 047d21r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ppm4q nominated_for 08rr3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ppm4q nominated_for 09k56b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ppm4q nominated_for 0fgpvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ppm4q nominated_for 028_yv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7965-02jtjz PRED entity: 02jtjz PRED relation: award PRED expected values: 0ck27z => 101 concepts (80 used for prediction) PRED predicted values (max 10 best out of 248): 09sb52 (0.67 #2061, 0.50 #1657, 0.45 #849), 0ck27z (0.56 #1304, 0.31 #10596, 0.25 #92), 05zr6wv (0.39 #1633, 0.27 #825, 0.27 #2037), 05b4l5x (0.33 #410, 0.17 #2834, 0.17 #2430), 05p09zm (0.27 #2548, 0.26 #2952, 0.18 #3356), 057xs89 (0.27 #969, 0.17 #1777, 0.14 #19798), 063y_ky (0.25 #132, 0.17 #536, 0.11 #17777), 05zrvfd (0.25 #111, 0.13 #32330, 0.12 #29500), 05zvj3m (0.22 #1709, 0.18 #901, 0.14 #19798), 0gqy2 (0.19 #2185, 0.15 #27883, 0.15 #31521) >> Best rule #2061 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 02q9kqf; >> query: (?x3866, 09sb52) <- award_nominee(?x3866, ?x1554), nominated_for(?x1554, ?x5129), ?x5129 = 0jqj5 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1304 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 0kctd; *> query: (?x3866, 0ck27z) <- nominated_for(?x3866, ?x8870), ?x8870 = 0fhzwl *> conf = 0.56 ranks of expected_values: 2 EVAL 02jtjz award 0ck27z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 80.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7964-0fbtbt PRED entity: 0fbtbt PRED relation: nominated_for PRED expected values: 039fgy 039c26 0dsx3f 0m123 07g9f 0300ml => 55 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1517): 039fgy (0.78 #10946, 0.78 #12512, 0.77 #20342), 0300ml (0.78 #10946, 0.78 #12512, 0.77 #20342), 02gl58 (0.53 #3127, 0.33 #2989, 0.33 #1425), 0dsx3f (0.53 #3127, 0.33 #2546, 0.33 #982), 07g9f (0.53 #3127, 0.33 #1407, 0.32 #10943), 039c26 (0.53 #3127, 0.33 #483, 0.32 #10943), 024hbv (0.53 #3127, 0.33 #1536, 0.27 #12511), 06qw_ (0.53 #3127, 0.32 #10943, 0.27 #14078), 03cf9ly (0.53 #3127, 0.32 #10943, 0.27 #14078), 0123qq (0.53 #3127, 0.32 #10943, 0.27 #14078) >> Best rule #10946 for best value: >> intensional similarity = 5 >> extensional distance = 67 >> proper extension: 02qyp19; 04ljl_l; 05b4l5x; 0f_nbyh; 05f4m9q; 03hkv_r; 05zr6wv; 099jhq; 0gkvb7; 0gr4k; ... >> query: (?x4921, ?x687) <- award(?x12138, ?x4921), award(?x3571, ?x4921), program(?x12138, ?x4932), tv_program(?x3571, ?x8316), award(?x687, ?x4921) >> conf = 0.78 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2, 4, 5, 6, 691 EVAL 0fbtbt nominated_for 0300ml CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 19.000 0.782 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fbtbt nominated_for 07g9f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 55.000 19.000 0.782 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fbtbt nominated_for 0m123 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 19.000 0.782 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fbtbt nominated_for 0dsx3f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 55.000 19.000 0.782 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fbtbt nominated_for 039c26 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 55.000 19.000 0.782 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0fbtbt nominated_for 039fgy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 19.000 0.782 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7963-02xj3rw PRED entity: 02xj3rw PRED relation: nominated_for PRED expected values: 02d49z 08nhfc1 => 60 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1859): 0m313 (0.33 #12, 0.21 #23755, 0.20 #25337), 03hmt9b (0.33 #595, 0.19 #24338, 0.19 #25920), 0dr_4 (0.33 #224, 0.19 #23967, 0.19 #25549), 05hjnw (0.33 #766, 0.19 #24509, 0.18 #26091), 0h95927 (0.33 #1157, 0.17 #23742, 0.16 #24900), 011yxg (0.33 #39, 0.16 #23782, 0.15 #25364), 019vhk (0.33 #414, 0.15 #24157, 0.14 #25739), 0661ql3 (0.33 #345, 0.14 #24088, 0.13 #25670), 09k56b7 (0.33 #283, 0.14 #24026, 0.13 #25608), 0bth54 (0.33 #73, 0.13 #23816, 0.13 #25398) >> Best rule #12 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 05pcn59; >> query: (?x9343, 0m313) <- award(?x3124, ?x9343), nominated_for(?x9343, ?x6619), ?x6619 = 02pw_n, award_winner(?x9343, ?x3961), nominated_for(?x68, ?x3124) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7908 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 0dt49; *> query: (?x9343, ?x197) <- award(?x8158, ?x9343), disciplines_or_subjects(?x9343, ?x373), award(?x8158, ?x1033), award(?x197, ?x1033) *> conf = 0.06 ranks of expected_values: 498, 673 EVAL 02xj3rw nominated_for 08nhfc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 60.000 18.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02xj3rw nominated_for 02d49z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 18.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7962-01hp5 PRED entity: 01hp5 PRED relation: film! PRED expected values: 02_p8v => 93 concepts (34 used for prediction) PRED predicted values (max 10 best out of 860): 06s26c (0.20 #2080, 0.10 #14560), 028lc8 (0.20 #266, 0.03 #2346), 0gn30 (0.20 #946, 0.02 #32140, 0.01 #36300), 02r_d4 (0.20 #103, 0.02 #10502, 0.02 #14663), 04xhwn (0.20 #1988, 0.01 #8227, 0.01 #14467), 01h1b (0.20 #1206, 0.01 #7445), 02t_vx (0.20 #1375, 0.01 #36729), 03bggl (0.20 #1857), 01g969 (0.20 #1669), 041rhq (0.20 #1188) >> Best rule #2080 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01zfzb; 06c0ns; 0k4bc; >> query: (?x751, ?x10522) <- genre(?x751, ?x53), film(?x5319, ?x751), ?x5319 = 02t_w8, produced_by(?x751, ?x10522) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #7163 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 044g_k; 03whyr; 09qljs; *> query: (?x751, 02_p8v) <- category(?x751, ?x134), film(?x3025, ?x751), profession(?x3025, ?x1032), story_by(?x751, ?x13339) *> conf = 0.06 ranks of expected_values: 31 EVAL 01hp5 film! 02_p8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 93.000 34.000 0.200 http://example.org/film/actor/film./film/performance/film #7961-073hkh PRED entity: 073hkh PRED relation: ceremony! PRED expected values: 0gq9h 0gvx_ => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 324): 0gvx_ (0.91 #3949, 0.90 #2993, 0.89 #2514), 0gq9h (0.90 #1727, 0.89 #3880, 0.87 #2924), 0gqzz (0.78 #7907, 0.38 #756, 0.30 #1716), 02x201b (0.78 #7907, 0.21 #1129, 0.18 #1439), 0czp_ (0.78 #7907, 0.14 #4979, 0.13 #5458), 025mb9 (0.25 #4678, 0.21 #5636, 0.20 #1199), 0257pw (0.25 #4781, 0.21 #5739, 0.19 #6459), 02hdky (0.25 #4749, 0.21 #5707, 0.19 #6427), 024_fw (0.25 #4702, 0.21 #5660, 0.19 #6380), 02nbqh (0.25 #4620, 0.21 #5578, 0.19 #6298) >> Best rule #3949 for best value: >> intensional similarity = 18 >> extensional distance = 44 >> proper extension: 05hmp6; >> query: (?x78, 0gvx_) <- award_winner(?x78, ?x398), ceremony(?x2222, ?x78), ceremony(?x484, ?x78), ceremony(?x77, ?x78), honored_for(?x78, ?x582), ?x484 = 0gq_v, ceremony(?x77, ?x7226), ceremony(?x77, ?x5703), ceremony(?x77, ?x4388), ceremony(?x77, ?x1084), award(?x1872, ?x77), ?x2222 = 0gs96, ?x1084 = 02yw5r, award(?x7554, ?x77), ?x7554 = 01mgw, ?x4388 = 0fz2y7, ?x7226 = 0c6vcj, ?x5703 = 02yvhx >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 073hkh ceremony! 0gvx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073hkh ceremony! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony #7960-03cx282 PRED entity: 03cx282 PRED relation: award PRED expected values: 02x258x => 108 concepts (62 used for prediction) PRED predicted values (max 10 best out of 257): 09sb52 (0.36 #3286, 0.28 #5719, 0.27 #4909), 02x258x (0.28 #938, 0.28 #533, 0.25 #1749), 02pqp12 (0.25 #71, 0.15 #24318, 0.14 #21885), 019f4v (0.25 #67, 0.15 #24318, 0.14 #21885), 054krc (0.25 #87, 0.15 #24318, 0.14 #21885), 0l8z1 (0.25 #64, 0.13 #22696, 0.13 #15401), 025m8l (0.25 #119, 0.06 #3364, 0.05 #3772), 025m8y (0.25 #99, 0.05 #3752, 0.04 #6182), 0fhpv4 (0.25 #196, 0.05 #1621, 0.03 #6279), 02g3ft (0.25 #85, 0.03 #895, 0.03 #490) >> Best rule #3286 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 03xsby; 0584j4n; 08c9b0; 01pk8v; 04bdzg; 08jfkw; 01r2c7; 026gb3v; 0b_4z; >> query: (?x4997, 09sb52) <- nominated_for(?x4997, ?x4751), award(?x4751, ?x2880), nominated_for(?x749, ?x4751), ?x2880 = 02ppm4q >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #938 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 04qvl7; 0gp9mp; 079hvk; 05dppk; 0693l; 0dqzkv; 07xr3w; 07mb57; 06nz46; 06g60w; ... *> query: (?x4997, 02x258x) <- award_winner(?x6616, ?x4997), nominated_for(?x4997, ?x4751), cinematography(?x1702, ?x4997), profession(?x4997, ?x2265) *> conf = 0.28 ranks of expected_values: 2 EVAL 03cx282 award 02x258x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 62.000 0.362 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7959-02vgh PRED entity: 02vgh PRED relation: artists! PRED expected values: 0cx7f 0781g => 88 concepts (51 used for prediction) PRED predicted values (max 10 best out of 285): 016clz (0.70 #11169, 0.69 #12412, 0.54 #11789), 0xhtw (0.61 #3426, 0.58 #2497, 0.57 #5287), 05w3f (0.57 #9029, 0.44 #1896, 0.44 #1584), 0cx7f (0.57 #6957, 0.52 #7576, 0.50 #2618), 08cyft (0.53 #3155, 0.46 #3774, 0.42 #5946), 0ggx5q (0.50 #11549, 0.42 #4105, 0.38 #5968), 02lnbg (0.46 #4085, 0.38 #3776, 0.36 #8427), 07v64s (0.40 #668, 0.22 #1910, 0.22 #1287), 025sc50 (0.39 #11520, 0.38 #5939, 0.32 #3148), 026z9 (0.38 #1003, 0.25 #384, 0.23 #4104) >> Best rule #11169 for best value: >> intensional similarity = 9 >> extensional distance = 110 >> proper extension: 01wbgdv; 018y2s; 01kx_81; 01w724; 0161c2; 0407f; 0dl567; 0180w8; 01vswx5; 02z4b_8; ... >> query: (?x6986, 016clz) <- artists(?x1572, ?x6986), artists(?x474, ?x6986), artist(?x2299, ?x6986), ?x1572 = 06by7, category(?x6986, ?x134), artists(?x474, ?x7683), artists(?x474, ?x1989), ?x7683 = 043c4j, ?x1989 = 04mn81 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #6957 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 51 *> proper extension: 0m19t; 01qqwp9; 07yg2; 047cx; 014_lq; 0ycp3; 070b4; 06br6t; 07n68; *> query: (?x6986, 0cx7f) <- artists(?x1380, ?x6986), group(?x75, ?x6986), artists(?x1380, ?x10043), artists(?x1380, ?x7810), artists(?x1380, ?x6067), artists(?x1380, ?x5494), ?x10043 = 0fb2l, ?x5494 = 018x3, ?x7810 = 0187x8, type_of_union(?x6067, ?x566) *> conf = 0.57 ranks of expected_values: 4, 28 EVAL 02vgh artists! 0781g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 88.000 51.000 0.696 http://example.org/music/genre/artists EVAL 02vgh artists! 0cx7f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 51.000 0.696 http://example.org/music/genre/artists #7958-01tpl1p PRED entity: 01tpl1p PRED relation: profession PRED expected values: 09jwl => 104 concepts (51 used for prediction) PRED predicted values (max 10 best out of 70): 09jwl (0.76 #459, 0.66 #2372, 0.65 #1342), 0nbcg (0.53 #2384, 0.47 #5923, 0.45 #2089), 0dz3r (0.42 #590, 0.41 #2061, 0.41 #5895), 0dxtg (0.35 #1043, 0.27 #2956, 0.26 #3545), 01d_h8 (0.32 #2212, 0.31 #1182, 0.29 #3685), 018gz8 (0.31 #1046, 0.27 #2959, 0.27 #3548), 039v1 (0.31 #1359, 0.30 #2389, 0.29 #1800), 01c72t (0.30 #169, 0.27 #7093, 0.25 #22), 03gjzk (0.29 #1044, 0.23 #2957, 0.22 #3546), 025352 (0.25 #58, 0.13 #205, 0.08 #2117) >> Best rule #459 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 09qr6; 01kv4mb; 01w02sy; 01wj18h; 01vsykc; 03y82t6; 01s21dg; 01kph_c; 015xp4; 01386_; ... >> query: (?x10607, 09jwl) <- profession(?x10607, ?x220), location(?x10607, ?x1523), ?x1523 = 030qb3t, ?x220 = 016z4k >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tpl1p profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 51.000 0.760 http://example.org/people/person/profession #7957-0bdlj PRED entity: 0bdlj PRED relation: nationality PRED expected values: 09c7w0 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 95): 09c7w0 (0.85 #4614, 0.85 #901, 0.85 #5215), 02jx1 (0.28 #2236, 0.23 #333, 0.21 #2036), 07ssc (0.23 #2218, 0.22 #115, 0.15 #315), 0f8l9c (0.15 #322, 0.11 #9341, 0.05 #2925), 0h7x (0.11 #9341, 0.11 #435, 0.06 #2238), 0d060g (0.11 #9341, 0.09 #1809, 0.06 #3614), 03_3d (0.11 #9341, 0.08 #2209, 0.03 #4511), 03rjj (0.11 #9341, 0.07 #1105, 0.04 #1506), 0345h (0.11 #9341, 0.07 #3135, 0.06 #4844), 06q1r (0.11 #9341, 0.06 #477, 0.05 #1879) >> Best rule #4614 for best value: >> intensional similarity = 3 >> extensional distance = 167 >> proper extension: 01gct2; >> query: (?x7262, 09c7w0) <- people(?x2510, ?x7262), award_winner(?x1088, ?x7262), ?x2510 = 0x67 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bdlj nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.852 http://example.org/people/person/nationality #7956-020h2v PRED entity: 020h2v PRED relation: film PRED expected values: 01jmyj => 85 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1827): 0pc62 (0.73 #1546, 0.63 #24728, 0.60 #17001), 0ds33 (0.73 #1546, 0.63 #24728, 0.60 #17001), 0crh5_f (0.73 #1546, 0.63 #24728, 0.56 #15454), 034r25 (0.73 #1546, 0.63 #24728, 0.56 #15454), 03mh_tp (0.44 #5073, 0.23 #11254, 0.22 #12799), 04z257 (0.33 #2064, 0.33 #518, 0.12 #8244), 0298n7 (0.33 #2718, 0.33 #1172, 0.12 #4263), 05zlld0 (0.33 #2080, 0.33 #534, 0.12 #3625), 035xwd (0.33 #1645, 0.22 #4735, 0.12 #7825), 0bz6sq (0.33 #2855, 0.22 #5945, 0.12 #4400) >> Best rule #1546 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01gb54; >> query: (?x7980, ?x392) <- production_companies(?x392, ?x7980), film(?x7980, ?x6176), ?x6176 = 0gmgwnv >> conf = 0.73 => this is the best rule for 4 predicted values *> Best rule #2814 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 054g1r; *> query: (?x7980, 01jmyj) <- film(?x7980, ?x8965), film(?x7980, ?x1108), ?x8965 = 01xvjb, ?x1108 = 0jjy0 *> conf = 0.33 ranks of expected_values: 55 EVAL 020h2v film 01jmyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 85.000 28.000 0.734 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7955-01jswq PRED entity: 01jswq PRED relation: campuses PRED expected values: 01jswq => 153 concepts (71 used for prediction) PRED predicted values (max 10 best out of 181): 065y4w7 (0.20 #12, 0.05 #2744, 0.04 #3290), 01pl14 (0.20 #8, 0.05 #2740, 0.04 #3286), 04hgpt (0.20 #142, 0.05 #2874, 0.03 #3966), 01mpwj (0.14 #641, 0.09 #1734, 0.02 #5012), 017v3q (0.05 #2421, 0.01 #7336, 0.01 #6790), 01stj9 (0.05 #2684, 0.01 #7053, 0.01 #8145), 02zr0z (0.05 #2702), 0438f (0.05 #2673), 05qgd9 (0.05 #2653), 03hpkp (0.05 #2569) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01pl14; 065y4w7; 04hgpt; >> query: (?x2711, 065y4w7) <- school(?x1883, ?x2711), school(?x700, ?x2711), currency(?x2711, ?x170), time_zones(?x2711, ?x2674) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #12562 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 102 *> proper extension: 06mkj; 0d05w3; *> query: (?x2711, ?x331) <- school(?x1883, ?x2711), contains(?x94, ?x2711), draft(?x387, ?x1883), school(?x1883, ?x331) *> conf = 0.02 ranks of expected_values: 83 EVAL 01jswq campuses 01jswq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 153.000 71.000 0.200 http://example.org/education/educational_institution/campuses #7954-0g9yrw PRED entity: 0g9yrw PRED relation: story_by PRED expected values: 03jm6c => 60 concepts (28 used for prediction) PRED predicted values (max 10 best out of 34): 0343h (0.06 #18, 0.04 #1298, 0.03 #1948), 04flrx (0.06 #109, 0.02 #325, 0.02 #541), 06z4wj (0.06 #121, 0.02 #337), 05gpy (0.06 #112, 0.02 #328), 0f7hc (0.06 #79, 0.02 #295), 02nygk (0.04 #427), 014nvr (0.03 #546), 09pl3f (0.02 #321), 05pq9 (0.02 #900), 01nr36 (0.02 #2598, 0.02 #1299, 0.01 #3681) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 049mql; >> query: (?x4032, 0343h) <- genre(?x4032, ?x1403), ?x1403 = 02l7c8, nominated_for(?x350, ?x4032), ?x350 = 05f4m9q >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g9yrw story_by 03jm6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 28.000 0.056 http://example.org/film/film/story_by #7953-0645k5 PRED entity: 0645k5 PRED relation: category PRED expected values: 08mbj5d => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.32 #3, 0.29 #5, 0.29 #27) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0d90m; 09sh8k; 0czyxs; 01hp5; 01qb5d; 08hmch; 01_mdl; 044g_k; 0cd2vh9; 05qbckf; ... >> query: (?x2896, 08mbj5d) <- film_release_region(?x2896, ?x87), film_crew_role(?x2896, ?x137), genre(?x2896, ?x6888), ?x6888 = 04pbhw >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0645k5 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.316 http://example.org/common/topic/webpage./common/webpage/category #7952-0dky9n PRED entity: 0dky9n PRED relation: type_of_union PRED expected values: 04ztj => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.84 #197, 0.84 #189, 0.83 #173), 01g63y (0.41 #274, 0.25 #2, 0.12 #369) >> Best rule #197 for best value: >> intensional similarity = 2 >> extensional distance = 327 >> proper extension: 01vrx3g; 03_0p; 06c0j; >> query: (?x877, 04ztj) <- people(?x9771, ?x877), award_winner(?x1703, ?x877) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dky9n type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.836 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7951-0428bc PRED entity: 0428bc PRED relation: profession PRED expected values: 02krf9 => 132 concepts (105 used for prediction) PRED predicted values (max 10 best out of 75): 01d_h8 (0.41 #900, 0.37 #602, 0.35 #2837), 03gjzk (0.33 #3740, 0.32 #6721, 0.24 #7764), 0dxtg (0.33 #4782, 0.30 #7763, 0.29 #4186), 0cbd2 (0.30 #305, 0.22 #2987, 0.20 #7), 02jknp (0.27 #1945, 0.26 #902, 0.25 #2392), 09jwl (0.26 #168, 0.21 #8514, 0.20 #6129), 01c72t (0.26 #173, 0.20 #769, 0.12 #1514), 0kyk (0.24 #328, 0.16 #3010, 0.13 #30), 018gz8 (0.16 #2848, 0.15 #7617, 0.14 #9406), 02hv44_ (0.15 #803, 0.13 #58, 0.12 #356) >> Best rule #900 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 0byfz; 04wqr; 012cj0; 019z7q; 0yfp; 030pr; 02lxj_; 0bmh4; 0jrny; 01dvms; ... >> query: (?x9977, 01d_h8) <- nominated_for(?x9977, ?x9616), people(?x7260, ?x9977), religion(?x9977, ?x1985) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #6733 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 803 *> proper extension: 01nzs7; 0kcd5; *> query: (?x9977, 02krf9) <- nominated_for(?x9977, ?x9616), nominated_for(?x375, ?x9616), languages(?x9616, ?x254) *> conf = 0.13 ranks of expected_values: 14 EVAL 0428bc profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 132.000 105.000 0.410 http://example.org/people/person/profession #7950-03hp2y1 PRED entity: 03hp2y1 PRED relation: production_companies PRED expected values: 03sb38 => 72 concepts (43 used for prediction) PRED predicted values (max 10 best out of 59): 05qd_ (0.44 #2952, 0.44 #3197, 0.37 #328), 016tw3 (0.44 #2952, 0.44 #3197, 0.37 #328), 054lpb6 (0.17 #15, 0.11 #179, 0.09 #1081), 05rrtf (0.17 #56, 0.03 #1864, 0.03 #1782), 04cygb3 (0.17 #46, 0.03 #210, 0.01 #292), 04rqd (0.17 #75, 0.02 #239, 0.01 #2128), 0gfmc_ (0.17 #48, 0.02 #212), 03sb38 (0.16 #381, 0.09 #789, 0.03 #625), 086k8 (0.13 #2954, 0.11 #2138, 0.11 #2791), 02slt7 (0.12 #358, 0.06 #766, 0.03 #194) >> Best rule #2952 for best value: >> intensional similarity = 4 >> extensional distance = 910 >> proper extension: 047msdk; 0k4d7; 085ccd; 02pb2bp; 0jymd; 0yxf4; 0dd6bf; 0jqb8; 0dtzkt; 0ktx_; >> query: (?x9981, ?x902) <- film(?x91, ?x9981), film(?x902, ?x9981), language(?x9981, ?x254), production_companies(?x9981, ?x4564) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #381 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 140 *> proper extension: 02vl9ln; *> query: (?x9981, 03sb38) <- country(?x9981, ?x789), country(?x9981, ?x94), ?x789 = 0f8l9c, nationality(?x51, ?x94) *> conf = 0.16 ranks of expected_values: 8 EVAL 03hp2y1 production_companies 03sb38 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 72.000 43.000 0.444 http://example.org/film/film/production_companies #7949-04mlh8 PRED entity: 04mlh8 PRED relation: actor! PRED expected values: 025x1t => 116 concepts (103 used for prediction) PRED predicted values (max 10 best out of 127): 01hvv0 (0.29 #151, 0.10 #1202, 0.07 #2256), 0vhm (0.21 #91, 0.08 #879, 0.08 #1142), 019g8j (0.21 #227, 0.08 #1278, 0.07 #2069), 0jwl2 (0.18 #335, 0.14 #73, 0.11 #861), 015w8_ (0.14 #46, 0.12 #308, 0.11 #834), 01h72l (0.14 #38, 0.12 #300, 0.09 #2932), 0ctzf1 (0.12 #397, 0.09 #660, 0.08 #923), 024rwx (0.12 #368, 0.09 #3000, 0.09 #2475), 025x1t (0.12 #482, 0.07 #220, 0.06 #1008), 0kfpm (0.12 #275, 0.06 #801, 0.05 #1064) >> Best rule #151 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 07gknc; >> query: (?x7288, 01hvv0) <- profession(?x7288, ?x1032), nationality(?x7288, ?x94), actor(?x5938, ?x7288), ?x5938 = 05f7w84 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #482 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 0h5g_; 015p37; 05z775; *> query: (?x7288, 025x1t) <- language(?x7288, ?x254), actor(?x5286, ?x7288), actor(?x8976, ?x7288), honored_for(?x2751, ?x8976) *> conf = 0.12 ranks of expected_values: 9 EVAL 04mlh8 actor! 025x1t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 116.000 103.000 0.286 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #7948-01k8rb PRED entity: 01k8rb PRED relation: film PRED expected values: 0m491 0kbwb => 117 concepts (87 used for prediction) PRED predicted values (max 10 best out of 846): 02k_4g (0.42 #123537, 0.40 #132489, 0.40 #132488), 026q3s3 (0.10 #5577, 0.09 #3786, 0.04 #7368), 02vw1w2 (0.09 #3796, 0.08 #5587, 0.04 #14539), 0f42nz (0.08 #8073, 0.08 #15234, 0.07 #9863), 07gbf (0.08 #75197, 0.08 #19697, 0.07 #80570), 03hp2y1 (0.08 #3401, 0.05 #1610, 0.03 #5192), 0ds5_72 (0.08 #3248, 0.05 #5039, 0.05 #6830), 06lpmt (0.08 #2476, 0.03 #4267, 0.03 #6058), 06w839_ (0.08 #2300, 0.03 #4091, 0.03 #5882), 0ptdz (0.08 #3549, 0.03 #5340, 0.03 #7131) >> Best rule #123537 for best value: >> intensional similarity = 3 >> extensional distance = 1309 >> proper extension: 05f7snc; >> query: (?x1397, ?x782) <- nominated_for(?x1397, ?x782), award_nominee(?x1397, ?x11884), people(?x1446, ?x11884) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #2080 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 03wpmd; *> query: (?x1397, 0m491) <- actor(?x782, ?x1397), special_performance_type(?x1397, ?x3558), award_nominee(?x1397, ?x2615) *> conf = 0.04 ranks of expected_values: 194 EVAL 01k8rb film 0kbwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 87.000 0.424 http://example.org/film/actor/film./film/performance/film EVAL 01k8rb film 0m491 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 117.000 87.000 0.424 http://example.org/film/actor/film./film/performance/film #7947-018dnt PRED entity: 018dnt PRED relation: film PRED expected values: 035_2h => 98 concepts (40 used for prediction) PRED predicted values (max 10 best out of 653): 03bx2lk (0.50 #1972, 0.08 #7334, 0.03 #35946), 0gmgwnv (0.50 #4654), 011ywj (0.33 #1433, 0.08 #8583, 0.06 #21097), 04w7rn (0.33 #237, 0.06 #9175, 0.04 #14537), 0ggbfwf (0.33 #1008, 0.02 #18882), 035w2k (0.33 #855, 0.02 #18729), 02_kd (0.33 #586, 0.02 #18460), 0gyy53 (0.33 #482, 0.02 #18356), 027pfb2 (0.33 #19663, 0.25 #19662, 0.20 #10726), 0symg (0.25 #5276, 0.25 #3488, 0.14 #7063) >> Best rule #1972 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01wbg84; 01vvb4m; >> query: (?x585, 03bx2lk) <- people(?x7322, ?x585), film(?x585, ?x7491), actor(?x4138, ?x585), ?x7491 = 01qb559 >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018dnt film 035_2h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 40.000 0.500 http://example.org/film/actor/film./film/performance/film #7946-01q_wyj PRED entity: 01q_wyj PRED relation: artist! PRED expected values: 01trtc => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 134): 015_1q (0.29 #301, 0.21 #1429, 0.19 #1711), 0bfp0l (0.29 #388, 0.02 #4337, 0.01 #3068), 023rwm (0.24 #2, 0.14 #1130, 0.06 #2540), 03rhqg (0.19 #297, 0.17 #579, 0.16 #861), 043g7l (0.19 #313, 0.07 #877, 0.07 #2569), 0181dw (0.14 #324, 0.12 #1029, 0.11 #2439), 0g768 (0.13 #1165, 0.12 #4550, 0.12 #3986), 011k1h (0.12 #432, 0.12 #1137, 0.12 #2830), 017l96 (0.12 #441, 0.12 #159, 0.10 #1428), 03qx_f (0.12 #497, 0.06 #215, 0.05 #356) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 01l03w2; >> query: (?x8282, 015_1q) <- artists(?x8798, ?x8282), instrumentalists(?x212, ?x8282), nationality(?x8282, ?x94), ?x8798 = 0gg8l >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #214 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 03kwtb; 04bpm6; 09prnq; 0137g1; 06x4l_; 03bnv; 0bkg4; 0180w8; 023l9y; 01mwsnc; ... *> query: (?x8282, 01trtc) <- profession(?x8282, ?x2348), role(?x8282, ?x4917), ?x4917 = 06w7v, ?x2348 = 0nbcg *> conf = 0.12 ranks of expected_values: 12 EVAL 01q_wyj artist! 01trtc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 76.000 76.000 0.286 http://example.org/music/record_label/artist #7945-05k4my PRED entity: 05k4my PRED relation: music PRED expected values: 02jxmr => 76 concepts (40 used for prediction) PRED predicted values (max 10 best out of 78): 01tc9r (0.13 #65, 0.07 #276, 0.05 #487), 0146pg (0.12 #221, 0.07 #642, 0.05 #2116), 015wc0 (0.10 #176, 0.01 #2704), 02bh9 (0.08 #683, 0.07 #262, 0.06 #473), 07hgkd (0.06 #82), 0jrqq (0.06 #5269, 0.06 #4846, 0.06 #3371), 086k8 (0.06 #5269, 0.06 #4846, 0.06 #3371), 023361 (0.06 #782, 0.05 #361, 0.03 #150), 0150t6 (0.05 #257, 0.05 #468, 0.05 #888), 0csdzz (0.05 #398, 0.05 #609, 0.03 #187) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 01dc0c; 01fwzk; >> query: (?x10422, 01tc9r) <- award(?x10422, ?x102), nominated_for(?x382, ?x10422), genre(?x10422, ?x3613), ?x3613 = 09blyk >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #1126 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 203 *> proper extension: 0c40vxk; *> query: (?x10422, 02jxmr) <- cinematography(?x10422, ?x7740), film_crew_role(?x10422, ?x137), genre(?x10422, ?x258) *> conf = 0.05 ranks of expected_values: 11 EVAL 05k4my music 02jxmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 76.000 40.000 0.129 http://example.org/film/film/music #7944-05hjnw PRED entity: 05hjnw PRED relation: genre PRED expected values: 0hfjk => 109 concepts (72 used for prediction) PRED predicted values (max 10 best out of 100): 05p553 (0.45 #2030, 0.42 #4419, 0.42 #480), 060__y (0.43 #1565, 0.42 #731, 0.26 #850), 0219x_ (0.33 #145, 0.33 #26, 0.25 #264), 02kdv5l (0.33 #121, 0.30 #4297, 0.29 #3460), 03k9fj (0.33 #130, 0.27 #2275, 0.27 #1322), 06cvj (0.33 #3, 0.25 #2029, 0.25 #241), 01t_vv (0.33 #54, 0.25 #292, 0.21 #530), 03p5xs (0.33 #89, 0.25 #327, 0.04 #1757), 04pbhw (0.33 #175, 0.06 #1367, 0.06 #1009), 01jfsb (0.33 #6217, 0.31 #4307, 0.31 #7887) >> Best rule #2030 for best value: >> intensional similarity = 4 >> extensional distance = 158 >> proper extension: 03kg2v; >> query: (?x4939, 05p553) <- film_crew_role(?x4939, ?x468), genre(?x4939, ?x1403), produced_by(?x4939, ?x3862), ?x1403 = 02l7c8 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #539 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 0d8w2n; *> query: (?x4939, 0hfjk) <- genre(?x4939, ?x1403), genre(?x4939, ?x714), ?x1403 = 02l7c8, ?x714 = 0hn10, film(?x902, ?x4939) *> conf = 0.04 ranks of expected_values: 54 EVAL 05hjnw genre 0hfjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 109.000 72.000 0.450 http://example.org/film/film/genre #7943-0134s5 PRED entity: 0134s5 PRED relation: artist! PRED expected values: 01fjfv => 126 concepts (102 used for prediction) PRED predicted values (max 10 best out of 4): 03gfvsz (0.75 #74, 0.73 #78, 0.69 #111), 01fjfv (0.38 #62, 0.38 #22, 0.24 #125), 04f73rc (0.02 #106, 0.02 #101, 0.01 #110), 0jrv_ (0.02 #105, 0.02 #100, 0.01 #109) >> Best rule #74 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 01vw20_; >> query: (?x3420, 03gfvsz) <- artist(?x11912, ?x3420), award(?x3420, ?x6126), artist(?x3265, ?x3420), artist(?x3265, ?x2683), ?x2683 = 01dw9z >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #62 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 30 *> proper extension: 04rcr; 0150jk; 0dtd6; 01czx; 01rm8b; 0mgcr; 0b1zz; 0ycfj; 014_xj; *> query: (?x3420, 01fjfv) <- artist(?x11912, ?x3420), award(?x3420, ?x6126), category(?x3420, ?x134), group(?x227, ?x3420), artist(?x2149, ?x3420) *> conf = 0.38 ranks of expected_values: 2 EVAL 0134s5 artist! 01fjfv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 102.000 0.750 http://example.org/broadcast/content/artist #7942-02_5x9 PRED entity: 02_5x9 PRED relation: group! PRED expected values: 02hnl 01v1d8 => 89 concepts (60 used for prediction) PRED predicted values (max 10 best out of 114): 02hnl (0.78 #2706, 0.77 #2546, 0.77 #1446), 018vs (0.71 #253, 0.68 #725, 0.68 #2058), 03bx0bm (0.68 #736, 0.67 #1442, 0.58 #2940), 07y_7 (0.57 #245, 0.25 #323, 0.21 #717), 03qjg (0.43 #286, 0.42 #758, 0.40 #203), 06ncr (0.43 #278, 0.39 #1187, 0.29 #908), 02sgy (0.43 #248, 0.32 #720, 0.26 #1500), 04rzd (0.43 #271, 0.32 #743, 0.25 #28), 013y1f (0.43 #266, 0.25 #23, 0.25 #323), 0mkg (0.43 #250, 0.25 #323, 0.21 #722) >> Best rule #2706 for best value: >> intensional similarity = 11 >> extensional distance = 173 >> proper extension: 0167_s; 0b1zz; 081wh1; 01jkqfz; 01_wfj; 016vn3; 0jg77; >> query: (?x1945, 02hnl) <- group(?x432, ?x1945), group(?x227, ?x1945), artists(?x7220, ?x1945), role(?x219, ?x227), group(?x227, ?x9589), group(?x227, ?x717), role(?x1647, ?x227), ?x9589 = 02cw1m, role(?x645, ?x432), ?x1647 = 05ljv7, ?x717 = 0150jk >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 27 EVAL 02_5x9 group! 01v1d8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 89.000 60.000 0.777 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 02_5x9 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 60.000 0.777 http://example.org/music/performance_role/regular_performances./music/group_membership/group #7941-0dgskx PRED entity: 0dgskx PRED relation: film PRED expected values: 016z7s => 78 concepts (52 used for prediction) PRED predicted values (max 10 best out of 541): 0ddd0gc (0.57 #5365, 0.46 #12522, 0.36 #71556), 011ywj (0.31 #1435, 0.06 #37567, 0.06 #39356), 0h3xztt (0.17 #171, 0.03 #59033, 0.03 #78715), 0194zl (0.10 #844, 0.03 #59033, 0.03 #78715), 05z43v (0.10 #1353, 0.03 #59033, 0.03 #78715), 03bx2lk (0.10 #184, 0.02 #12706, 0.02 #10916), 09gq0x5 (0.07 #281, 0.06 #37567, 0.06 #39356), 021y7yw (0.07 #389, 0.05 #41145, 0.03 #59033), 03177r (0.07 #462, 0.04 #2250, 0.03 #59033), 031786 (0.07 #1274, 0.03 #3062, 0.03 #59033) >> Best rule #5365 for best value: >> intensional similarity = 2 >> extensional distance = 188 >> proper extension: 014x77; 0htlr; 0456xp; 04shbh; 0prjs; 022_lg; 01mqz0; 03xmy1; 05hdf; 02dh86; ... >> query: (?x6612, ?x144) <- award_winner(?x144, ?x6612), spouse(?x988, ?x6612) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #334 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 02qflgv; 02w9895; 02pkpfs; 06ns98; 0263tn1; 027ht3n; *> query: (?x6612, 016z7s) <- award_nominee(?x6612, ?x2372), gender(?x6612, ?x231), ?x2372 = 0l6px *> conf = 0.03 ranks of expected_values: 108 EVAL 0dgskx film 016z7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 78.000 52.000 0.569 http://example.org/film/actor/film./film/performance/film #7940-0436kgz PRED entity: 0436kgz PRED relation: nationality PRED expected values: 06mkj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 71): 09c7w0 (0.77 #1208, 0.75 #2611, 0.75 #2711), 02jx1 (0.33 #233, 0.22 #133, 0.15 #734), 0gx1l (0.27 #8021), 01n4w (0.27 #8021), 0kpys (0.27 #8021), 07ssc (0.22 #215, 0.14 #1019, 0.12 #1423), 03rjj (0.12 #5, 0.05 #1814, 0.05 #1914), 03rk0 (0.11 #246, 0.08 #3256, 0.08 #3457), 0d060g (0.11 #107, 0.06 #2316, 0.05 #307), 0345h (0.03 #431, 0.02 #331, 0.02 #4144) >> Best rule #1208 for best value: >> intensional similarity = 3 >> extensional distance = 194 >> proper extension: 012gbb; >> query: (?x6658, 09c7w0) <- film(?x6658, ?x69), participant(?x6658, ?x2237), award(?x6658, ?x1336) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #10329 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4244 *> proper extension: 04s9n; *> query: (?x6658, ?x2146) <- profession(?x6658, ?x1032), profession(?x10076, ?x1032), nationality(?x10076, ?x2146) *> conf = 0.02 ranks of expected_values: 26 EVAL 0436kgz nationality 06mkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 104.000 104.000 0.765 http://example.org/people/person/nationality #7939-018x3 PRED entity: 018x3 PRED relation: influenced_by PRED expected values: 0bqch => 125 concepts (80 used for prediction) PRED predicted values (max 10 best out of 458): 0l12d (0.18 #5637, 0.16 #5203, 0.04 #3901), 032l1 (0.14 #7893, 0.10 #12232, 0.10 #5292), 02wh0 (0.13 #8186, 0.09 #12525, 0.09 #5585), 03_87 (0.13 #8005, 0.09 #12344, 0.08 #14078), 0lrh (0.13 #8238, 0.08 #1374, 0.07 #2241), 03sbs (0.12 #5424, 0.12 #8025, 0.09 #4990), 081k8 (0.12 #7958, 0.11 #12297, 0.09 #14031), 07c0j (0.11 #1757, 0.06 #4359, 0.03 #6527), 01s7qqw (0.11 #1896, 0.06 #6666, 0.05 #2329), 014z8v (0.11 #3588, 0.06 #8792, 0.06 #9226) >> Best rule #5637 for best value: >> intensional similarity = 2 >> extensional distance = 103 >> proper extension: 0399p; 02wh0; 047g6; 01h2_6; >> query: (?x5494, ?x1656) <- nationality(?x5494, ?x1310), peers(?x1656, ?x5494) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018x3 influenced_by 0bqch CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 80.000 0.176 http://example.org/influence/influence_node/influenced_by #7938-0g7pm1 PRED entity: 0g7pm1 PRED relation: film! PRED expected values: 0blbxk 08vr94 => 56 concepts (27 used for prediction) PRED predicted values (max 10 best out of 692): 01wbg84 (0.12 #45, 0.11 #2119, 0.03 #6267), 01q_ph (0.12 #55, 0.11 #2129, 0.03 #8351), 0mdqp (0.12 #117, 0.11 #2191, 0.02 #12561), 0d608 (0.12 #1298, 0.11 #3372, 0.02 #19971), 01bcq (0.12 #869, 0.11 #2943, 0.02 #7091), 04yqlk (0.12 #773, 0.11 #2847, 0.02 #6995), 0h953 (0.12 #1468, 0.11 #3542, 0.01 #13912), 01nm3s (0.12 #686, 0.11 #2760, 0.01 #42177), 01yf85 (0.07 #7725, 0.05 #9799, 0.05 #11873), 0h5g_ (0.06 #4220, 0.06 #72, 0.06 #2146) >> Best rule #45 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 0gxfz; 0glnm; 0jqb8; >> query: (?x6798, 01wbg84) <- genre(?x6798, ?x4150), film(?x237, ?x6798), ?x4150 = 06qm3, location(?x237, ?x2020), profession(?x237, ?x319) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #4350 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 31 *> proper extension: 05sxzwc; 05pbl56; 05qbckf; 05p1qyh; 05zy2cy; 047p7fr; 0ds2n; 04ydr95; 09lcsj; 05m_jsg; ... *> query: (?x6798, 0blbxk) <- genre(?x6798, ?x225), film_crew_role(?x6798, ?x5136), film(?x123, ?x6798), ?x225 = 02kdv5l, ?x5136 = 089g0h *> conf = 0.03 ranks of expected_values: 268, 552 EVAL 0g7pm1 film! 08vr94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 27.000 0.118 http://example.org/film/actor/film./film/performance/film EVAL 0g7pm1 film! 0blbxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 27.000 0.118 http://example.org/film/actor/film./film/performance/film #7937-0738b8 PRED entity: 0738b8 PRED relation: gender PRED expected values: 05zppz => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #39, 0.85 #27, 0.84 #49), 02zsn (0.63 #10, 0.45 #44, 0.38 #52) >> Best rule #39 for best value: >> intensional similarity = 2 >> extensional distance = 341 >> proper extension: 0hnlx; 09dt7; 02_j7t; 0126rp; 0j3v; 01x1cn2; 03pm9; 0dzkq; 085pr; 09qh1; ... >> query: (?x2437, 05zppz) <- location(?x2437, ?x335), influenced_by(?x2437, ?x9024) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0738b8 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.854 http://example.org/people/person/gender #7936-01pj7 PRED entity: 01pj7 PRED relation: service_location! PRED expected values: 018mxj => 157 concepts (130 used for prediction) PRED predicted values (max 10 best out of 133): 01c6k4 (0.52 #554, 0.45 #417, 0.32 #965), 018mxj (0.35 #421, 0.33 #558, 0.28 #695), 07zl6m (0.29 #681, 0.25 #544, 0.21 #1092), 064f29 (0.29 #608, 0.25 #471, 0.21 #1019), 05b5c (0.29 #676, 0.25 #539, 0.20 #813), 069b85 (0.29 #677, 0.25 #540, 0.20 #814), 0p4wb (0.25 #420, 0.24 #557, 0.20 #694), 04fv0k (0.25 #497, 0.24 #634, 0.20 #771), 0k9ts (0.24 #777, 0.24 #640, 0.20 #503), 0cv9b (0.24 #696, 0.19 #559, 0.16 #2204) >> Best rule #554 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 059g4; >> query: (?x1790, 01c6k4) <- film_release_region(?x141, ?x1790), adjoins(?x1790, ?x1003), film_release_region(?x7141, ?x1790) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 05kr_; *> query: (?x1790, 018mxj) <- adjoins(?x1790, ?x1003), film_release_region(?x7141, ?x1790), currency(?x1790, ?x170) *> conf = 0.35 ranks of expected_values: 2 EVAL 01pj7 service_location! 018mxj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 157.000 130.000 0.524 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #7935-04w58 PRED entity: 04w58 PRED relation: country! PRED expected values: 06f41 09wz9 03_8r => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 52): 071t0 (0.84 #1477, 0.80 #1321, 0.75 #1165), 03_8r (0.78 #956, 0.76 #384, 0.75 #488), 01cgz (0.75 #948, 0.72 #584, 0.72 #532), 01lb14 (0.72 #534, 0.71 #1470, 0.70 #1314), 03hr1p (0.70 #1322, 0.69 #1478, 0.60 #594), 06f41 (0.68 #585, 0.64 #533, 0.62 #325), 064vjs (0.62 #1329, 0.62 #1485, 0.54 #1069), 03fyrh (0.60 #182, 0.57 #1066, 0.53 #1170), 0194d (0.60 #201, 0.52 #617, 0.47 #981), 0w0d (0.58 #270, 0.53 #1466, 0.52 #582) >> Best rule #1477 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0h7x; 05b4w; 04w4s; 06sff; 04hqz; >> query: (?x3912, 071t0) <- country(?x4045, ?x3912), country(?x3309, ?x3912), ?x4045 = 06z6r, ?x3309 = 09w1n >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #956 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 027rn; 03_r3; 0162v; 03h64; 03gyl; 05r7t; 04ty8; *> query: (?x3912, 03_8r) <- vacationer(?x3912, ?x6730), olympics(?x3912, ?x418), participating_countries(?x1608, ?x3912) *> conf = 0.78 ranks of expected_values: 2, 6, 32 EVAL 04w58 country! 03_8r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 124.000 0.844 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 04w58 country! 09wz9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 124.000 124.000 0.844 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 04w58 country! 06f41 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 124.000 0.844 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #7934-0bvn25 PRED entity: 0bvn25 PRED relation: nominated_for! PRED expected values: 063y_ky => 64 concepts (60 used for prediction) PRED predicted values (max 10 best out of 185): 0ck27z (0.33 #549, 0.19 #12381, 0.11 #9283), 0bdx29 (0.33 #560, 0.19 #12381, 0.11 #9283), 0gkr9q (0.33 #685, 0.04 #3303, 0.04 #2827), 0fbtbt (0.33 #636, 0.03 #3492, 0.03 #3254), 0fbvqf (0.33 #515, 0.02 #3133, 0.02 #2657), 0bp_b2 (0.33 #493, 0.02 #3111, 0.02 #3349), 0cqhb3 (0.33 #675, 0.02 #3531, 0.02 #2817), 0gkts9 (0.33 #601, 0.02 #3219, 0.02 #2743), 02xcb6n (0.33 #677, 0.02 #2819, 0.02 #3295), 0cqh6z (0.33 #532, 0.01 #3150, 0.01 #7196) >> Best rule #549 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 05lfwd; >> query: (?x365, 0ck27z) <- nominated_for(?x1691, ?x365), nominated_for(?x5593, ?x365), ?x5593 = 025b5y >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #12381 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1588 *> proper extension: 09fb5; 01h1bf; 06g60w; 02kk_c; 0c3xpwy; 04glx0; 05sy0cv; 01b7h8; 07bz5; 03d17dg; ... *> query: (?x365, ?x1105) <- nominated_for(?x1104, ?x365), award(?x1104, ?x1105) *> conf = 0.19 ranks of expected_values: 45 EVAL 0bvn25 nominated_for! 063y_ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 64.000 60.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7933-03n3gl PRED entity: 03n3gl PRED relation: language PRED expected values: 02h40lc => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.96 #3569, 0.96 #1923, 0.95 #2262), 04306rv (0.21 #117, 0.11 #1362, 0.11 #174), 06nm1 (0.12 #67, 0.11 #518, 0.10 #123), 02bjrlw (0.12 #113, 0.10 #283, 0.08 #1358), 06b_j (0.10 #133, 0.06 #1378, 0.06 #190), 03_9r (0.07 #122, 0.06 #1082, 0.06 #686), 0653m (0.06 #181, 0.05 #857, 0.05 #519), 012w70 (0.05 #182, 0.03 #858, 0.03 #125), 05zjd (0.04 #136, 0.03 #250, 0.02 #80), 04h9h (0.04 #152, 0.03 #885, 0.03 #999) >> Best rule #3569 for best value: >> intensional similarity = 3 >> extensional distance = 1302 >> proper extension: 01fs__; >> query: (?x6365, 02h40lc) <- nominated_for(?x902, ?x6365), language(?x6365, ?x5607), major_field_of_study(?x5607, ?x90) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03n3gl language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.960 http://example.org/film/film/language #7932-01mw1 PRED entity: 01mw1 PRED relation: industry! PRED expected values: 01t7jy 0d2fd7 01qf54 02bm1v 046qpy 01nds 01_4mn 01qszl 01_30_ 04gbl3 => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 599): 0l8sx (0.56 #3078, 0.43 #5333, 0.42 #1717), 0k8z (0.43 #5333, 0.43 #2332, 0.33 #1001), 01dtcb (0.43 #5333, 0.42 #1717, 0.35 #3437), 049ql1 (0.43 #5333, 0.42 #1717, 0.35 #3437), 02bh8z (0.43 #5333, 0.33 #1381, 0.33 #428), 03mp8k (0.43 #5333, 0.33 #1446, 0.33 #493), 043g7l (0.43 #5333, 0.33 #1397, 0.33 #444), 07s363 (0.43 #5333, 0.33 #1490, 0.33 #537), 09glbnt (0.43 #5333, 0.33 #448, 0.33 #64), 0181hw (0.43 #5333, 0.33 #477, 0.33 #93) >> Best rule #3078 for best value: >> intensional similarity = 15 >> extensional distance = 7 >> proper extension: 0h6dj; >> query: (?x245, 0l8sx) <- industry(?x13349, ?x245), industry(?x11273, ?x245), industry(?x10377, ?x245), industry(?x6230, ?x245), industry(?x5112, ?x245), child(?x10377, ?x13872), category(?x5112, ?x134), state_province_region(?x10377, ?x335), company(?x346, ?x6230), service_language(?x13349, ?x254), ?x335 = 059rby, currency(?x10377, ?x170), ?x346 = 060c4, citytown(?x11273, ?x1860), company(?x554, ?x13349) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1702 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 1 *> proper extension: 020mfr; *> query: (?x245, 01_4mn) <- industry(?x14420, ?x245), industry(?x14246, ?x245), industry(?x14118, ?x245), industry(?x13954, ?x245), industry(?x13750, ?x245), industry(?x13650, ?x245), industry(?x13035, ?x245), industry(?x11070, ?x245), industry(?x10419, ?x245), industry(?x10377, ?x245), industry(?x9309, ?x245), industry(?x5961, ?x245), industry(?x5077, ?x245), ?x10377 = 01_4lx, ?x13954 = 07zl6m, company(?x346, ?x13035), ?x5077 = 0xwj, contact_category(?x13035, ?x897), citytown(?x13650, ?x9559), ?x14420 = 01yf92, ?x5961 = 0123j6, ?x13750 = 04kqk, ?x10419 = 08z84_, service_language(?x13035, ?x254), ?x9309 = 059wk, ?x11070 = 02brqp, child(?x14458, ?x14246), ?x14118 = 026wmz6 *> conf = 0.33 ranks of expected_values: 64, 65, 67, 68, 69, 70, 71, 73, 125 EVAL 01mw1 industry! 04gbl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 40.000 40.000 0.556 http://example.org/business/business_operation/industry EVAL 01mw1 industry! 01_30_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 40.000 40.000 0.556 http://example.org/business/business_operation/industry EVAL 01mw1 industry! 01qszl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 40.000 40.000 0.556 http://example.org/business/business_operation/industry EVAL 01mw1 industry! 01_4mn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 40.000 40.000 0.556 http://example.org/business/business_operation/industry EVAL 01mw1 industry! 01nds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 40.000 40.000 0.556 http://example.org/business/business_operation/industry EVAL 01mw1 industry! 046qpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 40.000 40.000 0.556 http://example.org/business/business_operation/industry EVAL 01mw1 industry! 02bm1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 40.000 40.000 0.556 http://example.org/business/business_operation/industry EVAL 01mw1 industry! 01qf54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 40.000 40.000 0.556 http://example.org/business/business_operation/industry EVAL 01mw1 industry! 0d2fd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 40.000 40.000 0.556 http://example.org/business/business_operation/industry EVAL 01mw1 industry! 01t7jy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 40.000 40.000 0.556 http://example.org/business/business_operation/industry #7931-018wrk PRED entity: 018wrk PRED relation: sports PRED expected values: 06z6r => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 48): 0crlz (0.81 #880, 0.80 #759, 0.80 #194), 06wrt (0.81 #860, 0.80 #739, 0.77 #298), 02bkg (0.77 #329, 0.77 #291, 0.73 #853), 06z6r (0.77 #329, 0.72 #752, 0.71 #147), 07jjt (0.77 #329, 0.67 #287, 0.65 #864), 071t0 (0.73 #866, 0.72 #745, 0.71 #140), 03_8r (0.71 #139, 0.62 #303, 0.60 #179), 096f8 (0.70 #171, 0.65 #857, 0.64 #736), 01sgl (0.67 #770, 0.66 #891, 0.42 #288), 0d1tm (0.65 #852, 0.64 #731, 0.62 #290) >> Best rule #880 for best value: >> intensional similarity = 13 >> extensional distance = 24 >> proper extension: 0l998; >> query: (?x358, 0crlz) <- sports(?x358, ?x1352), country(?x1352, ?x4073), country(?x1352, ?x2152), country(?x1352, ?x304), sports(?x584, ?x1352), ?x304 = 0d0vqn, ?x4073 = 07dvs, ?x584 = 0l98s, olympics(?x359, ?x358), film_release_region(?x11395, ?x2152), film_release_region(?x4290, ?x2152), ?x11395 = 05ypj5, ?x4290 = 0gtxj2q >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #329 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 11 *> proper extension: 0kbvb; 0jhn7; *> query: (?x358, ?x359) <- sports(?x358, ?x1352), sports(?x358, ?x471), ?x1352 = 0w0d, olympics(?x279, ?x358), ?x471 = 02vx4, sports(?x358, ?x359), combatants(?x279, ?x151), nationality(?x199, ?x279), countries_spoken_in(?x393, ?x279), country(?x136, ?x279) *> conf = 0.77 ranks of expected_values: 4 EVAL 018wrk sports 06z6r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 50.000 50.000 0.808 http://example.org/user/jg/default_domain/olympic_games/sports #7930-04pf4r PRED entity: 04pf4r PRED relation: music! PRED expected values: 07kh6f3 => 128 concepts (116 used for prediction) PRED predicted values (max 10 best out of 842): 01_0f7 (0.75 #6975, 0.11 #31881, 0.06 #69743), 0dr3sl (0.44 #2990, 0.40 #8968, 0.36 #997), 06gb1w (0.44 #2990, 0.03 #27895, 0.02 #2414), 025s1wg (0.44 #2990), 09d3b7 (0.09 #828, 0.04 #12785, 0.03 #19758), 01s7w3 (0.08 #12813, 0.06 #3846, 0.06 #16798), 03h3x5 (0.05 #1250, 0.05 #2246, 0.04 #3243), 078mm1 (0.05 #1809, 0.05 #2805, 0.03 #6790), 035s95 (0.05 #1201, 0.05 #2197, 0.02 #12161), 0y_pg (0.05 #1770, 0.02 #17711, 0.02 #6751) >> Best rule #6975 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 025vry; 09bx1k; >> query: (?x4019, ?x6531) <- music(?x363, ?x4019), artists(?x4910, ?x4019), award_winner(?x6531, ?x4019) >> conf = 0.75 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04pf4r music! 07kh6f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 116.000 0.750 http://example.org/film/film/music #7929-06cgy PRED entity: 06cgy PRED relation: produced_by! PRED expected values: 0bpx1k => 123 concepts (99 used for prediction) PRED predicted values (max 10 best out of 367): 03s5lz (0.44 #14131, 0.41 #14130, 0.41 #12246), 07gghl (0.44 #14131, 0.41 #14130, 0.41 #12246), 0by17xn (0.44 #14131, 0.41 #14130, 0.41 #12246), 0h95927 (0.44 #14131, 0.41 #14130, 0.41 #12246), 0jsf6 (0.44 #14131, 0.41 #14130, 0.41 #12246), 06cm5 (0.44 #14131, 0.41 #14130, 0.41 #12246), 04ltlj (0.44 #14131, 0.41 #14130, 0.41 #12246), 07cw4 (0.44 #14131, 0.41 #14130, 0.41 #12246), 0jqj5 (0.44 #14131, 0.41 #14130, 0.41 #12246), 0y_hb (0.44 #14131, 0.41 #14130, 0.41 #12246) >> Best rule #14131 for best value: >> intensional similarity = 3 >> extensional distance = 326 >> proper extension: 0ksf29; 04b19t; 04g865; 0br1w; 01n9d9; 01f7v_; 012rng; 01vsps; 015njf; 01r_t_; ... >> query: (?x1554, ?x4920) <- nominated_for(?x1554, ?x4920), produced_by(?x4174, ?x1554), genre(?x4920, ?x239) >> conf = 0.44 => this is the best rule for 13 predicted values *> Best rule #34860 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 804 *> proper extension: 01ycck; 0d_skg; 032dg7; *> query: (?x1554, ?x437) <- nominated_for(?x1554, ?x887), award_nominee(?x794, ?x1554), produced_by(?x437, ?x794) *> conf = 0.06 ranks of expected_values: 48 EVAL 06cgy produced_by! 0bpx1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 123.000 99.000 0.439 http://example.org/film/film/produced_by #7928-03h3x5 PRED entity: 03h3x5 PRED relation: language PRED expected values: 0345h => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 42): 064_8sq (0.17 #1009, 0.17 #79, 0.16 #1068), 04306rv (0.17 #120, 0.16 #352, 0.13 #876), 03_9r (0.17 #67, 0.16 #823, 0.12 #183), 06nm1 (0.17 #300, 0.12 #532, 0.12 #2103), 0653m (0.14 #475, 0.13 #592, 0.11 #359), 012w70 (0.12 #476, 0.12 #593, 0.08 #360), 06b_j (0.10 #894, 0.08 #1825, 0.07 #1127), 02bjrlw (0.08 #465, 0.08 #349, 0.08 #873), 0jzc (0.07 #891, 0.06 #1124, 0.05 #1240), 0c_v2 (0.06 #480, 0.06 #597, 0.05 #364) >> Best rule #1009 for best value: >> intensional similarity = 4 >> extensional distance = 119 >> proper extension: 064n1pz; 0bmc4cm; 0cp08zg; 02pcq92; >> query: (?x2642, 064_8sq) <- film(?x382, ?x2642), genre(?x2642, ?x258), region(?x2642, ?x512), country(?x2642, ?x94) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03h3x5 language 0345h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 105.000 0.174 http://example.org/film/film/language #7927-029q3k PRED entity: 029q3k PRED relation: team! PRED expected values: 07h1h5 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 70): 0djvzd (0.82 #3486, 0.81 #4059, 0.81 #4058), 0d1swh (0.33 #26, 0.27 #387, 0.25 #461), 0g7vxv (0.33 #61, 0.14 #279, 0.12 #800), 0841zn (0.33 #37, 0.14 #255, 0.12 #800), 0457w0 (0.33 #170, 0.13 #4062, 0.12 #1017), 09lhln (0.29 #232, 0.21 #814, 0.21 #595), 0f1pyf (0.25 #599, 0.20 #1164, 0.18 #379), 0879xc (0.25 #101, 0.19 #1090, 0.17 #828), 080dyk (0.25 #77, 0.19 #1090, 0.14 #804), 07zr66 (0.25 #132, 0.14 #277, 0.11 #3702) >> Best rule #3486 for best value: >> intensional similarity = 9 >> extensional distance = 178 >> proper extension: 02b15h; 056xx8; 02gys2; 04b4yg; 0y54; 01k2yr; 0371rb; 02b1mc; 01l3vx; 04jbyg; ... >> query: (?x9182, ?x7234) <- team(?x7234, ?x9182), position(?x9182, ?x60), team(?x63, ?x9182), athlete(?x471, ?x7234), team(?x7234, ?x5686), ?x60 = 02nzb8, nationality(?x7234, ?x1310), team(?x982, ?x5686), current_club(?x676, ?x5686) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1030 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 68 *> proper extension: 02s2lg; *> query: (?x9182, 07h1h5) <- team(?x7234, ?x9182), position(?x9182, ?x60), team(?x63, ?x9182), athlete(?x471, ?x7234), team(?x7234, ?x6353), ?x60 = 02nzb8, ?x471 = 02vx4, team(?x1696, ?x6353), team(?x9088, ?x6353), ?x9088 = 09l9tq *> conf = 0.07 ranks of expected_values: 40 EVAL 029q3k team! 07h1h5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 76.000 76.000 0.815 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #7926-058s57 PRED entity: 058s57 PRED relation: gender PRED expected values: 02zsn => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #121, 0.77 #167, 0.76 #191), 02zsn (0.46 #319, 0.45 #44, 0.45 #34) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 0459z; >> query: (?x1794, 05zppz) <- place_of_birth(?x1794, ?x3526), instrumentalists(?x227, ?x1794), nationality(?x1794, ?x94) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #319 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4064 *> proper extension: 0fv6dr; 09r1j5; 01kx1j; 0d3f83; 0dv1hh; 09m465; 02zbjwr; 06vnh2; 01llxp; 06s27s; ... *> query: (?x1794, ?x231) <- nationality(?x1794, ?x94), nationality(?x5298, ?x94), gender(?x5298, ?x231) *> conf = 0.46 ranks of expected_values: 2 EVAL 058s57 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 163.000 163.000 0.775 http://example.org/people/person/gender #7925-0dq630k PRED entity: 0dq630k PRED relation: role! PRED expected values: 03qjg => 68 concepts (55 used for prediction) PRED predicted values (max 10 best out of 118): 0l14qv (0.88 #2427, 0.88 #1858, 0.88 #1750), 0l14md (0.84 #225, 0.84 #1278, 0.83 #1045), 0mkg (0.80 #1411, 0.76 #2325, 0.75 #2910), 03bx0bm (0.79 #2932, 0.77 #1549, 0.76 #2005), 05148p4 (0.79 #2922, 0.76 #2337, 0.76 #3956), 04rzd (0.78 #1324, 0.70 #1397, 0.69 #226), 0dwt5 (0.75 #1830, 0.67 #2402, 0.65 #231), 06ncr (0.75 #868, 0.65 #231, 0.62 #1973), 018vs (0.73 #236, 0.70 #1397, 0.70 #582), 02w4b (0.73 #236, 0.70 #1397, 0.69 #226) >> Best rule #2427 for best value: >> intensional similarity = 20 >> extensional distance = 19 >> proper extension: 01wy6; 0gkd1; >> query: (?x2205, ?x228) <- role(?x2205, ?x432), role(?x2205, ?x314), role(?x2205, ?x228), role(?x2205, ?x227), role(?x2205, ?x212), group(?x2205, ?x4783), role(?x1165, ?x2205), ?x314 = 02sgy, ?x227 = 0342h, role(?x211, ?x432), instrumentalists(?x432, ?x4140), ?x4140 = 01sb5r, role(?x432, ?x75), instrumentalists(?x228, ?x140), role(?x228, ?x5480), role(?x228, ?x3328), ?x3328 = 016622, role(?x228, ?x74), ?x5480 = 01w4c9, role(?x642, ?x228) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1226 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 7 *> proper extension: 01s0ps; *> query: (?x2205, 03qjg) <- role(?x2205, ?x2048), role(?x2205, ?x1750), role(?x2205, ?x432), role(?x2205, ?x314), role(?x2205, ?x227), role(?x2205, ?x716), role(?x2205, ?x212), group(?x2205, ?x4783), role(?x1165, ?x2205), ?x314 = 02sgy, ?x227 = 0342h, ?x432 = 042v_gx, ?x1750 = 02hnl, role(?x74, ?x716), instrumentalists(?x716, ?x8143), instrumentalists(?x716, ?x5442), group(?x716, ?x2250), ?x8143 = 01wvxw1, location(?x5442, ?x5381), performance_role(?x1282, ?x212), ?x2250 = 0167_s, ?x2048 = 018j2, role(?x716, ?x433) *> conf = 0.67 ranks of expected_values: 29 EVAL 0dq630k role! 03qjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 68.000 55.000 0.879 http://example.org/music/performance_role/regular_performances./music/group_membership/role #7924-01vw8k PRED entity: 01vw8k PRED relation: film_crew_role PRED expected values: 0dxtw => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 24): 02r96rf (0.75 #173, 0.65 #139, 0.65 #1439), 09vw2b7 (0.67 #177, 0.62 #1716, 0.62 #1443), 01vx2h (0.60 #46, 0.38 #182, 0.36 #387), 0dxtw (0.42 #181, 0.36 #1447, 0.36 #1720), 089fss (0.40 #40, 0.25 #6, 0.08 #176), 0215hd (0.26 #85, 0.25 #17, 0.20 #51), 01xy5l_ (0.25 #14, 0.20 #48, 0.17 #82), 02_n3z (0.25 #1, 0.20 #35, 0.13 #69), 0263ycg (0.25 #16, 0.20 #50, 0.09 #84), 015h31 (0.20 #43, 0.12 #213, 0.10 #179) >> Best rule #173 for best value: >> intensional similarity = 4 >> extensional distance = 167 >> proper extension: 0bq8tmw; 0ct2tf5; 0466s8n; 047p798; >> query: (?x3979, 02r96rf) <- film(?x8764, ?x3979), participant(?x8764, ?x561), award_nominee(?x8764, ?x815), film_format(?x3979, ?x909) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #181 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 167 *> proper extension: 0bq8tmw; 0ct2tf5; 0466s8n; 047p798; *> query: (?x3979, 0dxtw) <- film(?x8764, ?x3979), participant(?x8764, ?x561), award_nominee(?x8764, ?x815), film_format(?x3979, ?x909) *> conf = 0.42 ranks of expected_values: 4 EVAL 01vw8k film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 106.000 106.000 0.751 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7923-058s57 PRED entity: 058s57 PRED relation: vacationer! PRED expected values: 0f2v0 => 133 concepts (117 used for prediction) PRED predicted values (max 10 best out of 56): 0160w (0.33 #2, 0.25 #127, 0.07 #627), 03h64 (0.33 #51, 0.25 #176, 0.07 #676), 03gh4 (0.13 #3584, 0.12 #4460, 0.07 #2081), 05qtj (0.12 #1572, 0.10 #447, 0.09 #2072), 0162v (0.10 #417, 0.03 #1542, 0.03 #3545), 02fzs (0.10 #499), 0cv3w (0.09 #1557, 0.08 #4061, 0.05 #2057), 0chghy (0.07 #635, 0.07 #885, 0.05 #1260), 015fr (0.07 #638, 0.07 #888, 0.05 #1263), 03gyl (0.07 #699, 0.07 #949, 0.05 #1324) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 025ldg; >> query: (?x1794, 0160w) <- award(?x1794, ?x2420), person(?x3480, ?x1794), ?x2420 = 026mfs, currency(?x1794, ?x170) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #813 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 03s9v; *> query: (?x1794, 0f2v0) <- diet(?x1794, ?x3130), profession(?x1794, ?x220), student(?x1368, ?x1794) *> conf = 0.07 ranks of expected_values: 11 EVAL 058s57 vacationer! 0f2v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 133.000 117.000 0.333 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #7922-04bfg PRED entity: 04bfg PRED relation: colors PRED expected values: 02rnmb => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.40 #722, 0.38 #81, 0.37 #682), 01g5v (0.28 #1244, 0.28 #864, 0.27 #1024), 0jc_p (0.27 #4, 0.10 #365, 0.10 #224), 06fvc (0.21 #102, 0.20 #22, 0.19 #62), 019sc (0.18 #1008, 0.18 #868, 0.18 #7), 038hg (0.18 #12, 0.14 #72, 0.09 #733), 067z2v (0.18 #9, 0.09 #89, 0.07 #129), 036k5h (0.17 #361, 0.10 #546, 0.10 #25), 09ggk (0.10 #56, 0.09 #16, 0.06 #697), 04mkbj (0.10 #371, 0.09 #871, 0.09 #1031) >> Best rule #722 for best value: >> intensional similarity = 4 >> extensional distance = 281 >> proper extension: 02d9nr; >> query: (?x6602, 083jv) <- colors(?x6602, ?x332), student(?x6602, ?x3025), profession(?x3025, ?x1032), ?x1032 = 02hrh1q >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #293 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 158 *> proper extension: 01hhvg; 03v6t; 02jyr8; 02bjhv; 01y17m; 01lnyf; 037njl; 01rgdw; 02zcz3; 02s8qk; ... *> query: (?x6602, 02rnmb) <- school(?x1632, ?x6602), institution(?x620, ?x6602), colors(?x6602, ?x332), contains(?x94, ?x6602) *> conf = 0.05 ranks of expected_values: 15 EVAL 04bfg colors 02rnmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 142.000 142.000 0.399 http://example.org/education/educational_institution/colors #7921-0prfz PRED entity: 0prfz PRED relation: award PRED expected values: 05ztrmj => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 288): 0gqy2 (0.38 #970, 0.14 #20719, 0.11 #11046), 0cqh46 (0.38 #857, 0.08 #1663, 0.05 #6096), 09sb52 (0.35 #15758, 0.34 #17773, 0.33 #14952), 04kxsb (0.25 #931, 0.13 #6573, 0.12 #11007), 099jhq (0.25 #825, 0.08 #1631, 0.07 #10901), 0bdw1g (0.25 #843, 0.07 #6485, 0.05 #8904), 05pcn59 (0.23 #15799, 0.22 #17814, 0.22 #10963), 02pqp12 (0.21 #1682, 0.12 #876, 0.08 #8131), 0ck27z (0.20 #495, 0.20 #92, 0.13 #28304), 02grdc (0.20 #434, 0.20 #31, 0.08 #1643) >> Best rule #970 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0143wl; 02c7lt; >> query: (?x399, 0gqy2) <- student(?x2486, ?x399), participant(?x399, ?x2891), ?x2486 = 015nl4 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #989 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0143wl; 02c7lt; *> query: (?x399, 05ztrmj) <- student(?x2486, ?x399), participant(?x399, ?x2891), ?x2486 = 015nl4 *> conf = 0.12 ranks of expected_values: 30 EVAL 0prfz award 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 150.000 150.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7920-03_vx9 PRED entity: 03_vx9 PRED relation: actor! PRED expected values: 02pqs8l => 131 concepts (89 used for prediction) PRED predicted values (max 10 best out of 88): 02d49z (0.11 #21537, 0.10 #18344, 0.10 #9296), 0431v3 (0.06 #98, 0.04 #894, 0.03 #2222), 01f3p_ (0.06 #52, 0.03 #1645, 0.03 #2176), 01kt_j (0.06 #209, 0.03 #475, 0.01 #7378), 07c72 (0.06 #48, 0.02 #844, 0.01 #2172), 03cv_gy (0.06 #94, 0.01 #1953, 0.01 #2483), 02rcwq0 (0.06 #88), 0d68qy (0.06 #37), 02zv4b (0.06 #291, 0.03 #1087, 0.03 #3477), 0180mw (0.05 #1447, 0.04 #3041, 0.03 #651) >> Best rule #21537 for best value: >> intensional similarity = 3 >> extensional distance = 1273 >> proper extension: 02j8nx; 044zvm; >> query: (?x950, ?x4596) <- nominated_for(?x950, ?x4596), film(?x950, ?x5092), nationality(?x950, ?x94) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_vx9 actor! 02pqs8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 89.000 0.105 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #7919-0137g1 PRED entity: 0137g1 PRED relation: artist! PRED expected values: 0fb0v 041n43 => 136 concepts (98 used for prediction) PRED predicted values (max 10 best out of 113): 03rhqg (0.24 #16, 0.20 #580, 0.18 #439), 01cl2y (0.24 #31, 0.16 #454, 0.15 #595), 015_1q (0.22 #866, 0.21 #2135, 0.20 #2417), 0g768 (0.18 #38, 0.17 #320, 0.13 #179), 041bnw (0.18 #70, 0.10 #634, 0.10 #493), 0fb0v (0.17 #148, 0.13 #712, 0.09 #994), 01dtcb (0.17 #330, 0.13 #189, 0.07 #1740), 0mzkr (0.16 #449, 0.13 #590, 0.08 #1436), 0n85g (0.14 #487, 0.13 #628, 0.13 #1051), 0181dw (0.13 #748, 0.12 #1594, 0.10 #1030) >> Best rule #16 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 04r1t; 018ndc; 0khth; 01cblr; 07m4c; 017959; 02vnpv; >> query: (?x2784, 03rhqg) <- artists(?x9750, ?x2784), artists(?x9630, ?x2784), artists(?x9630, ?x7570), ?x9750 = 016zgj, ?x7570 = 01dw_f >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #148 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 03fbc; 017j6; 01dwrc; 011z3g; 046p9; 09z1lg; 01f2q5; *> query: (?x2784, 0fb0v) <- artists(?x9630, ?x2784), ?x9630 = 012yc, origin(?x2784, ?x1523), award(?x2784, ?x1565) *> conf = 0.17 ranks of expected_values: 6, 27 EVAL 0137g1 artist! 041n43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 136.000 98.000 0.235 http://example.org/music/record_label/artist EVAL 0137g1 artist! 0fb0v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 136.000 98.000 0.235 http://example.org/music/record_label/artist #7918-01r97z PRED entity: 01r97z PRED relation: film! PRED expected values: 086k8 => 80 concepts (42 used for prediction) PRED predicted values (max 10 best out of 46): 03xq0f (0.62 #151, 0.60 #892, 0.58 #818), 031rq5 (0.46 #2969, 0.42 #1405, 0.40 #593), 086k8 (0.19 #1185, 0.18 #76, 0.18 #890), 05qd_ (0.17 #301, 0.17 #453, 0.16 #749), 016tt2 (0.16 #522, 0.14 #817, 0.13 #671), 016tw3 (0.15 #1341, 0.15 #529, 0.14 #2905), 0187y5 (0.15 #2080, 0.14 #443, 0.13 #74), 019pm_ (0.14 #443, 0.13 #74, 0.13 #442), 024rgt (0.12 #166, 0.06 #239, 0.05 #760), 0g1rw (0.11 #227, 0.10 #7, 0.09 #81) >> Best rule #151 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 03mgx6z; >> query: (?x770, 03xq0f) <- film(?x703, ?x770), film_distribution_medium(?x770, ?x81), genre(?x770, ?x604), ?x604 = 0lsxr >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1185 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 206 *> proper extension: 027ct7c; 0267wwv; *> query: (?x770, 086k8) <- award_winner(?x770, ?x2763), nominated_for(?x2763, ?x351), participant(?x2763, ?x248), produced_by(?x408, ?x2763) *> conf = 0.19 ranks of expected_values: 3 EVAL 01r97z film! 086k8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 80.000 42.000 0.625 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7917-016jhr PRED entity: 016jhr PRED relation: parent_genre PRED expected values: 05w3f 0gg8l => 71 concepts (53 used for prediction) PRED predicted values (max 10 best out of 290): 05w3f (0.80 #1320, 0.31 #1644, 0.21 #2591), 05r6t (0.75 #3623, 0.39 #4920, 0.29 #4107), 0155w (0.57 #231, 0.21 #2987, 0.20 #3150), 03lty (0.45 #5209, 0.35 #4071, 0.34 #3747), 0xhtw (0.33 #1308, 0.29 #6169, 0.20 #3582), 0dl5d (0.33 #15, 0.25 #1634, 0.20 #1310), 0133_p (0.29 #255, 0.25 #416, 0.11 #7314), 03_d0 (0.28 #5200, 0.23 #2927, 0.21 #3252), 02w4v (0.25 #353, 0.22 #678, 0.22 #516), 01lyv (0.22 #671, 0.22 #509, 0.14 #185) >> Best rule #1320 for best value: >> intensional similarity = 9 >> extensional distance = 13 >> proper extension: 012x7b; >> query: (?x837, 05w3f) <- parent_genre(?x837, ?x7083), artists(?x7083, ?x9868), artists(?x7083, ?x4182), artists(?x7083, ?x3472), artists(?x7083, ?x1751), ?x4182 = 07yg2, ?x1751 = 05crg7, profession(?x3472, ?x131), group(?x227, ?x9868) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 59 EVAL 016jhr parent_genre 0gg8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 71.000 53.000 0.800 http://example.org/music/genre/parent_genre EVAL 016jhr parent_genre 05w3f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 53.000 0.800 http://example.org/music/genre/parent_genre #7916-03nt59 PRED entity: 03nt59 PRED relation: award PRED expected values: 09qv3c => 92 concepts (88 used for prediction) PRED predicted values (max 10 best out of 184): 0cjyzs (0.45 #1252, 0.43 #3983, 0.42 #3046), 027gs1_ (0.43 #3983, 0.42 #3046, 0.41 #6793), 09qvc0 (0.43 #3983, 0.42 #3046, 0.41 #6793), 0cqhk0 (0.43 #3983, 0.42 #3046, 0.41 #6793), 03ccq3s (0.43 #3983, 0.42 #3046, 0.41 #6793), 09qs08 (0.35 #1278, 0.23 #1746, 0.22 #2216), 09qv3c (0.35 #1211, 0.22 #2149, 0.21 #2618), 09qj50 (0.29 #1207, 0.23 #2145, 0.23 #2614), 09qrn4 (0.29 #1328, 0.16 #1796, 0.16 #2266), 09qvf4 (0.20 #1783, 0.17 #2253, 0.17 #2722) >> Best rule #1252 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0557yqh; 099pks; 03nymk; 02rkkn1; >> query: (?x6070, 0cjyzs) <- genre(?x6070, ?x8534), producer_type(?x6070, ?x632), country_of_origin(?x6070, ?x94), ?x8534 = 0c4xc >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1211 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 0557yqh; 099pks; 03nymk; 02rkkn1; *> query: (?x6070, 09qv3c) <- genre(?x6070, ?x8534), producer_type(?x6070, ?x632), country_of_origin(?x6070, ?x94), ?x8534 = 0c4xc *> conf = 0.35 ranks of expected_values: 7 EVAL 03nt59 award 09qv3c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 92.000 88.000 0.452 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #7915-031zm1 PRED entity: 031zm1 PRED relation: position PRED expected values: 0dgrmp => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 9): 0dgrmp (0.82 #249, 0.82 #248, 0.82 #222), 02_j1w (0.82 #248, 0.82 #268, 0.82 #222), 03f0fp (0.20 #256, 0.19 #210, 0.19 #216), 02md_2 (0.20 #256, 0.19 #210, 0.19 #216), 05b3ts (0.20 #256, 0.19 #210, 0.19 #216), 04nfpk (0.20 #256, 0.19 #210, 0.19 #216), 02g_6x (0.20 #256, 0.19 #210, 0.19 #216), 01r3hr (0.20 #256, 0.19 #210, 0.19 #216), 02qvgy (0.04 #10, 0.01 #38) >> Best rule #249 for best value: >> intensional similarity = 11 >> extensional distance = 445 >> proper extension: 02b15h; 05kjc6; 02gys2; 08pgl8; 02jgm0; 02q3n9c; 047g6m; 024tsn; 02b1mc; 06yszk; ... >> query: (?x13233, ?x203) <- position(?x13233, ?x530), position(?x13233, ?x203), position(?x13233, ?x63), ?x63 = 02sdk9v, ?x530 = 02_j1w, team(?x203, ?x10443), team(?x203, ?x9182), team(?x203, ?x5850), ?x9182 = 029q3k, ?x10443 = 03j6_5, ?x5850 = 037mjv >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031zm1 position 0dgrmp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.822 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #7914-02rqwhl PRED entity: 02rqwhl PRED relation: language PRED expected values: 02h40lc => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.96 #233, 0.96 #347, 0.95 #5557), 064_8sq (0.19 #21, 0.16 #366, 0.15 #252), 03k50 (0.16 #8, 0.03 #871, 0.03 #1044), 06nm1 (0.12 #355, 0.12 #642, 0.11 #241), 02bjrlw (0.06 #60, 0.06 #346, 0.06 #403), 06b_j (0.06 #367, 0.05 #1001, 0.05 #1173), 03_9r (0.06 #641, 0.05 #698, 0.05 #584), 0653m (0.04 #413, 0.04 #643, 0.04 #874), 012w70 (0.04 #875, 0.04 #1048, 0.03 #644), 04h9h (0.04 #215, 0.04 #272, 0.03 #846) >> Best rule #233 for best value: >> intensional similarity = 3 >> extensional distance = 196 >> proper extension: 0jzw; 0872p_c; 05pbl56; 01hqhm; 06ybb1; 0c_j9x; 0ddjy; 05zlld0; 0qf2t; 02r_pp; ... >> query: (?x1420, 02h40lc) <- nominated_for(?x5706, ?x1420), language(?x1420, ?x732), nominated_for(?x1245, ?x1420) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rqwhl language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.960 http://example.org/film/film/language #7913-050l8 PRED entity: 050l8 PRED relation: partially_contains PRED expected values: 04ykz => 194 concepts (177 used for prediction) PRED predicted values (max 10 best out of 33): 0lm0n (0.35 #705, 0.33 #825, 0.33 #224), 0k3nk (0.27 #4105, 0.27 #4431, 0.25 #14), 04ykz (0.27 #4105, 0.27 #4431, 0.14 #232), 06c6l (0.25 #31, 0.05 #307, 0.03 #1108), 05lx3 (0.22 #69, 0.13 #547, 0.12 #108), 04yf_ (0.19 #328, 0.18 #249, 0.18 #810), 02cgp8 (0.12 #783, 0.12 #382, 0.12 #341), 0f2pf9 (0.11 #78, 0.04 #117, 0.03 #157), 0db94 (0.07 #156, 0.05 #353, 0.04 #555), 026zt (0.06 #2288, 0.05 #4085, 0.05 #3477) >> Best rule #705 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 05j49; >> query: (?x2049, 0lm0n) <- district_represented(?x605, ?x2049), first_level_division_of(?x2049, ?x94), state_province_region(?x8287, ?x2049) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #4105 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 169 *> proper extension: 0fmc5; *> query: (?x2049, ?x13214) <- adjoins(?x2049, ?x2768), adjoins(?x2049, ?x1351), partially_contains(?x1351, ?x13214), contains(?x2768, ?x2087) *> conf = 0.27 ranks of expected_values: 3 EVAL 050l8 partially_contains 04ykz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 194.000 177.000 0.347 http://example.org/location/location/partially_contains #7912-01rthc PRED entity: 01rthc PRED relation: artists PRED expected values: 04s5_s => 53 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2371): 01vrncs (0.71 #5465, 0.11 #14110, 0.11 #13029), 01w524f (0.50 #3611, 0.50 #1452, 0.44 #4690), 01tp5bj (0.50 #3428, 0.44 #4507, 0.40 #2350), 03wjb7 (0.50 #1860, 0.40 #2941, 0.33 #782), 04s5_s (0.50 #2123, 0.40 #3204, 0.33 #1045), 0l8g0 (0.44 #4879, 0.38 #3800, 0.25 #8116), 0gcs9 (0.43 #5641, 0.16 #15366, 0.11 #14286), 011z3g (0.40 #2762, 0.38 #9239, 0.27 #10319), 01wy61y (0.40 #2526, 0.38 #3604, 0.33 #4683), 02k5sc (0.40 #2869, 0.38 #3947, 0.33 #5026) >> Best rule #5465 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 07sbbz2; 0mhfr; 01lyv; 02w4v; 06j6l; 02yv6b; 016jny; 0155w; 017510; 016zgj; >> query: (?x12974, 01vrncs) <- artists(?x12974, ?x8114), place_of_birth(?x8114, ?x11299), role(?x8114, ?x212), profession(?x8114, ?x1359), ?x11299 = 0h1k6 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2123 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 016clz; *> query: (?x12974, 04s5_s) <- artists(?x12974, ?x8114), ?x8114 = 02mx98, parent_genre(?x12974, ?x302), artists(?x302, ?x10502), artists(?x302, ?x2930), ?x2930 = 0pkyh, award(?x10502, ?x1389) *> conf = 0.50 ranks of expected_values: 5 EVAL 01rthc artists 04s5_s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 17.000 0.714 http://example.org/music/genre/artists #7911-01m7f5r PRED entity: 01m7f5r PRED relation: gender PRED expected values: 05zppz => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.94 #21, 0.93 #17, 0.91 #59), 02zsn (0.46 #271, 0.46 #266, 0.46 #259) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 03h502k; 018x3; >> query: (?x9064, 05zppz) <- profession(?x9064, ?x1614), ?x1614 = 01c72t, music(?x1508, ?x9064), location(?x9064, ?x362) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m7f5r gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.939 http://example.org/people/person/gender #7910-011ywj PRED entity: 011ywj PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #46, 0.82 #131, 0.82 #36), 07c52 (0.06 #13, 0.04 #261, 0.04 #226), 02nxhr (0.04 #52, 0.04 #57, 0.04 #62), 07z4p (0.03 #248, 0.03 #263, 0.03 #228) >> Best rule #46 for best value: >> intensional similarity = 3 >> extensional distance = 290 >> proper extension: 026njb5; 04lqvlr; 04nlb94; >> query: (?x8367, 029j_) <- film_format(?x8367, ?x909), nominated_for(?x1587, ?x8367), award(?x276, ?x1587) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011ywj film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.825 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #7909-066yfh PRED entity: 066yfh PRED relation: award_nominee PRED expected values: 027km64 => 75 concepts (22 used for prediction) PRED predicted values (max 10 best out of 848): 0bjkpt (0.84 #2342, 0.83 #2341, 0.77 #25752), 01vhrz (0.84 #2342, 0.76 #51515, 0.76 #35121), 09d5d5 (0.29 #1895, 0.05 #42148, 0.03 #35122), 026fd (0.29 #1388, 0.05 #42148), 0fqyzz (0.29 #867, 0.05 #42148), 027km64 (0.18 #51514, 0.18 #28095, 0.17 #28094), 025hzx (0.18 #51514, 0.18 #28095, 0.17 #28094), 066yfh (0.18 #51514, 0.18 #28095, 0.14 #2301), 03v1w7 (0.18 #28095, 0.04 #3814, 0.02 #8496), 0dbbz (0.14 #2043) >> Best rule #2342 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 09d5d5; >> query: (?x12274, ?x9373) <- award_winner(?x12274, ?x2691), award_winner(?x9373, ?x12274), award_winner(?x11230, ?x12274), ?x11230 = 0fdtd7 >> conf = 0.84 => this is the best rule for 2 predicted values *> Best rule #51514 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1280 *> proper extension: 019_1h; 0clvcx; 06lgq8; 022769; 0f6_dy; 05218gr; 01r216; 08wr3kg; 0308kx; 070m12; ... *> query: (?x12274, ?x5202) <- award_winner(?x12274, ?x2691), award_winner(?x9373, ?x12274), nationality(?x12274, ?x1310), award_nominee(?x5202, ?x2691) *> conf = 0.18 ranks of expected_values: 6 EVAL 066yfh award_nominee 027km64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 75.000 22.000 0.842 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7908-0170qf PRED entity: 0170qf PRED relation: student! PRED expected values: 02mg7n => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 106): 0bwfn (0.08 #5534, 0.08 #6586, 0.08 #2904), 07tg4 (0.08 #2189, 0.06 #9027, 0.03 #10079), 07tgn (0.07 #2121, 0.04 #8959, 0.02 #12641), 026gvfj (0.07 #110, 0.04 #1688, 0.03 #636), 065y4w7 (0.05 #6326, 0.05 #5274, 0.04 #28421), 0fr9jp (0.05 #344, 0.03 #1922, 0.03 #870), 01d34b (0.05 #255, 0.03 #1833, 0.03 #781), 0m4yg (0.05 #364, 0.03 #9306, 0.02 #1942), 07vhb (0.05 #168, 0.02 #1746), 03ksy (0.04 #31142, 0.04 #46929, 0.04 #37460) >> Best rule #5534 for best value: >> intensional similarity = 3 >> extensional distance = 271 >> proper extension: 07nznf; 0q9kd; 0grwj; 016qtt; 0fvf9q; 04t2l2; 06dv3; 014zcr; 042l3v; 05ty4m; ... >> query: (?x2280, 0bwfn) <- award_nominee(?x2280, ?x57), produced_by(?x4668, ?x2280), nominated_for(?x2280, ?x278) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0170qf student! 02mg7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.081 http://example.org/education/educational_institution/students_graduates./education/education/student #7907-01kwld PRED entity: 01kwld PRED relation: gender PRED expected values: 05zppz => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #15, 0.71 #103, 0.71 #141), 02zsn (0.54 #45, 0.51 #64, 0.50 #8) >> Best rule #15 for best value: >> intensional similarity = 2 >> extensional distance = 467 >> proper extension: 032t2z; 03cvfg; 0fpj4lx; 0bkg4; 04h07s; 016lh0; 027dpx; 03l295; 018y81; 0n8bn; ... >> query: (?x628, 05zppz) <- nationality(?x628, ?x512), currency(?x628, ?x170) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kwld gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.723 http://example.org/people/person/gender #7906-01k_r5b PRED entity: 01k_r5b PRED relation: award PRED expected values: 02sp_v => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 264): 01by1l (0.32 #516, 0.31 #2128, 0.29 #6964), 01bgqh (0.26 #446, 0.23 #3670, 0.23 #6894), 09sb52 (0.24 #11729, 0.20 #25433, 0.20 #26239), 0c4z8 (0.22 #2087, 0.21 #475, 0.20 #878), 03qbh5 (0.21 #607, 0.19 #1010, 0.18 #2219), 054ks3 (0.19 #946, 0.18 #2155, 0.17 #543), 02f5qb (0.16 #9673, 0.13 #28617, 0.11 #9020), 02f6xy (0.16 #9673, 0.13 #28617, 0.11 #1005), 01c99j (0.16 #9673, 0.13 #28617, 0.11 #628), 02x17c2 (0.16 #9673, 0.13 #28617, 0.11 #1024) >> Best rule #516 for best value: >> intensional similarity = 3 >> extensional distance = 224 >> proper extension: 05gnf; 06lxn; >> query: (?x5265, 01by1l) <- award_winner(?x6467, ?x5265), category(?x5265, ?x134), instrumentalists(?x212, ?x6467) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #9673 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 646 *> proper extension: 0kk9v; 0fqy4p; 0c41qv; 076df9; 026v1z; *> query: (?x5265, ?x2430) <- award_nominee(?x5265, ?x367), category(?x5265, ?x134), award(?x367, ?x2430) *> conf = 0.16 ranks of expected_values: 25 EVAL 01k_r5b award 02sp_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 100.000 100.000 0.323 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7905-027n06w PRED entity: 027n06w PRED relation: award_winner PRED expected values: 04t2l2 026dg51 0bczgm 0cj2nl => 17 concepts (11 used for prediction) PRED predicted values (max 10 best out of 1666): 05np4c (0.60 #6610, 0.50 #3555, 0.43 #8138), 01hkhq (0.57 #7992, 0.40 #6464, 0.33 #352), 04mhxx (0.57 #9000, 0.40 #7472, 0.33 #1360), 0bwh6 (0.50 #3237, 0.40 #6292, 0.29 #7820), 0g5lhl7 (0.50 #3453, 0.40 #6508, 0.29 #8036), 025vl4m (0.50 #5671, 0.30 #3057, 0.30 #3056), 018ygt (0.43 #8595, 0.40 #7067, 0.33 #955), 02y_2y (0.43 #8324, 0.40 #6796, 0.33 #684), 025b5y (0.43 #8502, 0.40 #6974, 0.25 #3919), 01_njt (0.40 #7286, 0.29 #8814, 0.25 #4231) >> Best rule #6610 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 027hjff; >> query: (?x5469, 05np4c) <- award_winner(?x5469, ?x9011), award_winner(?x5469, ?x6868), award_winner(?x5469, ?x6580), ceremony(?x384, ?x5469), honored_for(?x5469, ?x10731), award_winner(?x6868, ?x2176), type_of_union(?x6868, ?x566), ?x10731 = 0cs134, award_nominee(?x6580, ?x912), nominated_for(?x384, ?x11213), ?x11213 = 0170xl, nominated_for(?x9011, ?x5808), award_nominee(?x9011, ?x1630), award(?x2648, ?x384), executive_produced_by(?x141, ?x2648), gender(?x6580, ?x514) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3057 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 1 *> proper extension: 0bq_mx; *> query: (?x5469, ?x2476) <- award_winner(?x5469, ?x9011), award_winner(?x5469, ?x6868), award_winner(?x5469, ?x3809), award_winner(?x5469, ?x2912), award_winner(?x5469, ?x2828), ceremony(?x384, ?x5469), honored_for(?x5469, ?x1135), award_winner(?x6868, ?x2476), award_winner(?x6868, ?x2176), ?x9011 = 03w9sgh, award_winner(?x2176, ?x912), award_winner(?x2829, ?x3809), award(?x3809, ?x2720), nominated_for(?x2828, ?x6080), award_winner(?x678, ?x2912), award_nominee(?x237, ?x2912), gender(?x3809, ?x231), award_nominee(?x2828, ?x6145), award_nominee(?x2544, ?x2176) *> conf = 0.30 ranks of expected_values: 47, 56, 78, 80 EVAL 027n06w award_winner 0cj2nl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 17.000 11.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 027n06w award_winner 0bczgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 17.000 11.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 027n06w award_winner 026dg51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 17.000 11.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 027n06w award_winner 04t2l2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 17.000 11.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7904-0b256b PRED entity: 0b256b PRED relation: sport PRED expected values: 02vx4 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.88 #310, 0.86 #38, 0.85 #193), 0z74 (0.50 #92, 0.50 #82, 0.27 #611), 018jz (0.10 #381, 0.10 #358, 0.10 #404), 0jm_ (0.10 #381, 0.09 #365, 0.09 #374), 03tmr (0.10 #381, 0.09 #354, 0.08 #491), 018w8 (0.10 #381, 0.09 #86, 0.05 #357), 039yzs (0.10 #381, 0.04 #479, 0.04 #472), 09xp_ (0.10 #381, 0.01 #478, 0.01 #487) >> Best rule #310 for best value: >> intensional similarity = 13 >> extensional distance = 199 >> proper extension: 0ytc; 0f6cl2; >> query: (?x6109, 02vx4) <- colors(?x6109, ?x663), colors(?x6823, ?x663), colors(?x2497, ?x663), colors(?x581, ?x663), major_field_of_study(?x581, ?x3490), major_field_of_study(?x2497, ?x6859), company(?x233, ?x581), student(?x581, ?x1299), institution(?x620, ?x2497), ?x3490 = 05qfh, position(?x6823, ?x2010), ?x620 = 07s6fsf, position(?x6109, ?x63) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b256b sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.876 http://example.org/sports/sports_team/sport #7903-01ck6v PRED entity: 01ck6v PRED relation: ceremony PRED expected values: 0gpjbt => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 134): 0gpjbt (0.75 #3354, 0.71 #295, 0.61 #697), 01c6qp (0.75 #3354, 0.71 #285, 0.57 #687), 01bx35 (0.75 #3354, 0.71 #273, 0.54 #675), 01mh_q (0.75 #3354, 0.71 #350, 0.54 #752), 02cg41 (0.75 #3354, 0.58 #789, 0.57 #387), 01s695 (0.75 #3354, 0.57 #270, 0.54 #672), 013b2h (0.75 #3354, 0.57 #342, 0.53 #744), 01mhwk (0.75 #3354, 0.57 #305, 0.52 #707), 01xqqp (0.75 #3354, 0.57 #357, 0.49 #759), 019bk0 (0.75 #3354, 0.54 #684, 0.48 #1354) >> Best rule #3354 for best value: >> intensional similarity = 4 >> extensional distance = 259 >> proper extension: 099c8n; 09tqxt; 03m73lj; 02qkk9_; 09v7wsg; 054knh; 02py7pj; 02py_sj; 06bwtj; 0bwgmzd; >> query: (?x7005, ?x342) <- ceremony(?x7005, ?x5656), ceremony(?x6090, ?x5656), ceremony(?x6090, ?x342), award_winner(?x5656, ?x367) >> conf = 0.75 => this is the best rule for 11 predicted values ranks of expected_values: 1 EVAL 01ck6v ceremony 0gpjbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony #7902-0q9kd PRED entity: 0q9kd PRED relation: produced_by! PRED expected values: 0bscw => 119 concepts (82 used for prediction) PRED predicted values (max 10 best out of 447): 011yqc (0.40 #22570, 0.40 #22569, 0.29 #13167), 01hq1 (0.40 #22570, 0.40 #22569, 0.29 #13167), 01b195 (0.40 #22570, 0.40 #22569, 0.29 #13167), 0q9jk (0.40 #22570, 0.40 #22569, 0.29 #13167), 030cx (0.40 #22570, 0.40 #22569, 0.29 #13167), 02pw_n (0.06 #4704, 0.05 #9405, 0.04 #15048), 0f4_l (0.06 #4704, 0.05 #9405, 0.04 #15048), 05q96q6 (0.05 #1975, 0.01 #15141, 0.01 #17961), 0gzlb9 (0.05 #3599, 0.01 #6420), 0bh8x1y (0.05 #2317) >> Best rule #22570 for best value: >> intensional similarity = 2 >> extensional distance = 344 >> proper extension: 03c9pqt; >> query: (?x71, ?x1496) <- produced_by(?x6198, ?x71), nominated_for(?x71, ?x1496) >> conf = 0.40 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 0q9kd produced_by! 0bscw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 82.000 0.397 http://example.org/film/film/produced_by #7901-01vsyjy PRED entity: 01vsyjy PRED relation: artists! PRED expected values: 0126t5 => 101 concepts (58 used for prediction) PRED predicted values (max 10 best out of 272): 06by7 (0.82 #3118, 0.68 #1567, 0.61 #6835), 05w3f (0.50 #36, 0.39 #654, 0.38 #1273), 064t9 (0.47 #12722, 0.44 #16135, 0.40 #13341), 0155w (0.44 #1652, 0.19 #12814, 0.19 #1961), 08jyyk (0.40 #67, 0.35 #1304, 0.33 #685), 05bt6j (0.39 #3140, 0.38 #17719, 0.29 #4069), 017_qw (0.37 #12153, 0.28 #10288, 0.18 #5638), 0cx7f (0.33 #3235, 0.31 #4164, 0.27 #1374), 03_d0 (0.30 #11, 0.23 #5587, 0.20 #16443), 09nwwf (0.30 #135, 0.23 #1372, 0.14 #1063) >> Best rule #3118 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 01t_xp_; 0dtd6; 01vrwfv; 0fcsd; 02vgh; 01kcms4; 0b_xm; 015cqh; 01l_w0; 0bk1p; ... >> query: (?x7272, 06by7) <- artists(?x1380, ?x7272), artists(?x302, ?x7272), ?x1380 = 0dl5d, artists(?x302, ?x5547), ?x5547 = 0dw4g >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 067mj; 05563d; 01shhf; 01_wfj; *> query: (?x7272, 0126t5) <- artists(?x1380, ?x7272), artists(?x1000, ?x7272), artists(?x302, ?x7272), ?x1380 = 0dl5d, ?x302 = 016clz, ?x1000 = 0xhtw *> conf = 0.10 ranks of expected_values: 54 EVAL 01vsyjy artists! 0126t5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 101.000 58.000 0.821 http://example.org/music/genre/artists #7900-0dscrwf PRED entity: 0dscrwf PRED relation: genre PRED expected values: 07s9rl0 => 55 concepts (54 used for prediction) PRED predicted values (max 10 best out of 86): 07s9rl0 (0.68 #733, 0.67 #1, 0.58 #3057), 03k9fj (0.42 #379, 0.39 #257, 0.37 #623), 02kdv5l (0.40 #369, 0.36 #491, 0.36 #613), 01jfsb (0.37 #380, 0.33 #746, 0.32 #1235), 05p553 (0.36 #1594, 0.36 #1960, 0.36 #1226), 01hmnh (0.23 #385, 0.21 #629, 0.21 #507), 06n90 (0.23 #503, 0.22 #625, 0.22 #381), 0lsxr (0.23 #132, 0.20 #254, 0.20 #742), 04xvlr (0.23 #124, 0.18 #1101, 0.18 #246), 060__y (0.19 #1117, 0.14 #1729, 0.14 #2708) >> Best rule #733 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 07w8fz; >> query: (?x511, 07s9rl0) <- film(?x3560, ?x511), country(?x511, ?x789), genre(?x511, ?x1403), ?x789 = 0f8l9c >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dscrwf genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 54.000 0.680 http://example.org/film/film/genre #7899-04qsdh PRED entity: 04qsdh PRED relation: award_winner! PRED expected values: 03gyp30 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 133): 092t4b (0.07 #191, 0.04 #51, 0.04 #751), 03nnm4t (0.06 #73, 0.04 #213, 0.03 #2873), 0bxs_d (0.06 #114, 0.03 #254, 0.02 #12042), 09qvms (0.05 #573, 0.05 #1693, 0.05 #1133), 09gkdln (0.05 #261, 0.04 #2921, 0.04 #1801), 092_25 (0.05 #211, 0.03 #491, 0.03 #631), 09qftb (0.05 #252, 0.02 #2912, 0.02 #1792), 013b2h (0.05 #2319, 0.05 #2459, 0.04 #4840), 09g90vz (0.05 #543, 0.05 #683, 0.04 #1803), 03gyp30 (0.05 #536, 0.04 #676, 0.04 #2776) >> Best rule #191 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 01bcq; 01dbgw; >> query: (?x8045, 092t4b) <- award(?x8045, ?x1132), award_winner(?x2257, ?x8045), ?x1132 = 0bdwft >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #536 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 540 *> proper extension: 076df9; *> query: (?x8045, 03gyp30) <- gender(?x8045, ?x514), actor(?x5116, ?x8045), award_nominee(?x100, ?x8045) *> conf = 0.05 ranks of expected_values: 10 EVAL 04qsdh award_winner! 03gyp30 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 95.000 95.000 0.068 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7898-05tbn PRED entity: 05tbn PRED relation: place_of_birth! PRED expected values: 0584j4n => 179 concepts (154 used for prediction) PRED predicted values (max 10 best out of 1710): 0bsb4j (0.42 #381597, 0.37 #211708, 0.34 #7841), 027vps (0.42 #381597, 0.37 #211708, 0.34 #7841), 02sjf5 (0.42 #381597, 0.37 #211708, 0.34 #7841), 016kb7 (0.42 #381597, 0.37 #211708, 0.34 #7841), 02bn75 (0.42 #381597, 0.37 #211708, 0.34 #7841), 024y6w (0.42 #381597, 0.37 #211708, 0.34 #7841), 040_t (0.42 #381597, 0.37 #211708, 0.34 #7841), 02vxyl5 (0.42 #381597, 0.37 #211708, 0.34 #7841), 06_6j3 (0.37 #211708, 0.34 #7841, 0.29 #368525), 02k4gv (0.34 #7841, 0.29 #368525, 0.29 #381596) >> Best rule #381597 for best value: >> intensional similarity = 3 >> extensional distance = 431 >> proper extension: 05mwx; >> query: (?x3670, ?x8225) <- location(?x8225, ?x3670), award_winner(?x1313, ?x8225), place_of_birth(?x8225, ?x4455) >> conf = 0.42 => this is the best rule for 8 predicted values No rule for expected values ranks of expected_values: EVAL 05tbn place_of_birth! 0584j4n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 179.000 154.000 0.422 http://example.org/people/person/place_of_birth #7897-03f7nt PRED entity: 03f7nt PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.79 #436, 0.78 #1132, 0.77 #933), 01xy5l_ (0.64 #77, 0.16 #176, 0.15 #938), 089g0h (0.55 #83, 0.16 #944, 0.15 #778), 0215hd (0.36 #82, 0.18 #777, 0.17 #943), 02ynfr (0.29 #46, 0.25 #13, 0.22 #774), 089fss (0.29 #38, 0.25 #5, 0.11 #137), 02rh1dz (0.25 #9, 0.21 #174, 0.20 #936), 015h31 (0.20 #769, 0.18 #935, 0.12 #173), 0d2b38 (0.19 #784, 0.18 #89, 0.17 #950), 02_n3z (0.18 #67, 0.12 #100, 0.12 #431) >> Best rule #436 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 048scx; 02rx2m5; 011ysn; 07kh6f3; 055td_; 0gkz3nz; 0n1s0; 027m5wv; 027pfg; >> query: (?x4848, 0ch6mp2) <- film(?x828, ?x4848), film_crew_role(?x4848, ?x137), nominated_for(?x1162, ?x4848), ?x1162 = 099c8n >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f7nt film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.793 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7896-04zwc PRED entity: 04zwc PRED relation: colors PRED expected values: 01l849 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.39 #649, 0.32 #98, 0.26 #763), 01l849 (0.34 #286, 0.31 #381, 0.28 #191), 06fvc (0.25 #2, 0.22 #648, 0.21 #97), 019sc (0.20 #26, 0.18 #387, 0.18 #767), 04mkbj (0.20 #29, 0.09 #447, 0.08 #371), 088fh (0.17 #63, 0.08 #158, 0.07 #970), 036k5h (0.11 #442, 0.10 #461, 0.10 #252), 09ggk (0.09 #300, 0.09 #205, 0.09 #319), 0jc_p (0.09 #650, 0.08 #460, 0.08 #156), 01jnf1 (0.07 #201, 0.07 #970, 0.06 #1199) >> Best rule #649 for best value: >> intensional similarity = 4 >> extensional distance = 333 >> proper extension: 02d9nr; >> query: (?x8507, 01g5v) <- colors(?x8507, ?x8047), colors(?x179, ?x8047), colors(?x10838, ?x8047), ?x10838 = 016sd3 >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #286 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 117 *> proper extension: 02zkz7; *> query: (?x8507, 01l849) <- school_type(?x8507, ?x3092), ?x3092 = 05jxkf, currency(?x8507, ?x7888), colors(?x8507, ?x663) *> conf = 0.34 ranks of expected_values: 2 EVAL 04zwc colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 140.000 140.000 0.394 http://example.org/education/educational_institution/colors #7895-043djx PRED entity: 043djx PRED relation: legislative_sessions PRED expected values: 01h7xx => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 50): 03ww_x (0.88 #1032, 0.86 #876, 0.77 #769), 060ny2 (0.88 #1061, 0.86 #905, 0.71 #583), 01gsvp (0.87 #270, 0.87 #269, 0.87 #268), 043djx (0.85 #273, 0.85 #272, 0.85 #271), 01h7xx (0.85 #273, 0.85 #272, 0.85 #271), 01grr2 (0.85 #273, 0.85 #272, 0.85 #271), 01grrf (0.85 #273, 0.85 #272, 0.85 #271), 01gsrl (0.85 #273, 0.85 #272, 0.85 #271), 01gst9 (0.85 #273, 0.85 #271, 0.85 #495), 01gstn (0.85 #273, 0.85 #271, 0.85 #495) >> Best rule #1032 for best value: >> intensional similarity = 31 >> extensional distance = 14 >> proper extension: 03tcbx; 03rtmz; >> query: (?x759, 03ww_x) <- district_represented(?x759, ?x7058), district_represented(?x759, ?x6895), district_represented(?x759, ?x3778), district_represented(?x759, ?x2831), district_represented(?x759, ?x961), district_represented(?x759, ?x335), ?x6895 = 05fjf, ?x335 = 059rby, jurisdiction_of_office(?x900, ?x7058), district_represented(?x6728, ?x7058), district_represented(?x5006, ?x7058), legislative_sessions(?x5401, ?x759), administrative_parent(?x11140, ?x7058), religion(?x2831, ?x962), administrative_parent(?x8350, ?x2831), location(?x396, ?x3778), state(?x9624, ?x7058), contains(?x7058, ?x3178), state_province_region(?x1201, ?x2831), ?x961 = 03s0w, contains(?x2831, ?x6952), source(?x11140, ?x958), ?x958 = 0jbk9, state_province_region(?x7071, ?x3778), contains(?x94, ?x7058), ?x6728 = 070mff, currency(?x7071, ?x170), ?x962 = 05sfs, origin(?x3171, ?x6952), school_type(?x3178, ?x1044), ?x5006 = 01gtc0 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #273 for first EXPECTED value: *> intensional similarity = 40 *> extensional distance = 2 *> proper extension: 070m6c; *> query: (?x759, ?x3973) <- district_represented(?x759, ?x7058), district_represented(?x759, ?x6895), district_represented(?x759, ?x4776), district_represented(?x759, ?x4622), district_represented(?x759, ?x3908), district_represented(?x759, ?x3778), district_represented(?x759, ?x2713), district_represented(?x759, ?x1906), district_represented(?x759, ?x1755), district_represented(?x759, ?x760), district_represented(?x759, ?x448), district_represented(?x759, ?x335), district_represented(?x759, ?x177), ?x6895 = 05fjf, ?x335 = 059rby, ?x7058 = 050ks, legislative_sessions(?x5252, ?x759), legislative_sessions(?x5006, ?x759), legislative_sessions(?x2712, ?x759), ?x3778 = 07h34, legislative_sessions(?x7714, ?x2712), legislative_sessions(?x3973, ?x2712), ?x1755 = 01x73, legislative_sessions(?x2860, ?x759), district_represented(?x5252, ?x3818), ?x2860 = 0b3wk, legislative_sessions(?x5401, ?x759), ?x1906 = 04rrx, ?x177 = 05kkh, ?x760 = 05fkf, legislative_sessions(?x9765, ?x5006), ?x3908 = 04ly1, ?x4776 = 06yxd, legislative_sessions(?x7891, ?x7714), ?x2713 = 06btq, ?x448 = 03v1s, state_province_region(?x2821, ?x4622), religion(?x4622, ?x109), district_represented(?x605, ?x4622), ?x605 = 077g7n *> conf = 0.85 ranks of expected_values: 5 EVAL 043djx legislative_sessions 01h7xx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 34.000 34.000 0.875 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #7894-03wh49y PRED entity: 03wh49y PRED relation: film_release_region PRED expected values: 04jpl 059j2 => 101 concepts (100 used for prediction) PRED predicted values (max 10 best out of 160): 03h64 (0.89 #2632, 0.77 #3655, 0.77 #7397), 06mkj (0.83 #2620, 0.83 #8065, 0.83 #8916), 0chghy (0.83 #2565, 0.78 #8010, 0.77 #8861), 05r4w (0.81 #7999, 0.81 #7319, 0.80 #8850), 059j2 (0.80 #7356, 0.80 #8887, 0.79 #8036), 03rjj (0.80 #7323, 0.77 #8854, 0.76 #8003), 03gj2 (0.80 #2583, 0.77 #3606, 0.73 #8879), 0k6nt (0.80 #8027, 0.79 #8878, 0.78 #7347), 05b4w (0.79 #3652, 0.67 #7394, 0.67 #8925), 0jgd (0.77 #2556, 0.75 #7321, 0.73 #8001) >> Best rule #2632 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 03g90h; 017gl1; 0bwfwpj; 05q96q6; 04hwbq; 0dtfn; 0gtvrv3; 04w7rn; 0bq8tmw; 04n52p6; ... >> query: (?x5517, 03h64) <- film_release_region(?x5517, ?x789), film_crew_role(?x5517, ?x137), story_by(?x5517, ?x9794), film_release_distribution_medium(?x5517, ?x81), ?x789 = 0f8l9c >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #7356 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 222 *> proper extension: 0k5g9; 01cm8w; *> query: (?x5517, 059j2) <- film_release_region(?x5517, ?x252), film(?x4923, ?x5517), award_winner(?x1193, ?x4923), ?x252 = 03_3d, genre(?x5517, ?x258) *> conf = 0.80 ranks of expected_values: 5, 94 EVAL 03wh49y film_release_region 059j2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 100.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03wh49y film_release_region 04jpl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 101.000 100.000 0.886 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7893-0hvb2 PRED entity: 0hvb2 PRED relation: award_winner! PRED expected values: 0275n3y => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 90): 0275n3y (0.24 #214, 0.04 #634, 0.03 #1754), 026kq4q (0.17 #46, 0.07 #186, 0.02 #326), 0bxs_d (0.17 #114, 0.03 #674, 0.02 #1654), 09p3h7 (0.17 #70, 0.02 #350, 0.02 #1330), 09pnw5 (0.17 #102, 0.02 #382, 0.02 #1362), 09g90vz (0.07 #263, 0.05 #683, 0.05 #963), 092c5f (0.07 #154, 0.05 #434, 0.04 #1414), 0g55tzk (0.07 #276, 0.04 #416, 0.04 #556), 0g5b0q5 (0.07 #160, 0.03 #300, 0.02 #1280), 0hndn2q (0.07 #180, 0.03 #320, 0.02 #460) >> Best rule #214 for best value: >> intensional similarity = 2 >> extensional distance = 27 >> proper extension: 01kt17; >> query: (?x1870, 0275n3y) <- award_nominee(?x3101, ?x1870), ?x3101 = 0dvmd >> conf = 0.24 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hvb2 award_winner! 0275n3y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.241 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7892-015kg1 PRED entity: 015kg1 PRED relation: artist PRED expected values: 0dvqq 01mr2g6 => 39 concepts (14 used for prediction) PRED predicted values (max 10 best out of 892): 02k5sc (0.56 #5578, 0.33 #566, 0.25 #3072), 01w02sy (0.50 #2706, 0.44 #6045, 0.40 #3539), 01vvyfh (0.50 #1938, 0.40 #4444, 0.40 #3607), 01w60_p (0.50 #2624, 0.25 #1788, 0.25 #952), 03f1d47 (0.50 #2865, 0.22 #6204, 0.15 #7037), 01vrz41 (0.50 #2566, 0.14 #9242, 0.13 #7571), 0565cz (0.44 #6038, 0.33 #193, 0.31 #6871), 020_4z (0.44 #5749, 0.33 #737, 0.25 #3243), 01jcxwp (0.40 #3843, 0.33 #6349, 0.25 #2174), 047cx (0.40 #3677, 0.25 #2008, 0.25 #1172) >> Best rule #5578 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 03mp8k; 05byxm; >> query: (?x3874, 02k5sc) <- artist(?x3874, ?x4387), artist(?x3874, ?x3875), instrumentalists(?x2798, ?x4387), instrumentalists(?x716, ?x4387), ?x2798 = 03qjg, ?x716 = 018vs, artists(?x2722, ?x3875), profession(?x4387, ?x220), award(?x3875, ?x6126), category(?x3875, ?x134), ?x2722 = 01g888 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #4783 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 3 *> proper extension: 096ysw; *> query: (?x3874, 01mr2g6) <- artist(?x3874, ?x6818), artist(?x3874, ?x4387), ?x4387 = 0kvnn, award(?x6818, ?x2180), artists(?x2995, ?x6818), origin(?x6818, ?x1523), artists(?x2995, ?x7781), artists(?x2995, ?x6942), category(?x6818, ?x134), ?x6942 = 04b7xr, ?x7781 = 089pg7, award_winner(?x7535, ?x6818), ?x134 = 08mbj5d *> conf = 0.20 ranks of expected_values: 317, 403 EVAL 015kg1 artist 01mr2g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 39.000 14.000 0.556 http://example.org/music/record_label/artist EVAL 015kg1 artist 0dvqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 14.000 0.556 http://example.org/music/record_label/artist #7891-017149 PRED entity: 017149 PRED relation: award PRED expected values: 099jhq 0cqh46 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 247): 0gq9h (0.40 #73, 0.10 #13572, 0.08 #5631), 0cqh46 (0.35 #842, 0.15 #27398, 0.15 #28590), 04kxsb (0.27 #915, 0.15 #27398, 0.15 #28590), 0ck27z (0.26 #2867, 0.25 #2073, 0.21 #1676), 0gqyl (0.25 #498, 0.15 #27398, 0.14 #25411), 099jhq (0.23 #812, 0.15 #27398, 0.15 #28590), 02w9sd7 (0.23 #959, 0.15 #27398, 0.15 #28590), 0gqwc (0.21 #467, 0.20 #70, 0.15 #27398), 02ppm4q (0.21 #549, 0.20 #152, 0.15 #27398), 0cqh6z (0.21 #460, 0.15 #27398, 0.15 #28590) >> Best rule #73 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0fvf9q; 0c3ns; 020_95; >> query: (?x525, 0gq9h) <- award_nominee(?x262, ?x525), nominated_for(?x525, ?x7432), ?x7432 = 01hv3t >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #842 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 03gm48; 046qq; 01y8cr; 016k6x; 0l786; 0jbp0; 021npv; *> query: (?x525, 0cqh46) <- award(?x525, ?x2192), award_winner(?x2215, ?x525), ?x2192 = 0bfvd4 *> conf = 0.35 ranks of expected_values: 2, 6 EVAL 017149 award 0cqh46 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 017149 award 099jhq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 97.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7890-0170s4 PRED entity: 0170s4 PRED relation: film PRED expected values: 0c3xw46 02d003 => 114 concepts (84 used for prediction) PRED predicted values (max 10 best out of 720): 01s81 (0.66 #33748, 0.64 #37301, 0.62 #87043), 0330r (0.66 #33748, 0.64 #37301, 0.62 #87043), 06q8qh (0.66 #33748, 0.64 #37301, 0.62 #87043), 058kh7 (0.15 #15987, 0.14 #10658, 0.02 #15775), 02qzh2 (0.06 #682, 0.03 #6011, 0.03 #2459), 017jd9 (0.06 #769, 0.03 #2546, 0.03 #57610), 017gl1 (0.06 #139, 0.03 #1916, 0.02 #56980), 0blpg (0.06 #646, 0.03 #2423, 0.02 #21961), 014lc_ (0.06 #2, 0.03 #1779, 0.02 #7107), 0ndwt2w (0.06 #988, 0.03 #2765, 0.02 #8093) >> Best rule #33748 for best value: >> intensional similarity = 3 >> extensional distance = 412 >> proper extension: 05hdf; 0c01c; 01pnn3; 039crh; 02zrv7; 06_bq1; 01p47r; 01gc7h; >> query: (?x2415, ?x3684) <- film(?x2415, ?x590), nominated_for(?x2415, ?x3684), participant(?x2415, ?x3329) >> conf = 0.66 => this is the best rule for 3 predicted values *> Best rule #616 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 014zfs; 042v2; 0b_dh; *> query: (?x2415, 0c3xw46) <- award(?x2415, ?x102), award_winner(?x945, ?x2415), notable_people_with_this_condition(?x8318, ?x2415) *> conf = 0.03 ranks of expected_values: 172, 349 EVAL 0170s4 film 02d003 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 114.000 84.000 0.665 http://example.org/film/actor/film./film/performance/film EVAL 0170s4 film 0c3xw46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 114.000 84.000 0.665 http://example.org/film/actor/film./film/performance/film #7889-04z_x4v PRED entity: 04z_x4v PRED relation: gender PRED expected values: 05zppz => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #69, 0.85 #85, 0.85 #91), 02zsn (0.46 #273, 0.36 #38, 0.33 #44) >> Best rule #69 for best value: >> intensional similarity = 2 >> extensional distance = 330 >> proper extension: 07c37; 042fk; >> query: (?x9067, 05zppz) <- place_of_death(?x9067, ?x1523), student(?x735, ?x9067) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04z_x4v gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.849 http://example.org/people/person/gender #7888-024my5 PRED entity: 024my5 PRED relation: actor! PRED expected values: 05f7w84 => 90 concepts (78 used for prediction) PRED predicted values (max 10 best out of 228): 0jwl2 (0.57 #1120, 0.50 #596, 0.50 #334), 05f7w84 (0.43 #892, 0.23 #1416, 0.17 #630), 0vhm (0.33 #614, 0.29 #876, 0.17 #352), 01h72l (0.33 #562, 0.17 #300, 0.14 #1086), 025x1t (0.33 #745, 0.17 #483, 0.14 #1269), 09g_31 (0.20 #165, 0.17 #427, 0.14 #1213), 0ctzf1 (0.20 #135, 0.17 #397, 0.08 #1970), 014gjp (0.17 #666, 0.17 #404, 0.14 #1190), 02648p (0.17 #590, 0.17 #328, 0.14 #1114), 07c72 (0.17 #571, 0.14 #833, 0.03 #1619) >> Best rule #1120 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02wrhj; 02y0yt; >> query: (?x7811, 0jwl2) <- actor(?x3144, ?x7811), film(?x7811, ?x10072), ?x10072 = 099bhp, location(?x7811, ?x739) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #892 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 01n7qlf; 08jtv5; *> query: (?x7811, 05f7w84) <- actor(?x11599, ?x7811), film(?x7811, ?x1919), ?x11599 = 019g8j, profession(?x7811, ?x1032) *> conf = 0.43 ranks of expected_values: 2 EVAL 024my5 actor! 05f7w84 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 78.000 0.571 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #7887-01385g PRED entity: 01385g PRED relation: award PRED expected values: 0f4x7 => 134 concepts (113 used for prediction) PRED predicted values (max 10 best out of 290): 02z13jg (0.70 #28765, 0.68 #27144, 0.68 #44566), 09sb52 (0.46 #11786, 0.40 #40, 0.26 #14625), 027dtxw (0.40 #4, 0.11 #11750, 0.11 #1624), 040njc (0.29 #1628, 0.20 #9323, 0.17 #12161), 0gqy2 (0.28 #2595, 0.23 #3405, 0.22 #7050), 0f4x7 (0.26 #2460, 0.23 #3675, 0.21 #6915), 02ppm4q (0.20 #157, 0.13 #12152, 0.13 #11903), 04kxsb (0.20 #126, 0.13 #2556, 0.13 #11872), 094qd5 (0.20 #44, 0.12 #854, 0.10 #11790), 0gqwc (0.20 #74, 0.11 #11820, 0.09 #26406) >> Best rule #28765 for best value: >> intensional similarity = 4 >> extensional distance = 1146 >> proper extension: 021yc7p; 0288fyj; 0hwd8; 04gmp_z; 02sj1x; 03q8ch; 0c_drn; 095zvfg; 05683cn; >> query: (?x12677, ?x850) <- nationality(?x12677, ?x1310), award(?x12677, ?x458), place_of_birth(?x12677, ?x9929), award_winner(?x850, ?x12677) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2460 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 95 *> proper extension: 0jvtp; *> query: (?x12677, 0f4x7) <- student(?x2999, ?x12677), film(?x12677, ?x3549), people(?x9933, ?x12677), award(?x12677, ?x458) *> conf = 0.26 ranks of expected_values: 6 EVAL 01385g award 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 134.000 113.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7886-0kpl PRED entity: 0kpl PRED relation: religion! PRED expected values: 01csvq 021yw7 0b78hw 056wb 0c12h 0chw_ 012x2b 06y7d 02rsz0 => 32 concepts (25 used for prediction) PRED predicted values (max 10 best out of 4193): 0948xk (0.43 #3413, 0.33 #2502, 0.25 #6138), 02xyl (0.33 #2700, 0.33 #1790, 0.29 #3611), 041mt (0.33 #1945, 0.33 #1035, 0.29 #2856), 032r1 (0.33 #2635, 0.33 #816, 0.18 #2728), 03_87 (0.33 #2277, 0.33 #458, 0.18 #2728), 0jmj (0.33 #2117, 0.33 #298, 0.14 #3028), 0kjrx (0.33 #1479, 0.33 #570, 0.14 #3300), 0lrh (0.33 #1085, 0.29 #2906, 0.25 #3815), 0cwtm (0.33 #1601, 0.29 #3422, 0.25 #4331), 01g23m (0.33 #1179, 0.29 #3000, 0.25 #3909) >> Best rule #3413 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 01spm; >> query: (?x2694, 0948xk) <- religion(?x10682, ?x2694), religion(?x5346, ?x2694), religion(?x2608, ?x2694), student(?x892, ?x2608), place_of_death(?x5346, ?x739), politician(?x9679, ?x2608), profession(?x10682, ?x563), influenced_by(?x2127, ?x5346), story_by(?x6924, ?x5346) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #836 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 0kq2; *> query: (?x2694, 06y7d) <- religion(?x8375, ?x2694), religion(?x5346, ?x2694), religion(?x2608, ?x2694), ?x5346 = 049gc, student(?x892, ?x2608), award_nominee(?x1711, ?x8375), profession(?x2608, ?x2225), profession(?x8375, ?x1032) *> conf = 0.33 ranks of expected_values: 398, 474, 1860, 2265, 2286, 2651, 3048, 3375, 3585 EVAL 0kpl religion! 02rsz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 25.000 0.429 http://example.org/people/person/religion EVAL 0kpl religion! 06y7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 32.000 25.000 0.429 http://example.org/people/person/religion EVAL 0kpl religion! 012x2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 25.000 0.429 http://example.org/people/person/religion EVAL 0kpl religion! 0chw_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 25.000 0.429 http://example.org/people/person/religion EVAL 0kpl religion! 0c12h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 25.000 0.429 http://example.org/people/person/religion EVAL 0kpl religion! 056wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 25.000 0.429 http://example.org/people/person/religion EVAL 0kpl religion! 0b78hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 25.000 0.429 http://example.org/people/person/religion EVAL 0kpl religion! 021yw7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 25.000 0.429 http://example.org/people/person/religion EVAL 0kpl religion! 01csvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 25.000 0.429 http://example.org/people/person/religion #7885-02rg_4 PRED entity: 02rg_4 PRED relation: institution! PRED expected values: 02h4rq6 => 166 concepts (114 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.87 #95, 0.82 #350, 0.80 #374), 03bwzr4 (0.68 #106, 0.68 #385, 0.66 #361), 02_xgp2 (0.68 #359, 0.65 #220, 0.65 #614), 016t_3 (0.60 #212, 0.55 #351, 0.55 #375), 0bkj86 (0.54 #355, 0.47 #216, 0.46 #610), 07s6fsf (0.43 #348, 0.40 #93, 0.40 #603), 04zx3q1 (0.43 #349, 0.37 #210, 0.37 #628), 013zdg (0.37 #628, 0.23 #99, 0.22 #76), 01rr_d (0.37 #628, 0.16 #364, 0.15 #225), 0bjrnt (0.37 #628, 0.15 #353, 0.13 #98) >> Best rule #95 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 07szy; 05mv4; >> query: (?x4293, 02h4rq6) <- major_field_of_study(?x4293, ?x6870), organization(?x346, ?x4293), institution(?x1368, ?x4293), ?x6870 = 01540 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rg_4 institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 114.000 0.867 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #7884-0c_zj PRED entity: 0c_zj PRED relation: student PRED expected values: 02cpb7 02lfwp => 159 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1129): 05y5kf (0.20 #848, 0.14 #5034, 0.11 #17592), 0bd2n4 (0.20 #601, 0.14 #2694, 0.08 #6880), 0kr5_ (0.20 #92, 0.14 #2185, 0.08 #6371), 034bgm (0.14 #4601, 0.12 #15066, 0.11 #19252), 0kh6b (0.14 #4802, 0.11 #17360, 0.11 #19453), 01lwx (0.14 #4075, 0.11 #18726, 0.08 #10354), 0448r (0.14 #3493, 0.11 #18144, 0.08 #9772), 0l6qt (0.14 #4202, 0.11 #16760, 0.08 #8388), 05m0h (0.14 #6009, 0.11 #18567, 0.08 #10195), 03ym1 (0.14 #5179, 0.11 #17737, 0.08 #9365) >> Best rule #848 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 05zhg; >> query: (?x4365, 05y5kf) <- contains(?x3302, ?x4365), contains(?x1310, ?x4365), contains(?x512, ?x4365), ?x1310 = 02jx1, ?x512 = 07ssc, ?x3302 = 01w0v >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #17551 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 02hmw9; 01s753; *> query: (?x4365, 02cpb7) <- citytown(?x4365, ?x3301), organization(?x2361, ?x4365), ?x3301 = 0978r *> conf = 0.06 ranks of expected_values: 83 EVAL 0c_zj student 02lfwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 53.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0c_zj student 02cpb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 159.000 53.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #7883-01wl38s PRED entity: 01wl38s PRED relation: role PRED expected values: 0l14md => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 106): 03bx0bm (0.52 #381, 0.25 #201, 0.19 #930), 05148p4 (0.19 #971, 0.19 #925, 0.18 #847), 02hnl (0.19 #387, 0.15 #813, 0.08 #508), 03gvt (0.18 #847, 0.16 #1946, 0.16 #849), 018vs (0.17 #252, 0.15 #372, 0.14 #798), 01vj9c (0.12 #193, 0.11 #253, 0.07 #373), 03_vpw (0.12 #220, 0.07 #400, 0.05 #340), 028tv0 (0.11 #1042, 0.09 #920, 0.08 #553), 026t6 (0.10 #2072, 0.09 #1332, 0.09 #1576), 07brj (0.10 #2072, 0.09 #1332, 0.09 #1576) >> Best rule #381 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 01mwsnc; 0gs6vr; 020hh3; 01nrz4; >> query: (?x565, 03bx0bm) <- profession(?x565, ?x987), person(?x10796, ?x565), role(?x565, ?x227) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #792 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 98 *> proper extension: 07_3qd; *> query: (?x565, 0l14md) <- instrumentalists(?x227, ?x565), artists(?x1000, ?x565), performance_role(?x565, ?x1466) *> conf = 0.10 ranks of expected_values: 14 EVAL 01wl38s role 0l14md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 105.000 105.000 0.519 http://example.org/music/group_member/membership./music/group_membership/role #7882-02fqrf PRED entity: 02fqrf PRED relation: nominated_for! PRED expected values: 05ztjjw => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 198): 05b1610 (0.56 #2614, 0.10 #6609, 0.09 #7549), 07bdd_ (0.54 #2635, 0.10 #6630, 0.09 #285), 099c8n (0.45 #524, 0.38 #759, 0.35 #994), 05f4m9q (0.43 #2596, 0.10 #6591, 0.09 #246), 02x17s4 (0.40 #562, 0.33 #92, 0.31 #1032), 0gq_v (0.38 #959, 0.32 #8479, 0.30 #489), 07cbcy (0.37 #2645, 0.12 #9695, 0.11 #6640), 04ljl_l (0.37 #2588, 0.09 #238, 0.09 #9638), 0gs96 (0.35 #557, 0.31 #1027, 0.23 #8547), 05ztjjw (0.33 #714, 0.29 #1419, 0.27 #1184) >> Best rule #2614 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 0pk1p; >> query: (?x3498, 05b1610) <- nominated_for(?x1500, ?x3498), film(?x489, ?x3498), nominated_for(?x154, ?x3498), ?x154 = 05b4l5x >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #714 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 0fq7dv_; 0fpgp26; *> query: (?x3498, 05ztjjw) <- film_release_region(?x3498, ?x1917), film_release_region(?x3498, ?x756), ?x756 = 06npd, ?x1917 = 01p1v, nominated_for(?x154, ?x3498) *> conf = 0.33 ranks of expected_values: 10 EVAL 02fqrf nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 98.000 98.000 0.557 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7881-05krk PRED entity: 05krk PRED relation: institution! PRED expected values: 0bkj86 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 13): 0bkj86 (0.56 #45, 0.54 #31, 0.47 #115), 04zx3q1 (0.44 #43, 0.43 #29, 0.31 #155), 027f2w (0.40 #46, 0.39 #32, 0.29 #158), 03mkk4 (0.25 #48, 0.24 #34, 0.20 #160), 0bjrnt (0.19 #44, 0.19 #30, 0.14 #156), 01rr_d (0.14 #122, 0.14 #52, 0.13 #150), 02m4yg (0.08 #51, 0.07 #37, 0.06 #178), 071tyz (0.08 #202, 0.06 #372, 0.05 #47), 02cq61 (0.07 #53, 0.06 #25, 0.06 #151), 01ysy9 (0.05 #69, 0.05 #210, 0.04 #111) >> Best rule #45 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 083pr; 06449; 0mj0c; 014635; 07cbs; 0x3r3; 01prf3; 07hyk; >> query: (?x388, 0bkj86) <- organization(?x388, ?x5487), country(?x5487, ?x94) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05krk institution! 0bkj86 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.562 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #7880-0168t PRED entity: 0168t PRED relation: official_language PRED expected values: 02h40lc => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.69 #222, 0.57 #706, 0.56 #134), 06nm1 (0.60 #536, 0.50 #800, 0.50 #668), 064_8sq (0.17 #2173, 0.16 #896, 0.16 #1732), 0jzc (0.11 #1730, 0.11 #366, 0.11 #1466), 03x42 (0.07 #300, 0.02 #1092, 0.02 #1224), 02bv9 (0.06 #769, 0.06 #857, 0.06 #417), 04306rv (0.06 #2030, 0.05 #885, 0.05 #1325), 0k0sv (0.05 #898, 0.03 #1338, 0.03 #1382), 0653m (0.05 #889, 0.03 #1329, 0.03 #1373), 06b_j (0.05 #1557, 0.05 #1601, 0.04 #1910) >> Best rule #222 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 02wmy; 035v3; >> query: (?x11553, 02h40lc) <- contains(?x7273, ?x11553), form_of_government(?x11553, ?x6065), form_of_government(?x11553, ?x1926), ?x7273 = 07c5l, ?x6065 = 01q20, ?x1926 = 018wl5 >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0168t official_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.692 http://example.org/location/country/official_language #7879-01qbjg PRED entity: 01qbjg PRED relation: producer_type PRED expected values: 0ckd1 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.32 #44, 0.24 #9, 0.20 #2) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 577 >> proper extension: 0m32_; 01mt1fy; 01d5vk; 02dlfh; >> query: (?x7932, 0ckd1) <- profession(?x7932, ?x1041), gender(?x7932, ?x231), ?x1041 = 03gjzk >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qbjg producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.323 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #7878-03ksy PRED entity: 03ksy PRED relation: company! PRED expected values: 03gkn5 => 103 concepts (65 used for prediction) PRED predicted values (max 10 best out of 268): 028rk (0.25 #289, 0.10 #1485, 0.08 #1964), 042kg (0.25 #456, 0.07 #4286, 0.02 #8837), 0157m (0.25 #267, 0.07 #4097, 0.02 #8648), 0203v (0.25 #266, 0.04 #8647, 0.03 #4096), 034ls (0.25 #389, 0.03 #4219, 0.02 #8770), 0d06m5 (0.25 #302, 0.03 #4132, 0.02 #5090), 0d3k14 (0.25 #452, 0.03 #4282, 0.02 #5240), 07hyk (0.25 #444, 0.03 #4274, 0.02 #5232), 042fk (0.25 #477, 0.03 #4307, 0.02 #5265), 06c0j (0.25 #473, 0.03 #4303, 0.02 #5261) >> Best rule #289 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 059j2; >> query: (?x3439, 028rk) <- company(?x346, ?x3439), contains(?x2020, ?x3439), location(?x8665, ?x3439) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1499 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 06y3r; *> query: (?x3439, 03gkn5) <- list(?x3439, ?x2197), organizations_founded(?x3439, ?x5487), currency(?x5487, ?x170) *> conf = 0.20 ranks of expected_values: 19 EVAL 03ksy company! 03gkn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 103.000 65.000 0.250 http://example.org/people/person/employment_history./business/employment_tenure/company #7877-0czhv7 PRED entity: 0czhv7 PRED relation: type_of_union PRED expected values: 04ztj => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.71 #92, 0.70 #88, 0.69 #5), 01g63y (0.32 #79, 0.22 #108, 0.21 #137), 0jgjn (0.22 #108, 0.21 #137, 0.19 #17) >> Best rule #92 for best value: >> intensional similarity = 2 >> extensional distance = 205 >> proper extension: 0cfywh; >> query: (?x10571, 04ztj) <- nationality(?x10571, ?x2146), ?x2146 = 03rk0 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0czhv7 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.705 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7876-0hg5 PRED entity: 0hg5 PRED relation: country! PRED expected values: 0bynt => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 50): 0bynt (0.86 #1861, 0.86 #2511, 0.86 #861), 01cgz (0.64 #1315, 0.63 #1065, 0.63 #965), 03hr1p (0.64 #22, 0.59 #122, 0.53 #272), 06wrt (0.64 #17, 0.57 #267, 0.56 #117), 01sgl (0.64 #40, 0.53 #140, 0.40 #290), 06f41 (0.60 #266, 0.59 #116, 0.56 #416), 07gyv (0.56 #407, 0.55 #457, 0.53 #557), 0194d (0.55 #293, 0.55 #43, 0.51 #493), 0w0d (0.55 #13, 0.53 #113, 0.50 #413), 07bs0 (0.55 #14, 0.53 #114, 0.40 #264) >> Best rule #1861 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 04gzd; 06npd; 019rg5; 03gj2; 01znc_; 01mjq; 03h64; 0697s; 06t8v; 04w8f; ... >> query: (?x2756, 0bynt) <- currency(?x2756, ?x170), adjoins(?x2756, ?x789), country(?x2266, ?x2756) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hg5 country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.862 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #7875-09306z PRED entity: 09306z PRED relation: award_winner PRED expected values: 01cbt3 => 31 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1557): 0gcs9 (0.29 #14303, 0.10 #15405, 0.05 #3519), 02fn5r (0.26 #14239), 02pqgt8 (0.23 #1539, 0.22 #3078, 0.11 #8338), 01cbt3 (0.23 #1539, 0.22 #3078, 0.11 #3898), 09fb5 (0.23 #1539, 0.22 #3078, 0.10 #15405), 020fgy (0.23 #1539, 0.22 #3078, 0.10 #15405), 08mhyd (0.23 #1539, 0.22 #3078, 0.10 #15405), 0c12h (0.23 #1539, 0.22 #3078, 0.10 #15405), 0c0k1 (0.23 #1539, 0.22 #3078, 0.03 #15106), 0c3ns (0.23 #1539, 0.22 #3078, 0.02 #15404) >> Best rule #14303 for best value: >> intensional similarity = 13 >> extensional distance = 33 >> proper extension: 02rjjll; 01bx35; 019bk0; 01c6qp; 056878; 01mhwk; 09n4nb; 0466p0j; 013b2h; 01mh_q; ... >> query: (?x7884, 0gcs9) <- ceremony(?x77, ?x7884), ceremony(?x77, ?x3254), award_winner(?x7884, ?x12574), award_winner(?x7884, ?x9008), honored_for(?x3254, ?x324), award(?x9149, ?x77), award_winner(?x3254, ?x771), nominated_for(?x77, ?x303), award_winner(?x77, ?x2086), film(?x12574, ?x4749), award(?x9149, ?x1180), ?x1180 = 02n9nmz, group(?x9008, ?x1271) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1539 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 15 *> proper extension: 02yw5r; 0bzm81; 02yv_b; 0bvfqq; 073h9x; 0bz6l9; 0bc773; 0bz6sb; 02ywhz; 03tn9w; ... *> query: (?x7884, ?x2179) <- ceremony(?x4573, ?x7884), ceremony(?x3617, ?x7884), ceremony(?x3066, ?x7884), ceremony(?x2209, ?x7884), ceremony(?x1972, ?x7884), ceremony(?x1323, ?x7884), ceremony(?x1313, ?x7884), ceremony(?x1079, ?x7884), ceremony(?x77, ?x7884), ?x77 = 0gqng, ?x3617 = 0gvx_, ?x1313 = 0gs9p, honored_for(?x7884, ?x7883), ?x1079 = 0l8z1, ?x4573 = 0gq_d, ?x1323 = 0gqz2, ?x2209 = 0gr42, award_winner(?x7884, ?x12574), award_winner(?x7884, ?x3339), nominated_for(?x3339, ?x1395), ?x3066 = 0gqy2, nominated_for(?x2179, ?x7883), ?x1972 = 0gqyl, nationality(?x12574, ?x94), titles(?x812, ?x7883) *> conf = 0.23 ranks of expected_values: 4 EVAL 09306z award_winner 01cbt3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 31.000 16.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7874-02bbyw PRED entity: 02bbyw PRED relation: major_field_of_study PRED expected values: 05qjt => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 122): 01mkq (0.52 #641, 0.50 #266, 0.46 #141), 04rjg (0.45 #646, 0.40 #1021, 0.33 #1397), 03g3w (0.44 #1028, 0.39 #653, 0.34 #1404), 02j62 (0.43 #1283, 0.41 #1032, 0.37 #2033), 02lp1 (0.41 #637, 0.35 #1012, 0.30 #1388), 04x_3 (0.33 #277, 0.33 #152, 0.25 #652), 01lj9 (0.32 #1042, 0.26 #1418, 0.23 #1293), 05qjt (0.32 #633, 0.31 #1008, 0.30 #1259), 062z7 (0.32 #654, 0.31 #1029, 0.30 #1405), 05qfh (0.30 #1289, 0.25 #288, 0.25 #163) >> Best rule #641 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 06pwq; 0kz2w; 0gkkf; 02gr81; 0ks67; 0c5x_; 0ymcz; >> query: (?x6784, 01mkq) <- school_type(?x6784, ?x4994), student(?x6784, ?x2584), ?x4994 = 07tf8, major_field_of_study(?x6784, ?x5179), organization(?x4095, ?x6784) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #633 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 42 *> proper extension: 06pwq; 0kz2w; 0gkkf; 02gr81; 0ks67; 0c5x_; 0ymcz; *> query: (?x6784, 05qjt) <- school_type(?x6784, ?x4994), student(?x6784, ?x2584), ?x4994 = 07tf8, major_field_of_study(?x6784, ?x5179), organization(?x4095, ?x6784) *> conf = 0.32 ranks of expected_values: 8 EVAL 02bbyw major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 146.000 146.000 0.523 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7873-02p86pb PRED entity: 02p86pb PRED relation: genre PRED expected values: 01fc50 => 93 concepts (67 used for prediction) PRED predicted values (max 10 best out of 127): 02kdv5l (0.82 #5261, 0.52 #1402, 0.50 #117), 01jfsb (0.51 #1413, 0.42 #5272, 0.41 #945), 05p553 (0.43 #3975, 0.35 #6424, 0.35 #936), 03k9fj (0.40 #944, 0.38 #1412, 0.34 #5271), 02l7c8 (0.38 #7364, 0.31 #5507, 0.31 #6552), 0lsxr (0.33 #8, 0.25 #124, 0.22 #3159), 02n4kr (0.33 #7, 0.14 #2924, 0.14 #1989), 01hmnh (0.29 #949, 0.27 #1417, 0.18 #248), 06n90 (0.26 #1414, 0.23 #5273, 0.18 #946), 04xvh5 (0.18 #613, 0.14 #1664, 0.13 #1780) >> Best rule #5261 for best value: >> intensional similarity = 5 >> extensional distance = 561 >> proper extension: 0cnztc4; 0gj9qxr; 043sct5; 0h95zbp; 03_wm6; 015qy1; 04svwx; >> query: (?x9060, 02kdv5l) <- genre(?x9060, ?x3515), genre(?x7834, ?x3515), genre(?x6531, ?x3515), ?x6531 = 01_0f7, ?x7834 = 01cycq >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1727 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 148 *> proper extension: 0192hw; *> query: (?x9060, 01fc50) <- genre(?x9060, ?x3515), genre(?x9060, ?x162), ?x3515 = 082gq, titles(?x162, ?x144) *> conf = 0.05 ranks of expected_values: 42 EVAL 02p86pb genre 01fc50 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 93.000 67.000 0.815 http://example.org/film/film/genre #7872-0mhdz PRED entity: 0mhdz PRED relation: contains! PRED expected values: 0l2xl => 123 concepts (69 used for prediction) PRED predicted values (max 10 best out of 183): 0l2xl (0.40 #433, 0.23 #5365, 0.17 #1790), 02qkt (0.40 #23591, 0.39 #33433, 0.36 #34330), 04_1l0v (0.38 #21906, 0.36 #47406, 0.35 #18329), 0l2vz (0.23 #2063, 0.20 #3850, 0.08 #11002), 0kpys (0.22 #12694, 0.17 #10907, 0.12 #24321), 05tbn (0.20 #30629, 0.07 #51207, 0.07 #55680), 02j9z (0.18 #23274, 0.18 #34013, 0.17 #33116), 0j0k (0.18 #33464, 0.17 #34361, 0.15 #23622), 03v0t (0.17 #1124, 0.10 #5595, 0.10 #11852), 0kq1l (0.15 #2205, 0.13 #3992, 0.03 #20978) >> Best rule #433 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0r6ff; >> query: (?x12143, 0l2xl) <- adjoins(?x6703, ?x12143), contains(?x94, ?x12143), ?x94 = 09c7w0, ?x6703 = 0f04v >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mhdz contains! 0l2xl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 69.000 0.400 http://example.org/location/location/contains #7871-021b_ PRED entity: 021b_ PRED relation: film PRED expected values: 03kx49 => 98 concepts (68 used for prediction) PRED predicted values (max 10 best out of 403): 0q9jk (0.62 #35707, 0.61 #17853, 0.59 #51777), 01fx1l (0.47 #71416, 0.34 #98199, 0.34 #91055), 03176f (0.14 #704, 0.02 #13200, 0.01 #11415), 02z3r8t (0.14 #107, 0.02 #1893, 0.02 #3678), 035s95 (0.14 #340, 0.02 #34261, 0.02 #16407), 09qycb (0.14 #1641, 0.02 #8782), 0f4_l (0.14 #349, 0.02 #23558, 0.01 #16416), 02ywwy (0.14 #1441, 0.01 #13937), 056xkh (0.14 #1595, 0.01 #17662, 0.01 #15876), 0642xf3 (0.14 #871, 0.01 #16938) >> Best rule #35707 for best value: >> intensional similarity = 3 >> extensional distance = 751 >> proper extension: 0184jc; 02s2ft; 05vsxz; 01k7d9; 02bfmn; 06dv3; 0byfz; 01p7yb; 0bl2g; 025h4z; ... >> query: (?x10588, ?x8132) <- film(?x10588, ?x3781), award_winner(?x8132, ?x10588), written_by(?x3781, ?x7855) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #78109 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1301 *> proper extension: 01vs14j; 01wk7b7; 01x1cn2; 06_6j3; 04h6mm; 0277c3; 01dw_f; 01vtg4q; 02_t2t; 03kxdw; ... *> query: (?x10588, 03kx49) <- film(?x10588, ?x7299), film(?x3118, ?x7299), artist(?x1693, ?x3118) *> conf = 0.01 ranks of expected_values: 178 EVAL 021b_ film 03kx49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 98.000 68.000 0.621 http://example.org/film/actor/film./film/performance/film #7870-0jymd PRED entity: 0jymd PRED relation: film_release_region PRED expected values: 03rjj 03_3d 0k6nt => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 120): 09c7w0 (0.93 #1284, 0.93 #8984, 0.92 #9145), 03rjj (0.85 #3692, 0.83 #3852, 0.80 #3371), 0k6nt (0.81 #4033, 0.80 #3713, 0.79 #2750), 0jgd (0.80 #3850, 0.80 #3690, 0.78 #3369), 03gj2 (0.80 #3714, 0.79 #3874, 0.73 #3393), 05qhw (0.79 #3704, 0.76 #3864, 0.72 #3383), 03_3d (0.78 #3373, 0.75 #3694, 0.75 #3854), 0d060g (0.77 #3695, 0.76 #3855, 0.65 #3374), 05b4w (0.73 #3755, 0.72 #3915, 0.64 #4075), 0b90_r (0.73 #3691, 0.72 #3851, 0.61 #3370) >> Best rule #1284 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 07gp9; 01k1k4; 0dtw1x; 0bth54; 0gkz15s; 01vksx; 0bwfwpj; 017gm7; 01f7gh; 04zyhx; ... >> query: (?x3986, 09c7w0) <- film_format(?x3986, ?x6392), crewmember(?x3986, ?x12398), film_release_region(?x3986, ?x87) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #3692 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 225 *> proper extension: 0fq27fp; 0c40vxk; *> query: (?x3986, 03rjj) <- film_release_region(?x3986, ?x583), film_release_region(?x3986, ?x87), ?x583 = 015fr, ?x87 = 05r4w *> conf = 0.85 ranks of expected_values: 2, 3, 7 EVAL 0jymd film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.934 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jymd film_release_region 03_3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.934 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jymd film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.934 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7869-058cm PRED entity: 058cm PRED relation: place! PRED expected values: 058cm => 113 concepts (74 used for prediction) PRED predicted values (max 10 best out of 224): 0lphb (0.08 #175, 0.08 #691, 0.04 #1206), 0q6lr (0.08 #361, 0.08 #877), 0q8jl (0.08 #292, 0.08 #808), 0q8s4 (0.08 #110, 0.08 #626), 0qc7l (0.08 #478, 0.02 #12377), 0fttg (0.08 #894, 0.01 #12755), 0q48z (0.08 #832), 0d35y (0.04 #1134, 0.04 #1649, 0.03 #2164), 0dc95 (0.04 #1080, 0.04 #1595, 0.03 #2110), 0dzt9 (0.04 #1295, 0.04 #1810, 0.03 #2325) >> Best rule #175 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 01wdl3; 02jyr8; 0325dj; >> query: (?x13429, 0lphb) <- contains(?x2831, ?x13429), contains(?x94, ?x13429), ?x94 = 09c7w0, ?x2831 = 0gyh, category(?x13429, ?x134) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 058cm place! 058cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 74.000 0.083 http://example.org/location/hud_county_place/place #7868-04fv0k PRED entity: 04fv0k PRED relation: list PRED expected values: 01ptsx 01pd60 => 257 concepts (257 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.84 #201, 0.82 #68, 0.77 #369), 04k4rt (0.63 #200, 0.57 #579, 0.56 #130), 01pd60 (0.62 #27, 0.60 #41, 0.56 #132), 09g7thr (0.23 #99, 0.21 #1250, 0.18 #78) >> Best rule #201 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 02630g; >> query: (?x9517, 01ptsx) <- citytown(?x9517, ?x5962), organization(?x4682, ?x9517), currency(?x9517, ?x170), company(?x265, ?x9517), state_province_region(?x9517, ?x1767), ?x265 = 0dq3c >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 04fv0k list 01pd60 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 257.000 257.000 0.842 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 04fv0k list 01ptsx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 257.000 257.000 0.842 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #7867-0b_6rk PRED entity: 0b_6rk PRED relation: locations PRED expected values: 0f1sm => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 335): 0fsb8 (0.60 #649, 0.56 #2038, 0.46 #2905), 071cn (0.60 #590, 0.44 #1979, 0.38 #2846), 09c7w0 (0.57 #1387, 0.40 #2429, 0.15 #5740), 0fpzwf (0.50 #1136, 0.40 #790, 0.33 #98), 0c1d0 (0.50 #1159, 0.33 #121, 0.25 #467), 0156q (0.45 #3852, 0.38 #5247, 0.38 #4897), 0d9jr (0.40 #2348, 0.40 #613, 0.33 #94), 0d9y6 (0.40 #785, 0.38 #2695, 0.38 #1827), 0fr0t (0.40 #592, 0.33 #73, 0.30 #2327), 010h9y (0.40 #844, 0.29 #3100, 0.24 #5038) >> Best rule #649 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 0b_6x2; 0b_6pv; 0b_6_l; >> query: (?x5897, 0fsb8) <- locations(?x5897, ?x2879), locations(?x5897, ?x2552), team(?x5897, ?x10171), team(?x5897, ?x9909), team(?x5897, ?x9576), team(?x5897, ?x6003), ?x10171 = 026w398, ?x9576 = 02qk2d5, ?x6003 = 02py8_w, featured_film_locations(?x155, ?x2552), administrative_division(?x2552, ?x6815), location(?x496, ?x2552), ?x9909 = 026wlnm, ?x2879 = 0ftxw >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1348 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 5 *> proper extension: 0b_6q5; *> query: (?x5897, 0f1sm) <- locations(?x5897, ?x2552), team(?x5897, ?x10171), team(?x5897, ?x9983), team(?x5897, ?x9833), team(?x5897, ?x9576), team(?x5897, ?x6003), ?x10171 = 026w398, ?x9576 = 02qk2d5, ?x6003 = 02py8_w, featured_film_locations(?x155, ?x2552), administrative_division(?x2552, ?x6815), location(?x496, ?x2552), ?x9833 = 03y9p40, ?x9983 = 02q4ntp *> conf = 0.29 ranks of expected_values: 29 EVAL 0b_6rk locations 0f1sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 74.000 74.000 0.600 http://example.org/time/event/locations #7866-0hz55 PRED entity: 0hz55 PRED relation: actor PRED expected values: 01yhvv 030xr_ => 116 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1102): 07lwsz (0.48 #4611, 0.44 #19370, 0.42 #9219), 09_99w (0.48 #4611, 0.44 #19370, 0.42 #9219), 04wvhz (0.48 #4611, 0.44 #19370, 0.42 #9219), 01541z (0.48 #4611, 0.44 #19370, 0.42 #9219), 01kwh5j (0.27 #2528, 0.02 #26504, 0.02 #32035), 01nsyf (0.18 #2657, 0.06 #5422, 0.03 #32164), 03cz9_ (0.18 #2712, 0.03 #26688, 0.02 #32219), 02h8hr (0.18 #2249, 0.03 #26225, 0.02 #31756), 0392kz (0.18 #2612, 0.03 #32119, 0.02 #36726), 01qvtwm (0.18 #2720, 0.02 #26696, 0.02 #32227) >> Best rule #4611 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 03d34x8; 02pqs8l; 01rf57; 04f6hhm; 0fhzwl; 07gbf; 0d7vtk; 01kt_j; >> query: (?x4932, ?x1039) <- genre(?x4932, ?x812), nominated_for(?x435, ?x4932), nominated_for(?x1039, ?x4932), ?x812 = 01jfsb >> conf = 0.48 => this is the best rule for 4 predicted values *> Best rule #27667 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 146 *> proper extension: 0cwrr; 0n2bh; 01h72l; 01h1bf; 03y3bp7; 015g28; 08cx5g; 06mr2s; 02kk_c; 04glx0; ... *> query: (?x4932, ?x1410) <- genre(?x4932, ?x225), nominated_for(?x4933, ?x4932), country_of_origin(?x4932, ?x94), award_nominee(?x1410, ?x4933) *> conf = 0.07 ranks of expected_values: 215 EVAL 0hz55 actor 030xr_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 82.000 0.476 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 0hz55 actor 01yhvv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 116.000 82.000 0.476 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #7865-0ddt_ PRED entity: 0ddt_ PRED relation: nominated_for! PRED expected values: 0p9sw 05ztrmj => 128 concepts (125 used for prediction) PRED predicted values (max 10 best out of 197): 0gq9h (0.48 #747, 0.38 #3508, 0.37 #1438), 0l8z1 (0.42 #1658, 0.29 #2118, 0.28 #277), 019f4v (0.42 #738, 0.33 #3499, 0.32 #12700), 0gs9p (0.39 #749, 0.33 #12711, 0.30 #3510), 04dn09n (0.36 #720, 0.31 #3481, 0.27 #5551), 0gqy2 (0.34 #805, 0.29 #1496, 0.27 #3566), 0gq_v (0.33 #17, 0.25 #12669, 0.24 #1628), 0k611 (0.33 #758, 0.30 #528, 0.28 #1449), 0gr4k (0.33 #714, 0.25 #1405, 0.25 #484), 0p9sw (0.31 #1629, 0.27 #18, 0.25 #2089) >> Best rule #747 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 011yxg; 0dqytn; 0jzw; 0pv2t; 06_wqk4; 0kv2hv; 017gl1; 09q5w2; 0jyx6; 03m4mj; ... >> query: (?x2899, 0gq9h) <- honored_for(?x2899, ?x1386), film(?x489, ?x2899), film(?x1387, ?x2899), award(?x2899, ?x102) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1629 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 76 *> proper extension: 025x1t; *> query: (?x2899, 0p9sw) <- nominated_for(?x10262, ?x2899), award_winner(?x1452, ?x10262), ?x1452 = 0jqn5 *> conf = 0.31 ranks of expected_values: 10, 22 EVAL 0ddt_ nominated_for! 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 128.000 125.000 0.478 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0ddt_ nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 128.000 125.000 0.478 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7864-05xbx PRED entity: 05xbx PRED relation: program PRED expected values: 0cwrr => 151 concepts (132 used for prediction) PRED predicted values (max 10 best out of 257): 0bx_hnp (0.25 #418, 0.17 #2113, 0.13 #4293), 015g28 (0.25 #296, 0.17 #1991, 0.13 #4171), 0124k9 (0.25 #261, 0.17 #1956, 0.13 #4136), 02rkkn1 (0.25 #462, 0.17 #2157, 0.11 #3368), 01ft14 (0.25 #423, 0.17 #2118, 0.11 #3329), 02qjv1p (0.25 #386, 0.17 #2081, 0.11 #3292), 08bytj (0.25 #366, 0.17 #2061, 0.11 #3272), 0524b41 (0.25 #354, 0.17 #2049, 0.11 #3260), 03nt59 (0.25 #335, 0.17 #2030, 0.11 #3241), 064r97z (0.25 #328, 0.17 #2023, 0.11 #3234) >> Best rule #418 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01j53q; >> query: (?x5007, 0bx_hnp) <- award_winner(?x2776, ?x5007), state_province_region(?x5007, ?x1426), ?x2776 = 0g5lhl7 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05xbx program 0cwrr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 151.000 132.000 0.250 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #7863-01gbb4 PRED entity: 01gbb4 PRED relation: religion PRED expected values: 03_gx => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 18): 0c8wxp (0.46 #270, 0.37 #446, 0.37 #534), 03_gx (0.30 #233, 0.18 #321, 0.17 #409), 03j6c (0.07 #416, 0.07 #768, 0.07 #944), 0flw86 (0.06 #398, 0.05 #310, 0.05 #926), 092bf5 (0.05 #279, 0.04 #411, 0.04 #323), 0kq2 (0.05 #457, 0.05 #545, 0.05 #413), 01lp8 (0.05 #309, 0.04 #925, 0.04 #397), 0n2g (0.04 #12, 0.04 #1674, 0.03 #760), 01hng3 (0.04 #1674), 04pk9 (0.04 #415, 0.03 #327, 0.03 #459) >> Best rule #270 for best value: >> intensional similarity = 4 >> extensional distance = 316 >> proper extension: 01hkhq; 02j9lm; 01438g; 01wb8bs; 01z7_f; 03ym1; 012gbb; 03k545; 0739z6; >> query: (?x7137, 0c8wxp) <- award_winner(?x458, ?x7137), nationality(?x7137, ?x94), religion(?x7137, ?x2694), film(?x7137, ?x3218) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 247 *> proper extension: 07h1q; *> query: (?x7137, 03_gx) <- gender(?x7137, ?x231), ?x231 = 05zppz, people(?x1050, ?x7137), ?x1050 = 041rx *> conf = 0.30 ranks of expected_values: 2 EVAL 01gbb4 religion 03_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.456 http://example.org/people/person/religion #7862-027kp3 PRED entity: 027kp3 PRED relation: featured_film_locations! PRED expected values: 046f3p => 95 concepts (46 used for prediction) PRED predicted values (max 10 best out of 638): 0ds2n (0.40 #959, 0.10 #7538, 0.05 #9732), 061681 (0.40 #777, 0.07 #7356, 0.07 #5894), 024l2y (0.40 #844, 0.07 #7423, 0.07 #5961), 033srr (0.40 #1008, 0.05 #9781, 0.05 #7587), 01q2nx (0.40 #1122, 0.05 #7701, 0.04 #9895), 05pbl56 (0.40 #839, 0.05 #7418, 0.04 #9612), 0ds33 (0.40 #760, 0.05 #7339, 0.04 #9533), 047csmy (0.20 #1123, 0.12 #1854, 0.10 #3316), 04gv3db (0.20 #1050, 0.11 #6167, 0.05 #7629), 01gkp1 (0.20 #1077, 0.10 #2539, 0.08 #5463) >> Best rule #959 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0rh6k; 02_286; 01cgxp; >> query: (?x4794, 0ds2n) <- featured_film_locations(?x10918, ?x4794), featured_film_locations(?x1744, ?x4794), film_crew_role(?x10918, ?x137), ?x1744 = 035yn8, ?x137 = 09zzb8 >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027kp3 featured_film_locations! 046f3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 46.000 0.400 http://example.org/film/film/featured_film_locations #7861-0c3ybss PRED entity: 0c3ybss PRED relation: category PRED expected values: 08mbj5d => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.43 #1, 0.41 #15, 0.39 #17) >> Best rule #1 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 02d44q; 0125xq; 01d259; 0cp0t91; 0g5qmbz; >> query: (?x249, 08mbj5d) <- language(?x249, ?x254), film_release_region(?x249, ?x5274), film_release_region(?x249, ?x3749), ?x5274 = 04g61, religion(?x3749, ?x492), contains(?x6304, ?x3749) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c3ybss category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.429 http://example.org/common/topic/webpage./common/webpage/category #7860-02yr3z PRED entity: 02yr3z PRED relation: school! PRED expected values: 0jmj7 => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 90): 0jmj7 (0.37 #6897, 0.35 #7180, 0.34 #6803), 01yjl (0.14 #219, 0.12 #313, 0.05 #5580), 01d5z (0.14 #198, 0.10 #386, 0.04 #1702), 0jmk7 (0.14 #279, 0.10 #467, 0.04 #1971), 0jm3v (0.14 #197, 0.10 #385, 0.04 #855), 0bwjj (0.12 #358, 0.07 #546, 0.05 #2144), 0jmfv (0.07 #484, 0.03 #1894, 0.02 #1142), 05m_8 (0.06 #5740, 0.06 #6777, 0.06 #5552), 01slc (0.06 #2503, 0.06 #2597, 0.06 #1751), 04wmvz (0.06 #1772, 0.04 #5817, 0.04 #6478) >> Best rule #6897 for best value: >> intensional similarity = 4 >> extensional distance = 250 >> proper extension: 01jt2w; >> query: (?x6904, 0jmj7) <- currency(?x6904, ?x170), ?x170 = 09nqf, institution(?x620, ?x6904), category(?x6904, ?x134) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02yr3z school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 187.000 0.369 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #7859-0w7c PRED entity: 0w7c PRED relation: student PRED expected values: 045gzq => 63 concepts (45 used for prediction) PRED predicted values (max 10 best out of 347): 012x2b (0.29 #1064, 0.25 #397, 0.20 #3513), 0bg539 (0.29 #910, 0.25 #243, 0.17 #687), 0kn4c (0.27 #2247, 0.19 #3808, 0.14 #3138), 03l3ln (0.25 #356, 0.17 #800, 0.17 #578), 083q7 (0.25 #18, 0.17 #461, 0.14 #3131), 06ltr (0.25 #335, 0.17 #779, 0.14 #1002), 04sry (0.25 #363, 0.17 #807, 0.14 #1030), 02qzjj (0.25 #432, 0.17 #876, 0.14 #1099), 06pjs (0.25 #392, 0.17 #836, 0.14 #1059), 0f4vbz (0.25 #258, 0.17 #702, 0.14 #925) >> Best rule #1064 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 07c52; >> query: (?x6760, 012x2b) <- major_field_of_study(?x10332, ?x6760), major_field_of_study(?x6637, ?x6760), major_field_of_study(?x6611, ?x6760), major_field_of_study(?x3387, ?x6760), major_field_of_study(?x2909, ?x6760), ?x6611 = 04b_46, contains(?x94, ?x2909), currency(?x3387, ?x170), institution(?x734, ?x6637) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0w7c student 045gzq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 45.000 0.286 http://example.org/education/field_of_study/students_majoring./education/education/student #7858-06xpp7 PRED entity: 06xpp7 PRED relation: category PRED expected values: 08mbj5d => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #21, 0.88 #12, 0.88 #9) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 388 >> proper extension: 03v6t; 0ymc8; 0p5wz; 0143hl; 037njl; 01xrlm; 05xb7q; 01zn4y; 06b19; 01wv24; ... >> query: (?x5522, 08mbj5d) <- contains(?x682, ?x5522), citytown(?x5522, ?x1523), place_of_birth(?x4383, ?x682) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06xpp7 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.879 http://example.org/common/topic/webpage./common/webpage/category #7857-047lj PRED entity: 047lj PRED relation: adjoins! PRED expected values: 06bnz => 89 concepts (88 used for prediction) PRED predicted values (max 10 best out of 417): 07t_x (0.82 #42298, 0.82 #30550, 0.81 #45430), 06bnz (0.21 #68173, 0.20 #2354, 0.17 #8716), 03rk0 (0.21 #68173, 0.20 #2354, 0.09 #12661), 05b7q (0.21 #68173, 0.20 #2354, 0.08 #1107), 05sb1 (0.21 #68173, 0.20 #2354, 0.07 #8746), 01crd5 (0.21 #68173, 0.20 #2354, 0.07 #9025), 0jdd (0.21 #68173, 0.20 #2354, 0.05 #2523), 07dvs (0.21 #68173, 0.20 #2354, 0.05 #2558), 047lj (0.21 #68173, 0.20 #2354, 0.05 #2376), 04xn_ (0.21 #68173, 0.20 #2354, 0.05 #2635) >> Best rule #42298 for best value: >> intensional similarity = 2 >> extensional distance = 188 >> proper extension: 0j3b; 05rgl; 059qw; 02j7k; 02613; 04swx; 0d8h4; >> query: (?x404, ?x9455) <- adjoins(?x404, ?x9455), form_of_government(?x9455, ?x48) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #68173 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 783 *> proper extension: 0mmr1; 0ntwb; 0l2mg; 0mvxt; 018f94; 0msck; *> query: (?x404, ?x252) <- adjoins(?x404, ?x2346), adjoins(?x252, ?x2346) *> conf = 0.21 ranks of expected_values: 2 EVAL 047lj adjoins! 06bnz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 88.000 0.819 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #7856-0mkg PRED entity: 0mkg PRED relation: instrumentalists PRED expected values: 03f7m4h => 95 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1053): 0161sp (0.75 #20102, 0.50 #9817, 0.50 #3173), 050z2 (0.68 #3623, 0.67 #15942, 0.61 #10872), 016ntp (0.68 #3623, 0.61 #10872, 0.60 #9661), 053y0s (0.68 #3623, 0.61 #10872, 0.60 #9661), 03ryks (0.68 #3623, 0.61 #10872, 0.60 #9661), 0180w8 (0.68 #3623, 0.61 #10872, 0.60 #9661), 01wdqrx (0.68 #3623, 0.61 #10872, 0.60 #9661), 05qhnq (0.68 #3623, 0.61 #10872, 0.60 #9661), 01nkxvx (0.68 #3623, 0.61 #10872, 0.60 #9661), 04bpm6 (0.68 #3623, 0.61 #10872, 0.60 #9661) >> Best rule #20102 for best value: >> intensional similarity = 16 >> extensional distance = 10 >> proper extension: 057cc; >> query: (?x614, 0161sp) <- instrumentalists(?x614, ?x4855), instrumentalists(?x614, ?x4206), role(?x4855, ?x2377), role(?x4855, ?x1267), role(?x4855, ?x316), role(?x4855, ?x212), artists(?x284, ?x4855), ?x1267 = 07brj, ?x212 = 026t6, ?x316 = 05r5c, award_winner(?x1381, ?x4206), role(?x3328, ?x2377), role(?x75, ?x2377), ?x3328 = 016622, student(?x12737, ?x4855), ?x75 = 07y_7 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3620 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 2 *> proper extension: 018vs; *> query: (?x614, ?x211) <- role(?x614, ?x716), role(?x614, ?x615), role(?x614, ?x2923), instrumentalists(?x614, ?x317), group(?x614, ?x11929), ?x615 = 0dwsp, ?x11929 = 07n3s, ?x2923 = 02k856, ?x317 = 0c9d9, role(?x130, ?x614), group(?x716, ?x379), instrumentalists(?x716, ?x3316), instrumentalists(?x716, ?x211), role(?x214, ?x716), ?x3316 = 0407f *> conf = 0.46 ranks of expected_values: 318 EVAL 0mkg instrumentalists 03f7m4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 95.000 50.000 0.750 http://example.org/music/instrument/instrumentalists #7855-03f4k PRED entity: 03f4k PRED relation: artists! PRED expected values: 0557q => 164 concepts (101 used for prediction) PRED predicted values (max 10 best out of 228): 064t9 (0.75 #30621, 0.67 #3447, 0.55 #5320), 0ggq0m (0.72 #11563, 0.57 #2198, 0.50 #1885), 03_d0 (0.58 #23750, 0.36 #8751, 0.32 #7502), 05bt6j (0.56 #3478, 0.45 #4727, 0.36 #5351), 02vjzr (0.50 #448, 0.44 #3569, 0.38 #3257), 026z9 (0.50 #390, 0.44 #3511, 0.38 #3199), 06q6jz (0.50 #2061, 0.43 #2374, 0.31 #6119), 02x8m (0.50 #331, 0.38 #3140, 0.36 #4701), 06by7 (0.44 #3455, 0.43 #22509, 0.40 #26258), 021dvj (0.43 #2862, 0.43 #2238, 0.38 #5983) >> Best rule #30621 for best value: >> intensional similarity = 5 >> extensional distance = 467 >> proper extension: 01pfr3; 07c0j; 01v0sx2; 01fl3; 03fbc; 016fmf; 0dm5l; 0249kn; 018ndc; 03xhj6; ... >> query: (?x9593, 064t9) <- artists(?x4910, ?x9593), artists(?x4910, ?x1894), artists(?x4910, ?x1489), ?x1894 = 02fgpf, nominated_for(?x1489, ?x1077) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2978 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0bvzp; *> query: (?x9593, 0557q) <- place_of_birth(?x9593, ?x2850), artists(?x4910, ?x9593), artists(?x888, ?x9593), ?x888 = 05lls, ?x4910 = 017_qw *> conf = 0.29 ranks of expected_values: 18 EVAL 03f4k artists! 0557q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 164.000 101.000 0.751 http://example.org/music/genre/artists #7854-01vb6z PRED entity: 01vb6z PRED relation: nationality PRED expected values: 09c7w0 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.84 #8909, 0.77 #1601, 0.74 #8407), 0l2q3 (0.33 #11516), 01n7q (0.33 #11516), 02jx1 (0.20 #533, 0.19 #733, 0.17 #2833), 07ssc (0.17 #715, 0.17 #2815, 0.16 #415), 0d060g (0.09 #7, 0.06 #807, 0.06 #307), 0345h (0.08 #5031, 0.07 #4931, 0.07 #5432), 03rk0 (0.05 #11662, 0.05 #11562, 0.05 #11862), 0f8l9c (0.05 #4522, 0.05 #5022, 0.04 #4922), 05kyr (0.04 #68, 0.03 #368, 0.02 #668) >> Best rule #8909 for best value: >> intensional similarity = 2 >> extensional distance = 1519 >> proper extension: 0784v1; 07m69t; 05fh2; >> query: (?x6698, ?x94) <- place_of_birth(?x6698, ?x11000), country(?x11000, ?x94) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vb6z nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.837 http://example.org/people/person/nationality #7853-06v99d PRED entity: 06v99d PRED relation: citytown PRED expected values: 01pt5w => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 63): 02_286 (0.41 #3328, 0.33 #4801, 0.33 #751), 052p7 (0.33 #783, 0.17 #1887, 0.11 #2209), 07dfk (0.21 #5367, 0.18 #6471, 0.15 #9047), 04vmp (0.20 #1638, 0.12 #2743, 0.08 #4583), 0f2w0 (0.20 #1507, 0.08 #2244, 0.06 #2612), 04jpl (0.19 #2584, 0.17 #1847, 0.15 #2216), 0r04p (0.15 #3786, 0.12 #4522, 0.08 #2314), 030qb3t (0.12 #5550, 0.12 #4445, 0.12 #5918), 024bqj (0.12 #5353, 0.12 #3512, 0.11 #2209), 0r00l (0.12 #2858, 0.11 #2209, 0.10 #3962) >> Best rule #3328 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 0gsg7; 09d5h; 01y67v; 0cjdk; 03mdt; 05xbx; 05gnf; 01j7pt; 01zcrv; 026v1z; ... >> query: (?x13340, 02_286) <- category(?x13340, ?x134), ?x134 = 08mbj5d, award_winner(?x3486, ?x13340), program(?x13340, ?x14278), languages(?x14278, ?x254), genre(?x14278, ?x258) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #8405 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 03jl0_; 017vb_; *> query: (?x13340, 01pt5w) <- category(?x13340, ?x134), ?x134 = 08mbj5d, program(?x13340, ?x14278), genre(?x14278, ?x258), languages(?x14278, ?x254) *> conf = 0.03 ranks of expected_values: 34 EVAL 06v99d citytown 01pt5w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 72.000 72.000 0.412 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #7852-0gkvb7 PRED entity: 0gkvb7 PRED relation: nominated_for PRED expected values: 0ph24 => 46 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1413): 05hjnw (0.43 #7156, 0.33 #771, 0.24 #15148), 09gq0x5 (0.38 #6642, 0.33 #257, 0.24 #16235), 011yl_ (0.38 #6918, 0.33 #533, 0.19 #16511), 02c638 (0.38 #6695, 0.33 #310, 0.19 #11491), 0f4_l (0.36 #6705, 0.20 #11501, 0.20 #14697), 03hkch7 (0.36 #6847, 0.20 #11643, 0.18 #18038), 0gmcwlb (0.33 #6569, 0.33 #184, 0.21 #16162), 09q5w2 (0.33 #6537, 0.33 #152, 0.18 #16130), 07w8fz (0.33 #6848, 0.33 #463, 0.18 #11644), 0gmgwnv (0.33 #967, 0.31 #7352, 0.23 #16945) >> Best rule #7156 for best value: >> intensional similarity = 6 >> extensional distance = 40 >> proper extension: 0gqng; >> query: (?x537, 05hjnw) <- award(?x8543, ?x537), award(?x3324, ?x537), nominated_for(?x537, ?x3626), award_nominee(?x3324, ?x221), participant(?x1567, ?x8543), place_of_death(?x8543, ?x242) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #9540 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 42 *> proper extension: 02p_7cr; *> query: (?x537, 0ph24) <- award(?x6651, ?x537), award(?x3324, ?x537), nominated_for(?x537, ?x3626), film(?x3324, ?x1045), tv_program(?x1040, ?x3626), profession(?x6651, ?x220) *> conf = 0.05 ranks of expected_values: 896 EVAL 0gkvb7 nominated_for 0ph24 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 13.000 0.429 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7851-039crh PRED entity: 039crh PRED relation: profession PRED expected values: 03gjzk => 157 concepts (63 used for prediction) PRED predicted values (max 10 best out of 79): 03gjzk (0.50 #452, 0.50 #14, 0.44 #891), 0dxtg (0.48 #5563, 0.45 #13, 0.42 #3664), 0cbd2 (0.47 #5994, 0.46 #6432, 0.44 #2197), 02jknp (0.38 #5557, 0.35 #3658, 0.35 #2490), 015cjr (0.37 #485, 0.33 #924, 0.27 #47), 018gz8 (0.30 #454, 0.27 #600, 0.27 #16), 0np9r (0.26 #458, 0.20 #897, 0.18 #7761), 0d1pc (0.23 #1071, 0.23 #2093, 0.20 #3845), 09jwl (0.22 #2063, 0.21 #4545, 0.20 #3669), 0d8qb (0.22 #515, 0.15 #954, 0.09 #77) >> Best rule #452 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 025p38; 03ldxq; 0bz5v2; 02dh86; 01gbbz; 049_zz; 0p8r1; 04gycf; 029_3; 01k70_; ... >> query: (?x4407, 03gjzk) <- location(?x4407, ?x1523), profession(?x4407, ?x319), program(?x4407, ?x4891), nominated_for(?x4407, ?x3413) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039crh profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 63.000 0.500 http://example.org/people/person/profession #7850-02fybl PRED entity: 02fybl PRED relation: role PRED expected values: 0l14qv => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 125): 02sgy (0.52 #2512, 0.46 #4511, 0.41 #2093), 05r5c (0.43 #5662, 0.42 #3251, 0.41 #3464), 018vs (0.42 #744, 0.36 #2520, 0.33 #639), 01vdm0 (0.38 #3275, 0.27 #1701, 0.27 #553), 05148p4 (0.38 #4192, 0.31 #4715, 0.29 #1357), 05842k (0.36 #600, 0.29 #1122, 0.27 #1331), 0l14qv (0.36 #526, 0.27 #1257, 0.18 #1570), 042v_gx (0.34 #2096, 0.33 #4514, 0.32 #1991), 026t6 (0.30 #419, 0.20 #5657, 0.18 #2823), 03bx0bm (0.29 #1357, 0.28 #4086, 0.26 #939) >> Best rule #2512 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 01w923; 0p5mw; 01309x; 09r8l; 050z2; 02cx90; 02bgmr; 01wx756; >> query: (?x7164, 02sgy) <- profession(?x7164, ?x2659), profession(?x7164, ?x1614), ?x2659 = 039v1, role(?x7164, ?x227), ?x1614 = 01c72t >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #526 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 0137g1; 0bqsy; *> query: (?x7164, 0l14qv) <- profession(?x7164, ?x2659), profession(?x7164, ?x131), ?x2659 = 039v1, participant(?x2352, ?x7164), ?x131 = 0dz3r *> conf = 0.36 ranks of expected_values: 7 EVAL 02fybl role 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 145.000 145.000 0.515 http://example.org/music/artist/track_contributions./music/track_contribution/role #7849-04l3_z PRED entity: 04l3_z PRED relation: story_by! PRED expected values: 0cc5mcj => 144 concepts (104 used for prediction) PRED predicted values (max 10 best out of 279): 01jft4 (0.32 #1714, 0.13 #685, 0.12 #8906), 0d87hc (0.32 #1714, 0.13 #685, 0.12 #8906), 04kkz8 (0.32 #1714, 0.13 #685, 0.12 #8906), 0mbql (0.06 #230, 0.05 #572, 0.02 #1257), 0f3m1 (0.06 #275, 0.05 #617, 0.02 #1302), 0k_9j (0.06 #265, 0.05 #607, 0.02 #1292), 0dfw0 (0.06 #176, 0.05 #518, 0.02 #1203), 04mcw4 (0.06 #157, 0.05 #499, 0.02 #1184), 01hw5kk (0.06 #135, 0.05 #477, 0.02 #1162), 0f4yh (0.06 #118, 0.05 #460, 0.02 #1145) >> Best rule #1714 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 01q415; >> query: (?x975, ?x974) <- location(?x975, ?x10687), written_by(?x974, ?x975), story_by(?x2494, ?x975), profession(?x975, ?x353) >> conf = 0.32 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 04l3_z story_by! 0cc5mcj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 104.000 0.321 http://example.org/film/film/story_by #7848-0hsqf PRED entity: 0hsqf PRED relation: place_of_birth! PRED expected values: 044n3h => 198 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1871): 03l3jy (0.35 #290037, 0.33 #112355, 0.33 #91449), 01v27pl (0.28 #133260, 0.27 #180292, 0.27 #198586), 01q8wk7 (0.25 #7766, 0.01 #73087), 0djc3s (0.25 #7595, 0.01 #72916), 0bkg87 (0.25 #7567, 0.01 #72888), 071xj (0.25 #7329, 0.01 #72650), 04c636 (0.25 #6616, 0.01 #71937), 0674cw (0.25 #6120, 0.01 #71441), 01n8_g (0.25 #5661, 0.01 #70982), 0c01c (0.20 #13537, 0.12 #18762, 0.07 #21375) >> Best rule #290037 for best value: >> intensional similarity = 4 >> extensional distance = 312 >> proper extension: 05d49; >> query: (?x9310, ?x4389) <- location(?x4389, ?x9310), category(?x9310, ?x134), place_of_birth(?x823, ?x9310), ?x134 = 08mbj5d >> conf = 0.35 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hsqf place_of_birth! 044n3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 198.000 117.000 0.353 http://example.org/people/person/place_of_birth #7847-03p2xc PRED entity: 03p2xc PRED relation: country PRED expected values: 09c7w0 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 65): 09c7w0 (0.89 #1886, 0.89 #852, 0.88 #1704), 0f8l9c (0.61 #139, 0.21 #1111, 0.15 #200), 03rjj (0.40 #3218, 0.07 #1099, 0.04 #2489), 02jx1 (0.40 #3218, 0.04 #2489, 0.01 #1120), 0k6nt (0.40 #3218, 0.04 #2489), 0345h (0.24 #1119, 0.17 #147, 0.10 #330), 03mqtr (0.12 #181, 0.06 #3280, 0.06 #3279), 07s9rl0 (0.12 #181, 0.06 #3280, 0.06 #3279), 0l4h_ (0.12 #181, 0.06 #3280, 0.06 #3279), 03_3d (0.10 #1100, 0.04 #2489, 0.04 #189) >> Best rule #1886 for best value: >> intensional similarity = 4 >> extensional distance = 871 >> proper extension: 0gtsx8c; 02vxq9m; 0gzy02; 0dq626; 0gtv7pk; 0h1cdwq; 0cpllql; 0gx9rvq; 0401sg; 0fr63l; ... >> query: (?x7128, 09c7w0) <- country(?x7128, ?x512), production_companies(?x7128, ?x166), film(?x495, ?x7128), award_winner(?x5349, ?x495) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03p2xc country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.890 http://example.org/film/film/country #7846-054c1 PRED entity: 054c1 PRED relation: type_of_union PRED expected values: 04ztj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.76 #117, 0.75 #49, 0.75 #17), 01g63y (0.27 #34, 0.22 #26, 0.21 #154), 0jgjn (0.20 #411, 0.01 #104), 01bl8s (0.20 #411) >> Best rule #117 for best value: >> intensional similarity = 2 >> extensional distance = 118 >> proper extension: 012x1l; >> query: (?x11924, 04ztj) <- inductee(?x12338, ?x11924), gender(?x11924, ?x231) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 054c1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.758 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7845-034bs PRED entity: 034bs PRED relation: gender PRED expected values: 05zppz => 249 concepts (249 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #103, 0.91 #151, 0.91 #183), 02zsn (0.73 #237, 0.72 #322, 0.46 #492) >> Best rule #103 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 01vb403; 02vr7; 01cspq; 015cbq; 026670; >> query: (?x4055, 05zppz) <- influenced_by(?x2485, ?x4055), nationality(?x4055, ?x1310), religion(?x4055, ?x2694), award_winner(?x10270, ?x4055) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034bs gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 249.000 249.000 0.924 http://example.org/people/person/gender #7844-02rybfn PRED entity: 02rybfn PRED relation: nationality PRED expected values: 09c7w0 => 134 concepts (118 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.87 #2614, 0.87 #3618, 0.85 #2312), 02xry (0.52 #6650, 0.37 #5837, 0.33 #5128), 0jrxx (0.37 #5837, 0.33 #5128, 0.32 #5433), 0vmt (0.26 #2613), 02jx1 (0.17 #937, 0.15 #736, 0.14 #3750), 03rt9 (0.17 #113, 0.06 #313, 0.04 #716), 07ssc (0.12 #2526, 0.12 #3933, 0.12 #3531), 0d05w3 (0.06 #250, 0.05 #552, 0.04 #1357), 0d0vqn (0.06 #209, 0.05 #511, 0.04 #813), 0chghy (0.06 #1317, 0.05 #1517, 0.05 #1217) >> Best rule #2614 for best value: >> intensional similarity = 4 >> extensional distance = 157 >> proper extension: 07nznf; 0fvf9q; 05kfs; 0mdqp; 02lk1s; 07q1v4; 03pmty; 01jrz5j; 0456xp; 021_rm; ... >> query: (?x9552, 09c7w0) <- award(?x9552, ?x1243), profession(?x9552, ?x2265), place_of_birth(?x9552, ?x739), ?x739 = 02_286 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rybfn nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 118.000 0.868 http://example.org/people/person/nationality #7843-0993r PRED entity: 0993r PRED relation: nationality PRED expected values: 09c7w0 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.85 #701, 0.80 #1103, 0.80 #902), 07ssc (0.50 #115, 0.40 #215, 0.34 #9912), 02jx1 (0.38 #633, 0.34 #9912, 0.17 #333), 0345h (0.34 #9912, 0.05 #531, 0.05 #5837), 0j5g9 (0.34 #9912, 0.04 #662, 0.01 #5868), 03rt9 (0.34 #9912, 0.02 #5819, 0.02 #2418), 0d060g (0.33 #8910, 0.27 #4706, 0.27 #6708), 0f8l9c (0.33 #8910, 0.27 #4706, 0.27 #6708), 06q1r (0.20 #277, 0.01 #4983), 03rk0 (0.06 #746, 0.06 #6954, 0.06 #2451) >> Best rule #701 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 035wq7; >> query: (?x3034, 09c7w0) <- film(?x3034, ?x835), person(?x3480, ?x3034), award(?x835, ?x154) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0993r nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.848 http://example.org/people/person/nationality #7842-0hd7j PRED entity: 0hd7j PRED relation: school! PRED expected values: 01y49 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 91): 0jmj7 (0.81 #392, 0.69 #847, 0.68 #3031), 05m_8 (0.21 #1004, 0.18 #1186, 0.17 #913), 051vz (0.18 #1023, 0.16 #1205, 0.14 #932), 07l8x (0.17 #429, 0.15 #1066, 0.14 #1248), 01yjl (0.17 #29, 0.14 #1485, 0.13 #848), 07l4z (0.17 #69, 0.13 #1070, 0.13 #1252), 01slc (0.17 #57, 0.13 #1058, 0.13 #876), 0713r (0.17 #35, 0.12 #1491, 0.12 #1855), 01yhm (0.17 #19, 0.12 #929, 0.12 #1202), 04wmvz (0.17 #441, 0.12 #1260, 0.12 #1078) >> Best rule #392 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 027mdh; >> query: (?x4603, 0jmj7) <- major_field_of_study(?x4603, ?x1154), institution(?x1519, ?x4603), school(?x684, ?x4603), ?x1519 = 013zdg, category(?x4603, ?x134) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #749 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 43 *> proper extension: 05f7s1; 07xpm; 01dyk8; 04jhp; 0lk0l; *> query: (?x4603, 01y49) <- major_field_of_study(?x4603, ?x1154), institution(?x4981, ?x4603), institution(?x734, ?x4603), contains(?x94, ?x4603), ?x4981 = 03bwzr4, ?x734 = 04zx3q1 *> conf = 0.09 ranks of expected_values: 41 EVAL 0hd7j school! 01y49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 153.000 153.000 0.806 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #7841-0mm1q PRED entity: 0mm1q PRED relation: profession PRED expected values: 01d_h8 02jknp 012t_z => 128 concepts (115 used for prediction) PRED predicted values (max 10 best out of 78): 02jknp (0.87 #6329, 0.87 #5594, 0.86 #5300), 01d_h8 (0.78 #5299, 0.78 #5593, 0.77 #5887), 03gjzk (0.67 #160, 0.60 #601, 0.56 #2071), 02krf9 (0.50 #25, 0.38 #9704, 0.35 #1054), 0d1pc (0.38 #9704, 0.33 #490, 0.29 #343), 0kyk (0.38 #9704, 0.32 #9437, 0.25 #3823), 016z4k (0.38 #9704, 0.30 #739, 0.25 #3823), 09jwl (0.38 #9704, 0.29 #311, 0.27 #7809), 018gz8 (0.36 #2514, 0.36 #2367, 0.33 #2073), 0nbcg (0.26 #1941, 0.23 #912, 0.23 #1794) >> Best rule #6329 for best value: >> intensional similarity = 3 >> extensional distance = 246 >> proper extension: 02rchht; 0byfz; 0qf43; 042l3v; 03f2_rc; 02lf0c; 0kr5_; 042rnl; 03_gd; 058kqy; ... >> query: (?x5565, 02jknp) <- gender(?x5565, ?x231), film(?x5565, ?x1707), profession(?x5565, ?x353) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 27 EVAL 0mm1q profession 012t_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 128.000 115.000 0.871 http://example.org/people/person/profession EVAL 0mm1q profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 115.000 0.871 http://example.org/people/person/profession EVAL 0mm1q profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 115.000 0.871 http://example.org/people/person/profession #7840-0d9v9q PRED entity: 0d9v9q PRED relation: team PRED expected values: 02b15h => 123 concepts (98 used for prediction) PRED predicted values (max 10 best out of 265): 02b185 (0.68 #4248, 0.68 #4247, 0.67 #2921), 01z1r (0.40 #363, 0.06 #3549, 0.06 #3814), 0223bl (0.40 #270, 0.05 #1595, 0.04 #3456), 050fh (0.20 #325, 0.13 #1385, 0.06 #2450), 0284h6 (0.20 #429, 0.11 #1754, 0.09 #3615), 02b1mc (0.20 #289, 0.10 #1349, 0.08 #1614), 02pp1 (0.20 #458, 0.10 #1518, 0.06 #2583), 01zhs3 (0.20 #359, 0.08 #2484, 0.07 #3810), 06l22 (0.20 #366, 0.06 #2491, 0.04 #3552), 011v3 (0.20 #329, 0.06 #2454, 0.04 #3515) >> Best rule #4248 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 02qjj7; 037gjc; >> query: (?x7212, ?x6503) <- team(?x7212, ?x6503), nationality(?x7212, ?x512), team(?x63, ?x6503), profession(?x7212, ?x7623) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1328 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 06yj20; *> query: (?x7212, 02b15h) <- profession(?x7212, ?x7623), nationality(?x7212, ?x512), ?x7623 = 0gl2ny2, jurisdiction_of_office(?x1328, ?x512) *> conf = 0.03 ranks of expected_values: 151 EVAL 0d9v9q team 02b15h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 123.000 98.000 0.683 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #7839-042gr4 PRED entity: 042gr4 PRED relation: film PRED expected values: 08fbnx => 82 concepts (33 used for prediction) PRED predicted values (max 10 best out of 802): 08fbnx (0.46 #21477, 0.11 #817, 0.04 #2606), 026q3s3 (0.33 #204, 0.25 #1993, 0.22 #5571), 0dh8v4 (0.33 #942, 0.21 #2731, 0.15 #6309), 02vw1w2 (0.25 #2003, 0.22 #5581, 0.20 #9161), 056k77g (0.22 #1539, 0.12 #3328, 0.11 #6906), 07ghv5 (0.17 #2958, 0.15 #6536, 0.13 #10116), 0dd6bf (0.17 #3024, 0.15 #6602, 0.13 #10182), 07ng9k (0.17 #1995, 0.15 #5573, 0.13 #9153), 02z9hqn (0.17 #1918, 0.15 #5496, 0.11 #129), 02z5x7l (0.17 #2997, 0.11 #6575, 0.11 #1208) >> Best rule #21477 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 07cjqy; 0f502; 028k57; 0gs6vr; 02b9g4; 0d608; >> query: (?x13336, ?x4770) <- special_performance_type(?x13336, ?x296), film(?x13336, ?x1628), prequel(?x1628, ?x4770), genre(?x1628, ?x258) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042gr4 film 08fbnx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 33.000 0.463 http://example.org/film/actor/film./film/performance/film #7838-0146mv PRED entity: 0146mv PRED relation: program PRED expected values: 05f7w84 => 148 concepts (98 used for prediction) PRED predicted values (max 10 best out of 247): 05h95s (0.71 #2176, 0.69 #1208, 0.66 #1209), 05qbbfb (0.71 #2176, 0.69 #1208, 0.47 #483), 04glx0 (0.40 #1070, 0.38 #829, 0.24 #2038), 0bx_hnp (0.40 #419, 0.29 #661, 0.12 #903), 015g28 (0.40 #295, 0.29 #537, 0.12 #779), 0124k9 (0.40 #260, 0.29 #502, 0.12 #744), 097h2 (0.38 #880, 0.30 #1121, 0.19 #1606), 045qmr (0.33 #137, 0.12 #862, 0.12 #8598), 020qr4 (0.33 #5, 0.12 #1939, 0.11 #7260), 05f7w84 (0.33 #92, 0.05 #7347, 0.04 #4204) >> Best rule #2176 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 01nzs7; 027_tg; 01j7pt; 0kctd; >> query: (?x11453, ?x6053) <- award_winner(?x6053, ?x11453), program(?x11453, ?x11454), nominated_for(?x11788, ?x11454) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #92 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1 *> proper extension: 025snf; *> query: (?x11453, 05f7w84) <- program(?x11453, ?x11454), ?x11454 = 07vqnc *> conf = 0.33 ranks of expected_values: 10 EVAL 0146mv program 05f7w84 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 148.000 98.000 0.712 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #7837-02cttt PRED entity: 02cttt PRED relation: major_field_of_study PRED expected values: 02lp1 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 117): 01mkq (0.76 #1000, 0.71 #877, 0.71 #262), 02h40lc (0.72 #496, 0.29 #865, 0.26 #373), 02lp1 (0.65 #873, 0.65 #996, 0.63 #258), 02j62 (0.58 #892, 0.57 #523, 0.57 #1015), 04rjg (0.56 #267, 0.56 #882, 0.56 #1005), 05qjt (0.52 #992, 0.48 #869, 0.45 #377), 062z7 (0.50 #1012, 0.46 #889, 0.40 #397), 03g3w (0.48 #1011, 0.45 #27, 0.44 #888), 01tbp (0.45 #430, 0.42 #922, 0.39 #307), 037mh8 (0.45 #69, 0.39 #1053, 0.35 #930) >> Best rule #1000 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 08815; 01f1r4; 02zd460; 0ks67; 0160nk; >> query: (?x918, 01mkq) <- school_type(?x918, ?x3092), major_field_of_study(?x918, ?x2502), organization(?x918, ?x5487), institution(?x1771, ?x918) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #873 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 01jssp; 01pq4w; 05zl0; 01qd_r; 0187nd; *> query: (?x918, 02lp1) <- category(?x918, ?x134), organization(?x918, ?x5487), major_field_of_study(?x918, ?x2502), student(?x918, ?x919) *> conf = 0.65 ranks of expected_values: 3 EVAL 02cttt major_field_of_study 02lp1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 129.000 0.759 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7836-02vtnf PRED entity: 02vtnf PRED relation: gender PRED expected values: 05zppz => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #25, 0.87 #29, 0.87 #13), 02zsn (0.46 #36, 0.44 #44, 0.44 #48) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 234 >> proper extension: 042rnl; 04g865; 013t9y; 05cgy8; 02mz_6; 01p87y; 05z_p6; 04vlh5; 04dyqk; >> query: (?x10920, 05zppz) <- nationality(?x10920, ?x94), film(?x10920, ?x3909), film(?x788, ?x3909) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vtnf gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.877 http://example.org/people/person/gender #7835-0pd64 PRED entity: 0pd64 PRED relation: genre PRED expected values: 02kdv5l => 84 concepts (56 used for prediction) PRED predicted values (max 10 best out of 151): 05p553 (0.51 #4258, 0.32 #2011, 0.32 #594), 02kdv5l (0.50 #2, 0.50 #3190, 0.49 #3427), 02l7c8 (0.50 #14, 0.43 #4268, 0.32 #2965), 0c3351 (0.50 #36, 0.15 #1570, 0.14 #862), 0bkbm (0.50 #38, 0.08 #1336, 0.08 #746), 060__y (0.32 #133, 0.23 #251, 0.23 #723), 03k9fj (0.31 #1899, 0.29 #600, 0.28 #2371), 04xvlr (0.30 #237, 0.21 #3782, 0.19 #4137), 01g6gs (0.25 #19, 0.23 #1671, 0.21 #1081), 082gq (0.23 #265, 0.20 #2154, 0.20 #2272) >> Best rule #4258 for best value: >> intensional similarity = 4 >> extensional distance = 452 >> proper extension: 02_1sj; 07g_0c; 0c8tkt; 0j_tw; 08052t3; 07x4qr; 0pvms; 014zwb; 0crc2cp; 0dgpwnk; ... >> query: (?x7711, 05p553) <- featured_film_locations(?x7711, ?x108), genre(?x7711, ?x600), genre(?x4688, ?x600), ?x4688 = 09jcj6 >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 05css_; *> query: (?x7711, 02kdv5l) <- featured_film_locations(?x7711, ?x6310), genre(?x7711, ?x600), ?x600 = 02n4kr, ?x6310 = 02nd_ *> conf = 0.50 ranks of expected_values: 2 EVAL 0pd64 genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 56.000 0.513 http://example.org/film/film/genre #7834-01y3_q PRED entity: 01y3_q PRED relation: parent_genre PRED expected values: 0hh2s => 60 concepts (51 used for prediction) PRED predicted values (max 10 best out of 206): 0y3_8 (0.59 #693, 0.26 #1189, 0.25 #1684), 06by7 (0.58 #3148, 0.53 #4469, 0.46 #509), 0glt670 (0.46 #1155, 0.46 #1020, 0.29 #2665), 0m0jc (0.46 #1155, 0.36 #3297, 0.29 #4949), 05w3f (0.46 #519, 0.43 #851, 0.28 #1348), 08cyft (0.36 #3297, 0.29 #823, 0.29 #700), 0mmp3 (0.33 #68, 0.31 #1224, 0.30 #1719), 03lty (0.33 #19, 0.31 #512, 0.29 #4805), 0fd3y (0.33 #8, 0.24 #825, 0.24 #656), 0g_bh (0.33 #83, 0.20 #2147, 0.13 #657) >> Best rule #693 for best value: >> intensional similarity = 7 >> extensional distance = 15 >> proper extension: 0133k0; >> query: (?x7279, 0y3_8) <- parent_genre(?x7279, ?x7280), parent_genre(?x7280, ?x3916), parent_genre(?x7280, ?x505), artists(?x7280, ?x1732), ?x3916 = 08cyft, artists(?x505, ?x12623), ?x12623 = 01nn3m >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #825 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 15 *> proper extension: 0133k0; *> query: (?x7279, ?x283) <- parent_genre(?x7279, ?x7280), parent_genre(?x7280, ?x3916), parent_genre(?x7280, ?x505), parent_genre(?x7280, ?x283), artists(?x7280, ?x1732), ?x3916 = 08cyft, artists(?x505, ?x12623), ?x12623 = 01nn3m *> conf = 0.24 ranks of expected_values: 25 EVAL 01y3_q parent_genre 0hh2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 60.000 51.000 0.588 http://example.org/music/genre/parent_genre #7833-0nv99 PRED entity: 0nv99 PRED relation: contains! PRED expected values: 03v0t => 119 concepts (60 used for prediction) PRED predicted values (max 10 best out of 118): 04ych (0.80 #1859, 0.12 #2757, 0.07 #960), 09c7w0 (0.76 #18836, 0.60 #1798, 0.51 #49331), 03v0t (0.69 #32288, 0.66 #46639, 0.59 #50229), 04_1l0v (0.37 #4040, 0.28 #4937, 0.25 #451), 0nv99 (0.25 #49332, 0.25 #52024, 0.25 #53820), 07c5l (0.25 #395, 0.07 #4881, 0.06 #50624), 07b_l (0.21 #2915, 0.19 #12779, 0.18 #13674), 02qkt (0.18 #50576, 0.12 #32635, 0.10 #35326), 059rby (0.15 #27823, 0.14 #31411, 0.14 #26028), 01n7q (0.14 #21602, 0.14 #8151, 0.14 #39541) >> Best rule #1859 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 019pwv; 02tz9z; >> query: (?x12859, 04ych) <- contains(?x11703, ?x12859), contains(?x11703, ?x11877), contains(?x11703, ?x10877), ?x11877 = 0ndh6, adjoins(?x4356, ?x10877) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #32288 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 246 *> proper extension: 0mx4_; 0mw93; 0m7fm; 0n5fl; 0fr59; 0mxcf; 0mx6c; 0m2lt; 0mk7z; 0p0cw; ... *> query: (?x12859, ?x3818) <- adjoins(?x12859, ?x10877), currency(?x12859, ?x170), contains(?x11703, ?x12859), contains(?x3818, ?x10877) *> conf = 0.69 ranks of expected_values: 3 EVAL 0nv99 contains! 03v0t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 60.000 0.800 http://example.org/location/location/contains #7832-0h407 PRED entity: 0h407 PRED relation: languages_spoken! PRED expected values: 03bkbh => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 106): 07hwkr (0.79 #561, 0.62 #286, 0.53 #1114), 033tf_ (0.43 #344, 0.40 #212, 0.33 #75), 03bkbh (0.43 #344, 0.40 #231, 0.25 #162), 03w9bjf (0.43 #344, 0.33 #527, 0.25 #182), 0x67 (0.43 #344, 0.33 #78, 0.25 #146), 0ffjqy (0.43 #344, 0.33 #122, 0.25 #190), 013b6_ (0.43 #344, 0.25 #181, 0.23 #319), 04czx7 (0.43 #344, 0.25 #202, 0.21 #547), 0d2by (0.43 #344, 0.25 #163, 0.21 #508), 078vc (0.43 #344, 0.25 #176, 0.20 #245) >> Best rule #561 for best value: >> intensional similarity = 13 >> extensional distance = 26 >> proper extension: 04h9h; >> query: (?x13258, 07hwkr) <- languages_spoken(?x5741, ?x13258), languages_spoken(?x5042, ?x13258), people(?x5042, ?x4019), people(?x5042, ?x1190), people(?x5042, ?x473), music(?x363, ?x4019), award_nominee(?x473, ?x374), award_nominee(?x1223, ?x473), people(?x5741, ?x1365), award_winner(?x2060, ?x1365), award_winner(?x1191, ?x1190), gender(?x1365, ?x231), ?x2060 = 054ky1 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #344 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 11 *> proper extension: 02hwyss; *> query: (?x13258, ?x743) <- countries_spoken_in(?x13258, ?x6371), contains(?x11138, ?x6371), official_language(?x6371, ?x254), jurisdiction_of_office(?x182, ?x6371), adjoins(?x11138, ?x1144), languages_spoken(?x412, ?x13258), capital(?x6371, ?x362), ?x1144 = 0j3b, language(?x54, ?x254), languages(?x118, ?x254), countries_spoken_in(?x254, ?x126), languages_spoken(?x743, ?x254) *> conf = 0.43 ranks of expected_values: 3 EVAL 0h407 languages_spoken! 03bkbh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 29.000 29.000 0.786 http://example.org/people/ethnicity/languages_spoken #7831-05f4_n0 PRED entity: 05f4_n0 PRED relation: edited_by PRED expected values: 0bn3jg => 80 concepts (57 used for prediction) PRED predicted values (max 10 best out of 19): 027pdrh (0.11 #10, 0.02 #219, 0.01 #251), 03q8ch (0.04 #131, 0.03 #222, 0.03 #254), 02qggqc (0.02 #698, 0.02 #578, 0.02 #151), 02lp3c (0.02 #106, 0.01 #195, 0.01 #135), 03hbzj (0.02 #241, 0.02 #273, 0.01 #209), 04cy8rb (0.02 #30, 0.02 #274, 0.02 #179), 0bn3jg (0.02 #57, 0.01 #206, 0.01 #146), 0bs1yy (0.02 #40), 03crcpt (0.01 #318), 03_gd (0.01 #93, 0.01 #63) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 058kh7; >> query: (?x4287, 027pdrh) <- film(?x6259, ?x4287), genre(?x4287, ?x225), country(?x4287, ?x94), ?x6259 = 01x4sb >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #57 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 0d_wms; 042fgh; 091xrc; *> query: (?x4287, 0bn3jg) <- film(?x1289, ?x4287), genre(?x4287, ?x6888), country(?x4287, ?x94), ?x6888 = 04pbhw *> conf = 0.02 ranks of expected_values: 7 EVAL 05f4_n0 edited_by 0bn3jg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 80.000 57.000 0.111 http://example.org/film/film/edited_by #7830-01b9ck PRED entity: 01b9ck PRED relation: produced_by! PRED expected values: 03bdkd => 125 concepts (82 used for prediction) PRED predicted values (max 10 best out of 827): 0bcndz (0.39 #32052, 0.39 #35821, 0.30 #20740), 0cwy47 (0.37 #20741, 0.34 #26396, 0.34 #23569), 0kb1g (0.20 #858, 0.08 #3685, 0.03 #3770), 0jdr0 (0.20 #820, 0.08 #3647, 0.02 #12133), 0k5px (0.20 #901, 0.04 #3728, 0.04 #4671), 0kbhf (0.20 #556, 0.04 #3383, 0.03 #3770), 0ktpx (0.20 #553, 0.04 #3380, 0.03 #3770), 04mzf8 (0.20 #117, 0.04 #2944, 0.03 #3770), 026fs38 (0.20 #697, 0.04 #3524, 0.01 #9182), 0c8qq (0.20 #297, 0.04 #3124, 0.01 #8782) >> Best rule #32052 for best value: >> intensional similarity = 3 >> extensional distance = 274 >> proper extension: 07nznf; 0q9kd; 0grwj; 016qtt; 0fvf9q; 04t2l2; 05ty4m; 0z4s; 054_mz; 07f8wg; ... >> query: (?x1300, ?x1745) <- award_nominee(?x382, ?x1300), produced_by(?x5183, ?x1300), nominated_for(?x1300, ?x1745) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #3770 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 016z1c; 016ghw; *> query: (?x1300, ?x4504) <- award_winner(?x1300, ?x6857), nominated_for(?x6857, ?x4504), place_of_burial(?x1300, ?x3153) *> conf = 0.03 ranks of expected_values: 164 EVAL 01b9ck produced_by! 03bdkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 125.000 82.000 0.391 http://example.org/film/film/produced_by #7829-02bvt PRED entity: 02bvt PRED relation: people! PRED expected values: 07jq_ => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 37): 0gk4g (0.13 #1000, 0.13 #142, 0.12 #1066), 04p3w (0.10 #143, 0.05 #1067, 0.05 #1199), 0dq9p (0.09 #1205, 0.09 #479, 0.08 #281), 0qcr0 (0.08 #265, 0.08 #991, 0.08 #133), 02k6hp (0.06 #697, 0.06 #499, 0.05 #1159), 02y0js (0.06 #266, 0.06 #1190, 0.05 #1916), 0m32h (0.05 #155, 0.03 #485, 0.03 #1079), 0d19y2 (0.05 #319, 0.03 #517, 0.02 #1177), 02knxx (0.04 #1022, 0.04 #164, 0.03 #1088), 04psf (0.04 #271, 0.02 #667, 0.02 #469) >> Best rule #1000 for best value: >> intensional similarity = 2 >> extensional distance = 275 >> proper extension: 0h1_w; 07xr3w; 01p7b6b; 07djnx; 0m9c1; 01vq3nl; 071jrc; 03gt0c5; >> query: (?x4806, 0gk4g) <- place_of_death(?x4806, ?x739), nominated_for(?x4806, ?x4517) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02bvt people! 07jq_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 122.000 0.134 http://example.org/people/cause_of_death/people #7828-02whj PRED entity: 02whj PRED relation: instrumentalists! PRED expected values: 018vs => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 122): 05r5c (0.67 #1112, 0.63 #602, 0.62 #1623), 018vs (0.58 #1713, 0.55 #1543, 0.42 #3586), 026t6 (0.39 #1534, 0.31 #1704, 0.19 #4173), 03qjg (0.36 #474, 0.26 #814, 0.24 #899), 07brj (0.34 #1787, 0.28 #3149, 0.28 #1361), 05842k (0.34 #1787, 0.28 #3149, 0.28 #1361), 03gvt (0.26 #658, 0.18 #1424, 0.18 #1850), 018j2 (0.24 #1567, 0.21 #1737, 0.16 #631), 0l14qv (0.19 #1706, 0.18 #1536, 0.12 #1366), 04rzd (0.18 #1566, 0.18 #460, 0.17 #1736) >> Best rule #1112 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 01nqfh_; 0kvjrw; 0hr3g; 02_33l; >> query: (?x1092, 05r5c) <- artists(?x505, ?x1092), profession(?x1092, ?x563), ?x563 = 01c8w0, instrumentalists(?x227, ?x1092) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1713 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 01s7qqw; 0326tc; 04m2zj; *> query: (?x1092, 018vs) <- artists(?x2249, ?x1092), profession(?x1092, ?x131), role(?x1092, ?x227), ?x2249 = 03lty *> conf = 0.58 ranks of expected_values: 2 EVAL 02whj instrumentalists! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 191.000 191.000 0.667 http://example.org/music/instrument/instrumentalists #7827-01jb26 PRED entity: 01jb26 PRED relation: film PRED expected values: 0prrm => 87 concepts (56 used for prediction) PRED predicted values (max 10 best out of 715): 0n6ds (0.14 #1630, 0.06 #68075, 0.02 #24915), 02v5_g (0.14 #793, 0.05 #6166, 0.04 #7958), 0gfzfj (0.14 #1697, 0.03 #7070, 0.03 #10653), 013q07 (0.14 #357, 0.03 #21851, 0.02 #36183), 013q0p (0.14 #808, 0.02 #9764, 0.01 #20511), 01738w (0.14 #1131, 0.02 #6504, 0.02 #11879), 043h78 (0.14 #1519, 0.02 #6892, 0.01 #12267), 056xkh (0.14 #1601, 0.02 #37427, 0.02 #10557), 07024 (0.14 #482, 0.02 #18394, 0.02 #13021), 01k1k4 (0.14 #58, 0.02 #10806, 0.02 #9014) >> Best rule #1630 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02s_qz; 014y6; >> query: (?x5268, 0n6ds) <- nationality(?x5268, ?x94), actor(?x9843, ?x5268), gender(?x5268, ?x514), ?x9843 = 0cskb >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2653 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 0993r; 04gycf; *> query: (?x5268, 0prrm) <- profession(?x5268, ?x1032), participant(?x5268, ?x3673), gender(?x5268, ?x514), person(?x9277, ?x5268) *> conf = 0.04 ranks of expected_values: 33 EVAL 01jb26 film 0prrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 87.000 56.000 0.143 http://example.org/film/actor/film./film/performance/film #7826-09_94 PRED entity: 09_94 PRED relation: olympics PRED expected values: 018ctl => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 40): 018ctl (0.78 #321, 0.75 #278, 0.67 #234), 0kbws (0.70 #628, 0.63 #837, 0.62 #584), 0swff (0.67 #357, 0.67 #335, 0.67 #204), 0sx8l (0.62 #268, 0.60 #266, 0.60 #183), 06sks6 (0.61 #639, 0.60 #380, 0.59 #766), 0sx7r (0.60 #266, 0.60 #183, 0.58 #267), 0124ld (0.60 #266, 0.60 #183, 0.58 #267), 015l4k (0.60 #266, 0.60 #183, 0.58 #267), 019n8z (0.60 #266, 0.60 #183, 0.58 #267), 015pkt (0.60 #266, 0.60 #183, 0.58 #267) >> Best rule #321 for best value: >> intensional similarity = 63 >> extensional distance = 7 >> proper extension: 03tmr; >> query: (?x2752, 018ctl) <- country(?x2752, ?x1892), country(?x2752, ?x789), ?x1892 = 02vzc, olympics(?x2752, ?x418), sports(?x2630, ?x2752), jurisdiction_of_office(?x182, ?x789), film_release_region(?x11395, ?x789), film_release_region(?x10246, ?x789), film_release_region(?x8682, ?x789), film_release_region(?x8373, ?x789), film_release_region(?x7700, ?x789), film_release_region(?x7651, ?x789), film_release_region(?x6168, ?x789), film_release_region(?x5517, ?x789), film_release_region(?x5509, ?x789), film_release_region(?x5162, ?x789), film_release_region(?x5092, ?x789), film_release_region(?x5067, ?x789), film_release_region(?x4355, ?x789), film_release_region(?x4041, ?x789), film_release_region(?x2628, ?x789), film_release_region(?x2441, ?x789), film_release_region(?x2394, ?x789), film_release_region(?x785, ?x789), ?x6168 = 0gj96ln, second_level_divisions(?x789, ?x2144), ?x10246 = 023vcd, ?x2394 = 0661ql3, country(?x8054, ?x789), country(?x4441, ?x789), service_location(?x555, ?x789), ?x5067 = 01rwpj, ?x2628 = 06wbm8q, exported_to(?x291, ?x789), administrative_parent(?x790, ?x789), ?x8373 = 0bs8hvm, ?x5162 = 0j3d9tn, ?x5092 = 0gg5qcw, combatants(?x151, ?x789), olympics(?x789, ?x3971), ?x4355 = 08tq4x, organization(?x789, ?x127), ?x4041 = 0gy2y8r, ?x11395 = 05ypj5, combatants(?x1140, ?x789), ?x8054 = 03ydlnj, olympics(?x789, ?x391), ?x7700 = 0cp08zg, ?x7651 = 0h95927, nationality(?x8933, ?x789), nationality(?x2824, ?x789), music(?x5509, ?x3811), film(?x11251, ?x5509), ?x2630 = 0swff, country(?x6423, ?x789), ?x2824 = 02w4fkq, ?x2441 = 0cc5mcj, ?x3971 = 0jhn7, ?x4441 = 0125xq, nominated_for(?x941, ?x8682), ?x8933 = 04glr5h, ?x5517 = 03wh49y, ?x785 = 03hjv97 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09_94 olympics 018ctl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.778 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #7825-01fx4k PRED entity: 01fx4k PRED relation: nominated_for! PRED expected values: 0gs9p => 82 concepts (77 used for prediction) PRED predicted values (max 10 best out of 224): 09cn0c (0.67 #2084, 0.66 #8338, 0.66 #9036), 0gq9h (0.64 #2834, 0.62 #1444, 0.61 #1676), 0gs9p (0.61 #2836, 0.56 #59, 0.52 #1446), 0f4x7 (0.58 #1644, 0.57 #1412, 0.38 #2802), 02qyntr (0.53 #1560, 0.53 #2950, 0.53 #1792), 04dn09n (0.50 #1421, 0.50 #1653, 0.41 #2811), 0k611 (0.49 #1455, 0.48 #1687, 0.47 #2845), 0l8z1 (0.44 #1206, 0.32 #1437, 0.32 #1669), 054krc (0.42 #1220, 0.29 #1451, 0.28 #1683), 02r22gf (0.41 #1183, 0.26 #2804, 0.23 #1414) >> Best rule #2084 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 047q2k1; 06wzvr; 090s_0; 06krf3; 0416y94; 02q6gfp; 0gxfz; 0cq8qq; 01s3vk; 0y_yw; ... >> query: (?x10049, ?x1245) <- award(?x10049, ?x1245), film(?x2173, ?x10049), nominated_for(?x198, ?x10049), costume_design_by(?x10049, ?x12364) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2836 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 139 *> proper extension: 0k4f3; 07w8fz; 014l6_; 0fy66; 063hp4; 027pfg; 0k4bc; 04165w; 0b85mm; 034hzj; *> query: (?x10049, 0gs9p) <- nominated_for(?x198, ?x10049), country(?x10049, ?x512), titles(?x162, ?x10049), ?x198 = 040njc *> conf = 0.61 ranks of expected_values: 3 EVAL 01fx4k nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 77.000 0.671 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7824-09c7b PRED entity: 09c7b PRED relation: category PRED expected values: 08mbj5d => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.79 #11, 0.77 #15, 0.77 #12) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 07xpm; 0hsb3; >> query: (?x14375, 08mbj5d) <- company(?x10512, ?x14375), influenced_by(?x10512, ?x236), student(?x6545, ?x10512) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09c7b category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.790 http://example.org/common/topic/webpage./common/webpage/category #7823-0f8j13 PRED entity: 0f8j13 PRED relation: genre PRED expected values: 07s9rl0 05p553 => 123 concepts (58 used for prediction) PRED predicted values (max 10 best out of 104): 05p553 (0.91 #2777, 0.91 #2547, 0.77 #1504), 01jfsb (0.87 #3361, 0.62 #931, 0.39 #2438), 0lsxr (0.75 #929, 0.33 #1738, 0.32 #3359), 07s9rl0 (0.62 #5660, 0.61 #2888, 0.60 #2428), 01hmnh (0.59 #6598, 0.32 #4752, 0.29 #3483), 02kdv5l (0.50 #348, 0.50 #3353, 0.49 #4738), 02l7c8 (0.43 #475, 0.36 #1168, 0.33 #130), 06n90 (0.33 #1049, 0.29 #702, 0.28 #4747), 06cvj (0.33 #119, 0.29 #464, 0.18 #1157), 0219x_ (0.33 #25, 0.25 #1409, 0.24 #1639) >> Best rule #2777 for best value: >> intensional similarity = 6 >> extensional distance = 101 >> proper extension: 02w86hz; 06823p; >> query: (?x9478, 05p553) <- film(?x858, ?x9478), genre(?x9478, ?x271), film_format(?x9478, ?x909), genre(?x4326, ?x271), ?x4326 = 0fz3b1, profession(?x858, ?x955) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0f8j13 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 58.000 0.913 http://example.org/film/film/genre EVAL 0f8j13 genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 58.000 0.913 http://example.org/film/film/genre #7822-02y_rq5 PRED entity: 02y_rq5 PRED relation: award_winner PRED expected values: 0n6f8 04r7p => 42 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1246): 09l3p (0.60 #942, 0.57 #5852, 0.40 #3397), 046zh (0.60 #1178, 0.57 #6088, 0.40 #3633), 05dbf (0.60 #461, 0.43 #5371, 0.40 #2916), 0dvld (0.44 #22095, 0.44 #20963, 0.41 #24551), 01p7yb (0.43 #4968, 0.40 #2513, 0.40 #58), 0n6f8 (0.43 #5162, 0.40 #252, 0.35 #31921), 0l6px (0.40 #2940, 0.40 #485, 0.38 #20125), 019f2f (0.40 #541, 0.36 #14732, 0.36 #12818), 01bj6y (0.40 #2271, 0.36 #14548, 0.35 #31921), 01vwllw (0.40 #691, 0.35 #31921, 0.33 #46654) >> Best rule #942 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 094qd5; >> query: (?x1716, 09l3p) <- award(?x5951, ?x1716), nominated_for(?x1716, ?x1803), ?x5951 = 0dvld, ?x1803 = 0g9wdmc >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #5162 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 02qyp19; *> query: (?x1716, 0n6f8) <- award(?x241, ?x1716), nominated_for(?x1716, ?x4067), nominated_for(?x1716, ?x1803), ?x4067 = 02d478, ?x1803 = 0g9wdmc *> conf = 0.43 ranks of expected_values: 6, 43 EVAL 02y_rq5 award_winner 04r7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 42.000 20.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02y_rq5 award_winner 0n6f8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 42.000 20.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #7821-01f8gz PRED entity: 01f8gz PRED relation: nominated_for! PRED expected values: 02404v => 89 concepts (39 used for prediction) PRED predicted values (max 10 best out of 670): 02404v (0.48 #32714, 0.47 #56080, 0.13 #3990), 03cp7b3 (0.38 #28041, 0.35 #4675, 0.22 #2338), 0146pg (0.22 #121, 0.11 #25825, 0.05 #53864), 01d1yr (0.13 #3736, 0.03 #8409, 0.01 #27102), 0534v (0.11 #1169, 0.11 #5844, 0.05 #12852), 06pj8 (0.11 #434, 0.06 #26138, 0.04 #12117), 0jlv5 (0.11 #1462, 0.05 #8473, 0.04 #6137), 02rgz4 (0.11 #96, 0.04 #4771, 0.02 #11779), 03q8ch (0.11 #904, 0.03 #26608, 0.03 #7915), 07h07 (0.11 #855, 0.03 #7866, 0.02 #12538) >> Best rule #32714 for best value: >> intensional similarity = 4 >> extensional distance = 155 >> proper extension: 07w8fz; 01qvz8; 0y_9q; >> query: (?x1625, ?x7740) <- nominated_for(?x7215, ?x1625), nominated_for(?x4169, ?x1625), cinematography(?x1625, ?x7740), film_crew_role(?x1625, ?x137) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f8gz nominated_for! 02404v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 39.000 0.478 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #7820-0y_yw PRED entity: 0y_yw PRED relation: currency PRED expected values: 09nqf => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.82 #22, 0.81 #127, 0.81 #1), 01nv4h (0.04 #51, 0.03 #198, 0.03 #100), 02l6h (0.01 #333, 0.01 #305, 0.01 #340), 02gsvk (0.01 #244) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 075cph; 05m_jsg; 0fsw_7; >> query: (?x6097, 09nqf) <- award(?x6097, ?x154), nominated_for(?x6213, ?x6097), nominated_for(?x1554, ?x6213), film(?x719, ?x6097) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0y_yw currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.819 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #7819-02q42j_ PRED entity: 02q42j_ PRED relation: gender PRED expected values: 05zppz => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #11, 0.87 #9, 0.87 #31), 02zsn (0.27 #126, 0.27 #70, 0.26 #58) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 04l3_z; 0jt90f5; 017l4; 03swmf; 02465; 08gf93; 05wm88; >> query: (?x5973, 05zppz) <- award(?x5973, ?x198), place_of_birth(?x5973, ?x1310), executive_produced_by(?x1209, ?x5973) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q42j_ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.877 http://example.org/people/person/gender #7818-034zc0 PRED entity: 034zc0 PRED relation: award_nominee PRED expected values: 02t_vx => 104 concepts (38 used for prediction) PRED predicted values (max 10 best out of 636): 0z4s (0.82 #4660, 0.81 #81526, 0.16 #88515), 015vq_ (0.82 #4660, 0.81 #81526, 0.16 #88515), 0410cp (0.82 #4660, 0.81 #81526, 0.16 #88515), 02ct_k (0.82 #4660, 0.81 #81526, 0.16 #88515), 042z_g (0.82 #4660, 0.81 #81526, 0.16 #88515), 017149 (0.82 #4660, 0.81 #81526, 0.07 #86185), 07r1h (0.16 #88515, 0.07 #86185, 0.06 #1428), 019pm_ (0.16 #88515, 0.07 #86185, 0.04 #42537), 034zc0 (0.16 #88515, 0.07 #86185, 0.03 #43284), 02t_vx (0.16 #88515, 0.07 #86185, 0.03 #43678) >> Best rule #4660 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 03_0p; >> query: (?x5806, ?x450) <- award_nominee(?x5806, ?x1677), people(?x4322, ?x5806), award_nominee(?x450, ?x5806) >> conf = 0.82 => this is the best rule for 6 predicted values *> Best rule #88515 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1277 *> proper extension: 05m63c; 033hqf; 01csrl; 02yplc; 021yzs; 029pnn; 019l3m; 06d6y; 02x08c; 06wvfq; ... *> query: (?x5806, ?x986) <- location(?x5806, ?x3125), film(?x5806, ?x6174), nominated_for(?x986, ?x6174) *> conf = 0.16 ranks of expected_values: 10 EVAL 034zc0 award_nominee 02t_vx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 104.000 38.000 0.819 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7817-016wzw PRED entity: 016wzw PRED relation: film_release_region! PRED expected values: 05p1tzf 087wc7n 053rxgm 017gm7 0bq8tmw 0407yfx 04f52jw 03mgx6z 04cppj 09gmmt6 0421v9q 03nsm5x => 149 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1233): 05p1tzf (0.88 #8386, 0.88 #2436, 0.83 #9576), 0by1wkq (0.88 #2581, 0.83 #7341, 0.77 #4961), 02vr3gz (0.88 #2788, 0.82 #8738, 0.77 #5168), 045j3w (0.88 #2702, 0.82 #8652, 0.73 #7462), 0cc5mcj (0.88 #2637, 0.80 #7397, 0.79 #8587), 0645k5 (0.88 #2687, 0.77 #5067, 0.77 #7447), 0ds35l9 (0.88 #2389, 0.73 #4769, 0.70 #8339), 053rxgm (0.85 #8450, 0.85 #4880, 0.80 #9640), 017gm7 (0.83 #2520, 0.82 #1327, 0.82 #8470), 0421v9q (0.83 #7918, 0.82 #9108, 0.79 #3158) >> Best rule #8386 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 03rt9; 06mzp; 015qh; >> query: (?x2843, 05p1tzf) <- taxonomy(?x2843, ?x939), film_release_region(?x2471, ?x2843), ?x2471 = 08052t3 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8, 9, 10, 11, 14, 15, 47, 48, 98, 99, 152 EVAL 016wzw film_release_region! 03nsm5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 0421v9q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 09gmmt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 04cppj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 03mgx6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 04f52jw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 0407yfx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 0bq8tmw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 017gm7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 053rxgm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 087wc7n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 016wzw film_release_region! 05p1tzf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7816-02grjf PRED entity: 02grjf PRED relation: registering_agency PRED expected values: 03z19 => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.85 #18, 0.85 #28, 0.84 #25) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 02g839; 03zw80; >> query: (?x11807, 03z19) <- contains(?x94, ?x11807), colors(?x11807, ?x332), currency(?x11807, ?x170), organization(?x346, ?x11807) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02grjf registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.853 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #7815-052smk PRED entity: 052smk PRED relation: parent_genre PRED expected values: 02r6mf => 69 concepts (43 used for prediction) PRED predicted values (max 10 best out of 184): 06by7 (0.60 #1006, 0.50 #676, 0.50 #180), 03lty (0.56 #842, 0.24 #3130, 0.22 #2982), 0ggq0m (0.47 #1493, 0.09 #3473, 0.04 #4961), 03mb9 (0.44 #1387, 0.05 #1552, 0.03 #3532), 05r6t (0.25 #715, 0.22 #878, 0.22 #3018), 0jrv_ (0.22 #932, 0.05 #3072, 0.05 #991), 03_d0 (0.22 #3472, 0.19 #2151, 0.19 #1327), 0133_p (0.20 #1087, 0.14 #1251, 0.12 #757), 07gxw (0.19 #1357, 0.11 #1522, 0.05 #4164), 05w3f (0.17 #190, 0.14 #355, 0.12 #686) >> Best rule #1006 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 0xhtw; 09jw2; >> query: (?x13686, 06by7) <- artists(?x13686, ?x10144), artists(?x13686, ?x4712), artists(?x13686, ?x4182), ?x4712 = 03f0fnk, influenced_by(?x8864, ?x4182), group(?x75, ?x4182), student(?x6127, ?x10144), instrumentalists(?x74, ?x10144) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1483 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 01vw77; *> query: (?x13686, ?x2808) <- parent_genre(?x13686, ?x497), artists(?x497, ?x7859), artists(?x497, ?x5751), ?x7859 = 03j1p2n, parent_genre(?x497, ?x2808), group(?x227, ?x5751) *> conf = 0.04 ranks of expected_values: 114 EVAL 052smk parent_genre 02r6mf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 69.000 43.000 0.600 http://example.org/music/genre/parent_genre #7814-07bcn PRED entity: 07bcn PRED relation: dog_breed PRED expected values: 01t032 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 1): 01t032 (0.87 #9, 0.85 #11, 0.78 #15) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0rh6k; 02_286; 030qb3t; 094jv; 0f2w0; 01_d4; 04f_d; 0dclg; 0f__1; 0ply0; ... >> query: (?x5893, 01t032) <- source(?x5893, ?x958), location(?x558, ?x5893), locations(?x6583, ?x5893), dog_breed(?x5893, ?x1706) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07bcn dog_breed 01t032 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.871 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #7813-01r47h PRED entity: 01r47h PRED relation: institution! PRED expected values: 016t_3 => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.75 #340, 0.74 #220, 0.73 #196), 019v9k (0.75 #202, 0.74 #178, 0.66 #226), 02_xgp2 (0.69 #182, 0.67 #206, 0.65 #350), 03bwzr4 (0.64 #208, 0.64 #184, 0.62 #232), 016t_3 (0.53 #197, 0.53 #221, 0.52 #341), 0bkj86 (0.52 #177, 0.48 #201, 0.47 #225), 07s6fsf (0.40 #338, 0.39 #194, 0.36 #170), 04zx3q1 (0.39 #219, 0.34 #339, 0.31 #195), 02m4yg (0.30 #2241, 0.29 #1285, 0.22 #17), 01ysy9 (0.30 #2241, 0.29 #1285, 0.05 #1703) >> Best rule #340 for best value: >> intensional similarity = 2 >> extensional distance = 136 >> proper extension: 03bwzr4; >> query: (?x11480, 02h4rq6) <- major_field_of_study(?x11480, ?x1668), ?x1668 = 01mkq >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #197 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 62 *> proper extension: 03np_7; *> query: (?x11480, 016t_3) <- contains(?x6895, ?x11480), currency(?x11480, ?x170), major_field_of_study(?x11480, ?x1668), ?x1668 = 01mkq, institution(?x1368, ?x11480) *> conf = 0.53 ranks of expected_values: 5 EVAL 01r47h institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 167.000 167.000 0.754 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #7812-043mk4y PRED entity: 043mk4y PRED relation: nominated_for! PRED expected values: 03jvmp => 79 concepts (32 used for prediction) PRED predicted values (max 10 best out of 609): 029m83 (0.81 #25686, 0.80 #30356, 0.80 #30355), 02ryx0 (0.45 #21014, 0.42 #23351), 015wnl (0.35 #42035, 0.34 #53710, 0.33 #46705), 03n52j (0.35 #42035, 0.34 #53710, 0.33 #46705), 05nzw6 (0.35 #42035, 0.34 #53710, 0.31 #46704), 0d05fv (0.35 #42035, 0.34 #53710, 0.31 #46704), 0bxtg (0.33 #83, 0.15 #74730, 0.13 #72394), 0415svh (0.33 #141, 0.15 #74730, 0.13 #72394), 01kb2j (0.33 #1133, 0.02 #22147, 0.02 #24484), 017149 (0.33 #92, 0.02 #21106, 0.02 #18771) >> Best rule #25686 for best value: >> intensional similarity = 4 >> extensional distance = 224 >> proper extension: 03kq98; 02d44q; 07k2mq; >> query: (?x7768, ?x2789) <- titles(?x53, ?x7768), ?x53 = 07s9rl0, award_winner(?x7768, ?x2789), award(?x7768, ?x4838) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #16797 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 183 *> proper extension: 0cwrr; 06mmr; *> query: (?x7768, 03jvmp) <- award_winner(?x7768, ?x2789), category(?x7768, ?x134), award_winner(?x4838, ?x2789), award_winner(?x5585, ?x2789) *> conf = 0.03 ranks of expected_values: 223 EVAL 043mk4y nominated_for! 03jvmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 79.000 32.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #7811-089g0h PRED entity: 089g0h PRED relation: film_crew_role! PRED expected values: 053rxgm 0416y94 08052t3 05c46y6 03q0r1 0243cq 05_5_22 02z2mr7 09hy79 06fpsx 087vnr5 => 32 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1647): 09sh8k (0.86 #16260, 0.78 #11610, 0.75 #10448), 05f4_n0 (0.82 #14424, 0.70 #13262, 0.67 #15587), 016dj8 (0.75 #15836, 0.75 #10029, 0.73 #14673), 09gmmt6 (0.75 #11219, 0.64 #17031, 0.60 #13541), 0dzlbx (0.75 #9878, 0.62 #11038, 0.60 #13360), 062zjtt (0.75 #9778, 0.60 #13260, 0.56 #12100), 01xvjb (0.73 #14932, 0.50 #16095, 0.50 #13770), 014kq6 (0.70 #13015, 0.67 #11855, 0.62 #9533), 04n52p6 (0.70 #12954, 0.64 #16444, 0.62 #10632), 06znpjr (0.70 #13685, 0.64 #14847, 0.62 #11363) >> Best rule #16260 for best value: >> intensional similarity = 16 >> extensional distance = 12 >> proper extension: 02zdwq; >> query: (?x5136, 09sh8k) <- film_crew_role(?x8089, ?x5136), film_crew_role(?x4047, ?x5136), film_crew_role(?x1644, ?x5136), award_winner(?x4047, ?x163), nominated_for(?x3209, ?x4047), nominated_for(?x704, ?x4047), award(?x4047, ?x289), ?x704 = 09sb52, genre(?x1644, ?x53), ?x3209 = 02w9sd7, film(?x510, ?x8089), film_release_region(?x4047, ?x1453), film_release_region(?x8137, ?x1453), film_release_region(?x1012, ?x1453), ?x8137 = 0gtx63s, ?x1012 = 0bwfwpj >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #16878 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 12 *> proper extension: 02zdwq; *> query: (?x5136, 05_5_22) <- film_crew_role(?x8089, ?x5136), film_crew_role(?x4047, ?x5136), film_crew_role(?x1644, ?x5136), award_winner(?x4047, ?x163), nominated_for(?x3209, ?x4047), nominated_for(?x704, ?x4047), award(?x4047, ?x289), ?x704 = 09sb52, genre(?x1644, ?x53), ?x3209 = 02w9sd7, film(?x510, ?x8089), film_release_region(?x4047, ?x1453), film_release_region(?x8137, ?x1453), film_release_region(?x1012, ?x1453), ?x8137 = 0gtx63s, ?x1012 = 0bwfwpj *> conf = 0.64 ranks of expected_values: 17, 27, 53, 92, 124, 133, 161, 409, 411, 619, 962 EVAL 089g0h film_crew_role! 087vnr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089g0h film_crew_role! 06fpsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089g0h film_crew_role! 09hy79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089g0h film_crew_role! 02z2mr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089g0h film_crew_role! 05_5_22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089g0h film_crew_role! 0243cq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089g0h film_crew_role! 03q0r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089g0h film_crew_role! 05c46y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089g0h film_crew_role! 08052t3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089g0h film_crew_role! 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089g0h film_crew_role! 053rxgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 32.000 15.000 0.857 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7810-02zft0 PRED entity: 02zft0 PRED relation: award_nominee! PRED expected values: 0146pg => 99 concepts (42 used for prediction) PRED predicted values (max 10 best out of 897): 01kv4mb (0.82 #27868, 0.81 #83605, 0.80 #83606), 020fgy (0.82 #27868, 0.81 #83605, 0.80 #83606), 0146pg (0.82 #27868, 0.81 #83605, 0.80 #83606), 02bh9 (0.29 #770, 0.25 #3092, 0.02 #7736), 02zft0 (0.27 #55735, 0.14 #1397, 0.14 #18578), 016jll (0.27 #55735, 0.05 #95218, 0.04 #97541), 0gs1_ (0.16 #62703, 0.14 #1493, 0.12 #3815), 030h95 (0.16 #62703, 0.14 #18578, 0.03 #9663), 01nvmd_ (0.16 #62703, 0.14 #18578), 016ks_ (0.16 #62703, 0.01 #73039) >> Best rule #27868 for best value: >> intensional similarity = 3 >> extensional distance = 794 >> proper extension: 04lgymt; 04rcr; 02r3zy; 011zf2; 0ggl02; 03g5jw; 05crg7; 0288fyj; 0dvqq; 03fbc; ... >> query: (?x6011, ?x669) <- award_nominee(?x6237, ?x6011), artists(?x671, ?x6237), award_nominee(?x6011, ?x669) >> conf = 0.82 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 02zft0 award_nominee! 0146pg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 42.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7809-01p95y0 PRED entity: 01p95y0 PRED relation: instrumentalists! PRED expected values: 013y1f 0gkd1 => 124 concepts (89 used for prediction) PRED predicted values (max 10 best out of 121): 0342h (0.79 #732, 0.78 #1215, 0.77 #812), 018vs (0.60 #656, 0.48 #2272, 0.47 #1786), 05r5c (0.56 #1452, 0.56 #1379, 0.55 #413), 0l14md (0.53 #2990, 0.44 #2989, 0.41 #2259), 04rzd (0.53 #2990, 0.44 #2989, 0.41 #2259), 01xqw (0.53 #2990, 0.44 #2989, 0.41 #2259), 01hww_ (0.53 #2990, 0.44 #2989, 0.41 #2259), 02hnl (0.38 #114, 0.36 #274, 0.27 #1242), 05842k (0.30 #2018, 0.30 #3883, 0.30 #5344), 026t6 (0.27 #245, 0.18 #2829, 0.17 #2263) >> Best rule #732 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 016qtt; 05cljf; 01kh2m1; 01t110; 020_4z; >> query: (?x10239, 0342h) <- profession(?x10239, ?x2659), people(?x6736, ?x10239), artists(?x378, ?x10239), ?x2659 = 039v1 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #271 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 023l9y; *> query: (?x10239, 013y1f) <- profession(?x10239, ?x131), artist(?x5891, ?x10239), role(?x10239, ?x615), ?x615 = 0dwsp, artists(?x378, ?x10239) *> conf = 0.18 ranks of expected_values: 15, 22 EVAL 01p95y0 instrumentalists! 0gkd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 124.000 89.000 0.786 http://example.org/music/instrument/instrumentalists EVAL 01p95y0 instrumentalists! 013y1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 124.000 89.000 0.786 http://example.org/music/instrument/instrumentalists #7808-02vr3gz PRED entity: 02vr3gz PRED relation: language PRED expected values: 06nm1 => 99 concepts (77 used for prediction) PRED predicted values (max 10 best out of 46): 02h40lc (0.90 #1499, 0.89 #2521, 0.89 #2280), 04306rv (0.21 #184, 0.14 #846, 0.12 #424), 064_8sq (0.19 #500, 0.18 #560, 0.16 #320), 06nm1 (0.15 #70, 0.14 #1031, 0.13 #190), 06b_j (0.12 #442, 0.11 #202, 0.09 #1043), 02bjrlw (0.10 #539, 0.10 #479, 0.09 #902), 03_9r (0.07 #248, 0.07 #10, 0.07 #1566), 0c_v2 (0.07 #1139), 071fb (0.06 #77, 0.05 #256, 0.03 #138), 0653m (0.06 #853, 0.05 #973, 0.05 #2047) >> Best rule #1499 for best value: >> intensional similarity = 7 >> extensional distance = 316 >> proper extension: 02sg5v; 02qrv7; 01_1hw; >> query: (?x3757, 02h40lc) <- genre(?x3757, ?x600), produced_by(?x3757, ?x12856), genre(?x11735, ?x600), genre(?x3614, ?x600), ?x11735 = 02x2jl_, ?x3614 = 0fy66, genre(?x2078, ?x600) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #70 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 32 *> proper extension: 0ds11z; 04fzfj; 06g77c; 07j94; 03t79f; 02p76f9; *> query: (?x3757, 06nm1) <- genre(?x3757, ?x571), produced_by(?x3757, ?x12856), nominated_for(?x533, ?x3757), country(?x3757, ?x2152), ?x571 = 03npn, film_release_region(?x66, ?x2152) *> conf = 0.15 ranks of expected_values: 4 EVAL 02vr3gz language 06nm1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 77.000 0.896 http://example.org/film/film/language #7807-02725hs PRED entity: 02725hs PRED relation: film! PRED expected values: 01vvb4m => 134 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1229): 09y20 (0.32 #14834, 0.06 #35673, 0.05 #8583), 0bl2g (0.29 #14640, 0.03 #47979, 0.03 #37562), 032xhg (0.25 #64, 0.20 #2148, 0.14 #8398), 0169dl (0.25 #402, 0.20 #2486, 0.12 #4570), 02t_st (0.25 #1289, 0.20 #3373, 0.10 #17958), 0151w_ (0.25 #164, 0.20 #2248, 0.10 #8498), 053xw6 (0.25 #1254, 0.20 #3338, 0.05 #9588), 025t9b (0.25 #668, 0.20 #2752, 0.05 #9002), 02j490 (0.25 #1823, 0.20 #3907, 0.05 #10157), 041ly3 (0.25 #113, 0.20 #2197, 0.05 #8447) >> Best rule #14834 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 03p2xc; >> query: (?x2289, 09y20) <- film(?x166, ?x2289), film(?x3780, ?x2289), produced_by(?x2289, ?x3568), film(?x3780, ?x9292), ?x9292 = 0h14ln >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #10941 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 19 *> proper extension: 05znbh7; *> query: (?x2289, 01vvb4m) <- currency(?x2289, ?x170), genre(?x2289, ?x8681), genre(?x2289, ?x162), ?x162 = 04xvlr, nominated_for(?x1007, ?x2289), student(?x8681, ?x1795) *> conf = 0.05 ranks of expected_values: 278 EVAL 02725hs film! 01vvb4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 134.000 27.000 0.321 http://example.org/film/actor/film./film/performance/film #7806-06x8y PRED entity: 06x8y PRED relation: languages_spoken! PRED expected values: 07hwkr => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 67): 07hwkr (0.71 #660, 0.63 #732, 0.62 #516), 02vsw1 (0.50 #190, 0.31 #550, 0.29 #694), 0d7wh (0.43 #232, 0.33 #304, 0.23 #448), 03w9bjf (0.30 #913, 0.18 #985, 0.17 #1489), 03bkbh (0.29 #245, 0.22 #317, 0.15 #461), 033tf_ (0.29 #223, 0.22 #295, 0.15 #439), 03lmx1 (0.29 #230, 0.22 #302, 0.15 #446), 0g96wd (0.29 #275, 0.22 #347, 0.15 #491), 04gfy7 (0.26 #924, 0.15 #1500, 0.12 #1716), 019lrz (0.24 #683, 0.21 #755, 0.12 #1043) >> Best rule #660 for best value: >> intensional similarity = 39 >> extensional distance = 15 >> proper extension: 01bkv; >> query: (?x14139, 07hwkr) <- countries_spoken_in(?x14139, ?x6435), film_release_region(?x9194, ?x6435), film_release_region(?x2340, ?x6435), film_release_region(?x1956, ?x6435), film_release_region(?x1228, ?x6435), film_release_region(?x1178, ?x6435), ?x2340 = 0fpv_3_, currency(?x6435, ?x170), ?x9194 = 0fpgp26, country(?x3015, ?x6435), country(?x2978, ?x6435), country(?x1121, ?x6435), ?x1178 = 053rxgm, ?x1121 = 0bynt, ?x1228 = 05z_kps, participating_countries(?x1931, ?x6435), contains(?x455, ?x6435), jurisdiction_of_office(?x182, ?x6435), administrative_area_type(?x6435, ?x2792), ?x2978 = 03_8r, olympics(?x8588, ?x1931), olympics(?x205, ?x1931), olympics(?x172, ?x1931), participating_countries(?x1931, ?x7479), participating_countries(?x1931, ?x2756), locations(?x1931, ?x206), ?x172 = 0154j, official_language(?x6435, ?x5814), sports(?x1931, ?x668), ?x3015 = 071t0, olympics(?x344, ?x1931), ?x7479 = 0165b, language(?x3088, ?x5814), ?x1956 = 05qbckf, olympics(?x150, ?x1931), ?x205 = 03rjj, ?x8588 = 0jhd, ?x2756 = 0hg5, ?x170 = 09nqf >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06x8y languages_spoken! 07hwkr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.706 http://example.org/people/ethnicity/languages_spoken #7805-049w1q PRED entity: 049w1q PRED relation: film_release_region PRED expected values: 0b90_r 0k6nt 059j2 01mjq => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 167): 0d060g (0.92 #161, 0.77 #315, 0.76 #934), 059j2 (0.89 #959, 0.88 #1269, 0.88 #186), 0345h (0.85 #1271, 0.84 #961, 0.79 #651), 03h64 (0.84 #992, 0.83 #1302, 0.80 #1456), 0k6nt (0.83 #797, 0.82 #1725, 0.82 #1416), 03spz (0.83 #250, 0.77 #404, 0.67 #1023), 0b90_r (0.83 #158, 0.72 #931, 0.70 #312), 03_3d (0.82 #623, 0.81 #778, 0.78 #1397), 06t2t (0.79 #214, 0.75 #368, 0.70 #987), 06bnz (0.73 #971, 0.73 #1281, 0.73 #352) >> Best rule #161 for best value: >> intensional similarity = 7 >> extensional distance = 22 >> proper extension: 087wc7n; 0dgst_d; 0fpv_3_; 03z9585; >> query: (?x10860, 0d060g) <- film_release_region(?x10860, ?x1536), film_release_region(?x10860, ?x1003), film_release_region(?x10860, ?x311), language(?x10860, ?x254), ?x311 = 0j1z8, ?x1003 = 03gj2, ?x1536 = 06c1y >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #959 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 146 *> proper extension: 0407yfx; *> query: (?x10860, 059j2) <- film(?x617, ?x10860), film_release_region(?x10860, ?x390), film_release_region(?x10860, ?x304), film_release_region(?x10860, ?x172), film_release_region(?x10860, ?x142), ?x390 = 0chghy, ?x304 = 0d0vqn, ?x172 = 0154j, ?x142 = 0jgd *> conf = 0.89 ranks of expected_values: 2, 5, 7, 23 EVAL 049w1q film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 98.000 98.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 049w1q film_release_region 059j2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 049w1q film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 049w1q film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 98.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7804-06k02 PRED entity: 06k02 PRED relation: performance_role PRED expected values: 02snj9 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 21): 03bx0bm (0.13 #277, 0.09 #321, 0.08 #757), 0l14md (0.06 #265, 0.05 #221, 0.05 #1008), 026t6 (0.05 #873, 0.05 #1005, 0.05 #1051), 013y1f (0.04 #1094, 0.04 #784, 0.04 #1093), 05r5c (0.04 #1094, 0.04 #784, 0.04 #1093), 01v1d8 (0.04 #1094, 0.04 #784, 0.04 #1093), 0l1589 (0.04 #1094, 0.04 #784, 0.04 #1093), 05148p4 (0.04 #1094, 0.04 #784, 0.04 #1093), 0dwsp (0.04 #1094, 0.04 #784, 0.04 #1093), 0680x0 (0.04 #784, 0.04 #1093, 0.04 #1047) >> Best rule #277 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 01vvydl; 07s3vqk; 0lbj1; 01vw87c; 01vrx3g; 01vrncs; 018y2s; 01vrz41; 01k5t_3; 0137n0; ... >> query: (?x2306, 03bx0bm) <- film(?x2306, ?x3157), artists(?x284, ?x2306), role(?x2306, ?x228) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #337 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 115 *> proper extension: 01yznp; 03qd_; 0bg539; 021bk; 01wk7b7; *> query: (?x2306, 02snj9) <- film(?x2306, ?x3157), instrumentalists(?x316, ?x2306), award(?x2306, ?x1079) *> conf = 0.02 ranks of expected_values: 16 EVAL 06k02 performance_role 02snj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 109.000 109.000 0.126 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #7803-01kkx2 PRED entity: 01kkx2 PRED relation: inductee! PRED expected values: 06szd3 => 142 concepts (102 used for prediction) PRED predicted values (max 10 best out of 5): 06szd3 (0.06 #245, 0.05 #146, 0.05 #101), 0qjfl (0.05 #30, 0.04 #84, 0.04 #39), 0g2c8 (0.03 #217, 0.03 #109, 0.03 #127), 04dm2n (0.03 #89, 0.02 #116, 0.02 #80), 04045y (0.02 #51) >> Best rule #245 for best value: >> intensional similarity = 3 >> extensional distance = 344 >> proper extension: 04l3_z; 0f1vrl; 0g51l1; 021lby; 0p3r8; 02dbp7; 01kws3; 05rx__; >> query: (?x12037, 06szd3) <- location(?x12037, ?x739), profession(?x12037, ?x1041), ?x1041 = 03gjzk >> conf = 0.06 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kkx2 inductee! 06szd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 102.000 0.058 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #7802-01hkhq PRED entity: 01hkhq PRED relation: award PRED expected values: 0bb57s => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 261): 099tbz (0.70 #29736, 0.70 #23864, 0.70 #24649), 02y_rq5 (0.70 #29736, 0.70 #23864, 0.70 #24649), 0bdwft (0.70 #29736, 0.70 #23864, 0.70 #24649), 05zvq6g (0.70 #29736, 0.70 #23864, 0.70 #24649), 02z0dfh (0.70 #29736, 0.70 #23864, 0.70 #24649), 02z1nbg (0.70 #29736, 0.70 #23864, 0.70 #24649), 02y_j8g (0.70 #29736, 0.70 #23864, 0.70 #24649), 0gqy2 (0.33 #155, 0.15 #28561, 0.13 #546), 07cbcy (0.33 #73, 0.15 #28561, 0.08 #464), 04ljl_l (0.33 #3, 0.15 #28561, 0.08 #394) >> Best rule #29736 for best value: >> intensional similarity = 2 >> extensional distance = 1585 >> proper extension: 06hzsx; 024y6w; >> query: (?x2493, ?x618) <- award_nominee(?x2493, ?x374), award_winner(?x618, ?x2493) >> conf = 0.70 => this is the best rule for 7 predicted values *> Best rule #28561 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1553 *> proper extension: 01jq34; *> query: (?x2493, ?x154) <- award_winner(?x2493, ?x7045), award(?x7045, ?x154) *> conf = 0.15 ranks of expected_values: 27 EVAL 01hkhq award 0bb57s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 107.000 107.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7801-033nzk PRED entity: 033nzk PRED relation: current_club PRED expected values: 03b04g 0544vh => 81 concepts (54 used for prediction) PRED predicted values (max 10 best out of 193): 01rly6 (0.50 #806, 0.33 #1514, 0.33 #241), 047fwlg (0.33 #758, 0.33 #193, 0.22 #1466), 03x6m (0.33 #354, 0.23 #2480, 0.22 #1485), 0y54 (0.33 #8, 0.22 #1423, 0.18 #2277), 075q_ (0.33 #4, 0.20 #429, 0.11 #1419), 045xx (0.33 #62, 0.17 #769, 0.14 #2331), 01gjlw (0.33 #29, 0.17 #736, 0.11 #1444), 01k2xy (0.33 #201, 0.17 #766, 0.11 #1474), 0j46b (0.33 #238, 0.17 #803, 0.11 #1511), 04mrhq (0.33 #278, 0.17 #843, 0.11 #1551) >> Best rule #806 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0cnk2q; 02s2lg; 03zrhb; >> query: (?x676, 01rly6) <- team(?x60, ?x676), current_club(?x676, ?x5686), position(?x676, ?x530), team(?x5685, ?x5686), ?x5685 = 0f1pyf, sport(?x676, ?x471), team(?x2201, ?x676) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #284 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 03_44z; *> query: (?x676, ?x2202) <- team(?x60, ?x676), current_club(?x676, ?x9860), current_club(?x676, ?x5686), position(?x676, ?x530), ?x5686 = 085v7, team(?x2201, ?x676), team(?x8860, ?x9860), team(?x2201, ?x2202) *> conf = 0.16 ranks of expected_values: 71 EVAL 033nzk current_club 0544vh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 54.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club EVAL 033nzk current_club 03b04g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 81.000 54.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #7800-035yn8 PRED entity: 035yn8 PRED relation: nominated_for! PRED expected values: 0k611 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 205): 094qd5 (0.57 #953, 0.22 #19789, 0.20 #19098), 0k611 (0.56 #526, 0.37 #2366, 0.31 #6507), 040njc (0.42 #467, 0.39 #927, 0.32 #2307), 02qyntr (0.40 #632, 0.29 #1092, 0.29 #402), 054krc (0.38 #522, 0.29 #2362, 0.23 #62), 02r22gf (0.38 #486, 0.22 #19789, 0.21 #1636), 02pqp12 (0.37 #975, 0.31 #515, 0.25 #2355), 09qwmm (0.37 #945, 0.22 #19789, 0.20 #19098), 0l8z1 (0.36 #509, 0.31 #2349, 0.26 #49), 0f4x7 (0.33 #944, 0.32 #2324, 0.25 #7615) >> Best rule #953 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 02rjv2w; >> query: (?x1744, 094qd5) <- award(?x1744, ?x500), featured_film_locations(?x1744, ?x108), nominated_for(?x1245, ?x1744), ?x1245 = 0gqwc >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #526 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 04v8x9; *> query: (?x1744, 0k611) <- award(?x1744, ?x500), currency(?x1744, ?x170), ?x500 = 0p9sw, film(?x2589, ?x1744) *> conf = 0.56 ranks of expected_values: 2 EVAL 035yn8 nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.569 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7799-01914 PRED entity: 01914 PRED relation: place_of_death! PRED expected values: 04xzm => 292 concepts (208 used for prediction) PRED predicted values (max 10 best out of 719): 04g_wd (0.14 #5848, 0.14 #5092, 0.11 #8113), 08959 (0.14 #4512, 0.10 #10553, 0.09 #11308), 0835q (0.14 #4434, 0.10 #10475, 0.09 #11230), 03_nq (0.14 #4238, 0.10 #10279, 0.09 #11034), 0c_jc (0.14 #4041, 0.10 #10082, 0.09 #10837), 0dq2k (0.14 #4022, 0.10 #10063, 0.09 #10818), 083pr (0.14 #3839, 0.10 #9880, 0.09 #10635), 0jf1b (0.14 #3797, 0.10 #9838, 0.09 #10593), 01_4z (0.14 #3823, 0.09 #10619, 0.08 #13640), 016ghw (0.14 #4518, 0.09 #11314, 0.08 #13580) >> Best rule #5848 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0h9vh; >> query: (?x206, 04g_wd) <- country(?x206, ?x2346), contains(?x206, ?x9409), ?x2346 = 0d05w3 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #81602 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 01q0l; *> query: (?x206, ?x754) <- capital(?x2346, ?x206), contains(?x6304, ?x2346), nationality(?x754, ?x2346) *> conf = 0.01 ranks of expected_values: 681 EVAL 01914 place_of_death! 04xzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 292.000 208.000 0.143 http://example.org/people/deceased_person/place_of_death #7798-0dtfn PRED entity: 0dtfn PRED relation: film_release_region PRED expected values: 0b90_r 03rjj 01pj7 04g5k 0165v => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 75): 03rjj (0.87 #754, 0.72 #5755, 0.67 #254), 0b90_r (0.71 #753, 0.60 #3, 0.55 #5754), 06bnz (0.67 #779, 0.56 #5780, 0.40 #29), 0d060g (0.64 #756, 0.63 #5757, 0.60 #6), 047yc (0.49 #768, 0.36 #5769, 0.20 #18), 06npd (0.44 #764, 0.18 #5765, 0.13 #1389), 05r7t (0.40 #81, 0.11 #12760, 0.11 #331), 0165v (0.40 #103, 0.11 #353, 0.11 #228), 01ppq (0.40 #99, 0.11 #349, 0.11 #224), 01pj7 (0.31 #783, 0.25 #5784, 0.20 #33) >> Best rule #754 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0fq27fp; 0bhwhj; >> query: (?x1386, 03rjj) <- film_release_region(?x1386, ?x1453), crewmember(?x1386, ?x1585), ?x1453 = 06qd3 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 8, 10, 27 EVAL 0dtfn film_release_region 0165v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 106.000 106.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dtfn film_release_region 04g5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 106.000 106.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dtfn film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 106.000 106.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dtfn film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dtfn film_release_region 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7797-02bb47 PRED entity: 02bb47 PRED relation: institution! PRED expected values: 016t_3 03bwzr4 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.84 #515, 0.84 #281, 0.83 #564), 019v9k (0.74 #288, 0.69 #571, 0.69 #522), 014mlp (0.70 #357, 0.68 #637, 0.66 #823), 016t_3 (0.68 #282, 0.66 #516, 0.60 #188), 03bwzr4 (0.68 #293, 0.62 #527, 0.60 #199), 027f2w (0.67 #33, 0.42 #289, 0.40 #195), 013zdg (0.55 #616, 0.41 #520, 0.37 #286), 04zx3q1 (0.53 #280, 0.50 #24, 0.47 #186), 0bkj86 (0.52 #360, 0.50 #31, 0.47 #287), 03mkk4 (0.27 #197, 0.26 #291, 0.26 #364) >> Best rule #515 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 02w2bc; 01jtp7; 07wrz; 0217m9; 01qd_r; 02nvg1; 02yxjs; 01rc6f; 0k__z; 026ssfj; ... >> query: (?x3212, 02h4rq6) <- major_field_of_study(?x3212, ?x3213), major_field_of_study(?x3212, ?x1154), ?x3213 = 0g4gr, major_field_of_study(?x5068, ?x1154), ?x5068 = 01h8rk >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #282 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 065y4w7; 07w0v; 027xx3; *> query: (?x3212, 016t_3) <- major_field_of_study(?x3212, ?x3213), major_field_of_study(?x3212, ?x1154), ?x3213 = 0g4gr, ?x1154 = 02lp1 *> conf = 0.68 ranks of expected_values: 4, 5 EVAL 02bb47 institution! 03bwzr4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 125.000 125.000 0.844 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02bb47 institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 125.000 125.000 0.844 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #7796-01x2_q PRED entity: 01x2_q PRED relation: place_of_birth PRED expected values: 0ptj2 => 70 concepts (10 used for prediction) PRED predicted values (max 10 best out of 97): 0fhzf (0.18 #1164), 0cr3d (0.12 #2210, 0.04 #2917, 0.02 #3626), 01_d4 (0.12 #1475, 0.04 #2182, 0.04 #2889), 0156q (0.10 #5003, 0.02 #4299), 0vp5f (0.09 #1272), 0r0ls (0.09 #1268), 01c1nm (0.09 #1122), 01k4f (0.09 #918), 0hptm (0.06 #1634, 0.04 #2341, 0.04 #3048), 0b_cr (0.06 #2055, 0.04 #2762, 0.04 #3469) >> Best rule #1164 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 051q39; >> query: (?x13210, 0fhzf) <- athlete(?x453, ?x13210), sports(?x12388, ?x453), country(?x453, ?x7430), ?x7430 = 01mk6, sports(?x418, ?x453), sports(?x12388, ?x520) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #4427 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 06y9c2; 0k4gf; 032v0v; 0j3v; 0136pk; 05whq_9; 0150t6; 0jcx; 04k15; 099bk; ... *> query: (?x13210, 0ptj2) <- gender(?x13210, ?x231), nationality(?x13210, ?x1264), ?x231 = 05zppz, ?x1264 = 0345h *> conf = 0.02 ranks of expected_values: 60 EVAL 01x2_q place_of_birth 0ptj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 70.000 10.000 0.182 http://example.org/people/person/place_of_birth #7795-0978r PRED entity: 0978r PRED relation: place_of_death! PRED expected values: 02m7r => 168 concepts (147 used for prediction) PRED predicted values (max 10 best out of 788): 09gnn (0.19 #9057, 0.11 #10571, 0.11 #9814), 047g6 (0.11 #4442, 0.10 #5199, 0.09 #5953), 06myp (0.11 #4348, 0.10 #5105, 0.09 #5859), 048cl (0.11 #4124, 0.10 #4881, 0.09 #5635), 0kn3g (0.11 #4275, 0.10 #5032, 0.09 #5786), 041xl (0.11 #4111, 0.10 #4868, 0.09 #5622), 073bb (0.11 #3833, 0.10 #4590, 0.09 #5344), 016ghw (0.11 #4512, 0.10 #5269, 0.09 #6023), 011zwl (0.11 #4495, 0.10 #5252, 0.09 #6006), 01b0k1 (0.11 #4464, 0.10 #5221, 0.09 #5975) >> Best rule #9057 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0s5cg; 0rhp6; 0r22d; 0t_07; 0kc40; 0sc6p; >> query: (?x3301, ?x10499) <- place_of_birth(?x10499, ?x3301), peers(?x10499, ?x4309), people(?x4322, ?x10499) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #61915 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 173 *> proper extension: 0f2wj; 02hrh0_; 015y2q; 07mgr; 01zst8; 019fv4; 018_7x; *> query: (?x3301, ?x2397) <- contains(?x3301, ?x11614), student(?x11614, ?x2397), citytown(?x1098, ?x3301) *> conf = 0.02 ranks of expected_values: 583 EVAL 0978r place_of_death! 02m7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 168.000 147.000 0.185 http://example.org/people/deceased_person/place_of_death #7794-01p5yn PRED entity: 01p5yn PRED relation: company! PRED expected values: 060c4 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 23): 09d6p2 (0.43 #114, 0.33 #208, 0.18 #396), 014l7h (0.33 #29, 0.29 #499, 0.28 #781), 060c4 (0.29 #473, 0.28 #755, 0.21 #3491), 0dq_5 (0.29 #65, 0.26 #3506, 0.22 #253), 0krdk (0.24 #3495, 0.20 #3024, 0.17 #3448), 02k13d (0.22 #766, 0.21 #484, 0.17 #14), 0dq3c (0.17 #2, 0.15 #3490, 0.15 #1082), 05_wyz (0.14 #113, 0.14 #66, 0.12 #3507), 01yc02 (0.14 #56, 0.13 #3497, 0.13 #3026), 04192r (0.14 #89, 0.11 #277, 0.10 #371) >> Best rule #114 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 018_q8; >> query: (?x3944, 09d6p2) <- award_winner(?x3486, ?x3944), ?x3486 = 0m7yy, award_winner(?x3944, ?x1762), industry(?x3944, ?x373) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #473 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0gsg7; 09d5h; 0cjdk; 01_8w2; 05gnf; 0146mv; 05s34b; *> query: (?x3944, 060c4) <- award_winner(?x3486, ?x3944), ?x3486 = 0m7yy, award_winner(?x3944, ?x1762), child(?x1908, ?x3944) *> conf = 0.29 ranks of expected_values: 3 EVAL 01p5yn company! 060c4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 105.000 0.429 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #7793-013y1f PRED entity: 013y1f PRED relation: group PRED expected values: 02mq_y => 86 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1047): 0dvqq (0.67 #1500, 0.62 #2828, 0.57 #1830), 02_5x9 (0.67 #1496, 0.57 #1826, 0.56 #3985), 06nv27 (0.67 #3687, 0.56 #4017, 0.50 #4347), 04qmr (0.67 #1347, 0.50 #688, 0.50 #522), 06br6t (0.67 #1433, 0.50 #774, 0.44 #4086), 047cx (0.60 #4340, 0.52 #6831, 0.50 #2682), 0163m1 (0.60 #856, 0.50 #691, 0.44 #4003), 02r1tx7 (0.60 #1008, 0.50 #513, 0.44 #3991), 03qkcn9 (0.56 #4127, 0.56 #3797, 0.50 #4457), 014pg1 (0.56 #4059, 0.53 #5716, 0.52 #6381) >> Best rule #1500 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 042v_gx; 01vj9c; >> query: (?x1495, 0dvqq) <- performance_role(?x1495, ?x212), role(?x4425, ?x1495), role(?x4311, ?x1495), role(?x2888, ?x1495), role(?x716, ?x1495), role(?x7549, ?x1495), ?x716 = 018vs, role(?x1495, ?x7869), ?x2888 = 02fsn, ?x4425 = 0979zs, role(?x1662, ?x7869), award_nominee(?x7549, ?x6027), ?x4311 = 01xqw, performance_role(?x1260, ?x1495) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1529 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 042v_gx; 01vj9c; *> query: (?x1495, 02mq_y) <- performance_role(?x1495, ?x212), role(?x4425, ?x1495), role(?x4311, ?x1495), role(?x2888, ?x1495), role(?x716, ?x1495), role(?x7549, ?x1495), ?x716 = 018vs, role(?x1495, ?x7869), ?x2888 = 02fsn, ?x4425 = 0979zs, role(?x1662, ?x7869), award_nominee(?x7549, ?x6027), ?x4311 = 01xqw, performance_role(?x1260, ?x1495) *> conf = 0.50 ranks of expected_values: 73 EVAL 013y1f group 02mq_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 86.000 46.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group #7792-02q52q PRED entity: 02q52q PRED relation: award PRED expected values: 0gqz2 => 109 concepts (104 used for prediction) PRED predicted values (max 10 best out of 202): 0k611 (0.45 #1009, 0.27 #2649, 0.24 #1946), 0gq_v (0.36 #955, 0.26 #4216, 0.25 #8432), 02qvyrt (0.36 #1032, 0.09 #1500, 0.08 #1969), 0gs9p (0.29 #2640, 0.21 #1937, 0.18 #1000), 0gq9h (0.27 #998, 0.26 #2638, 0.26 #4216), 0p9sw (0.18 #956, 0.17 #1893, 0.16 #2596), 0gr0m (0.18 #995, 0.16 #2635, 0.13 #8256), 0gr4k (0.18 #1196, 0.15 #2602, 0.15 #8223), 0gs96 (0.18 #1025, 0.15 #4070, 0.15 #1259), 0gqyl (0.18 #1016, 0.10 #4061, 0.09 #2656) >> Best rule #1009 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 06cgf; >> query: (?x1804, 0k611) <- titles(?x307, ?x1804), list(?x1804, ?x3004), category(?x1804, ?x134) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1235 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 048rn; 0jqb8; *> query: (?x1804, 0gqz2) <- costume_design_by(?x1804, ?x13187), film_art_direction_by(?x1804, ?x2449), genre(?x1804, ?x53) *> conf = 0.06 ranks of expected_values: 53 EVAL 02q52q award 0gqz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 109.000 104.000 0.455 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #7791-0dyb1 PRED entity: 0dyb1 PRED relation: film_release_region PRED expected values: 0d0vqn 01ls2 07twz => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 131): 09c7w0 (0.95 #2149, 0.94 #8591, 0.94 #4131), 0d0vqn (0.92 #1824, 0.92 #1329, 0.90 #2981), 0f8l9c (0.90 #2997, 0.88 #2336, 0.88 #1840), 05r4w (0.88 #2974, 0.86 #1322, 0.85 #2644), 07ssc (0.87 #679, 0.80 #1834, 0.80 #2661), 059j2 (0.86 #1357, 0.86 #3009, 0.86 #1852), 03_3d (0.86 #1823, 0.81 #2319, 0.80 #1328), 03gj2 (0.85 #1349, 0.82 #3001, 0.81 #1844), 03rjj (0.85 #2648, 0.84 #2978, 0.82 #1821), 02vzc (0.84 #1875, 0.81 #2371, 0.76 #3032) >> Best rule #2149 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 0m313; 016fyc; 034qrh; 03s6l2; 0pc62; 0fgpvf; 0jzw; 0164qt; 06_wqk4; 0p9lw; ... >> query: (?x3053, 09c7w0) <- currency(?x3053, ?x170), film_release_region(?x3053, ?x142), nominated_for(?x3053, ?x1259) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #1824 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 124 *> proper extension: 0h1cdwq; 0fq27fp; 0jjy0; 03bx2lk; 03twd6; 0gd0c7x; 08052t3; 07f_7h; 047p7fr; 045j3w; ... *> query: (?x3053, 0d0vqn) <- currency(?x3053, ?x170), film_release_region(?x3053, ?x1453), ?x1453 = 06qd3 *> conf = 0.92 ranks of expected_values: 2, 28, 44 EVAL 0dyb1 film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 96.000 96.000 0.948 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dyb1 film_release_region 01ls2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 96.000 96.000 0.948 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dyb1 film_release_region 0d0vqn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.948 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7790-01cbt3 PRED entity: 01cbt3 PRED relation: award_winner! PRED expected values: 03tn9w 073hd1 09306z => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 135): 073hd1 (0.60 #99, 0.01 #1350, 0.01 #6215), 0bzm__ (0.33 #226, 0.05 #1338, 0.03 #3006), 0bzjgq (0.22 #256, 0.05 #673, 0.04 #812), 09n4nb (0.20 #47, 0.10 #1298, 0.08 #464), 0hn821n (0.20 #129, 0.05 #407, 0.02 #5272), 0gkxgfq (0.20 #106, 0.02 #6639, 0.02 #7056), 0d__c3 (0.16 #8480, 0.11 #262, 0.05 #1235), 0c53zb (0.16 #8480, 0.11 #199, 0.03 #755), 0ftlkg (0.16 #8480, 0.11 #164, 0.03 #998), 0fzrhn (0.16 #8480, 0.11 #275, 0.01 #831) >> Best rule #99 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 027t8fw; >> query: (?x5251, 073hd1) <- award_winner(?x5304, ?x5251), award(?x5251, ?x1232), ?x5304 = 0y_9q >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11, 134 EVAL 01cbt3 award_winner! 09306z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 130.000 130.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01cbt3 award_winner! 073hd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01cbt3 award_winner! 03tn9w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 130.000 130.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7789-012lzr PRED entity: 012lzr PRED relation: citytown PRED expected values: 06y57 => 177 concepts (121 used for prediction) PRED predicted values (max 10 best out of 161): 06y57 (0.31 #22118, 0.05 #18794, 0.05 #33551), 0chghy (0.26 #7739, 0.25 #9581, 0.25 #7002), 02_286 (0.21 #21762, 0.21 #22502, 0.17 #1487), 0978r (0.19 #3390, 0.14 #5970, 0.13 #3758), 01b8jj (0.17 #1380, 0.07 #17316, 0.07 #20640), 05l5n (0.14 #5932, 0.13 #3720, 0.12 #8145), 04jpl (0.11 #7377, 0.09 #9219, 0.09 #6640), 052p7 (0.06 #2256, 0.06 #2624, 0.04 #2993), 030qb3t (0.06 #21775, 0.05 #7767, 0.04 #30261), 0fpzwf (0.06 #2336, 0.04 #3073, 0.04 #3442) >> Best rule #22118 for best value: >> intensional similarity = 5 >> extensional distance = 334 >> proper extension: 025504; >> query: (?x9181, ?x5036) <- state_province_region(?x9181, ?x8506), contains(?x8506, ?x8507), contains(?x8506, ?x5036), category(?x8507, ?x134), film_release_region(?x1035, ?x5036) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012lzr citytown 06y57 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 121.000 0.306 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #7788-02k21g PRED entity: 02k21g PRED relation: people! PRED expected values: 041rx => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 44): 065b6q (0.25 #3, 0.04 #1751, 0.04 #1827), 07bch9 (0.22 #174, 0.20 #98, 0.15 #326), 041rx (0.20 #80, 0.19 #3045, 0.17 #916), 033tf_ (0.20 #83, 0.18 #387, 0.17 #691), 09vc4s (0.20 #85, 0.14 #389, 0.11 #161), 01qhm_ (0.18 #386, 0.11 #766, 0.10 #690), 0xnvg (0.14 #392, 0.13 #468, 0.13 #1608), 013xrm (0.11 #171, 0.10 #247, 0.08 #323), 019kn7 (0.11 #197, 0.10 #273, 0.08 #349), 038723 (0.11 #220, 0.08 #372, 0.03 #600) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0gthm; >> query: (?x4490, 065b6q) <- film(?x4490, ?x1444), ?x1444 = 0bscw, religion(?x4490, ?x11490) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #80 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 07ymr5; 08vr94; 086nl7; *> query: (?x4490, 041rx) <- cast_members(?x5620, ?x4490), ?x5620 = 05drr9, award_winner(?x5446, ?x4490) *> conf = 0.20 ranks of expected_values: 3 EVAL 02k21g people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.250 http://example.org/people/ethnicity/people #7787-026k4d PRED entity: 026k4d PRED relation: artist PRED expected values: 0244r8 => 56 concepts (36 used for prediction) PRED predicted values (max 10 best out of 968): 0bk1p (0.70 #3193, 0.64 #4037, 0.54 #5726), 013rfk (0.67 #1421, 0.33 #5060, 0.31 #5059), 016lmg (0.50 #605, 0.44 #1445, 0.33 #5060), 07c0j (0.46 #5115, 0.33 #8498, 0.30 #2582), 03xhj6 (0.44 #1149, 0.33 #5060, 0.31 #5059), 048xh (0.41 #16583, 0.30 #26740, 0.30 #3067), 01323p (0.40 #3088, 0.36 #3932, 0.31 #5621), 0p76z (0.40 #3251, 0.36 #4095, 0.31 #5784), 0kr_t (0.40 #2922, 0.36 #3766, 0.31 #5455), 03h_fqv (0.40 #2069, 0.12 #18122, 0.10 #2913) >> Best rule #3193 for best value: >> intensional similarity = 13 >> extensional distance = 8 >> proper extension: 011k1h; 017l96; 0mzkr; 02p3cr5; 0k_kr; 01dtcb; >> query: (?x10123, 0bk1p) <- artist(?x10123, ?x4942), artist(?x8721, ?x4942), influenced_by(?x1573, ?x4942), group(?x645, ?x4942), ?x645 = 028tv0, influenced_by(?x4942, ?x10502), influenced_by(?x4942, ?x8149), ?x8721 = 01cf93, artists(?x1000, ?x8149), award(?x10502, ?x1389), profession(?x8149, ?x220), artist(?x6672, ?x10502), ?x220 = 016z4k >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026k4d artist 0244r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 36.000 0.700 http://example.org/music/record_label/artist #7786-05q9g1 PRED entity: 05q9g1 PRED relation: award_winner! PRED expected values: 05pd94v => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 134): 05pd94v (0.26 #3532, 0.25 #3815, 0.20 #991), 04n2r9h (0.26 #3532, 0.25 #3815, 0.20 #991), 0418154 (0.26 #3532, 0.25 #3815, 0.20 #991), 04110lv (0.26 #3532, 0.25 #3815, 0.20 #991), 09gkdln (0.20 #263, 0.04 #3512, 0.04 #547), 013b2h (0.18 #788, 0.17 #929, 0.16 #1494), 01bx35 (0.17 #291, 0.11 #1562, 0.11 #1844), 01mh_q (0.16 #373, 0.08 #1362, 0.08 #1785), 02rjjll (0.15 #289, 0.11 #1842, 0.11 #1983), 019bk0 (0.15 #300, 0.10 #1430, 0.10 #1571) >> Best rule #3532 for best value: >> intensional similarity = 4 >> extensional distance = 549 >> proper extension: 0f721s; 0gsg7; 0g5lhl7; 01w92; 01p5yn; 0hm0k; 05s34b; >> query: (?x10076, ?x139) <- award_winner(?x3890, ?x10076), award_winner(?x139, ?x3890), nominated_for(?x3890, ?x4007), category(?x3890, ?x134) >> conf = 0.26 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 05q9g1 award_winner! 05pd94v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.257 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7785-01zh29 PRED entity: 01zh29 PRED relation: nationality PRED expected values: 03rk0 => 122 concepts (120 used for prediction) PRED predicted values (max 10 best out of 75): 09c7w0 (0.82 #1201, 0.79 #1701, 0.78 #1601), 03rk0 (0.40 #446, 0.35 #8619, 0.30 #346), 02jx1 (0.11 #2134, 0.11 #4236, 0.11 #2434), 07ssc (0.10 #3617, 0.10 #2416, 0.10 #4118), 0d060g (0.10 #207, 0.09 #707, 0.06 #507), 0345h (0.10 #231, 0.05 #1331, 0.02 #4836), 0chghy (0.10 #210, 0.03 #1010, 0.03 #4113), 03rjj (0.10 #205, 0.03 #305, 0.02 #4810), 01xbgx (0.10 #281, 0.02 #881, 0.02 #781), 03shp (0.06 #456) >> Best rule #1201 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 01z7_f; >> query: (?x8073, 09c7w0) <- award(?x8073, ?x1937), student(?x1368, ?x8073), ?x1368 = 014mlp >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #446 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 09r1j5; *> query: (?x8073, 03rk0) <- gender(?x8073, ?x231), religion(?x8073, ?x492), ?x492 = 0flw86 *> conf = 0.40 ranks of expected_values: 2 EVAL 01zh29 nationality 03rk0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 120.000 0.821 http://example.org/people/person/nationality #7784-03mbdx_ PRED entity: 03mbdx_ PRED relation: organization! PRED expected values: 0mgkg 0hmyfsv => 163 concepts (108 used for prediction) PRED predicted values (max 10 best out of 525): 07ssc (0.57 #3500, 0.57 #1605, 0.50 #973), 0345h (0.57 #1631, 0.50 #3526, 0.36 #2580), 02vzc (0.57 #3551, 0.45 #2605, 0.43 #1656), 0k6nt (0.57 #3515, 0.45 #2569, 0.43 #1620), 0d0vqn (0.57 #3488, 0.45 #2542, 0.43 #1593), 03rjj (0.57 #3484, 0.45 #2538, 0.43 #1589), 059j2 (0.57 #3524, 0.45 #2578, 0.43 #1629), 09c7w0 (0.57 #1582, 0.43 #3477, 0.36 #2531), 03rt9 (0.50 #3497, 0.50 #970, 0.45 #2551), 0h7x (0.50 #3530, 0.45 #2584, 0.43 #1635) >> Best rule #3500 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01rz1; 0b6css; 018cqq; >> query: (?x14299, 07ssc) <- organization(?x5072, ?x14299), company(?x4792, ?x5072), company(?x4792, ?x11636), ?x11636 = 03s7h >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2844 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 02jxk; 04k4l; 0gkjy; 041288; *> query: (?x14299, ?x502) <- citytown(?x14299, ?x739), organization(?x5072, ?x14299), company(?x4792, ?x5072), company(?x4792, ?x10699), company(?x4792, ?x502), ?x10699 = 0206k5 *> conf = 0.03 ranks of expected_values: 349 EVAL 03mbdx_ organization! 0hmyfsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 163.000 108.000 0.571 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 03mbdx_ organization! 0mgkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 163.000 108.000 0.571 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #7783-0rw2x PRED entity: 0rw2x PRED relation: source PRED expected values: 0jbk9 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #19, 0.83 #7, 0.81 #10) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x13295, 0jbk9) <- category(?x13295, ?x134), ?x134 = 08mbj5d, place(?x13295, ?x13295) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rw2x source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #7782-05tjm3 PRED entity: 05tjm3 PRED relation: place_of_birth PRED expected values: 01_d4 => 71 concepts (63 used for prediction) PRED predicted values (max 10 best out of 47): 02_286 (0.11 #3541, 0.11 #4247, 0.11 #7069), 0r3w7 (0.08 #705, 0.07 #1410, 0.06 #16214), 01_d4 (0.06 #3588, 0.05 #6410, 0.05 #5705), 030qb3t (0.05 #5693, 0.04 #31771, 0.04 #35294), 0cr3d (0.04 #3616, 0.04 #2208, 0.03 #25471), 0chrx (0.03 #16215, 0.01 #15509), 013gxt (0.03 #16215, 0.01 #15509), 0135p7 (0.03 #16215), 0dclg (0.03 #78, 0.03 #783, 0.02 #5717), 05qtj (0.03 #167, 0.02 #1577, 0.02 #872) >> Best rule #3541 for best value: >> intensional similarity = 4 >> extensional distance = 341 >> proper extension: 05218gr; >> query: (?x12534, 02_286) <- place_of_death(?x12534, ?x13207), nationality(?x12534, ?x94), award(?x12534, ?x1313), ?x94 = 09c7w0 >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #3588 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 341 *> proper extension: 05218gr; *> query: (?x12534, 01_d4) <- place_of_death(?x12534, ?x13207), nationality(?x12534, ?x94), award(?x12534, ?x1313), ?x94 = 09c7w0 *> conf = 0.06 ranks of expected_values: 3 EVAL 05tjm3 place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 63.000 0.111 http://example.org/people/person/place_of_birth #7781-04mcw4 PRED entity: 04mcw4 PRED relation: film! PRED expected values: 06pj8 => 123 concepts (91 used for prediction) PRED predicted values (max 10 best out of 162): 030_3z (0.57 #4961, 0.28 #8552, 0.24 #8000), 0343h (0.57 #4961, 0.24 #3582, 0.20 #4684), 02q_cc (0.28 #8552, 0.24 #8000, 0.23 #276), 03q8ch (0.17 #1106, 0.14 #3030), 05qd_ (0.17 #4960, 0.14 #3858, 0.13 #12404), 0kx4m (0.17 #4960, 0.14 #3858, 0.13 #12404), 06pj8 (0.13 #2803, 0.09 #877, 0.07 #2527), 02kxbwx (0.08 #850, 0.04 #2776, 0.02 #3879), 02kxbx3 (0.06 #916, 0.05 #2842, 0.04 #87), 04sry (0.06 #997, 0.05 #2923, 0.03 #5960) >> Best rule #4961 for best value: >> intensional similarity = 4 >> extensional distance = 250 >> proper extension: 0kfpm; 0sxfd; 02qkq0; 01gvts; 05sy0cv; 03_8kz; >> query: (?x4551, ?x1387) <- award_winner(?x4551, ?x1387), award(?x4551, ?x507), award_nominee(?x574, ?x1387), film(?x1387, ?x1812) >> conf = 0.57 => this is the best rule for 2 predicted values *> Best rule #2803 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 117 *> proper extension: 0147sh; 0prrm; *> query: (?x4551, 06pj8) <- production_companies(?x4551, ?x847), language(?x4551, ?x254), edited_by(?x4551, ?x4215) *> conf = 0.13 ranks of expected_values: 7 EVAL 04mcw4 film! 06pj8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 123.000 91.000 0.570 http://example.org/film/director/film #7780-01tgwv PRED entity: 01tgwv PRED relation: award! PRED expected values: 0gd_s => 47 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2280): 07w21 (0.80 #23695, 0.71 #10209, 0.67 #20322), 01k56k (0.75 #16762, 0.71 #13391, 0.70 #26877), 04r68 (0.75 #18336, 0.55 #28451, 0.50 #25080), 09dt7 (0.71 #10424, 0.70 #23910, 0.67 #7053), 0gd_s (0.70 #26259, 0.67 #22886, 0.67 #9402), 05x8n (0.67 #8684, 0.62 #15426, 0.60 #25541), 01g6bk (0.67 #9939, 0.62 #20052, 0.57 #13310), 0fpzt5 (0.67 #9301, 0.60 #26158, 0.57 #12672), 04mhl (0.67 #8005, 0.60 #24862, 0.57 #11376), 0mfc0 (0.60 #26301, 0.57 #12815, 0.50 #16186) >> Best rule #23695 for best value: >> intensional similarity = 9 >> extensional distance = 8 >> proper extension: 02662b; 02664f; 045xh; >> query: (?x11263, 07w21) <- award(?x10438, ?x11263), award(?x5087, ?x11263), award(?x1752, ?x11263), ?x1752 = 01dzz7, disciplines_or_subjects(?x11263, ?x5864), company(?x10438, ?x4257), ?x5087 = 018fq, location(?x10438, ?x4316), award_winner(?x575, ?x10438) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #26259 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 8 *> proper extension: 02662b; 02664f; 045xh; *> query: (?x11263, 0gd_s) <- award(?x10438, ?x11263), award(?x5087, ?x11263), award(?x1752, ?x11263), ?x1752 = 01dzz7, disciplines_or_subjects(?x11263, ?x5864), company(?x10438, ?x4257), ?x5087 = 018fq, location(?x10438, ?x4316), award_winner(?x575, ?x10438) *> conf = 0.70 ranks of expected_values: 5 EVAL 01tgwv award! 0gd_s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 47.000 27.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7779-0nh57 PRED entity: 0nh57 PRED relation: contains! PRED expected values: 04ykg => 153 concepts (72 used for prediction) PRED predicted values (max 10 best out of 130): 04ykg (0.62 #2694, 0.59 #3593, 0.51 #5389), 09c7w0 (0.52 #49408, 0.51 #13472, 0.46 #50312), 04_1l0v (0.45 #4045, 0.44 #6739, 0.38 #10332), 0nh57 (0.35 #54796, 0.26 #62884, 0.23 #41321), 01n7q (0.24 #13551, 0.22 #7264, 0.21 #9061), 07b_l (0.21 #3816, 0.18 #50531, 0.17 #11001), 0f8l9c (0.16 #4538, 0.05 #2742, 0.04 #34182), 03v0t (0.15 #11012, 0.11 #10114, 0.10 #22693), 07ssc (0.14 #8116, 0.13 #35965, 0.12 #16200), 03s0w (0.13 #5448, 0.07 #50367, 0.07 #10837) >> Best rule #2694 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0123_x; >> query: (?x10566, ?x1274) <- contains(?x10566, ?x7328), second_level_divisions(?x94, ?x10566), jurisdiction_of_office(?x1195, ?x7328), capital(?x1274, ?x7328) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nh57 contains! 04ykg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 72.000 0.625 http://example.org/location/location/contains #7778-02w7fs PRED entity: 02w7fs PRED relation: ceremony PRED expected values: 01mhwk => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 125): 01mhwk (0.90 #284, 0.84 #409, 0.76 #534), 0gx1673 (0.50 #607, 0.45 #357, 0.42 #732), 092868 (0.42 #1754, 0.41 #1253, 0.31 #252), 08pc1x (0.42 #1754, 0.41 #1253, 0.31 #252), 0bvfqq (0.42 #1754, 0.41 #1253, 0.31 #252), 0bvhz9 (0.42 #1754, 0.41 #1253, 0.31 #252), 0275n3y (0.42 #1754, 0.41 #1253, 0.31 #252), 05c1t6z (0.19 #1514, 0.18 #1138, 0.18 #1264), 02q690_ (0.18 #1182, 0.17 #1308, 0.17 #1558), 0gvstc3 (0.17 #1153, 0.17 #1279, 0.17 #1529) >> Best rule #284 for best value: >> intensional similarity = 6 >> extensional distance = 56 >> proper extension: 02gx2k; 025m8l; 025mb9; 0248jb; 02v703; 02fm4d; 024_dt; >> query: (?x11010, 01mhwk) <- award(?x883, ?x11010), ceremony(?x11010, ?x6869), ceremony(?x11010, ?x725), ?x6869 = 01xqqp, award_winner(?x11010, ?x3200), ?x725 = 01bx35 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02w7fs ceremony 01mhwk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.897 http://example.org/award/award_category/winners./award/award_honor/ceremony #7777-05xvj PRED entity: 05xvj PRED relation: draft PRED expected values: 047dpm0 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 15): 02x2khw (0.89 #221, 0.81 #156, 0.78 #609), 047dpm0 (0.81 #156, 0.79 #232, 0.79 #344), 02pq_x5 (0.81 #156, 0.78 #609, 0.75 #16), 0g3zpp (0.64 #471, 0.58 #205, 0.43 #440), 092j54 (0.63 #210, 0.61 #476, 0.49 #445), 03nt7j (0.63 #209, 0.50 #475, 0.49 #444), 09l0x9 (0.61 #478, 0.58 #212, 0.46 #447), 05vsb7 (0.61 #470, 0.53 #204, 0.40 #439), 0f4vx0 (0.43 #158, 0.43 #157, 0.43 #63), 038c0q (0.43 #158, 0.43 #157, 0.43 #63) >> Best rule #221 for best value: >> intensional similarity = 16 >> extensional distance = 17 >> proper extension: 051wf; >> query: (?x12042, 02x2khw) <- school(?x12042, ?x8202), school(?x12042, ?x6925), season(?x12042, ?x701), major_field_of_study(?x6925, ?x8925), major_field_of_study(?x6925, ?x1695), major_field_of_study(?x6925, ?x1682), draft(?x12042, ?x1633), ?x8925 = 01zc2w, school(?x1161, ?x8202), institution(?x865, ?x8202), institution(?x1390, ?x6925), citytown(?x8202, ?x4733), ?x1682 = 02ky346, ?x1695 = 06ms6, ?x1390 = 0bjrnt, student(?x6925, ?x981) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #156 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 13 *> proper extension: 05tfm; 05tg3; 02c_4; *> query: (?x12042, ?x1633) <- team(?x2010, ?x12042), school(?x12042, ?x10666), school(?x12042, ?x4556), school(?x12042, ?x735), organization(?x346, ?x10666), institution(?x1771, ?x10666), ?x1771 = 019v9k, ?x735 = 065y4w7, school(?x10600, ?x10666), team(?x2010, ?x11919), team(?x2010, ?x1823), ?x10600 = 04f4z1k, draft(?x1823, ?x1633), state_province_region(?x10666, ?x4622), colors(?x11919, ?x332), school(?x1823, ?x546), major_field_of_study(?x4556, ?x888) *> conf = 0.81 ranks of expected_values: 2 EVAL 05xvj draft 047dpm0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 62.000 62.000 0.895 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #7776-02rx2m5 PRED entity: 02rx2m5 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.84 #85, 0.82 #26, 0.81 #298), 07c52 (0.21 #372, 0.04 #144, 0.03 #211), 02nxhr (0.21 #372, 0.04 #163, 0.04 #7), 07z4p (0.21 #372, 0.04 #146, 0.03 #62), 0735l (0.21 #372) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 543 >> proper extension: 03lrqw; 0p3_y; 02rjv2w; 0c34mt; 032zq6; 059lwy; 0bl3nn; 0h14ln; 0m5s5; 023cjg; >> query: (?x1866, 029j_) <- nominated_for(?x1162, ?x1866), genre(?x1866, ?x53), film(?x1865, ?x1866), featured_film_locations(?x1866, ?x5777) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rx2m5 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.844 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #7775-070yzk PRED entity: 070yzk PRED relation: student! PRED expected values: 014mlp => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 14): 014mlp (0.36 #126, 0.34 #406, 0.33 #166), 019v9k (0.09 #130, 0.07 #410, 0.04 #170), 0bkj86 (0.08 #409, 0.07 #129, 0.04 #169), 03mkk4 (0.07 #173, 0.06 #133, 0.06 #413), 016t_3 (0.06 #404, 0.06 #124, 0.06 #164), 04zx3q1 (0.06 #122, 0.04 #402), 028dcg (0.06 #178, 0.05 #418, 0.03 #678), 02h4rq6 (0.05 #63, 0.04 #403, 0.04 #123), 02_xgp2 (0.04 #134, 0.03 #414), 013zdg (0.02 #128, 0.01 #408) >> Best rule #126 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 017yfz; >> query: (?x8544, 014mlp) <- place_of_birth(?x8544, ?x108), student(?x4268, ?x8544), people(?x5606, ?x8544) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 070yzk student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.358 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #7774-031786 PRED entity: 031786 PRED relation: story_by PRED expected values: 042xh => 75 concepts (28 used for prediction) PRED predicted values (max 10 best out of 64): 0343h (0.15 #235, 0.06 #1104, 0.04 #1537), 042xh (0.11 #215, 0.10 #432, 0.03 #1734), 0fx02 (0.06 #713, 0.06 #1797, 0.05 #2233), 056wb (0.04 #759, 0.01 #3590), 01y8d4 (0.03 #1006, 0.02 #3183, 0.02 #3401), 02nygk (0.03 #1080, 0.02 #3695, 0.01 #4565), 0l99s (0.03 #562), 041h0 (0.03 #441), 079vf (0.03 #1305, 0.03 #1739, 0.02 #655), 011s9r (0.03 #1067, 0.02 #3244, 0.02 #3462) >> Best rule #235 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 07kb7vh; 0372j5; >> query: (?x7305, 0343h) <- film(?x5699, ?x7305), nominated_for(?x7305, ?x4235), award_winner(?x5699, ?x989), film_distribution_medium(?x4235, ?x627) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #215 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 09v9mks; *> query: (?x7305, 042xh) <- film(?x3860, ?x7305), genre(?x7305, ?x600), ?x3860 = 062dn7 *> conf = 0.11 ranks of expected_values: 2 EVAL 031786 story_by 042xh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 28.000 0.146 http://example.org/film/film/story_by #7773-065dc4 PRED entity: 065dc4 PRED relation: production_companies PRED expected values: 08wjc1 04rtpt => 105 concepts (95 used for prediction) PRED predicted values (max 10 best out of 93): 04rtpt (0.58 #418, 0.33 #49, 0.10 #383), 017s11 (0.34 #3427, 0.32 #5350, 0.31 #3092), 06jntd (0.34 #3427, 0.32 #5350, 0.31 #3092), 016tw3 (0.22 #12, 0.12 #598, 0.11 #513), 08wjc1 (0.22 #28, 0.03 #362, 0.03 #1114), 05qd_ (0.17 #428, 0.13 #93, 0.11 #845), 05h4t7 (0.16 #347, 0.04 #1265, 0.04 #1432), 086k8 (0.16 #85, 0.12 #420, 0.12 #170), 05rrtf (0.13 #392, 0.04 #1310, 0.03 #1560), 054lpb6 (0.12 #684, 0.10 #349, 0.09 #183) >> Best rule #418 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 02gpkt; >> query: (?x3953, ?x6560) <- genre(?x3953, ?x812), produced_by(?x3953, ?x1039), ?x812 = 01jfsb, organizations_founded(?x1039, ?x6560) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 065dc4 production_companies 04rtpt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 95.000 0.584 http://example.org/film/film/production_companies EVAL 065dc4 production_companies 08wjc1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 95.000 0.584 http://example.org/film/film/production_companies #7772-015qh PRED entity: 015qh PRED relation: medal PRED expected values: 02lq67 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.85 #13, 0.81 #14, 0.74 #6) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 03_3d; 0d0vqn; 01ls2; 0h7x; 01znc_; 01mjq; 03rj0; 06t2t; 03h64; 016wzw; ... >> query: (?x1497, 02lq67) <- film_release_region(?x3482, ?x1497), country(?x668, ?x1497), ?x3482 = 017z49 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015qh medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.846 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #7771-0498y PRED entity: 0498y PRED relation: contains PRED expected values: 0f__1 => 176 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2834): 02t4yc (0.80 #61593, 0.71 #205328, 0.71 #214128), 0498y (0.51 #131996, 0.49 #167199, 0.49 #266931), 09c7w0 (0.51 #131996, 0.49 #167199, 0.49 #266931), 0f__1 (0.51 #131996, 0.49 #167199, 0.49 #266931), 0f2pf9 (0.51 #131996, 0.49 #167199, 0.49 #266931), 0rh6k (0.13 #32263, 0.05 #199461, 0.03 #17603), 01t38b (0.11 #6617, 0.09 #12482, 0.04 #56475), 02lwv5 (0.11 #7604, 0.06 #19334, 0.05 #10537), 03dm7 (0.11 #7741, 0.06 #19471, 0.05 #10674), 04d5v9 (0.11 #6321, 0.06 #18051, 0.05 #9254) >> Best rule #61593 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 0195pd; >> query: (?x4061, ?x3696) <- state_province_region(?x3696, ?x4061), location_of_ceremony(?x566, ?x4061), contains(?x94, ?x3696) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #131996 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 04j53; 05g2v; 04pnx; 09b69; 03v9w; *> query: (?x4061, ?x94) <- contains(?x4061, ?x5259), contains(?x94, ?x5259), partially_contains(?x4061, ?x4540) *> conf = 0.51 ranks of expected_values: 4 EVAL 0498y contains 0f__1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 176.000 91.000 0.802 http://example.org/location/location/contains #7770-06dn58 PRED entity: 06dn58 PRED relation: award PRED expected values: 0789_m 0cqhk0 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 217): 02x8n1n (0.25 #117, 0.15 #21311, 0.05 #8159), 0c4z8 (0.25 #70, 0.06 #13340, 0.05 #16556), 07cbcy (0.25 #77, 0.05 #8119, 0.04 #9325), 0cqhk0 (0.18 #2047, 0.18 #3254, 0.17 #841), 09qj50 (0.16 #2815, 0.16 #12064, 0.13 #23324), 02xj3rw (0.16 #2815, 0.16 #12064, 0.13 #23324), 0cjyzs (0.16 #2815, 0.16 #12064, 0.12 #22518), 0cqhmg (0.16 #2815, 0.16 #12064, 0.12 #22518), 09qv3c (0.16 #2815, 0.16 #12064, 0.12 #22518), 09qrn4 (0.16 #2815, 0.16 #12064, 0.12 #22518) >> Best rule #117 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0161c2; >> query: (?x7776, 02x8n1n) <- award(?x7776, ?x693), film(?x7776, ?x5576), ?x5576 = 0gbfn9 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2047 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 399 *> proper extension: 03gm48; 01xsbh; 0241wg; 016gkf; 02tkzn; 01wc7p; 0gm34; 03kxp7; 0hsn_; 015np0; ... *> query: (?x7776, 0cqhk0) <- actor(?x493, ?x7776), award_winner(?x2293, ?x7776), award(?x7776, ?x693) *> conf = 0.18 ranks of expected_values: 4, 86 EVAL 06dn58 award 0cqhk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 76.000 0.250 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 06dn58 award 0789_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 76.000 76.000 0.250 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7769-011ycb PRED entity: 011ycb PRED relation: film! PRED expected values: 06z8gn => 86 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1068): 0gs1_ (0.64 #95441, 0.45 #24896, 0.42 #85064), 015wnl (0.33 #649, 0.03 #46293, 0.02 #25545), 02s2ft (0.17 #7, 0.06 #2082, 0.03 #22828), 0h5g_ (0.17 #74, 0.03 #87140, 0.03 #80914), 05gnf9 (0.17 #1270, 0.03 #87140, 0.03 #80914), 0h7pj (0.17 #1539, 0.03 #26435, 0.03 #20210), 0170s4 (0.17 #398, 0.03 #2473, 0.03 #4547), 0dzf_ (0.17 #808, 0.03 #2883, 0.03 #97518), 06jzh (0.17 #88, 0.03 #2163, 0.03 #97518), 04yywz (0.17 #19, 0.03 #4168, 0.02 #62259) >> Best rule #95441 for best value: >> intensional similarity = 2 >> extensional distance = 860 >> proper extension: 0clpml; >> query: (?x5013, ?x6558) <- nominated_for(?x6558, ?x5013), participant(?x6558, ?x1126) >> conf = 0.64 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 011ycb film! 06z8gn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 51.000 0.638 http://example.org/film/actor/film./film/performance/film #7768-0262s1 PRED entity: 0262s1 PRED relation: nominated_for PRED expected values: 014nq4 => 74 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1507): 0f4yh (0.83 #3187, 0.82 #3186, 0.78 #1593), 0k2sk (0.83 #3187, 0.82 #3186, 0.78 #1593), 0ddjy (0.83 #3187, 0.82 #3186, 0.78 #1593), 0hx4y (0.83 #3187, 0.82 #3186, 0.78 #1593), 017gl1 (0.83 #3187, 0.82 #3186, 0.78 #1593), 0dnqr (0.83 #3187, 0.82 #3186, 0.78 #1593), 04gcyg (0.83 #3187, 0.82 #3186, 0.78 #1593), 01s3vk (0.83 #3187, 0.82 #3186, 0.78 #1593), 03fts (0.83 #3187, 0.82 #3186, 0.78 #1593), 02dwj (0.83 #3187, 0.82 #3186, 0.78 #1593) >> Best rule #3187 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 040njc; >> query: (?x10747, ?x9484) <- award(?x2182, ?x10747), nominated_for(?x10747, ?x2770), award(?x9484, ?x10747), award(?x5228, ?x10747), ?x5228 = 02dwj, titles(?x7323, ?x9484) >> conf = 0.83 => this is the best rule for 17 predicted values No rule for expected values ranks of expected_values: EVAL 0262s1 nominated_for 014nq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 37.000 0.833 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7767-04sry PRED entity: 04sry PRED relation: award PRED expected values: 0gq9h => 129 concepts (116 used for prediction) PRED predicted values (max 10 best out of 322): 02sp_v (0.78 #37285, 0.72 #41600, 0.72 #41994), 02x4wr9 (0.72 #41600, 0.72 #41994, 0.71 #23941), 02wkmx (0.72 #41600, 0.72 #41994, 0.71 #23941), 02w_6xj (0.72 #41600, 0.72 #41994, 0.71 #23941), 09d28z (0.72 #41600, 0.72 #41994, 0.71 #23941), 027b9ly (0.72 #41600, 0.72 #41994, 0.71 #23941), 02wypbh (0.72 #41600, 0.72 #41994, 0.71 #23941), 0gq9h (0.69 #2424, 0.54 #1639, 0.42 #16162), 05pcn59 (0.67 #466, 0.35 #1250, 0.22 #2035), 09sb52 (0.44 #430, 0.37 #21228, 0.33 #37) >> Best rule #37285 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 02mslq; 0lzb8; 0kzy0; 01wv9xn; 0cg9y; 0hwd8; 0frsw; 0137g1; 01vrwfv; 0134s5; ... >> query: (?x7310, ?x1198) <- award_winner(?x1198, ?x7310), award(?x276, ?x1198), ceremony(?x1198, ?x2032) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2424 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 03qmx_f; 02bfxb; 02pq9yv; 07b3r9; 0grrq8; 03_80b; 02q42j_; 058frd; 02z2xdf; 0bkf72; ... *> query: (?x7310, 0gq9h) <- award_winner(?x198, ?x7310), award_nominee(?x2444, ?x7310), ?x198 = 040njc *> conf = 0.69 ranks of expected_values: 8 EVAL 04sry award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 129.000 116.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7766-06cs95 PRED entity: 06cs95 PRED relation: actor PRED expected values: 02zhkz 03lmzl 02__ww => 94 concepts (79 used for prediction) PRED predicted values (max 10 best out of 899): 06pj8 (0.53 #2764, 0.48 #5529, 0.40 #9217), 0kcd5 (0.53 #2764, 0.48 #5529, 0.40 #9217), 05wqr1 (0.50 #2456, 0.20 #3378, 0.12 #4299), 0f0kz (0.33 #1159, 0.11 #20276, 0.09 #36869), 01rzxl (0.33 #828, 0.02 #42306, 0.01 #11888), 03c5f7l (0.25 #2574, 0.20 #3496, 0.12 #4417), 0f4dx2 (0.25 #2101, 0.20 #3023, 0.12 #18432), 0785v8 (0.25 #1900, 0.20 #2822, 0.11 #20276), 0f6_dy (0.25 #2004, 0.20 #2926, 0.06 #3847), 03mcwq3 (0.25 #2041, 0.20 #2963, 0.06 #3884) >> Best rule #2764 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 039c26; 06zsk51; >> query: (?x531, ?x2135) <- titles(?x8280, ?x531), actor(?x531, ?x532), ?x8280 = 0hfjk, nominated_for(?x2135, ?x531) >> conf = 0.53 => this is the best rule for 2 predicted values *> Best rule #2516 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 039c26; 06zsk51; *> query: (?x531, 03lmzl) <- titles(?x8280, ?x531), actor(?x531, ?x532), ?x8280 = 0hfjk, nominated_for(?x2135, ?x531) *> conf = 0.25 ranks of expected_values: 11, 354 EVAL 06cs95 actor 02__ww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 94.000 79.000 0.526 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 06cs95 actor 03lmzl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 94.000 79.000 0.526 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 06cs95 actor 02zhkz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 79.000 0.526 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #7765-03mgx6z PRED entity: 03mgx6z PRED relation: language PRED expected values: 02h40lc => 123 concepts (90 used for prediction) PRED predicted values (max 10 best out of 58): 02h40lc (0.98 #4257, 0.97 #4848, 0.96 #5192), 06nm1 (0.21 #1044, 0.16 #1746, 0.15 #1101), 02bjrlw (0.17 #1445, 0.16 #228, 0.14 #287), 04306rv (0.16 #637, 0.14 #580, 0.13 #1740), 0653m (0.12 #68, 0.10 #1045, 0.10 #298), 06b_j (0.12 #77, 0.10 #2220, 0.08 #4435), 05zjd (0.11 #136, 0.08 #4435, 0.08 #4254), 03hkp (0.11 #127, 0.08 #4435, 0.08 #4254), 05qqm (0.11 #151, 0.08 #4435, 0.08 #4254), 03_9r (0.10 #1043, 0.08 #4435, 0.08 #4254) >> Best rule #4257 for best value: >> intensional similarity = 6 >> extensional distance = 593 >> proper extension: 0d7vtk; >> query: (?x5791, 02h40lc) <- country(?x5791, ?x94), language(?x5791, ?x5359), titles(?x4205, ?x5791), produced_by(?x5791, ?x4685), countries_spoken_in(?x5359, ?x279), major_field_of_study(?x5359, ?x6364) >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mgx6z language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 90.000 0.976 http://example.org/film/film/language #7764-04wf_b PRED entity: 04wf_b PRED relation: film PRED expected values: 0cc97st => 91 concepts (66 used for prediction) PRED predicted values (max 10 best out of 281): 0hz55 (0.59 #62594, 0.58 #35770, 0.41 #67959), 023g6w (0.10 #1479, 0.03 #98362), 03cp4cn (0.10 #1103, 0.03 #98362), 01hqhm (0.10 #330, 0.03 #98362), 07w8fz (0.10 #513, 0.01 #18398, 0.01 #5879), 01gglm (0.10 #1403, 0.01 #3192, 0.01 #4980), 095zlp (0.10 #60, 0.01 #5426, 0.01 #17945), 02b6n9 (0.10 #1571, 0.01 #19456), 03s9kp (0.10 #1761), 02x0fs9 (0.10 #1651) >> Best rule #62594 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 065d1h; >> query: (?x9218, ?x4932) <- profession(?x9218, ?x319), film(?x9218, ?x2709), nominated_for(?x9218, ?x4932) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04wf_b film 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 66.000 0.585 http://example.org/film/actor/film./film/performance/film #7763-0b6css PRED entity: 0b6css PRED relation: organization! PRED expected values: 015fr 04wgh 04gqr 01p1b 07fj_ 04tr1 06v36 01rxw 02kcz 01nyl 06srk 07f5x 0hdx8 01nqj => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 360): 03rt9 (0.75 #1056, 0.73 #1316, 0.62 #1578), 035qy (0.62 #1077, 0.55 #1337, 0.50 #296), 04tr1 (0.60 #911, 0.60 #651, 0.58 #2347), 07f5x (0.60 #994, 0.58 #2347, 0.58 #2346), 02kcz (0.60 #974, 0.58 #2347, 0.58 #2346), 06dfg (0.60 #956, 0.58 #2347, 0.58 #2346), 06v36 (0.60 #945, 0.58 #2347, 0.58 #2346), 01p1b (0.60 #893, 0.58 #2347, 0.58 #2346), 05sb1 (0.60 #580, 0.58 #2347, 0.58 #2346), 0chghy (0.60 #531, 0.50 #1052, 0.50 #271) >> Best rule #1056 for best value: >> intensional similarity = 12 >> extensional distance = 6 >> proper extension: 01rz1; 0_2v; 04k4l; 018cqq; >> query: (?x5701, 03rt9) <- organization(?x8857, ?x5701), organization(?x5680, ?x5701), organization(?x2152, ?x5701), currency(?x8857, ?x170), ?x170 = 09nqf, ?x2152 = 06mkj, country(?x4045, ?x5680), country(?x2315, ?x5680), ?x2315 = 06wrt, jurisdiction_of_office(?x182, ?x8857), ?x4045 = 06z6r, religion(?x8857, ?x109) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #911 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 0gkjy; 041288; *> query: (?x5701, 04tr1) <- organization(?x9251, ?x5701), organization(?x8857, ?x5701), organization(?x7037, ?x5701), organization(?x87, ?x5701), ?x8857 = 0164v, ?x7037 = 04hzj, ?x9251 = 07tp2, film_release_region(?x5564, ?x87), film_release_region(?x1150, ?x87), ?x5564 = 03yvf2, medal(?x87, ?x422), ?x1150 = 0h3xztt *> conf = 0.60 ranks of expected_values: 3, 4, 5, 7, 8, 22, 23, 41, 42, 43, 44, 56, 90, 174 EVAL 0b6css organization! 01nqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 0hdx8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 07f5x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 06srk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 01nyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 02kcz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 01rxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 06v36 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 04tr1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 07fj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 01p1b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 04gqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 04wgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0b6css organization! 015fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 12.000 12.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #7762-01nmgc PRED entity: 01nmgc PRED relation: colors PRED expected values: 038hg => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.39 #23, 0.35 #65, 0.32 #44), 01g5v (0.26 #46, 0.24 #25, 0.24 #67), 01l849 (0.23 #43, 0.21 #64, 0.21 #232), 06fvc (0.17 #24, 0.16 #66, 0.16 #45), 019sc (0.13 #239, 0.12 #386, 0.12 #344), 036k5h (0.12 #6, 0.08 #153, 0.06 #48), 03wkwg (0.09 #16, 0.05 #37, 0.05 #79), 038hg (0.07 #244, 0.06 #391, 0.06 #328), 09ggk (0.07 #17, 0.06 #80, 0.06 #59), 04mkbj (0.06 #368, 0.06 #263, 0.06 #326) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 071_8; >> query: (?x9440, 083jv) <- institution(?x865, ?x9440), institution(?x620, ?x9440), ?x865 = 02h4rq6, ?x620 = 07s6fsf >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #244 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 324 *> proper extension: 0mbwf; *> query: (?x9440, 038hg) <- institution(?x865, ?x9440), ?x865 = 02h4rq6 *> conf = 0.07 ranks of expected_values: 8 EVAL 01nmgc colors 038hg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 52.000 52.000 0.388 http://example.org/education/educational_institution/colors #7761-06qw_ PRED entity: 06qw_ PRED relation: languages PRED expected values: 02h40lc => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 21): 02h40lc (0.92 #278, 0.92 #443, 0.91 #632), 06nm1 (0.18 #115, 0.14 #148, 0.13 #159), 03_9r (0.14 #379, 0.12 #191, 0.12 #520), 064_8sq (0.12 #520, 0.11 #18, 0.11 #475), 02bv9 (0.12 #520, 0.11 #20, 0.11 #475), 04306rv (0.12 #520, 0.11 #14, 0.11 #475), 02bjrlw (0.12 #520, 0.11 #12, 0.11 #475), 05zjd (0.12 #520, 0.11 #19, 0.11 #475), 0t_2 (0.12 #520, 0.11 #475, 0.03 #658), 04jq2 (0.02 #276) >> Best rule #278 for best value: >> intensional similarity = 6 >> extensional distance = 61 >> proper extension: 03_8kz; >> query: (?x13070, 02h40lc) <- program_creator(?x13070, ?x11373), actor(?x13070, ?x585), program(?x5431, ?x13070), gender(?x11373, ?x231), producer_type(?x11373, ?x632), award_nominee(?x4299, ?x11373) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06qw_ languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.921 http://example.org/tv/tv_program/languages #7760-0dln8jk PRED entity: 0dln8jk PRED relation: featured_film_locations PRED expected values: 030qb3t 0fn2g => 110 concepts (82 used for prediction) PRED predicted values (max 10 best out of 68): 030qb3t (0.38 #39, 0.14 #1480, 0.14 #999), 02_286 (0.18 #2184, 0.16 #3867, 0.15 #740), 0cv3w (0.12 #70, 0.08 #790, 0.03 #7047), 04jpl (0.09 #7466, 0.08 #1932, 0.08 #1691), 06y57 (0.09 #343, 0.08 #583, 0.04 #1304), 02dtg (0.09 #252, 0.04 #492, 0.03 #972), 080h2 (0.08 #2670, 0.08 #2428, 0.06 #1225), 0rh6k (0.05 #721, 0.05 #961, 0.05 #241), 01_d4 (0.05 #1007, 0.05 #287, 0.04 #2933), 03gh4 (0.05 #1075, 0.03 #1556, 0.03 #2038) >> Best rule #39 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 047vp1n; >> query: (?x4847, 030qb3t) <- film_release_distribution_medium(?x4847, ?x81), film(?x6700, ?x4847), film_crew_role(?x4847, ?x137), ?x6700 = 02_0d2 >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dln8jk featured_film_locations 0fn2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 82.000 0.375 http://example.org/film/film/featured_film_locations EVAL 0dln8jk featured_film_locations 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 82.000 0.375 http://example.org/film/film/featured_film_locations #7759-030hcs PRED entity: 030hcs PRED relation: film PRED expected values: 0g3zrd => 131 concepts (90 used for prediction) PRED predicted values (max 10 best out of 930): 06_wqk4 (0.15 #23180, 0.05 #7257, 0.03 #60750), 02q56mk (0.15 #23180, 0.02 #23596, 0.02 #16463), 0422v0 (0.15 #23180, 0.01 #24957), 01shy7 (0.08 #2205, 0.07 #11120, 0.07 #12903), 02qzh2 (0.06 #2474, 0.05 #691, 0.03 #7823), 02wgk1 (0.05 #755, 0.04 #9670, 0.02 #7887), 0cqr0q (0.05 #1491, 0.03 #6840, 0.03 #5057), 020bv3 (0.05 #317, 0.03 #21713, 0.03 #60942), 0p9lw (0.05 #144, 0.03 #1927, 0.03 #9059), 0prrm (0.05 #858, 0.03 #2641, 0.02 #6207) >> Best rule #23180 for best value: >> intensional similarity = 3 >> extensional distance = 152 >> proper extension: 01rnxn; 01gw4f; 078g3l; 0lfbm; 0gm34; 0cg9f; 03f4w4; 03p9hl; >> query: (?x1815, ?x857) <- film(?x1815, ?x7722), spouse(?x1815, ?x10491), nominated_for(?x7722, ?x857) >> conf = 0.15 => this is the best rule for 3 predicted values *> Best rule #3933 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 01pcvn; 02r3cn; 022q32; *> query: (?x1815, 0g3zrd) <- spouse(?x1815, ?x10491), profession(?x1815, ?x1032), celebrity(?x1890, ?x1815) *> conf = 0.01 ranks of expected_values: 776 EVAL 030hcs film 0g3zrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 131.000 90.000 0.151 http://example.org/film/actor/film./film/performance/film #7758-02jqjm PRED entity: 02jqjm PRED relation: award_winner! PRED expected values: 02nhxf => 103 concepts (68 used for prediction) PRED predicted values (max 10 best out of 204): 01c9jp (0.47 #10343, 0.47 #10342, 0.46 #9481), 01c427 (0.47 #10343, 0.47 #10342, 0.46 #9481), 02f716 (0.47 #10343, 0.47 #10342, 0.46 #9481), 02nhxf (0.47 #10343, 0.47 #10342, 0.46 #9481), 0c4z8 (0.33 #9552, 0.27 #10844, 0.22 #12134), 02f73p (0.33 #615, 0.19 #8371, 0.17 #12246), 026mff (0.33 #162, 0.09 #23265, 0.08 #2745), 01c99j (0.30 #8842, 0.22 #9703, 0.16 #12285), 054ks3 (0.29 #15654, 0.19 #19525, 0.19 #9621), 0gqz2 (0.23 #19465, 0.22 #15594, 0.17 #11713) >> Best rule #10343 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 05pdbs; 02qlg7s; 01vd7hn; 028qdb; 01wwvd2; 07hgkd; 016732; 01vttb9; 01wyq0w; 01m7pwq; ... >> query: (?x5512, ?x3647) <- award_winner(?x2139, ?x5512), ?x2139 = 01by1l, award(?x5512, ?x3647), award_winner(?x3647, ?x538) >> conf = 0.47 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 02jqjm award_winner! 02nhxf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 68.000 0.473 http://example.org/award/award_category/winners./award/award_honor/award_winner #7757-0bt3j9 PRED entity: 0bt3j9 PRED relation: film_crew_role PRED expected values: 09zzb8 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 30): 09zzb8 (0.77 #36, 0.75 #921, 0.70 #1098), 0dxtw (0.38 #10, 0.38 #930, 0.34 #2171), 01pvkk (0.38 #47, 0.27 #2386, 0.27 #1109), 01vx2h (0.37 #294, 0.36 #258, 0.35 #11), 04pyp5 (0.21 #52, 0.09 #229, 0.07 #937), 02ynfr (0.19 #936, 0.17 #263, 0.16 #1077), 0d2b38 (0.15 #25, 0.10 #308, 0.10 #1086), 0215hd (0.15 #54, 0.13 #1080, 0.13 #1116), 02rh1dz (0.12 #292, 0.12 #256, 0.12 #9), 089fss (0.12 #6, 0.07 #926, 0.07 #253) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0j8f09z; >> query: (?x5142, 09zzb8) <- nominated_for(?x143, ?x5142), film_crew_role(?x5142, ?x6473), genre(?x5142, ?x258), ?x6473 = 02vs3x5 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bt3j9 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.766 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7756-02dpl9 PRED entity: 02dpl9 PRED relation: film_crew_role PRED expected values: 0dxtw => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 30): 02r96rf (0.90 #1460, 0.78 #267, 0.72 #1758), 09vw2b7 (0.77 #271, 0.68 #1464, 0.65 #965), 0215hd (0.57 #281, 0.17 #83, 0.15 #1474), 089g0h (0.52 #282, 0.13 #2623, 0.12 #51), 0d2b38 (0.48 #288, 0.13 #2623, 0.12 #1481), 0dxtw (0.39 #1068, 0.38 #969, 0.37 #1468), 02_n3z (0.27 #265, 0.17 #1, 0.14 #67), 02ynfr (0.21 #278, 0.19 #1471, 0.18 #972), 02rh1dz (0.18 #274, 0.13 #1467, 0.11 #571), 015h31 (0.17 #273, 0.09 #1466, 0.09 #3025) >> Best rule #1460 for best value: >> intensional similarity = 6 >> extensional distance = 831 >> proper extension: 0c34mt; 05r3qc; >> query: (?x3897, 02r96rf) <- film(?x8587, ?x3897), film_crew_role(?x3897, ?x2472), film_crew_role(?x2742, ?x2472), film_crew_role(?x2471, ?x2472), ?x2742 = 05c46y6, ?x2471 = 08052t3 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1068 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 612 *> proper extension: 0416y94; 01kff7; 0bscw; 014zwb; 0g54xkt; 0kv9d3; 0bpbhm; 0bs5k8r; 01s3vk; 02psgq; ... *> query: (?x3897, 0dxtw) <- film(?x8587, ?x3897), film_crew_role(?x3897, ?x137), nominated_for(?x9891, ?x3897), language(?x3897, ?x3271), ?x137 = 09zzb8 *> conf = 0.39 ranks of expected_values: 6 EVAL 02dpl9 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 93.000 93.000 0.900 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7755-0cmpn PRED entity: 0cmpn PRED relation: religion PRED expected values: 01spm => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 23): 03j6c (0.53 #291, 0.38 #516, 0.37 #561), 01spm (0.33 #37, 0.02 #712), 0kq2 (0.25 #198, 0.25 #153, 0.22 #243), 0kpl (0.25 #190, 0.25 #145, 0.20 #100), 0c8wxp (0.25 #51, 0.19 #681, 0.19 #591), 01lp8 (0.25 #46, 0.10 #902, 0.09 #1311), 02rxj (0.25 #52, 0.05 #1175, 0.05 #1539), 0b06q (0.25 #62, 0.05 #1175, 0.05 #1539), 0flw86 (0.21 #272, 0.10 #723, 0.10 #768), 03_gx (0.17 #239, 0.12 #149, 0.10 #599) >> Best rule #291 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 05d7rk; 0292l3; 015npr; 02vmzp; 03wpmd; 01n8_g; 04cbtrw; 0jrqq; 01gg59; 061zc_; ... >> query: (?x9919, 03j6c) <- gender(?x9919, ?x231), award_winner(?x9918, ?x9919), nationality(?x9919, ?x2146), ?x231 = 05zppz, ?x2146 = 03rk0 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 0bk4s; *> query: (?x9919, 01spm) <- gender(?x9919, ?x231), nationality(?x9919, ?x512), place_of_burial(?x9919, ?x13771), place_of_burial(?x9919, ?x13247), ?x13771 = 0bqyhk, ?x13247 = 0chgsm, type_of_union(?x9919, ?x566) *> conf = 0.33 ranks of expected_values: 2 EVAL 0cmpn religion 01spm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 60.000 60.000 0.535 http://example.org/people/person/religion #7754-059_c PRED entity: 059_c PRED relation: currency PRED expected values: 09nqf => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.88 #10, 0.83 #34, 0.83 #64), 02l6h (0.03 #9, 0.02 #78, 0.02 #132), 0ptk_ (0.03 #35, 0.03 #47, 0.03 #53) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 0rh6k; 05kkh; 059rby; 03v1s; 07ssc; 05kj_; 059f4; 05fkf; 0vmt; 03s0w; ... >> query: (?x1138, 09nqf) <- contains(?x94, ?x1138), adjoins(?x726, ?x1138), state_province_region(?x3367, ?x1138), religion(?x1138, ?x109) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059_c currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.880 http://example.org/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency #7753-01srq2 PRED entity: 01srq2 PRED relation: film_crew_role PRED expected values: 09zzb8 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 32): 09zzb8 (0.65 #915, 0.65 #610, 0.61 #419), 02r96rf (0.64 #918, 0.61 #460, 0.54 #651), 09vw2b7 (0.58 #922, 0.50 #46, 0.47 #1992), 01pvkk (0.38 #280, 0.30 #623, 0.29 #509), 01vx2h (0.37 #660, 0.32 #469, 0.30 #127), 0dxtw (0.33 #926, 0.33 #50, 0.32 #430), 015h31 (0.21 #466, 0.16 #657, 0.09 #962), 02ynfr (0.20 #132, 0.17 #18, 0.15 #474), 02rh1dz (0.17 #49, 0.15 #467, 0.12 #963), 0d2b38 (0.17 #66, 0.12 #484, 0.11 #942) >> Best rule #915 for best value: >> intensional similarity = 2 >> extensional distance = 321 >> proper extension: 047qxs; 01q2nx; >> query: (?x7246, 09zzb8) <- film(?x574, ?x7246), film_format(?x7246, ?x6392) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01srq2 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.653 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7752-0frsw PRED entity: 0frsw PRED relation: award_winner! PRED expected values: 01ckrr => 90 concepts (72 used for prediction) PRED predicted values (max 10 best out of 218): 02f72_ (0.38 #4964, 0.35 #5169, 0.32 #28005), 01by1l (0.35 #5169, 0.32 #28005, 0.32 #27574), 02f716 (0.35 #5169, 0.32 #28005, 0.32 #27574), 02f5qb (0.35 #5169, 0.32 #28005, 0.32 #27574), 01bgqh (0.35 #5169, 0.32 #28005, 0.32 #27574), 01ckcd (0.35 #5169, 0.32 #28005, 0.32 #27574), 02f73b (0.35 #5169, 0.32 #28005, 0.32 #27574), 02f6yz (0.35 #5169, 0.32 #28005, 0.32 #27574), 01ckrr (0.35 #5169, 0.32 #28005, 0.32 #27574), 0m7yy (0.29 #612, 0.11 #1472, 0.09 #29045) >> Best rule #4964 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 015mrk; >> query: (?x2521, 02f72_) <- category(?x2521, ?x134), award(?x2521, ?x2877), ?x2877 = 02f5qb, instrumentalists(?x314, ?x2521), artists(?x283, ?x2521) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #5169 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 27 *> proper extension: 015mrk; *> query: (?x2521, ?x724) <- category(?x2521, ?x134), award(?x2521, ?x2877), award(?x2521, ?x724), ?x2877 = 02f5qb, instrumentalists(?x314, ?x2521), artists(?x283, ?x2521) *> conf = 0.35 ranks of expected_values: 9 EVAL 0frsw award_winner! 01ckrr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 90.000 72.000 0.379 http://example.org/award/award_category/winners./award/award_honor/award_winner #7751-06fpsx PRED entity: 06fpsx PRED relation: nominated_for! PRED expected values: 02z0dfh 03hj5vf 03hl6lc => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 184): 05zvj3m (0.67 #5238, 0.66 #8574, 0.66 #8097), 0gq9h (0.31 #4584, 0.30 #6253, 0.30 #5300), 0gs9p (0.27 #6255, 0.27 #5302, 0.26 #4586), 0k611 (0.25 #311, 0.23 #4595, 0.22 #5311), 019f4v (0.25 #4575, 0.25 #5291, 0.25 #6244), 057xs89 (0.24 #357, 0.12 #119, 0.08 #595), 0gq_v (0.22 #4540, 0.22 #5256, 0.22 #6209), 07cbcy (0.21 #539, 0.12 #9291, 0.12 #301), 040njc (0.21 #4529, 0.20 #6198, 0.20 #5245), 04dn09n (0.20 #4556, 0.20 #5272, 0.19 #6225) >> Best rule #5238 for best value: >> intensional similarity = 4 >> extensional distance = 784 >> proper extension: 01fs__; 0fpxp; >> query: (?x7702, ?x1691) <- nominated_for(?x91, ?x7702), nominated_for(?x298, ?x7702), award(?x7702, ?x1691), award_winner(?x91, ?x710) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #11196 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1204 *> proper extension: 0n2bh; 03y3bp7; 02sqkh; 028k2x; 05fgr_; 06w7mlh; 06r1k; 025x1t; 03czz87; 03_b1g; *> query: (?x7702, ?x401) <- nominated_for(?x1736, ?x7702), titles(?x2480, ?x7702), profession(?x1736, ?x987), award(?x1736, ?x401) *> conf = 0.20 ranks of expected_values: 16, 17, 19 EVAL 06fpsx nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 73.000 73.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 06fpsx nominated_for! 03hj5vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 73.000 73.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 06fpsx nominated_for! 02z0dfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 73.000 73.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7750-029g_vk PRED entity: 029g_vk PRED relation: major_field_of_study! PRED expected values: 02fy0z 08qnnv => 81 concepts (54 used for prediction) PRED predicted values (max 10 best out of 637): 09f2j (0.75 #9073, 0.71 #4918, 0.62 #11455), 03ksy (0.75 #9013, 0.57 #18507, 0.57 #4858), 07szy (0.71 #4782, 0.58 #8937, 0.53 #26149), 02zd460 (0.67 #9089, 0.56 #17397, 0.51 #23332), 01w5m (0.65 #15542, 0.65 #13763, 0.62 #19699), 06pwq (0.58 #8906, 0.56 #23149, 0.53 #26118), 0bwfn (0.58 #9195, 0.55 #13353, 0.52 #14539), 017j69 (0.58 #9056, 0.43 #15586, 0.42 #20927), 01j_cy (0.57 #4781, 0.33 #8936, 0.33 #43), 01nnsv (0.57 #4951, 0.33 #9106, 0.28 #19194) >> Best rule #9073 for best value: >> intensional similarity = 8 >> extensional distance = 10 >> proper extension: 02ky346; 04rjg; 02jfc; >> query: (?x6575, 09f2j) <- major_field_of_study(?x2948, ?x6575), student(?x6575, ?x3150), major_field_of_study(?x1368, ?x6575), location(?x3150, ?x739), nationality(?x3150, ?x94), ?x2948 = 0j_sncb, profession(?x3150, ?x4725), student(?x3149, ?x3150) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #27292 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 49 *> proper extension: 02csf; *> query: (?x6575, ?x3149) <- major_field_of_study(?x6455, ?x6575), student(?x6575, ?x3150), profession(?x3150, ?x4725), student(?x3149, ?x3150), category(?x6455, ?x134), nationality(?x3150, ?x94), major_field_of_study(?x6575, ?x2606) *> conf = 0.56 ranks of expected_values: 13, 67 EVAL 029g_vk major_field_of_study! 08qnnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 81.000 54.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 029g_vk major_field_of_study! 02fy0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 81.000 54.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7749-07t2k PRED entity: 07t2k PRED relation: taxonomy PRED expected values: 04n6k => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.50 #7, 0.43 #8, 0.42 #16) >> Best rule #7 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 083p7; 0bymv; 09bg4l; 09b6zr; 0dq2k; 06c97; 0rlz; 034ls; 042f1; 038w8; >> query: (?x7558, 04n6k) <- gender(?x7558, ?x231), people(?x7185, ?x7558), ?x7185 = 063k3h, ?x231 = 05zppz, basic_title(?x7558, ?x346) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07t2k taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.500 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #7748-0gqyl PRED entity: 0gqyl PRED relation: category_of PRED expected values: 0g_w => 48 concepts (38 used for prediction) PRED predicted values (max 10 best out of 3): 0g_w (0.50 #24, 0.33 #3, 0.19 #129), 0c4ys (0.36 #410, 0.36 #455, 0.36 #498), 0gcf2r (0.26 #128, 0.23 #107, 0.20 #65) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0gqwc; >> query: (?x1972, 0g_w) <- award(?x4280, ?x1972), nominated_for(?x1972, ?x86), ?x4280 = 097zcz, award(?x91, ?x1972) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gqyl category_of 0g_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 38.000 0.500 http://example.org/award/award_category/category_of #7747-023tp8 PRED entity: 023tp8 PRED relation: gender PRED expected values: 02zsn => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #55, 0.72 #251, 0.72 #17), 02zsn (0.52 #30, 0.50 #38, 0.50 #36) >> Best rule #55 for best value: >> intensional similarity = 2 >> extensional distance = 262 >> proper extension: 06v8s0; 0bbxd3; 06z9yh; >> query: (?x376, 05zppz) <- profession(?x376, ?x1943), ?x1943 = 02krf9 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 176 *> proper extension: 04bdxl; 0prfz; 0h5g_; 0c4f4; 0bxtg; 01rr9f; 0147dk; 03f2_rc; 06jzh; 04bs3j; ... *> query: (?x376, 02zsn) <- spouse(?x2692, ?x376), participant(?x376, ?x4400), film(?x376, ?x377) *> conf = 0.52 ranks of expected_values: 2 EVAL 023tp8 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 132.000 0.814 http://example.org/people/person/gender #7746-06fq2 PRED entity: 06fq2 PRED relation: school! PRED expected values: 02r6gw6 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 14): 02qw1zx (0.41 #45, 0.23 #59, 0.22 #73), 025tn92 (0.31 #52, 0.20 #10, 0.17 #206), 09l0x9 (0.28 #51, 0.27 #9, 0.16 #205), 05vsb7 (0.21 #43, 0.20 #1, 0.18 #197), 038c0q (0.21 #46, 0.13 #449, 0.12 #200), 092j54 (0.20 #7, 0.17 #203, 0.16 #245), 0g3zpp (0.20 #2, 0.17 #44, 0.13 #198), 02z6872 (0.20 #8, 0.14 #50, 0.13 #449), 02pq_rp (0.20 #6, 0.13 #449, 0.12 #202), 03nt7j (0.15 #33, 0.14 #201, 0.14 #47) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 06mkj; 0d05w3; >> query: (?x8202, 02qw1zx) <- school(?x10600, ?x8202), school(?x4979, ?x8202), draft(?x260, ?x10600), ?x4979 = 0f4vx0 >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0l2tk; 01qgr3; *> query: (?x8202, 02r6gw6) <- student(?x8202, ?x2248), school(?x8901, ?x8202), ?x8901 = 07l4z *> conf = 0.13 ranks of expected_values: 13 EVAL 06fq2 school! 02r6gw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 108.000 108.000 0.414 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #7745-06npd PRED entity: 06npd PRED relation: film_release_region! PRED expected values: 02vxq9m 087wc7n 01vksx 02yvct 0661ql3 0dgpwnk 0cc97st 064lsn => 162 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1753): 0jjy0 (0.92 #12702, 0.92 #11444, 0.85 #17734), 0gwjw0c (0.92 #13453, 0.92 #12195, 0.79 #18485), 09k56b7 (0.92 #12805, 0.92 #11547, 0.79 #17837), 0872p_c (0.92 #12708, 0.92 #11450, 0.76 #17740), 0dtfn (0.88 #12732, 0.88 #11474, 0.85 #17764), 024mpp (0.88 #13048, 0.88 #11790, 0.85 #18080), 0661ql3 (0.88 #12858, 0.88 #11600, 0.78 #9084), 0cc5mcj (0.88 #11605, 0.84 #12863, 0.76 #17895), 0j8f09z (0.88 #12452, 0.84 #13710, 0.70 #18742), 062zm5h (0.85 #18233, 0.84 #13201, 0.83 #11943) >> Best rule #12702 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 0154j; >> query: (?x756, 0jjy0) <- film_release_region(?x8258, ?x756), film_release_region(?x385, ?x756), organization(?x756, ?x127), ?x8258 = 05ldxl, award_winner(?x385, ?x624) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #12858 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 0154j; *> query: (?x756, 0661ql3) <- film_release_region(?x8258, ?x756), film_release_region(?x385, ?x756), organization(?x756, ?x127), ?x8258 = 05ldxl, award_winner(?x385, ?x624) *> conf = 0.88 ranks of expected_values: 7, 11, 18, 23, 71, 94, 133, 180 EVAL 06npd film_release_region! 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 162.000 53.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06npd film_release_region! 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 162.000 53.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06npd film_release_region! 0dgpwnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 162.000 53.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06npd film_release_region! 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 162.000 53.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06npd film_release_region! 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 162.000 53.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06npd film_release_region! 01vksx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 162.000 53.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06npd film_release_region! 087wc7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 162.000 53.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06npd film_release_region! 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 162.000 53.000 0.920 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7744-0mnm2 PRED entity: 0mnm2 PRED relation: county PRED expected values: 0mnm2 => 150 concepts (99 used for prediction) PRED predicted values (max 10 best out of 144): 07z1m (0.18 #5898, 0.17 #3140, 0.15 #7278), 0mp3l (0.17 #9, 0.14 #205, 0.10 #597), 0m2fr (0.12 #2231, 0.09 #3016, 0.06 #1250), 0y62n (0.12 #472, 0.02 #3614, 0.01 #4204), 0k3kg (0.12 #423, 0.01 #5339, 0.01 #5929), 0kpys (0.11 #3743, 0.10 #6107, 0.09 #3349), 0mnm2 (0.08 #981, 0.01 #10626, 0.01 #11222), 09c7w0 (0.08 #981, 0.01 #8657, 0.01 #7080), 0dzt9 (0.06 #878, 0.06 #1271, 0.05 #1664), 0k3l5 (0.06 #836, 0.06 #1229, 0.04 #2210) >> Best rule #5898 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 0ftxw; 0y2dl; 09b8m; 0xpp5; 01smm; 0bxbb; 0pc6x; 0fsb8; 0rj4g; 0xszy; ... >> query: (?x7548, ?x1426) <- time_zones(?x7548, ?x2674), contains(?x1426, ?x7548), ?x2674 = 02hcv8, citytown(?x3949, ?x7548) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #981 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 03fb3t; 0mpbx; *> query: (?x7548, ?x94) <- state(?x7548, ?x1426), contains(?x7548, ?x11185), currency(?x7548, ?x170), contains(?x94, ?x11185) *> conf = 0.08 ranks of expected_values: 7 EVAL 0mnm2 county 0mnm2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 150.000 99.000 0.180 http://example.org/location/hud_county_place/county #7743-019z7q PRED entity: 019z7q PRED relation: place_of_death PRED expected values: 0cc56 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 47): 030qb3t (0.14 #10321, 0.13 #14402, 0.12 #14985), 02_286 (0.11 #13, 0.09 #10312, 0.07 #14976), 0p9z5 (0.11 #134, 0.01 #2078), 0vzm (0.11 #47), 0k049 (0.08 #10302, 0.05 #14383, 0.04 #14966), 04jpl (0.07 #4283, 0.06 #1951, 0.05 #5256), 05fjf (0.06 #7194, 0.05 #4665, 0.03 #6417), 0cr3d (0.06 #7194, 0.05 #4665, 0.03 #6417), 0xq63 (0.06 #7194, 0.05 #4665, 0.03 #6417), 06_kh (0.05 #395, 0.05 #590, 0.04 #784) >> Best rule #10321 for best value: >> intensional similarity = 2 >> extensional distance = 327 >> proper extension: 0dky9n; 030pr; 0hwd8; 03_0p; 0c_drn; 03bw6; 0gm34; >> query: (?x916, 030qb3t) <- award_winner(?x350, ?x916), people(?x11563, ?x916) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 019z7q place_of_death 0cc56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 144.000 0.137 http://example.org/people/deceased_person/place_of_death #7742-02778yp PRED entity: 02778yp PRED relation: award_winner! PRED expected values: 03gt46z => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 111): 05c1t6z (0.70 #15, 0.39 #295, 0.38 #155), 02q690_ (0.40 #65, 0.31 #205, 0.28 #3503), 03nnm4t (0.40 #74, 0.28 #3503, 0.28 #3362), 0418154 (0.40 #107, 0.28 #3503, 0.28 #3362), 03gt46z (0.30 #63, 0.16 #2941, 0.12 #203), 09g90vz (0.28 #3503, 0.28 #3362, 0.16 #2941), 05zksls (0.28 #3503, 0.28 #3362, 0.16 #2941), 0drtv8 (0.28 #3503, 0.28 #3362, 0.16 #2941), 027hjff (0.28 #3503, 0.28 #3362, 0.16 #2941), 0hr3c8y (0.28 #3503, 0.28 #3362, 0.16 #2941) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02773nt; 02773m2; 02778pf; 0284gcb; 02778qt; 026w_gk; 018ygt; 02778tk; >> query: (?x5264, 05c1t6z) <- award_nominee(?x5264, ?x1266), profession(?x5264, ?x987), ?x1266 = 0277470 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #63 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 02773nt; 02773m2; 02778pf; 0284gcb; 02778qt; 026w_gk; 018ygt; 02778tk; *> query: (?x5264, 03gt46z) <- award_nominee(?x5264, ?x1266), profession(?x5264, ?x987), ?x1266 = 0277470 *> conf = 0.30 ranks of expected_values: 5 EVAL 02778yp award_winner! 03gt46z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 78.000 0.700 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7741-03rt9 PRED entity: 03rt9 PRED relation: country! PRED expected values: 01hp22 01cgz 071t0 03rbzn => 207 concepts (207 used for prediction) PRED predicted values (max 10 best out of 41): 071t0 (0.89 #1615, 0.88 #344, 0.88 #1410), 0w0d (0.79 #624, 0.76 #952, 0.75 #911), 07jbh (0.79 #637, 0.75 #678, 0.70 #1252), 07gyv (0.79 #661, 0.71 #579, 0.67 #1153), 01cgz (0.72 #2922, 0.72 #2717, 0.71 #708), 02y8z (0.71 #629, 0.67 #1531, 0.67 #957), 01gqfm (0.71 #693, 0.64 #611, 0.61 #652), 07bs0 (0.71 #666, 0.62 #1240, 0.62 #1158), 01hp22 (0.70 #949, 0.69 #908, 0.68 #826), 019tzd (0.68 #684, 0.64 #643, 0.64 #971) >> Best rule #1615 for best value: >> intensional similarity = 2 >> extensional distance = 45 >> proper extension: 0h7x; 09lxtg; >> query: (?x429, 071t0) <- country(?x1591, ?x429), film_release_region(?x86, ?x429) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 9, 11 EVAL 03rt9 country! 03rbzn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 207.000 207.000 0.894 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03rt9 country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 207.000 207.000 0.894 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03rt9 country! 01cgz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 207.000 207.000 0.894 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03rt9 country! 01hp22 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 207.000 207.000 0.894 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #7740-027rfxc PRED entity: 027rfxc PRED relation: type_of_union PRED expected values: 04ztj => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.73 #29, 0.73 #45, 0.71 #49), 01g63y (0.14 #50, 0.13 #22, 0.13 #42) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 564 >> proper extension: 0q9kd; 05d7rk; 02qgqt; 0l6qt; 0byfz; 0qf43; 014zcr; 0h0jz; 01qscs; 09fb5; ... >> query: (?x8469, 04ztj) <- nationality(?x8469, ?x94), award(?x8469, ?x1703), nominated_for(?x1703, ?x9056), ?x9056 = 09sr0 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027rfxc type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.730 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7739-0byfz PRED entity: 0byfz PRED relation: profession PRED expected values: 0dxtg => 148 concepts (104 used for prediction) PRED predicted values (max 10 best out of 90): 0dxtg (0.82 #4275, 0.71 #2952, 0.69 #3834), 03gjzk (0.39 #4276, 0.37 #6334, 0.32 #3835), 09jwl (0.37 #11189, 0.37 #8249, 0.37 #10601), 0fj9f (0.35 #641, 0.21 #1817, 0.13 #3140), 0kyk (0.33 #28, 0.20 #1792, 0.15 #910), 0cbd2 (0.31 #594, 0.28 #4269, 0.28 #1770), 0nbcg (0.27 #11202, 0.26 #912, 0.26 #8262), 016z4k (0.23 #11176, 0.23 #10588, 0.23 #8236), 02krf9 (0.23 #3847, 0.20 #2965, 0.16 #4288), 0dz3r (0.22 #11174, 0.22 #10586, 0.21 #10880) >> Best rule #4275 for best value: >> intensional similarity = 3 >> extensional distance = 303 >> proper extension: 054187; 03p01x; >> query: (?x269, 0dxtg) <- profession(?x269, ?x319), written_by(?x3514, ?x269), nationality(?x269, ?x1310) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0byfz profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 104.000 0.816 http://example.org/people/person/profession #7738-0b_xm PRED entity: 0b_xm PRED relation: group! PRED expected values: 0ftps => 82 concepts (36 used for prediction) PRED predicted values (max 10 best out of 99): 01vrnsk (0.20 #316, 0.14 #512, 0.11 #708), 01vsl3_ (0.20 #243, 0.14 #439, 0.11 #635), 01bczm (0.20 #785), 01vsykc (0.20 #785), 02pt27 (0.14 #562, 0.10 #955, 0.06 #1152), 0fhxv (0.14 #477, 0.10 #870, 0.06 #1067), 018x3 (0.14 #492, 0.10 #885, 0.06 #1082), 0qf3p (0.14 #435, 0.10 #828, 0.06 #1025), 02whj (0.11 #605, 0.08 #1394, 0.02 #1791), 01w724 (0.11 #633, 0.04 #1422, 0.02 #1819) >> Best rule #316 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 07c0j; 0134s5; 0178kd; >> query: (?x7653, 01vrnsk) <- artist(?x9243, ?x7653), ?x9243 = 03qy3l, artists(?x1000, ?x7653), group(?x227, ?x7653), award_winner(?x4912, ?x7653) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0b_xm group! 0ftps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 36.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group #7737-0fjfh PRED entity: 0fjfh PRED relation: nutrient PRED expected values: 0466p20 02p0tjr 025tkqy 027g6p7 => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 34): 025tkqy (0.78 #300, 0.73 #315, 0.69 #15), 02p0tjr (0.69 #15, 0.64 #314, 0.56 #299), 0dcfv (0.69 #15, 0.56 #308, 0.56 #294), 014d7f (0.69 #15, 0.55 #312, 0.44 #297), 061xhr (0.69 #15, 0.36 #313, 0.33 #252), 02kc_w5 (0.69 #15, 0.33 #289, 0.33 #244), 01w_3 (0.69 #15, 0.33 #290, 0.33 #166), 0f4k5 (0.69 #15, 0.33 #291, 0.33 #167), 0466p20 (0.69 #15, 0.33 #280, 0.33 #249), 075pwf (0.69 #15, 0.33 #286, 0.33 #241) >> Best rule #300 for best value: >> intensional similarity = 60 >> extensional distance = 7 >> proper extension: 06x4c; >> query: (?x5009, 025tkqy) <- nutrient(?x5009, ?x13944), nutrient(?x5009, ?x12454), nutrient(?x5009, ?x9795), nutrient(?x5009, ?x8442), nutrient(?x5009, ?x6192), nutrient(?x5009, ?x6026), nutrient(?x5009, ?x5549), nutrient(?x5009, ?x5451), nutrient(?x5009, ?x5337), nutrient(?x5009, ?x2702), nutrient(?x5009, ?x2018), nutrient(?x10612, ?x9795), nutrient(?x9005, ?x9795), nutrient(?x7719, ?x9795), nutrient(?x7057, ?x9795), nutrient(?x6285, ?x9795), nutrient(?x6191, ?x9795), nutrient(?x6159, ?x9795), nutrient(?x5373, ?x9795), nutrient(?x4068, ?x9795), nutrient(?x3900, ?x9795), nutrient(?x2701, ?x9795), nutrient(?x1959, ?x9795), nutrient(?x1303, ?x9795), ?x1959 = 0f25w9, nutrient(?x9732, ?x8442), nutrient(?x8298, ?x8442), nutrient(?x6032, ?x8442), nutrient(?x3468, ?x8442), nutrient(?x1257, ?x8442), ?x3900 = 061_f, ?x6026 = 025sf8g, ?x6159 = 033cnk, nutrient(?x9489, ?x13944), ?x6192 = 06jry, ?x9732 = 05z55, ?x8298 = 037ls6, ?x9005 = 04zpv, ?x1303 = 0fj52s, ?x7719 = 0dj75, ?x5549 = 025s7j4, ?x6191 = 014j1m, taxonomy(?x5451, ?x939), ?x2701 = 0hkxq, ?x7057 = 0fbdb, ?x3468 = 0cxn2, ?x1257 = 09728, ?x10612 = 0frq6, ?x5373 = 0971v, ?x939 = 04n6k, ?x2018 = 01sh2, ?x9489 = 07j87, ?x6032 = 01nkt, ?x2702 = 0838f, ?x12454 = 025rw19, ?x4068 = 0fbw6, ?x6285 = 01645p, nutrient(?x3264, ?x5337), ?x3264 = 0dcfv, nutrient(?x1257, ?x13944) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 9, 13 EVAL 0fjfh nutrient 027g6p7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 22.000 22.000 0.778 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fjfh nutrient 025tkqy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.778 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fjfh nutrient 02p0tjr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 22.000 0.778 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0fjfh nutrient 0466p20 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 22.000 22.000 0.778 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #7736-0gyh PRED entity: 0gyh PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 231 concepts (231 used for prediction) PRED predicted values (max 10 best out of 20): 0fkvn (0.84 #236, 0.79 #635, 0.78 #404), 0pqc5 (0.69 #2611, 0.69 #2653, 0.60 #2485), 060bp (0.55 #1808, 0.39 #3323, 0.38 #674), 060c4 (0.54 #1810, 0.45 #3325, 0.41 #2798), 01t7n9 (0.37 #3597, 0.36 #3344, 0.35 #3090), 0fkzq (0.26 #415, 0.26 #162, 0.24 #331), 0789n (0.25 #9, 0.23 #30, 0.21 #135), 01gkgk (0.25 #6, 0.18 #27, 0.14 #1976), 02079p (0.25 #10, 0.14 #1976, 0.09 #31), 0dq3c (0.25 #2, 0.14 #1976, 0.09 #2797) >> Best rule #236 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 03s0w; >> query: (?x2831, 0fkvn) <- category(?x2831, ?x134), contains(?x2831, ?x1201), district_represented(?x176, ?x2831) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gyh jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 231.000 231.000 0.838 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #7735-0225bv PRED entity: 0225bv PRED relation: school! PRED expected values: 038c0q => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 18): 0f4vx0 (0.53 #154, 0.41 #64, 0.26 #226), 02qw1zx (0.36 #58, 0.30 #220, 0.25 #148), 025tn92 (0.31 #156, 0.27 #66, 0.20 #235), 092j54 (0.27 #62, 0.24 #224, 0.20 #235), 038c0q (0.25 #149, 0.20 #235, 0.14 #398), 05vsb7 (0.25 #217, 0.23 #55, 0.20 #235), 09l0x9 (0.25 #65, 0.22 #227, 0.20 #235), 02pq_x5 (0.20 #70, 0.14 #232, 0.14 #251), 03nt7j (0.20 #235, 0.20 #222, 0.18 #60), 09th87 (0.20 #235, 0.18 #158, 0.16 #68) >> Best rule #154 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 06mkj; 0d05w3; >> query: (?x12485, 0f4vx0) <- school(?x12852, ?x12485), contains(?x94, ?x12485), draft(?x5154, ?x12852), ?x5154 = 0jm8l >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 06mkj; 0d05w3; *> query: (?x12485, 038c0q) <- school(?x12852, ?x12485), contains(?x94, ?x12485), draft(?x5154, ?x12852), ?x5154 = 0jm8l *> conf = 0.25 ranks of expected_values: 5 EVAL 0225bv school! 038c0q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 119.000 119.000 0.529 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #7734-012vd6 PRED entity: 012vd6 PRED relation: influenced_by! PRED expected values: 044k8 => 92 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1517): 03j24kf (0.11 #13704, 0.07 #17257, 0.03 #1196), 0bk1p (0.11 #13704, 0.07 #17257, 0.02 #870), 01pq5j7 (0.11 #13704, 0.06 #712, 0.05 #19290), 0478__m (0.11 #13704, 0.04 #683, 0.03 #1191), 02ktrs (0.11 #13704, 0.02 #965, 0.02 #1473), 03f3yfj (0.11 #13704, 0.02 #816, 0.02 #1324), 0167xy (0.11 #1442, 0.09 #3474, 0.08 #427), 05ty4m (0.10 #514, 0.07 #2036, 0.05 #6099), 0683n (0.10 #5408, 0.10 #8963, 0.08 #10992), 02yl42 (0.09 #5206, 0.08 #8761, 0.07 #10790) >> Best rule #13704 for best value: >> intensional similarity = 2 >> extensional distance = 353 >> proper extension: 07scx; >> query: (?x5310, ?x5225) <- influenced_by(?x4474, ?x5310), influenced_by(?x5225, ?x4474) >> conf = 0.11 => this is the best rule for 6 predicted values *> Best rule #14212 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 354 *> proper extension: 0372p; 07yg2; 0qmny; 070b4; 06lxn; *> query: (?x5310, ?x523) <- influenced_by(?x4474, ?x5310), award(?x4474, ?x4481), award(?x523, ?x4481) *> conf = 0.01 ranks of expected_values: 983 EVAL 012vd6 influenced_by! 044k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 92.000 55.000 0.112 http://example.org/influence/influence_node/influenced_by #7733-07m77x PRED entity: 07m77x PRED relation: award_nominee PRED expected values: 028k57 => 89 concepts (43 used for prediction) PRED predicted values (max 10 best out of 854): 07s6prs (0.82 #20890, 0.81 #20889, 0.81 #39462), 028k57 (0.82 #32494, 0.81 #20889, 0.81 #39462), 07k51gd (0.81 #20889, 0.81 #39462, 0.81 #44104), 07y8l9 (0.81 #20889, 0.81 #44104, 0.81 #46425), 07m77x (0.71 #1918, 0.69 #6561, 0.68 #8882), 06_vpyq (0.18 #78931, 0.05 #8068), 02tr7d (0.10 #2670, 0.04 #21239, 0.03 #37489), 02qgqt (0.10 #2341, 0.04 #30192, 0.04 #20910), 0dlglj (0.10 #2655, 0.03 #21224, 0.03 #30506), 02l4pj (0.10 #3088, 0.03 #21657, 0.03 #30939) >> Best rule #20890 for best value: >> intensional similarity = 3 >> extensional distance = 570 >> proper extension: 0phx4; 05qhnq; >> query: (?x8896, ?x806) <- award_nominee(?x806, ?x8896), award_nominee(?x8896, ?x2383), instrumentalists(?x716, ?x806) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #32494 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 891 *> proper extension: 01vs14j; 06x58; 07ss8_; 06k02; 01wwvt2; 0j_c; 0892sx; 0k7pf; 0m8_v; 0pyww; ... *> query: (?x8896, ?x3258) <- award_nominee(?x3258, ?x8896), film(?x8896, ?x1318), participant(?x3258, ?x692) *> conf = 0.82 ranks of expected_values: 2 EVAL 07m77x award_nominee 028k57 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 43.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7732-03w4sh PRED entity: 03w4sh PRED relation: award_nominee PRED expected values: 03zqc1 05dxl5 => 75 concepts (36 used for prediction) PRED predicted values (max 10 best out of 681): 03x16f (0.83 #2333, 0.82 #4667, 0.81 #16334), 03zqc1 (0.80 #96, 0.76 #2430, 0.69 #7096), 07z1_q (0.80 #731, 0.76 #3065, 0.59 #7731), 04vmqg (0.76 #4415, 0.73 #2081, 0.53 #9081), 02s_qz (0.76 #4163, 0.73 #1829, 0.47 #8829), 06b0d2 (0.71 #2559, 0.67 #225, 0.56 #7225), 0gd_b_ (0.71 #3013, 0.67 #679, 0.50 #7679), 01rs5p (0.67 #2170, 0.65 #4504, 0.44 #9170), 05dxl5 (0.59 #3233, 0.56 #7899, 0.53 #899), 030znt (0.53 #279, 0.53 #2613, 0.38 #7279) >> Best rule #2333 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 01dw4q; 035gjq; 038g2x; 026zvx7; 0gd_b_; 07z1_q; 04psyp; 05dxl5; 05683p; 05lb30; ... >> query: (?x6538, ?x444) <- award_nominee(?x8746, ?x6538), award_nominee(?x444, ?x6538), ?x8746 = 03x16f, gender(?x6538, ?x231) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #96 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 01dw4q; 035gjq; 038g2x; 026zvx7; 0gd_b_; 07z1_q; 04psyp; 05dxl5; 05683p; 05lb30; ... *> query: (?x6538, 03zqc1) <- award_nominee(?x8746, ?x6538), ?x8746 = 03x16f, gender(?x6538, ?x231) *> conf = 0.80 ranks of expected_values: 2, 9 EVAL 03w4sh award_nominee 05dxl5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 75.000 36.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 03w4sh award_nominee 03zqc1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 36.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7731-0q1lp PRED entity: 0q1lp PRED relation: gender PRED expected values: 05zppz => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #5, 0.81 #79, 0.81 #73), 02zsn (0.46 #161, 0.35 #30, 0.32 #20) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 02hblj; >> query: (?x9650, 05zppz) <- profession(?x9650, ?x1383), profession(?x9650, ?x319), ?x319 = 01d_h8, ?x1383 = 0np9r >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q1lp gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.843 http://example.org/people/person/gender #7730-03k50 PRED entity: 03k50 PRED relation: service_language! PRED expected values: 06_9lg => 48 concepts (36 used for prediction) PRED predicted values (max 10 best out of 164): 01c6k4 (0.61 #1295, 0.60 #1582, 0.39 #1870), 0p4wb (0.39 #1298, 0.35 #1585, 0.33 #9), 064f29 (0.33 #1352, 0.33 #63, 0.30 #1639), 069b85 (0.33 #135, 0.29 #1280, 0.28 #1424), 05w3y (0.33 #65, 0.29 #1210, 0.25 #780), 018mxj (0.33 #10, 0.29 #1155, 0.25 #725), 04sv4 (0.33 #87, 0.29 #1232, 0.25 #802), 05b5c (0.33 #134, 0.29 #1279, 0.25 #849), 0gvbw (0.33 #24, 0.29 #1169, 0.25 #739), 049mr (0.33 #100, 0.29 #1245, 0.25 #815) >> Best rule #1295 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 0c_v2; 0459q4; >> query: (?x1882, 01c6k4) <- language(?x5247, ?x1882), titles(?x2146, ?x5247), languages_spoken(?x5025, ?x1882), service_language(?x1492, ?x1882), film(?x1936, ?x5247), countries_spoken_in(?x1882, ?x792) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #101 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x1882, 06_9lg) <- language(?x5247, ?x1882), language(?x2381, ?x1882), languages(?x12309, ?x1882), languages(?x11725, ?x1882), countries_spoken_in(?x1882, ?x792), film(?x5568, ?x5247), ?x11725 = 04cmrt, ?x12309 = 0894_x, ?x2381 = 04q00lw *> conf = 0.33 ranks of expected_values: 62 EVAL 03k50 service_language! 06_9lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 48.000 36.000 0.611 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #7729-03902 PRED entity: 03902 PRED relation: place_of_birth! PRED expected values: 07cn2c => 189 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1947): 01qq_lp (0.33 #104528, 0.33 #164641, 0.33 #81010), 07ym0 (0.33 #104528, 0.33 #164641, 0.33 #81010), 01my95 (0.20 #2410, 0.17 #10248, 0.02 #62509), 04jzj (0.20 #202, 0.17 #8040, 0.02 #60301), 01nd6v (0.17 #7833, 0.06 #15672, 0.05 #18286), 04zn7g (0.17 #7792, 0.06 #15631, 0.05 #18245), 01fxfk (0.17 #7735, 0.06 #15574, 0.05 #18188), 08141d (0.17 #7728, 0.06 #15567, 0.05 #18181), 02bc74 (0.17 #7720, 0.06 #15559, 0.05 #18173), 03j9ml (0.17 #7665, 0.06 #15504, 0.05 #18118) >> Best rule #104528 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 013m43; 0dqyw; 0pc56; >> query: (?x10610, ?x3931) <- location(?x3931, ?x10610), citytown(?x4031, ?x10610), jurisdiction_of_office(?x1195, ?x10610), ?x1195 = 0pqc5 >> conf = 0.33 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 03902 place_of_birth! 07cn2c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 189.000 66.000 0.332 http://example.org/people/person/place_of_birth #7728-07ghq PRED entity: 07ghq PRED relation: list PRED expected values: 05glt => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 3): 05glt (0.17 #16, 0.16 #163, 0.13 #114), 09g7thr (0.02 #631), 01ptsx (0.01 #635) >> Best rule #16 for best value: >> intensional similarity = 7 >> extensional distance = 28 >> proper extension: 08984j; >> query: (?x8370, 05glt) <- genre(?x8370, ?x225), currency(?x8370, ?x170), featured_film_locations(?x8370, ?x1523), film(?x9001, ?x8370), film(?x788, ?x8370), ?x788 = 0g1rw, child(?x9001, ?x13497) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ghq list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.167 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #7727-05z7c PRED entity: 05z7c PRED relation: nominated_for PRED expected values: 0k0rf => 64 concepts (25 used for prediction) PRED predicted values (max 10 best out of 134): 05z7c (0.53 #2437, 0.51 #1705, 0.23 #59), 0k0rf (0.53 #2437, 0.51 #1705, 0.23 #142), 025twgf (0.53 #2437, 0.51 #1705, 0.02 #465), 026p_bs (0.53 #2437, 0.51 #1705, 0.02 #255), 016fyc (0.08 #8, 0.08 #4631, 0.01 #1226), 0n_hp (0.08 #224, 0.01 #1442), 0284b56 (0.08 #151), 0bm2x (0.08 #145), 02rq8k8 (0.08 #108), 0661ql3 (0.08 #72) >> Best rule #2437 for best value: >> intensional similarity = 4 >> extensional distance = 232 >> proper extension: 02fn5r; >> query: (?x2094, ?x5134) <- nominated_for(?x2094, ?x5095), nominated_for(?x2094, ?x1708), nominated_for(?x198, ?x5095), nominated_for(?x1708, ?x5134) >> conf = 0.53 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 05z7c nominated_for 0k0rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 25.000 0.530 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #7726-014z8v PRED entity: 014z8v PRED relation: influenced_by! PRED expected values: 01xdf5 05rx__ 0q9t7 => 161 concepts (76 used for prediction) PRED predicted values (max 10 best out of 491): 04l19_ (0.40 #747, 0.25 #254, 0.11 #32582), 01xdf5 (0.40 #496, 0.12 #1484, 0.11 #32582), 04bs3j (0.25 #13, 0.20 #506, 0.11 #32582), 04gr35 (0.25 #398, 0.20 #891, 0.11 #32582), 014z8v (0.25 #148, 0.11 #32582, 0.07 #30605), 03lgg (0.25 #188, 0.11 #32582, 0.04 #25668), 09jm8 (0.25 #406, 0.11 #32582, 0.04 #25668), 0738b8 (0.25 #75, 0.11 #32582, 0.04 #25668), 0mbw0 (0.25 #315, 0.11 #32582, 0.04 #25668), 0bqs56 (0.20 #1226, 0.11 #11098, 0.11 #32582) >> Best rule #747 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 049fgvm; >> query: (?x4112, 04l19_) <- influenced_by(?x7872, ?x4112), influenced_by(?x4112, ?x4554), ?x7872 = 03g5_y >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #496 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 049fgvm; *> query: (?x4112, 01xdf5) <- influenced_by(?x7872, ?x4112), influenced_by(?x4112, ?x4554), ?x7872 = 03g5_y *> conf = 0.40 ranks of expected_values: 2, 33, 288 EVAL 014z8v influenced_by! 0q9t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 161.000 76.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 014z8v influenced_by! 05rx__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 161.000 76.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 014z8v influenced_by! 01xdf5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 161.000 76.000 0.400 http://example.org/influence/influence_node/influenced_by #7725-0gqwc PRED entity: 0gqwc PRED relation: award! PRED expected values: 02jt1k 0kszw 02mqc4 0fbx6 0k8y7 01gvyp 0g476 04rfq => 58 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2605): 0bq2g (0.79 #3269, 0.79 #22894, 0.79 #45782), 01933d (0.79 #3269, 0.79 #22894, 0.79 #45782), 0m6x4 (0.79 #3269, 0.79 #22894, 0.79 #35973), 0gmtm (0.79 #3269, 0.79 #22894, 0.79 #35973), 03knl (0.79 #3269, 0.79 #45782, 0.79 #35972), 01qq_lp (0.79 #3269, 0.79 #45782, 0.79 #35972), 018417 (0.79 #3269, 0.79 #45782, 0.79 #35972), 02x0dzw (0.79 #3269, 0.79 #45782, 0.79 #35972), 01dbhb (0.79 #3269, 0.79 #45782, 0.79 #35972), 0kjrx (0.50 #2277, 0.06 #51331, 0.06 #54605) >> Best rule #3269 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 05b4l5x; >> query: (?x1245, ?x396) <- award_winner(?x1245, ?x5821), award_winner(?x1245, ?x396), award(?x197, ?x1245), ?x5821 = 0hwbd, award(?x241, ?x1245) >> conf = 0.79 => this is the best rule for 9 predicted values *> Best rule #1942 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 05b4l5x; *> query: (?x1245, 01gvyp) <- award_winner(?x1245, ?x5821), award(?x197, ?x1245), ?x5821 = 0hwbd, award(?x241, ?x1245) *> conf = 0.50 ranks of expected_values: 15, 32, 63, 524, 710, 1224, 2074 EVAL 0gqwc award! 04rfq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 24.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqwc award! 0g476 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 58.000 24.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqwc award! 01gvyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 58.000 24.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqwc award! 0k8y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 24.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqwc award! 0fbx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 24.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqwc award! 02mqc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 58.000 24.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqwc award! 0kszw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 24.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqwc award! 02jt1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 24.000 0.794 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7724-0gpjbt PRED entity: 0gpjbt PRED relation: award_winner PRED expected values: 012x4t 01vs_v8 06x4l_ 0m_v0 02_jkc 01htxr => 47 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1864): 01vw20h (0.60 #8158, 0.57 #11153, 0.56 #15648), 02cx90 (0.60 #8129, 0.57 #11124, 0.50 #20118), 06fmdb (0.60 #6769, 0.57 #11261, 0.46 #17256), 02l840 (0.60 #7583, 0.50 #4590, 0.43 #12076), 01m3b1t (0.60 #8528, 0.50 #5535, 0.43 #13021), 04rcr (0.57 #10556, 0.56 #15051, 0.40 #6064), 0hl3d (0.57 #18006, 0.54 #16507, 0.50 #13509), 0g824 (0.57 #12920, 0.50 #5434, 0.50 #3935), 09hnb (0.50 #13849, 0.50 #3365, 0.43 #12350), 02qwg (0.50 #13967, 0.44 #15465, 0.43 #12468) >> Best rule #8158 for best value: >> intensional similarity = 20 >> extensional distance = 3 >> proper extension: 05pd94v; >> query: (?x2054, 01vw20h) <- ceremony(?x12835, ?x2054), ceremony(?x10316, ?x2054), ceremony(?x6652, ?x2054), ceremony(?x3313, ?x2054), ceremony(?x3103, ?x2054), ceremony(?x2324, ?x2054), ceremony(?x1801, ?x2054), ceremony(?x1361, ?x2054), ?x12835 = 03r00m, ?x6652 = 01cw7s, ?x1801 = 01c92g, ?x2324 = 02581c, ?x10316 = 02ddq4, ?x3313 = 02flpc, award_winner(?x2054, ?x367), award_winner(?x367, ?x2300), ?x1361 = 01c9f2, profession(?x367, ?x131), ?x3103 = 03tcnt, artist(?x2299, ?x367) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3793 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 2 *> proper extension: 056878; *> query: (?x2054, 02_jkc) <- ceremony(?x12835, ?x2054), ceremony(?x12701, ?x2054), ceremony(?x6652, ?x2054), ceremony(?x3103, ?x2054), ceremony(?x3094, ?x2054), ceremony(?x1801, ?x2054), ?x12835 = 03r00m, ?x6652 = 01cw7s, award(?x7549, ?x1801), award(?x3358, ?x1801), award(?x2963, ?x1801), award_winner(?x1801, ?x3290), ?x3358 = 01n8gr, ?x3094 = 026mff, ?x12701 = 024fxq, ?x2963 = 0gcs9, award_winner(?x2054, ?x367), artist(?x2039, ?x7549), ?x3103 = 03tcnt, role(?x7549, ?x1466) *> conf = 0.50 ranks of expected_values: 18, 25, 41, 62, 98, 143 EVAL 0gpjbt award_winner 01htxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 47.000 31.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gpjbt award_winner 02_jkc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 47.000 31.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gpjbt award_winner 0m_v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 47.000 31.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gpjbt award_winner 06x4l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 47.000 31.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gpjbt award_winner 01vs_v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 47.000 31.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gpjbt award_winner 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 47.000 31.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7723-06jk5_ PRED entity: 06jk5_ PRED relation: colors PRED expected values: 01l849 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.42 #22, 0.41 #182, 0.41 #82), 01g5v (0.29 #184, 0.28 #84, 0.27 #144), 01l849 (0.26 #21, 0.26 #781, 0.26 #941), 019sc (0.19 #1348, 0.18 #908, 0.18 #1028), 06fvc (0.16 #803, 0.16 #203, 0.15 #323), 036k5h (0.16 #46, 0.14 #6, 0.11 #26), 038hg (0.14 #12, 0.10 #672, 0.10 #1152), 0jc_p (0.10 #345, 0.09 #85, 0.09 #65), 09ggk (0.08 #136, 0.07 #196, 0.07 #676), 02rnmb (0.08 #153, 0.07 #193, 0.07 #233) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 07xpm; >> query: (?x1476, 083jv) <- institution(?x865, ?x1476), ?x865 = 02h4rq6, colors(?x1476, ?x7179), ?x7179 = 04mkbj >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 07xpm; *> query: (?x1476, 01l849) <- institution(?x865, ?x1476), ?x865 = 02h4rq6, colors(?x1476, ?x7179), ?x7179 = 04mkbj *> conf = 0.26 ranks of expected_values: 3 EVAL 06jk5_ colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 142.000 142.000 0.421 http://example.org/education/educational_institution/colors #7722-0lbbj PRED entity: 0lbbj PRED relation: olympics! PRED expected values: 0k6nt => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 239): 0k6nt (0.88 #3051, 0.88 #2946, 0.87 #2842), 015fr (0.82 #1996, 0.80 #2838, 0.67 #1157), 0h7x (0.80 #1797, 0.79 #3374, 0.78 #5704), 0154j (0.76 #3040, 0.72 #3145, 0.71 #4308), 06mkj (0.73 #2021, 0.67 #1182, 0.53 #2863), 05b4w (0.67 #5298, 0.67 #4979, 0.67 #2869), 01ls2 (0.67 #1155, 0.55 #1994, 0.47 #2836), 03_r3 (0.64 #1995, 0.60 #1784, 0.53 #2837), 0hzlz (0.60 #2841, 0.58 #2210, 0.56 #2945), 035qy (0.60 #2849, 0.55 #2007, 0.50 #4326) >> Best rule #3051 for best value: >> intensional similarity = 17 >> extensional distance = 15 >> proper extension: 0jhn7; 018qb4; >> query: (?x2369, 0k6nt) <- sports(?x2369, ?x5182), sports(?x2369, ?x779), sports(?x2369, ?x471), sports(?x2369, ?x359), olympics(?x4737, ?x2369), olympics(?x2051, ?x2369), taxonomy(?x2051, ?x939), olympics(?x779, ?x391), medal(?x2369, ?x422), ?x471 = 02vx4, countries_within(?x2467, ?x2051), olympics(?x766, ?x2369), country(?x779, ?x87), contains(?x2051, ?x12330), film_release_region(?x86, ?x4737), ?x5182 = 0crlz, ?x359 = 02bkg >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lbbj olympics! 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.882 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #7721-0f8l9c PRED entity: 0f8l9c PRED relation: combatants! PRED expected values: 06bnz 024pcx => 282 concepts (192 used for prediction) PRED predicted values (max 10 best out of 357): 0chghy (0.81 #4881, 0.62 #1381, 0.58 #959), 0d060g (0.81 #4881, 0.54 #1380, 0.47 #958), 01fvhp (0.81 #4881, 0.25 #7321, 0.21 #9617), 0f8l9c (0.63 #963, 0.62 #1385, 0.55 #1017), 01pj7 (0.44 #865, 0.25 #7321, 0.21 #9617), 06c1y (0.39 #860, 0.25 #7321, 0.21 #9617), 03gj2 (0.39 #857, 0.25 #7321, 0.21 #9617), 06bnz (0.35 #1391, 0.32 #969, 0.29 #1602), 07f1x (0.33 #885, 0.26 #3537, 0.25 #7321), 06v9sf (0.33 #179, 0.25 #7321, 0.20 #656) >> Best rule #4881 for best value: >> intensional similarity = 2 >> extensional distance = 48 >> proper extension: 0c4b8; 01h3dj; >> query: (?x789, ?x94) <- organization(?x789, ?x127), combatants(?x789, ?x94) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #1391 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 014tss; *> query: (?x789, 06bnz) <- nationality(?x317, ?x789), combatants(?x94, ?x789), country(?x251, ?x789) *> conf = 0.35 ranks of expected_values: 8, 19 EVAL 0f8l9c combatants! 024pcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 282.000 192.000 0.810 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants EVAL 0f8l9c combatants! 06bnz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 282.000 192.000 0.810 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #7720-05r6t PRED entity: 05r6t PRED relation: parent_genre PRED expected values: 09jw2 => 74 concepts (56 used for prediction) PRED predicted values (max 10 best out of 290): 03lty (0.82 #3764, 0.36 #2953, 0.25 #834), 05r6t (0.76 #4123, 0.56 #2827, 0.43 #6380), 05bt6j (0.50 #1822, 0.50 #1008, 0.33 #520), 064t9 (0.50 #991, 0.33 #1805, 0.33 #503), 011j5x (0.33 #348, 0.29 #1978, 0.25 #1000), 016clz (0.33 #332, 0.29 #1962, 0.20 #1148), 0827d (0.33 #166, 0.25 #2285, 0.25 #819), 02x8m (0.33 #505, 0.25 #2296, 0.25 #993), 09jw2 (0.33 #593, 0.25 #3365, 0.25 #1081), 06cqb (0.33 #493, 0.25 #981, 0.20 #1473) >> Best rule #3764 for best value: >> intensional similarity = 5 >> extensional distance = 43 >> proper extension: 0jf1v; >> query: (?x5934, 03lty) <- parent_genre(?x5934, ?x1000), artists(?x1000, ?x9589), artists(?x1000, ?x8640), ?x8640 = 020hh3, ?x9589 = 02cw1m >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 059kh; *> query: (?x5934, 09jw2) <- parent_genre(?x11737, ?x5934), artists(?x5934, ?x10639), artists(?x5934, ?x7125), artists(?x5934, ?x6102), ?x11737 = 01b4p4, ?x6102 = 07h76, artist(?x8738, ?x7125), group(?x227, ?x10639) *> conf = 0.33 ranks of expected_values: 9 EVAL 05r6t parent_genre 09jw2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 74.000 56.000 0.822 http://example.org/music/genre/parent_genre #7719-0ckd1 PRED entity: 0ckd1 PRED relation: producer_type! PRED expected values: 02_1q9 02k_4g 08jgk1 01h72l 0hz55 039cq4 02rlj20 02qfh 05sy0cv 07vqnc 043qqt5 => 25 concepts (18 used for prediction) No prediction ranks of expected_values: EVAL 0ckd1 producer_type! 043qqt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 07vqnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 05sy0cv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 02qfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 02rlj20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 039cq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 0hz55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 01h72l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 08jgk1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 02k_4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type EVAL 0ckd1 producer_type! 02_1q9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 18.000 0.000 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #7718-01gc8c PRED entity: 01gc8c PRED relation: contains! PRED expected values: 059g4 => 130 concepts (66 used for prediction) PRED predicted values (max 10 best out of 176): 09c7w0 (0.85 #52788, 0.69 #34892, 0.69 #57263), 02qkt (0.33 #1239, 0.04 #28972, 0.04 #21813), 02j9z (0.33 #921, 0.04 #8968, 0.02 #28654), 07ssc (0.33 #51921, 0.29 #53710, 0.28 #55501), 02jx1 (0.29 #49289, 0.18 #51976, 0.16 #53765), 01n7q (0.22 #49280, 0.15 #44806, 0.12 #16176), 015jr (0.17 #4882, 0.13 #6670, 0.11 #8458), 0h7h6 (0.12 #3682, 0.10 #5470, 0.07 #8152), 059rby (0.11 #38485, 0.11 #39380, 0.07 #8960), 05fjf (0.11 #13789, 0.11 #14683, 0.08 #38838) >> Best rule #52788 for best value: >> intensional similarity = 4 >> extensional distance = 797 >> proper extension: 07vfz; >> query: (?x13164, 09c7w0) <- category(?x13164, ?x134), contains(?x279, ?x13164), film_release_region(?x1861, ?x279), ?x1861 = 0b76t12 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #4037 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 01y8zd; 016ndm; 01y9st; 06b19; 0gf14; 01glqw; 011kn2; 01dq0z; 018djs; 018gmr; *> query: (?x13164, 059g4) <- category(?x13164, ?x134), contains(?x1905, ?x13164), contains(?x279, ?x13164), ?x279 = 0d060g, ?x1905 = 05kr_ *> conf = 0.04 ranks of expected_values: 49 EVAL 01gc8c contains! 059g4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 130.000 66.000 0.850 http://example.org/location/location/contains #7717-033x5p PRED entity: 033x5p PRED relation: major_field_of_study PRED expected values: 02j62 0g26h => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 121): 01mkq (0.50 #140, 0.46 #4267, 0.38 #5143), 02822 (0.50 #167, 0.33 #42, 0.17 #6676), 02j62 (0.46 #4283, 0.44 #1531, 0.38 #6665), 062z7 (0.38 #4280, 0.38 #1528, 0.34 #6662), 0g26h (0.38 #169, 0.37 #4296, 0.37 #6176), 03g3w (0.38 #152, 0.36 #1527, 0.32 #4279), 04rjg (0.38 #145, 0.35 #4272, 0.35 #1520), 02_7t (0.38 #192, 0.29 #4319, 0.29 #5195), 037mh8 (0.38 #195, 0.21 #6704, 0.21 #1570), 0fdys (0.38 #165, 0.20 #6674, 0.20 #1540) >> Best rule #140 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 09f2j; 08qnnv; 0cwx_; >> query: (?x4363, 01mkq) <- institution(?x865, ?x4363), student(?x4363, ?x158), award_nominee(?x158, ?x3632), ?x3632 = 01309x >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4283 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 95 *> proper extension: 01nkcn; *> query: (?x4363, 02j62) <- institution(?x1771, ?x4363), institution(?x865, ?x4363), ?x865 = 02h4rq6, school(?x1160, ?x4363), ?x1771 = 019v9k *> conf = 0.46 ranks of expected_values: 3, 5 EVAL 033x5p major_field_of_study 0g26h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 176.000 176.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 033x5p major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 176.000 176.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7716-07hbxm PRED entity: 07hbxm PRED relation: gender PRED expected values: 02zsn => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.76 #7, 0.72 #129, 0.71 #99), 02zsn (0.35 #4, 0.33 #20, 0.32 #24) >> Best rule #7 for best value: >> intensional similarity = 2 >> extensional distance = 19 >> proper extension: 0fp_xp; >> query: (?x2284, 05zppz) <- nationality(?x2284, ?x4221), ?x4221 = 0j5g9 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 04rsd2; 01qrbf; *> query: (?x2284, 02zsn) <- award_nominee(?x8566, ?x2284), award_nominee(?x2559, ?x2284), ?x2559 = 06mmb, film(?x8566, ?x2029) *> conf = 0.35 ranks of expected_values: 2 EVAL 07hbxm gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.762 http://example.org/people/person/gender #7715-01wgx4 PRED entity: 01wgx4 PRED relation: film PRED expected values: 01wb95 => 69 concepts (38 used for prediction) PRED predicted values (max 10 best out of 686): 031hcx (0.21 #4858, 0.08 #8441, 0.03 #12024), 01qz5 (0.21 #5000, 0.06 #8583, 0.03 #12166), 01vw8k (0.14 #4236, 0.08 #7819, 0.03 #6027), 011ywj (0.14 #5020, 0.08 #8603, 0.02 #12186), 031778 (0.14 #3898, 0.06 #5689, 0.04 #7481), 031786 (0.14 #4859, 0.06 #6650, 0.04 #8442), 017kct (0.14 #4165, 0.06 #7748, 0.03 #11331), 011yg9 (0.14 #4612, 0.06 #8195, 0.02 #11778), 0pv54 (0.14 #4541, 0.04 #11707, 0.04 #8124), 03x7hd (0.14 #4144, 0.04 #7727, 0.03 #11310) >> Best rule #4858 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0b_dy; >> query: (?x13812, 031hcx) <- award(?x13812, ?x3247), student(?x2486, ?x13812), ?x2486 = 015nl4, ?x3247 = 0bdwqv >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #9580 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 0382m4; 018qql; *> query: (?x13812, 01wb95) <- type_of_union(?x13812, ?x566), ?x566 = 04ztj, award(?x13812, ?x2071), ?x2071 = 0bdw6t *> conf = 0.05 ranks of expected_values: 158 EVAL 01wgx4 film 01wb95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 69.000 38.000 0.214 http://example.org/film/actor/film./film/performance/film #7714-0h1sg PRED entity: 0h1sg PRED relation: nutrient! PRED expected values: 0fbw6 => 59 concepts (56 used for prediction) PRED predicted values (max 10 best out of 11): 0fbw6 (0.89 #150, 0.89 #68, 0.89 #81), 06x4c (0.89 #150, 0.89 #68, 0.89 #81), 0dcfv (0.89 #150, 0.89 #68, 0.89 #81), 025rw19 (0.02 #144, 0.01 #326), 025tkqy (0.02 #144, 0.01 #326), 014d7f (0.02 #144, 0.01 #326), 06jry (0.02 #144, 0.01 #326), 025s7j4 (0.02 #144, 0.01 #326), 01sh2 (0.02 #144, 0.01 #326), 025sf8g (0.02 #144) >> Best rule #150 for best value: >> intensional similarity = 118 >> extensional distance = 13 >> proper extension: 025sf0_; 025rw19; >> query: (?x9490, ?x3264) <- nutrient(?x10612, ?x9490), nutrient(?x9732, ?x9490), nutrient(?x9489, ?x9490), nutrient(?x9005, ?x9490), nutrient(?x8298, ?x9490), nutrient(?x7057, ?x9490), nutrient(?x6285, ?x9490), nutrient(?x6191, ?x9490), nutrient(?x6159, ?x9490), nutrient(?x6032, ?x9490), nutrient(?x5373, ?x9490), nutrient(?x5009, ?x9490), nutrient(?x3900, ?x9490), nutrient(?x3468, ?x9490), nutrient(?x2701, ?x9490), nutrient(?x1959, ?x9490), nutrient(?x1303, ?x9490), nutrient(?x1257, ?x9490), ?x5373 = 0971v, ?x9489 = 07j87, ?x6285 = 01645p, ?x9005 = 04zpv, ?x8298 = 037ls6, ?x1257 = 09728, ?x3900 = 061_f, ?x1303 = 0fj52s, ?x2701 = 0hkxq, ?x6191 = 014j1m, nutrient(?x3468, ?x13944), nutrient(?x3468, ?x13545), nutrient(?x3468, ?x13126), nutrient(?x3468, ?x12902), nutrient(?x3468, ?x12336), nutrient(?x3468, ?x12083), nutrient(?x3468, ?x11758), nutrient(?x3468, ?x11270), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10098), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x9840), nutrient(?x3468, ?x9733), nutrient(?x3468, ?x9436), nutrient(?x3468, ?x9426), nutrient(?x3468, ?x8442), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x7894), nutrient(?x3468, ?x7720), nutrient(?x3468, ?x7652), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7364), nutrient(?x3468, ?x7362), nutrient(?x3468, ?x7219), nutrient(?x3468, ?x7135), nutrient(?x3468, ?x6586), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x6160), nutrient(?x3468, ?x6026), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5526), nutrient(?x3468, ?x5451), nutrient(?x3468, ?x5337), nutrient(?x3468, ?x5010), nutrient(?x3468, ?x3469), nutrient(?x3468, ?x3203), nutrient(?x3468, ?x2702), nutrient(?x3468, ?x1960), nutrient(?x3468, ?x1304), nutrient(?x3468, ?x1258), ?x1960 = 07hnp, ?x11758 = 0q01m, ?x7894 = 0f4hc, ?x13126 = 02kc_w5, ?x1959 = 0f25w9, ?x11270 = 02kc008, ?x2702 = 0838f, ?x5010 = 0h1vz, ?x10098 = 0h1_c, ?x6192 = 06jry, ?x7057 = 0fbdb, ?x5526 = 09pbb, ?x7219 = 0h1vg, ?x8413 = 02kc4sf, ?x12336 = 0f4l5, ?x5549 = 025s7j4, ?x12902 = 0fzjh, ?x9733 = 0h1tz, ?x3469 = 0h1zw, ?x9436 = 025sqz8, ?x9915 = 025tkqy, ?x1258 = 0h1wg, ?x13545 = 01w_3, ?x9732 = 05z55, ?x5451 = 05wvs, ?x9426 = 0h1yy, ?x6159 = 033cnk, ?x12083 = 01n78x, ?x7362 = 02kc5rj, ?x8442 = 02kcv4x, ?x1304 = 08lb68, ?x13944 = 0f4kp, ?x7364 = 09gvd, ?x7135 = 025rsfk, ?x7431 = 09gwd, ?x7720 = 025s7x6, ?x9840 = 02p0tjr, ?x10891 = 0g5gq, ?x6586 = 05gh50, taxonomy(?x5337, ?x939), nutrient(?x4068, ?x5337), nutrient(?x3264, ?x5337), ?x3203 = 04kl74p, ?x4068 = 0fbw6, ?x6160 = 041r51, ?x6026 = 025sf8g, ?x10612 = 0frq6, ?x5009 = 0fjfh, ?x7652 = 025s0s0, ?x6032 = 01nkt >> conf = 0.89 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0h1sg nutrient! 0fbw6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 56.000 0.894 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #7713-0fv4v PRED entity: 0fv4v PRED relation: olympics PRED expected values: 0l6ny => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 43): 06sks6 (0.71 #69, 0.57 #974, 0.56 #628), 0kbvb (0.67 #50, 0.56 #609, 0.53 #955), 0kbws (0.65 #58, 0.56 #963, 0.55 #101), 0jhn7 (0.61 #72, 0.54 #977, 0.53 #891), 0jdk_ (0.59 #71, 0.51 #976, 0.51 #630), 0l6m5 (0.45 #53, 0.42 #612, 0.39 #958), 0l6mp (0.43 #62, 0.37 #621, 0.36 #967), 0lgxj (0.41 #73, 0.35 #632, 0.32 #116), 0l6ny (0.39 #95, 0.35 #52, 0.32 #611), 0l98s (0.37 #48, 0.30 #607, 0.28 #953) >> Best rule #69 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 06sff; 06s9y; >> query: (?x7360, 06sks6) <- country(?x1352, ?x7360), ?x1352 = 0w0d, adjustment_currency(?x7360, ?x170) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #95 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 54 *> proper extension: 0853g; *> query: (?x7360, 0l6ny) <- contains(?x2467, ?x7360), exported_to(?x7360, ?x10450) *> conf = 0.39 ranks of expected_values: 9 EVAL 0fv4v olympics 0l6ny CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 83.000 83.000 0.706 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #7712-030qb3t PRED entity: 030qb3t PRED relation: origin! PRED expected values: 04rcr 0fpj4lx 01mr2g6 => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 1023): 02cyfz (0.25 #22702, 0.18 #22701, 0.18 #70967), 01nqfh_ (0.25 #22702, 0.18 #22701, 0.18 #70967), 02jxmr (0.25 #22702, 0.18 #22701, 0.09 #58668), 07qy0b (0.25 #22702, 0.18 #22701, 0.09 #58668), 01d4cb (0.25 #22702, 0.18 #22701, 0.09 #58668), 0bxtyq (0.25 #22702, 0.18 #22701, 0.09 #58668), 03xnq9_ (0.25 #22702, 0.18 #70967, 0.09 #58668), 02bc74 (0.25 #22702, 0.18 #70967, 0.09 #58668), 01vw26l (0.25 #22702, 0.09 #58668, 0.09 #54410), 0hgqq (0.25 #22702, 0.09 #58668, 0.09 #54410) >> Best rule #22702 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 0fs_s; >> query: (?x1523, ?x5657) <- place_of_birth(?x5657, ?x1523), place_of_birth(?x4428, ?x1523), artists(?x302, ?x5657), music(?x218, ?x4428) >> conf = 0.25 => this is the best rule for 10 predicted values *> Best rule #53464 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 168 *> proper extension: 010016; 0dzs0; *> query: (?x1523, ?x65) <- citytown(?x735, ?x1523), contains(?x1523, ?x682), student(?x735, ?x65) *> conf = 0.01 ranks of expected_values: 880 EVAL 030qb3t origin! 01mr2g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 181.000 181.000 0.254 http://example.org/music/artist/origin EVAL 030qb3t origin! 0fpj4lx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 181.000 0.254 http://example.org/music/artist/origin EVAL 030qb3t origin! 04rcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 181.000 0.254 http://example.org/music/artist/origin #7711-06sn8m PRED entity: 06sn8m PRED relation: profession PRED expected values: 01d_h8 => 145 concepts (53 used for prediction) PRED predicted values (max 10 best out of 59): 0dxtg (0.35 #3272, 0.33 #3716, 0.31 #4308), 018gz8 (0.31 #3275, 0.31 #3867, 0.31 #3719), 03gjzk (0.31 #607, 0.31 #2828, 0.31 #4013), 01d_h8 (0.30 #5633, 0.30 #3560, 0.28 #6818), 02krf9 (0.29 #766, 0.25 #618, 0.21 #4024), 02jknp (0.23 #3562, 0.21 #2377, 0.21 #4006), 01c72t (0.17 #23, 0.09 #5354, 0.06 #3577), 0cbd2 (0.16 #5634, 0.16 #7115, 0.15 #6819), 0kyk (0.15 #5656, 0.14 #7285, 0.13 #7137), 0nbcg (0.11 #3585, 0.06 #5362, 0.06 #623) >> Best rule #3272 for best value: >> intensional similarity = 5 >> extensional distance = 94 >> proper extension: 04cr6qv; 02jyhv; >> query: (?x6962, 0dxtg) <- profession(?x6962, ?x1383), profession(?x6962, ?x1032), people(?x1446, ?x6962), ?x1032 = 02hrh1q, ?x1383 = 0np9r >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #5633 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 434 *> proper extension: 05m63c; 04pxcx; 054fvj; 0cymln; 059y0; *> query: (?x6962, 01d_h8) <- student(?x10497, ?x6962), location(?x6962, ?x739), people(?x1446, ?x6962), currency(?x10497, ?x170) *> conf = 0.30 ranks of expected_values: 4 EVAL 06sn8m profession 01d_h8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 53.000 0.354 http://example.org/people/person/profession #7710-015cjr PRED entity: 015cjr PRED relation: profession! PRED expected values: 01pctb 043zg 015pvh 01nbq4 05hjmd 02v2jy => 56 concepts (31 used for prediction) PRED predicted values (max 10 best out of 3981): 0dpqk (0.73 #63837, 0.67 #76289, 0.67 #59688), 015pxr (0.69 #20754, 0.65 #20753, 0.60 #37946), 0151w_ (0.69 #20754, 0.65 #20753, 0.60 #33454), 051wwp (0.69 #20754, 0.65 #20753, 0.60 #34760), 027cxsm (0.69 #20754, 0.65 #20753, 0.60 #33638), 0bz5v2 (0.69 #20754, 0.65 #20753, 0.54 #24904), 0bczgm (0.69 #20754, 0.65 #20753, 0.54 #24904), 0170pk (0.69 #20754, 0.65 #20753, 0.54 #24904), 06j8wx (0.69 #20754, 0.65 #20753, 0.54 #24904), 05cj4r (0.69 #20754, 0.65 #20753, 0.54 #24904) >> Best rule #63837 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 016z4k; 0cbd2; >> query: (?x4725, 0dpqk) <- profession(?x12194, ?x4725), profession(?x7398, ?x4725), student(?x3439, ?x7398), instrumentalists(?x1212, ?x7398), artists(?x2480, ?x12194), ?x2480 = 01z4y, ?x1212 = 07xzm >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #24755 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 03gjzk; *> query: (?x4725, 02v2jy) <- profession(?x12479, ?x4725), profession(?x12194, ?x4725), profession(?x11208, ?x4725), profession(?x7040, ?x4725), profession(?x5413, ?x4725), profession(?x2127, ?x4725), ?x12194 = 01mbwlb, ?x5413 = 01yg9y, ?x7040 = 02b9g4, type_of_union(?x12479, ?x566), ?x2127 = 01j7rd, award_nominee(?x11208, ?x8609) *> conf = 0.50 ranks of expected_values: 776, 1036, 1163, 1944, 3500 EVAL 015cjr profession! 02v2jy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 31.000 0.727 http://example.org/people/person/profession EVAL 015cjr profession! 05hjmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 31.000 0.727 http://example.org/people/person/profession EVAL 015cjr profession! 01nbq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 31.000 0.727 http://example.org/people/person/profession EVAL 015cjr profession! 015pvh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 31.000 0.727 http://example.org/people/person/profession EVAL 015cjr profession! 043zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 31.000 0.727 http://example.org/people/person/profession EVAL 015cjr profession! 01pctb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 31.000 0.727 http://example.org/people/person/profession #7709-05c26ss PRED entity: 05c26ss PRED relation: film! PRED expected values: 05fnl9 016_mj => 76 concepts (59 used for prediction) PRED predicted values (max 10 best out of 943): 056ws9 (0.62 #6220, 0.50 #66349, 0.45 #45613), 018ygt (0.41 #26952, 0.02 #21846, 0.02 #23920), 02lj6p (0.41 #26952, 0.02 #22221, 0.02 #26368), 016_mj (0.41 #26952, 0.02 #10659, 0.02 #25171), 020ffd (0.41 #26952, 0.01 #23889, 0.01 #25962), 0c33pl (0.41 #26952), 01w9wwg (0.41 #26952), 02v0ff (0.41 #26952), 01svw8n (0.41 #26952), 0p_47 (0.29 #2745, 0.03 #46285, 0.02 #21403) >> Best rule #6220 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0407yfx; >> query: (?x3839, ?x5970) <- film_release_region(?x3839, ?x7748), genre(?x3839, ?x258), ?x7748 = 01xbgx, nominated_for(?x5970, ?x3839) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #26952 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 152 *> proper extension: 0140g4; 01hr1; 0g5pv3; 02vw1w2; 018nnz; 01kf3_9; 01f7kl; 01kf4tt; 0dnqr; 03l6q0; ... *> query: (?x3839, ?x1835) <- film_release_distribution_medium(?x3839, ?x81), film(?x396, ?x3839), prequel(?x3839, ?x7806), film(?x1835, ?x7806) *> conf = 0.41 ranks of expected_values: 4, 243 EVAL 05c26ss film! 016_mj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 59.000 0.625 http://example.org/film/actor/film./film/performance/film EVAL 05c26ss film! 05fnl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 76.000 59.000 0.625 http://example.org/film/actor/film./film/performance/film #7708-072zl1 PRED entity: 072zl1 PRED relation: currency PRED expected values: 09nqf => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.76 #64, 0.75 #57, 0.75 #50), 02l6h (0.21 #18, 0.06 #32, 0.01 #81), 01nv4h (0.11 #16, 0.10 #9, 0.06 #30) >> Best rule #64 for best value: >> intensional similarity = 3 >> extensional distance = 902 >> proper extension: 0cp08zg; 0267wwv; >> query: (?x7320, 09nqf) <- genre(?x7320, ?x53), production_companies(?x7320, ?x6554), nominated_for(?x3327, ?x7320) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 072zl1 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.764 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #7707-02_7t PRED entity: 02_7t PRED relation: major_field_of_study! PRED expected values: 014mlp => 77 concepts (73 used for prediction) PRED predicted values (max 10 best out of 14): 014mlp (0.79 #589, 0.78 #728, 0.77 #440), 01ysy9 (0.33 #79, 0.33 #65, 0.33 #39), 02m4yg (0.33 #100, 0.33 #60, 0.33 #34), 01gkg3 (0.33 #86, 0.33 #59, 0.33 #33), 0bjrnt (0.33 #69, 0.33 #171, 0.32 #265), 028dcg (0.33 #50, 0.33 #171, 0.30 #628), 03mkk4 (0.33 #171, 0.30 #628, 0.29 #712), 01rr_d (0.33 #171, 0.30 #628, 0.29 #712), 013zdg (0.33 #171, 0.30 #628, 0.29 #712), 02cq61 (0.33 #171, 0.30 #628, 0.29 #712) >> Best rule #589 for best value: >> intensional similarity = 10 >> extensional distance = 79 >> proper extension: 0557q; >> query: (?x7134, 014mlp) <- major_field_of_study(?x865, ?x7134), institution(?x865, ?x12276), institution(?x865, ?x11229), institution(?x865, ?x5844), institution(?x865, ?x4603), major_field_of_study(?x7134, ?x1527), category(?x11229, ?x134), company(?x3484, ?x12276), ?x4603 = 0hd7j, student(?x5844, ?x9232) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_7t major_field_of_study! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 73.000 0.790 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #7706-0gct_ PRED entity: 0gct_ PRED relation: gender PRED expected values: 05zppz => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 5): 05zppz (0.91 #114, 0.90 #92, 0.89 #88), 02zsn (0.46 #179, 0.46 #217, 0.45 #200), 0fltx (0.12 #145, 0.12 #136), 01hbgs (0.12 #145, 0.12 #136), 0c58k (0.12 #145, 0.12 #136) >> Best rule #114 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 03j90; >> query: (?x4679, 05zppz) <- nationality(?x4679, ?x789), place_of_death(?x4679, ?x12542), influenced_by(?x4679, ?x10923), nationality(?x10923, ?x10382) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gct_ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.912 http://example.org/people/person/gender #7705-0j_tw PRED entity: 0j_tw PRED relation: film_crew_role PRED expected values: 09zzb8 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.80 #1260, 0.75 #1373, 0.71 #1821), 0ch6mp2 (0.77 #1268, 0.74 #1829, 0.74 #1381), 09vw2b7 (0.69 #1267, 0.65 #1380, 0.64 #1828), 02r96rf (0.67 #1263, 0.65 #1376, 0.64 #1787), 01vx2h (0.41 #1272, 0.33 #1385, 0.32 #1796), 01pvkk (0.30 #1423, 0.29 #199, 0.29 #347), 04pyp5 (0.23 #93, 0.15 #56, 0.12 #19), 02ynfr (0.18 #1390, 0.17 #1277, 0.16 #1838), 0215hd (0.16 #428, 0.15 #761, 0.14 #1841), 02rh1dz (0.16 #1271, 0.11 #1384, 0.11 #1570) >> Best rule #1260 for best value: >> intensional similarity = 3 >> extensional distance = 385 >> proper extension: 0d_2fb; >> query: (?x2104, 09zzb8) <- film_crew_role(?x2104, ?x2095), ?x2095 = 0dxtw, film(?x1104, ?x2104) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j_tw film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.796 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7704-026zvx7 PRED entity: 026zvx7 PRED relation: award_nominee! PRED expected values: 035gjq 05lb65 => 81 concepts (40 used for prediction) PRED predicted values (max 10 best out of 846): 04vmqg (0.82 #4643, 0.82 #9287, 0.81 #18574), 07z1_q (0.82 #4643, 0.82 #9287, 0.81 #18574), 035gjq (0.82 #4643, 0.82 #9287, 0.81 #18574), 0443y3 (0.82 #4643, 0.82 #9287, 0.81 #6965), 026zvx7 (0.80 #5199, 0.79 #2877, 0.67 #7521), 05lb65 (0.73 #8512, 0.67 #10834, 0.60 #6190), 06b0d2 (0.57 #9507, 0.57 #2541, 0.53 #4863), 05lb87 (0.56 #16524, 0.36 #2594, 0.34 #18847), 01rs5p (0.50 #4484, 0.48 #11450, 0.47 #9128), 030znt (0.43 #2595, 0.40 #4917, 0.38 #9561) >> Best rule #4643 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 05lb65; 04vmqg; >> query: (?x2579, ?x444) <- award_nominee(?x2579, ?x7842), award_nominee(?x2579, ?x4976), award_nominee(?x2579, ?x444), ?x7842 = 048hf, ?x4976 = 05683p >> conf = 0.82 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 6 EVAL 026zvx7 award_nominee! 05lb65 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 81.000 40.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 026zvx7 award_nominee! 035gjq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 81.000 40.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7703-02p65p PRED entity: 02p65p PRED relation: award_winner! PRED expected values: 09bymc => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 98): 0hndn2q (0.12 #3059, 0.11 #594, 0.04 #733), 026kqs9 (0.12 #3059, 0.11 #506, 0.09 #89), 02hn5v (0.12 #3059, 0.09 #40, 0.07 #318), 02yv_b (0.12 #3059, 0.09 #23, 0.07 #301), 09g90vz (0.12 #3059, 0.08 #261, 0.05 #1095), 092t4b (0.12 #3059, 0.08 #189, 0.05 #1023), 0clfdj (0.12 #3059, 0.08 #143, 0.04 #977), 0gpjbt (0.12 #3059, 0.08 #166, 0.03 #4337), 056878 (0.12 #3059, 0.08 #169, 0.03 #4340), 09bymc (0.12 #3059, 0.08 #258, 0.02 #1092) >> Best rule #3059 for best value: >> intensional similarity = 3 >> extensional distance = 1426 >> proper extension: 0l56b; >> query: (?x192, ?x1112) <- award_nominee(?x192, ?x3308), award_winner(?x704, ?x192), award_winner(?x1112, ?x3308) >> conf = 0.12 => this is the best rule for 25 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 10 EVAL 02p65p award_winner! 09bymc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 79.000 79.000 0.116 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7702-02m92h PRED entity: 02m92h PRED relation: nationality PRED expected values: 09c7w0 => 76 concepts (55 used for prediction) PRED predicted values (max 10 best out of 14): 09c7w0 (0.83 #301, 0.83 #101, 0.82 #1), 02jx1 (0.11 #1233, 0.11 #2233, 0.10 #1033), 07ssc (0.09 #1315, 0.08 #1215, 0.08 #1815), 0d060g (0.06 #407, 0.06 #507, 0.05 #1107), 03rk0 (0.05 #5247, 0.05 #246, 0.04 #1246), 0chghy (0.03 #410, 0.02 #1410, 0.02 #1510), 0345h (0.02 #1331, 0.02 #1231, 0.02 #231), 0f8l9c (0.02 #422, 0.02 #722, 0.02 #5223), 0d05w3 (0.02 #650), 03rt9 (0.02 #913, 0.01 #1313, 0.01 #613) >> Best rule #301 for best value: >> intensional similarity = 2 >> extensional distance = 184 >> proper extension: 02k76g; >> query: (?x8519, 09c7w0) <- profession(?x8519, ?x319), tv_program(?x8519, ?x6884) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02m92h nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 55.000 0.828 http://example.org/people/person/nationality #7701-02ynfr PRED entity: 02ynfr PRED relation: specialization_of PRED expected values: 0n1h => 62 concepts (45 used for prediction) PRED predicted values (max 10 best out of 27): 0n1h (0.56 #734, 0.50 #69, 0.33 #352), 0cbd2 (0.46 #602, 0.10 #1120, 0.10 #1087), 02hrh1q (0.46 #574, 0.05 #1225, 0.05 #1292), 01c979 (0.42 #560, 0.33 #55, 0.25 #151), 09jwl (0.19 #1060, 0.15 #1293, 0.14 #1392), 03nfmq (0.12 #425, 0.11 #456), 03qh03g (0.11 #452), 06q2q (0.09 #1198, 0.09 #1164, 0.08 #1265), 015cjr (0.08 #583, 0.04 #1433, 0.03 #1234), 02ynfr (0.03 #1086, 0.03 #1085, 0.03 #697) >> Best rule #734 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 05wkw; >> query: (?x3197, 0n1h) <- specialization_of(?x3197, ?x1527), specialization_of(?x11804, ?x1527), profession(?x7077, ?x11804), profession(?x6444, ?x11804), ?x7077 = 016xk5, ?x6444 = 012q4n >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ynfr specialization_of 0n1h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 45.000 0.562 http://example.org/people/profession/specialization_of #7700-07ylj PRED entity: 07ylj PRED relation: film_release_region! PRED expected values: 0h1cdwq 0jjy0 04w7rn 040rmy 047svrl 0kv238 05zlld0 02vr3gz 017jd9 062zm5h 0gmd3k7 02825nf => 111 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1293): 017jd9 (0.93 #4321, 0.74 #6835, 0.72 #11863), 0jjy0 (0.89 #3888, 0.71 #6402, 0.59 #11430), 0872p_c (0.89 #3893, 0.70 #11435, 0.65 #6407), 087wc7n (0.89 #3855, 0.65 #6369, 0.62 #11397), 04w7rn (0.89 #3934, 0.61 #11476, 0.53 #6448), 062zm5h (0.85 #4381, 0.75 #11923, 0.65 #6895), 0by1wkq (0.85 #3984, 0.68 #6498, 0.65 #2727), 02vr3gz (0.85 #4208, 0.68 #6722, 0.58 #2951), 0661m4p (0.85 #4028, 0.67 #11570, 0.62 #6542), 0bh8tgs (0.85 #4395, 0.66 #11937, 0.59 #6909) >> Best rule #4321 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 05r4w; 0jgd; 0b90_r; 0154j; 03rjj; 0d060g; 0d0vqn; 0chghy; 03rt9; 05qhw; ... >> query: (?x1203, 017jd9) <- film_release_region(?x6446, ?x1203), film_release_region(?x3377, ?x1203), ?x3377 = 0gj8nq2, ?x6446 = 089j8p >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 6, 8, 17, 25, 45, 67, 87, 107, 172 EVAL 07ylj film_release_region! 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 0gmd3k7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 062zm5h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 017jd9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 02vr3gz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 05zlld0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 0kv238 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 047svrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 040rmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 04w7rn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 0jjy0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07ylj film_release_region! 0h1cdwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 111.000 54.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7699-06pj8 PRED entity: 06pj8 PRED relation: people! PRED expected values: 041rx => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 58): 041rx (0.63 #2740, 0.25 #80, 0.24 #156), 0x67 (0.19 #4570, 0.18 #7080, 0.18 #5102), 033tf_ (0.18 #539, 0.15 #463, 0.13 #919), 07bch9 (0.12 #631, 0.10 #1163, 0.09 #707), 02ctzb (0.12 #623, 0.09 #1155, 0.09 #471), 07hwkr (0.12 #468, 0.06 #88, 0.06 #2976), 0xnvg (0.12 #1381, 0.08 #1837, 0.08 #2217), 01qhm_ (0.10 #614, 0.10 #158, 0.07 #538), 02w7gg (0.10 #1522, 0.10 #2206, 0.09 #7072), 03bkbh (0.09 #488, 0.03 #2540, 0.03 #2692) >> Best rule #2740 for best value: >> intensional similarity = 2 >> extensional distance = 164 >> proper extension: 0mcf4; >> query: (?x2135, 041rx) <- religion(?x2135, ?x7131), ?x7131 = 03_gx >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06pj8 people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.627 http://example.org/people/ethnicity/people #7698-080lkt7 PRED entity: 080lkt7 PRED relation: genre PRED expected values: 0219x_ => 94 concepts (81 used for prediction) PRED predicted values (max 10 best out of 132): 017fp (0.63 #592, 0.57 #7581, 0.56 #5804), 03k9fj (0.60 #2733, 0.35 #4040, 0.35 #3803), 01hmnh (0.53 #2740, 0.33 #372, 0.26 #4047), 05p553 (0.50 #358, 0.46 #2726, 0.43 #595), 02kdv5l (0.42 #4386, 0.39 #5567, 0.37 #6041), 01jfsb (0.41 #1908, 0.39 #4396, 0.38 #5577), 02l7c8 (0.34 #3213, 0.34 #3094, 0.33 #370), 06n90 (0.33 #367, 0.22 #2145, 0.22 #2027), 015w9s (0.33 #31, 0.17 #979, 0.17 #859), 0lsxr (0.29 #1905, 0.25 #127, 0.21 #2849) >> Best rule #592 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 07w8fz; 03p2xc; 0fzm0g; >> query: (?x4643, ?x162) <- film(?x8066, ?x4643), titles(?x162, ?x4643), film_release_distribution_medium(?x4643, ?x81), ?x8066 = 031k24 >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #3104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 125 *> proper extension: 0bs5k8r; 0f42nz; 02z0f6l; 09rvwmy; *> query: (?x4643, 0219x_) <- film(?x123, ?x4643), film_festivals(?x4643, ?x9080), titles(?x162, ?x4643), film_release_distribution_medium(?x4643, ?x81) *> conf = 0.13 ranks of expected_values: 32 EVAL 080lkt7 genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 94.000 81.000 0.632 http://example.org/film/film/genre #7697-016732 PRED entity: 016732 PRED relation: organizations_founded PRED expected values: 05byxm => 94 concepts (59 used for prediction) PRED predicted values (max 10 best out of 98): 034h1h (0.17 #338, 0.11 #1151), 043g7l (0.10 #912, 0.08 #1117, 0.07 #1422), 09xwz (0.08 #77, 0.07 #381, 0.06 #583), 01rz1 (0.06 #1128), 017jv5 (0.06 #322, 0.05 #423, 0.05 #524), 07wbk (0.05 #19, 0.04 #424, 0.04 #525), 02jd_7 (0.05 #63, 0.03 #367, 0.03 #468), 05f4p (0.05 #73, 0.03 #377, 0.03 #478), 0d6qjf (0.05 #80, 0.03 #384, 0.03 #485), 01cl2y (0.05 #35, 0.03 #339, 0.03 #440) >> Best rule #338 for best value: >> intensional similarity = 2 >> extensional distance = 69 >> proper extension: 08815; 06pwq; 01w3v; 07szy; 09kvv; 01w5m; 03ksy; 07tds; 02zd460; 01p5xy; ... >> query: (?x6792, 034h1h) <- organizations_founded(?x6792, ?x2190), category(?x2190, ?x134) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016732 organizations_founded 05byxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 59.000 0.169 http://example.org/organization/organization_founder/organizations_founded #7696-07p7g PRED entity: 07p7g PRED relation: category PRED expected values: 08mbj5d => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.58 #8, 0.58 #5, 0.56 #4) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 0fngy; >> query: (?x13844, 08mbj5d) <- capital(?x4120, ?x13844), adjoins(?x4120, ?x291), country(?x1121, ?x4120) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07p7g category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.583 http://example.org/common/topic/webpage./common/webpage/category #7695-07kjk7c PRED entity: 07kjk7c PRED relation: ceremony PRED expected values: 02q690_ 0bx6zs 0hn821n => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 132): 02q690_ (0.73 #193, 0.54 #325, 0.43 #721), 0gpjbt (0.51 #1877, 0.48 #2009, 0.47 #1613), 0hn821n (0.50 #254, 0.39 #386, 0.25 #782), 09n4nb (0.49 #1893, 0.47 #1629, 0.47 #2025), 05pd94v (0.49 #1851, 0.46 #1587, 0.46 #1983), 0466p0j (0.49 #1919, 0.46 #2051, 0.46 #1655), 02rjjll (0.49 #1854, 0.46 #1986, 0.46 #1590), 056878 (0.49 #1879, 0.46 #2011, 0.46 #1615), 02cg41 (0.48 #1966, 0.46 #2098, 0.45 #1702), 01c6qp (0.47 #1867, 0.45 #1999, 0.44 #1603) >> Best rule #193 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 0bfvw2; 0bp_b2; 09qvc0; 09qj50; 09qv3c; 0bdwft; 0cjyzs; 0bdx29; 0bdw6t; 0bfvd4; ... >> query: (?x7850, 02q690_) <- award(?x293, ?x7850), award(?x361, ?x7850), ceremony(?x7850, ?x2213), ?x2213 = 0gvstc3, nominated_for(?x7850, ?x1994) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 17 EVAL 07kjk7c ceremony 0hn821n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 43.000 43.000 0.731 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 07kjk7c ceremony 0bx6zs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 43.000 43.000 0.731 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 07kjk7c ceremony 02q690_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.731 http://example.org/award/award_category/winners./award/award_honor/ceremony #7694-03bx0bm PRED entity: 03bx0bm PRED relation: group PRED expected values: 07c0j 0khth 0ycp3 0knhk 09z1lg 016ppr 016vj5 => 79 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1484): 02dw1_ (0.67 #2947, 0.67 #2542, 0.67 #1062), 01qqwp9 (0.67 #1130, 0.67 #1047, 0.67 #887), 06gcn (0.67 #1483, 0.67 #1155, 0.67 #912), 0134wr (0.67 #1160, 0.67 #1077, 0.62 #2142), 011_vz (0.67 #1169, 0.67 #1086, 0.57 #1331), 0163m1 (0.67 #1461, 0.67 #890, 0.53 #3097), 0qmpd (0.67 #1498, 0.67 #927, 0.50 #1170), 01q99h (0.67 #1474, 0.67 #1146, 0.50 #1063), 0mjn2 (0.67 #1180, 0.60 #856, 0.57 #1260), 017_hq (0.67 #1101, 0.60 #781, 0.57 #1346) >> Best rule #2947 for best value: >> intensional similarity = 14 >> extensional distance = 16 >> proper extension: 0mkg; 0gghm; 07_l6; 01xqw; >> query: (?x1466, 02dw1_) <- role(?x8819, ?x1466), role(?x7162, ?x1466), role(?x1291, ?x1466), role(?x1466, ?x745), group(?x1466, ?x13039), group(?x1466, ?x11425), group(?x1466, ?x5329), ?x745 = 01vj9c, profession(?x8819, ?x131), award_nominee(?x1292, ?x1291), award_winner(?x2634, ?x5329), ?x11425 = 02vnpv, award_nominee(?x7162, ?x954), artists(?x302, ?x13039) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2286 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 12 *> proper extension: 01vj9c; *> query: (?x1466, 0khth) <- role(?x1291, ?x1466), role(?x1466, ?x745), role(?x1466, ?x645), group(?x1466, ?x442), role(?x211, ?x745), performance_role(?x248, ?x1466), ?x645 = 028tv0, person(?x1619, ?x1291), profession(?x1291, ?x131), award_nominee(?x1292, ?x1291) *> conf = 0.57 ranks of expected_values: 24, 29, 34, 44, 72, 75, 886 EVAL 03bx0bm group 016vj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 79.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 03bx0bm group 016ppr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 79.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 03bx0bm group 09z1lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 79.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 03bx0bm group 0knhk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 79.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 03bx0bm group 0ycp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 79.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 03bx0bm group 0khth CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 79.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 03bx0bm group 07c0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 79.000 53.000 0.667 http://example.org/music/performance_role/regular_performances./music/group_membership/group #7693-033071 PRED entity: 033071 PRED relation: film PRED expected values: 0n08r => 67 concepts (49 used for prediction) PRED predicted values (max 10 best out of 593): 05sy0cv (0.60 #32205, 0.42 #12525, 0.39 #66206), 02bqvs (0.20 #1498), 01kff7 (0.10 #1997, 0.08 #50102, 0.07 #39366), 016z9n (0.10 #370, 0.08 #76943, 0.05 #2159), 03kx49 (0.10 #1343, 0.08 #76943, 0.05 #3132), 011ykb (0.10 #1142, 0.05 #2931, 0.02 #6510), 07phbc (0.10 #1641, 0.05 #3430), 0sxns (0.10 #1078, 0.05 #4657, 0.01 #20759), 06t2t2 (0.10 #1659, 0.04 #85892, 0.02 #5238), 049xgc (0.10 #973, 0.04 #85892, 0.02 #6341) >> Best rule #32205 for best value: >> intensional similarity = 4 >> extensional distance = 754 >> proper extension: 049tjg; 03f1zdw; 030znt; 02wrhj; 01hkhq; 03q1vd; 02j9lm; 0807ml; 0bl60p; 036hf4; ... >> query: (?x11972, ?x8837) <- type_of_union(?x11972, ?x566), film(?x11972, ?x4166), location(?x11972, ?x739), nominated_for(?x11972, ?x8837) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 033071 film 0n08r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 49.000 0.598 http://example.org/film/actor/film./film/performance/film #7692-04tz52 PRED entity: 04tz52 PRED relation: film! PRED expected values: 0347xz => 77 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1165): 0bzyh (0.23 #4163, 0.19 #10408, 0.15 #68689), 0164r9 (0.20 #1316, 0.02 #3397, 0.02 #7560), 01w1kyf (0.20 #909, 0.02 #40454, 0.02 #42535), 0d6d2 (0.20 #1428, 0.01 #13917, 0.01 #38892), 02zfg3 (0.20 #2037, 0.01 #39501, 0.01 #22850), 040z9 (0.20 #1292, 0.01 #49162), 076689 (0.20 #1893), 05ggt_ (0.20 #1715), 0ly5n (0.20 #653), 0p8r1 (0.19 #13075, 0.11 #21399, 0.03 #50539) >> Best rule #4163 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 07k2mq; >> query: (?x2816, ?x3960) <- film(?x190, ?x2816), film(?x902, ?x2816), film_release_region(?x2816, ?x142), film_release_region(?x2816, ?x94), film(?x3960, ?x2816) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #26688 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 96 *> proper extension: 0b85mm; *> query: (?x2816, 0347xz) <- film(?x7754, ?x2816), film_release_region(?x2816, ?x142), gender(?x7754, ?x514), genre(?x2816, ?x225), film_release_region(?x2816, ?x94) *> conf = 0.01 ranks of expected_values: 1161 EVAL 04tz52 film! 0347xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 77.000 52.000 0.227 http://example.org/film/actor/film./film/performance/film #7691-03b1l8 PRED entity: 03b1l8 PRED relation: award PRED expected values: 09cn0c 03ybrwc => 77 concepts (63 used for prediction) PRED predicted values (max 10 best out of 182): 0gr0m (0.27 #4840, 0.27 #4839, 0.26 #1152), 0k611 (0.27 #4840, 0.27 #4839, 0.26 #1152), 054krc (0.27 #4840, 0.27 #4839, 0.26 #1152), 0l8z1 (0.27 #4840, 0.27 #4839, 0.26 #1152), 0gqwc (0.27 #4840, 0.27 #4839, 0.26 #1152), 02qvyrt (0.27 #4840, 0.27 #4839, 0.26 #1152), 094qd5 (0.27 #4840, 0.27 #4839, 0.26 #1152), 03hl6lc (0.27 #4840, 0.27 #4839, 0.26 #1152), 0gq_v (0.16 #940, 0.14 #19, 0.06 #1633), 019f4v (0.15 #973, 0.07 #1436, 0.06 #1666) >> Best rule #4840 for best value: >> intensional similarity = 2 >> extensional distance = 1002 >> proper extension: 0g60z; 02_1q9; 080dwhx; 02_1rq; 03kq98; 072kp; 039fgy; 0kfpm; 02k_4g; 02nf2c; ... >> query: (?x7941, ?x1443) <- award(?x7941, ?x637), nominated_for(?x1443, ?x7941) >> conf = 0.27 => this is the best rule for 8 predicted values *> Best rule #5302 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x7941, ?x9130) <- award(?x7941, ?x1716), award(?x5107, ?x1716), award(?x5107, ?x9130) *> conf = 0.05 ranks of expected_values: 50 EVAL 03b1l8 award 03ybrwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 63.000 0.269 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 03b1l8 award 09cn0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 77.000 63.000 0.269 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #7690-048j4l PRED entity: 048j4l PRED relation: instrumentalists PRED expected values: 0kzy0 => 46 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1374): 01vvycq (0.80 #4339, 0.75 #3725, 0.60 #649), 01vw20_ (0.70 #4471, 0.67 #1395, 0.62 #3857), 01sb5r (0.70 #4545, 0.62 #3931, 0.60 #855), 018y81 (0.70 #4657, 0.62 #4043, 0.60 #967), 0473q (0.70 #4711, 0.62 #4097, 0.60 #1021), 01vrnsk (0.67 #2847, 0.67 #1615, 0.50 #3462), 018gkb (0.67 #3034, 0.67 #1802, 0.50 #4878), 01gg59 (0.67 #3298, 0.64 #10672, 0.62 #3913), 095x_ (0.67 #3525, 0.62 #4140, 0.60 #1064), 012x4t (0.67 #2547, 0.60 #701, 0.50 #4391) >> Best rule #4339 for best value: >> intensional similarity = 19 >> extensional distance = 8 >> proper extension: 0342h; 018vs; 03gvt; >> query: (?x7938, 01vvycq) <- instrumentalists(?x7938, ?x4052), instrumentalists(?x7938, ?x2945), award(?x2945, ?x1079), role(?x2945, ?x314), gender(?x2945, ?x231), profession(?x2945, ?x6565), profession(?x2945, ?x1183), profession(?x2945, ?x131), ?x131 = 0dz3r, ?x6565 = 0fnpj, ?x1183 = 09jwl, role(?x4052, ?x2048), artists(?x284, ?x4052), award(?x9946, ?x1079), ?x9946 = 015wc0, ceremony(?x1079, ?x78), nominated_for(?x1079, ?x167), ?x2048 = 018j2, artists(?x302, ?x2945) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1873 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 4 *> proper extension: 0l14qv; *> query: (?x7938, 0kzy0) <- instrumentalists(?x7938, ?x2945), instrumentalists(?x7938, ?x1322), instrumentalists(?x7938, ?x677), ?x2945 = 01271h, role(?x1322, ?x716), role(?x1322, ?x432), origin(?x1322, ?x9846), award(?x1322, ?x1323), profession(?x1322, ?x2348), spouse(?x677, ?x2697), instrumentalists(?x432, ?x6067), ?x6067 = 018y81, role(?x432, ?x75), student(?x5149, ?x677), role(?x1652, ?x432), ?x2348 = 0nbcg, ?x1652 = 01l1sq *> conf = 0.33 ranks of expected_values: 196 EVAL 048j4l instrumentalists 0kzy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 19.000 0.800 http://example.org/music/instrument/instrumentalists #7689-02g9p4 PRED entity: 02g9p4 PRED relation: role! PRED expected values: 037hgm => 83 concepts (53 used for prediction) PRED predicted values (max 10 best out of 815): 016h9b (0.50 #1834, 0.40 #4490, 0.40 #1248), 04mx7s (0.50 #1987, 0.40 #4643, 0.40 #1107), 0167v4 (0.50 #2005, 0.40 #1419, 0.40 #1125), 016jfw (0.50 #756, 0.40 #1635, 0.33 #1928), 02mx98 (0.44 #4047, 0.43 #2571, 0.40 #4932), 01mwsnc (0.44 #3959, 0.30 #11332, 0.30 #4254), 01kx_81 (0.43 #6792, 0.43 #2374, 0.40 #4735), 01vsnff (0.43 #2398, 0.33 #1811, 0.25 #6226), 0bg539 (0.40 #4445, 0.40 #1203, 0.36 #5031), 0473q (0.40 #4614, 0.36 #5200, 0.33 #1958) >> Best rule #1834 for best value: >> intensional similarity = 23 >> extensional distance = 4 >> proper extension: 0342h; >> query: (?x1482, 016h9b) <- role(?x2944, ?x1482), role(?x2048, ?x1482), role(?x1148, ?x1482), role(?x885, ?x1482), role(?x314, ?x1482), family(?x1482, ?x2377), role(?x2923, ?x1482), instrumentalists(?x885, ?x2784), role(?x3239, ?x885), ?x2048 = 018j2, ?x2944 = 0l14j_, ?x314 = 02sgy, role(?x4918, ?x2923), role(?x212, ?x2923), role(?x1482, ?x2785), role(?x885, ?x5676), ?x1148 = 02qjv, ?x5676 = 0151b0, role(?x1997, ?x885), ?x2785 = 0jtg0, performance_role(?x3239, ?x214), role(?x2923, ?x2205), instrumentalists(?x1482, ?x2662) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1306 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 3 *> proper extension: 0l14md; *> query: (?x1482, 037hgm) <- role(?x4917, ?x1482), role(?x3716, ?x1482), role(?x1969, ?x1482), role(?x314, ?x1482), role(?x1482, ?x315), ?x3716 = 03gvt, performance_role(?x1482, ?x1212), instrumentalists(?x1482, ?x10239), ?x314 = 02sgy, ?x1969 = 04rzd, instrumentalists(?x1212, ?x1992), role(?x1482, ?x2785), role(?x1212, ?x9987), role(?x1212, ?x4616), ?x2785 = 0jtg0, role(?x211, ?x1482), nationality(?x10239, ?x512), ?x4616 = 01rhl, ?x1992 = 01wz3cx, ?x9987 = 037c9s, ?x4917 = 06w7v, type_of_union(?x10239, ?x1873) *> conf = 0.40 ranks of expected_values: 18 EVAL 02g9p4 role! 037hgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 83.000 53.000 0.500 http://example.org/music/group_member/membership./music/group_membership/role #7688-0r3w7 PRED entity: 0r3w7 PRED relation: place_of_death! PRED expected values: 02lz1s => 112 concepts (86 used for prediction) PRED predicted values (max 10 best out of 857): 040z9 (0.17 #344, 0.13 #6735, 0.11 #1093), 02v2jy (0.17 #707, 0.13 #6735, 0.11 #1456), 01j851 (0.17 #485, 0.13 #6735, 0.11 #1234), 01p1z_ (0.17 #320, 0.13 #6735, 0.11 #1069), 01nrq5 (0.17 #123, 0.13 #6735, 0.11 #872), 014dq7 (0.17 #65, 0.13 #6735, 0.11 #814), 02whj (0.17 #31, 0.13 #6735, 0.11 #780), 057d89 (0.17 #30, 0.13 #6735, 0.11 #779), 02h48 (0.17 #669, 0.11 #1418, 0.05 #2167), 0h953 (0.17 #414, 0.11 #1163, 0.05 #1912) >> Best rule #344 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0f2tj; >> query: (?x13207, 040z9) <- time_zones(?x13207, ?x2950), place_of_death(?x8974, ?x13207), award_winner(?x8974, ?x5650), profession(?x8974, ?x1041), ?x1041 = 03gjzk >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r3w7 place_of_death! 02lz1s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 86.000 0.167 http://example.org/people/deceased_person/place_of_death #7687-03x16f PRED entity: 03x16f PRED relation: award PRED expected values: 0gkts9 => 116 concepts (92 used for prediction) PRED predicted values (max 10 best out of 267): 0bdw1g (0.74 #14178, 0.71 #9722, 0.70 #23498), 09sb52 (0.34 #16244, 0.31 #2470, 0.30 #2875), 0ck27z (0.32 #10624, 0.32 #11434, 0.27 #8598), 05pcn59 (0.30 #2916, 0.24 #2511, 0.21 #3321), 03c7tr1 (0.24 #2893, 0.21 #2488, 0.12 #4108), 05p09zm (0.22 #2959, 0.19 #2554, 0.19 #3364), 0bdwqv (0.22 #173, 0.12 #2198, 0.10 #5843), 09qj50 (0.21 #450, 0.13 #36462, 0.12 #855), 01by1l (0.20 #11859, 0.18 #13884, 0.15 #16721), 05b4l5x (0.17 #2841, 0.16 #2436, 0.15 #4056) >> Best rule #14178 for best value: >> intensional similarity = 2 >> extensional distance = 745 >> proper extension: 0kc6x; 065y4w7; 01y67v; 01jq34; 03yxwq; 0gsgr; 099ks0; 0kc8y; 02p10m; 05s34b; ... >> query: (?x8746, ?x686) <- category(?x8746, ?x134), award_winner(?x686, ?x8746) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #37273 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2312 *> proper extension: 0162c8; 03jvmp; 06rnl9; 0kk9v; 03n93; 031rx9; 0dh73w; 0fqy4p; 037hgm; 06hzsx; ... *> query: (?x8746, ?x678) <- award_nominee(?x8746, ?x10004), award_nominee(?x6851, ?x10004), award(?x6851, ?x678) *> conf = 0.07 ranks of expected_values: 69 EVAL 03x16f award 0gkts9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 116.000 92.000 0.739 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7686-09fqgj PRED entity: 09fqgj PRED relation: country PRED expected values: 07ssc => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 38): 07ssc (0.82 #597, 0.75 #16, 0.70 #910), 02jx1 (0.82 #597, 0.44 #6149, 0.40 #6806), 03k9fj (0.58 #955, 0.25 #954, 0.08 #1790), 03rt9 (0.44 #6149, 0.40 #6806, 0.37 #2028), 03rjj (0.44 #6149, 0.40 #6806, 0.37 #2028), 01hmnh (0.25 #954, 0.08 #1790, 0.08 #1789), 0345h (0.22 #205, 0.15 #1756, 0.15 #145), 0f8l9c (0.16 #913, 0.12 #3483, 0.12 #3782), 04v3q (0.12 #24, 0.08 #83, 0.04 #4898), 03_3d (0.08 #66, 0.07 #125, 0.06 #722) >> Best rule #597 for best value: >> intensional similarity = 5 >> extensional distance = 72 >> proper extension: 01jnc_; >> query: (?x10509, ?x512) <- produced_by(?x10509, ?x3858), influenced_by(?x5345, ?x3858), film(?x294, ?x10509), nationality(?x3858, ?x512), award(?x3858, ?x1375) >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 09fqgj country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.824 http://example.org/film/film/country #7685-0cj8x PRED entity: 0cj8x PRED relation: location_of_ceremony PRED expected values: 094jv => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 12): 0cv3w (0.03 #631, 0.02 #1468, 0.02 #273), 03gh4 (0.02 #63), 02_286 (0.01 #847, 0.01 #132, 0.01 #251), 0k049 (0.01 #123, 0.01 #1437, 0.01 #1077), 0kc40 (0.01 #222, 0.01 #341), 0k_q_ (0.01 #148, 0.01 #267), 0f2w0 (0.01 #141, 0.01 #260), 0r0m6 (0.01 #1483), 05qtj (0.01 #889), 0b90_r (0.01 #837) >> Best rule #631 for best value: >> intensional similarity = 2 >> extensional distance = 157 >> proper extension: 05m63c; 0d_84; 02qjj7; 08f3b1; 01wjrn; 01pl9g; 03rl84; 012s5j; 02mjmr; 04264n; ... >> query: (?x3002, 0cv3w) <- people(?x1446, ?x3002), ?x1446 = 033tf_ >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cj8x location_of_ceremony 094jv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 100.000 0.031 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #7684-016qtt PRED entity: 016qtt PRED relation: nationality PRED expected values: 09c7w0 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 55): 09c7w0 (0.82 #1101, 0.81 #1001, 0.81 #801), 02jx1 (0.19 #1634, 0.16 #1734, 0.16 #3736), 0d060g (0.14 #107, 0.08 #307, 0.07 #507), 07ssc (0.11 #215, 0.10 #6018, 0.09 #1516), 0ctw_b (0.11 #227, 0.04 #2903, 0.04 #8205), 03rk0 (0.06 #7149, 0.06 #9051, 0.06 #11253), 05jbn (0.05 #2302, 0.02 #1501), 0f8l9c (0.04 #2903, 0.04 #8205, 0.03 #9406), 03_3d (0.04 #2903, 0.04 #8205, 0.03 #9406), 03rjj (0.04 #2903, 0.04 #8205, 0.03 #9406) >> Best rule #1101 for best value: >> intensional similarity = 2 >> extensional distance = 60 >> proper extension: 0frmb1; >> query: (?x133, 09c7w0) <- person(?x3480, ?x133), type_of_union(?x133, ?x566) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016qtt nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.823 http://example.org/people/person/nationality #7683-0c4b8 PRED entity: 0c4b8 PRED relation: official_language PRED expected values: 02bv9 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 60): 0349s (0.33 #33, 0.20 #75, 0.10 #411), 04306rv (0.29 #718, 0.27 #550, 0.23 #2945), 064_8sq (0.29 #183, 0.20 #141, 0.20 #99), 02bv9 (0.23 #2945, 0.20 #62, 0.14 #2019), 0jzc (0.23 #2945, 0.14 #2019, 0.13 #1695), 02hxcvy (0.23 #2945, 0.14 #2019, 0.11 #277), 05zjd (0.23 #2945, 0.14 #2019, 0.11 #229), 03hkp (0.23 #2945, 0.14 #2019, 0.04 #1102), 0121sr (0.23 #2945, 0.14 #2019, 0.03 #3114), 09s02 (0.23 #2945, 0.14 #2019) >> Best rule #33 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 088q1s; >> query: (?x5738, 0349s) <- official_language(?x5738, ?x254), entity_involved(?x12031, ?x5738), ?x12031 = 02kxjx, capital(?x5738, ?x8751), combatants(?x12789, ?x5738), ?x12789 = 02h2z_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2945 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 59 *> proper extension: 04hzj; *> query: (?x5738, ?x254) <- capital(?x5738, ?x13319), form_of_government(?x5738, ?x6065), contains(?x792, ?x13319), adjoins(?x792, ?x3432), countries_spoken_in(?x254, ?x792), jurisdiction_of_office(?x182, ?x792) *> conf = 0.23 ranks of expected_values: 4 EVAL 0c4b8 official_language 02bv9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 123.000 123.000 0.333 http://example.org/location/country/official_language #7682-0blpg PRED entity: 0blpg PRED relation: titles! PRED expected values: 01z4y => 58 concepts (41 used for prediction) PRED predicted values (max 10 best out of 53): 01z4y (0.58 #1773, 0.50 #35, 0.46 #340), 04xvlr (0.45 #208, 0.30 #411, 0.29 #513), 07s9rl0 (0.36 #205, 0.33 #2661, 0.31 #2249), 07ssc (0.31 #1747, 0.13 #827, 0.12 #930), 024qqx (0.24 #589, 0.20 #182, 0.13 #1513), 02l7c8 (0.22 #2351, 0.21 #2454, 0.20 #2865), 05p553 (0.22 #2351, 0.21 #2454, 0.19 #2864), 06cvj (0.22 #2351, 0.21 #2454, 0.19 #2864), 01jfsb (0.18 #223, 0.15 #426, 0.14 #528), 06l3bl (0.15 #461, 0.09 #258, 0.04 #768) >> Best rule #1773 for best value: >> intensional similarity = 3 >> extensional distance = 430 >> proper extension: 01cjhz; 03j63k; 0jq2r; 06f0k; >> query: (?x3988, 01z4y) <- titles(?x307, ?x3988), titles(?x307, ?x9016), ?x9016 = 0bz6sq >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0blpg titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 41.000 0.579 http://example.org/media_common/netflix_genre/titles #7681-015t56 PRED entity: 015t56 PRED relation: film PRED expected values: 01q2nx => 96 concepts (79 used for prediction) PRED predicted values (max 10 best out of 511): 017jd9 (0.47 #67611, 0.42 #88963, 0.42 #42701), 084qpk (0.13 #121, 0.03 #1900, 0.03 #3679), 048vhl (0.13 #1486), 06q8qh (0.10 #2380, 0.10 #4159, 0.03 #97864), 02b6n9 (0.10 #5122, 0.07 #3343, 0.03 #97864), 031t2d (0.07 #2030, 0.07 #3809, 0.07 #251), 02v5_g (0.07 #2565, 0.07 #4344, 0.03 #97864), 01l_pn (0.07 #2740, 0.07 #4519, 0.03 #97864), 011ysn (0.07 #2340, 0.07 #4119, 0.03 #97864), 02qlp4 (0.07 #3463, 0.07 #5242, 0.03 #97864) >> Best rule #67611 for best value: >> intensional similarity = 2 >> extensional distance = 1275 >> proper extension: 05_pkf; 01m7f5r; 09myny; >> query: (?x2762, ?x972) <- location(?x2762, ?x5972), nominated_for(?x2762, ?x972) >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015t56 film 01q2nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 79.000 0.473 http://example.org/film/actor/film./film/performance/film #7680-03g5jw PRED entity: 03g5jw PRED relation: influenced_by PRED expected values: 014_lq 07mvp => 94 concepts (69 used for prediction) PRED predicted values (max 10 best out of 372): 07c0j (0.24 #3445, 0.14 #8574, 0.12 #13253), 04sd0 (0.24 #3831, 0.05 #17930, 0.04 #16222), 0dw4g (0.23 #11970, 0.23 #13252, 0.22 #14959), 03d9d6 (0.23 #11970, 0.23 #13252, 0.22 #14959), 01lc5 (0.22 #377, 0.06 #805, 0.05 #5935), 081nh (0.22 #63, 0.06 #491, 0.05 #5621), 02wh0 (0.19 #10208, 0.14 #11917, 0.14 #13199), 01vsy3q (0.19 #1430, 0.17 #2713, 0.17 #2286), 014zfs (0.19 #13276, 0.10 #17545, 0.08 #17971), 03sbs (0.17 #11760, 0.16 #13042, 0.16 #12613) >> Best rule #3445 for best value: >> intensional similarity = 2 >> extensional distance = 27 >> proper extension: 02_j7t; >> query: (?x1573, 07c0j) <- influenced_by(?x1573, ?x10561), group(?x7053, ?x10561) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #13253 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 0gs7x; 01ty4; *> query: (?x1573, ?x7902) <- peers(?x1573, ?x5547), influenced_by(?x1573, ?x5310), influenced_by(?x5310, ?x7902) *> conf = 0.12 ranks of expected_values: 51 EVAL 03g5jw influenced_by 07mvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 94.000 69.000 0.241 http://example.org/influence/influence_node/influenced_by EVAL 03g5jw influenced_by 014_lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 69.000 0.241 http://example.org/influence/influence_node/influenced_by #7679-09z1lg PRED entity: 09z1lg PRED relation: artists! PRED expected values: 02c8d7 08vlns 0233qs => 64 concepts (34 used for prediction) PRED predicted values (max 10 best out of 252): 016clz (0.67 #926, 0.64 #312, 0.62 #7391), 064t9 (0.61 #4943, 0.60 #935, 0.55 #321), 0gywn (0.45 #366, 0.40 #980, 0.29 #4988), 02x8m (0.45 #327, 0.33 #941, 0.23 #2173), 016jny (0.43 #104, 0.16 #1640, 0.14 #719), 0xhtw (0.40 #3709, 0.40 #4019, 0.39 #4636), 025sc50 (0.40 #972, 0.37 #2204, 0.36 #358), 03_d0 (0.36 #319, 0.27 #933, 0.21 #3396), 01lyv (0.36 #649, 0.31 #1263, 0.29 #34), 06j6l (0.34 #4978, 0.29 #5899, 0.29 #663) >> Best rule #926 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 01vw20_; 01vxlbm; 01dw_f; >> query: (?x9631, 016clz) <- artists(?x9630, ?x9631), artists(?x1572, ?x9631), ?x9630 = 012yc, ?x1572 = 06by7, award(?x9631, ?x884) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #641 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 0gcs9; 01m3b1t; *> query: (?x9631, 02c8d7) <- award_winner(?x486, ?x9631), award_winner(?x139, ?x9631), ?x139 = 05pd94v, ?x486 = 02rjjll, award_nominee(?x9631, ?x1566) *> conf = 0.07 ranks of expected_values: 99, 150, 218 EVAL 09z1lg artists! 0233qs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 64.000 34.000 0.667 http://example.org/music/genre/artists EVAL 09z1lg artists! 08vlns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 64.000 34.000 0.667 http://example.org/music/genre/artists EVAL 09z1lg artists! 02c8d7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 64.000 34.000 0.667 http://example.org/music/genre/artists #7678-0646qh PRED entity: 0646qh PRED relation: profession PRED expected values: 0dxtg => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 46): 0dxtg (0.88 #164, 0.87 #314, 0.82 #764), 02hrh1q (0.82 #1216, 0.80 #1666, 0.80 #1516), 03gjzk (0.70 #466, 0.67 #616, 0.65 #766), 01d_h8 (0.30 #456, 0.30 #3457, 0.30 #3757), 02jknp (0.27 #4202, 0.25 #8554, 0.25 #7053), 0cbd2 (0.24 #457, 0.20 #757, 0.19 #157), 02krf9 (0.20 #478, 0.18 #628, 0.17 #178), 09jwl (0.18 #2421, 0.18 #2271, 0.18 #1821), 018gz8 (0.17 #468, 0.17 #768, 0.17 #618), 016z4k (0.15 #905, 0.12 #1805, 0.12 #1655) >> Best rule #164 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 05qsxy; 02j8nx; 0cj2nl; 06jrhz; >> query: (?x6868, 0dxtg) <- award_nominee(?x415, ?x6868), award_winner(?x439, ?x6868), tv_program(?x6868, ?x4721) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0646qh profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.877 http://example.org/people/person/profession #7677-06rqw PRED entity: 06rqw PRED relation: parent_genre! PRED expected values: 059kh => 68 concepts (23 used for prediction) PRED predicted values (max 10 best out of 259): 0y3_8 (0.50 #1093, 0.50 #567, 0.33 #1884), 059kh (0.50 #569, 0.40 #3207, 0.33 #1095), 01h0kx (0.50 #656, 0.33 #1182, 0.33 #919), 0grjmv (0.50 #646, 0.33 #1172, 0.33 #909), 0xv2x (0.43 #1443, 0.33 #2235, 0.33 #1180), 03xnwz (0.33 #1080, 0.33 #817, 0.29 #1343), 0xjl2 (0.33 #1091, 0.33 #828, 0.29 #1354), 0bt7w (0.33 #1142, 0.33 #879, 0.29 #1405), 0g_bh (0.33 #1161, 0.33 #898, 0.25 #1688), 01gbcf (0.33 #1056, 0.33 #793, 0.25 #1583) >> Best rule #1093 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 08cyft; >> query: (?x6101, 0y3_8) <- artists(?x6101, ?x8060), artists(?x6101, ?x3894), artists(?x6101, ?x2521), award_winner(?x8060, ?x10209), ?x3894 = 01vxlbm, ?x10209 = 023p29, parent_genre(?x283, ?x6101), award(?x2521, ?x8994), ?x8994 = 02f6yz >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #569 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 064t9; 06by7; *> query: (?x6101, 059kh) <- artists(?x6101, ?x8060), artists(?x6101, ?x4628), artists(?x6101, ?x3894), award_winner(?x8060, ?x10209), ?x3894 = 01vxlbm, group(?x227, ?x8060), award_winner(?x2054, ?x8060), award(?x8060, ?x2139), ?x4628 = 016fnb *> conf = 0.50 ranks of expected_values: 2 EVAL 06rqw parent_genre! 059kh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 23.000 0.500 http://example.org/music/genre/parent_genre #7676-01hc1j PRED entity: 01hc1j PRED relation: major_field_of_study PRED expected values: 05qjt => 175 concepts (165 used for prediction) PRED predicted values (max 10 best out of 119): 02lp1 (0.67 #1452, 0.52 #5178, 0.50 #5778), 03g3w (0.58 #1346, 0.52 #7836, 0.50 #3389), 062z7 (0.48 #1467, 0.47 #1347, 0.42 #4352), 01540 (0.44 #4385, 0.33 #1500, 0.33 #5226), 01tbp (0.43 #1499, 0.37 #5225, 0.28 #3182), 0fdys (0.42 #1357, 0.42 #3400, 0.40 #3160), 05qjt (0.42 #1329, 0.39 #8060, 0.39 #4334), 0g26h (0.39 #5928, 0.38 #1481, 0.37 #6048), 02h40lc (0.38 #1445, 0.37 #1325, 0.33 #484), 04sh3 (0.38 #1514, 0.26 #1394, 0.23 #4399) >> Best rule #1452 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 0j_sncb; 03ksy; >> query: (?x11768, 02lp1) <- contains(?x4743, ?x11768), institution(?x2636, ?x11768), institution(?x620, ?x11768), ?x620 = 07s6fsf, ?x2636 = 027f2w, major_field_of_study(?x11768, ?x947) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1329 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 17 *> proper extension: 08815; 07wrz; 01mpwj; *> query: (?x11768, 05qjt) <- organization(?x346, ?x11768), major_field_of_study(?x11768, ?x8221), major_field_of_study(?x11768, ?x2981), major_field_of_study(?x11768, ?x2014), ?x2014 = 04rjg, ?x2981 = 02j62, ?x8221 = 037mh8 *> conf = 0.42 ranks of expected_values: 7 EVAL 01hc1j major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 175.000 165.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7675-0gx1bnj PRED entity: 0gx1bnj PRED relation: film! PRED expected values: 01l2fn 04j_gs => 114 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1234): 01f6zc (0.20 #943, 0.06 #3025, 0.05 #5107), 0147dk (0.20 #81, 0.04 #22979, 0.03 #31304), 023v4_ (0.20 #883, 0.03 #7129, 0.03 #9210), 0pmhf (0.20 #441, 0.03 #8768, 0.02 #23339), 015rkw (0.20 #281, 0.03 #8608, 0.02 #83540), 059m45 (0.20 #1232, 0.03 #9559), 0159h6 (0.20 #72, 0.03 #10482, 0.03 #12564), 01ggc9 (0.20 #1729, 0.02 #68335, 0.01 #24627), 03h_fqv (0.20 #955, 0.02 #19691, 0.01 #73806), 01c65z (0.20 #1981, 0.02 #20717, 0.01 #22798) >> Best rule #943 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 09fc83; >> query: (?x343, 01f6zc) <- genre(?x343, ?x12924), genre(?x343, ?x53), language(?x343, ?x254), ?x53 = 07s9rl0, ?x12924 = 0hc1z, film(?x1342, ?x343), award_nominee(?x444, ?x1342) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #79357 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 250 *> proper extension: 02gd6x; *> query: (?x343, 01l2fn) <- genre(?x343, ?x258), genre(?x343, ?x53), language(?x343, ?x254), ?x53 = 07s9rl0, production_companies(?x343, ?x1414), genre(?x7656, ?x258), genre(?x419, ?x258), ?x7656 = 011x_4 *> conf = 0.02 ranks of expected_values: 737 EVAL 0gx1bnj film! 04j_gs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 52.000 0.200 http://example.org/film/actor/film./film/performance/film EVAL 0gx1bnj film! 01l2fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 114.000 52.000 0.200 http://example.org/film/actor/film./film/performance/film #7674-06g60w PRED entity: 06g60w PRED relation: award_winner! PRED expected values: 0dznvw => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 132): 0275n3y (0.20 #75, 0.14 #215, 0.10 #355), 0ftlkg (0.20 #26, 0.14 #166, 0.10 #306), 02jp5r (0.20 #69, 0.14 #209, 0.10 #349), 0bzm__ (0.20 #87, 0.14 #227, 0.10 #367), 09p30_ (0.20 #85, 0.14 #225, 0.10 #365), 09qftb (0.20 #112, 0.14 #252, 0.10 #392), 01s695 (0.20 #3, 0.10 #283, 0.07 #703), 01bx35 (0.20 #7, 0.10 #287, 0.07 #707), 05c1t6z (0.14 #155, 0.10 #295, 0.09 #435), 0c53zb (0.06 #2021, 0.06 #2161, 0.05 #2581) >> Best rule #75 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01jkqfz; >> query: (?x4863, 0275n3y) <- award_nominee(?x4863, ?x9552), award_winner(?x6323, ?x4863), nominated_for(?x9138, ?x4863) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #10781 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1907 *> proper extension: 01wv9xn; 0frsw; 02jqjm; 0178_w; 07r1_; 0b_xm; 046p9; 01lf293; 017959; 016376; ... *> query: (?x4863, ?x78) <- award_winner(?x1243, ?x4863), ceremony(?x1243, ?x78) *> conf = 0.04 ranks of expected_values: 41 EVAL 06g60w award_winner! 0dznvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 114.000 114.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7673-0gcrg PRED entity: 0gcrg PRED relation: country PRED expected values: 09c7w0 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.87 #490, 0.86 #1466, 0.86 #429), 0d060g (0.48 #1954, 0.44 #6304, 0.06 #2944), 03rt9 (0.35 #1892, 0.03 #2153, 0.02 #2214), 0b90_r (0.35 #1892, 0.01 #2327), 07ssc (0.35 #1176, 0.31 #2216, 0.27 #261), 0345h (0.27 #272, 0.20 #2350, 0.19 #2166), 0chghy (0.13 #257, 0.05 #1172, 0.04 #2765), 0f8l9c (0.13 #4361, 0.10 #5281, 0.10 #5587), 03h64 (0.07 #718, 0.07 #413, 0.07 #901), 03_3d (0.07 #435, 0.06 #679, 0.05 #496) >> Best rule #490 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 04hk0w; >> query: (?x3909, 09c7w0) <- film_format(?x3909, ?x14581), music(?x3909, ?x12188), cinematography(?x3909, ?x10720), genre(?x3909, ?x53) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gcrg country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.868 http://example.org/film/film/country #7672-034rd PRED entity: 034rd PRED relation: profession PRED expected values: 0g0vx => 120 concepts (100 used for prediction) PRED predicted values (max 10 best out of 115): 02hrh1q (0.76 #8430, 0.76 #8140, 0.75 #7705), 02tx6q (0.73 #2662, 0.16 #14368), 04gc2 (0.60 #1492, 0.50 #1347, 0.46 #3813), 0cbd2 (0.50 #2182, 0.42 #1747, 0.35 #2763), 0dz3r (0.47 #2612, 0.12 #14078, 0.12 #14224), 01d_h8 (0.42 #8566, 0.41 #6099, 0.41 #7841), 0dxtg (0.37 #14090, 0.37 #14236, 0.30 #7123), 09j9h (0.35 #14222, 0.35 #14369, 0.34 #7255), 02jknp (0.32 #4359, 0.26 #6101, 0.24 #7843), 0nbcg (0.30 #7141, 0.23 #8447, 0.21 #8157) >> Best rule #8430 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 01vvycq; 017yfz; 01520h; >> query: (?x5609, 02hrh1q) <- notable_people_with_this_condition(?x4291, ?x5609), profession(?x5609, ?x1682), location(?x5609, ?x12702) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1411 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 042fk; *> query: (?x5609, 0g0vx) <- religion(?x5609, ?x4641), legislative_sessions(?x5609, ?x12714), profession(?x5609, ?x1682), taxonomy(?x5609, ?x939) *> conf = 0.25 ranks of expected_values: 14 EVAL 034rd profession 0g0vx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 120.000 100.000 0.759 http://example.org/people/person/profession #7671-0g9zljd PRED entity: 0g9zljd PRED relation: honored_for! PRED expected values: 0hndn2q => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 115): 09gkdln (0.20 #464, 0.10 #824, 0.09 #2882), 04n2r9h (0.17 #154, 0.07 #274, 0.07 #394), 0hndn2q (0.12 #30, 0.10 #510, 0.10 #870), 0418154 (0.12 #91, 0.08 #211, 0.06 #1051), 0bvhz9 (0.12 #112, 0.07 #472, 0.03 #832), 05zksls (0.12 #26, 0.05 #986, 0.05 #866), 0h_cssd (0.10 #501, 0.08 #141, 0.08 #861), 05qb8vx (0.10 #406, 0.05 #766, 0.04 #646), 0n8_m93 (0.10 #941, 0.08 #1181, 0.08 #581), 0275n3y (0.09 #2882, 0.09 #10813, 0.09 #10812) >> Best rule #464 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 011yxg; 0ds11z; 026p4q7; 01qxc7; >> query: (?x6270, 09gkdln) <- award(?x6270, ?x77), nominated_for(?x5959, ?x6270), region(?x6270, ?x512), honored_for(?x1442, ?x6270) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 04lqvly; *> query: (?x6270, 0hndn2q) <- award(?x6270, ?x77), nominated_for(?x5959, ?x6270), film_release_distribution_medium(?x6270, ?x81), ?x5959 = 024rdh *> conf = 0.12 ranks of expected_values: 3 EVAL 0g9zljd honored_for! 0hndn2q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 112.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #7670-0r6ff PRED entity: 0r6ff PRED relation: time_zones PRED expected values: 02lcqs => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 12): 02lcqs (0.82 #495, 0.81 #57, 0.80 #44), 02hcv8 (0.50 #511, 0.50 #484, 0.49 #498), 02fqwt (0.25 #640, 0.18 #274, 0.18 #561), 02hczc (0.25 #640, 0.16 #1419, 0.16 #1378), 02lcrv (0.25 #640, 0.16 #1419, 0.16 #1378), 042g7t (0.16 #1378, 0.16 #1392, 0.15 #1364), 03bdv (0.14 #136, 0.06 #253, 0.05 #305), 02llzg (0.11 #342, 0.09 #355, 0.09 #446), 03plfd (0.05 #348, 0.04 #374, 0.04 #361), 052vwh (0.02 #129, 0.02 #155, 0.01 #220) >> Best rule #495 for best value: >> intensional similarity = 3 >> extensional distance = 323 >> proper extension: 0mrf1; >> query: (?x11315, ?x2950) <- adjoins(?x6703, ?x11315), source(?x11315, ?x958), time_zones(?x6703, ?x2950) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r6ff time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.819 http://example.org/location/location/time_zones #7669-06xkst PRED entity: 06xkst PRED relation: actor PRED expected values: 0f8grf => 37 concepts (21 used for prediction) PRED predicted values (max 10 best out of 316): 01kwh5j (0.33 #689, 0.25 #1620, 0.20 #2551), 01rmnp (0.33 #704, 0.25 #1635, 0.20 #2566), 03ydry (0.33 #911, 0.25 #1842, 0.20 #2773), 066l3y (0.33 #477, 0.25 #1408, 0.20 #2339), 01nsyf (0.25 #1750, 0.15 #3612, 0.14 #4543), 01vs8ng (0.25 #1848, 0.15 #3710, 0.14 #4641), 048hf (0.20 #2477, 0.05 #8065, 0.04 #8996), 03x16f (0.20 #2537, 0.02 #8125, 0.02 #18380), 026zvx7 (0.20 #2070, 0.02 #7658, 0.02 #13250), 0443y3 (0.20 #2028, 0.02 #7616, 0.02 #8547) >> Best rule #689 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 02v5xg; >> query: (?x10555, 01kwh5j) <- genre(?x10555, ?x2540), genre(?x10555, ?x1403), genre(?x10555, ?x1013), genre(?x10555, ?x53), ?x53 = 07s9rl0, ?x2540 = 0hcr, actor(?x10555, ?x10231), ?x10231 = 0392kz, country_of_origin(?x10555, ?x252), ?x252 = 03_3d, ?x1013 = 06n90, ?x1403 = 02l7c8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3685 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 11 *> proper extension: 07ng9k; 099pks; 0dr1c2; 01lk02; 06r1k; 0q6g3; 0gxr1c; 02rhwjr; 051kd; *> query: (?x10555, 0f8grf) <- genre(?x10555, ?x2540), genre(?x10555, ?x1403), genre(?x10555, ?x53), ?x53 = 07s9rl0, ?x2540 = 0hcr, actor(?x10555, ?x10231), genre(?x10082, ?x1403), genre(?x6445, ?x1403), genre(?x5712, ?x1403), genre(?x5162, ?x1403), genre(?x4041, ?x1403), genre(?x2833, ?x1403), ?x5162 = 0j3d9tn, ?x2833 = 04jwly, ?x10082 = 05zwrg0, ?x5712 = 0k4p0, ?x4041 = 0gy2y8r, ?x6445 = 05v38p *> conf = 0.08 ranks of expected_values: 41 EVAL 06xkst actor 0f8grf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 37.000 21.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #7668-015qq1 PRED entity: 015qq1 PRED relation: film PRED expected values: 037xlx => 116 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1076): 030p35 (0.59 #26840, 0.49 #87686, 0.48 #44733), 0gjcrrw (0.20 #2420, 0.01 #9577), 0g_zyp (0.20 #3382, 0.01 #12328), 027pfb2 (0.12 #1791, 0.12 #689, 0.09 #59050), 01gglm (0.12 #1406, 0.10 #3197, 0.03 #15721), 032016 (0.12 #504, 0.10 #2295, 0.03 #14819), 04954r (0.12 #616, 0.05 #4196, 0.04 #14931), 017kct (0.12 #582, 0.04 #9530, 0.03 #14897), 01qz5 (0.12 #1417, 0.04 #10365, 0.03 #15732), 027r7k (0.12 #1722, 0.04 #10670, 0.03 #16037) >> Best rule #26840 for best value: >> intensional similarity = 3 >> extensional distance = 209 >> proper extension: 0b7t3p; 01vn0t_; 03cws8h; 02vtnf; >> query: (?x11380, ?x4639) <- type_of_union(?x11380, ?x1873), ?x1873 = 01g63y, nominated_for(?x11380, ?x4639) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #9941 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 77 *> proper extension: 048lv; 046qq; *> query: (?x11380, 037xlx) <- award(?x11380, ?x3247), ?x3247 = 0bdwqv, student(?x581, ?x11380) *> conf = 0.01 ranks of expected_values: 895 EVAL 015qq1 film 037xlx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 86.000 0.594 http://example.org/film/actor/film./film/performance/film #7667-01vksx PRED entity: 01vksx PRED relation: nominated_for! PRED expected values: 0gqxm => 95 concepts (90 used for prediction) PRED predicted values (max 10 best out of 199): 02r0csl (0.66 #12665, 0.66 #12664, 0.66 #6555), 099c8n (0.62 #1859, 0.28 #2311, 0.24 #7284), 0gq9h (0.56 #7290, 0.55 #6385, 0.50 #1865), 0gs9p (0.51 #6387, 0.48 #7292, 0.40 #1867), 019f4v (0.48 #6376, 0.39 #7281, 0.38 #1856), 0k611 (0.45 #6395, 0.43 #1875, 0.42 #7300), 040njc (0.44 #6335, 0.39 #1815, 0.31 #7240), 04dn09n (0.41 #6357, 0.40 #1837, 0.31 #7262), 02qyntr (0.40 #6496, 0.29 #1976, 0.25 #7401), 02pqp12 (0.38 #6381, 0.32 #1861, 0.24 #7286) >> Best rule #12665 for best value: >> intensional similarity = 2 >> extensional distance = 966 >> proper extension: 0cwrr; 04glx0; 05sy0cv; 07bz5; >> query: (?x908, ?x507) <- nominated_for(?x629, ?x908), award(?x908, ?x507) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #11079 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 826 *> proper extension: 07s8z_l; *> query: (?x908, ?x704) <- titles(?x811, ?x908), award_winner(?x908, ?x629), award_winner(?x704, ?x629) *> conf = 0.25 ranks of expected_values: 30 EVAL 01vksx nominated_for! 0gqxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 95.000 90.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7666-0tz1j PRED entity: 0tz1j PRED relation: currency PRED expected values: 09nqf => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.67 #1, 0.52 #16, 0.45 #24) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0tz41; >> query: (?x12919, 09nqf) <- contains(?x6905, ?x12919), time_zones(?x12919, ?x2674), ?x2674 = 02hcv8, ?x6905 = 0k3hn >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tz1j currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.667 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #7665-0ghtf PRED entity: 0ghtf PRED relation: source PRED expected values: 0jbk9 => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #5, 0.76 #13, 0.74 #6) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x14339, 0jbk9) <- category(?x14339, ?x134), ?x134 = 08mbj5d, place(?x14339, ?x14339) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ghtf source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #7664-01cz7r PRED entity: 01cz7r PRED relation: currency PRED expected values: 09nqf => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.85 #78, 0.83 #64, 0.82 #85), 01nv4h (0.14 #9, 0.12 #16, 0.11 #23), 02gsvk (0.04 #27, 0.02 #132), 02l6h (0.01 #102) >> Best rule #78 for best value: >> intensional similarity = 4 >> extensional distance = 193 >> proper extension: 031t2d; 0gfsq9; 01q2nx; 011xg5; >> query: (?x7645, 09nqf) <- featured_film_locations(?x7645, ?x362), genre(?x7645, ?x239), executive_produced_by(?x7645, ?x3568), produced_by(?x2029, ?x3568) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cz7r currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.846 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #7663-0bkmf PRED entity: 0bkmf PRED relation: location PRED expected values: 0z20d => 129 concepts (127 used for prediction) PRED predicted values (max 10 best out of 207): 02_286 (0.55 #22543, 0.33 #37, 0.29 #1644), 04jpl (0.29 #1624, 0.11 #12073, 0.11 #2427), 030qb3t (0.27 #28213, 0.26 #20983, 0.25 #40262), 0y3k9 (0.14 #1998), 01n7q (0.12 #22569, 0.07 #6491, 0.07 #5688), 059rby (0.12 #22522, 0.05 #12072, 0.04 #20113), 0r0m6 (0.11 #5842, 0.09 #8254, 0.07 #5039), 0cr3d (0.11 #5770, 0.09 #8182, 0.07 #77283), 05k7sb (0.10 #3323, 0.08 #4127, 0.04 #11361), 0cc56 (0.08 #22563, 0.06 #18548, 0.06 #2467) >> Best rule #22543 for best value: >> intensional similarity = 3 >> extensional distance = 177 >> proper extension: 033hqf; 05myd2; >> query: (?x9355, 02_286) <- participant(?x9355, ?x5239), location(?x9355, ?x3670), partially_contains(?x3670, ?x10710) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #12431 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 024dgj; *> query: (?x9355, 0z20d) <- spouse(?x4057, ?x9355), nationality(?x9355, ?x94), participant(?x5079, ?x9355) *> conf = 0.02 ranks of expected_values: 148 EVAL 0bkmf location 0z20d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 129.000 127.000 0.547 http://example.org/people/person/places_lived./people/place_lived/location #7662-03t5b6 PRED entity: 03t5b6 PRED relation: ceremony PRED expected values: 01bx35 => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 129): 05pd94v (0.59 #898, 0.57 #1026, 0.52 #1282), 02cg41 (0.59 #1137, 0.58 #1009, 0.51 #1393), 01bx35 (0.54 #901, 0.54 #1029, 0.50 #389), 019bk0 (0.54 #910, 0.54 #1038, 0.48 #1294), 0gx1673 (0.44 #1921, 0.40 #619, 0.38 #875), 03gyp30 (0.44 #1921, 0.27 #4485, 0.26 #3971), 0hhtgcw (0.44 #1921, 0.27 #4485, 0.26 #3971), 02hn5v (0.27 #4485, 0.26 #3971, 0.26 #4228), 09qvms (0.21 #4871, 0.21 #4870, 0.19 #2818), 09bymc (0.21 #4871, 0.21 #4870, 0.19 #2818) >> Best rule #898 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 07n52; 02xzd9; >> query: (?x3978, 05pd94v) <- category_of(?x3978, ?x2421), category_of(?x1088, ?x2421), disciplines_or_subjects(?x1088, ?x8681) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #901 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 125 *> proper extension: 07n52; 02xzd9; *> query: (?x3978, 01bx35) <- category_of(?x3978, ?x2421), category_of(?x1088, ?x2421), disciplines_or_subjects(?x1088, ?x8681) *> conf = 0.54 ranks of expected_values: 3 EVAL 03t5b6 ceremony 01bx35 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 45.000 45.000 0.591 http://example.org/award/award_category/winners./award/award_honor/ceremony #7661-087c7 PRED entity: 087c7 PRED relation: award_winner! PRED expected values: 0m7yy => 212 concepts (212 used for prediction) PRED predicted values (max 10 best out of 14): 0m7yy (0.17 #13572, 0.16 #15300, 0.14 #5364), 05p1dby (0.12 #3564, 0.09 #12636, 0.09 #6588), 0gq9h (0.12 #3534, 0.05 #14766, 0.05 #29455), 07bdd_ (0.09 #12594, 0.09 #6546, 0.09 #13026), 02x1z2s (0.08 #8406, 0.07 #2358, 0.06 #3654), 01l29r (0.08 #7510, 0.05 #19606, 0.05 #6214), 01lk0l (0.06 #3734, 0.05 #6758, 0.03 #12806), 018wng (0.02 #29419), 05p09zm (0.01 #65790), 0p9sw (0.01 #35881, 0.01 #36745, 0.01 #40201) >> Best rule #13572 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0g5lhl7; >> query: (?x502, 0m7yy) <- citytown(?x502, ?x503), company(?x265, ?x502), place_of_death(?x158, ?x503), contact_category(?x502, ?x897) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087c7 award_winner! 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 212.000 0.171 http://example.org/award/award_category/winners./award/award_honor/award_winner #7660-02x4x18 PRED entity: 02x4x18 PRED relation: nominated_for PRED expected values: 016kv6 02_06s 04gp58p => 38 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1469): 0h95927 (0.66 #31450, 0.66 #31449, 0.66 #31448), 011yhm (0.66 #31450, 0.66 #31449, 0.66 #31448), 0f4_l (0.62 #5030, 0.33 #3459, 0.18 #14458), 0209xj (0.62 #4811, 0.27 #26731, 0.23 #6382), 0sxmx (0.62 #5442, 0.15 #7013, 0.12 #31451), 026p4q7 (0.62 #6640, 0.33 #3498, 0.27 #26731), 0bmhvpr (0.62 #6846, 0.33 #3704, 0.25 #5275), 0gmgwnv (0.54 #7238, 0.50 #4096, 0.22 #15095), 02rcdc2 (0.54 #6708, 0.38 #5137, 0.12 #31451), 095zlp (0.54 #6341, 0.27 #26731, 0.23 #9483) >> Best rule #31450 for best value: >> intensional similarity = 1 >> extensional distance = 234 >> proper extension: 0dt49; >> query: (?x2478, ?x7283) <- award(?x7283, ?x2478) >> conf = 0.66 => this is the best rule for 2 predicted values *> Best rule #5228 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 02rdxsh; *> query: (?x2478, 016kv6) <- nominated_for(?x2478, ?x7470), nominated_for(?x2478, ?x2251), ?x2251 = 01qncf, film(?x902, ?x7470), country(?x7470, ?x512) *> conf = 0.50 ranks of expected_values: 31, 146, 267 EVAL 02x4x18 nominated_for 04gp58p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 38.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x4x18 nominated_for 02_06s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 38.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x4x18 nominated_for 016kv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 38.000 20.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7659-076_74 PRED entity: 076_74 PRED relation: company PRED expected values: 01w5m => 101 concepts (68 used for prediction) PRED predicted values (max 10 best out of 24): 030_1_ (0.07 #604, 0.03 #1184, 0.02 #1573), 06rq1k (0.03 #801, 0.02 #2157, 0.02 #1188), 02jd_7 (0.03 #920, 0.02 #1307, 0.02 #1502), 0kx4m (0.02 #595, 0.02 #1175, 0.02 #788), 01gb54 (0.02 #649, 0.02 #842), 02j_j0 (0.02 #677), 016tt2 (0.02 #589), 032j_n (0.02 #1311, 0.01 #2280), 061dn_ (0.02 #1210, 0.01 #2179), 0jz9f (0.02 #1162, 0.01 #2131) >> Best rule #604 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0fvf9q; 014zcr; 05ty4m; 054_mz; 02lf0c; 03_gd; 02kxbwx; 02q_cc; 04wvhz; 05m883; ... >> query: (?x3862, 030_1_) <- award(?x3862, ?x277), ?x277 = 0f_nbyh, produced_by(?x2085, ?x3862), nominated_for(?x3862, ?x3863) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 076_74 company 01w5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 68.000 0.073 http://example.org/people/person/employment_history./business/employment_tenure/company #7658-01zqy6t PRED entity: 01zqy6t PRED relation: state PRED expected values: 01n7q => 150 concepts (71 used for prediction) PRED predicted values (max 10 best out of 53): 01n7q (0.68 #1299, 0.68 #1226, 0.50 #261), 0l2xl (0.32 #5569, 0.26 #3126, 0.26 #1298), 06pvr (0.32 #5569, 0.26 #3126, 0.26 #1298), 09c7w0 (0.17 #952, 0.15 #1126, 0.13 #5745), 05kj_ (0.14 #352, 0.05 #610, 0.03 #3390), 01zqy6t (0.13 #5745, 0.08 #3040, 0.07 #3300), 05k7sb (0.09 #2716, 0.06 #457, 0.06 #889), 02xry (0.08 #1589, 0.06 #1852, 0.05 #5510), 081yw (0.07 #395, 0.05 #653, 0.02 #5358), 015jr (0.07 #408, 0.03 #925, 0.03 #1012) >> Best rule #1299 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0284jb; 0l1pj; 0281s1; 0281rp; 0r785; 0r111; >> query: (?x13529, ?x1227) <- contains(?x1227, ?x13529), country(?x13529, ?x94), ?x94 = 09c7w0, ?x1227 = 01n7q >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01zqy6t state 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 71.000 0.682 http://example.org/base/biblioness/bibs_location/state #7657-076lxv PRED entity: 076lxv PRED relation: film_sets_designed PRED expected values: 0cq8nx => 112 concepts (59 used for prediction) PRED predicted values (max 10 best out of 90): 0cwy47 (0.21 #96, 0.17 #184, 0.12 #272), 048rn (0.21 #130, 0.12 #306, 0.11 #218), 0bkq7 (0.14 #157, 0.11 #245, 0.08 #333), 0kvb6p (0.14 #158, 0.11 #246, 0.08 #334), 02q_4ph (0.14 #121, 0.11 #209, 0.08 #297), 0bcndz (0.14 #97, 0.11 #185, 0.08 #273), 014knw (0.14 #167, 0.11 #255, 0.08 #343), 04wddl (0.14 #165, 0.11 #253, 0.08 #341), 029jt9 (0.14 #162, 0.11 #250, 0.08 #338), 0dnw1 (0.14 #141, 0.11 #229, 0.08 #317) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 051x52f; >> query: (?x786, 0cwy47) <- film_sets_designed(?x786, ?x9572), genre(?x9572, ?x1805), ?x1805 = 01g6gs >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 076lxv film_sets_designed 0cq8nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 59.000 0.214 http://example.org/film/film_set_designer/film_sets_designed #7656-07xzm PRED entity: 07xzm PRED relation: group PRED expected values: 01k_yf => 85 concepts (56 used for prediction) PRED predicted values (max 10 best out of 572): 02vnpv (0.75 #5481, 0.75 #2808, 0.73 #6440), 02cw1m (0.75 #2781, 0.62 #2975, 0.58 #5261), 07mvp (0.75 #2727, 0.62 #7118, 0.61 #6548), 06nv27 (0.75 #2703, 0.53 #6143, 0.50 #6524), 0134wr (0.67 #6196, 0.67 #3520, 0.62 #2756), 05563d (0.67 #3447, 0.62 #2683, 0.60 #6123), 01czx (0.67 #3433, 0.62 #2669, 0.50 #5149), 02dw1_ (0.67 #3862, 0.58 #5388, 0.57 #7491), 02t3ln (0.67 #2316, 0.57 #2506, 0.56 #3843), 0khth (0.62 #2888, 0.62 #2694, 0.56 #3841) >> Best rule #5481 for best value: >> intensional similarity = 18 >> extensional distance = 10 >> proper extension: 0l14md; 013y1f; >> query: (?x1212, 02vnpv) <- role(?x1294, ?x1212), role(?x1212, ?x4917), role(?x1212, ?x2310), role(?x1212, ?x228), ?x4917 = 06w7v, instrumentalists(?x1212, ?x4062), instrumentalists(?x1212, ?x672), role(?x4616, ?x1212), role(?x716, ?x1212), ?x228 = 0l14qv, artist(?x648, ?x4062), participant(?x7830, ?x4062), artists(?x284, ?x4062), ?x716 = 018vs, family(?x4616, ?x7256), role(?x2310, ?x645), role(?x2310, ?x1436), student(?x2821, ?x672) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6528 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 16 *> proper extension: 05148p4; 0319l; *> query: (?x1212, 01k_yf) <- role(?x1294, ?x1212), role(?x1212, ?x4917), role(?x1212, ?x2888), role(?x75, ?x1212), role(?x10738, ?x4917), role(?x9735, ?x4917), role(?x9246, ?x4917), role(?x9128, ?x4917), role(?x2784, ?x4917), role(?x736, ?x4917), ?x9246 = 0pk41, instrumentalists(?x1212, ?x672), role(?x4917, ?x569), ?x9735 = 01wxdn3, ?x2888 = 02fsn, ?x10738 = 017f4y, ?x9128 = 01d4cb, artists(?x302, ?x2784), award(?x2784, ?x1565), ?x1565 = 01c4_6, award_winner(?x6487, ?x2784) *> conf = 0.50 ranks of expected_values: 39 EVAL 07xzm group 01k_yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 85.000 56.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group #7655-03cs_xw PRED entity: 03cs_xw PRED relation: place_of_birth PRED expected values: 01sn3 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 146): 02_286 (0.30 #1427, 0.15 #3539, 0.13 #7060), 0h30_ (0.25 #368), 030qb3t (0.20 #1462, 0.04 #10615, 0.04 #38082), 09c7w0 (0.17 #705, 0.06 #7041, 0.06 #2817), 094jv (0.10 #1469, 0.02 #2877, 0.02 #3581), 0r0m6 (0.10 #1558), 0fw2y (0.10 #1500), 01_d4 (0.09 #3586, 0.09 #7811, 0.07 #12740), 0cr3d (0.08 #2910, 0.06 #5726, 0.06 #16289), 01531 (0.06 #4329, 0.03 #9962, 0.02 #29680) >> Best rule #1427 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02t__3; 03c9pqt; >> query: (?x10898, 02_286) <- student(?x1103, ?x10898), ?x1103 = 01k2wn, nominated_for(?x10898, ?x8775) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #7190 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 03qd_; 02lk1s; 03xp8d5; 025vl4m; 02m92h; 05cqhl; 08nz99; 01lct6; *> query: (?x10898, 01sn3) <- award_nominee(?x1341, ?x10898), tv_program(?x10898, ?x8775), profession(?x10898, ?x987), type_of_union(?x10898, ?x566) *> conf = 0.03 ranks of expected_values: 19 EVAL 03cs_xw place_of_birth 01sn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 106.000 106.000 0.300 http://example.org/people/person/place_of_birth #7654-01wt4wc PRED entity: 01wt4wc PRED relation: location PRED expected values: 03s0w => 122 concepts (83 used for prediction) PRED predicted values (max 10 best out of 176): 030qb3t (0.58 #33823, 0.33 #45074, 0.12 #55523), 08809 (0.33 #567, 0.25 #1370, 0.20 #2173), 02_286 (0.20 #1643, 0.17 #64318, 0.17 #47440), 0cr3d (0.20 #1751, 0.07 #64426, 0.06 #11389), 0z1vw (0.20 #2189), 01cx_ (0.19 #13013, 0.10 #17031, 0.09 #33903), 0cc56 (0.18 #3269, 0.10 #2466, 0.05 #6481), 03s0w (0.12 #17672, 0.09 #27315, 0.09 #26510), 0dc95 (0.10 #2539, 0.09 #3342, 0.06 #4145), 07z1m (0.10 #2488, 0.09 #3291, 0.03 #12929) >> Best rule #33823 for best value: >> intensional similarity = 4 >> extensional distance = 430 >> proper extension: 05m63c; 023v4_; 03hh89; 03q3sy; 01nms7; 0cymln; 04bdqk; 014g9y; 03fwln; >> query: (?x8012, 030qb3t) <- nationality(?x8012, ?x94), location(?x8012, ?x4362), administrative_division(?x4362, ?x961), dog_breed(?x4362, ?x1706) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #17672 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 175 *> proper extension: 0p51w; 0399p; 026rm_y; 0bm9xk; 047g6; 0ngg; *> query: (?x8012, ?x961) <- gender(?x8012, ?x231), place_of_birth(?x8012, ?x4362), capital(?x961, ?x4362), time_zones(?x4362, ?x1638) *> conf = 0.12 ranks of expected_values: 8 EVAL 01wt4wc location 03s0w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 122.000 83.000 0.576 http://example.org/people/person/places_lived./people/place_lived/location #7653-0fz20l PRED entity: 0fz20l PRED relation: ceremony! PRED expected values: 0czp_ => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 335): 0gs96 (0.89 #563, 0.88 #1295, 0.87 #1051), 0gr07 (0.81 #2842, 0.81 #1620, 0.79 #3331), 0gq_v (0.80 #991, 0.79 #1235, 0.78 #1479), 0gqng (0.79 #2933, 0.78 #1467, 0.78 #1711), 0l8z1 (0.75 #3666, 0.75 #2728, 0.75 #2238), 018wdw (0.75 #3666, 0.75 #4890, 0.73 #6606), 0czp_ (0.75 #3666, 0.75 #4890, 0.73 #6606), 0gqxm (0.75 #3666, 0.75 #4890, 0.73 #6606), 0gqzz (0.75 #3666, 0.75 #4890, 0.73 #6606), 02x201b (0.75 #3666, 0.75 #4890, 0.73 #6606) >> Best rule #563 for best value: >> intensional similarity = 20 >> extensional distance = 25 >> proper extension: 02yw5r; 0bzm81; 0dth6b; 0bvfqq; 050yyb; 0bzkgg; 073h9x; 0fzrtf; 0bz6sb; 02jp5r; ... >> query: (?x3518, 0gs96) <- ceremony(?x3617, ?x3518), ceremony(?x3066, ?x3518), ceremony(?x1862, ?x3518), ceremony(?x1313, ?x3518), ceremony(?x1243, ?x3518), ceremony(?x601, ?x3518), ceremony(?x500, ?x3518), instance_of_recurring_event(?x3518, ?x3459), ?x1313 = 0gs9p, ?x1243 = 0gr0m, award_winner(?x3518, ?x6766), award_winner(?x3518, ?x5165), ?x3066 = 0gqy2, ?x601 = 0gr4k, ?x500 = 0p9sw, student(?x4750, ?x5165), ?x1862 = 0gr51, award_nominee(?x6766, ?x2304), gender(?x6766, ?x231), ?x3617 = 0gvx_ >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #3666 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 57 *> proper extension: 03gwpw2; 0fqpc7d; 04n2r9h; 09bymc; 09gkdln; *> query: (?x3518, ?x77) <- ceremony(?x1243, ?x3518), honored_for(?x3518, ?x5220), nominated_for(?x1243, ?x6680), nominated_for(?x1243, ?x6345), nominated_for(?x1243, ?x3681), nominated_for(?x1243, ?x2699), nominated_for(?x1243, ?x407), ceremony(?x1243, ?x8150), award(?x7384, ?x1243), ?x3681 = 01sxdy, ceremony(?x77, ?x8150), written_by(?x2699, ?x2182), currency(?x407, ?x170), genre(?x6345, ?x53), ?x7384 = 02vx4c2, honored_for(?x7226, ?x6680) *> conf = 0.75 ranks of expected_values: 7 EVAL 0fz20l ceremony! 0czp_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 30.000 30.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony #7652-05qhw PRED entity: 05qhw PRED relation: medal PRED expected values: 02lq5w => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 1): 02lq5w (0.79 #8, 0.79 #6, 0.77 #34) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 0ctw_b; 06f32; >> query: (?x456, 02lq5w) <- film_release_region(?x2628, ?x456), country(?x6265, ?x456), ?x2628 = 06wbm8q >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qhw medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.793 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #7651-031778 PRED entity: 031778 PRED relation: film! PRED expected values: 016nff 0356dp => 72 concepts (42 used for prediction) PRED predicted values (max 10 best out of 751): 0146pg (0.48 #57994, 0.48 #8284, 0.47 #31065), 0bbxx9b (0.48 #57994, 0.48 #8284, 0.47 #31065), 094tsh6 (0.48 #57994, 0.48 #8284, 0.47 #31065), 0d5wn3 (0.48 #57994, 0.48 #8284, 0.47 #31065), 03y1mlp (0.48 #57994, 0.48 #8284, 0.47 #31065), 016nff (0.25 #5356, 0.14 #1214, 0.02 #9498), 0jdhp (0.18 #2244, 0.01 #14669, 0.01 #18812), 015rkw (0.17 #4421, 0.06 #2072, 0.03 #8563), 0159h6 (0.17 #4215, 0.06 #2072, 0.03 #35281), 0jfx1 (0.17 #4543, 0.03 #14898, 0.03 #25254) >> Best rule #57994 for best value: >> intensional similarity = 5 >> extensional distance = 749 >> proper extension: 02qjv1p; >> query: (?x2006, ?x669) <- nominated_for(?x3861, ?x2006), nominated_for(?x669, ?x2006), participant(?x3861, ?x981), genre(?x2006, ?x600), film(?x3861, ?x2869) >> conf = 0.48 => this is the best rule for 5 predicted values *> Best rule #5356 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 04x4gw; *> query: (?x2006, 016nff) <- nominated_for(?x669, ?x2006), film(?x1469, ?x2006), ?x1469 = 05sq84, genre(?x2006, ?x600) *> conf = 0.25 ranks of expected_values: 6, 566 EVAL 031778 film! 0356dp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 42.000 0.481 http://example.org/film/actor/film./film/performance/film EVAL 031778 film! 016nff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 72.000 42.000 0.481 http://example.org/film/actor/film./film/performance/film #7650-05b4w PRED entity: 05b4w PRED relation: combatants! PRED expected values: 03gqgt3 => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 64): 03gqgt3 (0.43 #56, 0.36 #641, 0.34 #771), 0cm2xh (0.36 #11, 0.30 #76, 0.29 #141), 01h6pn (0.33 #142, 0.21 #12, 0.17 #857), 048n7 (0.31 #608, 0.30 #1648, 0.30 #738), 06k75 (0.29 #15, 0.21 #145, 0.17 #2225), 07j9n (0.29 #29, 0.16 #2759, 0.15 #2369), 0d06vc (0.25 #69, 0.21 #4, 0.18 #394), 07_nf (0.24 #602, 0.23 #732, 0.22 #862), 01gjd0 (0.21 #2, 0.20 #67, 0.17 #132), 018w0j (0.21 #36, 0.20 #101, 0.16 #361) >> Best rule #56 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 01d8l; >> query: (?x2513, 03gqgt3) <- combatants(?x94, ?x2513), olympics(?x2513, ?x5395), ?x5395 = 018qb4 >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05b4w combatants! 03gqgt3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.429 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #7649-02pptm PRED entity: 02pptm PRED relation: school! PRED expected values: 07l4z => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 85): 06x68 (0.27 #6, 0.19 #91, 0.12 #431), 01yjl (0.25 #112, 0.12 #367, 0.11 #622), 07l4z (0.25 #149, 0.09 #64, 0.09 #659), 03m1n (0.25 #163, 0.09 #78, 0.07 #673), 01slc (0.19 #139, 0.12 #479, 0.12 #649), 01ypc (0.19 #86, 0.09 #1, 0.08 #341), 049n7 (0.19 #95, 0.08 #350, 0.08 #435), 0487_ (0.19 #143, 0.05 #398, 0.04 #653), 04wmvz (0.18 #72, 0.12 #157, 0.08 #412), 07l8f (0.18 #52, 0.06 #137, 0.04 #222) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 015fsv; >> query: (?x9131, 06x68) <- contains(?x94, ?x9131), school(?x7357, ?x9131), ?x7357 = 04mjl >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 0frm7n; *> query: (?x9131, 07l4z) <- school(?x4243, ?x9131), ?x4243 = 0713r *> conf = 0.25 ranks of expected_values: 3 EVAL 02pptm school! 07l4z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 66.000 0.273 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #7648-016vj5 PRED entity: 016vj5 PRED relation: group! PRED expected values: 03bx0bm => 91 concepts (71 used for prediction) PRED predicted values (max 10 best out of 124): 03bx0bm (0.68 #1243, 0.64 #895, 0.62 #634), 028tv0 (0.47 #1231, 0.45 #1666, 0.44 #1840), 03qjg (0.30 #918, 0.26 #1963, 0.24 #2051), 05r5c (0.28 #878, 0.25 #1226, 0.24 #1748), 01vj9c (0.28 #2279, 0.27 #2541, 0.27 #2629), 0l14qv (0.27 #1833, 0.27 #2009, 0.27 #615), 04rzd (0.19 #641, 0.18 #2035, 0.16 #1685), 06ncr (0.15 #648, 0.14 #1083, 0.14 #1954), 0l14j_ (0.15 #661, 0.14 #2705, 0.14 #2794), 013y1f (0.14 #2705, 0.14 #2794, 0.13 #2468) >> Best rule #1243 for best value: >> intensional similarity = 5 >> extensional distance = 79 >> proper extension: 04rcr; 05k79; 01rm8b; 0mgcr; 047cx; 01cblr; 01fmz6; 01k_yf; 02jqjm; 013w2r; ... >> query: (?x11906, 03bx0bm) <- artist(?x1543, ?x11906), group(?x716, ?x11906), ?x716 = 018vs, artists(?x302, ?x11906), award(?x11906, ?x3365) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016vj5 group! 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 71.000 0.679 http://example.org/music/performance_role/regular_performances./music/group_membership/group #7647-07ym0 PRED entity: 07ym0 PRED relation: influenced_by PRED expected values: 03s9v => 128 concepts (41 used for prediction) PRED predicted values (max 10 best out of 267): 042q3 (0.57 #1665, 0.50 #2102, 0.45 #2537), 0gz_ (0.45 #2714, 0.40 #3586, 0.36 #3150), 015n8 (0.43 #1711, 0.33 #842, 0.29 #3456), 0j3v (0.43 #1362, 0.33 #493, 0.21 #3107), 03sbs (0.36 #3267, 0.33 #3703, 0.33 #653), 02wh0 (0.36 #3428, 0.33 #3864, 0.30 #2120), 026lj (0.33 #478, 0.27 #2656, 0.21 #3092), 015k7 (0.33 #707, 0.14 #1576, 0.09 #5493), 01vh096 (0.30 #2031, 0.27 #2466, 0.21 #3339), 01lwx (0.29 #1709, 0.20 #2146, 0.18 #2581) >> Best rule #1665 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 03f0324; 048cl; 04xzm; 06myp; >> query: (?x8390, 042q3) <- influenced_by(?x11097, ?x8390), influenced_by(?x1236, ?x8390), gender(?x8390, ?x231), ?x1236 = 045bg, location(?x8390, ?x362), influenced_by(?x118, ?x11097) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #5440 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 049dyj; 0gdh5; 02f8lw; 0bkg4; 01xv77; 018yj6; 01wj5hp; 0l_dv; *> query: (?x8390, 03s9v) <- profession(?x8390, ?x353), diet(?x8390, ?x3130), ?x3130 = 07_jd, ?x353 = 0cbd2 *> conf = 0.09 ranks of expected_values: 52 EVAL 07ym0 influenced_by 03s9v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 128.000 41.000 0.571 http://example.org/influence/influence_node/influenced_by #7646-06crk PRED entity: 06crk PRED relation: peers! PRED expected values: 059y0 => 107 concepts (54 used for prediction) PRED predicted values (max 10 best out of 29): 0jcx (0.06 #146, 0.04 #266, 0.03 #867), 02m7r (0.06 #140, 0.04 #260, 0.02 #500), 029rk (0.06 #208, 0.02 #568, 0.02 #688), 059y0 (0.06 #217, 0.01 #938, 0.01 #1178), 06g4_ (0.04 #339, 0.03 #459, 0.02 #579), 09gnn (0.02 #1173, 0.01 #1654, 0.01 #933), 06y7d (0.02 #591, 0.02 #711, 0.01 #1072), 04107 (0.02 #517, 0.02 #637, 0.01 #998), 06pj8 (0.02 #496, 0.02 #616, 0.01 #1337), 0343h (0.02 #489, 0.02 #609, 0.01 #1330) >> Best rule #146 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 05qmj; 0m93; >> query: (?x6342, 0jcx) <- nationality(?x6342, ?x94), profession(?x6342, ?x11056), ?x11056 = 05snw >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #217 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 05qmj; 0m93; *> query: (?x6342, 059y0) <- nationality(?x6342, ?x94), profession(?x6342, ?x11056), ?x11056 = 05snw *> conf = 0.06 ranks of expected_values: 4 EVAL 06crk peers! 059y0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 54.000 0.056 http://example.org/influence/influence_node/peers./influence/peer_relationship/peers #7645-07z31v PRED entity: 07z31v PRED relation: award_winner PRED expected values: 084m3 0g2lq 04ns3gy 02ct_k => 39 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2085): 01z7_f (0.56 #12912, 0.50 #9848, 0.33 #3719), 05bnq3j (0.50 #8380, 0.42 #15317, 0.41 #12254), 02661h (0.50 #5747, 0.38 #11878, 0.38 #10345), 04ns3gy (0.50 #8981, 0.36 #15108, 0.33 #2851), 0mz73 (0.50 #7259, 0.33 #4195, 0.12 #10324), 032xhg (0.50 #6172, 0.22 #29115, 0.21 #27583), 01wmxfs (0.50 #6230, 0.07 #4593, 0.06 #15423), 01j5ts (0.50 #6150, 0.06 #15343, 0.04 #27608), 01dy7j (0.44 #12695, 0.38 #9631, 0.33 #3502), 04crrxr (0.42 #15317, 0.41 #12254, 0.40 #16849) >> Best rule #12912 for best value: >> intensional similarity = 14 >> extensional distance = 7 >> proper extension: 092_25; >> query: (?x2126, 01z7_f) <- award_winner(?x2126, ?x3880), award_winner(?x2126, ?x3625), honored_for(?x2126, ?x2436), honored_for(?x2126, ?x1849), award_winner(?x3625, ?x236), producer_type(?x3625, ?x632), ?x1849 = 0kfv9, nominated_for(?x436, ?x2436), ceremony(?x686, ?x2126), nominated_for(?x678, ?x2436), award_winner(?x2436, ?x5899), award_nominee(?x3880, ?x364), program(?x3625, ?x3626), nominated_for(?x3880, ?x1135) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #8981 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 6 *> proper extension: 0lp_cd3; 0gx_st; 07y9ts; 03nnm4t; *> query: (?x2126, 04ns3gy) <- award_winner(?x2126, ?x3762), award_winner(?x2126, ?x3625), award_winner(?x2126, ?x2733), award_winner(?x2126, ?x1871), honored_for(?x2126, ?x337), ?x3625 = 02xs0q, profession(?x3762, ?x987), award_nominee(?x1871, ?x2275), award(?x1871, ?x686), film(?x2733, ?x3404), student(?x2486, ?x1871), award_nominee(?x488, ?x2733), type_of_union(?x3762, ?x566), nominated_for(?x2733, ?x2734), actor(?x1766, ?x2275) *> conf = 0.50 ranks of expected_values: 4, 46, 50, 143 EVAL 07z31v award_winner 02ct_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 39.000 24.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 07z31v award_winner 04ns3gy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 39.000 24.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 07z31v award_winner 0g2lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 39.000 24.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 07z31v award_winner 084m3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 39.000 24.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7644-064n1pz PRED entity: 064n1pz PRED relation: language PRED expected values: 02h40lc => 101 concepts (71 used for prediction) PRED predicted values (max 10 best out of 51): 02h40lc (0.94 #3966, 0.94 #3609, 0.93 #3788), 064_8sq (0.50 #429, 0.50 #371, 0.50 #255), 06nm1 (0.40 #69, 0.33 #186, 0.33 #11), 04306rv (0.40 #122, 0.22 #2717, 0.16 #1237), 02bjrlw (0.22 #2717, 0.20 #409, 0.20 #118), 03_9r (0.22 #2717, 0.20 #418, 0.17 #302), 06b_j (0.22 #2717, 0.11 #1254, 0.09 #1667), 012w70 (0.22 #2717, 0.09 #1658, 0.05 #4143), 04h9h (0.20 #159, 0.20 #100, 0.17 #217), 02hwyss (0.20 #158, 0.03 #4084, 0.03 #1213) >> Best rule #3966 for best value: >> intensional similarity = 5 >> extensional distance = 825 >> proper extension: 02vw1w2; 0d1qmz; >> query: (?x2098, 02h40lc) <- genre(?x2098, ?x9549), film_release_distribution_medium(?x2098, ?x81), genre(?x1708, ?x9549), ?x1708 = 05cj_j, language(?x2098, ?x4442) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 064n1pz language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 71.000 0.943 http://example.org/film/film/language #7643-03lh3v PRED entity: 03lh3v PRED relation: student! PRED expected values: 01jsn5 => 121 concepts (81 used for prediction) PRED predicted values (max 10 best out of 102): 078bz (0.40 #1131, 0.40 #604, 0.29 #1658), 033x5p (0.33 #142), 01pl14 (0.20 #1062, 0.20 #535, 0.14 #1589), 09f2j (0.20 #686, 0.14 #1740, 0.12 #7011), 0bx8pn (0.20 #1099, 0.14 #1626, 0.09 #2680), 01jq4b (0.14 #1775, 0.11 #2302, 0.08 #5992), 0lyjf (0.12 #3319, 0.12 #3846, 0.09 #2792), 01jpqb (0.11 #2468, 0.06 #3522, 0.06 #4576), 02fjzt (0.11 #2247, 0.06 #3301, 0.06 #4355), 07szy (0.09 #2675, 0.07 #7946, 0.06 #3202) >> Best rule #1131 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 054c1; >> query: (?x4834, 078bz) <- team(?x4834, ?x5032), location(?x4834, ?x13692), team(?x13045, ?x5032), team(?x6002, ?x5032), ?x6002 = 0cc8q3, ?x13045 = 0bqthy >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03lh3v student! 01jsn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 81.000 0.400 http://example.org/education/educational_institution/students_graduates./education/education/student #7642-02qwzkm PRED entity: 02qwzkm PRED relation: nominated_for PRED expected values: 0cw3yd => 27 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1228): 06kl78 (0.76 #4790, 0.75 #6387, 0.73 #6388), 0dl9_4 (0.76 #4790, 0.75 #6387, 0.73 #6388), 02c638 (0.50 #3504, 0.40 #5101, 0.22 #6698), 0gvvm6l (0.50 #2841, 0.33 #4438, 0.27 #6035), 02vp1f_ (0.50 #29, 0.33 #1626, 0.20 #4820), 080dfr7 (0.50 #1475, 0.27 #6266, 0.17 #4669), 03f7xg (0.50 #491, 0.20 #5282, 0.17 #3685), 027r7k (0.50 #1527, 0.20 #6318, 0.17 #4721), 03cwwl (0.50 #1429, 0.17 #4623, 0.17 #3026), 014bpd (0.50 #1217, 0.17 #4411, 0.17 #2814) >> Best rule #4790 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 02wkmx; 0gr4k; 02rdxsh; 0gs9p; 02rdyk7; 02x17s4; 02w9sd7; 027cyf7; >> query: (?x11943, ?x4772) <- award(?x5185, ?x11943), award(?x4772, ?x11943), nominated_for(?x11943, ?x8277), ?x8277 = 02r858_, executive_produced_by(?x5185, ?x4857), nominated_for(?x591, ?x5185), language(?x5185, ?x403) >> conf = 0.76 => this is the best rule for 2 predicted values *> Best rule #3603 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 02wkmx; 0gr4k; 02rdxsh; 0gs9p; 02rdyk7; 02x17s4; 02w9sd7; 027cyf7; *> query: (?x11943, 0cw3yd) <- award(?x5185, ?x11943), nominated_for(?x11943, ?x8277), ?x8277 = 02r858_, executive_produced_by(?x5185, ?x4857), nominated_for(?x591, ?x5185), language(?x5185, ?x403) *> conf = 0.17 ranks of expected_values: 218 EVAL 02qwzkm nominated_for 0cw3yd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 27.000 12.000 0.756 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7641-0ll3 PRED entity: 0ll3 PRED relation: gender PRED expected values: 05zppz => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.22 #27), 02zsn (0.06 #28) >> Best rule #27 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x4925, 05zppz) <- >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ll3 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.217 http://example.org/people/person/gender #7640-02t1wn PRED entity: 02t1wn PRED relation: place_of_birth PRED expected values: 0rh6k => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 39): 0cr3d (0.10 #94, 0.03 #19134, 0.03 #20543), 02_286 (0.08 #2136, 0.08 #6364, 0.08 #3545), 0f2wj (0.07 #5640, 0.07 #1412, 0.07 #7051), 01_d4 (0.05 #3592, 0.04 #17696, 0.03 #10645), 030qb3t (0.05 #3580, 0.04 #14865, 0.04 #2876), 01531 (0.04 #2222, 0.02 #14211, 0.02 #14916), 0dclg (0.03 #78, 0.02 #784, 0.02 #1490), 0cc56 (0.03 #33, 0.01 #2855, 0.01 #3559), 02h98sm (0.03 #699, 0.01 #2816), 0rydq (0.03 #681) >> Best rule #94 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 02sj1x; >> query: (?x13103, 0cr3d) <- nationality(?x13103, ?x94), place_of_death(?x13103, ?x682), ?x682 = 0f2wj, ?x94 = 09c7w0 >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #14108 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1042 *> proper extension: 03zqc1; 0308kx; 0bl60p; *> query: (?x13103, 0rh6k) <- nationality(?x13103, ?x94), ?x94 = 09c7w0, film(?x13103, ?x1133), type_of_union(?x13103, ?x566) *> conf = 0.01 ranks of expected_values: 33 EVAL 02t1wn place_of_birth 0rh6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 57.000 57.000 0.103 http://example.org/people/person/place_of_birth #7639-0th3k PRED entity: 0th3k PRED relation: contains! PRED expected values: 09c7w0 => 149 concepts (106 used for prediction) PRED predicted values (max 10 best out of 273): 09c7w0 (0.74 #58192, 0.72 #81472, 0.70 #78786), 01n7q (0.37 #23349, 0.27 #69008, 0.25 #71695), 01_d4 (0.33 #3704, 0.02 #94923, 0.01 #94024), 07ssc (0.29 #5403, 0.25 #42998, 0.20 #10773), 02jx1 (0.25 #43053, 0.17 #10828, 0.16 #5458), 059rby (0.21 #68055, 0.20 #68950, 0.17 #23291), 03rk0 (0.19 #23408, 0.07 #54744, 0.06 #45791), 04_1l0v (0.17 #13876, 0.13 #16561, 0.12 #15666), 0d9y6 (0.15 #2098, 0.10 #3888, 0.02 #80575), 03v0t (0.14 #3814, 0.07 #71850, 0.07 #29770) >> Best rule #58192 for best value: >> intensional similarity = 5 >> extensional distance = 479 >> proper extension: 01fq7; 01jssp; 01j_9c; 02w2bc; 0288zy; 02cttt; 01hhvg; 07w0v; 01k2wn; 02g839; ... >> query: (?x13370, 09c7w0) <- contains(?x4061, ?x13370), category(?x13370, ?x134), district_represented(?x3463, ?x4061), ?x3463 = 02bqmq, adjoins(?x177, ?x4061) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0th3k contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 106.000 0.744 http://example.org/location/location/contains #7638-0gqmvn PRED entity: 0gqmvn PRED relation: ceremony PRED expected values: 05c1t6z => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 135): 05c1t6z (0.91 #960, 0.90 #825, 0.89 #1095), 0hn821n (0.62 #665, 0.62 #935, 0.59 #1070), 0bx6zs (0.56 #661, 0.48 #931, 0.45 #1066), 0gpjbt (0.53 #2594, 0.53 #2729, 0.51 #2864), 0bxs_d (0.52 #919, 0.50 #1054, 0.50 #649), 0466p0j (0.52 #2637, 0.51 #2772, 0.49 #2907), 09n4nb (0.51 #2746, 0.51 #2611, 0.50 #2881), 05pd94v (0.51 #2568, 0.51 #2703, 0.49 #2838), 02rjjll (0.51 #2571, 0.50 #2706, 0.48 #2841), 02cg41 (0.50 #2821, 0.50 #2686, 0.48 #2956) >> Best rule #960 for best value: >> intensional similarity = 6 >> extensional distance = 20 >> proper extension: 09v82c0; >> query: (?x7041, 05c1t6z) <- ceremony(?x7041, ?x2292), ceremony(?x7041, ?x2213), award(?x3694, ?x7041), ?x2213 = 0gvstc3, ?x2292 = 0gx_st, student(?x6501, ?x3694) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gqmvn ceremony 05c1t6z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony #7637-01vvyfh PRED entity: 01vvyfh PRED relation: artists! PRED expected values: 02x8m 059kh 02lnbg => 99 concepts (62 used for prediction) PRED predicted values (max 10 best out of 231): 06by7 (0.61 #16511, 0.49 #5215, 0.49 #7350), 0glt670 (0.44 #40, 0.35 #4319, 0.33 #345), 02lnbg (0.35 #969, 0.33 #359, 0.23 #2752), 016clz (0.35 #920, 0.23 #16495, 0.23 #5199), 059kh (0.33 #961, 0.10 #5240, 0.10 #14703), 016cjb (0.31 #70, 0.23 #2752, 0.22 #375), 0xhtw (0.30 #5210, 0.23 #2752, 0.23 #626), 0m0jc (0.28 #924, 0.23 #2752, 0.09 #7338), 08cyft (0.23 #968, 0.07 #7382, 0.06 #14710), 08jyyk (0.23 #2752, 0.21 #672, 0.18 #1588) >> Best rule #16511 for best value: >> intensional similarity = 3 >> extensional distance = 639 >> proper extension: 04r1t; 02r1tx7; 05563d; 0394y; 01j59b0; 02mq_y; 07m4c; 013rfk; 01516r; 01shhf; ... >> query: (?x3929, 06by7) <- artists(?x3243, ?x3929), artists(?x3243, ?x8693), ?x8693 = 0bdxs5 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #969 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 03xhj6; 02vgh; 02hzz; 012vm6; 02twdq; *> query: (?x3929, 02lnbg) <- artists(?x3243, ?x3929), ?x3243 = 0y3_8, artist(?x2299, ?x3929) *> conf = 0.35 ranks of expected_values: 3, 5, 13 EVAL 01vvyfh artists! 02lnbg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 62.000 0.607 http://example.org/music/genre/artists EVAL 01vvyfh artists! 059kh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 62.000 0.607 http://example.org/music/genre/artists EVAL 01vvyfh artists! 02x8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 99.000 62.000 0.607 http://example.org/music/genre/artists #7636-0gz5hs PRED entity: 0gz5hs PRED relation: profession PRED expected values: 0dxtg 0np9r => 135 concepts (130 used for prediction) PRED predicted values (max 10 best out of 84): 0dxtg (0.71 #2496, 0.67 #3372, 0.67 #2788), 01d_h8 (0.62 #152, 0.60 #298, 0.55 #444), 01c72t (0.41 #2358, 0.20 #22, 0.15 #6446), 09jwl (0.37 #6441, 0.37 #8924, 0.36 #8048), 02jknp (0.32 #446, 0.25 #3220, 0.24 #3074), 012t_z (0.32 #451, 0.23 #1181, 0.20 #1473), 0nbcg (0.30 #2365, 0.28 #6453, 0.27 #905), 0kyk (0.27 #319, 0.23 #173, 0.14 #4407), 0cbd2 (0.26 #15186, 0.26 #16209, 0.22 #2489), 0np9r (0.26 #15186, 0.26 #16209, 0.21 #8634) >> Best rule #2496 for best value: >> intensional similarity = 2 >> extensional distance = 174 >> proper extension: 0f721s; 04rtpt; >> query: (?x1986, 0dxtg) <- program(?x1986, ?x1876), tv_program(?x1875, ?x1876) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 0gz5hs profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 135.000 130.000 0.710 http://example.org/people/person/profession EVAL 0gz5hs profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 130.000 0.710 http://example.org/people/person/profession #7635-0mcl0 PRED entity: 0mcl0 PRED relation: nominated_for! PRED expected values: 027dtxw => 97 concepts (85 used for prediction) PRED predicted values (max 10 best out of 203): 027571b (0.68 #8444, 0.67 #13119, 0.67 #8443), 02qyntr (0.56 #1497, 0.53 #2163, 0.42 #831), 040njc (0.47 #451, 0.46 #1340, 0.45 #674), 02pqp12 (0.47 #1383, 0.46 #2049, 0.40 #717), 09qwmm (0.41 #24, 0.35 #468, 0.34 #691), 04kxsb (0.37 #1414, 0.36 #748, 0.36 #2080), 099c8n (0.36 #49, 0.34 #1382, 0.33 #2048), 0gr51 (0.36 #64, 0.30 #508, 0.28 #731), 0f4x7 (0.33 #1800, 0.33 #6020, 0.32 #6242), 0gqyl (0.32 #510, 0.31 #733, 0.27 #6063) >> Best rule #8444 for best value: >> intensional similarity = 4 >> extensional distance = 452 >> proper extension: 06mmr; >> query: (?x3882, ?x1243) <- honored_for(?x7884, ?x3882), award(?x3882, ?x1243), award_winner(?x3882, ?x2551), nominated_for(?x1243, ?x144) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1337 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 111 *> proper extension: 015qsq; 028_yv; 0yyg4; 0m9p3; 09p7fh; 04f52jw; 0g68zt; 0571m; 016kz1; 02n9bh; ... *> query: (?x3882, 027dtxw) <- nominated_for(?x2379, ?x3882), ?x2379 = 02qvyrt, award_winner(?x3882, ?x2551), genre(?x3882, ?x53) *> conf = 0.30 ranks of expected_values: 13 EVAL 0mcl0 nominated_for! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 97.000 85.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7634-01_r9k PRED entity: 01_r9k PRED relation: student PRED expected values: 06pjs => 202 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1552): 0306ds (0.20 #4592, 0.06 #17144, 0.06 #21328), 02cx72 (0.20 #4787, 0.04 #17339, 0.04 #21523), 020_95 (0.20 #5131, 0.04 #17683, 0.03 #28143), 046zh (0.14 #906, 0.12 #2998, 0.09 #7182), 01vw37m (0.14 #1091, 0.12 #3183, 0.09 #7367), 02vntj (0.10 #4888, 0.06 #17440, 0.04 #40452), 015wc0 (0.10 #5879, 0.04 #18431, 0.04 #22615), 01l1rw (0.10 #5183, 0.04 #17735, 0.04 #21919), 03rs8y (0.10 #4230, 0.04 #16782, 0.04 #20966), 0892sx (0.10 #4609, 0.04 #17161, 0.04 #21345) >> Best rule #4592 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 02m0b0; >> query: (?x10170, 0306ds) <- state_province_region(?x10170, ?x3038), major_field_of_study(?x10170, ?x4268), country(?x10170, ?x94), ?x4268 = 02822 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #177829 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 251 *> proper extension: 08tyb_; *> query: (?x10170, ?x624) <- category(?x10170, ?x134), student(?x10170, ?x5462), citytown(?x10170, ?x2277), location(?x624, ?x2277) *> conf = 0.02 ranks of expected_values: 698 EVAL 01_r9k student 06pjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 202.000 153.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #7633-06r3p2 PRED entity: 06r3p2 PRED relation: type_of_union PRED expected values: 04ztj => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.80 #13, 0.74 #25, 0.74 #21), 01g63y (0.26 #18, 0.21 #22, 0.21 #26) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 01q9b9; >> query: (?x12630, 04ztj) <- profession(?x12630, ?x1032), award(?x12630, ?x3989), ?x3989 = 0bsjcw >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06r3p2 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.800 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7632-017gl1 PRED entity: 017gl1 PRED relation: nominated_for! PRED expected values: 02x17s4 02qyntr => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 176): 0gqxm (0.68 #6216, 0.67 #5800, 0.67 #6215), 05ztjjw (0.68 #6216, 0.67 #5800, 0.67 #6215), 02g3ft (0.68 #6216, 0.67 #5800, 0.67 #6215), 0262s1 (0.68 #6216, 0.67 #5800, 0.67 #6215), 0gqyl (0.52 #681, 0.49 #888, 0.47 #267), 02qyntr (0.50 #770, 0.50 #356, 0.47 #977), 04dn09n (0.50 #233, 0.48 #647, 0.47 #854), 0f4x7 (0.37 #2505, 0.36 #227, 0.33 #641), 094qd5 (0.29 #855, 0.29 #648, 0.28 #27), 02ppm4q (0.29 #919, 0.29 #712, 0.25 #298) >> Best rule #6216 for best value: >> intensional similarity = 3 >> extensional distance = 511 >> proper extension: 06mmr; >> query: (?x972, ?x3911) <- honored_for(?x472, ?x972), award(?x972, ?x3911), nominated_for(?x3911, ?x124) >> conf = 0.68 => this is the best rule for 4 predicted values *> Best rule #770 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 40 *> proper extension: 0c_j9x; 026p4q7; 0p4v_; 0mcl0; 03hmt9b; 0hfzr; 0cq8qq; 0y_9q; 05v38p; 0p_rk; *> query: (?x972, 02qyntr) <- nominated_for(?x1079, ?x972), nominated_for(?x601, ?x972), ?x1079 = 0l8z1, ?x601 = 0gr4k, award_winner(?x972, ?x629) *> conf = 0.50 ranks of expected_values: 6, 33 EVAL 017gl1 nominated_for! 02qyntr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 90.000 0.682 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 017gl1 nominated_for! 02x17s4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 90.000 90.000 0.682 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7631-01yf85 PRED entity: 01yf85 PRED relation: profession PRED expected values: 0d1pc => 150 concepts (149 used for prediction) PRED predicted values (max 10 best out of 69): 01d_h8 (0.51 #453, 0.45 #304, 0.42 #900), 03gjzk (0.35 #8945, 0.34 #462, 0.32 #7901), 0d1pc (0.35 #8945, 0.32 #7901, 0.30 #3578), 012t_z (0.35 #8945, 0.32 #7901, 0.30 #3578), 01c979 (0.35 #8945, 0.32 #7901, 0.30 #3578), 0dxtg (0.30 #5380, 0.29 #461, 0.29 #312), 0cbd2 (0.29 #7, 0.16 #10442, 0.15 #11784), 01c72t (0.29 #24, 0.07 #21194, 0.07 #20449), 09jwl (0.28 #1361, 0.27 #466, 0.25 #615), 02jknp (0.24 #12381, 0.24 #12083, 0.23 #10145) >> Best rule #453 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 03f2_rc; 0p__8; >> query: (?x8716, 01d_h8) <- nominated_for(?x8716, ?x146), friend(?x4782, ?x8716), currency(?x8716, ?x170) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #8945 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 517 *> proper extension: 0cbm64; *> query: (?x8716, ?x1032) <- participant(?x8716, ?x10371), participant(?x10371, ?x2422), profession(?x10371, ?x1032) *> conf = 0.35 ranks of expected_values: 3 EVAL 01yf85 profession 0d1pc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 150.000 149.000 0.512 http://example.org/people/person/profession #7630-01fl3 PRED entity: 01fl3 PRED relation: artists! PRED expected values: 06by7 => 82 concepts (30 used for prediction) PRED predicted values (max 10 best out of 282): 06by7 (0.82 #7084, 0.66 #5854, 0.65 #3397), 03_d0 (0.67 #1544, 0.20 #1225, 0.15 #4615), 0dl5d (0.62 #1246, 0.61 #4008, 0.60 #632), 0xhtw (0.57 #2778, 0.56 #2471, 0.53 #4313), 016clz (0.56 #1537, 0.40 #7067, 0.39 #7986), 02yv6b (0.43 #2858, 0.42 #1937, 0.41 #6235), 08jyyk (0.38 #1291, 0.36 #3133, 0.32 #4053), 03lty (0.30 #4938, 0.27 #5859, 0.26 #8008), 016jny (0.29 #4399, 0.24 #5323, 0.20 #409), 059kh (0.27 #3422, 0.25 #47, 0.20 #659) >> Best rule #7084 for best value: >> intensional similarity = 9 >> extensional distance = 127 >> proper extension: 015cxv; >> query: (?x1749, 06by7) <- group(?x227, ?x1749), artists(?x9063, ?x1749), artists(?x1748, ?x1749), artists(?x1748, ?x8323), ?x8323 = 01r0t_j, artists(?x9063, ?x5543), artists(?x9063, ?x4646), ?x4646 = 0fhxv, ?x5543 = 01kd57 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fl3 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 30.000 0.822 http://example.org/music/genre/artists #7629-05xb7q PRED entity: 05xb7q PRED relation: campuses! PRED expected values: 05xb7q => 130 concepts (64 used for prediction) PRED predicted values (max 10 best out of 143): 05ftw3 (0.10 #329), 03x83_ (0.10 #131), 0hsb3 (0.06 #748, 0.02 #3479, 0.01 #4025), 02bqy (0.06 #723, 0.01 #4547), 01s0_f (0.06 #599, 0.01 #4423), 02hp70 (0.06 #972), 015fsv (0.06 #881), 0h6rm (0.06 #680), 01v3ht (0.06 #666), 02w2bc (0.06 #556) >> Best rule #329 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0xnt5; 023vwt; 0n84k; >> query: (?x5968, 05ftw3) <- contains(?x5967, ?x5968), contains(?x2365, ?x5967), contains(?x2236, ?x5967), ?x2236 = 05sb1, adjoins(?x2365, ?x2146), ?x2146 = 03rk0 >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05xb7q campuses! 05xb7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 64.000 0.100 http://example.org/education/educational_institution/campuses #7628-01vw26l PRED entity: 01vw26l PRED relation: artists! PRED expected values: 0glt670 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 227): 064t9 (0.56 #939, 0.54 #322, 0.51 #2175), 06by7 (0.54 #1257, 0.53 #1566, 0.51 #948), 05bt6j (0.44 #1280, 0.42 #1589, 0.29 #354), 0xhtw (0.35 #1561, 0.34 #1252, 0.17 #7419), 017_qw (0.30 #3151, 0.13 #5925, 0.11 #6233), 025sc50 (0.29 #978, 0.26 #361, 0.25 #2214), 02k_kn (0.28 #1303, 0.27 #1612, 0.16 #994), 06j6l (0.28 #4368, 0.26 #2212, 0.26 #359), 01lyv (0.27 #961, 0.20 #1888, 0.19 #6820), 0glt670 (0.27 #4360, 0.24 #4976, 0.22 #8985) >> Best rule #939 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 02yygk; >> query: (?x3494, 064t9) <- languages(?x3494, ?x254), instrumentalists(?x1166, ?x3494), award_nominee(?x3494, ?x286) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #4360 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 289 *> proper extension: 03rl84; 0lrh; 01l9v7n; 01fs_4; 01l79yc; 02wb6d; 08c7cz; 09h_q; 023361; 01lqf49; ... *> query: (?x3494, 0glt670) <- award(?x3494, ?x401), artists(?x6513, ?x3494), people(?x2510, ?x3494) *> conf = 0.27 ranks of expected_values: 10 EVAL 01vw26l artists! 0glt670 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 96.000 96.000 0.564 http://example.org/music/genre/artists #7627-05qzv PRED entity: 05qzv PRED relation: story_by! PRED expected values: 017n9 => 150 concepts (138 used for prediction) PRED predicted values (max 10 best out of 248): 0h2zvzr (0.12 #612, 0.08 #1296, 0.07 #1638), 0kvbl6 (0.11 #910, 0.03 #2620, 0.02 #3304), 0bv8h2 (0.06 #2514, 0.04 #3198, 0.03 #4224), 02q_4ph (0.04 #6644, 0.02 #8012, 0.02 #12117), 08cfr1 (0.04 #2287, 0.03 #2629, 0.02 #3655), 03ntbmw (0.04 #2392, 0.03 #3076, 0.02 #3418), 0gtvrv3 (0.04 #2098, 0.03 #2782, 0.02 #4150), 03prz_ (0.04 #2256, 0.03 #2940, 0.02 #4308), 025rvx0 (0.04 #2258, 0.02 #3284, 0.02 #4310), 06kl78 (0.04 #2223, 0.02 #3249, 0.02 #4275) >> Best rule #612 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01hb6v; >> query: (?x9982, 0h2zvzr) <- influenced_by(?x9982, ?x2161), influenced_by(?x476, ?x9982), ?x2161 = 040db, place_of_birth(?x9982, ?x1860) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05qzv story_by! 017n9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 138.000 0.125 http://example.org/film/film/story_by #7626-01sxdy PRED entity: 01sxdy PRED relation: genre PRED expected values: 07s9rl0 02l7c8 => 87 concepts (60 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.83 #121, 0.81 #721, 0.79 #1), 02kdv5l (0.75 #3489, 0.42 #4575, 0.37 #842), 02l7c8 (0.69 #2540, 0.40 #135, 0.37 #15), 01jfsb (0.66 #251, 0.65 #491, 0.64 #371), 05p553 (0.61 #1807, 0.39 #1447, 0.39 #1567), 03k9fj (0.47 #4583, 0.34 #3497, 0.26 #4221), 060__y (0.37 #16, 0.32 #136, 0.30 #736), 06n90 (0.35 #3499, 0.25 #372, 0.24 #252), 04xvh5 (0.33 #154, 0.19 #754, 0.18 #995), 082gq (0.26 #30, 0.20 #630, 0.16 #750) >> Best rule #121 for best value: >> intensional similarity = 5 >> extensional distance = 61 >> proper extension: 0yyts; 09p7fh; 0b1y_2; 0c9k8; 0pd6l; 02ll45; 07bxqz; >> query: (?x3681, 07s9rl0) <- nominated_for(?x484, ?x3681), genre(?x3681, ?x162), ?x484 = 0gq_v, nominated_for(?x382, ?x3681), ?x162 = 04xvlr >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01sxdy genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 60.000 0.825 http://example.org/film/film/genre EVAL 01sxdy genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 60.000 0.825 http://example.org/film/film/genre #7625-02kk_c PRED entity: 02kk_c PRED relation: actor PRED expected values: 042z_g => 84 concepts (70 used for prediction) PRED predicted values (max 10 best out of 991): 04wvhz (0.39 #16627, 0.38 #8312, 0.38 #15703), 07ym6ss (0.39 #16627, 0.38 #8312, 0.38 #15703), 04m_zp (0.39 #16627, 0.38 #8312, 0.38 #15703), 044zvm (0.39 #16627, 0.38 #8312, 0.38 #15703), 0g2lq (0.39 #16627, 0.38 #15703, 0.37 #20321), 0bxtg (0.39 #16627, 0.38 #15703, 0.37 #20321), 03mdt (0.39 #16627, 0.38 #15703, 0.37 #20321), 064jjy (0.25 #633, 0.09 #15701, 0.08 #21244), 03mcwq3 (0.25 #1123, 0.09 #15701, 0.08 #21244), 059m45 (0.25 #547, 0.09 #15701, 0.08 #21244) >> Best rule #16627 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 090s_0; 02_1rq; 03kq98; 072kp; 039fgy; 0124k9; 02bg8v; 01j67j; 01b66d; 015w8_; ... >> query: (?x4881, ?x496) <- actor(?x4881, ?x4882), nominated_for(?x1039, ?x4881), award_winner(?x4881, ?x496), location(?x4882, ?x1523) >> conf = 0.39 => this is the best rule for 7 predicted values No rule for expected values ranks of expected_values: EVAL 02kk_c actor 042z_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 70.000 0.393 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #7624-02yy9r PRED entity: 02yy9r PRED relation: nominated_for! PRED expected values: 02qyp19 => 117 concepts (101 used for prediction) PRED predicted values (max 10 best out of 205): 0fq9zdn (0.58 #283, 0.13 #994, 0.12 #19207), 0gr0m (0.51 #3083, 0.51 #2905, 0.39 #3084), 0gq9h (0.48 #2908, 0.39 #4096, 0.36 #8125), 019f4v (0.43 #8116, 0.39 #2899, 0.30 #4087), 0gq_v (0.40 #2864, 0.34 #4052, 0.24 #8081), 02rdyk7 (0.39 #3084, 0.26 #3082, 0.26 #18255), 0k611 (0.36 #2919, 0.28 #8136, 0.26 #4107), 0gs9p (0.34 #8127, 0.34 #2910, 0.29 #777), 0gqy2 (0.33 #2968, 0.26 #4156, 0.23 #8185), 094qd5 (0.33 #37, 0.18 #3595, 0.16 #748) >> Best rule #283 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 021y7yw; >> query: (?x13183, 0fq9zdn) <- titles(?x512, ?x13183), nominated_for(?x13107, ?x13183), production_companies(?x13183, ?x9518), ?x13107 = 0fq9zcx >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #712 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 02qrv7; 05p3738; 01kf3_9; 04qw17; 0gyy53; 02_kd; 05c5z8j; 03cfkrw; 05vxdh; 0prh7; ... *> query: (?x13183, 02qyp19) <- titles(?x512, ?x13183), currency(?x13183, ?x170), ?x512 = 07ssc, genre(?x13183, ?x53) *> conf = 0.15 ranks of expected_values: 47 EVAL 02yy9r nominated_for! 02qyp19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 117.000 101.000 0.583 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7623-07bxqz PRED entity: 07bxqz PRED relation: film! PRED expected values: 0gz5hs 01cpqk 085q5 => 46 concepts (27 used for prediction) PRED predicted values (max 10 best out of 616): 02vxyl5 (0.46 #24947, 0.42 #51973, 0.41 #51972), 0grrq8 (0.42 #51973, 0.41 #51972, 0.41 #54053), 0bj9k (0.25 #327, 0.07 #4483, 0.03 #8643), 0p_47 (0.12 #674, 0.04 #43656, 0.01 #4830), 039bp (0.12 #179, 0.03 #35341, 0.02 #8495), 016fjj (0.12 #634, 0.03 #4790, 0.02 #8950), 0252fh (0.12 #1353, 0.03 #5509, 0.01 #9669), 0gyx4 (0.12 #773, 0.03 #45735), 015c4g (0.12 #780, 0.02 #9096, 0.01 #4936), 09fb5 (0.12 #58, 0.02 #12532, 0.02 #27083) >> Best rule #24947 for best value: >> intensional similarity = 3 >> extensional distance = 845 >> proper extension: 07s8z_l; 01j95; 06mmr; >> query: (?x11417, ?x12092) <- award_winner(?x11417, ?x12092), award(?x12092, ?x484), location(?x12092, ?x3670) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1719 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0y_yw; *> query: (?x11417, 085q5) <- genre(?x11417, ?x53), film(?x719, ?x11417), ?x719 = 01csvq *> conf = 0.12 ranks of expected_values: 11, 94, 612 EVAL 07bxqz film! 085q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 46.000 27.000 0.457 http://example.org/film/actor/film./film/performance/film EVAL 07bxqz film! 01cpqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 27.000 0.457 http://example.org/film/actor/film./film/performance/film EVAL 07bxqz film! 0gz5hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 27.000 0.457 http://example.org/film/actor/film./film/performance/film #7622-09b3v PRED entity: 09b3v PRED relation: production_companies! PRED expected values: 023gxx 03whyr => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1122): 05zlld0 (0.33 #9358, 0.25 #4884, 0.21 #22775), 0dl6fv (0.33 #936, 0.25 #4293, 0.20 #6530), 01cssf (0.33 #9011, 0.18 #17955, 0.18 #16837), 0d4htf (0.33 #2844, 0.12 #16263, 0.04 #45333), 0jnwx (0.33 #2443, 0.12 #15862, 0.03 #30398), 0k2sk (0.33 #2352, 0.06 #30307, 0.06 #15771), 01ry_x (0.33 #3305, 0.06 #16724, 0.03 #31260), 0cc97st (0.33 #2866, 0.06 #16285, 0.03 #30821), 0fgrm (0.33 #2749, 0.06 #16168, 0.03 #30704), 0crfwmx (0.33 #2344, 0.06 #15763, 0.03 #30299) >> Best rule #9358 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0hpt3; 01gb54; >> query: (?x3920, 05zlld0) <- production_companies(?x148, ?x3920), child(?x3920, ?x166), company(?x346, ?x3920) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #15997 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 02w_l9; *> query: (?x3920, 023gxx) <- citytown(?x3920, ?x11930), ?x11930 = 0r00l *> conf = 0.06 ranks of expected_values: 675, 677 EVAL 09b3v production_companies! 03whyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 105.000 105.000 0.333 http://example.org/film/film/production_companies EVAL 09b3v production_companies! 023gxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 105.000 105.000 0.333 http://example.org/film/film/production_companies #7621-02dwj PRED entity: 02dwj PRED relation: list PRED expected values: 05glt => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.50 #2, 0.22 #100, 0.20 #93) >> Best rule #2 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 0283_zv; 0pd4f; >> query: (?x5228, 05glt) <- film(?x7138, ?x5228), ?x7138 = 0l786 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02dwj list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #7620-09snz PRED entity: 09snz PRED relation: country PRED expected values: 09c7w0 => 168 concepts (119 used for prediction) PRED predicted values (max 10 best out of 48): 09c7w0 (0.80 #1135, 0.80 #1828, 0.79 #2263), 081yw (0.28 #6094, 0.27 #5311, 0.26 #7493), 0mlw1 (0.26 #7493, 0.26 #7058, 0.26 #7145), 04_1l0v (0.14 #6620, 0.12 #8465, 0.02 #10220), 03rk0 (0.07 #1959, 0.04 #5791, 0.04 #6053), 0d060g (0.05 #5146, 0.05 #5059, 0.05 #1399), 05kj_ (0.04 #6884, 0.02 #8374, 0.02 #8283), 015jr (0.04 #6884, 0.02 #8283), 041_3z (0.04 #6884), 0mmpm (0.04 #6884) >> Best rule #1135 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 0l_q9; >> query: (?x9141, 09c7w0) <- county_seat(?x10733, ?x9141), time_zones(?x9141, ?x2950), source(?x9141, ?x958), state(?x9141, ?x4600) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09snz country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 119.000 0.804 http://example.org/base/biblioness/bibs_location/country #7619-0jcx PRED entity: 0jcx PRED relation: nationality PRED expected values: 059z0 => 183 concepts (180 used for prediction) PRED predicted values (max 10 best out of 50): 07ssc (0.50 #299, 0.37 #3242, 0.36 #8877), 03rt9 (0.37 #3242, 0.36 #8877, 0.27 #9642), 02jx1 (0.33 #315, 0.29 #2603, 0.26 #8205), 01kk32 (0.28 #6107), 08966 (0.28 #6107), 03rk0 (0.27 #9642, 0.26 #8205, 0.26 #8301), 06bnz (0.27 #9642, 0.26 #8205, 0.26 #8301), 014tss (0.26 #8205, 0.26 #8301, 0.25 #10594), 059j2 (0.26 #8205, 0.26 #8301, 0.21 #3720), 0f8l9c (0.20 #1258, 0.13 #3933, 0.08 #4219) >> Best rule #299 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03s9v; >> query: (?x3335, 07ssc) <- student(?x6811, ?x3335), organization(?x3335, ?x5250), ?x5250 = 02hcxm, religion(?x3335, ?x2694) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jcx nationality 059z0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 183.000 180.000 0.500 http://example.org/people/person/nationality #7618-049g_xj PRED entity: 049g_xj PRED relation: profession PRED expected values: 018gz8 => 106 concepts (105 used for prediction) PRED predicted values (max 10 best out of 74): 01d_h8 (0.40 #1795, 0.39 #1944, 0.38 #6), 03gjzk (0.31 #313, 0.29 #1804, 0.28 #1953), 0dxtg (0.29 #5827, 0.28 #6276, 0.28 #6425), 02jknp (0.26 #306, 0.25 #4032, 0.24 #6120), 09jwl (0.25 #10288, 0.21 #3000, 0.21 #3447), 0np9r (0.25 #10288, 0.17 #1512, 0.14 #11352), 0nbcg (0.23 #1224, 0.22 #181, 0.21 #628), 016z4k (0.19 #153, 0.19 #4, 0.19 #1491), 0kyk (0.19 #626, 0.19 #1491, 0.17 #924), 0d1pc (0.19 #51, 0.19 #1491, 0.17 #200) >> Best rule #1795 for best value: >> intensional similarity = 3 >> extensional distance = 278 >> proper extension: 02wb6yq; >> query: (?x1530, 01d_h8) <- award_winner(?x1531, ?x1530), gender(?x1530, ?x514), participant(?x1987, ?x1530) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1491 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 120 *> proper extension: 032l1; 05wh0sh; 0zm1; 07_m9_; 080r3; 0ct9_; 06y3r; 02yy8; *> query: (?x1530, ?x319) <- notable_people_with_this_condition(?x8318, ?x1530), notable_people_with_this_condition(?x8318, ?x6613), profession(?x6613, ?x319) *> conf = 0.19 ranks of expected_values: 12 EVAL 049g_xj profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 106.000 105.000 0.404 http://example.org/people/person/profession #7617-0127s7 PRED entity: 0127s7 PRED relation: artists! PRED expected values: 06by7 02ny8t => 153 concepts (149 used for prediction) PRED predicted values (max 10 best out of 237): 06by7 (0.66 #1819, 0.50 #319, 0.47 #619), 08cyft (0.51 #652, 0.12 #952, 0.07 #6052), 016clz (0.47 #605, 0.29 #7505, 0.29 #7805), 02x8m (0.30 #8116, 0.16 #616, 0.12 #1816), 01lyv (0.28 #1832, 0.20 #17132, 0.19 #16832), 02yv6b (0.28 #1892, 0.17 #392, 0.12 #3992), 017_qw (0.24 #14756, 0.15 #6956, 0.12 #16856), 02w4v (0.23 #1841, 0.13 #641, 0.10 #20141), 07sbbz2 (0.20 #1808, 0.15 #308, 0.10 #8108), 0y3_8 (0.20 #644, 0.13 #2144, 0.11 #7544) >> Best rule #1819 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 01vrx3g; 01vvycq; 01wdqrx; 01wz_ml; 01s21dg; 0134tg; 015cxv; 011z3g; 023p29; >> query: (?x5906, 06by7) <- award_winner(?x1389, ?x5906), artists(?x7440, ?x5906), ?x7440 = 0155w >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1, 30 EVAL 0127s7 artists! 02ny8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 153.000 149.000 0.656 http://example.org/music/genre/artists EVAL 0127s7 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 149.000 0.656 http://example.org/music/genre/artists #7616-042g97 PRED entity: 042g97 PRED relation: film! PRED expected values: 01hbq0 => 94 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1002): 019x62 (0.46 #16658, 0.44 #33315, 0.43 #31232), 0jbp0 (0.19 #1758, 0.06 #5922, 0.03 #51727), 042ly5 (0.14 #1266, 0.11 #43723, 0.06 #5430), 03knl (0.14 #157, 0.08 #4321, 0.04 #14733), 079vf (0.14 #8, 0.06 #4172, 0.06 #49977), 03h_9lg (0.14 #132, 0.06 #4296, 0.03 #68838), 011s9r (0.14 #8328, 0.14 #8327, 0.09 #56213), 01y8d4 (0.14 #8328, 0.14 #8327, 0.09 #56213), 039bp (0.11 #41641, 0.11 #43723, 0.10 #180), 0kb3n (0.11 #41641, 0.07 #12492, 0.07 #22904) >> Best rule #16658 for best value: >> intensional similarity = 4 >> extensional distance = 131 >> proper extension: 07gp9; 01ln5z; 06z8s_; 017gm7; 09146g; 024lff; 0198b6; 057lbk; 06gb1w; 0432_5; ... >> query: (?x12214, ?x7088) <- nominated_for(?x7088, ?x12214), genre(?x12214, ?x225), film(?x773, ?x12214), prequel(?x3672, ?x12214) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #12434 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 112 *> proper extension: 02k_4g; 0q9jk; 0pc_l; *> query: (?x12214, 01hbq0) <- nominated_for(?x7088, ?x12214), honored_for(?x1072, ?x12214), titles(?x811, ?x12214), nominated_for(?x669, ?x1072) *> conf = 0.02 ranks of expected_values: 628 EVAL 042g97 film! 01hbq0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 94.000 35.000 0.465 http://example.org/film/actor/film./film/performance/film #7615-01wb8bs PRED entity: 01wb8bs PRED relation: film PRED expected values: 034qbx => 90 concepts (75 used for prediction) PRED predicted values (max 10 best out of 812): 03ln8b (0.60 #16093, 0.59 #46496, 0.58 #21458), 011ywj (0.20 #12163, 0.08 #3223, 0.03 #21104), 03177r (0.19 #11191, 0.08 #2251, 0.02 #134133), 02_fz3 (0.17 #3170, 0.11 #1382, 0.08 #8534), 0cf8qb (0.17 #3130, 0.05 #12070, 0.02 #134133), 078sj4 (0.17 #2241, 0.04 #11181, 0.02 #134133), 02krdz (0.17 #2350, 0.02 #134133, 0.02 #11290), 06v9_x (0.17 #2151, 0.02 #134133, 0.02 #11091), 031786 (0.15 #12002, 0.08 #3062, 0.02 #134133), 031hcx (0.14 #12001, 0.02 #37038, 0.01 #28097) >> Best rule #16093 for best value: >> intensional similarity = 3 >> extensional distance = 285 >> proper extension: 01pqy_; 01zh29; 0g476; 02j490; >> query: (?x3955, ?x2078) <- film(?x3955, ?x1877), award_winner(?x2078, ?x3955), religion(?x3955, ?x1985) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #8313 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 01541z; *> query: (?x3955, 034qbx) <- award_winner(?x6851, ?x3955), award_winner(?x1116, ?x3955), award_nominee(?x6851, ?x444), ?x1116 = 06b0d2 *> conf = 0.04 ranks of expected_values: 116 EVAL 01wb8bs film 034qbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 90.000 75.000 0.600 http://example.org/film/actor/film./film/performance/film #7614-01933d PRED entity: 01933d PRED relation: type_of_union PRED expected values: 04ztj => 248 concepts (248 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.92 #33, 0.91 #65, 0.91 #89), 01g63y (0.39 #138, 0.35 #146, 0.32 #266), 0jgjn (0.01 #80, 0.01 #208) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 015njf; 0484q; 025jbj; 04rfq; >> query: (?x8103, 04ztj) <- spouse(?x8103, ?x12525), people(?x9771, ?x8103), gender(?x8103, ?x514) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01933d type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 248.000 248.000 0.919 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7613-07h1q PRED entity: 07h1q PRED relation: influenced_by PRED expected values: 039n1 => 158 concepts (89 used for prediction) PRED predicted values (max 10 best out of 740): 0gz_ (0.58 #5610, 0.52 #22515, 0.50 #2642), 05qmj (0.52 #22515, 0.50 #2729, 0.49 #12068), 01h2_6 (0.52 #22515, 0.44 #4661, 0.43 #3388), 099bk (0.52 #22515, 0.25 #3074, 0.25 #2650), 0w6w (0.52 #22515, 0.25 #2961, 0.22 #2964), 037jz (0.52 #22515, 0.22 #2964, 0.21 #12729), 045m1_ (0.52 #22515, 0.12 #1512, 0.09 #2359), 0j3v (0.46 #3871, 0.46 #3447, 0.43 #4720), 026lj (0.40 #43, 0.32 #5551, 0.26 #12348), 0tfc (0.40 #404, 0.22 #2964, 0.21 #8034) >> Best rule #5610 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 026lj; 0j3v; 0372p; 01dvtx; 0b78hw; 03sbs; 039n1; 02wh0; 0tfc; >> query: (?x10110, 0gz_) <- people(?x1050, ?x10110), influenced_by(?x10110, ?x862), influenced_by(?x8232, ?x10110), interests(?x10110, ?x713) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #4978 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 06jkm; *> query: (?x10110, 039n1) <- influenced_by(?x10110, ?x7250), influenced_by(?x10110, ?x862), gender(?x10110, ?x231), ?x7250 = 03sbs, peers(?x12592, ?x10110), place_of_death(?x862, ?x1523) *> conf = 0.36 ranks of expected_values: 11 EVAL 07h1q influenced_by 039n1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 158.000 89.000 0.579 http://example.org/influence/influence_node/influenced_by #7612-0dscrwf PRED entity: 0dscrwf PRED relation: music PRED expected values: 01l79yc => 63 concepts (35 used for prediction) PRED predicted values (max 10 best out of 56): 02bh9 (0.10 #261, 0.08 #891, 0.07 #1101), 0150t6 (0.06 #256, 0.05 #886, 0.04 #1516), 0391jz (0.06 #7385, 0.01 #6961, 0.01 #5476), 0csdzz (0.05 #607, 0.05 #1447, 0.05 #1027), 01tc9r (0.05 #485, 0.04 #1115, 0.04 #275), 06fxnf (0.05 #489, 0.04 #69, 0.04 #1539), 01x6v6 (0.05 #543, 0.04 #1593, 0.04 #1383), 0146pg (0.05 #850, 0.04 #1060, 0.04 #220), 02jxmr (0.04 #1544, 0.04 #1124, 0.04 #284), 01x1fq (0.04 #1855) >> Best rule #261 for best value: >> intensional similarity = 5 >> extensional distance = 50 >> proper extension: 0g5qs2k; 01mgw; 02825nf; >> query: (?x511, 02bh9) <- film_release_region(?x511, ?x2843), film_release_region(?x511, ?x550), ?x2843 = 016wzw, produced_by(?x511, ?x4946), ?x550 = 05v8c >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #1794 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 140 *> proper extension: 06g77c; 011yfd; 0prhz; 02gd6x; 02754c9; 0bz3jx; 0qmfz; 05y0cr; 02vl9ln; *> query: (?x511, 01l79yc) <- country(?x511, ?x789), country(?x511, ?x512), ?x789 = 0f8l9c, combatants(?x151, ?x512), film_release_region(?x66, ?x512) *> conf = 0.02 ranks of expected_values: 21 EVAL 0dscrwf music 01l79yc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 63.000 35.000 0.096 http://example.org/film/film/music #7611-02sh8y PRED entity: 02sh8y PRED relation: nominated_for PRED expected values: 01rp13 => 117 concepts (54 used for prediction) PRED predicted values (max 10 best out of 454): 016ky6 (0.78 #45436, 0.78 #51929, 0.78 #55174), 0sxlb (0.54 #1623, 0.41 #47060, 0.37 #3246), 0ds2n (0.54 #1623, 0.37 #3246, 0.27 #56798), 01d259 (0.54 #1623, 0.37 #3246, 0.25 #47059), 01wb95 (0.54 #1623, 0.37 #3246, 0.25 #47059), 059lwy (0.54 #1623, 0.37 #3246, 0.25 #47059), 074rg9 (0.33 #890, 0.01 #9004, 0.01 #10627), 03phtz (0.33 #1607), 025s1wg (0.33 #1538), 05nyqk (0.33 #1385) >> Best rule #45436 for best value: >> intensional similarity = 3 >> extensional distance = 604 >> proper extension: 02404v; >> query: (?x5813, ?x5812) <- award(?x5813, ?x693), people(?x3591, ?x5813), award_winner(?x5812, ?x5813) >> conf = 0.78 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02sh8y nominated_for 01rp13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 54.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #7610-0xnt5 PRED entity: 0xnt5 PRED relation: country PRED expected values: 05sb1 => 128 concepts (95 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.62 #1480, 0.61 #1916, 0.57 #874), 05sb1 (0.59 #3753, 0.55 #4278, 0.48 #4455), 065zr (0.33 #3752, 0.30 #4277, 0.29 #5244), 03rk0 (0.31 #7442, 0.08 #571, 0.06 #3889), 07ssc (0.20 #802, 0.15 #715, 0.11 #3242), 0xnt5 (0.13 #6037, 0.12 #2878, 0.09 #4631), 0d060g (0.12 #794, 0.04 #2887, 0.04 #707), 0j0k (0.11 #8059), 02qkt (0.11 #8059), 02jx1 (0.08 #3259, 0.06 #3876, 0.06 #4840) >> Best rule #1480 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 0t015; 010dft; 01w0v; 0j0k; 0d23k; 059g4; 01pt5w; 0qxhc; 013d_f; >> query: (?x7593, 09c7w0) <- contains(?x7593, ?x4344), major_field_of_study(?x4344, ?x2014), state_province_region(?x4344, ?x2364), ?x2014 = 04rjg >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3753 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 222 *> proper extension: 0ttxp; 09hrc; *> query: (?x7593, ?x2236) <- contains(?x7593, ?x4344), contains(?x2236, ?x7593), student(?x4344, ?x10074), country(?x1121, ?x2236) *> conf = 0.59 ranks of expected_values: 2 EVAL 0xnt5 country 05sb1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 95.000 0.616 http://example.org/base/biblioness/bibs_location/country #7609-0133sq PRED entity: 0133sq PRED relation: profession PRED expected values: 01d_h8 => 125 concepts (96 used for prediction) PRED predicted values (max 10 best out of 81): 01d_h8 (0.87 #1904, 0.86 #4533, 0.85 #2196), 02hrh1q (0.79 #6292, 0.78 #2057, 0.77 #4102), 0cbd2 (0.60 #445, 0.54 #1175, 0.54 #1467), 0np9r (0.50 #165, 0.45 #311, 0.21 #895), 0kyk (0.47 #465, 0.39 #1487, 0.35 #1195), 02krf9 (0.45 #317, 0.28 #1631, 0.26 #6450), 02hv44_ (0.30 #493, 0.17 #639, 0.16 #1515), 0n1h (0.25 #157, 0.18 #303, 0.13 #1909), 012t_z (0.25 #158, 0.08 #3078, 0.08 #2786), 0nbcg (0.24 #13465, 0.13 #9084, 0.13 #10398) >> Best rule #1904 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 052gzr; 02_l96; 013zyw; 01qbjg; 081l_; 01vl17; 03h2p5; 015zql; 0yxl; 0jpdn; ... >> query: (?x10854, 01d_h8) <- story_by(?x2539, ?x10854), profession(?x10854, ?x1966), film_crew_role(?x83, ?x1966) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0133sq profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 96.000 0.866 http://example.org/people/person/profession #7608-031x_3 PRED entity: 031x_3 PRED relation: award_winner! PRED expected values: 01vsy7t => 140 concepts (73 used for prediction) PRED predicted values (max 10 best out of 702): 01ww2fs (0.86 #16067, 0.83 #30531, 0.83 #57847), 0x3b7 (0.27 #102840, 0.14 #101231, 0.08 #26424), 02cx90 (0.27 #102840, 0.14 #101231, 0.07 #26449), 03cfjg (0.27 #102840, 0.14 #101231, 0.07 #26269), 0ggjt (0.27 #102840, 0.14 #101231, 0.06 #26224), 01l47f5 (0.27 #102840, 0.14 #101231, 0.05 #98016), 01k_r5b (0.27 #102840, 0.14 #101231, 0.05 #98016), 05sq20 (0.27 #102840, 0.14 #101231, 0.05 #98016), 01lmj3q (0.27 #102840, 0.14 #101231, 0.05 #98016), 051m56 (0.27 #102840, 0.14 #101231, 0.05 #98016) >> Best rule #16067 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 02ryx0; 0cj2w; >> query: (?x8583, ?x2300) <- award_winner(?x352, ?x8583), award_winner(?x8583, ?x2300), performance_role(?x8583, ?x14713) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #102840 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 818 *> proper extension: 01l2fn; 024jwt; 016ghw; *> query: (?x8583, ?x367) <- award_winner(?x8583, ?x2300), location(?x8583, ?x2410), award_winner(?x2300, ?x367) *> conf = 0.27 ranks of expected_values: 14 EVAL 031x_3 award_winner! 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 140.000 73.000 0.860 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #7607-05jxkf PRED entity: 05jxkf PRED relation: school_type! PRED expected values: 0bx8pn 0j_sncb 02183k 01k3s2 02zcnq 01rgdw 07vhb 01vs5c 0ks67 07vjm 01qd_r 02f8zw 01g6l8 06b19 039d4 01jpqb 0m4yg 026m3y 023zl 02m_41 013719 03x1s8 027b43 07wkd 01gwck 07wm6 => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 447): 04rwx (0.50 #630, 0.44 #1547, 0.44 #3672), 02km0m (0.50 #741, 0.33 #1962, 0.33 #1658), 0kz2w (0.44 #3672, 0.33 #616, 0.33 #8), 01w3v (0.44 #3672, 0.33 #613, 0.33 #309), 06pwq (0.44 #3672, 0.33 #611, 0.33 #3), 03ksy (0.44 #3672, 0.33 #673, 0.33 #65), 065y4w7 (0.44 #3672, 0.33 #612, 0.33 #4), 0yls9 (0.44 #3672, 0.33 #134, 0.22 #1963), 0bx8pn (0.44 #3672, 0.33 #28, 0.22 #1553), 017z88 (0.44 #3672, 0.22 #1261, 0.17 #957) >> Best rule #630 for best value: >> intensional similarity = 27 >> extensional distance = 4 >> proper extension: 05pcjw; 01rs41; >> query: (?x3092, 04rwx) <- school_type(?x13770, ?x3092), school_type(?x12746, ?x3092), school_type(?x9025, ?x3092), school_type(?x5486, ?x3092), school_type(?x5068, ?x3092), school_type(?x4096, ?x3092), school_type(?x2522, ?x3092), school_type(?x1306, ?x3092), school_type(?x1201, ?x3092), school_type(?x546, ?x3092), currency(?x1306, ?x2244), contains(?x512, ?x12746), major_field_of_study(?x4096, ?x2605), contains(?x1658, ?x1306), student(?x12746, ?x1469), organization(?x346, ?x5068), contains(?x94, ?x5486), major_field_of_study(?x9025, ?x2981), state_province_region(?x1201, ?x2831), ?x2981 = 02j62, fraternities_and_sororities(?x1201, ?x4348), currency(?x5486, ?x170), institution(?x865, ?x2522), school(?x1823, ?x546), student(?x4096, ?x7400), ?x346 = 060c4, time_zones(?x13770, ?x5327) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3672 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 13 *> proper extension: 0m4mb; *> query: (?x3092, ?x8357) <- school_type(?x11821, ?x3092), school_type(?x8822, ?x3092), school_type(?x7546, ?x3092), school_type(?x5288, ?x3092), school_type(?x2327, ?x3092), school_type(?x639, ?x3092), category(?x639, ?x134), company(?x3131, ?x5288), student(?x5288, ?x460), contains(?x279, ?x2327), student(?x2327, ?x1422), student(?x7546, ?x3281), citytown(?x11821, ?x362), award(?x3281, ?x618), award_winner(?x3281, ?x628), award_nominee(?x3281, ?x230), student(?x8357, ?x3281), nominated_for(?x3281, ?x972), state_province_region(?x8822, ?x7058) *> conf = 0.44 ranks of expected_values: 9, 30, 41, 49, 50, 112, 122, 127, 133, 322, 323, 324, 327, 353, 358, 361, 364, 368, 369, 379, 382, 386, 411, 416, 446 EVAL 05jxkf school_type! 07wm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 01gwck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 07wkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 027b43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 03x1s8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 013719 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 02m_41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 023zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 026m3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 0m4yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 01jpqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 039d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 06b19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 01g6l8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 02f8zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 01qd_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 07vjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 0ks67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 01vs5c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 07vhb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 01rgdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 02zcnq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 01k3s2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 02183k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 0j_sncb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type EVAL 05jxkf school_type! 0bx8pn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 17.000 17.000 0.500 http://example.org/education/educational_institution/school_type #7606-0cc5mcj PRED entity: 0cc5mcj PRED relation: featured_film_locations PRED expected values: 05fjy => 86 concepts (81 used for prediction) PRED predicted values (max 10 best out of 80): 02_286 (0.20 #986, 0.19 #5799, 0.19 #1708), 030qb3t (0.14 #39, 0.10 #6299, 0.10 #1967), 080h2 (0.10 #749, 0.09 #990, 0.08 #1471), 0rh6k (0.08 #4094, 0.07 #6741, 0.07 #2891), 04jpl (0.07 #3862, 0.07 #8682, 0.07 #3622), 04sqj (0.06 #8914), 01_d4 (0.05 #1975, 0.04 #1013, 0.04 #2937), 02dtg (0.04 #12, 0.02 #1459, 0.02 #3142), 02frhbc (0.04 #165, 0.01 #2333, 0.01 #3536), 03gh4 (0.04 #840, 0.04 #1081, 0.03 #1562) >> Best rule #986 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0d_2fb; 048htn; 031hcx; 031786; 02bj22; 03cwwl; >> query: (?x2441, 02_286) <- film_distribution_medium(?x2441, ?x2099), film_crew_role(?x2441, ?x1284), ?x1284 = 0ch6mp2, production_companies(?x2441, ?x1478) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cc5mcj featured_film_locations 05fjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 81.000 0.195 http://example.org/film/film/featured_film_locations #7605-01htxr PRED entity: 01htxr PRED relation: award_winner PRED expected values: 012x4t => 127 concepts (63 used for prediction) PRED predicted values (max 10 best out of 712): 012x4t (0.81 #88737, 0.81 #93575, 0.81 #82282), 01pq5j7 (0.81 #88737, 0.81 #93575, 0.81 #82282), 0127s7 (0.48 #53234, 0.45 #83896, 0.41 #95188), 0b_j2 (0.48 #53234, 0.41 #95188, 0.41 #87124), 015rkw (0.17 #270, 0.14 #1883, 0.04 #72871), 0h10vt (0.17 #1430, 0.14 #3043, 0.03 #15946), 0169dl (0.17 #384, 0.14 #1997, 0.03 #14900), 02t__3 (0.17 #1011, 0.14 #2624, 0.03 #15527), 01swck (0.17 #774, 0.14 #2387, 0.03 #15290), 030hcs (0.17 #279, 0.14 #1892, 0.03 #14795) >> Best rule #88737 for best value: >> intensional similarity = 3 >> extensional distance = 855 >> proper extension: 07g2b; 039x1k; 029ghl; 016z1c; 0gdqy; 04vlh5; >> query: (?x6207, ?x1660) <- award(?x6207, ?x537), award_winner(?x1660, ?x6207), place_of_birth(?x6207, ?x12738) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01htxr award_winner 012x4t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 63.000 0.814 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #7604-01v1d8 PRED entity: 01v1d8 PRED relation: role! PRED expected values: 01w9wwg => 84 concepts (41 used for prediction) PRED predicted values (max 10 best out of 856): 050z2 (0.80 #10381, 0.73 #10842, 0.70 #8521), 05qhnq (0.64 #10965, 0.56 #7716, 0.55 #11429), 023l9y (0.62 #14118, 0.59 #15048, 0.50 #18303), 01wxdn3 (0.60 #10602, 0.60 #9207, 0.60 #8742), 02s6sh (0.60 #8767, 0.60 #4133, 0.60 #3671), 04bpm6 (0.60 #3311, 0.56 #13049, 0.56 #15372), 023slg (0.60 #4148, 0.50 #10642, 0.50 #8782), 01w9wwg (0.60 #3974, 0.50 #8608, 0.50 #4899), 01vn35l (0.60 #3366, 0.50 #10322, 0.50 #8462), 0140t7 (0.60 #3174, 0.50 #10593, 0.50 #2250) >> Best rule #10381 for best value: >> intensional similarity = 19 >> extensional distance = 8 >> proper extension: 0l1589; >> query: (?x3161, 050z2) <- role(?x3161, ?x5926), role(?x3161, ?x885), performance_role(?x3161, ?x212), ?x5926 = 0cfdd, role(?x4917, ?x3161), role(?x1969, ?x3161), role(?x1268, ?x3161), role(?x885, ?x614), role(?x885, ?x3716), role(?x885, ?x1574), role(?x2306, ?x3161), performance_role(?x764, ?x3161), ?x1574 = 0l15bq, role(?x3967, ?x1268), role(?x736, ?x4917), ?x3716 = 03gvt, role(?x2310, ?x4917), ?x1969 = 04rzd, ?x3967 = 01p970 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3974 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 3 *> proper extension: 01vj9c; *> query: (?x3161, 01w9wwg) <- role(?x3161, ?x5926), role(?x3161, ?x885), performance_role(?x3161, ?x212), ?x5926 = 0cfdd, role(?x2675, ?x3161), role(?x1969, ?x3161), role(?x1436, ?x3161), role(?x74, ?x3161), ?x885 = 0dwtp, role(?x3161, ?x1147), ?x2675 = 020w2, role(?x3399, ?x3161), group(?x3161, ?x3682), ?x74 = 03q5t, ?x1147 = 07kc_, role(?x2306, ?x3161), ?x1969 = 04rzd, role(?x615, ?x1436), role(?x1930, ?x1436) *> conf = 0.60 ranks of expected_values: 8 EVAL 01v1d8 role! 01w9wwg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 84.000 41.000 0.800 http://example.org/music/artist/track_contributions./music/track_contribution/role #7603-06x68 PRED entity: 06x68 PRED relation: draft PRED expected values: 02x2khw 04f4z1k => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 15): 04f4z1k (0.83 #301, 0.81 #608, 0.80 #687), 02x2khw (0.82 #395, 0.81 #608, 0.80 #687), 02z6872 (0.81 #608, 0.80 #687, 0.79 #765), 092j54 (0.50 #539, 0.47 #662, 0.46 #523), 05vsb7 (0.50 #517, 0.47 #533, 0.45 #656), 09l0x9 (0.47 #542, 0.46 #526, 0.45 #665), 0g3zpp (0.47 #534, 0.46 #518, 0.45 #657), 03nt7j (0.40 #538, 0.39 #522, 0.38 #661), 02qw1zx (0.40 #536, 0.37 #704, 0.36 #671), 025tn92 (0.40 #101, 0.37 #704, 0.36 #671) >> Best rule #301 for best value: >> intensional similarity = 17 >> extensional distance = 10 >> proper extension: 0cqt41; >> query: (?x700, 04f4z1k) <- draft(?x700, ?x1633), position(?x700, ?x2010), season(?x700, ?x9498), season(?x700, ?x8529), season(?x700, ?x8517), season(?x700, ?x701), school(?x700, ?x4846), ?x8529 = 025ygws, ?x9498 = 027pwzc, sport(?x700, ?x5063), season(?x12042, ?x8517), season(?x8894, ?x8517), ?x701 = 05kcgsf, ?x8894 = 02d02, ?x12042 = 05xvj, school_type(?x4846, ?x1044), teams(?x4356, ?x700) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 06x68 draft 04f4z1k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.833 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 06x68 draft 02x2khw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.833 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #7602-054lpb6 PRED entity: 054lpb6 PRED relation: organization! PRED expected values: 0dq_5 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 32): 0dq_5 (0.98 #736, 0.89 #176, 0.88 #146), 07xl34 (0.17 #1009, 0.16 #1029, 0.16 #1230), 05k17c (0.09 #1065, 0.08 #1095, 0.07 #1005), 0hm4q (0.06 #1006, 0.06 #1026, 0.05 #1167), 033smt (0.05 #881, 0.05 #1102), 02y6fz (0.05 #881, 0.05 #1102), 09d6p2 (0.05 #881, 0.05 #1102), 05c0jwl (0.05 #933, 0.04 #1013, 0.04 #1023), 04n1q6 (0.05 #1102, 0.01 #934, 0.01 #894), 0dq3c (0.05 #1102, 0.01 #791) >> Best rule #736 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 018mxj; 0gztl; 0300cp; 04vgq5; 0hm0k; 02d6ph; 05md3l; 064f29; 019rl6; 01qygl; ... >> query: (?x1478, 0dq_5) <- organization(?x346, ?x1478), category(?x1478, ?x134), industry(?x1478, ?x373) >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 054lpb6 organization! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.976 http://example.org/organization/role/leaders./organization/leadership/organization #7601-04grkmd PRED entity: 04grkmd PRED relation: film_crew_role PRED expected values: 01vx2h => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 31): 089g0h (0.57 #960, 0.19 #246, 0.17 #48), 0d2b38 (0.50 #966, 0.14 #3253, 0.14 #771), 01vx2h (0.45 #952, 0.39 #530, 0.36 #1702), 01xy5l_ (0.43 #955, 0.33 #43, 0.15 #208), 02_n3z (0.43 #945, 0.17 #33, 0.14 #99), 01pvkk (0.33 #140, 0.31 #206, 0.30 #1933), 02vs3x5 (0.25 #250, 0.14 #118, 0.14 #3253), 02ynfr (0.22 #665, 0.21 #1707, 0.20 #1511), 033smt (0.20 #968, 0.15 #773, 0.14 #3253), 02rh1dz (0.19 #529, 0.17 #39, 0.16 #854) >> Best rule #960 for best value: >> intensional similarity = 5 >> extensional distance = 117 >> proper extension: 092vkg; 05z_kps; 05qbckf; 02yvct; 07yk1xz; 04q00lw; 065z3_x; 05h43ls; 05c46y6; 0b1y_2; ... >> query: (?x3512, 089g0h) <- film(?x1104, ?x3512), film(?x399, ?x3512), film_crew_role(?x3512, ?x4305), production_companies(?x144, ?x1104), ?x4305 = 0215hd >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #952 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 117 *> proper extension: 092vkg; 05z_kps; 05qbckf; 02yvct; 07yk1xz; 04q00lw; 065z3_x; 05h43ls; 05c46y6; 0b1y_2; ... *> query: (?x3512, 01vx2h) <- film(?x1104, ?x3512), film(?x399, ?x3512), film_crew_role(?x3512, ?x4305), production_companies(?x144, ?x1104), ?x4305 = 0215hd *> conf = 0.45 ranks of expected_values: 3 EVAL 04grkmd film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 104.000 0.571 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7600-0ftlkg PRED entity: 0ftlkg PRED relation: ceremony! PRED expected values: 0gq_v 018wng 0gs96 => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 271): 0gs96 (0.84 #5092, 0.79 #3760, 0.75 #3838), 0gq9h (0.84 #5065, 0.79 #3760, 0.75 #3811), 018wng (0.83 #3787, 0.79 #3760, 0.78 #5041), 0gr51 (0.81 #5081, 0.79 #3760, 0.75 #3827), 0gs9p (0.81 #5066, 0.79 #3760, 0.75 #3812), 01cw7s (0.81 #3506, 0.78 #3507, 0.78 #3424), 01by1l (0.81 #3506, 0.78 #3331, 0.60 #1327), 02nbqh (0.81 #3506, 0.78 #3334, 0.60 #1330), 025m98 (0.81 #3506, 0.78 #3411, 0.60 #1407), 01bgqh (0.81 #3506, 0.78 #3285, 0.60 #1281) >> Best rule #5092 for best value: >> intensional similarity = 19 >> extensional distance = 35 >> proper extension: 02yvhx; 0c53vt; >> query: (?x1821, 0gs96) <- award_winner(?x1821, ?x6544), award_winner(?x1821, ?x3931), ceremony(?x3066, ?x1821), gender(?x6544, ?x514), award_winner(?x9372, ?x6544), award(?x406, ?x9372), award_nominee(?x538, ?x3931), nationality(?x3931, ?x205), category_of(?x9372, ?x2421), award(?x6544, ?x375), contains(?x205, ?x1356), film_release_region(?x3252, ?x205), film_release_region(?x2093, ?x205), service_location(?x555, ?x205), ?x2093 = 0gydcp7, country(?x89, ?x205), ?x3252 = 0gh8zks, country(?x10538, ?x205), ?x3066 = 0gqy2 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 87 EVAL 0ftlkg ceremony! 0gs96 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.838 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0ftlkg ceremony! 018wng CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 29.000 29.000 0.838 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0ftlkg ceremony! 0gq_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 29.000 29.000 0.838 http://example.org/award/award_category/winners./award/award_honor/ceremony #7599-0fq27fp PRED entity: 0fq27fp PRED relation: film_release_region PRED expected values: 0d05w3 => 107 concepts (97 used for prediction) PRED predicted values (max 10 best out of 267): 06t2t (0.92 #4911, 0.91 #5062, 0.91 #5364), 01znc_ (0.89 #5344, 0.87 #5949, 0.84 #7309), 0b90_r (0.87 #4858, 0.86 #5009, 0.86 #6067), 07ssc (0.87 #1528, 0.85 #4566, 0.85 #1075), 05b4w (0.86 #6123, 0.85 #5972, 0.84 #4308), 05v8c (0.85 #1076, 0.82 #925, 0.75 #5323), 03_3d (0.83 #5313, 0.81 #6069, 0.78 #5918), 0jgd (0.83 #7275, 0.81 #6973, 0.80 #1819), 04gzd (0.78 #765, 0.71 #7281, 0.71 #6072), 0ctw_b (0.77 #1083, 0.74 #2600, 0.73 #1536) >> Best rule #4911 for best value: >> intensional similarity = 16 >> extensional distance = 37 >> proper extension: 0872p_c; 0gj8t_b; 011yqc; 03qnvdl; 0gvrws1; 0fpv_3_; 03qnc6q; 0645k5; 0gj8nq2; 03q0r1; ... >> query: (?x622, 06t2t) <- film_release_region(?x622, ?x3277), film_release_region(?x622, ?x1892), film_release_region(?x622, ?x1497), film_release_region(?x622, ?x1229), film_release_region(?x622, ?x789), film_release_region(?x622, ?x205), film_release_region(?x622, ?x87), ?x1497 = 015qh, ?x1229 = 059j2, ?x87 = 05r4w, ?x1892 = 02vzc, genre(?x622, ?x53), ?x789 = 0f8l9c, film_crew_role(?x622, ?x137), ?x3277 = 06t8v, ?x205 = 03rjj >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #3031 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 18 *> proper extension: 0c40vxk; 0by1wkq; 09k56b7; 047svrl; 0gh65c5; 03cw411; 0dt8xq; 0gj96ln; 0gmd3k7; 0gy7bj4; *> query: (?x622, ?x142) <- film_release_region(?x622, ?x3277), film_release_region(?x622, ?x1497), film_release_region(?x622, ?x1229), film_release_region(?x622, ?x789), ?x1497 = 015qh, ?x1229 = 059j2, film_festivals(?x622, ?x2686), ?x3277 = 06t8v, film_festivals(?x2655, ?x2686), ?x789 = 0f8l9c, film_release_distribution_medium(?x2655, ?x81), film_release_region(?x2655, ?x142), film(?x1641, ?x2655) *> conf = 0.53 ranks of expected_values: 36 EVAL 0fq27fp film_release_region 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 107.000 97.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7598-01pj5q PRED entity: 01pj5q PRED relation: award_winner PRED expected values: 0h10vt => 87 concepts (29 used for prediction) PRED predicted values (max 10 best out of 568): 0f4vbz (0.83 #8054, 0.82 #17714, 0.82 #20936), 018ygt (0.83 #8054, 0.82 #17714, 0.82 #20936), 030hcs (0.83 #8054, 0.82 #17714, 0.82 #20936), 0h10vt (0.60 #1426, 0.28 #45099, 0.13 #35435), 01pj5q (0.60 #1230, 0.28 #45099, 0.13 #35435), 07yp0f (0.28 #45099, 0.20 #648, 0.19 #38654), 042xrr (0.28 #45099, 0.20 #791, 0.19 #38654), 0gy6z9 (0.28 #45099, 0.20 #544, 0.19 #38654), 0dvmd (0.28 #45099, 0.20 #509, 0.19 #38654), 09fb5 (0.28 #45099, 0.20 #47, 0.16 #41876) >> Best rule #8054 for best value: >> intensional similarity = 3 >> extensional distance = 537 >> proper extension: 04cy8rb; 043kzcr; 01trhmt; 01m15br; 014g22; 0gyx4; 01fmz6; 02q42j_; 02j_j0; 01jkqfz; ... >> query: (?x7733, ?x1815) <- award_winner(?x1815, ?x7733), award_winner(?x7733, ?x2422), spouse(?x1815, ?x10491) >> conf = 0.83 => this is the best rule for 3 predicted values *> Best rule #1426 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 016khd; 01wz01; *> query: (?x7733, 0h10vt) <- award_winner(?x1815, ?x7733), award_winner(?x7733, ?x2422), ?x1815 = 030hcs *> conf = 0.60 ranks of expected_values: 4 EVAL 01pj5q award_winner 0h10vt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 29.000 0.826 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #7597-09p0q PRED entity: 09p0q PRED relation: award_winner! PRED expected values: 018wng => 86 concepts (75 used for prediction) PRED predicted values (max 10 best out of 261): 0cjyzs (0.33 #867, 0.33 #434, 0.33 #107), 040njc (0.33 #867, 0.33 #8, 0.33 #3893), 02py7pj (0.25 #1175, 0.05 #2471, 0.05 #2904), 0fbtbt (0.25 #665, 0.05 #20752, 0.04 #22916), 040_9s0 (0.25 #749, 0.01 #10376), 0262yt (0.25 #699, 0.01 #10376), 0262zm (0.25 #518, 0.01 #10376), 02662b (0.25 #511, 0.01 #10376), 039yzf (0.25 #785), 0262x6 (0.25 #748) >> Best rule #867 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 07lwsz; 03772; >> query: (?x8662, ?x198) <- award_nominee(?x4383, ?x8662), ?x4383 = 07b3r9, award(?x8662, ?x198), student(?x1098, ?x8662) >> conf = 0.33 => this is the best rule for 2 predicted values *> Best rule #32436 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3385 *> proper extension: 07k2d; *> query: (?x8662, ?x757) <- award(?x8662, ?x2016), award(?x7795, ?x2016), award(?x7795, ?x757) *> conf = 0.01 ranks of expected_values: 199 EVAL 09p0q award_winner! 018wng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 86.000 75.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #7596-04qw17 PRED entity: 04qw17 PRED relation: film! PRED expected values: 01nwwl => 109 concepts (55 used for prediction) PRED predicted values (max 10 best out of 671): 022_q8 (0.67 #60478, 0.57 #87588, 0.48 #12517), 0c_gcr (0.57 #87588, 0.55 #95931, 0.48 #12517), 03crcpt (0.57 #87588, 0.48 #12517, 0.47 #60477), 024rgt (0.48 #12517, 0.47 #60477, 0.45 #75077), 031rx9 (0.48 #12517, 0.47 #60477, 0.45 #75077), 03ym1 (0.36 #11446, 0.02 #46899, 0.02 #48984), 0f0kz (0.27 #10948, 0.08 #25546, 0.06 #42231), 02ck7w (0.27 #11373, 0.02 #46826, 0.02 #48911), 0241jw (0.27 #10727, 0.02 #46180, 0.02 #48265), 01v9l67 (0.27 #10897, 0.02 #46350, 0.02 #48435) >> Best rule #60478 for best value: >> intensional similarity = 3 >> extensional distance = 480 >> proper extension: 084qpk; 0fq7dv_; 0fz3b1; 02q0k7v; 0234j5; 03ynwqj; 02t_h3; >> query: (?x1863, ?x5591) <- nominated_for(?x5591, ?x1863), currency(?x1863, ?x170), languages(?x5591, ?x254) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #25533 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 0ch26b_; *> query: (?x1863, 01nwwl) <- nominated_for(?x5971, ?x1863), award(?x1863, ?x749), edited_by(?x835, ?x5971), film_crew_role(?x1863, ?x137) *> conf = 0.04 ranks of expected_values: 136 EVAL 04qw17 film! 01nwwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 109.000 55.000 0.667 http://example.org/film/actor/film./film/performance/film #7595-01chpn PRED entity: 01chpn PRED relation: language PRED expected values: 02h40lc => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.96 #4172, 0.94 #2704, 0.92 #1998), 064_8sq (0.20 #432, 0.17 #315, 0.15 #606), 04306rv (0.17 #589, 0.16 #415, 0.13 #1003), 06nm1 (0.14 #1185, 0.12 #69, 0.11 #11), 02bjrlw (0.14 #353, 0.13 #585, 0.12 #999), 06b_j (0.12 #81, 0.11 #607, 0.08 #1197), 0653m (0.10 #129, 0.10 #305, 0.07 #188), 0jzc (0.08 #604, 0.07 #1312, 0.07 #1370), 02hxcvy (0.06 #92, 0.03 #1208, 0.03 #502), 03hkp (0.06 #73, 0.03 #483, 0.03 #1131) >> Best rule #4172 for best value: >> intensional similarity = 4 >> extensional distance = 981 >> proper extension: 0gtsx8c; >> query: (?x6288, 02h40lc) <- film_release_region(?x6288, ?x94), language(?x6288, ?x11038), film(?x92, ?x6288), award_winner(?x989, ?x92) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01chpn language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.956 http://example.org/film/film/language #7594-05hrq4 PRED entity: 05hrq4 PRED relation: student! PRED expected values: 026036 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 136): 065y4w7 (0.11 #14, 0.08 #2118, 0.07 #3696), 0bwfn (0.08 #9218, 0.08 #11848, 0.08 #29207), 03ksy (0.07 #105, 0.06 #2209, 0.06 #14835), 01k2wn (0.07 #24, 0.04 #3706, 0.04 #4232), 01w5m (0.05 #15886, 0.05 #11678, 0.04 #10100), 04b_46 (0.05 #9170, 0.03 #12852, 0.03 #11274), 01jq34 (0.05 #2161, 0.04 #57, 0.03 #2687), 025v3k (0.05 #1697, 0.03 #645, 0.03 #1171), 06thjt (0.04 #10393, 0.04 #11445, 0.04 #10919), 015nl4 (0.04 #29000, 0.04 #10589, 0.03 #28474) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 0bbxd3; >> query: (?x9097, 065y4w7) <- profession(?x9097, ?x1943), program_creator(?x9098, ?x9097), ?x1943 = 02krf9 >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #11966 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 207 *> proper extension: 0fpj4lx; 01r0t_j; 02qnbs; 03h2p5; 07lz9l; 062yh9; 03c9pqt; 02gnj2; 02k76g; 089z0z; *> query: (?x9097, 026036) <- profession(?x9097, ?x1032), place_of_birth(?x9097, ?x739), ?x739 = 02_286 *> conf = 0.02 ranks of expected_values: 76 EVAL 05hrq4 student! 026036 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 110.000 110.000 0.107 http://example.org/education/educational_institution/students_graduates./education/education/student #7593-011s0 PRED entity: 011s0 PRED relation: major_field_of_study! PRED expected values: 028dcg => 80 concepts (70 used for prediction) PRED predicted values (max 10 best out of 19): 02_xgp2 (0.80 #565, 0.79 #508, 0.78 #488), 03bwzr4 (0.78 #232, 0.72 #566, 0.71 #452), 0bkj86 (0.68 #504, 0.68 #561, 0.67 #484), 04zx3q1 (0.62 #298, 0.60 #74, 0.58 #243), 022h5x (0.56 #708, 0.56 #707, 0.54 #242), 01kxxq (0.56 #708, 0.56 #707, 0.54 #242), 027f2w (0.54 #538, 0.53 #670, 0.51 #787), 028dcg (0.54 #538, 0.53 #670, 0.51 #787), 02m4yg (0.40 #85, 0.34 #920, 0.31 #709), 013zdg (0.39 #499, 0.39 #222, 0.37 #825) >> Best rule #565 for best value: >> intensional similarity = 8 >> extensional distance = 23 >> proper extension: 02ky346; 01jzxy; 01540; 0l5mz; 02stgt; >> query: (?x5615, 02_xgp2) <- major_field_of_study(?x1675, ?x5615), student(?x1675, ?x1875), colors(?x1675, ?x8047), ?x8047 = 038hg, major_field_of_study(?x5615, ?x3213), country(?x1675, ?x94), major_field_of_study(?x620, ?x3213), state_province_region(?x1675, ?x1906) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #538 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 21 *> proper extension: 06nm1; 064_8sq; 03qsdpk; 05qdh; *> query: (?x5615, ?x620) <- major_field_of_study(?x1675, ?x5615), student(?x1675, ?x1875), colors(?x1675, ?x8047), ?x8047 = 038hg, major_field_of_study(?x5615, ?x3213), major_field_of_study(?x1675, ?x8221), ?x8221 = 037mh8, institution(?x620, ?x1675), school(?x580, ?x1675) *> conf = 0.54 ranks of expected_values: 8 EVAL 011s0 major_field_of_study! 028dcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 80.000 70.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #7592-05jt_ PRED entity: 05jt_ PRED relation: artists PRED expected values: 07g2v 0c9l1 => 59 concepts (17 used for prediction) PRED predicted values (max 10 best out of 3993): 01w8n89 (0.67 #3530, 0.60 #2458, 0.58 #7817), 048tgl (0.67 #4118, 0.60 #3046, 0.50 #10548), 01vsxdm (0.67 #3316, 0.60 #2244, 0.44 #4388), 0fpj4lx (0.67 #3537, 0.55 #5681, 0.50 #7824), 014_lq (0.67 #3692, 0.45 #5836, 0.44 #4764), 0ycp3 (0.67 #3824, 0.45 #5968, 0.44 #4896), 01vng3b (0.67 #3771, 0.45 #5915, 0.44 #4843), 0pkyh (0.67 #3454, 0.45 #5598, 0.42 #7741), 01gf5h (0.67 #3276, 0.44 #4348, 0.40 #2204), 0150jk (0.67 #3262, 0.40 #2190, 0.38 #9692) >> Best rule #3530 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 016clz; >> query: (?x8289, 01w8n89) <- parent_genre(?x8289, ?x1572), artists(?x8289, ?x8012), artists(?x8289, ?x5227), artists(?x8289, ?x2987), ?x5227 = 01j59b0, profession(?x8012, ?x131), category(?x8012, ?x134), ?x2987 = 01vw20_ >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3079 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 0xhtw; 03lty; *> query: (?x8289, 0c9l1) <- parent_genre(?x8289, ?x3753), artists(?x8289, ?x9463), artists(?x8289, ?x8012), artists(?x8289, ?x5227), ?x5227 = 01j59b0, ?x8012 = 01wt4wc, artists(?x3753, ?x717), ?x9463 = 01shhf, parent_genre(?x3753, ?x302) *> conf = 0.60 ranks of expected_values: 14, 80 EVAL 05jt_ artists 0c9l1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 59.000 17.000 0.667 http://example.org/music/genre/artists EVAL 05jt_ artists 07g2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 59.000 17.000 0.667 http://example.org/music/genre/artists #7591-0dzkq PRED entity: 0dzkq PRED relation: religion PRED expected values: 01hng3 => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 32): 0c8wxp (0.37 #622, 0.36 #6053, 0.35 #666), 0kpl (0.33 #230, 0.32 #2305, 0.31 #1287), 03j6c (0.25 #108, 0.19 #4214, 0.09 #5846), 0kq2 (0.20 #193, 0.15 #1030, 0.13 #1676), 01lp8 (0.13 #1676, 0.13 #3224, 0.12 #4592), 02vxy_ (0.13 #1676, 0.13 #3224, 0.12 #4592), 05sfs (0.13 #1676, 0.13 #3224, 0.12 #4592), 01t7j (0.13 #1676, 0.13 #3224, 0.12 #4592), 0n2g (0.13 #2308, 0.10 #2176, 0.10 #1290), 092bf5 (0.11 #1160, 0.10 #675, 0.10 #896) >> Best rule #622 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 03pvt; >> query: (?x3428, 0c8wxp) <- type_of_appearance(?x3428, ?x3429), ?x3429 = 01jdpf, religion(?x3428, ?x7131), gender(?x3428, ?x231) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #3129 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 135 *> proper extension: 01r42_g; 03_vx9; 01t6b4; 015rmq; 04dqdk; 02d9k; 0gz5hs; 06mfvc; 05wjnt; 0137g1; ... *> query: (?x3428, 01hng3) <- people(?x1050, ?x3428), ?x1050 = 041rx, nationality(?x3428, ?x291), religion(?x3428, ?x7131) *> conf = 0.01 ranks of expected_values: 31 EVAL 0dzkq religion 01hng3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 194.000 194.000 0.368 http://example.org/people/person/religion #7590-09yrh PRED entity: 09yrh PRED relation: location_of_ceremony PRED expected values: 0r0m6 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 29): 0cv3w (0.05 #394, 0.05 #752, 0.04 #1111), 0k049 (0.04 #721, 0.03 #363, 0.03 #123), 0f25y (0.04 #80, 0.03 #199, 0.01 #1754), 01x73 (0.04 #23), 059rby (0.04 #8), 0lhn5 (0.03 #419, 0.01 #1853), 0d9jr (0.03 #180, 0.01 #539, 0.01 #658), 030qb3t (0.02 #497, 0.02 #616, 0.02 #239), 0r0m6 (0.02 #647, 0.02 #289, 0.02 #886), 02_286 (0.02 #252, 0.02 #1806, 0.02 #239) >> Best rule #394 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 0167v4; >> query: (?x4536, 0cv3w) <- award(?x4536, ?x757), spouse(?x2499, ?x4536), celebrity(?x4536, ?x629) >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #647 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 98 *> proper extension: 02jg92; *> query: (?x4536, 0r0m6) <- participant(?x4536, ?x2499), location(?x4536, ?x739) *> conf = 0.02 ranks of expected_values: 9 EVAL 09yrh location_of_ceremony 0r0m6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 130.000 130.000 0.049 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #7589-01771z PRED entity: 01771z PRED relation: nominated_for! PRED expected values: 07bdd_ => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 184): 07bdd_ (0.58 #1939, 0.50 #51, 0.33 #3069), 05f4m9q (0.54 #1899, 0.50 #11, 0.33 #3069), 0gq9h (0.43 #1241, 0.41 #1005, 0.38 #297), 05b4l5x (0.42 #5, 0.35 #1893, 0.33 #3069), 05p09zm (0.42 #92, 0.30 #1980, 0.25 #13703), 019f4v (0.41 #996, 0.37 #1232, 0.34 #760), 04dn09n (0.39 #978, 0.35 #1214, 0.28 #270), 0k611 (0.35 #1015, 0.30 #1251, 0.28 #307), 0gqy2 (0.34 #828, 0.29 #592, 0.28 #1064), 05p1dby (0.33 #80, 0.20 #1968, 0.09 #3622) >> Best rule #1939 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 0g9yrw; 091rc5; 043h78; 0419kt; >> query: (?x2749, 07bdd_) <- nominated_for(?x102, ?x2749), genre(?x2749, ?x225), nominated_for(?x1888, ?x2749), ?x102 = 04ljl_l >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01771z nominated_for! 07bdd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.581 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7588-03qgjwc PRED entity: 03qgjwc PRED relation: award! PRED expected values: 0gjvqm 02bkdn 011_3s 0fgg4 0jlv5 023mdt => 39 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2390): 0l6px (0.67 #10622, 0.54 #13959, 0.53 #17297), 0161h5 (0.67 #6338, 0.50 #9675, 0.47 #19686), 05typm (0.67 #4656, 0.50 #7993, 0.40 #18004), 011_3s (0.67 #7558, 0.50 #4221, 0.33 #17569), 01fx5l (0.67 #5151, 0.38 #15161, 0.33 #18499), 039x1k (0.67 #5522, 0.33 #18870, 0.33 #8859), 06r3p2 (0.67 #6582, 0.33 #9919, 0.27 #19930), 0jlv5 (0.65 #30032, 0.50 #36709, 0.46 #36708), 0lpjn (0.62 #14103, 0.33 #757, 0.27 #17441), 02jsgf (0.54 #14481, 0.33 #4471, 0.27 #17819) >> Best rule #10622 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 09qj50; 0bdwft; 0bdx29; 09td7p; 0cqgl9; >> query: (?x3499, 0l6px) <- award(?x8966, ?x3499), award(?x4165, ?x3499), ?x4165 = 02mqc4, award(?x2742, ?x3499), award_nominee(?x157, ?x8966) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7558 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0bfvw2; 0cqh6z; 0ck27z; 0gkts9; *> query: (?x3499, 011_3s) <- award(?x4165, ?x3499), award(?x1641, ?x3499), ?x4165 = 02mqc4, award(?x2742, ?x3499), ?x1641 = 07s8r0 *> conf = 0.67 ranks of expected_values: 4, 8, 55, 65, 967, 973 EVAL 03qgjwc award! 023mdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 16.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qgjwc award! 0jlv5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 39.000 16.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qgjwc award! 0fgg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 16.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qgjwc award! 011_3s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 39.000 16.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qgjwc award! 02bkdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 39.000 16.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qgjwc award! 0gjvqm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 39.000 16.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7587-01c427 PRED entity: 01c427 PRED relation: ceremony PRED expected values: 01bx35 019bk0 013b2h => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 123): 01bx35 (0.75 #1897, 0.67 #887, 0.60 #761), 019bk0 (0.74 #1906, 0.70 #757, 0.67 #896), 013b2h (0.73 #1963, 0.67 #953, 0.60 #827), 02jp5r (0.70 #757, 0.30 #2526, 0.29 #2906), 09p2r9 (0.70 #757, 0.30 #2526, 0.29 #2906), 04n2r9h (0.45 #2272, 0.41 #2399, 0.30 #2526), 0hhtgcw (0.45 #2272, 0.41 #2399, 0.30 #2526), 09q_6t (0.45 #2272, 0.41 #2399, 0.30 #2526), 073hkh (0.45 #2272, 0.41 #2399, 0.29 #2906), 0n8_m93 (0.41 #2399, 0.30 #2526, 0.29 #2906) >> Best rule #1897 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 054knh; >> query: (?x1389, 01bx35) <- ceremony(?x1389, ?x6869), award_winner(?x6869, ?x4239), ?x4239 = 0x3b7 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 01c427 ceremony 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.752 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c427 ceremony 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.752 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c427 ceremony 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.752 http://example.org/award/award_category/winners./award/award_honor/ceremony #7586-0g_w PRED entity: 0g_w PRED relation: category_of! PRED expected values: 0l8z1 0gs9p 0gqyl 0gr42 => 49 concepts (40 used for prediction) PRED predicted values (max 10 best out of 369): 0gr42 (0.90 #279, 0.23 #976, 0.23 #977), 0gqyl (0.90 #279, 0.23 #976, 0.23 #977), 0gs9p (0.90 #279, 0.23 #976, 0.23 #977), 0l8z1 (0.90 #279, 0.23 #976, 0.23 #977), 024fxq (0.25 #266, 0.23 #976, 0.23 #977), 026mmy (0.25 #255, 0.23 #976, 0.23 #977), 02ddq4 (0.25 #249, 0.23 #976, 0.23 #977), 03qpp9 (0.25 #246, 0.23 #976, 0.23 #977), 01ckcd (0.25 #245, 0.23 #976, 0.23 #977), 024dzn (0.25 #240, 0.23 #976, 0.23 #977) >> Best rule #279 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0c4ys; 0gcf2r; >> query: (?x3459, ?x1079) <- instance_of_recurring_event(?x7884, ?x3459), category_of(?x1053, ?x3459), nominated_for(?x1053, ?x6216), award(?x975, ?x1053), ceremony(?x1079, ?x7884), film_release_region(?x6216, ?x87) >> conf = 0.90 => this is the best rule for 4 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0g_w category_of! 0gr42 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 40.000 0.903 http://example.org/award/award_category/category_of EVAL 0g_w category_of! 0gqyl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 40.000 0.903 http://example.org/award/award_category/category_of EVAL 0g_w category_of! 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 40.000 0.903 http://example.org/award/award_category/category_of EVAL 0g_w category_of! 0l8z1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 40.000 0.903 http://example.org/award/award_category/category_of #7585-02g3gj PRED entity: 02g3gj PRED relation: award_winner PRED expected values: 03j1p2n => 52 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2053): 0140t7 (0.57 #6902, 0.20 #9367, 0.08 #16768), 01vs_v8 (0.50 #2923, 0.44 #10318, 0.38 #22189), 01htxr (0.43 #6301, 0.30 #8766, 0.08 #16167), 09889g (0.43 #6049, 0.25 #3585, 0.20 #8514), 0m_v0 (0.43 #5669, 0.20 #8134, 0.10 #20465), 02qwg (0.43 #5662, 0.20 #8127, 0.10 #15528), 0ddkf (0.43 #6440, 0.20 #8905, 0.10 #16306), 01wwvc5 (0.43 #5507, 0.20 #7972, 0.08 #15373), 02dbp7 (0.43 #5952, 0.20 #8417, 0.07 #20748), 018x3 (0.43 #6164, 0.20 #8629, 0.04 #56719) >> Best rule #6902 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 01bgqh; 01c92g; 02nhxf; 01by1l; 031b3h; >> query: (?x528, 0140t7) <- award(?x9008, ?x528), ceremony(?x528, ?x5656), ?x5656 = 0466p0j, award_winner(?x528, ?x1896), ?x9008 = 016vqk >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #9094 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 02f76h; *> query: (?x528, 03j1p2n) <- award(?x5760, ?x528), award(?x4394, ?x528), participant(?x4394, ?x6835), ?x5760 = 01dwrc, ?x6835 = 06mt91 *> conf = 0.10 ranks of expected_values: 263 EVAL 02g3gj award_winner 03j1p2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 52.000 26.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner #7584-01_lhg PRED entity: 01_lhg PRED relation: current_club PRED expected values: 048xg8 => 72 concepts (53 used for prediction) PRED predicted values (max 10 best out of 741): 03x6m (0.40 #368, 0.33 #73, 0.31 #660), 049dzz (0.40 #387, 0.25 #533, 0.19 #679), 075q_ (0.40 #298, 0.18 #737, 0.17 #444), 0xbm (0.33 #460, 0.33 #168, 0.31 #606), 049f05 (0.33 #548, 0.29 #841, 0.26 #1279), 0y54 (0.33 #156, 0.25 #594, 0.25 #448), 06l22 (0.33 #56, 0.25 #497, 0.22 #1228), 04ltf (0.33 #219, 0.25 #511, 0.19 #657), 0266bd5 (0.33 #263, 0.25 #555, 0.19 #701), 02rh_0 (0.33 #91, 0.18 #825, 0.17 #1263) >> Best rule #368 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 03y_f8; 03ys48; 032jlh; >> query: (?x4485, 03x6m) <- team(?x8594, ?x4485), current_club(?x4485, ?x10389), current_club(?x4485, ?x9107), position(?x4485, ?x530), position(?x4485, ?x203), ?x203 = 0dgrmp, team(?x7109, ?x10389), team(?x8360, ?x10389), team(?x60, ?x10389), ?x530 = 02_j1w, ?x9107 = 0138mv >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #767 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 15 *> proper extension: 03xh50; 0329r5; 03zrhb; 02s9vc; 03_44z; *> query: (?x4485, 048xg8) <- position(?x4485, ?x530), position(?x4485, ?x63), ?x63 = 02sdk9v, current_club(?x4485, ?x3823), current_club(?x4485, ?x202), team(?x5191, ?x3823), team(?x9106, ?x202), ?x530 = 02_j1w, colors(?x3823, ?x3621), team(?x5471, ?x202) *> conf = 0.06 ranks of expected_values: 118 EVAL 01_lhg current_club 048xg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 72.000 53.000 0.400 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #7583-01r4zfk PRED entity: 01r4zfk PRED relation: profession PRED expected values: 01d_h8 => 68 concepts (60 used for prediction) PRED predicted values (max 10 best out of 122): 01d_h8 (0.75 #3612, 0.73 #3756, 0.58 #582), 0nbcg (0.56 #892, 0.55 #1327, 0.55 #747), 0dz3r (0.45 #867, 0.45 #1302, 0.41 #722), 039v1 (0.38 #897, 0.37 #1332, 0.37 #752), 02jknp (0.37 #3614, 0.37 #3758, 0.34 #584), 016z4k (0.37 #1304, 0.36 #1158, 0.36 #869), 02krf9 (0.36 #2476, 0.30 #599, 0.30 #1612), 0cbd2 (0.34 #4189, 0.22 #439, 0.20 #295), 01c72t (0.33 #3049, 0.28 #740, 0.28 #1174), 0np9r (0.26 #593, 0.21 #305, 0.19 #1027) >> Best rule #3612 for best value: >> intensional similarity = 5 >> extensional distance = 1301 >> proper extension: 06151l; 0qf43; 07f8wg; 0168cl; 02kxbwx; 05_k56; 05m883; 02lnhv; 01t6b4; 03kwtb; ... >> query: (?x8784, 01d_h8) <- profession(?x8784, ?x1041), profession(?x4784, ?x1041), profession(?x4676, ?x1041), ?x4784 = 05zh9c, ?x4676 = 04cl1 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r4zfk profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 60.000 0.748 http://example.org/people/person/profession #7582-0j0k PRED entity: 0j0k PRED relation: locations! PRED expected values: 0cm2xh => 147 concepts (97 used for prediction) PRED predicted values (max 10 best out of 118): 086m1 (0.30 #571, 0.25 #317, 0.21 #9501), 01w1sx (0.25 #343, 0.22 #470, 0.21 #1361), 0k4y6 (0.25 #327, 0.22 #454, 0.21 #9501), 0845v (0.25 #265, 0.22 #392, 0.21 #9501), 01y998 (0.25 #316, 0.21 #9501, 0.20 #570), 0f6rc (0.25 #198, 0.15 #891, 0.11 #452), 0j5ym (0.25 #248, 0.11 #502, 0.08 #1784), 0gfq9 (0.25 #155, 0.11 #409, 0.07 #663), 01hwkn (0.21 #9501, 0.20 #617, 0.13 #744), 07_nf (0.21 #9501, 0.15 #891, 0.10 #3908) >> Best rule #571 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0n2m7; >> query: (?x6956, 086m1) <- partially_contains(?x6956, ?x1499), teams(?x1499, ?x4306), adjoins(?x455, ?x6956), time_zones(?x1499, ?x10735) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #9501 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 03khn; *> query: (?x6956, ?x5503) <- adjoins(?x455, ?x6956), locations(?x326, ?x6956), locations(?x326, ?x9122), locations(?x5503, ?x9122) *> conf = 0.21 ranks of expected_values: 16 EVAL 0j0k locations! 0cm2xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 147.000 97.000 0.300 http://example.org/time/event/locations #7581-02khs PRED entity: 02khs PRED relation: organization PRED expected values: 041288 => 93 concepts (90 used for prediction) PRED predicted values (max 10 best out of 16): 041288 (0.73 #114, 0.71 #94, 0.70 #74), 04k4l (0.58 #822, 0.36 #144, 0.32 #304), 085h1 (0.58 #822, 0.32 #1205, 0.07 #10), 01rz1 (0.52 #141, 0.42 #261, 0.42 #221), 0_2v (0.44 #143, 0.34 #343, 0.32 #423), 018cqq (0.36 #149, 0.32 #1205, 0.28 #309), 0j7v_ (0.35 #65, 0.32 #1205, 0.30 #505), 02jxk (0.32 #142, 0.32 #1205, 0.24 #302), 059dn (0.32 #1205, 0.12 #153, 0.10 #233), 034h1h (0.22 #1152, 0.18 #1192, 0.10 #288) >> Best rule #114 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 06sw9; 06v36; 01nyl; >> query: (?x1756, 041288) <- countries_within(?x2467, ?x1756), organization(?x1756, ?x127), ?x2467 = 0dg3n1, country(?x1121, ?x1756) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02khs organization 041288 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 90.000 0.732 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #7580-0pf2 PRED entity: 0pf2 PRED relation: major_field_of_study! PRED expected values: 02h4rq6 => 53 concepts (27 used for prediction) PRED predicted values (max 10 best out of 15): 02h4rq6 (0.82 #156, 0.81 #241, 0.79 #224), 022h5x (0.64 #154, 0.44 #188, 0.38 #205), 07s6fsf (0.64 #154, 0.44 #188, 0.38 #205), 01gkg3 (0.64 #154, 0.22 #115, 0.22 #99), 01ysy9 (0.52 #323, 0.50 #75, 0.44 #122), 071tyz (0.52 #323, 0.50 #36, 0.44 #188), 02m4yg (0.52 #323, 0.44 #188, 0.38 #205), 01rr_d (0.44 #188, 0.38 #205, 0.37 #257), 013zdg (0.44 #188, 0.38 #205, 0.37 #257), 027f2w (0.44 #188, 0.38 #205, 0.37 #257) >> Best rule #156 for best value: >> intensional similarity = 13 >> extensional distance = 9 >> proper extension: 04rjg; >> query: (?x3400, 02h4rq6) <- major_field_of_study(?x5167, ?x3400), major_field_of_study(?x1390, ?x3400), ?x5167 = 015cz0, institution(?x1390, ?x4199), institution(?x1390, ?x1391), institution(?x1390, ?x892), major_field_of_study(?x1390, ?x8221), major_field_of_study(?x1390, ?x6364), ?x4199 = 016ndm, ?x1391 = 05f7s1, ?x892 = 07tgn, ?x6364 = 05qt0, ?x8221 = 037mh8 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pf2 major_field_of_study! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 27.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #7579-0345h PRED entity: 0345h PRED relation: adjoins PRED expected values: 0h7x => 262 concepts (145 used for prediction) PRED predicted values (max 10 best out of 525): 0154j (0.85 #46658, 0.85 #29059, 0.83 #87984), 0d05w3 (0.25 #18465, 0.16 #11584, 0.15 #8525), 0hg5 (0.25 #6241, 0.12 #10066, 0.09 #15418), 09krp (0.25 #1898, 0.07 #64627, 0.05 #41669), 017v_ (0.25 #1614, 0.05 #64343, 0.02 #44446), 09hrc (0.25 #2044, 0.05 #64773, 0.02 #44876), 04p0c (0.25 #2460, 0.04 #64425, 0.02 #41467), 03hrz (0.25 #2435, 0.02 #107884, 0.02 #22940), 09hzw (0.25 #2861, 0.02 #107884, 0.02 #22940), 0j3b (0.23 #8464, 0.17 #17639, 0.16 #11523) >> Best rule #46658 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 04kcn; >> query: (?x1264, ?x789) <- location_of_ceremony(?x566, ?x1264), adjoins(?x789, ?x1264), film_release_region(?x66, ?x789) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #104057 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 95 *> proper extension: 0mvsg; *> query: (?x1264, ?x756) <- partially_contains(?x1264, ?x10517), partially_contains(?x756, ?x10517) *> conf = 0.20 ranks of expected_values: 21 EVAL 0345h adjoins 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 262.000 145.000 0.854 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #7578-010cw1 PRED entity: 010cw1 PRED relation: place PRED expected values: 010cw1 => 112 concepts (74 used for prediction) PRED predicted values (max 10 best out of 149): 0xkq4 (0.41 #2579, 0.27 #8256, 0.25 #536), 0xkyn (0.41 #2579, 0.27 #8256, 0.25 #5674), 010cw1 (0.41 #2579, 0.27 #8256, 0.25 #5674), 0xl08 (0.41 #2579, 0.07 #12386, 0.02 #26329), 0hptm (0.11 #1187, 0.07 #1702, 0.01 #2217), 0xr0t (0.11 #1440, 0.01 #2470), 0xmlp (0.11 #1171), 0xn7q (0.07 #1891, 0.01 #2406, 0.01 #3956), 0xszy (0.07 #1816, 0.01 #2331, 0.01 #3881), 0h6l4 (0.07 #1921, 0.01 #2955) >> Best rule #2579 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 0f04v; >> query: (?x11407, ?x1189) <- county_seat(?x321, ?x11407), contains(?x321, ?x1189), adjoins(?x321, ?x4789), county_seat(?x4789, ?x12931) >> conf = 0.41 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 010cw1 place 010cw1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 74.000 0.408 http://example.org/location/hud_county_place/place #7577-01k9lpl PRED entity: 01k9lpl PRED relation: influenced_by! PRED expected values: 014z8v => 103 concepts (63 used for prediction) PRED predicted values (max 10 best out of 751): 01xwqn (0.50 #2431, 0.43 #2932, 0.25 #1931), 02p21g (0.40 #43, 0.38 #1544, 0.21 #2545), 016_mj (0.40 #2052, 0.36 #2553, 0.33 #1052), 01s7qqw (0.40 #203, 0.36 #2705, 0.30 #2204), 0bqs56 (0.40 #243, 0.29 #2745, 0.25 #1744), 046lt (0.38 #1603, 0.29 #2604, 0.21 #501), 03g5_y (0.33 #1305, 0.29 #2806, 0.21 #501), 0ph2w (0.33 #650, 0.25 #1650, 0.20 #149), 02633g (0.33 #1311, 0.21 #2812, 0.21 #501), 0p_pd (0.33 #508, 0.20 #7, 0.17 #1008) >> Best rule #2431 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 049gc; >> query: (?x9024, 01xwqn) <- influenced_by(?x2817, ?x9024), influenced_by(?x2127, ?x9024), ?x2127 = 01j7rd, award_winner(?x1711, ?x2817), place_of_birth(?x2817, ?x2850) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #501 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0ph2w; 0127xk; *> query: (?x9024, ?x1835) <- influenced_by(?x7183, ?x9024), influenced_by(?x1725, ?x9024), ?x1725 = 01n4f8, place_of_death(?x9024, ?x1523), influenced_by(?x1835, ?x7183) *> conf = 0.21 ranks of expected_values: 23 EVAL 01k9lpl influenced_by! 014z8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 103.000 63.000 0.500 http://example.org/influence/influence_node/influenced_by #7576-02_0d2 PRED entity: 02_0d2 PRED relation: student! PRED expected values: 01lhf => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 13): 02822 (0.11 #31, 0.06 #93, 0.04 #714), 02vxn (0.11 #4), 01x3g (0.06 #119), 06ms6 (0.06 #74), 05qfh (0.02 #275), 0fdys (0.02 #525, 0.01 #712, 0.01 #339), 01zc2w (0.02 #668, 0.01 #731), 03qsdpk (0.02 #1154, 0.01 #719, 0.01 #1341), 03g3w (0.02 #145, 0.01 #1139, 0.01 #207), 04rlf (0.02 #171, 0.01 #233) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02_j7t; >> query: (?x6700, 02822) <- nationality(?x6700, ?x94), film(?x6700, ?x5113), ?x5113 = 03h4fq7 >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02_0d2 student! 01lhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 102.000 0.111 http://example.org/education/field_of_study/students_majoring./education/education/student #7575-06mj4 PRED entity: 06mj4 PRED relation: artists! PRED expected values: 07ym47 01fh36 => 95 concepts (30 used for prediction) PRED predicted values (max 10 best out of 277): 06by7 (0.77 #4911, 0.67 #4299, 0.65 #3381), 03lty (0.50 #5528, 0.46 #3388, 0.46 #4000), 02yv6b (0.49 #1927, 0.49 #4375, 0.39 #1622), 0dl5d (0.47 #1849, 0.32 #3379, 0.31 #4909), 064t9 (0.46 #623, 0.46 #318, 0.46 #2760), 0glt670 (0.46 #345, 0.38 #650, 0.24 #2482), 01lyv (0.43 #33, 0.36 #7066, 0.20 #7033), 02w4v (0.43 #43, 0.23 #349, 0.15 #654), 0gywn (0.38 #363, 0.31 #668, 0.20 #7701), 05bt6j (0.38 #958, 0.36 #5238, 0.32 #4933) >> Best rule #4911 for best value: >> intensional similarity = 5 >> extensional distance = 133 >> proper extension: 01w8n89; 0bkg4; 018y81; >> query: (?x8060, 06by7) <- artists(?x2809, ?x8060), artists(?x1000, ?x8060), ?x1000 = 0xhtw, artists(?x2809, ?x2799), ?x2799 = 01vsl3_ >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1915 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 012zng; 09prnq; 01nn6c; 01vsy3q; 01vng3b; 01386_; 02l_7y; 018d6l; 01lz4tf; 01w9ph_; ... *> query: (?x8060, 01fh36) <- artists(?x2809, ?x8060), artists(?x1000, ?x8060), ?x1000 = 0xhtw, ?x2809 = 05w3f *> conf = 0.28 ranks of expected_values: 15, 145 EVAL 06mj4 artists! 01fh36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 95.000 30.000 0.770 http://example.org/music/genre/artists EVAL 06mj4 artists! 07ym47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 95.000 30.000 0.770 http://example.org/music/genre/artists #7574-02yxjs PRED entity: 02yxjs PRED relation: major_field_of_study PRED expected values: 02jfc => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 115): 03g3w (0.45 #1429, 0.38 #961, 0.36 #844), 02lp1 (0.44 #363, 0.43 #1299, 0.38 #831), 062z7 (0.44 #377, 0.42 #962, 0.39 #845), 05qjt (0.43 #827, 0.39 #944, 0.32 #1412), 0g26h (0.38 #1326, 0.37 #1560, 0.33 #390), 01540 (0.38 #875, 0.38 #992, 0.24 #1343), 037mh8 (0.35 #999, 0.33 #882, 0.28 #1467), 01lj9 (0.33 #855, 0.31 #972, 0.28 #1440), 02_7t (0.31 #1581, 0.26 #411, 0.23 #879), 0fdys (0.29 #1439, 0.28 #854, 0.26 #971) >> Best rule #1429 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 0ks67; 08qnnv; 0373qt; >> query: (?x8016, 03g3w) <- student(?x8016, ?x2182), institution(?x620, ?x8016), major_field_of_study(?x8016, ?x2014), ?x2014 = 04rjg >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1482 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 98 *> proper extension: 0ks67; 08qnnv; 0373qt; *> query: (?x8016, 02jfc) <- student(?x8016, ?x2182), institution(?x620, ?x8016), major_field_of_study(?x8016, ?x2014), ?x2014 = 04rjg *> conf = 0.17 ranks of expected_values: 24 EVAL 02yxjs major_field_of_study 02jfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 122.000 122.000 0.450 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7573-02pg45 PRED entity: 02pg45 PRED relation: country PRED expected values: 09c7w0 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 104): 09c7w0 (0.85 #246, 0.84 #307, 0.83 #1284), 03rjj (0.36 #3429, 0.08 #556, 0.08 #373), 02jx1 (0.36 #3429), 07ssc (0.21 #200, 0.21 #3261, 0.21 #3322), 0f8l9c (0.15 #569, 0.12 #81, 0.10 #264), 0d060g (0.14 #192, 0.08 #314, 0.07 #2995), 0345h (0.10 #272, 0.09 #1249, 0.09 #1371), 03_3d (0.07 #2995, 0.07 #191, 0.06 #374), 0chghy (0.07 #2995, 0.06 #379, 0.05 #562), 03rt9 (0.07 #2995, 0.05 #564, 0.03 #381) >> Best rule #246 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 04sntd; >> query: (?x5358, 09c7w0) <- film(?x8898, ?x5358), film(?x4764, ?x5358), genre(?x5358, ?x258), award(?x4764, ?x112), ?x8898 = 0h7pj >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pg45 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.850 http://example.org/film/film/country #7572-083skw PRED entity: 083skw PRED relation: film! PRED expected values: 076689 => 86 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1190): 026v_78 (0.40 #110388, 0.39 #91646, 0.39 #97893), 05v1sb (0.40 #110388, 0.39 #91646, 0.39 #97893), 0g1rw (0.40 #110388, 0.39 #91646, 0.39 #97893), 0638kv (0.40 #110388, 0.39 #91646, 0.39 #97893), 0hqcy (0.40 #110388, 0.39 #91646, 0.39 #97893), 042kbj (0.14 #60403), 03bw6 (0.14 #60403), 01y8cr (0.12 #745, 0.09 #2827, 0.03 #11157), 01mmslz (0.12 #400, 0.09 #2482, 0.03 #6646), 02vg0 (0.12 #1301, 0.04 #3383, 0.04 #7547) >> Best rule #110388 for best value: >> intensional similarity = 3 >> extensional distance = 902 >> proper extension: 0170z3; 014lc_; 02d413; 0b76d_m; 0ds35l9; 015qsq; 03qcfvw; 0g56t9t; 09sh8k; 0m313; ... >> query: (?x2612, ?x4251) <- production_companies(?x2612, ?x788), nominated_for(?x4251, ?x2612), genre(?x2612, ?x53) >> conf = 0.40 => this is the best rule for 5 predicted values *> Best rule #3976 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0j_tw; *> query: (?x2612, 076689) <- production_companies(?x2612, ?x788), films(?x11988, ?x2612), list(?x2612, ?x3004) *> conf = 0.04 ranks of expected_values: 150 EVAL 083skw film! 076689 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 86.000 54.000 0.400 http://example.org/film/actor/film./film/performance/film #7571-05q2c PRED entity: 05q2c PRED relation: student PRED expected values: 0151w_ => 157 concepts (127 used for prediction) PRED predicted values (max 10 best out of 1718): 01hbq0 (0.17 #4146, 0.12 #6236, 0.09 #10416), 015qq1 (0.17 #3981, 0.12 #6071, 0.08 #8161), 04t969 (0.17 #3369, 0.12 #5459, 0.06 #9639), 02vntj (0.17 #2793, 0.09 #11153, 0.07 #13245), 03ft8 (0.17 #2347, 0.06 #8617, 0.06 #4437), 023361 (0.17 #3542, 0.06 #9812, 0.06 #5632), 01cj6y (0.17 #2820, 0.06 #4910, 0.06 #11180), 0bw87 (0.17 #3241, 0.06 #5331, 0.04 #7421), 08s_lw (0.17 #3076, 0.06 #5166, 0.04 #7256), 01kt17 (0.17 #3681, 0.06 #5771, 0.04 #7861) >> Best rule #4146 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 025txrl; >> query: (?x8565, 01hbq0) <- citytown(?x8565, ?x1523), country(?x8565, ?x94), ?x1523 = 030qb3t >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #18815 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 49 *> proper extension: 02sdwt; *> query: (?x8565, ?x105) <- student(?x8565, ?x7352), student(?x8565, ?x2669), contains(?x94, ?x8565), friend(?x105, ?x2669), nominated_for(?x7352, ?x1481) *> conf = 0.09 ranks of expected_values: 40 EVAL 05q2c student 0151w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 157.000 127.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #7570-01fwj8 PRED entity: 01fwj8 PRED relation: award PRED expected values: 054ks3 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 286): 01by1l (0.35 #2114, 0.15 #16149, 0.15 #13743), 094qd5 (0.33 #43, 0.16 #845, 0.13 #1246), 05zvq6g (0.33 #58, 0.11 #459, 0.04 #2464), 01bgqh (0.30 #2046, 0.13 #16081, 0.13 #13675), 03qbh5 (0.27 #2207, 0.09 #13836, 0.09 #16242), 0c4z8 (0.24 #2075, 0.10 #13704, 0.09 #16110), 0gqwc (0.22 #73, 0.16 #875, 0.15 #1276), 0f4x7 (0.22 #31, 0.14 #2437, 0.14 #2838), 057xs89 (0.22 #157, 0.11 #2563, 0.11 #2964), 09qwmm (0.22 #34, 0.09 #836, 0.08 #10059) >> Best rule #2114 for best value: >> intensional similarity = 3 >> extensional distance = 189 >> proper extension: 01pbxb; 016srn; 01m15br; 01bczm; 02s2wq; 02z4b_8; 051m56; >> query: (?x1690, 01by1l) <- award_nominee(?x989, ?x1690), profession(?x1690, ?x220), ?x220 = 016z4k >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #2143 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 189 *> proper extension: 01pbxb; 016srn; 01m15br; 01bczm; 02s2wq; 02z4b_8; 051m56; *> query: (?x1690, 054ks3) <- award_nominee(?x989, ?x1690), profession(?x1690, ?x220), ?x220 = 016z4k *> conf = 0.18 ranks of expected_values: 13 EVAL 01fwj8 award 054ks3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 103.000 103.000 0.346 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7569-0d3k14 PRED entity: 0d3k14 PRED relation: profession PRED expected values: 0fj9f => 239 concepts (191 used for prediction) PRED predicted values (max 10 best out of 116): 02hrh1q (0.90 #23394, 0.89 #15307, 0.88 #25601), 0fj9f (0.86 #11523, 0.83 #9610, 0.82 #11082), 01d_h8 (0.67 #7357, 0.60 #1917, 0.53 #8680), 04gc2 (0.60 #2098, 0.60 #1804, 0.54 #6362), 02jknp (0.45 #18536, 0.40 #2212, 0.38 #3977), 018gz8 (0.41 #13251, 0.37 #7367, 0.36 #8690), 03gjzk (0.36 #8688, 0.34 #11337, 0.33 #7365), 03jgz (0.33 #2563, 0.33 #505, 0.25 #3299), 016fly (0.33 #514, 0.20 #2131, 0.17 #2572), 062z7 (0.33 #475, 0.20 #2092, 0.17 #2533) >> Best rule #23394 for best value: >> intensional similarity = 3 >> extensional distance = 381 >> proper extension: 01j5x6; 01713c; 01l2fn; 0k8y7; 0gx_p; 02w5q6; 06b_0; 01qqtr; 01cwkq; >> query: (?x11088, 02hrh1q) <- participant(?x11088, ?x543), people(?x1446, ?x11088), profession(?x11088, ?x353) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #11523 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 0466k4; *> query: (?x11088, 0fj9f) <- basic_title(?x11088, ?x346), religion(?x11088, ?x1985), profession(?x11088, ?x353) *> conf = 0.86 ranks of expected_values: 2 EVAL 0d3k14 profession 0fj9f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 239.000 191.000 0.898 http://example.org/people/person/profession #7568-018ljb PRED entity: 018ljb PRED relation: sports PRED expected values: 071t0 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 45): 096f8 (0.88 #345, 0.87 #515, 0.87 #259), 071t0 (0.83 #681, 0.83 #527, 0.82 #357), 06f41 (0.83 #681, 0.82 #1106, 0.70 #521), 01cgz (0.83 #520, 0.82 #350, 0.82 #95), 07jjt (0.82 #355, 0.81 #312, 0.80 #269), 07bs0 (0.75 #340, 0.74 #383, 0.74 #128), 01sgl (0.75 #340, 0.74 #383, 0.74 #128), 0486tv (0.70 #538, 0.67 #453, 0.67 #282), 064vjs (0.65 #362, 0.62 #319, 0.60 #276), 07_53 (0.59 #370, 0.56 #327, 0.55 #115) >> Best rule #345 for best value: >> intensional similarity = 13 >> extensional distance = 15 >> proper extension: 0l6vl; 0l998; 0lk8j; 0lbbj; 0jdk_; 0jkvj; >> query: (?x7051, 096f8) <- medal(?x7051, ?x422), sports(?x7051, ?x4876), sports(?x7051, ?x2315), sports(?x7051, ?x359), olympics(?x1557, ?x7051), ?x2315 = 06wrt, olympics(?x10801, ?x7051), olympics(?x2513, ?x7051), olympics(?x94, ?x7051), ?x359 = 02bkg, ?x4876 = 0d1t3, film_release_region(?x66, ?x2513), combatants(?x10801, ?x1611) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #681 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 25 *> proper extension: 09n48; *> query: (?x7051, ?x779) <- medal(?x7051, ?x422), sports(?x7051, ?x2315), olympics(?x1557, ?x7051), country(?x2315, ?x7479), sports(?x1081, ?x2315), sports(?x7051, ?x779), ?x7479 = 0165b, olympics(?x291, ?x1081), olympics(?x2315, ?x1931) *> conf = 0.83 ranks of expected_values: 2 EVAL 018ljb sports 071t0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 58.000 0.882 http://example.org/olympics/olympic_games/sports #7567-0372j5 PRED entity: 0372j5 PRED relation: titles! PRED expected values: 01z4y => 115 concepts (77 used for prediction) PRED predicted values (max 10 best out of 67): 01z4y (0.36 #5491, 0.32 #438, 0.29 #6607), 07s9rl0 (0.33 #1311, 0.33 #1210, 0.32 #5557), 04xvlr (0.26 #1815, 0.21 #7592, 0.21 #5054), 01hmnh (0.25 #27, 0.21 #227, 0.20 #1033), 07c52 (0.19 #4574, 0.14 #3454, 0.10 #7107), 01jfsb (0.18 #1128, 0.17 #522, 0.14 #5576), 07ssc (0.12 #5060, 0.11 #7087, 0.11 #7598), 017fp (0.10 #1233, 0.10 #1334, 0.08 #1835), 03k9fj (0.10 #119, 0.09 #521, 0.07 #2940), 0c3351 (0.09 #654, 0.07 #2973, 0.07 #2468) >> Best rule #5491 for best value: >> intensional similarity = 4 >> extensional distance = 490 >> proper extension: 052_mn; 043h78; >> query: (?x6751, 01z4y) <- film(?x2046, ?x6751), profession(?x2046, ?x1146), ?x1146 = 018gz8, titles(?x7323, ?x6751) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0372j5 titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 77.000 0.364 http://example.org/media_common/netflix_genre/titles #7566-02x4wb PRED entity: 02x4wb PRED relation: award_winner PRED expected values: 0c9l1 => 54 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1945): 01xzb6 (0.50 #1200, 0.09 #11095, 0.05 #20996), 07r1_ (0.50 #1571, 0.07 #11466, 0.05 #21367), 01vs_v8 (0.40 #462, 0.22 #10357, 0.15 #20258), 03h502k (0.38 #9894, 0.38 #7420, 0.37 #14844), 011hdn (0.38 #9894, 0.38 #7420, 0.37 #14844), 04rcr (0.38 #9894, 0.38 #7420, 0.37 #14844), 0c9l1 (0.38 #9894, 0.38 #7420, 0.37 #14844), 011_vz (0.38 #9894, 0.38 #7420, 0.37 #14844), 0bsj9 (0.38 #9894, 0.38 #7420, 0.37 #14844), 0560w (0.38 #9894, 0.38 #7420, 0.37 #14844) >> Best rule #1200 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 02f5qb; 02f716; 02f73p; 02f72_; 02f77l; >> query: (?x11068, 01xzb6) <- award(?x13142, ?x11068), award(?x5126, ?x11068), profession(?x5126, ?x220), instrumentalists(?x212, ?x5126), ?x13142 = 0jg77, role(?x5126, ?x74) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9894 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 62 *> proper extension: 02g8mp; 01ckbq; 026mff; 031b3h; 02gdjb; 025mbn; 026mml; 03ncb2; 02gm9n; *> query: (?x11068, ?x646) <- award(?x646, ?x11068), ceremony(?x11068, ?x6487), ceremony(?x11068, ?x5766), ceremony(?x11068, ?x1362), ?x1362 = 019bk0, award_winner(?x5766, ?x352), ?x6487 = 01mh_q *> conf = 0.38 ranks of expected_values: 7 EVAL 02x4wb award_winner 0c9l1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 54.000 20.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #7565-01t7n9 PRED entity: 01t7n9 PRED relation: basic_title! PRED expected values: 083pr => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 115): 0424m (0.43 #417, 0.38 #807, 0.33 #884), 042fk (0.43 #455, 0.38 #845, 0.33 #922), 07cbs (0.40 #1115, 0.40 #1036, 0.38 #799), 08f3b1 (0.38 #696, 0.33 #2, 0.33 #849), 0dq2k (0.38 #801, 0.33 #878, 0.33 #849), 042d1 (0.33 #282, 0.33 #54, 0.33 #849), 01mvpv (0.33 #300, 0.33 #72, 0.33 #849), 042f1 (0.33 #52, 0.33 #849, 0.32 #1167), 0835q (0.33 #65, 0.33 #849, 0.32 #1167), 0rlz (0.33 #32, 0.33 #849, 0.32 #1167) >> Best rule #417 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 060c4; >> query: (?x12562, 0424m) <- jurisdiction_of_office(?x12562, ?x4600), jurisdiction_of_office(?x12562, ?x94), basic_title(?x7891, ?x12562), featured_film_locations(?x5044, ?x4600), ?x94 = 09c7w0, adjoins(?x4600, ?x726), contains(?x4600, ?x5068), institution(?x734, ?x5068), partially_contains(?x4600, ?x6195), adjoins(?x1879, ?x4600), major_field_of_study(?x5068, ?x1154), colors(?x5068, ?x3364) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 0fkvn; *> query: (?x12562, 083pr) <- jurisdiction_of_office(?x12562, ?x7058), jurisdiction_of_office(?x12562, ?x4622), jurisdiction_of_office(?x12562, ?x4600), jurisdiction_of_office(?x12562, ?x1426), jurisdiction_of_office(?x12562, ?x177), jurisdiction_of_office(?x12562, ?x94), basic_title(?x7891, ?x12562), ?x4600 = 081yw, ?x7058 = 050ks, ?x4622 = 04tgp, ?x7891 = 0fd_1, ?x177 = 05kkh, ?x1426 = 07z1m, nationality(?x10770, ?x94), country(?x99, ?x94), contains(?x94, ?x95), service_location(?x127, ?x94), gender(?x10770, ?x514) *> conf = 0.33 ranks of expected_values: 11 EVAL 01t7n9 basic_title! 083pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 21.000 21.000 0.429 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #7564-01nqfh_ PRED entity: 01nqfh_ PRED relation: music! PRED expected values: 0bq6ntw => 98 concepts (50 used for prediction) PRED predicted values (max 10 best out of 946): 01s7w3 (0.08 #2883, 0.06 #6915, 0.05 #14979), 02ht1k (0.04 #7422, 0.03 #9438, 0.02 #18510), 0gvvm6l (0.04 #6851, 0.02 #14915, 0.02 #16931), 02rrfzf (0.04 #14435, 0.03 #16451, 0.03 #8387), 035s95 (0.03 #2221, 0.03 #5245, 0.03 #6253), 03_gz8 (0.03 #2667, 0.03 #6699, 0.02 #8715), 09146g (0.03 #2197, 0.03 #6229, 0.02 #8245), 08l0x2 (0.03 #2765, 0.03 #6797, 0.02 #17885), 05qm9f (0.03 #2691, 0.03 #6723, 0.02 #10755), 035yn8 (0.03 #2183, 0.03 #6215, 0.02 #11255) >> Best rule #2883 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 03qd_; 0p5mw; 021bk; 02qfhb; 020jqv; >> query: (?x562, 01s7w3) <- place_of_birth(?x562, ?x1523), music(?x1178, ?x562), instrumentalists(?x227, ?x562) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nqfh_ music! 0bq6ntw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 50.000 0.082 http://example.org/film/film/music #7563-01kph_c PRED entity: 01kph_c PRED relation: artists! PRED expected values: 02w4v => 131 concepts (41 used for prediction) PRED predicted values (max 10 best out of 244): 064t9 (0.57 #3692, 0.53 #625, 0.51 #8907), 06j6l (0.51 #6485, 0.30 #3724, 0.29 #657), 0155w (0.40 #1942, 0.29 #715, 0.23 #3782), 05w3f (0.36 #1874, 0.13 #9235, 0.13 #647), 02w4v (0.35 #41, 0.29 #1266, 0.28 #1573), 0gywn (0.32 #667, 0.29 #6495, 0.25 #973), 06924p (0.30 #173, 0.26 #479, 0.16 #1398), 0gg8l (0.30 #128, 0.23 #434, 0.14 #2580), 016zgj (0.30 #148, 0.23 #454, 0.09 #2600), 03lty (0.27 #1865, 0.16 #5546, 0.15 #2171) >> Best rule #3692 for best value: >> intensional similarity = 4 >> extensional distance = 162 >> proper extension: 01j4ls; 01r9fv; 049qx; 0x3n; 01x0yrt; >> query: (?x4790, 064t9) <- artists(?x1572, ?x4790), profession(?x4790, ?x220), award_winner(?x9828, ?x4790), ?x1572 = 06by7 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #41 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 06x4l_; *> query: (?x4790, 02w4v) <- artists(?x1928, ?x4790), award_nominee(?x2963, ?x4790), ?x1928 = 0mhfr, award_winner(?x9431, ?x4790) *> conf = 0.35 ranks of expected_values: 5 EVAL 01kph_c artists! 02w4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 131.000 41.000 0.573 http://example.org/music/genre/artists #7562-020trj PRED entity: 020trj PRED relation: languages PRED expected values: 02h40lc => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.34 #470, 0.30 #158, 0.30 #80), 064_8sq (0.06 #483, 0.05 #210, 0.05 #171), 02bjrlw (0.03 #79, 0.03 #118, 0.03 #352), 03k50 (0.03 #901, 0.03 #940, 0.03 #1174), 04306rv (0.02 #120, 0.01 #354, 0.01 #81), 0t_2 (0.01 #87, 0.01 #48), 07c9s (0.01 #949, 0.01 #1183, 0.01 #1144), 06nm1 (0.01 #1293, 0.01 #825) >> Best rule #470 for best value: >> intensional similarity = 3 >> extensional distance = 349 >> proper extension: 0137g1; 0gv40; 07bsj; >> query: (?x5833, 02h40lc) <- nationality(?x5833, ?x94), participant(?x5834, ?x5833), people(?x1050, ?x5833) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 020trj languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.336 http://example.org/people/person/languages #7561-016zgj PRED entity: 016zgj PRED relation: artists PRED expected values: 016s_5 018phr => 72 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1162): 0fpj4lx (0.73 #10935, 0.33 #8811, 0.33 #7747), 015882 (0.67 #3312, 0.62 #6496, 0.57 #4373), 0gcs9 (0.67 #3423, 0.57 #4484, 0.50 #6607), 01kph_c (0.62 #5724, 0.56 #7847, 0.38 #6785), 06rgq (0.62 #6081, 0.56 #8204, 0.38 #7142), 0249kn (0.62 #6599, 0.50 #3415, 0.43 #4476), 02jq1 (0.57 #4727, 0.54 #10038, 0.50 #6850), 01vrx3g (0.57 #4266, 0.50 #3205, 0.38 #6389), 01wp8w7 (0.57 #4350, 0.50 #3289, 0.38 #6473), 03h_fk5 (0.54 #9776, 0.50 #2342, 0.38 #6588) >> Best rule #10935 for best value: >> intensional similarity = 8 >> extensional distance = 20 >> proper extension: 02278y; >> query: (?x9750, 0fpj4lx) <- artists(?x9750, ?x10738), artists(?x9750, ?x4909), role(?x10738, ?x227), profession(?x10738, ?x131), artists(?x8187, ?x4909), artists(?x7329, ?x4909), ?x7329 = 016jny, ?x8187 = 05jg58 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #3667 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 07sbbz2; 0mhfr; 02w4v; *> query: (?x9750, 016s_5) <- artists(?x9750, ?x3426), artists(?x9750, ?x1089), parent_genre(?x11997, ?x9750), artist(?x3265, ?x3426), award(?x3426, ?x7691), award(?x3426, ?x1361), ?x1361 = 01c9f2, ?x1089 = 01vrncs, ?x7691 = 026m9w *> conf = 0.50 ranks of expected_values: 13, 14 EVAL 016zgj artists 018phr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 72.000 22.000 0.727 http://example.org/music/genre/artists EVAL 016zgj artists 016s_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 72.000 22.000 0.727 http://example.org/music/genre/artists #7560-015np0 PRED entity: 015np0 PRED relation: type_of_union PRED expected values: 04ztj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.78 #5, 0.73 #61, 0.73 #25), 01g63y (0.18 #14, 0.18 #26, 0.14 #94), 0jgjn (0.01 #28) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 015dnt; >> query: (?x8772, 04ztj) <- film(?x8772, ?x4971), ?x4971 = 01jwxx, award(?x8772, ?x112) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015np0 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.778 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7559-047jhq PRED entity: 047jhq PRED relation: film PRED expected values: 0f42nz => 126 concepts (84 used for prediction) PRED predicted values (max 10 best out of 750): 0f42nz (0.33 #910, 0.29 #15246, 0.27 #8078), 07vfy4 (0.33 #3390, 0.09 #6974, 0.08 #12350), 04jwjq (0.14 #18012, 0.09 #19804, 0.07 #28764), 050kh5 (0.10 #39431, 0.09 #62731, 0.09 #53769), 047q2k1 (0.09 #7200, 0.07 #14368, 0.05 #35846), 052_mn (0.09 #21116, 0.07 #19324, 0.06 #22908), 0gl02yg (0.09 #20722, 0.07 #18930, 0.06 #22514), 030z4z (0.08 #14022, 0.07 #17606, 0.05 #35846), 0233bn (0.07 #19229, 0.06 #22813, 0.04 #21021), 031ldd (0.07 #18963, 0.06 #22547, 0.04 #20755) >> Best rule #910 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 02n1gr; >> query: (?x12616, 0f42nz) <- location(?x12616, ?x12524), location(?x12616, ?x7412), ?x12524 = 02c7tb, profession(?x12616, ?x319), place_of_birth(?x491, ?x7412), religion(?x12616, ?x8967) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047jhq film 0f42nz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 84.000 0.333 http://example.org/film/actor/film./film/performance/film #7558-01mgw PRED entity: 01mgw PRED relation: language PRED expected values: 0653m => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 48): 02h40lc (0.89 #4445, 0.89 #3547, 0.89 #3666), 0653m (0.67 #12, 0.08 #2055, 0.06 #1031), 012w70 (0.44 #13, 0.06 #2056, 0.05 #73), 0459q4 (0.33 #37, 0.04 #2080, 0.03 #696), 064_8sq (0.20 #1280, 0.19 #501, 0.17 #922), 04306rv (0.16 #484, 0.12 #1263, 0.11 #1805), 06nm1 (0.13 #1509, 0.12 #1210, 0.11 #11), 02bjrlw (0.12 #480, 0.11 #61, 0.09 #1259), 03_9r (0.11 #10, 0.10 #429, 0.08 #910), 03k50 (0.11 #9, 0.07 #2052, 0.05 #849) >> Best rule #4445 for best value: >> intensional similarity = 3 >> extensional distance = 788 >> proper extension: 047qxs; 0ptdz; >> query: (?x7554, 02h40lc) <- genre(?x7554, ?x53), film(?x6211, ?x7554), film(?x2086, ?x7554) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0dckvs; *> query: (?x7554, 0653m) <- film_regional_debut_venue(?x7554, ?x6601), nominated_for(?x9217, ?x7554), film_release_region(?x7554, ?x94), ?x9217 = 09v51c2 *> conf = 0.67 ranks of expected_values: 2 EVAL 01mgw language 0653m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 122.000 0.892 http://example.org/film/film/language #7557-0bwx3 PRED entity: 0bwx3 PRED relation: influenced_by! PRED expected values: 01tz6vs => 70 concepts (38 used for prediction) PRED predicted values (max 10 best out of 295): 02kz_ (0.40 #224, 0.05 #2780, 0.04 #8397), 07h1q (0.35 #917, 0.05 #2962, 0.05 #3473), 07dnx (0.26 #871, 0.20 #360, 0.07 #7152), 045bg (0.26 #547, 0.05 #6675, 0.04 #2592), 03jht (0.20 #379, 0.17 #890, 0.07 #7152), 03cdg (0.20 #463, 0.17 #974, 0.04 #3019), 040db (0.20 #76, 0.13 #587, 0.09 #511), 03f0324 (0.20 #198, 0.13 #709, 0.09 #511), 04hcw (0.20 #290, 0.13 #801, 0.09 #511), 084w8 (0.20 #2, 0.09 #511, 0.09 #513) >> Best rule #224 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 015k7; >> query: (?x5811, 02kz_) <- influenced_by(?x8383, ?x5811), ?x8383 = 04xfb >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #229 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 3 *> proper extension: 015k7; *> query: (?x5811, 01tz6vs) <- influenced_by(?x8383, ?x5811), ?x8383 = 04xfb *> conf = 0.20 ranks of expected_values: 11 EVAL 0bwx3 influenced_by! 01tz6vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 70.000 38.000 0.400 http://example.org/influence/influence_node/influenced_by #7556-04vvh9 PRED entity: 04vvh9 PRED relation: film_release_region PRED expected values: 082fr => 88 concepts (86 used for prediction) PRED predicted values (max 10 best out of 266): 09c7w0 (0.93 #5111, 0.93 #4768, 0.93 #4940), 0f8l9c (0.91 #2409, 0.90 #707, 0.90 #2579), 03rjj (0.90 #1537, 0.90 #856, 0.89 #686), 059j2 (0.89 #2421, 0.87 #1570, 0.87 #719), 03h64 (0.87 #2462, 0.83 #420, 0.81 #760), 0chghy (0.83 #694, 0.83 #864, 0.83 #2226), 03_3d (0.83 #348, 0.83 #1369, 0.83 #1028), 0345h (0.83 #721, 0.80 #1402, 0.80 #1061), 0jgd (0.81 #2216, 0.79 #2386, 0.76 #2556), 035qy (0.81 #1063, 0.81 #723, 0.80 #2255) >> Best rule #5111 for best value: >> intensional similarity = 4 >> extensional distance = 767 >> proper extension: 09rfh9; >> query: (?x3638, 09c7w0) <- film_release_region(?x3638, ?x87), nominated_for(?x484, ?x3638), titles(?x53, ?x3638), genre(?x3638, ?x3515) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #3317 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 333 *> proper extension: 026p_bs; 053tj7; 03mgx6z; 02qk3fk; *> query: (?x3638, 082fr) <- film_release_region(?x3638, ?x985), film_release_region(?x3638, ?x87), ?x985 = 0k6nt, film_release_region(?x8891, ?x87), ?x8891 = 0gwlfnb *> conf = 0.20 ranks of expected_values: 52 EVAL 04vvh9 film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 88.000 86.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7555-05qjc PRED entity: 05qjc PRED relation: major_field_of_study! PRED expected values: 02ldmw => 44 concepts (19 used for prediction) PRED predicted values (max 10 best out of 665): 03ksy (0.70 #2492, 0.69 #3084, 0.52 #5459), 07wjk (0.67 #662, 0.63 #2439, 0.60 #68), 01w5m (0.67 #714, 0.60 #120, 0.56 #4271), 08815 (0.67 #596, 0.60 #2, 0.56 #2373), 07tgn (0.67 #612, 0.60 #18, 0.44 #2389), 07t90 (0.67 #763, 0.60 #169, 0.41 #2540), 01_qgp (0.67 #899, 0.60 #305, 0.27 #1494), 07tg4 (0.60 #94, 0.50 #688, 0.41 #2465), 0dzst (0.60 #383, 0.50 #977, 0.37 #2754), 015cz0 (0.60 #193, 0.50 #787, 0.33 #2564) >> Best rule #2492 for best value: >> intensional similarity = 12 >> extensional distance = 25 >> proper extension: 02h40lc; 036hv; 02lp1; 01lhy; 01mkq; 02ky346; 04rjg; 03g3w; 0pf2; 0193x; ... >> query: (?x5740, 03ksy) <- major_field_of_study(?x7918, ?x5740), colors(?x7918, ?x3189), colors(?x7918, ?x663), ?x663 = 083jv, organization(?x5510, ?x7918), institution(?x3437, ?x7918), institution(?x1200, ?x7918), ?x3189 = 01g5v, student(?x7918, ?x248), major_field_of_study(?x5739, ?x5740), ?x1200 = 016t_3, ?x3437 = 02_xgp2 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #8305 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 56 *> proper extension: 01r2l; *> query: (?x5740, ?x122) <- major_field_of_study(?x7918, ?x5740), colors(?x7918, ?x3189), colors(?x7918, ?x663), ?x663 = 083jv, ?x3189 = 01g5v, institution(?x1200, ?x7918), major_field_of_study(?x5739, ?x5740), institution(?x1200, ?x12823), institution(?x1200, ?x2775), institution(?x1200, ?x122), ?x2775 = 078bz, ?x12823 = 0lk0l *> conf = 0.10 ranks of expected_values: 484 EVAL 05qjc major_field_of_study! 02ldmw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 19.000 0.704 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7554-041mt PRED entity: 041mt PRED relation: influenced_by PRED expected values: 032l1 => 240 concepts (127 used for prediction) PRED predicted values (max 10 best out of 426): 03f0324 (0.30 #1007, 0.14 #26250, 0.12 #10416), 084w8 (0.30 #858, 0.11 #31654, 0.11 #53464), 081k8 (0.20 #1011, 0.19 #26254, 0.19 #4002), 0448r (0.20 #1542, 0.11 #31654, 0.11 #53464), 01tz6vs (0.20 #1031, 0.11 #31654, 0.11 #53464), 06whf (0.20 #980, 0.11 #53464, 0.11 #19679), 040db (0.20 #910, 0.09 #26153, 0.07 #48756), 0lcx (0.20 #970, 0.07 #54321, 0.05 #41486), 05qmj (0.17 #21154, 0.16 #20724, 0.11 #12594), 01hmk9 (0.16 #14331, 0.12 #3636, 0.12 #4063) >> Best rule #1007 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01m42d0; >> query: (?x2208, 03f0324) <- type_of_union(?x2208, ?x566), influenced_by(?x1089, ?x2208), student(?x3424, ?x2208), ?x3424 = 01w5m >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #26187 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 189 *> proper extension: 03g5jw; *> query: (?x2208, 032l1) <- influenced_by(?x2208, ?x9595), profession(?x9595, ?x3746), ?x3746 = 05z96 *> conf = 0.16 ranks of expected_values: 11 EVAL 041mt influenced_by 032l1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 240.000 127.000 0.300 http://example.org/influence/influence_node/influenced_by #7553-026y05 PRED entity: 026y05 PRED relation: taxonomy PRED expected values: 04n6k => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.03 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14813, 04n6k) <- >> conf = 0.03 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026y05 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.030 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #7552-0k4fz PRED entity: 0k4fz PRED relation: nominated_for! PRED expected values: 0gr51 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 202): 02z1nbg (0.68 #12371, 0.66 #15584, 0.66 #15583), 040njc (0.55 #2297, 0.43 #8023, 0.42 #5275), 0gr4k (0.53 #712, 0.50 #254, 0.50 #25), 0p9sw (0.50 #1394, 0.45 #2310, 0.38 #1165), 0gr51 (0.38 #2360, 0.29 #13519, 0.29 #13060), 027dtxw (0.35 #5272, 0.20 #6417, 0.20 #7791), 0gs96 (0.33 #1226, 0.33 #310, 0.31 #2829), 02pqp12 (0.33 #2346, 0.32 #8072, 0.29 #6469), 0l8z1 (0.33 #2340, 0.30 #6234, 0.28 #6463), 02qyntr (0.31 #5439, 0.31 #8187, 0.29 #6584) >> Best rule #12371 for best value: >> intensional similarity = 4 >> extensional distance = 454 >> proper extension: 0gzy02; 0kfpm; 02qm_f; 0358x_; 0ddd0gc; 02hct1; 01b64v; 012mrr; 02vqsll; 0phrl; ... >> query: (?x4841, ?x3902) <- honored_for(?x11428, ?x4841), nominated_for(?x484, ?x4841), award_winner(?x4841, ?x2716), award(?x4841, ?x3902) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2360 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 07xtqq; 0hmr4; 0_92w; 0dtfn; 0jqn5; 0jym0; 0bm2g; 0bx0l; 0h6r5; 0hfzr; ... *> query: (?x4841, 0gr51) <- honored_for(?x11428, ?x4841), titles(?x3613, ?x4841), nominated_for(?x484, ?x4841), list(?x4841, ?x3004) *> conf = 0.38 ranks of expected_values: 5 EVAL 0k4fz nominated_for! 0gr51 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 97.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7551-015fr PRED entity: 015fr PRED relation: combatants! PRED expected values: 059j2 01mk6 => 183 concepts (145 used for prediction) PRED predicted values (max 10 best out of 285): 0b90_r (0.84 #3008, 0.83 #1939, 0.83 #2208), 059j2 (0.81 #676, 0.50 #2152, 0.50 #544), 01mk6 (0.62 #710, 0.50 #376, 0.42 #1917), 015fr (0.56 #669, 0.37 #870, 0.32 #1876), 0bq0p9 (0.50 #337, 0.44 #671, 0.38 #539), 03b79 (0.40 #226, 0.29 #425, 0.27 #6038), 05v8c (0.36 #334, 0.31 #536, 0.31 #5498), 059z0 (0.34 #3193, 0.33 #3126, 0.31 #715), 0d04z6 (0.31 #5498, 0.27 #6038, 0.27 #6037), 0d05q4 (0.31 #5498, 0.27 #6038, 0.27 #6037) >> Best rule #3008 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 05kyr; >> query: (?x583, ?x94) <- combatants(?x583, ?x94), nationality(?x6390, ?x583), combatants(?x390, ?x583) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #676 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0bq0p9; *> query: (?x583, 059j2) <- combatants(?x583, ?x1264), nationality(?x6390, ?x583), ?x1264 = 0345h *> conf = 0.81 ranks of expected_values: 2, 3 EVAL 015fr combatants! 01mk6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 145.000 0.836 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants EVAL 015fr combatants! 059j2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 183.000 145.000 0.836 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #7550-012x4t PRED entity: 012x4t PRED relation: role PRED expected values: 0bxl5 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 110): 0342h (0.43 #3154, 0.38 #2957, 0.37 #3744), 05842k (0.40 #369, 0.25 #73, 0.20 #859), 01vdm0 (0.39 #128, 0.31 #3178, 0.29 #324), 018vs (0.33 #308, 0.32 #3052, 0.24 #4733), 0l14md (0.33 #886, 0.32 #885, 0.19 #302), 05148p4 (0.32 #3052, 0.24 #4733, 0.24 #2264), 03gvt (0.32 #3052, 0.24 #4733, 0.24 #2264), 0g2dz (0.32 #3052, 0.24 #4733, 0.24 #2264), 02hnl (0.32 #3052, 0.24 #4733, 0.24 #2264), 02dlh2 (0.32 #3052, 0.24 #2264, 0.24 #2560) >> Best rule #3154 for best value: >> intensional similarity = 3 >> extensional distance = 399 >> proper extension: 06br6t; >> query: (?x1660, 0342h) <- role(?x1660, ?x1574), role(?x3991, ?x1574), ?x3991 = 05842k >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #361 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 02s6sh; *> query: (?x1660, 0bxl5) <- role(?x1660, ?x212), profession(?x1660, ?x220), ?x212 = 026t6 *> conf = 0.07 ranks of expected_values: 22 EVAL 012x4t role 0bxl5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 110.000 110.000 0.426 http://example.org/music/artist/track_contributions./music/track_contribution/role #7549-02pqp12 PRED entity: 02pqp12 PRED relation: award! PRED expected values: 03_gd 0b_c7 0184dt 0jgwf 04353 015nvj => 64 concepts (30 used for prediction) PRED predicted values (max 10 best out of 2787): 0jf1b (0.64 #69829, 0.64 #83131, 0.63 #89784), 02vyw (0.64 #69829, 0.64 #83131, 0.63 #89784), 01p87y (0.64 #69829, 0.64 #83131, 0.63 #89784), 0h1p (0.64 #69829, 0.64 #83131, 0.63 #89784), 01f8ld (0.64 #69829, 0.64 #83131, 0.63 #89784), 01ycck (0.64 #69829, 0.63 #89784, 0.63 #89783), 0c12h (0.60 #11765, 0.56 #25067, 0.50 #31719), 03hy3g (0.60 #11797, 0.50 #31751, 0.50 #1823), 0bs8d (0.60 #11519, 0.50 #1545, 0.44 #24821), 01p1z_ (0.60 #11970, 0.50 #31924, 0.44 #25272) >> Best rule #69829 for best value: >> intensional similarity = 5 >> extensional distance = 136 >> proper extension: 05qck; 02qkk9_; 0d085; 02py7pj; 058vy5; 0bqsk5; 0154yf; >> query: (?x1198, ?x698) <- award_winner(?x1198, ?x4353), award_winner(?x1198, ?x698), award(?x4353, ?x198), people(?x1050, ?x4353), people(?x4322, ?x4353) >> conf = 0.64 => this is the best rule for 6 predicted values *> Best rule #30093 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 04dn09n; 02qyntr; *> query: (?x1198, 03_gd) <- award(?x276, ?x1198), nominated_for(?x1198, ?x2550), nominated_for(?x1198, ?x718), nominated_for(?x1198, ?x144), currency(?x2550, ?x1099), ?x144 = 0m313, ?x718 = 0hmr4 *> conf = 0.50 ranks of expected_values: 50, 104, 105, 108, 124, 132 EVAL 02pqp12 award! 015nvj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 64.000 30.000 0.645 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02pqp12 award! 04353 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 64.000 30.000 0.645 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02pqp12 award! 0jgwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 64.000 30.000 0.645 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02pqp12 award! 0184dt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 64.000 30.000 0.645 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02pqp12 award! 0b_c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 64.000 30.000 0.645 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02pqp12 award! 03_gd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 64.000 30.000 0.645 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7548-0djvzd PRED entity: 0djvzd PRED relation: profession PRED expected values: 0gl2ny2 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 144): 0gl2ny2 (0.68 #1269, 0.66 #1119, 0.64 #669), 02hrh1q (0.63 #2567, 0.62 #2867, 0.61 #3767), 01d_h8 (0.28 #4809, 0.27 #5559, 0.27 #5709), 0dxtg (0.26 #4817, 0.25 #8268, 0.25 #5717), 01445t (0.19 #474, 0.18 #1826, 0.16 #1374), 02jknp (0.18 #4811, 0.18 #5711, 0.18 #5561), 03gjzk (0.18 #4819, 0.17 #4218, 0.17 #6319), 01c72t (0.18 #1527, 0.11 #2577, 0.11 #775), 09jwl (0.17 #3472, 0.17 #770, 0.16 #1522), 0nbcg (0.17 #783, 0.12 #1535, 0.10 #8887) >> Best rule #1269 for best value: >> intensional similarity = 5 >> extensional distance = 72 >> proper extension: 07nv3_; 07h1h5; 0dhrqx; 02qny_; >> query: (?x7234, 0gl2ny2) <- team(?x7234, ?x9255), team(?x7234, ?x9182), position(?x9255, ?x63), ?x63 = 02sdk9v, team(?x203, ?x9182) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0djvzd profession 0gl2ny2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.676 http://example.org/people/person/profession #7547-01yf85 PRED entity: 01yf85 PRED relation: location PRED expected values: 0wq36 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 194): 030qb3t (0.50 #1688, 0.46 #4903, 0.42 #4099), 0r0ss (0.49 #40169, 0.44 #75510, 0.42 #59445), 02_286 (0.27 #5662, 0.22 #4054, 0.20 #7268), 01qh7 (0.20 #959, 0.17 #1762, 0.04 #4173), 0h7h6 (0.20 #892, 0.07 #52217, 0.05 #42579), 04n3l (0.20 #982, 0.02 #5804, 0.01 #7410), 052p7 (0.17 #1732, 0.01 #10573), 0vzm (0.14 #2581, 0.07 #52217, 0.05 #42579), 0f2w0 (0.14 #2502), 0cc56 (0.09 #8091, 0.06 #16126, 0.06 #12110) >> Best rule #1688 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01gy7r; >> query: (?x8716, 030qb3t) <- film(?x8716, ?x6005), ?x6005 = 051ys82, type_of_union(?x8716, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01yf85 location 0wq36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 121.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #7546-03tbg6 PRED entity: 03tbg6 PRED relation: crewmember PRED expected values: 051z6rz => 116 concepts (77 used for prediction) PRED predicted values (max 10 best out of 35): 021yc7p (0.29 #8, 0.18 #55, 0.03 #528), 05bm4sm (0.14 #26, 0.09 #73, 0.02 #452), 094wz7q (0.14 #19, 0.09 #66, 0.02 #492), 0bbxx9b (0.12 #447, 0.06 #211, 0.04 #541), 02q9kqf (0.10 #124, 0.04 #361, 0.02 #886), 02lp3c (0.10 #125, 0.04 #362, 0.02 #268), 04ktcgn (0.07 #1303, 0.07 #106, 0.07 #391), 0284n42 (0.06 #1295, 0.06 #667, 0.05 #1198), 04wp63 (0.06 #279, 0.06 #184, 0.05 #753), 03m49ly (0.06 #366, 0.05 #508, 0.04 #795) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01cssf; 026390q; 0qm8b; 0cz_ym; 07tlfx; >> query: (?x10455, 021yc7p) <- produced_by(?x10455, ?x2803), film(?x478, ?x10455), award_winner(?x10455, ?x6664), ?x2803 = 06chf >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #313 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 076xkdz; *> query: (?x10455, 051z6rz) <- genre(?x10455, ?x225), production_companies(?x10455, ?x382), ?x382 = 086k8, award(?x10455, ?x102) *> conf = 0.04 ranks of expected_values: 20 EVAL 03tbg6 crewmember 051z6rz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 116.000 77.000 0.286 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #7545-02q253 PRED entity: 02q253 PRED relation: school! PRED expected values: 092j54 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 20): 02qw1zx (0.30 #145, 0.26 #161, 0.20 #186), 0f4vx0 (0.26 #151, 0.21 #172, 0.20 #192), 05vsb7 (0.26 #161, 0.25 #141, 0.17 #1), 092j54 (0.26 #161, 0.24 #149, 0.18 #170), 09l0x9 (0.26 #161, 0.22 #152, 0.16 #193), 03nt7j (0.26 #161, 0.20 #147, 0.17 #188), 0g3zpp (0.26 #161, 0.18 #142, 0.11 #183), 038c0q (0.20 #46, 0.14 #66, 0.11 #146), 06439y (0.20 #60, 0.13 #160, 0.10 #100), 025tn92 (0.17 #153, 0.17 #13, 0.14 #174) >> Best rule #145 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 0fht9f; >> query: (?x13141, 02qw1zx) <- school(?x4469, ?x13141), team(?x1114, ?x4469), draft(?x4469, ?x465), position(?x387, ?x1114) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 0fht9f; *> query: (?x13141, ?x465) <- school(?x4469, ?x13141), team(?x1114, ?x4469), draft(?x4469, ?x465), position(?x387, ?x1114) *> conf = 0.26 ranks of expected_values: 4 EVAL 02q253 school! 092j54 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 143.000 143.000 0.303 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #7544-013b2h PRED entity: 013b2h PRED relation: award_winner PRED expected values: 04qmr 01vd7hn 0dzc16 02f1c 01kp_1t 01mskc3 => 34 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1627): 0gcs9 (0.75 #12300, 0.71 #9331, 0.69 #16755), 01vw20h (0.60 #8085, 0.57 #9572, 0.54 #16996), 01lmj3q (0.60 #7452, 0.54 #16363, 0.50 #13391), 02cx90 (0.60 #8054, 0.46 #16965, 0.45 #15480), 0fpjd_g (0.50 #13560, 0.50 #6136, 0.46 #16532), 01w60_p (0.50 #10682, 0.43 #9199, 0.40 #13651), 02r3zy (0.50 #6071, 0.40 #13495, 0.40 #7556), 04rcr (0.50 #6010, 0.40 #7495, 0.38 #10465), 011zf2 (0.50 #6115, 0.40 #7600, 0.37 #20972), 03h_fk5 (0.50 #6333, 0.40 #7818, 0.33 #399) >> Best rule #12300 for best value: >> intensional similarity = 22 >> extensional distance = 6 >> proper extension: 02rjjll; >> query: (?x5766, 0gcs9) <- ceremony(?x12458, ?x5766), ceremony(?x9594, ?x5766), ceremony(?x8369, ?x5766), ceremony(?x4958, ?x5766), ceremony(?x3647, ?x5766), ceremony(?x3313, ?x5766), ceremony(?x3033, ?x5766), ceremony(?x2139, ?x5766), ?x3647 = 01c9jp, award_winner(?x5766, ?x4875), award_winner(?x5766, ?x1089), ?x4958 = 03qbnj, ?x3313 = 02flpc, ?x3033 = 0257yf, ?x8369 = 02fv3t, role(?x4875, ?x227), ?x9594 = 02flqd, music(?x3275, ?x1089), ?x12458 = 024_dt, ?x2139 = 01by1l, instrumentalists(?x75, ?x1089), profession(?x1089, ?x131) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #5730 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 1 *> proper extension: 09n4nb; *> query: (?x5766, 01kp_1t) <- ceremony(?x12819, ?x5766), ceremony(?x8369, ?x5766), ceremony(?x4958, ?x5766), ceremony(?x3647, ?x5766), ceremony(?x3313, ?x5766), ceremony(?x3033, ?x5766), ceremony(?x724, ?x5766), ?x3647 = 01c9jp, award_winner(?x5766, ?x9220), award_winner(?x5766, ?x4875), award_winner(?x5766, ?x1832), award_winner(?x5766, ?x1089), ?x4958 = 03qbnj, ?x3313 = 02flpc, ?x3033 = 0257yf, ?x8369 = 02fv3t, role(?x4875, ?x227), ?x12819 = 0257__, religion(?x1089, ?x109), ?x1832 = 01ky2h, film(?x1089, ?x10931), award_winner(?x9220, ?x3200), ?x724 = 01bgqh *> conf = 0.33 ranks of expected_values: 58, 117, 122, 166, 227, 231 EVAL 013b2h award_winner 01mskc3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 34.000 20.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 013b2h award_winner 01kp_1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 34.000 20.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 013b2h award_winner 02f1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 013b2h award_winner 0dzc16 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 34.000 20.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 013b2h award_winner 01vd7hn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 013b2h award_winner 04qmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 34.000 20.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7543-030pr PRED entity: 030pr PRED relation: award PRED expected values: 019f4v 0gq9h => 193 concepts (180 used for prediction) PRED predicted values (max 10 best out of 327): 019f4v (0.77 #13371, 0.76 #42150, 0.72 #55936), 040njc (0.70 #5275, 0.62 #7706, 0.60 #13784), 0gq9h (0.59 #6560, 0.56 #4940, 0.54 #19125), 07bdd_ (0.54 #2902, 0.53 #19518, 0.50 #19923), 02pqp12 (0.46 #13847, 0.45 #7769, 0.40 #14253), 04dn09n (0.45 #1259, 0.36 #1664, 0.27 #18280), 05p1dby (0.42 #2943, 0.39 #19964, 0.37 #19559), 04kxsb (0.36 #1341, 0.29 #1746, 0.20 #2151), 0gr51 (0.35 #7798, 0.34 #13876, 0.30 #18336), 02rdyk7 (0.34 #13867, 0.33 #7789, 0.31 #14273) >> Best rule #13371 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 013rds; >> query: (?x1134, ?x1313) <- person(?x6093, ?x1134), award_winner(?x1313, ?x1134), ceremony(?x1313, ?x78) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 030pr award 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 193.000 180.000 0.774 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 030pr award 019f4v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 180.000 0.774 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7542-01jwxx PRED entity: 01jwxx PRED relation: genre PRED expected values: 02kdv5l 03g3w => 62 concepts (58 used for prediction) PRED predicted values (max 10 best out of 91): 07ssc (0.60 #1568, 0.54 #362, 0.52 #1929), 02kdv5l (0.44 #2, 0.34 #242, 0.31 #122), 05p553 (0.41 #124, 0.34 #2536, 0.34 #3381), 02l7c8 (0.35 #379, 0.31 #17, 0.31 #1705), 01jfsb (0.33 #495, 0.30 #1942, 0.30 #2424), 060__y (0.31 #18, 0.22 #1465, 0.21 #380), 04xvlr (0.29 #241, 0.26 #1448, 0.25 #1), 03k9fj (0.26 #1095, 0.25 #494, 0.25 #855), 0lsxr (0.25 #9, 0.20 #1212, 0.19 #1334), 02n4kr (0.25 #8, 0.12 #610, 0.12 #851) >> Best rule #1568 for best value: >> intensional similarity = 5 >> extensional distance = 494 >> proper extension: 03kq98; >> query: (?x4971, ?x512) <- titles(?x512, ?x4971), titles(?x512, ?x6900), titles(?x512, ?x1228), ?x6900 = 02z0f6l, ?x1228 = 05z_kps >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 083shs; 016ks5; *> query: (?x4971, 02kdv5l) <- film(?x7269, ?x4971), film(?x269, ?x4971), language(?x4971, ?x254), ?x7269 = 0gnbw, award_winner(?x102, ?x269) *> conf = 0.44 ranks of expected_values: 2, 15 EVAL 01jwxx genre 03g3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 62.000 58.000 0.599 http://example.org/film/film/genre EVAL 01jwxx genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 62.000 58.000 0.599 http://example.org/film/film/genre #7541-0ctw_b PRED entity: 0ctw_b PRED relation: country! PRED expected values: 02vx4 03_8r 09qgm 035d1m 03rbzn 0152n0 => 213 concepts (213 used for prediction) PRED predicted values (max 10 best out of 30): 03_8r (0.85 #639, 0.80 #2229, 0.79 #1239), 0w0d (0.81 #787, 0.71 #1567, 0.70 #757), 07jbh (0.78 #797, 0.78 #767, 0.77 #647), 03rbzn (0.74 #762, 0.67 #942, 0.67 #792), 01hp22 (0.67 #934, 0.65 #1144, 0.63 #754), 01gqfm (0.67 #776, 0.61 #566, 0.60 #626), 02vx4 (0.65 #543, 0.56 #303, 0.56 #783), 03fyrh (0.65 #1033, 0.63 #943, 0.63 #343), 0486tv (0.63 #801, 0.61 #561, 0.61 #831), 019w9j (0.62 #285, 0.54 #195, 0.53 #255) >> Best rule #639 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 05r4w; 02k54; 07ylj; 06bnz; 07f1x; >> query: (?x1023, 03_8r) <- film_release_region(?x1999, ?x1023), ?x1999 = 0gd0c7x, exported_to(?x1023, ?x94) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 7, 11, 17, 19 EVAL 0ctw_b country! 0152n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 213.000 213.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0ctw_b country! 03rbzn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 213.000 213.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0ctw_b country! 035d1m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 213.000 213.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0ctw_b country! 09qgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 213.000 213.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0ctw_b country! 03_8r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 213.000 213.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0ctw_b country! 02vx4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 213.000 213.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #7540-01zp33 PRED entity: 01zp33 PRED relation: film PRED expected values: 030z4z => 150 concepts (72 used for prediction) PRED predicted values (max 10 best out of 603): 0209hj (0.33 #99, 0.15 #12623, 0.07 #5466), 052_mn (0.33 #4980, 0.09 #13926, 0.04 #24660), 01p3ty (0.33 #3996, 0.06 #12942, 0.06 #14731), 030z4z (0.17 #5054, 0.17 #3265, 0.11 #15789), 04q00lw (0.17 #3960, 0.17 #2171, 0.07 #5749), 021pqy (0.17 #4350, 0.07 #6139, 0.06 #15085), 0h2zvzr (0.17 #5018, 0.06 #15753, 0.04 #26487), 047q2k1 (0.17 #3610, 0.06 #14345, 0.04 #8977), 09yxcz (0.17 #5259, 0.06 #15994, 0.03 #26728), 09fn1w (0.17 #4321, 0.03 #13267, 0.03 #15056) >> Best rule #99 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0276g40; >> query: (?x7504, 0209hj) <- place_of_birth(?x7504, ?x7412), people(?x11101, ?x7504), ?x11101 = 03kbr, religion(?x7504, ?x8967), nationality(?x7504, ?x2146) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5054 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0292l3; 01zh29; 0bxy67; 0tj9; *> query: (?x7504, 030z4z) <- place_of_birth(?x7504, ?x7412), film(?x7504, ?x657), award(?x7504, ?x10156), ?x657 = 04jwjq *> conf = 0.17 ranks of expected_values: 4 EVAL 01zp33 film 030z4z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 150.000 72.000 0.333 http://example.org/film/actor/film./film/performance/film #7539-0cm03 PRED entity: 0cm03 PRED relation: student! PRED expected values: 0yls9 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 209): 07tg4 (0.43 #1138, 0.12 #2716, 0.08 #3242), 0h6rm (0.29 #1196, 0.12 #2774, 0.08 #3826), 03ksy (0.17 #7470, 0.17 #2736, 0.16 #10100), 086xm (0.17 #92, 0.04 #2722, 0.03 #3774), 065y4w7 (0.16 #3170, 0.12 #5800, 0.09 #11060), 07tgn (0.14 #1069, 0.09 #2121, 0.08 #2647), 05zl0 (0.14 #1254, 0.08 #2832, 0.05 #3884), 013nky (0.14 #1434, 0.04 #3012, 0.03 #3538), 0138t4 (0.14 #1455, 0.04 #3033, 0.03 #3559), 014zws (0.14 #1383, 0.04 #2961, 0.03 #4013) >> Best rule #1138 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0n00; 0l99s; >> query: (?x4689, 07tg4) <- gender(?x4689, ?x231), nationality(?x4689, ?x512), organization(?x4689, ?x8603), ?x512 = 07ssc >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cm03 student! 0yls9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 81.000 0.429 http://example.org/education/educational_institution/students_graduates./education/education/student #7538-0jqj5 PRED entity: 0jqj5 PRED relation: nominated_for! PRED expected values: 02r22gf 094qd5 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 180): 0k611 (0.68 #10717, 0.68 #11615, 0.68 #12063), 02z0dfh (0.68 #10717, 0.68 #11615, 0.68 #12063), 09d28z (0.68 #10717, 0.68 #11615, 0.68 #12063), 02w_6xj (0.68 #10717, 0.68 #11615, 0.68 #12063), 02qt02v (0.68 #12063, 0.68 #10942, 0.67 #7142), 02r22gf (0.48 #472, 0.27 #25, 0.18 #4934), 02qvyrt (0.43 #530, 0.20 #4992, 0.18 #83), 0gq_v (0.38 #242, 0.30 #4927, 0.28 #2250), 054krc (0.38 #506, 0.27 #59, 0.20 #4968), 0l8z1 (0.38 #493, 0.24 #4955, 0.23 #270) >> Best rule #10717 for best value: >> intensional similarity = 3 >> extensional distance = 938 >> proper extension: 0g60z; 02_1q9; 080dwhx; 02_1rq; 03kq98; 072kp; 039fgy; 0kfpm; 02k_4g; 0358x_; ... >> query: (?x5129, ?x1107) <- nominated_for(?x1554, ?x5129), award(?x5129, ?x1107), award(?x276, ?x1107) >> conf = 0.68 => this is the best rule for 4 predicted values *> Best rule #472 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 0hv1t; 016kz1; 02mt51; 0qmhk; 0404j37; 0p9tm; *> query: (?x5129, 02r22gf) <- production_companies(?x5129, ?x1104), award(?x5129, ?x6909), ?x6909 = 02qyntr *> conf = 0.48 ranks of expected_values: 6, 18 EVAL 0jqj5 nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 100.000 100.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0jqj5 nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 100.000 100.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7537-02qvl7 PRED entity: 02qvl7 PRED relation: position! PRED expected values: 0jnng => 24 concepts (10 used for prediction) PRED predicted values (max 10 best out of 362): 0j5m6 (0.78 #76, 0.78 #34, 0.76 #78), 0bszz (0.78 #76, 0.78 #34, 0.76 #78), 0jnr_ (0.78 #76, 0.78 #34, 0.76 #78), 02r7lqg (0.78 #76, 0.78 #34, 0.76 #78), 0b6p3qf (0.78 #76, 0.78 #34, 0.76 #78), 04l590 (0.78 #76, 0.78 #34, 0.76 #78), 0hmt3 (0.78 #76, 0.78 #34, 0.76 #78), 0jnng (0.78 #76, 0.78 #34, 0.75 #58), 0hmtk (0.78 #76, 0.78 #34, 0.75 #58), 0jnm2 (0.78 #76, 0.78 #34, 0.74 #37) >> Best rule #76 for best value: >> intensional similarity = 38 >> extensional distance = 3 >> proper extension: 02qvkj; >> query: (?x2918, ?x3298) <- team(?x2918, ?x5233), team(?x2918, ?x3298), team(?x2918, ?x2919), position(?x12734, ?x2918), position(?x7174, ?x2918), colors(?x5233, ?x3189), colors(?x5233, ?x663), colors(?x5233, ?x332), position(?x5233, ?x5234), position(?x5233, ?x3724), position(?x5233, ?x3299), colors(?x13154, ?x3189), colors(?x12780, ?x3189), colors(?x11645, ?x3189), colors(?x3188, ?x3189), colors(?x1297, ?x3189), sport(?x7174, ?x453), ?x11645 = 03tc5p, colors(?x6127, ?x3189), colors(?x5306, ?x3189), ?x3188 = 04k3r_, ?x5306 = 0217m9, ?x6127 = 0gjv_, ?x3724 = 02qvzf, ?x1297 = 03x746, ?x3299 = 02qvgy, colors(?x11632, ?x332), colors(?x10104, ?x332), colors(?x7394, ?x332), ?x5234 = 02qvdc, ?x10104 = 0177sq, team(?x13270, ?x12734), ?x11632 = 0mbwf, ?x12780 = 019mdt, ?x13154 = 02w64f, ?x663 = 083jv, ?x7394 = 02h7qr, ?x2919 = 0c41y70 >> conf = 0.78 => this is the best rule for 12 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 8 EVAL 02qvl7 position! 0jnng CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 24.000 10.000 0.776 http://example.org/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position #7536-0d060g PRED entity: 0d060g PRED relation: medal PRED expected values: 02lpp7 => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.85 #23, 0.80 #55, 0.80 #19) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 014tss; >> query: (?x279, 02lpp7) <- nationality(?x199, ?x279), combatants(?x279, ?x94), country(?x136, ?x279) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d060g medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.852 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #7535-03f47xl PRED entity: 03f47xl PRED relation: people! PRED expected values: 0g6ff => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 48): 041rx (0.20 #312, 0.19 #158, 0.19 #620), 02w7gg (0.14 #156, 0.10 #541, 0.08 #1157), 013b6_ (0.14 #207, 0.09 #361, 0.08 #130), 0x67 (0.11 #1319, 0.11 #1935, 0.10 #1704), 033tf_ (0.11 #392, 0.11 #315, 0.09 #777), 03w9bjf (0.10 #54, 0.05 #208, 0.02 #4391), 09vc4s (0.10 #9, 0.03 #394, 0.02 #240), 07bch9 (0.10 #2003, 0.10 #793, 0.08 #947), 03ts0c (0.10 #2003, 0.08 #103, 0.06 #565), 063k3h (0.10 #2003, 0.04 #801, 0.04 #955) >> Best rule #312 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 04gcd1; 0kh6b; 0hfml; 06y7d; >> query: (?x6504, 041rx) <- profession(?x6504, ?x353), gender(?x6504, ?x231), ?x353 = 0cbd2, company(?x6504, ?x741) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2003 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 232 *> proper extension: 03g5jw; 016ppr; *> query: (?x6504, ?x12510) <- influenced_by(?x6504, ?x11075), award(?x6504, ?x601), people(?x12510, ?x11075) *> conf = 0.10 ranks of expected_values: 11 EVAL 03f47xl people! 0g6ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 117.000 117.000 0.196 http://example.org/people/ethnicity/people #7534-0mp3l PRED entity: 0mp3l PRED relation: location! PRED expected values: 0h1nt => 211 concepts (135 used for prediction) PRED predicted values (max 10 best out of 2255): 013w8y (0.44 #20124, 0.35 #10063, 0.24 #2516), 02lt8 (0.29 #796, 0.27 #8343, 0.18 #18404), 012v1t (0.29 #1216, 0.22 #6248, 0.18 #8763), 0x3r3 (0.29 #1180, 0.18 #8727, 0.17 #21304), 01wg982 (0.29 #437, 0.12 #18045, 0.09 #28106), 0sx5w (0.27 #9686, 0.23 #14717, 0.18 #19747), 0pyww (0.25 #16074, 0.17 #36195, 0.12 #3497), 0d3k14 (0.25 #4719, 0.09 #34902, 0.07 #44962), 09bg4l (0.25 #3199, 0.09 #33382, 0.07 #43442), 06wvj (0.22 #5499, 0.14 #467, 0.09 #8014) >> Best rule #20124 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0mn0v; 0mn8t; 0y62n; >> query: (?x2298, ?x8913) <- location(?x1165, ?x2298), currency(?x2298, ?x170), ?x170 = 09nqf, origin(?x8913, ?x2298) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #15303 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0ftkx; *> query: (?x2298, 0h1nt) <- citytown(?x5486, ?x2298), place_of_death(?x5254, ?x2298), contains(?x1426, ?x2298), basic_title(?x5254, ?x265) *> conf = 0.06 ranks of expected_values: 990 EVAL 0mp3l location! 0h1nt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 211.000 135.000 0.442 http://example.org/people/person/places_lived./people/place_lived/location #7533-05fjf PRED entity: 05fjf PRED relation: place_founded! PRED expected values: 0152x_ => 190 concepts (149 used for prediction) PRED predicted values (max 10 best out of 80): 04htfd (0.25 #148, 0.08 #260, 0.04 #1820), 05njw (0.08 #298, 0.06 #409, 0.04 #743), 0fvppk (0.08 #285, 0.06 #396, 0.04 #730), 01qf54 (0.08 #278, 0.06 #389, 0.04 #723), 01_4mn (0.08 #326, 0.05 #549, 0.04 #771), 0dq23 (0.08 #315, 0.05 #538, 0.04 #760), 01ynvx (0.08 #313, 0.05 #536, 0.04 #758), 01hlwv (0.08 #302, 0.05 #525, 0.04 #747), 032j_n (0.08 #289, 0.05 #512, 0.04 #734), 01dfb6 (0.08 #281, 0.05 #504, 0.04 #726) >> Best rule #148 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0n5j_; >> query: (?x6895, 04htfd) <- contains(?x6895, ?x6563), adjoins(?x335, ?x6895), ?x6563 = 0xl08 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05fjf place_founded! 0152x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 149.000 0.250 http://example.org/organization/organization/place_founded #7532-043zg PRED entity: 043zg PRED relation: profession PRED expected values: 02hrh1q 015cjr => 119 concepts (118 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.92 #7851, 0.92 #2480, 0.90 #4077), 09jwl (0.64 #9016, 0.62 #7273, 0.59 #308), 01d_h8 (0.63 #3051, 0.48 #8422, 0.44 #4648), 0dxtg (0.55 #8430, 0.55 #3059, 0.32 #2176), 016z4k (0.46 #7258, 0.46 #873, 0.45 #583), 0n1h (0.35 #301, 0.33 #881, 0.32 #591), 02jknp (0.32 #2176, 0.32 #3053, 0.26 #8424), 015cjr (0.31 #1208, 0.24 #193, 0.12 #3094), 039v1 (0.30 #905, 0.23 #9033, 0.20 #7290), 018gz8 (0.30 #3062, 0.29 #1176, 0.28 #161) >> Best rule #7851 for best value: >> intensional similarity = 3 >> extensional distance = 523 >> proper extension: 01l1b90; 05m63c; 02g8h; 0d_84; 01ty7ll; 033hqf; 0456xp; 03rl84; 02mhfy; 02fb1n; ... >> query: (?x5364, 02hrh1q) <- profession(?x5364, ?x131), participant(?x5364, ?x4777), film(?x5364, ?x3507) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 043zg profession 015cjr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 119.000 118.000 0.916 http://example.org/people/person/profession EVAL 043zg profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 118.000 0.916 http://example.org/people/person/profession #7531-04dyqk PRED entity: 04dyqk PRED relation: gender PRED expected values: 05zppz => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #11, 0.87 #23, 0.86 #71), 02zsn (0.46 #180, 0.28 #139, 0.28 #79) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 01d5vk; 0gry51; >> query: (?x11573, 05zppz) <- people(?x14040, ?x11573), profession(?x11573, ?x319), ?x319 = 01d_h8 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04dyqk gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.910 http://example.org/people/person/gender #7530-02c4s PRED entity: 02c4s PRED relation: gender PRED expected values: 05zppz => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #53, 0.83 #33, 0.83 #31), 02zsn (0.46 #299, 0.46 #326, 0.45 #82) >> Best rule #53 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 08849; >> query: (?x1590, 05zppz) <- jurisdiction_of_office(?x1590, ?x512), entity_involved(?x6829, ?x1590), country(?x150, ?x512), contains(?x512, ?x362), olympics(?x512, ?x391) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02c4s gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.833 http://example.org/people/person/gender #7529-078ds PRED entity: 078ds PRED relation: languages_spoken PRED expected values: 07c9s 02002f => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 67): 0t_2 (0.45 #782, 0.36 #562, 0.35 #838), 064_8sq (0.36 #293, 0.34 #348, 0.29 #458), 06nm1 (0.28 #228, 0.24 #393, 0.22 #448), 06b_j (0.19 #404, 0.11 #901, 0.11 #956), 0880p (0.16 #208, 0.10 #704, 0.10 #759), 03hkp (0.16 #177, 0.10 #673, 0.10 #728), 03k50 (0.13 #116, 0.10 #1160, 0.10 #1159), 032f6 (0.13 #215, 0.08 #711, 0.08 #435), 03x42 (0.12 #46, 0.11 #101, 0.10 #156), 01r2l (0.12 #21, 0.11 #76, 0.10 #131) >> Best rule #782 for best value: >> intensional similarity = 12 >> extensional distance = 49 >> proper extension: 065b6q; 07mqps; 07bch9; 063k3h; 09kr66; 04gfy7; 038723; >> query: (?x11778, 0t_2) <- languages_spoken(?x11778, ?x254), languages(?x3520, ?x254), language(?x6932, ?x254), language(?x3946, ?x254), language(?x3430, ?x254), language(?x1045, ?x254), student(?x1200, ?x3520), film(?x2156, ?x3946), languages(?x50, ?x254), country(?x3430, ?x512), film(?x5834, ?x1045), films(?x9203, ?x6932) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #125 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 28 *> proper extension: 0dryh9k; *> query: (?x11778, 07c9s) <- languages_spoken(?x11778, ?x254), languages(?x3520, ?x254), language(?x4841, ?x254), language(?x3946, ?x254), student(?x1200, ?x3520), film(?x2156, ?x3946), film_release_region(?x4841, ?x87), language(?x51, ?x254), service_language(?x127, ?x254) *> conf = 0.10 ranks of expected_values: 15, 25 EVAL 078ds languages_spoken 02002f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 22.000 22.000 0.451 http://example.org/people/ethnicity/languages_spoken EVAL 078ds languages_spoken 07c9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 22.000 22.000 0.451 http://example.org/people/ethnicity/languages_spoken #7528-01clyb PRED entity: 01clyb PRED relation: contains! PRED expected values: 02jx1 => 132 concepts (113 used for prediction) PRED predicted values (max 10 best out of 278): 0978r (0.80 #45753, 0.79 #12554, 0.75 #5378), 01w0v (0.77 #15246, 0.76 #43061, 0.74 #68183), 02jx1 (0.71 #4568, 0.68 #2776, 0.62 #87), 09c7w0 (0.68 #43064, 0.68 #42167, 0.67 #26912), 07ssc (0.55 #62797, 0.53 #2721, 0.46 #84343), 02k54 (0.46 #40368), 02j9z (0.32 #55623, 0.21 #73570, 0.08 #4509), 0d060g (0.27 #6287, 0.22 #9875, 0.22 #7183), 04jpl (0.17 #4503, 0.16 #8089, 0.15 #10783), 05kr_ (0.13 #1023, 0.10 #6400, 0.08 #9988) >> Best rule #45753 for best value: >> intensional similarity = 5 >> extensional distance = 233 >> proper extension: 01zn4y; >> query: (?x10348, ?x3301) <- currency(?x10348, ?x1099), category(?x10348, ?x134), citytown(?x10348, ?x3301), ?x134 = 08mbj5d, contains(?x3301, ?x1369) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #4568 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 01v3k2; 01g4yw; *> query: (?x10348, 02jx1) <- colors(?x10348, ?x3189), currency(?x10348, ?x1099), major_field_of_study(?x10348, ?x2981), ?x1099 = 01nv4h, citytown(?x10348, ?x3301) *> conf = 0.71 ranks of expected_values: 3 EVAL 01clyb contains! 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 132.000 113.000 0.797 http://example.org/location/location/contains #7527-014bpd PRED entity: 014bpd PRED relation: music PRED expected values: 01l1rw => 85 concepts (38 used for prediction) PRED predicted values (max 10 best out of 59): 01tc9r (0.33 #65, 0.04 #1537, 0.04 #695), 0146pg (0.09 #640, 0.08 #1061, 0.07 #1693), 02lf0c (0.06 #5480, 0.06 #6324, 0.06 #7379), 0150t6 (0.06 #676, 0.05 #2994, 0.05 #2784), 03h610 (0.06 #287, 0.05 #497, 0.05 #917), 02bh9 (0.05 #1313, 0.05 #2367, 0.04 #3421), 02cyfz (0.05 #664, 0.04 #1296, 0.03 #2982), 02jxmr (0.05 #704, 0.04 #3022, 0.04 #2812), 06fxnf (0.05 #2807, 0.04 #3017, 0.03 #3439), 01x1fq (0.04 #595, 0.04 #1015, 0.02 #385) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 06kl78; >> query: (?x7927, 01tc9r) <- genre(?x7927, ?x2753), nominated_for(?x11230, ?x7927), ?x2753 = 0219x_, ?x11230 = 0fdtd7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1997 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 247 *> proper extension: 080dwhx; 039fgy; 0kfpm; 02k_4g; 030k94; 02rzdcp; 05f4vxd; 0828jw; 0b005; 02r1ysd; ... *> query: (?x7927, 01l1rw) <- award(?x7927, ?x2599), award_winner(?x7927, ?x595), film(?x595, ?x5045), student(?x3091, ?x595) *> conf = 0.02 ranks of expected_values: 26 EVAL 014bpd music 01l1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 85.000 38.000 0.333 http://example.org/film/film/music #7526-08cl7s PRED entity: 08cl7s PRED relation: genre PRED expected values: 0hcr => 87 concepts (71 used for prediction) PRED predicted values (max 10 best out of 154): 0hcr (0.96 #3985, 0.95 #3569, 0.90 #2488), 07s9rl0 (0.87 #5315, 0.84 #4980, 0.80 #5064), 05p553 (0.76 #4644, 0.68 #987, 0.63 #3301), 01htzx (0.70 #3070, 0.66 #3485, 0.63 #2568), 01hmnh (0.69 #3816, 0.68 #987, 0.63 #3301), 06n90 (0.68 #987, 0.67 #1246, 0.63 #2564), 02l7c8 (0.68 #987, 0.63 #3301, 0.61 #2886), 01z4y (0.48 #4657, 0.31 #4486, 0.31 #1664), 0pr6f (0.38 #2026, 0.33 #3020, 0.33 #49), 0c4xc (0.33 #4681, 0.21 #4510, 0.20 #4596) >> Best rule #3985 for best value: >> intensional similarity = 12 >> extensional distance = 49 >> proper extension: 0vhm; 019g8j; 03k99c; >> query: (?x8077, 0hcr) <- actor(?x8077, ?x7764), genre(?x8077, ?x5937), genre(?x10826, ?x5937), genre(?x5936, ?x5937), genre(?x2153, ?x5937), ?x10826 = 0564x, genre(?x14241, ?x5937), genre(?x9523, ?x5937), ?x14241 = 03lyp4, ?x5936 = 02q3fdr, ?x9523 = 03d3ht, ?x2153 = 02qhqz4 >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08cl7s genre 0hcr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 71.000 0.961 http://example.org/tv/tv_program/genre #7525-0289q PRED entity: 0289q PRED relation: draft PRED expected values: 03nt7j => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 16): 05vsb7 (0.83 #210, 0.82 #258, 0.82 #386), 03nt7j (0.79 #230, 0.78 #1146, 0.75 #691), 02pq_rp (0.50 #22, 0.45 #729, 0.33 #631), 02r6gw6 (0.50 #26, 0.41 #733, 0.34 #935), 02pq_x5 (0.50 #29, 0.39 #736, 0.34 #935), 02rl201 (0.50 #19, 0.37 #726, 0.34 #935), 04f4z1k (0.41 #737, 0.34 #935, 0.34 #934), 047dpm0 (0.41 #738, 0.28 #932, 0.28 #916), 02z6872 (0.39 #730, 0.27 #616, 0.27 #924), 02x2khw (0.35 #725, 0.25 #18, 0.24 #919) >> Best rule #210 for best value: >> intensional similarity = 14 >> extensional distance = 10 >> proper extension: 01y3c; 07l24; 02c_4; 06x76; >> query: (?x4856, 05vsb7) <- draft(?x4856, ?x685), position_s(?x4856, ?x3113), position_s(?x4856, ?x2247), team(?x1717, ?x4856), team(?x180, ?x4856), ?x180 = 01r3hr, ?x3113 = 0b13yt, ?x1717 = 02g_6x, school(?x4856, ?x5844), position(?x387, ?x2247), teams(?x659, ?x4856), position_s(?x4986, ?x2247), ?x4986 = 04ls81, currency(?x5844, ?x170) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #230 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 12 *> proper extension: 043vc; *> query: (?x4856, 03nt7j) <- draft(?x4856, ?x685), position_s(?x4856, ?x7079), position_s(?x4856, ?x3113), position_s(?x4856, ?x2147), position_s(?x4856, ?x1114), team(?x180, ?x4856), ?x180 = 01r3hr, position_s(?x11061, ?x3113), position_s(?x9172, ?x3113), ?x1114 = 047g8h, ?x2147 = 04nfpk, ?x9172 = 06rpd, team(?x11323, ?x4856), team(?x7079, ?x6379), ?x6379 = 0bjkk9, ?x11061 = 06x76 *> conf = 0.79 ranks of expected_values: 2 EVAL 0289q draft 03nt7j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.833 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #7524-01gstn PRED entity: 01gstn PRED relation: district_represented PRED expected values: 05tbn 0498y => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 161): 05tbn (0.88 #1346, 0.88 #329, 0.87 #330), 0498y (0.88 #329, 0.87 #330, 0.86 #1153), 04ych (0.88 #329, 0.87 #330, 0.85 #47), 04rrx (0.88 #329, 0.87 #330, 0.85 #47), 0vbk (0.88 #329, 0.87 #330, 0.85 #47), 02xry (0.77 #521, 0.70 #327, 0.61 #896), 03s0w (0.77 #521, 0.70 #327, 0.61 #896), 0824r (0.77 #521, 0.70 #327, 0.61 #896), 07b_l (0.77 #521, 0.70 #327, 0.61 #896), 01n7q (0.77 #521, 0.70 #327, 0.61 #896) >> Best rule #1346 for best value: >> intensional similarity = 38 >> extensional distance = 24 >> proper extension: 03tcbx; 03rtmz; 02gkzs; 02cg7g; 02glc4; >> query: (?x5005, 05tbn) <- district_represented(?x5005, ?x7518), district_represented(?x5005, ?x6895), district_represented(?x5005, ?x4622), district_represented(?x5005, ?x1426), religion(?x1426, ?x8249), ?x6895 = 05fjf, ?x4622 = 04tgp, adjoins(?x108, ?x1426), contains(?x1426, ?x11046), contains(?x1426, ?x9443), contains(?x1426, ?x6919), location(?x1838, ?x1426), ?x8249 = 021_0p, district_represented(?x10291, ?x7518), district_represented(?x6712, ?x7518), district_represented(?x5252, ?x7518), district_represented(?x2019, ?x7518), district_represented(?x759, ?x7518), district_represented(?x605, ?x7518), ?x605 = 077g7n, ?x6712 = 01gst9, place_of_birth(?x460, ?x11046), jurisdiction_of_office(?x900, ?x1426), taxonomy(?x7518, ?x939), state_province_region(?x4077, ?x1426), contains(?x94, ?x1426), legislative_sessions(?x5005, ?x3669), ?x759 = 043djx, ?x10291 = 01gtdd, currency(?x7518, ?x170), ?x5252 = 01gtcq, time_zones(?x7518, ?x2674), ?x2019 = 01gtbb, location(?x9038, ?x11046), artists(?x302, ?x1838), major_field_of_study(?x9443, ?x1154), colors(?x6919, ?x332), religion(?x7518, ?x2591) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01gstn district_represented 0498y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.885 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gstn district_represented 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.885 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #7523-05j82v PRED entity: 05j82v PRED relation: film_release_region PRED expected values: 0d060g => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 7): 09c7w0 (0.06 #256, 0.05 #358, 0.05 #153), 07ssc (0.03 #2092, 0.03 #1325, 0.03 #1835), 0d060g (0.02 #56, 0.01 #362, 0.01 #412), 0345h (0.02 #493, 0.02 #214, 0.02 #316), 0jgd (0.02 #205, 0.02 #231, 0.01 #867), 06mkj (0.02 #143, 0.01 #68), 01znc_ (0.01 #318) >> Best rule #256 for best value: >> intensional similarity = 3 >> extensional distance = 207 >> proper extension: 018nnz; 025twgf; 0fztbq; >> query: (?x1493, 09c7w0) <- genre(?x1493, ?x53), film_release_distribution_medium(?x1493, ?x81), nominated_for(?x1493, ?x2734) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 053tj7; *> query: (?x1493, 0d060g) <- genre(?x1493, ?x1316), film_release_distribution_medium(?x1493, ?x81), ?x1316 = 017fp *> conf = 0.02 ranks of expected_values: 3 EVAL 05j82v film_release_region 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 90.000 0.057 http://example.org/film/film/runtime./film/film_cut/film_release_region #7522-02txdf PRED entity: 02txdf PRED relation: major_field_of_study PRED expected values: 01tbp => 153 concepts (145 used for prediction) PRED predicted values (max 10 best out of 122): 01mkq (0.69 #765, 0.47 #2765, 0.43 #1640), 02lp1 (0.59 #761, 0.46 #2761, 0.44 #11), 0g26h (0.51 #794, 0.38 #2169, 0.38 #1419), 062z7 (0.47 #778, 0.33 #2778, 0.33 #1403), 02j62 (0.46 #781, 0.45 #156, 0.45 #906), 04rjg (0.46 #770, 0.36 #645, 0.33 #1645), 01lj9 (0.44 #791, 0.36 #666, 0.32 #916), 05qfh (0.41 #787, 0.28 #912, 0.24 #662), 01tbp (0.37 #812, 0.28 #687, 0.25 #1437), 04x_3 (0.37 #776, 0.27 #901, 0.25 #1401) >> Best rule #765 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 08qnnv; >> query: (?x8789, 01mkq) <- fraternities_and_sororities(?x8789, ?x4348), institution(?x4981, ?x8789), currency(?x8789, ?x170), ?x4981 = 03bwzr4 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #812 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 08qnnv; *> query: (?x8789, 01tbp) <- fraternities_and_sororities(?x8789, ?x4348), institution(?x4981, ?x8789), currency(?x8789, ?x170), ?x4981 = 03bwzr4 *> conf = 0.37 ranks of expected_values: 9 EVAL 02txdf major_field_of_study 01tbp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 153.000 145.000 0.695 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7521-03fpg PRED entity: 03fpg PRED relation: genre! PRED expected values: 095sx6 => 34 concepts (33 used for prediction) PRED predicted values (max 10 best out of 310): 01f39b (0.74 #2935, 0.73 #2640, 0.59 #3524), 01b7h8 (0.60 #795, 0.39 #293, 0.33 #503), 016zfm (0.54 #1758, 0.42 #6174, 0.39 #293), 02_1q9 (0.54 #1758, 0.42 #6174, 0.39 #293), 02hct1 (0.52 #1172, 0.39 #4111, 0.39 #293), 0jwl2 (0.52 #1172, 0.39 #4111, 0.39 #293), 0124k9 (0.52 #1172, 0.39 #4111, 0.38 #5290), 06qv_ (0.52 #1172, 0.39 #4111, 0.38 #5290), 07vqnc (0.47 #2002, 0.26 #3178, 0.24 #3473), 0584r4 (0.47 #1201, 0.40 #907, 0.39 #1494) >> Best rule #2935 for best value: >> intensional similarity = 14 >> extensional distance = 24 >> proper extension: 07s9rl0; 02n4kr; 0lsxr; 0vgkd; 02l7c8; 01hmnh; 01z4y; 0hcr; 06nbt; 0djd22; ... >> query: (?x12120, ?x5684) <- genre(?x12387, ?x12120), genre(?x12165, ?x12120), genre(?x7904, ?x12120), actor(?x7904, ?x2378), nominated_for(?x2750, ?x7904), program(?x6678, ?x7904), actor(?x12165, ?x2382), program_creator(?x12165, ?x6937), actor(?x5684, ?x2378), people(?x4659, ?x2378), producer_type(?x7904, ?x632), honored_for(?x1265, ?x12387), languages(?x12387, ?x3592), ?x632 = 0ckd1 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #289 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 05p553; *> query: (?x12120, 095sx6) <- genre(?x9580, ?x12120), genre(?x7904, ?x12120), genre(?x7433, ?x12120), ?x7904 = 0fpxp, actor(?x7433, ?x649), languages(?x7433, ?x254), program(?x12476, ?x9580), ?x254 = 02h40lc, program(?x14163, ?x7433), award_winner(?x12476, ?x11453), type_of_union(?x649, ?x566), artists(?x671, ?x649), ?x671 = 064t9, artist(?x648, ?x649) *> conf = 0.33 ranks of expected_values: 180 EVAL 03fpg genre! 095sx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 34.000 33.000 0.743 http://example.org/tv/tv_program/genre #7520-0cm2xh PRED entity: 0cm2xh PRED relation: locations PRED expected values: 02j9z 0j0k => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 265): 02j9z (0.45 #3340, 0.42 #3892, 0.36 #4267), 0j3b (0.40 #396, 0.28 #10188, 0.28 #11297), 059g4 (0.30 #1056, 0.28 #10188, 0.28 #11297), 073q1 (0.28 #10188, 0.28 #11297, 0.24 #10373), 05rgl (0.28 #10188, 0.28 #11297, 0.24 #10373), 0j0k (0.28 #10188, 0.28 #11297, 0.24 #10373), 04swx (0.28 #10188, 0.28 #11297, 0.24 #10373), 02qkt (0.28 #10188, 0.28 #11297, 0.24 #10373), 03rk0 (0.28 #10188, 0.28 #11297, 0.24 #10373), 05v8c (0.28 #10188, 0.28 #11297, 0.24 #10373) >> Best rule #3340 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 0727h; >> query: (?x5503, 02j9z) <- locations(?x5503, ?x2467), contains(?x2467, ?x9251), contains(?x2467, ?x4121), entity_involved(?x5503, ?x1159), administrative_area_type(?x9251, ?x2792), jurisdiction_of_office(?x182, ?x4121) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 0cm2xh locations 0j0k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 82.000 82.000 0.450 http://example.org/time/event/locations EVAL 0cm2xh locations 02j9z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.450 http://example.org/time/event/locations #7519-05clg8 PRED entity: 05clg8 PRED relation: artist PRED expected values: 01wqmm8 032nl2 => 109 concepts (74 used for prediction) PRED predicted values (max 10 best out of 976): 016376 (0.43 #8267, 0.40 #3251, 0.33 #13280), 01wg25j (0.40 #3121, 0.33 #2287, 0.33 #618), 0178kd (0.40 #2953, 0.33 #2119, 0.33 #450), 0565cz (0.40 #2694, 0.33 #1860, 0.33 #191), 01wg6y (0.40 #3162, 0.33 #2328, 0.33 #659), 0134s5 (0.40 #2738, 0.33 #1904, 0.33 #235), 01lmj3q (0.40 #2518, 0.33 #1684, 0.33 #15), 03f3yfj (0.40 #3075, 0.33 #2241, 0.33 #572), 01kp_1t (0.40 #3195, 0.33 #2361, 0.33 #692), 0knhk (0.40 #3069, 0.33 #566, 0.30 #15871) >> Best rule #8267 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 027lf1; >> query: (?x12171, 016376) <- child(?x7793, ?x12171), child(?x12171, ?x7448), category(?x12171, ?x134), industry(?x7793, ?x3368), child(?x1104, ?x7793) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2211 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 033hn8; *> query: (?x12171, 01wqmm8) <- artist(?x12171, ?x6659), artist(?x12171, ?x6208), ?x6208 = 07r4c, artists(?x2937, ?x6659), instrumentalists(?x1437, ?x6659), child(?x7793, ?x12171), ?x1437 = 01vdm0, profession(?x6659, ?x131) *> conf = 0.33 ranks of expected_values: 177, 386 EVAL 05clg8 artist 032nl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 109.000 74.000 0.429 http://example.org/music/record_label/artist EVAL 05clg8 artist 01wqmm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 109.000 74.000 0.429 http://example.org/music/record_label/artist #7518-03bwzr4 PRED entity: 03bwzr4 PRED relation: institution PRED expected values: 0bthb 01jsn5 0dy04 01swxv 0820xz 02bb47 04chyn 02d_zc 057bxr 06b19 02x9g_ 01hjy5 02pptm 0dzst 0160nk 0325dj 01y06y 0jksm => 55 concepts (41 used for prediction) PRED predicted values (max 10 best out of 471): 01jsk6 (0.75 #5350, 0.71 #4436, 0.67 #3978), 0trv (0.75 #5265, 0.71 #4351, 0.60 #6180), 01pj48 (0.75 #5417, 0.70 #5873, 0.60 #6332), 01j_9c (0.75 #5031, 0.67 #3659, 0.60 #5946), 016ndm (0.71 #4666, 0.62 #5122, 0.60 #6037), 01vs5c (0.71 #4703, 0.50 #6074, 0.50 #5615), 09vzz (0.71 #4978, 0.50 #6349, 0.50 #5890), 017cy9 (0.70 #5593, 0.62 #5137, 0.60 #3309), 01jq34 (0.70 #5519, 0.62 #5063, 0.60 #3235), 01jtp7 (0.67 #3690, 0.57 #4148, 0.50 #5062) >> Best rule #5350 for best value: >> intensional similarity = 13 >> extensional distance = 6 >> proper extension: 02_xgp2; >> query: (?x4981, 01jsk6) <- major_field_of_study(?x4981, ?x9079), major_field_of_study(?x4981, ?x254), institution(?x4981, ?x10297), institution(?x4981, ?x5055), institution(?x4981, ?x4980), ?x254 = 02h40lc, student(?x4981, ?x118), ?x9079 = 0l5mz, school(?x8786, ?x10297), fraternities_and_sororities(?x10297, ?x3697), ?x3697 = 0325pb, ?x4980 = 01n6r0, service_location(?x5055, ?x94) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3883 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 07s6fsf; *> query: (?x4981, 01hjy5) <- major_field_of_study(?x4981, ?x254), institution(?x4981, ?x11278), institution(?x4981, ?x10297), institution(?x4981, ?x2999), institution(?x4981, ?x1783), institution(?x4981, ?x581), ?x11278 = 037q2p, major_field_of_study(?x5621, ?x254), ?x5621 = 01vs5c, major_field_of_study(?x1783, ?x10264), major_field_of_study(?x254, ?x2605), ?x2999 = 07tg4, ?x581 = 06pwq, currency(?x10297, ?x170) *> conf = 0.67 ranks of expected_values: 11, 27, 28, 33, 35, 45, 57, 59, 72, 177, 212, 224, 278, 279, 280, 284, 297, 332 EVAL 03bwzr4 institution 0jksm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 01y06y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 0325dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 0160nk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 0dzst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 02pptm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 01hjy5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 02x9g_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 06b19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 057bxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 02d_zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 04chyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 02bb47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 0820xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 01swxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 0dy04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 01jsn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03bwzr4 institution 0bthb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 55.000 41.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #7517-01q3_2 PRED entity: 01q3_2 PRED relation: place_of_birth PRED expected values: 010tkc => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 87): 02_286 (0.21 #1427, 0.19 #2131, 0.08 #32406), 0cr3d (0.07 #1502, 0.06 #2206, 0.04 #2910), 030qb3t (0.04 #5686, 0.04 #48642, 0.04 #49347), 01_d4 (0.04 #6402, 0.04 #11330, 0.04 #13442), 01531 (0.04 #2217, 0.03 #105, 0.02 #1513), 0n90z (0.03 #685, 0.03 #1389, 0.02 #2093), 0h1k6 (0.03 #445, 0.03 #1149, 0.02 #1853), 094jv (0.03 #61, 0.03 #765, 0.02 #1469), 0t6sb (0.03 #611, 0.02 #3427, 0.01 #4131), 0nbrp (0.03 #532, 0.02 #3348, 0.01 #4052) >> Best rule #1427 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 01wl38s; 04r7jc; 01jrz5j; 01vrncs; 01wp8w7; 012x4t; 0136p1; 0144l1; 01_x6v; 0c_mvb; ... >> query: (?x9731, 02_286) <- profession(?x9731, ?x6476), award_winner(?x9731, ?x568), ?x6476 = 025352 >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01q3_2 place_of_birth 010tkc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 93.000 0.214 http://example.org/people/person/place_of_birth #7516-02pq_rp PRED entity: 02pq_rp PRED relation: school PRED expected values: 0bx8pn 02zd460 => 15 concepts (15 used for prediction) PRED predicted values (max 10 best out of 717): 07vyf (0.57 #1016, 0.50 #1246, 0.50 #1133), 01pl14 (0.53 #1434, 0.50 #1094, 0.47 #1322), 03tw2s (0.50 #859, 0.50 #821, 0.33 #1389), 06fq2 (0.50 #730, 0.43 #1056, 0.38 #1286), 0jkhr (0.50 #498, 0.43 #933, 0.33 #284), 02pptm (0.50 #838, 0.33 #300, 0.33 #189), 01jq0j (0.50 #721, 0.33 #392, 0.30 #1086), 01vs5c (0.47 #1377, 0.33 #377, 0.33 #160), 09f2j (0.43 #913, 0.38 #1142, 0.29 #964), 07ccs (0.43 #929, 0.33 #280, 0.30 #1084) >> Best rule #1016 for best value: >> intensional similarity = 55 >> extensional distance = 5 >> proper extension: 0f4vx0; >> query: (?x3334, 07vyf) <- school(?x3334, ?x581), draft(?x1632, ?x3334), draft(?x1010, ?x3334), draft(?x700, ?x3334), school(?x1010, ?x6953), school(?x1010, ?x6814), school(?x1010, ?x6177), school(?x1010, ?x3948), school(?x1010, ?x2497), ?x2497 = 0f1nl, team(?x4244, ?x1010), school(?x580, ?x6953), major_field_of_study(?x6953, ?x3213), institution(?x1519, ?x581), school(?x1632, ?x6602), category(?x581, ?x134), citytown(?x6953, ?x2624), major_field_of_study(?x581, ?x1695), major_field_of_study(?x581, ?x1682), ?x1682 = 02ky346, colors(?x6953, ?x332), ?x1519 = 013zdg, state_province_region(?x581, ?x1227), student(?x581, ?x11088), student(?x581, ?x2789), sport(?x1632, ?x5063), ?x2789 = 01zfmm, school_type(?x581, ?x1044), ?x580 = 05m_8, team(?x5412, ?x1632), colors(?x581, ?x663), school(?x700, ?x10945), school(?x700, ?x4556), school(?x700, ?x4257), ?x10945 = 01jsk6, team(?x2010, ?x1632), ?x3948 = 025v3k, ?x4257 = 01q0kg, major_field_of_study(?x373, ?x1695), major_field_of_study(?x1043, ?x1695), organization(?x346, ?x6177), state_province_region(?x6602, ?x177), organizations_founded(?x11088, ?x11089), ?x1043 = 0kz2w, service_language(?x6177, ?x254), student(?x6177, ?x1270), ?x4556 = 01lnyf, gender(?x11088, ?x231), student(?x6953, ?x117), colors(?x700, ?x1101), profession(?x11088, ?x353), company(?x233, ?x581), contains(?x94, ?x6814), student(?x1695, ?x3806), teams(?x739, ?x1632) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1086 for first EXPECTED value: *> intensional similarity = 55 *> extensional distance = 5 *> proper extension: 0f4vx0; *> query: (?x3334, ?x6953) <- school(?x3334, ?x581), draft(?x1632, ?x3334), draft(?x1010, ?x3334), draft(?x700, ?x3334), school(?x1010, ?x6953), school(?x1010, ?x6814), school(?x1010, ?x6177), school(?x1010, ?x3948), school(?x1010, ?x2497), ?x2497 = 0f1nl, team(?x4244, ?x1010), school(?x580, ?x6953), major_field_of_study(?x6953, ?x3213), institution(?x1519, ?x581), school(?x1632, ?x6602), category(?x581, ?x134), citytown(?x6953, ?x2624), major_field_of_study(?x581, ?x1695), major_field_of_study(?x581, ?x1682), ?x1682 = 02ky346, colors(?x6953, ?x332), ?x1519 = 013zdg, state_province_region(?x581, ?x1227), student(?x581, ?x11088), student(?x581, ?x2789), sport(?x1632, ?x5063), ?x2789 = 01zfmm, school_type(?x581, ?x1044), ?x580 = 05m_8, team(?x5412, ?x1632), colors(?x581, ?x663), school(?x700, ?x10945), school(?x700, ?x4556), school(?x700, ?x4257), ?x10945 = 01jsk6, team(?x2010, ?x1632), ?x3948 = 025v3k, ?x4257 = 01q0kg, major_field_of_study(?x373, ?x1695), major_field_of_study(?x1043, ?x1695), organization(?x346, ?x6177), state_province_region(?x6602, ?x177), organizations_founded(?x11088, ?x11089), ?x1043 = 0kz2w, service_language(?x6177, ?x254), student(?x6177, ?x1270), ?x4556 = 01lnyf, gender(?x11088, ?x231), student(?x6953, ?x117), colors(?x700, ?x1101), profession(?x11088, ?x353), company(?x233, ?x581), contains(?x94, ?x6814), student(?x1695, ?x3806), teams(?x739, ?x1632) *> conf = 0.30 ranks of expected_values: 38, 97 EVAL 02pq_rp school 02zd460 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 15.000 15.000 0.571 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 02pq_rp school 0bx8pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 15.000 15.000 0.571 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #7515-07phbc PRED entity: 07phbc PRED relation: genre PRED expected values: 0lsxr => 109 concepts (76 used for prediction) PRED predicted values (max 10 best out of 113): 07s9rl0 (0.70 #1676, 0.62 #4779, 0.60 #6339), 01z4y (0.57 #478, 0.57 #1076, 0.51 #5498), 01jfsb (0.54 #3949, 0.50 #4430, 0.47 #1327), 03k9fj (0.48 #727, 0.44 #1804, 0.42 #3948), 02l7c8 (0.36 #4072, 0.31 #3594, 0.29 #134), 01hmnh (0.36 #17, 0.31 #734, 0.29 #853), 06n90 (0.28 #4431, 0.28 #3950, 0.28 #12), 0lsxr (0.22 #4426, 0.22 #1323, 0.20 #724), 06cvj (0.20 #3582, 0.20 #4060, 0.13 #3225), 04xvlr (0.18 #4780, 0.17 #5739, 0.16 #6340) >> Best rule #1676 for best value: >> intensional similarity = 5 >> extensional distance = 140 >> proper extension: 02vl9ln; >> query: (?x10268, 07s9rl0) <- country(?x10268, ?x789), country(?x10268, ?x94), ?x789 = 0f8l9c, contains(?x94, ?x95), nationality(?x51, ?x94) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4426 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 442 *> proper extension: 0cks1m; 0dr1c2; *> query: (?x10268, 0lsxr) <- genre(?x10268, ?x225), ?x225 = 02kdv5l, film(?x3604, ?x10268) *> conf = 0.22 ranks of expected_values: 8 EVAL 07phbc genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 109.000 76.000 0.697 http://example.org/film/film/genre #7514-01wg982 PRED entity: 01wg982 PRED relation: award PRED expected values: 02x4wb => 129 concepts (119 used for prediction) PRED predicted values (max 10 best out of 351): 02x4wb (0.43 #764, 0.16 #18226, 0.13 #41313), 01by1l (0.33 #6998, 0.32 #1328, 0.30 #6188), 0gq9h (0.32 #8583, 0.32 #5748, 0.16 #888), 01bgqh (0.28 #6928, 0.26 #1258, 0.26 #1663), 07cbcy (0.26 #889, 0.13 #2914, 0.10 #3319), 040njc (0.26 #5678, 0.24 #8513, 0.11 #23094), 09sb52 (0.25 #19077, 0.23 #29608, 0.23 #25557), 03qbh5 (0.24 #7092, 0.22 #6282, 0.19 #1422), 0c4z8 (0.23 #6147, 0.21 #6957, 0.20 #1692), 02f72n (0.23 #1362, 0.14 #552, 0.12 #4602) >> Best rule #764 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01vsxdm; 03j_hq; >> query: (?x2408, 02x4wb) <- artists(?x14090, ?x2408), award(?x2408, ?x9462), ?x14090 = 02lw8j >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wg982 award 02x4wb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 119.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7513-043s3 PRED entity: 043s3 PRED relation: influenced_by! PRED expected values: 07cbs 0420y => 154 concepts (66 used for prediction) PRED predicted values (max 10 best out of 393): 02wh0 (0.50 #2456, 0.33 #5992, 0.33 #4982), 039n1 (0.50 #2399, 0.33 #2903, 0.33 #888), 047g6 (0.50 #2485, 0.33 #974, 0.28 #8041), 07cbs (0.40 #1719, 0.11 #2727, 0.09 #10597), 01dvtx (0.33 #2162, 0.33 #651, 0.33 #147), 04hcw (0.33 #5833, 0.33 #4823, 0.33 #282), 0372p (0.33 #2161, 0.33 #650, 0.33 #146), 043s3 (0.33 #2165, 0.33 #654, 0.33 #150), 0399p (0.33 #4356, 0.33 #321, 0.32 #3852), 045bg (0.33 #5586, 0.33 #4576, 0.29 #6090) >> Best rule #2456 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 05qmj; >> query: (?x4033, 02wh0) <- influenced_by(?x11837, ?x4033), influenced_by(?x4547, ?x4033), influenced_by(?x2240, ?x4033), ?x4547 = 03_hd, ?x2240 = 0j3v, influenced_by(?x3864, ?x11837), gender(?x11837, ?x231) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1719 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 03s9v; *> query: (?x4033, 07cbs) <- influenced_by(?x12441, ?x4033), influenced_by(?x1857, ?x4033), religion(?x4033, ?x4641), nationality(?x4033, ?x512), ?x1857 = 026lj, influenced_by(?x4308, ?x12441) *> conf = 0.40 ranks of expected_values: 4, 13 EVAL 043s3 influenced_by! 0420y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 154.000 66.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 043s3 influenced_by! 07cbs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 154.000 66.000 0.500 http://example.org/influence/influence_node/influenced_by #7512-01pcz9 PRED entity: 01pcz9 PRED relation: nationality PRED expected values: 0chghy => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 23): 0d060g (0.34 #8325, 0.08 #105, 0.06 #1993), 02jx1 (0.16 #1621, 0.15 #628, 0.15 #728), 07ssc (0.14 #1603, 0.12 #1703, 0.12 #710), 03rk0 (0.05 #8964, 0.05 #9261, 0.05 #9360), 0chghy (0.04 #207, 0.03 #1003, 0.02 #2392), 03gyl (0.04 #263), 03_3d (0.03 #2982, 0.03 #3180, 0.02 #2883), 03h64 (0.02 #449, 0.02 #1442, 0.02 #1641), 06q1r (0.02 #672, 0.02 #772, 0.02 #1665), 0f8l9c (0.02 #2107, 0.02 #1015, 0.02 #1511) >> Best rule #8325 for best value: >> intensional similarity = 2 >> extensional distance = 2240 >> proper extension: 09jm8; >> query: (?x5899, ?x94) <- award_nominee(?x436, ?x5899), nationality(?x436, ?x94) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #207 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 24 *> proper extension: 01f2w0; *> query: (?x5899, 0chghy) <- award_winner(?x8762, ?x5899), ?x8762 = 09bymc *> conf = 0.04 ranks of expected_values: 5 EVAL 01pcz9 nationality 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 98.000 0.336 http://example.org/people/person/nationality #7511-02d413 PRED entity: 02d413 PRED relation: produced_by PRED expected values: 04353 => 105 concepts (77 used for prediction) PRED predicted values (max 10 best out of 170): 0bxtg (0.40 #12428, 0.12 #2326, 0.12 #12427), 0pmhf (0.40 #12428, 0.12 #2326, 0.12 #12427), 04353 (0.32 #10872, 0.01 #1863, 0.01 #3026), 0gcs9 (0.12 #18253, 0.11 #20199, 0.11 #14372), 0fvf9q (0.10 #393, 0.07 #1557, 0.05 #2720), 0grrq8 (0.06 #2102, 0.02 #2490, 0.02 #3266), 03xp8d5 (0.06 #156, 0.03 #2094, 0.03 #931), 03h304l (0.06 #186, 0.02 #1737, 0.01 #2900), 0js9s (0.06 #228, 0.02 #1779, 0.01 #2942), 03h8_g (0.06 #356) >> Best rule #12428 for best value: >> intensional similarity = 3 >> extensional distance = 605 >> proper extension: 0n2bh; >> query: (?x69, ?x496) <- nominated_for(?x496, ?x69), award_winner(?x496, ?x525), produced_by(?x797, ?x496) >> conf = 0.40 => this is the best rule for 2 predicted values *> Best rule #10872 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 553 *> proper extension: 0dckvs; *> query: (?x69, ?x9313) <- nominated_for(?x68, ?x69), titles(?x53, ?x69), film(?x9313, ?x69) *> conf = 0.32 ranks of expected_values: 3 EVAL 02d413 produced_by 04353 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 77.000 0.402 http://example.org/film/film/produced_by #7510-09bjv PRED entity: 09bjv PRED relation: place_of_birth! PRED expected values: 07f8wg => 159 concepts (78 used for prediction) PRED predicted values (max 10 best out of 1820): 0479b (0.39 #44397, 0.39 #54843, 0.35 #75736), 02t_st (0.39 #44397, 0.39 #54843, 0.35 #75736), 0fp_v1x (0.39 #44397, 0.39 #54843, 0.35 #75736), 03d9wk (0.09 #10433, 0.06 #13044, 0.05 #15656), 0kbg6 (0.09 #10417, 0.06 #13028, 0.05 #15640), 09jd9 (0.09 #10411, 0.06 #13022, 0.05 #15634), 026sb55 (0.09 #10394, 0.06 #13005, 0.05 #15617), 06lhbl (0.09 #10367, 0.06 #12978, 0.05 #15590), 03f4w4 (0.09 #10317, 0.06 #12928, 0.05 #15540), 01b0k1 (0.09 #10316, 0.06 #12927, 0.05 #15539) >> Best rule #44397 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 0ftn8; 05hcy; 0c1xm; >> query: (?x461, ?x460) <- capital(?x7413, ?x461), location(?x460, ?x461), participating_countries(?x418, ?x7413), administrative_parent(?x7413, ?x551) >> conf = 0.39 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 09bjv place_of_birth! 07f8wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 78.000 0.388 http://example.org/people/person/place_of_birth #7509-0gvt53w PRED entity: 0gvt53w PRED relation: film_crew_role PRED expected values: 02r96rf => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.73 #1453, 0.70 #147, 0.68 #75), 01vx2h (0.45 #119, 0.41 #83, 0.38 #335), 0dxtw (0.44 #334, 0.41 #1460, 0.40 #1278), 02rh1dz (0.35 #1668, 0.20 #117, 0.20 #333), 0215hd (0.35 #1668, 0.17 #55, 0.16 #127), 089g0h (0.35 #1668, 0.15 #56, 0.14 #20), 02_n3z (0.35 #1668, 0.15 #37, 0.14 #1), 04pyp5 (0.35 #1668, 0.07 #17, 0.06 #1285), 02vs3x5 (0.35 #1668, 0.06 #421, 0.05 #2308), 01pvkk (0.34 #409, 0.32 #626, 0.31 #300) >> Best rule #1453 for best value: >> intensional similarity = 4 >> extensional distance = 636 >> proper extension: 02bj22; >> query: (?x9432, 02r96rf) <- film(?x157, ?x9432), film_crew_role(?x9432, ?x1171), film(?x3462, ?x9432), ?x1171 = 09vw2b7 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gvt53w film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.726 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7508-04t6fk PRED entity: 04t6fk PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.75 #888, 0.74 #1117, 0.73 #1345), 09zzb8 (0.74 #881, 0.71 #1110, 0.71 #1338), 09vw2b7 (0.64 #887, 0.63 #1116, 0.60 #1344), 01vx2h (0.47 #89, 0.37 #356, 0.36 #815), 0dxtw (0.42 #317, 0.40 #355, 0.40 #241), 02rh1dz (0.41 #87, 0.21 #354, 0.20 #496), 02ynfr (0.35 #94, 0.20 #496, 0.17 #1127), 01pvkk (0.27 #1351, 0.26 #243, 0.26 #894), 015h31 (0.25 #10, 0.20 #496, 0.20 #48), 0d2b38 (0.25 #28, 0.20 #496, 0.20 #66) >> Best rule #888 for best value: >> intensional similarity = 4 >> extensional distance = 349 >> proper extension: 03q8xj; >> query: (?x2699, 0ch6mp2) <- country(?x2699, ?x94), ?x94 = 09c7w0, film_crew_role(?x2699, ?x468), executive_produced_by(?x2699, ?x3405) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04t6fk film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.746 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7507-04vh83 PRED entity: 04vh83 PRED relation: film_crew_role PRED expected values: 0dxtw => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 30): 0ch6mp2 (0.72 #528, 0.69 #1574, 0.69 #1499), 02r96rf (0.67 #671, 0.58 #1569, 0.58 #1494), 09vw2b7 (0.65 #527, 0.57 #1573, 0.56 #675), 0dxtw (0.40 #680, 0.33 #1503, 0.33 #1578), 01vx2h (0.32 #681, 0.27 #87, 0.27 #1579), 02ynfr (0.19 #685, 0.17 #202, 0.16 #537), 02_n3z (0.16 #521, 0.15 #223, 0.13 #706), 0d2b38 (0.16 #249, 0.13 #547, 0.11 #732), 0215hd (0.16 #540, 0.13 #242, 0.13 #725), 02rh1dz (0.15 #679, 0.12 #903, 0.12 #233) >> Best rule #528 for best value: >> intensional similarity = 5 >> extensional distance = 150 >> proper extension: 02y_lrp; 011yxg; 0ds11z; 04qw17; 031778; 02vqsll; 07w8fz; 03hmt9b; 05c5z8j; 01hqk; ... >> query: (?x3514, 0ch6mp2) <- country(?x3514, ?x512), currency(?x3514, ?x1099), ?x512 = 07ssc, nominated_for(?x198, ?x3514), film_crew_role(?x3514, ?x137) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #680 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 175 *> proper extension: 0sxg4; 09z2b7; 02q56mk; 04t6fk; 09p4w8; 02psgq; 063hp4; 05dptj; 03cvvlg; 02qlp4; *> query: (?x3514, 0dxtw) <- country(?x3514, ?x512), nominated_for(?x198, ?x3514), film_crew_role(?x3514, ?x137), film(?x269, ?x3514), story_by(?x3514, ?x5004) *> conf = 0.40 ranks of expected_values: 4 EVAL 04vh83 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 79.000 79.000 0.717 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7506-02lq10 PRED entity: 02lq10 PRED relation: type_of_union PRED expected values: 04ztj => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #57, 0.74 #53, 0.73 #141), 01g63y (0.17 #2, 0.16 #30, 0.15 #26) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 716 >> proper extension: 02s2ft; 05vsxz; 0byfz; 03x3qv; 0bl2g; 044rvb; 0kr5_; 05b__vr; 04hpck; 05_k56; ... >> query: (?x2217, 04ztj) <- film(?x2217, ?x197), gender(?x2217, ?x231), ?x231 = 05zppz, student(?x892, ?x2217) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lq10 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.742 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7505-0l6vl PRED entity: 0l6vl PRED relation: olympics! PRED expected values: 015qh => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 427): 0b90_r (0.85 #1916, 0.83 #1692, 0.82 #1244), 06qd3 (0.83 #1711, 0.77 #1935, 0.71 #1162), 03_3d (0.82 #1582, 0.79 #2138, 0.78 #1361), 05b4w (0.82 #1244, 0.78 #1291, 0.69 #1956), 015qh (0.82 #1244, 0.73 #1603, 0.71 #1165), 0ctw_b (0.82 #1244, 0.73 #1593, 0.71 #2149), 0hzlz (0.82 #1244, 0.67 #1023, 0.66 #1245), 09c7w0 (0.81 #2922, 0.80 #2700, 0.79 #583), 01znc_ (0.80 #1496, 0.78 #1274, 0.71 #1166), 03shp (0.80 #1527, 0.75 #1746, 0.73 #1635) >> Best rule #1916 for best value: >> intensional similarity = 64 >> extensional distance = 11 >> proper extension: 09x3r; >> query: (?x391, 0b90_r) <- sports(?x391, ?x3659), olympics(?x2152, ?x391), olympics(?x1536, ?x391), olympics(?x1353, ?x391), ?x1536 = 06c1y, film_release_region(?x11209, ?x1353), film_release_region(?x10535, ?x1353), film_release_region(?x9839, ?x1353), film_release_region(?x9501, ?x1353), film_release_region(?x6886, ?x1353), film_release_region(?x6882, ?x1353), film_release_region(?x6095, ?x1353), film_release_region(?x5827, ?x1353), film_release_region(?x4610, ?x1353), film_release_region(?x2783, ?x1353), film_release_region(?x2340, ?x1353), film_release_region(?x1701, ?x1353), film_release_region(?x1602, ?x1353), film_release_region(?x1364, ?x1353), film_release_region(?x664, ?x1353), ?x2783 = 0879bpq, film_release_region(?x10241, ?x2152), film_release_region(?x8377, ?x2152), film_release_region(?x7628, ?x2152), film_release_region(?x5286, ?x2152), film_release_region(?x1552, ?x2152), combatants(?x326, ?x1353), ?x1552 = 0gj9qxr, ?x1602 = 0gxtknx, country(?x150, ?x2152), nationality(?x395, ?x2152), ?x2340 = 0fpv_3_, ?x6882 = 043tvp3, ?x8377 = 0ds2l81, ?x1364 = 047msdk, ?x9839 = 0gy7bj4, combatants(?x1353, ?x279), contains(?x6304, ?x1353), country(?x689, ?x2152), ?x3659 = 0dwxr, exported_to(?x1353, ?x7833), capital(?x2152, ?x4698), location(?x4536, ?x1353), ?x5286 = 02gs6r, country(?x1649, ?x2152), ?x279 = 0d060g, participating_countries(?x418, ?x1353), ?x11209 = 04fjzv, ?x5827 = 0ggbfwf, nominated_for(?x749, ?x7628), award(?x10241, ?x746), location_of_ceremony(?x566, ?x1353), genre(?x10241, ?x239), olympics(?x94, ?x391), ?x664 = 0401sg, location(?x4055, ?x2152), film_regional_debut_venue(?x10535, ?x1658), film(?x1104, ?x7628), ?x6095 = 0bq6ntw, ?x9501 = 0g5qmbz, ?x6886 = 0gwjw0c, ?x4610 = 017jd9, genre(?x10535, ?x571), ?x1701 = 0bh8yn3 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1244 for first EXPECTED value: *> intensional similarity = 78 *> extensional distance = 5 *> proper extension: 0lgxj; *> query: (?x391, ?x1023) <- sports(?x391, ?x3641), sports(?x391, ?x2867), olympics(?x2152, ?x391), olympics(?x1536, ?x391), olympics(?x1355, ?x391), olympics(?x1353, ?x391), olympics(?x456, ?x391), ?x1536 = 06c1y, film_release_region(?x8580, ?x1353), film_release_region(?x8292, ?x1353), film_release_region(?x8025, ?x1353), film_release_region(?x7678, ?x1353), film_release_region(?x7554, ?x1353), film_release_region(?x6536, ?x1353), film_release_region(?x6492, ?x1353), film_release_region(?x6095, ?x1353), film_release_region(?x5992, ?x1353), film_release_region(?x5980, ?x1353), film_release_region(?x5271, ?x1353), film_release_region(?x5142, ?x1353), film_release_region(?x4950, ?x1353), film_release_region(?x4707, ?x1353), film_release_region(?x4111, ?x1353), film_release_region(?x2893, ?x1353), film_release_region(?x2628, ?x1353), film_release_region(?x2350, ?x1353), film_release_region(?x1724, ?x1353), film_release_region(?x1518, ?x1353), film_release_region(?x1315, ?x1353), film_release_region(?x1228, ?x1353), film_release_region(?x1150, ?x1353), film_release_region(?x664, ?x1353), film_release_region(?x622, ?x1353), film_release_region(?x511, ?x1353), film_release_region(?x409, ?x1353), film_release_region(?x303, ?x1353), ?x2152 = 06mkj, ?x664 = 0401sg, ?x456 = 05qhw, ?x1355 = 0h7x, ?x5980 = 0hv81, ?x8025 = 03nsm5x, ?x4111 = 0cmc26r, ?x6492 = 0ds6bmk, ?x5992 = 0g5q34q, ?x409 = 0gtv7pk, ?x1724 = 02r8hh_, country(?x668, ?x1353), nationality(?x1068, ?x1353), ?x2867 = 02y8z, ?x622 = 0fq27fp, ?x1150 = 0h3xztt, contains(?x1353, ?x7575), ?x2628 = 06wbm8q, ?x6095 = 0bq6ntw, jurisdiction_of_office(?x182, ?x1353), combatants(?x1023, ?x1353), ?x5271 = 047vnkj, ?x182 = 060bp, ?x303 = 011yrp, ?x2893 = 01jrbb, ?x3641 = 03fyrh, ?x2350 = 0661m4p, ?x8580 = 0hhggmy, ?x1518 = 04w7rn, ?x7678 = 0gvvf4j, ?x6536 = 09gmmt6, ?x4950 = 07k2mq, organization(?x1353, ?x127), ?x7554 = 01mgw, ?x4707 = 02xbyr, ?x1315 = 053tj7, ?x8292 = 0cmf0m0, ?x511 = 0dscrwf, ?x5142 = 0bt3j9, olympics(?x1353, ?x778), teams(?x1023, ?x10085), ?x1228 = 05z_kps *> conf = 0.82 ranks of expected_values: 5 EVAL 0l6vl olympics! 015qh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 31.000 31.000 0.846 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #7504-0p7qm PRED entity: 0p7qm PRED relation: films! PRED expected values: 0f6rc => 91 concepts (31 used for prediction) PRED predicted values (max 10 best out of 39): 01w1sx (0.50 #91, 0.02 #1819, 0.02 #1189), 0fx2s (0.07 #544, 0.05 #387, 0.03 #1171), 081pw (0.07 #317, 0.06 #474, 0.05 #945), 05489 (0.05 #366, 0.03 #2099, 0.03 #1150), 07s2s (0.04 #729, 0.02 #1513, 0.02 #1670), 03r8gp (0.04 #720, 0.02 #1504, 0.02 #246), 0fzyg (0.04 #996, 0.04 #1152, 0.03 #1625), 01vq3 (0.04 #197, 0.02 #827, 0.02 #1299), 06d4h (0.03 #357, 0.03 #2090, 0.03 #3201), 0g1x2_ (0.03 #341, 0.02 #498, 0.01 #2074) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04cbbz; >> query: (?x2924, 01w1sx) <- film(?x5440, ?x2924), award(?x2924, ?x484), genre(?x2924, ?x225), ?x5440 = 016z51 >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0p7qm films! 0f6rc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 31.000 0.500 http://example.org/film/film_subject/films #7503-09gb_4p PRED entity: 09gb_4p PRED relation: featured_film_locations PRED expected values: 0f2r6 => 81 concepts (44 used for prediction) PRED predicted values (max 10 best out of 82): 02_286 (0.27 #3133, 0.19 #20, 0.16 #739), 030qb3t (0.13 #3152, 0.07 #5073, 0.07 #7712), 04jpl (0.11 #3122, 0.09 #5762, 0.08 #1924), 0rh6k (0.07 #3114, 0.03 #1916, 0.03 #2156), 080h2 (0.06 #3137, 0.03 #5058, 0.03 #5298), 052p7 (0.04 #3170, 0.02 #776, 0.02 #5331), 02nd_ (0.03 #2270, 0.03 #1073, 0.03 #2030), 01_d4 (0.03 #3160, 0.02 #1245, 0.02 #5561), 0h7h6 (0.03 #762, 0.03 #3156, 0.03 #4596), 05qtj (0.03 #814, 0.02 #1293, 0.02 #1771) >> Best rule #3133 for best value: >> intensional similarity = 5 >> extensional distance = 265 >> proper extension: 02_1sj; 03ckwzc; 03t97y; 07sc6nw; 07g_0c; 03twd6; 028cg00; 02vqhv0; 047qxs; 06v9_x; ... >> query: (?x4602, 02_286) <- film_crew_role(?x4602, ?x1171), film_crew_role(?x4602, ?x468), ?x468 = 02r96rf, ?x1171 = 09vw2b7, featured_film_locations(?x4602, ?x2256) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09gb_4p featured_film_locations 0f2r6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 44.000 0.270 http://example.org/film/film/featured_film_locations #7502-0prh7 PRED entity: 0prh7 PRED relation: film! PRED expected values: 0chsq => 61 concepts (37 used for prediction) PRED predicted values (max 10 best out of 618): 0prjs (0.73 #49794, 0.50 #18672, 0.48 #68464), 0dvld (0.73 #49794, 0.48 #68464, 0.45 #49793), 06cgy (0.14 #2322, 0.06 #25148, 0.02 #60413), 0169dl (0.14 #399, 0.05 #12847, 0.03 #4547), 01kb2j (0.14 #906, 0.04 #13354, 0.03 #5054), 05nzw6 (0.14 #1188, 0.03 #5336, 0.02 #26088), 01nr36 (0.14 #1475, 0.03 #5623, 0.01 #22223), 01j5ts (0.14 #2102, 0.03 #4176, 0.01 #16624), 02q4mt (0.14 #1910, 0.03 #6058), 0p8r1 (0.14 #2657, 0.03 #21331, 0.02 #19255) >> Best rule #49794 for best value: >> intensional similarity = 4 >> extensional distance = 925 >> proper extension: 0dkv90; >> query: (?x4874, ?x1371) <- currency(?x4874, ?x170), nominated_for(?x1371, ?x4874), film(?x1371, ?x1263), award(?x1371, ?x102) >> conf = 0.73 => this is the best rule for 2 predicted values *> Best rule #24978 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 450 *> proper extension: 0hgnl3t; 01gglm; *> query: (?x4874, 0chsq) <- film(?x7391, ?x4874), award_winner(?x591, ?x7391), ?x591 = 0f4x7 *> conf = 0.02 ranks of expected_values: 256 EVAL 0prh7 film! 0chsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 37.000 0.726 http://example.org/film/actor/film./film/performance/film #7501-04qmr PRED entity: 04qmr PRED relation: award_winner! PRED expected values: 013b2h => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 108): 02rjjll (0.26 #565, 0.16 #3362, 0.14 #2246), 05pd94v (0.23 #562, 0.16 #3362, 0.12 #2803), 0466p0j (0.16 #3362, 0.15 #635, 0.14 #2316), 019bk0 (0.16 #3362, 0.15 #576, 0.12 #2257), 02cg41 (0.16 #3362, 0.14 #545, 0.13 #685), 056878 (0.16 #3362, 0.13 #592, 0.12 #8263), 0gx1673 (0.16 #3362, 0.13 #679, 0.12 #8263), 013b2h (0.16 #3362, 0.13 #2320, 0.12 #3300), 01bx35 (0.16 #3362, 0.12 #8263, 0.12 #8264), 01xqqp (0.16 #3362, 0.12 #8263, 0.12 #8264) >> Best rule #565 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 01wmxfs; >> query: (?x3682, 02rjjll) <- award(?x3682, ?x4837), award_nominee(?x3682, ?x1060), ?x4837 = 03t5kl >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #3362 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 376 *> proper extension: 01qkqwg; 01jllg1; 024y6w; *> query: (?x3682, ?x486) <- award_nominee(?x3682, ?x1060), award_winner(?x4837, ?x3682), artists(?x302, ?x3682), award_winner(?x486, ?x1060) *> conf = 0.16 ranks of expected_values: 8 EVAL 04qmr award_winner! 013b2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 89.000 89.000 0.256 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7500-050r1z PRED entity: 050r1z PRED relation: film_crew_role PRED expected values: 0ch6mp2 0d2b38 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 28): 0ch6mp2 (0.59 #87, 0.56 #677, 0.56 #912), 09zzb8 (0.58 #79, 0.56 #669, 0.54 #1455), 02r96rf (0.50 #4, 0.50 #434, 0.48 #907), 09vw2b7 (0.50 #8, 0.48 #676, 0.48 #1462), 0dxtw (0.32 #91, 0.29 #1467, 0.28 #286), 01vx2h (0.26 #444, 0.24 #1468, 0.23 #92), 02ynfr (0.25 #19, 0.15 #136, 0.14 #97), 01xy5l_ (0.25 #17, 0.11 #95, 0.10 #290), 089g0h (0.25 #23, 0.10 #691, 0.09 #926), 02_n3z (0.25 #2, 0.07 #984, 0.06 #670) >> Best rule #87 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 026p_bs; 035w2k; 03mgx6z; 03z9585; 078mm1; >> query: (?x586, 0ch6mp2) <- language(?x586, ?x5607), ?x5607 = 064_8sq, produced_by(?x586, ?x2332), film(?x585, ?x586) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1, 17 EVAL 050r1z film_crew_role 0d2b38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 91.000 91.000 0.593 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 050r1z film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.593 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7499-018vs PRED entity: 018vs PRED relation: group PRED expected values: 01t_xp_ 01wv9xn 0d193h 0g_g2 0kr_t 07bzp 0178kd 01kcms4 089pg7 017lb_ 01lf293 09z1lg 0p76z 03q_w5 0jltp 01518s 0fsyx => 64 concepts (62 used for prediction) PRED predicted values (max 10 best out of 684): 05563d (0.71 #988, 0.67 #1588, 0.67 #1438), 0khth (0.69 #1818, 0.52 #1498, 0.50 #1367), 0dvqq (0.67 #1434, 0.60 #686, 0.56 #1584), 01czx (0.67 #909, 0.60 #534, 0.56 #1583), 01wv9xn (0.67 #904, 0.60 #529, 0.52 #1498), 0kr_t (0.67 #925, 0.60 #550, 0.52 #1498), 0g_g2 (0.67 #921, 0.56 #1595, 0.56 #1445), 01lf293 (0.67 #1543, 0.56 #1467, 0.52 #1498), 0178kd (0.67 #931, 0.52 #1498, 0.46 #521), 0d193h (0.67 #917, 0.52 #1498, 0.46 #521) >> Best rule #988 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 06ncr; 06w7v; >> query: (?x716, 05563d) <- role(?x3418, ?x716), role(?x2048, ?x716), instrumentalists(?x716, ?x7084), instrumentalists(?x716, ?x1291), artists(?x378, ?x1291), ?x2048 = 018j2, group(?x716, ?x379), ?x7084 = 01vs4ff, role(?x212, ?x3418), award_nominee(?x1292, ?x1291) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #904 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 028tv0; *> query: (?x716, 01wv9xn) <- role(?x74, ?x716), group(?x716, ?x10938), group(?x716, ?x9868), group(?x716, ?x7612), ?x10938 = 09jvl, ?x9868 = 0134pk, role(?x248, ?x716), role(?x716, ?x228), ?x7612 = 01w5n51 *> conf = 0.67 ranks of expected_values: 5, 6, 7, 8, 9, 10, 14, 21, 24, 27, 32, 34, 39, 40, 49, 61, 63 EVAL 018vs group 0fsyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 01518s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 0jltp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 03q_w5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 0p76z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 09z1lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 01lf293 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 017lb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 089pg7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 01kcms4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 0178kd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 07bzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 0kr_t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 0g_g2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 0d193h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 01wv9xn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 018vs group 01t_xp_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 64.000 62.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #7498-02w3w PRED entity: 02w3w PRED relation: role! PRED expected values: 02fn5r => 67 concepts (42 used for prediction) PRED predicted values (max 10 best out of 786): 01p95y0 (0.60 #2894, 0.37 #4955, 0.35 #4656), 04mx7s (0.50 #1690, 0.42 #4921, 0.40 #2860), 016h9b (0.50 #1538, 0.42 #4769, 0.40 #2415), 0167v4 (0.50 #1708, 0.33 #2001, 0.33 #1123), 01vrx3g (0.50 #1470, 0.33 #1763, 0.33 #885), 03c7ln (0.50 #1467, 0.33 #1760, 0.33 #590), 01vs4ff (0.50 #1650, 0.33 #1943, 0.33 #1356), 01cv3n (0.50 #1479, 0.33 #1772, 0.33 #1185), 01mxnvc (0.40 #2903, 0.33 #1439, 0.33 #856), 0ftps (0.40 #2374, 0.33 #3545, 0.33 #1203) >> Best rule #2894 for best value: >> intensional similarity = 16 >> extensional distance = 8 >> proper extension: 07xzm; 01hww_; 06ncr; 0jtg0; 01xqw; >> query: (?x5417, 01p95y0) <- instrumentalists(?x5417, ?x7345), instrumentalists(?x5417, ?x3419), role(?x1482, ?x5417), role(?x716, ?x5417), profession(?x7345, ?x2348), profession(?x7345, ?x1183), role(?x1332, ?x5417), award_winner(?x3419, ?x1413), location(?x7345, ?x659), ?x716 = 018vs, award_winner(?x341, ?x3419), role(?x5417, ?x315), ?x1482 = 02g9p4, ?x1183 = 09jwl, nationality(?x7345, ?x94), ?x2348 = 0nbcg >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #650 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x5417, 02fn5r) <- instrumentalists(?x5417, ?x7345), instrumentalists(?x5417, ?x3419), role(?x5990, ?x5417), role(?x4429, ?x5417), role(?x75, ?x5417), ?x3419 = 03cfjg, ?x75 = 07y_7, profession(?x7345, ?x2348), ?x2348 = 0nbcg, student(?x7716, ?x7345), ?x4429 = 0g33q, people(?x1446, ?x7345), location(?x7345, ?x659), category(?x7345, ?x134), instrumentalists(?x5990, ?x4689), major_field_of_study(?x7716, ?x742) *> conf = 0.33 ranks of expected_values: 110 EVAL 02w3w role! 02fn5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 67.000 42.000 0.600 http://example.org/music/group_member/membership./music/group_membership/role #7497-07y_p6 PRED entity: 07y_p6 PRED relation: ceremony! PRED expected values: 09qvf4 027gs1_ => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 279): 027gs1_ (0.82 #1924, 0.82 #1674, 0.80 #1425), 0gqy2 (0.81 #3342, 0.79 #3590, 0.66 #3839), 0gqwc (0.76 #3282, 0.75 #3530, 0.62 #3779), 0gq9h (0.76 #3283, 0.71 #3531, 0.59 #3780), 0gs9p (0.76 #3284, 0.71 #3532, 0.59 #3781), 0gq_d (0.75 #3378, 0.74 #3626, 0.61 #3875), 0k611 (0.75 #3295, 0.74 #3543, 0.61 #3792), 0gvx_ (0.75 #3356, 0.72 #3604, 0.60 #3853), 0f4x7 (0.73 #3248, 0.72 #3496, 0.60 #3745), 018wng (0.73 #3257, 0.71 #3505, 0.59 #3754) >> Best rule #1924 for best value: >> intensional similarity = 18 >> extensional distance = 9 >> proper extension: 02q690_; >> query: (?x7085, 027gs1_) <- ceremony(?x5235, ?x7085), ceremony(?x4386, ?x7085), ceremony(?x686, ?x7085), instance_of_recurring_event(?x7085, ?x2758), honored_for(?x7085, ?x337), award(?x7796, ?x4386), award(?x1725, ?x4386), ?x1725 = 01n4f8, award(?x6694, ?x4386), ?x5235 = 09qrn4, award_winner(?x1180, ?x7796), profession(?x7796, ?x987), award_nominee(?x7796, ?x635), award_winner(?x7085, ?x3751), written_by(?x1163, ?x3751), award_nominee(?x1039, ?x3751), ?x686 = 0bdw1g, ?x987 = 0dxtg >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 16 EVAL 07y_p6 ceremony! 027gs1_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.818 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 07y_p6 ceremony! 09qvf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 24.000 24.000 0.818 http://example.org/award/award_category/winners./award/award_honor/ceremony #7496-01z4y PRED entity: 01z4y PRED relation: major_field_of_study! PRED expected values: 017d77 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 616): 02zd460 (0.50 #5543, 0.38 #7325, 0.36 #15049), 08815 (0.50 #5348, 0.38 #7130, 0.33 #2), 07t90 (0.50 #5515, 0.38 #7297, 0.33 #169), 01vs5c (0.50 #5553, 0.38 #7335, 0.33 #207), 03ksy (0.42 #14973, 0.38 #21507, 0.37 #23292), 06pwq (0.39 #21399, 0.38 #14865, 0.36 #23184), 09f2j (0.38 #15033, 0.36 #21567, 0.33 #181), 01w5m (0.37 #14972, 0.37 #21506, 0.35 #23291), 017j69 (0.33 #5510, 0.33 #164, 0.30 #15016), 07wjk (0.33 #5414, 0.33 #68, 0.25 #7196) >> Best rule #5543 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 02h40lc; 0g4gr; 04gb7; >> query: (?x2480, 02zd460) <- split_to(?x2480, ?x258), major_field_of_study(?x1368, ?x2480), ?x1368 = 014mlp >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #21982 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 90 *> proper extension: 02ky346; 04x_3; 0mkz; 0db86; 0fzyg; 02_7t; 06mnr; 03ytc; 01zc2w; 01bt59; ... *> query: (?x2480, ?x99) <- major_field_of_study(?x1368, ?x2480), student(?x1368, ?x123), institution(?x1368, ?x13491), institution(?x1368, ?x99), ?x13491 = 0f11p *> conf = 0.07 ranks of expected_values: 337 EVAL 01z4y major_field_of_study! 017d77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 63.000 63.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7495-02d4ct PRED entity: 02d4ct PRED relation: place_of_birth PRED expected values: 0f2tj => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 52): 03dm7 (0.17 #459, 0.01 #56333), 0r0ls (0.17 #564), 0hyxv (0.17 #147), 02_286 (0.08 #4244, 0.08 #2131, 0.08 #8469), 030qb3t (0.06 #758, 0.06 #8504, 0.06 #12728), 01_d4 (0.04 #1474, 0.03 #71185, 0.03 #28234), 0cr3d (0.03 #26854, 0.03 #22627, 0.03 #43048), 0rh6k (0.03 #1410, 0.01 #22535, 0.01 #23944), 0cc56 (0.03 #1441, 0.02 #2849, 0.02 #737), 06_kh (0.02 #2821, 0.02 #5638, 0.02 #7046) >> Best rule #459 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 06688p; 06cgy; 06mr6; >> query: (?x2374, 03dm7) <- film(?x2374, ?x195), gender(?x2374, ?x514), ?x195 = 0b2v79 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #2360 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 194 *> proper extension: 03l295; 0bx_q; 01xyt7; 0bw6y; 03d0ns; 0d3k14; 01g0jn; *> query: (?x2374, 0f2tj) <- participant(?x2374, ?x3195), award_winner(?x1254, ?x2374), student(?x122, ?x2374) *> conf = 0.01 ranks of expected_values: 51 EVAL 02d4ct place_of_birth 0f2tj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 110.000 110.000 0.167 http://example.org/people/person/place_of_birth #7494-01c57n PRED entity: 01c57n PRED relation: institution! PRED expected values: 019v9k => 160 concepts (101 used for prediction) PRED predicted values (max 10 best out of 23): 02_xgp2 (0.82 #264, 0.78 #252, 0.75 #323), 016t_3 (0.82 #264, 0.78 #244, 0.71 #361), 03bwzr4 (0.82 #264, 0.74 #504, 0.71 #361), 0bjrnt (0.82 #264, 0.71 #361, 0.49 #1126), 071tyz (0.82 #264, 0.71 #361, 0.49 #1126), 02h4rq6 (0.79 #340, 0.78 #243, 0.75 #314), 04zx3q1 (0.78 #242, 0.50 #339, 0.50 #313), 019v9k (0.68 #288, 0.65 #1277, 0.65 #1924), 01kxxq (0.68 #288, 0.42 #336, 0.36 #979), 01gkg3 (0.68 #288, 0.36 #979, 0.35 #1943) >> Best rule #264 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 0gl5_; >> query: (?x12489, ?x1200) <- major_field_of_study(?x12489, ?x12158), major_field_of_study(?x12489, ?x6364), major_field_of_study(?x2605, ?x6364), major_field_of_study(?x7950, ?x6364), major_field_of_study(?x1043, ?x6364), ?x12158 = 09s1f, major_field_of_study(?x1368, ?x6364), major_field_of_study(?x1200, ?x6364), ?x7950 = 01dbns, ?x1368 = 014mlp, ?x1043 = 0kz2w >> conf = 0.82 => this is the best rule for 5 predicted values *> Best rule #288 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 8 *> proper extension: 02bqy; 0ks67; 01dbns; 0373qt; *> query: (?x12489, ?x865) <- major_field_of_study(?x12489, ?x12158), major_field_of_study(?x12489, ?x6364), organization(?x5510, ?x12489), ?x6364 = 05qt0, major_field_of_study(?x10104, ?x12158), major_field_of_study(?x4916, ?x12158), ?x4916 = 019dwp, major_field_of_study(?x865, ?x12158), colors(?x10104, ?x332), contains(?x94, ?x10104) *> conf = 0.68 ranks of expected_values: 8 EVAL 01c57n institution! 019v9k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 160.000 101.000 0.825 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #7493-0jwmp PRED entity: 0jwmp PRED relation: films! PRED expected values: 01vq3 => 93 concepts (42 used for prediction) PRED predicted values (max 10 best out of 65): 01vq3 (0.20 #41, 0.04 #2399, 0.03 #985), 081pw (0.10 #159, 0.08 #473, 0.06 #1103), 07_nf (0.10 #223, 0.05 #537, 0.03 #695), 03hzt (0.08 #605, 0.05 #763, 0.05 #291), 07s2s (0.06 #2299, 0.03 #1199, 0.03 #2929), 05489 (0.06 #365, 0.04 #996, 0.04 #1308), 0fzyg (0.05 #210, 0.04 #1625, 0.04 #998), 03r8gp (0.05 #246, 0.03 #560, 0.02 #2920), 01cgz (0.05 #175, 0.03 #489, 0.02 #647), 0d3k14 (0.05 #250, 0.03 #564, 0.02 #722) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 04zl8; >> query: (?x3392, 01vq3) <- film(?x4297, ?x3392), ?x4297 = 04yt7, nominated_for(?x484, ?x3392) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jwmp films! 01vq3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 42.000 0.200 http://example.org/film/film_subject/films #7492-07cyl PRED entity: 07cyl PRED relation: film_release_region PRED expected values: 01znc_ 077qn => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 160): 05qhw (0.87 #1086, 0.81 #1852, 0.72 #167), 035qy (0.84 #1105, 0.80 #1871, 0.72 #186), 05v8c (0.81 #169, 0.68 #1088, 0.65 #1854), 0154j (0.79 #1076, 0.78 #1842, 0.67 #4602), 06bnz (0.79 #1117, 0.72 #198, 0.69 #1883), 06t2t (0.78 #215, 0.70 #1900, 0.69 #1134), 01znc_ (0.78 #1112, 0.77 #1878, 0.72 #4638), 04gzd (0.75 #161, 0.51 #1080, 0.46 #1846), 0b90_r (0.72 #1075, 0.72 #156, 0.71 #1841), 03rt9 (0.70 #1851, 0.68 #1085, 0.66 #166) >> Best rule #1086 for best value: >> intensional similarity = 5 >> extensional distance = 66 >> proper extension: 0fq27fp; 047svrl; >> query: (?x3471, 05qhw) <- film_release_region(?x3471, ?x774), film_release_region(?x3471, ?x279), ?x774 = 06mzp, currency(?x3471, ?x170), ?x279 = 0d060g >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1112 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 66 *> proper extension: 0fq27fp; 047svrl; *> query: (?x3471, 01znc_) <- film_release_region(?x3471, ?x774), film_release_region(?x3471, ?x279), ?x774 = 06mzp, currency(?x3471, ?x170), ?x279 = 0d060g *> conf = 0.78 ranks of expected_values: 7, 32 EVAL 07cyl film_release_region 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 111.000 111.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07cyl film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 111.000 111.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7491-0g5879y PRED entity: 0g5879y PRED relation: film_release_region PRED expected values: 0chghy => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 161): 0345h (0.81 #3378, 0.80 #2769, 0.80 #791), 0chghy (0.81 #3356, 0.81 #2747, 0.77 #3051), 03gj2 (0.77 #2762, 0.74 #3066, 0.73 #3371), 05qhw (0.76 #3360, 0.74 #2751, 0.73 #773), 06bnz (0.71 #2783, 0.70 #3392, 0.67 #2631), 03rt9 (0.64 #2750, 0.62 #3359, 0.60 #2598), 03rj0 (0.60 #2797, 0.59 #3406, 0.58 #2645), 05v8c (0.56 #2753, 0.55 #2601, 0.54 #3362), 04gzd (0.54 #2593, 0.47 #3354, 0.44 #2745), 01mjq (0.53 #2781, 0.50 #3085, 0.49 #2629) >> Best rule #3378 for best value: >> intensional similarity = 4 >> extensional distance = 243 >> proper extension: 0401sg; 087wc7n; 03bx2lk; 053tj7; 03mgx6z; 02qk3fk; 02825cv; 0hz6mv2; >> query: (?x2685, 0345h) <- genre(?x2685, ?x53), film_release_region(?x2685, ?x172), ?x172 = 0154j, language(?x2685, ?x254) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #3356 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 243 *> proper extension: 0401sg; 087wc7n; 03bx2lk; 053tj7; 03mgx6z; 02qk3fk; 02825cv; 0hz6mv2; *> query: (?x2685, 0chghy) <- genre(?x2685, ?x53), film_release_region(?x2685, ?x172), ?x172 = 0154j, language(?x2685, ?x254) *> conf = 0.81 ranks of expected_values: 2 EVAL 0g5879y film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7490-0521rl1 PRED entity: 0521rl1 PRED relation: film PRED expected values: 0d4htf => 78 concepts (28 used for prediction) PRED predicted values (max 10 best out of 109): 0h6r5 (0.25 #680, 0.02 #26886), 01xbxn (0.12 #1396, 0.02 #26886), 03sxd2 (0.12 #306, 0.02 #26886), 09rvwmy (0.06 #1696, 0.02 #26886), 09gdh6k (0.06 #1313, 0.02 #26886), 09hy79 (0.06 #1234, 0.02 #26886), 0286vp (0.06 #1228, 0.02 #26886), 0277j40 (0.06 #1226, 0.02 #26886), 0gg5kmg (0.06 #1080, 0.02 #26886), 06cm5 (0.06 #1072, 0.02 #26886) >> Best rule #680 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02lg9w; >> query: (?x840, 0h6r5) <- gender(?x840, ?x231), award_winner(?x840, ?x1059), ?x1059 = 021_rm >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0521rl1 film 0d4htf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 28.000 0.250 http://example.org/film/actor/film./film/performance/film #7489-05q54f5 PRED entity: 05q54f5 PRED relation: film! PRED expected values: 05nzw6 => 56 concepts (30 used for prediction) PRED predicted values (max 10 best out of 729): 0sz28 (0.64 #35353, 0.59 #51993, 0.44 #37434), 0kjrx (0.11 #1421, 0.02 #26372, 0.02 #5579), 0z4s (0.06 #2147, 0.05 #8384, 0.03 #6305), 020_95 (0.06 #966, 0.05 #51994, 0.01 #13441), 0f5xn (0.06 #969, 0.04 #19682, 0.04 #17603), 0169dl (0.06 #402, 0.03 #6639, 0.02 #14957), 0159h6 (0.06 #73, 0.03 #6310, 0.01 #8389), 0h5g_ (0.06 #74, 0.03 #2153, 0.03 #8390), 0h0wc (0.06 #425, 0.03 #2504, 0.03 #8741), 01gbn6 (0.06 #1626, 0.03 #3705, 0.01 #9942) >> Best rule #35353 for best value: >> intensional similarity = 2 >> extensional distance = 860 >> proper extension: 0123qq; >> query: (?x2892, ?x1208) <- nominated_for(?x1208, ?x2892), participant(?x1208, ?x872) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #15746 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 358 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x2892, 05nzw6) <- titles(?x3506, ?x2892), titles(?x3506, ?x5074), titles(?x3506, ?x2336), ?x5074 = 05mrf_p, nominated_for(?x591, ?x2336) *> conf = 0.03 ranks of expected_values: 136 EVAL 05q54f5 film! 05nzw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 56.000 30.000 0.638 http://example.org/film/actor/film./film/performance/film #7488-0p9gg PRED entity: 0p9gg PRED relation: film PRED expected values: 01lbcqx => 82 concepts (39 used for prediction) PRED predicted values (max 10 best out of 397): 0c_j9x (0.20 #373, 0.01 #11107, 0.01 #7529), 0ft18 (0.20 #1407, 0.01 #8563), 04vq33 (0.20 #1776), 0bj25 (0.08 #3280), 0k4f3 (0.08 #2237), 01kf3_9 (0.08 #2078), 02qr3k8 (0.05 #4866, 0.03 #13811, 0.03 #10233), 01lbcqx (0.04 #5027, 0.02 #8605), 0gzy02 (0.04 #3622), 0jvt9 (0.04 #7695, 0.03 #4117, 0.03 #5906) >> Best rule #373 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 07xr3w; >> query: (?x13159, 0c_j9x) <- nationality(?x13159, ?x94), nominated_for(?x13159, ?x1973), profession(?x13159, ?x1032), ?x1973 = 070fnm >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #5027 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 125 *> proper extension: 0cj2w; *> query: (?x13159, 01lbcqx) <- award(?x13159, ?x591), ?x591 = 0f4x7, award_winner(?x5180, ?x13159) *> conf = 0.04 ranks of expected_values: 8 EVAL 0p9gg film 01lbcqx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 82.000 39.000 0.200 http://example.org/film/actor/film./film/performance/film #7487-01bcwk PRED entity: 01bcwk PRED relation: list PRED expected values: 09g7thr => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 4): 09g7thr (0.46 #22, 0.37 #85, 0.33 #50), 01ptsx (0.13 #110, 0.09 #131, 0.09 #565), 04k4rt (0.08 #109, 0.07 #564, 0.06 #515), 01pd60 (0.08 #111, 0.06 #566, 0.05 #132) >> Best rule #22 for best value: >> intensional similarity = 7 >> extensional distance = 37 >> proper extension: 017ztv; >> query: (?x5035, 09g7thr) <- institution(?x1771, ?x5035), institution(?x1368, ?x5035), institution(?x734, ?x5035), ?x1771 = 019v9k, contains(?x390, ?x5035), ?x734 = 04zx3q1, ?x1368 = 014mlp >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bcwk list 09g7thr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.462 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #7486-04cj79 PRED entity: 04cj79 PRED relation: film! PRED expected values: 017s11 => 93 concepts (60 used for prediction) PRED predicted values (max 10 best out of 60): 016tw3 (0.21 #977, 0.19 #234, 0.16 #681), 05qd_ (0.21 #83, 0.14 #901, 0.12 #3226), 0jz9f (0.20 #1, 0.14 #75, 0.13 #150), 086k8 (0.17 #76, 0.16 #1340, 0.16 #2165), 016tt2 (0.16 #1342, 0.14 #227, 0.13 #674), 017s11 (0.14 #226, 0.13 #301, 0.12 #2844), 0g1rw (0.13 #306, 0.12 #900, 0.10 #974), 03xq0f (0.12 #1045, 0.12 #601, 0.11 #1343), 054g1r (0.10 #35, 0.10 #184, 0.08 #556), 020h2v (0.10 #44, 0.08 #267, 0.06 #1382) >> Best rule #977 for best value: >> intensional similarity = 4 >> extensional distance = 151 >> proper extension: 09p35z; 02sg5v; 04gknr; 05q96q6; 02qrv7; 0gd0c7x; 02725hs; 04g9gd; 08k40m; 024mxd; ... >> query: (?x3605, 016tw3) <- country(?x3605, ?x512), language(?x3605, ?x254), ?x512 = 07ssc, produced_by(?x3605, ?x2733) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #226 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 04v8x9; 0kv2hv; 0c5dd; 014l6_; *> query: (?x3605, 017s11) <- country(?x3605, ?x94), film_release_region(?x3605, ?x1499), award_winner(?x3605, ?x4294), produced_by(?x3605, ?x2733) *> conf = 0.14 ranks of expected_values: 6 EVAL 04cj79 film! 017s11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 93.000 60.000 0.209 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7485-03v0t PRED entity: 03v0t PRED relation: contains PRED expected values: 0nvd8 0ntwb 0nv99 => 177 concepts (90 used for prediction) PRED predicted values (max 10 best out of 2682): 0ftxc (0.88 #110138, 0.87 #121733, 0.86 #156516), 0s6jm (0.83 #84052, 0.80 #46374, 0.59 #211594), 0nv99 (0.59 #211594, 0.50 #95646, 0.49 #171013), 03v0t (0.59 #211594, 0.50 #95646, 0.49 #171013), 09c7w0 (0.59 #211594, 0.50 #95646, 0.49 #171013), 02v3m7 (0.59 #211594, 0.50 #95646, 0.49 #171013), 0nvd8 (0.59 #211594, 0.50 #95646, 0.49 #171013), 02bf58 (0.52 #28984, 0.50 #5536, 0.49 #130430), 0b5hj5 (0.52 #28984, 0.50 #4495, 0.49 #130430), 07wrz (0.52 #28984, 0.49 #130430, 0.49 #127532) >> Best rule #110138 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 01gh6z; >> query: (?x3818, ?x11811) <- contains(?x3818, ?x1964), capital(?x3818, ?x11811), country(?x1964, ?x94) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #211594 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 142 *> proper extension: 027rn; 04v3q; 09lxtg; 03gyl; 01ppq; *> query: (?x3818, ?x94) <- contains(?x3818, ?x13979), currency(?x3818, ?x170), contains(?x94, ?x13979) *> conf = 0.59 ranks of expected_values: 3, 7 EVAL 03v0t contains 0nv99 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 177.000 90.000 0.880 http://example.org/location/location/contains EVAL 03v0t contains 0ntwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 177.000 90.000 0.880 http://example.org/location/location/contains EVAL 03v0t contains 0nvd8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 177.000 90.000 0.880 http://example.org/location/location/contains #7484-018mxj PRED entity: 018mxj PRED relation: service_language PRED expected values: 04306rv 05qqm => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 17): 04306rv (0.58 #222, 0.38 #137, 0.38 #290), 03_9r (0.38 #71, 0.33 #3, 0.21 #1057), 05zjd (0.33 #26, 0.33 #9, 0.26 #230), 01r2l (0.33 #8, 0.21 #229, 0.17 #59), 06b_j (0.33 #7, 0.17 #58, 0.12 #75), 02hwhyv (0.33 #12, 0.17 #63, 0.12 #80), 01gp_d (0.33 #13, 0.17 #64, 0.12 #81), 05f_3 (0.33 #10, 0.17 #61, 0.12 #78), 06mp7 (0.33 #4, 0.17 #55, 0.12 #72), 03115z (0.21 #1057, 0.17 #1279, 0.16 #834) >> Best rule #222 for best value: >> intensional similarity = 10 >> extensional distance = 17 >> proper extension: 02slt7; 01m_zd; >> query: (?x896, 04306rv) <- service_location(?x896, ?x985), film_release_region(?x8682, ?x985), film_release_region(?x5220, ?x985), film_release_region(?x5092, ?x985), film_release_region(?x1035, ?x985), ?x1035 = 08hmch, ?x5092 = 0gg5qcw, ?x8682 = 0bmfnjs, exported_to(?x4164, ?x985), ?x5220 = 0kbf1 >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018mxj service_language 05qqm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 135.000 135.000 0.579 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 018mxj service_language 04306rv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.579 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #7483-0cvkv5 PRED entity: 0cvkv5 PRED relation: award PRED expected values: 09ly2r6 => 83 concepts (76 used for prediction) PRED predicted values (max 10 best out of 195): 054knh (0.60 #414, 0.47 #459, 0.43 #644), 0gqwc (0.47 #459, 0.42 #689, 0.42 #690), 02x1dht (0.47 #459, 0.42 #689, 0.42 #690), 09qwmm (0.47 #459, 0.42 #689, 0.42 #690), 099cng (0.47 #459, 0.42 #689, 0.42 #690), 0gq6s3 (0.47 #459, 0.42 #689, 0.42 #690), 09ly2r6 (0.47 #459, 0.42 #689, 0.42 #690), 0m7yy (0.29 #2877, 0.25 #1049, 0.19 #1965), 0fm3h2 (0.25 #217, 0.20 #447, 0.14 #677), 02pqp12 (0.20 #288, 0.17 #976, 0.14 #518) >> Best rule #414 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 04nl83; 06823p; 0gpx6; >> query: (?x8496, 054knh) <- nominated_for(?x11083, ?x8496), nominated_for(?x1245, ?x8496), ?x11083 = 0fms83, genre(?x8496, ?x53), ceremony(?x1245, ?x78), award_winner(?x1245, ?x396) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #459 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 04nl83; 06823p; 0gpx6; *> query: (?x8496, ?x533) <- nominated_for(?x11083, ?x8496), nominated_for(?x1245, ?x8496), nominated_for(?x533, ?x8496), ?x11083 = 0fms83, genre(?x8496, ?x53), ceremony(?x1245, ?x78), award_winner(?x1245, ?x396) *> conf = 0.47 ranks of expected_values: 7 EVAL 0cvkv5 award 09ly2r6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 83.000 76.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #7482-02z6l5f PRED entity: 02z6l5f PRED relation: award PRED expected values: 04g2jz2 => 128 concepts (101 used for prediction) PRED predicted values (max 10 best out of 313): 0fbtbt (0.44 #2670, 0.40 #10791, 0.38 #9979), 09sb52 (0.35 #853, 0.29 #24813, 0.25 #26437), 0cjyzs (0.35 #4574, 0.32 #11477, 0.31 #10664), 0gq9h (0.25 #9011, 0.22 #4951, 0.22 #13072), 0fc9js (0.25 #217, 0.14 #8744, 0.13 #1435), 0gkvb7 (0.25 #27, 0.12 #9366, 0.10 #10178), 02grdc (0.25 #32, 0.11 #6123, 0.06 #4093), 019bnn (0.25 #270, 0.10 #5549, 0.09 #4331), 047byns (0.25 #53, 0.07 #8580, 0.06 #5332), 040njc (0.24 #8941, 0.23 #1632, 0.21 #13002) >> Best rule #2670 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 03ktjq; 02779r4; 0697kh; >> query: (?x4857, 0fbtbt) <- producer_type(?x4857, ?x632), executive_produced_by(?x4329, ?x4857), genre(?x4329, ?x258), award_nominee(?x2803, ?x4857) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #10964 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 113 *> proper extension: 01r216; *> query: (?x4857, ?x68) <- producer_type(?x4857, ?x632), nominated_for(?x4857, ?x7982), award_winner(?x6238, ?x4857), nominated_for(?x68, ?x7982) *> conf = 0.17 ranks of expected_values: 26 EVAL 02z6l5f award 04g2jz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 128.000 101.000 0.436 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7481-03mfqm PRED entity: 03mfqm PRED relation: costume_design_by! PRED expected values: 09g7vfw 0c57yj => 89 concepts (25 used for prediction) PRED predicted values (max 10 best out of 179): 09d38d (0.11 #533, 0.10 #712, 0.08 #891), 0f42nz (0.07 #995, 0.04 #816), 04jn6y7 (0.06 #358, 0.05 #537, 0.05 #716), 015gm8 (0.06 #356, 0.05 #535, 0.05 #714), 01c9d (0.06 #355, 0.05 #534, 0.05 #713), 04fjzv (0.06 #353, 0.05 #532, 0.05 #711), 0k419 (0.06 #349, 0.05 #528, 0.05 #707), 0d6_s (0.06 #348, 0.05 #527, 0.05 #706), 0h0wd9 (0.06 #347, 0.05 #526, 0.05 #705), 01xlqd (0.06 #345, 0.05 #524, 0.05 #703) >> Best rule #533 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 03cp7b3; >> query: (?x6327, 09d38d) <- costume_design_by(?x708, ?x6327), nominated_for(?x484, ?x708), genre(?x708, ?x600), film_crew_role(?x708, ?x137) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03mfqm costume_design_by! 0c57yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 25.000 0.105 http://example.org/film/film/costume_design_by EVAL 03mfqm costume_design_by! 09g7vfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 25.000 0.105 http://example.org/film/film/costume_design_by #7480-026dg51 PRED entity: 026dg51 PRED relation: nominated_for PRED expected values: 01b66t => 72 concepts (31 used for prediction) PRED predicted values (max 10 best out of 161): 0358x_ (0.81 #8101, 0.80 #14583, 0.80 #8100), 034fl9 (0.80 #14583, 0.80 #8100, 0.80 #14582), 01b66t (0.65 #11340, 0.65 #6478, 0.65 #8099), 0phrl (0.23 #531, 0.07 #3769, 0.07 #5390), 02_1q9 (0.15 #54, 0.07 #3292, 0.07 #14637), 01b64v (0.07 #3657, 0.07 #5278, 0.07 #8520), 0828jw (0.06 #15494, 0.05 #17116, 0.05 #4149), 039cq4 (0.06 #2700, 0.06 #12424, 0.06 #10803), 07c72 (0.06 #2094, 0.05 #16681, 0.04 #19921), 01j7mr (0.05 #15131, 0.04 #3786, 0.04 #5407) >> Best rule #8101 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 02pbp9; >> query: (?x912, ?x1280) <- tv_program(?x912, ?x589), award_winner(?x1280, ?x912), honored_for(?x2751, ?x1280) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #11340 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 029_3; *> query: (?x912, ?x589) <- tv_program(?x912, ?x589), award_winner(?x1280, ?x912), award_nominee(?x438, ?x912) *> conf = 0.65 ranks of expected_values: 3 EVAL 026dg51 nominated_for 01b66t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 31.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #7479-0fphgb PRED entity: 0fphgb PRED relation: music PRED expected values: 03975z => 100 concepts (58 used for prediction) PRED predicted values (max 10 best out of 95): 02cyfz (0.20 #34, 0.09 #665, 0.03 #8478), 015wc0 (0.14 #387, 0.09 #597, 0.04 #2918), 016szr (0.14 #292, 0.09 #502, 0.04 #1768), 01x6v6 (0.14 #334, 0.03 #1387, 0.02 #1810), 04bpm6 (0.14 #237, 0.02 #1713, 0.02 #3192), 0146pg (0.13 #1274, 0.11 #4232, 0.11 #3389), 05y7hc (0.11 #970, 0.07 #1180, 0.03 #1390), 01x1fq (0.09 #806, 0.07 #1229, 0.05 #1019), 0150t6 (0.09 #677, 0.05 #1522, 0.05 #1944), 01m5m5b (0.09 #609, 0.02 #1875, 0.02 #5047) >> Best rule #34 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 033pf1; >> query: (?x3619, 02cyfz) <- currency(?x3619, ?x170), production_companies(?x3619, ?x902), film(?x4228, ?x3619), genre(?x3619, ?x258), ?x4228 = 01gy7r >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #3119 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 111 *> proper extension: 014zwb; *> query: (?x3619, 03975z) <- film(?x1774, ?x3619), film(?x3593, ?x3619), featured_film_locations(?x3619, ?x8811), language(?x3619, ?x254), film(?x3593, ?x1015) *> conf = 0.03 ranks of expected_values: 30 EVAL 0fphgb music 03975z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 100.000 58.000 0.200 http://example.org/film/film/music #7478-02_p8v PRED entity: 02_p8v PRED relation: influenced_by! PRED expected values: 0q5hw => 125 concepts (68 used for prediction) PRED predicted values (max 10 best out of 395): 0d4jl (0.21 #631, 0.09 #2173, 0.08 #2687), 0q5hw (0.14 #103, 0.12 #6687, 0.11 #13381), 07h1q (0.14 #922, 0.12 #1436, 0.07 #5551), 045bg (0.14 #550, 0.12 #1064, 0.06 #2092), 0dzkq (0.14 #640, 0.12 #1154, 0.06 #2182), 043tg (0.14 #841, 0.12 #1355, 0.06 #2383), 01hb6v (0.14 #608, 0.10 #7295, 0.07 #11413), 040db (0.14 #590, 0.09 #6248, 0.08 #7277), 0n6kf (0.14 #705, 0.06 #7392, 0.06 #2247), 0p8jf (0.14 #626, 0.06 #3712, 0.06 #2168) >> Best rule #631 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 08304; 047g6; >> query: (?x5188, 0d4jl) <- place_of_death(?x5188, ?x362), ?x362 = 04jpl, influenced_by(?x4066, ?x5188) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #103 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 05xd_v; *> query: (?x5188, 0q5hw) <- type_of_union(?x5188, ?x566), ?x566 = 04ztj, film(?x5188, ?x7881), ?x7881 = 01hq1 *> conf = 0.14 ranks of expected_values: 2 EVAL 02_p8v influenced_by! 0q5hw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 68.000 0.214 http://example.org/influence/influence_node/influenced_by #7477-02m501 PRED entity: 02m501 PRED relation: profession PRED expected values: 0dxtg => 130 concepts (129 used for prediction) PRED predicted values (max 10 best out of 125): 01d_h8 (0.52 #900, 0.49 #1496, 0.48 #304), 0dxtg (0.43 #3143, 0.38 #908, 0.36 #1504), 0gl2ny2 (0.40 #1707, 0.19 #1260, 0.18 #1111), 02jknp (0.35 #4323, 0.31 #1498, 0.28 #2094), 09jwl (0.35 #4323, 0.27 #4173, 0.27 #2254), 0nbcg (0.35 #4323, 0.27 #4173, 0.22 #2267), 0dz3r (0.35 #4323, 0.27 #4173, 0.18 #2237), 016z4k (0.35 #4323, 0.27 #4173, 0.17 #2239), 0n1h (0.35 #4323, 0.27 #4173, 0.12 #2247), 025352 (0.35 #4323, 0.27 #4173, 0.03 #9600) >> Best rule #900 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 014zfs; 015_30; 09b6zr; 01vxqyl; >> query: (?x9886, 01d_h8) <- award(?x9886, ?x1312), ?x1312 = 07cbcy, nationality(?x9886, ?x94) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #3143 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 315 *> proper extension: 01m15br; 01l03w2; 05sq0m; *> query: (?x9886, 0dxtg) <- award(?x9886, ?x1312), award(?x3917, ?x1312), ?x3917 = 0p_47 *> conf = 0.43 ranks of expected_values: 2 EVAL 02m501 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 129.000 0.516 http://example.org/people/person/profession #7476-0c0yh4 PRED entity: 0c0yh4 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.67 #79, 0.54 #1253, 0.53 #196), 0ch6mp2 (0.66 #87, 0.55 #1261, 0.54 #48), 02r96rf (0.59 #82, 0.54 #43, 0.47 #1256), 09vw2b7 (0.59 #86, 0.47 #1260, 0.46 #398), 01pvkk (0.34 #93, 0.20 #1854, 0.20 #405), 0dxtw (0.31 #91, 0.31 #169, 0.29 #1265), 01vx2h (0.28 #92, 0.23 #53, 0.22 #1266), 02ynfr (0.17 #97, 0.13 #175, 0.13 #1271), 01xy5l_ (0.11 #95, 0.08 #56, 0.08 #173), 0215hd (0.10 #100, 0.09 #2137, 0.09 #1274) >> Best rule #79 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 04zyhx; 064n1pz; 04nm0n0; 04yg13l; 0gs973; 0992d9; 02h22; 0bl3nn; 03bzjpm; >> query: (?x278, 09zzb8) <- titles(?x162, ?x278), film_release_distribution_medium(?x278, ?x81), country(?x278, ?x1264), ?x1264 = 0345h >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 101 *> proper extension: 04zyhx; 064n1pz; 04nm0n0; 04yg13l; 0gs973; 0992d9; 02h22; 0bl3nn; 03bzjpm; *> query: (?x278, 0ch6mp2) <- titles(?x162, ?x278), film_release_distribution_medium(?x278, ?x81), country(?x278, ?x1264), ?x1264 = 0345h *> conf = 0.66 ranks of expected_values: 2 EVAL 0c0yh4 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 92.000 0.670 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7475-07yg2 PRED entity: 07yg2 PRED relation: artists! PRED expected values: 016clz 06by7 0m0fw => 94 concepts (39 used for prediction) PRED predicted values (max 10 best out of 281): 06by7 (0.79 #8620, 0.79 #2474, 0.70 #3706), 016clz (0.65 #6450, 0.54 #2148, 0.52 #3072), 0xhtw (0.64 #2469, 0.58 #8615, 0.58 #4008), 0dl5d (0.57 #3088, 0.48 #4011, 0.43 #2472), 01fh36 (0.50 #1007, 0.29 #2540, 0.25 #701), 064t9 (0.41 #3390, 0.37 #3697, 0.36 #4312), 0155w (0.40 #1637, 0.37 #3790, 0.35 #8704), 0m0fw (0.40 #1287, 0.33 #369, 0.25 #675), 01ydtg (0.40 #1401, 0.33 #483, 0.25 #789), 06j6l (0.40 #1580, 0.31 #8341, 0.31 #8954) >> Best rule #8620 for best value: >> intensional similarity = 7 >> extensional distance = 82 >> proper extension: 015196; >> query: (?x4182, 06by7) <- artists(?x13686, ?x4182), artists(?x7329, ?x4182), artists(?x7083, ?x4182), artists(?x7329, ?x11446), parent_genre(?x13686, ?x497), ?x11446 = 016t00, ?x7083 = 02yv6b >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 8 EVAL 07yg2 artists! 0m0fw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 94.000 39.000 0.786 http://example.org/music/genre/artists EVAL 07yg2 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 39.000 0.786 http://example.org/music/genre/artists EVAL 07yg2 artists! 016clz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 39.000 0.786 http://example.org/music/genre/artists #7474-02pq9yv PRED entity: 02pq9yv PRED relation: produced_by! PRED expected values: 04jn6y7 => 114 concepts (21 used for prediction) PRED predicted values (max 10 best out of 693): 04gknr (0.33 #82, 0.06 #1020, 0.05 #1959), 01q2nx (0.33 #497, 0.04 #13142, 0.03 #2374), 04jpg2p (0.33 #773, 0.04 #4526, 0.03 #2650), 04pk1f (0.33 #575, 0.03 #2452, 0.03 #4328), 0ds11z (0.33 #40, 0.03 #1917, 0.03 #3793), 02vyyl8 (0.33 #528, 0.03 #2405, 0.01 #4281), 0yyts (0.33 #206, 0.03 #2083, 0.01 #3959), 050f0s (0.19 #1104, 0.01 #4857, 0.01 #16124), 08s6mr (0.12 #1644, 0.03 #2583, 0.03 #4459), 0gkz3nz (0.12 #1372, 0.03 #2311, 0.03 #4187) >> Best rule #82 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03v1w7; >> query: (?x3528, 04gknr) <- produced_by(?x3423, ?x3528), award_nominee(?x2689, ?x3528), ?x3423 = 09g7vfw, award(?x2689, ?x198) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02pq9yv produced_by! 04jn6y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 21.000 0.333 http://example.org/film/film/produced_by #7473-04h4c9 PRED entity: 04h4c9 PRED relation: language PRED expected values: 04306rv => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.88 #2707, 0.88 #1626, 0.88 #3365), 03_9r (0.26 #10, 0.09 #249, 0.07 #1393), 04306rv (0.19 #124, 0.18 #244, 0.13 #65), 064_8sq (0.18 #261, 0.14 #320, 0.13 #141), 0jzc (0.18 #80, 0.12 #139, 0.10 #259), 03k50 (0.15 #9, 0.09 #307, 0.02 #1332), 06b_j (0.12 #142, 0.08 #262, 0.07 #23), 06nm1 (0.11 #11, 0.10 #549, 0.10 #1151), 02bjrlw (0.08 #478, 0.08 #419, 0.08 #721), 0653m (0.08 #310, 0.04 #131, 0.04 #1395) >> Best rule #2707 for best value: >> intensional similarity = 4 >> extensional distance = 1369 >> proper extension: 05f67hw; >> query: (?x8670, 02h40lc) <- country(?x8670, ?x789), country(?x8670, ?x94), ?x94 = 09c7w0, currency(?x789, ?x170) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 050r1z; 03ckwzc; 02prw4h; 04kzqz; 01_1pv; 05dy7p; 083skw; 04t6fk; 016kv6; 0dx8gj; ... *> query: (?x8670, 04306rv) <- genre(?x8670, ?x3515), music(?x8670, ?x9891), titles(?x789, ?x8670), ?x3515 = 082gq *> conf = 0.19 ranks of expected_values: 3 EVAL 04h4c9 language 04306rv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 65.000 0.882 http://example.org/film/film/language #7472-027pfg PRED entity: 027pfg PRED relation: film_crew_role PRED expected values: 09zzb8 09vw2b7 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 23): 09zzb8 (0.75 #999, 0.71 #837, 0.71 #707), 09vw2b7 (0.67 #1004, 0.60 #712, 0.59 #842), 01vx2h (0.50 #331, 0.40 #107, 0.34 #75), 0dxtw (0.41 #330, 0.38 #1008, 0.35 #846), 01pvkk (0.35 #44, 0.30 #848, 0.29 #718), 02rh1dz (0.20 #329, 0.15 #73, 0.15 #137), 015h31 (0.18 #328, 0.09 #72, 0.08 #104), 02ynfr (0.18 #1013, 0.17 #143, 0.17 #15), 04pyp5 (0.13 #48, 0.08 #336, 0.06 #1014), 094hwz (0.10 #334, 0.04 #496, 0.04 #46) >> Best rule #999 for best value: >> intensional similarity = 4 >> extensional distance = 812 >> proper extension: 03_wm6; >> query: (?x6932, 09zzb8) <- film_crew_role(?x6932, ?x1284), ?x1284 = 0ch6mp2, country(?x6932, ?x94), genre(?x6932, ?x53) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 027pfg film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.746 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 027pfg film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.746 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7471-05fcbk7 PRED entity: 05fcbk7 PRED relation: currency PRED expected values: 09nqf => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.82 #57, 0.77 #50, 0.74 #120), 088n7 (0.03 #42, 0.03 #28), 01nv4h (0.03 #79, 0.02 #37, 0.02 #142), 02l6h (0.01 #102, 0.01 #109, 0.01 #158), 0kz1h (0.01 #40) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 229 >> proper extension: 0581vn8; >> query: (?x2847, 09nqf) <- country(?x2847, ?x94), nominated_for(?x3911, ?x2847), film_crew_role(?x2847, ?x2154), ?x2154 = 01vx2h >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fcbk7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.818 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #7470-06mkj PRED entity: 06mkj PRED relation: contains PRED expected values: 07q3s => 223 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2910): 035hm (0.33 #13098, 0.10 #83586, 0.07 #124700), 035v3 (0.33 #13921, 0.10 #46224, 0.07 #28606), 0b90_r (0.33 #11752, 0.10 #44055, 0.05 #284871), 05c74 (0.33 #12813, 0.10 #45116, 0.05 #42179), 0164b (0.33 #13290, 0.10 #45593, 0.05 #42656), 01p8s (0.33 #13199, 0.10 #45502, 0.05 #42565), 0345_ (0.33 #12376, 0.10 #44679, 0.05 #41742), 03h2c (0.33 #12207, 0.10 #44510, 0.05 #41573), 02vzc (0.33 #11971, 0.07 #82459, 0.07 #26656), 02kxx1 (0.25 #4921, 0.07 #28414, 0.06 #31350) >> Best rule #13098 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 07c5l; 04pnx; >> query: (?x2152, 035hm) <- contains(?x2152, ?x4698), film_release_region(?x3638, ?x4698), ?x3638 = 04vvh9 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06mkj contains 07q3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 223.000 115.000 0.333 http://example.org/location/location/contains #7469-026m9w PRED entity: 026m9w PRED relation: award! PRED expected values: 0x3b7 => 45 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2855): 0dzlk (0.77 #43812, 0.76 #43811, 0.25 #3117), 03yf3z (0.77 #43812, 0.76 #43811, 0.22 #4074), 01vs_v8 (0.62 #582, 0.56 #3951, 0.50 #7321), 0lbj1 (0.50 #6783, 0.50 #44, 0.44 #3413), 03j24kf (0.50 #8102, 0.50 #1363, 0.44 #4732), 01vrz41 (0.50 #7033, 0.50 #294, 0.44 #3663), 04xrx (0.50 #7438, 0.50 #699, 0.44 #4068), 0dw4g (0.50 #8375, 0.50 #1636, 0.44 #5005), 01vsgrn (0.50 #1630, 0.44 #4999, 0.42 #8369), 01wwvc5 (0.50 #737, 0.44 #4106, 0.42 #7476) >> Best rule #43812 for best value: >> intensional similarity = 4 >> extensional distance = 147 >> proper extension: 02qkk9_; >> query: (?x7691, ?x3122) <- ceremony(?x7691, ?x3121), award_winner(?x7691, ?x3122), award_winner(?x3121, ?x226), artists(?x2664, ?x3122) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #7929 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0c4z8; 01c427; 054ks3; *> query: (?x7691, 0x3b7) <- ceremony(?x7691, ?x5766), award(?x4080, ?x7691), ?x4080 = 0dl567, award_winner(?x5766, ?x352) *> conf = 0.33 ranks of expected_values: 53 EVAL 026m9w award! 0x3b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 45.000 16.000 0.773 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7468-03jsvl PRED entity: 03jsvl PRED relation: artists PRED expected values: 018y2s 04r1t 01c8v0 => 69 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1178): 01vw20_ (0.67 #5571, 0.67 #4506, 0.56 #8773), 04r1t (0.67 #5462, 0.50 #4397, 0.50 #3331), 01kph_c (0.58 #11085, 0.56 #7886, 0.55 #10018), 013rfk (0.57 #7106, 0.33 #6040, 0.33 #711), 0fsyx (0.57 #7443, 0.33 #1048, 0.25 #4246), 07mvp (0.56 #9110, 0.50 #5908, 0.50 #3777), 01kx_81 (0.56 #8615, 0.50 #3282, 0.50 #2216), 09889g (0.55 #12175, 0.29 #13243, 0.17 #7461), 01vrncs (0.55 #9662, 0.50 #10729, 0.44 #7530), 0gr69 (0.50 #4898, 0.50 #3832, 0.50 #1700) >> Best rule #5571 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0xhtw; >> query: (?x10318, 01vw20_) <- artists(?x10318, ?x7238), artists(?x10318, ?x5385), award_nominee(?x483, ?x7238), instrumentalists(?x227, ?x7238), special_performance_type(?x7238, ?x4832), ?x5385 = 0134tg, ?x4832 = 01pb34 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5462 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 0xhtw; *> query: (?x10318, 04r1t) <- artists(?x10318, ?x7238), artists(?x10318, ?x5385), award_nominee(?x483, ?x7238), instrumentalists(?x227, ?x7238), special_performance_type(?x7238, ?x4832), ?x5385 = 0134tg, ?x4832 = 01pb34 *> conf = 0.67 ranks of expected_values: 2, 45, 92 EVAL 03jsvl artists 01c8v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 69.000 31.000 0.667 http://example.org/music/genre/artists EVAL 03jsvl artists 04r1t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 31.000 0.667 http://example.org/music/genre/artists EVAL 03jsvl artists 018y2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 69.000 31.000 0.667 http://example.org/music/genre/artists #7467-0tygl PRED entity: 0tygl PRED relation: origin! PRED expected values: 018pj3 => 123 concepts (108 used for prediction) PRED predicted values (max 10 best out of 252): 018pj3 (0.26 #3104, 0.25 #8279, 0.07 #30013), 04n2vgk (0.25 #413, 0.04 #1964, 0.04 #2999), 0g824 (0.25 #277, 0.04 #794, 0.02 #1310), 01wf86y (0.25 #331, 0.04 #848, 0.02 #1364), 01vvyc_ (0.25 #247, 0.04 #764, 0.02 #1280), 01wdqrx (0.25 #35, 0.04 #552, 0.02 #1068), 05crg7 (0.25 #50, 0.04 #2636, 0.02 #1083), 0892sx (0.25 #97, 0.02 #1648, 0.02 #2683), 06lxn (0.25 #515, 0.02 #2066, 0.02 #3101), 011xhx (0.25 #508, 0.02 #2059, 0.02 #3094) >> Best rule #3104 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 0r540; 0rhp6; 0v1xg; >> query: (?x6295, ?x2575) <- place_of_birth(?x2575, ?x6295), contains(?x94, ?x6295), ?x94 = 09c7w0, role(?x2575, ?x614) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tygl origin! 018pj3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 108.000 0.256 http://example.org/music/artist/origin #7466-03f5mt PRED entity: 03f5mt PRED relation: role! PRED expected values: 0l14qv => 48 concepts (42 used for prediction) PRED predicted values (max 10 best out of 109): 0mkg (0.82 #1506, 0.81 #3420, 0.81 #3731), 0l14qv (0.75 #2006, 0.74 #794, 0.74 #1704), 0l14j_ (0.74 #794, 0.72 #1355, 0.72 #2055), 02hnl (0.74 #794, 0.72 #1334, 0.68 #3008), 0dwt5 (0.74 #794, 0.70 #1078, 0.70 #1677), 03f5mt (0.74 #794, 0.67 #1700, 0.67 #1401), 01vdm0 (0.74 #794, 0.62 #1301, 0.61 #1601), 06ch55 (0.74 #794, 0.62 #1301, 0.59 #3111), 0bxl5 (0.74 #1660, 0.72 #1361, 0.69 #1862), 01s0ps (0.71 #596, 0.67 #545, 0.61 #1601) >> Best rule #1506 for best value: >> intensional similarity = 13 >> extensional distance = 20 >> proper extension: 02sgy; 0l14md; 042v_gx; 018vs; 0dwtp; 013y1f; 04rzd; 06ncr; 0jtg0; 03qjg; ... >> query: (?x8957, 0mkg) <- role(?x745, ?x8957), instrumentalists(?x8957, ?x8978), instrumentalists(?x8957, ?x2698), award_winner(?x4012, ?x2698), profession(?x8978, ?x131), artist(?x2931, ?x2698), award_winner(?x2698, ?x217), gender(?x8978, ?x231), ?x2931 = 03rhqg, artists(?x302, ?x8978), ceremony(?x4012, ?x139), role(?x2698, ?x228), ?x745 = 01vj9c >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #2006 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 30 *> proper extension: 0l14v3; *> query: (?x8957, 0l14qv) <- role(?x716, ?x8957), role(?x316, ?x8957), role(?x75, ?x8957), ?x716 = 018vs, ?x75 = 07y_7, role(?x9321, ?x316), role(?x316, ?x4311), ?x9321 = 0140t7, role(?x74, ?x316), group(?x316, ?x997), ?x4311 = 01xqw, role(?x642, ?x316), instrumentalists(?x316, ?x2987), ?x2987 = 01vw20_ *> conf = 0.75 ranks of expected_values: 2 EVAL 03f5mt role! 0l14qv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 48.000 42.000 0.818 http://example.org/music/performance_role/regular_performances./music/group_membership/role #7465-04g7x PRED entity: 04g7x PRED relation: student PRED expected values: 0b78hw => 68 concepts (41 used for prediction) PRED predicted values (max 10 best out of 293): 031v3p (0.33 #712, 0.25 #952, 0.14 #2154), 02kxbwx (0.33 #492, 0.25 #732, 0.14 #1934), 01dvtx (0.33 #566, 0.25 #806, 0.14 #2008), 099bk (0.33 #565, 0.25 #805, 0.14 #2007), 04jzj (0.33 #500, 0.25 #740, 0.14 #1942), 0kn4c (0.33 #26, 0.17 #3390, 0.17 #2427), 01zwy (0.33 #411, 0.14 #2093, 0.14 #1853), 01tdnyh (0.33 #355, 0.14 #2037, 0.14 #1797), 059y0 (0.33 #454, 0.14 #2136, 0.14 #1896), 0jcx (0.33 #305, 0.14 #1987, 0.14 #1747) >> Best rule #712 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 037mh8; >> query: (?x8962, 031v3p) <- major_field_of_study(?x734, ?x8962), major_field_of_study(?x12877, ?x8962), major_field_of_study(?x11583, ?x8962), major_field_of_study(?x6894, ?x8962), major_field_of_study(?x5288, ?x8962), major_field_of_study(?x3439, ?x8962), major_field_of_study(?x1665, ?x8962), major_field_of_study(?x122, ?x8962), ?x122 = 08815, taxonomy(?x8962, ?x939), ?x3439 = 03ksy, ?x1665 = 04rwx, ?x6894 = 0cwx_, ?x5288 = 02zd460, category(?x12877, ?x134), list(?x11583, ?x2197), student(?x11583, ?x7264) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5777 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 26 *> proper extension: 01zc2w; 01r4k; *> query: (?x8962, ?x123) <- major_field_of_study(?x1368, ?x8962), major_field_of_study(?x122, ?x8962), ?x122 = 08815, institution(?x1368, ?x11185), institution(?x1368, ?x9110), institution(?x1368, ?x8216), institution(?x1368, ?x4344), institution(?x1368, ?x4199), ?x4344 = 03x83_, ?x8216 = 01rgn3, student(?x1368, ?x123), ?x4199 = 016ndm, ?x11185 = 01n4w_, ?x9110 = 07tjf *> conf = 0.02 ranks of expected_values: 245 EVAL 04g7x student 0b78hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 68.000 41.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #7464-09gmmt6 PRED entity: 09gmmt6 PRED relation: film! PRED expected values: 054lpb6 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 95): 018tnx (0.45 #3261, 0.02 #3550), 086k8 (0.22 #74, 0.18 #1517, 0.17 #2826), 017s11 (0.22 #75, 0.16 #580, 0.15 #1156), 03xq0f (0.20 #149, 0.19 #942, 0.18 #437), 016tw3 (0.18 #803, 0.16 #1816, 0.15 #3196), 016tt2 (0.15 #2828, 0.15 #1519, 0.14 #436), 01795t (0.15 #233, 0.15 #377, 0.14 #1458), 024rdh (0.13 #324, 0.13 #180, 0.10 #901), 01gb54 (0.12 #28, 0.11 #100, 0.08 #1761), 04f525m (0.12 #9, 0.11 #81, 0.07 #225) >> Best rule #3261 for best value: >> intensional similarity = 4 >> extensional distance = 788 >> proper extension: 016z43; >> query: (?x6536, ?x13838) <- titles(?x512, ?x6536), language(?x6536, ?x254), genre(?x6536, ?x53), production_companies(?x6536, ?x13838) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #3550 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 891 *> proper extension: 02bqvs; *> query: (?x6536, ?x382) <- film(?x902, ?x6536), music(?x6536, ?x4727), genre(?x6536, ?x1510), genre(?x7672, ?x1510), production_companies(?x7672, ?x382) *> conf = 0.02 ranks of expected_values: 48 EVAL 09gmmt6 film! 054lpb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 93.000 93.000 0.447 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7463-0f1vrl PRED entity: 0f1vrl PRED relation: gender PRED expected values: 05zppz => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #41, 0.85 #57, 0.84 #53), 02zsn (0.49 #291, 0.29 #144, 0.29 #142) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 04n7njg; 06brp0; 02_2v2; 01r216; 01wyy_; 026_dcw; 01y0y6; 01d8yn; 08n__5; 09pl3f; ... >> query: (?x1798, 05zppz) <- program(?x1798, ?x50), program_creator(?x12886, ?x1798), place_of_birth(?x1798, ?x10321), type_of_union(?x1798, ?x566) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f1vrl gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 179.000 0.851 http://example.org/people/person/gender #7462-0lzb8 PRED entity: 0lzb8 PRED relation: profession PRED expected values: 0kyk => 106 concepts (81 used for prediction) PRED predicted values (max 10 best out of 90): 03gjzk (0.80 #2220, 0.80 #1191, 0.72 #603), 0dxtg (0.70 #2219, 0.62 #1190, 0.60 #602), 01d_h8 (0.64 #9860, 0.61 #1182, 0.56 #2211), 0np9r (0.62 #168, 0.60 #315, 0.60 #21), 02jknp (0.52 #3095, 0.44 #9862, 0.36 #1625), 018gz8 (0.39 #1193, 0.32 #605, 0.31 #752), 09jwl (0.38 #6196, 0.37 #5018, 0.37 #8107), 0kyk (0.30 #470, 0.28 #764, 0.20 #2381), 016z4k (0.27 #5003, 0.27 #6181, 0.25 #4267), 0nbcg (0.26 #6208, 0.26 #4294, 0.26 #6355) >> Best rule #2220 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 01r216; >> query: (?x593, 03gjzk) <- student(?x735, ?x593), producer_type(?x593, ?x632), ?x632 = 0ckd1, award(?x593, ?x435) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #470 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 03ldxq; 05yjhm; 026_dq6; 019n7x; *> query: (?x593, 0kyk) <- student(?x735, ?x593), profession(?x593, ?x4725), category(?x593, ?x134), ?x4725 = 015cjr *> conf = 0.30 ranks of expected_values: 8 EVAL 0lzb8 profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 106.000 81.000 0.799 http://example.org/people/person/profession #7461-019dwp PRED entity: 019dwp PRED relation: student PRED expected values: 01nrq5 => 157 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1138): 01h1b (0.25 #1191, 0.17 #3283, 0.12 #5375), 06bss (0.25 #1168, 0.17 #3260, 0.12 #5352), 0147jt (0.25 #1573, 0.17 #3665, 0.12 #5757), 02bfmn (0.25 #21, 0.17 #2113, 0.12 #4205), 04vlh5 (0.09 #8092, 0.08 #10184, 0.06 #14368), 015qq1 (0.09 #8169, 0.08 #10261, 0.06 #14445), 02pv_d (0.09 #7673, 0.08 #9765, 0.06 #13949), 03l3ln (0.09 #7433, 0.08 #9525, 0.06 #13709), 05xd_v (0.09 #8102, 0.08 #10194, 0.06 #14378), 0fwy0h (0.09 #7119, 0.06 #13395, 0.06 #11303) >> Best rule #1191 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0fw2f; >> query: (?x4916, 01h1b) <- contains(?x5775, ?x4916), contains(?x2977, ?x4916), contains(?x94, ?x4916), ?x2977 = 081mh, ?x94 = 09c7w0, source(?x5775, ?x958) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #6277 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0mlm_; *> query: (?x4916, ?x1222) <- contains(?x2977, ?x4916), contains(?x94, ?x4916), ?x2977 = 081mh, location(?x1222, ?x94) *> conf = 0.03 ranks of expected_values: 660 EVAL 019dwp student 01nrq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 157.000 79.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #7460-0f__1 PRED entity: 0f__1 PRED relation: category PRED expected values: 08mbj5d => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #19, 0.82 #23, 0.81 #78) >> Best rule #19 for best value: >> intensional similarity = 2 >> extensional distance = 58 >> proper extension: 0qlrh; >> query: (?x2740, 08mbj5d) <- county(?x2740, ?x10845), place_of_death(?x4475, ?x2740) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f__1 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.850 http://example.org/common/topic/webpage./common/webpage/category #7459-01k165 PRED entity: 01k165 PRED relation: entity_involved! PRED expected values: 0gfhg1y => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 32): 0d06vc (0.33 #532, 0.25 #268, 0.17 #995), 0jnh (0.25 #170, 0.08 #1161, 0.03 #3405), 09r3f (0.25 #177, 0.03 #3412, 0.02 #3742), 086m1 (0.18 #1605, 0.12 #1473, 0.12 #681), 0cbvg (0.17 #557, 0.15 #1152, 0.06 #1548), 0cwt70 (0.17 #438, 0.10 #835, 0.08 #1099), 0cm2xh (0.17 #407, 0.10 #804, 0.08 #1068), 018w0j (0.17 #1027, 0.10 #2017, 0.09 #2083), 01_3rn (0.17 #559, 0.08 #1154, 0.06 #1550), 0py8j (0.17 #541, 0.08 #1136, 0.06 #1532) >> Best rule #532 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 012bk; >> query: (?x3099, 0d06vc) <- basic_title(?x3099, ?x182), jurisdiction_of_office(?x3099, ?x279), profession(?x3099, ?x5805), religion(?x3099, ?x9091), type_of_union(?x3099, ?x566), ?x182 = 060bp >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #834 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 01tdnyh; 01mr2g6; 01zwy; 06g4_; *> query: (?x3099, 0gfhg1y) <- type_of_union(?x3099, ?x566), student(?x2327, ?x3099), profession(?x3099, ?x10210), profession(?x12441, ?x10210), student(?x2981, ?x3099), ?x12441 = 0tfc *> conf = 0.10 ranks of expected_values: 18 EVAL 01k165 entity_involved! 0gfhg1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 182.000 182.000 0.333 http://example.org/base/culturalevent/event/entity_involved #7458-0c422z4 PRED entity: 0c422z4 PRED relation: nominated_for PRED expected values: 0ds3t5x 09k56b7 => 40 concepts (9 used for prediction) PRED predicted values (max 10 best out of 1484): 0ds35l9 (0.80 #3171, 0.31 #1592, 0.14 #6), 0b6tzs (0.62 #3299, 0.29 #128, 0.19 #1714), 026p4q7 (0.62 #3525, 0.25 #1940, 0.21 #354), 017gl1 (0.62 #3302, 0.21 #131, 0.18 #8057), 011yqc (0.62 #3379, 0.19 #1794, 0.15 #8134), 0gmgwnv (0.59 #4128, 0.50 #957, 0.25 #2543), 05hjnw (0.59 #3933, 0.43 #762, 0.25 #2348), 07s846j (0.59 #3772, 0.36 #601, 0.16 #5356), 09gq0x5 (0.56 #3424, 0.50 #253, 0.31 #1839), 05c46y6 (0.56 #1977, 0.34 #3562, 0.18 #5146) >> Best rule #3171 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 05b4l5x; 09qwmm; 09sb52; 094qd5; 099tbz; 0gqwc; 05pcn59; 0gqyl; 05p09zm; 02x4x18; ... >> query: (?x2597, ?x86) <- award(?x4295, ?x2597), nominated_for(?x2597, ?x186), ?x4295 = 09l3p, award(?x86, ?x2597) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1633 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 05b4l5x; 09qwmm; 09sb52; 094qd5; 099tbz; 0gqwc; 05pcn59; 0gqyl; 05p09zm; 02x4x18; ... *> query: (?x2597, 0ds3t5x) <- award(?x4295, ?x2597), nominated_for(?x2597, ?x186), ?x4295 = 09l3p, award(?x86, ?x2597) *> conf = 0.44 ranks of expected_values: 36, 57 EVAL 0c422z4 nominated_for 09k56b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 40.000 9.000 0.805 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0c422z4 nominated_for 0ds3t5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 40.000 9.000 0.805 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7457-04954 PRED entity: 04954 PRED relation: award_winner! PRED expected values: 05zksls 09k5jh7 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 104): 09q_6t (0.33 #8, 0.02 #988, 0.02 #2528), 0418154 (0.17 #108, 0.04 #248, 0.04 #388), 073h1t (0.17 #27, 0.04 #307, 0.01 #5207), 09p3h7 (0.17 #71, 0.03 #1051, 0.02 #2731), 0bxs_d (0.17 #115, 0.02 #2775, 0.01 #5295), 0hn821n (0.17 #130, 0.01 #5310, 0.01 #3770), 059x66 (0.17 #18, 0.01 #5198), 0gpjbt (0.14 #449, 0.12 #589, 0.09 #729), 01s695 (0.12 #423, 0.09 #563, 0.09 #703), 02cg41 (0.12 #545, 0.09 #685, 0.08 #1945) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0bxtg; 017149; 01s7zw; 02ldv0; >> query: (?x7530, 09q_6t) <- award_winner(?x7530, ?x526), profession(?x7530, ?x1032), ?x526 = 05hj0n >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1015 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 217 *> proper extension: 01k5zk; 0b80__; 01tnbn; 03d1y3; 01g969; *> query: (?x7530, 05zksls) <- award_nominee(?x7530, ?x496), nationality(?x7530, ?x94), spouse(?x7530, ?x8113) *> conf = 0.02 ranks of expected_values: 54, 67 EVAL 04954 award_winner! 09k5jh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 93.000 93.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 04954 award_winner! 05zksls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 93.000 93.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7456-02s529 PRED entity: 02s529 PRED relation: student! PRED expected values: 02zd2b => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 60): 0gl5_ (0.17 #244, 0.03 #771, 0.01 #25018), 0fr9jp (0.17 #345, 0.02 #2453, 0.02 #1399), 07tgn (0.12 #544, 0.02 #12139, 0.02 #13720), 07tg4 (0.06 #613, 0.02 #5356, 0.02 #4302), 015ln1 (0.06 #724), 0bwfn (0.06 #1856, 0.06 #6072, 0.06 #10816), 015nl4 (0.05 #3229, 0.03 #21152, 0.03 #14826), 09f2j (0.04 #1213, 0.04 #6483, 0.03 #2267), 017z88 (0.03 #4825, 0.03 #6406, 0.03 #3771), 03ksy (0.03 #633, 0.03 #7484, 0.03 #18554) >> Best rule #244 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01r7t9; 01zz8t; >> query: (?x12282, 0gl5_) <- gender(?x12282, ?x231), film(?x12282, ?x4460), ?x4460 = 0yxm1 >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02s529 student! 02zd2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 102.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #7455-018grr PRED entity: 018grr PRED relation: celebrities_impersonated PRED expected values: 015_30 => 94 concepts (52 used for prediction) PRED predicted values (max 10 best out of 123): 0157m (0.38 #14, 0.01 #1492), 0f502 (0.25 #38, 0.01 #284, 0.01 #1392), 06c0j (0.25 #114), 01m42d0 (0.25 #77), 044qx (0.25 #36), 0ph2w (0.25 #35), 02_fj (0.25 #29), 0tc7 (0.25 #22), 0chsq (0.25 #4), 09fb5 (0.25 #2) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 03m6t5; 04s430; >> query: (?x2101, 0157m) <- profession(?x2101, ?x319), location(?x2101, ?x578), celebrities_impersonated(?x2101, ?x4196) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 03m6t5; 04s430; *> query: (?x2101, 015_30) <- profession(?x2101, ?x319), location(?x2101, ?x578), celebrities_impersonated(?x2101, ?x4196) *> conf = 0.12 ranks of expected_values: 112 EVAL 018grr celebrities_impersonated 015_30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 94.000 52.000 0.375 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #7454-03dq9 PRED entity: 03dq9 PRED relation: student! PRED expected values: 01k8q5 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 65): 015nl4 (0.10 #3749, 0.09 #4275, 0.05 #9536), 0bwfn (0.08 #274, 0.08 #11321, 0.08 #11847), 065y4w7 (0.06 #14, 0.05 #1592, 0.05 #1066), 01w5m (0.05 #630, 0.05 #104, 0.05 #1156), 03ksy (0.04 #4839, 0.04 #1683, 0.04 #25354), 09f2j (0.04 #158, 0.04 #10153, 0.04 #9627), 0m4yg (0.04 #4046, 0.03 #4572, 0.02 #9833), 017z88 (0.04 #11129, 0.04 #10077, 0.04 #11655), 07tgn (0.04 #4225, 0.04 #3699, 0.02 #1595), 08815 (0.03 #9471, 0.03 #9997, 0.03 #8945) >> Best rule #3749 for best value: >> intensional similarity = 3 >> extensional distance = 281 >> proper extension: 06p0s1; >> query: (?x10394, 015nl4) <- award(?x10394, ?x10747), nationality(?x10394, ?x1310), ?x1310 = 02jx1 >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03dq9 student! 01k8q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 80.000 0.095 http://example.org/education/educational_institution/students_graduates./education/education/student #7453-04fzfj PRED entity: 04fzfj PRED relation: film_crew_role PRED expected values: 02rh1dz => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 22): 01pvkk (0.35 #265, 0.30 #1110, 0.30 #1142), 0215hd (0.19 #207, 0.18 #303, 0.15 #335), 0d2b38 (0.18 #214, 0.17 #310, 0.14 #407), 02rh1dz (0.17 #489, 0.17 #264, 0.17 #136), 089g0h (0.17 #208, 0.14 #304, 0.14 #401), 015h31 (0.17 #263, 0.13 #488, 0.12 #585), 01xy5l_ (0.15 #203, 0.14 #331, 0.14 #299), 02_n3z (0.13 #193, 0.13 #289, 0.11 #321), 089fss (0.08 #133, 0.08 #293, 0.08 #325), 033smt (0.08 #312, 0.07 #344, 0.06 #280) >> Best rule #265 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 0gtvrv3; >> query: (?x723, 01pvkk) <- film(?x1689, ?x723), story_by(?x723, ?x1799), film_crew_role(?x723, ?x137), language(?x723, ?x254) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #489 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 222 *> proper extension: 0k2m6; 09rfpk; *> query: (?x723, 02rh1dz) <- genre(?x723, ?x225), story_by(?x723, ?x1799), language(?x723, ?x254), film_crew_role(?x723, ?x137) *> conf = 0.17 ranks of expected_values: 4 EVAL 04fzfj film_crew_role 02rh1dz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 109.000 0.348 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7452-01w0yrc PRED entity: 01w0yrc PRED relation: award PRED expected values: 09qv3c => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 261): 0cjyzs (0.67 #105, 0.12 #5747, 0.08 #3329), 09sb52 (0.33 #6084, 0.32 #15756, 0.32 #7696), 0ck27z (0.31 #5330, 0.26 #6942, 0.21 #8957), 03ccq3s (0.22 #198, 0.14 #24584, 0.04 #5840), 027gs1_ (0.22 #283, 0.14 #24584, 0.03 #3507), 0bdw6t (0.20 #915, 0.05 #6960, 0.05 #5348), 01by1l (0.19 #1320, 0.19 #3335, 0.18 #2126), 0bfvd4 (0.18 #920, 0.07 #6965, 0.07 #13010), 0gqy2 (0.17 #970, 0.12 #6209, 0.12 #13060), 01bgqh (0.16 #3265, 0.10 #1250, 0.10 #2056) >> Best rule #105 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 09gffmz; 06yrj6; >> query: (?x10153, 0cjyzs) <- award_winner(?x10153, ?x2817), award_winner(?x6600, ?x10153), ?x2817 = 0q5hw >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #34257 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2183 *> proper extension: 0fb0v; *> query: (?x10153, ?x678) <- award_nominee(?x4816, ?x10153), award_winner(?x678, ?x4816) *> conf = 0.15 ranks of expected_values: 15 EVAL 01w0yrc award 09qv3c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 110.000 110.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7451-0h2zvzr PRED entity: 0h2zvzr PRED relation: film! PRED expected values: 016tt2 => 110 concepts (68 used for prediction) PRED predicted values (max 10 best out of 58): 054lpb6 (0.48 #4224, 0.47 #4832, 0.46 #3392), 016tw3 (0.25 #11, 0.21 #913, 0.18 #3931), 017s11 (0.25 #3, 0.19 #1132, 0.18 #1587), 086k8 (0.23 #1283, 0.20 #2339, 0.20 #2639), 03xq0f (0.16 #532, 0.15 #757, 0.14 #1740), 025jfl (0.15 #232, 0.14 #307, 0.08 #457), 061dn_ (0.15 #174, 0.14 #400, 0.12 #99), 05qd_ (0.15 #2271, 0.14 #1970, 0.13 #2873), 016tt2 (0.14 #2341, 0.12 #4, 0.12 #3924), 01795t (0.14 #770, 0.12 #18, 0.10 #545) >> Best rule #4224 for best value: >> intensional similarity = 4 >> extensional distance = 582 >> proper extension: 0yx7h; 01zfzb; >> query: (?x8381, ?x1478) <- production_companies(?x8381, ?x1478), film(?x8380, ?x8381), award_winner(?x8381, ?x2934), gender(?x8380, ?x514) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2341 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 204 *> proper extension: 02n9bh; *> query: (?x8381, 016tt2) <- story_by(?x8381, ?x2934), country(?x8381, ?x512), genre(?x8381, ?x53), award(?x2934, ?x8842) *> conf = 0.14 ranks of expected_values: 9 EVAL 0h2zvzr film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 110.000 68.000 0.476 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7450-0gbwp PRED entity: 0gbwp PRED relation: gender PRED expected values: 02zsn => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #53, 0.80 #51, 0.79 #19), 02zsn (0.47 #16, 0.47 #18, 0.46 #48) >> Best rule #53 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 0frmb1; 0cl_m; >> query: (?x3997, 05zppz) <- nationality(?x3997, ?x94), ?x94 = 09c7w0, company(?x3997, ?x8489) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 01vvydl; 0lbj1; 01vvycq; 01w61th; 01vrt_c; 01vrz41; 01v_pj6; 0j1yf; 0136p1; 07ss8_; ... *> query: (?x3997, 02zsn) <- award(?x3997, ?x3488), ?x3488 = 02f71y, award_nominee(?x2732, ?x3997) *> conf = 0.47 ranks of expected_values: 2 EVAL 0gbwp gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 120.000 0.812 http://example.org/people/person/gender #7449-01cx_ PRED entity: 01cx_ PRED relation: featured_film_locations! PRED expected values: 04vr_f => 194 concepts (150 used for prediction) PRED predicted values (max 10 best out of 819): 04dsnp (0.33 #65, 0.16 #6657, 0.15 #5192), 05pxnmb (0.33 #565, 0.06 #7889, 0.06 #7157), 0473rc (0.15 #5576, 0.08 #18021, 0.07 #26806), 061681 (0.14 #1511, 0.12 #6639, 0.10 #8103), 03k8th (0.14 #2164, 0.10 #6559, 0.05 #11684), 02yvct (0.14 #1617, 0.08 #5280, 0.06 #7477), 0m9p3 (0.14 #1632, 0.05 #31650, 0.04 #4563), 03ydlnj (0.14 #2048, 0.04 #4979, 0.04 #5711), 04pmnt (0.14 #1918, 0.04 #4849, 0.04 #5581), 01v1ln (0.14 #1975, 0.04 #4906, 0.03 #6370) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02cl1; >> query: (?x3052, 04dsnp) <- location(?x5097, ?x3052), location(?x2390, ?x3052), ?x2390 = 01_x6v, award_nominee(?x5097, ?x157) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #99574 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 289 *> proper extension: 022_6; 0dbdy; 020skc; 094vy; 0dm0f; *> query: (?x3052, ?x2207) <- location(?x5488, ?x3052), contains(?x3052, ?x1151), film(?x5488, ?x2207) *> conf = 0.03 ranks of expected_values: 675 EVAL 01cx_ featured_film_locations! 04vr_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 194.000 150.000 0.333 http://example.org/film/film/featured_film_locations #7448-03w9bjf PRED entity: 03w9bjf PRED relation: people PRED expected values: 016k6x => 27 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2819): 0197tq (0.50 #1747, 0.33 #21, 0.25 #5209), 03vrnh (0.35 #5185, 0.34 #8645, 0.33 #7960), 08d6bd (0.35 #5185, 0.34 #8645, 0.33 #7826), 047jhq (0.35 #5185, 0.34 #8645, 0.33 #8591), 04v7k2 (0.35 #5185, 0.34 #8645, 0.31 #10374), 02n1p5 (0.35 #5185, 0.34 #8645, 0.31 #10374), 05g3ss (0.35 #5185, 0.34 #8645, 0.31 #10374), 06gn7r (0.35 #5185, 0.34 #8645, 0.31 #10374), 02xgdv (0.35 #5185, 0.34 #8645, 0.31 #10374), 02qvhbb (0.35 #5185, 0.34 #8645, 0.31 #10374) >> Best rule #1747 for best value: >> intensional similarity = 16 >> extensional distance = 2 >> proper extension: 0bpjh3; >> query: (?x11665, 0197tq) <- languages_spoken(?x11665, ?x11341), languages_spoken(?x11665, ?x254), ?x11341 = 01c7y, language(?x10173, ?x254), language(?x7305, ?x254), language(?x6200, ?x254), language(?x6148, ?x254), languages(?x12004, ?x254), languages(?x1735, ?x254), nominated_for(?x3879, ?x10173), film(?x981, ?x7305), nominated_for(?x601, ?x6200), location(?x1735, ?x1523), participant(?x521, ?x1735), genre(?x6148, ?x53), people(?x5741, ?x12004) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4163 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 0dryh9k; *> query: (?x11665, 016k6x) <- languages_spoken(?x11665, ?x5121), languages_spoken(?x11665, ?x1882), ?x1882 = 03k50, languages(?x12675, ?x5121), languages(?x11285, ?x5121), languages(?x9994, ?x5121), languages(?x9139, ?x5121), languages(?x5120, ?x5121), profession(?x5120, ?x319), people(?x11665, ?x57), ?x9994 = 0kst7v, ?x9139 = 05nqq3, people(?x13008, ?x11285), ?x12675 = 040nwr *> conf = 0.25 ranks of expected_values: 62 EVAL 03w9bjf people 016k6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 27.000 15.000 0.500 http://example.org/people/ethnicity/people #7447-014ck4 PRED entity: 014ck4 PRED relation: adjoins PRED expected values: 0jk_8 => 179 concepts (65 used for prediction) PRED predicted values (max 10 best out of 466): 0jk_8 (0.86 #21684, 0.85 #20131, 0.85 #37198), 01914 (0.29 #4653, 0.25 #9290, 0.25 #8524), 0df4y (0.23 #24013, 0.23 #24789, 0.21 #24790), 0kzcv (0.23 #24013, 0.21 #24790, 0.21 #36424), 0qb0j (0.23 #24013, 0.21 #24790, 0.21 #36424), 014ck4 (0.21 #36424, 0.14 #6080, 0.14 #5305), 06bnz (0.20 #2410, 0.14 #6282, 0.10 #10152), 03rk0 (0.20 #2436, 0.14 #6308, 0.10 #10178), 0jdd (0.20 #2489, 0.14 #6361, 0.10 #10231), 04hhv (0.20 #2688, 0.14 #6560, 0.10 #10430) >> Best rule #21684 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 0fr61; 0mnlq; >> query: (?x12531, ?x14204) <- adjoins(?x14204, ?x12531), adjoins(?x206, ?x14204), contains(?x2346, ?x12531), mode_of_transportation(?x206, ?x4272) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014ck4 adjoins 0jk_8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 65.000 0.862 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #7446-01w4c9 PRED entity: 01w4c9 PRED relation: role! PRED expected values: 0l15bq => 67 concepts (48 used for prediction) PRED predicted values (max 10 best out of 110): 0dwsp (0.86 #103, 0.85 #104, 0.85 #2366), 026g73 (0.86 #103, 0.85 #104, 0.85 #2366), 01vdm0 (0.86 #103, 0.85 #104, 0.85 #1006), 01w4dy (0.86 #103, 0.85 #104, 0.85 #1006), 02bxd (0.86 #103, 0.85 #104, 0.85 #1006), 013y1f (0.85 #2754, 0.82 #1730, 0.81 #3434), 02sgy (0.83 #5454, 0.83 #5344, 0.79 #2841), 05148p4 (0.82 #1835, 0.79 #224, 0.74 #220), 0bxl5 (0.81 #3469, 0.79 #3695, 0.77 #2789), 0l15bq (0.81 #3435, 0.78 #1390, 0.77 #2755) >> Best rule #103 for best value: >> intensional similarity = 32 >> extensional distance = 1 >> proper extension: 026t6; >> query: (?x5480, ?x212) <- role(?x5480, ?x5676), role(?x5480, ?x1662), role(?x5480, ?x1268), role(?x5480, ?x1225), role(?x5480, ?x316), role(?x5480, ?x228), role(?x5480, ?x227), role(?x5480, ?x212), ?x5676 = 0151b0, ?x1268 = 0bm02, role(?x3967, ?x5480), role(?x1166, ?x5480), role(?x432, ?x5480), ?x1662 = 02bxd, ?x228 = 0l14qv, role(?x2888, ?x5480), ?x432 = 042v_gx, role(?x1225, ?x569), ?x316 = 05r5c, performance_role(?x1166, ?x75), instrumentalists(?x1166, ?x4836), instrumentalists(?x1166, ?x4646), instrumentalists(?x1166, ?x3069), instrumentalists(?x1166, ?x677), ?x3069 = 0150t6, ?x4836 = 0837ql, role(?x1433, ?x3967), role(?x1166, ?x614), ?x4646 = 0fhxv, role(?x645, ?x1225), ?x677 = 06y9c2, ?x227 = 0342h >> conf = 0.86 => this is the best rule for 5 predicted values *> Best rule #3435 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 14 *> proper extension: 018j2; *> query: (?x5480, 0l15bq) <- role(?x5480, ?x5676), role(?x5480, ?x1432), role(?x5480, ?x228), role(?x5480, ?x212), role(?x1166, ?x5676), role(?x716, ?x5676), role(?x8172, ?x5676), role(?x885, ?x5676), role(?x780, ?x5676), role(?x5480, ?x1212), ?x8172 = 06rvn, ?x228 = 0l14qv, role(?x5676, ?x3703), ?x1212 = 07xzm, ?x780 = 01qzyz, role(?x5676, ?x1332), ?x3703 = 02dlh2, ?x885 = 0dwtp, ?x1166 = 05148p4, role(?x3062, ?x5480), ?x1432 = 0395lw, ?x716 = 018vs, role(?x5883, ?x5676), role(?x4425, ?x212), ?x4425 = 0979zs *> conf = 0.81 ranks of expected_values: 10 EVAL 01w4c9 role! 0l15bq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 67.000 48.000 0.859 http://example.org/music/performance_role/track_performances./music/track_contribution/role #7445-035bcl PRED entity: 035bcl PRED relation: film_crew_role PRED expected values: 0d2b38 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 22): 09vw2b7 (0.66 #913, 0.63 #459, 0.63 #700), 01vx2h (0.37 #221, 0.37 #159, 0.35 #281), 01pvkk (0.29 #796, 0.28 #1163, 0.28 #918), 02ynfr (0.17 #285, 0.16 #315, 0.16 #225), 015h31 (0.16 #157, 0.09 #552, 0.08 #279), 02rh1dz (0.12 #128, 0.11 #158, 0.11 #553), 0d2b38 (0.11 #170, 0.09 #565, 0.09 #1020), 033smt (0.09 #172, 0.05 #567, 0.04 #415), 094hwz (0.08 #162, 0.04 #132, 0.04 #284), 04pyp5 (0.08 #14, 0.07 #468, 0.06 #922) >> Best rule #913 for best value: >> intensional similarity = 3 >> extensional distance = 1108 >> proper extension: 0fq27fp; >> query: (?x5829, 09vw2b7) <- film_crew_role(?x5829, ?x281), film_crew_role(?x2085, ?x281), ?x2085 = 0kvgxk >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #170 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 252 *> proper extension: 04svwx; *> query: (?x5829, 0d2b38) <- genre(?x5829, ?x1510), ?x1510 = 01hmnh *> conf = 0.11 ranks of expected_values: 7 EVAL 035bcl film_crew_role 0d2b38 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 65.000 65.000 0.661 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7444-0bh8yn3 PRED entity: 0bh8yn3 PRED relation: film! PRED expected values: 01nr36 => 73 concepts (37 used for prediction) PRED predicted values (max 10 best out of 813): 02w29z (0.50 #1409, 0.05 #7643, 0.03 #11797), 03ym1 (0.29 #3088, 0.04 #17632, 0.03 #7245), 01ps2h8 (0.29 #3016, 0.02 #62330, 0.01 #15481), 0c35b1 (0.25 #1349, 0.14 #3426, 0.01 #5506), 02g8h (0.25 #42, 0.03 #4199, 0.02 #16663), 04yj5z (0.25 #122, 0.03 #4279, 0.01 #45831), 01h910 (0.25 #1089, 0.02 #5246), 03n08b (0.25 #235, 0.02 #10623, 0.02 #6469), 073x6y (0.25 #1189, 0.01 #30275), 01wgcvn (0.25 #643, 0.01 #4800) >> Best rule #1409 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0fpgp26; >> query: (?x1701, 02w29z) <- film_release_region(?x1701, ?x8588), film_release_region(?x1701, ?x4737), film_release_region(?x1701, ?x1264), ?x1264 = 0345h, ?x8588 = 0jhd, ?x4737 = 07twz >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5634 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 04nlb94; *> query: (?x1701, 01nr36) <- film_crew_role(?x1701, ?x137), film_release_distribution_medium(?x1701, ?x81), region(?x1701, ?x512) *> conf = 0.02 ranks of expected_values: 229 EVAL 0bh8yn3 film! 01nr36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 73.000 37.000 0.500 http://example.org/film/actor/film./film/performance/film #7443-0jg77 PRED entity: 0jg77 PRED relation: award PRED expected values: 01c4_6 => 90 concepts (65 used for prediction) PRED predicted values (max 10 best out of 245): 01by1l (0.62 #907, 0.43 #2098, 0.38 #4084), 03tcnt (0.50 #3740, 0.35 #3343, 0.34 #4534), 01ckcd (0.46 #6683, 0.43 #3904, 0.41 #4698), 02f72n (0.44 #1337, 0.40 #3720, 0.27 #6499), 02f6xy (0.43 #2183, 0.25 #992, 0.16 #4169), 02f6yz (0.40 #3888, 0.40 #2697, 0.38 #3491), 02x17c2 (0.39 #2202, 0.38 #1011, 0.16 #4188), 01bgqh (0.39 #2028, 0.34 #4014, 0.30 #11557), 054ks3 (0.39 #2127, 0.25 #936, 0.22 #4113), 01c92g (0.39 #2083, 0.25 #892, 0.16 #4069) >> Best rule #907 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 011zf2; 02fn5r; 01wwvc5; 0x3b7; 0fhxv; 01k23t; >> query: (?x13142, 01by1l) <- award_winner(?x6869, ?x13142), award_winner(?x2704, ?x13142), award(?x13142, ?x2322), ?x2704 = 01mhwk, artist(?x3888, ?x13142), ?x6869 = 01xqqp >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2473 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 04rcr; 02r3zy; 03g5jw; 016fmf; 017j6; 0134s5; 04qmr; 0dw4g; 0b1zz; 07h76; ... *> query: (?x13142, 01c4_6) <- award_winner(?x2704, ?x13142), group(?x716, ?x13142), ?x716 = 018vs, artist(?x3888, ?x13142), artists(?x497, ?x13142) *> conf = 0.24 ranks of expected_values: 25 EVAL 0jg77 award 01c4_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 90.000 65.000 0.625 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7442-0190y4 PRED entity: 0190y4 PRED relation: artists PRED expected values: 03fbc => 69 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1095): 01yzl2 (0.50 #3747, 0.38 #4333, 0.33 #2664), 01dwrc (0.50 #3775, 0.38 #4859, 0.36 #11358), 01k3qj (0.50 #3937, 0.38 #5021, 0.18 #11520), 01w806h (0.50 #3512, 0.38 #4596, 0.16 #12178), 01gx5f (0.50 #3544, 0.36 #8959, 0.30 #10043), 0277c3 (0.50 #3801, 0.36 #4334, 0.33 #551), 04vrxh (0.50 #4153, 0.32 #20585, 0.26 #16248), 04mn81 (0.50 #3394, 0.32 #20585, 0.26 #16248), 01wy61y (0.50 #3618, 0.29 #9033, 0.25 #5785), 017j6 (0.50 #3542, 0.29 #8957, 0.25 #5709) >> Best rule #3747 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 02x8m; 0glt670; >> query: (?x11724, 01yzl2) <- parent_genre(?x12818, ?x11724), parent_genre(?x7280, ?x11724), parent_genre(?x11724, ?x14580), parent_genre(?x11724, ?x283), artists(?x12818, ?x2732), artists(?x283, ?x460), parent_genre(?x14580, ?x6101), ?x7280 = 0283d, ?x2732 = 01wgxtl >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13201 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 45 *> proper extension: 041738; *> query: (?x11724, 03fbc) <- parent_genre(?x2491, ?x11724), artists(?x11724, ?x1732), parent_genre(?x2491, ?x1127), artists(?x1127, ?x5364), artists(?x1127, ?x1720), ?x5364 = 043zg, ?x1720 = 01qkqwg *> conf = 0.23 ranks of expected_values: 293 EVAL 0190y4 artists 03fbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 32.000 0.500 http://example.org/music/genre/artists #7441-07bdd_ PRED entity: 07bdd_ PRED relation: award! PRED expected values: 03xsby 01vvb4m 02xnjd 016dmx 06rq2l 0cv9fc => 50 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2325): 0127m7 (0.78 #13164, 0.70 #3290, 0.70 #46091), 0pz91 (0.78 #13164, 0.70 #3290, 0.70 #46091), 04zwtdy (0.78 #13164, 0.70 #3290, 0.70 #46091), 086k8 (0.78 #13164, 0.70 #3290, 0.70 #46091), 030_1m (0.78 #13164, 0.70 #3290, 0.70 #46091), 047q2wc (0.78 #13164, 0.70 #3290, 0.70 #46091), 081bls (0.78 #13164, 0.70 #3290, 0.70 #46091), 0lx2l (0.38 #3941, 0.11 #13817, 0.10 #17110), 0p_47 (0.38 #4353, 0.11 #14229, 0.06 #24105), 01g1lp (0.33 #15380, 0.25 #2213, 0.14 #29627) >> Best rule #13164 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0p9sw; 0gq9h; 05p1dby; 0gr42; 02x1z2s; 0b6jkkg; >> query: (?x1105, ?x382) <- award_winner(?x1105, ?x5908), award_winner(?x1105, ?x382), nominated_for(?x1105, ?x103), production_companies(?x770, ?x5908) >> conf = 0.78 => this is the best rule for 7 predicted values *> Best rule #12227 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0p9sw; 0gq9h; 05p1dby; 0gr42; 02x1z2s; 0b6jkkg; *> query: (?x1105, 016dmx) <- award_winner(?x1105, ?x5908), nominated_for(?x1105, ?x103), production_companies(?x770, ?x5908) *> conf = 0.25 ranks of expected_values: 19, 20, 28, 45, 215 EVAL 07bdd_ award! 0cv9fc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 50.000 19.000 0.776 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07bdd_ award! 06rq2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 50.000 19.000 0.776 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07bdd_ award! 016dmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 50.000 19.000 0.776 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07bdd_ award! 02xnjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 19.000 0.776 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07bdd_ award! 01vvb4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 50.000 19.000 0.776 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 07bdd_ award! 03xsby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 50.000 19.000 0.776 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7440-05sbv3 PRED entity: 05sbv3 PRED relation: nominated_for! PRED expected values: 040njc 0gr4k => 77 concepts (70 used for prediction) PRED predicted values (max 10 best out of 212): 0gq_v (0.74 #257, 0.69 #474, 0.68 #7098), 0gq9h (0.71 #536, 0.69 #474, 0.68 #7098), 0gqz2 (0.69 #474, 0.68 #7098, 0.68 #7810), 0p9sw (0.64 #495, 0.27 #1204, 0.26 #258), 02qyntr (0.51 #652, 0.30 #415, 0.25 #1361), 040njc (0.49 #481, 0.40 #244, 0.34 #1190), 04dn09n (0.40 #510, 0.27 #1219, 0.25 #273), 02r0csl (0.40 #242, 0.13 #479, 0.12 #1188), 0gr4k (0.38 #501, 0.30 #1446, 0.27 #2155), 0f4x7 (0.36 #500, 0.32 #263, 0.30 #1445) >> Best rule #257 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 0k2m6; 05y0cr; >> query: (?x11348, 0gq_v) <- award(?x11348, ?x2222), country(?x11348, ?x94), ?x2222 = 0gs96 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #481 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 53 *> proper extension: 061681; 0k2sk; 04vr_f; 0dtfn; 0qm8b; 0dr_4; 011yd2; 07cz2; 07024; 0ywrc; ... *> query: (?x11348, 040njc) <- award(?x11348, ?x1703), nominated_for(?x3348, ?x11348), ?x1703 = 0k611 *> conf = 0.49 ranks of expected_values: 6, 9 EVAL 05sbv3 nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 77.000 70.000 0.736 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05sbv3 nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 70.000 0.736 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7439-05b7q PRED entity: 05b7q PRED relation: country! PRED expected values: 07jbh => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 46): 06z6r (0.89 #1267, 0.88 #530, 0.88 #991), 0bynt (0.88 #3090, 0.87 #2216, 0.86 #2124), 06wrt (0.83 #241, 0.71 #149, 0.63 #333), 03hr1p (0.79 #155, 0.78 #247, 0.63 #293), 07bs0 (0.74 #285, 0.64 #147, 0.61 #239), 06f41 (0.72 #240, 0.71 #148, 0.69 #1299), 01z27 (0.72 #242, 0.71 #150, 0.58 #288), 02y8z (0.72 #244, 0.71 #152, 0.53 #1763), 01sgl (0.72 #266, 0.57 #174, 0.53 #312), 09qgm (0.72 #249, 0.50 #157, 0.47 #295) >> Best rule #1267 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 0jdx; >> query: (?x7287, 06z6r) <- religion(?x7287, ?x109), participating_countries(?x784, ?x7287), country(?x359, ?x7287) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #164 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 06mzp; *> query: (?x7287, 07jbh) <- religion(?x7287, ?x109), olympics(?x7287, ?x7688), ?x7688 = 0jkvj *> conf = 0.64 ranks of expected_values: 19 EVAL 05b7q country! 07jbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 133.000 133.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #7438-03xp8d5 PRED entity: 03xp8d5 PRED relation: award_winner! PRED expected values: 0bzm__ => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 137): 02q690_ (0.15 #624, 0.11 #1744, 0.10 #764), 0gvstc3 (0.14 #1294, 0.09 #1994, 0.08 #1714), 0gx_st (0.13 #597, 0.09 #1717, 0.06 #737), 027n06w (0.12 #1332, 0.08 #632, 0.07 #3432), 05c1t6z (0.12 #1275, 0.11 #575, 0.11 #1695), 019bk0 (0.12 #296, 0.08 #5476, 0.06 #1836), 0hndn2q (0.10 #180, 0.07 #460, 0.06 #880), 01bx35 (0.10 #287, 0.08 #5467, 0.05 #1827), 01mhwk (0.10 #321, 0.08 #5501, 0.06 #1441), 09v0p2c (0.10 #1342, 0.06 #2042, 0.06 #3442) >> Best rule #624 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 023jq1; >> query: (?x4385, 02q690_) <- award_winner(?x4385, ?x2661), producer_type(?x4385, ?x632), student(?x7545, ?x4385) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #8401 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1125 *> proper extension: 0lzkm; 06lxn; *> query: (?x4385, ?x78) <- award_winner(?x4385, ?x2661), award_winner(?x1313, ?x4385), ceremony(?x1313, ?x78) *> conf = 0.05 ranks of expected_values: 76 EVAL 03xp8d5 award_winner! 0bzm__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 113.000 113.000 0.149 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7437-0gj9tn5 PRED entity: 0gj9tn5 PRED relation: film! PRED expected values: 0gy6z9 => 78 concepts (54 used for prediction) PRED predicted values (max 10 best out of 929): 035kl6 (0.26 #12482, 0.07 #12203, 0.01 #35086), 0197tq (0.26 #12482), 015wnl (0.25 #2729, 0.14 #11049, 0.03 #31852), 02r_d4 (0.25 #2184, 0.09 #6344, 0.02 #25067), 03x31g (0.25 #1845, 0.08 #10165, 0.07 #14327), 03m3nzf (0.25 #1566, 0.08 #9886, 0.07 #14048), 0f502 (0.25 #762, 0.07 #11162, 0.03 #17405), 027xbpw (0.25 #2656, 0.07 #81130, 0.07 #89457), 03jldb (0.25 #2325, 0.07 #81130, 0.07 #89457), 0gz5hs (0.25 #2399, 0.03 #16962, 0.02 #14881) >> Best rule #12482 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0d_wms; 058kh7; >> query: (?x1785, ?x217) <- film(?x3673, ?x1785), music(?x1785, ?x6891), film(?x2969, ?x1785), person(?x1785, ?x217) >> conf = 0.26 => this is the best rule for 2 predicted values *> Best rule #81130 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 711 *> proper extension: 0g60z; 080dwhx; 06cs95; 072kp; 02k_4g; 019nnl; 0ddd0gc; 0124k9; 08jgk1; 0584r4; ... *> query: (?x1785, ?x3293) <- nominated_for(?x4956, ?x1785), nominated_for(?x298, ?x1785), participant(?x4956, ?x123), award_nominee(?x4956, ?x3293) *> conf = 0.07 ranks of expected_values: 246 EVAL 0gj9tn5 film! 0gy6z9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 78.000 54.000 0.256 http://example.org/film/actor/film./film/performance/film #7436-015gsv PRED entity: 015gsv PRED relation: film PRED expected values: 0340hj => 125 concepts (61 used for prediction) PRED predicted values (max 10 best out of 474): 0340hj (0.50 #237), 0g0x9c (0.12 #1365), 0298n7 (0.12 #1349), 01pvxl (0.12 #908), 024mpp (0.12 #649), 04sntd (0.12 #490), 0dnvn3 (0.12 #55), 083shs (0.12 #19), 02qr3k8 (0.08 #4869, 0.05 #10239, 0.03 #53199), 0n08r (0.06 #1705, 0.04 #5285) >> Best rule #237 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0g2mbn; >> query: (?x8905, 0340hj) <- profession(?x8905, ?x1032), film(?x8905, ?x4502), ?x4502 = 02wgk1, nationality(?x8905, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015gsv film 0340hj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 61.000 0.500 http://example.org/film/actor/film./film/performance/film #7435-047d21r PRED entity: 047d21r PRED relation: produced_by PRED expected values: 0cc63l => 93 concepts (78 used for prediction) PRED predicted values (max 10 best out of 125): 06m6z6 (0.34 #15846, 0.16 #16233, 0.16 #16232), 04t38b (0.23 #160, 0.02 #3632, 0.02 #2475), 04wx2v (0.16 #16233, 0.16 #16232, 0.16 #14298), 0b25vg (0.16 #16233, 0.16 #16232, 0.16 #14298), 01wy5m (0.16 #16233, 0.16 #16232, 0.16 #14298), 0829rj (0.15 #349, 0.02 #3821), 029m83 (0.11 #658, 0.05 #1044, 0.03 #2588), 0bwh6 (0.08 #47, 0.04 #1591, 0.03 #2747), 04wvhz (0.08 #36, 0.04 #421, 0.03 #17810), 04y8r (0.08 #71, 0.04 #456, 0.02 #842) >> Best rule #15846 for best value: >> intensional similarity = 3 >> extensional distance = 566 >> proper extension: 01g3gq; 0m3gy; >> query: (?x3743, ?x3961) <- film_release_region(?x3743, ?x94), genre(?x3743, ?x53), film(?x3961, ?x3743) >> conf = 0.34 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 047d21r produced_by 0cc63l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 78.000 0.344 http://example.org/film/film/produced_by #7434-0dn8b PRED entity: 0dn8b PRED relation: adjoins! PRED expected values: 0cv0r => 158 concepts (34 used for prediction) PRED predicted values (max 10 best out of 427): 0bx9y (0.25 #24317, 0.22 #1258, 0.20 #783), 0fr61 (0.25 #24317, 0.20 #783, 0.20 #337), 0cv0r (0.25 #24317, 0.20 #783, 0.11 #1550), 0dn8b (0.25 #24317, 0.20 #783, 0.11 #1431), 0cv1h (0.22 #1426, 0.20 #642, 0.18 #2211), 0mwq7 (0.22 #1507, 0.20 #723, 0.18 #2292), 0mnlq (0.22 #1451, 0.20 #667, 0.18 #2236), 0cc07 (0.22 #1430, 0.20 #646, 0.18 #2215), 0cv13 (0.11 #1376, 0.09 #2161, 0.04 #4517), 0mwcz (0.11 #1297, 0.09 #2082, 0.04 #4438) >> Best rule #24317 for best value: >> intensional similarity = 5 >> extensional distance = 150 >> proper extension: 0m2gk; 0nvd8; 0k3ll; 0mws3; 0n5y4; 0mlzk; 0f4zv; 0mvxt; 0mww2; >> query: (?x12275, ?x7420) <- adjoins(?x12680, ?x12275), adjoins(?x10601, ?x12275), county(?x7284, ?x12275), contains(?x1767, ?x12680), adjoins(?x7420, ?x10601) >> conf = 0.25 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0dn8b adjoins! 0cv0r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 158.000 34.000 0.253 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #7433-0cc8q3 PRED entity: 0cc8q3 PRED relation: instance_of_recurring_event PRED expected values: 02jp2w => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1): 02jp2w (0.71 #215, 0.71 #213, 0.70 #240) >> Best rule #215 for best value: >> intensional similarity = 78 >> extensional distance = 5 >> proper extension: 0b_770; >> query: (?x6002, ?x10863) <- team(?x6002, ?x12370), team(?x6002, ?x10171), team(?x6002, ?x9983), team(?x6002, ?x9909), team(?x6002, ?x8728), team(?x6002, ?x8528), team(?x6002, ?x5551), team(?x6002, ?x5032), team(?x6002, ?x4369), team(?x6002, ?x3798), team(?x6002, ?x2303), team(?x12162, ?x10171), team(?x11210, ?x10171), team(?x9974, ?x10171), team(?x9956, ?x10171), team(?x9908, ?x10171), team(?x8992, ?x10171), team(?x7378, ?x10171), team(?x6802, ?x10171), team(?x5897, ?x10171), team(?x4803, ?x10171), team(?x4368, ?x10171), team(?x3797, ?x10171), team(?x2302, ?x10171), ?x5897 = 0b_6rk, colors(?x10171, ?x4557), ?x8728 = 026xxv_, ?x2302 = 0b_77q, ?x9956 = 0bzrsh, ?x4369 = 02pqcfz, ?x9909 = 026wlnm, ?x7378 = 0bzrxn, ?x3797 = 0b_6zk, ?x8528 = 091tgz, team(?x13045, ?x5551), team(?x10673, ?x5551), team(?x8824, ?x5551), ?x4803 = 0b_6jz, ?x12162 = 0b_6_l, locations(?x4368, ?x8993), locations(?x4368, ?x5267), locations(?x4368, ?x3983), locations(?x4368, ?x3786), team(?x4570, ?x12370), ?x9974 = 0b_6pv, instance_of_recurring_event(?x4368, ?x10863), ?x8993 = 0fsb8, ?x8824 = 05g_nr, colors(?x546, ?x4557), sport(?x2303, ?x12913), ?x5267 = 0d9jr, team(?x1348, ?x5551), teams(?x1248, ?x12370), colors(?x12370, ?x9778), ?x1348 = 01pv51, ?x11210 = 0b_6q5, team(?x4368, ?x10846), team(?x4368, ?x6803), ?x6802 = 0br1x_, ?x3798 = 02ptzz0, ?x13045 = 0bqthy, ?x10863 = 02jp2w, ?x6803 = 03by7wc, ?x3786 = 071cn, ?x10846 = 02pzy52, locations(?x8992, ?x2740), locations(?x8992, ?x2504), ?x2740 = 0f__1, ?x3983 = 0fr0t, ?x9908 = 0b_6lb, ?x10673 = 0b_6mr, ?x2504 = 029cr, ?x9983 = 02q4ntp, team(?x4834, ?x5032), colors(?x6732, ?x9778), colors(?x5780, ?x9778), ?x6732 = 0gdm1, ?x5780 = 02zcz3 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cc8q3 instance_of_recurring_event 02jp2w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.714 http://example.org/time/event/instance_of_recurring_event #7432-07_f2 PRED entity: 07_f2 PRED relation: district_represented! PRED expected values: 03ww_x 070m6c 02bp37 01grpq 01gsvb 01grq1 => 192 concepts (192 used for prediction) PRED predicted values (max 10 best out of 19): 02bp37 (0.87 #24, 0.71 #82, 0.59 #196), 070m6c (0.85 #233, 0.84 #81, 0.83 #119), 03ww_x (0.60 #22, 0.45 #58, 0.19 #137), 01gsvb (0.47 #31, 0.45 #58, 0.43 #146), 01grq1 (0.45 #58, 0.40 #35, 0.34 #131), 01grpq (0.45 #58, 0.40 #27, 0.33 #8), 01grmk (0.45 #58, 0.33 #34, 0.33 #15), 05rrw9 (0.45 #58, 0.33 #38, 0.33 #19), 0495ys (0.45 #58, 0.27 #20, 0.09 #192), 060ny2 (0.45 #58, 0.20 #29, 0.09 #201) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0g0syc; >> query: (?x7405, 02bp37) <- district_represented(?x2976, ?x7405), district_represented(?x2861, ?x7405), ?x2861 = 03tcbx, ?x2976 = 03rtmz >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6 EVAL 07_f2 district_represented! 01grq1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.867 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07_f2 district_represented! 01gsvb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.867 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07_f2 district_represented! 01grpq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.867 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07_f2 district_represented! 02bp37 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.867 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07_f2 district_represented! 070m6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.867 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07_f2 district_represented! 03ww_x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 192.000 0.867 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #7431-011s9r PRED entity: 011s9r PRED relation: place_of_death PRED expected values: 030qb3t => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 66): 09c7w0 (0.33 #1, 0.14 #195, 0.07 #585), 05qtj (0.21 #648, 0.20 #843, 0.09 #1425), 02_286 (0.14 #597, 0.13 #792, 0.10 #1180), 0161jj (0.14 #387, 0.02 #1360, 0.02 #4086), 0h7h6 (0.09 #1556, 0.08 #779, 0.06 #2141), 030qb3t (0.08 #1967, 0.05 #20667, 0.05 #6644), 0cp6w (0.08 #558, 0.02 #2311, 0.01 #2701), 04jpl (0.07 #591, 0.07 #1368, 0.07 #786), 0p9z5 (0.07 #718, 0.07 #913, 0.02 #1301), 0978r (0.07 #632, 0.07 #827, 0.02 #1409) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01y8d4; >> query: (?x11928, 09c7w0) <- story_by(?x7425, ?x11928), location(?x11928, ?x1658), gender(?x11928, ?x231), ?x7425 = 042fgh >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1967 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 01_k0d; *> query: (?x11928, 030qb3t) <- profession(?x11928, ?x353), peers(?x11928, ?x8209), nationality(?x11928, ?x94), place_of_birth(?x11928, ?x1658) *> conf = 0.08 ranks of expected_values: 6 EVAL 011s9r place_of_death 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 140.000 140.000 0.333 http://example.org/people/deceased_person/place_of_death #7430-09qj50 PRED entity: 09qj50 PRED relation: award_winner PRED expected values: 025mb_ => 56 concepts (29 used for prediction) PRED predicted values (max 10 best out of 2236): 02l3_5 (0.62 #14074, 0.25 #4216, 0.22 #19003), 0pz7h (0.60 #7559, 0.38 #12322, 0.36 #69010), 02x7vq (0.50 #3703, 0.25 #1239, 0.12 #13561), 015cbq (0.50 #6977, 0.12 #16835, 0.06 #22181), 0dqcm (0.50 #6857, 0.05 #48757, 0.04 #41365), 07s8r0 (0.50 #329, 0.04 #22510, 0.03 #24976), 081nh (0.50 #5432, 0.03 #35013, 0.03 #59650), 044qx (0.50 #5855, 0.03 #40363, 0.03 #42827), 0k9j_ (0.50 #6848, 0.02 #41356, 0.02 #21636), 043gj (0.50 #5978, 0.02 #40486, 0.02 #20766) >> Best rule #14074 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 05b4l5x; 0gqyl; 02ppm4q; 09qvf4; >> query: (?x757, 02l3_5) <- nominated_for(?x757, ?x758), award_winner(?x757, ?x495), award(?x10073, ?x757), ?x10073 = 05ggt_ >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #12322 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0cjyzs; 09qs08; 09qrn4; 027gs1_; *> query: (?x757, ?x444) <- nominated_for(?x757, ?x9951), award_winner(?x757, ?x495), ?x9951 = 023ny6, award(?x444, ?x757) *> conf = 0.38 ranks of expected_values: 14 EVAL 09qj50 award_winner 025mb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 56.000 29.000 0.625 http://example.org/award/award_category/winners./award/award_honor/award_winner #7429-07f5x PRED entity: 07f5x PRED relation: adjoins PRED expected values: 01699 => 77 concepts (70 used for prediction) PRED predicted values (max 10 best out of 377): 01699 (0.83 #16202, 0.82 #51721, 0.82 #23916), 0164v (0.83 #16202, 0.82 #51721, 0.82 #23916), 0fv4v (0.22 #50948, 0.22 #53268, 0.07 #319), 05cc1 (0.22 #53268, 0.10 #293, 0.06 #13117), 04v09 (0.22 #53268, 0.08 #407, 0.06 #13117), 05cgv (0.22 #53268, 0.06 #13117, 0.05 #65), 07f5x (0.22 #53268, 0.06 #13117, 0.05 #405), 088xp (0.17 #136, 0.11 #2450, 0.09 #5537), 06tw8 (0.12 #246, 0.06 #13117, 0.06 #10276), 0d05w3 (0.11 #5523, 0.10 #6295, 0.10 #20181) >> Best rule #16202 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 0n3g; 035hm; >> query: (?x8948, ?x6431) <- adjoins(?x8948, ?x2051), form_of_government(?x8948, ?x48), adjoins(?x6431, ?x8948) >> conf = 0.83 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 07f5x adjoins 01699 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 70.000 0.826 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #7428-01vsy7t PRED entity: 01vsy7t PRED relation: influenced_by! PRED expected values: 07hgm => 124 concepts (56 used for prediction) PRED predicted values (max 10 best out of 337): 03g5jw (0.12 #4156, 0.12 #4671, 0.08 #7759), 05rx__ (0.10 #6995, 0.04 #3906, 0.03 #22436), 0167xy (0.09 #4031, 0.06 #8148, 0.06 #7634), 01xwv7 (0.09 #7111, 0.07 #5051, 0.07 #7625), 05ty4m (0.09 #6694, 0.06 #7208, 0.05 #3605), 0bqs56 (0.09 #6936, 0.04 #3847, 0.03 #7964), 0lrh (0.08 #7820, 0.05 #25734, 0.03 #16058), 01s7qqw (0.07 #3807, 0.06 #6896, 0.06 #7924), 07c0j (0.07 #22643, 0.07 #5142, 0.04 #5143), 01vsy7t (0.07 #22643, 0.05 #25734, 0.03 #7898) >> Best rule #4156 for best value: >> intensional similarity = 2 >> extensional distance = 78 >> proper extension: 05xq9; 01kcms4; 070b4; 0167xy; 04sd0; >> query: (?x4620, 03g5jw) <- artist(?x3265, ?x4620), influenced_by(?x4620, ?x1029) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #3984 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 54 *> proper extension: 0459z; *> query: (?x4620, 07hgm) <- instrumentalists(?x227, ?x4620), influenced_by(?x4593, ?x4620) *> conf = 0.04 ranks of expected_values: 93 EVAL 01vsy7t influenced_by! 07hgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 124.000 56.000 0.125 http://example.org/influence/influence_node/influenced_by #7427-01pv91 PRED entity: 01pv91 PRED relation: film! PRED expected values: 0c1pj 0525b => 112 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1066): 01gb54 (0.42 #62505, 0.41 #20830, 0.41 #54170), 0133sq (0.41 #20830, 0.40 #75010, 0.40 #72926), 0bxtg (0.25 #77, 0.07 #14581, 0.05 #4242), 07nx9j (0.25 #1319, 0.02 #11732, 0.02 #45066), 046qq (0.12 #743, 0.10 #4908, 0.10 #2826), 01f6zc (0.12 #7192, 0.07 #14581, 0.05 #13441), 01r93l (0.12 #749, 0.07 #14581, 0.05 #4914), 03fbb6 (0.12 #980, 0.07 #14581, 0.05 #5145), 025j1t (0.12 #1078, 0.07 #14581, 0.05 #5243), 0cjsxp (0.12 #660, 0.07 #14581, 0.05 #4825) >> Best rule #62505 for best value: >> intensional similarity = 4 >> extensional distance = 386 >> proper extension: 0170z3; 02d413; 0g22z; 01br2w; 0140g4; 0b2v79; 01jc6q; 028_yv; 09m6kg; 0c0yh4; ... >> query: (?x2539, ?x4564) <- films(?x5954, ?x2539), nominated_for(?x4564, ?x2539), nominated_for(?x640, ?x2539), award_nominee(?x902, ?x4564) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #14581 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 95 *> proper extension: 02z2mr7; *> query: (?x2539, ?x5913) <- films(?x5954, ?x2539), films(?x5954, ?x12899), story_by(?x2539, ?x10854), film(?x5913, ?x12899) *> conf = 0.07 ranks of expected_values: 121, 968 EVAL 01pv91 film! 0525b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 112.000 66.000 0.421 http://example.org/film/actor/film./film/performance/film EVAL 01pv91 film! 0c1pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 112.000 66.000 0.421 http://example.org/film/actor/film./film/performance/film #7426-04ly1 PRED entity: 04ly1 PRED relation: jurisdiction_of_office! PRED expected values: 0f6c3 => 185 concepts (185 used for prediction) PRED predicted values (max 10 best out of 20): 0f6c3 (0.91 #196, 0.89 #133, 0.88 #280), 0pqc5 (0.69 #1685, 0.45 #1160, 0.41 #1622), 060c4 (0.50 #1831, 0.49 #2126, 0.46 #2147), 060bp (0.44 #1829, 0.43 #2124, 0.41 #1346), 0789n (0.33 #51, 0.31 #30, 0.29 #72), 0fkzq (0.31 #36, 0.29 #78, 0.28 #225), 01t7n9 (0.20 #59, 0.18 #80, 0.15 #38), 0p5vf (0.17 #95, 0.14 #1230, 0.10 #494), 01gkgk (0.15 #26, 0.14 #89, 0.13 #47), 04syw (0.14 #90, 0.11 #2129, 0.09 #1834) >> Best rule #196 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0rh6k; >> query: (?x3908, 0f6c3) <- state_province_region(?x466, ?x3908), location(?x1299, ?x3908), religion(?x3908, ?x109) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ly1 jurisdiction_of_office! 0f6c3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 185.000 185.000 0.913 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #7425-0h7h6 PRED entity: 0h7h6 PRED relation: place_of_birth! PRED expected values: 03q43g => 249 concepts (194 used for prediction) PRED predicted values (max 10 best out of 2324): 0m2l9 (0.46 #31143, 0.39 #303689, 0.34 #282922), 0p__8 (0.46 #31143, 0.34 #282922, 0.33 #417902), 02kz_ (0.46 #31143, 0.34 #282922, 0.33 #417902), 01g6bk (0.46 #31143, 0.34 #282922, 0.33 #417902), 0gd5z (0.46 #31143, 0.34 #282922, 0.33 #417902), 01rzqj (0.46 #31143, 0.34 #282922, 0.33 #417902), 01ycbq (0.46 #31143, 0.34 #282922, 0.33 #417902), 0479b (0.46 #31143, 0.34 #282922, 0.33 #417902), 0391jz (0.46 #31143, 0.34 #282922, 0.33 #417902), 045zr (0.46 #31143, 0.34 #282922, 0.33 #417902) >> Best rule #31143 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0619_; 01hvzr; >> query: (?x1658, ?x483) <- teams(?x1658, ?x6179), location(?x483, ?x1658), second_level_divisions(?x279, ?x1658) >> conf = 0.46 => this is the best rule for 23 predicted values No rule for expected values ranks of expected_values: EVAL 0h7h6 place_of_birth! 03q43g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 249.000 194.000 0.461 http://example.org/people/person/place_of_birth #7424-01vsgrn PRED entity: 01vsgrn PRED relation: award_nominee PRED expected values: 09qr6 => 122 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1372): 01vvydl (0.87 #6983, 0.85 #13966, 0.83 #44225), 01vvyc_ (0.87 #6983, 0.85 #13966, 0.83 #44225), 09qr6 (0.87 #6983, 0.85 #13966, 0.83 #44225), 057xn_m (0.87 #6983, 0.85 #13966, 0.83 #13965), 01wd9lv (0.33 #1466, 0.17 #3793, 0.06 #6121), 016vqk (0.33 #1940, 0.17 #4267, 0.06 #6595), 0gbwp (0.33 #907, 0.17 #3234, 0.06 #5562), 0478__m (0.17 #3409, 0.11 #10392, 0.06 #24358), 03j24kf (0.17 #3437, 0.11 #5765, 0.06 #8093), 0417z2 (0.17 #4374, 0.11 #6702, 0.04 #11357) >> Best rule #6983 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01vw26l; >> query: (?x5536, ?x140) <- artist(?x8738, ?x5536), film(?x5536, ?x2695), award_nominee(?x140, ?x5536) >> conf = 0.87 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 01vsgrn award_nominee 09qr6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 122.000 73.000 0.873 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7423-03m6zs PRED entity: 03m6zs PRED relation: category PRED expected values: 08mbj5d => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.71 #137, 0.71 #136, 0.71 #135) >> Best rule #137 for best value: >> intensional similarity = 6 >> extensional distance = 292 >> proper extension: 0hsb3; 02mw6c; >> query: (?x10026, ?x134) <- company(?x8411, ?x10026), company(?x8411, ?x9476), list(?x9476, ?x5997), category(?x9476, ?x134), service_location(?x9476, ?x94), state_province_region(?x9476, ?x1906) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03m6zs category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.707 http://example.org/common/topic/webpage./common/webpage/category #7422-010h9y PRED entity: 010h9y PRED relation: category PRED expected values: 08mbj5d => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.81 #44, 0.79 #56, 0.79 #53) >> Best rule #44 for best value: >> intensional similarity = 4 >> extensional distance = 142 >> proper extension: 0yc84; 013ksx; 0rp46; 0m2rv; 0rj0z; 0sb1r; 0gjcy; 0h778; 0zlgm; 0s5cg; ... >> query: (?x11669, 08mbj5d) <- source(?x11669, ?x958), location(?x12194, ?x11669), county(?x11669, ?x12599), adjoins(?x8808, ?x12599) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 010h9y category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.806 http://example.org/common/topic/webpage./common/webpage/category #7421-0f2sx4 PRED entity: 0f2sx4 PRED relation: language PRED expected values: 02h40lc => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 33): 02h40lc (0.96 #120, 0.92 #180, 0.90 #775), 064_8sq (0.22 #22, 0.15 #259, 0.14 #318), 03hkp (0.22 #15, 0.02 #1385, 0.02 #1146), 06nm1 (0.17 #11, 0.12 #189, 0.11 #724), 04306rv (0.11 #5, 0.09 #301, 0.09 #242), 02bjrlw (0.08 #774, 0.07 #835, 0.07 #534), 06b_j (0.06 #796, 0.06 #497, 0.05 #319), 03_9r (0.06 #10, 0.05 #1856, 0.04 #3766), 0c_v2 (0.06 #17, 0.01 #195, 0.01 #491), 02bv9 (0.05 #87, 0.01 #383, 0.01 #442) >> Best rule #120 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 02q8ms8; 09dv8h; 01xlqd; >> query: (?x7967, 02h40lc) <- nominated_for(?x1104, ?x7967), genre(?x7967, ?x239), ?x239 = 06cvj, film_release_region(?x7967, ?x94) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2sx4 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.956 http://example.org/film/film/language #7420-02fcs2 PRED entity: 02fcs2 PRED relation: award PRED expected values: 0gr4k => 102 concepts (86 used for prediction) PRED predicted values (max 10 best out of 272): 09d28z (0.77 #396, 0.74 #792, 0.74 #2769), 0gr4k (0.71 #2799, 0.68 #1613, 0.68 #1218), 02x17s4 (0.53 #1304, 0.50 #1699, 0.45 #2885), 0gs9p (0.38 #2050, 0.28 #1655, 0.27 #2841), 040njc (0.35 #1985, 0.33 #1590, 0.29 #2776), 019f4v (0.35 #2039, 0.28 #1644, 0.27 #2830), 02pqp12 (0.33 #2042, 0.23 #1647, 0.23 #1252), 09sb52 (0.30 #11104, 0.28 #6757, 0.26 #7943), 02x1dht (0.30 #2028, 0.25 #1633, 0.25 #1238), 0cjyzs (0.26 #3262, 0.10 #8399, 0.09 #494) >> Best rule #396 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 03d1y3; >> query: (?x2367, ?x8364) <- award(?x2367, ?x1862), award(?x2367, ?x688), award_winner(?x8364, ?x2367), ?x688 = 05b1610, ceremony(?x1862, ?x78) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2799 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 0qf43; 034bgm; 03hy3g; 013t9y; 05cgy8; 0mb5x; 026670; 06s1qy; *> query: (?x2367, 0gr4k) <- award(?x2367, ?x1180), written_by(?x2366, ?x2367), ?x1180 = 02n9nmz, profession(?x2367, ?x319) *> conf = 0.71 ranks of expected_values: 2 EVAL 02fcs2 award 0gr4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 86.000 0.773 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7419-0g9zcgx PRED entity: 0g9zcgx PRED relation: nationality PRED expected values: 09c7w0 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 85): 09c7w0 (0.70 #2810, 0.70 #3513, 0.69 #6316), 02jx1 (0.40 #134, 0.11 #838, 0.11 #1039), 07ssc (0.13 #906, 0.11 #518, 0.11 #820), 0ctw_b (0.07 #430, 0.07 #329, 0.06 #630), 03rk0 (0.05 #8965, 0.04 #1554, 0.03 #8565), 0chghy (0.04 #513, 0.03 #916, 0.03 #815), 0d060g (0.04 #2015, 0.04 #3519, 0.04 #1013), 0f8l9c (0.02 #425, 0.02 #704, 0.02 #827), 03rt9 (0.02 #516, 0.02 #704, 0.01 #705), 0hzlz (0.02 #526) >> Best rule #2810 for best value: >> intensional similarity = 3 >> extensional distance = 1106 >> proper extension: 023jq1; >> query: (?x6546, 09c7w0) <- award_winner(?x10416, ?x6546), gender(?x6546, ?x514), location(?x10416, ?x12820) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g9zcgx nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.697 http://example.org/people/person/nationality #7418-077qn PRED entity: 077qn PRED relation: nationality! PRED expected values: 03xyp_ => 174 concepts (94 used for prediction) PRED predicted values (max 10 best out of 4116): 03xyp_ (0.62 #121924, 0.33 #146313, 0.08 #10870), 01s7ns (0.62 #121924, 0.03 #68481, 0.03 #72545), 06qjgc (0.17 #11207, 0.13 #23399, 0.12 #27463), 0841zn (0.17 #10591, 0.13 #22783, 0.12 #26847), 01jb26 (0.17 #1621, 0.06 #66647, 0.05 #91031), 01wyzyl (0.17 #604, 0.06 #65630, 0.05 #90014), 0k525 (0.13 #23799, 0.12 #27863, 0.11 #31927), 059xvg (0.12 #66078, 0.12 #25436, 0.11 #29500), 07m69t (0.12 #67730, 0.09 #100243, 0.08 #104307), 01cspq (0.12 #27150, 0.11 #31214, 0.10 #39342) >> Best rule #121924 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 02qkt; 04wsz; >> query: (?x4059, ?x8696) <- contains(?x4059, ?x11540), locations(?x9939, ?x4059), location(?x8696, ?x11540) >> conf = 0.62 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 077qn nationality! 03xyp_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 94.000 0.622 http://example.org/people/person/nationality #7417-05mt_q PRED entity: 05mt_q PRED relation: profession PRED expected values: 0nbcg => 124 concepts (117 used for prediction) PRED predicted values (max 10 best out of 78): 0dz3r (0.71 #443, 0.69 #737, 0.67 #149), 09jwl (0.67 #166, 0.64 #6640, 0.64 #7083), 0nbcg (0.58 #31, 0.52 #472, 0.50 #325), 0cbd2 (0.58 #1477, 0.35 #16196, 0.15 #2212), 01d_h8 (0.51 #12524, 0.43 #2064, 0.40 #4417), 0dxtg (0.50 #12532, 0.38 #6931, 0.37 #16062), 0n1h (0.50 #306, 0.35 #1041, 0.33 #453), 016z4k (0.49 #1327, 0.46 #886, 0.46 #4710), 03gjzk (0.44 #6932, 0.33 #12533, 0.30 #2073), 0kyk (0.36 #1499, 0.16 #2234, 0.14 #2822) >> Best rule #443 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01ws9n6; >> query: (?x1388, 0dz3r) <- award(?x1388, ?x5799), ?x5799 = 03t5n3, nationality(?x1388, ?x279) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 026yqrr; *> query: (?x1388, 0nbcg) <- award(?x1388, ?x1389), award_nominee(?x3607, ?x1388), ?x3607 = 0412f5y *> conf = 0.58 ranks of expected_values: 3 EVAL 05mt_q profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 124.000 117.000 0.714 http://example.org/people/person/profession #7416-03cs_z7 PRED entity: 03cs_z7 PRED relation: producer_type PRED expected values: 0ckd1 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.82 #5, 0.78 #8, 0.77 #11) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 01r216; >> query: (?x1340, 0ckd1) <- award_nominee(?x1341, ?x1340), nationality(?x1340, ?x94), student(?x1103, ?x1340), program_creator(?x8775, ?x1340) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cs_z7 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.824 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #7415-01kx_81 PRED entity: 01kx_81 PRED relation: award_winner! PRED expected values: 09p2r9 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 119): 01s695 (0.21 #285, 0.13 #1131, 0.12 #1272), 056878 (0.21 #455, 0.11 #1019, 0.10 #1865), 0hndn2q (0.20 #40, 0.09 #181, 0.04 #1168), 02rjjll (0.18 #146, 0.17 #1274, 0.17 #428), 01xqqp (0.18 #237, 0.09 #1929, 0.09 #4467), 0466p0j (0.17 #499, 0.14 #217, 0.12 #1345), 02cg41 (0.17 #1254, 0.12 #549, 0.11 #4497), 013b2h (0.16 #3887, 0.14 #4451, 0.14 #221), 01c6qp (0.14 #160, 0.12 #301, 0.11 #5377), 0jzphpx (0.14 #180, 0.12 #462, 0.10 #1872) >> Best rule #285 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0k7pf; 04pf4r; 08c9b0; 01cbt3; 03h_fqv; 018gqj; 01p0vf; >> query: (?x1291, 01s695) <- artists(?x378, ?x1291), film(?x1291, ?x1619), music(?x8358, ?x1291) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2772 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 109 *> proper extension: 06zd1c; *> query: (?x1291, 09p2r9) <- award_nominee(?x1291, ?x1292), profession(?x1291, ?x131), music(?x8358, ?x1291) *> conf = 0.03 ranks of expected_values: 68 EVAL 01kx_81 award_winner! 09p2r9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 116.000 116.000 0.208 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7414-025x7g_ PRED entity: 025x7g_ PRED relation: taxonomy PRED expected values: 04n6k => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.03 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14816, 04n6k) <- >> conf = 0.03 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025x7g_ taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.030 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #7413-016z1t PRED entity: 016z1t PRED relation: award PRED expected values: 01bgqh => 93 concepts (90 used for prediction) PRED predicted values (max 10 best out of 251): 01c92g (0.78 #10077, 0.76 #9673, 0.74 #24996), 01bgqh (0.60 #43, 0.46 #3670, 0.27 #9716), 054ks3 (0.50 #141, 0.27 #544, 0.17 #3768), 01ck6h (0.31 #525, 0.14 #1331, 0.11 #3346), 0c4z8 (0.30 #72, 0.27 #3699, 0.23 #475), 0f4x7 (0.28 #2046, 0.28 #2449, 0.27 #1643), 03qbh5 (0.23 #607, 0.21 #3831, 0.20 #204), 026mfs (0.23 #532, 0.20 #129, 0.11 #9802), 02f5qb (0.20 #155, 0.17 #3782, 0.15 #9828), 02wh75 (0.20 #9, 0.15 #412, 0.12 #1218) >> Best rule #10077 for best value: >> intensional similarity = 3 >> extensional distance = 463 >> proper extension: 06lxn; >> query: (?x4718, ?x1801) <- award_winner(?x1801, ?x4718), artist(?x2241, ?x4718), artists(?x671, ?x4718) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 015_30; 0gdh5; 02jq1; *> query: (?x4718, 01bgqh) <- profession(?x4718, ?x1032), artists(?x1572, ?x4718), ?x1572 = 06by7, celebrities_impersonated(?x3649, ?x4718) *> conf = 0.60 ranks of expected_values: 2 EVAL 016z1t award 01bgqh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 90.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7412-08849 PRED entity: 08849 PRED relation: people! PRED expected values: 08g5q7 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 33): 01psyx (0.25 #111, 0.07 #1035, 0.06 #1563), 02y0js (0.12 #1124, 0.10 #1322, 0.09 #1520), 0dq9p (0.11 #2525, 0.11 #1271, 0.10 #1733), 02k6hp (0.10 #829, 0.10 #1951, 0.09 #2083), 04p3w (0.10 #803, 0.07 #1001, 0.03 #1595), 0gk4g (0.10 #2914, 0.09 #3112, 0.06 #2518), 012hw (0.08 #1768, 0.08 #1702, 0.07 #1966), 014w_8 (0.08 #963, 0.07 #1095, 0.05 #1293), 06z5s (0.07 #2005, 0.06 #1543, 0.05 #1807), 0kh3 (0.06 #1140, 0.05 #1206, 0.05 #1404) >> Best rule #111 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01kd57; >> query: (?x11617, 01psyx) <- award_winner(?x11617, ?x10552), profession(?x11617, ?x8498), ?x8498 = 09j9h >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1890 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 02yy8; *> query: (?x11617, 08g5q7) <- jurisdiction_of_office(?x11617, ?x10569), profession(?x11617, ?x5805), student(?x11229, ?x11617), ?x5805 = 0fj9f, school_type(?x11229, ?x3092) *> conf = 0.05 ranks of expected_values: 17 EVAL 08849 people! 08g5q7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 136.000 136.000 0.250 http://example.org/people/cause_of_death/people #7411-0fgg4 PRED entity: 0fgg4 PRED relation: notable_people_with_this_condition! PRED expected values: 0h99n => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 4): 0h99n (0.05 #76, 0.04 #120, 0.04 #142), 068p_ (0.03 #64, 0.01 #86), 029sk (0.02 #331, 0.02 #177, 0.02 #441), 01g2q (0.02 #273, 0.02 #207, 0.01 #185) >> Best rule #76 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 024y6w; >> query: (?x4949, 0h99n) <- profession(?x4949, ?x4773), ?x4773 = 0d1pc, award_winner(?x1972, ?x4949) >> conf = 0.05 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fgg4 notable_people_with_this_condition! 0h99n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.047 http://example.org/medicine/disease/notable_people_with_this_condition #7410-01jmyj PRED entity: 01jmyj PRED relation: films! PRED expected values: 07jq_ => 81 concepts (27 used for prediction) PRED predicted values (max 10 best out of 44): 081pw (0.08 #2192, 0.07 #1878, 0.06 #1094), 0bq3x (0.07 #30, 0.04 #1121, 0.04 #2219), 01w1sx (0.07 #90, 0.03 #1965, 0.03 #2279), 06d4h (0.06 #1918, 0.06 #2232, 0.05 #1134), 0fx2s (0.05 #1947, 0.05 #2261, 0.04 #1163), 07c52 (0.05 #1111, 0.03 #1895, 0.03 #2209), 04gb7 (0.05 #45, 0.03 #1920, 0.03 #2234), 07s2s (0.04 #2287, 0.04 #1973, 0.03 #1189), 05489 (0.04 #1927, 0.04 #2241, 0.02 #1143), 01vq3 (0.04 #2230, 0.04 #1916, 0.03 #1132) >> Best rule #2192 for best value: >> intensional similarity = 4 >> extensional distance = 516 >> proper extension: 01cgz; >> query: (?x8605, 081pw) <- films(?x5954, ?x8605), films(?x5954, ?x667), nominated_for(?x666, ?x667), film(?x3842, ?x667) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #1172 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 230 *> proper extension: 01gglm; *> query: (?x8605, 07jq_) <- titles(?x4205, ?x8605), film_release_distribution_medium(?x8605, ?x81), film_crew_role(?x8605, ?x137), films(?x5954, ?x8605) *> conf = 0.03 ranks of expected_values: 18 EVAL 01jmyj films! 07jq_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 81.000 27.000 0.075 http://example.org/film/film_subject/films #7409-02y_rq5 PRED entity: 02y_rq5 PRED relation: award! PRED expected values: 01w1kyf 02nwxc 0chw_ 023mdt 0cwtm => 45 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2146): 01hkhq (0.82 #23274, 0.67 #39905, 0.67 #49881), 01csvq (0.82 #23274, 0.67 #39905, 0.67 #49881), 015q43 (0.82 #23274, 0.67 #39905, 0.67 #49881), 0h1mt (0.82 #23274, 0.67 #39905, 0.67 #49881), 0h0wc (0.82 #23274, 0.67 #39905, 0.67 #49881), 01kb2j (0.80 #18087, 0.75 #21412, 0.67 #8112), 05dbf (0.67 #7226, 0.62 #10551, 0.53 #17201), 03mp9s (0.67 #8636, 0.62 #11961, 0.53 #18611), 01p7yb (0.67 #6717, 0.62 #10042, 0.53 #16692), 02jsgf (0.67 #17751, 0.62 #21076, 0.50 #11101) >> Best rule #23274 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 0bsjcw; >> query: (?x1716, ?x719) <- award(?x5330, ?x1716), award(?x2028, ?x1716), ?x2028 = 028knk, award_nominee(?x3225, ?x5330), award_winner(?x1716, ?x719), film(?x5330, ?x1490) >> conf = 0.82 => this is the best rule for 5 predicted values *> Best rule #12524 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 05zvj3m; *> query: (?x1716, 0chw_) <- award(?x2028, ?x1716), award(?x1299, ?x1716), nominated_for(?x1716, ?x718), award_nominee(?x92, ?x2028), ?x1299 = 0n6f8, place_of_birth(?x2028, ?x5093) *> conf = 0.50 ranks of expected_values: 20, 28, 57, 539, 569 EVAL 02y_rq5 award! 0cwtm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 45.000 16.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02y_rq5 award! 023mdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 16.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02y_rq5 award! 0chw_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 45.000 16.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02y_rq5 award! 02nwxc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 16.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02y_rq5 award! 01w1kyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 45.000 16.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7408-0fb0v PRED entity: 0fb0v PRED relation: child PRED expected values: 0181hw => 76 concepts (60 used for prediction) PRED predicted values (max 10 best out of 90): 04gmlt (0.25 #251, 0.12 #1109, 0.10 #1626), 056ws9 (0.25 #225, 0.12 #1083, 0.10 #1600), 032j_n (0.25 #446, 0.10 #1648, 0.10 #1303), 0fqy4p (0.25 #385, 0.08 #2792, 0.08 #2445), 013x0b (0.20 #521, 0.12 #863, 0.08 #2238), 0c41qv (0.15 #2474, 0.14 #3165, 0.10 #1616), 024rgt (0.10 #1571, 0.10 #1226, 0.08 #2429), 031rq5 (0.10 #1598, 0.10 #1253, 0.08 #2456), 07733f (0.10 #1695, 0.10 #1350, 0.08 #2553), 03yxwq (0.10 #1610, 0.10 #1265, 0.08 #2468) >> Best rule #251 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 056ws9; >> query: (?x1954, 04gmlt) <- organizations_founded(?x9373, ?x1954), ?x9373 = 01vhrz, award(?x1954, ?x3105), award_winner(?x3105, ?x1047) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fb0v child 0181hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 60.000 0.250 http://example.org/organization/organization/child./organization/organization_relationship/child #7407-042xh PRED entity: 042xh PRED relation: influenced_by PRED expected values: 0dz46 => 178 concepts (64 used for prediction) PRED predicted values (max 10 best out of 364): 0g5ff (0.33 #3235, 0.22 #8447, 0.17 #2366), 02lt8 (0.33 #120, 0.21 #9677, 0.19 #1857), 0ky1 (0.33 #360, 0.17 #2531, 0.10 #27814), 03_87 (0.31 #9760, 0.29 #17581, 0.29 #18452), 040_9 (0.28 #2269, 0.24 #9655, 0.10 #27814), 032l1 (0.26 #9646, 0.17 #17467, 0.16 #18338), 01v9724 (0.25 #17556, 0.24 #18427, 0.17 #2349), 03hnd (0.25 #3139, 0.18 #8351, 0.17 #13131), 06bng (0.25 #3320, 0.14 #8687, 0.14 #8532), 09dt7 (0.25 #3072, 0.14 #8284, 0.11 #13064) >> Best rule #3235 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 01dzz7; 05jm7; 04mhl; 02y49; 0gd_s; 05qzv; 05cv8; 03v36; 01k56k; >> query: (?x13644, 0g5ff) <- influenced_by(?x13644, ?x2343), award(?x13644, ?x3337), ?x3337 = 01yz0x, profession(?x13644, ?x319) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2476 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 06hmd; *> query: (?x13644, 0dz46) <- influenced_by(?x13644, ?x6810), ?x6810 = 037jz, profession(?x13644, ?x319) *> conf = 0.06 ranks of expected_values: 151 EVAL 042xh influenced_by 0dz46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 178.000 64.000 0.333 http://example.org/influence/influence_node/influenced_by #7406-03_0p PRED entity: 03_0p PRED relation: artists! PRED expected values: 0ggq0m => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 192): 06by7 (0.46 #7280, 0.44 #10119, 0.43 #6964), 03_d0 (0.39 #958, 0.39 #642, 0.33 #1904), 064t9 (0.39 #5378, 0.38 #6955, 0.36 #3799), 06j6l (0.30 #997, 0.23 #5415, 0.23 #8571), 016clz (0.27 #10101, 0.21 #10732, 0.20 #4106), 0gywn (0.26 #3846, 0.22 #5425, 0.19 #9212), 017_qw (0.25 #66, 0.24 #3536, 0.14 #1958), 021dvj (0.25 #54, 0.10 #1946, 0.10 #684), 0d6n1 (0.25 #144, 0.10 #2036, 0.06 #774), 06q6jz (0.25 #192, 0.10 #2715, 0.10 #822) >> Best rule #7280 for best value: >> intensional similarity = 3 >> extensional distance = 219 >> proper extension: 0c9d9; 0274ck; 01w923; 012zng; 02jg92; 01tp5bj; 03xl77; 0gkg6; 01vv6_6; 0bkg4; ... >> query: (?x5150, 06by7) <- category(?x5150, ?x134), role(?x5150, ?x75), type_of_union(?x5150, ?x566) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #2536 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 0k4gf; 0hgqq; 03llf8; 03_f0; 01vsqvs; 0hqgp; 0c73z; 0459z; 0c73g; *> query: (?x5150, 0ggq0m) <- gender(?x5150, ?x231), people(?x10199, ?x5150), instrumentalists(?x75, ?x5150) *> conf = 0.14 ranks of expected_values: 27 EVAL 03_0p artists! 0ggq0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 126.000 126.000 0.462 http://example.org/music/genre/artists #7405-0p5mw PRED entity: 0p5mw PRED relation: role PRED expected values: 0680x0 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 114): 0342h (0.71 #304, 0.66 #804, 0.61 #604), 05r5c (0.55 #1407, 0.53 #1307, 0.51 #407), 042v_gx (0.54 #808, 0.42 #308, 0.25 #608), 01vdm0 (0.34 #431, 0.29 #2131, 0.29 #1831), 0l14qv (0.29 #405, 0.29 #105, 0.27 #205), 013y1f (0.29 #536, 0.29 #436, 0.23 #236), 05842k (0.27 #875, 0.24 #175, 0.20 #475), 026t6 (0.26 #302, 0.18 #802, 0.16 #1802), 05148p4 (0.23 #422, 0.20 #522, 0.20 #222), 01vj9c (0.20 #414, 0.17 #514, 0.17 #214) >> Best rule #304 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0161sp; 03h502k; 082brv; 01d4cb; >> query: (?x1887, 0342h) <- profession(?x1887, ?x1614), ?x1614 = 01c72t, role(?x1887, ?x314), ?x314 = 02sgy >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #271 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 01w923; 0lgm5; 024zq; 0lsw9; 01r0t_j; 02rn_bj; 023322; *> query: (?x1887, 0680x0) <- profession(?x1887, ?x1614), ?x1614 = 01c72t, category(?x1887, ?x134), performance_role(?x1887, ?x75) *> conf = 0.10 ranks of expected_values: 27 EVAL 0p5mw role 0680x0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 65.000 65.000 0.710 http://example.org/music/artist/track_contributions./music/track_contribution/role #7404-0mnsf PRED entity: 0mnsf PRED relation: contains! PRED expected values: 07z1m => 138 concepts (61 used for prediction) PRED predicted values (max 10 best out of 215): 09c7w0 (0.75 #11655, 0.74 #19720, 0.73 #36752), 07z1m (0.71 #31371, 0.70 #32268, 0.68 #46609), 0mnsf (0.53 #28682, 0.53 #51098, 0.53 #51097), 04_1l0v (0.37 #15688, 0.25 #451, 0.21 #54241), 05k7sb (0.33 #18954, 0.32 #5511, 0.32 #18058), 07c5l (0.25 #395, 0.03 #15632, 0.01 #53289), 01n7q (0.23 #11730, 0.22 #7248, 0.22 #2767), 01x73 (0.20 #5493, 0.17 #4597, 0.15 #18040), 059rby (0.17 #2709, 0.13 #3606, 0.12 #35872), 02_286 (0.13 #3629, 0.13 #2732, 0.11 #1836) >> Best rule #11655 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 0rgxp; 04pry; >> query: (?x7478, 09c7w0) <- source(?x7478, ?x958), location(?x8996, ?x7478), ?x958 = 0jbk9, athlete(?x4833, ?x8996) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #31371 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 134 *> proper extension: 01qq80; *> query: (?x7478, ?x1426) <- citytown(?x347, ?x7478), category(?x7478, ?x134), time_zones(?x7478, ?x2674), state_province_region(?x347, ?x1426) *> conf = 0.71 ranks of expected_values: 2 EVAL 0mnsf contains! 07z1m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 61.000 0.755 http://example.org/location/location/contains #7403-016ckq PRED entity: 016ckq PRED relation: artist PRED expected values: 012z8_ 03q2t9 01mxqyk 0163kf => 116 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1101): 01vxlbm (0.50 #2702, 0.43 #4332, 0.33 #257), 01k23t (0.43 #4625, 0.33 #2995, 0.17 #13589), 0136p1 (0.33 #920, 0.33 #103, 0.15 #9068), 03f1d47 (0.33 #1165, 0.33 #348, 0.14 #4423), 01lw3kh (0.33 #1248, 0.33 #431, 0.08 #9396), 0g824 (0.33 #2886, 0.29 #4516, 0.18 #7776), 011z3g (0.33 #2908, 0.29 #4538, 0.10 #21647), 0ffgh (0.33 #2938, 0.29 #4568, 0.09 #15975), 01vrt_c (0.33 #2502, 0.29 #4132, 0.08 #8207), 019g40 (0.33 #917, 0.25 #8250, 0.23 #9065) >> Best rule #2702 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 06wcbk7; 01cszh; 01f_3w; 01q940; >> query: (?x7448, 01vxlbm) <- artist(?x7448, ?x8874), artist(?x7448, ?x483), ?x8874 = 03f7jfh, influenced_by(?x483, ?x1136), gender(?x483, ?x231), award(?x483, ?x247) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8114 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 046b0s; 024rgt; 02j_j0; 05gnf; 0c41qv; *> query: (?x7448, 0163kf) <- citytown(?x7448, ?x739), company(?x4960, ?x7448), child(?x12171, ?x7448), artist(?x12171, ?x2352) *> conf = 0.09 ranks of expected_values: 397, 418, 486, 771 EVAL 016ckq artist 0163kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 116.000 44.000 0.500 http://example.org/music/record_label/artist EVAL 016ckq artist 01mxqyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 116.000 44.000 0.500 http://example.org/music/record_label/artist EVAL 016ckq artist 03q2t9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 116.000 44.000 0.500 http://example.org/music/record_label/artist EVAL 016ckq artist 012z8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 116.000 44.000 0.500 http://example.org/music/record_label/artist #7402-05t0zfv PRED entity: 05t0zfv PRED relation: genre PRED expected values: 0hcr => 88 concepts (41 used for prediction) PRED predicted values (max 10 best out of 93): 0hcr (0.81 #2715, 0.80 #2105, 0.78 #2593), 01jfsb (0.79 #3056, 0.79 #2947, 0.77 #5019), 02kdv5l (0.77 #5019, 0.71 #1225, 0.68 #1345), 03k9fj (0.77 #5019, 0.70 #4035, 0.68 #4034), 05p553 (0.73 #1350, 0.47 #3428, 0.44 #3794), 06n90 (0.68 #1345, 0.66 #5022, 0.66 #5021), 03npn (0.68 #1345, 0.66 #5022, 0.66 #5021), 01hmnh (0.62 #1120, 0.59 #1855, 0.57 #2465), 07s9rl0 (0.53 #2812, 0.50 #3302, 0.49 #3668), 03q4nz (0.50 #631, 0.43 #2466, 0.40 #2222) >> Best rule #2715 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 04svwx; >> query: (?x10187, 0hcr) <- genre(?x10187, ?x5937), ?x5937 = 0jxy, country(?x10187, ?x252), ?x252 = 03_3d >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05t0zfv genre 0hcr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 41.000 0.812 http://example.org/film/film/genre #7401-03f1zdw PRED entity: 03f1zdw PRED relation: award_nominee! PRED expected values: 05vsxz => 103 concepts (48 used for prediction) PRED predicted values (max 10 best out of 826): 0lpjn (0.81 #23024, 0.81 #59868, 0.81 #96709), 0dgskx (0.81 #23024, 0.81 #59868, 0.81 #96709), 01tspc6 (0.81 #23024, 0.81 #59868, 0.81 #96709), 0m2wm (0.81 #23024, 0.81 #59868, 0.81 #96709), 020_95 (0.81 #23024, 0.81 #59868, 0.81 #96709), 02zq43 (0.81 #23024, 0.81 #59868, 0.81 #96709), 06mmb (0.81 #23024, 0.81 #59868, 0.81 #96709), 015rkw (0.81 #23024, 0.81 #59868, 0.81 #96709), 02l4rh (0.81 #23024, 0.81 #59868, 0.81 #96709), 03f1zdw (0.65 #4845, 0.32 #57565, 0.15 #108222) >> Best rule #23024 for best value: >> intensional similarity = 3 >> extensional distance = 122 >> proper extension: 023tp8; 01fwj8; 01gq0b; 0993r; 02g0mx; 01d1st; 036dyy; >> query: (?x1222, ?x57) <- award(?x1222, ?x1336), award_nominee(?x1222, ?x57), ?x1336 = 05pcn59 >> conf = 0.81 => this is the best rule for 9 predicted values *> Best rule #4610 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 18 *> proper extension: 01qrbf; 0djywgn; *> query: (?x1222, 05vsxz) <- award_nominee(?x3034, ?x1222), ?x3034 = 0993r *> conf = 0.65 ranks of expected_values: 11 EVAL 03f1zdw award_nominee! 05vsxz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 103.000 48.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7400-02gqm3 PRED entity: 02gqm3 PRED relation: language PRED expected values: 02h40lc => 52 concepts (45 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.92 #358, 0.91 #658, 0.90 #837), 06mp7 (0.25 #16, 0.09 #75, 0.08 #134), 064_8sq (0.20 #259, 0.20 #496, 0.17 #140), 04306rv (0.18 #600, 0.17 #479, 0.15 #539), 04h9h (0.18 #102, 0.17 #161, 0.15 #221), 06nm1 (0.11 #905, 0.10 #965, 0.10 #1025), 0jzc (0.10 #554, 0.07 #615, 0.06 #376), 03_9r (0.09 #425, 0.09 #69, 0.08 #128), 0653m (0.09 #71, 0.08 #130, 0.08 #190), 0349s (0.09 #104, 0.08 #163, 0.08 #223) >> Best rule #358 for best value: >> intensional similarity = 5 >> extensional distance = 47 >> proper extension: 0gcrg; 0cq8nx; >> query: (?x10047, 02h40lc) <- film_art_direction_by(?x10047, ?x8719), film(?x382, ?x10047), genre(?x10047, ?x225), profession(?x8719, ?x1032), nationality(?x8719, ?x512) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02gqm3 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 45.000 0.918 http://example.org/film/film/language #7399-0gj9tn5 PRED entity: 0gj9tn5 PRED relation: nominated_for! PRED expected values: 0gy6z9 => 62 concepts (23 used for prediction) PRED predicted values (max 10 best out of 829): 05_swj (0.34 #35069, 0.20 #1486, 0.17 #21041), 0jbp0 (0.32 #9353, 0.23 #44422, 0.23 #44421), 03fbb6 (0.32 #9353, 0.23 #44422, 0.23 #44421), 02tqkf (0.32 #9353, 0.23 #44422, 0.23 #44421), 0pz91 (0.25 #4943, 0.08 #14293, 0.03 #9619), 027j79k (0.20 #1963, 0.11 #21040, 0.06 #49099), 027xbpw (0.20 #707, 0.11 #21040, 0.06 #49099), 03jldb (0.20 #304, 0.11 #21040, 0.06 #49099), 0d9_96 (0.20 #706, 0.06 #49099, 0.02 #5383), 0146pg (0.20 #16486, 0.05 #18823, 0.03 #46882) >> Best rule #35069 for best value: >> intensional similarity = 3 >> extensional distance = 656 >> proper extension: 01kf3_9; 03lrqw; 01f7kl; 0p3_y; 01kf4tt; 059lwy; 08l0x2; >> query: (?x1785, ?x6891) <- film_release_distribution_medium(?x1785, ?x81), nominated_for(?x298, ?x1785), music(?x1785, ?x6891) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #21040 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 227 *> proper extension: 08cx5g; *> query: (?x1785, ?x3293) <- nominated_for(?x4956, ?x1785), nominated_for(?x3673, ?x1785), program(?x3673, ?x1395), award_nominee(?x4956, ?x3293), award_winner(?x6891, ?x3673) *> conf = 0.11 ranks of expected_values: 14 EVAL 0gj9tn5 nominated_for! 0gy6z9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 62.000 23.000 0.339 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #7398-0k269 PRED entity: 0k269 PRED relation: nominated_for PRED expected values: 0180mw => 80 concepts (55 used for prediction) PRED predicted values (max 10 best out of 378): 07tj4c (0.82 #25851, 0.80 #66255, 0.80 #56559), 05mrf_p (0.82 #25851, 0.80 #66255, 0.80 #56559), 0dfw0 (0.29 #25852, 0.28 #9694, 0.26 #32315), 0ddt_ (0.29 #25852, 0.28 #9694, 0.26 #32315), 0f4_2k (0.29 #25852, 0.28 #9694, 0.26 #32315), 0660b9b (0.29 #25852, 0.28 #9694, 0.26 #32315), 047p7fr (0.29 #25852, 0.28 #9694, 0.26 #32315), 0c40vxk (0.29 #25852, 0.28 #9694, 0.26 #32315), 0415ggl (0.29 #25852, 0.28 #9694, 0.26 #32315), 0qm8b (0.29 #25852, 0.28 #9694, 0.26 #32315) >> Best rule #25851 for best value: >> intensional similarity = 3 >> extensional distance = 882 >> proper extension: 06_bq1; >> query: (?x3580, ?x5074) <- nominated_for(?x3580, ?x308), award_winner(?x5074, ?x3580), film(?x3580, ?x633) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #26886 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 904 *> proper extension: 01v3vp; *> query: (?x3580, 0180mw) <- nominated_for(?x3580, ?x5109), honored_for(?x873, ?x5109), location(?x3580, ?x1523) *> conf = 0.02 ranks of expected_values: 83 EVAL 0k269 nominated_for 0180mw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 80.000 55.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #7397-0gt14 PRED entity: 0gt14 PRED relation: award PRED expected values: 0czp_ => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 191): 0gs9p (0.32 #65, 0.28 #1473, 0.26 #300), 0gq9h (0.31 #235, 0.26 #1471, 0.25 #63), 0gq_v (0.31 #235, 0.24 #704, 0.23 #706), 0gr4k (0.31 #235, 0.24 #704, 0.23 #705), 0gqy2 (0.31 #235, 0.24 #704, 0.22 #15239), 0k611 (0.28 #1482, 0.27 #74, 0.25 #309), 0p9sw (0.20 #489, 0.18 #20, 0.18 #255), 0gr0m (0.18 #529, 0.17 #1468, 0.14 #2170), 0gs96 (0.18 #2199, 0.15 #558, 0.10 #1497), 019f4v (0.18 #1462, 0.18 #54, 0.16 #289) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 03pc89; >> query: (?x12113, 0gs9p) <- list(?x12113, ?x3004), nominated_for(?x484, ?x12113), currency(?x12113, ?x170), film_release_region(?x12113, ?x94) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1597 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 07bz5; *> query: (?x12113, 0czp_) <- list(?x12113, ?x3004), nominated_for(?x541, ?x12113), award(?x12113, ?x1862), award_winner(?x770, ?x541) *> conf = 0.02 ranks of expected_values: 116 EVAL 0gt14 award 0czp_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 73.000 73.000 0.321 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #7396-02l424 PRED entity: 02l424 PRED relation: school! PRED expected values: 0jmj7 => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 93): 0jmj7 (0.71 #5626, 0.70 #1708, 0.65 #7116), 01slc (0.42 #523, 0.21 #616, 0.20 #337), 02d02 (0.33 #69, 0.27 #441, 0.14 #162), 04wmvz (0.33 #79, 0.27 #451, 0.14 #172), 01d6g (0.33 #72, 0.27 #444, 0.14 #165), 01ypc (0.33 #1, 0.18 #373, 0.14 #559), 0487_ (0.33 #62, 0.18 #434, 0.14 #155), 03wnh (0.33 #51, 0.18 #423, 0.14 #144), 06rpd (0.33 #74, 0.17 #539, 0.14 #632), 07l4z (0.33 #70, 0.14 #1749, 0.14 #628) >> Best rule #5626 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 02zc7f; >> query: (?x9620, 0jmj7) <- category(?x9620, ?x134), colors(?x9620, ?x663), currency(?x9620, ?x170), school(?x4571, ?x9620) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02l424 school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 180.000 180.000 0.714 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #7395-06rgq PRED entity: 06rgq PRED relation: award PRED expected values: 0c4z8 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 288): 054krc (0.58 #4409, 0.30 #6767, 0.25 #11483), 02qvyrt (0.42 #4447, 0.23 #6805, 0.21 #11521), 0l8z1 (0.40 #4386, 0.24 #6744, 0.20 #11460), 02f71y (0.38 #5679, 0.18 #4107, 0.16 #9609), 09sb52 (0.34 #27158, 0.32 #18512, 0.31 #19298), 02f705 (0.34 #5650, 0.19 #9580, 0.16 #4078), 02f73b (0.32 #5779, 0.23 #4207, 0.21 #2635), 01c9f2 (0.32 #6370, 0.15 #48735, 0.15 #47555), 0gqz2 (0.31 #6761, 0.31 #4403, 0.26 #11477), 05zr6wv (0.30 #1589, 0.25 #1982, 0.21 #11807) >> Best rule #4409 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 02rgz4; 01nqfh_; 0p5mw; 05_pkf; >> query: (?x8490, 054krc) <- instrumentalists(?x227, ?x8490), nominated_for(?x8490, ?x3854), music(?x6994, ?x8490) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #17756 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 160 *> proper extension: 04lgymt; 0jdhp; 01x15dc; 02_jkc; 016732; 01wyq0w; 010xjr; 05mxw33; *> query: (?x8490, 0c4z8) <- award_nominee(?x248, ?x8490), award(?x8490, ?x2139), ?x2139 = 01by1l *> conf = 0.30 ranks of expected_values: 12 EVAL 06rgq award 0c4z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 145.000 145.000 0.578 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7394-0g1rw PRED entity: 0g1rw PRED relation: award_nominee PRED expected values: 017s11 => 115 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1189): 02hy9p (0.81 #149373, 0.79 #9336, 0.77 #93349), 016z1c (0.77 #151708, 0.77 #165715, 0.77 #163380), 017s11 (0.27 #9443, 0.20 #21111, 0.19 #32778), 016tt2 (0.24 #21117, 0.23 #2446, 0.20 #9449), 0jrqq (0.23 #5548, 0.23 #3213, 0.23 #880), 0kx4m (0.23 #4839, 0.23 #2504, 0.23 #171), 0343h (0.23 #2625, 0.15 #4960, 0.15 #292), 03k545 (0.21 #121360, 0.18 #168050), 0gv40 (0.21 #121360, 0.08 #5772, 0.08 #3437), 0g1rw (0.21 #121360, 0.08 #4813, 0.07 #23482) >> Best rule #149373 for best value: >> intensional similarity = 3 >> extensional distance = 1110 >> proper extension: 06vqdf; 0cbxl0; >> query: (?x788, ?x1172) <- nominated_for(?x788, ?x1804), award_winner(?x788, ?x1850), award_nominee(?x1172, ?x788) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #9443 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 05gnf; *> query: (?x788, 017s11) <- nominated_for(?x788, ?x1804), film(?x788, ?x186), organization(?x4682, ?x788) *> conf = 0.27 ranks of expected_values: 3 EVAL 0g1rw award_nominee 017s11 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 73.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7393-0404j37 PRED entity: 0404j37 PRED relation: nominated_for! PRED expected values: 02hsq3m 09sb52 02x1dht 0l8z1 019f4v 09qv_s => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 202): 0gr51 (0.67 #4810, 0.67 #658, 0.66 #6344), 02r22gf (0.67 #4810, 0.67 #658, 0.66 #6344), 0279c15 (0.67 #4810, 0.67 #658, 0.66 #6344), 099c8n (0.67 #4810, 0.67 #658, 0.66 #6344), 094qd5 (0.39 #250, 0.25 #31, 0.15 #3964), 027dtxw (0.39 #222, 0.16 #1316, 0.15 #1534), 0gqy2 (0.35 #328, 0.27 #1640, 0.27 #1860), 019f4v (0.33 #1579, 0.33 #1799, 0.32 #2891), 05ztjjw (0.33 #446, 0.17 #226, 0.13 #665), 0gqyl (0.30 #288, 0.20 #2912, 0.19 #4002) >> Best rule #4810 for best value: >> intensional similarity = 3 >> extensional distance = 655 >> proper extension: 04glx0; >> query: (?x6448, ?x68) <- award_winner(?x6448, ?x9449), award(?x6448, ?x68), award_winner(?x2670, ?x9449) >> conf = 0.67 => this is the best rule for 4 predicted values *> Best rule #1579 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 302 *> proper extension: 02d413; 0m313; 0g22z; 0b2v79; 01jc6q; 028_yv; 09m6kg; 0c0yh4; 0yyg4; 011yrp; ... *> query: (?x6448, 019f4v) <- genre(?x6448, ?x53), award(?x6448, ?x68), films(?x2391, ?x6448), nominated_for(?x68, ?x69) *> conf = 0.33 ranks of expected_values: 8, 11, 12, 15, 35, 37 EVAL 0404j37 nominated_for! 09qv_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 70.000 70.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0404j37 nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 70.000 70.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0404j37 nominated_for! 0l8z1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 70.000 70.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0404j37 nominated_for! 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 70.000 70.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0404j37 nominated_for! 09sb52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 70.000 70.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0404j37 nominated_for! 02hsq3m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 70.000 70.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7392-0jqzt PRED entity: 0jqzt PRED relation: story_by PRED expected values: 04_by => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 43): 079vf (0.04 #866, 0.03 #218, 0.02 #2163), 04l3_z (0.03 #1091, 0.02 #1307, 0.02 #1523), 04hw4b (0.03 #988, 0.02 #340, 0.01 #2935), 04zd4m (0.03 #881, 0.02 #233, 0.01 #2178), 0fx02 (0.02 #5683, 0.01 #6548, 0.01 #7414), 04jspq (0.02 #332, 0.01 #548, 0.01 #764), 0343h (0.02 #234, 0.01 #450, 0.01 #666), 0jt90f5 (0.02 #248, 0.01 #2843, 0.01 #896), 041h0 (0.02 #221, 0.01 #869, 0.01 #2166), 02nygk (0.02 #427, 0.01 #1075) >> Best rule #866 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 01d259; 0g57wgv; >> query: (?x11074, 079vf) <- film_release_region(?x11074, ?x4737), film_release_region(?x11074, ?x1499), ?x4737 = 07twz, olympics(?x1499, ?x584) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #609 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 0cq7tx; 0jsqk; 03kx49; *> query: (?x11074, 04_by) <- film(?x1104, ?x11074), genre(?x11074, ?x571), list(?x11074, ?x3004), titles(?x13390, ?x11074) *> conf = 0.01 ranks of expected_values: 15 EVAL 0jqzt story_by 04_by CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 53.000 53.000 0.038 http://example.org/film/film/story_by #7391-03f0fnk PRED entity: 03f0fnk PRED relation: profession PRED expected values: 0dz3r => 150 concepts (106 used for prediction) PRED predicted values (max 10 best out of 91): 02hrh1q (0.83 #11486, 0.82 #10758, 0.81 #8719), 0dxtg (0.63 #4074, 0.52 #3929, 0.50 #158), 0cbd2 (0.52 #7550, 0.49 #7695, 0.48 #4938), 02jknp (0.50 #442, 0.38 #152, 0.33 #7), 01c72t (0.47 #1328, 0.41 #2198, 0.41 #3069), 0dz3r (0.47 #7256, 0.46 #4353, 0.45 #1017), 01d_h8 (0.46 #3921, 0.43 #440, 0.39 #4066), 0n1h (0.38 #156, 0.35 #1896, 0.32 #1026), 018gz8 (0.36 #4078, 0.29 #3933, 0.24 #4804), 0kyk (0.35 #7573, 0.34 #5541, 0.33 #4526) >> Best rule #11486 for best value: >> intensional similarity = 3 >> extensional distance = 500 >> proper extension: 013bd1; 045n3p; >> query: (?x4712, 02hrh1q) <- profession(?x4712, ?x220), award(?x4712, ?x2322), languages(?x4712, ?x254) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #7256 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 258 *> proper extension: 025tdwc; *> query: (?x4712, 0dz3r) <- profession(?x4712, ?x2348), profession(?x4712, ?x1183), ?x1183 = 09jwl, ?x2348 = 0nbcg *> conf = 0.47 ranks of expected_values: 6 EVAL 03f0fnk profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 150.000 106.000 0.835 http://example.org/people/person/profession #7390-0b_dy PRED entity: 0b_dy PRED relation: award_winner! PRED expected values: 027b9j5 => 81 concepts (79 used for prediction) PRED predicted values (max 10 best out of 194): 09sb52 (0.37 #15375, 0.36 #12385, 0.34 #16230), 0f4x7 (0.37 #15375, 0.36 #12385, 0.34 #16230), 0gqy2 (0.37 #15375, 0.36 #12385, 0.34 #16230), 027dtxw (0.37 #15375, 0.36 #12385, 0.34 #16230), 0ck27z (0.11 #946, 0.11 #92, 0.10 #519), 0cqhk0 (0.08 #891, 0.07 #37, 0.07 #464), 099tbz (0.07 #911, 0.07 #18367, 0.07 #57), 0gq9h (0.07 #18367, 0.05 #29479, 0.05 #26485), 040njc (0.07 #18367, 0.05 #29479, 0.05 #26485), 02x73k6 (0.07 #18367, 0.05 #29479, 0.05 #26485) >> Best rule #15375 for best value: >> intensional similarity = 2 >> extensional distance = 1462 >> proper extension: 030_1_; 014hr0; 0khth; 014l4w; 07mvp; 032dg7; 04k05; 014g91; 07k2d; >> query: (?x3139, ?x112) <- award(?x3139, ?x112), award_winner(?x156, ?x3139) >> conf = 0.37 => this is the best rule for 4 predicted values *> Best rule #29479 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2301 *> proper extension: 0181hw; *> query: (?x3139, ?x401) <- award_nominee(?x3139, ?x7946), award(?x7946, ?x401) *> conf = 0.05 ranks of expected_values: 53 EVAL 0b_dy award_winner! 027b9j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 81.000 79.000 0.368 http://example.org/award/award_category/winners./award/award_honor/award_winner #7389-0697kh PRED entity: 0697kh PRED relation: award_winner! PRED expected values: 07y9ts => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 116): 07y9ts (0.19 #68, 0.10 #9175, 0.03 #2014), 0gx_st (0.16 #37, 0.05 #1010, 0.04 #1983), 02q690_ (0.13 #65, 0.07 #1038, 0.06 #2011), 07z31v (0.13 #31, 0.04 #1004, 0.03 #1977), 05c1t6z (0.10 #15, 0.07 #154, 0.07 #571), 09v0p2c (0.10 #83, 0.07 #639, 0.06 #222), 03gt46z (0.10 #63, 0.05 #619, 0.05 #202), 09q_6t (0.10 #8, 0.04 #425, 0.04 #286), 0bxs_d (0.10 #114, 0.04 #1087, 0.03 #392), 0gvstc3 (0.09 #173, 0.08 #590, 0.06 #34) >> Best rule #68 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 05cqhl; >> query: (?x8337, 07y9ts) <- award_nominee(?x2650, ?x8337), award_winner(?x4921, ?x8337), ?x4921 = 0fbtbt >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0697kh award_winner! 07y9ts CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.194 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7388-03_0p PRED entity: 03_0p PRED relation: place_of_death PRED expected values: 02_286 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 59): 030qb3t (0.18 #1964, 0.17 #992, 0.17 #2546), 02_286 (0.13 #1165, 0.13 #595, 0.13 #2537), 04swd (0.13 #1165, 0.07 #5630, 0.06 #896), 0k049 (0.11 #585, 0.08 #973, 0.07 #1945), 01tlmw (0.09 #10, 0.03 #398, 0.02 #786), 05jbn (0.09 #459, 0.06 #847, 0.04 #1236), 06_kh (0.07 #587, 0.06 #393, 0.06 #1364), 04jpl (0.06 #395, 0.06 #1172, 0.06 #783), 0r3w7 (0.06 #565, 0.03 #1147, 0.03 #2119), 0f2wj (0.06 #3507, 0.05 #982, 0.04 #3313) >> Best rule #1964 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 076lxv; 01jrz5j; 057d89; 01tcf7; 02lxj_; 05x2t7; 015gw6; 03r1pr; 098n5; 02q5xsx; ... >> query: (?x5150, 030qb3t) <- award_winner(?x5150, ?x1399), people(?x10199, ?x5150), award(?x5150, ?x2324) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #1165 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 0grmhb; *> query: (?x5150, ?x739) <- award_winner(?x5150, ?x5132), people(?x10199, ?x5150), place_of_death(?x5132, ?x739) *> conf = 0.13 ranks of expected_values: 2 EVAL 03_0p place_of_death 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 122.000 0.179 http://example.org/people/deceased_person/place_of_death #7387-01z9v6 PRED entity: 01z9v6 PRED relation: position! PRED expected values: 05g76 => 29 concepts (23 used for prediction) PRED predicted values (max 10 best out of 307): 0x2p (0.86 #49, 0.86 #67, 0.83 #85), 01ync (0.86 #49, 0.86 #67, 0.83 #85), 03m1n (0.86 #49, 0.86 #67, 0.83 #85), 06wpc (0.83 #85, 0.83 #50, 0.83 #48), 04b5l3 (0.83 #48, 0.83 #14, 0.83 #39), 05g76 (0.75 #51, 0.60 #32, 0.55 #77), 051wf (0.36 #38, 0.35 #13, 0.27 #83), 04vn5 (0.36 #38, 0.35 #13, 0.27 #83), 0jmj7 (0.36 #38, 0.35 #13, 0.27 #83), 01xvb (0.36 #38, 0.35 #13, 0.27 #83) >> Best rule #49 for best value: >> intensional similarity = 29 >> extensional distance = 4 >> proper extension: 02dwpf; >> query: (?x8520, ?x4487) <- position(?x13733, ?x8520), position(?x8894, ?x8520), position(?x8521, ?x8520), position(?x7060, ?x8520), position(?x4243, ?x8520), position(?x700, ?x8520), position(?x260, ?x8520), team(?x8520, ?x4487), team(?x8520, ?x2405), school(?x700, ?x12761), school(?x700, ?x9745), ?x4243 = 0713r, ?x9745 = 01jpqb, state_province_region(?x12761, ?x2020), season(?x700, ?x701), major_field_of_study(?x12761, ?x1154), draft(?x2405, ?x3334), draft(?x700, ?x1633), category(?x12761, ?x134), ?x260 = 01ypc, team(?x11844, ?x4487), team(?x5412, ?x2405), sport(?x8521, ?x5063), school(?x4487, ?x581), institution(?x1200, ?x12761), teams(?x479, ?x8894), ?x7060 = 01slc, ?x1200 = 016t_3, colors(?x13733, ?x332) >> conf = 0.86 => this is the best rule for 3 predicted values *> Best rule #51 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 6 *> proper extension: 02dwn9; 02s7tr; 02rsl1; *> query: (?x8520, 05g76) <- position(?x4243, ?x8520), position(?x1632, ?x8520), position(?x700, ?x8520), team(?x8520, ?x2405), school(?x700, ?x12761), school(?x700, ?x9745), school(?x700, ?x4296), ?x4243 = 0713r, ?x9745 = 01jpqb, state_province_region(?x12761, ?x2020), season(?x700, ?x701), major_field_of_study(?x12761, ?x1154), ?x2405 = 0x2p, colors(?x12761, ?x1101), teams(?x739, ?x1632), draft(?x700, ?x1633), institution(?x620, ?x12761), school(?x1632, ?x735), major_field_of_study(?x4296, ?x1668), ?x701 = 05kcgsf, currency(?x12761, ?x170), student(?x4296, ?x3927), colors(?x4296, ?x8271), team(?x5412, ?x1632), colors(?x1632, ?x663) *> conf = 0.75 ranks of expected_values: 6 EVAL 01z9v6 position! 05g76 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 29.000 23.000 0.865 http://example.org/sports/sports_team/roster./baseball/baseball_roster_position/position #7386-01rr31 PRED entity: 01rr31 PRED relation: organization! PRED expected values: 060c4 => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 19): 060c4 (0.79 #277, 0.79 #394, 0.78 #342), 07xl34 (0.49 #182, 0.42 #143, 0.38 #90), 0dq_5 (0.16 #1314, 0.16 #1327, 0.15 #1353), 05k17c (0.14 #386, 0.13 #217, 0.11 #647), 05c0jwl (0.14 #163, 0.13 #137, 0.10 #176), 0dq3c (0.09 #1148, 0.07 #588, 0.07 #1266), 0fj45 (0.09 #1148, 0.07 #588, 0.07 #27), 0hm4q (0.09 #179, 0.07 #140, 0.06 #87), 04n1q6 (0.07 #1266, 0.03 #164, 0.02 #85), 09d6p2 (0.07 #1266, 0.01 #220) >> Best rule #277 for best value: >> intensional similarity = 5 >> extensional distance = 186 >> proper extension: 01t8sr; 02jyr8; 02t4yc; 021l5s; 0269kx; 02fs_d; 02xpy5; 04bfg; 01hnb; 02yr3z; ... >> query: (?x4845, 060c4) <- school_type(?x4845, ?x1044), state_province_region(?x4845, ?x13030), contains(?x550, ?x4845), currency(?x4845, ?x170), colors(?x4845, ?x663) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rr31 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 170.000 0.787 http://example.org/organization/role/leaders./organization/leadership/organization #7385-04gp58p PRED entity: 04gp58p PRED relation: language PRED expected values: 02h40lc => 74 concepts (73 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.89 #1777, 0.88 #2316, 0.88 #3804), 02bjrlw (0.33 #1, 0.20 #60, 0.14 #1124), 0jzc (0.22 #138, 0.14 #1124, 0.12 #256), 064_8sq (0.20 #553, 0.19 #612, 0.17 #671), 04306rv (0.19 #241, 0.17 #359, 0.17 #300), 03k50 (0.14 #1124, 0.11 #186, 0.11 #127), 02hwyss (0.14 #1124, 0.11 #219, 0.11 #160), 0880p (0.14 #1124, 0.11 #223, 0.06 #282), 012w70 (0.14 #1124, 0.11 #190, 0.06 #249), 0121sr (0.14 #1124, 0.11 #225, 0.06 #284) >> Best rule #1777 for best value: >> intensional similarity = 4 >> extensional distance = 702 >> proper extension: 02gs6r; >> query: (?x8283, 02h40lc) <- genre(?x8283, ?x1403), film(?x3585, ?x8283), film(?x9313, ?x8283), country(?x8283, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gp58p language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 73.000 0.893 http://example.org/film/film/language #7384-0418154 PRED entity: 0418154 PRED relation: award_winner PRED expected values: 0277470 => 33 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2746): 0h0wc (0.60 #7987, 0.50 #9520, 0.50 #6462), 04sry (0.50 #7176, 0.43 #14819, 0.40 #8701), 07g7h2 (0.50 #5553, 0.29 #14721, 0.25 #7078), 09v6gc9 (0.50 #5365, 0.29 #14533, 0.25 #6890), 01gq0b (0.50 #3308, 0.29 #14005, 0.25 #4837), 01wy5m (0.50 #3801, 0.25 #5330, 0.21 #19853), 03f1zdw (0.50 #3206, 0.25 #4735, 0.17 #29040), 0bj9k (0.50 #3329, 0.25 #4858, 0.14 #14026), 02zj61 (0.50 #4560, 0.25 #6089, 0.14 #15257), 01d8yn (0.50 #3609, 0.25 #5138, 0.14 #14306) >> Best rule #7987 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 0275n3y; >> query: (?x7767, 0h0wc) <- award_winner(?x7767, ?x8269), award_winner(?x7767, ?x6324), ceremony(?x2585, ?x7767), award(?x8532, ?x2585), award(?x5720, ?x2585), award(?x1231, ?x2585), award(?x300, ?x2585), ?x1231 = 01vrz41, ?x6324 = 018ygt, honored_for(?x7767, ?x1448), category(?x300, ?x134), award_winner(?x810, ?x8532), award_nominee(?x8532, ?x3662), award_winner(?x3519, ?x5720), award(?x8269, ?x102), award_winner(?x2585, ?x1292) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #19853 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 13 *> proper extension: 026kq4q; *> query: (?x7767, ?x3051) <- award_winner(?x7767, ?x829), award_winner(?x7767, ?x496), ceremony(?x2585, ?x7767), ?x2585 = 054ks3, honored_for(?x7767, ?x5736), honored_for(?x7767, ?x3822), honored_for(?x7767, ?x3310), nominated_for(?x2880, ?x5736), written_by(?x5736, ?x986), nominated_for(?x1116, ?x3822), award_winner(?x829, ?x830), nominated_for(?x435, ?x3310), award(?x156, ?x2880), award_nominee(?x525, ?x496), award_winner(?x591, ?x496), film(?x496, ?x69), award_winner(?x3310, ?x3051) *> conf = 0.21 ranks of expected_values: 134 EVAL 0418154 award_winner 0277470 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 33.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7383-015qqg PRED entity: 015qqg PRED relation: honored_for! PRED expected values: 03tn9w => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 113): 0bzlrh (0.33 #89, 0.16 #2198, 0.09 #7815), 0n8_m93 (0.17 #225, 0.03 #957, 0.02 #1831), 0c53zb (0.16 #2198, 0.09 #7815, 0.08 #7326), 03tn9w (0.16 #2198, 0.09 #7815, 0.08 #7326), 0bzjgq (0.16 #2198, 0.09 #7815, 0.08 #7326), 0dth6b (0.16 #2198, 0.09 #7815, 0.08 #7326), 0bzknt (0.16 #2198, 0.02 #1831, 0.02 #313), 026kq4q (0.16 #2198, 0.02 #281, 0.01 #891), 02yxh9 (0.09 #7815, 0.08 #7326, 0.03 #452), 0ftlkg (0.09 #7815, 0.08 #7326, 0.02 #1831) >> Best rule #89 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0p_qr; >> query: (?x4870, 0bzlrh) <- film(?x574, ?x4870), award_winner(?x4870, ?x1126), nominated_for(?x3690, ?x4870), ?x1126 = 0h1mt >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2198 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 665 *> proper extension: 07s8z_l; *> query: (?x4870, ?x1793) <- award_winner(?x4870, ?x2800), award_winner(?x1793, ?x2800), titles(?x53, ?x4870) *> conf = 0.16 ranks of expected_values: 4 EVAL 015qqg honored_for! 03tn9w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 86.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #7382-05dl1s PRED entity: 05dl1s PRED relation: film! PRED expected values: 05f260 => 108 concepts (74 used for prediction) PRED predicted values (max 10 best out of 53): 086k8 (0.40 #227, 0.17 #2554, 0.16 #3756), 016tw3 (0.22 #11, 0.14 #3091, 0.13 #536), 024rdh (0.22 #37, 0.10 #337, 0.04 #4239), 03xsby (0.22 #16, 0.06 #316, 0.04 #465), 05qd_ (0.20 #83, 0.19 #159, 0.18 #1509), 016tt2 (0.16 #78, 0.15 #304, 0.15 #154), 017s11 (0.15 #377, 0.14 #603, 0.14 #753), 03xq0f (0.13 #305, 0.09 #5038, 0.08 #1505), 0jz9f (0.12 #301, 0.08 #1277, 0.07 #825), 06jntd (0.11 #31, 0.08 #331, 0.02 #5064) >> Best rule #227 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 01j8wk; >> query: (?x11037, 086k8) <- country(?x11037, ?x390), film_release_distribution_medium(?x11037, ?x81), nominated_for(?x1197, ?x11037), ?x390 = 0chghy >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #971 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 346 *> proper extension: 0dgq_kn; *> query: (?x11037, 05f260) <- film(?x1197, ?x11037), award(?x11037, ?x8843), titles(?x53, ?x11037), country(?x11037, ?x279) *> conf = 0.01 ranks of expected_values: 52 EVAL 05dl1s film! 05f260 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 108.000 74.000 0.400 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7381-0300ml PRED entity: 0300ml PRED relation: award PRED expected values: 02xcb6n => 84 concepts (75 used for prediction) PRED predicted values (max 10 best out of 211): 0m7yy (0.59 #2004, 0.56 #2239, 0.52 #1769), 0fbvqf (0.44 #5153, 0.44 #7257, 0.44 #2107), 0bp_b2 (0.44 #5153, 0.44 #7257, 0.44 #2107), 0gkts9 (0.44 #5153, 0.44 #7257, 0.44 #2107), 0bdw6t (0.38 #1722, 0.33 #554, 0.32 #1957), 0cqhb3 (0.33 #1362, 0.32 #2063, 0.29 #1828), 0ck27z (0.32 #1944, 0.29 #1709, 0.25 #1243), 0gkr9q (0.32 #2073, 0.29 #1838, 0.23 #2543), 0bdx29 (0.29 #1721, 0.28 #2426, 0.27 #1956), 0cqh6z (0.29 #1692, 0.23 #1927, 0.19 #2162) >> Best rule #2004 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0g60z; 03kq98; 02k_4g; 0kfv9; 03d34x8; 02rzdcp; 0d66j2; 02md2d; 030p35; 0hz55; ... >> query: (?x12324, 0m7yy) <- genre(?x12324, ?x604), award(?x12324, ?x686), nominated_for(?x435, ?x12324), ?x435 = 0bp_b2, languages(?x12324, ?x254) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #2066 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 0g60z; 03kq98; 02k_4g; 0kfv9; 03d34x8; 02rzdcp; 0d66j2; 02md2d; 030p35; 0hz55; ... *> query: (?x12324, 02xcb6n) <- genre(?x12324, ?x604), award(?x12324, ?x686), nominated_for(?x435, ?x12324), ?x435 = 0bp_b2, languages(?x12324, ?x254) *> conf = 0.18 ranks of expected_values: 20 EVAL 0300ml award 02xcb6n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 84.000 75.000 0.591 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #7380-04353 PRED entity: 04353 PRED relation: award PRED expected values: 02pqp12 02x4wr9 => 122 concepts (76 used for prediction) PRED predicted values (max 10 best out of 284): 09d28z (0.70 #29014, 0.70 #27806, 0.69 #14904), 027c924 (0.70 #29014, 0.70 #27806, 0.69 #14904), 02pqp12 (0.60 #2082, 0.58 #1680, 0.55 #2887), 0gq9h (0.60 #478, 0.44 #3296, 0.41 #4101), 0gr4k (0.52 #434, 0.42 #2045, 0.40 #1643), 04dn09n (0.48 #445, 0.31 #2056, 0.30 #2861), 0gr51 (0.44 #499, 0.42 #2915, 0.40 #2110), 02x4wr9 (0.38 #1744, 0.35 #2146, 0.32 #2951), 03hl6lc (0.36 #578, 0.24 #2416, 0.22 #1787), 03hkv_r (0.36 #417, 0.24 #2416, 0.21 #2028) >> Best rule #29014 for best value: >> intensional similarity = 4 >> extensional distance = 1859 >> proper extension: 01vvydl; 09fqtq; 01wbgdv; 01k5t_3; 031zkw; 0c3kw; 01dzz7; 058s57; 040db; 01trhmt; ... >> query: (?x9313, ?x289) <- award_winner(?x289, ?x9313), nationality(?x9313, ?x94), profession(?x9313, ?x319), award(?x9313, ?x198) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #2082 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 01vqrm; 0405l; *> query: (?x9313, 02pqp12) <- award(?x9313, ?x1587), ?x1587 = 02rdyk7, film(?x9313, ?x69), nominated_for(?x68, ?x69) *> conf = 0.60 ranks of expected_values: 3, 8 EVAL 04353 award 02x4wr9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 122.000 76.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04353 award 02pqp12 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 122.000 76.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7379-04shbh PRED entity: 04shbh PRED relation: location_of_ceremony PRED expected values: 02_286 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 30): 03lrc (0.05 #465), 0cv3w (0.04 #1469, 0.04 #1588, 0.04 #2307), 0k049 (0.04 #603, 0.03 #841, 0.03 #722), 02_286 (0.02 #373, 0.02 #492, 0.02 #3841), 0b90_r (0.02 #363, 0.02 #1078, 0.01 #3473), 04lh6 (0.02 #437, 0.02 #1195, 0.01 #2870), 0f8l9c (0.02 #374, 0.01 #493), 0ggyr (0.02 #452, 0.01 #1526, 0.01 #1645), 0n3g (0.02 #422, 0.01 #1734), 0b_yz (0.02 #458) >> Best rule #465 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 01sxd1; >> query: (?x1018, 03lrc) <- spouse(?x548, ?x1018), nationality(?x1018, ?x1310), ?x1310 = 02jx1 >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #373 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 01sxd1; *> query: (?x1018, 02_286) <- spouse(?x548, ?x1018), nationality(?x1018, ?x1310), ?x1310 = 02jx1 *> conf = 0.02 ranks of expected_values: 4 EVAL 04shbh location_of_ceremony 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 123.000 123.000 0.048 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #7378-053ksp PRED entity: 053ksp PRED relation: written_by! PRED expected values: 07jqjx => 108 concepts (23 used for prediction) PRED predicted values (max 10 best out of 426): 064lsn (0.71 #660, 0.52 #11858, 0.46 #7905), 0sxg4 (0.55 #659, 0.51 #8564, 0.47 #1319), 07l450 (0.11 #589, 0.09 #1908, 0.09 #1249), 09gdh6k (0.11 #496, 0.09 #1815, 0.09 #1156), 0g9lm2 (0.11 #290, 0.09 #1609, 0.09 #950), 02vqsll (0.11 #193, 0.09 #1512, 0.09 #853), 0gyy53 (0.11 #187, 0.09 #1506, 0.09 #847), 0glbqt (0.11 #616, 0.09 #1935, 0.09 #1276), 0cq806 (0.11 #559, 0.09 #1878, 0.09 #1219), 01fx6y (0.11 #453, 0.09 #1772, 0.09 #1113) >> Best rule #660 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02645b; >> query: (?x10381, ?x2903) <- award(?x10381, ?x7606), ?x7606 = 01l78d, story_by(?x161, ?x10381), nominated_for(?x10381, ?x2903) >> conf = 0.71 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 053ksp written_by! 07jqjx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 23.000 0.706 http://example.org/film/film/written_by #7377-0fxmbn PRED entity: 0fxmbn PRED relation: genre PRED expected values: 03k9fj => 111 concepts (79 used for prediction) PRED predicted values (max 10 best out of 140): 01hmnh (0.80 #381, 0.64 #744, 0.40 #139), 07s9rl0 (0.64 #4723, 0.64 #5814, 0.61 #5329), 05p553 (0.62 #9331, 0.39 #6059, 0.36 #730), 03k9fj (0.60 #375, 0.52 #2675, 0.47 #4371), 0bkbm (0.50 #282, 0.31 #887, 0.28 #1008), 0lsxr (0.46 #8487, 0.43 #8851, 0.35 #8972), 06n90 (0.44 #3283, 0.43 #3767, 0.40 #134), 02l7c8 (0.41 #2558, 0.39 #2074, 0.35 #1832), 04pbhw (0.40 #178, 0.33 #662, 0.31 #3327), 0btmb (0.40 #210, 0.20 #89, 0.19 #3359) >> Best rule #381 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0cpllql; >> query: (?x7713, 01hmnh) <- film(?x3028, ?x7713), story_by(?x7713, ?x3686), ?x3028 = 0f0kz, currency(?x7713, ?x170), genre(?x7713, ?x225) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #375 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 0cpllql; *> query: (?x7713, 03k9fj) <- film(?x3028, ?x7713), story_by(?x7713, ?x3686), ?x3028 = 0f0kz, currency(?x7713, ?x170), genre(?x7713, ?x225) *> conf = 0.60 ranks of expected_values: 4 EVAL 0fxmbn genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 79.000 0.800 http://example.org/film/film/genre #7376-01bx35 PRED entity: 01bx35 PRED relation: award_winner PRED expected values: 01vvydl 014z8v 03f1d47 01vsgrn 06rgq 02f1c 02h9_l => 35 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1261): 02fn5r (0.78 #10866, 0.58 #13865, 0.57 #15365), 01vw20h (0.71 #9677, 0.50 #15675, 0.50 #12677), 02cx90 (0.71 #9646, 0.50 #15644, 0.50 #12646), 0gcs9 (0.57 #15425, 0.57 #9427, 0.50 #12427), 0hl3d (0.50 #13526, 0.50 #12028, 0.44 #10527), 032nwy (0.50 #15049, 0.50 #6052, 0.44 #10550), 05pdbs (0.50 #6158, 0.43 #15155, 0.33 #3159), 0pkyh (0.50 #7923, 0.43 #9422, 0.33 #3424), 09hnb (0.50 #7879, 0.40 #12378, 0.36 #15376), 01vvyvk (0.50 #6676, 0.36 #15673, 0.33 #11174) >> Best rule #10866 for best value: >> intensional similarity = 24 >> extensional distance = 7 >> proper extension: 09n4nb; >> query: (?x725, 02fn5r) <- ceremony(?x11048, ?x725), ceremony(?x9462, ?x725), ceremony(?x9295, ?x725), ceremony(?x8409, ?x725), ceremony(?x7594, ?x725), ceremony(?x6378, ?x725), ceremony(?x3835, ?x725), ceremony(?x2139, ?x725), ceremony(?x1565, ?x725), ?x1565 = 01c4_6, award_winner(?x725, ?x7549), ?x2139 = 01by1l, ?x7594 = 02v703, award(?x7086, ?x9462), award(?x5623, ?x9462), ?x8409 = 03ncb2, ?x6378 = 0249fn, ?x11048 = 03nl5k, ?x9295 = 023vrq, ?x5623 = 01vsyg9, ?x3835 = 01cky2, artist(?x2039, ?x7549), group(?x227, ?x7086), profession(?x7549, ?x1183) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #17335 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 15 *> proper extension: 0hhtgcw; *> query: (?x725, 01vsgrn) <- award_winner(?x725, ?x10924), award_winner(?x725, ?x5547), award_winner(?x10169, ?x5547), award_winner(?x2585, ?x5547), award_nominee(?x1573, ?x5547), award(?x5547, ?x7535), ?x10169 = 02f79n, ?x7535 = 02f73b, award_winner(?x6869, ?x5547), people(?x10798, ?x10924), ceremony(?x12458, ?x6869), ceremony(?x8369, ?x6869), ceremony(?x3835, ?x6869), ?x8369 = 02fv3t, ?x12458 = 024_dt, ceremony(?x2585, ?x944), ?x3835 = 01cky2 *> conf = 0.35 ranks of expected_values: 27, 35, 54, 257, 398, 409, 487 EVAL 01bx35 award_winner 02h9_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 35.000 15.000 0.778 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01bx35 award_winner 02f1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 35.000 15.000 0.778 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01bx35 award_winner 06rgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 35.000 15.000 0.778 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01bx35 award_winner 01vsgrn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 35.000 15.000 0.778 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01bx35 award_winner 03f1d47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 35.000 15.000 0.778 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01bx35 award_winner 014z8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 35.000 15.000 0.778 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01bx35 award_winner 01vvydl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 35.000 15.000 0.778 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7375-02f77y PRED entity: 02f77y PRED relation: award_winner PRED expected values: 01vrt_c => 45 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1753): 01vs_v8 (0.65 #5406, 0.39 #37107, 0.38 #2472), 018n6m (0.50 #3522, 0.38 #2472, 0.34 #4945), 01vrt_c (0.50 #2706, 0.33 #233, 0.18 #5179), 015f7 (0.39 #37107, 0.38 #2472, 0.35 #32159), 03y82t6 (0.39 #37107, 0.38 #2472, 0.35 #32159), 02z4b_8 (0.38 #2472, 0.35 #6520, 0.34 #12368), 06mt91 (0.38 #2472, 0.35 #9894, 0.35 #14841), 0127s7 (0.38 #2472, 0.34 #4945, 0.31 #39582), 01wyz92 (0.38 #2472, 0.34 #4945, 0.31 #39582), 0bqsy (0.38 #2472, 0.34 #4945, 0.31 #39582) >> Best rule #5406 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 05b4l5x; 03c7tr1; 01c427; 05p09zm; 02f716; 02f71y; 02f73p; 05q8pss; 01c99j; 02f72_; ... >> query: (?x6416, 01vs_v8) <- award(?x10740, ?x6416), award(?x3397, ?x6416), ?x3397 = 015f7, award_winner(?x6416, ?x883), artist(?x3265, ?x10740) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #2706 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 02f79n; *> query: (?x6416, 01vrt_c) <- award(?x10740, ?x6416), award(?x3397, ?x6416), ?x3397 = 015f7, award_winner(?x6416, ?x883), ?x10740 = 016ppr *> conf = 0.50 ranks of expected_values: 3 EVAL 02f77y award_winner 01vrt_c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 45.000 17.000 0.647 http://example.org/award/award_category/winners./award/award_honor/award_winner #7374-01hr11 PRED entity: 01hr11 PRED relation: major_field_of_study PRED expected values: 09s1f => 117 concepts (113 used for prediction) PRED predicted values (max 10 best out of 116): 01mkq (0.46 #752, 0.45 #2476, 0.39 #876), 02j62 (0.35 #768, 0.35 #2492, 0.35 #892), 062z7 (0.34 #765, 0.27 #889, 0.26 #2489), 0g26h (0.33 #2505, 0.31 #781, 0.28 #905), 04rjg (0.33 #757, 0.33 #881, 0.28 #2481), 05qjt (0.32 #746, 0.28 #870, 0.24 #2470), 03g3w (0.29 #764, 0.28 #888, 0.23 #2488), 01540 (0.27 #800, 0.22 #2524, 0.20 #924), 01lj9 (0.25 #778, 0.19 #902, 0.19 #2502), 05qfh (0.24 #2498, 0.23 #774, 0.20 #898) >> Best rule #752 for best value: >> intensional similarity = 3 >> extensional distance = 153 >> proper extension: 01w_sh; >> query: (?x8643, 01mkq) <- institution(?x4981, ?x8643), school_type(?x8643, ?x3205), ?x4981 = 03bwzr4 >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #2563 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 301 *> proper extension: 03bwzr4; *> query: (?x8643, 09s1f) <- major_field_of_study(?x8643, ?x7134), major_field_of_study(?x6271, ?x7134), ?x6271 = 015q1n *> conf = 0.12 ranks of expected_values: 26 EVAL 01hr11 major_field_of_study 09s1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 117.000 113.000 0.465 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7373-07q0g5 PRED entity: 07q0g5 PRED relation: award_nominee! PRED expected values: 05mlqj => 88 concepts (43 used for prediction) PRED predicted values (max 10 best out of 728): 04lp8k (0.81 #86160, 0.81 #86159, 0.81 #74514), 05mlqj (0.81 #86160, 0.81 #86159, 0.81 #74514), 02jtjz (0.81 #86160, 0.81 #86159, 0.81 #74514), 069ld1 (0.81 #86160, 0.81 #86159, 0.81 #74514), 07q0g5 (0.50 #1727, 0.29 #53554, 0.28 #37255), 03f4xvm (0.36 #53553, 0.29 #53554, 0.28 #37255), 03lmzl (0.29 #53554, 0.28 #37255, 0.28 #62868), 059j4x (0.23 #95476, 0.15 #97805, 0.12 #4613), 0kctd (0.23 #95476), 07v4dm (0.23 #95476) >> Best rule #86160 for best value: >> intensional similarity = 3 >> extensional distance = 1495 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 04bdxl; 02s2ft; 05vsxz; 06qgvf; 0grwj; ... >> query: (?x7804, ?x7367) <- gender(?x7804, ?x514), award_nominee(?x7804, ?x7367), film(?x7367, ?x5323) >> conf = 0.81 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07q0g5 award_nominee! 05mlqj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 43.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7372-0157m PRED entity: 0157m PRED relation: participant! PRED expected values: 06pj8 => 197 concepts (111 used for prediction) PRED predicted values (max 10 best out of 401): 014zcr (0.84 #29281, 0.12 #1928, 0.11 #2564), 01trhmt (0.23 #6540, 0.10 #20545, 0.10 #13544), 0gx_p (0.22 #2965, 0.13 #9966, 0.10 #13149), 06mt91 (0.15 #6806, 0.05 #13810, 0.05 #13174), 03h_0_z (0.15 #6768, 0.03 #20773, 0.02 #26505), 0d06m5 (0.14 #22915, 0.11 #22278, 0.11 #34372), 01l9p (0.12 #2024, 0.11 #2660, 0.05 #13480), 0d05fv (0.12 #2229, 0.11 #2865, 0.05 #13685), 03f77 (0.12 #2264, 0.11 #2900, 0.05 #13720), 0cqt90 (0.12 #11094, 0.07 #19367, 0.06 #20004) >> Best rule #29281 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 03qcq; 0p3r8; 0163t3; 022q32; >> query: (?x1620, ?x286) <- profession(?x1620, ?x2225), ?x2225 = 0kyk, participant(?x1620, ?x286) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #27517 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 01pfkw; *> query: (?x1620, 06pj8) <- profession(?x1620, ?x2225), company(?x1620, ?x94), participant(?x1384, ?x1620) *> conf = 0.02 ranks of expected_values: 292 EVAL 0157m participant! 06pj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 197.000 111.000 0.838 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #7371-0160w PRED entity: 0160w PRED relation: olympics PRED expected values: 0l6m5 => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 39): 0jhn7 (0.79 #1549, 0.75 #533, 0.67 #1627), 0l6m5 (0.70 #517, 0.64 #244, 0.62 #1533), 0l6ny (0.65 #516, 0.58 #125, 0.58 #789), 018ctl (0.61 #235, 0.55 #1250, 0.52 #2852), 0l6mp (0.60 #525, 0.57 #252, 0.55 #1541), 0lgxj (0.60 #1550, 0.57 #261, 0.55 #534), 09x3r (0.54 #948, 0.50 #246, 0.48 #1104), 0l998 (0.53 #436, 0.50 #943, 0.50 #514), 0lbbj (0.53 #448, 0.46 #955, 0.43 #253), 018qb4 (0.50 #264, 0.50 #146, 0.26 #459) >> Best rule #1549 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 084n_; >> query: (?x126, 0jhn7) <- form_of_government(?x126, ?x1926), country(?x3693, ?x126), organization(?x126, ?x127) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #517 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 03_r3; *> query: (?x126, 0l6m5) <- vacationer(?x126, ?x872), olympics(?x126, ?x1931), ?x1931 = 0kbws *> conf = 0.70 ranks of expected_values: 2 EVAL 0160w olympics 0l6m5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 163.000 163.000 0.786 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #7370-087pfc PRED entity: 087pfc PRED relation: genre PRED expected values: 07s9rl0 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.66 #2503, 0.66 #2742, 0.65 #3580), 01z4y (0.61 #7280, 0.52 #3101, 0.52 #5967), 03k9fj (0.49 #1203, 0.40 #250, 0.37 #488), 02kdv5l (0.41 #122, 0.40 #1432, 0.40 #360), 01jfsb (0.41 #132, 0.36 #370, 0.36 #251), 04xvlr (0.31 #2026, 0.28 #2863, 0.19 #2504), 04t36 (0.25 #6, 0.09 #3346, 0.09 #4063), 0hcr (0.22 #1213, 0.16 #498, 0.14 #856), 0lsxr (0.21 #1438, 0.19 #2272, 0.18 #2392), 082gq (0.19 #2770, 0.19 #2531, 0.11 #1577) >> Best rule #2503 for best value: >> intensional similarity = 3 >> extensional distance = 471 >> proper extension: 0140g4; 02_fm2; 02_1sj; 03ckwzc; 0963mq; 0340hj; 01kf3_9; 047qxs; 03lrqw; 01f7kl; ... >> query: (?x9174, 07s9rl0) <- film(?x609, ?x9174), films(?x11523, ?x9174), genre(?x9174, ?x258) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087pfc genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.662 http://example.org/film/film/genre #7369-02yw5r PRED entity: 02yw5r PRED relation: award_winner PRED expected values: 048lv 0gnbw 0164w8 => 36 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1134): 0bytkq (0.50 #458, 0.20 #1999, 0.17 #3540), 02pv_d (0.50 #1172, 0.20 #2713, 0.17 #4254), 02s2ft (0.28 #4625, 0.22 #13874, 0.22 #15414), 028knk (0.28 #4625, 0.22 #13874, 0.22 #15414), 0301yj (0.28 #4625, 0.22 #13874, 0.22 #15414), 01x_d8 (0.28 #4625, 0.22 #13874, 0.22 #15414), 02bkdn (0.28 #4625, 0.22 #13874, 0.22 #15414), 048lv (0.28 #4625, 0.22 #13874, 0.22 #15414), 0c3jz (0.28 #4625, 0.22 #13874, 0.22 #15414), 01x6v6 (0.28 #4625, 0.22 #13874, 0.22 #15414) >> Best rule #458 for best value: >> intensional similarity = 21 >> extensional distance = 2 >> proper extension: 0gmdkyy; 050yyb; >> query: (?x1084, 0bytkq) <- ceremony(?x6860, ?x1084), ceremony(?x4573, ?x1084), ceremony(?x3458, ?x1084), ceremony(?x1972, ?x1084), ceremony(?x1862, ?x1084), ceremony(?x484, ?x1084), ?x1862 = 0gr51, honored_for(?x1084, ?x9452), ?x484 = 0gq_v, ?x1972 = 0gqyl, ?x6860 = 018wdw, award(?x9452, ?x3435), award(?x9452, ?x2853), ?x2853 = 09qv_s, nominated_for(?x92, ?x9452), ?x4573 = 0gq_d, nominated_for(?x637, ?x9452), ?x637 = 02r22gf, ?x3458 = 0gqxm, award(?x237, ?x3435), nominated_for(?x3435, ?x69) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4625 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 10 *> proper extension: 059x66; 0bvfqq; 05qb8vx; 02jp5r; 05q7cj; 02yxh9; *> query: (?x1084, ?x1384) <- ceremony(?x6860, ?x1084), ceremony(?x4573, ?x1084), ceremony(?x1972, ?x1084), ceremony(?x1862, ?x1084), ceremony(?x484, ?x1084), ?x1862 = 0gr51, honored_for(?x1084, ?x9452), ?x484 = 0gq_v, ?x1972 = 0gqyl, ?x6860 = 018wdw, award(?x9452, ?x2853), ?x2853 = 09qv_s, nominated_for(?x1384, ?x9452), ?x4573 = 0gq_d, nominated_for(?x68, ?x9452), award_winner(?x1867, ?x1384), film(?x4389, ?x9452), profession(?x1384, ?x319) *> conf = 0.28 ranks of expected_values: 8, 31, 35 EVAL 02yw5r award_winner 0164w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 36.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02yw5r award_winner 0gnbw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 36.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02yw5r award_winner 048lv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 36.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7368-043gj PRED entity: 043gj PRED relation: award PRED expected values: 054ky1 => 160 concepts (146 used for prediction) PRED predicted values (max 10 best out of 320): 054ky1 (0.76 #25059, 0.74 #37182, 0.71 #50119), 05qck (0.76 #25059, 0.74 #37182, 0.71 #50119), 07cbcy (0.52 #885, 0.19 #1693, 0.16 #2906), 05ztrmj (0.48 #992, 0.18 #9074, 0.13 #5841), 05p09zm (0.43 #931, 0.23 #4164, 0.15 #1739), 0gs9p (0.43 #9372, 0.42 #1290, 0.36 #10585), 019f4v (0.41 #9360, 0.36 #8148, 0.33 #10573), 05zr6wv (0.38 #825, 0.21 #8907, 0.16 #5674), 04kxsb (0.38 #933, 0.21 #1337, 0.19 #1741), 05pcn59 (0.38 #888, 0.21 #4121, 0.18 #8970) >> Best rule #25059 for best value: >> intensional similarity = 3 >> extensional distance = 477 >> proper extension: 03fbc; 0163m1; 0hvbj; 01fmz6; 016890; 07bzp; 02j_j0; 01dq9q; 0187x8; 016lmg; ... >> query: (?x4647, ?x591) <- award_nominee(?x4647, ?x6745), category(?x4647, ?x134), award_winner(?x591, ?x4647) >> conf = 0.76 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 043gj award 054ky1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 146.000 0.757 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7367-06w2sn5 PRED entity: 06w2sn5 PRED relation: award PRED expected values: 01c427 03qbnj => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 279): 01bgqh (0.50 #43, 0.42 #1249, 0.42 #445), 01by1l (0.50 #113, 0.41 #6947, 0.38 #1319), 02f716 (0.50 #176, 0.29 #1784, 0.28 #2588), 02f73p (0.50 #187, 0.25 #589, 0.21 #2599), 03t5kl (0.50 #226, 0.22 #34171, 0.22 #2236), 03t5n3 (0.50 #248, 0.22 #34171, 0.14 #7082), 099vwn (0.50 #215, 0.17 #617, 0.14 #7049), 0c4z8 (0.42 #474, 0.38 #1278, 0.29 #1680), 09sb52 (0.41 #3659, 0.30 #7679, 0.30 #34212), 05p09zm (0.38 #929, 0.36 #4145, 0.28 #5753) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01vsgrn; >> query: (?x1462, 01bgqh) <- participant(?x1462, ?x3244), friend(?x6577, ?x1462), award_winner(?x2855, ?x1462), ?x2855 = 02f705 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1438 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 01vrz41; 0j1yf; 01wk7b7; 01vsl3_; 01w02sy; 016fnb; 01s21dg; 04cr6qv; 04f7c55; 06rgq; ... *> query: (?x1462, 03qbnj) <- participant(?x1462, ?x3244), friend(?x6577, ?x1462), instrumentalists(?x227, ?x1462), profession(?x1462, ?x131) *> conf = 0.25 ranks of expected_values: 21, 26 EVAL 06w2sn5 award 03qbnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 168.000 168.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 06w2sn5 award 01c427 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 168.000 168.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7366-07qg8v PRED entity: 07qg8v PRED relation: film_release_region PRED expected values: 0d0vqn 01mjq 06bnz 0d05w3 03h64 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 141): 0d0vqn (0.92 #153, 0.91 #1043, 0.91 #895), 03h64 (0.87 #209, 0.81 #1099, 0.80 #951), 015fr (0.86 #163, 0.76 #905, 0.75 #1053), 0chghy (0.83 #1196, 0.83 #158, 0.83 #1048), 0b90_r (0.83 #151, 0.70 #1041, 0.70 #893), 06bnz (0.82 #189, 0.68 #931, 0.68 #1079), 05v8c (0.73 #162, 0.58 #1052, 0.57 #904), 04gzd (0.73 #156, 0.51 #898, 0.49 #1046), 0ctw_b (0.71 #171, 0.54 #1061, 0.53 #913), 015qh (0.65 #184, 0.45 #926, 0.45 #1074) >> Best rule #153 for best value: >> intensional similarity = 6 >> extensional distance = 99 >> proper extension: 014lc_; 087wc7n; 0h3xztt; 0407yj_; 045j3w; 0gffmn8; 0gwjw0c; 0m63c; 0fpgp26; >> query: (?x1421, 0d0vqn) <- film_release_region(?x1421, ?x2513), film_release_region(?x1421, ?x1174), film_release_region(?x1421, ?x205), ?x2513 = 05b4w, ?x205 = 03rjj, ?x1174 = 047yc >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6, 12, 31 EVAL 07qg8v film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07qg8v film_release_region 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 96.000 96.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07qg8v film_release_region 06bnz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 96.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07qg8v film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 96.000 96.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07qg8v film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7365-011yxg PRED entity: 011yxg PRED relation: nominated_for! PRED expected values: 040njc 019f4v => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 172): 054krc (0.67 #6239, 0.67 #5593, 0.66 #10329), 027b9k6 (0.67 #6239, 0.67 #5593, 0.66 #10329), 02x2gy0 (0.67 #6239, 0.67 #5593, 0.66 #10329), 0gr51 (0.61 #708, 0.22 #4149, 0.21 #5439), 019f4v (0.53 #45, 0.39 #4132, 0.38 #5422), 0gs9p (0.49 #51, 0.40 #5428, 0.37 #697), 04dn09n (0.47 #27, 0.42 #673, 0.30 #888), 0gqy2 (0.41 #104, 0.28 #5481, 0.26 #4191), 0gr4k (0.35 #21, 0.24 #5398, 0.22 #4108), 040njc (0.32 #650, 0.32 #5381, 0.31 #4091) >> Best rule #6239 for best value: >> intensional similarity = 3 >> extensional distance = 461 >> proper extension: 02gl58; >> query: (?x308, ?x112) <- award_winner(?x308, ?x574), award(?x308, ?x112), honored_for(?x472, ?x308) >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #45 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 0d_wms; 042g97; *> query: (?x308, 019f4v) <- honored_for(?x472, ?x308), currency(?x308, ?x170), honored_for(?x308, ?x4696) *> conf = 0.53 ranks of expected_values: 5, 10 EVAL 011yxg nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 98.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 011yxg nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 98.000 98.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7364-0jrhl PRED entity: 0jrhl PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 132 concepts (49 used for prediction) PRED predicted values (max 10 best out of 6): 09c7w0 (0.87 #249, 0.87 #223, 0.86 #195), 01n7q (0.11 #154, 0.10 #336, 0.09 #260), 02xry (0.11 #390, 0.10 #336, 0.09 #260), 06pvr (0.07 #289, 0.07 #319), 03rt9 (0.02 #409, 0.02 #543, 0.02 #279), 02jx1 (0.02 #386, 0.01 #548, 0.01 #347) >> Best rule #249 for best value: >> intensional similarity = 5 >> extensional distance = 223 >> proper extension: 0mkdm; >> query: (?x13582, 09c7w0) <- time_zones(?x13582, ?x2674), adjoins(?x13582, ?x11164), currency(?x13582, ?x170), contains(?x2623, ?x11164), source(?x11164, ?x958) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jrhl second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 49.000 0.871 http://example.org/location/country/second_level_divisions #7363-027j79k PRED entity: 027j79k PRED relation: profession PRED expected values: 0dxtg => 98 concepts (97 used for prediction) PRED predicted values (max 10 best out of 58): 0dxtg (0.88 #903, 0.87 #607, 0.87 #1347), 01d_h8 (0.85 #6519, 0.84 #4891, 0.84 #6371), 02hrh1q (0.79 #163, 0.78 #8304, 0.77 #7268), 02jknp (0.58 #2821, 0.57 #5633, 0.56 #5337), 02krf9 (0.40 #915, 0.37 #619, 0.32 #3135), 018gz8 (0.33 #17, 0.23 #445, 0.23 #6234), 0cbd2 (0.30 #2820, 0.28 #6076, 0.28 #5928), 015h31 (0.23 #445, 0.22 #27, 0.10 #916), 01c72t (0.23 #445, 0.11 #23, 0.09 #9348), 09jwl (0.17 #9344, 0.16 #10676, 0.16 #10380) >> Best rule #903 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0gd9k; >> query: (?x9564, 0dxtg) <- producer_type(?x9564, ?x632), written_by(?x10942, ?x9564), nationality(?x9564, ?x550) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027j79k profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 97.000 0.885 http://example.org/people/person/profession #7362-0bnzd PRED entity: 0bnzd PRED relation: nominated_for! PRED expected values: 099c8n 0gqyl 02x4sn8 => 81 concepts (80 used for prediction) PRED predicted values (max 10 best out of 228): 0gs9p (0.68 #1850, 0.60 #954, 0.57 #1402), 09cn0c (0.67 #3811, 0.67 #5156, 0.67 #8970), 027571b (0.67 #3811, 0.67 #5156, 0.67 #8970), 09cm54 (0.67 #3811, 0.67 #5156, 0.67 #8970), 027c924 (0.67 #3811, 0.67 #5156, 0.67 #8970), 027dtxw (0.65 #1124, 0.27 #8744, 0.25 #676), 019f4v (0.60 #1842, 0.53 #946, 0.49 #1394), 0k611 (0.55 #1858, 0.38 #1410, 0.38 #1186), 040njc (0.47 #1799, 0.43 #903, 0.42 #1351), 099c8n (0.45 #1173, 0.29 #2294, 0.27 #1845) >> Best rule #1850 for best value: >> intensional similarity = 4 >> extensional distance = 241 >> proper extension: 01jc6q; 0jzw; 0jqn5; 09k56b7; 0jym0; 0yyts; 03hkch7; 02rn00y; 093dqjy; 0pd6l; ... >> query: (?x7087, 0gs9p) <- award_winner(?x7087, ?x166), nominated_for(?x1307, ?x7087), award(?x7087, ?x289), ?x1307 = 0gq9h >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1173 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 162 *> proper extension: 0284b56; *> query: (?x7087, 099c8n) <- award_winner(?x7087, ?x166), nominated_for(?x704, ?x7087), award_winner(?x704, ?x3604), ?x3604 = 03v3xp *> conf = 0.45 ranks of expected_values: 10, 17, 27 EVAL 0bnzd nominated_for! 02x4sn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 81.000 80.000 0.683 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bnzd nominated_for! 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 81.000 80.000 0.683 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bnzd nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 81.000 80.000 0.683 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7361-0416y94 PRED entity: 0416y94 PRED relation: film! PRED expected values: 017s11 => 82 concepts (53 used for prediction) PRED predicted values (max 10 best out of 56): 0jz9f (0.26 #76, 0.09 #909, 0.08 #230), 016tt2 (0.20 #4, 0.14 #383, 0.12 #459), 0g1rw (0.20 #8, 0.08 #991, 0.07 #1292), 061dn_ (0.20 #24, 0.04 #1384, 0.04 #177), 03xq0f (0.19 #158, 0.14 #309, 0.11 #384), 086k8 (0.18 #381, 0.17 #608, 0.16 #457), 05qd_ (0.17 #842, 0.15 #313, 0.14 #1293), 016tw3 (0.17 #240, 0.16 #86, 0.14 #919), 017s11 (0.14 #534, 0.14 #1591, 0.13 #1062), 01gb54 (0.10 #104, 0.09 #408, 0.06 #560) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 02pqs8l; >> query: (?x1318, 0jz9f) <- nominated_for(?x9743, ?x1318), titles(?x53, ?x1318), award_nominee(?x844, ?x9743), ?x844 = 03h_9lg >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #534 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 232 *> proper extension: 01br2w; 0267wwv; *> query: (?x1318, 017s11) <- film(?x1774, ?x1318), music(?x1318, ?x10634), film_crew_role(?x1318, ?x137), ?x137 = 09zzb8 *> conf = 0.14 ranks of expected_values: 9 EVAL 0416y94 film! 017s11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 82.000 53.000 0.258 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7360-07g9f PRED entity: 07g9f PRED relation: languages PRED expected values: 02h40lc => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 5): 02h40lc (0.90 #134, 0.89 #145, 0.89 #222), 03_9r (0.07 #70, 0.05 #15, 0.04 #334), 0t_2 (0.04 #61, 0.04 #83, 0.04 #39), 06nm1 (0.04 #71, 0.03 #82, 0.03 #115), 064_8sq (0.02 #84, 0.02 #117, 0.01 #194) >> Best rule #134 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 02nf2c; >> query: (?x10089, 02h40lc) <- award_winner(?x10089, ?x2554), nominated_for(?x435, ?x10089), genre(?x10089, ?x53) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07g9f languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.903 http://example.org/tv/tv_program/languages #7359-0m0bj PRED entity: 0m0bj PRED relation: contains PRED expected values: 01tzfz => 82 concepts (20 used for prediction) PRED predicted values (max 10 best out of 23): 02gw_w (0.01 #2551, 0.01 #8445, 0.01 #5497), 01314k (0.01 #852, 0.01 #6746, 0.01 #3798), 0f8j6 (0.01 #2854, 0.01 #5800), 0202wk (0.01 #2806, 0.01 #5752), 0ym20 (0.01 #2746, 0.01 #5692), 0ym4t (0.01 #2682, 0.01 #5628), 03lkp (0.01 #2531, 0.01 #5477), 0f485 (0.01 #2502, 0.01 #5448), 0yl_w (0.01 #2029, 0.01 #4975), 01z53w (0.01 #1992, 0.01 #4938) >> Best rule #2551 for best value: >> intensional similarity = 2 >> extensional distance = 254 >> proper extension: 04jpl; 0ymbl; 0dhdp; 0fm2_; 022_6; 02jx1; 0zc6f; 0crjn65; 0dbdy; 05l5n; ... >> query: (?x13528, 02gw_w) <- contains(?x512, ?x13528), ?x512 = 07ssc >> conf = 0.01 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0m0bj contains 01tzfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 20.000 0.012 http://example.org/location/location/contains #7358-01dpsv PRED entity: 01dpsv PRED relation: award PRED expected values: 02v1m7 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 275): 026mfs (0.78 #9297, 0.78 #5659, 0.78 #22234), 01by1l (0.33 #4557, 0.30 #5366, 0.30 #4153), 01bgqh (0.28 #4488, 0.27 #5297, 0.25 #4084), 09sb52 (0.23 #21060, 0.20 #19036, 0.20 #19845), 03qbh5 (0.22 #206, 0.22 #4247, 0.20 #4651), 0c4z8 (0.22 #4517, 0.20 #5731, 0.19 #4113), 054ks3 (0.18 #4587, 0.17 #5801, 0.17 #4992), 01c427 (0.17 #84, 0.15 #1297, 0.14 #2509), 01c92g (0.15 #1310, 0.15 #2522, 0.14 #4138), 02f5qb (0.15 #5410, 0.12 #9047, 0.12 #7431) >> Best rule #9297 for best value: >> intensional similarity = 3 >> extensional distance = 586 >> proper extension: 015cxv; >> query: (?x12659, ?x7691) <- award_winner(?x7691, ?x12659), award(?x872, ?x7691), artists(?x1928, ?x12659) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #5367 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 463 *> proper extension: 089tm; 01pfr3; 04rcr; 02r3zy; 07c0j; 01v0sx2; 01vsxdm; 03g5jw; 01wv9xn; 0dvqq; ... *> query: (?x12659, 02v1m7) <- artist(?x4868, ?x12659), award_winner(?x2420, ?x12659), artists(?x1928, ?x12659) *> conf = 0.11 ranks of expected_values: 33 EVAL 01dpsv award 02v1m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 89.000 89.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7357-01vhb0 PRED entity: 01vhb0 PRED relation: profession PRED expected values: 02hrh1q => 164 concepts (160 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.92 #4010, 0.92 #6528, 0.92 #4898), 01d_h8 (0.67 #14524, 0.55 #13632, 0.48 #4299), 03gjzk (0.43 #754, 0.39 #4159, 0.38 #8145), 09jwl (0.43 #1350, 0.38 #1498, 0.37 #17052), 0dz3r (0.40 #1482, 0.27 #1630, 0.26 #2666), 018gz8 (0.38 #8145, 0.33 #16, 0.31 #9035), 0np9r (0.38 #8145, 0.31 #5053, 0.30 #14072), 015cjr (0.38 #8145, 0.30 #14072, 0.29 #12737), 0nbcg (0.38 #1363, 0.29 #2695, 0.27 #17509), 0d1pc (0.33 #790, 0.31 #938, 0.27 #1678) >> Best rule #4010 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 0162c8; 06vsbt; 06fc0b; 01p47r; 01507p; 044zvm; >> query: (?x2308, 02hrh1q) <- participant(?x2894, ?x2308), nominated_for(?x2308, ?x416), actor(?x416, ?x1594) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vhb0 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 160.000 0.924 http://example.org/people/person/profession #7356-0963mq PRED entity: 0963mq PRED relation: genre PRED expected values: 06nbt 0gsy3b => 108 concepts (98 used for prediction) PRED predicted values (max 10 best out of 110): 01z4y (0.61 #9864, 0.61 #9744, 0.59 #6178), 04jjy (0.59 #6178, 0.57 #1661, 0.55 #9863), 02kdv5l (0.50 #120, 0.35 #950, 0.35 #10341), 03k9fj (0.50 #128, 0.32 #840, 0.26 #1432), 06n90 (0.50 #130, 0.18 #1434, 0.16 #3218), 02l7c8 (0.40 #251, 0.37 #2149, 0.35 #3103), 01jfsb (0.38 #10114, 0.37 #1433, 0.37 #3217), 04xvlr (0.33 #1, 0.27 #1542, 0.27 #356), 0lsxr (0.33 #8, 0.24 #838, 0.22 #2380), 06cvj (0.26 #2137, 0.23 #1901, 0.22 #3091) >> Best rule #9864 for best value: >> intensional similarity = 4 >> extensional distance = 1218 >> proper extension: 015w8_; 0vhm; 024rwx; 0ctzf1; 028k2x; 01hvv0; 09g_31; 0170k0; 06r1k; 025x1t; ... >> query: (?x943, ?x2480) <- titles(?x2480, ?x943), titles(?x2480, ?x5712), genre(?x631, ?x2480), nominated_for(?x198, ?x5712) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #2159 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 272 *> proper extension: 01q_y0; *> query: (?x943, 06nbt) <- titles(?x2480, ?x943), titles(?x2480, ?x5142), titles(?x2480, ?x3404), film_release_region(?x5142, ?x87), ?x3404 = 01jrbv *> conf = 0.08 ranks of expected_values: 37, 50 EVAL 0963mq genre 0gsy3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 108.000 98.000 0.614 http://example.org/film/film/genre EVAL 0963mq genre 06nbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 108.000 98.000 0.614 http://example.org/film/film/genre #7355-01qnfc PRED entity: 01qnfc PRED relation: profession PRED expected values: 02hrh1q 02pjxr => 76 concepts (39 used for prediction) PRED predicted values (max 10 best out of 54): 02hrh1q (0.85 #4978, 0.69 #596, 0.67 #2057), 03gjzk (0.49 #2934, 0.39 #3665, 0.37 #2204), 0np9r (0.41 #457, 0.12 #2064, 0.11 #2210), 0cbd2 (0.38 #2781, 0.35 #3365, 0.26 #2051), 09jwl (0.34 #4107, 0.27 #4253, 0.19 #3669), 0kyk (0.28 #2657, 0.18 #3826, 0.17 #2803), 02krf9 (0.21 #1924, 0.20 #1631, 0.18 #1485), 0dz3r (0.20 #3654, 0.18 #4092, 0.10 #4238), 018gz8 (0.20 #2060, 0.17 #2352, 0.17 #2206), 0lgw7 (0.19 #629, 0.18 #191, 0.18 #45) >> Best rule #4978 for best value: >> intensional similarity = 6 >> extensional distance = 2657 >> proper extension: 02zq43; 0436f4; 01ty7ll; 01gvr1; 01mvth; 04bd8y; 066m4g; 03gm48; 0f0p0; 0sz28; ... >> query: (?x12722, 02hrh1q) <- gender(?x12722, ?x231), profession(?x12722, ?x987), profession(?x4134, ?x987), profession(?x523, ?x987), ?x523 = 06cv1, ?x4134 = 07cn2c >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qnfc profession 02pjxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 39.000 0.852 http://example.org/people/person/profession EVAL 01qnfc profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 39.000 0.852 http://example.org/people/person/profession #7354-0kxbc PRED entity: 0kxbc PRED relation: profession PRED expected values: 0nbcg => 97 concepts (67 used for prediction) PRED predicted values (max 10 best out of 61): 016z4k (0.68 #1611, 0.64 #734, 0.57 #881), 01d_h8 (0.60 #6, 0.36 #4247, 0.36 #3367), 0nbcg (0.59 #760, 0.56 #2513, 0.52 #907), 0dz3r (0.53 #440, 0.50 #2047, 0.49 #1463), 03gjzk (0.40 #15, 0.26 #1184, 0.25 #3816), 0dxtg (0.40 #14, 0.25 #9677, 0.24 #9239), 0np9r (0.40 #20, 0.15 #1773, 0.13 #2211), 015h31 (0.40 #26, 0.04 #1487, 0.02 #903), 0n1h (0.35 #2057, 0.33 #1473, 0.31 #889), 0fnpj (0.24 #935, 0.20 #58, 0.19 #1519) >> Best rule #1611 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 094xh; >> query: (?x5635, 016z4k) <- award(?x5635, ?x247), instrumentalists(?x2798, ?x5635), artists(?x302, ?x5635), ?x2798 = 03qjg >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #760 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 03m6pk; 0d608; *> query: (?x5635, 0nbcg) <- award(?x5635, ?x247), participant(?x5635, ?x5507), role(?x5635, ?x227) *> conf = 0.59 ranks of expected_values: 3 EVAL 0kxbc profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 67.000 0.677 http://example.org/people/person/profession #7353-04h41v PRED entity: 04h41v PRED relation: language PRED expected values: 02h40lc => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 28): 02h40lc (0.91 #122, 0.88 #597, 0.88 #2337), 064_8sq (0.14 #794, 0.13 #498, 0.13 #853), 06nm1 (0.11 #70, 0.09 #546, 0.09 #131), 07zrf (0.10 #3, 0.01 #123, 0.01 #598), 04306rv (0.08 #777, 0.08 #1315, 0.08 #1496), 02bjrlw (0.06 #299, 0.06 #1010, 0.06 #1853), 06b_j (0.05 #736, 0.05 #677, 0.05 #1032), 03_9r (0.04 #3303, 0.04 #3122, 0.04 #1923), 03k50 (0.04 #188, 0.04 #68, 0.02 #1319), 012w70 (0.04 #72, 0.02 #785, 0.02 #1383) >> Best rule #122 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 0b7l4x; >> query: (?x5966, 02h40lc) <- film(?x2216, ?x5966), genre(?x5966, ?x809), ?x809 = 0vgkd >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04h41v language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.912 http://example.org/film/film/language #7352-01ycbq PRED entity: 01ycbq PRED relation: film PRED expected values: 01jwxx 076xkps => 99 concepts (59 used for prediction) PRED predicted values (max 10 best out of 752): 04glx0 (0.60 #40880, 0.59 #67541, 0.50 #3555), 02gjrc (0.13 #5333, 0.06 #39102, 0.06 #33768), 06gb1w (0.11 #727, 0.07 #7838, 0.05 #9615), 02_fm2 (0.11 #25, 0.07 #7136, 0.05 #8913), 0ds2n (0.11 #521, 0.05 #9409, 0.03 #58653), 01xdxy (0.11 #1555, 0.05 #5110, 0.03 #8666), 01pvxl (0.11 #899, 0.05 #4454, 0.03 #8010), 0992d9 (0.11 #983, 0.05 #4538, 0.03 #8094), 0gjcrrw (0.11 #624, 0.05 #4179, 0.03 #7735), 02c7k4 (0.11 #1093, 0.03 #8204, 0.03 #9981) >> Best rule #40880 for best value: >> intensional similarity = 3 >> extensional distance = 931 >> proper extension: 02k6rq; >> query: (?x2033, ?x253) <- award_winner(?x112, ?x2033), nominated_for(?x2033, ?x253), film(?x2033, ?x1481) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01ycbq film 076xkps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 59.000 0.595 http://example.org/film/actor/film./film/performance/film EVAL 01ycbq film 01jwxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 59.000 0.595 http://example.org/film/actor/film./film/performance/film #7351-06qd3 PRED entity: 06qd3 PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 204 concepts (204 used for prediction) PRED predicted values (max 10 best out of 26): 060bp (0.71 #505, 0.71 #253, 0.70 #715), 0pqc5 (0.48 #2776, 0.39 #2923, 0.38 #3007), 0f6c3 (0.38 #2779, 0.34 #1266, 0.33 #1392), 0fkvn (0.37 #2607, 0.37 #1388, 0.34 #3489), 09n5b9 (0.35 #2783, 0.28 #2615, 0.26 #892), 01zq91 (0.26 #538, 0.23 #139, 0.23 #517), 0p5vf (0.26 #662, 0.25 #914, 0.25 #410), 04syw (0.21 #236, 0.18 #845, 0.18 #404), 0789n (0.19 #176, 0.18 #155, 0.17 #239), 0377k9 (0.18 #413, 0.18 #161, 0.17 #308) >> Best rule #505 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 02_286; >> query: (?x1453, 060bp) <- film_release_region(?x8682, ?x1453), film_release_region(?x4040, ?x1453), nominated_for(?x4040, ?x7580), ?x8682 = 0bmfnjs >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06qd3 jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 204.000 204.000 0.710 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #7350-0mzww PRED entity: 0mzww PRED relation: origin! PRED expected values: 0ycfj => 158 concepts (119 used for prediction) PRED predicted values (max 10 best out of 494): 023361 (0.27 #44878, 0.09 #28363, 0.09 #49524), 01j590z (0.19 #48491, 0.15 #15464, 0.06 #29914), 01jw4r (0.09 #28363, 0.09 #49524, 0.08 #44877), 05_2h8 (0.09 #28363, 0.09 #49524, 0.08 #44877), 05hj0n (0.08 #44877, 0.07 #6699, 0.06 #29396), 059m45 (0.08 #44877, 0.07 #6699, 0.06 #29396), 02xbw2 (0.06 #29914, 0.06 #29913, 0.06 #40232), 0443c (0.06 #29914, 0.06 #29913, 0.06 #40232), 012v9y (0.06 #29914, 0.06 #29913, 0.06 #40232), 05crg7 (0.05 #1081, 0.03 #2112, 0.03 #50) >> Best rule #44878 for best value: >> intensional similarity = 3 >> extensional distance = 270 >> proper extension: 01gln9; >> query: (?x6987, ?x8374) <- place_of_birth(?x8374, ?x6987), contains(?x94, ?x6987), artists(?x1067, ?x8374) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0mzww origin! 0ycfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 119.000 0.274 http://example.org/music/artist/origin #7349-0ftlxj PRED entity: 0ftlxj PRED relation: award_winner PRED expected values: 0c921 => 29 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1529): 0cw67g (0.19 #1408, 0.16 #12173, 0.15 #7557), 01jpmpv (0.18 #3074, 0.17 #4612, 0.16 #3075), 02bn75 (0.18 #3074, 0.17 #4612, 0.16 #3075), 040z9 (0.18 #3074, 0.06 #6149, 0.05 #19543), 0byfz (0.18 #3074, 0.06 #6149, 0.01 #24608), 025cn2 (0.18 #3074, 0.05 #20935, 0.05 #25557), 043gj (0.18 #3074, 0.03 #8424, 0.03 #11500), 06mn7 (0.18 #3074, 0.02 #22202, 0.02 #20660), 070bjw (0.18 #3074, 0.02 #24611, 0.02 #24612), 0c921 (0.18 #3074, 0.01 #10765) >> Best rule #1408 for best value: >> intensional similarity = 19 >> extensional distance = 19 >> proper extension: 0bzmt8; >> query: (?x5369, 0cw67g) <- ceremony(?x5409, ?x5369), ceremony(?x2209, ?x5369), ceremony(?x1703, ?x5369), ceremony(?x1243, ?x5369), ceremony(?x500, ?x5369), ceremony(?x484, ?x5369), ?x484 = 0gq_v, ?x5409 = 0gr07, award(?x197, ?x500), ceremony(?x500, ?x11087), nominated_for(?x500, ?x5711), nominated_for(?x500, ?x1118), ?x11087 = 073h5b, ?x1243 = 0gr0m, ?x2209 = 0gr42, award_winner(?x500, ?x902), ?x1118 = 0_92w, ?x1703 = 0k611, ?x5711 = 0bl5c >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #3074 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 19 *> proper extension: 073hkh; 02yw5r; 0bzm81; 0gmdkyy; 050yyb; 02hn5v; 0bzkgg; 0bzk2h; 0bc773; 05qb8vx; ... *> query: (?x5369, ?x7391) <- ceremony(?x5409, ?x5369), ceremony(?x1703, ?x5369), ceremony(?x1243, ?x5369), ceremony(?x591, ?x5369), ceremony(?x500, ?x5369), ceremony(?x484, ?x5369), ?x484 = 0gq_v, ?x5409 = 0gr07, ?x500 = 0p9sw, honored_for(?x5369, ?x4648), honored_for(?x5369, ?x2958), ?x1703 = 0k611, award_winner(?x5369, ?x7380), ?x1243 = 0gr0m, ?x591 = 0f4x7, genre(?x4648, ?x53), music(?x4648, ?x3483), location(?x7380, ?x2254), film_release_region(?x2958, ?x87), nominated_for(?x7391, ?x2958) *> conf = 0.18 ranks of expected_values: 10 EVAL 0ftlxj award_winner 0c921 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 29.000 18.000 0.190 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7348-0l6ny PRED entity: 0l6ny PRED relation: sports PRED expected values: 0bynt => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 37): 0bynt (0.87 #137, 0.85 #172, 0.84 #103), 03hr1p (0.87 #137, 0.85 #172, 0.84 #103), 01hp22 (0.87 #137, 0.85 #172, 0.84 #103), 02y8z (0.87 #137, 0.85 #172, 0.84 #103), 03_8r (0.87 #137, 0.85 #172, 0.84 #103), 01sgl (0.64 #273, 0.58 #206, 0.43 #171), 01gqfm (0.57 #201, 0.50 #97, 0.43 #171), 07jbh (0.50 #87, 0.43 #191, 0.43 #171), 018jz (0.50 #88, 0.43 #192, 0.43 #171), 019tzd (0.50 #90, 0.43 #194, 0.43 #171) >> Best rule #137 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 0l998; 0kbvb; >> query: (?x867, ?x359) <- olympics(?x5453, ?x867), olympics(?x2629, ?x867), olympics(?x1353, ?x867), olympics(?x1203, ?x867), olympics(?x421, ?x867), olympics(?x291, ?x867), sports(?x867, ?x359), ?x2629 = 06f32, ?x421 = 03_r3, ?x1203 = 07ylj, organization(?x5453, ?x312), ?x1353 = 035qy, exported_to(?x87, ?x291) >> conf = 0.87 => this is the best rule for 5 predicted values ranks of expected_values: 1 EVAL 0l6ny sports 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.870 http://example.org/olympics/olympic_games/sports #7347-09wz9 PRED entity: 09wz9 PRED relation: country PRED expected values: 07ssc 04w58 => 27 concepts (23 used for prediction) PRED predicted values (max 10 best out of 286): 0d05w3 (0.82 #1382, 0.82 #1319, 0.82 #1184), 07t21 (0.82 #3088, 0.81 #2710, 0.74 #1325), 06mkj (0.82 #1375, 0.79 #1757, 0.75 #3486), 0hzlz (0.82 #1346, 0.71 #1728, 0.67 #3457), 0b90_r (0.82 #1333, 0.59 #1317, 0.57 #2873), 0d0vqn (0.81 #2687, 0.79 #1718, 0.75 #2488), 05b4w (0.79 #1766, 0.78 #994, 0.75 #1571), 07ssc (0.79 #1916, 0.76 #2882, 0.75 #4215), 01znc_ (0.77 #1323, 0.56 #3439, 0.55 #1362), 0jgd (0.74 #1325, 0.73 #1332, 0.71 #1714) >> Best rule #1382 for best value: >> intensional similarity = 58 >> extensional distance = 9 >> proper extension: 01sgl; >> query: (?x2884, 0d05w3) <- country(?x2884, ?x5482), country(?x2884, ?x3040), country(?x2884, ?x1453), country(?x2884, ?x1264), country(?x2884, ?x1229), country(?x2884, ?x1023), country(?x2884, ?x774), country(?x2884, ?x205), contains(?x5482, ?x8212), country(?x6564, ?x5482), country(?x3015, ?x5482), country(?x2978, ?x5482), country(?x520, ?x5482), ?x1023 = 0ctw_b, ?x1264 = 0345h, ?x520 = 01dys, adjoins(?x5482, ?x344), olympics(?x5482, ?x2630), olympics(?x5482, ?x1277), ?x1277 = 0swbd, ?x205 = 03rjj, ?x774 = 06mzp, adjustment_currency(?x5482, ?x170), ?x6564 = 0152n0, locations(?x5352, ?x5482), ?x2630 = 0swff, member_states(?x2106, ?x5482), ?x3015 = 071t0, form_of_government(?x3040, ?x1926), ?x1229 = 059j2, film_release_region(?x9174, ?x1453), film_release_region(?x7554, ?x1453), film_release_region(?x7016, ?x1453), film_release_region(?x6751, ?x1453), film_release_region(?x6394, ?x1453), film_release_region(?x4615, ?x1453), film_release_region(?x3897, ?x1453), film_release_region(?x3850, ?x1453), film_release_region(?x2961, ?x1453), ?x7554 = 01mgw, olympics(?x2884, ?x4424), ?x2978 = 03_8r, ?x7016 = 07g1sm, film_release_region(?x559, ?x5482), medal(?x3040, ?x422), countries_spoken_in(?x7926, ?x1453), ?x4615 = 0dlngsd, countries_spoken_in(?x732, ?x3040), ?x3897 = 02dpl9, ?x9174 = 087pfc, contains(?x455, ?x5482), ?x6394 = 0cmdwwg, olympics(?x5482, ?x2131), olympics(?x1453, ?x584), ?x3850 = 047fjjr, ?x6751 = 0372j5, ?x2131 = 0lk8j, ?x2961 = 047p7fr >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1916 for first EXPECTED value: *> intensional similarity = 58 *> extensional distance = 12 *> proper extension: 0486tv; 0152n0; *> query: (?x2884, 07ssc) <- country(?x2884, ?x5482), country(?x2884, ?x1603), country(?x2884, ?x1355), country(?x2884, ?x1229), country(?x2884, ?x789), ?x5482 = 04g5k, olympics(?x2884, ?x418), location(?x10895, ?x1355), country(?x4503, ?x1355), country(?x2266, ?x1355), ?x4503 = 06z68, nationality(?x681, ?x1355), film_release_region(?x11074, ?x1355), film_release_region(?x6181, ?x1355), film_release_region(?x4047, ?x1355), film_release_region(?x2094, ?x1355), olympics(?x1355, ?x778), adjoins(?x1355, ?x1003), sports(?x8189, ?x2884), film_release_region(?x7524, ?x1229), film_release_region(?x5992, ?x1229), film_release_region(?x5713, ?x1229), film_release_region(?x5588, ?x1229), film_release_region(?x3830, ?x1229), film_release_region(?x3565, ?x1229), film_release_region(?x3135, ?x1229), film_release_region(?x2441, ?x1229), film_release_region(?x1932, ?x1229), film_release_region(?x1701, ?x1229), film_release_region(?x86, ?x1229), ?x3135 = 0bmc4cm, combatants(?x326, ?x1229), country(?x3407, ?x1229), contains(?x1229, ?x2351), combatants(?x151, ?x1229), ?x6181 = 0hv27, ?x5713 = 0cc97st, organization(?x1229, ?x127), ?x3565 = 0cp0ph6, ?x2266 = 01lb14, ?x5992 = 0g5q34q, jurisdiction_of_office(?x182, ?x1229), countries_spoken_in(?x732, ?x1355), ?x1932 = 0btyf5z, olympics(?x304, ?x8189), ?x5588 = 0gtt5fb, ?x1603 = 06bnz, ?x11074 = 0jqzt, ?x789 = 0f8l9c, ?x2094 = 05z7c, ?x86 = 0ds35l9, ?x2441 = 0cc5mcj, ?x4047 = 07s846j, nominated_for(?x9084, ?x1701), ?x7524 = 01cm8w, ?x3830 = 0gjcrrw, adjustment_currency(?x1355, ?x170), film_crew_role(?x1701, ?x137) *> conf = 0.79 ranks of expected_values: 8, 35 EVAL 09wz9 country 04w58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 27.000 23.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09wz9 country 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 27.000 23.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #7346-035wq7 PRED entity: 035wq7 PRED relation: people! PRED expected values: 041rx => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 43): 0x67 (0.26 #314, 0.23 #1227, 0.19 #390), 041rx (0.26 #4, 0.25 #1221, 0.23 #80), 033tf_ (0.13 #1224, 0.13 #1072, 0.13 #7), 02ctzb (0.11 #547, 0.09 #319, 0.09 #395), 02w7gg (0.10 #1067, 0.09 #1371, 0.08 #2207), 0xnvg (0.09 #1230, 0.08 #1078, 0.07 #1382), 07hwkr (0.08 #1229, 0.05 #544, 0.05 #1077), 0dryh9k (0.06 #1385, 0.05 #2221, 0.04 #929), 07bch9 (0.06 #1240, 0.05 #1088, 0.05 #327), 09kr66 (0.06 #118, 0.04 #346, 0.03 #422) >> Best rule #314 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 05cljf; 01v3s2_; 0pz7h; 07ymr5; 01j7rd; 047sxrj; 04xrx; 01vx5w7; 0fb1q; 02v0ff; ... >> query: (?x11885, 0x67) <- profession(?x11885, ?x319), program(?x11885, ?x2583), gender(?x11885, ?x231), people(?x9428, ?x11885) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 0mdyn; 01rzxl; *> query: (?x11885, 041rx) <- profession(?x11885, ?x319), program(?x11885, ?x2583), type_of_union(?x11885, ?x566), ?x319 = 01d_h8 *> conf = 0.26 ranks of expected_values: 2 EVAL 035wq7 people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 57.000 57.000 0.263 http://example.org/people/ethnicity/people #7345-04hhv PRED entity: 04hhv PRED relation: organization PRED expected values: 02vk52z => 91 concepts (89 used for prediction) PRED predicted values (max 10 best out of 50): 02vk52z (0.87 #489, 0.86 #622, 0.86 #710), 0b6css (0.64 #76, 0.50 #142, 0.48 #120), 01rz1 (0.52 #90, 0.46 #490, 0.39 #156), 041288 (0.41 #126, 0.37 #192, 0.36 #82), 0gkjy (0.36 #73, 0.33 #117, 0.32 #1398), 018cqq (0.36 #165, 0.32 #1398, 0.31 #254), 04k4l (0.34 #559, 0.33 #247, 0.32 #625), 0j7v_ (0.32 #1398, 0.30 #137, 0.30 #115), 02jxk (0.32 #1398, 0.23 #491, 0.20 #91), 059dn (0.32 #1398, 0.16 #103, 0.15 #169) >> Best rule #489 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 0154j; 04gzd; 047lj; 03rt9; 06mzp; 03gj2; 035qy; 0h7x; 015qh; 01znc_; ... >> query: (?x8033, 02vk52z) <- adjoins(?x2346, ?x8033), contains(?x6304, ?x8033), ?x6304 = 02qkt >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04hhv organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 89.000 0.871 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #7344-06mq7 PRED entity: 06mq7 PRED relation: major_field_of_study! PRED expected values: 0bwfn => 76 concepts (47 used for prediction) PRED predicted values (max 10 best out of 661): 03ksy (0.75 #6023, 0.65 #17240, 0.64 #19607), 02zd460 (0.75 #6688, 0.58 #16724, 0.56 #19091), 09f2j (0.67 #3720, 0.62 #14937, 0.52 #17298), 07szy (0.67 #3587, 0.58 #14804, 0.52 #16575), 0bwfn (0.67 #3842, 0.50 #889, 0.48 #16830), 0bx8pn (0.67 #3594, 0.50 #641, 0.40 #3004), 04rwx (0.67 #3584, 0.50 #631, 0.39 #16572), 06pwq (0.62 #14773, 0.62 #6508, 0.61 #16544), 01w3v (0.62 #14776, 0.62 #6511, 0.54 #11233), 08815 (0.62 #7088, 0.50 #6497, 0.50 #5906) >> Best rule #6023 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 01540; 036nz; >> query: (?x12907, 03ksy) <- taxonomy(?x12907, ?x939), major_field_of_study(?x4296, ?x12907), major_field_of_study(?x3513, ?x12907), ?x3513 = 0pspl, major_field_of_study(?x1368, ?x12907), ?x1368 = 014mlp, major_field_of_study(?x12907, ?x2014), school(?x700, ?x4296), student(?x4296, ?x14008), ?x14008 = 0cbgl >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3842 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 02_7t; *> query: (?x12907, 0bwfn) <- major_field_of_study(?x4981, ?x12907), ?x4981 = 03bwzr4, major_field_of_study(?x4296, ?x12907), major_field_of_study(?x3513, ?x12907), major_field_of_study(?x3178, ?x12907), ?x3513 = 0pspl, ?x4296 = 07vyf, organization(?x346, ?x3178), school_type(?x3178, ?x1044), state_province_region(?x3178, ?x7058) *> conf = 0.67 ranks of expected_values: 5 EVAL 06mq7 major_field_of_study! 0bwfn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 76.000 47.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7343-0qmjd PRED entity: 0qmjd PRED relation: films! PRED expected values: 04gb7 => 67 concepts (28 used for prediction) PRED predicted values (max 10 best out of 51): 05489 (0.15 #364, 0.06 #834, 0.04 #520), 0fx2s (0.07 #697, 0.05 #1796, 0.04 #1170), 081pw (0.06 #1726, 0.04 #1100, 0.03 #627), 07_nf (0.04 #379, 0.03 #1164, 0.02 #1790), 0fzyg (0.04 #366, 0.03 #1620, 0.02 #3516), 03r8gp (0.04 #402, 0.02 #3552, 0.01 #558), 0g1x2_ (0.04 #339, 0.01 #4279, 0.01 #651), 01fzpw (0.04 #431), 05qt0 (0.04 #368), 06d4h (0.04 #825, 0.03 #1140, 0.03 #1766) >> Best rule #364 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 015qsq; 0jzw; 04vr_f; 04hwbq; 05j82v; 09cr8; 026gyn_; 0fy34l; 02c638; 011yd2; ... >> query: (?x6915, 05489) <- nominated_for(?x6915, ?x1822), film(?x5330, ?x6915), nominated_for(?x601, ?x6915), ?x601 = 0gr4k >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #1925 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 209 *> proper extension: 023g6w; *> query: (?x6915, 04gb7) <- nominated_for(?x6915, ?x7265), film(?x5330, ?x6915), nominated_for(?x2344, ?x6915), genre(?x7265, ?x53) *> conf = 0.02 ranks of expected_values: 24 EVAL 0qmjd films! 04gb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 67.000 28.000 0.154 http://example.org/film/film_subject/films #7342-04hgpt PRED entity: 04hgpt PRED relation: institution! PRED expected values: 022h5x => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 18): 0bkj86 (0.73 #45, 0.64 #206, 0.64 #420), 07s6fsf (0.68 #357, 0.66 #121, 0.65 #318), 04zx3q1 (0.62 #61, 0.55 #41, 0.50 #416), 013zdg (0.55 #44, 0.54 #64, 0.41 #125), 027f2w (0.50 #26, 0.45 #421, 0.42 #207), 028dcg (0.43 #142, 0.38 #34, 0.36 #54), 03mkk4 (0.43 #142, 0.28 #423, 0.27 #728), 022h5x (0.43 #142, 0.27 #728, 0.25 #136), 0bjrnt (0.43 #142, 0.27 #728, 0.25 #23), 02m4yg (0.43 #142, 0.27 #728, 0.16 #1921) >> Best rule #45 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 01gwck; >> query: (?x4750, 0bkj86) <- major_field_of_study(?x4750, ?x10391), major_field_of_study(?x4750, ?x7134), state_province_region(?x4750, ?x3670), ?x10391 = 02jfc, ?x7134 = 02_7t >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #142 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 30 *> proper extension: 0pspl; 01jzyx; 08qnnv; 0gy3w; *> query: (?x4750, ?x734) <- major_field_of_study(?x4750, ?x7134), major_field_of_study(?x4750, ?x1682), ?x7134 = 02_7t, school(?x4171, ?x4750), major_field_of_study(?x734, ?x1682) *> conf = 0.43 ranks of expected_values: 8 EVAL 04hgpt institution! 022h5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 111.000 111.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #7341-01pj5q PRED entity: 01pj5q PRED relation: religion PRED expected values: 0c8wxp => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 12): 0c8wxp (0.18 #233, 0.18 #369, 0.14 #640), 03_gx (0.10 #59, 0.08 #422, 0.08 #2899), 0kpl (0.07 #55, 0.05 #914, 0.04 #2895), 092bf5 (0.03 #106, 0.02 #197, 0.02 #424), 04pk9 (0.03 #65, 0.01 #789, 0.01 #474), 0kq2 (0.02 #517, 0.02 #562, 0.02 #2093), 03j6c (0.02 #2952, 0.02 #925, 0.02 #2906), 01lp8 (0.02 #137, 0.02 #319, 0.02 #364), 0flw86 (0.02 #138, 0.02 #2933, 0.02 #320), 06nzl (0.02 #469, 0.02 #60, 0.01 #105) >> Best rule #233 for best value: >> intensional similarity = 3 >> extensional distance = 532 >> proper extension: 05cljf; 01l1b90; 0prfz; 06cv1; 04bs3j; 01n5309; 01vlj1g; 03ds3; 0170vn; 018y2s; ... >> query: (?x7733, 0c8wxp) <- award(?x7733, ?x102), profession(?x7733, ?x1032), participant(?x1554, ?x7733) >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pj5q religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.182 http://example.org/people/person/religion #7340-07cw4 PRED entity: 07cw4 PRED relation: language PRED expected values: 06nm1 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 35): 064_8sq (0.18 #79, 0.17 #312, 0.16 #254), 04306rv (0.14 #4, 0.13 #62, 0.11 #586), 06nm1 (0.14 #10, 0.12 #534, 0.10 #826), 06b_j (0.14 #22, 0.09 #138, 0.08 #604), 02bjrlw (0.09 #117, 0.08 #1519, 0.08 #234), 03_9r (0.07 #475, 0.06 #67, 0.05 #3055), 05zjd (0.06 #83, 0.02 #1603, 0.02 #491), 04h9h (0.05 #508, 0.03 #1858, 0.03 #158), 0jzc (0.04 #135, 0.04 #252, 0.04 #310), 0653m (0.04 #1179, 0.04 #710, 0.04 #1003) >> Best rule #79 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 03kwtb; 05whq_9; 081l_; >> query: (?x5930, 064_8sq) <- category(?x5930, ?x134), film_festivals(?x5930, ?x7988), ?x134 = 08mbj5d >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 07cyl; 09v71cj; 026lgs; 0gg8z1f; 01gvts; *> query: (?x5930, 06nm1) <- film(?x541, ?x5930), film(?x9033, ?x5930), language(?x5930, ?x254), ?x9033 = 0chw_ *> conf = 0.14 ranks of expected_values: 3 EVAL 07cw4 language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 109.000 0.177 http://example.org/film/film/language #7339-04w7rn PRED entity: 04w7rn PRED relation: film_release_region PRED expected values: 07ylj 04g5k => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 107): 07ssc (0.80 #421, 0.79 #557, 0.77 #1237), 015fr (0.80 #422, 0.79 #558, 0.74 #1238), 04gzd (0.75 #416, 0.53 #552, 0.44 #1232), 01mjq (0.64 #579, 0.52 #443, 0.48 #1259), 0ctw_b (0.63 #566, 0.62 #430, 0.47 #1246), 01p1v (0.61 #451, 0.51 #587, 0.41 #1267), 06mzp (0.58 #562, 0.42 #1242, 0.38 #426), 06c1y (0.46 #442, 0.42 #578, 0.28 #1258), 047lj (0.44 #418, 0.30 #554, 0.29 #1234), 03ryn (0.42 #478, 0.28 #614, 0.25 #1294) >> Best rule #421 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 0c40vxk; >> query: (?x1518, 07ssc) <- production_companies(?x1518, ?x2549), film_release_region(?x1518, ?x2146), ?x2146 = 03rk0 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #433 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 77 *> proper extension: 0c40vxk; *> query: (?x1518, 07ylj) <- production_companies(?x1518, ?x2549), film_release_region(?x1518, ?x2146), ?x2146 = 03rk0 *> conf = 0.37 ranks of expected_values: 13, 26 EVAL 04w7rn film_release_region 04g5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 89.000 89.000 0.797 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04w7rn film_release_region 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 89.000 89.000 0.797 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7338-01kyvx PRED entity: 01kyvx PRED relation: special_performance_type! PRED expected values: 01wzs_q => 49 concepts (20 used for prediction) PRED predicted values (max 10 best out of 160): 07cjqy (0.50 #206, 0.40 #363, 0.33 #680), 0l_dv (0.25 #317, 0.20 #474, 0.17 #791), 04v7k2 (0.25 #316, 0.20 #473, 0.17 #790), 045hz5 (0.25 #315, 0.20 #472, 0.17 #789), 06101p (0.25 #314, 0.20 #471, 0.17 #788), 0tj9 (0.25 #313, 0.20 #470, 0.17 #787), 063b4k (0.25 #312, 0.20 #469, 0.17 #786), 040nwr (0.25 #311, 0.20 #468, 0.17 #785), 0ql36 (0.25 #310, 0.20 #467, 0.17 #784), 03f4w4 (0.25 #309, 0.20 #466, 0.17 #783) >> Best rule #206 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 09_gdc; 01pb34; >> query: (?x296, 07cjqy) <- film(?x296, ?x1807), country(?x1807, ?x94), special_performance_type(?x256, ?x296), language(?x1807, ?x254), genre(?x1807, ?x53), film_distribution_medium(?x1807, ?x81) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kyvx special_performance_type! 01wzs_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 20.000 0.500 http://example.org/film/actor/film./film/performance/special_performance_type #7337-081lh PRED entity: 081lh PRED relation: award_winner! PRED expected values: 03hl6lc 09d28z => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 329): 0f4x7 (0.31 #36971, 0.31 #36970, 0.30 #46219), 04kxsb (0.31 #36971, 0.31 #36970, 0.30 #46219), 019f4v (0.31 #36971, 0.31 #36970, 0.30 #46219), 0gq6s3 (0.31 #36971, 0.31 #36970, 0.30 #46219), 02grdc (0.31 #36971, 0.31 #36970, 0.30 #46219), 03hj5vf (0.31 #36971, 0.31 #36970, 0.30 #46218), 027c95y (0.28 #5607, 0.20 #147, 0.15 #567), 027986c (0.23 #5504, 0.10 #44, 0.08 #464), 02w9sd7 (0.21 #5617, 0.20 #157, 0.15 #577), 09cm54 (0.19 #5549, 0.10 #89, 0.08 #509) >> Best rule #36971 for best value: >> intensional similarity = 2 >> extensional distance = 1601 >> proper extension: 01w8sf; 0dfrq; >> query: (?x986, ?x2375) <- award(?x986, ?x2375), student(?x2730, ?x986) >> conf = 0.31 => this is the best rule for 6 predicted values *> Best rule #2392 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 0b478; *> query: (?x986, 09d28z) <- spouse(?x986, ?x6525), written_by(?x306, ?x986), profession(?x986, ?x353) *> conf = 0.12 ranks of expected_values: 17, 29 EVAL 081lh award_winner! 09d28z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 116.000 116.000 0.308 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 081lh award_winner! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 116.000 116.000 0.308 http://example.org/award/award_category/winners./award/award_honor/award_winner #7336-03f22dp PRED entity: 03f22dp PRED relation: profession PRED expected values: 02jknp => 157 concepts (59 used for prediction) PRED predicted values (max 10 best out of 78): 0dxtg (0.48 #8305, 0.47 #2974, 0.30 #6528), 02jknp (0.47 #451, 0.46 #8299, 0.42 #2968), 03gjzk (0.30 #2975, 0.29 #8306, 0.21 #4899), 0cbd2 (0.25 #2671, 0.25 #3559, 0.23 #5484), 0fj9f (0.24 #498, 0.23 #942, 0.19 #1090), 015cjr (0.19 #937, 0.12 #1085, 0.12 #493), 0kyk (0.18 #1658, 0.17 #6544, 0.17 #3582), 0nbcg (0.17 #5806, 0.10 #8175, 0.10 #4324), 09jwl (0.16 #6089, 0.13 #7274, 0.13 #8310), 0d1pc (0.15 #642, 0.07 #8638, 0.07 #3011) >> Best rule #8305 for best value: >> intensional similarity = 5 >> extensional distance = 560 >> proper extension: 02zyy4; 01fxck; 06101p; 0gry51; 02qnhk1; >> query: (?x12200, 0dxtg) <- profession(?x12200, ?x1032), profession(?x12200, ?x319), ?x319 = 01d_h8, gender(?x12200, ?x231), ?x1032 = 02hrh1q >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #451 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 08hhm6; 01x2tm8; 03j367r; *> query: (?x12200, 02jknp) <- award(?x12200, ?x1937), profession(?x12200, ?x319), ?x1937 = 03r8tl, ?x319 = 01d_h8 *> conf = 0.47 ranks of expected_values: 2 EVAL 03f22dp profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 157.000 59.000 0.475 http://example.org/people/person/profession #7335-027rn PRED entity: 027rn PRED relation: film_release_region! PRED expected values: 07l50vn => 112 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1320): 08hmch (0.86 #15959, 0.69 #22559, 0.64 #27839), 017jd9 (0.86 #16432, 0.68 #23032, 0.66 #5872), 0bpm4yw (0.82 #16385, 0.68 #22985, 0.65 #36186), 04f52jw (0.82 #16171, 0.66 #22771, 0.66 #5611), 043tvp3 (0.82 #16760, 0.60 #23360, 0.59 #6200), 0661m4p (0.82 #16123, 0.57 #22723, 0.56 #5563), 0gd0c7x (0.80 #16081, 0.66 #22681, 0.63 #27961), 0dzlbx (0.78 #16490, 0.66 #23090, 0.61 #28370), 03nm_fh (0.78 #16445, 0.65 #23045, 0.60 #28325), 05zlld0 (0.78 #16311, 0.62 #22911, 0.57 #28191) >> Best rule #15959 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 03rj0; 06t2t; 03h64; 07dfk; 0fhzf; >> query: (?x47, 08hmch) <- film_release_region(?x5089, ?x47), ?x5089 = 0bh8tgs, jurisdiction_of_office(?x346, ?x47) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #16560 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 03rj0; 06t2t; 03h64; 07dfk; 0fhzf; *> query: (?x47, 07l50vn) <- film_release_region(?x5089, ?x47), ?x5089 = 0bh8tgs, jurisdiction_of_office(?x346, ?x47) *> conf = 0.56 ranks of expected_values: 138 EVAL 027rn film_release_region! 07l50vn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 112.000 61.000 0.860 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7334-0cq7kw PRED entity: 0cq7kw PRED relation: award_winner PRED expected values: 0c0tzp => 98 concepts (54 used for prediction) PRED predicted values (max 10 best out of 366): 012wg (0.50 #21339, 0.50 #26265, 0.49 #16414), 02gyl0 (0.24 #4921, 0.24 #6563, 0.21 #8205), 03cdg (0.10 #13130), 086k8 (0.09 #70577, 0.05 #55806, 0.05 #65652), 04__f (0.09 #70577, 0.02 #1247, 0.01 #12735), 01b9ck (0.09 #70577, 0.02 #208), 01lc5 (0.09 #70577), 0k9j_ (0.09 #70577), 01d6jf (0.09 #70577), 03_bcg (0.09 #70577) >> Best rule #21339 for best value: >> intensional similarity = 4 >> extensional distance = 372 >> proper extension: 07k2mq; >> query: (?x4504, ?x4505) <- film(?x4655, ?x4504), award_winner(?x4504, ?x6857), nominated_for(?x198, ?x4504), written_by(?x4504, ?x4505) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1614 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 06mmr; *> query: (?x4504, 0c0tzp) <- award(?x4504, ?x500), award_winner(?x4504, ?x6857), ?x500 = 0p9sw *> conf = 0.02 ranks of expected_values: 179 EVAL 0cq7kw award_winner 0c0tzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 98.000 54.000 0.501 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #7333-02py4c8 PRED entity: 02py4c8 PRED relation: genre PRED expected values: 0d63kt => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 89): 015w9s (0.72 #10808, 0.72 #10322, 0.60 #10807), 0hn10 (0.72 #10808, 0.72 #10322, 0.60 #10807), 01z77k (0.60 #10807, 0.60 #10321, 0.52 #8503), 03mdt (0.60 #10807, 0.60 #10321, 0.52 #8503), 07c52 (0.60 #10807, 0.60 #10321, 0.52 #8503), 05p553 (0.52 #11179, 0.33 #7778, 0.32 #11056), 03k9fj (0.50 #7786, 0.33 #11187, 0.32 #2195), 04xvh5 (0.43 #156, 0.21 #521, 0.20 #642), 01jfsb (0.42 #11309, 0.30 #6813, 0.26 #13618), 02kdv5l (0.41 #11177, 0.33 #7776, 0.33 #11298) >> Best rule #10808 for best value: >> intensional similarity = 4 >> extensional distance = 1028 >> proper extension: 02v63m; 02vqhv0; 04tz52; 048rn; 01svry; 0bxsk; 028kj0; 09qljs; 04sh80; 09v8clw; >> query: (?x715, ?x714) <- titles(?x714, ?x715), film(?x488, ?x715), genre(?x11619, ?x714), region(?x11619, ?x512) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #451 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 02bg8v; 064r97z; 0b6m5fy; 043mk4y; 02rlj20; 04f6df0; 06zsk51; 045r_9; 03cffvv; *> query: (?x715, 0d63kt) <- nominated_for(?x2192, ?x715), ?x2192 = 0bfvd4, language(?x715, ?x254), titles(?x714, ?x715) *> conf = 0.09 ranks of expected_values: 33 EVAL 02py4c8 genre 0d63kt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 116.000 116.000 0.722 http://example.org/film/film/genre #7332-0g_bh PRED entity: 0g_bh PRED relation: parent_genre PRED expected values: 0fd3y 0190_q => 63 concepts (48 used for prediction) PRED predicted values (max 10 best out of 304): 05r6t (0.84 #3742, 0.72 #4821, 0.56 #1275), 0xhtw (0.40 #930, 0.33 #3858, 0.33 #470), 064t9 (0.40 #2930, 0.18 #1388, 0.14 #2624), 011j5x (0.38 #1090, 0.33 #1244, 0.23 #1535), 05w3f (0.36 #3869, 0.25 #2018, 0.25 #634), 05bt6j (0.33 #176, 0.25 #637, 0.24 #2640), 0mmp3 (0.33 #519, 0.25 #672, 0.20 #979), 05c6073 (0.33 #572, 0.23 #1535, 0.20 #1032), 0190_q (0.33 #327, 0.20 #787, 0.19 #2017), 0fd3y (0.33 #313, 0.20 #773, 0.13 #3389) >> Best rule #3742 for best value: >> intensional similarity = 8 >> extensional distance = 30 >> proper extension: 020ngt; 011j5x; 02srgf; 0jmwg; 01738f; 02z7f3; 03p7rp; 01b4p4; 0kvtr; 04f73rc; ... >> query: (?x8747, 05r6t) <- parent_genre(?x13652, ?x8747), parent_genre(?x8747, ?x302), artists(?x302, ?x8215), artists(?x302, ?x6774), artists(?x302, ?x3740), ?x3740 = 0fpj4lx, ?x6774 = 01ydzx, ?x8215 = 04_jsg >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #327 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 016y3j; *> query: (?x8747, 0190_q) <- parent_genre(?x13652, ?x8747), parent_genre(?x8747, ?x5379), parent_genre(?x8747, ?x2996), parent_genre(?x8747, ?x302), ?x302 = 016clz, artists(?x8747, ?x7972), ?x7972 = 0326tc, ?x2996 = 01243b, artists(?x5379, ?x483) *> conf = 0.33 ranks of expected_values: 9, 10 EVAL 0g_bh parent_genre 0190_q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 63.000 48.000 0.844 http://example.org/music/genre/parent_genre EVAL 0g_bh parent_genre 0fd3y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 63.000 48.000 0.844 http://example.org/music/genre/parent_genre #7331-0h6rm PRED entity: 0h6rm PRED relation: colors PRED expected values: 01g5v => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 19): 01l849 (0.33 #780, 0.27 #1084, 0.27 #1160), 01g5v (0.31 #136, 0.27 #2397, 0.27 #592), 019sc (0.28 #520, 0.21 #83, 0.20 #1090), 06fvc (0.25 #21, 0.24 #135, 0.20 #40), 09ggk (0.25 #15, 0.10 #243, 0.10 #53), 03wkwg (0.19 #185, 0.17 #71, 0.13 #242), 036k5h (0.13 #233, 0.12 #347, 0.10 #461), 0jc_p (0.11 #61, 0.10 #821, 0.10 #232), 04mkbj (0.11 #542, 0.10 #580, 0.10 #162), 01jnf1 (0.11 #87, 0.06 #68, 0.05 #1417) >> Best rule #780 for best value: >> intensional similarity = 5 >> extensional distance = 121 >> proper extension: 033q4k; 01ptt7; 01y9pk; 07xpm; 01jsn5; 01r3y2; 0pmcz; 016ndm; 02fjzt; 0hd7j; ... >> query: (?x4390, 01l849) <- student(?x4390, ?x1857), school_type(?x4390, ?x3092), colors(?x4390, ?x663), institution(?x865, ?x4390), ?x3092 = 05jxkf >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #136 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 024cg8; *> query: (?x4390, 01g5v) <- institution(?x1390, ?x4390), colors(?x4390, ?x663), ?x1390 = 0bjrnt, school_type(?x4390, ?x3092) *> conf = 0.31 ranks of expected_values: 2 EVAL 0h6rm colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 203.000 203.000 0.333 http://example.org/education/educational_institution/colors #7330-01f1kd PRED entity: 01f1kd PRED relation: medal PRED expected values: 02lpp7 => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.92 #17, 0.92 #21, 0.89 #24) >> Best rule #17 for best value: >> intensional similarity = 58 >> extensional distance = 8 >> proper extension: 06sks6; >> query: (?x7775, ?x2132) <- olympics(?x1023, ?x7775), olympics(?x512, ?x7775), medal(?x7775, ?x422), olympics(?x11872, ?x7775), medal(?x11872, ?x2132), film_release_region(?x204, ?x11872), ?x1023 = 0ctw_b, nationality(?x7943, ?x512), nationality(?x4858, ?x512), nationality(?x2492, ?x512), nationality(?x1181, ?x512), nationality(?x248, ?x512), film_release_region(?x499, ?x512), combatants(?x151, ?x512), country(?x1156, ?x512), country(?x2896, ?x512), country(?x1944, ?x512), country(?x1863, ?x512), contains(?x512, ?x362), adjoins(?x512, ?x429), region(?x5128, ?x512), award_winner(?x2372, ?x4858), film_release_region(?x5270, ?x512), film_release_region(?x5255, ?x512), film_release_region(?x4690, ?x512), film_release_region(?x3081, ?x512), film_release_region(?x1915, ?x512), film_release_region(?x1859, ?x512), film_release_region(?x791, ?x512), film_release_region(?x781, ?x512), ?x3081 = 023gxx, nominated_for(?x618, ?x1863), film(?x988, ?x1863), instrumentalists(?x227, ?x248), ?x791 = 087wc7n, country(?x2044, ?x512), ?x2044 = 06f41, ?x1859 = 0m491, ?x2372 = 0l6px, edited_by(?x1863, ?x5971), ?x1915 = 0fq7dv_, nominated_for(?x4858, ?x1434), award_nominee(?x248, ?x3403), nominated_for(?x500, ?x1944), country(?x9042, ?x512), award(?x1181, ?x724), ?x5270 = 0bc1yhb, film_release_region(?x2896, ?x87), ?x4690 = 0gkz3nz, film(?x609, ?x5128), ?x618 = 09qwmm, ?x5255 = 01sby_, profession(?x7943, ?x319), ?x781 = 0gkz15s, artists(?x302, ?x2492), award_winner(?x217, ?x1181), film(?x788, ?x1944), award_winner(?x342, ?x248) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f1kd medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 23.000 23.000 0.923 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #7329-016zp5 PRED entity: 016zp5 PRED relation: award_winner! PRED expected values: 092c5f => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 104): 092c5f (0.67 #296, 0.48 #437, 0.30 #578), 09qvms (0.43 #577, 0.09 #7193, 0.06 #295), 02wzl1d (0.11 #4231, 0.09 #7193, 0.08 #152), 03gwpw2 (0.11 #4231, 0.09 #7193, 0.06 #291), 092t4b (0.11 #4231, 0.09 #7193, 0.04 #2731), 0clfdj (0.11 #4231, 0.03 #2683, 0.03 #1978), 0bzm81 (0.11 #4231, 0.02 #1009, 0.02 #1291), 0bzknt (0.11 #4231, 0.02 #1210), 02yvhx (0.11 #4231, 0.01 #1205, 0.01 #3461), 073hgx (0.11 #4231) >> Best rule #296 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 02bfmn; 01kwld; 09wj5; 02gvwz; 01v9l67; 015t56; 016ypb; 0f0kz; 01846t; 0294fd; ... >> query: (?x5495, 092c5f) <- award_nominee(?x5495, ?x1424), nominated_for(?x5495, ?x972), ?x1424 = 01rh0w >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016zp5 award_winner! 092c5f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7328-01wk3c PRED entity: 01wk3c PRED relation: nationality PRED expected values: 06q1r => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 43): 09c7w0 (0.78 #10811, 0.77 #4067, 0.77 #8331), 06q1r (0.75 #373, 0.65 #571, 0.33 #274), 02jx1 (0.46 #428, 0.40 #5950, 0.40 #6150), 0d060g (0.12 #7, 0.05 #601, 0.04 #9924), 0hzlz (0.08 #418, 0.03 #3570), 03rk0 (0.06 #10855, 0.05 #10459, 0.05 #10954), 0h7x (0.05 #628, 0.03 #3570, 0.01 #4793), 0j5g9 (0.03 #952), 0chghy (0.03 #3570, 0.02 #2091, 0.02 #2191), 0f8l9c (0.03 #3570, 0.02 #813, 0.02 #10831) >> Best rule #10811 for best value: >> intensional similarity = 2 >> extensional distance = 3538 >> proper extension: 047f9jp; >> query: (?x10886, 09c7w0) <- nationality(?x10886, ?x512), region(?x54, ?x512) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #373 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 03f77; *> query: (?x10886, 06q1r) <- student(?x13639, ?x10886), location(?x10886, ?x4030), ?x4030 = 0hyxv *> conf = 0.75 ranks of expected_values: 2 EVAL 01wk3c nationality 06q1r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.777 http://example.org/people/person/nationality #7327-02xv8m PRED entity: 02xv8m PRED relation: film PRED expected values: 0bv8h2 => 82 concepts (43 used for prediction) PRED predicted values (max 10 best out of 306): 0g60z (0.48 #7133, 0.35 #23182, 0.34 #24967), 07pd_j (0.06 #42805, 0.03 #1181, 0.03 #74912), 05k2xy (0.06 #42805, 0.03 #362), 02qpt1w (0.06 #42805, 0.03 #74912), 092vkg (0.06 #42805, 0.01 #1939), 06fpsx (0.06 #42805), 07kh6f3 (0.06 #42805), 078sj4 (0.05 #451, 0.03 #74912, 0.02 #2234), 03bx2lk (0.05 #184, 0.02 #1967, 0.02 #18016), 02cbhg (0.05 #1397, 0.02 #3180, 0.01 #12096) >> Best rule #7133 for best value: >> intensional similarity = 3 >> extensional distance = 616 >> proper extension: 079ws; 0gdhhy; >> query: (?x3876, ?x337) <- award_winner(?x3876, ?x91), nominated_for(?x3876, ?x337), location(?x3876, ?x108) >> conf = 0.48 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02xv8m film 0bv8h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 43.000 0.479 http://example.org/film/actor/film./film/performance/film #7326-05qbbfb PRED entity: 05qbbfb PRED relation: film! PRED expected values: 02fgm7 => 87 concepts (36 used for prediction) PRED predicted values (max 10 best out of 847): 0jrqq (0.74 #18756, 0.57 #54193, 0.55 #33346), 030_3z (0.48 #18755, 0.45 #25009, 0.45 #66695), 0146mv (0.48 #18755, 0.45 #25009, 0.45 #66695), 02gf_l (0.20 #1269, 0.04 #5438, 0.02 #11690), 016ypb (0.13 #500, 0.03 #15088, 0.03 #17171), 057_yx (0.13 #1842, 0.02 #6011, 0.02 #14347), 03dn9v (0.13 #1840, 0.02 #6009, 0.02 #14345), 0151w_ (0.13 #164, 0.02 #4333, 0.02 #10585), 083wr9 (0.13 #2057, 0.02 #6226, 0.01 #14562), 04hxyv (0.13 #2042, 0.02 #6211, 0.01 #14547) >> Best rule #18756 for best value: >> intensional similarity = 4 >> extensional distance = 233 >> proper extension: 0m313; 034qmv; 083shs; 02vxq9m; 028_yv; 011yxg; 07gp9; 01k1k4; 095zlp; 034qrh; ... >> query: (?x6053, ?x3873) <- nominated_for(?x3873, ?x6053), nominated_for(?x154, ?x6053), film_format(?x6053, ?x6392), film(?x3873, ?x1108) >> conf = 0.74 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05qbbfb film! 02fgm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 36.000 0.744 http://example.org/film/actor/film./film/performance/film #7325-03ryn PRED entity: 03ryn PRED relation: film_release_region! PRED expected values: 0c40vxk 0872p_c 05qbckf 07x4qr 06wbm8q 0407yj_ 0gffmn8 05zlld0 05c26ss 02mt51 0432_5 04yg13l 0glqh5_ 0h95zbp 05b6rdt 01mgw 0ddbjy4 => 161 concepts (108 used for prediction) PRED predicted values (max 10 best out of 1731): 0gj9tn5 (0.89 #10164, 0.87 #3929, 0.86 #2682), 0cc5mcj (0.87 #4008, 0.86 #18972, 0.86 #10243), 0jjy0 (0.87 #3860, 0.86 #10095, 0.86 #2613), 0by1wkq (0.87 #3950, 0.86 #10185, 0.86 #2703), 0cp0ph6 (0.87 #4149, 0.86 #10384, 0.86 #2902), 07l50vn (0.87 #4411, 0.86 #10646, 0.86 #3164), 03qnc6q (0.87 #4027, 0.86 #2780, 0.83 #20238), 0g9wdmc (0.87 #3932, 0.86 #2685, 0.82 #10167), 01vksx (0.87 #3836, 0.86 #2589, 0.81 #20047), 0ds3t5x (0.87 #3781, 0.86 #2534, 0.81 #19992) >> Best rule #10164 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 05r4w; 09c7w0; 0154j; 0chghy; 03rt9; 05qhw; 07ssc; 05v8c; 0f8l9c; 0ctw_b; ... >> query: (?x3749, 0gj9tn5) <- film_release_region(?x1170, ?x3749), film_release_region(?x249, ?x3749), ?x249 = 0c3ybss, films(?x14661, ?x1170), service_location(?x1492, ?x3749) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #3805 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 03_3d; *> query: (?x3749, 0c40vxk) <- film_release_region(?x1170, ?x3749), film_release_region(?x249, ?x3749), ?x249 = 0c3ybss, ?x1170 = 09gdm7q *> conf = 0.87 ranks of expected_values: 16, 17, 18, 19, 20, 23, 25, 38, 57, 66, 77, 83, 99, 103, 111, 279, 292 EVAL 03ryn film_release_region! 0ddbjy4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 01mgw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 05b6rdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 0h95zbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 0glqh5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 04yg13l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 0432_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 02mt51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 05zlld0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 0gffmn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 0407yj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 06wbm8q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 07x4qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 05qbckf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 0872p_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03ryn film_release_region! 0c40vxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 161.000 108.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7324-057_yx PRED entity: 057_yx PRED relation: award_nominee! PRED expected values: 01vyv9 => 59 concepts (28 used for prediction) PRED predicted values (max 10 best out of 579): 057_yx (0.45 #6955, 0.16 #44054, 0.15 #48691), 01s7z0 (0.45 #6955), 01vyv9 (0.16 #44054, 0.15 #48691, 0.15 #25503), 05kfs (0.16 #44054, 0.15 #48691), 0652ty (0.16 #44054), 05cl2w (0.16 #44054), 0k2mxq (0.16 #44054), 02lnhv (0.16 #44054), 04w391 (0.15 #48691, 0.15 #25503, 0.15 #37098), 02qzjj (0.15 #48691, 0.15 #37098, 0.15 #27823) >> Best rule #6955 for best value: >> intensional similarity = 2 >> extensional distance = 258 >> proper extension: 0f721s; 04rtpt; 025504; >> query: (?x11100, ?x12775) <- program(?x11100, ?x11414), program(?x12775, ?x11414) >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #44054 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1755 *> proper extension: 0kc9f; *> query: (?x11100, ?x450) <- nominated_for(?x11100, ?x2336), film(?x450, ?x2336) *> conf = 0.16 ranks of expected_values: 3 EVAL 057_yx award_nominee! 01vyv9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 59.000 28.000 0.446 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7323-01xq8v PRED entity: 01xq8v PRED relation: award PRED expected values: 018wdw => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 176): 02r0csl (0.48 #5, 0.30 #232, 0.26 #6707), 02hsq3m (0.30 #232, 0.26 #6707, 0.26 #8096), 0gq_v (0.30 #232, 0.26 #6707, 0.26 #8096), 0gr0m (0.19 #1679, 0.15 #1910, 0.13 #2603), 0gr42 (0.18 #7170, 0.14 #87, 0.08 #1476), 02qyp19 (0.18 #7170, 0.06 #5318, 0.05 #10644), 027b9j5 (0.18 #7170, 0.05 #10644, 0.05 #151), 0gq9h (0.18 #1682, 0.14 #62, 0.14 #526), 0p9sw (0.18 #1640, 0.12 #1871, 0.12 #2564), 0gs9p (0.16 #1684, 0.11 #4226, 0.11 #2608) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 02vp1f_; >> query: (?x7741, 02r0csl) <- film(?x450, ?x7741), award(?x7741, ?x3458), ?x3458 = 0gqxm, nominated_for(?x143, ?x7741) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #169 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 02vp1f_; *> query: (?x7741, 018wdw) <- film(?x450, ?x7741), award(?x7741, ?x3458), ?x3458 = 0gqxm, nominated_for(?x143, ?x7741) *> conf = 0.10 ranks of expected_values: 26 EVAL 01xq8v award 018wdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 92.000 92.000 0.476 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #7322-03r00m PRED entity: 03r00m PRED relation: ceremony PRED expected values: 09n4nb 02cg41 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 134): 09n4nb (0.75 #3888, 0.62 #981, 0.62 #847), 02cg41 (0.75 #3888, 0.60 #1057, 0.60 #923), 01c6qp (0.75 #3888, 0.60 #16, 0.59 #954), 01bx35 (0.75 #3888, 0.60 #5, 0.57 #943), 01mh_q (0.75 #3888, 0.60 #82, 0.56 #1020), 01s695 (0.75 #3888, 0.60 #2, 0.55 #940), 013b2h (0.75 #3888, 0.60 #74, 0.55 #1012), 01mhwk (0.75 #3888, 0.60 #36, 0.55 #974), 01xqqp (0.75 #3888, 0.52 #893, 0.52 #1027), 0jzphpx (0.75 #3888, 0.47 #972, 0.46 #838) >> Best rule #3888 for best value: >> intensional similarity = 5 >> extensional distance = 259 >> proper extension: 099c8n; 09tqxt; 03m73lj; 054knh; 02py_sj; 06bwtj; 0bwgmzd; >> query: (?x12835, ?x342) <- ceremony(?x12835, ?x2186), ceremony(?x6090, ?x2186), ceremony(?x341, ?x2186), award(?x1413, ?x341), ceremony(?x6090, ?x342) >> conf = 0.75 => this is the best rule for 11 predicted values ranks of expected_values: 1, 2 EVAL 03r00m ceremony 02cg41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03r00m ceremony 09n4nb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony #7321-0w7s PRED entity: 0w7s PRED relation: major_field_of_study! PRED expected values: 07w0v 03v6t => 68 concepts (48 used for prediction) PRED predicted values (max 10 best out of 694): 06pwq (0.74 #16840, 0.70 #20336, 0.69 #19751), 09f2j (0.71 #17001, 0.70 #18745, 0.63 #19912), 03ksy (0.69 #16361, 0.69 #13458, 0.56 #14039), 01w5m (0.67 #5335, 0.62 #13457, 0.60 #17523), 07wrz (0.67 #5284, 0.52 #13406, 0.40 #2966), 017j69 (0.67 #5378, 0.49 #17566, 0.48 #13500), 01w3v (0.66 #13357, 0.60 #2917, 0.51 #17423), 02zd460 (0.60 #17598, 0.60 #16435, 0.59 #13532), 0bwfn (0.60 #6674, 0.60 #3196, 0.59 #13056), 01f1r4 (0.60 #6518, 0.60 #3040, 0.50 #2460) >> Best rule #16840 for best value: >> intensional similarity = 12 >> extensional distance = 40 >> proper extension: 05qjt; 036hv; 02ky346; 06ms6; 04rjg; 0h5k; 04x_3; 03g3w; 062z7; 06n6p; ... >> query: (?x11820, 06pwq) <- major_field_of_study(?x865, ?x11820), major_field_of_study(?x2497, ?x11820), school(?x12141, ?x2497), school(?x3333, ?x2497), ?x12141 = 0jmk7, colors(?x2497, ?x332), list(?x2497, ?x2197), institution(?x620, ?x2497), draft(?x3333, ?x1161), ?x865 = 02h4rq6, state_province_region(?x2497, ?x3038), team(?x2010, ?x3333) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #2923 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 3 *> proper extension: 01lj9; *> query: (?x11820, 07w0v) <- major_field_of_study(?x1771, ?x11820), major_field_of_study(?x734, ?x11820), major_field_of_study(?x6333, ?x11820), major_field_of_study(?x2497, ?x11820), major_field_of_study(?x735, ?x11820), ?x2497 = 0f1nl, ?x734 = 04zx3q1, school_type(?x6333, ?x4994), ?x735 = 065y4w7, school(?x700, ?x6333), major_field_of_study(?x6333, ?x6870), ?x1771 = 019v9k, school(?x4171, ?x6333), ?x6870 = 01540 *> conf = 0.60 ranks of expected_values: 11, 23 EVAL 0w7s major_field_of_study! 03v6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 68.000 48.000 0.738 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0w7s major_field_of_study! 07w0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 68.000 48.000 0.738 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7320-07s8hms PRED entity: 07s8hms PRED relation: award_winner! PRED expected values: 0g55tzk => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 110): 0g55tzk (0.50 #413, 0.25 #552, 0.24 #1252), 092t4b (0.47 #468, 0.18 #4730, 0.17 #5427), 0clfdj (0.44 #421, 0.18 #4730, 0.17 #5427), 02wzl1d (0.25 #149, 0.18 #4730, 0.17 #5427), 02yxh9 (0.25 #239, 0.18 #4730, 0.17 #5427), 03gwpw2 (0.25 #148, 0.18 #4730, 0.17 #5427), 03gt46z (0.25 #201, 0.18 #4730, 0.17 #5427), 0g5b0q5 (0.24 #1252, 0.18 #4730, 0.17 #5427), 0gx_st (0.18 #4730, 0.17 #5427, 0.17 #5148), 04n2r9h (0.18 #4730, 0.17 #5427, 0.17 #5148) >> Best rule #413 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0dyztm; >> query: (?x3841, 0g55tzk) <- award_winner(?x3841, ?x6031), ?x6031 = 02l6dy, nominated_for(?x3841, ?x493) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07s8hms award_winner! 0g55tzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7319-0838f PRED entity: 0838f PRED relation: nutrient! PRED expected values: 0dcfv 061_f 0971v => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 71): 061_f (0.95 #462, 0.95 #459, 0.94 #488), 0971v (0.90 #429, 0.90 #417, 0.89 #70), 0dcfv (0.89 #70, 0.89 #57, 0.89 #182), 04k8n (0.03 #32, 0.03 #31, 0.03 #48), 0f4kp (0.03 #32, 0.03 #31, 0.03 #48), 0fzjh (0.03 #32, 0.03 #31, 0.03 #48), 025rw19 (0.03 #32, 0.03 #31, 0.03 #48), 07zqy (0.03 #32, 0.03 #31, 0.03 #48), 0q01m (0.03 #32, 0.03 #31, 0.03 #48), 025sf0_ (0.03 #32, 0.03 #31, 0.03 #48) >> Best rule #462 for best value: >> intensional similarity = 118 >> extensional distance = 35 >> proper extension: 0466p20; >> query: (?x2702, ?x3900) <- nutrient(?x10612, ?x2702), nutrient(?x9732, ?x2702), nutrient(?x9005, ?x2702), nutrient(?x8298, ?x2702), nutrient(?x7719, ?x2702), nutrient(?x7057, ?x2702), nutrient(?x6285, ?x2702), ?x7057 = 0fbdb, nutrient(?x7719, ?x13944), nutrient(?x7719, ?x12902), nutrient(?x7719, ?x12868), nutrient(?x7719, ?x12454), nutrient(?x7719, ?x11784), nutrient(?x7719, ?x11592), nutrient(?x7719, ?x11270), nutrient(?x7719, ?x10709), nutrient(?x7719, ?x9915), nutrient(?x7719, ?x9855), nutrient(?x7719, ?x9840), nutrient(?x7719, ?x9795), nutrient(?x7719, ?x9733), nutrient(?x7719, ?x9708), nutrient(?x7719, ?x9619), nutrient(?x7719, ?x9490), nutrient(?x7719, ?x9436), nutrient(?x7719, ?x9426), nutrient(?x7719, ?x8413), nutrient(?x7719, ?x8243), nutrient(?x7719, ?x7720), nutrient(?x7719, ?x7652), nutrient(?x7719, ?x7431), nutrient(?x7719, ?x7364), nutrient(?x7719, ?x7362), nutrient(?x7719, ?x7219), nutrient(?x7719, ?x7135), nutrient(?x7719, ?x6586), nutrient(?x7719, ?x6286), nutrient(?x7719, ?x6192), nutrient(?x7719, ?x6026), nutrient(?x7719, ?x5526), nutrient(?x7719, ?x5374), nutrient(?x7719, ?x5010), nutrient(?x7719, ?x4069), nutrient(?x7719, ?x3469), nutrient(?x7719, ?x3264), nutrient(?x7719, ?x2018), nutrient(?x7719, ?x1258), ?x10709 = 0h1sz, ?x9619 = 0h1tg, ?x12868 = 03d49, ?x11592 = 025sf0_, ?x7135 = 025rsfk, ?x10612 = 0frq6, ?x9840 = 02p0tjr, ?x7431 = 09gwd, ?x5526 = 09pbb, nutrient(?x8298, ?x12083), nutrient(?x8298, ?x11409), nutrient(?x8298, ?x10891), nutrient(?x8298, ?x10453), nutrient(?x8298, ?x10098), nutrient(?x8298, ?x6160), nutrient(?x8298, ?x6033), nutrient(?x8298, ?x5549), nutrient(?x8298, ?x5451), ?x8413 = 02kc4sf, ?x6026 = 025sf8g, ?x6033 = 04zjxcz, ?x10453 = 075pwf, ?x11270 = 02kc008, nutrient(?x9732, ?x14210), nutrient(?x9732, ?x13545), nutrient(?x9732, ?x12336), nutrient(?x9732, ?x6517), nutrient(?x9732, ?x1304), ?x6285 = 01645p, ?x6286 = 02y_3rf, ?x7364 = 09gvd, ?x2018 = 01sh2, ?x5374 = 025s0zp, ?x9436 = 025sqz8, ?x7720 = 025s7x6, ?x12454 = 025rw19, ?x4069 = 0hqw8p_, ?x6586 = 05gh50, ?x11409 = 0h1yf, ?x12902 = 0fzjh, ?x9005 = 04zpv, ?x13545 = 01w_3, ?x9708 = 061xhr, ?x10098 = 0h1_c, ?x5451 = 05wvs, ?x3264 = 0dcfv, ?x9795 = 05v_8y, ?x7652 = 025s0s0, ?x9426 = 0h1yy, ?x1304 = 08lb68, ?x3469 = 0h1zw, ?x12336 = 0f4l5, ?x7219 = 0h1vg, ?x12083 = 01n78x, ?x14210 = 0f4k5, ?x10891 = 0g5gq, ?x6517 = 02kd8zw, ?x9733 = 0h1tz, nutrient(?x3900, ?x5549), ?x3900 = 061_f, ?x7362 = 02kc5rj, ?x13944 = 0f4kp, ?x9915 = 025tkqy, ?x9855 = 0d9t0, ?x1258 = 0h1wg, ?x8243 = 014d7f, ?x11784 = 07zqy, ?x6192 = 06jry, ?x9490 = 0h1sg, ?x6160 = 041r51, ?x5010 = 0h1vz >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0838f nutrient! 0971v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.946 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0838f nutrient! 061_f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.946 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0838f nutrient! 0dcfv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.946 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #7318-02sg5v PRED entity: 02sg5v PRED relation: film! PRED expected values: 02v406 => 149 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1344): 03kpvp (0.50 #2715, 0.43 #21458, 0.38 #23542), 0h5g_ (0.50 #6320, 0.33 #12568, 0.29 #20899), 02f2dn (0.50 #6695, 0.33 #12943, 0.29 #21274), 06mr6 (0.33 #26034, 0.33 #17702, 0.33 #9371), 04gc65 (0.33 #1975, 0.12 #39470, 0.12 #41553), 0b_dy (0.33 #535, 0.12 #38030, 0.12 #40113), 031k24 (0.33 #1409, 0.12 #38904, 0.12 #40987), 01515w (0.33 #1086, 0.07 #30246, 0.06 #38581), 014x77 (0.33 #92, 0.07 #29252, 0.06 #37587), 0blq0z (0.33 #455, 0.07 #29615, 0.06 #37950) >> Best rule #2715 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 02qrv7; >> query: (?x836, 03kpvp) <- nominated_for(?x836, ?x11120), nominated_for(?x836, ?x6077), nominated_for(?x836, ?x1851), ?x11120 = 0fztbq, ?x1851 = 01kf3_9, genre(?x836, ?x225), film(?x788, ?x836), ?x6077 = 0g5pvv >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #119471 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 66 *> proper extension: 01bb9r; 09wnnb; *> query: (?x836, 02v406) <- nominated_for(?x836, ?x11120), film_release_region(?x11120, ?x94), written_by(?x11120, ?x3686), genre(?x11120, ?x812), production_companies(?x836, ?x788), student(?x13297, ?x3686) *> conf = 0.01 ranks of expected_values: 1133 EVAL 02sg5v film! 02v406 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 149.000 111.000 0.500 http://example.org/film/actor/film./film/performance/film #7317-029pnn PRED entity: 029pnn PRED relation: award PRED expected values: 05zvj3m => 85 concepts (60 used for prediction) PRED predicted values (max 10 best out of 256): 09sb52 (0.27 #3281, 0.26 #2471, 0.26 #9761), 04ljl_l (0.20 #4053, 0.08 #813, 0.07 #5268), 05p09zm (0.18 #4174, 0.10 #934, 0.09 #5389), 05zr6wv (0.18 #827, 0.13 #1637, 0.13 #422), 05b4l5x (0.16 #4056, 0.06 #24307, 0.06 #3651), 0cqhk0 (0.15 #1252, 0.15 #442, 0.12 #3277), 0cjyzs (0.15 #1321, 0.06 #24307, 0.05 #511), 07bdd_ (0.15 #4116, 0.06 #24307, 0.05 #13836), 03c7tr1 (0.15 #4109, 0.06 #24307, 0.05 #59), 05ztrmj (0.15 #995, 0.08 #4235, 0.07 #590) >> Best rule #3281 for best value: >> intensional similarity = 4 >> extensional distance = 177 >> proper extension: 016gr2; 01fwj8; 027xbpw; 02g5h5; 0fby2t; 03dpqd; 08hsww; 016zp5; 03kxp7; 05w6cw; ... >> query: (?x8257, 09sb52) <- profession(?x8257, ?x1383), award(?x8257, ?x1312), ?x1383 = 0np9r, film(?x8257, ?x4717) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #15391 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1535 *> proper extension: 0gsg7; 09d5h; 03jvmp; 0cjdk; 0kk9v; 027_tg; 05xbx; 05gnf; 01j7pt; 0kctd; ... *> query: (?x8257, ?x640) <- nominated_for(?x8257, ?x7444), award_winner(?x1312, ?x8257), nominated_for(?x640, ?x7444) *> conf = 0.13 ranks of expected_values: 14 EVAL 029pnn award 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 85.000 60.000 0.274 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7316-04wp63 PRED entity: 04wp63 PRED relation: gender PRED expected values: 05zppz => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #57, 0.83 #43, 0.80 #39), 02zsn (0.32 #42, 0.32 #52, 0.32 #54) >> Best rule #57 for best value: >> intensional similarity = 2 >> extensional distance = 661 >> proper extension: 02qjj7; 04rs03; 042rnl; 04l3_z; 01g4zr; 016hvl; 01p45_v; 01c59k; 01c58j; 0177s6; ... >> query: (?x10262, 05zppz) <- profession(?x10262, ?x524), ?x524 = 02jknp >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wp63 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.839 http://example.org/people/person/gender #7315-02zfg3 PRED entity: 02zfg3 PRED relation: gender PRED expected values: 05zppz => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #31, 0.91 #21, 0.86 #71), 02zsn (0.36 #64, 0.33 #102, 0.32 #104) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 01w23w; >> query: (?x13194, 05zppz) <- award(?x13194, ?x3066), nominated_for(?x13194, ?x6111), ?x3066 = 0gqy2 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zfg3 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.917 http://example.org/people/person/gender #7314-07xr3w PRED entity: 07xr3w PRED relation: award PRED expected values: 0gr0m => 147 concepts (95 used for prediction) PRED predicted values (max 10 best out of 317): 0gr0m (0.77 #1698, 0.75 #2917, 0.75 #2510), 02x258x (0.33 #2972, 0.33 #941, 0.32 #3378), 09sb52 (0.28 #11004, 0.27 #24813, 0.27 #12222), 02rdyk7 (0.21 #4965, 0.13 #6183, 0.11 #904), 0gq9h (0.20 #484, 0.20 #78, 0.19 #16651), 0gq_v (0.20 #428, 0.20 #22, 0.19 #16651), 0gqyl (0.20 #106, 0.19 #16651, 0.13 #32085), 0gr4k (0.20 #439, 0.19 #16651, 0.13 #32085), 0gqz2 (0.20 #487, 0.19 #16651, 0.12 #37777), 0gqwc (0.20 #75, 0.13 #32085, 0.13 #32086) >> Best rule #1698 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 070bjw; >> query: (?x3348, 0gr0m) <- profession(?x3348, ?x2265), ?x2265 = 0dgd_, award_winner(?x9611, ?x3348), place_of_death(?x3348, ?x1523) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07xr3w award 0gr0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 95.000 0.769 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7313-03c602 PRED entity: 03c602 PRED relation: award_winner! PRED expected values: 08pc1x => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 110): 05pd94v (0.29 #554, 0.27 #416, 0.22 #278), 0466p0j (0.25 #627, 0.24 #489, 0.24 #351), 056878 (0.24 #445, 0.24 #307, 0.23 #583), 019bk0 (0.24 #291, 0.20 #429, 0.19 #567), 01c6qp (0.20 #294, 0.18 #432, 0.15 #570), 0gpjbt (0.20 #304, 0.18 #442, 0.15 #580), 0gx1673 (0.17 #671, 0.17 #395, 0.16 #533), 01bx35 (0.17 #282, 0.16 #420, 0.15 #558), 01mh_q (0.17 #364, 0.16 #502, 0.13 #640), 013b2h (0.17 #4633, 0.15 #5047, 0.14 #1873) >> Best rule #554 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 01vrx3g; 02r3zy; 02k5sc; 09z1lg; 016l09; 0mjn2; >> query: (?x10477, 05pd94v) <- award_winner(?x486, ?x10477), ?x486 = 02rjjll, award_winner(?x3666, ?x10477) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #13665 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1857 *> proper extension: 02zq43; 01j5x6; 0b05xm; 073749; *> query: (?x10477, ?x725) <- profession(?x10477, ?x220), award_nominee(?x10477, ?x9220), award_winner(?x725, ?x9220) *> conf = 0.10 ranks of expected_values: 15 EVAL 03c602 award_winner! 08pc1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 143.000 143.000 0.288 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7312-04j53 PRED entity: 04j53 PRED relation: contains! PRED expected values: 02qkt => 134 concepts (98 used for prediction) PRED predicted values (max 10 best out of 143): 02qkt (0.82 #2137, 0.81 #5718, 0.76 #3927), 02j71 (0.61 #85133, 0.59 #74375, 0.57 #86929), 09c7w0 (0.48 #17917, 0.46 #17020, 0.46 #15227), 04_1l0v (0.44 #18364, 0.44 #17467, 0.44 #15674), 07c5l (0.41 #1289, 0.24 #24582, 0.23 #38021), 06n3y (0.35 #1620, 0.13 #24913, 0.11 #15054), 0dg3n1 (0.32 #28821, 0.31 #36886, 0.28 #31509), 0j0k (0.27 #30836, 0.26 #29939, 0.24 #43380), 04pnx (0.24 #1319, 0.14 #24612, 0.13 #37156), 0345h (0.21 #62718, 0.19 #86928, 0.15 #43981) >> Best rule #2137 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 01g_k3; >> query: (?x3040, 02qkt) <- contains(?x455, ?x3040), teams(?x3040, ?x10189), ?x455 = 02j9z >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04j53 contains! 02qkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 98.000 0.818 http://example.org/location/location/contains #7311-01rwpj PRED entity: 01rwpj PRED relation: nominated_for! PRED expected values: 02x1dht 02x73k6 09sdmz => 84 concepts (76 used for prediction) PRED predicted values (max 10 best out of 208): 019f4v (0.77 #1483, 0.50 #2673, 0.49 #2435), 0gq9h (0.66 #1492, 0.61 #2444, 0.59 #2682), 0gs9p (0.59 #1494, 0.50 #2446, 0.49 #2684), 0k611 (0.49 #1503, 0.42 #2693, 0.41 #2455), 04dn09n (0.46 #1464, 0.37 #2654, 0.35 #2416), 040njc (0.43 #1436, 0.35 #2626, 0.34 #2388), 0f4x7 (0.42 #1454, 0.35 #2644, 0.35 #2406), 0gr4k (0.41 #1455, 0.38 #2645, 0.38 #2407), 0gq_v (0.40 #1448, 0.35 #2400, 0.34 #2638), 0gqyl (0.35 #1510, 0.31 #2700, 0.30 #2462) >> Best rule #1483 for best value: >> intensional similarity = 5 >> extensional distance = 118 >> proper extension: 0bmpm; >> query: (?x5067, 019f4v) <- nominated_for(?x617, ?x5067), nominated_for(?x5886, ?x5067), nominated_for(?x3066, ?x5067), ?x3066 = 0gqy2, disciplines_or_subjects(?x5886, ?x373) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1574 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 118 *> proper extension: 0bmpm; *> query: (?x5067, 09sdmz) <- nominated_for(?x617, ?x5067), nominated_for(?x5886, ?x5067), nominated_for(?x3066, ?x5067), ?x3066 = 0gqy2, disciplines_or_subjects(?x5886, ?x373) *> conf = 0.33 ranks of expected_values: 11, 20, 57 EVAL 01rwpj nominated_for! 09sdmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 84.000 76.000 0.767 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01rwpj nominated_for! 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 84.000 76.000 0.767 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01rwpj nominated_for! 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 84.000 76.000 0.767 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7310-051q5 PRED entity: 051q5 PRED relation: team! PRED expected values: 059yj => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 8): 059yj (0.88 #213, 0.85 #189, 0.83 #141), 0h69c (0.29 #94, 0.25 #22, 0.23 #318), 0355pl (0.14 #484, 0.14 #516, 0.13 #508), 07y9k (0.09 #566, 0.07 #575, 0.06 #485), 03zv9 (0.09 #507, 0.06 #475, 0.06 #547), 01ddbl (0.06 #544, 0.05 #496, 0.05 #279), 021q23 (0.06 #312, 0.05 #280, 0.02 #497), 0356lc (0.05 #563, 0.04 #572, 0.03 #482) >> Best rule #213 for best value: >> intensional similarity = 15 >> extensional distance = 31 >> proper extension: 05tfm; 01y49; 03b3j; 02c_4; >> query: (?x4222, 059yj) <- position(?x4222, ?x2312), position(?x4222, ?x1240), position_s(?x4222, ?x3113), school(?x4222, ?x546), position(?x9172, ?x2312), position(?x9115, ?x2312), position(?x6379, ?x2312), position(?x4546, ?x2312), draft(?x4222, ?x465), team(?x2312, ?x179), ?x9115 = 0g0z58, ?x4546 = 05gg4, position(?x706, ?x1240), ?x6379 = 0bjkk9, ?x9172 = 06rpd >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051q5 team! 059yj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.879 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #7309-02kcz PRED entity: 02kcz PRED relation: administrative_parent PRED expected values: 02j71 => 146 concepts (103 used for prediction) PRED predicted values (max 10 best out of 53): 02j71 (0.91 #148, 0.88 #2335, 0.87 #964), 09c7w0 (0.28 #6847, 0.25 #8092, 0.22 #10157), 0dg3n1 (0.15 #6984, 0.14 #14032, 0.13 #12780), 03rjj (0.06 #7957, 0.06 #7680, 0.05 #7541), 0d060g (0.03 #12649, 0.03 #13062, 0.02 #13619), 07ssc (0.03 #2196, 0.03 #2607, 0.02 #3289), 049nq (0.03 #640, 0.02 #1732, 0.02 #2281), 03_3d (0.03 #13339, 0.02 #13471), 0345h (0.02 #3304, 0.02 #4128, 0.02 #13471), 0b90_r (0.02 #3281, 0.01 #2462, 0.01 #8642) >> Best rule #148 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 04wgh; 04w58; 06srk; >> query: (?x7807, 02j71) <- organization(?x7807, ?x127), form_of_government(?x7807, ?x48), countries_spoken_in(?x5607, ?x7807), ?x5607 = 064_8sq >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02kcz administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 103.000 0.909 http://example.org/base/aareas/schema/administrative_area/administrative_parent #7308-0mnyn PRED entity: 0mnyn PRED relation: time_zones PRED expected values: 02hcv8 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.86 #185, 0.84 #146, 0.84 #237), 03bdv (0.24 #136, 0.19 #58, 0.18 #110), 02lcqs (0.21 #122, 0.19 #83, 0.18 #304), 02fqwt (0.18 #1028, 0.18 #508, 0.18 #755), 02llzg (0.15 #134, 0.14 #108, 0.10 #173), 02hczc (0.08 #652, 0.07 #782, 0.07 #847), 03plfd (0.04 #257, 0.03 #283, 0.02 #751), 0d2t4g (0.04 #126), 042g7t (0.03 #154, 0.03 #245, 0.02 #1051), 02lcrv (0.01 #254) >> Best rule #185 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0mp3l; 02xry; 01zmqw; 0tr3p; 0d739; 0mnwd; >> query: (?x13182, 02hcv8) <- location(?x5097, ?x13182), currency(?x13182, ?x170), contains(?x1426, ?x13182), category(?x13182, ?x134) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mnyn time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.857 http://example.org/location/location/time_zones #7307-0432_5 PRED entity: 0432_5 PRED relation: category PRED expected values: 08mbj5d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.31 #8, 0.29 #31, 0.29 #50) >> Best rule #8 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 0dx8gj; 02_sr1; 05znbh7; 0cqr0q; 0kbwb; 01qdmh; 02x2jl_; >> query: (?x4604, 08mbj5d) <- country(?x4604, ?x2316), language(?x4604, ?x3271), genre(?x4604, ?x225), ?x3271 = 012w70, nominated_for(?x7739, ?x4604) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0432_5 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.308 http://example.org/common/topic/webpage./common/webpage/category #7306-0fd3y PRED entity: 0fd3y PRED relation: artists PRED expected values: 043c4j 0326tc 01w9mnm => 56 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1040): 01vxlbm (0.71 #9868, 0.60 #7749, 0.50 #4570), 03f5spx (0.71 #9591, 0.50 #4293, 0.20 #7472), 01w5n51 (0.60 #14460, 0.50 #4913, 0.44 #11272), 05k79 (0.60 #7559, 0.50 #4380, 0.43 #9678), 01dwrc (0.60 #7927, 0.43 #10046, 0.25 #15356), 0415mzy (0.60 #7909, 0.43 #10028, 0.25 #4730), 0c7ct (0.60 #7455, 0.29 #9574, 0.25 #4276), 07g2v (0.60 #7709, 0.25 #4530, 0.14 #9828), 016t0h (0.57 #10533, 0.50 #9473, 0.33 #2056), 01pfr3 (0.57 #9560, 0.50 #4262, 0.33 #3203) >> Best rule #9868 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 064t9; 06by7; 0y3_8; >> query: (?x497, 01vxlbm) <- parent_genre(?x497, ?x2808), artists(?x497, ?x11689), artists(?x497, ?x9757), artists(?x497, ?x1489), parent_genre(?x2439, ?x497), ?x11689 = 06p03s, role(?x9757, ?x212), nominated_for(?x1489, ?x1910), nominated_for(?x384, ?x1910) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4920 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 016clz; 0m0jc; *> query: (?x497, 043c4j) <- parent_genre(?x497, ?x2808), artists(?x497, ?x13142), artists(?x497, ?x11689), artists(?x497, ?x9757), artists(?x497, ?x1489), parent_genre(?x2439, ?x497), ?x11689 = 06p03s, ?x9757 = 06br6t, music(?x1077, ?x1489), award(?x13142, ?x2322) *> conf = 0.50 ranks of expected_values: 34, 83, 448 EVAL 0fd3y artists 01w9mnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 28.000 0.714 http://example.org/music/genre/artists EVAL 0fd3y artists 0326tc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 56.000 28.000 0.714 http://example.org/music/genre/artists EVAL 0fd3y artists 043c4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 56.000 28.000 0.714 http://example.org/music/genre/artists #7305-06t8b PRED entity: 06t8b PRED relation: award_winner! PRED expected values: 02x4wr9 => 153 concepts (122 used for prediction) PRED predicted values (max 10 best out of 273): 0gr0m (0.83 #3484, 0.48 #3910, 0.44 #5619), 02pqp12 (0.45 #2560, 0.45 #4265, 0.44 #5974), 04dn09n (0.45 #2560, 0.45 #4265, 0.44 #5974), 0gr51 (0.45 #2560, 0.45 #4265, 0.44 #5974), 03hl6lc (0.45 #2560, 0.45 #4265, 0.44 #5974), 02qyp19 (0.45 #2560, 0.45 #4265, 0.44 #5974), 02x258x (0.45 #2560, 0.45 #4265, 0.44 #5974), 019f4v (0.38 #1343, 0.22 #11154, 0.21 #6891), 02qyntr (0.35 #2399, 0.32 #2826, 0.30 #3252), 0k611 (0.30 #517, 0.27 #2651, 0.26 #3077) >> Best rule #3484 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0627sn; >> query: (?x7903, 0gr0m) <- award_winner(?x1820, ?x7903), award_winner(?x1819, ?x7903), award_winner(?x289, ?x7903), cinematography(?x633, ?x7903) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1410 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0693l; 026dx; 01j2xj; 02hfp_; 027zz; *> query: (?x7903, 02x4wr9) <- award_winner(?x1820, ?x7903), award(?x7903, ?x1198), executive_produced_by(?x603, ?x7903), ?x1198 = 02pqp12 *> conf = 0.25 ranks of expected_values: 15 EVAL 06t8b award_winner! 02x4wr9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 153.000 122.000 0.833 http://example.org/award/award_category/winners./award/award_honor/award_winner #7304-01hmk9 PRED entity: 01hmk9 PRED relation: award PRED expected values: 0bp_b2 0gkvb7 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 299): 03x3wf (0.72 #10512, 0.70 #36383, 0.70 #45278), 0bp_b2 (0.43 #17, 0.09 #12533, 0.05 #1229), 09sb52 (0.32 #20654, 0.30 #22677, 0.29 #16613), 0gr4k (0.30 #8118, 0.28 #8522, 0.26 #10948), 0ck27z (0.29 #92, 0.23 #16261, 0.14 #30007), 0gqy2 (0.29 #165, 0.21 #7038, 0.14 #9867), 09qv3c (0.29 #50, 0.05 #1262, 0.04 #9348), 0bs0bh (0.29 #103, 0.04 #19908, 0.03 #22335), 0gr51 (0.27 #8186, 0.25 #8590, 0.23 #11824), 04dn09n (0.27 #8129, 0.24 #8533, 0.22 #11767) >> Best rule #10512 for best value: >> intensional similarity = 3 >> extensional distance = 291 >> proper extension: 04f525m; 0kk9v; 056ws9; 01j7pt; 04rcl7; 02x2097; 01njxvw; 0kctd; 0kcd5; >> query: (?x7183, ?x1088) <- nominated_for(?x7183, ?x1210), category(?x7183, ?x134), award_winner(?x1088, ?x7183) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 09d5h; *> query: (?x7183, 0bp_b2) <- nominated_for(?x7183, ?x5594), award_winner(?x7183, ?x2300), ?x5594 = 01fx1l *> conf = 0.43 ranks of expected_values: 2, 29 EVAL 01hmk9 award 0gkvb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 135.000 135.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01hmk9 award 0bp_b2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 135.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7303-03176f PRED entity: 03176f PRED relation: honored_for! PRED expected values: 0clfdj => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 99): 02yvhx (0.14 #65, 0.09 #309, 0.03 #1407), 0clfdj (0.14 #2, 0.09 #246, 0.03 #1344), 0bzlrh (0.14 #89, 0.09 #333, 0.01 #3383), 09p3h7 (0.14 #60, 0.09 #304, 0.01 #6404), 092t4b (0.14 #42, 0.09 #286), 073hkh (0.10 #6955, 0.05 #733, 0.02 #1099), 03tn9w (0.10 #6955, 0.04 #446, 0.04 #568), 0bzmt8 (0.10 #6955, 0.03 #816, 0.03 #1670), 05c1t6z (0.10 #6955, 0.02 #6355, 0.02 #6843), 0bzjvm (0.10 #6955, 0.02 #1072, 0.02 #1926) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0h3xztt; 011ywj; 0ckrnn; >> query: (?x4235, 02yvhx) <- film(?x2372, ?x4235), titles(?x1510, ?x4235), ?x2372 = 0l6px, music(?x4235, ?x669) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0h3xztt; 011ywj; 0ckrnn; *> query: (?x4235, 0clfdj) <- film(?x2372, ?x4235), titles(?x1510, ?x4235), ?x2372 = 0l6px, music(?x4235, ?x669) *> conf = 0.14 ranks of expected_values: 2 EVAL 03176f honored_for! 0clfdj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #7302-01g6bk PRED entity: 01g6bk PRED relation: profession PRED expected values: 0d8qb => 136 concepts (90 used for prediction) PRED predicted values (max 10 best out of 103): 02hrh1q (0.74 #7859, 0.74 #8600, 0.74 #9488), 0dxtg (0.61 #1937, 0.54 #2677, 0.54 #1493), 01d_h8 (0.50 #1930, 0.48 #2078, 0.46 #2818), 03gjzk (0.46 #1939, 0.35 #2235, 0.34 #2087), 09jwl (0.39 #6828, 0.38 #11716, 0.37 #12456), 018gz8 (0.38 #2089, 0.36 #2829, 0.35 #1941), 015btn (0.33 #546, 0.11 #990, 0.04 #3210), 02jknp (0.31 #10815, 0.30 #10074, 0.29 #9925), 03jgz (0.31 #8438, 0.31 #6809, 0.17 #509), 0d8qb (0.31 #8438, 0.31 #6809, 0.17 #523) >> Best rule #7859 for best value: >> intensional similarity = 3 >> extensional distance = 436 >> proper extension: 05d7rk; 01tvz5j; 04rs03; 014x77; 0c1pj; 012t1; 030pr; 015rmq; 02g87m; 04nw9; ... >> query: (?x12009, 02hrh1q) <- award(?x12009, ?x575), religion(?x12009, ?x7131), people(?x1050, ?x12009) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #8438 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 444 *> proper extension: 07h1q; *> query: (?x12009, ?x353) <- gender(?x12009, ?x231), influenced_by(?x12009, ?x9738), profession(?x9738, ?x353) *> conf = 0.31 ranks of expected_values: 10 EVAL 01g6bk profession 0d8qb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 136.000 90.000 0.744 http://example.org/people/person/profession #7301-0bh8x1y PRED entity: 0bh8x1y PRED relation: written_by PRED expected values: 05183k => 75 concepts (43 used for prediction) PRED predicted values (max 10 best out of 43): 081k8 (0.24 #2359), 02ld6x (0.11 #1430, 0.06 #1092, 0.06 #755), 0h32q (0.11 #6740, 0.10 #7752, 0.08 #12134), 02mt4k (0.08 #493, 0.06 #1167, 0.06 #830), 0170qf (0.08 #11459, 0.08 #10447, 0.08 #11796), 02z2xdf (0.07 #5057), 081lh (0.06 #1041, 0.06 #704, 0.06 #1379), 05y5fw (0.06 #1173, 0.06 #836, 0.06 #1511), 01q_ph (0.06 #1019, 0.06 #682, 0.06 #1357), 02fcs2 (0.02 #2090) >> Best rule #2359 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 02n9bh; >> query: (?x4668, ?x5004) <- titles(?x53, ?x4668), award(?x4668, ?x13107), story_by(?x4668, ?x5004) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #4093 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 331 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x4668, 05183k) <- award(?x4668, ?x13107), disciplines_or_subjects(?x13107, ?x373) *> conf = 0.01 ranks of expected_values: 32 EVAL 0bh8x1y written_by 05183k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 75.000 43.000 0.243 http://example.org/film/film/written_by #7300-0kcn7 PRED entity: 0kcn7 PRED relation: nominated_for! PRED expected values: 0gr4k => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 247): 0l8z1 (0.78 #7092, 0.77 #10407, 0.77 #4490), 0gqwc (0.78 #7092, 0.77 #10407, 0.77 #4490), 0gr42 (0.78 #7092, 0.77 #10407, 0.77 #4490), 0k611 (0.77 #10407, 0.77 #4490, 0.68 #3306), 019f4v (0.49 #2415, 0.49 #290, 0.42 #3596), 02qvyrt (0.47 #330, 0.37 #2455, 0.22 #3163), 04dn09n (0.41 #271, 0.37 #2396, 0.29 #3104), 0gr4k (0.40 #1443, 0.39 #970, 0.34 #1207), 02qyntr (0.39 #414, 0.30 #2539, 0.25 #3720), 0f4x7 (0.39 #969, 0.37 #1442, 0.34 #1206) >> Best rule #7092 for best value: >> intensional similarity = 4 >> extensional distance = 532 >> proper extension: 07bz5; >> query: (?x2640, ?x1079) <- award(?x2640, ?x1079), award_winner(?x1079, ?x3890), ceremony(?x1079, ?x78), category(?x3890, ?x134) >> conf = 0.78 => this is the best rule for 3 predicted values *> Best rule #1443 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 0gcrg; 06pyc2; 0gy4k; 03mr85; *> query: (?x2640, 0gr4k) <- film_release_distribution_medium(?x2640, ?x81), nominated_for(?x484, ?x2640), film_art_direction_by(?x2640, ?x12512), nominated_for(?x1779, ?x2640) *> conf = 0.40 ranks of expected_values: 8 EVAL 0kcn7 nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 113.000 113.000 0.784 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7299-08jgk1 PRED entity: 08jgk1 PRED relation: genre PRED expected values: 06nbt => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 78): 07s9rl0 (0.53 #2107, 0.52 #391, 0.51 #1483), 06n90 (0.23 #480, 0.19 #2118, 0.16 #2431), 0hcr (0.22 #2123, 0.19 #2202, 0.19 #251), 01htzx (0.19 #484, 0.18 #1498, 0.17 #796), 01hmnh (0.17 #483, 0.15 #2121, 0.14 #327), 03k9fj (0.17 #2116, 0.16 #478, 0.14 #2195), 06nbt (0.16 #487, 0.14 #253, 0.14 #175), 0vgkd (0.16 #87, 0.14 #165, 0.14 #711), 01jfsb (0.15 #401, 0.14 #557, 0.11 #791), 06q7n (0.15 #508, 0.13 #1912, 0.13 #352) >> Best rule #2107 for best value: >> intensional similarity = 2 >> extensional distance = 244 >> proper extension: 0283ph; 088tp3; 07qht4; >> query: (?x1631, 07s9rl0) <- genre(?x1631, ?x258), genre(?x86, ?x258) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #487 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 86 *> proper extension: 070ltt; 04x4gj; 0d_rw; *> query: (?x1631, 06nbt) <- tv_program(?x2819, ?x1631), genre(?x1631, ?x239) *> conf = 0.16 ranks of expected_values: 7 EVAL 08jgk1 genre 06nbt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 71.000 71.000 0.533 http://example.org/tv/tv_program/genre #7298-01vx5w7 PRED entity: 01vx5w7 PRED relation: artists! PRED expected values: 02c8d7 => 100 concepts (48 used for prediction) PRED predicted values (max 10 best out of 257): 016clz (0.59 #4905, 0.23 #5, 0.23 #11950), 06by7 (0.46 #22, 0.42 #11967, 0.41 #4922), 08cyft (0.46 #56, 0.06 #1895, 0.06 #4650), 06j6l (0.39 #1887, 0.35 #48, 0.29 #2499), 0m0jc (0.38 #9, 0.11 #4909, 0.11 #1848), 0y3_8 (0.31 #47, 0.09 #4947, 0.07 #1886), 07gxw (0.23 #55, 0.03 #14703, 0.03 #4955), 05r6t (0.23 #4979, 0.12 #79, 0.10 #1918), 059kh (0.19 #49, 0.12 #4949, 0.07 #11994), 01lyv (0.19 #4629, 0.19 #5819, 0.18 #2180) >> Best rule #4905 for best value: >> intensional similarity = 3 >> extensional distance = 352 >> proper extension: 01tp5bj; 01m65sp; 037hgm; 02mq_y; 03xnq9_; 0326tc; 02mx98; 03wjb7; 04bbv7; 06br6t; ... >> query: (?x2925, 016clz) <- artists(?x2937, ?x2925), artists(?x2937, ?x3977), ?x3977 = 01wn718 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #1866 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 122 *> proper extension: 0m19t; 01q7cb_; 03xl77; 01gx5f; 0phx4; 01wy61y; 01wbsdz; 01vw917; 01k3qj; 02bwjv; ... *> query: (?x2925, 02c8d7) <- artist(?x1124, ?x2925), artists(?x2937, ?x2925), ?x2937 = 0glt670 *> conf = 0.04 ranks of expected_values: 106 EVAL 01vx5w7 artists! 02c8d7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 100.000 48.000 0.590 http://example.org/music/genre/artists #7297-06nbt PRED entity: 06nbt PRED relation: genre! PRED expected values: 08jgk1 0431v3 => 75 concepts (33 used for prediction) PRED predicted values (max 10 best out of 292): 05p9_ql (0.67 #3221, 0.67 #1531, 0.50 #687), 04p5cr (0.67 #3204, 0.67 #1514, 0.50 #670), 0l76z (0.60 #1198, 0.56 #3168, 0.50 #1478), 05f7w84 (0.60 #941, 0.50 #660, 0.40 #1224), 03nt59 (0.60 #1226, 0.44 #3196, 0.38 #2913), 08jgk1 (0.60 #1146, 0.44 #3116, 0.33 #1708), 07zhjj (0.60 #1290, 0.44 #3260, 0.33 #1570), 05pbsry (0.60 #1394, 0.44 #3364, 0.33 #1674), 06f0k (0.60 #1372, 0.33 #3342, 0.33 #1934), 023ny6 (0.60 #1323, 0.33 #3293, 0.33 #479) >> Best rule #3221 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 0djd22; >> query: (?x2700, 05p9_ql) <- genre(?x7551, ?x2700), genre(?x3102, ?x2700), genre(?x2555, ?x2700), ?x7551 = 014gjp, nominated_for(?x1765, ?x2555), actor(?x3102, ?x3673), actor(?x2555, ?x478), category(?x3102, ?x134) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1146 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 0c4xc; *> query: (?x2700, 08jgk1) <- genre(?x9787, ?x2700), genre(?x7551, ?x2700), genre(?x6884, ?x2700), genre(?x1876, ?x2700), genre(?x1542, ?x2700), genre(?x1395, ?x2700), ?x7551 = 014gjp, ?x9787 = 06y_n, ?x1876 = 0584r4, ?x1395 = 019nnl, ?x1542 = 0124k9, tv_program(?x236, ?x6884) *> conf = 0.60 ranks of expected_values: 6, 31 EVAL 06nbt genre! 0431v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 75.000 33.000 0.667 http://example.org/tv/tv_program/genre EVAL 06nbt genre! 08jgk1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 75.000 33.000 0.667 http://example.org/tv/tv_program/genre #7296-07j8r PRED entity: 07j8r PRED relation: genre PRED expected values: 01jfsb => 69 concepts (68 used for prediction) PRED predicted values (max 10 best out of 93): 07ssc (0.51 #5409, 0.48 #6732, 0.48 #4325), 02kdv5l (0.45 #122, 0.29 #723, 0.28 #3485), 0jxy (0.45 #165, 0.02 #6296, 0.02 #7618), 0hcr (0.43 #143, 0.14 #744, 0.11 #624), 05p553 (0.37 #4, 0.33 #725, 0.32 #4689), 03k9fj (0.33 #732, 0.32 #131, 0.28 #612), 01jfsb (0.32 #733, 0.32 #3495, 0.30 #3735), 06n90 (0.30 #133, 0.15 #614, 0.15 #734), 01hmnh (0.29 #137, 0.21 #3964, 0.18 #738), 03q4nz (0.23 #138, 0.06 #5305, 0.05 #7591) >> Best rule #5409 for best value: >> intensional similarity = 3 >> extensional distance = 1297 >> proper extension: 035xwd; 0436yk; 027pfb2; 0dh8v4; 0h1fktn; 01f39b; 02z2mr7; 05css_; 03mnn0; 03ffcz; ... >> query: (?x2550, ?x512) <- language(?x2550, ?x254), titles(?x512, ?x2550), genre(?x2550, ?x53) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #733 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 188 *> proper extension: 03g90h; 047msdk; 0jqn5; 09146g; 0ch26b_; 0900j5; 0c3xw46; 06zn2v2; 0dlngsd; 02xbyr; ... *> query: (?x2550, 01jfsb) <- film_release_region(?x2550, ?x550), film(?x2938, ?x2550), ?x550 = 05v8c *> conf = 0.32 ranks of expected_values: 7 EVAL 07j8r genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 69.000 68.000 0.514 http://example.org/film/film/genre #7295-07jdr PRED entity: 07jdr PRED relation: films PRED expected values: 0c_j9x => 59 concepts (18 used for prediction) PRED predicted values (max 10 best out of 897): 06_x996 (0.33 #725, 0.20 #1780, 0.20 #1253), 0hfzr (0.29 #2320, 0.21 #4431, 0.20 #2847), 0ds11z (0.29 #2135, 0.17 #3719, 0.14 #4246), 02yvct (0.29 #4857, 0.14 #2218, 0.13 #6968), 02pxst (0.29 #2475, 0.10 #5641, 0.10 #3002), 08xvpn (0.20 #3107, 0.17 #4164, 0.17 #3637), 025rvx0 (0.20 #2921, 0.17 #3978, 0.14 #4505), 09qycb (0.20 #3121, 0.17 #4178, 0.14 #4705), 0bl5c (0.20 #2915, 0.17 #3972, 0.14 #4499), 01jwxx (0.20 #2886, 0.17 #3943, 0.14 #4470) >> Best rule #725 for best value: >> intensional similarity = 19 >> extensional distance = 1 >> proper extension: 025t3bg; >> query: (?x4272, 06_x996) <- mode_of_transportation(?x11743, ?x4272), mode_of_transportation(?x8252, ?x4272), mode_of_transportation(?x5719, ?x4272), mode_of_transportation(?x5267, ?x4272), mode_of_transportation(?x5168, ?x4272), mode_of_transportation(?x3052, ?x4272), mode_of_transportation(?x1649, ?x4272), mode_of_transportation(?x1458, ?x4272), mode_of_transportation(?x206, ?x4272), ?x5267 = 0d9jr, ?x11743 = 0853g, ?x3052 = 01cx_, ?x5168 = 06mxs, ?x8252 = 0k3p, ?x206 = 01914, featured_film_locations(?x3693, ?x1458), ?x1649 = 01f62, contains(?x1558, ?x1458), ?x5719 = 0f2rq >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3166 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 8 *> proper extension: 0chghy; 0fy91; *> query: (?x4272, ?x599) <- films(?x4272, ?x8477), films(?x4272, ?x5155), film_release_region(?x8477, ?x94), genre(?x8477, ?x4205), genre(?x8477, ?x1626), genre(?x8477, ?x225), country(?x8477, ?x205), production_companies(?x5155, ?x574), nominated_for(?x2393, ?x5155), ?x1626 = 03q4nz, titles(?x4205, ?x599), genre(?x2009, ?x4205), genre(?x11066, ?x225), ?x11066 = 025s1wg *> conf = 0.02 ranks of expected_values: 807 EVAL 07jdr films 0c_j9x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 59.000 18.000 0.333 http://example.org/film/film_subject/films #7294-0n5_t PRED entity: 0n5_t PRED relation: adjoins PRED expected values: 0nm3n => 152 concepts (51 used for prediction) PRED predicted values (max 10 best out of 452): 059f4 (0.44 #13902, 0.12 #5440, 0.04 #38648), 09c7w0 (0.44 #13902, 0.05 #4633, 0.03 #5408), 0n5xb (0.33 #701, 0.29 #36325, 0.27 #15449), 0nm6k (0.29 #1856, 0.03 #6491, 0.01 #14988), 0nm9y (0.29 #2295, 0.03 #6930), 0nm8n (0.29 #2097, 0.03 #6732), 0nm42 (0.29 #1873, 0.03 #6508), 0n5yh (0.29 #36325, 0.27 #15449, 0.27 #24728), 0n5y4 (0.25 #21634, 0.25 #3571, 0.25 #2800), 0n5_t (0.25 #21634, 0.25 #11584, 0.25 #20862) >> Best rule #13902 for best value: >> intensional similarity = 4 >> extensional distance = 217 >> proper extension: 01dbxr; >> query: (?x12433, ?x728) <- adjoins(?x12433, ?x7565), second_level_divisions(?x94, ?x12433), contains(?x7565, ?x7564), contains(?x728, ?x7564) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #21634 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 261 *> proper extension: 0msck; *> query: (?x12433, ?x4825) <- adjoins(?x12433, ?x7565), second_level_divisions(?x94, ?x12433), source(?x12433, ?x958), ?x958 = 0jbk9, adjoins(?x4825, ?x7565) *> conf = 0.25 ranks of expected_values: 13 EVAL 0n5_t adjoins 0nm3n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 152.000 51.000 0.441 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #7293-03fbb6 PRED entity: 03fbb6 PRED relation: gender PRED expected values: 05zppz => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #3, 0.72 #179, 0.71 #25), 02zsn (0.37 #18, 0.35 #24, 0.33 #40) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 02s_qz; >> query: (?x5500, 05zppz) <- film(?x5500, ?x2928), award_nominee(?x496, ?x5500), ?x2928 = 07024 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03fbb6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.727 http://example.org/people/person/gender #7292-05fkf PRED entity: 05fkf PRED relation: religion PRED expected values: 05sfs 072w0 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 22): 05sfs (0.86 #77, 0.81 #177, 0.73 #302), 021_0p (0.61 #185, 0.56 #310, 0.55 #85), 0flw86 (0.38 #101, 0.37 #1306, 0.37 #1331), 092bf5 (0.38 #107, 0.31 #182, 0.29 #607), 072w0 (0.29 #65, 0.27 #90, 0.23 #1356), 02t7t (0.28 #188, 0.27 #88, 0.25 #313), 03j6c (0.23 #1356, 0.12 #111, 0.09 #1316), 0n2g (0.23 #1356, 0.08 #105, 0.03 #1310), 078tg (0.23 #1356, 0.04 #121, 0.03 #1326), 06yyp (0.23 #1356, 0.04 #112, 0.02 #1317) >> Best rule #77 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 01bkb; >> query: (?x760, 05sfs) <- country(?x760, ?x94), religion(?x760, ?x109), location_of_ceremony(?x566, ?x760) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 05fkf religion 072w0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 142.000 142.000 0.864 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 05fkf religion 05sfs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.864 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #7291-02lk60 PRED entity: 02lk60 PRED relation: film_crew_role PRED expected values: 015h31 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 33): 0ch6mp2 (0.82 #371, 0.78 #117, 0.77 #627), 09zzb8 (0.81 #620, 0.74 #400, 0.73 #364), 09vw2b7 (0.71 #370, 0.68 #626, 0.65 #442), 015h31 (0.32 #47, 0.29 #83, 0.16 #192), 01pvkk (0.31 #157, 0.30 #631, 0.29 #447), 033smt (0.23 #65, 0.20 #101, 0.09 #2851), 0d2b38 (0.19 #63, 0.17 #99, 0.16 #389), 02ynfr (0.19 #379, 0.17 #451, 0.17 #635), 02rh1dz (0.18 #446, 0.16 #630, 0.15 #193), 01xy5l_ (0.17 #123, 0.16 #196, 0.15 #232) >> Best rule #371 for best value: >> intensional similarity = 4 >> extensional distance = 181 >> proper extension: 061681; 0gkz15s; 06z8s_; 0c0nhgv; 0bscw; 0fpkhkz; 02r1c18; 0qm8b; 0gxtknx; 047n8xt; ... >> query: (?x4656, 0ch6mp2) <- nominated_for(?x1053, ?x4656), film_crew_role(?x4656, ?x468), film_format(?x4656, ?x6392), ?x468 = 02r96rf >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 0b60sq; *> query: (?x4656, 015h31) <- nominated_for(?x1723, ?x4656), nominated_for(?x4564, ?x4656), ?x1723 = 09tqxt, language(?x4656, ?x254) *> conf = 0.32 ranks of expected_values: 4 EVAL 02lk60 film_crew_role 015h31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 79.000 79.000 0.820 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7290-07z31v PRED entity: 07z31v PRED relation: honored_for PRED expected values: 01j7mr => 28 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1183): 01j7mr (0.64 #3169, 0.25 #1986, 0.20 #2577), 039cq4 (0.45 #3368, 0.25 #2185, 0.20 #2776), 07s8z_l (0.45 #3511, 0.14 #4103, 0.11 #6485), 01rp13 (0.40 #2753, 0.27 #3345, 0.25 #2162), 030cx (0.40 #2633, 0.25 #2042, 0.18 #3225), 01bv8b (0.40 #2521, 0.25 #1930, 0.18 #3113), 07zhjj (0.36 #3454, 0.16 #4642, 0.14 #4046), 06mr2s (0.36 #3240, 0.16 #4428, 0.14 #3832), 01b7h8 (0.36 #3490, 0.16 #4678, 0.14 #4082), 04xbq3 (0.36 #3469, 0.16 #4657, 0.14 #4061) >> Best rule #3169 for best value: >> intensional similarity = 16 >> extensional distance = 9 >> proper extension: 05c1t6z; 0gvstc3; 0gx_st; 02q690_; 03nnm4t; 0hn821n; >> query: (?x2126, 01j7mr) <- honored_for(?x2126, ?x3303), honored_for(?x2126, ?x782), honored_for(?x2126, ?x715), ceremony(?x870, ?x2126), actor(?x3303, ?x818), award_winner(?x782, ?x1343), award_winner(?x3303, ?x3381), titles(?x714, ?x715), nominated_for(?x5595, ?x715), film(?x5595, ?x787), program(?x1394, ?x782), profession(?x5595, ?x967), award(?x5595, ?x458), award_winner(?x2126, ?x1039), genre(?x715, ?x53), ?x870 = 09qv3c >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07z31v honored_for 01j7mr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 21.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #7289-0c4hgj PRED entity: 0c4hgj PRED relation: award_winner PRED expected values: 0hskw => 50 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2481): 02cx90 (0.71 #9920, 0.60 #6835, 0.46 #20714), 0lbj1 (0.71 #10820, 0.16 #29326, 0.14 #9276), 01vw20h (0.60 #6866, 0.57 #9951, 0.46 #20745), 02l840 (0.60 #6269, 0.43 #9354, 0.38 #20148), 01vrx3g (0.60 #6200, 0.43 #9285, 0.23 #20079), 06mt91 (0.60 #7190, 0.43 #10275, 0.23 #21069), 01wd9vs (0.60 #4627, 0.27 #19570, 0.25 #16485), 02zft0 (0.60 #4627, 0.25 #4012, 0.23 #3083), 06rrzn (0.60 #4627, 0.23 #3083, 0.23 #26215), 0gcs9 (0.57 #9696, 0.54 #20490, 0.40 #17406) >> Best rule #9920 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 019bk0; >> query: (?x6606, 02cx90) <- award_winner(?x6606, ?x6660), award_winner(?x6606, ?x5720), category(?x5720, ?x134), ceremony(?x5409, ?x6606), profession(?x5720, ?x563), people(?x268, ?x5720), participant(?x6660, ?x4058), award_winner(?x5409, ?x1853), participant(?x5239, ?x6660), award_nominee(?x5720, ?x6011), risk_factors(?x6483, ?x268) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #12338 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 5 *> proper extension: 01s695; *> query: (?x6606, ?x382) <- award_winner(?x6606, ?x6660), award_winner(?x6606, ?x5720), category(?x5720, ?x134), ceremony(?x2209, ?x6606), profession(?x5720, ?x7998), location(?x6660, ?x1755), ?x7998 = 01d30f, participant(?x6660, ?x5336), award_nominee(?x5720, ?x6011), award_winner(?x749, ?x6660), nominated_for(?x2209, ?x324), award(?x382, ?x2209) *> conf = 0.07 ranks of expected_values: 1084 EVAL 0c4hgj award_winner 0hskw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 27.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7288-01lp8 PRED entity: 01lp8 PRED relation: religion! PRED expected values: 01lbp 0157m 0pmhf 01fx2g 01f6zc 04r7p => 35 concepts (26 used for prediction) PRED predicted values (max 10 best out of 4201): 04rfq (0.33 #2023, 0.33 #1001, 0.29 #5090), 0mb5x (0.29 #2716, 0.27 #8850, 0.27 #7828), 0cqt90 (0.29 #4381, 0.22 #5403, 0.17 #1314), 043gj (0.29 #4460, 0.22 #5482, 0.17 #1393), 042f1 (0.29 #4876, 0.22 #5898, 0.17 #1809), 041mt (0.29 #2185, 0.18 #9342, 0.18 #8319), 04hcw (0.29 #2627, 0.18 #9784, 0.18 #8761), 0q9kd (0.29 #2046, 0.18 #9203, 0.18 #8180), 019f2f (0.29 #2217, 0.18 #9374, 0.18 #8351), 02xyl (0.29 #3045, 0.18 #10202, 0.18 #9179) >> Best rule #2023 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 05w5d; >> query: (?x109, 04rfq) <- religion(?x5622, ?x109), religion(?x4622, ?x109), religion(?x2623, ?x109), religion(?x2049, ?x109), religion(?x8693, ?x109), artists(?x482, ?x8693), ?x4622 = 04tgp, location(?x8693, ?x1523), ?x2623 = 02xry, ?x2049 = 050l8, ?x5622 = 0l3h >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5209 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 7 *> proper extension: 01s5nb; *> query: (?x109, 0157m) <- religion(?x3670, ?x109), religion(?x2713, ?x109), religion(?x2146, ?x109), ?x3670 = 05tbn, film_release_region(?x4464, ?x2146), taxonomy(?x2146, ?x939), contains(?x2146, ?x1391), nominated_for(?x298, ?x4464), ?x2713 = 06btq, adjoins(?x2236, ?x2146) *> conf = 0.22 ranks of expected_values: 21, 1155, 1227, 1401, 1935, 2698 EVAL 01lp8 religion! 04r7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 26.000 0.333 http://example.org/people/person/religion EVAL 01lp8 religion! 01f6zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 26.000 0.333 http://example.org/people/person/religion EVAL 01lp8 religion! 01fx2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 35.000 26.000 0.333 http://example.org/people/person/religion EVAL 01lp8 religion! 0pmhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 26.000 0.333 http://example.org/people/person/religion EVAL 01lp8 religion! 0157m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 35.000 26.000 0.333 http://example.org/people/person/religion EVAL 01lp8 religion! 01lbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 26.000 0.333 http://example.org/people/person/religion #7287-01wmgrf PRED entity: 01wmgrf PRED relation: participant PRED expected values: 02ktrs => 119 concepts (85 used for prediction) PRED predicted values (max 10 best out of 207): 02ktrs (0.80 #21894, 0.80 #7724, 0.80 #24467), 02lk95 (0.08 #3218, 0.06 #26398, 0.06 #20605), 0bbf1f (0.06 #841, 0.06 #1485, 0.03 #2772), 01trhmt (0.06 #816, 0.04 #1460, 0.04 #5321), 014zcr (0.05 #7098, 0.04 #1305, 0.03 #14181), 07r1h (0.05 #1058, 0.04 #1702, 0.03 #2989), 01vrz41 (0.05 #725, 0.03 #1369, 0.02 #2656), 01vswwx (0.05 #1005, 0.02 #1649, 0.02 #5510), 015f7 (0.04 #5383, 0.03 #878, 0.03 #4740), 0227vl (0.04 #5690, 0.03 #1185, 0.03 #3116) >> Best rule #21894 for best value: >> intensional similarity = 2 >> extensional distance = 540 >> proper extension: 01bpnd; 0sx5w; >> query: (?x3122, ?x11519) <- participant(?x11519, ?x3122), award(?x3122, ?x1361) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wmgrf participant 02ktrs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 85.000 0.803 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #7286-03f5spx PRED entity: 03f5spx PRED relation: instrumentalists! PRED expected values: 04rzd => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 123): 05r5c (0.55 #348, 0.47 #433, 0.47 #3420), 026t6 (0.33 #343, 0.27 #936, 0.26 #1278), 0l14md (0.31 #347, 0.18 #432, 0.14 #943), 03qjg (0.31 #389, 0.18 #1241, 0.17 #985), 01vdm0 (0.27 #936, 0.26 #1278, 0.25 #1364), 0l14qv (0.17 #345, 0.16 #430, 0.16 #260), 01v1d8 (0.12 #481, 0.07 #651, 0.05 #821), 06ncr (0.12 #382, 0.11 #212, 0.08 #1064), 018j2 (0.12 #376, 0.09 #1058, 0.09 #3362), 07y_7 (0.11 #2, 0.07 #342, 0.06 #257) >> Best rule #348 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 02y7sr; >> query: (?x959, 05r5c) <- origin(?x959, ?x2673), instrumentalists(?x1750, ?x959), ?x1750 = 02hnl >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1057 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 248 *> proper extension: 0pmw9; 014v1q; *> query: (?x959, 04rzd) <- award_winner(?x486, ?x959), profession(?x959, ?x131), instrumentalists(?x227, ?x959) *> conf = 0.10 ranks of expected_values: 23 EVAL 03f5spx instrumentalists! 04rzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 103.000 103.000 0.548 http://example.org/music/instrument/instrumentalists #7285-0bzjvm PRED entity: 0bzjvm PRED relation: award_winner PRED expected values: 09fb5 => 39 concepts (15 used for prediction) PRED predicted values (max 10 best out of 626): 04glr5h (0.60 #3078, 0.60 #3077, 0.50 #4618), 0cp9f9 (0.60 #3078, 0.60 #3077, 0.50 #4618), 04gnbv1 (0.60 #3078, 0.50 #4618, 0.16 #18481), 02f9wb (0.60 #3077, 0.43 #4617), 081nh (0.46 #15743, 0.45 #12661, 0.43 #17283), 02sj1x (0.46 #14387, 0.36 #17469, 0.31 #15929), 015nhn (0.33 #10437, 0.33 #2739, 0.25 #4278), 09r9m7 (0.33 #10140, 0.25 #3981, 0.23 #16304), 01tcf7 (0.33 #167, 0.25 #4785, 0.20 #7865), 03qhyn8 (0.33 #1526, 0.25 #6144, 0.20 #9224) >> Best rule #3078 for best value: >> intensional similarity = 26 >> extensional distance = 1 >> proper extension: 0bz6sb; >> query: (?x7940, ?x4618) <- ceremony(?x4573, ?x7940), ceremony(?x3617, ?x7940), ceremony(?x1972, ?x7940), ceremony(?x1323, ?x7940), ceremony(?x484, ?x7940), ?x4573 = 0gq_d, award_winner(?x7940, ?x12287), award_winner(?x7940, ?x8830), award_winner(?x7940, ?x4948), ?x1972 = 0gqyl, profession(?x4948, ?x987), profession(?x4948, ?x353), award_nominee(?x4618, ?x4948), award(?x8830, ?x537), gender(?x4948, ?x231), ?x353 = 0cbd2, profession(?x8830, ?x1032), location(?x12287, ?x739), ?x1323 = 0gqz2, place_of_burial(?x8830, ?x1227), award_winner(?x5958, ?x4948), award_winner(?x1132, ?x12287), award_winner(?x8459, ?x8830), ?x987 = 0dxtg, ?x484 = 0gq_v, ?x3617 = 0gvx_ >> conf = 0.60 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0bzjvm award_winner 09fb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 15.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7284-015d3h PRED entity: 015d3h PRED relation: people! PRED expected values: 01psyx => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 36): 0gk4g (0.50 #10, 0.22 #604, 0.21 #934), 0qcr0 (0.20 #67, 0.04 #2509, 0.03 #529), 02knxx (0.20 #98, 0.03 #1550, 0.03 #1682), 0dq9p (0.16 #413, 0.13 #1271, 0.13 #1073), 0x2fg (0.10 #170, 0.06 #236, 0.03 #566), 034qg (0.10 #165, 0.06 #231, 0.02 #825), 051_y (0.10 #180, 0.06 #246, 0.02 #840), 02k6hp (0.10 #895, 0.08 #1225, 0.08 #697), 02y0js (0.09 #596, 0.08 #398, 0.06 #1652), 01dcqj (0.08 #408, 0.06 #606, 0.05 #672) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02_fj; 01g42; >> query: (?x4587, 0gk4g) <- film(?x4587, ?x2112), celebrities_impersonated(?x3649, ?x4587), nominated_for(?x4587, ?x6482), ?x2112 = 0bm2g >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #771 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 0q9kd; 09fb5; 0bxtg; 0chsq; 0187y5; 081lh; 01vrncs; 0pz91; 0j582; 016_mj; ... *> query: (?x4587, 01psyx) <- film(?x4587, ?x2112), celebrities_impersonated(?x3649, ?x4587), nominated_for(?x4587, ?x6482), nominated_for(?x198, ?x2112) *> conf = 0.02 ranks of expected_values: 31 EVAL 015d3h people! 01psyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 121.000 121.000 0.500 http://example.org/people/cause_of_death/people #7283-01nkxvx PRED entity: 01nkxvx PRED relation: location PRED expected values: 0pswc => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 120): 0kpys (0.57 #7239, 0.55 #44238, 0.50 #5629), 02_286 (0.21 #6471, 0.15 #43470, 0.13 #40253), 01n7q (0.20 #867, 0.04 #4083, 0.03 #4887), 04p3c (0.20 #1039), 05fhy (0.20 #52), 01531 (0.16 #6592, 0.04 #43591, 0.03 #58878), 030qb3t (0.12 #57193, 0.11 #57998, 0.11 #6517), 094jv (0.09 #6527, 0.03 #2505, 0.03 #7332), 0ccvx (0.08 #6656, 0.03 #28374, 0.03 #18723), 0cr3d (0.08 #4969, 0.07 #43578, 0.06 #18646) >> Best rule #7239 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 049tjg; >> query: (?x8599, ?x2949) <- place_of_birth(?x8599, ?x2949), people(?x11321, ?x8599), currency(?x2949, ?x170) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2189 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 01l1b90; 089tm; 01vrt_c; 03g5jw; 09k2t1; 07ss8_; 01trhmt; 0161c2; 014488; 016dsy; ... *> query: (?x8599, 0pswc) <- award(?x8599, ?x884), artists(?x302, ?x8599), artist(?x7089, ?x8599), ?x7089 = 0181dw *> conf = 0.02 ranks of expected_values: 61 EVAL 01nkxvx location 0pswc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 126.000 126.000 0.575 http://example.org/people/person/places_lived./people/place_lived/location #7282-0p__8 PRED entity: 0p__8 PRED relation: award PRED expected values: 0gkvb7 => 129 concepts (115 used for prediction) PRED predicted values (max 10 best out of 327): 05zr6wv (0.74 #3978, 0.73 #2785, 0.72 #36196), 099tbz (0.74 #3978, 0.73 #2785, 0.72 #36196), 05p1dby (0.45 #1297, 0.33 #3683, 0.18 #34208), 0gq9h (0.42 #6439, 0.35 #12407, 0.26 #5246), 04dn09n (0.40 #5213, 0.33 #8793, 0.33 #7599), 0gr4k (0.40 #5204, 0.36 #8784, 0.33 #7590), 05pcn59 (0.38 #78, 0.24 #2465, 0.23 #476), 040njc (0.37 #6372, 0.29 #5179, 0.27 #12340), 0gr51 (0.37 #5267, 0.34 #8847, 0.34 #7653), 04ljl_l (0.31 #401, 0.14 #36991, 0.09 #1992) >> Best rule #3978 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 081bls; >> query: (?x5940, ?x401) <- award_winner(?x3646, ?x5940), award_winner(?x401, ?x5940), nominated_for(?x3646, ?x5608), ?x5608 = 01l_pn >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #823 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 0kzy0; 0126rp; 0pkyh; 02qwg; 01vvyfh; 01vvyvk; 03j24kf; 0bqs56; 03f3yfj; 07d3x; ... *> query: (?x5940, 0gkvb7) <- award_winner(?x401, ?x5940), currency(?x5940, ?x170), influenced_by(?x5940, ?x4988) *> conf = 0.23 ranks of expected_values: 24 EVAL 0p__8 award 0gkvb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 129.000 115.000 0.741 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7281-0cc8q3 PRED entity: 0cc8q3 PRED relation: team PRED expected values: 03y9p40 => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 343): 03y9p40 (0.75 #193, 0.72 #213, 0.67 #40), 02pzy52 (0.72 #213, 0.67 #40, 0.67 #38), 03by7wc (0.72 #213, 0.67 #40, 0.67 #38), 0263cyj (0.67 #40, 0.65 #115, 0.65 #188), 051vz (0.50 #81, 0.40 #20, 0.40 #6), 0512p (0.50 #35, 0.40 #20, 0.40 #6), 0jmbv (0.50 #81, 0.38 #168, 0.23 #211), 0jmgb (0.50 #35, 0.23 #211, 0.22 #166), 051q5 (0.50 #35, 0.11 #66, 0.05 #60), 07l4z (0.40 #67, 0.40 #37, 0.40 #36) >> Best rule #193 for best value: >> intensional similarity = 99 >> extensional distance = 6 >> proper extension: 0b_6qj; >> query: (?x6002, 03y9p40) <- team(?x6002, ?x12370), team(?x6002, ?x9576), team(?x6002, ?x5032), team(?x6002, ?x4938), team(?x6002, ?x4804), team(?x6002, ?x4369), ?x4938 = 027yf83, team(?x13045, ?x4804), team(?x12451, ?x4804), team(?x12162, ?x4804), team(?x10736, ?x4804), team(?x4937, ?x4804), team(?x4368, ?x4804), team(?x2302, ?x4804), ?x12370 = 026dqjm, colors(?x4804, ?x3189), colors(?x4804, ?x332), team(?x7378, ?x5032), team(?x7042, ?x5032), team(?x5897, ?x5032), ?x12451 = 0b_6xf, ?x4937 = 0br1xn, colors(?x11881, ?x3189), colors(?x10993, ?x3189), colors(?x9865, ?x3189), colors(?x8822, ?x3189), colors(?x8715, ?x3189), colors(?x8427, ?x3189), colors(?x7777, ?x3189), colors(?x6548, ?x3189), colors(?x5941, ?x3189), colors(?x4846, ?x3189), colors(?x918, ?x3189), colors(?x13989, ?x3189), colors(?x13166, ?x3189), colors(?x12526, ?x3189), colors(?x11312, ?x3189), colors(?x10956, ?x3189), colors(?x9922, ?x3189), colors(?x9338, ?x3189), colors(?x9003, ?x3189), colors(?x8689, ?x3189), colors(?x7641, ?x3189), colors(?x7485, ?x3189), colors(?x7122, ?x3189), colors(?x6153, ?x3189), colors(?x5233, ?x3189), colors(?x3188, ?x3189), colors(?x2074, ?x3189), colors(?x1599, ?x3189), colors(?x978, ?x3189), colors(?x387, ?x3189), ?x8822 = 020ddc, colors(?x5032, ?x663), ?x8715 = 01wv24, ?x4846 = 037njl, ?x387 = 02896, ?x11312 = 03w7kx, ?x978 = 03y_f8, position(?x4369, ?x1348), ?x8689 = 03v9yw, ?x5897 = 0b_6rk, ?x1599 = 025txtg, ?x2302 = 0b_77q, ?x10736 = 0f9rw9, ?x7042 = 0b_72t, ?x9003 = 05ls3r, ?x10956 = 056zf9, ?x9865 = 04gd8j, ?x12526 = 0bg4f9, ?x9338 = 024_ql, ?x9576 = 02qk2d5, ?x7485 = 019m60, ?x13989 = 0d9qmn, ?x2074 = 0j2pg, ?x9922 = 0284h6, ?x7641 = 049fbh, ?x10993 = 01fy2s, ?x918 = 02cttt, ?x13166 = 0j6tr, ?x7122 = 01zhs3, ?x11881 = 01s7pm, ?x3188 = 04k3r_, ?x6153 = 016gp5, ?x13045 = 0bqthy, ?x663 = 083jv, colors(?x12175, ?x332), colors(?x2171, ?x332), ?x6548 = 0yls9, ?x7777 = 057wlm, ?x7378 = 0bzrxn, ?x8427 = 021996, locations(?x4368, ?x674), ?x1348 = 01pv51, ?x5233 = 0j5m6, ?x12175 = 036hnm, ?x2171 = 01jq34, ?x5941 = 017v71, ?x12162 = 0b_6_l >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cc8q3 team 03y9p40 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 28.000 0.750 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #7280-06z9yh PRED entity: 06z9yh PRED relation: student! PRED expected values: 02301 => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 62): 0bwfn (0.08 #275, 0.06 #1329, 0.06 #802), 065y4w7 (0.05 #1068, 0.04 #541, 0.04 #14), 04b_46 (0.04 #754, 0.04 #227, 0.03 #1281), 03ksy (0.04 #3268, 0.04 #2214, 0.04 #106), 09f2j (0.03 #3848, 0.03 #1213, 0.02 #2794), 01w5m (0.03 #3794, 0.03 #7483, 0.02 #2740), 08815 (0.02 #1583, 0.02 #5799, 0.02 #3691), 01rtm4 (0.02 #4, 0.02 #531, 0.01 #1058), 023znp (0.02 #119, 0.01 #2227), 05nrkb (0.02 #349) >> Best rule #275 for best value: >> intensional similarity = 6 >> extensional distance = 83 >> proper extension: 0bkf72; >> query: (?x13392, 0bwfn) <- profession(?x13392, ?x1943), profession(?x13392, ?x1041), profession(?x13392, ?x319), ?x1041 = 03gjzk, ?x1943 = 02krf9, ?x319 = 01d_h8 >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06z9yh student! 02301 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 45.000 45.000 0.082 http://example.org/education/educational_institution/students_graduates./education/education/student #7279-05ljv7 PRED entity: 05ljv7 PRED relation: role PRED expected values: 05842k => 64 concepts (57 used for prediction) PRED predicted values (max 10 best out of 112): 05842k (0.89 #3153, 0.89 #2383, 0.89 #1726), 0l14md (0.88 #1443, 0.84 #1542, 0.82 #2978), 04rzd (0.85 #1916, 0.84 #1329, 0.83 #659), 0cfdd (0.84 #1542, 0.76 #104, 0.71 #326), 0l14qv (0.84 #1329, 0.83 #3958, 0.82 #1878), 02sgy (0.84 #1329, 0.83 #659, 0.82 #1878), 0l1589 (0.78 #547, 0.76 #104, 0.70 #325), 0bxl5 (0.78 #1717, 0.69 #1391, 0.68 #2422), 0l15bq (0.76 #2016, 0.68 #3112, 0.67 #3223), 05148p4 (0.76 #104, 0.71 #326, 0.71 #2529) >> Best rule #3153 for best value: >> intensional similarity = 20 >> extensional distance = 36 >> proper extension: 07c6l; 01xqw; 02w3w; >> query: (?x1647, 05842k) <- role(?x745, ?x1647), role(?x228, ?x1647), role(?x3296, ?x228), role(?x3161, ?x228), role(?x3112, ?x228), role(?x7549, ?x228), role(?x3890, ?x228), ?x745 = 01vj9c, role(?x645, ?x228), role(?x4162, ?x1647), ?x3161 = 01v1d8, ?x7549 = 02p2zq, instrumentalists(?x228, ?x140), group(?x228, ?x7810), ?x3112 = 0mbct, ?x3296 = 07_l6, role(?x228, ?x214), role(?x1291, ?x228), ?x7810 = 0187x8, artists(?x284, ?x3890) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05ljv7 role 05842k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 57.000 0.895 http://example.org/music/performance_role/track_performances./music/track_contribution/role #7278-04mhxx PRED entity: 04mhxx PRED relation: film PRED expected values: 05n6sq => 121 concepts (109 used for prediction) PRED predicted values (max 10 best out of 454): 0cs134 (0.58 #112795, 0.58 #53710, 0.45 #32226), 01svry (0.11 #1192, 0.04 #2982, 0.01 #8352), 02x3lt7 (0.11 #84, 0.03 #128909, 0.02 #91311), 0422v0 (0.11 #1784, 0.03 #128909, 0.02 #91311), 016kv6 (0.11 #574, 0.03 #128909, 0.02 #91311), 09cr8 (0.11 #285, 0.02 #52204, 0.02 #89804), 02pg45 (0.11 #931, 0.01 #4511, 0.01 #15251), 025s1wg (0.11 #1706, 0.01 #17816, 0.01 #21397), 0270k40 (0.11 #1742), 02jxrw (0.11 #1618) >> Best rule #112795 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 0m32_; >> query: (?x10053, ?x10731) <- nominated_for(?x10053, ?x10731), film(?x10053, ?x2627) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04mhxx film 05n6sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 109.000 0.585 http://example.org/film/actor/film./film/performance/film #7277-02vjp3 PRED entity: 02vjp3 PRED relation: featured_film_locations PRED expected values: 030qb3t => 73 concepts (54 used for prediction) PRED predicted values (max 10 best out of 42): 030qb3t (0.13 #3392, 0.12 #4112, 0.09 #516), 04jpl (0.12 #3363, 0.12 #4083, 0.12 #2884), 052p7 (0.08 #57, 0.03 #1253, 0.03 #3411), 03rjj (0.08 #6, 0.02 #4314, 0.02 #4080), 02301 (0.08 #63), 0rh6k (0.06 #3355, 0.06 #4075, 0.04 #958), 080h2 (0.04 #4097, 0.04 #3377, 0.03 #501), 01_d4 (0.04 #4120, 0.03 #3400, 0.02 #3640), 03pzf (0.03 #414, 0.02 #653, 0.02 #3529), 0h7h6 (0.03 #4116, 0.03 #3396, 0.03 #1477) >> Best rule #3392 for best value: >> intensional similarity = 3 >> extensional distance = 506 >> proper extension: 0192hw; 0hv81; >> query: (?x7480, 030qb3t) <- film_crew_role(?x7480, ?x137), language(?x7480, ?x254), featured_film_locations(?x7480, ?x739) >> conf = 0.13 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vjp3 featured_film_locations 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 54.000 0.132 http://example.org/film/film/featured_film_locations #7276-05tbn PRED entity: 05tbn PRED relation: religion PRED expected values: 02t7t => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 25): 021_0p (0.65 #687, 0.59 #712, 0.58 #410), 0flw86 (0.50 #126, 0.42 #1454, 0.39 #427), 092bf5 (0.50 #132, 0.33 #107, 0.33 #32), 058x5 (0.40 #679, 0.37 #729, 0.36 #654), 03j6c (0.33 #36, 0.25 #86, 0.25 #61), 0kpl (0.33 #29, 0.25 #79, 0.25 #54), 07w8f (0.33 #44, 0.25 #94, 0.25 #69), 02t7t (0.27 #213, 0.27 #690, 0.26 #539), 072w0 (0.27 #215, 0.25 #692, 0.24 #742), 06yyp (0.05 #614, 0.03 #438, 0.02 #2293) >> Best rule #687 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05kkh; 059rby; 03v1s; 05kj_; 059f4; 05fkf; 0vmt; 03s0w; 05fhy; 059_c; ... >> query: (?x3670, 021_0p) <- contains(?x3670, ?x331), district_represented(?x176, ?x3670), religion(?x3670, ?x109) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #213 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 01bkb; *> query: (?x3670, 02t7t) <- religion(?x3670, ?x109), location_of_ceremony(?x566, ?x3670), country(?x3670, ?x94) *> conf = 0.27 ranks of expected_values: 8 EVAL 05tbn religion 02t7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 194.000 194.000 0.646 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #7275-02jm0n PRED entity: 02jm0n PRED relation: film PRED expected values: 07tw_b 035gnh => 73 concepts (37 used for prediction) PRED predicted values (max 10 best out of 285): 039cq4 (0.39 #39278, 0.38 #33922, 0.33 #53561), 03nfnx (0.15 #3185, 0.05 #1400, 0.03 #6756), 03bx2lk (0.11 #184, 0.08 #1969, 0.04 #5540), 0ds5_72 (0.11 #1454, 0.08 #3239, 0.03 #6810), 08952r (0.11 #716, 0.08 #2501, 0.02 #13213), 0ds35l9 (0.11 #6, 0.08 #1791, 0.02 #5362), 02f6g5 (0.11 #280, 0.08 #2065, 0.01 #5636), 03cyslc (0.11 #1203, 0.08 #2988, 0.01 #6559), 03s5lz (0.11 #196, 0.08 #1981), 0bvn25 (0.08 #1835, 0.05 #50, 0.04 #12547) >> Best rule #39278 for best value: >> intensional similarity = 3 >> extensional distance = 1177 >> proper extension: 05ty4m; 05cj4r; 0436f4; 03f2_rc; 03qd_; 05ml_s; 04bd8y; 03gm48; 015grj; 0bz5v2; ... >> query: (?x1787, ?x6884) <- profession(?x1787, ?x1032), student(?x8220, ?x1787), nominated_for(?x1787, ?x6884) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #680 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 02lk1s; 01lct6; *> query: (?x1787, 07tw_b) <- profession(?x1787, ?x1032), student(?x8220, ?x1787), nominated_for(?x1787, ?x6884), ?x6884 = 039cq4 *> conf = 0.05 ranks of expected_values: 37 EVAL 02jm0n film 035gnh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 37.000 0.386 http://example.org/film/actor/film./film/performance/film EVAL 02jm0n film 07tw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 73.000 37.000 0.386 http://example.org/film/actor/film./film/performance/film #7274-0ft0s PRED entity: 0ft0s PRED relation: contains! PRED expected values: 0d05q4 => 4 concepts (4 used for prediction) PRED predicted values (max 10 best out of 28): 09c7w0 (0.27 #899, 0.27 #1799, 0.27 #3), 07ssc (0.09 #1828, 0.09 #32, 0.09 #928), 02jx1 (0.07 #1883, 0.07 #87, 0.07 #983), 01n7q (0.06 #78, 0.06 #974, 0.06 #1874), 04_1l0v (0.05 #451, 0.05 #1347, 0.05 #2247), 02qkt (0.04 #347, 0.04 #1243, 0.04 #2143), 0345h (0.04 #82, 0.04 #978, 0.04 #1878), 03rk0 (0.03 #137, 0.03 #1033, 0.03 #1933), 04jpl (0.03 #1818, 0.03 #22, 0.03 #918), 07c5l (0.03 #395, 0.03 #1291, 0.03 #2191) >> Best rule #899 for best value: >> intensional similarity = 28 >> extensional distance = 464 >> proper extension: 03gh4; 0kwgs; >> query: (?x13397, 09c7w0) <- location_of_ceremony(?x566, ?x13397), type_of_union(?x10929, ?x566), type_of_union(?x8716, ?x566), type_of_union(?x8061, ?x566), type_of_union(?x7201, ?x566), type_of_union(?x6957, ?x566), type_of_union(?x4949, ?x566), type_of_union(?x4911, ?x566), type_of_union(?x4740, ?x566), type_of_union(?x4366, ?x566), type_of_union(?x3732, ?x566), type_of_union(?x2698, ?x566), location_of_ceremony(?x566, ?x13384), ?x4949 = 0fgg4, award_winner(?x486, ?x2698), role(?x2698, ?x228), profession(?x4740, ?x220), ?x8716 = 01yf85, award(?x2698, ?x1079), inductee(?x9953, ?x10929), ?x6957 = 03s9b, music(?x1932, ?x4911), award_nominee(?x2698, ?x217), ?x4366 = 01vxxb, time_zones(?x13384, ?x2864), ?x8061 = 0sw6g, student(?x2909, ?x3732), group(?x7201, ?x3390) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ft0s contains! 0d05q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 4.000 4.000 0.268 http://example.org/location/location/contains #7273-03t79f PRED entity: 03t79f PRED relation: currency PRED expected values: 09nqf => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.87 #36, 0.85 #22, 0.83 #57), 01nv4h (0.25 #561, 0.04 #30, 0.03 #240), 02l6h (0.02 #242, 0.01 #277, 0.01 #431), 088n7 (0.01 #49, 0.01 #56, 0.01 #140) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 0k2m6; >> query: (?x5372, 09nqf) <- titles(?x571, ?x5372), edited_by(?x5372, ?x4215), film_crew_role(?x5372, ?x137), film_release_distribution_medium(?x5372, ?x81) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t79f currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.868 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #7272-05k17c PRED entity: 05k17c PRED relation: company PRED expected values: 01jtp7 => 32 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2218): 06pwq (0.80 #5253, 0.73 #6230, 0.50 #663), 060ppp (0.70 #6132, 0.50 #5803, 0.43 #2854), 087c7 (0.70 #5902, 0.50 #5573, 0.43 #2624), 0300cp (0.60 #5944, 0.50 #5615, 0.47 #7580), 019rl6 (0.60 #6048, 0.50 #5719, 0.43 #2770), 0vlf (0.60 #6174, 0.50 #5845, 0.43 #2896), 0z90c (0.60 #6059, 0.50 #5730, 0.43 #2781), 01s73z (0.60 #6002, 0.50 #5673, 0.43 #2724), 01qygl (0.60 #6081, 0.50 #5752, 0.43 #2803), 02r5dz (0.60 #5965, 0.40 #5636, 0.40 #2033) >> Best rule #5253 for best value: >> intensional similarity = 14 >> extensional distance = 8 >> proper extension: 01rk91; 021q1c; 01___w; 05smlt; >> query: (?x3484, 06pwq) <- company(?x3484, ?x735), company(?x3484, ?x122), school(?x8995, ?x735), school(?x3333, ?x735), major_field_of_study(?x735, ?x254), major_field_of_study(?x122, ?x8681), major_field_of_study(?x122, ?x5179), student(?x122, ?x123), season(?x3333, ?x701), ?x8995 = 01d6g, school_type(?x122, ?x1044), student(?x735, ?x65), ?x8681 = 04rlf, ?x5179 = 04gb7 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5897 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 8 *> proper extension: 01yc02; *> query: (?x3484, ?x7065) <- company(?x3484, ?x3696), organization(?x3484, ?x8982), organization(?x3484, ?x7065), state_province_region(?x7065, ?x2020), citytown(?x8982, ?x3125), category(?x8982, ?x134), ?x134 = 08mbj5d, location(?x399, ?x3125), citytown(?x7065, ?x3052), organization(?x346, ?x3696) *> conf = 0.22 ranks of expected_values: 195 EVAL 05k17c company 01jtp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 32.000 25.000 0.800 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #7271-0688f PRED entity: 0688f PRED relation: language! PRED expected values: 02tcgh => 51 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1842): 030z4z (0.75 #3463, 0.49 #8656, 0.33 #4876), 052_mn (0.75 #3463, 0.40 #8268, 0.33 #4808), 047q2k1 (0.75 #3463, 0.40 #6949, 0.33 #3489), 02tcgh (0.75 #3463, 0.33 #5110, 0.33 #3379), 0f42nz (0.75 #3463, 0.33 #4332, 0.33 #2601), 021pqy (0.75 #3463, 0.33 #4204, 0.33 #2473), 012mrr (0.57 #9110, 0.44 #16034, 0.40 #7377), 08nvyr (0.57 #9392, 0.33 #16316, 0.33 #736), 024l2y (0.57 #8902, 0.33 #15826, 0.33 #246), 0f4_2k (0.56 #16565, 0.43 #9641, 0.38 #30421) >> Best rule #3463 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 02hxcvy; >> query: (?x10323, ?x257) <- languages(?x7517, ?x10323), countries_spoken_in(?x10323, ?x2236), ?x2236 = 05sb1, type_of_union(?x7517, ?x566), award(?x7517, ?x1937), ?x566 = 04ztj, place_of_birth(?x7517, ?x9466), languages(?x7517, ?x13017), ?x13017 = 09s02, award_winner(?x1937, ?x1445), nominated_for(?x1937, ?x257), language(?x2882, ?x10323) >> conf = 0.75 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 0688f language! 02tcgh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 51.000 26.000 0.750 http://example.org/film/film/language #7270-0459z PRED entity: 0459z PRED relation: profession PRED expected values: 01c72t 05vyk => 171 concepts (167 used for prediction) PRED predicted values (max 10 best out of 105): 09jwl (0.78 #3020, 0.71 #7072, 0.71 #9173), 02hrh1q (0.69 #9018, 0.68 #16822, 0.67 #19824), 0cbd2 (0.60 #3757, 0.55 #4657, 0.54 #2707), 0nbcg (0.51 #3033, 0.49 #7085, 0.48 #9786), 01c72t (0.50 #625, 0.49 #1675, 0.46 #1225), 0dxtg (0.47 #2864, 0.46 #3614, 0.46 #3764), 016z4k (0.46 #3004, 0.44 #8706, 0.40 #8256), 0kyk (0.39 #331, 0.38 #5882, 0.38 #2731), 0dz3r (0.38 #9755, 0.38 #8254, 0.38 #13205), 03gjzk (0.33 #766, 0.28 #3766, 0.27 #3316) >> Best rule #3020 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 0136p1; 01wz3cx; 03j0br4; 02wb6yq; 0407f; 063t3j; >> query: (?x11512, 09jwl) <- location(?x11512, ?x2985), currency(?x2985, ?x5696), jurisdiction_of_office(?x1195, ?x2985), instrumentalists(?x316, ?x11512) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #625 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 032t2z; *> query: (?x11512, 01c72t) <- place_of_death(?x11512, ?x863), people(?x268, ?x11512), instrumentalists(?x316, ?x11512), ?x316 = 05r5c *> conf = 0.50 ranks of expected_values: 5, 11 EVAL 0459z profession 05vyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 171.000 167.000 0.780 http://example.org/people/person/profession EVAL 0459z profession 01c72t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 171.000 167.000 0.780 http://example.org/people/person/profession #7269-0gq_d PRED entity: 0gq_d PRED relation: award! PRED expected values: 04vlh5 => 48 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2874): 01c1px (0.80 #30301, 0.79 #37035, 0.79 #13466), 08xz51 (0.80 #30301, 0.79 #37035, 0.79 #13466), 03rwz3 (0.60 #2169, 0.28 #3366, 0.20 #33667), 020h2v (0.60 #2305, 0.28 #3366, 0.19 #15771), 01gb54 (0.60 #1311, 0.28 #3366, 0.19 #14777), 06cgy (0.52 #30684, 0.15 #10483, 0.15 #7117), 07r1h (0.43 #32101, 0.28 #3366, 0.20 #1800), 0237fw (0.43 #30941, 0.15 #10740, 0.15 #7374), 0gyx4 (0.40 #1248, 0.35 #31549, 0.25 #34915), 024rgt (0.40 #673, 0.28 #3366, 0.20 #33667) >> Best rule #30301 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 0gq_v; 0gr4k; 0gr51; 0gr07; >> query: (?x4573, ?x2426) <- ceremony(?x4573, ?x8478), ceremony(?x4573, ?x1819), award_winner(?x4573, ?x2426), ?x8478 = 0bzjgq, award_winner(?x1819, ?x262), honored_for(?x1819, ?x1077) >> conf = 0.80 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0gq_d award! 04vlh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 19.000 0.795 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7268-01qdmh PRED entity: 01qdmh PRED relation: country PRED expected values: 09c7w0 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 50): 09c7w0 (0.91 #1408, 0.88 #1174, 0.88 #762), 07ssc (0.58 #249, 0.37 #426, 0.35 #367), 01znc_ (0.41 #527, 0.36 #586, 0.04 #2286), 02jx1 (0.37 #4466, 0.04 #2286, 0.03 #1172), 03rjj (0.19 #240, 0.07 #358, 0.07 #298), 0d0vqn (0.16 #243, 0.06 #361, 0.06 #3821), 0d060g (0.13 #241, 0.08 #418, 0.07 #359), 0k6nt (0.13 #253, 0.06 #3821, 0.04 #371), 0chghy (0.10 #245, 0.10 #70, 0.06 #3821), 059j2 (0.10 #258, 0.04 #2286, 0.03 #376) >> Best rule #1408 for best value: >> intensional similarity = 4 >> extensional distance = 684 >> proper extension: 0cq8nx; >> query: (?x11148, 09c7w0) <- country(?x11148, ?x1264), film_release_region(?x6121, ?x1264), film(?x9785, ?x11148), ?x6121 = 064lsn >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qdmh country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.914 http://example.org/film/film/country #7267-03bxwtd PRED entity: 03bxwtd PRED relation: award_nominee! PRED expected values: 01pfkw => 120 concepts (51 used for prediction) PRED predicted values (max 10 best out of 910): 06x4l_ (0.82 #53537, 0.81 #44226, 0.81 #114056), 05vzw3 (0.82 #53537, 0.81 #44226, 0.81 #114056), 067nsm (0.82 #53537, 0.81 #44226, 0.81 #114056), 03h_fk5 (0.31 #9310, 0.31 #116385, 0.15 #16293), 06mj4 (0.31 #9310, 0.31 #116385, 0.15 #16293), 018ndc (0.31 #9310, 0.31 #116385, 0.15 #16293), 01vw20h (0.31 #116385, 0.15 #16293, 0.06 #1057), 0154qm (0.18 #7720, 0.04 #114795, 0.03 #100826), 02l840 (0.15 #16293, 0.07 #25760, 0.06 #76967), 01wgxtl (0.15 #16293, 0.06 #601, 0.05 #26205) >> Best rule #53537 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 028q6; 032nwy; 0146pg; 08wq0g; 03qd_; 081lh; 01wdqrx; 0bg539; 01vs14j; 0pz91; ... >> query: (?x3062, ?x2862) <- instrumentalists(?x227, ?x3062), award_nominee(?x527, ?x3062), award_nominee(?x3062, ?x2862) >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #16293 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 133 *> proper extension: 01hrqc; *> query: (?x3062, ?x2807) <- role(?x3062, ?x227), award_nominee(?x10209, ?x3062), award_nominee(?x10209, ?x2807) *> conf = 0.15 ranks of expected_values: 40 EVAL 03bxwtd award_nominee! 01pfkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 120.000 51.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7266-0q5hw PRED entity: 0q5hw PRED relation: profession PRED expected values: 03gjzk => 108 concepts (104 used for prediction) PRED predicted values (max 10 best out of 79): 03gjzk (0.83 #1911, 0.82 #2496, 0.79 #304), 02jknp (0.45 #5411, 0.28 #590, 0.28 #7742), 0kyk (0.38 #27, 0.32 #2072, 0.31 #4118), 02krf9 (0.30 #1923, 0.28 #2508, 0.28 #7742), 018gz8 (0.30 #598, 0.28 #7742, 0.26 #11832), 09jwl (0.30 #746, 0.27 #1038, 0.25 #1622), 0d1pc (0.25 #778, 0.19 #1508, 0.19 #1654), 0np9r (0.21 #5569, 0.20 #5277, 0.12 #602), 0nbcg (0.20 #759, 0.19 #4266, 0.17 #1635), 0dz3r (0.20 #4239, 0.13 #1024, 0.13 #1316) >> Best rule #1911 for best value: >> intensional similarity = 2 >> extensional distance = 198 >> proper extension: 04rtpt; >> query: (?x2817, 03gjzk) <- program(?x2817, ?x631), award(?x631, ?x678) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q5hw profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 104.000 0.830 http://example.org/people/person/profession #7265-02z0f6l PRED entity: 02z0f6l PRED relation: genre PRED expected values: 01f9r0 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 88): 07s9rl0 (0.82 #5423, 0.81 #1203, 0.77 #4096), 04xvlr (0.72 #4820, 0.59 #4819, 0.59 #4217), 07ssc (0.59 #4819, 0.59 #4217, 0.58 #1323), 05p553 (0.42 #1692, 0.37 #3019, 0.37 #2536), 0lsxr (0.33 #130, 0.33 #10, 0.20 #250), 01jfsb (0.32 #4351, 0.28 #4231, 0.28 #5315), 03k9fj (0.30 #253, 0.29 #373, 0.22 #133), 060__y (0.28 #1219, 0.21 #4112, 0.18 #1704), 02kdv5l (0.27 #4340, 0.26 #5304, 0.25 #4943), 06cvj (0.24 #1691, 0.13 #484, 0.10 #3018) >> Best rule #5423 for best value: >> intensional similarity = 3 >> extensional distance = 1224 >> proper extension: 0c0wvx; >> query: (?x6900, 07s9rl0) <- genre(?x6900, ?x1403), genre(?x2112, ?x1403), ?x2112 = 0bm2g >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1156 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 257 *> proper extension: 05f67hw; *> query: (?x6900, 01f9r0) <- country(?x6900, ?x94), films(?x11911, ?x6900), produced_by(?x6900, ?x3528) *> conf = 0.06 ranks of expected_values: 37 EVAL 02z0f6l genre 01f9r0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 74.000 74.000 0.821 http://example.org/film/film/genre #7264-01vhb0 PRED entity: 01vhb0 PRED relation: award PRED expected values: 02pzz3p => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 294): 02pzz3p (0.77 #24593, 0.76 #23786, 0.74 #15320), 0gqwc (0.41 #1282, 0.19 #2491, 0.17 #3297), 0gqyl (0.37 #1313, 0.20 #910, 0.20 #507), 09sb52 (0.35 #5280, 0.34 #2055, 0.33 #4877), 0cqgl9 (0.33 #1401, 0.09 #7852, 0.08 #998), 05p09zm (0.32 #2138, 0.30 #4960, 0.28 #2944), 05pcn59 (0.32 #2095, 0.29 #2901, 0.28 #4514), 0ck27z (0.31 #21054, 0.28 #21458, 0.27 #22264), 0bdwft (0.30 #1276, 0.20 #470, 0.12 #7727), 0gkts9 (0.26 #1377, 0.07 #7828, 0.06 #25568) >> Best rule #24593 for best value: >> intensional similarity = 3 >> extensional distance = 704 >> proper extension: 06lxn; >> query: (?x2308, ?x2773) <- category(?x2308, ?x134), award_winner(?x2773, ?x2308), award(?x540, ?x2773) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vhb0 award 02pzz3p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7263-02sfnv PRED entity: 02sfnv PRED relation: film! PRED expected values: 01nrq5 => 67 concepts (32 used for prediction) PRED predicted values (max 10 best out of 788): 052hl (0.50 #3265, 0.33 #1189), 02ch1w (0.50 #3112, 0.33 #1036), 027l0b (0.33 #475, 0.25 #2551, 0.02 #6704), 02xwgr (0.25 #3009, 0.03 #7162, 0.01 #11315), 0p_47 (0.25 #2749, 0.03 #6902), 01vy_v8 (0.25 #2808, 0.02 #9038, 0.01 #11114), 01337_ (0.25 #3718, 0.01 #9948), 02zfg3 (0.25 #4108), 0534nr (0.25 #3880), 020jqv (0.25 #3851) >> Best rule #3265 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03kx49; >> query: (?x5187, 052hl) <- film(?x9849, ?x5187), film(?x3017, ?x5187), country(?x5187, ?x94), ?x9849 = 02nrdp, award_nominee(?x3017, ?x2461) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02sfnv film! 01nrq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 32.000 0.500 http://example.org/film/actor/film./film/performance/film #7262-09qftb PRED entity: 09qftb PRED relation: honored_for PRED expected values: 011ypx => 25 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1330): 0d68qy (0.35 #3116, 0.27 #1334, 0.26 #3713), 03ln8b (0.33 #592, 0.33 #591, 0.33 #122), 02pqs8l (0.33 #591, 0.29 #1182, 0.25 #2369), 02k_4g (0.33 #591, 0.29 #1182, 0.25 #2369), 090s_0 (0.33 #591, 0.29 #1182, 0.25 #2369), 024hbv (0.33 #591, 0.29 #1182, 0.25 #2369), 01cvtf (0.33 #591, 0.29 #1182, 0.25 #2369), 02qkq0 (0.33 #591, 0.29 #1182, 0.25 #2369), 02md2d (0.33 #591, 0.29 #1182, 0.25 #2369), 05lfwd (0.33 #343, 0.20 #935, 0.15 #3309) >> Best rule #3116 for best value: >> intensional similarity = 18 >> extensional distance = 24 >> proper extension: 0hr3c8y; 09qvms; 092t4b; 058m5m4; 027hjff; 092_25; 03gyp30; 09g90vz; 0g55tzk; >> query: (?x8128, 0d68qy) <- ceremony(?x1443, ?x8128), award_winner(?x1443, ?x5556), nominated_for(?x1443, ?x6215), nominated_for(?x1443, ?x4607), nominated_for(?x1443, ?x3573), nominated_for(?x1443, ?x586), award(?x308, ?x1443), award(?x3690, ?x1443), award(?x460, ?x1443), instrumentalists(?x212, ?x460), award_winner(?x2707, ?x5556), film_release_region(?x6215, ?x94), ?x4607 = 0h03fhx, written_by(?x586, ?x2332), ?x3573 = 011yl_, film_release_region(?x6215, ?x87), music(?x3311, ?x3690), titles(?x162, ?x586) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #590 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 1 *> proper extension: 09pj68; *> query: (?x8128, ?x144) <- ceremony(?x1443, ?x8128), ?x1443 = 054krc, award_winner(?x8128, ?x1343), award_winner(?x8128, ?x989), award_nominee(?x1343, ?x6632), actor(?x2078, ?x1343), ?x6632 = 05lb30, nominated_for(?x1343, ?x293), award_winner(?x969, ?x989), award_nominee(?x5461, ?x1343), award_winner(?x989, ?x92), participant(?x989, ?x287), profession(?x989, ?x319), award_winner(?x198, ?x989), award_winner(?x144, ?x989), location(?x989, ?x3007) *> conf = 0.08 ranks of expected_values: 94 EVAL 09qftb honored_for 011ypx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 25.000 15.000 0.346 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #7261-0dz96 PRED entity: 0dz96 PRED relation: profession! PRED expected values: 01yznp => 45 concepts (14 used for prediction) PRED predicted values (max 10 best out of 4307): 0dpqk (0.50 #5856, 0.50 #1614, 0.35 #14341), 01vsl3_ (0.50 #5068, 0.50 #826, 0.32 #13553), 01vrncs (0.50 #4527, 0.50 #285, 0.29 #13012), 03lgg (0.50 #5836, 0.50 #1594, 0.29 #10078), 01bbwp (0.50 #7445, 0.50 #3203, 0.26 #15930), 02g3w (0.50 #7958, 0.50 #3716, 0.24 #16443), 01nz1q6 (0.50 #7835, 0.50 #3593, 0.23 #12077), 01wj5hp (0.50 #7157, 0.50 #2915, 0.21 #32616), 017yfz (0.50 #5532, 0.50 #1290, 0.18 #14017), 02nygk (0.50 #8377, 0.50 #4135, 0.13 #12619) >> Best rule #5856 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 09jwl; 0196pc; >> query: (?x14622, 0dpqk) <- profession(?x8753, ?x14622), profession(?x7117, ?x14622), ?x8753 = 0yxl, location(?x7117, ?x6683), actor(?x5852, ?x7117), film(?x7117, ?x141) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4332 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 09jwl; 0196pc; *> query: (?x14622, 01yznp) <- profession(?x8753, ?x14622), profession(?x7117, ?x14622), ?x8753 = 0yxl, location(?x7117, ?x6683), actor(?x5852, ?x7117), film(?x7117, ?x141) *> conf = 0.25 ranks of expected_values: 447 EVAL 0dz96 profession! 01yznp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 14.000 0.500 http://example.org/people/person/profession #7260-04fjzv PRED entity: 04fjzv PRED relation: film_release_region PRED expected values: 0jgd 07ssc => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 136): 0jgd (0.84 #472, 0.78 #3286, 0.78 #2659), 03gj2 (0.84 #493, 0.77 #3307, 0.75 #2680), 05qhw (0.84 #481, 0.77 #2668, 0.70 #3295), 07ssc (0.79 #483, 0.79 #2670, 0.77 #1890), 01znc_ (0.74 #511, 0.70 #3325, 0.69 #2698), 06bnz (0.74 #516, 0.65 #3330, 0.64 #2703), 03spz (0.73 #565, 0.59 #2752, 0.59 #3379), 06t2t (0.66 #532, 0.61 #2719, 0.59 #3346), 0ctw_b (0.65 #494, 0.52 #2681, 0.47 #3308), 05v8c (0.63 #484, 0.55 #2671, 0.49 #3298) >> Best rule #472 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 0gx1bnj; 08hmch; 0gj8t_b; 0661m4p; 0gj8nq2; 047vnkj; 0gtt5fb; 0dll_t2; 0bq6ntw; 0ds2l81; ... >> query: (?x11209, 0jgd) <- film_crew_role(?x11209, ?x137), film_release_region(?x11209, ?x2629), produced_by(?x11209, ?x4353), ?x2629 = 06f32 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 04fjzv film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 96.000 0.839 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04fjzv film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.839 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7259-05dy7p PRED entity: 05dy7p PRED relation: featured_film_locations PRED expected values: 04wgh => 93 concepts (78 used for prediction) PRED predicted values (max 10 best out of 102): 02_286 (0.21 #1702, 0.17 #4348, 0.15 #1222), 04jpl (0.09 #971, 0.08 #489, 0.08 #8907), 030qb3t (0.08 #4608, 0.07 #1961, 0.07 #9418), 0rh6k (0.05 #1443, 0.04 #5773, 0.04 #1203), 01_d4 (0.04 #1009, 0.03 #3893, 0.03 #2449), 06y57 (0.04 #1305, 0.04 #1545, 0.03 #2025), 01yj2 (0.04 #631, 0.03 #391), 0qr8z (0.03 #390, 0.03 #630, 0.02 #871), 03rjj (0.03 #1688, 0.03 #2408, 0.03 #968), 0d6lp (0.03 #1754, 0.02 #4159, 0.02 #4641) >> Best rule #1702 for best value: >> intensional similarity = 4 >> extensional distance = 119 >> proper extension: 02n72k; >> query: (?x2402, 02_286) <- genre(?x2402, ?x53), film_release_distribution_medium(?x2402, ?x81), language(?x2402, ?x254), film_production_design_by(?x2402, ?x3080) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05dy7p featured_film_locations 04wgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 78.000 0.207 http://example.org/film/film/featured_film_locations #7258-01l29r PRED entity: 01l29r PRED relation: award! PRED expected values: 098n5 026g4l_ 052hl 03m9c8 03rwz3 026v1z => 37 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2463): 02645b (0.83 #10032, 0.82 #13375, 0.79 #50159), 03m9c8 (0.83 #10032, 0.82 #13375, 0.79 #50159), 0b13g7 (0.83 #10032, 0.82 #13375, 0.79 #50159), 012t1 (0.83 #10032, 0.82 #13375, 0.79 #50159), 052hl (0.83 #10032, 0.82 #13375, 0.79 #50159), 012wg (0.83 #10032, 0.82 #13375, 0.79 #50159), 01vrlqd (0.83 #10032, 0.82 #13375, 0.79 #50159), 098n5 (0.83 #10032, 0.82 #13375, 0.79 #50159), 03kpvp (0.83 #10032, 0.82 #13375, 0.79 #50159), 0lpjn (0.62 #20811, 0.61 #14129, 0.24 #24153) >> Best rule #10032 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 040njc; 0gq9h; 01lj_c; >> query: (?x3105, ?x1047) <- award(?x8275, ?x3105), award(?x4060, ?x3105), award(?x163, ?x3105), ?x4060 = 05hj_k, ?x163 = 0fvf9q, nationality(?x8275, ?x94), award_winner(?x3105, ?x1047), religion(?x8275, ?x2694) >> conf = 0.83 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 5, 8, 206, 412 EVAL 01l29r award! 026v1z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 19.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01l29r award! 03rwz3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 19.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01l29r award! 03m9c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 37.000 19.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01l29r award! 052hl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 37.000 19.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01l29r award! 026g4l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 37.000 19.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01l29r award! 098n5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 37.000 19.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7257-015wc0 PRED entity: 015wc0 PRED relation: crewmember! PRED expected values: 01s9vc => 160 concepts (115 used for prediction) PRED predicted values (max 10 best out of 3): 01lsl (0.03 #20309, 0.02 #21922, 0.02 #29019), 07cw4 (0.02 #21599, 0.02 #18374, 0.01 #28051), 02q7yfq (0.01 #566) >> Best rule #20309 for best value: >> intensional similarity = 3 >> extensional distance = 877 >> proper extension: 031rx9; >> query: (?x9946, ?x9185) <- award_nominee(?x10412, ?x9946), nominated_for(?x9946, ?x9185), award_winner(?x7589, ?x9946) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015wc0 crewmember! 01s9vc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 115.000 0.025 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #7256-02zmh5 PRED entity: 02zmh5 PRED relation: award_nominee! PRED expected values: 0412f5y => 94 concepts (40 used for prediction) PRED predicted values (max 10 best out of 825): 015f7 (0.80 #46611, 0.79 #32624, 0.77 #83903), 067nsm (0.80 #46611, 0.79 #32624, 0.04 #15482), 02zmh5 (0.23 #58266, 0.18 #83904, 0.17 #44280), 01gg59 (0.23 #58266, 0.18 #83904, 0.17 #44280), 01vsgrn (0.18 #83904, 0.17 #44280, 0.08 #15283), 02l840 (0.12 #14138, 0.08 #2487, 0.07 #157), 01vw20h (0.12 #15040, 0.07 #1059, 0.05 #31352), 05vzw3 (0.11 #1091, 0.07 #15072, 0.02 #26723), 01vvydl (0.09 #16, 0.05 #13997, 0.03 #25648), 026yqrr (0.08 #15430, 0.04 #45729, 0.04 #48060) >> Best rule #46611 for best value: >> intensional similarity = 3 >> extensional distance = 423 >> proper extension: 037hgm; 024y6w; 0knjh; >> query: (?x2083, ?x3397) <- artists(?x671, ?x2083), nationality(?x2083, ?x304), award_nominee(?x2083, ?x3397) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #14800 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 124 *> proper extension: 04mn81; 01vx5w7; 0hvbj; 043zg; 016890; 01dwrc; 03h_0_z; 02vwckw; 01lqf49; 02h9_l; ... *> query: (?x2083, 0412f5y) <- artists(?x3562, ?x2083), artists(?x1572, ?x2083), ?x3562 = 025sc50, parent_genre(?x114, ?x1572) *> conf = 0.05 ranks of expected_values: 39 EVAL 02zmh5 award_nominee! 0412f5y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 94.000 40.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7255-02n9nmz PRED entity: 02n9nmz PRED relation: award! PRED expected values: 02kxbwx 0jgwf => 48 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2341): 07s93v (0.92 #3333, 0.86 #13333, 0.86 #16667), 013tcv (0.92 #3333, 0.86 #13333, 0.86 #16667), 06b_0 (0.57 #32199, 0.50 #8862, 0.40 #12195), 02kxbwx (0.57 #30176, 0.50 #6839, 0.38 #33510), 04y8r (0.50 #7263, 0.43 #30600, 0.40 #10596), 0c12h (0.50 #8466, 0.43 #31803, 0.40 #11799), 0693l (0.50 #7510, 0.43 #30847, 0.22 #17512), 0dbbz (0.50 #9367, 0.40 #12700, 0.36 #32704), 01q4qv (0.50 #7528, 0.40 #10861, 0.36 #30865), 0jgwf (0.50 #9118, 0.40 #12451, 0.33 #15786) >> Best rule #3333 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0gr4k; >> query: (?x1180, ?x1616) <- nominated_for(?x1180, ?x161), award(?x6654, ?x1180), award(?x276, ?x1180), award_winner(?x1180, ?x1616), ?x276 = 0qf43, ?x6654 = 01wd3l >> conf = 0.92 => this is the best rule for 2 predicted values *> Best rule #30176 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 0f_nbyh; 04dn09n; 019f4v; 02pqp12; 0gq9h; 02g3ft; 02rdyk7; 02x17s4; 02x4wr9; 0fq9zdv; *> query: (?x1180, 02kxbwx) <- nominated_for(?x1180, ?x161), award(?x11705, ?x1180), award(?x276, ?x1180), award_winner(?x1180, ?x1616), ?x276 = 0qf43, nominated_for(?x11705, ?x9456) *> conf = 0.57 ranks of expected_values: 4, 10 EVAL 02n9nmz award! 0jgwf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 48.000 25.000 0.923 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02n9nmz award! 02kxbwx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 48.000 25.000 0.923 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7254-01k47c PRED entity: 01k47c PRED relation: people! PRED expected values: 01ddth => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 44): 0gk4g (0.33 #1310, 0.25 #2741, 0.22 #1245), 0qcr0 (0.17 #1301, 0.16 #326, 0.12 #3512), 0dq9p (0.15 #1317, 0.14 #82, 0.13 #2748), 02y0js (0.14 #457, 0.11 #1302, 0.08 #2733), 02k6hp (0.12 #1336, 0.08 #1792, 0.08 #2572), 04p3w (0.09 #2742, 0.07 #4497, 0.07 #466), 01l2m3 (0.07 #2747, 0.05 #3527, 0.05 #4502), 02knxx (0.07 #2762, 0.06 #3542, 0.05 #1787), 0m32h (0.06 #1779, 0.05 #2754, 0.05 #2039), 04psf (0.06 #137, 0.04 #852, 0.04 #462) >> Best rule #1310 for best value: >> intensional similarity = 5 >> extensional distance = 104 >> proper extension: 07_grx; 015wfg; 04107; 03bw6; 015qq1; >> query: (?x9074, 0gk4g) <- place_of_birth(?x9074, ?x1156), people(?x7260, ?x9074), nationality(?x9074, ?x512), student(?x7021, ?x9074), risk_factors(?x9510, ?x7260) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01k47c people! 01ddth CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 140.000 0.330 http://example.org/people/cause_of_death/people #7253-015fr PRED entity: 015fr PRED relation: film_release_region! PRED expected values: 0g56t9t 0gkz15s 0872p_c 03bx2lk 05z_kps 02c6d 04hwbq 04jkpgv 02r1c18 09k56b7 0gz6b6g 05q4y12 0j43swk 023gxx 0gffmn8 0gjc4d3 0blpg 09v71cj 0db94w 0dr_9t7 017jd9 07bzz7 0gbfn9 01d259 064lsn 089j8p 0dc_ms 07pd_j 0gvvf4j 0m63c 0fphf3v 03z9585 0hhggmy 02wtp6 => 211 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1547): 017jd9 (0.89 #23567, 0.89 #14107, 0.88 #10953), 04hwbq (0.84 #9573, 0.81 #23238, 0.78 #14829), 09k56b7 (0.84 #9637, 0.78 #18046, 0.73 #10688), 03z9585 (0.80 #10281, 0.78 #18690, 0.73 #23946), 07qg8v (0.80 #9582, 0.64 #3275, 0.62 #17991), 0gffmn8 (0.79 #47592, 0.76 #9753, 0.75 #291), 0gkz15s (0.79 #29499, 0.78 #23193, 0.77 #10579), 03bx2lk (0.78 #23233, 0.78 #17977, 0.75 #106), 04w7rn (0.76 #9594, 0.75 #18003, 0.73 #10645), 04jkpgv (0.76 #9593, 0.72 #18002, 0.62 #131) >> Best rule #23567 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 07tp2; >> query: (?x583, 017jd9) <- organization(?x583, ?x312), participating_countries(?x418, ?x583), service_location(?x127, ?x583) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 14, 16, 19, 20, 22, 23, 24, 26, 27, 30, 31, 33, 34, 35, 44, 45, 46, 48, 61, 78, 84, 87, 110, 180 EVAL 015fr film_release_region! 02wtp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0hhggmy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 03z9585 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0fphf3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0m63c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0gvvf4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 07pd_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 089j8p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 064lsn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 01d259 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0gbfn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 07bzz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 017jd9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0dr_9t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0db94w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 09v71cj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0blpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0gjc4d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0gffmn8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 023gxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0j43swk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 05q4y12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0gz6b6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 09k56b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 02r1c18 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 04jkpgv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 04hwbq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 02c6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 05z_kps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 03bx2lk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0872p_c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 015fr film_release_region! 0g56t9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 211.000 125.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7252-05dxl5 PRED entity: 05dxl5 PRED relation: award_nominee! PRED expected values: 03w4sh => 74 concepts (34 used for prediction) PRED predicted values (max 10 best out of 910): 05lb30 (0.86 #3820, 0.82 #11584, 0.81 #27799), 01wb8bs (0.82 #11584, 0.81 #27799, 0.81 #39383), 026zvx7 (0.82 #11584, 0.81 #27799, 0.81 #39383), 02s_qz (0.82 #11584, 0.81 #27799, 0.81 #39383), 038g2x (0.78 #13901, 0.77 #71830, 0.77 #67191), 030znt (0.75 #76464, 0.75 #64873, 0.75 #64872), 05dxl5 (0.72 #14798, 0.69 #5529, 0.64 #3213), 03w4sh (0.44 #15383, 0.33 #13066, 0.25 #6114), 044lyq (0.33 #13208, 0.02 #24790, 0.01 #17841), 048q6x (0.33 #12771, 0.02 #24353, 0.01 #31303) >> Best rule #3820 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 07z1_q; 03w4sh; 02s_qz; 04vmqg; 01rs5p; >> query: (?x3956, 05lb30) <- award_nominee(?x7842, ?x3956), award_nominee(?x2129, ?x3956), ?x2129 = 0443y3, ?x7842 = 048hf >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #15383 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 026zvx7; 048hf; 06hgym; *> query: (?x3956, 03w4sh) <- award_nominee(?x2129, ?x3956), award_nominee(?x444, ?x3956), nominated_for(?x2129, ?x2078), ?x444 = 01dw4q *> conf = 0.44 ranks of expected_values: 8 EVAL 05dxl5 award_nominee! 03w4sh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 74.000 34.000 0.857 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7251-04w7rn PRED entity: 04w7rn PRED relation: genre PRED expected values: 06www => 96 concepts (80 used for prediction) PRED predicted values (max 10 best out of 119): 05p553 (0.69 #6085, 0.39 #2268, 0.36 #3345), 02kdv5l (0.58 #1075, 0.49 #3223, 0.48 #2504), 01jfsb (0.53 #3233, 0.46 #1085, 0.45 #727), 06n90 (0.33 #490, 0.32 #1086, 0.25 #728), 0hcr (0.32 #260, 0.29 #1214, 0.27 #856), 02l7c8 (0.31 #3833, 0.29 #7054, 0.29 #373), 0lsxr (0.28 #3230, 0.21 #2631, 0.20 #3110), 0hn10 (0.27 #367, 0.06 #1321, 0.06 #4660), 04pbhw (0.22 #532, 0.14 #174, 0.14 #293), 04xvlr (0.21 #358, 0.20 #1, 0.19 #4175) >> Best rule #6085 for best value: >> intensional similarity = 5 >> extensional distance = 841 >> proper extension: 06ybb1; 01mszz; 099bhp; 0gfzfj; >> query: (?x1518, 05p553) <- genre(?x1518, ?x811), genre(?x9701, ?x811), genre(?x6110, ?x811), ?x9701 = 0h1x5f, ?x6110 = 05pdd86 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #8472 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1223 *> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 02xhwm; *> query: (?x1518, ?x53) <- titles(?x1510, ?x1518), genre(?x6726, ?x1510), genre(?x6726, ?x53) *> conf = 0.07 ranks of expected_values: 37 EVAL 04w7rn genre 06www CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 96.000 80.000 0.692 http://example.org/film/film/genre #7250-01gzm2 PRED entity: 01gzm2 PRED relation: type_of_union PRED expected values: 04ztj => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.84 #61, 0.81 #81, 0.80 #17), 01g63y (0.19 #321, 0.14 #142, 0.14 #146), 01bl8s (0.19 #321, 0.01 #71), 0jgjn (0.19 #321) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 354 >> proper extension: 0hwd8; 04n_g; 012gbb; 0b5x23; 0436zq; 0131kb; >> query: (?x1774, 04ztj) <- award(?x1774, ?x68), people(?x3799, ?x1774), award(?x306, ?x68) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gzm2 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.840 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7249-018dcy PRED entity: 018dcy PRED relation: time_zones PRED expected values: 02hcv8 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.62 #29, 0.60 #16, 0.48 #94), 03bdv (0.26 #84, 0.07 #188, 0.06 #474), 02lcqs (0.20 #1028, 0.20 #57, 0.17 #655), 02fqwt (0.20 #1028, 0.19 #131, 0.19 #144), 042g7t (0.20 #1028, 0.17 #1491, 0.17 #1451), 02hczc (0.20 #1028, 0.17 #1491, 0.17 #1451), 05jphn (0.20 #1028, 0.17 #1491, 0.17 #1451), 02llzg (0.12 #407, 0.10 #82, 0.08 #537), 03plfd (0.01 #426, 0.01 #673, 0.01 #1142) >> Best rule #29 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 018lc_; >> query: (?x6196, 02hcv8) <- location(?x413, ?x6196), contains(?x1905, ?x6196), contains(?x279, ?x6196), ?x1905 = 05kr_, contains(?x279, ?x13959), ?x13959 = 018djs >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018dcy time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.615 http://example.org/location/location/time_zones #7248-07ymr5 PRED entity: 07ymr5 PRED relation: award_winner PRED expected values: 05gnf => 91 concepts (52 used for prediction) PRED predicted values (max 10 best out of 615): 092ggq (0.54 #75821, 0.48 #67753, 0.44 #53238), 01vwllw (0.54 #75821, 0.48 #67753, 0.44 #53238), 0pz91 (0.54 #75821, 0.48 #67753, 0.34 #70979), 07ymr5 (0.25 #301, 0.20 #53240, 0.17 #3528), 05gnf (0.25 #1104, 0.17 #4331, 0.11 #7559), 05drr9 (0.25 #960, 0.06 #72594, 0.05 #7415), 03f5spx (0.25 #138, 0.06 #72594, 0.05 #6593), 04vrxh (0.25 #1458, 0.05 #7913, 0.02 #9527), 04mn81 (0.25 #308, 0.05 #6763, 0.02 #8377), 015cbq (0.25 #1442) >> Best rule #75821 for best value: >> intensional similarity = 3 >> extensional distance = 1282 >> proper extension: 014hr0; 0c9l1; >> query: (?x1942, ?x1335) <- award_winner(?x1942, ?x8139), award_nominee(?x1335, ?x1942), place_of_birth(?x8139, ?x3976) >> conf = 0.54 => this is the best rule for 3 predicted values *> Best rule #1104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 05gnf; *> query: (?x1942, 05gnf) <- award_winner(?x1942, ?x8139), award_winner(?x1942, ?x6447), ?x8139 = 09px1w, ?x6447 = 091yn0 *> conf = 0.25 ranks of expected_values: 5 EVAL 07ymr5 award_winner 05gnf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 52.000 0.539 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #7247-01lqf49 PRED entity: 01lqf49 PRED relation: nationality PRED expected values: 09c7w0 => 102 concepts (97 used for prediction) PRED predicted values (max 10 best out of 55): 09c7w0 (0.85 #2410, 0.85 #1406, 0.78 #3813), 02jx1 (0.21 #234, 0.17 #334, 0.14 #3445), 0d060g (0.17 #7, 0.06 #1312, 0.06 #1010), 071vr (0.17 #101, 0.06 #1104, 0.05 #1806), 01ls2 (0.17 #11, 0.04 #412, 0.02 #8418), 07ssc (0.13 #717, 0.12 #316, 0.11 #817), 0345h (0.07 #3843, 0.02 #7347, 0.02 #7747), 05r7t (0.06 #179, 0.04 #479, 0.02 #1081), 03rk0 (0.06 #8765, 0.06 #9065, 0.05 #4159), 06q1r (0.05 #779, 0.04 #879, 0.04 #378) >> Best rule #2410 for best value: >> intensional similarity = 2 >> extensional distance = 282 >> proper extension: 0443c; >> query: (?x8848, 09c7w0) <- people(?x2510, ?x8848), ?x2510 = 0x67 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lqf49 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 97.000 0.852 http://example.org/people/person/nationality #7246-01z9l_ PRED entity: 01z9l_ PRED relation: parent_genre PRED expected values: 01g_bs => 56 concepts (42 used for prediction) PRED predicted values (max 10 best out of 200): 06by7 (0.86 #3491, 0.77 #4490, 0.68 #4822), 0m0jc (0.64 #1655, 0.29 #833, 0.25 #1162), 05r6t (0.62 #2031, 0.36 #4198, 0.31 #1867), 064t9 (0.46 #1824, 0.13 #4485, 0.12 #1988), 03_d0 (0.36 #1658, 0.33 #9, 0.25 #174), 08cyft (0.33 #368, 0.33 #39, 0.29 #866), 0glt670 (0.33 #28, 0.30 #1348, 0.25 #193), 0fd3y (0.33 #8, 0.29 #670, 0.27 #1657), 02x8m (0.33 #14, 0.27 #2823, 0.25 #179), 0190y4 (0.33 #113, 0.25 #278, 0.21 #2307) >> Best rule #3491 for best value: >> intensional similarity = 10 >> extensional distance = 47 >> proper extension: 01gbcf; 018ysx; >> query: (?x13883, 06by7) <- parent_genre(?x13883, ?x3915), artists(?x3915, ?x7865), artists(?x3915, ?x7018), artists(?x3915, ?x5760), artists(?x3915, ?x475), parent_genre(?x7279, ?x13883), ?x7865 = 02k5sc, ?x475 = 01pfr3, ?x7018 = 01sxd1, award_nominee(?x1573, ?x5760) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1156 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 0xv2x; *> query: (?x13883, ?x497) <- artists(?x13883, ?x8636), artists(?x7280, ?x8636), artists(?x497, ?x8636), artist(?x12666, ?x8636), ?x7280 = 0283d, parent_genre(?x13883, ?x7220), ?x7220 = 0mmp3, artist(?x12666, ?x12357), ?x12357 = 012x1l *> conf = 0.10 ranks of expected_values: 59 EVAL 01z9l_ parent_genre 01g_bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 56.000 42.000 0.857 http://example.org/music/genre/parent_genre #7245-0l2nd PRED entity: 0l2nd PRED relation: source PRED expected values: 0jbk9 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #10, 0.93 #7, 0.93 #6) >> Best rule #10 for best value: >> intensional similarity = 5 >> extensional distance = 171 >> proper extension: 0f4y_; 0nvd8; 0n5_g; 0k3ll; 0mws3; 0n5y4; 0cc1v; 043z0; 09dfcj; 0mlzk; ... >> query: (?x13522, 0jbk9) <- adjoins(?x5892, ?x13522), adjoins(?x13522, ?x10702), second_level_divisions(?x94, ?x13522), time_zones(?x13522, ?x2950), contains(?x5892, ?x5893) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l2nd source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.936 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #7244-03f02ct PRED entity: 03f02ct PRED relation: languages PRED expected values: 03k50 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 14): 03k50 (0.50 #82, 0.43 #4, 0.35 #121), 02h40lc (0.35 #470, 0.30 #665, 0.29 #626), 0688f (0.25 #107, 0.14 #29, 0.12 #146), 09s02 (0.14 #36, 0.12 #114, 0.07 #309), 07c9s (0.13 #364, 0.10 #286, 0.09 #403), 02hxcvy (0.07 #1992, 0.07 #1913, 0.05 #260), 0999q (0.06 #374, 0.06 #296, 0.05 #413), 0121sr (0.06 #150, 0.04 #189, 0.03 #228), 055qm (0.04 #180, 0.04 #375, 0.04 #336), 01c7y (0.04 #343, 0.04 #265, 0.03 #382) >> Best rule #82 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 047jhq; >> query: (?x10570, 03k50) <- profession(?x10570, ?x524), award_winner(?x1937, ?x10570), people(?x7838, ?x10570), ?x7838 = 02sch9 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f02ct languages 03k50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.500 http://example.org/people/person/languages #7243-0pmhf PRED entity: 0pmhf PRED relation: participant PRED expected values: 0c1pj => 136 concepts (109 used for prediction) PRED predicted values (max 10 best out of 299): 0237fw (0.25 #161, 0.07 #7208, 0.03 #25140), 05dbf (0.25 #148, 0.03 #7195, 0.02 #18724), 0chw_ (0.25 #549), 0c1pj (0.25 #39), 05cljf (0.25 #12), 0bl2g (0.14 #1306, 0.02 #8992, 0.01 #7711), 014zcr (0.11 #8986, 0.06 #8345, 0.05 #5145), 0c6qh (0.09 #9134, 0.06 #7213, 0.04 #4653), 07r1h (0.07 #9381, 0.04 #14507, 0.04 #25392), 016_mj (0.07 #3320, 0.02 #5242, 0.01 #5882) >> Best rule #161 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 06dv3; 0bgrsl; >> query: (?x2596, 0237fw) <- award_nominee(?x5461, ?x2596), produced_by(?x3532, ?x2596), ?x5461 = 014v6f >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #39 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 06dv3; 0bgrsl; *> query: (?x2596, 0c1pj) <- award_nominee(?x5461, ?x2596), produced_by(?x3532, ?x2596), ?x5461 = 014v6f *> conf = 0.25 ranks of expected_values: 4 EVAL 0pmhf participant 0c1pj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 136.000 109.000 0.250 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #7242-089fss PRED entity: 089fss PRED relation: film_crew_role! PRED expected values: 05sxzwc 04n52p6 08052t3 03nm_fh 02nt3d 0bs8ndx 04jpg2p 04180vy => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1203): 024l2y (0.78 #9813, 0.71 #7405, 0.71 #6201), 0fdv3 (0.78 #9832, 0.71 #7424, 0.71 #6220), 0dp7wt (0.78 #10571, 0.71 #8163, 0.71 #6959), 02b61v (0.78 #10338, 0.71 #7930, 0.71 #6726), 0pc62 (0.78 #9696, 0.71 #7288, 0.71 #6084), 0b2km_ (0.78 #10730, 0.71 #8322, 0.71 #7118), 0bmch_x (0.78 #10217, 0.71 #7809, 0.71 #6605), 01hqk (0.78 #10140, 0.71 #7732, 0.71 #6528), 03whyr (0.78 #10700, 0.71 #8292, 0.71 #7088), 08c6k9 (0.78 #10666, 0.71 #8258, 0.71 #7054) >> Best rule #9813 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02r96rf; >> query: (?x1078, 024l2y) <- film_crew_role(?x4591, ?x1078), film_crew_role(?x2081, ?x1078), ?x2081 = 01j8wk, prequel(?x4194, ?x4591), music(?x4591, ?x3410) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #5828 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 01pvkk; 0d2b38; *> query: (?x1078, 04jpg2p) <- film_crew_role(?x10268, ?x1078), profession(?x199, ?x1078), country(?x10268, ?x94), nominated_for(?x7526, ?x10268), ?x7526 = 03rwz3 *> conf = 0.71 ranks of expected_values: 22, 26, 68, 141, 196, 232, 315, 609 EVAL 089fss film_crew_role! 04180vy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 43.000 43.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089fss film_crew_role! 04jpg2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 43.000 43.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089fss film_crew_role! 0bs8ndx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 43.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089fss film_crew_role! 02nt3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 43.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089fss film_crew_role! 03nm_fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 43.000 43.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089fss film_crew_role! 08052t3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 43.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089fss film_crew_role! 04n52p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 43.000 43.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 089fss film_crew_role! 05sxzwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 43.000 43.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7241-0261m PRED entity: 0261m PRED relation: contains PRED expected values: 06s0l => 174 concepts (46 used for prediction) PRED predicted values (max 10 best out of 2388): 0j3b (0.68 #79326, 0.66 #108711, 0.53 #117525), 07ww5 (0.68 #79326, 0.66 #108711, 0.52 #41128), 0164b (0.50 #4482, 0.33 #1545, 0.29 #22107), 01p8s (0.50 #4390, 0.33 #1453, 0.29 #22015), 05c74 (0.50 #4005, 0.33 #1068, 0.29 #21630), 0345_ (0.50 #3569, 0.33 #632, 0.29 #21194), 03h2c (0.50 #3400, 0.33 #463, 0.29 #21025), 0b90_r (0.50 #2945, 0.33 #8, 0.29 #20570), 0165v (0.50 #4404, 0.33 #1467, 0.29 #22029), 016wzw (0.50 #3273, 0.33 #336, 0.29 #20898) >> Best rule #79326 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 02qkt; 073q1; 09b69; 02613; >> query: (?x9729, ?x1317) <- contains(?x9729, ?x11553), form_of_government(?x11553, ?x1926), adjoins(?x11553, ?x1317), locations(?x1777, ?x9729) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #3899 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 07c5l; *> query: (?x9729, 06s0l) <- contains(?x9729, ?x6559), contains(?x9729, ?x1957), taxonomy(?x9729, ?x939), ?x6559 = 05r7t, countries_spoken_in(?x254, ?x1957) *> conf = 0.25 ranks of expected_values: 260 EVAL 0261m contains 06s0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 174.000 46.000 0.681 http://example.org/location/location/contains #7240-025hwq PRED entity: 025hwq PRED relation: award_nominee! PRED expected values: 02mc79 => 83 concepts (41 used for prediction) PRED predicted values (max 10 best out of 823): 02_l96 (0.81 #32643, 0.80 #86273, 0.80 #65287), 016tt2 (0.44 #111, 0.22 #2443, 0.18 #72281), 05qd_ (0.27 #9505, 0.24 #7173, 0.23 #14169), 086k8 (0.23 #9388, 0.22 #62, 0.20 #14052), 01gb54 (0.22 #1082, 0.18 #72281, 0.18 #5745), 03rwz3 (0.22 #4010, 0.18 #72281, 0.16 #93268), 025hwq (0.22 #4089, 0.18 #72281, 0.16 #93268), 0cv9fc (0.22 #2249, 0.18 #72281, 0.16 #93268), 06jz0 (0.22 #4474, 0.16 #93268, 0.10 #2332), 03ktjq (0.18 #72281, 0.16 #93268, 0.12 #8351) >> Best rule #32643 for best value: >> intensional similarity = 4 >> extensional distance = 143 >> proper extension: 05bxwh; 0181hw; 0qdwr; 016ghw; >> query: (?x7935, ?x1561) <- award_nominee(?x7935, ?x1561), award_nominee(?x7935, ?x541), production_companies(?x80, ?x541), film(?x541, ?x186) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #93268 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1239 *> proper extension: 07s6tbm; 01d1yr; 01mkn_d; *> query: (?x7935, ?x1689) <- award_nominee(?x1561, ?x7935), award_winner(?x4709, ?x7935), award_nominee(?x1689, ?x1561) *> conf = 0.16 ranks of expected_values: 31 EVAL 025hwq award_nominee! 02mc79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 83.000 41.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7239-050r1z PRED entity: 050r1z PRED relation: titles! PRED expected values: 07s9rl0 => 61 concepts (24 used for prediction) PRED predicted values (max 10 best out of 120): 07s9rl0 (0.45 #104, 0.33 #723, 0.33 #1), 02l7c8 (0.25 #206, 0.22 #103, 0.20 #721), 04xvh5 (0.25 #206, 0.22 #103, 0.20 #721), 082gq (0.25 #206, 0.22 #103, 0.20 #721), 060__y (0.25 #206, 0.22 #103, 0.20 #721), 01z4y (0.25 #2192, 0.20 #549, 0.17 #1780), 07ssc (0.17 #112, 0.12 #9, 0.12 #215), 017fp (0.13 #126, 0.08 #333, 0.08 #2077), 03mqtr (0.12 #45, 0.10 #251, 0.10 #148), 06l3bl (0.12 #54, 0.10 #260, 0.04 #878) >> Best rule #104 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 0c0nhgv; 03hj3b3; 03r0g9; 0p9tm; 0h0wd9; >> query: (?x586, 07s9rl0) <- language(?x586, ?x254), nominated_for(?x2375, ?x586), genre(?x586, ?x53), ?x2375 = 04kxsb >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050r1z titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 24.000 0.452 http://example.org/media_common/netflix_genre/titles #7238-0gyv0b4 PRED entity: 0gyv0b4 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.88 #82, 0.88 #77, 0.88 #57), 02nxhr (0.20 #2, 0.07 #53, 0.07 #48), 0735l (0.12 #11), 07z4p (0.05 #26, 0.03 #332, 0.03 #367), 07c52 (0.03 #365, 0.03 #330, 0.03 #355) >> Best rule #82 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 0p9lw; 04hwbq; 03n785; 05c26ss; 0432_5; 01kf5lf; 031ldd; 01k0vq; 0cmf0m0; 01f7jt; >> query: (?x10446, 029j_) <- nominated_for(?x10445, ?x10446), prequel(?x10446, ?x4375), award_winner(?x2915, ?x10445), genre(?x10446, ?x53) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gyv0b4 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #7237-0yz30 PRED entity: 0yz30 PRED relation: place! PRED expected values: 0yz30 => 94 concepts (47 used for prediction) PRED predicted values (max 10 best out of 16): 0n25q (0.07 #2062, 0.06 #3608), 0z1l8 (0.04 #514, 0.03 #1030), 0yw93 (0.04 #485, 0.03 #1001), 0yzyn (0.04 #340, 0.03 #856), 0z1vw (0.04 #331, 0.03 #847), 0z20d (0.04 #203, 0.03 #719), 029cr (0.04 #48, 0.03 #564), 07l5z (0.04 #313), 01smm (0.04 #161), 013jz2 (0.04 #33) >> Best rule #2062 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 0f04v; >> query: (?x14177, ?x14240) <- county_seat(?x14240, ?x14177), source(?x14177, ?x958), time_zones(?x14177, ?x2674) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0yz30 place! 0yz30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 47.000 0.069 http://example.org/location/hud_county_place/place #7236-031sn PRED entity: 031sn PRED relation: source PRED expected values: 0jbk9 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #55, 0.93 #35, 0.93 #51) >> Best rule #55 for best value: >> intensional similarity = 5 >> extensional distance = 257 >> proper extension: 0xqf3; >> query: (?x13803, 0jbk9) <- county(?x13803, ?x7497), contains(?x94, ?x13803), contains(?x94, ?x10563), jurisdiction_of_office(?x652, ?x94), origin(?x1974, ?x10563) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031sn source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.931 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #7235-03qzj4 PRED entity: 03qzj4 PRED relation: contains! PRED expected values: 02xry => 55 concepts (24 used for prediction) PRED predicted values (max 10 best out of 247): 02xry (0.90 #7171, 0.83 #17941, 0.83 #15249), 09c7w0 (0.90 #7171, 0.73 #17047, 0.71 #21528), 0n1rj (0.29 #1793, 0.22 #3586, 0.16 #4482), 01n7q (0.28 #17122, 0.27 #9943, 0.26 #11738), 059rby (0.19 #17064, 0.16 #5399, 0.15 #916), 07ssc (0.18 #19766, 0.17 #20662, 0.02 #18870), 02jx1 (0.17 #19821, 0.17 #20717), 06pvr (0.17 #1062, 0.10 #2855, 0.08 #3752), 0kpzy (0.15 #1265, 0.09 #3058, 0.06 #3955), 05k7sb (0.14 #17177, 0.11 #8203, 0.10 #1029) >> Best rule #7171 for best value: >> intensional similarity = 5 >> extensional distance = 136 >> proper extension: 0lbp_; >> query: (?x14113, ?x2623) <- contains(?x13776, ?x14113), second_level_divisions(?x94, ?x13776), county_seat(?x13776, ?x6084), location(?x5153, ?x6084), contains(?x2623, ?x6084) >> conf = 0.90 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 03qzj4 contains! 02xry CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 24.000 0.896 http://example.org/location/location/contains #7234-021j72 PRED entity: 021j72 PRED relation: award PRED expected values: 03rbj2 => 157 concepts (128 used for prediction) PRED predicted values (max 10 best out of 288): 0b6k___ (0.79 #9324, 0.76 #9323, 0.73 #40926), 03rbj2 (0.44 #9141, 0.38 #6301, 0.33 #224), 03r8v_ (0.31 #6419, 0.21 #9259, 0.17 #342), 0gs9p (0.26 #12240, 0.20 #17103, 0.16 #26422), 040njc (0.25 #12168, 0.16 #26350, 0.16 #17031), 09sb52 (0.23 #8552, 0.21 #40560, 0.20 #40967), 0bdw6t (0.20 #1325, 0.18 #2946, 0.15 #3757), 0f4x7 (0.16 #10165, 0.15 #6513, 0.15 #7324), 0gq9h (0.15 #12238, 0.15 #26420, 0.15 #30066), 019f4v (0.15 #12227, 0.15 #26409, 0.15 #17090) >> Best rule #9324 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 099ks0; 02x2097; >> query: (?x10750, ?x1937) <- award_winner(?x1937, ?x10750), nominated_for(?x1937, ?x657), ?x657 = 04jwjq >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #9141 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 099ks0; 02x2097; *> query: (?x10750, 03rbj2) <- award_winner(?x1937, ?x10750), nominated_for(?x1937, ?x657), ?x657 = 04jwjq *> conf = 0.44 ranks of expected_values: 2 EVAL 021j72 award 03rbj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 157.000 128.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7233-04sqj PRED entity: 04sqj PRED relation: adjoins PRED expected values: 01gfhk => 245 concepts (133 used for prediction) PRED predicted values (max 10 best out of 466): 01gfhk (0.83 #29379, 0.83 #82757, 0.82 #99767), 04sqj (0.33 #372, 0.02 #55279, 0.02 #57599), 0b90_r (0.30 #14688, 0.06 #13919, 0.03 #63420), 06bnz (0.18 #12457, 0.14 #52673, 0.12 #58861), 0f8l9c (0.18 #12408, 0.11 #13953, 0.08 #76602), 01mjq (0.18 #12454, 0.11 #13999, 0.05 #52670), 07t21 (0.18 #12449, 0.07 #54987, 0.06 #13994), 0h7x (0.18 #12446, 0.05 #54984, 0.05 #76640), 0345h (0.17 #13980, 0.12 #12435, 0.07 #54973), 05fjf (0.14 #16534, 0.12 #21174, 0.11 #14214) >> Best rule #29379 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 04xn_; 06k5_; >> query: (?x8181, ?x13910) <- place_of_birth(?x2442, ?x8181), location(?x883, ?x8181), adjoins(?x13910, ?x8181), administrative_parent(?x8181, ?x151) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sqj adjoins 01gfhk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 245.000 133.000 0.827 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #7232-01vfqh PRED entity: 01vfqh PRED relation: nominated_for! PRED expected values: 0gs96 0gqy2 => 106 concepts (95 used for prediction) PRED predicted values (max 10 best out of 201): 02z13jg (0.68 #14183, 0.68 #2643, 0.67 #11778), 02wkmx (0.68 #14183, 0.68 #2643, 0.67 #11778), 02wypbh (0.68 #14183, 0.68 #2643, 0.67 #11778), 0gq9h (0.57 #4628, 0.56 #4869, 0.56 #1744), 0gs9p (0.49 #1746, 0.45 #4871, 0.45 #4630), 019f4v (0.48 #4619, 0.48 #4860, 0.46 #1735), 0gr0m (0.47 #1741, 0.38 #4625, 0.38 #4866), 0k611 (0.46 #1755, 0.41 #4639, 0.40 #4880), 0gs96 (0.42 #4897, 0.42 #4656, 0.40 #5858), 04dn09n (0.41 #1716, 0.36 #8169, 0.33 #1475) >> Best rule #14183 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 0h3mh3q; >> query: (?x1331, ?x372) <- award_winner(?x1331, ?x185), award(?x1331, ?x372), award(?x1365, ?x372) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #4897 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 185 *> proper extension: 05y0cr; *> query: (?x1331, 0gs96) <- nominated_for(?x484, ?x1331), ?x484 = 0gq_v, genre(?x1331, ?x162), award(?x1331, ?x372) *> conf = 0.42 ranks of expected_values: 9, 14 EVAL 01vfqh nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 106.000 95.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01vfqh nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 106.000 95.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7231-07tp2 PRED entity: 07tp2 PRED relation: location! PRED expected values: 03h8_g => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 843): 032r1 (0.07 #7354, 0.06 #12392, 0.05 #19949), 099d4 (0.07 #7404, 0.04 #12442, 0.04 #14961), 06g4_ (0.06 #4718), 0465_ (0.05 #13892, 0.05 #18930, 0.05 #16411), 01w02sy (0.05 #13191, 0.05 #18229, 0.05 #5634), 03rl84 (0.05 #12957, 0.05 #17995, 0.05 #20514), 0227tr (0.05 #5518, 0.04 #33227, 0.04 #8037), 0prfz (0.05 #5087, 0.04 #10125, 0.04 #12644), 03hzl42 (0.05 #5937, 0.04 #10975, 0.04 #13494), 01ps2h8 (0.05 #6110, 0.04 #11148, 0.03 #33819) >> Best rule #7354 for best value: >> intensional similarity = 2 >> extensional distance = 39 >> proper extension: 034cm; >> query: (?x9251, 032r1) <- medal(?x9251, ?x1242), service_location(?x9968, ?x9251) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #34971 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 88 *> proper extension: 020skc; 01d88c; 0c8tk; 06y57; 09c6w; 027wvb; 04vmp; 029kpy; 0cvw9; 0chgzm; ... *> query: (?x9251, 03h8_g) <- contains(?x2467, ?x9251), service_location(?x9968, ?x9251) *> conf = 0.03 ranks of expected_values: 21 EVAL 07tp2 location! 03h8_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 88.000 88.000 0.073 http://example.org/people/person/places_lived./people/place_lived/location #7230-03cffvv PRED entity: 03cffvv PRED relation: language PRED expected values: 02h40lc => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 27): 02h40lc (0.96 #230, 0.95 #2578, 0.93 #1201), 04306rv (0.11 #233, 0.08 #405, 0.08 #2581), 03_9r (0.09 #124, 0.05 #353, 0.05 #2586), 06nm1 (0.09 #754, 0.09 #411, 0.08 #2587), 02bjrlw (0.07 #229, 0.06 #2577, 0.06 #401), 06b_j (0.06 #250, 0.05 #765, 0.05 #1450), 03hkp (0.05 #186, 0.02 #243, 0.01 #300), 0880p (0.05 #215), 0jzc (0.03 #248, 0.02 #2024, 0.02 #2596), 0653m (0.03 #1211, 0.03 #2016, 0.03 #1554) >> Best rule #230 for best value: >> intensional similarity = 3 >> extensional distance = 192 >> proper extension: 0cnztc4; >> query: (?x11610, 02h40lc) <- language(?x11610, ?x5607), genre(?x11610, ?x1509), ?x1509 = 060__y >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cffvv language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.959 http://example.org/film/film/language #7229-09p4w8 PRED entity: 09p4w8 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #11, 0.82 #80, 0.81 #112), 07c52 (0.04 #13, 0.03 #212, 0.03 #170), 02nxhr (0.04 #124, 0.04 #41, 0.04 #113), 07z4p (0.02 #79, 0.02 #328, 0.02 #371) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 191 >> proper extension: 02pcq92; >> query: (?x4853, 029j_) <- film(?x382, ?x4853), genre(?x4853, ?x604), titles(?x600, ?x4853), ?x604 = 0lsxr >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09p4w8 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.829 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #7228-065_cjc PRED entity: 065_cjc PRED relation: film! PRED expected values: 016tw3 => 100 concepts (91 used for prediction) PRED predicted values (max 10 best out of 58): 016tw3 (0.60 #11, 0.29 #86, 0.18 #5518), 02j_j0 (0.44 #894, 0.44 #742, 0.44 #819), 054lpb6 (0.44 #894, 0.44 #742, 0.44 #819), 017s11 (0.43 #78, 0.25 #596, 0.20 #971), 016tt2 (0.33 #227, 0.33 #153, 0.26 #671), 086k8 (0.22 #225, 0.21 #299, 0.21 #5509), 03xq0f (0.22 #228, 0.17 #450, 0.16 #598), 054g1r (0.20 #34, 0.10 #405, 0.07 #2419), 05qd_ (0.15 #5516, 0.14 #306, 0.14 #4477), 01gb54 (0.14 #103, 0.11 #251, 0.11 #177) >> Best rule #11 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 05k2xy; 09r94m; 0ds2l81; >> query: (?x6752, 016tw3) <- film_crew_role(?x6752, ?x3197), film_release_region(?x6752, ?x94), production_companies(?x6752, ?x6554), ?x3197 = 02ynfr, ?x6554 = 02j_j0, film(?x436, ?x6752) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065_cjc film! 016tw3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 91.000 0.600 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7227-014635 PRED entity: 014635 PRED relation: location PRED expected values: 0f1sm => 127 concepts (114 used for prediction) PRED predicted values (max 10 best out of 280): 07_f2 (0.25 #347, 0.11 #2741, 0.03 #12320), 04vmp (0.25 #349, 0.11 #2743, 0.03 #12322), 06pr6 (0.25 #339, 0.04 #9120, 0.02 #21095), 030qb3t (0.18 #67956, 0.16 #43193, 0.16 #71947), 059rby (0.18 #43127, 0.15 #10393, 0.14 #50316), 04jpl (0.18 #63100, 0.09 #8000, 0.08 #19975), 02_286 (0.17 #8020, 0.17 #75095, 0.16 #19995), 0rh6k (0.17 #7987, 0.15 #10381, 0.14 #11977), 01n7q (0.17 #43173, 0.13 #50362, 0.12 #51160), 01xd9 (0.14 #882, 0.12 #1680, 0.08 #3276) >> Best rule #347 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0h25; >> query: (?x3969, 07_f2) <- gender(?x3969, ?x231), influenced_by(?x8433, ?x3969), location(?x3969, ?x1025), ?x8433 = 06bng >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #5245 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0320jz; 021yw7; 015d3h; 0hnjt; 03_l8m; 025b5y; 02l6dy; 0bw87; 03d_zl4; 027j79k; ... *> query: (?x3969, 0f1sm) <- gender(?x3969, ?x231), profession(?x3969, ?x353), location(?x3969, ?x1755), ?x1755 = 01x73 *> conf = 0.07 ranks of expected_values: 43 EVAL 014635 location 0f1sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 127.000 114.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #7226-0h3y PRED entity: 0h3y PRED relation: medal PRED expected values: 02lq67 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.82 #48, 0.80 #16, 0.79 #11) >> Best rule #48 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 05v8c; >> query: (?x291, 02lq67) <- medal(?x291, ?x1242), country(?x3015, ?x291), ?x3015 = 071t0 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h3y medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.821 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #7225-0167v PRED entity: 0167v PRED relation: member_states! PRED expected values: 085h1 => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 13): 085h1 (0.76 #3, 0.68 #96, 0.67 #88), 018cqq (0.29 #2, 0.15 #56, 0.15 #64), 059dn (0.17 #4, 0.12 #97, 0.11 #102), 02jxk (0.15 #1, 0.14 #18, 0.13 #94), 0_2v (0.09 #5, 0.07 #147, 0.06 #98), 07t65 (0.09 #5, 0.07 #147, 0.06 #98), 02vk52z (0.09 #5, 0.07 #147, 0.06 #98), 041288 (0.07 #147), 0b6css (0.07 #147), 0gkjy (0.07 #147) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 07bxhl; >> query: (?x5445, 085h1) <- organization(?x5445, ?x3750), ?x3750 = 0_2v, adjustment_currency(?x5445, ?x170) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0167v member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 43.000 0.756 http://example.org/user/ktrueman/default_domain/international_organization/member_states #7224-013y1f PRED entity: 013y1f PRED relation: role! PRED expected values: 016622 => 78 concepts (56 used for prediction) PRED predicted values (max 10 best out of 66): 0l14md (0.88 #2711, 0.86 #245, 0.85 #308), 07kc_ (0.86 #245, 0.85 #61, 0.85 #308), 07xzm (0.86 #245, 0.85 #308, 0.84 #809), 02pprs (0.86 #245, 0.85 #308, 0.84 #809), 03m5k (0.86 #245, 0.85 #308, 0.84 #809), 0cfdd (0.86 #245, 0.85 #308, 0.84 #809), 0bm02 (0.86 #245, 0.85 #308, 0.84 #809), 0dwvl (0.86 #245, 0.85 #308, 0.84 #809), 01bns_ (0.86 #245, 0.85 #308, 0.84 #809), 02snj9 (0.85 #61, 0.75 #998, 0.74 #368) >> Best rule #2711 for best value: >> intensional similarity = 11 >> extensional distance = 23 >> proper extension: 07kc_; 0dq630k; 02w4b; 02dlh2; 0bmnm; 0l14v3; >> query: (?x1495, 0l14md) <- role(?x7549, ?x1495), role(?x3399, ?x1495), role(?x3703, ?x1495), role(?x2206, ?x1495), role(?x1495, ?x74), group(?x1495, ?x997), role(?x2566, ?x3703), artist(?x2039, ?x7549), ?x2206 = 07gql, student(?x1151, ?x7549), type_of_union(?x3399, ?x566) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1033 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 5 *> proper extension: 06ncr; *> query: (?x1495, 016622) <- role(?x7121, ?x1495), role(?x3168, ?x1495), role(?x654, ?x1495), role(?x75, ?x1495), role(?x1495, ?x214), group(?x1495, ?x997), role(?x642, ?x1495), artists(?x302, ?x7121), role(?x2309, ?x1495), ?x3168 = 016ntp, artist(?x2149, ?x654), ?x214 = 02pprs *> conf = 0.71 ranks of expected_values: 29 EVAL 013y1f role! 016622 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 78.000 56.000 0.880 http://example.org/music/performance_role/track_performances./music/track_contribution/role #7223-06_bq1 PRED entity: 06_bq1 PRED relation: award_winner PRED expected values: 02s2ft => 137 concepts (93 used for prediction) PRED predicted values (max 10 best out of 832): 06_bq1 (0.44 #124076, 0.43 #114408, 0.28 #148243), 02s2ft (0.44 #124076, 0.43 #114408, 0.28 #148243), 02xv8m (0.43 #114408, 0.28 #148243, 0.28 #145021), 03m8lq (0.43 #114408, 0.28 #148243, 0.28 #145021), 04bdxl (0.43 #114408, 0.28 #148243, 0.28 #145021), 049k07 (0.43 #114408, 0.28 #148243, 0.28 #145021), 02114t (0.43 #114408, 0.28 #148243, 0.28 #145021), 026_w57 (0.43 #114408, 0.28 #148243, 0.28 #145021), 01713c (0.43 #114408, 0.16 #140186, 0.15 #141798), 0170pk (0.28 #148243, 0.28 #145021, 0.16 #140186) >> Best rule #124076 for best value: >> intensional similarity = 3 >> extensional distance = 808 >> proper extension: 0kr_t; 02x0bdb; 05zjx; 07sbk; 03y3dk; 027d5g5; 01fh0q; 04n2vgk; 01kp_1t; 05g7q; ... >> query: (?x7046, ?x92) <- award_winner(?x2122, ?x7046), religion(?x2122, ?x1985), award_winner(?x92, ?x2122) >> conf = 0.44 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 06_bq1 award_winner 02s2ft CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 93.000 0.440 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #7222-04sylm PRED entity: 04sylm PRED relation: educational_institution! PRED expected values: 04sylm => 143 concepts (104 used for prediction) PRED predicted values (max 10 best out of 358): 04sylm (0.36 #17251, 0.24 #21025, 0.21 #36131), 03qdm (0.36 #17251, 0.24 #21025, 0.21 #36131), 031n5b (0.36 #17251, 0.21 #36131, 0.19 #25881), 03fgm (0.36 #17251, 0.19 #25881, 0.18 #40448), 017z88 (0.19 #25881, 0.18 #40448, 0.05 #1151), 021q2j (0.05 #1386, 0.05 #847, 0.04 #1925), 03bmmc (0.05 #1260, 0.05 #721, 0.04 #1799), 04ftdq (0.05 #1382, 0.05 #843, 0.04 #1921), 09k9d0 (0.05 #1535, 0.05 #996, 0.04 #2074), 01vg13 (0.05 #1283, 0.05 #744, 0.04 #1822) >> Best rule #17251 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 01b1mj; 0yjf0; 022xml; 0dplh; 01jq34; 07w4j; 01k7xz; 0cchk3; 01wdj_; 02rff2; ... >> query: (?x2767, ?x10869) <- student(?x2767, ?x7906), award(?x7906, ?x537), student(?x10869, ?x7906), instrumentalists(?x316, ?x7906) >> conf = 0.36 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 04sylm educational_institution! 04sylm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 104.000 0.359 http://example.org/education/educational_institution_campus/educational_institution #7221-0fvvz PRED entity: 0fvvz PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 217 concepts (217 used for prediction) PRED predicted values (max 10 best out of 21): 0pqc5 (0.73 #168, 0.69 #283, 0.64 #260), 060c4 (0.29 #1730, 0.26 #2007, 0.24 #2053), 060bp (0.26 #2005, 0.26 #1728, 0.22 #2051), 0f6c3 (0.21 #2380, 0.19 #148, 0.18 #769), 09n5b9 (0.20 #2384, 0.19 #1440, 0.16 #3051), 0fkvn (0.18 #2376, 0.17 #2974, 0.15 #3043), 01q24l (0.12 #591, 0.11 #1189, 0.10 #1143), 0fkzq (0.07 #2389, 0.05 #3056, 0.04 #2987), 01zq91 (0.06 #2019, 0.06 #1650, 0.04 #2479), 01t7n9 (0.05 #780, 0.05 #366, 0.05 #159) >> Best rule #168 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0fw2y; 0k9p4; 0q_0z; >> query: (?x1248, 0pqc5) <- origin(?x10025, ?x1248), source(?x1248, ?x958), featured_film_locations(?x3500, ?x1248), state(?x1248, ?x3908) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fvvz jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 217.000 217.000 0.727 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #7220-0fg04 PRED entity: 0fg04 PRED relation: genre PRED expected values: 0lsxr 01jfsb => 105 concepts (73 used for prediction) PRED predicted values (max 10 best out of 91): 07s9rl0 (0.83 #6878, 0.81 #581, 0.77 #7576), 05p553 (0.63 #7464, 0.37 #1170, 0.35 #7348), 01jfsb (0.62 #361, 0.62 #944, 0.62 #709), 03npn (0.54 #931, 0.51 #3851, 0.50 #5131), 03k9fj (0.51 #1293, 0.50 #1761, 0.50 #12), 02kdv5l (0.50 #3, 0.40 #1868, 0.40 #1985), 02l7c8 (0.40 #597, 0.34 #2466, 0.33 #17), 0lsxr (0.39 #705, 0.37 #357, 0.36 #940), 04xvlr (0.34 #582, 0.19 #4085, 0.19 #815), 06n90 (0.33 #14, 0.24 #2113, 0.24 #1295) >> Best rule #6878 for best value: >> intensional similarity = 4 >> extensional distance = 827 >> proper extension: 03s5lz; 056xkh; >> query: (?x708, 07s9rl0) <- film_crew_role(?x708, ?x137), genre(?x708, ?x600), genre(?x2116, ?x600), ?x2116 = 02c638 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #361 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 120 *> proper extension: 0dq626; 0fh694; 09p0ct; 07qg8v; 07nt8p; 03177r; 0ctb4g; 0gh65c5; 02vr3gz; 047fjjr; ... *> query: (?x708, 01jfsb) <- film_crew_role(?x708, ?x137), genre(?x708, ?x600), ?x600 = 02n4kr, film(?x1469, ?x708) *> conf = 0.62 ranks of expected_values: 3, 8 EVAL 0fg04 genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 73.000 0.831 http://example.org/film/film/genre EVAL 0fg04 genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 105.000 73.000 0.831 http://example.org/film/film/genre #7219-017b2p PRED entity: 017b2p PRED relation: gender PRED expected values: 02zsn => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #25, 0.84 #71, 0.81 #39), 02zsn (0.57 #28, 0.57 #24, 0.55 #56) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 04r7jc; 025tdwc; 02xs0q; 01v6480; 01x2tm8; 01pbwwl; 0c408_; >> query: (?x8947, 05zppz) <- profession(?x8947, ?x6476), nationality(?x8947, ?x252), ?x6476 = 025352 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #28 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 85 *> proper extension: 089pg7; *> query: (?x8947, 02zsn) <- artist(?x4079, ?x8947), artists(?x5876, ?x8947), ?x5876 = 0ggx5q *> conf = 0.57 ranks of expected_values: 2 EVAL 017b2p gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 139.000 139.000 0.864 http://example.org/people/person/gender #7218-0j5g9 PRED entity: 0j5g9 PRED relation: place_of_birth! PRED expected values: 03y1mlp => 182 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1329): 066yfh (0.20 #2440, 0.09 #7667, 0.09 #5054), 02g40r (0.20 #2182, 0.09 #7409, 0.09 #4796), 02q42j_ (0.20 #1229, 0.09 #6456, 0.09 #3843), 04_1nk (0.20 #1135, 0.09 #6362, 0.09 #3749), 02pq9yv (0.20 #679, 0.09 #5906, 0.09 #3293), 026g801 (0.20 #1061, 0.08 #8901, 0.06 #16740), 0135xb (0.09 #6721, 0.07 #14560), 0288fyj (0.09 #3041, 0.03 #26559, 0.02 #94517), 029b9k (0.09 #7082), 0171cm (0.08 #10929, 0.03 #44903) >> Best rule #2440 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 07ssc; 02jx1; 06q1r; >> query: (?x4221, 066yfh) <- time_zones(?x4221, ?x5327), state_province_region(?x4220, ?x4221), nationality(?x450, ?x4221) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0j5g9 place_of_birth! 03y1mlp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 182.000 105.000 0.200 http://example.org/people/person/place_of_birth #7217-0c_v2 PRED entity: 0c_v2 PRED relation: language! PRED expected values: 0dln8jk => 49 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1864): 0gl02yg (0.96 #5185, 0.77 #6913, 0.67 #14785), 05g8pg (0.96 #5185, 0.77 #6913, 0.67 #14348), 08j7lh (0.96 #5185, 0.77 #6913, 0.67 #15293), 0mb8c (0.96 #5185, 0.77 #6913, 0.67 #14690), 05znbh7 (0.96 #5185, 0.77 #6913, 0.67 #14869), 012mrr (0.96 #5185, 0.77 #6913, 0.60 #10825), 0cq806 (0.96 #5185, 0.77 #6913, 0.60 #11793), 034xyf (0.96 #5185, 0.77 #6913, 0.60 #11752), 08nvyr (0.96 #5185, 0.77 #6913, 0.60 #11106), 061681 (0.96 #5185, 0.77 #6913, 0.60 #10469) >> Best rule #5185 for best value: >> intensional similarity = 19 >> extensional distance = 1 >> proper extension: 02h40lc; >> query: (?x4605, ?x1185) <- service_language(?x11303, ?x4605), language(?x7713, ?x4605), language(?x7502, ?x4605), language(?x6069, ?x4605), language(?x4920, ?x4605), language(?x3076, ?x4605), language(?x1745, ?x4605), ?x7713 = 0fxmbn, languages_spoken(?x10322, ?x4605), languages_spoken(?x9979, ?x4605), ?x6069 = 0bs4r, ?x10322 = 078vc, ?x7502 = 0233bn, ?x4920 = 033fqh, languages_spoken(?x9979, ?x10296), language(?x1185, ?x10296), ?x1745 = 0bcndz, ?x3076 = 0g5838s, service_language(?x11188, ?x10296) >> conf = 0.96 => this is the best rule for 1619 predicted values *> Best rule #1728 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 1 *> proper extension: 012w70; *> query: (?x4605, ?x66) <- languages_spoken(?x9979, ?x4605), language(?x7502, ?x4605), language(?x4604, ?x4605), language(?x3076, ?x4605), ?x4604 = 0432_5, ?x9979 = 04l_pt, ?x7502 = 0233bn, countries_spoken_in(?x4605, ?x7747), film_release_region(?x3076, ?x1174), film_release_region(?x6168, ?x1174), film_release_region(?x1080, ?x1174), film_release_region(?x66, ?x1174), organization(?x1174, ?x127), film_crew_role(?x3076, ?x468), ?x6168 = 0gj96ln, ?x1080 = 01c22t *> conf = 0.02 ranks of expected_values: 1850 EVAL 0c_v2 language! 0dln8jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 10.000 0.957 http://example.org/film/film/language #7216-05sb1 PRED entity: 05sb1 PRED relation: countries_spoken_in! PRED expected values: 0swlx => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 53): 02h40lc (0.69 #2872, 0.69 #4495, 0.69 #5365), 02ztjwg (0.33 #245, 0.29 #353, 0.13 #407), 0jzc (0.29 #556, 0.20 #882, 0.19 #1530), 06nm1 (0.24 #2825, 0.24 #711, 0.22 #2609), 04306rv (0.22 #383, 0.17 #491, 0.17 #221), 064_8sq (0.19 #2511, 0.19 #1154, 0.19 #5926), 02hwhyv (0.17 #404, 0.13 #458, 0.13 #783), 07c9s (0.14 #69, 0.13 #177, 0.13 #123), 0cjk9 (0.14 #328, 0.13 #707, 0.11 #220), 02bjrlw (0.14 #649, 0.13 #487, 0.13 #704) >> Best rule #2872 for best value: >> intensional similarity = 2 >> extensional distance = 85 >> proper extension: 0j11; >> query: (?x2236, ?x254) <- official_language(?x2236, ?x254), film_release_region(?x66, ?x2236) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #102 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 0393g; *> query: (?x2236, 0swlx) <- adjoins(?x2146, ?x2236), place_of_birth(?x14088, ?x2236), nationality(?x111, ?x2146) *> conf = 0.07 ranks of expected_values: 30 EVAL 05sb1 countries_spoken_in! 0swlx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 172.000 172.000 0.692 http://example.org/language/human_language/countries_spoken_in #7215-021_z5 PRED entity: 021_z5 PRED relation: parent_genre PRED expected values: 0gywn => 45 concepts (44 used for prediction) PRED predicted values (max 10 best out of 172): 06j6l (0.33 #687, 0.33 #33, 0.27 #524), 02x8m (0.32 #831, 0.26 #994, 0.26 #1157), 064t9 (0.29 #991, 0.09 #1154, 0.07 #1317), 0y3_8 (0.29 #1175, 0.06 #1012, 0.06 #1338), 016_rm (0.28 #949, 0.21 #1112, 0.20 #1275), 06by7 (0.28 #2303, 0.26 #1649, 0.25 #1812), 01243b (0.25 #1497, 0.09 #1008, 0.08 #5911), 0gywn (0.20 #367, 0.18 #531, 0.17 #694), 016_nr (0.20 #864, 0.15 #1027, 0.14 #1190), 05r6t (0.20 #1523, 0.18 #1034, 0.16 #5937) >> Best rule #687 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 064t9; 0glt670; 0gywn; 016cjb; 01fm07; 05lwjc; 02qcqkl; >> query: (?x1952, 06j6l) <- artists(?x1952, ?x7162), artists(?x1952, ?x6666), ?x7162 = 0ffgh, artist(?x9224, ?x6666), award(?x6666, ?x2180), award_nominee(?x1367, ?x6666), gender(?x6666, ?x514) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #367 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 06j6l; *> query: (?x1952, 0gywn) <- artists(?x1952, ?x11455), artists(?x1952, ?x7162), artists(?x1952, ?x6666), artists(?x1952, ?x4960), ?x7162 = 0ffgh, ?x6666 = 05szp, artists(?x14063, ?x11455), ?x14063 = 01h96, ?x4960 = 09889g *> conf = 0.20 ranks of expected_values: 8 EVAL 021_z5 parent_genre 0gywn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 45.000 44.000 0.333 http://example.org/music/genre/parent_genre #7214-07ddz9 PRED entity: 07ddz9 PRED relation: award PRED expected values: 02x73k6 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 220): 0ck27z (0.32 #493, 0.18 #90, 0.14 #16121), 099vwn (0.27 #215, 0.14 #16121, 0.13 #19752), 0bdx29 (0.21 #509, 0.14 #16121, 0.12 #18944), 0bdw6t (0.21 #914, 0.12 #18944, 0.12 #16928), 0bfvd4 (0.21 #919, 0.08 #2934, 0.06 #3337), 05pcn59 (0.18 #79, 0.14 #16121, 0.13 #19752), 04ljl_l (0.18 #3, 0.14 #16121, 0.10 #809), 057xs89 (0.18 #159, 0.14 #16121, 0.06 #965), 0bdwqv (0.17 #977, 0.11 #574, 0.09 #171), 0gqy2 (0.17 #969, 0.10 #2984, 0.10 #7820) >> Best rule #493 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0gsg7; >> query: (?x10167, 0ck27z) <- nominated_for(?x10167, ?x6740), nominated_for(?x10167, ?x782), ?x782 = 02k_4g, titles(?x53, ?x6740) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #18944 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2257 *> proper extension: 05d6q1; 0fvppk; 0kcd5; *> query: (?x10167, ?x435) <- nominated_for(?x10167, ?x782), nominated_for(?x435, ?x782) *> conf = 0.12 ranks of expected_values: 54 EVAL 07ddz9 award 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 68.000 68.000 0.316 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7213-0838y PRED entity: 0838y PRED relation: artists! PRED expected values: 01243b => 108 concepts (50 used for prediction) PRED predicted values (max 10 best out of 266): 06by7 (0.75 #3127, 0.71 #6241, 0.71 #4060), 05r6t (0.61 #2878, 0.44 #704, 0.36 #2257), 0xhtw (0.60 #947, 0.56 #1257, 0.49 #6547), 05bt6j (0.52 #2838, 0.50 #44, 0.33 #8127), 064t9 (0.49 #9340, 0.49 #4987, 0.48 #5607), 02yv6b (0.40 #1031, 0.33 #1341, 0.26 #4139), 025sc50 (0.37 #5645, 0.36 #5025, 0.29 #6893), 05w3f (0.36 #4077, 0.34 #3144, 0.33 #969), 06j6l (0.36 #5023, 0.35 #5643, 0.29 #5333), 0jmwg (0.33 #733, 0.23 #4663, 0.23 #3727) >> Best rule #3127 for best value: >> intensional similarity = 6 >> extensional distance = 30 >> proper extension: 02r1tx7; 05563d; 0394y; 06nv27; 02dw1_; 01kcms4; 012vm6; 02vnpv; 03qkcn9; >> query: (?x6818, 06by7) <- group(?x2798, ?x6818), group(?x1166, ?x6818), artists(?x302, ?x6818), artist(?x1954, ?x6818), ?x2798 = 03qjg, ?x1166 = 05148p4 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #973 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 012zng; 01vng3b; *> query: (?x6818, 01243b) <- artist(?x7793, ?x6818), artists(?x9935, ?x6818), ?x9935 = 0133_p, child(?x1104, ?x7793) *> conf = 0.27 ranks of expected_values: 20 EVAL 0838y artists! 01243b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 108.000 50.000 0.750 http://example.org/music/genre/artists #7212-05f3q PRED entity: 05f3q PRED relation: award_winner PRED expected values: 07t65 02jxk 08849 => 44 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1729): 016ggh (0.33 #4748, 0.10 #7217, 0.05 #12157), 015c2f (0.33 #3077, 0.07 #5546, 0.04 #15428), 02xs0q (0.33 #3253, 0.07 #5722, 0.03 #18075), 01j7rd (0.33 #2902, 0.07 #5371, 0.03 #17724), 0cj8x (0.33 #3119, 0.07 #8057, 0.07 #10528), 01wd9lv (0.33 #3888, 0.07 #11297, 0.06 #28594), 016k6x (0.33 #3598, 0.07 #11007, 0.06 #13478), 0bvzp (0.33 #3892, 0.05 #6361, 0.05 #13772), 018417 (0.33 #4903, 0.05 #7372, 0.04 #14783), 025mb_ (0.33 #4414, 0.05 #6883, 0.04 #16765) >> Best rule #4748 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 02grdc; >> query: (?x8493, 016ggh) <- award_winner(?x8493, ?x11290), award_winner(?x8493, ?x9385), award_winner(?x8493, ?x8494), ?x8494 = 051cc, profession(?x9385, ?x3802), religion(?x9385, ?x8613), ?x11290 = 042kg, ?x8613 = 04pk9 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05f3q award_winner 08849 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 12.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 05f3q award_winner 02jxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 12.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 05f3q award_winner 07t65 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 12.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #7211-07twz PRED entity: 07twz PRED relation: country! PRED expected values: 06wrt 01gqfm => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 50): 06wrt (0.81 #114, 0.81 #314, 0.80 #164), 07jbh (0.81 #129, 0.79 #79, 0.71 #279), 01lb14 (0.77 #163, 0.75 #313, 0.68 #63), 064vjs (0.74 #77, 0.67 #327, 0.67 #177), 035d1m (0.74 #72, 0.52 #122, 0.50 #272), 0194d (0.70 #193, 0.69 #343, 0.63 #493), 07gyv (0.68 #56, 0.67 #106, 0.64 #506), 0w0d (0.68 #61, 0.67 #161, 0.64 #311), 07bs0 (0.68 #62, 0.67 #112, 0.63 #162), 07rlg (0.68 #51, 0.57 #101, 0.47 #251) >> Best rule #114 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 05r4w; 01p1v; 06t2t; 03h64; 016wzw; 077qn; >> query: (?x4737, 06wrt) <- film_release_region(?x9859, ?x4737), film_release_region(?x7275, ?x4737), ?x9859 = 0g57wgv, ?x7275 = 0g4vmj8 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 15 EVAL 07twz country! 01gqfm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 103.000 103.000 0.810 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07twz country! 06wrt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.810 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #7210-01l3mk3 PRED entity: 01l3mk3 PRED relation: type_of_union PRED expected values: 04ztj => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.75 #25, 0.72 #33, 0.71 #9), 01g63y (0.26 #14, 0.16 #174, 0.16 #178), 0jgjn (0.01 #24, 0.01 #32, 0.01 #28) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 02qfhb; >> query: (?x7955, 04ztj) <- award_nominee(?x538, ?x7955), nominated_for(?x7955, ?x2755), instrumentalists(?x316, ?x7955) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l3mk3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.747 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7209-01y81r PRED entity: 01y81r PRED relation: company! PRED expected values: 014l7h => 45 concepts (42 used for prediction) PRED predicted values (max 10 best out of 30): 0dq_5 (0.53 #726, 0.53 #1527, 0.50 #963), 014l7h (0.50 #614, 0.50 #547, 0.50 #453), 09d6p2 (0.50 #614, 0.45 #566, 0.28 #897), 060c4 (0.50 #427, 0.34 #1702, 0.33 #1512), 0krdk (0.39 #1516, 0.38 #1800, 0.37 #1706), 0dq3c (0.33 #426, 0.27 #947, 0.25 #1701), 05_wyz (0.32 #964, 0.25 #1528, 0.24 #1434), 02211by (0.21 #1419, 0.20 #712, 0.18 #949), 0fkvn (0.20 #99, 0.04 #1044, 0.03 #1232), 01yc02 (0.19 #1518, 0.18 #1849, 0.18 #1896) >> Best rule #726 for best value: >> intensional similarity = 12 >> extensional distance = 13 >> proper extension: 0226k3; >> query: (?x5919, 0dq_5) <- service_location(?x5919, ?x8602), service_language(?x5919, ?x254), film_release_region(?x11065, ?x8602), contains(?x390, ?x8602), featured_film_locations(?x570, ?x8602), location(?x72, ?x8602), category(?x5919, ?x134), ?x11065 = 0n08r, award(?x72, ?x880), film(?x72, ?x924), ?x134 = 08mbj5d, award_nominee(?x71, ?x72) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #614 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 8 *> proper extension: 02qbjm; 07zlqp; 01fkr_; *> query: (?x5919, ?x5161) <- program(?x5919, ?x8444), program(?x5919, ?x6793), category(?x5919, ?x134), ?x134 = 08mbj5d, program(?x14343, ?x8444), languages(?x6793, ?x254), company(?x5161, ?x14343), child(?x14343, ?x13890), country_of_origin(?x6793, ?x390), country(?x901, ?x390), nationality(?x72, ?x390) *> conf = 0.50 ranks of expected_values: 2 EVAL 01y81r company! 014l7h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 45.000 42.000 0.533 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #7208-01vmv_ PRED entity: 01vmv_ PRED relation: contains! PRED expected values: 05bcl => 164 concepts (72 used for prediction) PRED predicted values (max 10 best out of 263): 09c7w0 (0.89 #52822, 0.87 #17898, 0.80 #59982), 05bcl (0.82 #34903, 0.75 #42072, 0.65 #55503), 0yl27 (0.80 #34902, 0.77 #61768, 0.71 #42071), 02jx1 (0.75 #54694, 0.67 #9033, 0.57 #2770), 07ssc (0.73 #8978, 0.65 #55503, 0.63 #55502), 03rt9 (0.50 #1813, 0.47 #64454, 0.34 #30424), 02cft (0.33 #349, 0.25 #2137, 0.04 #5715), 02j9z (0.32 #6289, 0.07 #2711, 0.07 #8974), 0j0k (0.32 #6638, 0.02 #20062, 0.01 #17377), 05l5n (0.29 #2805, 0.10 #11751, 0.07 #19807) >> Best rule #52822 for best value: >> intensional similarity = 6 >> extensional distance = 290 >> proper extension: 06xpp7; 02lwv5; >> query: (?x11459, 09c7w0) <- contains(?x12190, ?x11459), contains(?x3699, ?x11459), student(?x11459, ?x489), origin(?x1521, ?x12190), film_release_region(?x5827, ?x3699), ?x5827 = 0ggbfwf >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #34903 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 166 *> proper extension: 0194_r; *> query: (?x11459, ?x4071) <- contains(?x3699, ?x11459), currency(?x11459, ?x1099), student(?x11459, ?x489), state_province_region(?x11459, ?x4070), first_level_division_of(?x4070, ?x4071) *> conf = 0.82 ranks of expected_values: 2 EVAL 01vmv_ contains! 05bcl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 164.000 72.000 0.890 http://example.org/location/location/contains #7207-039x1k PRED entity: 039x1k PRED relation: award_winner! PRED expected values: 0gqyl 0bsjcw => 104 concepts (99 used for prediction) PRED predicted values (max 10 best out of 236): 0gkts9 (0.40 #431, 0.40 #164, 0.39 #19324), 0gqyl (0.39 #19324, 0.39 #21473, 0.39 #6875), 0bfvw2 (0.39 #19324, 0.39 #21473, 0.39 #6875), 0bb57s (0.39 #19324, 0.39 #21473, 0.39 #6875), 0bsjcw (0.39 #19324, 0.39 #21473, 0.39 #6875), 0gqwc (0.20 #74, 0.20 #936, 0.12 #505), 09sb52 (0.17 #2193, 0.13 #3911, 0.13 #1334), 02z1nbg (0.17 #1054, 0.13 #192, 0.08 #623), 09qs08 (0.14 #573, 0.13 #142, 0.02 #1004), 09qvf4 (0.13 #206, 0.12 #637, 0.03 #1068) >> Best rule #431 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 01j5ts; 02d4ct; 02kxwk; 04pp9s; 0mbs8; >> query: (?x7615, ?x3184) <- award_winner(?x1747, ?x7615), award(?x7615, ?x3184), ?x3184 = 0gkts9 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #19324 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1368 *> proper extension: 05218gr; 02xb2bt; 014hr0; 01vd7hn; 01fmz6; 0k9ctht; 016890; 07bzp; 07mvp; 02j_j0; ... *> query: (?x7615, ?x3184) <- award(?x7615, ?x3184), award_winner(?x7615, ?x12809), ceremony(?x3184, ?x1265) *> conf = 0.39 ranks of expected_values: 2, 5 EVAL 039x1k award_winner! 0bsjcw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 104.000 99.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 039x1k award_winner! 0gqyl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 99.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #7206-0swlx PRED entity: 0swlx PRED relation: countries_spoken_in PRED expected values: 05sb1 => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 344): 0d060g (0.72 #2759, 0.67 #3487, 0.50 #1475), 03rk0 (0.50 #1341, 0.44 #548, 0.38 #2074), 0161c (0.50 #448, 0.44 #548, 0.33 #85), 0hzlz (0.48 #2041, 0.44 #548, 0.31 #1491), 05sb1 (0.44 #548, 0.43 #3841, 0.37 #2560), 0697s (0.44 #548, 0.33 #2091, 0.29 #624), 07t_x (0.44 #548, 0.33 #116, 0.26 #1832), 07dvs (0.44 #548, 0.33 #91, 0.25 #454), 05l8y (0.44 #548, 0.33 #127, 0.25 #490), 0162b (0.44 #548, 0.25 #351, 0.23 #729) >> Best rule #2759 for best value: >> intensional similarity = 16 >> extensional distance = 23 >> proper extension: 02h40lc; 07zrf; 04306rv; 02hxc3j; 06nm1; 07c9s; 06b_j; 01r2l; 05zjd; 02hwhyv; ... >> query: (?x13468, 0d060g) <- countries_spoken_in(?x13468, ?x3730), countries_spoken_in(?x13468, ?x3352), languages_spoken(?x3584, ?x13468), contains(?x3730, ?x5237), olympics(?x3730, ?x584), form_of_government(?x3730, ?x4763), country(?x10585, ?x3730), country(?x668, ?x3730), contains(?x6304, ?x3730), ?x584 = 0l98s, locations(?x7241, ?x3730), ?x10585 = 01gqfm, participating_countries(?x1608, ?x3352), country(?x6270, ?x3730), medal(?x3730, ?x422), ?x668 = 07gyv >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #548 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 0jzc; *> query: (?x13468, ?x279) <- countries_spoken_in(?x13468, ?x3730), languages_spoken(?x14168, ?x13468), ?x3730 = 03shp, languages_spoken(?x14168, ?x13310), geographic_distribution(?x14168, ?x2236), people(?x14168, ?x10452), film_release_region(?x66, ?x2236), nationality(?x10452, ?x2146), adjoins(?x2346, ?x2236), language(?x1724, ?x13310), place_of_birth(?x14088, ?x2236), contains(?x2236, ?x2364), countries_spoken_in(?x13310, ?x279), administrative_parent(?x2236, ?x551) *> conf = 0.44 ranks of expected_values: 5 EVAL 0swlx countries_spoken_in 05sb1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 34.000 34.000 0.720 http://example.org/language/human_language/countries_spoken_in #7205-0b6tzs PRED entity: 0b6tzs PRED relation: genre PRED expected values: 07s9rl0 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 98): 07s9rl0 (0.88 #241, 0.80 #1321, 0.78 #721), 01jfsb (0.61 #10704, 0.54 #10703, 0.53 #5050), 05p553 (0.54 #9865, 0.36 #2286, 0.35 #2528), 02l7c8 (0.42 #9877, 0.37 #1217, 0.36 #857), 03k9fj (0.33 #372, 0.30 #972, 0.24 #1572), 0219x_ (0.33 #27, 0.18 #747, 0.16 #1107), 02kdv5l (0.32 #963, 0.30 #1563, 0.29 #6255), 082gq (0.26 #631, 0.25 #391, 0.16 #1471), 04xvlr (0.23 #2162, 0.23 #842, 0.20 #9862), 06n90 (0.23 #974, 0.17 #1574, 0.14 #3500) >> Best rule #241 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 083shs; 017gl1; 050gkf; 02c638; 02yvct; 011yd2; 0b1y_2; 0b44shh; 02lxrv; 011ypx; ... >> query: (?x945, 07s9rl0) <- award(?x945, ?x4091), award_winner(?x945, ?x163), film(?x4533, ?x945), ?x4091 = 09sdmz >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b6tzs genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.875 http://example.org/film/film/genre #7204-01hp5 PRED entity: 01hp5 PRED relation: language PRED expected values: 064_8sq => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 35): 064_8sq (0.15 #493, 0.15 #552, 0.14 #377), 06nm1 (0.13 #366, 0.13 #600, 0.10 #1250), 04306rv (0.11 #181, 0.11 #1596, 0.11 #300), 02bjrlw (0.09 #297, 0.08 #357, 0.08 #178), 06b_j (0.09 #612, 0.08 #378, 0.07 #259), 0jzc (0.06 #196, 0.05 #375, 0.05 #609), 03_9r (0.06 #9, 0.06 #127, 0.06 #186), 0653m (0.04 #1014, 0.04 #895, 0.04 #1900), 04h9h (0.04 #42, 0.04 #338, 0.04 #219), 012w70 (0.04 #368, 0.03 #602, 0.03 #896) >> Best rule #493 for best value: >> intensional similarity = 4 >> extensional distance = 329 >> proper extension: 016ztl; >> query: (?x751, 064_8sq) <- film(?x4314, ?x751), titles(?x8581, ?x751), film_release_distribution_medium(?x751, ?x81), production_companies(?x751, ?x382) >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hp5 language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.148 http://example.org/film/film/language #7203-0pkyh PRED entity: 0pkyh PRED relation: artists! PRED expected values: 02yv6b => 123 concepts (107 used for prediction) PRED predicted values (max 10 best out of 249): 064t9 (0.58 #3373, 0.57 #1844, 0.56 #19556), 0glt670 (0.32 #1867, 0.29 #4006, 0.26 #3396), 02yv6b (0.31 #1622, 0.24 #3761, 0.21 #20765), 05bt6j (0.30 #19582, 0.30 #4314, 0.28 #2175), 025sc50 (0.28 #3406, 0.28 #1877, 0.25 #4016), 06j6l (0.27 #8593, 0.27 #8288, 0.27 #1875), 02w4v (0.27 #1566, 0.24 #345, 0.21 #20765), 0ggx5q (0.25 #1906, 0.24 #3435, 0.23 #4045), 02lnbg (0.25 #1886, 0.24 #3415, 0.23 #4025), 03_d0 (0.25 #10, 0.19 #11612, 0.19 #6117) >> Best rule #3373 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 01m65sp; >> query: (?x2930, 064t9) <- participant(?x6208, ?x2930), artists(?x284, ?x2930), category(?x2930, ?x134) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1622 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 0m19t; 07yg2; 0394y; 01kcms4; 08w4pm; 01l_w0; 0p76z; *> query: (?x2930, 02yv6b) <- artist(?x3006, ?x2930), artists(?x7329, ?x2930), ?x7329 = 016jny *> conf = 0.31 ranks of expected_values: 3 EVAL 0pkyh artists! 02yv6b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 107.000 0.580 http://example.org/music/genre/artists #7202-07gkgp PRED entity: 07gkgp PRED relation: nationality PRED expected values: 0d060g => 66 concepts (62 used for prediction) PRED predicted values (max 10 best out of 97): 09c7w0 (0.76 #101, 0.73 #1201, 0.72 #1101), 0d060g (0.12 #307, 0.10 #107, 0.09 #707), 02jx1 (0.12 #1833, 0.11 #1633, 0.11 #1733), 03_3d (0.11 #906, 0.06 #6, 0.05 #206), 07ssc (0.09 #415, 0.09 #1815, 0.08 #1615), 03rk0 (0.07 #2447, 0.07 #2347, 0.07 #2547), 0j5g9 (0.02 #862, 0.02 #4103, 0.02 #5308), 0345h (0.02 #1431, 0.02 #1531, 0.02 #1731), 03rt9 (0.02 #213, 0.02 #313, 0.02 #4103), 0chghy (0.02 #210, 0.02 #310, 0.02 #4103) >> Best rule #101 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 084x96; >> query: (?x11118, 09c7w0) <- profession(?x11118, ?x1383), language(?x11118, ?x254), ?x254 = 02h40lc, ?x1383 = 0np9r, category(?x11118, ?x134) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #307 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 55 *> proper extension: 0ccqd7; *> query: (?x11118, 0d060g) <- profession(?x11118, ?x1383), profession(?x11118, ?x1032), language(?x11118, ?x254), ?x254 = 02h40lc, ?x1383 = 0np9r, ?x1032 = 02hrh1q *> conf = 0.12 ranks of expected_values: 2 EVAL 07gkgp nationality 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 62.000 0.759 http://example.org/people/person/nationality #7201-049mql PRED entity: 049mql PRED relation: language PRED expected values: 02h40lc => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.89 #2098, 0.89 #2938, 0.88 #2277), 04306rv (0.18 #5, 0.18 #4265, 0.12 #838), 064_8sq (0.18 #4265, 0.16 #82, 0.15 #1096), 06b_j (0.18 #4265, 0.15 #23, 0.07 #856), 02bjrlw (0.18 #4265, 0.13 #1, 0.10 #834), 06nm1 (0.18 #4265, 0.10 #131, 0.10 #310), 03_9r (0.18 #4265, 0.07 #70, 0.06 #1144), 04h9h (0.18 #4265, 0.05 #43, 0.04 #938), 0653m (0.18 #4265, 0.05 #549, 0.04 #489), 03k50 (0.18 #4265, 0.03 #1083, 0.02 #2883) >> Best rule #2098 for best value: >> intensional similarity = 4 >> extensional distance = 649 >> proper extension: 027pfb2; >> query: (?x4127, 02h40lc) <- film(?x7164, ?x4127), genre(?x4127, ?x53), celebrity(?x7164, ?x3705), profession(?x7164, ?x131) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049mql language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.891 http://example.org/film/film/language #7200-01j6t0 PRED entity: 01j6t0 PRED relation: symptom_of PRED expected values: 035482 0dcqh 01qqwn => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 71): 0h1wz (0.50 #238, 0.50 #213, 0.43 #264), 011zdm (0.50 #229, 0.50 #204, 0.38 #283), 01_qc_ (0.50 #177, 0.38 #282, 0.35 #244), 014w_8 (0.36 #363, 0.35 #244, 0.33 #124), 01qqwn (0.36 #363, 0.35 #244, 0.33 #131), 07x16 (0.36 #363, 0.35 #244, 0.33 #135), 087z2 (0.36 #363, 0.33 #137, 0.33 #115), 0542n (0.36 #363, 0.33 #134, 0.25 #240), 035482 (0.35 #244, 0.33 #456, 0.33 #99), 02y0js (0.35 #244, 0.33 #26, 0.27 #392) >> Best rule #238 for best value: >> intensional similarity = 22 >> extensional distance = 2 >> proper extension: 012qjw; >> query: (?x4905, 0h1wz) <- symptom_of(?x4905, ?x13131), symptom_of(?x4905, ?x11659), symptom_of(?x4905, ?x9933), symptom_of(?x4905, ?x9898), symptom_of(?x4905, ?x7007), symptom_of(?x4905, ?x4959), risk_factors(?x11659, ?x12536), notable_people_with_this_condition(?x9933, ?x8596), notable_people_with_this_condition(?x9933, ?x3792), notable_people_with_this_condition(?x9933, ?x2807), people(?x4959, ?x8473), people(?x4959, ?x7961), ?x12536 = 0dcp_, ?x9898 = 09jg8, ?x8473 = 0gyy0, ?x13131 = 0d19y2, student(?x1428, ?x7961), type_of_union(?x2807, ?x566), award_winner(?x486, ?x2807), risk_factors(?x9119, ?x7007), award_winner(?x435, ?x8596), award_nominee(?x788, ?x3792) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #363 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 10 *> proper extension: 01l2m3; 0dq9p; *> query: (?x4905, ?x10613) <- symptom_of(?x4905, ?x13131), symptom_of(?x4905, ?x11678), symptom_of(?x13373, ?x13131), symptom_of(?x10717, ?x13131), symptom_of(?x3679, ?x13131), ?x13373 = 0f3kl, risk_factors(?x13744, ?x11678), ?x3679 = 02tfl8, symptom_of(?x11393, ?x13744), people(?x10717, ?x9392), symptom_of(?x10717, ?x10613) *> conf = 0.36 ranks of expected_values: 5, 9, 23 EVAL 01j6t0 symptom_of 01qqwn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 21.000 21.000 0.500 http://example.org/medicine/symptom/symptom_of EVAL 01j6t0 symptom_of 0dcqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 21.000 21.000 0.500 http://example.org/medicine/symptom/symptom_of EVAL 01j6t0 symptom_of 035482 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 21.000 21.000 0.500 http://example.org/medicine/symptom/symptom_of #7199-010t4v PRED entity: 010t4v PRED relation: time_zones PRED expected values: 02lcqs => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 10): 02lcqs (0.85 #327, 0.83 #627, 0.82 #44), 02hcv8 (0.55 #199, 0.53 #251, 0.48 #173), 02fqwt (0.53 #27, 0.23 #80, 0.22 #119), 02llzg (0.13 #383, 0.13 #265, 0.11 #1374), 02hczc (0.12 #133, 0.11 #81, 0.11 #107), 03bdv (0.07 #32, 0.06 #333, 0.05 #528), 03plfd (0.02 #1380, 0.02 #1393, 0.02 #1432), 042g7t (0.02 #103, 0.02 #129, 0.02 #900), 052vwh (0.02 #914, 0.01 #1148, 0.01 #1135), 02lcrv (0.01 #620) >> Best rule #327 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 02j3w; 0mmzt; 05jbn; 0c_m3; 02_n7; 0mndw; 0mn8t; 0fw4v; 0ynfz; 0dzt9; ... >> query: (?x9973, ?x2950) <- category(?x9973, ?x134), place_of_birth(?x8803, ?x9973), county_seat(?x11569, ?x9973), time_zones(?x11569, ?x2950) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 010t4v time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.845 http://example.org/location/location/time_zones #7198-01cpqk PRED entity: 01cpqk PRED relation: category PRED expected values: 08mbj5d => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.63 #53, 0.48 #25, 0.44 #16) >> Best rule #53 for best value: >> intensional similarity = 3 >> extensional distance = 472 >> proper extension: 089tm; 04rcr; 01vsxdm; 01wv9xn; 03t9sp; 0dtd6; 0frsw; 016fmf; 01vrwfv; 01rm8b; ... >> query: (?x6525, 08mbj5d) <- award(?x6525, ?x1007), award(?x3397, ?x1007), ?x3397 = 015f7 >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cpqk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.631 http://example.org/common/topic/webpage./common/webpage/category #7197-0dplh PRED entity: 0dplh PRED relation: major_field_of_study PRED expected values: 0fdys => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 104): 05qjt (0.59 #126, 0.43 #603, 0.41 #722), 04rjg (0.55 #138, 0.46 #734, 0.44 #615), 02lp1 (0.54 #726, 0.47 #607, 0.36 #130), 037mh8 (0.50 #185, 0.33 #66, 0.33 #781), 03g3w (0.41 #144, 0.39 #740, 0.35 #978), 0fdys (0.41 #156, 0.36 #752, 0.32 #633), 02h40lc (0.41 #122, 0.22 #599, 0.21 #718), 01lj9 (0.41 #753, 0.38 #634, 0.32 #157), 062z7 (0.38 #741, 0.34 #622, 0.29 #264), 01540 (0.36 #178, 0.34 #774, 0.32 #655) >> Best rule #126 for best value: >> intensional similarity = 2 >> extensional distance = 20 >> proper extension: 071_8; 01nm8w; >> query: (?x2142, 05qjt) <- major_field_of_study(?x2142, ?x8962), ?x8962 = 04g7x >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #156 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 20 *> proper extension: 071_8; 01nm8w; *> query: (?x2142, 0fdys) <- major_field_of_study(?x2142, ?x8962), ?x8962 = 04g7x *> conf = 0.41 ranks of expected_values: 6 EVAL 0dplh major_field_of_study 0fdys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 30.000 30.000 0.591 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7196-02p0szs PRED entity: 02p0szs PRED relation: genre! PRED expected values: 020y73 0h03fhx 011ycb 043n0v_ 0k20s => 41 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2009): 04pmnt (0.75 #17586, 0.67 #15753, 0.60 #10253), 0gyy53 (0.67 #15152, 0.62 #16985, 0.60 #9652), 03kg2v (0.67 #15149, 0.56 #20649, 0.50 #22484), 02w9k1c (0.67 #15680, 0.50 #17513, 0.50 #6512), 02ljhg (0.67 #16031, 0.50 #17864, 0.49 #9168), 023p7l (0.67 #15291, 0.50 #17124, 0.40 #9791), 0fgpvf (0.62 #16603, 0.60 #9270, 0.50 #14770), 041td_ (0.62 #17619, 0.60 #10286, 0.50 #15786), 047myg9 (0.62 #17638, 0.50 #15805, 0.50 #6637), 02dr9j (0.60 #14107, 0.60 #12272, 0.50 #23274) >> Best rule #17586 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 017fp; >> query: (?x3312, 04pmnt) <- genre(?x4197, ?x3312), genre(?x2852, ?x3312), genre(?x1283, ?x3312), film_release_region(?x1283, ?x404), film_release_region(?x1283, ?x390), ?x390 = 0chghy, ?x4197 = 01242_, nominated_for(?x1703, ?x2852), form_of_government(?x404, ?x4763), ?x1703 = 0k611, film_crew_role(?x2852, ?x137) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #12746 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 3 *> proper extension: 03q4nz; *> query: (?x3312, 0k20s) <- genre(?x4399, ?x3312), genre(?x3992, ?x3312), genre(?x3135, ?x3312), genre(?x1283, ?x3312), ?x1283 = 0cnztc4, genre(?x4399, ?x4757), genre(?x4399, ?x162), films(?x12672, ?x4399), ?x162 = 04xvlr, nominated_for(?x112, ?x4399), ?x3135 = 0bmc4cm, award_winner(?x3992, ?x6514), nominated_for(?x484, ?x3992), titles(?x4757, ?x499) *> conf = 0.60 ranks of expected_values: 14, 356, 474, 608, 1440 EVAL 02p0szs genre! 0k20s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 41.000 20.000 0.750 http://example.org/film/film/genre EVAL 02p0szs genre! 043n0v_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 41.000 20.000 0.750 http://example.org/film/film/genre EVAL 02p0szs genre! 011ycb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 20.000 0.750 http://example.org/film/film/genre EVAL 02p0szs genre! 0h03fhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 20.000 0.750 http://example.org/film/film/genre EVAL 02p0szs genre! 020y73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 20.000 0.750 http://example.org/film/film/genre #7195-0qmny PRED entity: 0qmny PRED relation: artists! PRED expected values: 05w3f => 71 concepts (32 used for prediction) PRED predicted values (max 10 best out of 247): 016clz (0.66 #4035, 0.66 #3723, 0.64 #2481), 06by7 (0.55 #2186, 0.55 #639, 0.53 #3739), 03lty (0.55 #2193, 0.34 #3746, 0.34 #4058), 064t9 (0.50 #5591, 0.47 #1870, 0.44 #1560), 06j6l (0.50 #358, 0.38 #1595, 0.32 #3145), 02yv6b (0.50 #100, 0.32 #2476, 0.28 #5268), 01fh36 (0.45 #706, 0.36 #1325, 0.31 #1634), 0xhtw (0.45 #2183, 0.39 #4048, 0.34 #3736), 01738f (0.40 #2281, 0.27 #4146, 0.26 #3834), 05r6t (0.34 #3717, 0.29 #1320, 0.28 #5268) >> Best rule #4035 for best value: >> intensional similarity = 6 >> extensional distance = 39 >> proper extension: 01y_rz; 01w20rx; >> query: (?x8637, 016clz) <- artists(?x9013, ?x8637), artists(?x3928, ?x8637), ?x9013 = 09nwwf, artists(?x3928, ?x5405), gender(?x5405, ?x514), award_nominee(?x2698, ?x5405) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #2476 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 18 *> proper extension: 067mj; 01vw20_; 01gx5f; 01w8n89; 0fpj4lx; 0fhxv; 0191h5; 020_4z; *> query: (?x8637, ?x6173) <- artists(?x13401, ?x8637), artists(?x9013, ?x8637), ?x9013 = 09nwwf, artist(?x2931, ?x8637), parent_genre(?x13401, ?x6173), parent_genre(?x13401, ?x2809), ?x2809 = 05w3f *> conf = 0.32 ranks of expected_values: 13 EVAL 0qmny artists! 05w3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 71.000 32.000 0.659 http://example.org/music/genre/artists #7194-06fqlk PRED entity: 06fqlk PRED relation: region PRED expected values: 07ssc => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 10): 07ssc (0.86 #265, 0.47 #122, 0.29 #29), 09c7w0 (0.12 #140, 0.04 #117, 0.03 #165), 0d05w3 (0.12 #140, 0.02 #188, 0.02 #1360), 0345h (0.12 #140, 0.02 #188, 0.02 #1360), 09nm_ (0.03 #163, 0.01 #234, 0.01 #211), 059j2 (0.02 #125, 0.02 #173), 0d060g (0.02 #120), 06mx8 (0.02 #182), 03rt9 (0.01 #1738, 0.01 #1885, 0.01 #1218), 02jx1 (0.01 #1738, 0.01 #1885, 0.01 #1218) >> Best rule #265 for best value: >> intensional similarity = 4 >> extensional distance = 115 >> proper extension: 0522wp; >> query: (?x6489, 07ssc) <- film_distribution_medium(?x6489, ?x2099), film(?x609, ?x6489), ?x2099 = 0735l, ?x609 = 03xq0f >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06fqlk region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.863 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #7193-02zv4b PRED entity: 02zv4b PRED relation: actor PRED expected values: 04shbh 05szp => 63 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1102): 029_3 (0.79 #5570, 0.72 #3713, 0.71 #927), 0261x8t (0.79 #5570, 0.72 #3713, 0.71 #927), 01j7rd (0.50 #2021, 0.25 #3879, 0.25 #2950), 01vx5w7 (0.33 #234, 0.13 #6731, 0.12 #3949), 0163t3 (0.33 #682, 0.13 #7179, 0.12 #4397), 0gps0z (0.33 #725, 0.12 #4440, 0.12 #3511), 01pfkw (0.33 #353, 0.12 #4068, 0.12 #3139), 0c7ct (0.33 #49, 0.12 #3764, 0.12 #2835), 02r_d4 (0.25 #1907, 0.12 #3765, 0.12 #2836), 01jbx1 (0.25 #1190, 0.12 #3977, 0.12 #3048) >> Best rule #5570 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 03czz87; >> query: (?x1766, ?x4065) <- category(?x1766, ?x134), program(?x4065, ?x1766), country_of_origin(?x1766, ?x94), actor(?x1766, ?x397), languages(?x1766, ?x254) >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #11136 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 03y3bp7; *> query: (?x1766, ?x1018) <- category(?x1766, ?x134), ?x134 = 08mbj5d, actor(?x1766, ?x2275), award_winner(?x308, ?x2275), participant(?x2275, ?x1018) *> conf = 0.08 ranks of expected_values: 159 EVAL 02zv4b actor 05szp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 41.000 0.786 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 02zv4b actor 04shbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 63.000 41.000 0.786 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #7192-05mvd62 PRED entity: 05mvd62 PRED relation: award PRED expected values: 0gq9h => 127 concepts (113 used for prediction) PRED predicted values (max 10 best out of 281): 09sb52 (0.58 #443, 0.34 #17776, 0.31 #21000), 0gq9h (0.44 #12973, 0.38 #9749, 0.38 #12167), 02g3gw (0.40 #295, 0.18 #33864, 0.17 #14914), 05b1610 (0.40 #38, 0.16 #27814, 0.16 #32249), 03hkv_r (0.40 #15, 0.14 #9688, 0.12 #12106), 0f_nbyh (0.27 #1218, 0.17 #9682, 0.17 #4039), 0gs9p (0.26 #9751, 0.24 #12169, 0.23 #12572), 019f4v (0.25 #9738, 0.23 #12962, 0.23 #12156), 0fbtbt (0.21 #1440, 0.15 #35074, 0.13 #27007), 05f4m9q (0.20 #12, 0.18 #818, 0.16 #27814) >> Best rule #443 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 030hcs; 03hzl42; >> query: (?x7094, 09sb52) <- nominated_for(?x7094, ?x7141), award_nominee(?x902, ?x7094), ?x7141 = 027r9t >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #12973 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 260 *> proper extension: 014dm6; *> query: (?x7094, 0gq9h) <- nominated_for(?x7094, ?x5277), produced_by(?x2928, ?x7094), award(?x2928, ?x500) *> conf = 0.44 ranks of expected_values: 2 EVAL 05mvd62 award 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 113.000 0.583 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7191-059g4 PRED entity: 059g4 PRED relation: locations! PRED expected values: 022840 => 160 concepts (108 used for prediction) PRED predicted values (max 10 best out of 130): 081pw (0.40 #1602, 0.40 #862, 0.38 #1354), 05t2fh4 (0.27 #1840, 0.25 #731, 0.22 #1592), 01w1sx (0.25 #1440, 0.25 #1317, 0.25 #702), 01hwkn (0.25 #1335, 0.25 #351, 0.20 #1706), 024jvz (0.25 #687, 0.20 #1056, 0.20 #810), 022840 (0.25 #675, 0.20 #1044, 0.20 #798), 03jv8d (0.25 #352, 0.20 #967, 0.12 #1459), 0dr7s (0.25 #349, 0.20 #964, 0.12 #1456), 0cwt70 (0.25 #340, 0.20 #955, 0.12 #1447), 01_3rn (0.25 #328, 0.20 #943, 0.12 #1435) >> Best rule #1602 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 03rz4; >> query: (?x8483, 081pw) <- locations(?x1777, ?x8483), partially_contains(?x8483, ?x94), nationality(?x51, ?x94), film_release_region(?x54, ?x94) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #675 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09c7w0; 05k7sb; *> query: (?x8483, 022840) <- contains(?x8483, ?x10440), partially_contains(?x8483, ?x94), adjoins(?x12315, ?x8483), ?x10440 = 0typ5 *> conf = 0.25 ranks of expected_values: 6 EVAL 059g4 locations! 022840 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 160.000 108.000 0.400 http://example.org/time/event/locations #7190-05szp PRED entity: 05szp PRED relation: actor! PRED expected values: 02zv4b => 103 concepts (100 used for prediction) PRED predicted values (max 10 best out of 73): 026bfsh (0.33 #97, 0.20 #627, 0.12 #1954), 01b7h8 (0.26 #1327, 0.12 #1061, 0.11 #18560), 03gvm3t (0.12 #934, 0.07 #1200, 0.02 #1996), 05631 (0.07 #787, 0.03 #2114, 0.02 #2909), 03bww6 (0.07 #663, 0.02 #1990, 0.01 #4905), 0cpz4k (0.05 #1123, 0.04 #857), 039cq4 (0.05 #1190, 0.01 #4371, 0.01 #7288), 0kfv9 (0.04 #1619, 0.01 #8246, 0.01 #16994), 0jwl2 (0.04 #1665), 0d68qy (0.02 #1098, 0.01 #4014, 0.01 #4279) >> Best rule #97 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 02yygk; >> query: (?x6666, 026bfsh) <- award_nominee(?x1367, ?x6666), artists(?x1952, ?x6666), ?x1367 = 0136g9 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4002 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 132 *> proper extension: 02v60l; *> query: (?x6666, 02zv4b) <- participant(?x4420, ?x6666), gender(?x6666, ?x514), spouse(?x6666, ?x5834) *> conf = 0.01 ranks of expected_values: 44 EVAL 05szp actor! 02zv4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 103.000 100.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #7189-01m7pwq PRED entity: 01m7pwq PRED relation: role PRED expected values: 02sgy => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 120): 02sgy (0.50 #210, 0.45 #924, 0.43 #1435), 05r5c (0.50 #212, 0.40 #2155, 0.39 #3286), 018vs (0.50 #217, 0.26 #931, 0.26 #1544), 05842k (0.50 #281, 0.23 #689, 0.20 #1506), 01vj9c (0.50 #219, 0.22 #1546, 0.22 #1444), 0395lw (0.40 #232, 0.05 #742, 0.04 #4000), 05148p4 (0.32 #3792, 0.24 #4415, 0.24 #4414), 026t6 (0.31 #615, 0.30 #207, 0.23 #1023), 0l14qv (0.30 #209, 0.20 #5, 0.17 #1434), 013y1f (0.30 #239, 0.20 #35, 0.17 #1464) >> Best rule #210 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 07_3qd; >> query: (?x9830, 02sgy) <- artists(?x1572, ?x9830), role(?x9830, ?x1267), role(?x9830, ?x227), ?x227 = 0342h, ?x1267 = 07brj >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m7pwq role 02sgy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #7188-034qrh PRED entity: 034qrh PRED relation: nominated_for! PRED expected values: 05zr6wv 05zvj3m => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 191): 0gq9h (0.27 #11300, 0.27 #11778, 0.26 #15841), 05pcn59 (0.27 #4782, 0.22 #1674, 0.19 #10280), 05zr6wv (0.27 #4782, 0.22 #1674, 0.19 #10280), 05b4l5x (0.27 #4782, 0.22 #1674, 0.18 #1919), 0hnf5vm (0.27 #4782, 0.22 #1674, 0.02 #1094), 02x1z2s (0.25 #861, 0.09 #1339, 0.08 #4447), 0gqzz (0.25 #768, 0.05 #4354, 0.03 #8657), 0gs9p (0.24 #11779, 0.24 #11301, 0.23 #13930), 02hsq3m (0.24 #1225, 0.20 #986, 0.14 #6246), 019f4v (0.23 #13920, 0.23 #11769, 0.23 #11291) >> Best rule #11300 for best value: >> intensional similarity = 4 >> extensional distance = 619 >> proper extension: 09xbpt; 03s6l2; 03s5lz; 01b195; 0pdp8; 01hvjx; 0bby9p5; 08gg47; 02rrfzf; 0dgpwnk; ... >> query: (?x437, 0gq9h) <- film_release_region(?x437, ?x94), nominated_for(?x794, ?x437), award_winner(?x437, ?x7205), language(?x437, ?x254) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #4782 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 194 *> proper extension: 0g60z; 0180mw; *> query: (?x437, ?x1312) <- nominated_for(?x437, ?x6198), nominated_for(?x794, ?x437), nominated_for(?x6198, ?x2102), nominated_for(?x1312, ?x6198) *> conf = 0.27 ranks of expected_values: 3, 17 EVAL 034qrh nominated_for! 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 117.000 117.000 0.272 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 034qrh nominated_for! 05zr6wv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 117.000 0.272 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7187-06wxw PRED entity: 06wxw PRED relation: place_of_birth! PRED expected values: 01nm3s => 159 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1863): 016pns (0.35 #238912, 0.34 #194757, 0.33 #163591), 0m_v0 (0.35 #238912, 0.34 #194757, 0.33 #163591), 01kstn9 (0.35 #238912, 0.34 #194757, 0.33 #163591), 01vwyqp (0.35 #238912, 0.34 #194757, 0.33 #163591), 01gbn6 (0.35 #238912, 0.34 #194757, 0.33 #163591), 072bb1 (0.35 #238912, 0.34 #194757, 0.33 #163591), 01_j71 (0.35 #238912, 0.34 #194757, 0.33 #163591), 06jw0s (0.35 #238912, 0.34 #194757, 0.33 #163591), 06cc_1 (0.35 #238912, 0.34 #194757, 0.33 #163591), 0c4y8 (0.35 #238912, 0.34 #194757, 0.33 #163591) >> Best rule #238912 for best value: >> intensional similarity = 3 >> extensional distance = 335 >> proper extension: 02p3my; >> query: (?x4356, ?x568) <- place_of_birth(?x9055, ?x4356), film(?x9055, ?x3783), location(?x568, ?x4356) >> conf = 0.35 => this is the best rule for 14 predicted values No rule for expected values ranks of expected_values: EVAL 06wxw place_of_birth! 01nm3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 114.000 0.347 http://example.org/people/person/place_of_birth #7186-0bq_mx PRED entity: 0bq_mx PRED relation: award_winner PRED expected values: 06jrhz 06y9bd 09hd6f => 48 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1709): 01j7rd (0.75 #4904, 0.64 #9506, 0.60 #11041), 0h0wc (0.67 #1900, 0.50 #3435, 0.38 #6505), 02xs0q (0.58 #5149, 0.50 #9751, 0.47 #11286), 0pz7h (0.57 #7794, 0.22 #20074, 0.20 #23146), 04sry (0.50 #2616, 0.35 #17966, 0.33 #1082), 0cp9f9 (0.43 #8862, 0.40 #4258, 0.33 #5793), 04ns3gy (0.42 #5929, 0.36 #10531, 0.33 #12066), 018ygt (0.40 #4032, 0.36 #8636, 0.33 #2497), 02661h (0.33 #5765, 0.33 #1161, 0.27 #11902), 05dbf (0.33 #1856, 0.33 #322, 0.20 #3391) >> Best rule #4904 for best value: >> intensional similarity = 12 >> extensional distance = 10 >> proper extension: 0lp_cd3; 07z31v; 0gx_st; 07y9ts; 07y_p6; >> query: (?x10809, 01j7rd) <- honored_for(?x10809, ?x2829), award_winner(?x10809, ?x8375), award_winner(?x10809, ?x4022), student(?x2605, ?x8375), profession(?x4022, ?x987), award_nominee(?x1711, ?x8375), award_nominee(?x4022, ?x5387), ceremony(?x384, ?x10809), producer_type(?x8375, ?x632), place_of_birth(?x8375, ?x2850), influenced_by(?x8375, ?x986), ?x986 = 081lh >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #24560 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 38 *> proper extension: 0hr6lkl; 0fqpc7d; 0fy6bh; 0bzk2h; 0bx6zs; *> query: (?x10809, ?x1711) <- honored_for(?x10809, ?x2829), award_winner(?x10809, ?x8375), award_winner(?x10809, ?x4022), award_winner(?x10809, ?x3260), award_winner(?x10809, ?x2248), award_nominee(?x1711, ?x8375), program_creator(?x631, ?x8375), student(?x216, ?x2248), people(?x1050, ?x8375), profession(?x3260, ?x319), award_winner(?x10340, ?x4022), nationality(?x3260, ?x279), award(?x2248, ?x384), award_nominee(?x4022, ?x5387) *> conf = 0.23 ranks of expected_values: 65, 105, 194 EVAL 0bq_mx award_winner 09hd6f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 48.000 26.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bq_mx award_winner 06y9bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 48.000 26.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bq_mx award_winner 06jrhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 48.000 26.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7185-017g21 PRED entity: 017g21 PRED relation: profession PRED expected values: 0dz3r 016z4k 025352 => 164 concepts (96 used for prediction) PRED predicted values (max 10 best out of 92): 016z4k (0.69 #148, 0.60 #1734, 0.58 #2743), 0dz3r (0.50 #5484, 0.48 #3608, 0.48 #434), 01d_h8 (0.50 #870, 0.30 #8811, 0.28 #12861), 02jknp (0.46 #872, 0.18 #1161, 0.18 #8813), 01c72t (0.43 #11285, 0.32 #5358, 0.32 #6801), 0kyk (0.36 #891, 0.30 #2478, 0.16 #2045), 0cbd2 (0.32 #2458, 0.22 #2025, 0.18 #871), 03gjzk (0.29 #877, 0.27 #13, 0.20 #2464), 018gz8 (0.25 #879, 0.18 #15, 0.14 #2466), 0fnpj (0.24 #344, 0.22 #488, 0.19 #3373) >> Best rule #148 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 021bk; >> query: (?x7252, 016z4k) <- place_of_birth(?x7252, ?x11432), role(?x7252, ?x1466), instrumentalists(?x1166, ?x7252), religion(?x7252, ?x2694), ?x1466 = 03bx0bm >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 14 EVAL 017g21 profession 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 164.000 96.000 0.688 http://example.org/people/person/profession EVAL 017g21 profession 016z4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 96.000 0.688 http://example.org/people/person/profession EVAL 017g21 profession 0dz3r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 96.000 0.688 http://example.org/people/person/profession #7184-02bp37 PRED entity: 02bp37 PRED relation: district_represented PRED expected values: 059_c 01n7q 06mz5 050ks 07_f2 => 40 concepts (32 used for prediction) PRED predicted values (max 10 best out of 964): 06yxd (0.88 #1179, 0.88 #1156, 0.88 #315), 0d0x8 (0.88 #1151, 0.88 #315, 0.87 #140), 026mj (0.88 #1160, 0.88 #315, 0.87 #140), 07_f2 (0.88 #315, 0.87 #140, 0.84 #386), 07h34 (0.88 #315, 0.87 #140, 0.84 #386), 05kkh (0.88 #315, 0.87 #140, 0.84 #386), 0vbk (0.88 #315, 0.87 #140, 0.84 #386), 01n7q (0.88 #315, 0.87 #140, 0.84 #386), 03s5t (0.88 #315, 0.87 #140, 0.84 #386), 05fhy (0.88 #315, 0.87 #140, 0.84 #386) >> Best rule #1179 for best value: >> intensional similarity = 33 >> extensional distance = 24 >> proper extension: 01grrf; >> query: (?x1829, ?x4776) <- district_represented(?x1829, ?x5575), district_represented(?x1829, ?x4198), district_represented(?x1829, ?x2713), district_represented(?x1829, ?x728), legislative_sessions(?x1829, ?x845), legislative_sessions(?x1829, ?x605), ?x728 = 059f4, legislative_sessions(?x652, ?x1829), district_represented(?x845, ?x4776), district_represented(?x845, ?x1138), religion(?x4198, ?x109), jurisdiction_of_office(?x900, ?x4198), district_represented(?x605, ?x2982), district_represented(?x605, ?x1274), contains(?x1138, ?x3026), legislative_sessions(?x845, ?x1027), first_level_division_of(?x4198, ?x94), legislative_sessions(?x2669, ?x845), contains(?x2982, ?x659), adjoins(?x4198, ?x1275), state(?x3987, ?x2982), capital(?x1274, ?x7328), religion(?x2982, ?x2591), location(?x117, ?x2982), taxonomy(?x1138, ?x939), ?x4776 = 06yxd, time_zones(?x4198, ?x1638), legislative_sessions(?x2860, ?x845), ?x2713 = 06btq, location(?x1461, ?x1274), contains(?x8260, ?x2982), adjoins(?x1274, ?x1905), location(?x338, ?x5575) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 44 *> extensional distance = 2 *> proper extension: 077g7n; *> query: (?x1829, ?x760) <- district_represented(?x1829, ?x4198), district_represented(?x1829, ?x3670), district_represented(?x1829, ?x3086), district_represented(?x1829, ?x2977), district_represented(?x1829, ?x2049), district_represented(?x1829, ?x1906), district_represented(?x1829, ?x961), ?x4198 = 05fky, legislative_sessions(?x6728, ?x1829), legislative_sessions(?x2976, ?x1829), legislative_sessions(?x1137, ?x1829), legislative_sessions(?x1028, ?x1829), legislative_sessions(?x952, ?x1829), legislative_sessions(?x653, ?x1829), legislative_sessions(?x606, ?x1829), legislative_sessions(?x605, ?x1829), legislative_sessions(?x355, ?x1829), ?x1906 = 04rrx, legislative_sessions(?x9334, ?x1829), legislative_sessions(?x2357, ?x1829), ?x2976 = 03rtmz, legislative_sessions(?x1829, ?x3463), ?x2977 = 081mh, ?x952 = 06f0dc, ?x653 = 070m6c, ?x2049 = 050l8, ?x3086 = 0846v, ?x9334 = 02hy5d, ?x1028 = 032ft5, legislative_sessions(?x11440, ?x605), ?x3463 = 02bqmq, ?x3670 = 05tbn, ?x1137 = 02bqn1, ?x355 = 0495ys, ?x2357 = 0bymv, religion(?x961, ?x109), legislative_sessions(?x2860, ?x606), legislative_sessions(?x6742, ?x606), ?x6728 = 070mff, district_represented(?x605, ?x7518), district_represented(?x605, ?x760), contains(?x961, ?x310), ?x7518 = 026mj, country(?x961, ?x94) *> conf = 0.88 ranks of expected_values: 4, 8, 11, 17, 18 EVAL 02bp37 district_represented 07_f2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 40.000 32.000 0.885 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 02bp37 district_represented 050ks CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 40.000 32.000 0.885 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 02bp37 district_represented 06mz5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 40.000 32.000 0.885 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 02bp37 district_represented 01n7q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 40.000 32.000 0.885 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 02bp37 district_represented 059_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 40.000 32.000 0.885 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #7183-0qt85 PRED entity: 0qt85 PRED relation: contains! PRED expected values: 0vbk => 104 concepts (86 used for prediction) PRED predicted values (max 10 best out of 251): 05kkh (0.33 #8, 0.05 #67155, 0.04 #27770), 0n2k5 (0.33 #764, 0.03 #3450, 0.02 #4347), 04_1l0v (0.24 #4929, 0.17 #7616, 0.13 #25527), 01n7q (0.19 #59169, 0.17 #17991, 0.14 #50215), 07ssc (0.18 #26003, 0.16 #43009, 0.16 #42114), 07b_l (0.18 #19926, 0.15 #24403, 0.14 #27088), 059rby (0.17 #1810, 0.14 #2705, 0.12 #3602), 02jx1 (0.13 #76194, 0.12 #26058, 0.12 #43064), 03v0t (0.12 #1127, 0.11 #2023, 0.09 #5607), 02_286 (0.11 #1833, 0.10 #2728, 0.09 #3625) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01snm; >> query: (?x12036, 05kkh) <- place_of_birth(?x1357, ?x12036), award_winner(?x1357, ?x6331), award_winner(?x9095, ?x1357), ?x9095 = 0dqcm >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #19991 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 145 *> proper extension: 0ml25; 0nh0f; 0nvd8; 0ntwb; 0nh57; 0_wm_; 0mrf1; 0msck; *> query: (?x12036, 0vbk) <- time_zones(?x12036, ?x1638), ?x1638 = 02fqwt, source(?x12036, ?x958) *> conf = 0.04 ranks of expected_values: 54 EVAL 0qt85 contains! 0vbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 104.000 86.000 0.333 http://example.org/location/location/contains #7182-0rh6k PRED entity: 0rh6k PRED relation: mode_of_transportation PRED expected values: 025t3bg => 238 concepts (238 used for prediction) PRED predicted values (max 10 best out of 3): 025t3bg (0.83 #73, 0.81 #19, 0.80 #76), 06d_3 (0.20 #6, 0.06 #24, 0.04 #114), 0k4j (0.02 #185, 0.02 #83, 0.02 #113) >> Best rule #73 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 01914; 02cl1; 02_286; 0fhp9; 080h2; 05ywg; 030qb3t; 0156q; 0h7h6; 01_d4; ... >> query: (?x108, 025t3bg) <- month(?x108, ?x2255), place_of_birth(?x236, ?x108), ?x2255 = 040fv >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rh6k mode_of_transportation 025t3bg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 238.000 238.000 0.829 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #7181-0g_w PRED entity: 0g_w PRED relation: instance_of_recurring_event! PRED expected values: 0gmdkyy 0fy6bh 0bc773 0bzn6_ 0c53zb 05hmp6 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 117): 02cg41 (0.25 #108, 0.20 #173, 0.17 #238), 0gx1673 (0.25 #102, 0.20 #167, 0.17 #232), 01xqqp (0.25 #97, 0.20 #162, 0.17 #227), 01mh_q (0.25 #95, 0.20 #160, 0.17 #225), 013b2h (0.25 #93, 0.20 #158, 0.17 #223), 0466p0j (0.25 #92, 0.20 #157, 0.17 #222), 09n4nb (0.25 #81, 0.20 #146, 0.17 #211), 01mhwk (0.25 #80, 0.20 #145, 0.17 #210), 056878 (0.25 #76, 0.20 #141, 0.17 #206), 0gpjbt (0.25 #74, 0.20 #139, 0.17 #204) >> Best rule #108 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0c4ys; 0gcf2r; >> query: (?x3459, 02cg41) <- category_of(?x3458, ?x3459), instance_of_recurring_event(?x7038, ?x3459), ceremony(?x1079, ?x7038), nominated_for(?x3458, ?x69) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #457 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 0j6j8; 01ppdy; 02tzwd; *> query: (?x3459, ?x11428) <- category_of(?x500, ?x3459), category_of(?x484, ?x3459), award_winner(?x500, ?x902), award(?x12186, ?x484), award(?x382, ?x500), award_winner(?x11428, ?x12186) *> conf = 0.11 ranks of expected_values: 83, 93, 96, 98, 100, 107 EVAL 0g_w instance_of_recurring_event! 05hmp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 58.000 58.000 0.250 http://example.org/time/event/instance_of_recurring_event EVAL 0g_w instance_of_recurring_event! 0c53zb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 58.000 58.000 0.250 http://example.org/time/event/instance_of_recurring_event EVAL 0g_w instance_of_recurring_event! 0bzn6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 58.000 58.000 0.250 http://example.org/time/event/instance_of_recurring_event EVAL 0g_w instance_of_recurring_event! 0bc773 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 58.000 58.000 0.250 http://example.org/time/event/instance_of_recurring_event EVAL 0g_w instance_of_recurring_event! 0fy6bh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 58.000 58.000 0.250 http://example.org/time/event/instance_of_recurring_event EVAL 0g_w instance_of_recurring_event! 0gmdkyy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 58.000 58.000 0.250 http://example.org/time/event/instance_of_recurring_event #7180-0ct5zc PRED entity: 0ct5zc PRED relation: film_crew_role PRED expected values: 01pvkk => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 25): 09vw2b7 (0.74 #620, 0.65 #1199, 0.63 #1127), 02r96rf (0.70 #616, 0.69 #111, 0.68 #75), 01vx2h (0.45 #624, 0.33 #119, 0.32 #1131), 01pvkk (0.33 #625, 0.31 #228, 0.29 #1204), 01xy5l_ (0.20 #14, 0.11 #1461, 0.11 #1134), 02ynfr (0.20 #629, 0.18 #1208, 0.18 #196), 02rh1dz (0.19 #623, 0.13 #190, 0.12 #82), 0215hd (0.18 #19, 0.16 #632, 0.14 #1466), 089g0h (0.18 #20, 0.11 #633, 0.11 #1467), 02_n3z (0.14 #1, 0.09 #976, 0.09 #397) >> Best rule #620 for best value: >> intensional similarity = 4 >> extensional distance = 308 >> proper extension: 03t97y; 08sk8l; 01gglm; 07p12s; >> query: (?x2342, 09vw2b7) <- language(?x2342, ?x254), film_crew_role(?x2342, ?x2095), currency(?x2342, ?x170), ?x2095 = 0dxtw >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #625 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 308 *> proper extension: 03t97y; 08sk8l; 01gglm; 07p12s; *> query: (?x2342, 01pvkk) <- language(?x2342, ?x254), film_crew_role(?x2342, ?x2095), currency(?x2342, ?x170), ?x2095 = 0dxtw *> conf = 0.33 ranks of expected_values: 4 EVAL 0ct5zc film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 93.000 0.739 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7179-01wdqrx PRED entity: 01wdqrx PRED relation: instrumentalists! PRED expected values: 018vs => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 74): 0342h (0.65 #2796, 0.65 #1222, 0.64 #3060), 05r5c (0.47 #5596, 0.45 #5245, 0.45 #5771), 0l14md (0.42 #2880, 0.38 #2792, 0.29 #3579), 02hnl (0.42 #2880, 0.38 #2792, 0.23 #2826), 018vs (0.41 #2805, 0.41 #2717, 0.36 #3940), 05842k (0.29 #3579, 0.27 #4015, 0.26 #3056), 0mkg (0.29 #3579, 0.27 #4015, 0.26 #3056), 07brj (0.29 #3579, 0.27 #4015, 0.26 #3056), 0l15bq (0.29 #3579, 0.27 #4015, 0.26 #3056), 03qjg (0.22 #1269, 0.19 #2843, 0.17 #3542) >> Best rule #2796 for best value: >> intensional similarity = 3 >> extensional distance = 228 >> proper extension: 07_3qd; 04mx7s; >> query: (?x1282, 0342h) <- instrumentalists(?x212, ?x1282), gender(?x1282, ?x231), role(?x1282, ?x315) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #2805 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 228 *> proper extension: 07_3qd; 04mx7s; *> query: (?x1282, 018vs) <- instrumentalists(?x212, ?x1282), gender(?x1282, ?x231), role(?x1282, ?x315) *> conf = 0.41 ranks of expected_values: 5 EVAL 01wdqrx instrumentalists! 018vs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 121.000 121.000 0.652 http://example.org/music/instrument/instrumentalists #7178-0m_h6 PRED entity: 0m_h6 PRED relation: country PRED expected values: 09c7w0 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 31): 09c7w0 (0.84 #2, 0.80 #612, 0.80 #185), 07ssc (0.28 #78, 0.21 #873, 0.21 #627), 0f8l9c (0.13 #81, 0.11 #814, 0.10 #3265), 0345h (0.09 #3334, 0.09 #822, 0.09 #3517), 0d060g (0.07 #558, 0.06 #2939, 0.06 #131), 03rjj (0.06 #2939, 0.05 #68, 0.04 #801), 03_3d (0.06 #2939, 0.04 #4173, 0.04 #5641), 0chghy (0.06 #2939, 0.03 #807, 0.03 #931), 01mjq (0.06 #2939, 0.02 #219, 0.02 #280), 0ctw_b (0.06 #2939, 0.02 #85, 0.02 #1677) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 04v8x9; 0ds33; 0jzw; 0cwy47; 0bcndz; 0k4kk; 02q52q; 070fnm; 083skw; 0kcn7; ... >> query: (?x9059, 09c7w0) <- award_winner(?x9059, ?x3519), nominated_for(?x484, ?x9059), film(?x7091, ?x9059), film_sets_designed(?x4423, ?x9059) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m_h6 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.841 http://example.org/film/film/country #7177-07vfy4 PRED entity: 07vfy4 PRED relation: film! PRED expected values: 07f0tw => 70 concepts (49 used for prediction) PRED predicted values (max 10 best out of 992): 01m5m5b (0.45 #33344, 0.44 #2084, 0.43 #37512), 025jfl (0.45 #33344, 0.44 #2084, 0.43 #37512), 08y7b9 (0.33 #1944, 0.06 #4028, 0.05 #6112), 01zh29 (0.33 #1413, 0.06 #3497, 0.04 #5581), 0tj9 (0.33 #2022, 0.06 #4106, 0.04 #6190), 0292l3 (0.25 #232, 0.06 #2316, 0.03 #8570), 03x31g (0.25 #1850, 0.04 #3934, 0.02 #6018), 0bxy67 (0.25 #1777, 0.04 #3861, 0.02 #5945), 01zp33 (0.25 #1306, 0.04 #3390, 0.02 #5474), 0241wg (0.17 #534, 0.04 #2618, 0.02 #4702) >> Best rule #33344 for best value: >> intensional similarity = 4 >> extensional distance = 679 >> proper extension: 0gtvrv3; >> query: (?x9805, ?x617) <- film_release_region(?x9805, ?x94), ?x94 = 09c7w0, nominated_for(?x617, ?x9805), film_crew_role(?x9805, ?x468) >> conf = 0.45 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 07vfy4 film! 07f0tw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 49.000 0.447 http://example.org/film/actor/film./film/performance/film #7176-0mwk9 PRED entity: 0mwk9 PRED relation: currency PRED expected values: 09nqf => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.84 #23, 0.84 #21, 0.84 #28) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 144 >> proper extension: 02cl1; 0235l; 0mnyn; >> query: (?x12296, 09nqf) <- county_seat(?x12296, ?x12295), contains(?x3670, ?x12296), source(?x12296, ?x958), district_represented(?x176, ?x3670) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwk9 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.842 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #7175-037lyl PRED entity: 037lyl PRED relation: nationality PRED expected values: 09c7w0 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 54): 09c7w0 (0.85 #3312, 0.74 #2609, 0.74 #3712), 04_1l0v (0.20 #2810), 02jx1 (0.13 #533, 0.13 #633, 0.13 #3444), 07ssc (0.12 #115, 0.10 #615, 0.09 #715), 0d060g (0.12 #107, 0.06 #607, 0.05 #807), 03rk0 (0.08 #1549, 0.05 #9465, 0.05 #11471), 0f8l9c (0.07 #322, 0.06 #122, 0.06 #222), 0345h (0.06 #131, 0.06 #831, 0.06 #1534), 059rby (0.05 #1503, 0.04 #2004, 0.04 #2506), 06q1r (0.04 #377, 0.04 #277, 0.03 #477) >> Best rule #3312 for best value: >> intensional similarity = 2 >> extensional distance = 550 >> proper extension: 02x8mt; >> query: (?x4013, 09c7w0) <- student(?x2909, ?x4013), registering_agency(?x2909, ?x1982) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 037lyl nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.848 http://example.org/people/person/nationality #7174-0p_qr PRED entity: 0p_qr PRED relation: genre PRED expected values: 02l7c8 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 105): 05p553 (0.66 #5294, 0.33 #6014, 0.33 #5894), 02l7c8 (0.38 #859, 0.37 #618, 0.35 #137), 02kdv5l (0.34 #723, 0.27 #6732, 0.26 #7812), 04xvlr (0.29 #121, 0.24 #481, 0.23 #2407), 01jfsb (0.29 #6743, 0.28 #1817, 0.28 #7823), 0lsxr (0.27 #9, 0.26 #249, 0.19 #2415), 03bxz7 (0.27 #55, 0.17 #175, 0.17 #295), 060__y (0.24 #5308, 0.23 #18, 0.20 #498), 03k9fj (0.21 #7822, 0.21 #8902, 0.21 #9022), 017fp (0.19 #136, 0.13 #256, 0.11 #2543) >> Best rule #5294 for best value: >> intensional similarity = 3 >> extensional distance = 888 >> proper extension: 04svwx; >> query: (?x3505, 05p553) <- genre(?x3505, ?x3515), genre(?x5747, ?x3515), ?x5747 = 0660b9b >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #859 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 157 *> proper extension: 0c00zd0; 05cj_j; 0m491; 075cph; 0gyy53; 0gy2y8r; 0bbw2z6; 01xlqd; 02wwmhc; *> query: (?x3505, 02l7c8) <- nominated_for(?x1126, ?x3505), genre(?x3505, ?x53), film(?x1850, ?x3505), costume_design_by(?x3505, ?x3685) *> conf = 0.38 ranks of expected_values: 2 EVAL 0p_qr genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 92.000 0.655 http://example.org/film/film/genre #7173-0488g PRED entity: 0488g PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 20): 0fkvn (0.78 #151, 0.78 #109, 0.78 #88), 0pqc5 (0.58 #1098, 0.49 #2232, 0.40 #1518), 060c4 (0.58 #1894, 0.55 #1705, 0.54 #1831), 060bp (0.50 #1892, 0.49 #1703, 0.48 #1829), 0fkzq (0.38 #2396, 0.26 #183, 0.25 #309), 0p5vf (0.25 #768, 0.10 #347, 0.09 #1293), 01t7n9 (0.17 #484, 0.17 #101, 0.17 #38), 0789n (0.17 #484, 0.15 #177, 0.15 #93), 02079p (0.17 #484, 0.13 #31, 0.13 #10), 01gkgk (0.17 #484, 0.13 #27, 0.13 #6) >> Best rule #151 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 018jcq; >> query: (?x1782, 0fkvn) <- administrative_parent(?x1782, ?x94), jurisdiction_of_office(?x3959, ?x1782), state_province_region(?x1783, ?x1782) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0488g jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 182.000 0.783 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #7172-09hnb PRED entity: 09hnb PRED relation: artist! PRED expected values: 0mcf4 => 129 concepts (79 used for prediction) PRED predicted values (max 10 best out of 99): 0n85g (0.25 #60, 0.19 #740, 0.12 #1012), 0g768 (0.25 #35, 0.12 #7523, 0.12 #6569), 01trtc (0.25 #69, 0.09 #4695, 0.08 #3333), 0fb0v (0.19 #7, 0.14 #687, 0.10 #143), 01cl0d (0.19 #52, 0.09 #596, 0.08 #732), 01cl2y (0.17 #708, 0.14 #572, 0.08 #844), 01clyr (0.14 #167, 0.08 #1799, 0.07 #1391), 03qy3l (0.14 #197, 0.06 #1013, 0.06 #741), 011k1h (0.14 #690, 0.11 #554, 0.11 #6544), 01cf93 (0.14 #735, 0.11 #599, 0.08 #871) >> Best rule #60 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0m19t; >> query: (?x2698, 0n85g) <- artists(?x2936, ?x2698), category(?x2698, ?x134), ?x2936 = 029h7y >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1416 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 02zrv7; *> query: (?x2698, 0mcf4) <- gender(?x2698, ?x231), profession(?x2698, ?x1183), performance_role(?x2698, ?x316) *> conf = 0.06 ranks of expected_values: 32 EVAL 09hnb artist! 0mcf4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 129.000 79.000 0.250 http://example.org/music/record_label/artist #7171-09p06 PRED entity: 09p06 PRED relation: people! PRED expected values: 04p3w => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 40): 0gk4g (0.29 #335, 0.29 #140, 0.23 #1115), 0dq9p (0.17 #81, 0.12 #341, 0.12 #2356), 02knxx (0.14 #161, 0.08 #226, 0.07 #1136), 02k6hp (0.14 #166, 0.07 #2246, 0.07 #2376), 0qcr0 (0.14 #1106, 0.10 #2211, 0.10 #1431), 01psyx (0.10 #304, 0.04 #2254, 0.04 #2319), 04p3w (0.08 #2351, 0.08 #2546, 0.08 #661), 02y0js (0.08 #912, 0.08 #2797, 0.08 #2927), 0jdk0 (0.08 #200, 0.07 #265, 0.04 #395), 01mtqf (0.07 #264, 0.06 #394, 0.04 #199) >> Best rule #335 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 05hjmd; >> query: (?x3637, 0gk4g) <- people(?x5801, ?x3637), nominated_for(?x3637, ?x3638), place_of_burial(?x3637, ?x14112) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2351 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 319 *> proper extension: 02m30v; *> query: (?x3637, 04p3w) <- profession(?x3637, ?x1032), ?x1032 = 02hrh1q, people(?x5801, ?x3637) *> conf = 0.08 ranks of expected_values: 7 EVAL 09p06 people! 04p3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 121.000 121.000 0.293 http://example.org/people/cause_of_death/people #7170-01z7_f PRED entity: 01z7_f PRED relation: award PRED expected values: 0789_m => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 283): 0ck27z (0.70 #8061, 0.70 #18542, 0.69 #14510), 01by1l (0.36 #3335, 0.22 #15427, 0.12 #8575), 0fbtbt (0.34 #3859, 0.31 #4665, 0.29 #7083), 0cjyzs (0.29 #3733, 0.28 #6957, 0.27 #4539), 0cqhk0 (0.25 #1246, 0.25 #843, 0.21 #8098), 01bgqh (0.21 #3266, 0.16 #15358, 0.08 #8506), 0bdx29 (0.20 #511, 0.19 #32250, 0.18 #31442), 09qvc0 (0.20 #443, 0.13 #46360, 0.11 #27813), 09qv3c (0.20 #453, 0.11 #27813, 0.05 #6095), 0bb57s (0.20 #647, 0.11 #27813, 0.04 #2259) >> Best rule #8061 for best value: >> intensional similarity = 3 >> extensional distance = 254 >> proper extension: 03wpmd; >> query: (?x4328, ?x1670) <- award_winner(?x369, ?x4328), religion(?x4328, ?x1985), award_winner(?x1670, ?x4328) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #423 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 015qyf; *> query: (?x4328, 0789_m) <- award_winner(?x9450, ?x4328), ?x9450 = 0bx6zs, film(?x4328, ?x1644) *> conf = 0.10 ranks of expected_values: 73 EVAL 01z7_f award 0789_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 124.000 124.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7169-0j1z8 PRED entity: 0j1z8 PRED relation: contains! PRED expected values: 0j1z8 04wsz => 102 concepts (68 used for prediction) PRED predicted values (max 10 best out of 95): 02j71 (0.58 #58182, 0.55 #38489, 0.45 #60871), 02j9z (0.45 #41201, 0.43 #19716, 0.40 #21505), 04wsz (0.39 #2289, 0.29 #5867, 0.27 #12131), 09c7w0 (0.36 #29540, 0.25 #52814, 0.24 #59080), 0dg3n1 (0.34 #27005, 0.34 #25214, 0.30 #1947), 07c5l (0.25 #32615, 0.23 #24558, 0.21 #45147), 04_1l0v (0.22 #29986, 0.20 #50573, 0.20 #55946), 05nrg (0.19 #35473, 0.13 #14882, 0.11 #16672), 059g4 (0.18 #41634, 0.06 #24626, 0.06 #29104), 073q1 (0.14 #3989, 0.13 #7568, 0.11 #12042) >> Best rule #58182 for best value: >> intensional similarity = 3 >> extensional distance = 225 >> proper extension: 01w0v; 0290rb; 09f07; >> query: (?x311, ?x551) <- contains(?x311, ?x3838), category(?x3838, ?x134), administrative_parent(?x311, ?x551) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #2289 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 21 *> proper extension: 0h44w; *> query: (?x311, 04wsz) <- countries_spoken_in(?x5359, ?x311), ?x5359 = 0jzc *> conf = 0.39 ranks of expected_values: 3, 47 EVAL 0j1z8 contains! 04wsz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 68.000 0.577 http://example.org/location/location/contains EVAL 0j1z8 contains! 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 102.000 68.000 0.577 http://example.org/location/location/contains #7168-0x25q PRED entity: 0x25q PRED relation: nominated_for PRED expected values: 07cz2 => 117 concepts (67 used for prediction) PRED predicted values (max 10 best out of 215): 07cz2 (0.84 #9535, 0.78 #6522, 0.78 #6521), 03tbg6 (0.14 #240, 0.02 #3998, 0.01 #4500), 0fsw_7 (0.12 #5164, 0.10 #4160, 0.07 #652), 02qrv7 (0.12 #5048, 0.10 #4044, 0.07 #536), 01kf4tt (0.12 #5086, 0.10 #4082, 0.07 #574), 02sg5v (0.12 #5033, 0.10 #4029, 0.07 #521), 01kf3_9 (0.12 #5065, 0.10 #4061, 0.06 #8083), 0g5pvv (0.11 #5180, 0.09 #4176, 0.05 #2923), 02n72k (0.11 #5199, 0.09 #4195, 0.05 #8217), 025twgt (0.09 #5258, 0.09 #4254, 0.07 #746) >> Best rule #9535 for best value: >> intensional similarity = 3 >> extensional distance = 226 >> proper extension: 02fn5r; >> query: (?x3055, ?x2770) <- nominated_for(?x3055, ?x1807), nominated_for(?x2770, ?x3055), nominated_for(?x298, ?x2770) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0x25q nominated_for 07cz2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 67.000 0.840 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #7167-0c8tk PRED entity: 0c8tk PRED relation: category PRED expected values: 08mbj5d => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.75 #97, 0.74 #19, 0.74 #18) >> Best rule #97 for best value: >> intensional similarity = 4 >> extensional distance = 167 >> proper extension: 0jpkg; 0dzs0; >> query: (?x4335, 08mbj5d) <- citytown(?x9399, ?x4335), colors(?x9399, ?x9464), institution(?x865, ?x9399), state_province_region(?x9399, ?x9305) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c8tk category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.746 http://example.org/common/topic/webpage./common/webpage/category #7166-0l14qv PRED entity: 0l14qv PRED relation: instrumentalists PRED expected values: 0135xb 01mr2g6 => 81 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1113): 01gg59 (0.73 #571, 0.71 #11048, 0.71 #569), 032t2z (0.73 #571, 0.71 #569, 0.70 #2283), 04kjrv (0.73 #571, 0.71 #569, 0.70 #2283), 01wg6y (0.73 #571, 0.71 #569, 0.70 #2283), 023slg (0.73 #571, 0.71 #569, 0.70 #2283), 0326tc (0.73 #571, 0.71 #569, 0.70 #2283), 050z2 (0.73 #571, 0.71 #569, 0.70 #572), 06k02 (0.73 #571, 0.71 #569, 0.70 #572), 07r4c (0.73 #571, 0.71 #569, 0.70 #572), 0f0qfz (0.73 #571, 0.71 #569, 0.70 #572) >> Best rule #571 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 0342h; >> query: (?x228, ?x3160) <- role(?x228, ?x894), role(?x228, ?x780), group(?x228, ?x1573), ?x780 = 01qzyz, role(?x4741, ?x228), role(?x3160, ?x228), performance_role(?x568, ?x228), artists(?x302, ?x3160), ?x4741 = 01s21dg, ?x894 = 03m5k, role(?x1291, ?x228), instrumentalists(?x228, ?x140) >> conf = 0.73 => this is the best rule for 57 predicted values *> Best rule #5501 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 05148p4; *> query: (?x228, 0135xb) <- role(?x228, ?x2059), role(?x228, ?x780), group(?x228, ?x1573), role(?x219, ?x780), role(?x228, ?x4583), role(?x228, ?x645), role(?x130, ?x228), instrumentalists(?x228, ?x140), ?x4583 = 0bmnm, ?x2059 = 0dwr4, ?x645 = 028tv0 *> conf = 0.50 ranks of expected_values: 92, 159 EVAL 0l14qv instrumentalists 01mr2g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 81.000 51.000 0.733 http://example.org/music/instrument/instrumentalists EVAL 0l14qv instrumentalists 0135xb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 81.000 51.000 0.733 http://example.org/music/instrument/instrumentalists #7165-02__x PRED entity: 02__x PRED relation: draft PRED expected values: 02x2khw 02z6872 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 14): 02z6872 (0.77 #910, 0.76 #867, 0.76 #866), 02x2khw (0.77 #910, 0.76 #867, 0.76 #866), 092j54 (0.55 #620, 0.55 #612, 0.50 #246), 05vsb7 (0.55 #620, 0.55 #606, 0.50 #240), 09l0x9 (0.55 #620, 0.55 #615, 0.50 #249), 0g3zpp (0.55 #620, 0.55 #607, 0.38 #853), 03nt7j (0.55 #620, 0.52 #611, 0.42 #245), 02qw1zx (0.55 #620, 0.41 #609, 0.33 #243), 038c0q (0.54 #553, 0.44 #683, 0.40 #104), 0f4vx0 (0.51 #730, 0.50 #557, 0.40 #108) >> Best rule #910 for best value: >> intensional similarity = 10 >> extensional distance = 63 >> proper extension: 01jvgt; >> query: (?x6074, ?x1161) <- sport(?x6074, ?x5063), team(?x5727, ?x6074), teams(?x3501, ?x6074), team(?x5727, ?x11919), team(?x5727, ?x1160), school(?x6074, ?x2948), category(?x1160, ?x134), teams(?x2017, ?x1160), colors(?x11919, ?x332), draft(?x1160, ?x1161) >> conf = 0.77 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2 EVAL 02__x draft 02z6872 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.767 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02__x draft 02x2khw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.767 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #7164-013h1c PRED entity: 013h1c PRED relation: source PRED expected values: 0jbk9 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #4, 0.75 #14, 0.75 #13) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x11255, 0jbk9) <- category(?x11255, ?x134), ?x134 = 08mbj5d, place(?x11255, ?x11255) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013h1c source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #7163-05nqq3 PRED entity: 05nqq3 PRED relation: type_of_union PRED expected values: 04ztj => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.82 #9, 0.76 #13, 0.76 #33), 01g63y (0.25 #309, 0.16 #70, 0.16 #74), 0jgjn (0.25 #309, 0.01 #28) >> Best rule #9 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 012d40; 03jldb; 032w8h; 0gz5hs; 02mhfy; 01j7rd; 01wyzyl; 02qgyv; 0738b8; 046lt; ... >> query: (?x9139, 04ztj) <- location(?x9139, ?x12033), profession(?x9139, ?x1146), languages(?x9139, ?x1882), nationality(?x9139, ?x2146), ?x1146 = 018gz8 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05nqq3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.818 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7162-08fn5b PRED entity: 08fn5b PRED relation: featured_film_locations PRED expected values: 01_d4 => 122 concepts (103 used for prediction) PRED predicted values (max 10 best out of 94): 02_286 (0.21 #980, 0.19 #261, 0.18 #3140), 030qb3t (0.10 #1479, 0.10 #279, 0.09 #3878), 04jpl (0.08 #11053, 0.08 #4089, 0.07 #5529), 0rh6k (0.06 #2881, 0.05 #961, 0.04 #3841), 01_d4 (0.05 #2926, 0.04 #3166, 0.04 #2687), 080h2 (0.05 #3384, 0.04 #1465, 0.04 #1225), 06y57 (0.04 #1543, 0.03 #3462, 0.03 #343), 0h7h6 (0.04 #283, 0.03 #4122, 0.03 #3882), 035p3 (0.04 #1673, 0.03 #3112, 0.03 #3592), 03rjj (0.03 #2167, 0.03 #1447, 0.02 #3366) >> Best rule #980 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 0b2v79; 0gzy02; 01m13b; 09q5w2; 020fcn; 0hv1t; 0340hj; 01f7kl; 04t6fk; 059rc; ... >> query: (?x4167, 02_286) <- nominated_for(?x451, ?x4167), production_companies(?x4167, ?x382), written_by(?x4167, ?x3260), films(?x326, ?x4167) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2926 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 163 *> proper extension: 03hj3b3; 0bm2g; 0pd4f; 0m_q0; 0k5fg; 0m_h6; 03bdkd; *> query: (?x4167, 01_d4) <- nominated_for(?x500, ?x4167), film(?x382, ?x4167), film(?x3051, ?x4167), ?x500 = 0p9sw *> conf = 0.05 ranks of expected_values: 5 EVAL 08fn5b featured_film_locations 01_d4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 122.000 103.000 0.215 http://example.org/film/film/featured_film_locations #7161-0gg5kmg PRED entity: 0gg5kmg PRED relation: film_release_region PRED expected values: 0d060g 07ssc 0d0kn 05b4w => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 129): 0d060g (0.88 #144, 0.83 #424, 0.81 #284), 07ssc (0.88 #572, 0.87 #152, 0.86 #432), 05b4w (0.85 #193, 0.84 #333, 0.82 #473), 03rt9 (0.85 #150, 0.81 #290, 0.81 #430), 01ls2 (0.75 #148, 0.55 #428, 0.54 #288), 01mjq (0.64 #458, 0.63 #178, 0.62 #318), 016wzw (0.62 #195, 0.51 #335, 0.48 #475), 06c1y (0.60 #177, 0.46 #317, 0.45 #457), 047yc (0.58 #163, 0.51 #583, 0.49 #443), 077qn (0.52 #213, 0.40 #493, 0.38 #353) >> Best rule #144 for best value: >> intensional similarity = 7 >> extensional distance = 50 >> proper extension: 0gtsx8c; 0g5qs2k; 0gkz15s; 087wc7n; 08hmch; 0gj8t_b; 017gm7; 0gxtknx; 0bq8tmw; 0gj9tn5; ... >> query: (?x6175, 0d060g) <- film_release_region(?x6175, ?x1917), film_release_region(?x6175, ?x1790), film_release_region(?x6175, ?x142), ?x1790 = 01pj7, ?x142 = 0jgd, ?x1917 = 01p1v, film(?x2499, ?x6175) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 22 EVAL 0gg5kmg film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gg5kmg film_release_region 0d0kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 57.000 57.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gg5kmg film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gg5kmg film_release_region 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7160-01n6c PRED entity: 01n6c PRED relation: countries_spoken_in! PRED expected values: 064_8sq => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 50): 064_8sq (0.76 #1105, 0.71 #827, 0.69 #938), 02h40lc (0.38 #1051, 0.36 #718, 0.36 #1713), 0jzc (0.19 #15, 0.17 #290, 0.17 #70), 06nm1 (0.19 #724, 0.17 #890, 0.17 #1057), 012v8 (0.12 #372, 0.12 #206, 0.12 #482), 02ztjwg (0.12 #138, 0.11 #248, 0.11 #359), 02bjrlw (0.12 #166, 0.10 #442, 0.09 #221), 05zjd (0.08 #407, 0.07 #1015, 0.07 #1070), 04306rv (0.08 #446, 0.08 #170, 0.08 #501), 0x82 (0.07 #433, 0.03 #874, 0.03 #985) >> Best rule #1105 for best value: >> intensional similarity = 2 >> extensional distance = 140 >> proper extension: 0g8bw; 088q1s; >> query: (?x2468, ?x5607) <- official_language(?x2468, ?x5607), countries_spoken_in(?x5003, ?x2468) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n6c countries_spoken_in! 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.759 http://example.org/language/human_language/countries_spoken_in #7159-0gj96ln PRED entity: 0gj96ln PRED relation: film_release_region PRED expected values: 0k6nt 02vzc 03h64 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 157): 0d0vqn (0.93 #552, 0.90 #1648, 0.90 #963), 03h64 (0.90 #1697, 0.89 #2245, 0.89 #875), 02vzc (0.82 #1138, 0.82 #1549, 0.79 #2234), 0k6nt (0.82 #1526, 0.82 #978, 0.81 #1663), 016wzw (0.73 #876, 0.72 #1150, 0.71 #602), 05qx1 (0.64 #580, 0.61 #854, 0.60 #991), 06c1y (0.62 #582, 0.59 #856, 0.53 #993), 09pmkv (0.59 #1117, 0.58 #569, 0.54 #843), 03ryn (0.55 #893, 0.49 #1167, 0.42 #619), 047lj (0.54 #830, 0.50 #1104, 0.40 #1515) >> Best rule #552 for best value: >> intensional similarity = 8 >> extensional distance = 43 >> proper extension: 0407yfx; >> query: (?x6168, 0d0vqn) <- film_release_region(?x6168, ?x2267), film_release_region(?x6168, ?x429), film_release_region(?x6168, ?x410), film_release_region(?x6168, ?x344), ?x410 = 01ls2, ?x429 = 03rt9, ?x2267 = 03rj0, ?x344 = 04gzd >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1697 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 100 *> proper extension: 0gkz15s; *> query: (?x6168, 03h64) <- film_release_region(?x6168, ?x2267), film_release_region(?x6168, ?x429), film_release_region(?x6168, ?x410), ?x410 = 01ls2, ?x429 = 03rt9, member_states(?x7695, ?x2267) *> conf = 0.90 ranks of expected_values: 2, 3, 4 EVAL 0gj96ln film_release_region 03h64 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gj96ln film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gj96ln film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.933 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7158-0h03fhx PRED entity: 0h03fhx PRED relation: nominated_for! PRED expected values: 02qvyrt 0gqy2 0fhpv4 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 213): 0gq9h (0.79 #222, 0.71 #7929, 0.69 #1763), 019f4v (0.79 #222, 0.71 #7929, 0.69 #1763), 0gr4k (0.79 #222, 0.71 #7929, 0.69 #1763), 02qvyrt (0.59 #964, 0.28 #221, 0.27 #3385), 0gs9p (0.54 #935, 0.31 #1595, 0.30 #5558), 0gr0m (0.44 #930, 0.23 #1150, 0.22 #3351), 02r22gf (0.44 #905, 0.20 #1125, 0.16 #3326), 0gqy2 (0.38 #990, 0.28 #221, 0.24 #11235), 0f4x7 (0.33 #902, 0.28 #221, 0.24 #11235), 0gq_v (0.33 #897, 0.25 #3318, 0.25 #5520) >> Best rule #222 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0kv9d3; >> query: (?x4607, ?x198) <- award_winner(?x4607, ?x10634), award_winner(?x4607, ?x286), ?x10634 = 0csdzz, award(?x4607, ?x198), award(?x286, ?x68) >> conf = 0.79 => this is the best rule for 3 predicted values *> Best rule #964 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 0c_j9x; *> query: (?x4607, 02qvyrt) <- award_winner(?x4607, ?x10634), nominated_for(?x1198, ?x4607), music(?x1318, ?x10634), ?x1198 = 02pqp12 *> conf = 0.59 ranks of expected_values: 4, 8, 48 EVAL 0h03fhx nominated_for! 0fhpv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 83.000 83.000 0.789 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0h03fhx nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 83.000 83.000 0.789 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0h03fhx nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 83.000 0.789 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7157-0bsjcw PRED entity: 0bsjcw PRED relation: award! PRED expected values: 05slvm 0h96g 0mbhr => 38 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2574): 03z509 (0.82 #33513, 0.77 #40215, 0.73 #40216), 01dvms (0.82 #33513, 0.73 #40216, 0.73 #40214), 03yk8z (0.82 #33513, 0.73 #40216, 0.73 #40214), 01kb2j (0.75 #31644, 0.67 #18239, 0.50 #24942), 02jsgf (0.70 #24605, 0.62 #31307, 0.38 #27956), 02kxwk (0.70 #24692, 0.54 #28043, 0.38 #31394), 0lpjn (0.67 #20868, 0.62 #30922, 0.50 #24220), 019f2f (0.67 #20795, 0.60 #24147, 0.50 #30849), 0mz73 (0.67 #19011, 0.56 #32416, 0.50 #22362), 0dvld (0.67 #21847, 0.56 #31901, 0.50 #18496) >> Best rule #33513 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 09qwmm; 03c7tr1; 02z0dfh; 099cng; 02ppm4q; 0cqgl9; >> query: (?x3989, ?x4349) <- award(?x3705, ?x3989), award(?x2028, ?x3989), ?x2028 = 028knk, film(?x3705, ?x695), award_nominee(?x1582, ?x3705), award_winner(?x3989, ?x4349) >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #11433 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 0gqwc; *> query: (?x3989, 0h96g) <- award(?x9777, ?x3989), award(?x8307, ?x3989), award(?x2028, ?x3989), award(?x1253, ?x3989), ?x2028 = 028knk, ?x8307 = 015nhn, ?x1253 = 0gjvqm, place_of_burial(?x9777, ?x3691) *> conf = 0.33 ranks of expected_values: 119, 138, 2327 EVAL 0bsjcw award! 0mbhr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 38.000 22.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bsjcw award! 0h96g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 38.000 22.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bsjcw award! 05slvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 38.000 22.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7156-04jb97 PRED entity: 04jb97 PRED relation: profession PRED expected values: 02hrh1q => 96 concepts (42 used for prediction) PRED predicted values (max 10 best out of 46): 02hrh1q (0.92 #4723, 0.92 #5311, 0.92 #4870), 01d_h8 (0.69 #888, 0.67 #2216, 0.67 #2363), 03gjzk (0.40 #749, 0.38 #1782, 0.37 #3401), 018gz8 (0.34 #751, 0.15 #1621, 0.15 #1931), 015cjr (0.33 #48, 0.17 #342, 0.15 #1621), 0dgd_ (0.33 #323, 0.06 #911, 0.06 #2386), 02krf9 (0.24 #907, 0.21 #2235, 0.21 #2529), 0cbd2 (0.24 #1331, 0.22 #1479, 0.22 #1775), 01c72t (0.23 #463, 0.15 #1621, 0.09 #1494), 0nbcg (0.23 #471, 0.06 #3858, 0.06 #4152) >> Best rule #4723 for best value: >> intensional similarity = 4 >> extensional distance = 1265 >> proper extension: 0m2wm; 02zq43; 04wqr; 07lmxq; 03m8lq; 01j5x6; 01v3s2_; 0bz5v2; 04cf09; 01wjrn; ... >> query: (?x8104, 02hrh1q) <- nominated_for(?x8104, ?x7502), film(?x8104, ?x4604), profession(?x8104, ?x524), film_release_region(?x4604, ?x94) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04jb97 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 42.000 0.922 http://example.org/people/person/profession #7155-0xrz2 PRED entity: 0xrz2 PRED relation: category PRED expected values: 08mbj5d => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #10, 0.79 #31, 0.79 #37) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 06_kh; 02dtg; 0f2r6; 0wh3; 0yc84; 0mnzd; 030qb3t; 0r2l7; 0k_q_; 0f__1; ... >> query: (?x8343, 08mbj5d) <- source(?x8343, ?x958), place_of_birth(?x1736, ?x8343), county(?x8343, ?x7492), administrative_division(?x3807, ?x7492) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xrz2 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.819 http://example.org/common/topic/webpage./common/webpage/category #7154-0jqzt PRED entity: 0jqzt PRED relation: language PRED expected values: 02h40lc => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.99 #2253, 0.95 #1419, 0.95 #1781), 064_8sq (0.23 #22, 0.14 #140, 0.13 #791), 06nm1 (0.23 #11, 0.12 #129, 0.10 #69), 02bjrlw (0.23 #1, 0.07 #770, 0.07 #119), 0jzc (0.10 #138, 0.03 #789, 0.03 #20), 04306rv (0.10 #5, 0.09 #123, 0.09 #476), 06b_j (0.10 #81, 0.08 #141, 0.06 #315), 03_9r (0.07 #10, 0.06 #302, 0.05 #1190), 0653m (0.07 #12, 0.04 #483, 0.03 #720), 05qqm (0.07 #41, 0.02 #159, 0.01 #512) >> Best rule #2253 for best value: >> intensional similarity = 4 >> extensional distance = 1615 >> proper extension: 05dy7p; 02n9bh; 0gcrg; 04lqvly; 011yfd; 027ct7c; 05_61y; 04cf_l; 0cq8nx; 0c5qvw; ... >> query: (?x11074, 02h40lc) <- genre(?x11074, ?x571), language(?x11074, ?x11038), language(?x6269, ?x11038), ?x6269 = 0286gm1 >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jqzt language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.988 http://example.org/film/film/language #7153-016clz PRED entity: 016clz PRED relation: artists PRED expected values: 01wl38s 01vs14j 03g5jw 01wj18h 0phx4 0mgcr 07yg2 016fnb 01k_yf 01bczm 044mfr 04b7xr 02rn_bj 01d_h 0cbm64 03q_w5 0jg77 07n68 => 65 concepts (22 used for prediction) PRED predicted values (max 10 best out of 3917): 01dw_f (0.67 #9328, 0.40 #11959, 0.33 #12837), 0frsw (0.67 #8934, 0.33 #10688, 0.33 #2793), 07h76 (0.67 #9220, 0.33 #10974, 0.33 #3079), 01jcxwp (0.60 #8415, 0.38 #10169, 0.33 #11046), 01wt4wc (0.60 #8484, 0.33 #11115, 0.33 #2343), 03sww (0.60 #8259, 0.33 #9136, 0.33 #2995), 015196 (0.60 #8673, 0.33 #2532, 0.33 #1655), 03j_hq (0.60 #8711, 0.33 #2570, 0.17 #9588), 01w03jv (0.60 #8744, 0.33 #2603, 0.17 #9621), 01x1cn2 (0.50 #11567, 0.50 #8936, 0.33 #15080) >> Best rule #9328 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 06cqb; 059kh; >> query: (?x302, 01dw_f) <- artists(?x302, ?x9241), artists(?x302, ?x8560), artists(?x302, ?x8048), profession(?x8560, ?x655), origin(?x8560, ?x5267), parent_genre(?x302, ?x1572), role(?x8048, ?x716), ?x9241 = 01w5gg6 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #9107 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 06cqb; 059kh; *> query: (?x302, 016fnb) <- artists(?x302, ?x9241), artists(?x302, ?x8560), artists(?x302, ?x8048), profession(?x8560, ?x655), origin(?x8560, ?x5267), parent_genre(?x302, ?x1572), role(?x8048, ?x716), ?x9241 = 01w5gg6 *> conf = 0.50 ranks of expected_values: 14, 57, 58, 59, 66, 81, 179, 189, 191, 194, 200, 240, 334, 451, 460, 473, 523, 599 EVAL 016clz artists 07n68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 0jg77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 03q_w5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 0cbm64 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 01d_h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 02rn_bj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 04b7xr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 044mfr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 01bczm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 01k_yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 016fnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 07yg2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 0mgcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 0phx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 01wj18h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 03g5jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 01vs14j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 65.000 22.000 0.667 http://example.org/music/genre/artists EVAL 016clz artists 01wl38s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 65.000 22.000 0.667 http://example.org/music/genre/artists #7152-06x76 PRED entity: 06x76 PRED relation: colors PRED expected values: 01g5v => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.89 #1546, 0.82 #923, 0.77 #1565), 019sc (0.77 #796, 0.76 #1474, 0.73 #1494), 01g5v (0.76 #1509, 0.64 #697, 0.40 #3), 06fvc (0.70 #1410, 0.37 #924, 0.36 #1547), 02rnmb (0.48 #1091, 0.34 #821, 0.23 #214), 038hg (0.44 #1166, 0.23 #214, 0.23 #213), 03vtbc (0.40 #8, 0.38 #27, 0.31 #298), 0jc_p (0.28 #211, 0.26 #980, 0.23 #214), 067z2v (0.28 #211, 0.26 #980, 0.23 #214), 09ggk (0.28 #211, 0.25 #983, 0.23 #214) >> Best rule #1546 for best value: >> intensional similarity = 13 >> extensional distance = 240 >> proper extension: 075q_; 0d_q40; 0266sb_; 044crp; 04mnts; 0f5hyg; 024d8w; 0b6p3qf; 050fh; 0b256b; ... >> query: (?x11061, 083jv) <- team(?x180, ?x11061), colors(?x11061, ?x332), colors(?x6988, ?x332), colors(?x5679, ?x332), colors(?x2171, ?x332), colors(?x10908, ?x332), colors(?x580, ?x332), ?x5679 = 022jr5, season(?x580, ?x2406), team(?x12323, ?x580), ?x10908 = 03915c, ?x2171 = 01jq34, organization(?x346, ?x6988) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1509 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 133 *> proper extension: 05jx2d; 02gys2; 03x746; 025txtg; 0j2pg; 04k3r_; 01fjz9; 0bl8l; 04zw9hs; 05hywl; ... *> query: (?x11061, 01g5v) <- team(?x2573, ?x11061), team(?x2573, ?x3114), colors(?x11061, ?x332), colors(?x6637, ?x332), colors(?x6177, ?x332), colors(?x11789, ?x332), ?x11789 = 02pyyld, colors(?x3114, ?x5325), school_type(?x6177, ?x3205), sport(?x3114, ?x1083), contains(?x94, ?x6177), institution(?x734, ?x6637) *> conf = 0.76 ranks of expected_values: 3 EVAL 06x76 colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 88.000 88.000 0.893 http://example.org/sports/sports_team/colors #7151-07mvp PRED entity: 07mvp PRED relation: group! PRED expected values: 01vsksr => 138 concepts (56 used for prediction) PRED predicted values (max 10 best out of 199): 0285c (0.20 #223, 0.10 #1995, 0.10 #615), 053y0s (0.20 #1, 0.06 #2166, 0.06 #785), 01vrx3g (0.20 #6, 0.06 #1185, 0.03 #2565), 01vrx35 (0.20 #142, 0.06 #1321, 0.03 #2701), 01vrnsk (0.20 #315, 0.06 #1494, 0.04 #1691), 01vsl3_ (0.20 #241, 0.06 #1420, 0.04 #1617), 01w9wwg (0.20 #305, 0.03 #2077, 0.02 #4044), 01svw8n (0.20 #268, 0.03 #2040, 0.02 #4007), 048tgl (0.16 #2340, 0.07 #6671, 0.07 #8047), 0jfx1 (0.11 #2165) >> Best rule #223 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01dwrc; >> query: (?x6475, 0285c) <- group(?x1291, ?x6475), award_winner(?x2180, ?x6475), ?x2180 = 02v1m7, category(?x6475, ?x134) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07mvp group! 01vsksr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 56.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group #7150-092t4b PRED entity: 092t4b PRED relation: honored_for PRED expected values: 09m6kg => 29 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1047): 0hz55 (0.50 #3265, 0.33 #292, 0.29 #4454), 0d68qy (0.38 #4310, 0.33 #743, 0.29 #5497), 09m6kg (0.33 #1792, 0.33 #1199, 0.25 #2387), 011yxg (0.33 #1795, 0.33 #1202, 0.25 #2390), 0dr3sl (0.33 #1947, 0.33 #1354, 0.25 #2542), 01rwpj (0.33 #2083, 0.25 #2678, 0.24 #593), 0bnzd (0.33 #2200, 0.25 #2795, 0.11 #3985), 09gq0x5 (0.33 #694, 0.24 #593, 0.18 #13088), 05zr0xl (0.33 #1080, 0.19 #4647, 0.17 #3458), 063ykwt (0.33 #815, 0.19 #4382, 0.11 #3786) >> Best rule #3265 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 0hr3c8y; 092c5f; 03gyp30; >> query: (?x3460, 0hz55) <- award_winner(?x3460, ?x9815), award_winner(?x3460, ?x1223), ceremony(?x8459, ?x3460), honored_for(?x3460, ?x898), film(?x2443, ?x898), honored_for(?x3706, ?x898), film_crew_role(?x898, ?x137), award(?x9815, ?x678), student(?x1440, ?x9815), student(?x2999, ?x1223), ?x8459 = 02py7pj, award_nominee(?x72, ?x1223) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1792 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 0clfdj; *> query: (?x3460, 09m6kg) <- award_winner(?x3460, ?x4969), award_winner(?x3460, ?x4411), award_winner(?x3460, ?x1669), award_winner(?x3460, ?x926), award_winner(?x3460, ?x473), ceremony(?x2257, ?x3460), ?x473 = 09fqtq, honored_for(?x3460, ?x337), nominated_for(?x2257, ?x5950), nominated_for(?x2257, ?x3743), nominated_for(?x2257, ?x385), ?x1669 = 02tr7d, ?x4969 = 016k6x, gender(?x4411, ?x514), ?x5950 = 011yg9, ?x926 = 01sp81, ?x385 = 0ds3t5x, ?x3743 = 047d21r *> conf = 0.33 ranks of expected_values: 3 EVAL 092t4b honored_for 09m6kg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 29.000 24.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #7149-01kb2j PRED entity: 01kb2j PRED relation: film PRED expected values: 0djlxb 07bwr 0315rp 0ddf2bm => 79 concepts (64 used for prediction) PRED predicted values (max 10 best out of 658): 01b66t (0.57 #21292, 0.57 #12420, 0.45 #8871), 0cfhfz (0.50 #2263, 0.04 #79865, 0.03 #101158), 07bwr (0.44 #42591, 0.44 #53240, 0.40 #42590), 07l50_1 (0.44 #42591, 0.44 #53240, 0.40 #42590), 021y7yw (0.25 #389, 0.01 #3937), 016fyc (0.25 #56), 01cssf (0.12 #88, 0.05 #70988, 0.05 #1862), 07cyl (0.12 #558, 0.05 #70988, 0.04 #79865), 049xgc (0.12 #962, 0.05 #2736, 0.04 #90512), 0prhz (0.12 #788, 0.05 #2562, 0.03 #23067) >> Best rule #21292 for best value: >> intensional similarity = 2 >> extensional distance = 939 >> proper extension: 0d_84; 0h1_w; 014x77; 025p38; 0kr5_; 012c6x; 0htlr; 03_vx9; 03gm48; 0456xp; ... >> query: (?x5097, ?x4721) <- film(?x5097, ?x414), award_winner(?x4721, ?x5097) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #42591 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1248 *> proper extension: 0cbxl0; *> query: (?x5097, ?x4083) <- award_nominee(?x192, ?x5097), nominated_for(?x5097, ?x4083), film(?x525, ?x4083) *> conf = 0.44 ranks of expected_values: 3, 51, 66 EVAL 01kb2j film 0ddf2bm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 64.000 0.573 http://example.org/film/actor/film./film/performance/film EVAL 01kb2j film 0315rp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 79.000 64.000 0.573 http://example.org/film/actor/film./film/performance/film EVAL 01kb2j film 07bwr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 64.000 0.573 http://example.org/film/actor/film./film/performance/film EVAL 01kb2j film 0djlxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 79.000 64.000 0.573 http://example.org/film/actor/film./film/performance/film #7148-01d34b PRED entity: 01d34b PRED relation: student PRED expected values: 09yrh 02lymt 04bdqk => 69 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1888): 01vh18t (0.33 #1607, 0.05 #7828, 0.05 #9901), 042xrr (0.33 #779, 0.05 #9073, 0.04 #13221), 0blbxk (0.33 #184, 0.04 #2258, 0.03 #29213), 0bv7t (0.33 #897, 0.04 #2971, 0.03 #34072), 031x_3 (0.33 #1478, 0.04 #3552, 0.03 #7699), 013w7j (0.33 #1060, 0.04 #3134, 0.03 #7281), 0277c3 (0.33 #1057, 0.04 #3131, 0.03 #7278), 095b70 (0.33 #1035, 0.04 #3109, 0.03 #7256), 0ff3y (0.10 #10344, 0.09 #12418, 0.08 #16565), 01cv3n (0.10 #4236, 0.07 #8383, 0.06 #12531) >> Best rule #1607 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0217m9; >> query: (?x7075, 01vh18t) <- student(?x7075, ?x5064), student(?x7075, ?x1282), student(?x7075, ?x999), people(?x2510, ?x1282), award_nominee(?x5650, ?x999), ?x5064 = 02_l96, artists(?x378, ?x1282) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10054 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 39 *> proper extension: 01r3y2; *> query: (?x7075, 04bdqk) <- student(?x7075, ?x7823), student(?x7075, ?x1282), gender(?x1282, ?x231), origin(?x1282, ?x739), participant(?x7823, ?x2352), category(?x1282, ?x134) *> conf = 0.05 ranks of expected_values: 145, 1691 EVAL 01d34b student 04bdqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 69.000 40.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01d34b student 02lymt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 69.000 40.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01d34b student 09yrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 40.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #7147-09ksp PRED entity: 09ksp PRED relation: location! PRED expected values: 0m2wm => 211 concepts (120 used for prediction) PRED predicted values (max 10 best out of 1683): 042q3 (0.25 #2144, 0.22 #14734, 0.17 #7180), 0459z (0.25 #2275, 0.11 #14865, 0.07 #24938), 0465_ (0.18 #18922, 0.10 #44104, 0.10 #36550), 01w02sy (0.18 #18222, 0.10 #43404, 0.09 #53476), 0gs1_ (0.18 #18950, 0.08 #21469, 0.06 #29023), 0738b8 (0.18 #18071, 0.08 #20590, 0.06 #28144), 0dn3n (0.18 #18214, 0.08 #20733, 0.06 #28287), 01pllx (0.18 #19442, 0.08 #21961, 0.06 #29515), 0cqt90 (0.18 #18378, 0.08 #20897, 0.06 #28451), 01j7rd (0.18 #18009, 0.08 #20528, 0.06 #28082) >> Best rule #2144 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03hrz; 09krp; >> query: (?x7934, 042q3) <- adjoins(?x7934, ?x8264), adjoins(?x7934, ?x3623), ?x3623 = 04p0c, location(?x4884, ?x7934), contains(?x1264, ?x8264) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #30218 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 018jcq; *> query: (?x7934, ?x380) <- administrative_parent(?x7934, ?x1264), administrative_parent(?x14383, ?x7934), citytown(?x610, ?x14383), nationality(?x380, ?x1264) *> conf = 0.02 ranks of expected_values: 1378 EVAL 09ksp location! 0m2wm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 211.000 120.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #7146-024rbz PRED entity: 024rbz PRED relation: award_winner! PRED expected values: 03gwpw2 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 136): 0fqpc7d (0.25 #36, 0.14 #1164, 0.12 #1446), 058m5m4 (0.22 #10013, 0.17 #8179, 0.11 #337), 04110lv (0.22 #10013, 0.17 #8179, 0.11 #392), 02glmx (0.22 #10013, 0.17 #8179, 0.09 #1209), 03gwpw2 (0.22 #10013, 0.17 #8179, 0.08 #1419), 0418154 (0.22 #10013, 0.17 #8179, 0.04 #6453), 09gkdln (0.22 #10013, 0.13 #2096, 0.10 #2942), 0g5b0q5 (0.22 #10013, 0.05 #1148, 0.04 #7775), 0h_cssd (0.22 #10013, 0.05 #1156, 0.03 #6373), 0275n3y (0.17 #216, 0.11 #498, 0.11 #2049) >> Best rule #36 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 016tw3; 03sb38; >> query: (?x1414, 0fqpc7d) <- film(?x1414, ?x6540), film(?x1414, ?x1820), film(?x192, ?x1820), ?x6540 = 03_wm6, genre(?x1820, ?x53) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #10013 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1128 *> proper extension: 07fq1y; 04wqr; 03m8lq; 01nzs7; 02wrhj; 049k07; 05x2t7; 03n0q5; 08wr3kg; 015v3r; ... *> query: (?x1414, ?x3609) <- nominated_for(?x1414, ?x3124), genre(?x3124, ?x53), honored_for(?x3609, ?x3124) *> conf = 0.22 ranks of expected_values: 5 EVAL 024rbz award_winner! 03gwpw2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 95.000 95.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7145-095x_ PRED entity: 095x_ PRED relation: instrumentalists! PRED expected values: 026t6 => 98 concepts (62 used for prediction) PRED predicted values (max 10 best out of 121): 03qjg (0.33 #939, 0.33 #289, 0.30 #696), 01vdm0 (0.29 #1546, 0.27 #2459, 0.27 #1464), 05842k (0.29 #1546, 0.27 #2459, 0.27 #1464), 0l14md (0.29 #413, 0.20 #575, 0.19 #1389), 03gvt (0.20 #710, 0.20 #141, 0.18 #791), 026t6 (0.17 #246, 0.14 #2045, 0.14 #2128), 0l14qv (0.17 #1387, 0.13 #980, 0.12 #1881), 06w7v (0.17 #310, 0.13 #1042, 0.09 #1123), 0l14j_ (0.17 #292, 0.07 #942, 0.06 #1024), 05kms (0.17 #315, 0.07 #965, 0.06 #1128) >> Best rule #939 for best value: >> intensional similarity = 6 >> extensional distance = 28 >> proper extension: 06cc_1; 01vrz41; 07_3qd; 09qr6; 01wp8w7; 015_30; 01wwvc5; 01vsl3_; 016ntp; 01309x; ... >> query: (?x8035, 03qjg) <- artists(?x5300, ?x8035), artists(?x1380, ?x8035), role(?x8035, ?x227), ?x5300 = 02k_kn, artists(?x1380, ?x11916), ?x11916 = 023slg >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #246 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 016h9b; 0fhxv; 01vs4ff; 0140t7; *> query: (?x8035, 026t6) <- artists(?x5300, ?x8035), artists(?x1380, ?x8035), role(?x8035, ?x227), ?x5300 = 02k_kn, ?x1380 = 0dl5d, instrumentalists(?x316, ?x8035) *> conf = 0.17 ranks of expected_values: 6 EVAL 095x_ instrumentalists! 026t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 98.000 62.000 0.333 http://example.org/music/instrument/instrumentalists #7144-08hmch PRED entity: 08hmch PRED relation: genre PRED expected values: 02kdv5l 06n90 => 82 concepts (78 used for prediction) PRED predicted values (max 10 best out of 95): 02kdv5l (0.75 #3, 0.38 #360, 0.37 #1550), 07s9rl0 (0.68 #3099, 0.66 #3576, 0.61 #239), 06n90 (0.50 #14, 0.29 #371, 0.21 #1442), 05p553 (0.44 #600, 0.39 #1195, 0.37 #3342), 03k9fj (0.38 #727, 0.38 #370, 0.37 #1560), 02l7c8 (0.32 #612, 0.28 #3952, 0.27 #2398), 0lsxr (0.27 #129, 0.25 #5379, 0.21 #2152), 060__y (0.26 #256, 0.21 #494, 0.16 #3593), 04xvlr (0.20 #3100, 0.19 #3577, 0.15 #2264), 082gq (0.19 #3606, 0.18 #3129, 0.12 #7751) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01hr1; 0d_wms; 01hqk; 042fgh; >> query: (?x1035, 02kdv5l) <- film(?x574, ?x1035), genre(?x1035, ?x6888), written_by(?x1035, ?x5033), ?x6888 = 04pbhw >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 08hmch genre 06n90 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 78.000 0.750 http://example.org/film/film/genre EVAL 08hmch genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 78.000 0.750 http://example.org/film/film/genre #7143-02bc74 PRED entity: 02bc74 PRED relation: artists! PRED expected values: 0y4f8 => 114 concepts (73 used for prediction) PRED predicted values (max 10 best out of 260): 03_d0 (0.72 #5851, 0.38 #319, 0.38 #12), 0gywn (0.62 #58, 0.23 #4051, 0.21 #4665), 06by7 (0.50 #21, 0.44 #5551, 0.43 #7398), 016clz (0.47 #1847, 0.31 #3690, 0.27 #2769), 025sc50 (0.38 #50, 0.31 #4657, 0.29 #2814), 06j6l (0.38 #48, 0.31 #4655, 0.29 #2812), 0155w (0.38 #107, 0.19 #1949, 0.17 #7484), 0glt670 (0.35 #4648, 0.30 #3112, 0.28 #3419), 02lnbg (0.29 #2823, 0.28 #3130, 0.28 #4666), 05lls (0.27 #2471, 0.09 #5853, 0.09 #628) >> Best rule #5851 for best value: >> intensional similarity = 3 >> extensional distance = 189 >> proper extension: 067mj; 03t9sp; 02qlg7s; 017j6; 044gyq; 02lbrd; 01rm8b; 01vvyfh; 0163m1; 037lyl; ... >> query: (?x12743, 03_d0) <- artists(?x597, ?x12743), artists(?x597, ?x4184), ?x4184 = 01m3x5p >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #417 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 03d6q; *> query: (?x12743, 0y4f8) <- artists(?x597, ?x12743), ?x597 = 0ggq0m, category(?x12743, ?x134) *> conf = 0.03 ranks of expected_values: 127 EVAL 02bc74 artists! 0y4f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 114.000 73.000 0.723 http://example.org/music/genre/artists #7142-0kz10 PRED entity: 0kz10 PRED relation: parent_genre! PRED expected values: 01hydr => 70 concepts (44 used for prediction) PRED predicted values (max 10 best out of 295): 0283d (0.65 #5190, 0.33 #3310, 0.25 #2500), 01ym9b (0.41 #5145, 0.25 #2455, 0.25 #578), 07lnk (0.33 #3518, 0.33 #3251, 0.25 #2441), 0193f (0.33 #3324, 0.22 #3591, 0.18 #5204), 02w6s3 (0.33 #3447, 0.22 #3714, 0.13 #5060), 0ccxx6 (0.33 #219, 0.14 #2097, 0.13 #5057), 0y3_8 (0.28 #5953, 0.15 #6760, 0.15 #8105), 0mmp3 (0.25 #2498, 0.25 #621, 0.20 #1424), 01hydr (0.25 #781, 0.20 #5081, 0.20 #1584), 0kz10 (0.25 #775, 0.20 #5075, 0.20 #1578) >> Best rule #5190 for best value: >> intensional similarity = 8 >> extensional distance = 15 >> proper extension: 06__c; >> query: (?x14233, 0283d) <- parent_genre(?x7279, ?x14233), parent_genre(?x7279, ?x13883), parent_genre(?x13883, ?x3915), artists(?x7279, ?x8636), artists(?x13883, ?x8199), ?x3915 = 07gxw, ?x8636 = 0k60, ?x8199 = 016lmg >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #781 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 07gxw; 0283d; *> query: (?x14233, 01hydr) <- parent_genre(?x7279, ?x14233), artists(?x14233, ?x8636), parent_genre(?x14233, ?x474), ?x8636 = 0k60, artists(?x474, ?x10924), artists(?x474, ?x7331), artists(?x474, ?x2237), ?x2237 = 01vs_v8, ?x10924 = 01nz1q6, award_winner(?x342, ?x7331), award_winner(?x528, ?x7331) *> conf = 0.25 ranks of expected_values: 9 EVAL 0kz10 parent_genre! 01hydr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 70.000 44.000 0.647 http://example.org/music/genre/parent_genre #7141-0prjs PRED entity: 0prjs PRED relation: award PRED expected values: 027dtxw => 117 concepts (108 used for prediction) PRED predicted values (max 10 best out of 297): 027c924 (0.73 #35877, 0.72 #37852, 0.71 #35876), 019f4v (0.38 #13065, 0.31 #3214, 0.18 #62), 040njc (0.37 #13010, 0.32 #3159, 0.25 #401), 05pcn59 (0.36 #75, 0.26 #2045, 0.19 #1257), 0gr51 (0.36 #94, 0.22 #3246, 0.21 #13097), 09sb52 (0.35 #2007, 0.31 #11464, 0.29 #14222), 027dtxw (0.31 #11430, 0.25 #397, 0.15 #791), 0gq9h (0.30 #13075, 0.25 #466, 0.23 #3224), 02x73k6 (0.29 #11483, 0.09 #56, 0.08 #450), 04dn09n (0.27 #40, 0.25 #434, 0.19 #3192) >> Best rule #35877 for best value: >> intensional similarity = 3 >> extensional distance = 2245 >> proper extension: 089tm; 01pfr3; 02mslq; 04rcr; 01v0sx2; 011zf2; 01vsxdm; 0ggl02; 03g5jw; 01wv9xn; ... >> query: (?x1371, ?x1198) <- award_winner(?x1198, ?x1371), award(?x1371, ?x102), award(?x276, ?x1198) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #11430 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 164 *> proper extension: 02bfmn; 019_1h; 018swb; 02mxw0; 015v3r; 02t_v1; 015wfg; 02xwgr; 031y07; 02sh8y; ... *> query: (?x1371, 027dtxw) <- award(?x1371, ?x3066), ?x3066 = 0gqy2, film(?x1371, ?x1263) *> conf = 0.31 ranks of expected_values: 7 EVAL 0prjs award 027dtxw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 117.000 108.000 0.728 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7140-05w1vf PRED entity: 05w1vf PRED relation: profession PRED expected values: 02jknp => 101 concepts (75 used for prediction) PRED predicted values (max 10 best out of 48): 01d_h8 (0.62 #6769, 0.61 #6916, 0.53 #2946), 0cbd2 (0.49 #1330, 0.43 #1036, 0.42 #1183), 02jknp (0.46 #2948, 0.42 #3536, 0.42 #6771), 03gjzk (0.36 #3542, 0.36 #2954, 0.32 #6924), 0kyk (0.35 #1352, 0.33 #1058, 0.33 #1205), 0np9r (0.33 #461, 0.25 #20, 0.22 #608), 05z96 (0.21 #1365, 0.15 #1218, 0.14 #777), 018gz8 (0.20 #6926, 0.18 #2956, 0.17 #3544), 025352 (0.16 #1234, 0.16 #940, 0.15 #1087), 09jwl (0.16 #8696, 0.15 #8990, 0.15 #10019) >> Best rule #6769 for best value: >> intensional similarity = 5 >> extensional distance = 1573 >> proper extension: 01c59k; >> query: (?x11529, 01d_h8) <- profession(?x11529, ?x987), profession(?x4988, ?x987), profession(?x2648, ?x987), ?x4988 = 041c4, ?x2648 = 034bgm >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2948 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 653 *> proper extension: 0c8hct; 032md; *> query: (?x11529, 02jknp) <- type_of_union(?x11529, ?x566), ?x566 = 04ztj, profession(?x11529, ?x987), ?x987 = 0dxtg *> conf = 0.46 ranks of expected_values: 3 EVAL 05w1vf profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 75.000 0.619 http://example.org/people/person/profession #7139-018grr PRED entity: 018grr PRED relation: film PRED expected values: 02ntb8 => 118 concepts (79 used for prediction) PRED predicted values (max 10 best out of 969): 05fm6m (0.71 #8912, 0.60 #96254, 0.59 #98037), 034qzw (0.60 #96254, 0.59 #98037, 0.58 #101602), 0gc_c_ (0.60 #96254, 0.59 #98037, 0.58 #101602), 087vnr5 (0.60 #96254, 0.59 #98037, 0.58 #101602), 039cq4 (0.60 #96254, 0.59 #98037, 0.58 #101602), 02825cv (0.18 #4700, 0.05 #18961, 0.03 #16042), 0g9z_32 (0.18 #4835, 0.03 #16042, 0.03 #19096), 0830vk (0.17 #2372, 0.03 #7719, 0.03 #11285), 09g8vhw (0.17 #2106, 0.03 #9236, 0.02 #16366), 03tps5 (0.17 #2515, 0.03 #9645, 0.02 #16775) >> Best rule #8912 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 0443y3; 01pfkw; >> query: (?x2101, ?x4304) <- award_nominee(?x237, ?x2101), vacationer(?x304, ?x2101), award_winner(?x4304, ?x2101) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #49913 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 601 *> proper extension: 05gnf; *> query: (?x2101, ?x4888) <- nominated_for(?x2101, ?x2102), nominated_for(?x4888, ?x2102) *> conf = 0.12 ranks of expected_values: 47 EVAL 018grr film 02ntb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 118.000 79.000 0.712 http://example.org/film/actor/film./film/performance/film #7138-0gyh2wm PRED entity: 0gyh2wm PRED relation: film_release_region PRED expected values: 0b90_r 0345h 035qy 06bnz => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 94): 0345h (0.83 #947, 0.82 #1253, 0.80 #794), 03_3d (0.81 #618, 0.78 #924, 0.78 #1230), 02vzc (0.81 #663, 0.78 #1275, 0.78 #969), 035qy (0.79 #796, 0.75 #949, 0.73 #1255), 01znc_ (0.74 #958, 0.72 #1264, 0.72 #652), 0b90_r (0.72 #1228, 0.72 #922, 0.71 #769), 06bnz (0.68 #962, 0.68 #809, 0.67 #1268), 03spz (0.66 #859, 0.64 #1012, 0.64 #1318), 03rj0 (0.58 #1284, 0.57 #978, 0.56 #825), 05v8c (0.56 #932, 0.55 #1238, 0.54 #779) >> Best rule #947 for best value: >> intensional similarity = 6 >> extensional distance = 206 >> proper extension: 0gtsx8c; 0gtv7pk; 0h1cdwq; 0gx9rvq; 0401sg; 087wc7n; 0jjy0; 0gj8t_b; 03bx2lk; 053tj7; ... >> query: (?x3958, 0345h) <- film_release_region(?x3958, ?x2645), film_release_region(?x3958, ?x304), film_release_region(?x3958, ?x142), ?x142 = 0jgd, ?x2645 = 03h64, ?x304 = 0d0vqn >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 6, 7 EVAL 0gyh2wm film_release_region 06bnz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 68.000 0.827 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gyh2wm film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 68.000 0.827 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gyh2wm film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.827 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gyh2wm film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 68.000 0.827 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7137-03j9ml PRED entity: 03j9ml PRED relation: film PRED expected values: 050f0s => 87 concepts (37 used for prediction) PRED predicted values (max 10 best out of 566): 0k54q (0.29 #937, 0.04 #6314, 0.03 #4521), 040_lv (0.18 #2841, 0.02 #10011, 0.02 #8219), 0888c3 (0.18 #3209, 0.02 #10379, 0.02 #8587), 01ry_x (0.14 #1709, 0.06 #5293, 0.02 #14256), 0k2sk (0.14 #163, 0.05 #3747, 0.03 #5540), 0fgrm (0.14 #789, 0.05 #4373, 0.02 #13336), 03h3x5 (0.14 #423, 0.05 #4007, 0.02 #5800), 0291ck (0.14 #1568, 0.02 #5152, 0.01 #6945), 0p9tm (0.14 #1368, 0.02 #4952, 0.01 #6745), 07bxqz (0.14 #1737, 0.02 #5321) >> Best rule #937 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 019803; >> query: (?x12280, 0k54q) <- profession(?x12280, ?x1032), actor(?x9340, ?x12280), ?x1032 = 02hrh1q, ?x9340 = 05nlzq, type_of_union(?x12280, ?x566) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2102 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 0bl2g; 0fby2t; 03n52j; 029k55; *> query: (?x12280, 050f0s) <- profession(?x12280, ?x1383), place_of_birth(?x12280, ?x1523), ?x1383 = 0np9r, gender(?x12280, ?x514), ?x1523 = 030qb3t *> conf = 0.09 ranks of expected_values: 17 EVAL 03j9ml film 050f0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 87.000 37.000 0.286 http://example.org/film/actor/film./film/performance/film #7136-0fdv3 PRED entity: 0fdv3 PRED relation: film! PRED expected values: 02lnhv => 85 concepts (50 used for prediction) PRED predicted values (max 10 best out of 946): 04wp63 (0.44 #91380, 0.44 #14539, 0.44 #72693), 0169dl (0.16 #401, 0.12 #2478, 0.06 #10786), 0c0k1 (0.13 #1505, 0.12 #3582, 0.07 #5659), 0151w_ (0.10 #164, 0.09 #2241, 0.03 #33396), 0gn30 (0.09 #13407, 0.08 #23791, 0.08 #30020), 0hvb2 (0.09 #2376, 0.06 #299, 0.06 #10684), 0dvmd (0.09 #2604, 0.06 #527, 0.04 #10912), 0bxtg (0.09 #2154, 0.03 #77, 0.03 #4231), 01swck (0.07 #9105, 0.03 #4951, 0.02 #50646), 0h5g_ (0.07 #6305, 0.05 #4228, 0.05 #27073) >> Best rule #91380 for best value: >> intensional similarity = 3 >> extensional distance = 747 >> proper extension: 0170z3; 014_x2; 09sh8k; 034qmv; 01br2w; 02v8kmz; 0c3ybss; 09xbpt; 0bvn25; 0m2kd; ... >> query: (?x1812, ?x3580) <- nominated_for(?x3580, ?x1812), film_crew_role(?x1812, ?x137), currency(?x1812, ?x170) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #8500 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 99 *> proper extension: 02vqhv0; *> query: (?x1812, 02lnhv) <- crewmember(?x1812, ?x10262), films(?x8435, ?x1812), film_crew_role(?x1812, ?x137) *> conf = 0.02 ranks of expected_values: 454 EVAL 0fdv3 film! 02lnhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 85.000 50.000 0.442 http://example.org/film/actor/film./film/performance/film #7135-01l2fn PRED entity: 01l2fn PRED relation: film PRED expected values: 0gx1bnj => 106 concepts (55 used for prediction) PRED predicted values (max 10 best out of 884): 011yg9 (0.18 #2799, 0.03 #8130, 0.02 #6353), 03wjm2 (0.18 #3524, 0.01 #33733), 0q9b0 (0.14 #1264, 0.05 #4818, 0.02 #10149), 031hcx (0.12 #3042, 0.07 #1265, 0.03 #33251), 03177r (0.12 #2239, 0.07 #462, 0.03 #32448), 0879bpq (0.12 #2224, 0.07 #447, 0.02 #4001), 08k40m (0.12 #2258, 0.01 #32467), 03ydlnj (0.12 #3163, 0.01 #6717), 02t_h3 (0.12 #3525), 06_wqk4 (0.07 #127, 0.07 #9012, 0.07 #10789) >> Best rule #2799 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 05vsxz; 05tk7y; 0993r; >> query: (?x1634, 011yg9) <- nominated_for(?x1634, ?x908), award_nominee(?x1634, ?x6122), ?x6122 = 016xh5 >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01l2fn film 0gx1bnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 55.000 0.176 http://example.org/film/actor/film./film/performance/film #7134-02r96rf PRED entity: 02r96rf PRED relation: film_crew_role! PRED expected values: 014_x2 09m6kg 04nl83 08720 08gsvw 09p35z 0b73_1d 04gknr 04kkz8 05q96q6 0k2sk 0jjy0 09gdm7q 072x7s 0bh8yn3 04n52p6 0g9wdmc 0ch26b_ 050gkf 0kvgxk 07p62k 011yd2 0d_2fb 02q6gfp 065z3_x 048htn 05h43ls 01shy7 05zy2cy 0hx4y 0ctb4g 03r0g9 0gjcrrw 03z106 04lqvlr 0gtxj2q 0243cq 01rxyb 07k8rt4 04gv3db 02xs6_ 05mrf_p 01q2nx 0cc97st 0415ggl 0127ps 031ldd 05r3qc 071nw5 06fcqw 02tktw 0cmdwwg 095z4q 03t95n 01svry 02nx2k 0b6l1st 0292qb 031786 0bdjd 01qb559 04165w 05fm6m 0h95927 046f3p 04b2qn 02_fz3 03nsm5x 04j14qc 03cvvlg 03shpq 087pfc 02qd04y 0gvt53w 0dcz8_ 0fh2v5 0bs5f0b 0bm2nq 07p12s 049w1q 0by17xn 08c4yn 0fzm0g 07ykkx5 => 27 concepts (27 used for prediction) PRED predicted values (max 10 best out of 500): 07k8rt4 (0.78 #8215, 0.60 #5212, 0.57 #6714), 05zy2cy (0.78 #8124, 0.60 #5121, 0.57 #6623), 07p12s (0.78 #8480, 0.60 #5477, 0.50 #7980), 05q7874 (0.78 #8301, 0.60 #5298, 0.50 #4798), 04n52p6 (0.71 #6574, 0.67 #8075, 0.62 #7074), 0g7pm1 (0.71 #6842, 0.67 #8343, 0.60 #5841), 027m5wv (0.67 #8299, 0.67 #6297, 0.50 #7799), 0415ggl (0.67 #8276, 0.60 #5774, 0.60 #5273), 04jplwp (0.67 #8400, 0.60 #5898, 0.50 #7900), 0pv54 (0.67 #6263, 0.50 #3262, 0.50 #2762) >> Best rule #8215 for best value: >> intensional similarity = 13 >> extensional distance = 7 >> proper extension: 0215hd; >> query: (?x468, 07k8rt4) <- film_crew_role(?x6624, ?x468), film_crew_role(?x5155, ?x468), film_crew_role(?x4131, ?x468), film_crew_role(?x3745, ?x468), film_crew_role(?x3532, ?x468), film_crew_role(?x3276, ?x468), ?x5155 = 076zy_g, film(?x262, ?x3276), genre(?x3745, ?x53), ?x3532 = 04ydr95, country(?x4131, ?x94), music(?x3276, ?x3069), titles(?x812, ?x6624) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 22, 26, 32, 33, 34, 36, 43, 44, 47, 49, 51, 53, 54, 55, 56, 57, 58, 59, 63, 64, 67, 70, 71, 74, 80, 87, 90, 92, 94, 95, 96, 98, 99, 100, 117, 121, 123, 137, 141, 144, 145, 150, 151, 157, 172, 178, 189, 201, 214, 215, 222, 223, 225, 227, 233, 234, 241, 250, 275, 278, 302, 319, 353, 356, 357, 359, 376, 401, 473, 475, 494 EVAL 02r96rf film_crew_role! 07ykkx5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0fzm0g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 08c4yn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0by17xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 049w1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 07p12s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0bm2nq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0bs5f0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0fh2v5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0dcz8_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0gvt53w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 02qd04y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 087pfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 03shpq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 03cvvlg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 04j14qc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 03nsm5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 02_fz3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 04b2qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 046f3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0h95927 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 05fm6m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 04165w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 01qb559 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0bdjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 031786 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0292qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0b6l1st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 02nx2k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 01svry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 03t95n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 095z4q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0cmdwwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 02tktw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 06fcqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 071nw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 05r3qc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 031ldd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0127ps CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0415ggl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 01q2nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 05mrf_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 02xs6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 04gv3db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 07k8rt4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 01rxyb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0243cq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0gtxj2q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 04lqvlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 03z106 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0gjcrrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 03r0g9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0ctb4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0hx4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 05zy2cy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 01shy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 05h43ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 048htn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 065z3_x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 02q6gfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0d_2fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 011yd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 07p62k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0kvgxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 050gkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0ch26b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0g9wdmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 04n52p6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0bh8yn3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 072x7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 09gdm7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0jjy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0k2sk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 05q96q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 04kkz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 04gknr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 0b73_1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 09p35z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 08gsvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 08720 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 04nl83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 09m6kg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02r96rf film_crew_role! 014_x2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 27.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7133-0d05q4 PRED entity: 0d05q4 PRED relation: countries_spoken_in! PRED expected values: 032f6 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 55): 02h40lc (0.57 #332, 0.39 #222, 0.38 #4347), 06nm1 (0.23 #2538, 0.22 #338, 0.21 #1273), 064_8sq (0.20 #402, 0.19 #4362, 0.19 #2327), 032f6 (0.16 #4951, 0.09 #1368, 0.07 #213), 0swlx (0.16 #4951, 0.07 #214, 0.04 #1369), 02hwyss (0.16 #4951, 0.04 #1798, 0.04 #753), 02ztjwg (0.14 #303, 0.10 #2668, 0.09 #633), 04306rv (0.13 #885, 0.11 #225, 0.10 #1160), 05zjd (0.11 #241, 0.11 #2331, 0.09 #351), 02hxcvy (0.11 #250, 0.10 #415, 0.09 #360) >> Best rule #332 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0604m; >> query: (?x4092, 02h40lc) <- adjustment_currency(?x4092, ?x170), jurisdiction_of_office(?x12920, ?x4092), country(?x1121, ?x4092) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4951 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 142 *> proper extension: 04fh3; 0mhhw; 0hyyq; 0p_x; *> query: (?x4092, ?x5359) <- adjoins(?x4092, ?x4302), film_release_region(?x559, ?x4302), countries_spoken_in(?x5359, ?x4302) *> conf = 0.16 ranks of expected_values: 4 EVAL 0d05q4 countries_spoken_in! 032f6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 159.000 159.000 0.565 http://example.org/language/human_language/countries_spoken_in #7132-01gvr1 PRED entity: 01gvr1 PRED relation: location PRED expected values: 02_286 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 151): 0f94t (0.70 #50595, 0.49 #12849, 0.47 #17668), 030qb3t (0.33 #17751, 0.28 #886, 0.24 #2492), 02_286 (0.33 #17705, 0.19 #1643, 0.17 #12082), 0cr3d (0.09 #947, 0.08 #5765, 0.08 #2553), 04jpl (0.09 #1623, 0.07 #13669, 0.06 #8850), 0k049 (0.08 #2417, 0.06 #811, 0.05 #5629), 059rby (0.08 #16, 0.04 #17684, 0.04 #1622), 0r0m6 (0.06 #1020, 0.06 #2626, 0.04 #5838), 01n7q (0.06 #866, 0.05 #7290, 0.05 #8093), 0rh6k (0.06 #2413, 0.04 #4822, 0.04 #6428) >> Best rule #50595 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 07m69t; 01h2_6; >> query: (?x624, ?x1005) <- place_of_birth(?x624, ?x1005), location(?x624, ?x2277) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #17705 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 587 *> proper extension: 03qcq; 01j5x6; 04cf09; 01v3bn; 073749; 07_grx; 0g2mbn; 07y8l9; 01wbsdz; 04j_gs; ... *> query: (?x624, 02_286) <- award_nominee(?x1244, ?x624), location(?x624, ?x2277), dog_breed(?x2277, ?x3095) *> conf = 0.33 ranks of expected_values: 3 EVAL 01gvr1 location 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 106.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #7131-03f7xg PRED entity: 03f7xg PRED relation: film! PRED expected values: 04mkft => 112 concepts (89 used for prediction) PRED predicted values (max 10 best out of 56): 031rp3 (0.53 #720, 0.50 #5663, 0.49 #3768), 016tw3 (0.33 #9, 0.17 #3705, 0.17 #2396), 01tc9r (0.17 #793, 0.11 #1153, 0.06 #792), 05qd_ (0.17 #364, 0.17 #5234, 0.16 #292), 0g1rw (0.17 #6, 0.08 #5233, 0.08 #291), 06jntd (0.17 #29, 0.02 #966, 0.02 #5620), 0gfmc_ (0.17 #38, 0.02 #1335, 0.01 #2280), 017s11 (0.16 #216, 0.16 #5229, 0.15 #648), 01gb54 (0.12 #384, 0.10 #527, 0.09 #600), 0jz9f (0.11 #72, 0.07 #215, 0.07 #5228) >> Best rule #720 for best value: >> intensional similarity = 4 >> extensional distance = 340 >> proper extension: 0pd57; >> query: (?x3306, ?x11695) <- nominated_for(?x1867, ?x3306), film(?x382, ?x3306), featured_film_locations(?x3306, ?x1658), production_companies(?x3306, ?x11695) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #177 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 05r3qc; *> query: (?x3306, 04mkft) <- film_crew_role(?x3306, ?x1284), nominated_for(?x507, ?x3306), film_production_design_by(?x3306, ?x8844) *> conf = 0.08 ranks of expected_values: 18 EVAL 03f7xg film! 04mkft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 112.000 89.000 0.527 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7130-015ynm PRED entity: 015ynm PRED relation: genre PRED expected values: 06n90 => 109 concepts (87 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.69 #1910, 0.63 #4773, 0.63 #5488), 09b3v (0.62 #2387, 0.53 #7279, 0.52 #9192), 02kdv5l (0.50 #2866, 0.40 #3, 0.35 #2985), 01zhp (0.50 #435, 0.46 #792, 0.43 #554), 082gq (0.41 #268, 0.17 #4323, 0.16 #7879), 01jfsb (0.37 #2399, 0.34 #2994, 0.34 #3949), 02l7c8 (0.35 #1566, 0.35 #255, 0.33 #2044), 04t36 (0.27 #1080, 0.24 #841, 0.20 #1318), 06n90 (0.24 #2876, 0.20 #13, 0.20 #2995), 03g3w (0.24 #262, 0.16 #7879, 0.11 #1693) >> Best rule #1910 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 06mmr; >> query: (?x8359, 07s9rl0) <- award_winner(?x8359, ?x7701), award(?x7701, ?x1079), award_winner(?x3121, ?x7701), ?x1079 = 0l8z1 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2876 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 216 *> proper extension: 0bmc4cm; 043sct5; 076xkdz; *> query: (?x8359, 06n90) <- film_release_distribution_medium(?x8359, ?x81), film_release_region(?x8359, ?x94), genre(?x8359, ?x811), ?x811 = 03k9fj *> conf = 0.24 ranks of expected_values: 9 EVAL 015ynm genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 109.000 87.000 0.689 http://example.org/film/film/genre #7129-04vn5 PRED entity: 04vn5 PRED relation: position PRED expected values: 05zm34 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 10): 02g_7z (0.89 #333, 0.89 #329, 0.87 #421), 05zm34 (0.83 #285, 0.81 #204, 0.81 #180), 01_9c1 (0.82 #15, 0.81 #367, 0.78 #352), 03h42s4 (0.82 #15, 0.78 #352, 0.78 #251), 0b13yt (0.82 #15, 0.76 #210, 0.76 #186), 0bgv8y (0.62 #370, 0.61 #387, 0.60 #254), 0bgv4g (0.62 #370, 0.61 #387, 0.60 #254), 05fyy5 (0.62 #370, 0.60 #254, 0.59 #627), 02vkdwz (0.61 #387, 0.59 #411, 0.58 #253), 01snvb (0.56 #628, 0.51 #265, 0.48 #533) >> Best rule #333 for best value: >> intensional similarity = 17 >> extensional distance = 25 >> proper extension: 025_64l; 0fjzsy; >> query: (?x6976, ?x3346) <- position(?x6976, ?x3346), position(?x6976, ?x2147), ?x3346 = 02g_7z, team(?x2247, ?x6976), position(?x5773, ?x2147), position(?x4256, ?x2147), position(?x4222, ?x2147), position(?x4189, ?x2147), sport(?x6976, ?x1083), ?x4189 = 026lg0s, ?x4222 = 051q5, position_s(?x4661, ?x2147), position_s(?x2526, ?x2147), ?x4661 = 0frm7n, ?x2526 = 03hfx6c, ?x4256 = 03lsq, ?x5773 = 06rny >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #285 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 21 *> proper extension: 01ct6; 051q5; 0289q; *> query: (?x6976, 05zm34) <- draft(?x6976, ?x465), school(?x6976, ?x388), position(?x6976, ?x2573), position(?x6976, ?x1517), position(?x6976, ?x1114), ?x1114 = 047g8h, team(?x3346, ?x6976), ?x2573 = 05b3ts, ?x1517 = 02g_6j, position_s(?x1239, ?x3346), ?x1239 = 01xvb, team(?x3346, ?x5822), team(?x3346, ?x4723), team(?x3346, ?x705), ?x5822 = 03wnh, ?x4723 = 043tz8m, ?x705 = 07k53y *> conf = 0.83 ranks of expected_values: 2 EVAL 04vn5 position 05zm34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.889 http://example.org/sports/sports_team/roster./american_football/football_roster_position/position #7128-05gc0h PRED entity: 05gc0h PRED relation: people! PRED expected values: 04mvp8 => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 29): 0dryh9k (0.35 #16, 0.34 #247, 0.33 #93), 041rx (0.15 #313, 0.14 #698, 0.14 #544), 0x67 (0.08 #319, 0.08 #473, 0.07 #2552), 033tf_ (0.06 #470, 0.06 #316, 0.05 #161), 01rv7x (0.06 #39, 0.05 #116, 0.05 #270), 02sch9 (0.05 #35, 0.04 #266, 0.04 #112), 0xnvg (0.05 #167, 0.04 #322, 0.04 #476), 02w7gg (0.04 #1774, 0.04 #1928, 0.04 #2005), 04mvp8 (0.04 #144, 0.04 #298, 0.03 #67), 048z7l (0.03 #194, 0.03 #503, 0.03 #349) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 0cfywh; >> query: (?x7999, 0dryh9k) <- nationality(?x7999, ?x2146), type_of_union(?x7999, ?x566), ?x2146 = 03rk0, ?x566 = 04ztj >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 152 *> proper extension: 01qx13; 0265z9l; 015k7; 0dhqyw; 0cmpn; 0b5x23; 081hvm; 0frpd5; 02qfk4j; *> query: (?x7999, 04mvp8) <- nationality(?x7999, ?x2146), gender(?x7999, ?x231), ?x2146 = 03rk0, ?x231 = 05zppz *> conf = 0.04 ranks of expected_values: 9 EVAL 05gc0h people! 04mvp8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 48.000 48.000 0.354 http://example.org/people/ethnicity/people #7127-03p5xs PRED entity: 03p5xs PRED relation: genre! PRED expected values: 027qgy 0f7hw => 27 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1860): 0gd92 (0.75 #8769, 0.71 #6911, 0.60 #5053), 0sxns (0.75 #8538, 0.71 #6680, 0.60 #4822), 02x0fs9 (0.75 #9141, 0.71 #7283, 0.60 #5425), 03rz2b (0.75 #7911, 0.71 #6053, 0.60 #4195), 0g7pm1 (0.75 #1859, 0.48 #1858, 0.38 #8667), 0d8w2n (0.71 #7408, 0.62 #9266, 0.60 #5550), 02rv_dz (0.71 #5821, 0.62 #7679, 0.60 #3963), 0ddf2bm (0.62 #9170, 0.60 #5454, 0.57 #7312), 03c7twt (0.62 #9163, 0.60 #5447, 0.57 #7305), 0sxfd (0.62 #7652, 0.60 #3936, 0.57 #5794) >> Best rule #8769 for best value: >> intensional similarity = 14 >> extensional distance = 6 >> proper extension: 06cvj; >> query: (?x11464, 0gd92) <- genre(?x8367, ?x11464), genre(?x7062, ?x11464), genre(?x696, ?x11464), nominated_for(?x926, ?x8367), film(?x473, ?x8367), student(?x2486, ?x473), ?x696 = 0209xj, genre(?x8367, ?x714), nominated_for(?x1307, ?x8367), ?x1307 = 0gq9h, honored_for(?x472, ?x8367), currency(?x7062, ?x170), genre(?x7735, ?x714), ?x7735 = 0gpx6 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #7459 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 6 *> proper extension: 06cvj; *> query: (?x11464, 027qgy) <- genre(?x8367, ?x11464), genre(?x7062, ?x11464), genre(?x696, ?x11464), nominated_for(?x926, ?x8367), film(?x473, ?x8367), student(?x2486, ?x473), ?x696 = 0209xj, genre(?x8367, ?x714), nominated_for(?x1307, ?x8367), ?x1307 = 0gq9h, honored_for(?x472, ?x8367), currency(?x7062, ?x170), genre(?x7735, ?x714), ?x7735 = 0gpx6 *> conf = 0.50 ranks of expected_values: 98, 178 EVAL 03p5xs genre! 0f7hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 27.000 12.000 0.750 http://example.org/film/film/genre EVAL 03p5xs genre! 027qgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 27.000 12.000 0.750 http://example.org/film/film/genre #7126-01xcfy PRED entity: 01xcfy PRED relation: film PRED expected values: 0g54xkt => 122 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1100): 02dr9j (0.73 #123064, 0.71 #26751, 0.69 #14268), 02d478 (0.69 #14268, 0.34 #110577, 0.33 #117711), 01shy7 (0.09 #423, 0.08 #3990, 0.07 #7556), 013q07 (0.09 #357, 0.05 #3924, 0.05 #11057), 02z3r8t (0.09 #108, 0.05 #3675, 0.04 #7241), 0cn_b8 (0.09 #612, 0.05 #4179, 0.03 #23796), 0blpg (0.09 #653, 0.05 #4220, 0.03 #13137), 09lxv9 (0.09 #1501, 0.05 #5068, 0.03 #21119), 0830vk (0.09 #590, 0.05 #4157, 0.02 #5940), 04165w (0.09 #1312, 0.05 #4879, 0.01 #22713) >> Best rule #123064 for best value: >> intensional similarity = 3 >> extensional distance = 1111 >> proper extension: 049tjg; >> query: (?x2891, ?x7214) <- nominated_for(?x2891, ?x7214), film(?x2891, ?x2547), film(?x4337, ?x7214) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2310 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 02d9k; 01pcrw; 016dsy; 03f77; 04kjrv; *> query: (?x2891, 0g54xkt) <- nationality(?x2891, ?x512), participant(?x844, ?x2891), ?x512 = 07ssc *> conf = 0.03 ranks of expected_values: 329 EVAL 01xcfy film 0g54xkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 122.000 73.000 0.730 http://example.org/film/actor/film./film/performance/film #7125-05q54f5 PRED entity: 05q54f5 PRED relation: film! PRED expected values: 04f525m => 74 concepts (54 used for prediction) PRED predicted values (max 10 best out of 55): 03xq0f (0.55 #453, 0.11 #1053, 0.09 #1128), 0jz9f (0.29 #449, 0.11 #224, 0.09 #374), 017s11 (0.25 #3, 0.13 #1503, 0.13 #1277), 086k8 (0.16 #2935, 0.16 #1953, 0.16 #1502), 016tw3 (0.15 #85, 0.14 #310, 0.14 #234), 05qd_ (0.15 #83, 0.14 #1434, 0.13 #1359), 016tt2 (0.12 #1504, 0.12 #303, 0.12 #1127), 04f525m (0.10 #458, 0.03 #233, 0.03 #309), 017jv5 (0.09 #89, 0.07 #163, 0.04 #1214), 0fvppk (0.08 #504, 0.03 #130, 0.02 #578) >> Best rule #453 for best value: >> intensional similarity = 3 >> extensional distance = 246 >> proper extension: 0522wp; >> query: (?x2892, 03xq0f) <- film(?x6082, ?x2892), film(?x6082, ?x4352), ?x4352 = 09v71cj >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #458 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 246 *> proper extension: 0522wp; *> query: (?x2892, 04f525m) <- film(?x6082, ?x2892), film(?x6082, ?x4352), ?x4352 = 09v71cj *> conf = 0.10 ranks of expected_values: 8 EVAL 05q54f5 film! 04f525m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 74.000 54.000 0.548 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7124-07b1gq PRED entity: 07b1gq PRED relation: honored_for PRED expected values: 06ybb1 0cwfgz => 76 concepts (26 used for prediction) PRED predicted values (max 10 best out of 170): 06c0ns (0.83 #2470, 0.83 #2783, 0.83 #3096), 0cwfgz (0.64 #416, 0.64 #262, 0.54 #2627), 06ybb1 (0.64 #360, 0.55 #206, 0.54 #2627), 07b1gq (0.57 #374, 0.54 #2627, 0.46 #1390), 08984j (0.46 #1390, 0.45 #1235, 0.45 #926), 0cf08 (0.46 #1390, 0.45 #1235), 01_mdl (0.08 #485, 0.05 #639, 0.05 #948), 042g97 (0.08 #616, 0.05 #770, 0.05 #1079), 0ddt_ (0.08 #523, 0.05 #831, 0.05 #1140), 0d_wms (0.08 #533, 0.04 #687, 0.04 #996) >> Best rule #2470 for best value: >> intensional similarity = 4 >> extensional distance = 137 >> proper extension: 092vkg; 0bshwmp; 026390q; 024l2y; 0fphgb; 05m_jsg; 02b61v; 0286gm1; 08s6mr; 0bj25; ... >> query: (?x3640, ?x5667) <- honored_for(?x5667, ?x3640), film(?x3651, ?x3640), film_release_distribution_medium(?x5667, ?x81), country(?x5667, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #416 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 0140g4; 0946bb; 02ny6g; 074rg9; *> query: (?x3640, 0cwfgz) <- honored_for(?x3640, ?x12648), honored_for(?x3640, ?x4538), ?x4538 = 0q9sg, film(?x3651, ?x3640), ?x12648 = 03phtz *> conf = 0.64 ranks of expected_values: 2, 3 EVAL 07b1gq honored_for 0cwfgz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 26.000 0.834 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for EVAL 07b1gq honored_for 06ybb1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 26.000 0.834 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #7123-03v1jf PRED entity: 03v1jf PRED relation: participant! PRED expected values: 0lx2l => 102 concepts (52 used for prediction) PRED predicted values (max 10 best out of 109): 0lx2l (0.84 #11741, 0.82 #7176, 0.81 #5872), 01p4vl (0.84 #11741, 0.82 #7176, 0.81 #5872), 01x9_8 (0.10 #540, 0.09 #1192, 0.08 #1844), 0gyx4 (0.05 #4223, 0.04 #4875, 0.04 #5526), 016vg8 (0.04 #2937, 0.02 #5547, 0.02 #8808), 02dlfh (0.04 #3121, 0.01 #10298, 0.01 #3773), 01p47r (0.04 #3200), 0f276 (0.04 #3192), 02b9g4 (0.04 #3071), 023kzp (0.04 #3012) >> Best rule #11741 for best value: >> intensional similarity = 2 >> extensional distance = 593 >> proper extension: 02qjj7; 06y9c2; 01pl9g; 02d9k; 0zjpz; 063vn; 01wz3cx; 01vv126; 015z4j; 01pcrw; ... >> query: (?x5216, ?x2534) <- participant(?x12743, ?x5216), participant(?x5216, ?x2534) >> conf = 0.84 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 03v1jf participant! 0lx2l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 52.000 0.839 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #7122-0bzk2h PRED entity: 0bzk2h PRED relation: ceremony! PRED expected values: 0gvx_ => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 358): 0gvx_ (0.91 #3270, 0.89 #5938, 0.88 #4481), 0gs96 (0.89 #2254, 0.89 #2012, 0.87 #3223), 0gr42 (0.84 #969, 0.79 #3948, 0.79 #4432), 018wdw (0.84 #969, 0.74 #2350, 0.73 #8007), 0gqxm (0.84 #969, 0.73 #8007, 0.73 #7032), 0gqzz (0.84 #969, 0.73 #8007, 0.73 #7032), 0czp_ (0.84 #969, 0.73 #8007, 0.73 #7032), 02x201b (0.84 #969, 0.73 #8007, 0.73 #7032), 054krc (0.40 #1506, 0.33 #4605, 0.32 #7033), 04dn09n (0.40 #1480, 0.33 #4605, 0.32 #7033) >> Best rule #3270 for best value: >> intensional similarity = 16 >> extensional distance = 21 >> proper extension: 0bc773; 0bzn6_; >> query: (?x3173, 0gvx_) <- ceremony(?x1703, ?x3173), honored_for(?x3173, ?x6767), honored_for(?x3173, ?x6213), honored_for(?x3173, ?x2345), ?x1703 = 0k611, award_winner(?x3173, ?x8408), award_winner(?x3173, ?x3736), nominated_for(?x3617, ?x6767), gender(?x8408, ?x231), nominated_for(?x6213, ?x6097), award_winner(?x6213, ?x2035), student(?x2730, ?x8408), award_nominee(?x902, ?x3736), written_by(?x5008, ?x3736), nominated_for(?x4867, ?x6213), titles(?x162, ?x2345) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bzk2h ceremony! 0gvx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.913 http://example.org/award/award_category/winners./award/award_honor/ceremony #7121-04dsnp PRED entity: 04dsnp PRED relation: language PRED expected values: 064_8sq => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 46): 064_8sq (0.20 #697, 0.20 #76, 0.19 #1602), 04306rv (0.17 #1586, 0.14 #1018, 0.13 #1812), 0jzc (0.14 #18, 0.13 #74, 0.09 #1032), 02hwhyv (0.14 #27, 0.05 #195, 0.02 #1212), 02bjrlw (0.13 #57, 0.11 #1583, 0.11 #1015), 03_9r (0.13 #65, 0.11 #2099, 0.11 #177), 03hkp (0.12 #125, 0.05 #464, 0.05 #520), 05zjd (0.11 #191, 0.04 #1491, 0.03 #1717), 02hxcvy (0.08 #368, 0.05 #538, 0.04 #255), 01r2l (0.07 #78, 0.04 #302, 0.02 #641) >> Best rule #697 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 06_wqk4; 02r79_h; 075wx7_; 065z3_x; 03hmt9b; 07jxpf; 05_5_22; 051ys82; 05q7874; 03xf_m; ... >> query: (?x1015, 064_8sq) <- film_crew_role(?x1015, ?x2472), featured_film_locations(?x1015, ?x362), ?x2472 = 01xy5l_ >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04dsnp language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.204 http://example.org/film/film/language #7120-037mh8 PRED entity: 037mh8 PRED relation: major_field_of_study! PRED expected values: 0277jc 07szy 016ndm 06bw5 0pz6q => 115 concepts (38 used for prediction) PRED predicted values (max 10 best out of 611): 07szy (0.71 #12153, 0.67 #8458, 0.61 #11099), 017j69 (0.71 #4340, 0.67 #11729, 0.57 #4868), 065y4w7 (0.64 #6853, 0.61 #11606, 0.54 #7382), 07t90 (0.60 #8564, 0.57 #12259, 0.50 #11205), 07vyf (0.57 #4862, 0.54 #7499, 0.50 #2232), 0bx8pn (0.57 #4244, 0.50 #2142, 0.46 #7409), 012mzw (0.57 #5522, 0.48 #12380, 0.40 #8685), 01wv24 (0.57 #4512, 0.38 #7677, 0.33 #6093), 07w0v (0.56 #11081, 0.54 #7386, 0.53 #8440), 025v3k (0.56 #5898, 0.45 #6953, 0.44 #11706) >> Best rule #12153 for best value: >> intensional similarity = 7 >> extensional distance = 19 >> proper extension: 03nfmq; >> query: (?x8221, 07szy) <- major_field_of_study(?x7545, ?x8221), major_field_of_study(?x741, ?x8221), student(?x7545, ?x7039), student(?x7545, ?x2359), ?x7039 = 041_y, ?x741 = 01w3v, award_winner(?x822, ?x2359) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 86, 396, 485, 565 EVAL 037mh8 major_field_of_study! 0pz6q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 115.000 38.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 037mh8 major_field_of_study! 06bw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 115.000 38.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 037mh8 major_field_of_study! 016ndm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 115.000 38.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 037mh8 major_field_of_study! 07szy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 38.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 037mh8 major_field_of_study! 0277jc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 115.000 38.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7119-02bqn1 PRED entity: 02bqn1 PRED relation: district_represented PRED expected values: 07srw => 39 concepts (36 used for prediction) PRED predicted values (max 10 best out of 214): 059f4 (0.86 #91, 0.84 #357, 0.82 #1121), 0vbk (0.86 #91, 0.84 #357, 0.78 #1163), 02xry (0.86 #91, 0.84 #357, 0.78 #1163), 06yxd (0.86 #91, 0.84 #357, 0.78 #1163), 0d0x8 (0.86 #91, 0.84 #357, 0.78 #1163), 03v0t (0.86 #91, 0.84 #357, 0.78 #1163), 04ly1 (0.86 #91, 0.84 #357, 0.78 #1163), 05kj_ (0.86 #91, 0.84 #357, 0.78 #1163), 07z1m (0.86 #91, 0.84 #357, 0.78 #1163), 05mph (0.86 #91, 0.84 #357, 0.78 #1163) >> Best rule #91 for best value: >> intensional similarity = 53 >> extensional distance = 1 >> proper extension: 06f0dc; >> query: (?x1137, ?x177) <- legislative_sessions(?x1137, ?x6728), legislative_sessions(?x1137, ?x5977), legislative_sessions(?x1137, ?x4821), legislative_sessions(?x1137, ?x4730), legislative_sessions(?x1137, ?x3765), legislative_sessions(?x1137, ?x1829), legislative_sessions(?x1137, ?x952), legislative_sessions(?x1137, ?x605), ?x6728 = 070mff, district_represented(?x1137, ?x7405), district_represented(?x1137, ?x6895), district_represented(?x1137, ?x6226), district_represented(?x1137, ?x4198), district_represented(?x1137, ?x4105), district_represented(?x1137, ?x4061), district_represented(?x1137, ?x3670), district_represented(?x1137, ?x2977), district_represented(?x1137, ?x1767), legislative_sessions(?x1830, ?x1137), ?x4198 = 05fky, ?x3670 = 05tbn, ?x5977 = 06r713, ?x1829 = 02bp37, ?x4821 = 02bqm0, ?x7405 = 07_f2, legislative_sessions(?x9334, ?x1137), legislative_sessions(?x8607, ?x1137), legislative_sessions(?x5932, ?x1137), ?x6895 = 05fjf, legislative_sessions(?x6742, ?x952), legislative_sessions(?x3445, ?x952), district_represented(?x952, ?x13269), district_represented(?x952, ?x3086), district_represented(?x952, ?x2768), district_represented(?x952, ?x1274), district_represented(?x952, ?x177), ?x3445 = 0d06m5, ?x2977 = 081mh, ?x1274 = 04ykg, ?x3765 = 04gp1d, ?x4061 = 0498y, ?x2768 = 03s5t, ?x1767 = 04rrd, ?x6742 = 06bss, ?x6226 = 03gh4, ?x8607 = 0226cw, ?x5932 = 012v1t, ?x605 = 077g7n, ?x13269 = 0czr9_, ?x4730 = 02cg7g, ?x4105 = 0824r, ?x3086 = 0846v, ?x9334 = 02hy5d >> conf = 0.86 => this is the best rule for 29 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 27 EVAL 02bqn1 district_represented 07srw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 39.000 36.000 0.860 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #7118-0631_ PRED entity: 0631_ PRED relation: religion! PRED expected values: 0bt7ws 026dx 0cg9f => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1702): 0948xk (0.50 #1855, 0.33 #802, 0.25 #22943), 0bymv (0.50 #4381, 0.25 #11754, 0.25 #10701), 0157m (0.50 #4323, 0.25 #11696, 0.25 #10643), 09b6zr (0.50 #5603, 0.20 #6656, 0.17 #8764), 0d05fv (0.50 #4583, 0.17 #22506, 0.17 #8797), 06bss (0.50 #4778, 0.17 #22701, 0.17 #8992), 09bg4l (0.50 #4492, 0.17 #22415, 0.17 #8706), 08f3b1 (0.44 #13746, 0.33 #19018, 0.29 #9528), 04rfq (0.40 #8407, 0.33 #9460, 0.33 #1028), 02xyl (0.40 #8406, 0.33 #1027, 0.25 #4191) >> Best rule #1855 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 0n2g; >> query: (?x2591, 0948xk) <- religion(?x12525, ?x2591), religion(?x4240, ?x2591), religion(?x2768, ?x2591), religion(?x728, ?x2591), celebrities_impersonated(?x6707, ?x12525), film(?x4240, ?x3294), ?x3294 = 0jvt9, location(?x5620, ?x728), contains(?x8260, ?x2768), languages(?x6707, ?x732), type_of_union(?x6707, ?x566), currency(?x728, ?x170) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #12646 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 6 *> proper extension: 01lp8; 05sfs; 051kv; 019cr; 04pk9; *> query: (?x2591, ?x2416) <- religion(?x12525, ?x2591), religion(?x4240, ?x2591), religion(?x1138, ?x2591), religion(?x961, ?x2591), celebrities_impersonated(?x3649, ?x12525), film(?x4240, ?x11395), film(?x4240, ?x3294), participant(?x4240, ?x3002), ?x1138 = 059_c, nationality(?x4240, ?x94), ?x961 = 03s0w, language(?x11395, ?x254), film(?x2416, ?x3294) *> conf = 0.11 ranks of expected_values: 994 EVAL 0631_ religion! 0cg9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 42.000 0.500 http://example.org/people/person/religion EVAL 0631_ religion! 026dx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 42.000 0.500 http://example.org/people/person/religion EVAL 0631_ religion! 0bt7ws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 42.000 0.500 http://example.org/people/person/religion #7117-0127s7 PRED entity: 0127s7 PRED relation: award PRED expected values: 02f716 02f73p => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 301): 03qbh5 (0.78 #8625, 0.77 #31367, 0.76 #35289), 02f716 (0.56 #6053, 0.19 #1741, 0.17 #7229), 01bgqh (0.53 #827, 0.41 #5923, 0.34 #8275), 02f73b (0.49 #6158, 0.25 #1846, 0.24 #1062), 01by1l (0.43 #5991, 0.41 #1679, 0.40 #8343), 02f73p (0.43 #6063, 0.19 #1751, 0.18 #967), 02v1m7 (0.41 #5992, 0.18 #3248, 0.16 #1680), 02f72n (0.41 #6024, 0.18 #928, 0.16 #33328), 09sb52 (0.34 #6313, 0.32 #49837, 0.32 #31408), 0ck27z (0.32 #39692, 0.28 #27143, 0.20 #45966) >> Best rule #8625 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 01vvydl; 012d40; 0kzy0; 016kjs; 014zfs; 01wdqrx; 05mt_q; 03xgm3; 01l1sq; 01bpc9; ... >> query: (?x5906, ?x1389) <- currency(?x5906, ?x170), artists(?x474, ?x5906), award_winner(?x1389, ?x5906) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6053 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 73 *> proper extension: 01v0sx2; 0dtd6; 01vn35l; 01wbz9; 01wzlxj; 01vw8mh; 01xzb6; 0b1zz; 0ycfj; *> query: (?x5906, 02f716) <- award(?x5906, ?x2877), ?x2877 = 02f5qb *> conf = 0.56 ranks of expected_values: 2, 6 EVAL 0127s7 award 02f73p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 164.000 164.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0127s7 award 02f716 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 164.000 164.000 0.779 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7116-02v60l PRED entity: 02v60l PRED relation: program PRED expected values: 06hwzy => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 19): 06hwzy (0.30 #240, 0.30 #292, 0.06 #422), 01j7mr (0.08 #242, 0.05 #294, 0.02 #138), 0cpz4k (0.08 #243, 0.04 #295, 0.02 #113), 01b7h8 (0.06 #227, 0.05 #305, 0.05 #253), 01h1bf (0.05 #241, 0.04 #293, 0.01 #579), 02zv4b (0.05 #55, 0.03 #237, 0.03 #289), 026bfsh (0.05 #245, 0.03 #297, 0.02 #323), 0304nh (0.05 #244, 0.03 #296, 0.02 #218), 025ljp (0.03 #252, 0.01 #304, 0.01 #876), 0275kr (0.03 #255, 0.01 #307) >> Best rule #240 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 0bz5v2; 09k2t1; 01wj9y9; 06mmb; 0p8r1; 012gq6; 0253b6; 01k70_; 01vt9p3; 0g2mbn; ... >> query: (?x4611, 06hwzy) <- profession(?x4611, ?x319), location(?x4611, ?x4612), person(?x3480, ?x4611) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v60l program 06hwzy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.303 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #7115-0brkwj PRED entity: 0brkwj PRED relation: award_winner! PRED expected values: 06jrhz => 89 concepts (34 used for prediction) PRED predicted values (max 10 best out of 508): 0884hk (0.82 #46567, 0.81 #43353, 0.81 #43352), 0d7hg4 (0.82 #46567, 0.81 #43353, 0.81 #43352), 0brkwj (0.55 #2888, 0.55 #1283, 0.28 #43354), 0h5jg5 (0.51 #11239, 0.49 #3210, 0.48 #32114), 08q3s0 (0.49 #3210, 0.48 #1605, 0.45 #11238), 01xndd (0.48 #32114, 0.45 #2286, 0.37 #35324), 06jrhz (0.45 #2595, 0.45 #990, 0.28 #43354), 04wvhz (0.28 #43354, 0.28 #54592, 0.09 #1752), 059j4x (0.28 #25695), 01rzqj (0.28 #25695) >> Best rule #46567 for best value: >> intensional similarity = 3 >> extensional distance = 1246 >> proper extension: 0g51l1; 01wz_ml; 02c0mv; 08xz51; 06vqdf; 0cbxl0; >> query: (?x8094, ?x4023) <- award_winner(?x8094, ?x4023), profession(?x8094, ?x353), place_of_birth(?x4023, ?x8181) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #2595 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 09hd6f; *> query: (?x8094, 06jrhz) <- award_nominee(?x8094, ?x4023), ?x4023 = 09hd16, nationality(?x8094, ?x94) *> conf = 0.45 ranks of expected_values: 7 EVAL 0brkwj award_winner! 06jrhz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 89.000 34.000 0.815 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #7114-0194zl PRED entity: 0194zl PRED relation: genre PRED expected values: 03bxz7 => 100 concepts (75 used for prediction) PRED predicted values (max 10 best out of 93): 018h2 (0.61 #3676, 0.59 #2491, 0.56 #2490), 05p553 (0.50 #4, 0.44 #714, 0.40 #360), 04xvlr (0.36 #475, 0.33 #120, 0.31 #239), 03bxz7 (0.34 #527, 0.14 #1002, 0.14 #409), 01jfsb (0.33 #3213, 0.32 #1317, 0.32 #3807), 0d63kt (0.30 #84, 0.09 #440, 0.04 #203), 02kdv5l (0.29 #3203, 0.29 #2611, 0.28 #2018), 03k9fj (0.27 #603, 0.22 #1079, 0.22 #1672), 06cvj (0.26 #713, 0.11 #241, 0.10 #3), 03g3w (0.25 #496, 0.10 #2393, 0.09 #971) >> Best rule #3676 for best value: >> intensional similarity = 5 >> extensional distance = 740 >> proper extension: 016ztl; 0564x; >> query: (?x4963, ?x53) <- film(?x166, ?x4963), titles(?x714, ?x4963), titles(?x53, ?x4963), film_release_distribution_medium(?x4963, ?x81), genre(?x161, ?x714) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #527 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 0413cff; *> query: (?x4963, 03bxz7) <- genre(?x4963, ?x1316), country(?x4963, ?x94), ?x1316 = 017fp *> conf = 0.34 ranks of expected_values: 4 EVAL 0194zl genre 03bxz7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 100.000 75.000 0.607 http://example.org/film/film/genre #7113-02snj9 PRED entity: 02snj9 PRED relation: performance_role! PRED expected values: 06k02 02s6sh => 73 concepts (42 used for prediction) PRED predicted values (max 10 best out of 726): 02rn_bj (0.50 #461, 0.43 #1935, 0.43 #1812), 09hnb (0.50 #397, 0.33 #31, 0.29 #1871), 02s6sh (0.40 #971, 0.33 #1707, 0.33 #1584), 050z2 (0.40 #910, 0.33 #1523, 0.33 #171), 053y0s (0.40 #863, 0.33 #1476, 0.33 #124), 07r4c (0.33 #1669, 0.33 #1546, 0.33 #194), 0fpjd_g (0.33 #241, 0.33 #137, 0.25 #259), 01hrqc (0.33 #1678, 0.33 #203, 0.25 #325), 01vrncs (0.33 #1605, 0.25 #2225, 0.25 #2100), 01kd57 (0.33 #188, 0.25 #310, 0.20 #1052) >> Best rule #461 for best value: >> intensional similarity = 24 >> extensional distance = 2 >> proper extension: 05r5c; >> query: (?x3214, 02rn_bj) <- role(?x3214, ?x8957), role(?x3214, ?x3215), role(?x3214, ?x2297), role(?x3214, ?x1647), role(?x3214, ?x1466), role(?x3214, ?x1147), role(?x3214, ?x614), ?x1147 = 07kc_, ?x1647 = 05ljv7, performance_role(?x2876, ?x3214), ?x3215 = 0bxl5, ?x8957 = 03f5mt, artist(?x3265, ?x2876), ?x614 = 0mkg, ?x3265 = 015_1q, group(?x3214, ?x8058), performance_role(?x212, ?x3214), performance_role(?x3214, ?x4975), role(?x317, ?x2297), gender(?x2876, ?x231), group(?x75, ?x8058), award_nominee(?x2876, ?x3403), ?x75 = 07y_7, ?x1466 = 03bx0bm >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #971 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 3 *> proper extension: 013y1f; *> query: (?x3214, 02s6sh) <- role(?x3214, ?x3161), role(?x3214, ?x2888), role(?x3214, ?x1166), role(?x3214, ?x1147), role(?x3214, ?x614), ?x3161 = 01v1d8, ?x614 = 0mkg, instrumentalists(?x1147, ?x2242), group(?x3214, ?x498), role(?x1147, ?x212), instrumentalists(?x3214, ?x2945), role(?x2888, ?x75), role(?x642, ?x3214), instrumentalists(?x2888, ?x8972), instrumentalists(?x2888, ?x3890), performance_role(?x3214, ?x1574), role(?x74, ?x2888), role(?x317, ?x1147), performance_role(?x764, ?x3214), role(?x1260, ?x2888), ?x1166 = 05148p4, ?x8972 = 01fh0q, ?x3890 = 01gg59 *> conf = 0.40 ranks of expected_values: 3, 15 EVAL 02snj9 performance_role! 02s6sh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 73.000 42.000 0.500 http://example.org/music/artist/contribution./music/recording_contribution/performance_role EVAL 02snj9 performance_role! 06k02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 73.000 42.000 0.500 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #7112-02c638 PRED entity: 02c638 PRED relation: nominated_for! PRED expected values: 0fhpv4 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 192): 027b9j5 (0.68 #8775, 0.67 #9846, 0.67 #14563), 027c95y (0.68 #8775, 0.67 #9846, 0.67 #14563), 027b9ly (0.68 #8775, 0.67 #9846, 0.67 #14563), 04jhhng (0.68 #8775, 0.67 #9846, 0.67 #14563), 040njc (0.58 #2788, 0.36 #862, 0.36 #4072), 02pqp12 (0.55 #2829, 0.39 #261, 0.30 #903), 02qyntr (0.54 #2938, 0.34 #370, 0.29 #4222), 0k611 (0.49 #2841, 0.47 #273, 0.41 #915), 0gq_v (0.42 #231, 0.39 #873, 0.33 #9648), 0gr0m (0.34 #904, 0.34 #262, 0.32 #4114) >> Best rule #8775 for best value: >> intensional similarity = 4 >> extensional distance = 364 >> proper extension: 04lqvlr; 04qk12; 08j7lh; 0j8f09z; 03xj05; >> query: (?x2116, ?x2915) <- film_crew_role(?x2116, ?x1284), nominated_for(?x112, ?x2116), ?x1284 = 0ch6mp2, award(?x2116, ?x2915) >> conf = 0.68 => this is the best rule for 4 predicted values *> Best rule #21424 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1588 *> proper extension: 06g60w; *> query: (?x2116, ?x880) <- nominated_for(?x7530, ?x2116), award(?x7530, ?x880) *> conf = 0.19 ranks of expected_values: 40 EVAL 02c638 nominated_for! 0fhpv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 107.000 107.000 0.680 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7111-04ycjk PRED entity: 04ycjk PRED relation: currency PRED expected values: 09nqf => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.80 #67, 0.76 #97, 0.75 #55), 01nv4h (0.14 #200, 0.11 #544, 0.11 #550), 02l6h (0.12 #415, 0.02 #268, 0.02 #419), 0ptk_ (0.07 #27, 0.07 #21, 0.05 #57), 0kz1h (0.02 #197, 0.02 #299, 0.02 #125) >> Best rule #67 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 017j69; >> query: (?x7065, 09nqf) <- institution(?x1519, ?x7065), ?x1519 = 013zdg, citytown(?x7065, ?x3052), student(?x7065, ?x6913), program(?x6913, ?x782) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ycjk currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.800 http://example.org/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency #7110-04rzd PRED entity: 04rzd PRED relation: role! PRED expected values: 03gvt => 78 concepts (55 used for prediction) PRED predicted values (max 10 best out of 57): 026t6 (0.85 #1783, 0.83 #1627, 0.82 #1028), 0l14qv (0.82 #1028, 0.81 #107, 0.81 #2096), 0mkg (0.82 #1028, 0.81 #107, 0.81 #2096), 01hww_ (0.82 #1028, 0.81 #107, 0.81 #2096), 02hnl (0.82 #1028, 0.81 #107, 0.81 #2096), 0319l (0.82 #1028, 0.81 #107, 0.81 #2096), 0dwvl (0.82 #1028, 0.81 #107, 0.80 #2094), 05ljv7 (0.82 #1028, 0.81 #107, 0.80 #2094), 01qzyz (0.82 #1028, 0.81 #107, 0.80 #2094), 02hrlh (0.82 #1028, 0.81 #107, 0.80 #2094) >> Best rule #1783 for best value: >> intensional similarity = 10 >> extensional distance = 24 >> proper extension: 028tv0; >> query: (?x1969, 026t6) <- role(?x1969, ?x3214), role(?x1969, ?x615), ?x3214 = 02snj9, role(?x3991, ?x1969), role(?x2059, ?x1969), group(?x1969, ?x1929), role(?x211, ?x3991), group(?x615, ?x3516), instrumentalists(?x2059, ?x3241), role(?x1321, ?x615) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1989 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 25 *> proper extension: 04q7r; *> query: (?x1969, ?x3716) <- instrumentalists(?x1969, ?x4875), instrumentalists(?x1969, ?x2731), instrumentalists(?x1969, ?x1413), role(?x2158, ?x1969), award(?x2731, ?x567), award_winner(?x2431, ?x2731), ?x2158 = 01dnws, artists(?x671, ?x2731), award_winner(?x4875, ?x1051), role(?x2731, ?x3716), artist(?x2190, ?x2731), award_winner(?x2124, ?x1413) *> conf = 0.67 ranks of expected_values: 33 EVAL 04rzd role! 03gvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 78.000 55.000 0.846 http://example.org/music/performance_role/track_performances./music/track_contribution/role #7109-02bg55 PRED entity: 02bg55 PRED relation: film_release_distribution_medium PRED expected values: 029j_ 07z4p => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (1.00 #318, 0.99 #207, 0.99 #167), 07z4p (0.22 #4, 0.21 #332, 0.08 #95), 07c52 (0.21 #332, 0.08 #97, 0.08 #93), 0735l (0.19 #150), 0dq6p (0.19 #150) >> Best rule #318 for best value: >> intensional similarity = 6 >> extensional distance = 1457 >> proper extension: 05jyb2; >> query: (?x6520, 029j_) <- genre(?x6520, ?x812), film_release_distribution_medium(?x6520, ?x627), film_distribution_medium(?x2006, ?x627), film_distribution_medium(?x1035, ?x627), ?x2006 = 031778, ?x1035 = 08hmch >> conf = 1.00 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02bg55 film_release_distribution_medium 07z4p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.997 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 02bg55 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.997 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #7108-031n5b PRED entity: 031n5b PRED relation: institution! PRED expected values: 016t_3 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 26): 014mlp (0.77 #664, 0.73 #492, 0.72 #1218), 019v9k (0.69 #178, 0.67 #202, 0.64 #496), 02h4rq6 (0.67 #661, 0.67 #1215, 0.66 #171), 016t_3 (0.57 #172, 0.52 #196, 0.46 #490), 02_xgp2 (0.56 #500, 0.54 #182, 0.54 #672), 0bkj86 (0.54 #495, 0.51 #667, 0.47 #104), 03bwzr4 (0.53 #111, 0.48 #502, 0.46 #184), 07s6fsf (0.46 #170, 0.40 #194, 0.36 #512), 028dcg (0.32 #140, 0.29 #3148, 0.28 #3322), 027f2w (0.32 #669, 0.31 #497, 0.27 #106) >> Best rule #664 for best value: >> intensional similarity = 5 >> extensional distance = 93 >> proper extension: 03cz83; >> query: (?x9612, 014mlp) <- school_type(?x9612, ?x3205), category(?x9612, ?x134), ?x134 = 08mbj5d, major_field_of_study(?x9612, ?x8681), genre(?x903, ?x8681) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #172 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 33 *> proper extension: 04bfg; 02htv6; *> query: (?x9612, 016t_3) <- student(?x9612, ?x9891), organization(?x3484, ?x9612), category(?x9612, ?x134), state_province_region(?x9612, ?x335), music(?x5029, ?x9891), nominated_for(?x914, ?x5029) *> conf = 0.57 ranks of expected_values: 4 EVAL 031n5b institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 145.000 0.768 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #7107-03_nq PRED entity: 03_nq PRED relation: taxonomy PRED expected values: 04n6k => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.68 #18, 0.64 #6, 0.62 #19) >> Best rule #18 for best value: >> intensional similarity = 6 >> extensional distance = 20 >> proper extension: 083p7; 083q7; 083pr; 0f7fy; 034ls; 07t2k; 0c_md_; 0b22w; 0d3k14; 042kg; ... >> query: (?x9046, 04n6k) <- basic_title(?x9046, ?x5402), basic_title(?x9046, ?x346), jurisdiction_of_office(?x9046, ?x94), ?x346 = 060c4, politician(?x10510, ?x9046), jurisdiction_of_office(?x5402, ?x177) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_nq taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.682 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #7106-0l14md PRED entity: 0l14md PRED relation: instrumentalists PRED expected values: 03qmj9 015882 0pkyh 02s2wq 01vtg4q 01k_0fp => 92 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1027): 01vn35l (0.67 #5010, 0.67 #2230, 0.62 #7938), 012x4t (0.67 #5010, 0.67 #2230, 0.57 #6759), 03h502k (0.67 #4726, 0.67 #4168, 0.57 #5283), 0l12d (0.67 #5010, 0.67 #2230, 0.56 #4453), 03j24kf (0.67 #5010, 0.67 #2230, 0.56 #4453), 018x3 (0.67 #5010, 0.67 #2230, 0.56 #4453), 01kv4mb (0.67 #5010, 0.67 #2230, 0.56 #4453), 01w9mnm (0.67 #5010, 0.67 #2230, 0.56 #4453), 02s6sh (0.67 #5010, 0.67 #2230, 0.56 #4453), 01wxdn3 (0.67 #5010, 0.67 #2230, 0.56 #4453) >> Best rule #5010 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 026t6; >> query: (?x315, ?x115) <- role(?x1969, ?x315), group(?x315, ?x379), ?x1969 = 04rzd, role(?x460, ?x315), performance_role(?x115, ?x315), instrumentalists(?x315, ?x7237), instrumentalists(?x315, ?x1826), type_of_union(?x7237, ?x566), ?x1826 = 09mq4m >> conf = 0.67 => this is the best rule for 19 predicted values *> Best rule #8215 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 05r5c; *> query: (?x315, 01vtg4q) <- role(?x1969, ?x315), group(?x315, ?x379), ?x1969 = 04rzd, role(?x5514, ?x315), performance_role(?x115, ?x315), instrumentalists(?x315, ?x7237), ?x7237 = 0473q, sibling(?x5514, ?x10353) *> conf = 0.62 ranks of expected_values: 34, 285, 303, 331, 338, 416 EVAL 0l14md instrumentalists 01k_0fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 92.000 67.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 0l14md instrumentalists 01vtg4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 92.000 67.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 0l14md instrumentalists 02s2wq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 92.000 67.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 0l14md instrumentalists 0pkyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 92.000 67.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 0l14md instrumentalists 015882 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 92.000 67.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 0l14md instrumentalists 03qmj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 92.000 67.000 0.667 http://example.org/music/instrument/instrumentalists #7105-06q8qh PRED entity: 06q8qh PRED relation: produced_by PRED expected values: 02_340 => 110 concepts (83 used for prediction) PRED predicted values (max 10 best out of 257): 0fvf9q (0.20 #6, 0.08 #781, 0.05 #13989), 02l5rm (0.20 #103, 0.04 #878, 0.01 #2820), 03kpvp (0.13 #513, 0.02 #8278, 0.02 #12165), 0h0wc (0.12 #1552, 0.12 #3495, 0.11 #11652), 0170s4 (0.12 #1552, 0.12 #3495, 0.11 #11652), 01pgzn_ (0.12 #1552, 0.12 #3495, 0.11 #11652), 01kwsg (0.12 #1552, 0.12 #3495, 0.11 #11652), 01gbn6 (0.12 #1552, 0.12 #3495, 0.11 #11652), 02k21g (0.12 #1552, 0.12 #3495, 0.11 #11652), 02qgyv (0.12 #1552, 0.12 #3495, 0.11 #11652) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0jyx6; 0jqj5; 03cvvlg; >> query: (?x3684, 0fvf9q) <- cinematography(?x3684, ?x10583), nominated_for(?x2551, ?x3684), award(?x3684, ?x1254), ?x2551 = 0h0wc >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06q8qh produced_by 02_340 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 83.000 0.200 http://example.org/film/film/produced_by #7104-031ns1 PRED entity: 031ns1 PRED relation: student PRED expected values: 01cwcr => 64 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1520): 037lyl (0.20 #2746, 0.14 #4831, 0.03 #11086), 02bn75 (0.20 #3442, 0.14 #5527, 0.03 #11782), 01x1fq (0.20 #3771, 0.14 #5856, 0.03 #12111), 06h2w (0.20 #3015, 0.14 #5100, 0.03 #11355), 012201 (0.14 #5639, 0.10 #3554, 0.03 #11894), 0pcc0 (0.14 #4288, 0.02 #27227, 0.02 #31397), 0kn3g (0.10 #3750, 0.10 #1663, 0.09 #7920), 0kh6b (0.10 #613, 0.09 #6870, 0.03 #27724), 0xnc3 (0.10 #1438, 0.09 #7695, 0.02 #18122), 01lwx (0.10 #1975, 0.09 #8232, 0.02 #18659) >> Best rule #2746 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 031n8c; 06mvyf; >> query: (?x13639, 037lyl) <- category(?x13639, ?x134), ?x134 = 08mbj5d, school_type(?x13639, ?x9240), ?x9240 = 01y64 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 031ns1 student 01cwcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 36.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #7103-0kb3n PRED entity: 0kb3n PRED relation: category PRED expected values: 08mbj5d => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.35 #36, 0.33 #9, 0.32 #58) >> Best rule #36 for best value: >> intensional similarity = 3 >> extensional distance = 639 >> proper extension: 01r42_g; 0f830f; 08w7vj; 08m4c8; 05dxl5; 02cm2m; 02z6l5f; 0cj36c; 03_wpf; 0191h5; ... >> query: (?x8408, 08mbj5d) <- location(?x8408, ?x1131), award_nominee(?x1314, ?x8408), award_winner(?x2822, ?x8408) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kb3n category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.349 http://example.org/common/topic/webpage./common/webpage/category #7102-0154qm PRED entity: 0154qm PRED relation: language PRED expected values: 02h40lc => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 13): 02h40lc (0.09 #1, 0.05 #54, 0.05 #57), 03_9r (0.02 #65, 0.01 #286), 03k50 (0.02 #14, 0.02 #26, 0.01 #39), 01jb8r (0.01 #286), 04h9h (0.01 #286), 07qv_ (0.01 #286), 05zjd (0.01 #286), 06b_j (0.01 #286), 064_8sq (0.01 #286), 0jzc (0.01 #286) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0p_pd; 01q_ph; 0mdqp; 039bp; 01y_px; 03hzl42; 0205dx; 01f7dd; 030vnj; >> query: (?x3281, 02h40lc) <- film(?x3281, ?x972), award_nominee(?x3281, ?x241), ?x241 = 01j5ts >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0154qm language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.091 http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language #7101-067pl7 PRED entity: 067pl7 PRED relation: student! PRED expected values: 0885n => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 77): 07wjk (0.12 #1117, 0.09 #590), 03ksy (0.12 #106, 0.06 #1687, 0.05 #1160), 052nd (0.06 #536, 0.03 #1063), 0bwfn (0.06 #5545, 0.06 #3964, 0.06 #4491), 065y4w7 (0.05 #1595, 0.04 #14, 0.03 #3703), 08815 (0.04 #2, 0.02 #21618, 0.02 #8961), 01vg13 (0.04 #219, 0.02 #10014), 07w0v (0.04 #20, 0.02 #10014), 07tds (0.04 #149, 0.01 #2784, 0.01 #1730), 02zd460 (0.04 #170, 0.01 #8075, 0.01 #1751) >> Best rule #1117 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 02wrhj; >> query: (?x2691, 07wjk) <- place_of_birth(?x2691, ?x4826), nationality(?x2691, ?x279), ?x279 = 0d060g >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #781 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 77 *> proper extension: 0dky9n; 019fnv; *> query: (?x2691, 0885n) <- award_winner(?x3263, ?x2691), nationality(?x2691, ?x279), ?x279 = 0d060g *> conf = 0.01 ranks of expected_values: 45 EVAL 067pl7 student! 0885n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 82.000 82.000 0.121 http://example.org/education/educational_institution/students_graduates./education/education/student #7100-06_wqk4 PRED entity: 06_wqk4 PRED relation: genre PRED expected values: 02l7c8 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 87): 01z4y (0.61 #7284, 0.53 #3515, 0.53 #7162), 07s9rl0 (0.60 #5582, 0.60 #5459, 0.60 #2667), 01jfsb (0.44 #133, 0.36 #2556, 0.34 #1829), 02l7c8 (0.37 #1349, 0.29 #501, 0.29 #6203), 02kdv5l (0.36 #610, 0.34 #246, 0.34 #2547), 03k9fj (0.33 #132, 0.27 #739, 0.25 #618), 0lsxr (0.26 #251, 0.23 #372, 0.22 #493), 01hmnh (0.22 #139, 0.17 #3290, 0.15 #3897), 03npn (0.22 #127, 0.07 #3278, 0.07 #3036), 04xvlr (0.17 #5460, 0.17 #6189, 0.17 #5583) >> Best rule #7284 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 02xhwm; >> query: (?x857, ?x2480) <- titles(?x2480, ?x857), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1349 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 368 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x857, 02l7c8) <- titles(?x2480, ?x857), titles(?x2480, ?x616), ?x616 = 011yph *> conf = 0.37 ranks of expected_values: 4 EVAL 06_wqk4 genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 86.000 0.612 http://example.org/film/film/genre #7099-0134w7 PRED entity: 0134w7 PRED relation: currency PRED expected values: 09nqf => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.42 #13, 0.41 #16, 0.41 #31), 01nv4h (0.12 #220, 0.02 #29, 0.01 #104) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 033wx9; 01vswwx; 0261x8t; >> query: (?x981, 09nqf) <- award(?x981, ?x401), vacationer(?x910, ?x981), location(?x981, ?x789) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0134w7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.415 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #7098-01wy6 PRED entity: 01wy6 PRED relation: role! PRED expected values: 09hnb => 85 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1585): 050z2 (0.70 #9619, 0.67 #14814, 0.67 #6315), 04bpm6 (0.67 #8555, 0.67 #6198, 0.60 #3845), 023l9y (0.67 #7283, 0.64 #12013, 0.60 #3987), 01wxdn3 (0.67 #6541, 0.60 #9370, 0.60 #4188), 082brv (0.67 #6397, 0.60 #4044, 0.60 #3102), 0l12d (0.60 #4780, 0.60 #4311, 0.60 #3841), 0lzkm (0.60 #3947, 0.60 #3005, 0.50 #7713), 01vsnff (0.60 #2923, 0.56 #8575, 0.50 #7631), 02s6sh (0.60 #4214, 0.55 #12240, 0.50 #7510), 03h502k (0.60 #4011, 0.55 #12037, 0.50 #2125) >> Best rule #9619 for best value: >> intensional similarity = 16 >> extensional distance = 8 >> proper extension: 03m5k; >> query: (?x2460, 050z2) <- role(?x2460, ?x4913), role(?x2460, ?x1473), role(?x2460, ?x1495), role(?x716, ?x2460), instrumentalists(?x2460, ?x3503), ?x1473 = 0g2dz, role(?x12557, ?x1495), role(?x8539, ?x1495), performance_role(?x1495, ?x1147), group(?x2460, ?x3516), ?x8539 = 01w9mnm, role(?x1495, ?x432), friend(?x1896, ?x3503), instrumentalists(?x1495, ?x483), ?x12557 = 04s5_s, ?x4913 = 03ndd >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #7536 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 4 *> proper extension: 0214km; *> query: (?x2460, ?x158) <- role(?x6039, ?x2460), role(?x2764, ?x2460), role(?x2309, ?x2460), role(?x1495, ?x2460), role(?x1472, ?x2460), ?x6039 = 05kms, role(?x1818, ?x2764), role(?x1563, ?x2764), role(?x158, ?x2764), ?x1563 = 0fpjd_g, ?x1495 = 013y1f, role(?x2764, ?x7033), group(?x2764, ?x11425), ?x1472 = 0319l, ?x11425 = 02vnpv, ?x1818 = 0770cd, ?x2309 = 06ncr, role(?x2764, ?x1166), role(?x1148, ?x2764), ?x7033 = 0gkd1 *> conf = 0.34 ranks of expected_values: 167 EVAL 01wy6 role! 09hnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 85.000 41.000 0.700 http://example.org/music/artist/track_contributions./music/track_contribution/role #7097-09h4b5 PRED entity: 09h4b5 PRED relation: artists! PRED expected values: 064t9 02ny8t => 155 concepts (96 used for prediction) PRED predicted values (max 10 best out of 218): 064t9 (0.73 #14, 0.71 #323, 0.68 #4031), 05bt6j (0.53 #45, 0.38 #354, 0.29 #1281), 06by7 (0.47 #640, 0.42 #28765, 0.42 #25982), 02ny8t (0.47 #132, 0.29 #1368, 0.25 #441), 0glt670 (0.35 #10239, 0.34 #4059, 0.33 #8076), 016clz (0.33 #623, 0.29 #5876, 0.26 #4640), 0y3_8 (0.33 #49, 0.21 #358, 0.15 #1285), 0155w (0.29 #4122, 0.15 #12775, 0.14 #10611), 02x8m (0.26 #4036, 0.12 #12689, 0.11 #18251), 03_d0 (0.24 #4029, 0.18 #12682, 0.17 #15463) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 07qnf; 02twdq; 016vn3; 01v27pl; >> query: (?x8018, 064t9) <- category(?x8018, ?x134), artists(?x996, ?x8018), ?x996 = 0dn16 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 09h4b5 artists! 02ny8t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 155.000 96.000 0.733 http://example.org/music/genre/artists EVAL 09h4b5 artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 96.000 0.733 http://example.org/music/genre/artists #7096-05k7sb PRED entity: 05k7sb PRED relation: location_of_ceremony! PRED expected values: 04ztj => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.72 #117, 0.71 #45, 0.69 #237), 01g63y (0.36 #341, 0.05 #94, 0.05 #174), 01bl8s (0.36 #341, 0.01 #179), 0jgjn (0.04 #76, 0.02 #180, 0.02 #64) >> Best rule #117 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 0k049; 0ftxw; 0hptm; 0ggh3; 0bdg5; 0t_3w; 0r00l; 0hqzr; >> query: (?x2020, 04ztj) <- location(?x6320, ?x2020), jurisdiction_of_office(?x900, ?x2020), story_by(?x351, ?x6320) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05k7sb location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.719 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #7095-0ccqd7 PRED entity: 0ccqd7 PRED relation: film PRED expected values: 0gfzfj => 79 concepts (69 used for prediction) PRED predicted values (max 10 best out of 675): 047csmy (0.13 #4494, 0.10 #2704, 0.10 #8074), 05sw5b (0.13 #4395, 0.10 #7975, 0.09 #9765), 099bhp (0.13 #3408, 0.08 #19514, 0.07 #1618), 0872p_c (0.10 #3755, 0.07 #7335, 0.07 #9125), 0crfwmx (0.10 #3731, 0.07 #7311, 0.07 #9101), 0241y7 (0.10 #2861, 0.07 #1071, 0.05 #18967), 0g56t9t (0.10 #1800, 0.07 #3590, 0.06 #5380), 03d8jd1 (0.10 #3514, 0.07 #5304, 0.05 #19620), 023p7l (0.10 #2408, 0.05 #18514, 0.05 #9568), 03q0r1 (0.07 #637, 0.07 #2427, 0.06 #11377) >> Best rule #4494 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 0582cf; >> query: (?x9894, 047csmy) <- nationality(?x9894, ?x94), actor(?x9698, ?x9894), place_of_birth(?x9894, ?x739), film(?x9894, ?x2709) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #14224 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 0h5g_; 044rvb; 01pcq3; 0pz7h; 03d_w3h; 01mqz0; 030hcs; 02wgln; 0169dl; 03pmzt; ... *> query: (?x9894, 0gfzfj) <- language(?x9894, ?x254), profession(?x9894, ?x1032), film(?x9894, ?x2709), student(?x4955, ?x9894) *> conf = 0.04 ranks of expected_values: 57 EVAL 0ccqd7 film 0gfzfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 79.000 69.000 0.133 http://example.org/film/actor/film./film/performance/film #7094-05x_5 PRED entity: 05x_5 PRED relation: school! PRED expected values: 0g3zpp => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 17): 092j54 (0.29 #262, 0.26 #296, 0.23 #313), 09l0x9 (0.29 #264, 0.25 #298, 0.25 #315), 02pq_x5 (0.27 #235, 0.20 #48, 0.19 #99), 03nt7j (0.24 #260, 0.22 #294, 0.22 #311), 025tn92 (0.23 #129, 0.21 #231, 0.19 #299), 0g3zpp (0.22 #256, 0.21 #290, 0.20 #307), 02z6872 (0.19 #229, 0.14 #263, 0.13 #297), 038c0q (0.16 #123, 0.14 #565, 0.13 #259), 09th87 (0.16 #131, 0.13 #301, 0.13 #318), 02rl201 (0.16 #224, 0.11 #3, 0.10 #564) >> Best rule #262 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 05kj_; >> query: (?x6973, 092j54) <- school(?x465, ?x6973), draft(?x4170, ?x465), ?x4170 = 05l71 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #256 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 05kj_; *> query: (?x6973, 0g3zpp) <- school(?x465, ?x6973), draft(?x4170, ?x465), ?x4170 = 05l71 *> conf = 0.22 ranks of expected_values: 6 EVAL 05x_5 school! 0g3zpp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 141.000 141.000 0.286 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #7093-0bbf1f PRED entity: 0bbf1f PRED relation: film PRED expected values: 06__m6 => 134 concepts (115 used for prediction) PRED predicted values (max 10 best out of 1127): 01shy7 (0.07 #2206, 0.07 #25411, 0.06 #9346), 013q07 (0.07 #5710, 0.05 #7495, 0.04 #3925), 03bzjpm (0.06 #1311, 0.05 #8451, 0.04 #26301), 06_wqk4 (0.06 #9051, 0.06 #23331, 0.06 #10836), 0fphf3v (0.06 #10283, 0.06 #12068, 0.05 #24563), 0prrm (0.06 #2642, 0.04 #6212, 0.03 #18707), 01flv_ (0.06 #2847, 0.04 #18912, 0.02 #11772), 01633c (0.06 #3108, 0.03 #19173, 0.02 #8463), 04yc76 (0.06 #2225, 0.03 #18290, 0.02 #30785), 02qzh2 (0.05 #6045, 0.05 #7830, 0.05 #690) >> Best rule #2206 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 014g9y; >> query: (?x2857, 01shy7) <- award(?x2857, ?x1336), ?x1336 = 05pcn59, student(?x3424, ?x2857) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bbf1f film 06__m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 115.000 0.075 http://example.org/film/actor/film./film/performance/film #7092-08z39v PRED entity: 08z39v PRED relation: type_of_union PRED expected values: 04ztj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.85 #73, 0.85 #61, 0.84 #117), 01g63y (0.25 #389, 0.15 #110, 0.11 #154), 0jgjn (0.25 #389, 0.01 #100) >> Best rule #73 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 04wqr; 01d1yr; >> query: (?x10078, 04ztj) <- profession(?x10078, ?x2265), people(?x6720, ?x10078), award_winner(?x3882, ?x10078) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08z39v type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.848 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7091-06fxnf PRED entity: 06fxnf PRED relation: award PRED expected values: 02qvyrt => 93 concepts (82 used for prediction) PRED predicted values (max 10 best out of 262): 054krc (0.67 #1302, 0.51 #2517, 0.43 #2922), 0gqz2 (0.46 #1295, 0.36 #2510, 0.35 #1700), 02qvyrt (0.42 #1342, 0.39 #2557, 0.35 #2962), 025m8y (0.33 #1314, 0.26 #1719, 0.24 #2529), 054ks3 (0.31 #1762, 0.30 #1357, 0.26 #2572), 09sb52 (0.30 #12191, 0.30 #8951, 0.29 #8546), 01by1l (0.30 #5782, 0.28 #6592, 0.20 #4972), 0fhpv4 (0.26 #1411, 0.18 #2626, 0.15 #1816), 02x17c2 (0.25 #220, 0.19 #1840, 0.16 #2650), 0c4z8 (0.23 #1691, 0.20 #6551, 0.20 #5741) >> Best rule #1302 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 02qmncd; 025cn2; 06lk0_; >> query: (?x4020, 054krc) <- award(?x4020, ?x1079), ?x1079 = 0l8z1, nationality(?x4020, ?x512) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1342 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 02qmncd; 025cn2; 06lk0_; *> query: (?x4020, 02qvyrt) <- award(?x4020, ?x1079), ?x1079 = 0l8z1, nationality(?x4020, ?x512) *> conf = 0.42 ranks of expected_values: 3 EVAL 06fxnf award 02qvyrt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 82.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7090-05bmq PRED entity: 05bmq PRED relation: taxonomy PRED expected values: 04n6k => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.73 #40, 0.72 #9, 0.71 #4) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 166 >> proper extension: 0rh6k; 05kkh; 059rby; 03v1s; 05kj_; 059f4; 05fkf; 0hjy; 05fhy; 04ych; ... >> query: (?x9458, 04n6k) <- contains(?x2467, ?x9458), adjoins(?x792, ?x9458), jurisdiction_of_office(?x182, ?x9458), currency(?x9458, ?x170) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05bmq taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.732 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #7089-01_njt PRED entity: 01_njt PRED relation: award PRED expected values: 0bdx29 => 107 concepts (81 used for prediction) PRED predicted values (max 10 best out of 253): 099tbz (0.70 #17746, 0.70 #16131, 0.70 #16939), 099jhq (0.33 #19, 0.22 #422, 0.14 #23394), 09sdmz (0.33 #205, 0.17 #608, 0.14 #23394), 0bfvd4 (0.33 #113, 0.13 #516, 0.09 #4950), 0cqh46 (0.33 #51, 0.06 #5291, 0.06 #6098), 0gqy2 (0.22 #566, 0.17 #163, 0.14 #23394), 0fbtbt (0.22 #634, 0.13 #21778, 0.13 #30654), 027dtxw (0.22 #407, 0.09 #5244, 0.08 #6859), 0cqhk0 (0.17 #4471, 0.17 #37, 0.12 #3665), 0f4x7 (0.17 #434, 0.14 #5271, 0.14 #1643) >> Best rule #17746 for best value: >> intensional similarity = 3 >> extensional distance = 1363 >> proper extension: 02pp_q_; 01qkqwg; 02cm2m; 0191h5; 027d5g5; 08xz51; 051m56; 0drdv; >> query: (?x8167, ?x995) <- nationality(?x8167, ?x279), award_nominee(?x848, ?x8167), award_winner(?x995, ?x8167) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #21778 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1535 *> proper extension: 0kcd5; *> query: (?x8167, ?x435) <- nominated_for(?x8167, ?x5808), award_winner(?x995, ?x8167), nominated_for(?x435, ?x5808) *> conf = 0.13 ranks of expected_values: 49 EVAL 01_njt award 0bdx29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 107.000 81.000 0.702 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7088-05nn4k PRED entity: 05nn4k PRED relation: profession PRED expected values: 03gjzk => 117 concepts (116 used for prediction) PRED predicted values (max 10 best out of 112): 02hrh1q (0.79 #15365, 0.72 #4485, 0.71 #3740), 0dxtg (0.62 #4633, 0.61 #6274, 0.61 #5230), 02jknp (0.56 #4627, 0.51 #6268, 0.51 #5224), 03gjzk (0.52 #462, 0.49 #7617, 0.47 #5232), 012t_z (0.33 #12, 0.28 #3428, 0.20 #608), 09jwl (0.33 #19, 0.28 #3428, 0.17 #16264), 0np9r (0.33 #170, 0.13 #915, 0.12 #1660), 0kyk (0.31 #1222, 0.28 #1073, 0.22 #4054), 0cbd2 (0.29 #1198, 0.28 #1049, 0.28 #3428), 02krf9 (0.28 #3428, 0.18 #7629, 0.17 #4647) >> Best rule #15365 for best value: >> intensional similarity = 3 >> extensional distance = 3159 >> proper extension: 0f1vrl; 054187; 03yf4d; 02pbp9; >> query: (?x4660, 02hrh1q) <- profession(?x4660, ?x319), profession(?x9313, ?x319), ?x9313 = 04353 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #462 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 079vf; *> query: (?x4660, 03gjzk) <- produced_by(?x153, ?x4660), award_winner(?x1561, ?x4660), company(?x4660, ?x3323) *> conf = 0.52 ranks of expected_values: 4 EVAL 05nn4k profession 03gjzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 116.000 0.790 http://example.org/people/person/profession #7087-04y5j64 PRED entity: 04y5j64 PRED relation: film_crew_role PRED expected values: 01pvkk => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 30): 02r96rf (0.76 #299, 0.74 #410, 0.71 #336), 09vw2b7 (0.70 #414, 0.69 #303, 0.68 #340), 01vx2h (0.46 #271, 0.33 #945, 0.32 #419), 0dxtw (0.41 #270, 0.38 #944, 0.37 #344), 01pvkk (0.30 #272, 0.29 #1812, 0.28 #946), 02ynfr (0.25 #54, 0.20 #424, 0.19 #988), 015h31 (0.19 #268, 0.09 #1055, 0.09 #305), 089g0h (0.15 #428, 0.13 #465, 0.12 #169), 0215hd (0.14 #689, 0.14 #427, 0.13 #279), 0d2b38 (0.14 #286, 0.14 #434, 0.12 #471) >> Best rule #299 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 034qmv; 083shs; 02_fm2; 011yxg; 0dnvn3; 01k1k4; 04fzfj; 061681; 05jzt3; 01vksx; ... >> query: (?x4166, 02r96rf) <- titles(?x53, ?x4166), film_crew_role(?x4166, ?x137), film_format(?x4166, ?x909), music(?x4166, ?x8374) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #272 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 132 *> proper extension: 091z_p; *> query: (?x4166, 01pvkk) <- titles(?x2286, ?x4166), film_crew_role(?x4166, ?x137), films(?x2286, ?x197) *> conf = 0.30 ranks of expected_values: 5 EVAL 04y5j64 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 92.000 0.757 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #7086-0534nr PRED entity: 0534nr PRED relation: gender PRED expected values: 05zppz => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #53, 0.78 #3, 0.72 #95), 02zsn (0.35 #12, 0.34 #14, 0.34 #16) >> Best rule #53 for best value: >> intensional similarity = 3 >> extensional distance = 1152 >> proper extension: 02qjj7; 042rnl; 01pr_j6; 01c59k; 016ntp; 02rgz97; 0674cw; 0h005; 0fr7nt; 087yty; ... >> query: (?x10785, 05zppz) <- nationality(?x10785, ?x94), profession(?x10785, ?x13327), film_crew_role(?x1372, ?x13327) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0534nr gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.785 http://example.org/people/person/gender #7085-01q7q2 PRED entity: 01q7q2 PRED relation: student PRED expected values: 04411 => 127 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1830): 05kfs (0.12 #2189, 0.03 #23099, 0.03 #10553), 022411 (0.12 #3777, 0.03 #24687, 0.03 #28871), 06pwf6 (0.08 #461, 0.08 #4643, 0.06 #6734), 0ff3y (0.08 #2068, 0.06 #12523, 0.04 #4159), 01hbq0 (0.08 #2057, 0.06 #8330, 0.04 #6239), 03h40_7 (0.08 #1812, 0.06 #8085, 0.04 #5994), 0306ds (0.08 #2497, 0.04 #406, 0.04 #4588), 05bnp0 (0.08 #2102, 0.04 #23012, 0.03 #31378), 07ymr5 (0.08 #2381, 0.03 #23291, 0.03 #10745), 0405l (0.08 #3943, 0.03 #24853, 0.03 #12307) >> Best rule #2189 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 02kth6; 08qnnv; 01dthg; 031n5b; >> query: (?x8008, 05kfs) <- student(?x8008, ?x838), major_field_of_study(?x8008, ?x8681), institution(?x1368, ?x8008), ?x8681 = 04rlf >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #10578 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 032r4n; *> query: (?x8008, 04411) <- student(?x8008, ?x838), institution(?x3437, ?x8008), ?x3437 = 02_xgp2, artists(?x302, ?x838) *> conf = 0.03 ranks of expected_values: 593 EVAL 01q7q2 student 04411 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 127.000 96.000 0.120 http://example.org/education/educational_institution/students_graduates./education/education/student #7084-01vh3r PRED entity: 01vh3r PRED relation: film PRED expected values: 03nfnx => 87 concepts (50 used for prediction) PRED predicted values (max 10 best out of 804): 0gs973 (0.33 #914, 0.25 #2698, 0.04 #4482), 011ywj (0.33 #1433, 0.25 #3217, 0.02 #6785), 03wjm2 (0.33 #1755, 0.25 #3539, 0.02 #5323), 0g9lm2 (0.33 #727, 0.25 #2511, 0.02 #4295), 01w8g3 (0.33 #662, 0.25 #2446, 0.02 #6014), 02ctc6 (0.33 #521, 0.25 #2305, 0.01 #20146), 02x2jl_ (0.33 #1750, 0.25 #3534, 0.01 #21375), 049w1q (0.33 #1686, 0.25 #3470), 0crs0b8 (0.33 #1537, 0.25 #3321), 04gcyg (0.33 #1381, 0.25 #3165) >> Best rule #914 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01hkhq; >> query: (?x11985, 0gs973) <- film(?x11985, ?x2111), award(?x11985, ?x112), ?x2111 = 016z7s, people(?x743, ?x11985) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6752 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 125 *> proper extension: 01n8_g; 0241wg; 0hwbd; 01wc7p; 013bd1; *> query: (?x11985, 03nfnx) <- film(?x11985, ?x144), award(?x11985, ?x112), religion(?x11985, ?x1985), languages(?x11985, ?x254) *> conf = 0.03 ranks of expected_values: 133 EVAL 01vh3r film 03nfnx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 87.000 50.000 0.333 http://example.org/film/actor/film./film/performance/film #7083-02nygk PRED entity: 02nygk PRED relation: story_by! PRED expected values: 0btpm6 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 332): 062zjtt (0.17 #477, 0.11 #1823, 0.10 #814), 0bc1yhb (0.17 #521, 0.11 #1867, 0.04 #5563), 05qbckf (0.17 #395, 0.11 #1741, 0.04 #5437), 024mpp (0.17 #463, 0.11 #1809, 0.04 #5505), 057lbk (0.17 #482, 0.11 #1828, 0.04 #5524), 02wgk1 (0.17 #487, 0.06 #1833, 0.04 #2841), 012s1d (0.17 #522, 0.06 #1868, 0.04 #2876), 0fqt1ns (0.17 #498, 0.06 #1844, 0.04 #2852), 0340hj (0.17 #382, 0.06 #1728, 0.04 #2736), 0dzlbx (0.17 #510, 0.06 #1856, 0.02 #5552) >> Best rule #477 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 013km; >> query: (?x13339, 062zjtt) <- story_by(?x339, ?x13339), place_of_birth(?x13339, ?x739), company(?x13339, ?x4585), film_release_region(?x204, ?x739) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #2934 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 03jm6c; 03nbbv; 05lnk0; 02gnj2; *> query: (?x13339, 0btpm6) <- place_of_birth(?x13339, ?x739), profession(?x13339, ?x8310), ?x8310 = 0196pc *> conf = 0.04 ranks of expected_values: 92 EVAL 02nygk story_by! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 140.000 140.000 0.167 http://example.org/film/film/story_by #7082-0tz41 PRED entity: 0tz41 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.38 #29, 0.35 #125, 0.35 #121) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 019fh; >> query: (?x11398, 04ztj) <- county(?x11398, ?x6905), source(?x6905, ?x958), state(?x11398, ?x2020) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tz41 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.382 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #7081-07_f2 PRED entity: 07_f2 PRED relation: contains! PRED expected values: 029jpy => 215 concepts (167 used for prediction) PRED predicted values (max 10 best out of 236): 029jpy (0.64 #83258, 0.56 #56394, 0.17 #215), 0d060g (0.64 #83258, 0.56 #56394, 0.16 #40283), 059g4 (0.64 #83258, 0.56 #56394, 0.06 #117751), 02qkt (0.34 #109577, 0.34 #87182, 0.32 #88972), 0kpys (0.33 #180, 0.24 #1074, 0.15 #1970), 030qb3t (0.33 #100, 0.12 #994, 0.07 #1890), 07_f2 (0.27 #132512, 0.16 #124450, 0.10 #100274), 0hpyv (0.27 #132512, 0.16 #124450, 0.10 #100274), 02j9z (0.24 #68061, 0.19 #61797, 0.18 #69850), 07ssc (0.21 #66277, 0.20 #115532, 0.18 #72540) >> Best rule #83258 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 0lfyd; 025r_t; 01gpy4; 0qb62; 0m_cg; >> query: (?x7405, ?x3448) <- adjoins(?x2020, ?x7405), country(?x7405, ?x94), contains(?x3448, ?x2020) >> conf = 0.64 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 07_f2 contains! 029jpy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 215.000 167.000 0.638 http://example.org/location/location/contains #7080-0147sh PRED entity: 0147sh PRED relation: genre PRED expected values: 07s9rl0 => 52 concepts (51 used for prediction) PRED predicted values (max 10 best out of 86): 07s9rl0 (0.96 #1786, 0.79 #120, 0.75 #239), 082gq (0.46 #150, 0.44 #269, 0.33 #31), 05p553 (0.43 #599, 0.42 #837, 0.42 #956), 03k9fj (0.33 #12, 0.25 #250, 0.25 #131), 01jfsb (0.29 #2631, 0.28 #5608, 0.28 #5846), 060__y (0.29 #493, 0.25 #17, 0.23 #255), 02kdv5l (0.29 #2, 0.27 #240, 0.27 #121), 06cvj (0.27 #598, 0.25 #955, 0.23 #836), 02p0szs (0.23 #267, 0.23 #148, 0.21 #29), 03g3w (0.23 #263, 0.21 #144, 0.17 #25) >> Best rule #1786 for best value: >> intensional similarity = 5 >> extensional distance = 1049 >> proper extension: 0c0wvx; 02qjv1p; >> query: (?x878, 07s9rl0) <- genre(?x878, ?x4757), genre(?x4971, ?x4757), genre(?x573, ?x4757), ?x4971 = 01jwxx, nominated_for(?x198, ?x573) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0147sh genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 51.000 0.957 http://example.org/film/film/genre #7079-017_pb PRED entity: 017_pb PRED relation: place_of_birth PRED expected values: 0f94t => 121 concepts (109 used for prediction) PRED predicted values (max 10 best out of 150): 01_d4 (0.33 #66, 0.06 #31066, 0.05 #7110), 0r7fy (0.20 #753, 0.06 #3571, 0.06 #2867), 02_286 (0.12 #28199, 0.10 #7767, 0.09 #12698), 06wxw (0.11 #2975, 0.11 #1566, 0.10 #2270), 02z0j (0.11 #1740, 0.04 #13714, 0.03 #6671), 0b2lw (0.11 #1676, 0.03 #6607, 0.02 #8721), 0t_07 (0.11 #1857, 0.03 #7492, 0.02 #8902), 01cx_ (0.10 #2222, 0.08 #7153, 0.06 #10676), 01ly5m (0.10 #2210, 0.04 #4324, 0.02 #9959), 0yzyn (0.10 #2588, 0.03 #7519, 0.02 #8223) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 017yfz; >> query: (?x7513, 01_d4) <- student(?x4016, ?x7513), profession(?x7513, ?x3746), ?x3746 = 05z96, ?x4016 = 09r4xx >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #31028 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 229 *> proper extension: 080knyg; *> query: (?x7513, 0f94t) <- people(?x2510, ?x7513), ?x2510 = 0x67, award(?x7513, ?x11471) *> conf = 0.03 ranks of expected_values: 48 EVAL 017_pb place_of_birth 0f94t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 121.000 109.000 0.333 http://example.org/people/person/place_of_birth #7078-03f3yfj PRED entity: 03f3yfj PRED relation: award PRED expected values: 02f5qb 02f79n => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 303): 01by1l (0.39 #897, 0.33 #8778, 0.31 #7990), 0c4z8 (0.27 #857, 0.22 #8738, 0.21 #11102), 0gqz2 (0.26 #11111, 0.12 #13869, 0.11 #12687), 09sb52 (0.25 #10678, 0.23 #16588, 0.21 #11466), 01c92g (0.24 #882, 0.15 #11127, 0.13 #8763), 054ks3 (0.22 #11172, 0.18 #927, 0.17 #13930), 03qbnj (0.20 #2195, 0.15 #11258, 0.14 #8894), 02f5qb (0.20 #2123, 0.15 #13156, 0.14 #11186), 02f6ym (0.19 #2220, 0.14 #8525, 0.13 #8919), 02x17c2 (0.18 #1000, 0.15 #11245, 0.09 #12821) >> Best rule #897 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 02fybl; 032qgs; >> query: (?x7909, 01by1l) <- profession(?x7909, ?x319), profession(?x7909, ?x220), ?x319 = 01d_h8, ?x220 = 016z4k >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #2123 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 02r3cn; *> query: (?x7909, 02f5qb) <- artists(?x671, ?x7909), participant(?x7909, ?x5798), nationality(?x7909, ?x94) *> conf = 0.20 ranks of expected_values: 8, 18 EVAL 03f3yfj award 02f79n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 94.000 94.000 0.394 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03f3yfj award 02f5qb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 94.000 94.000 0.394 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7077-0dzc16 PRED entity: 0dzc16 PRED relation: award PRED expected values: 02681vq 02681xs => 94 concepts (66 used for prediction) PRED predicted values (max 10 best out of 290): 02681vq (0.50 #54, 0.15 #24185, 0.15 #24184), 02681_5 (0.50 #387, 0.10 #1596, 0.05 #1999), 01bgqh (0.49 #1252, 0.29 #3670, 0.29 #1655), 02f6ym (0.38 #1468, 0.29 #1871, 0.25 #259), 02f71y (0.38 #1392, 0.26 #1795, 0.25 #183), 01by1l (0.37 #3739, 0.33 #6561, 0.32 #4142), 03qbh5 (0.36 #1415, 0.25 #206, 0.25 #1818), 02f777 (0.36 #1519, 0.25 #310, 0.23 #1922), 02f5qb (0.36 #1365, 0.25 #156, 0.21 #1768), 01c99j (0.36 #1436, 0.21 #1839, 0.18 #6449) >> Best rule #54 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01wj18h; 01wv9p; >> query: (?x4258, 02681vq) <- award_winner(?x4258, ?x9220), award_winner(?x7594, ?x4258), ?x9220 = 0cc5tgk >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 20 EVAL 0dzc16 award 02681xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 94.000 66.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0dzc16 award 02681vq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 66.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7076-04lg6 PRED entity: 04lg6 PRED relation: profession PRED expected values: 09jwl 0mn6 => 137 concepts (107 used for prediction) PRED predicted values (max 10 best out of 123): 01d_h8 (0.94 #13268, 0.44 #12404, 0.38 #1015), 02hrh1q (0.79 #7219, 0.78 #8947, 0.73 #10103), 0dxtg (0.75 #12410, 0.55 #3759, 0.45 #13274), 0kyk (0.58 #6803, 0.53 #10985, 0.53 #5650), 05snw (0.45 #1386, 0.18 #1154, 0.17 #11534), 018gz8 (0.43 #3763, 0.33 #16, 0.18 #8230), 02jknp (0.43 #13269, 0.40 #2601, 0.34 #12405), 05z96 (0.42 #5477, 0.35 #5044, 0.26 #2924), 0mn6 (0.42 #5477, 0.35 #5044, 0.17 #1517), 0nbcg (0.38 #1040, 0.36 #1761, 0.33 #752) >> Best rule #13268 for best value: >> intensional similarity = 6 >> extensional distance = 935 >> proper extension: 07nznf; 05bnp0; 0dbpyd; 02p65p; 01l1b90; 07f8wg; 02pp_q_; 0415svh; 02kxbwx; 03h_9lg; ... >> query: (?x9393, 01d_h8) <- profession(?x9393, ?x3802), nationality(?x9393, ?x205), profession(?x10251, ?x3802), profession(?x3711, ?x3802), ?x10251 = 07bty, influenced_by(?x3711, ?x712) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #5477 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 65 *> proper extension: 01zkxv; 06kb_; 0j0pf; 0bwx3; 01vs4f3; *> query: (?x9393, ?x3746) <- peers(?x9393, ?x9520), profession(?x9393, ?x353), nationality(?x9393, ?x205), location(?x9393, ?x8956), profession(?x9520, ?x3746) *> conf = 0.42 ranks of expected_values: 9, 11 EVAL 04lg6 profession 0mn6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 137.000 107.000 0.942 http://example.org/people/person/profession EVAL 04lg6 profession 09jwl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 137.000 107.000 0.942 http://example.org/people/person/profession #7075-03qcfvw PRED entity: 03qcfvw PRED relation: film! PRED expected values: 05zbm4 02cbs0 => 94 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1047): 0hpt3 (0.43 #83094, 0.42 #128802, 0.42 #116338), 05qd_ (0.43 #83094, 0.42 #128802, 0.42 #116338), 01q_ph (0.20 #57, 0.03 #18750, 0.03 #51989), 01nfys (0.20 #1569, 0.03 #14031, 0.02 #16108), 0fgg4 (0.20 #882, 0.02 #7113, 0.01 #15421), 01pk3z (0.20 #986, 0.01 #34221, 0.01 #13448), 06m6p7 (0.20 #1367, 0.01 #51222, 0.01 #74073), 06mmb (0.20 #426, 0.01 #12888, 0.01 #14965), 02mc79 (0.20 #1406, 0.01 #13868, 0.01 #15945), 01lbp (0.20 #150, 0.01 #12612, 0.01 #14689) >> Best rule #83094 for best value: >> intensional similarity = 3 >> extensional distance = 537 >> proper extension: 04dsnp; 02phtzk; 0hv81; 03q8xj; 02wk7b; 02zk08; 0581vn8; 02yy9r; >> query: (?x103, ?x902) <- featured_film_locations(?x103, ?x108), language(?x103, ?x5607), nominated_for(?x902, ?x103) >> conf = 0.43 => this is the best rule for 2 predicted values *> Best rule #8460 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 0gtv7pk; 080dfr7; *> query: (?x103, 05zbm4) <- film_distribution_medium(?x103, ?x2099), genre(?x103, ?x225), ?x225 = 02kdv5l, nominated_for(?x102, ?x103) *> conf = 0.02 ranks of expected_values: 575, 770 EVAL 03qcfvw film! 02cbs0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 94.000 63.000 0.430 http://example.org/film/actor/film./film/performance/film EVAL 03qcfvw film! 05zbm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 94.000 63.000 0.430 http://example.org/film/actor/film./film/performance/film #7074-02qkq0 PRED entity: 02qkq0 PRED relation: genre PRED expected values: 07s9rl0 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 77): 07s9rl0 (0.65 #3529, 0.60 #1, 0.54 #757), 05p553 (0.51 #89, 0.51 #341, 0.50 #1181), 01z4y (0.35 #103, 0.34 #355, 0.34 #1195), 0c4xc (0.28 #379, 0.26 #295, 0.26 #127), 0hcr (0.23 #3633, 0.19 #3718, 0.18 #4055), 06n90 (0.20 #14, 0.20 #3627, 0.16 #3712), 01hmnh (0.20 #17, 0.15 #3630, 0.14 #3545), 0vgkd (0.20 #11, 0.14 #347, 0.13 #1187), 01t_vv (0.19 #1211, 0.18 #1463, 0.18 #1883), 06q7n (0.18 #129, 0.16 #2229, 0.15 #1305) >> Best rule #3529 for best value: >> intensional similarity = 3 >> extensional distance = 201 >> proper extension: 07qht4; >> query: (?x6706, 07s9rl0) <- genre(?x6706, ?x7120), genre(?x5386, ?x7120), ?x5386 = 0vjr >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qkq0 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.645 http://example.org/tv/tv_program/genre #7073-02t_vx PRED entity: 02t_vx PRED relation: student! PRED expected values: 07szy => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 54): 0bwfn (0.08 #7112, 0.07 #13426, 0.07 #5008), 01w5m (0.06 #105, 0.05 #1683, 0.05 #631), 015nl4 (0.06 #67, 0.05 #593, 0.05 #1119), 08815 (0.06 #2, 0.05 #528, 0.05 #1054), 053mhx (0.06 #294, 0.05 #820, 0.05 #1346), 0dzst (0.06 #336, 0.05 #862, 0.05 #1388), 025v3k (0.06 #119, 0.05 #645, 0.05 #1171), 02fgdx (0.06 #102, 0.05 #628, 0.05 #1154), 04rwx (0.06 #38, 0.05 #564, 0.05 #1090), 015zyd (0.06 #1, 0.05 #527, 0.05 #1053) >> Best rule #7112 for best value: >> intensional similarity = 2 >> extensional distance = 1150 >> proper extension: 0flpy; 01m3b1t; 01p0w_; >> query: (?x7923, 0bwfn) <- award_nominee(?x7923, ?x450), student(?x3564, ?x7923) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02t_vx student! 07szy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 78.000 0.082 http://example.org/education/educational_institution/students_graduates./education/education/student #7072-09pl3s PRED entity: 09pl3s PRED relation: nationality PRED expected values: 09c7w0 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 73): 09c7w0 (0.86 #4460, 0.86 #100, 0.86 #5057), 07ssc (0.17 #3779, 0.11 #707, 0.11 #3679), 02jx1 (0.16 #3797, 0.11 #428, 0.11 #5188), 0d060g (0.11 #699, 0.10 #303, 0.06 #4961), 0f8l9c (0.07 #3786, 0.05 #3765, 0.04 #2675), 03rk0 (0.06 #12249, 0.06 #12645, 0.06 #12942), 0ctw_b (0.05 #3765, 0.04 #2675, 0.04 #521), 0345h (0.05 #3765, 0.04 #2675, 0.04 #3795), 03rjj (0.05 #3765, 0.04 #2675, 0.04 #3769), 0chghy (0.05 #3765, 0.04 #2675, 0.03 #5165) >> Best rule #4460 for best value: >> intensional similarity = 3 >> extensional distance = 520 >> proper extension: 07_grx; >> query: (?x2442, 09c7w0) <- student(?x1011, ?x2442), place_of_birth(?x2442, ?x8181), fraternities_and_sororities(?x1011, ?x3697) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09pl3s nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.864 http://example.org/people/person/nationality #7071-09r9dp PRED entity: 09r9dp PRED relation: award_winner PRED expected values: 0blq0z => 120 concepts (63 used for prediction) PRED predicted values (max 10 best out of 531): 0gd_b_ (0.82 #68807, 0.82 #99209, 0.82 #91212), 0blq0z (0.82 #68807, 0.82 #99209, 0.82 #91212), 027n4zv (0.82 #68807, 0.82 #99209, 0.82 #91212), 04glr5h (0.53 #44799, 0.48 #31994, 0.48 #84810), 04gnbv1 (0.53 #44799, 0.48 #31994, 0.48 #84810), 07s95_l (0.53 #44799, 0.48 #31994, 0.38 #81609), 0cp9f9 (0.53 #44799, 0.48 #31994, 0.38 #81609), 048lv (0.16 #51200, 0.16 #73608, 0.16 #100811), 09r9dp (0.16 #51200, 0.16 #73608, 0.16 #100811), 028knk (0.16 #51200, 0.16 #73608, 0.16 #100811) >> Best rule #68807 for best value: >> intensional similarity = 3 >> extensional distance = 896 >> proper extension: 012ljv; 0fvf9q; 0520r2x; 0cb77r; 06gp3f; 04cy8rb; 076lxv; 0415svh; 066m4g; 03f5spx; ... >> query: (?x3789, ?x92) <- award_winner(?x92, ?x3789), award_winner(?x3789, ?x4697), place_of_birth(?x3789, ?x4356) >> conf = 0.82 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 09r9dp award_winner 0blq0z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 63.000 0.824 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #7070-01n073 PRED entity: 01n073 PRED relation: company! PRED expected values: 09d6p2 => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 36): 0dq3c (0.62 #3033, 0.59 #2445, 0.57 #297), 09d6p2 (0.57 #1448, 0.50 #1109, 0.38 #899), 01yc02 (0.53 #2450, 0.50 #1565, 0.50 #848), 01kr6k (0.44 #1116, 0.43 #1455, 0.40 #191), 02211by (0.40 #45, 0.29 #256, 0.22 #1180), 04192r (0.28 #1214, 0.20 #710, 0.15 #5474), 01rk91 (0.20 #127, 0.20 #85, 0.20 #43), 0142rn (0.16 #1285, 0.16 #2296, 0.15 #2675), 09lq2c (0.15 #5474, 0.13 #5517, 0.13 #6786), 021q0l (0.15 #5474, 0.13 #5517, 0.13 #6786) >> Best rule #3033 for best value: >> intensional similarity = 5 >> extensional distance = 43 >> proper extension: 01zpmq; >> query: (?x3253, 0dq3c) <- company(?x346, ?x3253), list(?x3253, ?x7472), service_location(?x3253, ?x94), organization(?x346, ?x99), jurisdiction_of_office(?x346, ?x47) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1448 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 0206k5; *> query: (?x3253, 09d6p2) <- company(?x4792, ?x3253), company(?x346, ?x3253), industry(?x3253, ?x245), ?x4792 = 05_wyz, ?x346 = 060c4 *> conf = 0.57 ranks of expected_values: 2 EVAL 01n073 company! 09d6p2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 180.000 180.000 0.622 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #7069-01h2_6 PRED entity: 01h2_6 PRED relation: gender PRED expected values: 05zppz => 273 concepts (273 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.94 #128, 0.92 #211, 0.92 #200), 02zsn (0.81 #206, 0.77 #290, 0.73 #405) >> Best rule #128 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 08304; >> query: (?x12592, 05zppz) <- student(?x13316, ?x12592), influenced_by(?x3428, ?x12592), place_of_death(?x12592, ?x2152), major_field_of_study(?x13316, ?x2172) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01h2_6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 273.000 273.000 0.935 http://example.org/people/person/gender #7068-019kyn PRED entity: 019kyn PRED relation: produced_by PRED expected values: 081nh => 64 concepts (48 used for prediction) PRED predicted values (max 10 best out of 150): 0b82vw (0.11 #13184, 0.11 #6591, 0.10 #4652), 0b13g7 (0.10 #118, 0.03 #7873, 0.02 #15241), 02bfxb (0.05 #889, 0.04 #2439, 0.02 #3604), 06pj8 (0.05 #2780, 0.03 #5108, 0.03 #2004), 081nh (0.05 #76, 0.05 #1238, 0.04 #1625), 0h1p (0.05 #66, 0.03 #841, 0.01 #3556), 02q42j_ (0.05 #210, 0.03 #7965, 0.02 #15333), 02hy9p (0.05 #281, 0.01 #2218), 05ty4m (0.05 #12, 0.01 #12418, 0.01 #15135), 015c4g (0.05 #159, 0.01 #2872) >> Best rule #13184 for best value: >> intensional similarity = 4 >> extensional distance = 745 >> proper extension: 0gh8zks; >> query: (?x4669, ?x1934) <- film(?x12065, ?x4669), film(?x5537, ?x4669), award_winner(?x4669, ?x1934), country(?x4669, ?x94) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 02s4l6; 02725hs; 04grkmd; 0fgrm; 06t6dz; 01pvxl; 039zft; 05q7874; 03cyslc; 02825kb; ... *> query: (?x4669, 081nh) <- genre(?x4669, ?x8681), genre(?x4669, ?x53), ?x53 = 07s9rl0, currency(?x4669, ?x170), ?x8681 = 04rlf *> conf = 0.05 ranks of expected_values: 5 EVAL 019kyn produced_by 081nh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 48.000 0.114 http://example.org/film/film/produced_by #7067-0f04v PRED entity: 0f04v PRED relation: mode_of_transportation PRED expected values: 01bjv => 257 concepts (257 used for prediction) PRED predicted values (max 10 best out of 3): 01bjv (0.88 #22, 0.84 #91, 0.83 #28), 06d_3 (0.07 #114, 0.04 #144, 0.03 #93), 0k4j (0.07 #17, 0.06 #149, 0.06 #26) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 0jpkg; >> query: (?x6703, 01bjv) <- citytown(?x6404, ?x6703), adjoins(?x6703, ?x3794), mode_of_transportation(?x6703, ?x4272) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f04v mode_of_transportation 01bjv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 257.000 257.000 0.882 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #7066-0dlngsd PRED entity: 0dlngsd PRED relation: film! PRED expected values: 086k8 => 76 concepts (48 used for prediction) PRED predicted values (max 10 best out of 58): 086k8 (0.68 #152, 0.20 #1057, 0.18 #1132), 046b0s (0.50 #1731, 0.46 #980, 0.44 #2187), 05h4t7 (0.50 #1731, 0.46 #980, 0.44 #2187), 03xq0f (0.29 #5, 0.23 #80, 0.19 #381), 05qd_ (0.24 #9, 0.23 #84, 0.16 #1664), 01795t (0.24 #18, 0.14 #93, 0.11 #1298), 017s11 (0.21 #454, 0.18 #907, 0.18 #606), 054g1r (0.18 #110, 0.18 #35, 0.08 #1315), 016tw3 (0.18 #915, 0.18 #462, 0.17 #1742), 024rgt (0.14 #95, 0.12 #20, 0.08 #246) >> Best rule #152 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 05h43ls; 0x25q; 05r3qc; >> query: (?x4615, 086k8) <- film_crew_role(?x4615, ?x137), production_companies(?x4615, ?x2548), titles(?x162, ?x4615), ?x2548 = 046b0s >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dlngsd film! 086k8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 48.000 0.679 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #7065-01vxqyl PRED entity: 01vxqyl PRED relation: artists! PRED expected values: 0glt670 => 148 concepts (62 used for prediction) PRED predicted values (max 10 best out of 234): 064t9 (0.65 #2805, 0.64 #5286, 0.63 #3425), 016clz (0.56 #5, 0.50 #315, 0.33 #2485), 03lty (0.44 #29, 0.40 #339, 0.22 #18310), 02lnbg (0.38 #2851, 0.37 #3471, 0.35 #3161), 0ggx5q (0.38 #2871, 0.37 #3491, 0.33 #4111), 06j6l (0.38 #3151, 0.32 #2841, 0.32 #3461), 05bt6j (0.34 #13694, 0.32 #11522, 0.32 #16176), 0xhtw (0.33 #18, 0.30 #328, 0.28 #16149), 0glt670 (0.33 #42, 0.30 #2833, 0.29 #3453), 04n7jdv (0.33 #291, 0.10 #601, 0.06 #2771) >> Best rule #2805 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 081lh; >> query: (?x8782, 064t9) <- artists(?x1572, ?x8782), location(?x8782, ?x2623), celebrity(?x8782, ?x2237), contains(?x2623, ?x95) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 01j59b0; 01shhf; *> query: (?x8782, 0glt670) <- artists(?x8289, ?x8782), artists(?x1572, ?x8782), ?x1572 = 06by7, artist(?x9492, ?x8782), ?x8289 = 05jt_ *> conf = 0.33 ranks of expected_values: 9 EVAL 01vxqyl artists! 0glt670 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 148.000 62.000 0.649 http://example.org/music/genre/artists #7064-0chsq PRED entity: 0chsq PRED relation: film PRED expected values: 0prh7 => 137 concepts (113 used for prediction) PRED predicted values (max 10 best out of 702): 047wh1 (0.58 #64232, 0.48 #94576, 0.47 #92790), 0jvt9 (0.21 #11242, 0.09 #9458, 0.09 #18378), 048vhl (0.20 #6842, 0.18 #8626), 0gzy02 (0.18 #8964, 0.05 #10748, 0.04 #17884), 07g1sm (0.17 #1232, 0.05 #11936, 0.02 #15504), 0ch3qr1 (0.17 #974, 0.02 #15246, 0.01 #20598), 016z9n (0.17 #368, 0.02 #34266, 0.02 #39619), 06_wqk4 (0.17 #126, 0.02 #37593, 0.02 #59005), 0bxsk (0.17 #1207, 0.02 #38674, 0.01 #129701), 09xbpt (0.17 #47, 0.01 #58926, 0.01 #37514) >> Best rule #64232 for best value: >> intensional similarity = 3 >> extensional distance = 591 >> proper extension: 0cnl80; 032xhg; 04bs3j; 0bz5v2; 04y79_n; 02wrhj; 06x58; 07ymr5; 06lgq8; 01wxyx1; ... >> query: (?x510, ?x499) <- award_winner(?x7226, ?x510), nominated_for(?x510, ?x499), film(?x510, ?x3783) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #11538 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 02whj; 03h_fk5; 015qt5; 01t94_1; 02j4sk; *> query: (?x510, 0prh7) <- award_winner(?x7226, ?x510), people(?x5801, ?x510), celebrities_impersonated(?x3649, ?x510) *> conf = 0.05 ranks of expected_values: 161 EVAL 0chsq film 0prh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 137.000 113.000 0.579 http://example.org/film/actor/film./film/performance/film #7063-04vzv4 PRED entity: 04vzv4 PRED relation: costume_design_by! PRED expected values: 0gcrg => 113 concepts (76 used for prediction) PRED predicted values (max 10 best out of 196): 05dmmc (0.33 #201, 0.33 #90, 0.17 #402), 0ft18 (0.33 #160, 0.17 #361, 0.10 #963), 0cbn7c (0.33 #156, 0.17 #357, 0.10 #959), 027rpym (0.33 #98, 0.17 #299, 0.10 #901), 0gnjh (0.24 #603), 0f42nz (0.20 #711, 0.06 #1511), 015gm8 (0.12 #600, 0.10 #1001, 0.10 #801), 01gvsn (0.12 #593, 0.10 #994, 0.10 #794), 0k419 (0.12 #591, 0.10 #992, 0.10 #792), 0291ck (0.12 #583, 0.10 #984, 0.10 #784) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 05x2t7; >> query: (?x4526, ?x4513) <- award_winner(?x4513, ?x4526), ?x4513 = 05dmmc, costume_design_by(?x2612, ?x4526) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04vzv4 costume_design_by! 0gcrg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 76.000 0.333 http://example.org/film/film/costume_design_by #7062-023vrq PRED entity: 023vrq PRED relation: award_winner PRED expected values: 01vsgrn => 43 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1923): 01vs_v8 (0.67 #5388, 0.38 #15250, 0.25 #10321), 0fhxv (0.67 #5977, 0.31 #15839, 0.25 #10910), 015mrk (0.60 #3128, 0.43 #8060, 0.38 #15455), 0g824 (0.60 #3875, 0.38 #11273, 0.33 #13737), 01w7nww (0.44 #22186, 0.43 #24650, 0.39 #49285), 01vsgrn (0.44 #22186, 0.43 #24650, 0.39 #49285), 01s1zk (0.44 #22186, 0.43 #24650, 0.39 #49285), 01wyz92 (0.44 #22186, 0.43 #24650, 0.39 #49285), 0gbwp (0.44 #22186, 0.43 #24650, 0.39 #49285), 04mn81 (0.44 #22186, 0.43 #24650, 0.39 #49285) >> Best rule #5388 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02f72n; 02f73b; >> query: (?x9295, 01vs_v8) <- award(?x5536, ?x9295), award(?x4474, ?x9295), award(?x3176, ?x9295), ?x3176 = 01w7nww, artist(?x1954, ?x4474), ?x5536 = 01vsgrn >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #22186 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 026mg3; 02g8mp; 02gx2k; 02nbqh; 01dpdh; 02hgm4; 0257w4; 0257yf; 026mff; 02flpc; ... *> query: (?x9295, ?x3176) <- award(?x3176, ?x9295), ceremony(?x9295, ?x6487), award_winner(?x1362, ?x3176), ?x6487 = 01mh_q *> conf = 0.44 ranks of expected_values: 6 EVAL 023vrq award_winner 01vsgrn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 43.000 22.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #7061-01n8qg PRED entity: 01n8qg PRED relation: country! PRED expected values: 03_8r => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 57): 06z6r (0.80 #1287, 0.78 #1230, 0.77 #1173), 03_8r (0.68 #1163, 0.67 #1847, 0.67 #1106), 01cgz (0.63 #983, 0.62 #1040, 0.62 #926), 071t0 (0.59 #1164, 0.58 #1107, 0.58 #366), 01lb14 (0.47 #358, 0.46 #1270, 0.46 #1840), 03hr1p (0.47 #367, 0.39 #1165, 0.39 #1222), 07gyv (0.45 #1090, 0.44 #1147, 0.43 #1831), 0w0d (0.42 #354, 0.38 #12, 0.35 #1209), 0194d (0.42 #392, 0.35 #2623, 0.35 #1247), 06f41 (0.40 #1155, 0.39 #1098, 0.39 #357) >> Best rule #1287 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 04hvw; >> query: (?x9654, 06z6r) <- organization(?x9654, ?x127), adjustment_currency(?x9654, ?x170), adjoins(?x9654, ?x390) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1163 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 122 *> proper extension: 03_3d; 03h64; 0697s; 04w4s; 02lx0; 07twz; *> query: (?x9654, 03_8r) <- organization(?x9654, ?x127), currency(?x9654, ?x170), adjoins(?x9654, ?x390) *> conf = 0.68 ranks of expected_values: 2 EVAL 01n8qg country! 03_8r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.797 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #7060-037xlx PRED entity: 037xlx PRED relation: honored_for! PRED expected values: 059lwy => 116 concepts (65 used for prediction) PRED predicted values (max 10 best out of 504): 069q4f (0.90 #2322, 0.87 #2789, 0.86 #5596), 074rg9 (0.90 #2322, 0.87 #2789, 0.86 #5596), 0140g4 (0.90 #2322, 0.86 #5596, 0.86 #4503), 037xlx (0.80 #401, 0.72 #1176, 0.71 #246), 059lwy (0.75 #585, 0.69 #740, 0.67 #1205), 0cf08 (0.48 #3413, 0.47 #4505, 0.07 #8398), 08984j (0.48 #3413, 0.47 #4505, 0.04 #8399), 06sfk6 (0.20 #74, 0.05 #1313, 0.05 #1469), 0fdv3 (0.18 #967, 0.10 #1277, 0.08 #2516), 0dfw0 (0.18 #1011, 0.08 #2560, 0.08 #2715) >> Best rule #2322 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 026p_bs; >> query: (?x5731, ?x188) <- honored_for(?x5731, ?x188), honored_for(?x3639, ?x5731), written_by(?x5731, ?x5338), film(?x9403, ?x5731), country(?x5731, ?x94) >> conf = 0.90 => this is the best rule for 3 predicted values *> Best rule #585 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 069q4f; 07b1gq; 01mszz; 059lwy; *> query: (?x5731, 059lwy) <- honored_for(?x5731, ?x3330), honored_for(?x6963, ?x5731), film(?x879, ?x5731), ?x3330 = 0946bb, ?x6963 = 06c0ns *> conf = 0.75 ranks of expected_values: 5 EVAL 037xlx honored_for! 059lwy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 116.000 65.000 0.895 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #7059-0d5wn3 PRED entity: 0d5wn3 PRED relation: award_winner! PRED expected values: 0bzmt8 02yxh9 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 130): 0bzkvd (0.20 #113, 0.11 #393, 0.07 #673), 05zksls (0.20 #34, 0.04 #734, 0.02 #874), 0dth6b (0.20 #23), 0bzmt8 (0.19 #2941, 0.12 #238, 0.02 #798), 02yxh9 (0.19 #2941, 0.05 #800, 0.03 #940), 0bc773 (0.19 #2941, 0.02 #8263, 0.02 #7422), 02q690_ (0.12 #204, 0.06 #484, 0.04 #624), 013b2h (0.12 #219, 0.04 #4140, 0.04 #1899), 092t4b (0.12 #191, 0.04 #2151, 0.03 #1031), 0clfdj (0.12 #143, 0.03 #2103, 0.03 #1683) >> Best rule #113 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 016z2j; 01cbt3; 037w7r; >> query: (?x4449, 0bzkvd) <- profession(?x4449, ?x2450), nominated_for(?x4449, ?x3116), nominated_for(?x4449, ?x518), ?x518 = 016z5x, film(?x450, ?x3116) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2941 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1192 *> proper extension: 05typm; 05xbx; 01w23w; 0kcdl; *> query: (?x4449, ?x7100) <- nominated_for(?x4449, ?x2006), nominated_for(?x4449, ?x697), award(?x697, ?x484), film(?x981, ?x2006), honored_for(?x7100, ?x697) *> conf = 0.19 ranks of expected_values: 4, 5 EVAL 0d5wn3 award_winner! 02yxh9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 74.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0d5wn3 award_winner! 0bzmt8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 74.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7058-0dl6fv PRED entity: 0dl6fv PRED relation: nominated_for! PRED expected values: 0cqgl9 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 201): 0m7yy (0.69 #3122, 0.69 #4326, 0.69 #3845), 02g3v6 (0.50 #1461, 0.07 #9388, 0.07 #8908), 0bfvd4 (0.46 #1049, 0.31 #2250, 0.30 #2010), 0bdwft (0.46 #1016, 0.30 #776, 0.28 #2217), 07kjk7c (0.38 #1153, 0.30 #913, 0.25 #2354), 0bdwqv (0.38 #1090, 0.30 #850, 0.22 #2291), 027dtxw (0.33 #4, 0.23 #5531, 0.12 #8891), 04dn09n (0.33 #35, 0.19 #8922, 0.18 #10123), 02ppm4q (0.33 #118, 0.19 #15854, 0.19 #16816), 09v82c0 (0.31 #1148, 0.22 #2349, 0.20 #2109) >> Best rule #3122 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 04glx0; 06w7mlh; >> query: (?x8733, ?x3486) <- nominated_for(?x2372, ?x8733), languages(?x8733, ?x254), award(?x8733, ?x3486) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #16816 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1587 *> proper extension: 01tspc6; 06g60w; 04z_x4v; 0clpml; *> query: (?x8733, ?x1670) <- nominated_for(?x2372, ?x8733), award(?x2372, ?x1670), award_winner(?x1670, ?x56) *> conf = 0.19 ranks of expected_values: 53 EVAL 0dl6fv nominated_for! 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 80.000 80.000 0.694 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7057-09v7wsg PRED entity: 09v7wsg PRED relation: nominated_for PRED expected values: 0d68qy => 50 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1114): 01bv8b (0.78 #1963, 0.67 #3543, 0.21 #8288), 05p9_ql (0.78 #4273, 0.44 #2693, 0.17 #9018), 0124k9 (0.78 #1795, 0.33 #3375, 0.15 #8120), 0vjr (0.67 #3996, 0.67 #2416, 0.19 #8741), 0d68qy (0.67 #3521, 0.56 #1941, 0.21 #8266), 01q_y0 (0.67 #1909, 0.56 #3489, 0.19 #8234), 02h2vv (0.67 #2570, 0.56 #4150, 0.17 #8895), 0q9jk (0.67 #2810, 0.44 #4390, 0.14 #7554), 02hct1 (0.67 #1927, 0.44 #3507, 0.14 #6671), 0431v3 (0.67 #4016, 0.33 #2436, 0.12 #7180) >> Best rule #1963 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 09qvc0; 09qv3c; 03ccq3s; 09qrn4; >> query: (?x6724, 01bv8b) <- ceremony(?x6724, ?x4760), nominated_for(?x6724, ?x10731), nominated_for(?x6724, ?x4535), ?x4535 = 030cx, ?x4760 = 02q690_, actor(?x10731, ?x2194) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3521 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 0cqhk0; 09qj50; 0cjyzs; 09qs08; 02_3zj; 027gs1_; 0cqhmg; *> query: (?x6724, 0d68qy) <- ceremony(?x6724, ?x1265), nominated_for(?x6724, ?x11818), nominated_for(?x6724, ?x10731), ?x10731 = 0cs134, program_creator(?x11818, ?x3405), genre(?x11818, ?x53) *> conf = 0.67 ranks of expected_values: 5 EVAL 09v7wsg nominated_for 0d68qy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 50.000 10.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #7056-01y3v PRED entity: 01y3v PRED relation: team! PRED expected values: 019y64 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 120): 040j2_ (0.50 #385, 0.29 #1058, 0.29 #946), 019g65 (0.33 #303, 0.31 #2321, 0.28 #3668), 03n69x (0.29 #2599, 0.29 #1367, 0.28 #3610), 063g7l (0.29 #1435, 0.20 #649, 0.17 #761), 0hcs3 (0.29 #1218, 0.20 #3797, 0.14 #1105), 0cv72h (0.25 #1608, 0.17 #3627, 0.17 #3290), 019y64 (0.20 #565, 0.19 #4940, 0.17 #677), 03vrv9 (0.20 #2886, 0.18 #1878, 0.17 #2102), 0444x (0.20 #646, 0.17 #3675, 0.17 #871), 01xyt7 (0.20 #586, 0.17 #811, 0.17 #698) >> Best rule #385 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 051vz; 02__x; >> query: (?x2574, 040j2_) <- sport(?x2574, ?x1083), draft(?x2574, ?x465), team(?x11323, ?x2574), school(?x2574, ?x2948), team(?x180, ?x2574), colors(?x2574, ?x663), ?x2948 = 0j_sncb >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #565 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 0fsb_6; *> query: (?x2574, 019y64) <- position(?x2574, ?x1717), position(?x2574, ?x1114), position(?x2574, ?x935), position_s(?x2574, ?x1792), ?x1717 = 02g_6x, ?x1792 = 05zm34, teams(?x1860, ?x2574), ?x935 = 06b1q, ?x1114 = 047g8h, place_of_birth(?x5642, ?x1860), award_nominee(?x4933, ?x5642), colors(?x2574, ?x663) *> conf = 0.20 ranks of expected_values: 7 EVAL 01y3v team! 019y64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 104.000 104.000 0.500 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #7055-05njw PRED entity: 05njw PRED relation: industry PRED expected values: 01mf0 => 204 concepts (204 used for prediction) PRED predicted values (max 10 best out of 46): 020mfr (0.45 #882, 0.45 #833, 0.43 #65), 01mw1 (0.41 #866, 0.40 #817, 0.38 #2739), 01mf0 (0.29 #175, 0.19 #1281, 0.18 #945), 01mfj (0.29 #181, 0.18 #325, 0.14 #469), 02jjt (0.27 #296, 0.21 #440, 0.20 #584), 03qh03g (0.25 #197, 0.13 #533, 0.12 #1111), 0147gr (0.25 #233, 0.13 #569, 0.08 #1291), 02vxn (0.22 #1348, 0.22 #1300, 0.20 #482), 0191_7 (0.14 #184, 0.14 #136, 0.12 #712), 07c1v (0.14 #187, 0.13 #571, 0.12 #667) >> Best rule #882 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0nzm; >> query: (?x11504, 020mfr) <- place_founded(?x11504, ?x1227), state_province_region(?x6455, ?x1227), contains(?x1227, ?x191), major_field_of_study(?x6455, ?x2606) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #175 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 059wk; *> query: (?x11504, 01mf0) <- place_founded(?x11504, ?x1227), company(?x11503, ?x11504), state_province_region(?x11504, ?x335), contact_category(?x11504, ?x897) *> conf = 0.29 ranks of expected_values: 3 EVAL 05njw industry 01mf0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 204.000 204.000 0.455 http://example.org/business/business_operation/industry #7054-07l4z PRED entity: 07l4z PRED relation: draft PRED expected values: 02r6gw6 04f4z1k => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 14): 02r6gw6 (0.82 #212, 0.81 #342, 0.78 #284), 04f4z1k (0.81 #345, 0.79 #302, 0.78 #287), 092j54 (0.55 #217, 0.43 #479, 0.40 #63), 09l0x9 (0.55 #217, 0.40 #481, 0.40 #65), 0g3zpp (0.55 #217, 0.40 #475, 0.40 #59), 025tn92 (0.55 #217, 0.40 #38, 0.38 #560), 038981 (0.55 #217, 0.40 #41, 0.38 #560), 02qw1zx (0.55 #217, 0.35 #516, 0.35 #274), 03nt7j (0.43 #478, 0.35 #516, 0.35 #274), 05vsb7 (0.40 #58, 0.38 #474, 0.35 #516) >> Best rule #212 for best value: >> intensional similarity = 10 >> extensional distance = 15 >> proper extension: 0x0d; >> query: (?x8901, 02r6gw6) <- position(?x8901, ?x2010), school(?x8901, ?x3777), school(?x8901, ?x1884), school(?x3334, ?x3777), school_type(?x3777, ?x1507), teams(?x1658, ?x8901), season(?x8901, ?x2406), ?x3334 = 02pq_rp, currency(?x3777, ?x170), student(?x1884, ?x1815) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07l4z draft 04f4z1k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.824 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 07l4z draft 02r6gw6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.824 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #7053-09wwlj PRED entity: 09wwlj PRED relation: contains! PRED expected values: 015fr => 29 concepts (19 used for prediction) PRED predicted values (max 10 best out of 140): 02qkt (0.40 #347, 0.28 #12892, 0.26 #13788), 09c7w0 (0.36 #8963, 0.35 #14340, 0.34 #10755), 07ssc (0.32 #9888, 0.28 #2720, 0.26 #5408), 02jx1 (0.21 #2775, 0.19 #5463, 0.19 #9943), 02j9z (0.20 #28, 0.14 #12573, 0.14 #13469), 0345h (0.14 #5458, 0.11 #7250, 0.11 #3666), 03rjj (0.14 #1802, 0.13 #3594, 0.13 #6282), 0j0k (0.12 #8442, 0.12 #12923, 0.11 #12027), 07c5l (0.10 #12940, 0.10 #12044, 0.10 #8459), 06n3y (0.10 #726, 0.05 #13271, 0.04 #14167) >> Best rule #347 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 030qb3t; 06mkj; 06t2t; 06t8v; 077qn; 05qtj; 0pfd9; >> query: (?x14738, 02qkt) <- teams(?x14738, ?x12780), position(?x12780, ?x203), colors(?x12780, ?x3189), colors(?x12780, ?x1101), ?x203 = 0dgrmp, ?x1101 = 06fvc, ?x3189 = 01g5v >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2724 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 41 *> proper extension: 04jpl; 01cr28; 0dhdp; 06gmr; 0fm2_; 01ly5m; 09tlh; 0jp26; 04p3c; 0b2h3; ... *> query: (?x14738, 015fr) <- category(?x14738, ?x134), teams(?x14738, ?x12780), ?x134 = 08mbj5d, colors(?x12780, ?x663), sport(?x12780, ?x471), ?x471 = 02vx4 *> conf = 0.09 ranks of expected_values: 18 EVAL 09wwlj contains! 015fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 29.000 19.000 0.400 http://example.org/location/location/contains #7052-01mf0 PRED entity: 01mf0 PRED relation: industry! PRED expected values: 045c7b 01zpmq 0py9b 046qpy 05njw => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 806): 026v5 (0.66 #1448, 0.62 #4622, 0.49 #8038), 0l8sx (0.66 #1448, 0.62 #7307, 0.54 #7308), 01nn79 (0.66 #1448, 0.62 #7307, 0.54 #7308), 01tkfj (0.66 #1448, 0.62 #7307, 0.54 #7308), 01qxs3 (0.66 #1448, 0.54 #7308, 0.50 #2316), 0xwj (0.66 #1448, 0.54 #7308, 0.50 #2239), 059wk (0.66 #1448, 0.54 #7308, 0.50 #2293), 02brqp (0.66 #1448, 0.54 #7308, 0.50 #2326), 01n073 (0.66 #1448, 0.54 #7308, 0.50 #2217), 03_c8p (0.66 #1448, 0.54 #7308, 0.50 #2332) >> Best rule #1448 for best value: >> intensional similarity = 29 >> extensional distance = 1 >> proper extension: 03qh03g; >> query: (?x12987, ?x2270) <- industry(?x12452, ?x12987), industry(?x9469, ?x12987), industry(?x7218, ?x12987), industry(?x4549, ?x12987), organization(?x4682, ?x4549), company(?x7176, ?x4549), company(?x6010, ?x4549), company(?x1491, ?x4549), ?x1491 = 0krdk, category(?x4549, ?x134), ?x134 = 08mbj5d, company(?x6010, ?x9198), company(?x6010, ?x3253), ?x4682 = 0dq_5, ?x7218 = 019rl6, ?x7176 = 01kr6k, industry(?x9469, ?x13911), industry(?x9469, ?x12816), citytown(?x12452, ?x6703), list(?x9469, ?x5997), ?x9198 = 0mgkg, service_language(?x4549, ?x254), company(?x233, ?x9469), ?x3253 = 01n073, service_location(?x12452, ?x94), ?x13911 = 06xw2, mode_of_transportation(?x6703, ?x4272), industry(?x2270, ?x12816), teams(?x6703, ?x7766) >> conf = 0.66 => this is the best rule for 86 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 62, 83, 120, 236, 248 EVAL 01mf0 industry! 05njw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 42.000 0.657 http://example.org/business/business_operation/industry EVAL 01mf0 industry! 046qpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 42.000 42.000 0.657 http://example.org/business/business_operation/industry EVAL 01mf0 industry! 0py9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 42.000 42.000 0.657 http://example.org/business/business_operation/industry EVAL 01mf0 industry! 01zpmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 42.000 0.657 http://example.org/business/business_operation/industry EVAL 01mf0 industry! 045c7b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 42.000 42.000 0.657 http://example.org/business/business_operation/industry #7051-07vjm PRED entity: 07vjm PRED relation: contains! PRED expected values: 09c7w0 0gx1l => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 223): 09c7w0 (0.73 #43724, 0.70 #72274, 0.69 #39263), 02qkt (0.44 #25328, 0.34 #29788, 0.25 #40497), 07c5l (0.33 #392, 0.13 #25375, 0.13 #29835), 07ssc (0.27 #68734, 0.25 #1816, 0.24 #69626), 0dg3n1 (0.27 #25136, 0.21 #29596, 0.15 #40305), 02jx1 (0.25 #1870, 0.21 #69680, 0.17 #68788), 05tbn (0.25 #1114, 0.12 #2006, 0.09 #2898), 068p2 (0.25 #1162, 0.12 #2054, 0.06 #2946), 05l5n (0.25 #1904, 0.04 #5473, 0.03 #17070), 0jt5zcn (0.25 #1924, 0.02 #30475, 0.02 #5493) >> Best rule #43724 for best value: >> intensional similarity = 2 >> extensional distance = 330 >> proper extension: 01zn4y; >> query: (?x6637, 09c7w0) <- contains(?x1227, ?x6637), currency(?x6637, ?x170) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 178 EVAL 07vjm contains! 0gx1l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 92.000 92.000 0.726 http://example.org/location/location/contains EVAL 07vjm contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.726 http://example.org/location/location/contains #7050-0498y PRED entity: 0498y PRED relation: district_represented! PRED expected values: 01gtcc 01grpq 01gstn 01gtcq 01gst9 => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 23): 01gtcc (0.50 #331, 0.50 #216, 0.50 #170), 01gstn (0.47 #334, 0.47 #219, 0.47 #173), 01gtcq (0.42 #335, 0.41 #220, 0.40 #174), 01gst9 (0.41 #223, 0.40 #338, 0.40 #177), 03rtmz (0.33 #145, 0.28 #191, 0.28 #582), 03tcbx (0.33 #144, 0.25 #328, 0.25 #190), 01grpq (0.28 #218, 0.28 #333, 0.24 #586), 05rrw9 (0.25 #345, 0.25 #230, 0.22 #598), 01grmk (0.25 #342, 0.25 #227, 0.22 #595), 03z5xd (0.22 #143, 0.19 #189, 0.17 #580) >> Best rule #331 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 059f4; 03s0w; 05fhy; 059_c; 01x73; 05k7sb; 0gyh; 05j49; 05fky; >> query: (?x4061, 01gtcc) <- partially_contains(?x4061, ?x4540), district_represented(?x176, ?x4061), contains(?x94, ?x4061) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 7 EVAL 0498y district_represented! 01gst9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.500 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 0498y district_represented! 01gtcq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.500 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 0498y district_represented! 01gstn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.500 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 0498y district_represented! 01grpq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 186.000 186.000 0.500 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 0498y district_represented! 01gtcc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.500 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #7049-0147dk PRED entity: 0147dk PRED relation: artist! PRED expected values: 01trtc => 117 concepts (97 used for prediction) PRED predicted values (max 10 best out of 88): 03rhqg (0.29 #16, 0.14 #6182, 0.13 #4079), 0181dw (0.19 #41, 0.12 #3123, 0.10 #6207), 043g7l (0.18 #1011, 0.09 #4094, 0.08 #3113), 03mp8k (0.16 #1046, 0.09 #3148, 0.08 #4129), 01trtc (0.14 #72, 0.09 #4135, 0.07 #3434), 033hn8 (0.13 #994, 0.11 #3096, 0.11 #6180), 0g768 (0.12 #6202, 0.10 #3118, 0.10 #4099), 011k1h (0.11 #3092, 0.11 #990, 0.10 #6176), 0n85g (0.10 #62, 0.08 #6228, 0.07 #3144), 0mzkr (0.10 #25, 0.07 #1005, 0.06 #6191) >> Best rule #16 for best value: >> intensional similarity = 2 >> extensional distance = 19 >> proper extension: 0gr69; >> query: (?x521, 03rhqg) <- award_winner(?x3926, ?x521), ?x3926 = 02f6xy >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #72 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 19 *> proper extension: 0gr69; *> query: (?x521, 01trtc) <- award_winner(?x3926, ?x521), ?x3926 = 02f6xy *> conf = 0.14 ranks of expected_values: 5 EVAL 0147dk artist! 01trtc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 117.000 97.000 0.286 http://example.org/music/record_label/artist #7048-0m32h PRED entity: 0m32h PRED relation: people PRED expected values: 0dh73w 01r_t_ 076689 => 55 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1624): 014zn0 (0.50 #3262, 0.29 #5946, 0.25 #7287), 01938t (0.33 #947, 0.33 #278, 0.14 #7651), 02dth1 (0.33 #809, 0.25 #2818, 0.20 #9521), 0407f (0.33 #105, 0.25 #2783, 0.14 #7478), 05xpv (0.33 #386, 0.25 #3064, 0.14 #7759), 03fvqg (0.33 #57, 0.25 #2735, 0.14 #7430), 06c0j (0.33 #605, 0.25 #3283, 0.14 #7978), 018ty9 (0.33 #290, 0.25 #2968, 0.14 #7663), 0b22w (0.33 #480, 0.21 #7853, 0.19 #10531), 053yx (0.33 #91, 0.20 #9472, 0.18 #11481) >> Best rule #3262 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 0dcsx; >> query: (?x6720, 014zn0) <- people(?x6720, ?x7332), people(?x6720, ?x1984), people(?x6720, ?x1204), student(?x2999, ?x7332), gender(?x7332, ?x231), jurisdiction_of_office(?x1984, ?x279), religion(?x1984, ?x1985), film(?x1204, ?x4347), award_nominee(?x1204, ?x1205), notable_people_with_this_condition(?x6720, ?x3324), basic_title(?x1984, ?x182) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #10720 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 14 *> proper extension: 0kh3; *> query: (?x6720, ?x190) <- people(?x6720, ?x7332), people(?x6720, ?x1984), people(?x6720, ?x1204), student(?x2999, ?x7332), gender(?x7332, ?x231), jurisdiction_of_office(?x1984, ?x279), religion(?x1984, ?x1985), film(?x1204, ?x4347), award(?x1204, ?x6878), profession(?x1204, ?x1032), award(?x190, ?x6878) *> conf = 0.02 ranks of expected_values: 951 EVAL 0m32h people 076689 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 39.000 0.500 http://example.org/people/cause_of_death/people EVAL 0m32h people 01r_t_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 39.000 0.500 http://example.org/people/cause_of_death/people EVAL 0m32h people 0dh73w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 39.000 0.500 http://example.org/people/cause_of_death/people #7047-0ymcz PRED entity: 0ymcz PRED relation: institution! PRED expected values: 0bjrnt 02_xgp2 => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.77 #195, 0.77 #123, 0.76 #1719), 03bwzr4 (0.75 #39, 0.62 #426, 0.59 #1731), 019v9k (0.71 #201, 0.70 #33, 0.67 #129), 02_xgp2 (0.65 #37, 0.60 #424, 0.59 #569), 016t_3 (0.60 #196, 0.59 #391, 0.57 #560), 0bkj86 (0.60 #32, 0.49 #200, 0.48 #346), 04zx3q1 (0.50 #26, 0.45 #558, 0.40 #340), 013zdg (0.38 #55, 0.27 #563, 0.26 #199), 01rr_d (0.38 #66, 0.21 #574, 0.20 #42), 07s6fsf (0.37 #193, 0.37 #266, 0.36 #557) >> Best rule #195 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 09kvv; 03ksy; 0pspl; 0bqxw; 0217m9; 01bk1y; >> query: (?x11003, 02h4rq6) <- company(?x346, ?x11003), institution(?x1368, ?x11003), ?x346 = 060c4, major_field_of_study(?x11003, ?x742), citytown(?x11003, ?x1841) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 0dzbl; *> query: (?x11003, 02_xgp2) <- company(?x346, ?x11003), citytown(?x11003, ?x1841), school_type(?x11003, ?x4994), ?x4994 = 07tf8, category(?x11003, ?x134) *> conf = 0.65 ranks of expected_values: 4, 11 EVAL 0ymcz institution! 02_xgp2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 172.000 172.000 0.771 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0ymcz institution! 0bjrnt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 172.000 172.000 0.771 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #7046-057hz PRED entity: 057hz PRED relation: type_of_union PRED expected values: 04ztj => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #49, 0.87 #97, 0.86 #93), 01g63y (0.33 #30, 0.31 #107, 0.29 #105), 0jgjn (0.02 #44, 0.01 #117, 0.01 #121) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 0d1_f; 0ngg; >> query: (?x3644, 04ztj) <- location_of_ceremony(?x3644, ?x739), location(?x3644, ?x1227), state_province_region(?x99, ?x1227) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 057hz type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.869 http://example.org/people/person/spouse_s./people/marriage/type_of_union #7045-03lvwp PRED entity: 03lvwp PRED relation: film! PRED expected values: 02r6c_ => 104 concepts (88 used for prediction) PRED predicted values (max 10 best out of 102): 092kgw (0.22 #11278, 0.21 #1647, 0.21 #11003), 0l99s (0.16 #823, 0.11 #3026, 0.08 #1648), 03lvyj (0.13 #4679, 0.12 #9355, 0.12 #15954), 08664q (0.13 #4679, 0.12 #9355, 0.12 #15954), 02vyw (0.07 #638, 0.06 #364, 0.02 #4494), 06pj8 (0.05 #1696, 0.05 #1971, 0.05 #2247), 07rd7 (0.04 #1201, 0.04 #2579, 0.04 #1476), 081lh (0.04 #849, 0.02 #8829, 0.01 #6905), 0j_c (0.04 #6942, 0.02 #337, 0.02 #11066), 026670 (0.04 #507, 0.02 #1881, 0.02 #2156) >> Best rule #11278 for best value: >> intensional similarity = 3 >> extensional distance = 794 >> proper extension: 053tj7; >> query: (?x6020, ?x5527) <- produced_by(?x6020, ?x5527), film(?x752, ?x6020), nominated_for(?x5527, ?x1135) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #484 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 047bynf; *> query: (?x6020, 02r6c_) <- nominated_for(?x1033, ?x6020), film_release_distribution_medium(?x6020, ?x81), currency(?x6020, ?x170), ?x1033 = 02x73k6 *> conf = 0.02 ranks of expected_values: 36 EVAL 03lvwp film! 02r6c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 104.000 88.000 0.221 http://example.org/film/director/film #7044-03s9kp PRED entity: 03s9kp PRED relation: titles! PRED expected values: 07s9rl0 => 87 concepts (44 used for prediction) PRED predicted values (max 10 best out of 59): 07s9rl0 (0.50 #102, 0.48 #204, 0.32 #3091), 04xvlr (0.25 #105, 0.21 #308, 0.20 #1025), 01z4y (0.19 #136, 0.17 #1574, 0.16 #749), 024qqx (0.19 #181, 0.15 #487, 0.12 #693), 02n4kr (0.19 #4125, 0.18 #3917, 0.18 #4335), 05c3mp2 (0.19 #4125, 0.18 #3917, 0.18 #4335), 0lsxr (0.19 #4125, 0.18 #3917, 0.18 #4335), 09b3v (0.14 #49, 0.09 #4539, 0.07 #559), 02l7c8 (0.14 #25, 0.09 #4539, 0.04 #228), 01jfsb (0.13 #937, 0.12 #324, 0.11 #223) >> Best rule #102 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 08bytj; >> query: (?x11996, 07s9rl0) <- nominated_for(?x496, ?x11996), ?x496 = 0bxtg, titles(?x1510, ?x11996) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s9kp titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 44.000 0.500 http://example.org/media_common/netflix_genre/titles #7043-01y665 PRED entity: 01y665 PRED relation: award PRED expected values: 0bfvd4 => 95 concepts (76 used for prediction) PRED predicted values (max 10 best out of 262): 0ck27z (0.41 #3289, 0.16 #14090, 0.15 #15290), 0bp_b2 (0.33 #18, 0.19 #3218, 0.13 #27604), 05ztrmj (0.29 #581, 0.15 #981, 0.14 #2581), 05p09zm (0.24 #920, 0.19 #2920, 0.17 #3720), 0bfvd4 (0.22 #111, 0.20 #3311, 0.13 #30408), 099jhq (0.22 #19, 0.13 #30408, 0.13 #30407), 02x8n1n (0.22 #116, 0.13 #30408, 0.13 #30407), 05zr6wv (0.21 #417, 0.20 #2017, 0.20 #3617), 05pcn59 (0.20 #878, 0.19 #9679, 0.17 #10479), 0f4x7 (0.20 #830, 0.17 #3630, 0.17 #2030) >> Best rule #3289 for best value: >> intensional similarity = 2 >> extensional distance = 57 >> proper extension: 03gm48; 015rhv; 01ft2l; 03pp73; 03n52j; 02tkzn; 02vg0; 02h0f3; 0f13b; 05xpv; ... >> query: (?x3039, 0ck27z) <- award(?x3039, ?x783), ?x783 = 0fbvqf >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #111 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 015grj; 03pmty; 0151w_; 0f7h2v; 01y9xg; 01nm3s; 069nzr; *> query: (?x3039, 0bfvd4) <- award_nominee(?x3039, ?x2735), ?x2735 = 034g2b, nominated_for(?x3039, ?x2293) *> conf = 0.22 ranks of expected_values: 5 EVAL 01y665 award 0bfvd4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 95.000 76.000 0.407 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7042-01trhmt PRED entity: 01trhmt PRED relation: gender PRED expected values: 05zppz => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #77, 0.82 #59, 0.82 #41), 02zsn (0.65 #54, 0.50 #2, 0.49 #24) >> Best rule #77 for best value: >> intensional similarity = 2 >> extensional distance = 341 >> proper extension: 07c37; >> query: (?x2562, 05zppz) <- location(?x2562, ?x2004), influenced_by(?x2562, ?x4960) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01trhmt gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.854 http://example.org/people/person/gender #7041-06pwf6 PRED entity: 06pwf6 PRED relation: location PRED expected values: 09c6w => 159 concepts (98 used for prediction) PRED predicted values (max 10 best out of 217): 0c8tk (0.67 #12893, 0.61 #36254, 0.55 #25779), 04vmp (0.33 #1160, 0.23 #6798, 0.22 #20494), 02_286 (0.29 #7287, 0.24 #44343, 0.24 #12930), 030qb3t (0.21 #18612, 0.21 #12170, 0.18 #37947), 09c6w (0.20 #5106, 0.08 #6716, 0.03 #27663), 03p85 (0.17 #1569, 0.14 #2376, 0.07 #8818), 02p3my (0.17 #1570, 0.14 #2377, 0.03 #8819), 04bz2f (0.17 #1396, 0.04 #15093, 0.03 #8645), 09c7w0 (0.17 #809, 0.03 #8058, 0.03 #11283), 01l69g (0.17 #1605, 0.03 #8854, 0.03 #12079) >> Best rule #12893 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 09r_wb; >> query: (?x2873, ?x4335) <- nationality(?x2873, ?x2146), place_of_birth(?x2873, ?x4335), student(?x4981, ?x2873), languages(?x2873, ?x13017) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5106 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 0dfjb8; *> query: (?x2873, 09c6w) <- languages(?x2873, ?x13017), ?x13017 = 09s02, profession(?x2873, ?x319), gender(?x2873, ?x231) *> conf = 0.20 ranks of expected_values: 5 EVAL 06pwf6 location 09c6w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 159.000 98.000 0.667 http://example.org/people/person/places_lived./people/place_lived/location #7040-02f6s3 PRED entity: 02f6s3 PRED relation: celebrities_impersonated! PRED expected values: 03m6t5 => 113 concepts (61 used for prediction) PRED predicted values (max 10 best out of 4): 03m6t5 (0.21 #67, 0.13 #27, 0.12 #11), 01n5309 (0.06 #9, 0.03 #1, 0.03 #33), 0pz04 (0.05 #24, 0.03 #16, 0.03 #8), 03d_zl4 (0.03 #14, 0.03 #6, 0.03 #22) >> Best rule #67 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 07s3vqk; 08433; 01vsl3_; 03bnv; 04264n; 04n_g; 014z8v; 03bpn6; 09889g; 022g44; ... >> query: (?x6591, 03m6t5) <- award(?x6591, ?x3066), type_of_union(?x6591, ?x566), place_of_death(?x6591, ?x10865), film(?x6591, ?x3009) >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02f6s3 celebrities_impersonated! 03m6t5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 61.000 0.210 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #7039-03_hd PRED entity: 03_hd PRED relation: influenced_by! PRED expected values: 09gnn => 115 concepts (47 used for prediction) PRED predicted values (max 10 best out of 400): 03_hd (0.57 #689, 0.17 #2041, 0.17 #4770), 0j3v (0.43 #591, 0.20 #1611, 0.17 #2041), 09gnn (0.43 #927, 0.20 #12256, 0.17 #5008), 0683n (0.41 #6970, 0.24 #5440, 0.17 #7481), 04411 (0.40 #1557, 0.20 #12256, 0.17 #2041), 048cl (0.40 #1827, 0.18 #3868, 0.17 #2041), 0b78hw (0.30 #1696, 0.29 #676, 0.25 #4757), 02wh0 (0.30 #1978, 0.29 #958, 0.18 #5551), 01dvtx (0.30 #1680, 0.14 #660, 0.11 #6783), 04z0g (0.29 #748, 0.25 #2787, 0.25 #237) >> Best rule #689 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 05qmj; >> query: (?x4547, 03_hd) <- influenced_by(?x12216, ?x4547), influenced_by(?x5796, ?x4547), influenced_by(?x4055, ?x4547), ?x12216 = 047g6, interests(?x5796, ?x1858), company(?x5796, ?x741), place_of_burial(?x4055, ?x10603) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #927 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 05qmj; *> query: (?x4547, 09gnn) <- influenced_by(?x12216, ?x4547), influenced_by(?x5796, ?x4547), influenced_by(?x4055, ?x4547), ?x12216 = 047g6, interests(?x5796, ?x1858), company(?x5796, ?x741), place_of_burial(?x4055, ?x10603) *> conf = 0.43 ranks of expected_values: 3 EVAL 03_hd influenced_by! 09gnn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 47.000 0.571 http://example.org/influence/influence_node/influenced_by #7038-03x6rj PRED entity: 03x6rj PRED relation: position PRED expected values: 02_j1w => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 5): 02_j1w (0.82 #545, 0.82 #524, 0.82 #58), 0dgrmp (0.82 #58, 0.80 #167, 0.79 #207), 03f0fp (0.50 #591, 0.45 #252, 0.44 #546), 02qvgy (0.50 #591, 0.45 #252, 0.44 #546), 02md_2 (0.45 #252, 0.44 #546, 0.34 #238) >> Best rule #545 for best value: >> intensional similarity = 15 >> extensional distance = 585 >> proper extension: 03fn8k; 02v4vl; 03l7qs; 036kmk; 0b5ysl; 09xb4h; 0gw2y6; 033kqb; 02b1l7; 07_q87; ... >> query: (?x12890, ?x60) <- team(?x530, ?x12890), team(?x60, ?x12890), ?x530 = 02_j1w, position(?x13134, ?x60), position(?x12310, ?x60), position(?x8425, ?x60), position(?x4306, ?x60), position(?x4116, ?x60), position(?x2330, ?x60), ?x12310 = 03fn6z, ?x13134 = 03d0d7, ?x4306 = 037mp6, position(?x8425, ?x203), ?x4116 = 04mnts, ?x2330 = 024tsn >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x6rj position 02_j1w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.821 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #7037-0b90_r PRED entity: 0b90_r PRED relation: vacationer PRED expected values: 0lk90 => 231 concepts (231 used for prediction) PRED predicted values (max 10 best out of 349): 0bksh (0.19 #2683, 0.14 #6552, 0.12 #8163), 016fnb (0.16 #6549, 0.15 #2680, 0.15 #909), 05r5w (0.15 #2651, 0.14 #6520, 0.12 #8131), 0bbf1f (0.15 #2638, 0.11 #3122, 0.11 #6507), 03_6y (0.15 #882, 0.11 #3137, 0.09 #234), 01pgzn_ (0.15 #2622, 0.11 #6491, 0.10 #8263), 01dw4q (0.12 #2260, 0.09 #325, 0.09 #5160), 01xyt7 (0.12 #2700, 0.11 #5278, 0.11 #3184), 01gq0b (0.12 #2609, 0.11 #3093, 0.09 #352), 0lk90 (0.12 #2600, 0.11 #6469, 0.07 #12276) >> Best rule #2683 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0d1qn; 04vmp; 01bkb; >> query: (?x151, 0bksh) <- featured_film_locations(?x224, ?x151), vacationer(?x151, ?x286), location_of_ceremony(?x1149, ?x151) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #2600 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0d1qn; 04vmp; 01bkb; *> query: (?x151, 0lk90) <- featured_film_locations(?x224, ?x151), vacationer(?x151, ?x286), location_of_ceremony(?x1149, ?x151) *> conf = 0.12 ranks of expected_values: 10 EVAL 0b90_r vacationer 0lk90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 231.000 231.000 0.192 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #7036-059j2 PRED entity: 059j2 PRED relation: partially_contains! PRED expected values: 0157g9 => 265 concepts (188 used for prediction) PRED predicted values (max 10 best out of 35): 02j9z (0.33 #201, 0.15 #1267, 0.14 #588), 05rgl (0.29 #614, 0.22 #808, 0.22 #711), 059g4 (0.29 #655, 0.22 #849, 0.22 #752), 04swx (0.14 #672, 0.11 #866, 0.11 #769), 06n3y (0.14 #669, 0.11 #863, 0.11 #766), 03rz4 (0.14 #661, 0.11 #855, 0.11 #758), 0j3b (0.09 #2445, 0.08 #2736, 0.08 #3026), 059qw (0.09 #773, 0.07 #1257, 0.07 #1256), 0345h (0.09 #773, 0.07 #1257, 0.07 #1256), 0j0k (0.09 #6183, 0.07 #3266, 0.07 #3653) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 049nq; >> query: (?x1229, 02j9z) <- contains(?x1229, ?x12932), countries_spoken_in(?x7658, ?x1229), ?x12932 = 0fqxw >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 059j2 partially_contains! 0157g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 265.000 188.000 0.333 http://example.org/location/location/partially_contains #7035-06ybb1 PRED entity: 06ybb1 PRED relation: honored_for! PRED expected values: 07b1gq => 99 concepts (67 used for prediction) PRED predicted values (max 10 best out of 139): 02scbv (0.86 #2808, 0.86 #623, 0.86 #2495), 07sgdw (0.86 #2808, 0.86 #623, 0.86 #2495), 0q9sg (0.86 #2808, 0.86 #623, 0.86 #2495), 01mszz (0.86 #2808, 0.86 #623, 0.86 #2495), 07b1gq (0.50 #61, 0.44 #2339, 0.37 #372), 06ybb1 (0.44 #2339, 0.37 #356, 0.33 #45), 0kv2hv (0.18 #170, 0.13 #326, 0.06 #951), 07gghl (0.18 #273, 0.10 #429, 0.06 #1054), 06sfk6 (0.17 #74, 0.04 #229, 0.03 #541), 0bxxzb (0.14 #274, 0.07 #430, 0.05 #1055) >> Best rule #2808 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 023g6w; >> query: (?x2165, ?x4538) <- nominated_for(?x8799, ?x2165), honored_for(?x188, ?x2165), honored_for(?x2165, ?x4538), award(?x8799, ?x1232) >> conf = 0.86 => this is the best rule for 4 predicted values *> Best rule #61 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0140g4; 0ds33; 02scbv; 03phtz; *> query: (?x2165, 07b1gq) <- nominated_for(?x574, ?x2165), honored_for(?x188, ?x2165), nominated_for(?x4317, ?x2165), ?x4317 = 05q8pss *> conf = 0.50 ranks of expected_values: 5 EVAL 06ybb1 honored_for! 07b1gq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 99.000 67.000 0.859 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #7034-01w5gg6 PRED entity: 01w5gg6 PRED relation: student! PRED expected values: 02237m => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 77): 015nl4 (0.07 #21674, 0.07 #15877, 0.06 #14296), 07tg4 (0.07 #14315, 0.06 #21693, 0.06 #15896), 0bwfn (0.07 #1856, 0.05 #31895, 0.05 #31368), 07tgn (0.06 #14246, 0.05 #21624, 0.04 #15827), 02g839 (0.05 #3714, 0.04 #10565, 0.04 #3187), 09f2j (0.04 #2794, 0.04 #159, 0.03 #3321), 02mj7c (0.04 #165, 0.02 #1219, 0.02 #2273), 01dq5z (0.04 #96, 0.02 #1150, 0.02 #2204), 01d34b (0.04 #256, 0.02 #1310, 0.01 #16593), 025v3k (0.04 #120, 0.02 #1174) >> Best rule #21674 for best value: >> intensional similarity = 3 >> extensional distance = 597 >> proper extension: 09jd9; >> query: (?x9241, 015nl4) <- nationality(?x9241, ?x1310), nationality(?x3849, ?x1310), ?x3849 = 0f8pz >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #3032 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 103 *> proper extension: 01yzl2; *> query: (?x9241, 02237m) <- artist(?x3265, ?x9241), nationality(?x9241, ?x512), artists(?x302, ?x9241), ?x302 = 016clz *> conf = 0.02 ranks of expected_values: 30 EVAL 01w5gg6 student! 02237m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 101.000 101.000 0.073 http://example.org/education/educational_institution/students_graduates./education/education/student #7033-0gg5qcw PRED entity: 0gg5qcw PRED relation: film_release_region PRED expected values: 059j2 07twz 03spz => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 114): 059j2 (0.88 #875, 0.85 #1158, 0.84 #449), 03_3d (0.78 #430, 0.77 #1139, 0.77 #856), 03spz (0.73 #933, 0.67 #507, 0.63 #1216), 03rt9 (0.68 #862, 0.65 #1145, 0.57 #436), 06qd3 (0.68 #454, 0.52 #880, 0.49 #1163), 03rj0 (0.65 #899, 0.60 #1182, 0.59 #473), 05v8c (0.62 #864, 0.56 #1147, 0.56 #438), 01mjq (0.60 #885, 0.56 #1168, 0.54 #459), 01ls2 (0.50 #860, 0.43 #1143, 0.32 #2133), 06t8v (0.46 #915, 0.42 #1198, 0.37 #631) >> Best rule #875 for best value: >> intensional similarity = 4 >> extensional distance = 178 >> proper extension: 0gtsx8c; >> query: (?x5092, 059j2) <- film_release_region(?x5092, ?x2316), film_release_region(?x5092, ?x512), ?x512 = 07ssc, ?x2316 = 06t2t >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 22 EVAL 0gg5qcw film_release_region 03spz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.883 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gg5qcw film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 80.000 80.000 0.883 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gg5qcw film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.883 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7032-06g77c PRED entity: 06g77c PRED relation: film! PRED expected values: 070yzk => 74 concepts (48 used for prediction) PRED predicted values (max 10 best out of 435): 09zmys (0.70 #20774, 0.66 #39473, 0.64 #41552), 026dx (0.41 #47789, 0.35 #60255, 0.34 #64411), 0pz91 (0.20 #2289, 0.02 #18908, 0.02 #33451), 01vw8mh (0.20 #2934), 0gnbw (0.12 #1267, 0.03 #5421, 0.02 #22042), 0h5g_ (0.12 #74, 0.03 #10460, 0.02 #14615), 01kb2j (0.12 #907, 0.02 #21682, 0.02 #5061), 0h0wc (0.12 #422, 0.02 #68991, 0.02 #21197), 07m77x (0.12 #1540, 0.02 #16081, 0.02 #7771), 0k269 (0.12 #608, 0.02 #15149, 0.02 #19304) >> Best rule #20774 for best value: >> intensional similarity = 4 >> extensional distance = 379 >> proper extension: 0clpml; >> query: (?x2547, ?x5521) <- nominated_for(?x5521, ?x2547), award(?x5521, ?x375), participant(?x8143, ?x5521), type_of_union(?x5521, ?x566) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #5636 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 107 *> proper extension: 0413cff; 07s3m4g; *> query: (?x2547, 070yzk) <- language(?x2547, ?x254), genre(?x2547, ?x2753), titles(?x600, ?x2547), ?x2753 = 0219x_ *> conf = 0.02 ranks of expected_values: 184 EVAL 06g77c film! 070yzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 74.000 48.000 0.704 http://example.org/film/actor/film./film/performance/film #7031-0h7h6 PRED entity: 0h7h6 PRED relation: citytown! PRED expected values: 018_q8 => 185 concepts (25 used for prediction) PRED predicted values (max 10 best out of 391): 0146mv (0.22 #4562, 0.22 #3765, 0.22 #2968), 06182p (0.22 #4374, 0.22 #3577, 0.22 #2780), 01dtcb (0.22 #4364, 0.22 #3567, 0.22 #2770), 0338lq (0.22 #4011, 0.22 #3214, 0.22 #2417), 0jpkw (0.17 #2082, 0.06 #10851, 0.03 #18027), 01t3h6 (0.14 #11958, 0.02 #15147), 018_q8 (0.11 #4337, 0.11 #3540, 0.11 #2743), 049ql1 (0.11 #4564, 0.11 #3767, 0.11 #2970), 0ky6d (0.11 #4745, 0.11 #3948, 0.11 #3151), 01w5gp (0.11 #4328, 0.11 #3531, 0.11 #2734) >> Best rule #4562 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02fzs; >> query: (?x1658, 0146mv) <- contains(?x279, ?x1658), film_regional_debut_venue(?x1283, ?x1658), vacationer(?x1658, ?x1897) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #4337 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 02fzs; *> query: (?x1658, 018_q8) <- contains(?x279, ?x1658), film_regional_debut_venue(?x1283, ?x1658), vacationer(?x1658, ?x1897) *> conf = 0.11 ranks of expected_values: 7 EVAL 0h7h6 citytown! 018_q8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 185.000 25.000 0.222 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #7030-0d_2fb PRED entity: 0d_2fb PRED relation: music PRED expected values: 02jxmr => 132 concepts (96 used for prediction) PRED predicted values (max 10 best out of 95): 02rgz4 (0.25 #426, 0.05 #636, 0.02 #8003), 04bpm6 (0.22 #236, 0.12 #26, 0.02 #4865), 0146pg (0.22 #220, 0.08 #431, 0.08 #1692), 02jxkw (0.12 #142, 0.09 #4771, 0.08 #1404), 06fxnf (0.12 #69, 0.08 #1121, 0.05 #1962), 0150t6 (0.12 #1308, 0.09 #1518, 0.06 #2989), 02bh9 (0.11 #261, 0.11 #1733, 0.08 #5311), 04pf4r (0.10 #699, 0.08 #489, 0.07 #3011), 07qy0b (0.10 #680, 0.06 #2362, 0.04 #3414), 02fgpf (0.10 #661, 0.04 #872, 0.02 #7606) >> Best rule #426 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 08fbnx; >> query: (?x2339, 02rgz4) <- country(?x2339, ?x94), titles(?x1510, ?x2339), genre(?x2339, ?x6459), genre(?x2339, ?x811), ?x6459 = 0bj8m2, ?x811 = 03k9fj >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1336 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 24 *> proper extension: 0170z3; 03qcfvw; 0dq626; 0ds11z; 01qb5d; 0bwfwpj; 0872p_c; 0bh8yn3; 0btyf5z; 07024; ... *> query: (?x2339, 02jxmr) <- production_companies(?x2339, ?x7935), region(?x2339, ?x512), film_crew_role(?x2339, ?x2095), film_crew_role(?x2339, ?x1284), film(?x3461, ?x2339), ?x2095 = 0dxtw, ?x1284 = 0ch6mp2 *> conf = 0.08 ranks of expected_values: 18 EVAL 0d_2fb music 02jxmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 132.000 96.000 0.250 http://example.org/film/film/music #7029-09g90vz PRED entity: 09g90vz PRED relation: award_winner PRED expected values: 06vsbt 08qxx9 07m77x 0bbvr84 => 27 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1831): 01j7rd (0.50 #10831, 0.40 #4805, 0.20 #18368), 04ns3gy (0.40 #5806, 0.28 #11832, 0.17 #8819), 02tr7d (0.40 #3238, 0.25 #7754, 0.20 #4741), 0bj9k (0.40 #3288, 0.20 #4791, 0.18 #12325), 01_x6d (0.40 #5191, 0.17 #11217, 0.17 #8204), 01_x6v (0.40 #4838, 0.17 #10864, 0.17 #7851), 02xs0q (0.39 #11073, 0.30 #5047, 0.17 #8060), 0cp9f9 (0.35 #10190, 0.33 #8685, 0.30 #5672), 0h0wc (0.34 #15416, 0.33 #1859, 0.33 #354), 0m_v0 (0.33 #2008, 0.33 #503, 0.10 #20086) >> Best rule #10831 for best value: >> intensional similarity = 15 >> extensional distance = 16 >> proper extension: 05c1t6z; 0lp_cd3; 07z31v; 0gvstc3; 0gx_st; 058m5m4; 02q690_; 07y9ts; 03nnm4t; 07y_p6; ... >> query: (?x9306, 01j7rd) <- award_winner(?x9306, ?x1902), award_winner(?x9306, ?x1796), profession(?x1902, ?x1032), ceremony(?x678, ?x9306), person(?x5639, ?x1796), award(?x5410, ?x678), award(?x1116, ?x678), nominated_for(?x678, ?x7317), ?x7317 = 05p9_ql, award_nominee(?x516, ?x1116), award_winner(?x678, ?x679), ?x1032 = 02hrh1q, place_of_birth(?x1902, ?x5719), nominated_for(?x1116, ?x2078), profession(?x5410, ?x319) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1232 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 09gkdln; *> query: (?x9306, 07m77x) <- award_winner(?x9306, ?x7797), award_winner(?x9306, ?x1902), award_winner(?x9306, ?x1796), profession(?x1902, ?x1032), ceremony(?x678, ?x9306), ceremony(?x618, ?x9306), person(?x5639, ?x1796), award(?x4816, ?x678), nominated_for(?x678, ?x1631), location(?x4816, ?x108), nominated_for(?x618, ?x2742), ?x7797 = 02qw2xb, award_winner(?x618, ?x396), film_release_region(?x2742, ?x94) *> conf = 0.33 ranks of expected_values: 12, 13, 94, 95 EVAL 09g90vz award_winner 0bbvr84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 27.000 15.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09g90vz award_winner 07m77x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 27.000 15.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09g90vz award_winner 08qxx9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 27.000 15.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09g90vz award_winner 06vsbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 27.000 15.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #7028-02pb53 PRED entity: 02pb53 PRED relation: profession PRED expected values: 01d_h8 => 87 concepts (72 used for prediction) PRED predicted values (max 10 best out of 73): 0dxtg (0.71 #13, 0.63 #895, 0.54 #1777), 01d_h8 (0.55 #1035, 0.54 #1182, 0.54 #888), 03gjzk (0.46 #455, 0.40 #896, 0.39 #749), 0kyk (0.35 #2380, 0.32 #2674, 0.31 #3851), 02jknp (0.33 #1183, 0.33 #889, 0.32 #1036), 09jwl (0.31 #5885, 0.29 #311, 0.26 #10594), 0nbcg (0.31 #5885, 0.26 #10594, 0.26 #10593), 02krf9 (0.31 #5885, 0.26 #10594, 0.26 #10593), 025352 (0.31 #5885, 0.26 #10594, 0.26 #10593), 0np9r (0.30 #4118, 0.18 #166, 0.14 #7521) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0277990; >> query: (?x1726, 0dxtg) <- award_nominee(?x1726, ?x5620), ?x5620 = 05drr9, student(?x122, ?x1726) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1035 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 07s3vqk; 01xdf5; 0p_pd; 04wqr; 0gt_k; 01vb403; 086qd; 01w60_p; 0j_c; 03h_fk5; ... *> query: (?x1726, 01d_h8) <- award_nominee(?x1726, ?x1422), influenced_by(?x1725, ?x1726), people(?x1050, ?x1726) *> conf = 0.55 ranks of expected_values: 2 EVAL 02pb53 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 72.000 0.714 http://example.org/people/person/profession #7027-0jvt9 PRED entity: 0jvt9 PRED relation: film! PRED expected values: 02_pft => 88 concepts (67 used for prediction) PRED predicted values (max 10 best out of 849): 04vzv4 (0.49 #118171, 0.44 #128534, 0.43 #132681), 087v17 (0.44 #128534, 0.43 #132681, 0.42 #35239), 058vfp4 (0.44 #128534, 0.43 #132681, 0.42 #35239), 06hzsx (0.44 #128534, 0.43 #132681, 0.42 #35239), 09cdxn (0.44 #128534, 0.43 #132681, 0.42 #35239), 05bht9 (0.23 #10363, 0.15 #103658, 0.14 #47689), 0454s1 (0.23 #10363, 0.15 #103658, 0.14 #47689), 01pp3p (0.23 #10363, 0.15 #103658, 0.14 #47689), 0btj0 (0.20 #1993, 0.05 #10283, 0.04 #6137), 0g10g (0.20 #1819, 0.04 #5963, 0.02 #8036) >> Best rule #118171 for best value: >> intensional similarity = 3 >> extensional distance = 963 >> proper extension: 03g9xj; >> query: (?x3294, ?x4526) <- nominated_for(?x4526, ?x3294), titles(?x4757, ?x3294), people(?x4195, ?x4526) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #14262 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 016kz1; *> query: (?x3294, 02_pft) <- film_sets_designed(?x9825, ?x3294), genre(?x3294, ?x53), ?x53 = 07s9rl0, nominated_for(?x484, ?x3294) *> conf = 0.02 ranks of expected_values: 385 EVAL 0jvt9 film! 02_pft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 88.000 67.000 0.485 http://example.org/film/actor/film./film/performance/film #7026-03061d PRED entity: 03061d PRED relation: award PRED expected values: 0ck27z => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 272): 09sb52 (0.37 #12596, 0.33 #16241, 0.33 #11381), 0ck27z (0.34 #9408, 0.32 #9003, 0.32 #14268), 0bdx29 (0.25 #109, 0.13 #23896, 0.13 #39287), 0bfvw2 (0.25 #15, 0.13 #23896, 0.13 #39287), 0cqhk0 (0.21 #442, 0.19 #7732, 0.18 #12997), 0gkvb7 (0.19 #432, 0.12 #2862, 0.11 #3267), 05pcn59 (0.18 #5347, 0.16 #4942, 0.16 #4132), 05zr6wv (0.17 #422, 0.13 #1232, 0.12 #5282), 05p09zm (0.16 #5389, 0.13 #4984, 0.12 #7009), 0cjyzs (0.14 #512, 0.11 #917, 0.09 #19142) >> Best rule #12596 for best value: >> intensional similarity = 3 >> extensional distance = 553 >> proper extension: 01sl1q; 0197tq; 06gp3f; 01j5ts; 01r42_g; 02zq43; 01qscs; 03rs8y; 0z4s; 058ncz; ... >> query: (?x11884, 09sb52) <- award_nominee(?x1397, ?x11884), people(?x1446, ?x11884), participant(?x406, ?x1397) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #9408 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 432 *> proper extension: 02lfl4; 02pkpfs; 07f3xb; 05tk7y; 02lg9w; 02bkdn; 0443y3; 02xb2bt; 02d4ct; 0f4dx2; ... *> query: (?x11884, 0ck27z) <- award_nominee(?x1397, ?x11884), actor(?x782, ?x11884), participant(?x406, ?x1397) *> conf = 0.34 ranks of expected_values: 2 EVAL 03061d award 0ck27z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.366 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7025-0138mv PRED entity: 0138mv PRED relation: colors PRED expected values: 083jv 019sc => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 17): 083jv (0.50 #424, 0.50 #62, 0.47 #343), 01g5v (0.50 #84, 0.38 #305, 0.29 #426), 019sc (0.43 #188, 0.33 #48, 0.33 #8), 06fvc (0.33 #143, 0.33 #3, 0.31 #324), 088fh (0.26 #803, 0.22 #241, 0.19 #308), 038hg (0.25 #866, 0.25 #865, 0.25 #113), 09ggk (0.25 #866, 0.25 #865, 0.22 #1009), 02rnmb (0.25 #74, 0.22 #241, 0.14 #214), 01l849 (0.22 #241, 0.11 #1812, 0.10 #783), 06kqt3 (0.22 #241, 0.11 #1812, 0.10 #1837) >> Best rule #424 for best value: >> intensional similarity = 10 >> extensional distance = 46 >> proper extension: 01xn7x1; >> query: (?x9107, 083jv) <- team(?x9106, ?x9107), position(?x9107, ?x530), position(?x9107, ?x203), position(?x9107, ?x63), position(?x9107, ?x60), ?x63 = 02sdk9v, ?x203 = 0dgrmp, ?x60 = 02nzb8, sport(?x9107, ?x471), ?x530 = 02_j1w >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0138mv colors 019sc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.500 http://example.org/sports/sports_team/colors EVAL 0138mv colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.500 http://example.org/sports/sports_team/colors #7024-024l2y PRED entity: 024l2y PRED relation: genre PRED expected values: 07s9rl0 => 90 concepts (67 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.79 #4381, 0.74 #4736, 0.69 #951), 03k9fj (0.43 #1673, 0.42 #5809, 0.38 #2145), 05p553 (0.40 #1311, 0.35 #5329, 0.35 #1901), 0lsxr (0.38 #1197, 0.37 #1079, 0.34 #1788), 02l7c8 (0.34 #4750, 0.31 #253, 0.31 #1322), 01hmnh (0.30 #17, 0.21 #5814, 0.20 #136), 02n4kr (0.26 #1196, 0.25 #1787, 0.25 #1078), 060__y (0.20 #372, 0.20 #16, 0.18 #728), 04xvlr (0.19 #952, 0.19 #2844, 0.16 #2017), 082gq (0.19 #2872, 0.19 #980, 0.17 #268) >> Best rule #4381 for best value: >> intensional similarity = 4 >> extensional distance = 703 >> proper extension: 06krf3; 011yfd; 06nr2h; 0p_tz; 08cfr1; 0kt_4; 05y0cr; 03cffvv; 0c5qvw; >> query: (?x1688, 07s9rl0) <- award(?x1688, ?x640), genre(?x1688, ?x812), genre(?x5704, ?x812), ?x5704 = 0h95zbp >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024l2y genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 67.000 0.794 http://example.org/film/film/genre #7023-0781g PRED entity: 0781g PRED relation: artists PRED expected values: 02r3cn 02vgh 0187x8 => 62 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1075): 011z3g (0.67 #5980, 0.62 #9202, 0.38 #11354), 03xhj6 (0.67 #5768, 0.62 #8990, 0.31 #11142), 01shhf (0.67 #4090, 0.60 #1940, 0.47 #12691), 01w8n89 (0.67 #3541, 0.60 #1391, 0.38 #11068), 0187x8 (0.67 #3926, 0.60 #1776, 0.38 #9301), 049qx (0.67 #5761, 0.50 #8983, 0.31 #11135), 0326tc (0.60 #1793, 0.50 #3943, 0.33 #720), 01t8399 (0.60 #3108, 0.43 #8482, 0.33 #4181), 0fhxv (0.56 #16125, 0.50 #9011, 0.50 #20428), 02pt27 (0.56 #16125, 0.50 #20428, 0.48 #20429) >> Best rule #5980 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0m0jc; 064t9; 026z9; >> query: (?x11106, 011z3g) <- parent_genre(?x11106, ?x1380), artists(?x11106, ?x7237), artists(?x11106, ?x7121), participant(?x7121, ?x3503), artist(?x648, ?x7121), ?x7237 = 0473q, ?x648 = 013x0b >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3926 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 06by7; *> query: (?x11106, 0187x8) <- parent_genre(?x11106, ?x1380), artists(?x11106, ?x7121), ?x7121 = 04kjrv, artists(?x1380, ?x8335), artists(?x1380, ?x3867), artist(?x3265, ?x8335), instrumentalists(?x227, ?x3867) *> conf = 0.67 ranks of expected_values: 5, 15, 715 EVAL 0781g artists 0187x8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 34.000 0.667 http://example.org/music/genre/artists EVAL 0781g artists 02vgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 62.000 34.000 0.667 http://example.org/music/genre/artists EVAL 0781g artists 02r3cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 34.000 0.667 http://example.org/music/genre/artists #7022-03y0pn PRED entity: 03y0pn PRED relation: genre PRED expected values: 02kdv5l => 108 concepts (107 used for prediction) PRED predicted values (max 10 best out of 108): 07s9rl0 (0.74 #606, 0.71 #3516, 0.69 #3274), 02kdv5l (0.54 #850, 0.49 #2668, 0.47 #487), 05p553 (0.52 #8492, 0.41 #1336, 0.36 #1094), 01jfsb (0.48 #860, 0.46 #497, 0.40 #134), 0hcr (0.44 #24, 0.24 #1355, 0.22 #1476), 02l7c8 (0.41 #8504, 0.32 #3290, 0.32 #1833), 03q4nz (0.33 #19, 0.07 #624, 0.07 #140), 0bj8m2 (0.33 #50, 0.04 #1381, 0.04 #1139), 06n90 (0.26 #498, 0.25 #2679, 0.25 #861), 04xvlr (0.26 #123, 0.21 #1818, 0.19 #3638) >> Best rule #606 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 02rb607; 0gcrg; 04lqvly; 0g9zljd; 0cvkv5; 0dmn0x; >> query: (?x7207, 07s9rl0) <- genre(?x7207, ?x811), film_format(?x7207, ?x909), nominated_for(?x1634, ?x7207), honored_for(?x5592, ?x7207) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #850 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 168 *> proper extension: 0gtsx8c; *> query: (?x7207, 02kdv5l) <- prequel(?x7207, ?x908), language(?x7207, ?x254), film(?x629, ?x7207) *> conf = 0.54 ranks of expected_values: 2 EVAL 03y0pn genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 107.000 0.744 http://example.org/film/film/genre #7021-0hskw PRED entity: 0hskw PRED relation: profession PRED expected values: 0q04f => 90 concepts (70 used for prediction) PRED predicted values (max 10 best out of 61): 0dxtg (0.97 #3638, 0.72 #1026, 0.70 #1606), 03gjzk (0.45 #1462, 0.43 #1172, 0.42 #1753), 0cbd2 (0.38 #295, 0.22 #3632, 0.21 #585), 0np9r (0.25 #6384, 0.24 #1467, 0.11 #3644), 0kyk (0.25 #6384, 0.23 #316, 0.15 #606), 02krf9 (0.23 #1618, 0.22 #1038, 0.22 #748), 09jwl (0.20 #2626, 0.20 #2481, 0.17 #3497), 02hv44_ (0.20 #489, 0.08 #3681, 0.05 #2085), 012t_z (0.15 #445, 0.08 #155, 0.08 #1751), 016z4k (0.13 #2469, 0.12 #2614, 0.11 #2760) >> Best rule #3638 for best value: >> intensional similarity = 3 >> extensional distance = 988 >> proper extension: 079vf; 05g8ky; 02qjj7; 01pr_j6; 04n7njg; 01p45_v; 03ft8; 064p92m; 01c58j; 0177s6; ... >> query: (?x2733, 0dxtg) <- profession(?x2733, ?x106), profession(?x1172, ?x106), ?x1172 = 0207wx >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 0p51w; 03bw6; *> query: (?x2733, 0q04f) <- award_winner(?x1313, ?x2733), ?x1313 = 0gs9p, place_of_birth(?x2733, ?x1646) *> conf = 0.08 ranks of expected_values: 18 EVAL 0hskw profession 0q04f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 90.000 70.000 0.966 http://example.org/people/person/profession #7020-053yx PRED entity: 053yx PRED relation: artists! PRED expected values: 03_d0 01f9y_ => 156 concepts (112 used for prediction) PRED predicted values (max 10 best out of 223): 06by7 (0.49 #4938, 0.43 #1557, 0.40 #25215), 064t9 (0.47 #22744, 0.47 #23051, 0.47 #10764), 06j6l (0.37 #4966, 0.29 #6194, 0.28 #22781), 0155w (0.37 #5023, 0.28 #4100, 0.24 #6251), 0gywn (0.31 #4976, 0.28 #6204, 0.25 #5590), 05w3f (0.30 #1574, 0.23 #4032, 0.21 #653), 03_d0 (0.29 #4927, 0.28 #4004, 0.27 #9533), 017_qw (0.27 #372, 0.23 #9894, 0.19 #14809), 07sbbz2 (0.27 #314, 0.22 #4923, 0.10 #9529), 0xhtw (0.26 #1552, 0.25 #4010, 0.24 #4933) >> Best rule #4938 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0dbb3; >> query: (?x2835, 06by7) <- profession(?x2835, ?x955), influenced_by(?x1872, ?x2835), artist(?x3240, ?x2835) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #4927 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 0dbb3; *> query: (?x2835, 03_d0) <- profession(?x2835, ?x955), influenced_by(?x1872, ?x2835), artist(?x3240, ?x2835) *> conf = 0.29 ranks of expected_values: 7 EVAL 053yx artists! 01f9y_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 156.000 112.000 0.490 http://example.org/music/genre/artists EVAL 053yx artists! 03_d0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 156.000 112.000 0.490 http://example.org/music/genre/artists #7019-0glqh5_ PRED entity: 0glqh5_ PRED relation: film_release_region PRED expected values: 0154j 015qh 06f32 03ryn => 94 concepts (66 used for prediction) PRED predicted values (max 10 best out of 152): 03_3d (0.88 #144, 0.87 #5, 0.84 #283), 0154j (0.83 #837, 0.82 #281, 0.81 #1254), 01p1v (0.72 #182, 0.72 #460, 0.71 #321), 015qh (0.70 #451, 0.67 #312, 0.65 #173), 06mzp (0.67 #16, 0.55 #850, 0.52 #989), 06t8v (0.66 #897, 0.64 #480, 0.62 #341), 03ryn (0.64 #349, 0.60 #488, 0.56 #210), 06qd3 (0.64 #30, 0.58 #169, 0.58 #308), 0h7x (0.64 #27, 0.49 #166, 0.48 #1139), 01mjq (0.64 #453, 0.63 #870, 0.63 #175) >> Best rule #144 for best value: >> intensional similarity = 7 >> extensional distance = 41 >> proper extension: 0407yj_; 0gffmn8; 0gwjw0c; 0fpgp26; >> query: (?x5315, 03_3d) <- film_release_region(?x5315, ?x1790), film_release_region(?x5315, ?x1174), ?x1790 = 01pj7, genre(?x5315, ?x53), film(?x643, ?x5315), ?x1174 = 047yc, film(?x1104, ?x5315) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #837 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 63 *> proper extension: 0gtsx8c; 0gtvrv3; *> query: (?x5315, 0154j) <- film_release_region(?x5315, ?x1790), film_release_region(?x5315, ?x1603), film_release_region(?x5315, ?x456), ?x1790 = 01pj7, ?x456 = 05qhw, ?x1603 = 06bnz, film(?x1104, ?x5315) *> conf = 0.83 ranks of expected_values: 2, 4, 7, 14 EVAL 0glqh5_ film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 66.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0glqh5_ film_release_region 06f32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 94.000 66.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0glqh5_ film_release_region 015qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 66.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0glqh5_ film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 66.000 0.884 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7018-01h0kx PRED entity: 01h0kx PRED relation: artists PRED expected values: 05k79 => 44 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1201): 02ndj5 (0.56 #7455, 0.50 #5270, 0.47 #10734), 01w5n51 (0.54 #9431, 0.44 #7246, 0.43 #6153), 0l8g0 (0.50 #4933, 0.44 #7118, 0.43 #6025), 06br6t (0.44 #7449, 0.38 #9634, 0.33 #1990), 067mj (0.40 #3375, 0.33 #4466, 0.29 #5558), 0187x8 (0.33 #7263, 0.33 #5078, 0.25 #2896), 01wv9xn (0.33 #6671, 0.33 #4486, 0.25 #2304), 06gd4 (0.33 #6890, 0.31 #9075, 0.25 #2523), 0pqp3 (0.33 #5352, 0.29 #6444, 0.25 #3170), 0191h5 (0.33 #7204, 0.26 #11576, 0.25 #19224) >> Best rule #7455 for best value: >> intensional similarity = 14 >> extensional distance = 7 >> proper extension: 0fd3y; 0cx7f; >> query: (?x9881, 02ndj5) <- parent_genre(?x9881, ?x2809), parent_genre(?x9881, ?x1000), ?x2809 = 05w3f, artists(?x1000, ?x12449), artists(?x1000, ?x12211), artists(?x1000, ?x7252), artists(?x1000, ?x5279), artists(?x1000, ?x4658), group(?x227, ?x12449), ?x7252 = 017g21, ?x12211 = 0jltp, artist(?x2299, ?x4658), ?x5279 = 06nv27, parent_genre(?x7577, ?x9881) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #11071 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 17 *> proper extension: 0ggq0m; 025tm81; *> query: (?x9881, 05k79) <- parent_genre(?x7577, ?x9881), artists(?x7577, ?x3667), artists(?x7577, ?x3503), ?x3667 = 0phx4, origin(?x3503, ?x362), student(?x2142, ?x3503), role(?x3503, ?x227), profession(?x3503, ?x131) *> conf = 0.21 ranks of expected_values: 163 EVAL 01h0kx artists 05k79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 29.000 0.556 http://example.org/music/genre/artists #7017-0355pl PRED entity: 0355pl PRED relation: team PRED expected values: 01rly6 01fwqn => 9 concepts (9 used for prediction) PRED predicted values (max 10 best out of 845): 0272vm (0.33 #238, 0.33 #148, 0.28 #147), 02ryyk (0.33 #288, 0.33 #148, 0.28 #147), 02s2ys (0.33 #241, 0.33 #148, 0.28 #147), 039_ym (0.33 #269, 0.28 #147, 0.25 #714), 02_lt (0.33 #148, 0.28 #147, 0.11 #296), 050fh (0.33 #148, 0.28 #147, 0.11 #296), 027pwl (0.33 #148, 0.28 #147, 0.11 #296), 0j47s (0.33 #148, 0.28 #147, 0.11 #296), 01rly6 (0.33 #148, 0.28 #147, 0.11 #296), 085v7 (0.33 #148, 0.28 #147, 0.11 #296) >> Best rule #238 for best value: >> intensional similarity = 29 >> extensional distance = 1 >> proper extension: 07y9k; >> query: (?x8194, 0272vm) <- team(?x8194, ?x11748), team(?x8194, ?x7122), team(?x8194, ?x6064), team(?x8194, ?x4511), team(?x8194, ?x1697), team(?x203, ?x4511), team(?x63, ?x4511), category(?x8194, ?x134), ?x134 = 08mbj5d, team(?x5420, ?x1697), team(?x1696, ?x1697), position(?x4511, ?x60), ?x63 = 02sdk9v, ?x60 = 02nzb8, teams(?x7213, ?x1697), team(?x208, ?x4511), teams(?x13494, ?x7122), team(?x2666, ?x7122), ?x203 = 0dgrmp, nationality(?x5420, ?x512), team(?x5420, ?x5993), sport(?x6064, ?x471), team(?x3047, ?x11748), ?x1696 = 080dyk, position(?x11748, ?x12598), teams(?x10790, ?x6064), team(?x5420, ?x983), gender(?x5420, ?x231), ?x5993 = 0196bp >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #148 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 1 *> proper extension: 03zv9; *> query: (?x8194, ?x1899) <- team(?x8194, ?x11748), team(?x8194, ?x11337), team(?x8194, ?x9434), team(?x8194, ?x8885), team(?x8194, ?x8195), team(?x8194, ?x4511), team(?x8194, ?x1697), team(?x530, ?x4511), colors(?x4511, ?x3189), team(?x11781, ?x4511), teams(?x11747, ?x11748), team(?x3047, ?x11748), team(?x6063, ?x8885), sport(?x1697, ?x471), ?x3189 = 01g5v, position(?x11337, ?x60), team(?x927, ?x1697), ?x530 = 02_j1w, team(?x8598, ?x8195), team(?x8860, ?x9434), place_of_birth(?x8598, ?x1406), team(?x11781, ?x1899), contains(?x512, ?x11747), team(?x7907, ?x8885), nationality(?x11781, ?x429), team(?x5685, ?x11337), current_club(?x6180, ?x9434) *> conf = 0.33 ranks of expected_values: 9, 13 EVAL 0355pl team 01fwqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 9.000 9.000 0.333 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team EVAL 0355pl team 01rly6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 9.000 9.000 0.333 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #7016-03fbb6 PRED entity: 03fbb6 PRED relation: place_of_birth PRED expected values: 030qb3t => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 23): 01_d4 (0.13 #1474, 0.11 #770, 0.03 #6402), 0rh6k (0.11 #706, 0.01 #12675, 0.01 #15493), 0z1vw (0.11 #1167), 0y62n (0.11 #1043), 0sf9_ (0.11 #846), 02_286 (0.07 #50013, 0.07 #16918, 0.06 #51422), 06_kh (0.07 #1413, 0.02 #2117, 0.02 #4229), 013f1h (0.07 #1939), 0qymv (0.07 #1818), 030qb3t (0.04 #3574, 0.04 #2870, 0.04 #50048) >> Best rule #1474 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0bxtg; 02lkcc; 02d4ct; 0169dl; 03jqw5; 04wp3s; 025j1t; 015pvh; 0z05l; 04wg38; ... >> query: (?x5500, 01_d4) <- award_nominee(?x5500, ?x12425), film(?x5500, ?x573), ?x12425 = 04m064 >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #3574 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 458 *> proper extension: 05m63c; 04bs3j; 0151ns; 025p38; 09byk; 0htlr; 04shbh; 02r34n; 0n6f8; 0prjs; ... *> query: (?x5500, 030qb3t) <- languages(?x5500, ?x254), film(?x5500, ?x573) *> conf = 0.04 ranks of expected_values: 10 EVAL 03fbb6 place_of_birth 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 79.000 79.000 0.133 http://example.org/people/person/place_of_birth #7015-08phg9 PRED entity: 08phg9 PRED relation: films! PRED expected values: 07s2s => 115 concepts (83 used for prediction) PRED predicted values (max 10 best out of 59): 0cm2xh (0.17 #47, 0.03 #673, 0.02 #1144), 0bq3x (0.07 #1127, 0.06 #656, 0.05 #1912), 07s2s (0.06 #4181, 0.04 #412, 0.03 #569), 0ddct (0.06 #714, 0.06 #1813, 0.05 #1970), 081pw (0.06 #629, 0.05 #943, 0.05 #6592), 02jx1 (0.06 #169, 0.04 #326, 0.03 #483), 0d06vc (0.06 #180, 0.03 #650, 0.02 #964), 01d5g (0.05 #894, 0.05 #1050, 0.03 #736), 01vq3 (0.05 #825, 0.03 #511, 0.03 #2081), 0fx2s (0.05 #2901, 0.04 #3999, 0.04 #4784) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01gc7; 026p4q7; 0639bg; 04k9y6; >> query: (?x5128, 0cm2xh) <- region(?x5128, ?x512), award(?x5128, ?x3458), film_crew_role(?x5128, ?x281), ?x3458 = 0gqxm >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #4181 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 212 *> proper extension: 06n90; *> query: (?x5128, 07s2s) <- genre(?x5128, ?x1013), ?x1013 = 06n90 *> conf = 0.06 ranks of expected_values: 3 EVAL 08phg9 films! 07s2s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 83.000 0.167 http://example.org/film/film_subject/films #7014-0dq_5 PRED entity: 0dq_5 PRED relation: organization PRED expected values: 086k8 04f525m 0g2c8 054lpb6 084l5 04f0xq 07zlqp 049mr 01frpd => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 820): 054lpb6 (0.60 #3652, 0.50 #1242, 0.33 #35), 01xk7r (0.50 #2085, 0.33 #277, 0.29 #7513), 01q460 (0.40 #4331, 0.33 #111, 0.29 #6744), 017cy9 (0.40 #4389, 0.33 #169, 0.29 #6802), 0mbwf (0.40 #4711, 0.33 #491, 0.29 #7124), 022jr5 (0.40 #4422, 0.33 #202, 0.29 #6835), 01bcwk (0.40 #4396, 0.33 #176, 0.29 #6809), 07wjk (0.40 #4291, 0.33 #71, 0.29 #6704), 029qzx (0.40 #4668, 0.33 #448, 0.29 #7081), 011kn2 (0.40 #4799, 0.33 #579, 0.29 #7212) >> Best rule #3652 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 01yc02; >> query: (?x4682, 054lpb6) <- organization(?x4682, ?x3381), organization(?x4682, ?x3265), organization(?x4682, ?x166), company(?x4682, ?x8237), state_province_region(?x3265, ?x335), ?x8237 = 07xyn1, film(?x166, ?x8436), film(?x166, ?x167), citytown(?x166, ?x739), currency(?x8436, ?x170), child(?x3381, ?x2246), film(?x643, ?x167) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 698, 767, 775, 819 EVAL 0dq_5 organization 01frpd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.600 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 0dq_5 organization 049mr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.600 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 0dq_5 organization 07zlqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.600 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 0dq_5 organization 04f0xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.600 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 0dq_5 organization 084l5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.600 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 0dq_5 organization 054lpb6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.600 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 0dq_5 organization 0g2c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.600 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 0dq_5 organization 04f525m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 34.000 0.600 http://example.org/organization/role/leaders./organization/leadership/organization EVAL 0dq_5 organization 086k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.600 http://example.org/organization/role/leaders./organization/leadership/organization #7013-0835q PRED entity: 0835q PRED relation: gender PRED expected values: 05zppz => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #67, 0.86 #77, 0.85 #162), 02zsn (0.51 #209, 0.47 #157, 0.27 #20) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 099bk; 026m0; >> query: (?x11956, 05zppz) <- student(?x4672, ?x11956), politician(?x1912, ?x11956), type_of_union(?x11956, ?x566) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0835q gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.867 http://example.org/people/person/gender #7012-04gdr PRED entity: 04gdr PRED relation: category PRED expected values: 08mbj5d => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.75 #76, 0.74 #21, 0.74 #54) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 131 >> proper extension: 01y06y; >> query: (?x10739, 08mbj5d) <- contains(?x4627, ?x10739), place_of_birth(?x8233, ?x4627), time_zones(?x4627, ?x2864), influenced_by(?x8233, ?x118), location_of_ceremony(?x566, ?x4627) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gdr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 194.000 0.752 http://example.org/common/topic/webpage./common/webpage/category #7011-014kq6 PRED entity: 014kq6 PRED relation: film! PRED expected values: 018p4y => 101 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1138): 017lqp (0.40 #3687, 0.02 #70217, 0.02 #72297), 0716t2 (0.25 #1907, 0.08 #8144, 0.05 #16460), 01vh3r (0.25 #1931, 0.05 #112279, 0.05 #54055), 01yfm8 (0.25 #1290, 0.05 #15843, 0.04 #17922), 01pg1d (0.25 #1813, 0.02 #18445, 0.02 #51709), 01fdc0 (0.25 #599, 0.02 #17231, 0.02 #38021), 0347db (0.25 #1245, 0.02 #17877, 0.01 #22035), 06mr6 (0.20 #3116, 0.03 #69646, 0.03 #77968), 01wbg84 (0.20 #4205, 0.03 #20837, 0.02 #39548), 079vf (0.17 #6245, 0.10 #18719, 0.10 #14561) >> Best rule #3687 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0g5pv3; 01kf3_9; 01kf4tt; 0fsw_7; 02n72k; >> query: (?x2160, 017lqp) <- nominated_for(?x11120, ?x2160), language(?x2160, ?x254), ?x254 = 02h40lc, ?x11120 = 0fztbq >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #89244 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 416 *> proper extension: 04svwx; *> query: (?x2160, 018p4y) <- genre(?x2160, ?x225), ?x225 = 02kdv5l, country(?x2160, ?x94) *> conf = 0.01 ranks of expected_values: 873 EVAL 014kq6 film! 018p4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 101.000 60.000 0.400 http://example.org/film/actor/film./film/performance/film #7010-026v1z PRED entity: 026v1z PRED relation: award PRED expected values: 01l29r => 72 concepts (55 used for prediction) PRED predicted values (max 10 best out of 245): 0m7yy (0.71 #13405, 0.71 #13812, 0.70 #14219), 01l78d (0.67 #698, 0.33 #291, 0.28 #6904), 01lk0l (0.50 #688, 0.33 #281, 0.28 #6904), 07bdd_ (0.45 #4127, 0.39 #3721, 0.35 #6563), 05p1dby (0.45 #4169, 0.35 #3763, 0.29 #6605), 01lj_c (0.33 #709, 0.33 #302, 0.28 #6904), 01l29r (0.28 #6904, 0.25 #407, 0.25 #8937), 0gq9h (0.26 #8202, 0.24 #8608, 0.24 #6575), 02x1z2s (0.23 #4262, 0.16 #3856, 0.05 #6698), 09sb52 (0.21 #11009, 0.18 #12633, 0.18 #13039) >> Best rule #13405 for best value: >> intensional similarity = 4 >> extensional distance = 1193 >> proper extension: 0chsq; 0mdqp; 0134w7; 01fkv0; 05drq5; 0jfx1; 0pmhf; 02f8lw; 026_w57; 01cj6y; ... >> query: (?x11078, ?x3486) <- award_winner(?x7274, ?x11078), award_nominee(?x7274, ?x5714), award_winner(?x3486, ?x11078), type_of_union(?x5714, ?x566) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6904 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 02qgqt; 0fvf9q; 0l6qt; 02rchht; 07f8wg; 017s11; 058kqy; 02q_cc; 0kx4m; 05qd_; ... *> query: (?x11078, ?x3105) <- award_winner(?x7274, ?x11078), award_nominee(?x7274, ?x902), award_winner(?x3105, ?x7274), ?x902 = 05qd_ *> conf = 0.28 ranks of expected_values: 7 EVAL 026v1z award 01l29r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 72.000 55.000 0.707 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #7009-01vc5m PRED entity: 01vc5m PRED relation: major_field_of_study PRED expected values: 04rjg => 129 concepts (126 used for prediction) PRED predicted values (max 10 best out of 116): 01mkq (0.44 #3257, 0.43 #3377, 0.39 #495), 04rjg (0.39 #3262, 0.37 #620, 0.36 #380), 03g3w (0.38 #3269, 0.38 #507, 0.38 #3389), 02lp1 (0.36 #131, 0.35 #371, 0.35 #491), 05qfh (0.31 #515, 0.31 #635, 0.31 #155), 04x_3 (0.28 #506, 0.28 #146, 0.27 #626), 01540 (0.28 #540, 0.27 #660, 0.26 #180), 0fdys (0.25 #638, 0.25 #518, 0.22 #158), 06ms6 (0.25 #497, 0.24 #617, 0.22 #137), 0g26h (0.25 #281, 0.24 #2083, 0.23 #3764) >> Best rule #3257 for best value: >> intensional similarity = 5 >> extensional distance = 308 >> proper extension: 01c0cc; 01jssp; 0ym8f; 01k7xz; 0j_sncb; 0373qg; 01pq4w; 0kw4j; 0ymdn; 01ymvk; ... >> query: (?x3178, 01mkq) <- major_field_of_study(?x3178, ?x742), major_field_of_study(?x6315, ?x742), major_field_of_study(?x4672, ?x742), ?x4672 = 07tds, ?x6315 = 08qnnv >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #3262 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 308 *> proper extension: 01c0cc; 01jssp; 0ym8f; 01k7xz; 0j_sncb; 0373qg; 01pq4w; 0kw4j; 0ymdn; 01ymvk; ... *> query: (?x3178, 04rjg) <- major_field_of_study(?x3178, ?x742), major_field_of_study(?x6315, ?x742), major_field_of_study(?x4672, ?x742), ?x4672 = 07tds, ?x6315 = 08qnnv *> conf = 0.39 ranks of expected_values: 2 EVAL 01vc5m major_field_of_study 04rjg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 126.000 0.442 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #7008-0jhd PRED entity: 0jhd PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.88 #44, 0.87 #56, 0.87 #45) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 05rznz; >> query: (?x8588, 0hzc9wc) <- administrative_parent(?x8588, ?x551), form_of_government(?x8588, ?x48), organization(?x8588, ?x127) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jhd administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.879 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #7007-05f4p PRED entity: 05f4p PRED relation: company! PRED expected values: 0hfml => 96 concepts (56 used for prediction) PRED predicted values (max 10 best out of 75): 028rk (0.47 #9373, 0.47 #8632, 0.47 #8633), 0d3k14 (0.47 #8632, 0.47 #8633, 0.46 #12335), 03gkn5 (0.33 #62, 0.25 #5235, 0.25 #1540), 034ls (0.33 #150, 0.25 #1628, 0.25 #1136), 0343h (0.27 #7172, 0.27 #6677, 0.20 #8410), 02sdx (0.23 #11842, 0.19 #11841, 0.05 #11808), 04z0g (0.23 #11842, 0.19 #11841, 0.05 #11709), 01dvtx (0.23 #11842, 0.19 #11841, 0.05 #11667), 0d_w7 (0.23 #11842, 0.19 #11841), 0969fd (0.23 #11842, 0.19 #11841) >> Best rule #9373 for best value: >> intensional similarity = 9 >> extensional distance = 15 >> proper extension: 0gh4g0; 03d96s; 0n85g; 01f9wm; 03vtfp; 0gy1_; >> query: (?x11089, ?x2663) <- organizations_founded(?x2663, ?x11089), profession(?x2663, ?x8290), religion(?x2663, ?x2591), company(?x2663, ?x94), profession(?x11543, ?x8290), profession(?x10855, ?x8290), specialization_of(?x14341, ?x8290), ?x10855 = 05xd_v, ?x11543 = 01pj3h >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05f4p company! 0hfml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 56.000 0.471 http://example.org/people/person/employment_history./business/employment_tenure/company #7006-0ds3t5x PRED entity: 0ds3t5x PRED relation: film_release_region PRED expected values: 0154j 047yc => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 79): 0154j (0.77 #1385, 0.76 #1247, 0.71 #418), 06qd3 (0.60 #443, 0.50 #1410, 0.47 #1272), 0j1z8 (0.59 #5532, 0.50 #415, 0.45 #830), 05v8c (0.57 #426, 0.57 #1393, 0.54 #1255), 0ctw_b (0.54 #1400, 0.53 #1262, 0.51 #433), 0h7x (0.50 #440, 0.37 #1407, 0.34 #1269), 06f32 (0.48 #1432, 0.47 #1294, 0.42 #465), 047yc (0.48 #1264, 0.46 #1402, 0.44 #435), 06t8v (0.42 #1305, 0.41 #1443, 0.41 #476), 06c1y (0.38 #448, 0.37 #1277, 0.33 #1415) >> Best rule #1385 for best value: >> intensional similarity = 4 >> extensional distance = 203 >> proper extension: 053tj7; 0hz6mv2; >> query: (?x385, 0154j) <- film_release_region(?x385, ?x7747), film_release_region(?x385, ?x2267), ?x2267 = 03rj0, country(?x6054, ?x7747) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 0ds3t5x film_release_region 047yc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 77.000 0.771 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds3t5x film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.771 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #7005-029_l PRED entity: 029_l PRED relation: award_nominee! PRED expected values: 015pkc => 68 concepts (26 used for prediction) PRED predicted values (max 10 best out of 611): 015pkc (0.82 #4661, 0.81 #4660, 0.81 #11653), 01w7nww (0.82 #4661, 0.81 #4660, 0.81 #11652), 0k269 (0.25 #809, 0.16 #60586, 0.15 #53595), 0gpprt (0.25 #1915, 0.01 #11237), 08jfkw (0.25 #1752), 04jspq (0.25 #1525), 04bdzg (0.25 #1442), 05_k56 (0.25 #212), 025h4z (0.25 #74), 039bp (0.24 #13986, 0.19 #13985, 0.02 #2554) >> Best rule #4661 for best value: >> intensional similarity = 3 >> extensional distance = 264 >> proper extension: 0m2l9; 02l840; 03f5spx; 06b0d2; 016kjs; 01wbgdv; 0136g9; 01wcp_g; 04mz10g; 0p_2r; ... >> query: (?x5377, ?x1733) <- award_nominee(?x5377, ?x1733), location(?x5377, ?x1658), diet(?x1733, ?x3130) >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 029_l award_nominee! 015pkc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 26.000 0.823 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7004-026qnh6 PRED entity: 026qnh6 PRED relation: genre PRED expected values: 0hfjk => 100 concepts (85 used for prediction) PRED predicted values (max 10 best out of 115): 060__y (0.62 #15, 0.22 #4209, 0.20 #2456), 02kdv5l (0.52 #117, 0.48 #2094, 0.38 #1), 01jfsb (0.39 #243, 0.33 #4321, 0.31 #5722), 05p553 (0.38 #351, 0.38 #2795, 0.37 #2444), 01hmnh (0.38 #16, 0.33 #2109, 0.27 #132), 06n90 (0.30 #128, 0.25 #244, 0.25 #2105), 04xvh5 (0.25 #31, 0.12 #4225, 0.10 #2472), 02xlf (0.25 #49, 0.07 #165, 0.04 #861), 02p0szs (0.25 #26, 0.05 #2119, 0.04 #4220), 06cvj (0.23 #2443, 0.09 #931, 0.09 #1047) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 017gl1; 017gm7; 017jd9; >> query: (?x4810, 060__y) <- film(?x574, ?x4810), nominated_for(?x507, ?x4810), film(?x5282, ?x4810), ?x5282 = 02ck7w >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2152 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 334 *> proper extension: 02vw1w2; *> query: (?x4810, 0hfjk) <- language(?x4810, ?x254), genre(?x4810, ?x811), ?x811 = 03k9fj *> conf = 0.05 ranks of expected_values: 37 EVAL 026qnh6 genre 0hfjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 100.000 85.000 0.625 http://example.org/film/film/genre #7003-0336mc PRED entity: 0336mc PRED relation: film PRED expected values: 0bmch_x => 112 concepts (67 used for prediction) PRED predicted values (max 10 best out of 814): 02z3r8t (0.20 #1896, 0.04 #10831, 0.03 #7257), 017jd9 (0.20 #2566, 0.03 #43669, 0.03 #63328), 017gl1 (0.20 #1931, 0.03 #43034, 0.02 #7292), 03bx2lk (0.10 #1973, 0.04 #7334, 0.04 #16269), 06z8s_ (0.10 #1918, 0.03 #14427, 0.03 #103655), 0cd2vh9 (0.10 #2040, 0.03 #103655, 0.03 #67912), 0crc2cp (0.10 #2307, 0.03 #103655, 0.03 #67912), 0gd0c7x (0.10 #2103, 0.03 #103655, 0.03 #67912), 02p76f9 (0.10 #3214, 0.03 #103655, 0.02 #8575), 09xbpt (0.10 #1835, 0.03 #103655, 0.02 #7196) >> Best rule #1896 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02xs5v; 0356dp; >> query: (?x8764, 02z3r8t) <- award_nominee(?x8764, ?x815), film(?x8764, ?x3979), ?x3979 = 01vw8k >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0336mc film 0bmch_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 67.000 0.200 http://example.org/film/actor/film./film/performance/film #7002-01x5fb PRED entity: 01x5fb PRED relation: contains! PRED expected values: 02j9z => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 146): 0cxgc (0.76 #23284, 0.72 #17909, 0.71 #20596), 09c7w0 (0.67 #16121, 0.65 #22391, 0.65 #17016), 07ssc (0.64 #11671, 0.55 #27763, 0.42 #25075), 06q1r (0.42 #25075, 0.01 #33487, 0.01 #34383), 05tbn (0.40 #223, 0.07 #1118, 0.06 #2909), 03_3d (0.22 #14325, 0.02 #2698, 0.02 #34044), 0d060g (0.20 #13, 0.09 #1803, 0.08 #2699), 068p2 (0.20 #271, 0.07 #1166, 0.04 #2061), 015jr (0.20 #413, 0.07 #1308, 0.02 #2203), 0dclg (0.20 #143, 0.02 #2829, 0.02 #3724) >> Best rule #23284 for best value: >> intensional similarity = 2 >> extensional distance = 471 >> proper extension: 02583l; 01nkcn; 049dk; 031n8c; 0ymc8; 02_2kg; 0143hl; 020923; 017y26; 09krm_; ... >> query: (?x13827, ?x11432) <- state_province_region(?x13827, ?x11432), contains(?x1310, ?x13827) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #5399 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 08815; 07tgn; 0dplh; 07tg4; 0p5wz; 01f1r4; 02zd460; 01tpvt; 01dyk8; 0jhjl; ... *> query: (?x13827, 02j9z) <- list(?x13827, ?x2197), major_field_of_study(?x13827, ?x6364), school_type(?x13827, ?x3092) *> conf = 0.05 ranks of expected_values: 28 EVAL 01x5fb contains! 02j9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 42.000 42.000 0.762 http://example.org/location/location/contains #7001-039bp PRED entity: 039bp PRED relation: award_nominee! PRED expected values: 0bq2g => 136 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1444): 02x7vq (0.81 #102336, 0.81 #120943, 0.81 #72102), 0bq2g (0.81 #102336, 0.81 #120943, 0.81 #72102), 0205dx (0.81 #102336, 0.81 #120943, 0.81 #72102), 030vnj (0.81 #102336, 0.81 #120943, 0.81 #72102), 01z7_f (0.81 #102336, 0.81 #120943, 0.81 #72102), 0bmh4 (0.25 #2864, 0.04 #32560, 0.02 #27909), 019_1h (0.25 #2528, 0.02 #27909, 0.02 #11830), 01wz01 (0.25 #3291), 0h1mt (0.25 #2546), 039bp (0.18 #62798, 0.16 #88379, 0.08 #125594) >> Best rule #102336 for best value: >> intensional similarity = 3 >> extensional distance = 1020 >> proper extension: 03ywyk; >> query: (?x1119, ?x71) <- film(?x1119, ?x7711), award_nominee(?x1119, ?x71), featured_film_locations(?x7711, ?x108) >> conf = 0.81 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 039bp award_nominee! 0bq2g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 57.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #7000-0l14_3 PRED entity: 0l14_3 PRED relation: family! PRED expected values: 07_l6 01xqw => 38 concepts (37 used for prediction) PRED predicted values (max 10 best out of 126): 018vs (0.50 #100, 0.33 #10, 0.25 #269), 01xqw (0.33 #63, 0.25 #322, 0.25 #153), 03m5k (0.33 #14, 0.09 #89, 0.09 #84), 07_l6 (0.33 #57, 0.09 #89, 0.09 #84), 0l14_3 (0.33 #79, 0.09 #89, 0.09 #84), 014zz1 (0.33 #71, 0.07 #83, 0.06 #1792), 0fx80y (0.33 #74, 0.07 #83, 0.05 #2038), 042v_gx (0.25 #174, 0.25 #96, 0.17 #515), 023r2x (0.25 #332, 0.25 #163, 0.14 #750), 07m2y (0.25 #337, 0.25 #168, 0.14 #755) >> Best rule #100 for best value: >> intensional similarity = 19 >> extensional distance = 2 >> proper extension: 0342h; 01vj9c; >> query: (?x9885, 018vs) <- family(?x75, ?x9885), role(?x2782, ?x9885), ?x2782 = 014q2g, role(?x3716, ?x75), role(?x745, ?x75), role(?x432, ?x75), role(?x315, ?x75), role(?x227, ?x75), ?x745 = 01vj9c, instrumentalists(?x75, ?x535), ?x3716 = 03gvt, role(?x75, ?x1472), role(?x2328, ?x75), role(?x75, ?x2158), ?x1472 = 0319l, ?x432 = 042v_gx, ?x315 = 0l14md, ?x227 = 0342h, group(?x75, ?x1751) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #63 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 0d8lm; *> query: (?x9885, 01xqw) <- family(?x2888, ?x9885), role(?x1574, ?x9885), performance_role(?x9885, ?x315), ?x315 = 0l14md, ?x2888 = 02fsn, role(?x1166, ?x1574), role(?x868, ?x1574), role(?x228, ?x1574), ?x868 = 0dwvl, role(?x228, ?x74), instrumentalists(?x228, ?x140), performance_role(?x130, ?x228), role(?x642, ?x228), role(?x1432, ?x228), role(?x1260, ?x1574) *> conf = 0.33 ranks of expected_values: 2, 4 EVAL 0l14_3 family! 01xqw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 38.000 37.000 0.500 http://example.org/music/instrument/family EVAL 0l14_3 family! 07_l6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 38.000 37.000 0.500 http://example.org/music/instrument/family #6999-029j_ PRED entity: 029j_ PRED relation: film_release_distribution_medium! PRED expected values: 02vxq9m 0b2v79 0c3ybss 0fq27fp 0fr63l 035xwd 02sg5v 0fh694 02qm_f 053rxgm 02v63m 01f7gh 02rv_dz 02q52q 02rx2m5 0ch26b_ 047qxs 09g8vhw 0260bz 02c638 0bx0l 0f4_l 0fvr1 08052t3 048htn 03h3x5 0879bpq 04q24zv 0cw3yd 0b1y_2 07024 0407yj_ 0ggbhy7 0j43swk 01dvbd 0htww 0gyfp9c 0jvt9 0jswp 0ctb4g 0dgpwnk 017z49 09lcsj 011yl_ 01sxdy 024lff 0blpg 0n04r 0g9yrw 0kv9d3 0h6r5 07tw_b 02qzh2 05c5z8j 057lbk 06nr2h 0cq7tx 016y_f 0hgnl3t 0432_5 0jsqk 019kyn 013q0p 0dqcs3 09p4w8 02xs6_ 035w2k 0191n 043n0v_ 0dl9_4 05_5_22 0mb8c 012s1d 0fjyzt 02tjl3 0gbfn9 049xgc 0h21v2 03mgx6z 02w9k1c 011ypx 02q3fdr 05r3qc 0sxns 02ph9tm 02c7k4 0kvbl6 01738w 06fqlk 02bg55 0h63gl9 07nxnw 02scbv 08cfr1 02825kb 02r9p0c 0dd6bf 06rzwx 02_06s 03clwtw 0q9b0 07xvf 026fs38 02fj8n 07bx6 01k0vq 02825nf 0jqkh 0gpx6 01hq1 08zrbl 015bpl 04f6df0 0296vv 0bwhdbl 06_sc3 011xg5 011ywj 07ghq 0ds2l81 04jpg2p 04cf_l 0f61tk 057__d 03b1sb 0bz6sq 02bj22 09y6pb 032sl_ 0g5qmbz 03whyr 0hz6mv2 07l450 058kh7 09p5mwg 0fh2v5 03hp2y1 0kb1g 065ym0c 09qycb 02x0fs9 0gyv0b4 0gldyz 09fqgj 0kvf3b 01gvsn 05sbv3 02q_ncg 04jn6y7 => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 391): 0mb8c (0.33 #52, 0.33 #10, 0.20 #103), 0kvbl6 (0.33 #33, 0.31 #40, 0.20 #106), 05ypj5 (0.33 #61, 0.20 #112, 0.02 #115), 0k4fz (0.33 #51, 0.20 #102, 0.02 #115), 02fttd (0.33 #50, 0.20 #101, 0.02 #115), 0htww (0.33 #48, 0.20 #99, 0.02 #115), 0h1v19 (0.33 #47, 0.20 #98, 0.02 #115), 040rmy (0.33 #46, 0.20 #97, 0.02 #115), 05z_kps (0.33 #44, 0.20 #95, 0.02 #115), 03hjv97 (0.33 #43, 0.20 #94, 0.02 #115) >> Best rule #52 for best value: >> intensional similarity = 48 >> extensional distance = 1 >> proper extension: 07c52; >> query: (?x81, 0mb8c) <- film_release_distribution_medium(?x9533, ?x81), film_release_distribution_medium(?x9452, ?x81), film_release_distribution_medium(?x8580, ?x81), film_release_distribution_medium(?x5519, ?x81), film_release_distribution_medium(?x5372, ?x81), film_release_distribution_medium(?x5243, ?x81), film_release_distribution_medium(?x3820, ?x81), film_release_distribution_medium(?x3455, ?x81), film_release_distribution_medium(?x2954, ?x81), film_release_distribution_medium(?x2943, ?x81), film_release_distribution_medium(?x2218, ?x81), film_release_distribution_medium(?x1728, ?x81), film_release_distribution_medium(?x903, ?x81), film_release_distribution_medium(?x675, ?x81), film_release_distribution_medium(?x557, ?x81), film_release_distribution_medium(?x308, ?x81), film(?x4360, ?x2218), film(?x2070, ?x2218), titles(?x1316, ?x675), honored_for(?x1747, ?x2943), language(?x5519, ?x254), film_release_region(?x8580, ?x1892), titles(?x162, ?x5519), currency(?x1728, ?x170), film_crew_role(?x3455, ?x281), film(?x902, ?x2943), ?x2954 = 0crh5_f, film(?x2033, ?x3455), nominated_for(?x157, ?x9533), film(?x4832, ?x5243), award_winner(?x308, ?x574), nominated_for(?x102, ?x3820), film(?x804, ?x308), film(?x643, ?x5243), award(?x2943, ?x112), production_companies(?x557, ?x10629), ?x1892 = 02vzc, ?x903 = 04969y, genre(?x2943, ?x3515), cinematography(?x3820, ?x10704), participant(?x2416, ?x2070), ?x4360 = 0f502, country(?x5372, ?x94), film(?x400, ?x5372), films(?x1967, ?x675), genre(?x5243, ?x307), music(?x9452, ?x6783), film_distribution_medium(?x97, ?x81) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6, 14, 15, 16, 19, 20, 21, 29, 31, 35, 36, 39, 41, 43, 44, 48, 49, 57, 58, 59, 64, 68, 71, 72, 76, 77, 78, 81, 83, 84, 85, 88, 89, 91, 94, 99, 102, 105, 107, 108, 109, 110, 112, 114, 118, 119, 121, 125, 127, 130, 134, 136, 143, 144, 147, 151, 153, 156, 159, 160, 163, 168, 169, 173, 176, 177, 180, 181, 182, 185, 191, 195, 197, 199, 205, 207, 208, 209, 210, 211, 212, 221, 222, 224, 226, 227, 228, 229, 235, 237, 240, 241, 243, 246, 247, 255, 258, 266, 268, 270, 272, 277, 280, 283, 284, 287, 290, 292, 296, 305, 316, 317, 318, 320, 321, 327, 336, 337, 338, 339, 340, 345, 347, 350, 352, 355, 357, 358, 360, 365, 368, 371, 372, 379, 383, 385, 387, 390, 391 EVAL 029j_ film_release_distribution_medium! 04jn6y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02q_ncg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 05sbv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 01gvsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0kvf3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 09fqgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0gldyz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0gyv0b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02x0fs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 09qycb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 065ym0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0kb1g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 03hp2y1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0fh2v5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 09p5mwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 058kh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 07l450 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0hz6mv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 03whyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0g5qmbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 032sl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 09y6pb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02bj22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0bz6sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 03b1sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 057__d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0f61tk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 04cf_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 04jpg2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0ds2l81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 07ghq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 011ywj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 011xg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 06_sc3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0bwhdbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0296vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 04f6df0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 015bpl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 08zrbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 01hq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0gpx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0jqkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 01k0vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 07bx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02fj8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 026fs38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 07xvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0q9b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 03clwtw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02_06s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 06rzwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0dd6bf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02r9p0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02825kb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 08cfr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02scbv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 07nxnw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0h63gl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02bg55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 06fqlk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 01738w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0kvbl6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02c7k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02ph9tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0sxns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 05r3qc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02q3fdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 011ypx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02w9k1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 03mgx6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0h21v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 049xgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0gbfn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02tjl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0fjyzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 012s1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0mb8c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 05_5_22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0dl9_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 043n0v_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0191n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 035w2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02xs6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 09p4w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0dqcs3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 013q0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 019kyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0jsqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0432_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0hgnl3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 016y_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0cq7tx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 06nr2h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 057lbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 05c5z8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02qzh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 07tw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0h6r5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0kv9d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0g9yrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0n04r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0blpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 024lff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 01sxdy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 011yl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 09lcsj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 017z49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0dgpwnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0ctb4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0jswp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0jvt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0gyfp9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0htww CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 01dvbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0j43swk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0ggbhy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0407yj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 07024 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0b1y_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0cw3yd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 04q24zv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0879bpq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 048htn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 08052t3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0fvr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0f4_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0bx0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02c638 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0260bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 09g8vhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 047qxs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0ch26b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02rx2m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02q52q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02rv_dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 01f7gh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02v63m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 053rxgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02qm_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0fh694 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02sg5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 035xwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0fr63l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0fq27fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0c3ybss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 0b2v79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium EVAL 029j_ film_release_distribution_medium! 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 6.000 6.000 0.333 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6998-0f4x7 PRED entity: 0f4x7 PRED relation: award_winner PRED expected values: 015qt5 => 49 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1883): 01ycbq (0.43 #12507, 0.40 #17349, 0.31 #7665), 016gr2 (0.43 #5069, 0.21 #12333, 0.20 #17175), 03pp73 (0.43 #5976, 0.15 #8398, 0.14 #13240), 026rm_y (0.36 #13931, 0.33 #18773, 0.23 #9089), 0js9s (0.36 #11109, 0.13 #15952, 0.09 #20793), 0zcbl (0.31 #8777, 0.29 #13619, 0.27 #18461), 016ggh (0.31 #9497, 0.21 #14339, 0.20 #19181), 015grj (0.29 #55687, 0.29 #31475, 0.29 #31474), 01713c (0.29 #55687, 0.29 #31475, 0.29 #31474), 01kwsg (0.29 #55687, 0.29 #31475, 0.29 #31474) >> Best rule #12507 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0bs0bh; >> query: (?x591, 01ycbq) <- award(?x9886, ?x591), award(?x968, ?x591), ?x968 = 015grj, participant(?x9886, ?x950) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #31475 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 121 *> proper extension: 06196; 0fqnzts; *> query: (?x591, ?x2670) <- award(?x2670, ?x591), award(?x69, ?x591), ceremony(?x591, ?x78) *> conf = 0.29 ranks of expected_values: 70 EVAL 0f4x7 award_winner 015qt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 49.000 23.000 0.429 http://example.org/award/award_category/winners./award/award_honor/award_winner #6997-07kdkfj PRED entity: 07kdkfj PRED relation: language PRED expected values: 02bjrlw => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 56): 064_8sq (0.16 #705, 0.14 #306, 0.14 #1512), 06nm1 (0.11 #694, 0.10 #637, 0.10 #67), 04306rv (0.10 #175, 0.09 #688, 0.09 #803), 02bjrlw (0.07 #172, 0.07 #800, 0.07 #685), 06b_j (0.07 #193, 0.07 #706, 0.07 #478), 0653m (0.06 #125, 0.04 #695, 0.04 #638), 03_9r (0.05 #636, 0.05 #3165, 0.04 #4203), 0jzc (0.04 #646, 0.04 #361, 0.03 #992), 012w70 (0.04 #126, 0.03 #411, 0.03 #754), 04h9h (0.03 #898, 0.03 #611, 0.03 #155) >> Best rule #705 for best value: >> intensional similarity = 4 >> extensional distance = 403 >> proper extension: 0d90m; 04kkz8; 0_b3d; 02v63m; 032_wv; 02rx2m5; 02c638; 0f4_l; 085ccd; 08052t3; ... >> query: (?x7722, 064_8sq) <- film_crew_role(?x7722, ?x137), language(?x7722, ?x254), currency(?x7722, ?x170), featured_film_locations(?x7722, ?x739) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #172 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 207 *> proper extension: 0c5qvw; *> query: (?x7722, 02bjrlw) <- genre(?x7722, ?x53), cinematography(?x7722, ?x6062), ?x53 = 07s9rl0 *> conf = 0.07 ranks of expected_values: 4 EVAL 07kdkfj language 02bjrlw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 75.000 0.158 http://example.org/film/film/language #6996-0fy34l PRED entity: 0fy34l PRED relation: currency PRED expected values: 09nqf => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.89 #15, 0.84 #78, 0.83 #64), 01nv4h (0.11 #491, 0.06 #86, 0.04 #198), 088n7 (0.11 #491, 0.03 #42), 02l6h (0.11 #491, 0.02 #221, 0.02 #200), 02gsvk (0.11 #491, 0.02 #202, 0.01 #223), 0kz1h (0.11 #491), 0ptk_ (0.11 #491) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01jw67; 0h1x5f; >> query: (?x1948, 09nqf) <- nominated_for(?x1033, ?x1948), genre(?x1948, ?x53), nominated_for(?x5976, ?x1948), ?x1033 = 02x73k6 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fy34l currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.889 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #6995-01hvv0 PRED entity: 01hvv0 PRED relation: program! PRED expected values: 025snf => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 60): 0cjdk (0.33 #61, 0.32 #117, 0.21 #342), 0b275x (0.22 #19, 0.06 #976, 0.06 #638), 03mdt (0.20 #738, 0.16 #795, 0.12 #1416), 05gnf (0.20 #2555, 0.20 #576, 0.20 #1993), 0gsg7 (0.20 #507, 0.19 #1240, 0.18 #1981), 02hmvw (0.17 #211, 0.12 #379, 0.11 #42), 09d5h (0.15 #791, 0.12 #1241, 0.12 #2882), 0187wh (0.15 #531, 0.14 #82, 0.14 #138), 03lpbx (0.14 #88, 0.14 #425, 0.09 #369), 0ljc_ (0.14 #84, 0.12 #365, 0.11 #421) >> Best rule #61 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 0cwrr; 03y3bp7; 06r1k; >> query: (?x8017, 0cjdk) <- languages(?x8017, ?x254), actor(?x8017, ?x2594), genre(?x8017, ?x2540), ?x2540 = 0hcr, program(?x12154, ?x8017) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #204 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 22 *> proper extension: 088tp3; *> query: (?x8017, 025snf) <- genre(?x8017, ?x2700), genre(?x8017, ?x1510), ?x1510 = 01hmnh, genre(?x1395, ?x2700), ?x1395 = 019nnl, genre(?x240, ?x2700) *> conf = 0.12 ranks of expected_values: 11 EVAL 01hvv0 program! 025snf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 91.000 91.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #6994-07bx6 PRED entity: 07bx6 PRED relation: film_crew_role PRED expected values: 01pvkk => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 29): 01vx2h (0.38 #208, 0.38 #42, 0.36 #242), 01pvkk (0.31 #475, 0.30 #209, 0.30 #843), 02rh1dz (0.19 #207, 0.18 #241, 0.14 #41), 02ynfr (0.18 #47, 0.18 #479, 0.18 #213), 0215hd (0.16 #50, 0.14 #482, 0.14 #449), 089g0h (0.15 #51, 0.12 #483, 0.12 #217), 01xy5l_ (0.15 #45, 0.11 #477, 0.11 #312), 02_n3z (0.12 #34, 0.09 #466, 0.09 #400), 015h31 (0.11 #206, 0.11 #240, 0.09 #40), 04pyp5 (0.10 #15, 0.09 #3036, 0.08 #82) >> Best rule #208 for best value: >> intensional similarity = 3 >> extensional distance = 306 >> proper extension: 0dtw1x; 0fq27fp; >> query: (?x7482, 01vx2h) <- genre(?x7482, ?x225), crewmember(?x7482, ?x666), film_crew_role(?x7482, ?x137) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #475 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 374 *> proper extension: 0bs8hvm; *> query: (?x7482, 01pvkk) <- film(?x11876, ?x7482), film_crew_role(?x7482, ?x1284), ?x1284 = 0ch6mp2 *> conf = 0.31 ranks of expected_values: 2 EVAL 07bx6 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 92.000 0.380 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6993-0l6mp PRED entity: 0l6mp PRED relation: sports PRED expected values: 0w0d 03hr1p => 26 concepts (26 used for prediction) PRED predicted values (max 10 best out of 37): 06f41 (0.87 #221, 0.86 #184, 0.85 #481), 01hp22 (0.87 #221, 0.86 #184, 0.85 #481), 0486tv (0.87 #221, 0.86 #184, 0.85 #481), 03_8r (0.87 #221, 0.86 #184, 0.85 #481), 0w0d (0.87 #221, 0.86 #184, 0.85 #481), 07jbh (0.87 #221, 0.86 #184, 0.85 #481), 01sgl (0.78 #295, 0.75 #483, 0.74 #260), 02y74 (0.78 #295, 0.75 #483, 0.74 #260), 03hr1p (0.67 #461, 0.59 #294, 0.57 #112), 018jz (0.67 #466, 0.53 #149, 0.52 #484) >> Best rule #221 for best value: >> intensional similarity = 69 >> extensional distance = 2 >> proper extension: 0l998; >> query: (?x2233, ?x171) <- olympics(?x390, ?x2233), olympics(?x151, ?x2233), sports(?x2233, ?x6150), sports(?x2233, ?x3641), sports(?x2233, ?x171), olympics(?x359, ?x2233), olympics(?x8558, ?x2233), olympics(?x1917, ?x2233), olympics(?x1355, ?x2233), olympics(?x583, ?x2233), ?x1355 = 0h7x, ?x151 = 0b90_r, ?x6150 = 07_53, adjoins(?x8558, ?x3635), adjoins(?x8558, ?x1756), time_zones(?x8558, ?x6582), film_release_region(?x10860, ?x1917), film_release_region(?x9652, ?x1917), film_release_region(?x5089, ?x1917), film_release_region(?x4607, ?x1917), film_release_region(?x4352, ?x1917), film_release_region(?x1370, ?x1917), film_release_region(?x1219, ?x1917), film_release_region(?x908, ?x1917), film_release_region(?x141, ?x1917), ?x908 = 01vksx, ?x1756 = 02khs, olympics(?x3635, ?x784), medal(?x8558, ?x422), ?x3641 = 03fyrh, ?x141 = 0gtsx8c, ?x1219 = 03bx2lk, nationality(?x10179, ?x390), nationality(?x7327, ?x390), nationality(?x844, ?x390), ?x1370 = 0gmcwlb, ?x4352 = 09v71cj, film_release_region(?x7379, ?x390), film_release_region(?x7170, ?x390), film_release_region(?x5849, ?x390), film_release_region(?x5070, ?x390), film_release_region(?x4446, ?x390), film_release_region(?x3988, ?x390), film_release_region(?x3886, ?x390), film_release_region(?x3524, ?x390), film_release_region(?x1988, ?x390), ?x10860 = 049w1q, ?x9652 = 0ddbjy4, ?x5089 = 0bh8tgs, geographic_distribution(?x1571, ?x390), combatants(?x613, ?x390), team(?x10179, ?x59), ?x3988 = 0blpg, ?x5849 = 02h22, cinematography(?x83, ?x7327), adjoins(?x1917, ?x142), ?x4446 = 0db94w, ?x7170 = 02pxst, ?x1988 = 09k56b7, ?x583 = 015fr, ?x3886 = 0198b6, ?x3524 = 06r2_, country(?x150, ?x390), ?x4607 = 0h03fhx, ?x613 = 0bq0p9, ?x5070 = 0dt8xq, taxonomy(?x1917, ?x939), award_nominee(?x844, ?x628), ?x7379 = 032clf >> conf = 0.87 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 9 EVAL 0l6mp sports 03hr1p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 26.000 26.000 0.870 http://example.org/olympics/olympic_games/sports EVAL 0l6mp sports 0w0d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 26.000 26.000 0.870 http://example.org/olympics/olympic_games/sports #6992-01pny5 PRED entity: 01pny5 PRED relation: profession PRED expected values: 0nbcg => 102 concepts (60 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.81 #4283, 0.73 #8128, 0.67 #8571), 0nbcg (0.68 #472, 0.68 #913, 0.67 #1354), 016z4k (0.56 #1326, 0.53 #2061, 0.53 #1914), 01c72t (0.37 #3849, 0.35 #1199, 0.35 #2670), 0n1h (0.31 #305, 0.27 #1334, 0.26 #11), 0fnpj (0.25 #353, 0.22 #1529, 0.18 #2706), 01d_h8 (0.22 #8562, 0.22 #7380, 0.20 #6196), 0dxtg (0.18 #7388, 0.17 #3248, 0.16 #6204), 0cbd2 (0.15 #3241, 0.10 #7381, 0.10 #6197), 02jknp (0.13 #3242, 0.11 #7382, 0.10 #4276) >> Best rule #4283 for best value: >> intensional similarity = 7 >> extensional distance = 423 >> proper extension: 0184jc; 06dv3; 0z4s; 04bs3j; 018db8; 015grj; 01sxq9; 0blbxk; 0bg539; 0fsm8c; ... >> query: (?x12791, 02hrh1q) <- profession(?x12791, ?x2659), profession(?x12791, ?x1183), ?x1183 = 09jwl, profession(?x6461, ?x2659), profession(?x3401, ?x2659), ?x6461 = 01t110, ?x3401 = 01wz_ml >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #472 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 55 *> proper extension: 01cv3n; 01gf5h; 02whj; 0l12d; 0144l1; 01tp5bj; 01vsl3_; 01w02sy; 01m65sp; 03bnv; ... *> query: (?x12791, 0nbcg) <- profession(?x12791, ?x2659), artists(?x1000, ?x12791), ?x2659 = 039v1, group(?x12791, ?x10670), category(?x12791, ?x134) *> conf = 0.68 ranks of expected_values: 2 EVAL 01pny5 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 60.000 0.807 http://example.org/people/person/profession #6991-01m3x5p PRED entity: 01m3x5p PRED relation: type_of_union PRED expected values: 04ztj => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.77 #13, 0.75 #161, 0.75 #21), 01g63y (0.15 #22, 0.13 #246, 0.13 #18), 01bl8s (0.01 #19) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 01z7s_; 01pllx; >> query: (?x4184, 04ztj) <- profession(?x4184, ?x319), award_nominee(?x4184, ?x5172), sibling(?x1583, ?x4184) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m3x5p type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.772 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6990-07h34 PRED entity: 07h34 PRED relation: origin! PRED expected values: 03g5jw => 164 concepts (151 used for prediction) PRED predicted values (max 10 best out of 364): 06nv27 (0.25 #219, 0.06 #1769, 0.04 #4870), 0cbm64 (0.25 #411, 0.06 #1961, 0.04 #5062), 01wv9p (0.25 #169, 0.03 #1719, 0.03 #2236), 03j0br4 (0.25 #90, 0.03 #1640, 0.03 #2157), 015bwt (0.25 #476, 0.03 #2026, 0.03 #2543), 0837ql (0.25 #203, 0.03 #1753, 0.03 #2270), 0d193h (0.25 #173, 0.03 #1723, 0.03 #2240), 0153nq (0.25 #516, 0.03 #2066, 0.03 #2583), 01tpl1p (0.25 #457, 0.03 #2007, 0.03 #2524), 01jgkj2 (0.25 #407, 0.03 #1957, 0.03 #2474) >> Best rule #219 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0mtdx; >> query: (?x3778, 06nv27) <- contains(?x3778, ?x4978), contains(?x3778, ?x2004), ?x2004 = 0pzpz, location(?x105, ?x4978) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07h34 origin! 03g5jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 164.000 151.000 0.250 http://example.org/music/artist/origin #6989-0clvcx PRED entity: 0clvcx PRED relation: film PRED expected values: 02wk7b => 86 concepts (61 used for prediction) PRED predicted values (max 10 best out of 276): 0ddd0gc (0.40 #82473, 0.39 #39443, 0.38 #57371), 0194zl (0.17 #847, 0.10 #46615, 0.04 #71716), 0407yfx (0.11 #2137, 0.01 #7515), 0h3xztt (0.10 #46615, 0.08 #172, 0.04 #71716), 01f69m (0.10 #46615, 0.08 #1739, 0.04 #71716), 06gjk9 (0.10 #46615, 0.08 #538, 0.04 #2330), 03cwwl (0.10 #46615, 0.08 #1614, 0.04 #3406), 06_sc3 (0.10 #46615, 0.08 #1423, 0.04 #3215), 04f6df0 (0.10 #46615, 0.08 #1397, 0.04 #3189), 01_0f7 (0.10 #46615, 0.08 #1157, 0.04 #2949) >> Best rule #82473 for best value: >> intensional similarity = 3 >> extensional distance = 1432 >> proper extension: 02rgz4; 02zrv7; 01gc7h; >> query: (?x1435, ?x1434) <- nationality(?x1435, ?x6401), type_of_union(?x1435, ?x566), nominated_for(?x1435, ?x1434) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0clvcx film 02wk7b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 61.000 0.401 http://example.org/film/actor/film./film/performance/film #6988-02xry PRED entity: 02xry PRED relation: location! PRED expected values: 0738b8 048tgl 01mskc3 => 154 concepts (131 used for prediction) PRED predicted values (max 10 best out of 2186): 01797x (0.33 #2078, 0.25 #9575, 0.25 #4577), 032r1 (0.33 #2297, 0.25 #4796, 0.05 #77278), 04z0g (0.33 #1174, 0.25 #3673, 0.04 #11171), 099d4 (0.33 #2346, 0.25 #4845, 0.04 #39839), 0b78hw (0.33 #846, 0.25 #3345, 0.03 #15843), 01zwy (0.33 #1710, 0.25 #4209, 0.03 #16707), 03f1zdw (0.33 #206, 0.25 #2705, 0.03 #15203), 02756j (0.33 #1274, 0.25 #3773, 0.03 #16271), 03_2y (0.33 #2048, 0.25 #4547, 0.03 #17045), 099p5 (0.33 #1885, 0.25 #4384, 0.03 #16882) >> Best rule #2078 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09c7w0; >> query: (?x2623, 01797x) <- contains(?x2623, ?x9598), location(?x91, ?x2623), ?x9598 = 0rrwt >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10438 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 078lk; *> query: (?x2623, 0738b8) <- contains(?x2623, ?x11164), vacationer(?x2623, ?x5625), second_level_divisions(?x94, ?x11164) *> conf = 0.08 ranks of expected_values: 363, 405, 1307 EVAL 02xry location! 01mskc3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 154.000 131.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02xry location! 048tgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 154.000 131.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02xry location! 0738b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 154.000 131.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #6987-0900j5 PRED entity: 0900j5 PRED relation: language PRED expected values: 02h40lc => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.90 #833, 0.90 #1071, 0.89 #1012), 064_8sq (0.16 #200, 0.14 #972, 0.14 #496), 06nm1 (0.14 #189, 0.13 #1080, 0.13 #1139), 06b_j (0.10 #201, 0.09 #1151, 0.08 #1092), 04306rv (0.10 #836, 0.09 #1074, 0.09 #955), 02bjrlw (0.07 #951, 0.06 #1488, 0.06 #1070), 03_9r (0.06 #950, 0.06 #10, 0.06 #2988), 012w70 (0.06 #950, 0.06 #13, 0.03 #1500), 0653m (0.06 #950, 0.05 #190, 0.04 #962), 04h9h (0.06 #950, 0.03 #517, 0.03 #457) >> Best rule #833 for best value: >> intensional similarity = 4 >> extensional distance = 332 >> proper extension: 0c3ybss; 0dnvn3; 0c40vxk; 0401sg; 02sg5v; 04gknr; 07sc6nw; 02qrv7; 0bh8yn3; 05cj_j; ... >> query: (?x3588, 02h40lc) <- currency(?x3588, ?x170), genre(?x3588, ?x812), ?x812 = 01jfsb, film(?x574, ?x3588) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0900j5 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.901 http://example.org/film/film/language #6986-048htn PRED entity: 048htn PRED relation: film_crew_role PRED expected values: 02r96rf => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 25): 02r96rf (0.69 #112, 0.67 #1794, 0.64 #1462), 0dxtw (0.38 #119, 0.38 #1801, 0.35 #1469), 01vx2h (0.38 #120, 0.36 #84, 0.31 #1802), 01pvkk (0.38 #121, 0.28 #1803, 0.28 #703), 02ynfr (0.20 #125, 0.17 #1807, 0.16 #633), 02rh1dz (0.16 #191, 0.14 #45, 0.12 #154), 0215hd (0.14 #1810, 0.13 #347, 0.12 #128), 01xy5l_ (0.12 #123, 0.11 #342, 0.11 #87), 02_n3z (0.12 #110, 0.09 #875, 0.09 #329), 0d2b38 (0.11 #99, 0.10 #135, 0.10 #354) >> Best rule #112 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 0bs8hvm; >> query: (?x2571, 02r96rf) <- genre(?x2571, ?x53), country(?x2571, ?x1264), ?x1264 = 0345h, film_crew_role(?x2571, ?x137) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 048htn film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.691 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6985-04t6fk PRED entity: 04t6fk PRED relation: film! PRED expected values: 036c_0 01h4rj => 91 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1275): 08t7nz (0.46 #51979, 0.44 #101878, 0.41 #83169), 03ym1 (0.43 #3090, 0.12 #5169, 0.04 #68614), 015t56 (0.43 #2547, 0.04 #68614, 0.03 #42053), 0241jw (0.43 #2373, 0.04 #68614, 0.02 #21086), 0jfx1 (0.38 #4563, 0.06 #8722, 0.06 #6643), 0kszw (0.38 #4576, 0.06 #8735, 0.05 #23289), 016k6x (0.33 #890, 0.14 #2968, 0.06 #72773), 02yxwd (0.33 #743, 0.14 #2821, 0.05 #23613), 05sq84 (0.33 #235, 0.14 #2313, 0.03 #8551), 04sry (0.33 #1277, 0.14 #3355, 0.03 #32465) >> Best rule #51979 for best value: >> intensional similarity = 4 >> extensional distance = 237 >> proper extension: 0gtvrv3; >> query: (?x2699, ?x7758) <- film(?x1031, ?x2699), nominated_for(?x7758, ?x2699), story_by(?x2699, ?x2182), type_of_union(?x7758, ?x566) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #14134 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 01qvz8; 01zfzb; 016ky6; 0f2sx4; 0yx1m; *> query: (?x2699, 01h4rj) <- film(?x1031, ?x2699), titles(?x2480, ?x2699), cinematography(?x2699, ?x7758), ?x2480 = 01z4y, production_companies(?x2699, ?x541) *> conf = 0.03 ranks of expected_values: 588 EVAL 04t6fk film! 01h4rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 91.000 62.000 0.459 http://example.org/film/actor/film./film/performance/film EVAL 04t6fk film! 036c_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 62.000 0.459 http://example.org/film/actor/film./film/performance/film #6984-04mvk7 PRED entity: 04mvk7 PRED relation: colors PRED expected values: 019sc => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.86 #612, 0.79 #688, 0.76 #458), 019sc (0.54 #905, 0.48 #520, 0.40 #83), 01g5v (0.51 #421, 0.37 #747, 0.37 #307), 038hg (0.21 #717, 0.21 #69, 0.20 #12), 01l849 (0.21 #153, 0.21 #514, 0.19 #592), 04mkbj (0.20 #10, 0.15 #840, 0.15 #1208), 02rnmb (0.18 #222, 0.18 #533, 0.17 #165), 088fh (0.18 #533, 0.15 #840, 0.15 #1208), 06kqt3 (0.18 #533, 0.15 #840, 0.15 #1208), 0jc_p (0.18 #533, 0.15 #840, 0.15 #1208) >> Best rule #612 for best value: >> intensional similarity = 7 >> extensional distance = 125 >> proper extension: 020wyp; >> query: (?x10238, 083jv) <- colors(?x10238, ?x1101), teams(?x13220, ?x10238), sport(?x10238, ?x471), colors(?x13148, ?x1101), colors(?x12231, ?x1101), ?x12231 = 09cvbq, currency(?x13148, ?x170) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #905 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 185 *> proper extension: 026w398; *> query: (?x10238, 019sc) <- colors(?x10238, ?x1101), colors(?x6179, ?x1101), colors(?x700, ?x1101), colors(?x6675, ?x1101), institution(?x865, ?x6675), ?x6179 = 0cgwt8, season(?x700, ?x701) *> conf = 0.54 ranks of expected_values: 2 EVAL 04mvk7 colors 019sc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.858 http://example.org/sports/sports_team/colors #6983-0bq6ntw PRED entity: 0bq6ntw PRED relation: film_release_region PRED expected values: 0jgd 03gj2 047yc => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 117): 03gj2 (0.87 #534, 0.86 #663, 0.86 #792), 0jgd (0.85 #649, 0.82 #907, 0.81 #1424), 03spz (0.79 #589, 0.76 #847, 0.75 #976), 047yc (0.69 #537, 0.65 #924, 0.62 #666), 0ctw_b (0.66 #922, 0.65 #664, 0.65 #535), 06mzp (0.53 #788, 0.51 #530, 0.50 #1047), 06t8v (0.50 #958, 0.50 #700, 0.50 #571), 01pj7 (0.46 #551, 0.41 #938, 0.39 #809), 02k54 (0.44 #527, 0.33 #10, 0.31 #785), 077qn (0.42 #710, 0.40 #968, 0.33 #1356) >> Best rule #534 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 01mgw; >> query: (?x6095, 03gj2) <- film(?x56, ?x6095), film_release_region(?x6095, ?x1122), category(?x56, ?x134), ?x1122 = 09pmkv >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 0bq6ntw film_release_region 047yc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bq6ntw film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bq6ntw film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6982-0cg9f PRED entity: 0cg9f PRED relation: religion PRED expected values: 0631_ => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 27): 0c8wxp (0.22 #456, 0.22 #321, 0.20 #501), 0kpl (0.13 #776, 0.11 #911, 0.10 #1136), 06nzl (0.10 #285, 0.06 #240, 0.03 #555), 03_gx (0.08 #3395, 0.08 #3532, 0.08 #3486), 03j6c (0.06 #201, 0.03 #1282, 0.03 #606), 051kv (0.06 #185, 0.03 #320, 0.02 #500), 01spm (0.06 #217, 0.02 #803, 0.02 #938), 02rsw (0.06 #204, 0.01 #1240, 0.01 #519), 07x21 (0.06 #218, 0.01 #1254), 01t7j (0.06 #220) >> Best rule #456 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 01_ztw; 01d5vk; 01wk51; 02lyx4; 01nglk; >> query: (?x12584, 0c8wxp) <- spouse(?x12584, ?x2524), film(?x12584, ?x951), film_release_region(?x951, ?x142) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #323 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 67 *> proper extension: 014dq7; 06c97; 036jp8; *> query: (?x12584, 0631_) <- people(?x10900, ?x12584), profession(?x12584, ?x1032), celebrities_impersonated(?x3649, ?x12584) *> conf = 0.04 ranks of expected_values: 13 EVAL 0cg9f religion 0631_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 114.000 114.000 0.221 http://example.org/people/person/religion #6981-0w6w PRED entity: 0w6w PRED relation: gender PRED expected values: 05zppz => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #59, 0.90 #55, 0.89 #69), 02zsn (0.76 #112, 0.74 #107, 0.74 #117) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 02lt8; 06c44; 043tg; 07dnx; >> query: (?x13901, 05zppz) <- influenced_by(?x712, ?x13901), influenced_by(?x3711, ?x712), interests(?x712, ?x713), company(?x3711, ?x9745) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0w6w gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.905 http://example.org/people/person/gender #6980-0pspl PRED entity: 0pspl PRED relation: major_field_of_study PRED expected values: 02j62 => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 135): 02lp1 (0.67 #695, 0.65 #239, 0.60 #1265), 0fdys (0.59 #263, 0.42 #719, 0.35 #1631), 0g26h (0.58 #609, 0.48 #723, 0.47 #837), 02j62 (0.56 #1052, 0.52 #710, 0.50 #140), 04sh3 (0.53 #296, 0.44 #1322, 0.43 #1664), 05qjt (0.53 #235, 0.39 #691, 0.38 #1033), 037mh8 (0.53 #290, 0.35 #1088, 0.33 #176), 04x_3 (0.47 #821, 0.42 #707, 0.33 #1049), 05qfh (0.47 #260, 0.45 #716, 0.38 #1628), 01lj9 (0.42 #606, 0.41 #264, 0.35 #834) >> Best rule #695 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 01tx9m; >> query: (?x3513, 02lp1) <- institution(?x2636, ?x3513), student(?x3513, ?x2015), ?x2636 = 027f2w, school(?x799, ?x3513) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1052 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 01g6l8; *> query: (?x3513, 02j62) <- institution(?x1200, ?x3513), ?x1200 = 016t_3, major_field_of_study(?x3513, ?x2606), ?x2606 = 062z7 *> conf = 0.56 ranks of expected_values: 4 EVAL 0pspl major_field_of_study 02j62 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 193.000 193.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #6979-04vh83 PRED entity: 04vh83 PRED relation: prequel PRED expected values: 0kt_4 => 84 concepts (16 used for prediction) PRED predicted values (max 10 best out of 10): 0fdv3 (0.02 #34, 0.01 #396, 0.01 #758), 06bc59 (0.02 #344, 0.01 #706), 033qdy (0.02 #296, 0.01 #658), 0btpm6 (0.01 #499), 027m67 (0.01 #491), 06gb1w (0.01 #442), 0198b6 (0.01 #430), 014nq4 (0.01 #417), 017gm7 (0.01 #387), 017gl1 (0.01 #379) >> Best rule #34 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 0j_tw; >> query: (?x3514, 0fdv3) <- genre(?x3514, ?x53), film_release_region(?x3514, ?x2984), currency(?x3514, ?x1099), ?x2984 = 082fr >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04vh83 prequel 0kt_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 16.000 0.019 http://example.org/film/film/prequel #6978-035sc2 PRED entity: 035sc2 PRED relation: type_of_union PRED expected values: 04ztj => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #17, 0.74 #41, 0.73 #29), 01g63y (0.16 #6, 0.15 #34, 0.15 #2) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 529 >> proper extension: 01vvycq; 034np8; 01wj9y9; 0jfx1; 0j_c; 062ftr; 01ycck; 0gv5c; 01pp3p; 03flwk; ... >> query: (?x8680, 04ztj) <- profession(?x8680, ?x1032), profession(?x8680, ?x987), ?x1032 = 02hrh1q, ?x987 = 0dxtg >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035sc2 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.740 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6977-01dy7j PRED entity: 01dy7j PRED relation: award_winner! PRED expected values: 03gyp30 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 116): 03gyp30 (0.57 #387, 0.50 #250, 0.17 #10962), 0fqpc7d (0.20 #36, 0.17 #10962, 0.02 #4146), 0n8_m93 (0.20 #114, 0.01 #4224, 0.01 #5457), 0hr3c8y (0.17 #10962, 0.14 #147, 0.10 #11648), 0g55tzk (0.17 #10962, 0.10 #11648, 0.10 #11786), 07z31v (0.17 #10962, 0.10 #11648, 0.10 #11786), 0bx6zs (0.17 #10962, 0.10 #11648, 0.10 #11786), 027hjff (0.17 #10962, 0.09 #1153, 0.08 #1701), 09g90vz (0.17 #10962, 0.09 #1764, 0.08 #668), 0gx_st (0.17 #10962, 0.07 #9728, 0.04 #1681) >> Best rule #387 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 0521rl1; 02lgj6; 01l1sq; 02p_ycc; 059gkk; 01z7_f; 02lg3y; 0bl60p; >> query: (?x2965, 03gyp30) <- award_nominee(?x447, ?x2965), award_nominee(?x2965, ?x1059), ?x447 = 02lfcm >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dy7j award_winner! 03gyp30 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6976-02f93t PRED entity: 02f93t PRED relation: award PRED expected values: 03hl6lc => 91 concepts (79 used for prediction) PRED predicted values (max 10 best out of 288): 02w_6xj (0.70 #23801, 0.70 #24596, 0.70 #22610), 02wypbh (0.70 #23801, 0.70 #24596, 0.70 #22610), 0gqy2 (0.34 #12050, 0.20 #14032, 0.12 #11256), 0gr4k (0.34 #4786, 0.33 #5580, 0.30 #8753), 09sb52 (0.31 #11139, 0.28 #14311, 0.26 #9157), 0f_nbyh (0.31 #1592, 0.31 #800, 0.23 #2783), 0f4x7 (0.28 #11923, 0.16 #13905, 0.11 #11129), 03hkv_r (0.24 #5563, 0.21 #4769, 0.18 #8736), 03hl6lc (0.23 #5719, 0.20 #4925, 0.16 #5322), 02n9nmz (0.20 #4821, 0.20 #5615, 0.18 #1650) >> Best rule #23801 for best value: >> intensional similarity = 4 >> extensional distance = 2248 >> proper extension: 089tm; 02mslq; 04rcr; 01v0sx2; 01vsxdm; 01wv9xn; 03yf3z; 02lbrd; 04qmr; 01vd7hn; ... >> query: (?x9430, ?x5398) <- award(?x9430, ?x198), award_winner(?x5398, ?x9430), award(?x7837, ?x198), award_winner(?x253, ?x7837) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #5719 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 211 *> proper extension: 016hvl; *> query: (?x9430, 03hl6lc) <- award_winner(?x1587, ?x9430), profession(?x9430, ?x319), written_by(?x6616, ?x9430), award(?x1118, ?x1587) *> conf = 0.23 ranks of expected_values: 9 EVAL 02f93t award 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 91.000 79.000 0.702 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6975-01tx9m PRED entity: 01tx9m PRED relation: educational_institution! PRED expected values: 01tx9m => 170 concepts (108 used for prediction) PRED predicted values (max 10 best out of 281): 05zl0 (0.08 #189, 0.02 #3964, 0.02 #7739), 07tgn (0.08 #15, 0.02 #3790), 017z88 (0.08 #73, 0.02 #4928, 0.02 #6006), 02bqy (0.08 #168, 0.02 #7718, 0.01 #12030), 02zcnq (0.08 #130, 0.01 #11992), 01rtm4 (0.08 #4, 0.01 #12944, 0.01 #15101), 02kbtf (0.08 #334, 0.01 #17589), 0221g_ (0.05 #654, 0.05 #1193, 0.03 #25343), 0558_1 (0.05 #981, 0.05 #1520, 0.02 #3678), 02ngbs (0.05 #884, 0.02 #4660, 0.01 #9512) >> Best rule #189 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 02l9wl; >> query: (?x6177, 05zl0) <- student(?x6177, ?x3852), award_nominee(?x3852, ?x4719), award_nominee(?x3852, ?x275), ?x275 = 083chw, ?x4719 = 08hsww >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #25343 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 111 *> proper extension: 0frm7n; *> query: (?x6177, ?x1011) <- school(?x1010, ?x6177), school(?x1010, ?x1011), position(?x1010, ?x2010), draft(?x1010, ?x1161) *> conf = 0.03 ranks of expected_values: 70 EVAL 01tx9m educational_institution! 01tx9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 170.000 108.000 0.083 http://example.org/education/educational_institution_campus/educational_institution #6974-03k48_ PRED entity: 03k48_ PRED relation: profession PRED expected values: 018gz8 => 90 concepts (67 used for prediction) PRED predicted values (max 10 best out of 53): 01d_h8 (0.49 #8622, 0.49 #1028, 0.48 #2489), 03gjzk (0.48 #1036, 0.37 #2497, 0.30 #1753), 02jknp (0.42 #2491, 0.39 #1030, 0.34 #8624), 018gz8 (0.30 #1753, 0.26 #8909, 0.23 #162), 015cjr (0.30 #1753, 0.26 #8909, 0.05 #1947), 09jwl (0.22 #602, 0.21 #1917, 0.20 #5568), 0cbd2 (0.22 #2490, 0.19 #1029, 0.15 #3805), 02krf9 (0.20 #1047, 0.15 #2508, 0.11 #8641), 0dz3r (0.18 #586, 0.14 #5552, 0.14 #2193), 0nbcg (0.15 #614, 0.15 #5580, 0.13 #2221) >> Best rule #8622 for best value: >> intensional similarity = 3 >> extensional distance = 1970 >> proper extension: 023l9y; >> query: (?x10620, 01d_h8) <- profession(?x10620, ?x1383), profession(?x2426, ?x1383), ?x2426 = 081nh >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #1753 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 562 *> proper extension: 017s11; 016tt2; 0kx4m; 05qd_; 016tw3; 0ggl02; 05crg7; 017jv5; 0288fyj; 0dvqq; ... *> query: (?x10620, ?x319) <- award_nominee(?x10620, ?x6008), influenced_by(?x6008, ?x397), profession(?x6008, ?x319) *> conf = 0.30 ranks of expected_values: 4 EVAL 03k48_ profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 67.000 0.494 http://example.org/people/person/profession #6973-01t6b4 PRED entity: 01t6b4 PRED relation: student! PRED expected values: 07vyf => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 166): 0trv (0.17 #319, 0.02 #5589, 0.02 #6116), 02yr1q (0.17 #370), 04gd8j (0.17 #368), 0bwfn (0.12 #802, 0.10 #17142, 0.10 #11344), 01q0kg (0.12 #661, 0.06 #2242, 0.02 #6458), 017v3q (0.12 #772, 0.04 #1826, 0.03 #2880), 01mpwj (0.12 #634, 0.03 #8012, 0.02 #11703), 02gr81 (0.12 #659, 0.03 #2767, 0.02 #9619), 07szy (0.12 #567, 0.02 #11636, 0.02 #15853), 01hc1j (0.12 #977) >> Best rule #319 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0dxmyh; >> query: (?x1285, 0trv) <- nationality(?x1285, ?x94), location(?x1285, ?x938), ?x938 = 0vmt >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #3827 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 0h3mrc; 0bz60q; 02vqpx8; *> query: (?x1285, 07vyf) <- people(?x1050, ?x1285), award_nominee(?x2156, ?x1285), program(?x1285, ?x10447) *> conf = 0.02 ranks of expected_values: 111 EVAL 01t6b4 student! 07vyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 128.000 128.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #6972-01cwcr PRED entity: 01cwcr PRED relation: location PRED expected values: 02_286 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 111): 0hyxv (0.47 #211, 0.03 #7459), 02_286 (0.21 #843, 0.17 #2454, 0.15 #3259), 02m77 (0.20 #331, 0.02 #7579), 030qb3t (0.15 #6526, 0.15 #8137, 0.13 #15376), 04jpl (0.14 #7265, 0.05 #15310, 0.04 #16115), 0cc56 (0.10 #863, 0.07 #2474, 0.04 #8111), 059rby (0.09 #5636, 0.08 #5635, 0.07 #6443), 0nqv1 (0.08 #5635, 0.07 #6443, 0.07 #7248), 0cr3d (0.07 #3367, 0.07 #4171, 0.05 #8199), 01531 (0.07 #964, 0.05 #2575, 0.03 #9017) >> Best rule #211 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0tfc; 011zwl; >> query: (?x7277, 0hyxv) <- nationality(?x7277, ?x6401), ?x6401 = 06q1r, people(?x3715, ?x7277) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #843 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 0jf1b; *> query: (?x7277, 02_286) <- award(?x7277, ?x6878), award_nominee(?x1205, ?x7277), ?x6878 = 08_vwq *> conf = 0.21 ranks of expected_values: 2 EVAL 01cwcr location 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.467 http://example.org/people/person/places_lived./people/place_lived/location #6971-04jpl PRED entity: 04jpl PRED relation: origin! PRED expected values: 014hr0 01pq5j7 01wgjj5 06p03s => 211 concepts (177 used for prediction) PRED predicted values (max 10 best out of 649): 04pf4r (0.20 #150, 0.17 #628, 0.04 #4455), 047cx (0.20 #183, 0.17 #661, 0.04 #4488), 0fcsd (0.20 #163, 0.17 #641, 0.04 #4468), 0167_s (0.20 #73, 0.17 #551, 0.04 #4378), 01w923 (0.20 #47, 0.17 #525, 0.04 #4352), 01v0fn1 (0.16 #60836, 0.10 #31121, 0.09 #67059), 0150t6 (0.16 #60836, 0.07 #69933, 0.07 #69932), 0140t7 (0.16 #60836, 0.07 #69933, 0.07 #69932), 01vsyg9 (0.16 #60836, 0.07 #69933, 0.07 #69932), 01nz1q6 (0.16 #60836, 0.07 #69933, 0.07 #69932) >> Best rule #150 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01_c4; >> query: (?x362, 04pf4r) <- contains(?x362, ?x10940), contains(?x362, ?x9844), ?x9844 = 0m4yg, category(?x10940, ?x134) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1671 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 0_j_z; *> query: (?x362, 01wgjj5) <- citytown(?x2776, ?x362), award_winner(?x2246, ?x2776) *> conf = 0.06 ranks of expected_values: 276 EVAL 04jpl origin! 06p03s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 211.000 177.000 0.200 http://example.org/music/artist/origin EVAL 04jpl origin! 01wgjj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 211.000 177.000 0.200 http://example.org/music/artist/origin EVAL 04jpl origin! 01pq5j7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 211.000 177.000 0.200 http://example.org/music/artist/origin EVAL 04jpl origin! 014hr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 211.000 177.000 0.200 http://example.org/music/artist/origin #6970-09zmys PRED entity: 09zmys PRED relation: profession PRED expected values: 02hrh1q 03gjzk => 125 concepts (124 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.91 #9489, 0.90 #2826, 0.90 #4752), 01d_h8 (0.67 #5781, 0.62 #599, 0.43 #1783), 03gjzk (0.47 #607, 0.35 #6085, 0.35 #6220), 09jwl (0.35 #6220, 0.32 #4295, 0.30 #1055), 01c72t (0.35 #6220, 0.32 #4295, 0.27 #9773), 02krf9 (0.35 #6220, 0.32 #4295, 0.27 #9773), 0dgd_ (0.35 #6220, 0.32 #4295, 0.27 #9773), 018gz8 (0.29 #609, 0.27 #165, 0.20 #2385), 0d1pc (0.25 #1827, 0.20 #2863, 0.19 #3604), 0cbd2 (0.22 #7, 0.18 #1488, 0.17 #2968) >> Best rule #9489 for best value: >> intensional similarity = 3 >> extensional distance = 1117 >> proper extension: 013rds; >> query: (?x5521, 02hrh1q) <- award_winner(?x7451, ?x5521), film(?x5521, ?x2075), profession(?x5521, ?x524) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 09zmys profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 124.000 0.906 http://example.org/people/person/profession EVAL 09zmys profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 124.000 0.906 http://example.org/people/person/profession #6969-01qdhx PRED entity: 01qdhx PRED relation: school_type PRED expected values: 01rs41 => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 23): 01rs41 (0.56 #629, 0.53 #797, 0.53 #557), 05pcjw (0.53 #313, 0.49 #673, 0.48 #529), 05jxkf (0.42 #2506, 0.42 #3226, 0.42 #1180), 01_srz (0.18 #147, 0.16 #555, 0.14 #579), 01_9fk (0.17 #2, 0.16 #506, 0.14 #2261), 07tf8 (0.16 #1185, 0.16 #777, 0.15 #465), 04qbv (0.11 #136, 0.07 #160, 0.06 #760), 01y64 (0.09 #420, 0.07 #468, 0.06 #1332), 06cs1 (0.07 #150, 0.05 #342, 0.04 #558), 02dk5q (0.07 #271, 0.05 #967, 0.05 #1039) >> Best rule #629 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 037s9x; >> query: (?x13109, 01rs41) <- currency(?x13109, ?x170), contains(?x94, ?x13109), major_field_of_study(?x13109, ?x8681), category(?x13109, ?x134) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qdhx school_type 01rs41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 188.000 0.561 http://example.org/education/educational_institution/school_type #6968-01gvxv PRED entity: 01gvxv PRED relation: student! PRED expected values: 06182p => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 100): 0bwfn (0.12 #275, 0.11 #802, 0.08 #2910), 01w5m (0.12 #105, 0.05 #632, 0.05 #1159), 02vnp2 (0.12 #358, 0.05 #885, 0.05 #1412), 065y4w7 (0.12 #14, 0.05 #541, 0.05 #9500), 02301 (0.12 #74, 0.05 #601, 0.03 #2709), 021w0_ (0.12 #324, 0.05 #851, 0.03 #2959), 0217m9 (0.12 #171, 0.05 #698), 08815 (0.12 #2, 0.03 #14758, 0.03 #3691), 01pcj4 (0.12 #369, 0.01 #9328, 0.01 #5639), 09f2j (0.05 #2794, 0.04 #3848, 0.03 #14388) >> Best rule #275 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01vh18t; >> query: (?x11577, 0bwfn) <- gender(?x11577, ?x514), spouse(?x6993, ?x11577), award(?x11577, ?x375), type_of_appearance(?x11577, ?x3429) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #825 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0lbj1; 0gyx4; *> query: (?x11577, 06182p) <- award_winner(?x8762, ?x11577), film(?x11577, ?x1724), type_of_appearance(?x11577, ?x3429) *> conf = 0.05 ranks of expected_values: 11 EVAL 01gvxv student! 06182p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 127.000 127.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #6967-0y3_8 PRED entity: 0y3_8 PRED relation: parent_genre PRED expected values: 0mmp3 => 45 concepts (31 used for prediction) PRED predicted values (max 10 best out of 268): 05r6t (0.89 #2383, 0.66 #2539, 0.33 #1292), 09jw2 (0.33 #1339, 0.30 #1961, 0.27 #2118), 02w4v (0.33 #1737, 0.25 #960, 0.20 #1115), 05w3f (0.33 #335, 0.11 #1578, 0.11 #1422), 0xhtw (0.33 #324, 0.11 #1567, 0.09 #2347), 0155w (0.33 #377, 0.11 #1620, 0.07 #3029), 02yv6b (0.33 #370, 0.11 #1613, 0.05 #4074), 0p9xd (0.33 #405, 0.11 #1648, 0.02 #2647), 01243b (0.27 #2517, 0.25 #493, 0.24 #2361), 06j6l (0.25 #962, 0.20 #1117, 0.17 #1273) >> Best rule #2383 for best value: >> intensional similarity = 5 >> extensional distance = 44 >> proper extension: 028cl7; 088vmr; >> query: (?x3243, 05r6t) <- parent_genre(?x3243, ?x2491), artists(?x2491, ?x13505), artists(?x2491, ?x5883), ?x5883 = 01wgjj5, ?x13505 = 07n68 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #3918 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 163 *> proper extension: 02p4l6s; *> query: (?x3243, ?x671) <- parent_genre(?x996, ?x3243), parent_genre(?x996, ?x671) *> conf = 0.10 ranks of expected_values: 27 EVAL 0y3_8 parent_genre 0mmp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 45.000 31.000 0.891 http://example.org/music/genre/parent_genre #6966-03ds3 PRED entity: 03ds3 PRED relation: type_of_union PRED expected values: 04ztj => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.94 #46, 0.93 #311, 0.93 #184), 0jgjn (0.03 #60, 0.01 #87) >> Best rule #46 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 06mmb; 016ksk; 03xx9l; 0mbw0; 0427y; 01mbwlb; >> query: (?x858, 04ztj) <- person(?x3480, ?x858), type_of_union(?x858, ?x1873), film(?x858, ?x8677), nominated_for(?x538, ?x8677) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03ds3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.939 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6965-027rn PRED entity: 027rn PRED relation: location! PRED expected values: 01sl1q => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 1724): 033m23 (0.25 #1571, 0.06 #4089, 0.03 #19199), 03nb5v (0.25 #1323, 0.06 #79397, 0.06 #74360), 023s8 (0.25 #2111, 0.05 #9666, 0.05 #80185), 01520h (0.25 #1363, 0.05 #8918, 0.03 #59286), 01pcdn (0.25 #971, 0.05 #8526, 0.03 #58894), 059gkk (0.25 #630, 0.05 #8185, 0.03 #28330), 0sx5w (0.25 #2142, 0.04 #42433, 0.04 #65105), 06crk (0.25 #1290, 0.04 #13882, 0.03 #16400), 032wdd (0.25 #1763, 0.02 #82355, 0.02 #42054), 05ztm4r (0.25 #321, 0.02 #80913, 0.02 #40612) >> Best rule #1571 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0ccvx; 05fjf; >> query: (?x47, 033m23) <- location(?x4252, ?x47), location_of_ceremony(?x566, ?x47), ?x4252 = 05qg6g >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #15111 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 0h44w; *> query: (?x47, 01sl1q) <- location(?x4252, ?x47), countries_spoken_in(?x2502, ?x47), film(?x4252, ?x573) *> conf = 0.03 ranks of expected_values: 583 EVAL 027rn location! 01sl1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 141.000 141.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #6964-03f2_rc PRED entity: 03f2_rc PRED relation: profession PRED expected values: 01d_h8 => 117 concepts (116 used for prediction) PRED predicted values (max 10 best out of 59): 01d_h8 (0.85 #2153, 0.67 #7316, 0.37 #5163), 09jwl (0.62 #300, 0.57 #5315, 0.56 #7038), 0dz3r (0.50 #288, 0.43 #2, 0.42 #431), 0nbcg (0.48 #455, 0.47 #598, 0.43 #3750), 01c72t (0.38 #2310, 0.35 #878, 0.35 #162), 0n1h (0.26 #581, 0.26 #438, 0.24 #295), 0d1pc (0.20 #45, 0.20 #761, 0.14 #2909), 039v1 (0.19 #8059, 0.19 #5332, 0.18 #6625), 01c8w0 (0.17 #149, 0.15 #865, 0.11 #2297), 0fnpj (0.15 #341, 0.12 #55, 0.09 #4496) >> Best rule #2153 for best value: >> intensional similarity = 2 >> extensional distance = 276 >> proper extension: 024c1b; >> query: (?x538, 01d_h8) <- produced_by(?x8677, ?x538), music(?x8677, ?x2124) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f2_rc profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 116.000 0.845 http://example.org/people/person/profession #6963-042kg PRED entity: 042kg PRED relation: type_of_union PRED expected values: 04ztj => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.89 #85, 0.89 #129, 0.88 #49), 01g63y (0.13 #230, 0.12 #14, 0.12 #154), 01bl8s (0.03 #83, 0.01 #179), 0jgjn (0.02 #156, 0.01 #164) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 09bg4l; >> query: (?x11290, 04ztj) <- basic_title(?x11290, ?x346), gender(?x11290, ?x231), organization(?x346, ?x99), jurisdiction_of_office(?x346, ?x47) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042kg type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.892 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6962-031b3h PRED entity: 031b3h PRED relation: ceremony PRED expected values: 0gpjbt 013b2h => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 123): 0gpjbt (0.88 #531, 0.88 #404, 0.86 #658), 013b2h (0.84 #578, 0.84 #451, 0.76 #705), 01xqqp (0.81 #592, 0.79 #465, 0.72 #719), 0jzphpx (0.77 #413, 0.76 #540, 0.66 #667), 09306z (0.22 #2795, 0.17 #224, 0.13 #1113), 0bzn6_ (0.22 #2795, 0.17 #174, 0.13 #1063), 0drtv8 (0.22 #2795, 0.03 #3614, 0.03 #3233), 04n2r9h (0.22 #2795, 0.02 #3467, 0.02 #3594), 05c1t6z (0.19 #1154, 0.18 #1281, 0.17 #1789), 02q690_ (0.18 #1326, 0.17 #1199, 0.17 #1834) >> Best rule #531 for best value: >> intensional similarity = 5 >> extensional distance = 73 >> proper extension: 02flpc; 02flqd; 03nl5k; >> query: (?x3937, 0gpjbt) <- award(?x11897, ?x3937), award_winner(?x3937, ?x2335), ceremony(?x3937, ?x6487), ?x6487 = 01mh_q, artists(?x302, ?x11897) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 031b3h ceremony 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.880 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 031b3h ceremony 0gpjbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.880 http://example.org/award/award_category/winners./award/award_honor/ceremony #6961-018vs PRED entity: 018vs PRED relation: role PRED expected values: 02snj9 => 79 concepts (72 used for prediction) PRED predicted values (max 10 best out of 58): 018vs (0.88 #1816, 0.84 #2184, 0.83 #3172), 0mkg (0.85 #1289, 0.84 #1813, 0.77 #1340), 0342h (0.84 #613, 0.84 #1389, 0.83 #1544), 03bx0bm (0.84 #613, 0.84 #1389, 0.83 #1646), 042v_gx (0.84 #613, 0.84 #1389, 0.83 #1646), 0l14j_ (0.84 #613, 0.84 #1389, 0.83 #1646), 04rzd (0.84 #613, 0.84 #1389, 0.83 #1646), 01v1d8 (0.84 #613, 0.84 #1389, 0.83 #1646), 028tv0 (0.84 #613, 0.84 #1389, 0.83 #1646), 0bxl5 (0.84 #613, 0.84 #1389, 0.83 #1646) >> Best rule #1816 for best value: >> intensional similarity = 9 >> extensional distance = 23 >> proper extension: 02k84w; 01dnws; 0gghm; 07_l6; 03gvt; >> query: (?x716, 018vs) <- instrumentalists(?x716, ?x11916), role(?x1437, ?x716), ?x1437 = 01vdm0, group(?x716, ?x11425), group(?x716, ?x7620), role(?x75, ?x716), ?x11425 = 02vnpv, role(?x11916, ?x212), origin(?x7620, ?x362) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3319 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 62 *> proper extension: 048j4l; 06ch55; 057cc; *> query: (?x716, ?x645) <- instrumentalists(?x716, ?x10744), instrumentalists(?x716, ?x8215), instrumentalists(?x716, ?x3657), role(?x8215, ?x227), group(?x10744, ?x11551), type_of_union(?x10744, ?x566), artist(?x1954, ?x8215), role(?x3657, ?x645) *> conf = 0.65 ranks of expected_values: 42 EVAL 018vs role 02snj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 79.000 72.000 0.880 http://example.org/music/performance_role/regular_performances./music/group_membership/role #6960-05fhy PRED entity: 05fhy PRED relation: religion PRED expected values: 04pk9 => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 23): 04pk9 (0.83 #386, 0.81 #412, 0.80 #513), 01y0s9 (0.65 #404, 0.65 #378, 0.61 #505), 03_gx (0.49 #509, 0.48 #408, 0.48 #382), 092bf5 (0.48 #981, 0.42 #1812, 0.38 #2163), 072w0 (0.48 #981, 0.42 #1812, 0.37 #2164), 0flw86 (0.39 #1435, 0.37 #606, 0.37 #1586), 03j6c (0.23 #1636, 0.09 #1597, 0.09 #1622), 0kpl (0.23 #1636, 0.04 #4, 0.02 #229), 07w8f (0.23 #1636, 0.04 #19, 0.02 #244), 0n2g (0.23 #1636, 0.03 #55, 0.03 #1439) >> Best rule #386 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05kkh; 059rby; 03v1s; 05kj_; 059f4; 05fkf; 0vmt; 03s0w; 059_c; 01n7q; ... >> query: (?x1024, 04pk9) <- religion(?x1024, ?x109), state_province_region(?x4846, ?x1024), district_represented(?x605, ?x1024) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fhy religion 04pk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 187.000 0.833 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #6959-021dvj PRED entity: 021dvj PRED relation: artists PRED expected values: 0kn3g => 51 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1124): 043d4 (0.60 #6032, 0.50 #3889, 0.42 #8568), 0kn3g (0.60 #6236, 0.50 #4093, 0.40 #5165), 0k4gf (0.60 #5433, 0.50 #3290, 0.33 #2220), 0hqgp (0.60 #6342, 0.50 #4199, 0.33 #3129), 0pcc0 (0.60 #5409, 0.50 #3266, 0.33 #2196), 06449 (0.60 #4522, 0.40 #5593, 0.33 #1309), 0c73g (0.60 #6415, 0.33 #3202, 0.33 #2131), 0383f (0.60 #6354, 0.33 #3141, 0.33 #2070), 0c73z (0.50 #4203, 0.40 #6346, 0.38 #12853), 03f4k (0.40 #5155, 0.33 #1942, 0.33 #871) >> Best rule #6032 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 0ggq0m; >> query: (?x3597, 043d4) <- artists(?x3597, ?x9480), artists(?x3597, ?x7386), artists(?x3597, ?x4831), ?x9480 = 063tn, profession(?x4831, ?x1032), student(?x4955, ?x4831), people(?x1158, ?x4831), nationality(?x4831, ?x94), type_of_union(?x7386, ?x566) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6236 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 0ggq0m; *> query: (?x3597, 0kn3g) <- artists(?x3597, ?x9480), artists(?x3597, ?x7386), artists(?x3597, ?x4831), ?x9480 = 063tn, profession(?x4831, ?x1032), student(?x4955, ?x4831), people(?x1158, ?x4831), nationality(?x4831, ?x94), type_of_union(?x7386, ?x566) *> conf = 0.60 ranks of expected_values: 2 EVAL 021dvj artists 0kn3g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 51.000 31.000 0.600 http://example.org/music/genre/artists #6958-09qv_s PRED entity: 09qv_s PRED relation: award! PRED expected values: 02p65p 018db8 0237fw 0171cm 057176 => 57 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2891): 0bxtg (0.76 #13332, 0.75 #13333, 0.68 #60017), 01tt43d (0.76 #13332, 0.75 #13333, 0.68 #60017), 016yvw (0.76 #13332, 0.75 #13333, 0.68 #60017), 03f1zdw (0.76 #13332, 0.75 #13333, 0.68 #60017), 02qgqt (0.76 #13332, 0.75 #13333, 0.68 #60017), 06cgy (0.71 #373, 0.29 #3707, 0.14 #10372), 018db8 (0.71 #157, 0.19 #46679, 0.14 #10156), 0237fw (0.71 #627, 0.14 #10626, 0.14 #3961), 015grj (0.57 #213, 0.29 #3547, 0.14 #10212), 01kwsg (0.57 #1332, 0.21 #11331, 0.19 #46679) >> Best rule #13332 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 02py7pj; >> query: (?x2853, ?x157) <- ceremony(?x2853, ?x1112), ?x1112 = 09qvms, award_winner(?x2853, ?x2444), award_winner(?x2853, ?x157), film(?x2444, ?x485) >> conf = 0.76 => this is the best rule for 5 predicted values *> Best rule #157 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 099jhq; 0f4x7; 09sb52; 057xs89; 0gqy2; *> query: (?x2853, 018db8) <- award(?x406, ?x2853), award(?x253, ?x2853), ?x406 = 09fb5 *> conf = 0.71 ranks of expected_values: 7, 8, 11, 132, 162 EVAL 09qv_s award! 057176 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 57.000 18.000 0.756 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qv_s award! 0171cm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 57.000 18.000 0.756 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qv_s award! 0237fw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 57.000 18.000 0.756 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qv_s award! 018db8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 57.000 18.000 0.756 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qv_s award! 02p65p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 57.000 18.000 0.756 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6957-073h9x PRED entity: 073h9x PRED relation: ceremony! PRED expected values: 0k611 018wdw => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 363): 0k611 (0.92 #1734, 0.92 #4370, 0.92 #1256), 018wdw (0.83 #1124, 0.77 #2320, 0.70 #2802), 0gqzz (0.42 #996, 0.27 #1475, 0.23 #2192), 094qd5 (0.34 #3355, 0.31 #2398, 0.31 #2397), 04kxsb (0.34 #3355, 0.31 #2398, 0.31 #2397), 05ztjjw (0.34 #3355, 0.31 #2398, 0.31 #2397), 02r22gf (0.34 #3355, 0.31 #2398, 0.31 #2397), 02qyntr (0.34 #3355, 0.31 #2398, 0.31 #2397), 04dn09n (0.34 #3355, 0.31 #2398, 0.31 #2397), 019f4v (0.34 #3355, 0.31 #2398, 0.31 #2397) >> Best rule #1734 for best value: >> intensional similarity = 17 >> extensional distance = 24 >> proper extension: 02yvhx; >> query: (?x3254, 0k611) <- ceremony(?x1862, ?x3254), ceremony(?x1323, ?x3254), ceremony(?x1313, ?x3254), ceremony(?x591, ?x3254), ?x1313 = 0gs9p, ?x591 = 0f4x7, award_winner(?x3254, ?x4393), award_winner(?x3254, ?x2870), award_nominee(?x1983, ?x4393), crewmember(?x1386, ?x4393), ?x1323 = 0gqz2, award_winner(?x324, ?x4393), ?x1862 = 0gr51, nominated_for(?x4393, ?x573), award_nominee(?x929, ?x2870), nominated_for(?x2870, ?x1012), student(?x6988, ?x2870) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 073h9x ceremony! 018wdw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.923 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 073h9x ceremony! 0k611 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.923 http://example.org/award/award_category/winners./award/award_honor/ceremony #6956-03v1s PRED entity: 03v1s PRED relation: contains PRED expected values: 03y5ky 0nt4s => 203 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2733): 0ftxw (0.87 #289747, 0.86 #275114, 0.84 #280969), 0sl2w (0.81 #73151, 0.68 #196083, 0.49 #283896), 0snty (0.81 #73151, 0.49 #283896, 0.46 #269262), 0l3kx (0.64 #35113, 0.59 #81930, 0.06 #128756), 0nvt9 (0.64 #35113, 0.59 #81930, 0.06 #128756), 07xyn1 (0.53 #163885, 0.52 #196084, 0.48 #64373), 02zy1z (0.53 #163885, 0.52 #196084, 0.48 #64373), 03y5ky (0.53 #163885, 0.52 #196084, 0.48 #64373), 035tlh (0.53 #163885, 0.52 #196084, 0.48 #64373), 03v1s (0.49 #283896, 0.46 #269262, 0.25 #41) >> Best rule #289747 for best value: >> intensional similarity = 3 >> extensional distance = 178 >> proper extension: 0cb4j; 022_6; 0mtdx; 0jt5zcn; 0123_x; 0ntpv; 028n3; 0c7hq; 0nrqh; 0mkdm; ... >> query: (?x448, ?x2879) <- administrative_division(?x2879, ?x448), contains(?x448, ?x10584), time_zones(?x10584, ?x1638) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #163885 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 0cr7m; *> query: (?x448, ?x12897) <- location(?x5346, ?x448), state_province_region(?x12897, ?x448), category(?x12897, ?x134) *> conf = 0.53 ranks of expected_values: 8, 1549 EVAL 03v1s contains 0nt4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 203.000 99.000 0.867 http://example.org/location/location/contains EVAL 03v1s contains 03y5ky CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 203.000 99.000 0.867 http://example.org/location/location/contains #6955-0bzkgg PRED entity: 0bzkgg PRED relation: award_winner PRED expected values: 02vyw => 37 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1249): 0cw67g (0.20 #1410, 0.16 #13731, 0.13 #18352), 06rnl9 (0.20 #422, 0.12 #1961, 0.11 #3500), 04wp63 (0.20 #1395, 0.12 #2934, 0.11 #4473), 02q9kqf (0.20 #956, 0.12 #2495, 0.11 #4034), 05kfs (0.20 #91, 0.10 #4709, 0.07 #23198), 03r1pr (0.20 #423, 0.10 #5041, 0.06 #12744), 02fgp0 (0.20 #1239, 0.10 #5857, 0.06 #15100), 0c0tzp (0.17 #4590, 0.12 #3051, 0.11 #10750), 0bytkq (0.14 #5077, 0.08 #25108, 0.07 #23566), 081nh (0.13 #29619, 0.12 #31161, 0.12 #24990) >> Best rule #1410 for best value: >> intensional similarity = 20 >> extensional distance = 8 >> proper extension: 0bc773; >> query: (?x2822, 0cw67g) <- ceremony(?x5409, ?x2822), ceremony(?x3617, ?x2822), ceremony(?x1972, ?x2822), ceremony(?x1243, ?x2822), ceremony(?x1079, ?x2822), ceremony(?x601, ?x2822), ceremony(?x591, ?x2822), ceremony(?x484, ?x2822), ?x484 = 0gq_v, ?x5409 = 0gr07, ?x601 = 0gr4k, ?x591 = 0f4x7, award_winner(?x2822, ?x767), ?x1243 = 0gr0m, ?x3617 = 0gvx_, award_winner(?x197, ?x767), ?x1972 = 0gqyl, award_winner(?x198, ?x767), place_of_death(?x767, ?x108), ?x1079 = 0l8z1 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2092 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 15 *> proper extension: 0bzk8w; 0bzm81; 0dth6b; 02hn5v; 0bzk2h; 073h9x; 0bz6l9; 0bz6sb; 0bzmt8; 02yxh9; ... *> query: (?x2822, 02vyw) <- instance_of_recurring_event(?x2822, ?x3459), ceremony(?x4573, ?x2822), ceremony(?x1972, ?x2822), ceremony(?x1862, ?x2822), ceremony(?x1313, ?x2822), ceremony(?x1079, ?x2822), ceremony(?x77, ?x2822), ?x1079 = 0l8z1, ?x1313 = 0gs9p, award_winner(?x2822, ?x10146), ?x1862 = 0gr51, ?x77 = 0gqng, ?x1972 = 0gqyl, people(?x6260, ?x10146), profession(?x10146, ?x563), honored_for(?x2822, ?x7016), ?x4573 = 0gq_d, ?x3459 = 0g_w *> conf = 0.12 ranks of expected_values: 27 EVAL 0bzkgg award_winner 02vyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 37.000 21.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6954-01817f PRED entity: 01817f PRED relation: artist! PRED expected values: 015_1q => 131 concepts (69 used for prediction) PRED predicted values (max 10 best out of 114): 0g768 (0.55 #3324, 0.27 #582, 0.13 #3049), 015_1q (0.48 #3991, 0.27 #564, 0.22 #2757), 017l96 (0.40 #426, 0.22 #837, 0.12 #3990), 011k1h (0.29 #3985, 0.20 #421, 0.17 #2751), 02p11jq (0.25 #4124, 0.20 #423, 0.09 #1794), 0fb0v (0.24 #4119, 0.10 #281, 0.09 #3022), 03mp8k (0.23 #1022, 0.18 #2804, 0.16 #1845), 02p3cr5 (0.20 #298, 0.10 #435, 0.06 #1257), 01cl0d (0.20 #463, 0.09 #1422, 0.09 #1285), 01cl2y (0.19 #712, 0.18 #575, 0.09 #849) >> Best rule #3324 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 0130sy; >> query: (?x4537, 0g768) <- artist(?x2299, ?x4537), artist(?x2299, ?x9528), nationality(?x4537, ?x94), ?x9528 = 01kp_1t >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #3991 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 205 *> proper extension: 032t2z; 04cr6qv; *> query: (?x4537, 015_1q) <- artist(?x2299, ?x4537), artist(?x2299, ?x11442), nationality(?x4537, ?x94), ?x11442 = 01vzz1c *> conf = 0.48 ranks of expected_values: 2 EVAL 01817f artist! 015_1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 69.000 0.553 http://example.org/music/record_label/artist #6953-03wh95l PRED entity: 03wh95l PRED relation: award_nominee! PRED expected values: 0b1f49 => 87 concepts (45 used for prediction) PRED predicted values (max 10 best out of 840): 0b1f49 (0.81 #44315, 0.81 #62975, 0.80 #51312), 0crx5w (0.50 #319, 0.29 #83970, 0.26 #32653), 0b2_xp (0.50 #1812, 0.29 #83970, 0.26 #32653), 02773m2 (0.38 #159, 0.29 #83970, 0.26 #32653), 0b7t3p (0.38 #1479, 0.29 #83970, 0.26 #32653), 015p37 (0.30 #41982, 0.29 #83970, 0.26 #32653), 02js_6 (0.30 #41982, 0.29 #83970, 0.26 #32653), 02r_d4 (0.30 #41982, 0.29 #83970, 0.26 #32653), 011_3s (0.30 #41982, 0.06 #24056, 0.02 #31054), 01j7rd (0.30 #41982, 0.03 #25654, 0.02 #23768) >> Best rule #44315 for best value: >> intensional similarity = 3 >> extensional distance = 814 >> proper extension: 037hgm; 03b78r; 024y6w; 02v49c; >> query: (?x11581, ?x364) <- award_nominee(?x11581, ?x364), type_of_union(?x11581, ?x566), student(?x6545, ?x11581) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03wh95l award_nominee! 0b1f49 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 45.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #6952-0kbwb PRED entity: 0kbwb PRED relation: film! PRED expected values: 01k8rb 039g82 => 80 concepts (47 used for prediction) PRED predicted values (max 10 best out of 989): 0p__8 (0.57 #81156, 0.49 #56181, 0.44 #56180), 0253b6 (0.49 #56181, 0.44 #56180, 0.44 #62424), 01xllf (0.18 #1724, 0.09 #12130, 0.09 #14210), 0p_pd (0.18 #54, 0.09 #10460, 0.09 #12540), 0p_2r (0.14 #2081, 0.13 #6243, 0.11 #85322), 02_p5w (0.12 #6892, 0.12 #8973, 0.10 #2728), 02gf_l (0.12 #7515, 0.12 #9596, 0.10 #3351), 01q_ph (0.12 #57, 0.09 #10463, 0.09 #20863), 02rf1y (0.12 #964, 0.09 #11370, 0.09 #13450), 01j5ts (0.12 #29, 0.06 #10435, 0.06 #12515) >> Best rule #81156 for best value: >> intensional similarity = 3 >> extensional distance = 737 >> proper extension: 06mmr; >> query: (?x9330, ?x5940) <- award_winner(?x9330, ?x5940), award_nominee(?x5940, ?x815), film(?x5940, ?x146) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0kbwb film! 039g82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 47.000 0.567 http://example.org/film/actor/film./film/performance/film EVAL 0kbwb film! 01k8rb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 47.000 0.567 http://example.org/film/actor/film./film/performance/film #6951-07ykkx5 PRED entity: 07ykkx5 PRED relation: film_crew_role PRED expected values: 02r96rf 0215hd => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 29): 02r96rf (0.69 #1372, 0.68 #653, 0.67 #750), 0dxtw (0.41 #1215, 0.38 #757, 0.38 #856), 01pvkk (0.35 #42, 0.32 #1216, 0.31 #463), 02ynfr (0.18 #665, 0.18 #762, 0.18 #861), 0215hd (0.16 #49, 0.14 #864, 0.14 #765), 01xy5l_ (0.11 #44, 0.11 #663, 0.11 #760), 02rh1dz (0.11 #104, 0.10 #1378, 0.10 #1214), 015h31 (0.09 #2652, 0.08 #298, 0.08 #103), 089fss (0.09 #2652, 0.08 #656, 0.07 #753), 04pyp5 (0.09 #2652, 0.07 #207, 0.06 #1058) >> Best rule #1372 for best value: >> intensional similarity = 6 >> extensional distance = 1079 >> proper extension: 0g56t9t; 09146g; 06wbm8q; 05q4y12; 0cw3yd; 0kv9d3; 0bs5k8r; 043n0v_; 03wh49y; 0ddj0x; ... >> query: (?x13178, 02r96rf) <- film(?x5197, ?x13178), film_crew_role(?x13178, ?x1284), film_crew_role(?x5185, ?x1284), film_crew_role(?x4007, ?x1284), ?x5185 = 0dl9_4, ?x4007 = 03hmt9b >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 07ykkx5 film_crew_role 0215hd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 82.000 0.694 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 07ykkx5 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.694 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6950-0gsrz4 PRED entity: 0gsrz4 PRED relation: time_zones! PRED expected values: 0fngy 0fq5j => 10 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1405): 0d060g (0.75 #6250, 0.71 #5001, 0.67 #1258), 09c7w0 (0.67 #1249, 0.57 #4992, 0.57 #3744), 01z215 (0.64 #11235, 0.59 #11237, 0.26 #1244), 05g2v (0.59 #11237, 0.10 #11238, 0.10 #7481), 0dg3n1 (0.59 #11237, 0.10 #7481), 02j71 (0.52 #6232), 04pnx (0.50 #1703, 0.43 #5446, 0.43 #4198), 06mzp (0.50 #1242, 0.33 #1243, 0.33 #35), 07fj_ (0.50 #1242, 0.33 #252, 0.26 #1244), 03rjj (0.50 #1242, 0.33 #7, 0.26 #1244) >> Best rule #6250 for best value: >> intensional similarity = 54 >> extensional distance = 6 >> proper extension: 05jphn; >> query: (?x6582, 0d060g) <- time_zones(?x13481, ?x6582), time_zones(?x8558, ?x6582), time_zones(?x2468, ?x6582), time_zones(?x1756, ?x6582), time_zones(?x910, ?x6582), organization(?x1756, ?x4753), adjustment_currency(?x1756, ?x170), official_language(?x910, ?x254), olympics(?x910, ?x2966), olympics(?x910, ?x1277), olympics(?x910, ?x775), olympics(?x910, ?x584), film_release_region(?x1150, ?x910), ?x2966 = 06sks6, medal(?x910, ?x1242), ?x1277 = 0swbd, ?x1242 = 02lq5w, jurisdiction_of_office(?x346, ?x1756), organization(?x910, ?x4403), administrative_area_type(?x2468, ?x2792), ?x584 = 0l98s, official_language(?x8558, ?x5607), ?x254 = 02h40lc, location_of_ceremony(?x566, ?x13481), film_release_region(?x1150, ?x3277), film_release_region(?x1150, ?x2513), film_release_region(?x1150, ?x2316), film_release_region(?x1150, ?x1353), film_release_region(?x1150, ?x789), film_release_region(?x1150, ?x456), film_release_region(?x1150, ?x429), film_release_region(?x1150, ?x410), film_release_region(?x1150, ?x304), film_release_region(?x1150, ?x47), ?x789 = 0f8l9c, ?x3277 = 06t8v, country(?x1121, ?x2468), ?x2316 = 06t2t, ?x1353 = 035qy, film(?x2372, ?x1150), nominated_for(?x704, ?x1150), ?x456 = 05qhw, ?x429 = 03rt9, ?x170 = 09nqf, ?x47 = 027rn, organization(?x6431, ?x4753), nationality(?x9672, ?x910), ?x775 = 0l998, ?x6431 = 01699, ?x410 = 01ls2, country(?x668, ?x910), contains(?x2467, ?x8558), ?x304 = 0d0vqn, ?x2513 = 05b4w >> conf = 0.75 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gsrz4 time_zones! 0fq5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 10.000 10.000 0.750 http://example.org/location/location/time_zones EVAL 0gsrz4 time_zones! 0fngy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 10.000 10.000 0.750 http://example.org/location/location/time_zones #6949-01flzq PRED entity: 01flzq PRED relation: parent_genre! PRED expected values: 036jv => 56 concepts (39 used for prediction) PRED predicted values (max 10 best out of 258): 06cp5 (0.50 #341, 0.33 #76, 0.29 #872), 036jv (0.50 #427, 0.33 #162, 0.29 #958), 04h6m (0.50 #327, 0.33 #62, 0.29 #858), 01flzq (0.50 #363, 0.33 #98, 0.29 #894), 0339z0 (0.50 #457, 0.33 #192, 0.29 #988), 01ym9b (0.33 #40, 0.27 #1366, 0.25 #305), 016_nr (0.33 #63, 0.25 #328, 0.20 #1389), 016_rm (0.33 #198, 0.25 #463, 0.17 #729), 0190zg (0.33 #214, 0.25 #479, 0.17 #745), 012yc (0.33 #124, 0.25 #389, 0.17 #655) >> Best rule #341 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 016_nr; >> query: (?x8123, 06cp5) <- artists(?x8123, ?x11123), artists(?x8123, ?x8124), artists(?x8123, ?x6264), ?x8124 = 03j149k, ?x11123 = 0k6yt1, participant(?x6264, ?x3054) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #427 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 016_nr; *> query: (?x8123, 036jv) <- artists(?x8123, ?x11123), artists(?x8123, ?x8124), artists(?x8123, ?x6264), ?x8124 = 03j149k, ?x11123 = 0k6yt1, participant(?x6264, ?x3054) *> conf = 0.50 ranks of expected_values: 2 EVAL 01flzq parent_genre! 036jv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 39.000 0.500 http://example.org/music/genre/parent_genre #6948-06gjk9 PRED entity: 06gjk9 PRED relation: country PRED expected values: 07ssc 04g61 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.84 #430, 0.82 #247, 0.82 #308), 07ssc (0.80 #139, 0.43 #17, 0.38 #5854), 03rt9 (0.49 #5792, 0.38 #5854, 0.37 #6347), 02jx1 (0.49 #5792, 0.38 #5854, 0.37 #6347), 06q1r (0.38 #5854, 0.37 #6347, 0.37 #3940), 0f8l9c (0.29 #20, 0.15 #6346, 0.11 #2174), 07s9rl0 (0.16 #2217, 0.09 #2216, 0.08 #1103), 01mjq (0.15 #6346, 0.14 #36, 0.01 #1140), 0345h (0.15 #6346, 0.13 #1503, 0.13 #640), 03rjj (0.15 #6346, 0.07 #313, 0.07 #190) >> Best rule #430 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 0c3ybss; 09m6kg; 011yrp; 0ds33; 0164qt; 0b6tzs; 0p9lw; 048scx; 0dtfn; 0168ls; ... >> query: (?x3283, 09c7w0) <- film(?x902, ?x3283), film_crew_role(?x3283, ?x6473), ?x6473 = 02vs3x5, film(?x3282, ?x3283) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #139 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 06nr2h; *> query: (?x3283, 07ssc) <- film(?x902, ?x3283), production_companies(?x3283, ?x9518), film(?x3282, ?x3283), ?x9518 = 0283xx2 *> conf = 0.80 ranks of expected_values: 2, 18 EVAL 06gjk9 country 04g61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 106.000 106.000 0.837 http://example.org/film/film/country EVAL 06gjk9 country 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 106.000 0.837 http://example.org/film/film/country #6947-0gys2jp PRED entity: 0gys2jp PRED relation: costume_design_by PRED expected values: 03cp7b3 => 76 concepts (48 used for prediction) PRED predicted values (max 10 best out of 15): 02pqgt8 (0.08 #40, 0.03 #68, 0.02 #354), 03mfqm (0.08 #46, 0.02 #448, 0.02 #911), 03y1mlp (0.08 #30, 0.02 #87, 0.02 #230), 0bytkq (0.08 #38), 02cqbx (0.03 #129, 0.03 #158, 0.03 #215), 0bytfv (0.02 #353, 0.02 #383, 0.02 #618), 02mxbd (0.02 #447, 0.02 #476, 0.02 #505), 03cp7b3 (0.02 #518, 0.02 #372, 0.02 #578), 014hdb (0.02 #518, 0.02 #372, 0.02 #578), 06w33f8 (0.02 #88, 0.01 #174) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0bz3jx; >> query: (?x11701, 02pqgt8) <- film(?x10271, ?x11701), language(?x11701, ?x2890), ?x2890 = 0653m, production_companies(?x11701, ?x1478) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #518 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 628 *> proper extension: 015qsq; 05jf85; 011yxg; 01hr1; 0ds11z; 050r1z; 0n0bp; 0170_p; 0209xj; 02py4c8; ... *> query: (?x11701, ?x9086) <- film(?x10271, ?x11701), nominated_for(?x7965, ?x11701), nominated_for(?x9086, ?x11701), language(?x11701, ?x254) *> conf = 0.02 ranks of expected_values: 8 EVAL 0gys2jp costume_design_by 03cp7b3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 76.000 48.000 0.077 http://example.org/film/film/costume_design_by #6946-01gvr1 PRED entity: 01gvr1 PRED relation: award_winner! PRED expected values: 0hr3c8y => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 132): 0hr3c8y (0.67 #10, 0.23 #433, 0.12 #151), 0clfdj (0.17 #4, 0.06 #145, 0.05 #1837), 09p3h7 (0.17 #71, 0.06 #212, 0.04 #1622), 0hhtgcw (0.17 #86, 0.05 #368, 0.05 #509), 0418154 (0.12 #249, 0.12 #672, 0.10 #390), 0hndn2q (0.12 #181, 0.08 #604, 0.05 #322), 09gkdln (0.12 #263, 0.05 #404, 0.05 #1955), 04n2r9h (0.12 #186, 0.05 #327, 0.04 #609), 09bymc (0.12 #262, 0.05 #1954, 0.02 #1672), 092_25 (0.12 #213, 0.05 #495, 0.04 #11422) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0h1nt; 09yhzs; 014g22; 03kbb8; >> query: (?x624, 0hr3c8y) <- award_winner(?x1253, ?x624), ?x1253 = 0gjvqm, award_winner(?x686, ?x624) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gvr1 award_winner! 0hr3c8y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6945-057_yx PRED entity: 057_yx PRED relation: award_nominee PRED expected values: 01vyv9 => 83 concepts (26 used for prediction) PRED predicted values (max 10 best out of 762): 057_yx (0.86 #4540, 0.80 #2207, 0.75 #6873), 015vq_ (0.81 #7000, 0.81 #6998, 0.81 #32653), 016ks_ (0.81 #7000, 0.81 #6998, 0.81 #4665), 02ct_k (0.81 #7000, 0.81 #6998, 0.81 #4665), 03q95r (0.81 #7000, 0.81 #6998, 0.81 #4665), 01vyv9 (0.71 #3404, 0.60 #1071, 0.55 #5737), 02yxwd (0.28 #32654, 0.24 #25654, 0.17 #7001), 0gnbw (0.28 #32654, 0.24 #25654, 0.17 #7001), 022g44 (0.28 #32654, 0.24 #25654, 0.17 #7001), 0739z6 (0.28 #32654, 0.17 #7001, 0.16 #60643) >> Best rule #4540 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 017149; 021vwt; 03q1vd; 02jsgf; 0410cp; 014g22; 016ks_; 03q95r; 034zc0; 02ch1w; ... >> query: (?x11100, 057_yx) <- award_nominee(?x4128, ?x11100), award_nominee(?x3932, ?x11100), ?x4128 = 015vq_, ?x3932 = 050t68, award(?x11100, ?x704) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #3404 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 017149; 021vwt; 03q1vd; 02jsgf; 0410cp; 014g22; 016ks_; 03q95r; 034zc0; 02ch1w; ... *> query: (?x11100, 01vyv9) <- award_nominee(?x4128, ?x11100), award_nominee(?x3932, ?x11100), ?x4128 = 015vq_, ?x3932 = 050t68, award(?x11100, ?x704) *> conf = 0.71 ranks of expected_values: 6 EVAL 057_yx award_nominee 01vyv9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 83.000 26.000 0.857 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #6944-02s4l6 PRED entity: 02s4l6 PRED relation: nominated_for! PRED expected values: 02r0csl 02x4wr9 => 76 concepts (63 used for prediction) PRED predicted values (max 10 best out of 215): 0gq_v (0.56 #20, 0.43 #5060, 0.42 #260), 0gq9h (0.54 #5104, 0.32 #5344, 0.29 #3904), 0gs9p (0.42 #5106, 0.27 #546, 0.27 #5346), 0k611 (0.41 #5115, 0.27 #315, 0.24 #555), 019f4v (0.38 #5095, 0.32 #55, 0.27 #5335), 0gqy2 (0.36 #604, 0.27 #5164, 0.23 #2284), 0p9sw (0.33 #5061, 0.24 #1461, 0.24 #21), 099c8n (0.33 #538, 0.21 #2698, 0.21 #5098), 0gr0m (0.30 #5101, 0.24 #3661, 0.20 #3901), 02pqp12 (0.30 #540, 0.22 #5100, 0.20 #2940) >> Best rule #20 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 06wzvr; 02q_4ph; >> query: (?x2287, 0gq_v) <- titles(?x714, ?x2287), titles(?x307, ?x2287), ?x307 = 04t36, costume_design_by(?x2287, ?x5613), genre(?x161, ?x714) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #13448 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1265 *> proper extension: 0cc97st; 0cmf0m0; *> query: (?x2287, ?x746) <- nominated_for(?x4320, ?x2287), film(?x2125, ?x2287), nominated_for(?x2222, ?x2287), award(?x4320, ?x746) *> conf = 0.20 ranks of expected_values: 30, 43 EVAL 02s4l6 nominated_for! 02x4wr9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 76.000 63.000 0.560 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02s4l6 nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 76.000 63.000 0.560 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6943-09qvf4 PRED entity: 09qvf4 PRED relation: ceremony PRED expected values: 07y_p6 0bx6zs => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 132): 0gpjbt (0.36 #3305, 0.34 #3699, 0.34 #3174), 09n4nb (0.35 #3321, 0.34 #3715, 0.33 #3584), 0466p0j (0.35 #3346, 0.33 #3740, 0.33 #3215), 02cg41 (0.35 #3394, 0.33 #3788, 0.32 #3263), 02rjjll (0.34 #3283, 0.33 #3677, 0.33 #3152), 056878 (0.34 #3307, 0.34 #3701, 0.33 #3176), 05pd94v (0.33 #3280, 0.33 #3674, 0.33 #3149), 01c6qp (0.33 #3296, 0.33 #3690, 0.32 #3559), 01mh_q (0.32 #3358, 0.31 #3752, 0.31 #3621), 01bx35 (0.32 #3285, 0.32 #3679, 0.31 #3548) >> Best rule #3305 for best value: >> intensional similarity = 3 >> extensional distance = 229 >> proper extension: 0bwgmzd; >> query: (?x4225, 0gpjbt) <- ceremony(?x4225, ?x4760), honored_for(?x4760, ?x1631), award_winner(?x4760, ?x829) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #263 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 52 *> proper extension: 02py_sj; *> query: (?x4225, ?x747) <- award(?x5386, ?x4225), award(?x2528, ?x4225), honored_for(?x747, ?x5386), nominated_for(?x2307, ?x5386), languages(?x2528, ?x254) *> conf = 0.25 ranks of expected_values: 26, 27 EVAL 09qvf4 ceremony 0bx6zs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 52.000 52.000 0.364 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09qvf4 ceremony 07y_p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 52.000 52.000 0.364 http://example.org/award/award_category/winners./award/award_honor/ceremony #6942-01ls2 PRED entity: 01ls2 PRED relation: medal PRED expected values: 02lq5w 02lpp7 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 02lpp7 (0.81 #12, 0.76 #14, 0.74 #6), 02lq5w (0.79 #13, 0.78 #11, 0.74 #5) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 05r4w; 09c7w0; 0b90_r; 0154j; 03rjj; 0d060g; 0d0vqn; 0chghy; 03rt9; 05qhw; ... >> query: (?x410, 02lpp7) <- film_release_region(?x5142, ?x410), ?x5142 = 0bt3j9, adjoins(?x583, ?x410) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01ls2 medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.812 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 01ls2 medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.812 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #6941-0415mzy PRED entity: 0415mzy PRED relation: profession PRED expected values: 047rgpy => 103 concepts (58 used for prediction) PRED predicted values (max 10 best out of 70): 09jwl (0.76 #759, 0.71 #611, 0.68 #907), 02hrh1q (0.66 #6400, 0.66 #8331, 0.63 #5212), 016z4k (0.51 #1631, 0.45 #1779, 0.39 #2374), 01c72t (0.32 #1060, 0.29 #616, 0.28 #764), 01d_h8 (0.31 #6687, 0.30 #6391, 0.29 #8322), 03gjzk (0.29 #15, 0.23 #6697, 0.22 #8332), 0fnpj (0.28 #800, 0.25 #1096, 0.25 #948), 0dxtg (0.28 #6695, 0.24 #8330, 0.24 #8478), 012t_z (0.27 #2670, 0.14 #12, 0.06 #7429), 0n1h (0.24 #1787, 0.24 #1639, 0.23 #1491) >> Best rule #759 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 01vvydl; 01vvycq; 03f5spx; 02r4qs; 012x4t; 09mq4m; 0892sx; 01wwvc5; 03bxwtd; 01w806h; ... >> query: (?x5544, 09jwl) <- award(?x5544, ?x1827), ?x1827 = 02nhxf, artists(?x474, ?x5544), profession(?x5544, ?x131) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1145 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 01wwvd2; 02_jkc; 05mxw33; 02qtywd; *> query: (?x5544, 047rgpy) <- award(?x5544, ?x1827), ?x1827 = 02nhxf, award_nominee(?x527, ?x5544), profession(?x5544, ?x131) *> conf = 0.07 ranks of expected_values: 28 EVAL 0415mzy profession 047rgpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 103.000 58.000 0.760 http://example.org/people/person/profession #6940-0cz_ym PRED entity: 0cz_ym PRED relation: production_companies PRED expected values: 04rtpt => 67 concepts (49 used for prediction) PRED predicted values (max 10 best out of 65): 05qd_ (0.31 #3115, 0.31 #1355, 0.31 #637), 03sb38 (0.25 #51, 0.05 #130, 0.03 #210), 086k8 (0.12 #1838, 0.12 #1037, 0.12 #1357), 030_1_ (0.11 #253, 0.05 #332, 0.04 #1130), 046b0s (0.10 #101, 0.03 #260, 0.03 #418), 017s11 (0.09 #1118, 0.09 #1038, 0.08 #1839), 016tt2 (0.09 #1119, 0.08 #1359, 0.08 #1840), 01gb54 (0.07 #273, 0.07 #431, 0.06 #751), 0kx4m (0.07 #247, 0.02 #405, 0.02 #1124), 014v6f (0.06 #1436, 0.05 #1195, 0.05 #1435) >> Best rule #3115 for best value: >> intensional similarity = 2 >> extensional distance = 1225 >> proper extension: 0bmc4cm; 016kz1; 011yfd; 076xkdz; 05y0cr; >> query: (?x1877, ?x902) <- film(?x902, ?x1877), nominated_for(?x68, ?x1877) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #761 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 591 *> proper extension: 0fq27fp; *> query: (?x1877, 04rtpt) <- film_crew_role(?x1877, ?x1171), currency(?x1877, ?x170), ?x1171 = 09vw2b7 *> conf = 0.03 ranks of expected_values: 40 EVAL 0cz_ym production_companies 04rtpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 67.000 49.000 0.313 http://example.org/film/film/production_companies #6939-0jfqp PRED entity: 0jfqp PRED relation: teams PRED expected values: 026xxv_ => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 159): 02ptzz0 (0.06 #69, 0.04 #429, 0.02 #789), 0263cyj (0.06 #199, 0.04 #559, 0.02 #2720), 01k8vh (0.06 #268, 0.04 #628, 0.01 #4950), 01y3c (0.06 #20, 0.04 #380, 0.01 #4702), 0cqt41 (0.04 #2551, 0.03 #3991, 0.02 #750), 0jmfv (0.02 #745, 0.02 #1106, 0.02 #1826), 01d6g (0.02 #913, 0.02 #1274, 0.02 #1994), 02__x (0.02 #835, 0.02 #1196, 0.02 #1916), 0jm2v (0.02 #748, 0.02 #1109, 0.02 #1829), 0jnr_ (0.02 #981, 0.02 #1342, 0.02 #2062) >> Best rule #69 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0n4mk; 0n491; >> query: (?x8322, 02ptzz0) <- source(?x8322, ?x958), contains(?x760, ?x8322), ?x760 = 05fkf >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jfqp teams 026xxv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 155.000 0.062 http://example.org/sports/sports_team_location/teams #6938-01my4f PRED entity: 01my4f PRED relation: currency PRED expected values: 09nqf => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.38 #19, 0.37 #7, 0.33 #22) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 05cj4r; 0pz7h; 01_x6d; 04g3p5; 0bdt8; 04pg29; >> query: (?x6913, 09nqf) <- award(?x6913, ?x2016), program_creator(?x782, ?x6913), award_winner(?x747, ?x6913) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01my4f currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.383 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #6937-016732 PRED entity: 016732 PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 49): 09c7w0 (0.79 #1604, 0.78 #1102, 0.78 #2006), 02jx1 (0.34 #8319, 0.25 #133, 0.14 #1334), 03gyl (0.34 #8319, 0.20 #66, 0.02 #10925), 07ssc (0.34 #8319, 0.12 #115, 0.10 #1316), 0d060g (0.07 #507, 0.07 #1308, 0.05 #407), 043g7l (0.06 #1502, 0.04 #1804, 0.03 #2005), 03rk0 (0.06 #7661, 0.06 #10169, 0.05 #5058), 0345h (0.06 #1432, 0.03 #1935, 0.03 #1734), 03rt9 (0.04 #213, 0.03 #1014, 0.03 #313), 03spz (0.04 #267, 0.03 #668, 0.03 #1168) >> Best rule #1604 for best value: >> intensional similarity = 3 >> extensional distance = 193 >> proper extension: 0pmw9; 0280mv7; 06z4wj; 01h5f8; >> query: (?x6792, 09c7w0) <- award_winner(?x6819, ?x6792), artists(?x283, ?x6819), student(?x7545, ?x6792) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016732 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.790 http://example.org/people/person/nationality #6936-07gp9 PRED entity: 07gp9 PRED relation: film_release_region PRED expected values: 03gj2 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 111): 03rjj (0.84 #1927, 0.82 #2407, 0.80 #1767), 05r4w (0.83 #1923, 0.82 #2403, 0.81 #1763), 03gj2 (0.81 #1947, 0.77 #2267, 0.77 #2427), 035qy (0.80 #1957, 0.75 #2437, 0.73 #2277), 05qhw (0.77 #1937, 0.74 #2417, 0.69 #2257), 0154j (0.77 #1926, 0.72 #2406, 0.68 #2246), 0b90_r (0.75 #1925, 0.70 #2405, 0.67 #2245), 05b4w (0.73 #1989, 0.71 #2469, 0.67 #2309), 01znc_ (0.73 #1965, 0.72 #1004, 0.71 #1164), 06bnz (0.71 #1970, 0.67 #2450, 0.64 #2290) >> Best rule #1927 for best value: >> intensional similarity = 5 >> extensional distance = 220 >> proper extension: 09v42sf; >> query: (?x324, 03rjj) <- film(?x2387, ?x324), film_release_region(?x324, ?x1229), film_release_region(?x324, ?x583), ?x1229 = 059j2, ?x583 = 015fr >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1947 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 220 *> proper extension: 09v42sf; *> query: (?x324, 03gj2) <- film(?x2387, ?x324), film_release_region(?x324, ?x1229), film_release_region(?x324, ?x583), ?x1229 = 059j2, ?x583 = 015fr *> conf = 0.81 ranks of expected_values: 3 EVAL 07gp9 film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 106.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6935-0294fd PRED entity: 0294fd PRED relation: award_winner! PRED expected values: 099tbz => 75 concepts (59 used for prediction) PRED predicted values (max 10 best out of 204): 09sb52 (0.74 #41, 0.63 #473, 0.34 #12963), 099tbz (0.68 #58, 0.58 #490, 0.09 #14693), 07h0cl (0.34 #12963, 0.31 #22473, 0.31 #23339), 0ck27z (0.11 #957, 0.11 #3981, 0.09 #1389), 05zr6wv (0.11 #450, 0.11 #18, 0.09 #14693), 0cqhk0 (0.09 #901, 0.08 #3925, 0.06 #1333), 02n9nmz (0.09 #14693, 0.07 #15126, 0.05 #19448), 0gqyl (0.09 #14693, 0.07 #6913, 0.05 #538), 09td7p (0.09 #14693, 0.07 #6913, 0.05 #554), 099cng (0.09 #14693, 0.07 #6913, 0.05 #519) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 02gvwz; 0241jw; >> query: (?x4153, 09sb52) <- award_nominee(?x4153, ?x5282), ?x5282 = 02ck7w, award_winner(?x2728, ?x4153) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 02gvwz; 0241jw; *> query: (?x4153, 099tbz) <- award_nominee(?x4153, ?x5282), ?x5282 = 02ck7w, award_winner(?x2728, ?x4153) *> conf = 0.68 ranks of expected_values: 2 EVAL 0294fd award_winner! 099tbz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 59.000 0.737 http://example.org/award/award_category/winners./award/award_honor/award_winner #6934-02pqs8l PRED entity: 02pqs8l PRED relation: nominated_for! PRED expected values: 0gkts9 => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 169): 0gq9h (0.36 #4349, 0.35 #4587, 0.34 #4825), 0gs9p (0.32 #4351, 0.31 #4589, 0.31 #4827), 019f4v (0.32 #4340, 0.30 #4578, 0.30 #4816), 0k611 (0.27 #4359, 0.26 #4597, 0.26 #4835), 0gq_v (0.26 #4305, 0.26 #4543, 0.26 #4781), 040njc (0.26 #4292, 0.25 #4530, 0.25 #4768), 04dn09n (0.24 #5000, 0.23 #4321, 0.21 #4559), 0gqyl (0.24 #5000, 0.20 #4366, 0.20 #4604), 09qs08 (0.24 #5000, 0.19 #8813, 0.19 #8812), 0gr51 (0.24 #5000, 0.19 #8813, 0.19 #8812) >> Best rule #4349 for best value: >> intensional similarity = 2 >> extensional distance = 503 >> proper extension: 0hmr4; 044g_k; 02rjv2w; 019kyn; 01c9d; 072hx4; 06mmr; >> query: (?x3822, 0gq9h) <- award_winner(?x3822, ?x843), honored_for(?x1265, ?x3822) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #5000 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 558 *> proper extension: 04dsnp; 04m1bm; 091z_p; 047n8xt; 0j6b5; 078sj4; 07yvsn; 0pd6l; 02kfzz; 0k5fg; ... *> query: (?x3822, ?x1670) <- nominated_for(?x2076, ?x3822), award(?x2076, ?x1670), honored_for(?x1265, ?x3822) *> conf = 0.24 ranks of expected_values: 15 EVAL 02pqs8l nominated_for! 0gkts9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 41.000 41.000 0.364 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6933-0t015 PRED entity: 0t015 PRED relation: category PRED expected values: 08mbj5d => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #16, 0.82 #38, 0.82 #37) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 013f9v; 0114m0; >> query: (?x310, 08mbj5d) <- time_zones(?x310, ?x1638), county_seat(?x4212, ?x310), contains(?x961, ?x310), ?x1638 = 02fqwt >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0t015 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.828 http://example.org/common/topic/webpage./common/webpage/category #6932-017m2y PRED entity: 017m2y PRED relation: participant PRED expected values: 037gjc => 162 concepts (57 used for prediction) PRED predicted values (max 10 best out of 494): 02dlfh (0.84 #26547, 0.82 #31731, 0.82 #32380), 037gjc (0.84 #26547, 0.82 #31731, 0.82 #32380), 0zjpz (0.54 #5826, 0.52 #7124, 0.47 #6474), 01q7cb_ (0.25 #711, 0.09 #2005, 0.09 #2651), 02qjj7 (0.25 #666, 0.09 #2606, 0.04 #4550), 015882 (0.20 #115, 0.10 #1409, 0.03 #7239), 0kszw (0.20 #172, 0.10 #1466, 0.01 #7296), 01ccr8 (0.20 #520, 0.10 #1814, 0.01 #7644), 01vhb0 (0.20 #153, 0.10 #1447, 0.01 #7277), 02fybl (0.13 #2409, 0.09 #3055, 0.06 #8240) >> Best rule #26547 for best value: >> intensional similarity = 4 >> extensional distance = 194 >> proper extension: 06y9c2; 01pl9g; >> query: (?x9276, ?x4882) <- participant(?x9276, ?x3422), spouse(?x9276, ?x1970), profession(?x9276, ?x319), participant(?x4882, ?x9276) >> conf = 0.84 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 017m2y participant 037gjc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 162.000 57.000 0.838 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #6931-04rlf PRED entity: 04rlf PRED relation: genre! PRED expected values: 01b7h8 => 103 concepts (31 used for prediction) PRED predicted values (max 10 best out of 276): 03ln8b (0.60 #4799, 0.43 #8368, 0.38 #8965), 05f4vxd (0.50 #3067, 0.43 #8428, 0.38 #9025), 0cwrr (0.50 #2990, 0.29 #8053, 0.25 #3587), 02rkkn1 (0.50 #3246, 0.25 #3843, 0.14 #8607), 016zfm (0.50 #3089, 0.25 #3686, 0.14 #8450), 0266s9 (0.43 #8590, 0.40 #5021, 0.38 #9187), 099pks (0.43 #8438, 0.38 #9035, 0.33 #699), 0123qq (0.43 #8575, 0.38 #9172, 0.33 #836), 0cs134 (0.43 #8568, 0.38 #9165, 0.33 #829), 02py9yf (0.43 #8567, 0.38 #9164, 0.33 #828) >> Best rule #4799 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 02n4kr; 02l7c8; >> query: (?x8681, 03ln8b) <- genre(?x3201, ?x8681), genre(?x2287, ?x8681), genre(?x903, ?x8681), ?x3201 = 01ffx4, film_release_region(?x903, ?x1353), film(?x8118, ?x903), music(?x2287, ?x4644), produced_by(?x2287, ?x7324), ?x1353 = 035qy >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04rlf genre! 01b7h8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 31.000 0.600 http://example.org/tv/tv_program/genre #6930-0kp2_ PRED entity: 0kp2_ PRED relation: nationality PRED expected values: 09c7w0 => 199 concepts (194 used for prediction) PRED predicted values (max 10 best out of 88): 09c7w0 (0.89 #14122, 0.88 #4103, 0.85 #11809), 0345h (0.40 #401, 0.25 #432, 0.12 #1332), 06pvr (0.34 #15627, 0.33 #14121), 07ssc (0.33 #15, 0.24 #2417, 0.22 #3417), 02jx1 (0.26 #4737, 0.22 #5637, 0.21 #5337), 01x73 (0.20 #11407, 0.01 #4504), 01p726 (0.20 #11407), 059g4 (0.20 #11407), 0d060g (0.16 #2609, 0.08 #7111, 0.07 #3609), 035qy (0.10 #635, 0.02 #18235) >> Best rule #14122 for best value: >> intensional similarity = 3 >> extensional distance = 913 >> proper extension: 02mslq; 05typm; >> query: (?x6795, 09c7w0) <- place_of_birth(?x6795, ?x3125), location(?x399, ?x3125), dog_breed(?x3125, ?x1706) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kp2_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 194.000 0.887 http://example.org/people/person/nationality #6929-0b_5d PRED entity: 0b_5d PRED relation: film_release_region PRED expected values: 030qb3t 03h64 082fr => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 89): 09c7w0 (0.92 #9721, 0.92 #9243, 0.91 #6536), 06mkj (0.82 #1975, 0.32 #6594, 0.27 #9301), 0chghy (0.77 #1926, 0.29 #6545, 0.25 #9252), 0345h (0.75 #1951, 0.29 #6570, 0.25 #9277), 0jgd (0.74 #1919, 0.28 #6538, 0.24 #9245), 03h64 (0.74 #1986, 0.26 #6605, 0.22 #9312), 03gj2 (0.71 #1942, 0.36 #5102, 0.34 #8762), 015fr (0.68 #1934, 0.25 #6553, 0.21 #9260), 05qhw (0.67 #1931, 0.24 #6550, 0.20 #9257), 0d060g (0.65 #1922, 0.34 #8762, 0.31 #10674) >> Best rule #9721 for best value: >> intensional similarity = 2 >> extensional distance = 1319 >> proper extension: 02d413; 0g22z; 018js4; 0b2v79; 01jc6q; 027qgy; 047q2k1; 0ckr7s; 08lr6s; 016fyc; ... >> query: (?x2958, 09c7w0) <- film_release_region(?x2958, ?x87), genre(?x2958, ?x53) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1986 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 326 *> proper extension: 014lc_; 0401sg; 087wc7n; 04969y; 0m_mm; 0h3xztt; 03bx2lk; 053tj7; 0fq7dv_; 01fmys; ... *> query: (?x2958, 03h64) <- film_release_region(?x2958, ?x205), genre(?x2958, ?x53), ?x205 = 03rjj *> conf = 0.74 ranks of expected_values: 6, 45, 61 EVAL 0b_5d film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 78.000 78.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b_5d film_release_region 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 78.000 78.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b_5d film_release_region 030qb3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 78.000 78.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6928-0633p0 PRED entity: 0633p0 PRED relation: gender PRED expected values: 02zsn => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #7, 0.84 #5, 0.76 #157), 02zsn (0.39 #16, 0.34 #20, 0.34 #10) >> Best rule #7 for best value: >> intensional similarity = 2 >> extensional distance = 113 >> proper extension: 04xjp; 0d5_f; 01v9724; 014nvr; 0739y; 07ym0; 0c1fs; 06hgj; 01vh096; 0h336; ... >> query: (?x9397, 05zppz) <- profession(?x9397, ?x6421), ?x6421 = 02hv44_ >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 287 *> proper extension: 0fb7c; 01xg_w; *> query: (?x9397, 02zsn) <- award(?x9397, ?x678), award(?x5944, ?x678), ?x5944 = 03q3sy *> conf = 0.39 ranks of expected_values: 2 EVAL 0633p0 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.870 http://example.org/people/person/gender #6927-01d0fp PRED entity: 01d0fp PRED relation: film PRED expected values: 035s95 => 94 concepts (62 used for prediction) PRED predicted values (max 10 best out of 790): 0dr_4 (0.62 #14305, 0.46 #28611, 0.43 #28610), 01shy7 (0.05 #9362, 0.04 #18303, 0.03 #14727), 0blpg (0.04 #655, 0.03 #2443, 0.03 #4231), 03bzjpm (0.04 #1314, 0.03 #4890, 0.03 #8466), 0ch3qr1 (0.04 #975, 0.03 #4551, 0.02 #2763), 09cr8 (0.04 #2071, 0.04 #3859, 0.03 #5647), 011ycb (0.04 #2644, 0.03 #856, 0.02 #4432), 011ywj (0.03 #21104, 0.03 #6799, 0.03 #28256), 08r4x3 (0.03 #9093, 0.03 #14458, 0.03 #21610), 02z3r8t (0.03 #9047, 0.02 #12623, 0.02 #21564) >> Best rule #14305 for best value: >> intensional similarity = 2 >> extensional distance = 304 >> proper extension: 01t07j; 04cbtrw; 0gv40; 012vct; >> query: (?x4930, ?x1496) <- participant(?x4324, ?x4930), award_winner(?x1496, ?x4930) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #12855 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 304 *> proper extension: 01t07j; 04cbtrw; 0gv40; 012vct; *> query: (?x4930, 035s95) <- participant(?x4324, ?x4930), award_winner(?x1496, ?x4930) *> conf = 0.03 ranks of expected_values: 35 EVAL 01d0fp film 035s95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 94.000 62.000 0.622 http://example.org/film/actor/film./film/performance/film #6926-04jvt PRED entity: 04jvt PRED relation: notable_people_with_this_condition! PRED expected values: 02vrr => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 16): 0g02vk (0.20 #279, 0.14 #390, 0.12 #211), 02vrr (0.14 #180, 0.14 #158, 0.14 #136), 06vr2 (0.12 #216, 0.11 #239, 0.08 #527), 068p_ (0.11 #264, 0.10 #331, 0.05 #642), 0dcsx (0.11 #248, 0.10 #315, 0.04 #558), 01g2q (0.10 #298, 0.06 #788, 0.06 #587), 012hw (0.10 #267, 0.03 #578, 0.03 #577), 051_y (0.10 #267, 0.03 #578, 0.03 #577), 03p41 (0.08 #361, 0.05 #494, 0.02 #762), 07jwr (0.07 #380, 0.02 #714, 0.02 #781) >> Best rule #279 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 07ssc; 04g61; >> query: (?x9851, 0g02vk) <- organizations_founded(?x9851, ?x12362), entity_involved(?x7241, ?x9851), locations(?x7241, ?x8588), combatants(?x7241, ?x94), country(?x668, ?x8588) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #180 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 03pn9; 01h3dj; 012m_; 0212ny; *> query: (?x9851, 02vrr) <- entity_involved(?x7241, ?x9851), ?x7241 = 06k75 *> conf = 0.14 ranks of expected_values: 2 EVAL 04jvt notable_people_with_this_condition! 02vrr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 135.000 0.200 http://example.org/medicine/disease/notable_people_with_this_condition #6925-07cw4 PRED entity: 07cw4 PRED relation: currency PRED expected values: 09nqf => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.85 #15, 0.80 #22, 0.79 #127), 01nv4h (0.05 #72, 0.05 #51, 0.04 #100), 02l6h (0.04 #102, 0.04 #123, 0.03 #151), 02gsvk (0.02 #174, 0.01 #160, 0.01 #167), 0kz1h (0.02 #54), 088n7 (0.01 #147) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 02ll45; 0h95927; 01xbxn; >> query: (?x5930, 09nqf) <- film(?x1554, ?x5930), film_crew_role(?x5930, ?x137), award_winner(?x5930, ?x9946), ?x1554 = 06cgy >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07cw4 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.846 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #6924-03h_0_z PRED entity: 03h_0_z PRED relation: nationality PRED expected values: 09c7w0 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.78 #2610, 0.77 #1704, 0.76 #9334), 0nzny (0.27 #10642, 0.27 #11451, 0.23 #4325), 0mpzm (0.27 #10642, 0.27 #11451, 0.23 #4325), 0d0x8 (0.27 #10642, 0.27 #11451, 0.23 #4325), 02jx1 (0.14 #2240, 0.13 #5460, 0.12 #6361), 03rk0 (0.10 #5673, 0.06 #11497, 0.05 #11798), 0d060g (0.09 #407, 0.07 #6735, 0.06 #9340), 07ssc (0.07 #12068, 0.07 #5042, 0.07 #11767), 0345h (0.07 #331, 0.04 #1033, 0.04 #833), 0vzm (0.05 #2609, 0.04 #2006) >> Best rule #2610 for best value: >> intensional similarity = 3 >> extensional distance = 339 >> proper extension: 0mdyn; >> query: (?x6144, 09c7w0) <- participant(?x5798, ?x6144), participant(?x6144, ?x1896), profession(?x6144, ?x131) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03h_0_z nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.777 http://example.org/people/person/nationality #6923-040b5k PRED entity: 040b5k PRED relation: film_release_region PRED expected values: 05r4w 0154j 05qhw 0345h 06qd3 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 157): 06mkj (0.87 #1458, 0.86 #1881, 0.84 #47), 05qhw (0.87 #13, 0.67 #1424, 0.66 #719), 05r4w (0.84 #1413, 0.81 #990, 0.81 #708), 0154j (0.81 #4, 0.70 #1415, 0.68 #992), 0345h (0.77 #26, 0.75 #1437, 0.74 #1860), 06qd3 (0.65 #30, 0.55 #736, 0.54 #1018), 04gzd (0.51 #8, 0.39 #1419, 0.39 #714), 0ctw_b (0.49 #21, 0.43 #1432, 0.43 #1855), 016wzw (0.49 #55, 0.43 #1466, 0.39 #1889), 05qx1 (0.48 #33, 0.35 #1444, 0.35 #739) >> Best rule #1458 for best value: >> intensional similarity = 5 >> extensional distance = 171 >> proper extension: 02d44q; 047svrl; 0gh8zks; 0hgnl3t; 07k2mq; 0372j5; >> query: (?x2889, 06mkj) <- film_release_region(?x2889, ?x252), film_release_region(?x2889, ?x205), ?x205 = 03rjj, nominated_for(?x3574, ?x2889), ?x252 = 03_3d >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 73 *> proper extension: 01shy7; *> query: (?x2889, 05qhw) <- genre(?x2889, ?x53), film_release_region(?x2889, ?x2267), film_release_region(?x2889, ?x1355), ?x1355 = 0h7x, ?x2267 = 03rj0 *> conf = 0.87 ranks of expected_values: 2, 3, 4, 5, 6 EVAL 040b5k film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 040b5k film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 040b5k film_release_region 05qhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 040b5k film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 040b5k film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6922-01vdm0 PRED entity: 01vdm0 PRED relation: instrumentalists PRED expected values: 0ftqr => 62 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1031): 01vsy7t (0.72 #1237, 0.71 #1236, 0.67 #7419), 06cc_1 (0.72 #1237, 0.71 #1236, 0.67 #7419), 01vrncs (0.72 #1237, 0.71 #1236, 0.67 #7419), 0140t7 (0.72 #1237, 0.71 #1236, 0.67 #7419), 01wl38s (0.72 #1237, 0.71 #1236, 0.67 #7419), 018x3 (0.72 #1237, 0.71 #1236, 0.67 #7419), 01vsy95 (0.72 #1237, 0.71 #1236, 0.67 #7419), 01w724 (0.72 #1237, 0.71 #1236, 0.67 #7419), 01xzb6 (0.72 #1237, 0.71 #1236, 0.67 #7419), 0473q (0.72 #1237, 0.71 #1236, 0.67 #7419) >> Best rule #1237 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 0342h; >> query: (?x1437, ?x2784) <- role(?x6609, ?x1437), role(?x5170, ?x1437), role(?x4693, ?x1437), role(?x2784, ?x1437), role(?x1715, ?x1437), ?x5170 = 01vswx5, role(?x1437, ?x4471), role(?x1437, ?x3703), instrumentalists(?x4471, ?x1073), ?x6609 = 01tv3x2, role(?x316, ?x1437), ?x1715 = 04bpm6, ?x4693 = 01vwbts, award(?x2784, ?x1565), role(?x645, ?x3703) >> conf = 0.72 => this is the best rule for 93 predicted values *> Best rule #1241 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x1437, ?x1073) <- role(?x6609, ?x1437), role(?x5170, ?x1437), role(?x4693, ?x1437), role(?x2784, ?x1437), role(?x1715, ?x1437), ?x5170 = 01vswx5, role(?x1437, ?x4471), role(?x1437, ?x3703), instrumentalists(?x4471, ?x1073), ?x6609 = 01tv3x2, role(?x316, ?x1437), ?x1715 = 04bpm6, ?x4693 = 01vwbts, award(?x2784, ?x1565), role(?x645, ?x3703) *> conf = 0.55 ranks of expected_values: 379 EVAL 01vdm0 instrumentalists 0ftqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 50.000 0.724 http://example.org/music/instrument/instrumentalists #6921-0fsd9t PRED entity: 0fsd9t PRED relation: film! PRED expected values: 0g1rw => 78 concepts (66 used for prediction) PRED predicted values (max 10 best out of 53): 0283xx2 (0.40 #1785, 0.38 #1189, 0.38 #1264), 086k8 (0.33 #1, 0.25 #75, 0.22 #446), 016tw3 (0.25 #306, 0.19 #678, 0.17 #1720), 025jfl (0.25 #79, 0.06 #301, 0.05 #227), 0338lq (0.25 #80, 0.03 #377, 0.02 #1866), 05qd_ (0.15 #453, 0.13 #379, 0.13 #1047), 017jv5 (0.15 #459, 0.05 #1053, 0.04 #1874), 017s11 (0.15 #224, 0.14 #1414, 0.13 #1266), 024rgt (0.12 #315, 0.11 #390, 0.06 #1132), 054g1r (0.12 #182, 0.10 #924, 0.08 #1298) >> Best rule #1785 for best value: >> intensional similarity = 5 >> extensional distance = 551 >> proper extension: 0gtsx8c; 0gh8zks; 07kb7vh; 07k2mq; 01gglm; >> query: (?x8729, ?x9518) <- film_crew_role(?x8729, ?x1284), film(?x891, ?x8729), ?x1284 = 0ch6mp2, production_companies(?x8729, ?x9518), language(?x8729, ?x254) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #155 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 05z43v; *> query: (?x8729, 0g1rw) <- genre(?x8729, ?x53), film(?x4859, ?x8729), film(?x891, ?x8729), award_nominee(?x891, ?x3866), country(?x8729, ?x512), film_release_distribution_medium(?x8729, ?x81), ?x4859 = 05y5kf *> conf = 0.12 ranks of expected_values: 11 EVAL 0fsd9t film! 0g1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 78.000 66.000 0.403 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #6920-0j1yf PRED entity: 0j1yf PRED relation: category PRED expected values: 08mbj5d => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #4, 0.79 #30, 0.78 #36) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 012x4t; 01trhmt; >> query: (?x1896, 08mbj5d) <- award_nominee(?x1896, ?x959), friend(?x1896, ?x3503), artist(?x5836, ?x1896) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j1yf category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.846 http://example.org/common/topic/webpage./common/webpage/category #6919-05rznz PRED entity: 05rznz PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.90 #7, 0.87 #35, 0.86 #23) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 0160w; 01ppq; >> query: (?x13717, 0hzc9wc) <- time_zones(?x13717, ?x6582), organization(?x13717, ?x312), form_of_government(?x13717, ?x48) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05rznz administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.896 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #6918-0125xq PRED entity: 0125xq PRED relation: genre PRED expected values: 03k9fj => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 117): 07s9rl0 (0.70 #848, 0.66 #1211, 0.64 #4116), 01hmnh (0.61 #7636, 0.53 #5572, 0.49 #6907), 0lsxr (0.60 #9, 0.42 #130, 0.24 #1219), 01jfsb (0.50 #134, 0.49 #1102, 0.46 #497), 05p553 (0.49 #2061, 0.40 #730, 0.36 #3392), 03k9fj (0.47 #4611, 0.47 #1101, 0.45 #496), 02l7c8 (0.28 #5466, 0.27 #7530, 0.27 #1226), 04xvlr (0.17 #6665, 0.17 #5452, 0.16 #7516), 02n4kr (0.17 #371, 0.16 #250, 0.14 #2791), 060__y (0.17 #1227, 0.16 #2316, 0.15 #5467) >> Best rule #848 for best value: >> intensional similarity = 2 >> extensional distance = 140 >> proper extension: 02vl9ln; >> query: (?x4441, 07s9rl0) <- country(?x4441, ?x789), ?x789 = 0f8l9c >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4611 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 784 *> proper extension: 06n90; *> query: (?x4441, 03k9fj) <- genre(?x4441, ?x225), genre(?x12214, ?x225), ?x12214 = 042g97 *> conf = 0.47 ranks of expected_values: 6 EVAL 0125xq genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 85.000 85.000 0.697 http://example.org/film/film/genre #6917-015fsv PRED entity: 015fsv PRED relation: school! PRED expected values: 05xvj => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 92): 05m_8 (0.21 #555, 0.19 #647, 0.18 #371), 051vz (0.19 #299, 0.16 #575, 0.13 #851), 01slc (0.17 #58, 0.15 #334, 0.11 #518), 02c_4 (0.17 #64, 0.12 #156, 0.09 #248), 0jm7n (0.17 #80, 0.12 #172, 0.09 #264), 0jmcv (0.17 #67, 0.12 #159, 0.09 #251), 06rny (0.17 #50, 0.09 #234, 0.08 #418), 0jmjr (0.17 #76, 0.09 #260, 0.05 #2577), 01d5z (0.15 #286, 0.13 #562, 0.10 #838), 01ypc (0.15 #277, 0.07 #553, 0.07 #829) >> Best rule #555 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 03wv2g; >> query: (?x9249, 05m_8) <- fraternities_and_sororities(?x9249, ?x4348), colors(?x9249, ?x663), school_type(?x9249, ?x3092), school(?x2820, ?x9249) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #547 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 62 *> proper extension: 0gy3w; *> query: (?x9249, 05xvj) <- institution(?x1526, ?x9249), school(?x2820, ?x9249), major_field_of_study(?x9249, ?x10391), ?x1526 = 0bkj86 *> conf = 0.11 ranks of expected_values: 27 EVAL 015fsv school! 05xvj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 117.000 117.000 0.206 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #6916-01my95 PRED entity: 01my95 PRED relation: type_of_union PRED expected values: 04ztj => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.81 #13, 0.81 #9, 0.74 #213), 01g63y (0.20 #2, 0.19 #78, 0.16 #142) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 09r1j5; >> query: (?x12130, 04ztj) <- athlete(?x1557, ?x12130), gender(?x12130, ?x231), religion(?x12130, ?x1985) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01my95 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.815 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6915-04ykz PRED entity: 04ykz PRED relation: partially_contains! PRED expected values: 050l8 => 47 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1174): 09c7w0 (0.50 #91, 0.40 #185, 0.22 #184), 0d060g (0.50 #95, 0.40 #189, 0.17 #1161), 04ykg (0.40 #759, 0.40 #2111, 0.39 #1728), 050l8 (0.40 #759, 0.40 #2111, 0.39 #1728), 0824r (0.40 #759, 0.40 #2111, 0.39 #1728), 03v1s (0.40 #759, 0.40 #2111, 0.39 #1728), 0846v (0.40 #759, 0.40 #2111, 0.39 #1728), 01n4w (0.40 #759, 0.40 #2111, 0.39 #1728), 0pmq2 (0.40 #759, 0.40 #2111, 0.39 #1728), 05mph (0.40 #2111, 0.39 #1728, 0.39 #1345) >> Best rule #91 for best value: >> intensional similarity = 15 >> extensional distance = 2 >> proper extension: 06c6l; >> query: (?x13214, 09c7w0) <- partially_contains(?x4198, ?x13214), partially_contains(?x1351, ?x13214), partially_contains(?x961, ?x13214), time_zones(?x4198, ?x1638), contains(?x8260, ?x4198), location(?x6808, ?x4198), adjoins(?x1351, ?x1274), currency(?x1351, ?x170), religion(?x961, ?x109), ?x109 = 01lp8, contains(?x1274, ?x3204), contains(?x4198, ?x7067), contains(?x961, ?x4362), state_province_region(?x11648, ?x1274), ?x4362 = 02j3w >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #759 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 8 *> proper extension: 0k3nk; 02cgp8; 0lm0n; 02v3m7; 05lx3; 0db94; 0f2pf9; *> query: (?x13214, ?x1275) <- partially_contains(?x4198, ?x13214), time_zones(?x4198, ?x1638), district_represented(?x653, ?x4198), adjoins(?x4198, ?x1275), state_province_region(?x11516, ?x4198), location(?x6808, ?x4198), religion(?x4198, ?x109), location(?x483, ?x1275), contains(?x1275, ?x12737), ?x653 = 070m6c, jurisdiction_of_office(?x900, ?x4198), first_level_division_of(?x4198, ?x94) *> conf = 0.40 ranks of expected_values: 4 EVAL 04ykz partially_contains! 050l8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 47.000 33.000 0.500 http://example.org/location/location/partially_contains #6914-03_gd PRED entity: 03_gd PRED relation: award PRED expected values: 02pqp12 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 284): 02qt02v (0.70 #22418, 0.70 #16911, 0.69 #13367), 02pqp12 (0.44 #851, 0.32 #458, 0.22 #2817), 0gr4k (0.37 #2390, 0.30 #817, 0.22 #424), 0gr51 (0.35 #2450, 0.29 #484, 0.29 #877), 09sb52 (0.31 #9867, 0.29 #7902, 0.28 #11832), 02rdyk7 (0.29 #869, 0.25 #476, 0.18 #2835), 03hkv_r (0.26 #2373, 0.14 #1980, 0.14 #407), 02n9nmz (0.21 #2423, 0.14 #850, 0.14 #457), 02x17s4 (0.20 #2475, 0.12 #1688, 0.08 #116), 02qyp19 (0.19 #2360, 0.18 #787, 0.15 #394) >> Best rule #22418 for best value: >> intensional similarity = 4 >> extensional distance = 1897 >> proper extension: 06lxn; >> query: (?x800, ?x688) <- award_winner(?x1703, ?x800), award_winner(?x688, ?x800), ceremony(?x1703, ?x78), award(?x707, ?x1703) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #851 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 0p51w; 03bw6; *> query: (?x800, 02pqp12) <- award(?x800, ?x198), ?x198 = 040njc, film(?x800, ?x1597) *> conf = 0.44 ranks of expected_values: 2 EVAL 03_gd award 02pqp12 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 81.000 0.701 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6913-03bxp5 PRED entity: 03bxp5 PRED relation: film! PRED expected values: 01wy5m => 66 concepts (21 used for prediction) PRED predicted values (max 10 best out of 691): 01x6v6 (0.42 #8311, 0.39 #16623, 0.39 #18702), 072twv (0.42 #8311, 0.39 #16623, 0.39 #18702), 076lxv (0.42 #8311, 0.39 #16623, 0.39 #18702), 0z4s (0.05 #8379, 0.04 #4223, 0.04 #14613), 015pkc (0.05 #41558, 0.04 #41557, 0.02 #31448), 0h0wc (0.05 #41558, 0.04 #423, 0.04 #19125), 01kb2j (0.05 #41558, 0.04 #2984, 0.03 #19608), 017149 (0.05 #41558, 0.03 #83, 0.02 #2161), 05dbf (0.05 #41558, 0.03 #4519, 0.03 #8675), 0pz91 (0.05 #41558, 0.02 #12677, 0.02 #6445) >> Best rule #8311 for best value: >> intensional similarity = 4 >> extensional distance = 181 >> proper extension: 02y_lrp; 0140g4; 0ds3t5x; 09q5w2; 0fpmrm3; 01771z; 07w8fz; 03176f; 0dln8jk; 0ptxj; ... >> query: (?x6199, ?x786) <- nominated_for(?x384, ?x6199), country(?x6199, ?x94), nominated_for(?x786, ?x6199), nominated_for(?x5950, ?x6199) >> conf = 0.42 => this is the best rule for 3 predicted values *> Best rule #9166 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 188 *> proper extension: 043sct5; *> query: (?x6199, 01wy5m) <- genre(?x6199, ?x162), film(?x541, ?x6199), ?x162 = 04xvlr *> conf = 0.03 ranks of expected_values: 83 EVAL 03bxp5 film! 01wy5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 66.000 21.000 0.424 http://example.org/film/actor/film./film/performance/film #6912-07phbc PRED entity: 07phbc PRED relation: film! PRED expected values: 01gbn6 => 66 concepts (35 used for prediction) PRED predicted values (max 10 best out of 807): 017s11 (0.47 #6247, 0.47 #8329, 0.46 #10411), 03rwz3 (0.47 #6247, 0.47 #8329, 0.46 #10411), 05bpg3 (0.25 #959, 0.02 #7206, 0.02 #9288), 042xrr (0.25 #817, 0.02 #7064, 0.01 #9146), 0993r (0.25 #519, 0.02 #6766, 0.01 #8848), 01pllx (0.25 #1546, 0.02 #7793, 0.01 #9875), 02t_99 (0.25 #825, 0.02 #7072, 0.01 #9154), 01wgcvn (0.25 #645, 0.02 #6892, 0.01 #8974), 0126y2 (0.25 #469, 0.02 #6716, 0.01 #8798), 01pk3z (0.25 #987, 0.01 #9316) >> Best rule #6247 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 0c_j9x; 011yr9; 0q9sg; 0277j40; 026fs38; 02gpkt; 01bn3l; 06yykb; 02p76f9; 0g5ptf; ... >> query: (?x10268, ?x541) <- nominated_for(?x541, ?x10268), genre(?x10268, ?x225), prequel(?x9774, ?x10268), nominated_for(?x154, ?x10268) >> conf = 0.47 => this is the best rule for 2 predicted values *> Best rule #30781 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 342 *> proper extension: 014lc_; 0g22z; 0140g4; 0b2v79; 0gzy02; 01hr1; 016z5x; 01sxly; 050r1z; 0b60sq; ... *> query: (?x10268, 01gbn6) <- nominated_for(?x541, ?x10268), genre(?x10268, ?x225), written_by(?x10268, ?x3917), production_companies(?x10268, ?x788) *> conf = 0.01 ranks of expected_values: 758 EVAL 07phbc film! 01gbn6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 66.000 35.000 0.469 http://example.org/film/actor/film./film/performance/film #6911-03rjj PRED entity: 03rjj PRED relation: film_release_region! PRED expected values: 0gtsx8c 0g5qs2k 0gx9rvq 08720 0crfwmx 0_92w 0gmcwlb 0dtfn 03twd6 0cc7hmk 02yvct 07j8r 02x6dqb 026njb5 0jymd 0n04r 06tpmy 04nm0n0 07bzz7 0kbf1 0hv8w 0dll_t2 0cc97st 03mgx6z 064lsn 05dss7 07s3m4g 0btpm6 042zrm 078mm1 0ndsl1x 0gy7bj4 0j8f09z 09tcg4 0jz71 => 248 concepts (187 used for prediction) PRED predicted values (max 10 best out of 1471): 0dtfn (0.88 #18028, 0.82 #49879, 0.76 #44901), 0btpm6 (0.84 #47496, 0.79 #50483, 0.70 #45505), 0gmcwlb (0.82 #49877, 0.82 #46890, 0.81 #18026), 07s3m4g (0.82 #50407, 0.79 #45429, 0.75 #18556), 02yvct (0.81 #18096, 0.79 #49947, 0.79 #46960), 0dll_t2 (0.77 #50294, 0.76 #47307, 0.75 #18443), 03mgx6z (0.76 #45331, 0.75 #15470, 0.69 #18458), 0gtsx8c (0.75 #14935, 0.74 #49774, 0.73 #44796), 0ndsl1x (0.75 #18745, 0.74 #47609, 0.72 #50596), 0bq8tmw (0.75 #15058, 0.73 #44919, 0.72 #49897) >> Best rule #18028 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 07ssc; 02vzc; 082fr; >> query: (?x205, 0dtfn) <- film_release_region(?x4971, ?x205), olympics(?x205, ?x391), ?x4971 = 01jwxx >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 14, 16, 17, 18, 19, 23, 28, 30, 31, 32, 34, 35, 55, 59, 60, 63, 64, 65, 72, 78, 109, 111, 112, 120, 195 EVAL 03rjj film_release_region! 0jz71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 09tcg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0j8f09z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0gy7bj4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0ndsl1x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 078mm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 042zrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0btpm6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 07s3m4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 05dss7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 03mgx6z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0cc97st CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0dll_t2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0hv8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0kbf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 07bzz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 04nm0n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 06tpmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0n04r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0jymd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 026njb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 02x6dqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 07j8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 02yvct CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0cc7hmk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 03twd6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0dtfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0gmcwlb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0_92w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0crfwmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 08720 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0gx9rvq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0g5qs2k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rjj film_release_region! 0gtsx8c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 248.000 187.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6910-01sbhvd PRED entity: 01sbhvd PRED relation: religion PRED expected values: 03_gx => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 12): 0c8wxp (0.21 #96, 0.21 #141, 0.21 #51), 0kpl (0.07 #415, 0.07 #955, 0.06 #280), 03_gx (0.07 #959, 0.06 #419, 0.06 #779), 092bf5 (0.04 #151, 0.03 #106, 0.03 #196), 01lp8 (0.03 #361, 0.02 #586, 0.02 #271), 019cr (0.03 #146, 0.02 #191, 0.02 #101), 03j6c (0.02 #2766, 0.02 #2406, 0.02 #2316), 0flw86 (0.02 #542, 0.02 #272, 0.02 #362), 0kq2 (0.02 #288, 0.02 #468, 0.02 #1143), 06nzl (0.02 #60, 0.01 #510, 0.01 #285) >> Best rule #96 for best value: >> intensional similarity = 4 >> extensional distance = 179 >> proper extension: 04shbh; 012gbb; >> query: (?x11200, 0c8wxp) <- award(?x11200, ?x1312), film(?x11200, ?x9920), nationality(?x11200, ?x94), participant(?x10805, ?x11200) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #959 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1025 *> proper extension: 084w8; 0hl3d; 07w21; 041h0; 01zkxv; 0168cl; 012cph; 0m77m; 09dt7; 01963w; ... *> query: (?x11200, 03_gx) <- award(?x11200, ?x1312), profession(?x11200, ?x987), profession(?x4301, ?x987), ?x4301 = 0d5_f *> conf = 0.07 ranks of expected_values: 3 EVAL 01sbhvd religion 03_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.210 http://example.org/people/person/religion #6909-0233bn PRED entity: 0233bn PRED relation: nominated_for! PRED expected values: 09v4bym => 99 concepts (94 used for prediction) PRED predicted values (max 10 best out of 204): 09v8db5 (0.68 #13108, 0.68 #12154, 0.67 #9051), 019f4v (0.45 #9344, 0.35 #5053, 0.31 #3625), 0gq9h (0.42 #5062, 0.38 #302, 0.37 #9353), 0gs9p (0.35 #9355, 0.34 #7445, 0.32 #8161), 04dn09n (0.33 #5034, 0.29 #6701, 0.28 #9325), 0gr0m (0.32 #5059, 0.31 #299, 0.29 #3631), 0k611 (0.32 #5073, 0.29 #9364, 0.28 #3645), 0gq_v (0.30 #5018, 0.27 #3590, 0.25 #7639), 02g3v6 (0.28 #974, 0.25 #2640, 0.25 #3354), 054krc (0.28 #9360, 0.21 #3641, 0.19 #1975) >> Best rule #13108 for best value: >> intensional similarity = 4 >> extensional distance = 847 >> proper extension: 02_1ky; >> query: (?x7502, ?x5923) <- award_winner(?x7502, ?x1864), award(?x7502, ?x5923), award(?x754, ?x5923), nominated_for(?x5923, ?x467) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #21205 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1501 *> proper extension: 01vrwfv; *> query: (?x7502, ?x2393) <- nominated_for(?x10573, ?x7502), nominated_for(?x7740, ?x7502), profession(?x7740, ?x524), gender(?x10573, ?x231), award(?x7740, ?x2393) *> conf = 0.20 ranks of expected_values: 29 EVAL 0233bn nominated_for! 09v4bym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 99.000 94.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6908-016t_3 PRED entity: 016t_3 PRED relation: major_field_of_study PRED expected values: 02822 036nz 03ytc 040p_q 06q83 01400v 09s1f 02mgp 01h788 => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 86): 0193x (0.60 #662, 0.56 #596, 0.53 #322), 088tb (0.60 #764, 0.56 #596, 0.53 #322), 05r79 (0.60 #658, 0.56 #596, 0.53 #322), 01lhy (0.60 #656, 0.56 #596, 0.53 #322), 09s1f (0.56 #596, 0.53 #322, 0.52 #542), 03ytc (0.56 #596, 0.53 #322, 0.52 #542), 06mq7 (0.56 #596, 0.53 #322, 0.52 #542), 0dc_v (0.56 #596, 0.53 #322, 0.52 #542), 02822 (0.56 #596, 0.53 #322, 0.52 #542), 036nz (0.56 #596, 0.53 #322, 0.52 #542) >> Best rule #662 for best value: >> intensional similarity = 27 >> extensional distance = 3 >> proper extension: 0bkj86; >> query: (?x1200, 0193x) <- institution(?x1200, ?x11229), institution(?x1200, ?x10666), institution(?x1200, ?x8706), institution(?x1200, ?x6056), institution(?x1200, ?x5068), institution(?x1200, ?x4278), institution(?x1200, ?x3439), institution(?x1200, ?x2497), institution(?x1200, ?x481), major_field_of_study(?x1200, ?x5359), major_field_of_study(?x1200, ?x1527), school(?x4979, ?x2497), student(?x1200, ?x665), ?x1527 = 04_tv, organization(?x346, ?x5068), ?x481 = 052nd, language(?x136, ?x5359), ?x3439 = 03ksy, student(?x10666, ?x8744), school(?x4171, ?x5068), institution(?x7636, ?x11229), languages_spoken(?x1176, ?x5359), ?x8706 = 0trv, ?x4979 = 0f4vx0, ?x7636 = 01rr_d, ?x6056 = 05zl0, contains(?x2020, ?x4278) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #596 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 3 *> proper extension: 01rr_d; *> query: (?x1200, ?x11038) <- institution(?x1200, ?x11229), institution(?x1200, ?x10666), institution(?x1200, ?x8479), institution(?x1200, ?x4755), institution(?x1200, ?x2497), institution(?x1200, ?x2327), major_field_of_study(?x1200, ?x6364), major_field_of_study(?x1200, ?x4321), school(?x685, ?x2497), student(?x1200, ?x665), ?x11229 = 02w6bq, school(?x3333, ?x2497), ?x2327 = 07wjk, company(?x920, ?x4755), contains(?x726, ?x8479), ?x3333 = 01yjl, major_field_of_study(?x216, ?x4321), student(?x4321, ?x744), student(?x10666, ?x8744), major_field_of_study(?x11038, ?x6364) *> conf = 0.56 ranks of expected_values: 5, 6, 9, 10, 12, 16, 34, 47, 53 EVAL 016t_3 major_field_of_study 01h788 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 25.000 25.000 0.600 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 016t_3 major_field_of_study 02mgp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 25.000 25.000 0.600 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 016t_3 major_field_of_study 09s1f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 25.000 25.000 0.600 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 016t_3 major_field_of_study 01400v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 25.000 25.000 0.600 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 016t_3 major_field_of_study 06q83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 25.000 25.000 0.600 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 016t_3 major_field_of_study 040p_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 25.000 25.000 0.600 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 016t_3 major_field_of_study 03ytc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 25.000 25.000 0.600 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 016t_3 major_field_of_study 036nz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 25.000 25.000 0.600 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 016t_3 major_field_of_study 02822 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 25.000 25.000 0.600 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #6907-07t65 PRED entity: 07t65 PRED relation: combatants! PRED expected values: 048n7 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 62): 081pw (0.48 #1585, 0.39 #1915, 0.38 #2245), 03gqgt3 (0.28 #1641, 0.24 #1971, 0.23 #2301), 07_nf (0.28 #1602, 0.20 #2328, 0.19 #2394), 0ql7q (0.25 #345, 0.05 #2259, 0.04 #2788), 048n7 (0.24 #1938, 0.24 #1608, 0.21 #2334), 0cm2xh (0.21 #2586, 0.20 #1926, 0.19 #2520), 01h6pn (0.18 #1927, 0.17 #1597, 0.15 #2323), 06k75 (0.18 #2590, 0.18 #2524, 0.17 #2987), 07j9n (0.16 #2670, 0.15 #2803, 0.15 #2737), 0c3mz (0.14 #1954, 0.14 #1624, 0.12 #2350) >> Best rule #1585 for best value: >> intensional similarity = 2 >> extensional distance = 27 >> proper extension: 0b90_r; 0154j; 03rjj; 0d060g; 0chghy; 05qhw; 05v8c; 015fr; 0bq0p9; 0f8l9c; ... >> query: (?x312, 081pw) <- combatants(?x94, ?x312), ?x94 = 09c7w0 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1938 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 09c7w0; 07ssc; 0k6nt; 059j2; 015qh; 025ndl; 03bxbql; 03b79; 02psqkz; 03spz; ... *> query: (?x312, 048n7) <- combatants(?x94, ?x312), second_level_divisions(?x94, ?x321), country(?x54, ?x94), nationality(?x51, ?x94) *> conf = 0.24 ranks of expected_values: 5 EVAL 07t65 combatants! 048n7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 86.000 0.483 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #6906-01chpn PRED entity: 01chpn PRED relation: nominated_for! PRED expected values: 099c8n 02n9nmz 04kxsb 099t8j => 114 concepts (103 used for prediction) PRED predicted values (max 10 best out of 252): 099jhq (0.70 #2970, 0.70 #457, 0.69 #2740), 0gq9h (0.59 #4396, 0.51 #3940, 0.40 #1654), 0gs9p (0.49 #4398, 0.44 #3942, 0.36 #5994), 0k611 (0.41 #4407, 0.40 #3951, 0.34 #5091), 040njc (0.35 #3889, 0.33 #4345, 0.29 #6171), 0gq_v (0.35 #4358, 0.33 #3902, 0.27 #9607), 0p9sw (0.34 #3903, 0.25 #5043, 0.24 #5955), 099c8n (0.33 #4618, 0.29 #6216, 0.29 #964), 02qyntr (0.30 #4053, 0.28 #4737, 0.28 #1539), 0gr0m (0.30 #3937, 0.27 #4393, 0.27 #1651) >> Best rule #2970 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 06mmr; >> query: (?x6288, ?x3066) <- award(?x6288, ?x3066), honored_for(?x6238, ?x6288), category(?x6288, ?x134), award(?x92, ?x3066) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4618 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 208 *> proper extension: 0bx_hnp; *> query: (?x6288, 099c8n) <- film_crew_role(?x6288, ?x1284), ?x1284 = 0ch6mp2, honored_for(?x6238, ?x6288) *> conf = 0.33 ranks of expected_values: 8, 30, 38, 42 EVAL 01chpn nominated_for! 099t8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 114.000 103.000 0.701 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01chpn nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 114.000 103.000 0.701 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01chpn nominated_for! 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 114.000 103.000 0.701 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01chpn nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 114.000 103.000 0.701 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6905-03j6c PRED entity: 03j6c PRED relation: religion! PRED expected values: 084z0w 03l3ln 02ply6j 03m2fg 03f02ct 05wdgq 040nwr 01ggbx => 33 concepts (19 used for prediction) PRED predicted values (max 10 best out of 4196): 0mb5x (0.50 #1666, 0.40 #2665, 0.33 #4661), 04hcw (0.50 #1583, 0.40 #2582, 0.33 #587), 0q9kd (0.50 #997, 0.40 #1996, 0.33 #1), 03_87 (0.40 #2522, 0.33 #527, 0.25 #1523), 0jmj (0.40 #2344, 0.33 #349, 0.25 #1345), 032r1 (0.40 #2909, 0.33 #914, 0.25 #1910), 0jcx (0.40 #2243, 0.25 #1244, 0.22 #5239), 047g6 (0.40 #2935, 0.25 #1936, 0.12 #11916), 0knjh (0.40 #2702, 0.25 #1703, 0.12 #11683), 02kz_ (0.33 #435, 0.29 #3429, 0.25 #1431) >> Best rule #1666 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 03_gx; >> query: (?x8967, 0mb5x) <- religion(?x5246, ?x8967), religion(?x4662, ?x8967), film(?x4662, ?x408), award_nominee(?x450, ?x4662), ?x450 = 0z4s, participant(?x4662, ?x2443), film(?x5246, ?x603), participant(?x286, ?x5246), award(?x5246, ?x154) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #10975 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 15 *> proper extension: 0flw86; 051kv; 0n2g; 02rsw; 05w5d; 02vxy_; 01hng3; *> query: (?x8967, ?x450) <- religion(?x8530, ?x8967), religion(?x7039, ?x8967), religion(?x4662, ?x8967), film(?x4662, ?x408), award_nominee(?x450, ?x4662), profession(?x4662, ?x1032), place_of_birth(?x8530, ?x12210), film(?x450, ?x518), influenced_by(?x1900, ?x7039) *> conf = 0.08 ranks of expected_values: 1045, 1506, 1511, 1525, 1809, 1902, 2188, 2618 EVAL 03j6c religion! 01ggbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 19.000 0.500 http://example.org/people/person/religion EVAL 03j6c religion! 040nwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 19.000 0.500 http://example.org/people/person/religion EVAL 03j6c religion! 05wdgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 19.000 0.500 http://example.org/people/person/religion EVAL 03j6c religion! 03f02ct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 19.000 0.500 http://example.org/people/person/religion EVAL 03j6c religion! 03m2fg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 19.000 0.500 http://example.org/people/person/religion EVAL 03j6c religion! 02ply6j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 19.000 0.500 http://example.org/people/person/religion EVAL 03j6c religion! 03l3ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 19.000 0.500 http://example.org/people/person/religion EVAL 03j6c religion! 084z0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 19.000 0.500 http://example.org/people/person/religion #6904-0ccvx PRED entity: 0ccvx PRED relation: contains PRED expected values: 01qcx_ => 122 concepts (78 used for prediction) PRED predicted values (max 10 best out of 1840): 01jzyx (0.76 #108996, 0.67 #106051, 0.66 #88377), 01nl79 (0.33 #8838, 0.17 #8094, 0.13 #14731), 02hrh0_ (0.33 #6564, 0.17 #12456, 0.09 #18349), 02g839 (0.17 #11897, 0.17 #6005, 0.14 #14843), 0225v9 (0.17 #14048, 0.17 #8156, 0.14 #16994), 01jsk6 (0.17 #13513, 0.17 #7621, 0.14 #16459), 02s838 (0.17 #13423, 0.17 #7531, 0.14 #16369), 01wqg8 (0.17 #13061, 0.17 #7169, 0.14 #16007), 04ycjk (0.17 #12745, 0.17 #6853, 0.14 #15691), 01l9vr (0.17 #12407, 0.17 #6515, 0.14 #15353) >> Best rule #108996 for best value: >> intensional similarity = 2 >> extensional distance = 266 >> proper extension: 0fngy; >> query: (?x4253, ?x5426) <- citytown(?x5426, ?x4253), contains(?x94, ?x5426) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #8150 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 4 *> proper extension: 06rny; *> query: (?x4253, 01qcx_) <- split_to(?x12563, ?x4253), place_of_birth(?x4060, ?x12563) *> conf = 0.17 ranks of expected_values: 160 EVAL 0ccvx contains 01qcx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 122.000 78.000 0.761 http://example.org/location/location/contains #6903-0gjw_ PRED entity: 0gjw_ PRED relation: locations PRED expected values: 09lgt => 48 concepts (42 used for prediction) PRED predicted values (max 10 best out of 263): 04wsz (0.52 #3542, 0.37 #2423, 0.20 #4103), 0dg3n1 (0.52 #3542, 0.37 #2423, 0.20 #4103), 05qtj (0.50 #1022, 0.40 #1393, 0.17 #2702), 0156q (0.45 #3764, 0.45 #3578, 0.18 #6396), 013yq (0.43 #2851, 0.35 #3224, 0.31 #4156), 02j9z (0.37 #2423, 0.25 #573, 0.20 #2996), 0f8l9c (0.37 #2423, 0.20 #1138, 0.20 #186), 0154j (0.37 #2423, 0.20 #1122, 0.20 #4103), 0hzlz (0.33 #395, 0.05 #5983, 0.02 #4499), 06v36 (0.33 #487, 0.05 #5983, 0.02 #4591) >> Best rule #3542 for best value: >> intensional similarity = 7 >> extensional distance = 17 >> proper extension: 024jvz; 09x7p1; 0gfhg1y; >> query: (?x10413, ?x2467) <- entity_involved(?x10413, ?x13289), entity_involved(?x5503, ?x13289), films(?x5503, ?x2133), combatants(?x5503, ?x87), student(?x9651, ?x13289), people(?x3715, ?x13289), locations(?x5503, ?x2467) >> conf = 0.52 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0gjw_ locations 09lgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 42.000 0.520 http://example.org/time/event/locations #6902-030znt PRED entity: 030znt PRED relation: award_winner PRED expected values: 05lb65 => 97 concepts (31 used for prediction) PRED predicted values (max 10 best out of 505): 01wb8bs (0.82 #11219, 0.82 #36872, 0.81 #38474), 0308kx (0.82 #11219, 0.82 #36872, 0.81 #38474), 05dxl5 (0.82 #11219, 0.82 #36872, 0.81 #38474), 05lb65 (0.82 #11219, 0.82 #36872, 0.81 #38474), 030znt (0.69 #193, 0.35 #1796, 0.29 #32061), 07z1_q (0.59 #1603, 0.52 #49695, 0.52 #38475), 05ry0p (0.59 #1603, 0.52 #49695, 0.52 #38475), 01q9b9 (0.59 #1603, 0.52 #49695, 0.52 #38475), 086sj (0.59 #1603, 0.52 #49695, 0.52 #38475), 05slvm (0.59 #1603, 0.52 #49695, 0.52 #38475) >> Best rule #11219 for best value: >> intensional similarity = 3 >> extensional distance = 784 >> proper extension: 01sl1q; 0184jc; 04bdxl; 01vvydl; 012d40; 07fq1y; 02qgqt; 0fvf9q; 0l6qt; 07s3vqk; ... >> query: (?x1343, ?x444) <- award_winner(?x444, ?x1343), award_nominee(?x1116, ?x1343), location(?x1343, ?x859) >> conf = 0.82 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 030znt award_winner 05lb65 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 31.000 0.816 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #6901-065y4w7 PRED entity: 065y4w7 PRED relation: student PRED expected values: 04wvhz 06b0d2 0c94fn 0bs1yy 07qy0b 026spg 06qgjh 04wp63 028qyn 02_33l 0b_4z => 128 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1551): 02t_w8 (0.18 #4923, 0.05 #31187, 0.05 #29167), 01ft2l (0.10 #2571, 0.09 #4591, 0.03 #44997), 01x0sy (0.10 #3591, 0.07 #9651, 0.05 #15712), 08f3b1 (0.10 #2111, 0.07 #8171, 0.05 #14232), 03xx9l (0.10 #3294, 0.05 #31578, 0.05 #29558), 01mqh5 (0.10 #3839, 0.05 #30103, 0.05 #15960), 06pwf6 (0.10 #2460, 0.05 #14581, 0.03 #38825), 0c_md_ (0.10 #3622, 0.05 #15743, 0.02 #31906), 01g0jn (0.10 #3930, 0.05 #16051, 0.02 #32214), 01whg97 (0.10 #3382, 0.05 #15503, 0.02 #31666) >> Best rule #4923 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 02zc7f; >> query: (?x735, 02t_w8) <- school(?x1160, ?x735), student(?x735, ?x65), ?x1160 = 049n7 >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #29693 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 03v6t; 03tw2s; *> query: (?x735, 06qgjh) <- institution(?x734, ?x735), ?x734 = 04zx3q1, school(?x580, ?x735) *> conf = 0.02 ranks of expected_values: 596, 716, 917, 1272, 1345 EVAL 065y4w7 student 0b_4z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 065y4w7 student 02_33l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 065y4w7 student 028qyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 065y4w7 student 04wp63 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 065y4w7 student 06qgjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 065y4w7 student 026spg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 065y4w7 student 07qy0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 065y4w7 student 0bs1yy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 065y4w7 student 0c94fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 065y4w7 student 06b0d2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 065y4w7 student 04wvhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 117.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student #6900-05dtwm PRED entity: 05dtwm PRED relation: award PRED expected values: 0ck27z => 83 concepts (63 used for prediction) PRED predicted values (max 10 best out of 254): 0ck27z (0.60 #93, 0.32 #2123, 0.32 #3341), 09sb52 (0.42 #853, 0.37 #1665, 0.36 #7349), 099jhq (0.25 #831, 0.06 #1643, 0.05 #6515), 0bdw6t (0.20 #111, 0.13 #25178, 0.13 #22741), 0bdx29 (0.20 #109, 0.13 #25178, 0.13 #22741), 0fbtbt (0.20 #234, 0.13 #25178, 0.13 #22741), 0cqhb3 (0.20 #307, 0.13 #25178, 0.13 #22741), 0cqh46 (0.20 #52, 0.13 #25178, 0.13 #22741), 057xs89 (0.20 #162, 0.13 #25178, 0.13 #22741), 02pzxlw (0.20 #187, 0.13 #25178, 0.13 #22741) >> Best rule #93 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01541z; >> query: (?x5542, 0ck27z) <- award_nominee(?x9218, ?x5542), actor(?x4932, ?x5542), ?x9218 = 04wf_b >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05dtwm award 0ck27z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 63.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6899-0219x_ PRED entity: 0219x_ PRED relation: genre! PRED expected values: 027qgy 05jf85 07xtqq 02v63m 032_wv 0fq7dv_ 0f4_l 082scv 03hkch7 0blpg 0320fn 01242_ 080lkt7 07k2mq 03c_cxn 0294mx 0jqkh 0h1x5f 07bxqz => 73 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1756): 0bs5k8r (0.67 #28564, 0.57 #32046, 0.50 #14617), 047myg9 (0.67 #28962, 0.57 #32444, 0.38 #37670), 05q_dw (0.62 #19164, 0.61 #19162, 0.60 #27877), 03hfmm (0.62 #38005, 0.33 #4901, 0.33 #3160), 03hkch7 (0.62 #37079, 0.33 #3975, 0.33 #2234), 0p9lw (0.61 #19162, 0.60 #27877, 0.60 #27876), 0djlxb (0.61 #19162, 0.60 #27877, 0.60 #27876), 023g6w (0.61 #19162, 0.60 #27877, 0.60 #27876), 04zyhx (0.61 #19162, 0.60 #27876, 0.60 #19161), 0dx8gj (0.57 #31975, 0.50 #28493, 0.50 #12803) >> Best rule #28564 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 060__y; 03q4nz; >> query: (?x2753, 0bs5k8r) <- genre(?x9527, ?x2753), genre(?x4312, ?x2753), genre(?x2840, ?x2753), genre(?x1941, ?x2753), nominated_for(?x484, ?x9527), film(?x3056, ?x9527), film_release_distribution_medium(?x4312, ?x81), ?x2840 = 0f4vx, ?x484 = 0gq_v, crewmember(?x1941, ?x9151) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #37079 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 0hn10; 03bxz7; 0d63kt; *> query: (?x2753, 03hkch7) <- genre(?x11213, ?x2753), genre(?x9527, ?x2753), genre(?x4312, ?x2753), nominated_for(?x154, ?x9527), film(?x3056, ?x9527), film_release_distribution_medium(?x4312, ?x81), ?x11213 = 0170xl *> conf = 0.62 ranks of expected_values: 5, 107, 234, 392, 469, 488, 512, 546, 574, 601, 610, 787, 788, 1014, 1052, 1108, 1196, 1267, 1723 EVAL 0219x_ genre! 07bxqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 0h1x5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 0jqkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 0294mx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 03c_cxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 07k2mq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 080lkt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 01242_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 0320fn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 0blpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 03hkch7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 082scv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 0f4_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 0fq7dv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 032_wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 02v63m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 07xtqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 05jf85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 39.000 0.667 http://example.org/film/film/genre EVAL 0219x_ genre! 027qgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 39.000 0.667 http://example.org/film/film/genre #6898-0bdt8 PRED entity: 0bdt8 PRED relation: participant PRED expected values: 0c2tf => 126 concepts (93 used for prediction) PRED predicted values (max 10 best out of 264): 0c2tf (0.82 #25946, 0.82 #27892, 0.82 #22705), 022q4j (0.11 #1230, 0.09 #1879, 0.09 #5119), 0d_84 (0.10 #11, 0.04 #2604, 0.03 #18820), 02v60l (0.10 #323, 0.04 #2916, 0.02 #7455), 01gbn6 (0.10 #572, 0.03 #7055, 0.02 #7704), 01q7cb_ (0.10 #65, 0.03 #14333, 0.02 #2658), 014zcr (0.10 #7, 0.02 #2600, 0.02 #3248), 01j2xj (0.10 #338, 0.02 #2931, 0.02 #3579), 0170s4 (0.10 #157, 0.02 #2750, 0.02 #3398), 026r8q (0.10 #476, 0.02 #3069, 0.02 #19285) >> Best rule #25946 for best value: >> intensional similarity = 3 >> extensional distance = 305 >> proper extension: 0prfz; 0h5g_; 06cv1; 01vlj1g; 04yj5z; 0htlr; 03d_w3h; 0prjs; 013cr; 01xcqc; ... >> query: (?x6440, ?x1606) <- award_winner(?x1132, ?x6440), nominated_for(?x6440, ?x1973), participant(?x1606, ?x6440) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bdt8 participant 0c2tf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 93.000 0.818 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #6897-031778 PRED entity: 031778 PRED relation: titles! PRED expected values: 024qqx => 91 concepts (71 used for prediction) PRED predicted values (max 10 best out of 60): 024qqx (0.50 #81, 0.33 #183, 0.29 #3079), 01hmnh (0.43 #3287, 0.43 #3210, 0.33 #27), 07s9rl0 (0.29 #3079, 0.29 #4010, 0.28 #2462), 04xvlr (0.29 #3079, 0.27 #106, 0.19 #1849), 01z4y (0.25 #548, 0.22 #960, 0.20 #1676), 02n4kr (0.18 #6597, 0.18 #7220, 0.18 #7219), 02xlf (0.18 #6597, 0.18 #7220, 0.18 #7219), 03k9fj (0.18 #6597, 0.18 #7220, 0.18 #7219), 01jfsb (0.15 #2685, 0.14 #5686, 0.14 #2996), 07ssc (0.09 #1445, 0.09 #2471, 0.09 #3813) >> Best rule #81 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 09v8clw; >> query: (?x2006, 024qqx) <- nominated_for(?x2006, ?x2869), film_crew_role(?x2006, ?x137), film(?x1469, ?x2006), ?x1469 = 05sq84 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031778 titles! 024qqx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 71.000 0.500 http://example.org/media_common/netflix_genre/titles #6896-05mkhs PRED entity: 05mkhs PRED relation: award PRED expected values: 099ck7 => 104 concepts (97 used for prediction) PRED predicted values (max 10 best out of 249): 05p09zm (0.24 #929, 0.17 #1332, 0.17 #1735), 0bfvd4 (0.22 #11398, 0.11 #114, 0.06 #21876), 01by1l (0.21 #16634, 0.15 #5753, 0.14 #5350), 05b4l5x (0.20 #812, 0.14 #1618, 0.14 #2424), 0gqyl (0.18 #14612, 0.16 #15418, 0.13 #31035), 03c7tr1 (0.17 #864, 0.14 #3685, 0.13 #461), 0gqwc (0.17 #74, 0.17 #15388, 0.13 #14582), 0ck27z (0.17 #91, 0.15 #18629, 0.14 #19435), 057xs89 (0.17 #159, 0.10 #4995, 0.09 #1368), 02y_rq5 (0.17 #94, 0.09 #15408, 0.07 #14602) >> Best rule #929 for best value: >> intensional similarity = 3 >> extensional distance = 141 >> proper extension: 024dgj; 01wvxw1; >> query: (?x3816, 05p09zm) <- award(?x3816, ?x704), participant(?x10777, ?x3816), participant(?x3816, ?x1410) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #31035 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2016 *> proper extension: 02xnjd; *> query: (?x3816, ?x102) <- gender(?x3816, ?x231), award_nominee(?x123, ?x3816), award(?x123, ?x102) *> conf = 0.13 ranks of expected_values: 22 EVAL 05mkhs award 099ck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 104.000 97.000 0.245 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6895-0h0jz PRED entity: 0h0jz PRED relation: religion PRED expected values: 0c8wxp => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 22): 0c8wxp (0.22 #726, 0.19 #953, 0.17 #1403), 0kpl (0.11 #100, 0.11 #325, 0.10 #280), 03_gx (0.09 #1638, 0.08 #915, 0.08 #1592), 0kq2 (0.07 #108, 0.06 #288, 0.04 #378), 03j6c (0.05 #561, 0.05 #1058, 0.05 #1193), 0n2g (0.04 #688, 0.03 #463, 0.02 #328), 0631_ (0.04 #98, 0.03 #278, 0.03 #368), 019cr (0.04 #101, 0.02 #281, 0.02 #731), 092bf5 (0.03 #286, 0.03 #61, 0.03 #736), 051kv (0.03 #275, 0.02 #365, 0.01 #50) >> Best rule #726 for best value: >> intensional similarity = 3 >> extensional distance = 383 >> proper extension: 02w5q6; 034ls; 016cff; 019n7x; 0dq9wx; >> query: (?x294, 0c8wxp) <- profession(?x294, ?x1032), participant(?x294, ?x9797), people(?x5056, ?x294) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h0jz religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.216 http://example.org/people/person/religion #6894-095zlp PRED entity: 095zlp PRED relation: titles! PRED expected values: 04xvlr => 78 concepts (30 used for prediction) PRED predicted values (max 10 best out of 61): 07s9rl0 (0.45 #1, 0.44 #199, 0.39 #2196), 07c52 (0.43 #822, 0.15 #1222, 0.11 #1925), 01hmnh (0.43 #819, 0.10 #2524, 0.09 #223), 04xvlr (0.35 #202, 0.31 #4, 0.25 #1098), 07ssc (0.27 #995, 0.27 #904, 0.18 #1807), 04xvh5 (0.24 #1193, 0.23 #99, 0.23 #2096), 060__y (0.24 #1193, 0.23 #99, 0.23 #2096), 01z4y (0.20 #2532, 0.19 #528, 0.17 #2731), 017fp (0.16 #22, 0.11 #1216, 0.11 #717), 01jfsb (0.15 #217, 0.13 #2518, 0.12 #2115) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 064n1pz; 011yfd; 02phtzk; 064lsn; 05y0cr; >> query: (?x414, 07s9rl0) <- genre(?x414, ?x53), honored_for(?x2707, ?x414), nominated_for(?x1180, ?x414), ?x1180 = 02n9nmz >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #202 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 125 *> proper extension: 0cnztc4; *> query: (?x414, 04xvlr) <- genre(?x414, ?x1509), film(?x166, ?x414), ?x1509 = 060__y, film_crew_role(?x414, ?x137) *> conf = 0.35 ranks of expected_values: 4 EVAL 095zlp titles! 04xvlr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 78.000 30.000 0.453 http://example.org/media_common/netflix_genre/titles #6893-0dzc16 PRED entity: 0dzc16 PRED relation: award_winner! PRED expected values: 013b2h => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 103): 01c6qp (0.20 #18, 0.15 #1674, 0.11 #570), 02rjjll (0.20 #5, 0.13 #281, 0.11 #557), 013b2h (0.18 #6626, 0.17 #7179, 0.16 #631), 056878 (0.18 #6626, 0.17 #7179, 0.16 #3175), 01mhwk (0.18 #6626, 0.17 #7179, 0.16 #3175), 0jzphpx (0.18 #6626, 0.17 #7179, 0.16 #3175), 05pd94v (0.17 #140, 0.16 #278, 0.11 #554), 0466p0j (0.16 #1731, 0.10 #75, 0.09 #1593), 02cg41 (0.16 #1781, 0.11 #401, 0.10 #125), 09n4nb (0.15 #1703, 0.10 #599, 0.09 #1565) >> Best rule #18 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 09z1lg; >> query: (?x4258, 01c6qp) <- artists(?x671, ?x4258), award(?x4258, ?x11439), ?x11439 = 03m79j_ >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #6626 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1277 *> proper extension: 026v1z; *> query: (?x4258, ?x725) <- award_winner(?x4258, ?x9418), award_winner(?x725, ?x9418), profession(?x9418, ?x353) *> conf = 0.18 ranks of expected_values: 3 EVAL 0dzc16 award_winner! 013b2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 95.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6892-04bpm6 PRED entity: 04bpm6 PRED relation: profession PRED expected values: 04f2zj => 103 concepts (76 used for prediction) PRED predicted values (max 10 best out of 57): 02hrh1q (0.88 #4288, 0.87 #3253, 0.86 #3992), 01c72t (0.68 #1200, 0.66 #2231, 0.59 #1789), 039v1 (0.44 #182, 0.38 #329, 0.30 #771), 0n1h (0.44 #158, 0.38 #305, 0.25 #452), 016z4k (0.38 #4865, 0.38 #739, 0.37 #3685), 01d_h8 (0.34 #4720, 0.33 #8699, 0.32 #6935), 01c8w0 (0.34 #1185, 0.34 #1038, 0.28 #2216), 0fnpj (0.33 #206, 0.23 #353, 0.18 #1531), 0dxtg (0.31 #8707, 0.29 #8854, 0.27 #11062), 0d1pc (0.25 #490, 0.13 #3288, 0.13 #4617) >> Best rule #4288 for best value: >> intensional similarity = 3 >> extensional distance = 536 >> proper extension: 04bs3j; 031zkw; 01mqz0; 01csrl; 044qx; 01fwf1; 06b_0; 01kgg9; 033p3_; 04bdqk; >> query: (?x1715, 02hrh1q) <- award(?x1715, ?x1237), participant(?x4933, ?x1715), profession(?x1715, ?x131) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #978 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 015882; *> query: (?x1715, 04f2zj) <- award_nominee(?x1715, ?x827), performance_role(?x1715, ?x315), award_nominee(?x827, ?x527) *> conf = 0.06 ranks of expected_values: 29 EVAL 04bpm6 profession 04f2zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 103.000 76.000 0.885 http://example.org/people/person/profession #6891-0kc8y PRED entity: 0kc8y PRED relation: award_winner PRED expected values: 0g5lhl7 => 70 concepts (37 used for prediction) PRED predicted values (max 10 best out of 790): 05gnf (0.54 #4335, 0.38 #5951, 0.16 #20493), 05xbx (0.50 #2485, 0.33 #871, 0.19 #5716), 03mdt (0.50 #2171, 0.33 #557, 0.15 #3786), 03jvmp (0.33 #352, 0.25 #1966, 0.15 #27465), 0g5lhl7 (0.32 #56566, 0.28 #56567, 0.26 #56564), 01w92 (0.28 #56567, 0.26 #56564, 0.25 #51716), 0dbpwb (0.28 #56567, 0.26 #56564, 0.25 #51716), 04cw0j (0.28 #56567, 0.26 #56564, 0.25 #51716), 0kc8y (0.28 #56567, 0.26 #56564, 0.25 #51716), 05qd_ (0.23 #19514, 0.15 #6589, 0.15 #8204) >> Best rule #4335 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 014hdb; >> query: (?x10844, 05gnf) <- award_winner(?x3486, ?x10844), award_winner(?x10844, ?x10166), citytown(?x10166, ?x10059), award_winner(?x10166, ?x2776), ?x2776 = 0g5lhl7 >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #56566 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 506 *> proper extension: 02c0mv; 023jq1; 08f3yq; *> query: (?x10844, ?x2776) <- award_winner(?x11078, ?x10844), award_winner(?x11078, ?x2776), program(?x2776, ?x10234) *> conf = 0.32 ranks of expected_values: 5 EVAL 0kc8y award_winner 0g5lhl7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 70.000 37.000 0.538 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #6890-01j5x6 PRED entity: 01j5x6 PRED relation: gender PRED expected values: 05zppz => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #225, 0.71 #189, 0.71 #223), 02zsn (0.52 #164, 0.51 #10, 0.48 #12) >> Best rule #225 for best value: >> intensional similarity = 2 >> extensional distance = 2862 >> proper extension: 099bk; 0bk4s; 0frmb1; 015k7; 03d6q; 0cmpn; 07zr66; 0cw10; 0jrg; 04cw0n4; ... >> query: (?x891, 05zppz) <- type_of_union(?x891, ?x566), ?x566 = 04ztj >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j5x6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.716 http://example.org/people/person/gender #6889-01k2wn PRED entity: 01k2wn PRED relation: major_field_of_study PRED expected values: 05qfh => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 108): 062z7 (0.53 #372, 0.50 #24, 0.45 #2583), 0g26h (0.53 #386, 0.38 #38, 0.31 #10145), 05qjt (0.46 #2215, 0.39 #3030, 0.36 #3378), 01mkq (0.45 #7451, 0.42 #2106, 0.42 #10354), 01lj9 (0.41 #2244, 0.39 #2594, 0.37 #2127), 02lp1 (0.41 #2569, 0.37 #10117, 0.34 #3034), 01540 (0.39 #2615, 0.36 #3080, 0.32 #2265), 05qfh (0.35 #2591, 0.34 #3056, 0.32 #2241), 02_7t (0.35 #408, 0.24 #5290, 0.23 #10167), 06ms6 (0.33 #132, 0.29 #2575, 0.27 #1992) >> Best rule #372 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 027mdh; >> query: (?x1103, 062z7) <- institution(?x1526, ?x1103), currency(?x1103, ?x170), ?x1526 = 0bkj86, colors(?x1103, ?x4557) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #2591 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 0p7tb; *> query: (?x1103, 05qfh) <- institution(?x1200, ?x1103), company(?x1159, ?x1103), colors(?x1103, ?x4557), major_field_of_study(?x1103, ?x254) *> conf = 0.35 ranks of expected_values: 8 EVAL 01k2wn major_field_of_study 05qfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 194.000 194.000 0.529 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #6888-057lbk PRED entity: 057lbk PRED relation: film_crew_role PRED expected values: 01d_h8 01pvkk => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 18): 02rh1dz (0.33 #5, 0.31 #30, 0.22 #254), 01pvkk (0.33 #56, 0.31 #31, 0.30 #336), 02zdwq (0.22 #254, 0.20 #330, 0.19 #837), 02ynfr (0.18 #339, 0.17 #465, 0.17 #34), 094hwz (0.17 #33, 0.10 #58, 0.08 #161), 089fss (0.17 #78, 0.09 #333, 0.08 #459), 0263ycg (0.14 #86, 0.03 #544, 0.03 #341), 05smlt (0.14 #37, 0.10 #62, 0.09 #139), 020xn5 (0.14 #29, 0.10 #54, 0.05 #79), 04pyp5 (0.09 #340, 0.08 #466, 0.06 #163) >> Best rule #5 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 09sh8k; 0287477; 0bl3nn; 034b6k; >> query: (?x4378, 02rh1dz) <- genre(?x4378, ?x6888), ?x6888 = 04pbhw, film(?x8716, ?x4378), country(?x4378, ?x1264), ?x1264 = 0345h >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 0522wp; *> query: (?x4378, 01pvkk) <- region(?x4378, ?x512), category(?x4378, ?x134), film_distribution_medium(?x4378, ?x2099), ?x2099 = 0735l, ?x512 = 07ssc *> conf = 0.33 ranks of expected_values: 2 EVAL 057lbk film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 52.000 0.333 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 057lbk film_crew_role 01d_h8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 52.000 0.333 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6887-0jswp PRED entity: 0jswp PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.80 #167, 0.80 #41, 0.80 #203), 07c52 (0.04 #23, 0.03 #68, 0.03 #73), 02nxhr (0.03 #57, 0.03 #183, 0.03 #215), 07z4p (0.03 #15, 0.03 #288, 0.02 #85) >> Best rule #167 for best value: >> intensional similarity = 4 >> extensional distance = 696 >> proper extension: 02vxq9m; 027qgy; 0164qt; 04vr_f; 0dgst_d; 044g_k; 0qm8b; 035yn8; 02q5g1z; 0fdv3; ... >> query: (?x3369, 029j_) <- nominated_for(?x601, ?x3369), country(?x3369, ?x94), award_winner(?x3369, ?x3483), ?x94 = 09c7w0 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jswp film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.804 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6886-0k8y7 PRED entity: 0k8y7 PRED relation: gender PRED expected values: 02zsn => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #33, 0.82 #31, 0.82 #29), 02zsn (0.81 #6, 0.49 #46, 0.49 #20) >> Best rule #33 for best value: >> intensional similarity = 1 >> extensional distance = 145 >> proper extension: 0454s1; >> query: (?x4285, 05zppz) <- place_of_burial(?x4285, ?x3153) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 01l_vgt; *> query: (?x4285, 02zsn) <- nationality(?x4285, ?x94), participant(?x4284, ?x4285), influenced_by(?x7717, ?x4284) *> conf = 0.81 ranks of expected_values: 2 EVAL 0k8y7 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.837 http://example.org/people/person/gender #6885-016srn PRED entity: 016srn PRED relation: gender PRED expected values: 05zppz => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #39, 0.77 #33, 0.76 #25), 02zsn (0.43 #2, 0.33 #4, 0.31 #60) >> Best rule #39 for best value: >> intensional similarity = 2 >> extensional distance = 546 >> proper extension: 0cm03; >> query: (?x3159, 05zppz) <- nationality(?x3159, ?x94), instrumentalists(?x227, ?x3159) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016srn gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.776 http://example.org/people/person/gender #6884-05_pkf PRED entity: 05_pkf PRED relation: music! PRED expected values: 025n07 02x8fs => 137 concepts (14 used for prediction) PRED predicted values (max 10 best out of 867): 02rrfzf (0.06 #2333, 0.04 #7363, 0.04 #4345), 01s7w3 (0.05 #9919, 0.05 #6901, 0.04 #10925), 0gvvm6l (0.05 #2812, 0.02 #9854, 0.02 #10860), 02ht1k (0.04 #4387, 0.03 #9417, 0.02 #3381), 0pdp8 (0.04 #4243, 0.02 #9273, 0.02 #3237), 03_gz8 (0.04 #1653, 0.03 #2659, 0.02 #6683), 013q0p (0.04 #1482, 0.03 #4500, 0.02 #6512), 04tqtl (0.04 #1311, 0.02 #7347, 0.02 #9359), 07bzz7 (0.03 #2535, 0.03 #11589, 0.02 #6559), 0y_pg (0.03 #2792, 0.03 #4804, 0.02 #7822) >> Best rule #2333 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 0p5mw; 0b6yp2; 01jpmpv; 02sj1x; 01pr6q7; 02w670; 0gv07g; 02bn75; 01mh8zn; 03975z; ... >> query: (?x3805, 02rrfzf) <- nationality(?x3805, ?x94), music(?x2318, ?x3805), film_release_region(?x2318, ?x1892), ?x1892 = 02vzc >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05_pkf music! 02x8fs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 14.000 0.061 http://example.org/film/film/music EVAL 05_pkf music! 025n07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 14.000 0.061 http://example.org/film/film/music #6883-03t97y PRED entity: 03t97y PRED relation: film_crew_role PRED expected values: 09zzb8 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.75 #870, 0.73 #733, 0.73 #332), 02ynfr (0.33 #13, 0.29 #244, 0.25 #145), 02rh1dz (0.21 #340, 0.21 #273, 0.20 #42), 089fss (0.20 #39, 0.19 #2007, 0.17 #72), 094hwz (0.20 #45, 0.19 #2007, 0.09 #575), 05smlt (0.20 #51, 0.19 #2007, 0.07 #216), 014kbl (0.20 #63, 0.02 #762, 0.02 #795), 0d2b38 (0.19 #2007, 0.16 #354, 0.12 #420), 01xy5l_ (0.19 #2007, 0.16 #342, 0.12 #408), 015h31 (0.19 #2007, 0.16 #571, 0.12 #239) >> Best rule #870 for best value: >> intensional similarity = 4 >> extensional distance = 483 >> proper extension: 07l50vn; 072r5v; 0dmn0x; >> query: (?x1074, 09zzb8) <- featured_film_locations(?x1074, ?x362), film_crew_role(?x1074, ?x468), genre(?x1074, ?x225), country(?x1074, ?x94) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t97y film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.755 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6882-02fqrf PRED entity: 02fqrf PRED relation: film_distribution_medium PRED expected values: 02nxhr => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 4): 02nxhr (0.33 #1, 0.32 #5, 0.30 #13), 0dq6p (0.24 #6, 0.19 #91, 0.18 #87), 07z4p (0.05 #12, 0.02 #89, 0.02 #93), 07c52 (0.01 #88, 0.01 #92) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0g5qs2k; 0bh8yn3; >> query: (?x3498, 02nxhr) <- film_release_region(?x3498, ?x3277), film_distribution_medium(?x3498, ?x81), ?x3277 = 06t8v, film(?x489, ?x3498) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02fqrf film_distribution_medium 02nxhr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.333 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #6881-01_vfy PRED entity: 01_vfy PRED relation: student! PRED expected values: 01w5m => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 126): 0bwfn (0.30 #275, 0.14 #7115, 0.12 #9745), 015nl4 (0.20 #67, 0.05 #3223, 0.04 #3749), 03ksy (0.11 #1158, 0.09 #11680, 0.06 #19044), 0gl5_ (0.10 #244, 0.01 #29177, 0.01 #33912), 0cwx_ (0.10 #241, 0.01 #24440, 0.01 #33383), 02mj7c (0.10 #165), 065y4w7 (0.09 #4222, 0.09 #1592, 0.08 #6854), 01w5m (0.09 #11679, 0.08 #19043, 0.06 #19569), 04b_46 (0.09 #1805, 0.04 #9697, 0.04 #6014), 0lk0l (0.06 #1016, 0.05 #1542, 0.03 #2594) >> Best rule #275 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 09mfvx; >> query: (?x2344, 0bwfn) <- nominated_for(?x2344, ?x5706), nominated_for(?x2344, ?x2345), ?x5706 = 0284b56, nominated_for(?x198, ?x2345) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #11679 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 172 *> proper extension: 0j3v; 0dzkq; 07_grx; 07c37; *> query: (?x2344, 01w5m) <- gender(?x2344, ?x231), people(?x3538, ?x2344), student(?x9479, ?x2344), location(?x2344, ?x2254) *> conf = 0.09 ranks of expected_values: 8 EVAL 01_vfy student! 01w5m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 146.000 146.000 0.300 http://example.org/education/educational_institution/students_graduates./education/education/student #6880-05whq_9 PRED entity: 05whq_9 PRED relation: profession PRED expected values: 0dxtg => 93 concepts (91 used for prediction) PRED predicted values (max 10 best out of 72): 0dxtg (0.87 #4246, 0.82 #4976, 0.75 #3662), 03gjzk (0.70 #2933, 0.66 #2349, 0.61 #4685), 09jwl (0.42 #5711, 0.38 #5419, 0.37 #6149), 018gz8 (0.40 #2351, 0.22 #3227, 0.16 #4687), 0cbd2 (0.33 #6, 0.29 #4240, 0.28 #4970), 0fj9f (0.32 #1658, 0.29 #1804, 0.27 #344), 0nbcg (0.27 #5723, 0.26 #5431, 0.26 #6161), 016z4k (0.27 #5406, 0.24 #5990, 0.23 #6136), 0np9r (0.26 #2355, 0.21 #895, 0.18 #1187), 0dz3r (0.24 #5404, 0.23 #5696, 0.22 #5988) >> Best rule #4246 for best value: >> intensional similarity = 3 >> extensional distance = 206 >> proper extension: 01r216; >> query: (?x2595, 0dxtg) <- nationality(?x2595, ?x1264), student(?x7154, ?x2595), written_by(?x7700, ?x2595) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05whq_9 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 91.000 0.865 http://example.org/people/person/profession #6879-07t90 PRED entity: 07t90 PRED relation: major_field_of_study PRED expected values: 01mkq 04rjg 03g3w => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 100): 01mkq (0.72 #632, 0.71 #220, 0.70 #735), 037mh8 (0.62 #258, 0.41 #670, 0.35 #773), 04rjg (0.62 #636, 0.57 #224, 0.47 #739), 03g3w (0.52 #230, 0.44 #642, 0.43 #539), 0fdys (0.48 #237, 0.46 #649, 0.33 #855), 02ky346 (0.48 #221, 0.33 #633, 0.29 #530), 0h5k (0.43 #227, 0.31 #639, 0.21 #536), 02h40lc (0.33 #210, 0.28 #622, 0.25 #519), 0l5mz (0.33 #264, 0.23 #676, 0.18 #882), 0pf2 (0.33 #234, 0.21 #646, 0.18 #543) >> Best rule #632 for best value: >> intensional similarity = 2 >> extensional distance = 37 >> proper extension: 0d06m5; 0d05fv; >> query: (?x4599, 01mkq) <- list(?x4599, ?x2197), organization(?x4599, ?x5487) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4 EVAL 07t90 major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.718 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07t90 major_field_of_study 04rjg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.718 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07t90 major_field_of_study 01mkq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.718 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #6878-09qj50 PRED entity: 09qj50 PRED relation: award! PRED expected values: 0bw6y 02l3_5 01934k => 47 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2894): 0c4f4 (0.79 #13376, 0.68 #43474, 0.66 #46820), 01z5tr (0.79 #13376, 0.68 #43474, 0.66 #46820), 057hz (0.79 #13376, 0.68 #43474, 0.66 #46820), 018ygt (0.40 #1830, 0.16 #20063, 0.14 #40126), 01h910 (0.40 #1789, 0.16 #20063, 0.14 #40126), 04t2l2 (0.40 #42, 0.16 #20063, 0.14 #40126), 0q5hw (0.40 #765, 0.14 #40126, 0.12 #50164), 04cl1 (0.40 #1345, 0.12 #46819, 0.06 #4688), 01y0y6 (0.40 #1025, 0.12 #50164, 0.12 #50163), 0q9zc (0.40 #2407, 0.06 #5750, 0.03 #12438) >> Best rule #13376 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 09v7wsg; >> query: (?x757, ?x495) <- award(?x758, ?x757), nominated_for(?x757, ?x2063), ceremony(?x757, ?x1265), award_winner(?x757, ?x495) >> conf = 0.79 => this is the best rule for 3 predicted values *> Best rule #40126 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 198 *> proper extension: 0g_w; 0h53c_5; 0dgr5xp; 09v1lrz; 07t_l23; *> query: (?x757, ?x822) <- award(?x7797, ?x757), award(?x444, ?x757), nominated_for(?x757, ?x758), award_winner(?x515, ?x444), award_winner(?x7797, ?x822) *> conf = 0.14 ranks of expected_values: 235, 283, 1095 EVAL 09qj50 award! 01934k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 47.000 17.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qj50 award! 02l3_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 47.000 17.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qj50 award! 0bw6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 17.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6877-0pb33 PRED entity: 0pb33 PRED relation: award PRED expected values: 02qyntr => 102 concepts (92 used for prediction) PRED predicted values (max 10 best out of 187): 0k611 (0.38 #72, 0.26 #1845, 0.25 #8765), 0gs9p (0.36 #63, 0.20 #1677, 0.12 #4674), 0gq9h (0.34 #61, 0.22 #1675, 0.12 #4672), 0gq_v (0.30 #19, 0.14 #1633, 0.10 #4630), 05zr6wv (0.26 #1845, 0.25 #8765, 0.25 #6920), 05pcn59 (0.26 #1845, 0.25 #8765, 0.25 #6920), 05ztjjw (0.26 #1845, 0.25 #8765, 0.25 #6920), 019f4v (0.25 #52, 0.17 #1666, 0.10 #4663), 04ljl_l (0.24 #2998, 0.21 #11765, 0.18 #5766), 03c7tr1 (0.24 #2998, 0.21 #11765, 0.18 #5766) >> Best rule #72 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 06mmr; >> query: (?x1450, 0k611) <- award_winner(?x1450, ?x3308), award(?x1450, ?x500), ?x500 = 0p9sw >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1784 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 191 *> proper extension: 03bdkd; *> query: (?x1450, 02qyntr) <- nominated_for(?x1703, ?x1450), film(?x820, ?x1450), ?x1703 = 0k611, award_winner(?x1450, ?x4284) *> conf = 0.10 ranks of expected_values: 30 EVAL 0pb33 award 02qyntr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 102.000 92.000 0.377 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #6876-04v3q PRED entity: 04v3q PRED relation: member_states! PRED expected values: 085h1 => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 12): 085h1 (0.96 #303, 0.96 #284, 0.93 #104), 018cqq (0.52 #78, 0.47 #74, 0.46 #25), 059dn (0.38 #115, 0.36 #80, 0.35 #105), 0j7v_ (0.16 #116, 0.16 #70, 0.15 #77), 01rz1 (0.16 #116, 0.16 #70, 0.15 #77), 07t65 (0.16 #116, 0.16 #70, 0.15 #77), 041288 (0.07 #506, 0.03 #564), 0b6css (0.07 #506, 0.03 #564), 04k4l (0.07 #506, 0.03 #564), 0_2v (0.07 #506, 0.03 #564) >> Best rule #303 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 02jxk; >> query: (?x1061, 085h1) <- member_states(?x2106, ?x1061), organization(?x304, ?x2106), jurisdiction_of_office(?x182, ?x1061), countries_spoken_in(?x4442, ?x304) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04v3q member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.962 http://example.org/user/ktrueman/default_domain/international_organization/member_states #6875-03z20c PRED entity: 03z20c PRED relation: nominated_for! PRED expected values: 05f4m9q => 72 concepts (67 used for prediction) PRED predicted values (max 10 best out of 178): 0gs9p (0.50 #537, 0.31 #774, 0.19 #6230), 0gr0m (0.50 #533, 0.28 #770, 0.16 #6226), 0gq_v (0.48 #731, 0.38 #494, 0.18 #6187), 0gq9h (0.38 #536, 0.34 #773, 0.25 #4806), 0gqy2 (0.38 #596, 0.31 #833, 0.17 #1307), 019f4v (0.38 #527, 0.28 #764, 0.20 #2660), 0gr4k (0.38 #501, 0.17 #738, 0.15 #4771), 040njc (0.38 #481, 0.15 #6174, 0.14 #718), 05p09zm (0.28 #3557, 0.26 #4507, 0.25 #4508), 05p1dby (0.28 #3557, 0.26 #4507, 0.25 #4508) >> Best rule #537 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0k4f3; 0kxf1; >> query: (?x2907, 0gs9p) <- film(?x376, ?x2907), crewmember(?x2907, ?x666), film_release_region(?x2907, ?x94), film_sets_designed(?x8814, ?x2907) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3557 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 216 *> proper extension: 01p9hgt; 01kv4mb; 0ggjt; 0bhvtc; 03cfjg; 0p_47; 0pmw9; *> query: (?x2907, ?x102) <- nominated_for(?x7967, ?x2907), nominated_for(?x154, ?x2907), nominated_for(?x102, ?x7967) *> conf = 0.28 ranks of expected_values: 13 EVAL 03z20c nominated_for! 05f4m9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 72.000 67.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6874-07h07 PRED entity: 07h07 PRED relation: nominated_for PRED expected values: 0jwmp => 86 concepts (38 used for prediction) PRED predicted values (max 10 best out of 558): 0y_pg (0.17 #2851, 0.06 #6087, 0.03 #43700), 08xvpn (0.17 #3055, 0.06 #6291, 0.02 #12763), 0qmjd (0.17 #2706, 0.06 #5942, 0.01 #7560), 0n1s0 (0.17 #2556, 0.06 #5792, 0.01 #7410), 0b2v79 (0.17 #1639, 0.06 #4875, 0.01 #6493), 0gxsh4 (0.17 #3178, 0.06 #6414), 0304nh (0.17 #2389, 0.06 #5625), 055td_ (0.17 #2287, 0.06 #5523), 07s846j (0.11 #5465, 0.03 #43700, 0.02 #13555), 03cw411 (0.11 #5417, 0.03 #10271, 0.01 #7035) >> Best rule #2851 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0yfp; 0fb1q; 02b29; 04135; >> query: (?x4008, 0y_pg) <- award_nominee(?x4008, ?x6866), ?x6866 = 03m9c8, influenced_by(?x4008, ?x4028) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07h07 nominated_for 0jwmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 38.000 0.167 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #6873-082xp PRED entity: 082xp PRED relation: profession PRED expected values: 099md => 97 concepts (57 used for prediction) PRED predicted values (max 10 best out of 120): 02hrh1q (0.88 #6788, 0.85 #6932, 0.80 #2030), 09jwl (0.67 #2611, 0.50 #18, 0.29 #595), 0cbd2 (0.58 #1160, 0.53 #3179, 0.52 #7070), 01d_h8 (0.55 #1447, 0.55 #1591, 0.41 #3034), 0nbcg (0.50 #30, 0.36 #2623, 0.29 #607), 04gc2 (0.44 #2345, 0.37 #2201, 0.35 #2489), 03gjzk (0.39 #1455, 0.36 #1599, 0.35 #4050), 016z4k (0.39 #2597, 0.29 #581, 0.25 #4), 02jknp (0.34 #1449, 0.32 #1593, 0.30 #3180), 012t_z (0.27 #1597, 0.24 #1453, 0.18 #3040) >> Best rule #6788 for best value: >> intensional similarity = 7 >> extensional distance = 165 >> proper extension: 04yywz; 01l1b90; 01csvq; 0yfp; 05r5w; 017xm3; 0p3r8; 039crh; 02t_99; 015njf; ... >> query: (?x11492, 02hrh1q) <- profession(?x11492, ?x2225), profession(?x11492, ?x955), ?x2225 = 0kyk, profession(?x4741, ?x955), profession(?x2737, ?x955), ?x4741 = 01s21dg, ?x2737 = 0126y2 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #2374 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 012v1t; *> query: (?x11492, 099md) <- basic_title(?x11492, ?x10118), politician(?x10498, ?x11492), jurisdiction_of_office(?x10118, ?x390) *> conf = 0.15 ranks of expected_values: 19 EVAL 082xp profession 099md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 97.000 57.000 0.880 http://example.org/people/person/profession #6872-07fb6 PRED entity: 07fb6 PRED relation: organization PRED expected values: 0_2v 0j7v_ => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 49): 0j7v_ (0.74 #89, 0.66 #194, 0.59 #152), 0_2v (0.42 #45, 0.41 #108, 0.41 #129), 0b6css (0.41 #115, 0.39 #136, 0.38 #304), 01rz1 (0.33 #127, 0.32 #106, 0.32 #1116), 04k4l (0.32 #1116, 0.31 #298, 0.31 #445), 0gkjy (0.32 #1116, 0.31 #91, 0.30 #196), 018cqq (0.32 #1116, 0.23 #116, 0.22 #137), 02jxk (0.32 #1116, 0.16 #296, 0.16 #107), 059dn (0.32 #1116, 0.11 #120, 0.10 #141), 085h1 (0.32 #1116, 0.07 #117, 0.06 #138) >> Best rule #89 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0j5g9; 049nq; >> query: (?x8378, 0j7v_) <- official_language(?x8378, ?x254), ?x254 = 02h40lc, administrative_parent(?x8378, ?x551) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07fb6 organization 0j7v_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.738 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 07fb6 organization 0_2v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.738 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #6871-01fxfk PRED entity: 01fxfk PRED relation: award_winner! PRED expected values: 0c4hx0 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 134): 01bx35 (0.15 #7, 0.05 #8608, 0.04 #6775), 0c4hx0 (0.15 #128, 0.04 #2948, 0.04 #1961), 0d__c3 (0.08 #407, 0.08 #266, 0.07 #1112), 01xqqp (0.08 #378, 0.06 #237, 0.05 #1929), 0gpjbt (0.08 #29, 0.05 #8630, 0.05 #593), 02cg41 (0.08 #126, 0.05 #8727, 0.04 #1959), 07y9ts (0.08 #68, 0.05 #914, 0.05 #773), 0c53zb (0.07 #1894, 0.06 #3022, 0.05 #2599), 02rjjll (0.07 #8606, 0.03 #12273, 0.03 #13119), 013b2h (0.06 #8681, 0.03 #13194, 0.03 #12348) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01bcq; 01cpqk; 0kbg6; >> query: (?x12872, 01bx35) <- award(?x12872, ?x1862), student(?x9879, ?x12872), nationality(?x12872, ?x94), ?x9879 = 01pcj4 >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 01bcq; 01cpqk; 0kbg6; *> query: (?x12872, 0c4hx0) <- award(?x12872, ?x1862), student(?x9879, ?x12872), nationality(?x12872, ?x94), ?x9879 = 01pcj4 *> conf = 0.15 ranks of expected_values: 2 EVAL 01fxfk award_winner! 0c4hx0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 137.000 0.154 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6870-0178_w PRED entity: 0178_w PRED relation: group! PRED expected values: 0342h => 78 concepts (50 used for prediction) PRED predicted values (max 10 best out of 113): 0342h (0.92 #1140, 0.91 #1546, 0.90 #977), 018vs (0.76 #661, 0.73 #499, 0.69 #1149), 03bx0bm (0.70 #997, 0.67 #672, 0.66 #1078), 028tv0 (0.45 #985, 0.44 #1066, 0.42 #660), 0l14qv (0.36 #653, 0.36 #1141, 0.35 #491), 04rzd (0.33 #192, 0.25 #30, 0.20 #111), 05r5c (0.26 #1061, 0.26 #1143, 0.25 #1224), 07gql (0.25 #34, 0.20 #115, 0.15 #1007), 07brj (0.25 #19, 0.20 #100, 0.14 #1136), 06w7v (0.25 #67, 0.20 #148, 0.14 #1136) >> Best rule #1140 for best value: >> intensional similarity = 5 >> extensional distance = 70 >> proper extension: 01qqwp9; 012vm6; 0qmpd; 06br6t; 09jm8; 0pqp3; >> query: (?x6854, 0342h) <- group(?x315, ?x6854), ?x315 = 0l14md, artists(?x3061, ?x6854), artists(?x3061, ?x7620), ?x7620 = 06gcn >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0178_w group! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 50.000 0.917 http://example.org/music/performance_role/regular_performances./music/group_membership/group #6869-0psss PRED entity: 0psss PRED relation: place_of_birth PRED expected values: 04jpl => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 72): 0r2dp (0.33 #401, 0.02 #1105, 0.01 #1809), 05qtj (0.30 #4226, 0.28 #4931, 0.27 #51413), 04jpl (0.07 #712, 0.02 #12684, 0.02 #18320), 02_286 (0.07 #2836, 0.06 #12695, 0.06 #5654), 0cr3d (0.05 #2207, 0.05 #2911, 0.04 #14883), 06_kh (0.05 #709, 0.01 #13386), 030qb3t (0.05 #2871, 0.04 #5689, 0.04 #12026), 0694j (0.03 #13381), 01_d4 (0.03 #7109, 0.03 #45137, 0.03 #28941), 0dclg (0.03 #3599, 0.02 #8529, 0.02 #782) >> Best rule #401 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 016fnb; >> query: (?x3280, 0r2dp) <- award_nominee(?x3280, ?x3281), ?x3281 = 0154qm, artists(?x2936, ?x3280) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #712 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 02bfmn; 01j5ts; 0p_pd; 01q_ph; 09wj5; 02gvwz; 07vc_9; 01v42g; 0blbxk; 01rh0w; ... *> query: (?x3280, 04jpl) <- award_nominee(?x3280, ?x3281), ?x3281 = 0154qm, nominated_for(?x3280, ?x1490) *> conf = 0.07 ranks of expected_values: 3 EVAL 0psss place_of_birth 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 91.000 0.333 http://example.org/people/person/place_of_birth #6868-03xl77 PRED entity: 03xl77 PRED relation: artist! PRED expected values: 01trtc => 166 concepts (131 used for prediction) PRED predicted values (max 10 best out of 126): 0n85g (0.33 #61, 0.20 #1451, 0.18 #5344), 01trtc (0.33 #71, 0.20 #488, 0.17 #4937), 03mp8k (0.33 #65, 0.20 #482, 0.14 #2984), 011k1h (0.33 #9, 0.17 #3623, 0.13 #5292), 01f_3w (0.33 #33, 0.11 #4342, 0.10 #4620), 01cszh (0.33 #10, 0.09 #4876, 0.09 #2929), 043g7l (0.33 #30, 0.08 #10875, 0.08 #1142), 01q940 (0.33 #50, 0.07 #467, 0.06 #6306), 015_1q (0.22 #5301, 0.21 #5579, 0.21 #2381), 033hn8 (0.19 #985, 0.14 #2375, 0.14 #1124) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01vxlbm; >> query: (?x2946, 0n85g) <- participant(?x8793, ?x2946), artists(?x6101, ?x2946), category(?x2946, ?x134), ?x6101 = 06rqw >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #71 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 01vxlbm; *> query: (?x2946, 01trtc) <- participant(?x8793, ?x2946), artists(?x6101, ?x2946), category(?x2946, ?x134), ?x6101 = 06rqw *> conf = 0.33 ranks of expected_values: 2 EVAL 03xl77 artist! 01trtc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 166.000 131.000 0.333 http://example.org/music/record_label/artist #6867-03h_0_z PRED entity: 03h_0_z PRED relation: award PRED expected values: 02f777 => 132 concepts (126 used for prediction) PRED predicted values (max 10 best out of 289): 09sb52 (0.65 #24720, 0.30 #14768, 0.30 #11982), 01by1l (0.48 #3296, 0.45 #12849, 0.43 #13645), 01bgqh (0.43 #3227, 0.39 #7606, 0.35 #12780), 02f73b (0.40 #4659, 0.33 #3067, 0.28 #7446), 02f777 (0.38 #3090, 0.33 #4682, 0.32 #2294), 03qbnj (0.38 #3014, 0.30 #4606, 0.30 #7791), 02f6xy (0.37 #2188, 0.29 #3382, 0.27 #4576), 02f6ym (0.33 #4630, 0.33 #3038, 0.32 #5028), 0c4z8 (0.33 #3256, 0.27 #12809, 0.26 #13605), 054ks3 (0.33 #3325, 0.21 #7704, 0.19 #12878) >> Best rule #24720 for best value: >> intensional similarity = 3 >> extensional distance = 614 >> proper extension: 01v42g; 01fwj8; 0f4dx2; 0278x6s; 042z_g; 03fbb6; 025j1t; 03nkts; 02t_vx; 013zs9; ... >> query: (?x6144, 09sb52) <- award(?x6144, ?x4837), award(?x3930, ?x4837), ?x3930 = 01svw8n >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #3090 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 01l_vgt; *> query: (?x6144, 02f777) <- artists(?x5876, ?x6144), ?x5876 = 0ggx5q, participant(?x5798, ?x6144), award_nominee(?x5798, ?x140) *> conf = 0.38 ranks of expected_values: 5 EVAL 03h_0_z award 02f777 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 132.000 126.000 0.653 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6866-027m67 PRED entity: 027m67 PRED relation: prequel! PRED expected values: 01f8gz => 118 concepts (54 used for prediction) PRED predicted values (max 10 best out of 145): 01f8f7 (0.17 #294, 0.17 #115, 0.06 #652), 0bpm4yw (0.17 #74, 0.05 #1148, 0.05 #1327), 03y0pn (0.17 #121, 0.05 #1195, 0.04 #1553), 0bc1yhb (0.17 #273, 0.05 #1347, 0.04 #1526), 03nfnx (0.17 #137, 0.05 #1390, 0.04 #1569), 05qbckf (0.17 #222, 0.05 #1296, 0.04 #1475), 02lk60 (0.17 #85, 0.05 #1338, 0.04 #1517), 031ldd (0.12 #462, 0.05 #1178, 0.05 #1896), 05nlx4 (0.12 #478, 0.05 #1194, 0.02 #1912), 09xbpt (0.12 #367, 0.02 #1801, 0.02 #1980) >> Best rule #294 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 05qbckf; 0198b6; 0dzlbx; 01f85k; >> query: (?x7293, 01f8f7) <- prequel(?x6376, ?x7293), film_release_region(?x7293, ?x1353), ?x1353 = 035qy, film_production_design_by(?x7293, ?x9086) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027m67 prequel! 01f8gz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 54.000 0.167 http://example.org/film/film/prequel #6865-017g21 PRED entity: 017g21 PRED relation: student! PRED expected values: 0fvd03 => 174 concepts (127 used for prediction) PRED predicted values (max 10 best out of 177): 01t0dy (0.20 #217, 0.17 #744, 0.12 #1271), 07tg4 (0.13 #13789, 0.09 #19586, 0.08 #14843), 0h6rm (0.11 #5414, 0.03 #12793, 0.02 #22806), 09f2j (0.11 #3321, 0.05 #9119, 0.03 #12808), 01w5m (0.11 #3794, 0.04 #39633, 0.04 #5375), 02g839 (0.10 #6350, 0.06 #16363, 0.06 #8985), 05zjtn4 (0.10 #1584, 0.01 #9490, 0.01 #25302), 029qzx (0.10 #1986, 0.01 #9892, 0.01 #12000), 015nl4 (0.09 #14824, 0.07 #13770, 0.07 #19567), 0bwfn (0.08 #21883, 0.06 #2383, 0.06 #59306) >> Best rule #217 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 01q99h; >> query: (?x7252, 01t0dy) <- artist(?x5666, ?x7252), artist(?x2149, ?x7252), artists(?x7329, ?x7252), ?x7329 = 016jny, ?x5666 = 043g7l, ?x2149 = 011k1h >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2608 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 021bk; 09g0h; *> query: (?x7252, 0fvd03) <- group(?x7252, ?x1684), instrumentalists(?x716, ?x7252), religion(?x7252, ?x2694), ?x2694 = 0kpl, profession(?x7252, ?x655) *> conf = 0.06 ranks of expected_values: 14 EVAL 017g21 student! 0fvd03 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 174.000 127.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #6864-035qy PRED entity: 035qy PRED relation: country! PRED expected values: 06z6r 0crlz 01sgl => 218 concepts (218 used for prediction) PRED predicted values (max 10 best out of 35): 06z6r (0.86 #297, 0.85 #2152, 0.85 #1452), 0194d (0.75 #484, 0.68 #309, 0.67 #204), 07bs0 (0.75 #464, 0.64 #289, 0.63 #1129), 0d1t3 (0.71 #474, 0.68 #299, 0.45 #264), 03fyrh (0.70 #1450, 0.62 #1030, 0.59 #2360), 01sgl (0.67 #481, 0.64 #306, 0.51 #1146), 01z27 (0.67 #465, 0.64 #290, 0.51 #1130), 07rlg (0.67 #456, 0.64 #281, 0.49 #1366), 09w1n (0.62 #468, 0.59 #293, 0.53 #1378), 0d1tm (0.54 #457, 0.50 #282, 0.39 #177) >> Best rule #297 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0h7x; 082fr; 07twz; >> query: (?x1353, 06z6r) <- film_release_region(?x2717, ?x1353), contains(?x1353, ?x7575), ?x2717 = 0k5g9 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 28 EVAL 035qy country! 01sgl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 218.000 218.000 0.864 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 035qy country! 0crlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 218.000 218.000 0.864 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 035qy country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 218.000 218.000 0.864 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #6863-016h4r PRED entity: 016h4r PRED relation: profession PRED expected values: 09jwl => 129 concepts (128 used for prediction) PRED predicted values (max 10 best out of 77): 09jwl (0.72 #610, 0.71 #5962, 0.69 #3434), 01d_h8 (0.62 #5, 0.49 #449, 0.34 #4906), 0nbcg (0.61 #179, 0.54 #623, 0.52 #2555), 0dz3r (0.51 #594, 0.46 #1338, 0.43 #5946), 03gjzk (0.46 #14, 0.30 #458, 0.25 #1052), 0dxtg (0.38 #13, 0.37 #457, 0.29 #9969), 01c72t (0.36 #4180, 0.35 #2250, 0.32 #1952), 02jknp (0.31 #451, 0.23 #7, 0.21 #12926), 0n1h (0.29 #1347, 0.28 #603, 0.25 #2089), 039v1 (0.28 #5980, 0.27 #6426, 0.26 #3452) >> Best rule #610 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 04bgy; >> query: (?x3495, 09jwl) <- artists(?x1572, ?x3495), film(?x3495, ?x3275), role(?x3495, ?x227) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016h4r profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 128.000 0.724 http://example.org/people/person/profession #6862-0bbvr84 PRED entity: 0bbvr84 PRED relation: nationality PRED expected values: 09c7w0 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 12): 09c7w0 (0.76 #501, 0.76 #1302, 0.75 #201), 02jx1 (0.36 #4105, 0.35 #2303, 0.09 #433), 07ssc (0.36 #4105, 0.35 #2303, 0.08 #315), 0f8l9c (0.08 #322, 0.02 #2825, 0.02 #3425), 03rk0 (0.06 #7456, 0.05 #8158, 0.05 #8259), 0d060g (0.04 #5012, 0.04 #708, 0.04 #407), 0chghy (0.02 #2012, 0.02 #1211, 0.02 #1812), 03rt9 (0.02 #413, 0.02 #714, 0.01 #814), 03rjj (0.02 #1106, 0.02 #1707, 0.02 #1807), 0345h (0.02 #8143, 0.02 #1532, 0.01 #7541) >> Best rule #501 for best value: >> intensional similarity = 2 >> extensional distance = 346 >> proper extension: 02wrhj; >> query: (?x10138, 09c7w0) <- award_winner(?x3609, ?x10138), actor(?x3310, ?x10138) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bbvr84 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.759 http://example.org/people/person/nationality #6861-09c6w PRED entity: 09c6w PRED relation: location! PRED expected values: 06pwf6 => 170 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1779): 04fkg4 (0.56 #17587, 0.53 #50246, 0.52 #55272), 02756j (0.40 #8818, 0.17 #38963, 0.12 #41475), 02xfrd (0.33 #861, 0.09 #36032, 0.08 #38544), 02qvhbb (0.33 #2436, 0.09 #37607, 0.05 #47656), 03fwln (0.30 #34819, 0.18 #37331, 0.17 #17234), 019fz (0.29 #22471, 0.25 #27495, 0.22 #30007), 0738b8 (0.29 #18032, 0.20 #30592, 0.11 #43152), 0gs1_ (0.29 #18911, 0.20 #31471, 0.11 #44031), 0dn3n (0.29 #18175, 0.20 #30735, 0.11 #43295), 01pllx (0.29 #19403, 0.20 #31963, 0.11 #44523) >> Best rule #17587 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 016v46; >> query: (?x5384, ?x11002) <- country(?x5384, ?x2146), category(?x5384, ?x134), administrative_division(?x5384, ?x11812), place_of_birth(?x11002, ?x5384) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #55271 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 0jcg8; 0ftkx; *> query: (?x5384, ?x111) <- country(?x5384, ?x2146), place_of_death(?x5383, ?x5384), place_of_birth(?x11002, ?x5384), film_release_region(?x80, ?x2146), nationality(?x111, ?x2146) *> conf = 0.03 ranks of expected_values: 1264 EVAL 09c6w location! 06pwf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 170.000 49.000 0.558 http://example.org/people/person/places_lived./people/place_lived/location #6860-07024 PRED entity: 07024 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.86 #109, 0.85 #51, 0.84 #6), 0735l (0.16 #71, 0.15 #77), 02nxhr (0.06 #37, 0.04 #172, 0.04 #218), 07c52 (0.04 #100, 0.04 #300, 0.04 #224), 07z4p (0.03 #102, 0.03 #134, 0.03 #226) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 226 >> proper extension: 0fztbq; >> query: (?x2928, 029j_) <- film(?x2858, ?x2928), language(?x2928, ?x254), nominated_for(?x2928, ?x2262), profession(?x2858, ?x319) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07024 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.855 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6859-033x5p PRED entity: 033x5p PRED relation: student PRED expected values: 0hwqz => 186 concepts (153 used for prediction) PRED predicted values (max 10 best out of 1700): 0fpzt5 (0.09 #1536, 0.05 #9900, 0.04 #16174), 0c9xjl (0.09 #949, 0.05 #9313, 0.03 #17678), 03ktjq (0.09 #1004, 0.04 #11459, 0.04 #13551), 01_xtx (0.09 #628, 0.04 #11083, 0.04 #34086), 015qq1 (0.08 #3982, 0.03 #60441, 0.03 #24893), 0ff3y (0.06 #22979, 0.04 #2068, 0.04 #45981), 0306ds (0.06 #10861, 0.05 #17135, 0.04 #406), 04t969 (0.06 #28463, 0.05 #22190, 0.04 #3370), 01_f_5 (0.06 #7364, 0.04 #11546, 0.04 #3182), 0405l (0.05 #22764, 0.04 #1853, 0.02 #39493) >> Best rule #1536 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 01pl14; 02c9dj; >> query: (?x4363, 0fpzt5) <- registering_agency(?x4363, ?x1982), major_field_of_study(?x4363, ?x1154), student(?x4363, ?x158), school(?x1160, ?x4363) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #21936 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 02sdwt; *> query: (?x4363, 0hwqz) <- student(?x4363, ?x1398), artists(?x378, ?x1398), film(?x1398, ?x2565), award(?x1398, ?x2322) *> conf = 0.02 ranks of expected_values: 1048 EVAL 033x5p student 0hwqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 186.000 153.000 0.087 http://example.org/education/educational_institution/students_graduates./education/education/student #6858-045bg PRED entity: 045bg PRED relation: influenced_by! PRED expected values: 0mb0 => 149 concepts (51 used for prediction) PRED predicted values (max 10 best out of 418): 01d494 (0.50 #3115, 0.24 #7199, 0.22 #9237), 0683n (0.38 #1357, 0.20 #3400, 0.19 #4421), 02yl42 (0.33 #644, 0.25 #1667, 0.22 #2688), 047g6 (0.31 #4052, 0.28 #7625, 0.24 #9663), 0b78hw (0.31 #3741, 0.25 #6802, 0.24 #7314), 032r1 (0.31 #4046, 0.22 #3024, 0.21 #7107), 0dzkq (0.30 #3190, 0.24 #9312, 0.20 #124), 032l1 (0.26 #6247, 0.10 #9700, 0.08 #9306), 0969fd (0.25 #2473, 0.25 #1452, 0.20 #3495), 03_hd (0.25 #3754, 0.24 #7327, 0.17 #6815) >> Best rule #3115 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 099bk; >> query: (?x1236, 01d494) <- interests(?x1236, ?x713), influenced_by(?x1029, ?x1236), influenced_by(?x1236, ?x9600), ?x9600 = 039n1 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #25027 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 187 *> proper extension: 0134tg; 07mvp; 03c3yf; 033s6; 016vn3; *> query: (?x1236, ?x3980) <- influenced_by(?x4265, ?x1236), award(?x1236, ?x8153), influenced_by(?x3980, ?x4265) *> conf = 0.11 ranks of expected_values: 106 EVAL 045bg influenced_by! 0mb0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 149.000 51.000 0.500 http://example.org/influence/influence_node/influenced_by #6857-0fttg PRED entity: 0fttg PRED relation: featured_film_locations! PRED expected values: 027pfg => 128 concepts (86 used for prediction) PRED predicted values (max 10 best out of 380): 02cbhg (0.33 #1329, 0.25 #2803, 0.25 #2066), 02wwmhc (0.25 #2904, 0.04 #11011, 0.03 #14696), 0dt8xq (0.20 #3324, 0.17 #4061, 0.06 #8483), 02yvct (0.15 #6788, 0.13 #7525, 0.12 #4577), 072x7s (0.15 #6746, 0.12 #4535, 0.11 #11168), 06fqlk (0.13 #7855, 0.12 #4907, 0.09 #5644), 061681 (0.13 #7417, 0.08 #6680, 0.07 #16261), 0ywrc (0.12 #4649, 0.09 #5386, 0.08 #6123), 0f4_2k (0.12 #4860, 0.09 #5597, 0.08 #6334), 04180vy (0.12 #5140, 0.09 #5877, 0.08 #6614) >> Best rule #1329 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0dzt9; >> query: (?x12384, 02cbhg) <- capital(?x8866, ?x12384), administrative_division(?x12384, ?x2831), contains(?x94, ?x12384), ?x8866 = 020d5 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fttg featured_film_locations! 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 86.000 0.333 http://example.org/film/film/featured_film_locations #6856-05k7sb PRED entity: 05k7sb PRED relation: adjoins! PRED expected values: 01x73 => 140 concepts (61 used for prediction) PRED predicted values (max 10 best out of 419): 05tbn (0.25 #961, 0.20 #39852, 0.13 #6428), 026mj (0.25 #1123, 0.09 #2683, 0.09 #3464), 0d060g (0.25 #11, 0.08 #792, 0.06 #43772), 05rgl (0.25 #101, 0.06 #2442, 0.06 #12595), 0b90_r (0.25 #5, 0.04 #7815, 0.03 #16403), 01qh7 (0.25 #149, 0.02 #47674, 0.02 #4686), 07h34 (0.20 #39852, 0.17 #4873, 0.17 #968), 081mh (0.20 #39852, 0.17 #926, 0.14 #3267), 0694j (0.20 #39852, 0.17 #1082, 0.12 #1862), 05k7sb (0.20 #39852, 0.17 #891, 0.09 #1671) >> Best rule #961 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 059rby; 059f4; 05fkf; 01x73; 04rrd; 06btq; 0d0x8; 05tbn; 06yxd; 05fjf; >> query: (?x2020, 05tbn) <- district_represented(?x10638, ?x2020), location(?x237, ?x2020), ?x10638 = 01grmk >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #39852 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 95 *> proper extension: 0mvsg; *> query: (?x2020, ?x335) <- partially_contains(?x2020, ?x10954), partially_contains(?x335, ?x10954) *> conf = 0.20 ranks of expected_values: 12 EVAL 05k7sb adjoins! 01x73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 140.000 61.000 0.250 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #6855-0bq6ntw PRED entity: 0bq6ntw PRED relation: film_crew_role PRED expected values: 09zzb8 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.85 #282, 0.81 #531, 0.81 #422), 01vx2h (0.60 #116, 0.60 #81, 0.58 #291), 01pvkk (0.35 #257, 0.32 #292, 0.30 #117), 02ynfr (0.30 #121, 0.30 #86, 0.29 #50), 02rh1dz (0.29 #44, 0.19 #290, 0.18 #466), 015h31 (0.29 #43, 0.14 #289, 0.14 #1493), 0215hd (0.18 #299, 0.16 #620, 0.15 #475), 089g0h (0.18 #300, 0.14 #54, 0.14 #1493), 089fss (0.17 #147, 0.14 #182, 0.14 #1493), 0d2b38 (0.16 #306, 0.14 #60, 0.14 #1493) >> Best rule #282 for best value: >> intensional similarity = 6 >> extensional distance = 89 >> proper extension: 01gglm; >> query: (?x6095, 09zzb8) <- film_crew_role(?x6095, ?x2095), film_crew_role(?x6095, ?x1284), ?x2095 = 0dxtw, language(?x6095, ?x254), film_format(?x6095, ?x10390), ?x1284 = 0ch6mp2 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bq6ntw film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.846 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6854-07c1v PRED entity: 07c1v PRED relation: industry! PRED expected values: 0k8z => 116 concepts (98 used for prediction) PRED predicted values (max 10 best out of 744): 061v5m (0.96 #2217, 0.77 #2957, 0.33 #77), 018p5f (0.96 #2217, 0.77 #2957, 0.33 #93), 0l8sx (0.40 #1747, 0.33 #8898, 0.29 #15056), 09glbnt (0.33 #566, 0.20 #2042, 0.20 #1550), 025hwq (0.33 #592, 0.20 #2068, 0.12 #4041), 02x2097 (0.33 #882, 0.17 #3099, 0.12 #4331), 018tnx (0.33 #956, 0.17 #3173, 0.12 #4405), 027jw0c (0.33 #869, 0.17 #3086, 0.12 #4318), 0178g (0.33 #291, 0.12 #3742, 0.11 #10400), 02b07b (0.29 #15056, 0.29 #15055, 0.27 #6379) >> Best rule #2217 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 0hcr; >> query: (?x14555, ?x6386) <- major_field_of_study(?x1200, ?x14555), industry(?x5072, ?x14555), industry(?x5072, ?x5615), major_field_of_study(?x3439, ?x14555), company(?x4480, ?x5072), industry(?x6386, ?x5615), major_field_of_study(?x4955, ?x5615), ?x4955 = 09f2j, citytown(?x5072, ?x12691) >> conf = 0.96 => this is the best rule for 2 predicted values *> Best rule #15056 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 09j2d; *> query: (?x14555, ?x3230) <- industry(?x9469, ?x14555), industry(?x9469, ?x13981), company(?x233, ?x9469), service_language(?x9469, ?x732), industry(?x3230, ?x13981) *> conf = 0.29 ranks of expected_values: 11 EVAL 07c1v industry! 0k8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 116.000 98.000 0.965 http://example.org/business/business_operation/industry #6853-01xcr4 PRED entity: 01xcr4 PRED relation: profession PRED expected values: 03gjzk => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 97): 02hrh1q (0.89 #2956, 0.87 #1485, 0.85 #1044), 0dxtg (0.87 #2219, 0.83 #3838, 0.79 #3985), 03gjzk (0.85 #5017, 0.83 #6046, 0.83 #5899), 01d_h8 (0.51 #5154, 0.49 #3536, 0.48 #6036), 018gz8 (0.36 #312, 0.36 #1782, 0.33 #753), 02krf9 (0.31 #2233, 0.30 #5911, 0.29 #5029), 0cbd2 (0.27 #5302, 0.25 #595, 0.24 #1624), 02jknp (0.26 #3538, 0.24 #6038, 0.24 #5156), 09jwl (0.26 #6933, 0.24 #7374, 0.20 #7962), 0np9r (0.24 #1051, 0.20 #3405, 0.20 #1492) >> Best rule #2956 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 01jbx1; 05r5w; 0164nb; 039crh; 02w5q6; 0261x8t; 02l3_5; 0mbw0; 0163t3; 02_wxh; ... >> query: (?x4259, 02hrh1q) <- program(?x4259, ?x3075), profession(?x4259, ?x2225), people(?x1050, ?x4259) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #5017 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 144 *> proper extension: 02f9wb; *> query: (?x4259, 03gjzk) <- award_winner(?x3183, ?x4259), gender(?x4259, ?x514), program(?x4259, ?x4891) *> conf = 0.85 ranks of expected_values: 3 EVAL 01xcr4 profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 120.000 0.885 http://example.org/people/person/profession #6852-0kv238 PRED entity: 0kv238 PRED relation: film! PRED expected values: 0z4s => 101 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1094): 0cw67g (0.61 #95811, 0.56 #2083, 0.52 #4166), 016z2j (0.18 #390, 0.16 #2473, 0.03 #14969), 062dn7 (0.18 #662, 0.16 #2745, 0.02 #15241), 0c6qh (0.13 #21241, 0.05 #56651, 0.05 #48319), 07lt7b (0.12 #114, 0.11 #2197, 0.04 #8444), 0c9xjl (0.12 #972, 0.11 #3055, 0.03 #15551), 01chc7 (0.12 #560, 0.11 #2643, 0.02 #40134), 07swvb (0.12 #698, 0.11 #2781, 0.02 #15277), 07ldhs (0.12 #888, 0.11 #2971, 0.02 #15467), 02114t (0.12 #636, 0.11 #2719, 0.02 #79784) >> Best rule #95811 for best value: >> intensional similarity = 4 >> extensional distance = 840 >> proper extension: 016zfm; 01b7h8; >> query: (?x2714, ?x10416) <- nominated_for(?x10416, ?x2714), award_winner(?x10416, ?x6546), location(?x10416, ?x12820), film(?x10416, ?x1673) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #20894 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 154 *> proper extension: 06nr2h; 04t9c0; 01qbg5; 0y_pg; 03b1l8; 02p76f9; 06zsk51; 01kqq7; *> query: (?x2714, 0z4s) <- nominated_for(?x10416, ?x2714), film(?x4968, ?x2714), location_of_ceremony(?x4968, ?x10582), participant(?x6262, ?x4968) *> conf = 0.04 ranks of expected_values: 199 EVAL 0kv238 film! 0z4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 101.000 60.000 0.610 http://example.org/film/actor/film./film/performance/film #6851-01s560x PRED entity: 01s560x PRED relation: category PRED expected values: 08mbj5d => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #46, 0.86 #56, 0.86 #53) >> Best rule #46 for best value: >> intensional similarity = 6 >> extensional distance = 267 >> proper extension: 03n0q5; 08n__5; >> query: (?x10745, 08mbj5d) <- award(?x10745, ?x2180), award(?x7477, ?x2180), award(?x5547, ?x2180), ?x5547 = 0dw4g, origin(?x10745, ?x8771), participant(?x7477, ?x5246) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01s560x category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.862 http://example.org/common/topic/webpage./common/webpage/category #6850-02wb6yq PRED entity: 02wb6yq PRED relation: location PRED expected values: 07b_l => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 315): 0f2rq (0.51 #17674, 0.38 #47395, 0.38 #6427), 030qb3t (0.40 #6509, 0.38 #2492, 0.33 #20167), 02_286 (0.21 #14497, 0.21 #48235, 0.20 #58675), 04jpl (0.18 #1624, 0.18 #68291, 0.08 #3231), 0cr3d (0.16 #4161, 0.11 #11392, 0.10 #25850), 05jbn (0.10 #1055, 0.07 #56229, 0.04 #24352), 03l2n (0.10 #1046, 0.05 #2653, 0.04 #4260), 04tgp (0.10 #1042, 0.05 #2649, 0.03 #15502), 0r2dp (0.10 #1315, 0.05 #2922, 0.02 #6135), 0k9p4 (0.10 #1262, 0.05 #2869, 0.02 #6082) >> Best rule #17674 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 02fybl; 03zz8b; >> query: (?x3244, ?x5719) <- participant(?x1462, ?x3244), profession(?x3244, ?x220), origin(?x3244, ?x5719) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #68460 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 663 *> proper extension: 06qgvf; 084w8; 02zq43; 0151ns; 027dtv3; 01zkxv; 04bd8y; 04411; 0bn9sc; 0487c3; ... *> query: (?x3244, 07b_l) <- profession(?x3244, ?x220), location(?x3244, ?x1227), state_province_region(?x99, ?x1227) *> conf = 0.03 ranks of expected_values: 98 EVAL 02wb6yq location 07b_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 131.000 131.000 0.506 http://example.org/people/person/places_lived./people/place_lived/location #6849-04wlz2 PRED entity: 04wlz2 PRED relation: major_field_of_study PRED expected values: 01540 => 185 concepts (185 used for prediction) PRED predicted values (max 10 best out of 108): 02j62 (0.38 #32, 0.35 #8283, 0.33 #7782), 0g26h (0.36 #5669, 0.35 #6419, 0.32 #4669), 02lp1 (0.34 #5637, 0.33 #6387, 0.30 #4637), 01mkq (0.31 #4891, 0.31 #5766, 0.31 #6766), 0_jm (0.31 #185, 0.28 #4685, 0.27 #6435), 02_7t (0.31 #192, 0.28 #567, 0.24 #6442), 01lj9 (0.31 #166, 0.25 #41, 0.19 #6416), 03g3w (0.30 #8279, 0.27 #7028, 0.27 #7904), 04rjg (0.30 #1396, 0.29 #7021, 0.29 #5771), 062z7 (0.28 #7779, 0.28 #5654, 0.27 #6404) >> Best rule #32 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 020yvh; >> query: (?x347, 02j62) <- school_type(?x347, ?x3205), school_type(?x347, ?x1962), student(?x347, ?x4558), ?x1962 = 01_srz, ?x3205 = 01rs41 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #63 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 020yvh; *> query: (?x347, 01540) <- school_type(?x347, ?x3205), school_type(?x347, ?x1962), student(?x347, ?x4558), ?x1962 = 01_srz, ?x3205 = 01rs41 *> conf = 0.25 ranks of expected_values: 12 EVAL 04wlz2 major_field_of_study 01540 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 185.000 185.000 0.375 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #6848-02v5xg PRED entity: 02v5xg PRED relation: genre PRED expected values: 06n90 => 102 concepts (68 used for prediction) PRED predicted values (max 10 best out of 119): 05p553 (0.90 #7046, 0.54 #7292, 0.51 #8022), 02l7c8 (0.87 #1700, 0.84 #1944, 0.82 #1336), 06n90 (0.87 #1700, 0.84 #1944, 0.82 #1336), 02kdv5l (0.87 #1700, 0.84 #1944, 0.81 #1094), 03k9fj (0.73 #1348, 0.57 #864, 0.57 #744), 01hmnh (0.60 #1113, 0.57 #871, 0.57 #751), 01jfsb (0.34 #6688, 0.22 #8153, 0.22 #8031), 01t_vv (0.32 #7096, 0.10 #1026, 0.10 #5759), 03q4nz (0.31 #2330, 0.31 #5967, 0.31 #5725), 04t36 (0.27 #3044, 0.23 #3285, 0.16 #5591) >> Best rule #7046 for best value: >> intensional similarity = 9 >> extensional distance = 242 >> proper extension: 0gj8t_b; 0yyts; 08rr3p; 01gkp1; 0b44shh; 0dnw1; 0p_tz; 01hv3t; 0h95927; 04b2qn; ... >> query: (?x8717, 05p553) <- genre(?x8717, ?x5937), genre(?x8717, ?x2540), genre(?x8717, ?x53), ?x53 = 07s9rl0, film(?x9263, ?x8717), genre(?x1366, ?x5937), ?x1366 = 07ng9k, genre(?x3180, ?x2540), ?x3180 = 07c72 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1700 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 02gd6x; *> query: (?x8717, ?x1013) <- genre(?x8717, ?x53), ?x53 = 07s9rl0, genre(?x8717, ?x1013), film(?x9263, ?x8717), country(?x8717, ?x252), genre(?x97, ?x1013) *> conf = 0.87 ranks of expected_values: 3 EVAL 02v5xg genre 06n90 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 68.000 0.898 http://example.org/film/film/genre #6847-01b_d4 PRED entity: 01b_d4 PRED relation: organization! PRED expected values: 07xl34 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 17): 07xl34 (0.77 #76, 0.59 #89, 0.55 #206), 060c4 (0.72 #665, 0.69 #639, 0.69 #392), 05c0jwl (0.56 #31, 0.35 #44, 0.28 #187), 0dq_5 (0.25 #568, 0.20 #711, 0.19 #1049), 0hm4q (0.22 #21, 0.11 #203, 0.09 #60), 05k17c (0.11 #215, 0.11 #735, 0.10 #982), 08jcfy (0.08 #1288, 0.06 #480, 0.06 #25), 04n1q6 (0.08 #1288, 0.06 #32, 0.06 #19), 02wlwtm (0.08 #1288, 0.06 #39, 0.03 #91), 07t3gd (0.08 #1288) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 02kzfw; >> query: (?x5539, 07xl34) <- school_type(?x5539, ?x3092), contains(?x1310, ?x5539), ?x1310 = 02jx1, ?x3092 = 05jxkf, institution(?x1368, ?x5539) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01b_d4 organization! 07xl34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.769 http://example.org/organization/role/leaders./organization/leadership/organization #6846-09pl3f PRED entity: 09pl3f PRED relation: profession PRED expected values: 01d_h8 => 128 concepts (118 used for prediction) PRED predicted values (max 10 best out of 58): 01d_h8 (0.87 #5005, 0.85 #5299, 0.83 #3388), 02hrh1q (0.73 #9424, 0.72 #10602, 0.72 #3690), 02jknp (0.59 #4272, 0.58 #3537, 0.57 #3684), 0cbd2 (0.48 #2947, 0.48 #2212, 0.47 #2065), 0kyk (0.27 #2968, 0.27 #2233, 0.25 #2086), 0np9r (0.19 #20, 0.18 #14137, 0.17 #1490), 018gz8 (0.18 #1486, 0.17 #6044, 0.15 #3692), 09jwl (0.17 #5752, 0.17 #606, 0.17 #6634), 02hv44_ (0.16 #2849, 0.15 #2996, 0.15 #2261), 01c72t (0.14 #23, 0.10 #905, 0.09 #5757) >> Best rule #5005 for best value: >> intensional similarity = 3 >> extensional distance = 297 >> proper extension: 079vf; 05cv94; 0gg9_5q; 037q1z; 024t0y; 01g04k; >> query: (?x6001, 01d_h8) <- type_of_union(?x6001, ?x566), produced_by(?x2441, ?x6001), profession(?x6001, ?x987) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09pl3f profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 118.000 0.866 http://example.org/people/person/profession #6845-08qnnv PRED entity: 08qnnv PRED relation: institution! PRED expected values: 07s6fsf => 163 concepts (151 used for prediction) PRED predicted values (max 10 best out of 15): 07s6fsf (0.61 #458, 0.61 #302, 0.59 #413), 013zdg (0.53 #260, 0.44 #229, 0.42 #461), 028dcg (0.51 #941, 0.33 #312, 0.33 #2192), 022h5x (0.42 #329, 0.33 #313, 0.33 #298), 01rr_d (0.41 #265, 0.29 #1503, 0.26 #421), 0bjrnt (0.35 #259, 0.33 #2192, 0.31 #2315), 02m4yg (0.33 #2192, 0.31 #2315, 0.29 #1503), 01ysy9 (0.33 #2192, 0.31 #2315, 0.29 #1503), 071tyz (0.33 #2192, 0.31 #2315, 0.29 #1503), 01gkg3 (0.33 #2192, 0.31 #2315, 0.19 #2157) >> Best rule #458 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 01j_9c; 02w2bc; 07w0v; 049dk; 0bx8pn; 01jsn5; 0f1nl; 0f102; 02183k; 04hgpt; ... >> query: (?x6315, 07s6fsf) <- school_type(?x6315, ?x3092), institution(?x734, ?x6315), company(?x346, ?x6315), fraternities_and_sororities(?x6315, ?x3697), major_field_of_study(?x6315, ?x742) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08qnnv institution! 07s6fsf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 151.000 0.613 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #6844-020mfr PRED entity: 020mfr PRED relation: industry! PRED expected values: 09j_g 01swdw 01qvcr 0dwcl => 27 concepts (27 used for prediction) PRED predicted values (max 10 best out of 416): 04sv4 (0.70 #2325, 0.67 #1518, 0.60 #507), 045c7b (0.50 #1266, 0.40 #454, 0.30 #2272), 0k8z (0.50 #1451, 0.30 #2258, 0.28 #1207), 0dwcl (0.47 #599, 0.43 #3622, 0.33 #171), 02qdyj (0.47 #599, 0.43 #3622, 0.33 #67), 09j_g (0.47 #599, 0.18 #2211, 0.17 #1211), 02wbnv (0.47 #599, 0.17 #1211, 0.14 #1209), 02b07b (0.43 #3622, 0.43 #2194, 0.40 #2600), 07733f (0.43 #3622, 0.41 #2212, 0.40 #1809), 09k0h5 (0.43 #3622, 0.40 #1809, 0.39 #2210) >> Best rule #2325 for best value: >> intensional similarity = 40 >> extensional distance = 8 >> proper extension: 0hz28; >> query: (?x10022, 04sv4) <- industry(?x13954, ?x10022), industry(?x10419, ?x10022), industry(?x5077, ?x10022), service_language(?x13954, ?x2502), service_location(?x13954, ?x279), ?x279 = 0d060g, language(?x10515, ?x2502), language(?x8063, ?x2502), language(?x6531, ?x2502), language(?x6306, ?x2502), language(?x6079, ?x2502), language(?x5736, ?x2502), language(?x5608, ?x2502), language(?x5277, ?x2502), language(?x4174, ?x2502), language(?x3700, ?x2502), language(?x2968, ?x2502), language(?x1688, ?x2502), countries_spoken_in(?x2502, ?x6559), ?x6559 = 05r7t, languages(?x11026, ?x2502), languages(?x1825, ?x2502), currency(?x5077, ?x170), ?x5608 = 01l_pn, ?x6079 = 05sy_5, ?x4174 = 07nxvj, ?x6531 = 01_0f7, ?x11026 = 01s7ns, ?x8063 = 01718w, ?x6306 = 016dj8, major_field_of_study(?x481, ?x2502), ?x1825 = 0806vbn, ?x1688 = 024l2y, ?x10515 = 0dnkmq, ?x5736 = 02qpt1w, company(?x9105, ?x5077), ?x2968 = 025n07, citytown(?x10419, ?x9559), ?x5277 = 047csmy, ?x3700 = 024lff >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #599 for first EXPECTED value: *> intensional similarity = 33 *> extensional distance = 3 *> proper extension: 06xw2; 07c1v; *> query: (?x10022, ?x6141) <- industry(?x14014, ?x10022), industry(?x13954, ?x10022), industry(?x11303, ?x10022), industry(?x11071, ?x10022), industry(?x9806, ?x10022), industry(?x5077, ?x10022), industry(?x4878, ?x10022), service_language(?x13954, ?x2502), service_location(?x13954, ?x2152), service_location(?x13954, ?x279), ?x279 = 0d060g, ?x2502 = 06nm1, organization(?x4682, ?x9806), currency(?x4878, ?x170), category(?x14014, ?x134), company(?x9105, ?x5077), citytown(?x11071, ?x9559), ?x134 = 08mbj5d, place_founded(?x5077, ?x739), state_province_region(?x9806, ?x1227), olympics(?x2152, ?x391), child(?x11303, ?x6141), country(?x150, ?x2152), olympics(?x2152, ?x452), service_language(?x11303, ?x2164), film_release_region(?x6270, ?x2152), film_release_region(?x4610, ?x2152), film_release_region(?x908, ?x2152), ?x908 = 01vksx, profession(?x9105, ?x967), combatants(?x2391, ?x2152), ?x4610 = 017jd9, ?x6270 = 0g9zljd *> conf = 0.47 ranks of expected_values: 4, 6, 21, 27 EVAL 020mfr industry! 0dwcl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 27.000 27.000 0.700 http://example.org/business/business_operation/industry EVAL 020mfr industry! 01qvcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 27.000 27.000 0.700 http://example.org/business/business_operation/industry EVAL 020mfr industry! 01swdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 27.000 27.000 0.700 http://example.org/business/business_operation/industry EVAL 020mfr industry! 09j_g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 27.000 27.000 0.700 http://example.org/business/business_operation/industry #6843-0p_47 PRED entity: 0p_47 PRED relation: company PRED expected values: 02fzs => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 16): 07wj1 (0.10 #533, 0.03 #1112, 0.02 #919), 06rq1k (0.03 #1188, 0.03 #995, 0.03 #1381), 02jd_7 (0.02 #1307, 0.02 #1500, 0.01 #1114), 016ckq (0.02 #886, 0.01 #1079), 01xcgf (0.02 #961), 01wsj0 (0.02 #940), 01cl0d (0.02 #901), 01k2wn (0.02 #792), 07wg3 (0.01 #1149), 07wh1 (0.01 #1147) >> Best rule #533 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 01wp_jm; 0sx5w; >> query: (?x3917, 07wj1) <- influenced_by(?x10963, ?x3917), ?x10963 = 01xwqn >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0p_47 company 02fzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 52.000 0.100 http://example.org/people/person/employment_history./business/employment_tenure/company #6842-0m77m PRED entity: 0m77m PRED relation: influenced_by PRED expected values: 0c1fs => 159 concepts (61 used for prediction) PRED predicted values (max 10 best out of 340): 02wh0 (0.50 #2121, 0.36 #3861, 0.33 #2991), 01v9724 (0.50 #1915, 0.33 #2785, 0.21 #4525), 02lt8 (0.50 #1858, 0.33 #2728, 0.18 #4468), 03sbs (0.50 #656, 0.28 #6748, 0.25 #4135), 058vp (0.50 #618, 0.20 #1487, 0.17 #2357), 081k8 (0.40 #1459, 0.33 #1894, 0.33 #155), 03_dj (0.33 #2586, 0.33 #2151, 0.25 #847), 04xjp (0.33 #1795, 0.33 #56, 0.22 #2665), 0379s (0.33 #1817, 0.31 #3992, 0.24 #4427), 0448r (0.33 #261, 0.25 #696, 0.20 #1565) >> Best rule #2121 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 032l1; >> query: (?x1235, 02wh0) <- influenced_by(?x1235, ?x1236), influenced_by(?x10974, ?x1235), religion(?x1235, ?x492), ?x10974 = 01vdrw, interests(?x1236, ?x713) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9139 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 126 *> proper extension: 07kb5; 03_hd; 0hky; 0ct9_; 07h1q; *> query: (?x1235, ?x118) <- influenced_by(?x1235, ?x1236), influenced_by(?x10974, ?x1235), religion(?x1235, ?x492), influenced_by(?x10974, ?x118), student(?x2775, ?x10974) *> conf = 0.09 ranks of expected_values: 89 EVAL 0m77m influenced_by 0c1fs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 159.000 61.000 0.500 http://example.org/influence/influence_node/influenced_by #6841-0nvd8 PRED entity: 0nvd8 PRED relation: contains! PRED expected values: 03v0t => 120 concepts (49 used for prediction) PRED predicted values (max 10 best out of 135): 03v0t (0.57 #11681, 0.50 #1131, 0.42 #14377), 09c7w0 (0.54 #40439, 0.47 #33252, 0.46 #36851), 0nvd8 (0.36 #43137, 0.36 #26960, 0.35 #22465), 04_1l0v (0.23 #14828, 0.21 #30106, 0.21 #33704), 03v1s (0.20 #26, 0.17 #924, 0.04 #35049), 01n7q (0.19 #10860, 0.17 #3671, 0.16 #15354), 07b_l (0.16 #2019, 0.16 #9205, 0.14 #37070), 059rby (0.14 #40460, 0.13 #19789, 0.12 #41357), 06pvr (0.14 #10948, 0.06 #19935, 0.05 #4657), 03s0w (0.13 #1855, 0.05 #9041, 0.05 #36906) >> Best rule #11681 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 01cz_1; >> query: (?x8552, ?x3818) <- adjoins(?x11150, ?x8552), adjoins(?x11150, ?x10134), time_zones(?x11150, ?x1638), state(?x10134, ?x3818) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nvd8 contains! 03v0t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 49.000 0.567 http://example.org/location/location/contains #6840-06y9c2 PRED entity: 06y9c2 PRED relation: artists! PRED expected values: 02z7f3 => 174 concepts (102 used for prediction) PRED predicted values (max 10 best out of 300): 064t9 (0.92 #26784, 0.79 #6472, 0.78 #4628), 06by7 (0.66 #6481, 0.63 #10479, 0.63 #11096), 05r6t (0.64 #1926, 0.34 #20998, 0.25 #696), 02lnbg (0.42 #6517, 0.41 #4673, 0.35 #8668), 06j6l (0.38 #30205, 0.38 #3123, 0.34 #26818), 0ggx5q (0.37 #6537, 0.33 #8688, 0.30 #4693), 0xhtw (0.36 #12934, 0.34 #14165, 0.29 #23391), 059kh (0.33 #8658, 0.32 #6507, 0.30 #4663), 025sc50 (0.32 #8659, 0.32 #6508, 0.30 #26820), 0glt670 (0.30 #6194, 0.30 #12345, 0.29 #10192) >> Best rule #26784 for best value: >> intensional similarity = 6 >> extensional distance = 332 >> proper extension: 01pfr3; 07c0j; 01v0sx2; 01fl3; 016fmf; 0249kn; 018ndc; 01rm8b; 0hvbj; 01fmz6; ... >> query: (?x677, 064t9) <- artists(?x3243, ?x677), artist(?x4483, ?x677), artists(?x3243, ?x8185), artists(?x3243, ?x2635), ?x8185 = 02vwckw, ?x2635 = 03fbc >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #9397 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 67 *> proper extension: 028q6; 0lbj1; 0fp_v1x; 01cv3n; 025xt8y; 01vrncs; 0137n0; 01wdqrx; 01kx_81; 01qvgl; ... *> query: (?x677, 02z7f3) <- instrumentalists(?x227, ?x677), role(?x677, ?x716), profession(?x677, ?x353), artists(?x302, ?x677), student(?x5149, ?x677) *> conf = 0.01 ranks of expected_values: 248 EVAL 06y9c2 artists! 02z7f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 174.000 102.000 0.922 http://example.org/music/genre/artists #6839-01bpnd PRED entity: 01bpnd PRED relation: profession PRED expected values: 0nbcg 039v1 => 199 concepts (178 used for prediction) PRED predicted values (max 10 best out of 101): 02hrh1q (0.90 #9118, 0.88 #7625, 0.86 #18233), 016z4k (0.76 #1346, 0.69 #2541, 0.67 #1047), 0nbcg (0.66 #2418, 0.64 #6000, 0.64 #926), 039v1 (0.60 #1080, 0.59 #3022, 0.53 #2423), 0dz3r (0.58 #3732, 0.56 #10149, 0.55 #6567), 01c72t (0.56 #322, 0.38 #3754, 0.34 #13908), 01d_h8 (0.48 #8063, 0.46 #10004, 0.45 #8512), 0fnpj (0.44 #508, 0.27 #3046, 0.20 #3493), 0n1h (0.43 #5234, 0.42 #3444, 0.39 #5532), 0dxtg (0.37 #1655, 0.36 #9565, 0.36 #12087) >> Best rule #9118 for best value: >> intensional similarity = 4 >> extensional distance = 141 >> proper extension: 03m8lq; 08m4c8; 01wkmgb; 01yzhn; >> query: (?x5872, 02hrh1q) <- participant(?x2444, ?x5872), nominated_for(?x2444, ?x224), nationality(?x5872, ?x1310), languages(?x5872, ?x254) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2418 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 0285c; 0zjpz; 01lz4tf; 020hh3; *> query: (?x5872, 0nbcg) <- participant(?x2444, ?x5872), role(?x5872, ?x745), role(?x5872, ?x1466), group(?x1466, ?x442) *> conf = 0.66 ranks of expected_values: 3, 4 EVAL 01bpnd profession 039v1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 199.000 178.000 0.902 http://example.org/people/person/profession EVAL 01bpnd profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 199.000 178.000 0.902 http://example.org/people/person/profession #6838-0127m7 PRED entity: 0127m7 PRED relation: nationality PRED expected values: 09c7w0 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.88 #3102, 0.88 #2902, 0.79 #1401), 02jx1 (0.12 #3034, 0.12 #3836, 0.12 #4336), 07ssc (0.11 #815, 0.10 #4518, 0.10 #215), 03rjj (0.10 #205, 0.05 #705, 0.03 #1405), 03rk0 (0.08 #5249, 0.07 #3247, 0.07 #3548), 0d060g (0.05 #3208, 0.04 #11122, 0.04 #7614), 0345h (0.04 #231, 0.04 #731, 0.03 #631), 0d05w3 (0.03 #1750, 0.02 #3552, 0.01 #2150), 0chghy (0.03 #3913, 0.02 #410, 0.02 #4113), 03gj2 (0.03 #1226, 0.01 #1126) >> Best rule #3102 for best value: >> intensional similarity = 3 >> extensional distance = 580 >> proper extension: 03h40_7; >> query: (?x2451, 09c7w0) <- student(?x10297, ?x2451), award_nominee(?x2451, ?x382), school(?x1160, ?x10297) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0127m7 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.885 http://example.org/people/person/nationality #6837-04ynx7 PRED entity: 04ynx7 PRED relation: country PRED expected values: 09c7w0 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.83 #861, 0.83 #369, 0.81 #2), 07ssc (0.31 #139, 0.23 #200, 0.23 #445), 0345h (0.16 #456, 0.12 #272, 0.12 #762), 0f8l9c (0.13 #448, 0.10 #1186, 0.09 #1617), 03h64 (0.07 #47, 0.03 #475, 0.02 #719), 0hfjk (0.06 #2461, 0.06 #2460, 0.06 #3382), 04xvlr (0.06 #2461, 0.06 #2460, 0.06 #3382), 0chghy (0.05 #257, 0.04 #810, 0.04 #380), 0d060g (0.05 #743, 0.05 #929, 0.05 #315), 03rjj (0.05 #435, 0.03 #1604, 0.03 #2591) >> Best rule #861 for best value: >> intensional similarity = 4 >> extensional distance = 707 >> proper extension: 0b76kw1; 016z9n; 058kh7; >> query: (?x9872, 09c7w0) <- film(?x338, ?x9872), genre(?x9872, ?x162), participant(?x338, ?x2258), participant(?x2221, ?x338) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ynx7 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.832 http://example.org/film/film/country #6836-0ddf2bm PRED entity: 0ddf2bm PRED relation: film! PRED expected values: 01g257 01kb2j 04954 => 83 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1012): 0c6qh (0.33 #414, 0.04 #8739, 0.04 #10822), 0sz28 (0.33 #192, 0.04 #6435, 0.03 #8517), 0jfx1 (0.15 #4568, 0.11 #2487, 0.07 #10814), 0pz91 (0.15 #4373, 0.11 #2292, 0.04 #48089), 02yxwd (0.15 #4906, 0.02 #61110, 0.02 #42379), 02bkdn (0.13 #10407, 0.13 #10408, 0.11 #2381), 03kbb8 (0.13 #10407, 0.13 #10408, 0.11 #3328), 01gvr1 (0.13 #10407, 0.13 #10408, 0.11 #2179), 0gjvqm (0.13 #10407, 0.13 #10408, 0.11 #2281), 0h1nt (0.13 #10407, 0.13 #10408, 0.11 #2277) >> Best rule #414 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03qnc6q; >> query: (?x10769, 0c6qh) <- honored_for(?x385, ?x10769), ?x385 = 0ds3t5x, film(?x382, ?x10769), film_crew_role(?x10769, ?x137) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13399 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 80 *> proper extension: 0d8w2n; *> query: (?x10769, 01kb2j) <- genre(?x10769, ?x6674), genre(?x10769, ?x258), country(?x10769, ?x94), ?x6674 = 01t_vv, ?x258 = 05p553 *> conf = 0.02 ranks of expected_values: 229, 291, 534 EVAL 0ddf2bm film! 04954 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 83.000 55.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 0ddf2bm film! 01kb2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 83.000 55.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 0ddf2bm film! 01g257 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 83.000 55.000 0.333 http://example.org/film/actor/film./film/performance/film #6835-02h48 PRED entity: 02h48 PRED relation: nationality PRED expected values: 09c7w0 => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 56): 09c7w0 (0.85 #401, 0.84 #5722, 0.84 #12566), 02xry (0.42 #7231, 0.28 #7232, 0.28 #3812), 02_286 (0.33 #7538, 0.27 #14172, 0.01 #3206), 0jgk3 (0.28 #7232, 0.28 #3812, 0.27 #7437), 0345h (0.17 #131, 0.10 #4848, 0.09 #4950), 07ssc (0.16 #1115, 0.15 #2618, 0.15 #3525), 02jx1 (0.15 #533, 0.15 #7366, 0.14 #5554), 03rk0 (0.09 #3253, 0.07 #1948, 0.06 #1146), 06bnz (0.08 #941, 0.04 #6868, 0.03 #6768), 0f8l9c (0.08 #4941, 0.07 #4839, 0.07 #3127) >> Best rule #401 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 036jb; 0bw87; 012j8z; 040z9; 01g42; 02zfg3; >> query: (?x12334, 09c7w0) <- nominated_for(?x12334, ?x8536), people(?x4322, ?x12334), award_winner(?x8459, ?x12334), ?x8459 = 02py7pj >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02h48 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.846 http://example.org/people/person/nationality #6834-049k07 PRED entity: 049k07 PRED relation: award_winner PRED expected values: 06mmb => 86 concepts (39 used for prediction) PRED predicted values (max 10 best out of 481): 049k07 (0.19 #8037, 0.16 #40177, 0.15 #41784), 028k57 (0.19 #8037, 0.14 #4821, 0.13 #56250), 06mmb (0.19 #8037, 0.14 #4821, 0.13 #56250), 024rdh (0.19 #8037), 04954 (0.16 #40177, 0.15 #41784, 0.15 #27319), 01dbk6 (0.16 #40177, 0.15 #41784, 0.15 #27319), 086sj (0.16 #40177, 0.15 #41784, 0.15 #27319), 05slvm (0.16 #40177, 0.15 #41784, 0.15 #27319), 01pkhw (0.16 #40177, 0.15 #41784, 0.15 #27319), 071ywj (0.16 #40177, 0.15 #41784, 0.15 #27319) >> Best rule #8037 for best value: >> intensional similarity = 3 >> extensional distance = 357 >> proper extension: 02wb6yq; >> query: (?x1773, ?x1460) <- award_winner(?x6684, ?x1773), religion(?x1773, ?x109), nominated_for(?x1460, ?x6684) >> conf = 0.19 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 049k07 award_winner 06mmb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 39.000 0.194 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #6833-0b4rf3 PRED entity: 0b4rf3 PRED relation: type_of_union PRED expected values: 04ztj => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.75 #141, 0.75 #133, 0.75 #21), 01g63y (0.13 #146, 0.13 #70, 0.13 #78) >> Best rule #141 for best value: >> intensional similarity = 2 >> extensional distance = 661 >> proper extension: 01d5vk; 03d8njj; 0gry51; >> query: (?x11603, 04ztj) <- profession(?x11603, ?x524), ?x524 = 02jknp >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b4rf3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.753 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6832-01xvjb PRED entity: 01xvjb PRED relation: film! PRED expected values: 028k57 => 102 concepts (66 used for prediction) PRED predicted values (max 10 best out of 972): 0g2lq (0.63 #120666, 0.62 #122751, 0.44 #31205), 04wvhz (0.12 #108179, 0.12 #49927, 0.11 #128996), 0f5xn (0.10 #971, 0.09 #3052, 0.07 #5133), 0c0k1 (0.10 #1510, 0.07 #3591, 0.06 #7752), 0l6px (0.08 #388, 0.06 #8710, 0.06 #10790), 06ltr (0.08 #948, 0.06 #9270, 0.05 #13430), 09y20 (0.08 #248, 0.06 #8570, 0.05 #12730), 0134w7 (0.08 #160, 0.06 #8482, 0.05 #12642), 065jlv (0.08 #313, 0.06 #8635, 0.05 #12795), 013_vh (0.08 #663, 0.06 #8985, 0.05 #11065) >> Best rule #120666 for best value: >> intensional similarity = 4 >> extensional distance = 881 >> proper extension: 03_b1g; >> query: (?x8965, ?x8858) <- nominated_for(?x8858, ?x8965), nominated_for(?x7837, ?x8965), participant(?x3917, ?x8858), award(?x7837, ?x198) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #21595 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 139 *> proper extension: 0gtsx8c; 0c3ybss; 03g90h; 0dq626; 0cpllql; 09gdm7q; 053rxgm; 0cz8mkh; 0cd2vh9; 050f0s; ... *> query: (?x8965, 028k57) <- film_distribution_medium(?x8965, ?x2007), country(?x8965, ?x94), film_crew_role(?x8965, ?x137), film(?x496, ?x8965) *> conf = 0.01 ranks of expected_values: 666 EVAL 01xvjb film! 028k57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 102.000 66.000 0.626 http://example.org/film/actor/film./film/performance/film #6831-01l8t8 PRED entity: 01l8t8 PRED relation: major_field_of_study PRED expected values: 01lj9 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 115): 02lp1 (0.65 #844, 0.62 #1201, 0.60 #487), 02j62 (0.60 #504, 0.57 #861, 0.57 #1218), 04rjg (0.53 #1208, 0.52 #851, 0.50 #494), 062z7 (0.47 #1215, 0.46 #858, 0.46 #1096), 04x_3 (0.44 #500, 0.43 #857, 0.42 #1214), 01tbp (0.44 #534, 0.42 #1248, 0.41 #891), 01lj9 (0.40 #1227, 0.37 #870, 0.35 #1108), 01540 (0.40 #1368, 0.33 #892, 0.32 #1249), 0g26h (0.35 #873, 0.35 #1230, 0.32 #1111), 0fdys (0.35 #1226, 0.33 #1107, 0.33 #869) >> Best rule #844 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 02cttt; 01j_cy; 07szy; 09kvv; 0bx8pn; 07wrz; 03ksy; 0bqxw; 0c5x_; 01hjy5; ... >> query: (?x10659, 02lp1) <- colors(?x10659, ?x3364), organization(?x10659, ?x5487), institution(?x1200, ?x10659) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #1227 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 01prf3; *> query: (?x10659, 01lj9) <- organization(?x10659, ?x5487), organization(?x1665, ?x5487), registering_agency(?x1665, ?x1982) *> conf = 0.40 ranks of expected_values: 7 EVAL 01l8t8 major_field_of_study 01lj9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 91.000 91.000 0.648 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #6830-0fjyzt PRED entity: 0fjyzt PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.85 #37, 0.84 #173, 0.83 #22), 02nxhr (0.06 #23, 0.04 #70, 0.04 #49), 07z4p (0.05 #52, 0.04 #83, 0.03 #36), 07c52 (0.04 #29, 0.03 #107, 0.03 #365), 0735l (0.01 #30) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 0872p_c; 031t2d; 0btbyn; >> query: (?x5465, 029j_) <- executive_produced_by(?x5465, ?x8042), category(?x5465, ?x134), film_crew_role(?x5465, ?x1284), ?x1284 = 0ch6mp2 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fjyzt film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.849 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6829-0171cm PRED entity: 0171cm PRED relation: film PRED expected values: 02mt51 => 83 concepts (41 used for prediction) PRED predicted values (max 10 best out of 876): 0bnzd (0.36 #42582, 0.33 #58554), 0h03fhx (0.22 #4317, 0.03 #60330, 0.03 #14194), 01l_pn (0.20 #956, 0.15 #2730, 0.02 #11601), 01y9jr (0.20 #1150, 0.15 #2924, 0.01 #11795), 013q07 (0.13 #351, 0.10 #2125, 0.04 #5673), 035s95 (0.13 #335, 0.10 #2109, 0.03 #5657), 056xkh (0.13 #1584, 0.10 #3358, 0.03 #6906), 03bzjpm (0.13 #1303, 0.10 #3077, 0.03 #33711), 03nfnx (0.13 #1389, 0.10 #3163, 0.02 #12034), 0f4_l (0.13 #344, 0.10 #2118, 0.02 #10989) >> Best rule #42582 for best value: >> intensional similarity = 3 >> extensional distance = 1128 >> proper extension: 025vry; 072vj; 0c3dzk; 02rf51g; >> query: (?x2556, ?x144) <- nationality(?x2556, ?x512), award_winner(?x112, ?x2556), award_winner(?x144, ?x2556) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #662 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 019g40; *> query: (?x2556, 02mt51) <- award(?x2556, ?x3646), place_of_birth(?x2556, ?x4510), ?x3646 = 0hnf5vm *> conf = 0.07 ranks of expected_values: 85 EVAL 0171cm film 02mt51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 83.000 41.000 0.357 http://example.org/film/actor/film./film/performance/film #6828-051wwp PRED entity: 051wwp PRED relation: nationality PRED expected values: 09c7w0 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 16): 09c7w0 (0.80 #501, 0.75 #101, 0.74 #1102), 02jx1 (0.37 #433, 0.12 #333, 0.10 #3537), 07ssc (0.19 #315, 0.19 #415, 0.12 #115), 0d060g (0.14 #207, 0.12 #307, 0.12 #107), 0chghy (0.11 #410, 0.03 #810, 0.03 #1010), 03rk0 (0.07 #2049, 0.05 #7353, 0.05 #7553), 03rt9 (0.04 #413, 0.02 #513, 0.01 #613), 0f8l9c (0.04 #422, 0.02 #4926, 0.02 #1925), 0ctw_b (0.04 #427), 03rjj (0.02 #1908, 0.02 #2308, 0.02 #1507) >> Best rule #501 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 03gm48; 022_lg; 0d06m5; 0lzkm; 01pbs9w; 030vmc; 023w9s; 033p3_; 0fx0j2; 021npv; ... >> query: (?x4928, 09c7w0) <- award_winner(?x472, ?x4928), place_of_birth(?x4928, ?x1860), ?x1860 = 01_d4 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051wwp nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.804 http://example.org/people/person/nationality #6827-05f7snc PRED entity: 05f7snc PRED relation: award_winner! PRED expected values: 0jt3qpk => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 109): 0jt3qpk (0.69 #463, 0.67 #183, 0.62 #323), 09n4nb (0.11 #8826, 0.09 #8265, 0.09 #48), 073hd1 (0.11 #8826, 0.09 #8265, 0.09 #100), 05c1t6z (0.10 #715, 0.09 #1275, 0.05 #855), 0lp_cd3 (0.10 #723, 0.07 #583, 0.05 #863), 0gvstc3 (0.10 #734, 0.05 #874, 0.04 #1434), 02q690_ (0.08 #1325, 0.07 #765, 0.04 #1465), 03nnm4t (0.07 #1334, 0.07 #774, 0.04 #1474), 027hjff (0.07 #617, 0.06 #1317, 0.05 #897), 0466p0j (0.07 #636, 0.05 #916, 0.04 #2457) >> Best rule #463 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 06n7h7; 05sj55; >> query: (?x4762, 0jt3qpk) <- award_winner(?x6170, ?x4762), award_winner(?x690, ?x4762), nominated_for(?x690, ?x3075), ?x6170 = 02qssrm >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05f7snc award_winner! 0jt3qpk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.688 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6826-0417z2 PRED entity: 0417z2 PRED relation: instrumentalists! PRED expected values: 0342h => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 62): 0342h (0.64 #1767, 0.62 #1062, 0.61 #1679), 05r5c (0.48 #1066, 0.45 #2035, 0.42 #1771), 018vs (0.37 #1071, 0.29 #1776, 0.26 #2040), 02hnl (0.22 #1092, 0.16 #1797, 0.16 #2061), 03qjg (0.19 #1109, 0.15 #1814, 0.15 #1726), 0l14md (0.17 #1065, 0.12 #1770, 0.11 #1682), 026t6 (0.14 #1060, 0.12 #2029, 0.10 #1677), 0l14qv (0.13 #1063, 0.09 #1680, 0.09 #1151), 03gvt (0.13 #1123, 0.06 #66, 0.06 #1828), 013y1f (0.11 #1089, 0.05 #2058, 0.05 #1794) >> Best rule #1767 for best value: >> intensional similarity = 3 >> extensional distance = 446 >> proper extension: 01w923; 0bkg4; 04cr6qv; 02r3cn; 01ydzx; 04_jsg; 01w9mnm; >> query: (?x9719, 0342h) <- profession(?x9719, ?x1614), category(?x9719, ?x134), instrumentalists(?x1166, ?x9719) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0417z2 instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.643 http://example.org/music/instrument/instrumentalists #6825-01x1fq PRED entity: 01x1fq PRED relation: role PRED expected values: 01vdm0 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 114): 01vdm0 (0.51 #340, 0.27 #1887, 0.27 #2716), 0342h (0.41 #1965, 0.40 #314, 0.40 #2585), 013y1f (0.34 #345, 0.18 #448, 0.15 #2616), 05842k (0.30 #387, 0.18 #1418, 0.18 #490), 01vj9c (0.29 #324, 0.17 #1355, 0.17 #1975), 02sgy (0.26 #1966, 0.25 #2586, 0.24 #315), 042v_gx (0.24 #317, 0.23 #1968, 0.23 #1452), 018vs (0.21 #322, 0.17 #1353, 0.17 #2593), 01s0ps (0.20 #370, 0.09 #2684, 0.08 #1917), 026t6 (0.17 #312, 0.17 #2583, 0.16 #1343) >> Best rule #340 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 032t2z; >> query: (?x9891, 01vdm0) <- role(?x9891, ?x228), artists(?x505, ?x9891), ?x228 = 0l14qv >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x1fq role 01vdm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.514 http://example.org/music/artist/track_contributions./music/track_contribution/role #6824-04tng0 PRED entity: 04tng0 PRED relation: titles! PRED expected values: 03mqtr => 120 concepts (96 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.77 #7006, 0.59 #7814, 0.58 #5376), 082gq (0.34 #5477, 0.34 #5476, 0.33 #405), 02p0szs (0.34 #5477, 0.34 #5476, 0.33 #405), 02kdv5l (0.34 #5477, 0.34 #5476, 0.33 #405), 024qqx (0.33 #584, 0.27 #1088, 0.22 #1594), 07c52 (0.32 #6524, 0.12 #9479, 0.12 #9684), 03k9fj (0.30 #1129, 0.14 #826, 0.11 #625), 04t36 (0.29 #815, 0.21 #915, 0.21 #1927), 01jfsb (0.25 #7832, 0.22 #726, 0.18 #3461), 07ssc (0.25 #312, 0.25 #111, 0.20 #1120) >> Best rule #7006 for best value: >> intensional similarity = 3 >> extensional distance = 523 >> proper extension: 03kq98; 01q_y0; 05jyb2; >> query: (?x7265, 07s9rl0) <- titles(?x162, ?x7265), genre(?x9801, ?x162), ?x9801 = 08xvpn >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #348 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 0kv238; 04vvh9; 0jymd; *> query: (?x7265, 03mqtr) <- film(?x8151, ?x7265), film_release_region(?x7265, ?x4698), genre(?x7265, ?x53), nominated_for(?x1443, ?x7265), ?x4698 = 056_y *> conf = 0.12 ranks of expected_values: 17 EVAL 04tng0 titles! 03mqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 120.000 96.000 0.766 http://example.org/media_common/netflix_genre/titles #6823-0847q PRED entity: 0847q PRED relation: jurisdiction_of_office! PRED expected values: 0fkx3 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 21): 0f6c3 (0.90 #359, 0.82 #403, 0.80 #425), 09n5b9 (0.88 #363, 0.78 #407, 0.76 #429), 0p5vf (0.71 #78, 0.62 #100, 0.60 #56), 0fkx3 (0.60 #64, 0.57 #86, 0.50 #108), 060c4 (0.54 #1017, 0.54 #1127, 0.47 #1303), 060bp (0.48 #1015, 0.47 #1125, 0.40 #1301), 02079p (0.38 #1455, 0.14 #76, 0.12 #98), 0pqc5 (0.36 #1856, 0.36 #1768, 0.24 #1348), 0fj45 (0.33 #19, 0.25 #41, 0.15 #1500), 0fkzq (0.24 #368, 0.22 #434, 0.22 #412) >> Best rule #359 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 05kr_; >> query: (?x12125, 0f6c3) <- state(?x8823, ?x12125), jurisdiction_of_office(?x900, ?x12125), district_represented(?x11189, ?x12125), state_province_region(?x10889, ?x12125) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #64 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 07cfx; 0chgr2; 0g39h; *> query: (?x12125, 0fkx3) <- contains(?x390, ?x12125), capital(?x12125, ?x8823), ?x390 = 0chghy, location(?x927, ?x12125) *> conf = 0.60 ranks of expected_values: 4 EVAL 0847q jurisdiction_of_office! 0fkx3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 147.000 147.000 0.902 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #6822-07bzp PRED entity: 07bzp PRED relation: group! PRED expected values: 018vs => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 69): 02hnl (0.85 #446, 0.83 #950, 0.77 #2043), 018vs (0.65 #935, 0.64 #2028, 0.62 #2112), 028tv0 (0.45 #934, 0.37 #2027, 0.36 #2111), 01vj9c (0.39 #432, 0.33 #96, 0.28 #2113), 0l14j_ (0.33 #132, 0.18 #468, 0.11 #2149), 06ncr (0.33 #120, 0.15 #2053, 0.15 #2137), 07gql (0.33 #118, 0.09 #454, 0.09 #706), 07c6l (0.33 #91, 0.06 #427, 0.06 #2024), 0l14qv (0.30 #424, 0.24 #2021, 0.24 #2105), 07y_7 (0.24 #422, 0.17 #86, 0.11 #2103) >> Best rule #446 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 07qnf; 067mj; 01fl3; 05563d; 07yg2; 0394y; 047cx; 06nv27; 0l8g0; 0k1bs; ... >> query: (?x6241, 02hnl) <- artist(?x3050, ?x6241), artists(?x2809, ?x6241), ?x2809 = 05w3f, group(?x212, ?x6241) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #935 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 02t3ln; *> query: (?x6241, 018vs) <- artists(?x1000, ?x6241), group(?x227, ?x6241), ?x227 = 0342h, ?x1000 = 0xhtw *> conf = 0.65 ranks of expected_values: 2 EVAL 07bzp group! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.848 http://example.org/music/performance_role/regular_performances./music/group_membership/group #6821-0fwc0 PRED entity: 0fwc0 PRED relation: currency PRED expected values: 09nqf => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.84 #46, 0.82 #49, 0.75 #5) >> Best rule #46 for best value: >> intensional similarity = 3 >> extensional distance = 315 >> proper extension: 0mww2; >> query: (?x10428, 09nqf) <- second_level_divisions(?x94, ?x10428), ?x94 = 09c7w0, time_zones(?x10428, ?x2674) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fwc0 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.842 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #6820-0c4xc PRED entity: 0c4xc PRED relation: genre! PRED expected values: 0kfpm 03y3bp7 01b9w3 02h2vv 01rp13 0sw0q 07vqnc => 44 concepts (34 used for prediction) PRED predicted values (max 10 best out of 449): 05p9_ql (0.71 #1646, 0.53 #1021, 0.40 #1135), 01j67j (0.71 #1562, 0.53 #1021, 0.33 #285), 0fkwzs (0.62 #1923, 0.60 #900, 0.53 #1021), 01hvv0 (0.62 #1917, 0.53 #1021, 0.42 #2430), 025x1t (0.62 #1993, 0.53 #1021, 0.42 #2506), 03y3bp7 (0.60 #1055, 0.53 #1021, 0.50 #2078), 02r1ysd (0.60 #870, 0.53 #1021, 0.43 #1637), 0123qq (0.60 #967, 0.53 #1021, 0.43 #1734), 0cskb (0.60 #943, 0.53 #1021, 0.43 #1710), 04p5cr (0.57 #1628, 0.53 #1021, 0.40 #1117) >> Best rule #1646 for best value: >> intensional similarity = 15 >> extensional distance = 5 >> proper extension: 07s9rl0; 0djd22; 01t_vv; 04gm78f; >> query: (?x8534, 05p9_ql) <- genre(?x7365, ?x8534), genre(?x5236, ?x8534), genre(?x2555, ?x8534), genre(?x1395, ?x8534), language(?x2555, ?x3592), genre(?x2555, ?x2700), nominated_for(?x1765, ?x2555), actor(?x1395, ?x1537), languages(?x1395, ?x254), genre(?x240, ?x2700), ?x7365 = 01fs__, country_of_origin(?x2555, ?x94), honored_for(?x2213, ?x1395), award(?x5236, ?x870), tv_program(?x8713, ?x1395) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1055 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 3 *> proper extension: 0vgkd; *> query: (?x8534, 03y3bp7) <- genre(?x10249, ?x8534), genre(?x8396, ?x8534), genre(?x2555, ?x8534), genre(?x2063, ?x8534), genre(?x1395, ?x8534), genre(?x808, ?x8534), ?x808 = 07hpv3, ?x1395 = 019nnl, program(?x1394, ?x2555), nominated_for(?x757, ?x2063), genre(?x2555, ?x10159), tv_program(?x7002, ?x10249), ?x10159 = 025s89p, titles(?x2008, ?x2555), ?x8396 = 03nymk, ?x2008 = 07c52 *> conf = 0.60 ranks of expected_values: 6, 33, 34, 35, 36, 37, 66 EVAL 0c4xc genre! 07vqnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 44.000 34.000 0.714 http://example.org/tv/tv_program/genre EVAL 0c4xc genre! 0sw0q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 44.000 34.000 0.714 http://example.org/tv/tv_program/genre EVAL 0c4xc genre! 01rp13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 44.000 34.000 0.714 http://example.org/tv/tv_program/genre EVAL 0c4xc genre! 02h2vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 44.000 34.000 0.714 http://example.org/tv/tv_program/genre EVAL 0c4xc genre! 01b9w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 44.000 34.000 0.714 http://example.org/tv/tv_program/genre EVAL 0c4xc genre! 03y3bp7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 44.000 34.000 0.714 http://example.org/tv/tv_program/genre EVAL 0c4xc genre! 0kfpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 44.000 34.000 0.714 http://example.org/tv/tv_program/genre #6819-011w20 PRED entity: 011w20 PRED relation: profession PRED expected values: 0196pc => 98 concepts (81 used for prediction) PRED predicted values (max 10 best out of 102): 02hrh1q (0.90 #9702, 0.87 #8360, 0.85 #8509), 0dxtg (0.73 #5974, 0.62 #9403, 0.48 #8061), 03gjzk (0.58 #6572, 0.32 #8063, 0.30 #7318), 0kyk (0.53 #5394, 0.51 #5543, 0.50 #3457), 01d_h8 (0.53 #8054, 0.48 #7309, 0.44 #5967), 0np9r (0.45 #6429, 0.17 #7324, 0.14 #11328), 02jknp (0.43 #9397, 0.34 #5968, 0.33 #8055), 05z96 (0.19 #1086, 0.19 #937, 0.18 #43), 02hv44_ (0.17 #1101, 0.16 #952, 0.14 #4975), 02krf9 (0.17 #9417, 0.14 #6584, 0.14 #11328) >> Best rule #9702 for best value: >> intensional similarity = 6 >> extensional distance = 1778 >> proper extension: 0184jc; 07fq1y; 02qgqt; 05m63c; 0h0jz; 01r42_g; 0m2wm; 02zq43; 0h5g_; 033hqf; ... >> query: (?x12152, 02hrh1q) <- location(?x12152, ?x739), profession(?x12152, ?x353), profession(?x11088, ?x353), profession(?x2653, ?x353), sibling(?x6138, ?x11088), ?x2653 = 03t0k1 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #4246 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 286 *> proper extension: 06msq2; 067xw; 0gthm; 02yy8; *> query: (?x12152, 0196pc) <- profession(?x12152, ?x353), ?x353 = 0cbd2, nationality(?x12152, ?x94), ?x94 = 09c7w0 *> conf = 0.06 ranks of expected_values: 30 EVAL 011w20 profession 0196pc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 98.000 81.000 0.898 http://example.org/people/person/profession #6818-0dh73w PRED entity: 0dh73w PRED relation: people! PRED expected values: 0m32h => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 36): 02k6hp (0.18 #103, 0.07 #565, 0.05 #499), 0gk4g (0.17 #538, 0.13 #934, 0.12 #1198), 0dq9p (0.11 #545, 0.09 #677, 0.09 #743), 02knxx (0.10 #230, 0.07 #362, 0.07 #296), 0qcr0 (0.08 #529, 0.06 #793, 0.06 #859), 01dcqj (0.06 #78, 0.05 #606, 0.02 #804), 0m32h (0.06 #89, 0.04 #551, 0.03 #815), 01_qc_ (0.06 #94, 0.04 #556, 0.03 #886), 01mtqf (0.06 #70, 0.03 #532, 0.02 #664), 01bcp7 (0.06 #79, 0.01 #607) >> Best rule #103 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 01h4rj; >> query: (?x4168, 02k6hp) <- student(?x735, ?x4168), place_of_death(?x4168, ?x6987), ?x735 = 065y4w7 >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #89 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 01h4rj; *> query: (?x4168, 0m32h) <- student(?x735, ?x4168), place_of_death(?x4168, ?x6987), ?x735 = 065y4w7 *> conf = 0.06 ranks of expected_values: 7 EVAL 0dh73w people! 0m32h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 97.000 0.176 http://example.org/people/cause_of_death/people #6817-01c6qp PRED entity: 01c6qp PRED relation: award_winner PRED expected values: 02fn5r 03h_fk5 => 46 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1234): 02fn5r (0.64 #19826, 0.50 #16833, 0.50 #15335), 0gcs9 (0.62 #16893, 0.60 #9402, 0.56 #18389), 01w60_p (0.50 #13770, 0.50 #6284, 0.50 #4786), 01htxr (0.50 #14390, 0.50 #5406, 0.45 #20379), 01vsy95 (0.50 #13957, 0.50 #6471, 0.38 #15455), 015882 (0.50 #13712, 0.50 #4728, 0.38 #15210), 032nwy (0.50 #16522, 0.45 #19515, 0.44 #18018), 0dw4g (0.50 #12806, 0.45 #20295, 0.38 #15804), 01vvyvk (0.50 #17136, 0.44 #18632, 0.40 #9645), 058s57 (0.50 #7719, 0.40 #9214, 0.38 #16705) >> Best rule #19826 for best value: >> intensional similarity = 15 >> extensional distance = 9 >> proper extension: 0jzphpx; >> query: (?x1480, 02fn5r) <- award_winner(?x1480, ?x6562), award_winner(?x1480, ?x6025), award_winner(?x1480, ?x4343), award_winner(?x3235, ?x6025), ceremony(?x7534, ?x1480), ceremony(?x6378, ?x1480), ceremony(?x2322, ?x1480), award_winner(?x158, ?x4343), instrumentalists(?x227, ?x6562), ?x6378 = 0249fn, award_nominee(?x1400, ?x6025), award(?x11446, ?x2322), profession(?x4343, ?x131), ?x11446 = 016t00, ?x7534 = 02flpq >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 29 EVAL 01c6qp award_winner 03h_fk5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 46.000 24.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01c6qp award_winner 02fn5r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 24.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6816-03f4xvm PRED entity: 03f4xvm PRED relation: profession PRED expected values: 03gjzk => 118 concepts (116 used for prediction) PRED predicted values (max 10 best out of 75): 0dz3r (0.72 #2, 0.48 #298, 0.43 #1782), 09jwl (0.70 #167, 0.69 #2984, 0.66 #2095), 016z4k (0.55 #152, 0.42 #1784, 0.41 #1932), 01c72t (0.43 #764, 0.35 #1210, 0.32 #1359), 01d_h8 (0.38 #2378, 0.35 #6681, 0.33 #2674), 0n1h (0.32 #12, 0.24 #1495, 0.21 #1643), 0dxtg (0.29 #2386, 0.29 #11577, 0.28 #7580), 039v1 (0.27 #3001, 0.25 #184, 0.23 #2112), 02jknp (0.25 #2380, 0.24 #2528, 0.24 #9203), 03gjzk (0.24 #7433, 0.24 #8025, 0.23 #8469) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 0phx4; >> query: (?x4548, 0dz3r) <- gender(?x4548, ?x231), artists(?x9630, ?x4548), ?x9630 = 012yc >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #7433 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1029 *> proper extension: 0h32q; 02tkzn; *> query: (?x4548, 03gjzk) <- award_winner(?x4337, ?x4548), gender(?x4548, ?x231), nominated_for(?x4548, ?x4514) *> conf = 0.24 ranks of expected_values: 10 EVAL 03f4xvm profession 03gjzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 118.000 116.000 0.720 http://example.org/people/person/profession #6815-01x73 PRED entity: 01x73 PRED relation: adjoins PRED expected values: 05k7sb => 176 concepts (109 used for prediction) PRED predicted values (max 10 best out of 533): 059_c (0.29 #2370, 0.20 #3141, 0.15 #6222), 05kj_ (0.29 #2343, 0.20 #3114, 0.15 #6195), 05fjf (0.25 #1069, 0.21 #51677, 0.15 #5692), 05tbn (0.25 #946, 0.21 #51677, 0.14 #2487), 0j3b (0.25 #59, 0.19 #7767, 0.15 #4683), 05rgl (0.25 #101, 0.14 #2413, 0.11 #8579), 0l2vz (0.25 #1761, 0.09 #4072, 0.04 #10239), 035p3 (0.25 #2304, 0.09 #4615, 0.04 #10782), 0kpzy (0.25 #1833, 0.09 #4144, 0.04 #10311), 0l2hf (0.25 #1718, 0.09 #4029, 0.04 #10196) >> Best rule #2370 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 03gh4; >> query: (?x1755, 059_c) <- location_of_ceremony(?x1545, ?x1755), location(?x1897, ?x1755), state(?x503, ?x1755) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #5504 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 03v1s; 059f4; 04ych; 04rrd; 05k7sb; 0gyh; 05tbn; 04tgp; 07_f2; *> query: (?x1755, 05k7sb) <- district_represented(?x4821, ?x1755), district_represented(?x2019, ?x1755), ?x4821 = 02bqm0, ?x2019 = 01gtbb *> conf = 0.23 ranks of expected_values: 14 EVAL 01x73 adjoins 05k7sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 176.000 109.000 0.286 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #6814-0ghvb PRED entity: 0ghvb PRED relation: institution! PRED expected values: 019v9k => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.79 #282, 0.77 #165, 0.77 #236), 019v9k (0.72 #171, 0.68 #195, 0.67 #288), 03bwzr4 (0.56 #294, 0.55 #318, 0.54 #177), 0bkj86 (0.53 #217, 0.51 #194, 0.41 #146), 02_xgp2 (0.53 #175, 0.51 #222, 0.49 #292), 016t_3 (0.47 #213, 0.46 #142, 0.45 #1400), 04zx3q1 (0.32 #140, 0.27 #211, 0.26 #281), 027f2w (0.27 #148, 0.24 #219, 0.23 #172), 013zdg (0.27 #216, 0.23 #145, 0.22 #169), 022h5x (0.26 #207, 0.25 #20, 0.23 #230) >> Best rule #282 for best value: >> intensional similarity = 5 >> extensional distance = 88 >> proper extension: 0b1xl; 01nnsv; 0ks67; 0g2jl; >> query: (?x11467, 02h4rq6) <- institution(?x620, ?x11467), fraternities_and_sororities(?x11467, ?x3697), major_field_of_study(?x11467, ?x3490), category(?x11467, ?x134), organization(?x346, ?x11467) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 72 *> proper extension: 0gl5_; *> query: (?x11467, 019v9k) <- institution(?x620, ?x11467), fraternities_and_sororities(?x11467, ?x3697), major_field_of_study(?x11467, ?x3490), category(?x11467, ?x134), currency(?x11467, ?x170) *> conf = 0.72 ranks of expected_values: 2 EVAL 0ghvb institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 141.000 0.789 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #6813-026m9w PRED entity: 026m9w PRED relation: award_winner PRED expected values: 0x3b7 01h5f8 => 44 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1630): 01hkhq (0.56 #7923, 0.50 #10390, 0.06 #49864), 05sq20 (0.47 #2466, 0.39 #14806, 0.39 #12337), 017xm3 (0.47 #2466, 0.39 #14806, 0.39 #12337), 01h5f8 (0.47 #2466, 0.39 #14806, 0.39 #12337), 05sq0m (0.47 #2466, 0.39 #14806, 0.39 #12337), 0h1nt (0.47 #2466, 0.39 #14806, 0.39 #12337), 06rgq (0.47 #2466, 0.39 #14806, 0.39 #17274), 0152cw (0.47 #2466, 0.39 #14806, 0.39 #17274), 0h0wc (0.44 #7939, 0.42 #10406, 0.04 #49880), 0g824 (0.33 #6352, 0.33 #3884, 0.10 #18691) >> Best rule #7923 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 02z0dfh; 02y_rq5; 02x4x18; >> query: (?x7691, 01hkhq) <- award(?x1794, ?x7691), award(?x1244, ?x7691), artists(?x671, ?x1794), ?x1244 = 0h1nt, profession(?x1794, ?x220), currency(?x1794, ?x170) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2466 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 026mfs; *> query: (?x7691, ?x872) <- award(?x6562, ?x7691), award(?x1817, ?x7691), award(?x1794, ?x7691), award(?x872, ?x7691), ?x1794 = 058s57, ?x1817 = 015882, ?x6562 = 05sq20, ceremony(?x7691, ?x139) *> conf = 0.47 ranks of expected_values: 4, 329 EVAL 026m9w award_winner 01h5f8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 44.000 21.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 026m9w award_winner 0x3b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 21.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner #6812-0jdk_ PRED entity: 0jdk_ PRED relation: olympics! PRED expected values: 0jgd 03rt9 0697s 0jgx 07t_x 07f1x 01crd5 => 56 concepts (50 used for prediction) PRED predicted values (max 10 best out of 195): 05b4w (0.74 #216, 0.68 #1180, 0.68 #1395), 087vz (0.74 #216, 0.62 #533, 0.56 #72), 0345h (0.74 #216, 0.56 #72, 0.50 #513), 047lj (0.74 #216, 0.56 #72, 0.50 #436), 0jhd (0.74 #216, 0.56 #72, 0.50 #277), 03spz (0.74 #216, 0.56 #72, 0.45 #504), 07t21 (0.74 #216, 0.56 #72, 0.45 #504), 03__y (0.74 #216, 0.56 #72, 0.45 #504), 035qy (0.67 #370, 0.50 #515, 0.50 #298), 05vz3zq (0.52 #984, 0.50 #1130, 0.45 #689) >> Best rule #216 for best value: >> intensional similarity = 13 >> extensional distance = 1 >> proper extension: 0jhn7; >> query: (?x3729, ?x404) <- olympics(?x9458, ?x3729), olympics(?x2188, ?x3729), olympics(?x1229, ?x3729), ?x9458 = 05bmq, sports(?x3729, ?x4045), sports(?x3729, ?x2867), sports(?x3729, ?x1967), ?x2867 = 02y8z, olympics(?x404, ?x3729), ?x1229 = 059j2, ?x4045 = 06z6r, ?x1967 = 01cgz, ?x2188 = 0163v >> conf = 0.74 => this is the best rule for 8 predicted values *> Best rule #560 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 6 *> proper extension: 09x3r; 0jkvj; *> query: (?x3729, 07f1x) <- olympics(?x8420, ?x3729), olympics(?x2152, ?x3729), olympics(?x291, ?x3729), olympics(?x252, ?x3729), ?x252 = 03_3d, sports(?x3729, ?x7687), sports(?x3729, ?x5989), ?x7687 = 03krj, country(?x4045, ?x8420), olympics(?x2044, ?x3729), adjoins(?x4120, ?x291), country(?x5989, ?x142), film_release_region(?x66, ?x2152) *> conf = 0.50 ranks of expected_values: 11, 12, 22, 29, 31, 39, 91 EVAL 0jdk_ olympics! 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 56.000 50.000 0.741 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jdk_ olympics! 07f1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 56.000 50.000 0.741 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jdk_ olympics! 07t_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 56.000 50.000 0.741 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jdk_ olympics! 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 56.000 50.000 0.741 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jdk_ olympics! 0697s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 56.000 50.000 0.741 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jdk_ olympics! 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 56.000 50.000 0.741 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jdk_ olympics! 0jgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 56.000 50.000 0.741 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #6811-04wmvz PRED entity: 04wmvz PRED relation: teams! PRED expected values: 0k9p4 => 85 concepts (76 used for prediction) PRED predicted values (max 10 best out of 109): 0d6lp (0.33 #637, 0.25 #1451, 0.17 #1722), 0nqph (0.33 #259, 0.25 #1071, 0.11 #2698), 0nlh7 (0.33 #480, 0.07 #4541, 0.07 #4814), 01_d4 (0.25 #1145, 0.21 #5475, 0.18 #3039), 0fpzwf (0.25 #1494, 0.17 #1765, 0.14 #2307), 01cx_ (0.25 #1179, 0.14 #2263, 0.09 #3343), 04f_d (0.17 #1691, 0.11 #2503, 0.09 #2773), 02_286 (0.14 #1921, 0.10 #5707, 0.09 #3271), 01531 (0.14 #1989, 0.09 #3339, 0.09 #3069), 0dc95 (0.11 #2517, 0.09 #2787, 0.07 #18705) >> Best rule #637 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 0713r; >> query: (?x10279, 0d6lp) <- school(?x10279, ?x9768), school(?x10279, ?x6814), school(?x10279, ?x1011), school(?x10279, ?x466), draft(?x10279, ?x11905), ?x1011 = 07w0v, currency(?x6814, ?x170), institution(?x865, ?x9768), ?x466 = 01pl14, organization(?x346, ?x9768), ?x11905 = 047dpm0, team(?x12323, ?x10279), team(?x4244, ?x10279), school(?x465, ?x6814), ?x4244 = 028c_8 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04wmvz teams! 0k9p4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 76.000 0.333 http://example.org/sports/sports_team_location/teams #6810-01w923 PRED entity: 01w923 PRED relation: student! PRED expected values: 07tg4 => 127 concepts (126 used for prediction) PRED predicted values (max 10 best out of 134): 017rbx (0.11 #1396, 0.09 #3504, 0.03 #5085), 09f2j (0.11 #1740, 0.09 #2794, 0.08 #159), 03ksy (0.11 #1687, 0.09 #2741, 0.04 #11173), 015nl4 (0.09 #16405, 0.07 #25365, 0.04 #11661), 07tg4 (0.08 #11680, 0.06 #6937, 0.06 #16424), 02g839 (0.08 #25, 0.05 #1606, 0.05 #13200), 0778p (0.08 #110, 0.05 #1691, 0.04 #2745), 0ks67 (0.08 #189, 0.05 #1770), 07wrz (0.08 #62, 0.04 #2697, 0.01 #5859), 01g0p5 (0.08 #4950, 0.06 #1261, 0.05 #1788) >> Best rule #1396 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 0d4jl; 07rzf; >> query: (?x1694, 017rbx) <- profession(?x1694, ?x1614), nationality(?x1694, ?x1310), nationality(?x1694, ?x512), ?x1614 = 01c72t, ?x1310 = 02jx1, ?x512 = 07ssc >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #11680 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 129 *> proper extension: 07m69t; *> query: (?x1694, 07tg4) <- nationality(?x1694, ?x1310), nationality(?x1694, ?x512), ?x1310 = 02jx1, ?x512 = 07ssc *> conf = 0.08 ranks of expected_values: 5 EVAL 01w923 student! 07tg4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 127.000 126.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #6809-05mcjs PRED entity: 05mcjs PRED relation: award PRED expected values: 04dn09n => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 284): 0gr4k (0.46 #2033, 0.32 #3233, 0.30 #4434), 04dn09n (0.41 #2044, 0.30 #3244, 0.26 #4044), 0cjyzs (0.38 #2506, 0.33 #906, 0.32 #3706), 0gs9p (0.32 #2080, 0.25 #3280, 0.22 #4402), 03hkv_r (0.31 #2016, 0.22 #3216, 0.20 #4016), 09sb52 (0.27 #14843, 0.26 #11242, 0.24 #13242), 019f4v (0.26 #2067, 0.22 #4402, 0.22 #3267), 0gq9h (0.26 #2078, 0.22 #4402, 0.19 #3278), 040njc (0.26 #2008, 0.21 #3208, 0.19 #6809), 02n9nmz (0.25 #2070, 0.19 #3270, 0.17 #4070) >> Best rule #2033 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 01y8d4; >> query: (?x6673, 0gr4k) <- profession(?x6673, ?x319), written_by(?x8367, ?x6673), honored_for(?x472, ?x8367) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #2044 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 134 *> proper extension: 01y8d4; *> query: (?x6673, 04dn09n) <- profession(?x6673, ?x319), written_by(?x8367, ?x6673), honored_for(?x472, ?x8367) *> conf = 0.41 ranks of expected_values: 2 EVAL 05mcjs award 04dn09n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.463 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6808-0yxf4 PRED entity: 0yxf4 PRED relation: films! PRED expected values: 081pw => 92 concepts (12 used for prediction) PRED predicted values (max 10 best out of 49): 081pw (0.20 #945, 0.16 #3, 0.05 #1580), 0fx2s (0.08 #73, 0.05 #1650, 0.03 #1492), 07_nf (0.07 #224, 0.06 #1009, 0.05 #67), 04gb7 (0.07 #202, 0.03 #1780, 0.02 #1148), 0cm2xh (0.05 #47, 0.05 #989, 0.03 #1466), 07jq_ (0.05 #82, 0.04 #1024, 0.02 #553), 0fzyg (0.05 #682, 0.03 #996, 0.03 #525), 0kbq (0.04 #1047, 0.04 #1208, 0.03 #1524), 01w1sx (0.04 #248, 0.04 #1033, 0.03 #91), 03r8gp (0.04 #247, 0.03 #875, 0.03 #90) >> Best rule #945 for best value: >> intensional similarity = 5 >> extensional distance = 155 >> proper extension: 0c0wvx; >> query: (?x6616, 081pw) <- genre(?x6616, ?x3515), genre(?x6616, ?x258), ?x3515 = 082gq, genre(?x974, ?x258), film(?x157, ?x974) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0yxf4 films! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 12.000 0.197 http://example.org/film/film_subject/films #6807-01w5m PRED entity: 01w5m PRED relation: student PRED expected values: 032_jg 05drq5 0453t 01_vfy 01jqr_5 01m65sp 0g824 011lvx 016dgz 01k56k 02qx5h => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1517): 04411 (0.15 #18220, 0.15 #22269, 0.10 #26319), 0d_w7 (0.15 #18220, 0.15 #22269, 0.10 #26319), 0969fd (0.15 #18220, 0.15 #22269, 0.10 #26319), 0d4jl (0.15 #18220, 0.15 #22269, 0.10 #26319), 01dvtx (0.15 #18220, 0.15 #22269, 0.10 #26319), 0kvsb (0.15 #18220, 0.15 #22269, 0.10 #40496), 02sdx (0.15 #18220, 0.15 #22269, 0.10 #40496), 028rk (0.15 #18220, 0.15 #22269, 0.10 #40496), 0d3k14 (0.12 #1792, 0.06 #17987, 0.05 #22036), 02hsgn (0.08 #795, 0.06 #2820, 0.04 #16990) >> Best rule #18220 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 053mhx; 02_gzx; >> query: (?x3424, ?x920) <- student(?x3424, ?x13298), company(?x920, ?x3424), location(?x13298, ?x2850) >> conf = 0.15 => this is the best rule for 8 predicted values *> Best rule #5295 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 031n8c; 036921; *> query: (?x3424, 011lvx) <- currency(?x3424, ?x170), company(?x346, ?x3424), ?x346 = 060c4 *> conf = 0.05 ranks of expected_values: 48, 446, 567, 714, 940, 1164 EVAL 01w5m student 02qx5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w5m student 01k56k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w5m student 016dgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w5m student 011lvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w5m student 0g824 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w5m student 01m65sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w5m student 01jqr_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w5m student 01_vfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w5m student 0453t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w5m student 05drq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w5m student 032_jg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student #6806-09blyk PRED entity: 09blyk PRED relation: genre! PRED expected values: 05cj_j 0jdr0 07nnp_ => 52 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1818): 06gjk9 (0.80 #1805, 0.79 #7224, 0.79 #3613), 084302 (0.80 #1805, 0.79 #7224, 0.79 #3613), 0k4fz (0.80 #1805, 0.79 #7224, 0.79 #3613), 04mzf8 (0.80 #1805, 0.79 #7224, 0.79 #3613), 049w1q (0.80 #1805, 0.79 #7224, 0.79 #3613), 0k7tq (0.80 #1805, 0.79 #7224, 0.79 #3613), 01j8wk (0.80 #1805, 0.79 #7224, 0.79 #3613), 02pcq92 (0.80 #1805, 0.79 #7224, 0.79 #3613), 026njb5 (0.80 #1805, 0.79 #7224, 0.79 #3613), 07nnp_ (0.80 #1805, 0.79 #7224, 0.79 #3613) >> Best rule #1805 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 07s9rl0; >> query: (?x3613, ?x1308) <- genre(?x11313, ?x3613), genre(?x5134, ?x3613), genre(?x4136, ?x3613), titles(?x3613, ?x3196), titles(?x3613, ?x1308), genre(?x5047, ?x3613), ?x5134 = 0k0rf, ?x4136 = 02jr6k, ?x3196 = 084302, film_release_region(?x11313, ?x87) >> conf = 0.80 => this is the best rule for 23 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 10, 26, 162 EVAL 09blyk genre! 07nnp_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 52.000 23.000 0.797 http://example.org/film/film/genre EVAL 09blyk genre! 0jdr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 52.000 23.000 0.797 http://example.org/film/film/genre EVAL 09blyk genre! 05cj_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 23.000 0.797 http://example.org/film/film/genre #6805-01tt43d PRED entity: 01tt43d PRED relation: profession PRED expected values: 02krf9 => 88 concepts (49 used for prediction) PRED predicted values (max 10 best out of 55): 01d_h8 (0.82 #148, 0.78 #1292, 0.77 #1435), 09jwl (0.62 #730, 0.23 #3590, 0.21 #3447), 0cbd2 (0.50 #1150, 0.42 #864, 0.28 #2008), 03gjzk (0.45 #1013, 0.40 #155, 0.39 #2014), 0dz3r (0.41 #717, 0.14 #3434, 0.12 #5008), 0np9r (0.40 #17, 0.25 #1018, 0.11 #4165), 0nbcg (0.35 #743, 0.15 #3460, 0.14 #4605), 0n1h (0.27 #725, 0.07 #3442, 0.06 #1154), 0kyk (0.26 #1170, 0.25 #884, 0.20 #26), 039v1 (0.25 #748, 0.05 #3465, 0.05 #3608) >> Best rule #148 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 01vvycq; 04v048; >> query: (?x6426, 01d_h8) <- award(?x6426, ?x2325), award(?x6426, ?x350), profession(?x6426, ?x220), ?x350 = 05f4m9q, nominated_for(?x2325, ?x136) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1596 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 262 *> proper extension: 0162c8; 04b19t; 01twdk; 0cm89v; 013zyw; 03ysmg; 0454s1; 0jpdn; 03g62; 072vj; *> query: (?x6426, 02krf9) <- film(?x6426, ?x6425), language(?x6425, ?x90), film(?x4935, ?x6425) *> conf = 0.23 ranks of expected_values: 11 EVAL 01tt43d profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 88.000 49.000 0.820 http://example.org/people/person/profession #6804-076xkps PRED entity: 076xkps PRED relation: produced_by PRED expected values: 04q5zw => 110 concepts (53 used for prediction) PRED predicted values (max 10 best out of 163): 0fvf9q (0.25 #6, 0.05 #6981, 0.05 #11624), 02779r4 (0.25 #232, 0.02 #3334, 0.02 #2944), 01t6b4 (0.07 #1980, 0.06 #2367, 0.06 #2755), 02xnjd (0.07 #2210, 0.04 #5311, 0.04 #1434), 03ktjq (0.06 #2138, 0.04 #7566, 0.03 #973), 02lf0c (0.06 #795, 0.03 #1184, 0.02 #3125), 06pj8 (0.05 #3943, 0.05 #2391, 0.04 #3555), 0b13g7 (0.05 #890, 0.04 #1279, 0.04 #7870), 02tn0_ (0.05 #1100, 0.04 #2265, 0.03 #5366), 0c00lh (0.05 #962, 0.03 #1351) >> Best rule #6 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0dgq_kn; >> query: (?x8886, 0fvf9q) <- produced_by(?x8886, ?x12894), film(?x9317, ?x8886), ?x9317 = 03_2td, film(?x382, ?x8886), film_crew_role(?x8886, ?x137) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #880 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 84 *> proper extension: 07yk1xz; 0292qb; *> query: (?x8886, 04q5zw) <- produced_by(?x8886, ?x12894), film(?x9317, ?x8886), award_nominee(?x2194, ?x9317), film_crew_role(?x8886, ?x4305), ?x4305 = 0215hd *> conf = 0.02 ranks of expected_values: 38 EVAL 076xkps produced_by 04q5zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 110.000 53.000 0.250 http://example.org/film/film/produced_by #6803-04h41v PRED entity: 04h41v PRED relation: film_crew_role PRED expected values: 01pvkk => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 31): 09zzb8 (0.71 #345, 0.54 #77, 0.54 #307), 02r96rf (0.59 #348, 0.51 #234, 0.48 #310), 09vw2b7 (0.58 #352, 0.49 #122, 0.47 #314), 0dxtw (0.35 #356, 0.27 #318, 0.27 #662), 01vx2h (0.28 #357, 0.24 #243, 0.22 #663), 01pvkk (0.26 #358, 0.23 #128, 0.20 #740), 02ynfr (0.15 #362, 0.12 #324, 0.12 #668), 0215hd (0.14 #97, 0.13 #135, 0.12 #173), 01xy5l_ (0.13 #130, 0.09 #246, 0.08 #92), 089g0h (0.10 #252, 0.09 #174, 0.09 #136) >> Best rule #345 for best value: >> intensional similarity = 4 >> extensional distance = 704 >> proper extension: 0hgnl3t; >> query: (?x5966, 09zzb8) <- country(?x5966, ?x94), ?x94 = 09c7w0, nominated_for(?x1254, ?x5966), film_crew_role(?x5966, ?x1284) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #358 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 704 *> proper extension: 0hgnl3t; *> query: (?x5966, 01pvkk) <- country(?x5966, ?x94), ?x94 = 09c7w0, nominated_for(?x1254, ?x5966), film_crew_role(?x5966, ?x1284) *> conf = 0.26 ranks of expected_values: 6 EVAL 04h41v film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 69.000 69.000 0.707 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6802-03shpq PRED entity: 03shpq PRED relation: featured_film_locations PRED expected values: 02_286 => 74 concepts (63 used for prediction) PRED predicted values (max 10 best out of 59): 0rh6k (0.25 #1, 0.06 #1204, 0.05 #1445), 02_286 (0.15 #2187, 0.15 #983, 0.15 #742), 03__y (0.12 #82), 02dtg (0.12 #12), 04jpl (0.08 #490, 0.08 #1212, 0.08 #1453), 030qb3t (0.07 #2206, 0.07 #5585, 0.07 #5827), 0h7h6 (0.07 #524, 0.06 #765, 0.04 #1006), 080h2 (0.04 #265, 0.03 #505, 0.03 #746), 0d6lp (0.04 #313, 0.02 #9166, 0.02 #553), 03rjj (0.04 #247, 0.02 #1450, 0.02 #487) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0gj8nq2; >> query: (?x8446, 0rh6k) <- production_companies(?x8446, ?x902), film(?x7779, ?x8446), ?x7779 = 0c35b1 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2187 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 433 *> proper extension: 04969y; *> query: (?x8446, 02_286) <- nominated_for(?x399, ?x8446), genre(?x8446, ?x53), film(?x9313, ?x8446), produced_by(?x8446, ?x163) *> conf = 0.15 ranks of expected_values: 2 EVAL 03shpq featured_film_locations 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 63.000 0.250 http://example.org/film/film/featured_film_locations #6801-01vsyjy PRED entity: 01vsyjy PRED relation: people! PRED expected values: 07bch9 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 37): 041rx (0.33 #4, 0.11 #3084, 0.09 #2622), 0x67 (0.28 #1242, 0.27 #472, 0.27 #857), 02w7gg (0.26 #1927, 0.23 #3159, 0.22 #2235), 07hwkr (0.17 #12, 0.14 #89, 0.07 #320), 063k3h (0.14 #108, 0.05 #416, 0.03 #647), 02g7sp (0.14 #480, 0.08 #249, 0.07 #326), 0xnvg (0.12 #783, 0.06 #2708, 0.06 #2477), 06gbnc (0.09 #181, 0.01 #1798, 0.01 #951), 033tf_ (0.09 #2163, 0.08 #2625, 0.08 #2548), 0d7wh (0.07 #325, 0.06 #1942, 0.06 #3174) >> Best rule #4 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 067mj; >> query: (?x7272, 041rx) <- artists(?x7329, ?x7272), artists(?x7083, ?x7272), artists(?x302, ?x7272), ?x7083 = 02yv6b, ?x7329 = 016jny, ?x302 = 016clz >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #408 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 01gf5h; 01p9hgt; 01l1sq; 0zjpz; 0144l1; 09prnq; 03h_fk5; 0565cz; 01kstn9; 01w8n89; ... *> query: (?x7272, 07bch9) <- instrumentalists(?x1969, ?x7272), instrumentalists(?x227, ?x7272), profession(?x7272, ?x2348), ?x1969 = 04rzd, ?x227 = 0342h, ?x2348 = 0nbcg *> conf = 0.05 ranks of expected_values: 12 EVAL 01vsyjy people! 07bch9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 104.000 104.000 0.333 http://example.org/people/ethnicity/people #6800-03fbb6 PRED entity: 03fbb6 PRED relation: film PRED expected values: 0g3zrd 0pvms 02wwmhc => 80 concepts (49 used for prediction) PRED predicted values (max 10 best out of 268): 02rrh1w (0.40 #1349), 0bq6ntw (0.20 #1054, 0.08 #2835, 0.03 #32059), 0322yj (0.20 #1765, 0.08 #3546), 0ct2tf5 (0.20 #1550, 0.08 #3331), 02qr3k8 (0.20 #1283, 0.02 #4845, 0.02 #35123), 01vksx (0.20 #133, 0.02 #3695, 0.01 #5476), 08phg9 (0.20 #881, 0.01 #20472, 0.01 #22253), 01d2v1 (0.20 #1707), 02q7yfq (0.20 #1198), 065_cjc (0.20 #1190) >> Best rule #1349 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 0h96g; >> query: (?x5500, 02rrh1w) <- film(?x5500, ?x573), ?x573 = 0bth54 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2191 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 0bxtg; 0c94fn; 06pj8; 025n3p; 03jqw5; 0b6mgp_; 025j1t; 05mvd62; 04wg38; 02qjpv5; ... *> query: (?x5500, 0pvms) <- award_winner(?x5500, ?x496), award_winner(?x2928, ?x5500), ?x2928 = 07024 *> conf = 0.08 ranks of expected_values: 67 EVAL 03fbb6 film 02wwmhc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 49.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 03fbb6 film 0pvms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 80.000 49.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 03fbb6 film 0g3zrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 49.000 0.400 http://example.org/film/actor/film./film/performance/film #6799-04wp3s PRED entity: 04wp3s PRED relation: film PRED expected values: 03fts 05qbckf => 87 concepts (56 used for prediction) PRED predicted values (max 10 best out of 464): 0gxtknx (0.60 #40995, 0.56 #14259, 0.55 #16042), 04cv9m (0.17 #698, 0.01 #32781, 0.01 #14957), 0gzlb9 (0.08 #1458, 0.03 #1783, 0.03 #80212), 07w8fz (0.08 #510, 0.03 #1783, 0.02 #32593), 011yqc (0.08 #232, 0.03 #1783, 0.01 #32315), 011ydl (0.08 #521, 0.03 #1783), 04jwly (0.08 #455, 0.03 #1783), 02qr3k8 (0.08 #1285, 0.03 #36932, 0.03 #45847), 04tqtl (0.08 #506, 0.03 #80212, 0.03 #44562), 0gfzfj (0.08 #1691, 0.03 #80212, 0.03 #44562) >> Best rule #40995 for best value: >> intensional similarity = 3 >> extensional distance = 968 >> proper extension: 049tjg; >> query: (?x5492, ?x603) <- location(?x5492, ?x739), nominated_for(?x5492, ?x603), film(?x5492, ?x1219) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #9219 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 256 *> proper extension: 0grwj; 01vvydl; 012d40; 01xdf5; 04t2l2; 01qscs; 0p_pd; 09fb5; 03rs8y; 025h4z; ... *> query: (?x5492, 05qbckf) <- location(?x5492, ?x739), award_nominee(?x496, ?x5492), currency(?x5492, ?x170) *> conf = 0.01 ranks of expected_values: 368 EVAL 04wp3s film 05qbckf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 87.000 56.000 0.596 http://example.org/film/actor/film./film/performance/film EVAL 04wp3s film 03fts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 56.000 0.596 http://example.org/film/actor/film./film/performance/film #6798-0dfjb8 PRED entity: 0dfjb8 PRED relation: people! PRED expected values: 01rv7x => 127 concepts (123 used for prediction) PRED predicted values (max 10 best out of 79): 01rv7x (0.40 #114, 0.33 #266, 0.25 #494), 0x67 (0.40 #618, 0.26 #922, 0.20 #6579), 04mvp8 (0.31 #826, 0.27 #902, 0.11 #598), 041rx (0.29 #308, 0.28 #1834, 0.24 #6573), 033tf_ (0.20 #2066, 0.17 #1837, 0.14 #1149), 07hwkr (0.17 #164, 0.11 #2300, 0.08 #2680), 013b6_ (0.14 #356, 0.04 #1882, 0.03 #1576), 02sch9 (0.12 #414, 0.11 #566, 0.09 #718), 06j2v (0.12 #449, 0.11 #601, 0.09 #753), 0xnvg (0.11 #2072, 0.09 #1383, 0.08 #1690) >> Best rule #114 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01gg59; >> query: (?x5120, 01rv7x) <- location(?x5120, ?x4335), religion(?x5120, ?x492), profession(?x5120, ?x319), type_of_union(?x5120, ?x566), ?x4335 = 0c8tk >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dfjb8 people! 01rv7x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 123.000 0.400 http://example.org/people/ethnicity/people #6797-01rtm4 PRED entity: 01rtm4 PRED relation: school_type PRED expected values: 01rs41 => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 21): 01rs41 (0.52 #653, 0.52 #677, 0.51 #605), 05pcjw (0.47 #601, 0.42 #649, 0.42 #553), 05jxkf (0.44 #1708, 0.43 #2188, 0.42 #2932), 07tf8 (0.31 #33, 0.23 #249, 0.20 #273), 03ss47 (0.15 #37, 0.01 #1381, 0.01 #1477), 01_9fk (0.15 #506, 0.13 #266, 0.13 #146), 01_srz (0.14 #603, 0.13 #651, 0.12 #555), 01y64 (0.11 #12, 0.05 #684, 0.05 #84), 02p0qmm (0.09 #178, 0.07 #394, 0.06 #154), 0m4mb (0.08 #35, 0.04 #1475, 0.03 #2363) >> Best rule #653 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 02bjhv; 04cnp4; 0558_1; >> query: (?x263, 01rs41) <- currency(?x263, ?x170), contains(?x94, ?x263), ?x170 = 09nqf, major_field_of_study(?x263, ?x254) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rtm4 school_type 01rs41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.522 http://example.org/education/educational_institution/school_type #6796-01d259 PRED entity: 01d259 PRED relation: film_crew_role PRED expected values: 0dxtw => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 35): 02r96rf (0.70 #299, 0.65 #188, 0.64 #225), 09vw2b7 (0.62 #303, 0.59 #1055, 0.55 #192), 01vx2h (0.41 #308, 0.32 #719, 0.31 #606), 0dxtw (0.35 #307, 0.35 #1059, 0.31 #643), 01pvkk (0.30 #309, 0.27 #1061, 0.25 #161), 02ynfr (0.24 #91, 0.20 #1464, 0.17 #313), 02rh1dz (0.20 #195, 0.20 #1464, 0.19 #306), 0d2b38 (0.20 #1464, 0.15 #175, 0.12 #138), 089g0h (0.20 #1464, 0.12 #21, 0.12 #1504), 0215hd (0.20 #1464, 0.12 #20, 0.12 #1504) >> Best rule #299 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 03_wm6; >> query: (?x5721, 02r96rf) <- genre(?x5721, ?x571), currency(?x5721, ?x170), ?x571 = 03npn, film_crew_role(?x5721, ?x137) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #307 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 03_wm6; *> query: (?x5721, 0dxtw) <- genre(?x5721, ?x571), currency(?x5721, ?x170), ?x571 = 03npn, film_crew_role(?x5721, ?x137) *> conf = 0.35 ranks of expected_values: 4 EVAL 01d259 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 56.000 56.000 0.698 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6795-01900g PRED entity: 01900g PRED relation: gender PRED expected values: 05zppz => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #23, 0.86 #15, 0.85 #43), 02zsn (0.38 #4, 0.29 #82, 0.28 #154) >> Best rule #23 for best value: >> intensional similarity = 2 >> extensional distance = 281 >> proper extension: 04jzj; 04xjp; 01wj9y9; 02m7r; 01lcxbb; 081k8; 03bxh; 05gpy; 0d0mbj; 03s9v; ... >> query: (?x4462, 05zppz) <- location(?x4462, ?x1523), influenced_by(?x5450, ?x4462) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01900g gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.873 http://example.org/people/person/gender #6794-0ht8h PRED entity: 0ht8h PRED relation: place_of_death! PRED expected values: 09gnn => 121 concepts (60 used for prediction) PRED predicted values (max 10 best out of 477): 08z39v (0.20 #1286, 0.17 #2042, 0.10 #2798), 016ghw (0.10 #3011, 0.03 #8304, 0.03 #9061), 011zwl (0.10 #2994, 0.03 #8287, 0.03 #9044), 01b0k1 (0.10 #2963, 0.03 #8256, 0.03 #9013), 02vkvcz (0.10 #2947, 0.03 #8240, 0.03 #8997), 047g6 (0.10 #2941, 0.03 #8234, 0.03 #8991), 01tw31 (0.10 #2849, 0.03 #8142, 0.03 #8899), 06myp (0.10 #2847, 0.03 #8140, 0.03 #8897), 02784z (0.10 #2820, 0.03 #8113, 0.03 #8870), 0239zv (0.10 #2796, 0.03 #8089, 0.03 #8846) >> Best rule #1286 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 01z53w; >> query: (?x7737, 08z39v) <- contains(?x7736, ?x7737), contains(?x1310, ?x7737), contains(?x512, ?x7737), ?x512 = 07ssc, ?x1310 = 02jx1, ?x7736 = 0d6br >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ht8h place_of_death! 09gnn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 60.000 0.200 http://example.org/people/deceased_person/place_of_death #6793-05zy2cy PRED entity: 05zy2cy PRED relation: film_release_region PRED expected values: 0jgd => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 22): 0345h (0.29 #35, 0.19 #396, 0.16 #276), 01znc_ (0.14 #37, 0.12 #61, 0.11 #85), 06mkj (0.14 #41, 0.06 #402, 0.06 #137), 05v8c (0.14 #33, 0.06 #225, 0.04 #153), 0jgd (0.11 #218, 0.08 #387, 0.08 #267), 06qd3 (0.08 #228, 0.04 #277, 0.04 #397), 0d060g (0.08 #270, 0.06 #390, 0.06 #221), 07ssc (0.06 #393, 0.04 #273, 0.03 #224), 0d0vqn (0.06 #222, 0.04 #271, 0.03 #391), 082fr (0.06 #238, 0.04 #287, 0.02 #407) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01_1pv; >> query: (?x2649, 0345h) <- titles(?x3920, ?x2649), ?x3920 = 09b3v, film(?x5636, ?x2649), film_release_region(?x2649, ?x94) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #218 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0k419; *> query: (?x2649, 0jgd) <- written_by(?x2649, ?x4022), film_release_region(?x2649, ?x94), award_winner(?x2649, ?x4324), award(?x4022, ?x4921) *> conf = 0.11 ranks of expected_values: 5 EVAL 05zy2cy film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 110.000 110.000 0.286 http://example.org/film/film/runtime./film/film_cut/film_release_region #6792-0mz73 PRED entity: 0mz73 PRED relation: actor! PRED expected values: 0n2bh => 130 concepts (97 used for prediction) PRED predicted values (max 10 best out of 88): 04q827 (0.12 #11683, 0.12 #11950, 0.12 #17792), 02nczh (0.12 #11683, 0.12 #11950, 0.12 #17792), 0gfzgl (0.10 #33, 0.02 #829, 0.02 #1094), 026bfsh (0.04 #1953, 0.04 #2485, 0.02 #5935), 0180mw (0.03 #916, 0.03 #1181, 0.01 #1446), 01kt_j (0.03 #1005, 0.03 #1270, 0.01 #2597), 08l0x2 (0.03 #410, 0.02 #676), 02zv4b (0.03 #290, 0.01 #1351), 0330r (0.03 #454, 0.01 #3373), 01p4wv (0.03 #358, 0.01 #3277) >> Best rule #11683 for best value: >> intensional similarity = 3 >> extensional distance = 889 >> proper extension: 0h1_w; 04shbh; 019_1h; 0f6_dy; 015wfg; >> query: (?x7831, ?x10806) <- film(?x7831, ?x5724), award_winner(?x10806, ?x7831), award(?x7831, ?x618) >> conf = 0.12 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0mz73 actor! 0n2bh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 97.000 0.123 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #6791-0209hj PRED entity: 0209hj PRED relation: film! PRED expected values: 0hvb2 0141kz => 99 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1164): 016k6x (0.40 #105893, 0.39 #60214, 0.38 #43604), 0kr5_ (0.40 #105893, 0.38 #43604, 0.38 #91359), 0pj8m (0.40 #105893, 0.38 #43604, 0.38 #91359), 03975z (0.40 #105893, 0.38 #43604, 0.38 #91359), 01xsbh (0.40 #105893, 0.38 #43604, 0.38 #91359), 0d5wn3 (0.40 #105893, 0.38 #43604, 0.38 #91359), 0f4dx2 (0.18 #559), 0h0wc (0.12 #2500, 0.08 #4578, 0.05 #23265), 0h32q (0.12 #2849, 0.08 #4927, 0.04 #13233), 0gr36 (0.12 #2574, 0.03 #8804, 0.03 #10882) >> Best rule #105893 for best value: >> intensional similarity = 4 >> extensional distance = 703 >> proper extension: 0dckvs; 0dkv90; 0g4pl7z; 0581vn8; >> query: (?x697, ?x2424) <- nominated_for(?x112, ?x697), produced_by(?x697, ?x698), nominated_for(?x2424, ?x697), genre(?x697, ?x53) >> conf = 0.40 => this is the best rule for 6 predicted values *> Best rule #21062 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 0g5pv3; *> query: (?x697, 0hvb2) <- music(?x697, ?x7995), film_production_design_by(?x697, ?x4449), nominated_for(?x698, ?x697), film(?x111, ?x697) *> conf = 0.03 ranks of expected_values: 261, 437 EVAL 0209hj film! 0141kz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 99.000 57.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 0209hj film! 0hvb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 99.000 57.000 0.400 http://example.org/film/actor/film./film/performance/film #6790-0fb7sd PRED entity: 0fb7sd PRED relation: country PRED expected values: 09c7w0 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 91): 09c7w0 (0.89 #796, 0.88 #2326, 0.88 #1775), 07ssc (0.37 #5750, 0.37 #6480, 0.37 #6419), 02jx1 (0.37 #5750, 0.37 #6480, 0.37 #6419), 0f8l9c (0.28 #142, 0.25 #203, 0.18 #80), 0d060g (0.20 #131, 0.18 #192, 0.18 #69), 03h64 (0.14 #46, 0.03 #1022, 0.03 #1572), 03_3d (0.08 #130, 0.07 #191, 0.06 #68), 0chghy (0.08 #499, 0.08 #683, 0.05 #438), 0j1z8 (0.06 #72, 0.04 #134, 0.04 #195), 0hzlz (0.06 #81, 0.04 #204, 0.01 #446) >> Best rule #796 for best value: >> intensional similarity = 5 >> extensional distance = 120 >> proper extension: 05f67hw; >> query: (?x4967, 09c7w0) <- language(?x4967, ?x254), film_release_region(?x4967, ?x94), produced_by(?x4967, ?x2332), producer_type(?x2332, ?x632), country(?x4967, ?x1264) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fb7sd country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.893 http://example.org/film/film/country #6789-0h99n PRED entity: 0h99n PRED relation: notable_people_with_this_condition PRED expected values: 01rh0w 0bksh 0fgg4 => 30 concepts (16 used for prediction) PRED predicted values (max 10 best out of 463): 01rh0w (0.33 #11, 0.20 #207, 0.17 #403), 03j90 (0.33 #84, 0.20 #280, 0.17 #476), 024jwt (0.33 #83, 0.20 #279, 0.17 #475), 016nvh (0.33 #81, 0.20 #277, 0.17 #473), 02wyc0 (0.33 #78, 0.20 #274, 0.17 #470), 01wk7ql (0.33 #77, 0.20 #273, 0.17 #469), 042v2 (0.33 #66, 0.20 #262, 0.17 #458), 01qbjg (0.33 #60, 0.20 #256, 0.17 #452), 060_7 (0.33 #57, 0.20 #253, 0.17 #449), 01vtj38 (0.33 #56, 0.20 #252, 0.17 #448) >> Best rule #11 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 029sk; >> query: (?x8318, 01rh0w) <- notable_people_with_this_condition(?x8318, ?x1503), notable_people_with_this_condition(?x8318, ?x981), profession(?x981, ?x1032), people(?x5042, ?x981), film(?x981, ?x2869), participant(?x3861, ?x981), currency(?x2869, ?x170), ?x1503 = 01pw2f1, location(?x981, ?x789) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 92 EVAL 0h99n notable_people_with_this_condition 0fgg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 16.000 0.333 http://example.org/medicine/disease/notable_people_with_this_condition EVAL 0h99n notable_people_with_this_condition 0bksh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 30.000 16.000 0.333 http://example.org/medicine/disease/notable_people_with_this_condition EVAL 0h99n notable_people_with_this_condition 01rh0w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 16.000 0.333 http://example.org/medicine/disease/notable_people_with_this_condition #6788-03m9c8 PRED entity: 03m9c8 PRED relation: award PRED expected values: 01l29r => 65 concepts (61 used for prediction) PRED predicted values (max 10 best out of 250): 01l29r (0.71 #10485, 0.71 #10889, 0.71 #9677), 02qkk9_ (0.71 #10485, 0.71 #10889, 0.71 #9677), 07bdd_ (0.53 #2081, 0.53 #3292, 0.33 #1678), 09sb52 (0.47 #4073, 0.42 #5685, 0.30 #6491), 05p1dby (0.47 #2123, 0.43 #3334, 0.33 #1720), 02qyp19 (0.33 #807, 0.15 #16534, 0.15 #17341), 0gq9h (0.31 #3304, 0.25 #481, 0.25 #78), 02x1z2s (0.27 #2216, 0.24 #1813, 0.17 #3023), 019f4v (0.25 #67, 0.15 #20569, 0.15 #22990), 02pqp12 (0.25 #71, 0.15 #20569, 0.15 #22990) >> Best rule #10485 for best value: >> intensional similarity = 3 >> extensional distance = 1219 >> proper extension: 012ljv; 0411q; 0134w7; 015rmq; 0244r8; 01sbf2; 030_1_; 06k02; 010hn; 01dw9z; ... >> query: (?x6866, ?x3105) <- award_winner(?x6866, ?x163), award(?x6866, ?x7285), award_winner(?x3105, ?x6866) >> conf = 0.71 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 03m9c8 award 01l29r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 61.000 0.715 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6787-02__34 PRED entity: 02__34 PRED relation: genre PRED expected values: 082gq 0hfjk => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 79): 07s9rl0 (0.66 #121, 0.66 #1323, 0.66 #1444), 05p553 (0.41 #124, 0.34 #3370, 0.33 #4573), 01jfsb (0.36 #493, 0.35 #1817, 0.34 #2178), 02kdv5l (0.29 #843, 0.29 #1806, 0.29 #2887), 03k9fj (0.25 #2777, 0.24 #2897, 0.23 #3258), 06cvj (0.24 #123, 0.08 #2408, 0.08 #6016), 0lsxr (0.20 #9, 0.20 #1813, 0.19 #1211), 082gq (0.20 #30, 0.19 #1352, 0.19 #1473), 04xvh5 (0.20 #34, 0.09 #634, 0.09 #154), 01hmnh (0.17 #2782, 0.17 #2902, 0.16 #3263) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 317 >> proper extension: 0fq27fp; >> query: (?x2133, 07s9rl0) <- currency(?x2133, ?x170), genre(?x2133, ?x1403), ?x1403 = 02l7c8 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 039fgy; *> query: (?x2133, 082gq) <- nominated_for(?x10151, ?x2133), nominated_for(?x484, ?x2133), ?x10151 = 05strv *> conf = 0.20 ranks of expected_values: 8, 42 EVAL 02__34 genre 0hfjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 79.000 79.000 0.665 http://example.org/film/film/genre EVAL 02__34 genre 082gq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 79.000 79.000 0.665 http://example.org/film/film/genre #6786-03clwtw PRED entity: 03clwtw PRED relation: film! PRED expected values: 032xhg => 101 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1041): 0dlglj (0.33 #259, 0.05 #6501, 0.04 #10404), 07h565 (0.33 #1008, 0.04 #10404, 0.03 #39538), 013knm (0.33 #638, 0.04 #10404, 0.03 #39538), 0crvfq (0.33 #1374, 0.04 #10404, 0.03 #39538), 03vgp7 (0.33 #551, 0.04 #10404, 0.03 #39538), 02vy5j (0.33 #369, 0.04 #10404, 0.03 #39538), 04smkr (0.33 #368, 0.04 #10404, 0.03 #39538), 030h95 (0.33 #291, 0.04 #10404, 0.03 #39538), 0b_dy (0.33 #535, 0.04 #10404, 0.03 #39538), 028r4y (0.33 #970, 0.04 #10404, 0.03 #81160) >> Best rule #259 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 09cr8; >> query: (?x7145, 0dlglj) <- film(?x4248, ?x7145), executive_produced_by(?x7145, ?x4060), film(?x963, ?x7145), production_companies(?x2128, ?x963), ?x4248 = 01zg98 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4225 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 30 *> proper extension: 06_wqk4; 05pbl56; 0bq8tmw; 028cg00; 0kv238; 0j43swk; 0gc_c_; 0830vk; 02vrgnr; 0fqt1ns; ... *> query: (?x7145, 032xhg) <- production_companies(?x7145, ?x1478), genre(?x7145, ?x225), music(?x7145, ?x562), film(?x574, ?x7145), ?x1478 = 054lpb6 *> conf = 0.09 ranks of expected_values: 52 EVAL 03clwtw film! 032xhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 101.000 68.000 0.333 http://example.org/film/actor/film./film/performance/film #6785-0m2l9 PRED entity: 0m2l9 PRED relation: artist! PRED expected values: 01cl2y => 112 concepts (71 used for prediction) PRED predicted values (max 10 best out of 112): 015_1q (0.30 #159, 0.24 #1700, 0.24 #999), 03rhqg (0.19 #155, 0.18 #295, 0.15 #1275), 0181dw (0.18 #462, 0.14 #602, 0.13 #1022), 01trtc (0.16 #1192, 0.09 #2595, 0.09 #352), 011k1h (0.16 #429, 0.15 #989, 0.12 #1409), 0g768 (0.15 #37, 0.13 #1157, 0.13 #457), 0bfp0l (0.15 #105, 0.02 #2768, 0.02 #525), 01w40h (0.14 #168, 0.12 #588, 0.11 #308), 033hn8 (0.13 #433, 0.12 #6179, 0.11 #2115), 017l96 (0.13 #438, 0.12 #998, 0.11 #1699) >> Best rule #159 for best value: >> intensional similarity = 2 >> extensional distance = 41 >> proper extension: 026dx; 0djywgn; >> query: (?x483, 015_1q) <- influenced_by(?x1573, ?x483), role(?x483, ?x227) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #170 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 41 *> proper extension: 026dx; 0djywgn; *> query: (?x483, 01cl2y) <- influenced_by(?x1573, ?x483), role(?x483, ?x227) *> conf = 0.12 ranks of expected_values: 16 EVAL 0m2l9 artist! 01cl2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 112.000 71.000 0.302 http://example.org/music/record_label/artist #6784-06chf PRED entity: 06chf PRED relation: award_nominee! PRED expected values: 02z6l5f => 132 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1322): 04wvhz (0.81 #158605, 0.81 #146942, 0.81 #116620), 02z6l5f (0.81 #146942, 0.81 #116620, 0.81 #111955), 02__7n (0.28 #95628, 0.03 #78611, 0.02 #41293), 0b9dmk (0.28 #95628, 0.02 #87017, 0.01 #94014), 01z_g6 (0.28 #95628, 0.01 #5869, 0.01 #8201), 0mbs8 (0.28 #95628), 0hz_1 (0.28 #95628), 01cwcr (0.28 #95628), 0f6_dy (0.28 #95628), 04cf09 (0.28 #95628) >> Best rule #158605 for best value: >> intensional similarity = 3 >> extensional distance = 1139 >> proper extension: 07_grx; >> query: (?x2803, ?x1039) <- award_nominee(?x2803, ?x1039), student(?x6120, ?x2803), award_nominee(?x1039, ?x496) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #146942 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1092 *> proper extension: 01r42_g; 01qkqwg; 08m4c8; 0l56b; 07sgfsl; 06jvj7; 05dxl5; 0fwy0h; 04crrxr; 06jw0s; ... *> query: (?x2803, ?x1039) <- award_nominee(?x2803, ?x1039), profession(?x2803, ?x319), award_winner(?x2213, ?x2803) *> conf = 0.81 ranks of expected_values: 2 EVAL 06chf award_nominee! 02z6l5f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 76.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #6783-02vz6dn PRED entity: 02vz6dn PRED relation: film_release_region PRED expected values: 02vzc 03h64 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 301): 05r4w (0.89 #2856, 0.88 #2064, 0.85 #1588), 0chghy (0.88 #2072, 0.85 #2864, 0.81 #4605), 035qy (0.87 #2889, 0.87 #2097, 0.86 #669), 03h64 (0.86 #2129, 0.83 #2921, 0.76 #4662), 07ssc (0.86 #650, 0.82 #2078, 0.82 #2870), 02vzc (0.83 #2590, 0.82 #4647, 0.82 #3856), 015fr (0.82 #2080, 0.82 #2872, 0.80 #652), 06t2t (0.78 #2125, 0.73 #2917, 0.67 #697), 0b90_r (0.75 #2066, 0.75 #2858, 0.67 #638), 03spz (0.72 #2160, 0.69 #2952, 0.68 #732) >> Best rule #2856 for best value: >> intensional similarity = 6 >> extensional distance = 161 >> proper extension: 0gx1bnj; 09v42sf; >> query: (?x7393, 05r4w) <- genre(?x7393, ?x53), film_release_region(?x7393, ?x1499), film_release_region(?x7393, ?x172), film_crew_role(?x7393, ?x137), ?x1499 = 01znc_, ?x172 = 0154j >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #2129 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 135 *> proper extension: 0gkz15s; 0c0nhgv; 0dgst_d; 07g_0c; 04n52p6; 0fpv_3_; 06wbm8q; 01shy7; 0fpmrm3; 0dlngsd; ... *> query: (?x7393, 03h64) <- genre(?x7393, ?x53), film_release_region(?x7393, ?x1499), film_release_region(?x7393, ?x172), film_release_region(?x7393, ?x142), film_crew_role(?x7393, ?x137), ?x1499 = 01znc_, ?x172 = 0154j, ?x142 = 0jgd *> conf = 0.86 ranks of expected_values: 4, 6 EVAL 02vz6dn film_release_region 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 90.000 0.890 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vz6dn film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.890 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6782-0b_yz PRED entity: 0b_yz PRED relation: location_of_ceremony! PRED expected values: 04ztj => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.94 #121, 0.89 #285, 0.89 #225), 01g63y (0.17 #26, 0.06 #122, 0.06 #58), 0jgjn (0.17 #28, 0.06 #60, 0.05 #228), 01bl8s (0.06 #51, 0.04 #107, 0.04 #111) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 03_r3; >> query: (?x11072, 04ztj) <- location_of_ceremony(?x5951, ?x11072), contains(?x512, ?x11072), teams(?x11072, ?x6153) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_yz location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 200.000 200.000 0.935 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #6781-0b90_r PRED entity: 0b90_r PRED relation: contains PRED expected values: 039cpd 0pswc => 221 concepts (130 used for prediction) PRED predicted values (max 10 best out of 2857): 0ldff (0.87 #20572, 0.86 #117531, 0.86 #102841), 01zlx (0.87 #20572, 0.86 #117531, 0.86 #102841), 039cpd (0.71 #11755, 0.62 #26451, 0.40 #143976), 07b_l (0.69 #96965, 0.62 #358465, 0.07 #12205), 05fjy (0.69 #96965, 0.07 #12479, 0.05 #18358), 0vmt (0.69 #96965, 0.07 #11840, 0.05 #17719), 01kmyh (0.18 #5406, 0.12 #2468, 0.09 #49485), 064xp (0.18 #5401, 0.12 #2463, 0.09 #49480), 079yb (0.18 #4845, 0.12 #1907, 0.09 #48924), 031y2 (0.18 #4374, 0.12 #1436, 0.09 #48453) >> Best rule #20572 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 018jcq; >> query: (?x151, ?x5474) <- administrative_parent(?x8181, ?x151), administrative_parent(?x5474, ?x151), contains(?x7273, ?x151), film_release_region(?x1421, ?x8181) >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #11755 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 03lrc; *> query: (?x151, ?x3285) <- administrative_parent(?x8852, ?x151), location_of_ceremony(?x1149, ?x151), contains(?x8852, ?x3285) *> conf = 0.71 ranks of expected_values: 3 EVAL 0b90_r contains 0pswc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 221.000 130.000 0.872 http://example.org/location/location/contains EVAL 0b90_r contains 039cpd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 221.000 130.000 0.872 http://example.org/location/location/contains #6780-05xls PRED entity: 05xls PRED relation: specialization_of PRED expected values: 09j9h => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 23): 09jwl (0.40 #105, 0.36 #39, 0.33 #72), 0n1h (0.16 #395, 0.16 #427, 0.13 #525), 0cbd2 (0.12 #293, 0.12 #260, 0.12 #228), 06q2q (0.07 #145, 0.07 #177, 0.07 #1036), 04_tv (0.05 #530, 0.05 #400, 0.04 #929), 01c979 (0.05 #808, 0.04 #447, 0.04 #545), 02hrh1q (0.05 #1125, 0.05 #131, 0.05 #98), 02jknp (0.05 #1125, 0.05 #131, 0.05 #98), 01d_h8 (0.05 #131, 0.05 #98, 0.04 #65), 01c72t (0.05 #131, 0.05 #98, 0.04 #65) >> Best rule #105 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 0gbbt; 0fnpj; >> query: (?x14138, 09jwl) <- profession(?x2408, ?x14138), artists(?x9831, ?x2408), artists(?x2249, ?x2408), ?x9831 = 0xv2x, parent_genre(?x2249, ?x1572) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1045 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 129 *> proper extension: 0dl08; 07lqg0; 025rxky; 02p0s5r; 036n1; 01pxg; *> query: (?x14138, 09j9h) <- profession(?x2408, ?x14138), type_of_union(?x2408, ?x566), location(?x2408, ?x3052) *> conf = 0.03 ranks of expected_values: 15 EVAL 05xls specialization_of 09j9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 37.000 37.000 0.400 http://example.org/people/profession/specialization_of #6779-016z1t PRED entity: 016z1t PRED relation: profession PRED expected values: 0nbcg => 166 concepts (129 used for prediction) PRED predicted values (max 10 best out of 68): 0nbcg (0.60 #4602, 0.59 #4158, 0.59 #2680), 016z4k (0.56 #1770, 0.54 #2065, 0.53 #3096), 0dz3r (0.55 #884, 0.50 #3537, 0.49 #3684), 0n1h (0.38 #2073, 0.37 #1778, 0.33 #3104), 01c72t (0.32 #11689, 0.31 #905, 0.30 #8588), 01d_h8 (0.30 #17561, 0.28 #16238, 0.27 #13738), 0dxtg (0.29 #17569, 0.24 #18893, 0.24 #1633), 03gjzk (0.22 #162, 0.19 #16982, 0.18 #1634), 02jknp (0.22 #17563, 0.21 #1185, 0.21 #2363), 0fnpj (0.22 #941, 0.18 #1972, 0.17 #2855) >> Best rule #4602 for best value: >> intensional similarity = 4 >> extensional distance = 223 >> proper extension: 01s21dg; 01wxdn3; 06p03s; >> query: (?x4718, 0nbcg) <- role(?x4718, ?x227), artists(?x671, ?x4718), profession(?x4718, ?x1032), artist(?x2241, ?x4718) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016z1t profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 129.000 0.596 http://example.org/people/person/profession #6778-0c_j9x PRED entity: 0c_j9x PRED relation: film! PRED expected values: 016dgz => 69 concepts (25 used for prediction) PRED predicted values (max 10 best out of 870): 02z81h (0.49 #22835, 0.47 #8306, 0.46 #45671), 01_vfy (0.49 #22835, 0.47 #8306, 0.46 #45671), 06qn87 (0.49 #22835, 0.47 #8306, 0.46 #45671), 027pdrh (0.49 #22835, 0.47 #8306, 0.46 #45671), 0ft7sr (0.49 #22835, 0.47 #8306, 0.46 #45671), 0170pk (0.17 #281, 0.06 #2358, 0.04 #4434), 04__f (0.17 #1379, 0.03 #3456, 0.03 #5532), 0h0jz (0.17 #39, 0.03 #2116, 0.03 #4192), 0cg9f (0.17 #1986, 0.03 #10292, 0.01 #12367), 0g_92 (0.17 #1549, 0.02 #9855, 0.01 #11930) >> Best rule #22835 for best value: >> intensional similarity = 4 >> extensional distance = 212 >> proper extension: 06tpmy; 0b6l1st; >> query: (?x2345, ?x1779) <- nominated_for(?x1779, ?x2345), film(?x1607, ?x2345), genre(?x2345, ?x604), ?x604 = 0lsxr >> conf = 0.49 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 0c_j9x film! 016dgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 25.000 0.487 http://example.org/film/actor/film./film/performance/film #6777-0pz7h PRED entity: 0pz7h PRED relation: award PRED expected values: 03ccq3s => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 279): 0cjyzs (0.80 #4513, 0.73 #4112, 0.71 #23667), 03ccq3s (0.71 #23667, 0.71 #26879, 0.70 #24872), 09qvf4 (0.71 #23667, 0.71 #26879, 0.70 #24872), 0cqhmg (0.71 #23667, 0.71 #26879, 0.70 #24872), 0789_m (0.33 #19, 0.15 #27682, 0.13 #4831), 09qrn4 (0.33 #236, 0.15 #27682, 0.13 #36107), 0bdwqv (0.33 #169, 0.15 #27682, 0.12 #2174), 04ljl_l (0.33 #3, 0.15 #27682, 0.12 #2008), 0gqy2 (0.33 #161, 0.15 #27682, 0.12 #2166), 09sdmz (0.33 #203, 0.15 #27682, 0.12 #2208) >> Best rule #4513 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 02773nt; 0bczgm; 06jnvs; >> query: (?x906, 0cjyzs) <- award_nominee(?x3082, ?x906), award_nominee(?x829, ?x906), ?x3082 = 02778qt, award_winner(?x1265, ?x829) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #23667 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1424 *> proper extension: 030pr; 030_1_; 03jvmp; 0g5lhl7; 01w92; 05xbx; 024rdh; 04glx0; 018p5f; 0cbm64; ... *> query: (?x906, ?x2016) <- award_nominee(?x3082, ?x906), award_winner(?x1265, ?x3082), award_winner(?x2016, ?x906) *> conf = 0.71 ranks of expected_values: 2 EVAL 0pz7h award 03ccq3s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6776-0chw_ PRED entity: 0chw_ PRED relation: religion PRED expected values: 0kpl => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 18): 0c8wxp (0.25 #456, 0.22 #591, 0.22 #186), 0kpl (0.12 #55, 0.06 #235, 0.06 #640), 0kq2 (0.12 #63, 0.03 #513, 0.02 #153), 03_gx (0.10 #962, 0.08 #1504, 0.08 #1913), 03j6c (0.05 #1105, 0.04 #1195, 0.04 #969), 0n2g (0.04 #238, 0.04 #103, 0.03 #373), 019cr (0.04 #101, 0.04 #326, 0.02 #596), 01lp8 (0.04 #91, 0.03 #406, 0.03 #271), 092bf5 (0.03 #331, 0.03 #376, 0.03 #466), 0flw86 (0.03 #272, 0.03 #950, 0.02 #1176) >> Best rule #456 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 0162c8; >> query: (?x9033, 0c8wxp) <- profession(?x9033, ?x319), participant(?x9033, ?x4400), nominated_for(?x9033, ?x3471) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 092ys_y; 0bbxx9b; 01zwy; *> query: (?x9033, 0kpl) <- award(?x9033, ?x2478), nominated_for(?x9033, ?x5418), ?x5418 = 026lgs, award(?x1988, ?x2478) *> conf = 0.12 ranks of expected_values: 2 EVAL 0chw_ religion 0kpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.251 http://example.org/people/person/religion #6775-01cssf PRED entity: 01cssf PRED relation: genre PRED expected values: 0lsxr 02l7c8 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 95): 081k8 (0.62 #851, 0.55 #6566, 0.53 #10830), 02kdv5l (0.59 #366, 0.50 #2, 0.49 #609), 03k9fj (0.49 #376, 0.37 #254, 0.36 #1348), 060__y (0.45 #502, 0.23 #746, 0.17 #989), 02l7c8 (0.40 #501, 0.32 #5608, 0.30 #1959), 05p553 (0.38 #5474, 0.38 #6205, 0.37 #3041), 01hmnh (0.36 #2204, 0.34 #382, 0.28 #625), 0lsxr (0.32 #130, 0.25 #9, 0.24 #1102), 06n90 (0.27 #1228, 0.24 #985, 0.24 #864), 04xvlr (0.27 #486, 0.23 #1094, 0.21 #730) >> Best rule #851 for best value: >> intensional similarity = 5 >> extensional distance = 110 >> proper extension: 0413cff; >> query: (?x638, ?x5004) <- featured_film_locations(?x638, ?x5232), titles(?x5004, ?x638), titles(?x53, ?x638), film_release_region(?x638, ?x94), ?x53 = 07s9rl0 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #501 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 92 *> proper extension: 064n1pz; *> query: (?x638, 02l7c8) <- titles(?x53, ?x638), nominated_for(?x1180, ?x638), ?x1180 = 02n9nmz *> conf = 0.40 ranks of expected_values: 5, 8 EVAL 01cssf genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 109.000 109.000 0.624 http://example.org/film/film/genre EVAL 01cssf genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 109.000 109.000 0.624 http://example.org/film/film/genre #6774-0cyhq PRED entity: 0cyhq PRED relation: profession PRED expected values: 025352 => 119 concepts (98 used for prediction) PRED predicted values (max 10 best out of 93): 02hrh1q (0.87 #4012, 0.82 #4605, 0.80 #4754), 01d_h8 (0.67 #598, 0.42 #4003, 0.40 #1782), 09jwl (0.60 #7727, 0.57 #8619, 0.56 #8321), 02jknp (0.44 #600, 0.23 #1784, 0.23 #4005), 0dz3r (0.39 #7709, 0.39 #7857, 0.38 #8006), 016z4k (0.39 #7264, 0.38 #7859, 0.38 #8752), 01c8w0 (0.39 #2377, 0.33 #749, 0.25 #6529), 0dxtg (0.34 #1346, 0.33 #606, 0.33 #1642), 0cbd2 (0.33 #599, 0.32 #1487, 0.26 #1043), 018gz8 (0.33 #610, 0.20 #18, 0.17 #314) >> Best rule #4012 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 0p_pd; 03w1v2; 03wpmd; 01n8_g; 07cjqy; 028k57; 0pyww; 018fmr; 02k4b2; 02b9g4; ... >> query: (?x10883, 02hrh1q) <- nationality(?x10883, ?x94), award(?x10883, ?x1869), special_performance_type(?x10883, ?x4832) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #59 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0bdlj; *> query: (?x10883, 025352) <- place_of_burial(?x10883, ?x14112), type_of_union(?x10883, ?x566), organization(?x10883, ?x8603), ?x8603 = 02_l9 *> conf = 0.20 ranks of expected_values: 17 EVAL 0cyhq profession 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 119.000 98.000 0.870 http://example.org/people/person/profession #6773-0jml5 PRED entity: 0jml5 PRED relation: teams! PRED expected values: 0d35y => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 78): 0ply0 (0.33 #101, 0.25 #912, 0.25 #372), 0f2tj (0.25 #423, 0.20 #1233, 0.14 #2043), 0cr3d (0.25 #624, 0.17 #1434, 0.14 #1704), 0f2v0 (0.25 #914, 0.12 #3077, 0.12 #2806), 030qb3t (0.20 #3566, 0.20 #1131, 0.17 #1401), 01cx_ (0.17 #1445, 0.14 #1985, 0.12 #2526), 0fsb8 (0.14 #2076, 0.12 #2347, 0.10 #3701), 01_d4 (0.14 #1951, 0.09 #4117, 0.08 #4389), 02_286 (0.14 #13841, 0.12 #2725, 0.11 #3267), 0fpzwf (0.12 #3112, 0.12 #2841, 0.09 #4195) >> Best rule #101 for best value: >> intensional similarity = 21 >> extensional distance = 1 >> proper extension: 0jm4b; >> query: (?x5483, 0ply0) <- draft(?x5483, ?x12852), draft(?x5483, ?x8586), draft(?x5483, ?x8542), draft(?x5483, ?x4979), team(?x5755, ?x5483), team(?x4747, ?x5483), ?x4979 = 0f4vx0, team(?x13931, ?x5483), ?x8586 = 038981, ?x12852 = 06439y, sport(?x5483, ?x4833), ?x4747 = 02sf_r, school(?x5483, ?x581), ?x5755 = 0355dz, participant(?x3054, ?x13931), ?x8542 = 09th87, people(?x2510, ?x13931), religion(?x13931, ?x1985), place_of_birth(?x13931, ?x3125), type_of_union(?x13931, ?x566), ?x2510 = 0x67 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #11779 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 42 *> proper extension: 06x68; *> query: (?x5483, 0d35y) <- draft(?x5483, ?x4979), team(?x1348, ?x5483), school(?x4979, ?x8202), school(?x4979, ?x6953), school(?x4979, ?x3779), school(?x4979, ?x2775), school(?x4979, ?x466), sport(?x5483, ?x4833), institution(?x620, ?x2775), student(?x466, ?x3134), student(?x2775, ?x1447), ?x620 = 07s6fsf, registering_agency(?x466, ?x1982), major_field_of_study(?x2775, ?x1154), citytown(?x466, ?x1248), major_field_of_study(?x466, ?x947), ?x8202 = 06fq2, ?x3779 = 01pq4w, school_type(?x6953, ?x3092), school(?x260, ?x466) *> conf = 0.02 ranks of expected_values: 56 EVAL 0jml5 teams! 0d35y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 60.000 60.000 0.333 http://example.org/sports/sports_team_location/teams #6772-0pv3x PRED entity: 0pv3x PRED relation: nominated_for! PRED expected values: 03hkv_r 0gr4k 09sb52 094qd5 => 102 concepts (96 used for prediction) PRED predicted values (max 10 best out of 184): 0gq_v (0.74 #3182, 0.69 #4031, 0.68 #11461), 02x258x (0.74 #3182, 0.69 #4031, 0.68 #11461), 0gr4k (0.68 #871, 0.41 #1083, 0.40 #1295), 03hkv_r (0.60 #861, 0.33 #13, 0.29 #1073), 02w9sd7 (0.40 #100, 0.34 #1160, 0.23 #11674), 027dtxw (0.40 #4, 0.29 #1276, 0.29 #1064), 099t8j (0.37 #505, 0.37 #717, 0.33 #81), 0gqy2 (0.34 #1369, 0.31 #3065, 0.27 #945), 0gr51 (0.33 #60, 0.33 #1332, 0.29 #484), 02rdxsh (0.33 #44, 0.27 #680, 0.25 #468) >> Best rule #3182 for best value: >> intensional similarity = 4 >> extensional distance = 219 >> proper extension: 06mmr; >> query: (?x1199, ?x2341) <- award(?x1199, ?x2341), award_winner(?x1199, ?x84), nominated_for(?x2341, ?x3596), ?x3596 = 0cc5qkt >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #871 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 011ydl; *> query: (?x1199, 0gr4k) <- nominated_for(?x1180, ?x1199), award_winner(?x1199, ?x84), nominated_for(?x57, ?x1199), ?x1180 = 02n9nmz *> conf = 0.68 ranks of expected_values: 3, 4, 12, 13 EVAL 0pv3x nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 102.000 96.000 0.744 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0pv3x nominated_for! 09sb52 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 102.000 96.000 0.744 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0pv3x nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 96.000 0.744 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0pv3x nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 96.000 0.744 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6771-01f8gz PRED entity: 01f8gz PRED relation: film_release_region PRED expected values: 05r4w 06mkj => 93 concepts (91 used for prediction) PRED predicted values (max 10 best out of 163): 06mkj (0.88 #3566, 0.87 #3884, 0.86 #3725), 03h64 (0.83 #3895, 0.83 #3577, 0.78 #3418), 05r4w (0.83 #3828, 0.82 #3510, 0.80 #4786), 015fr (0.83 #3843, 0.82 #3525, 0.81 #3684), 01znc_ (0.82 #3549, 0.79 #3867, 0.75 #2592), 03_3d (0.80 #3356, 0.79 #3674, 0.78 #3515), 0154j (0.79 #3513, 0.78 #3831, 0.76 #2556), 0b90_r (0.79 #3512, 0.78 #3830, 0.70 #2555), 03spz (0.75 #3925, 0.74 #3448, 0.74 #3607), 06bnz (0.74 #3554, 0.74 #3872, 0.70 #2597) >> Best rule #3566 for best value: >> intensional similarity = 7 >> extensional distance = 134 >> proper extension: 0gtsx8c; >> query: (?x1625, 06mkj) <- film_release_region(?x1625, ?x1229), film_release_region(?x1625, ?x550), film_release_region(?x1625, ?x390), ?x1229 = 059j2, film(?x11657, ?x1625), ?x550 = 05v8c, ?x390 = 0chghy >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01f8gz film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 91.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01f8gz film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 91.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6770-0blpg PRED entity: 0blpg PRED relation: genre PRED expected values: 0219x_ => 70 concepts (69 used for prediction) PRED predicted values (max 10 best out of 108): 07s9rl0 (0.72 #481, 0.71 #6497, 0.69 #961), 04t36 (0.52 #4574, 0.51 #3003, 0.50 #4453), 03k9fj (0.44 #131, 0.39 #4221, 0.33 #851), 02kdv5l (0.41 #123, 0.37 #1323, 0.34 #1083), 01jfsb (0.37 #132, 0.36 #2653, 0.36 #1332), 0219x_ (0.33 #26, 0.09 #986, 0.09 #3150), 06n90 (0.27 #133, 0.23 #4223, 0.21 #1213), 0lsxr (0.23 #1088, 0.20 #1448, 0.20 #1328), 01hmnh (0.22 #137, 0.21 #4227, 0.19 #377), 04xvlr (0.18 #962, 0.17 #4455, 0.17 #2) >> Best rule #481 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 01kqq7; >> query: (?x3988, 07s9rl0) <- nominated_for(?x986, ?x3988), country(?x3988, ?x94), film_regional_debut_venue(?x3988, ?x739), profession(?x986, ?x353) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 07bxqz; *> query: (?x3988, 0219x_) <- nominated_for(?x6157, ?x3988), nominated_for(?x4562, ?x3988), location(?x6157, ?x739), ?x4562 = 0grrq8 *> conf = 0.33 ranks of expected_values: 6 EVAL 0blpg genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 70.000 69.000 0.720 http://example.org/film/film/genre #6769-021bk PRED entity: 021bk PRED relation: influenced_by! PRED expected values: 015pxr => 129 concepts (103 used for prediction) PRED predicted values (max 10 best out of 66): 040rjq (0.10 #487, 0.02 #18046, 0.02 #20629), 0cbgl (0.10 #514), 01hc9_ (0.10 #364), 0343h (0.10 #41), 0167xy (0.05 #2501, 0.03 #1985, 0.03 #4050), 03g5jw (0.05 #2110, 0.03 #1594, 0.03 #4692), 05ty4m (0.05 #5172, 0.02 #17566, 0.02 #20149), 086qd (0.04 #1107, 0.04 #590, 0.03 #2140), 017yfz (0.04 #1193, 0.02 #3775, 0.01 #16168), 01vsy95 (0.04 #1156, 0.02 #3738, 0.01 #3221) >> Best rule #487 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 05cgy8; >> query: (?x2328, 040rjq) <- film(?x2328, ?x2384), profession(?x2328, ?x524), music(?x2329, ?x2328) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 021bk influenced_by! 015pxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 103.000 0.100 http://example.org/influence/influence_node/influenced_by #6768-0gnbw PRED entity: 0gnbw PRED relation: film PRED expected values: 0bpm4yw 05nyqk => 90 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1062): 0sxns (0.48 #56816, 0.41 #44385, 0.39 #72798), 017kct (0.44 #2349, 0.04 #4124, 0.02 #14775), 011yxg (0.40 #41, 0.01 #3591, 0.01 #5366), 04vh83 (0.22 #2340, 0.03 #4115, 0.01 #14766), 01_mdl (0.22 #1935, 0.02 #9035, 0.01 #12586), 026zlh9 (0.22 #2835), 024mxd (0.20 #596, 0.11 #2371, 0.01 #4146), 03mh94 (0.20 #63, 0.07 #3613, 0.07 #5388), 0f4k49 (0.20 #816, 0.04 #4366, 0.03 #6141), 0660b9b (0.20 #988, 0.04 #6313, 0.03 #4538) >> Best rule #56816 for best value: >> intensional similarity = 3 >> extensional distance = 1178 >> proper extension: 04yywz; 02g8h; 0d_84; 02nb2s; 0151ns; 03_vx9; 0456xp; 04shbh; 03rl84; 02mhfy; ... >> query: (?x7269, ?x167) <- award(?x7269, ?x102), nominated_for(?x7269, ?x167), location(?x7269, ?x2235) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #6041 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 01337_; *> query: (?x7269, 0bpm4yw) <- award(?x7269, ?x4091), film(?x7269, ?x167), ?x4091 = 09sdmz *> conf = 0.04 ranks of expected_values: 102 EVAL 0gnbw film 05nyqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 58.000 0.482 http://example.org/film/actor/film./film/performance/film EVAL 0gnbw film 0bpm4yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 90.000 58.000 0.482 http://example.org/film/actor/film./film/performance/film #6767-04k25 PRED entity: 04k25 PRED relation: type_of_union PRED expected values: 04ztj => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.82 #45, 0.81 #21, 0.78 #33), 01g63y (0.23 #6, 0.21 #18, 0.20 #14) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 230 >> proper extension: 04rs03; 012cj0; 01g4zr; 01n4f8; 0738b8; 081_zm; 016ksk; 03pvt; 0164nb; 02v0ff; ... >> query: (?x2671, 04ztj) <- award(?x2671, ?x372), religion(?x2671, ?x1985), profession(?x2671, ?x319), ?x319 = 01d_h8 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04k25 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.823 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6766-02zcnq PRED entity: 02zcnq PRED relation: institution! PRED expected values: 019v9k => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 22): 019v9k (0.70 #376, 0.64 #446, 0.63 #975), 03bwzr4 (0.53 #382, 0.53 #452, 0.51 #498), 02_xgp2 (0.53 #380, 0.47 #450, 0.46 #496), 0bkj86 (0.50 #7, 0.42 #859, 0.39 #882), 016t_3 (0.43 #371, 0.42 #878, 0.42 #164), 07s6fsf (0.42 #899, 0.39 #968, 0.39 #162), 04zx3q1 (0.27 #370, 0.26 #854, 0.25 #877), 027f2w (0.23 #377, 0.22 #170, 0.20 #2498), 013zdg (0.22 #674, 0.21 #904, 0.21 #167), 022h5x (0.20 #388, 0.20 #2498, 0.19 #458) >> Best rule #376 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 02cttt; 01wdl3; 017zq0; 01j_cy; 07szy; 0bx8pn; 07wrz; 01r3y2; 03ksy; 019dwp; ... >> query: (?x4555, 019v9k) <- contains(?x1227, ?x4555), student(?x4555, ?x496), major_field_of_study(?x4555, ?x6756), fraternities_and_sororities(?x4555, ?x4348) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zcnq institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.700 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #6765-02zv4b PRED entity: 02zv4b PRED relation: genre PRED expected values: 0m1xv => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 83): 07s9rl0 (0.54 #936, 0.53 #681, 0.53 #851), 05p553 (0.49 #600, 0.44 #430, 0.43 #1195), 09lmb (0.41 #202, 0.33 #287, 0.33 #32), 01z4y (0.38 #529, 0.37 #614, 0.36 #784), 06ntj (0.33 #72, 0.07 #157, 0.06 #242), 01t_vv (0.31 #545, 0.28 #460, 0.26 #630), 0hcr (0.27 #2230, 0.21 #1210, 0.19 #1040), 0c4xc (0.27 #554, 0.26 #639, 0.24 #469), 06nbt (0.27 #107, 0.26 #362, 0.24 #192), 05jhg (0.27 #143, 0.26 #398, 0.17 #313) >> Best rule #936 for best value: >> intensional similarity = 5 >> extensional distance = 55 >> proper extension: 0123qq; >> query: (?x1766, 07s9rl0) <- actor(?x1766, ?x2275), participant(?x2275, ?x1018), award_winner(?x308, ?x2275), award_nominee(?x748, ?x2275), award_winner(?x2707, ?x2275) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 070ltt; *> query: (?x1766, 0m1xv) <- program(?x4065, ?x1766), person(?x9723, ?x4065), profession(?x4065, ?x1032), nationality(?x4065, ?x94) *> conf = 0.27 ranks of expected_values: 11 EVAL 02zv4b genre 0m1xv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 52.000 52.000 0.544 http://example.org/tv/tv_program/genre #6764-02fzs PRED entity: 02fzs PRED relation: company! PRED expected values: 0p_47 => 92 concepts (77 used for prediction) PRED predicted values (max 10 best out of 8): 0cbgl (0.01 #3426), 07n39 (0.01 #3371), 0ct9_ (0.01 #3347), 06crk (0.01 #3310), 01dvtx (0.01 #3255), 01g6bk (0.01 #3899), 099p5 (0.01 #3853), 03v40v (0.01 #3777) >> Best rule #3426 for best value: >> intensional similarity = 5 >> extensional distance = 78 >> proper extension: 0288zy; 01hhvg; 01bzw5; 0r7fy; 033q4k; 0r2l7; 0r540; 07vht; 0kpys; 027xx3; ... >> query: (?x13079, 0cbgl) <- category(?x13079, ?x134), contains(?x1227, ?x13079), contains(?x94, ?x13079), ?x94 = 09c7w0, ?x1227 = 01n7q >> conf = 0.01 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02fzs company! 0p_47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 77.000 0.013 http://example.org/people/person/employment_history./business/employment_tenure/company #6763-0830vk PRED entity: 0830vk PRED relation: film! PRED expected values: 01vsn38 => 87 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1044): 01r93l (0.70 #22865, 0.69 #29106, 0.66 #103946), 01f7dd (0.29 #1207, 0.06 #3285, 0.05 #7441), 04fzk (0.29 #707, 0.06 #2785, 0.05 #6941), 044rvb (0.29 #100, 0.06 #2178, 0.05 #6334), 0716t2 (0.29 #1906, 0.06 #3984, 0.03 #8140), 015pkc (0.29 #277, 0.06 #2355, 0.03 #6511), 01h8f (0.29 #929, 0.06 #3007, 0.03 #7163), 079vf (0.14 #8, 0.12 #2086, 0.09 #4164), 024bbl (0.14 #837, 0.05 #7071, 0.05 #4993), 046zh (0.14 #935, 0.04 #21720, 0.04 #27961) >> Best rule #22865 for best value: >> intensional similarity = 3 >> extensional distance = 224 >> proper extension: 02_1q9; 0358x_; 030cx; 05lfwd; 0gvsh7l; 06ys2; >> query: (?x3601, ?x4294) <- nominated_for(?x4294, ?x3601), participant(?x1017, ?x4294), participant(?x4294, ?x2275) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #6007 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 0ckrgs; *> query: (?x3601, 01vsn38) <- film(?x123, ?x3601), film(?x4832, ?x3601), film_crew_role(?x3601, ?x137) *> conf = 0.03 ranks of expected_values: 131 EVAL 0830vk film! 01vsn38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 87.000 56.000 0.703 http://example.org/film/actor/film./film/performance/film #6762-069ld1 PRED entity: 069ld1 PRED relation: award_nominee PRED expected values: 016yr0 => 64 concepts (32 used for prediction) PRED predicted values (max 10 best out of 499): 05mlqj (0.81 #63112, 0.81 #42074, 0.81 #74801), 07q0g5 (0.81 #63112, 0.81 #42074, 0.81 #74801), 022yb4 (0.81 #63112, 0.81 #42074, 0.81 #74801), 069ld1 (0.50 #180, 0.28 #9350, 0.21 #7012), 016yr0 (0.28 #9350, 0.21 #7012, 0.16 #18701), 03lmzl (0.28 #9350, 0.21 #7012), 03f4xvm (0.28 #9350, 0.21 #7012), 03yj_0n (0.16 #18701, 0.16 #51425, 0.06 #813), 06151l (0.16 #18701, 0.16 #51425, 0.06 #35), 083chw (0.16 #18701, 0.16 #51425, 0.06 #47) >> Best rule #63112 for best value: >> intensional similarity = 3 >> extensional distance = 1878 >> proper extension: 02vyh; 026v1z; >> query: (?x890, ?x7804) <- award_nominee(?x890, ?x5645), award_nominee(?x7804, ?x890), student(?x7545, ?x5645) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #9350 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 899 *> proper extension: 0kcd5; *> query: (?x890, ?x879) <- nominated_for(?x890, ?x8870), actor(?x8870, ?x879) *> conf = 0.28 ranks of expected_values: 5 EVAL 069ld1 award_nominee 016yr0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 32.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #6761-02822 PRED entity: 02822 PRED relation: major_field_of_study! PRED expected values: 016t_3 => 70 concepts (62 used for prediction) PRED predicted values (max 10 best out of 19): 02_xgp2 (0.87 #396, 0.78 #286, 0.76 #507), 016t_3 (0.83 #352, 0.83 #389, 0.80 #297), 03mkk4 (0.77 #645, 0.50 #100, 0.47 #774), 04zx3q1 (0.71 #259, 0.70 #388, 0.67 #278), 03bwzr4 (0.64 #323, 0.61 #397, 0.56 #287), 0bjrnt (0.57 #244, 0.50 #299, 0.50 #115), 022h5x (0.47 #774, 0.47 #773, 0.46 #794), 07s6fsf (0.47 #774, 0.47 #773, 0.46 #794), 071tyz (0.47 #774, 0.47 #773, 0.46 #794), 01gkg3 (0.47 #774, 0.47 #773, 0.46 #794) >> Best rule #396 for best value: >> intensional similarity = 9 >> extensional distance = 21 >> proper extension: 01tbp; >> query: (?x4268, 02_xgp2) <- student(?x4268, ?x906), major_field_of_study(?x6501, ?x4268), major_field_of_study(?x4257, ?x4268), major_field_of_study(?x1440, ?x4268), currency(?x6501, ?x170), colors(?x6501, ?x3364), student(?x1440, ?x8896), ?x8896 = 07m77x, school(?x580, ?x4257) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #352 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 16 *> proper extension: 02h40lc; 05qjt; 06ms6; 0fdys; 04g51; 040p_q; 04sh3; 041y2; *> query: (?x4268, 016t_3) <- major_field_of_study(?x4268, ?x3995), major_field_of_study(?x122, ?x4268), student(?x4268, ?x6113), major_field_of_study(?x11963, ?x3995), major_field_of_study(?x2730, ?x3995), ?x2730 = 02301, ?x11963 = 01bzs9, nominated_for(?x6113, ?x4084) *> conf = 0.83 ranks of expected_values: 2 EVAL 02822 major_field_of_study! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 62.000 0.870 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #6760-0kjgl PRED entity: 0kjgl PRED relation: people! PRED expected values: 09kr66 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 50): 0x67 (0.25 #159, 0.21 #1959, 0.20 #1809), 033tf_ (0.15 #381, 0.14 #1581, 0.13 #1656), 0xnvg (0.14 #12, 0.09 #387, 0.08 #1587), 07mqps (0.14 #18, 0.03 #393, 0.02 #1368), 0g8_vp (0.14 #21, 0.02 #1671, 0.01 #1596), 02w7gg (0.12 #1652, 0.12 #1577, 0.10 #1802), 06gbnc (0.11 #101, 0.07 #176, 0.01 #1601), 09vc4s (0.11 #83, 0.05 #383, 0.05 #683), 07hwkr (0.10 #461, 0.10 #236, 0.09 #536), 07bch9 (0.08 #397, 0.07 #172, 0.05 #1597) >> Best rule #159 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 04sx9_; >> query: (?x7946, 0x67) <- award_winner(?x192, ?x7946), film(?x7946, ?x153), ?x192 = 02p65p >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #416 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 013ybx; *> query: (?x7946, 09kr66) <- award_winner(?x192, ?x7946), people(?x1050, ?x7946), spouse(?x3708, ?x7946) *> conf = 0.03 ranks of expected_values: 29 EVAL 0kjgl people! 09kr66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 104.000 104.000 0.250 http://example.org/people/ethnicity/people #6759-0djlxb PRED entity: 0djlxb PRED relation: category PRED expected values: 08mbj5d => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.53 #3, 0.50 #1, 0.43 #4) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 0ds3t5x; 021y7yw; 0gyfp9c; 01cmp9; 0gmgwnv; 03cvvlg; >> query: (?x3275, 08mbj5d) <- nominated_for(?x2577, ?x3275), nominated_for(?x2257, ?x3275), film(?x2437, ?x3275), ?x2257 = 09td7p, ?x2577 = 099t8j >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0djlxb category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.533 http://example.org/common/topic/webpage./common/webpage/category #6758-020qr4 PRED entity: 020qr4 PRED relation: genre PRED expected values: 025s89p => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 198): 01z4y (0.47 #1166, 0.38 #317, 0.33 #1633), 0c4xc (0.33 #1189, 0.32 #841, 0.25 #188), 01z77k (0.33 #23, 0.32 #841, 0.25 #99), 02n4kr (0.33 #235, 0.32 #841, 0.24 #465), 025s89p (0.32 #841, 0.29 #809, 0.25 #197), 095bb (0.32 #841, 0.27 #795, 0.18 #950), 01jfsb (0.32 #841, 0.25 #162, 0.25 #86), 01t_vv (0.32 #841, 0.25 #331, 0.20 #1023), 0vgkd (0.32 #841, 0.25 #161, 0.12 #313), 06nbt (0.32 #841, 0.18 #779, 0.13 #1168) >> Best rule #1166 for best value: >> intensional similarity = 16 >> extensional distance = 124 >> proper extension: 0gxsh4; >> query: (?x419, 01z4y) <- country_of_origin(?x419, ?x252), genre(?x419, ?x2540), genre(?x419, ?x1844), genre(?x419, ?x53), genre(?x10669, ?x1844), genre(?x4037, ?x1844), category(?x10669, ?x134), program_creator(?x10669, ?x329), honored_for(?x1265, ?x10669), languages(?x10669, ?x254), genre(?x11377, ?x2540), ?x11377 = 025x1t, program(?x2776, ?x4037), genre(?x5386, ?x53), nominated_for(?x2307, ?x5386), award_winner(?x5386, ?x2554) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #841 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 47 *> proper extension: 05hd32; 05x72k; 088tp3; 03q4hl; *> query: (?x419, ?x2480) <- country_of_origin(?x419, ?x252), genre(?x419, ?x2540), genre(?x419, ?x1844), genre(?x10669, ?x1844), genre(?x6839, ?x1844), genre(?x5852, ?x1844), genre(?x4037, ?x1844), category(?x10669, ?x134), program_creator(?x10669, ?x329), honored_for(?x1265, ?x10669), languages(?x10669, ?x254), ?x2540 = 0hcr, ?x6839 = 0dr1c2, program(?x6678, ?x10669), genre(?x10669, ?x2480), ?x5852 = 024rwx, film(?x3575, ?x4037) *> conf = 0.32 ranks of expected_values: 5 EVAL 020qr4 genre 025s89p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 40.000 40.000 0.468 http://example.org/tv/tv_program/genre #6757-04vlh5 PRED entity: 04vlh5 PRED relation: award PRED expected values: 0gq_d => 124 concepts (101 used for prediction) PRED predicted values (max 10 best out of 283): 05b1610 (0.71 #20258, 0.71 #38907, 0.69 #13371), 019f4v (0.46 #4525, 0.32 #3310, 0.16 #5336), 0gs9p (0.45 #4538, 0.33 #3323, 0.17 #5349), 07bdd_ (0.40 #2497, 0.15 #1687, 0.15 #29176), 040njc (0.40 #4466, 0.28 #3251, 0.18 #29583), 02x1z2s (0.32 #1821, 0.13 #39313, 0.06 #2631), 0gq9h (0.31 #4536, 0.24 #3321, 0.20 #78), 05p1dby (0.31 #2539, 0.23 #2838, 0.17 #1729), 09sb52 (0.30 #7335, 0.24 #17463, 0.23 #17058), 02pqp12 (0.29 #4529, 0.22 #3314, 0.11 #5340) >> Best rule #20258 for best value: >> intensional similarity = 3 >> extensional distance = 1229 >> proper extension: 018ndc; 0hvbj; >> query: (?x10730, ?x688) <- award_winner(?x688, ?x10730), award_winner(?x3692, ?x10730), award(?x10730, ?x2706) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #628 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 33 *> proper extension: 03k1vm; 0b57p6; 0jnb0; 01c5d5; *> query: (?x10730, 0gq_d) <- profession(?x10730, ?x1966), ?x1966 = 015h31 *> conf = 0.20 ranks of expected_values: 27 EVAL 04vlh5 award 0gq_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 124.000 101.000 0.715 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6756-0gwjw0c PRED entity: 0gwjw0c PRED relation: film_release_region PRED expected values: 03gj2 06c1y 01p1v => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 126): 03gj2 (0.86 #867, 0.86 #1009, 0.84 #1150), 03spz (0.76 #1071, 0.76 #929, 0.75 #1212), 05v8c (0.71 #434, 0.70 #1000, 0.70 #1141), 03rk0 (0.64 #469, 0.63 #893, 0.62 #1035), 01p1v (0.63 #1031, 0.63 #889, 0.62 #1172), 016wzw (0.56 #902, 0.56 #1185, 0.55 #1044), 06qd3 (0.53 #1301, 0.52 #312, 0.48 #1865), 01ls2 (0.53 #431, 0.52 #855, 0.52 #997), 06mzp (0.53 #298, 0.49 #1287, 0.48 #1851), 06f32 (0.52 #901, 0.51 #1184, 0.50 #477) >> Best rule #867 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 0b76d_m; 0ds35l9; 0g56t9t; 0gtsx8c; 02vxq9m; 0gx1bnj; 0ds3t5x; 0gtv7pk; 0h1cdwq; 0g5qs2k; ... >> query: (?x6886, 03gj2) <- film(?x8740, ?x6886), film_release_region(?x6886, ?x344), ?x344 = 04gzd, location(?x8740, ?x362) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 11 EVAL 0gwjw0c film_release_region 01p1v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 73.000 73.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gwjw0c film_release_region 06c1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 73.000 73.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gwjw0c film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6755-0nbrp PRED entity: 0nbrp PRED relation: place_of_birth! PRED expected values: 01rzqj => 98 concepts (57 used for prediction) PRED predicted values (max 10 best out of 138): 0dx_q (0.33 #91206, 0.33 #104235, 0.33 #104234), 0prjs (0.33 #225, 0.02 #10427), 01pny5 (0.33 #2495), 016z68 (0.33 #2299), 01wd02c (0.33 #1405), 0czkbt (0.07 #3569, 0.02 #39090), 016xh5 (0.07 #3857, 0.01 #46912, 0.01 #70361), 016dp0 (0.07 #5106), 040rjq (0.07 #5054), 0h1q6 (0.07 #5040) >> Best rule #91206 for best value: >> intensional similarity = 4 >> extensional distance = 459 >> proper extension: 013wf1; >> query: (?x12461, ?x7605) <- contains(?x362, ?x12461), place_of_birth(?x8348, ?x12461), gender(?x8348, ?x231), location(?x7605, ?x12461) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0nbrp place_of_birth! 01rzqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 57.000 0.334 http://example.org/people/person/place_of_birth #6754-03gj2 PRED entity: 03gj2 PRED relation: olympics PRED expected values: 0sx7r => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 28): 018ctl (0.63 #212, 0.53 #290, 0.51 #317), 0swbd (0.50 #372, 0.48 #215, 0.45 #137), 0c_tl (0.48 #313, 0.45 #496, 0.44 #628), 016r9z (0.48 #313, 0.45 #496, 0.44 #628), 0lv1x (0.48 #313, 0.45 #496, 0.44 #628), 0l6m5 (0.48 #313, 0.45 #496, 0.44 #628), 0l6vl (0.48 #313, 0.45 #496, 0.44 #628), 018qb4 (0.48 #313, 0.45 #496, 0.44 #628), 0ldqf (0.48 #313, 0.45 #496, 0.44 #628), 018ljb (0.48 #313, 0.45 #496, 0.44 #628) >> Best rule #212 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 04j53; >> query: (?x1003, 018ctl) <- olympics(?x1003, ?x418), medal(?x1003, ?x422), partially_contains(?x1003, ?x10517) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #211 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 04j53; *> query: (?x1003, 0sx7r) <- olympics(?x1003, ?x418), medal(?x1003, ?x422), partially_contains(?x1003, ?x10517) *> conf = 0.22 ranks of expected_values: 20 EVAL 03gj2 olympics 0sx7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 190.000 190.000 0.630 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #6753-0b05xm PRED entity: 0b05xm PRED relation: produced_by! PRED expected values: 0cks1m => 67 concepts (48 used for prediction) PRED predicted values (max 10 best out of 99): 01g3gq (0.26 #7578, 0.24 #10422, 0.06 #11370), 0443v1 (0.17 #2819, 0.10 #4714, 0.06 #5661), 07g9f (0.10 #24636, 0.10 #23688, 0.09 #43588), 015g28 (0.06 #11370, 0.06 #15159, 0.01 #7932), 0bwfwpj (0.03 #5773, 0.02 #7668, 0.01 #8616), 0b6l1st (0.03 #6362, 0.02 #9205, 0.01 #11101), 03tbg6 (0.03 #6561), 047vnkj (0.03 #6184), 0gd0c7x (0.03 #5852), 0cz_ym (0.03 #5845) >> Best rule #7578 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 06z4wj; 054187; 0gd9k; 03p01x; 0133sq; >> query: (?x3570, ?x7366) <- profession(?x3570, ?x1041), written_by(?x7366, ?x3570), ?x1041 = 03gjzk >> conf = 0.26 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0b05xm produced_by! 0cks1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 48.000 0.261 http://example.org/film/film/produced_by #6752-04s5_s PRED entity: 04s5_s PRED relation: artists! PRED expected values: 01rthc => 94 concepts (30 used for prediction) PRED predicted values (max 10 best out of 263): 06by7 (0.55 #22, 0.52 #5692, 0.51 #6323), 064t9 (0.45 #3157, 0.45 #13, 0.45 #2526), 0dl5d (0.45 #20, 0.40 #1590, 0.32 #2533), 05w3f (0.40 #1609, 0.39 #981, 0.26 #2552), 0xhtw (0.35 #1587, 0.28 #1273, 0.28 #959), 08jyyk (0.33 #1012, 0.23 #698, 0.23 #2583), 03_d0 (0.32 #2840, 0.27 #11, 0.25 #1581), 05bt6j (0.32 #2558, 0.30 #1615, 0.27 #3189), 016jny (0.31 #736, 0.24 #2937, 0.19 #2307), 0cx7f (0.28 #1397, 0.27 #2340, 0.25 #1711) >> Best rule #22 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 050z2; 01w9wwg; 016s0m; >> query: (?x12557, 06by7) <- role(?x12557, ?x3991), role(?x12557, ?x315), ?x3991 = 05842k, instrumentalists(?x1166, ?x12557), ?x1166 = 05148p4, ?x315 = 0l14md >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #7245 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 242 *> proper extension: 0bpk2; 01dwrc; 07sbk; *> query: (?x12557, ?x1000) <- artists(?x302, ?x12557), artists(?x302, ?x10574), artists(?x302, ?x5227), artists(?x1000, ?x5227), ?x10574 = 02g40r *> conf = 0.04 ranks of expected_values: 134 EVAL 04s5_s artists! 01rthc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 94.000 30.000 0.545 http://example.org/music/genre/artists #6751-01p7b6b PRED entity: 01p7b6b PRED relation: people! PRED expected values: 02y0js => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 41): 0gk4g (0.31 #1961, 0.27 #855, 0.25 #1375), 0qcr0 (0.12 #1366, 0.12 #1301, 0.11 #1561), 02y0js (0.11 #1953, 0.08 #2019, 0.07 #847), 04p3w (0.10 #141, 0.09 #1962, 0.09 #271), 02k6hp (0.08 #1987, 0.08 #36, 0.06 #1336), 01psyx (0.08 #44, 0.05 #304, 0.04 #109), 097ns (0.08 #26, 0.04 #91, 0.03 #286), 02knxx (0.07 #1331, 0.07 #1396, 0.06 #226), 0dcsx (0.07 #340, 0.03 #1575, 0.03 #1315), 01l2m3 (0.06 #1967, 0.04 #2488, 0.04 #2293) >> Best rule #1961 for best value: >> intensional similarity = 2 >> extensional distance = 455 >> proper extension: 084w8; 01jrz5j; 083q7; 0k4gf; 0136p1; 028rk; 0d4jl; 01v3bn; 09r8l; 014635; ... >> query: (?x10146, 0gk4g) <- people(?x6260, ?x10146), symptom_of(?x4905, ?x6260) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #1953 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 455 *> proper extension: 084w8; 01jrz5j; 083q7; 0k4gf; 0136p1; 028rk; 0d4jl; 01v3bn; 09r8l; 014635; ... *> query: (?x10146, 02y0js) <- people(?x6260, ?x10146), symptom_of(?x4905, ?x6260) *> conf = 0.11 ranks of expected_values: 3 EVAL 01p7b6b people! 02y0js CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 88.000 88.000 0.311 http://example.org/people/cause_of_death/people #6750-03xsby PRED entity: 03xsby PRED relation: film PRED expected values: 0cw3yd 059rc 03nqnnk 072hx4 => 147 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1831): 0cqr0q (0.75 #3106, 0.75 #3105, 0.64 #52754), 0170yd (0.75 #3106, 0.75 #3105, 0.64 #52754), 05pt0l (0.75 #3106, 0.75 #3105, 0.64 #52754), 0gkz15s (0.75 #3106, 0.75 #3105, 0.64 #52754), 02r79_h (0.75 #3106, 0.75 #3105, 0.64 #52754), 04fv5b (0.75 #3106, 0.75 #3105, 0.64 #52754), 047vp1n (0.75 #3106, 0.75 #3105, 0.64 #52754), 03clwtw (0.75 #3106, 0.75 #3105, 0.64 #52754), 09rfh9 (0.75 #3106, 0.75 #3105, 0.64 #52754), 0436yk (0.75 #3106, 0.75 #3105, 0.64 #52754) >> Best rule #3106 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 017s11; >> query: (?x1914, ?x4993) <- production_companies(?x4993, ?x1914), film(?x1914, ?x8679), ?x8679 = 023g6w >> conf = 0.75 => this is the best rule for 10 predicted values *> Best rule #37207 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0cjdk; *> query: (?x1914, 072hx4) <- citytown(?x1914, ?x242), nominated_for(?x1914, ?x11213), executive_produced_by(?x11213, ?x11214) *> conf = 0.09 ranks of expected_values: 839, 894, 1739, 1786 EVAL 03xsby film 072hx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 147.000 70.000 0.753 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xsby film 03nqnnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 147.000 70.000 0.753 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xsby film 059rc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 147.000 70.000 0.753 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03xsby film 0cw3yd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 147.000 70.000 0.753 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #6749-019n9w PRED entity: 019n9w PRED relation: school_type PRED expected values: 02p0qmm => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 21): 05pcjw (0.52 #484, 0.51 #668, 0.49 #875), 05jxkf (0.50 #2489, 0.46 #1960, 0.45 #1638), 02dk5q (0.33 #6, 0.10 #167, 0.08 #3414), 0bpgx (0.33 #20, 0.10 #181, 0.02 #1009), 04qbv (0.25 #61, 0.08 #3414, 0.06 #935), 01_9fk (0.19 #1014, 0.13 #370, 0.13 #1636), 07tf8 (0.15 #399, 0.15 #790, 0.14 #146), 01_srz (0.15 #670, 0.12 #739, 0.12 #923), 02p0qmm (0.10 #308, 0.08 #3414, 0.07 #147), 04399 (0.08 #128, 0.08 #3414, 0.04 #220) >> Best rule #484 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 0kz2w; 07w3r; 02607j; 0217m9; 0cwx_; 01xk7r; 01bk1y; 04ftdq; 02qw_v; 03k7dn; >> query: (?x8525, 05pcjw) <- student(?x8525, ?x3495), registering_agency(?x8525, ?x1982), currency(?x8525, ?x170), colors(?x8525, ?x663) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #308 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 07wj1; *> query: (?x8525, 02p0qmm) <- company(?x14008, ?x8525), people(?x6821, ?x14008), influenced_by(?x14008, ?x117) *> conf = 0.10 ranks of expected_values: 9 EVAL 019n9w school_type 02p0qmm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 201.000 201.000 0.517 http://example.org/education/educational_institution/school_type #6748-02pzz3p PRED entity: 02pzz3p PRED relation: nominated_for PRED expected values: 02_1rq 034fl9 02_1ky => 54 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1356): 02_1rq (0.67 #1665, 0.62 #3255, 0.60 #75), 0147w8 (0.50 #3141, 0.40 #1551, 0.38 #4731), 034fl9 (0.33 #2925, 0.25 #4515, 0.22 #25461), 0ddd0gc (0.30 #4965, 0.25 #6555, 0.24 #9737), 026p4q7 (0.29 #13085, 0.27 #14677, 0.21 #16268), 09gq0x5 (0.26 #12982, 0.24 #14574, 0.22 #16165), 0m313 (0.26 #12739, 0.24 #14331, 0.20 #15922), 0gmgwnv (0.26 #13687, 0.23 #15279, 0.20 #16870), 01g03q (0.26 #6141, 0.25 #7731, 0.21 #9322), 0kfv9 (0.26 #5030, 0.23 #6620, 0.19 #9802) >> Best rule #1665 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 02pz3j5; >> query: (?x2773, 02_1rq) <- nominated_for(?x2773, ?x3544), nominated_for(?x2773, ?x2829), award(?x540, ?x2773), ?x2829 = 01b64v, ?x3544 = 0phrl >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 44 EVAL 02pzz3p nominated_for 02_1ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 54.000 18.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pzz3p nominated_for 034fl9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 54.000 18.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02pzz3p nominated_for 02_1rq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 18.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6747-027ct7c PRED entity: 027ct7c PRED relation: language PRED expected values: 03hkp => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 46): 064_8sq (0.17 #1608, 0.17 #1016, 0.17 #1668), 03_9r (0.17 #9, 0.06 #769, 0.06 #595), 01r2l (0.17 #24, 0.03 #82, 0.02 #668), 06nm1 (0.14 #244, 0.14 #68, 0.11 #1597), 04306rv (0.14 #764, 0.13 #590, 0.13 #648), 02bjrlw (0.11 #587, 0.11 #118, 0.10 #761), 06b_j (0.11 #80, 0.09 #256, 0.09 #197), 0653m (0.11 #69, 0.08 #128, 0.07 #245), 03k50 (0.06 #476, 0.05 #359, 0.04 #1062), 02hxcvy (0.06 #501, 0.05 #384, 0.03 #968) >> Best rule #1608 for best value: >> intensional similarity = 5 >> extensional distance = 342 >> proper extension: 0ds35l9; 0m313; 0g22z; 083shs; 02vxq9m; 0b2v79; 09m6kg; 01gc7; 011yrp; 011yxg; ... >> query: (?x5533, 064_8sq) <- genre(?x5533, ?x53), award_winner(?x5533, ?x4307), honored_for(?x5924, ?x5533), country(?x5533, ?x94), language(?x5533, ?x254) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #424 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 92 *> proper extension: 0jqb8; *> query: (?x5533, 03hkp) <- genre(?x5533, ?x258), genre(?x5533, ?x53), film_release_distribution_medium(?x5533, ?x81), ?x53 = 07s9rl0, ?x258 = 05p553, film(?x4307, ?x5533) *> conf = 0.02 ranks of expected_values: 32 EVAL 027ct7c language 03hkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 104.000 104.000 0.172 http://example.org/film/film/language #6746-0ntxg PRED entity: 0ntxg PRED relation: time_zones PRED expected values: 02fqwt => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 12): 02fqwt (0.61 #1239, 0.58 #28, 0.50 #27), 02hcv8 (0.54 #395, 0.53 #512, 0.52 #317), 02lcqs (0.47 #136, 0.40 #176, 0.33 #123), 02hczc (0.25 #15, 0.16 #212, 0.16 #238), 042g7t (0.25 #24, 0.03 #103, 0.03 #286), 02lcrv (0.25 #20, 0.01 #581, 0.01 #711), 02llzg (0.18 #461, 0.17 #201, 0.16 #630), 03plfd (0.07 #467, 0.06 #89, 0.06 #688), 03bdv (0.05 #697, 0.04 #1258, 0.03 #632), 0gsrz4 (0.04 #972, 0.03 #87, 0.02 #1325) >> Best rule #1239 for best value: >> intensional similarity = 5 >> extensional distance = 428 >> proper extension: 0bwtj; 01gfhk; >> query: (?x10877, ?x1638) <- adjoins(?x10877, ?x12859), adjoins(?x10877, ?x11877), contains(?x11877, ?x4356), contains(?x11703, ?x12859), time_zones(?x11877, ?x1638) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ntxg time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.614 http://example.org/location/location/time_zones #6745-01cwcr PRED entity: 01cwcr PRED relation: student! PRED expected values: 031ns1 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 139): 07tg4 (0.09 #86, 0.07 #10102, 0.06 #4831), 015nl4 (0.09 #67, 0.06 #10083, 0.04 #19574), 031hxk (0.09 #367, 0.02 #894, 0.01 #1421), 01g0p5 (0.09 #207, 0.02 #10223), 011xy1 (0.09 #318, 0.01 #2426), 01s753 (0.09 #502), 0dzbl (0.09 #501), 05zjtn4 (0.09 #3), 07tgn (0.06 #10033, 0.04 #4762, 0.02 #14777), 01d34b (0.06 #783, 0.04 #1310, 0.04 #1837) >> Best rule #86 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0cgfb; >> query: (?x7277, 07tg4) <- film(?x7277, ?x9016), gender(?x7277, ?x231), ?x9016 = 0bz6sq >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #3154 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 94 *> proper extension: 06c0j; *> query: (?x7277, 031ns1) <- location_of_ceremony(?x7277, ?x335), award_winner(?x6878, ?x7277), contains(?x335, ?x322) *> conf = 0.01 ranks of expected_values: 131 EVAL 01cwcr student! 031ns1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 101.000 101.000 0.091 http://example.org/education/educational_institution/students_graduates./education/education/student #6744-01qcx_ PRED entity: 01qcx_ PRED relation: time_zones PRED expected values: 02hcv8 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.44 #185, 0.38 #3, 0.35 #198), 02lcqs (0.21 #161, 0.21 #174, 0.21 #148), 02fqwt (0.17 #196, 0.16 #326, 0.16 #755), 02hczc (0.16 #755, 0.12 #145, 0.08 #158), 02lcrv (0.16 #755), 02llzg (0.07 #264, 0.07 #316, 0.07 #17), 03bdv (0.05 #500, 0.05 #448, 0.05 #32), 03plfd (0.02 #88, 0.02 #140, 0.02 #270), 052vwh (0.02 #90, 0.02 #142, 0.02 #25), 042g7t (0.02 #154, 0.02 #37, 0.01 #492) >> Best rule #185 for best value: >> intensional similarity = 2 >> extensional distance = 123 >> proper extension: 0fm9_; 01mc11; 0drsm; 02607j; 0dlhg; 0f6_4; 0f6_j; 0dc3_; 0f63n; 0fc2c; ... >> query: (?x12738, 02hcv8) <- contains(?x335, ?x12738), ?x335 = 059rby >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qcx_ time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.440 http://example.org/location/location/time_zones #6743-05tg3 PRED entity: 05tg3 PRED relation: school PRED expected values: 01rc6f => 102 concepts (79 used for prediction) PRED predicted values (max 10 best out of 362): 01vs5c (0.50 #2154, 0.46 #2907, 0.44 #1588), 01rc6f (0.50 #508, 0.23 #2761, 0.19 #4268), 07w0v (0.43 #949, 0.40 #2076, 0.38 #2829), 05krk (0.36 #3012, 0.32 #4895, 0.27 #3576), 01lnyf (0.33 #255, 0.30 #2132, 0.23 #2885), 01pl14 (0.33 #5, 0.29 #943, 0.23 #10200), 01qd_r (0.33 #314, 0.25 #502, 0.19 #3885), 025v3k (0.33 #242, 0.23 #2872, 0.20 #2119), 0trv (0.33 #139, 0.23 #2957, 0.15 #10334), 01dzg0 (0.33 #351, 0.20 #2228, 0.19 #3922) >> Best rule #2154 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 0512p; 01yjl; 02__x; >> query: (?x3674, 01vs5c) <- team(?x180, ?x3674), sport(?x3674, ?x1083), colors(?x3674, ?x663), school(?x3674, ?x735), ?x663 = 083jv, teams(?x2254, ?x3674), category(?x3674, ?x134), origin(?x3176, ?x2254) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #508 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 03b3j; 05l71; *> query: (?x3674, 01rc6f) <- team(?x11424, ?x3674), position(?x3674, ?x1792), school(?x3674, ?x9768), teams(?x2254, ?x3674), team(?x7749, ?x3674), colors(?x3674, ?x663), institution(?x865, ?x9768), ?x11424 = 0bgv8y, major_field_of_study(?x9768, ?x1154), currency(?x9768, ?x170) *> conf = 0.50 ranks of expected_values: 2 EVAL 05tg3 school 01rc6f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 79.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #6742-05b4l5x PRED entity: 05b4l5x PRED relation: nominated_for PRED expected values: 03nx8mj 07sgdw 047wh1 01s7w3 => 41 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1434): 0q9sg (0.85 #3058, 0.83 #1530, 0.77 #4589), 0y_yw (0.85 #3058, 0.83 #1530, 0.77 #4589), 047wh1 (0.85 #3058, 0.83 #1530, 0.77 #4589), 05c46y6 (0.75 #1902, 0.19 #4961, 0.17 #6489), 02ywwy (0.67 #1205, 0.22 #18345, 0.22 #18343), 08lr6s (0.67 #41, 0.22 #18345, 0.22 #18343), 048qrd (0.67 #277, 0.22 #18345, 0.22 #18343), 05b_gq (0.67 #927, 0.22 #18345, 0.22 #18343), 09y6pb (0.67 #1315, 0.22 #18345, 0.22 #18343), 075wx7_ (0.67 #218, 0.14 #3276, 0.12 #1748) >> Best rule #3058 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 09sb52; 02z0dfh; 09td7p; 063y_ky; 02ppm4q; 03qgjwc; >> query: (?x154, ?x103) <- award(?x1564, ?x154), award(?x103, ?x154), ?x1564 = 01g257, nominated_for(?x154, ?x270) >> conf = 0.85 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 209, 210, 1216 EVAL 05b4l5x nominated_for 01s7w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 17.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05b4l5x nominated_for 047wh1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 17.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05b4l5x nominated_for 07sgdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 41.000 17.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05b4l5x nominated_for 03nx8mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 41.000 17.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6741-02qkt PRED entity: 02qkt PRED relation: contains PRED expected values: 06npd 0d0kn 03rk0 04j53 03spz 03_xj 02w9s => 77 concepts (53 used for prediction) PRED predicted values (max 10 best out of 2744): 012wgb (0.66 #57172, 0.51 #128650, 0.46 #97199), 02jx1 (0.66 #57172, 0.47 #100059, 0.47 #108637), 014tss (0.66 #57172, 0.06 #17926, 0.05 #40796), 01j_x (0.66 #57172), 0g5y6 (0.66 #57172), 013xrm (0.66 #57172), 04swx (0.60 #34304, 0.57 #82903, 0.51 #128650), 0fhnf (0.60 #34304, 0.57 #82903, 0.47 #100059), 070zc (0.60 #34304, 0.57 #82903, 0.47 #100059), 05bcl (0.60 #34304, 0.57 #82903, 0.47 #100059) >> Best rule #57172 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 02_286; 03gh4; 014wxc; >> query: (?x6304, ?x8649) <- contains(?x6304, ?x3041), contains(?x6304, ?x1003), jurisdiction_of_office(?x182, ?x3041), split_to(?x8649, ?x1003) >> conf = 0.66 => this is the best rule for 6 predicted values *> Best rule #34304 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 18 *> proper extension: 0hzlz; *> query: (?x6304, ?x2517) <- contains(?x6304, ?x3041), contains(?x6304, ?x512), contains(?x6304, ?x344), jurisdiction_of_office(?x182, ?x3041), combatants(?x326, ?x512), adjoins(?x344, ?x2517) *> conf = 0.60 ranks of expected_values: 11, 13, 16, 19, 27, 713 EVAL 02qkt contains 02w9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 53.000 0.659 http://example.org/location/location/contains EVAL 02qkt contains 03_xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 77.000 53.000 0.659 http://example.org/location/location/contains EVAL 02qkt contains 03spz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 77.000 53.000 0.659 http://example.org/location/location/contains EVAL 02qkt contains 04j53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 77.000 53.000 0.659 http://example.org/location/location/contains EVAL 02qkt contains 03rk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 77.000 53.000 0.659 http://example.org/location/location/contains EVAL 02qkt contains 0d0kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 77.000 53.000 0.659 http://example.org/location/location/contains EVAL 02qkt contains 06npd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 77.000 53.000 0.659 http://example.org/location/location/contains #6740-02lz1s PRED entity: 02lz1s PRED relation: place_of_death PRED expected values: 0r3w7 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 15): 04jpl (0.08 #201, 0.05 #395, 0.03 #784), 0fhp9 (0.08 #208, 0.05 #402, 0.02 #1373), 030qb3t (0.08 #799, 0.08 #993, 0.07 #604), 02_286 (0.08 #790, 0.06 #1566, 0.06 #1178), 071cn (0.05 #777, 0.03 #7958, 0.02 #9900), 0k049 (0.03 #585, 0.02 #780, 0.02 #974), 06_kh (0.03 #587, 0.01 #1558, 0.01 #782), 071vr (0.03 #684, 0.01 #1655), 0f2wj (0.03 #1371, 0.02 #1177, 0.01 #594), 0nbwf (0.01 #699, 0.01 #894) >> Best rule #201 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 04k15; 03j24kf; 01l4g5; 03f7m4h; 01k47c; >> query: (?x1852, 04jpl) <- profession(?x1852, ?x11127), profession(?x1852, ?x2348), profession(?x1852, ?x1614), ?x2348 = 0nbcg, ?x11127 = 05vyk, ?x1614 = 01c72t >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02lz1s place_of_death 0r3w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 71.000 0.083 http://example.org/people/deceased_person/place_of_death #6739-0r0ls PRED entity: 0r0ls PRED relation: contains! PRED expected values: 01n7q => 104 concepts (29 used for prediction) PRED predicted values (max 10 best out of 277): 01n7q (0.67 #971, 0.65 #1865, 0.57 #19751), 03rk0 (0.25 #136, 0.07 #21599, 0.05 #17127), 06mzp (0.25 #45, 0.04 #2727, 0.03 #3622), 07ssc (0.21 #6291, 0.21 #3608, 0.18 #21494), 02jx1 (0.17 #6346, 0.14 #21549, 0.08 #7240), 04_1l0v (0.17 #16545, 0.09 #21912, 0.06 #23701), 06pvr (0.12 #19839, 0.12 #12685, 0.08 #5531), 04jpl (0.12 #6281, 0.07 #21484, 0.06 #10753), 05kj_ (0.11 #19714, 0.07 #12560, 0.05 #5406), 015jr (0.11 #5778, 0.07 #12932, 0.05 #20086) >> Best rule #971 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0qjfl; >> query: (?x12824, 01n7q) <- contains(?x2949, ?x12824), time_zones(?x12824, ?x2950), ?x2950 = 02lcqs, ?x2949 = 0kpys >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r0ls contains! 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 29.000 0.667 http://example.org/location/location/contains #6738-02bh8z PRED entity: 02bh8z PRED relation: company! PRED expected values: 09d6p2 => 226 concepts (226 used for prediction) PRED predicted values (max 10 best out of 41): 060c4 (0.68 #7583, 0.67 #7627, 0.62 #4097), 01yc02 (0.62 #1284, 0.51 #4102, 0.50 #1372), 0dq3c (0.54 #3347, 0.51 #3964, 0.51 #4008), 09d6p2 (0.50 #325, 0.50 #237, 0.49 #3362), 01kr6k (0.50 #245, 0.33 #7802, 0.31 #3987), 014l7h (0.50 #202, 0.25 #2931, 0.20 #2711), 02y6fz (0.33 #7802, 0.18 #1078, 0.15 #1298), 033smt (0.33 #7802, 0.14 #5946, 0.13 #8423), 0142rn (0.25 #244, 0.17 #4339, 0.16 #3986), 02k13d (0.25 #189, 0.16 #2698, 0.14 #2918) >> Best rule #7583 for best value: >> intensional similarity = 5 >> extensional distance = 191 >> proper extension: 09c7w0; 025jfl; 0f8l9c; 03rj0; 01jsn5; 0f1nl; 0j_sncb; 012fvq; 07vfj; 02hcxm; ... >> query: (?x3887, 060c4) <- company(?x1491, ?x3887), company(?x1491, ?x12471), company(?x1491, ?x3920), ?x3920 = 09b3v, ?x12471 = 01npw8 >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #325 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0c_j5d; 05qd_; 0bwfn; *> query: (?x3887, 09d6p2) <- company(?x1491, ?x3887), child(?x3887, ?x648), company(?x6151, ?x3887), currency(?x3887, ?x170) *> conf = 0.50 ranks of expected_values: 4 EVAL 02bh8z company! 09d6p2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 226.000 226.000 0.684 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #6737-04nnpw PRED entity: 04nnpw PRED relation: country PRED expected values: 0f8l9c => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 139): 0f8l9c (0.63 #1194, 0.14 #135, 0.12 #429), 02vzc (0.50 #117, 0.36 #650, 0.18 #176), 07ssc (0.34 #1191, 0.29 #132, 0.24 #1894), 0345h (0.20 #1202, 0.15 #320, 0.14 #143), 03h64 (0.12 #1220, 0.05 #2999, 0.05 #1631), 0d060g (0.11 #716, 0.10 #657, 0.09 #775), 01mjq (0.08 #328, 0.07 #386, 0.06 #1210), 0d05w3 (0.08 #335, 0.07 #393, 0.06 #1217), 0chghy (0.08 #247, 0.06 #540, 0.05 #2999), 0d0vqn (0.08 #245, 0.05 #2999, 0.03 #538) >> Best rule #1194 for best value: >> intensional similarity = 5 >> extensional distance = 223 >> proper extension: 02vl9ln; >> query: (?x4696, 0f8l9c) <- country(?x4696, ?x2152), film_release_region(?x7711, ?x2152), film_release_region(?x6376, ?x2152), ?x7711 = 0pd64, ?x6376 = 01f85k >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04nnpw country 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.627 http://example.org/film/film/country #6736-01x53m PRED entity: 01x53m PRED relation: award PRED expected values: 0grw_ => 120 concepts (90 used for prediction) PRED predicted values (max 10 best out of 330): 0ddd9 (0.81 #15329, 0.76 #33476, 0.75 #18557), 01l78d (0.66 #8765, 0.29 #1500, 0.21 #3516), 0g9wd99 (0.43 #1580, 0.29 #3193, 0.22 #4808), 040vk98 (0.39 #6889, 0.33 #14551, 0.29 #1642), 0265vt (0.37 #7187, 0.33 #327, 0.30 #2343), 01yz0x (0.37 #7037, 0.23 #14699, 0.18 #12279), 03c7tr1 (0.33 #462, 0.30 #11758, 0.08 #2478), 0gqwc (0.33 #478, 0.19 #12581, 0.11 #11774), 05pcn59 (0.33 #485, 0.16 #11781, 0.09 #12588), 02x4x18 (0.33 #537, 0.16 #12640, 0.09 #31190) >> Best rule #15329 for best value: >> intensional similarity = 6 >> extensional distance = 136 >> proper extension: 0m2l9; 01n5309; 02whj; 07c0j; 022_lg; 01wp8w7; 03g5jw; 034np8; 01t07j; 086qd; ... >> query: (?x9173, ?x921) <- influenced_by(?x9173, ?x118), award_winner(?x921, ?x9173), award(?x11492, ?x921), award(?x6810, ?x921), influenced_by(?x477, ?x6810), celebrities_impersonated(?x3649, ?x11492) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1929 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 02h761; *> query: (?x9173, 0grw_) <- award(?x9173, ?x11388), award(?x9173, ?x11020), award(?x1235, ?x11388), ?x11020 = 01f7d, student(?x1011, ?x9173), ?x1235 = 0m77m *> conf = 0.29 ranks of expected_values: 20 EVAL 01x53m award 0grw_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 120.000 90.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6735-01hq1 PRED entity: 01hq1 PRED relation: genre PRED expected values: 0lsxr => 104 concepts (65 used for prediction) PRED predicted values (max 10 best out of 98): 07s9rl0 (0.68 #3784, 0.67 #3904, 0.66 #3665), 024qqx (0.52 #6980, 0.52 #6743, 0.50 #4848), 05p553 (0.50 #240, 0.39 #4379, 0.36 #831), 03k9fj (0.49 #5334, 0.44 #130, 0.39 #4979), 0lsxr (0.34 #3081, 0.23 #3436, 0.23 #2253), 06n90 (0.32 #2257, 0.31 #4980, 0.31 #131), 02l7c8 (0.29 #6877, 0.28 #6640, 0.28 #5928), 02n4kr (0.25 #8, 0.24 #3080, 0.14 #1897), 01t_vv (0.25 #54, 0.10 #3957, 0.08 #4902), 0gf28 (0.25 #63, 0.06 #4438, 0.06 #2425) >> Best rule #3784 for best value: >> intensional similarity = 4 >> extensional distance = 429 >> proper extension: 01cgz; >> query: (?x7881, 07s9rl0) <- films(?x5011, ?x7881), films(?x5011, ?x2958), cinematography(?x2958, ?x7537), language(?x2958, ?x254) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #3081 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 319 *> proper extension: 0ddcbd5; 08c6k9; *> query: (?x7881, 0lsxr) <- production_companies(?x7881, ?x752), genre(?x7881, ?x812), film(?x71, ?x7881), ?x812 = 01jfsb *> conf = 0.34 ranks of expected_values: 5 EVAL 01hq1 genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 104.000 65.000 0.682 http://example.org/film/film/genre #6734-05t4q PRED entity: 05t4q PRED relation: profession! PRED expected values: 07kb5 039n1 0h336 06myp => 60 concepts (31 used for prediction) PRED predicted values (max 10 best out of 4081): 0gthm (0.67 #24420, 0.50 #37118, 0.50 #15954), 081lh (0.67 #21422, 0.50 #12956, 0.46 #42328), 0mb5x (0.67 #23920, 0.50 #15454, 0.42 #53552), 0d5_f (0.67 #22515, 0.50 #14049, 0.42 #52147), 05wm88 (0.67 #24975, 0.50 #16509, 0.40 #37673), 02b29 (0.67 #23400, 0.50 #14934, 0.40 #36098), 0drdv (0.67 #25055, 0.50 #16589, 0.40 #37753), 016yzz (0.67 #22393, 0.50 #13927, 0.40 #35091), 0hcvy (0.67 #24826, 0.50 #16360, 0.40 #37524), 01v9724 (0.67 #22926, 0.50 #14460, 0.40 #35624) >> Best rule #24420 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0dxtg; 02hv44_; >> query: (?x6630, 0gthm) <- profession(?x4377, ?x6630), profession(?x1236, ?x6630), ?x1236 = 045bg, award_nominee(?x9038, ?x4377), nationality(?x9038, ?x94), student(?x2711, ?x9038), award_winner(?x9038, ?x5574), type_of_union(?x4377, ?x566) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #71966 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 12 *> proper extension: 04_tv; *> query: (?x6630, ?x8232) <- specialization_of(?x13995, ?x6630), profession(?x8232, ?x13995), profession(?x5148, ?x13995), influenced_by(?x8232, ?x3712), influenced_by(?x3428, ?x8232), student(?x1220, ?x5148), influenced_by(?x5148, ?x2240) *> conf = 0.54 ranks of expected_values: 18, 40, 227, 464 EVAL 05t4q profession! 06myp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 60.000 31.000 0.667 http://example.org/people/person/profession EVAL 05t4q profession! 0h336 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 60.000 31.000 0.667 http://example.org/people/person/profession EVAL 05t4q profession! 039n1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 60.000 31.000 0.667 http://example.org/people/person/profession EVAL 05t4q profession! 07kb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 60.000 31.000 0.667 http://example.org/people/person/profession #6733-050ks PRED entity: 050ks PRED relation: district_represented! PRED expected values: 06f0dc 02bn_p 02bp37 01gtbb 01gtdd => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 41): 06f0dc (0.85 #824, 0.84 #332, 0.83 #783), 02bn_p (0.68 #333, 0.64 #825, 0.64 #784), 01gtdd (0.68 #363, 0.55 #1272, 0.43 #855), 01gtbb (0.65 #339, 0.55 #1272, 0.42 #831), 02bp37 (0.61 #337, 0.58 #829, 0.57 #788), 02bqmq (0.55 #1272, 0.55 #342, 0.53 #834), 01gssz (0.55 #1272, 0.55 #362, 0.38 #198), 02bqn1 (0.55 #1272, 0.52 #335, 0.43 #827), 01gssm (0.55 #1272, 0.52 #346, 0.38 #182), 01gst9 (0.55 #1272, 0.52 #355, 0.38 #191) >> Best rule #824 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 0h5qxv; >> query: (?x7058, 06f0dc) <- jurisdiction_of_office(?x900, ?x7058), contains(?x94, ?x7058), district_represented(?x176, ?x7058) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 050ks district_represented! 01gtdd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 189.000 189.000 0.849 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 050ks district_represented! 01gtbb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 189.000 189.000 0.849 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 050ks district_represented! 02bp37 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 189.000 189.000 0.849 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 050ks district_represented! 02bn_p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 189.000 189.000 0.849 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 050ks district_represented! 06f0dc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 189.000 189.000 0.849 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #6732-02qw1zx PRED entity: 02qw1zx PRED relation: school PRED expected values: 0lyjf => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 791): 06pwq (0.73 #1626, 0.60 #1330, 0.50 #1529), 07w0v (0.71 #1434, 0.60 #1235, 0.50 #1141), 012vwb (0.60 #1259, 0.50 #1165, 0.44 #372), 0f1nl (0.50 #864, 0.50 #767, 0.44 #372), 0pspl (0.50 #1067, 0.50 #874, 0.33 #306), 0lyjf (0.50 #1178, 0.44 #372, 0.40 #1370), 01vs5c (0.50 #1184, 0.44 #372, 0.40 #1278), 0frm7n (0.50 #1175, 0.44 #372, 0.38 #371), 09f2j (0.50 #1083, 0.33 #322, 0.29 #1472), 078bz (0.50 #1060, 0.33 #299, 0.25 #1621) >> Best rule #1626 for best value: >> intensional similarity = 49 >> extensional distance = 9 >> proper extension: 02z6872; >> query: (?x1883, 06pwq) <- school(?x1883, ?x2959), school(?x1883, ?x331), draft(?x7312, ?x1883), draft(?x6976, ?x1883), draft(?x5773, ?x1883), draft(?x4170, ?x1883), major_field_of_study(?x2959, ?x4321), colors(?x4170, ?x4557), team(?x2147, ?x6976), school_type(?x2959, ?x1507), major_field_of_study(?x331, ?x6859), team(?x180, ?x4170), position(?x1717, ?x2147), institution(?x1526, ?x331), country(?x2959, ?x94), school(?x4170, ?x546), sport(?x5773, ?x1083), school(?x5773, ?x6856), ?x94 = 09c7w0, ?x6859 = 01tbp, ?x4321 = 0g26h, position(?x180, ?x10168), major_field_of_study(?x1526, ?x2981), major_field_of_study(?x1526, ?x742), team(?x10361, ?x4170), ?x6856 = 0jkhr, institution(?x1526, ?x11975), institution(?x1526, ?x10861), institution(?x1526, ?x5167), institution(?x1526, ?x4199), institution(?x1526, ?x3948), institution(?x1526, ?x1675), institution(?x1526, ?x892), ?x10861 = 02mzg9, ?x4199 = 016ndm, fraternities_and_sororities(?x331, ?x3697), ?x1675 = 01j_cy, ?x2981 = 02j62, ?x892 = 07tgn, student(?x1526, ?x476), ?x11975 = 050xpd, contains(?x3670, ?x331), student(?x331, ?x2993), category(?x7312, ?x134), ?x742 = 05qjt, currency(?x2959, ?x170), school(?x7312, ?x4750), ?x3948 = 025v3k, ?x5167 = 015cz0 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1178 for first EXPECTED value: *> intensional similarity = 59 *> extensional distance = 2 *> proper extension: 0g3zpp; *> query: (?x1883, 0lyjf) <- school(?x1883, ?x10945), school(?x1883, ?x6973), school(?x1883, ?x6083), school(?x1883, ?x2959), draft(?x11061, ?x1883), draft(?x9172, ?x1883), draft(?x7643, ?x1883), draft(?x6976, ?x1883), draft(?x4469, ?x1883), draft(?x2198, ?x1883), draft(?x2114, ?x1883), draft(?x684, ?x1883), ?x6976 = 04vn5, currency(?x2959, ?x170), colors(?x2959, ?x332), institution(?x1390, ?x2959), institution(?x620, ?x2959), ?x620 = 07s6fsf, ?x7643 = 02c_4, ?x11061 = 06x76, ?x2114 = 01y49, institution(?x4981, ?x6083), category(?x6083, ?x134), school_type(?x6973, ?x1507), institution(?x1390, ?x10197), institution(?x1390, ?x6925), institution(?x1390, ?x4955), institution(?x1390, ?x3513), institution(?x1390, ?x3416), colors(?x6973, ?x4557), major_field_of_study(?x1390, ?x3490), major_field_of_study(?x1390, ?x742), student(?x6083, ?x11630), school(?x700, ?x10945), ?x684 = 01ct6, ?x2198 = 05g3v, colors(?x6083, ?x8047), ?x9172 = 06rpd, ?x4955 = 09f2j, major_field_of_study(?x6973, ?x2601), major_field_of_study(?x6973, ?x1668), ?x742 = 05qjt, ?x6925 = 01bm_, ?x3490 = 05qfh, ?x4469 = 043vc, ?x3513 = 0pspl, student(?x10945, ?x8485), school_type(?x2959, ?x3205), colors(?x10945, ?x7179), ?x10197 = 013nky, ?x3416 = 02183k, ?x4981 = 03bwzr4, award(?x8485, ?x783), contains(?x94, ?x6973), ?x1668 = 01mkq, written_by(?x7989, ?x8485), ?x2601 = 04x_3, film(?x8485, ?x3524), fraternities_and_sororities(?x6973, ?x4348) *> conf = 0.50 ranks of expected_values: 6 EVAL 02qw1zx school 0lyjf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 20.000 20.000 0.727 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #6731-03kx49 PRED entity: 03kx49 PRED relation: genre PRED expected values: 03k9fj 04228s => 50 concepts (48 used for prediction) PRED predicted values (max 10 best out of 90): 03k9fj (0.57 #128, 0.37 #837, 0.23 #4735), 0hcr (0.47 #850, 0.39 #141, 0.20 #23), 04xvlr (0.40 #1, 0.20 #2362, 0.18 #2244), 03g3w (0.33 #851, 0.09 #142, 0.08 #969), 02l7c8 (0.31 #370, 0.30 #488, 0.30 #2376), 01jfsb (0.31 #1546, 0.30 #1900, 0.28 #4263), 01hmnh (0.30 #135, 0.27 #844, 0.16 #4742), 02kdv5l (0.26 #2127, 0.26 #2717, 0.26 #5435), 01g6gs (0.24 #493, 0.22 #375, 0.20 #611), 060__y (0.20 #16, 0.15 #2259, 0.15 #2377) >> Best rule #128 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 016ztl; >> query: (?x7723, 03k9fj) <- film(?x4800, ?x7723), genre(?x7723, ?x6459), ?x6459 = 0bj8m2 >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 35 EVAL 03kx49 genre 04228s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 50.000 48.000 0.565 http://example.org/film/film/genre EVAL 03kx49 genre 03k9fj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 48.000 0.565 http://example.org/film/film/genre #6730-02bn75 PRED entity: 02bn75 PRED relation: artists! PRED expected values: 017_qw => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 157): 017_qw (0.61 #381, 0.47 #2271, 0.46 #1641), 064t9 (0.27 #9783, 0.27 #7263, 0.26 #10728), 06by7 (0.26 #9792, 0.25 #10737, 0.21 #10107), 06j6l (0.15 #7300, 0.15 #9820, 0.14 #6040), 0557q (0.14 #1117, 0.14 #1432, 0.12 #2062), 03_d0 (0.14 #642, 0.13 #957, 0.12 #2533), 0glt670 (0.13 #7292, 0.11 #10127, 0.11 #9182), 05bt6j (0.13 #10760, 0.12 #9815, 0.11 #10130), 01lyv (0.13 #7285, 0.11 #9805, 0.11 #6025), 016clz (0.13 #10719, 0.12 #9774, 0.09 #8199) >> Best rule #381 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 01vtmw6; 02_33l; >> query: (?x7857, 017_qw) <- nationality(?x7857, ?x94), ?x94 = 09c7w0, music(?x951, ?x7857), film_release_region(?x951, ?x142) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bn75 artists! 017_qw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.607 http://example.org/music/genre/artists #6729-02z4b_8 PRED entity: 02z4b_8 PRED relation: artists! PRED expected values: 0gywn => 102 concepts (77 used for prediction) PRED predicted values (max 10 best out of 243): 0glt670 (0.35 #639, 0.28 #6366, 0.26 #5763), 02lnbg (0.31 #654, 0.20 #1558, 0.15 #7285), 016jny (0.31 #1304, 0.19 #1002, 0.12 #4619), 0ggx5q (0.29 #674, 0.22 #1578, 0.18 #5798), 0gywn (0.27 #954, 0.27 #7284, 0.26 #3968), 0xhtw (0.25 #2726, 0.24 #316, 0.23 #1521), 02k_kn (0.25 #1866, 0.24 #59, 0.20 #962), 017_qw (0.22 #3369, 0.12 #56, 0.12 #13015), 05r6t (0.21 #4596, 0.17 #2787, 0.12 #678), 05w3f (0.19 #336, 0.10 #11487, 0.10 #938) >> Best rule #639 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 015mrk; >> query: (?x7115, 0glt670) <- award(?x7115, ?x2877), ?x2877 = 02f5qb, artists(?x302, ?x7115), award_nominee(?x1826, ?x7115) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #954 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 028q6; 05crg7; 02zmh5; 02qlg7s; 017vkx; 01kd57; 04n32; *> query: (?x7115, 0gywn) <- award(?x7115, ?x724), role(?x7115, ?x227), artists(?x302, ?x7115), ?x724 = 01bgqh *> conf = 0.27 ranks of expected_values: 5 EVAL 02z4b_8 artists! 0gywn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 102.000 77.000 0.354 http://example.org/music/genre/artists #6728-0194_r PRED entity: 0194_r PRED relation: company! PRED expected values: 0b78hw => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 212): 03s9v (0.29 #1113, 0.08 #2086, 0.06 #1842), 0x3r3 (0.17 #358, 0.14 #1087, 0.10 #2303), 0nk72 (0.17 #406, 0.10 #2351, 0.08 #2838), 083q7 (0.17 #262, 0.07 #2207, 0.05 #3424), 0d4jl (0.17 #301, 0.05 #2489, 0.04 #2733), 01hc9_ (0.17 #414, 0.05 #2602, 0.03 #2359), 01vdrw (0.17 #452, 0.03 #2397, 0.02 #2640), 0bv7t (0.17 #348, 0.03 #2293, 0.02 #2536), 01d494 (0.17 #271, 0.03 #2216, 0.02 #2459), 03gkn5 (0.14 #1033, 0.14 #2249, 0.13 #3466) >> Best rule #1113 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02hcxm; >> query: (?x12284, 03s9v) <- company(?x2998, ?x12284), ?x2998 = 021q0l, company(?x3335, ?x12284) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2268 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 09c7w0; *> query: (?x12284, 0b78hw) <- contains(?x12221, ?x12284), company(?x2998, ?x12284), company(?x3335, ?x12284) *> conf = 0.03 ranks of expected_values: 62 EVAL 0194_r company! 0b78hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 92.000 92.000 0.286 http://example.org/people/person/employment_history./business/employment_tenure/company #6727-03b79 PRED entity: 03b79 PRED relation: contains! PRED expected values: 02j9z => 84 concepts (58 used for prediction) PRED predicted values (max 10 best out of 55): 02j9z (0.62 #7218, 0.60 #9020, 0.50 #924), 02qkt (0.48 #30909, 0.47 #8089, 0.46 #28213), 07c5l (0.39 #23764, 0.22 #15676, 0.21 #12081), 0j0k (0.25 #30940, 0.24 #14761, 0.22 #39047), 04wsz (0.24 #14882, 0.12 #24766, 0.11 #28365), 0dg3n1 (0.23 #49629, 0.08 #27123, 0.08 #38824), 05nrg (0.20 #4159, 0.17 #50041, 0.17 #5958), 04pnx (0.19 #23794, 0.17 #15706, 0.09 #40895), 059g4 (0.17 #5854, 0.13 #18439, 0.13 #23832), 06n3y (0.16 #24095, 0.12 #22295, 0.09 #39395) >> Best rule #7218 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0h7x; >> query: (?x3142, 02j9z) <- nationality(?x11479, ?x3142), nationality(?x11479, ?x10003), nationality(?x11479, ?x8687), ?x10003 = 084n_, combatants(?x5114, ?x8687), contains(?x455, ?x8687), ?x5114 = 05vz3zq, gender(?x11479, ?x231) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03b79 contains! 02j9z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 58.000 0.625 http://example.org/location/location/contains #6726-0mwq7 PRED entity: 0mwq7 PRED relation: currency PRED expected values: 09nqf => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.85 #20, 0.85 #19, 0.84 #24) >> Best rule #20 for best value: >> intensional similarity = 5 >> extensional distance = 243 >> proper extension: 0mkqr; >> query: (?x13304, ?x170) <- adjoins(?x12233, ?x13304), adjoins(?x11384, ?x13304), time_zones(?x13304, ?x2674), second_level_divisions(?x94, ?x12233), currency(?x11384, ?x170) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwq7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.849 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #6725-07_l6 PRED entity: 07_l6 PRED relation: family PRED expected values: 0l14_3 => 91 concepts (58 used for prediction) PRED predicted values (max 10 best out of 127): 0fx80y (0.50 #53, 0.44 #558, 0.43 #493), 01vj9c (0.40 #185, 0.33 #312, 0.33 #7), 0342h (0.30 #631, 0.29 #440, 0.22 #537), 0l14md (0.27 #733, 0.18 #1842, 0.17 #339), 085jw (0.25 #876, 0.25 #524, 0.25 #83), 05148p4 (0.21 #901, 0.20 #672, 0.20 #160), 0l14_3 (0.20 #175, 0.17 #723, 0.17 #359), 02qjv (0.17 #313, 0.14 #478, 0.10 #606), 026t6 (0.17 #336, 0.10 #599, 0.09 #730), 018vs (0.15 #626, 0.07 #920, 0.07 #332) >> Best rule #53 for best value: >> intensional similarity = 19 >> extensional distance = 2 >> proper extension: 04rzd; >> query: (?x3296, 0fx80y) <- role(?x3214, ?x3296), role(?x960, ?x3296), role(?x75, ?x3296), role(?x3296, ?x8014), role(?x3296, ?x316), role(?x3296, ?x212), instrumentalists(?x3296, ?x7559), ?x75 = 07y_7, role(?x4595, ?x8014), role(?x4206, ?x8014), role(?x8014, ?x2785), ?x316 = 05r5c, ?x960 = 04q7r, ?x3214 = 02snj9, ?x4206 = 028qdb, ?x2785 = 0jtg0, artists(?x597, ?x7559), ?x212 = 026t6, ?x4595 = 023l9y >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #175 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 3 *> proper extension: 06ch55; *> query: (?x3296, 0l14_3) <- instrumentalists(?x3296, ?x7937), instrumentalists(?x3296, ?x7386), artist(?x9492, ?x7937), profession(?x7937, ?x131), award_nominee(?x2575, ?x7937), artists(?x1928, ?x7937), ?x9492 = 03mp8k, organization(?x7386, ?x8603), instrumentalists(?x227, ?x7937), origin(?x7386, ?x13577), role(?x219, ?x227), role(?x74, ?x227), group(?x227, ?x9791), group(?x227, ?x2005), ?x2005 = 05k79, ?x9791 = 016l09 *> conf = 0.20 ranks of expected_values: 7 EVAL 07_l6 family 0l14_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 91.000 58.000 0.500 http://example.org/music/instrument/family #6724-029jt9 PRED entity: 029jt9 PRED relation: film! PRED expected values: 0161h5 => 92 concepts (39 used for prediction) PRED predicted values (max 10 best out of 974): 02cqbx (0.40 #64641, 0.39 #79239, 0.39 #60468), 05683cn (0.40 #64641, 0.39 #79239, 0.39 #60468), 071jv5 (0.40 #64641, 0.39 #79239, 0.39 #60468), 057bc6m (0.40 #64641, 0.39 #79239, 0.39 #60468), 0dck27 (0.40 #64641, 0.39 #79239, 0.29 #62554), 02w670 (0.39 #60468, 0.38 #79238, 0.38 #72980), 07h1tr (0.39 #60468, 0.38 #79238, 0.38 #72980), 0c6g29 (0.39 #60468, 0.38 #79238, 0.38 #72980), 05_2h8 (0.39 #60468, 0.38 #79238, 0.38 #72980), 01v5h (0.16 #22937, 0.15 #33363, 0.14 #8341) >> Best rule #64641 for best value: >> intensional similarity = 4 >> extensional distance = 485 >> proper extension: 01xbxn; >> query: (?x8941, ?x2110) <- genre(?x8941, ?x53), production_companies(?x8941, ?x902), award_winner(?x8941, ?x2110), film_release_distribution_medium(?x8941, ?x81) >> conf = 0.40 => this is the best rule for 5 predicted values *> Best rule #8085 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 02r_pp; *> query: (?x8941, 0161h5) <- genre(?x8941, ?x53), costume_design_by(?x8941, ?x2110), film(?x8942, ?x8941), film_sets_designed(?x2716, ?x8941) *> conf = 0.04 ranks of expected_values: 195 EVAL 029jt9 film! 0161h5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 92.000 39.000 0.403 http://example.org/film/actor/film./film/performance/film #6723-01dvtx PRED entity: 01dvtx PRED relation: influenced_by PRED expected values: 02wh0 => 137 concepts (70 used for prediction) PRED predicted values (max 10 best out of 302): 0j3v (0.50 #1773, 0.38 #3057, 0.30 #2201), 043s3 (0.41 #4395, 0.33 #1399, 0.32 #5680), 02wh0 (0.38 #3374, 0.33 #378, 0.29 #5086), 03_87 (0.38 #3196, 0.26 #21192, 0.15 #3624), 042q3 (0.33 #2072, 0.33 #1644, 0.30 #2500), 048cl (0.33 #231, 0.29 #4511, 0.27 #5796), 04hcw (0.33 #221, 0.24 #4501, 0.20 #2361), 03f0324 (0.33 #151, 0.23 #3575, 0.23 #13429), 081k8 (0.33 #2723, 0.23 #3151, 0.20 #5291), 02ln1 (0.33 #276, 0.20 #2416, 0.18 #4984) >> Best rule #1773 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0jcx; >> query: (?x4003, 0j3v) <- company(?x4003, ?x2313), influenced_by(?x4003, ?x12259), profession(?x4003, ?x353), ?x12259 = 015n8 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3374 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 099bk; 06whf; 0d5_f; 03f0324; 013pp3; 040_t; 06hgj; 042q3; *> query: (?x4003, 02wh0) <- student(?x8221, ?x4003), influenced_by(?x4003, ?x3993), interests(?x3993, ?x8405), nationality(?x3993, ?x1264) *> conf = 0.38 ranks of expected_values: 3 EVAL 01dvtx influenced_by 02wh0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 137.000 70.000 0.500 http://example.org/influence/influence_node/influenced_by #6722-015p3p PRED entity: 015p3p PRED relation: film PRED expected values: 07q1m => 85 concepts (60 used for prediction) PRED predicted values (max 10 best out of 360): 03s9kp (0.58 #49766, 0.42 #78213, 0.41 #63990), 08bytj (0.58 #49766, 0.38 #53321, 0.38 #63989), 026lgs (0.33 #934, 0.04 #14217, 0.03 #55099), 023g6w (0.17 #1471, 0.04 #14217, 0.03 #55099), 017gl1 (0.17 #142, 0.03 #26798, 0.02 #30354), 035s95 (0.17 #339, 0.02 #5670, 0.02 #12778), 0ndwt2w (0.17 #996, 0.01 #27652, 0.01 #31208), 0symg (0.17 #1690, 0.01 #3467), 02z3r8t (0.17 #107, 0.01 #48095, 0.01 #26763), 065_cjc (0.17 #1188, 0.01 #6519, 0.01 #11850) >> Best rule #49766 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 049tjg; >> query: (?x6221, ?x7756) <- nominated_for(?x6221, ?x7756), film(?x6221, ?x224) >> conf = 0.58 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 015p3p film 07q1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 60.000 0.585 http://example.org/film/actor/film./film/performance/film #6721-0dlglj PRED entity: 0dlglj PRED relation: film PRED expected values: 08nvyr => 81 concepts (56 used for prediction) PRED predicted values (max 10 best out of 303): 0cc7hmk (0.44 #3863, 0.06 #17862, 0.03 #100034), 01chpn (0.29 #1107, 0.03 #100034, 0.03 #30368), 04xg2f (0.14 #1552, 0.12 #5124, 0.03 #16075), 0284b56 (0.14 #982, 0.07 #2768, 0.06 #17862), 02jkkv (0.14 #1551, 0.07 #3337, 0.03 #30368), 07jxpf (0.14 #681, 0.07 #2467, 0.03 #30368), 0djlxb (0.14 #533, 0.06 #4105, 0.06 #17862), 048scx (0.14 #156, 0.06 #3728, 0.06 #17862), 0c0zq (0.14 #1560, 0.06 #5132, 0.03 #100034), 043mk4y (0.14 #1350, 0.06 #4922, 0.03 #100034) >> Best rule #3863 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 048lv; 016ywr; 0c6g1l; 024n3z; 01chc7; 018009; 0884fm; 03l3jy; 02c6pq; 02js9p; ... >> query: (?x1596, 0cc7hmk) <- nominated_for(?x1596, ?x1064), award_winner(?x5999, ?x1596), ?x5999 = 0d02km >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dlglj film 08nvyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 56.000 0.438 http://example.org/film/actor/film./film/performance/film #6720-047rgpy PRED entity: 047rgpy PRED relation: specialization_of! PRED expected values: 0dz3r => 43 concepts (35 used for prediction) PRED predicted values (max 10 best out of 107): 04s84y (0.14 #433, 0.03 #543, 0.02 #653), 028sdw (0.14 #432, 0.03 #542, 0.02 #652), 0fgsq2 (0.14 #428, 0.03 #538, 0.02 #648), 05798 (0.14 #418, 0.03 #528, 0.02 #638), 011s0 (0.14 #357, 0.03 #467, 0.02 #577), 01xr66 (0.04 #591, 0.04 #701, 0.04 #924), 07s467s (0.03 #1110, 0.03 #1330, 0.03 #1771), 0nbcg (0.03 #455, 0.02 #883, 0.02 #565), 0dxtg (0.03 #447, 0.02 #883, 0.02 #557), 064xm0 (0.03 #478, 0.02 #588, 0.02 #698) >> Best rule #433 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 05sxg2; 0dz3r; 012t_z; 03gjzk; >> query: (?x12718, 04s84y) <- profession(?x9623, ?x12718), profession(?x9373, ?x12718), award_winner(?x827, ?x9623), ?x9373 = 01vhrz, award(?x9623, ?x1232), type_of_union(?x9623, ?x566) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #771 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 48 *> proper extension: 01bs9f; *> query: (?x12718, ?x131) <- profession(?x9623, ?x12718), profession(?x9373, ?x12718), profession(?x7259, ?x12718), profession(?x6792, ?x12718), award_winner(?x827, ?x9623), organizations_founded(?x9373, ?x1954), award_winner(?x2139, ?x6792), profession(?x7259, ?x131) *> conf = 0.02 ranks of expected_values: 78 EVAL 047rgpy specialization_of! 0dz3r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 43.000 35.000 0.143 http://example.org/people/profession/specialization_of #6719-06crk PRED entity: 06crk PRED relation: student! PRED expected values: 019v9k => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 18): 014mlp (0.70 #976, 0.52 #1052, 0.52 #767), 0bkj86 (0.30 #123, 0.29 #47, 0.22 #85), 019v9k (0.27 #295, 0.22 #238, 0.21 #980), 02h4rq6 (0.25 #3, 0.17 #22, 0.16 #345), 02mjs7 (0.25 #5, 0.17 #24, 0.04 #347), 01rr_d (0.20 #130, 0.15 #187, 0.09 #301), 04zx3q1 (0.20 #344, 0.14 #362, 0.14 #478), 016t_3 (0.14 #362, 0.11 #61, 0.09 #308), 03mkk4 (0.14 #362, 0.11 #70, 0.07 #983), 013zdg (0.14 #362, 0.10 #122, 0.10 #522) >> Best rule #976 for best value: >> intensional similarity = 5 >> extensional distance = 160 >> proper extension: 0bkg4; 04z_x4v; 0bq4j6; >> query: (?x6342, 014mlp) <- student(?x3437, ?x6342), institution(?x3437, ?x11614), institution(?x3437, ?x11607), ?x11614 = 07tk7, ?x11607 = 02hwww >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #295 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 084w8; 04jzj; 01d494; 03gyh_z; 0zm1; 036jb; 02t_w8; 0282x; 01gj8_; 0239zv; ... *> query: (?x6342, 019v9k) <- student(?x3437, ?x6342), people(?x268, ?x6342), type_of_union(?x6342, ?x566), nationality(?x6342, ?x94) *> conf = 0.27 ranks of expected_values: 3 EVAL 06crk student! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 150.000 150.000 0.704 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #6718-01v3s2_ PRED entity: 01v3s2_ PRED relation: instrumentalists! PRED expected values: 026t6 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 38): 026t6 (0.70 #359, 0.04 #2495, 0.04 #7121), 02hnl (0.61 #392, 0.06 #2172, 0.06 #2350), 0l14md (0.39 #364, 0.04 #2500, 0.04 #2322), 05r5c (0.30 #365, 0.17 #2501, 0.17 #2145), 0342h (0.26 #361, 0.26 #1429, 0.26 #2319), 05148p4 (0.26 #378, 0.12 #2514, 0.12 #2336), 018vs (0.22 #370, 0.11 #2328, 0.11 #2506), 06ncr (0.09 #402, 0.03 #2182, 0.03 #2360), 042v_gx (0.09 #366, 0.02 #455, 0.01 #2502), 013y1f (0.09 #389, 0.02 #2525, 0.02 #2169) >> Best rule #359 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0fp_v1x; 02pzc4; 04x4s2; 01v0fn1; 018y81; 01vng3b; 024dw0; 043c4j; 01wt4wc; 02mx98; ... >> query: (?x905, 026t6) <- place_of_birth(?x905, ?x12883), profession(?x905, ?x1359), ?x1359 = 09lbv >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v3s2_ instrumentalists! 026t6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.696 http://example.org/music/instrument/instrumentalists #6717-0d0x8 PRED entity: 0d0x8 PRED relation: district_represented! PRED expected values: 01gtcc => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 27): 024tkd (0.74 #508, 0.69 #589, 0.64 #454), 02bn_p (0.74 #490, 0.67 #571, 0.65 #652), 02bp37 (0.63 #493, 0.60 #574, 0.59 #439), 02bqm0 (0.61 #503, 0.57 #449, 0.55 #1054), 02bqmq (0.57 #497, 0.55 #1054, 0.55 #443), 01gtcc (0.55 #1054, 0.52 #175, 0.46 #1217), 02bqn1 (0.55 #1054, 0.46 #573, 0.46 #492), 02gkzs (0.55 #1054, 0.46 #1217, 0.43 #501), 02cg7g (0.55 #1054, 0.44 #583, 0.43 #502), 03rtmz (0.55 #1054, 0.30 #496, 0.30 #172) >> Best rule #508 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0gj4fx; >> query: (?x3038, 024tkd) <- contains(?x3038, ?x2277), district_represented(?x845, ?x3038), ?x845 = 07p__7 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1054 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 65 *> proper extension: 0h5qxv; *> query: (?x3038, ?x355) <- district_represented(?x952, ?x3038), legislative_sessions(?x355, ?x952) *> conf = 0.55 ranks of expected_values: 6 EVAL 0d0x8 district_represented! 01gtcc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 196.000 196.000 0.739 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #6716-017r2 PRED entity: 017r2 PRED relation: type_of_union PRED expected values: 04ztj => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.82 #81, 0.82 #133, 0.81 #89), 01g63y (0.55 #181, 0.55 #186, 0.19 #34), 01bl8s (0.08 #47, 0.03 #79, 0.03 #43), 0jgjn (0.07 #24, 0.03 #40) >> Best rule #81 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 04bdlg; >> query: (?x1645, 04ztj) <- award(?x1645, ?x1869), people(?x4322, ?x1645), people(?x5540, ?x1645), location(?x1645, ?x12866) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017r2 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.825 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6715-01nx_8 PRED entity: 01nx_8 PRED relation: place_of_birth PRED expected values: 0lhql => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 90): 013n0n (0.40 #2115, 0.40 #1923, 0.33 #2820), 0cr3d (0.25 #798, 0.22 #2209, 0.12 #3620), 0cc56 (0.25 #33, 0.09 #2854, 0.07 #5672), 02_286 (0.13 #5658, 0.09 #15519, 0.09 #14110), 0f94t (0.11 #2143, 0.09 #2849, 0.06 #3554), 0hptm (0.11 #2340, 0.09 #3046, 0.06 #3751), 01531 (0.11 #2220, 0.06 #3631, 0.06 #6448), 06wxw (0.09 #2978, 0.06 #3683, 0.05 #4388), 0rh6k (0.06 #3528, 0.05 #4233, 0.04 #4937), 05qtj (0.06 #6510, 0.03 #8624, 0.02 #7215) >> Best rule #2115 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01j851; >> query: (?x10631, ?x12222) <- location(?x10631, ?x12222), type_of_union(?x10631, ?x566), ?x12222 = 013n0n, ?x566 = 04ztj >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nx_8 place_of_birth 0lhql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 106.000 0.400 http://example.org/people/person/place_of_birth #6714-0f0p0 PRED entity: 0f0p0 PRED relation: award PRED expected values: 0fbvqf => 125 concepts (104 used for prediction) PRED predicted values (max 10 best out of 292): 027c95y (0.70 #22299, 0.69 #29197, 0.69 #33656), 02z13jg (0.70 #22299, 0.69 #29197, 0.69 #33656), 04kxsb (0.51 #1752, 0.50 #940, 0.44 #533), 09sb52 (0.50 #854, 0.38 #13421, 0.38 #13826), 0gqy2 (0.44 #572, 0.40 #979, 0.35 #1791), 02w9sd7 (0.40 #985, 0.36 #1797, 0.32 #578), 040njc (0.34 #2040, 0.25 #8929, 0.24 #7713), 0gs9p (0.33 #2111, 0.27 #7784, 0.26 #9000), 027dtxw (0.32 #411, 0.17 #4, 0.16 #1630), 0gqwc (0.30 #74, 0.11 #5754, 0.09 #19533) >> Best rule #22299 for best value: >> intensional similarity = 4 >> extensional distance = 1098 >> proper extension: 01j4ls; 01w60_p; 07ss8_; 047sxrj; 016sp_; 01wyz92; 016h4r; 01svw8n; 047c9l; 01k_mc; ... >> query: (?x1021, ?x591) <- award_winner(?x591, ?x1021), profession(?x1021, ?x524), film(?x1021, ?x2729), award(?x123, ?x591) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #37710 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1980 *> proper extension: 0frnff; *> query: (?x1021, ?x435) <- gender(?x1021, ?x231), nominated_for(?x1021, ?x6415), nominated_for(?x435, ?x6415) *> conf = 0.13 ranks of expected_values: 53 EVAL 0f0p0 award 0fbvqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 125.000 104.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6713-0dw4b0 PRED entity: 0dw4b0 PRED relation: film_release_region PRED expected values: 0b90_r 0154j 0d0vqn => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 257): 0d0vqn (0.90 #514, 0.89 #1183, 0.89 #1016), 06mkj (0.86 #736, 0.86 #235, 0.85 #569), 059j2 (0.85 #541, 0.83 #207, 0.82 #1210), 05r4w (0.84 #1174, 0.82 #171, 0.82 #672), 03rjj (0.83 #677, 0.82 #510, 0.82 #1179), 02vzc (0.83 #1232, 0.82 #730, 0.79 #1065), 0k6nt (0.82 #1203, 0.78 #701, 0.77 #200), 03h64 (0.80 #245, 0.78 #579, 0.76 #746), 0154j (0.80 #509, 0.73 #1178, 0.72 #676), 0jgd (0.78 #507, 0.78 #1176, 0.77 #173) >> Best rule #514 for best value: >> intensional similarity = 4 >> extensional distance = 169 >> proper extension: 0fq27fp; >> query: (?x10346, 0d0vqn) <- film_crew_role(?x10346, ?x468), film_release_region(?x10346, ?x390), ?x390 = 0chghy, ?x468 = 02r96rf >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9, 16 EVAL 0dw4b0 film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dw4b0 film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 81.000 81.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dw4b0 film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 81.000 81.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6712-01n7q PRED entity: 01n7q PRED relation: contains PRED expected values: 06xpp7 0r80l 07vfz 0r15k 0l2mg 0qxzd 0r0ls 0dwh5 => 155 concepts (123 used for prediction) PRED predicted values (max 10 best out of 2798): 07bcn (0.86 #219697, 0.84 #166853, 0.83 #133485), 06pwq (0.84 #166853, 0.83 #133485, 0.83 #144608), 0d6lp (0.84 #166853, 0.83 #133485, 0.83 #144608), 0r02m (0.84 #166853, 0.83 #133485, 0.83 #144608), 0r5lz (0.84 #166853, 0.83 #133485, 0.83 #144608), 0mzww (0.84 #166853, 0.83 #133485, 0.83 #144608), 0b2ds (0.84 #166853, 0.83 #133485, 0.83 #144608), 0r5y9 (0.84 #166853, 0.83 #133485, 0.83 #144608), 0r2bv (0.84 #166853, 0.83 #133485, 0.83 #144608), 0r771 (0.84 #166853, 0.83 #133485, 0.83 #144608) >> Best rule #219697 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 0mczk; 0gxmj; >> query: (?x1227, ?x5893) <- contains(?x1227, ?x191), contains(?x94, ?x1227), capital(?x1227, ?x5893) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #180762 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 01vsb_; *> query: (?x1227, ?x3794) <- contains(?x94, ?x1227), state_province_region(?x3793, ?x1227), citytown(?x3793, ?x3794) *> conf = 0.70 ranks of expected_values: 26, 42, 124, 500, 729, 735, 2124 EVAL 01n7q contains 0dwh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 123.000 0.862 http://example.org/location/location/contains EVAL 01n7q contains 0r0ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 155.000 123.000 0.862 http://example.org/location/location/contains EVAL 01n7q contains 0qxzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 123.000 0.862 http://example.org/location/location/contains EVAL 01n7q contains 0l2mg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 155.000 123.000 0.862 http://example.org/location/location/contains EVAL 01n7q contains 0r15k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 155.000 123.000 0.862 http://example.org/location/location/contains EVAL 01n7q contains 07vfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 123.000 0.862 http://example.org/location/location/contains EVAL 01n7q contains 0r80l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 155.000 123.000 0.862 http://example.org/location/location/contains EVAL 01n7q contains 06xpp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 155.000 123.000 0.862 http://example.org/location/location/contains #6711-019kyn PRED entity: 019kyn PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.89 #6, 0.86 #1, 0.81 #164), 02nxhr (0.10 #2, 0.08 #27, 0.06 #17), 07c52 (0.08 #28, 0.04 #53, 0.03 #48), 07z4p (0.06 #20, 0.05 #30, 0.05 #5) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 016ztl; >> query: (?x4669, 029j_) <- music(?x4669, ?x1934), production_companies(?x4669, ?x3920), ?x3920 = 09b3v, genre(?x4669, ?x53) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019kyn film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6710-02_7t PRED entity: 02_7t PRED relation: major_field_of_study! PRED expected values: 01jtp7 07wrz 03ksy 0b1xl 03tw2s 05x_5 01jt2w 02nvg1 021996 => 86 concepts (77 used for prediction) PRED predicted values (max 10 best out of 580): 03ksy (0.75 #8012, 0.60 #5903, 0.57 #6958), 025v3k (0.75 #8026, 0.60 #5917, 0.56 #8553), 05zl0 (0.75 #8107, 0.60 #5998, 0.53 #11798), 0j_sncb (0.67 #8513, 0.57 #6405, 0.50 #11149), 07ccs (0.67 #8645, 0.50 #8118, 0.50 #2847), 02bqy (0.62 #8083, 0.60 #5974, 0.57 #7029), 09kvv (0.62 #7946, 0.60 #5837, 0.40 #11637), 07tds (0.62 #8054, 0.58 #12800, 0.56 #8581), 07t90 (0.62 #8053, 0.50 #11216, 0.47 #11744), 0lfgr (0.62 #7949, 0.47 #11640, 0.44 #8476) >> Best rule #8012 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 02j62; 01540; >> query: (?x7134, 03ksy) <- major_field_of_study(?x10910, ?x7134), major_field_of_study(?x6912, ?x7134), major_field_of_study(?x3813, ?x7134), major_field_of_study(?x2959, ?x7134), ?x6912 = 0gl5_, major_field_of_study(?x7134, ?x1527), contains(?x94, ?x2959), major_field_of_study(?x10910, ?x6760), ?x3813 = 07vfj, ?x6760 = 0w7c >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 20, 27, 51, 62, 85, 104, 133, 150 EVAL 02_7t major_field_of_study! 021996 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 86.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02_7t major_field_of_study! 02nvg1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 86.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02_7t major_field_of_study! 01jt2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 86.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02_7t major_field_of_study! 05x_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 86.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02_7t major_field_of_study! 03tw2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 86.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02_7t major_field_of_study! 0b1xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 86.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02_7t major_field_of_study! 03ksy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02_7t major_field_of_study! 07wrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 86.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02_7t major_field_of_study! 01jtp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 86.000 77.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #6709-0gqxm PRED entity: 0gqxm PRED relation: nominated_for PRED expected values: 01vksx 02z0f6l 05nlx4 01hq1 05y0cr 0dcz8_ => 46 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1234): 017gl1 (0.83 #4732, 0.77 #18445, 0.77 #18444), 0gmgwnv (0.67 #2460, 0.61 #5535, 0.48 #7073), 09gq0x5 (0.65 #4851, 0.48 #6389, 0.44 #1776), 0y_9q (0.61 #5407, 0.48 #6945, 0.33 #2332), 011yqc (0.61 #4808, 0.44 #1733, 0.39 #6346), 049xgc (0.61 #5450, 0.43 #6988, 0.40 #3913), 0gmcwlb (0.61 #4782, 0.43 #6320, 0.32 #14005), 01mgw (0.61 #5720, 0.40 #4183, 0.39 #7258), 03hmt9b (0.61 #5180, 0.35 #6718, 0.30 #14403), 09q5w2 (0.57 #4751, 0.48 #6289, 0.30 #7826) >> Best rule #4732 for best value: >> intensional similarity = 6 >> extensional distance = 21 >> proper extension: 02r0csl; 03hkv_r; 0gq_v; 0p9sw; 0gr4k; 02r22gf; 02hsq3m; 09sb52; 099tbz; 0l8z1; ... >> query: (?x3458, 017gl1) <- nominated_for(?x3458, ?x11276), nominated_for(?x3458, ?x4610), ceremony(?x3458, ?x78), award(?x2871, ?x3458), ?x4610 = 017jd9, film(?x1371, ?x11276) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #115 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 05zvj3m; *> query: (?x3458, 01vksx) <- nominated_for(?x3458, ?x10274), nominated_for(?x3458, ?x9424), nominated_for(?x3458, ?x174), genre(?x9424, ?x53), ?x10274 = 0d87hc, films(?x9829, ?x174) *> conf = 0.50 ranks of expected_values: 28, 29, 197, 471, 483, 1170 EVAL 0gqxm nominated_for 0dcz8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 14.000 0.826 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqxm nominated_for 05y0cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 14.000 0.826 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqxm nominated_for 01hq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 14.000 0.826 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqxm nominated_for 05nlx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 46.000 14.000 0.826 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqxm nominated_for 02z0f6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 14.000 0.826 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqxm nominated_for 01vksx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 46.000 14.000 0.826 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6708-0drc1 PRED entity: 0drc1 PRED relation: award_winner! PRED expected values: 02qkk9_ => 121 concepts (112 used for prediction) PRED predicted values (max 10 best out of 321): 0gqz2 (0.46 #1713, 0.46 #7708, 0.43 #1714), 0c4z8 (0.46 #1713, 0.46 #7708, 0.43 #8994), 0l8z1 (0.44 #1778, 0.33 #64, 0.25 #6486), 04mqgr (0.31 #1009, 0.21 #1437, 0.17 #3579), 054ks3 (0.29 #1425, 0.27 #12560, 0.18 #8705), 025m8y (0.24 #12950, 0.23 #9950, 0.18 #15091), 054krc (0.21 #1372, 0.21 #12938, 0.21 #3942), 03x3wf (0.20 #493, 0.11 #10343, 0.10 #7344), 02v1m7 (0.20 #541, 0.05 #9535, 0.05 #13819), 02f73p (0.20 #610, 0.05 #9604, 0.04 #13888) >> Best rule #1713 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 02dbp7; 01mh8zn; >> query: (?x8275, ?x1232) <- award(?x8275, ?x3467), award(?x8275, ?x1323), award(?x8275, ?x1232), ?x1323 = 0gqz2, ?x3467 = 02h3d1 >> conf = 0.46 => this is the best rule for 2 predicted values *> Best rule #1518 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 02dbp7; 01mh8zn; *> query: (?x8275, 02qkk9_) <- award(?x8275, ?x3467), award(?x8275, ?x1323), ?x1323 = 0gqz2, ?x3467 = 02h3d1 *> conf = 0.07 ranks of expected_values: 59 EVAL 0drc1 award_winner! 02qkk9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 121.000 112.000 0.462 http://example.org/award/award_category/winners./award/award_honor/award_winner #6707-05b4w PRED entity: 05b4w PRED relation: olympics PRED expected values: 0blg2 01f1kd 01f1jf => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 15): 0jdk_ (0.83 #40, 0.75 #70, 0.75 #10), 0sx7r (0.74 #226, 0.59 #1491, 0.58 #1702), 0124ld (0.74 #226, 0.59 #1491, 0.58 #1702), 0l998 (0.67 #4, 0.57 #34, 0.54 #94), 0jkvj (0.58 #13, 0.54 #28, 0.52 #163), 0lbd9 (0.54 #26, 0.53 #71, 0.52 #252), 0lbbj (0.50 #23, 0.49 #98, 0.48 #234), 0l6vl (0.50 #2, 0.43 #152, 0.41 #228), 0lk8j (0.50 #6, 0.40 #96, 0.37 #262), 0sx8l (0.44 #317, 0.28 #588, 0.27 #1355) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 077qn; >> query: (?x2513, 0jdk_) <- film_release_region(?x6527, ?x2513), country(?x359, ?x2513), ?x6527 = 0gfh84d >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #263 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 44 *> proper extension: 0195pd; *> query: (?x2513, 0blg2) <- film_release_region(?x1108, ?x2513), ?x1108 = 0jjy0 *> conf = 0.43 ranks of expected_values: 11, 12, 13 EVAL 05b4w olympics 01f1jf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 174.000 174.000 0.833 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 05b4w olympics 01f1kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 174.000 174.000 0.833 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 05b4w olympics 0blg2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 174.000 174.000 0.833 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #6706-01sxly PRED entity: 01sxly PRED relation: produced_by PRED expected values: 0d_skg => 86 concepts (49 used for prediction) PRED predicted values (max 10 best out of 112): 041c4 (0.25 #5436, 0.25 #7377, 0.25 #3495), 0fvf9q (0.14 #6, 0.04 #1947, 0.04 #1557), 04t38b (0.14 #162, 0.02 #939, 0.02 #4044), 03fqv5 (0.14 #382), 06s26c (0.14 #344), 01qg7c (0.14 #324), 0grrq8 (0.14 #552, 0.03 #2105, 0.02 #1715), 01gbn6 (0.13 #13589, 0.12 #3105, 0.12 #776), 04yt7 (0.13 #13589, 0.12 #3105, 0.12 #776), 0hwqz (0.13 #13589, 0.12 #3105, 0.12 #776) >> Best rule #5436 for best value: >> intensional similarity = 4 >> extensional distance = 432 >> proper extension: 08hmch; 0g5pv3; 065zlr; 0bbw2z6; 035w2k; 047vnkj; 0b6l1st; 0291hr; >> query: (?x582, ?x4988) <- nominated_for(?x5884, ?x582), written_by(?x582, ?x4988), film(?x788, ?x582), award_winner(?x2880, ?x5884) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3105 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 318 *> proper extension: 01gglm; *> query: (?x582, ?x4297) <- nominated_for(?x4297, ?x582), written_by(?x582, ?x4988), titles(?x4150, ?x582), genre(?x1133, ?x4150) *> conf = 0.12 ranks of expected_values: 11 EVAL 01sxly produced_by 0d_skg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 86.000 49.000 0.254 http://example.org/film/film/produced_by #6705-04mby PRED entity: 04mby PRED relation: story_by! PRED expected values: 027fwmt => 98 concepts (82 used for prediction) PRED predicted values (max 10 best out of 242): 02q_4ph (0.06 #832, 0.06 #1518, 0.05 #2204), 0bc1yhb (0.05 #532, 0.04 #1218, 0.04 #1561), 05qbckf (0.05 #403, 0.04 #1089, 0.04 #1432), 08ct6 (0.05 #513, 0.04 #1885, 0.02 #856), 063hp4 (0.04 #919, 0.04 #1262, 0.04 #1605), 063y9fp (0.04 #975, 0.04 #1318, 0.04 #1661), 0g_zyp (0.04 #991, 0.04 #1334, 0.04 #1677), 0ccd3x (0.04 #845, 0.04 #1188, 0.04 #1531), 014nq4 (0.04 #791, 0.02 #448, 0.02 #1134), 01cmp9 (0.04 #1586, 0.04 #1929, 0.03 #2272) >> Best rule #832 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 08nz99; >> query: (?x9467, 02q_4ph) <- profession(?x9467, ?x319), place_of_death(?x9467, ?x682), story_by(?x1804, ?x9467), type_of_union(?x9467, ?x566) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04mby story_by! 027fwmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 82.000 0.059 http://example.org/film/film/story_by #6704-03np3w PRED entity: 03np3w PRED relation: type_of_union PRED expected values: 04ztj => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #5, 0.71 #97, 0.71 #125), 01g63y (0.14 #66, 0.14 #26, 0.14 #74) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 415 >> proper extension: 06sn8m; >> query: (?x3522, 04ztj) <- award(?x3522, ?x678), actor(?x10731, ?x3522), student(?x1276, ?x3522) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03np3w type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.724 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6703-0chghy PRED entity: 0chghy PRED relation: currency PRED expected values: 0kz1h => 232 concepts (232 used for prediction) PRED predicted values (max 10 best out of 4): 0ptk_ (0.08 #26, 0.03 #113, 0.02 #152), 01nv4h (0.07 #40, 0.03 #79, 0.03 #94), 02l6h (0.05 #549, 0.05 #303, 0.05 #312), 0kz1h (0.03 #562) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01zv_; >> query: (?x390, 0ptk_) <- contains(?x390, ?x5036), featured_film_locations(?x3573, ?x390), month(?x5036, ?x1459) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #562 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 210 *> proper extension: 0plyy; 0rt80; *> query: (?x390, ?x7888) <- contains(?x390, ?x2013), location(?x4468, ?x390), currency(?x2013, ?x7888) *> conf = 0.03 ranks of expected_values: 4 EVAL 0chghy currency 0kz1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 232.000 232.000 0.083 http://example.org/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency #6702-02fcs2 PRED entity: 02fcs2 PRED relation: award_winner! PRED expected values: 05h5nb8 => 72 concepts (57 used for prediction) PRED predicted values (max 10 best out of 247): 0gq9h (0.31 #20700, 0.31 #20699, 0.31 #12074), 04dn09n (0.31 #20700, 0.31 #20699, 0.31 #12074), 0gr51 (0.31 #20700, 0.31 #20699, 0.31 #12074), 02qyp19 (0.31 #20700, 0.31 #20699, 0.31 #12074), 05p1dby (0.31 #20700, 0.31 #20699, 0.31 #12074), 07bdd_ (0.31 #20700, 0.31 #20699, 0.31 #12074), 05f4m9q (0.31 #20700, 0.31 #20699, 0.31 #12074), 02n9nmz (0.31 #20700, 0.31 #20699, 0.31 #12074), 05b1610 (0.31 #20700, 0.31 #20699, 0.31 #12074), 03hl6lc (0.31 #20700, 0.31 #20699, 0.31 #12074) >> Best rule #20700 for best value: >> intensional similarity = 3 >> extensional distance = 2195 >> proper extension: 04lgymt; 0ggl02; 03j43; 0288fyj; 0hwd8; 01x15dc; 04n_g; 09b3v; 050t68; 0163m1; ... >> query: (?x2367, ?x1862) <- gender(?x2367, ?x231), award(?x2367, ?x1862), ?x231 = 05zppz >> conf = 0.31 => this is the best rule for 11 predicted values *> Best rule #319 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 272 *> proper extension: 0jf1b; 01q415; 01q4qv; 02bfxb; 013t9y; 043hg; 0dbc1s; 012vct; 0d608; 06t8b; ... *> query: (?x2367, 05h5nb8) <- profession(?x2367, ?x987), ?x987 = 0dxtg, written_by(?x2366, ?x2367) *> conf = 0.02 ranks of expected_values: 100 EVAL 02fcs2 award_winner! 05h5nb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 72.000 57.000 0.306 http://example.org/award/award_category/winners./award/award_honor/award_winner #6701-015n8 PRED entity: 015n8 PRED relation: influenced_by! PRED expected values: 040db 039n1 02wh0 => 187 concepts (99 used for prediction) PRED predicted values (max 10 best out of 439): 02wh0 (0.67 #2923, 0.50 #4914, 0.50 #4416), 0bk5r (0.50 #1198, 0.33 #2691, 0.33 #202), 07dnx (0.50 #1346, 0.33 #2839, 0.33 #350), 03f0324 (0.50 #1186, 0.33 #2679, 0.33 #190), 06whf (0.50 #1154, 0.33 #2647, 0.33 #158), 06c44 (0.50 #1244, 0.33 #2737, 0.33 #248), 039n1 (0.50 #2869, 0.30 #4860, 0.25 #4362), 0dzkq (0.40 #5596, 0.33 #5099, 0.32 #7096), 04411 (0.38 #4008, 0.25 #523, 0.22 #4480), 0x3r3 (0.38 #4215, 0.25 #730, 0.20 #7207) >> Best rule #2923 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0372p; 0420y; >> query: (?x12259, 02wh0) <- religion(?x12259, ?x2694), influenced_by(?x10111, ?x12259), company(?x10111, ?x13316), company(?x10111, ?x4096), ?x13316 = 01stzp, ?x4096 = 0pmcz >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 29 EVAL 015n8 influenced_by! 02wh0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 99.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 015n8 influenced_by! 039n1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 187.000 99.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 015n8 influenced_by! 040db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 187.000 99.000 0.667 http://example.org/influence/influence_node/influenced_by #6700-09sb52 PRED entity: 09sb52 PRED relation: award! PRED expected values: 04b2qn => 43 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1678): 0hfzr (0.53 #8501, 0.36 #10527, 0.33 #2432), 0gmcwlb (0.53 #8213, 0.36 #10239, 0.22 #16196), 07s846j (0.53 #8486, 0.36 #10512, 0.22 #16196), 0btpm6 (0.50 #3780, 0.38 #5803, 0.33 #2769), 0b44shh (0.50 #3547, 0.38 #5570, 0.33 #2536), 0_92w (0.50 #3134, 0.33 #2123, 0.25 #5157), 0gwjw0c (0.50 #3726, 0.33 #2715, 0.25 #5749), 0jym0 (0.50 #3229, 0.33 #2218, 0.25 #5252), 075wx7_ (0.50 #4201, 0.09 #10117, 0.07 #17211), 03nm_fh (0.50 #4508, 0.09 #10117, 0.05 #5057) >> Best rule #8501 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 099c8n; >> query: (?x704, 0hfzr) <- nominated_for(?x704, ?x4007), nominated_for(?x704, ?x2928), ?x2928 = 07024, ?x4007 = 03hmt9b >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #5841 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 02x73k6; 02x4w6g; 09sdmz; *> query: (?x704, 04b2qn) <- award(?x237, ?x704), award(?x92, ?x704), ?x92 = 02s2ft, award_winner(?x274, ?x237), award_winner(?x237, ?x3924) *> conf = 0.25 ranks of expected_values: 87 EVAL 09sb52 award! 04b2qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 43.000 20.000 0.529 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #6699-0c34mt PRED entity: 0c34mt PRED relation: genre PRED expected values: 01585b => 76 concepts (43 used for prediction) PRED predicted values (max 10 best out of 113): 02kdv5l (0.60 #344, 0.54 #2631, 0.51 #1375), 09blyk (0.56 #141, 0.25 #600, 0.21 #1285), 01hmnh (0.36 #241, 0.33 #13, 0.25 #1615), 03k9fj (0.36 #1610, 0.34 #2066, 0.33 #1952), 06n90 (0.33 #9, 0.30 #352, 0.26 #2639), 0fdjb (0.33 #156, 0.08 #843, 0.08 #615), 01585b (0.33 #161, 0.05 #620, 0.05 #734), 02l7c8 (0.33 #2872, 0.30 #3790, 0.29 #2299), 05p553 (0.32 #4813, 0.32 #3092, 0.31 #2404), 06nbt (0.22 #135, 0.07 #479, 0.04 #4810) >> Best rule #344 for best value: >> intensional similarity = 5 >> extensional distance = 61 >> proper extension: 0h95zbp; >> query: (?x3531, 02kdv5l) <- genre(?x3531, ?x812), film_crew_role(?x3531, ?x3197), ?x812 = 01jfsb, film_release_distribution_medium(?x3531, ?x81), ?x3197 = 02ynfr >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 01kjr0; *> query: (?x3531, 01585b) <- genre(?x3531, ?x604), genre(?x3531, ?x571), film(?x1289, ?x3531), ?x604 = 0lsxr, film_release_distribution_medium(?x3531, ?x81), ?x571 = 03npn *> conf = 0.33 ranks of expected_values: 7 EVAL 0c34mt genre 01585b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 76.000 43.000 0.603 http://example.org/film/film/genre #6698-057bc6m PRED entity: 057bc6m PRED relation: gender PRED expected values: 05zppz => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #29, 0.85 #23, 0.85 #31), 02zsn (0.25 #28, 0.25 #55, 0.25 #59) >> Best rule #29 for best value: >> intensional similarity = 2 >> extensional distance = 681 >> proper extension: 07c37; >> query: (?x8401, 05zppz) <- place_of_death(?x8401, ?x1523), place_of_birth(?x338, ?x1523) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 057bc6m gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.849 http://example.org/people/person/gender #6697-0fw3f PRED entity: 0fw3f PRED relation: category PRED expected values: 08mbj5d => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #25, 0.82 #35, 0.81 #33) >> Best rule #25 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 0ydpd; 02cl1; 03v_5; 0r5lz; 0rqyx; 0235l; 0l4vc; 010t4v; >> query: (?x12805, 08mbj5d) <- source(?x12805, ?x958), location_of_ceremony(?x566, ?x12805), administrative_division(?x12805, ?x3086), ?x958 = 0jbk9 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fw3f category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.882 http://example.org/common/topic/webpage./common/webpage/category #6696-012s1d PRED entity: 012s1d PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #126, 0.84 #105, 0.83 #21), 07z4p (0.09 #5, 0.06 #25, 0.04 #60), 07c52 (0.09 #3, 0.05 #33, 0.04 #43), 02nxhr (0.06 #57, 0.06 #27, 0.05 #37) >> Best rule #126 for best value: >> intensional similarity = 4 >> extensional distance = 533 >> proper extension: 047svrl; >> query: (?x5305, 029j_) <- film(?x156, ?x5305), featured_film_locations(?x5305, ?x739), titles(?x1510, ?x5305), student(?x2486, ?x156) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012s1d film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.852 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6695-095nx PRED entity: 095nx PRED relation: actor! PRED expected values: 026bfsh => 143 concepts (109 used for prediction) PRED predicted values (max 10 best out of 94): 03kq98 (0.10 #1599, 0.05 #5574, 0.05 #3454), 016tvq (0.10 #1221, 0.05 #3341, 0.05 #3606), 0fpxp (0.10 #1209, 0.05 #3329, 0.04 #4654), 06qv_ (0.10 #1271, 0.05 #3391, 0.04 #4716), 0k0q73t (0.10 #1567, 0.05 #3687, 0.04 #4482), 01j7mr (0.10 #1645, 0.03 #5090, 0.03 #5620), 0124k9 (0.10 #1611, 0.03 #5056, 0.03 #5586), 02qkq0 (0.10 #1715, 0.03 #5690), 026y3cf (0.10 #5815, 0.02 #6080, 0.02 #14030), 050kh5 (0.09 #2095, 0.03 #5275, 0.02 #6070) >> Best rule #1599 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 03cvv4; >> query: (?x13842, 03kq98) <- profession(?x13842, ?x319), award_winner(?x594, ?x13842), ?x594 = 02grdc, ?x319 = 01d_h8 >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #13877 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 229 *> proper extension: 031x_3; *> query: (?x13842, 026bfsh) <- people(?x2510, ?x13842), award(?x13842, ?x594), ?x2510 = 0x67 *> conf = 0.05 ranks of expected_values: 16 EVAL 095nx actor! 026bfsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 143.000 109.000 0.100 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #6694-03czqs PRED entity: 03czqs PRED relation: month PRED expected values: 03_ly => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 10): 04w_7 (0.93 #241, 0.90 #251, 0.90 #362), 03_ly (0.91 #298, 0.91 #408, 0.90 #251), 040fb (0.90 #251, 0.90 #223, 0.89 #412), 0ll3 (0.90 #251, 0.89 #299, 0.89 #412), 028kb (0.90 #251, 0.89 #301, 0.89 #412), 040fv (0.90 #251, 0.89 #412, 0.86 #244), 05lf_ (0.90 #251, 0.89 #412, 0.85 #296), 0lkm (0.90 #251, 0.89 #412, 0.85 #229), 06vkl (0.90 #251, 0.89 #412, 0.82 #403), 05cw8 (0.88 #367, 0.88 #407, 0.87 #297) >> Best rule #241 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 0h3tv; >> query: (?x11103, 04w_7) <- month(?x11103, ?x7298), month(?x11103, ?x4869), ?x7298 = 04wzr, location_of_ceremony(?x566, ?x11103), seasonal_months(?x1459, ?x4869) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #298 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 45 *> proper extension: 01914; 02cl1; 0fhp9; 080h2; 05ywg; 030qb3t; 01f62; 01_d4; 0dclg; 013yq; ... *> query: (?x11103, 03_ly) <- month(?x11103, ?x7298), month(?x11103, ?x4869), ?x7298 = 04wzr, mode_of_transportation(?x11103, ?x6665), ?x4869 = 02xx5 *> conf = 0.91 ranks of expected_values: 2 EVAL 03czqs month 03_ly CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 144.000 0.932 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #6693-03cd0x PRED entity: 03cd0x PRED relation: film_format PRED expected values: 07fb8_ => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.17 #83, 0.17 #39, 0.16 #45), 0cj16 (0.13 #213, 0.11 #282, 0.11 #148), 017fx5 (0.05 #42, 0.05 #59, 0.04 #53) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 672 >> proper extension: 0pvms; 03wh49y; 0gh6j94; 0h63q6t; >> query: (?x5388, 07fb8_) <- country(?x5388, ?x94), film_crew_role(?x5388, ?x1171), ?x1171 = 09vw2b7 >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cd0x film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.168 http://example.org/film/film/film_format #6692-0bqxw PRED entity: 0bqxw PRED relation: major_field_of_study PRED expected values: 03g3w => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 115): 02j62 (0.65 #499, 0.61 #1207, 0.58 #1443), 02lp1 (0.63 #1427, 0.62 #601, 0.59 #1191), 062z7 (0.52 #496, 0.47 #1440, 0.47 #1204), 05qjt (0.47 #1423, 0.47 #1187, 0.46 #597), 03g3w (0.46 #849, 0.46 #1439, 0.45 #495), 0fdys (0.46 #626, 0.36 #1452, 0.34 #862), 04x_3 (0.45 #494, 0.45 #376, 0.43 #1202), 05qfh (0.44 #1449, 0.44 #623, 0.43 #1213), 01tbp (0.43 #1000, 0.43 #1236, 0.42 #1472), 01540 (0.36 #1119, 0.33 #647, 0.32 #1709) >> Best rule #499 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 09c7w0; 01j_9c; 07w0v; 059j2; 03rj0; 07vfj; 04hgpt; 05zl0; 07ccs; 05x_5; ... >> query: (?x4338, 02j62) <- contains(?x94, ?x4338), organization(?x4338, ?x5487), company(?x346, ?x4338) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #849 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 39 *> proper extension: 0cv_2; 02z_b; *> query: (?x4338, 03g3w) <- organization(?x4338, ?x5487), company(?x346, ?x4338) *> conf = 0.46 ranks of expected_values: 5 EVAL 0bqxw major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 98.000 0.645 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #6691-02jg92 PRED entity: 02jg92 PRED relation: profession PRED expected values: 01b30l => 118 concepts (78 used for prediction) PRED predicted values (max 10 best out of 60): 016z4k (0.59 #2048, 0.54 #1756, 0.52 #734), 0dz3r (0.56 #2, 0.56 #8795, 0.54 #1462), 01b30l (0.56 #54, 0.19 #1222, 0.11 #2246), 039v1 (0.47 #2079, 0.42 #1495, 0.42 #1933), 01c72t (0.44 #23, 0.37 #2215, 0.35 #1191), 0n1h (0.32 #888, 0.26 #304, 0.26 #596), 01d_h8 (0.31 #4249, 0.29 #7333, 0.29 #9679), 0dxtg (0.26 #9687, 0.24 #10714, 0.22 #4257), 0d1pc (0.25 #195, 0.16 #341, 0.13 #633), 02jknp (0.21 #4251, 0.21 #7335, 0.20 #9681) >> Best rule #2048 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 011_vz; >> query: (?x2269, 016z4k) <- artists(?x482, ?x2269), artist(?x12017, ?x2269), role(?x2269, ?x1466), ?x1466 = 03bx0bm >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #54 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 01309x; 01wzlxj; 0b_j2; 01mr2g6; *> query: (?x2269, 01b30l) <- instrumentalists(?x3716, ?x2269), profession(?x2269, ?x1032), place_of_birth(?x2269, ?x4978), ?x3716 = 03gvt *> conf = 0.56 ranks of expected_values: 3 EVAL 02jg92 profession 01b30l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 118.000 78.000 0.593 http://example.org/people/person/profession #6690-05b6rdt PRED entity: 05b6rdt PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 30): 0ch6mp2 (0.83 #674, 0.82 #994, 0.81 #111), 01vx2h (0.47 #187, 0.41 #679, 0.40 #468), 0dxtw (0.44 #998, 0.43 #643, 0.43 #678), 01pvkk (0.29 #1882, 0.29 #1070, 0.28 #223), 089fss (0.21 #110, 0.11 #1340, 0.10 #3010), 02rh1dz (0.20 #185, 0.17 #114, 0.16 #997), 0215hd (0.18 #651, 0.15 #475, 0.15 #1500), 0d2b38 (0.15 #482, 0.15 #693, 0.13 #1188), 089g0h (0.15 #230, 0.14 #476, 0.13 #687), 01xy5l_ (0.13 #471, 0.12 #1496, 0.12 #1177) >> Best rule #674 for best value: >> intensional similarity = 5 >> extensional distance = 221 >> proper extension: 0bscw; 0qm8b; 0bs5k8r; 05ch98; >> query: (?x6235, 0ch6mp2) <- film_crew_role(?x6235, ?x468), genre(?x6235, ?x571), film_format(?x6235, ?x6392), ?x468 = 02r96rf, film(?x4771, ?x6235) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05b6rdt film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.834 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6689-0l_v1 PRED entity: 0l_v1 PRED relation: contains! PRED expected values: 0hjy => 74 concepts (27 used for prediction) PRED predicted values (max 10 best out of 195): 05k7sb (0.25 #132, 0.07 #5502, 0.06 #3712), 01n7q (0.19 #9028, 0.17 #17991, 0.14 #6343), 07ssc (0.18 #19738, 0.18 #20634, 0.16 #21532), 059rby (0.13 #18830, 0.12 #914, 0.12 #8970), 02jx1 (0.11 #19793, 0.10 #20689, 0.09 #21587), 01x73 (0.10 #114, 0.03 #5484, 0.02 #11751), 0vmt (0.08 #54, 0.04 #6320, 0.03 #9005), 05tbn (0.07 #2013, 0.06 #1118, 0.06 #2908), 05kkh (0.06 #17025, 0.06 #903, 0.05 #3588), 0d060g (0.06 #19719, 0.06 #20615, 0.05 #21513) >> Best rule #132 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 0cb4j; 02xry; 0d6lp; 01m1_t; 0rd5k; 01zmqw; 0mb2b; 0p9z5; 0l2q3; 0rd6b; ... >> query: (?x14180, 05k7sb) <- currency(?x14180, ?x170), contains(?x94, ?x14180), ?x170 = 09nqf, ?x94 = 09c7w0 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 0cb4j; 02xry; 0d6lp; 01m1_t; 0rd5k; 01zmqw; 0mb2b; 0p9z5; 0l2q3; 0rd6b; ... *> query: (?x14180, 0hjy) <- currency(?x14180, ?x170), contains(?x94, ?x14180), ?x170 = 09nqf, ?x94 = 09c7w0 *> conf = 0.03 ranks of expected_values: 30 EVAL 0l_v1 contains! 0hjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 74.000 27.000 0.247 http://example.org/location/location/contains #6688-037gjc PRED entity: 037gjc PRED relation: participant! PRED expected values: 017m2y => 134 concepts (96 used for prediction) PRED predicted values (max 10 best out of 221): 017m2y (0.81 #24148, 0.81 #15006, 0.81 #25453), 03xmy1 (0.07 #762, 0.06 #1415, 0.02 #5980), 029b9k (0.07 #1198, 0.06 #1851, 0.02 #6416), 01jw4r (0.07 #1179, 0.06 #1832, 0.02 #6397), 029ql (0.07 #1083, 0.06 #1736, 0.02 #6301), 020trj (0.07 #1044, 0.06 #1697, 0.02 #6262), 033_1p (0.07 #1243, 0.03 #2548, 0.03 #3852), 02ts3h (0.07 #1118, 0.02 #6336, 0.01 #6989), 01w02sy (0.06 #1519, 0.02 #6084, 0.01 #6737), 01jbx1 (0.03 #2183, 0.03 #6096, 0.03 #6749) >> Best rule #24148 for best value: >> intensional similarity = 3 >> extensional distance = 406 >> proper extension: 06cv1; 01vvycq; 01vv7sc; 035gjq; 01b9ck; 0162c8; 06w2sn5; 015882; 0721cy; 045zr; ... >> query: (?x4882, ?x9276) <- award_nominee(?x4882, ?x3815), participant(?x4882, ?x9276), profession(?x4882, ?x1032) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 037gjc participant! 017m2y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 96.000 0.813 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #6687-061fhg PRED entity: 061fhg PRED relation: artists PRED expected values: 05563d => 77 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1243): 070b4 (0.67 #4053, 0.60 #2975, 0.50 #5130), 03sww (0.67 #5830, 0.33 #441, 0.17 #6908), 0285c (0.67 #5529, 0.25 #1218, 0.20 #2296), 01jcxwp (0.67 #6027, 0.13 #18956, 0.13 #21110), 0191h5 (0.60 #2804, 0.50 #4959, 0.50 #3882), 01m65sp (0.60 #2426, 0.50 #4581, 0.50 #3504), 06br6t (0.60 #3048, 0.50 #5203, 0.50 #4126), 03fbc (0.60 #2359, 0.50 #4514, 0.50 #3437), 016ntp (0.60 #2419, 0.50 #4574, 0.50 #3497), 0l8g0 (0.60 #2718, 0.50 #4873, 0.50 #3796) >> Best rule #4053 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 01rthc; >> query: (?x1555, 070b4) <- artists(?x1555, ?x11929), artists(?x1555, ?x5279), artists(?x1555, ?x4850), artists(?x1555, ?x1092), ?x11929 = 07n3s, artist(?x7793, ?x4850), group(?x75, ?x5279), award_winner(?x4912, ?x1092), role(?x75, ?x74), industry(?x7793, ?x3368) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1387 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 016clz; 08jyyk; *> query: (?x1555, 05563d) <- artists(?x1555, ?x11929), artists(?x1555, ?x4850), ?x11929 = 07n3s, artist(?x2931, ?x4850), nominated_for(?x4850, ?x4920), film(?x400, ?x4920), nominated_for(?x401, ?x4920), category(?x4850, ?x134) *> conf = 0.50 ranks of expected_values: 20 EVAL 061fhg artists 05563d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 77.000 41.000 0.667 http://example.org/music/genre/artists #6686-03l5m1 PRED entity: 03l5m1 PRED relation: capital PRED expected values: 095w_ => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 122): 04swd (0.36 #1242, 0.24 #2810, 0.20 #4252), 09bkv (0.33 #49, 0.09 #1012, 0.05 #3663), 04jpl (0.27 #1329, 0.19 #2416, 0.18 #1088), 05qtj (0.25 #261, 0.18 #1225, 0.13 #11804), 01q0l (0.25 #400, 0.18 #882, 0.11 #3292), 0fhsz (0.25 #316, 0.14 #1883, 0.14 #1762), 0d34_ (0.25 #460, 0.14 #700, 0.11 #821), 02z0j (0.25 #401, 0.14 #641, 0.11 #762), 01f62 (0.25 #254, 0.11 #735, 0.09 #976), 0156q (0.24 #3025, 0.18 #5072, 0.16 #6155) >> Best rule #1242 for best value: >> intensional similarity = 13 >> extensional distance = 9 >> proper extension: 0b90_r; >> query: (?x9940, 04swd) <- capital(?x9940, ?x8601), capital(?x756, ?x8601), olympics(?x756, ?x784), film_release_region(?x6932, ?x756), film_release_region(?x5425, ?x756), film_release_region(?x2893, ?x756), ?x5425 = 02prwdh, country(?x150, ?x756), combatants(?x1003, ?x756), ?x6932 = 027pfg, ?x784 = 018ctl, ?x2893 = 01jrbb, combatants(?x326, ?x756) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #610 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 5 *> proper extension: 03gj2; 01fvhp; *> query: (?x9940, 095w_) <- entity_involved(?x12992, ?x9940), capital(?x9940, ?x8601), category(?x8601, ?x134), ?x134 = 08mbj5d, combatants(?x12992, ?x11329), combatants(?x12992, ?x9328), locations(?x12992, ?x4743), contains(?x4743, ?x5167), adjoins(?x608, ?x4743), ?x9328 = 024pcx, location(?x12255, ?x4743), entity_involved(?x4373, ?x11329) *> conf = 0.14 ranks of expected_values: 14 EVAL 03l5m1 capital 095w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 122.000 122.000 0.364 http://example.org/location/country/capital #6685-0165b PRED entity: 0165b PRED relation: olympics PRED expected values: 06sks6 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 40): 06sks6 (0.91 #2593, 0.88 #2473, 0.87 #1631), 0kbws (0.60 #1377, 0.54 #495, 0.54 #454), 0kbvb (0.54 #488, 0.50 #528, 0.44 #610), 09n48 (0.47 #562, 0.46 #484, 0.37 #481), 0lgxj (0.47 #562, 0.37 #481, 0.37 #2693), 09x3r (0.47 #562, 0.37 #481, 0.37 #2693), 0jdk_ (0.42 #507, 0.37 #547, 0.34 #1389), 0sxrz (0.42 #502, 0.35 #381, 0.31 #624), 0swbd (0.42 #492, 0.33 #532, 0.30 #371), 0jkvj (0.39 #1404, 0.38 #1525, 0.36 #1566) >> Best rule #2593 for best value: >> intensional similarity = 4 >> extensional distance = 159 >> proper extension: 0168t; >> query: (?x7479, 06sks6) <- contains(?x1144, ?x7479), participating_countries(?x418, ?x7479), country(?x150, ?x7479), olympics(?x7479, ?x3110) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0165b olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.907 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #6684-0b_dy PRED entity: 0b_dy PRED relation: type_of_union PRED expected values: 04ztj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.79 #9, 0.74 #85, 0.72 #13), 01g63y (0.47 #157, 0.45 #314, 0.21 #18) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 016bx2; 01cspq; 01lc5; >> query: (?x3139, 04ztj) <- award_nominee(?x156, ?x3139), award_winner(?x2523, ?x3139), ?x2523 = 03nqnk3 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_dy type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.786 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6683-04fv0k PRED entity: 04fv0k PRED relation: contact_category PRED expected values: 03w5xm => 195 concepts (195 used for prediction) PRED predicted values (max 10 best out of 3): 03w5xm (0.80 #116, 0.79 #96, 0.78 #113), 02zdwq (0.45 #42, 0.43 #51, 0.35 #54), 014dgf (0.30 #32, 0.25 #175, 0.22 #149) >> Best rule #116 for best value: >> intensional similarity = 5 >> extensional distance = 44 >> proper extension: 0hm0k; >> query: (?x9517, 03w5xm) <- service_language(?x9517, ?x732), industry(?x9517, ?x12380), organization(?x4682, ?x9517), ?x4682 = 0dq_5, company(?x265, ?x9517) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04fv0k contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 195.000 0.804 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #6682-03t97y PRED entity: 03t97y PRED relation: genre PRED expected values: 0g092b => 91 concepts (38 used for prediction) PRED predicted values (max 10 best out of 88): 0h9qh (0.71 #4316, 0.70 #2333, 0.59 #2332), 07s9rl0 (0.63 #4199, 0.62 #1865, 0.62 #1165), 05p553 (0.60 #120, 0.57 #236, 0.50 #352), 06n90 (0.40 #127, 0.34 #2344, 0.27 #1993), 06nbt (0.40 #139, 0.10 #255, 0.07 #371), 0lsxr (0.34 #1756, 0.27 #2926, 0.26 #3042), 02l7c8 (0.30 #1178, 0.30 #1878, 0.28 #2814), 02n4kr (0.25 #7, 0.24 #1755, 0.17 #2925), 060__y (0.25 #15, 0.17 #2230, 0.16 #1179), 01q03 (0.20 #121, 0.05 #237, 0.03 #1169) >> Best rule #4316 for best value: >> intensional similarity = 3 >> extensional distance = 746 >> proper extension: 08j7lh; >> query: (?x1074, ?x6154) <- titles(?x6154, ?x1074), genre(?x723, ?x6154), film_crew_role(?x1074, ?x468) >> conf = 0.71 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03t97y genre 0g092b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 38.000 0.707 http://example.org/film/film/genre #6681-0bdjd PRED entity: 0bdjd PRED relation: nominated_for! PRED expected values: 0gq_v 04kxsb => 93 concepts (84 used for prediction) PRED predicted values (max 10 best out of 195): 0gq9h (0.72 #699, 0.68 #11043, 0.68 #10393), 0k611 (0.68 #11043, 0.68 #10393, 0.68 #11261), 0279c15 (0.68 #11043, 0.68 #10393, 0.68 #11261), 027c95y (0.68 #11043, 0.68 #10393, 0.68 #11261), 04kxsb (0.51 #513, 0.46 #946, 0.45 #729), 099c8n (0.44 #1129, 0.42 #913, 0.39 #480), 02w9sd7 (0.43 #755, 0.24 #539, 0.24 #972), 0gq_v (0.40 #1314, 0.27 #1746, 0.25 #882), 0gs96 (0.39 #1373, 0.22 #941, 0.22 #508), 0gqyl (0.37 #1364, 0.37 #499, 0.36 #1796) >> Best rule #699 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 04v8x9; 0n0bp; 0pv2t; 0c5dd; 0bm2x; >> query: (?x7336, 0gq9h) <- nominated_for(?x496, ?x7336), award(?x7336, ?x591), nominated_for(?x198, ?x7336), ?x591 = 0f4x7 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #513 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 0n1s0; *> query: (?x7336, 04kxsb) <- music(?x7336, ?x7701), nominated_for(?x1180, ?x7336), nominated_for(?x601, ?x7336), ?x1180 = 02n9nmz, ?x601 = 0gr4k *> conf = 0.51 ranks of expected_values: 5, 8 EVAL 0bdjd nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 84.000 0.717 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bdjd nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 93.000 84.000 0.717 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6680-09c17 PRED entity: 09c17 PRED relation: place_of_birth! PRED expected values: 090gpr => 203 concepts (67 used for prediction) PRED predicted values (max 10 best out of 2013): 07f0tw (0.28 #83564, 0.28 #117515, 0.28 #109680), 0bxy67 (0.12 #2165, 0.10 #4776, 0.08 #7387), 02756j (0.12 #1296, 0.10 #3907, 0.08 #6518), 015npr (0.12 #374, 0.10 #2985, 0.08 #5596), 0bvls5 (0.12 #2597, 0.10 #5208, 0.08 #7819), 02x20c9 (0.12 #2588, 0.10 #5199, 0.08 #7810), 03cprft (0.12 #2581, 0.10 #5192, 0.08 #7803), 04qp06 (0.12 #2571, 0.10 #5182, 0.08 #7793), 08s0m7 (0.12 #2476, 0.10 #5087, 0.08 #7698), 090gk3 (0.12 #2466, 0.10 #5077, 0.08 #7688) >> Best rule #83564 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 0rh6k; 01914; 04jpl; 0f2wj; 0r62v; 080h2; 095w_; 01r32; 0156q; 094jv; ... >> query: (?x12210, ?x8530) <- featured_film_locations(?x257, ?x12210), country(?x12210, ?x2146), location(?x8530, ?x12210), location_of_ceremony(?x566, ?x12210) >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09c17 place_of_birth! 090gpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 203.000 67.000 0.283 http://example.org/people/person/place_of_birth #6679-03rl84 PRED entity: 03rl84 PRED relation: student! PRED expected values: 017j69 => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 183): 0bwfn (0.40 #802, 0.05 #57737, 0.05 #49821), 04b_46 (0.20 #754, 0.05 #12348, 0.04 #17091), 0m4yg (0.12 #5635, 0.04 #4581, 0.04 #11432), 01bzs9 (0.11 #2041, 0.08 #2568, 0.07 #3622), 015nl4 (0.08 #2175, 0.06 #5337, 0.06 #3756), 08815 (0.08 #2110, 0.06 #3691, 0.04 #18974), 0lyjf (0.08 #2265, 0.06 #3846, 0.03 #5427), 033gn8 (0.08 #2486, 0.06 #4067, 0.03 #5648), 02zd460 (0.08 #2278, 0.06 #3859, 0.03 #5440), 017cy9 (0.08 #2260, 0.06 #3841, 0.03 #5422) >> Best rule #802 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0jrqq; >> query: (?x2012, 0bwfn) <- film(?x2012, ?x1246), award(?x2012, ?x757), ?x1246 = 02pxmgz >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #8577 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 03vrv9; *> query: (?x2012, 017j69) <- film(?x2012, ?x7141), genre(?x7141, ?x53), notable_people_with_this_condition(?x8318, ?x2012) *> conf = 0.03 ranks of expected_values: 42 EVAL 03rl84 student! 017j69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 151.000 151.000 0.400 http://example.org/education/educational_institution/students_graduates./education/education/student #6678-0r02m PRED entity: 0r02m PRED relation: time_zones PRED expected values: 02lcqs => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 13): 02lcqs (0.86 #187, 0.85 #148, 0.80 #200), 02hcv8 (0.46 #1186, 0.45 #692, 0.45 #926), 02fqwt (0.38 #118, 0.29 #326, 0.28 #339), 02hczc (0.16 #1951, 0.12 #327, 0.11 #470), 02lcrv (0.16 #1951, 0.02 #215, 0.01 #345), 02llzg (0.09 #1122, 0.09 #1109, 0.08 #771), 03bdv (0.06 #58, 0.06 #617, 0.06 #435), 03plfd (0.05 #1115, 0.05 #1128, 0.03 #1375), 0gsrz4 (0.02 #1373, 0.02 #1399, 0.02 #1412), 042g7t (0.02 #960, 0.02 #947, 0.02 #973) >> Best rule #187 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 06pwq; >> query: (?x13255, 02lcqs) <- category(?x13255, ?x134), state(?x13255, ?x1227), ?x1227 = 01n7q >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r02m time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.861 http://example.org/location/location/time_zones #6677-0k049 PRED entity: 0k049 PRED relation: location! PRED expected values: 03m8lq => 99 concepts (55 used for prediction) PRED predicted values (max 10 best out of 2120): 013cr (0.43 #107127, 0.42 #109618, 0.39 #49830), 0gps0z (0.33 #1934, 0.17 #4424, 0.04 #31833), 01g1lp (0.33 #1560, 0.17 #4050, 0.03 #16504), 08959 (0.33 #2463, 0.17 #4953, 0.03 #17407), 02mxw0 (0.33 #505, 0.17 #2995, 0.03 #15449), 03qcq (0.33 #7, 0.17 #2497, 0.03 #14951), 02wr6r (0.33 #1963, 0.17 #4453, 0.03 #16907), 026ck (0.33 #2360, 0.17 #4850, 0.03 #17304), 02x0dzw (0.33 #1753, 0.17 #4243, 0.03 #16697), 07q0g5 (0.33 #1544, 0.17 #4034, 0.03 #16488) >> Best rule #107127 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 0bxbb; 0nmj; 0r6ff; 0fttg; 0jpy_; 01p726; >> query: (?x191, ?x1401) <- source(?x191, ?x958), citytown(?x574, ?x191), place_of_birth(?x1401, ?x191) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k049 location! 03m8lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 55.000 0.434 http://example.org/people/person/places_lived./people/place_lived/location #6676-0cc56 PRED entity: 0cc56 PRED relation: contains PRED expected values: 0f94t => 119 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2578): 0gsg7 (0.36 #214767, 0.28 #44134, 0.21 #32363), 0cc56 (0.36 #232419, 0.25 #3052, 0.07 #11878), 02_286 (0.36 #232419, 0.07 #11837, 0.05 #250070), 059rby (0.36 #232419, 0.04 #11798, 0.02 #117688), 09c7w0 (0.36 #232419, 0.02 #117688), 0rd6b (0.33 #1668, 0.05 #250070, 0.04 #13436), 0f2nf (0.33 #1372, 0.05 #250070, 0.02 #117688), 01fpvz (0.33 #56, 0.04 #11824, 0.02 #114802), 03kmyy (0.33 #1395, 0.04 #13163, 0.02 #116141), 02bq1j (0.33 #669, 0.04 #12437, 0.02 #115415) >> Best rule #214767 for best value: >> intensional similarity = 2 >> extensional distance = 355 >> proper extension: 0tln7; 0pc6x; 0d8s8; 0t_4_; 0cy41; 0k_mf; 0gp5l6; 0qpsn; 02w70; 01qq80; ... >> query: (?x1131, ?x1762) <- contains(?x335, ?x1131), citytown(?x1762, ?x1131) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #3037 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 04jpl; *> query: (?x1131, 0f94t) <- location(?x13195, ?x1131), location(?x5454, ?x1131), ?x13195 = 0dszr0, nominated_for(?x5454, ?x2029) *> conf = 0.25 ranks of expected_values: 39 EVAL 0cc56 contains 0f94t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 119.000 111.000 0.362 http://example.org/location/location/contains #6675-01_xtx PRED entity: 01_xtx PRED relation: film PRED expected values: 06_wqk4 => 103 concepts (87 used for prediction) PRED predicted values (max 10 best out of 839): 05p1tzf (0.66 #39228, 0.63 #41012, 0.59 #98082), 04gv3db (0.25 #752, 0.12 #2535, 0.09 #4318), 0bvn25 (0.25 #50, 0.12 #1833, 0.09 #3616), 040_lv (0.25 #1044, 0.12 #2827, 0.09 #4610), 02825cv (0.25 #1139, 0.12 #2922, 0.09 #4705), 0c0nhgv (0.25 #172, 0.12 #1955, 0.09 #3738), 02825kb (0.25 #1225, 0.12 #3008, 0.09 #4791), 05zpghd (0.25 #953, 0.12 #2736, 0.09 #4519), 087pfc (0.25 #1525, 0.12 #3308, 0.09 #5091), 04f52jw (0.25 #440, 0.12 #2223, 0.09 #4006) >> Best rule #39228 for best value: >> intensional similarity = 3 >> extensional distance = 412 >> proper extension: 01p47r; >> query: (?x3865, ?x559) <- nominated_for(?x3865, ?x559), film(?x3865, ?x755), participant(?x3865, ?x4782) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #10824 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 126 *> proper extension: 0fb7c; *> query: (?x3865, 06_wqk4) <- award(?x3865, ?x1336), ?x1336 = 05pcn59, film(?x3865, ?x755) *> conf = 0.03 ranks of expected_values: 91 EVAL 01_xtx film 06_wqk4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 103.000 87.000 0.664 http://example.org/film/actor/film./film/performance/film #6674-0d22f PRED entity: 0d22f PRED relation: adjoins PRED expected values: 0mx3k => 178 concepts (64 used for prediction) PRED predicted values (max 10 best out of 442): 0mx3k (0.82 #37836, 0.82 #46331, 0.81 #37835), 0mx6c (0.60 #108, 0.25 #2426, 0.23 #3197), 0mx4_ (0.40 #33, 0.25 #2351, 0.20 #804), 0mxbq (0.40 #453, 0.25 #2771, 0.20 #1224), 0d22f (0.33 #1698, 0.26 #21619, 0.25 #16988), 0mx48 (0.33 #2014, 0.26 #21619, 0.25 #16988), 0mx5p (0.31 #3743, 0.22 #5289, 0.20 #654), 0d1xx (0.26 #21619, 0.24 #35522, 0.24 #33203), 0l339 (0.20 #1211, 0.20 #440, 0.15 #3529), 0mlyw (0.20 #4048, 0.02 #7912, 0.02 #11772) >> Best rule #37836 for best value: >> intensional similarity = 4 >> extensional distance = 312 >> proper extension: 05kr_; >> query: (?x3067, ?x11062) <- adjoins(?x11062, ?x3067), source(?x11062, ?x958), contains(?x726, ?x11062), time_zones(?x3067, ?x2950) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d22f adjoins 0mx3k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 64.000 0.819 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #6673-01p79b PRED entity: 01p79b PRED relation: institution! PRED expected values: 016t_3 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 19): 019v9k (0.78 #307, 0.73 #650, 0.69 #206), 02_xgp2 (0.61 #513, 0.52 #189, 0.51 #169), 03bwzr4 (0.59 #131, 0.54 #191, 0.52 #171), 016t_3 (0.54 #506, 0.50 #122, 0.49 #182), 04zx3q1 (0.40 #1, 0.36 #708, 0.30 #161), 013zdg (0.40 #4, 0.35 #124, 0.26 #164), 071tyz (0.36 #708, 0.17 #956, 0.07 #187), 022h5x (0.27 #37, 0.25 #157, 0.25 #137), 027f2w (0.24 #166, 0.22 #126, 0.20 #6), 028dcg (0.20 #16, 0.19 #136, 0.18 #36) >> Best rule #307 for best value: >> intensional similarity = 5 >> extensional distance = 241 >> proper extension: 01fpvz; 065y4w7; 03x83_; 0hd7j; 01vs5c; 0172jm; 08qnnv; 02sjgpq; 0ylsr; 02z6fs; ... >> query: (?x7920, 019v9k) <- institution(?x1368, ?x7920), institution(?x620, ?x7920), ?x1368 = 014mlp, institution(?x620, ?x11740), ?x11740 = 07wtc >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #506 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 300 *> proper extension: 03p7gb; 01dbns; *> query: (?x7920, 016t_3) <- organization(?x346, ?x7920), institution(?x620, ?x7920), institution(?x620, ?x6193), ?x6193 = 02kzfw *> conf = 0.54 ranks of expected_values: 4 EVAL 01p79b institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 106.000 106.000 0.782 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #6672-01wwvt2 PRED entity: 01wwvt2 PRED relation: film PRED expected values: 0c9t0y => 127 concepts (120 used for prediction) PRED predicted values (max 10 best out of 200): 07bzz7 (0.07 #13414, 0.05 #18781, 0.04 #16992), 04jpk2 (0.07 #13110, 0.03 #16688, 0.03 #18477), 01s7w3 (0.06 #12524, 0.03 #23259, 0.02 #28627), 0888c3 (0.06 #10361, 0.05 #13940, 0.05 #22885), 02ht1k (0.06 #9576, 0.05 #13155, 0.05 #22100), 01738w (0.06 #10075, 0.05 #13654, 0.03 #22599), 04zl8 (0.06 #9869, 0.05 #13448, 0.03 #18815), 027fwmt (0.06 #12327, 0.03 #23062, 0.02 #28430), 01shy7 (0.05 #12948, 0.05 #424, 0.04 #4002), 035gnh (0.05 #3082, 0.05 #1293, 0.03 #6660) >> Best rule #13414 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 03qd_; 0pgjm; 021bk; 02bh9; 04gycf; 04yt7; 0dpqk; 04bgy; 03dq9; 09g0h; >> query: (?x2392, 07bzz7) <- gender(?x2392, ?x231), group(?x2392, ?x1945), film(?x2392, ?x5066) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wwvt2 film 0c9t0y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 120.000 0.071 http://example.org/film/actor/film./film/performance/film #6671-05q_mg PRED entity: 05q_mg PRED relation: category PRED expected values: 08mbj5d => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.45 #2, 0.39 #9, 0.35 #10) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 05dxl5; 04fzk; 062hgx; 06_6j3; 081jbk; 01x_d8; 03d_zl4; 09wlpl; 024my5; 01zh29; ... >> query: (?x12803, 08mbj5d) <- location(?x12803, ?x3052), actor(?x11154, ?x12803), film(?x7764, ?x11154), genre(?x11154, ?x225) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05q_mg category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.455 http://example.org/common/topic/webpage./common/webpage/category #6670-02pt27 PRED entity: 02pt27 PRED relation: profession PRED expected values: 0nbcg => 109 concepts (61 used for prediction) PRED predicted values (max 10 best out of 54): 016z4k (0.69 #738, 0.64 #1179, 0.54 #591), 0nbcg (0.64 #1942, 0.63 #1501, 0.63 #3562), 02hrh1q (0.60 #8568, 0.53 #6491, 0.53 #7381), 01c72t (0.44 #905, 0.42 #1346, 0.35 #2966), 0fnpj (0.41 #941, 0.27 #1382, 0.24 #3002), 0n1h (0.24 #893, 0.22 #4277, 0.21 #8565), 025352 (0.20 #58, 0.15 #793, 0.15 #646), 0dxtg (0.20 #5310, 0.17 #6490, 0.16 #7380), 0kyk (0.19 #764, 0.12 #1058, 0.11 #5326), 01d_h8 (0.18 #5302, 0.16 #6482, 0.16 #7372) >> Best rule #738 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 01vw20_; 01vtqml; 0qf11; 018d6l; 019389; 015196; 016t00; >> query: (?x9693, 016z4k) <- instrumentalists(?x2798, ?x9693), artists(?x1000, ?x9693), ?x1000 = 0xhtw, ?x2798 = 03qjg, gender(?x9693, ?x231) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1942 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 89 *> proper extension: 01hw6wq; 01wg982; 044k8; 01dw_f; 02p68d; 01whg97; 01k47c; 01wqpnm; 01vvybv; 01y_rz; ... *> query: (?x9693, 0nbcg) <- instrumentalists(?x2798, ?x9693), artists(?x1000, ?x9693), ?x1000 = 0xhtw, role(?x2798, ?x75) *> conf = 0.64 ranks of expected_values: 2 EVAL 02pt27 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 61.000 0.692 http://example.org/people/person/profession #6669-015vql PRED entity: 015vql PRED relation: place_of_death PRED expected values: 05l5n => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 19): 04jpl (0.07 #591, 0.06 #785, 0.05 #1372), 030qb3t (0.04 #5274, 0.04 #4109, 0.04 #4497), 02_286 (0.02 #6238, 0.02 #4488, 0.02 #4100), 0k049 (0.02 #4090, 0.02 #4478, 0.02 #5255), 0235n9 (0.01 #382, 0.01 #187), 0nbfm (0.01 #341, 0.01 #146), 09bkv (0.01 #331, 0.01 #136), 0qpn9 (0.01 #302, 0.01 #107), 06c62 (0.01 #296, 0.01 #101), 0167q3 (0.01 #294, 0.01 #99) >> Best rule #591 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 07m69t; >> query: (?x12889, 04jpl) <- nationality(?x12889, ?x1310), nationality(?x12889, ?x512), ?x1310 = 02jx1, ?x512 = 07ssc >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015vql place_of_death 05l5n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 81.000 0.069 http://example.org/people/deceased_person/place_of_death #6668-0bxtyq PRED entity: 0bxtyq PRED relation: nationality PRED expected values: 09c7w0 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 25): 09c7w0 (0.90 #802, 0.82 #1905, 0.76 #2005), 01xbgx (0.40 #7224), 02jx1 (0.16 #1737, 0.13 #934, 0.12 #333), 07ssc (0.11 #315, 0.11 #515, 0.10 #916), 03rk0 (0.10 #146, 0.05 #7670, 0.05 #7970), 0f8l9c (0.07 #22, 0.03 #1125, 0.03 #1225), 0345h (0.06 #131, 0.06 #631, 0.04 #1434), 0d060g (0.05 #507, 0.05 #607, 0.05 #1008), 03rjj (0.04 #405, 0.04 #5, 0.03 #1108), 0jgd (0.04 #2, 0.02 #402, 0.01 #102) >> Best rule #802 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 04l3_z; 02jm0n; 047hpm; 01yd8v; 01_rh4; 01n7qlf; 0gg9_5q; 04mhl; 0b0pf; 03n52j; ... >> query: (?x10079, 09c7w0) <- place_of_birth(?x10079, ?x1523), gender(?x10079, ?x231), ?x1523 = 030qb3t >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bxtyq nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.905 http://example.org/people/person/nationality #6667-04_jsg PRED entity: 04_jsg PRED relation: gender PRED expected values: 05zppz => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #17, 0.84 #11, 0.83 #7), 02zsn (0.47 #71, 0.46 #128, 0.30 #36) >> Best rule #17 for best value: >> intensional similarity = 7 >> extensional distance = 53 >> proper extension: 0fsm8c; 07ymr5; 03jqw5; 062dn7; 01_x6d; 028k57; 0479b; 01fxck; 01g1lp; 036dyy; ... >> query: (?x8215, 05zppz) <- profession(?x8215, ?x1183), profession(?x8215, ?x1032), profession(?x8215, ?x319), ?x1032 = 02hrh1q, ?x319 = 01d_h8, ?x1183 = 09jwl, film(?x8215, ?x6684) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04_jsg gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.855 http://example.org/people/person/gender #6666-06j0md PRED entity: 06j0md PRED relation: place_of_birth PRED expected values: 030qb3t => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 90): 0cr3d (0.17 #94, 0.11 #798, 0.11 #2206), 0xc9x (0.17 #582, 0.11 #1286, 0.10 #1990), 0c9cw (0.17 #671, 0.11 #1375), 02dtg (0.11 #714, 0.02 #3530, 0.02 #4234), 02frhbc (0.11 #1066, 0.02 #3882, 0.01 #5290), 02_286 (0.11 #4947, 0.09 #5652, 0.09 #4243), 030qb3t (0.10 #1462, 0.07 #4278, 0.05 #48638), 013n2h (0.10 #1715), 01_d4 (0.07 #4994, 0.07 #5699, 0.05 #2882), 0rh6k (0.07 #2114, 0.04 #3522, 0.03 #7044) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 03xp8d5; 02qlkc3; 01jgpsh; >> query: (?x201, 0cr3d) <- award_winner(?x2293, ?x201), award_nominee(?x2285, ?x201), ?x2285 = 0721cy >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1462 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0170s4; 02bvt; 041c4; 0988cp; 03wh8kl; 03wh8pq; 02wk_43; 06w58f; *> query: (?x201, 030qb3t) <- award_winner(?x4517, ?x201), award_nominee(?x2285, ?x201), ?x4517 = 01s81 *> conf = 0.10 ranks of expected_values: 7 EVAL 06j0md place_of_birth 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 107.000 107.000 0.167 http://example.org/people/person/place_of_birth #6665-03fg0r PRED entity: 03fg0r PRED relation: student! PRED expected values: 09f2j => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 90): 065y4w7 (0.11 #14, 0.07 #5814, 0.07 #2123), 06xpp7 (0.11 #177, 0.02 #1231, 0.01 #1759), 05nrkb (0.11 #349, 0.01 #16162, 0.01 #19324), 05bjp6 (0.11 #416, 0.01 #1470, 0.01 #1998), 02yxjs (0.11 #294), 0bwfn (0.09 #11345, 0.08 #8710, 0.08 #10818), 03ksy (0.06 #7487, 0.06 #9595, 0.06 #8014), 04b_46 (0.06 #3390, 0.04 #6027, 0.04 #1281), 01w5m (0.04 #11702, 0.04 #12756, 0.03 #8540), 01k2wn (0.04 #551, 0.04 #2133, 0.02 #4241) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 086k8; 024rgt; 03mdt; >> query: (?x4589, 065y4w7) <- award_nominee(?x4589, ?x2548), ?x2548 = 046b0s, award_winner(?x2586, ?x4589) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #3849 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 135 *> proper extension: 01_k1z; 021r7r; *> query: (?x4589, 09f2j) <- profession(?x4589, ?x987), currency(?x4589, ?x170), ?x987 = 0dxtg *> conf = 0.04 ranks of expected_values: 13 EVAL 03fg0r student! 09f2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 99.000 99.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #6664-012cph PRED entity: 012cph PRED relation: nationality PRED expected values: 09c7w0 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 95): 09c7w0 (0.88 #5620, 0.81 #3515, 0.81 #3213), 07ssc (0.46 #1217, 0.38 #1117, 0.33 #715), 02jx1 (0.40 #433, 0.38 #1235, 0.31 #1135), 03rt9 (0.29 #513, 0.14 #7525, 0.13 #8227), 03rk0 (0.25 #2052, 0.25 #46, 0.23 #2353), 0f8l9c (0.14 #522, 0.14 #7525, 0.13 #1928), 06bnz (0.14 #541, 0.14 #7525, 0.13 #8227), 0345h (0.14 #7525, 0.13 #8227, 0.12 #1534), 0h7x (0.14 #7525, 0.13 #8227, 0.05 #1739), 0jgd (0.14 #7525, 0.13 #8227, 0.03 #2609) >> Best rule #5620 for best value: >> intensional similarity = 4 >> extensional distance = 203 >> proper extension: 076psv; >> query: (?x1030, 09c7w0) <- place_of_death(?x1030, ?x739), profession(?x1030, ?x353), location(?x2068, ?x739), ?x2068 = 0gl88b >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012cph nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.878 http://example.org/people/person/nationality #6663-04k9y6 PRED entity: 04k9y6 PRED relation: produced_by PRED expected values: 05prs8 => 85 concepts (20 used for prediction) PRED predicted values (max 10 best out of 121): 0lx2l (0.10 #6983, 0.09 #4267, 0.09 #3102), 03mfqm (0.10 #6983, 0.09 #4267, 0.09 #3102), 01x6v6 (0.10 #6983, 0.09 #3102, 0.07 #7760), 0kp2_ (0.10 #4268, 0.08 #3101), 02kxbwx (0.06 #418, 0.06 #805, 0.02 #1968), 02xnjd (0.06 #1435, 0.04 #1822, 0.03 #2597), 030_3z (0.04 #1712, 0.03 #1325, 0.03 #3265), 092kgw (0.04 #583, 0.04 #970, 0.02 #2133), 054_mz (0.04 #403, 0.04 #790, 0.01 #1565), 02kxbx3 (0.04 #506, 0.04 #893, 0.01 #2056) >> Best rule #6983 for best value: >> intensional similarity = 3 >> extensional distance = 413 >> proper extension: 01h72l; >> query: (?x6018, ?x2534) <- honored_for(?x2294, ?x6018), genre(?x6018, ?x53), award_winner(?x6018, ?x2534) >> conf = 0.10 => this is the best rule for 3 predicted values *> Best rule #4709 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 259 *> proper extension: 05h95s; 01j95; *> query: (?x6018, 05prs8) <- category(?x6018, ?x134), award_winner(?x6018, ?x2534), ?x134 = 08mbj5d *> conf = 0.02 ranks of expected_values: 61 EVAL 04k9y6 produced_by 05prs8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 85.000 20.000 0.104 http://example.org/film/film/produced_by #6662-063b4k PRED entity: 063b4k PRED relation: profession PRED expected values: 01d_h8 0cbd2 => 126 concepts (86 used for prediction) PRED predicted values (max 10 best out of 78): 01d_h8 (0.86 #1759, 0.85 #3950, 0.85 #3511), 03gjzk (0.55 #1328, 0.47 #1474, 0.46 #2350), 0cbd2 (0.53 #299, 0.48 #1906, 0.30 #737), 0kyk (0.39 #320, 0.27 #1927, 0.20 #758), 02krf9 (0.25 #463, 0.24 #3384, 0.24 #2800), 0dz3r (0.25 #2, 0.19 #4238, 0.17 #4092), 016z4k (0.25 #4, 0.14 #4094, 0.13 #4240), 0n1h (0.25 #11, 0.10 #4101, 0.09 #6583), 0mn6 (0.25 #75), 09jwl (0.24 #4253, 0.22 #4107, 0.20 #6589) >> Best rule #1759 for best value: >> intensional similarity = 3 >> extensional distance = 150 >> proper extension: 013t9y; 0d608; >> query: (?x12856, 01d_h8) <- award(?x12856, ?x68), produced_by(?x1786, ?x12856), written_by(?x4422, ?x12856) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 063b4k profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 86.000 0.855 http://example.org/people/person/profession EVAL 063b4k profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 86.000 0.855 http://example.org/people/person/profession #6661-0265z9l PRED entity: 0265z9l PRED relation: languages PRED expected values: 03k50 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 23): 03k50 (0.56 #255, 0.42 #361, 0.40 #39), 07c9s (0.31 #264, 0.13 #374, 0.11 #300), 0999q (0.16 #274, 0.06 #3613, 0.06 #384), 064_8sq (0.15 #338, 0.11 #412, 0.11 #556), 09bnf (0.11 #288, 0.06 #3613, 0.03 #398), 09s02 (0.09 #285, 0.07 #69, 0.06 #3613), 02hxcvy (0.08 #132, 0.07 #276, 0.07 #60), 02bjrlw (0.08 #325, 0.07 #399, 0.06 #543), 04306rv (0.07 #326, 0.04 #724, 0.03 #1014), 01c7y (0.07 #281, 0.07 #65, 0.06 #3613) >> Best rule #255 for best value: >> intensional similarity = 5 >> extensional distance = 43 >> proper extension: 04rs03; 0292l3; 040wdl; 02vmzp; 03wpmd; 01n8_g; 06pwf6; 0jrqq; 084z0w; 01gj8_; ... >> query: (?x7082, 03k50) <- gender(?x7082, ?x231), ?x231 = 05zppz, languages(?x7082, ?x254), nationality(?x7082, ?x2146), ?x2146 = 03rk0 >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0265z9l languages 03k50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.556 http://example.org/people/person/languages #6660-0686zv PRED entity: 0686zv PRED relation: film PRED expected values: 07f_t4 => 97 concepts (59 used for prediction) PRED predicted values (max 10 best out of 562): 0djlxb (0.27 #4099, 0.03 #78460, 0.03 #105216), 08phg9 (0.13 #4448, 0.02 #8015, 0.02 #6231), 0btpm6 (0.13 #4867, 0.02 #6650), 09cr8 (0.12 #2067, 0.07 #3850, 0.03 #78460), 01l_pn (0.12 #2747, 0.07 #4530, 0.03 #78460), 01jrbv (0.12 #2333, 0.07 #4116, 0.03 #78460), 06_wqk4 (0.12 #1910, 0.07 #3693, 0.03 #78460), 01rxyb (0.12 #2512, 0.07 #4295, 0.03 #78460), 01y9jr (0.12 #2941, 0.07 #4724, 0.03 #78460), 0bt3j9 (0.12 #2671, 0.07 #4454, 0.03 #78460) >> Best rule #4099 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0psss; 086sj; 01wy5m; 01yfm8; >> query: (?x3079, 0djlxb) <- award_nominee(?x7157, ?x3079), award_nominee(?x3078, ?x3079), ?x3078 = 01438g, gender(?x7157, ?x231) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0686zv film 07f_t4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 59.000 0.267 http://example.org/film/actor/film./film/performance/film #6659-01grmk PRED entity: 01grmk PRED relation: legislative_sessions! PRED expected values: 042d1 => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 27): 0rlz (0.52 #421, 0.50 #336, 0.50 #92), 042d1 (0.52 #421, 0.50 #336, 0.48 #420), 0fd_1 (0.52 #421, 0.50 #336, 0.48 #420), 03_nq (0.52 #421, 0.50 #336, 0.22 #280), 021sv1 (0.49 #738, 0.40 #801, 0.40 #767), 0226cw (0.49 #753, 0.40 #816, 0.40 #782), 02hy5d (0.47 #756, 0.38 #819, 0.38 #785), 024_vw (0.45 #761, 0.36 #824, 0.36 #790), 0194xc (0.43 #757, 0.34 #820, 0.34 #786), 06bss (0.40 #750, 0.33 #813, 0.33 #779) >> Best rule #421 for best value: >> intensional similarity = 36 >> extensional distance = 11 >> proper extension: 06f0dc; >> query: (?x10638, ?x7891) <- district_represented(?x10638, ?x6895), district_represented(?x10638, ?x4776), district_represented(?x10638, ?x3038), district_represented(?x10638, ?x2020), district_represented(?x10638, ?x1755), district_represented(?x10638, ?x760), district_represented(?x10638, ?x728), district_represented(?x10638, ?x335), legislative_sessions(?x11142, ?x10638), legislative_sessions(?x4812, ?x10638), legislative_sessions(?x1754, ?x10638), ?x760 = 05fkf, ?x6895 = 05fjf, legislative_sessions(?x4665, ?x10638), legislative_sessions(?x2860, ?x10638), ?x1755 = 01x73, ?x3038 = 0d0x8, district_represented(?x1754, ?x4061), legislative_sessions(?x5978, ?x10638), ?x2860 = 0b3wk, district_represented(?x11142, ?x4754), district_represented(?x11142, ?x3778), ?x3778 = 07h34, ?x4776 = 06yxd, ?x2020 = 05k7sb, ?x4754 = 0g0syc, ?x4665 = 07t58, legislative_sessions(?x7714, ?x11142), ?x4061 = 0498y, ?x335 = 059rby, legislative_sessions(?x7891, ?x7714), legislative_sessions(?x5742, ?x4812), district_represented(?x12714, ?x728), district_represented(?x3463, ?x728), ?x3463 = 02bqmq, ?x12714 = 05rrw9 >> conf = 0.52 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01grmk legislative_sessions! 042d1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 30.000 30.000 0.516 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #6658-0byq0v PRED entity: 0byq0v PRED relation: colors PRED expected values: 0jc_p => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 17): 083jv (0.85 #1764, 0.80 #1079, 0.79 #1098), 06fvc (0.69 #852, 0.68 #563, 0.64 #1688), 01g5v (0.47 #698, 0.37 #1727, 0.36 #1442), 038hg (0.40 #289, 0.36 #521, 0.33 #69), 01l849 (0.31 #1401, 0.22 #1782, 0.16 #714), 036k5h (0.22 #1782, 0.16 #714, 0.15 #1822), 088fh (0.16 #714, 0.15 #1822, 0.15 #1823), 0jc_p (0.16 #714, 0.15 #1822, 0.15 #1823), 09ggk (0.16 #714, 0.15 #1822, 0.15 #1823), 03vtbc (0.16 #714, 0.11 #811, 0.10 #1066) >> Best rule #1764 for best value: >> intensional similarity = 17 >> extensional distance = 260 >> proper extension: 04088s0; 026xxv_; 03dkx; >> query: (?x12207, 083jv) <- colors(?x12207, ?x4557), colors(?x13326, ?x4557), colors(?x12792, ?x4557), colors(?x12043, ?x4557), colors(?x9543, ?x4557), colors(?x12761, ?x4557), colors(?x5981, ?x4557), colors(?x2351, ?x4557), ?x13326 = 0hm2b, contains(?x94, ?x5981), ?x9543 = 07s8qm7, ?x12043 = 03jb2n, ?x94 = 09c7w0, colors(?x12761, ?x1101), ?x12792 = 03x726, ?x2351 = 0q19t, ?x1101 = 06fvc >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #714 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 36 *> proper extension: 047g98; 0dkb83; *> query: (?x12207, ?x663) <- position(?x12207, ?x63), position(?x12207, ?x60), team(?x203, ?x12207), current_club(?x6180, ?x12207), ?x63 = 02sdk9v, colors(?x12207, ?x4557), ?x60 = 02nzb8, colors(?x13090, ?x4557), colors(?x10066, ?x4557), colors(?x8750, ?x4557), colors(?x6816, ?x4557), colors(?x10066, ?x663), contains(?x94, ?x6816), ?x13090 = 0lmm3, team(?x2201, ?x8750) *> conf = 0.16 ranks of expected_values: 8 EVAL 0byq0v colors 0jc_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 113.000 113.000 0.847 http://example.org/sports/sports_team/colors #6657-07b1gq PRED entity: 07b1gq PRED relation: honored_for! PRED expected values: 06ybb1 0cwfgz => 74 concepts (28 used for prediction) PRED predicted values (max 10 best out of 84): 02scbv (0.86 #2336, 0.83 #1556, 0.83 #1712), 03phtz (0.86 #2336, 0.83 #1712, 0.83 #2025), 037xlx (0.86 #2336, 0.83 #1712, 0.83 #2025), 06ybb1 (0.54 #1399, 0.51 #2493, 0.51 #2492), 0cwfgz (0.54 #1399, 0.51 #2493, 0.51 #2492), 07b1gq (0.54 #1399, 0.51 #2493, 0.51 #2492), 024mxd (0.50 #62, 0.04 #1618, 0.04 #1931), 044g_k (0.40 #25, 0.03 #1581, 0.03 #1894), 042g97 (0.40 #152, 0.03 #1708, 0.03 #2021), 0d_wms (0.40 #66, 0.03 #1622, 0.03 #1935) >> Best rule #2336 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 0g60z; 02xhpl; 0180mw; 0q9jk; >> query: (?x3640, ?x5731) <- honored_for(?x5667, ?x3640), honored_for(?x3640, ?x5731), nominated_for(?x5338, ?x3640), nominated_for(?x382, ?x5667) >> conf = 0.86 => this is the best rule for 3 predicted values *> Best rule #1399 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 125 *> proper extension: 02k_4g; *> query: (?x3640, ?x2165) <- honored_for(?x5667, ?x3640), honored_for(?x1311, ?x3640), award_winner(?x3640, ?x5338), honored_for(?x5667, ?x2165), film_release_distribution_medium(?x1311, ?x81) *> conf = 0.54 ranks of expected_values: 4, 5 EVAL 07b1gq honored_for! 0cwfgz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 28.000 0.856 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for EVAL 07b1gq honored_for! 06ybb1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 74.000 28.000 0.856 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #6656-032w8h PRED entity: 032w8h PRED relation: award_nominee PRED expected values: 01fx5l => 82 concepts (45 used for prediction) PRED predicted values (max 10 best out of 940): 05slvm (0.81 #58332, 0.81 #23335, 0.81 #72333), 05ty4m (0.21 #2334, 0.15 #102668, 0.15 #23336), 09r9dp (0.21 #2334, 0.15 #23336, 0.15 #25671), 072bb1 (0.18 #67666, 0.15 #102668, 0.14 #9334), 0bt4r4 (0.18 #67666, 0.14 #9334, 0.04 #652), 0h3mrc (0.18 #67666, 0.14 #9334, 0.04 #888), 0cj2nl (0.18 #67666, 0.14 #9334, 0.04 #883), 015grj (0.18 #67666, 0.14 #9334, 0.04 #197), 060j8b (0.18 #67666, 0.14 #9334, 0.03 #13117), 0bt7ws (0.18 #67666, 0.14 #9334, 0.03 #12535) >> Best rule #58332 for best value: >> intensional similarity = 4 >> extensional distance = 1259 >> proper extension: 016ppr; >> query: (?x1736, ?x237) <- award_nominee(?x8440, ?x1736), award_nominee(?x237, ?x1736), category(?x8440, ?x134), gender(?x8440, ?x231) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #102668 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1755 *> proper extension: 0kc9f; *> query: (?x1736, ?x643) <- nominated_for(?x1736, ?x167), film(?x643, ?x167) *> conf = 0.15 ranks of expected_values: 39 EVAL 032w8h award_nominee 01fx5l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 82.000 45.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #6655-04cy8rb PRED entity: 04cy8rb PRED relation: award PRED expected values: 02qyntr => 117 concepts (80 used for prediction) PRED predicted values (max 10 best out of 255): 0k611 (0.79 #27647, 0.78 #24799, 0.77 #15447), 02qyntr (0.45 #274, 0.38 #1087, 0.38 #680), 0p9sw (0.43 #1648, 0.31 #1242, 0.08 #836), 02r22gf (0.34 #1660, 0.21 #1254, 0.08 #848), 0gs9p (0.31 #486, 0.27 #80, 0.23 #893), 09sb52 (0.31 #3697, 0.28 #9389, 0.27 #4510), 019f4v (0.23 #473, 0.19 #880, 0.19 #21546), 040njc (0.23 #414, 0.16 #21954, 0.15 #30493), 0gq_v (0.21 #1241, 0.14 #1647, 0.07 #31308), 02pqp12 (0.19 #477, 0.14 #71, 0.12 #884) >> Best rule #27647 for best value: >> intensional similarity = 3 >> extensional distance = 1380 >> proper extension: 01vw87c; >> query: (?x323, ?x1703) <- award_winner(?x1703, ?x323), ceremony(?x1703, ?x78), nominated_for(?x1703, ?x144) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #274 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 04_m9gk; *> query: (?x323, 02qyntr) <- edited_by(?x5051, ?x323), produced_by(?x5051, ?x2596), award_nominee(?x100, ?x2596) *> conf = 0.45 ranks of expected_values: 2 EVAL 04cy8rb award 02qyntr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 80.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6654-06s6l PRED entity: 06s6l PRED relation: member_states! PRED expected values: 085h1 => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 13): 085h1 (0.69 #60, 0.69 #36, 0.67 #40), 018cqq (0.26 #14, 0.20 #19, 0.20 #23), 059dn (0.16 #8, 0.15 #12, 0.14 #25), 02jxk (0.16 #22, 0.15 #13, 0.14 #82), 07t65 (0.11 #17, 0.07 #163, 0.05 #94), 0j7v_ (0.11 #17, 0.07 #163, 0.05 #94), 041288 (0.07 #163), 0b6css (0.07 #163), 0gkjy (0.07 #163), 04k4l (0.07 #163) >> Best rule #60 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 04thp; >> query: (?x1925, 085h1) <- currency(?x1925, ?x170), contains(?x7273, ?x1925), official_language(?x1925, ?x254) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06s6l member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.691 http://example.org/user/ktrueman/default_domain/international_organization/member_states #6653-017vkx PRED entity: 017vkx PRED relation: artists! PRED expected values: 07sbbz2 06by7 => 135 concepts (61 used for prediction) PRED predicted values (max 10 best out of 235): 064t9 (0.81 #11704, 0.77 #12629, 0.75 #320), 06j6l (0.72 #4353, 0.68 #1584, 0.68 #5890), 025sc50 (0.62 #356, 0.53 #3125, 0.50 #663), 016clz (0.62 #8923, 0.39 #3695, 0.38 #2157), 06by7 (0.59 #16016, 0.59 #16324, 0.59 #2789), 0ggx5q (0.50 #382, 0.36 #2227, 0.31 #689), 02x8m (0.49 #7399, 0.35 #3095, 0.31 #4324), 05bt6j (0.42 #2196, 0.38 #658, 0.38 #351), 0155w (0.41 #1640, 0.27 #3180, 0.23 #5946), 02lnbg (0.38 #2208, 0.38 #363, 0.27 #3132) >> Best rule #11704 for best value: >> intensional similarity = 5 >> extensional distance = 430 >> proper extension: 0c7ct; 01pr_j6; 01qkqwg; 013v5j; 02wb6yq; 01l_vgt; 01wz_ml; 0p3r8; 01wy61y; 03xhj6; ... >> query: (?x3856, 064t9) <- artists(?x3928, ?x3856), artists(?x3928, ?x10712), artists(?x3928, ?x4675), ?x4675 = 026spg, ?x10712 = 016376 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #16016 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 653 *> proper extension: 01nqfh_; 07qnf; 04r1t; 09prnq; 02r1tx7; 0565cz; 05563d; 0394y; 018gm9; 01j59b0; ... *> query: (?x3856, 06by7) <- artists(?x3928, ?x3856), artists(?x3928, ?x3997), ?x3997 = 0gbwp, parent_genre(?x1127, ?x3928) *> conf = 0.59 ranks of expected_values: 5, 35 EVAL 017vkx artists! 06by7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 135.000 61.000 0.815 http://example.org/music/genre/artists EVAL 017vkx artists! 07sbbz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 135.000 61.000 0.815 http://example.org/music/genre/artists #6652-094vy PRED entity: 094vy PRED relation: contains PRED expected values: 0m75g => 104 concepts (32 used for prediction) PRED predicted values (max 10 best out of 2678): 094vy (0.61 #5882, 0.54 #52945, 0.54 #61769), 02jx1 (0.61 #5882, 0.54 #52945, 0.54 #61769), 07ssc (0.61 #5882, 0.54 #52945, 0.54 #61769), 0138kk (0.60 #5375, 0.50 #2434, 0.12 #8316), 04p3c (0.60 #3520, 0.50 #579, 0.12 #6461), 0134bf (0.57 #11764, 0.54 #52945, 0.54 #8823), 01z53w (0.50 #1989, 0.40 #4930, 0.16 #7871), 04lh6 (0.50 #1308, 0.40 #4249, 0.12 #7190), 0d6yv (0.50 #1543, 0.40 #4484, 0.12 #7425), 01zfrt (0.50 #2057, 0.40 #4998, 0.12 #7939) >> Best rule #5882 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0134bf; >> query: (?x9985, ?x512) <- contains(?x9985, ?x12237), contains(?x9985, ?x10786), contains(?x9985, ?x8755), ?x8755 = 020d8d, contains(?x512, ?x12237), location(?x4015, ?x12237), category(?x10786, ?x134) >> conf = 0.61 => this is the best rule for 3 predicted values *> Best rule #986 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 07ssc; 02jx1; *> query: (?x9985, 0m75g) <- contains(?x9985, ?x12237), contains(?x9985, ?x8755), ?x8755 = 020d8d, ?x12237 = 013wf1, location(?x11750, ?x9985) *> conf = 0.50 ranks of expected_values: 91 EVAL 094vy contains 0m75g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 104.000 32.000 0.607 http://example.org/location/location/contains #6651-01vsxdm PRED entity: 01vsxdm PRED relation: origin PRED expected values: 0rj0z => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 98): 01_d4 (0.33 #512, 0.25 #748, 0.17 #2164), 0l3q2 (0.33 #444, 0.06 #3276, 0.02 #4933), 0q_0z (0.25 #915, 0.17 #2567, 0.17 #2331), 030qb3t (0.20 #1214, 0.11 #2866, 0.09 #4049), 0rh7t (0.20 #1287, 0.11 #2939, 0.04 #3885), 04jpl (0.20 #1658, 0.08 #4257, 0.07 #7803), 0c_m3 (0.17 #2461, 0.14 #2697, 0.06 #3169), 0dqyw (0.17 #2539, 0.14 #2775, 0.06 #3247), 0tz14 (0.17 #2313, 0.06 #3257), 0ply0 (0.14 #2665, 0.06 #3137, 0.02 #5266) >> Best rule #512 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 01fchy; >> query: (?x1467, 01_d4) <- artists(?x9248, ?x1467), artists(?x2249, ?x1467), ?x2249 = 03lty, ?x9248 = 02t8gf, award(?x1467, ?x2634), group(?x5126, ?x1467) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01vsxdm origin 0rj0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 99.000 0.333 http://example.org/music/artist/origin #6650-0k525 PRED entity: 0k525 PRED relation: film PRED expected values: 05q96q6 03cp4cn 02gpkt 060__7 => 111 concepts (60 used for prediction) PRED predicted values (max 10 best out of 697): 02gpkt (0.40 #1302, 0.02 #15527), 02_sr1 (0.40 #662), 03q0r1 (0.29 #4188, 0.01 #55753, 0.01 #68199), 087pfc (0.25 #6854, 0.01 #17523), 0f7hw (0.20 #3326, 0.12 #6882, 0.08 #15773), 03mh94 (0.20 #1842, 0.12 #5398, 0.04 #14289), 0pc62 (0.20 #1872, 0.12 #5428, 0.02 #14319), 034qmv (0.20 #15, 0.12 #5349), 03nx8mj (0.20 #2470, 0.08 #7804), 051zy_b (0.20 #2354, 0.04 #14801, 0.02 #27247) >> Best rule #1302 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01900g; 06b_0; >> query: (?x11155, 02gpkt) <- film(?x11155, ?x7514), film(?x11155, ?x7415), ?x7514 = 06x43v, award(?x11155, ?x591), film(?x1104, ?x7415) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 472, 618 EVAL 0k525 film 060__7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 111.000 60.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 0k525 film 02gpkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 60.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 0k525 film 03cp4cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 60.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 0k525 film 05q96q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 111.000 60.000 0.400 http://example.org/film/actor/film./film/performance/film #6649-02w6bq PRED entity: 02w6bq PRED relation: institution! PRED expected values: 022h5x => 178 concepts (70 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.87 #1034, 0.86 #1294, 0.83 #105), 0bkj86 (0.71 #66, 0.67 #108, 0.65 #87), 02_xgp2 (0.68 #173, 0.59 #70, 0.56 #362), 03bwzr4 (0.67 #114, 0.65 #93, 0.58 #135), 027f2w (0.59 #88, 0.59 #67, 0.56 #109), 013zdg (0.54 #398, 0.43 #168, 0.37 #234), 04zx3q1 (0.50 #104, 0.43 #165, 0.41 #83), 07s6fsf (0.44 #103, 0.41 #61, 0.41 #164), 0bjrnt (0.44 #106, 0.41 #64, 0.35 #85), 02m4yg (0.39 #595, 0.36 #782, 0.19 #220) >> Best rule #1034 for best value: >> intensional similarity = 7 >> extensional distance = 317 >> proper extension: 0373qg; >> query: (?x11229, 014mlp) <- organization(?x346, ?x11229), institution(?x7636, ?x11229), contains(?x608, ?x11229), institution(?x7636, ?x11607), institution(?x7636, ?x7596), ?x7596 = 012mzw, ?x11607 = 02hwww >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #119 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 16 *> proper extension: 03ksy; 050xpd; *> query: (?x11229, 022h5x) <- institution(?x7636, ?x11229), school_type(?x11229, ?x3092), ?x7636 = 01rr_d, major_field_of_study(?x11229, ?x2606), student(?x11229, ?x1020), ?x2606 = 062z7 *> conf = 0.22 ranks of expected_values: 17 EVAL 02w6bq institution! 022h5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 178.000 70.000 0.871 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #6648-01h8rk PRED entity: 01h8rk PRED relation: contains! PRED expected values: 09c7w0 => 192 concepts (64 used for prediction) PRED predicted values (max 10 best out of 276): 09c7w0 (0.88 #17910, 0.83 #42989, 0.82 #16120), 059rby (0.60 #36735, 0.53 #41215, 0.46 #48379), 05kj_ (0.52 #8097, 0.04 #3621, 0.04 #9891), 0d0vqn (0.52 #35820), 04jpl (0.22 #41217, 0.04 #45694, 0.04 #7183), 02jx1 (0.21 #41282, 0.13 #45759, 0.11 #39490), 0d9jr (0.18 #308, 0.17 #1203, 0.08 #3888), 0mmpz (0.18 #655, 0.08 #4235, 0.04 #8711), 02_286 (0.16 #36758, 0.11 #41238, 0.10 #49298), 01n7q (0.14 #15300, 0.13 #5448, 0.11 #26047) >> Best rule #17910 for best value: >> intensional similarity = 5 >> extensional distance = 93 >> proper extension: 06mkj; 0d05w3; >> query: (?x5068, 09c7w0) <- school(?x4171, ?x5068), contains(?x4600, ?x5068), contains(?x4600, ?x5267), location(?x1165, ?x5267), month(?x5267, ?x1459) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01h8rk contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 192.000 64.000 0.884 http://example.org/location/location/contains #6647-09v3jyg PRED entity: 09v3jyg PRED relation: film_crew_role PRED expected values: 01pvkk => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 35): 0ch6mp2 (0.83 #459, 0.82 #684, 0.80 #497), 09zzb8 (0.82 #452, 0.81 #826, 0.78 #714), 09vw2b7 (0.79 #458, 0.72 #496, 0.71 #421), 01vx2h (0.52 #463, 0.41 #501, 0.41 #837), 01pvkk (0.30 #838, 0.28 #1327, 0.28 #464), 02rh1dz (0.27 #11, 0.23 #462, 0.17 #836), 02ynfr (0.22 #468, 0.19 #431, 0.19 #506), 015h31 (0.19 #197, 0.14 #825, 0.13 #2175), 01xy5l_ (0.16 #504, 0.15 #429, 0.14 #691), 0215hd (0.16 #696, 0.15 #509, 0.15 #434) >> Best rule #459 for best value: >> intensional similarity = 7 >> extensional distance = 261 >> proper extension: 03bzyn4; >> query: (?x6931, 0ch6mp2) <- film(?x3785, ?x6931), language(?x6931, ?x254), film_crew_role(?x6931, ?x2095), film_crew_role(?x6931, ?x468), ?x2095 = 0dxtw, ?x468 = 02r96rf, genre(?x6931, ?x307) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #838 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 392 *> proper extension: 014zwb; 02nczh; *> query: (?x6931, 01pvkk) <- film(?x3785, ?x6931), language(?x6931, ?x254), film_crew_role(?x6931, ?x2095), film_crew_role(?x6931, ?x468), ?x2095 = 0dxtw, film_crew_role(?x13292, ?x468), ?x13292 = 076tw54 *> conf = 0.30 ranks of expected_values: 5 EVAL 09v3jyg film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 67.000 67.000 0.825 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6646-016vh2 PRED entity: 016vh2 PRED relation: genre! PRED expected values: 0pk1p => 31 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1894): 06z8s_ (0.67 #3869, 0.60 #2002, 0.50 #134), 02jkkv (0.64 #9082, 0.58 #10950, 0.50 #12820), 03s6l2 (0.64 #7557, 0.58 #9425, 0.50 #3820), 0bxsk (0.60 #3115, 0.50 #10587, 0.50 #4982), 02mpyh (0.58 #10851, 0.55 #8983, 0.41 #16457), 0dlngsd (0.55 #8275, 0.50 #10143, 0.50 #4538), 061681 (0.55 #7584, 0.50 #13190, 0.50 #9452), 03kg2v (0.55 #7965, 0.50 #9833, 0.50 #493), 03twd6 (0.55 #7706, 0.50 #9574, 0.50 #234), 07nt8p (0.55 #7839, 0.50 #9707, 0.47 #15313) >> Best rule #3869 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 05p553; >> query: (?x13420, 06z8s_) <- genre(?x10130, ?x13420), genre(?x7428, ?x13420), genre(?x3990, ?x13420), genre(?x2500, ?x13420), ?x7428 = 035gnh, ?x2500 = 0418wg, language(?x3990, ?x2502), ?x2502 = 06nm1, film(?x2156, ?x3990), film_format(?x3990, ?x909), film(?x1194, ?x3990), country(?x10130, ?x94), produced_by(?x3990, ?x1285), film(?x525, ?x10130) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5248 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 05p553; *> query: (?x13420, 0pk1p) <- genre(?x10130, ?x13420), genre(?x7428, ?x13420), genre(?x3990, ?x13420), genre(?x2500, ?x13420), ?x7428 = 035gnh, ?x2500 = 0418wg, language(?x3990, ?x2502), ?x2502 = 06nm1, film(?x2156, ?x3990), film_format(?x3990, ?x909), film(?x1194, ?x3990), country(?x10130, ?x94), produced_by(?x3990, ?x1285), film(?x525, ?x10130) *> conf = 0.33 ranks of expected_values: 357 EVAL 016vh2 genre! 0pk1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 31.000 17.000 0.667 http://example.org/film/film/genre #6645-024yxd PRED entity: 024yxd PRED relation: artists! PRED expected values: 064t9 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 184): 064t9 (0.53 #1589, 0.46 #2534, 0.43 #7574), 06by7 (0.44 #7583, 0.41 #9158, 0.39 #2543), 05bt6j (0.30 #1621, 0.24 #2566, 0.23 #7606), 06j6l (0.28 #2571, 0.25 #1626, 0.23 #7611), 025sc50 (0.27 #2573, 0.24 #1628, 0.18 #7613), 03_d0 (0.25 #1272, 0.24 #957, 0.17 #2532), 0glt670 (0.25 #2563, 0.22 #988, 0.19 #7603), 016clz (0.23 #7565, 0.21 #9140, 0.20 #950), 02k_kn (0.23 #1644, 0.12 #2589, 0.12 #4479), 0gywn (0.20 #2581, 0.19 #1636, 0.16 #1321) >> Best rule #1589 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 0lbj1; 06cc_1; 01vvycq; 02l840; 01vrz41; 012x4t; 09mq4m; 02fgpf; 04xrx; 0259r0; ... >> query: (?x11219, 064t9) <- award(?x11219, ?x1232), award_winner(?x11219, ?x6025), ?x1232 = 0c4z8 >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024yxd artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.527 http://example.org/music/genre/artists #6644-01wtlq PRED entity: 01wtlq PRED relation: artists PRED expected values: 0b_j2 => 72 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1475): 01w8n89 (0.62 #12200, 0.33 #315, 0.29 #17603), 02ndj5 (0.60 #10621, 0.33 #897, 0.25 #1979), 0kn3g (0.50 #5211, 0.50 #3050, 0.43 #4131), 02ck1 (0.50 #4532, 0.50 #2371, 0.43 #3452), 06wvj (0.50 #2355, 0.40 #4516, 0.29 #3436), 018x3 (0.50 #1580, 0.33 #498, 0.29 #3741), 06k02 (0.50 #2335, 0.31 #7734, 0.29 #3416), 01nqfh_ (0.50 #2195, 0.30 #5436, 0.29 #3276), 01pbs9w (0.50 #2684, 0.30 #5925, 0.29 #3765), 02sjp (0.50 #3006, 0.30 #6247, 0.29 #4087) >> Best rule #12200 for best value: >> intensional similarity = 7 >> extensional distance = 45 >> proper extension: 01738f; 07bbw; 0jrv_; >> query: (?x1067, 01w8n89) <- artists(?x1067, ?x2940), artists(?x497, ?x2940), artist(?x8721, ?x2940), ?x497 = 0fd3y, role(?x2940, ?x1166), profession(?x2940, ?x1183), role(?x2940, ?x316) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2756 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 0ggq0m; 017_qw; *> query: (?x1067, 0b_j2) <- artists(?x1067, ?x2940), artists(?x1067, ?x1068), artists(?x1067, ?x352), ?x2940 = 06449, award_winner(?x9431, ?x352), ?x9431 = 02cg41, award_winner(?x352, ?x8583), artist(?x2149, ?x1068) *> conf = 0.25 ranks of expected_values: 75 EVAL 01wtlq artists 0b_j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 72.000 32.000 0.617 http://example.org/music/genre/artists #6643-06dfg PRED entity: 06dfg PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 21): 060bp (0.70 #133, 0.67 #155, 0.67 #705), 0dq3c (0.39 #727, 0.38 #1970, 0.34 #1593), 0pqc5 (0.36 #2132, 0.27 #400, 0.25 #202), 04syw (0.34 #138, 0.25 #6, 0.22 #50), 0fkvn (0.34 #1817, 0.33 #69, 0.28 #179), 0f6c3 (0.31 #73, 0.29 #1310, 0.27 #1400), 09n5b9 (0.28 #77, 0.25 #1314, 0.24 #1604), 02079p (0.27 #2062, 0.11 #1704, 0.06 #538), 0fj45 (0.25 #19, 0.23 #151, 0.14 #591), 0p5vf (0.19 #78, 0.19 #56, 0.19 #12) >> Best rule #133 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 088q1s; >> query: (?x7035, 060bp) <- form_of_government(?x7035, ?x1926), countries_spoken_in(?x254, ?x7035), ?x1926 = 018wl5 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06dfg jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.702 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #6642-02qx5h PRED entity: 02qx5h PRED relation: instrumentalists! PRED expected values: 018j2 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 44): 0342h (0.40 #2052, 0.39 #895, 0.37 #628), 05r5c (0.25 #632, 0.25 #2056, 0.25 #1077), 05148p4 (0.19 #2069, 0.17 #1090, 0.17 #645), 018vs (0.18 #2061, 0.17 #904, 0.17 #637), 02hnl (0.12 #2083, 0.11 #1727, 0.10 #1549), 03qjg (0.09 #1121, 0.09 #2100, 0.08 #1566), 0l14md (0.08 #631, 0.08 #898, 0.08 #1254), 026t6 (0.08 #1694, 0.07 #2050, 0.07 #1783), 0l14qv (0.05 #1519, 0.05 #629, 0.05 #1074), 07y_7 (0.05 #1070, 0.03 #625, 0.03 #892) >> Best rule #2052 for best value: >> intensional similarity = 4 >> extensional distance = 342 >> proper extension: 01vzz1c; >> query: (?x12788, 0342h) <- profession(?x12788, ?x1183), profession(?x12788, ?x1032), ?x1032 = 02hrh1q, ?x1183 = 09jwl >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2087 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 342 *> proper extension: 01vzz1c; *> query: (?x12788, 018j2) <- profession(?x12788, ?x1183), profession(?x12788, ?x1032), ?x1032 = 02hrh1q, ?x1183 = 09jwl *> conf = 0.04 ranks of expected_values: 12 EVAL 02qx5h instrumentalists! 018j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 102.000 102.000 0.401 http://example.org/music/instrument/instrumentalists #6641-03d9d6 PRED entity: 03d9d6 PRED relation: artists! PRED expected values: 059kh => 81 concepts (55 used for prediction) PRED predicted values (max 10 best out of 238): 064t9 (0.85 #931, 0.55 #11653, 0.49 #12878), 02lnbg (0.46 #974, 0.16 #6489, 0.14 #3730), 0ggx5q (0.42 #993, 0.18 #3749, 0.16 #6508), 0xhtw (0.40 #4609, 0.40 #4916, 0.39 #5223), 015pdg (0.35 #2144, 0.07 #2764, 0.05 #7973), 059kh (0.35 #964, 0.15 #4945, 0.15 #4638), 017_qw (0.32 #6186, 0.30 #5574, 0.26 #4346), 025sc50 (0.31 #965, 0.26 #6480, 0.20 #11687), 0m0jc (0.31 #926, 0.17 #12560, 0.16 #14705), 06j6l (0.29 #6478, 0.27 #963, 0.26 #11685) >> Best rule #931 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 03f5spx; 01x1cn2; 01vxlbm; 0137hn; >> query: (?x5618, 064t9) <- artists(?x3243, ?x5618), artists(?x1572, ?x5618), ?x1572 = 06by7, ?x3243 = 0y3_8 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #964 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 03f5spx; 01x1cn2; 01vxlbm; 0137hn; *> query: (?x5618, 059kh) <- artists(?x3243, ?x5618), artists(?x1572, ?x5618), ?x1572 = 06by7, ?x3243 = 0y3_8 *> conf = 0.35 ranks of expected_values: 6 EVAL 03d9d6 artists! 059kh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 55.000 0.846 http://example.org/music/genre/artists #6640-019v9k PRED entity: 019v9k PRED relation: major_field_of_study PRED expected values: 02lp1 06ms6 01jzxy 064_8sq 04rlf 02mgp => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 124): 02lp1 (0.70 #814, 0.64 #981, 0.57 #703), 06ms6 (0.64 #929, 0.60 #816, 0.57 #705), 0jjw (0.60 #495, 0.57 #712, 0.55 #214), 05qt0 (0.60 #609, 0.56 #773, 0.55 #214), 04rlf (0.60 #505, 0.55 #214, 0.53 #1032), 01jzxy (0.60 #599, 0.55 #214, 0.53 #1032), 04g7x (0.55 #214, 0.55 #947, 0.53 #1032), 0cd78 (0.55 #214, 0.53 #1032, 0.53 #977), 07c52 (0.55 #214, 0.53 #1032, 0.53 #977), 06bvp (0.55 #214, 0.53 #1032, 0.53 #977) >> Best rule #814 for best value: >> intensional similarity = 14 >> extensional distance = 8 >> proper extension: 01ysy9; >> query: (?x1771, 02lp1) <- institution(?x1771, ?x5907), institution(?x1771, ?x5158), institution(?x1771, ?x4257), institution(?x1771, ?x4209), institution(?x1771, ?x621), major_field_of_study(?x1771, ?x12158), major_field_of_study(?x1771, ?x10391), colors(?x5158, ?x332), school_type(?x621, ?x3092), student(?x4209, ?x123), school(?x4779, ?x4257), ?x10391 = 02jfc, major_field_of_study(?x581, ?x12158), currency(?x5907, ?x170) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 6, 11, 33 EVAL 019v9k major_field_of_study 02mgp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 24.000 24.000 0.700 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 019v9k major_field_of_study 04rlf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 24.000 24.000 0.700 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 019v9k major_field_of_study 064_8sq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 24.000 24.000 0.700 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 019v9k major_field_of_study 01jzxy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 24.000 24.000 0.700 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 019v9k major_field_of_study 06ms6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.700 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 019v9k major_field_of_study 02lp1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.700 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #6639-05v8c PRED entity: 05v8c PRED relation: teams PRED expected values: 04k3r_ => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 237): 0hmtk (0.11 #677, 0.08 #1038, 0.08 #1398), 05g76 (0.11 #395, 0.08 #756, 0.08 #1116), 0cqt41 (0.11 #390, 0.08 #751, 0.08 #1111), 0jm3v (0.11 #373, 0.08 #734, 0.08 #1094), 086x3 (0.11 #720, 0.08 #1441, 0.07 #1801), 0ckf6 (0.11 #679, 0.07 #1760, 0.03 #5002), 01z1r (0.11 #511, 0.07 #1592, 0.03 #4834), 02_lt (0.11 #484, 0.07 #1565, 0.03 #4807), 024nj1 (0.08 #1075, 0.07 #1795, 0.04 #3956), 035l_9 (0.08 #1036, 0.07 #1756, 0.04 #3917) >> Best rule #677 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0mgp; >> query: (?x550, 0hmtk) <- place_founded(?x12640, ?x550), location(?x2141, ?x550), service_location(?x1492, ?x550) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05v8c teams 04k3r_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 158.000 0.111 http://example.org/sports/sports_team_location/teams #6638-01c92g PRED entity: 01c92g PRED relation: ceremony PRED expected values: 01s695 019bk0 056878 01mhwk => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 125): 056878 (0.62 #1180, 0.60 #1308, 0.60 #283), 019bk0 (0.62 #1166, 0.54 #1678, 0.50 #13), 01s695 (0.60 #1283, 0.60 #386, 0.60 #258), 01mhwk (0.60 #1316, 0.60 #419, 0.60 #291), 0gx1673 (0.60 #364, 0.60 #236, 0.57 #749), 05c1t6z (0.38 #513, 0.23 #3202, 0.21 #4483), 0gvstc3 (0.38 #513, 0.23 #3202, 0.21 #4483), 02q690_ (0.38 #513, 0.23 #3202, 0.21 #4483), 0bzm81 (0.38 #513, 0.23 #3202, 0.21 #4483), 03nnm4t (0.38 #513, 0.23 #3202, 0.21 #4483) >> Best rule #1180 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 026mfs; 01dpdh; 026mff; >> query: (?x1801, 056878) <- award(?x9321, ?x1801), award(?x3358, ?x1801), award(?x2321, ?x1801), artists(?x505, ?x9321), ?x3358 = 01n8gr, participant(?x6236, ?x2321) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 01c92g ceremony 01mhwk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.625 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c92g ceremony 056878 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.625 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c92g ceremony 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.625 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c92g ceremony 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.625 http://example.org/award/award_category/winners./award/award_honor/ceremony #6637-088vmr PRED entity: 088vmr PRED relation: parent_genre PRED expected values: 011j5x => 60 concepts (52 used for prediction) PRED predicted values (max 10 best out of 241): 06by7 (0.87 #5608, 0.83 #1290, 0.80 #5926), 016clz (0.67 #2077, 0.45 #3997, 0.44 #797), 0xhtw (0.58 #1287, 0.52 #2568, 0.50 #1607), 011j5x (0.50 #339, 0.44 #815, 0.36 #2739), 02x8m (0.50 #172, 0.24 #1432, 0.22 #3032), 06cqb (0.50 #159, 0.24 #1432, 0.22 #3032), 01243b (0.44 #2102, 0.44 #1943, 0.44 #4180), 03lty (0.37 #6406, 0.33 #19, 0.30 #6564), 0296y (0.33 #57, 0.09 #2392, 0.09 #5591), 01jwt (0.33 #45, 0.09 #2392, 0.06 #1959) >> Best rule #5608 for best value: >> intensional similarity = 16 >> extensional distance = 66 >> proper extension: 01_sz1; 01_qp_; 0133k0; >> query: (?x14058, 06by7) <- parent_genre(?x14058, ?x10306), parent_genre(?x14058, ?x5934), parent_genre(?x14058, ?x3167), artists(?x3167, ?x13039), artists(?x10306, ?x4712), artists(?x10306, ?x2600), location_of_ceremony(?x4712, ?x3987), ?x2600 = 0qf3p, artists(?x5934, ?x9407), artists(?x5934, ?x9241), artists(?x5934, ?x4594), award_nominee(?x4594, ?x1989), ?x13039 = 0fsyx, ?x9241 = 01w5gg6, ?x9407 = 024qwq, artist(?x2931, ?x4712) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #339 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 2 *> proper extension: 0xjl2; 0y2tr; *> query: (?x14058, 011j5x) <- parent_genre(?x14058, ?x14252), parent_genre(?x14058, ?x10306), parent_genre(?x14058, ?x5934), parent_genre(?x14058, ?x3108), ?x10306 = 09jw2, ?x5934 = 05r6t, artists(?x14252, ?x565), parent_genre(?x14252, ?x1572), artists(?x3108, ?x12565), artists(?x3108, ?x7902), artists(?x3108, ?x5543), artists(?x3108, ?x872), ?x5543 = 01kd57, ?x12565 = 063t3j, ?x565 = 01wl38s, influenced_by(?x1378, ?x7902), people(?x268, ?x7902), artist(?x6474, ?x872), instrumentalists(?x316, ?x7902) *> conf = 0.50 ranks of expected_values: 4 EVAL 088vmr parent_genre 011j5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 60.000 52.000 0.868 http://example.org/music/genre/parent_genre #6636-02b9g4 PRED entity: 02b9g4 PRED relation: vacationer! PRED expected values: 0f2v0 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 71): 05qtj (0.17 #71, 0.10 #320, 0.08 #2063), 0b90_r (0.17 #3, 0.07 #2244, 0.06 #1995), 0r0m6 (0.14 #317, 0.05 #441, 0.04 #2309), 03gh4 (0.14 #2197, 0.11 #2321, 0.11 #2072), 0cv3w (0.10 #305, 0.08 #2048, 0.08 #2297), 0f2v0 (0.08 #62, 0.06 #2676, 0.06 #2800), 0261m (0.08 #101, 0.05 #1844, 0.04 #2093), 0jbs5 (0.08 #99, 0.03 #2091, 0.02 #2340), 06c62 (0.08 #86, 0.03 #3444, 0.03 #4189), 04jpl (0.06 #2126, 0.06 #1752, 0.06 #2001) >> Best rule #71 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 05wjnt; >> query: (?x7040, 05qtj) <- film(?x7040, ?x365), location(?x7040, ?x1523), ?x365 = 0bvn25 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #62 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 05wjnt; *> query: (?x7040, 0f2v0) <- film(?x7040, ?x365), location(?x7040, ?x1523), ?x365 = 0bvn25 *> conf = 0.08 ranks of expected_values: 6 EVAL 02b9g4 vacationer! 0f2v0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 126.000 126.000 0.167 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #6635-04f7c55 PRED entity: 04f7c55 PRED relation: instrumentalists! PRED expected values: 01xqw => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 122): 0342h (0.74 #1945, 0.71 #1101, 0.71 #3209), 05r5c (0.72 #2281, 0.72 #2202, 0.60 #344), 018vs (0.52 #3469, 0.47 #5823, 0.44 #2207), 03bx0bm (0.48 #2280, 0.43 #6738, 0.42 #7756), 013y1f (0.28 #2224, 0.11 #5924, 0.09 #3486), 03qjg (0.27 #1820, 0.24 #3505, 0.23 #5859), 0l14qv (0.25 #2200, 0.15 #3210, 0.15 #5816), 02snj9 (0.25 #140, 0.11 #898, 0.09 #10793), 04rzd (0.22 #876, 0.21 #1131, 0.20 #287), 03gvt (0.22 #2257, 0.20 #399, 0.14 #5957) >> Best rule #1945 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0gdh5; 0282x; >> query: (?x5691, 0342h) <- friend(?x10277, ?x5691), instrumentalists(?x1750, ?x5691), role(?x74, ?x1750), role(?x211, ?x1750) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #5960 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 131 *> proper extension: 020jqv; *> query: (?x5691, 01xqw) <- category(?x5691, ?x134), gender(?x5691, ?x231), instrumentalists(?x1166, ?x5691), ?x1166 = 05148p4 *> conf = 0.06 ranks of expected_values: 62 EVAL 04f7c55 instrumentalists! 01xqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 183.000 183.000 0.742 http://example.org/music/instrument/instrumentalists #6634-01vrnsk PRED entity: 01vrnsk PRED relation: instrumentalists! PRED expected values: 0342h => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 116): 0342h (0.73 #1396, 0.72 #1551, 0.68 #1783), 018vs (0.48 #1790, 0.44 #88, 0.42 #1869), 02hnl (0.44 #108, 0.42 #1858, 0.40 #187), 03gvt (0.31 #1857, 0.28 #1936, 0.27 #2168), 026g73 (0.31 #1857, 0.28 #1936, 0.27 #2168), 02dlh2 (0.31 #1857, 0.28 #1936, 0.27 #2168), 07brj (0.31 #1857, 0.28 #1936, 0.27 #2168), 011k_j (0.31 #1857, 0.28 #1936, 0.27 #2168), 05842k (0.31 #1857, 0.28 #1936, 0.27 #2168), 0l15bq (0.31 #1857, 0.28 #1936, 0.27 #2168) >> Best rule #1396 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 01p0vf; >> query: (?x6947, 0342h) <- role(?x6947, ?x212), group(?x6947, ?x1136), award_nominee(?x1089, ?x6947) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrnsk instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.729 http://example.org/music/instrument/instrumentalists #6633-08815 PRED entity: 08815 PRED relation: institution! PRED expected values: 07s6fsf 02cq61 028dcg => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 9): 07s6fsf (0.74 #354, 0.55 #11, 0.49 #122), 028dcg (0.74 #354, 0.27 #16, 0.25 #26), 02mjs7 (0.33 #22, 0.27 #12, 0.19 #173), 022h5x (0.26 #67, 0.22 #118, 0.20 #108), 02cq61 (0.25 #25, 0.18 #15, 0.13 #65), 01ysy9 (0.11 #9, 0.10 #516, 0.07 #120), 01kxxq (0.10 #516, 0.03 #179, 0.03 #78), 0g26h (0.10 #516, 0.03 #84, 0.02 #154), 01gkg3 (0.10 #516, 0.02 #296, 0.01 #398) >> Best rule #354 for best value: >> intensional similarity = 3 >> extensional distance = 122 >> proper extension: 0194_r; >> query: (?x122, ?x1368) <- student(?x122, ?x7625), currency(?x122, ?x170), student(?x1368, ?x7625) >> conf = 0.74 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2, 5 EVAL 08815 institution! 028dcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.738 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 08815 institution! 02cq61 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 135.000 135.000 0.738 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 08815 institution! 07s6fsf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.738 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #6632-07cw4 PRED entity: 07cw4 PRED relation: featured_film_locations PRED expected values: 02nd_ => 86 concepts (61 used for prediction) PRED predicted values (max 10 best out of 45): 01n6r0 (0.20 #101, 0.02 #1057), 030qb3t (0.13 #6510, 0.09 #3390, 0.08 #5792), 04jpl (0.12 #6481, 0.07 #4803, 0.06 #5763), 0rh6k (0.07 #5755, 0.06 #6473, 0.06 #4795), 02nd_ (0.05 #1310, 0.02 #3707, 0.01 #5869), 080h2 (0.04 #6495, 0.03 #5777, 0.02 #4817), 0h7h6 (0.03 #6514, 0.02 #4357, 0.02 #5796), 01_d4 (0.03 #3398, 0.03 #6518, 0.03 #1241), 03dm7 (0.03 #13202), 0ccvx (0.03 #13202) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 05whq_9; >> query: (?x5930, 01n6r0) <- category(?x5930, ?x134), film_festivals(?x5930, ?x7988), list(?x5930, ?x3004) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1310 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 0hmr4; *> query: (?x5930, 02nd_) <- genre(?x5930, ?x604), nominated_for(?x198, ?x5930), list(?x5930, ?x3004) *> conf = 0.05 ranks of expected_values: 5 EVAL 07cw4 featured_film_locations 02nd_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 61.000 0.200 http://example.org/film/film/featured_film_locations #6631-0yxl PRED entity: 0yxl PRED relation: influenced_by PRED expected values: 04x56 => 125 concepts (37 used for prediction) PRED predicted values (max 10 best out of 354): 0l99s (0.32 #4920, 0.15 #2358, 0.08 #10476), 084w8 (0.31 #2138, 0.16 #4700, 0.11 #10256), 03j2gxx (0.31 #2511, 0.14 #5073, 0.08 #14103), 0h25 (0.25 #346, 0.14 #2562, 0.04 #8034), 041h0 (0.24 #4707, 0.23 #2145, 0.08 #14103), 03_87 (0.24 #4896, 0.18 #9169, 0.17 #10452), 01v9724 (0.23 #2309, 0.19 #4871, 0.14 #2562), 03f0324 (0.23 #2283, 0.17 #9118, 0.15 #10401), 01_k0d (0.22 #4271, 0.18 #6407, 0.16 #11964), 037jz (0.21 #4051, 0.15 #2342, 0.12 #6186) >> Best rule #4920 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 07w21; 019z7q; 03g5jw; 086qd; 01hb6v; 0pkyh; 0p8jf; 085pr; 07h07; 07rd7; ... >> query: (?x8753, 0l99s) <- influenced_by(?x8753, ?x10000), influenced_by(?x8753, ?x7851), peers(?x10000, ?x5334), profession(?x7851, ?x353), nominated_for(?x7851, ?x5305) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #14103 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 132 *> proper extension: 04rcr; 0134tg; 07mvp; 04k05; *> query: (?x8753, ?x1029) <- award_winner(?x10270, ?x8753), influenced_by(?x6723, ?x8753), influenced_by(?x6723, ?x1029), category(?x6723, ?x134) *> conf = 0.08 ranks of expected_values: 69 EVAL 0yxl influenced_by 04x56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 125.000 37.000 0.324 http://example.org/influence/influence_node/influenced_by #6630-0j3d9tn PRED entity: 0j3d9tn PRED relation: language PRED expected values: 02h40lc => 84 concepts (76 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.99 #3706, 0.95 #2037, 0.95 #3585), 0jzc (0.20 #4407, 0.08 #197, 0.06 #496), 03_9r (0.20 #4407, 0.05 #1023, 0.05 #1864), 04306rv (0.11 #481, 0.09 #1138, 0.09 #124), 06nm1 (0.10 #1024, 0.10 #1144, 0.09 #2046), 02bjrlw (0.10 #477, 0.10 #178, 0.09 #1), 0c_v2 (0.09 #17), 06b_j (0.08 #81, 0.07 #498, 0.07 #1155), 0653m (0.05 #71, 0.05 #1025, 0.04 #2106), 04h9h (0.04 #397, 0.04 #457, 0.03 #279) >> Best rule #3706 for best value: >> intensional similarity = 5 >> extensional distance = 1362 >> proper extension: 0979n; >> query: (?x5162, 02h40lc) <- film(?x748, ?x5162), language(?x5162, ?x5607), film(?x5959, ?x5162), language(?x8284, ?x5607), ?x8284 = 02p76f9 >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j3d9tn language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 76.000 0.986 http://example.org/film/film/language #6629-0d35y PRED entity: 0d35y PRED relation: film_regional_debut_venue! PRED expected values: 0crh5_f => 198 concepts (158 used for prediction) PRED predicted values (max 10 best out of 22): 0crh5_f (0.13 #2102, 0.09 #2662, 0.08 #5085), 01sby_ (0.07 #2146, 0.06 #2706, 0.05 #3639), 0b44shh (0.07 #2143, 0.06 #2703, 0.05 #3636), 0blpg (0.07 #2120, 0.06 #2680, 0.05 #3613), 09v42sf (0.06 #738, 0.06 #1110, 0.04 #1482), 043sct5 (0.06 #640, 0.06 #1012, 0.04 #1384), 0cnztc4 (0.06 #576, 0.06 #948, 0.04 #1320), 0gffmn8 (0.06 #2666, 0.06 #5648, 0.05 #6207), 0btpm6 (0.06 #2750, 0.04 #4055, 0.04 #1446), 01s9vc (0.06 #2784, 0.04 #1480, 0.04 #5766) >> Best rule #2102 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 02cl1; 0f2r6; 030qb3t; 094jv; 04f_d; 013yq; 0f__1; 0cv3w; 0vzm; 0f2v0; ... >> query: (?x4419, 0crh5_f) <- location(?x2226, ?x4419), dog_breed(?x4419, ?x1706), featured_film_locations(?x2094, ?x4419) >> conf = 0.13 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d35y film_regional_debut_venue! 0crh5_f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 158.000 0.133 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #6628-02p_7cr PRED entity: 02p_7cr PRED relation: award! PRED expected values: 0gj50 => 65 concepts (42 used for prediction) PRED predicted values (max 10 best out of 859): 0gj50 (0.79 #4082, 0.75 #3059, 0.71 #8161), 0phrl (0.79 #4082, 0.75 #3059, 0.70 #5101), 02_1kl (0.70 #5103, 0.70 #5101, 0.69 #4081), 0147w8 (0.70 #5103, 0.70 #5101, 0.69 #4081), 02_1q9 (0.70 #5101, 0.69 #4081, 0.67 #2038), 034vds (0.70 #5101, 0.69 #4081, 0.67 #2038), 0kfv9 (0.26 #9356, 0.21 #13437, 0.20 #10376), 0180mw (0.24 #9846, 0.19 #7810, 0.17 #13927), 015ppk (0.24 #9892, 0.19 #7856, 0.16 #10912), 01q_y0 (0.23 #7367, 0.18 #9403, 0.16 #10423) >> Best rule #4082 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 02q1tc5; >> query: (?x588, ?x4011) <- nominated_for(?x588, ?x7175), nominated_for(?x588, ?x4114), nominated_for(?x588, ?x4011), ?x7175 = 02_1kl, genre(?x4011, ?x8805), nominated_for(?x438, ?x4011), tv_program(?x2544, ?x4011), ?x4114 = 01b65l >> conf = 0.79 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 02p_7cr award! 0gj50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 42.000 0.789 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #6627-01g4bk PRED entity: 01g4bk PRED relation: profession PRED expected values: 018gz8 => 95 concepts (68 used for prediction) PRED predicted values (max 10 best out of 65): 0nbcg (0.86 #3894, 0.43 #28, 0.33 #886), 0cbd2 (0.81 #1293, 0.71 #4445, 0.60 #1579), 09jwl (0.53 #3882, 0.37 #731, 0.36 #874), 0dz3r (0.35 #3868, 0.13 #7162, 0.12 #7307), 03gjzk (0.34 #5455, 0.29 #4165, 0.23 #1871), 02hv44_ (0.33 #338, 0.33 #195, 0.30 #481), 016z4k (0.30 #433, 0.28 #3870, 0.27 #862), 0n1h (0.29 #10, 0.21 #868, 0.20 #296), 039v1 (0.21 #3899, 0.14 #33, 0.10 #462), 01c72t (0.19 #3887, 0.14 #21, 0.12 #1022) >> Best rule #3894 for best value: >> intensional similarity = 5 >> extensional distance = 495 >> proper extension: 01vrx3g; 032t2z; 0c7ct; 06y9c2; 01cv3n; 01gf5h; 03kwtb; 0ftps; 01p9hgt; 06w2sn5; ... >> query: (?x9747, 0nbcg) <- profession(?x9747, ?x3746), profession(?x8485, ?x3746), profession(?x3893, ?x3746), ?x8485 = 0f13b, ?x3893 = 01v40wd >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #5457 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1011 *> proper extension: 07nznf; 079vf; 05bnp0; 0dbpyd; 06j0md; 03ckxdg; 050023; 026dcvf; 04rs03; 04bs3j; ... *> query: (?x9747, 018gz8) <- profession(?x9747, ?x3746), profession(?x8485, ?x3746), profession(?x3279, ?x3746), ?x8485 = 0f13b, ?x3279 = 0d4jl *> conf = 0.16 ranks of expected_values: 12 EVAL 01g4bk profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 95.000 68.000 0.855 http://example.org/people/person/profession #6626-0ldqf PRED entity: 0ldqf PRED relation: olympics! PRED expected values: 0chghy => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 234): 059j2 (0.78 #997, 0.70 #1190, 0.64 #2356), 0d0vqn (0.70 #1170, 0.67 #977, 0.64 #1751), 0chghy (0.70 #1173, 0.67 #980, 0.64 #1754), 0345h (0.56 #999, 0.54 #3919, 0.52 #3724), 03gj2 (0.56 #991, 0.50 #3521, 0.50 #2350), 0ctw_b (0.51 #3299, 0.50 #1357, 0.50 #1185), 035qy (0.51 #3299, 0.49 #1356, 0.48 #1162), 015fr (0.51 #3299, 0.49 #1356, 0.48 #1162), 05b4w (0.50 #1227, 0.49 #1356, 0.48 #1162), 02vzc (0.49 #1356, 0.48 #1162, 0.47 #3498) >> Best rule #997 for best value: >> intensional similarity = 16 >> extensional distance = 7 >> proper extension: 0kbvb; 0lv1x; 0nbjq; 06sks6; 0jdk_; 0jhn7; 0lgxj; >> query: (?x7441, 059j2) <- sports(?x7441, ?x3659), sports(?x7441, ?x2885), sports(?x7441, ?x2867), sports(?x7441, ?x2044), sports(?x7441, ?x1967), sports(?x7441, ?x1121), sports(?x7441, ?x779), ?x2044 = 06f41, olympics(?x1023, ?x7441), ?x1967 = 01cgz, ?x1023 = 0ctw_b, ?x3659 = 0dwxr, ?x2867 = 02y8z, ?x1121 = 0bynt, ?x779 = 096f8, ?x2885 = 07jjt >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1173 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 8 *> proper extension: 0sxrz; *> query: (?x7441, 0chghy) <- sports(?x7441, ?x4876), sports(?x7441, ?x3659), sports(?x7441, ?x2867), sports(?x7441, ?x2044), sports(?x7441, ?x1967), sports(?x7441, ?x1121), ?x2044 = 06f41, olympics(?x1023, ?x7441), ?x1967 = 01cgz, ?x1023 = 0ctw_b, ?x3659 = 0dwxr, ?x2867 = 02y8z, sports(?x358, ?x1121), country(?x1121, ?x47), ?x4876 = 0d1t3 *> conf = 0.70 ranks of expected_values: 3 EVAL 0ldqf olympics! 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 56.000 56.000 0.778 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #6625-085pr PRED entity: 085pr PRED relation: place_of_birth PRED expected values: 01_d4 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 113): 0s9z_ (0.28 #54238, 0.28 #47898, 0.27 #68331), 02_286 (0.16 #4243, 0.15 #5652, 0.14 #4948), 0cr3d (0.09 #2206, 0.09 #94, 0.09 #6431), 01_d4 (0.09 #770, 0.08 #1474, 0.05 #8516), 0chrx (0.09 #305, 0.05 #2417, 0.04 #1713), 0x335 (0.09 #420, 0.04 #1828, 0.02 #3236), 0_vn7 (0.09 #156, 0.04 #1564, 0.02 #2972), 0r3tq (0.09 #430, 0.04 #1838, 0.02 #2542), 0n96z (0.09 #674, 0.04 #2082, 0.02 #4194), 03l2n (0.08 #1577, 0.04 #7211, 0.03 #3689) >> Best rule #54238 for best value: >> intensional similarity = 3 >> extensional distance = 1657 >> proper extension: 07m69t; >> query: (?x3527, ?x11639) <- nationality(?x3527, ?x94), ?x94 = 09c7w0, location(?x3527, ?x11639) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #770 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 0g5ff; 09jd9; *> query: (?x3527, 01_d4) <- student(?x3424, ?x3527), award(?x3527, ?x8909), ?x8909 = 040_9s0 *> conf = 0.09 ranks of expected_values: 4 EVAL 085pr place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 117.000 0.277 http://example.org/people/person/place_of_birth #6624-0gd9k PRED entity: 0gd9k PRED relation: profession PRED expected values: 02hrh1q => 114 concepts (69 used for prediction) PRED predicted values (max 10 best out of 86): 02hrh1q (0.89 #9521, 0.87 #9088, 0.86 #300), 0cbd2 (0.54 #5912, 0.47 #6777, 0.47 #5480), 09jwl (0.42 #8370, 0.39 #3617, 0.37 #9235), 0kyk (0.36 #26, 0.36 #5932, 0.34 #4204), 015cjr (0.36 #46, 0.27 #622, 0.08 #3504), 02krf9 (0.33 #1320, 0.33 #1464, 0.32 #3193), 0np9r (0.33 #2594, 0.25 #1170, 0.21 #305), 016z4k (0.32 #3606, 0.23 #7927, 0.23 #9224), 0nbcg (0.31 #3630, 0.27 #8383, 0.26 #9248), 0dz3r (0.30 #3604, 0.23 #8357, 0.21 #9222) >> Best rule #9521 for best value: >> intensional similarity = 3 >> extensional distance = 979 >> proper extension: 049tjg; 02wrhj; 02k6rq; 050_qx; >> query: (?x7984, 02hrh1q) <- nominated_for(?x7984, ?x994), film(?x7984, ?x5024), films(?x14068, ?x5024) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gd9k profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 69.000 0.886 http://example.org/people/person/profession #6623-02tktw PRED entity: 02tktw PRED relation: film_crew_role PRED expected values: 02r96rf => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 27): 02r96rf (0.85 #277, 0.74 #1225, 0.72 #381), 02rh1dz (0.50 #9, 0.33 #283, 0.28 #213), 02ynfr (0.50 #14, 0.29 #48, 0.27 #184), 01pvkk (0.42 #78, 0.38 #284, 0.31 #249), 0215hd (0.21 #85, 0.21 #291, 0.15 #1204), 015h31 (0.21 #212, 0.21 #282, 0.14 #491), 089g0h (0.21 #292, 0.16 #86, 0.13 #2928), 0d2b38 (0.20 #298, 0.16 #92, 0.13 #2928), 01xy5l_ (0.16 #286, 0.13 #2928, 0.13 #1199), 033smt (0.14 #300, 0.13 #2928, 0.09 #230) >> Best rule #277 for best value: >> intensional similarity = 5 >> extensional distance = 95 >> proper extension: 0bx_hnp; >> query: (?x6293, 02r96rf) <- genre(?x6293, ?x53), crewmember(?x6293, ?x3782), film_release_distribution_medium(?x6293, ?x81), film_crew_role(?x6293, ?x2154), ?x2154 = 01vx2h >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tktw film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.845 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6622-016tt2 PRED entity: 016tt2 PRED relation: award_nominee PRED expected values: 01gb54 020h2v => 153 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1150): 03v1w7 (0.81 #232775, 0.80 #239760, 0.80 #242089), 020h2v (0.81 #232775, 0.80 #239760, 0.80 #242089), 01gb54 (0.81 #232775, 0.80 #239760, 0.80 #242089), 03xsby (0.81 #232775, 0.80 #239760, 0.80 #242089), 0py5b (0.81 #232775, 0.80 #239760, 0.80 #242089), 05bxwh (0.81 #232775, 0.80 #239760, 0.80 #242089), 05hjmd (0.76 #258392, 0.76 #244418, 0.76 #246748), 017jv5 (0.76 #258392, 0.76 #244418, 0.75 #228117), 05v1sb (0.50 #7960), 016tt2 (0.33 #2439, 0.25 #11749, 0.25 #4767) >> Best rule #232775 for best value: >> intensional similarity = 3 >> extensional distance = 1158 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 012ljv; 02s2ft; 05vsxz; 0grwj; 05bnp0; ... >> query: (?x574, ?x541) <- award_winner(?x1105, ?x574), nominated_for(?x574, ?x308), award_nominee(?x541, ?x574) >> conf = 0.81 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3 EVAL 016tt2 award_nominee 020h2v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 153.000 111.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 016tt2 award_nominee 01gb54 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 153.000 111.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #6621-0ph2w PRED entity: 0ph2w PRED relation: award_winner! PRED expected values: 05qck => 145 concepts (83 used for prediction) PRED predicted values (max 10 best out of 331): 0f4x7 (0.50 #3471, 0.42 #4331, 0.31 #6481), 019bnn (0.33 #4996, 0.25 #1126, 0.22 #10586), 03x3wf (0.33 #65, 0.23 #6085, 0.20 #7805), 01by1l (0.33 #1833, 0.23 #6133, 0.18 #12583), 02v1m7 (0.33 #114, 0.23 #6134, 0.17 #1834), 02ddq4 (0.33 #343, 0.15 #6363, 0.12 #2493), 01dpdh (0.33 #131, 0.12 #2281, 0.10 #7871), 024fz9 (0.33 #207, 0.12 #2357, 0.10 #7947), 02f73p (0.33 #184, 0.12 #2334, 0.09 #4054), 01c9f2 (0.33 #83, 0.12 #2233, 0.09 #3953) >> Best rule #3471 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0cj8x; 09qh1; 044qx; 043gj; 0c2tf; 0l5yl; 063_t; 0bkmf; >> query: (?x4066, 0f4x7) <- participant(?x4065, ?x4066), place_of_death(?x4066, ?x1523), celebrities_impersonated(?x3649, ?x4066), profession(?x4066, ?x1032) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1052 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 014zfs; 01h910; *> query: (?x4066, 05qck) <- participant(?x4065, ?x4066), award(?x4066, ?x4386), influenced_by(?x4066, ?x2283), ?x4065 = 029_3 *> conf = 0.25 ranks of expected_values: 18 EVAL 0ph2w award_winner! 05qck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 145.000 83.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #6620-02jxk PRED entity: 02jxk PRED relation: award_winner! PRED expected values: 05f3q => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 10): 0m7yy (0.09 #14869, 0.08 #16165, 0.07 #13141), 05p1dby (0.04 #13069, 0.02 #27327, 0.02 #24735), 07bdd_ (0.03 #13027, 0.02 #21236, 0.02 #27285), 02x1z2s (0.03 #13159, 0.02 #21368, 0.02 #27417), 01lj_c (0.02 #14985, 0.02 #16281, 0.01 #18873), 01l78d (0.02 #14976, 0.02 #16272, 0.01 #18864), 0gq9h (0.02 #13039), 01l29r (0.01 #14855, 0.01 #16151, 0.01 #23929), 01lk0l (0.01 #14967, 0.01 #16263), 0gvx_ (0.01 #8825, 0.01 #9257, 0.01 #10121) >> Best rule #14869 for best value: >> intensional similarity = 6 >> extensional distance = 143 >> proper extension: 0jz9f; 0c_j5d; 09d5h; 01xdn1; 02301; 04sylm; 017z88; 0gvbw; 01hb1t; 015_1q; ... >> query: (?x2106, 0m7yy) <- citytown(?x2106, ?x4826), country(?x4826, ?x172), month(?x4826, ?x4869), month(?x4826, ?x2255), ?x4869 = 02xx5, ?x2255 = 040fv >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02jxk award_winner! 05f3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 82.000 0.090 http://example.org/award/award_category/winners./award/award_honor/award_winner #6619-025j1t PRED entity: 025j1t PRED relation: film PRED expected values: 035s95 0bxsk => 84 concepts (42 used for prediction) PRED predicted values (max 10 best out of 660): 011ywj (0.27 #4998, 0.03 #15699, 0.03 #17482), 02_1sj (0.25 #80, 0.20 #1863, 0.03 #33883), 07vn_9 (0.25 #1677, 0.20 #3460, 0.03 #33883), 02j69w (0.25 #798, 0.20 #2581, 0.03 #33883), 0830vk (0.25 #590, 0.20 #2373, 0.02 #4156), 0992d9 (0.25 #989, 0.20 #2772, 0.02 #4555), 01s7w3 (0.20 #3307, 0.03 #39234, 0.03 #33883), 04vr_f (0.10 #3736, 0.06 #7135, 0.04 #65989), 0h7t36 (0.10 #5244, 0.01 #15945), 04cj79 (0.08 #4158, 0.02 #5943, 0.01 #7727) >> Best rule #4998 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 02k6rq; >> query: (?x6068, 011ywj) <- award_winner(?x6068, ?x5500), film(?x5500, ?x8084), ?x8084 = 02cbhg >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #4771 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 02k6rq; *> query: (?x6068, 0bxsk) <- award_winner(?x6068, ?x5500), film(?x5500, ?x8084), ?x8084 = 02cbhg *> conf = 0.04 ranks of expected_values: 69, 135 EVAL 025j1t film 0bxsk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 84.000 42.000 0.275 http://example.org/film/actor/film./film/performance/film EVAL 025j1t film 035s95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 84.000 42.000 0.275 http://example.org/film/actor/film./film/performance/film #6618-0zq7r PRED entity: 0zq7r PRED relation: category PRED expected values: 08mbj5d => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14805, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0zq7r category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #6617-0blq0z PRED entity: 0blq0z PRED relation: award_winner PRED expected values: 0ksrf8 => 119 concepts (63 used for prediction) PRED predicted values (max 10 best out of 535): 03_wj_ (0.82 #41833, 0.82 #46663, 0.82 #43443), 0278x6s (0.82 #41833, 0.82 #46663, 0.82 #43443), 0ksrf8 (0.82 #41833, 0.82 #46663, 0.82 #43443), 02s2ft (0.82 #41833, 0.82 #46663, 0.82 #43443), 06_bq1 (0.82 #41833, 0.82 #43443, 0.82 #46662), 02114t (0.66 #4825, 0.64 #6435, 0.53 #43442), 035rnz (0.66 #4825, 0.64 #6435, 0.53 #43442), 058s44 (0.66 #4825, 0.64 #6435, 0.53 #43442), 03_wvl (0.61 #4193, 0.38 #5803, 0.34 #6438), 01sl1q (0.61 #3217, 0.35 #6437, 0.35 #4827) >> Best rule #41833 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 0g51l1; 0c_mvb; 0lzkm; 079ws; 01y8d4; 037q1z; 023jq1; 027zz; 011s9r; 08f3yq; >> query: (?x2670, ?x72) <- place_of_birth(?x2670, ?x1406), award_winner(?x72, ?x2670), gender(?x2670, ?x231) >> conf = 0.82 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0blq0z award_winner 0ksrf8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 63.000 0.822 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #6616-03qbm PRED entity: 03qbm PRED relation: list PRED expected values: 01pd60 => 232 concepts (232 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.76 #145, 0.71 #75, 0.70 #33), 04k4rt (0.56 #431, 0.56 #60, 0.53 #473), 01pd60 (0.53 #76, 0.53 #111, 0.50 #230), 09g7thr (0.14 #1017, 0.14 #1136, 0.13 #905) >> Best rule #145 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 02zs4; 087c7; 03mnk; 08z129; 02bh8z; 01yfp7; 01ym8l; 077w0b; 01nn79; 0sxdg; ... >> query: (?x11080, 01ptsx) <- industry(?x11080, ?x11520), currency(?x11080, ?x170), citytown(?x11080, ?x739), state_province_region(?x11080, ?x335), company(?x1907, ?x11080), taxonomy(?x11520, ?x939) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 0300cp; 0plw; *> query: (?x11080, 01pd60) <- industry(?x11080, ?x11520), citytown(?x11080, ?x739), organization(?x4682, ?x11080), company(?x1907, ?x11080), ?x4682 = 0dq_5, ?x1907 = 01yc02 *> conf = 0.53 ranks of expected_values: 3 EVAL 03qbm list 01pd60 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 232.000 232.000 0.762 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #6615-02j9z PRED entity: 02j9z PRED relation: contains PRED expected values: 03rjj 03rt9 03b79 087vz 026zt 01lvrm 01x5fb => 164 concepts (56 used for prediction) PRED predicted values (max 10 best out of 2913): 06bnz (0.73 #20122, 0.67 #5750, 0.63 #69001), 0f8l9c (0.73 #20122, 0.67 #5750, 0.63 #69001), 01znc_ (0.73 #20122, 0.67 #5750, 0.63 #69001), 035qy (0.73 #20122, 0.67 #5750, 0.63 #69001), 059j2 (0.73 #20122, 0.67 #5750, 0.63 #69001), 06npd (0.73 #20122, 0.67 #5750, 0.63 #69001), 04fh3 (0.73 #20122, 0.67 #5750, 0.63 #69001), 04w4s (0.73 #20122, 0.67 #5750, 0.63 #69001), 0154j (0.73 #20122, 0.67 #5750, 0.63 #69001), 047lj (0.73 #20122, 0.67 #5750, 0.33 #34) >> Best rule #20122 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0j0k; >> query: (?x455, ?x87) <- contains(?x455, ?x1790), countries_within(?x455, ?x87), adjoins(?x455, ?x1144), film_release_region(?x141, ?x1790) >> conf = 0.73 => this is the best rule for 11 predicted values *> Best rule #69001 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 24 *> proper extension: 0b90_r; 04rrx; 04_1l0v; 01w65s; *> query: (?x455, ?x756) <- contains(?x455, ?x8958), contains(?x455, ?x1558), locations(?x9939, ?x8958), film_release_region(?x124, ?x1558), adjoins(?x756, ?x1558) *> conf = 0.63 ranks of expected_values: 15, 28, 87, 557, 1995, 2512 EVAL 02j9z contains 01x5fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 164.000 56.000 0.731 http://example.org/location/location/contains EVAL 02j9z contains 01lvrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 164.000 56.000 0.731 http://example.org/location/location/contains EVAL 02j9z contains 026zt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 164.000 56.000 0.731 http://example.org/location/location/contains EVAL 02j9z contains 087vz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 164.000 56.000 0.731 http://example.org/location/location/contains EVAL 02j9z contains 03b79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 164.000 56.000 0.731 http://example.org/location/location/contains EVAL 02j9z contains 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 164.000 56.000 0.731 http://example.org/location/location/contains EVAL 02j9z contains 03rjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 164.000 56.000 0.731 http://example.org/location/location/contains #6614-01n7qlf PRED entity: 01n7qlf PRED relation: category PRED expected values: 08mbj5d => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #8, 0.83 #11, 0.70 #36) >> Best rule #8 for best value: >> intensional similarity = 5 >> extensional distance = 118 >> proper extension: 01wcp_g; 04mn81; 09k2t1; 01w724; 01k98nm; 01w272y; 0fpj4lx; 02cx90; 0kvnn; 01vwbts; ... >> query: (?x3611, 08mbj5d) <- profession(?x3611, ?x1183), profession(?x3611, ?x220), ?x1183 = 09jwl, place_of_birth(?x3611, ?x1523), ?x220 = 016z4k >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n7qlf category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.833 http://example.org/common/topic/webpage./common/webpage/category #6613-09f2j PRED entity: 09f2j PRED relation: school! PRED expected values: 09l0x9 04f4z1k => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 17): 02qw1zx (0.41 #158, 0.33 #5, 0.25 #22), 09l0x9 (0.36 #96, 0.33 #11, 0.28 #164), 05vsb7 (0.33 #1, 0.29 #86, 0.25 #18), 03nt7j (0.33 #7, 0.29 #92, 0.25 #24), 092j54 (0.33 #9, 0.25 #26, 0.21 #94), 02pq_rp (0.33 #8, 0.25 #25, 0.21 #93), 047dpm0 (0.33 #16, 0.25 #33, 0.21 #101), 02pq_x5 (0.33 #14, 0.25 #31, 0.19 #715), 0g3zpp (0.29 #87, 0.19 #715, 0.17 #155), 02x2khw (0.29 #88, 0.19 #715, 0.16 #428) >> Best rule #158 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 06mkj; 0d05w3; >> query: (?x4955, 02qw1zx) <- school(?x8542, ?x4955), school(?x4979, ?x4955), draft(?x799, ?x8542), ?x4979 = 0f4vx0 >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #96 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 016sd3; *> query: (?x4955, 09l0x9) <- currency(?x4955, ?x170), school(?x8111, ?x4955), ?x8111 = 07147 *> conf = 0.36 ranks of expected_values: 2, 16 EVAL 09f2j school! 04f4z1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 118.000 118.000 0.414 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 09f2j school! 09l0x9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.414 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #6612-0146pg PRED entity: 0146pg PRED relation: music! PRED expected values: 0fdv3 => 130 concepts (124 used for prediction) PRED predicted values (max 10 best out of 652): 0f3m1 (0.72 #9662, 0.06 #34782, 0.06 #89870), 0ptxj (0.72 #9662, 0.06 #34782, 0.06 #89870), 09sr0 (0.72 #9662, 0.06 #34782, 0.06 #89870), 01s7w3 (0.08 #3727, 0.08 #828, 0.07 #5659), 07bzz7 (0.08 #500, 0.05 #3399, 0.04 #7263), 02ht1k (0.08 #346, 0.05 #4211, 0.03 #9041), 033g4d (0.08 #108, 0.03 #3007, 0.02 #4939), 05qm9f (0.08 #644, 0.03 #3543, 0.02 #5475), 0pdp8 (0.08 #214, 0.03 #4079, 0.02 #5045), 0888c3 (0.08 #770, 0.03 #4635, 0.02 #7533) >> Best rule #9662 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 07y8l9; >> query: (?x669, ?x5212) <- music(?x1072, ?x669), nominated_for(?x112, ?x1072), nominated_for(?x669, ?x5212) >> conf = 0.72 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0146pg music! 0fdv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 124.000 0.722 http://example.org/film/film/music #6611-01_1hw PRED entity: 01_1hw PRED relation: country PRED expected values: 09c7w0 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 159): 09c7w0 (0.85 #2041, 0.83 #803, 0.83 #2477), 03gj2 (0.63 #5511, 0.02 #6372, 0.02 #6619), 07ssc (0.56 #386, 0.39 #943, 0.37 #7542), 0d060g (0.37 #7542, 0.37 #3279, 0.31 #254), 03rk0 (0.37 #7542, 0.37 #3279, 0.02 #6372), 02jx1 (0.37 #7542, 0.37 #3279), 0345h (0.33 #28, 0.24 #582, 0.20 #1322), 0f8l9c (0.33 #20, 0.20 #142, 0.12 #760), 02_286 (0.26 #5387, 0.26 #5138, 0.25 #1358), 0mw1j (0.26 #5387, 0.26 #5138, 0.25 #1358) >> Best rule #2041 for best value: >> intensional similarity = 6 >> extensional distance = 177 >> proper extension: 01gglm; >> query: (?x8631, 09c7w0) <- film(?x8898, ?x8631), film(?x1867, ?x8631), executive_produced_by(?x8631, ?x8208), participant(?x2763, ?x8898), award_nominee(?x8898, ?x396), award_winner(?x1868, ?x1867) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_1hw country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.849 http://example.org/film/film/country #6610-0232lm PRED entity: 0232lm PRED relation: location PRED expected values: 059rby => 112 concepts (109 used for prediction) PRED predicted values (max 10 best out of 193): 01zqy6t (0.37 #18427), 030qb3t (0.30 #10494, 0.27 #59371, 0.24 #61776), 04jpl (0.13 #10430, 0.12 #17, 0.08 #63316), 04lh6 (0.12 #434, 0.04 #2837, 0.04 #1235), 0ccvx (0.12 #220, 0.04 #61915, 0.03 #24255), 013yq (0.12 #117, 0.04 #19346, 0.03 #59407), 0hptm (0.12 #301, 0.02 #26739, 0.02 #1903), 0179qv (0.12 #768, 0.02 #2370), 0167q3 (0.12 #332, 0.02 #1934), 0rd5k (0.12 #180, 0.02 #1782) >> Best rule #18427 for best value: >> intensional similarity = 4 >> extensional distance = 215 >> proper extension: 025cn2; >> query: (?x8873, ?x13529) <- gender(?x8873, ?x231), ?x231 = 05zppz, origin(?x8873, ?x13529), nationality(?x8873, ?x94) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #10429 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 113 *> proper extension: 0459z; *> query: (?x8873, 059rby) <- location(?x8873, ?x739), instrumentalists(?x227, ?x8873), film_release_region(?x204, ?x739), place_of_death(?x340, ?x739) *> conf = 0.05 ranks of expected_values: 20 EVAL 0232lm location 059rby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 112.000 109.000 0.369 http://example.org/people/person/places_lived./people/place_lived/location #6609-014nq4 PRED entity: 014nq4 PRED relation: genre PRED expected values: 03k9fj => 132 concepts (64 used for prediction) PRED predicted values (max 10 best out of 113): 05p553 (0.79 #1773, 0.63 #5320, 0.59 #2600), 03k9fj (0.73 #3198, 0.65 #3790, 0.50 #1663), 0lsxr (0.40 #3550, 0.35 #834, 0.31 #598), 01hmnh (0.38 #3794, 0.34 #3202, 0.33 #15), 0jxy (0.33 #43, 0.07 #5951, 0.06 #633), 06cvj (0.29 #474, 0.29 #238, 0.28 #1300), 04xvh5 (0.29 #150, 0.28 #1330, 0.19 #976), 082gq (0.29 #146, 0.17 #6888, 0.15 #736), 04xvlr (0.26 #945, 0.20 #5436, 0.20 #6861), 04pbhw (0.25 #644, 0.17 #1706, 0.16 #2296) >> Best rule #1773 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 01g3gq; >> query: (?x3221, 05p553) <- prequel(?x66, ?x3221), genre(?x3221, ?x1403), film_release_distribution_medium(?x3221, ?x81), genre(?x12899, ?x1403), ?x12899 = 0ckt6 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #3198 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 89 *> proper extension: 02vw1w2; 02_qt; *> query: (?x3221, 03k9fj) <- prequel(?x66, ?x3221), genre(?x3221, ?x1403), film_release_distribution_medium(?x3221, ?x81), genre(?x3294, ?x1403), ?x3294 = 0jvt9 *> conf = 0.73 ranks of expected_values: 2 EVAL 014nq4 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 64.000 0.786 http://example.org/film/film/genre #6608-06nnj PRED entity: 06nnj PRED relation: administrative_parent PRED expected values: 02j71 => 156 concepts (103 used for prediction) PRED predicted values (max 10 best out of 21): 02j71 (0.87 #4552, 0.86 #3316, 0.86 #4690), 09c7w0 (0.28 #6480, 0.27 #8266, 0.26 #8404), 049nq (0.20 #96, 0.02 #782, 0.02 #919), 06n3y (0.15 #10067, 0.15 #10066, 0.15 #10763), 07c5l (0.15 #10067, 0.15 #10066, 0.15 #10763), 0jgd (0.06 #139), 0345h (0.06 #9396, 0.03 #12601, 0.03 #13580), 03rjj (0.05 #11045, 0.01 #9374, 0.01 #9792), 0d05w3 (0.05 #14020, 0.04 #9835, 0.03 #1558), 0d060g (0.04 #10630, 0.04 #8409, 0.03 #11324) >> Best rule #4552 for best value: >> intensional similarity = 5 >> extensional distance = 99 >> proper extension: 04w4s; >> query: (?x9051, 02j71) <- jurisdiction_of_office(?x346, ?x9051), country(?x4045, ?x9051), adjoins(?x9051, ?x9459), taxonomy(?x9051, ?x939), contains(?x7273, ?x9051) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06nnj administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 103.000 0.871 http://example.org/base/aareas/schema/administrative_area/administrative_parent #6607-0f7hc PRED entity: 0f7hc PRED relation: award PRED expected values: 027dtxw 09qv3c 02tj96 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 313): 019bnn (0.76 #48107, 0.70 #44975, 0.70 #48106), 0gq9h (0.35 #19231, 0.29 #11800, 0.25 #11018), 063y_ky (0.33 #121, 0.09 #5986, 0.08 #512), 01bgqh (0.32 #2772, 0.26 #18415, 0.25 #22716), 01by1l (0.31 #20046, 0.31 #18482, 0.31 #22783), 054krc (0.31 #9463, 0.28 #5552, 0.08 #30580), 054ks3 (0.30 #5605, 0.20 #9516, 0.17 #1304), 0l8z1 (0.28 #9441, 0.18 #5530, 0.07 #15698), 05pcn59 (0.27 #5937, 0.21 #16105, 0.21 #13758), 040njc (0.27 #19168, 0.22 #11737, 0.20 #10955) >> Best rule #48107 for best value: >> intensional similarity = 2 >> extensional distance = 1907 >> proper extension: 01963w; 0hwd8; 02gyl0; 04r68; 0g5ff; 01t265; 06z4wj; 0210f1; 0fpzt5; 03swmf; ... >> query: (?x4657, ?x4091) <- award_winner(?x4091, ?x4657), ceremony(?x4091, ?x873) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #43018 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1508 *> proper extension: 04n7njg; *> query: (?x4657, ?x1312) <- nominated_for(?x4657, ?x7729), award_winner(?x4656, ?x4657), nominated_for(?x1312, ?x7729) *> conf = 0.13 ranks of expected_values: 88, 90, 244 EVAL 0f7hc award 02tj96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 150.000 150.000 0.764 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f7hc award 09qv3c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 150.000 150.000 0.764 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f7hc award 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 150.000 150.000 0.764 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6606-02g0mx PRED entity: 02g0mx PRED relation: religion PRED expected values: 06nzl => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 12): 0c8wxp (0.20 #276, 0.19 #906, 0.19 #726), 03_gx (0.06 #59, 0.06 #149, 0.05 #14), 0kpl (0.05 #640, 0.05 #505, 0.04 #415), 092bf5 (0.03 #16, 0.03 #61, 0.03 #106), 01lp8 (0.03 #1, 0.02 #226, 0.02 #451), 03j6c (0.02 #2857, 0.02 #2992, 0.02 #3577), 019cr (0.02 #56, 0.01 #146, 0.01 #101), 0flw86 (0.02 #227, 0.02 #1442, 0.02 #947), 06nzl (0.02 #195, 0.02 #60, 0.01 #150), 0kq2 (0.02 #63, 0.01 #243, 0.01 #288) >> Best rule #276 for best value: >> intensional similarity = 3 >> extensional distance = 377 >> proper extension: 01hrqc; >> query: (?x3100, 0c8wxp) <- participant(?x2499, ?x3100), award_nominee(?x3100, ?x1554), gender(?x3100, ?x514) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #195 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 350 *> proper extension: 02wb6yq; *> query: (?x3100, 06nzl) <- participant(?x2499, ?x3100), nominated_for(?x3100, ?x2815), participant(?x2499, ?x91) *> conf = 0.02 ranks of expected_values: 9 EVAL 02g0mx religion 06nzl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 102.000 102.000 0.195 http://example.org/people/person/religion #6605-0cbkc PRED entity: 0cbkc PRED relation: languages PRED expected values: 02h40lc 064_8sq => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 19): 02h40lc (0.92 #1111, 0.92 #667, 0.91 #1777), 064_8sq (0.40 #87, 0.19 #198, 0.13 #753), 0t_2 (0.33 #7, 0.03 #451, 0.03 #673), 06nm1 (0.10 #78, 0.04 #2409, 0.04 #1114), 04h9h (0.10 #102, 0.01 #361, 0.01 #398), 03hkp (0.10 #82, 0.01 #341, 0.01 #378), 0349s (0.10 #104), 03k50 (0.08 #2407, 0.07 #1704, 0.07 #1741), 07c9s (0.04 #2416, 0.03 #1713, 0.03 #1750), 06b_j (0.02 #199) >> Best rule #1111 for best value: >> intensional similarity = 3 >> extensional distance = 283 >> proper extension: 012d40; 07s3vqk; 01wbg84; 02zq43; 01p7yb; 0p_pd; 0z4s; 0chsq; 01yk13; 015grj; ... >> query: (?x8888, 02h40lc) <- film(?x8888, ?x1009), languages(?x8888, ?x90), award_nominee(?x12218, ?x8888) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0cbkc languages 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.919 http://example.org/people/person/languages EVAL 0cbkc languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.919 http://example.org/people/person/languages #6604-02z6872 PRED entity: 02z6872 PRED relation: draft! PRED expected values: 061xq 02__x 051wf => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 279): 0cqt41 (0.80 #1088, 0.78 #946, 0.75 #871), 01d5z (0.80 #1088, 0.78 #946, 0.58 #362), 061xq (0.78 #946, 0.75 #871, 0.73 #725), 02__x (0.78 #946, 0.75 #871, 0.73 #725), 06x68 (0.78 #946, 0.75 #871, 0.73 #725), 03lpp_ (0.78 #946, 0.47 #945, 0.33 #874), 051wf (0.78 #946, 0.33 #431, 0.33 #357), 0jmj7 (0.64 #797, 0.58 #362, 0.58 #361), 0jmk7 (0.58 #362, 0.58 #361, 0.58 #798), 0jml5 (0.58 #362, 0.58 #361, 0.58 #798) >> Best rule #1088 for best value: >> intensional similarity = 56 >> extensional distance = 5 >> proper extension: 09th87; >> query: (?x4779, ?x1010) <- school(?x4779, ?x2522), school(?x4779, ?x2171), school(?x4779, ?x581), draft(?x8901, ?x4779), draft(?x4487, ?x4779), draft(?x1160, ?x4779), citytown(?x2522, ?x13225), major_field_of_study(?x2522, ?x6756), major_field_of_study(?x2522, ?x4321), major_field_of_study(?x581, ?x12158), major_field_of_study(?x581, ?x2601), major_field_of_study(?x581, ?x1527), major_field_of_study(?x581, ?x947), school(?x4171, ?x581), ?x4171 = 092j54, school(?x8901, ?x10297), school(?x8901, ?x2830), student(?x581, ?x5611), school(?x2820, ?x581), contains(?x94, ?x2522), institution(?x734, ?x2171), taxonomy(?x947, ?x939), team(?x12323, ?x8901), major_field_of_study(?x6912, ?x12158), ?x10297 = 02rv1w, major_field_of_study(?x8008, ?x2601), major_field_of_study(?x7950, ?x2601), major_field_of_study(?x6056, ?x2601), major_field_of_study(?x5167, ?x2601), major_field_of_study(?x122, ?x2601), student(?x2601, ?x2873), ?x6912 = 0gl5_, currency(?x581, ?x170), student(?x2171, ?x6072), colors(?x2171, ?x332), team(?x2010, ?x4487), team(?x12323, ?x1010), ?x5167 = 015cz0, major_field_of_study(?x11397, ?x1527), ?x2830 = 01wdj_, ?x122 = 08815, school_type(?x2522, ?x3092), ?x8008 = 01q7q2, award_nominee(?x5611, ?x2109), company(?x4682, ?x1160), major_field_of_study(?x620, ?x1527), award_winner(?x6072, ?x6071), location(?x6072, ?x108), ?x7950 = 01dbns, ?x4321 = 0g26h, organization(?x346, ?x2171), gender(?x12323, ?x231), major_field_of_study(?x254, ?x12158), ?x11397 = 02hp70, ?x6056 = 05zl0, ?x6756 = 0_jm >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #946 for first EXPECTED value: *> intensional similarity = 54 *> extensional distance = 4 *> proper extension: 092j54; *> query: (?x4779, ?x662) <- school(?x4779, ?x2522), school(?x4779, ?x1884), school(?x4779, ?x581), draft(?x11361, ?x4779), draft(?x10939, ?x4779), draft(?x8894, ?x4779), draft(?x7725, ?x4779), draft(?x4487, ?x4779), ?x581 = 06pwq, draft(?x11361, ?x3334), major_field_of_study(?x2522, ?x2981), category(?x2522, ?x134), school(?x11361, ?x7338), school(?x11361, ?x735), sport(?x10939, ?x5063), team(?x2010, ?x11361), currency(?x2522, ?x170), colors(?x7725, ?x1101), company(?x4196, ?x7725), ?x7338 = 01qgr3, team(?x11844, ?x4487), school(?x7725, ?x8706), school(?x7725, ?x7716), institution(?x3437, ?x1884), institution(?x1519, ?x1884), institution(?x620, ?x1884), team(?x261, ?x7725), major_field_of_study(?x1884, ?x7134), major_field_of_study(?x1884, ?x2014), ?x1519 = 013zdg, ?x620 = 07s6fsf, contains(?x94, ?x7716), school(?x8894, ?x5486), draft(?x662, ?x3334), team(?x12826, ?x10939), citytown(?x8706, ?x4419), ?x2014 = 04rjg, student(?x1884, ?x2543), major_field_of_study(?x3424, ?x7134), major_field_of_study(?x3212, ?x7134), award_winner(?x2543, ?x415), ?x3437 = 02_xgp2, ?x5486 = 0g8rj, nominated_for(?x2543, ?x3104), ?x3424 = 01w5m, major_field_of_study(?x8706, ?x947), school(?x2820, ?x1884), colors(?x481, ?x1101), ?x3212 = 02bb47, student(?x735, ?x65), time_zones(?x735, ?x2950), colors(?x1639, ?x1101), ?x1639 = 07l24, major_field_of_study(?x735, ?x254) *> conf = 0.78 ranks of expected_values: 3, 4, 7 EVAL 02z6872 draft! 051wf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 19.000 19.000 0.800 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02z6872 draft! 02__x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 19.000 19.000 0.800 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02z6872 draft! 061xq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 19.000 19.000 0.800 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #6603-02y_rq5 PRED entity: 02y_rq5 PRED relation: award! PRED expected values: 0_b9f => 47 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1357): 07cyl (0.50 #2351, 0.33 #334, 0.33 #6053), 01gvts (0.50 #2733, 0.33 #716, 0.25 #6769), 07xtqq (0.50 #2049, 0.33 #32, 0.25 #6085), 01jc6q (0.50 #2029, 0.33 #12, 0.25 #6065), 02py4c8 (0.43 #4101, 0.40 #1074, 0.25 #5110), 0b76kw1 (0.40 #1198, 0.29 #4225, 0.17 #5234), 0h3mh3q (0.40 #1905, 0.17 #5941, 0.14 #4932), 04qw17 (0.33 #1009, 0.33 #177, 0.33 #6053), 0g9wdmc (0.33 #2183, 0.33 #166, 0.33 #6053), 02q6gfp (0.33 #2248, 0.33 #231, 0.33 #6053) >> Best rule #2351 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0gqwc; 0gs9p; 02ppm4q; >> query: (?x1716, 07cyl) <- nominated_for(?x1716, ?x5584), nominated_for(?x1716, ?x1863), award(?x241, ?x1716), ?x1863 = 04qw17, ?x5584 = 0yyn5 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2488 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0gqwc; 0gs9p; 02ppm4q; *> query: (?x1716, 0_b9f) <- nominated_for(?x1716, ?x5584), nominated_for(?x1716, ?x1863), award(?x241, ?x1716), ?x1863 = 04qw17, ?x5584 = 0yyn5 *> conf = 0.33 ranks of expected_values: 14 EVAL 02y_rq5 award! 0_b9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 47.000 13.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #6602-018ygt PRED entity: 018ygt PRED relation: award PRED expected values: 09sb52 0bfvd4 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 246): 027cyf7 (0.70 #18480, 0.69 #8258, 0.69 #7864), 09sb52 (0.38 #6718, 0.37 #4753, 0.37 #1607), 0fbtbt (0.35 #2187, 0.33 #1401, 0.16 #615), 05pcn59 (0.24 #2826, 0.23 #3220, 0.22 #1646), 0gqwc (0.21 #1639, 0.21 #2819, 0.16 #3213), 047byns (0.18 #46, 0.15 #25164, 0.06 #439), 05zr6wv (0.18 #1587, 0.17 #2767, 0.15 #3161), 0f4x7 (0.17 #1599, 0.15 #2779, 0.15 #25164), 04kxsb (0.16 #1690, 0.15 #25164, 0.14 #2870), 0gqyl (0.16 #1670, 0.15 #25164, 0.13 #2850) >> Best rule #18480 for best value: >> intensional similarity = 2 >> extensional distance = 1304 >> proper extension: 0g51l1; 01_8w2; 01p5yn; 03yxwq; 0gsgr; 015zql; 05g7q; 0kc8y; 04rqd; 05s34b; ... >> query: (?x6324, ?x678) <- award_winner(?x406, ?x6324), award_winner(?x678, ?x6324) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #6718 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 746 *> proper extension: 03b78r; *> query: (?x6324, 09sb52) <- award_winner(?x1135, ?x6324), award_nominee(?x6324, ?x406), film(?x6324, ?x667) *> conf = 0.38 ranks of expected_values: 2, 35 EVAL 018ygt award 0bfvd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 99.000 99.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 018ygt award 09sb52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6601-0nzw2 PRED entity: 0nzw2 PRED relation: source PRED expected values: 0jbk9 => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #46, 0.93 #34, 0.93 #9) >> Best rule #46 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 0mnzd; 0k1jg; >> query: (?x12545, 0jbk9) <- second_level_divisions(?x94, ?x12545), time_zones(?x12545, ?x2674), ?x94 = 09c7w0, ?x2674 = 02hcv8 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nzw2 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.940 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #6600-024bbl PRED entity: 024bbl PRED relation: student! PRED expected values: 0cwx_ => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 64): 012lzr (0.33 #336), 09f2j (0.10 #1211, 0.04 #18569, 0.04 #6997), 065y4w7 (0.10 #540, 0.07 #1066, 0.05 #6852), 0bwfn (0.08 #3957, 0.08 #10269, 0.08 #18685), 03ksy (0.05 #18516, 0.05 #3788, 0.04 #1684), 017z88 (0.05 #608, 0.04 #3764, 0.04 #11128), 078bz (0.05 #603, 0.02 #3759, 0.02 #18487), 02m0b0 (0.05 #924, 0.01 #29987), 02zcnq (0.05 #672, 0.01 #29987), 086xm (0.05 #618, 0.01 #29987) >> Best rule #336 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 013t9y; >> query: (?x4681, 012lzr) <- nominated_for(?x4681, ?x3219), ?x3219 = 011ydl, award(?x4681, ?x435) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1293 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 110 *> proper extension: 0gg9_5q; 0hgqq; 0bbxd3; 05rnp1; 0444x; 09zw90; *> query: (?x4681, 0cwx_) <- place_of_birth(?x4681, ?x1523), ?x1523 = 030qb3t *> conf = 0.02 ranks of expected_values: 31 EVAL 024bbl student! 0cwx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 88.000 88.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #6599-0kc6x PRED entity: 0kc6x PRED relation: state_province_region PRED expected values: 01x73 => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 88): 01n7q (0.75 #1002, 0.68 #3098, 0.59 #3839), 059rby (0.57 #10516, 0.41 #2714, 0.37 #2467), 0d0x8 (0.33 #44, 0.20 #413, 0.14 #782), 0kpys (0.27 #12989, 0.25 #18817, 0.25 #18942), 0gx1l (0.25 #18817, 0.25 #18942, 0.23 #19440), 09c7w0 (0.25 #18817, 0.25 #18942, 0.23 #19440), 07z1m (0.20 #391, 0.14 #637, 0.12 #883), 02jx1 (0.18 #1373, 0.07 #1745, 0.04 #4707), 05kr_ (0.16 #3973, 0.04 #12521, 0.04 #13388), 03v0t (0.14 #791, 0.12 #2393, 0.06 #5484) >> Best rule #1002 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 01frpd; >> query: (?x234, 01n7q) <- company(?x233, ?x234), category(?x234, ?x134), citytown(?x234, ?x1523), ?x1523 = 030qb3t, ?x134 = 08mbj5d >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2365 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 01pq4w; 017j69; 059wk; *> query: (?x234, 01x73) <- service_location(?x234, ?x94), service_language(?x234, ?x254), contact_category(?x234, ?x897), company(?x4451, ?x234), ?x254 = 02h40lc *> conf = 0.06 ranks of expected_values: 16 EVAL 0kc6x state_province_region 01x73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 173.000 173.000 0.750 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #6598-05glt PRED entity: 05glt PRED relation: list! PRED expected values: 0m_mm 0_7w6 0gxfz 0k4f3 0jswp 0f4yh 027rpym 02dwj 0jwvf 0jsf6 07g1sm 0k4bc 0ft18 07ghq 0cq8nx => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 187): 02ktt7 (0.50 #561, 0.43 #748, 0.40 #373), 07gyp7 (0.50 #558, 0.43 #745, 0.40 #370), 0dq23 (0.50 #550, 0.43 #737, 0.40 #362), 0hkqn (0.50 #546, 0.43 #733, 0.40 #358), 0lwkh (0.50 #544, 0.43 #731, 0.40 #356), 03s7h (0.50 #534, 0.43 #721, 0.40 #346), 0k9ts (0.50 #518, 0.43 #705, 0.40 #330), 01dfb6 (0.50 #517, 0.43 #704, 0.40 #329), 035nm (0.50 #512, 0.43 #699, 0.40 #324), 04sv4 (0.50 #511, 0.43 #698, 0.40 #323) >> Best rule #561 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 04k4rt; >> query: (?x3004, 02ktt7) <- list(?x4559, ?x3004), list(?x4216, ?x3004), category(?x4216, ?x134), ?x134 = 08mbj5d, award(?x4559, ?x484) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05glt list! 0cq8nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 07ghq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 0ft18 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 0k4bc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 07g1sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 0jsf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 0jwvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 02dwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 027rpym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 0f4yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 0jswp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 0k4f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 0gxfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 0_7w6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 05glt list! 0m_mm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 5.000 5.000 0.500 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #6597-04sd0 PRED entity: 04sd0 PRED relation: group! PRED expected values: 041c4 => 86 concepts (43 used for prediction) PRED predicted values (max 10 best out of 174): 01q3_2 (0.20 #369, 0.17 #959, 0.06 #2741), 01jgkj2 (0.20 #365, 0.17 #955, 0.06 #2737), 016vqk (0.20 #363, 0.17 #953, 0.06 #2735), 02vcp0 (0.20 #351, 0.17 #941, 0.06 #2723), 01wf86y (0.20 #339, 0.17 #929, 0.06 #2711), 0d608 (0.20 #336, 0.17 #926, 0.06 #2708), 05qhnq (0.20 #326, 0.17 #916, 0.06 #2698), 0ddkf (0.20 #316, 0.17 #906, 0.06 #2688), 0b_j2 (0.20 #312, 0.17 #902, 0.06 #2684), 01l87db (0.20 #304, 0.17 #894, 0.06 #2676) >> Best rule #369 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01v0sx2; 06nv27; 02mq_y; >> query: (?x12459, 01q3_2) <- category(?x12459, ?x134), group(?x11797, ?x12459), profession(?x11797, ?x353), written_by(?x3998, ?x11797), ?x134 = 08mbj5d >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04sd0 group! 041c4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 43.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group #6596-02fqrf PRED entity: 02fqrf PRED relation: film_release_region PRED expected values: 05r4w 0b90_r 0154j 0j1z8 0k6nt 02vzc 05b4w => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 107): 09c7w0 (0.92 #6798, 0.92 #7065, 0.85 #668), 05r4w (0.87 #1731, 0.87 #1598, 0.87 #534), 0154j (0.86 #405, 0.85 #538, 0.83 #139), 0k6nt (0.85 #1615, 0.84 #684, 0.83 #817), 07ssc (0.83 #679, 0.82 #546, 0.82 #280), 02vzc (0.82 #1768, 0.82 #1635, 0.81 #704), 0b90_r (0.81 #404, 0.80 #138, 0.80 #537), 05b4w (0.79 #447, 0.78 #181, 0.78 #713), 04gzd (0.75 #143, 0.65 #409, 0.65 #542), 0ctw_b (0.73 #153, 0.73 #419, 0.68 #552) >> Best rule #6798 for best value: >> intensional similarity = 4 >> extensional distance = 1322 >> proper extension: 0170z3; 014lc_; 02d413; 0b76d_m; 014_x2; 0d90m; 03qcfvw; 0g56t9t; 09sh8k; 0m313; ... >> query: (?x3498, 09c7w0) <- film_release_region(?x3498, ?x1499), country(?x668, ?x1499), film_release_region(?x6247, ?x1499), ?x6247 = 09v9mks >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1731 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 165 *> proper extension: 0c3ybss; 08hmch; 0gtvrv3; 0bh8yn3; 07x4qr; 047svrl; 023gxx; 0c3xw46; 05c26ss; 02dpl9; ... *> query: (?x3498, 05r4w) <- film_release_region(?x3498, ?x1499), ?x1499 = 01znc_, nominated_for(?x1500, ?x3498), film_crew_role(?x3498, ?x137) *> conf = 0.87 ranks of expected_values: 2, 3, 4, 6, 7, 8, 21 EVAL 02fqrf film_release_region 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02fqrf film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02fqrf film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 77.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02fqrf film_release_region 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 77.000 77.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02fqrf film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 77.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02fqrf film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02fqrf film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 77.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6595-017jd9 PRED entity: 017jd9 PRED relation: nominated_for! PRED expected values: 027dtxw 040njc 02qvyrt 02x258x => 97 concepts (80 used for prediction) PRED predicted values (max 10 best out of 192): 040njc (0.68 #11851, 0.67 #8401, 0.67 #11634), 054krc (0.68 #11851, 0.67 #8401, 0.67 #11634), 02g3ft (0.68 #11851, 0.67 #8401, 0.67 #11634), 09d28z (0.68 #11851, 0.67 #8401, 0.67 #11634), 02w_6xj (0.68 #11851, 0.67 #8401, 0.67 #11634), 0gqyl (0.43 #492, 0.33 #4587, 0.31 #708), 04dn09n (0.40 #1317, 0.35 #8643, 0.34 #4551), 027dtxw (0.40 #219, 0.32 #1295, 0.29 #8617), 0f4x7 (0.38 #4545, 0.38 #666, 0.29 #8637), 0gr0m (0.38 #691, 0.37 #8662, 0.32 #2196) >> Best rule #11851 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 07bz5; >> query: (?x4610, ?x198) <- award_winner(?x4610, ?x628), award(?x4610, ?x198), award(?x71, ?x198) >> conf = 0.68 => this is the best rule for 5 predicted values ranks of expected_values: 1, 8, 12, 44 EVAL 017jd9 nominated_for! 02x258x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 97.000 80.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 017jd9 nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 97.000 80.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 017jd9 nominated_for! 040njc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 80.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 017jd9 nominated_for! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 80.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6594-0fgg4 PRED entity: 0fgg4 PRED relation: film PRED expected values: 0c38gj => 114 concepts (70 used for prediction) PRED predicted values (max 10 best out of 934): 09m6kg (0.50 #57059, 0.50 #1813, 0.36 #65976), 020fcn (0.25 #1968, 0.05 #67760, 0.03 #64192), 02wgk1 (0.25 #4322, 0.02 #34634, 0.02 #54248), 0qm98 (0.17 #222, 0.12 #2005, 0.05 #67760), 0fphf3v (0.17 #1357, 0.05 #8489, 0.04 #12055), 016z9n (0.17 #369, 0.03 #64192, 0.02 #66345), 035s95 (0.17 #340, 0.02 #16387, 0.02 #21736), 056xkh (0.17 #1593, 0.02 #6942, 0.02 #10508), 016z5x (0.17 #69, 0.02 #5418, 0.01 #25031), 01gglm (0.17 #1400, 0.02 #10315, 0.02 #22796) >> Best rule #57059 for best value: >> intensional similarity = 3 >> extensional distance = 983 >> proper extension: 04dyqk; >> query: (?x4949, ?x253) <- profession(?x4949, ?x1032), award_winner(?x253, ?x4949), ?x1032 = 02hrh1q >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fgg4 film 0c38gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 70.000 0.500 http://example.org/film/actor/film./film/performance/film #6593-07zhd7 PRED entity: 07zhd7 PRED relation: music! PRED expected values: 0cbn7c => 137 concepts (20 used for prediction) PRED predicted values (max 10 best out of 917): 027rpym (0.72 #12157, 0.70 #17225, 0.68 #20266), 016z7s (0.25 #206, 0.05 #6284, 0.03 #8310), 09146g (0.12 #183, 0.03 #11326, 0.02 #16394), 0btpm6 (0.12 #742, 0.03 #11885, 0.02 #16953), 02fqrf (0.12 #338, 0.03 #11481, 0.02 #16549), 0ktx_ (0.12 #993, 0.03 #5045, 0.02 #6058), 04wddl (0.12 #872, 0.03 #4924, 0.02 #5937), 034r25 (0.12 #438, 0.02 #16649, 0.02 #6516), 09q5w2 (0.12 #101, 0.02 #16312, 0.02 #6179), 034hzj (0.12 #1006, 0.02 #7084, 0.02 #8097) >> Best rule #12157 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 03bnv; >> query: (?x12188, ?x4865) <- music(?x2779, ?x12188), award_winner(?x4865, ?x12188), genre(?x2779, ?x258), ?x258 = 05p553 >> conf = 0.72 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07zhd7 music! 0cbn7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 20.000 0.722 http://example.org/film/film/music #6592-09g8vhw PRED entity: 09g8vhw PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.87 #61, 0.86 #78, 0.86 #123), 02nxhr (0.06 #37, 0.05 #84, 0.04 #32), 07c52 (0.04 #23, 0.04 #28, 0.03 #211), 07z4p (0.03 #357, 0.03 #310, 0.03 #250) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 016fyc; 0pc62; 0fgpvf; 0164qt; 0p9lw; 0bshwmp; 04vr_f; 0872p_c; 026390q; 04hwbq; ... >> query: (?x2075, 029j_) <- film(?x398, ?x2075), film_release_region(?x2075, ?x94), nominated_for(?x857, ?x2075), production_companies(?x2075, ?x902) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09g8vhw film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.868 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6591-014zcr PRED entity: 014zcr PRED relation: award_winner! PRED expected values: 09cm54 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 271): 0ck27z (0.39 #19087, 0.37 #27144, 0.37 #28418), 019f4v (0.39 #19087, 0.37 #27144, 0.37 #28418), 05p09zm (0.39 #19087, 0.37 #27144, 0.37 #28418), 05pcn59 (0.39 #19087, 0.37 #27144, 0.37 #28418), 0789r6 (0.39 #19087, 0.37 #27144, 0.37 #28418), 02pqp12 (0.39 #19087, 0.37 #27144, 0.37 #28418), 0gr4k (0.39 #19087, 0.37 #27144, 0.37 #28418), 099tbz (0.39 #19087, 0.37 #27144, 0.37 #28418), 04dn09n (0.39 #19087, 0.37 #27144, 0.37 #28418), 02qyp19 (0.39 #19087, 0.37 #27144, 0.37 #28418) >> Best rule #19087 for best value: >> intensional similarity = 3 >> extensional distance = 950 >> proper extension: 044k8; 0khth; 014l4w; 04k05; 014g91; >> query: (?x286, ?x68) <- award_winner(?x286, ?x426), award_winner(?x1442, ?x286), award(?x286, ?x68) >> conf = 0.39 => this is the best rule for 19 predicted values *> Best rule #943 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 26 *> proper extension: 0jvs0; *> query: (?x286, 09cm54) <- list(?x286, ?x5160), gender(?x286, ?x231) *> conf = 0.07 ranks of expected_values: 71 EVAL 014zcr award_winner! 09cm54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 112.000 112.000 0.392 http://example.org/award/award_category/winners./award/award_honor/award_winner #6590-026kqs9 PRED entity: 026kqs9 PRED relation: award_winner PRED expected values: 0l786 => 32 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2274): 06pj8 (0.50 #4907, 0.33 #300, 0.27 #9519), 09r9m7 (0.40 #3969, 0.07 #11656, 0.07 #27040), 0gl88b (0.40 #3357, 0.05 #26428, 0.05 #27965), 02qwg (0.37 #15877, 0.33 #14341, 0.33 #11266), 02fn5r (0.33 #14209, 0.33 #11134, 0.32 #15745), 05m883 (0.33 #1691, 0.33 #156, 0.27 #9375), 02pv_d (0.33 #2704, 0.33 #1169, 0.25 #5776), 07g7h2 (0.33 #7130, 0.33 #2519, 0.19 #13276), 0hl3d (0.33 #13862, 0.32 #15398, 0.27 #10787), 0h0wc (0.33 #365, 0.31 #12657, 0.25 #4972) >> Best rule #4907 for best value: >> intensional similarity = 14 >> extensional distance = 6 >> proper extension: 02wzl1d; >> query: (?x6595, 06pj8) <- award_winner(?x6595, ?x4248), award_winner(?x6595, ?x1365), honored_for(?x6595, ?x758), ceremony(?x746, ?x6595), ?x746 = 04dn09n, award_nominee(?x2200, ?x4248), award_winner(?x289, ?x1365), category(?x4248, ?x134), nationality(?x1365, ?x94), ?x289 = 027c924, spouse(?x9817, ?x1365), award_winner(?x3235, ?x1365), award(?x2200, ?x704), location(?x4248, ?x1523) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #18442 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 22 *> proper extension: 03gwpw2; 0h_cssd; 04n2r9h; 0275n3y; 09bymc; *> query: (?x6595, ?x879) <- award_winner(?x6595, ?x538), honored_for(?x6595, ?x758), ceremony(?x746, ?x6595), award(?x6534, ?x746), award(?x3961, ?x746), award_winner(?x746, ?x826), award(?x1744, ?x746), featured_film_locations(?x1744, ?x108), nominated_for(?x746, ?x8367), ceremony(?x746, ?x2220), ?x6534 = 01_6dw, ?x3961 = 06m6z6, award_winner(?x2220, ?x879), film(?x374, ?x8367) *> conf = 0.12 ranks of expected_values: 301 EVAL 026kqs9 award_winner 0l786 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 32.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6589-0453t PRED entity: 0453t PRED relation: influenced_by! PRED expected values: 0343h 01w9ph_ => 148 concepts (54 used for prediction) PRED predicted values (max 10 best out of 363): 0683n (0.43 #1374, 0.20 #340, 0.17 #10682), 041jlr (0.29 #1396, 0.07 #10704, 0.03 #12258), 0969fd (0.29 #1470, 0.06 #6120, 0.05 #10778), 02yl42 (0.22 #1688, 0.20 #135, 0.14 #10477), 04107 (0.21 #9823, 0.21 #3104, 0.19 #8787), 0dzkq (0.20 #126, 0.17 #10468, 0.14 #1160), 06whf (0.20 #165, 0.14 #1199, 0.09 #10507), 0gd_s (0.20 #379, 0.14 #1413, 0.05 #10721), 0399p (0.20 #330, 0.14 #10672, 0.09 #6014), 0n6kf (0.20 #192, 0.13 #2779, 0.12 #10534) >> Best rule #1374 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 07h1q; >> query: (?x2239, 0683n) <- influenced_by(?x2239, ?x10895), ?x10895 = 06myp, gender(?x2239, ?x231), peers(?x2239, ?x4808) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2907 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 05jm7; *> query: (?x2239, 01w9ph_) <- profession(?x2239, ?x2225), profession(?x2239, ?x353), ?x353 = 0cbd2, ?x2225 = 0kyk, peers(?x4808, ?x2239) *> conf = 0.13 ranks of expected_values: 34, 284 EVAL 0453t influenced_by! 01w9ph_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 148.000 54.000 0.429 http://example.org/influence/influence_node/influenced_by EVAL 0453t influenced_by! 0343h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 148.000 54.000 0.429 http://example.org/influence/influence_node/influenced_by #6588-0h1zw PRED entity: 0h1zw PRED relation: nutrient! PRED expected values: 09728 0fj52s 037ls6 05z55 => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 13): 0fj52s (0.93 #579, 0.92 #557, 0.92 #548), 037ls6 (0.90 #174, 0.89 #69, 0.89 #41), 05z55 (0.90 #174, 0.89 #69, 0.89 #41), 09728 (0.90 #174, 0.89 #69, 0.89 #41), 0dcfv (0.90 #174, 0.89 #69, 0.89 #41), 06x4c (0.90 #174, 0.89 #69, 0.89 #41), 01sh2 (0.03 #736, 0.02 #337, 0.02 #97), 04k8n (0.03 #736), 025rw19 (0.02 #337, 0.02 #97), 025tkqy (0.02 #337, 0.02 #97) >> Best rule #579 for best value: >> intensional similarity = 121 >> extensional distance = 27 >> proper extension: 03d49; >> query: (?x3469, 0fj52s) <- nutrient(?x9005, ?x3469), nutrient(?x7719, ?x3469), nutrient(?x7057, ?x3469), nutrient(?x6285, ?x3469), nutrient(?x6191, ?x3469), nutrient(?x6032, ?x3469), nutrient(?x5009, ?x3469), nutrient(?x4068, ?x3469), nutrient(?x3900, ?x3469), nutrient(?x3468, ?x3469), nutrient(?x1959, ?x3469), ?x7719 = 0dj75, ?x4068 = 0fbw6, nutrient(?x3468, ?x13944), nutrient(?x3468, ?x12902), nutrient(?x3468, ?x12083), nutrient(?x3468, ?x11784), nutrient(?x3468, ?x11758), nutrient(?x3468, ?x11409), nutrient(?x3468, ?x11270), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10709), nutrient(?x3468, ?x10195), nutrient(?x3468, ?x10098), nutrient(?x3468, ?x9949), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x9733), nutrient(?x3468, ?x9490), nutrient(?x3468, ?x9436), nutrient(?x3468, ?x9426), nutrient(?x3468, ?x9365), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x7894), nutrient(?x3468, ?x7720), nutrient(?x3468, ?x7652), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7364), nutrient(?x3468, ?x7362), nutrient(?x3468, ?x7219), nutrient(?x3468, ?x7135), nutrient(?x3468, ?x6586), nutrient(?x3468, ?x6286), nutrient(?x3468, ?x6033), nutrient(?x3468, ?x6026), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5526), nutrient(?x3468, ?x5451), nutrient(?x3468, ?x5010), nutrient(?x3468, ?x4069), nutrient(?x3468, ?x3203), nutrient(?x3468, ?x2018), nutrient(?x3468, ?x1960), nutrient(?x3468, ?x1304), nutrient(?x3468, ?x1258), ?x6286 = 02y_3rf, ?x1960 = 07hnp, ?x11758 = 0q01m, ?x13944 = 0f4kp, ?x10891 = 0g5gq, ?x4069 = 0hqw8p_, ?x7364 = 09gvd, ?x3203 = 04kl74p, ?x6026 = 025sf8g, ?x5451 = 05wvs, ?x9915 = 025tkqy, ?x7431 = 09gwd, ?x9365 = 04k8n, ?x1304 = 08lb68, nutrient(?x9005, ?x13498), nutrient(?x9005, ?x12454), nutrient(?x9005, ?x11592), nutrient(?x9005, ?x9795), nutrient(?x9005, ?x8487), nutrient(?x9005, ?x5374), nutrient(?x9005, ?x3901), ?x9426 = 0h1yy, ?x12083 = 01n78x, ?x7362 = 02kc5rj, ?x11592 = 025sf0_, ?x9733 = 0h1tz, ?x9949 = 02kd0rh, ?x11409 = 0h1yf, ?x6285 = 01645p, ?x3901 = 0466p20, nutrient(?x6032, ?x8243), nutrient(?x6032, ?x3264), ?x7894 = 0f4hc, ?x7057 = 0fbdb, nutrient(?x3900, ?x9855), ?x9795 = 05v_8y, ?x5374 = 025s0zp, ?x9436 = 025sqz8, ?x7720 = 025s7x6, nutrient(?x1959, ?x6517), ?x6191 = 014j1m, ?x10709 = 0h1sz, ?x5009 = 0fjfh, ?x7135 = 025rsfk, ?x8243 = 014d7f, ?x11784 = 07zqy, ?x12454 = 025rw19, ?x9490 = 0h1sg, ?x6586 = 05gh50, ?x12902 = 0fzjh, ?x5549 = 025s7j4, ?x11270 = 02kc008, ?x10195 = 0hkwr, ?x8487 = 014yzm, ?x9855 = 0d9t0, ?x8413 = 02kc4sf, ?x6033 = 04zjxcz, ?x7652 = 025s0s0, ?x5010 = 0h1vz, ?x2018 = 01sh2, ?x5526 = 09pbb, ?x7219 = 0h1vg, ?x3264 = 0dcfv, ?x10098 = 0h1_c, ?x13498 = 07q0m, ?x1258 = 0h1wg, ?x6517 = 02kd8zw >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0h1zw nutrient! 05z55 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.931 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0h1zw nutrient! 037ls6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.931 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0h1zw nutrient! 0fj52s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.931 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0h1zw nutrient! 09728 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.931 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #6587-06n9lt PRED entity: 06n9lt PRED relation: award PRED expected values: 0gs9p => 90 concepts (81 used for prediction) PRED predicted values (max 10 best out of 279): 0gr51 (0.80 #15034, 0.80 #5689, 0.76 #23982), 0czp_ (0.80 #15034, 0.76 #23982, 0.72 #26018), 09sb52 (0.37 #8168, 0.26 #14261, 0.24 #16295), 0gr4k (0.30 #4096, 0.28 #6128, 0.26 #3690), 0gq9h (0.28 #5359, 0.22 #4141, 0.19 #4547), 040njc (0.27 #5289, 0.22 #4071, 0.20 #8949), 04dn09n (0.27 #4107, 0.24 #6139, 0.23 #4513), 0gs9p (0.23 #4143, 0.22 #9021, 0.22 #5361), 019f4v (0.22 #5348, 0.21 #4130, 0.21 #9008), 03hkv_r (0.19 #4079, 0.19 #3673, 0.19 #6111) >> Best rule #15034 for best value: >> intensional similarity = 3 >> extensional distance = 853 >> proper extension: 04lgymt; 05qd_; 02r3zy; 07c0j; 0249kn; 01x15dc; 018ndc; 0bbxx9b; 0b6mgp_; 0dw4g; ... >> query: (?x5146, ?x1862) <- award_winner(?x1862, ?x5146), nominated_for(?x1862, ?x69), category_of(?x1862, ?x3459) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #4143 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 216 *> proper extension: 0l6qt; 04t2l2; 0byfz; 014zcr; 05ty4m; 01q_ph; 02lfcm; 0159h6; 0bxtg; 06cv1; ... *> query: (?x5146, 0gs9p) <- type_of_union(?x5146, ?x566), nominated_for(?x5146, ?x3599), profession(?x5146, ?x319), written_by(?x8677, ?x5146) *> conf = 0.23 ranks of expected_values: 8 EVAL 06n9lt award 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 90.000 81.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6586-011k4g PRED entity: 011k4g PRED relation: artists! PRED expected values: 017_qw => 91 concepts (70 used for prediction) PRED predicted values (max 10 best out of 207): 016clz (0.65 #5973, 0.21 #16656, 0.20 #5345), 017_qw (0.58 #380, 0.43 #66, 0.39 #5406), 06by7 (0.44 #651, 0.42 #5991, 0.40 #16674), 0ggq0m (0.43 #13, 0.25 #327, 0.21 #5981), 08cyft (0.43 #60, 0.25 #374, 0.15 #5400), 064t9 (0.40 #16981, 0.39 #15092, 0.38 #5354), 0ggx5q (0.33 #5422, 0.14 #82, 0.12 #17049), 0m0jc (0.29 #9, 0.22 #5349, 0.17 #323), 0fd3y (0.29 #11, 0.17 #325, 0.10 #5351), 03ckfl9 (0.29 #166, 0.17 #480, 0.08 #1108) >> Best rule #5973 for best value: >> intensional similarity = 3 >> extensional distance = 322 >> proper extension: 0150jk; 02r3zy; 067mj; 01vsxdm; 01wv9xn; 01fl3; 0dtd6; 0167_s; 0dvqq; 016fmf; ... >> query: (?x11457, 016clz) <- artists(?x7052, ?x11457), artists(?x7052, ?x7053), ?x7053 = 01p0vf >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #380 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 10 *> proper extension: 014hr0; *> query: (?x11457, 017_qw) <- artists(?x7052, ?x11457), ?x7052 = 0l14gg *> conf = 0.58 ranks of expected_values: 2 EVAL 011k4g artists! 017_qw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 70.000 0.645 http://example.org/music/genre/artists #6585-0xv2x PRED entity: 0xv2x PRED relation: artists PRED expected values: 01fchy => 66 concepts (27 used for prediction) PRED predicted values (max 10 best out of 992): 02ndj5 (0.68 #10519, 0.50 #4100, 0.40 #5169), 070b4 (0.62 #6162, 0.60 #5091, 0.50 #2950), 01gx5f (0.60 #4567, 0.50 #3498, 0.50 #2426), 03lgg (0.60 #4720, 0.50 #2579, 0.40 #3207), 09lwrt (0.59 #4278, 0.40 #3207, 0.33 #1666), 0838y (0.59 #4278, 0.40 #3207, 0.33 #604), 02hzz (0.59 #4278, 0.33 #1809, 0.25 #3949), 081wh1 (0.59 #4278, 0.33 #1693, 0.25 #3833), 01jcxwp (0.59 #4278, 0.25 #2771, 0.24 #5348), 01wy61y (0.50 #3208, 0.50 #2500, 0.40 #3207) >> Best rule #10519 for best value: >> intensional similarity = 10 >> extensional distance = 29 >> proper extension: 016jhr; 0g_bh; 0g293; 0p9xd; 015wd7; 01rthc; 0b_6yv; 08s6r6; >> query: (?x9831, 02ndj5) <- artists(?x9831, ?x9603), artists(?x9831, ?x475), artists(?x10969, ?x9603), artists(?x10933, ?x9603), artists(?x5934, ?x9603), ?x5934 = 05r6t, artist(?x9121, ?x9603), ?x10933 = 03p7rp, group(?x227, ?x475), ?x10969 = 029fbr >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #3207 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 05r6t; *> query: (?x9831, ?x133) <- artists(?x9831, ?x9603), artists(?x9831, ?x8199), artists(?x9831, ?x5227), ?x9603 = 012ycy, artists(?x8289, ?x5227), artists(?x302, ?x5227), ?x8289 = 05jt_, award_nominee(?x5618, ?x8199), artists(?x302, ?x4162), artists(?x302, ?x2824), artists(?x302, ?x133), ?x2824 = 02w4fkq, ?x4162 = 01wy61y *> conf = 0.40 ranks of expected_values: 181 EVAL 0xv2x artists 01fchy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 66.000 27.000 0.677 http://example.org/music/genre/artists #6584-01h8f PRED entity: 01h8f PRED relation: gender PRED expected values: 05zppz => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.82 #33, 0.81 #15, 0.81 #29), 02zsn (0.45 #26, 0.32 #48, 0.30 #28) >> Best rule #33 for best value: >> intensional similarity = 4 >> extensional distance = 223 >> proper extension: 02rchht; 01g4zr; 016hvl; 0136g9; 0162c8; 07s93v; 01gzm2; 052gzr; 01f7j9; 02fcs2; ... >> query: (?x5217, 05zppz) <- place_of_birth(?x5217, ?x7321), student(?x11215, ?x5217), profession(?x5217, ?x524), ?x524 = 02jknp >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01h8f gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.822 http://example.org/people/person/gender #6583-096f8 PRED entity: 096f8 PRED relation: country PRED expected values: 01z215 => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 383): 03rjj (0.89 #6271, 0.89 #6838, 0.89 #6650), 05qhw (0.87 #1706, 0.86 #183, 0.82 #192), 0ctw_b (0.87 #1706, 0.86 #183, 0.82 #192), 07f1x (0.87 #1706, 0.86 #183, 0.82 #192), 01z215 (0.87 #1706, 0.86 #183, 0.82 #192), 05v8c (0.87 #1706, 0.82 #192, 0.77 #381), 06qd3 (0.86 #183, 0.85 #3441, 0.82 #192), 0163v (0.86 #183, 0.82 #192, 0.82 #2897), 03gj2 (0.86 #183, 0.82 #192, 0.81 #1321), 0h7x (0.86 #183, 0.82 #192, 0.81 #1321) >> Best rule #6271 for best value: >> intensional similarity = 43 >> extensional distance = 36 >> proper extension: 09xp_; >> query: (?x779, 03rjj) <- sports(?x778, ?x779), country(?x779, ?x252), country(?x779, ?x151), sports(?x778, ?x4833), sports(?x778, ?x1121), olympics(?x150, ?x778), olympics(?x2346, ?x778), olympics(?x311, ?x778), olympics(?x4833, ?x1931), film_release_region(?x8891, ?x151), film_release_region(?x5142, ?x151), film_release_region(?x5089, ?x151), film_release_region(?x2961, ?x151), film_release_region(?x2714, ?x151), film_release_region(?x2342, ?x151), film_release_region(?x1392, ?x151), film_release_region(?x1370, ?x151), film_release_region(?x1150, ?x151), film_release_region(?x903, ?x151), film_release_region(?x141, ?x151), ?x1370 = 0gmcwlb, ?x2714 = 0kv238, athlete(?x4833, ?x1213), adjoins(?x151, ?x1227), combatants(?x326, ?x151), ?x903 = 04969y, olympics(?x1592, ?x778), film_crew_role(?x8891, ?x137), location_of_ceremony(?x1149, ?x151), ?x5142 = 0bt3j9, medal(?x778, ?x422), film(?x788, ?x2342), ?x2961 = 047p7fr, ?x5089 = 0bh8tgs, film(?x2372, ?x1150), countries_spoken_in(?x5359, ?x311), ?x141 = 0gtsx8c, film_release_region(?x504, ?x311), titles(?x2346, ?x3157), exported_to(?x2346, ?x291), ?x1392 = 017gm7, ?x1121 = 0bynt, adjustment_currency(?x252, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1706 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 3 *> proper extension: 03hr1p; *> query: (?x779, ?x1781) <- sports(?x7688, ?x779), sports(?x2496, ?x779), sports(?x584, ?x779), sports(?x391, ?x779), ?x584 = 0l98s, ?x7688 = 0jkvj, country(?x779, ?x5073), country(?x779, ?x4302), country(?x779, ?x1264), country(?x779, ?x410), country(?x779, ?x390), country(?x779, ?x279), country(?x779, ?x142), ?x391 = 0l6vl, combatants(?x1781, ?x4302), adjoins(?x4302, ?x1499), ?x410 = 01ls2, sports(?x867, ?x779), ?x390 = 0chghy, medal(?x4302, ?x422), ?x1264 = 0345h, ?x142 = 0jgd, combatants(?x13684, ?x4302), countries_spoken_in(?x5359, ?x4302), ?x279 = 0d060g, combatants(?x4302, ?x1780), administrative_parent(?x4302, ?x551), jurisdiction_of_office(?x182, ?x4302), capital(?x1781, ?x14491), organization(?x1781, ?x127), film_release_region(?x1178, ?x4302), form_of_government(?x1781, ?x4763), contains(?x6304, ?x1781), entity_involved(?x13684, ?x8437), country(?x14657, ?x1781), olympics(?x779, ?x1931), teams(?x4302, ?x3060), olympics(?x205, ?x2496), taxonomy(?x5073, ?x939), olympics(?x47, ?x867), ?x1178 = 053rxgm *> conf = 0.87 ranks of expected_values: 5 EVAL 096f8 country 01z215 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 39.000 39.000 0.895 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #6582-016ksk PRED entity: 016ksk PRED relation: artist! PRED expected values: 011k1h 04fcjt 04gm7n => 111 concepts (101 used for prediction) PRED predicted values (max 10 best out of 96): 04fcjt (0.27 #171, 0.23 #453, 0.08 #312), 01f_3w (0.20 #740, 0.10 #35, 0.10 #1022), 033hn8 (0.19 #1001, 0.16 #719, 0.13 #3400), 015_1q (0.19 #3406, 0.18 #2983, 0.18 #1148), 0181dw (0.18 #184, 0.16 #748, 0.15 #466), 06wcbk7 (0.18 #145, 0.15 #427, 0.08 #709), 03d96s (0.18 #190, 0.15 #472, 0.06 #1177), 0g768 (0.16 #3142, 0.14 #1166, 0.13 #1730), 03rhqg (0.14 #7211, 0.13 #5660, 0.12 #6083), 01trtc (0.14 #1202, 0.11 #1766, 0.10 #74) >> Best rule #171 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 02x_h0; 02vwckw; 01wlt3k; 03f0qd7; >> query: (?x3707, 04fcjt) <- currency(?x3707, ?x170), artists(?x8184, ?x3707), ?x8184 = 016_v3 >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14, 28 EVAL 016ksk artist! 04gm7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 111.000 101.000 0.273 http://example.org/music/record_label/artist EVAL 016ksk artist! 04fcjt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 101.000 0.273 http://example.org/music/record_label/artist EVAL 016ksk artist! 011k1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 111.000 101.000 0.273 http://example.org/music/record_label/artist #6581-049m19 PRED entity: 049m19 PRED relation: film PRED expected values: 047q2k1 => 180 concepts (142 used for prediction) PRED predicted values (max 10 best out of 1376): 0f42nz (0.60 #910, 0.39 #8075, 0.13 #104797), 030z4z (0.20 #1478, 0.11 #8643, 0.07 #14018), 02tcgh (0.20 #1710, 0.07 #8875, 0.07 #16041), 04q00lw (0.20 #383, 0.04 #3965, 0.04 #5757), 013q07 (0.14 #12897, 0.09 #18270, 0.08 #39763), 01mszz (0.12 #2879, 0.07 #17210, 0.05 #42285), 016dj8 (0.10 #13656, 0.09 #19029, 0.08 #2907), 056xkh (0.10 #14141, 0.07 #19514, 0.06 #21305), 07ghv5 (0.10 #11918, 0.05 #35203, 0.03 #88936), 050kh5 (0.09 #28660, 0.08 #12540, 0.08 #164786) >> Best rule #910 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 04y0yc; >> query: (?x11799, 0f42nz) <- special_performance_type(?x11799, ?x4832), film(?x11799, ?x9805), place_of_birth(?x11799, ?x7412), ?x7412 = 04vmp >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1823 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 03mv0b; *> query: (?x11799, 047q2k1) <- special_performance_type(?x11799, ?x4832), category(?x11799, ?x134), profession(?x11799, ?x319), ?x319 = 01d_h8 *> conf = 0.04 ranks of expected_values: 171 EVAL 049m19 film 047q2k1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 180.000 142.000 0.600 http://example.org/film/actor/film./film/performance/film #6580-01qrbf PRED entity: 01qrbf PRED relation: artists! PRED expected values: 02vjzr => 106 concepts (81 used for prediction) PRED predicted values (max 10 best out of 207): 06by7 (0.63 #1592, 0.53 #1278, 0.53 #2220), 06j6l (0.36 #6330, 0.33 #7587, 0.32 #4760), 05bt6j (0.35 #6325, 0.33 #7582, 0.31 #1615), 0glt670 (0.33 #3182, 0.29 #4438, 0.29 #3810), 025sc50 (0.32 #6332, 0.32 #3192, 0.30 #7589), 0xhtw (0.31 #1587, 0.25 #1273, 0.20 #8811), 0gywn (0.27 #4770, 0.26 #6340, 0.25 #2258), 0ggx5q (0.24 #6361, 0.22 #7618, 0.20 #1965), 016clz (0.24 #9741, 0.23 #9427, 0.23 #8799), 0155w (0.24 #2308, 0.17 #4820, 0.15 #2936) >> Best rule #1592 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 02r1tx7; 0dm5l; 08w4pm; 04k05; >> query: (?x7186, 06by7) <- origin(?x7186, ?x362), artist(?x5744, ?x7186), ?x362 = 04jpl >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #6418 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 307 *> proper extension: 01v0sx2; 01fl3; 01rm8b; 01323p; 02twdq; *> query: (?x7186, 02vjzr) <- artist(?x5744, ?x7186), artists(?x671, ?x7186), ?x671 = 064t9 *> conf = 0.13 ranks of expected_values: 24 EVAL 01qrbf artists! 02vjzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 106.000 81.000 0.629 http://example.org/music/genre/artists #6579-05cx7x PRED entity: 05cx7x PRED relation: nationality PRED expected values: 09c7w0 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 97): 09c7w0 (0.78 #605, 0.77 #405, 0.74 #906), 07ssc (0.34 #4318, 0.14 #115, 0.12 #216), 0b90_r (0.34 #4318, 0.05 #3, 0.02 #3314), 0j5g9 (0.34 #4318, 0.05 #62), 02jx1 (0.16 #234, 0.13 #133, 0.12 #335), 03rk0 (0.07 #1754, 0.06 #6370, 0.06 #3460), 0d060g (0.05 #7, 0.05 #511, 0.04 #2618), 03rt9 (0.05 #13, 0.02 #1319, 0.02 #717), 0345h (0.04 #435, 0.03 #635, 0.02 #3646), 06q1r (0.03 #177, 0.02 #379, 0.02 #278) >> Best rule #605 for best value: >> intensional similarity = 3 >> extensional distance = 447 >> proper extension: 012v1t; >> query: (?x7487, 09c7w0) <- people(?x1050, ?x7487), people(?x1050, ?x2837), ?x2837 = 047hpm >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05cx7x nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.777 http://example.org/people/person/nationality #6578-04x4vj PRED entity: 04x4vj PRED relation: nominated_for! PRED expected values: 0gqy2 => 88 concepts (80 used for prediction) PRED predicted values (max 10 best out of 181): 0gq9h (0.41 #4111, 0.37 #8159, 0.30 #1966), 019f4v (0.38 #292, 0.32 #1958, 0.29 #8151), 0k611 (0.38 #311, 0.28 #8170, 0.27 #4122), 0f4x7 (0.38 #263, 0.25 #4074, 0.21 #8122), 099c8n (0.38 #295, 0.25 #2677, 0.25 #771), 040njc (0.38 #245, 0.25 #4056, 0.24 #8104), 02qyntr (0.38 #418, 0.20 #8277, 0.18 #2084), 02qvyrt (0.38 #335, 0.17 #8194, 0.17 #811), 04kxsb (0.38 #334, 0.17 #8193, 0.14 #5954), 0gs9p (0.33 #4113, 0.31 #8161, 0.28 #1968) >> Best rule #4111 for best value: >> intensional similarity = 3 >> extensional distance = 328 >> proper extension: 06r1k; 0123qq; >> query: (?x4591, 0gq9h) <- nominated_for(?x8288, ?x4591), profession(?x8288, ?x2265), people(?x5118, ?x8288) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #8220 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 719 *> proper extension: 04z_x4v; *> query: (?x4591, 0gqy2) <- nominated_for(?x4091, ?x4591), nominated_for(?x4091, ?x2920), ?x2920 = 0b1y_2 *> conf = 0.26 ranks of expected_values: 15 EVAL 04x4vj nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 88.000 80.000 0.409 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6577-0bbgvp PRED entity: 0bbgvp PRED relation: genre PRED expected values: 07s9rl0 => 96 concepts (69 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.95 #4843, 0.87 #2717, 0.79 #473), 01jfsb (0.62 #5091, 0.35 #3790, 0.35 #3436), 0hcr (0.61 #612, 0.07 #6874, 0.06 #5101), 05p553 (0.42 #2602, 0.41 #2366, 0.41 #2011), 0jxy (0.31 #633, 0.03 #5122, 0.01 #2405), 04xvlr (0.30 #1300, 0.27 #1418, 0.21 #474), 01hmnh (0.29 #606, 0.16 #5569, 0.16 #5451), 06n90 (0.27 #603, 0.27 #5092, 0.17 #367), 06cvj (0.25 #2601, 0.25 #2365, 0.24 #2010), 0lsxr (0.25 #9, 0.25 #5088, 0.20 #835) >> Best rule #4843 for best value: >> intensional similarity = 4 >> extensional distance = 739 >> proper extension: 015qy1; >> query: (?x11998, 07s9rl0) <- film_release_region(?x11998, ?x94), genre(?x11998, ?x1509), genre(?x6704, ?x1509), ?x6704 = 02wyzmv >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bbgvp genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 69.000 0.949 http://example.org/film/film/genre #6576-0k33p PRED entity: 0k33p PRED relation: location! PRED expected values: 01mxnvc => 144 concepts (78 used for prediction) PRED predicted values (max 10 best out of 2070): 0bz5v2 (0.47 #118086, 0.46 #105522, 0.45 #135674), 026y23w (0.47 #118086, 0.46 #105522, 0.45 #135674), 081k8 (0.33 #1016, 0.25 #6040, 0.05 #153266), 016m5c (0.29 #82910, 0.28 #120599, 0.28 #138188), 04k05 (0.29 #82910, 0.28 #120599, 0.28 #138188), 012vm6 (0.29 #82910, 0.28 #120599, 0.28 #138188), 02vgh (0.29 #82910, 0.28 #120599, 0.28 #138188), 040dv (0.17 #9353, 0.04 #39504, 0.04 #14378), 01vsy3q (0.10 #26109, 0.07 #33651, 0.05 #81385), 023kzp (0.10 #26333, 0.06 #79097, 0.06 #43923) >> Best rule #118086 for best value: >> intensional similarity = 3 >> extensional distance = 177 >> proper extension: 0hzlz; 0x335; 05d49; >> query: (?x9878, ?x1040) <- location(?x477, ?x9878), influenced_by(?x476, ?x477), place_of_birth(?x1040, ?x9878) >> conf = 0.47 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0k33p location! 01mxnvc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 78.000 0.475 http://example.org/people/person/places_lived./people/place_lived/location #6575-01_mdl PRED entity: 01_mdl PRED relation: film! PRED expected values: 01fkv0 04__f => 105 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1096): 0kb3n (0.45 #51943, 0.44 #68563, 0.44 #120509), 06qn87 (0.45 #51943, 0.44 #68563, 0.44 #120509), 05drq5 (0.45 #51943, 0.44 #68563, 0.44 #120509), 0146pg (0.45 #51943, 0.44 #68563, 0.44 #120509), 0h32q (0.33 #4926, 0.02 #38169, 0.01 #54793), 05kh_ (0.25 #999, 0.20 #3076, 0.17 #7230), 044bn (0.25 #1845, 0.20 #3922, 0.17 #8076), 015nvj (0.25 #1800, 0.20 #3877, 0.17 #8031), 0m0hw (0.25 #1166, 0.20 #3243, 0.17 #7397), 021lby (0.21 #37397, 0.19 #27009, 0.18 #35319) >> Best rule #51943 for best value: >> intensional similarity = 4 >> extensional distance = 204 >> proper extension: 0bx_hnp; >> query: (?x1072, ?x669) <- film_crew_role(?x1072, ?x281), film_release_region(?x1072, ?x512), ?x512 = 07ssc, nominated_for(?x669, ?x1072) >> conf = 0.45 => this is the best rule for 4 predicted values *> Best rule #11765 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 042fgh; *> query: (?x1072, 04__f) <- film(?x773, ?x1072), production_companies(?x1072, ?x382), honored_for(?x1072, ?x1385), story_by(?x1072, ?x8209) *> conf = 0.15 ranks of expected_values: 40, 209 EVAL 01_mdl film! 04__f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 105.000 60.000 0.445 http://example.org/film/actor/film./film/performance/film EVAL 01_mdl film! 01fkv0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 105.000 60.000 0.445 http://example.org/film/actor/film./film/performance/film #6574-02p5hf PRED entity: 02p5hf PRED relation: type_of_union PRED expected values: 01g63y => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 3): 01g63y (0.33 #52, 0.29 #103, 0.22 #109), 0jgjn (0.02 #21, 0.01 #24, 0.01 #27), 01bl8s (0.01 #29) >> Best rule #52 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 01wk7b7; >> query: (?x10445, 01g63y) <- gender(?x10445, ?x231), spouse(?x8626, ?x10445), participant(?x10445, ?x8134) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02p5hf type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.326 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6573-085q5 PRED entity: 085q5 PRED relation: film PRED expected values: 0dyb1 07bxqz => 79 concepts (59 used for prediction) PRED predicted values (max 10 best out of 730): 03q0r1 (0.40 #633, 0.07 #9548, 0.06 #5982), 07y9w5 (0.20 #223, 0.14 #3789, 0.03 #105207), 0407yj_ (0.20 #479, 0.05 #9394, 0.03 #5828), 0640m69 (0.20 #1755, 0.05 #5321, 0.03 #105207), 047vp1n (0.20 #1274, 0.05 #4840, 0.03 #105207), 02c7k4 (0.20 #1099, 0.05 #6448, 0.03 #105207), 0gj96ln (0.20 #1071, 0.03 #4637, 0.03 #9986), 0f2sx4 (0.20 #1381, 0.03 #4947, 0.03 #105207), 0symg (0.20 #1696, 0.03 #5262, 0.03 #105207), 0ds33 (0.20 #68, 0.03 #3634, 0.03 #105207) >> Best rule #633 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01wbg84; 015pvh; 01h1b; >> query: (?x10121, 03q0r1) <- profession(?x10121, ?x987), ?x987 = 0dxtg, film(?x10121, ?x1080), ?x1080 = 01c22t >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #105207 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2056 *> proper extension: 022769; 080knyg; *> query: (?x10121, ?x2973) <- film(?x10121, ?x1046), film(?x4234, ?x1046), language(?x1046, ?x254), film(?x4234, ?x2973) *> conf = 0.03 ranks of expected_values: 187, 275 EVAL 085q5 film 07bxqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 79.000 59.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 085q5 film 0dyb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 79.000 59.000 0.400 http://example.org/film/actor/film./film/performance/film #6572-02z3r8t PRED entity: 02z3r8t PRED relation: music PRED expected values: 0jn5l => 93 concepts (75 used for prediction) PRED predicted values (max 10 best out of 94): 03c_8t (0.29 #624, 0.07 #1043, 0.03 #1882), 02jxkw (0.25 #142, 0.14 #558, 0.04 #10196), 020fgy (0.25 #164, 0.03 #1838, 0.02 #2046), 05_pkf (0.25 #61, 0.02 #6968, 0.01 #10115), 023361 (0.17 #358, 0.09 #774, 0.03 #4959), 03h610 (0.14 #912, 0.10 #1751, 0.07 #1541), 02bh9 (0.14 #467, 0.09 #675, 0.07 #886), 01x6v6 (0.14 #958, 0.06 #1377, 0.04 #3678), 07v4dm (0.14 #607, 0.04 #1445, 0.03 #1655), 06fxnf (0.14 #904, 0.03 #4878, 0.03 #2993) >> Best rule #624 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 04yc76; >> query: (?x755, 03c_8t) <- film_crew_role(?x755, ?x468), film(?x3865, ?x755), ?x3865 = 01_xtx, music(?x755, ?x10574) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1770 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 0d6b7; *> query: (?x755, 0jn5l) <- film_crew_role(?x755, ?x468), music(?x755, ?x10574), film_festivals(?x755, ?x9189) *> conf = 0.03 ranks of expected_values: 34 EVAL 02z3r8t music 0jn5l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 93.000 75.000 0.286 http://example.org/film/film/music #6571-02jhc PRED entity: 02jhc PRED relation: interests! PRED expected values: 0gz_ => 51 concepts (42 used for prediction) PRED predicted values (max 10 best out of 209): 039n1 (0.60 #88, 0.50 #41, 0.28 #279), 01dvtx (0.50 #31, 0.40 #78, 0.33 #6), 0nk72 (0.50 #39, 0.40 #86, 0.33 #14), 043s3 (0.50 #32, 0.40 #79, 0.25 #97), 04hcw (0.50 #35, 0.40 #82, 0.25 #97), 03j43 (0.50 #26, 0.40 #73, 0.25 #97), 0gz_ (0.40 #74, 0.33 #2, 0.25 #50), 07h1q (0.40 #89, 0.25 #42, 0.25 #97), 0399p (0.40 #85, 0.25 #38, 0.25 #97), 01h2_6 (0.40 #94, 0.25 #47, 0.22 #48) >> Best rule #88 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0x0w; >> query: (?x6978, 039n1) <- interests(?x12259, ?x6978), interests(?x8430, ?x6978), interests(?x7250, ?x6978), interests(?x5797, ?x6978), location(?x5797, ?x4627), influenced_by(?x2608, ?x5797), ?x7250 = 03sbs, influenced_by(?x7509, ?x12259), profession(?x8430, ?x353), ?x7509 = 048cl, influenced_by(?x8430, ?x2161) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #74 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 0x0w; *> query: (?x6978, 0gz_) <- interests(?x12259, ?x6978), interests(?x8430, ?x6978), interests(?x7250, ?x6978), interests(?x5797, ?x6978), location(?x5797, ?x4627), influenced_by(?x2608, ?x5797), ?x7250 = 03sbs, influenced_by(?x7509, ?x12259), profession(?x8430, ?x353), ?x7509 = 048cl, influenced_by(?x8430, ?x2161) *> conf = 0.40 ranks of expected_values: 7 EVAL 02jhc interests! 0gz_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 51.000 42.000 0.600 http://example.org/user/alexander/philosophy/philosopher/interests #6570-01d38t PRED entity: 01d38t PRED relation: award! PRED expected values: 0150jk 016m5c 011xhx => 33 concepts (9 used for prediction) PRED predicted values (max 10 best out of 2008): 02r3zy (0.81 #6728, 0.79 #26905, 0.78 #3363), 04qmr (0.81 #6728, 0.79 #26905, 0.78 #3363), 0kr_t (0.67 #4979, 0.50 #1614, 0.14 #8343), 01pfr3 (0.67 #3459, 0.50 #94, 0.07 #6823), 03d9d6 (0.50 #5024, 0.50 #1659, 0.09 #8388), 0gr69 (0.50 #5449, 0.50 #2084, 0.05 #8813), 02qwg (0.50 #932, 0.33 #4297, 0.26 #7661), 0gcs9 (0.50 #817, 0.33 #4182, 0.23 #7546), 03g5jw (0.50 #396, 0.33 #3761, 0.22 #3365), 02vr7 (0.50 #2405, 0.33 #5770, 0.18 #9134) >> Best rule #6728 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 02f716; 02f72_; >> query: (?x9462, ?x646) <- award_winner(?x9462, ?x10813), award_winner(?x9462, ?x8060), award_winner(?x9462, ?x646), ?x8060 = 06mj4, artist(?x2931, ?x10813), artists(?x1000, ?x10813), inductee(?x1091, ?x10813) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #152 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 03tcnt; 01ckcd; *> query: (?x9462, 0150jk) <- award_winner(?x9462, ?x8060), award_winner(?x9462, ?x2930), ?x8060 = 06mj4, ceremony(?x9462, ?x9431), ?x9431 = 02cg41, award_nominee(?x2929, ?x2930) *> conf = 0.50 ranks of expected_values: 18, 545 EVAL 01d38t award! 011xhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 9.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01d38t award! 016m5c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 33.000 9.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01d38t award! 0150jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 33.000 9.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6569-0c5tl PRED entity: 0c5tl PRED relation: influenced_by PRED expected values: 0448r => 169 concepts (89 used for prediction) PRED predicted values (max 10 best out of 335): 042q3 (0.60 #797, 0.50 #5569, 0.19 #6943), 032l1 (0.50 #5294, 0.33 #19176, 0.33 #6598), 03_87 (0.50 #3235, 0.29 #19288, 0.24 #6710), 02lt8 (0.45 #2719, 0.38 #4022, 0.25 #3154), 02wh0 (0.44 #5587, 0.27 #18601, 0.20 #815), 03sbs (0.40 #654, 0.33 #1087, 0.28 #5426), 015n8 (0.40 #842, 0.33 #1275, 0.28 #5614), 0gz_ (0.40 #536, 0.19 #6943, 0.19 #6612), 0420y (0.40 #836, 0.17 #1269, 0.14 #6912), 07ym0 (0.40 #710, 0.10 #6786, 0.09 #36475) >> Best rule #797 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 02wh0; >> query: (?x5091, 042q3) <- influenced_by(?x5091, ?x12146), influenced_by(?x5091, ?x5004), gender(?x5091, ?x231), religion(?x5091, ?x2694), ?x5004 = 081k8, ?x12146 = 01lwx >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6770 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 19 *> proper extension: 0j3v; *> query: (?x5091, 0448r) <- influenced_by(?x5091, ?x12146), influenced_by(?x5091, ?x5004), gender(?x5091, ?x231), religion(?x5091, ?x2694), ?x5004 = 081k8, influenced_by(?x12146, ?x1857) *> conf = 0.14 ranks of expected_values: 56 EVAL 0c5tl influenced_by 0448r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 169.000 89.000 0.600 http://example.org/influence/influence_node/influenced_by #6568-015fsv PRED entity: 015fsv PRED relation: organization! PRED expected values: 060c4 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 9): 060c4 (0.77 #262, 0.76 #289, 0.76 #302), 0dq_5 (0.33 #139, 0.29 #9, 0.27 #22), 07xl34 (0.29 #167, 0.25 #258, 0.23 #50), 05k17c (0.16 #59, 0.10 #280, 0.10 #437), 0hm4q (0.09 #164, 0.05 #438, 0.05 #764), 05c0jwl (0.04 #435, 0.04 #553, 0.04 #344), 08jcfy (0.02 #259, 0.02 #547, 0.02 #194), 04n1q6 (0.01 #554, 0.01 #462, 0.01 #397), 0dq3c (0.01 #131) >> Best rule #262 for best value: >> intensional similarity = 4 >> extensional distance = 176 >> proper extension: 01pl14; 01hhvg; 01b1mj; 01j_06; 01nkcn; 02jyr8; 022lly; 01y17m; 0pspl; 03x33n; ... >> query: (?x9249, 060c4) <- institution(?x620, ?x9249), school(?x2820, ?x9249), institution(?x620, ?x11244), ?x11244 = 02gnmp >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015fsv organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.770 http://example.org/organization/role/leaders./organization/leadership/organization #6567-01w5jwb PRED entity: 01w5jwb PRED relation: film PRED expected values: 0gj96ln => 111 concepts (99 used for prediction) PRED predicted values (max 10 best out of 377): 0fpgp26 (0.14 #1539, 0.12 #3331, 0.06 #5123), 0c8tkt (0.10 #5644, 0.04 #11020, 0.03 #14604), 05c46y6 (0.07 #440, 0.06 #4024, 0.06 #2232), 017d93 (0.07 #1114, 0.06 #4698, 0.06 #2906), 047svrl (0.07 #430, 0.06 #4014, 0.06 #2222), 01jnc_ (0.07 #6947, 0.05 #12323, 0.04 #19491), 03l6q0 (0.05 #16672, 0.04 #13088, 0.02 #27424), 0jqkh (0.04 #24628, 0.02 #13876, 0.01 #101684), 01719t (0.03 #23527, 0.03 #12775, 0.01 #100583), 0b7l4x (0.03 #6417, 0.02 #13585, 0.02 #17169) >> Best rule #1539 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 01yzl2; 06mt91; 01mskc3; >> query: (?x8722, 0fpgp26) <- award_nominee(?x8722, ?x2335), ?x2335 = 0288fyj, artist(?x2190, ?x8722) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w5jwb film 0gj96ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 99.000 0.143 http://example.org/film/actor/film./film/performance/film #6566-049t4g PRED entity: 049t4g PRED relation: award PRED expected values: 09sb52 => 91 concepts (49 used for prediction) PRED predicted values (max 10 best out of 216): 09sb52 (0.62 #1656, 0.32 #8122, 0.30 #5698), 04dn09n (0.48 #2469, 0.10 #6913, 0.08 #13755), 0gr4k (0.31 #2458, 0.13 #6902, 0.05 #2054), 0bdwqv (0.29 #2194, 0.15 #2598, 0.10 #3002), 02x73k6 (0.29 #2082, 0.20 #60, 0.16 #2486), 0ck27z (0.27 #4538, 0.22 #4942, 0.22 #5346), 09sdmz (0.26 #2228, 0.18 #2632, 0.09 #1822), 0f4x7 (0.26 #2052, 0.17 #6900, 0.17 #2456), 0bfvd4 (0.24 #2137, 0.20 #115, 0.13 #2541), 0gr51 (0.23 #2526, 0.10 #6970, 0.08 #13755) >> Best rule #1656 for best value: >> intensional similarity = 4 >> extensional distance = 115 >> proper extension: 02s2ft; 05bnp0; 0337vz; 04t2l2; 01wbg84; 05cj4r; 01q_ph; 032xhg; 09fqtq; 044rvb; ... >> query: (?x9578, 09sb52) <- film(?x9578, ?x299), award(?x299, ?x3435), award(?x9578, ?x112), ?x3435 = 03hl6lc >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049t4g award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 49.000 0.615 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6565-01kymm PRED entity: 01kymm PRED relation: artist! PRED expected values: 01w565 => 152 concepts (134 used for prediction) PRED predicted values (max 10 best out of 122): 017l96 (0.30 #871, 0.25 #1297, 0.12 #3005), 015_1q (0.29 #730, 0.24 #3006, 0.23 #2151), 02swsm (0.29 #806, 0.20 #238, 0.18 #1090), 033hn8 (0.25 #1292, 0.22 #3713, 0.20 #866), 0181dw (0.25 #1321, 0.20 #895, 0.14 #6449), 011k1h (0.23 #2710, 0.22 #2996, 0.16 #3853), 01clyr (0.23 #2734, 0.18 #3020, 0.14 #3733), 03mp8k (0.20 #920, 0.17 #1346, 0.17 #494), 01trtc (0.20 #926, 0.17 #1352, 0.17 #500), 01dtcb (0.20 #48, 0.17 #474, 0.14 #758) >> Best rule #871 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 01vsgrn; >> query: (?x6118, 017l96) <- origin(?x6118, ?x9559), profession(?x6118, ?x220), special_performance_type(?x6118, ?x296), artists(?x671, ?x6118), profession(?x9797, ?x220), ?x9797 = 010xjr >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #899 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 01vsgrn; *> query: (?x6118, 01w565) <- origin(?x6118, ?x9559), profession(?x6118, ?x220), special_performance_type(?x6118, ?x296), artists(?x671, ?x6118), profession(?x9797, ?x220), ?x9797 = 010xjr *> conf = 0.10 ranks of expected_values: 34 EVAL 01kymm artist! 01w565 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 152.000 134.000 0.300 http://example.org/music/record_label/artist #6564-01cl0d PRED entity: 01cl0d PRED relation: artist PRED expected values: 014pg1 0gps0z 0b1hw => 77 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1011): 01vtj38 (0.60 #2110, 0.11 #7726, 0.08 #8530), 0c9d9 (0.50 #12, 0.25 #813, 0.12 #8035), 01q99h (0.50 #423, 0.21 #6839, 0.20 #2026), 01323p (0.50 #537, 0.21 #6953, 0.16 #8560), 028qyn (0.50 #4009, 0.21 #16863, 0.20 #6416), 01s560x (0.50 #722, 0.20 #2325, 0.14 #7138), 046p9 (0.50 #573, 0.20 #2176, 0.14 #6989), 01wj18h (0.50 #200, 0.20 #1803, 0.14 #6616), 01fh0q (0.50 #634, 0.20 #2237, 0.14 #7050), 020_4z (0.50 #1507, 0.20 #2309, 0.08 #8729) >> Best rule #2110 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0fb0v; 015_1q; 0g768; >> query: (?x8489, 01vtj38) <- artist(?x8489, ?x6162), artist(?x8489, ?x3997), artist(?x8489, ?x1001), ?x6162 = 01w9wwg, category(?x8489, ?x134), artists(?x302, ?x1001), award(?x3997, ?x528) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1473 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 01clyr; 01dtcb; *> query: (?x8489, 0gps0z) <- artist(?x8489, ?x8391), artist(?x8489, ?x6162), artist(?x8489, ?x2521), ?x2521 = 0frsw, profession(?x8391, ?x220), award_nominee(?x6162, ?x827), instrumentalists(?x716, ?x6162) *> conf = 0.50 ranks of expected_values: 12, 716, 938 EVAL 01cl0d artist 0b1hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 77.000 27.000 0.600 http://example.org/music/record_label/artist EVAL 01cl0d artist 0gps0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 77.000 27.000 0.600 http://example.org/music/record_label/artist EVAL 01cl0d artist 014pg1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 77.000 27.000 0.600 http://example.org/music/record_label/artist #6563-04jpg2p PRED entity: 04jpg2p PRED relation: genre PRED expected values: 03k9fj => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 97): 07s9rl0 (0.77 #969, 0.76 #2788, 0.69 #4368), 024qqx (0.54 #3638, 0.54 #1211, 0.53 #3516), 03k9fj (0.53 #134, 0.53 #497, 0.51 #1102), 060__y (0.50 #19, 0.27 #140, 0.25 #261), 02kdv5l (0.44 #245, 0.40 #124, 0.39 #366), 05p553 (0.42 #1094, 0.39 #1458, 0.37 #731), 01jfsb (0.36 #2922, 0.35 #4259, 0.33 #2679), 02l7c8 (0.36 #986, 0.33 #2805, 0.32 #3290), 04xvh5 (0.25 #36, 0.20 #157, 0.19 #278), 04xvlr (0.25 #849, 0.20 #970, 0.19 #2182) >> Best rule #969 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 016kz1; >> query: (?x8570, 07s9rl0) <- nominated_for(?x1443, ?x8570), award_winner(?x8570, ?x930), ?x1443 = 054krc >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #134 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0g56t9t; 0cpllql; 0fdv3; 0ndwt2w; *> query: (?x8570, 03k9fj) <- film(?x3028, ?x8570), country(?x8570, ?x94), ?x3028 = 0f0kz, nominated_for(?x3410, ?x8570) *> conf = 0.53 ranks of expected_values: 3 EVAL 04jpg2p genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 89.000 0.772 http://example.org/film/film/genre #6562-0kb57 PRED entity: 0kb57 PRED relation: nominated_for! PRED expected values: 0gq_v 0gr42 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 189): 0gs9p (0.60 #3103, 0.58 #2635, 0.37 #1699), 019f4v (0.56 #3095, 0.50 #2627, 0.35 #1691), 0gq_v (0.47 #1892, 0.39 #3062, 0.35 #1658), 040njc (0.44 #3049, 0.41 #2581, 0.28 #1645), 04dn09n (0.44 #3076, 0.37 #2608, 0.24 #1672), 0p9sw (0.44 #3063, 0.26 #1893, 0.24 #13809), 04kxsb (0.42 #2666, 0.30 #3134, 0.20 #2198), 0gqy2 (0.38 #1289, 0.35 #3161, 0.34 #2693), 02qyntr (0.38 #3219, 0.25 #2751, 0.18 #3921), 02pqp12 (0.34 #3100, 0.26 #2632, 0.16 #3802) >> Best rule #3103 for best value: >> intensional similarity = 4 >> extensional distance = 193 >> proper extension: 0m313; 083shs; 07gp9; 0gzy02; 07xtqq; 095zlp; 04v8x9; 0bth54; 05jzt3; 0m_mm; ... >> query: (?x3009, 0gs9p) <- language(?x3009, ?x254), nominated_for(?x1703, ?x3009), film(?x6591, ?x3009), ?x1703 = 0k611 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1892 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 92 *> proper extension: 026p_bs; 075cph; 0k0rf; 048rn; 0fsw_7; 0k7tq; 0fztbq; *> query: (?x3009, 0gq_v) <- language(?x3009, ?x3592), languages_spoken(?x913, ?x3592), film_sets_designed(?x12725, ?x3009), languages(?x1093, ?x3592) *> conf = 0.47 ranks of expected_values: 3, 48 EVAL 0kb57 nominated_for! 0gr42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 69.000 69.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0kb57 nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 69.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6561-03f6fl0 PRED entity: 03f6fl0 PRED relation: artists! PRED expected values: 016ybr => 156 concepts (95 used for prediction) PRED predicted values (max 10 best out of 268): 064t9 (0.94 #18462, 0.76 #2828, 0.58 #23151), 05bt6j (0.47 #2859, 0.43 #356, 0.34 #18493), 05w3f (0.43 #350, 0.24 #2227, 0.21 #975), 0cx7f (0.43 #452, 0.16 #2329, 0.15 #2641), 03ckfl9 (0.43 #476, 0.09 #2039, 0.09 #2353), 0xhtw (0.37 #954, 0.35 #13773, 0.31 #642), 06j6l (0.36 #1299, 0.32 #2864, 0.32 #18498), 03_d0 (0.36 #1261, 0.19 #10010, 0.19 #16270), 03lty (0.36 #13784, 0.26 #965, 0.24 #2217), 02vjzr (0.31 #2951, 0.13 #18585, 0.11 #5139) >> Best rule #18462 for best value: >> intensional similarity = 5 >> extensional distance = 371 >> proper extension: 0123r4; >> query: (?x4977, 064t9) <- artists(?x5300, ?x4977), artists(?x5300, ?x7112), artists(?x5300, ?x1800), ?x1800 = 015_30, ?x7112 = 0133x7 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #2944 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 0134wr; *> query: (?x4977, 016ybr) <- artists(?x5300, ?x4977), artist(?x2299, ?x4977), gender(?x4977, ?x231), ?x5300 = 02k_kn *> conf = 0.05 ranks of expected_values: 115 EVAL 03f6fl0 artists! 016ybr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 156.000 95.000 0.944 http://example.org/music/genre/artists #6560-02mpyh PRED entity: 02mpyh PRED relation: nominated_for! PRED expected values: 0154qm => 94 concepts (31 used for prediction) PRED predicted values (max 10 best out of 837): 029m83 (0.46 #58273, 0.10 #67603, 0.09 #1706), 04fhn_ (0.28 #23309, 0.27 #39625, 0.26 #44287), 04t7ts (0.28 #23309, 0.27 #39625, 0.26 #44287), 09rp4r_ (0.27 #48950, 0.02 #25956, 0.01 #49267), 05qd_ (0.15 #32630, 0.11 #67602, 0.11 #69934), 05bm4sm (0.13 #3586, 0.13 #1256, 0.10 #8246), 01tc9r (0.13 #3156, 0.10 #7816, 0.09 #826), 06dv3 (0.13 #33, 0.10 #2363, 0.07 #7023), 09v6tz (0.13 #1651, 0.10 #3981, 0.07 #8641), 03mfqm (0.11 #15365, 0.10 #22359, 0.06 #38674) >> Best rule #58273 for best value: >> intensional similarity = 4 >> extensional distance = 306 >> proper extension: 05f67hw; >> query: (?x8574, ?x8041) <- film_release_region(?x8574, ?x94), produced_by(?x8574, ?x8041), ?x94 = 09c7w0, film(?x8041, ?x2323) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #3021 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 28 *> proper extension: 09m6kg; 095zlp; 0m2kd; 0b6tzs; 017gl1; 04vr_f; 011yqc; 011yth; 0ch26b_; 02c638; ... *> query: (?x8574, 0154qm) <- nominated_for(?x1107, ?x8574), nominated_for(?x384, ?x8574), ?x384 = 03hkv_r, nominated_for(?x84, ?x8574), ?x1107 = 019f4v, film_crew_role(?x8574, ?x137) *> conf = 0.07 ranks of expected_values: 31 EVAL 02mpyh nominated_for! 0154qm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 94.000 31.000 0.463 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #6559-0k4gf PRED entity: 0k4gf PRED relation: influenced_by! PRED expected values: 0c73g => 221 concepts (87 used for prediction) PRED predicted values (max 10 best out of 474): 0459z (0.33 #465, 0.22 #980, 0.12 #3558), 02wh0 (0.33 #451, 0.20 #2512, 0.11 #966), 04k15 (0.22 #653, 0.17 #138, 0.12 #3231), 07lp1 (0.20 #1449, 0.17 #418, 0.11 #5575), 07h1q (0.20 #2470, 0.12 #6597, 0.12 #14845), 01vrncs (0.20 #1061, 0.11 #7249, 0.11 #5187), 01w9ph_ (0.20 #1351, 0.11 #5477, 0.07 #7539), 07dnx (0.17 #363, 0.13 #2424, 0.11 #878), 0dzkq (0.17 #126, 0.13 #2187, 0.09 #14047), 0399p (0.17 #330, 0.13 #2391, 0.09 #14251) >> Best rule #465 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 039n1; 042q3; >> query: (?x1211, 0459z) <- influenced_by(?x1211, ?x8177), nationality(?x1211, ?x1264), gender(?x1211, ?x231), ?x8177 = 03_f0, people(?x1050, ?x1211) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #507 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 039n1; 042q3; *> query: (?x1211, 0c73g) <- influenced_by(?x1211, ?x8177), nationality(?x1211, ?x1264), gender(?x1211, ?x231), ?x8177 = 03_f0, people(?x1050, ?x1211) *> conf = 0.17 ranks of expected_values: 28 EVAL 0k4gf influenced_by! 0c73g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 221.000 87.000 0.333 http://example.org/influence/influence_node/influenced_by #6558-0lk8j PRED entity: 0lk8j PRED relation: olympics! PRED expected values: 02jx1 => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 315): 0d060g (0.80 #209, 0.63 #615, 0.62 #817), 07ssc (0.65 #1232, 0.65 #423, 0.63 #626), 03rjj (0.63 #613, 0.59 #410, 0.57 #815), 059j2 (0.53 #606, 0.53 #437, 0.50 #403), 03gj2 (0.53 #431, 0.53 #634, 0.52 #836), 0345h (0.50 #3271, 0.48 #4284, 0.48 #844), 06mkj (0.50 #262, 0.44 #4451, 0.41 #3437), 06c1y (0.50 #247, 0.41 #450, 0.38 #855), 0b90_r (0.47 #610, 0.43 #1216, 0.43 #812), 0chghy (0.44 #4451, 0.42 #620, 0.41 #417) >> Best rule #209 for best value: >> intensional similarity = 9 >> extensional distance = 8 >> proper extension: 0l6vl; 0kbvb; 0l6ny; 0l6m5; 0lv1x; 0lbbj; 06sks6; 0jhn7; >> query: (?x2131, 0d060g) <- medal(?x2131, ?x422), sports(?x2131, ?x1967), sports(?x2131, ?x779), olympics(?x1229, ?x2131), olympics(?x429, ?x2131), ?x779 = 096f8, ?x1967 = 01cgz, ?x1229 = 059j2, ?x429 = 03rt9 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #644 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 17 *> proper extension: 0sxrz; 0lgxj; 0ldqf; *> query: (?x2131, 02jx1) <- medal(?x2131, ?x1242), sports(?x2131, ?x1967), sports(?x2131, ?x779), olympics(?x1229, ?x2131), ?x779 = 096f8, ?x1967 = 01cgz, country(?x150, ?x1229), ?x1242 = 02lq5w *> conf = 0.16 ranks of expected_values: 61 EVAL 0lk8j olympics! 02jx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 46.000 46.000 0.800 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #6557-01hmk9 PRED entity: 01hmk9 PRED relation: people! PRED expected values: 0gk4g => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 42): 0gk4g (0.40 #10, 0.23 #1180, 0.23 #1960), 0qcr0 (0.14 #1171, 0.10 #1951, 0.10 #3706), 0dq9p (0.12 #1967, 0.10 #3722, 0.09 #1187), 04p3w (0.10 #1961, 0.08 #1181, 0.06 #3716), 02k6hp (0.08 #102, 0.07 #1987, 0.06 #1207), 01l2m3 (0.08 #81, 0.04 #3721, 0.04 #1446), 02y0js (0.07 #3707, 0.06 #847, 0.05 #1952), 0d19y2 (0.07 #184, 0.05 #704, 0.05 #834), 06z5s (0.07 #155, 0.03 #3730, 0.03 #675), 02knxx (0.06 #1982, 0.06 #1202, 0.05 #3737) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 063_t; >> query: (?x7183, 0gk4g) <- people(?x2510, ?x7183), influenced_by(?x4563, ?x7183), ?x4563 = 0dzf_ >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hmk9 people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.400 http://example.org/people/cause_of_death/people #6556-015g1w PRED entity: 015g1w PRED relation: contains! PRED expected values: 04jpl => 97 concepts (51 used for prediction) PRED predicted values (max 10 best out of 195): 07ssc (0.76 #30458, 0.64 #25085, 0.60 #16140), 09c7w0 (0.71 #35800, 0.66 #37590, 0.65 #26847), 049nq (0.58 #27739, 0.54 #33111, 0.52 #19689), 09pmkv (0.54 #33111, 0.48 #34901, 0.42 #44747), 04jpl (0.50 #1811, 0.46 #25948, 0.26 #9867), 036wy (0.46 #25948, 0.03 #25818, 0.02 #31191), 0978r (0.30 #10051, 0.20 #10946, 0.15 #11841), 01n7q (0.25 #7239, 0.08 #35875, 0.08 #38560), 01w0v (0.15 #10052, 0.10 #10947, 0.09 #11842), 0fcrg (0.13 #27741, 0.10 #33112) >> Best rule #30458 for best value: >> intensional similarity = 5 >> extensional distance = 332 >> proper extension: 02jx1; 0zc6f; 0dbdy; 0jcg8; 0jt5zcn; 06y9v; 0978r; 0hyxv; 05bcl; 0j5g9; ... >> query: (?x8052, 07ssc) <- contains(?x1310, ?x8052), nationality(?x12565, ?x1310), nationality(?x5370, ?x1310), ?x12565 = 063t3j, ?x5370 = 016gkf >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1811 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09bkv; 0nbfm; *> query: (?x8052, 04jpl) <- contains(?x13447, ?x8052), contains(?x1310, ?x8052), ?x1310 = 02jx1, ?x13447 = 0f485 *> conf = 0.50 ranks of expected_values: 5 EVAL 015g1w contains! 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 51.000 0.763 http://example.org/location/location/contains #6555-0mbql PRED entity: 0mbql PRED relation: film! PRED expected values: 0svqs => 162 concepts (115 used for prediction) PRED predicted values (max 10 best out of 1636): 03lmzl (0.33 #1538, 0.08 #9865, 0.05 #36930), 01twdk (0.33 #845, 0.06 #23745, 0.05 #34156), 056rgc (0.33 #547, 0.06 #25530, 0.05 #31776), 0bq2g (0.33 #606, 0.06 #23506, 0.04 #42246), 085q5 (0.33 #1721, 0.05 #35032, 0.04 #105816), 02bj6k (0.33 #1386, 0.05 #40944, 0.05 #38861), 016vg8 (0.33 #833, 0.03 #86190, 0.03 #57047), 01vlj1g (0.33 #111, 0.03 #56325, 0.03 #58406), 01qqtr (0.33 #1551, 0.02 #128547, 0.02 #86908), 0f0kz (0.29 #6761, 0.22 #29663, 0.22 #42156) >> Best rule #1538 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 03vyw8; >> query: (?x6620, 03lmzl) <- genre(?x6620, ?x258), featured_film_locations(?x6620, ?x726), film(?x12148, ?x6620), ?x12148 = 0mbs8, language(?x6620, ?x90), production_companies(?x6620, ?x1686) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #9203 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 01jrbb; *> query: (?x6620, 0svqs) <- genre(?x6620, ?x258), film_release_region(?x6620, ?x279), written_by(?x6620, ?x9354), crewmember(?x6620, ?x6166), ?x279 = 0d060g, executive_produced_by(?x6620, ?x846) *> conf = 0.17 ranks of expected_values: 43 EVAL 0mbql film! 0svqs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 162.000 115.000 0.333 http://example.org/film/actor/film./film/performance/film #6554-09v478h PRED entity: 09v478h PRED relation: nominated_for PRED expected values: 01f8gz => 75 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1790): 017gl1 (0.69 #12885, 0.67 #9694, 0.60 #3319), 095zlp (0.67 #3239, 0.62 #9614, 0.44 #12805), 011yqc (0.67 #9771, 0.60 #3396, 0.53 #12962), 011yg9 (0.67 #4109, 0.57 #10484, 0.44 #13675), 03hmt9b (0.62 #10161, 0.60 #3786, 0.50 #13352), 0ctb4g (0.60 #3693, 0.48 #10068, 0.44 #13259), 05c46y6 (0.60 #3583, 0.48 #9958, 0.34 #13149), 05g8pg (0.60 #499, 0.24 #11656, 0.23 #16440), 0df92l (0.60 #902, 0.19 #13654, 0.14 #16843), 065ym0c (0.60 #1432, 0.17 #12589, 0.17 #17373) >> Best rule #12885 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 0gq_v; 02g3v6; 02r22gf; 02hsq3m; 0gr0m; 0k611; 0gs96; 02x2gy0; >> query: (?x11115, 017gl1) <- nominated_for(?x11115, ?x7554), award(?x1207, ?x11115), ?x7554 = 01mgw, nationality(?x1207, ?x94), ?x94 = 09c7w0 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #227 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 09v4bym; *> query: (?x11115, 01f8gz) <- nominated_for(?x11115, ?x5826), disciplines_or_subjects(?x11115, ?x373), award(?x1207, ?x11115), nationality(?x1207, ?x94), ?x5826 = 0gl02yg *> conf = 0.60 ranks of expected_values: 14 EVAL 09v478h nominated_for 01f8gz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 75.000 30.000 0.688 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6553-0cv3w PRED entity: 0cv3w PRED relation: dog_breed PRED expected values: 01_gx_ => 238 concepts (238 used for prediction) PRED predicted values (max 10 best out of 1): 01_gx_ (0.86 #32, 0.83 #18, 0.82 #31) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0ftxw; 0fvzg; 03l2n; 0c_m3; 0n1rj; 01smm; 01snm; 0f04v; 0chrx; >> query: (?x3026, 01_gx_) <- dog_breed(?x3026, ?x11363), ?x11363 = 01k3tq, origin(?x5618, ?x3026) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cv3w dog_breed 01_gx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 238.000 238.000 0.857 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #6552-01hvjx PRED entity: 01hvjx PRED relation: film! PRED expected values: 0163r3 => 76 concepts (55 used for prediction) PRED predicted values (max 10 best out of 897): 029k55 (0.50 #1819, 0.06 #3896, 0.05 #5973), 0436kgz (0.50 #1162, 0.03 #9471, 0.01 #17782), 02661h (0.50 #1393, 0.02 #9702, 0.02 #49182), 06qgvf (0.50 #10), 05qd_ (0.45 #31166, 0.44 #39478, 0.42 #83110), 02f_k_ (0.25 #1119, 0.05 #5273, 0.03 #3196), 01wbg84 (0.25 #46, 0.03 #8355, 0.03 #16666), 06x58 (0.25 #300, 0.03 #2377, 0.03 #4454), 015t56 (0.25 #468, 0.03 #10854, 0.03 #12932), 0gn30 (0.25 #944, 0.03 #71590, 0.02 #59121) >> Best rule #1819 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02rrfzf; >> query: (?x2349, 029k55) <- genre(?x2349, ?x258), language(?x2349, ?x254), film(?x10124, ?x2349), ?x10124 = 01p8r8 >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01hvjx film! 0163r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 55.000 0.500 http://example.org/film/actor/film./film/performance/film #6551-0dryh9k PRED entity: 0dryh9k PRED relation: languages_spoken PRED expected values: 02h40lc => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 54): 02h40lc (0.89 #218, 0.50 #110, 0.41 #814), 0688f (0.50 #143, 0.14 #197, 0.11 #251), 0t_2 (0.47 #443, 0.46 #389, 0.44 #335), 01c7y (0.33 #40, 0.25 #148, 0.14 #202), 07c9s (0.31 #541, 0.31 #596, 0.25 #123), 09s02 (0.31 #541, 0.31 #596, 0.25 #155), 0121sr (0.31 #541, 0.31 #596, 0.25 #152), 0999q (0.31 #541, 0.31 #596, 0.14 #189), 032f6 (0.25 #157, 0.14 #211, 0.08 #861), 0swlx (0.25 #158, 0.14 #212, 0.05 #862) >> Best rule #218 for best value: >> intensional similarity = 11 >> extensional distance = 26 >> proper extension: 071x0k; 078ds; 0fk3s; 04czx7; 0c41n; >> query: (?x5025, 02h40lc) <- languages_spoken(?x5025, ?x9113), languages_spoken(?x5025, ?x1882), countries_spoken_in(?x9113, ?x279), languages(?x10783, ?x1882), languages(?x9253, ?x1882), languages(?x2145, ?x1882), location(?x2145, ?x8297), people(?x6781, ?x2145), language(?x257, ?x1882), ?x10783 = 03fwln, ?x9253 = 01x2tm8 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dryh9k languages_spoken 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 21.000 21.000 0.893 http://example.org/people/ethnicity/languages_spoken #6550-01th4s PRED entity: 01th4s PRED relation: artist PRED expected values: 02mq_y => 40 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1072): 01vxlbm (0.60 #2772, 0.50 #3607, 0.40 #1101), 01vsnff (0.50 #118, 0.40 #1789, 0.26 #10145), 06gcn (0.44 #4728, 0.29 #7233, 0.27 #8070), 06k02 (0.43 #6812, 0.40 #7649, 0.38 #8484), 0qf3p (0.40 #2657, 0.40 #986, 0.36 #6833), 0ycp3 (0.40 #2992, 0.40 #1321, 0.33 #3827), 01323p (0.40 #3062, 0.40 #1391, 0.33 #3897), 01vwyqp (0.40 #2721, 0.40 #1050, 0.33 #3556), 0c9l1 (0.40 #3247, 0.40 #1576, 0.33 #4082), 09qr6 (0.40 #2571, 0.40 #900, 0.33 #3406) >> Best rule #2772 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0fb0v; >> query: (?x6648, 01vxlbm) <- artist(?x6648, ?x6949), artist(?x6648, ?x565), role(?x6949, ?x1750), role(?x6949, ?x615), ?x615 = 0dwsp, category(?x6949, ?x134), ?x1750 = 02hnl, role(?x565, ?x227), group(?x565, ?x5303), artists(?x1000, ?x565), ?x227 = 0342h >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4176 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 0mcf4; *> query: (?x6648, ?x5303) <- artist(?x6648, ?x6949), artist(?x6648, ?x565), role(?x6949, ?x1750), role(?x6949, ?x615), ?x615 = 0dwsp, category(?x6949, ?x134), ?x1750 = 02hnl, role(?x565, ?x227), group(?x565, ?x5303), artists(?x1000, ?x565), instrumentalists(?x227, ?x115) *> conf = 0.26 ranks of expected_values: 41 EVAL 01th4s artist 02mq_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 40.000 23.000 0.600 http://example.org/music/record_label/artist #6549-07sbk PRED entity: 07sbk PRED relation: award_winner! PRED expected values: 01ckrr => 96 concepts (59 used for prediction) PRED predicted values (max 10 best out of 223): 01ckrr (0.39 #24118, 0.39 #24117, 0.39 #8616), 05zkcn5 (0.39 #24118, 0.39 #24117, 0.39 #8616), 01c4_6 (0.39 #24118, 0.39 #24117, 0.39 #8616), 02f777 (0.39 #24118, 0.39 #24117, 0.39 #8616), 01by1l (0.36 #4420, 0.23 #6571, 0.21 #5710), 02f72n (0.20 #576, 0.09 #4452, 0.08 #144), 01bgqh (0.20 #4350, 0.15 #42, 0.12 #2196), 02v1m7 (0.15 #113, 0.12 #4421, 0.11 #975), 02f73p (0.15 #184, 0.11 #1046, 0.08 #2768), 01c9jp (0.15 #186, 0.11 #4494, 0.08 #2770) >> Best rule #24118 for best value: >> intensional similarity = 3 >> extensional distance = 950 >> proper extension: 044mz_; 0184jc; 02s2ft; 02qgqt; 0fvf9q; 02p65p; 0520r2x; 0cb77r; 06151l; 06gp3f; ... >> query: (?x8332, ?x4912) <- award_winner(?x8332, ?x8874), award(?x8332, ?x4912), award_winner(?x486, ?x8332) >> conf = 0.39 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 07sbk award_winner! 01ckrr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 59.000 0.392 http://example.org/award/award_category/winners./award/award_honor/award_winner #6548-024rwx PRED entity: 024rwx PRED relation: genre PRED expected values: 06n90 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 81): 05p553 (0.87 #1546, 0.84 #1708, 0.83 #2028), 07s9rl0 (0.78 #2600, 0.63 #1379, 0.62 #972), 03k9fj (0.75 #983, 0.71 #1066, 0.56 #822), 06n90 (0.70 #1392, 0.50 #1068, 0.50 #824), 01z4y (0.62 #1559, 0.60 #1721, 0.58 #1640), 06nbt (0.45 #181, 0.44 #910, 0.22 #1561), 0c4xc (0.42 #1582, 0.40 #1744, 0.39 #1663), 01hmnh (0.33 #827, 0.30 #1395, 0.29 #665), 01t_vv (0.32 #1574, 0.29 #1655, 0.28 #1736), 02n4kr (0.27 #251, 0.18 #331, 0.18 #729) >> Best rule #1546 for best value: >> intensional similarity = 9 >> extensional distance = 77 >> proper extension: 072kp; 039fgy; 0124k9; 08jgk1; 03ln8b; 01q_y0; 0d68qy; 01j67j; 01bv8b; 030k94; ... >> query: (?x5852, 05p553) <- genre(?x5852, ?x10023), actor(?x5852, ?x3660), genre(?x11477, ?x10023), genre(?x8837, ?x10023), genre(?x5219, ?x10023), titles(?x7712, ?x5852), ?x11477 = 043qqt5, award(?x8837, ?x3486), ?x5219 = 0vhm >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1392 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 65 *> proper extension: 0283ph; 06w7mlh; 03cf9ly; 04x4gj; 01j95; 0d_rw; *> query: (?x5852, 06n90) <- genre(?x5852, ?x1844), genre(?x10284, ?x1844), genre(?x9649, ?x1844), genre(?x4108, ?x1844), genre(?x3413, ?x1844), ?x3413 = 01f3p_, ?x9649 = 03g9xj, ?x10284 = 02gl58, ?x4108 = 02648p *> conf = 0.70 ranks of expected_values: 4 EVAL 024rwx genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 58.000 58.000 0.873 http://example.org/tv/tv_program/genre #6547-0l15bq PRED entity: 0l15bq PRED relation: role! PRED expected values: 01w4c9 => 76 concepts (58 used for prediction) PRED predicted values (max 10 best out of 79): 0395lw (0.86 #370, 0.85 #2171, 0.84 #2630), 04rzd (0.86 #370, 0.84 #1928, 0.83 #1852), 07gql (0.86 #370, 0.84 #1928, 0.83 #1852), 07brj (0.86 #370, 0.84 #1928, 0.83 #1852), 0dwsp (0.86 #370, 0.84 #1928, 0.83 #1852), 02k84w (0.86 #370, 0.84 #1928, 0.83 #1852), 07xzm (0.86 #370, 0.84 #1928, 0.83 #1852), 0xzly (0.86 #370, 0.84 #1928, 0.83 #1852), 01qzyz (0.86 #370, 0.84 #1928, 0.83 #1852), 0l15bq (0.86 #2404, 0.78 #1851, 0.78 #1874) >> Best rule #370 for best value: >> intensional similarity = 18 >> extensional distance = 2 >> proper extension: 0395lw; >> query: (?x1574, ?x1432) <- role(?x227, ?x1574), role(?x3296, ?x1574), role(?x2957, ?x1574), role(?x1437, ?x1574), role(?x614, ?x1574), ?x2957 = 01v8y9, performance_role(?x11947, ?x1574), role(?x2784, ?x1437), ?x614 = 0mkg, ?x2784 = 0137g1, role(?x3409, ?x3296), role(?x2377, ?x3296), ?x3409 = 0680x0, role(?x1574, ?x1432), ?x2377 = 01bns_, role(?x1147, ?x1437), ?x11947 = 04mky3, role(?x3296, ?x645) >> conf = 0.86 => this is the best rule for 9 predicted values *> Best rule #743 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 3 *> proper extension: 01vdm0; *> query: (?x1574, ?x615) <- role(?x227, ?x1574), role(?x3112, ?x1574), role(?x2957, ?x1574), role(?x2944, ?x1574), ?x2957 = 01v8y9, role(?x7987, ?x1574), role(?x3321, ?x1574), role(?x2944, ?x4471), role(?x2944, ?x1831), role(?x2944, ?x615), instrumentalists(?x2944, ?x120), ?x1831 = 03t22m, award(?x3321, ?x4488), ?x4471 = 026g73, performance_role(?x214, ?x2944), ?x3112 = 0mbct, inductee(?x1091, ?x3321), ?x4488 = 02gdjb, ?x7987 = 0j6cj *> conf = 0.66 ranks of expected_values: 58 EVAL 0l15bq role! 01w4c9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 76.000 58.000 0.858 http://example.org/music/performance_role/track_performances./music/track_contribution/role #6546-013v5j PRED entity: 013v5j PRED relation: actor! PRED expected values: 0k0q73t => 113 concepts (78 used for prediction) PRED predicted values (max 10 best out of 64): 026bfsh (0.25 #362, 0.12 #892, 0.12 #627), 0k0q73t (0.12 #1037), 016tvq (0.07 #1221, 0.02 #2016), 0h3mh3q (0.07 #1247, 0.01 #2837), 0cpz4k (0.05 #1917), 01s81 (0.04 #1930), 05jyb2 (0.04 #1913), 05631 (0.03 #3172, 0.02 #2377, 0.01 #3437), 0sw0q (0.02 #3890, 0.01 #2830, 0.01 #2565), 02_1q9 (0.02 #1330, 0.02 #13257, 0.01 #2920) >> Best rule #362 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 015srx; >> query: (?x2274, 026bfsh) <- artists(?x671, ?x2274), origin(?x2274, ?x10584), ?x671 = 064t9, ?x10584 = 03b12 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1037 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 01zmpg; 04cr6qv; 04f7c55; 013rds; *> query: (?x2274, 0k0q73t) <- profession(?x2274, ?x1183), profession(?x2274, ?x220), ?x1183 = 09jwl, nationality(?x2274, ?x94), ?x220 = 016z4k, sibling(?x2273, ?x2274) *> conf = 0.12 ranks of expected_values: 2 EVAL 013v5j actor! 0k0q73t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 78.000 0.250 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #6545-0fpkhkz PRED entity: 0fpkhkz PRED relation: film_crew_role PRED expected values: 01vx2h => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 30): 09zzb8 (0.71 #673, 0.71 #861, 0.69 #1275), 09vw2b7 (0.67 #304, 0.57 #679, 0.56 #867), 01vx2h (0.55 #309, 0.31 #198, 0.30 #160), 01pvkk (0.50 #13, 0.29 #50, 0.28 #310), 0dxtw (0.47 #308, 0.33 #871, 0.32 #1285), 089fss (0.25 #6, 0.15 #117, 0.10 #266), 02rh1dz (0.22 #307, 0.17 #84, 0.14 #47), 02ynfr (0.21 #314, 0.14 #1291, 0.14 #689), 01xy5l_ (0.17 #89, 0.14 #52, 0.12 #163), 0215hd (0.16 #243, 0.15 #168, 0.14 #506) >> Best rule #673 for best value: >> intensional similarity = 4 >> extensional distance = 761 >> proper extension: 02v63m; 0c00zd0; 01j8wk; 0gyy53; 014zwb; 07bwr; 0415ggl; 047rkcm; 03cyslc; 03m5y9p; ... >> query: (?x1490, 09zzb8) <- genre(?x1490, ?x1013), nominated_for(?x2671, ?x1490), film_crew_role(?x1490, ?x468), titles(?x53, ?x1490) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #309 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 109 *> proper extension: 02_fm2; 05r3qc; 0bl3nn; *> query: (?x1490, 01vx2h) <- genre(?x1490, ?x1013), nominated_for(?x372, ?x1490), film_crew_role(?x1490, ?x468), ?x1013 = 06n90 *> conf = 0.55 ranks of expected_values: 3 EVAL 0fpkhkz film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.713 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6544-044mfr PRED entity: 044mfr PRED relation: artists! PRED expected values: 016clz 05bt6j 0gywn 09nwwf => 165 concepts (126 used for prediction) PRED predicted values (max 10 best out of 258): 0glt670 (0.65 #3455, 0.53 #9973, 0.52 #11526), 0gywn (0.58 #11542, 0.53 #4402, 0.53 #9989), 02lnbg (0.56 #5024, 0.54 #11543, 0.50 #9990), 0ggx5q (0.53 #5044, 0.44 #10010, 0.44 #11563), 05bt6j (0.50 #1284, 0.37 #36044, 0.34 #15253), 0xhtw (0.49 #5915, 0.43 #10880, 0.38 #947), 02ny8t (0.43 #753, 0.19 #2615, 0.12 #1373), 01cbwl (0.43 #662, 0.12 #1282, 0.12 #972), 016clz (0.38 #5903, 0.38 #12420, 0.36 #9315), 03lty (0.38 #958, 0.30 #5926, 0.25 #10891) >> Best rule #3455 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 02l840; 0pyg6; 016pns; 024dgj; 0gbwp; 01vvyvk; 018n6m; 013w7j; 0g824; 05w6cw; >> query: (?x5589, 0glt670) <- participant(?x1897, ?x5589), artists(?x3319, ?x5589), profession(?x5589, ?x1032), ?x3319 = 06j6l >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #11542 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 69 *> proper extension: 0hvbj; 016890; 01dwrc; 011z3g; 016376; 016ppr; *> query: (?x5589, 0gywn) <- artists(?x3562, ?x5589), artists(?x3319, ?x5589), ?x3319 = 06j6l, ?x3562 = 025sc50, artist(?x8489, ?x5589) *> conf = 0.58 ranks of expected_values: 2, 5, 9, 23 EVAL 044mfr artists! 09nwwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 165.000 126.000 0.654 http://example.org/music/genre/artists EVAL 044mfr artists! 0gywn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 165.000 126.000 0.654 http://example.org/music/genre/artists EVAL 044mfr artists! 05bt6j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 165.000 126.000 0.654 http://example.org/music/genre/artists EVAL 044mfr artists! 016clz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 165.000 126.000 0.654 http://example.org/music/genre/artists #6543-0myfz PRED entity: 0myfz PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 136 concepts (53 used for prediction) PRED predicted values (max 10 best out of 14): 09c7w0 (0.88 #249, 0.86 #165, 0.85 #273), 05kkh (0.10 #464, 0.09 #666, 0.09 #414), 0snty (0.06 #570), 0sq2v (0.06 #570), 0smfm (0.06 #570), 02_n7 (0.06 #570), 0ftxw (0.06 #570), 052p7 (0.06 #570), 0h7h6 (0.06 #570), 059rby (0.06 #570) >> Best rule #249 for best value: >> intensional similarity = 4 >> extensional distance = 240 >> proper extension: 0mlyw; >> query: (?x13453, 09c7w0) <- adjoins(?x8178, ?x13453), source(?x13453, ?x958), ?x958 = 0jbk9, currency(?x13453, ?x170) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0myfz second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 53.000 0.880 http://example.org/location/country/second_level_divisions #6542-0l76z PRED entity: 0l76z PRED relation: languages PRED expected values: 02h40lc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.93 #79, 0.93 #68, 0.92 #101), 0t_2 (0.06 #61, 0.05 #83, 0.05 #215), 03_9r (0.05 #411, 0.04 #499, 0.04 #422), 06nm1 (0.03 #247, 0.03 #269, 0.03 #291), 064_8sq (0.02 #73, 0.02 #84, 0.02 #249), 02bv9 (0.02 #75, 0.02 #86, 0.01 #97), 04306rv (0.02 #69, 0.02 #80, 0.01 #91), 02bjrlw (0.02 #67, 0.02 #78, 0.01 #89), 07qv_ (0.01 #175), 05zjd (0.01 #173) >> Best rule #79 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 025ljp; 02gl58; 07s8z_l; 02py9yf; >> query: (?x4588, 02h40lc) <- program_creator(?x4588, ?x4589), program(?x846, ?x4588), honored_for(?x1193, ?x4588) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l76z languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.930 http://example.org/tv/tv_program/languages #6541-01jq34 PRED entity: 01jq34 PRED relation: school! PRED expected values: 05vsb7 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 17): 02qw1zx (0.39 #243, 0.39 #107, 0.25 #124), 05vsb7 (0.39 #103, 0.25 #120, 0.18 #239), 03nt7j (0.39 #109, 0.18 #245, 0.15 #551), 0g3zpp (0.33 #104, 0.14 #240, 0.14 #546), 02rl201 (0.28 #106, 0.15 #123, 0.11 #242), 047dpm0 (0.28 #118, 0.11 #254, 0.10 #135), 092j54 (0.25 #128, 0.22 #111, 0.20 #553), 04f4z1k (0.25 #134, 0.11 #117, 0.09 #576), 02pq_rp (0.22 #110, 0.15 #127, 0.14 #246), 09th87 (0.22 #114, 0.14 #556, 0.11 #250) >> Best rule #243 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 06mkj; 0d05w3; >> query: (?x2171, 02qw1zx) <- school(?x4779, ?x2171), organization(?x2171, ?x5487), contains(?x94, ?x2171) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #103 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 05kj_; *> query: (?x2171, 05vsb7) <- school(?x6462, ?x2171), contains(?x94, ?x2171), ?x6462 = 09l0x9 *> conf = 0.39 ranks of expected_values: 2 EVAL 01jq34 school! 05vsb7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.393 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #6540-016z43 PRED entity: 016z43 PRED relation: country PRED expected values: 09c7w0 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 43): 09c7w0 (0.83 #246, 0.80 #124, 0.78 #802), 0d060g (0.41 #1602, 0.41 #1664, 0.14 #800), 02jx1 (0.41 #1664, 0.01 #334), 07ssc (0.24 #444, 0.22 #568, 0.21 #1188), 0f8l9c (0.17 #325, 0.14 #800, 0.10 #447), 0345h (0.16 #579, 0.16 #455, 0.14 #800), 03_3d (0.14 #800, 0.10 #130, 0.08 #252), 0chghy (0.14 #800, 0.05 #318, 0.04 #440), 0ctw_b (0.14 #800, 0.02 #451, 0.02 #575), 01hmnh (0.08 #489, 0.07 #1233, 0.06 #2524) >> Best rule #246 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 02_1sj; 01738w; >> query: (?x12401, 09c7w0) <- film(?x806, ?x12401), ?x806 = 03qd_, genre(?x12401, ?x53), currency(?x12401, ?x170) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016z43 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.833 http://example.org/film/film/country #6539-02rrfzf PRED entity: 02rrfzf PRED relation: edited_by PRED expected values: 04cy8rb => 98 concepts (77 used for prediction) PRED predicted values (max 10 best out of 31): 03q8ch (0.31 #209, 0.20 #180, 0.20 #295), 02qggqc (0.17 #114, 0.17 #199, 0.15 #170), 02kxbx3 (0.14 #39, 0.14 #151, 0.10 #123), 02kxbwx (0.14 #32, 0.14 #144, 0.09 #172), 06q8hf (0.12 #197, 0.03 #663, 0.02 #1156), 03crcpt (0.12 #155, 0.06 #212, 0.04 #357), 02lp3c (0.12 #156, 0.06 #358, 0.06 #389), 03nqbvz (0.10 #153, 0.07 #125, 0.05 #296), 0gd9k (0.10 #20, 0.08 #160, 0.07 #132), 04cy8rb (0.10 #85, 0.09 #374, 0.08 #169) >> Best rule #209 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 08sk8l; >> query: (?x3344, 03q8ch) <- edited_by(?x3344, ?x523), production_companies(?x3344, ?x10884), music(?x3344, ?x3410) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 0cc5mcj; 0879bpq; *> query: (?x3344, 04cy8rb) <- film(?x166, ?x3344), film(?x101, ?x3344), music(?x3344, ?x4019), ?x4019 = 04pf4r *> conf = 0.10 ranks of expected_values: 10 EVAL 02rrfzf edited_by 04cy8rb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 98.000 77.000 0.305 http://example.org/film/film/edited_by #6538-02ghq PRED entity: 02ghq PRED relation: profession PRED expected values: 01d_h8 => 110 concepts (46 used for prediction) PRED predicted values (max 10 best out of 77): 02hrh1q (0.73 #3103, 0.72 #1631, 0.68 #1925), 0kyk (0.70 #4296, 0.50 #323, 0.50 #29), 01d_h8 (0.50 #6, 0.50 #5892, 0.36 #594), 02jknp (0.43 #5893, 0.25 #7, 0.20 #154), 09jwl (0.42 #6345, 0.37 #5757, 0.30 #1930), 03gjzk (0.39 #5900, 0.35 #2942, 0.31 #4709), 02hv44_ (0.35 #2942, 0.31 #4709, 0.29 #1618), 025352 (0.35 #2942, 0.31 #4709, 0.29 #1618), 0fj9f (0.35 #2942, 0.31 #4709, 0.29 #1618), 03sbb (0.35 #2942, 0.31 #4709, 0.29 #1618) >> Best rule #3103 for best value: >> intensional similarity = 3 >> extensional distance = 344 >> proper extension: 02vntj; >> query: (?x10978, 02hrh1q) <- religion(?x10978, ?x1985), profession(?x10978, ?x353), ?x1985 = 0c8wxp >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 042xh; *> query: (?x10978, 01d_h8) <- influenced_by(?x10978, ?x6796), nationality(?x10978, ?x94), type_of_union(?x10978, ?x566), ?x6796 = 01wd02c *> conf = 0.50 ranks of expected_values: 3 EVAL 02ghq profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 46.000 0.731 http://example.org/people/person/profession #6537-0jjy0 PRED entity: 0jjy0 PRED relation: film! PRED expected values: 018db8 => 97 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1203): 0gy6z9 (0.33 #567, 0.03 #23478, 0.03 #25560), 03rl84 (0.33 #324, 0.02 #16985, 0.01 #31563), 04yj5z (0.33 #121, 0.02 #70929, 0.02 #73012), 030_3z (0.17 #8330, 0.15 #22911, 0.12 #18744), 0h5g_ (0.17 #2156, 0.05 #14653, 0.03 #22985), 0170qf (0.17 #2449, 0.04 #35773, 0.03 #23278), 018swb (0.17 #2424, 0.04 #4506, 0.03 #8672), 018_lb (0.17 #3941, 0.04 #6023, 0.03 #12272), 014g22 (0.17 #2800, 0.04 #4882, 0.03 #11131), 016k6x (0.17 #2973, 0.03 #9221, 0.03 #13387) >> Best rule #567 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02pxmgz; >> query: (?x1108, 0gy6z9) <- cinematography(?x1108, ?x10542), film(?x3873, ?x1108), ?x3873 = 0jrqq, genre(?x1108, ?x53) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #23028 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 56 *> proper extension: 0ds11z; 031778; 0pvms; 03cfkrw; 01s3vk; 0292qb; *> query: (?x1108, 018db8) <- film_release_distribution_medium(?x1108, ?x81), costume_design_by(?x1108, ?x3685), film_crew_role(?x1108, ?x1171), film(?x3873, ?x1108), film(?x556, ?x1108) *> conf = 0.02 ranks of expected_values: 820 EVAL 0jjy0 film! 018db8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 97.000 69.000 0.333 http://example.org/film/actor/film./film/performance/film #6536-0g_g2 PRED entity: 0g_g2 PRED relation: award PRED expected values: 03tcnt 01c9jp => 116 concepts (92 used for prediction) PRED predicted values (max 10 best out of 240): 01by1l (0.65 #23375, 0.54 #8134, 0.51 #5728), 02f716 (0.64 #5791, 0.40 #4587, 0.33 #15014), 02f5qb (0.62 #5771, 0.40 #4567, 0.35 #4166), 02f72n (0.62 #5761, 0.36 #14984, 0.30 #4156), 01bgqh (0.59 #8064, 0.51 #23305, 0.44 #5658), 02f72_ (0.58 #5844, 0.35 #4239, 0.33 #15067), 02f73p (0.53 #5802, 0.39 #15025, 0.31 #2593), 03qbh5 (0.46 #8226, 0.31 #5820, 0.24 #17450), 02v1m7 (0.44 #5729, 0.36 #4525, 0.27 #17359), 02f6yz (0.44 #4728, 0.30 #4327, 0.30 #9140) >> Best rule #23375 for best value: >> intensional similarity = 3 >> extensional distance = 288 >> proper extension: 04lgymt; 0jdhp; 05pdbs; 01x15dc; 01vd7hn; 02_jkc; 0kftt; 01wyq0w; 010xjr; 05mxw33; ... >> query: (?x4957, 01by1l) <- award(?x4957, ?x4958), award_winner(?x4958, ?x1238), ?x1238 = 05pdbs >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #4577 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 01yzl2; 01dwrc; 016ppr; *> query: (?x4957, 03tcnt) <- origin(?x4957, ?x6895), group(?x300, ?x4957), award(?x4957, ?x2420), category(?x4957, ?x134), award_winner(?x2054, ?x4957) *> conf = 0.36 ranks of expected_values: 15, 16 EVAL 0g_g2 award 01c9jp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 116.000 92.000 0.652 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0g_g2 award 03tcnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 116.000 92.000 0.652 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6535-0d68qy PRED entity: 0d68qy PRED relation: nominated_for! PRED expected values: 09r9dp 03q43g 087qxp => 61 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1109): 02773nt (0.67 #30038, 0.65 #18485, 0.60 #32350), 02773m2 (0.67 #30038, 0.65 #18485, 0.60 #32350), 02778qt (0.67 #30038, 0.65 #18485, 0.60 #32350), 01gq0b (0.33 #373, 0.20 #2683, 0.05 #78559), 016tt2 (0.33 #108, 0.20 #2418, 0.02 #78667), 020h2v (0.33 #1680, 0.20 #3990), 02qfhb (0.33 #1083, 0.20 #3393), 03cfjg (0.20 #3019, 0.09 #4621), 02fn5r (0.20 #2854, 0.09 #4621), 01kv4mb (0.20 #2734, 0.09 #4621) >> Best rule #30038 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 090s_0; 0g60z; 080dwhx; 06cs95; 02_1rq; 072kp; 039fgy; 0kfpm; 02k_4g; 0358x_; ... >> query: (?x2528, ?x829) <- nominated_for(?x678, ?x2528), nominated_for(?x368, ?x2528), program(?x829, ?x2528) >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #32349 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 140 *> proper extension: 0cpz4k; 05f7w84; 03r0rq; *> query: (?x2528, ?x2307) <- actor(?x2528, ?x3557), award_nominee(?x2307, ?x3557), program(?x829, ?x2528) *> conf = 0.12 ranks of expected_values: 38, 381, 660 EVAL 0d68qy nominated_for! 087qxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 61.000 42.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0d68qy nominated_for! 03q43g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 42.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0d68qy nominated_for! 09r9dp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 42.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #6534-01ft2l PRED entity: 01ft2l PRED relation: film PRED expected values: 0c57yj => 118 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1081): 090s_0 (0.64 #57260, 0.62 #41155, 0.58 #84107), 01s81 (0.64 #57260, 0.62 #41155, 0.58 #84107), 02rcwq0 (0.64 #57260, 0.62 #41155, 0.58 #84107), 0b3n61 (0.33 #1360, 0.25 #3149, 0.06 #8517), 076tq0z (0.33 #461, 0.25 #2250, 0.05 #16564), 03tbg6 (0.33 #1655, 0.25 #3444, 0.04 #14179), 07nxvj (0.33 #696, 0.25 #2485, 0.04 #13220), 02nt3d (0.33 #1084, 0.25 #2873, 0.04 #13608), 03vfr_ (0.33 #1643, 0.25 #3432, 0.04 #15957), 01gkp1 (0.33 #816, 0.25 #2605, 0.04 #15130) >> Best rule #57260 for best value: >> intensional similarity = 3 >> extensional distance = 441 >> proper extension: 0m2wm; 04smkr; 01pnn3; 02wb6yq; 02qfhb; 03ds83; >> query: (?x3633, ?x293) <- profession(?x3633, ?x319), nominated_for(?x3633, ?x293), participant(?x3183, ?x3633) >> conf = 0.64 => this is the best rule for 3 predicted values *> Best rule #16742 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 02tqkf; 0bkq_8; *> query: (?x3633, 0c57yj) <- profession(?x3633, ?x319), actor(?x293, ?x3633), student(?x8398, ?x3633) *> conf = 0.02 ranks of expected_values: 684 EVAL 01ft2l film 0c57yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 118.000 85.000 0.642 http://example.org/film/actor/film./film/performance/film #6533-0hmm7 PRED entity: 0hmm7 PRED relation: film_format PRED expected values: 0cj16 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 4): 07fb8_ (0.17 #72, 0.16 #87, 0.15 #92), 0cj16 (0.14 #3, 0.13 #8, 0.12 #130), 017fx5 (0.06 #60, 0.05 #75, 0.05 #55), 01dc60 (0.01 #46, 0.01 #20) >> Best rule #72 for best value: >> intensional similarity = 3 >> extensional distance = 207 >> proper extension: 03h_yy; 04xx9s; >> query: (?x2047, 07fb8_) <- crewmember(?x2047, ?x6233), nominated_for(?x3662, ?x2047), currency(?x2047, ?x170) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0k7tq; *> query: (?x2047, 0cj16) <- honored_for(?x6213, ?x2047), titles(?x3613, ?x2047), ?x3613 = 09blyk *> conf = 0.14 ranks of expected_values: 2 EVAL 0hmm7 film_format 0cj16 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.167 http://example.org/film/film/film_format #6532-0jkvj PRED entity: 0jkvj PRED relation: olympics! PRED expected values: 0chghy 04w8f => 47 concepts (43 used for prediction) PRED predicted values (max 10 best out of 197): 07ssc (0.88 #847, 0.83 #638, 0.75 #428), 0chghy (0.76 #843, 0.67 #634, 0.62 #424), 019pcs (0.75 #474, 0.67 #684, 0.67 #265), 0hzlz (0.71 #851, 0.50 #432, 0.50 #327), 01znc_ (0.67 #233, 0.62 #442, 0.58 #652), 019rg5 (0.67 #224, 0.62 #433, 0.58 #643), 05b4w (0.65 #879, 0.64 #2356, 0.62 #355), 035qy (0.62 #439, 0.58 #649, 0.50 #230), 04w8f (0.62 #472, 0.50 #682, 0.50 #263), 06mkj (0.62 #454, 0.50 #664, 0.50 #245) >> Best rule #847 for best value: >> intensional similarity = 8 >> extensional distance = 15 >> proper extension: 06sks6; >> query: (?x7688, 07ssc) <- olympics(?x87, ?x7688), sports(?x7688, ?x5182), sports(?x7688, ?x4876), sports(?x7688, ?x3659), ?x3659 = 0dwxr, locations(?x7688, ?x2474), ?x5182 = 0crlz, ?x4876 = 0d1t3 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #843 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 15 *> proper extension: 06sks6; *> query: (?x7688, 0chghy) <- olympics(?x87, ?x7688), sports(?x7688, ?x5182), sports(?x7688, ?x4876), sports(?x7688, ?x3659), ?x3659 = 0dwxr, locations(?x7688, ?x2474), ?x5182 = 0crlz, ?x4876 = 0d1t3 *> conf = 0.76 ranks of expected_values: 2, 9 EVAL 0jkvj olympics! 04w8f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 47.000 43.000 0.882 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jkvj olympics! 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 43.000 0.882 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #6531-0c4ys PRED entity: 0c4ys PRED relation: instance_of_recurring_event! PRED expected values: 0jzphpx => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 157): 0hn821n (0.33 #90, 0.28 #1500, 0.26 #1071), 0bx6zs (0.33 #80, 0.28 #1500, 0.26 #1071), 07y_p6 (0.33 #56, 0.28 #1500, 0.26 #1071), 03nnm4t (0.33 #41, 0.28 #1500, 0.26 #1071), 07y9ts (0.33 #37, 0.28 #1500, 0.26 #1071), 02q690_ (0.33 #31, 0.28 #1500, 0.26 #1071), 0gx_st (0.33 #16, 0.28 #1500, 0.26 #1071), 0gvstc3 (0.33 #15, 0.28 #1500, 0.26 #1071), 07z31v (0.33 #13, 0.28 #1500, 0.26 #1071), 0lp_cd3 (0.33 #8, 0.28 #1500, 0.26 #1071) >> Best rule #90 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 0gcf2r; >> query: (?x2421, 0hn821n) <- category_of(?x1565, ?x2421), award(?x6234, ?x1565), award(?x5547, ?x1565), award(?x1656, ?x1565), instance_of_recurring_event(?x2054, ?x2421), award_winner(?x2054, ?x367), instrumentalists(?x227, ?x1656), award_winner(?x1565, ?x3735), artist(?x2931, ?x6234), category(?x2421, ?x134), award_nominee(?x5547, ?x1573), profession(?x1656, ?x131) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1500 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 01b8bn; *> query: (?x2421, ?x139) <- category_of(?x5799, ?x2421), category_of(?x2561, ?x2421), award_winner(?x2561, ?x8972), award_winner(?x2561, ?x2698), profession(?x8972, ?x655), student(?x2767, ?x2698), award_winner(?x5799, ?x4476), award_winner(?x139, ?x4476) *> conf = 0.28 ranks of expected_values: 124 EVAL 0c4ys instance_of_recurring_event! 0jzphpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 23.000 23.000 0.333 http://example.org/time/event/instance_of_recurring_event #6530-02cx72 PRED entity: 02cx72 PRED relation: award PRED expected values: 02x17c2 => 117 concepts (103 used for prediction) PRED predicted values (max 10 best out of 293): 0gqz2 (0.76 #33305, 0.71 #26478, 0.70 #28488), 025m8l (0.76 #33305, 0.71 #26478, 0.70 #28488), 02h3d1 (0.76 #33305, 0.71 #26478, 0.70 #28488), 02581q (0.56 #7, 0.14 #32902, 0.12 #30093), 01by1l (0.37 #4122, 0.27 #2117, 0.22 #5726), 02sp_v (0.36 #1364, 0.11 #160, 0.06 #2166), 0c4z8 (0.36 #2078, 0.21 #1276, 0.21 #2479), 09sb52 (0.29 #12075, 0.28 #8063, 0.27 #19295), 01bgqh (0.27 #2049, 0.25 #4054, 0.23 #2450), 02qvyrt (0.24 #2934, 0.23 #3335, 0.20 #2533) >> Best rule #33305 for best value: >> intensional similarity = 2 >> extensional distance = 1907 >> proper extension: 01wv9xn; 0frsw; 01vrwfv; 02jqjm; 07mvp; 0178_w; 07r1_; 0b_xm; 01323p; 046p9; ... >> query: (?x3732, ?x3467) <- award_winner(?x3467, ?x3732), ceremony(?x3467, ?x139) >> conf = 0.76 => this is the best rule for 3 predicted values *> Best rule #2222 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 119 *> proper extension: 01cblr; *> query: (?x3732, 02x17c2) <- award(?x3732, ?x2585), ?x2585 = 054ks3 *> conf = 0.22 ranks of expected_values: 11 EVAL 02cx72 award 02x17c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 117.000 103.000 0.764 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6529-02z6872 PRED entity: 02z6872 PRED relation: school PRED expected values: 0ks67 => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1062): 015q1n (0.71 #1903, 0.58 #314, 0.57 #1797), 065y4w7 (0.66 #1079, 0.60 #1521, 0.58 #314), 07w0v (0.66 #1079, 0.58 #314, 0.50 #1300), 01qgr3 (0.66 #1079, 0.58 #314, 0.50 #707), 021w0_ (0.66 #1079, 0.58 #314, 0.39 #1624), 0187nd (0.66 #1079, 0.58 #314, 0.39 #1624), 07t90 (0.66 #1079, 0.58 #314, 0.39 #1624), 01jq4b (0.66 #1079, 0.58 #314, 0.35 #1515), 02gr81 (0.66 #1079, 0.58 #314, 0.33 #458), 019pwv (0.66 #1079, 0.58 #314, 0.33 #496) >> Best rule #1903 for best value: >> intensional similarity = 46 >> extensional distance = 5 >> proper extension: 038c0q; >> query: (?x4779, 015q1n) <- school(?x4779, ?x2711), school(?x4779, ?x1884), school(?x4779, ?x581), draft(?x7357, ?x4779), draft(?x1823, ?x4779), student(?x1884, ?x11373), major_field_of_study(?x1884, ?x10046), major_field_of_study(?x1884, ?x3489), major_field_of_study(?x1884, ?x2606), ?x3489 = 0193x, school(?x7136, ?x1884), school(?x5822, ?x1884), major_field_of_study(?x4410, ?x10046), major_field_of_study(?x2948, ?x10046), sport(?x7357, ?x5063), ?x4410 = 017j69, school(?x7357, ?x6953), school(?x7357, ?x3779), team(?x2010, ?x7357), institution(?x865, ?x2711), category(?x2711, ?x134), institution(?x620, ?x1884), position(?x5822, ?x2573), ?x7136 = 0jm74, ?x2573 = 05b3ts, team(?x935, ?x5822), colors(?x1823, ?x663), currency(?x1884, ?x170), contains(?x94, ?x2711), ?x2948 = 0j_sncb, ?x6953 = 01jq0j, profession(?x11373, ?x987), school(?x700, ?x2711), teams(?x1523, ?x7357), major_field_of_study(?x2711, ?x1154), student(?x581, ?x4748), major_field_of_study(?x8008, ?x2606), student(?x2606, ?x677), ?x134 = 08mbj5d, fraternities_and_sororities(?x2711, ?x3697), company(?x3520, ?x3779), organization(?x581, ?x5487), gender(?x4748, ?x231), film(?x4748, ?x3433), student(?x10046, ?x690), ?x8008 = 01q7q2 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1681 for first EXPECTED value: *> intensional similarity = 50 *> extensional distance = 3 *> proper extension: 09th87; *> query: (?x4779, 0ks67) <- school(?x4779, ?x2171), school(?x4779, ?x1884), draft(?x8894, ?x4779), draft(?x7357, ?x4779), draft(?x1823, ?x4779), draft(?x1438, ?x4779), student(?x1884, ?x11373), major_field_of_study(?x1884, ?x10046), major_field_of_study(?x1884, ?x3995), major_field_of_study(?x1884, ?x3489), ?x3489 = 0193x, school(?x7136, ?x1884), school(?x2820, ?x1884), ?x10046 = 041y2, producer_type(?x11373, ?x632), organization(?x1884, ?x5487), school(?x1823, ?x4296), school(?x1823, ?x3779), school(?x1823, ?x2895), ?x2820 = 0jmj7, school(?x7357, ?x3387), ?x4296 = 07vyf, list(?x1884, ?x2197), sport(?x7357, ?x5063), school_type(?x1884, ?x4994), institution(?x2636, ?x1884), institution(?x1368, ?x1884), team(?x261, ?x1438), ?x3995 = 0fdys, school(?x1632, ?x2171), contains(?x94, ?x2171), ?x1368 = 014mlp, ?x2636 = 027f2w, major_field_of_study(?x2895, ?x6859), award_nominee(?x11373, ?x5431), colors(?x7136, ?x663), colors(?x8894, ?x8271), contains(?x2256, ?x2895), ?x6859 = 01tbp, school(?x8894, ?x12736), school(?x8894, ?x6455), school(?x8894, ?x4556), ?x4556 = 01lnyf, fraternities_and_sororities(?x2171, ?x3697), athlete(?x5063, ?x5412), ?x3779 = 01pq4w, student(?x2171, ?x3338), ?x12736 = 01stj9, contact_category(?x3387, ?x897), ?x6455 = 026vcc *> conf = 0.20 ranks of expected_values: 133 EVAL 02z6872 school 0ks67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 20.000 20.000 0.714 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #6528-05l3g_ PRED entity: 05l3g_ PRED relation: people PRED expected values: 03d9v8 => 31 concepts (20 used for prediction) PRED predicted values (max 10 best out of 3690): 0k9j_ (0.50 #4719, 0.37 #3445, 0.30 #6442), 01pk3z (0.40 #5954, 0.33 #4231, 0.33 #786), 029ql (0.37 #3445, 0.10 #10337, 0.10 #13785), 04__f (0.37 #3445, 0.10 #13785, 0.09 #8615), 01d6jf (0.37 #3445, 0.10 #13785, 0.09 #8615), 032_jg (0.33 #3552, 0.33 #1829, 0.33 #107), 0lkr7 (0.33 #4154, 0.33 #709, 0.30 #5877), 03mstc (0.33 #4762, 0.33 #1317, 0.20 #6485), 01fwj8 (0.33 #1933, 0.33 #211, 0.17 #3656), 02184q (0.33 #3104, 0.33 #1382, 0.17 #4827) >> Best rule #4719 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 033tf_; 07hwkr; 07bch9; >> query: (?x12278, 0k9j_) <- people(?x12278, ?x9095), award_winner(?x4504, ?x9095), award_winner(?x9000, ?x9095), award_winner(?x6331, ?x9095), award(?x9095, ?x2060), ?x6331 = 029ql, ?x2060 = 054ky1, languages(?x9095, ?x90), award_winner(?x2431, ?x9095), film(?x9000, ?x327) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3017 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 02w7gg; *> query: (?x12278, 03d9v8) <- people(?x12278, ?x9095), people(?x12278, ?x406), award_winner(?x4504, ?x9095), award_winner(?x6331, ?x9095), award(?x9095, ?x1245), place_of_birth(?x6331, ?x6555), diet(?x6331, ?x3130), nationality(?x6331, ?x1264), ?x406 = 09fb5, type_of_union(?x9095, ?x566) *> conf = 0.33 ranks of expected_values: 66 EVAL 05l3g_ people 03d9v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 31.000 20.000 0.500 http://example.org/people/ethnicity/people #6527-02_286 PRED entity: 02_286 PRED relation: vacationer PRED expected values: 01k53x => 186 concepts (185 used for prediction) PRED predicted values (max 10 best out of 185): 016tbr (0.20 #159, 0.09 #841, 0.08 #1352), 01vv126 (0.20 #52, 0.09 #2610, 0.08 #1245), 0bksh (0.19 #1295, 0.11 #4885, 0.10 #272), 0261x8t (0.16 #4918, 0.15 #1328, 0.12 #4233), 0bbf1f (0.15 #1250, 0.12 #2103, 0.11 #3126), 01pgzn_ (0.15 #1232, 0.11 #4822, 0.07 #1914), 05r5w (0.15 #1263, 0.10 #240, 0.09 #2116), 01xyt7 (0.12 #1314, 0.11 #2679, 0.10 #1996), 033wx9 (0.12 #1247, 0.11 #2612, 0.10 #54), 01f492 (0.12 #1339, 0.10 #316, 0.10 #146) >> Best rule #159 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 07751; 02fzs; >> query: (?x739, 016tbr) <- film_regional_debut_venue(?x2047, ?x739), vacationer(?x739, ?x444) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #670 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 18 *> proper extension: 06bnz; 082fr; 03f2w; *> query: (?x739, 01k53x) <- film_release_region(?x11218, ?x739), ?x11218 = 0ccck7 *> conf = 0.05 ranks of expected_values: 73 EVAL 02_286 vacationer 01k53x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 186.000 185.000 0.200 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #6526-01fmys PRED entity: 01fmys PRED relation: genre PRED expected values: 0lsxr => 94 concepts (76 used for prediction) PRED predicted values (max 10 best out of 119): 07s9rl0 (0.73 #2620, 0.71 #2859, 0.66 #4409), 02kdv5l (0.52 #122, 0.42 #6917, 0.41 #479), 01jfsb (0.39 #7880, 0.36 #250, 0.33 #2751), 01hmnh (0.36 #375, 0.31 #1208, 0.30 #1803), 06n90 (0.33 #132, 0.29 #251, 0.25 #608), 02l7c8 (0.29 #3472, 0.28 #6572, 0.28 #7288), 06cvj (0.25 #4, 0.15 #3460, 0.11 #6918), 02b5_l (0.25 #49, 0.10 #406, 0.09 #1239), 01t_vv (0.25 #54, 0.10 #3510, 0.09 #7922), 01q03 (0.25 #5, 0.05 #6556, 0.04 #3818) >> Best rule #2620 for best value: >> intensional similarity = 4 >> extensional distance = 248 >> proper extension: 0yyg4; 0n0bp; 0170_p; 0fdv3; 09cr8; 0260bz; 07yk1xz; 02s4l6; 05dy7p; 0kb57; ... >> query: (?x2050, 07s9rl0) <- film(?x9354, ?x2050), films(?x5011, ?x2050), genre(?x2050, ?x258), nominated_for(?x669, ?x2050) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2748 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 252 *> proper extension: 015qsq; 0419kt; 016z43; *> query: (?x2050, 0lsxr) <- film(?x9354, ?x2050), nominated_for(?x669, ?x2050), currency(?x2050, ?x170), featured_film_locations(?x2050, ?x1860) *> conf = 0.21 ranks of expected_values: 11 EVAL 01fmys genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 94.000 76.000 0.732 http://example.org/film/film/genre #6525-02z13jg PRED entity: 02z13jg PRED relation: film_festivals PRED expected values: 05f5rsr => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 18): 05f5rsr (0.15 #111, 0.09 #471, 0.07 #131), 04_m9gk (0.14 #473, 0.12 #553, 0.10 #673), 0bmj62v (0.13 #472, 0.11 #552, 0.09 #672), 04grdgy (0.11 #469, 0.10 #549, 0.08 #669), 0kfhjq0 (0.11 #465, 0.08 #665, 0.02 #545), 0gg7gsl (0.10 #541, 0.08 #661, 0.02 #461), 0g57ws5 (0.10 #467, 0.08 #547, 0.07 #667), 0j63cyr (0.10 #463, 0.08 #543, 0.07 #663), 09rwjly (0.08 #548, 0.07 #668, 0.02 #468), 0hrcs29 (0.08 #474, 0.07 #554, 0.06 #674) >> Best rule #111 for best value: >> intensional similarity = 2 >> extensional distance = 24 >> proper extension: 0h3y; 042rnl; 01m13b; 03kwtb; 02qmsr; 01f7v_; 07k2mq; 01c6l; 04ld94; 01f85k; ... >> query: (?x850, 05f5rsr) <- film_festivals(?x850, ?x11231), ?x11231 = 03wf1p2 >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02z13jg film_festivals 05f5rsr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.154 http://example.org/film/film/film_festivals #6524-0xqf3 PRED entity: 0xqf3 PRED relation: contains! PRED expected values: 09c7w0 => 95 concepts (58 used for prediction) PRED predicted values (max 10 best out of 158): 09c7w0 (0.73 #37567, 0.72 #39357, 0.70 #20571), 02xry (0.65 #5527, 0.08 #1057, 0.07 #25203), 02_286 (0.33 #43, 0.08 #937, 0.06 #1831), 04_1l0v (0.33 #18779, 0.16 #4920, 0.15 #4026), 01n7q (0.25 #13490, 0.20 #16174, 0.18 #39432), 07ssc (0.17 #43860, 0.15 #50125, 0.14 #29545), 01cx_ (0.17 #5561, 0.02 #42235, 0.02 #44920), 02qkt (0.15 #1241, 0.12 #2135, 0.05 #3923), 0d060g (0.15 #42051, 0.14 #44736, 0.05 #32208), 02jx1 (0.13 #43915, 0.11 #50180, 0.11 #29600) >> Best rule #37567 for best value: >> intensional similarity = 2 >> extensional distance = 532 >> proper extension: 018mlg; >> query: (?x8944, 09c7w0) <- contains(?x10054, ?x8944), source(?x10054, ?x958) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xqf3 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 58.000 0.727 http://example.org/location/location/contains #6523-02lf1j PRED entity: 02lf1j PRED relation: film PRED expected values: 0dgrwqr => 116 concepts (60 used for prediction) PRED predicted values (max 10 best out of 827): 0dtw1x (0.22 #32104, 0.18 #17835, 0.18 #19619), 09lxv9 (0.20 #1501, 0.14 #3284, 0.03 #10416), 03q0r1 (0.20 #635, 0.08 #9550, 0.06 #16686), 04hwbq (0.20 #191, 0.05 #9106, 0.04 #14458), 02qr3k8 (0.20 #1285, 0.05 #4851, 0.05 #6634), 0ndsl1x (0.20 #1510, 0.05 #5076, 0.04 #8642), 0g56t9t (0.20 #10, 0.05 #5359, 0.04 #8925), 02ctc6 (0.20 #521, 0.05 #5870, 0.03 #41543), 08sk8l (0.20 #1117, 0.05 #6466, 0.02 #42139), 01s3vk (0.20 #900, 0.05 #6249, 0.02 #33004) >> Best rule #32104 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 080knyg; >> query: (?x2564, ?x424) <- person(?x424, ?x2564), film(?x2564, ?x9193), genre(?x9193, ?x239) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02lf1j film 0dgrwqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 60.000 0.223 http://example.org/film/actor/film./film/performance/film #6522-02f75t PRED entity: 02f75t PRED relation: award! PRED expected values: 0147dk 013w7j 07pzc => 32 concepts (12 used for prediction) PRED predicted values (max 10 best out of 2312): 01vw20h (0.86 #6733, 0.81 #16833, 0.80 #23566), 05mt_q (0.78 #340, 0.30 #3706, 0.16 #7074), 01vs_v8 (0.60 #3951, 0.42 #7319, 0.22 #585), 02z4b_8 (0.50 #5428, 0.26 #8796, 0.22 #2062), 0478__m (0.50 #4691, 0.26 #8059, 0.22 #1325), 02l840 (0.44 #180, 0.30 #3546, 0.22 #6914), 07ss8_ (0.44 #583, 0.19 #37034, 0.14 #7317), 0gbwp (0.40 #4478, 0.36 #7846, 0.22 #1112), 0gdh5 (0.40 #4125, 0.33 #759, 0.25 #7493), 01wf86y (0.40 #5552, 0.33 #2186, 0.19 #8920) >> Best rule #6733 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 02f5qb; 02f716; 02f73p; 02f6xy; 05q8pss; 099vwn; 02x17c2; 01c99j; >> query: (?x6287, ?x140) <- award_winner(?x6287, ?x140), award(?x4693, ?x6287), ?x4693 = 01vwbts, location(?x140, ?x1523), award_nominee(?x140, ?x527) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #3476 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 02f5qb; 02f716; 02f73p; 02f6xy; 05q8pss; 099vwn; 02x17c2; 01c99j; *> query: (?x6287, 0147dk) <- award_winner(?x6287, ?x140), award(?x4693, ?x6287), ?x4693 = 01vwbts, location(?x140, ?x1523), award_nominee(?x140, ?x527) *> conf = 0.30 ranks of expected_values: 41, 79, 175 EVAL 02f75t award! 07pzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 32.000 12.000 0.864 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f75t award! 013w7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 32.000 12.000 0.864 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f75t award! 0147dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 32.000 12.000 0.864 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6521-07hnp PRED entity: 07hnp PRED relation: nutrient! PRED expected values: 0hkxq 07j87 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 7): 0hkxq (0.92 #569, 0.91 #537, 0.90 #398), 07j87 (0.89 #36, 0.89 #27, 0.89 #25), 06x4c (0.89 #36, 0.89 #27, 0.89 #25), 0dcfv (0.89 #36, 0.89 #27, 0.89 #25), 04k8n (0.02 #487), 05wvs (0.02 #487), 01sh2 (0.02 #487) >> Best rule #569 for best value: >> intensional similarity = 116 >> extensional distance = 49 >> proper extension: 02y_3rt; >> query: (?x1960, 0hkxq) <- nutrient(?x9732, ?x1960), nutrient(?x9005, ?x1960), nutrient(?x6285, ?x1960), nutrient(?x6191, ?x1960), nutrient(?x5373, ?x1960), nutrient(?x5009, ?x1960), nutrient(?x4068, ?x1960), nutrient(?x9732, ?x13944), nutrient(?x9732, ?x13498), nutrient(?x9732, ?x12902), nutrient(?x9732, ?x12454), nutrient(?x9732, ?x11758), nutrient(?x9732, ?x11592), nutrient(?x9732, ?x11409), nutrient(?x9732, ?x11270), nutrient(?x9732, ?x10891), nutrient(?x9732, ?x10709), nutrient(?x9732, ?x10098), nutrient(?x9732, ?x9733), nutrient(?x9732, ?x9436), nutrient(?x9732, ?x9365), nutrient(?x9732, ?x8442), nutrient(?x9732, ?x8413), nutrient(?x9732, ?x7894), nutrient(?x9732, ?x7720), nutrient(?x9732, ?x7652), nutrient(?x9732, ?x7364), nutrient(?x9732, ?x7362), nutrient(?x9732, ?x7219), nutrient(?x9732, ?x7135), nutrient(?x9732, ?x6586), nutrient(?x9732, ?x6160), nutrient(?x9732, ?x6033), nutrient(?x9732, ?x5549), nutrient(?x9732, ?x5526), nutrient(?x9732, ?x5451), nutrient(?x9732, ?x5010), nutrient(?x9732, ?x2702), nutrient(?x9732, ?x2018), nutrient(?x9732, ?x1304), nutrient(?x9732, ?x1258), ?x5010 = 0h1vz, ?x2702 = 0838f, nutrient(?x5373, ?x13126), nutrient(?x5373, ?x9795), nutrient(?x5373, ?x9619), nutrient(?x5373, ?x8487), nutrient(?x5373, ?x7431), nutrient(?x5373, ?x6192), nutrient(?x5373, ?x6026), nutrient(?x5373, ?x3469), ?x9365 = 04k8n, ?x1304 = 08lb68, ?x7720 = 025s7x6, ?x7431 = 09gwd, ?x7219 = 0h1vg, ?x2018 = 01sh2, ?x5526 = 09pbb, nutrient(?x9005, ?x9840), nutrient(?x9005, ?x6286), nutrient(?x9005, ?x5337), nutrient(?x9005, ?x4069), nutrient(?x9005, ?x3901), nutrient(?x6285, ?x12868), nutrient(?x6285, ?x12481), nutrient(?x6285, ?x11784), nutrient(?x6285, ?x10195), nutrient(?x6285, ?x9855), ?x9795 = 05v_8y, ?x9436 = 025sqz8, ?x9840 = 02p0tjr, ?x13126 = 02kc_w5, ?x10891 = 0g5gq, ?x11758 = 0q01m, ?x6026 = 025sf8g, ?x4068 = 0fbw6, ?x10098 = 0h1_c, ?x11784 = 07zqy, ?x9733 = 0h1tz, ?x10709 = 0h1sz, nutrient(?x6191, ?x3264), ?x7894 = 0f4hc, ?x5009 = 0fjfh, ?x13498 = 07q0m, ?x5549 = 025s7j4, ?x8413 = 02kc4sf, ?x6160 = 041r51, ?x10195 = 0hkwr, ?x9855 = 0d9t0, ?x8442 = 02kcv4x, ?x7652 = 025s0s0, ?x6286 = 02y_3rf, ?x11270 = 02kc008, ?x1258 = 0h1wg, ?x8487 = 014yzm, nutrient(?x9489, ?x9619), ?x12454 = 025rw19, ?x3264 = 0dcfv, ?x5337 = 06x4c, ?x4069 = 0hqw8p_, ?x6033 = 04zjxcz, ?x12902 = 0fzjh, ?x9489 = 07j87, ?x11409 = 0h1yf, ?x13944 = 0f4kp, ?x6192 = 06jry, ?x5451 = 05wvs, ?x7135 = 025rsfk, ?x12868 = 03d49, ?x6586 = 05gh50, ?x3469 = 0h1zw, ?x12481 = 027g6p7, ?x11592 = 025sf0_, ?x7364 = 09gvd, ?x3901 = 0466p20, ?x7362 = 02kc5rj >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07hnp nutrient! 07j87 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.922 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 07hnp nutrient! 0hkxq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.922 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #6520-06_wqk4 PRED entity: 06_wqk4 PRED relation: film! PRED expected values: 01_xtx 026l37 => 81 concepts (62 used for prediction) PRED predicted values (max 10 best out of 797): 0dl567 (0.64 #78592, 0.64 #80663, 0.58 #78591), 03qd_ (0.40 #2190, 0.33 #4258, 0.02 #33207), 051wwp (0.40 #2938, 0.33 #5006, 0.01 #67051), 07y8l9 (0.40 #3032, 0.33 #5100, 0.01 #34049), 02qx69 (0.40 #2620, 0.33 #4688, 0.01 #8822), 07m77x (0.40 #3597, 0.33 #5665, 0.01 #42888), 028k57 (0.40 #2853, 0.33 #4921), 021bk (0.40 #2443, 0.33 #4511), 0pgjm (0.40 #2281, 0.33 #4349), 02ndbd (0.40 #4135, 0.20 #35153, 0.16 #62044) >> Best rule #78592 for best value: >> intensional similarity = 4 >> extensional distance = 851 >> proper extension: 01f3p_; 0cskb; >> query: (?x857, ?x7830) <- nominated_for(?x7830, ?x857), nominated_for(?x3756, ?x857), participant(?x7830, ?x2763), film(?x3756, ?x3317) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #13066 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 130 *> proper extension: 03mh_tp; 0c3xw46; 0dll_t2; 02q8ms8; 09dv8h; 047rkcm; 03cyslc; 047vp1n; 03bzjpm; 0456zg; ... *> query: (?x857, 01_xtx) <- film(?x828, ?x857), genre(?x857, ?x239), ?x239 = 06cvj *> conf = 0.02 ranks of expected_values: 514, 717 EVAL 06_wqk4 film! 026l37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 81.000 62.000 0.642 http://example.org/film/actor/film./film/performance/film EVAL 06_wqk4 film! 01_xtx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 81.000 62.000 0.642 http://example.org/film/actor/film./film/performance/film #6519-04r7p PRED entity: 04r7p PRED relation: award_winner! PRED expected values: 02y_rq5 => 124 concepts (104 used for prediction) PRED predicted values (max 10 best out of 261): 0gqwc (0.38 #24468, 0.37 #31341, 0.37 #31340), 02y_rq5 (0.38 #24468, 0.37 #31341, 0.37 #31340), 094qd5 (0.38 #24468, 0.37 #31341, 0.37 #31340), 0bb57s (0.38 #24468, 0.37 #31341, 0.37 #31340), 027c924 (0.21 #11, 0.13 #440, 0.11 #1298), 0789r6 (0.21 #396, 0.13 #825, 0.04 #1254), 09sb52 (0.19 #9054, 0.17 #12490, 0.16 #11201), 02wypbh (0.17 #776, 0.14 #347, 0.04 #1205), 0bdwft (0.17 #24898, 0.15 #33920, 0.09 #42513), 0gqyl (0.17 #24898, 0.15 #33920, 0.09 #42513) >> Best rule #24468 for best value: >> intensional similarity = 3 >> extensional distance = 1248 >> proper extension: 04rcr; 0g1rw; 03h26tm; 011zf2; 0hpt3; 05218gr; 05qsxy; 04gmp_z; 014hr0; 0249kn; ... >> query: (?x6958, ?x749) <- award(?x6958, ?x749), award_winner(?x6958, ?x6440), award_winner(?x3029, ?x6440) >> conf = 0.38 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 04r7p award_winner! 02y_rq5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 104.000 0.376 http://example.org/award/award_category/winners./award/award_honor/award_winner #6518-0b90_r PRED entity: 0b90_r PRED relation: olympics PRED expected values: 0lk8j => 219 concepts (219 used for prediction) PRED predicted values (max 10 best out of 28): 0lv1x (0.65 #373, 0.61 #233, 0.56 #177), 018ctl (0.62 #701, 0.61 #369, 0.61 #926), 09n48 (0.62 #701, 0.61 #926, 0.60 #1095), 016r9z (0.62 #701, 0.61 #926, 0.60 #1095), 0nbjq (0.57 #376, 0.56 #236, 0.50 #320), 0ldqf (0.56 #192, 0.55 #332, 0.53 #528), 0kbvv (0.52 #381, 0.48 #353, 0.46 #409), 0blg2 (0.50 #235, 0.49 #768, 0.45 #319), 0lk8j (0.48 #374, 0.44 #542, 0.43 #767), 0swbd (0.48 #370, 0.38 #538, 0.35 #342) >> Best rule #373 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 02j71; >> query: (?x151, 0lv1x) <- currency(?x151, ?x170), administrative_parent(?x5474, ?x151), service_location(?x1540, ?x151) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #374 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 02j71; *> query: (?x151, 0lk8j) <- currency(?x151, ?x170), administrative_parent(?x5474, ?x151), service_location(?x1540, ?x151) *> conf = 0.48 ranks of expected_values: 9 EVAL 0b90_r olympics 0lk8j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 219.000 219.000 0.652 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #6517-09v71cj PRED entity: 09v71cj PRED relation: film_crew_role PRED expected values: 09vw2b7 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 32): 09zzb8 (0.82 #401, 0.79 #1172, 0.77 #1281), 09vw2b7 (0.72 #1214, 0.70 #1287, 0.69 #2136), 01pvkk (0.54 #84, 0.35 #302, 0.33 #1219), 0dxtw (0.48 #1218, 0.40 #191, 0.40 #1182), 02rh1dz (0.40 #10, 0.29 #46, 0.27 #263), 02ynfr (0.27 #269, 0.26 #562, 0.23 #1223), 01xy5l_ (0.25 #158, 0.21 #267, 0.20 #450), 0d2b38 (0.25 #170, 0.20 #26, 0.18 #279), 015h31 (0.23 #225, 0.20 #9, 0.18 #1216), 0215hd (0.21 #127, 0.21 #272, 0.19 #163) >> Best rule #401 for best value: >> intensional similarity = 6 >> extensional distance = 47 >> proper extension: 02v63m; >> query: (?x4352, 09zzb8) <- film_crew_role(?x4352, ?x468), film(?x166, ?x4352), language(?x4352, ?x254), ?x166 = 0jz9f, ?x254 = 02h40lc, film(?x556, ?x4352) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1214 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 254 *> proper extension: 04fzfj; 0b73_1d; 0963mq; 02qm_f; 048scx; 020fcn; 02pxmgz; 07y9w5; 0340hj; 0c00zd0; ... *> query: (?x4352, 09vw2b7) <- film_crew_role(?x4352, ?x2154), film(?x166, ?x4352), country(?x4352, ?x94), language(?x4352, ?x254), ?x254 = 02h40lc, ?x2154 = 01vx2h *> conf = 0.72 ranks of expected_values: 2 EVAL 09v71cj film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.816 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6516-0fcyj PRED entity: 0fcyj PRED relation: location_of_ceremony! PRED expected values: 04ztj => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.71 #41, 0.71 #25, 0.68 #21), 0jgjn (0.14 #8, 0.12 #4, 0.12 #16), 01g63y (0.12 #2, 0.07 #6, 0.06 #14), 01bl8s (0.01 #123) >> Best rule #41 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 0d6hn; >> query: (?x13229, 04ztj) <- capital(?x1203, ?x13229), country(?x359, ?x1203), film_release_region(?x5496, ?x1203), olympics(?x1203, ?x391), ?x5496 = 07l50vn >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fcyj location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.714 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #6515-04v3q PRED entity: 04v3q PRED relation: featured_film_locations! PRED expected values: 072x7s => 189 concepts (119 used for prediction) PRED predicted values (max 10 best out of 693): 02dwj (0.19 #4810, 0.14 #1129, 0.13 #4074), 03k8th (0.14 #1440, 0.10 #5857, 0.09 #8067), 02yvct (0.14 #891, 0.09 #2364, 0.07 #25191), 0m9p3 (0.14 #906, 0.09 #2379, 0.07 #3851), 01v1ln (0.14 #1252, 0.09 #2725, 0.07 #4197), 04nm0n0 (0.14 #1106, 0.09 #2579, 0.07 #4051), 0hfzr (0.14 #1042, 0.09 #2515, 0.07 #3987), 015g28 (0.14 #1022, 0.09 #2495, 0.07 #3967), 03twd6 (0.14 #837, 0.09 #2310, 0.07 #3782), 072x7s (0.14 #849, 0.07 #14838, 0.06 #4530) >> Best rule #4810 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 06c6l; >> query: (?x1061, 02dwj) <- contains(?x455, ?x1061), featured_film_locations(?x7373, ?x1061), partially_contains(?x455, ?x404), countries_within(?x455, ?x87) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #849 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0ftns; *> query: (?x1061, 072x7s) <- contains(?x455, ?x1061), featured_film_locations(?x7373, ?x1061), ?x455 = 02j9z, film_crew_role(?x7373, ?x137) *> conf = 0.14 ranks of expected_values: 10 EVAL 04v3q featured_film_locations! 072x7s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 189.000 119.000 0.188 http://example.org/film/film/featured_film_locations #6514-01lhf PRED entity: 01lhf PRED relation: student PRED expected values: 02_0d2 => 57 concepts (27 used for prediction) PRED predicted values (max 10 best out of 237): 0kn4c (0.43 #737, 0.30 #1209, 0.19 #2631), 02z1yj (0.33 #191, 0.18 #1610, 0.17 #2084), 012x2b (0.33 #187, 0.18 #1606, 0.17 #2080), 033jkj (0.33 #342, 0.18 #1522, 0.17 #1996), 0c3p7 (0.33 #134, 0.18 #1553, 0.17 #2027), 06whf (0.33 #338, 0.17 #574, 0.15 #2467), 083q7 (0.33 #494, 0.17 #1675, 0.14 #966), 02r34n (0.33 #259, 0.17 #495, 0.14 #967), 016h4r (0.33 #310, 0.17 #546, 0.14 #1018), 02tc5y (0.33 #435, 0.17 #671, 0.14 #1143) >> Best rule #737 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 04_tv; >> query: (?x11378, 0kn4c) <- major_field_of_study(?x6912, ?x11378), major_field_of_study(?x4672, ?x11378), major_field_of_study(?x4410, ?x11378), major_field_of_study(?x581, ?x11378), major_field_of_study(?x865, ?x11378), ?x4410 = 017j69, ?x581 = 06pwq, major_field_of_study(?x6912, ?x11820), major_field_of_study(?x6912, ?x9829), institution(?x734, ?x6912), ?x734 = 04zx3q1, films(?x9829, ?x174), ?x4672 = 07tds, ?x11820 = 0w7s >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01lhf student 02_0d2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 27.000 0.429 http://example.org/education/field_of_study/students_majoring./education/education/student #6513-0bp_b2 PRED entity: 0bp_b2 PRED relation: ceremony PRED expected values: 07z31v 0gx_st => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 132): 0gx_st (0.68 #558, 0.37 #689, 0.37 #820), 0hn821n (0.54 #645, 0.29 #514, 0.27 #776), 0gpjbt (0.51 #1600, 0.48 #1731, 0.38 #3042), 09n4nb (0.49 #1618, 0.47 #1749, 0.37 #3060), 0466p0j (0.49 #1643, 0.46 #1774, 0.37 #3085), 05pd94v (0.49 #1575, 0.46 #1706, 0.35 #3017), 056878 (0.49 #1603, 0.46 #1734, 0.36 #3045), 02cg41 (0.48 #1690, 0.46 #1821, 0.37 #3132), 02rjjll (0.48 #1578, 0.46 #1709, 0.36 #3020), 01c6qp (0.47 #1591, 0.45 #1722, 0.35 #3033) >> Best rule #558 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 09v7wsg; >> query: (?x435, 0gx_st) <- nominated_for(?x435, ?x337), award_winner(?x435, ?x879), ceremony(?x435, ?x4760), ?x4760 = 02q690_ >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1, 21 EVAL 0bp_b2 ceremony 0gx_st CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.679 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bp_b2 ceremony 07z31v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 55.000 55.000 0.679 http://example.org/award/award_category/winners./award/award_honor/ceremony #6512-05qx1 PRED entity: 05qx1 PRED relation: olympics PRED expected values: 0kbws => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 42): 0jdk_ (0.77 #69, 0.66 #237, 0.54 #153), 06sks6 (0.74 #67, 0.69 #235, 0.64 #757), 0kbws (0.72 #57, 0.64 #757, 0.63 #225), 0kbvb (0.72 #49, 0.63 #217, 0.56 #133), 0jhn7 (0.63 #154, 0.63 #70, 0.61 #238), 0l6mp (0.56 #60, 0.46 #228, 0.37 #480), 0l6m5 (0.53 #52, 0.48 #220, 0.42 #472), 0lgxj (0.53 #71, 0.43 #239, 0.37 #155), 0l6ny (0.53 #51, 0.40 #219, 0.34 #471), 0lbd9 (0.47 #75, 0.37 #243, 0.29 #495) >> Best rule #69 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 05r4w; 09c7w0; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 04gzd; 0chghy; ... >> query: (?x1475, 0jdk_) <- film_release_region(?x4336, ?x1475), film_release_region(?x4047, ?x1475), ?x4336 = 0bpm4yw, ?x4047 = 07s846j >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #57 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 05r4w; 09c7w0; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 04gzd; 0chghy; ... *> query: (?x1475, 0kbws) <- film_release_region(?x4336, ?x1475), film_release_region(?x4047, ?x1475), ?x4336 = 0bpm4yw, ?x4047 = 07s846j *> conf = 0.72 ranks of expected_values: 3 EVAL 05qx1 olympics 0kbws CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 108.000 0.767 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #6511-05pcjw PRED entity: 05pcjw PRED relation: school_type! PRED expected values: 02s62q 04d5v9 01pq4w 0kw4j 017j69 02bqy 027mdh 01jq4b 021s9n 05zl0 01bk1y 02gkxp 02ckl3 03b8c4 01wrwf => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 766): 01pl14 (0.40 #2734, 0.40 #2279, 0.33 #4101), 020yvh (0.33 #1261, 0.33 #806, 0.25 #4448), 01ljpm (0.33 #1100, 0.33 #645, 0.25 #2010), 04wlz2 (0.33 #912, 0.33 #457, 0.25 #1822), 01yqqv (0.33 #1201, 0.33 #746, 0.25 #2111), 01k2wn (0.33 #927, 0.33 #472, 0.25 #1837), 02x9cv (0.33 #715, 0.33 #260, 0.25 #2080), 01fxg8 (0.33 #1714, 0.33 #1259, 0.21 #6273), 01tx9m (0.33 #1086, 0.33 #176, 0.20 #2906), 0558_1 (0.33 #1281, 0.33 #371, 0.20 #3101) >> Best rule #2734 for best value: >> intensional similarity = 20 >> extensional distance = 8 >> proper extension: 01_9fk; 05jxkf; 02p0qmm; 04qbv; >> query: (?x1044, 01pl14) <- school_type(?x12175, ?x1044), school_type(?x11711, ?x1044), school_type(?x11474, ?x1044), school_type(?x11278, ?x1044), school_type(?x10824, ?x1044), school_type(?x9307, ?x1044), school_type(?x6925, ?x1044), institution(?x1368, ?x6925), institution(?x1519, ?x11711), contains(?x94, ?x11711), registering_agency(?x11278, ?x1982), citytown(?x11474, ?x2879), currency(?x9307, ?x170), company(?x3131, ?x6925), colors(?x12175, ?x332), ?x1368 = 014mlp, ?x94 = 09c7w0, student(?x6925, ?x981), category(?x10824, ?x134), major_field_of_study(?x6925, ?x254) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1516 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 04399; *> query: (?x1044, 027mdh) <- school_type(?x12175, ?x1044), school_type(?x11711, ?x1044), school_type(?x11278, ?x1044), school_type(?x10576, ?x1044), school_type(?x9307, ?x1044), school_type(?x6925, ?x1044), school_type(?x5145, ?x1044), school_type(?x3228, ?x1044), institution(?x1368, ?x6925), major_field_of_study(?x10576, ?x1154), institution(?x1519, ?x11711), contains(?x94, ?x11711), registering_agency(?x11278, ?x1982), currency(?x9307, ?x170), company(?x3131, ?x6925), colors(?x12175, ?x332), ?x1368 = 014mlp, ?x94 = 09c7w0, citytown(?x10576, ?x4356), ?x5145 = 0b1xl, state_province_region(?x3228, ?x108) *> conf = 0.33 ranks of expected_values: 12, 86, 99, 130, 275, 281, 283, 486, 709, 713, 738, 746, 747 EVAL 05pcjw school_type! 01wrwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 03b8c4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 02ckl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 02gkxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 01bk1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 05zl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 021s9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 01jq4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 027mdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 02bqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 017j69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 0kw4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 01pq4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 04d5v9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type EVAL 05pcjw school_type! 02s62q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 19.000 19.000 0.400 http://example.org/education/educational_institution/school_type #6510-06fcqw PRED entity: 06fcqw PRED relation: film_release_region PRED expected values: 07ssc 0f2wj 04g5k => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 111): 07ssc (0.85 #936, 0.82 #1464, 0.82 #1200), 0ctw_b (0.83 #16, 0.77 #413, 0.76 #281), 03rk0 (0.78 #39, 0.68 #436, 0.67 #304), 047yc (0.72 #19, 0.65 #284, 0.64 #416), 06c1y (0.69 #29, 0.62 #426, 0.61 #294), 01ls2 (0.65 #933, 0.64 #7, 0.60 #404), 09pmkv (0.61 #18, 0.57 #415, 0.55 #283), 06t8v (0.61 #56, 0.54 #982, 0.53 #453), 05qx1 (0.53 #424, 0.53 #27, 0.49 #292), 07twz (0.53 #73, 0.49 #470, 0.49 #338) >> Best rule #936 for best value: >> intensional similarity = 5 >> extensional distance = 72 >> proper extension: 064lsn; >> query: (?x6216, 07ssc) <- film_release_region(?x6216, ?x1917), film_release_region(?x6216, ?x94), ?x94 = 09c7w0, nominated_for(?x1053, ?x6216), ?x1917 = 01p1v >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 20, 47 EVAL 06fcqw film_release_region 04g5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 90.000 90.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06fcqw film_release_region 0f2wj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 90.000 90.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06fcqw film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6509-03h3x5 PRED entity: 03h3x5 PRED relation: film! PRED expected values: 01pb34 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 4): 01pb34 (0.22 #18, 0.12 #13, 0.08 #78), 01kyvx (0.19 #31, 0.01 #465, 0.01 #443), 02t8yb (0.14 #24, 0.03 #34, 0.01 #74), 09_gdc (0.04 #77, 0.04 #72, 0.03 #62) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02bqvs; >> query: (?x2642, 01pb34) <- film(?x3917, ?x2642), film(?x382, ?x2642), ?x3917 = 0p_47, music(?x2642, ?x669) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03h3x5 film! 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.222 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #6508-07h5d PRED entity: 07h5d PRED relation: influenced_by PRED expected values: 05qzv => 110 concepts (70 used for prediction) PRED predicted values (max 10 best out of 133): 032l1 (0.06 #7969, 0.06 #8406, 0.01 #2717), 081k8 (0.06 #8036, 0.06 #8473, 0.01 #3222), 01v9724 (0.05 #8058, 0.05 #8495, 0.02 #3244), 03_87 (0.05 #8520, 0.05 #8083, 0.01 #22084), 03f0324 (0.05 #8032, 0.04 #8469, 0.02 #2780), 02lt8 (0.04 #8000, 0.04 #8437, 0.01 #3186), 02wh0 (0.04 #8264, 0.04 #8701), 0379s (0.04 #8395, 0.03 #7958), 0j3v (0.03 #7940, 0.03 #8377), 041mt (0.03 #496, 0.02 #2248) >> Best rule #7969 for best value: >> intensional similarity = 3 >> extensional distance = 431 >> proper extension: 0p3sf; 08n9ng; 01dvtx; 06whf; 0d5_f; 058vp; 02f9wb; 07ym0; 06hgj; 027y_; ... >> query: (?x7352, 032l1) <- nationality(?x7352, ?x512), profession(?x7352, ?x353), ?x353 = 0cbd2 >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #1652 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 149 *> proper extension: 022_lg; 04b19t; 01ycck; 012vct; *> query: (?x7352, 05qzv) <- place_of_birth(?x7352, ?x5771), nominated_for(?x7352, ?x1481), film(?x7352, ?x1842) *> conf = 0.02 ranks of expected_values: 37 EVAL 07h5d influenced_by 05qzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 110.000 70.000 0.065 http://example.org/influence/influence_node/influenced_by #6507-07bwr PRED entity: 07bwr PRED relation: genre PRED expected values: 05p553 0lsxr => 80 concepts (23 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.80 #1916, 0.68 #1795, 0.67 #716), 05p553 (0.67 #124, 0.41 #243, 0.36 #1558), 01z4y (0.53 #1673), 02kdv5l (0.43 #3, 0.33 #839, 0.30 #2641), 01jfsb (0.36 #2651, 0.35 #849, 0.34 #489), 06nbt (0.33 #146, 0.07 #27, 0.05 #622), 02l7c8 (0.29 #1092, 0.28 #1332, 0.26 #972), 03k9fj (0.29 #12, 0.25 #369, 0.25 #848), 0lsxr (0.25 #366, 0.22 #1924, 0.19 #2045), 04xvlr (0.25 #597, 0.25 #717, 0.21 #1796) >> Best rule #1916 for best value: >> intensional similarity = 4 >> extensional distance = 438 >> proper extension: 0d8w2n; >> query: (?x5066, 07s9rl0) <- genre(?x5066, ?x271), films(?x14068, ?x5066), genre(?x6288, ?x271), ?x6288 = 01chpn >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 04cf_l; *> query: (?x5066, 05p553) <- genre(?x5066, ?x271), ?x271 = 01q03, music(?x5066, ?x4644) *> conf = 0.67 ranks of expected_values: 2, 9 EVAL 07bwr genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 80.000 23.000 0.795 http://example.org/film/film/genre EVAL 07bwr genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 23.000 0.795 http://example.org/film/film/genre #6506-02jxrw PRED entity: 02jxrw PRED relation: films! PRED expected values: 018h2 => 77 concepts (20 used for prediction) PRED predicted values (max 10 best out of 54): 05489 (0.09 #52, 0.05 #1466, 0.04 #994), 081pw (0.08 #1417, 0.06 #3, 0.05 #1887), 01w1sx (0.07 #249, 0.04 #877, 0.04 #405), 0fx2s (0.07 #1487, 0.04 #1957, 0.04 #387), 06d4h (0.05 #1457, 0.05 #513, 0.05 #670), 0kbq (0.05 #104, 0.03 #262, 0.03 #731), 0fzyg (0.04 #1468, 0.03 #54, 0.02 #1938), 07c52 (0.04 #1434, 0.02 #178, 0.02 #1591), 01s5q (0.03 #1526, 0.03 #270, 0.02 #426), 018h2 (0.03 #1436, 0.03 #22, 0.03 #492) >> Best rule #52 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 03bxp5; 0296rz; >> query: (?x10060, 05489) <- genre(?x10060, ?x162), ?x162 = 04xvlr, nominated_for(?x1880, ?x10060), featured_film_locations(?x10060, ?x151) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #1436 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 290 *> proper extension: 08cfr1; *> query: (?x10060, 018h2) <- award(?x10060, ?x143), genre(?x10060, ?x53), film(?x1738, ?x10060), films(?x11683, ?x10060) *> conf = 0.03 ranks of expected_values: 10 EVAL 02jxrw films! 018h2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 77.000 20.000 0.091 http://example.org/film/film_subject/films #6505-0dclg PRED entity: 0dclg PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 23): 0pqc5 (0.62 #97, 0.60 #764, 0.60 #580), 0f6c3 (0.58 #1020, 0.41 #1595, 0.31 #2032), 060bp (0.58 #1335, 0.46 #1795, 0.45 #1266), 060c4 (0.56 #1337, 0.52 #1268, 0.49 #1797), 09n5b9 (0.54 #1024, 0.38 #1599, 0.28 #2036), 0fkvn (0.51 #1016, 0.39 #1591, 0.28 #2028), 0fkzq (0.17 #1029, 0.12 #1604, 0.09 #937), 01q24l (0.15 #83, 0.15 #175, 0.14 #60), 0p5vf (0.13 #1278, 0.11 #1347, 0.09 #1692), 0dq3c (0.12 #209, 0.11 #1267, 0.10 #1336) >> Best rule #97 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 05l64; >> query: (?x2254, 0pqc5) <- month(?x2254, ?x1459), time_zones(?x2254, ?x2674), origin(?x3176, ?x2254) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dclg jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.625 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #6504-0bh8yn3 PRED entity: 0bh8yn3 PRED relation: film_release_region PRED expected values: 05r4w 03rj0 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 118): 05r4w (0.87 #1063, 0.81 #1855, 0.80 #2383), 07ssc (0.78 #1865, 0.77 #1073, 0.76 #1205), 03rt9 (0.63 #142, 0.62 #1203, 0.62 #1071), 03rj0 (0.60 #178, 0.54 #1107, 0.53 #1899), 04gzd (0.54 #1068, 0.47 #139, 0.45 #1200), 01mjq (0.49 #1887, 0.47 #1095, 0.44 #1227), 06mzp (0.44 #149, 0.43 #1870, 0.39 #2002), 06f32 (0.43 #1112, 0.39 #1244, 0.36 #1904), 0h7x (0.38 #2012, 0.37 #1880, 0.37 #2408), 06c1y (0.36 #1094, 0.30 #165, 0.27 #1226) >> Best rule #1063 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 0b76d_m; 0g56t9t; 0gtsx8c; 02vxq9m; 07gp9; 0gx1bnj; 0ds3t5x; 0dscrwf; 0djb3vw; 0c40vxk; ... >> query: (?x1701, 05r4w) <- production_companies(?x1701, ?x382), film_crew_role(?x1701, ?x137), film_release_region(?x1701, ?x279), ?x279 = 0d060g >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0bh8yn3 film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bh8yn3 film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6503-01jtp7 PRED entity: 01jtp7 PRED relation: student PRED expected values: 0cqt90 => 115 concepts (96 used for prediction) PRED predicted values (max 10 best out of 585): 04fcx7 (0.10 #870, 0.08 #9242, 0.04 #7149), 02dth1 (0.10 #697, 0.05 #4883, 0.04 #6976), 0641g8 (0.10 #858, 0.05 #5044, 0.04 #7137), 0cqt90 (0.10 #638, 0.05 #4824, 0.04 #6917), 0835q (0.10 #1960, 0.05 #6146, 0.04 #8239), 0716t2 (0.10 #1945, 0.05 #6131, 0.04 #8224), 03xpfzg (0.10 #1939, 0.05 #6125, 0.04 #8218), 0b455l (0.10 #1713, 0.05 #5899, 0.04 #7992), 0c1jh (0.10 #1636, 0.05 #5822, 0.04 #7915), 0438pz (0.10 #1532, 0.05 #5718, 0.04 #7811) >> Best rule #870 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 01prf3; >> query: (?x2166, 04fcx7) <- citytown(?x2166, ?x2254), ?x2254 = 0dclg >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #638 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 8 *> proper extension: 01prf3; *> query: (?x2166, 0cqt90) <- citytown(?x2166, ?x2254), ?x2254 = 0dclg *> conf = 0.10 ranks of expected_values: 4 EVAL 01jtp7 student 0cqt90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 96.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student #6502-02wm6l PRED entity: 02wm6l PRED relation: capital PRED expected values: 07_pf => 7 concepts (7 used for prediction) PRED predicted values (max 10 best out of 51): 0fw4v (0.02 #752, 0.01 #629, 0.01 #36), 0889d (0.02 #243, 0.02 #751, 0.02 #624), 04jpl (0.02 #633, 0.02 #506), 0c1xm (0.02 #245, 0.01 #629, 0.01 #116), 0dlqv (0.02 #245, 0.01 #629, 0.01 #95), 0lpfh (0.02 #245, 0.01 #629, 0.01 #63), 06c62 (0.02 #403, 0.02 #755, 0.01 #629), 0c499 (0.02 #757, 0.01 #629, 0.01 #89), 067z4 (0.02 #757, 0.01 #629, 0.01 #54), 01yj2 (0.02 #757, 0.01 #629, 0.01 #39) >> Best rule #752 for best value: >> intensional similarity = 191 >> extensional distance = 152 >> proper extension: 0chghy; 0bq0p9; 047yc; 02jx1; 0161c; 05bcl; 0j5g9; 01k6y1; 0n3g; 0c4b8; ... >> query: (?x13687, ?x8428) <- form_of_government(?x13687, ?x48), form_of_government(?x11289, ?x48), form_of_government(?x9458, ?x48), form_of_government(?x8857, ?x48), form_of_government(?x8845, ?x48), form_of_government(?x7413, ?x48), form_of_government(?x6974, ?x48), form_of_government(?x6559, ?x48), form_of_government(?x4714, ?x48), form_of_government(?x4092, ?x48), form_of_government(?x3855, ?x48), form_of_government(?x2629, ?x48), form_of_government(?x2188, ?x48), form_of_government(?x1780, ?x48), form_of_government(?x792, ?x48), olympics(?x9458, ?x2966), film_release_region(?x11839, ?x3855), film_release_region(?x11313, ?x3855), film_release_region(?x9194, ?x3855), film_release_region(?x8292, ?x3855), film_release_region(?x5644, ?x3855), film_release_region(?x5016, ?x3855), film_release_region(?x4615, ?x3855), film_release_region(?x4336, ?x3855), film_release_region(?x2709, ?x3855), film_release_region(?x1490, ?x3855), film_release_region(?x1456, ?x3855), film_release_region(?x1012, ?x3855), capital(?x3855, ?x13391), organization(?x3855, ?x127), country(?x5396, ?x3855), country(?x3309, ?x3855), country(?x1967, ?x3855), adjoins(?x8857, ?x1241), film_release_region(?x5825, ?x6559), film_release_region(?x4610, ?x6559), film_release_region(?x1392, ?x6559), medal(?x3855, ?x1242), administrative_area_type(?x3855, ?x2792), ?x1967 = 01cgz, ?x5016 = 062zm5h, ?x11839 = 072hx4, ?x9194 = 0fpgp26, ?x4610 = 017jd9, countries_within(?x2467, ?x8857), location_of_ceremony(?x566, ?x6559), participating_countries(?x784, ?x3855), ?x5825 = 067ghz, origin(?x6406, ?x3855), country(?x2266, ?x9458), adjustment_currency(?x6974, ?x170), ?x1456 = 0cz8mkh, ?x2792 = 0hzc9wc, adjoins(?x1780, ?x1781), religion(?x8857, ?x109), film_release_region(?x10475, ?x2629), film_release_region(?x8646, ?x2629), film_release_region(?x7678, ?x2629), film_release_region(?x6882, ?x2629), film_release_region(?x5826, ?x2629), film_release_region(?x5713, ?x2629), film_release_region(?x5564, ?x2629), film_release_region(?x5418, ?x2629), film_release_region(?x4998, ?x2629), film_release_region(?x4604, ?x2629), film_release_region(?x4041, ?x2629), film_release_region(?x3850, ?x2629), film_release_region(?x3748, ?x2629), film_release_region(?x3292, ?x2629), film_release_region(?x3076, ?x2629), film_release_region(?x2896, ?x2629), film_release_region(?x2168, ?x2629), film_release_region(?x1916, ?x2629), film_release_region(?x1602, ?x2629), film_release_region(?x1470, ?x2629), film_release_region(?x511, ?x2629), film_release_region(?x141, ?x2629), ?x10475 = 047p798, ?x1012 = 0bwfwpj, combatants(?x151, ?x2629), ?x6882 = 043tvp3, ?x5396 = 0486tv, country(?x3598, ?x2188), country(?x2885, ?x2188), country(?x1352, ?x2188), country(?x471, ?x2188), ?x3309 = 09w1n, ?x784 = 018ctl, jurisdiction_of_office(?x182, ?x2188), ?x3748 = 05zlld0, contains(?x6304, ?x2629), member_states(?x7695, ?x2629), ?x3076 = 0g5838s, geographic_distribution(?x5590, ?x2188), countries_spoken_in(?x12312, ?x792), countries_spoken_in(?x9113, ?x792), ?x1242 = 02lq5w, film_release_region(?x7493, ?x792), film_release_region(?x6761, ?x792), film_release_region(?x3482, ?x792), film_release_region(?x1496, ?x792), film_release_region(?x791, ?x792), film_release_region(?x622, ?x792), ?x1352 = 0w0d, country(?x2204, ?x792), administrative_parent(?x841, ?x792), ?x9113 = 02hxcvy, olympics(?x2629, ?x867), olympics(?x2629, ?x775), geographic_distribution(?x9148, ?x792), ?x151 = 0b90_r, ?x471 = 02vx4, contains(?x6974, ?x14027), ?x4604 = 0432_5, ?x1392 = 017gm7, teams(?x11289, ?x10493), ?x3598 = 03rbzn, ?x867 = 0l6ny, ?x3292 = 0gvs1kt, nationality(?x477, ?x792), ?x11313 = 0by17xn, ?x1490 = 0fpkhkz, contains(?x7273, ?x6559), ?x2896 = 0645k5, nationality(?x690, ?x2629), country(?x2203, ?x792), organization(?x792, ?x5701), country(?x171, ?x792), ?x1470 = 03twd6, capital(?x8845, ?x6959), service_location(?x11636, ?x2629), ?x1781 = 01z215, ?x12312 = 0121sr, ?x511 = 0dscrwf, nationality(?x395, ?x6559), entity_involved(?x7455, ?x2629), ?x622 = 0fq27fp, olympics(?x792, ?x2496), ?x4041 = 0gy2y8r, ?x4336 = 0bpm4yw, ?x4615 = 0dlngsd, administrative_parent(?x11289, ?x551), medal(?x2629, ?x2132), ?x5418 = 026lgs, official_language(?x8845, ?x11590), olympics(?x2188, ?x1277), country(?x11656, ?x4714), ?x6761 = 05ft32, adjoins(?x3855, ?x1499), ?x5713 = 0cc97st, ?x1496 = 011yqc, ?x3850 = 047fjjr, ?x7678 = 0gvvf4j, film_release_region(?x249, ?x2188), ?x1602 = 0gxtknx, ?x2885 = 07jjt, ?x1916 = 0ch26b_, ?x2168 = 0bx0l, ?x2709 = 06ztvyx, ?x7493 = 0btpm6, ?x11590 = 0349s, ?x5564 = 03yvf2, locations(?x5352, ?x2188), locations(?x7241, ?x3855), ?x141 = 0gtsx8c, ?x5701 = 0b6css, origin(?x7547, ?x6559), combatants(?x7419, ?x4092), ?x3482 = 017z49, adjoins(?x4421, ?x792), countries_within(?x8483, ?x4714), jurisdiction_of_office(?x12920, ?x4092), ?x4998 = 0dzlbx, ?x5826 = 0gl02yg, ?x2966 = 06sks6, capital(?x6559, ?x8428), ?x775 = 0l998, olympics(?x9458, ?x1081), jurisdiction_of_office(?x10118, ?x792), contains(?x7413, ?x461), olympics(?x792, ?x1608), combatants(?x583, ?x792), combatants(?x792, ?x1264), ?x791 = 087wc7n, film_release_region(?x7832, ?x7413), ?x8292 = 0cmf0m0, ?x8646 = 05zvzf3, ?x7832 = 0fphf3v, ?x551 = 02j71, ?x5644 = 0dll_t2, capital(?x4092, ?x13482) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02wm6l capital 07_pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 7.000 7.000 0.024 http://example.org/location/country/capital #6501-027x7z5 PRED entity: 027x7z5 PRED relation: currency PRED expected values: 09nqf => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.88 #71, 0.85 #148, 0.85 #85), 01nv4h (0.25 #827, 0.03 #156, 0.03 #443), 02l6h (0.03 #88, 0.03 #53, 0.02 #74), 088n7 (0.03 #189, 0.02 #217, 0.02 #238), 02gsvk (0.01 #139, 0.01 #146) >> Best rule #71 for best value: >> intensional similarity = 7 >> extensional distance = 41 >> proper extension: 0gbtbm; >> query: (?x8690, 09nqf) <- film_crew_role(?x8690, ?x3197), film_crew_role(?x8690, ?x2178), genre(?x8690, ?x571), ?x3197 = 02ynfr, language(?x8690, ?x254), ?x254 = 02h40lc, ?x2178 = 01pvkk >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027x7z5 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.884 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #6500-06qgvf PRED entity: 06qgvf PRED relation: nationality PRED expected values: 09c7w0 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 31): 09c7w0 (0.91 #698, 0.90 #199, 0.82 #797), 02jx1 (0.34 #5581, 0.25 #928, 0.12 #32), 0chghy (0.34 #5581, 0.11 #6480, 0.06 #108), 0ctw_b (0.34 #5581, 0.11 #6480, 0.06 #125), 07ssc (0.15 #910, 0.11 #6480, 0.10 #1703), 0345h (0.11 #6480, 0.10 #3987, 0.08 #926), 0d060g (0.11 #6480, 0.10 #3987, 0.05 #2896), 0jdx (0.11 #6480, 0.10 #3987, 0.01 #378), 0f8l9c (0.11 #6480, 0.06 #917, 0.02 #2408), 06q1r (0.11 #6480, 0.01 #2966, 0.01 #3265) >> Best rule #698 for best value: >> intensional similarity = 3 >> extensional distance = 245 >> proper extension: 0chrwb; 02rmxx; 02pzck; 038nv6; >> query: (?x101, 09c7w0) <- people(?x3591, ?x101), people(?x3591, ?x5999), ?x5999 = 0d02km >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06qgvf nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.907 http://example.org/people/person/nationality #6499-0pswc PRED entity: 0pswc PRED relation: contains! PRED expected values: 0b90_r => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 393): 09c7w0 (0.68 #25993, 0.64 #24201, 0.63 #62744), 0b90_r (0.58 #84254, 0.51 #41229, 0.48 #74392), 01n7q (0.58 #26068, 0.55 #92398, 0.54 #62819), 07ssc (0.29 #20645, 0.25 #26919, 0.24 #25126), 06pvr (0.21 #3750, 0.12 #62907, 0.12 #92486), 02qkt (0.20 #89082, 0.19 #82808, 0.15 #81910), 0kpys (0.20 #1973, 0.16 #26171, 0.16 #14520), 04_1l0v (0.20 #2243, 0.15 #80222, 0.14 #78430), 07c5l (0.20 #5771, 0.14 #3083, 0.14 #1291), 059_c (0.20 #1863, 0.06 #125497, 0.05 #36816) >> Best rule #25993 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 0gjcy; >> query: (?x11561, 09c7w0) <- location(?x11186, ?x11561), category(?x11561, ?x134), time_zones(?x11561, ?x2950), nationality(?x11186, ?x94), ?x2950 = 02lcqs >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #84254 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 161 *> proper extension: 06n8j; *> query: (?x11561, ?x94) <- location(?x11186, ?x11561), category(?x11561, ?x134), nationality(?x11186, ?x94), profession(?x11186, ?x1183), ?x1183 = 09jwl *> conf = 0.58 ranks of expected_values: 2 EVAL 0pswc contains! 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 143.000 0.680 http://example.org/location/location/contains #6498-0gh6j94 PRED entity: 0gh6j94 PRED relation: film_release_region PRED expected values: 05r4w 0d060g 03gj2 059j2 01mjq => 109 concepts (69 used for prediction) PRED predicted values (max 10 best out of 236): 03gj2 (0.93 #1666, 0.92 #1216, 0.91 #916), 03h64 (0.93 #1708, 0.91 #809, 0.89 #3361), 06t2t (0.91 #806, 0.89 #1705, 0.88 #1255), 059j2 (0.91 #1973, 0.88 #1523, 0.88 #3477), 01znc_ (0.89 #1683, 0.88 #1233, 0.88 #3336), 02vzc (0.87 #944, 0.86 #795, 0.86 #1694), 0d060g (0.87 #900, 0.86 #1650, 0.84 #1200), 03rk0 (0.83 #949, 0.82 #800, 0.80 #1249), 05r4w (0.82 #4051, 0.82 #1647, 0.82 #6149), 03rt9 (0.82 #1657, 0.80 #1207, 0.78 #907) >> Best rule #1666 for best value: >> intensional similarity = 12 >> extensional distance = 26 >> proper extension: 0cc97st; >> query: (?x7680, 03gj2) <- film_release_region(?x7680, ?x2513), film_release_region(?x7680, ?x2346), film_release_region(?x7680, ?x789), film_release_region(?x7680, ?x550), film_release_region(?x7680, ?x390), film_release_region(?x7680, ?x205), ?x789 = 0f8l9c, ?x390 = 0chghy, ?x550 = 05v8c, ?x2346 = 0d05w3, ?x2513 = 05b4w, location_of_ceremony(?x566, ?x205) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 7, 9, 15 EVAL 0gh6j94 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 109.000 69.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh6j94 film_release_region 059j2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 69.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh6j94 film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 69.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh6j94 film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 109.000 69.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh6j94 film_release_region 05r4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 109.000 69.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6497-016z7s PRED entity: 016z7s PRED relation: nominated_for! PRED expected values: 027dtxw 02r22gf 0gqyl => 94 concepts (80 used for prediction) PRED predicted values (max 10 best out of 216): 0gq9h (0.74 #746, 0.65 #1898, 0.65 #2359), 019f4v (0.74 #739, 0.60 #1661, 0.60 #2122), 0gs9p (0.72 #748, 0.61 #1900, 0.58 #2361), 02r0csl (0.69 #1612, 0.69 #2764, 0.67 #9449), 02y_j8g (0.69 #1612, 0.69 #2764, 0.67 #9449), 0k611 (0.52 #1679, 0.51 #2140, 0.50 #757), 04dn09n (0.50 #1643, 0.49 #721, 0.42 #2104), 0gr0m (0.38 #1665, 0.35 #743, 0.35 #2126), 0gqyl (0.38 #764, 0.35 #1916, 0.31 #2147), 099c8n (0.38 #1664, 0.30 #2125, 0.28 #2355) >> Best rule #746 for best value: >> intensional similarity = 5 >> extensional distance = 66 >> proper extension: 07w8fz; >> query: (?x2111, 0gq9h) <- country(?x2111, ?x512), nominated_for(?x591, ?x2111), nominated_for(?x198, ?x2111), ?x198 = 040njc, ?x591 = 0f4x7 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #764 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 66 *> proper extension: 07w8fz; *> query: (?x2111, 0gqyl) <- country(?x2111, ?x512), nominated_for(?x591, ?x2111), nominated_for(?x198, ?x2111), ?x198 = 040njc, ?x591 = 0f4x7 *> conf = 0.38 ranks of expected_values: 9, 12, 14 EVAL 016z7s nominated_for! 0gqyl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 94.000 80.000 0.735 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 016z7s nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 94.000 80.000 0.735 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 016z7s nominated_for! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 94.000 80.000 0.735 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6496-04mcw4 PRED entity: 04mcw4 PRED relation: category PRED expected values: 08mbj5d => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.40 #3, 0.39 #2, 0.36 #13) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 0bth54; 06_wqk4; 0416y94; 09p0ct; 02r79_h; 024l2y; 075wx7_; 02yvct; 07yk1xz; 026p4q7; ... >> query: (?x4551, 08mbj5d) <- film_crew_role(?x4551, ?x2472), ?x2472 = 01xy5l_, nominated_for(?x846, ?x4551), award(?x4551, ?x507) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mcw4 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.400 http://example.org/common/topic/webpage./common/webpage/category #6495-0131kb PRED entity: 0131kb PRED relation: profession PRED expected values: 02hrh1q => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 147): 02hrh1q (0.89 #8419, 0.89 #5568, 0.88 #12472), 0dxtg (0.46 #1664, 0.41 #4665, 0.40 #2864), 0cbd2 (0.43 #1657, 0.43 #5860, 0.42 #4658), 01d_h8 (0.40 #2856, 0.36 #10212, 0.34 #6759), 0fj9f (0.39 #506, 0.17 #4953, 0.13 #4107), 02jknp (0.39 #2858, 0.23 #1658, 0.23 #158), 0kyk (0.35 #2431, 0.35 #2581, 0.34 #3332), 0np9r (0.31 #4802, 0.29 #322, 0.23 #3301), 03gjzk (0.31 #4802, 0.23 #4217, 0.23 #3301), 018gz8 (0.31 #4802, 0.23 #3301, 0.20 #1668) >> Best rule #8419 for best value: >> intensional similarity = 4 >> extensional distance = 483 >> proper extension: 05vsxz; 01qscs; 01csvq; 018db8; 058kqy; 01wmxfs; 039bp; 016gr2; 048lv; 01fwj8; ... >> query: (?x12896, 02hrh1q) <- award(?x12896, ?x2192), nationality(?x12896, ?x512), award(?x5610, ?x2192), ?x5610 = 04mg6l >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0131kb profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.893 http://example.org/people/person/profession #6494-0cb4j PRED entity: 0cb4j PRED relation: source PRED expected values: 0jbk9 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #27, 0.91 #83, 0.90 #4) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 0fxyd; 0kv2r; 0mq17; 0n474; 0mmpm; 0nv99; 0nzny; >> query: (?x578, 0jbk9) <- contains(?x578, ?x1661), county(?x7152, ?x578), place_of_birth(?x1814, ?x1661) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cb4j source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.938 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #6493-053vcrp PRED entity: 053vcrp PRED relation: place_of_death PRED expected values: 071vr => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 19): 030qb3t (0.31 #606, 0.29 #995, 0.28 #800), 0b2ds (0.20 #303, 0.20 #109, 0.14 #498), 0f2wj (0.20 #12, 0.14 #401, 0.07 #985), 0c_m3 (0.08 #973, 0.02 #4278, 0.02 #6609), 0k049 (0.06 #587, 0.01 #3698, 0.01 #3892), 0rqf1 (0.06 #738), 02d6c (0.06 #727), 0k_p5 (0.06 #1255, 0.04 #1061, 0.03 #866), 06_kh (0.05 #783, 0.04 #978, 0.02 #1172), 02_286 (0.03 #3512, 0.03 #791, 0.02 #7205) >> Best rule #606 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0g1rw; 0j_c; 03gyh_z; 09qh1; 03thw4; 0584j4n; 02rybfn; 058vfp4; 012vby; >> query: (?x10609, 030qb3t) <- nominated_for(?x10609, ?x878), nominated_for(?x2801, ?x878), ?x2801 = 04gmp_z >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #1075 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 0521d_3; 034qt_; *> query: (?x10609, 071vr) <- award(?x10609, ?x484), ?x484 = 0gq_v, award_nominee(?x786, ?x10609) *> conf = 0.02 ranks of expected_values: 18 EVAL 053vcrp place_of_death 071vr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 59.000 59.000 0.312 http://example.org/people/deceased_person/place_of_death #6492-01vj9c PRED entity: 01vj9c PRED relation: role! PRED expected values: 0fpj9pm => 87 concepts (62 used for prediction) PRED predicted values (max 10 best out of 756): 016h9b (0.64 #8599, 0.56 #7223, 0.56 #6670), 0167v4 (0.60 #3527, 0.50 #2425, 0.45 #8758), 04mx7s (0.55 #8740, 0.50 #2407, 0.44 #7364), 02jg92 (0.50 #7755, 0.50 #3073, 0.43 #4725), 06h2w (0.50 #2474, 0.50 #823, 0.38 #4398), 0473q (0.50 #2381, 0.45 #8714, 0.40 #3483), 01mxnvc (0.50 #2450, 0.45 #8783, 0.38 #5478), 01kx_81 (0.50 #2498, 0.44 #7178, 0.44 #6901), 0bg539 (0.50 #2223, 0.44 #7455, 0.44 #7180), 01r0t_j (0.50 #2685, 0.44 #7088, 0.38 #12047) >> Best rule #8599 for best value: >> intensional similarity = 12 >> extensional distance = 9 >> proper extension: 01v8y9; >> query: (?x745, 016h9b) <- role(?x4288, ?x745), role(?x3206, ?x745), role(?x2206, ?x745), group(?x745, ?x498), role(?x1004, ?x745), award_winner(?x1089, ?x4288), category(?x3206, ?x134), role(?x2206, ?x645), group(?x2206, ?x1751), profession(?x3206, ?x131), role(?x75, ?x745), actor(?x5529, ?x3206) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #2380 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 02hnl; *> query: (?x745, 0fpj9pm) <- role(?x4288, ?x745), role(?x74, ?x745), group(?x745, ?x11929), group(?x745, ?x11551), role(?x7084, ?x745), performance_role(?x5356, ?x745), ?x11551 = 0cfgd, ?x11929 = 07n3s, role(?x745, ?x75), award_winner(?x4288, ?x1089), ?x1089 = 01vrncs, nationality(?x7084, ?x1310) *> conf = 0.50 ranks of expected_values: 15 EVAL 01vj9c role! 0fpj9pm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 87.000 62.000 0.636 http://example.org/music/group_member/membership./music/group_membership/role #6491-09n70c PRED entity: 09n70c PRED relation: location PRED expected values: 010nlt => 140 concepts (46 used for prediction) PRED predicted values (max 10 best out of 198): 04jpl (0.42 #21734, 0.19 #15295, 0.18 #16905), 0r03f (0.40 #3837, 0.03 #13489, 0.03 #15097), 0cr3d (0.33 #145, 0.25 #1752, 0.15 #6575), 0yj9v (0.33 #652, 0.25 #2259, 0.14 #5473), 0s9z_ (0.33 #586, 0.25 #2193, 0.14 #5407), 0f2v0 (0.33 #986, 0.10 #6613, 0.02 #9830), 056_y (0.33 #1046), 02_286 (0.27 #28199, 0.24 #30611, 0.23 #31415), 01f08r (0.25 #2610, 0.20 #3413, 0.17 #4217), 0hptm (0.25 #1910, 0.14 #5124, 0.10 #7537) >> Best rule #21734 for best value: >> intensional similarity = 5 >> extensional distance = 144 >> proper extension: 012d40; 0h5g_; 025p38; 09byk; 07lt7b; 018db8; 03d_w3h; 01tspc6; 01l2fn; 015rkw; ... >> query: (?x10193, 04jpl) <- film(?x10193, ?x3217), nationality(?x10193, ?x4059), profession(?x10193, ?x1581), location(?x10193, ?x11540), capital(?x12163, ?x11540) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09n70c location 010nlt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 46.000 0.418 http://example.org/people/person/places_lived./people/place_lived/location #6490-015z4j PRED entity: 015z4j PRED relation: currency PRED expected values: 09nqf => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.46 #100, 0.46 #31, 0.43 #115), 01nv4h (0.01 #218, 0.01 #272), 02l6h (0.01 #93) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 02hhtj; >> query: (?x3020, 09nqf) <- participant(?x3020, ?x2925), celebrity(?x1416, ?x3020), participant(?x3020, ?x2108), people(?x2510, ?x2925) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015z4j currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.464 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #6489-016hvl PRED entity: 016hvl PRED relation: profession PRED expected values: 0dxtg 0d8qb => 123 concepts (69 used for prediction) PRED predicted values (max 10 best out of 83): 0dxtg (0.81 #4016, 0.81 #4302, 0.77 #1585), 01d_h8 (0.71 #5869, 0.66 #4010, 0.63 #8732), 0nbcg (0.62 #172, 0.12 #2460, 0.12 #3461), 03gjzk (0.54 #7451, 0.44 #8453, 0.41 #4017), 09jwl (0.50 #160, 0.20 #2448, 0.17 #2734), 0n1h (0.38 #153, 0.12 #2441, 0.12 #1154), 016z4k (0.38 #147, 0.11 #7585, 0.09 #9160), 025352 (0.30 #7009, 0.12 #197, 0.12 #340), 04cvn_ (0.30 #7009, 0.02 #1278, 0.02 #1421), 018gz8 (0.23 #8455, 0.20 #5163, 0.20 #4162) >> Best rule #4016 for best value: >> intensional similarity = 3 >> extensional distance = 185 >> proper extension: 0bxtg; 06cv1; 04l3_z; 06pk8; 032v0v; 01fh9; 0c3ns; 01q415; 04gcd1; 01wg982; ... >> query: (?x1278, 0dxtg) <- location(?x1278, ?x5481), profession(?x1278, ?x353), written_by(?x5429, ?x1278) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 20 EVAL 016hvl profession 0d8qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 123.000 69.000 0.813 http://example.org/people/person/profession EVAL 016hvl profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 69.000 0.813 http://example.org/people/person/profession #6488-07l24 PRED entity: 07l24 PRED relation: school PRED expected values: 01pl14 => 105 concepts (77 used for prediction) PRED predicted values (max 10 best out of 371): 065y4w7 (0.60 #949, 0.43 #8117, 0.43 #2828), 0lyjf (0.44 #4020, 0.44 #4775, 0.43 #1389), 01jq0j (0.42 #2368, 0.29 #3685, 0.28 #4061), 0dzst (0.40 #1836, 0.29 #1272, 0.07 #8253), 06pwq (0.40 #760, 0.28 #8115, 0.22 #10009), 05krk (0.40 #568, 0.25 #379, 0.24 #4706), 01pl14 (0.38 #1508, 0.25 #2070, 0.24 #8113), 07w0v (0.33 #2077, 0.31 #2453, 0.27 #9634), 0j_sncb (0.33 #227, 0.30 #1730, 0.29 #1166), 01rc6f (0.33 #319, 0.25 #506, 0.20 #884) >> Best rule #949 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 05g3v; 0wsr; 02c_4; >> query: (?x1639, 065y4w7) <- draft(?x1639, ?x1883), draft(?x1639, ?x465), teams(?x6084, ?x1639), position(?x1639, ?x3346), position(?x1639, ?x1792), ?x1883 = 02qw1zx, colors(?x1639, ?x4557), ?x465 = 05vsb7, ?x1792 = 05zm34, ?x4557 = 019sc, ?x3346 = 02g_7z >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1508 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 6 *> proper extension: 02896; 0487_; 06rpd; *> query: (?x1639, 01pl14) <- draft(?x1639, ?x1883), draft(?x1639, ?x465), teams(?x6084, ?x1639), position(?x1639, ?x1792), ?x1883 = 02qw1zx, colors(?x1639, ?x4557), ?x465 = 05vsb7, ?x1792 = 05zm34, colors(?x546, ?x4557), school(?x1639, ?x2830) *> conf = 0.38 ranks of expected_values: 7 EVAL 07l24 school 01pl14 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 105.000 77.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #6487-03f19q4 PRED entity: 03f19q4 PRED relation: place_of_birth PRED expected values: 02_286 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 48): 0f94t (0.34 #9156, 0.30 #7746, 0.24 #19017), 0cr3d (0.33 #94, 0.15 #798, 0.05 #7840), 0xl08 (0.33 #52829, 0.33 #56351, 0.28 #62692), 02_286 (0.11 #19, 0.08 #1427, 0.08 #723), 0ftxw (0.11 #96, 0.08 #800, 0.01 #51419), 0f__1 (0.08 #797, 0.02 #1501, 0.01 #51419), 0f2tj (0.08 #952, 0.01 #51419, 0.01 #52124), 03v0t (0.08 #841), 030qb3t (0.05 #14140, 0.05 #11323, 0.05 #24706), 01_d4 (0.04 #13447, 0.04 #21196, 0.04 #52895) >> Best rule #9156 for best value: >> intensional similarity = 3 >> extensional distance = 271 >> proper extension: 0dhqyw; >> query: (?x5203, ?x1005) <- category(?x5203, ?x134), origin(?x5203, ?x1005), gender(?x5203, ?x231) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #19 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 026yqrr; *> query: (?x5203, 02_286) <- award_nominee(?x5203, ?x1125), award_nominee(?x3737, ?x5203), ?x3737 = 01q32bd *> conf = 0.11 ranks of expected_values: 4 EVAL 03f19q4 place_of_birth 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 110.000 0.344 http://example.org/people/person/place_of_birth #6486-01jq0j PRED entity: 01jq0j PRED relation: student PRED expected values: 01w9ph_ => 149 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1591): 03ft8 (0.10 #255, 0.04 #23201, 0.04 #10685), 01s7ns (0.10 #1842, 0.04 #12272, 0.04 #10186), 01rzxl (0.10 #1926, 0.04 #4012, 0.02 #8184), 04pz5c (0.10 #987, 0.02 #25033, 0.02 #45898), 01w7nwm (0.10 #503, 0.02 #17191, 0.02 #6761), 02x8z_ (0.10 #770, 0.02 #19544, 0.02 #7028), 0gn30 (0.10 #923, 0.02 #7181, 0.02 #11353), 02vy5j (0.10 #343, 0.02 #6601, 0.02 #10773), 04dyqk (0.10 #1914, 0.02 #8172, 0.02 #12344), 03h8_g (0.10 #1858, 0.02 #8116, 0.02 #12288) >> Best rule #255 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01jygk; >> query: (?x6953, 03ft8) <- state_province_region(?x6953, ?x2623), ?x2623 = 02xry, category(?x6953, ?x134), ?x134 = 08mbj5d >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #152304 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 366 *> proper extension: 019q50; *> query: (?x6953, ?x91) <- state_province_region(?x6953, ?x2623), contains(?x94, ?x6953), location(?x91, ?x2623), institution(?x865, ?x6953) *> conf = 0.01 ranks of expected_values: 1585 EVAL 01jq0j student 01w9ph_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 149.000 79.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student #6485-047msdk PRED entity: 047msdk PRED relation: film! PRED expected values: 03xq0f => 90 concepts (76 used for prediction) PRED predicted values (max 10 best out of 59): 03xq0f (0.88 #523, 0.18 #301, 0.15 #79), 016tw3 (0.26 #10, 0.15 #2763, 0.13 #676), 086k8 (0.18 #520, 0.16 #1559, 0.16 #668), 017s11 (0.17 #3, 0.15 #225, 0.13 #743), 05qd_ (0.16 #526, 0.14 #3358, 0.13 #2761), 016tt2 (0.14 #522, 0.13 #3354, 0.13 #893), 03xsby (0.10 #89, 0.10 #607, 0.08 #978), 01795t (0.09 #313, 0.08 #239, 0.08 #757), 024rdh (0.09 #406, 0.08 #999, 0.08 #1296), 01gb54 (0.09 #546, 0.06 #842, 0.06 #1659) >> Best rule #523 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 0522wp; >> query: (?x1364, 03xq0f) <- region(?x1364, ?x512), film(?x617, ?x1364), ?x512 = 07ssc >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047msdk film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 76.000 0.881 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #6484-01ym8l PRED entity: 01ym8l PRED relation: company! PRED expected values: 0sx5w => 198 concepts (160 used for prediction) PRED predicted values (max 10 best out of 127): 04ns3gy (0.14 #673, 0.11 #1406, 0.04 #2870), 06y3r (0.12 #2373, 0.08 #2129, 0.07 #3837), 01vw20h (0.11 #1309, 0.04 #2773, 0.03 #4972), 01wwvd2 (0.11 #1308, 0.04 #2772, 0.03 #4971), 01w_10 (0.09 #2843, 0.05 #7482, 0.05 #2599), 0frmb1 (0.09 #2839, 0.05 #7478, 0.05 #2595), 06q8hf (0.08 #8202, 0.05 #9669, 0.05 #17239), 05hj_k (0.08 #8130, 0.05 #9597, 0.05 #17167), 013w7j (0.07 #4029, 0.03 #4274, 0.03 #6716), 079ws (0.05 #2596, 0.05 #8211, 0.03 #16759) >> Best rule #673 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0l8sx; 0537b; 077w0b; 07_dn; >> query: (?x7151, 04ns3gy) <- currency(?x7151, ?x170), state_province_region(?x7151, ?x335), company(?x346, ?x7151), industry(?x7151, ?x6575), ?x6575 = 029g_vk >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01ym8l company! 0sx5w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 198.000 160.000 0.143 http://example.org/people/person/employment_history./business/employment_tenure/company #6483-03mkk4 PRED entity: 03mkk4 PRED relation: institution PRED expected values: 01j_9c 07wlf 07t90 09f2j 0g8rj 0bwfn 02m0b0 => 25 concepts (21 used for prediction) PRED predicted values (max 10 best out of 604): 09f2j (0.82 #9042, 0.82 #8451, 0.82 #7860), 03ksy (0.82 #8387, 0.82 #7796, 0.80 #10160), 0bwfn (0.82 #9160, 0.75 #9751, 0.73 #8569), 07wjk (0.82 #8932, 0.75 #9523, 0.73 #8341), 0g8rj (0.82 #9061, 0.75 #9652, 0.73 #7879), 0gl5_ (0.82 #9132, 0.75 #9723, 0.73 #7950), 017j69 (0.82 #9024, 0.73 #8433, 0.73 #7251), 07tgn (0.82 #8888, 0.73 #7706, 0.71 #5341), 06pwq (0.75 #9475, 0.73 #8884, 0.73 #7702), 01jsk6 (0.73 #9319, 0.73 #8728, 0.73 #7546) >> Best rule #9042 for best value: >> intensional similarity = 23 >> extensional distance = 9 >> proper extension: 04zx3q1; >> query: (?x3386, 09f2j) <- student(?x3386, ?x11133), student(?x3386, ?x10694), student(?x3386, ?x2551), institution(?x3386, ?x11215), institution(?x3386, ?x3123), institution(?x3386, ?x1681), institution(?x3386, ?x466), ?x1681 = 07szy, major_field_of_study(?x11215, ?x1527), major_field_of_study(?x3386, ?x373), award(?x2551, ?x704), school_type(?x11215, ?x3092), people(?x1050, ?x10694), profession(?x10694, ?x319), citytown(?x3123, ?x739), school(?x7312, ?x466), school(?x1438, ?x466), religion(?x11133, ?x109), currency(?x466, ?x170), ?x1527 = 04_tv, ?x1438 = 0512p, ?x7312 = 0487_, colors(?x11215, ?x332) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 5, 25, 32, 126, 372 EVAL 03mkk4 institution 02m0b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 25.000 21.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03mkk4 institution 0bwfn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 21.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03mkk4 institution 0g8rj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 25.000 21.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03mkk4 institution 09f2j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 21.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03mkk4 institution 07t90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 25.000 21.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03mkk4 institution 07wlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 25.000 21.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03mkk4 institution 01j_9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 25.000 21.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #6482-05v38p PRED entity: 05v38p PRED relation: film! PRED expected values: 0jz9f => 87 concepts (70 used for prediction) PRED predicted values (max 10 best out of 62): 05qd_ (0.33 #8, 0.26 #157, 0.20 #305), 0jz9f (0.28 #1, 0.14 #224, 0.11 #76), 01795t (0.21 #92, 0.08 #166, 0.07 #1878), 086k8 (0.21 #373, 0.21 #597, 0.20 #820), 016tt2 (0.19 #451, 0.16 #153, 0.15 #673), 016tw3 (0.14 #2390, 0.14 #233, 0.13 #1796), 017s11 (0.13 #1640, 0.13 #2087, 0.13 #969), 01gb54 (0.11 #28, 0.11 #697, 0.11 #475), 024rbz (0.11 #11, 0.05 #1424, 0.05 #2095), 054g1r (0.11 #109, 0.09 #2118, 0.08 #183) >> Best rule #8 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 011yg9; 0p9rz; >> query: (?x6445, 05qd_) <- nominated_for(?x2880, ?x6445), nominated_for(?x1443, ?x6445), ?x1443 = 054krc, award_winner(?x6445, ?x4254), ?x2880 = 02ppm4q >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 011yg9; 0p9rz; *> query: (?x6445, 0jz9f) <- nominated_for(?x2880, ?x6445), nominated_for(?x1443, ?x6445), ?x1443 = 054krc, award_winner(?x6445, ?x4254), ?x2880 = 02ppm4q *> conf = 0.28 ranks of expected_values: 2 EVAL 05v38p film! 0jz9f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 70.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #6481-03m6_z PRED entity: 03m6_z PRED relation: role PRED expected values: 05r5c => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 41): 03bx0bm (0.51 #214, 0.48 #22, 0.46 #406), 05148p4 (0.24 #465, 0.24 #401, 0.24 #1617), 05r5c (0.24 #200, 0.17 #8, 0.17 #1608), 02hnl (0.19 #477, 0.19 #93, 0.17 #413), 0l14md (0.19 #391, 0.16 #455, 0.14 #71), 028tv0 (0.18 #205, 0.14 #1613, 0.13 #13), 03qjg (0.17 #42, 0.16 #426, 0.14 #490), 042v_gx (0.12 #73, 0.10 #201, 0.10 #457), 02sgy (0.09 #6, 0.05 #454, 0.05 #390), 01vj9c (0.08 #206, 0.07 #78, 0.06 #1614) >> Best rule #214 for best value: >> intensional similarity = 2 >> extensional distance = 49 >> proper extension: 01vng3b; >> query: (?x7156, 03bx0bm) <- participant(?x4106, ?x7156), role(?x7156, ?x227) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #200 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 49 *> proper extension: 01vng3b; *> query: (?x7156, 05r5c) <- participant(?x4106, ?x7156), role(?x7156, ?x227) *> conf = 0.24 ranks of expected_values: 3 EVAL 03m6_z role 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 132.000 132.000 0.510 http://example.org/music/group_member/membership./music/group_membership/role #6480-01vsy9_ PRED entity: 01vsy9_ PRED relation: people! PRED expected values: 033tf_ => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 51): 0x67 (0.48 #541, 0.45 #161, 0.44 #313), 041rx (0.21 #2893, 0.17 #1296, 0.16 #3045), 033tf_ (0.20 #1755, 0.17 #2288, 0.13 #2896), 0xnvg (0.13 #1760, 0.09 #2293, 0.09 #2901), 02w7gg (0.13 #2891, 0.07 #3500, 0.06 #4718), 07bch9 (0.11 #934, 0.11 #1162, 0.09 #706), 02g7sp (0.09 #397, 0.04 #1385, 0.04 #1461), 02ctzb (0.08 #546, 0.07 #926, 0.07 #1154), 063k3h (0.07 #1170, 0.06 #942, 0.05 #714), 0222qb (0.05 #1791, 0.04 #2324, 0.03 #2932) >> Best rule #541 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 069d71; >> query: (?x8803, 0x67) <- athlete(?x14205, ?x8803), nationality(?x8803, ?x94), ?x94 = 09c7w0 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1755 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 176 *> proper extension: 099bk; 0cl_m; *> query: (?x8803, 033tf_) <- religion(?x8803, ?x1985), student(?x1087, ?x8803), ?x1985 = 0c8wxp *> conf = 0.20 ranks of expected_values: 3 EVAL 01vsy9_ people! 033tf_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 99.000 0.475 http://example.org/people/ethnicity/people #6479-0mdyn PRED entity: 0mdyn PRED relation: gender PRED expected values: 05zppz => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #209, 0.76 #131, 0.72 #277), 02zsn (0.56 #58, 0.54 #14, 0.53 #76) >> Best rule #209 for best value: >> intensional similarity = 4 >> extensional distance = 610 >> proper extension: 05dxl_; 04dz_y7; 0gry51; >> query: (?x7836, 05zppz) <- profession(?x7836, ?x1032), profession(?x7836, ?x319), ?x1032 = 02hrh1q, ?x319 = 01d_h8 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mdyn gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.781 http://example.org/people/person/gender #6478-0cj2k3 PRED entity: 0cj2k3 PRED relation: profession PRED expected values: 0dxtg => 93 concepts (92 used for prediction) PRED predicted values (max 10 best out of 49): 0dxtg (0.91 #753, 0.88 #605, 0.86 #309), 02hrh1q (0.77 #8308, 0.68 #8752, 0.66 #9344), 02jknp (0.56 #1931, 0.55 #2079, 0.45 #3855), 02krf9 (0.50 #174, 0.30 #6959, 0.30 #6662), 0cbd2 (0.42 #154, 0.30 #6959, 0.30 #6662), 018gz8 (0.33 #164, 0.30 #6959, 0.30 #6662), 0np9r (0.30 #6959, 0.30 #6662, 0.28 #7256), 0kyk (0.30 #6959, 0.30 #6662, 0.28 #7256), 015cjr (0.30 #6662, 0.25 #10959, 0.24 #7405), 08z956 (0.30 #6662, 0.25 #10959, 0.24 #7405) >> Best rule #753 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 011s9r; >> query: (?x8872, 0dxtg) <- award_winner(?x2143, ?x8872), profession(?x8872, ?x319), tv_program(?x8872, ?x1631) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cj2k3 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 92.000 0.910 http://example.org/people/person/profession #6477-059rby PRED entity: 059rby PRED relation: place_founded! PRED expected values: 04f0xq => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 206): 04htfd (0.33 #37, 0.25 #260, 0.17 #372), 01_4mn (0.33 #215, 0.07 #1331, 0.07 #1443), 032j_n (0.33 #178, 0.07 #7955, 0.07 #2573), 01hlwv (0.33 #191, 0.04 #1084, 0.04 #1195), 01dfb6 (0.33 #170, 0.04 #1063, 0.04 #1174), 043g7l (0.33 #143, 0.04 #1036, 0.04 #1147), 01xdn1 (0.33 #122, 0.04 #1015, 0.04 #1126), 0dq23 (0.33 #204, 0.04 #1097, 0.04 #1208), 01ynvx (0.33 #202, 0.04 #1095, 0.04 #1206), 0xwj (0.33 #141, 0.04 #1034, 0.04 #1145) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09c7w0; >> query: (?x335, 04htfd) <- contains(?x335, ?x3182), ?x3182 = 02ccqg, location(?x101, ?x335) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 059rby place_founded! 04f0xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 193.000 193.000 0.333 http://example.org/organization/organization/place_founded #6476-0rhp6 PRED entity: 0rhp6 PRED relation: county PRED expected values: 0jgj7 => 72 concepts (35 used for prediction) PRED predicted values (max 10 best out of 28): 0jgj7 (0.43 #1965, 0.18 #1964, 0.13 #394), 02xry (0.43 #1965, 0.18 #1964, 0.13 #394), 09c7w0 (0.18 #1964, 0.09 #393, 0.09 #3538), 0jrxx (0.04 #278), 0jgk3 (0.04 #266), 0kpys (0.04 #408, 0.04 #1780, 0.04 #1584), 0k3hn (0.03 #48, 0.02 #1031, 0.02 #1227), 0k3k1 (0.03 #79, 0.01 #2046, 0.01 #1650), 0k3jc (0.03 #175), 0n2k5 (0.03 #167) >> Best rule #1965 for best value: >> intensional similarity = 3 >> extensional distance = 340 >> proper extension: 03qzj4; >> query: (?x8005, ?x2623) <- contains(?x2623, ?x8005), contains(?x8260, ?x2623), currency(?x2623, ?x170) >> conf = 0.43 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0rhp6 county 0jgj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 35.000 0.428 http://example.org/location/hud_county_place/county #6475-0641kkh PRED entity: 0641kkh PRED relation: nominated_for PRED expected values: 01hq1 => 43 concepts (9 used for prediction) PRED predicted values (max 10 best out of 1353): 07gp9 (0.71 #14347, 0.69 #12750, 0.66 #12749), 0pb33 (0.71 #14347, 0.66 #12749, 0.61 #4781), 0bdjd (0.60 #4315, 0.50 #2721, 0.25 #1127), 07cz2 (0.50 #3589, 0.50 #1995, 0.25 #401), 0dr_4 (0.50 #3411, 0.50 #1817, 0.24 #11378), 0btpm6 (0.50 #4333, 0.50 #2739, 0.16 #12300), 020fcn (0.50 #3354, 0.50 #1760, 0.15 #11321), 05zlld0 (0.50 #2153, 0.40 #3747, 0.25 #559), 0ch26b_ (0.50 #1867, 0.40 #3461, 0.15 #11428), 0jqn5 (0.50 #1793, 0.40 #3387, 0.14 #11354) >> Best rule #14347 for best value: >> intensional similarity = 3 >> extensional distance = 151 >> proper extension: 0fqnzts; >> query: (?x9770, ?x324) <- award(?x2237, ?x9770), award(?x324, ?x9770), participant(?x1208, ?x2237) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #4395 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 02hsq3m; 0gr42; 0gqxm; 0262s1; *> query: (?x9770, 01hq1) <- award(?x971, ?x9770), award(?x1450, ?x9770), award(?x324, ?x9770), nominated_for(?x9770, ?x339), ?x324 = 07gp9, award_winner(?x1450, ?x3308) *> conf = 0.20 ranks of expected_values: 267 EVAL 0641kkh nominated_for 01hq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 9.000 0.705 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6474-014g91 PRED entity: 014g91 PRED relation: type_of_union PRED expected values: 04ztj => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #65, 0.84 #329, 0.84 #385), 01g63y (0.18 #18, 0.12 #514, 0.12 #38), 01bl8s (0.07 #31, 0.04 #55, 0.03 #71) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 01gzm2; 03fvqg; 01n9d9; 07cbs; 044f7; 01t265; 01938t; 096hm; 02h0f3; 01t9qj_; ... >> query: (?x10879, 04ztj) <- people(?x6260, ?x10879), nationality(?x10879, ?x94), award_winner(?x1088, ?x10879), ?x6260 = 0dq9p >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014g91 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.867 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6473-05f4p PRED entity: 05f4p PRED relation: organizations_founded! PRED expected values: 06c97 => 90 concepts (70 used for prediction) PRED predicted values (max 10 best out of 467): 09bg4l (0.73 #7348, 0.50 #2807, 0.50 #334), 07cbs (0.67 #3597, 0.60 #3708, 0.50 #4155), 01vhrz (0.62 #2622, 0.56 #3177, 0.50 #3844), 07_m9_ (0.60 #1594, 0.11 #3548, 0.11 #3547), 081nh (0.50 #2910, 0.50 #2686, 0.44 #3353), 06pj8 (0.50 #1909, 0.50 #1797, 0.38 #2573), 06c0j (0.50 #334, 0.36 #4553, 0.33 #989), 07hyk (0.50 #334, 0.36 #4553, 0.25 #1199), 03kdl (0.50 #334, 0.36 #4553, 0.23 #1883), 07t2k (0.50 #334, 0.36 #4553, 0.23 #1883) >> Best rule #7348 for best value: >> intensional similarity = 6 >> extensional distance = 40 >> proper extension: 07y2b; >> query: (?x11089, ?x3563) <- organizations_founded(?x11088, ?x11089), organizations_founded(?x2663, ?x11089), award_winner(?x14536, ?x11088), award(?x11290, ?x14536), organizations_founded(?x2663, ?x11817), organizations_founded(?x3563, ?x11817) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #334 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 061dn_; *> query: (?x11089, ?x1157) <- organizations_founded(?x11088, ?x11089), company(?x11088, ?x94), sibling(?x11088, ?x9569), sibling(?x11088, ?x6138), location(?x11088, ?x108), award_winner(?x3846, ?x9569), profession(?x6138, ?x3342), company(?x1157, ?x94) *> conf = 0.50 ranks of expected_values: 15 EVAL 05f4p organizations_founded! 06c97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 90.000 70.000 0.733 http://example.org/organization/organization_founder/organizations_founded #6472-0c8tk PRED entity: 0c8tk PRED relation: location! PRED expected values: 01vt5c_ => 198 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2250): 0b66qd (0.72 #140572, 0.71 #138061, 0.67 #175715), 046rfv (0.72 #140572, 0.71 #138061, 0.67 #175715), 06pwf6 (0.72 #140572, 0.71 #138061, 0.67 #175715), 04cdxc (0.57 #37656, 0.56 #122999, 0.56 #37655), 0gp_x9 (0.57 #37656, 0.56 #122999, 0.56 #37655), 0lkr7 (0.25 #3523, 0.20 #6033, 0.10 #33647), 011hdn (0.25 #3194, 0.20 #5704, 0.08 #15743), 094xh (0.25 #3584, 0.20 #6094, 0.06 #46261), 09yhzs (0.25 #3091, 0.20 #5601, 0.06 #10620), 0fb7c (0.25 #3768, 0.20 #6278, 0.06 #11297) >> Best rule #140572 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 0f04c; 0q34g; >> query: (?x4335, ?x10579) <- place_of_birth(?x10579, ?x4335), country(?x4335, ?x2146), citytown(?x9399, ?x4335), languages(?x10579, ?x254) >> conf = 0.72 => this is the best rule for 3 predicted values *> Best rule #19160 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 03lrc; *> query: (?x4335, 01vt5c_) <- location_of_ceremony(?x12595, ?x4335), place_of_birth(?x2873, ?x4335), country(?x4335, ?x2146), place_of_birth(?x12595, ?x7412) *> conf = 0.04 ranks of expected_values: 904 EVAL 0c8tk location! 01vt5c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 198.000 95.000 0.715 http://example.org/people/person/places_lived./people/place_lived/location #6471-0jhjl PRED entity: 0jhjl PRED relation: school_type PRED expected values: 047951 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 17): 05pcjw (0.34 #24, 0.31 #47, 0.25 #70), 07tf8 (0.29 #77, 0.25 #31, 0.16 #215), 01rs41 (0.27 #512, 0.25 #650, 0.24 #744), 01_9fk (0.15 #48, 0.12 #209, 0.12 #71), 01_srz (0.07 #49, 0.05 #418, 0.05 #511), 04399 (0.04 #13, 0.03 #243, 0.03 #266), 052q4j (0.04 #14), 02p0qmm (0.04 #517, 0.03 #354, 0.03 #655), 06cs1 (0.03 #51, 0.02 #28, 0.01 #74), 047951 (0.03 #76, 0.02 #30, 0.02 #168) >> Best rule #24 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 01x5fb; >> query: (?x9409, 05pcjw) <- major_field_of_study(?x9409, ?x4100), state_province_region(?x9409, ?x206), list(?x9409, ?x2197) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 66 *> proper extension: 01tpvt; 01nmgc; *> query: (?x9409, 047951) <- institution(?x865, ?x9409), category(?x9409, ?x134), list(?x9409, ?x2197) *> conf = 0.03 ranks of expected_values: 10 EVAL 0jhjl school_type 047951 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 57.000 57.000 0.340 http://example.org/education/educational_institution/school_type #6470-05ty4m PRED entity: 05ty4m PRED relation: nationality PRED expected values: 09c7w0 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 62): 09c7w0 (0.80 #1001, 0.78 #801, 0.77 #101), 02jx1 (0.14 #433, 0.13 #1133, 0.13 #2235), 07ssc (0.13 #615, 0.13 #2217, 0.13 #2017), 0345h (0.07 #1532, 0.06 #2033, 0.06 #3033), 03rk0 (0.07 #3548, 0.06 #4949, 0.05 #6754), 0d060g (0.06 #207, 0.04 #707, 0.04 #6211), 0f8l9c (0.04 #1523, 0.04 #1802, 0.04 #2324), 03rt9 (0.04 #1802, 0.04 #213, 0.03 #913), 0h7x (0.04 #1802, 0.03 #1536, 0.03 #1335), 03rjj (0.04 #1802, 0.03 #5, 0.02 #1205) >> Best rule #1001 for best value: >> intensional similarity = 2 >> extensional distance = 219 >> proper extension: 04rtpt; >> query: (?x364, 09c7w0) <- program(?x364, ?x14067), nominated_for(?x757, ?x14067) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05ty4m nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.796 http://example.org/people/person/nationality #6469-042rnl PRED entity: 042rnl PRED relation: award PRED expected values: 09v92_x => 108 concepts (102 used for prediction) PRED predicted values (max 10 best out of 291): 0dgr5xp (0.70 #22301, 0.69 #21084, 0.69 #24328), 09v51c2 (0.45 #1540, 0.09 #325, 0.07 #3648), 09v92_x (0.41 #1494, 0.09 #279, 0.07 #3648), 0gs9p (0.39 #6973, 0.37 #4945, 0.31 #3729), 02rdyk7 (0.38 #497, 0.29 #902, 0.26 #1712), 019f4v (0.37 #4932, 0.35 #6960, 0.31 #3716), 02pqp12 (0.36 #71, 0.33 #881, 0.31 #476), 040njc (0.35 #4873, 0.34 #6901, 0.33 #3657), 0gq9h (0.30 #4943, 0.28 #3727, 0.28 #6971), 02wkmx (0.27 #14, 0.19 #824, 0.17 #1634) >> Best rule #22301 for best value: >> intensional similarity = 4 >> extensional distance = 1656 >> proper extension: 04cy8rb; 0dky9n; >> query: (?x754, ?x5923) <- award_winner(?x5923, ?x754), award(?x12529, ?x5923), nominated_for(?x5923, ?x467), film(?x12529, ?x5826) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1494 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 02p59ry; 04dz_y7; *> query: (?x754, 09v92_x) <- award(?x754, ?x5923), profession(?x754, ?x319), nominated_for(?x5923, ?x3886), ?x3886 = 0198b6 *> conf = 0.41 ranks of expected_values: 3 EVAL 042rnl award 09v92_x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 108.000 102.000 0.697 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6468-0f8pz PRED entity: 0f8pz PRED relation: student! PRED expected values: 07tgn => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 117): 09f2j (0.12 #682, 0.06 #1730, 0.04 #3302), 017z88 (0.12 #81, 0.11 #605, 0.06 #1653), 02g839 (0.12 #25, 0.05 #549, 0.03 #1597), 0bwfn (0.09 #20186, 0.08 #14422, 0.08 #8658), 01w5m (0.08 #104, 0.04 #35740, 0.04 #628), 01qd_r (0.08 #280, 0.01 #8664, 0.01 #9188), 07tg4 (0.08 #2705, 0.07 #9517, 0.06 #6373), 015nl4 (0.08 #6354, 0.06 #9498, 0.06 #2686), 07tgn (0.07 #2637, 0.06 #9449, 0.05 #6305), 065y4w7 (0.07 #538, 0.05 #7874, 0.05 #14162) >> Best rule #682 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 0hr3g; >> query: (?x3849, 09f2j) <- place_of_birth(?x3849, ?x14442), music(?x4249, ?x3849), student(?x1978, ?x3849) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #2637 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 161 *> proper extension: 07m69t; *> query: (?x3849, 07tgn) <- nationality(?x3849, ?x512), ?x512 = 07ssc, place_of_birth(?x3849, ?x14442) *> conf = 0.07 ranks of expected_values: 9 EVAL 0f8pz student! 07tgn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 121.000 121.000 0.123 http://example.org/education/educational_institution/students_graduates./education/education/student #6467-013y1f PRED entity: 013y1f PRED relation: role PRED expected values: 016622 => 84 concepts (56 used for prediction) PRED predicted values (max 10 best out of 71): 013y1f (0.89 #2466, 0.86 #2600, 0.83 #1615), 0l14qv (0.86 #3577, 0.85 #1466, 0.85 #2452), 01xqw (0.85 #1466, 0.82 #1402, 0.82 #1400), 06ncr (0.85 #2844, 0.84 #1990, 0.82 #1402), 0859_ (0.85 #2844, 0.82 #1400, 0.82 #2451), 02fsn (0.84 #1990, 0.82 #1402, 0.82 #1400), 0dwt5 (0.83 #761, 0.82 #1402, 0.82 #1400), 018j2 (0.83 #761, 0.82 #1402, 0.82 #1400), 0gkd1 (0.82 #1402, 0.82 #1400, 0.82 #2451), 0g2dz (0.82 #1402, 0.82 #1400, 0.82 #2451) >> Best rule #2466 for best value: >> intensional similarity = 13 >> extensional distance = 17 >> proper extension: 025cbm; >> query: (?x1495, 013y1f) <- role(?x4425, ?x1495), role(?x2785, ?x1495), role(?x569, ?x1495), role(?x316, ?x1495), group(?x569, ?x1751), ?x316 = 05r5c, ?x1751 = 05crg7, ?x4425 = 0979zs, role(?x130, ?x1495), role(?x214, ?x569), group(?x2785, ?x1945), instrumentalists(?x2785, ?x1970), role(?x2785, ?x1663) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #989 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 01vdm0; *> query: (?x1495, 016622) <- role(?x569, ?x1495), ?x569 = 07c6l, instrumentalists(?x1495, ?x2765), role(?x2940, ?x1495), role(?x2784, ?x1495), role(?x1818, ?x1495), ?x1818 = 0770cd, role(?x315, ?x1495), award_winner(?x4974, ?x2940), ?x2784 = 0137g1, place_of_birth(?x2765, ?x4733), award_winner(?x414, ?x2940), award_winner(?x1930, ?x2765) *> conf = 0.67 ranks of expected_values: 37 EVAL 013y1f role 016622 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 84.000 56.000 0.895 http://example.org/music/performance_role/track_performances./music/track_contribution/role #6466-01vttb9 PRED entity: 01vttb9 PRED relation: award PRED expected values: 01bgqh => 128 concepts (101 used for prediction) PRED predicted values (max 10 best out of 275): 025m8y (0.78 #13433, 0.77 #13432, 0.77 #13830), 025m98 (0.78 #13433, 0.77 #13432, 0.77 #13830), 025mb9 (0.77 #13432, 0.77 #13830, 0.72 #28460), 02qvyrt (0.35 #6442, 0.31 #3677, 0.31 #6047), 01bgqh (0.28 #2018, 0.26 #13079, 0.24 #13477), 09sb52 (0.26 #4781, 0.25 #25331, 0.24 #24936), 01ckrr (0.23 #2199, 0.09 #5754, 0.08 #10890), 025m8l (0.20 #114, 0.19 #5249, 0.18 #33998), 04njml (0.20 #98, 0.16 #6418, 0.15 #33997), 0fhpv4 (0.20 #190, 0.15 #3745, 0.14 #585) >> Best rule #13433 for best value: >> intensional similarity = 3 >> extensional distance = 483 >> proper extension: 06lxn; >> query: (?x7556, ?x2212) <- award_winner(?x2212, ?x7556), artists(?x505, ?x7556), category_of(?x2212, ?x2421) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #2018 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 02mslq; 04zwjd; 0dtd6; 01vvpjj; 01l9v7n; 02lbrd; 0134tg; 015cxv; 0178_w; 07r1_; ... *> query: (?x7556, 01bgqh) <- award(?x7556, ?x4488), artists(?x505, ?x7556), ?x4488 = 02gdjb *> conf = 0.28 ranks of expected_values: 5 EVAL 01vttb9 award 01bgqh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 128.000 101.000 0.776 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6465-06x68 PRED entity: 06x68 PRED relation: season PRED expected values: 026fmqm => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 2): 026fmqm (0.84 #29, 0.81 #35, 0.60 #1), 04n36qk (0.14 #4, 0.09 #6, 0.09 #24) >> Best rule #29 for best value: >> intensional similarity = 9 >> extensional distance = 23 >> proper extension: 05m_8; 03lpp_; 01d5z; 0512p; 0cqt41; 0x2p; 01yjl; 061xq; 0713r; 01ync; ... >> query: (?x700, 026fmqm) <- school(?x700, ?x3208), institution(?x4981, ?x3208), institution(?x1771, ?x3208), draft(?x700, ?x1633), colors(?x3208, ?x1101), teams(?x4356, ?x700), ?x1771 = 019v9k, position(?x700, ?x2010), ?x4981 = 03bwzr4 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06x68 season 026fmqm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.840 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #6464-0259r0 PRED entity: 0259r0 PRED relation: artist! PRED expected values: 01cf93 => 127 concepts (100 used for prediction) PRED predicted values (max 10 best out of 120): 015_1q (0.23 #1430, 0.21 #3690, 0.21 #2276), 03rhqg (0.21 #298, 0.17 #439, 0.16 #3968), 016ckq (0.16 #325, 0.06 #1030, 0.05 #466), 03mp8k (0.15 #349, 0.12 #490, 0.10 #772), 01w40h (0.13 #311, 0.13 #29, 0.12 #452), 0181dw (0.13 #324, 0.12 #606, 0.11 #3712), 01cszh (0.13 #293, 0.06 #716, 0.06 #1703), 033hn8 (0.13 #14, 0.12 #296, 0.12 #719), 017l96 (0.12 #1429, 0.10 #3689, 0.10 #1711), 011k1h (0.11 #1702, 0.11 #3962, 0.10 #292) >> Best rule #1430 for best value: >> intensional similarity = 3 >> extensional distance = 249 >> proper extension: 0m0hw; 013pk3; 016jll; 020jqv; >> query: (?x2786, 015_1q) <- artist(?x6474, ?x2786), award_winner(?x4018, ?x2786), type_of_union(?x2786, ?x566) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #199 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 41 *> proper extension: 05xq9; 06br6t; *> query: (?x2786, 01cf93) <- artists(?x2996, ?x2786), ?x2996 = 01243b *> conf = 0.07 ranks of expected_values: 27 EVAL 0259r0 artist! 01cf93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 127.000 100.000 0.227 http://example.org/music/record_label/artist #6463-023r2x PRED entity: 023r2x PRED relation: role! PRED expected values: 07gql => 60 concepts (38 used for prediction) PRED predicted values (max 10 best out of 109): 02sgy (0.90 #2477, 0.90 #2369, 0.87 #1344), 02bxd (0.87 #1344, 0.86 #1569, 0.86 #2018), 07gql (0.87 #1344, 0.86 #1569, 0.86 #102), 013y1f (0.80 #2399, 0.76 #217, 0.74 #774), 01vdm0 (0.78 #2734, 0.76 #217, 0.76 #2506), 05r5c (0.78 #3385, 0.76 #2939, 0.76 #217), 018j2 (0.76 #217, 0.74 #3087, 0.74 #774), 04rzd (0.76 #217, 0.74 #774, 0.73 #104), 07y_7 (0.76 #217, 0.74 #774, 0.73 #104), 01v1d8 (0.76 #217, 0.73 #104, 0.73 #666) >> Best rule #2477 for best value: >> intensional similarity = 23 >> extensional distance = 18 >> proper extension: 01vj9c; 0680x0; >> query: (?x6938, ?x314) <- role(?x6938, ?x1831), role(?x6938, ?x314), role(?x6938, ?x228), role(?x6938, ?x214), ?x314 = 02sgy, role(?x1333, ?x1831), role(?x315, ?x1831), role(?x1495, ?x1831), role(?x1267, ?x1831), role(?x7772, ?x214), group(?x1831, ?x4010), ?x1267 = 07brj, ?x228 = 0l14qv, role(?x922, ?x214), ?x1333 = 01l4zqz, role(?x7706, ?x214), ?x922 = 050rj, ?x7706 = 0lsw9, ?x7772 = 0j862, role(?x75, ?x1495), role(?x130, ?x1495), instrumentalists(?x1495, ?x483), performance_role(?x1260, ?x1495) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1344 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 3 *> proper extension: 0214km; *> query: (?x6938, ?x214) <- role(?x6938, ?x2460), role(?x6938, ?x2157), role(?x6938, ?x1831), role(?x6938, ?x314), role(?x6938, ?x214), ?x314 = 02sgy, ?x1831 = 03t22m, ?x2460 = 01wy6, role(?x227, ?x6938), role(?x316, ?x2157), role(?x5990, ?x2157), role(?x5417, ?x2157), role(?x1267, ?x2157), role(?x74, ?x2157), ?x316 = 05r5c, ?x5417 = 02w3w, ?x74 = 03q5t, ?x5990 = 0192l, role(?x3492, ?x6938), ?x227 = 0342h, role(?x2157, ?x315), ?x1267 = 07brj, instrumentalists(?x2157, ?x10239), performance_role(?x6328, ?x2157), award_winner(?x5904, ?x3492), role(?x11443, ?x2157) *> conf = 0.87 ranks of expected_values: 3 EVAL 023r2x role! 07gql CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 38.000 0.900 http://example.org/music/performance_role/track_performances./music/track_contribution/role #6462-01ggbx PRED entity: 01ggbx PRED relation: profession PRED expected values: 02jknp => 146 concepts (78 used for prediction) PRED predicted values (max 10 best out of 74): 02hrh1q (0.89 #3270, 0.88 #5790, 0.87 #3122), 02jknp (0.75 #303, 0.63 #6524, 0.62 #155), 0cbd2 (0.61 #1042, 0.44 #1634, 0.32 #450), 018gz8 (0.50 #16, 0.32 #608, 0.26 #460), 09jwl (0.43 #7719, 0.42 #9347, 0.42 #906), 03gjzk (0.42 #606, 0.38 #7419, 0.38 #9047), 016z4k (0.39 #892, 0.31 #1780, 0.30 #2076), 0nbcg (0.33 #919, 0.28 #9952, 0.28 #7732), 0kyk (0.30 #1657, 0.25 #1065, 0.21 #621), 0dz3r (0.27 #890, 0.25 #7703, 0.25 #6223) >> Best rule #3270 for best value: >> intensional similarity = 4 >> extensional distance = 112 >> proper extension: 0fpj4lx; 02fybl; 01d_4t; 03d9wk; >> query: (?x13441, 02hrh1q) <- profession(?x13441, ?x987), diet(?x13441, ?x3130), profession(?x3138, ?x987), ?x3138 = 015v3r >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #303 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 05b1062; *> query: (?x13441, 02jknp) <- profession(?x13441, ?x987), award(?x13441, ?x4443), ?x987 = 0dxtg, ?x4443 = 0b6k___ *> conf = 0.75 ranks of expected_values: 2 EVAL 01ggbx profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 78.000 0.886 http://example.org/people/person/profession #6461-0292qb PRED entity: 0292qb PRED relation: film! PRED expected values: 02d45s => 98 concepts (56 used for prediction) PRED predicted values (max 10 best out of 964): 0bl2g (0.21 #10462, 0.02 #54172, 0.02 #91635), 0lpjn (0.15 #478, 0.06 #17129, 0.06 #2559), 04flrx (0.15 #41630, 0.15 #35385), 05txrz (0.11 #11173, 0.03 #2847, 0.03 #15336), 012d40 (0.11 #10423, 0.02 #43727, 0.02 #16667), 01wbg84 (0.09 #8371, 0.07 #12535, 0.02 #66651), 041c4 (0.09 #2974, 0.04 #893, 0.03 #17544), 0p8r1 (0.09 #2666, 0.02 #79678, 0.02 #65108), 011zd3 (0.09 #10781, 0.03 #4536, 0.03 #6617), 0f5xn (0.08 #13456, 0.06 #15538, 0.05 #9292) >> Best rule #10462 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 09fb5; >> query: (?x7263, 0bl2g) <- nominated_for(?x2258, ?x7263), film(?x2258, ?x2709), ?x2709 = 06ztvyx >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #24713 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 142 *> proper extension: 0d_wms; 01kf5lf; 0fxmbn; 025twgt; *> query: (?x7263, 02d45s) <- country(?x7263, ?x512), ?x512 = 07ssc, music(?x7263, ?x7701), film_release_distribution_medium(?x7263, ?x81) *> conf = 0.01 ranks of expected_values: 901 EVAL 0292qb film! 02d45s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 98.000 56.000 0.213 http://example.org/film/actor/film./film/performance/film #6460-02dth1 PRED entity: 02dth1 PRED relation: film PRED expected values: 091rc5 => 120 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1064): 01bv8b (0.64 #23223, 0.58 #94680, 0.57 #19650), 07g9f (0.64 #23223, 0.58 #94680, 0.57 #19650), 017180 (0.57 #92893, 0.39 #98254, 0.35 #155436), 0p9tm (0.33 #1364, 0.01 #10295, 0.01 #17441), 0hvvf (0.33 #1350), 03kx49 (0.17 #1340, 0.05 #19203, 0.04 #6698), 01ry_x (0.17 #1703, 0.04 #10634, 0.04 #26713), 04gv3db (0.17 #2537, 0.03 #7896, 0.03 #36477), 018f8 (0.17 #181, 0.03 #7326, 0.02 #14472), 07h9gp (0.17 #264, 0.03 #25274, 0.03 #9195) >> Best rule #23223 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 02wb6yq; >> query: (?x4204, ?x2710) <- award_winner(?x2710, ?x4204), friend(?x2799, ?x4204), profession(?x4204, ?x1032) >> conf = 0.64 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02dth1 film 091rc5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 98.000 0.644 http://example.org/film/actor/film./film/performance/film #6459-0bk5r PRED entity: 0bk5r PRED relation: influenced_by! PRED expected values: 0453t => 185 concepts (69 used for prediction) PRED predicted values (max 10 best out of 471): 04hcw (0.62 #2848, 0.14 #19254, 0.14 #2336), 06myp (0.50 #2997, 0.32 #13315, 0.27 #17421), 03f0324 (0.50 #2756, 0.25 #3268, 0.09 #15565), 0683n (0.50 #3410, 0.21 #30076, 0.20 #1363), 0j3v (0.50 #2639, 0.20 #1104, 0.11 #9298), 034bs (0.50 #3226, 0.14 #2202, 0.12 #2714), 0bk5r (0.50 #2768, 0.13 #21020, 0.13 #21019), 03f47xl (0.50 #3333, 0.12 #2821, 0.10 #14603), 047g6 (0.40 #988, 0.20 #2013, 0.14 #19443), 07h1q (0.40 #916, 0.17 #19371, 0.17 #15774) >> Best rule #2848 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 015k7; >> query: (?x5148, 04hcw) <- influenced_by(?x9308, ?x5148), gender(?x5148, ?x231), religion(?x5148, ?x109), ?x9308 = 03jht >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2638 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 015k7; *> query: (?x5148, 0453t) <- influenced_by(?x9308, ?x5148), gender(?x5148, ?x231), religion(?x5148, ?x109), ?x9308 = 03jht *> conf = 0.25 ranks of expected_values: 49 EVAL 0bk5r influenced_by! 0453t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 185.000 69.000 0.625 http://example.org/influence/influence_node/influenced_by #6458-0l14md PRED entity: 0l14md PRED relation: role! PRED expected values: 01wgjj5 => 87 concepts (59 used for prediction) PRED predicted values (max 10 best out of 858): 04bpm6 (0.78 #12211, 0.67 #8613, 0.60 #19408), 023l9y (0.75 #11452, 0.71 #10099, 0.71 #9199), 0l12d (0.71 #9956, 0.54 #17983, 0.52 #11687), 0lzkm (0.71 #10059, 0.50 #11412, 0.50 #8712), 082brv (0.67 #12404, 0.67 #8806, 0.64 #15102), 01wgjj5 (0.60 #7902, 0.57 #10150, 0.50 #8803), 0565cz (0.60 #5980, 0.50 #4180, 0.43 #9572), 0137g1 (0.57 #10006, 0.57 #9106, 0.50 #11359), 01vn35l (0.57 #10014, 0.54 #17983, 0.52 #11687), 023322 (0.57 #447, 0.54 #17983, 0.52 #11687) >> Best rule #12211 for best value: >> intensional similarity = 12 >> extensional distance = 7 >> proper extension: 01vdm0; >> query: (?x315, 04bpm6) <- role(?x315, ?x2205), role(?x315, ?x716), instrumentalists(?x315, ?x3316), role(?x315, ?x1495), role(?x315, ?x1466), ?x2205 = 0dq630k, ?x1466 = 03bx0bm, ?x716 = 018vs, award(?x3316, ?x567), role(?x1495, ?x2157), film(?x3316, ?x1743), role(?x130, ?x1495) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #7902 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 0151b0; *> query: (?x315, 01wgjj5) <- role(?x315, ?x5480), group(?x315, ?x8999), group(?x315, ?x8058), group(?x315, ?x5858), ?x5480 = 01w4c9, ?x8999 = 0bk1p, role(?x212, ?x315), role(?x315, ?x615), ?x212 = 026t6, group(?x3410, ?x8058), artists(?x671, ?x5858) *> conf = 0.60 ranks of expected_values: 6 EVAL 0l14md role! 01wgjj5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 87.000 59.000 0.778 http://example.org/music/artist/track_contributions./music/track_contribution/role #6457-094qd5 PRED entity: 094qd5 PRED relation: nominated_for PRED expected values: 0m313 0pv3x 0416y94 0j43swk 015whm 01k60v 0jqj5 01fx6y 09hy79 011yxy 0h95927 0cvkv5 => 54 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1446): 049xgc (0.81 #14192, 0.60 #12705, 0.47 #11217), 01jc6q (0.81 #11892, 0.81 #8919, 0.68 #34212), 07cyl (0.81 #11892, 0.81 #8919, 0.68 #34212), 09k56b7 (0.81 #11892, 0.81 #8919, 0.68 #34212), 09p3_s (0.81 #11892, 0.81 #8919, 0.68 #34212), 0_b9f (0.81 #11892, 0.81 #8919, 0.68 #34212), 03cw411 (0.81 #11892, 0.81 #8919, 0.68 #34212), 0209xj (0.81 #11892, 0.81 #8919, 0.68 #34212), 0194zl (0.81 #11892, 0.81 #8919, 0.68 #34212), 0m313 (0.75 #13391, 0.71 #5959, 0.67 #11904) >> Best rule #14192 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 02r22gf; 04dn09n; 02x1dht; 02z0dfh; 0k611; 0gqyl; 09td7p; 099t8j; 03hl6lc; >> query: (?x749, 049xgc) <- award_winner(?x749, ?x488), nominated_for(?x749, ?x7141), nominated_for(?x749, ?x1863), nominated_for(?x2549, ?x1863), ?x7141 = 027r9t, titles(?x53, ?x1863) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #13391 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 02r22gf; 04dn09n; 02x1dht; 02z0dfh; 0k611; 0gqyl; 09td7p; 099t8j; 03hl6lc; *> query: (?x749, 0m313) <- award_winner(?x749, ?x488), nominated_for(?x749, ?x7141), nominated_for(?x749, ?x1863), nominated_for(?x2549, ?x1863), ?x7141 = 027r9t, titles(?x53, ?x1863) *> conf = 0.75 ranks of expected_values: 10, 13, 23, 29, 40, 96, 104, 148, 276, 329, 460, 541 EVAL 094qd5 nominated_for 0cvkv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 0h95927 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 011yxy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 09hy79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 01fx6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 0jqj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 01k60v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 015whm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 0j43swk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 0pv3x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 094qd5 nominated_for 0m313 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 54.000 23.000 0.812 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6456-025rvx0 PRED entity: 025rvx0 PRED relation: currency PRED expected values: 09nqf => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.84 #29, 0.81 #113, 0.80 #57), 01nv4h (0.03 #205, 0.03 #121, 0.02 #107), 02l6h (0.03 #25, 0.02 #39, 0.02 #102), 02gsvk (0.01 #223, 0.01 #251) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 0g22z; 016fyc; 0ds11z; 0ds33; 016z5x; 0pc62; 0fg04; 01r97z; 06_wqk4; 0147sh; ... >> query: (?x5795, 09nqf) <- titles(?x53, ?x5795), edited_by(?x5795, ?x4215), nominated_for(?x484, ?x5795), production_companies(?x5795, ?x382) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025rvx0 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.838 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #6455-089pg7 PRED entity: 089pg7 PRED relation: group! PRED expected values: 018vs 02hnl => 83 concepts (68 used for prediction) PRED predicted values (max 10 best out of 123): 02hnl (0.79 #451, 0.78 #1813, 0.78 #1983), 018vs (0.67 #945, 0.66 #1030, 0.63 #1541), 03qjg (0.46 #980, 0.36 #470, 0.25 #1576), 013y1f (0.41 #959, 0.14 #2214, 0.13 #2152), 01vj9c (0.28 #1542, 0.27 #2139, 0.27 #1798), 04rzd (0.26 #964, 0.14 #2214, 0.12 #1816), 06ncr (0.18 #971, 0.17 #1567, 0.17 #1311), 042v_gx (0.15 #941, 0.14 #1281, 0.14 #431), 0l14j_ (0.15 #1580, 0.14 #2214, 0.11 #2177), 07gql (0.15 #1054, 0.11 #204, 0.11 #119) >> Best rule #451 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 02r3zy; 0dvqq; 04qmr; 0d193h; 0kr_t; 0143q0; 0838y; 016l09; 0134pk; 0c9l1; ... >> query: (?x7781, 02hnl) <- award(?x7781, ?x6126), award(?x7781, ?x3365), ?x3365 = 02f716, artists(?x302, ?x7781), group(?x227, ?x7781), ?x6126 = 02f77l >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 089pg7 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 68.000 0.786 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 089pg7 group! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 68.000 0.786 http://example.org/music/performance_role/regular_performances./music/group_membership/group #6454-01271h PRED entity: 01271h PRED relation: award PRED expected values: 03q27t => 133 concepts (115 used for prediction) PRED predicted values (max 10 best out of 283): 02f5qb (0.42 #552, 0.13 #18952, 0.12 #22152), 01by1l (0.38 #509, 0.32 #6509, 0.30 #109), 02f716 (0.38 #573, 0.12 #18973, 0.11 #2973), 0gqz2 (0.37 #3679, 0.37 #1679, 0.31 #2479), 09sb52 (0.37 #11640, 0.35 #11240, 0.35 #8040), 054ks3 (0.33 #1738, 0.30 #138, 0.28 #3738), 023vrq (0.33 #722, 0.06 #16722, 0.05 #45203), 02f76h (0.33 #574, 0.05 #16574, 0.04 #18974), 01bgqh (0.31 #2842, 0.29 #442, 0.26 #18842), 02f72n (0.29 #542, 0.18 #942, 0.13 #1342) >> Best rule #552 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 04n2vgk; 09z1lg; >> query: (?x2945, 02f5qb) <- award_winner(?x1443, ?x2945), artists(?x9630, ?x2945), ?x9630 = 012yc >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1965 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 85 *> proper extension: 02sj1x; 0c_drn; *> query: (?x2945, 03q27t) <- award_winner(?x1443, ?x2945), music(?x6007, ?x2945), award_winner(?x2945, ?x10574) *> conf = 0.02 ranks of expected_values: 223 EVAL 01271h award 03q27t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 133.000 115.000 0.417 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6453-02_fj PRED entity: 02_fj PRED relation: artists! PRED expected values: 0y4f8 => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 231): 064t9 (0.65 #4989, 0.57 #10587, 0.55 #13698), 017_qw (0.52 #1620, 0.12 #20593, 0.10 #3797), 06by7 (0.52 #3443, 0.49 #14329, 0.46 #4998), 06j6l (0.33 #13113, 0.32 #13735, 0.31 #5026), 05bt6j (0.32 #356, 0.29 #14352, 0.28 #6576), 0glt670 (0.31 #13105, 0.28 #13727, 0.27 #6573), 025sc50 (0.30 #5028, 0.30 #13737, 0.27 #13115), 0gywn (0.30 #13123, 0.28 #5036, 0.26 #13745), 016clz (0.29 #6536, 0.23 #14312, 0.22 #25200), 02lnbg (0.25 #372, 0.25 #6592, 0.19 #12501) >> Best rule #4989 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 01pfr3; 07r1_; 017959; >> query: (?x3017, 064t9) <- award(?x3017, ?x2139), award(?x3017, ?x591), ?x2139 = 01by1l, nominated_for(?x591, ?x54) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #424 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 01kymm; 01wphh2; 0392kz; 01vs8ng; *> query: (?x3017, 0y4f8) <- profession(?x3017, ?x319), special_performance_type(?x3017, ?x4832), artists(?x505, ?x3017) *> conf = 0.07 ranks of expected_values: 54 EVAL 02_fj artists! 0y4f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 152.000 152.000 0.654 http://example.org/music/genre/artists #6452-018f94 PRED entity: 018f94 PRED relation: place_of_birth! PRED expected values: 06vnh2 => 139 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1486): 01h2_6 (0.25 #2476, 0.09 #15536, 0.02 #86065), 0459z (0.25 #2323, 0.09 #15383, 0.02 #85912), 0l9k1 (0.25 #2290, 0.09 #15350, 0.02 #85879), 0277c3 (0.25 #1258, 0.09 #14318, 0.02 #84847), 0bqytm (0.25 #1030, 0.09 #14090, 0.02 #84619), 018dyl (0.25 #853, 0.09 #13913, 0.02 #84442), 0hskw (0.25 #523, 0.09 #13583, 0.02 #84112), 04kj2v (0.25 #467, 0.09 #13527, 0.02 #84056), 0k4gf (0.25 #200, 0.09 #13260, 0.02 #83789), 01kwld (0.25 #90, 0.09 #13150, 0.02 #83679) >> Best rule #2476 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0156q; 03hrz; >> query: (?x13992, 01h2_6) <- adjoins(?x9402, ?x13992), country(?x13992, ?x1264), contains(?x7934, ?x9402), ?x1264 = 0345h, place_of_birth(?x12564, ?x13992) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018f94 place_of_birth! 06vnh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 139.000 89.000 0.250 http://example.org/people/person/place_of_birth #6451-0f4vbz PRED entity: 0f4vbz PRED relation: profession PRED expected values: 0cbd2 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 62): 0dxtg (0.36 #2202, 0.31 #158, 0.30 #6874), 03gjzk (0.33 #2203, 0.31 #1619, 0.31 #159), 09jwl (0.27 #1623, 0.23 #1331, 0.22 #455), 02krf9 (0.23 #171, 0.10 #6887, 0.09 #2507), 0nbcg (0.21 #1636, 0.15 #468, 0.15 #322), 0dz3r (0.20 #1608, 0.14 #2484, 0.14 #1462), 018gz8 (0.17 #2205, 0.15 #1621, 0.14 #6001), 016z4k (0.16 #1610, 0.15 #296, 0.14 #734), 0cbd2 (0.15 #152, 0.15 #11394, 0.14 #6430), 0np9r (0.15 #2209, 0.15 #11553, 0.14 #11699) >> Best rule #2202 for best value: >> intensional similarity = 2 >> extensional distance = 255 >> proper extension: 02vptk_; >> query: (?x2258, 0dxtg) <- currency(?x2258, ?x170), student(?x3564, ?x2258) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #152 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 11 *> proper extension: 0d_w7; *> query: (?x2258, 0cbd2) <- participant(?x2499, ?x2258), ?x2499 = 0c6qh *> conf = 0.15 ranks of expected_values: 9 EVAL 0f4vbz profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 102.000 102.000 0.362 http://example.org/people/person/profession #6450-0l1589 PRED entity: 0l1589 PRED relation: role PRED expected values: 0dwt5 => 75 concepts (49 used for prediction) PRED predicted values (max 10 best out of 113): 0l14md (0.90 #3354, 0.88 #2459, 0.87 #2237), 01vdm0 (0.87 #3721, 0.87 #3801, 0.84 #4709), 05r5c (0.87 #3801, 0.82 #1674, 0.82 #5499), 0l15bq (0.82 #1826, 0.82 #1713, 0.80 #2266), 01v1d8 (0.82 #1674, 0.82 #5499, 0.81 #5276), 02sgy (0.82 #1674, 0.82 #5499, 0.81 #5276), 01s0ps (0.82 #1737, 0.82 #5499, 0.81 #5276), 05148p4 (0.82 #5499, 0.81 #5276, 0.81 #5386), 018j2 (0.82 #5499, 0.81 #5276, 0.81 #5386), 042v_gx (0.82 #5499, 0.81 #5276, 0.81 #5386) >> Best rule #3354 for best value: >> intensional similarity = 21 >> extensional distance = 18 >> proper extension: 0gghm; >> query: (?x2725, 0l14md) <- role(?x2725, ?x1466), role(?x2725, ?x745), role(?x2725, ?x7033), role(?x2725, ?x885), performance_role(?x3716, ?x2725), ?x745 = 01vj9c, instrumentalists(?x3716, ?x130), role(?x3716, ?x3239), role(?x3716, ?x1432), role(?x3716, ?x868), role(?x211, ?x3716), ?x1432 = 0395lw, ?x868 = 0dwvl, role(?x2306, ?x2725), role(?x2963, ?x885), role(?x7033, ?x1166), artists(?x284, ?x2306), profession(?x2306, ?x131), role(?x569, ?x885), ?x1466 = 03bx0bm, role(?x3238, ?x3239) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #106 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 1 *> proper extension: 018vs; *> query: (?x2725, ?x314) <- role(?x2725, ?x2764), role(?x2725, ?x745), role(?x2725, ?x716), role(?x2725, ?x316), role(?x2725, ?x7869), role(?x2725, ?x894), performance_role(?x3716, ?x2725), ?x745 = 01vj9c, ?x3716 = 03gvt, ?x894 = 03m5k, ?x2764 = 01s0ps, ?x316 = 05r5c, role(?x4186, ?x2725), role(?x1338, ?x716), role(?x7410, ?x7869), group(?x716, ?x12810), group(?x716, ?x9999), role(?x314, ?x716), ?x1338 = 09qr6, ?x12810 = 027kwc, ?x9999 = 01_wfj, role(?x2206, ?x7869) *> conf = 0.70 ranks of expected_values: 31 EVAL 0l1589 role 0dwt5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 75.000 49.000 0.900 http://example.org/music/performance_role/track_performances./music/track_contribution/role #6449-07jnt PRED entity: 07jnt PRED relation: nominated_for! PRED expected values: 0f4x7 0l8z1 0gr0m 09qv_s => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 212): 02qt02v (0.66 #18103, 0.66 #14575, 0.66 #15282), 0gs9p (0.54 #4526, 0.32 #8286, 0.30 #12284), 019f4v (0.47 #4516, 0.29 #991, 0.28 #12274), 02hsq3m (0.40 #27, 0.26 #967, 0.25 #2612), 040njc (0.39 #4472, 0.26 #947, 0.25 #477), 099c8n (0.36 #289, 0.33 #524, 0.25 #4519), 0gr42 (0.36 #85, 0.22 #320, 0.20 #555), 0f4x7 (0.35 #4489, 0.22 #12247, 0.21 #12011), 02n9nmz (0.35 #4520, 0.19 #290, 0.17 #525), 0gqy2 (0.35 #4584, 0.22 #12342, 0.21 #9521) >> Best rule #18103 for best value: >> intensional similarity = 3 >> extensional distance = 1002 >> proper extension: 03j63k; 097h2; 019g8j; 0147w8; 0300ml; 02rq7nd; >> query: (?x6782, ?x3233) <- nominated_for(?x500, ?x6782), award(?x6782, ?x3233), award(?x324, ?x500) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #4489 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 156 *> proper extension: 0sxg4; 0gzy02; 04v8x9; 0n0bp; 0b73_1d; 0c5dd; 04mzf8; 09z2b7; 0p_th; 09cr8; ... *> query: (?x6782, 0f4x7) <- film(?x382, ?x6782), language(?x6782, ?x254), nominated_for(?x601, ?x6782), ?x254 = 02h40lc, ?x601 = 0gr4k *> conf = 0.35 ranks of expected_values: 8, 14, 18, 33 EVAL 07jnt nominated_for! 09qv_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 115.000 115.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07jnt nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 115.000 115.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07jnt nominated_for! 0l8z1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 115.000 115.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07jnt nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 115.000 115.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6448-01z4y PRED entity: 01z4y PRED relation: genre! PRED expected values: 01bv8b 01fs__ 0q9jk 016tvq 0sw0q 0330r 01ft14 04svwx 0hr41p6 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 484): 05f7w84 (0.50 #3143, 0.50 #1965, 0.50 #1025), 01fs__ (0.50 #1281, 0.50 #1046, 0.43 #2694), 01f3p_ (0.50 #1218, 0.43 #2631, 0.33 #748), 01rf57 (0.50 #1231, 0.43 #2644, 0.33 #761), 01j67j (0.50 #1208, 0.43 #2621, 0.33 #738), 0d66j2 (0.50 #1220, 0.43 #2633, 0.33 #750), 0330r (0.50 #1099, 0.40 #1569, 0.33 #864), 01ft14 (0.50 #1114, 0.40 #1584, 0.33 #879), 016tvq (0.50 #1067, 0.40 #1537, 0.33 #832), 0q9jk (0.50 #1063, 0.40 #1533, 0.33 #828) >> Best rule #3143 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01htzx; 095bb; >> query: (?x2480, 05f7w84) <- genre(?x1876, ?x2480), genre(?x808, ?x2480), nominated_for(?x1986, ?x1876), ?x808 = 07hpv3 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1281 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 01t_vv; *> query: (?x2480, 01fs__) <- genre(?x8775, ?x2480), genre(?x7317, ?x2480), genre(?x3630, ?x2480), ?x3630 = 0557yqh, ?x7317 = 05p9_ql, ?x8775 = 07zhjj *> conf = 0.50 ranks of expected_values: 2, 7, 8, 9, 10, 11, 25, 45, 65 EVAL 01z4y genre! 0hr41p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 67.000 67.000 0.500 http://example.org/tv/tv_program/genre EVAL 01z4y genre! 04svwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 67.000 67.000 0.500 http://example.org/tv/tv_program/genre EVAL 01z4y genre! 01ft14 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 67.000 0.500 http://example.org/tv/tv_program/genre EVAL 01z4y genre! 0330r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 67.000 0.500 http://example.org/tv/tv_program/genre EVAL 01z4y genre! 0sw0q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 67.000 67.000 0.500 http://example.org/tv/tv_program/genre EVAL 01z4y genre! 016tvq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 67.000 0.500 http://example.org/tv/tv_program/genre EVAL 01z4y genre! 0q9jk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 67.000 0.500 http://example.org/tv/tv_program/genre EVAL 01z4y genre! 01fs__ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.500 http://example.org/tv/tv_program/genre EVAL 01z4y genre! 01bv8b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 67.000 0.500 http://example.org/tv/tv_program/genre #6447-01wf86y PRED entity: 01wf86y PRED relation: instrumentalists! PRED expected values: 018vs => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 121): 05r5c (0.48 #2448, 0.48 #595, 0.47 #679), 018vs (0.42 #935, 0.41 #599, 0.29 #2028), 05148p4 (0.42 #943, 0.38 #607, 0.32 #2460), 02hnl (0.25 #619, 0.24 #955, 0.16 #787), 06ncr (0.23 #41, 0.10 #965, 0.09 #629), 07gql (0.23 #39, 0.05 #711, 0.04 #963), 026t6 (0.21 #591, 0.19 #927, 0.12 #1011), 03qjg (0.20 #972, 0.17 #636, 0.16 #1056), 07y_7 (0.15 #2, 0.06 #2443, 0.06 #2529), 0l14j_ (0.15 #51, 0.06 #975, 0.04 #639) >> Best rule #2448 for best value: >> intensional similarity = 4 >> extensional distance = 509 >> proper extension: 0f0y8; 0c9d9; 01vvy; 06y9c2; 0274ck; 0pcc0; 01pr_j6; 0k4gf; 07_3qd; 01w923; ... >> query: (?x7581, 05r5c) <- nationality(?x7581, ?x94), instrumentalists(?x569, ?x7581), role(?x569, ?x2309), ?x2309 = 06ncr >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #935 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 158 *> proper extension: 053y0s; 03c7ln; 01q7cb_; 01p45_v; 0285c; 0zjpz; 09prnq; 02jg92; 01tp5bj; 0gkg6; ... *> query: (?x7581, 018vs) <- nationality(?x7581, ?x94), instrumentalists(?x227, ?x7581), group(?x7581, ?x1271), artists(?x671, ?x7581) *> conf = 0.42 ranks of expected_values: 2 EVAL 01wf86y instrumentalists! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.483 http://example.org/music/instrument/instrumentalists #6446-018db8 PRED entity: 018db8 PRED relation: sibling! PRED expected values: 015076 => 108 concepts (63 used for prediction) PRED predicted values (max 10 best out of 78): 015076 (0.82 #1047, 0.81 #1163, 0.74 #464), 02tf1y (0.08 #75, 0.03 #423, 0.02 #1006), 01wskg (0.05 #465), 010xjr (0.05 #465), 0m_v0 (0.05 #465), 01x15dc (0.05 #465), 016gr2 (0.05 #465), 06dv3 (0.05 #465), 032_jg (0.05 #116, 0.04 #8, 0.02 #356), 0479b (0.05 #116) >> Best rule #1047 for best value: >> intensional similarity = 2 >> extensional distance = 100 >> proper extension: 0c7ct; 013v5j; 0mj0c; 06hx2; 03ys2f; 03ysmg; 024dw0; 02x8kk; 02x8mt; 0dv1hh; ... >> query: (?x793, ?x11259) <- sibling(?x793, ?x11259), gender(?x793, ?x231) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018db8 sibling! 015076 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 63.000 0.821 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #6445-04dn09n PRED entity: 04dn09n PRED relation: award_winner PRED expected values: 02vyw => 61 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1708): 03_gd (0.36 #36778, 0.33 #135, 0.29 #34326), 0gyx4 (0.36 #36778, 0.33 #960, 0.29 #34326), 06pj8 (0.36 #36778, 0.29 #34326, 0.29 #14709), 052hl (0.36 #36778, 0.29 #34326, 0.29 #14709), 0184dt (0.36 #36778, 0.29 #34326, 0.29 #14709), 0499lc (0.36 #36778, 0.29 #34326, 0.29 #14709), 05ldnp (0.36 #36778, 0.29 #34326, 0.29 #14709), 02mt4k (0.36 #36778, 0.29 #34326, 0.29 #14709), 04r7jc (0.36 #36778, 0.29 #34326, 0.29 #14709), 0237jb (0.36 #36778, 0.29 #34326, 0.29 #14709) >> Best rule #36778 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 02f5qb; 02f73p; 02f6xy; 02f72_; 0c_dx; 02f73b; 040_9s0; 0g9wd99; >> query: (?x746, ?x3260) <- award_winner(?x746, ?x361), award(?x3260, ?x746), award_winner(?x8762, ?x3260), story_by(?x6079, ?x3260) >> conf = 0.36 => this is the best rule for 14 predicted values *> Best rule #780 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 019f4v; *> query: (?x746, 02vyw) <- ceremony(?x746, ?x747), nominated_for(?x746, ?x3246), nominated_for(?x746, ?x1813), ?x1813 = 09gq0x5, ?x3246 = 02tqm5 *> conf = 0.33 ranks of expected_values: 20 EVAL 04dn09n award_winner 02vyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 61.000 23.000 0.361 http://example.org/award/award_category/winners./award/award_honor/award_winner #6444-06yxd PRED entity: 06yxd PRED relation: location! PRED expected values: 01gz9n => 166 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1557): 02sjf5 (0.33 #2705, 0.09 #10220, 0.04 #137986), 01pl9g (0.33 #2776, 0.03 #10291, 0.03 #7786), 0q9zc (0.33 #4196, 0.03 #11711, 0.02 #139477), 0237fw (0.33 #2950, 0.03 #10465, 0.02 #43031), 01vrlqd (0.33 #4075, 0.03 #11590, 0.02 #44156), 06fc0b (0.33 #4061, 0.03 #11576, 0.02 #44142), 013423 (0.33 #3801, 0.03 #11316, 0.02 #43882), 01dy7j (0.33 #3073, 0.03 #10588, 0.02 #43154), 01wj5hp (0.33 #4290, 0.02 #139571, 0.01 #169632), 06chvn (0.33 #3769, 0.02 #139050, 0.01 #169111) >> Best rule #2705 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0cc56; 01531; >> query: (?x4776, 02sjf5) <- location(?x10920, ?x4776), nominated_for(?x10920, ?x6722), ?x6722 = 063hp4 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #280581 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 393 *> proper extension: 018jk2; 03v_5; 0k_s5; 0zpfy; 0p9nv; 0d9z_y; 01l69g; *> query: (?x4776, ?x9964) <- location(?x10920, ?x4776), nominated_for(?x10920, ?x6722), music(?x6722, ?x9964) *> conf = 0.03 ranks of expected_values: 542 EVAL 06yxd location! 01gz9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 166.000 117.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #6443-028r4y PRED entity: 028r4y PRED relation: gender PRED expected values: 05zppz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.77 #1, 0.72 #132, 0.71 #3), 02zsn (0.54 #125, 0.48 #6, 0.47 #12) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 02p65p; 01qscs; 0bwh6; 0dlglj; 030h95; 01tfck; 02vy5j; 0b_dy; 01zg98; 01tnxc; ... >> query: (?x5467, 05zppz) <- award_winner(?x5467, ?x7946), ?x7946 = 0kjgl, award(?x5467, ?x704) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028r4y gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.769 http://example.org/people/person/gender #6442-03_8kz PRED entity: 03_8kz PRED relation: genre PRED expected values: 07s9rl0 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 79): 07s9rl0 (0.73 #86, 0.68 #511, 0.65 #1), 05p553 (0.57 #600, 0.56 #3235, 0.56 #2555), 01z4y (0.44 #189, 0.43 #2569, 0.41 #274), 0c4xc (0.36 #2594, 0.35 #639, 0.34 #299), 01t_vv (0.24 #205, 0.23 #2585, 0.23 #3095), 02fgmn (0.24 #67, 0.20 #237, 0.15 #577), 0vgkd (0.23 #96, 0.20 #181, 0.17 #946), 0lsxr (0.22 #520, 0.18 #95, 0.18 #10), 06nbt (0.21 #277, 0.18 #702, 0.17 #1382), 01htzx (0.20 #698, 0.19 #1718, 0.18 #4778) >> Best rule #86 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0g60z; 080dwhx; 02k_4g; 0ddd0gc; 0kfv9; 03d34x8; 030k94; 02rzdcp; 02pqs8l; 030p35; ... >> query: (?x9551, 07s9rl0) <- program_creator(?x9551, ?x4303), award_winner(?x9551, ?x2062), nominated_for(?x4921, ?x9551), ?x4921 = 0fbtbt >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_8kz genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.727 http://example.org/tv/tv_program/genre #6441-01pj3h PRED entity: 01pj3h PRED relation: profession PRED expected values: 03gjzk 01xr66 => 88 concepts (83 used for prediction) PRED predicted values (max 10 best out of 60): 02jknp (0.48 #1467, 0.46 #2489, 0.45 #3657), 03gjzk (0.36 #1473, 0.32 #1911, 0.32 #2495), 0gl2ny2 (0.34 #504, 0.19 #212, 0.01 #11895), 01445t (0.24 #167, 0.15 #459, 0.02 #2065), 02krf9 (0.19 #25, 0.15 #1485, 0.14 #2507), 09jwl (0.17 #1039, 0.17 #5274, 0.16 #5858), 0cbd2 (0.16 #2050, 0.15 #4241, 0.15 #7309), 0d1pc (0.15 #633, 0.14 #1801, 0.14 #925), 0np9r (0.15 #8636, 0.15 #8782, 0.15 #19), 018gz8 (0.15 #307, 0.13 #8632, 0.13 #4104) >> Best rule #1467 for best value: >> intensional similarity = 3 >> extensional distance = 502 >> proper extension: 05g8ky; 02qjj7; 05cv94; 0162c8; 0c_mvb; 06pwf6; 01vv6_6; 03n93; 019r_1; 01_k1z; ... >> query: (?x11543, 02jknp) <- profession(?x11543, ?x319), ?x319 = 01d_h8, student(?x735, ?x11543) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1473 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 502 *> proper extension: 05g8ky; 02qjj7; 05cv94; 0162c8; 0c_mvb; 06pwf6; 01vv6_6; 03n93; 019r_1; 01_k1z; ... *> query: (?x11543, 03gjzk) <- profession(?x11543, ?x319), ?x319 = 01d_h8, student(?x735, ?x11543) *> conf = 0.36 ranks of expected_values: 2, 27 EVAL 01pj3h profession 01xr66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 88.000 83.000 0.476 http://example.org/people/person/profession EVAL 01pj3h profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 83.000 0.476 http://example.org/people/person/profession #6440-045c7b PRED entity: 045c7b PRED relation: industry PRED expected values: 03ytc 01mf0 => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 134): 02jjt (0.91 #1268, 0.27 #368, 0.25 #2394), 02vxn (0.59 #4820, 0.50 #3379, 0.47 #5407), 04rlf (0.40 #1903, 0.22 #1273, 0.21 #3751), 029g_vk (0.40 #1900, 0.21 #1585, 0.21 #3748), 01mw1 (0.34 #3694, 0.27 #5000, 0.27 #6488), 03qh03g (0.33 #3468, 0.24 #3833, 0.22 #545), 01mf0 (0.33 #3468, 0.21 #435, 0.20 #300), 0191_7 (0.25 #173, 0.18 #398, 0.14 #443), 020mfr (0.25 #3709, 0.24 #5015, 0.23 #4339), 01mfj (0.21 #440, 0.20 #305, 0.20 #260) >> Best rule #1268 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 02rr_z4; >> query: (?x5072, 02jjt) <- industry(?x5072, ?x5615), industry(?x6386, ?x5615), ?x6386 = 061v5m >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #3468 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 025tlyv; 0nzm; *> query: (?x5072, ?x2271) <- place_founded(?x5072, ?x581), state_province_region(?x5072, ?x1227), place_founded(?x7218, ?x581), industry(?x7218, ?x2271) *> conf = 0.33 ranks of expected_values: 7, 38 EVAL 045c7b industry 01mf0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 194.000 194.000 0.913 http://example.org/business/business_operation/industry EVAL 045c7b industry 03ytc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 194.000 194.000 0.913 http://example.org/business/business_operation/industry #6439-076tq0z PRED entity: 076tq0z PRED relation: nominated_for! PRED expected values: 016tw3 => 75 concepts (30 used for prediction) PRED predicted values (max 10 best out of 386): 0150t6 (0.37 #2340, 0.37 #32736, 0.37 #28059), 018ygt (0.35 #28057, 0.32 #25717, 0.31 #58455), 060j8b (0.35 #28057, 0.32 #25717, 0.31 #58455), 0p_47 (0.35 #28057, 0.32 #25717, 0.31 #58455), 044zvm (0.35 #28057, 0.32 #25717, 0.31 #58455), 053y4h (0.35 #28057, 0.32 #25717, 0.31 #58455), 016tw3 (0.11 #21041, 0.02 #2561, 0.02 #16586), 0146pg (0.06 #120, 0.05 #25838, 0.04 #30517), 086k8 (0.05 #4735, 0.04 #9409, 0.03 #11746), 05qd_ (0.04 #4851, 0.03 #23552, 0.03 #21214) >> Best rule #2340 for best value: >> intensional similarity = 4 >> extensional distance = 149 >> proper extension: 09p7fh; 0m63c; 0gyv0b4; >> query: (?x2846, ?x3069) <- film(?x6262, ?x2846), award(?x2846, ?x4135), music(?x2846, ?x3069), student(?x1368, ?x6262) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #21041 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 401 *> proper extension: 0b60sq; 011yfd; 076xkdz; 05y0cr; 06zn1c; 0d8w2n; *> query: (?x2846, ?x1104) <- genre(?x2846, ?x1403), ?x1403 = 02l7c8, film(?x1104, ?x2846) *> conf = 0.11 ranks of expected_values: 7 EVAL 076tq0z nominated_for! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 75.000 30.000 0.373 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #6438-0gmblvq PRED entity: 0gmblvq PRED relation: genre PRED expected values: 03bxz7 => 102 concepts (101 used for prediction) PRED predicted values (max 10 best out of 86): 0hcr (0.51 #1356, 0.48 #1840, 0.48 #1477), 04xvlr (0.43 #1, 0.38 #727, 0.31 #848), 01jfsb (0.42 #9101, 0.31 #3282, 0.30 #4251), 03k9fj (0.40 #1464, 0.37 #1343, 0.36 #1827), 02p0szs (0.36 #392, 0.29 #29, 0.16 #876), 05p553 (0.36 #7634, 0.33 #8484, 0.33 #3395), 02kdv5l (0.34 #8847, 0.33 #10548, 0.33 #9090), 017fp (0.34 #742, 0.31 #863, 0.14 #16), 082gq (0.32 #757, 0.26 #878, 0.25 #152), 02l7c8 (0.30 #2074, 0.30 #6920, 0.28 #6072) >> Best rule #1356 for best value: >> intensional similarity = 4 >> extensional distance = 195 >> proper extension: 0h1cdwq; 0crfwmx; 026q3s3; 02847m9; 0c8tkt; 018nnz; 02vqhv0; 02qhqz4; 01hvjx; 0k4d7; ... >> query: (?x4083, 0hcr) <- film(?x2246, ?x4083), genre(?x4083, ?x2605), major_field_of_study(?x122, ?x2605), student(?x2605, ?x445) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #782 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 035s95; 03kg2v; 09rsjpv; 03z106; 0bw20; *> query: (?x4083, 03bxz7) <- film(?x2246, ?x4083), genre(?x4083, ?x2605), ?x2605 = 03g3w, film_crew_role(?x4083, ?x137) *> conf = 0.21 ranks of expected_values: 13 EVAL 0gmblvq genre 03bxz7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 102.000 101.000 0.513 http://example.org/film/film/genre #6437-04j13sx PRED entity: 04j13sx PRED relation: nominated_for! PRED expected values: 0gqy2 02qyntr => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 182): 04dn09n (0.68 #5608, 0.66 #4038, 0.66 #5607), 02w_6xj (0.68 #5608, 0.66 #4038, 0.66 #5607), 02qyntr (0.53 #615, 0.27 #390, 0.21 #840), 0gr4k (0.41 #473, 0.38 #248, 0.24 #7856), 0gq_v (0.40 #242, 0.24 #2934, 0.23 #467), 0gqy2 (0.37 #333, 0.27 #1232, 0.26 #783), 0p9sw (0.36 #243, 0.28 #468, 0.25 #1142), 02qvyrt (0.34 #532, 0.16 #757, 0.15 #1206), 0l8z1 (0.31 #497, 0.22 #272, 0.18 #2964), 02n9nmz (0.30 #502, 0.12 #6735, 0.11 #2969) >> Best rule #5608 for best value: >> intensional similarity = 2 >> extensional distance = 987 >> proper extension: 06mmr; >> query: (?x6013, ?x1107) <- award(?x6013, ?x1107), award(?x276, ?x1107) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #615 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 122 *> proper extension: 0c_j9x; 09p7fh; 016ks5; 0h95927; 0yx_w; *> query: (?x6013, 02qyntr) <- nominated_for(?x2375, ?x6013), award(?x6013, ?x746), ?x2375 = 04kxsb *> conf = 0.53 ranks of expected_values: 3, 6 EVAL 04j13sx nominated_for! 02qyntr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 70.000 70.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04j13sx nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 70.000 70.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6436-01qn7n PRED entity: 01qn7n PRED relation: country_of_origin PRED expected values: 09c7w0 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 56): 09c7w0 (0.88 #309, 0.84 #161, 0.84 #378), 02jx1 (0.56 #390, 0.02 #217, 0.02 #365), 07ssc (0.13 #226, 0.12 #305, 0.12 #215), 03_3d (0.13 #323, 0.12 #356, 0.11 #345), 0d060g (0.10 #37, 0.05 #175, 0.04 #357), 0d0vqn (0.03 #108, 0.03 #120, 0.02 #131), 07c52 (0.03 #90, 0.03 #103, 0.02 #515), 07qht4 (0.03 #90, 0.03 #103, 0.02 #115), 03rjj (0.02 #128, 0.02 #140, 0.02 #151), 03rt9 (0.02 #365, 0.01 #179, 0.01 #190) >> Best rule #309 for best value: >> intensional similarity = 6 >> extensional distance = 137 >> proper extension: 063zky; >> query: (?x273, 09c7w0) <- actor(?x273, ?x969), nationality(?x969, ?x94), film(?x969, ?x3133), type_of_union(?x969, ?x566), profession(?x969, ?x1032), program(?x329, ?x273) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qn7n country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.885 http://example.org/tv/tv_program/country_of_origin #6435-0hgnl3t PRED entity: 0hgnl3t PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.81 #266, 0.78 #599, 0.77 #932), 09zzb8 (0.76 #593, 0.75 #852, 0.75 #38), 0dxtw (0.46 #603, 0.42 #714, 0.40 #862), 01vx2h (0.45 #197, 0.41 #345, 0.39 #937), 01pvkk (0.30 #198, 0.29 #605, 0.28 #716), 0215hd (0.25 #57, 0.25 #20, 0.20 #94), 02_n3z (0.25 #2, 0.20 #76, 0.12 #39), 089g0h (0.25 #21, 0.14 #280, 0.13 #95), 0d2b38 (0.25 #27, 0.14 #212, 0.13 #101), 01xy5l_ (0.25 #15, 0.13 #89, 0.13 #274) >> Best rule #266 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 0209xj; 0g54xkt; 0n1s0; 04b_jc; >> query: (?x4518, 0ch6mp2) <- film(?x2596, ?x4518), nominated_for(?x2853, ?x4518), ?x2853 = 09qv_s, language(?x4518, ?x254) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hgnl3t film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.810 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6434-017jd9 PRED entity: 017jd9 PRED relation: film! PRED expected values: 0f0kz => 67 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1196): 02bfxb (0.49 #43421, 0.47 #4135, 0.44 #55834), 015t56 (0.49 #43421, 0.47 #4135, 0.44 #55834), 05bm4sm (0.49 #43421, 0.47 #4135, 0.44 #55834), 04ktcgn (0.49 #43421, 0.47 #4135, 0.44 #55834), 02fgm7 (0.49 #43421, 0.47 #4135, 0.44 #55834), 02h1rt (0.49 #43421, 0.47 #4135, 0.44 #55834), 016zp5 (0.49 #43421, 0.44 #55834, 0.43 #53765), 024rgt (0.49 #43421, 0.44 #55834, 0.43 #53765), 01tc9r (0.47 #4135, 0.43 #14472, 0.42 #51695), 05hj_k (0.11 #33081, 0.10 #28943, 0.01 #68249) >> Best rule #43421 for best value: >> intensional similarity = 4 >> extensional distance = 614 >> proper extension: 02ljhg; >> query: (?x4610, ?x1194) <- currency(?x4610, ?x170), nominated_for(?x1424, ?x4610), nominated_for(?x1194, ?x4610), participant(?x1424, ?x2782) >> conf = 0.49 => this is the best rule for 8 predicted values *> Best rule #4643 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 0661m4p; 047svrl; 0gffmn8; 0gj8nq2; 06tpmy; 03mgx6z; 0bq6ntw; 0gtx63s; 07jqjx; *> query: (?x4610, 0f0kz) <- produced_by(?x4610, ?x3434), film_release_region(?x4610, ?x7747), ?x7747 = 07f1x *> conf = 0.07 ranks of expected_values: 25 EVAL 017jd9 film! 0f0kz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 67.000 36.000 0.488 http://example.org/film/actor/film./film/performance/film #6433-01kkfp PRED entity: 01kkfp PRED relation: current_club! PRED expected values: 03dj48 => 58 concepts (35 used for prediction) PRED predicted values (max 10 best out of 29): 033nzk (0.25 #2, 0.12 #32, 0.11 #62), 02bh_v (0.22 #80, 0.15 #110, 0.14 #142), 02ltg3 (0.15 #97, 0.14 #129, 0.11 #67), 01l3vx (0.12 #35, 0.12 #95, 0.11 #65), 03ylxn (0.12 #55, 0.11 #85, 0.08 #115), 02s2lg (0.12 #96, 0.11 #128, 0.03 #267), 03dj48 (0.11 #83, 0.08 #113, 0.07 #145), 0cnk2q (0.11 #61, 0.04 #91, 0.04 #123), 02pp1 (0.08 #116, 0.07 #148, 0.05 #185), 03yl2t (0.07 #235, 0.06 #429, 0.06 #302) >> Best rule #2 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 019lty; 0kwv2; >> query: (?x12981, 033nzk) <- position(?x12981, ?x203), position(?x12981, ?x63), position(?x12981, ?x60), team(?x530, ?x12981), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x60 = 02nzb8, ?x203 = 0dgrmp, category(?x12981, ?x134), sport(?x12981, ?x471), ?x134 = 08mbj5d >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #83 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 7 *> proper extension: 019lwb; 02gys2; 03fhm5; 0y9j; 03fnn5; *> query: (?x12981, 03dj48) <- position(?x12981, ?x203), position(?x12981, ?x63), position(?x12981, ?x60), team(?x530, ?x12981), ?x530 = 02_j1w, ?x63 = 02sdk9v, ?x60 = 02nzb8, ?x203 = 0dgrmp, category(?x12981, ?x134), ?x134 = 08mbj5d, position(?x12981, ?x530) *> conf = 0.11 ranks of expected_values: 7 EVAL 01kkfp current_club! 03dj48 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 58.000 35.000 0.250 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #6432-012fvq PRED entity: 012fvq PRED relation: educational_institution! PRED expected values: 012fvq => 186 concepts (127 used for prediction) PRED predicted values (max 10 best out of 334): 01g7_r (0.08 #235, 0.06 #774, 0.06 #1313), 0cwx_ (0.08 #225, 0.06 #764, 0.06 #1842), 02rg_4 (0.08 #122, 0.06 #661, 0.06 #1739), 01j_5k (0.08 #214, 0.06 #753, 0.06 #1831), 0bthb (0.08 #38, 0.06 #577, 0.06 #1655), 02htv6 (0.08 #465, 0.06 #1004, 0.06 #2082), 037s9x (0.08 #44, 0.06 #583, 0.03 #63131), 0qlnr (0.08 #312, 0.06 #1929, 0.06 #1390), 02hft3 (0.08 #43, 0.03 #63131, 0.02 #62591), 029d_ (0.06 #686, 0.06 #1764, 0.03 #63131) >> Best rule #235 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 02htv6; >> query: (?x3576, 01g7_r) <- state_province_region(?x3576, ?x3670), student(?x3576, ?x3497), ?x3670 = 05tbn, currency(?x3576, ?x170) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #63131 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 376 *> proper extension: 0ym4t; *> query: (?x3576, ?x6763) <- state_province_region(?x3576, ?x3670), school_type(?x3576, ?x1044), contains(?x3670, ?x6763), colors(?x6763, ?x663) *> conf = 0.03 ranks of expected_values: 46 EVAL 012fvq educational_institution! 012fvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 186.000 127.000 0.083 http://example.org/education/educational_institution_campus/educational_institution #6431-0jnng PRED entity: 0jnng PRED relation: position PRED expected values: 02qvl7 => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 3): 02qvl7 (0.89 #114, 0.86 #121, 0.86 #90), 02qvgy (0.68 #129, 0.60 #125, 0.57 #91), 02qvkj (0.60 #125, 0.57 #91, 0.53 #130) >> Best rule #114 for best value: >> intensional similarity = 25 >> extensional distance = 25 >> proper extension: 05pcr; 04l57x; >> query: (?x10690, ?x2918) <- position(?x10690, ?x5234), position(?x10690, ?x3724), ?x5234 = 02qvdc, ?x3724 = 02qvzf, team(?x2918, ?x10690), sport(?x10690, ?x453), ?x453 = 03tmr, team(?x2918, ?x13166), team(?x2918, ?x12977), team(?x2918, ?x12757), team(?x2918, ?x11826), team(?x2918, ?x10755), team(?x2918, ?x10034), team(?x2918, ?x8892), team(?x2918, ?x5380), position(?x11368, ?x2918), ?x11368 = 032yps, ?x13166 = 0j6tr, ?x8892 = 02fp3, ?x10034 = 0jnq8, ?x12757 = 0hmtk, ?x12977 = 0jnkr, ?x5380 = 0b6p3qf, ?x11826 = 0hn2q, ?x10755 = 0jbqf >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jnng position 02qvl7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.886 http://example.org/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position #6430-06mn7 PRED entity: 06mn7 PRED relation: award PRED expected values: 019f4v 0gq9h => 146 concepts (138 used for prediction) PRED predicted values (max 10 best out of 310): 0262s1 (0.73 #50069, 0.71 #48476, 0.71 #40126), 027b9ly (0.73 #50069, 0.71 #48476, 0.71 #40126), 027c924 (0.73 #50069, 0.71 #48476, 0.71 #40126), 0gr42 (0.73 #50069, 0.71 #48476, 0.71 #40126), 09d28z (0.73 #50069, 0.71 #48476, 0.71 #40126), 0gq9h (0.61 #7222, 0.45 #13973, 0.35 #21919), 019f4v (0.54 #7212, 0.43 #13168, 0.36 #13963), 04dn09n (0.37 #5203, 0.29 #14734, 0.26 #18310), 03hl6lc (0.36 #5334, 0.20 #14865, 0.20 #14071), 02qyp19 (0.35 #5164, 0.29 #1, 0.19 #13901) >> Best rule #50069 for best value: >> intensional similarity = 3 >> extensional distance = 2245 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01vsxdm; 01wv9xn; 0frsw; 016fmf; 01vrwfv; 0134s5; 02lbrd; ... >> query: (?x4353, ?x10747) <- award_winner(?x10747, ?x4353), award(?x4353, ?x350), award(?x2182, ?x10747) >> conf = 0.73 => this is the best rule for 5 predicted values *> Best rule #7222 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 119 *> proper extension: 04q5zw; 076_74; 04pqqb; 092kgw; 0d_skg; 03v1xb; 0d0xs5; 026gb3v; *> query: (?x4353, 0gq9h) <- nominated_for(?x4353, ?x1547), award(?x4353, ?x198), ?x198 = 040njc *> conf = 0.61 ranks of expected_values: 6, 7 EVAL 06mn7 award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 146.000 138.000 0.729 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 06mn7 award 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 146.000 138.000 0.729 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6429-05zr0xl PRED entity: 05zr0xl PRED relation: award PRED expected values: 0cjyzs => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 172): 0m7yy (0.48 #4501, 0.47 #359, 0.41 #3580), 09qvc0 (0.41 #3451, 0.39 #6213, 0.38 #6444), 0cqhmg (0.41 #3451, 0.39 #6213, 0.38 #6444), 02pz3j5 (0.33 #112, 0.12 #1492, 0.08 #1032), 02q1tc5 (0.33 #107, 0.12 #1487, 0.08 #1027), 027qq9b (0.33 #140, 0.09 #1520, 0.08 #1060), 0cjyzs (0.26 #540, 0.23 #310, 0.23 #770), 09qj50 (0.21 #726, 0.19 #1186, 0.17 #1876), 09qvf4 (0.16 #832, 0.15 #602, 0.15 #1292), 0fbtbt (0.13 #2912, 0.12 #3603, 0.11 #4294) >> Best rule #4501 for best value: >> intensional similarity = 3 >> extensional distance = 122 >> proper extension: 02nf2c; >> query: (?x8533, 0m7yy) <- genre(?x8533, ?x258), award_winner(?x8533, ?x1541), award(?x8533, ?x678) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #540 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 0dk0dj; *> query: (?x8533, 0cjyzs) <- genre(?x8533, ?x258), program_creator(?x8533, ?x1541), ?x258 = 05p553 *> conf = 0.26 ranks of expected_values: 7 EVAL 05zr0xl award 0cjyzs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 96.000 96.000 0.484 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #6428-0137g1 PRED entity: 0137g1 PRED relation: award PRED expected values: 05zkcn5 => 133 concepts (109 used for prediction) PRED predicted values (max 10 best out of 288): 01bgqh (0.50 #3187, 0.46 #11440, 0.42 #1222), 03qbh5 (0.50 #1377, 0.38 #3342, 0.22 #11595), 02f73p (0.50 #3325, 0.33 #1360, 0.19 #19831), 01c92g (0.50 #1276, 0.28 #2848, 0.27 #883), 0c4z8 (0.42 #1251, 0.33 #4788, 0.32 #3216), 02v1m7 (0.41 #3256, 0.25 #505, 0.17 #1291), 02f6ym (0.41 #3394, 0.16 #7324, 0.12 #11647), 01c99j (0.38 #3363, 0.16 #11616, 0.15 #7293), 03qbnj (0.32 #3369, 0.18 #1011, 0.18 #11622), 09sb52 (0.30 #13403, 0.28 #7508, 0.25 #13010) >> Best rule #3187 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 01wgxtl; >> query: (?x2784, 01bgqh) <- artists(?x302, ?x2784), award(?x2784, ?x3365), profession(?x2784, ?x131), ?x3365 = 02f716 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1200 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0167km; 016t00; *> query: (?x2784, 05zkcn5) <- artists(?x302, ?x2784), award_winner(?x2322, ?x2784), ?x2322 = 01ck6h, gender(?x2784, ?x231) *> conf = 0.17 ranks of expected_values: 34 EVAL 0137g1 award 05zkcn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 133.000 109.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6427-04xbr4 PRED entity: 04xbr4 PRED relation: place_of_birth PRED expected values: 0d9jr => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 49): 01531 (0.20 #105, 0.09 #1514, 0.03 #9259), 0dclg (0.20 #78, 0.09 #1487, 0.01 #6416), 06wxw (0.12 #861, 0.02 #5791, 0.02 #5086), 03dm7 (0.12 #1163), 0psxp (0.12 #915), 096gm (0.12 #879), 02_286 (0.11 #6357, 0.10 #3540, 0.09 #9877), 030qb3t (0.09 #1463, 0.08 #2167, 0.05 #12729), 0k9p4 (0.09 #1761), 0fr0t (0.09 #1553) >> Best rule #105 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01sg7_; >> query: (?x12436, 01531) <- profession(?x12436, ?x1032), student(?x3513, ?x12436), ?x3513 = 0pspl, people(?x1050, ?x12436) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04xbr4 place_of_birth 0d9jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 89.000 0.200 http://example.org/people/person/place_of_birth #6426-0735l PRED entity: 0735l PRED relation: film_distribution_medium! PRED expected values: 0170z3 0c00zd0 01fmys 0f4_l 0407yj_ 06w839_ 0bmc4cm 09gkx35 0cn_b8 05c9zr 062zjtt 03nm_fh 01l2b3 026hxwx 0mbql 03nfnx 06_sc3 0315rp 05567m => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 1767): 05567m (0.64 #345, 0.50 #418, 0.50 #329), 0jsf6 (0.64 #345, 0.47 #89, 0.29 #81), 0y_yw (0.64 #345, 0.47 #89, 0.12 #344), 02nt3d (0.64 #345, 0.47 #89, 0.12 #344), 0284b56 (0.64 #345, 0.47 #89, 0.04 #346), 034qrh (0.64 #345, 0.12 #344, 0.03 #349), 01fmys (0.50 #367, 0.50 #347, 0.50 #278), 03177r (0.50 #375, 0.50 #286, 0.47 #89), 0ddt_ (0.50 #376, 0.50 #287, 0.33 #202), 032clf (0.50 #403, 0.50 #314, 0.33 #229) >> Best rule #345 for best value: >> intensional similarity = 108 >> extensional distance = 4 >> proper extension: 07z4p; >> query: (?x2099, ?x437) <- film_distribution_medium(?x9914, ?x2099), film_distribution_medium(?x7700, ?x2099), film_distribution_medium(?x6751, ?x2099), film_distribution_medium(?x6422, ?x2099), film_distribution_medium(?x4409, ?x2099), film_distribution_medium(?x4131, ?x2099), film_distribution_medium(?x2102, ?x2099), film_distribution_medium(?x908, ?x2099), film_distribution_medium(?x97, ?x2099), film_distribution_medium(?x86, ?x2099), film_release_region(?x6751, ?x87), film(?x709, ?x6751), film_release_region(?x6422, ?x1229), nominated_for(?x2102, ?x437), award(?x6751, ?x1336), titles(?x7323, ?x6751), film_release_region(?x9941, ?x1229), film_release_region(?x9902, ?x1229), film_release_region(?x9194, ?x1229), film_release_region(?x8955, ?x1229), film_release_region(?x8258, ?x1229), film_release_region(?x8193, ?x1229), film_release_region(?x6181, ?x1229), film_release_region(?x6175, ?x1229), film_release_region(?x5873, ?x1229), film_release_region(?x5139, ?x1229), film_release_region(?x4336, ?x1229), film_release_region(?x3886, ?x1229), film_release_region(?x3423, ?x1229), film_release_region(?x3252, ?x1229), film_release_region(?x3088, ?x1229), film_release_region(?x3035, ?x1229), film_release_region(?x2656, ?x1229), film_release_region(?x2655, ?x1229), film_release_region(?x2628, ?x1229), film_release_region(?x2050, ?x1229), film_release_region(?x1392, ?x1229), film_release_region(?x1150, ?x1229), film_release_region(?x785, ?x1229), nationality(?x731, ?x1229), ?x2655 = 0fpmrm3, country(?x1121, ?x1229), country(?x1037, ?x1229), country(?x471, ?x1229), geographic_distribution(?x1571, ?x1229), language(?x97, ?x254), ?x785 = 03hjv97, written_by(?x2102, ?x2101), ?x8258 = 05ldxl, ?x1121 = 0bynt, ?x6175 = 0gg5kmg, organization(?x1229, ?x127), ?x5139 = 07bzz7, ?x8193 = 03z9585, film_release_distribution_medium(?x2102, ?x81), film_crew_role(?x4409, ?x137), olympics(?x1229, ?x3971), ?x4336 = 0bpm4yw, genre(?x9914, ?x225), ?x3088 = 06w839_, film_crew_role(?x97, ?x1171), combatants(?x613, ?x1229), ?x9194 = 0fpgp26, ?x1037 = 09_bl, ?x1392 = 017gm7, ?x6181 = 0hv27, film(?x237, ?x2102), ?x2656 = 03qnc6q, ?x3252 = 0gh8zks, service_location(?x610, ?x1229), ?x3971 = 0jhn7, ?x3423 = 09g7vfw, titles(?x2480, ?x2102), olympics(?x1229, ?x12388), ?x8955 = 0g4pl7z, film_release_region(?x908, ?x279), ?x471 = 02vx4, ?x5873 = 0cq86w, nominated_for(?x8394, ?x7700), nominated_for(?x298, ?x97), ?x1150 = 0h3xztt, ?x2050 = 01fmys, film(?x609, ?x86), film_regional_debut_venue(?x7700, ?x5416), contains(?x1229, ?x2351), nominated_for(?x500, ?x908), ?x9941 = 024lt6, location(?x2580, ?x1229), ?x9902 = 0j8f09z, taxonomy(?x1229, ?x939), film_crew_role(?x9209, ?x137), film_crew_role(?x7225, ?x137), film_crew_role(?x5517, ?x137), film_crew_role(?x5201, ?x137), film_crew_role(?x4399, ?x137), film_crew_role(?x2218, ?x137), currency(?x4131, ?x170), ?x7225 = 02mmwk, ?x5201 = 05_5_22, ?x9209 = 0crs0b8, ?x2628 = 06wbm8q, ?x4399 = 055td_, nominated_for(?x3789, ?x86), ?x3886 = 0198b6, ?x5517 = 03wh49y, ?x2218 = 013q07, ?x3035 = 0j43swk, ?x12388 = 015pkt >> conf = 0.64 => this is the best rule for 6 predicted values ranks of expected_values: 1, 7, 12, 61, 67, 101, 113, 182, 334, 434, 462, 530, 570, 1063, 1105, 1106, 1164, 1342, 1701 EVAL 0735l film_distribution_medium! 05567m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 0315rp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 06_sc3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 03nfnx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 0mbql CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 026hxwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 01l2b3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 03nm_fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 062zjtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 05c9zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 0cn_b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 09gkx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 0bmc4cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 06w839_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 0407yj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 0f4_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 01fmys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 0c00zd0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium EVAL 0735l film_distribution_medium! 0170z3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 6.000 6.000 0.636 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #6425-0xhj2 PRED entity: 0xhj2 PRED relation: place! PRED expected values: 0xhj2 => 113 concepts (66 used for prediction) PRED predicted values (max 10 best out of 106): 0n5yh (0.04 #1546), 071vr (0.03 #176, 0.01 #1206), 010r6f (0.03 #461), 0r6cx (0.03 #303), 0r5y9 (0.03 #167), 0bxbb (0.03 #162), 0bxbr (0.03 #149), 0d7k1z (0.03 #143), 0d9jr (0.03 #130), 0135g (0.03 #127) >> Best rule #1546 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 0fw4v; >> query: (?x11937, ?x5088) <- time_zones(?x11937, ?x2674), county_seat(?x5088, ?x11937), location(?x1335, ?x11937) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0xhj2 place! 0xhj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 66.000 0.041 http://example.org/location/hud_county_place/place #6424-025ldg PRED entity: 025ldg PRED relation: artist! PRED expected values: 0181dw => 114 concepts (74 used for prediction) PRED predicted values (max 10 best out of 104): 0g768 (0.29 #37, 0.19 #317, 0.15 #1998), 0181dw (0.26 #182, 0.24 #322, 0.16 #883), 015_1q (0.26 #1281, 0.24 #1981, 0.23 #2401), 01cszh (0.21 #11, 0.11 #151, 0.10 #1412), 03rhqg (0.19 #2538, 0.18 #1277, 0.17 #3098), 0n85g (0.19 #343, 0.10 #3145, 0.10 #1464), 02p11jq (0.18 #1274, 0.17 #2535, 0.08 #4638), 01trtc (0.16 #1473, 0.14 #2874, 0.11 #1613), 02swsm (0.16 #234, 0.11 #935, 0.10 #1075), 017l96 (0.15 #2541, 0.15 #1280, 0.12 #2400) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01fkxr; 02h9_l; 01vvybv; >> query: (?x4200, 0g768) <- award_winner(?x3121, ?x4200), artists(?x671, ?x4200), profession(?x4200, ?x131), person(?x1183, ?x4200) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #182 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 01pgzn_; *> query: (?x4200, 0181dw) <- artists(?x3319, ?x4200), vacationer(?x126, ?x4200), ?x3319 = 06j6l *> conf = 0.26 ranks of expected_values: 2 EVAL 025ldg artist! 0181dw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 74.000 0.286 http://example.org/music/record_label/artist #6423-01wskg PRED entity: 01wskg PRED relation: nationality PRED expected values: 02jx1 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 34): 04v3q (0.77 #906, 0.32 #1809, 0.31 #1911), 09c7w0 (0.74 #3214, 0.73 #3615, 0.73 #602), 02jx1 (0.63 #133, 0.33 #33, 0.33 #7526), 07ssc (0.36 #115, 0.33 #7526, 0.32 #3918), 04jpl (0.33 #7526, 0.32 #3918, 0.03 #4920), 02j9z (0.32 #1809, 0.31 #1911), 0d0vqn (0.11 #9, 0.03 #4920, 0.03 #2513), 03rk0 (0.06 #7370, 0.06 #2559, 0.06 #3360), 0chghy (0.04 #3816, 0.03 #4920, 0.03 #2513), 030qb3t (0.04 #3816, 0.03 #4920, 0.03 #2513) >> Best rule #906 for best value: >> intensional similarity = 3 >> extensional distance = 435 >> proper extension: 01k31p; >> query: (?x12435, ?x1061) <- place_of_death(?x12435, ?x13472), profession(?x12435, ?x1032), country(?x13472, ?x1061) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #133 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 141 *> proper extension: 01m65sp; 01sxd1; 040dv; *> query: (?x12435, 02jx1) <- people(?x743, ?x12435), profession(?x12435, ?x1032), ?x743 = 02w7gg *> conf = 0.63 ranks of expected_values: 3 EVAL 01wskg nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 92.000 0.769 http://example.org/people/person/nationality #6422-0fgrm PRED entity: 0fgrm PRED relation: film! PRED expected values: 054g1r => 94 concepts (82 used for prediction) PRED predicted values (max 10 best out of 58): 04rcl7 (0.47 #2767, 0.42 #2692), 03xq0f (0.33 #5, 0.25 #79, 0.14 #1945), 054g1r (0.33 #34, 0.20 #182, 0.17 #1003), 020h2v (0.33 #44, 0.05 #1087, 0.05 #1311), 016tw3 (0.25 #85, 0.17 #2628, 0.16 #233), 05qd_ (0.25 #83, 0.15 #1424, 0.15 #1052), 086k8 (0.17 #2016, 0.16 #1417, 0.16 #2694), 016tt2 (0.15 #1419, 0.12 #2696, 0.12 #301), 017s11 (0.13 #1943, 0.12 #2017, 0.12 #1719), 0g1rw (0.12 #305, 0.11 #902, 0.08 #754) >> Best rule #2767 for best value: >> intensional similarity = 4 >> extensional distance = 630 >> proper extension: 016ztl; >> query: (?x4650, ?x10685) <- music(?x4650, ?x8374), production_companies(?x4650, ?x10685), language(?x4650, ?x254), genre(?x4650, ?x53) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 0k2sk; *> query: (?x4650, 054g1r) <- film_crew_role(?x4650, ?x468), nominated_for(?x8374, ?x4650), film(?x12005, ?x4650), film(?x7266, ?x4650), ?x12005 = 019803, ?x7266 = 02gf_l *> conf = 0.33 ranks of expected_values: 3 EVAL 0fgrm film! 054g1r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 82.000 0.468 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #6421-0h6r5 PRED entity: 0h6r5 PRED relation: nominated_for! PRED expected values: 02x73k6 02rdxsh => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 185): 040njc (0.78 #5224, 0.68 #10464, 0.67 #10235), 02rdyk7 (0.68 #10464, 0.67 #10235, 0.67 #5223), 09d28z (0.68 #10464, 0.67 #10235, 0.67 #5223), 02w_6xj (0.68 #10464, 0.67 #10235, 0.67 #5223), 09cm54 (0.68 #10464, 0.67 #10235, 0.67 #5223), 0f4x7 (0.54 #1160, 0.36 #5020, 0.35 #3430), 0gq_v (0.36 #5014, 0.36 #473, 0.35 #3424), 0gr0m (0.35 #5050, 0.31 #509, 0.30 #282), 0l8z1 (0.34 #1185, 0.28 #5045, 0.27 #277), 02qvyrt (0.34 #1223, 0.27 #5083, 0.26 #542) >> Best rule #5224 for best value: >> intensional similarity = 3 >> extensional distance = 311 >> proper extension: 04m1bm; 091z_p; 02n9bh; 011yfd; 064lsn; 03q8xj; 0gpx6; 02wk7b; 02zk08; 05y0cr; ... >> query: (?x4093, ?x1180) <- award(?x4093, ?x1180), nominated_for(?x1180, ?x7580), ?x7580 = 04165w >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #8406 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 826 *> proper extension: 07s8z_l; *> query: (?x4093, ?x198) <- award_winner(?x4093, ?x4495), titles(?x53, ?x4093), award_winner(?x198, ?x4495) *> conf = 0.25 ranks of expected_values: 19, 63 EVAL 0h6r5 nominated_for! 02rdxsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 77.000 77.000 0.783 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0h6r5 nominated_for! 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 77.000 77.000 0.783 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6420-03s9v PRED entity: 03s9v PRED relation: influenced_by! PRED expected values: 07ym0 => 146 concepts (68 used for prediction) PRED predicted values (max 10 best out of 428): 0j3v (0.56 #4169, 0.29 #8770, 0.29 #3658), 03_hd (0.56 #4269, 0.15 #14320, 0.13 #8358), 01dvtx (0.44 #4239, 0.33 #149, 0.24 #8840), 0372p (0.44 #4238, 0.21 #4602, 0.14 #3727), 043s3 (0.44 #4242, 0.21 #4602, 0.14 #3731), 048cl (0.44 #4386, 0.18 #8987, 0.15 #14320), 04hcw (0.43 #3865, 0.29 #8977, 0.20 #8465), 03jht (0.43 #3957, 0.24 #9069, 0.20 #2422), 014ps4 (0.43 #3374, 0.21 #12582, 0.21 #9511), 0b78hw (0.40 #2721, 0.20 #1699, 0.15 #14320) >> Best rule #4169 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 07kb5; >> query: (?x7251, 0j3v) <- influenced_by(?x7250, ?x7251), influenced_by(?x3335, ?x7251), ?x7250 = 03sbs, location(?x3335, ?x1264) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #4427 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 07kb5; *> query: (?x7251, 07ym0) <- influenced_by(?x7250, ?x7251), influenced_by(?x3335, ?x7251), ?x7250 = 03sbs, location(?x3335, ?x1264) *> conf = 0.22 ranks of expected_values: 63 EVAL 03s9v influenced_by! 07ym0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 146.000 68.000 0.556 http://example.org/influence/influence_node/influenced_by #6419-02rlj20 PRED entity: 02rlj20 PRED relation: producer_type PRED expected values: 0ckd1 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.64 #6, 0.61 #5, 0.55 #3) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 025ljp; 07s8z_l; 09v38qj; 03czz87; >> query: (?x7895, 0ckd1) <- titles(?x3381, ?x7895), honored_for(?x4760, ?x7895), titles(?x3381, ?x1849), ?x1849 = 0kfv9 >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rlj20 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.645 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #6418-0435vm PRED entity: 0435vm PRED relation: film! PRED expected values: 0k269 => 102 concepts (80 used for prediction) PRED predicted values (max 10 best out of 874): 01b9z4 (0.33 #1646, 0.02 #5807, 0.01 #7888), 09pl3f (0.19 #27054, 0.18 #43700, 0.18 #45781), 02qzjj (0.17 #70755, 0.16 #68672, 0.15 #52024), 01rh0w (0.17 #229, 0.08 #81163, 0.03 #2310), 079vf (0.17 #8, 0.06 #6250, 0.06 #4169), 01q_ph (0.17 #56, 0.03 #2137, 0.03 #39594), 012q4n (0.17 #1136, 0.03 #9458, 0.03 #11540), 026c1 (0.17 #356, 0.02 #37813, 0.02 #23247), 01fh9 (0.17 #315, 0.02 #8637, 0.02 #10719), 06t74h (0.17 #694, 0.02 #2775, 0.02 #15261) >> Best rule #1646 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0gfzfj; >> query: (?x3925, 01b9z4) <- language(?x3925, ?x254), film(?x1522, ?x3925), produced_by(?x3925, ?x1533), genre(?x3925, ?x53), ?x1522 = 02lkcc >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2690 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 92 *> proper extension: 01ln5z; 02pxmgz; 0dtfn; 04w7rn; 05p1qyh; 08052t3; 0ddt_; 03459x; 09rsjpv; 0gh65c5; ... *> query: (?x3925, 0k269) <- language(?x3925, ?x254), film(?x368, ?x3925), film_crew_role(?x3925, ?x2091), country(?x3925, ?x94), ?x2091 = 02rh1dz *> conf = 0.03 ranks of expected_values: 118 EVAL 0435vm film! 0k269 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 102.000 80.000 0.333 http://example.org/film/actor/film./film/performance/film #6417-05q96q6 PRED entity: 05q96q6 PRED relation: genre PRED expected values: 07s9rl0 03k9fj => 89 concepts (73 used for prediction) PRED predicted values (max 10 best out of 104): 07s9rl0 (0.82 #4970, 0.77 #2785, 0.70 #8619), 03k9fj (0.42 #616, 0.37 #374, 0.37 #737), 05p553 (0.41 #3272, 0.38 #2303, 0.37 #1213), 03bxz7 (0.38 #177, 0.14 #2840, 0.11 #419), 01jfsb (0.37 #1343, 0.36 #738, 0.33 #3159), 02l7c8 (0.36 #4985, 0.32 #8634, 0.31 #2800), 01hmnh (0.32 #623, 0.25 #1470, 0.25 #744), 06n90 (0.31 #255, 0.24 #618, 0.24 #739), 0lsxr (0.25 #129, 0.21 #371, 0.18 #1218), 082gq (0.25 #152, 0.16 #394, 0.13 #2815) >> Best rule #4970 for best value: >> intensional similarity = 4 >> extensional distance = 858 >> proper extension: 0413cff; 015qy1; 0k20s; >> query: (?x1038, 07s9rl0) <- film_release_region(?x1038, ?x94), genre(?x1038, ?x162), genre(?x1753, ?x162), ?x1753 = 02q5g1z >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05q96q6 genre 03k9fj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 73.000 0.817 http://example.org/film/film/genre EVAL 05q96q6 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 73.000 0.817 http://example.org/film/film/genre #6416-01ttg5 PRED entity: 01ttg5 PRED relation: diet PRED expected values: 07_hy => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 1): 07_hy (0.25 #13, 0.05 #14, 0.05 #1) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 53 >> proper extension: 01pfkw; >> query: (?x3934, 07_hy) <- participant(?x3934, ?x7233), diet(?x3934, ?x3130) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ttg5 diet 07_hy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.255 http://example.org/base/eating/practicer_of_diet/diet #6415-0c4f4 PRED entity: 0c4f4 PRED relation: film PRED expected values: 0bby9p5 => 93 concepts (69 used for prediction) PRED predicted values (max 10 best out of 879): 0gyfp9c (0.70 #3575, 0.66 #8937, 0.65 #30388), 01s7w3 (0.70 #3575, 0.66 #8937, 0.65 #30388), 02czd5 (0.70 #3575, 0.66 #8937, 0.65 #30388), 016z9n (0.38 #3944, 0.04 #2156, 0.01 #54003), 0404j37 (0.17 #2923), 01k1k4 (0.11 #58, 0.04 #1845, 0.01 #30446), 04hwbq (0.11 #191, 0.02 #7340), 03wy8t (0.11 #1584, 0.02 #24823, 0.01 #15884), 0407yj_ (0.11 #483, 0.01 #11208, 0.01 #71993), 07bxqz (0.11 #1732, 0.01 #14244) >> Best rule #3575 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 07h565; >> query: (?x495, ?x1045) <- nominated_for(?x495, ?x1045), award_nominee(?x5022, ?x495), ?x5022 = 0278x6s >> conf = 0.70 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0c4f4 film 0bby9p5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 69.000 0.704 http://example.org/film/actor/film./film/performance/film #6414-049rl0 PRED entity: 049rl0 PRED relation: company! PRED expected values: 014l7h => 57 concepts (51 used for prediction) PRED predicted values (max 10 best out of 14): 014l7h (0.16 #170, 0.13 #217, 0.11 #358), 02k13d (0.08 #155, 0.06 #202, 0.06 #343), 060c4 (0.06 #144, 0.05 #191, 0.05 #332), 09d6p2 (0.06 #161, 0.05 #208, 0.05 #349), 0dq_5 (0.05 #159, 0.04 #206, 0.04 #753), 0krdk (0.04 #753, 0.04 #148, 0.03 #195), 0dq3c (0.04 #753, 0.03 #143, 0.02 #190), 01rk91 (0.04 #753, 0.03 #142, 0.02 #189), 05_wyz (0.04 #753, 0.03 #160, 0.02 #207), 05k17c (0.04 #753, 0.03 #154, 0.02 #201) >> Best rule #170 for best value: >> intensional similarity = 2 >> extensional distance = 75 >> proper extension: 0grwj; 0kc6x; 065y4w7; 05qd_; 0f721s; 030_1_; 0gsg7; 0g51l1; 09d5h; 06pj8; ... >> query: (?x14380, 014l7h) <- award_winner(?x3486, ?x14380), ?x3486 = 0m7yy >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049rl0 company! 014l7h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 51.000 0.156 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #6413-0rrwt PRED entity: 0rrwt PRED relation: time_zones PRED expected values: 02hcv8 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.62 #3, 0.43 #497, 0.40 #68), 02lcqs (0.25 #31, 0.22 #18, 0.20 #122), 02fqwt (0.18 #313, 0.18 #300, 0.18 #196), 02hczc (0.13 #859, 0.08 #275, 0.07 #210), 02lcrv (0.13 #859), 02llzg (0.07 #355, 0.07 #394, 0.06 #43), 03bdv (0.04 #409, 0.04 #396, 0.03 #930), 03plfd (0.02 #244, 0.02 #400, 0.01 #101), 042g7t (0.01 #375, 0.01 #427, 0.01 #440) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 0ftvz; 0n1rj; 0jrxx; >> query: (?x9598, 02hcv8) <- contains(?x2623, ?x9598), contains(?x94, ?x9598), ?x94 = 09c7w0, location(?x101, ?x9598), ?x2623 = 02xry >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rrwt time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.615 http://example.org/location/location/time_zones #6412-04xrx PRED entity: 04xrx PRED relation: vacationer! PRED expected values: 0162v => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 78): 03gh4 (0.35 #198, 0.15 #440, 0.15 #803), 0f2v0 (0.13 #181, 0.10 #1391, 0.07 #1755), 0160w (0.10 #123, 0.06 #2, 0.06 #607), 0chghy (0.10 #131, 0.05 #373, 0.04 #857), 02_286 (0.08 #620, 0.06 #136, 0.06 #741), 04jpl (0.07 #1946, 0.06 #130, 0.06 #735), 0b90_r (0.07 #1334, 0.06 #124, 0.06 #3), 0261m (0.06 #219, 0.06 #824, 0.05 #461), 030qb3t (0.06 #157, 0.05 #399, 0.03 #1367), 06c62 (0.06 #204, 0.05 #446, 0.03 #3353) >> Best rule #198 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 04d_mtq; >> query: (?x2614, 03gh4) <- type_of_union(?x2614, ?x566), artists(?x671, ?x2614), vacationer(?x2983, ?x2614) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #163 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 04d_mtq; *> query: (?x2614, 0162v) <- type_of_union(?x2614, ?x566), artists(?x671, ?x2614), vacationer(?x2983, ?x2614) *> conf = 0.06 ranks of expected_values: 12 EVAL 04xrx vacationer! 0162v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 149.000 149.000 0.355 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #6411-0llcx PRED entity: 0llcx PRED relation: nominated_for! PRED expected values: 0f4x7 0k611 => 77 concepts (64 used for prediction) PRED predicted values (max 10 best out of 190): 02pqp12 (0.71 #503, 0.69 #278, 0.53 #1178), 0k611 (0.67 #289, 0.67 #514, 0.53 #1639), 0gr4k (0.45 #1599, 0.40 #1149, 0.37 #249), 0gqy2 (0.43 #336, 0.41 #561, 0.37 #1686), 0f4x7 (0.41 #1598, 0.40 #1148, 0.39 #473), 099c8n (0.40 #276, 0.38 #726, 0.35 #501), 0p9sw (0.39 #18, 0.36 #243, 0.35 #468), 027dtxw (0.39 #228, 0.38 #453, 0.26 #1128), 02r22gf (0.38 #476, 0.33 #251, 0.23 #3853), 0gqyl (0.36 #295, 0.35 #520, 0.32 #1645) >> Best rule #503 for best value: >> intensional similarity = 5 >> extensional distance = 67 >> proper extension: 0qm8b; 07w8fz; 0yx7h; >> query: (?x7883, 02pqp12) <- nominated_for(?x6909, ?x7883), nominated_for(?x1313, ?x7883), ?x1313 = 0gs9p, ?x6909 = 02qyntr, film_release_distribution_medium(?x7883, ?x81) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #289 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 65 *> proper extension: 09gq0x5; 0y_9q; 0bdjd; 03pc89; *> query: (?x7883, 0k611) <- nominated_for(?x6909, ?x7883), nominated_for(?x1313, ?x7883), ?x1313 = 0gs9p, ?x6909 = 02qyntr, honored_for(?x7884, ?x7883) *> conf = 0.67 ranks of expected_values: 2, 5 EVAL 0llcx nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 64.000 0.710 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0llcx nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 64.000 0.710 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6410-04w8f PRED entity: 04w8f PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.87 #33, 0.86 #37, 0.85 #11) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 04fh3; >> query: (?x3357, 0hzc9wc) <- currency(?x3357, ?x170), adjoins(?x1603, ?x3357), ?x170 = 09nqf >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04w8f administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.866 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #6409-01kf3_9 PRED entity: 01kf3_9 PRED relation: genre PRED expected values: 01jfsb => 109 concepts (91 used for prediction) PRED predicted values (max 10 best out of 113): 01jfsb (0.75 #613, 0.73 #492, 0.71 #252), 0bkbm (0.75 #640, 0.73 #519, 0.68 #3375), 07s9rl0 (0.73 #3134, 0.71 #4698, 0.70 #3737), 07ssc (0.55 #7710, 0.53 #5541, 0.52 #5179), 05p553 (0.46 #6989, 0.37 #2776, 0.35 #7834), 01hmnh (0.42 #3993, 0.38 #4473, 0.34 #5317), 02l7c8 (0.40 #3510, 0.40 #4712, 0.35 #4231), 0lsxr (0.36 #1333, 0.35 #1454, 0.33 #9), 082gq (0.33 #6022, 0.33 #6384, 0.33 #1566), 0d2rhq (0.33 #6022, 0.33 #6384, 0.33 #1566) >> Best rule #613 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 0g5pv3; >> query: (?x1851, 01jfsb) <- nominated_for(?x6533, ?x1851), nominated_for(?x6077, ?x1851), film(?x2507, ?x1851), genre(?x1851, ?x225), language(?x1851, ?x90), ?x6533 = 02n72k, prequel(?x7713, ?x6077) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kf3_9 genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 91.000 0.750 http://example.org/film/film/genre #6408-01g257 PRED entity: 01g257 PRED relation: film PRED expected values: 0ddf2bm => 122 concepts (79 used for prediction) PRED predicted values (max 10 best out of 869): 0284b56 (0.71 #24968, 0.71 #23184, 0.69 #39235), 02qzh2 (0.12 #692, 0.10 #6042, 0.08 #13174), 035xwd (0.12 #115, 0.10 #5465, 0.06 #12597), 01qvz8 (0.12 #805, 0.06 #6155, 0.05 #2588), 0yxm1 (0.12 #750, 0.06 #6100, 0.04 #13232), 0fphf3v (0.12 #1356, 0.05 #22756, 0.05 #10272), 0m313 (0.12 #13, 0.05 #8929, 0.04 #10712), 01dc0c (0.12 #1446, 0.04 #13928, 0.04 #5013), 01shy7 (0.12 #3989, 0.10 #30741, 0.10 #32524), 0b3n61 (0.09 #3136, 0.08 #8486, 0.04 #4920) >> Best rule #24968 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 023tp8; 01csvq; 03xmy1; 018z_c; 04g4n; 0mm1q; 01wrcxr; 01vxqyl; 0f276; >> query: (?x1564, ?x4749) <- celebrity(?x1564, ?x2373), nominated_for(?x1564, ?x4749), award_winner(?x1336, ?x1564) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3456 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 01yg9y; *> query: (?x1564, 0ddf2bm) <- award_winner(?x1564, ?x157), award_winner(?x5706, ?x1564), program(?x1564, ?x631) *> conf = 0.05 ranks of expected_values: 212 EVAL 01g257 film 0ddf2bm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 122.000 79.000 0.713 http://example.org/film/actor/film./film/performance/film #6407-01fl3 PRED entity: 01fl3 PRED relation: artist! PRED expected values: 017l96 01cl2y => 79 concepts (59 used for prediction) PRED predicted values (max 10 best out of 131): 03rhqg (0.47 #1824, 0.25 #850, 0.25 #155), 033hn8 (0.47 #1683, 0.33 #292, 0.28 #1544), 0181dw (0.45 #737, 0.42 #876, 0.25 #181), 017l96 (0.37 #1132, 0.24 #1271, 0.23 #1966), 03mp8k (0.34 #1596, 0.25 #1457, 0.22 #1735), 043g7l (0.33 #448, 0.29 #1422, 0.25 #1561), 011k1h (0.33 #288, 0.25 #149, 0.19 #1540), 01clyr (0.33 #589, 0.24 #1285, 0.17 #311), 0g768 (0.33 #593, 0.21 #1010, 0.17 #454), 01cf93 (0.33 #58, 0.17 #614, 0.17 #475) >> Best rule #1824 for best value: >> intensional similarity = 6 >> extensional distance = 53 >> proper extension: 01pfr3; 04rcr; 05k79; 0frsw; 04qmr; 047cx; 013w2r; 0l8g0; 0178_w; 0ycp3; ... >> query: (?x1749, 03rhqg) <- artists(?x671, ?x1749), artist(?x3265, ?x1749), award(?x1749, ?x3045), group(?x227, ?x1749), artist(?x3265, ?x9210), ?x9210 = 03d2k >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1132 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 013qvn; *> query: (?x1749, 017l96) <- artists(?x9427, ?x1749), artists(?x2809, ?x1749), artist(?x3265, ?x1749), ?x9427 = 0m40d, parent_genre(?x497, ?x2809), parent_genre(?x2809, ?x505) *> conf = 0.37 ranks of expected_values: 4, 13 EVAL 01fl3 artist! 01cl2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 79.000 59.000 0.473 http://example.org/music/record_label/artist EVAL 01fl3 artist! 017l96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 79.000 59.000 0.473 http://example.org/music/record_label/artist #6406-0d1pc PRED entity: 0d1pc PRED relation: profession! PRED expected values: 0456xp 01fwk3 0bksh 01pctb 0lkr7 03ds83 01pcvn 026v437 05vk_d 01yf85 01x9_8 067sqt => 51 concepts (19 used for prediction) PRED predicted values (max 10 best out of 4035): 03j24kf (0.67 #21892, 0.60 #30077, 0.60 #17800), 014q2g (0.67 #21224, 0.60 #29409, 0.40 #17132), 01vvycq (0.67 #20611, 0.60 #12429, 0.40 #16519), 0144l1 (0.67 #21030, 0.53 #29215, 0.40 #16938), 01ydzx (0.67 #22563, 0.53 #30748, 0.40 #18471), 01l1sq (0.67 #20877, 0.53 #20449, 0.48 #24540), 07ss8_ (0.67 #21045, 0.53 #20449, 0.48 #24540), 01wk7ql (0.67 #23577, 0.53 #20449, 0.48 #24540), 0g824 (0.67 #22447, 0.48 #24540, 0.46 #16358), 02cx90 (0.67 #21752, 0.47 #29937, 0.40 #17660) >> Best rule #21892 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 016z4k; 0nbcg; >> query: (?x4773, 03j24kf) <- profession(?x9095, ?x4773), profession(?x7164, ?x4773), profession(?x2012, ?x4773), ?x7164 = 02fybl, award_winner(?x6331, ?x9095), award(?x9095, ?x1245), ?x2012 = 03rl84 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #13021 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0cbd2; *> query: (?x4773, 01fwk3) <- profession(?x9561, ?x4773), profession(?x9095, ?x4773), languages(?x9095, ?x7658), award_nominee(?x2531, ?x9561), ?x7658 = 02bv9 *> conf = 0.60 ranks of expected_values: 86, 169, 195, 252, 613, 884, 1479, 1614, 1709, 1721, 2314, 2581 EVAL 0d1pc profession! 067sqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 01x9_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 01yf85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 05vk_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 026v437 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 01pcvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 03ds83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 0lkr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 01pctb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 0bksh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 01fwk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 51.000 19.000 0.667 http://example.org/people/person/profession EVAL 0d1pc profession! 0456xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 19.000 0.667 http://example.org/people/person/profession #6405-0gf28 PRED entity: 0gf28 PRED relation: genre! PRED expected values: 02y_lrp 07h9gp 050f0s 0n1s0 01mszz 0pk1p 063_j5 => 43 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1828): 05cj_j (0.71 #12956, 0.67 #7517, 0.60 #3890), 03m4mj (0.62 #18329, 0.50 #2016, 0.43 #11081), 02x0fs9 (0.62 #19788, 0.50 #3475, 0.40 #7102), 0gd92 (0.62 #19428, 0.50 #3115, 0.40 #6742), 0sxns (0.62 #19204, 0.50 #2891, 0.40 #6518), 03rz2b (0.62 #18593, 0.50 #2280, 0.40 #5907), 0209xj (0.62 #18224, 0.50 #1911, 0.40 #5538), 03c7twt (0.62 #19809, 0.50 #3496, 0.33 #21620), 0sxfd (0.62 #18339, 0.50 #2026, 0.33 #216), 0jqb8 (0.62 #19667, 0.50 #3354, 0.33 #1544) >> Best rule #12956 for best value: >> intensional similarity = 15 >> extensional distance = 5 >> proper extension: 0vjs6; >> query: (?x8467, 05cj_j) <- genre(?x10873, ?x8467), genre(?x9484, ?x8467), genre(?x4888, ?x8467), genre(?x4717, ?x8467), genre(?x2207, ?x8467), genre(?x1769, ?x8467), ?x1769 = 06rmdr, nominated_for(?x2794, ?x9484), language(?x9484, ?x254), film(?x2647, ?x4717), award_winner(?x4888, ?x400), titles(?x307, ?x10873), nominated_for(?x298, ?x4717), participant(?x2647, ?x1231), executive_produced_by(?x2207, ?x496) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5124 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 02n4kr; 01jfsb; *> query: (?x8467, 063_j5) <- genre(?x9484, ?x8467), genre(?x2529, ?x8467), genre(?x1769, ?x8467), ?x1769 = 06rmdr, nominated_for(?x7648, ?x9484), genre(?x1631, ?x8467), film(?x643, ?x2529), cinematography(?x2529, ?x2530), award_winner(?x7648, ?x1585), film_crew_role(?x2529, ?x468) *> conf = 0.60 ranks of expected_values: 24, 189, 262, 472, 542, 550, 744 EVAL 0gf28 genre! 063_j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 43.000 15.000 0.714 http://example.org/film/film/genre EVAL 0gf28 genre! 0pk1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 15.000 0.714 http://example.org/film/film/genre EVAL 0gf28 genre! 01mszz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 15.000 0.714 http://example.org/film/film/genre EVAL 0gf28 genre! 0n1s0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 15.000 0.714 http://example.org/film/film/genre EVAL 0gf28 genre! 050f0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 15.000 0.714 http://example.org/film/film/genre EVAL 0gf28 genre! 07h9gp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 15.000 0.714 http://example.org/film/film/genre EVAL 0gf28 genre! 02y_lrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 43.000 15.000 0.714 http://example.org/film/film/genre #6404-03hkch7 PRED entity: 03hkch7 PRED relation: titles! PRED expected values: 03mqtr => 105 concepts (63 used for prediction) PRED predicted values (max 10 best out of 102): 03g3w (0.32 #1484, 0.31 #890, 0.24 #989), 03bxz7 (0.32 #1484, 0.31 #890, 0.24 #989), 01jfsb (0.28 #4085, 0.26 #4682, 0.19 #215), 07ssc (0.28 #799, 0.27 #1197, 0.26 #1690), 0jtdp (0.27 #117, 0.21 #316, 0.17 #513), 01z4y (0.22 #1615, 0.21 #1120, 0.18 #725), 01j28z (0.20 #187, 0.16 #386, 0.04 #1177), 01hmnh (0.19 #1903, 0.11 #2002, 0.10 #2994), 02n4kr (0.19 #210, 0.11 #4080, 0.10 #4677), 03mqtr (0.17 #1427, 0.12 #833, 0.12 #3212) >> Best rule #1484 for best value: >> intensional similarity = 5 >> extensional distance = 126 >> proper extension: 0bx_hnp; >> query: (?x3124, ?x53) <- genre(?x3124, ?x1316), genre(?x3124, ?x53), nominated_for(?x123, ?x3124), genre(?x5400, ?x1316), ?x5400 = 0bhwhj >> conf = 0.32 => this is the best rule for 2 predicted values *> Best rule #1427 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 126 *> proper extension: 0bx_hnp; *> query: (?x3124, 03mqtr) <- genre(?x3124, ?x1316), nominated_for(?x123, ?x3124), genre(?x5400, ?x1316), ?x5400 = 0bhwhj *> conf = 0.17 ranks of expected_values: 10 EVAL 03hkch7 titles! 03mqtr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 105.000 63.000 0.317 http://example.org/media_common/netflix_genre/titles #6403-0fthdk PRED entity: 0fthdk PRED relation: profession PRED expected values: 0np9r => 122 concepts (121 used for prediction) PRED predicted values (max 10 best out of 68): 0np9r (0.73 #1214, 0.55 #915, 0.52 #1064), 01d_h8 (0.47 #2540, 0.43 #3434, 0.39 #3136), 03gjzk (0.33 #2549, 0.31 #3443, 0.27 #5976), 0dxtg (0.29 #3442, 0.28 #2548, 0.28 #10895), 018gz8 (0.27 #762, 0.27 #315, 0.21 #1060), 09jwl (0.26 #3149, 0.25 #5980, 0.22 #1659), 0d1pc (0.25 #9987, 0.24 #1393, 0.23 #1691), 02krf9 (0.25 #9987, 0.09 #10610, 0.09 #11504), 0d8qb (0.25 #9987, 0.08 #80, 0.07 #229), 0dz3r (0.22 #3132, 0.21 #5963, 0.14 #1493) >> Best rule #1214 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 084x96; >> query: (?x9314, 0np9r) <- actor(?x10826, ?x9314), language(?x9314, ?x254), profession(?x9314, ?x1032) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fthdk profession 0np9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 121.000 0.730 http://example.org/people/person/profession #6402-06c62 PRED entity: 06c62 PRED relation: place_founded! PRED expected values: 026wmz6 => 260 concepts (248 used for prediction) PRED predicted values (max 10 best out of 107): 026wmz6 (0.11 #661, 0.08 #1328, 0.05 #1775), 0225z1 (0.11 #629, 0.08 #1296, 0.05 #1743), 05g8n (0.11 #625, 0.08 #1292, 0.05 #1739), 011k11 (0.11 #588, 0.08 #1255, 0.05 #1702), 0g5lhl7 (0.11 #566, 0.08 #1233, 0.05 #1680), 04fv0k (0.11 #608, 0.08 #1275, 0.05 #1722), 025txrl (0.11 #640, 0.07 #1643, 0.07 #1531), 03xsby (0.09 #895, 0.08 #1118, 0.08 #1006), 016tw3 (0.09 #893, 0.08 #1116, 0.08 #1004), 017s11 (0.09 #891, 0.08 #1114, 0.08 #1002) >> Best rule #661 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 06pr6; >> query: (?x6959, 026wmz6) <- location(?x914, ?x6959), location_of_ceremony(?x566, ?x6959), capital(?x205, ?x6959), taxonomy(?x6959, ?x939) >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06c62 place_founded! 026wmz6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 260.000 248.000 0.111 http://example.org/organization/organization/place_founded #6401-0jnh PRED entity: 0jnh PRED relation: locations PRED expected values: 0j3b => 90 concepts (54 used for prediction) PRED predicted values (max 10 best out of 323): 02j9z (0.57 #2940, 0.50 #1652, 0.44 #5148), 09c7w0 (0.57 #2557, 0.40 #6054, 0.23 #7520), 013yq (0.54 #4274, 0.42 #5373, 0.40 #5558), 0f2rq (0.46 #4326, 0.32 #5425, 0.26 #5979), 0f2r6 (0.40 #930, 0.35 #5524, 0.31 #4240), 0ftxw (0.40 #976, 0.25 #795, 0.23 #4286), 0f2tj (0.40 #1029, 0.25 #848, 0.11 #3597), 0j3b (0.40 #6054, 0.30 #4401, 0.24 #1092), 059f4 (0.40 #6054, 0.30 #4401, 0.24 #1092), 0h7h6 (0.40 #6054, 0.23 #7520, 0.21 #5358) >> Best rule #2940 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 01hwkn; >> query: (?x11109, 02j9z) <- combatants(?x11109, ?x13859), combatants(?x11109, ?x6371), combatants(?x10176, ?x13859), combatants(?x10119, ?x13859), entity_involved(?x11109, ?x5609), ?x10176 = 01gqg3, ?x6371 = 014tss, ?x10119 = 07j9n, locations(?x11109, ?x8483) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #6054 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 21 *> proper extension: 0b_6h7; *> query: (?x11109, ?x7273) <- locations(?x11109, ?x12697), locations(?x11109, ?x11920), locations(?x11109, ?x9729), locations(?x11109, ?x9283), contains(?x12094, ?x9283), adjoins(?x11920, ?x151), locations(?x6829, ?x12094), country(?x12697, ?x94), contains(?x9729, ?x6559), contains(?x7273, ?x6559) *> conf = 0.40 ranks of expected_values: 8 EVAL 0jnh locations 0j3b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 90.000 54.000 0.571 http://example.org/time/event/locations #6400-01vsykc PRED entity: 01vsykc PRED relation: award_winner! PRED expected values: 01mhwk => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 135): 013b2h (0.17 #644, 0.14 #4028, 0.13 #5861), 02rjjll (0.17 #569, 0.14 #1979, 0.13 #428), 09n4nb (0.17 #612, 0.11 #1176, 0.10 #3714), 09pnw5 (0.17 #385, 0.06 #2359, 0.05 #1090), 01c6qp (0.14 #19, 0.12 #160, 0.11 #5941), 05pd94v (0.14 #1130, 0.13 #4514, 0.11 #3668), 01s695 (0.14 #1131, 0.11 #4515, 0.10 #5925), 0jzphpx (0.13 #603, 0.11 #1167, 0.09 #2859), 05q7cj (0.12 #236, 0.02 #2492, 0.02 #1082), 073h5b (0.12 #275, 0.02 #2813, 0.01 #1685) >> Best rule #644 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 026ps1; 010hn; 02fn5r; 01dw9z; 053yx; 01309x; 0f8pz; 0gbwp; 036px; 0dzc16; ... >> query: (?x3290, 013b2h) <- spouse(?x3290, ?x4884), award_winner(?x3290, ?x1206), artists(?x671, ?x3290) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #605 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 026ps1; 010hn; 02fn5r; 01dw9z; 053yx; 01309x; 0f8pz; 0gbwp; 036px; 0dzc16; ... *> query: (?x3290, 01mhwk) <- spouse(?x3290, ?x4884), award_winner(?x3290, ?x1206), artists(?x671, ?x3290) *> conf = 0.10 ranks of expected_values: 20 EVAL 01vsykc award_winner! 01mhwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 129.000 129.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6399-0pd57 PRED entity: 0pd57 PRED relation: film! PRED expected values: 03bggl => 112 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1049): 039x1k (0.51 #81173, 0.45 #49944, 0.44 #56189), 0171lb (0.51 #81173, 0.44 #81172, 0.43 #56190), 02cqbx (0.51 #81173, 0.44 #81172, 0.43 #56190), 02sj1x (0.51 #81173, 0.44 #81172, 0.43 #56190), 01wmcbg (0.51 #66595, 0.45 #49944, 0.44 #56189), 07fzq3 (0.44 #81172, 0.43 #56190, 0.43 #64513), 09byk (0.25 #112, 0.07 #2192, 0.07 #4273), 09qh1 (0.25 #621, 0.07 #2701, 0.07 #4782), 015grj (0.25 #155, 0.05 #8477, 0.05 #10558), 0bl2g (0.25 #55, 0.04 #6297, 0.03 #18780) >> Best rule #81173 for best value: >> intensional similarity = 4 >> extensional distance = 528 >> proper extension: 02v63m; 01j8wk; 01qdmh; >> query: (?x4179, ?x7615) <- genre(?x4179, ?x225), featured_film_locations(?x4179, ?x1860), nominated_for(?x7615, ?x4179), type_of_union(?x7615, ?x566) >> conf = 0.51 => this is the best rule for 4 predicted values *> Best rule #16423 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 0gvs1kt; 0glnm; 0kb07; 0cbn7c; 0c5qvw; *> query: (?x4179, 03bggl) <- costume_design_by(?x4179, ?x5611), nominated_for(?x3519, ?x4179), nominated_for(?x1307, ?x4179), ?x1307 = 0gq9h *> conf = 0.02 ranks of expected_values: 657 EVAL 0pd57 film! 03bggl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 42.000 0.514 http://example.org/film/actor/film./film/performance/film #6398-0hj6h PRED entity: 0hj6h PRED relation: place_of_birth! PRED expected values: 045n3p => 126 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1851): 05b__vr (0.25 #121), 0cfz_z (0.14 #10433), 02rn_bj (0.06 #4344, 0.03 #9560, 0.02 #12169), 05zh9c (0.06 #3590, 0.03 #8806, 0.02 #11415), 01d8yn (0.06 #3333, 0.03 #8549, 0.02 #11158), 02cx72 (0.06 #3326, 0.03 #8542, 0.02 #11151), 0146pg (0.06 #2705, 0.03 #7921, 0.02 #10530), 02fn5r (0.06 #3106, 0.03 #8322, 0.02 #10931), 0d7hg4 (0.06 #3111, 0.03 #8327, 0.02 #10936), 02gnlz (0.06 #5162, 0.02 #12987, 0.02 #15595) >> Best rule #121 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0dhd5; >> query: (?x11914, 05b__vr) <- country(?x11914, ?x2236), ?x2236 = 05sb1, category(?x11914, ?x134), ?x134 = 08mbj5d >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hj6h place_of_birth! 045n3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 19.000 0.250 http://example.org/people/person/place_of_birth #6397-03r0g9 PRED entity: 03r0g9 PRED relation: genre PRED expected values: 07s9rl0 02kdv5l => 127 concepts (126 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.73 #1688, 0.73 #1567, 0.73 #1326), 02kdv5l (0.67 #967, 0.67 #3, 0.57 #8561), 02l7c8 (0.45 #135, 0.39 #1581, 0.36 #1461), 01hmnh (0.42 #860, 0.36 #2304, 0.36 #981), 05p553 (0.40 #1089, 0.37 #5791, 0.35 #10741), 06n90 (0.36 #855, 0.33 #734, 0.33 #372), 04xvlr (0.36 #242, 0.31 #1568, 0.27 #122), 0bkbm (0.33 #39, 0.09 #159, 0.08 #3775), 082gq (0.25 #7626, 0.19 #1717, 0.18 #1235), 060__y (0.22 #1341, 0.19 #5079, 0.18 #2546) >> Best rule #1688 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 016kz1; >> query: (?x3693, 07s9rl0) <- produced_by(?x3693, ?x3692), nominated_for(?x2379, ?x3693), ?x2379 = 02qvyrt >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03r0g9 genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 126.000 0.734 http://example.org/film/film/genre EVAL 03r0g9 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 126.000 0.734 http://example.org/film/film/genre #6396-01ckbq PRED entity: 01ckbq PRED relation: ceremony PRED expected values: 01s695 013b2h => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 133): 09n4nb (0.90 #1507, 0.75 #310, 0.70 #1108), 0gpjbt (0.87 #1489, 0.75 #292, 0.73 #558), 01s695 (0.86 #1466, 0.75 #269, 0.67 #402), 056878 (0.86 #1492, 0.67 #1093, 0.64 #561), 0466p0j (0.83 #1535, 0.75 #338, 0.73 #604), 05pd94v (0.83 #1465, 0.75 #268, 0.73 #534), 02rjjll (0.83 #1468, 0.67 #1069, 0.60 #138), 013b2h (0.83 #1539, 0.57 #1140, 0.53 #3002), 0jzphpx (0.70 #1499, 0.62 #302, 0.51 #1100), 0gx1673 (0.56 #512, 0.55 #645, 0.50 #379) >> Best rule #1507 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 02flpq; 02flqd; 01c9d1; 0257pw; >> query: (?x1479, 09n4nb) <- award(?x7581, ?x1479), ceremony(?x1479, ?x725), ?x725 = 01bx35 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1466 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 02flpq; 02flqd; 01c9d1; 0257pw; *> query: (?x1479, 01s695) <- award(?x7581, ?x1479), ceremony(?x1479, ?x725), ?x725 = 01bx35 *> conf = 0.86 ranks of expected_values: 3, 8 EVAL 01ckbq ceremony 013b2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 53.000 53.000 0.896 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01ckbq ceremony 01s695 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 53.000 53.000 0.896 http://example.org/award/award_category/winners./award/award_honor/ceremony #6395-0k_9j PRED entity: 0k_9j PRED relation: nominated_for! PRED expected values: 02qyntr => 133 concepts (126 used for prediction) PRED predicted values (max 10 best out of 222): 0p9sw (0.55 #1917, 0.50 #732, 0.46 #7131), 0gq_v (0.44 #494, 0.40 #1205, 0.40 #968), 0gq9h (0.43 #12865, 0.43 #4090, 0.40 #14288), 018wdw (0.40 #889, 0.33 #652, 0.30 #1126), 02g3v6 (0.40 #733, 0.33 #496, 0.29 #1444), 0k611 (0.38 #4101, 0.38 #1968, 0.33 #12876), 019f4v (0.37 #12856, 0.36 #4081, 0.34 #14279), 0gs9p (0.37 #12867, 0.36 #4092, 0.36 #14290), 04dn09n (0.36 #3115, 0.27 #4063, 0.27 #12838), 054krc (0.34 #1964, 0.23 #3860, 0.22 #3149) >> Best rule #1917 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 01fmys; 016z9n; >> query: (?x8107, 0p9sw) <- film(?x1387, ?x8107), nominated_for(?x669, ?x8107), titles(?x811, ?x8107), ?x669 = 0146pg >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #4208 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 06cs95; 02qjv1p; 025x1t; *> query: (?x8107, 02qyntr) <- nominated_for(?x10262, ?x8107), titles(?x811, ?x8107), edited_by(?x1812, ?x10262) *> conf = 0.33 ranks of expected_values: 11 EVAL 0k_9j nominated_for! 02qyntr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 133.000 126.000 0.552 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6394-01c65z PRED entity: 01c65z PRED relation: actor! PRED expected values: 0fkwzs => 117 concepts (71 used for prediction) PRED predicted values (max 10 best out of 123): 02kwcj (0.71 #2388, 0.61 #1589, 0.56 #1856), 02py4c8 (0.33 #12, 0.14 #542, 0.08 #1601), 03ffcz (0.33 #122, 0.14 #652, 0.03 #2244), 05ldxl (0.31 #9793, 0.31 #9264, 0.31 #7148), 0b6m5fy (0.20 #382, 0.03 #2239), 019nnl (0.14 #549, 0.01 #7960, 0.01 #3992), 01p4wv (0.14 #623, 0.01 #4332), 02md2d (0.14 #601), 0bxxzb (0.09 #2387, 0.09 #1588, 0.07 #2121), 04ydr95 (0.09 #2387, 0.09 #1588, 0.07 #2121) >> Best rule #2388 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 069z_5; 0223g8; >> query: (?x12448, ?x13179) <- film(?x12448, ?x13179), film(?x12448, ?x3532), location(?x12448, ?x4510), film_crew_role(?x3532, ?x137), actor(?x13179, ?x256) >> conf = 0.71 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01c65z actor! 0fkwzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 71.000 0.710 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #6393-0vg8x PRED entity: 0vg8x PRED relation: contains! PRED expected values: 02gt5s => 114 concepts (72 used for prediction) PRED predicted values (max 10 best out of 189): 09c7w0 (0.86 #1794, 0.79 #53744, 0.79 #49259), 0nj7b (0.75 #16121, 0.74 #19705, 0.72 #15225), 0nj07 (0.27 #1299, 0.14 #2194, 0.13 #3089), 0njlp (0.22 #608, 0.09 #3294, 0.07 #5084), 01n7q (0.21 #11717, 0.20 #14407, 0.20 #17096), 04_1l0v (0.19 #6716, 0.16 #12986, 0.14 #7612), 02gt5s (0.19 #5172, 0.17 #3382, 0.15 #4277), 059rby (0.13 #28680, 0.11 #43007, 0.08 #57349), 07ssc (0.12 #8985, 0.11 #45707, 0.11 #50189), 05k7sb (0.12 #7294, 0.09 #18941, 0.08 #6398) >> Best rule #1794 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 02w2bc; 01j_cy; 015fs3; 037q2p; 02grjf; 04gxp2; >> query: (?x9666, 09c7w0) <- contains(?x1906, ?x9666), category(?x9666, ?x134), ?x134 = 08mbj5d, ?x1906 = 04rrx >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #5172 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 07szy; *> query: (?x9666, 02gt5s) <- contains(?x1906, ?x9666), time_zones(?x9666, ?x2674), ?x1906 = 04rrx, ?x2674 = 02hcv8 *> conf = 0.19 ranks of expected_values: 7 EVAL 0vg8x contains! 02gt5s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 114.000 72.000 0.864 http://example.org/location/location/contains #6392-01bvx1 PRED entity: 01bvx1 PRED relation: service_location PRED expected values: 0d060g => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 144): 0d060g (0.43 #7927, 0.37 #5974, 0.33 #5484), 02j71 (0.33 #406, 0.30 #4024, 0.30 #3440), 0chghy (0.33 #595, 0.30 #888, 0.29 #497), 05v8c (0.29 #502, 0.22 #600, 0.20 #893), 03_3d (0.24 #10077, 0.23 #11842, 0.23 #11941), 07ssc (0.22 #599, 0.21 #6862, 0.20 #3925), 0f8l9c (0.22 #605, 0.20 #898, 0.18 #996), 0345h (0.17 #1295, 0.15 #7947, 0.15 #3547), 03rt9 (0.17 #401, 0.14 #498, 0.11 #596), 02vzc (0.17 #423, 0.14 #520, 0.11 #618) >> Best rule #7927 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 018mxj; 0hm0k; 064f29; 049mr; 02brqp; 0z07; 07zl6m; 06rfy5; >> query: (?x12044, 0d060g) <- industry(?x12044, ?x11691), contact_category(?x12044, ?x897), service_location(?x12044, ?x94), service_language(?x12044, ?x254), category(?x12044, ?x134) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bvx1 service_location 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 220.000 220.000 0.426 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #6391-02fj8n PRED entity: 02fj8n PRED relation: language PRED expected values: 01wgr => 106 concepts (99 used for prediction) PRED predicted values (max 10 best out of 43): 06b_j (0.33 #420, 0.17 #877, 0.16 #991), 064_8sq (0.29 #647, 0.25 #419, 0.23 #533), 02bjrlw (0.25 #343, 0.25 #172, 0.16 #742), 04306rv (0.25 #289, 0.25 #175, 0.16 #688), 06nm1 (0.18 #807, 0.17 #465, 0.16 #979), 01r2l (0.17 #479, 0.12 #365, 0.11 #764), 0jzc (0.15 #531, 0.08 #474, 0.08 #988), 012w70 (0.11 #809, 0.08 #581, 0.08 #524), 0653m (0.11 #808, 0.08 #1037, 0.06 #1209), 03_9r (0.08 #464, 0.08 #3816, 0.08 #521) >> Best rule #420 for best value: >> intensional similarity = 8 >> extensional distance = 10 >> proper extension: 01hr1; 01_mdl; 05qbckf; 024mpp; 0dc_ms; 042fgh; >> query: (?x7463, 06b_j) <- genre(?x7463, ?x11401), film(?x3495, ?x7463), genre(?x1046, ?x11401), genre(?x1035, ?x11401), story_by(?x7463, ?x8582), ?x1035 = 08hmch, written_by(?x7463, ?x7106), ?x1046 = 02qm_f >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #951 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 45 *> proper extension: 014lc_; 0bth54; 01cssf; 04fzfj; 02sg5v; 017gl1; 0bwfwpj; 0g5pv3; 0gj9tn5; 02vqhv0; ... *> query: (?x7463, 01wgr) <- genre(?x7463, ?x11401), film(?x3495, ?x7463), genre(?x1035, ?x11401), story_by(?x7463, ?x8582), ?x1035 = 08hmch, written_by(?x7463, ?x7106), music(?x7463, ?x3414) *> conf = 0.02 ranks of expected_values: 31 EVAL 02fj8n language 01wgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 106.000 99.000 0.333 http://example.org/film/film/language #6390-01xcqc PRED entity: 01xcqc PRED relation: film PRED expected values: 04sh80 => 137 concepts (83 used for prediction) PRED predicted values (max 10 best out of 709): 0gzy02 (0.33 #43, 0.14 #5410, 0.11 #7199), 0bx0l (0.33 #348, 0.14 #5715, 0.11 #7504), 032016 (0.33 #503, 0.14 #5870, 0.11 #7659), 0fsd9t (0.25 #3275, 0.01 #40845), 027fwmt (0.20 #5170, 0.10 #14115, 0.06 #30216), 08rr3p (0.20 #4021, 0.05 #12966, 0.03 #29067), 09g8vhw (0.20 #3903, 0.05 #12848, 0.03 #28949), 01gvpz (0.20 #5085, 0.05 #14030, 0.03 #30131), 02vnmc9 (0.20 #4925, 0.05 #13870, 0.03 #29971), 0qm98 (0.20 #3800, 0.05 #12745, 0.03 #28846) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0b_fw; >> query: (?x1606, 0gzy02) <- people(?x3984, ?x1606), participant(?x6440, ?x1606), ?x6440 = 0bdt8, nationality(?x1606, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #53630 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 163 *> proper extension: 0785v8; *> query: (?x1606, 04sh80) <- award(?x1606, ?x3066), ?x3066 = 0gqy2, nationality(?x1606, ?x94) *> conf = 0.02 ranks of expected_values: 288 EVAL 01xcqc film 04sh80 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 137.000 83.000 0.333 http://example.org/film/actor/film./film/performance/film #6389-0p51w PRED entity: 0p51w PRED relation: nationality PRED expected values: 0h7x => 160 concepts (159 used for prediction) PRED predicted values (max 10 best out of 70): 0h7x (0.80 #3777, 0.45 #3080, 0.44 #1787), 01nhhz (0.45 #3080, 0.44 #1787), 07ssc (0.40 #14, 0.35 #12234, 0.17 #1701), 02jx1 (0.33 #131, 0.20 #32, 0.17 #1719), 0345h (0.17 #228, 0.07 #1420, 0.06 #2514), 0d060g (0.11 #1396, 0.06 #899, 0.05 #501), 03rk0 (0.09 #5914, 0.08 #7904, 0.08 #4619), 0f8l9c (0.08 #1708, 0.07 #3001, 0.04 #3697), 03rjj (0.07 #698, 0.05 #499, 0.05 #301), 03gj2 (0.04 #1415, 0.02 #1712, 0.01 #3005) >> Best rule #3777 for best value: >> intensional similarity = 3 >> extensional distance = 403 >> proper extension: 045hz5; >> query: (?x2800, ?x1355) <- place_of_birth(?x2800, ?x863), award(?x2800, ?x198), country(?x863, ?x1355) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p51w nationality 0h7x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 159.000 0.802 http://example.org/people/person/nationality #6388-05jjl PRED entity: 05jjl PRED relation: student! PRED expected values: 02mzg9 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 97): 0lbfv (0.12 #747, 0.04 #1272, 0.01 #3372), 01w5m (0.11 #105, 0.06 #5355, 0.06 #1155), 03ksy (0.08 #631, 0.08 #7457, 0.05 #5356), 065y4w7 (0.06 #7365, 0.06 #1589, 0.05 #5264), 09f2j (0.06 #1733, 0.04 #7509, 0.04 #21689), 07tg4 (0.06 #86, 0.04 #611, 0.03 #1661), 02sdwt (0.06 #400, 0.04 #925, 0.03 #1975), 03bmmc (0.06 #195, 0.04 #720, 0.01 #1245), 07wjk (0.06 #63, 0.02 #2688, 0.01 #3738), 02g839 (0.06 #25, 0.02 #11577, 0.02 #21556) >> Best rule #747 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 01v9724; >> query: (?x8683, 0lbfv) <- profession(?x8683, ?x6421), profession(?x8683, ?x987), ?x987 = 0dxtg, religion(?x8683, ?x7131), ?x6421 = 02hv44_ >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05jjl student! 02mzg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.120 http://example.org/education/educational_institution/students_graduates./education/education/student #6387-015rmq PRED entity: 015rmq PRED relation: award PRED expected values: 024vjd => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 311): 02581c (0.79 #6876, 0.79 #2833, 0.78 #6875), 024vjd (0.79 #6876, 0.79 #2833, 0.78 #6875), 024_dt (0.79 #6876, 0.79 #2833, 0.78 #6875), 0257__ (0.43 #790, 0.02 #6450, 0.02 #2004), 01by1l (0.36 #2541, 0.35 #6583, 0.31 #5775), 0257w4 (0.33 #148, 0.29 #554, 0.06 #2172), 0257wh (0.33 #340, 0.14 #746, 0.04 #2364), 02sp_v (0.29 #570, 0.17 #164, 0.08 #976), 054krc (0.28 #6154, 0.14 #8583, 0.13 #2112), 01bgqh (0.27 #6919, 0.25 #5300, 0.25 #6513) >> Best rule #6876 for best value: >> intensional similarity = 4 >> extensional distance = 213 >> proper extension: 0cj2w; >> query: (?x1373, ?x12458) <- award_winner(?x12458, ?x1373), award_nominee(?x8583, ?x1373), role(?x1373, ?x316), award(?x352, ?x12458) >> conf = 0.79 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 015rmq award 024vjd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 127.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6386-07zl1 PRED entity: 07zl1 PRED relation: influenced_by! PRED expected values: 01zkxv => 107 concepts (36 used for prediction) PRED predicted values (max 10 best out of 558): 0j0pf (0.24 #2267, 0.24 #3299, 0.23 #2784), 05jm7 (0.20 #2718, 0.18 #5297, 0.18 #3233), 067xw (0.20 #800, 0.08 #5441, 0.07 #2345), 040db (0.18 #9357, 0.10 #14510, 0.08 #16056), 01zkxv (0.17 #2593, 0.15 #3108, 0.11 #4655), 01dzz7 (0.17 #1083, 0.14 #2113, 0.13 #2630), 0683n (0.14 #9620, 0.09 #14773, 0.07 #16319), 02yl42 (0.13 #4774, 0.12 #9416, 0.12 #5808), 01d494 (0.11 #3659, 0.11 #4175, 0.04 #9332), 03772 (0.11 #1233, 0.10 #2780, 0.09 #3295) >> Best rule #2267 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 02ghq; >> query: (?x10438, 0j0pf) <- award(?x10438, ?x8880), award(?x10438, ?x5050), ?x8880 = 0262x6, award_winner(?x5050, ?x1287) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #2593 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 07w21; 09dt7; 01963w; 0p8jf; 01dhmw; 04mhl; 03vrp; 018fq; 0g5ff; 05x8n; ... *> query: (?x10438, 01zkxv) <- award(?x10438, ?x9285), award_winner(?x575, ?x10438), ?x9285 = 0265vt, gender(?x10438, ?x231) *> conf = 0.17 ranks of expected_values: 5 EVAL 07zl1 influenced_by! 01zkxv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 107.000 36.000 0.241 http://example.org/influence/influence_node/influenced_by #6385-0157m PRED entity: 0157m PRED relation: basic_title PRED expected values: 060c4 => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 17): 060c4 (0.43 #615, 0.40 #479, 0.39 #360), 0dq3c (0.29 #19, 0.21 #478, 0.17 #359), 01gkgk (0.29 #141, 0.19 #464, 0.18 #311), 0789n (0.18 #315, 0.17 #366, 0.17 #468), 0p5vf (0.14 #147, 0.12 #79, 0.12 #62), 060bp (0.14 #137, 0.11 #681, 0.10 #460), 0pqc5 (0.12 #72, 0.12 #55, 0.09 #106), 01q24l (0.09 #114, 0.07 #148, 0.06 #318), 04syw (0.09 #108, 0.02 #482, 0.01 #686), 02079p (0.05 #367, 0.04 #486, 0.03 #673) >> Best rule #615 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 05wh0sh; 0424m; 012bk; 042d1; 0b22w; 030dr; 08849; 0835q; 079dy; 081t6; >> query: (?x1620, 060c4) <- type_of_union(?x1620, ?x566), basic_title(?x1620, ?x900), profession(?x1620, ?x2225) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0157m basic_title 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.429 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #6384-0pmw9 PRED entity: 0pmw9 PRED relation: profession PRED expected values: 028kk_ => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 56): 0nbcg (0.57 #1031, 0.55 #1890, 0.51 #744), 0dz3r (0.49 #1006, 0.46 #432, 0.45 #288), 016z4k (0.40 #721, 0.36 #1008, 0.36 #2441), 039v1 (0.39 #1036, 0.36 #1895, 0.31 #749), 01d_h8 (0.28 #3725, 0.27 #3301, 0.26 #2872), 0dxtg (0.28 #3725, 0.26 #4311, 0.26 #4597), 03gjzk (0.28 #3725, 0.25 #15, 0.22 #3310), 0fnpj (0.28 #3725, 0.21 #629, 0.17 #1059), 0n1h (0.28 #3725, 0.19 #298, 0.17 #442), 02hv44_ (0.28 #3725, 0.17 #52, 0.04 #2632) >> Best rule #1031 for best value: >> intensional similarity = 2 >> extensional distance = 205 >> proper extension: 01pbxb; 0f0y8; 03c7ln; 01vw87c; 01vrx3g; 0fp_v1x; 0m2l9; 032t2z; 0kzy0; 025xt8y; ... >> query: (?x4566, 0nbcg) <- role(?x4566, ?x1166), role(?x4566, ?x316) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #3725 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1538 *> proper extension: 032dg7; *> query: (?x4566, ?x220) <- award_winner(?x1413, ?x4566), profession(?x1413, ?x220) *> conf = 0.28 ranks of expected_values: 12 EVAL 0pmw9 profession 028kk_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 50.000 50.000 0.575 http://example.org/people/person/profession #6383-0n5_t PRED entity: 0n5_t PRED relation: adjoins! PRED expected values: 0nm3n => 142 concepts (44 used for prediction) PRED predicted values (max 10 best out of 438): 0n5y4 (0.50 #492, 0.43 #1275, 0.25 #13335), 0n5_t (0.25 #13335, 0.25 #8626, 0.25 #660), 0mpfn (0.25 #13335, 0.25 #8626, 0.25 #242), 0nm3n (0.25 #13335, 0.25 #326, 0.25 #19608), 0nm9h (0.25 #13335, 0.25 #19608, 0.24 #18039), 0n5yh (0.25 #246, 0.18 #29028, 0.14 #1029), 0n5xb (0.18 #29028, 0.14 #1491, 0.10 #3918), 0n5_g (0.14 #1183, 0.10 #3918, 0.09 #7840), 0gj4fx (0.14 #1476, 0.10 #3918, 0.08 #3044), 0czr9_ (0.14 #1502, 0.10 #3918, 0.08 #3070) >> Best rule #492 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0n5yv; 0n5xb; >> query: (?x12433, 0n5y4) <- source(?x12433, ?x958), adjoins(?x7565, ?x12433), contains(?x728, ?x12433), ?x728 = 059f4, ?x958 = 0jbk9 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13335 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 241 *> proper extension: 01279v; 035p3; *> query: (?x12433, ?x7330) <- adjoins(?x7954, ?x12433), adjoins(?x7565, ?x12433), adjoins(?x7954, ?x7330), contains(?x7058, ?x7954), contains(?x7565, ?x7564), second_level_divisions(?x94, ?x7565) *> conf = 0.25 ranks of expected_values: 4 EVAL 0n5_t adjoins! 0nm3n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 142.000 44.000 0.500 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #6382-058ncz PRED entity: 058ncz PRED relation: student! PRED expected values: 04b_46 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 74): 04b_46 (0.22 #2857, 0.07 #753, 0.06 #227), 078bz (0.07 #603, 0.06 #77, 0.04 #1655), 0trv (0.06 #318, 0.03 #844, 0.03 #1370), 01qgr3 (0.06 #267, 0.03 #793, 0.03 #1319), 0ks67 (0.06 #189, 0.03 #715, 0.03 #1241), 053mhx (0.06 #294, 0.03 #820, 0.02 #1872), 01k3s2 (0.06 #140, 0.03 #666, 0.02 #1718), 017zq0 (0.06 #33, 0.03 #559, 0.02 #1611), 015nl4 (0.05 #3223, 0.05 #14796, 0.04 #5853), 017z88 (0.05 #3764, 0.04 #4816, 0.04 #5868) >> Best rule #2857 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 04bs3j; 01wjrn; 012_53; 01z0rcq; 02yplc; 0c9xjl; 041_y; 02yy_j; 02_pft; 0405l; ... >> query: (?x515, 04b_46) <- student(?x7545, ?x515), nationality(?x515, ?x94), ?x7545 = 0bwfn >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 058ncz student! 04b_46 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.218 http://example.org/education/educational_institution/students_graduates./education/education/student #6381-0b_dy PRED entity: 0b_dy PRED relation: film PRED expected values: 08sfxj => 75 concepts (57 used for prediction) PRED predicted values (max 10 best out of 285): 05z43v (0.68 #7119, 0.65 #5339, 0.58 #56946), 0hv81 (0.68 #7119, 0.45 #26691, 0.41 #26690), 092vkg (0.20 #154, 0.08 #1934, 0.07 #7273), 0gg5qcw (0.20 #867, 0.03 #90761, 0.03 #33809), 035s95 (0.20 #335, 0.03 #33809, 0.01 #14571), 06z8s_ (0.15 #1907, 0.07 #7246, 0.07 #5466), 0dj0m5 (0.14 #7214, 0.14 #5434, 0.14 #3654), 07sgdw (0.10 #803, 0.08 #2583, 0.07 #7922), 0gvt53w (0.10 #1550, 0.06 #49827, 0.03 #90761), 03hj5lq (0.10 #1043, 0.06 #49827, 0.03 #90761) >> Best rule #7119 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02j9lm; >> query: (?x3139, ?x1820) <- award_nominee(?x3139, ?x8147), ?x8147 = 01tnxc, award_winner(?x1820, ?x3139) >> conf = 0.68 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0b_dy film 08sfxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 57.000 0.684 http://example.org/film/actor/film./film/performance/film #6380-034np8 PRED entity: 034np8 PRED relation: profession PRED expected values: 01d_h8 => 88 concepts (86 used for prediction) PRED predicted values (max 10 best out of 71): 01d_h8 (0.50 #1311, 0.49 #3196, 0.34 #6533), 0cbd2 (0.45 #2327, 0.44 #2182, 0.43 #1892), 02jknp (0.42 #3198, 0.33 #8, 0.31 #1313), 08z956 (0.33 #75, 0.02 #510, 0.02 #1815), 0kyk (0.32 #1476, 0.30 #2346, 0.30 #2201), 0q04f (0.27 #8995, 0.25 #10447, 0.04 #386), 021wpb (0.27 #8995, 0.25 #10447, 0.04 #339), 09jwl (0.25 #10447, 0.18 #1031, 0.16 #8575), 0d1pc (0.25 #10447, 0.16 #337, 0.11 #1062), 02krf9 (0.25 #168, 0.15 #3213, 0.13 #1763) >> Best rule #1311 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 085pr; 0l99s; 079ws; 02dlfh; 02js_6; >> query: (?x1814, 01d_h8) <- nominated_for(?x1814, ?x7141), profession(?x1814, ?x987), influenced_by(?x1814, ?x397) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034np8 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 86.000 0.504 http://example.org/people/person/profession #6379-03c6sl9 PRED entity: 03c6sl9 PRED relation: season! PRED expected values: 0512p => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 338): 0512p (0.76 #5, 0.64 #16, 0.64 #15), 051wf (0.76 #5, 0.49 #33, 0.37 #55), 0jmj7 (0.64 #16, 0.64 #15, 0.59 #41), 0jm64 (0.64 #16, 0.64 #15, 0.59 #41), 06rpd (0.64 #16, 0.64 #15, 0.59 #41), 0jmk7 (0.64 #16, 0.64 #15, 0.59 #41), 0jm3b (0.64 #16, 0.64 #15, 0.59 #41), 06rny (0.64 #16, 0.64 #15, 0.59 #41), 0jml5 (0.64 #16, 0.64 #15, 0.59 #41), 03wnh (0.64 #16, 0.64 #15, 0.49 #33) >> Best rule #5 for best value: >> intensional similarity = 104 >> extensional distance = 1 >> proper extension: 025ygws; >> query: (?x2406, ?x1438) <- season(?x12042, ?x2406), season(?x10939, ?x2406), season(?x10279, ?x2406), season(?x8901, ?x2406), season(?x8894, ?x2406), season(?x7399, ?x2406), season(?x7060, ?x2406), season(?x6823, ?x2406), season(?x6074, ?x2406), season(?x3333, ?x2406), season(?x2174, ?x2406), season(?x1823, ?x2406), season(?x1632, ?x2406), season(?x1160, ?x2406), season(?x700, ?x2406), season(?x580, ?x2406), season(?x260, ?x2406), ?x12042 = 05xvj, school(?x10279, ?x6814), school(?x10279, ?x735), school(?x10279, ?x466), season(?x8894, ?x10017), season(?x8894, ?x8517), season(?x8894, ?x3431), draft(?x10279, ?x10600), draft(?x10279, ?x4779), draft(?x10279, ?x1161), school(?x8894, ?x8202), school(?x8894, ?x6455), school(?x8894, ?x5907), school(?x8894, ?x5486), school(?x8894, ?x4556), ?x8901 = 07l4z, ?x3431 = 025ygqm, ?x5907 = 01jq4b, ?x10939 = 0x0d, team(?x4244, ?x8894), team(?x2010, ?x8894), ?x1632 = 0cqt41, ?x7060 = 01slc, school(?x260, ?x5621), school(?x260, ?x4209), school(?x260, ?x1428), institution(?x4981, ?x4556), ?x6074 = 02__x, ?x700 = 06x68, team(?x5412, ?x260), contains(?x3634, ?x8202), ?x1428 = 01j_06, team(?x11844, ?x260), major_field_of_study(?x4556, ?x888), organization(?x346, ?x4556), colors(?x260, ?x1101), colors(?x4556, ?x3621), institution(?x2636, ?x5486), position(?x260, ?x10822), position(?x260, ?x7724), major_field_of_study(?x5486, ?x254), ?x4779 = 02z6872, teams(?x4144, ?x6823), school(?x6823, ?x3779), ?x346 = 060c4, school(?x1883, ?x5486), ?x1883 = 02qw1zx, ?x1161 = 02x2khw, student(?x5486, ?x118), ?x1823 = 01yhm, citytown(?x4556, ?x3521), major_field_of_study(?x8202, ?x2606), ?x10017 = 026fmqm, season(?x1438, ?x8517), team(?x12323, ?x10279), school_type(?x4556, ?x3092), ?x2010 = 02lyr4, category(?x8202, ?x134), citytown(?x6455, ?x4151), ?x2174 = 051vz, ?x7399 = 06wpc, ?x10600 = 04f4z1k, contains(?x94, ?x5621), ?x7724 = 02rsl1, citytown(?x5486, ?x2298), school(?x685, ?x5621), ?x6814 = 03tw2s, contains(?x1426, ?x5486), school(?x580, ?x4916), colors(?x481, ?x1101), major_field_of_study(?x6455, ?x5900), ?x4244 = 028c_8, ?x2606 = 062z7, school_type(?x4209, ?x4994), ?x2636 = 027f2w, ?x735 = 065y4w7, colors(?x580, ?x332), school(?x8133, ?x4916), student(?x8202, ?x2248), ?x10822 = 017drs, sport(?x8894, ?x5063), team(?x10434, ?x260), citytown(?x4916, ?x5775), ?x5900 = 0db86, ?x466 = 01pl14, ?x3333 = 01yjl, ?x1160 = 049n7 >> conf = 0.76 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 03c6sl9 season! 0512p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.758 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #6378-0f4zv PRED entity: 0f4zv PRED relation: contains! PRED expected values: 059rby => 124 concepts (93 used for prediction) PRED predicted values (max 10 best out of 210): 059rby (0.78 #7180, 0.64 #18864, 0.62 #68273), 09c7w0 (0.48 #70070, 0.48 #69175, 0.47 #901), 0f4zv (0.40 #63781, 0.25 #70071, 0.24 #49408), 04_1l0v (0.35 #1349, 0.22 #12126, 0.20 #37284), 03rjj (0.25 #72769, 0.06 #28758, 0.04 #53909), 01n7q (0.24 #78, 0.16 #8157, 0.16 #41402), 02qkt (0.15 #71318, 0.14 #73116, 0.13 #80307), 07z1m (0.13 #1887, 0.11 #5477, 0.11 #2784), 05tbn (0.13 #22680, 0.12 #36158, 0.10 #46037), 07ssc (0.12 #35068, 0.12 #28780, 0.12 #16200) >> Best rule #7180 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 01w0v; 02ly_; 0ht8h; 0dmy0; 09cpb; 021y1s; 03lrc; 03msf; 0glh3; 0htx8; ... >> query: (?x11833, ?x335) <- contains(?x11833, ?x3689), second_level_divisions(?x94, ?x11833), state(?x3689, ?x335) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f4zv contains! 059rby CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 93.000 0.776 http://example.org/location/location/contains #6377-039n1 PRED entity: 039n1 PRED relation: profession PRED expected values: 05t4q => 164 concepts (128 used for prediction) PRED predicted values (max 10 best out of 110): 0dxtg (0.67 #907, 0.50 #1056, 0.48 #10601), 02hv44_ (0.62 #1101, 0.62 #1548, 0.50 #952), 05z96 (0.50 #1086, 0.46 #1533, 0.36 #2278), 02hrh1q (0.47 #11200, 0.45 #10751, 0.45 #10602), 0kyk (0.46 #1520, 0.46 #7036, 0.44 #5098), 06q2q (0.33 #641, 0.31 #16405, 0.26 #2876), 05snw (0.33 #689, 0.31 #16405, 0.18 #4862), 04s2z (0.33 #660, 0.31 #16405, 0.15 #2746), 01c72t (0.31 #16405, 0.29 #11186, 0.29 #1663), 016fly (0.31 #16405, 0.29 #11186, 0.23 #4545) >> Best rule #907 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 02lt8; 0dw6b; 01vh096; 0113sg; >> query: (?x9600, 0dxtg) <- influenced_by(?x3336, ?x9600), place_of_death(?x9600, ?x1646), ?x3336 = 032l1, influenced_by(?x9600, ?x3712), people(?x5540, ?x9600) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7606 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 83 *> proper extension: 053yx; 0btj0; *> query: (?x9600, ?x353) <- influenced_by(?x3336, ?x9600), influenced_by(?x1236, ?x9600), place_of_death(?x9600, ?x1646), people(?x5784, ?x3336), profession(?x1236, ?x2225), profession(?x1236, ?x353), ?x2225 = 0kyk *> conf = 0.22 ranks of expected_values: 22 EVAL 039n1 profession 05t4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 164.000 128.000 0.667 http://example.org/people/person/profession #6376-0306bt PRED entity: 0306bt PRED relation: award PRED expected values: 0ck27z => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 244): 09sb52 (0.56 #848, 0.37 #444, 0.36 #9736), 023vrq (0.33 #326, 0.03 #730, 0.02 #7194), 02f75t (0.33 #259, 0.03 #663, 0.02 #2279), 02f76h (0.33 #177, 0.03 #581, 0.01 #10681), 0ck27z (0.31 #4536, 0.30 #1304, 0.28 #2516), 0cqhk0 (0.20 #1248, 0.19 #1652, 0.18 #2460), 0gqwc (0.19 #3710, 0.18 #25859, 0.17 #4114), 0gkts9 (0.18 #25859, 0.16 #20201, 0.12 #37173), 05b4l5x (0.18 #25859, 0.16 #20201, 0.10 #4046), 0gqyl (0.17 #4145, 0.17 #105, 0.16 #3741) >> Best rule #848 for best value: >> intensional similarity = 3 >> extensional distance = 210 >> proper extension: 02bwjv; >> query: (?x9670, 09sb52) <- film(?x9670, ?x3317), award_winner(?x9670, ?x5821), celebrity(?x5821, ?x4397) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #4536 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 601 *> proper extension: 076df9; *> query: (?x9670, 0ck27z) <- award_nominee(?x9670, ?x1397), actor(?x782, ?x9670) *> conf = 0.31 ranks of expected_values: 5 EVAL 0306bt award 0ck27z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 108.000 0.557 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6375-016fjj PRED entity: 016fjj PRED relation: award_winner! PRED expected values: 0gqy2 => 92 concepts (73 used for prediction) PRED predicted values (max 10 best out of 165): 09sb52 (0.36 #30477, 0.31 #27469, 0.31 #28758), 057xs89 (0.36 #30477, 0.31 #27469, 0.31 #28758), 04ljl_l (0.36 #30477, 0.31 #27469, 0.31 #28758), 0bdwqv (0.36 #30477, 0.31 #27469, 0.31 #28758), 0789_m (0.36 #30477, 0.31 #27469, 0.31 #28758), 08_vwq (0.36 #30477, 0.31 #27469, 0.31 #28758), 027c95y (0.20 #156, 0.12 #585, 0.11 #1014), 0f4x7 (0.20 #30, 0.12 #459, 0.11 #888), 02w9sd7 (0.20 #166, 0.12 #595, 0.11 #1024), 09cm54 (0.20 #95, 0.12 #524, 0.11 #953) >> Best rule #30477 for best value: >> intensional similarity = 2 >> extensional distance = 2276 >> proper extension: 089tm; 01pfr3; 04f525m; 01v0sx2; 022_lg; 01vsxdm; 01wv9xn; 040db; 0frsw; 016fmf; ... >> query: (?x3701, ?x102) <- award_winner(?x112, ?x3701), award(?x3701, ?x102) >> conf = 0.36 => this is the best rule for 6 predicted values *> Best rule #16737 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1462 *> proper extension: 0hm0k; *> query: (?x3701, ?x500) <- award_winner(?x5129, ?x3701), award(?x5129, ?x500) *> conf = 0.16 ranks of expected_values: 11 EVAL 016fjj award_winner! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 92.000 73.000 0.364 http://example.org/award/award_category/winners./award/award_honor/award_winner #6374-083chw PRED entity: 083chw PRED relation: award_winner! PRED expected values: 092c5f => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 97): 027n06w (0.18 #211, 0.17 #5422, 0.11 #8620), 09g90vz (0.17 #5422, 0.14 #122, 0.11 #8620), 092c5f (0.17 #5422, 0.14 #14, 0.11 #8620), 03gwpw2 (0.17 #5422, 0.14 #9, 0.11 #8620), 0gx_st (0.17 #5422, 0.11 #8620, 0.04 #871), 05zksls (0.17 #5422, 0.11 #8620, 0.02 #1147), 03gt46z (0.17 #5422, 0.11 #8620, 0.02 #896), 09pj68 (0.17 #5422, 0.04 #382, 0.02 #2050), 09p3h7 (0.11 #8620, 0.04 #348, 0.02 #1182), 09qvms (0.06 #2376, 0.06 #847, 0.05 #2098) >> Best rule #211 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0cmt6q; >> query: (?x275, 027n06w) <- award_nominee(?x7752, ?x275), film(?x275, ?x1474), ?x7752 = 05l0j5 >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #5422 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1330 *> proper extension: 05b2f_k; *> query: (?x275, ?x3624) <- award_winner(?x275, ?x4333), award_nominee(?x4333, ?x679), award_winner(?x3624, ?x4333) *> conf = 0.17 ranks of expected_values: 3 EVAL 083chw award_winner! 092c5f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 95.000 0.182 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6373-069d71 PRED entity: 069d71 PRED relation: sibling! PRED expected values: 069d68 => 101 concepts (51 used for prediction) PRED predicted values (max 10 best out of 2): 016cff (0.03 #880, 0.02 #1111, 0.02 #1226), 015z4j (0.03 #836, 0.02 #1067, 0.02 #1182) >> Best rule #880 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 03l295; 01gct2; 095nx; 02cg2v; >> query: (?x13333, 016cff) <- athlete(?x1557, ?x13333), gender(?x13333, ?x231), country(?x1557, ?x4521), ?x4521 = 07fj_ >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 069d71 sibling! 069d68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 51.000 0.033 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #6372-011zd3 PRED entity: 011zd3 PRED relation: profession PRED expected values: 09jwl => 117 concepts (116 used for prediction) PRED predicted values (max 10 best out of 66): 02jknp (0.57 #155, 0.29 #303, 0.23 #600), 0dxtg (0.30 #6823, 0.29 #309, 0.29 #161), 03gjzk (0.29 #162, 0.27 #1791, 0.27 #2532), 0d1pc (0.29 #198, 0.25 #14657, 0.23 #1087), 09jwl (0.25 #14657, 0.21 #2980, 0.21 #3128), 018gz8 (0.25 #14657, 0.20 #2385, 0.14 #164), 0np9r (0.25 #14657, 0.17 #20, 0.16 #2389), 0dz3r (0.25 #14657, 0.14 #2223, 0.13 #891), 016z4k (0.25 #14657, 0.13 #3854, 0.13 #3114), 01xr66 (0.25 #14657, 0.06 #360, 0.02 #657) >> Best rule #155 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 041c4; >> query: (?x2307, 02jknp) <- film(?x2307, ?x5608), ?x5608 = 01l_pn, award_winner(?x5386, ?x2307) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #14657 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2267 *> proper extension: 0phx4; *> query: (?x2307, ?x220) <- award_nominee(?x2373, ?x2307), profession(?x2373, ?x220) *> conf = 0.25 ranks of expected_values: 5 EVAL 011zd3 profession 09jwl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 117.000 116.000 0.571 http://example.org/people/person/profession #6371-04smkr PRED entity: 04smkr PRED relation: people! PRED expected values: 03bkbh => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 44): 033tf_ (0.24 #229, 0.18 #897, 0.17 #674), 0x67 (0.23 #10, 0.21 #2603, 0.20 #307), 041rx (0.23 #2523, 0.22 #746, 0.22 #1116), 02w7gg (0.12 #2521, 0.11 #2595, 0.09 #1484), 0xnvg (0.11 #903, 0.10 #235, 0.10 #755), 07hwkr (0.10 #1494, 0.09 #86, 0.07 #1568), 07bch9 (0.08 #913, 0.08 #1135, 0.08 #765), 01qhm_ (0.08 #748, 0.08 #228, 0.06 #896), 09vc4s (0.08 #231, 0.07 #751, 0.06 #899), 0dryh9k (0.07 #1498, 0.05 #4980, 0.05 #4683) >> Best rule #229 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 02cg2v; >> query: (?x2281, 033tf_) <- participant(?x5881, ?x2281), people(?x6736, ?x2281), religion(?x2281, ?x7300) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 163 *> proper extension: 02cg2v; *> query: (?x2281, 03bkbh) <- participant(?x5881, ?x2281), people(?x6736, ?x2281), religion(?x2281, ?x7300) *> conf = 0.07 ranks of expected_values: 11 EVAL 04smkr people! 03bkbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 95.000 95.000 0.242 http://example.org/people/ethnicity/people #6370-034qbx PRED entity: 034qbx PRED relation: genre PRED expected values: 05p553 01jfsb => 75 concepts (68 used for prediction) PRED predicted values (max 10 best out of 116): 05p553 (0.99 #2153, 0.42 #1436, 0.40 #6574), 02kdv5l (0.80 #360, 0.58 #479, 0.34 #1314), 07s9rl0 (0.70 #6570, 0.67 #1073, 0.66 #597), 01z4y (0.62 #5851, 0.53 #2148, 0.50 #7048), 01jfsb (0.57 #370, 0.47 #489, 0.36 #132), 03k9fj (0.46 #488, 0.40 #369, 0.29 #1323), 02l7c8 (0.37 #1088, 0.37 #969, 0.36 #135), 04xvh5 (0.27 #153, 0.20 #272, 0.14 #34), 01hmnh (0.26 #494, 0.21 #1329, 0.17 #1449), 06cvj (0.20 #2152, 0.10 #957, 0.10 #2390) >> Best rule #2153 for best value: >> intensional similarity = 5 >> extensional distance = 588 >> proper extension: 04svwx; >> query: (?x6588, 05p553) <- genre(?x6588, ?x8467), genre(?x5513, ?x8467), genre(?x4304, ?x8467), ?x5513 = 0d4htf, ?x4304 = 08952r >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 034qbx genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 68.000 0.988 http://example.org/film/film/genre EVAL 034qbx genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 68.000 0.988 http://example.org/film/film/genre #6369-019pkm PRED entity: 019pkm PRED relation: student! PRED expected values: 0cwx_ => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 120): 0bwfn (0.10 #4491, 0.07 #7653, 0.06 #802), 065y4w7 (0.09 #2122, 0.07 #3703, 0.06 #4230), 03ksy (0.07 #2214, 0.06 #5376, 0.06 #6957), 01w5m (0.06 #4321, 0.03 #105, 0.02 #19078), 08815 (0.06 #2, 0.05 #2637, 0.04 #529), 01k2wn (0.05 #3186, 0.04 #3713, 0.03 #2132), 01jq34 (0.04 #2165, 0.04 #3219, 0.03 #3746), 0m4yg (0.04 #1419, 0.01 #5108, 0.01 #3527), 06xpp7 (0.04 #1231, 0.01 #4920), 04b_46 (0.04 #4443, 0.03 #6024, 0.03 #7605) >> Best rule #4491 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 02lg9w; 07_grx; 0grrq8; 03h610; 025cn2; 02q9kqf; 0c_drn; 095zvfg; 0521d_3; >> query: (?x9335, 0bwfn) <- place_of_birth(?x9335, ?x739), award_nominee(?x3018, ?x9335), ?x739 = 02_286 >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 019pkm student! 0cwx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 80.000 0.096 http://example.org/education/educational_institution/students_graduates./education/education/student #6368-018n6m PRED entity: 018n6m PRED relation: artists! PRED expected values: 0glt670 => 125 concepts (60 used for prediction) PRED predicted values (max 10 best out of 214): 0glt670 (0.82 #42, 0.35 #1885, 0.34 #2192), 0m0jc (0.67 #930, 0.27 #9, 0.17 #15975), 06by7 (0.56 #16304, 0.50 #636, 0.49 #1251), 01fm07 (0.45 #123, 0.12 #1659, 0.09 #1966), 0y3_8 (0.35 #970, 0.19 #663, 0.18 #49), 05bt6j (0.33 #659, 0.27 #16327, 0.27 #1274), 016clz (0.33 #926, 0.29 #619, 0.21 #16287), 03mb9 (0.30 #1020, 0.09 #99, 0.03 #4398), 03_d0 (0.29 #1548, 0.25 #1241, 0.24 #4311), 02x8m (0.27 #19, 0.26 #4318, 0.25 #1555) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0x3n; >> query: (?x4640, 0glt670) <- artists(?x11787, ?x4640), artists(?x3319, ?x4640), ?x3319 = 06j6l, ?x11787 = 05lwjc >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018n6m artists! 0glt670 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 60.000 0.818 http://example.org/music/genre/artists #6367-05hf_5 PRED entity: 05hf_5 PRED relation: institution! PRED expected values: 02h4rq6 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 22): 016t_3 (0.85 #1029, 0.55 #1378, 0.55 #374), 014mlp (0.78 #845, 0.72 #353, 0.72 #469), 02h4rq6 (0.74 #676, 0.72 #373, 0.71 #466), 02_xgp2 (0.69 #104, 0.61 #382, 0.59 #428), 03bwzr4 (0.63 #384, 0.52 #1388, 0.50 #804), 0bkj86 (0.45 #472, 0.43 #379, 0.40 #356), 01ysy9 (0.40 #68, 0.26 #815, 0.25 #886), 07s6fsf (0.38 #1375, 0.36 #371, 0.33 #464), 04zx3q1 (0.33 #465, 0.33 #372, 0.30 #117), 013zdg (0.28 #870, 0.26 #815, 0.25 #886) >> Best rule #1029 for best value: >> intensional similarity = 6 >> extensional distance = 187 >> proper extension: 03p7gb; >> query: (?x12330, 016t_3) <- school_type(?x12330, ?x3092), institution(?x2636, ?x12330), institution(?x2636, ?x10832), institution(?x2636, ?x5055), ?x5055 = 029d_, ?x10832 = 014jyk >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #676 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 116 *> proper extension: 08qnnv; 01p896; *> query: (?x12330, 02h4rq6) <- school_type(?x12330, ?x3092), institution(?x1771, ?x12330), major_field_of_study(?x12330, ?x2981), ?x1771 = 019v9k, ?x3092 = 05jxkf *> conf = 0.74 ranks of expected_values: 3 EVAL 05hf_5 institution! 02h4rq6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 143.000 143.000 0.852 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #6366-09v51c2 PRED entity: 09v51c2 PRED relation: award! PRED expected values: 014hdb 06101p => 49 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2564): 014hdb (0.33 #2888, 0.25 #6264, 0.17 #9640), 0h1p (0.33 #542, 0.25 #3918, 0.17 #7294), 0jlv5 (0.33 #8709, 0.13 #74303, 0.13 #60783), 054k_8 (0.25 #5002, 0.13 #77683, 0.13 #74303), 04jb97 (0.25 #5731, 0.13 #77683, 0.13 #74303), 0m9v7 (0.25 #6284, 0.13 #77683, 0.13 #74303), 02nfjp (0.25 #4876, 0.13 #77683, 0.13 #74303), 02wk4d (0.25 #5124, 0.13 #77683, 0.13 #60783), 07ftc0 (0.25 #5756, 0.06 #12508, 0.05 #19262), 042rnl (0.25 #3534, 0.03 #10286, 0.03 #13663) >> Best rule #2888 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 09v92_x; >> query: (?x9217, 014hdb) <- nominated_for(?x9217, ?x7554), nominated_for(?x9217, ?x467), award_winner(?x9217, ?x12529), ?x467 = 0dckvs, ?x7554 = 01mgw, ?x12529 = 0pksh >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09v51c2 award! 06101p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 23.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09v51c2 award! 014hdb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 23.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6365-032_jg PRED entity: 032_jg PRED relation: film PRED expected values: 01k0vq => 104 concepts (82 used for prediction) PRED predicted values (max 10 best out of 997): 06z8s_ (0.73 #82031, 0.65 #39230, 0.65 #82030), 03s6l2 (0.38 #82, 0.33 #1865, 0.05 #49931), 02704ff (0.25 #980, 0.22 #2763, 0.04 #53498), 0blpg (0.12 #653, 0.11 #2436, 0.06 #6003), 029k4p (0.12 #834, 0.11 #2617, 0.05 #49931), 01vw8k (0.12 #650, 0.11 #2433, 0.05 #49931), 01b195 (0.12 #359, 0.11 #2142, 0.05 #49931), 02yvct (0.12 #350, 0.11 #2133, 0.05 #49931), 05sns6 (0.12 #707, 0.11 #2490, 0.05 #49931), 07w8fz (0.12 #512, 0.11 #2295, 0.05 #49931) >> Best rule #82031 for best value: >> intensional similarity = 3 >> extensional distance = 1111 >> proper extension: 0n8bn; 065d1h; 01mylz; >> query: (?x875, ?x876) <- nominated_for(?x875, ?x876), film(?x875, ?x349), film(?x192, ?x876) >> conf = 0.73 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 032_jg film 01k0vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 82.000 0.730 http://example.org/film/actor/film./film/performance/film #6364-02wkmx PRED entity: 02wkmx PRED relation: award! PRED expected values: 01_vfy => 57 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1380): 06b_0 (0.62 #50620, 0.62 #53993, 0.62 #6748), 022_q8 (0.62 #50620, 0.62 #53993, 0.62 #6748), 03s9b (0.62 #50620, 0.62 #53993, 0.62 #6748), 02kxbx3 (0.62 #50620, 0.62 #53993, 0.62 #6748), 01t07j (0.62 #50620, 0.62 #53993, 0.62 #6748), 04sry (0.62 #50620, 0.62 #53993, 0.62 #6748), 0jw67 (0.62 #50620, 0.62 #53993, 0.62 #6748), 026dx (0.62 #50620, 0.62 #53993, 0.62 #6748), 02r6c_ (0.62 #50620, 0.62 #53993, 0.62 #6748), 02kxbwx (0.62 #50620, 0.62 #53993, 0.62 #6748) >> Best rule #50620 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 040vk98; 018wng; 03x3wf; 02662b; 0262zm; 054ky1; 01yz0x; 01cdjp; 02664f; 0265wl; ... >> query: (?x372, ?x767) <- award(?x8019, ?x372), award_winner(?x372, ?x767), disciplines_or_subjects(?x372, ?x373), award_nominee(?x9128, ?x8019) >> conf = 0.62 => this is the best rule for 16 predicted values *> Best rule #3986 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 040njc; 02rdyk7; 0789r6; *> query: (?x372, 01_vfy) <- award(?x810, ?x372), award_winner(?x372, ?x1872), ?x1872 = 01t07j, nominated_for(?x372, ?x303), award(?x1365, ?x372) *> conf = 0.40 ranks of expected_values: 31 EVAL 02wkmx award! 01_vfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 57.000 17.000 0.625 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6363-0dwt5 PRED entity: 0dwt5 PRED relation: role PRED expected values: 0l1589 => 84 concepts (58 used for prediction) PRED predicted values (max 10 best out of 79): 0342h (0.88 #3384, 0.86 #459, 0.85 #3537), 0bxl5 (0.88 #2180, 0.82 #689, 0.82 #688), 0l14j_ (0.86 #459, 0.82 #1832, 0.74 #2289), 05148p4 (0.86 #459, 0.82 #1832, 0.73 #2075), 05r5c (0.82 #689, 0.82 #688, 0.82 #3994), 01xqw (0.82 #689, 0.82 #688, 0.82 #462), 018j2 (0.82 #689, 0.82 #688, 0.82 #462), 01hww_ (0.82 #689, 0.82 #688, 0.82 #462), 01qbl (0.82 #689, 0.82 #688, 0.82 #462), 05ljv7 (0.82 #689, 0.82 #688, 0.82 #462) >> Best rule #3384 for best value: >> intensional similarity = 18 >> extensional distance = 23 >> proper extension: 0239kh; 0xzly; 0l14j_; 05842k; 011k_j; 0151b0; 0gkd1; >> query: (?x4769, 0342h) <- role(?x4769, ?x1750), role(?x4769, ?x615), role(?x565, ?x4769), role(?x2944, ?x4769), ?x615 = 0dwsp, role(?x316, ?x4769), role(?x2944, ?x1332), instrumentalists(?x2944, ?x4646), group(?x4769, ?x3516), ?x4646 = 0fhxv, instrumentalists(?x1750, ?x10756), instrumentalists(?x1750, ?x9117), group(?x1750, ?x13039), group(?x1750, ?x10671), ?x10671 = 04k05, artists(?x1000, ?x10756), ?x9117 = 0167v4, ?x13039 = 0fsyx >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1186 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 5 *> proper extension: 026t6; *> query: (?x4769, 0l1589) <- role(?x4769, ?x3991), role(?x4769, ?x3328), role(?x4769, ?x3161), role(?x4769, ?x2923), role(?x4769, ?x2888), role(?x4769, ?x2059), role(?x4769, ?x1574), role(?x4769, ?x615), role(?x565, ?x4769), role(?x227, ?x4769), instrumentalists(?x4769, ?x7211), ?x3991 = 05842k, role(?x316, ?x615), ?x3328 = 016622, ?x2923 = 02k856, group(?x615, ?x3516), instrumentalists(?x615, ?x1338), profession(?x7211, ?x1183), ?x3161 = 01v1d8, ?x1574 = 0l15bq, role(?x1997, ?x2059), ?x2888 = 02fsn *> conf = 0.71 ranks of expected_values: 17 EVAL 0dwt5 role 0l1589 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 84.000 58.000 0.880 http://example.org/music/performance_role/track_performances./music/track_contribution/role #6362-01k7xz PRED entity: 01k7xz PRED relation: company! PRED expected values: 021q1c => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 38): 060c4 (0.33 #50, 0.33 #3, 0.21 #335), 021q0l (0.33 #10, 0.17 #57, 0.16 #342), 05_wyz (0.33 #19, 0.17 #66, 0.10 #681), 05k17c (0.19 #3166, 0.17 #60, 0.15 #3826), 09d6p2 (0.17 #67, 0.06 #682, 0.06 #2288), 0krdk (0.13 #1188, 0.13 #669, 0.12 #1094), 0dq_5 (0.13 #1105, 0.13 #680, 0.12 #1199), 07t3gd (0.11 #355, 0.07 #213, 0.06 #544), 04n1q6 (0.11 #344, 0.05 #768, 0.04 #957), 0dq3c (0.09 #1089, 0.08 #664, 0.08 #1183) >> Best rule #50 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0204jh; >> query: (?x2484, 060c4) <- student(?x2484, ?x9246), student(?x2484, ?x2485), award(?x2485, ?x12418), ?x12418 = 045xh, inductee(?x1091, ?x9246) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1004 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 210 *> proper extension: 03p7gb; 01dbns; 0jksm; *> query: (?x2484, 021q1c) <- institution(?x3437, ?x2484), institution(?x1771, ?x2484), ?x3437 = 02_xgp2, student(?x1771, ?x744) *> conf = 0.08 ranks of expected_values: 11 EVAL 01k7xz company! 021q1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 111.000 111.000 0.333 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #6361-02qhm3 PRED entity: 02qhm3 PRED relation: type_of_union PRED expected values: 04ztj => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.85 #125, 0.83 #93, 0.83 #57), 01g63y (0.22 #130, 0.22 #174, 0.22 #218), 01bl8s (0.05 #39, 0.01 #83, 0.01 #87), 0jgjn (0.01 #84, 0.01 #88) >> Best rule #125 for best value: >> intensional similarity = 3 >> extensional distance = 206 >> proper extension: 01l3j; >> query: (?x11612, 04ztj) <- nationality(?x11612, ?x94), people(?x10199, ?x11612), film(?x11612, ?x7864) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qhm3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.851 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6360-064xm0 PRED entity: 064xm0 PRED relation: profession! PRED expected values: 018db8 01vvyfh => 56 concepts (17 used for prediction) PRED predicted values (max 10 best out of 4121): 086qd (0.67 #9071, 0.60 #4837, 0.55 #26012), 01q9b9 (0.62 #19378, 0.36 #27849, 0.30 #23614), 01vsn38 (0.60 #24828, 0.55 #29063, 0.47 #33297), 01wgcvn (0.60 #5377, 0.50 #9611, 0.45 #26552), 018grr (0.55 #25998, 0.50 #21763, 0.35 #30232), 03lgg (0.53 #31237, 0.45 #27003, 0.40 #22768), 0dpqk (0.53 #31257, 0.36 #27023, 0.33 #35493), 01vw8mh (0.50 #22734, 0.50 #10028, 0.47 #31203), 021yw7 (0.50 #22284, 0.47 #30753, 0.45 #26519), 052hl (0.50 #23372, 0.47 #31841, 0.45 #27607) >> Best rule #9071 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0nbcg; >> query: (?x6759, 086qd) <- profession(?x11876, ?x6759), profession(?x5364, ?x6759), ?x5364 = 043zg, award_nominee(?x11876, ?x1285), award_winner(?x350, ?x11876) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #30851 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 0d8qb; *> query: (?x6759, 01vvyfh) <- profession(?x5364, ?x6759), film(?x5364, ?x9361), participant(?x5364, ?x989), film(?x2317, ?x9361), ?x2317 = 04fhxp *> conf = 0.24 ranks of expected_values: 1123, 1318 EVAL 064xm0 profession! 01vvyfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 17.000 0.667 http://example.org/people/person/profession EVAL 064xm0 profession! 018db8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 17.000 0.667 http://example.org/people/person/profession #6359-0291hr PRED entity: 0291hr PRED relation: titles! PRED expected values: 01z4y => 66 concepts (9 used for prediction) PRED predicted values (max 10 best out of 39): 09q17 (0.59 #519, 0.50 #415, 0.50 #386), 01z4y (0.50 #346, 0.50 #36, 0.25 #661), 0hfjk (0.36 #495, 0.17 #185, 0.01 #599), 07s9rl0 (0.29 #729, 0.26 #520, 0.16 #834), 01jfsb (0.28 #414, 0.22 #518, 0.19 #832), 02n4kr (0.28 #414, 0.22 #518, 0.19 #832), 0gf28 (0.22 #518, 0.19 #832, 0.17 #623), 01hwc6 (0.22 #518, 0.19 #832, 0.17 #623), 05p553 (0.22 #518, 0.19 #832, 0.17 #623), 04xvlr (0.19 #419, 0.19 #732, 0.16 #523) >> Best rule #519 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 01kff7; 0f4k49; 0p9tm; 02rlj20; 02q5bx2; >> query: (?x8279, ?x7323) <- genre(?x8279, ?x7323), film(?x5840, ?x8279), titles(?x7323, ?x1210), ?x1210 = 018f8 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #346 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 04cf_l; *> query: (?x8279, 01z4y) <- genre(?x8279, ?x7323), genre(?x8279, ?x600), ?x7323 = 09q17, written_by(?x8279, ?x6771), titles(?x600, ?x394) *> conf = 0.50 ranks of expected_values: 2 EVAL 0291hr titles! 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 9.000 0.586 http://example.org/media_common/netflix_genre/titles #6358-011k11 PRED entity: 011k11 PRED relation: artist PRED expected values: 03h_yfh 031x_3 020_4z 0pqp3 01nn3m => 93 concepts (71 used for prediction) PRED predicted values (max 10 best out of 884): 033s6 (0.50 #2288, 0.33 #6347, 0.33 #665), 0163m1 (0.50 #1890, 0.33 #5949, 0.33 #267), 0167_s (0.50 #1746, 0.33 #5805, 0.33 #123), 01fchy (0.50 #2307, 0.33 #6366, 0.33 #684), 0153nq (0.50 #2433, 0.33 #6492, 0.33 #810), 02vr7 (0.50 #3030, 0.33 #596, 0.29 #15212), 09hnb (0.50 #2591, 0.33 #157, 0.25 #1780), 03d2k (0.50 #3093, 0.33 #659, 0.25 #2282), 0163kf (0.50 #3209, 0.14 #42252, 0.14 #42251), 01qdjm (0.50 #2596, 0.14 #42252, 0.14 #42251) >> Best rule #2288 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 01t04r; >> query: (?x5891, 033s6) <- artist(?x5891, ?x10239), artist(?x5891, ?x7859), artist(?x5891, ?x6475), ?x7859 = 03j1p2n, group(?x227, ?x6475), role(?x10239, ?x316), inductee(?x1091, ?x6475), award(?x6475, ?x2634) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3150 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 015_1q; 01xyqk; *> query: (?x5891, 020_4z) <- artist(?x5891, ?x10559), artist(?x5891, ?x7859), award_nominee(?x7859, ?x1206), nominated_for(?x7859, ?x408), child(?x7793, ?x5891), ?x10559 = 0dbb3, nationality(?x7859, ?x1310) *> conf = 0.25 ranks of expected_values: 138, 195, 481, 832, 851 EVAL 011k11 artist 01nn3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 93.000 71.000 0.500 http://example.org/music/record_label/artist EVAL 011k11 artist 0pqp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 93.000 71.000 0.500 http://example.org/music/record_label/artist EVAL 011k11 artist 020_4z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 93.000 71.000 0.500 http://example.org/music/record_label/artist EVAL 011k11 artist 031x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 93.000 71.000 0.500 http://example.org/music/record_label/artist EVAL 011k11 artist 03h_yfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 93.000 71.000 0.500 http://example.org/music/record_label/artist #6357-0hz6mv2 PRED entity: 0hz6mv2 PRED relation: genre PRED expected values: 04t36 => 114 concepts (58 used for prediction) PRED predicted values (max 10 best out of 216): 07s9rl0 (0.97 #6131, 0.97 #5769, 0.89 #6251), 05p553 (0.92 #3489, 0.48 #5050, 0.38 #1926), 0lsxr (0.89 #2532, 0.28 #5295, 0.25 #5778), 0hcr (0.75 #5070, 0.19 #2667, 0.16 #4229), 03g3w (0.69 #4950, 0.59 #986, 0.27 #626), 02kdv5l (0.68 #1203, 0.62 #843, 0.59 #1563), 01jfsb (0.66 #5299, 0.53 #2536, 0.45 #1214), 06n90 (0.50 #15, 0.36 #1215, 0.31 #1575), 03k9fj (0.48 #853, 0.47 #1573, 0.46 #5058), 04t36 (0.47 #6016, 0.23 #5052, 0.22 #367) >> Best rule #6131 for best value: >> intensional similarity = 10 >> extensional distance = 177 >> proper extension: 02psgq; >> query: (?x9565, 07s9rl0) <- film_format(?x9565, ?x6392), genre(?x9565, ?x6887), language(?x9565, ?x254), genre(?x7664, ?x6887), genre(?x3882, ?x6887), genre(?x3157, ?x6887), ?x7664 = 046f3p, ?x3157 = 0ywrc, ?x3882 = 0mcl0, country(?x9565, ?x94) >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #6016 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 175 *> proper extension: 023p33; *> query: (?x9565, 04t36) <- genre(?x9565, ?x6887), genre(?x6254, ?x6887), genre(?x5724, ?x6887), genre(?x3275, ?x6887), film_release_region(?x9565, ?x87), featured_film_locations(?x6254, ?x3125), ?x3275 = 0djlxb, nominated_for(?x521, ?x6254), titles(?x53, ?x5724) *> conf = 0.47 ranks of expected_values: 10 EVAL 0hz6mv2 genre 04t36 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 114.000 58.000 0.972 http://example.org/film/film/genre #6356-07b_l PRED entity: 07b_l PRED relation: location! PRED expected values: 09pl3s 02wb6yq 02p7_k 06hmd => 183 concepts (159 used for prediction) PRED predicted values (max 10 best out of 2085): 04z0g (0.20 #3669, 0.17 #8671, 0.12 #11172), 01zwy (0.20 #4213, 0.17 #9215, 0.12 #11716), 099p5 (0.20 #4389, 0.17 #9391, 0.12 #11892), 023mdt (0.20 #4354, 0.11 #16859, 0.07 #56876), 05ry0p (0.20 #4649, 0.11 #17154, 0.07 #57171), 022yb4 (0.20 #4199, 0.11 #16704, 0.07 #56721), 0443c (0.20 #4986, 0.11 #17491, 0.05 #57508), 03l26m (0.20 #4777, 0.11 #17282, 0.05 #57299), 01jz6x (0.20 #4602, 0.11 #17107, 0.05 #57124), 09nhvw (0.20 #4394, 0.11 #16899, 0.05 #56916) >> Best rule #3669 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0chrx; >> query: (?x3634, 04z0g) <- contains(?x3634, ?x216), location(?x4387, ?x3634), ?x4387 = 0kvnn >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #20622 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 01bkb; *> query: (?x3634, 02wb6yq) <- featured_film_locations(?x2656, ?x3634), religion(?x3634, ?x109), film_release_region(?x2656, ?x87) *> conf = 0.05 ranks of expected_values: 943, 959 EVAL 07b_l location! 06hmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 183.000 159.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location EVAL 07b_l location! 02p7_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 183.000 159.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location EVAL 07b_l location! 02wb6yq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 159.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location EVAL 07b_l location! 09pl3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 159.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location #6355-088gzp PRED entity: 088gzp PRED relation: student PRED expected values: 0ddfph 027lfrs => 98 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1337): 05bnp0 (0.07 #4187, 0.03 #16709, 0.03 #6274), 06hx2 (0.07 #7333, 0.04 #5246, 0.03 #11507), 0d3k14 (0.06 #6027, 0.05 #8114, 0.03 #18549), 0h0wc (0.06 #4568, 0.03 #17090, 0.02 #10829), 0ff3y (0.05 #8328, 0.04 #6241, 0.03 #10415), 0194xc (0.05 #7902, 0.04 #5815, 0.03 #12076), 03f22dp (0.05 #1984, 0.03 #4071), 0djc3s (0.05 #1945, 0.03 #4032), 03_nq (0.05 #7825, 0.03 #5738, 0.01 #9912), 041c4 (0.04 #5043, 0.04 #7130, 0.02 #11304) >> Best rule #4187 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 09wv__; 02l9wl; 053mhx; >> query: (?x13396, 05bnp0) <- student(?x13396, ?x8073), award_winner(?x657, ?x8073), film(?x8073, ?x2617), student(?x1368, ?x8073) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 088gzp student 027lfrs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 43.000 0.074 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 088gzp student 0ddfph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 43.000 0.074 http://example.org/education/educational_institution/students_graduates./education/education/student #6354-01fwj8 PRED entity: 01fwj8 PRED relation: location PRED expected values: 0162v => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 75): 030qb3t (0.40 #887, 0.23 #1692, 0.22 #15366), 059rby (0.20 #16, 0.05 #6450, 0.05 #12886), 01n7q (0.20 #63, 0.05 #6497, 0.05 #5693), 0d6lp (0.20 #972, 0.04 #2581, 0.04 #1777), 0k049 (0.20 #8, 0.03 #5638, 0.03 #4834), 05fjf (0.20 #332, 0.02 #2745, 0.02 #3549), 0f2v0 (0.20 #987, 0.01 #15466, 0.01 #9031), 0r771 (0.20 #1345), 02d6c (0.20 #524), 04ykg (0.20 #68) >> Best rule #887 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01gq0b; 086sj; 03_x5t; >> query: (?x1690, 030qb3t) <- participant(?x2422, ?x1690), ?x2422 = 0169dl, nationality(?x1690, ?x1310) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01fwj8 location 0162v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #6353-01lb14 PRED entity: 01lb14 PRED relation: country PRED expected values: 03rk0 016wzw 04w4s 088q4 077qn 0d04z6 06m_5 02k1b => 41 concepts (40 used for prediction) PRED predicted values (max 10 best out of 403): 016wzw (0.80 #2063, 0.78 #1655, 0.75 #1521), 015qh (0.78 #1640, 0.77 #2728, 0.75 #1506), 07f1x (0.78 #1714, 0.75 #1580, 0.71 #1305), 02k54 (0.77 #2720, 0.75 #1363, 0.67 #1632), 047lj (0.75 #1495, 0.75 #1360, 0.73 #2309), 0d04z6 (0.75 #1548, 0.71 #1273, 0.70 #2090), 07t_x (0.75 #1558, 0.71 #1283, 0.70 #2100), 0d0kn (0.75 #1512, 0.71 #1237, 0.70 #2054), 0jdx (0.75 #1583, 0.71 #1308, 0.67 #1717), 04w4s (0.75 #1388, 0.70 #2065, 0.67 #1791) >> Best rule #2063 for best value: >> intensional similarity = 47 >> extensional distance = 8 >> proper extension: 06f41; >> query: (?x2266, 016wzw) <- olympics(?x2266, ?x1931), country(?x2266, ?x7035), country(?x2266, ?x6435), country(?x2266, ?x3656), country(?x2266, ?x3016), country(?x2266, ?x2346), country(?x2266, ?x2188), country(?x2266, ?x1536), country(?x2266, ?x1264), country(?x2266, ?x774), country(?x2266, ?x291), country(?x2266, ?x142), ?x291 = 0h3y, ?x2346 = 0d05w3, ?x142 = 0jgd, administrative_parent(?x3656, ?x551), film_release_region(?x7393, ?x774), film_release_region(?x4684, ?x774), film_release_region(?x4607, ?x774), film_release_region(?x3745, ?x774), film_release_region(?x3076, ?x774), film_release_region(?x2893, ?x774), film_release_region(?x1012, ?x774), film_release_region(?x186, ?x774), ?x1536 = 06c1y, nationality(?x1221, ?x774), ?x4684 = 03nm_fh, member_states(?x7416, ?x774), ?x3745 = 03cw411, organization(?x3016, ?x127), countries_spoken_in(?x254, ?x7035), contains(?x774, ?x1220), ?x4607 = 0h03fhx, ?x1012 = 0bwfwpj, country(?x2315, ?x774), ?x7393 = 02vz6dn, film_release_region(?x1701, ?x3016), form_of_government(?x6435, ?x48), ?x186 = 02vxq9m, ?x1264 = 0345h, ?x2893 = 01jrbb, ?x2188 = 0163v, olympics(?x774, ?x584), ?x3076 = 0g5838s, ?x2315 = 06wrt, ?x584 = 0l98s, taxonomy(?x774, ?x939) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 10, 12, 27, 47, 48, 125 EVAL 01lb14 country 02k1b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 41.000 40.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01lb14 country 06m_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 41.000 40.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01lb14 country 0d04z6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 41.000 40.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01lb14 country 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 41.000 40.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01lb14 country 088q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 41.000 40.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01lb14 country 04w4s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 41.000 40.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01lb14 country 016wzw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 40.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01lb14 country 03rk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 41.000 40.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #6352-021lkq PRED entity: 021lkq PRED relation: location_of_ceremony! PRED expected values: 04ztj => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.20 #29, 0.19 #37, 0.19 #33) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 478 >> proper extension: 02vzc; 035v3; >> query: (?x14501, 04ztj) <- contains(?x512, ?x14501), region(?x54, ?x512), titles(?x512, ?x4197), film(?x2653, ?x4197) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021lkq location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.196 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #6351-0y62n PRED entity: 0y62n PRED relation: county_seat PRED expected values: 0y62n => 134 concepts (113 used for prediction) PRED predicted values (max 10 best out of 99): 02_286 (0.57 #2910, 0.40 #7282, 0.38 #6366), 013h9 (0.17 #138, 0.14 #321, 0.07 #686), 0dq16 (0.17 #55, 0.14 #238, 0.05 #1149), 0235l (0.14 #271, 0.05 #1182, 0.03 #2090), 0xrzh (0.07 #584, 0.07 #402, 0.06 #766), 02cl1 (0.07 #371, 0.06 #735, 0.05 #1099), 0mndw (0.07 #445, 0.06 #809, 0.05 #1173), 0mnyn (0.07 #534, 0.06 #898, 0.05 #1262), 0q_0z (0.07 #531, 0.05 #1077, 0.04 #1805), 0dc95 (0.07 #567, 0.05 #931, 0.04 #1659) >> Best rule #2910 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 0nt4s; >> query: (?x9233, ?x739) <- county(?x739, ?x9233), location(?x163, ?x739), origin(?x217, ?x739) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0y62n county_seat 0y62n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 113.000 0.574 http://example.org/location/us_county/county_seat #6350-09c7w0 PRED entity: 09c7w0 PRED relation: country! PRED expected values: 02bkg 06zgc => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 4): 02bkg (0.50 #81, 0.49 #205, 0.49 #193), 06zgc (0.45 #74, 0.44 #142, 0.42 #154), 03krj (0.43 #63, 0.40 #143, 0.38 #155), 09xp_ (0.09 #76, 0.08 #84, 0.07 #100) >> Best rule #81 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 06frc; >> query: (?x94, 02bkg) <- jurisdiction_of_office(?x652, ?x94), entity_involved(?x1140, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 09c7w0 country! 06zgc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 189.000 189.000 0.500 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09c7w0 country! 02bkg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 189.000 189.000 0.500 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #6349-03kpvp PRED entity: 03kpvp PRED relation: award PRED expected values: 0f_nbyh => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 292): 05b1610 (0.72 #45505, 0.70 #36563, 0.70 #36156), 01l29r (0.72 #45505, 0.70 #36563, 0.70 #36156), 01l78d (0.65 #697, 0.15 #34936, 0.13 #44691), 0gq9h (0.52 #890, 0.41 #5765, 0.38 #7390), 040njc (0.37 #5695, 0.33 #820, 0.31 #10162), 0gr4k (0.36 #3284, 0.31 #6126, 0.29 #11406), 09sb52 (0.36 #23194, 0.35 #15475, 0.33 #18319), 01lk0l (0.35 #687, 0.15 #34936, 0.13 #44691), 04ljl_l (0.33 #3, 0.08 #17875, 0.08 #13406), 04dn09n (0.32 #3295, 0.30 #6137, 0.28 #4919) >> Best rule #45505 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01vsxdm; 01wv9xn; 0frsw; 01vrwfv; 014_lq; 02jqjm; 0178kd; ... >> query: (?x3692, ?x688) <- award_winner(?x688, ?x3692), award(?x702, ?x688) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #4073 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 151 *> proper extension: 0m_v0; 081l_; 05bnx3j; *> query: (?x3692, 0f_nbyh) <- produced_by(?x835, ?x3692), award_winner(?x3692, ?x10730), student(?x2171, ?x10730) *> conf = 0.18 ranks of expected_values: 21 EVAL 03kpvp award 0f_nbyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 123.000 123.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6348-0fpmrm3 PRED entity: 0fpmrm3 PRED relation: nominated_for! PRED expected values: 03qgjwc => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 211): 0gq9h (0.34 #539, 0.30 #4589, 0.29 #1015), 0fbtbt (0.33 #160, 0.03 #8494, 0.03 #8970), 0bdx29 (0.33 #84, 0.03 #8418, 0.03 #5324), 0gkr9q (0.33 #209, 0.02 #5449, 0.02 #6401), 0cqhb3 (0.33 #199, 0.02 #5439, 0.02 #8533), 09v7wsg (0.33 #176, 0.02 #5416, 0.02 #8510), 02_3zj (0.33 #185), 019f4v (0.28 #531, 0.26 #1007, 0.23 #4581), 0gs9p (0.28 #3095, 0.26 #4288, 0.26 #4287), 099c8n (0.28 #3095, 0.26 #4288, 0.26 #4287) >> Best rule #539 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 0q9jk; 01dvry; >> query: (?x2655, 0gq9h) <- honored_for(?x2655, ?x385), titles(?x812, ?x2655), nominated_for(?x1254, ?x2655) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #14763 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1445 *> proper extension: 01tspc6; 016zfm; 06qwh; 0fpxp; 04z_x4v; 023ny6; 015pnb; *> query: (?x2655, ?x375) <- nominated_for(?x1254, ?x2655), nominated_for(?x1641, ?x2655), award(?x1641, ?x375) *> conf = 0.20 ranks of expected_values: 45 EVAL 0fpmrm3 nominated_for! 03qgjwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 75.000 75.000 0.336 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6347-0c0yh4 PRED entity: 0c0yh4 PRED relation: film_release_region PRED expected values: 09c7w0 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 91): 09c7w0 (0.66 #1624, 0.65 #10983, 0.65 #11526), 0d060g (0.45 #4683, 0.45 #4682, 0.43 #11883), 0345h (0.45 #4683, 0.45 #4682, 0.43 #11883), 0h7x (0.45 #4683, 0.45 #4682, 0.43 #11883), 07ssc (0.38 #2162, 0.30 #7923, 0.29 #8461), 02jx1 (0.38 #2162, 0.30 #7923, 0.29 #8461), 0f8l9c (0.30 #7923, 0.29 #8461, 0.21 #13531), 0d0vqn (0.21 #13511, 0.21 #13331, 0.21 #14588), 06mkj (0.19 #13575, 0.19 #13395, 0.19 #12497), 059j2 (0.18 #14621, 0.18 #13364, 0.18 #13544) >> Best rule #1624 for best value: >> intensional similarity = 3 >> extensional distance = 504 >> proper extension: 01cgz; >> query: (?x278, 09c7w0) <- films(?x14144, ?x278), films(?x14144, ?x3745), film_release_region(?x3745, ?x87) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c0yh4 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.656 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6346-02gs6r PRED entity: 02gs6r PRED relation: film_release_region PRED expected values: 0jgd 0b90_r => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 202): 0d0vqn (0.94 #4387, 0.93 #4737, 0.91 #5612), 09c7w0 (0.93 #10334, 0.93 #5254, 0.93 #9109), 0f8l9c (0.90 #3180, 0.88 #4931, 0.86 #6858), 07ssc (0.87 #4398, 0.85 #4748, 0.82 #3172), 059j2 (0.86 #4419, 0.84 #4769, 0.84 #4944), 0chghy (0.84 #4742, 0.84 #4392, 0.83 #5617), 02vzc (0.84 #4793, 0.83 #4443, 0.81 #6194), 03h64 (0.84 #4810, 0.83 #4460, 0.78 #3234), 05r4w (0.84 #3152, 0.82 #5603, 0.82 #4378), 03gj2 (0.84 #3185, 0.81 #4411, 0.81 #4936) >> Best rule #4387 for best value: >> intensional similarity = 6 >> extensional distance = 92 >> proper extension: 02d44q; 047svrl; 0hgnl3t; >> query: (?x5286, 0d0vqn) <- produced_by(?x5286, ?x5287), film_release_region(?x5286, ?x1264), film_release_region(?x5286, ?x252), ?x1264 = 0345h, ?x252 = 03_3d, film(?x5636, ?x5286) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #4731 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 99 *> proper extension: 014lc_; 0b76d_m; 0ds35l9; 0g56t9t; 028_yv; 07gp9; 0ds3t5x; 0g5qs2k; 0djb3vw; 04969y; ... *> query: (?x5286, 0jgd) <- produced_by(?x5286, ?x5287), film_release_region(?x5286, ?x1264), film_release_region(?x5286, ?x252), ?x1264 = 0345h, ?x252 = 03_3d, genre(?x5286, ?x811) *> conf = 0.82 ranks of expected_values: 12, 19 EVAL 02gs6r film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 113.000 113.000 0.936 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02gs6r film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 113.000 113.000 0.936 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6345-04pry PRED entity: 04pry PRED relation: place_of_birth! PRED expected values: 03nkts 06j8q_ => 151 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1900): 0py5b (0.37 #13057, 0.36 #78349, 0.36 #31337), 04hw4b (0.17 #4082, 0.06 #14529, 0.04 #19752), 05yh_t (0.17 #3791, 0.04 #22072, 0.04 #24683), 04yyhw (0.17 #5221, 0.04 #23502, 0.04 #26113), 01nr63 (0.17 #5068, 0.04 #23349, 0.04 #25960), 0814k3 (0.17 #5054, 0.04 #23335, 0.04 #25946), 031c2r (0.17 #5042, 0.04 #23323, 0.04 #25934), 01dbgw (0.17 #5030, 0.04 #23311, 0.04 #25922), 04mky3 (0.17 #4992, 0.04 #23273, 0.04 #25884), 06kbb6 (0.17 #4970, 0.04 #23251, 0.04 #25862) >> Best rule #13057 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0pmp2; 08966; >> query: (?x12488, ?x12602) <- location(?x12602, ?x12488), location(?x703, ?x12488), capital(?x1906, ?x12488), celebrities_impersonated(?x6707, ?x703) >> conf = 0.37 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04pry place_of_birth! 06j8q_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 151.000 86.000 0.369 http://example.org/people/person/place_of_birth EVAL 04pry place_of_birth! 03nkts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 151.000 86.000 0.369 http://example.org/people/person/place_of_birth #6344-07h07 PRED entity: 07h07 PRED relation: award_winner! PRED expected values: 03hl6lc => 93 concepts (91 used for prediction) PRED predicted values (max 10 best out of 286): 01l78d (0.50 #1143, 0.40 #18461, 0.37 #21039), 01l29r (0.50 #1022, 0.13 #1880, 0.10 #2738), 01lk0l (0.40 #1134, 0.09 #5579, 0.05 #30489), 01lj_c (0.40 #1152, 0.09 #5579, 0.05 #30489), 02qyp19 (0.40 #18461, 0.37 #21039, 0.37 #18891), 02n9nmz (0.40 #18461, 0.37 #21039, 0.37 #18891), 02x17s4 (0.40 #18461, 0.37 #21039, 0.37 #18891), 02x1dht (0.40 #18461, 0.37 #21039, 0.37 #18891), 05p1dby (0.21 #5255, 0.09 #5579, 0.05 #30489), 0d085 (0.20 #1535, 0.19 #1964, 0.10 #2822) >> Best rule #1143 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 061dn_; 0dbpwb; >> query: (?x4008, 01l78d) <- award_winner(?x6866, ?x4008), ?x6866 = 03m9c8, award_nominee(?x3462, ?x4008) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #25760 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1900 *> proper extension: 0h1_w; 03q1vd; 0p51w; 03q95r; 05typm; 06lht1; 03d9d6; 025cn2; 02756j; 0807ml; ... *> query: (?x4008, ?x198) <- award(?x4008, ?x68), nominated_for(?x4008, ?x6121), award(?x6121, ?x198) *> conf = 0.11 ranks of expected_values: 28 EVAL 07h07 award_winner! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 93.000 91.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #6343-01ccr8 PRED entity: 01ccr8 PRED relation: nominated_for PRED expected values: 0vjr => 124 concepts (59 used for prediction) PRED predicted values (max 10 best out of 383): 0vhm (0.42 #64864, 0.41 #42165, 0.38 #17837), 01pvxl (0.28 #34055, 0.23 #69729, 0.23 #66486), 02x6dqb (0.28 #34055, 0.23 #69729, 0.23 #66486), 03ln8b (0.12 #303, 0.04 #1924, 0.03 #3545), 01shy7 (0.12 #389, 0.04 #2010, 0.03 #3631), 06pyc2 (0.12 #1527, 0.04 #3148, 0.03 #4769), 0cs134 (0.12 #1510, 0.04 #3131, 0.03 #4752), 06t2t2 (0.12 #1498, 0.03 #4740, 0.03 #6362), 039cq4 (0.06 #20544, 0.03 #90272, 0.02 #78919), 0ds33 (0.06 #6548, 0.05 #8169, 0.04 #11411) >> Best rule #64864 for best value: >> intensional similarity = 3 >> extensional distance = 658 >> proper extension: 0clvcx; 02lg9w; 06lgq8; 0f6_dy; 02xb2bt; 050t68; 0308kx; 06lht1; 0131kb; >> query: (?x8412, ?x5219) <- actor(?x5219, ?x8412), award(?x8412, ?x1058), nationality(?x8412, ?x94) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #17071 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 155 *> proper extension: 03zqc1; 0gm34; *> query: (?x8412, 0vjr) <- actor(?x5219, ?x8412), award(?x8412, ?x1058), participant(?x8412, ?x3917) *> conf = 0.02 ranks of expected_values: 246 EVAL 01ccr8 nominated_for 0vjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 124.000 59.000 0.418 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #6342-01699 PRED entity: 01699 PRED relation: organization PRED expected values: 041288 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 48): 041288 (0.65 #262, 0.61 #34, 0.56 #241), 04k4l (0.34 #64, 0.34 #44, 0.33 #124), 01rz1 (0.32 #1083, 0.29 #81, 0.26 #181), 0_2v (0.32 #1083, 0.28 #405, 0.28 #203), 0j7v_ (0.32 #1083, 0.26 #367, 0.25 #25), 018cqq (0.32 #1083, 0.22 #89, 0.21 #49), 02jxk (0.32 #1083, 0.18 #82, 0.16 #42), 059dn (0.32 #1083, 0.05 #93, 0.05 #113), 085h1 (0.32 #1083, 0.03 #10, 0.02 #50), 034h1h (0.22 #970, 0.18 #1070, 0.02 #1561) >> Best rule #262 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 078lk; 068cn; 04fh3; 05c17; 082pc; >> query: (?x6431, ?x127) <- currency(?x6431, ?x170), adjoins(?x6431, ?x9035), organization(?x9035, ?x127) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01699 organization 041288 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.653 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #6341-02wycg2 PRED entity: 02wycg2 PRED relation: award PRED expected values: 05ztrmj => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 234): 09sb52 (0.36 #4507, 0.35 #13033, 0.33 #5319), 05ztrmj (0.30 #998, 0.22 #592, 0.13 #19897), 02x4w6g (0.30 #927, 0.15 #29238, 0.13 #19897), 099jhq (0.30 #831, 0.13 #19897, 0.13 #31270), 05zr6wv (0.20 #829, 0.17 #17, 0.13 #19897), 0gqy2 (0.20 #978, 0.12 #9910, 0.10 #7474), 09sdmz (0.20 #1020, 0.08 #13399, 0.07 #19084), 0ck27z (0.20 #4153, 0.18 #4559, 0.15 #16740), 063y_ky (0.17 #133, 0.13 #19897, 0.13 #31270), 02x8n1n (0.17 #121, 0.13 #19897, 0.13 #31270) >> Best rule #4507 for best value: >> intensional similarity = 3 >> extensional distance = 609 >> proper extension: 03g5jw; 0dvqq; >> query: (?x4085, 09sb52) <- award_nominee(?x123, ?x4085), student(?x8925, ?x123), film(?x123, ?x1219) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #998 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01vsn38; *> query: (?x4085, 05ztrmj) <- film(?x4085, ?x2886), award_nominee(?x4085, ?x123), ?x2886 = 02ryz24 *> conf = 0.30 ranks of expected_values: 2 EVAL 02wycg2 award 05ztrmj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.363 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6340-0cks1m PRED entity: 0cks1m PRED relation: produced_by PRED expected values: 0b05xm => 61 concepts (44 used for prediction) PRED predicted values (max 10 best out of 91): 030_3z (0.25 #1712, 0.08 #5198, 0.08 #5975), 02q_cc (0.25 #1582, 0.08 #5068, 0.08 #5845), 02fgp0 (0.12 #4170, 0.06 #7275, 0.03 #9996), 01rlxt (0.11 #4452, 0.03 #10278, 0.02 #10665), 02r251z (0.11 #4501, 0.02 #12653, 0.02 #15765), 01vhrz (0.11 #4576), 0534v (0.08 #6001, 0.06 #6777, 0.05 #8331), 0fvf9q (0.07 #13194, 0.05 #13583, 0.02 #15529), 01t6b4 (0.06 #10515, 0.05 #11290, 0.03 #12454), 013t9y (0.06 #7206, 0.03 #9927, 0.03 #10314) >> Best rule #1712 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 02q3fdr; >> query: (?x5633, 030_3z) <- actor(?x5633, ?x4134), film(?x2156, ?x5633), genre(?x5633, ?x5937), genre(?x5633, ?x2540), genre(?x5633, ?x1510), ?x5937 = 0jxy, country(?x5633, ?x252), ?x2156 = 01795t, ?x1510 = 01hmnh, ?x2540 = 0hcr, ?x252 = 03_3d >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cks1m produced_by 0b05xm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 44.000 0.250 http://example.org/film/film/produced_by #6339-0ptxj PRED entity: 0ptxj PRED relation: nominated_for PRED expected values: 0p9tm => 97 concepts (28 used for prediction) PRED predicted values (max 10 best out of 145): 0cq7tx (0.11 #376, 0.03 #874, 0.02 #1621), 01s9vc (0.09 #985, 0.06 #3227, 0.06 #4224), 01kf3_9 (0.08 #800, 0.06 #3042, 0.05 #5041), 05css_ (0.08 #906, 0.05 #3148, 0.05 #4145), 05cj_j (0.08 #793, 0.05 #3035, 0.05 #4032), 02r_pp (0.08 #891, 0.05 #3133, 0.05 #4130), 01kf4tt (0.07 #821, 0.05 #3063, 0.04 #5062), 02sg5v (0.07 #767, 0.05 #3009, 0.04 #5008), 0fsw_7 (0.07 #899, 0.05 #3141, 0.04 #5140), 025twgt (0.07 #991, 0.05 #3233, 0.04 #4230) >> Best rule #376 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 0d_wms; >> query: (?x5212, 0cq7tx) <- nominated_for(?x5212, ?x1822), honored_for(?x3173, ?x5212), cinematography(?x5212, ?x5862) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #965 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 118 *> proper extension: 02sg5v; 02qrv7; 0k0rf; 05css_; 0g5pvv; 02n72k; 08984j; 01jr4j; 042fgh; 025twgf; ... *> query: (?x5212, 0p9tm) <- nominated_for(?x5212, ?x7265), film(?x382, ?x5212), written_by(?x7265, ?x10819) *> conf = 0.02 ranks of expected_values: 112 EVAL 0ptxj nominated_for 0p9tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 97.000 28.000 0.111 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #6338-01vh3r PRED entity: 01vh3r PRED relation: languages PRED expected values: 02bjrlw => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 20): 02bjrlw (0.27 #1, 0.17 #75, 0.06 #38), 04306rv (0.18 #2, 0.10 #76, 0.06 #39), 06nm1 (0.09 #5, 0.07 #79, 0.04 #671), 06b_j (0.09 #14), 03k50 (0.07 #1150, 0.05 #1002, 0.04 #854), 07c9s (0.04 #1159, 0.03 #1011, 0.03 #863), 0349s (0.03 #104), 04h9h (0.03 #102), 03115z (0.03 #100), 0295r (0.03 #93) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 06wvj; 0g7k2g; >> query: (?x11985, 02bjrlw) <- location(?x11985, ?x4627), languages(?x11985, ?x254), ?x4627 = 05qtj >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vh3r languages 02bjrlw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.273 http://example.org/people/person/languages #6337-09lgt PRED entity: 09lgt PRED relation: locations! PRED expected values: 0gjw_ => 76 concepts (47 used for prediction) PRED predicted values (max 10 best out of 13): 0gjw_ (0.08 #605, 0.08 #476, 0.03 #734), 016r9z (0.08 #539, 0.08 #410, 0.03 #668), 0nbjq (0.08 #536, 0.08 #407, 0.03 #665), 06k75 (0.02 #3311), 03jqfx (0.01 #3336), 0b_6lb (0.01 #3465, 0.01 #3856, 0.01 #3596), 0bzrsh (0.01 #3858, 0.01 #3988, 0.01 #3727), 0b_6jz (0.01 #3812, 0.01 #3942), 0b_6v_ (0.01 #3712, 0.01 #3452, 0.01 #3843), 0b_6qj (0.01 #3715, 0.01 #3455) >> Best rule #605 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0mhlq; >> query: (?x13973, 0gjw_) <- second_level_divisions(?x789, ?x13973), ?x789 = 0f8l9c, country(?x13973, ?x789) >> conf = 0.08 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09lgt locations! 0gjw_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 47.000 0.077 http://example.org/time/event/locations #6336-02q56mk PRED entity: 02q56mk PRED relation: film_crew_role PRED expected values: 09zzb8 0ch6mp2 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.81 #987, 0.77 #7, 0.74 #151), 09zzb8 (0.77 #981, 0.74 #1, 0.70 #1528), 0dxtw (0.40 #990, 0.37 #335, 0.35 #190), 01vx2h (0.35 #991, 0.30 #191, 0.30 #119), 01pvkk (0.30 #120, 0.27 #1539, 0.27 #1611), 02ynfr (0.18 #996, 0.17 #160, 0.15 #1543), 0215hd (0.16 #19, 0.15 #999, 0.14 #127), 01xy5l_ (0.13 #122, 0.13 #14, 0.12 #994), 02_n3z (0.13 #110, 0.11 #146, 0.10 #2), 089g0h (0.13 #20, 0.12 #1000, 0.11 #164) >> Best rule #987 for best value: >> intensional similarity = 3 >> extensional distance = 729 >> proper extension: 0fq27fp; >> query: (?x2613, 0ch6mp2) <- film_crew_role(?x2613, ?x1171), genre(?x2613, ?x53), ?x1171 = 09vw2b7 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02q56mk film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.807 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02q56mk film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.807 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6335-065ym0c PRED entity: 065ym0c PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.98 #216, 0.98 #263, 0.98 #171), 07c52 (0.20 #7, 0.20 #3, 0.18 #11), 02nxhr (0.10 #54, 0.09 #128, 0.08 #42), 0735l (0.02 #40, 0.01 #48, 0.01 #56) >> Best rule #216 for best value: >> intensional similarity = 6 >> extensional distance = 470 >> proper extension: 015g28; >> query: (?x10080, 029j_) <- award(?x10080, ?x12715), titles(?x2346, ?x10080), award_winner(?x10080, ?x12529), film_release_distribution_medium(?x10080, ?x10850), award(?x6211, ?x12715), award(?x12529, ?x5039) >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065ym0c film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.983 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6334-0b3n61 PRED entity: 0b3n61 PRED relation: genre PRED expected values: 05p553 => 56 concepts (55 used for prediction) PRED predicted values (max 10 best out of 104): 05p553 (0.64 #718, 0.58 #599, 0.47 #480), 07s9rl0 (0.56 #2623, 0.54 #2028, 0.54 #3099), 01hmnh (0.50 #613, 0.49 #851, 0.40 #256), 06n90 (0.40 #132, 0.35 #370, 0.24 #1085), 01jfsb (0.38 #1084, 0.35 #369, 0.29 #1681), 02l7c8 (0.28 #969, 0.26 #2638, 0.25 #1447), 060__y (0.25 #17, 0.14 #2044, 0.13 #2639), 0219x_ (0.25 #26, 0.09 #979, 0.08 #2648), 0jxy (0.22 #878, 0.20 #164, 0.04 #6554), 04t36 (0.22 #601, 0.16 #482, 0.14 #839) >> Best rule #718 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 015ynm; >> query: (?x7806, 05p553) <- film(?x6171, ?x7806), award_nominee(?x690, ?x6171), nationality(?x6171, ?x94), program(?x6171, ?x3075) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b3n61 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 55.000 0.636 http://example.org/film/film/genre #6333-03k99c PRED entity: 03k99c PRED relation: actor PRED expected values: 019803 => 76 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1051): 02gf_l (0.35 #12665, 0.33 #570, 0.25 #7085), 029cpw (0.33 #552, 0.25 #7067, 0.23 #10787), 069z_5 (0.33 #778, 0.23 #28843, 0.12 #7293), 06czxq (0.33 #901, 0.12 #7416, 0.06 #19509), 03x16f (0.29 #5329, 0.17 #2536, 0.12 #7190), 03zyvw (0.25 #5881, 0.20 #8671, 0.17 #2157), 01vyv9 (0.25 #5954, 0.20 #8744, 0.06 #11534), 02h0f3 (0.23 #10822, 0.17 #4310, 0.17 #3379), 018p4y (0.20 #1774, 0.17 #4567, 0.17 #3636), 01vrnsk (0.20 #1477, 0.17 #4270, 0.17 #3339) >> Best rule #12665 for best value: >> intensional similarity = 8 >> extensional distance = 15 >> proper extension: 02z44tp; 02wyzmv; 02q5bx2; 02gl58; 0h63q6t; >> query: (?x12473, 02gf_l) <- languages(?x12473, ?x254), actor(?x12473, ?x10998), genre(?x12473, ?x2540), profession(?x10998, ?x353), actor(?x5946, ?x10998), ?x254 = 02h40lc, country(?x5946, ?x94), film_crew_role(?x5946, ?x2178) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #4579 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 043qqt5; *> query: (?x12473, 019803) <- languages(?x12473, ?x254), actor(?x12473, ?x10998), category(?x10998, ?x134), people(?x4322, ?x10998), gender(?x10998, ?x231), profession(?x10998, ?x353), location(?x10998, ?x11058) *> conf = 0.17 ranks of expected_values: 57 EVAL 03k99c actor 019803 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 76.000 40.000 0.353 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #6332-0288fyj PRED entity: 0288fyj PRED relation: award PRED expected values: 01by1l => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 257): 01c9dd (0.77 #16134, 0.74 #3628, 0.72 #22187), 031b3h (0.77 #16134, 0.74 #3628, 0.72 #22187), 01by1l (0.67 #111, 0.56 #514, 0.24 #2126), 0c4z8 (0.42 #71, 0.38 #474, 0.18 #14114), 01cky2 (0.33 #194, 0.18 #14114, 0.15 #14921), 03qbh5 (0.32 #608, 0.25 #205, 0.14 #2220), 01c99j (0.26 #629, 0.25 #226, 0.13 #21782), 03qbnj (0.26 #636, 0.25 #233, 0.18 #14114), 03t5kl (0.25 #227, 0.18 #14114, 0.15 #14921), 02f5qb (0.25 #155, 0.17 #558, 0.15 #14921) >> Best rule #16134 for best value: >> intensional similarity = 3 >> extensional distance = 1631 >> proper extension: 0dky9n; 034bs; >> query: (?x2335, ?x8705) <- nationality(?x2335, ?x94), award_winner(?x8705, ?x2335), ceremony(?x8705, ?x139) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #111 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 04lgymt; 0770cd; 04mn81; 01vs_v8; 04xrx; 01vw20h; 05vzw3; 016fnb; 02x_h0; 01nhkxp; *> query: (?x2335, 01by1l) <- award_nominee(?x2335, ?x5478), award_nominee(?x2335, ?x1566), ?x5478 = 01yzl2, award_winner(?x2054, ?x1566) *> conf = 0.67 ranks of expected_values: 3 EVAL 0288fyj award 01by1l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 71.000 0.766 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6331-0dckvs PRED entity: 0dckvs PRED relation: film_format PRED expected values: 0cj16 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 4): 0cj16 (0.40 #3, 0.22 #8, 0.20 #69), 07fb8_ (0.18 #77, 0.17 #131, 0.17 #27), 017fx5 (0.10 #19, 0.09 #45, 0.09 #14), 01dc60 (0.05 #645) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 05g8pg; 0198b6; 043n0v_; 0gl02yg; 08j7lh; >> query: (?x467, 0cj16) <- film_crew_role(?x467, ?x137), country(?x467, ?x2645), film_release_region(?x467, ?x94), language(?x467, ?x9980), ?x9980 = 0459q4 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dckvs film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.400 http://example.org/film/film/film_format #6330-024qwq PRED entity: 024qwq PRED relation: instrumentalists! PRED expected values: 05148p4 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 119): 0342h (0.71 #606, 0.65 #1208, 0.65 #3881), 05r5c (0.58 #953, 0.47 #4143, 0.47 #4403), 05148p4 (0.39 #1223, 0.38 #191, 0.36 #3896), 02hnl (0.25 #205, 0.19 #1753, 0.17 #3910), 07c6l (0.25 #9, 0.12 #95, 0.06 #611), 01wy6 (0.25 #46, 0.12 #132, 0.06 #304), 03qjg (0.24 #652, 0.16 #3580, 0.16 #3927), 03gvt (0.19 #236, 0.09 #1010, 0.07 #1268), 0l14qv (0.15 #1209, 0.12 #177, 0.12 #263), 06ncr (0.15 #645, 0.08 #989, 0.08 #1247) >> Best rule #606 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 04mx7s; >> query: (?x9407, 0342h) <- artists(?x3370, ?x9407), instrumentalists(?x212, ?x9407), ?x3370 = 059kh, nationality(?x9407, ?x94) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1223 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 122 *> proper extension: 03k0yw; 02ryx0; 02qtywd; *> query: (?x9407, 05148p4) <- profession(?x9407, ?x131), award_winner(?x3835, ?x9407), ?x131 = 0dz3r, instrumentalists(?x212, ?x9407) *> conf = 0.39 ranks of expected_values: 3 EVAL 024qwq instrumentalists! 05148p4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 129.000 0.706 http://example.org/music/instrument/instrumentalists #6329-06x4l_ PRED entity: 06x4l_ PRED relation: award_winner! PRED expected values: 0gpjbt => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 107): 0gpjbt (0.21 #2381, 0.20 #29, 0.13 #869), 0466p0j (0.21 #2381, 0.20 #76, 0.11 #1336), 0n8_m93 (0.21 #2381, 0.20 #118, 0.01 #2919), 013b2h (0.16 #360, 0.14 #2881, 0.13 #2320), 02cg41 (0.16 #405, 0.11 #965, 0.11 #1385), 01s695 (0.15 #283, 0.14 #143, 0.11 #843), 02rjjll (0.12 #1265, 0.12 #2245, 0.11 #2526), 01c6qp (0.12 #2259, 0.10 #2820, 0.10 #2540), 05pd94v (0.11 #1262, 0.10 #2242, 0.10 #2803), 01bx35 (0.11 #287, 0.11 #2247, 0.10 #2808) >> Best rule #2381 for best value: >> intensional similarity = 3 >> extensional distance = 327 >> proper extension: 0c_mvb; 06lxn; >> query: (?x2862, ?x5656) <- artists(?x302, ?x2862), award_winner(?x2862, ?x7115), award_winner(?x5656, ?x7115) >> conf = 0.21 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 06x4l_ award_winner! 0gpjbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.214 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6328-014kq6 PRED entity: 014kq6 PRED relation: nominated_for! PRED expected values: 02n72k 09y6pb 025twgt => 96 concepts (48 used for prediction) PRED predicted values (max 10 best out of 196): 01kf3_9 (0.86 #5878, 0.86 #6617, 0.85 #6615), 02n72k (0.86 #5878, 0.86 #6617, 0.85 #6615), 025twgt (0.75 #972, 0.73 #727, 0.54 #6618), 014kq6 (0.58 #794, 0.54 #6618, 0.51 #5879), 09y6pb (0.54 #6618, 0.51 #5879, 0.51 #5143), 0y_yw (0.25 #160, 0.02 #2604, 0.02 #3582), 0jsf6 (0.25 #166, 0.02 #2610, 0.02 #3588), 07g1sm (0.25 #190, 0.02 #3612, 0.02 #4104), 06_wqk4 (0.12 #996, 0.06 #2952, 0.04 #3686), 0ddt_ (0.12 #81, 0.04 #3503, 0.04 #3749) >> Best rule #5878 for best value: >> intensional similarity = 4 >> extensional distance = 201 >> proper extension: 01p9hgt; 01kv4mb; 0ggjt; 0bhvtc; 03cfjg; 0p_47; 0pmw9; >> query: (?x2160, ?x835) <- nominated_for(?x2160, ?x835), nominated_for(?x836, ?x2160), nominated_for(?x835, ?x11362), nominated_for(?x154, ?x2160) >> conf = 0.86 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3, 5 EVAL 014kq6 nominated_for! 025twgt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 48.000 0.856 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for EVAL 014kq6 nominated_for! 09y6pb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 48.000 0.856 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for EVAL 014kq6 nominated_for! 02n72k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 48.000 0.856 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #6327-025352 PRED entity: 025352 PRED relation: specialization_of PRED expected values: 0cbd2 => 48 concepts (40 used for prediction) PRED predicted values (max 10 best out of 59): 09jwl (0.43 #263, 0.33 #7, 0.30 #364), 0cbd2 (0.25 #193, 0.25 #161, 0.25 #129), 0n1h (0.20 #228, 0.14 #260, 0.10 #361), 02hrh1q (0.14 #330, 0.05 #530, 0.04 #892), 01c72t (0.06 #1218, 0.04 #1020, 0.03 #488), 0dz3r (0.06 #1218, 0.04 #1020, 0.03 #290), 06q2q (0.05 #1068, 0.03 #769, 0.03 #801), 0nbcg (0.04 #1020, 0.03 #488, 0.03 #290), 09lbv (0.04 #1020, 0.03 #290, 0.02 #688), 01c979 (0.04 #910, 0.04 #976, 0.04 #1045) >> Best rule #263 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 01c8w0; >> query: (?x6476, 09jwl) <- profession(?x7906, ?x6476), profession(?x4620, ?x6476), ?x7906 = 03kts, award_winner(?x4620, ?x1136), artist(?x3265, ?x4620), role(?x4620, ?x227) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #193 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 0dxtg; *> query: (?x6476, 0cbd2) <- profession(?x12724, ?x6476), profession(?x10591, ?x6476), profession(?x7906, ?x6476), profession(?x4987, ?x6476), profession(?x3625, ?x6476), profession(?x2479, ?x6476), ?x3625 = 02xs0q, award_winner(?x7491, ?x12724), languages(?x7906, ?x254), student(?x7545, ?x2479), ?x4987 = 0dpqk, category(?x10591, ?x134) *> conf = 0.25 ranks of expected_values: 2 EVAL 025352 specialization_of 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 48.000 40.000 0.429 http://example.org/people/profession/specialization_of #6326-081l_ PRED entity: 081l_ PRED relation: profession PRED expected values: 02hrh1q => 128 concepts (69 used for prediction) PRED predicted values (max 10 best out of 114): 02hrh1q (0.80 #6736, 0.79 #8595, 0.75 #6879), 0cbd2 (0.77 #1723, 0.55 #3869, 0.54 #4155), 03gjzk (0.57 #585, 0.52 #9312, 0.52 #1587), 09jwl (0.49 #2719, 0.41 #6169, 0.41 #4882), 0nbcg (0.49 #2719, 0.30 #6180, 0.29 #3748), 0dz3r (0.49 #2719, 0.27 #6154, 0.26 #3722), 01c72t (0.49 #2719, 0.26 #1166, 0.18 #8605), 029bkp (0.49 #2719, 0.03 #4436, 0.03 #6196), 01d30f (0.49 #2719, 0.03 #4436, 0.02 #3213), 03lgtv (0.49 #2719, 0.03 #4436, 0.01 #6260) >> Best rule #6736 for best value: >> intensional similarity = 4 >> extensional distance = 499 >> proper extension: 016qtt; 03gm48; 0993r; 055c8; 01pp3p; 015q43; 06wm0z; 023361; 04znsy; 0fthdk; ... >> query: (?x8019, 02hrh1q) <- profession(?x8019, ?x319), award_winner(?x372, ?x8019), award_winner(?x9501, ?x8019), people(?x5540, ?x8019) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 081l_ profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 69.000 0.800 http://example.org/people/person/profession #6325-026mmy PRED entity: 026mmy PRED relation: nominated_for PRED expected values: 02cbhg => 53 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1359): 01cmp9 (0.67 #20822, 0.66 #25630, 0.66 #35248), 02yvct (0.53 #3522, 0.21 #24028, 0.21 #28836), 0gmcwlb (0.53 #3384, 0.20 #19406, 0.18 #24214), 07w8fz (0.53 #3663, 0.15 #19685, 0.14 #24493), 0m313 (0.47 #3212, 0.20 #19234, 0.19 #24042), 0g9lm2 (0.47 #3862, 0.18 #19884, 0.17 #24692), 07024 (0.47 #3636, 0.16 #19658, 0.14 #24466), 011yl_ (0.47 #3733, 0.15 #6937, 0.15 #19755), 0209xj (0.47 #3294, 0.13 #19316, 0.13 #6498), 08nvyr (0.47 #3898, 0.12 #19920, 0.11 #24728) >> Best rule #20822 for best value: >> intensional similarity = 3 >> extensional distance = 171 >> proper extension: 02rdxsh; 02qysm0; 02qwzkm; >> query: (?x10881, ?x6048) <- award(?x6048, ?x10881), nominated_for(?x10881, ?x3854), production_companies(?x3854, ?x2156) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #8006 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 117 *> proper extension: 0d085; 0bqsk5; *> query: (?x10881, ?x7491) <- award_winner(?x10881, ?x9719), award_winner(?x10881, ?x1853), award(?x1853, ?x1307), award_winner(?x7491, ?x9719), story_by(?x6281, ?x1853) *> conf = 0.25 ranks of expected_values: 83 EVAL 026mmy nominated_for 02cbhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 53.000 22.000 0.674 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6324-010m55 PRED entity: 010m55 PRED relation: source PRED expected values: 0jbk9 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #40, 0.81 #25, 0.80 #3) >> Best rule #40 for best value: >> intensional similarity = 1 >> extensional distance = 514 >> proper extension: 010bnr; >> query: (?x10772, 0jbk9) <- place(?x10772, ?x10772) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 010m55 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #6323-011yth PRED entity: 011yth PRED relation: nominated_for! PRED expected values: 09qv_s => 78 concepts (59 used for prediction) PRED predicted values (max 10 best out of 224): 027c95y (0.66 #4731, 0.66 #8561, 0.66 #6083), 027986c (0.66 #4731, 0.66 #8561, 0.66 #6083), 0gq_v (0.44 #19, 0.33 #2271, 0.32 #2496), 0gqy2 (0.37 #111, 0.24 #4166, 0.24 #2363), 0l8z1 (0.37 #47, 0.22 #498, 0.20 #2524), 0gqyl (0.30 #69, 0.23 #2321, 0.20 #4124), 040njc (0.27 #2484, 0.26 #2259, 0.25 #4062), 0gs96 (0.26 #79, 0.21 #2331, 0.19 #2556), 027dtxw (0.23 #455, 0.19 #13296, 0.19 #13297), 02qyntr (0.23 #2644, 0.21 #2419, 0.21 #4222) >> Best rule #4731 for best value: >> intensional similarity = 3 >> extensional distance = 773 >> proper extension: 08cfr1; >> query: (?x1910, ?x834) <- award(?x1910, ?x834), film(?x2033, ?x1910), award_nominee(?x434, ?x2033) >> conf = 0.66 => this is the best rule for 2 predicted values *> Best rule #13296 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1494 *> proper extension: 09fb5; 0n2bh; 01vrwfv; 05gnf; 01b7h8; 06ys2; *> query: (?x1910, ?x591) <- nominated_for(?x2035, ?x1910), award(?x2035, ?x102), award_winner(?x591, ?x2035) *> conf = 0.19 ranks of expected_values: 14 EVAL 011yth nominated_for! 09qv_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 78.000 59.000 0.659 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6322-081bls PRED entity: 081bls PRED relation: film PRED expected values: 0gtv7pk => 117 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1784): 05b_gq (0.75 #7941, 0.75 #15884, 0.74 #31771), 03mh_tp (0.44 #5211, 0.43 #11565, 0.36 #8388), 02rb84n (0.40 #1839, 0.36 #6603, 0.33 #3427), 0dgq_kn (0.40 #2516, 0.33 #4104, 0.29 #12046), 014kq6 (0.40 #1893, 0.33 #3481, 0.29 #11423), 045j3w (0.40 #2023, 0.33 #3611, 0.22 #5199), 0gwjw0c (0.40 #2670, 0.33 #4258, 0.21 #12200), 043tvp3 (0.40 #2669, 0.33 #4257, 0.18 #7433), 0gtvpkw (0.40 #2091, 0.33 #3679, 0.17 #14798), 02vzpb (0.40 #3022, 0.33 #4610, 0.15 #10964) >> Best rule #7941 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 086k8; 030_1m; 01gb54; >> query: (?x6969, ?x6244) <- film(?x6969, ?x511), production_companies(?x6244, ?x6969), award_winner(?x1105, ?x6969), ?x1105 = 07bdd_ >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #49 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 06jntd; *> query: (?x6969, 0gtv7pk) <- film(?x6969, ?x10590), film(?x6969, ?x9329), award_winner(?x6244, ?x6969), ?x10590 = 080dfr7, currency(?x9329, ?x170) *> conf = 0.33 ranks of expected_values: 50 EVAL 081bls film 0gtv7pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 117.000 28.000 0.753 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #6321-01c427 PRED entity: 01c427 PRED relation: award! PRED expected values: 01pfr3 07c0j 01vrz41 06w2sn5 01vsykc 01f9zw 02h9_l => 38 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2536): 01l3mk3 (0.78 #49537, 0.78 #49536, 0.77 #52841), 01k98nm (0.78 #49537, 0.78 #49536, 0.77 #52841), 0kr_t (0.71 #18091, 0.44 #21393, 0.40 #11486), 016l09 (0.71 #19231, 0.38 #22533, 0.20 #12626), 0134pk (0.71 #19251, 0.38 #22553, 0.20 #6042), 0dtd6 (0.71 #17032, 0.25 #20334, 0.20 #3823), 01xzb6 (0.60 #11412, 0.60 #4808, 0.44 #21319), 01vrz41 (0.60 #10196, 0.60 #3592, 0.31 #20103), 01vw20h (0.60 #11161, 0.56 #21068, 0.40 #7859), 0fhxv (0.60 #11221, 0.50 #21128, 0.43 #17826) >> Best rule #49537 for best value: >> intensional similarity = 3 >> extensional distance = 139 >> proper extension: 09v7wsg; >> query: (?x1389, ?x6383) <- category_of(?x1389, ?x2421), award_winner(?x1389, ?x6383), ceremony(?x1389, ?x139) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #10196 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 01bgqh; 0c4z8; 01by1l; *> query: (?x1389, 01vrz41) <- award(?x7115, ?x1389), award(?x1181, ?x1389), ?x1181 = 0b68vs, ?x7115 = 02z4b_8, category_of(?x1389, ?x2421) *> conf = 0.60 ranks of expected_values: 8, 13, 57, 62, 253, 256, 476 EVAL 01c427 award! 02h9_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 38.000 22.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c427 award! 01f9zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 38.000 22.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c427 award! 01vsykc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 38.000 22.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c427 award! 06w2sn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 38.000 22.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c427 award! 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 38.000 22.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c427 award! 07c0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 38.000 22.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c427 award! 01pfr3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 38.000 22.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6320-035gnh PRED entity: 035gnh PRED relation: film_format PRED expected values: 07fb8_ => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.18 #6, 0.17 #93, 0.16 #45), 0cj16 (0.13 #145, 0.12 #140, 0.12 #195), 017fx5 (0.04 #14, 0.04 #48, 0.04 #37) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 312 >> proper extension: 07bz5; >> query: (?x7428, 07fb8_) <- nominated_for(?x2451, ?x7428), award_winner(?x4623, ?x2451), friend(?x8898, ?x2451), gender(?x2451, ?x231) >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035gnh film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.178 http://example.org/film/film/film_format #6319-02yplc PRED entity: 02yplc PRED relation: gender PRED expected values: 02zsn => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #27, 0.75 #37, 0.74 #83), 02zsn (0.46 #169, 0.34 #32, 0.33 #30) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 012v1t; >> query: (?x4263, 05zppz) <- religion(?x4263, ?x1985), type_of_union(?x4263, ?x566), student(?x8398, ?x4263) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #169 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4064 *> proper extension: 0jrg; *> query: (?x4263, ?x231) <- nationality(?x4263, ?x94), nationality(?x5048, ?x94), gender(?x5048, ?x231) *> conf = 0.46 ranks of expected_values: 2 EVAL 02yplc gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.809 http://example.org/people/person/gender #6318-05njw PRED entity: 05njw PRED relation: list PRED expected values: 01pd60 => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 4): 01pd60 (0.81 #653, 0.81 #647, 0.77 #536), 09g7thr (0.75 #126, 0.53 #496, 0.53 #486), 05glt (0.38 #643, 0.38 #649, 0.09 #532), 026cl_m (0.25 #433, 0.09 #644, 0.09 #650) >> Best rule #653 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 07bz5; >> query: (?x11504, ?x8915) <- list(?x11504, ?x7472), list(?x7471, ?x7472), list(?x7471, ?x8915) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05njw list 01pd60 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 180.000 180.000 0.814 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #6317-02_01w PRED entity: 02_01w PRED relation: people! PRED expected values: 02y0js => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 41): 0m32h (0.33 #23, 0.16 #551, 0.13 #353), 0gk4g (0.23 #1396, 0.23 #1198, 0.20 #1330), 0dq9p (0.20 #743, 0.15 #1403, 0.15 #611), 01psyx (0.20 #111, 0.02 #4665, 0.02 #4731), 0dcsx (0.16 #543, 0.13 #345, 0.11 #477), 04p3w (0.13 #341, 0.11 #473, 0.10 #2453), 02y0js (0.12 #398, 0.11 #134, 0.10 #1190), 02k6hp (0.11 #169, 0.10 #1093, 0.09 #1489), 012hw (0.11 #184, 0.07 #316, 0.06 #448), 0c58k (0.11 #162, 0.07 #294, 0.06 #426) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 07n39; >> query: (?x12004, 0m32h) <- place_of_death(?x12004, ?x191), ?x191 = 0k049, company(?x12004, ?x13490), type_of_union(?x12004, ?x566) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #398 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 042kg; *> query: (?x12004, 02y0js) <- celebrities_impersonated(?x3649, ?x12004), profession(?x12004, ?x1032), company(?x12004, ?x13490), nationality(?x12004, ?x94) *> conf = 0.12 ranks of expected_values: 7 EVAL 02_01w people! 02y0js CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 125.000 125.000 0.333 http://example.org/people/cause_of_death/people #6316-062zjtt PRED entity: 062zjtt PRED relation: film! PRED expected values: 0prjs => 92 concepts (33 used for prediction) PRED predicted values (max 10 best out of 85): 0693l (0.33 #84, 0.06 #3664, 0.04 #7243), 01twdk (0.12 #395, 0.07 #670, 0.06 #1221), 01vz80y (0.12 #448), 079vf (0.12 #6884, 0.11 #7987, 0.10 #7159), 046_v (0.12 #6884, 0.11 #7987, 0.10 #7159), 0br1w (0.12 #6884, 0.11 #7987, 0.10 #7159), 072vj (0.11 #1649, 0.07 #824, 0.06 #1375), 07rd7 (0.11 #1754, 0.05 #2857, 0.03 #3132), 01f7j9 (0.08 #1977, 0.06 #2528, 0.06 #877), 02qzjj (0.08 #2466, 0.06 #2742, 0.03 #2191) >> Best rule #84 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0gyv0b4; >> query: (?x4273, 0693l) <- executive_produced_by(?x4273, ?x96), film(?x5462, ?x4273), film(?x96, ?x97), ?x5462 = 0f5xn, region(?x4273, ?x512) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 062zjtt film! 0prjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 33.000 0.333 http://example.org/film/director/film #6315-01mxqyk PRED entity: 01mxqyk PRED relation: award_winner PRED expected values: 01wwvd2 => 137 concepts (66 used for prediction) PRED predicted values (max 10 best out of 655): 04xrx (0.50 #41923, 0.50 #64504, 0.44 #3225), 03q2t9 (0.44 #3225, 0.38 #46761, 0.37 #82246), 02qwg (0.15 #88697, 0.13 #19908, 0.04 #40869), 028qdb (0.15 #88697, 0.10 #20062, 0.02 #28122), 02qlg7s (0.15 #88697, 0.09 #19741, 0.03 #29414), 04dqdk (0.15 #88697, 0.09 #19557, 0.02 #43743), 01cwhp (0.15 #88697, 0.06 #19740, 0.02 #40701), 01m7pwq (0.15 #88697, 0.06 #20801, 0.02 #28861), 01vw20h (0.15 #88697, 0.05 #93537, 0.02 #12051), 01wgxtl (0.15 #88697, 0.05 #93537) >> Best rule #41923 for best value: >> intensional similarity = 3 >> extensional distance = 341 >> proper extension: 0770cd; 014hr0; 06fxnf; 09bx1k; 01yg9y; 01hrqc; 0bxtyq; 04f9r2; >> query: (?x11621, ?x2614) <- award_winner(?x11621, ?x2138), award_nominee(?x2614, ?x11621), artists(?x671, ?x11621) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #88697 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 845 *> proper extension: 01j53q; *> query: (?x11621, ?x1381) <- award_winner(?x11621, ?x9528), award_winner(?x11621, ?x4574), award_nominee(?x1381, ?x4574), category(?x9528, ?x134) *> conf = 0.15 ranks of expected_values: 11 EVAL 01mxqyk award_winner 01wwvd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 137.000 66.000 0.498 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #6314-0bqytm PRED entity: 0bqytm PRED relation: nationality PRED expected values: 0345h => 104 concepts (101 used for prediction) PRED predicted values (max 10 best out of 39): 0345h (0.84 #5629, 0.50 #631, 0.45 #1505), 09c7w0 (0.82 #2109, 0.81 #2211, 0.80 #1606), 0h3y (0.49 #3321, 0.38 #7034, 0.20 #108), 084n_ (0.45 #1505), 059z0 (0.45 #1505), 01k6y1 (0.45 #1505), 02jx1 (0.40 #8742, 0.33 #33, 0.16 #1437), 07ssc (0.40 #8742, 0.17 #1419, 0.13 #615), 03rjj (0.38 #7034, 0.11 #305, 0.08 #405), 0156q (0.16 #1203, 0.02 #2311, 0.02 #2209) >> Best rule #5629 for best value: >> intensional similarity = 2 >> extensional distance = 1519 >> proper extension: 0784v1; 01qx13; >> query: (?x5014, ?x1264) <- place_of_birth(?x5014, ?x1646), country(?x1646, ?x1264) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bqytm nationality 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 101.000 0.837 http://example.org/people/person/nationality #6313-09blyk PRED entity: 09blyk PRED relation: titles PRED expected values: 0c3ybss 01srq2 0dgrwqr => 68 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1812): 0296rz (0.60 #5843, 0.35 #16315, 0.33 #11825), 07z6xs (0.50 #3717, 0.44 #11195, 0.40 #5213), 01q7h2 (0.50 #4279, 0.44 #11757, 0.40 #5775), 0191n (0.50 #3697, 0.44 #11175, 0.40 #5193), 0c34mt (0.50 #1964, 0.40 #6450, 0.33 #7945), 07f_7h (0.50 #1841, 0.40 #6327, 0.33 #7822), 02rrh1w (0.50 #4103, 0.40 #5599, 0.33 #1112), 08720 (0.50 #3069, 0.40 #4565, 0.33 #78), 070fnm (0.50 #3249, 0.34 #4487, 0.33 #10727), 0jwvf (0.50 #3810, 0.34 #4487, 0.33 #819) >> Best rule #5843 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 02qfv5d; >> query: (?x3613, 0296rz) <- titles(?x3613, ?x6493), titles(?x3613, ?x4841), titles(?x3613, ?x3471), genre(?x5074, ?x3613), film_release_region(?x4841, ?x87), nominated_for(?x1180, ?x3471), ?x1180 = 02n9nmz, ?x5074 = 05mrf_p, film(?x541, ?x6493) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2556 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 03npn; *> query: (?x3613, 0dgrwqr) <- titles(?x3613, ?x5627), genre(?x5047, ?x3613), genre(?x2094, ?x3613), ?x2094 = 05z7c, award_winner(?x5627, ?x519), nominated_for(?x102, ?x5627) *> conf = 0.25 ranks of expected_values: 161, 176, 424 EVAL 09blyk titles 0dgrwqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 68.000 16.000 0.600 http://example.org/media_common/netflix_genre/titles EVAL 09blyk titles 01srq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 16.000 0.600 http://example.org/media_common/netflix_genre/titles EVAL 09blyk titles 0c3ybss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 68.000 16.000 0.600 http://example.org/media_common/netflix_genre/titles #6312-0gqwc PRED entity: 0gqwc PRED relation: ceremony PRED expected values: 050yyb 0bzn6_ 073hgx 0bzmt8 0c4hx0 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 81): 0bzn6_ (0.62 #522, 0.43 #927, 0.40 #603), 0gpjbt (0.61 #1720, 0.34 #2692, 0.34 #2773), 09n4nb (0.60 #1734, 0.34 #2706, 0.33 #33), 0466p0j (0.59 #1749, 0.33 #48, 0.33 #2721), 05pd94v (0.59 #1702, 0.33 #1, 0.33 #2674), 056878 (0.58 #1722, 0.34 #2694, 0.33 #21), 02rjjll (0.58 #1705, 0.33 #4, 0.33 #2677), 02cg41 (0.58 #1772, 0.33 #71, 0.33 #2744), 01c6qp (0.57 #1716, 0.33 #15, 0.33 #2688), 01bx35 (0.54 #1706, 0.33 #5, 0.32 #2678) >> Best rule #522 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0gq_v; 0gr4k; 054krc; 0gqyl; 0gqy2; 02x201b; >> query: (?x1245, 0bzn6_) <- award(?x197, ?x1245), ceremony(?x1245, ?x78), nominated_for(?x1245, ?x10752), ?x10752 = 01k5y0, award(?x241, ?x1245) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 16, 17, 18, 34 EVAL 0gqwc ceremony 0c4hx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 58.000 58.000 0.625 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqwc ceremony 0bzmt8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 58.000 58.000 0.625 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqwc ceremony 073hgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 58.000 58.000 0.625 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqwc ceremony 0bzn6_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.625 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gqwc ceremony 050yyb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 58.000 58.000 0.625 http://example.org/award/award_category/winners./award/award_honor/ceremony #6311-02fqrf PRED entity: 02fqrf PRED relation: film! PRED expected values: 01f6zc => 94 concepts (31 used for prediction) PRED predicted values (max 10 best out of 959): 01wy5m (0.44 #56158, 0.38 #2080, 0.32 #62400), 04hw4b (0.44 #56158, 0.38 #2080, 0.32 #62400), 03y1mlp (0.38 #2080, 0.32 #56157, 0.32 #60319), 02nygk (0.20 #6240, 0.14 #14559, 0.10 #39514), 01f6zc (0.15 #3021, 0.12 #941, 0.04 #5101), 0184dt (0.15 #20798, 0.04 #62401), 0p8r1 (0.12 #582, 0.09 #4742, 0.05 #13060), 0237fw (0.12 #404, 0.08 #2484, 0.04 #62401), 023nlj (0.12 #1514, 0.08 #3594, 0.03 #7754), 06t74h (0.12 #693, 0.08 #2773, 0.02 #4853) >> Best rule #56158 for best value: >> intensional similarity = 3 >> extensional distance = 317 >> proper extension: 0gfzgl; >> query: (?x3498, ?x4835) <- nominated_for(?x4835, ?x3498), category(?x3498, ?x134), location(?x4835, ?x87) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #3021 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 02jxbw; *> query: (?x3498, 01f6zc) <- music(?x3498, ?x3069), film(?x3186, ?x3498), ?x3186 = 055c8, nominated_for(?x154, ?x3498) *> conf = 0.15 ranks of expected_values: 5 EVAL 02fqrf film! 01f6zc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 31.000 0.440 http://example.org/film/actor/film./film/performance/film #6310-0k29f PRED entity: 0k29f PRED relation: gender PRED expected values: 05zppz => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #5, 0.83 #3, 0.80 #69), 02zsn (0.46 #107, 0.26 #22, 0.25 #76) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0dbpyd; 04snp2; 03nbbv; 05lnk0; 079ws; 03h2p5; 047cqr; 0jnb0; 02gnj2; 02gnlz; ... >> query: (?x11298, 05zppz) <- nationality(?x11298, ?x94), profession(?x11298, ?x8310), ?x94 = 09c7w0, ?x8310 = 0196pc >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k29f gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.879 http://example.org/people/person/gender #6309-015010 PRED entity: 015010 PRED relation: languages PRED expected values: 064_8sq => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 21): 064_8sq (0.29 #647, 0.17 #166, 0.10 #90), 02bjrlw (0.29 #647, 0.17 #153, 0.10 #77), 04306rv (0.29 #647, 0.10 #78, 0.10 #1028), 06mp7 (0.29 #647, 0.10 #86, 0.10 #1028), 02hwyss (0.29 #647, 0.10 #1028, 0.07 #1867), 03_9r (0.17 #156, 0.02 #460, 0.02 #536), 012w70 (0.08 #159, 0.01 #463, 0.01 #539), 02ztjwg (0.08 #176), 02hwhyv (0.08 #173), 03hkp (0.08 #161) >> Best rule #647 for best value: >> intensional similarity = 4 >> extensional distance = 409 >> proper extension: 01syr4; >> query: (?x12347, ?x90) <- film(?x12347, ?x2345), languages(?x12347, ?x254), nominated_for(?x198, ?x2345), language(?x2345, ?x90) >> conf = 0.29 => this is the best rule for 5 predicted values ranks of expected_values: 1 EVAL 015010 languages 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.293 http://example.org/people/person/languages #6308-051x52f PRED entity: 051x52f PRED relation: award_nominee PRED expected values: 0fmqp6 => 92 concepts (40 used for prediction) PRED predicted values (max 10 best out of 691): 0fmqp6 (0.81 #32773, 0.81 #91296, 0.80 #70226), 05233hy (0.81 #32773, 0.81 #91296, 0.80 #70226), 07h1tr (0.56 #599, 0.42 #21064, 0.31 #11701), 0584j4n (0.42 #21064, 0.33 #1162, 0.31 #11701), 057bc6m (0.42 #21064, 0.33 #1856, 0.31 #11701), 071jv5 (0.42 #21064, 0.33 #2298, 0.31 #11701), 051x52f (0.42 #21064, 0.31 #11701, 0.21 #72568), 04gmp_z (0.42 #21064, 0.31 #11701, 0.17 #9984), 058vfp4 (0.42 #21064, 0.31 #11701, 0.14 #11431), 05683cn (0.42 #21064, 0.31 #11701, 0.11 #2078) >> Best rule #32773 for best value: >> intensional similarity = 3 >> extensional distance = 715 >> proper extension: 01wbsdz; >> query: (?x7876, ?x786) <- profession(?x7876, ?x1078), award_nominee(?x786, ?x7876), film_crew_role(?x148, ?x1078) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 051x52f award_nominee 0fmqp6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 40.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #6307-02q690_ PRED entity: 02q690_ PRED relation: honored_for PRED expected values: 0kfv9 => 32 concepts (21 used for prediction) PRED predicted values (max 10 best out of 702): 06hwzy (0.50 #6993, 0.50 #6421, 0.40 #5280), 07c72 (0.50 #7027, 0.50 #3603, 0.40 #5314), 02rzdcp (0.50 #7036, 0.47 #2849, 0.40 #5323), 080dwhx (0.47 #2849, 0.40 #4580, 0.33 #6293), 0kfv9 (0.47 #2849, 0.36 #7519, 0.33 #8093), 01q_y0 (0.47 #2849, 0.25 #3555, 0.21 #8697), 05_z42 (0.47 #2849, 0.20 #5461, 0.20 #4319), 01j67j (0.47 #2849, 0.17 #3987, 0.16 #7413), 0557yqh (0.47 #2849, 0.17 #3987, 0.16 #7413), 099pks (0.47 #2849, 0.17 #3987) >> Best rule #6993 for best value: >> intensional similarity = 18 >> extensional distance = 4 >> proper extension: 05c1t6z; >> query: (?x4760, 06hwzy) <- award_winner(?x4760, ?x10053), award_winner(?x4760, ?x3762), award_winner(?x4760, ?x2493), ceremony(?x2016, ?x4760), ceremony(?x870, ?x4760), ceremony(?x757, ?x4760), nationality(?x3762, ?x94), ?x870 = 09qv3c, award_nominee(?x3762, ?x722), award_winner(?x618, ?x2493), film(?x2493, ?x2111), nominated_for(?x10053, ?x10731), honored_for(?x4760, ?x1631), award_winner(?x374, ?x2493), ?x757 = 09qj50, award_nominee(?x2194, ?x10053), place_of_birth(?x10053, ?x1523), ?x2016 = 0cjyzs >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2849 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 0gvstc3; *> query: (?x4760, ?x1849) <- award_winner(?x4760, ?x5263), award_winner(?x4760, ?x3762), award_winner(?x4760, ?x2657), ceremony(?x9640, ?x4760), ceremony(?x7041, ?x4760), ceremony(?x6724, ?x4760), ceremony(?x4225, ?x4760), ceremony(?x3906, ?x4760), ?x7041 = 0gqmvn, ?x9640 = 0gkr9q, ?x3906 = 03ccq3s, program(?x3762, ?x1849), ?x6724 = 09v7wsg, ?x4225 = 09qvf4, student(?x6602, ?x5263), honored_for(?x4760, ?x1631), award(?x2657, ?x704), film(?x2657, ?x153) *> conf = 0.47 ranks of expected_values: 5 EVAL 02q690_ honored_for 0kfv9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 32.000 21.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #6306-05c5z8j PRED entity: 05c5z8j PRED relation: nominated_for! PRED expected values: 0gr4k => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 188): 02r0csl (0.30 #5, 0.14 #479, 0.09 #3798), 0gq9h (0.29 #2197, 0.28 #2671, 0.27 #4804), 0gs96 (0.25 #91, 0.15 #2225, 0.14 #2699), 02qyp19 (0.25 #1, 0.10 #2135, 0.10 #2609), 019f4v (0.25 #2189, 0.23 #2663, 0.23 #4796), 0fq9zdn (0.24 #7349, 0.20 #46, 0.06 #757), 0gr4k (0.24 #7349, 0.19 #12801, 0.18 #4768), 0cjyzs (0.24 #7349, 0.19 #12801, 0.04 #1742), 09sb52 (0.24 #7349, 0.10 #35, 0.09 #746), 0bdw6t (0.24 #7349, 0.03 #6011, 0.03 #5537) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0fgpvf; 02r79_h; 0gj9qxr; 05p3738; 0g9wdmc; 09gq0x5; 04qw17; 047n8xt; 06gjk9; 09gkx35; ... >> query: (?x4329, 02r0csl) <- production_companies(?x4329, ?x9041), genre(?x4329, ?x258), ?x9041 = 05mgj0 >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #7349 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1095 *> proper extension: 01j95; *> query: (?x4329, ?x704) <- award_winner(?x4329, ?x4328), award(?x4328, ?x704) *> conf = 0.24 ranks of expected_values: 7 EVAL 05c5z8j nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 66.000 66.000 0.300 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6305-0p9sw PRED entity: 0p9sw PRED relation: ceremony PRED expected values: 0bzk8w 073h1t 0bzm__ 073hgx 0bzmt8 073hd1 0fz0c2 => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 83): 073hd1 (0.82 #559, 0.76 #476, 0.64 #310), 0bzmt8 (0.77 #558, 0.76 #475, 0.73 #309), 0bzm__ (0.77 #551, 0.76 #468, 0.64 #302), 073h1t (0.77 #517, 0.71 #434, 0.55 #268), 073hgx (0.73 #556, 0.71 #473, 0.67 #141), 0bzk8w (0.71 #420, 0.68 #503, 0.64 #254), 0fz0c2 (0.65 #479, 0.64 #313, 0.55 #562), 0gpjbt (0.61 #1266, 0.51 #1515, 0.39 #2263), 09n4nb (0.60 #1279, 0.49 #1528, 0.37 #2276), 0466p0j (0.59 #1293, 0.49 #1542, 0.37 #2290) >> Best rule #559 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0gr07; >> query: (?x500, 073hd1) <- ceremony(?x500, ?x5349), award(?x382, ?x500), ?x5349 = 02jp5r >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7 EVAL 0p9sw ceremony 0fz0c2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.818 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0p9sw ceremony 073hd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.818 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0p9sw ceremony 0bzmt8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.818 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0p9sw ceremony 073hgx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.818 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0p9sw ceremony 0bzm__ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.818 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0p9sw ceremony 073h1t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.818 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0p9sw ceremony 0bzk8w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.818 http://example.org/award/award_category/winners./award/award_honor/ceremony #6304-019bk0 PRED entity: 019bk0 PRED relation: award_winner PRED expected values: 01vx5w7 01l03w2 018n6m 0kr_t 01t110 => 40 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1439): 02fn5r (0.60 #12363, 0.57 #18372, 0.53 #21372), 0dw4g (0.60 #12830, 0.33 #20339, 0.33 #17336), 0hl3d (0.57 #15030, 0.50 #18034, 0.50 #13527), 06fmdb (0.57 #15782, 0.50 #18786, 0.50 #9777), 011zf2 (0.57 #15182, 0.50 #9177, 0.36 #18186), 01lmj3q (0.53 #21037, 0.50 #18037, 0.50 #9028), 0fpjd_g (0.50 #18208, 0.50 #9199, 0.47 #21208), 0x3b7 (0.50 #8121, 0.47 #20129, 0.43 #18629), 02qwg (0.50 #10987, 0.47 #19996, 0.43 #18496), 02qlg7s (0.50 #10836, 0.43 #15341, 0.36 #18345) >> Best rule #12363 for best value: >> intensional similarity = 18 >> extensional distance = 3 >> proper extension: 0jzphpx; >> query: (?x1362, 02fn5r) <- ceremony(?x11456, ?x1362), ceremony(?x4012, ?x1362), ceremony(?x567, ?x1362), award_winner(?x1362, ?x6207), award_winner(?x1362, ?x3175), award_winner(?x1362, ?x3122), award_winner(?x1362, ?x1818), award_winner(?x1362, ?x1128), ?x6207 = 01htxr, ?x4012 = 025mb9, ?x567 = 01d38g, award(?x1322, ?x11456), place_of_birth(?x1818, ?x7213), film(?x3175, ?x1877), award_nominee(?x12102, ?x1128), award_winner(?x3122, ?x4560), ?x12102 = 0163kf, people(?x2510, ?x3175) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #5205 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 09n4nb; *> query: (?x1362, 018n6m) <- ceremony(?x11456, ?x1362), ceremony(?x7691, ?x1362), ceremony(?x4012, ?x1362), ceremony(?x567, ?x1362), award_winner(?x1362, ?x6207), award_winner(?x1362, ?x3122), award_winner(?x1362, ?x2698), award_winner(?x1362, ?x1818), ?x6207 = 01htxr, ?x4012 = 025mb9, ?x567 = 01d38g, ?x11456 = 03q27t, ?x7691 = 026m9w, award_winner(?x1079, ?x2698), ?x3122 = 01wmgrf, award(?x2698, ?x1443), artists(?x505, ?x2698), group(?x1818, ?x11897) *> conf = 0.33 ranks of expected_values: 75, 104, 219, 329, 452 EVAL 019bk0 award_winner 01t110 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 40.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 019bk0 award_winner 0kr_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 40.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 019bk0 award_winner 018n6m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 40.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 019bk0 award_winner 01l03w2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 40.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 019bk0 award_winner 01vx5w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 40.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6303-03h3x5 PRED entity: 03h3x5 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.84 #65, 0.84 #141, 0.83 #176), 0735l (0.27 #31, 0.23 #43, 0.23 #54), 02nxhr (0.04 #126, 0.04 #157, 0.04 #56), 07z4p (0.04 #30, 0.03 #165, 0.02 #160), 07c52 (0.03 #132, 0.03 #158, 0.03 #163) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 125 >> proper extension: 0m313; 018js4; 028_yv; 095zlp; 034qrh; 0bth54; 04fzfj; 061681; 08r4x3; 04vr_f; ... >> query: (?x2642, 029j_) <- produced_by(?x2642, ?x496), nominated_for(?x3911, ?x2642), film_format(?x2642, ?x909), currency(?x2642, ?x170) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03h3x5 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.843 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6302-0ctw_b PRED entity: 0ctw_b PRED relation: olympics PRED expected values: 0l98s 0l6m5 => 218 concepts (218 used for prediction) PRED predicted values (max 10 best out of 25): 0kbvv (0.75 #952, 0.69 #1405, 0.67 #90), 01f1kd (0.75 #952, 0.69 #1405, 0.66 #2760), 0swbd (0.75 #952, 0.69 #1405, 0.66 #2760), 0l6m5 (0.71 #332, 0.67 #782, 0.67 #407), 018ctl (0.67 #80, 0.54 #1079, 0.54 #1078), 0blg2 (0.56 #86, 0.52 #412, 0.45 #251), 0lk8j (0.56 #85, 0.45 #251, 0.45 #185), 018qb4 (0.56 #93, 0.45 #251, 0.43 #294), 09n48 (0.56 #78, 0.45 #251, 0.41 #404), 0sx8l (0.54 #1079, 0.54 #1078, 0.45 #251) >> Best rule #952 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 01z88t; >> query: (?x1023, ?x778) <- country(?x150, ?x1023), combatants(?x1023, ?x94), olympics(?x1023, ?x778) >> conf = 0.75 => this is the best rule for 3 predicted values *> Best rule #332 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 01k6y1; *> query: (?x1023, 0l6m5) <- combatants(?x456, ?x1023), location(?x843, ?x1023), capital(?x456, ?x8989) *> conf = 0.71 ranks of expected_values: 4, 11 EVAL 0ctw_b olympics 0l6m5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 218.000 218.000 0.746 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0ctw_b olympics 0l98s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 218.000 218.000 0.746 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #6301-0d05w3 PRED entity: 0d05w3 PRED relation: country! PRED expected values: 07bs0 09w1n 07_53 => 242 concepts (242 used for prediction) PRED predicted values (max 10 best out of 17): 07bs0 (0.70 #123, 0.62 #684, 0.62 #735), 09w1n (0.59 #398, 0.57 #738, 0.56 #534), 02vx4 (0.50 #529, 0.50 #121, 0.48 #784), 0d1t3 (0.50 #501, 0.50 #127, 0.47 #535), 0152n0 (0.50 #130, 0.35 #2110, 0.35 #2417), 096f8 (0.48 #394, 0.47 #496, 0.43 #734), 0d1tm (0.43 #732, 0.42 #528, 0.41 #392), 09wz9 (0.42 #465, 0.42 #533, 0.40 #125), 09f6b (0.42 #542, 0.40 #134, 0.39 #474), 07_53 (0.42 #537, 0.40 #129, 0.39 #792) >> Best rule #123 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06mx8; >> query: (?x2346, 07bs0) <- taxonomy(?x2346, ?x939), contains(?x2346, ?x1885), titles(?x2346, ?x2889) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 10 EVAL 0d05w3 country! 07_53 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 242.000 242.000 0.700 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d05w3 country! 09w1n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 242.000 242.000 0.700 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d05w3 country! 07bs0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 242.000 242.000 0.700 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #6300-01jqr_5 PRED entity: 01jqr_5 PRED relation: student! PRED expected values: 01w5m => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 264): 02kj7g (0.33 #514, 0.03 #11014, 0.03 #11539), 07szy (0.25 #565, 0.12 #2140, 0.10 #2665), 02bq1j (0.25 #692, 0.12 #2267, 0.10 #2792), 04gxp2 (0.25 #1031, 0.12 #2606, 0.10 #3131), 05q2c (0.25 #837, 0.12 #2412, 0.03 #5562), 03ksy (0.24 #4831, 0.15 #44747, 0.10 #25836), 01j_06 (0.20 #1082, 0.12 #2132, 0.10 #2657), 09f2j (0.20 #1209, 0.11 #44800, 0.06 #9084), 02gn8s (0.20 #1300, 0.02 #9175, 0.01 #13900), 02m0sc (0.20 #1920, 0.02 #10320, 0.02 #10845) >> Best rule #514 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 019fz; >> query: (?x2511, 02kj7g) <- place_of_death(?x2511, ?x2254), student(?x5807, ?x2511), ?x2254 = 0dclg, gender(?x2511, ?x231), ?x231 = 05zppz >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10080 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 57 *> proper extension: 041mt; *> query: (?x2511, 01w5m) <- place_of_death(?x2511, ?x2254), student(?x5807, ?x2511), adjoins(?x2254, ?x4202), teams(?x2254, ?x3674), gender(?x2511, ?x231) *> conf = 0.17 ranks of expected_values: 14 EVAL 01jqr_5 student! 01w5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 163.000 163.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #6299-0gqng PRED entity: 0gqng PRED relation: award! PRED expected values: 0gvvm6l => 53 concepts (19 used for prediction) PRED predicted values (max 10 best out of 823): 0ch3qr1 (0.54 #5653, 0.20 #7687, 0.05 #15826), 0ccd3x (0.50 #3509, 0.43 #6558, 0.42 #8593), 01ffx4 (0.50 #1327, 0.10 #3363, 0.07 #6412), 0cp0790 (0.50 #1721, 0.01 #19011), 0gzlb9 (0.46 #5922, 0.27 #7956, 0.05 #16095), 04v8x9 (0.43 #6138, 0.42 #8173, 0.40 #3089), 017jd9 (0.43 #6564, 0.42 #8599, 0.40 #3515), 0ywrc (0.40 #3360, 0.37 #8444, 0.36 #6409), 0pv3x (0.40 #3160, 0.37 #8244, 0.36 #6209), 0dr_4 (0.40 #3204, 0.29 #6253, 0.26 #8288) >> Best rule #5653 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 04ljl_l; 05b4l5x; 05f4m9q; 03c7tr1; 07bdd_; 07cbcy; 05p1dby; 05p09zm; 05q8pss; 02g2wv; ... >> query: (?x77, 0ch3qr1) <- nominated_for(?x77, ?x303), award(?x1872, ?x77), award(?x5096, ?x77), category(?x77, ?x134), ?x134 = 08mbj5d >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #2033 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 02qsfzv; 02qrwjt; *> query: (?x77, ?x303) <- nominated_for(?x77, ?x9805), nominated_for(?x77, ?x303), award(?x6426, ?x77), award(?x5096, ?x77), ?x9805 = 07vfy4, award_winner(?x591, ?x6426) *> conf = 0.35 ranks of expected_values: 21 EVAL 0gqng award! 0gvvm6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 53.000 19.000 0.538 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #6298-01kx_81 PRED entity: 01kx_81 PRED relation: artist! PRED expected values: 0n85g => 113 concepts (98 used for prediction) PRED predicted values (max 10 best out of 98): 03rhqg (0.29 #16, 0.22 #298, 0.18 #721), 015_1q (0.25 #1853, 0.24 #1289, 0.23 #866), 01clyr (0.22 #316, 0.18 #739, 0.18 #598), 03y5g8 (0.22 #391, 0.14 #109, 0.09 #814), 01w40h (0.22 #311, 0.09 #734, 0.09 #5672), 043g7l (0.18 #737, 0.16 #1301, 0.14 #32), 017l96 (0.18 #583, 0.15 #1006, 0.14 #1429), 0mzkr (0.18 #590, 0.15 #1013, 0.08 #1718), 0g768 (0.18 #743, 0.14 #1448, 0.14 #1166), 03qy3l (0.18 #769, 0.14 #64, 0.12 #205) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 06pj8; >> query: (?x1291, 03rhqg) <- peers(?x1291, ?x4960), currency(?x1291, ?x1099), participant(?x692, ?x1291) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1191 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 12 *> proper extension: 017l4; *> query: (?x1291, 0n85g) <- executive_produced_by(?x1619, ?x1291), artists(?x378, ?x1291) *> conf = 0.14 ranks of expected_values: 14 EVAL 01kx_81 artist! 0n85g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 113.000 98.000 0.286 http://example.org/music/record_label/artist #6297-03nnm4t PRED entity: 03nnm4t PRED relation: ceremony! PRED expected values: 02_3zj 09v82c0 => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 293): 0gkr9q (0.67 #2316, 0.66 #1879, 0.64 #2349), 047sgz4 (0.67 #1965, 0.66 #1879, 0.60 #1260), 09qvc0 (0.67 #1904, 0.64 #2349, 0.60 #1199), 0cjcbg (0.66 #1879, 0.64 #2349, 0.64 #703), 0ck27z (0.66 #1879, 0.64 #2349, 0.64 #703), 03ccq3s (0.66 #1879, 0.64 #2349, 0.60 #1301), 02_3zj (0.66 #1879, 0.64 #2349, 0.57 #1644), 0cqhmg (0.66 #1879, 0.64 #2349, 0.57 #1644), 0cqhk0 (0.66 #1879, 0.64 #2349, 0.57 #1644), 04ldyx1 (0.66 #1879, 0.60 #1316, 0.57 #1644) >> Best rule #2316 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 0bx6zs; >> query: (?x5585, 0gkr9q) <- honored_for(?x5585, ?x6884), honored_for(?x5585, ?x3626), ?x6884 = 039cq4, award_winner(?x5585, ?x5504), award_winner(?x5585, ?x2813), award_winner(?x3626, ?x1040), award(?x3626, ?x3486), ceremony(?x7510, ?x5585), genre(?x3626, ?x258), ?x7510 = 027gs1_, award_nominee(?x3808, ?x2813), film(?x2813, ?x2231), award(?x5504, ?x704) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1879 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 0hhtgcw; *> query: (?x5585, ?x537) <- honored_for(?x5585, ?x9788), honored_for(?x5585, ?x6884), award_winner(?x5585, ?x3403), tv_program(?x397, ?x6884), influenced_by(?x3403, ?x2169), nominated_for(?x537, ?x6884), award(?x3403, ?x247), ?x9788 = 01b7h8, actor(?x6884, ?x692), artists(?x1000, ?x3403), award_nominee(?x248, ?x3403), award_nominee(?x3403, ?x4701) *> conf = 0.66 ranks of expected_values: 7, 12 EVAL 03nnm4t ceremony! 09v82c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 46.000 46.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 03nnm4t ceremony! 02_3zj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 46.000 46.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony #6296-0534v PRED entity: 0534v PRED relation: profession PRED expected values: 01d_h8 => 107 concepts (54 used for prediction) PRED predicted values (max 10 best out of 66): 01d_h8 (0.85 #3108, 0.83 #147, 0.81 #1980), 02hrh1q (0.68 #6078, 0.68 #6924, 0.68 #5796), 03gjzk (0.43 #4527, 0.39 #1423, 0.38 #3256), 0dgd_ (0.33 #167, 0.31 #449, 0.13 #731), 018gz8 (0.29 #1002, 0.23 #861, 0.22 #2412), 0np9r (0.25 #301, 0.18 #19, 0.14 #583), 0n1h (0.24 #997, 0.15 #4806, 0.09 #10), 02krf9 (0.23 #2139, 0.23 #1575, 0.19 #4538), 09jwl (0.21 #1004, 0.18 #7493, 0.18 #17), 02hv44_ (0.20 #898, 0.11 #3719, 0.11 #2731) >> Best rule #3108 for best value: >> intensional similarity = 3 >> extensional distance = 333 >> proper extension: 024c1b; >> query: (?x5287, 01d_h8) <- produced_by(?x5286, ?x5287), genre(?x5286, ?x811), film_release_region(?x5286, ?x252) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0534v profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 54.000 0.848 http://example.org/people/person/profession #6295-01mxt_ PRED entity: 01mxt_ PRED relation: profession PRED expected values: 02hrh1q => 156 concepts (118 used for prediction) PRED predicted values (max 10 best out of 86): 09jwl (0.75 #3149, 0.71 #9118, 0.70 #12849), 02hrh1q (0.67 #15387, 0.66 #16728, 0.66 #17176), 0nbcg (0.55 #1076, 0.55 #3162, 0.53 #2864), 0dz3r (0.55 #2833, 0.53 #3131, 0.45 #7904), 016z4k (0.50 #153, 0.49 #7906, 0.44 #7608), 01c72t (0.50 #472, 0.45 #1068, 0.44 #919), 0dxtg (0.49 #7320, 0.49 #4932, 0.46 #7171), 0cbd2 (0.48 #9403, 0.48 #1497, 0.47 #9701), 01d_h8 (0.44 #4924, 0.41 #751, 0.37 #1943), 0n1h (0.35 #1651, 0.34 #1055, 0.33 #906) >> Best rule #3149 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 01hw6wq; 01k47c; 020_4z; 01t8399; 04mky3; >> query: (?x5587, 09jwl) <- artists(?x1380, ?x5587), ?x1380 = 0dl5d, nationality(?x5587, ?x94), instrumentalists(?x227, ?x5587) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #15387 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1442 *> proper extension: 0bl60p; *> query: (?x5587, 02hrh1q) <- nationality(?x5587, ?x94), ?x94 = 09c7w0, type_of_union(?x5587, ?x566), award(?x5587, ?x4912) *> conf = 0.67 ranks of expected_values: 2 EVAL 01mxt_ profession 02hrh1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 156.000 118.000 0.755 http://example.org/people/person/profession #6294-014_lq PRED entity: 014_lq PRED relation: influenced_by! PRED expected values: 03g5jw => 106 concepts (65 used for prediction) PRED predicted values (max 10 best out of 312): 03g5jw (0.38 #44, 0.30 #3148, 0.27 #4183), 04rcr (0.28 #2584, 0.21 #2583, 0.16 #7765), 01czx (0.28 #2584, 0.21 #2583, 0.16 #7765), 0b1hw (0.28 #2584, 0.21 #2583, 0.16 #7765), 04k05 (0.21 #2583, 0.20 #2585, 0.11 #9841), 05xq9 (0.20 #718, 0.08 #4859, 0.08 #5376), 02yl42 (0.14 #16199, 0.12 #19314, 0.12 #20352), 01vvyfh (0.12 #4803, 0.12 #2730, 0.10 #662), 016_mj (0.12 #9378, 0.11 #17676, 0.11 #14044), 01xwv7 (0.12 #9749, 0.11 #14415, 0.11 #22721) >> Best rule #44 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01w3lzq; >> query: (?x5329, 03g5jw) <- influenced_by(?x5329, ?x2073), artist(?x11912, ?x2073), artists(?x302, ?x5329), group(?x227, ?x2073) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014_lq influenced_by! 03g5jw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 65.000 0.375 http://example.org/influence/influence_node/influenced_by #6293-02mjmr PRED entity: 02mjmr PRED relation: student! PRED expected values: 014zws => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 248): 08815 (0.29 #2098, 0.27 #7863, 0.25 #3670), 02bq1j (0.29 #3310, 0.27 #8027, 0.25 #166), 07tg4 (0.29 #1658, 0.20 #6375, 0.18 #7423), 03ksy (0.25 #105, 0.23 #13206, 0.21 #10062), 01mpwj (0.25 #106, 0.18 #13207, 0.14 #10063), 017v71 (0.25 #194, 0.14 #3338, 0.14 #2290), 07tgn (0.22 #4733, 0.10 #6830, 0.09 #8402), 07x4c (0.20 #7071, 0.18 #8643, 0.09 #19125), 05zl0 (0.20 #725, 0.14 #2821, 0.11 #5442), 014zws (0.20 #852, 0.14 #2948, 0.11 #5569) >> Best rule #2098 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0tc7; 03f77; 09k0f; >> query: (?x2669, 08815) <- student(?x2606, ?x2669), jurisdiction_of_office(?x2669, ?x94), participant(?x2669, ?x286) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #852 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0424m; 03_js; 0835q; *> query: (?x2669, 014zws) <- legislative_sessions(?x2669, ?x845), entity_involved(?x2391, ?x2669), student(?x3424, ?x2669) *> conf = 0.20 ranks of expected_values: 10 EVAL 02mjmr student! 014zws CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 155.000 155.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #6292-0c_gcr PRED entity: 0c_gcr PRED relation: film PRED expected values: 019vhk => 110 concepts (48 used for prediction) PRED predicted values (max 10 best out of 401): 04qw17 (0.53 #64033, 0.53 #62253, 0.53 #62252), 062zm5h (0.14 #4410, 0.07 #10673, 0.02 #9745), 02cbhg (0.14 #4952, 0.02 #8508, 0.01 #10287), 03whyr (0.14 #5118, 0.02 #6896), 02z0f6l (0.14 #4765, 0.02 #6543), 0ggbhy7 (0.14 #4054, 0.02 #5832), 0c0yh4 (0.14 #3593, 0.02 #5371), 0bscw (0.14 #3776, 0.01 #9111), 040_lv (0.14 #4599, 0.01 #8155, 0.01 #9934), 078sj4 (0.14 #4011, 0.01 #12903) >> Best rule #64033 for best value: >> intensional similarity = 4 >> extensional distance = 1466 >> proper extension: 0263tn1; >> query: (?x9643, ?x1863) <- profession(?x9643, ?x1032), nominated_for(?x9643, ?x1863), profession(?x2559, ?x1032), ?x2559 = 06mmb >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #9354 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 595 *> proper extension: 01rrwf6; 04smkr; 02rmfm; 02_n5d; 05hj_k; 049qx; 0k2mxq; 01vzx45; 02nfhx; 02hy9p; ... *> query: (?x9643, 019vhk) <- profession(?x9643, ?x1032), film(?x9643, ?x4067), honored_for(?x2116, ?x4067), nominated_for(?x68, ?x4067) *> conf = 0.02 ranks of expected_values: 123 EVAL 0c_gcr film 019vhk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 110.000 48.000 0.528 http://example.org/film/actor/film./film/performance/film #6291-0bg539 PRED entity: 0bg539 PRED relation: location PRED expected values: 0853g => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 238): 02_286 (0.27 #6469, 0.27 #5665, 0.18 #3253), 030qb3t (0.25 #9731, 0.25 #83, 0.23 #33856), 02jx1 (0.25 #71, 0.06 #4091, 0.04 #6503), 0b1t1 (0.25 #473, 0.06 #4493, 0.04 #6101), 0ctw_b (0.25 #51, 0.06 #4071, 0.03 #29753), 018dk_ (0.25 #658, 0.06 #4678, 0.01 #11110), 0d060g (0.25 #13, 0.06 #4033, 0.01 #12877), 087vz (0.25 #193, 0.06 #4213), 0hzlz (0.25 #41, 0.06 #4061), 0cr3d (0.11 #1753, 0.11 #8989, 0.10 #2557) >> Best rule #6469 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 031zkw; 03k7bd; 03jjzf; 02f8lw; 0f5xn; 03kbb8; 02rrsz; 02z1yj; 02p5hf; 0ywqc; >> query: (?x1294, 02_286) <- award(?x1294, ?x2016), student(?x4268, ?x1294), ?x4268 = 02822, type_of_union(?x1294, ?x566) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bg539 location 0853g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 117.000 0.269 http://example.org/people/person/places_lived./people/place_lived/location #6290-018y2s PRED entity: 018y2s PRED relation: artists! PRED expected values: 02vjzr 03jsvl => 92 concepts (61 used for prediction) PRED predicted values (max 10 best out of 203): 06j6l (0.38 #2837, 0.34 #3456, 0.34 #2528), 05bt6j (0.37 #352, 0.33 #2832, 0.33 #4069), 0glt670 (0.35 #969, 0.23 #2829, 0.23 #2520), 025sc50 (0.33 #2530, 0.33 #3458, 0.32 #2839), 0xhtw (0.28 #1254, 0.25 #635, 0.25 #8066), 0gywn (0.27 #2847, 0.24 #3466, 0.24 #2538), 0ggx5q (0.25 #3487, 0.25 #2559, 0.22 #4105), 0155w (0.23 #105, 0.23 #725, 0.17 #1344), 01lyv (0.23 #32, 0.21 #3131, 0.18 #1892), 02lnbg (0.22 #2539, 0.22 #3467, 0.22 #2848) >> Best rule #2837 for best value: >> intensional similarity = 3 >> extensional distance = 294 >> proper extension: 01l1b90; 01pfr3; 01v0sx2; 03t9sp; 01fl3; 019g40; 01wz3cx; 0cg9y; 01x1cn2; 016fmf; ... >> query: (?x1165, 06j6l) <- artists(?x671, ?x1165), ?x671 = 064t9, award(?x1165, ?x2322) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2923 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 294 *> proper extension: 01l1b90; 01pfr3; 01v0sx2; 03t9sp; 01fl3; 019g40; 01wz3cx; 0cg9y; 01x1cn2; 016fmf; ... *> query: (?x1165, 02vjzr) <- artists(?x671, ?x1165), ?x671 = 064t9, award(?x1165, ?x2322) *> conf = 0.15 ranks of expected_values: 21, 36 EVAL 018y2s artists! 03jsvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 92.000 61.000 0.378 http://example.org/music/genre/artists EVAL 018y2s artists! 02vjzr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 92.000 61.000 0.378 http://example.org/music/genre/artists #6289-018vs PRED entity: 018vs PRED relation: role! PRED expected values: 02snj9 => 72 concepts (71 used for prediction) PRED predicted values (max 10 best out of 56): 0gkd1 (0.85 #415, 0.84 #184, 0.83 #690), 026t6 (0.85 #415, 0.84 #184, 0.83 #690), 0l14qv (0.85 #415, 0.84 #184, 0.83 #690), 0239kh (0.85 #415, 0.84 #184, 0.83 #690), 02dlh2 (0.85 #415, 0.84 #184, 0.83 #690), 0j862 (0.85 #415, 0.84 #184, 0.83 #690), 07xzm (0.85 #415, 0.84 #184, 0.83 #690), 06w7v (0.85 #415, 0.84 #184, 0.83 #690), 01c3q (0.85 #415, 0.84 #184, 0.83 #690), 01wy6 (0.85 #415, 0.84 #184, 0.83 #690) >> Best rule #415 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 03bx0bm; >> query: (?x716, ?x75) <- role(?x211, ?x716), role(?x74, ?x716), role(?x716, ?x75), group(?x716, ?x10813), group(?x716, ?x8614), group(?x716, ?x6818), ?x6818 = 0838y, ?x10813 = 0ycfj, ?x8614 = 0jn38 >> conf = 0.85 => this is the best rule for 15 predicted values *> Best rule #828 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 057cc; *> query: (?x716, ?x74) <- instrumentalists(?x716, ?x2908), instrumentalists(?x716, ?x2865), instrumentalists(?x716, ?x702), ?x2908 = 0161sp, artists(?x302, ?x702), role(?x2865, ?x74), award(?x702, ?x350), diet(?x702, ?x11141) *> conf = 0.76 ranks of expected_values: 18 EVAL 018vs role! 02snj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 72.000 71.000 0.847 http://example.org/music/performance_role/regular_performances./music/group_membership/role #6288-02cw8s PRED entity: 02cw8s PRED relation: school_type PRED expected values: 01y64 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.44 #340, 0.42 #1540, 0.41 #1420), 05pcjw (0.29 #49, 0.28 #169, 0.27 #193), 01rs41 (0.26 #821, 0.26 #629, 0.26 #845), 07tf8 (0.20 #33, 0.19 #177, 0.18 #345), 01y64 (0.15 #84, 0.15 #108, 0.14 #228), 01_9fk (0.10 #626, 0.10 #1034, 0.10 #722), 01_srz (0.07 #675, 0.07 #843, 0.06 #1035), 0m4mb (0.06 #107, 0.06 #155, 0.05 #203), 01jlsn (0.06 #113, 0.06 #161, 0.05 #377), 0bpgx (0.05 #213, 0.04 #261, 0.04 #285) >> Best rule #340 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 0ym69; >> query: (?x2593, 05jxkf) <- major_field_of_study(?x2593, ?x227), split_to(?x227, ?x645), organization(?x346, ?x2593) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #84 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 45 *> proper extension: 02g839; 04bfg; 01d34b; 01722w; 02_gzx; 02sdwt; 019vv1; 02htv6; 02kj7g; *> query: (?x2593, 01y64) <- student(?x2593, ?x3690), student(?x2593, ?x317), music(?x3311, ?x3690), role(?x317, ?x227), artists(?x497, ?x317) *> conf = 0.15 ranks of expected_values: 5 EVAL 02cw8s school_type 01y64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 132.000 132.000 0.440 http://example.org/education/educational_institution/school_type #6287-013ksx PRED entity: 013ksx PRED relation: featured_film_locations! PRED expected values: 047csmy => 148 concepts (138 used for prediction) PRED predicted values (max 10 best out of 630): 02f6g5 (0.33 #123, 0.02 #3808, 0.02 #4545), 04fzfj (0.33 #45, 0.02 #3730, 0.01 #8152), 024mpp (0.33 #275, 0.02 #3960, 0.01 #26807), 0bbgvp (0.33 #724, 0.02 #4409), 0315rp (0.33 #605, 0.02 #4290), 05nlx4 (0.33 #527, 0.02 #4212), 02825kb (0.33 #515, 0.02 #4200), 03rg2b (0.33 #464, 0.02 #4149), 01l_pn (0.33 #416, 0.02 #4101), 0hx4y (0.33 #210, 0.02 #3895) >> Best rule #123 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03gh4; >> query: (?x3068, 02f6g5) <- category(?x3068, ?x134), location(?x10780, ?x3068), ?x10780 = 014g_s, ?x134 = 08mbj5d >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4818 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 0mskq; *> query: (?x3068, 047csmy) <- time_zones(?x3068, ?x2674), contains(?x3068, ?x1772), source(?x3068, ?x958), location_of_ceremony(?x566, ?x3068) *> conf = 0.04 ranks of expected_values: 46 EVAL 013ksx featured_film_locations! 047csmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 148.000 138.000 0.333 http://example.org/film/film/featured_film_locations #6286-07rnh PRED entity: 07rnh PRED relation: group! PRED expected values: 05148p4 02hnl => 78 concepts (65 used for prediction) PRED predicted values (max 10 best out of 125): 05148p4 (0.82 #719, 0.81 #1420, 0.81 #1332), 02hnl (0.79 #1786, 0.78 #1876, 0.78 #1695), 03bx0bm (0.65 #1426, 0.63 #1075, 0.63 #1338), 0l14qv (0.59 #705, 0.53 #792, 0.33 #4), 028tv0 (0.50 #185, 0.45 #1238, 0.45 #1500), 05r5c (0.50 #180, 0.33 #1144, 0.29 #1057), 013y1f (0.50 #201, 0.24 #728, 0.21 #815), 06ncr (0.42 #826, 0.41 #739, 0.33 #38), 03qjg (0.37 #835, 0.35 #748, 0.27 #1449), 0l14j_ (0.37 #839, 0.35 #752, 0.21 #876) >> Best rule #719 for best value: >> intensional similarity = 9 >> extensional distance = 15 >> proper extension: 05563d; 07yg2; 047cx; 06nv27; 02dw1_; 01q99h; 02vgh; 048xh; 03k3b; 014pg1; ... >> query: (?x9096, 05148p4) <- group(?x227, ?x9096), group(?x75, ?x9096), artist(?x14593, ?x9096), ?x75 = 07y_7, artists(?x302, ?x9096), artist(?x14593, ?x11633), influenced_by(?x11633, ?x7476), artists(?x3167, ?x11633), ?x227 = 0342h >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07rnh group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 65.000 0.824 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 07rnh group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 65.000 0.824 http://example.org/music/performance_role/regular_performances./music/group_membership/group #6285-014dq7 PRED entity: 014dq7 PRED relation: influenced_by PRED expected values: 0l99s => 167 concepts (81 used for prediction) PRED predicted values (max 10 best out of 377): 014z8v (0.38 #7423, 0.14 #21167, 0.12 #19447), 03f0324 (0.33 #578, 0.20 #2295, 0.11 #21627), 03_87 (0.33 #628, 0.15 #2345, 0.14 #9648), 02wh0 (0.33 #808, 0.15 #2525, 0.13 #33944), 01rgr (0.33 #749, 0.13 #30072, 0.12 #32224), 048cl (0.33 #660, 0.10 #16118, 0.08 #15260), 01v9724 (0.30 #2321, 0.13 #2750, 0.13 #33944), 05gpy (0.25 #2341, 0.17 #624, 0.08 #8786), 03j0d (0.25 #2479, 0.08 #1191, 0.06 #29642), 0gz_ (0.23 #5686, 0.12 #9551, 0.09 #29742) >> Best rule #7423 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 01kcms4; >> query: (?x1946, 014z8v) <- influenced_by(?x1946, ?x916), award_winner(?x350, ?x916), award(?x916, ?x688), person(?x1315, ?x916) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #33944 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 321 *> proper extension: 01d494; 099bk; 03sbs; 02ln1; *> query: (?x1946, ?x3336) <- influenced_by(?x1946, ?x916), influenced_by(?x916, ?x3336), student(?x3439, ?x916), gender(?x1946, ?x231) *> conf = 0.13 ranks of expected_values: 55 EVAL 014dq7 influenced_by 0l99s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 167.000 81.000 0.377 http://example.org/influence/influence_node/influenced_by #6284-0ckrnn PRED entity: 0ckrnn PRED relation: film! PRED expected values: 0f2df => 82 concepts (43 used for prediction) PRED predicted values (max 10 best out of 696): 065jlv (0.43 #2391, 0.05 #37414, 0.05 #35335), 06ltr (0.43 #3024, 0.04 #9259, 0.04 #11338), 0134w7 (0.43 #2239, 0.03 #8474, 0.03 #10553), 013_vh (0.43 #2740, 0.02 #8975, 0.02 #11054), 0161h5 (0.42 #85224, 0.42 #78987, 0.41 #81066), 09y20 (0.36 #2326, 0.04 #8561, 0.04 #12718), 05sq84 (0.29 #2313, 0.03 #10627, 0.02 #8548), 03y_46 (0.29 #3094, 0.02 #9329, 0.02 #11408), 015rkw (0.25 #282, 0.21 #2360, 0.05 #37414), 0kszw (0.25 #418, 0.21 #2496, 0.03 #6652) >> Best rule #2391 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 03hxsv; >> query: (?x10831, 065jlv) <- genre(?x10831, ?x600), film(?x2372, ?x10831), ?x2372 = 0l6px >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ckrnn film! 0f2df CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 43.000 0.429 http://example.org/film/actor/film./film/performance/film #6283-09dt7 PRED entity: 09dt7 PRED relation: people! PRED expected values: 033tf_ => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 35): 041rx (0.19 #2391, 0.17 #2237, 0.17 #1544), 02w7gg (0.12 #2, 0.11 #233, 0.07 #1003), 07hwkr (0.12 #12, 0.07 #1706, 0.06 #1937), 0x67 (0.10 #6171, 0.10 #5170, 0.10 #2012), 013xrm (0.10 #1406, 0.10 #1329, 0.09 #1637), 033tf_ (0.08 #5706, 0.08 #5167, 0.08 #5398), 07bch9 (0.06 #3720, 0.05 #3874, 0.05 #1948), 013b6_ (0.06 #669, 0.04 #1285, 0.04 #1593), 048z7l (0.06 #656, 0.04 #733, 0.04 #1734), 09kr66 (0.06 #659, 0.04 #736, 0.04 #813) >> Best rule #2391 for best value: >> intensional similarity = 4 >> extensional distance = 265 >> proper extension: 0cl_m; 011zwl; >> query: (?x1287, 041rx) <- student(?x3424, ?x1287), place_of_death(?x1287, ?x9341), gender(?x1287, ?x231), institution(?x620, ?x3424) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #5706 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1046 *> proper extension: 07m69t; *> query: (?x1287, 033tf_) <- location(?x1287, ?x9341), nationality(?x1287, ?x94), ?x94 = 09c7w0, state(?x9341, ?x5575) *> conf = 0.08 ranks of expected_values: 6 EVAL 09dt7 people! 033tf_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 115.000 115.000 0.187 http://example.org/people/ethnicity/people #6282-039n1 PRED entity: 039n1 PRED relation: influenced_by PRED expected values: 015n8 => 145 concepts (70 used for prediction) PRED predicted values (max 10 best out of 273): 026lj (0.60 #5646, 0.40 #5216, 0.35 #6507), 039n1 (0.56 #5063, 0.50 #4632, 0.40 #5494), 032l1 (0.50 #3967, 0.40 #2244, 0.26 #20333), 07c37 (0.50 #2771, 0.26 #7939, 0.21 #9234), 02wh0 (0.45 #6411, 0.39 #20622, 0.35 #21914), 043s3 (0.43 #3130, 0.40 #5716, 0.38 #4424), 015n8 (0.43 #3421, 0.38 #4715, 0.36 #17201), 0j3v (0.40 #2215, 0.38 #3938, 0.32 #20304), 01lwx (0.40 #2558, 0.38 #4713, 0.25 #4281), 07ym0 (0.40 #2429, 0.38 #4152, 0.25 #4584) >> Best rule #5646 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 09gnn; 01lwx; >> query: (?x9600, 026lj) <- influenced_by(?x9600, ?x12441), influenced_by(?x9600, ?x3712), interests(?x3712, ?x6364), influenced_by(?x920, ?x9600), ?x12441 = 0tfc >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3421 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0w6w; *> query: (?x9600, 015n8) <- influenced_by(?x9600, ?x3712), ?x3712 = 0gz_, influenced_by(?x1236, ?x9600), ?x1236 = 045bg *> conf = 0.43 ranks of expected_values: 7 EVAL 039n1 influenced_by 015n8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 145.000 70.000 0.600 http://example.org/influence/influence_node/influenced_by #6281-05dss7 PRED entity: 05dss7 PRED relation: film! PRED expected values: 01twdk => 98 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1151): 064jjy (0.38 #29159, 0.22 #35407, 0.20 #95815), 02lf1j (0.25 #431, 0.01 #69166, 0.01 #77498), 0807ml (0.25 #1126), 02qgqt (0.12 #18, 0.07 #2101, 0.06 #43739), 01nm3s (0.12 #691, 0.06 #43739, 0.04 #2774), 04954 (0.12 #1308, 0.06 #43739, 0.02 #55461), 014v6f (0.12 #969, 0.06 #43739, 0.01 #44709), 0k269 (0.12 #612, 0.05 #8943, 0.04 #31853), 09zmys (0.12 #985, 0.04 #3068, 0.04 #7233), 01f7dd (0.12 #1210, 0.04 #3293, 0.03 #11625) >> Best rule #29159 for best value: >> intensional similarity = 5 >> extensional distance = 151 >> proper extension: 02hxhz; 050gkf; 03m8y5; 01dvbd; 07yvsn; 07tw_b; 06lpmt; 08952r; 02825kb; 027r9t; ... >> query: (?x6556, ?x8235) <- film(?x752, ?x6556), written_by(?x6556, ?x8235), film_crew_role(?x6556, ?x468), language(?x6556, ?x254), film(?x8235, ?x4621) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #30005 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 163 *> proper extension: 0dckvs; 0djb3vw; 03bx2lk; 05z_kps; 03twd6; 06v9_x; 07j8r; 07f_7h; 05q4y12; 0crh5_f; ... *> query: (?x6556, 01twdk) <- film_release_region(?x6556, ?x1453), film_release_region(?x6556, ?x304), ?x304 = 0d0vqn, genre(?x6556, ?x258), ?x1453 = 06qd3 *> conf = 0.02 ranks of expected_values: 534 EVAL 05dss7 film! 01twdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 98.000 58.000 0.375 http://example.org/film/actor/film./film/performance/film #6280-01gq0b PRED entity: 01gq0b PRED relation: place_of_birth PRED expected values: 0cc56 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 121): 02_286 (0.12 #4950, 0.09 #8472, 0.09 #9880), 030qb3t (0.08 #2167, 0.08 #8507, 0.07 #6394), 0d6lp (0.07 #114, 0.03 #1522, 0.02 #11385), 02gw_w (0.07 #637, 0.02 #2750, 0.02 #3455), 0r04p (0.07 #172, 0.02 #2990, 0.02 #7216), 0y62n (0.07 #339, 0.02 #3862, 0.01 #8792), 04gxf (0.07 #285), 0dclg (0.05 #782, 0.03 #1486, 0.02 #11349), 01cx_ (0.05 #813, 0.02 #2222, 0.02 #2927), 0f2s6 (0.05 #1071) >> Best rule #4950 for best value: >> intensional similarity = 2 >> extensional distance = 88 >> proper extension: 03cvfg; >> query: (?x1890, 02_286) <- celebrity(?x1890, ?x1815), student(?x122, ?x1890) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #3556 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 64 *> proper extension: 01pcvn; 02r3cn; 0484q; 0167v4; 022q32; *> query: (?x1890, 0cc56) <- celebrity(?x1890, ?x1815), spouse(?x10224, ?x1890) *> conf = 0.03 ranks of expected_values: 17 EVAL 01gq0b place_of_birth 0cc56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 120.000 120.000 0.122 http://example.org/people/person/place_of_birth #6279-05zjd PRED entity: 05zjd PRED relation: language! PRED expected values: 087pfc 0dnkmq => 46 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1838): 0f4_2k (0.60 #2689, 0.55 #4404, 0.50 #6118), 0dr_4 (0.60 #1948, 0.45 #3663, 0.42 #5377), 034xyf (0.60 #3088, 0.45 #4803, 0.42 #6517), 03z9585 (0.60 #3058, 0.45 #4773, 0.42 #6487), 041td_ (0.60 #2762, 0.45 #4477, 0.33 #6191), 0c_j9x (0.60 #2063, 0.42 #5492, 0.36 #3778), 01ffx4 (0.60 #2205, 0.42 #5634, 0.36 #3920), 03twd6 (0.60 #1926, 0.36 #3641, 0.34 #3427), 02yvct (0.60 #2046, 0.36 #3761, 0.34 #3427), 012kyx (0.60 #2822, 0.36 #4537, 0.33 #6251) >> Best rule #2689 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 02bjrlw; 02h40lc; 064_8sq; >> query: (?x6753, 0f4_2k) <- language(?x5271, ?x6753), language(?x2029, ?x6753), official_language(?x583, ?x6753), ?x2029 = 020bv3, languages(?x419, ?x6753), film_release_region(?x66, ?x583), country(?x150, ?x583), ?x5271 = 047vnkj, combatants(?x94, ?x583) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3427 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 02bjrlw; 02h40lc; 064_8sq; *> query: (?x6753, ?x66) <- language(?x5271, ?x6753), language(?x2029, ?x6753), official_language(?x583, ?x6753), ?x2029 = 020bv3, languages(?x419, ?x6753), film_release_region(?x66, ?x583), country(?x150, ?x583), ?x5271 = 047vnkj, combatants(?x94, ?x583) *> conf = 0.34 ranks of expected_values: 258, 629 EVAL 05zjd language! 0dnkmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 24.000 0.600 http://example.org/film/film/language EVAL 05zjd language! 087pfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 24.000 0.600 http://example.org/film/film/language #6278-02ryx0 PRED entity: 02ryx0 PRED relation: music! PRED expected values: 0c9k8 => 105 concepts (87 used for prediction) PRED predicted values (max 10 best out of 676): 043n1r5 (0.08 #914, 0.03 #1922, 0.02 #4946), 01gvpz (0.08 #856, 0.03 #1864, 0.02 #4888), 0qmfz (0.08 #828, 0.03 #1836, 0.02 #4860), 0btpm6 (0.08 #741, 0.02 #6789, 0.02 #7797), 02fqrf (0.08 #339, 0.02 #6387, 0.02 #7395), 09146g (0.08 #183, 0.02 #6231, 0.02 #7239), 034r25 (0.08 #439, 0.02 #6487, 0.01 #42344), 09q5w2 (0.08 #101, 0.02 #6149, 0.01 #42344), 09v8clw (0.08 #1000, 0.01 #42344, 0.01 #7048), 0gy0n (0.08 #984, 0.01 #42344, 0.01 #7032) >> Best rule #914 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 06cc_1; 02r4qs; 045zr; 09hnb; 02dbp7; 02qmncd; 0ddkf; >> query: (?x5949, 043n1r5) <- award_winner(?x4012, ?x5949), role(?x5949, ?x316), ?x4012 = 025mb9 >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02ryx0 music! 0c9k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 87.000 0.083 http://example.org/film/film/music #6277-02y_lrp PRED entity: 02y_lrp PRED relation: genre PRED expected values: 06cvj 0gf28 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.64 #4329, 0.63 #4571, 0.63 #5414), 01z4y (0.61 #8904, 0.53 #7701, 0.52 #4449), 01jfsb (0.46 #975, 0.37 #2655, 0.37 #2055), 0jtdp (0.42 #15, 0.06 #11189, 0.02 #2177), 02kdv5l (0.36 #483, 0.35 #363, 0.35 #965), 03k9fj (0.35 #372, 0.34 #492, 0.29 #612), 0lsxr (0.32 #9, 0.22 #729, 0.21 #1091), 04rlf (0.26 #66, 0.06 #11189, 0.03 #2948), 06n90 (0.24 #6749, 0.21 #976, 0.21 #855), 0219x_ (0.21 #27, 0.11 #868, 0.11 #1589) >> Best rule #4329 for best value: >> intensional similarity = 4 >> extensional distance = 530 >> proper extension: 02d413; 015qsq; 0g22z; 028_yv; 02vp1f_; 09m6kg; 047q2k1; 0c0yh4; 0yyg4; 090s_0; ... >> query: (?x146, 07s9rl0) <- nominated_for(?x902, ?x146), award(?x146, ?x688), film_release_distribution_medium(?x146, ?x81), titles(?x2480, ?x146) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #4212 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 527 *> proper extension: 04svwx; *> query: (?x146, 06cvj) <- country(?x146, ?x94), genre(?x146, ?x258), ?x258 = 05p553 *> conf = 0.21 ranks of expected_values: 12, 28 EVAL 02y_lrp genre 0gf28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 101.000 101.000 0.639 http://example.org/film/film/genre EVAL 02y_lrp genre 06cvj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 101.000 101.000 0.639 http://example.org/film/film/genre #6276-01r42_g PRED entity: 01r42_g PRED relation: languages PRED expected values: 06nm1 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 16): 02h40lc (0.92 #306, 0.91 #116, 0.90 #724), 03k50 (0.11 #156, 0.08 #802, 0.07 #612), 064_8sq (0.09 #737, 0.09 #813, 0.09 #623), 02bjrlw (0.06 #1, 0.05 #723, 0.04 #609), 06nm1 (0.06 #6, 0.04 #310, 0.04 #82), 07c9s (0.05 #165, 0.04 #735, 0.04 #621), 0t_2 (0.03 #85, 0.02 #123, 0.02 #807), 04306rv (0.03 #725, 0.03 #801, 0.03 #611), 0999q (0.03 #175, 0.02 #631, 0.02 #745), 03115z (0.02 #66) >> Best rule #306 for best value: >> intensional similarity = 2 >> extensional distance = 357 >> proper extension: 0q59y; 03h40_7; 01mbwlb; >> query: (?x369, 02h40lc) <- award_nominee(?x368, ?x369), languages(?x369, ?x11590) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0bl60p; 01b9z4; 0c1ps1; *> query: (?x369, 06nm1) <- award_nominee(?x1652, ?x369), ?x1652 = 01l1sq, nationality(?x369, ?x94) *> conf = 0.06 ranks of expected_values: 5 EVAL 01r42_g languages 06nm1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 92.000 0.919 http://example.org/people/person/languages #6275-0k20s PRED entity: 0k20s PRED relation: genre PRED expected values: 02p0szs => 82 concepts (51 used for prediction) PRED predicted values (max 10 best out of 93): 0f8l9c (0.56 #5244, 0.51 #816, 0.50 #5595), 05p553 (0.35 #3151, 0.30 #5832, 0.30 #5364), 02kdv5l (0.34 #1166, 0.26 #3847, 0.26 #5597), 082gq (0.33 #27, 0.17 #609, 0.15 #1307), 04228s (0.33 #73, 0.06 #2915, 0.02 #3220), 02l7c8 (0.32 #2345, 0.32 #3510, 0.31 #3393), 060__y (0.25 #597, 0.20 #131, 0.19 #3628), 04xvlr (0.23 #700, 0.23 #5128, 0.21 #5712), 03k9fj (0.20 #3855, 0.20 #1406, 0.20 #2808), 03g3w (0.20 #138, 0.12 #370, 0.11 #254) >> Best rule #5244 for best value: >> intensional similarity = 5 >> extensional distance = 852 >> proper extension: 01qn7n; 07hpv3; 09kn9; 01cjhz; 05sy2k_; 02648p; 01p4wv; 099pks; 05r1_t; 0jq2r; ... >> query: (?x11110, ?x789) <- titles(?x3506, ?x11110), titles(?x789, ?x11110), titles(?x3506, ?x2943), nominated_for(?x9151, ?x2943), ?x9151 = 095zvfg >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2915 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 531 *> proper extension: 0g5qs2k; 05nlx4; *> query: (?x11110, ?x53) <- nominated_for(?x6909, ?x11110), nominated_for(?x6909, ?x6149), nominated_for(?x6909, ?x5825), nominated_for(?x6909, ?x1496), ?x5825 = 067ghz, genre(?x1496, ?x53), music(?x6149, ?x9408) *> conf = 0.06 ranks of expected_values: 36 EVAL 0k20s genre 02p0szs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 82.000 51.000 0.557 http://example.org/film/film/genre #6274-02778pf PRED entity: 02778pf PRED relation: award_winner! PRED expected values: 05c1t6z 02q690_ => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 109): 05c1t6z (0.56 #15, 0.52 #153, 0.17 #4417), 02q690_ (0.36 #203, 0.25 #65, 0.17 #4417), 09v0p2c (0.31 #82, 0.17 #4417, 0.16 #220), 058m5m4 (0.17 #4417, 0.17 #7592, 0.16 #193), 09g90vz (0.17 #4417, 0.17 #7592, 0.12 #122), 03gyp30 (0.17 #4417, 0.17 #7592, 0.12 #115), 027hjff (0.17 #4417, 0.17 #7592, 0.10 #8559), 0hr3c8y (0.17 #4417, 0.17 #7592, 0.10 #8559), 0drtv8 (0.17 #4417, 0.17 #7592, 0.10 #8559), 05zksls (0.17 #4417, 0.17 #7592, 0.10 #8559) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0277990; >> query: (?x832, 05c1t6z) <- award_nominee(?x832, ?x906), award_nominee(?x829, ?x832), ?x829 = 02773nt >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02778pf award_winner! 02q690_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.562 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02778pf award_winner! 05c1t6z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.562 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6273-0dn3n PRED entity: 0dn3n PRED relation: profession PRED expected values: 01d_h8 => 114 concepts (41 used for prediction) PRED predicted values (max 10 best out of 43): 03gjzk (0.40 #15, 0.29 #6113, 0.29 #6112), 01d_h8 (0.37 #3433, 0.36 #4178, 0.35 #2092), 0d1pc (0.29 #6113, 0.29 #6112, 0.29 #200), 0dxtg (0.29 #6113, 0.29 #6112, 0.28 #5365), 02jknp (0.29 #6113, 0.29 #6112, 0.28 #5365), 09jwl (0.29 #6113, 0.29 #6112, 0.28 #5365), 018gz8 (0.29 #6113, 0.29 #6112, 0.28 #5365), 02krf9 (0.29 #6113, 0.29 #6112, 0.28 #5365), 0np9r (0.16 #1064, 0.12 #2107, 0.11 #2703), 0nbcg (0.16 #330, 0.14 #3310, 0.13 #5546) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01hxs4; >> query: (?x3070, 03gjzk) <- participant(?x2647, ?x3070), ?x2647 = 01pnn3, award_nominee(?x3070, ?x3056) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3433 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 300 *> proper extension: 024dgj; 0bx_q; 03d0ns; 09px1w; 026_dq6; 01t_wfl; 09x8ms; *> query: (?x3070, 01d_h8) <- student(?x2399, ?x3070), participant(?x496, ?x3070), profession(?x3070, ?x1032) *> conf = 0.37 ranks of expected_values: 2 EVAL 0dn3n profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 41.000 0.400 http://example.org/people/person/profession #6272-098z9w PRED entity: 098z9w PRED relation: genre PRED expected values: 06ntj => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 70): 05p553 (0.58 #855, 0.54 #940, 0.52 #1110), 07s9rl0 (0.47 #2466, 0.46 #2041, 0.46 #1956), 01z4y (0.41 #784, 0.40 #1209, 0.39 #869), 066wd (0.40 #144, 0.33 #229, 0.25 #59), 0c4xc (0.36 #894, 0.35 #979, 0.34 #809), 01htzx (0.27 #528, 0.21 #698, 0.18 #273), 0hcr (0.26 #700, 0.23 #2570, 0.22 #2230), 06ntj (0.25 #72, 0.20 #157, 0.17 #242), 06nbt (0.22 #957, 0.21 #872, 0.20 #1127), 06n90 (0.20 #524, 0.18 #269, 0.16 #694) >> Best rule #855 for best value: >> intensional similarity = 9 >> extensional distance = 31 >> proper extension: 0g60z; 072kp; 0kfpm; 0124k9; 02zv4b; 0584r4; 02hct1; 0d68qy; 01j7mr; 05_z42; ... >> query: (?x11629, 05p553) <- actor(?x11629, ?x11630), type_of_union(?x11630, ?x566), profession(?x11630, ?x1041), ?x1041 = 03gjzk, currency(?x11630, ?x170), ?x566 = 04ztj, nationality(?x11630, ?x94), location(?x11630, ?x2623), contains(?x2623, ?x95) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #72 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 02skyy; 074j87; *> query: (?x11629, 06ntj) <- actor(?x11629, ?x11630), ?x11630 = 01rzxl, country_of_origin(?x11629, ?x94), ?x94 = 09c7w0 *> conf = 0.25 ranks of expected_values: 8 EVAL 098z9w genre 06ntj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 40.000 40.000 0.576 http://example.org/tv/tv_program/genre #6271-01bvx1 PRED entity: 01bvx1 PRED relation: contact_category PRED expected values: 02zdwq => 211 concepts (211 used for prediction) PRED predicted values (max 10 best out of 2): 02zdwq (0.44 #16, 0.38 #122, 0.36 #102), 014dgf (0.33 #23, 0.33 #15, 0.33 #3) >> Best rule #16 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02bh8z; 045c7b; 077w0b; 07_dn; >> query: (?x12044, 02zdwq) <- industry(?x12044, ?x11691), list(?x12044, ?x5997), citytown(?x12044, ?x9559), student(?x11691, ?x7753), major_field_of_study(?x11691, ?x373) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bvx1 contact_category 02zdwq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 211.000 211.000 0.444 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #6270-050l8 PRED entity: 050l8 PRED relation: time_zones PRED expected values: 02hczc => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 13): 02hczc (0.78 #1074, 0.76 #969, 0.74 #576), 02fqwt (0.64 #1714, 0.35 #92, 0.34 #144), 02lcqs (0.64 #1714, 0.33 #5, 0.24 #646), 02hcv8 (0.50 #29, 0.44 #998, 0.43 #1037), 02lcrv (0.26 #1127, 0.25 #20, 0.09 #1858), 042g7t (0.26 #1127, 0.25 #24, 0.09 #1858), 02llzg (0.13 #566, 0.13 #751, 0.13 #907), 03bdv (0.10 #674, 0.07 #1497, 0.07 #779), 03plfd (0.07 #572, 0.06 #874, 0.06 #900), 0gsrz4 (0.05 #425, 0.05 #360, 0.05 #478) >> Best rule #1074 for best value: >> intensional similarity = 4 >> extensional distance = 217 >> proper extension: 02v3m7; >> query: (?x2049, ?x2088) <- contains(?x2049, ?x13186), contains(?x2049, ?x12644), source(?x12644, ?x958), time_zones(?x13186, ?x2088) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050l8 time_zones 02hczc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 171.000 0.777 http://example.org/location/location/time_zones #6269-043h78 PRED entity: 043h78 PRED relation: film! PRED expected values: 03nkts => 97 concepts (60 used for prediction) PRED predicted values (max 10 best out of 969): 086k8 (0.47 #58302, 0.45 #122846, 0.45 #45804), 016xh5 (0.33 #3163, 0.03 #122847, 0.03 #91614), 0c5vh (0.33 #1945, 0.01 #16519, 0.01 #18601), 0223g8 (0.33 #1829), 069z_5 (0.33 #1754), 029cpw (0.33 #1228), 0686zv (0.17 #2606, 0.08 #4687, 0.02 #6770), 09y20 (0.17 #2330, 0.04 #14823, 0.04 #16905), 0h5g_ (0.17 #2155, 0.03 #122847, 0.03 #29217), 05vsxz (0.17 #2090, 0.03 #122847, 0.03 #14583) >> Best rule #58302 for best value: >> intensional similarity = 4 >> extensional distance = 357 >> proper extension: 04dsnp; 02n9bh; 02hfk5; 02wk7b; 06zn1c; >> query: (?x9031, ?x382) <- genre(?x9031, ?x258), nominated_for(?x382, ?x9031), titles(?x7323, ?x9031), ?x258 = 05p553 >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 043h78 film! 03nkts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 60.000 0.474 http://example.org/film/actor/film./film/performance/film #6268-05zrx3v PRED entity: 05zrx3v PRED relation: produced_by! PRED expected values: 03ntbmw => 76 concepts (69 used for prediction) PRED predicted values (max 10 best out of 595): 04q827 (0.33 #895, 0.25 #1838, 0.06 #33019), 09y6pb (0.33 #826, 0.06 #33019, 0.05 #21702), 03ntbmw (0.33 #933, 0.06 #33019, 0.01 #2819), 0gy0l_ (0.25 #1754, 0.02 #2697, 0.02 #3640), 08fn5b (0.25 #1317, 0.02 #2260, 0.02 #3203), 09gdh6k (0.25 #1650, 0.02 #2593, 0.01 #9201), 0g54xkt (0.25 #1231, 0.01 #13500, 0.01 #16330), 02qr69m (0.25 #1158, 0.01 #13427, 0.01 #16257), 04q01mn (0.25 #1886, 0.01 #2829, 0.01 #3772), 077q8x (0.25 #1536, 0.01 #2479, 0.01 #3422) >> Best rule #895 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 059x0w; >> query: (?x9380, 04q827) <- produced_by(?x6762, ?x9380), award_nominee(?x9316, ?x9380), gender(?x9380, ?x231), ?x6762 = 047rkcm >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #933 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 059x0w; *> query: (?x9380, 03ntbmw) <- produced_by(?x6762, ?x9380), award_nominee(?x9316, ?x9380), gender(?x9380, ?x231), ?x6762 = 047rkcm *> conf = 0.33 ranks of expected_values: 3 EVAL 05zrx3v produced_by! 03ntbmw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 76.000 69.000 0.333 http://example.org/film/film/produced_by #6267-0d6484 PRED entity: 0d6484 PRED relation: executive_produced_by! PRED expected values: 0yyn5 => 116 concepts (109 used for prediction) PRED predicted values (max 10 best out of 171): 0gm2_0 (0.14 #493, 0.02 #5849, 0.02 #5317), 01jft4 (0.14 #403, 0.01 #2527, 0.01 #3593), 02pxmgz (0.14 #61, 0.01 #2185, 0.01 #3251), 0drnwh (0.14 #381, 0.01 #2505, 0.01 #3571), 0287477 (0.14 #350, 0.01 #2474, 0.01 #3540), 01msrb (0.14 #263, 0.01 #2387, 0.01 #3453), 02y_lrp (0.14 #9, 0.01 #2133, 0.01 #3199), 03qcfvw (0.14 #5, 0.01 #2129, 0.01 #3195), 01q2nx (0.14 #302, 0.01 #4025, 0.01 #4556), 09q5w2 (0.14 #50, 0.01 #3773, 0.01 #4304) >> Best rule #493 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 027z0pl; >> query: (?x9743, 0gm2_0) <- nationality(?x9743, ?x94), award_nominee(?x9743, ?x4946), ?x4946 = 03h304l >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d6484 executive_produced_by! 0yyn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 109.000 0.143 http://example.org/film/film/executive_produced_by #6266-0d810y PRED entity: 0d810y PRED relation: award_winner! PRED expected values: 058m5m4 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 95): 058m5m4 (0.71 #195, 0.31 #55, 0.17 #3221), 09qvms (0.17 #3221, 0.11 #5462, 0.10 #5883), 02wzl1d (0.17 #3221, 0.11 #5462, 0.10 #5883), 092_25 (0.08 #72, 0.03 #632, 0.03 #1052), 09qftb (0.08 #113, 0.02 #673, 0.02 #533), 09p30_ (0.08 #85, 0.02 #505, 0.02 #645), 02jp5r (0.08 #69, 0.02 #629, 0.02 #1329), 0n8_m93 (0.08 #118, 0.01 #538), 02hn5v (0.08 #42, 0.01 #1162), 013b2h (0.05 #1340, 0.05 #780, 0.05 #920) >> Best rule #195 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0bbvr84; >> query: (?x5707, 058m5m4) <- award_winner(?x8424, ?x5707), award_winner(?x9306, ?x5707), ?x8424 = 027n4zv >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d810y award_winner! 058m5m4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6265-01q0kg PRED entity: 01q0kg PRED relation: colors PRED expected values: 019sc => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.39 #1522, 0.38 #876, 0.38 #1408), 01g5v (0.30 #1182, 0.30 #1163, 0.30 #1030), 02rnmb (0.25 #51, 0.06 #641, 0.05 #242), 019sc (0.19 #2136, 0.18 #578, 0.18 #2364), 06fvc (0.18 #1447, 0.16 #1029, 0.16 #1580), 038hg (0.12 #507, 0.09 #2141, 0.09 #1590), 0jc_p (0.11 #632, 0.11 #936, 0.10 #613), 03wkwg (0.10 #282, 0.09 #377, 0.08 #510), 036k5h (0.10 #1697, 0.10 #1450, 0.09 #1469), 09ggk (0.09 #302, 0.07 #511, 0.07 #1822) >> Best rule #1522 for best value: >> intensional similarity = 4 >> extensional distance = 257 >> proper extension: 019vv1; >> query: (?x4257, 083jv) <- student(?x4257, ?x9000), nominated_for(?x9000, ?x8617), colors(?x4257, ?x332), award(?x9000, ?x591) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #2136 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 361 *> proper extension: 05d9y_; *> query: (?x4257, 019sc) <- major_field_of_study(?x4257, ?x2601), organization(?x346, ?x4257), colors(?x4257, ?x332), major_field_of_study(?x865, ?x2601) *> conf = 0.19 ranks of expected_values: 4 EVAL 01q0kg colors 019sc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 177.000 177.000 0.386 http://example.org/education/educational_institution/colors #6264-0c4hgj PRED entity: 0c4hgj PRED relation: ceremony! PRED expected values: 0gqng 0gr0m 0gs9p 0gs96 => 35 concepts (34 used for prediction) PRED predicted values (max 10 best out of 357): 0gs9p (0.90 #2017, 0.88 #3740, 0.88 #1279), 0gqng (0.90 #1968, 0.80 #2460, 0.80 #739), 0gs96 (0.88 #2534, 0.87 #4747, 0.86 #2042), 0gq_v (0.86 #1980, 0.83 #4440, 0.83 #4194), 0gqz2 (0.83 #4723, 0.83 #4232, 0.81 #1280), 0gr0m (0.81 #4721, 0.80 #5211, 0.80 #3739), 0gqxm (0.77 #7373, 0.56 #1348, 0.40 #4791), 02x201b (0.77 #7373, 0.33 #175, 0.27 #1228), 0czp_ (0.77 #7373, 0.31 #1178, 0.21 #3392), 0gqzz (0.77 #7373, 0.25 #1267, 0.20 #4219) >> Best rule #2017 for best value: >> intensional similarity = 14 >> extensional distance = 19 >> proper extension: 0bzlrh; >> query: (?x6606, 0gs9p) <- award_winner(?x6606, ?x5720), award_winner(?x6606, ?x4951), ceremony(?x1862, ?x6606), award_winner(?x669, ?x4951), award_nominee(?x6519, ?x4951), award_winner(?x2826, ?x5720), profession(?x5720, ?x2348), music(?x1372, ?x5720), award(?x4951, ?x746), honored_for(?x6606, ?x3510), ?x2348 = 0nbcg, nominated_for(?x4951, ?x7760), award_nominee(?x5720, ?x6011), ?x1862 = 0gr51 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 6 EVAL 0c4hgj ceremony! 0gs96 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 34.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c4hgj ceremony! 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 34.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c4hgj ceremony! 0gr0m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 34.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c4hgj ceremony! 0gqng CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 34.000 0.905 http://example.org/award/award_category/winners./award/award_honor/ceremony #6263-0lhn5 PRED entity: 0lhn5 PRED relation: place_of_birth! PRED expected values: 069ld1 => 140 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1559): 01jcjt (0.31 #20896, 0.24 #78357, 0.23 #60076), 01vvlyt (0.31 #20896, 0.24 #78357, 0.23 #60076), 0djtky (0.07 #2352, 0.04 #12800, 0.04 #10188), 04myfb7 (0.07 #345, 0.04 #10793, 0.04 #8181), 049k07 (0.07 #304, 0.04 #10752, 0.04 #8140), 01w03jv (0.07 #2479, 0.04 #12927, 0.04 #10315), 01pcdn (0.07 #978, 0.04 #11426, 0.04 #8814), 057bc6m (0.07 #1757, 0.04 #9593, 0.03 #14817), 052gzr (0.07 #321, 0.04 #8157, 0.03 #13381), 0184jw (0.07 #1622, 0.04 #9458, 0.03 #14682) >> Best rule #20896 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 01xd9; >> query: (?x5211, ?x5405) <- location(?x13815, ?x5211), location(?x5405, ?x5211), entity_involved(?x11109, ?x13815), profession(?x5405, ?x131) >> conf = 0.31 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0lhn5 place_of_birth! 069ld1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 45.000 0.313 http://example.org/people/person/place_of_birth #6262-01qb559 PRED entity: 01qb559 PRED relation: film_crew_role PRED expected values: 02r96rf => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.75 #871, 0.72 #70, 0.72 #1137), 02rh1dz (0.33 #76, 0.27 #2606, 0.26 #176), 02ynfr (0.27 #2606, 0.23 #147, 0.20 #881), 0d2b38 (0.27 #2606, 0.18 #90, 0.11 #757), 089g0h (0.27 #2606, 0.16 #84, 0.11 #184), 01xy5l_ (0.27 #2606, 0.16 #78, 0.10 #577), 0215hd (0.27 #2606, 0.15 #83, 0.14 #317), 04pyp5 (0.27 #2606, 0.13 #81, 0.10 #148), 015h31 (0.27 #2606, 0.11 #175, 0.10 #75), 02_n3z (0.27 #2606, 0.10 #1, 0.09 #902) >> Best rule #871 for best value: >> intensional similarity = 5 >> extensional distance = 365 >> proper extension: 0h1cdwq; 02_1sj; 035xwd; 03ckwzc; 04gknr; 0963mq; 04kkz8; 08hmch; 09gdm7q; 02v63m; ... >> query: (?x7491, 02r96rf) <- film_crew_role(?x7491, ?x1284), film_crew_role(?x7491, ?x137), music(?x7491, ?x2363), ?x137 = 09zzb8, ?x1284 = 0ch6mp2 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qb559 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.749 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6261-0bq2g PRED entity: 0bq2g PRED relation: profession PRED expected values: 02hrh1q => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 66): 02hrh1q (0.91 #7616, 0.91 #8063, 0.90 #6722), 01d_h8 (0.52 #1049, 0.47 #1347, 0.46 #304), 03gjzk (0.46 #463, 0.36 #1506, 0.35 #1357), 0dxtg (0.44 #461, 0.38 #312, 0.36 #759), 02jknp (0.34 #3577, 0.29 #306, 0.29 #157), 0d1pc (0.34 #3577, 0.28 #1690, 0.24 #1392), 09jwl (0.34 #3577, 0.27 #1063, 0.26 #2404), 018gz8 (0.34 #3577, 0.25 #3147, 0.19 #763), 0np9r (0.34 #3577, 0.16 #3151, 0.15 #17308), 05sxg2 (0.34 #3577, 0.07 #597, 0.05 #2087) >> Best rule #7616 for best value: >> intensional similarity = 3 >> extensional distance = 412 >> proper extension: 01sl1q; 04bdxl; 01j5ts; 06dv3; 014zcr; 02g8h; 0d_84; 023tp8; 0m2wm; 01qscs; ... >> query: (?x3553, 02hrh1q) <- participant(?x3553, ?x989), nominated_for(?x3553, ?x144), film(?x3553, ?x2218) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bq2g profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.908 http://example.org/people/person/profession #6260-0nvrd PRED entity: 0nvrd PRED relation: adjoins PRED expected values: 0nvg4 => 119 concepts (52 used for prediction) PRED predicted values (max 10 best out of 424): 0nvg4 (0.82 #38650, 0.81 #40199, 0.81 #40198), 0nvd8 (0.50 #3474, 0.33 #1157, 0.32 #4632), 0nvrd (0.33 #109, 0.32 #4632, 0.26 #26272), 0nt6b (0.33 #549, 0.32 #4632, 0.26 #26272), 0nv6n (0.32 #4632, 0.26 #26272, 0.26 #16989), 0ns_4 (0.25 #2094, 0.03 #5954, 0.02 #8267), 01_d4 (0.17 #3962, 0.04 #7047, 0.02 #7818), 04ych (0.10 #7771, 0.10 #9315, 0.07 #11631), 0vbk (0.10 #7945, 0.10 #9489, 0.06 #11805), 0ms1n (0.10 #6005, 0.06 #6776, 0.05 #8318) >> Best rule #38650 for best value: >> intensional similarity = 4 >> extensional distance = 218 >> proper extension: 0l2l_; 0xn5b; 0nm3n; 0mwxz; 0xpq9; 0m25p; 0d1xh; 0nm6z; 0n5_g; 0xszy; ... >> query: (?x1963, ?x6410) <- adjoins(?x6410, ?x1963), adjoins(?x8552, ?x6410), county(?x1860, ?x6410), time_zones(?x6410, ?x1638) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nvrd adjoins 0nvg4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 52.000 0.815 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #6259-05sy_5 PRED entity: 05sy_5 PRED relation: honored_for! PRED expected values: 0bvfqq => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 119): 05zksls (0.17 #3361, 0.09 #8526, 0.09 #8887), 09bymc (0.17 #3361, 0.09 #8526, 0.09 #8887), 05qb8vx (0.17 #3361, 0.09 #8526, 0.09 #8887), 09g90vz (0.17 #3361, 0.09 #8526, 0.09 #8887), 0clfdj (0.17 #3361, 0.09 #8526, 0.09 #8887), 092t4b (0.17 #3361, 0.09 #8526, 0.09 #8887), 09q_6t (0.17 #3361, 0.09 #8526, 0.09 #8887), 056878 (0.17 #3361, 0.09 #8526, 0.09 #8887), 0gpjbt (0.17 #3361, 0.09 #8526, 0.09 #8887), 09gkdln (0.06 #465, 0.04 #3105, 0.04 #3225) >> Best rule #3361 for best value: >> intensional similarity = 4 >> extensional distance = 527 >> proper extension: 0c3xpwy; >> query: (?x6079, ?x2220) <- nominated_for(?x3308, ?x6079), honored_for(?x1112, ?x6079), award(?x3308, ?x154), award_winner(?x2220, ?x3308) >> conf = 0.17 => this is the best rule for 9 predicted values *> Best rule #505 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 140 *> proper extension: 0j8f09z; *> query: (?x6079, 0bvfqq) <- genre(?x6079, ?x53), nominated_for(?x1162, ?x6079), ?x1162 = 099c8n, film_crew_role(?x6079, ?x468) *> conf = 0.04 ranks of expected_values: 29 EVAL 05sy_5 honored_for! 0bvfqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 102.000 102.000 0.165 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #6258-03vtrv PRED entity: 03vtrv PRED relation: artist PRED expected values: 0167xy => 40 concepts (13 used for prediction) PRED predicted values (max 10 best out of 862): 01vrz41 (0.60 #6714, 0.50 #7546, 0.50 #5051), 0232lm (0.60 #7306, 0.50 #8138, 0.50 #5643), 02vr7 (0.50 #5601, 0.40 #7264, 0.33 #8096), 01wwvc5 (0.50 #5154, 0.40 #6817, 0.33 #7649), 0cg9y (0.50 #5125, 0.40 #6788, 0.33 #7620), 0dw4g (0.50 #5388, 0.40 #7051, 0.33 #7883), 0g_g2 (0.50 #5345, 0.40 #7008, 0.33 #7840), 0m19t (0.50 #5014, 0.40 #6677, 0.33 #7509), 0gbwp (0.50 #5265, 0.40 #6928, 0.33 #7760), 016vqk (0.50 #5653, 0.40 #7316, 0.33 #8148) >> Best rule #6714 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 01dtcb; >> query: (?x13110, 01vrz41) <- artist(?x13110, ?x9206), artist(?x13110, ?x2250), artist(?x13110, ?x646), ?x9206 = 017mbb, award_winner(?x9408, ?x646), award(?x646, ?x2634), award_winner(?x4912, ?x646), artist(?x4483, ?x2250), award_winner(?x5656, ?x646), ?x4483 = 0mzkr, ?x5656 = 0466p0j >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2409 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 01w40h; *> query: (?x13110, 0167xy) <- artist(?x13110, ?x9206), artist(?x13110, ?x4381), artist(?x13110, ?x2250), artist(?x13110, ?x646), ?x9206 = 017mbb, award_winner(?x9408, ?x646), award(?x646, ?x2634), award_winner(?x4912, ?x646), ?x2250 = 0167_s, ?x4381 = 0qf11, group(?x3024, ?x646), award_winner(?x342, ?x646) *> conf = 0.33 ranks of expected_values: 37 EVAL 03vtrv artist 0167xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 40.000 13.000 0.600 http://example.org/music/record_label/artist #6257-0m0bj PRED entity: 0m0bj PRED relation: citytown! PRED expected values: 01tzfz => 74 concepts (17 used for prediction) No prediction ranks of expected_values: EVAL 0m0bj citytown! 01tzfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 17.000 0.000 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #6256-07q1m PRED entity: 07q1m PRED relation: film! PRED expected values: 015p3p => 122 concepts (62 used for prediction) PRED predicted values (max 10 best out of 868): 026dx (0.50 #64640, 0.47 #77151, 0.45 #75065), 0627sn (0.43 #75066, 0.41 #114681, 0.40 #85491), 01x1fq (0.43 #75066, 0.41 #114681, 0.40 #85491), 02_p5w (0.25 #4814, 0.14 #6900, 0.01 #44435), 01v3vp (0.25 #4878, 0.14 #6964, 0.01 #44499), 01kwsg (0.25 #840, 0.04 #15432, 0.03 #9179), 02gf_l (0.21 #7524, 0.08 #5438, 0.02 #13777), 0p8r1 (0.21 #6840, 0.03 #8925, 0.02 #29775), 085q5 (0.14 #7977, 0.08 #5891, 0.03 #10062), 01fwpt (0.14 #6848, 0.08 #4762, 0.03 #8933) >> Best rule #64640 for best value: >> intensional similarity = 4 >> extensional distance = 508 >> proper extension: 01f8gz; >> query: (?x5646, ?x4703) <- award_winner(?x5646, ?x4703), film_crew_role(?x5646, ?x137), type_of_union(?x4703, ?x566), award_winner(?x372, ?x4703) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1096 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02qr3k8; *> query: (?x5646, 015p3p) <- film(?x1244, ?x5646), genre(?x5646, ?x811), country(?x5646, ?x94), ?x1244 = 0h1nt *> conf = 0.12 ranks of expected_values: 50 EVAL 07q1m film! 015p3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 122.000 62.000 0.503 http://example.org/film/actor/film./film/performance/film #6255-023v4_ PRED entity: 023v4_ PRED relation: gender PRED expected values: 02zsn => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #107, 0.72 #51, 0.71 #197), 02zsn (0.54 #6, 0.49 #16, 0.48 #8) >> Best rule #107 for best value: >> intensional similarity = 2 >> extensional distance = 1211 >> proper extension: 07_3qd; 0fv6dr; 0bw7ly; >> query: (?x4956, 05zppz) <- nationality(?x4956, ?x1471), teams(?x1471, ?x12288) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 0hwbd; 01cpqk; *> query: (?x4956, 02zsn) <- nominated_for(?x4956, ?x1785), celebrity(?x4956, ?x7830), participant(?x5881, ?x4956) *> conf = 0.54 ranks of expected_values: 2 EVAL 023v4_ gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.725 http://example.org/people/person/gender #6254-027571b PRED entity: 027571b PRED relation: award_winner PRED expected values: 0h0wc => 46 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1114): 015nhn (0.50 #4236, 0.45 #6689, 0.04 #9142), 0h0wc (0.45 #5436, 0.40 #2983, 0.12 #19636), 01bj6y (0.45 #7177, 0.40 #4724, 0.05 #9630), 0bdt8 (0.45 #6319, 0.40 #3866, 0.04 #8772), 01csvq (0.40 #2575, 0.27 #5028, 0.03 #7481), 0sz28 (0.40 #231, 0.12 #19636, 0.07 #7590), 01vvb4m (0.40 #656, 0.12 #19636, 0.06 #8015), 046qq (0.40 #934, 0.12 #19636, 0.04 #8293), 016ywr (0.40 #379, 0.12 #19636, 0.03 #7738), 09fb5 (0.40 #62, 0.11 #7421, 0.08 #9877) >> Best rule #4236 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 040njc; 027c924; 0gr4k; 094qd5; >> query: (?x7192, 015nhn) <- award(?x11429, ?x7192), award_winner(?x7192, ?x241), nominated_for(?x7192, ?x1490), ?x11429 = 0_9l_ >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5436 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0bdw1g; 0cqh6z; 0bdwft; 0gqwc; 02y_rq5; 0gkts9; 02z1nbg; 02y_j8g; 09cn0c; *> query: (?x7192, 0h0wc) <- award(?x414, ?x7192), award_winner(?x7192, ?x8612), nominated_for(?x7192, ?x1490), ?x8612 = 01jw4r *> conf = 0.45 ranks of expected_values: 2 EVAL 027571b award_winner 0h0wc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 10.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #6253-0571m PRED entity: 0571m PRED relation: featured_film_locations PRED expected values: 030qb3t => 98 concepts (81 used for prediction) PRED predicted values (max 10 best out of 58): 02_286 (0.19 #3873, 0.18 #4115, 0.17 #20), 03gh4 (0.17 #115, 0.02 #5175, 0.02 #5898), 04jpl (0.15 #970, 0.14 #1451, 0.12 #1692), 030qb3t (0.11 #280, 0.10 #520, 0.07 #1962), 0rh6k (0.10 #482, 0.08 #722, 0.07 #2889), 0h7h6 (0.06 #284, 0.05 #524, 0.03 #1966), 0hyxv (0.06 #327, 0.05 #567, 0.02 #2492), 03rjj (0.06 #247, 0.04 #2170, 0.04 #2412), 01cx_ (0.06 #312, 0.04 #792, 0.03 #2477), 0b90_r (0.06 #245, 0.04 #725, 0.03 #1927) >> Best rule #3873 for best value: >> intensional similarity = 4 >> extensional distance = 149 >> proper extension: 0gzy02; 0jyx6; 02vqsll; 0kbhf; 0m5s5; >> query: (?x3251, 02_286) <- film(?x1104, ?x3251), films(?x10705, ?x3251), written_by(?x3251, ?x4703), nominated_for(?x746, ?x3251) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #280 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 16 *> proper extension: 03h_yy; 0f4_l; 0f4m2z; 06kl78; 0191n; 07z6xs; 05pt0l; 09gdh6k; 035zr0; 016yxn; *> query: (?x3251, 030qb3t) <- titles(?x812, ?x3251), titles(?x53, ?x3251), ?x812 = 01jfsb, nominated_for(?x4703, ?x3251), film(?x133, ?x3251), ?x53 = 07s9rl0 *> conf = 0.11 ranks of expected_values: 4 EVAL 0571m featured_film_locations 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 81.000 0.192 http://example.org/film/film/featured_film_locations #6252-0s3pw PRED entity: 0s3pw PRED relation: adjoins! PRED expected values: 0s3y5 => 120 concepts (92 used for prediction) PRED predicted values (max 10 best out of 364): 0s3y5 (0.83 #16505, 0.82 #14935, 0.82 #12578), 01_d4 (0.40 #1675, 0.25 #888, 0.15 #4820), 0nvt9 (0.25 #1072, 0.10 #3432, 0.08 #13650), 0l3kx (0.25 #1390, 0.10 #3750, 0.04 #13968), 0nv6n (0.25 #1351, 0.10 #3711, 0.04 #13929), 059rby (0.21 #7881, 0.14 #10240, 0.12 #7095), 05tbn (0.21 #8045, 0.14 #10404, 0.06 #19045), 03v1s (0.20 #1598, 0.08 #4743, 0.08 #3957), 04kcn (0.20 #2172, 0.08 #5317, 0.08 #4531), 0psxp (0.20 #1842, 0.08 #4987, 0.08 #4201) >> Best rule #16505 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 03khn; >> query: (?x13681, ?x405) <- adjoins(?x13681, ?x405), country(?x13681, ?x94), category(?x13681, ?x134) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0s3pw adjoins! 0s3y5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 92.000 0.828 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #6251-062dn7 PRED entity: 062dn7 PRED relation: gender PRED expected values: 05zppz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #15, 0.72 #59, 0.71 #175), 02zsn (0.48 #2, 0.48 #4, 0.47 #8) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 251 >> proper extension: 0f0y8; 032t2z; 01ky2h; 0285c; 01m65sp; 04k15; 017yfz; 06_6j3; 01vsy3q; 01mwsnc; ... >> query: (?x3860, 05zppz) <- people(?x4195, ?x3860), profession(?x3860, ?x1183), ?x1183 = 09jwl >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 062dn7 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.779 http://example.org/people/person/gender #6250-02xry PRED entity: 02xry PRED relation: contains PRED expected values: 0rp46 0lhql 0rql_ 03qzj4 => 200 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2741): 0jrq9 (0.83 #228759, 0.79 #115824, 0.60 #324323), 0lhql (0.67 #251929, 0.60 #324323, 0.33 #518), 0rql_ (0.67 #251929, 0.33 #1123, 0.25 #15599), 03xpx0 (0.67 #251929, 0.25 #17351), 03qzj4 (0.67 #251929), 0l35f (0.60 #324323, 0.06 #38632, 0.06 #41528), 01jygk (0.52 #156363, 0.46 #205592), 02xry (0.48 #277993, 0.48 #222966, 0.48 #306951), 09c7w0 (0.48 #277993, 0.48 #222966, 0.48 #306951), 05cwl_ (0.33 #727, 0.25 #18099, 0.25 #15203) >> Best rule #228759 for best value: >> intensional similarity = 2 >> extensional distance = 88 >> proper extension: 09hzw; >> query: (?x2623, ?x9104) <- administrative_parent(?x2623, ?x94), administrative_parent(?x9104, ?x2623) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #251929 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 05bcl; 09lxtg; 05vz3zq; 0166b; 01mk6; 01d8l; 0msyb; 036wy; *> query: (?x2623, ?x14664) <- contains(?x2623, ?x8219), jurisdiction_of_office(?x900, ?x2623), contains(?x8219, ?x14664) *> conf = 0.67 ranks of expected_values: 2, 3, 5, 770 EVAL 02xry contains 03qzj4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 200.000 162.000 0.831 http://example.org/location/location/contains EVAL 02xry contains 0rql_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 200.000 162.000 0.831 http://example.org/location/location/contains EVAL 02xry contains 0lhql CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 200.000 162.000 0.831 http://example.org/location/location/contains EVAL 02xry contains 0rp46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 200.000 162.000 0.831 http://example.org/location/location/contains #6249-044mrh PRED entity: 044mrh PRED relation: place_of_birth PRED expected values: 0hv7l => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 33): 0chrx (0.08 #305, 0.07 #1713, 0.04 #2417), 0f2w0 (0.08 #62, 0.07 #1470, 0.03 #2878), 0nq_b (0.08 #589, 0.04 #2701, 0.03 #3405), 01_d4 (0.08 #770, 0.03 #20485, 0.03 #50763), 0cr3d (0.08 #798, 0.03 #4318, 0.03 #50791), 03l2n (0.08 #873, 0.01 #3689), 0s3pw (0.08 #1347), 0mpbx (0.08 #1146), 0ftxw (0.08 #800), 0f94t (0.08 #732) >> Best rule #305 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01sl1q; 07f3xb; 03_wj_; 02k4gv; 03x22w; 03_wvl; 03_wtr; 044mjy; 044mvs; 044n3h; >> query: (?x4965, 0chrx) <- award_nominee(?x4965, ?x1514), award_nominee(?x4965, ?x628), ?x628 = 01kwld, ?x1514 = 044mm6 >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 044mrh place_of_birth 0hv7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 87.000 0.083 http://example.org/people/person/place_of_birth #6248-01dqhq PRED entity: 01dqhq PRED relation: parent_genre! PRED expected values: 0jrv_ => 65 concepts (43 used for prediction) PRED predicted values (max 10 best out of 271): 0173b0 (0.43 #945, 0.40 #681, 0.33 #417), 0jrv_ (0.43 #939, 0.33 #411, 0.23 #1203), 0jrgr (0.40 #686, 0.33 #422, 0.33 #158), 06cp5 (0.40 #604, 0.33 #340, 0.29 #868), 0b_6yv (0.40 #741, 0.33 #477, 0.29 #1005), 04_sqm (0.40 #714, 0.33 #450, 0.29 #978), 0ccxx6 (0.33 #481, 0.28 #1800, 0.20 #745), 05jt_ (0.33 #366, 0.22 #1685, 0.20 #630), 02t8gf (0.33 #381, 0.22 #1700, 0.20 #645), 01_bkd (0.33 #311, 0.20 #575, 0.19 #1367) >> Best rule #945 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 07bbw; 04_sqm; >> query: (?x5762, 0173b0) <- parent_genre(?x6805, ?x5762), artists(?x5762, ?x12228), ?x12228 = 016m5c, parent_genre(?x5762, ?x2249), artists(?x6805, ?x8152), performance_role(?x8152, ?x228) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #939 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 07bbw; 04_sqm; *> query: (?x5762, 0jrv_) <- parent_genre(?x6805, ?x5762), artists(?x5762, ?x12228), ?x12228 = 016m5c, parent_genre(?x5762, ?x2249), artists(?x6805, ?x8152), performance_role(?x8152, ?x228) *> conf = 0.43 ranks of expected_values: 2 EVAL 01dqhq parent_genre! 0jrv_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 65.000 43.000 0.429 http://example.org/music/genre/parent_genre #6247-0241y7 PRED entity: 0241y7 PRED relation: film! PRED expected values: 01w0yrc => 91 concepts (61 used for prediction) PRED predicted values (max 10 best out of 995): 0p8r1 (0.50 #4744, 0.37 #2664, 0.30 #8905), 02cx72 (0.47 #79039, 0.43 #70718, 0.43 #72798), 01795t (0.47 #79039, 0.42 #68637, 0.41 #118550), 085q5 (0.23 #5877, 0.16 #3797, 0.13 #7957), 019vgs (0.21 #2738, 0.15 #8979, 0.14 #4818), 02gf_l (0.21 #3346, 0.13 #13747, 0.12 #9587), 015pvh (0.14 #5260, 0.13 #7340, 0.11 #3180), 01nm3s (0.14 #4847, 0.13 #6927, 0.07 #9008), 01h1b (0.14 #5365, 0.13 #7445, 0.07 #9526), 0jfx1 (0.13 #6645, 0.07 #19125, 0.07 #14966) >> Best rule #4744 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0b60sq; >> query: (?x6140, 0p8r1) <- film(?x2156, ?x6140), ?x2156 = 01795t, nominated_for(?x3732, ?x6140), nominated_for(?x3911, ?x6140), ?x3911 = 02x1z2s >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0241y7 film! 01w0yrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 61.000 0.500 http://example.org/film/actor/film./film/performance/film #6246-03qbnj PRED entity: 03qbnj PRED relation: award! PRED expected values: 06w2sn5 02w4fkq => 42 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2368): 04mn81 (0.79 #26608, 0.79 #59866, 0.78 #66517), 02qtywd (0.79 #26608, 0.79 #59866, 0.78 #66517), 05pdbs (0.79 #26608, 0.79 #59866, 0.78 #66517), 0ggl02 (0.79 #26608, 0.79 #59866, 0.78 #39910), 01dw9z (0.38 #708, 0.22 #19956, 0.20 #56540), 03f2_rc (0.31 #114, 0.22 #19956, 0.20 #56540), 0154qm (0.31 #887, 0.15 #43235, 0.15 #3326), 0gdh5 (0.31 #745, 0.14 #4071, 0.12 #30679), 0gx_p (0.31 #1806, 0.12 #73169, 0.05 #48366), 01wf86y (0.31 #2158, 0.10 #5484, 0.09 #32092) >> Best rule #26608 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 02r0d0; >> query: (?x4958, ?x3632) <- award_winner(?x4958, ?x3632), category_of(?x4958, ?x2421), award(?x3632, ?x159) >> conf = 0.79 => this is the best rule for 4 predicted values *> Best rule #76495 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 259 *> proper extension: 099c8n; 09tqxt; 03m73lj; 054knh; 02py_sj; 06bwtj; 0bwgmzd; *> query: (?x4958, ?x140) <- ceremony(?x4958, ?x139), ceremony(?x10881, ?x139), ceremony(?x1827, ?x139), award(?x140, ?x1827), award_winner(?x10881, ?x793) *> conf = 0.05 ranks of expected_values: 932, 1492 EVAL 03qbnj award! 02w4fkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 23.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03qbnj award! 06w2sn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 23.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6245-09nqf PRED entity: 09nqf PRED relation: currency! PRED expected values: 04ycjk 03fcbb => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 1550): 09k9d0 (0.75 #241, 0.70 #243, 0.67 #604), 019c57 (0.75 #241, 0.70 #243, 0.67 #604), 03np_7 (0.75 #241, 0.70 #243, 0.67 #604), 02grjf (0.75 #241, 0.70 #243, 0.67 #604), 02hp70 (0.75 #241, 0.70 #243, 0.67 #604), 02lwv5 (0.75 #241, 0.70 #243, 0.67 #604), 02klny (0.75 #241, 0.70 #243, 0.67 #604), 04gd8j (0.75 #241, 0.70 #243, 0.67 #604), 01jpqb (0.75 #241, 0.70 #243, 0.67 #604), 01qqv5 (0.75 #241, 0.70 #243, 0.67 #604) >> Best rule #241 for best value: >> intensional similarity = 27 >> extensional distance = 1 >> proper extension: 01nv4h; >> query: (?x170, ?x466) <- currency(?x9524, ?x170), currency(?x7922, ?x170), currency(?x6661, ?x170), currency(?x5024, ?x170), currency(?x4131, ?x170), currency(?x2518, ?x170), currency(?x1620, ?x170), currency(?x1206, ?x170), currency(?x3432, ?x170), currency(?x1665, ?x170), executive_produced_by(?x5024, ?x7324), currency(?x466, ?x170), currency(?x266, ?x170), celebrities_impersonated(?x3649, ?x1620), nominated_for(?x68, ?x7922), currency(?x1961, ?x170), award(?x1206, ?x1479), contains(?x3432, ?x12404), type_of_union(?x1206, ?x566), featured_film_locations(?x9524, ?x739), people(?x1050, ?x1206), currency(?x1783, ?x170), titles(?x812, ?x6661), film_distribution_medium(?x4131, ?x2099), participant(?x1620, ?x286), major_field_of_study(?x1665, ?x742), award_nominee(?x2518, ?x367) >> conf = 0.75 => this is the best rule for 61 predicted values *> Best rule #240 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 1 *> proper extension: 01nv4h; *> query: (?x170, ?x12404) <- currency(?x9524, ?x170), currency(?x7922, ?x170), currency(?x6661, ?x170), currency(?x5024, ?x170), currency(?x4131, ?x170), currency(?x2518, ?x170), currency(?x1620, ?x170), currency(?x1206, ?x170), currency(?x3432, ?x170), currency(?x1665, ?x170), executive_produced_by(?x5024, ?x7324), currency(?x466, ?x170), currency(?x266, ?x170), celebrities_impersonated(?x3649, ?x1620), nominated_for(?x68, ?x7922), currency(?x1961, ?x170), award(?x1206, ?x1479), contains(?x3432, ?x12404), type_of_union(?x1206, ?x566), featured_film_locations(?x9524, ?x739), people(?x1050, ?x1206), currency(?x1783, ?x170), titles(?x812, ?x6661), film_distribution_medium(?x4131, ?x2099), participant(?x1620, ?x286), major_field_of_study(?x1665, ?x742), award_nominee(?x2518, ?x367) *> conf = 0.19 ranks of expected_values: 348, 1178 EVAL 09nqf currency! 03fcbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 8.000 8.000 0.750 http://example.org/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04ycjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.750 http://example.org/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency #6244-04wvhz PRED entity: 04wvhz PRED relation: profession PRED expected values: 0dxtg => 111 concepts (110 used for prediction) PRED predicted values (max 10 best out of 82): 02hrh1q (0.68 #6676, 0.64 #5640, 0.64 #14966), 0dxtg (0.68 #1937, 0.66 #3418, 0.66 #2233), 02jknp (0.52 #3708, 0.50 #5041, 0.49 #5485), 02krf9 (0.40 #2665, 0.33 #1802, 0.30 #2246), 0np9r (0.40 #2665, 0.28 #8439, 0.13 #464), 0kyk (0.40 #2665, 0.28 #8439, 0.10 #1065), 012t_z (0.33 #308, 0.20 #1048, 0.16 #1640), 0cbd2 (0.25 #11696, 0.21 #1338, 0.19 #1930), 018gz8 (0.25 #11696, 0.16 #3125, 0.12 #6678), 01c72t (0.25 #11696, 0.09 #8165, 0.09 #9350) >> Best rule #6676 for best value: >> intensional similarity = 2 >> extensional distance = 899 >> proper extension: 049tjg; 0kcdl; 033071; >> query: (?x1039, 02hrh1q) <- nominated_for(?x1039, ?x4932), actor(?x4932, ?x3366) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1937 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 125 *> proper extension: 0d1mp3; 0bczgm; 0h3mrc; 02cm2m; 02_340; 0bz60q; 08xz51; *> query: (?x1039, 0dxtg) <- award_nominee(?x1104, ?x1039), award_winner(?x747, ?x1039), program(?x1039, ?x2436) *> conf = 0.68 ranks of expected_values: 2 EVAL 04wvhz profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 110.000 0.680 http://example.org/people/person/profession #6243-0jsqk PRED entity: 0jsqk PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.81 #21, 0.81 #1, 0.81 #46), 07c52 (0.24 #247, 0.04 #8, 0.03 #28), 02nxhr (0.24 #247, 0.03 #95, 0.03 #42), 07z4p (0.24 #247, 0.03 #10, 0.02 #230) >> Best rule #21 for best value: >> intensional similarity = 2 >> extensional distance = 209 >> proper extension: 03ckwzc; 0cnztc4; 02wyzmv; >> query: (?x4653, 029j_) <- genre(?x4653, ?x1509), ?x1509 = 060__y >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jsqk film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.810 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6242-02q5g1z PRED entity: 02q5g1z PRED relation: award PRED expected values: 027b9k6 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 191): 0gq_v (0.37 #254, 0.28 #471, 0.26 #1416), 0gq9h (0.31 #298, 0.19 #1243, 0.16 #1481), 0gr0m (0.29 #295, 0.17 #1240, 0.14 #1478), 0gs9p (0.29 #300, 0.16 #1245, 0.14 #1483), 0gs96 (0.28 #471, 0.27 #473, 0.27 #325), 0gqy2 (0.28 #471, 0.26 #1416, 0.25 #1655), 019f4v (0.28 #471, 0.26 #1416, 0.25 #1655), 02x17s4 (0.28 #471, 0.26 #1416, 0.25 #1655), 09qwmm (0.28 #471, 0.26 #1416, 0.25 #1655), 02n9nmz (0.28 #471, 0.26 #1416, 0.25 #1655) >> Best rule #254 for best value: >> intensional similarity = 5 >> extensional distance = 60 >> proper extension: 0m313; 01gc7; 0ds11z; 0n0bp; 0cwy47; 017gl1; 0m_mm; 09q5w2; 0pv3x; 020fcn; ... >> query: (?x1753, 0gq_v) <- nominated_for(?x2222, ?x1753), nominated_for(?x1107, ?x1753), ?x2222 = 0gs96, ?x1107 = 019f4v, film(?x3842, ?x1753) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #3068 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 245 *> proper extension: 02fn5r; *> query: (?x1753, ?x746) <- nominated_for(?x1753, ?x3745), nominated_for(?x746, ?x3745) *> conf = 0.07 ranks of expected_values: 48 EVAL 02q5g1z award 027b9k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 83.000 83.000 0.371 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #6241-0bwfwpj PRED entity: 0bwfwpj PRED relation: film_release_region PRED expected values: 015qh 01p1v 05b4w 07f1x => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 126): 05b4w (0.81 #1103, 0.78 #443, 0.77 #1763), 05v8c (0.72 #405, 0.69 #1065, 0.67 #1725), 03rj0 (0.69 #1100, 0.68 #1760, 0.68 #440), 0ctw_b (0.62 #1073, 0.61 #1733, 0.59 #413), 01mjq (0.60 #1087, 0.60 #1747, 0.58 #1879), 015qh (0.59 #1084, 0.58 #1744, 0.56 #1876), 06t8v (0.56 #1114, 0.52 #1774, 0.52 #1906), 01p1v (0.56 #1754, 0.55 #1094, 0.55 #434), 016wzw (0.55 #1105, 0.53 #445, 0.51 #1765), 06c1y (0.44 #1086, 0.41 #1746, 0.40 #1878) >> Best rule #1103 for best value: >> intensional similarity = 6 >> extensional distance = 141 >> proper extension: 0fq27fp; 040rmy; 07l50vn; 07s3m4g; 0g5qmbz; >> query: (?x1012, 05b4w) <- film_release_region(?x1012, ?x1892), film_release_region(?x1012, ?x1603), film_release_region(?x1012, ?x456), ?x456 = 05qhw, ?x1603 = 06bnz, ?x1892 = 02vzc >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 8, 11 EVAL 0bwfwpj film_release_region 07f1x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 91.000 91.000 0.811 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bwfwpj film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.811 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bwfwpj film_release_region 01p1v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 91.000 91.000 0.811 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bwfwpj film_release_region 015qh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 91.000 0.811 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6240-015qh PRED entity: 015qh PRED relation: olympics PRED expected values: 0l6vl 0l998 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 28): 0l6m5 (0.67 #259, 0.66 #231, 0.62 #371), 0l6ny (0.62 #174, 0.57 #483, 0.56 #258), 0swbd (0.61 #2019, 0.61 #2246, 0.59 #2728), 0swff (0.61 #2019, 0.61 #2246, 0.59 #2728), 09x3r (0.60 #449, 0.47 #205, 0.45 #121), 018ctl (0.60 #449, 0.44 #257, 0.42 #2161), 0c_tl (0.60 #449, 0.42 #2161, 0.42 #2160), 0blfl (0.60 #449, 0.42 #2161, 0.42 #2160), 0l998 (0.57 #228, 0.56 #172, 0.50 #481), 0lv1x (0.56 #179, 0.49 #235, 0.48 #488) >> Best rule #259 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 01ls2; 07t21; >> query: (?x1497, 0l6m5) <- film_release_region(?x2441, ?x1497), film_release_region(?x1108, ?x1497), ?x1108 = 0jjy0, ?x2441 = 0cc5mcj >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #228 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 0d0vqn; 04gzd; 0chghy; 03rt9; ... *> query: (?x1497, 0l998) <- member_states(?x2106, ?x1497), film_release_region(?x5070, ?x1497), ?x5070 = 0dt8xq *> conf = 0.57 ranks of expected_values: 9, 11 EVAL 015qh olympics 0l998 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 146.000 146.000 0.667 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 015qh olympics 0l6vl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 146.000 146.000 0.667 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #6239-017f4y PRED entity: 017f4y PRED relation: role PRED expected values: 02sgy => 156 concepts (128 used for prediction) PRED predicted values (max 10 best out of 121): 02sgy (0.60 #589, 0.32 #1562, 0.30 #2242), 018vs (0.32 #2922, 0.27 #595, 0.25 #778), 0l14md (0.32 #2922, 0.25 #778, 0.23 #2823), 01vdm0 (0.31 #4121, 0.29 #5296, 0.28 #2266), 05148p4 (0.31 #2431, 0.14 #2354, 0.14 #4112), 0l14qv (0.23 #2241, 0.20 #393, 0.19 #4195), 026t6 (0.20 #2239, 0.20 #4193, 0.18 #2336), 03qjg (0.13 #643, 0.11 #448, 0.11 #1226), 01s0ps (0.13 #641, 0.11 #446, 0.09 #2588), 04rzd (0.13 #625, 0.09 #917, 0.08 #1404) >> Best rule #589 for best value: >> intensional similarity = 5 >> extensional distance = 50 >> proper extension: 01qvgl; 082brv; 03mszl; 0326tc; 04m2zj; 01m7pwq; >> query: (?x10738, 02sgy) <- location(?x10738, ?x3908), profession(?x10738, ?x131), gender(?x10738, ?x231), role(?x10738, ?x432), ?x432 = 042v_gx >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017f4y role 02sgy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 128.000 0.596 http://example.org/music/artist/track_contributions./music/track_contribution/role #6238-08wjc1 PRED entity: 08wjc1 PRED relation: production_companies! PRED expected values: 053tj7 065dc4 => 150 concepts (134 used for prediction) PRED predicted values (max 10 best out of 1183): 07q1m (0.25 #1765, 0.17 #5188, 0.17 #4047), 04g73n (0.25 #2040, 0.17 #5463, 0.17 #4322), 039zft (0.25 #1757, 0.17 #5180, 0.17 #4039), 023p7l (0.25 #1549, 0.17 #4972, 0.17 #3831), 09wnnb (0.25 #2183, 0.17 #5606, 0.17 #4465), 06y611 (0.25 #2163, 0.17 #5586, 0.17 #4445), 05nlx4 (0.25 #1935, 0.17 #5358, 0.17 #4217), 06fcqw (0.25 #1835, 0.17 #5258, 0.17 #4117), 05sw5b (0.25 #1672, 0.17 #5095, 0.17 #3954), 019kyn (0.25 #1658, 0.17 #5081, 0.17 #3940) >> Best rule #1765 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 09b3v; 099ks0; >> query: (?x3085, 07q1m) <- industry(?x3085, ?x3368), category(?x3085, ?x134), production_companies(?x253, ?x3085), ?x3368 = 02jjt, place_founded(?x3085, ?x682) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #19535 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 0181hw; *> query: (?x3085, 053tj7) <- industry(?x3085, ?x3368), industry(?x9492, ?x3368), industry(?x7526, ?x3368), ?x7526 = 03rwz3, artist(?x9492, ?x838), place_founded(?x3085, ?x682) *> conf = 0.06 ranks of expected_values: 515 EVAL 08wjc1 production_companies! 065dc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 134.000 0.250 http://example.org/film/film/production_companies EVAL 08wjc1 production_companies! 053tj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 150.000 134.000 0.250 http://example.org/film/film/production_companies #6237-02dbp7 PRED entity: 02dbp7 PRED relation: award PRED expected values: 02gdjb => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 295): 0gq9h (0.36 #9951, 0.07 #39902, 0.06 #25754), 02v1m7 (0.36 #109, 0.15 #6824, 0.13 #899), 03qbh5 (0.31 #593, 0.26 #4543, 0.26 #4938), 01c92g (0.30 #885, 0.25 #490, 0.21 #95), 02x17c2 (0.29 #211, 0.17 #1001, 0.15 #36346), 040njc (0.28 #9883, 0.06 #15808, 0.06 #25686), 02f6xy (0.26 #984, 0.21 #194, 0.15 #2959), 09sb52 (0.25 #21767, 0.25 #16631, 0.24 #17421), 03qbnj (0.25 #620, 0.18 #30024, 0.18 #6940), 02gdjb (0.22 #1002, 0.14 #212, 0.12 #607) >> Best rule #9951 for best value: >> intensional similarity = 3 >> extensional distance = 322 >> proper extension: 024c1b; >> query: (?x4574, 0gq9h) <- produced_by(?x10742, ?x4574), nominated_for(?x666, ?x10742), film(?x1561, ?x10742) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1002 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 09hnb; 01w724; 03bxwtd; 02qwg; 01vvyfh; 028qdb; 018dyl; 01wwvd2; 01mvjl0; 01vrnsk; ... *> query: (?x4574, 02gdjb) <- award_winner(?x2139, ?x4574), role(?x4574, ?x1166), ?x2139 = 01by1l *> conf = 0.22 ranks of expected_values: 10 EVAL 02dbp7 award 02gdjb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 119.000 119.000 0.361 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6236-0222qb PRED entity: 0222qb PRED relation: languages_spoken PRED expected values: 02bjrlw => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 57): 02h40lc (0.93 #497, 0.39 #1378, 0.38 #332), 0t_2 (0.50 #287, 0.50 #177, 0.47 #948), 0880p (0.40 #263, 0.33 #318, 0.25 #373), 03hkp (0.40 #233, 0.33 #288, 0.25 #343), 04306rv (0.33 #5, 0.25 #170, 0.07 #996), 03k50 (0.33 #62, 0.07 #502, 0.06 #1383), 0688f (0.33 #92, 0.07 #532, 0.06 #1413), 07c9s (0.33 #71, 0.07 #511, 0.06 #787), 01c7y (0.33 #96, 0.05 #1417, 0.05 #1087), 055qm (0.33 #85, 0.04 #1186, 0.03 #1351) >> Best rule #497 for best value: >> intensional similarity = 11 >> extensional distance = 25 >> proper extension: 071x0k; 078vc; 078ds; 0fk3s; 04czx7; 0c41n; >> query: (?x10035, 02h40lc) <- languages_spoken(?x10035, ?x11038), official_language(?x1778, ?x11038), language(?x6897, ?x11038), language(?x6721, ?x11038), language(?x6178, ?x11038), language(?x708, ?x11038), ?x708 = 0fg04, ?x6897 = 02nx2k, ?x6178 = 02v_r7d, major_field_of_study(?x11038, ?x2605), ?x6721 = 017180 >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #221 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 3 *> proper extension: 0xnvg; *> query: (?x10035, 02bjrlw) <- people(?x10035, ?x10963), people(?x10035, ?x9363), people(?x10035, ?x4731), people(?x10035, ?x2584), people(?x10035, ?x1424), award_nominee(?x788, ?x9363), nominated_for(?x9363, ?x89), produced_by(?x3330, ?x9363), student(?x6784, ?x2584), influenced_by(?x10963, ?x397), ?x4731 = 01twdk, award_winner(?x1424, ?x628), religion(?x9363, ?x1985) *> conf = 0.20 ranks of expected_values: 17 EVAL 0222qb languages_spoken 02bjrlw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 32.000 32.000 0.926 http://example.org/people/ethnicity/languages_spoken #6235-07sgdw PRED entity: 07sgdw PRED relation: titles! PRED expected values: 01z4y => 73 concepts (43 used for prediction) PRED predicted values (max 10 best out of 51): 07s9rl0 (0.40 #1949, 0.34 #2156, 0.32 #2363), 01z4y (0.40 #1471, 0.39 #1266, 0.26 #242), 04xvlr (0.25 #1952, 0.21 #3196, 0.21 #2159), 05p553 (0.22 #2257, 0.22 #3294, 0.19 #1845), 01cgz (0.18 #29, 0.02 #235, 0.01 #440), 07c52 (0.16 #131, 0.10 #2703, 0.09 #1362), 024qqx (0.16 #286, 0.10 #1208, 0.10 #696), 01jfsb (0.14 #329, 0.13 #1968, 0.13 #1148), 07ssc (0.10 #2165, 0.10 #3202, 0.10 #1958), 01hmnh (0.10 #1462, 0.10 #1257, 0.09 #3219) >> Best rule #1949 for best value: >> intensional similarity = 3 >> extensional distance = 681 >> proper extension: 07s8z_l; >> query: (?x4749, 07s9rl0) <- award_winner(?x4749, ?x5338), titles(?x7323, ?x4749), genre(?x11454, ?x7323) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1471 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 416 *> proper extension: 02pg45; 01xbxn; *> query: (?x4749, 01z4y) <- genre(?x4749, ?x258), nominated_for(?x350, ?x4749), film(?x806, ?x4749), ?x258 = 05p553 *> conf = 0.40 ranks of expected_values: 2 EVAL 07sgdw titles! 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 43.000 0.404 http://example.org/media_common/netflix_genre/titles #6234-0m3gy PRED entity: 0m3gy PRED relation: genre PRED expected values: 01jfsb => 66 concepts (57 used for prediction) PRED predicted values (max 10 best out of 101): 01jfsb (0.92 #3683, 0.70 #4037, 0.64 #1669), 07s9rl0 (0.71 #4262, 0.66 #3195, 0.66 #3552), 05p553 (0.67 #242, 0.41 #4266, 0.40 #123), 09blyk (0.50 #31, 0.20 #149, 0.19 #3671), 02kdv5l (0.44 #3674, 0.36 #4028, 0.32 #359), 02l7c8 (0.35 #1082, 0.34 #1437, 0.33 #727), 06nbt (0.33 #263, 0.07 #6270, 0.06 #382), 0lsxr (0.31 #3679, 0.26 #4033, 0.25 #8), 060__y (0.31 #4042, 0.19 #3671, 0.17 #3211), 03k9fj (0.30 #1550, 0.25 #4154, 0.24 #4508) >> Best rule #3683 for best value: >> intensional similarity = 5 >> extensional distance = 525 >> proper extension: 02vw1w2; 0d1qmz; 05znbh7; 03ffcz; 052_mn; >> query: (?x9294, 01jfsb) <- genre(?x9294, ?x6625), genre(?x9786, ?x6625), genre(?x4663, ?x6625), ?x9786 = 06bc59, ?x4663 = 02v5_g >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m3gy genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 57.000 0.917 http://example.org/film/film/genre #6233-01cj6y PRED entity: 01cj6y PRED relation: award_winner! PRED expected values: 02w9sd7 => 96 concepts (75 used for prediction) PRED predicted values (max 10 best out of 201): 04kxsb (0.37 #17259, 0.37 #17258, 0.34 #5179), 09sb52 (0.37 #17259, 0.37 #17258, 0.34 #5179), 09qv_s (0.37 #17259, 0.37 #17258, 0.34 #5179), 02w9sd7 (0.37 #17259, 0.37 #17258, 0.34 #5179), 02x4w6g (0.37 #17259, 0.37 #17258, 0.34 #5179), 027c95y (0.26 #157, 0.06 #4039, 0.05 #1884), 027986c (0.17 #48, 0.04 #3930, 0.04 #1775), 0bfvd4 (0.16 #9492, 0.10 #10356, 0.09 #18122), 0bs0bh (0.16 #9492, 0.10 #10356, 0.09 #18122), 09cm54 (0.15 #96, 0.04 #3978, 0.04 #1823) >> Best rule #17259 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 012ljv; 0411q; 015rmq; 0244r8; 01sbf2; 030_1_; 010hn; 014hr0; 094wz7q; 076_74; ... >> query: (?x4337, ?x2183) <- award(?x4337, ?x2183), award_winner(?x4337, ?x3054) >> conf = 0.37 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 01cj6y award_winner! 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 75.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #6232-032zq6 PRED entity: 032zq6 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.83 #586, 0.82 #1817, 0.81 #328), 0215hd (0.33 #15, 0.28 #1455, 0.25 #79), 02_n3z (0.33 #1, 0.28 #1455, 0.25 #65), 033smt (0.33 #24, 0.28 #1455, 0.25 #88), 0ckd1 (0.33 #3, 0.28 #1455, 0.25 #67), 02ynfr (0.26 #592, 0.25 #44, 0.24 #1823), 089fss (0.25 #37, 0.12 #4295, 0.11 #3936), 094hwz (0.25 #43, 0.12 #4295, 0.11 #3936), 0d2b38 (0.16 #733, 0.16 #246, 0.15 #214), 089g0h (0.16 #240, 0.14 #596, 0.14 #338) >> Best rule #586 for best value: >> intensional similarity = 6 >> extensional distance = 96 >> proper extension: 03hkch7; >> query: (?x4152, 0ch6mp2) <- genre(?x4152, ?x53), featured_film_locations(?x4152, ?x2552), films(?x14046, ?x4152), film_crew_role(?x4152, ?x1171), film(?x541, ?x4152), ?x1171 = 09vw2b7 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 032zq6 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 141.000 0.827 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6231-0232lm PRED entity: 0232lm PRED relation: artists! PRED expected values: 0xhtw 0173b0 => 112 concepts (39 used for prediction) PRED predicted values (max 10 best out of 299): 016clz (0.82 #2462, 0.67 #2155, 0.64 #1234), 064t9 (0.77 #3393, 0.49 #5551, 0.45 #10174), 017_qw (0.52 #4671, 0.41 #4364, 0.40 #3750), 016ybr (0.44 #740, 0.36 #1354, 0.33 #432), 0pm85 (0.41 #2611, 0.30 #1076, 0.24 #2919), 02vjzr (0.34 #3511, 0.11 #5669, 0.09 #9984), 01g888 (0.33 #343, 0.27 #1265, 0.23 #1572), 0xhtw (0.33 #18, 0.25 #10178, 0.24 #11409), 06cp5 (0.33 #397, 0.22 #705, 0.20 #1012), 059kh (0.33 #48, 0.20 #2813, 0.17 #355) >> Best rule #2462 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 03d9d6; 09lwrt; 089pg7; >> query: (?x8873, 016clz) <- artists(?x2995, ?x8873), ?x2995 = 01cbwl, instrumentalists(?x227, ?x8873), ?x227 = 0342h >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #18 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 0kxbc; *> query: (?x8873, 0xhtw) <- profession(?x8873, ?x131), artists(?x2995, ?x8873), artists(?x2249, ?x8873), ?x2995 = 01cbwl, location(?x8873, ?x739), ?x2249 = 03lty *> conf = 0.33 ranks of expected_values: 8, 91 EVAL 0232lm artists! 0173b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 112.000 39.000 0.824 http://example.org/music/genre/artists EVAL 0232lm artists! 0xhtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 112.000 39.000 0.824 http://example.org/music/genre/artists #6230-03cw411 PRED entity: 03cw411 PRED relation: language PRED expected values: 0349s => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 53): 064_8sq (0.29 #134, 0.19 #249, 0.18 #418), 06nm1 (0.16 #294, 0.14 #123, 0.13 #577), 02bjrlw (0.14 #115, 0.13 #343, 0.10 #286), 06b_j (0.14 #135, 0.08 #475, 0.08 #1781), 03_9r (0.11 #8, 0.08 #1314, 0.08 #1543), 0t_2 (0.11 #183, 0.07 #69, 0.04 #523), 06mp7 (0.11 #14, 0.03 #7409, 0.03 #525), 0jzc (0.08 #303, 0.07 #1778, 0.05 #2118), 032f6 (0.07 #224, 0.05 #564, 0.05 #735), 07c9s (0.07 #131, 0.07 #74, 0.03 #7409) >> Best rule #134 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 03bx2lk; >> query: (?x3745, 064_8sq) <- produced_by(?x3745, ?x8041), film_release_region(?x3745, ?x7413), film_release_region(?x3745, ?x1355), ?x1355 = 0h7x, ?x7413 = 04hqz >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #7409 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1866 *> proper extension: 0vgkd; 06n90; 0c0wvx; *> query: (?x3745, ?x254) <- genre(?x3745, ?x1403), genre(?x7750, ?x1403), genre(?x6140, ?x1403), language(?x6140, ?x254), film_sets_designed(?x13444, ?x7750) *> conf = 0.03 ranks of expected_values: 25 EVAL 03cw411 language 0349s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 132.000 132.000 0.286 http://example.org/film/film/language #6229-01d_h PRED entity: 01d_h PRED relation: profession PRED expected values: 01c72t => 112 concepts (59 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.90 #4612, 0.68 #8178, 0.64 #6992), 09jwl (0.77 #4617, 0.76 #5658, 0.75 #4469), 01c72t (0.66 #1950, 0.64 #2099, 0.63 #3881), 0nbcg (0.64 #2257, 0.60 #2405, 0.59 #3741), 039v1 (0.53 #3746, 0.50 #4487, 0.31 #5676), 016z4k (0.49 #3712, 0.49 #3564, 0.47 #5194), 0kyk (0.40 #6711, 0.14 #622, 0.13 #1510), 01c8w0 (0.36 #3418, 0.34 #1934, 0.33 #2083), 05vyk (0.34 #2020, 0.33 #2169, 0.16 #4247), 01d_h8 (0.33 #153, 0.29 #8169, 0.27 #7873) >> Best rule #4612 for best value: >> intensional similarity = 4 >> extensional distance = 187 >> proper extension: 0jfx1; 0cj2w; >> query: (?x8806, 02hrh1q) <- profession(?x8806, ?x6183), role(?x8806, ?x227), profession(?x8876, ?x6183), ?x8876 = 01d_4t >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1950 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 02rgz4; 0146pg; 07q1v4; 0244r8; 0l12d; 01w923; 04bpm6; 04zwjd; 06k02; 0kvrb; ... *> query: (?x8806, 01c72t) <- profession(?x8806, ?x131), artists(?x4910, ?x8806), role(?x8806, ?x227), ?x4910 = 017_qw *> conf = 0.66 ranks of expected_values: 3 EVAL 01d_h profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 59.000 0.899 http://example.org/people/person/profession #6228-04n3l PRED entity: 04n3l PRED relation: place_of_birth! PRED expected values: 01qmy04 => 116 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1764): 0cqt90 (0.33 #3373, 0.11 #5984, 0.07 #11205), 0d0l91 (0.33 #4939, 0.11 #7550, 0.07 #12771), 05lnk0 (0.33 #4191, 0.11 #6802, 0.07 #12023), 05cx7x (0.33 #4154, 0.11 #6765, 0.07 #11986), 04sry (0.33 #4127, 0.11 #6738, 0.07 #11959), 057176 (0.33 #4051, 0.11 #6662, 0.07 #11883), 01h910 (0.33 #3878, 0.11 #6489, 0.07 #11710), 07ncs0 (0.33 #3875, 0.11 #6486, 0.07 #11707), 045cq (0.33 #3758, 0.11 #6369, 0.07 #11590), 07d3z7 (0.33 #3655, 0.11 #6266, 0.07 #11487) >> Best rule #3373 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0ccvx; >> query: (?x3415, 0cqt90) <- location(?x5798, ?x3415), contains(?x94, ?x3415), contains(?x3415, ?x3148), ?x5798 = 01vvyc_ >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04n3l place_of_birth! 01qmy04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 53.000 0.333 http://example.org/people/person/place_of_birth #6227-0dn_w PRED entity: 0dn_w PRED relation: contact_category PRED expected values: 03w5xm => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 3): 03w5xm (0.76 #76, 0.74 #34, 0.74 #52), 014dgf (0.25 #5, 0.25 #2, 0.24 #53), 02zdwq (0.25 #45, 0.24 #99, 0.23 #152) >> Best rule #76 for best value: >> intensional similarity = 6 >> extensional distance = 77 >> proper extension: 0j47s; 01_qgp; 013fn; >> query: (?x14036, 03w5xm) <- service_location(?x14036, ?x94), organization(?x4682, ?x14036), service_language(?x14036, ?x254), ?x254 = 02h40lc, ?x4682 = 0dq_5, country(?x54, ?x94) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dn_w contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.759 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #6226-0lh0c PRED entity: 0lh0c PRED relation: nationality PRED expected values: 09c7w0 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.87 #7935, 0.87 #7531, 0.85 #2205), 030qb3t (0.32 #8944, 0.32 #9147, 0.32 #9249), 01n7q (0.32 #8944, 0.32 #9147, 0.32 #9249), 059_c (0.26 #5314, 0.25 #6925, 0.24 #6118), 03rk0 (0.23 #5962, 0.22 #4757, 0.20 #5760), 07ssc (0.17 #4324, 0.15 #2821, 0.15 #6320), 02jx1 (0.17 #2035, 0.16 #6555, 0.15 #7059), 06c1y (0.15 #6320, 0.13 #5012, 0.11 #1541), 0d060g (0.15 #6320, 0.13 #5012, 0.10 #10754), 0h7x (0.15 #6320, 0.13 #5012, 0.05 #1537) >> Best rule #7935 for best value: >> intensional similarity = 5 >> extensional distance = 381 >> proper extension: 01vvydl; 05m63c; 0lzb8; 034x61; 01wbgdv; 01qdjm; 0gbwp; 02yplc; 02y_2y; 033w9g; ... >> query: (?x6246, 09c7w0) <- people(?x5741, ?x6246), type_of_union(?x6246, ?x566), ?x566 = 04ztj, people(?x5741, ?x6383), ?x6383 = 0g824 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lh0c nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.869 http://example.org/people/person/nationality #6225-01wj18h PRED entity: 01wj18h PRED relation: artists! PRED expected values: 016clz 0ggx5q => 151 concepts (86 used for prediction) PRED predicted values (max 10 best out of 288): 0gywn (0.59 #352, 0.59 #3367, 0.52 #2162), 0155w (0.36 #11553, 0.36 #2513, 0.32 #401), 0glt670 (0.35 #3354, 0.34 #5462, 0.33 #5764), 03_d0 (0.32 #312, 0.30 #2424, 0.28 #3327), 02x8m (0.32 #319, 0.28 #2129, 0.27 #7554), 0xhtw (0.30 #22629, 0.24 #3633, 0.20 #16290), 016clz (0.30 #3621, 0.26 #12059, 0.23 #908), 0ggx5q (0.29 #3387, 0.23 #5495, 0.23 #372), 07sbbz2 (0.28 #2420, 0.18 #308, 0.17 #7242), 01lyv (0.27 #333, 0.27 #9677, 0.26 #10883) >> Best rule #352 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0127s7; >> query: (?x3200, 0gywn) <- artists(?x3319, ?x3200), ?x3319 = 06j6l, award(?x3200, ?x2585), ?x2585 = 054ks3 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #3621 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 86 *> proper extension: 09g0h; *> query: (?x3200, 016clz) <- instrumentalists(?x1750, ?x3200), profession(?x3200, ?x220), gender(?x3200, ?x514), ?x1750 = 02hnl *> conf = 0.30 ranks of expected_values: 7, 8 EVAL 01wj18h artists! 0ggx5q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 151.000 86.000 0.591 http://example.org/music/genre/artists EVAL 01wj18h artists! 016clz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 151.000 86.000 0.591 http://example.org/music/genre/artists #6224-06c62 PRED entity: 06c62 PRED relation: teams PRED expected values: 0ytc => 301 concepts (301 used for prediction) PRED predicted values (max 10 best out of 297): 0cqt41 (0.33 #30, 0.06 #8670, 0.05 #10830), 0hmtk (0.33 #317, 0.06 #8957, 0.05 #11117), 05g76 (0.33 #35, 0.06 #8675, 0.05 #10835), 0jm3v (0.33 #13, 0.06 #8653, 0.05 #10813), 01_1kk (0.25 #2142, 0.25 #1782, 0.17 #3222), 03dj48 (0.25 #1687, 0.17 #2767, 0.06 #8527), 020wyp (0.17 #3213, 0.07 #6813, 0.06 #8613), 0cnk2q (0.17 #2881, 0.07 #6481, 0.06 #8281), 04h5_c (0.17 #2739, 0.06 #8139, 0.03 #17499), 02w64f (0.17 #2847, 0.05 #10047, 0.04 #12927) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02_286; >> query: (?x6959, 0cqt41) <- citytown(?x5994, ?x6959), featured_film_locations(?x11686, ?x6959), month(?x6959, ?x1459), ?x11686 = 04180vy >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06c62 teams 0ytc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 301.000 301.000 0.333 http://example.org/sports/sports_team_location/teams #6223-02frhbc PRED entity: 02frhbc PRED relation: place PRED expected values: 02frhbc => 183 concepts (160 used for prediction) PRED predicted values (max 10 best out of 250): 02frhbc (0.12 #57252, 0.07 #62927, 0.07 #75825), 0chrx (0.12 #57252, 0.07 #75825, 0.01 #12595), 07b_l (0.12 #57252, 0.07 #75825), 0d6lp (0.12 #57252, 0.07 #75825), 0cr3d (0.12 #57252, 0.07 #75825), 030qb3t (0.10 #13919, 0.08 #16500, 0.07 #545), 0d23k (0.08 #35592, 0.01 #58800), 05kj_ (0.07 #62927, 0.07 #75825, 0.04 #77892), 09c7w0 (0.07 #62927, 0.07 #75825, 0.04 #77892), 01cx_ (0.07 #579, 0.06 #1094, 0.06 #1610) >> Best rule #57252 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 01k6y1; 0ffmp; >> query: (?x9605, ?x2850) <- location(?x4387, ?x9605), place_of_death(?x4387, ?x1523), location(?x4387, ?x2850) >> conf = 0.12 => this is the best rule for 5 predicted values ranks of expected_values: 1 EVAL 02frhbc place 02frhbc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 160.000 0.117 http://example.org/location/hud_county_place/place #6222-05qfh PRED entity: 05qfh PRED relation: interests! PRED expected values: 0b78hw => 100 concepts (68 used for prediction) PRED predicted values (max 10 best out of 98): 03sbs (0.60 #367, 0.50 #173, 0.36 #678), 026lj (0.50 #157, 0.45 #662, 0.40 #351), 01bpn (0.50 #168, 0.40 #362, 0.36 #673), 045bg (0.50 #155, 0.40 #349, 0.36 #660), 07kb5 (0.50 #153, 0.40 #347, 0.36 #658), 015n8 (0.50 #188, 0.40 #382, 0.27 #693), 039n1 (0.40 #376, 0.36 #687, 0.25 #182), 0m93 (0.40 #330, 0.33 #99, 0.33 #61), 0gz_ (0.40 #355, 0.27 #666, 0.25 #161), 0399p (0.40 #372, 0.25 #178, 0.18 #683) >> Best rule #367 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0x0w; >> query: (?x3490, 03sbs) <- interests(?x3941, ?x3490), interests(?x2240, ?x3490), ?x2240 = 0j3v, profession(?x3941, ?x2225), peers(?x920, ?x3941), influenced_by(?x3941, ?x1857), influenced_by(?x1737, ?x3941) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #672 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 9 *> proper extension: 02jcc; 0gt_hv; *> query: (?x3490, 0b78hw) <- interests(?x2240, ?x3490), company(?x2240, ?x13316), influenced_by(?x1236, ?x2240), religion(?x2240, ?x7422), location(?x2240, ?x8977), religion(?x9782, ?x7422), ?x9782 = 0cwtm *> conf = 0.36 ranks of expected_values: 11 EVAL 05qfh interests! 0b78hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 100.000 68.000 0.600 http://example.org/user/alexander/philosophy/philosopher/interests #6221-03rk0 PRED entity: 03rk0 PRED relation: nationality! PRED expected values: 04b19t 0dfjb8 0f2c8g 03fw60 01zh29 06gn7r 04hqbbz 05yvfd 05_zc7 02jxsq 07t3x8 04m_kpx 03d8njj 03f22dp 047jhq 045hz5 02r99xw 027lfrs 048svj => 222 concepts (95 used for prediction) PRED predicted values (max 10 best out of 3876): 01zh29 (0.78 #61800, 0.71 #42487, 0.59 #166090), 0f2c8g (0.78 #61800, 0.59 #166090, 0.56 #131323), 027lfrs (0.78 #61800, 0.59 #166090, 0.56 #131323), 06kb_ (0.78 #61800, 0.59 #166090, 0.56 #131323), 045hz5 (0.78 #61800, 0.59 #166090, 0.56 #131323), 04b19t (0.78 #61800, 0.59 #166090, 0.56 #131323), 03f22dp (0.71 #42487, 0.46 #320593, 0.43 #162226), 06gn7r (0.71 #42487, 0.46 #320593, 0.43 #162226), 03d8njj (0.71 #42487, 0.46 #320593, 0.43 #162226), 07t3x8 (0.71 #42487, 0.46 #320593, 0.43 #162226) >> Best rule #61800 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 04pnx; 014wxc; >> query: (?x2146, ?x6249) <- contains(?x2146, ?x13082), contains(?x2146, ?x6250), religion(?x13082, ?x7422), place_of_birth(?x6249, ?x6250) >> conf = 0.78 => this is the best rule for 6 predicted values ranks of expected_values: 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 23 EVAL 03rk0 nationality! 048svj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 027lfrs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 02r99xw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 045hz5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 047jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 03f22dp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 03d8njj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 04m_kpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 07t3x8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 02jxsq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 05_zc7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 05yvfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 04hqbbz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 06gn7r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 01zh29 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 03fw60 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 0f2c8g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 0dfjb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 222.000 95.000 0.785 http://example.org/people/person/nationality EVAL 03rk0 nationality! 04b19t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 222.000 95.000 0.785 http://example.org/people/person/nationality #6220-023v4_ PRED entity: 023v4_ PRED relation: film PRED expected values: 01qbg5 => 102 concepts (92 used for prediction) PRED predicted values (max 10 best out of 868): 027fwmt (0.17 #1589, 0.12 #6945, 0.12 #3374), 07bzz7 (0.17 #887, 0.12 #2672, 0.11 #8028), 02qr3k8 (0.17 #1285, 0.12 #3070, 0.06 #6641), 03nqnnk (0.17 #1021, 0.12 #2806, 0.06 #6377), 02ndy4 (0.17 #1694, 0.12 #3479, 0.06 #7050), 02pg45 (0.17 #929, 0.12 #2714, 0.06 #6285), 014kq6 (0.17 #344, 0.12 #2129, 0.06 #5700), 035_2h (0.17 #916, 0.12 #2701, 0.06 #6272), 01fwzk (0.17 #1496, 0.12 #3281, 0.06 #6852), 05b_gq (0.17 #1097, 0.12 #2882, 0.06 #6453) >> Best rule #1589 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 03j24kf; >> query: (?x4956, 027fwmt) <- participant(?x4956, ?x4960), ?x4960 = 09889g, award_nominee(?x4956, ?x3293) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 023v4_ film 01qbg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 92.000 0.167 http://example.org/film/actor/film./film/performance/film #6219-05cc1 PRED entity: 05cc1 PRED relation: country! PRED expected values: 01cgz 02y8z => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 54): 071t0 (0.69 #508, 0.62 #1102, 0.61 #994), 01cgz (0.68 #338, 0.65 #446, 0.64 #554), 01lb14 (0.52 #339, 0.52 #501, 0.49 #1095), 03hr1p (0.49 #509, 0.48 #347, 0.46 #1103), 07jbh (0.49 #519, 0.45 #357, 0.42 #1113), 0w0d (0.48 #498, 0.45 #336, 0.40 #1092), 07gyv (0.45 #493, 0.45 #655, 0.44 #385), 03fyrh (0.44 #514, 0.42 #352, 0.41 #1189), 06wrt (0.44 #340, 0.43 #502, 0.40 #1096), 0194d (0.44 #371, 0.41 #1189, 0.36 #2000) >> Best rule #508 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 0j5g9; >> query: (?x6827, 071t0) <- adjoins(?x6827, ?x291), countries_spoken_in(?x5607, ?x6827), country(?x11110, ?x291) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #338 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 06c62; *> query: (?x6827, 01cgz) <- taxonomy(?x6827, ?x939), olympics(?x6827, ?x584), contains(?x2467, ?x6827) *> conf = 0.68 ranks of expected_values: 2, 12 EVAL 05cc1 country! 02y8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 91.000 91.000 0.692 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 05cc1 country! 01cgz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.692 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #6218-03kcyd PRED entity: 03kcyd PRED relation: profession PRED expected values: 012t_z => 96 concepts (95 used for prediction) PRED predicted values (max 10 best out of 45): 0np9r (0.70 #21, 0.21 #2852, 0.20 #2405), 03gjzk (0.33 #3145, 0.31 #1058, 0.25 #11028), 01d_h8 (0.31 #4030, 0.30 #3434, 0.29 #4328), 0dxtg (0.31 #4038, 0.30 #3144, 0.29 #908), 02jknp (0.25 #11028, 0.21 #8949, 0.21 #4032), 02krf9 (0.25 #11028, 0.14 #3157, 0.13 #1070), 09jwl (0.22 #3745, 0.17 #5831, 0.17 #4937), 0dz3r (0.16 #3728, 0.11 #4920, 0.11 #5814), 0nbcg (0.16 #3758, 0.12 #5397, 0.11 #8228), 018gz8 (0.14 #1358, 0.13 #166, 0.13 #1060) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 03j9ml; >> query: (?x9461, 0np9r) <- place_of_birth(?x9461, ?x10400), actor(?x9340, ?x9461), ?x9340 = 05nlzq >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #3441 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 949 *> proper extension: 06_bq1; 01y8d4; 01d6jf; 0gdhhy; 0knjh; 037q1z; *> query: (?x9461, 012t_z) <- award_winner(?x9461, ?x286), award_winner(?x427, ?x9461), type_of_union(?x9461, ?x566) *> conf = 0.03 ranks of expected_values: 27 EVAL 03kcyd profession 012t_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 96.000 95.000 0.700 http://example.org/people/person/profession #6217-032zg9 PRED entity: 032zg9 PRED relation: award_winner! PRED expected values: 09qftb => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 104): 09gkdln (0.25 #122, 0.17 #404, 0.14 #686), 05c1t6z (0.25 #15, 0.17 #297, 0.14 #579), 09pnw5 (0.25 #103, 0.17 #385, 0.14 #667), 09qvms (0.17 #154, 0.14 #436, 0.04 #3256), 09g90vz (0.17 #265, 0.14 #547, 0.03 #5059), 09k5jh7 (0.17 #225, 0.14 #507, 0.02 #930), 09pj68 (0.17 #387, 0.14 #669, 0.02 #1092), 0h98b3k (0.14 #705), 05pd94v (0.06 #848, 0.06 #1271, 0.04 #989), 01mhwk (0.06 #887, 0.04 #1310, 0.04 #1028) >> Best rule #122 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 07r1h; 053xw6; >> query: (?x4667, 09gkdln) <- film(?x4667, ?x5871), nationality(?x4667, ?x94), ?x5871 = 02b61v, place_of_birth(?x4667, ?x739) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2228 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 226 *> proper extension: 03xp8d5; 021r7r; 02465; 03f22dp; *> query: (?x4667, 09qftb) <- student(?x2909, ?x4667), people(?x2510, ?x4667), profession(?x4667, ?x319), ?x319 = 01d_h8 *> conf = 0.02 ranks of expected_values: 47 EVAL 032zg9 award_winner! 09qftb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 136.000 136.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6216-08qxx9 PRED entity: 08qxx9 PRED relation: award_winner! PRED expected values: 09g90vz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 117): 09g90vz (0.71 #123, 0.16 #3221, 0.10 #11342), 09gkdln (0.16 #3221, 0.10 #11342, 0.05 #401), 0n8_m93 (0.16 #3221, 0.10 #11342, 0.03 #257), 05zksls (0.16 #3221, 0.10 #11342, 0.03 #735), 05qb8vx (0.16 #3221, 0.10 #11342, 0.01 #479), 050yyb (0.10 #11342, 0.03 #178, 0.01 #4379), 073h9x (0.10 #11342, 0.01 #4391, 0.01 #4531), 09qvms (0.10 #153, 0.05 #1973, 0.05 #4354), 0418154 (0.10 #247, 0.05 #387, 0.04 #807), 058m5m4 (0.10 #195, 0.04 #755, 0.03 #1875) >> Best rule #123 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 058kqy; 0c6qh; 07m9cm; 0p__8; 0336mc; >> query: (?x8765, 09g90vz) <- award_nominee(?x8765, ?x8740), film(?x8765, ?x633), ?x8740 = 026rm_y >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08qxx9 award_winner! 09g90vz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6215-0gl2ny2 PRED entity: 0gl2ny2 PRED relation: profession! PRED expected values: 09lhln 09r1j5 0djvzd 01lqnff 07m69t 0d3f83 02y0dd => 21 concepts (15 used for prediction) PRED predicted values (max 10 best out of 4170): 01wgcvn (0.50 #1135, 0.25 #13716, 0.24 #22100), 017b2p (0.50 #2926, 0.21 #28083, 0.14 #23891), 0gbwp (0.50 #1224, 0.19 #22189, 0.19 #13805), 09h4b5 (0.50 #2601, 0.14 #23566, 0.12 #15182), 0fqjhm (0.50 #3311, 0.14 #24276, 0.12 #15892), 021yw7 (0.44 #13683, 0.41 #17875, 0.38 #22067), 015pxr (0.44 #13180, 0.41 #17372, 0.38 #21564), 0mdqp (0.44 #12770, 0.41 #16962, 0.38 #21154), 03b78r (0.44 #14977, 0.41 #19169, 0.33 #23361), 04cl1 (0.44 #14075, 0.41 #18267, 0.33 #22459) >> Best rule #1135 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 02hrh1q; 0d1pc; 01xr66; 02y5kn; >> query: (?x7623, 01wgcvn) <- profession(?x9672, ?x7623), profession(?x982, ?x7623), gender(?x982, ?x231), team(?x9672, ?x2355), team(?x982, ?x6153), nationality(?x982, ?x512), teams(?x11072, ?x6153) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #15133 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 14 *> proper extension: 01d_h8; 02jknp; 03gjzk; 018gz8; 0np9r; 01445t; 02krf9; 0kyk; 0fj9f; 0747nrk; ... *> query: (?x7623, 01lqnff) <- profession(?x9697, ?x7623), profession(?x9672, ?x7623), profession(?x982, ?x7623), gender(?x982, ?x231), type_of_union(?x9697, ?x566), team(?x9672, ?x983), place_of_birth(?x9672, ?x14311), team(?x9697, ?x3162) *> conf = 0.06 ranks of expected_values: 3430 EVAL 0gl2ny2 profession! 02y0dd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 15.000 0.500 http://example.org/people/person/profession EVAL 0gl2ny2 profession! 0d3f83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 15.000 0.500 http://example.org/people/person/profession EVAL 0gl2ny2 profession! 07m69t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 15.000 0.500 http://example.org/people/person/profession EVAL 0gl2ny2 profession! 01lqnff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 15.000 0.500 http://example.org/people/person/profession EVAL 0gl2ny2 profession! 0djvzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 15.000 0.500 http://example.org/people/person/profession EVAL 0gl2ny2 profession! 09r1j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 15.000 0.500 http://example.org/people/person/profession EVAL 0gl2ny2 profession! 09lhln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 15.000 0.500 http://example.org/people/person/profession #6214-01kp_1t PRED entity: 01kp_1t PRED relation: profession PRED expected values: 02hrh1q 0nbcg => 118 concepts (114 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.73 #10922, 0.71 #2250, 0.70 #8832), 0dxtg (0.71 #11368, 0.40 #8085, 0.34 #3593), 0cbd2 (0.70 #8078, 0.49 #3138, 0.47 #3586), 09jwl (0.70 #765, 0.62 #6443, 0.62 #1063), 0nbcg (0.52 #777, 0.49 #1075, 0.47 #6455), 016z4k (0.46 #6427, 0.46 #2985, 0.45 #2835), 01d_h8 (0.46 #304, 0.38 #11360, 0.37 #900), 0dz3r (0.43 #2, 0.42 #6425, 0.42 #2983), 02jknp (0.32 #11362, 0.25 #306, 0.23 #2094), 03gjzk (0.30 #11370, 0.25 #314, 0.22 #9579) >> Best rule #10922 for best value: >> intensional similarity = 2 >> extensional distance = 1288 >> proper extension: 02v49c; >> query: (?x9528, 02hrh1q) <- location(?x9528, ?x479), award_nominee(?x9528, ?x11621) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 01kp_1t profession 0nbcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 118.000 114.000 0.730 http://example.org/people/person/profession EVAL 01kp_1t profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 114.000 0.730 http://example.org/people/person/profession #6213-03kwtb PRED entity: 03kwtb PRED relation: music! PRED expected values: 0456zg => 121 concepts (95 used for prediction) PRED predicted values (max 10 best out of 757): 01s7w3 (0.07 #5936, 0.05 #6949, 0.05 #9988), 01hp5 (0.06 #3098, 0.02 #7150, 0.01 #9176), 02ht1k (0.04 #7460, 0.03 #6447, 0.02 #4421), 09d3b7 (0.04 #7933, 0.03 #6920, 0.02 #11985), 02rrfzf (0.03 #5391, 0.03 #10456, 0.03 #9443), 04tqtl (0.03 #5375, 0.02 #7401, 0.02 #12466), 013q0p (0.03 #2507, 0.02 #5546, 0.01 #9598), 09cxm4 (0.03 #2839, 0.02 #6891, 0.02 #7904), 0_7w6 (0.03 #2211, 0.02 #6263, 0.02 #7276), 0140g4 (0.03 #2039, 0.02 #6091, 0.01 #9130) >> Best rule #5936 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 01nqfh_; 089kpp; >> query: (?x1292, 01s7w3) <- music(?x148, ?x1292), profession(?x1292, ?x131), category(?x1292, ?x134) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #7906 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 02qfhb; 07y8l9; 0jn5l; 06zd1c; *> query: (?x1292, 0456zg) <- gender(?x1292, ?x231), award_nominee(?x1291, ?x1292), music(?x148, ?x1292) *> conf = 0.02 ranks of expected_values: 129 EVAL 03kwtb music! 0456zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 121.000 95.000 0.070 http://example.org/film/film/music #6212-0bymv PRED entity: 0bymv PRED relation: location PRED expected values: 0vmt => 104 concepts (81 used for prediction) PRED predicted values (max 10 best out of 295): 0rh6k (0.26 #8049, 0.25 #7243, 0.25 #4025), 030qb3t (0.24 #27444, 0.18 #16179, 0.16 #25031), 02_286 (0.20 #6472, 0.20 #37, 0.18 #24985), 0fpzwf (0.20 #282, 0.17 #1086, 0.06 #7521), 03s5t (0.20 #142, 0.17 #946, 0.03 #11406), 0hjy (0.20 #46, 0.17 #850, 0.03 #11310), 01n7q (0.18 #5694, 0.14 #3280, 0.06 #27424), 02m77 (0.17 #1135, 0.06 #7570, 0.03 #22867), 05fkf (0.14 #2450, 0.14 #1646, 0.09 #4864), 0cc56 (0.14 #2469, 0.14 #1665, 0.09 #4883) >> Best rule #8049 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 07hyk; >> query: (?x2357, 0rh6k) <- people(?x5741, ?x2357), profession(?x2357, ?x353), basic_title(?x2357, ?x2358), award_winner(?x5631, ?x2357) >> conf = 0.26 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bymv location 0vmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 81.000 0.263 http://example.org/people/person/places_lived./people/place_lived/location #6211-019rg5 PRED entity: 019rg5 PRED relation: form_of_government PRED expected values: 06cx9 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 6): 06cx9 (0.46 #79, 0.38 #7, 0.36 #319), 01fpfn (0.45 #39, 0.39 #75, 0.39 #69), 018wl5 (0.39 #110, 0.33 #2, 0.32 #20), 01q20 (0.35 #22, 0.31 #112, 0.30 #58), 01d9r3 (0.34 #83, 0.31 #11, 0.31 #281), 026wp (0.11 #6, 0.08 #78, 0.08 #72) >> Best rule #79 for best value: >> intensional similarity = 2 >> extensional distance = 57 >> proper extension: 05g2v; 0ftn8; 0lnfy; 0fnyc; 0dbks; 0c1xm; 01pxqx; 0fnc_; >> query: (?x910, 06cx9) <- contains(?x2467, ?x910), ?x2467 = 0dg3n1 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019rg5 form_of_government 06cx9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.458 http://example.org/location/country/form_of_government #6210-01pfkw PRED entity: 01pfkw PRED relation: award_nominee PRED expected values: 03bxwtd 01my_c => 143 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1523): 02b9g4 (0.88 #28099, 0.83 #173252, 0.82 #175595), 0136g9 (0.88 #28099, 0.82 #175595, 0.82 #67904), 02yygk (0.88 #28099, 0.82 #175595, 0.82 #184960), 01my_c (0.88 #28099, 0.82 #184960, 0.82 #72588), 01pfkw (0.71 #8064, 0.57 #10406, 0.21 #60880), 0603qp (0.33 #1342, 0.20 #59881, 0.12 #15392), 0q9zc (0.33 #1853, 0.20 #60392, 0.12 #15903), 017s11 (0.33 #2448, 0.16 #114832, 0.14 #11814), 0b7t3p (0.33 #1486, 0.15 #22561, 0.12 #15536), 0bbf1f (0.33 #2987, 0.14 #12353, 0.12 #14695) >> Best rule #28099 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 01vrncs; 012x4t; 015_30; 014q2g; 0gdh5; 03h_fk5; 02_fj; 0407f; 01n8gr; 01vvyfh; ... >> query: (?x4420, ?x527) <- award_nominee(?x527, ?x4420), celebrities_impersonated(?x5915, ?x4420), artist(?x2149, ?x4420) >> conf = 0.88 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 68 EVAL 01pfkw award_nominee 01my_c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 143.000 87.000 0.878 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 01pfkw award_nominee 03bxwtd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 143.000 87.000 0.878 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #6209-05jjl PRED entity: 05jjl PRED relation: profession PRED expected values: 0kyk => 115 concepts (53 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.78 #6150, 0.75 #5565, 0.74 #6735), 0cbd2 (0.68 #5997, 0.52 #6430, 0.49 #1758), 09jwl (0.65 #164, 0.47 #6301, 0.46 #6594), 02jknp (0.64 #1613, 0.63 #4975, 0.61 #1321), 0nbcg (0.56 #6314, 0.54 #6607, 0.50 #177), 01c72t (0.50 #607, 0.43 #4845, 0.40 #6306), 0kyk (0.46 #6020, 0.35 #1051, 0.35 #1197), 03gjzk (0.44 #2789, 0.43 #3374, 0.42 #2497), 0dz3r (0.32 #586, 0.29 #6285, 0.29 #6578), 016z4k (0.31 #4241, 0.30 #150, 0.29 #588) >> Best rule #6150 for best value: >> intensional similarity = 4 >> extensional distance = 722 >> proper extension: 06n7h7; 03g62; 02t_8z; >> query: (?x8683, 02hrh1q) <- profession(?x8683, ?x319), location(?x8683, ?x3014), student(?x4016, ?x8683), nominated_for(?x8683, ?x833) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6020 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 698 *> proper extension: 079vf; 06y9c2; 07kb5; 04zd4m; 03mz9r; 063vn; 0l56b; 0453t; 0bymv; 01gp_x; ... *> query: (?x8683, 0kyk) <- profession(?x8683, ?x6421), profession(?x4795, ?x6421), profession(?x1236, ?x6421), ?x4795 = 0n6kf, ?x1236 = 045bg *> conf = 0.46 ranks of expected_values: 7 EVAL 05jjl profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 115.000 53.000 0.783 http://example.org/people/person/profession #6208-0mlyw PRED entity: 0mlyw PRED relation: contains! PRED expected values: 081yw => 95 concepts (38 used for prediction) PRED predicted values (max 10 best out of 58): 081yw (0.71 #12604, 0.69 #23398, 0.67 #13504), 01n7q (0.53 #3678, 0.49 #4578, 0.45 #2778), 09c7w0 (0.35 #2703, 0.34 #9906, 0.33 #4503), 05kj_ (0.19 #3641, 0.12 #5442, 0.10 #1839), 06pvr (0.18 #2866, 0.17 #4666, 0.15 #5567), 059rby (0.17 #15328, 0.15 #17125, 0.15 #16227), 04_1l0v (0.14 #10354, 0.14 #3151, 0.10 #5852), 05tbn (0.12 #6526, 0.11 #13731, 0.10 #12829), 05fjf (0.11 #15682, 0.10 #6676, 0.10 #14781), 0l2xl (0.08 #3136, 0.04 #4936, 0.04 #5837) >> Best rule #12604 for best value: >> intensional similarity = 6 >> extensional distance = 172 >> proper extension: 0m2gk; 0l2l_; 0l2v0; 0k3kv; 0k3hn; 0l2jt; 0nm42; 0n5_g; 0k3k1; 0k3ll; ... >> query: (?x3840, ?x4600) <- source(?x3840, ?x958), second_level_divisions(?x94, ?x3840), adjoins(?x10733, ?x3840), ?x94 = 09c7w0, county_seat(?x10733, ?x9141), contains(?x4600, ?x10733) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mlyw contains! 081yw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 38.000 0.706 http://example.org/location/location/contains #6207-03vgp7 PRED entity: 03vgp7 PRED relation: film PRED expected values: 0299hs => 84 concepts (63 used for prediction) PRED predicted values (max 10 best out of 367): 09146g (0.17 #298, 0.01 #3876, 0.01 #7454), 03nfnx (0.17 #1402, 0.01 #6769, 0.01 #44339), 02qydsh (0.17 #1498, 0.01 #6865), 04tc1g (0.17 #133, 0.01 #5500), 017n9 (0.17 #1750), 0d87hc (0.17 #1640), 0f7hw (0.17 #1558), 0m_h6 (0.17 #1520), 0hv4t (0.17 #1180), 0bxxzb (0.17 #1176) >> Best rule #298 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0f7hc; 033m23; 06qgjh; 01mbwlb; >> query: (?x3229, 09146g) <- film(?x3229, ?x4650), award_nominee(?x192, ?x3229), ?x4650 = 0fgrm >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03vgp7 film 0299hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 63.000 0.167 http://example.org/film/actor/film./film/performance/film #6206-08849 PRED entity: 08849 PRED relation: religion PRED expected values: 078tg => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 38): 0c8wxp (0.53 #3756, 0.43 #2428, 0.40 #2692), 0kpl (0.33 #9, 0.20 #493, 0.20 #449), 02rsw (0.25 #375, 0.23 #551, 0.20 #463), 0631_ (0.25 #51, 0.17 #403, 0.16 #1107), 03_gx (0.24 #3764, 0.20 #1994, 0.20 #101), 025t7ly (0.20 #143, 0.04 #539, 0.03 #671), 051kv (0.17 #400, 0.13 #620, 0.12 #224), 019cr (0.12 #1286, 0.12 #1154, 0.12 #230), 05sfs (0.12 #222, 0.11 #266, 0.10 #970), 0v53x (0.12 #248, 0.11 #292, 0.07 #1172) >> Best rule #3756 for best value: >> intensional similarity = 3 >> extensional distance = 685 >> proper extension: 01w3v; 0mcf4; >> query: (?x11617, 0c8wxp) <- religion(?x11617, ?x492), religion(?x1755, ?x492), ?x1755 = 01x73 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #431 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 03_nq; 09k0f; 0c_md_; 042f1; 0b22w; 038w8; 0466k4; 06c0j; 081t6; 08959; *> query: (?x11617, 078tg) <- type_of_union(?x11617, ?x566), profession(?x11617, ?x5805), ?x5805 = 0fj9f, basic_title(?x11617, ?x346), ?x346 = 060c4 *> conf = 0.04 ranks of expected_values: 22 EVAL 08849 religion 078tg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 142.000 142.000 0.525 http://example.org/people/person/religion #6205-01d259 PRED entity: 01d259 PRED relation: country PRED expected values: 09c7w0 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 173): 09c7w0 (0.88 #4168, 0.87 #3188, 0.85 #550), 07ssc (0.43 #1486, 0.28 #688, 0.24 #3448), 03rjj (0.33 #122, 0.23 #305, 0.19 #4971), 0chghy (0.33 #122, 0.23 #305, 0.19 #4971), 0k6nt (0.33 #122, 0.23 #305, 0.19 #4971), 06mkj (0.33 #122, 0.23 #305, 0.19 #4971), 0d0vqn (0.33 #122, 0.23 #305, 0.19 #4971), 05r4w (0.33 #122, 0.23 #305, 0.19 #4971), 03_3d (0.33 #122, 0.23 #305, 0.19 #4971), 0b90_r (0.33 #122, 0.23 #305, 0.19 #4971) >> Best rule #4168 for best value: >> intensional similarity = 5 >> extensional distance = 1031 >> proper extension: 0bl5c; >> query: (?x5721, 09c7w0) <- film(?x5126, ?x5721), language(?x5721, ?x254), participant(?x2258, ?x5126), nationality(?x5126, ?x94), country(?x5721, ?x789) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d259 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.879 http://example.org/film/film/country #6204-02vr7 PRED entity: 02vr7 PRED relation: film PRED expected values: 03hfmm => 125 concepts (102 used for prediction) PRED predicted values (max 10 best out of 330): 02cbhg (0.42 #10741, 0.38 #116356, 0.38 #152162), 01jnc_ (0.08 #1569, 0.05 #3359, 0.04 #5149), 0c8tkt (0.06 #268, 0.03 #3848, 0.03 #7428), 0hmr4 (0.04 #103, 0.02 #7263, 0.01 #25164), 031hcx (0.03 #40656, 0.01 #83617, 0.01 #19176), 01shy7 (0.03 #9374, 0.03 #4004, 0.02 #30855), 02x3lt7 (0.03 #9034, 0.01 #26935), 04jpk2 (0.03 #2376, 0.01 #32807, 0.01 #45337), 03177r (0.03 #39846, 0.01 #82807), 03kx49 (0.03 #13874, 0.02 #1343, 0.02 #8503) >> Best rule #10741 for best value: >> intensional similarity = 2 >> extensional distance = 120 >> proper extension: 04qmr; 03d9d6; >> query: (?x8311, ?x8084) <- artist(?x382, ?x8311), nominated_for(?x8311, ?x8084) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #10429 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 120 *> proper extension: 04qmr; 03d9d6; *> query: (?x8311, 03hfmm) <- artist(?x382, ?x8311), nominated_for(?x8311, ?x8084) *> conf = 0.02 ranks of expected_values: 132 EVAL 02vr7 film 03hfmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 125.000 102.000 0.420 http://example.org/film/actor/film./film/performance/film #6203-0ck27z PRED entity: 0ck27z PRED relation: ceremony PRED expected values: 09g90vz 0g55tzk => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 132): 05c1t6z (0.45 #672, 0.33 #540, 0.25 #144), 02q690_ (0.39 #719, 0.33 #587, 0.27 #4490), 0gvstc3 (0.39 #691, 0.28 #559, 0.27 #4490), 03nnm4t (0.38 #727, 0.33 #595, 0.25 #199), 0gx_st (0.35 #694, 0.28 #562, 0.27 #4490), 0gpjbt (0.33 #2534, 0.32 #2666, 0.24 #3590), 0ds460j (0.33 #123, 0.29 #387, 0.22 #3697), 0h_9252 (0.33 #52, 0.29 #316, 0.07 #2692), 0bx6zs (0.33 #646, 0.27 #4490, 0.25 #250), 07y_p6 (0.33 #618, 0.27 #4490, 0.25 #222) >> Best rule #672 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 02rdxsh; 054knh; 02py_sj; >> query: (?x1670, 05c1t6z) <- nominated_for(?x1670, ?x5594), actor(?x5594, ?x101), nominated_for(?x914, ?x5594) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #4490 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 358 *> proper extension: 02q3s; *> query: (?x1670, ?x8347) <- award_winner(?x1670, ?x1485), award_winner(?x8347, ?x1485) *> conf = 0.27 ranks of expected_values: 28, 29 EVAL 0ck27z ceremony 0g55tzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 44.000 44.000 0.455 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0ck27z ceremony 09g90vz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 44.000 44.000 0.455 http://example.org/award/award_category/winners./award/award_honor/ceremony #6202-03_gz8 PRED entity: 03_gz8 PRED relation: produced_by PRED expected values: 0b13g7 => 92 concepts (69 used for prediction) PRED predicted values (max 10 best out of 190): 013tcv (0.22 #309, 0.02 #1081, 0.02 #1467), 0154qm (0.12 #2705, 0.11 #5803, 0.11 #5028), 0b13g7 (0.11 #118, 0.11 #2823, 0.05 #3985), 02tn0_ (0.11 #327, 0.03 #713, 0.01 #8066), 02xnjd (0.11 #272, 0.02 #15372, 0.02 #1817), 06chf (0.11 #99, 0.01 #3578, 0.01 #7066), 09zw90 (0.11 #359, 0.01 #2676), 052gzr (0.11 #64, 0.01 #2381), 0d_skg (0.11 #229), 05hj_k (0.06 #527, 0.02 #913, 0.02 #1299) >> Best rule #309 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0b2km_; >> query: (?x6362, 013tcv) <- music(?x6362, ?x6910), music(?x6362, ?x3890), ?x6910 = 05y7hc, country(?x6362, ?x512), profession(?x3890, ?x131) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #118 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0b2km_; *> query: (?x6362, 0b13g7) <- music(?x6362, ?x6910), music(?x6362, ?x3890), ?x6910 = 05y7hc, country(?x6362, ?x512), profession(?x3890, ?x131) *> conf = 0.11 ranks of expected_values: 3 EVAL 03_gz8 produced_by 0b13g7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 69.000 0.222 http://example.org/film/film/produced_by #6201-03sxd2 PRED entity: 03sxd2 PRED relation: film! PRED expected values: 01phtd 0428bc => 87 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1054): 0807ml (0.50 #1121, 0.01 #7350), 01vh18t (0.50 #1605), 01ycbq (0.25 #325, 0.06 #95511, 0.05 #2401), 09nz_c (0.25 #1691, 0.06 #95511, 0.01 #9996), 0f5xn (0.25 #965, 0.06 #23805, 0.04 #42491), 0pmhf (0.25 #440, 0.04 #112124, 0.04 #120429), 02dth1 (0.25 #721, 0.02 #4873), 029_l (0.25 #950, 0.02 #11331, 0.01 #50780), 01r7t9 (0.25 #1875, 0.02 #47553, 0.01 #41325), 04qsdh (0.25 #1399, 0.01 #7628, 0.01 #9704) >> Best rule #1121 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0jqkh; >> query: (?x1941, 0807ml) <- genre(?x1941, ?x53), film(?x9153, ?x1941), film(?x804, ?x1941), award(?x804, ?x458), ?x9153 = 06pjs >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5849 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 44 *> proper extension: 011yph; 0dzz6g; 03hmt9b; 06t6dz; 037q31; 0170yd; 08g_jw; *> query: (?x1941, 0428bc) <- production_companies(?x1941, ?x7980), film(?x4042, ?x1941), genre(?x1941, ?x2753), award_winner(?x192, ?x4042), ?x2753 = 0219x_ *> conf = 0.02 ranks of expected_values: 407, 506 EVAL 03sxd2 film! 0428bc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 66.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 03sxd2 film! 01phtd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 66.000 0.500 http://example.org/film/actor/film./film/performance/film #6200-0jqn5 PRED entity: 0jqn5 PRED relation: nominated_for! PRED expected values: 02r22gf => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 262): 02qvyrt (0.67 #12885, 0.66 #12884, 0.66 #8663), 02qt02v (0.67 #12885, 0.66 #12884, 0.66 #8663), 02g3gw (0.67 #12885, 0.66 #12884, 0.66 #8663), 02w_6xj (0.67 #12885, 0.66 #12884, 0.66 #8663), 025m8y (0.67 #12885, 0.66 #12884, 0.66 #8663), 04kxsb (0.45 #2745, 0.43 #2967, 0.23 #81), 0gq_v (0.38 #18, 0.33 #9570, 0.32 #5571), 0gqyl (0.38 #66, 0.27 #2952, 0.26 #2730), 02r22gf (0.38 #2912, 0.37 #2690, 0.19 #19335), 027dtxw (0.34 #2668, 0.33 #2890, 0.18 #5557) >> Best rule #12885 for best value: >> intensional similarity = 3 >> extensional distance = 981 >> proper extension: 075cph; 04q00lw; 05m_jsg; 015g28; 019kyn; 0fsw_7; 01kf5lf; 0k7tq; 08cfr1; 06w7mlh; ... >> query: (?x1452, ?x1587) <- award(?x1452, ?x1587), award_winner(?x1587, ?x986), nominated_for(?x1587, ?x696) >> conf = 0.67 => this is the best rule for 5 predicted values *> Best rule #2912 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 157 *> proper extension: 017kct; 016y_f; 01fwzk; 034hzj; *> query: (?x1452, 02r22gf) <- nominated_for(?x6909, ?x1452), ?x6909 = 02qyntr *> conf = 0.38 ranks of expected_values: 9 EVAL 0jqn5 nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 91.000 91.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6199-01qgr3 PRED entity: 01qgr3 PRED relation: contains! PRED expected values: 09c7w0 => 176 concepts (96 used for prediction) PRED predicted values (max 10 best out of 289): 09c7w0 (0.83 #14310, 0.81 #5367, 0.78 #11626), 05jbn (0.44 #1188, 0.14 #56351, 0.03 #14601), 02_286 (0.24 #40287, 0.13 #61759, 0.06 #58183), 01n7q (0.22 #61794, 0.17 #21538, 0.14 #55533), 059rby (0.19 #40264, 0.15 #61736, 0.11 #42053), 02jx1 (0.16 #19759, 0.15 #27808, 0.15 #34069), 05kkh (0.15 #3585, 0.12 #1797, 0.09 #9844), 07ssc (0.13 #19704, 0.13 #16128, 0.10 #22386), 030qb3t (0.13 #40345, 0.07 #61817, 0.04 #21561), 03v1s (0.12 #5390, 0.11 #26, 0.06 #11649) >> Best rule #14310 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 01t8sr; 02jyr8; 01fsv9; >> query: (?x7338, 09c7w0) <- school(?x5822, ?x7338), position(?x5822, ?x180), contains(?x3778, ?x7338), team(?x935, ?x5822) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qgr3 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 96.000 0.826 http://example.org/location/location/contains #6198-0829rj PRED entity: 0829rj PRED relation: place_of_birth PRED expected values: 02_286 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 58): 02_286 (0.29 #19, 0.14 #723, 0.09 #8468), 0c1d0 (0.14 #1002, 0.14 #298), 0s5cg (0.14 #885, 0.14 #181), 01sn3 (0.14 #853, 0.14 #149), 030qb3t (0.06 #4983, 0.05 #5687, 0.05 #3575), 01_d4 (0.04 #1475, 0.04 #9924, 0.04 #10628), 0cr3d (0.04 #7135, 0.04 #9952, 0.04 #4319), 0d6lp (0.02 #1523, 0.02 #2227, 0.02 #9267), 03l2n (0.02 #1578, 0.01 #7914, 0.01 #8618), 01531 (0.02 #2218, 0.02 #17004, 0.02 #12075) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01xcqc; 01l9v7n; 0l15n; >> query: (?x10925, 02_286) <- award_winner(?x1307, ?x10925), nominated_for(?x10925, ?x7370), ?x7370 = 0cf08 >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0829rj place_of_birth 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.286 http://example.org/people/person/place_of_birth #6197-027b9ly PRED entity: 027b9ly PRED relation: award! PRED expected values: 09r94m => 44 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1205): 0404j37 (0.60 #2680, 0.50 #1671, 0.40 #3689), 07s846j (0.53 #3426, 0.40 #2417, 0.33 #399), 042y1c (0.50 #1241, 0.40 #2250, 0.27 #3259), 0jqj5 (0.50 #1526, 0.40 #2535, 0.27 #3544), 0571m (0.50 #1324, 0.40 #2333, 0.27 #3342), 09r94m (0.50 #1549, 0.40 #2558, 0.27 #3567), 0ptx_ (0.50 #1628, 0.40 #2637, 0.13 #3646), 03hmt9b (0.40 #2410, 0.33 #3419, 0.25 #1401), 04b2qn (0.40 #2801, 0.27 #3810, 0.25 #1792), 0h03fhx (0.40 #2475, 0.27 #3484, 0.25 #1466) >> Best rule #2680 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 099c8n; >> query: (?x5516, 0404j37) <- award(?x9452, ?x5516), award(?x2490, ?x5516), award(?x1496, ?x5516), award(?x1230, ?x5516), ?x1496 = 011yqc, ?x9452 = 0c0zq, film(?x1678, ?x1230), film_crew_role(?x2490, ?x137) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1549 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 02w_6xj; 02qyntr; *> query: (?x5516, 09r94m) <- award(?x9452, ?x5516), award(?x1496, ?x5516), award(?x1230, ?x5516), ?x1496 = 011yqc, ?x9452 = 0c0zq, award_winner(?x5516, ?x826), nominated_for(?x2849, ?x1230) *> conf = 0.50 ranks of expected_values: 6 EVAL 027b9ly award! 09r94m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 44.000 13.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #6196-06zgc PRED entity: 06zgc PRED relation: country PRED expected values: 09c7w0 01mjq => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 341): 09c7w0 (0.96 #6461, 0.96 #6067, 0.93 #5289), 07ssc (0.91 #5686, 0.89 #1173, 0.85 #5110), 07t21 (0.89 #1173, 0.88 #4547, 0.83 #3370), 06mkj (0.89 #1173, 0.88 #4176, 0.85 #3580), 0jgd (0.89 #1173, 0.87 #3914, 0.84 #971), 015fr (0.89 #1173, 0.84 #971, 0.82 #1763), 035qy (0.89 #1173, 0.84 #971, 0.82 #1763), 0154j (0.89 #1173, 0.84 #971, 0.82 #1763), 0k6nt (0.89 #1173, 0.84 #971, 0.80 #1177), 06f32 (0.89 #1173, 0.84 #971, 0.80 #1177) >> Best rule #6461 for best value: >> intensional similarity = 59 >> extensional distance = 53 >> proper extension: 06br8; >> query: (?x5177, 09c7w0) <- country(?x5177, ?x1229), film_release_region(?x11809, ?x1229), film_release_region(?x11209, ?x1229), film_release_region(?x10080, ?x1229), film_release_region(?x7692, ?x1229), film_release_region(?x6394, ?x1229), film_release_region(?x5826, ?x1229), film_release_region(?x5791, ?x1229), film_release_region(?x5588, ?x1229), film_release_region(?x5347, ?x1229), film_release_region(?x4828, ?x1229), film_release_region(?x3425, ?x1229), film_release_region(?x2394, ?x1229), film_release_region(?x1743, ?x1229), film_release_region(?x1470, ?x1229), film_release_region(?x1463, ?x1229), film_release_region(?x1392, ?x1229), film_release_region(?x1386, ?x1229), film_release_region(?x785, ?x1229), film_release_region(?x664, ?x1229), film_release_region(?x504, ?x1229), film_release_region(?x249, ?x1229), ?x664 = 0401sg, olympics(?x5177, ?x418), olympics(?x1229, ?x1931), ?x1470 = 03twd6, member_states(?x2106, ?x1229), ?x10080 = 065ym0c, ?x785 = 03hjv97, combatants(?x8687, ?x1229), combatants(?x613, ?x1229), ?x7692 = 0bt4g, time_zones(?x1229, ?x2864), ?x5347 = 02ylg6, first_level_division_of(?x7655, ?x1229), ?x1931 = 0kbws, ?x504 = 0g5qs2k, ?x5826 = 0gl02yg, region(?x4664, ?x1229), ?x249 = 0c3ybss, ?x1743 = 0c8tkt, film(?x241, ?x6394), ?x613 = 0bq0p9, ?x1386 = 0dtfn, olympics(?x1229, ?x8189), ?x4828 = 02fttd, ?x1463 = 0gtvrv3, film_release_region(?x5791, ?x4737), ?x1392 = 017gm7, ?x4737 = 07twz, countries_spoken_in(?x7658, ?x1229), second_level_divisions(?x1229, ?x3408), ?x11209 = 04fjzv, ?x3425 = 0qm9n, ?x5588 = 0gtt5fb, ?x2394 = 0661ql3, ?x11809 = 0b85mm, ?x8189 = 015l4k, combatants(?x326, ?x8687) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1, 25 EVAL 06zgc country 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 35.000 35.000 0.964 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06zgc country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.964 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #6195-03fnmd PRED entity: 03fnmd PRED relation: position PRED expected values: 02sdk9v => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 5): 02sdk9v (0.90 #280, 0.88 #268, 0.88 #274), 02_j1w (0.89 #457, 0.86 #508, 0.85 #507), 03f0fp (0.58 #228, 0.46 #120, 0.43 #553), 02md_2 (0.46 #120, 0.43 #553, 0.31 #569), 02qvgy (0.46 #120, 0.43 #553) >> Best rule #280 for best value: >> intensional similarity = 11 >> extensional distance = 75 >> proper extension: 03x746; 025txtg; 01nd2c; 0hvjr; 01vqc7; 03j7cf; 03fnqj; 08vq2y; >> query: (?x5552, 02sdk9v) <- current_club(?x4406, ?x5552), team(?x63, ?x5552), current_club(?x4406, ?x13041), current_club(?x4406, ?x8585), sport(?x4406, ?x471), position(?x5552, ?x60), team(?x927, ?x8585), team(?x7484, ?x13041), ?x60 = 02nzb8, current_club(?x3587, ?x8585), ?x3587 = 02s2lg >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03fnmd position 02sdk9v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.896 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #6194-015grj PRED entity: 015grj PRED relation: nominated_for PRED expected values: 0330r => 104 concepts (44 used for prediction) PRED predicted values (max 10 best out of 431): 03rtz1 (0.30 #59924, 0.28 #25906, 0.28 #29147), 0f4k49 (0.30 #59924, 0.28 #25906, 0.28 #29147), 01s3vk (0.30 #59924, 0.28 #25906, 0.28 #29147), 0f61tk (0.30 #59924, 0.28 #25906, 0.28 #29147), 026wlxw (0.30 #59924, 0.28 #25906, 0.28 #29147), 091rc5 (0.30 #59924, 0.28 #25906, 0.28 #29147), 02qr3k8 (0.28 #25906, 0.28 #29147, 0.28 #27526), 0ndsl1x (0.28 #25906, 0.28 #29147, 0.28 #27526), 072kp (0.17 #86, 0.01 #9805, 0.01 #11423), 08jgk1 (0.16 #71263, 0.15 #56685, 0.03 #5089) >> Best rule #59924 for best value: >> intensional similarity = 3 >> extensional distance = 1147 >> proper extension: 029_3; 03sww; 01p0vf; >> query: (?x968, ?x1120) <- film(?x968, ?x1120), award_nominee(?x237, ?x968), nominated_for(?x154, ?x1120) >> conf = 0.30 => this is the best rule for 6 predicted values *> Best rule #56477 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1128 *> proper extension: 03fykz; 055sjw; *> query: (?x968, 0330r) <- award_winner(?x968, ?x1722), award_winner(?x9701, ?x1722), award_winner(?x3624, ?x1722) *> conf = 0.02 ranks of expected_values: 176 EVAL 015grj nominated_for 0330r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 104.000 44.000 0.302 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #6193-03c7ln PRED entity: 03c7ln PRED relation: profession PRED expected values: 039v1 04f2zj => 110 concepts (77 used for prediction) PRED predicted values (max 10 best out of 84): 01c72t (0.96 #1631, 0.45 #3092, 0.40 #1191), 02hrh1q (0.65 #5141, 0.64 #4404, 0.63 #4551), 0nbcg (0.60 #322, 0.60 #3538, 0.59 #3685), 039v1 (0.46 #2082, 0.44 #766, 0.43 #912), 025352 (0.32 #1666, 0.13 #2688, 0.11 #1518), 0fnpj (0.30 #2251, 0.27 #1081, 0.26 #789), 0n1h (0.30 #302, 0.26 #2641, 0.26 #1619), 0dxtg (0.24 #3962, 0.24 #5287, 0.23 #6313), 01d_h8 (0.22 #5279, 0.21 #6305, 0.20 #5572), 04f2zj (0.20 #1117, 0.19 #825, 0.18 #971) >> Best rule #1631 for best value: >> intensional similarity = 7 >> extensional distance = 45 >> proper extension: 02cj_f; >> query: (?x211, 01c72t) <- profession(?x211, ?x5654), profession(?x211, ?x220), ?x220 = 016z4k, profession(?x10574, ?x5654), profession(?x8415, ?x5654), ?x10574 = 02g40r, crewmember(?x3304, ?x8415) >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #2082 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 88 *> proper extension: 04dqdk; 01j4ls; 01ww2fs; 0m_v0; 0x3b7; 028hc2; 01nkxvx; 01hgwkr; 02s6sh; *> query: (?x211, 039v1) <- profession(?x211, ?x131), role(?x211, ?x1574), role(?x211, ?x432), artists(?x671, ?x211), ?x432 = 042v_gx, performance_role(?x1260, ?x1574) *> conf = 0.46 ranks of expected_values: 4, 10 EVAL 03c7ln profession 04f2zj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 110.000 77.000 0.957 http://example.org/people/person/profession EVAL 03c7ln profession 039v1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 77.000 0.957 http://example.org/people/person/profession #6192-0bszz PRED entity: 0bszz PRED relation: colors PRED expected values: 01g5v => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 19): 019sc (0.57 #513, 0.50 #369, 0.49 #936), 01g5v (0.53 #635, 0.40 #128, 0.39 #1194), 01l849 (0.39 #598, 0.39 #580, 0.33 #201), 038hg (0.30 #950, 0.28 #872, 0.25 #83), 088fh (0.30 #950, 0.28 #872, 0.20 #780), 036k5h (0.30 #950, 0.16 #891, 0.12 #1192), 03vtbc (0.28 #872, 0.25 #79, 0.20 #780), 02rnmb (0.28 #872, 0.23 #411, 0.20 #780), 0680m7 (0.28 #872, 0.20 #780, 0.20 #779), 0jc_p (0.25 #93, 0.25 #21, 0.20 #780) >> Best rule #513 for best value: >> intensional similarity = 19 >> extensional distance = 21 >> proper extension: 04l57x; >> query: (?x14258, 019sc) <- team(?x2918, ?x14258), colors(?x14258, ?x663), team(?x2918, ?x13326), team(?x2918, ?x7197), ?x13326 = 0hm2b, ?x7197 = 04l5f2, colors(?x13785, ?x663), colors(?x13542, ?x663), colors(?x9172, ?x663), colors(?x7725, ?x663), colors(?x2011, ?x663), colors(?x2327, ?x663), ?x2011 = 04913k, position(?x13785, ?x4570), team(?x8206, ?x7725), major_field_of_study(?x2327, ?x742), position(?x13542, ?x60), team(?x935, ?x9172), ?x935 = 06b1q >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #635 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 30 *> proper extension: 024nj1; *> query: (?x14258, 01g5v) <- colors(?x14258, ?x1101), teams(?x6842, ?x14258), contains(?x6842, ?x481), adjoins(?x335, ?x6842), colors(?x12042, ?x1101), colors(?x7725, ?x1101), colors(?x7389, ?x1101), colors(?x6526, ?x1101), colors(?x1010, ?x1101), ?x7389 = 01xn6mc, ?x12042 = 05xvj, colors(?x11278, ?x1101), colors(?x4220, ?x1101), ?x11278 = 037q2p, ?x6526 = 03c0t9, draft(?x1010, ?x1161), ?x4220 = 01v3ht, ?x7725 = 07l8x *> conf = 0.53 ranks of expected_values: 2 EVAL 0bszz colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 75.000 0.565 http://example.org/sports/sports_team/colors #6191-0ptk_ PRED entity: 0ptk_ PRED relation: currency! PRED expected values: 0g5q34q => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 1333): 02r858_ (0.78 #5282, 0.75 #5281, 0.33 #2382), 06kl78 (0.78 #5282, 0.75 #5281, 0.33 #1929), 0267wwv (0.78 #5282, 0.75 #5281), 0yxm1 (0.78 #5282, 0.75 #5281), 03f7xg (0.78 #5282, 0.75 #5281), 02bqxb (0.78 #5282, 0.33 #2623, 0.25 #6585), 05qm9f (0.78 #5282, 0.33 #2191, 0.25 #6153), 05rfst (0.78 #5282, 0.33 #2051, 0.25 #6013), 0gtvrv3 (0.78 #5282, 0.33 #1496, 0.25 #5458), 0170_p (0.78 #5282, 0.33 #1397, 0.25 #5359) >> Best rule #5282 for best value: >> intensional similarity = 78 >> extensional distance = 2 >> proper extension: 0kz1h; >> query: (?x2244, ?x675) <- currency(?x7066, ?x2244), currency(?x4199, ?x2244), currency(?x481, ?x2244), currency(?x13670, ?x2244), currency(?x9570, ?x2244), currency(?x8191, ?x2244), currency(?x2327, ?x2244), currency(?x2243, ?x2244), colors(?x4199, ?x3189), institution(?x1526, ?x4199), institution(?x620, ?x4199), currency(?x12356, ?x2244), student(?x4199, ?x2033), currency(?x1306, ?x2244), institution(?x2759, ?x7066), organization(?x346, ?x9570), major_field_of_study(?x8191, ?x1154), ?x1526 = 0bkj86, citytown(?x9570, ?x13523), major_field_of_study(?x7066, ?x742), major_field_of_study(?x4199, ?x5179), colors(?x8191, ?x332), major_field_of_study(?x2327, ?x6760), major_field_of_study(?x2327, ?x3995), contains(?x9311, ?x9570), student(?x2327, ?x4307), student(?x2327, ?x3827), school_type(?x2243, ?x3092), citytown(?x2243, ?x3877), ?x742 = 05qjt, student(?x6760, ?x665), disciplines_or_subjects(?x850, ?x6760), institution(?x620, ?x12127), institution(?x620, ?x11768), institution(?x620, ?x11244), institution(?x620, ?x9150), institution(?x620, ?x7707), institution(?x620, ?x7618), institution(?x620, ?x7071), institution(?x620, ?x4889), institution(?x620, ?x4410), institution(?x620, ?x1681), institution(?x620, ?x1201), ?x4889 = 02dq8f, nominated_for(?x2033, ?x253), ?x2759 = 071tyz, produced_by(?x8277, ?x3827), contains(?x1196, ?x13670), institution(?x1200, ?x13670), profession(?x3827, ?x319), film(?x2033, ?x1481), ?x11768 = 01hc1j, ?x9150 = 05ftw3, ?x12127 = 02tz9z, location(?x2033, ?x1658), award_nominee(?x2033, ?x262), category(?x4199, ?x134), film(?x4307, ?x675), ?x1681 = 07szy, ?x11244 = 02gnmp, major_field_of_study(?x620, ?x1527), award(?x3827, ?x372), adjoins(?x1275, ?x9311), ?x7071 = 02y9bj, ?x4410 = 017j69, profession(?x4307, ?x1041), location(?x10607, ?x9311), ?x7707 = 01jt2w, student(?x3995, ?x1188), institution(?x734, ?x2327), ?x7618 = 01bk1y, ?x3189 = 01g5v, ?x1201 = 01wdl3, major_field_of_study(?x3995, ?x90), award(?x2033, ?x451), major_field_of_study(?x10332, ?x6760), organization(?x5510, ?x481), institution(?x1305, ?x481) >> conf = 0.78 => this is the best rule for 13 predicted values No rule for expected values ranks of expected_values: EVAL 0ptk_ currency! 0g5q34q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.780 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #6190-0fby2t PRED entity: 0fby2t PRED relation: award PRED expected values: 05zvj3m 0gqy2 => 98 concepts (84 used for prediction) PRED predicted values (max 10 best out of 277): 09sb52 (0.43 #10065, 0.34 #13675, 0.34 #8461), 05pcn59 (0.25 #81, 0.20 #4492, 0.20 #4091), 05zr6wv (0.25 #16, 0.16 #4026, 0.16 #4427), 057xs89 (0.25 #159, 0.10 #4169, 0.09 #2565), 0gqy2 (0.19 #10188, 0.13 #21656, 0.10 #10589), 05p09zm (0.18 #1327, 0.15 #4535, 0.15 #4134), 05b4l5x (0.17 #1208, 0.10 #4015, 0.09 #3614), 0gq9h (0.16 #10102, 0.13 #6493, 0.13 #21656), 0ck27z (0.16 #8513, 0.15 #13727, 0.14 #15732), 03c7tr1 (0.14 #1261, 0.12 #2063, 0.11 #4469) >> Best rule #10065 for best value: >> intensional similarity = 3 >> extensional distance = 924 >> proper extension: 086k8; 017s11; 02qggqc; 0g1rw; 05qd_; 016tw3; 0p_th; 017jv5; 05218gr; 0hwd8; ... >> query: (?x4325, 09sb52) <- award(?x4325, ?x112), nominated_for(?x112, ?x167), ?x167 = 083shs >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #10188 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 924 *> proper extension: 086k8; 017s11; 02qggqc; 0g1rw; 05qd_; 016tw3; 0p_th; 017jv5; 05218gr; 0hwd8; ... *> query: (?x4325, 0gqy2) <- award(?x4325, ?x112), nominated_for(?x112, ?x167), ?x167 = 083shs *> conf = 0.19 ranks of expected_values: 5, 12 EVAL 0fby2t award 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 84.000 0.434 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0fby2t award 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 98.000 84.000 0.434 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6189-06t2t PRED entity: 06t2t PRED relation: month PRED expected values: 04w_7 040fb => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2): 04w_7 (0.94 #59, 0.93 #51, 0.89 #81), 040fb (0.88 #18, 0.84 #52, 0.83 #60) >> Best rule #59 for best value: >> intensional similarity = 2 >> extensional distance = 46 >> proper extension: 06mxs; >> query: (?x2316, 04w_7) <- month(?x2316, ?x2255), ?x2255 = 040fv >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 06t2t month 040fb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.938 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 06t2t month 04w_7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.938 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #6188-0524b41 PRED entity: 0524b41 PRED relation: genre PRED expected values: 03k9fj => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 81): 06n90 (0.61 #754, 0.60 #177, 0.56 #95), 05p553 (0.51 #993, 0.48 #1075, 0.46 #1732), 0hcr (0.45 #346, 0.44 #1170, 0.39 #758), 03k9fj (0.44 #93, 0.34 #752, 0.33 #11), 01z4y (0.36 #923, 0.34 #1087, 0.34 #510), 03npn (0.35 #171, 0.33 #89, 0.21 #336), 01t_vv (0.33 #114, 0.21 #939, 0.20 #196), 01jfsb (0.32 #424, 0.23 #753, 0.21 #670), 0lsxr (0.29 #667, 0.18 #1162, 0.13 #1901), 02kdv5l (0.26 #414, 0.16 #331, 0.16 #743) >> Best rule #754 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 0283ph; 06qxh; 01j95; >> query: (?x7119, 06n90) <- genre(?x7119, ?x1510), genre(?x4392, ?x1510), disciplines_or_subjects(?x1288, ?x1510), ?x4392 = 06gb1w >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #93 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 020qr4; *> query: (?x7119, 03k9fj) <- genre(?x7119, ?x1844), genre(?x7119, ?x1510), genre(?x7119, ?x53), ?x1510 = 01hmnh, ?x53 = 07s9rl0, ?x1844 = 01htzx *> conf = 0.44 ranks of expected_values: 4 EVAL 0524b41 genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 81.000 0.610 http://example.org/tv/tv_program/genre #6187-0gry51 PRED entity: 0gry51 PRED relation: place_of_birth PRED expected values: 095w_ => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 81): 02_286 (0.07 #19, 0.07 #5651, 0.07 #4243), 01_d4 (0.04 #66, 0.04 #11330, 0.04 #2178), 030qb3t (0.03 #16246, 0.03 #19063, 0.03 #20472), 013kcv (0.03 #23, 0.03 #727, 0.03 #1431), 0cr3d (0.03 #94, 0.03 #10654, 0.03 #12062), 0dclg (0.03 #782, 0.03 #2894, 0.03 #1486), 02dtg (0.02 #2122, 0.01 #10), 04vmp (0.02 #3788, 0.02 #4492, 0.02 #6604), 0cc56 (0.02 #2849, 0.01 #33, 0.01 #10593), 04f_d (0.02 #2889) >> Best rule #19 for best value: >> intensional similarity = 5 >> extensional distance = 65 >> proper extension: 0gm34; 014g91; 02wh0; 0436zq; >> query: (?x13488, 02_286) <- gender(?x13488, ?x231), ?x231 = 05zppz, people(?x5801, ?x13488), people(?x5801, ?x6928), ?x6928 = 018ty9 >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #752 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 66 *> proper extension: 0byfz; 0chsq; 02pp_q_; 019z7q; 0f0p0; 01g4zr; 016hvl; 01xcqc; 0177s6; 01t07j; ... *> query: (?x13488, 095w_) <- gender(?x13488, ?x231), profession(?x13488, ?x1032), profession(?x13488, ?x524), ?x524 = 02jknp, ?x231 = 05zppz, people(?x5801, ?x13488), ?x1032 = 02hrh1q *> conf = 0.01 ranks of expected_values: 41 EVAL 0gry51 place_of_birth 095w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 68.000 68.000 0.075 http://example.org/people/person/place_of_birth #6186-01sbf2 PRED entity: 01sbf2 PRED relation: artists! PRED expected values: 0827d => 117 concepts (55 used for prediction) PRED predicted values (max 10 best out of 216): 06by7 (0.59 #2493, 0.51 #3111, 0.50 #1256), 016clz (0.47 #6499, 0.26 #3096, 0.25 #2478), 017_qw (0.43 #3772, 0.21 #370, 0.20 #679), 0ggx5q (0.36 #3788, 0.18 #4716, 0.18 #2551), 06j6l (0.31 #4685, 0.30 #1902, 0.29 #1283), 025sc50 (0.29 #1904, 0.27 #2831, 0.26 #4687), 0glt670 (0.27 #2822, 0.23 #4369, 0.22 #1895), 05lls (0.26 #939, 0.10 #3723, 0.08 #321), 0gywn (0.24 #1912, 0.23 #4695, 0.22 #4386), 01lyv (0.24 #3434, 0.22 #4052, 0.21 #3124) >> Best rule #2493 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 06y9c2; 01p45_v; 04bgy; 0gs6vr; 0130sy; 01vs8ng; >> query: (?x1613, 06by7) <- artists(?x3061, ?x1613), profession(?x1613, ?x131), ?x3061 = 05bt6j >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #312 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 0146pg; 025xt8y; 015rmq; 011zf2; 0ftps; 0fpjd_g; 01kvqc; 06k02; 01hw6wq; 02b25y; ... *> query: (?x1613, 0827d) <- award(?x1613, ?x9372), artists(?x597, ?x1613), ?x597 = 0ggq0m, award_winner(?x9372, ?x4563) *> conf = 0.10 ranks of expected_values: 38 EVAL 01sbf2 artists! 0827d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 117.000 55.000 0.588 http://example.org/music/genre/artists #6185-01ljpm PRED entity: 01ljpm PRED relation: major_field_of_study PRED expected values: 02j62 => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 113): 02j62 (0.44 #29, 0.43 #1118, 0.41 #271), 037mh8 (0.44 #66, 0.36 #308, 0.29 #1760), 04rjg (0.43 #1957, 0.43 #1110, 0.41 #1715), 02lp1 (0.43 #617, 0.43 #133, 0.41 #980), 01mkq (0.41 #1105, 0.41 #2799, 0.40 #1952), 05qjt (0.35 #1097, 0.33 #8, 0.33 #2791), 01lj9 (0.33 #159, 0.30 #643, 0.30 #1127), 04x_3 (0.32 #1115, 0.31 #1720, 0.28 #2809), 0g26h (0.30 #645, 0.29 #161, 0.28 #1008), 02_7t (0.29 #184, 0.28 #1031, 0.27 #668) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 031n8c; >> query: (?x6501, 02j62) <- company(?x346, ?x6501), currency(?x6501, ?x170), school_type(?x6501, ?x3205), ?x3205 = 01rs41 >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ljpm major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.444 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #6184-05b1610 PRED entity: 05b1610 PRED relation: award! PRED expected values: 08cn_n => 32 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2567): 0b455l (0.79 #3339, 0.78 #3338, 0.65 #16692), 04vlh5 (0.79 #3339, 0.78 #3338, 0.65 #16692), 03kpvp (0.78 #3338, 0.65 #16692, 0.65 #13353), 05ldnp (0.67 #7559, 0.12 #17577, 0.11 #20918), 06m6z6 (0.67 #7771, 0.09 #21130, 0.08 #24472), 0h5f5n (0.67 #6736, 0.08 #23437, 0.08 #16754), 02mt4k (0.67 #8080, 0.07 #21439, 0.06 #24781), 0184jw (0.56 #8914, 0.14 #53437, 0.10 #18932), 02kxbx3 (0.56 #7650, 0.13 #17668, 0.12 #21009), 04xn2m (0.56 #8991, 0.12 #5654, 0.06 #22350) >> Best rule #3339 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02kgb7; >> query: (?x688, ?x1387) <- award_winner(?x688, ?x4638), award_winner(?x688, ?x1387), award(?x1387, ?x350), ?x4638 = 02t_99 >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #5664 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 05f4m9q; 03c7tr1; 07bdd_; 05p09zm; 05q5t0b; 05q8pss; *> query: (?x688, 08cn_n) <- award(?x5189, ?x688), nominated_for(?x688, ?x103), award(?x702, ?x688), ?x5189 = 0n83s *> conf = 0.12 ranks of expected_values: 322 EVAL 05b1610 award! 08cn_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 32.000 20.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6183-032sl_ PRED entity: 032sl_ PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.85 #78, 0.85 #67, 0.84 #93), 0dq6p (0.30 #6, 0.21 #28, 0.19 #61), 07z4p (0.05 #38, 0.04 #5, 0.03 #27), 02nxhr (0.04 #18, 0.04 #2, 0.04 #68), 07c52 (0.04 #36, 0.03 #105, 0.03 #224) >> Best rule #78 for best value: >> intensional similarity = 4 >> extensional distance = 348 >> proper extension: 047svrl; 0372j5; >> query: (?x9429, 029j_) <- currency(?x9429, ?x170), film_crew_role(?x9429, ?x137), featured_film_locations(?x9429, ?x108), nominated_for(?x521, ?x9429) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 032sl_ film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6182-019f9z PRED entity: 019f9z PRED relation: artists! PRED expected values: 02lnbg 0nk3g => 118 concepts (63 used for prediction) PRED predicted values (max 10 best out of 281): 0glt670 (0.75 #638, 0.40 #1539, 0.36 #2139), 016clz (0.47 #12613, 0.27 #8411, 0.25 #605), 05bt6j (0.43 #2742, 0.32 #16255, 0.31 #5745), 0155w (0.38 #399, 0.27 #999, 0.25 #1900), 02lnbg (0.33 #1555, 0.29 #3355, 0.28 #6059), 08vlns (0.29 #198, 0.18 #1098, 0.05 #1699), 01fh36 (0.26 #2480, 0.21 #4281, 0.18 #1880), 0xhtw (0.23 #8421, 0.22 #5719, 0.18 #915), 01lyv (0.21 #1232, 0.20 #5735, 0.20 #4534), 0dl5d (0.18 #917, 0.14 #12625, 0.14 #17) >> Best rule #638 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0j1yf; 04mn81; 04xrx; 01q32bd; 01vw20h; 0g824; >> query: (?x6651, 0glt670) <- award(?x6651, ?x537), participant(?x2562, ?x6651), ?x2562 = 01trhmt, award_nominee(?x568, ?x6651) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1555 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 0lk90; 0161sp; 04gycf; *> query: (?x6651, 02lnbg) <- award(?x6651, ?x537), participant(?x2562, ?x6651), artists(?x378, ?x6651), actor(?x5529, ?x2562) *> conf = 0.33 ranks of expected_values: 5, 65 EVAL 019f9z artists! 0nk3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 118.000 63.000 0.750 http://example.org/music/genre/artists EVAL 019f9z artists! 02lnbg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 118.000 63.000 0.750 http://example.org/music/genre/artists #6181-02qsjt PRED entity: 02qsjt PRED relation: artists! PRED expected values: 0175yg => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 216): 06by7 (0.59 #649, 0.51 #2840, 0.50 #23), 064t9 (0.47 #8791, 0.46 #13176, 0.46 #8164), 06j6l (0.35 #51, 0.32 #8828, 0.31 #677), 016clz (0.30 #2822, 0.26 #8468, 0.24 #9721), 0xhtw (0.30 #18, 0.24 #644, 0.20 #8481), 0gywn (0.29 #1000, 0.28 #1313, 0.27 #8838), 03_d0 (0.29 #951, 0.21 #4396, 0.21 #4082), 05bt6j (0.28 #1298, 0.23 #16971, 0.22 #18223), 0glt670 (0.28 #8820, 0.26 #13205, 0.24 #6938), 01lyv (0.27 #2853, 0.25 #6304, 0.22 #5363) >> Best rule #649 for best value: >> intensional similarity = 2 >> extensional distance = 27 >> proper extension: 07qnf; 0394y; 07m4c; 0knhk; 02hzz; >> query: (?x6939, 06by7) <- artist(?x3050, ?x6939), ?x3050 = 0229rs >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #527 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 0d06m5; *> query: (?x6939, 0175yg) <- location(?x6939, ?x3908), award_winner(?x6487, ?x6939), ?x6487 = 01mh_q *> conf = 0.08 ranks of expected_values: 45 EVAL 02qsjt artists! 0175yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 139.000 139.000 0.586 http://example.org/music/genre/artists #6180-092ys_y PRED entity: 092ys_y PRED relation: profession PRED expected values: 0ch6mp2 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 47): 02hrh1q (0.69 #11490, 0.69 #11639, 0.68 #10000), 01d_h8 (0.34 #1646, 0.33 #1944, 0.32 #5520), 0dxtg (0.29 #5528, 0.29 #6422, 0.27 #9999), 02jknp (0.25 #7154, 0.25 #8, 0.23 #5522), 0np9r (0.25 #7154, 0.25 #22, 0.09 #12690), 01c72t (0.25 #25, 0.13 #1069, 0.10 #1516), 03gjzk (0.24 #2848, 0.24 #2550, 0.23 #1209), 09jwl (0.19 #1511, 0.19 #4491, 0.18 #1809), 0nbcg (0.13 #1524, 0.13 #1822, 0.13 #4504), 0dz3r (0.13 #1493, 0.13 #1791, 0.12 #4473) >> Best rule #11490 for best value: >> intensional similarity = 3 >> extensional distance = 2543 >> proper extension: 0d4jl; 06kb_; 05x8n; 01wd02c; 06bng; 0yxl; 07zl1; 05cv8; 01k56k; >> query: (?x3782, 02hrh1q) <- award(?x3782, ?x500), profession(?x3782, ?x5654), award(?x197, ?x500) >> conf = 0.69 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 092ys_y profession 0ch6mp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 87.000 0.689 http://example.org/people/person/profession #6179-01gtdd PRED entity: 01gtdd PRED relation: district_represented PRED expected values: 05kkh 04rrx 050ks => 34 concepts (33 used for prediction) PRED predicted values (max 10 best out of 226): 04rrx (0.91 #178, 0.89 #584, 0.88 #402), 05kkh (0.91 #178, 0.89 #584, 0.88 #402), 050ks (0.91 #178, 0.89 #584, 0.88 #402), 02xry (0.91 #178, 0.89 #584, 0.88 #402), 03s0w (0.91 #178, 0.89 #584, 0.88 #402), 0824r (0.91 #178, 0.89 #584, 0.88 #402), 07b_l (0.91 #178, 0.89 #584, 0.88 #402), 01n7q (0.91 #178, 0.89 #584, 0.88 #402), 0g0syc (0.91 #178, 0.89 #584, 0.88 #402), 01_d4 (0.71 #1045, 0.61 #1275, 0.61 #1092) >> Best rule #178 for best value: >> intensional similarity = 47 >> extensional distance = 1 >> proper extension: 077g7n; >> query: (?x10291, ?x1906) <- district_represented(?x10291, ?x7518), district_represented(?x10291, ?x7405), district_represented(?x10291, ?x4776), district_represented(?x10291, ?x4758), district_represented(?x10291, ?x4622), district_represented(?x10291, ?x3908), district_represented(?x10291, ?x3818), district_represented(?x10291, ?x2713), district_represented(?x10291, ?x2020), district_represented(?x10291, ?x1767), district_represented(?x10291, ?x1755), district_represented(?x10291, ?x1025), district_represented(?x10291, ?x760), district_represented(?x10291, ?x728), district_represented(?x10291, ?x448), legislative_sessions(?x10291, ?x7944), legislative_sessions(?x10291, ?x5006), legislative_sessions(?x7973, ?x10291), ?x1025 = 04ych, ?x3908 = 04ly1, legislative_sessions(?x7914, ?x7973), district_represented(?x7973, ?x7058), district_represented(?x7973, ?x177), ?x728 = 059f4, ?x2020 = 05k7sb, legislative_sessions(?x2860, ?x7973), ?x448 = 03v1s, ?x7405 = 07_f2, ?x177 = 05kkh, ?x4758 = 0vbk, legislative_sessions(?x4787, ?x7914), legislative_sessions(?x9046, ?x7914), ?x3818 = 03v0t, ?x7058 = 050ks, ?x4776 = 06yxd, district_represented(?x7944, ?x1906), district_represented(?x7944, ?x961), ?x961 = 03s0w, ?x1767 = 04rrd, ?x2713 = 06btq, legislative_sessions(?x9765, ?x5006), ?x4622 = 04tgp, ?x1755 = 01x73, ?x7518 = 026mj, politician(?x8714, ?x9765), profession(?x9765, ?x3342), ?x760 = 05fkf >> conf = 0.91 => this is the best rule for 9 predicted values ranks of expected_values: 1, 2, 3 EVAL 01gtdd district_represented 050ks CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 33.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtdd district_represented 04rrx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 33.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtdd district_represented 05kkh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 33.000 0.909 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #6178-0ks67 PRED entity: 0ks67 PRED relation: colors PRED expected values: 04d18d => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 21): 083jv (0.38 #128, 0.35 #423, 0.34 #275), 01l849 (0.25 #316, 0.23 #106, 0.23 #422), 01g5v (0.24 #509, 0.23 #109, 0.22 #824), 019sc (0.18 #513, 0.17 #113, 0.17 #429), 036k5h (0.17 #27, 0.14 #90, 0.10 #174), 03wkwg (0.14 #100, 0.12 #184, 0.10 #205), 06fvc (0.14 #508, 0.14 #823, 0.14 #150), 0jc_p (0.11 #400, 0.10 #320, 0.10 #383), 038hg (0.11 #400, 0.09 #791, 0.09 #391), 09ggk (0.11 #400, 0.05 #858, 0.05 #438) >> Best rule #128 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 017ztv; >> query: (?x5807, 083jv) <- student(?x5807, ?x5790), institution(?x734, ?x5807), ?x734 = 04zx3q1, influenced_by(?x5790, ?x3711) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 045c7b; 03_c8p; 0cv_2; 02z_b; *> query: (?x5807, 04d18d) <- organization(?x346, ?x5807), organization(?x5807, ?x5487), company(?x346, ?x127), organization(?x47, ?x127) *> conf = 0.07 ranks of expected_values: 16 EVAL 0ks67 colors 04d18d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 77.000 77.000 0.380 http://example.org/education/educational_institution/colors #6177-07j8r PRED entity: 07j8r PRED relation: film! PRED expected values: 01w92 => 83 concepts (61 used for prediction) PRED predicted values (max 10 best out of 59): 016tw3 (0.25 #10, 0.18 #456, 0.15 #531), 086k8 (0.25 #1, 0.17 #372, 0.16 #1564), 07k2x (0.18 #264, 0.17 #338), 03xq0f (0.18 #153, 0.16 #599, 0.12 #4), 01ts_3 (0.15 #2763, 0.12 #1192, 0.12 #1042), 05qd_ (0.14 #752, 0.14 #900, 0.13 #2845), 025jfl (0.13 #79, 0.06 #376, 0.06 #600), 0fqy4p (0.13 #101, 0.04 #398, 0.02 #473), 017s11 (0.13 #672, 0.12 #1044, 0.12 #1269), 017jv5 (0.12 #14, 0.11 #385, 0.09 #535) >> Best rule #10 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0_816; >> query: (?x2550, 016tw3) <- nominated_for(?x3435, ?x2550), nominated_for(?x3209, ?x2550), nominated_for(?x1313, ?x2550), ?x3209 = 02w9sd7, ?x3435 = 03hl6lc, ?x1313 = 0gs9p >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07j8r film! 01w92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 61.000 0.250 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #6176-0l1589 PRED entity: 0l1589 PRED relation: role! PRED expected values: 0dwt5 => 66 concepts (52 used for prediction) PRED predicted values (max 10 best out of 107): 018vs (0.87 #4562, 0.86 #4672, 0.85 #1566), 01vj9c (0.86 #3411, 0.86 #2561, 0.86 #2451), 0dwt5 (0.86 #3475, 0.85 #2296, 0.79 #2438), 03gvt (0.85 #1251, 0.78 #1531, 0.75 #1216), 05842k (0.84 #1038, 0.84 #1784, 0.84 #1352), 07brj (0.82 #1897, 0.82 #1810, 0.70 #3500), 01679d (0.80 #1728, 0.78 #1296, 0.75 #1193), 07y_7 (0.78 #1358, 0.76 #3821, 0.76 #3607), 0dwr4 (0.78 #1394, 0.67 #1503, 0.67 #311), 0bmnm (0.78 #1429, 0.67 #311, 0.62 #1151) >> Best rule #4562 for best value: >> intensional similarity = 19 >> extensional distance = 36 >> proper extension: 028tv0; >> query: (?x2725, ?x3215) <- role(?x2725, ?x3215), role(?x2725, ?x1495), role(?x2908, ?x3215), role(?x2187, ?x3215), role(?x3215, ?x3161), role(?x9413, ?x3215), role(?x569, ?x3215), role(?x2725, ?x1647), ?x2908 = 0161sp, performance_role(?x764, ?x3161), performance_role(?x4186, ?x2725), role(?x3161, ?x1268), ?x2187 = 01vsnff, ?x9413 = 07m2y, role(?x3161, ?x1472), group(?x1495, ?x997), ?x1268 = 0bm02, ?x569 = 07c6l, role(?x130, ?x1495) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #3475 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 19 *> proper extension: 07brj; *> query: (?x2725, 0dwt5) <- role(?x2725, ?x3215), role(?x2725, ?x885), role(?x2725, ?x227), ?x3215 = 0bxl5, performance_role(?x315, ?x2725), role(?x2725, ?x75), ?x885 = 0dwtp, role(?x1467, ?x2725), instrumentalists(?x227, ?x10527), instrumentalists(?x227, ?x10144), instrumentalists(?x227, ?x5057), group(?x227, ?x10938), group(?x227, ?x7865), group(?x227, ?x2005), ?x10527 = 020jqv, ?x5057 = 01w3lzq, role(?x1247, ?x227), ?x7865 = 02k5sc, role(?x1292, ?x227), ?x2005 = 05k79, ?x10144 = 016wvy, ?x10938 = 09jvl, performance_role(?x227, ?x645) *> conf = 0.86 ranks of expected_values: 3 EVAL 0l1589 role! 0dwt5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 52.000 0.871 http://example.org/music/performance_role/track_performances./music/track_contribution/role #6175-01k60v PRED entity: 01k60v PRED relation: genre PRED expected values: 06lbpz => 57 concepts (55 used for prediction) PRED predicted values (max 10 best out of 84): 05p553 (0.47 #122, 0.41 #4732, 0.33 #5677), 02l7c8 (0.38 #369, 0.36 #133, 0.29 #2262), 04xvlr (0.36 #237, 0.22 #119, 0.20 #2248), 017fp (0.34 #250, 0.33 #14, 0.10 #709), 03mqtr (0.33 #29, 0.13 #265, 0.05 #2276), 02kdv5l (0.32 #474, 0.27 #1656, 0.27 #5203), 03k9fj (0.31 #483, 0.24 #5212, 0.23 #838), 0219x_ (0.25 #144, 0.11 #26, 0.10 #1562), 01t_vv (0.25 #172, 0.11 #54, 0.09 #2301), 060__y (0.22 #16, 0.17 #370, 0.16 #2263) >> Best rule #122 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 0d8w2n; >> query: (?x4448, 05p553) <- film(?x574, ?x4448), language(?x4448, ?x254), genre(?x4448, ?x714), ?x714 = 0hn10 >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #453 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 164 *> proper extension: 0c5qvw; *> query: (?x4448, 06lbpz) <- costume_design_by(?x4448, ?x3685), nominated_for(?x1774, ?x4448), nominated_for(?x1107, ?x4448), language(?x4448, ?x254) *> conf = 0.01 ranks of expected_values: 80 EVAL 01k60v genre 06lbpz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 57.000 55.000 0.475 http://example.org/film/film/genre #6174-03wjb7 PRED entity: 03wjb7 PRED relation: role PRED expected values: 026t6 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 127): 0342h (0.68 #1381, 0.49 #1593, 0.48 #1909), 02sgy (0.42 #1383, 0.31 #1595, 0.30 #1911), 05r5c (0.38 #4024, 0.38 #4131, 0.34 #1385), 01vdm0 (0.33 #138, 0.26 #4155, 0.26 #4048), 026t6 (0.33 #108, 0.20 #847, 0.20 #954), 05148p4 (0.33 #129, 0.14 #444, 0.13 #1082), 013y1f (0.30 #1058, 0.29 #951, 0.29 #1164), 03bx0bm (0.30 #1058, 0.29 #951, 0.29 #1164), 042v_gx (0.28 #1386, 0.20 #4132, 0.20 #4025), 018vs (0.26 #1391, 0.22 #1073, 0.20 #753) >> Best rule #1381 for best value: >> intensional similarity = 5 >> extensional distance = 103 >> proper extension: 02nfjp; >> query: (?x8403, 0342h) <- profession(?x8403, ?x2659), profession(?x8403, ?x1183), ?x1183 = 09jwl, ?x2659 = 039v1, role(?x8403, ?x745) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #108 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 06br6t; *> query: (?x8403, 026t6) <- artists(?x10721, ?x8403), artists(?x302, ?x8403), ?x10721 = 04z1v0, role(?x8403, ?x745), ?x302 = 016clz *> conf = 0.33 ranks of expected_values: 5 EVAL 03wjb7 role 026t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 100.000 100.000 0.676 http://example.org/music/artist/track_contributions./music/track_contribution/role #6173-09c7w0 PRED entity: 09c7w0 PRED relation: time_zones PRED expected values: 02lcqs => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 7): 02lcqs (0.58 #1209, 0.21 #890, 0.18 #1098), 02llzg (0.41 #377, 0.40 #49, 0.33 #137), 03bdv (0.32 #163, 0.25 #99, 0.22 #91), 03plfd (0.22 #382, 0.21 #118, 0.21 #110), 052vwh (0.14 #79, 0.07 #247, 0.07 #127), 0gsrz4 (0.09 #452, 0.09 #380, 0.09 #476), 05jphn (0.07 #136, 0.07 #128, 0.06 #144) >> Best rule #1209 for best value: >> intensional similarity = 2 >> extensional distance = 413 >> proper extension: 0n5j_; 03v1s; 0cb4j; 0jcgs; 05fkf; 0mwl2; 013kcv; 07tgn; 03s0w; 0mw89; ... >> query: (?x94, ?x1638) <- contains(?x94, ?x13556), time_zones(?x13556, ?x1638) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09c7w0 time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.580 http://example.org/location/location/time_zones #6172-05cc1 PRED entity: 05cc1 PRED relation: adjoins! PRED expected values: 0h3y => 91 concepts (86 used for prediction) PRED predicted values (max 10 best out of 387): 0h3y (0.83 #38164, 0.83 #64686, 0.83 #63124), 06tw8 (0.23 #65466, 0.22 #62345, 0.22 #64687), 05cc1 (0.23 #65466, 0.22 #62345, 0.22 #64687), 01nyl (0.23 #65466, 0.22 #62345, 0.22 #64687), 0fv4v (0.23 #65466, 0.22 #62345, 0.22 #64687), 01nln (0.23 #65466, 0.22 #62345, 0.22 #64687), 06srk (0.23 #65466, 0.22 #62345, 0.22 #64687), 07f5x (0.23 #65466, 0.22 #62345, 0.22 #64687), 04vjh (0.23 #65466, 0.22 #62345, 0.22 #64687), 02k54 (0.23 #65466, 0.22 #62345, 0.21 #66247) >> Best rule #38164 for best value: >> intensional similarity = 3 >> extensional distance = 182 >> proper extension: 0mhhw; 0f0sbl; 0hyyq; 0p_x; >> query: (?x6827, ?x8857) <- adjoins(?x6827, ?x8857), currency(?x8857, ?x170), adjoins(?x4120, ?x6827) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05cc1 adjoins! 0h3y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 86.000 0.834 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #6171-03cfkrw PRED entity: 03cfkrw PRED relation: nominated_for! PRED expected values: 02r0csl 019f4v => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 205): 0gr4k (0.66 #26, 0.36 #3953, 0.33 #257), 0l8z1 (0.62 #50, 0.47 #1667, 0.45 #512), 02qyntr (0.60 #404, 0.57 #635, 0.55 #2021), 0gq9h (0.57 #3984, 0.56 #57, 0.53 #288), 019f4v (0.53 #52, 0.51 #283, 0.49 #514), 0k611 (0.53 #67, 0.51 #298, 0.47 #529), 03hkv_r (0.53 #14, 0.36 #245, 0.35 #476), 0gs9p (0.51 #290, 0.50 #3986, 0.49 #521), 099c8n (0.47 #55, 0.41 #1672, 0.40 #286), 02r22gf (0.47 #27, 0.40 #1875, 0.38 #258) >> Best rule #26 for best value: >> intensional similarity = 6 >> extensional distance = 30 >> proper extension: 095zlp; 017gl1; 0pv3x; 09p0ct; 011yqc; 016z7s; 026p4q7; 0ggbhy7; 02n9bh; 07cyl; ... >> query: (?x4458, 0gr4k) <- titles(?x53, ?x4458), nominated_for(?x2379, ?x4458), nominated_for(?x1180, ?x4458), ?x2379 = 02qvyrt, ?x1180 = 02n9nmz, genre(?x4458, ?x604) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #52 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 30 *> proper extension: 095zlp; 017gl1; 0pv3x; 09p0ct; 011yqc; 016z7s; 026p4q7; 0ggbhy7; 02n9bh; 07cyl; ... *> query: (?x4458, 019f4v) <- titles(?x53, ?x4458), nominated_for(?x2379, ?x4458), nominated_for(?x1180, ?x4458), ?x2379 = 02qvyrt, ?x1180 = 02n9nmz, genre(?x4458, ?x604) *> conf = 0.53 ranks of expected_values: 5, 22 EVAL 03cfkrw nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 03cfkrw nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 90.000 90.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6170-02pq9yv PRED entity: 02pq9yv PRED relation: award PRED expected values: 0f_nbyh => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 268): 040njc (0.70 #18593, 0.70 #21424, 0.70 #30726), 0fbtbt (0.50 #233, 0.15 #25063, 0.15 #28703), 09sb52 (0.36 #12569, 0.32 #10144, 0.30 #14185), 019f4v (0.25 #67, 0.19 #7342, 0.19 #6533), 07bdd_ (0.25 #2491, 0.25 #470, 0.23 #874), 05p1dby (0.22 #2532, 0.19 #511, 0.18 #1319), 0f_nbyh (0.21 #2435, 0.17 #3244, 0.17 #414), 0gs9p (0.19 #6545, 0.19 #7354, 0.19 #7758), 0ck27z (0.19 #10195, 0.16 #9387, 0.15 #12620), 0cqhk0 (0.18 #9332, 0.16 #10140, 0.08 #13373) >> Best rule #18593 for best value: >> intensional similarity = 3 >> extensional distance = 1533 >> proper extension: 01sl1q; 044mz_; 0184jc; 02s2ft; 05vsxz; 05bnp0; 01vvydl; 012d40; 07fq1y; 02qgqt; ... >> query: (?x3528, ?x198) <- award_nominee(?x3528, ?x2444), award_nominee(?x2135, ?x3528), award_winner(?x198, ?x3528) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2435 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 148 *> proper extension: 01wg982; 02lymt; 0f13b; *> query: (?x3528, 0f_nbyh) <- produced_by(?x10778, ?x3528), produced_by(?x6900, ?x3528), film_format(?x6900, ?x6392), genre(?x10778, ?x53) *> conf = 0.21 ranks of expected_values: 7 EVAL 02pq9yv award 0f_nbyh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 92.000 92.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6169-0kh6b PRED entity: 0kh6b PRED relation: languages PRED expected values: 02h40lc => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 22): 02h40lc (0.45 #1445, 0.38 #314, 0.37 #1679), 03k50 (0.25 #43, 0.17 #238, 0.11 #355), 064_8sq (0.08 #639, 0.03 #4385, 0.03 #3410), 0688f (0.06 #848, 0.03 #1550, 0.03 #1784), 02bjrlw (0.05 #1444, 0.04 #625, 0.03 #2107), 02bv9 (0.04 #644), 07c9s (0.03 #2353, 0.02 #3369, 0.02 #1261), 06nm1 (0.03 #786, 0.03 #825, 0.02 #1956), 09s02 (0.03 #855, 0.02 #3392, 0.02 #2376), 0t_2 (0.03 #828, 0.02 #1140) >> Best rule #1445 for best value: >> intensional similarity = 5 >> extensional distance = 64 >> proper extension: 01p45_v; 01cwhp; 0161c2; 015c4g; 015d3h; 03y82t6; 09889g; 02zhkz; 022qw7; >> query: (?x3796, 02h40lc) <- profession(?x3796, ?x1032), company(?x3796, ?x2776), location(?x3796, ?x362), type_of_union(?x3796, ?x566), ?x1032 = 02hrh1q >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kh6b languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 182.000 0.455 http://example.org/people/person/languages #6168-01f7dd PRED entity: 01f7dd PRED relation: film PRED expected values: 049xgc 0170yd => 107 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1043): 017jd9 (0.30 #7868, 0.29 #9641, 0.12 #32694), 017gl1 (0.30 #7235, 0.29 #9008, 0.10 #32061), 017gm7 (0.28 #7302, 0.27 #9075, 0.10 #32128), 077q8x (0.25 #2842, 0.03 #6388, 0.02 #11708), 07k2mq (0.17 #837, 0.12 #2610, 0.03 #6156), 0bpm4yw (0.17 #720, 0.06 #13133, 0.05 #30865), 029zqn (0.17 #263, 0.06 #3809, 0.04 #14449), 03bxp5 (0.17 #1077, 0.06 #4623, 0.04 #15263), 016ywb (0.17 #1230, 0.05 #8323, 0.05 #10096), 011ysn (0.17 #562, 0.05 #7655, 0.05 #9428) >> Best rule #7868 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 02bfmn; 01j5ts; 0p_pd; 01q_ph; 09wj5; 02gvwz; 07vc_9; 01v42g; 0blbxk; 01rh0w; ... >> query: (?x6916, 017jd9) <- award_nominee(?x6916, ?x3281), ?x3281 = 0154qm, film(?x6916, ?x288) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #8058 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 02bfmn; 01j5ts; 0p_pd; 01q_ph; 09wj5; 02gvwz; 07vc_9; 01v42g; 0blbxk; 01rh0w; ... *> query: (?x6916, 049xgc) <- award_nominee(?x6916, ?x3281), ?x3281 = 0154qm, film(?x6916, ?x288) *> conf = 0.07 ranks of expected_values: 94 EVAL 01f7dd film 0170yd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 64.000 0.300 http://example.org/film/actor/film./film/performance/film EVAL 01f7dd film 049xgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 107.000 64.000 0.300 http://example.org/film/actor/film./film/performance/film #6167-01qcx_ PRED entity: 01qcx_ PRED relation: contains! PRED expected values: 0ccvx => 71 concepts (63 used for prediction) PRED predicted values (max 10 best out of 205): 02_286 (0.29 #935, 0.25 #11666, 0.21 #9877), 04jpl (0.22 #17904, 0.09 #44733, 0.06 #19692), 02jx1 (0.21 #17969, 0.13 #51956, 0.12 #52851), 01n7q (0.19 #6335, 0.18 #76, 0.16 #2758), 07ssc (0.16 #19702, 0.16 #18808, 0.16 #23278), 0kpys (0.13 #179, 0.09 #4650, 0.09 #6438), 081yw (0.10 #18160, 0.04 #44989, 0.02 #276), 04_1l0v (0.10 #7602, 0.10 #4026, 0.09 #5814), 0ccvx (0.09 #39345, 0.08 #40240, 0.08 #33085), 027l4q (0.09 #39345, 0.08 #40240, 0.08 #33085) >> Best rule #935 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 02cttt; 0xy28; 02kth6; 04sylm; 078bz; 017z88; 02q636; 01hb1t; 0ybkj; 02ccqg; ... >> query: (?x12738, 02_286) <- contains(?x335, ?x12738), contains(?x94, ?x12738), ?x335 = 059rby, ?x94 = 09c7w0 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #39345 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 502 *> proper extension: 01c1nm; *> query: (?x12738, ?x4253) <- place_of_birth(?x9132, ?x12738), location(?x9132, ?x4253), contains(?x94, ?x12738) *> conf = 0.09 ranks of expected_values: 9 EVAL 01qcx_ contains! 0ccvx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 71.000 63.000 0.292 http://example.org/location/location/contains #6166-07cdz PRED entity: 07cdz PRED relation: nominated_for! PRED expected values: 040njc 0gq9h => 97 concepts (95 used for prediction) PRED predicted values (max 10 best out of 224): 0gq9h (0.67 #1444, 0.62 #4678, 0.58 #751), 0k611 (0.51 #1454, 0.50 #761, 0.38 #4688), 040njc (0.51 #1394, 0.46 #701, 0.42 #4628), 0gq_v (0.44 #714, 0.40 #1407, 0.38 #2100), 0gr51 (0.42 #766, 0.33 #1459, 0.26 #4693), 04kxsb (0.40 #4710, 0.33 #89, 0.31 #783), 0p9sw (0.39 #1408, 0.29 #715, 0.25 #8108), 0gqy2 (0.38 #810, 0.36 #1503, 0.35 #4737), 0gr0m (0.37 #1442, 0.31 #749, 0.30 #2828), 09qwmm (0.34 #2799, 0.12 #15020, 0.09 #8113) >> Best rule #1444 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 07bz5; >> query: (?x3510, 0gq9h) <- award_winner(?x3510, ?x398), award(?x3510, ?x1107), list(?x3510, ?x3004) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 07cdz nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 95.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07cdz nominated_for! 040njc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 95.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6165-0h7x PRED entity: 0h7x PRED relation: olympics PRED expected values: 018qb4 0124ld => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 9): 0jhn7 (0.80 #59, 0.77 #295, 0.76 #223), 0kbws (0.77 #129, 0.76 #56, 0.73 #220), 0l6m5 (0.72 #55, 0.71 #128, 0.66 #835), 0blfl (0.66 #835, 0.61 #973, 0.39 #972), 0lbbj (0.52 #130, 0.51 #221, 0.50 #212), 0ldqf (0.49 #290, 0.49 #227, 0.48 #136), 0sxrz (0.39 #972, 0.32 #131, 0.29 #158), 0124ld (0.39 #972, 0.27 #962, 0.26 #135), 018qb4 (0.39 #134, 0.35 #161, 0.32 #225) >> Best rule #59 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 05r4w; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 03rt9; 07ssc; ... >> query: (?x1355, 0jhn7) <- film_release_region(?x2655, ?x1355), adjoins(?x1355, ?x205), ?x2655 = 0fpmrm3 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #972 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 148 *> proper extension: 07ww5; 04tr1; 036b_; 01rxw; 01c4pv; 04vjh; *> query: (?x1355, ?x584) <- adjoins(?x1355, ?x1003), olympics(?x1355, ?x418), olympics(?x1003, ?x584) *> conf = 0.39 ranks of expected_values: 8, 9 EVAL 0h7x olympics 0124ld CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 183.000 183.000 0.800 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0h7x olympics 018qb4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 183.000 183.000 0.800 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #6164-0ps1q PRED entity: 0ps1q PRED relation: place_of_death! PRED expected values: 01kx1j => 76 concepts (57 used for prediction) PRED predicted values (max 10 best out of 14): 0hqgp (0.03 #2137, 0.01 #2893), 0k_mt (0.03 #2054, 0.01 #2810), 04xm_ (0.03 #2045, 0.01 #2801), 03_f0 (0.03 #1904, 0.01 #2660), 08c7cz (0.03 #1871, 0.01 #2627), 0j3v (0.03 #1597, 0.01 #2353), 02wh0 (0.01 #2864), 042q3 (0.01 #2828), 0h336 (0.01 #2825), 039n1 (0.01 #2764) >> Best rule #2137 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 0m7yh; >> query: (?x14214, 0hqgp) <- contains(?x7934, ?x14214), contains(?x7934, ?x9402), country(?x7934, ?x1264), category(?x9402, ?x134), ?x1264 = 0345h >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ps1q place_of_death! 01kx1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 57.000 0.027 http://example.org/people/deceased_person/place_of_death #6163-06fq2 PRED entity: 06fq2 PRED relation: contains! PRED expected values: 09c7w0 => 90 concepts (80 used for prediction) PRED predicted values (max 10 best out of 225): 09c7w0 (0.90 #3579, 0.77 #20570, 0.76 #22358), 02qkt (0.34 #26276, 0.31 #28963, 0.25 #29860), 0dg3n1 (0.21 #26086, 0.19 #28773, 0.16 #29670), 02jx1 (0.19 #64489, 0.16 #21548, 0.13 #34971), 05k7sb (0.18 #133, 0.12 #1921, 0.08 #23382), 0mrhq (0.18 #28618), 0j0k (0.18 #26307, 0.16 #28994, 0.12 #29891), 059rby (0.17 #23269, 0.09 #51003, 0.09 #34010), 01n7q (0.17 #4548, 0.15 #24221, 0.14 #972), 07ssc (0.14 #68015, 0.14 #68911, 0.14 #64434) >> Best rule #3579 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0100mt; 0mq17; 0f2s6; 0mrhq; >> query: (?x8202, 09c7w0) <- contains(?x4733, ?x8202), contains(?x3634, ?x8202), ?x3634 = 07b_l, place_of_birth(?x1093, ?x4733) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06fq2 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 80.000 0.905 http://example.org/location/location/contains #6162-04g61 PRED entity: 04g61 PRED relation: olympics PRED expected values: 0sx7r => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 40): 0jdk_ (0.81 #306, 0.76 #506, 0.75 #547), 0jhn7 (0.81 #307, 0.75 #908, 0.75 #548), 06sks6 (0.81 #304, 0.75 #545, 0.71 #504), 0kbws (0.71 #294, 0.68 #494, 0.68 #535), 0l6m5 (0.67 #289, 0.62 #890, 0.59 #329), 09x3r (0.62 #291, 0.61 #521, 0.60 #251), 0l6mp (0.62 #298, 0.61 #498, 0.60 #178), 0lbbj (0.62 #299, 0.60 #179, 0.57 #59), 0l998 (0.62 #286, 0.58 #486, 0.57 #46), 0lbd9 (0.62 #311, 0.55 #271, 0.53 #511) >> Best rule #306 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 09c7w0; 0b90_r; 03rjj; 0d060g; 0chghy; 05qhw; 07ssc; 0f8l9c; 0hzlz; 03gj2; ... >> query: (?x5274, 0jdk_) <- country(?x695, ?x5274), capital(?x5274, ?x1464), combatants(?x172, ?x5274) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #284 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 09c7w0; 0b90_r; 03rjj; 0d060g; 0chghy; 05qhw; 07ssc; 0f8l9c; 0hzlz; 03gj2; ... *> query: (?x5274, 0sx7r) <- country(?x695, ?x5274), capital(?x5274, ?x1464), combatants(?x172, ?x5274) *> conf = 0.38 ranks of expected_values: 27 EVAL 04g61 olympics 0sx7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 121.000 121.000 0.810 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #6161-016ypb PRED entity: 016ypb PRED relation: location PRED expected values: 0d6yv 0j4q1 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 77): 02_286 (0.23 #36890, 0.20 #5646, 0.17 #2441), 030qb3t (0.22 #4090, 0.21 #2487, 0.20 #8898), 0cr3d (0.09 #36997, 0.08 #1747, 0.06 #5753), 04jpl (0.08 #36870, 0.06 #4024, 0.06 #819), 06_kh (0.06 #813, 0.02 #45667, 0.02 #2415), 0gyvgw (0.06 #1596, 0.02 #45667), 0dv9v (0.06 #1564, 0.02 #45667), 01b8jj (0.06 #1392, 0.02 #45667), 01rwf_ (0.06 #1368, 0.02 #45667), 07_fl (0.06 #1366, 0.02 #45667) >> Best rule #36890 for best value: >> intensional similarity = 2 >> extensional distance = 1538 >> proper extension: 07c37; >> query: (?x2922, 02_286) <- location(?x2922, ?x2204), teams(?x2204, ?x3814) >> conf = 0.23 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016ypb location 0j4q1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 88.000 0.227 http://example.org/people/person/places_lived./people/place_lived/location EVAL 016ypb location 0d6yv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 88.000 0.227 http://example.org/people/person/places_lived./people/place_lived/location #6160-04bdxl PRED entity: 04bdxl PRED relation: film PRED expected values: 09txzv => 95 concepts (71 used for prediction) PRED predicted values (max 10 best out of 840): 05k2xy (0.64 #17831, 0.48 #46361, 0.47 #60629), 02rx2m5 (0.64 #17831, 0.48 #46361, 0.47 #60629), 0sxns (0.15 #1076, 0.03 #65982, 0.03 #99877), 02cbhg (0.15 #1401, 0.03 #3184, 0.03 #99877), 07bwr (0.15 #868, 0.03 #99877), 02qcr (0.15 #1516, 0.02 #5082, 0.02 #6865), 05hjnw (0.08 #842, 0.05 #2625, 0.03 #65982), 0c57yj (0.08 #638, 0.05 #2421, 0.03 #99877), 01jrbv (0.08 #551, 0.03 #65982, 0.03 #99877), 02mpyh (0.08 #1461, 0.03 #65982, 0.03 #99877) >> Best rule #17831 for best value: >> intensional similarity = 3 >> extensional distance = 406 >> proper extension: 02g8h; 0d_84; 04bs3j; 0htlr; 03_vx9; 0456xp; 04shbh; 0h1m9; 0prjs; 013cr; ... >> query: (?x91, ?x1064) <- nationality(?x91, ?x94), nominated_for(?x91, ?x1064), participant(?x395, ?x91) >> conf = 0.64 => this is the best rule for 2 predicted values *> Best rule #2036 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 63 *> proper extension: 01skmp; *> query: (?x91, 09txzv) <- award(?x91, ?x2478), ?x2478 = 02x4x18 *> conf = 0.03 ranks of expected_values: 142 EVAL 04bdxl film 09txzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 95.000 71.000 0.640 http://example.org/film/actor/film./film/performance/film #6159-01vwyqp PRED entity: 01vwyqp PRED relation: artists! PRED expected values: 07sbbz2 064t9 06j6l => 106 concepts (56 used for prediction) PRED predicted values (max 10 best out of 224): 064t9 (0.89 #2422, 0.87 #2723, 0.85 #3024), 06j6l (0.50 #650, 0.44 #2456, 0.42 #3058), 02qdgx (0.39 #340, 0.19 #2146, 0.17 #13552), 0glt670 (0.39 #2449, 0.37 #2750, 0.37 #3051), 0xhtw (0.31 #4233, 0.31 #3932, 0.29 #3631), 016clz (0.28 #7234, 0.25 #1510, 0.24 #3919), 07sbbz2 (0.27 #610, 0.14 #7237, 0.10 #3621), 0y3_8 (0.23 #2455, 0.22 #2756, 0.22 #3057), 01lyv (0.23 #636, 0.21 #335, 0.21 #6056), 03_d0 (0.23 #614, 0.18 #313, 0.17 #6939) >> Best rule #2422 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 017b2p; >> query: (?x3256, 064t9) <- artists(?x5876, ?x3256), ?x5876 = 0ggx5q, location(?x3256, ?x3778) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 7 EVAL 01vwyqp artists! 06j6l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 56.000 0.891 http://example.org/music/genre/artists EVAL 01vwyqp artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 56.000 0.891 http://example.org/music/genre/artists EVAL 01vwyqp artists! 07sbbz2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 106.000 56.000 0.891 http://example.org/music/genre/artists #6158-030vnj PRED entity: 030vnj PRED relation: film PRED expected values: 01l_pn => 109 concepts (84 used for prediction) PRED predicted values (max 10 best out of 817): 01gkp1 (0.64 #51743, 0.64 #49958, 0.63 #48173), 01shy7 (0.33 #422, 0.06 #28969, 0.06 #3990), 04gv3db (0.33 #751, 0.05 #16057, 0.05 #21410), 0bmssv (0.33 #697, 0.05 #16057, 0.05 #21410), 033fqh (0.33 #838, 0.05 #16057, 0.05 #21410), 043t8t (0.33 #787, 0.05 #42820, 0.04 #137389), 09g8vhw (0.33 #325, 0.05 #26763, 0.03 #12813), 03tps5 (0.33 #736, 0.05 #26763, 0.03 #121325), 03lrht (0.33 #257, 0.05 #26763, 0.03 #121325), 03459x (0.33 #569, 0.04 #4137, 0.03 #5921) >> Best rule #51743 for best value: >> intensional similarity = 3 >> extensional distance = 349 >> proper extension: 04bpm6; 012vct; 07q0g5; >> query: (?x8291, ?x4768) <- award_nominee(?x241, ?x8291), nominated_for(?x8291, ?x4768), participant(?x2221, ?x8291) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #4532 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 0dl567; 02bwjv; 09h4b5; 01qn8k; *> query: (?x8291, 01l_pn) <- friend(?x8291, ?x794), celebrity(?x710, ?x794), film(?x8291, ?x148) *> conf = 0.07 ranks of expected_values: 35 EVAL 030vnj film 01l_pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 109.000 84.000 0.642 http://example.org/film/actor/film./film/performance/film #6157-02wwwv5 PRED entity: 02wwwv5 PRED relation: artists! PRED expected values: 064t9 06j6l => 84 concepts (82 used for prediction) PRED predicted values (max 10 best out of 211): 064t9 (0.70 #1262, 0.66 #1886, 0.64 #638), 06by7 (0.52 #1583, 0.44 #4080, 0.44 #2207), 06j6l (0.46 #361, 0.33 #673, 0.33 #1609), 05bt6j (0.45 #45, 0.45 #1917, 0.44 #669), 02lnbg (0.44 #1307, 0.44 #683, 0.38 #1931), 0ggx5q (0.44 #703, 0.42 #1327, 0.38 #79), 016clz (0.34 #1877, 0.28 #629, 0.28 #5), 0gywn (0.31 #370, 0.27 #1618, 0.24 #3178), 059kh (0.30 #1922, 0.28 #50, 0.23 #674), 0m0jc (0.30 #1881, 0.23 #633, 0.21 #1257) >> Best rule #1262 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 04cr6qv; >> query: (?x9623, 064t9) <- artists(?x3243, ?x9623), ?x3243 = 0y3_8, profession(?x9623, ?x220) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 02wwwv5 artists! 06j6l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 82.000 0.698 http://example.org/music/genre/artists EVAL 02wwwv5 artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 82.000 0.698 http://example.org/music/genre/artists #6156-083tk PRED entity: 083tk PRED relation: languages_spoken! PRED expected values: 0d7wh => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 177): 07hwkr (0.59 #3106, 0.57 #1001, 0.55 #2825), 02vsw1 (0.50 #610, 0.43 #398, 0.38 #893), 059_w (0.38 #661, 0.38 #520, 0.33 #237), 03w9bjf (0.36 #825, 0.33 #48, 0.30 #754), 0d7wh (0.33 #227, 0.33 #87, 0.33 #16), 0bbz66j (0.33 #255, 0.33 #44, 0.29 #397), 071x0k (0.33 #219, 0.33 #8, 0.25 #643), 0x67 (0.33 #221, 0.33 #10, 0.25 #645), 033tf_ (0.33 #218, 0.33 #7, 0.25 #148), 03bkbh (0.33 #239, 0.33 #28, 0.25 #169) >> Best rule #3106 for best value: >> intensional similarity = 8 >> extensional distance = 30 >> proper extension: 01bkv; >> query: (?x9617, 07hwkr) <- countries_spoken_in(?x9617, ?x6371), official_language(?x4221, ?x9617), nationality(?x1328, ?x6371), country(?x7713, ?x6371), languages_spoken(?x12950, ?x9617), people(?x12950, ?x11081), film(?x11081, ?x1797), nationality(?x11081, ?x613) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #227 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 0349s; *> query: (?x9617, 0d7wh) <- countries_spoken_in(?x9617, ?x6371), languages(?x7870, ?x9617), language(?x4452, ?x9617), nominated_for(?x7870, ?x3048), type_of_union(?x7870, ?x566), contains(?x11138, ?x6371), location(?x7870, ?x1523), ?x566 = 04ztj, titles(?x162, ?x4452), split_to(?x5073, ?x6371), country(?x4452, ?x94) *> conf = 0.33 ranks of expected_values: 5 EVAL 083tk languages_spoken! 0d7wh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 53.000 0.594 http://example.org/people/ethnicity/languages_spoken #6155-01lbp PRED entity: 01lbp PRED relation: religion PRED expected values: 01lp8 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 17): 0c8wxp (0.26 #141, 0.24 #6, 0.24 #96), 03_gx (0.07 #734, 0.06 #509, 0.05 #1635), 092bf5 (0.06 #511, 0.05 #691, 0.04 #601), 0kpl (0.05 #595, 0.05 #1406, 0.05 #1586), 04pk9 (0.04 #65, 0.02 #200, 0.01 #1146), 0flw86 (0.04 #47, 0.02 #1578, 0.02 #1128), 01lp8 (0.04 #766, 0.03 #361, 0.03 #856), 06nzl (0.04 #105, 0.03 #195, 0.03 #240), 019cr (0.04 #146, 0.03 #281, 0.03 #326), 0kq2 (0.03 #198, 0.03 #378, 0.03 #513) >> Best rule #141 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 014zcr; 05m63c; 01q_ph; 01dw4q; 03_vx9; 01q7cb_; 0151w_; 0456xp; 0lk90; 01rh0w; ... >> query: (?x932, 0c8wxp) <- film(?x932, ?x4500), participant(?x1213, ?x932), participant(?x828, ?x932) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #766 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 159 *> proper extension: 03n69x; 04d_mtq; *> query: (?x932, 01lp8) <- vacationer(?x1917, ?x932), nationality(?x932, ?x94) *> conf = 0.04 ranks of expected_values: 7 EVAL 01lbp religion 01lp8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 119.000 119.000 0.263 http://example.org/people/person/religion #6154-04j5fx PRED entity: 04j5fx PRED relation: actor! PRED expected values: 045qmr => 94 concepts (32 used for prediction) PRED predicted values (max 10 best out of 149): 03d3ht (0.29 #1248, 0.27 #1512, 0.25 #188), 031kyy (0.25 #151, 0.17 #1211, 0.15 #1475), 02v5xg (0.25 #170, 0.17 #1230, 0.15 #1494), 01lk02 (0.23 #429, 0.21 #1224, 0.19 #1488), 045nc5 (0.23 #525, 0.17 #790, 0.12 #1320), 02rhwjr (0.17 #1318, 0.15 #1582, 0.11 #2112), 03lyp4 (0.15 #527, 0.11 #792, 0.08 #1322), 01hvv0 (0.15 #417, 0.11 #682, 0.06 #1741), 02kwcj (0.12 #1315, 0.12 #1579, 0.10 #1844), 08cl7s (0.12 #1214, 0.12 #1478, 0.10 #1743) >> Best rule #1248 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 01kymm; 01kwh5j; 03ydry; >> query: (?x11146, 03d3ht) <- nationality(?x11146, ?x252), special_performance_type(?x11146, ?x296), profession(?x11146, ?x1383), ?x296 = 01kyvx, ?x252 = 03_3d >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04j5fx actor! 045qmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 32.000 0.292 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #6153-01wqlc PRED entity: 01wqlc PRED relation: artists PRED expected values: 06wvj 0hgqq 0h6sv => 70 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1643): 067mj (0.56 #5458, 0.31 #7602, 0.25 #3313), 01w8n89 (0.56 #5675, 0.28 #7819, 0.26 #8897), 05563d (0.50 #3522, 0.39 #5667, 0.33 #2449), 01gx5f (0.50 #3506, 0.33 #5651, 0.33 #2433), 0146pg (0.50 #3257, 0.33 #2184, 0.33 #40), 0b_j2 (0.50 #3809, 0.33 #2736, 0.28 #5954), 09hnb (0.50 #3425, 0.33 #2352, 0.21 #6642), 01qkqwg (0.50 #3336, 0.33 #2263, 0.17 #5481), 0fpjd_g (0.50 #3324, 0.33 #2251, 0.15 #6541), 0135xb (0.50 #3862, 0.33 #2789, 0.15 #7079) >> Best rule #5458 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 096jwc; 09qxq7; >> query: (?x5640, 067mj) <- artists(?x5640, ?x9074), place_of_death(?x9074, ?x362), profession(?x9074, ?x1183), instrumentalists(?x716, ?x9074), student(?x7021, ?x9074), ?x716 = 018vs >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2335 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 0ggq0m; *> query: (?x5640, 06wvj) <- artists(?x5640, ?x9728), artists(?x5640, ?x8730), parent_genre(?x497, ?x5640), ?x9728 = 0kn3g, music(?x2519, ?x8730), award_winner(?x8730, ?x4850), profession(?x8730, ?x563) *> conf = 0.33 ranks of expected_values: 30, 84, 90 EVAL 01wqlc artists 0h6sv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 70.000 28.000 0.556 http://example.org/music/genre/artists EVAL 01wqlc artists 0hgqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 70.000 28.000 0.556 http://example.org/music/genre/artists EVAL 01wqlc artists 06wvj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 70.000 28.000 0.556 http://example.org/music/genre/artists #6152-05vzw3 PRED entity: 05vzw3 PRED relation: award PRED expected values: 01by1l => 103 concepts (80 used for prediction) PRED predicted values (max 10 best out of 274): 01by1l (0.58 #111, 0.35 #1317, 0.34 #4935), 01cky2 (0.33 #193, 0.15 #18091, 0.15 #30964), 03t5kl (0.33 #226, 0.15 #18091, 0.15 #30964), 02f6ym (0.25 #257, 0.18 #24126, 0.17 #1463), 02f5qb (0.25 #155, 0.18 #24126, 0.15 #18091), 02f716 (0.25 #175, 0.18 #24126, 0.15 #18091), 023vrq (0.25 #324, 0.15 #18091, 0.15 #30964), 01d38g (0.24 #1233, 0.11 #4851, 0.10 #8469), 0c4z8 (0.22 #5698, 0.21 #7306, 0.21 #4894), 03qbh5 (0.22 #5028, 0.21 #1410, 0.19 #5832) >> Best rule #111 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 04lgymt; 01wmxfs; 0288fyj; >> query: (?x4594, 01by1l) <- award_nominee(?x6573, ?x4594), award(?x4594, ?x528), ?x6573 = 067nsm >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05vzw3 award 01by1l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 80.000 0.583 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6151-018wdw PRED entity: 018wdw PRED relation: award_winner PRED expected values: 04ktcgn => 50 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1785): 0js9s (0.37 #11356, 0.33 #13830, 0.16 #61880), 014zcr (0.33 #42, 0.10 #4992, 0.09 #7467), 01g257 (0.33 #313, 0.05 #5263, 0.05 #7738), 04fzk (0.33 #899, 0.05 #44553, 0.04 #24751), 0j5q3 (0.33 #1563, 0.02 #33739, 0.02 #43639), 06wm0z (0.33 #1149), 016vg8 (0.33 #1060), 03m49ly (0.32 #7425, 0.31 #9900, 0.05 #44553), 04sry (0.30 #11521, 0.27 #13995, 0.16 #61880), 04ls53 (0.24 #3552, 0.06 #20876, 0.06 #15925) >> Best rule #11356 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 027h4yd; >> query: (?x6860, 0js9s) <- award_winner(?x6860, ?x3879), crewmember(?x5513, ?x3879), film_crew_role(?x5513, ?x137), nominated_for(?x1336, ?x5513) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #61880 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 181 *> proper extension: 0m7yy; 02wwsh8; 03ybrwc; 0468g4r; *> query: (?x6860, ?x2818) <- award(?x6214, ?x6860), award(?x2770, ?x6860), film(?x3261, ?x6214), genre(?x6214, ?x225), award_winner(?x2770, ?x2818) *> conf = 0.16 ranks of expected_values: 76 EVAL 018wdw award_winner 04ktcgn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 50.000 25.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner #6150-025v3k PRED entity: 025v3k PRED relation: major_field_of_study PRED expected values: 036hv 09s1f => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 96): 0g26h (0.57 #248, 0.50 #34, 0.47 #356), 04rjg (0.57 #231, 0.41 #875, 0.41 #3559), 01lj9 (0.50 #31, 0.43 #245, 0.30 #1961), 0fdys (0.43 #244, 0.31 #674, 0.30 #566), 037mh8 (0.43 #271, 0.27 #915, 0.26 #593), 02_7t (0.36 #268, 0.35 #376, 0.30 #590), 06ms6 (0.36 #229, 0.24 #873, 0.22 #1194), 02jfc (0.36 #284, 0.24 #392, 0.22 #606), 0h5k (0.36 #234, 0.22 #556, 0.20 #3650), 0g4gr (0.29 #346, 0.25 #24, 0.23 #1525) >> Best rule #248 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 05krk; 06pwq; 04rwx; 07wrz; 03ksy; 017j69; 0hsb3; 08qnnv; 07ccs; 0bwfn; ... >> query: (?x3948, 0g26h) <- institution(?x4981, ?x3948), student(?x3948, ?x1068), service_language(?x3948, ?x254), ?x4981 = 03bwzr4 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 065y4w7; 02gnmp; *> query: (?x3948, 036hv) <- institution(?x620, ?x3948), student(?x3948, ?x13084), colors(?x3948, ?x332), ?x13084 = 01hbq0 *> conf = 0.25 ranks of expected_values: 17, 27 EVAL 025v3k major_field_of_study 09s1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 108.000 108.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 025v3k major_field_of_study 036hv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 108.000 108.000 0.571 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #6149-0fpjyd PRED entity: 0fpjyd PRED relation: music! PRED expected values: 0340hj 02q56mk 02wwmhc 04s1zr => 123 concepts (58 used for prediction) PRED predicted values (max 10 best out of 861): 027m5wv (0.81 #2021, 0.76 #7072, 0.75 #11115), 02rrfzf (0.09 #1335, 0.04 #8408, 0.04 #10429), 04hk0w (0.08 #1009, 0.05 #2019, 0.02 #3030), 0btpm6 (0.08 #742, 0.05 #1752, 0.02 #6803), 02fqrf (0.08 #338, 0.05 #1348, 0.02 #6399), 09146g (0.08 #182, 0.05 #1192, 0.02 #6243), 034r25 (0.08 #439, 0.02 #1449, 0.02 #6500), 09q5w2 (0.08 #100, 0.02 #1110, 0.02 #6161), 047fjjr (0.08 #373, 0.02 #1383, 0.02 #6434), 03h3x5 (0.05 #1269, 0.04 #259, 0.03 #3290) >> Best rule #2021 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 05_swj; >> query: (?x6907, ?x6081) <- music(?x3000, ?x6907), nominated_for(?x6907, ?x6081), film_release_region(?x3000, ?x429), ?x429 = 03rt9 >> conf = 0.81 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fpjyd music! 04s1zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 58.000 0.806 http://example.org/film/film/music EVAL 0fpjyd music! 02wwmhc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 58.000 0.806 http://example.org/film/film/music EVAL 0fpjyd music! 02q56mk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 58.000 0.806 http://example.org/film/film/music EVAL 0fpjyd music! 0340hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 58.000 0.806 http://example.org/film/film/music #6148-09c7w0 PRED entity: 09c7w0 PRED relation: nationality! PRED expected values: 06qgvf 016qtt 0cb77r 049tjg 05ty4m 01vrx3g 0436f4 01rr9f 03f2_rc 01ty7ll 06cc_1 01gvr1 0d4fqn 06y9c2 025vry 01n5309 01vvycq 01mvth 03qd_ 05ml_s 04bd8y 066m4g 02lk1s 07q1v4 03gm48 015grj 0f0p0 012cph 03lt8g 030pr 0sz28 01wdqrx 0blbxk 0n6f8 05drq5 01qvgl 0gcdzz 043q6n_ 022_lg 012x4t 02_hj4 05fnl9 02jt1k 0f1vrl 034np8 015882 04ktcgn 04mn81 01vs_v8 0f4vbz 01wj9y9 0311wg 01pgzn_ 080knyg 03jm6c 09pl3s 0jfx1 0127m7 043js 01dw9z 09ftwr 09hnb 01wwvc5 0m32_ 05728w1 027l0b 0gdh5 070w7s 053yx 0bt4r4 01vx5w7 0p8jf 0993r 0gd_b_ 02778qt 01w02sy 01wmgrf 04cw0j 055c8 01k98nm 01nrq5 07z1_q 01jbx1 01ksr1 02pzc4 01wz_ml 03_6y 04gycf 0b05xm 01n7qlf 01309x 03kpvp 03pvt 0lzkm 0hw1j 0cjsxp 019vgs 01svw8n 062ftr 0mj0c 037lyl 035rnz 04fzk 073749 01v3vp 017yfz 01wz01 028qdb 02vntj 05v1sb 03xp8d5 03l3jy 01mt1fy 02xwq9 086nl7 0gv5c 09yrh 03f4xvm 03bpn6 02t_99 019r_1 01s21dg 05f7snc 0n6kf 01pp3p 051wwp 06wm0z 03flwk 0d9xq 0127gn 03_0p 05m9f9 01pqy_ 09bx1k 0gn30 043zg 0854hr 03cn92 020_95 01c6l 01vsgrn 07j8kh 02tkzn 027kmrb 01_k1z 0c8hct 024zq 01l1rw 01wbsdz 023kzp 01xyt7 02ryx0 037d35 025j1t 0hnp7 06g2d1 02qlkc3 09cdxn 03h_0_z 01515w 0lh0c 0flpy 05gpy 029ql 0g9zcgx 0gs1_ 019f9z 01vw_dv 04l19_ 01vb6z 01skmp 01520h 016732 0kp2_ 03xn3s2 023n39 0ddkf 01gvyp 06sn8m 02wb6d 03mp9s 02js9p 06_bq1 0210f1 030b93 012vct 01lz4tf 01m3b1t 0bdlj 05gnf9 08h79x 01d5vk 011vx3 025vl4m 021r7r 05cx7x 04wg38 03h_yfh 01fxck 014ps4 019389 0454s1 01z5tr 02xnjd 017g2y 02jyhv 0c8br 02hy9p 02dlfh 02vwckw 023361 02clgg 02m92h 04v048 0bkf72 02v49c 02q6cv4 01lqf49 02y49 03_js 01sg7_ 0f6lx 06pjs 06rq2l 04znsy 04wf_b 04bbv7 0gd_s 0fthdk 01x0sy 01npcy7 04ns3gy 02rybfn 05cqhl 01nhkxp 01l1ls 0kbn5 01h4rj 042f1 0f1jhc 09nz_c 0f14q 0g476 03cvv4 03j3pg9 033cw 039xcr 0bxtyq 085q5 0bbvr84 02h9_l 07ddz9 023p29 076df9 0cw67g 0c1ps1 042d1 020jqv 0dbb3 0f87jy 065mm1 017f4y 03c6v3 02j490 01xwqn 06l6nj 0bt23 01hkck 03k1vm 08nz99 06pcz0 063g7l 03s2y9 05dxl_ 045931 09gb9xh 04d2yp 023slg 02qny_ 014zn0 033071 02hblj 01g0jn 03j9ml 02h48 06r3p2 04bz7q 0pgm3 02qx5h 091n7z 0g72r 01hbq0 014kg4 01svq8 0gry51 018qql 0qkj7 011lpr 045gzq 01wttr1 038nv6 => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 1592): 01lct6 (0.65 #21095, 0.30 #46148, 0.26 #43511), 02p8v8 (0.65 #21095, 0.30 #46148, 0.26 #43511), 01mvpv (0.65 #21095, 0.30 #46148, 0.26 #43511), 081t6 (0.65 #21095, 0.30 #46148, 0.26 #43511), 06c0j (0.65 #21095, 0.30 #46148, 0.26 #43511), 07hyk (0.65 #21095, 0.30 #46148, 0.26 #43511), 042d1 (0.65 #21095, 0.30 #46148, 0.26 #43511), 0c_md_ (0.65 #21095, 0.30 #46148, 0.26 #43511), 07t2k (0.65 #21095, 0.30 #46148, 0.26 #43511), 034ls (0.65 #21095, 0.30 #46148, 0.26 #43511) >> Best rule #21095 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 06frc; >> query: (?x94, ?x652) <- jurisdiction_of_office(?x652, ?x94), entity_involved(?x1140, ?x94) >> conf = 0.65 => this is the best rule for 11 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7, 177, 437, 526, 532, 551, 555, 561, 564, 588, 685, 687, 689, 691, 692, 695, 698, 699, 701, 703, 704, 705, 706, 707, 708, 711, 712, 713, 714, 716, 718, 719, 720, 724, 725, 726, 729, 732, 735, 741, 742, 743, 746, 747, 748, 749, 752, 753, 754, 755, 757, 758, 761, 762, 763, 764, 765, 766, 767, 768, 769, 771, 774, 776, 780, 782, 783, 784, 786, 788, 789, 790, 791, 795, 796, 798, 799, 801, 802, 804, 805, 807, 808, 809, 810, 812, 816, 817, 820, 821, 824, 825, 827, 828, 830, 831, 832, 836, 838, 839, 840, 841, 842, 844, 845, 846, 847, 849, 850, 853, 854, 855, 856, 857, 858, 859, 864, 865, 866, 868, 869, 870, 871, 874, 877, 878, 881, 883, 888, 890, 891, 892, 894, 896, 900, 905, 906, 908, 909, 910, 911, 913, 914, 916, 917, 920, 922, 924, 925, 929, 930, 931, 932, 951, 980, 997, 1018, 1030, 1049, 1065, 1076, 1096, 1097, 1128, 1326, 1397, 1401 EVAL 09c7w0 nationality! 038nv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01wttr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 045gzq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 011lpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0qkj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 018qql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0gry51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01svq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 014kg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01hbq0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0g72r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 091n7z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02qx5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0pgm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04bz7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06r3p2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02h48 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03j9ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01g0jn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02hblj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 033071 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 014zn0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02qny_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 023slg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04d2yp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 09gb9xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 045931 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05dxl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03s2y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 063g7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06pcz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 08nz99 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03k1vm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01hkck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0bt23 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06l6nj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01xwqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02j490 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03c6v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 017f4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 065mm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0f87jy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0dbb3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 020jqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 042d1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0c1ps1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0cw67g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 076df9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 023p29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 07ddz9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02h9_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0bbvr84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 085q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0bxtyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 039xcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 033cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03j3pg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03cvv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0g476 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0f14q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 09nz_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0f1jhc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 042f1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01h4rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0kbn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01l1ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01nhkxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05cqhl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02rybfn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04ns3gy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01npcy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01x0sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0fthdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0gd_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04bbv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04wf_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04znsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06rq2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06pjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0f6lx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01sg7_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03_js CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02y49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01lqf49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02q6cv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02v49c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0bkf72 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04v048 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02m92h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02clgg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 023361 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02vwckw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02dlfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02hy9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0c8br CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02jyhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 017g2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02xnjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01z5tr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0454s1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 019389 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 014ps4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01fxck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03h_yfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04wg38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05cx7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 021r7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 025vl4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 011vx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01d5vk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 08h79x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05gnf9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0bdlj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01m3b1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01lz4tf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 012vct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 030b93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0210f1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06_bq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02js9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03mp9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02wb6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06sn8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01gvyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0ddkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 023n39 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03xn3s2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0kp2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 016732 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01520h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01skmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01vb6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04l19_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01vw_dv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 019f9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0gs1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0g9zcgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 029ql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05gpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0flpy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0lh0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01515w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03h_0_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 09cdxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02qlkc3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06g2d1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0hnp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 025j1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 037d35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02ryx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01xyt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 023kzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01wbsdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01l1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 024zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0c8hct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01_k1z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 027kmrb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02tkzn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 07j8kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01vsgrn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01c6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 020_95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03cn92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0854hr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 043zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0gn30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 09bx1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01pqy_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05m9f9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03_0p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0127gn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0d9xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03flwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06wm0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 051wwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01pp3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0n6kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05f7snc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01s21dg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 019r_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02t_99 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03bpn6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03f4xvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 09yrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0gv5c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 086nl7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02xwq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01mt1fy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03l3jy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03xp8d5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05v1sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02vntj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 028qdb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01wz01 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 017yfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01v3vp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 073749 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04fzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 035rnz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 037lyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0mj0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 062ftr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01svw8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 019vgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0cjsxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0hw1j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0lzkm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03pvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03kpvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01309x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01n7qlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0b05xm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04gycf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03_6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01wz_ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02pzc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01ksr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01jbx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 07z1_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01nrq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01k98nm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 055c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04cw0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01wmgrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01w02sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02778qt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0gd_b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0993r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0p8jf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01vx5w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0bt4r4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 053yx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 070w7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0gdh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 027l0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05728w1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0m32_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01wwvc5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 09hnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 09ftwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01dw9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 043js CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0127m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0jfx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 09pl3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03jm6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 080knyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01pgzn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0311wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01wj9y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0f4vbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01vs_v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04mn81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04ktcgn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 015882 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 034np8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0f1vrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02jt1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05fnl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02_hj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 022_lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 043q6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0gcdzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01qvgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05drq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0n6f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0blbxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01wdqrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0sz28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 030pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03lt8g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 012cph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0f0p0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 015grj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03gm48 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 07q1v4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 02lk1s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 066m4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 04bd8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05ml_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03qd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01mvth CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01vvycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01n5309 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 025vry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06y9c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0d4fqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01gvr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06cc_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01ty7ll CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 03f2_rc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01rr9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0436f4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 01vrx3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 05ty4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 049tjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 0cb77r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 016qtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 171.000 171.000 0.650 http://example.org/people/person/nationality EVAL 09c7w0 nationality! 06qgvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 171.000 171.000 0.650 http://example.org/people/person/nationality #6147-084z0w PRED entity: 084z0w PRED relation: languages PRED expected values: 03k50 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 23): 03k50 (0.55 #3, 0.52 #40, 0.22 #558), 0999q (0.44 #58, 0.41 #21, 0.06 #687), 09bnf (0.30 #74, 0.18 #37, 0.06 #148), 09s02 (0.23 #34, 0.22 #71, 0.05 #589), 064_8sq (0.14 #13, 0.11 #50, 0.10 #938), 02hxcvy (0.09 #24, 0.07 #61, 0.07 #3559), 055qm (0.09 #22, 0.07 #59, 0.04 #577), 01c7y (0.09 #29, 0.07 #66, 0.03 #584), 0688f (0.07 #3559, 0.07 #3447, 0.01 #582), 02bjrlw (0.06 #815, 0.05 #926, 0.05 #1000) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0dfjb8; 02n1p5; 02qy3py; 09r_wb; 02wmbg; 05nqq3; 01x2tm8; 06zmg7m; 0kst7v; 06kl0k; ... >> query: (?x4645, 03k50) <- languages(?x4645, ?x5121), profession(?x4645, ?x1032), ?x5121 = 07c9s, ?x1032 = 02hrh1q >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 084z0w languages 03k50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.545 http://example.org/people/person/languages #6146-0621cs PRED entity: 0621cs PRED relation: parent_genre PRED expected values: 018ysx => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 163): 06j6l (0.50 #32, 0.14 #1465, 0.11 #510), 016clz (0.39 #322, 0.38 #163, 0.12 #1596), 03lty (0.39 #1133, 0.39 #1292, 0.14 #2569), 0jmwg (0.25 #233, 0.25 #74, 0.13 #392), 0dl5d (0.25 #174, 0.17 #333, 0.07 #652), 09jw2 (0.25 #98, 0.12 #257, 0.09 #1531), 011j5x (0.25 #21, 0.12 #658, 0.11 #818), 017371 (0.25 #102, 0.11 #580, 0.07 #739), 03p7rp (0.25 #264, 0.09 #423, 0.06 #2871), 01750n (0.25 #154, 0.06 #2871, 0.05 #3355) >> Best rule #32 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 06hzq3; 08cg36; >> query: (?x10366, 06j6l) <- parent_genre(?x10366, ?x9853), parent_genre(?x10366, ?x2996), parent_genre(?x10366, ?x1572), ?x1572 = 06by7, ?x9853 = 02qm5j, artists(?x2996, ?x498) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2871 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 265 *> proper extension: 0145m; *> query: (?x10366, ?x2664) <- parent_genre(?x10366, ?x1572), parent_genre(?x13294, ?x1572), parent_genre(?x13294, ?x2664) *> conf = 0.06 ranks of expected_values: 57 EVAL 0621cs parent_genre 018ysx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 25.000 25.000 0.500 http://example.org/music/genre/parent_genre #6145-0_jm PRED entity: 0_jm PRED relation: major_field_of_study! PRED expected values: 07s6fsf 01kxxq => 62 concepts (55 used for prediction) PRED predicted values (max 10 best out of 17): 02_xgp2 (0.89 #180, 0.81 #429, 0.80 #198), 01gkg3 (0.89 #180, 0.80 #198, 0.74 #381), 01kxxq (0.89 #180, 0.80 #198, 0.74 #381), 04zx3q1 (0.70 #235, 0.69 #438, 0.69 #38), 07s6fsf (0.70 #235, 0.69 #438, 0.69 #38), 071tyz (0.70 #235, 0.69 #438, 0.69 #38), 0bjrnt (0.70 #235, 0.69 #38, 0.62 #126), 01rr_d (0.70 #235, 0.69 #38, 0.62 #126), 013zdg (0.70 #235, 0.69 #38, 0.62 #126), 027f2w (0.70 #235, 0.69 #38, 0.62 #126) >> Best rule #180 for best value: >> intensional similarity = 15 >> extensional distance = 2 >> proper extension: 05qfh; >> query: (?x6756, ?x865) <- major_field_of_study(?x6756, ?x12158), major_field_of_study(?x7920, ?x6756), major_field_of_study(?x6257, ?x6756), major_field_of_study(?x2399, ?x6756), major_field_of_study(?x865, ?x12158), major_field_of_study(?x9525, ?x12158), major_field_of_study(?x4916, ?x12158), major_field_of_study(?x4296, ?x12158), ?x4916 = 019dwp, ?x4296 = 07vyf, currency(?x2399, ?x170), ?x6257 = 02s8qk, school(?x1823, ?x2399), institution(?x734, ?x9525), institution(?x620, ?x7920) >> conf = 0.89 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 5 EVAL 0_jm major_field_of_study! 01kxxq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 55.000 0.889 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 0_jm major_field_of_study! 07s6fsf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 62.000 55.000 0.889 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #6144-027nb PRED entity: 027nb PRED relation: administrative_parent PRED expected values: 02j71 => 93 concepts (78 used for prediction) PRED predicted values (max 10 best out of 23): 02j71 (0.87 #4946, 0.86 #7683, 0.86 #5357), 049nq (0.14 #232, 0.11 #368, 0.10 #505), 09c7w0 (0.14 #9333, 0.11 #9890, 0.09 #10309), 07c5l (0.09 #4248, 0.08 #4249, 0.08 #409), 0157g9 (0.09 #4248, 0.08 #4249, 0.08 #409), 0f8l9c (0.08 #4129), 03rjj (0.07 #8780, 0.07 #8227, 0.03 #3153), 05r7t (0.06 #757), 07ssc (0.05 #3298, 0.03 #4809, 0.02 #6177), 0d05w3 (0.04 #10075, 0.04 #10215, 0.03 #10635) >> Best rule #4946 for best value: >> intensional similarity = 5 >> extensional distance = 110 >> proper extension: 07bxhl; 01c4pv; >> query: (?x183, 02j71) <- organization(?x183, ?x312), country(?x1121, ?x183), countries_within(?x8483, ?x183), contains(?x7273, ?x183), ?x312 = 07t65 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027nb administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 78.000 0.866 http://example.org/base/aareas/schema/administrative_area/administrative_parent #6143-015njf PRED entity: 015njf PRED relation: religion PRED expected values: 0c8wxp => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 17): 0kq2 (0.25 #18, 0.07 #423, 0.05 #63), 0kpl (0.22 #100, 0.16 #550, 0.15 #145), 03_gx (0.22 #104, 0.11 #554, 0.10 #59), 0c8wxp (0.17 #366, 0.17 #996, 0.14 #1401), 03j6c (0.05 #66, 0.04 #1056, 0.03 #1371), 0n2g (0.05 #58, 0.04 #1273, 0.04 #1678), 019cr (0.03 #101, 0.02 #551, 0.02 #326), 06nzl (0.03 #105, 0.02 #1005, 0.01 #1410), 092bf5 (0.03 #106, 0.02 #1186, 0.02 #601), 0v53x (0.03 #119, 0.02 #569) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 015rhv; >> query: (?x4813, 0kq2) <- profession(?x4813, ?x4354), award_winner(?x289, ?x4813), people(?x9771, ?x4813), ?x4354 = 0lgw7 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #366 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 0htlr; 01fh9; 0bj9k; 01vs_v8; 0f4vbz; 01vhb0; 019pm_; 01vsl3_; 02_fj; 01vvb4m; ... *> query: (?x4813, 0c8wxp) <- profession(?x4813, ?x524), award_winner(?x289, ?x4813), spouse(?x12364, ?x4813), ?x524 = 02jknp *> conf = 0.17 ranks of expected_values: 4 EVAL 015njf religion 0c8wxp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 111.000 0.250 http://example.org/people/person/religion #6142-05typm PRED entity: 05typm PRED relation: award PRED expected values: 0bdw1g => 114 concepts (104 used for prediction) PRED predicted values (max 10 best out of 241): 09qvf4 (0.73 #36029, 0.72 #38433, 0.71 #18012), 0gqwc (0.46 #2873, 0.32 #1272, 0.17 #3273), 0gqyl (0.46 #2904, 0.23 #1303, 0.18 #3304), 0bdwft (0.45 #1266, 0.32 #2867, 0.14 #3267), 0bdwqv (0.36 #970, 0.12 #36430, 0.12 #30425), 09sb52 (0.32 #2841, 0.32 #1240, 0.29 #840), 094qd5 (0.32 #1244, 0.29 #2845, 0.13 #3245), 02ppm4q (0.31 #2956, 0.18 #1355, 0.14 #3356), 0bfvd4 (0.29 #913, 0.20 #513, 0.20 #113), 02z0dfh (0.27 #1273, 0.20 #2874, 0.11 #3274) >> Best rule #36029 for best value: >> intensional similarity = 3 >> extensional distance = 2245 >> proper extension: 089tm; 01pfr3; 04rcr; 01v0sx2; 01vsxdm; 03g5jw; 01wv9xn; 05crg7; 01dzz7; 0dvqq; ... >> query: (?x4630, ?x4225) <- award(?x4630, ?x375), award_winner(?x4225, ?x4630), award(?x488, ?x4225) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #36430 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2257 *> proper extension: 02j8nx; 0b05xm; 0fvppk; *> query: (?x4630, ?x375) <- nominated_for(?x4630, ?x11610), nominated_for(?x375, ?x11610) *> conf = 0.12 ranks of expected_values: 49 EVAL 05typm award 0bdw1g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 114.000 104.000 0.728 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6141-02qyntr PRED entity: 02qyntr PRED relation: nominated_for PRED expected values: 0bth54 0b6tzs 017gl1 0pb33 09cr8 05znxx 04j13sx 011yhm 0gwjw0c 01mgw 0b4lkx 0k_9j => 59 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1328): 017gl1 (0.86 #13107, 0.74 #8773, 0.53 #7329), 09q5w2 (0.79 #5769, 0.70 #31773, 0.70 #31772), 0hv1t (0.79 #5769, 0.70 #31773, 0.70 #31772), 0571m (0.79 #5769, 0.70 #31773, 0.70 #31772), 0p9tm (0.79 #5769, 0.70 #31773, 0.70 #31772), 011yg9 (0.73 #8022, 0.50 #809, 0.38 #13800), 07j8r (0.73 #4645, 0.50 #3203, 0.50 #1761), 01mgw (0.67 #8239, 0.59 #14017, 0.53 #9683), 0gmgwnv (0.67 #3732, 0.57 #13839, 0.53 #8061), 011ycb (0.67 #3565, 0.50 #2123, 0.36 #5007) >> Best rule #13107 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0gr0m; 054krc; 02rdyk7; 0gr42; 0gs96; 04kxsb; 02x2gy0; 057xs89; 05ztrmj; 02x1z2s; ... >> query: (?x6909, 017gl1) <- nominated_for(?x6909, ?x1392), award(?x800, ?x6909), nominated_for(?x628, ?x1392), ?x628 = 01kwld >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8, 15, 26, 30, 50, 54, 99, 100, 104, 349, 622 EVAL 02qyntr nominated_for 0k_9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 0b4lkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 01mgw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 0gwjw0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 011yhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 04j13sx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 05znxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 09cr8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 0pb33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 017gl1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 0b6tzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyntr nominated_for 0bth54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 59.000 28.000 0.865 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6140-0cq8nx PRED entity: 0cq8nx PRED relation: currency PRED expected values: 09nqf => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.86 #29, 0.78 #141, 0.77 #92), 02l6h (0.03 #39, 0.02 #60, 0.01 #102), 01nv4h (0.02 #114, 0.02 #135, 0.02 #93), 02gsvk (0.01 #153, 0.01 #160, 0.01 #167), 0kz1h (0.01 #54) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 0hmr4; >> query: (?x9611, 09nqf) <- genre(?x9611, ?x53), award_winner(?x9611, ?x788), award(?x9611, ?x1313), ?x1313 = 0gs9p >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cq8nx currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.860 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #6139-081hvm PRED entity: 081hvm PRED relation: people! PRED expected values: 0dryh9k => 118 concepts (102 used for prediction) PRED predicted values (max 10 best out of 36): 0dryh9k (0.47 #632, 0.44 #93, 0.43 #170), 041rx (0.13 #3394, 0.12 #5243, 0.12 #1391), 02sch9 (0.12 #343, 0.11 #728, 0.10 #420), 0bpjh3 (0.11 #102, 0.10 #256, 0.08 #333), 033tf_ (0.09 #1933, 0.09 #2010, 0.09 #2087), 0x67 (0.09 #2167, 0.09 #3631, 0.09 #2013), 03w9bjf (0.07 #208, 0.05 #747, 0.05 #285), 01rv7x (0.07 #501, 0.06 #886, 0.06 #1040), 0xnvg (0.06 #2016, 0.06 #1939, 0.06 #2093), 02w7gg (0.06 #2622, 0.06 #3238, 0.06 #4008) >> Best rule #632 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 01vzz1c; >> query: (?x12062, 0dryh9k) <- nationality(?x12062, ?x2146), location(?x12062, ?x13082), ?x2146 = 03rk0, category(?x13082, ?x134) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 081hvm people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 102.000 0.469 http://example.org/people/ethnicity/people #6138-0b_756 PRED entity: 0b_756 PRED relation: team PRED expected values: 04088s0 => 27 concepts (27 used for prediction) PRED predicted values (max 10 best out of 142): 03y9p40 (0.79 #169, 0.75 #180, 0.73 #145), 02pzy52 (0.70 #85, 0.67 #95, 0.67 #83), 026w398 (0.70 #85, 0.67 #86, 0.67 #82), 03d555l (0.70 #85, 0.67 #76, 0.65 #163), 026dqjm (0.70 #85, 0.65 #163, 0.65 #174), 02pqcfz (0.70 #85, 0.65 #163, 0.65 #174), 04088s0 (0.70 #85, 0.65 #163, 0.65 #174), 02pjzvh (0.70 #85, 0.65 #163, 0.65 #174), 03d5m8w (0.70 #85, 0.65 #163, 0.65 #174), 02r2qt7 (0.70 #85, 0.65 #174, 0.64 #138) >> Best rule #169 for best value: >> intensional similarity = 32 >> extensional distance = 12 >> proper extension: 0b_71r; >> query: (?x10594, 03y9p40) <- team(?x10594, ?x11789), team(?x10594, ?x9983), team(?x10594, ?x9909), team(?x10594, ?x9576), team(?x10594, ?x6003), team(?x10594, ?x3798), ?x9983 = 02q4ntp, instance_of_recurring_event(?x10594, ?x10863), ?x9576 = 02qk2d5, colors(?x6003, ?x3189), team(?x12798, ?x6003), team(?x12162, ?x6003), team(?x11210, ?x6003), team(?x9908, ?x6003), team(?x8824, ?x6003), team(?x7378, ?x6003), team(?x6583, ?x6003), team(?x4368, ?x6003), team(?x2302, ?x6003), team(?x1348, ?x11789), ?x9909 = 026wlnm, colors(?x11789, ?x332), ?x6583 = 0b_75k, ?x12798 = 0b_770, ?x11210 = 0b_6q5, ?x12162 = 0b_6_l, ?x4368 = 0b_6x2, ?x7378 = 0bzrxn, ?x8824 = 05g_nr, ?x2302 = 0b_77q, team(?x9266, ?x3798), ?x9908 = 0b_6lb >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 35 *> extensional distance = 4 *> proper extension: 0b_6x2; 0b_6_l; *> query: (?x10594, ?x4369) <- team(?x10594, ?x9983), team(?x10594, ?x9576), team(?x10594, ?x8528), team(?x10594, ?x6803), team(?x10594, ?x6003), team(?x10594, ?x3798), team(?x13045, ?x9983), team(?x12798, ?x9983), team(?x10673, ?x9983), team(?x9956, ?x9983), team(?x7042, ?x9983), team(?x6002, ?x9983), team(?x4803, ?x9983), team(?x2302, ?x9983), ?x12798 = 0b_770, ?x6003 = 02py8_w, ?x6803 = 03by7wc, ?x9956 = 0bzrsh, ?x9576 = 02qk2d5, ?x6002 = 0cc8q3, team(?x4747, ?x9983), colors(?x9983, ?x3315), colors(?x9983, ?x3189), ?x3189 = 01g5v, ?x3315 = 0jc_p, ?x8528 = 091tgz, ?x13045 = 0bqthy, ?x10673 = 0b_6mr, ?x2302 = 0b_77q, team(?x4803, ?x10171), team(?x4803, ?x4369), ?x7042 = 0b_72t, ?x10171 = 026w398, ?x3798 = 02ptzz0, locations(?x4803, ?x2017) *> conf = 0.70 ranks of expected_values: 7 EVAL 0b_756 team 04088s0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 27.000 27.000 0.786 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #6137-06y7d PRED entity: 06y7d PRED relation: religion PRED expected values: 0kpl => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 34): 0c8wxp (0.49 #5489, 0.40 #2480, 0.35 #5975), 0kpl (0.42 #406, 0.36 #1996, 0.33 #583), 03_gx (0.39 #1073, 0.38 #190, 0.33 #234), 019cr (0.27 #539, 0.14 #495, 0.11 #584), 092bf5 (0.11 #280, 0.10 #324, 0.08 #721), 051kv (0.11 #269, 0.10 #313, 0.07 #445), 03j6c (0.09 #2494, 0.09 #1124, 0.08 #5591), 01lp8 (0.09 #1016, 0.07 #441, 0.06 #5484), 0631_ (0.08 #360, 0.08 #757, 0.07 #492), 0v53x (0.08 #380, 0.07 #512, 0.07 #556) >> Best rule #5489 for best value: >> intensional similarity = 3 >> extensional distance = 742 >> proper extension: 01w3v; 0mcf4; >> query: (?x12147, 0c8wxp) <- religion(?x12147, ?x8140), religion(?x4466, ?x8140), program_creator(?x9787, ?x4466) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 099bk; *> query: (?x12147, 0kpl) <- student(?x3437, ?x12147), gender(?x12147, ?x231), type_of_union(?x12147, ?x566), ?x3437 = 02_xgp2 *> conf = 0.42 ranks of expected_values: 2 EVAL 06y7d religion 0kpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 201.000 201.000 0.485 http://example.org/people/person/religion #6136-0g9k4 PRED entity: 0g9k4 PRED relation: contains! PRED expected values: 0dj0x => 89 concepts (23 used for prediction) PRED predicted values (max 10 best out of 196): 09c7w0 (0.63 #14329, 0.62 #15225, 0.62 #16122), 01n7q (0.36 #3656, 0.26 #7237, 0.24 #8133), 04jpl (0.22 #1810, 0.16 #916, 0.12 #2705), 0d060g (0.16 #5382, 0.16 #6278, 0.13 #7173), 0345h (0.14 #5450, 0.14 #6346, 0.11 #7241), 03rjj (0.13 #5379, 0.12 #6275, 0.06 #9858), 05fjf (0.10 #3951, 0.07 #7532, 0.07 #8428), 059rby (0.09 #14346, 0.09 #15242, 0.09 #16139), 0978r (0.07 #20602, 0.06 #1099, 0.06 #1993), 06q1r (0.07 #20602, 0.06 #3033, 0.02 #17014) >> Best rule #14329 for best value: >> intensional similarity = 5 >> extensional distance = 1411 >> proper extension: 0n4m5; 0ygbf; 0n4mk; 0jkhr; 02rtlp5; 0n3ll; 0n474; 0yj9v; 013hvr; 02pdhz; ... >> query: (?x14732, 09c7w0) <- contains(?x1310, ?x14732), contains(?x1310, ?x7213), origin(?x1694, ?x1310), place_of_birth(?x1818, ?x7213), place_of_birth(?x3528, ?x1310) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #20602 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1681 *> proper extension: 03v6t; 0nr_q; 0nryt; 0nrqh; 01_qgp; 01nf9x; 02qjb7z; 0ghvb; 02c7tb; 0nrnz; ... *> query: (?x14732, ?x11868) <- contains(?x1310, ?x14732), contains(?x1310, ?x13696), state_province_region(?x963, ?x1310), contains(?x11868, ?x13696), location(?x981, ?x1310) *> conf = 0.07 ranks of expected_values: 28 EVAL 0g9k4 contains! 0dj0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 89.000 23.000 0.631 http://example.org/location/location/contains #6135-01vsl3_ PRED entity: 01vsl3_ PRED relation: artists! PRED expected values: 02yv6b => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 250): 06j6l (0.50 #47, 0.35 #8342, 0.33 #16639), 0xhtw (0.50 #11080, 0.31 #18145, 0.29 #6469), 025sc50 (0.50 #49, 0.31 #16641, 0.29 #8344), 0gywn (0.50 #57, 0.24 #16649, 0.22 #1899), 02vjzr (0.50 #133, 0.17 #440, 0.11 #1975), 02lnbg (0.41 #8353, 0.18 #9275, 0.17 #365), 02yv6b (0.39 #11160, 0.22 #2246, 0.20 #6549), 0dl5d (0.36 #11083, 0.19 #18148, 0.17 #2169), 03_d0 (0.35 #2161, 0.31 #3391, 0.30 #10458), 017_qw (0.34 #4977, 0.32 #7743, 0.31 #8050) >> Best rule #47 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0152cw; 01vrt_c; 01vsykc; >> query: (?x2799, 06j6l) <- profession(?x2799, ?x131), location_of_ceremony(?x2799, ?x9026), artist(?x6672, ?x2799) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #11160 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 82 *> proper extension: 05563d; 03k3b; *> query: (?x2799, 02yv6b) <- artists(?x2809, ?x2799), ?x2809 = 05w3f *> conf = 0.39 ranks of expected_values: 7 EVAL 01vsl3_ artists! 02yv6b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 148.000 148.000 0.500 http://example.org/music/genre/artists #6134-01f8gz PRED entity: 01f8gz PRED relation: nominated_for! PRED expected values: 09v478h => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 203): 0gq9h (0.46 #2671, 0.43 #5041, 0.40 #1960), 019f4v (0.43 #5032, 0.42 #2662, 0.42 #3136), 0gq_v (0.43 #4997, 0.42 #2627, 0.40 #20), 0gs9p (0.40 #2673, 0.40 #66, 0.38 #5043), 0gs96 (0.40 #92, 0.33 #329, 0.25 #2699), 0gr51 (0.40 #80, 0.29 #2687, 0.22 #317), 0f4x7 (0.33 #5003, 0.33 #2633, 0.28 #4292), 040njc (0.32 #4984, 0.31 #2614, 0.28 #1903), 03r8tl (0.32 #1266, 0.32 #555, 0.30 #792), 03rbj2 (0.32 #1342, 0.32 #631, 0.30 #868) >> Best rule #2671 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 06lpmt; >> query: (?x1625, 0gq9h) <- film_production_design_by(?x1625, ?x9086), nominated_for(?x7215, ?x1625), titles(?x2645, ?x1625), written_by(?x1625, ?x4169) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #219 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 02gd6x; *> query: (?x1625, 09v478h) <- genre(?x1625, ?x1626), film_release_region(?x1625, ?x2645), ?x1626 = 03q4nz, written_by(?x1625, ?x4169) *> conf = 0.20 ranks of expected_values: 43 EVAL 01f8gz nominated_for! 09v478h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 101.000 101.000 0.462 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6133-081nh PRED entity: 081nh PRED relation: produced_by! PRED expected values: 023p33 019kyn => 169 concepts (167 used for prediction) PRED predicted values (max 10 best out of 428): 029jt9 (0.20 #1745, 0.17 #2689, 0.12 #5521), 0gl3hr (0.20 #1543, 0.17 #2487, 0.12 #5319), 0bl5c (0.20 #1485, 0.17 #2429, 0.12 #5261), 0kb57 (0.20 #1215, 0.17 #2159, 0.12 #4991), 0kb1g (0.20 #1804, 0.17 #2748, 0.05 #13133), 0bbgly (0.17 #2809, 0.12 #5641, 0.05 #13194), 03hjv97 (0.17 #1955, 0.12 #4787, 0.05 #12340), 0hv81 (0.14 #4341, 0.10 #6229, 0.07 #8117), 0h3k3f (0.14 #4567, 0.10 #6455, 0.07 #8343), 0209xj (0.13 #8556, 0.05 #10445, 0.04 #14221) >> Best rule #1745 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 022p06; 01v5h; 05hjmd; >> query: (?x2426, 029jt9) <- place_of_burial(?x2426, ?x2495), organizations_founded(?x2426, ?x99), award_nominee(?x2426, ?x1377) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #9441 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 02nygk; *> query: (?x2426, ?x148) <- people(?x1423, ?x2426), company(?x2426, ?x3920), production_companies(?x148, ?x3920) *> conf = 0.09 ranks of expected_values: 58, 63 EVAL 081nh produced_by! 019kyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 169.000 167.000 0.200 http://example.org/film/film/produced_by EVAL 081nh produced_by! 023p33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 169.000 167.000 0.200 http://example.org/film/film/produced_by #6132-01zzk4 PRED entity: 01zzk4 PRED relation: contains! PRED expected values: 04jpl => 90 concepts (35 used for prediction) PRED predicted values (max 10 best out of 162): 07ssc (0.76 #11686, 0.64 #8101, 0.64 #8996), 09c7w0 (0.60 #24211, 0.59 #25108, 0.57 #26005), 0cxgc (0.58 #4484, 0.51 #5382, 0.50 #3587), 04jpl (0.50 #1815, 0.30 #2713, 0.24 #7196), 0978r (0.31 #3794, 0.23 #4692, 0.19 #6485), 05l5n (0.31 #3709, 0.23 #4607, 0.14 #5505), 01n7q (0.25 #19799, 0.14 #9938, 0.10 #28773), 02j9z (0.25 #10785, 0.10 #2719, 0.07 #6307), 0jt5zcn (0.23 #3729, 0.17 #4627, 0.09 #6420), 01w0v (0.19 #3795, 0.14 #4693, 0.14 #5591) >> Best rule #11686 for best value: >> intensional similarity = 5 >> extensional distance = 332 >> proper extension: 04jpl; 02jx1; 0zc6f; 0dbdy; 05l5n; 0jcg8; 07w4j; 0jt5zcn; 06y9v; 0978r; ... >> query: (?x13887, 07ssc) <- contains(?x1310, ?x13887), second_level_divisions(?x1310, ?x1156), nationality(?x57, ?x1310), contains(?x1310, ?x7918), ?x7918 = 0gl6f >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1815 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 01g0p5; 019vsw; 0m4yg; *> query: (?x13887, 04jpl) <- contains(?x1310, ?x13887), ?x1310 = 02jx1, state_province_region(?x13887, ?x11432), contains(?x11432, ?x10922), ?x10922 = 049kw *> conf = 0.50 ranks of expected_values: 4 EVAL 01zzk4 contains! 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 35.000 0.763 http://example.org/location/location/contains #6131-01bn3l PRED entity: 01bn3l PRED relation: film! PRED expected values: 01gn36 => 118 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1329): 0b455l (0.52 #20798, 0.52 #2080, 0.50 #2079), 03kpvp (0.52 #20798, 0.52 #2080, 0.50 #2079), 04vlh5 (0.52 #20798, 0.52 #2080, 0.50 #2079), 058frd (0.43 #41589, 0.42 #85247, 0.42 #143477), 016tw3 (0.43 #41589, 0.42 #85247, 0.42 #143477), 0c5vh (0.23 #18718, 0.20 #27035, 0.18 #6240), 0lpjn (0.18 #478, 0.06 #19196, 0.06 #27513), 06pj8 (0.15 #12480, 0.15 #10400, 0.13 #22878), 02q_cc (0.15 #12480, 0.15 #10400, 0.13 #22878), 0488g9 (0.15 #12480, 0.15 #10400, 0.13 #22878) >> Best rule #20798 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 013q0p; >> query: (?x7801, ?x10754) <- prequel(?x7800, ?x7801), award_winner(?x7801, ?x10754), produced_by(?x7801, ?x6086), profession(?x10754, ?x319) >> conf = 0.52 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 01bn3l film! 01gn36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 76.000 0.523 http://example.org/film/actor/film./film/performance/film #6130-0vmt PRED entity: 0vmt PRED relation: district_represented! PRED expected values: 024tcq => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 46): 024tcq (0.90 #336, 0.83 #796, 0.82 #658), 043djx (0.59 #465, 0.59 #327, 0.58 #649), 03rl1g (0.59 #323, 0.58 #645, 0.57 #783), 03rtmz (0.56 #1013, 0.52 #334, 0.31 #472), 02glc4 (0.56 #1013, 0.48 #347, 0.31 #485), 03tcbx (0.56 #1013, 0.45 #333, 0.28 #793), 03z5xd (0.56 #1013, 0.31 #330, 0.20 #698), 03ww_x (0.56 #1013, 0.31 #326, 0.19 #2900), 032ft5 (0.56 #1013, 0.19 #2900, 0.17 #328), 0495ys (0.56 #1013, 0.19 #2900, 0.14 #324) >> Best rule #336 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 04ych; 081yw; 03gh4; >> query: (?x938, 024tcq) <- location(?x1285, ?x938), district_represented(?x3463, ?x938), contains(?x938, ?x3983), ?x3463 = 02bqmq >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0vmt district_represented! 024tcq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.897 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #6129-01pjr7 PRED entity: 01pjr7 PRED relation: written_by! PRED expected values: 0j_tw => 108 concepts (83 used for prediction) PRED predicted values (max 10 best out of 391): 0473rc (0.55 #7903, 0.40 #5269, 0.34 #2633), 011x_4 (0.55 #7903, 0.40 #5269, 0.34 #2633), 02825cv (0.19 #7244, 0.16 #5268, 0.09 #3950), 0g_zyp (0.19 #7244, 0.16 #5268, 0.09 #3950), 028kj0 (0.19 #7244, 0.16 #5268, 0.09 #3950), 014bpd (0.19 #7244, 0.16 #5268, 0.09 #3950), 025rxjq (0.19 #7244, 0.16 #5268, 0.09 #3950), 011yn5 (0.19 #7244, 0.16 #5268, 0.09 #3950), 0bvn25 (0.19 #7244, 0.16 #5268, 0.09 #3950), 02ph9tm (0.07 #425, 0.04 #1083, 0.03 #3716) >> Best rule #7903 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 096hm; >> query: (?x7624, ?x6099) <- written_by(?x1295, ?x7624), gender(?x7624, ?x231), film(?x7624, ?x6099) >> conf = 0.55 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01pjr7 written_by! 0j_tw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 83.000 0.552 http://example.org/film/film/written_by #6128-01g4bk PRED entity: 01g4bk PRED relation: award PRED expected values: 04zx08r => 91 concepts (78 used for prediction) PRED predicted values (max 10 best out of 382): 0789r6 (0.70 #26743, 0.70 #25527, 0.70 #24716), 040njc (0.56 #818, 0.17 #1224, 0.15 #2439), 0gs9p (0.54 #890, 0.16 #3321, 0.16 #1296), 019f4v (0.53 #877, 0.16 #1283, 0.13 #2498), 0gq9h (0.45 #888, 0.15 #1294, 0.14 #4939), 02pqp12 (0.37 #881, 0.11 #1287, 0.10 #3312), 0gr51 (0.30 #911, 0.15 #1317, 0.14 #3342), 09sb52 (0.29 #11382, 0.26 #10977, 0.25 #20702), 02rdyk7 (0.29 #902, 0.09 #1308, 0.09 #3333), 04dn09n (0.23 #854, 0.12 #1260, 0.11 #4905) >> Best rule #26743 for best value: >> intensional similarity = 4 >> extensional distance = 2238 >> proper extension: 089tm; 086k8; 01pfr3; 04lgymt; 017s11; 02mslq; 016tt2; 025jfl; 04rcr; 0g1rw; ... >> query: (?x9747, ?x13075) <- award(?x9747, ?x6147), award_winner(?x13075, ?x9747), award_winner(?x13075, ?x2086), nominated_for(?x2086, ?x2085) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #247 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 02bxjp; *> query: (?x9747, 04zx08r) <- profession(?x9747, ?x987), ?x987 = 0dxtg, gender(?x9747, ?x231), location(?x9747, ?x9559), ?x9559 = 07dfk *> conf = 0.20 ranks of expected_values: 12 EVAL 01g4bk award 04zx08r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 91.000 78.000 0.703 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6127-027l0b PRED entity: 027l0b PRED relation: gender PRED expected values: 05zppz => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #11, 0.87 #25, 0.86 #29), 02zsn (0.32 #86, 0.32 #84, 0.32 #20) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 0785v8; >> query: (?x2794, 05zppz) <- award(?x2794, ?x3066), nominated_for(?x2794, ?x2293), ?x3066 = 0gqy2 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027l0b gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.917 http://example.org/people/person/gender #6126-07srw PRED entity: 07srw PRED relation: district_represented! PRED expected values: 02bqn1 => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 41): 02bqn1 (0.86 #88, 0.46 #1764, 0.46 #744), 043djx (0.64 #86, 0.61 #619, 0.55 #824), 03rl1g (0.64 #83, 0.61 #616, 0.54 #739), 01gtcc (0.57 #93, 0.43 #626, 0.41 #749), 01gtcq (0.57 #101, 0.41 #634, 0.38 #839), 01h7xx (0.54 #644, 0.50 #111, 0.49 #849), 01gt99 (0.50 #652, 0.50 #119, 0.47 #857), 01gtdd (0.50 #116, 0.48 #649, 0.45 #854), 01gtc0 (0.50 #100, 0.46 #633, 0.43 #838), 01gst_ (0.48 #624, 0.45 #829, 0.43 #91) >> Best rule #88 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0g0syc; >> query: (?x2256, 02bqn1) <- district_represented(?x6728, ?x2256), district_represented(?x2861, ?x2256), ?x6728 = 070mff, ?x2861 = 03tcbx >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07srw district_represented! 02bqn1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 220.000 220.000 0.857 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #6125-050sw4 PRED entity: 050sw4 PRED relation: artists PRED expected values: 016vqk => 49 concepts (6 used for prediction) PRED predicted values (max 10 best out of 3360): 01vtj38 (0.60 #666, 0.24 #1751, 0.22 #2841), 02z4b_8 (0.60 #641, 0.22 #1726, 0.19 #3902), 0127s7 (0.60 #543, 0.22 #2718, 0.21 #3804), 0197tq (0.60 #12, 0.21 #1097, 0.17 #4359), 01vvycq (0.50 #47, 0.21 #1132, 0.20 #2222), 025ldg (0.50 #374, 0.19 #1459, 0.17 #2549), 015882 (0.50 #130, 0.17 #1215, 0.17 #4477), 09889g (0.50 #453, 0.16 #2628, 0.16 #1538), 01vvyfh (0.50 #345, 0.16 #1430, 0.14 #2520), 0bdxs5 (0.50 #806, 0.16 #1891, 0.14 #2981) >> Best rule #666 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 03_d0; 064t9; 01lyv; 05bt6j; 06j6l; 0ggx5q; 02vjzr; 06924p; >> query: (?x14606, 01vtj38) <- artists(?x14606, ?x9184), ?x9184 = 01fkxr >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #837 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 8 *> proper extension: 03_d0; 064t9; 01lyv; 05bt6j; 06j6l; 0ggx5q; 02vjzr; 06924p; *> query: (?x14606, 016vqk) <- artists(?x14606, ?x9184), ?x9184 = 01fkxr *> conf = 0.50 ranks of expected_values: 13 EVAL 050sw4 artists 016vqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 49.000 6.000 0.600 http://example.org/music/genre/artists #6124-018gkb PRED entity: 018gkb PRED relation: performance_role PRED expected values: 042v_gx => 136 concepts (77 used for prediction) PRED predicted values (max 10 best out of 46): 026t6 (0.24 #1055, 0.24 #1010, 0.21 #595), 03bx0bm (0.22 #156, 0.19 #429, 0.13 #934), 0l14md (0.20 #189, 0.11 #144, 0.11 #96), 01vj9c (0.12 #55, 0.07 #240, 0.07 #286), 013y1f (0.11 #157, 0.11 #109, 0.10 #202), 0l14qv (0.11 #142, 0.11 #552, 0.10 #187), 0342h (0.11 #93, 0.10 #186, 0.06 #598), 02snj9 (0.11 #172, 0.10 #445, 0.06 #401), 02dlh2 (0.11 #175, 0.10 #448, 0.02 #953), 0l15bq (0.07 #249, 0.07 #295, 0.05 #431) >> Best rule #1055 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 03ds3; 04gycf; >> query: (?x11161, ?x212) <- instrumentalists(?x1166, ?x11161), instrumentalists(?x212, ?x11161), ?x212 = 026t6, group(?x1166, ?x442) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #3051 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 427 *> proper extension: 01k5t_3; 015f7; 0bvzp; 02z4b_8; 01d_h; 01m7f5r; *> query: (?x11161, ?x314) <- role(?x11161, ?x314), performance_role(?x212, ?x314), profession(?x11161, ?x2659), role(?x74, ?x314) *> conf = 0.05 ranks of expected_values: 21 EVAL 018gkb performance_role 042v_gx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 136.000 77.000 0.236 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #6123-02yy8 PRED entity: 02yy8 PRED relation: gender PRED expected values: 05zppz => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #135, 0.89 #117, 0.89 #145), 02zsn (0.46 #184, 0.46 #289, 0.45 #130) >> Best rule #135 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 0459z; >> query: (?x12571, 05zppz) <- influenced_by(?x12571, ?x7893), people(?x10900, ?x12571), nationality(?x12571, ?x94), profession(?x7893, ?x9682) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02yy8 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.900 http://example.org/people/person/gender #6122-026p_bs PRED entity: 026p_bs PRED relation: cinematography PRED expected values: 06hzsx => 77 concepts (53 used for prediction) PRED predicted values (max 10 best out of 37): 026sb55 (0.33 #252, 0.19 #441, 0.17 #567), 06nz46 (0.17 #76, 0.11 #202, 0.06 #391), 04flrx (0.14 #152, 0.10 #278, 0.06 #404), 06r_by (0.10 #275, 0.06 #464, 0.04 #1160), 02vx4c2 (0.06 #412, 0.06 #538, 0.05 #853), 03rqww (0.04 #988, 0.04 #609, 0.04 #672), 03ctv8m (0.04 #588, 0.04 #651, 0.03 #714), 09bxq9 (0.04 #607, 0.03 #733, 0.03 #796), 04qvl7 (0.03 #1772, 0.03 #1201, 0.02 #2344), 02404v (0.03 #731, 0.03 #794, 0.02 #984) >> Best rule #252 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 02sg5v; 02qrv7; 0g5pv3; >> query: (?x650, 026sb55) <- genre(?x650, ?x5104), ?x5104 = 0bkbm, nominated_for(?x8737, ?x650), film(?x8002, ?x650), language(?x650, ?x254), written_by(?x650, ?x4405) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026p_bs cinematography 06hzsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 53.000 0.333 http://example.org/film/film/cinematography #6121-05mrf_p PRED entity: 05mrf_p PRED relation: genre PRED expected values: 02n4kr 03mqtr => 76 concepts (51 used for prediction) PRED predicted values (max 10 best out of 79): 03mqtr (0.71 #4497, 0.59 #4496, 0.48 #5799), 07ssc (0.59 #4496, 0.48 #5799, 0.46 #4377), 02kdv5l (0.47 #828, 0.47 #1301, 0.45 #1064), 05p553 (0.35 #1539, 0.34 #594, 0.33 #5329), 02l7c8 (0.34 #723, 0.31 #4036, 0.31 #1432), 02n4kr (0.24 #1070, 0.24 #1307, 0.23 #8), 082gq (0.23 #30, 0.13 #1447, 0.12 #4051), 0bkbm (0.23 #38, 0.08 #864, 0.08 #1337), 03k9fj (0.22 #3795, 0.21 #837, 0.21 #5217), 04xvlr (0.20 #4378, 0.19 #2838, 0.19 #1418) >> Best rule #4497 for best value: >> intensional similarity = 3 >> extensional distance = 889 >> proper extension: 06cs95; >> query: (?x5074, ?x11405) <- nominated_for(?x963, ?x5074), titles(?x11405, ?x5074), genre(?x288, ?x11405) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 05mrf_p genre 03mqtr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 51.000 0.715 http://example.org/film/film/genre EVAL 05mrf_p genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 76.000 51.000 0.715 http://example.org/film/film/genre #6120-04rcl7 PRED entity: 04rcl7 PRED relation: production_companies! PRED expected values: 0_7w6 01_1pv 06fcqw 09v3jyg => 110 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1178): 06fcqw (0.50 #5223, 0.33 #1824, 0.29 #8622), 07q1m (0.50 #5155, 0.33 #1756, 0.29 #8554), 019kyn (0.50 #5051, 0.33 #1652, 0.29 #8450), 09wnnb (0.50 #5568, 0.33 #2169, 0.29 #8967), 06y611 (0.50 #5547, 0.33 #2148, 0.29 #8946), 05nlx4 (0.50 #5323, 0.33 #1924, 0.29 #8722), 05sw5b (0.50 #5064, 0.33 #1665, 0.29 #8463), 0kcn7 (0.50 #4814, 0.33 #1415, 0.29 #8213), 0dq626 (0.50 #4564, 0.33 #1165, 0.29 #7963), 034qmv (0.50 #4545, 0.33 #1146, 0.29 #7944) >> Best rule #5223 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01795t; >> query: (?x10685, 06fcqw) <- production_companies(?x11073, ?x10685), production_companies(?x8112, ?x10685), film_release_distribution_medium(?x11073, ?x81), titles(?x1510, ?x11073), ?x8112 = 04g73n >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12, 42, 48 EVAL 04rcl7 production_companies! 09v3jyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 110.000 90.000 0.500 http://example.org/film/film/production_companies EVAL 04rcl7 production_companies! 06fcqw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 90.000 0.500 http://example.org/film/film/production_companies EVAL 04rcl7 production_companies! 01_1pv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 110.000 90.000 0.500 http://example.org/film/film/production_companies EVAL 04rcl7 production_companies! 0_7w6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 110.000 90.000 0.500 http://example.org/film/film/production_companies #6119-02rx2m5 PRED entity: 02rx2m5 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 25): 09vw2b7 (0.71 #114, 0.71 #78, 0.66 #294), 01vx2h (0.57 #83, 0.43 #119, 0.33 #299), 0dxtw (0.42 #298, 0.36 #946, 0.34 #550), 01pvkk (0.31 #696, 0.29 #264, 0.29 #1817), 089g0h (0.29 #128, 0.29 #92, 0.12 #164), 02ynfr (0.29 #124, 0.20 #304, 0.18 #952), 0215hd (0.29 #91, 0.16 #307, 0.15 #451), 01xy5l_ (0.29 #86, 0.14 #122, 0.14 #266), 02rh1dz (0.29 #117, 0.14 #81, 0.12 #261), 0d2b38 (0.29 #98, 0.14 #134, 0.12 #278) >> Best rule #114 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0cp0t91; >> query: (?x1866, 09vw2b7) <- film(?x4046, ?x1866), featured_film_locations(?x1866, ?x5777), ?x4046 = 07swvb >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rx2m5 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.714 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #6118-01gx5f PRED entity: 01gx5f PRED relation: role PRED expected values: 0dwvl => 126 concepts (54 used for prediction) PRED predicted values (max 10 best out of 117): 02sgy (0.69 #956, 0.53 #1051, 0.40 #2099), 05148p4 (0.62 #970, 0.41 #1065, 0.38 #2883), 01vj9c (0.44 #962, 0.37 #1627, 0.30 #867), 026t6 (0.42 #1714, 0.38 #2382, 0.38 #2576), 03bx0bm (0.42 #1714, 0.38 #2382, 0.38 #2576), 0l14qv (0.40 #860, 0.39 #1718, 0.38 #955), 03gvt (0.30 #1784, 0.25 #1021, 0.21 #1686), 01s0ps (0.29 #1102, 0.25 #1007, 0.20 #1770), 0g2dz (0.27 #3533, 0.26 #3532, 0.25 #3151), 0gkd1 (0.25 #659, 0.25 #184, 0.24 #1134) >> Best rule #956 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 03k0yw; >> query: (?x3399, 02sgy) <- role(?x3399, ?x1437), role(?x3399, ?x432), profession(?x3399, ?x2348), ?x1437 = 01vdm0, ?x432 = 042v_gx, ?x2348 = 0nbcg >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0lsw9; 01w9mnm; *> query: (?x3399, 0dwvl) <- artists(?x11746, ?x3399), role(?x3399, ?x74), performance_role(?x3399, ?x212), ?x11746 = 03w94xt, nationality(?x3399, ?x94) *> conf = 0.25 ranks of expected_values: 12 EVAL 01gx5f role 0dwvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 126.000 54.000 0.688 http://example.org/music/artist/track_contributions./music/track_contribution/role #6117-0yyts PRED entity: 0yyts PRED relation: nominated_for! PRED expected values: 02pqp12 => 101 concepts (94 used for prediction) PRED predicted values (max 10 best out of 202): 027c95y (0.68 #2262, 0.67 #10181, 0.66 #12672), 0gs9p (0.59 #2092, 0.57 #57, 0.54 #284), 019f4v (0.57 #49, 0.53 #2084, 0.48 #276), 0gr51 (0.57 #69, 0.27 #522, 0.24 #1426), 04dn09n (0.46 #258, 0.38 #936, 0.38 #484), 02qyntr (0.43 #168, 0.35 #621, 0.32 #395), 02qyp19 (0.43 #1, 0.22 #454, 0.18 #16522), 03hl6lc (0.43 #119, 0.17 #572, 0.16 #1476), 04kxsb (0.38 #313, 0.34 #991, 0.32 #765), 02pqp12 (0.38 #506, 0.29 #2088, 0.29 #53) >> Best rule #2262 for best value: >> intensional similarity = 4 >> extensional distance = 155 >> proper extension: 0b73_1d; 0b6tzs; 04mzf8; 09p0ct; 05j82v; 09z2b7; 0p_th; 026gyn_; 03hj3b3; 016z7s; ... >> query: (?x2370, ?x384) <- award_winner(?x2370, ?x1197), award(?x2370, ?x384), nominated_for(?x601, ?x2370), ?x601 = 0gr4k >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #506 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 101 *> proper extension: 0yyn5; 01gvpz; *> query: (?x2370, 02pqp12) <- award_winner(?x2370, ?x1197), nominated_for(?x749, ?x2370), nominated_for(?x7522, ?x2370), ?x749 = 094qd5 *> conf = 0.38 ranks of expected_values: 10 EVAL 0yyts nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 101.000 94.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6116-0jvtp PRED entity: 0jvtp PRED relation: place_of_death PRED expected values: 04qdj => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 33): 04jpl (0.33 #7, 0.05 #4476, 0.04 #5059), 030qb3t (0.25 #798, 0.24 #2546, 0.17 #4297), 02_286 (0.25 #789, 0.09 #5648, 0.08 #7009), 01m1_d (0.20 #559, 0.14 #753), 0r0f7 (0.20 #312), 0k049 (0.12 #779, 0.08 #4278, 0.07 #5249), 0f2wj (0.08 #3508, 0.03 #8563, 0.03 #10116), 013wf1 (0.07 #5635, 0.06 #4858, 0.02 #18854), 06_kh (0.05 #4668, 0.04 #5445, 0.04 #5251), 0281y0 (0.05 #1276, 0.03 #2247, 0.02 #2441) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 08304; >> query: (?x8254, 04jpl) <- student(?x8833, ?x8254), ?x8833 = 0173s9, nationality(?x8254, ?x512) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jvtp place_of_death 04qdj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 134.000 0.333 http://example.org/people/deceased_person/place_of_death #6115-01q03 PRED entity: 01q03 PRED relation: genre! PRED expected values: 03n3gl 02fwfb 06bc59 => 70 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1849): 0gd92 (0.74 #9187, 0.33 #14193, 0.33 #3163), 09cxm4 (0.57 #16159, 0.50 #8804, 0.50 #6966), 07z6xs (0.57 #17438, 0.29 #21115, 0.23 #28473), 011wtv (0.57 #17329, 0.24 #21006, 0.23 #28364), 02v63m (0.50 #7532, 0.50 #5694, 0.43 #14887), 02v5_g (0.50 #8155, 0.50 #6317, 0.43 #15510), 02mc5v (0.50 #8774, 0.50 #6936, 0.43 #16129), 0291ck (0.50 #8946, 0.50 #7108, 0.43 #16301), 0cqr0q (0.50 #8877, 0.43 #16232, 0.41 #21748), 08k40m (0.50 #6003, 0.43 #15196, 0.33 #13360) >> Best rule #9187 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 03npn; >> query: (?x271, ?x7501) <- genre(?x11958, ?x271), genre(?x10873, ?x271), genre(?x6681, ?x271), genre(?x4880, ?x271), genre(?x994, ?x271), ?x11958 = 02t_h3, ?x6681 = 04y9mm8, ?x10873 = 06cgf, ?x4880 = 029k4p, prequel(?x994, ?x7501), film(?x989, ?x994) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #6653 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 0gf28; *> query: (?x271, 03n3gl) <- genre(?x11958, ?x271), genre(?x6681, ?x271), genre(?x2102, ?x271), ?x11958 = 02t_h3, film(?x7023, ?x6681), ?x2102 = 034qzw, film_crew_role(?x6681, ?x468), student(?x3439, ?x7023), genre(?x9633, ?x271), award(?x7023, ?x618) *> conf = 0.50 ranks of expected_values: 18, 143, 894 EVAL 01q03 genre! 06bc59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 70.000 24.000 0.740 http://example.org/film/film/genre EVAL 01q03 genre! 02fwfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 70.000 24.000 0.740 http://example.org/film/film/genre EVAL 01q03 genre! 03n3gl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 70.000 24.000 0.740 http://example.org/film/film/genre #6114-01xyy PRED entity: 01xyy PRED relation: contains! PRED expected values: 04swx => 191 concepts (61 used for prediction) PRED predicted values (max 10 best out of 314): 09c7w0 (0.78 #23293, 0.72 #37630, 0.70 #25981), 04_1l0v (0.60 #12986, 0.53 #9405, 0.53 #7614), 07ssc (0.53 #16151, 0.40 #19736, 0.30 #24218), 0d060g (0.44 #40326, 0.33 #52873, 0.27 #28679), 02jx1 (0.41 #16206, 0.24 #19791, 0.18 #24273), 03_3d (0.39 #6281, 0.12 #2697, 0.11 #22403), 02qkt (0.34 #5720, 0.30 #48377, 0.22 #15570), 0f8l9c (0.34 #25128, 0.26 #40360, 0.19 #52907), 01n7q (0.33 #14406, 0.24 #18886, 0.15 #23368), 0345h (0.32 #4560, 0.24 #17097, 0.22 #8142) >> Best rule #23293 for best value: >> intensional similarity = 6 >> extensional distance = 128 >> proper extension: 0d6hn; >> query: (?x11223, 09c7w0) <- contains(?x1353, ?x11223), location_of_ceremony(?x566, ?x11223), time_zones(?x11223, ?x10735), film_release_region(?x66, ?x1353), combatants(?x94, ?x1353), entity_involved(?x13053, ?x1353) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4342 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 0p_x; 0k2q; *> query: (?x11223, 04swx) <- contains(?x1353, ?x11223), locations(?x9939, ?x1353), adjoins(?x1353, ?x1499), ?x1499 = 01znc_ *> conf = 0.32 ranks of expected_values: 11 EVAL 01xyy contains! 04swx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 191.000 61.000 0.785 http://example.org/location/location/contains #6113-03dctt PRED entity: 03dctt PRED relation: profession PRED expected values: 02jknp => 130 concepts (49 used for prediction) PRED predicted values (max 10 best out of 59): 02hrh1q (0.78 #4010, 0.75 #7120, 0.74 #4602), 02jknp (0.69 #599, 0.57 #451, 0.49 #3116), 0d1pc (0.50 #346, 0.40 #198, 0.09 #1975), 03gjzk (0.39 #1198, 0.38 #2235, 0.32 #2679), 02t8yb (0.25 #81, 0.20 #229, 0.17 #377), 0dgd_ (0.25 #30, 0.20 #178, 0.17 #326), 0cbd2 (0.25 #2227, 0.24 #1190, 0.21 #1634), 09jwl (0.20 #1943, 0.20 #2831, 0.20 #2387), 018gz8 (0.20 #2237, 0.19 #1200, 0.14 #1348), 0np9r (0.20 #168, 0.17 #316, 0.14 #2241) >> Best rule #4010 for best value: >> intensional similarity = 5 >> extensional distance = 787 >> proper extension: 0z4s; 01csvq; 018db8; 0126rp; 01vvpjj; 015t56; 016h4r; 085pr; 018n6m; 03f19q4; ... >> query: (?x13550, 02hrh1q) <- award(?x13550, ?x4443), profession(?x13550, ?x319), gender(?x13550, ?x231), location(?x13550, ?x7297), people(?x13008, ?x13550) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #599 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 05q9g1; 03hzkq; 02g5bf; *> query: (?x13550, 02jknp) <- award(?x13550, ?x4443), type_of_union(?x13550, ?x566), ?x4443 = 0b6k___, ?x566 = 04ztj, nationality(?x13550, ?x2146) *> conf = 0.69 ranks of expected_values: 2 EVAL 03dctt profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 49.000 0.785 http://example.org/people/person/profession #6112-01vw20_ PRED entity: 01vw20_ PRED relation: category PRED expected values: 08mbj5d => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #20, 0.86 #25, 0.85 #48) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 0150jk; 0dtd6; 01vrwfv; 01rm8b; 013w2r; 01q99h; 033s6; 0134pk; 0mjn2; 016vn3; ... >> query: (?x2987, 08mbj5d) <- artists(?x114, ?x2987), artist(?x6672, ?x2987), ?x6672 = 03gfvsz >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vw20_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.879 http://example.org/common/topic/webpage./common/webpage/category #6111-01tz6vs PRED entity: 01tz6vs PRED relation: influenced_by! PRED expected values: 07w21 019z7q 080r3 03_87 03f47xl => 175 concepts (61 used for prediction) PRED predicted values (max 10 best out of 470): 03f47xl (0.57 #1263, 0.33 #256, 0.20 #3279), 040db (0.55 #4104, 0.50 #3098, 0.18 #17202), 073v6 (0.50 #3139, 0.45 #4145, 0.15 #8170), 03_87 (0.43 #1262, 0.33 #255, 0.18 #4284), 01tz6vs (0.43 #1230, 0.19 #6265, 0.13 #504), 01v_0b (0.40 #3496, 0.27 #4502, 0.14 #1480), 0d4jl (0.40 #3138, 0.27 #4144, 0.14 #22167), 0l99s (0.33 #283, 0.29 #1290, 0.13 #504), 03f0324 (0.33 #194, 0.29 #1201, 0.13 #504), 05qzv (0.33 #392, 0.29 #1399, 0.10 #3415) >> Best rule #1263 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 03pm9; 032l1; 0dw6b; 0113sg; >> query: (?x5434, 03f47xl) <- profession(?x5434, ?x353), influenced_by(?x8383, ?x5434), influenced_by(?x4292, ?x5434), ?x4292 = 0zm1, religion(?x8383, ?x8967) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 25, 42, 138 EVAL 01tz6vs influenced_by! 03f47xl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 61.000 0.571 http://example.org/influence/influence_node/influenced_by EVAL 01tz6vs influenced_by! 03_87 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 175.000 61.000 0.571 http://example.org/influence/influence_node/influenced_by EVAL 01tz6vs influenced_by! 080r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 175.000 61.000 0.571 http://example.org/influence/influence_node/influenced_by EVAL 01tz6vs influenced_by! 019z7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 175.000 61.000 0.571 http://example.org/influence/influence_node/influenced_by EVAL 01tz6vs influenced_by! 07w21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 175.000 61.000 0.571 http://example.org/influence/influence_node/influenced_by #6110-02704ff PRED entity: 02704ff PRED relation: titles! PRED expected values: 01z4y => 94 concepts (76 used for prediction) PRED predicted values (max 10 best out of 70): 07ssc (0.40 #112, 0.27 #1337, 0.27 #1243), 01z4y (0.33 #36, 0.20 #138, 0.18 #2832), 017fp (0.33 #24, 0.09 #330, 0.09 #3860), 03_gx (0.33 #72), 07s9rl0 (0.33 #617, 0.32 #2381, 0.32 #2277), 04xvlr (0.26 #310, 0.23 #1237, 0.22 #2280), 011ys5 (0.20 #2380, 0.20 #719, 0.20 #1338), 0gf28 (0.20 #2380, 0.20 #719, 0.20 #1338), 0vgkd (0.20 #2380, 0.20 #719, 0.20 #1338), 0lsxr (0.20 #2380, 0.20 #719, 0.20 #1338) >> Best rule #112 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0661m4p; >> query: (?x5694, 07ssc) <- film_crew_role(?x5694, ?x137), film(?x8568, ?x5694), film(?x396, ?x5694), ?x8568 = 050_qx, award_nominee(?x396, ?x157) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #36 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 02r1c18; *> query: (?x5694, 01z4y) <- film_crew_role(?x5694, ?x137), film(?x286, ?x5694), nominated_for(?x3572, ?x5694), ?x3572 = 02kxbx3 *> conf = 0.33 ranks of expected_values: 2 EVAL 02704ff titles! 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 76.000 0.400 http://example.org/media_common/netflix_genre/titles #6109-09q_6t PRED entity: 09q_6t PRED relation: award_winner PRED expected values: 09fb5 0hwbd => 50 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2026): 02mxbd (0.54 #1527, 0.54 #1526, 0.40 #33605), 0lpjn (0.54 #1527, 0.54 #1526, 0.40 #33605), 05hj_k (0.54 #1527, 0.54 #1526, 0.40 #33605), 0151w_ (0.54 #1527, 0.54 #1526, 0.40 #33605), 0171cm (0.54 #1527, 0.54 #1526, 0.40 #33605), 0170pk (0.54 #1527, 0.54 #1526, 0.40 #33605), 03y_46 (0.54 #1527, 0.54 #1526, 0.40 #33605), 0dgskx (0.54 #1527, 0.54 #1526, 0.40 #33605), 03t0k1 (0.54 #1527, 0.54 #1526, 0.40 #33605), 03f1zdw (0.54 #1527, 0.54 #1526, 0.40 #33605) >> Best rule #1527 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 0dthsy; >> query: (?x747, ?x988) <- ceremony(?x1443, ?x747), honored_for(?x747, ?x144), award_winner(?x747, ?x9000), award_winner(?x747, ?x7324), award(?x144, ?x112), award_winner(?x144, ?x988), award(?x10949, ?x1443), award(?x308, ?x1443), nominated_for(?x1443, ?x155), ?x10949 = 01njxvw, award_winner(?x7324, ?x6771), ?x9000 = 0k9j_ >> conf = 0.54 => this is the best rule for 45 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 35, 63 EVAL 09q_6t award_winner 0hwbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 50.000 26.000 0.538 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09q_6t award_winner 09fb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 50.000 26.000 0.538 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6108-016z68 PRED entity: 016z68 PRED relation: type_of_union PRED expected values: 04ztj => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.76 #37, 0.74 #21, 0.74 #17), 01g63y (0.22 #6, 0.19 #469, 0.17 #34), 0jgjn (0.19 #469, 0.02 #16), 01bl8s (0.19 #469) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 517 >> proper extension: 02sj1x; >> query: (?x11396, 04ztj) <- award_winner(?x3209, ?x11396), award(?x11396, ?x458), religion(?x11396, ?x962) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016z68 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.757 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6107-02tcgh PRED entity: 02tcgh PRED relation: language PRED expected values: 0688f => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 53): 02h40lc (0.99 #4294, 0.96 #401, 0.95 #1208), 01c7y (0.62 #1032, 0.61 #341, 0.60 #2288), 055qm (0.60 #2288), 01chg (0.26 #57, 0.06 #398, 0.03 #114), 03rk0 (0.26 #57, 0.06 #398, 0.03 #114), 0688f (0.25 #37, 0.05 #3782, 0.05 #3150), 064_8sq (0.15 #362, 0.15 #1513, 0.14 #534), 04306rv (0.13 #176, 0.11 #62, 0.10 #576), 06nm1 (0.11 #124, 0.10 #927, 0.10 #523), 07c9s (0.08 #18, 0.05 #3782, 0.05 #3150) >> Best rule #4294 for best value: >> intensional similarity = 4 >> extensional distance = 1618 >> proper extension: 05f67hw; >> query: (?x11114, 02h40lc) <- language(?x11114, ?x9113), language(?x2882, ?x9113), languages(?x6189, ?x9113), ?x2882 = 03rz2b >> conf = 0.99 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 047q2k1; 04jwjq; 04q00lw; 01p3ty; 02w86hz; 09fn1w; 021pqy; 0f42nz; 030z4z; 09yxcz; *> query: (?x11114, 0688f) <- language(?x11114, ?x1882), titles(?x3741, ?x11114), film(?x656, ?x11114), ?x3741 = 01chg *> conf = 0.25 ranks of expected_values: 6 EVAL 02tcgh language 0688f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 84.000 84.000 0.994 http://example.org/film/film/language #6106-0197tq PRED entity: 0197tq PRED relation: role PRED expected values: 0342h 0gkd1 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 107): 0342h (0.59 #201, 0.40 #1873, 0.36 #2758), 01vdm0 (0.27 #2195, 0.27 #2783, 0.26 #1898), 05148p4 (0.25 #1571, 0.24 #3643, 0.23 #3642), 06ch55 (0.25 #1571, 0.23 #3642, 0.23 #2066), 05842k (0.18 #270, 0.17 #1645, 0.17 #1942), 01vj9c (0.17 #1881, 0.15 #994, 0.15 #1092), 026t6 (0.16 #1871, 0.15 #2756, 0.13 #1574), 0l14qv (0.16 #1577, 0.15 #1874, 0.15 #987), 02qjv (0.14 #20, 0.05 #216, 0.03 #2773), 0dwsp (0.14 #11, 0.03 #1879, 0.03 #2764) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 0fpj4lx; >> query: (?x217, 0342h) <- artists(?x3108, ?x217), ?x3108 = 02w4v, role(?x217, ?x314) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1, 27 EVAL 0197tq role 0gkd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 103.000 103.000 0.591 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0197tq role 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.591 http://example.org/music/artist/track_contributions./music/track_contribution/role #6105-076lxv PRED entity: 076lxv PRED relation: place_of_death PRED expected values: 0f2wj => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 33): 030qb3t (0.38 #410, 0.35 #604, 0.33 #799), 0f2wj (0.25 #12, 0.17 #206, 0.07 #1179), 04jpl (0.25 #7, 0.05 #1174, 0.05 #1369), 0k049 (0.10 #1559, 0.06 #2338, 0.05 #2727), 02_286 (0.09 #1569, 0.07 #2737, 0.07 #3126), 0k_p5 (0.06 #476, 0.05 #2335, 0.04 #2140), 06_kh (0.06 #393, 0.05 #1367, 0.04 #587), 071vr (0.06 #490, 0.04 #684, 0.04 #879), 0284jb (0.06 #409, 0.04 #603, 0.04 #798), 0r62v (0.06 #403, 0.04 #597, 0.04 #792) >> Best rule #410 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0cb77r; 057dxsg; 0584j4n; 053j4w4; 051x52f; 058vfp4; 053vcrp; 0fd6qb; 051y1hd; 0c0tzp; ... >> query: (?x786, 030qb3t) <- award_nominee(?x786, ?x2449), film_sets_designed(?x786, ?x1804), gender(?x786, ?x231) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0bdt8; *> query: (?x786, 0f2wj) <- award_nominee(?x786, ?x2449), award_winner(?x1973, ?x786), ?x1973 = 070fnm *> conf = 0.25 ranks of expected_values: 2 EVAL 076lxv place_of_death 0f2wj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.375 http://example.org/people/deceased_person/place_of_death #6104-0bz5v2 PRED entity: 0bz5v2 PRED relation: type_of_union PRED expected values: 04ztj => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.71 #213, 0.70 #417, 0.70 #341), 01g63y (0.14 #194, 0.14 #318, 0.14 #26), 0jgjn (0.11 #16), 01bl8s (0.01 #83) >> Best rule #213 for best value: >> intensional similarity = 3 >> extensional distance = 654 >> proper extension: 04bs3j; 02wrhj; 044qx; 02y_2y; 02j4sk; 033p3_; 02zfg3; >> query: (?x1040, 04ztj) <- film(?x1040, ?x2512), award_winner(?x1265, ?x1040), film_release_region(?x2512, ?x87) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bz5v2 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.712 http://example.org/people/person/spouse_s./people/marriage/type_of_union #6103-02mx98 PRED entity: 02mx98 PRED relation: profession PRED expected values: 09jwl => 116 concepts (66 used for prediction) PRED predicted values (max 10 best out of 55): 02hrh1q (0.86 #8156, 0.58 #9195, 0.48 #2373), 09jwl (0.84 #3267, 0.83 #3118, 0.82 #1937), 0dz3r (0.55 #149, 0.52 #739, 0.50 #3249), 039v1 (0.50 #478, 0.47 #2100, 0.46 #2247), 016z4k (0.48 #2511, 0.48 #2068, 0.48 #2215), 01c72t (0.47 #9647, 0.37 #2678, 0.36 #5649), 0fnpj (0.29 #1239, 0.27 #1535, 0.22 #1977), 01d_h8 (0.28 #9186, 0.16 #8147, 0.16 #1185), 0dxtg (0.26 #9194, 0.14 #1193, 0.11 #1489), 0n1h (0.19 #2519, 0.19 #5039, 0.19 #8153) >> Best rule #8156 for best value: >> intensional similarity = 4 >> extensional distance = 464 >> proper extension: 0c7xjb; 01wj5hp; 01vs8ng; >> query: (?x8114, 02hrh1q) <- artists(?x302, ?x8114), profession(?x8114, ?x12647), profession(?x5742, ?x12647), taxonomy(?x5742, ?x939) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #3267 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 181 *> proper extension: 0zjpz; 01vv6_6; 01w8n89; 01vrkdt; 01tw31; *> query: (?x8114, 09jwl) <- role(?x8114, ?x432), role(?x8114, ?x212), role(?x432, ?x75), nationality(?x8114, ?x94), role(?x74, ?x432) *> conf = 0.84 ranks of expected_values: 2 EVAL 02mx98 profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 66.000 0.861 http://example.org/people/person/profession #6102-02yw1c PRED entity: 02yw1c PRED relation: artists PRED expected values: 01s7qqw => 57 concepts (22 used for prediction) PRED predicted values (max 10 best out of 949): 01w8n89 (0.45 #13383, 0.43 #14472, 0.43 #5759), 01wt4wc (0.43 #6173, 0.36 #9436, 0.26 #8348), 01gx5f (0.40 #8998, 0.38 #5735, 0.33 #295), 01386_ (0.33 #6024, 0.33 #584, 0.25 #2755), 01j59b0 (0.33 #5914, 0.31 #4824, 0.25 #1559), 011_vz (0.33 #850, 0.29 #6290, 0.27 #7375), 016lj_ (0.33 #919, 0.29 #6359, 0.25 #3090), 0274ck (0.33 #49, 0.29 #5489, 0.25 #2220), 03f0fnk (0.33 #420, 0.25 #2591, 0.25 #1505), 01vsxdm (0.33 #5543, 0.19 #4453, 0.15 #8806) >> Best rule #13383 for best value: >> intensional similarity = 12 >> extensional distance = 63 >> proper extension: 02w4v; 09qxq7; >> query: (?x8230, 01w8n89) <- artists(?x8230, ?x9463), artists(?x6350, ?x9463), artists(?x5379, ?x9463), artists(?x2249, ?x9463), artists(?x302, ?x9463), artists(?x6350, ?x3399), artists(?x6350, ?x1955), ?x5379 = 08jyyk, ?x2249 = 03lty, ?x302 = 016clz, ?x3399 = 01gx5f, ?x1955 = 0285c >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #5911 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 19 *> proper extension: 012xsy; *> query: (?x8230, 01s7qqw) <- artists(?x8230, ?x9463), ?x9463 = 01shhf *> conf = 0.14 ranks of expected_values: 123 EVAL 02yw1c artists 01s7qqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 57.000 22.000 0.446 http://example.org/music/genre/artists #6101-035wtd PRED entity: 035wtd PRED relation: school! PRED expected values: 0jmj7 => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 93): 0jmj7 (0.40 #2098, 0.35 #2757, 0.34 #3133), 07147 (0.08 #68, 0.07 #2137, 0.07 #162), 07l8x (0.08 #67, 0.07 #161, 0.04 #1102), 01yjl (0.08 #31, 0.05 #3135, 0.05 #3793), 07l4z (0.08 #71, 0.05 #165, 0.04 #2423), 01y3v (0.08 #28, 0.05 #122, 0.03 #1063), 01ypc (0.08 #1, 0.04 #2070, 0.03 #1036), 05xvj (0.08 #89, 0.04 #4321, 0.04 #5073), 0512p (0.08 #15, 0.03 #1050, 0.03 #109), 06wpc (0.08 #65, 0.03 #1100, 0.03 #159) >> Best rule #2098 for best value: >> intensional similarity = 3 >> extensional distance = 122 >> proper extension: 0373qt; >> query: (?x4145, 0jmj7) <- institution(?x620, ?x4145), category(?x4145, ?x134), ?x620 = 07s6fsf >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035wtd school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.403 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #6100-01gsvp PRED entity: 01gsvp PRED relation: district_represented PRED expected values: 05kkh 0gyh 04ly1 05fjf => 31 concepts (29 used for prediction) PRED predicted values (max 10 best out of 192): 05fjf (0.92 #536, 0.91 #1223, 0.90 #1188), 0gyh (0.92 #536, 0.88 #1103, 0.88 #956), 05kkh (0.92 #536, 0.88 #683, 0.85 #48), 04ly1 (0.88 #683, 0.85 #48, 0.84 #886), 0g0syc (0.88 #683, 0.83 #882, 0.82 #830), 04rrx (0.85 #48, 0.84 #886, 0.84 #881), 03s0w (0.85 #48, 0.84 #886, 0.84 #881), 0vbk (0.85 #48, 0.84 #886, 0.84 #881), 02xry (0.85 #48, 0.84 #886, 0.84 #881), 0824r (0.85 #48, 0.84 #886, 0.84 #881) >> Best rule #536 for best value: >> intensional similarity = 41 >> extensional distance = 3 >> proper extension: 01gt99; >> query: (?x6021, ?x6895) <- district_represented(?x6021, ?x7058), district_represented(?x6021, ?x4622), district_represented(?x6021, ?x3818), district_represented(?x6021, ?x3778), district_represented(?x6021, ?x3670), district_represented(?x6021, ?x2020), district_represented(?x6021, ?x1755), district_represented(?x6021, ?x1426), district_represented(?x6021, ?x760), district_represented(?x6021, ?x728), district_represented(?x6021, ?x335), legislative_sessions(?x6021, ?x10291), legislative_sessions(?x6021, ?x7944), legislative_sessions(?x6021, ?x4437), legislative_sessions(?x6021, ?x759), ?x7058 = 050ks, ?x3670 = 05tbn, ?x1755 = 01x73, legislative_sessions(?x4437, ?x6712), ?x10291 = 01gtdd, ?x1426 = 07z1m, ?x728 = 059f4, legislative_sessions(?x4665, ?x4437), ?x759 = 043djx, ?x6712 = 01gst9, legislative_sessions(?x9046, ?x4437), district_represented(?x4437, ?x6895), district_represented(?x4437, ?x177), ?x3778 = 07h34, ?x2020 = 05k7sb, ?x760 = 05fkf, gender(?x9046, ?x231), ?x335 = 059rby, ?x7944 = 01h7xx, ?x4622 = 04tgp, ?x4665 = 07t58, profession(?x9046, ?x5805), ?x3818 = 03v0t, ?x177 = 05kkh, people(?x4195, ?x9046), politician(?x10510, ?x9046) >> conf = 0.92 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 01gsvp district_represented 05fjf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 29.000 0.919 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gsvp district_represented 04ly1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 29.000 0.919 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gsvp district_represented 0gyh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 29.000 0.919 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gsvp district_represented 05kkh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 29.000 0.919 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #6099-0nzlp PRED entity: 0nzlp PRED relation: adjoins PRED expected values: 0nzw2 => 92 concepts (55 used for prediction) PRED predicted values (max 10 best out of 356): 0nzw2 (0.84 #8500, 0.84 #15453, 0.83 #27046), 0nzlp (0.26 #21633, 0.25 #6181, 0.25 #20088), 0nz_b (0.26 #21633, 0.25 #6181, 0.25 #20088), 0k3ll (0.04 #6620, 0.04 #8166, 0.04 #5847), 0694j (0.04 #6477, 0.03 #20384, 0.03 #17293), 0mwxz (0.04 #6519, 0.03 #8838, 0.03 #9610), 07z1m (0.04 #6260, 0.03 #8579, 0.03 #9351), 05tbn (0.04 #6358, 0.03 #8677, 0.03 #9449), 059rby (0.04 #6198, 0.03 #8517, 0.03 #9289), 0d060g (0.04 #6191, 0.03 #8510, 0.03 #9282) >> Best rule #8500 for best value: >> intensional similarity = 4 >> extensional distance = 203 >> proper extension: 02m4d; >> query: (?x10821, ?x12545) <- adjoins(?x10821, ?x13275), source(?x13275, ?x958), county_seat(?x13275, ?x2277), adjoins(?x12545, ?x10821) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nzlp adjoins 0nzw2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 55.000 0.839 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #6098-02lnhv PRED entity: 02lnhv PRED relation: profession PRED expected values: 02hrh1q => 124 concepts (81 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.91 #6725, 0.90 #11652, 0.90 #7771), 0dxtg (0.48 #8815, 0.48 #10310, 0.48 #10459), 02jknp (0.48 #5823, 0.48 #8809, 0.46 #10304), 0nbcg (0.44 #330, 0.41 #479, 0.27 #777), 03gjzk (0.43 #3444, 0.38 #4339, 0.37 #611), 0kyk (0.36 #1641, 0.32 #5070, 0.32 #5220), 018gz8 (0.36 #1641, 0.32 #5070, 0.32 #5220), 0747nrk (0.36 #1641, 0.32 #5070, 0.32 #5220), 09jwl (0.34 #8653, 0.31 #317, 0.29 #466), 0dz3r (0.34 #8653, 0.22 #2835, 0.22 #3133) >> Best rule #6725 for best value: >> intensional similarity = 4 >> extensional distance = 367 >> proper extension: 02qw2xb; >> query: (?x1207, 02hrh1q) <- award(?x1207, ?x11115), film(?x1207, ?x943), participant(?x1206, ?x1207), award_nominee(?x140, ?x1206) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lnhv profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 81.000 0.905 http://example.org/people/person/profession #6097-0dzt9 PRED entity: 0dzt9 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.80 #41, 0.79 #25, 0.75 #77), 01g63y (0.45 #289, 0.04 #14, 0.03 #22), 0jgjn (0.07 #24, 0.04 #16, 0.04 #48), 01bl8s (0.04 #15, 0.03 #23, 0.02 #135) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 09c7w0; 059rby; 013kcv; 04ych; 0wh3; 04ykg; 02h6_6p; 01531; 0_vn7; 06wxw; ... >> query: (?x9846, 04ztj) <- contains(?x94, ?x9846), place_of_birth(?x4397, ?x9846), contains(?x9846, ?x10104), story_by(?x240, ?x4397) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dzt9 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.800 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #6096-04jpl PRED entity: 04jpl PRED relation: featured_film_locations! PRED expected values: 02vxq9m 0ds11z 0kv238 04cj79 0dl9_4 02dwj => 240 concepts (240 used for prediction) PRED predicted values (max 10 best out of 970): 047csmy (0.25 #5567, 0.20 #4260, 0.20 #995), 01lsl (0.25 #5132, 0.20 #4478, 0.20 #1213), 0ds2n (0.25 #5425, 0.20 #4118, 0.20 #853), 0872p_c (0.25 #5291, 0.20 #3984, 0.20 #719), 0hmr4 (0.20 #3956, 0.20 #691, 0.17 #5263), 07nnp_ (0.20 #4563, 0.20 #1298, 0.17 #5870), 0g_zyp (0.20 #4501, 0.20 #1236, 0.17 #5808), 01z452 (0.20 #4483, 0.20 #1218, 0.17 #5790), 06x43v (0.20 #4404, 0.20 #1139, 0.17 #5711), 012s1d (0.20 #4263, 0.20 #998, 0.17 #5570) >> Best rule #5567 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0gp5l6; >> query: (?x362, 047csmy) <- citytown(?x752, ?x362), film_release_region(?x2738, ?x362), place_founded(?x5891, ?x362) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #79684 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 01l3k6; *> query: (?x362, ?x1470) <- featured_film_locations(?x2896, ?x362), film_release_region(?x2896, ?x1264), featured_film_locations(?x1470, ?x1264) *> conf = 0.05 ranks of expected_values: 458, 598, 631, 729, 770 EVAL 04jpl featured_film_locations! 02dwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 240.000 240.000 0.250 http://example.org/film/film/featured_film_locations EVAL 04jpl featured_film_locations! 0dl9_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 240.000 240.000 0.250 http://example.org/film/film/featured_film_locations EVAL 04jpl featured_film_locations! 04cj79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 240.000 240.000 0.250 http://example.org/film/film/featured_film_locations EVAL 04jpl featured_film_locations! 0kv238 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 240.000 240.000 0.250 http://example.org/film/film/featured_film_locations EVAL 04jpl featured_film_locations! 0ds11z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 240.000 240.000 0.250 http://example.org/film/film/featured_film_locations EVAL 04jpl featured_film_locations! 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 240.000 240.000 0.250 http://example.org/film/film/featured_film_locations #6095-01npcy7 PRED entity: 01npcy7 PRED relation: actor! PRED expected values: 0n2bh => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 106): 06zsk51 (0.06 #1773, 0.02 #3364, 0.02 #4424), 026bfsh (0.06 #627, 0.05 #8050, 0.05 #1422), 02zv4b (0.06 #555, 0.05 #820, 0.05 #1350), 0cs134 (0.06 #743, 0.05 #1008, 0.03 #4984), 02_1q9 (0.06 #1860, 0.04 #2655, 0.02 #9548), 03ln8b (0.06 #561, 0.03 #7719, 0.03 #5332), 0fhzwl (0.06 #706, 0.03 #2031, 0.03 #2296), 0cskb (0.06 #728, 0.02 #2583, 0.02 #5234), 0gj50 (0.06 #593, 0.02 #2448, 0.02 #5099), 01b64v (0.06 #572, 0.02 #5078, 0.01 #5343) >> Best rule #1773 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 063t3j; >> query: (?x9482, 06zsk51) <- participant(?x8638, ?x9482), profession(?x9482, ?x1032), notable_people_with_this_condition(?x8318, ?x8638), people(?x1446, ?x8638) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #1887 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 01nfys; *> query: (?x9482, 0n2bh) <- participant(?x8638, ?x9482), type_of_union(?x9482, ?x1873), ?x1873 = 01g63y, people(?x1446, ?x8638) *> conf = 0.03 ranks of expected_values: 39 EVAL 01npcy7 actor! 0n2bh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 157.000 157.000 0.061 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #6094-016z5x PRED entity: 016z5x PRED relation: film! PRED expected values: 057_yx => 98 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1055): 016z2j (0.69 #58137, 0.68 #51907, 0.58 #2076), 01cbt3 (0.58 #2076, 0.47 #80978, 0.42 #112120), 0d5wn3 (0.58 #2076, 0.47 #80978, 0.42 #112120), 01r93l (0.22 #6975, 0.05 #9052, 0.03 #25658), 079vf (0.22 #6237, 0.03 #20767, 0.03 #31149), 0bq2g (0.22 #6833, 0.03 #54589, 0.02 #29669), 0c9xjl (0.22 #7198, 0.02 #30034, 0.02 #27958), 0c_gcr (0.22 #7868, 0.02 #24474, 0.01 #22398), 01chc7 (0.22 #6787, 0.02 #46234, 0.01 #56618), 0391jz (0.22 #6835, 0.01 #50436, 0.01 #56666) >> Best rule #58137 for best value: >> intensional similarity = 2 >> extensional distance = 478 >> proper extension: 0gxsh4; 0clpml; >> query: (?x518, ?x2373) <- nominated_for(?x2373, ?x518), participant(?x10473, ?x2373) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #110044 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 942 *> proper extension: 01xbxn; *> query: (?x518, ?x940) <- currency(?x518, ?x170), film(?x3583, ?x518), nominated_for(?x143, ?x518), award_nominee(?x940, ?x3583) *> conf = 0.03 ranks of expected_values: 254 EVAL 016z5x film! 057_yx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 98.000 56.000 0.687 http://example.org/film/actor/film./film/performance/film #6093-016z4k PRED entity: 016z4k PRED relation: profession! PRED expected values: 01vrt_c 01vrz41 01ww2fs 016sp_ 01m65sp 0pj9t 09r8l 01w524f 01mt1fy 09889g 02bgmr 0127s7 02wk4d 0277c3 07r4c 017g21 01vt5c_ 0dt1cm 027hm_ 06s7rd 02vr7 0kj34 0140t7 01w58n3 01hgwkr 0739z6 01pgk0 => 55 concepts (23 used for prediction) PRED predicted values (max 10 best out of 3872): 017yfz (0.71 #36505, 0.60 #32577, 0.33 #40433), 01vsy7t (0.60 #32754, 0.56 #40610, 0.50 #24900), 0ddkf (0.60 #33449, 0.56 #41305, 0.50 #25595), 01vsy3q (0.60 #32832, 0.50 #24978, 0.50 #21051), 04n2vgk (0.60 #34206, 0.50 #26352, 0.50 #14571), 0l12d (0.60 #31832, 0.50 #23978, 0.50 #12197), 02l840 (0.60 #31605, 0.50 #15897, 0.50 #11970), 0484q (0.60 #33611, 0.50 #25757, 0.43 #37539), 016kjs (0.60 #31691, 0.50 #12056, 0.43 #35619), 086qd (0.60 #31970, 0.50 #12335, 0.43 #35898) >> Best rule #36505 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 0kyk; >> query: (?x220, 017yfz) <- profession(?x9117, ?x220), profession(?x6808, ?x220), profession(?x6406, ?x220), profession(?x4140, ?x220), award_nominee(?x6807, ?x6808), origin(?x6406, ?x461), gender(?x4140, ?x231), ?x9117 = 0167v4 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #21083 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 01c72t; *> query: (?x220, 09889g) <- profession(?x9103, ?x220), profession(?x6838, ?x220), profession(?x5691, ?x220), profession(?x3442, ?x220), ?x6838 = 0130sy, ?x3442 = 0m_v0, role(?x5691, ?x227), ?x9103 = 0147jt *> conf = 0.50 ranks of expected_values: 23, 59, 65, 69, 89, 131, 142, 148, 149, 150, 173, 278, 297, 328, 336, 344, 421, 453, 474, 522, 542, 659, 673, 797, 1454, 2679, 3549 EVAL 016z4k profession! 01pgk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 0739z6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 01hgwkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 01w58n3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 0140t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 0kj34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 02vr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 06s7rd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 027hm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 0dt1cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 01vt5c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 017g21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 07r4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 0277c3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 02wk4d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 0127s7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 02bgmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 09889g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 01mt1fy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 01w524f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 09r8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 0pj9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 01m65sp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 016sp_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 01ww2fs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 23.000 0.714 http://example.org/people/person/profession EVAL 016z4k profession! 01vrt_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 23.000 0.714 http://example.org/people/person/profession #6092-02rv_dz PRED entity: 02rv_dz PRED relation: genre PRED expected values: 01j1n2 => 80 concepts (76 used for prediction) PRED predicted values (max 10 best out of 95): 01z4y (0.61 #7037, 0.52 #6684, 0.49 #6566), 03k9fj (0.42 #4934, 0.34 #479, 0.24 #948), 06cvj (0.33 #120, 0.23 #2113, 0.15 #354), 02kdv5l (0.32 #470, 0.29 #4925, 0.26 #5510), 01jfsb (0.30 #3998, 0.29 #5520, 0.29 #2356), 01hmnh (0.26 #485, 0.22 #4940, 0.17 #2127), 04xvlr (0.19 #1406, 0.19 #1642, 0.18 #1289), 0lsxr (0.19 #8, 0.19 #828, 0.18 #710), 060__y (0.19 #16, 0.16 #1304, 0.16 #718), 02n4kr (0.19 #7, 0.13 #1061, 0.12 #6926) >> Best rule #7037 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 024rwx; 0ctzf1; 09g_31; >> query: (?x1531, ?x2480) <- titles(?x2480, ?x1531), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #291 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 103 *> proper extension: 027qgy; 0bvn25; 0b76t12; 0by1wkq; 0gkz3nz; 046488; 05q_dw; 01l2b3; 0bm2nq; 02x0fs9; *> query: (?x1531, 01j1n2) <- nominated_for(?x3435, ?x1531), ?x3435 = 03hl6lc *> conf = 0.05 ranks of expected_values: 44 EVAL 02rv_dz genre 01j1n2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 80.000 76.000 0.612 http://example.org/film/film/genre #6091-0pd64 PRED entity: 0pd64 PRED relation: nominated_for! PRED expected values: 0gr0m => 124 concepts (103 used for prediction) PRED predicted values (max 10 best out of 206): 09cm54 (0.70 #3636, 0.69 #6594, 0.69 #2954), 027c95y (0.70 #3636, 0.69 #2954, 0.68 #20473), 0gq_v (0.47 #702, 0.41 #3655, 0.39 #1156), 0gr0m (0.42 #1191, 0.38 #737, 0.36 #2326), 0gqyl (0.39 #2344, 0.38 #755, 0.38 #1890), 02rdyk7 (0.35 #517, 0.33 #63, 0.25 #2335), 0gr51 (0.33 #70, 0.32 #3706, 0.32 #2342), 094qd5 (0.33 #32, 0.32 #2304, 0.19 #486), 02rdxsh (0.33 #47, 0.25 #2319, 0.23 #501), 02ppm4q (0.33 #2380, 0.17 #6473, 0.16 #6365) >> Best rule #3636 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 0fpkhkz; 0fpv_3_; 06wbm8q; 0fpmrm3; 0hv27; 0bs8s1p; >> query: (?x7711, ?x591) <- award(?x7711, ?x591), film_release_region(?x7711, ?x151), genre(?x7711, ?x53), ?x151 = 0b90_r >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #1191 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 62 *> proper extension: 0pv2t; 0jym0; 026p4q7; 02rjv2w; 0kb57; 03hmt9b; 07j94; 0jsqk; 0jqj5; 0k4p0; ... *> query: (?x7711, 0gr0m) <- featured_film_locations(?x7711, ?x108), nominated_for(?x1307, ?x7711), production_companies(?x7711, ?x574), ?x1307 = 0gq9h *> conf = 0.42 ranks of expected_values: 4 EVAL 0pd64 nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 124.000 103.000 0.703 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6090-01wsl7c PRED entity: 01wsl7c PRED relation: profession PRED expected values: 0n1h => 173 concepts (88 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.75 #6889, 0.73 #6596, 0.72 #7035), 0dz3r (0.49 #4094, 0.48 #8051, 0.46 #3070), 0cbd2 (0.43 #444, 0.27 #736, 0.20 #12159), 0kyk (0.43 #467, 0.27 #759, 0.16 #1489), 01c72t (0.40 #607, 0.32 #8510, 0.32 #8656), 0n1h (0.33 #303, 0.31 #2495, 0.31 #1909), 0dxtg (0.30 #12166, 0.24 #6449, 0.24 #6595), 0fnpj (0.30 #642, 0.20 #3126, 0.19 #1956), 025352 (0.30 #641, 0.11 #2687, 0.09 #3125), 05z96 (0.29 #478, 0.27 #770, 0.15 #916) >> Best rule #6889 for best value: >> intensional similarity = 5 >> extensional distance = 271 >> proper extension: 02nwxc; 08h79x; >> query: (?x1997, 02hrh1q) <- nationality(?x1997, ?x512), type_of_union(?x1997, ?x566), gender(?x1997, ?x514), student(?x13827, ?x1997), ?x514 = 02zsn >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #303 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0259r0; 01t110; *> query: (?x1997, 0n1h) <- artists(?x12800, ?x1997), profession(?x1997, ?x220), origin(?x1997, ?x14595), ?x12800 = 09qxq7 *> conf = 0.33 ranks of expected_values: 6 EVAL 01wsl7c profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 173.000 88.000 0.755 http://example.org/people/person/profession #6089-029_3 PRED entity: 029_3 PRED relation: participant! PRED expected values: 015pvh => 145 concepts (68 used for prediction) PRED predicted values (max 10 best out of 238): 015pvh (0.80 #28708, 0.80 #30667, 0.80 #33275), 01fwj8 (0.25 #755, 0.03 #4670, 0.02 #5975), 046zh (0.08 #28707, 0.08 #30666, 0.07 #5220), 01h910 (0.08 #28707, 0.08 #30666, 0.07 #5220), 03f1r6t (0.08 #28707, 0.08 #30666, 0.07 #5220), 0ph2w (0.08 #28707, 0.08 #30666, 0.07 #5220), 014zfs (0.08 #28707, 0.08 #30666, 0.06 #20221), 01ztgm (0.05 #4648, 0.05 #5953, 0.03 #6604), 01vvycq (0.05 #4607, 0.05 #5912, 0.03 #6563), 01q7cb_ (0.05 #4629, 0.03 #7237, 0.03 #6585) >> Best rule #28708 for best value: >> intensional similarity = 3 >> extensional distance = 310 >> proper extension: 01xyt7; >> query: (?x4065, ?x2046) <- participant(?x4065, ?x1145), gender(?x4065, ?x231), participant(?x4065, ?x2046) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 029_3 participant! 015pvh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 68.000 0.802 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #6088-02h3d1 PRED entity: 02h3d1 PRED relation: award! PRED expected values: 01ccr8 04twmk => 42 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2383): 01vrlr4 (0.79 #3354, 0.74 #13414, 0.70 #77168), 02cx72 (0.79 #3354, 0.74 #13414, 0.70 #77168), 01wd9lv (0.79 #3354, 0.74 #13414, 0.70 #77168), 01r6jt2 (0.79 #3354, 0.74 #13414, 0.69 #50320), 02lfp4 (0.67 #18205, 0.62 #24911, 0.60 #11496), 01l3mk3 (0.67 #19057, 0.62 #25763, 0.40 #12348), 0ddkf (0.67 #18747, 0.50 #25453, 0.40 #12038), 01vvdm (0.67 #17808, 0.50 #24514, 0.40 #11099), 01c7p_ (0.67 #19488, 0.50 #26194, 0.40 #12779), 029h45 (0.60 #11721, 0.33 #18430, 0.33 #1661) >> Best rule #3354 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 04njml; >> query: (?x3467, ?x3732) <- award(?x12947, ?x3467), award(?x5223, ?x3467), ?x12947 = 0164y7, award_winner(?x3467, ?x3732), ?x5223 = 0178rl >> conf = 0.79 => this is the best rule for 4 predicted values *> Best rule #16768 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0262s1; *> query: (?x3467, ?x2280) <- award(?x12947, ?x3467), award(?x4987, ?x3467), award(?x3861, ?x3467), ?x4987 = 0dpqk, award_winner(?x12947, ?x6943), award_nominee(?x2280, ?x3861) *> conf = 0.28 ranks of expected_values: 281, 2071 EVAL 02h3d1 award! 04twmk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 26.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02h3d1 award! 01ccr8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 26.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6087-059s8 PRED entity: 059s8 PRED relation: contains PRED expected values: 04_lb => 223 concepts (71 used for prediction) PRED predicted values (max 10 best out of 2828): 0d060g (0.52 #206181, 0.47 #14719, 0.25 #2967), 059s8 (0.52 #206181, 0.25 #4832, 0.15 #17663), 07wkd (0.51 #38277, 0.51 #91295, 0.48 #50058), 0j11 (0.33 #10384, 0.30 #13327, 0.29 #16271), 0n3g (0.33 #9533, 0.25 #3647, 0.21 #15420), 052nd (0.33 #47, 0.25 #2993, 0.12 #5936), 01zh3_ (0.33 #2028, 0.25 #4974, 0.12 #7917), 0pml7 (0.33 #1723, 0.25 #4669, 0.12 #7612), 01fd26 (0.33 #1454, 0.25 #4400, 0.12 #7343), 0778_3 (0.33 #2317, 0.08 #22925, 0.08 #25870) >> Best rule #206181 for best value: >> intensional similarity = 3 >> extensional distance = 117 >> proper extension: 09bjv; 0g284; 0978r; 09d4_; 06pr6; 02yc5b; 0kqb0; >> query: (?x11542, ?x279) <- contains(?x11542, ?x12635), state_province_region(?x12356, ?x11542), contains(?x279, ?x12635) >> conf = 0.52 => this is the best rule for 2 predicted values *> Best rule #4821 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 07c5l; *> query: (?x11542, 04_lb) <- contains(?x11542, ?x12135), time_zones(?x12135, ?x11506), ?x11506 = 042g7t, place_of_birth(?x7155, ?x12135) *> conf = 0.25 ranks of expected_values: 40 EVAL 059s8 contains 04_lb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 223.000 71.000 0.523 http://example.org/location/location/contains #6086-02w3w PRED entity: 02w3w PRED relation: group PRED expected values: 01jkqfz => 72 concepts (39 used for prediction) PRED predicted values (max 10 best out of 234): 02vnpv (0.86 #2572, 0.80 #3321, 0.80 #3133), 02dw1_ (0.84 #4741, 0.78 #1917, 0.71 #3610), 07mvp (0.71 #2493, 0.67 #4182, 0.67 #3054), 047cx (0.71 #1714, 0.67 #1153, 0.60 #781), 01qqwp9 (0.71 #1326, 0.60 #952, 0.60 #767), 0qmpd (0.71 #1791, 0.60 #858, 0.60 #674), 05crg7 (0.71 #1685, 0.60 #752, 0.60 #568), 06gcn (0.71 #1760, 0.60 #827, 0.60 #643), 014pg1 (0.67 #1209, 0.57 #1770, 0.43 #2518), 0123r4 (0.60 #990, 0.60 #805, 0.57 #1364) >> Best rule #2572 for best value: >> intensional similarity = 17 >> extensional distance = 12 >> proper extension: 0l14qv; 02sgy; 0mkg; 02k84w; 01xqw; >> query: (?x5417, 02vnpv) <- role(?x4875, ?x5417), role(?x2923, ?x5417), role(?x1166, ?x5417), role(?x745, ?x5417), role(?x367, ?x5417), role(?x5417, ?x1332), ?x2923 = 02k856, group(?x1332, ?x3516), ?x1166 = 05148p4, instrumentalists(?x1332, ?x120), role(?x315, ?x1332), role(?x214, ?x1332), ?x214 = 02pprs, ?x745 = 01vj9c, ?x3516 = 05563d, instrumentalists(?x5417, ?x642), ?x315 = 0l14md >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1292 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 4 *> proper extension: 01wy6; *> query: (?x5417, ?x442) <- role(?x5417, ?x7772), role(?x5417, ?x6039), role(?x5417, ?x2888), role(?x5417, ?x2310), role(?x5417, ?x2309), role(?x5417, ?x1466), instrumentalists(?x5417, ?x367), role(?x5417, ?x745), ?x2309 = 06ncr, ?x2888 = 02fsn, role(?x212, ?x2310), role(?x1260, ?x2310), group(?x5417, ?x5407), ?x6039 = 05kms, artists(?x2542, ?x5407), performance_role(?x248, ?x1466), role(?x115, ?x1466), group(?x1466, ?x3875), group(?x1466, ?x442), ?x3875 = 0mgcr, ?x7772 = 0j862 *> conf = 0.41 ranks of expected_values: 157 EVAL 02w3w group 01jkqfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 72.000 39.000 0.857 http://example.org/music/performance_role/regular_performances./music/group_membership/group #6085-06qd3 PRED entity: 06qd3 PRED relation: country! PRED expected values: 07gyv 09_9n => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 27): 07gyv (0.85 #193, 0.75 #436, 0.75 #274), 01sgl (0.77 #209, 0.75 #290, 0.75 #236), 01gqfm (0.77 #212, 0.75 #293, 0.70 #725), 09w1n (0.76 #306, 0.69 #198, 0.69 #279), 019tzd (0.76 #611, 0.76 #584, 0.75 #449), 064vjs (0.75 #282, 0.75 #255, 0.75 #228), 07bs0 (0.75 #276, 0.71 #573, 0.69 #195), 0d1t3 (0.75 #284, 0.65 #311, 0.62 #257), 035d1m (0.75 #281, 0.65 #308, 0.62 #254), 09qgm (0.69 #199, 0.69 #280, 0.65 #361) >> Best rule #193 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 02vzc; >> query: (?x1453, 07gyv) <- film_release_region(?x7887, ?x1453), film_release_region(?x7494, ?x1453), film_release_region(?x1498, ?x1453), ?x1498 = 04jkpgv, ?x7494 = 0dgrwqr, ?x7887 = 04z_3pm >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 19 EVAL 06qd3 country! 09_9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 193.000 193.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06qd3 country! 07gyv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 193.000 193.000 0.846 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #6084-0qm9n PRED entity: 0qm9n PRED relation: nominated_for! PRED expected values: 0gqwc 0gq9h => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 200): 0gq9h (0.70 #990, 0.38 #291, 0.31 #524), 0gqwc (0.67 #3964, 0.66 #8868, 0.66 #7469), 09cn0c (0.67 #3964, 0.66 #8868, 0.66 #7469), 027571b (0.67 #3964, 0.66 #8868, 0.66 #7469), 02z1nbg (0.67 #3964, 0.66 #8868, 0.66 #7469), 0gs9p (0.61 #992, 0.31 #293, 0.29 #526), 019f4v (0.56 #982, 0.43 #50, 0.31 #283), 040njc (0.44 #939, 0.29 #7, 0.24 #473), 04dn09n (0.43 #964, 0.29 #32, 0.27 #498), 0gqy2 (0.43 #117, 0.36 #1049, 0.21 #350) >> Best rule #990 for best value: >> intensional similarity = 4 >> extensional distance = 212 >> proper extension: 01qz5; 0c5qvw; >> query: (?x3425, 0gq9h) <- genre(?x3425, ?x53), nominated_for(?x1244, ?x3425), nominated_for(?x1703, ?x3425), ?x1703 = 0k611 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0qm9n nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.701 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0qm9n nominated_for! 0gqwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.701 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6083-0mmp3 PRED entity: 0mmp3 PRED relation: artists PRED expected values: 01271h => 75 concepts (32 used for prediction) PRED predicted values (max 10 best out of 965): 03t9sp (0.67 #10885, 0.61 #1075, 0.60 #6580), 01vv7sc (0.67 #10825, 0.61 #1075, 0.56 #6456), 02ndj5 (0.61 #1075, 0.60 #10580, 0.60 #9504), 048tgl (0.61 #1075, 0.60 #17050, 0.60 #9517), 01dwrc (0.61 #1075, 0.60 #6980, 0.56 #6456), 02hzz (0.61 #1075, 0.60 #10429, 0.56 #6456), 06k02 (0.61 #1075, 0.56 #6456, 0.56 #6455), 05k79 (0.61 #1075, 0.56 #6456, 0.56 #6455), 01pfr3 (0.61 #1075, 0.56 #6456, 0.56 #6455), 01sxd1 (0.61 #1075, 0.56 #6456, 0.56 #6455) >> Best rule #10885 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0fd3y; 03mb9; >> query: (?x7220, 03t9sp) <- parent_genre(?x3915, ?x7220), parent_genre(?x2439, ?x7220), artists(?x3915, ?x5544), award_nominee(?x527, ?x5544), ?x2439 = 07lnk, parent_genre(?x474, ?x3915) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1075 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 0y3_8; *> query: (?x7220, ?x764) <- parent_genre(?x9248, ?x7220), parent_genre(?x3915, ?x7220), ?x3915 = 07gxw, artists(?x7220, ?x7476), ?x7476 = 048xh, parent_genre(?x7220, ?x3916), artists(?x9248, ?x764) *> conf = 0.61 ranks of expected_values: 23 EVAL 0mmp3 artists 01271h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 75.000 32.000 0.667 http://example.org/music/genre/artists #6082-04gm7n PRED entity: 04gm7n PRED relation: artist PRED expected values: 016ksk 013423 => 44 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1069): 019g40 (0.50 #936, 0.33 #102, 0.18 #5958), 01gf5h (0.50 #879, 0.33 #45, 0.15 #1713), 0fpj4lx (0.50 #1087, 0.33 #253, 0.15 #1921), 01vxlbm (0.33 #264, 0.25 #1098, 0.24 #4446), 0677ng (0.33 #521, 0.25 #1355, 0.23 #2189), 013w7j (0.33 #431, 0.25 #1265, 0.23 #2099), 01wgxtl (0.33 #165, 0.25 #999, 0.23 #1833), 0cg9y (0.33 #135, 0.25 #969, 0.18 #4317), 01w60_p (0.33 #117, 0.25 #951, 0.18 #4299), 016fnb (0.33 #319, 0.25 #1153, 0.15 #1987) >> Best rule #936 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 033hn8; >> query: (?x12719, 019g40) <- artist(?x12719, ?x4851), artist(?x12719, ?x3494), artist(?x12719, ?x140), ?x140 = 01vvydl, artists(?x12988, ?x3494), artists(?x11545, ?x3494), ?x11545 = 036jv, instrumentalists(?x1166, ?x3494), people(?x2510, ?x3494), ?x12988 = 016_rm, category(?x4851, ?x134) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1287 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 033hn8; *> query: (?x12719, 013423) <- artist(?x12719, ?x4851), artist(?x12719, ?x3494), artist(?x12719, ?x140), ?x140 = 01vvydl, artists(?x12988, ?x3494), artists(?x11545, ?x3494), ?x11545 = 036jv, instrumentalists(?x1166, ?x3494), people(?x2510, ?x3494), ?x12988 = 016_rm, category(?x4851, ?x134) *> conf = 0.25 ranks of expected_values: 106, 390 EVAL 04gm7n artist 013423 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 44.000 12.000 0.500 http://example.org/music/record_label/artist EVAL 04gm7n artist 016ksk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 12.000 0.500 http://example.org/music/record_label/artist #6081-0nmj PRED entity: 0nmj PRED relation: time_zones PRED expected values: 02fqwt => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 11): 02fqwt (0.83 #27, 0.71 #1, 0.50 #14), 02hcv8 (0.46 #653, 0.45 #1082, 0.45 #55), 02lcqs (0.36 #161, 0.26 #200, 0.24 #408), 02hczc (0.16 #1522, 0.11 #54, 0.10 #236), 042g7t (0.16 #1522, 0.03 #76, 0.03 #63), 02lcrv (0.16 #1522, 0.02 #59, 0.01 #72), 02llzg (0.08 #316, 0.07 #433, 0.07 #953), 03bdv (0.06 #721, 0.06 #266, 0.06 #708), 03plfd (0.02 #1063, 0.02 #920, 0.02 #959), 05jphn (0.02 #65, 0.01 #78, 0.01 #546) >> Best rule #27 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0nr_q; 0nryt; 0nrqh; 02d6c; 0nrnz; >> query: (?x10350, 02fqwt) <- source(?x10350, ?x958), ?x958 = 0jbk9, contains(?x961, ?x10350), ?x961 = 03s0w >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nmj time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.833 http://example.org/location/location/time_zones #6080-0hwpz PRED entity: 0hwpz PRED relation: film! PRED expected values: 0bsb4j => 67 concepts (28 used for prediction) PRED predicted values (max 10 best out of 411): 0tc7 (0.52 #24963, 0.45 #2080, 0.42 #47845), 03_gd (0.43 #10402, 0.42 #33283, 0.42 #35364), 0jfx1 (0.05 #6647, 0.04 #10809, 0.04 #4567), 023mdt (0.05 #18723, 0.01 #14058, 0.01 #3655), 0794g (0.05 #18723), 0gn30 (0.04 #5107, 0.04 #7187, 0.04 #11349), 0lpjn (0.04 #4639, 0.04 #6719, 0.04 #10881), 0bl2g (0.04 #4216, 0.04 #10458, 0.04 #6296), 0mdqp (0.04 #4279, 0.04 #6359, 0.04 #10521), 0p_pd (0.03 #4215, 0.03 #10457, 0.02 #22936) >> Best rule #24963 for best value: >> intensional similarity = 3 >> extensional distance = 649 >> proper extension: 01f3p_; 07wqr6; 03g9xj; 0cskb; 0123qq; >> query: (?x7444, ?x8257) <- nominated_for(?x8257, ?x7444), religion(?x8257, ?x7131), people(?x1050, ?x8257) >> conf = 0.52 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hwpz film! 0bsb4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 28.000 0.519 http://example.org/film/actor/film./film/performance/film #6079-048vhl PRED entity: 048vhl PRED relation: film! PRED expected values: 0gd9k => 101 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1189): 0151w_ (0.74 #87344, 0.68 #62380, 0.66 #47820), 01jfrg (0.74 #87344, 0.68 #62380, 0.66 #47820), 01f6zc (0.18 #3021, 0.03 #15490, 0.03 #11334), 0pz91 (0.15 #4370, 0.03 #29313, 0.03 #20997), 03y_46 (0.12 #3095, 0.11 #41580, 0.07 #72780), 09y20 (0.12 #2327, 0.04 #18955, 0.04 #21033), 06ltr (0.12 #3024, 0.04 #19652, 0.03 #21730), 055c8 (0.12 #2621, 0.03 #17170, 0.02 #10934), 01chc7 (0.12 #2638, 0.03 #10951, 0.03 #6795), 01wy5m (0.12 #2936, 0.03 #11249, 0.03 #13327) >> Best rule #87344 for best value: >> intensional similarity = 3 >> extensional distance = 763 >> proper extension: 04xbq3; >> query: (?x8787, ?x2108) <- nominated_for(?x2108, ?x8787), participant(?x2108, ?x56), film(?x96, ?x8787) >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #41579 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 390 *> proper extension: 0gj50; *> query: (?x8787, ?x545) <- award_winner(?x8787, ?x989), award_winner(?x969, ?x989), participant(?x989, ?x545) *> conf = 0.05 ranks of expected_values: 189 EVAL 048vhl film! 0gd9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 101.000 48.000 0.737 http://example.org/film/actor/film./film/performance/film #6078-043n1r5 PRED entity: 043n1r5 PRED relation: nominated_for! PRED expected values: 0gq_v => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 205): 0gq9h (0.66 #2186, 0.45 #1950, 0.43 #2894), 0gs9p (0.66 #2187, 0.38 #3367, 0.36 #2895), 05b1610 (0.61 #976, 0.50 #1448, 0.08 #5696), 07bdd_ (0.57 #997, 0.48 #1469, 0.07 #5717), 05f4m9q (0.54 #955, 0.48 #1427, 0.06 #7799), 0gqwc (0.53 #768, 0.25 #296, 0.23 #2184), 04dn09n (0.52 #2159, 0.32 #1687, 0.29 #1923), 0k611 (0.49 #2195, 0.33 #2903, 0.33 #1959), 040njc (0.48 #2130, 0.29 #714, 0.29 #2838), 04ljl_l (0.46 #947, 0.35 #1419, 0.07 #5667) >> Best rule #2186 for best value: >> intensional similarity = 3 >> extensional distance = 190 >> proper extension: 0hmr4; 02rjv2w; 06gjk9; 01c9d; >> query: (?x10077, 0gq9h) <- titles(?x53, ?x10077), nominated_for(?x1107, ?x10077), ?x1107 = 019f4v >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #2143 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 190 *> proper extension: 0hmr4; 02rjv2w; 06gjk9; 01c9d; *> query: (?x10077, 0gq_v) <- titles(?x53, ?x10077), nominated_for(?x1107, ?x10077), ?x1107 = 019f4v *> conf = 0.41 ranks of expected_values: 12 EVAL 043n1r5 nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 77.000 77.000 0.656 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6077-0mkg PRED entity: 0mkg PRED relation: group PRED expected values: 02r3zy => 97 concepts (51 used for prediction) PRED predicted values (max 10 best out of 920): 06nv27 (0.71 #3482, 0.57 #3311, 0.56 #5036), 027kwc (0.71 #3593, 0.57 #3422, 0.50 #2916), 01fmz6 (0.71 #3480, 0.57 #3309, 0.50 #2916), 01v0sxx (0.71 #3564, 0.50 #2916, 0.50 #2873), 012x1l (0.71 #3589, 0.50 #2916, 0.50 #2898), 01w5n51 (0.71 #3516, 0.50 #2916, 0.50 #2825), 0838y (0.71 #3504, 0.50 #2916, 0.50 #2813), 07h76 (0.71 #3495, 0.50 #2916, 0.50 #2804), 0bk1p (0.71 #3543, 0.50 #2916, 0.50 #2852), 016m5c (0.71 #3587, 0.50 #2916, 0.50 #2205) >> Best rule #3482 for best value: >> intensional similarity = 16 >> extensional distance = 5 >> proper extension: 028tv0; >> query: (?x614, 06nv27) <- group(?x614, ?x5858), role(?x3716, ?x614), role(?x3409, ?x614), role(?x4913, ?x614), role(?x2923, ?x614), role(?x2798, ?x614), role(?x2310, ?x614), role(?x2575, ?x614), ?x2798 = 03qjg, ?x4913 = 03ndd, ?x2310 = 0gghm, role(?x2923, ?x314), role(?x4918, ?x2923), role(?x1715, ?x3409), ?x5858 = 013w2r, instrumentalists(?x3716, ?x130) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3441 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 5 *> proper extension: 028tv0; *> query: (?x614, 02r3zy) <- group(?x614, ?x5858), role(?x3716, ?x614), role(?x3409, ?x614), role(?x4913, ?x614), role(?x2923, ?x614), role(?x2798, ?x614), role(?x2310, ?x614), role(?x2575, ?x614), ?x2798 = 03qjg, ?x4913 = 03ndd, ?x2310 = 0gghm, role(?x2923, ?x314), role(?x4918, ?x2923), role(?x1715, ?x3409), ?x5858 = 013w2r, instrumentalists(?x3716, ?x130) *> conf = 0.57 ranks of expected_values: 40 EVAL 0mkg group 02r3zy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 97.000 51.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #6076-0dfrq PRED entity: 0dfrq PRED relation: people! PRED expected values: 03bkbh => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 46): 03bkbh (0.34 #802, 0.05 #1804, 0.05 #2574), 041rx (0.33 #466, 0.30 #389, 0.25 #1930), 033tf_ (0.20 #1779, 0.20 #2549, 0.11 #3782), 0xnvg (0.13 #1785, 0.11 #2555, 0.07 #1862), 0x67 (0.11 #2244, 0.10 #1859, 0.10 #2475), 02w7gg (0.11 #1004, 0.06 #2005, 0.06 #618), 013b6_ (0.10 #53, 0.09 #130, 0.09 #669), 013xrm (0.09 #97, 0.08 #174, 0.07 #559), 0bpjh3 (0.08 #179, 0.04 #1002), 07hwkr (0.08 #1014, 0.07 #1168, 0.07 #1476) >> Best rule #802 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0f1pyf; 02y0dd; >> query: (?x9278, 03bkbh) <- nationality(?x9278, ?x429), ?x429 = 03rt9, gender(?x9278, ?x231), ?x231 = 05zppz >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dfrq people! 03bkbh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.342 http://example.org/people/ethnicity/people #6075-0123qq PRED entity: 0123qq PRED relation: titles! PRED expected values: 07c52 => 50 concepts (18 used for prediction) PRED predicted values (max 10 best out of 31): 07c52 (0.66 #1391, 0.64 #967, 0.63 #1601), 03mdt (0.13 #672, 0.10 #357, 0.09 #1194), 0187wh (0.11 #1148, 0.10 #1781), 0kctd (0.10 #402, 0.09 #507, 0.08 #612), 01z77k (0.08 #1632, 0.06 #1104, 0.05 #792), 07s9rl0 (0.08 #1675, 0.07 #1572, 0.07 #1044), 01hmnh (0.08 #1675, 0.04 #1674, 0.03 #1465), 01z4y (0.08 #1675, 0.04 #1674, 0.03 #1465), 03k9fj (0.08 #1675, 0.04 #1674, 0.03 #1465), 03npn (0.08 #1675, 0.04 #1674, 0.03 #1465) >> Best rule #1391 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 090s_0; 0358x_; 0cpz4k; 06mr2s; 03gvm3t; 05fgr_; 02qjv1p; 0q9nj; 01b7h8; 02pvqmz; ... >> query: (?x11203, 07c52) <- genre(?x11203, ?x571), program(?x7587, ?x11203), titles(?x571, ?x249) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0123qq titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 18.000 0.660 http://example.org/media_common/netflix_genre/titles #6074-02hyt PRED entity: 02hyt PRED relation: source PRED expected values: 0jbk9 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #23, 0.91 #21, 0.91 #11) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 0b_cr; >> query: (?x9758, 0jbk9) <- contains(?x1227, ?x9758), ?x1227 = 01n7q, place(?x9758, ?x9758) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hyt source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.922 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #6073-012x2b PRED entity: 012x2b PRED relation: religion PRED expected values: 0kpl => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 23): 0c8wxp (0.25 #771, 0.19 #2076, 0.19 #2256), 04pk9 (0.17 #20, 0.08 #110, 0.06 #290), 092bf5 (0.17 #151, 0.08 #736, 0.07 #826), 0kpl (0.15 #370, 0.14 #190, 0.11 #1000), 03_gx (0.15 #374, 0.14 #509, 0.09 #1094), 0n2g (0.08 #148, 0.07 #193, 0.04 #2038), 051kv (0.08 #140, 0.03 #455, 0.02 #545), 03j6c (0.08 #741, 0.07 #831, 0.05 #471), 0kq2 (0.04 #1818, 0.04 #1863, 0.04 #1503), 019cr (0.03 #1631, 0.02 #1676, 0.02 #1451) >> Best rule #771 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 0146pg; 01cwhp; 029ghl; >> query: (?x9601, 0c8wxp) <- languages(?x9601, ?x254), profession(?x9601, ?x987), nominated_for(?x9601, ?x6000), film_distribution_medium(?x6000, ?x2099) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #370 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 0cj2w; *> query: (?x9601, 0kpl) <- student(?x9823, ?x9601), profession(?x9601, ?x1146), ?x1146 = 018gz8, written_by(?x2939, ?x9601) *> conf = 0.15 ranks of expected_values: 4 EVAL 012x2b religion 0kpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 128.000 128.000 0.246 http://example.org/people/person/religion #6072-03f6fl0 PRED entity: 03f6fl0 PRED relation: location PRED expected values: 0xmp9 => 143 concepts (118 used for prediction) PRED predicted values (max 10 best out of 219): 02_286 (0.30 #1642, 0.28 #37, 0.26 #2443), 030qb3t (0.20 #36147, 0.19 #76215, 0.18 #78620), 04jpl (0.12 #17, 0.11 #1622, 0.10 #2423), 0cr3d (0.09 #70665, 0.07 #86700, 0.07 #69862), 04lh6 (0.08 #433, 0.08 #1234, 0.07 #2038), 02jx1 (0.07 #1676, 0.06 #2477, 0.04 #71), 0mgp (0.07 #20839, 0.07 #1605, 0.07 #1604), 0cv3w (0.07 #20839, 0.07 #1604, 0.06 #20037), 0d9jr (0.06 #3474, 0.05 #5076, 0.04 #267), 06y9v (0.06 #4164, 0.02 #4965, 0.02 #17787) >> Best rule #1642 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 03rl84; >> query: (?x4977, 02_286) <- gender(?x4977, ?x231), role(?x4977, ?x316), profession(?x4977, ?x131), languages(?x4977, ?x254), artists(?x302, ?x4977) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03f6fl0 location 0xmp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 118.000 0.296 http://example.org/people/person/places_lived./people/place_lived/location #6071-099p5 PRED entity: 099p5 PRED relation: company PRED expected values: 06pwq => 110 concepts (79 used for prediction) PRED predicted values (max 10 best out of 132): 02sjgpq (0.23 #388, 0.08 #109, 0.08 #498), 01hx2t (0.23 #388, 0.03 #2322, 0.02 #3674), 01w3v (0.17 #15, 0.15 #404, 0.15 #210), 07wrz (0.17 #36, 0.15 #425, 0.12 #619), 09c7w0 (0.16 #1745, 0.08 #3095, 0.07 #1937), 01w5m (0.15 #438, 0.12 #632, 0.08 #49), 07tg4 (0.15 #239, 0.08 #433, 0.06 #627), 0lvng (0.15 #306, 0.08 #500, 0.06 #694), 03ksy (0.12 #633, 0.08 #50, 0.08 #439), 0f1r9 (0.08 #184, 0.08 #573, 0.06 #767) >> Best rule #388 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01zwy; >> query: (?x9385, ?x7278) <- student(?x7278, ?x9385), profession(?x9385, ?x11056), location(?x9385, ?x94), ?x11056 = 05snw >> conf = 0.23 => this is the best rule for 2 predicted values *> Best rule #1561 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 01vvydl; 0fvf9q; 01xdf5; 01p45_v; 0gt_k; 040db; 01vsps; 01vw20h; 015d3h; 01vw8mh; ... *> query: (?x9385, 06pwq) <- location(?x9385, ?x2622), profession(?x9385, ?x3802), locations(?x4803, ?x2622), company(?x9385, ?x6637) *> conf = 0.01 ranks of expected_values: 72 EVAL 099p5 company 06pwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 110.000 79.000 0.227 http://example.org/people/person/employment_history./business/employment_tenure/company #6070-03339m PRED entity: 03339m PRED relation: parent_genre PRED expected values: 01jwt => 71 concepts (47 used for prediction) PRED predicted values (max 10 best out of 215): 06by7 (0.65 #4746, 0.53 #1315, 0.52 #1480), 05w3f (0.40 #188, 0.20 #350, 0.15 #487), 05r6t (0.35 #5771, 0.29 #701, 0.27 #1190), 07sbbz2 (0.33 #5, 0.17 #492, 0.09 #810), 02l96k (0.33 #69, 0.17 #556, 0.09 #810), 0jrv_ (0.30 #1080, 0.27 #1243, 0.16 #2877), 016clz (0.29 #3591, 0.23 #4079, 0.17 #491), 0xhtw (0.20 #4564, 0.20 #338, 0.20 #176), 01_bkd (0.20 #1011, 0.20 #362, 0.18 #1174), 02yv6b (0.20 #388, 0.20 #226, 0.14 #711) >> Best rule #4746 for best value: >> intensional similarity = 11 >> extensional distance = 89 >> proper extension: 01h0kx; 018ysx; 017ht; >> query: (?x10471, 06by7) <- parent_genre(?x10471, ?x2249), artists(?x2249, ?x9074), artists(?x2249, ?x3740), artists(?x2249, ?x3399), artists(?x2249, ?x1970), ?x3399 = 01gx5f, ?x3740 = 0fpj4lx, parent_genre(?x12808, ?x2249), ?x9074 = 01k47c, ?x12808 = 03fpx, ?x1970 = 0zjpz >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #857 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 5 *> proper extension: 0d4xmp; *> query: (?x10471, 01jwt) <- parent_genre(?x10471, ?x6350), parent_genre(?x10471, ?x5436), parent_genre(?x10471, ?x2249), ?x2249 = 03lty, ?x6350 = 0296y, artists(?x5436, ?x12266), artists(?x5436, ?x3657), artists(?x5436, ?x3024), ?x3024 = 0gkg6, parent_genre(?x5436, ?x1380), ?x12266 = 0889x, ?x3657 = 01w8n89 *> conf = 0.14 ranks of expected_values: 37 EVAL 03339m parent_genre 01jwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 71.000 47.000 0.648 http://example.org/music/genre/parent_genre #6069-02f71y PRED entity: 02f71y PRED relation: award! PRED expected values: 0147dk 01trhmt 049qx => 41 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2520): 016fnb (0.85 #13374, 0.84 #10029, 0.82 #20060), 049qx (0.85 #13374, 0.84 #10029, 0.82 #20060), 0kr_t (0.73 #11624, 0.67 #4938, 0.57 #8281), 03y82t6 (0.71 #8045, 0.67 #4702, 0.64 #11388), 01xzb6 (0.71 #8207, 0.64 #11550, 0.50 #14895), 0dvqq (0.67 #3964, 0.57 #13995, 0.57 #7307), 01vsgrn (0.67 #4951, 0.55 #11637, 0.47 #18325), 0fhxv (0.64 #14700, 0.58 #18043, 0.43 #8012), 016l09 (0.64 #12777, 0.57 #9434, 0.50 #6091), 0134pk (0.64 #12798, 0.57 #9455, 0.50 #6112) >> Best rule #13374 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 02f73p; 01c9jp; >> query: (?x3488, ?x4394) <- award(?x8156, ?x3488), award(?x7115, ?x3488), award(?x2784, ?x3488), award_winner(?x3488, ?x4394), ?x8156 = 046p9, participant(?x2784, ?x3581), award_nominee(?x7115, ?x1826) >> conf = 0.85 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 75, 270 EVAL 02f71y award! 049qx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 26.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f71y award! 01trhmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 41.000 26.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f71y award! 0147dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 41.000 26.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6068-01nyl PRED entity: 01nyl PRED relation: country! PRED expected values: 0w0d => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 52): 03_8r (0.69 #657, 0.67 #392, 0.67 #551), 071t0 (0.59 #658, 0.54 #393, 0.53 #923), 01lb14 (0.47 #650, 0.43 #544, 0.42 #915), 06f41 (0.40 #649, 0.38 #543, 0.36 #702), 03hr1p (0.40 #659, 0.33 #1401, 0.33 #924), 07jbh (0.37 #669, 0.36 #298, 0.35 #404), 0w0d (0.37 #647, 0.35 #382, 0.34 #276), 064vjs (0.36 #667, 0.33 #561, 0.31 #455), 03fyrh (0.34 #664, 0.34 #399, 0.33 #558), 06wrt (0.34 #651, 0.32 #492, 0.32 #333) >> Best rule #657 for best value: >> intensional similarity = 3 >> extensional distance = 129 >> proper extension: 0168t; >> query: (?x7871, 03_8r) <- adjoins(?x2804, ?x7871), participating_countries(?x1931, ?x7871), currency(?x2804, ?x170) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #647 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 129 *> proper extension: 0168t; *> query: (?x7871, 0w0d) <- adjoins(?x2804, ?x7871), participating_countries(?x1931, ?x7871), currency(?x2804, ?x170) *> conf = 0.37 ranks of expected_values: 7 EVAL 01nyl country! 0w0d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 73.000 73.000 0.695 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #6067-07147 PRED entity: 07147 PRED relation: colors PRED expected values: 083jv => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.67 #285, 0.62 #223, 0.62 #451), 02rnmb (0.43 #463, 0.42 #297, 0.40 #134), 06fvc (0.43 #203, 0.42 #286, 0.40 #123), 019sc (0.35 #705, 0.32 #769, 0.31 #498), 01l849 (0.30 #677, 0.30 #657, 0.27 #491), 01g5v (0.30 #677, 0.29 #204, 0.26 #473), 03vtbc (0.30 #677, 0.25 #395, 0.25 #69), 036k5h (0.30 #677, 0.22 #1054, 0.22 #1055), 0jc_p (0.30 #268, 0.26 #864, 0.25 #308), 038hg (0.22 #1054, 0.22 #1055, 0.20 #221) >> Best rule #285 for best value: >> intensional similarity = 19 >> extensional distance = 10 >> proper extension: 06x68; >> query: (?x8111, 083jv) <- season(?x8111, ?x11501), season(?x8111, ?x10017), season(?x8111, ?x9498), season(?x8111, ?x3431), ?x9498 = 027pwzc, school(?x8111, ?x5907), season(?x11361, ?x10017), season(?x7060, ?x10017), season(?x4487, ?x10017), season(?x2067, ?x10017), ?x11361 = 03m1n, ?x7060 = 01slc, ?x4487 = 01ync, draft(?x8111, ?x1161), ?x11501 = 027mvrc, ?x2067 = 05g76, ?x3431 = 025ygqm, student(?x5907, ?x3762), sport(?x8111, ?x5063) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07147 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.667 http://example.org/sports/sports_team/colors #6066-02l7c8 PRED entity: 02l7c8 PRED relation: genre! PRED expected values: 0ds35l9 0gx1bnj 0djb3vw 01cssf 04jwjq 0pv2t 06_wqk4 0cwy47 0jnwx 0k4d7 02rjv2w 0p4v_ 0_816 0p_qr 011yl_ 01sxdy 02qzmz6 0d_wms 05m_jsg 06lpmt 07kb7vh 0g83dv 09fn1w 09ps01 04nm0n0 02ylg6 0d4htf 0df92l 0ggbfwf 01jw67 0gl3hr 03_gz8 01l2b3 0421v9q 0gnjh 063hp4 01f8f7 0gnkb 0cf08 04qk12 0f7hw 0n6ds 023vcd 01c9d 0fzm0g => 53 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1411): 02_kd (0.80 #14180, 0.80 #1418, 0.80 #17018), 03nm_fh (0.80 #14180, 0.80 #1418, 0.80 #17018), 011yxg (0.80 #14180, 0.80 #1418, 0.80 #17018), 014l6_ (0.80 #14180, 0.80 #1418, 0.80 #17018), 0pv2t (0.80 #14180, 0.80 #1418, 0.80 #17018), 02rjv2w (0.80 #14180, 0.80 #1418, 0.80 #17018), 02nx2k (0.71 #15140, 0.71 #13721, 0.67 #20814), 0fvr1 (0.62 #15890, 0.57 #14471, 0.57 #13052), 05ch98 (0.62 #16679, 0.57 #15260, 0.57 #13841), 02fwfb (0.62 #18025, 0.45 #22281, 0.33 #3843) >> Best rule #14180 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 03k9fj; >> query: (?x1403, ?x144) <- titles(?x1403, ?x144), genre(?x6536, ?x1403), genre(?x2211, ?x1403), ?x2211 = 07nt8p, genre(?x2078, ?x1403), film_release_region(?x6536, ?x87) >> conf = 0.80 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 6, 62, 121, 161, 183, 200, 215, 232, 246, 362, 381, 469, 472, 479, 481, 508, 513, 562, 573, 678, 681, 686, 720, 729, 752, 819, 831, 834, 849, 870, 877, 900, 965, 968, 970, 975, 1007, 1154, 1161, 1187, 1241, 1351, 1378 EVAL 02l7c8 genre! 0fzm0g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 01c9d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 023vcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0n6ds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0f7hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 04qk12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0cf08 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0gnkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 01f8f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 063hp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0gnjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0421v9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 01l2b3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 03_gz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0gl3hr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 01jw67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0ggbfwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0df92l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0d4htf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 02ylg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 04nm0n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 09ps01 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 09fn1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0g83dv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 07kb7vh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 06lpmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 05m_jsg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0d_wms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 02qzmz6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 01sxdy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 011yl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0p_qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0_816 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0p4v_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 02rjv2w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0k4d7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0jnwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0cwy47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 06_wqk4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0pv2t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 04jwjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 01cssf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0djb3vw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0gx1bnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 21.000 0.798 http://example.org/film/film/genre EVAL 02l7c8 genre! 0ds35l9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 21.000 0.798 http://example.org/film/film/genre #6065-03rk0 PRED entity: 03rk0 PRED relation: country! PRED expected values: 0fkbh 01sv6k => 205 concepts (148 used for prediction) PRED predicted values (max 10 best out of 676): 055vr (0.59 #7669, 0.47 #31282, 0.41 #14750), 01hpnh (0.59 #7669, 0.47 #31282, 0.41 #14750), 049lr (0.59 #7669, 0.47 #31282, 0.41 #14750), 0byh8j (0.59 #7669, 0.47 #31282, 0.41 #14750), 086g2 (0.59 #7669, 0.47 #31282, 0.41 #14750), 0290rb (0.59 #7669, 0.47 #31282, 0.41 #14750), 03p85 (0.59 #7669, 0.47 #31282, 0.41 #14750), 01c0h6 (0.59 #7669, 0.47 #31282, 0.41 #14750), 06k5_ (0.59 #7669, 0.47 #31282, 0.41 #20065), 011hq1 (0.59 #7669, 0.47 #31282, 0.35 #12981) >> Best rule #7669 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 018jcq; >> query: (?x2146, ?x11134) <- administrative_parent(?x11134, ?x2146), location_of_ceremony(?x566, ?x2146), location(?x14055, ?x11134) >> conf = 0.59 => this is the best rule for 11 predicted values *> Best rule #2948 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 06mx8; *> query: (?x2146, ?x8940) <- contains(?x2146, ?x8940), titles(?x2146, ?x257), service_location(?x10867, ?x8940) *> conf = 0.43 ranks of expected_values: 21, 33 EVAL 03rk0 country! 01sv6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 205.000 148.000 0.590 http://example.org/base/biblioness/bibs_location/country EVAL 03rk0 country! 0fkbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 205.000 148.000 0.590 http://example.org/base/biblioness/bibs_location/country #6064-0dq9wx PRED entity: 0dq9wx PRED relation: location PRED expected values: 0d6lp => 195 concepts (167 used for prediction) PRED predicted values (max 10 best out of 334): 02_286 (0.32 #101958, 0.31 #102760, 0.30 #106771), 01n7q (0.14 #867, 0.09 #4881, 0.09 #16916), 04jpl (0.12 #101938, 0.11 #102740, 0.11 #106751), 0cc56 (0.11 #6480, 0.10 #861, 0.09 #16910), 0cr3d (0.10 #109285, 0.09 #113297, 0.09 #114901), 0r0m6 (0.10 #1021, 0.08 #12257, 0.07 #21884), 05jbn (0.10 #1056, 0.08 #252, 0.04 #16303), 0k049 (0.08 #11246, 0.08 #8, 0.07 #16861), 013yq (0.08 #1724, 0.07 #16169, 0.07 #4936), 06_kh (0.08 #1617, 0.07 #2420, 0.07 #6434) >> Best rule #101958 for best value: >> intensional similarity = 3 >> extensional distance = 1087 >> proper extension: 02k4b2; 015p3p; 05sq0m; 060_7; 08wjf4; 0btxr; 047s_cr; 02qvhbb; 0gzh; >> query: (?x12047, 02_286) <- location(?x12047, ?x1523), origin(?x250, ?x1523), location_of_ceremony(?x147, ?x1523) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #2576 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 01xzb6; *> query: (?x12047, 0d6lp) <- location(?x12047, ?x1523), ?x1523 = 030qb3t, nationality(?x12047, ?x94), celebrity(?x12047, ?x9374) *> conf = 0.07 ranks of expected_values: 25 EVAL 0dq9wx location 0d6lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 195.000 167.000 0.321 http://example.org/people/person/places_lived./people/place_lived/location #6063-05xbx PRED entity: 05xbx PRED relation: award_winner! PRED expected values: 04xbq3 => 145 concepts (126 used for prediction) PRED predicted values (max 10 best out of 346): 08y2fn (0.53 #12494, 0.48 #26126, 0.44 #120374), 0ddd0gc (0.53 #12494, 0.48 #26126, 0.44 #120374), 03ffcz (0.33 #754, 0.25 #7567, 0.14 #124920), 03ctqqf (0.33 #1114, 0.25 #7927, 0.14 #124920), 021gzd (0.29 #49986, 0.26 #24989, 0.26 #12493), 04xbq3 (0.29 #49986, 0.26 #24989, 0.26 #12493), 05lfwd (0.26 #52906, 0.03 #109669, 0.01 #119886), 01hn_t (0.26 #24989, 0.26 #12493, 0.25 #19308), 04glx0 (0.26 #24989, 0.26 #12493, 0.25 #19308), 01r97z (0.25 #19385, 0.25 #3481, 0.22 #21656) >> Best rule #12494 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0146mv; 0kctd; >> query: (?x5007, ?x1434) <- program(?x5007, ?x4275), nominated_for(?x5007, ?x1434), organization(?x4682, ?x5007) >> conf = 0.53 => this is the best rule for 2 predicted values *> Best rule #49986 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0187wh; 02hmvw; 02fp82; 0hmxn; *> query: (?x5007, ?x7465) <- program(?x5007, ?x7465), actor(?x7465, ?x1250), award(?x7465, ?x3486) *> conf = 0.29 ranks of expected_values: 6 EVAL 05xbx award_winner! 04xbq3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 145.000 126.000 0.528 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #6062-0395lw PRED entity: 0395lw PRED relation: role PRED expected values: 0xzly => 90 concepts (60 used for prediction) PRED predicted values (max 10 best out of 73): 03qjg (0.86 #2142, 0.86 #2110, 0.83 #3788), 02qjv (0.83 #2993, 0.83 #1496, 0.83 #1565), 0gkd1 (0.83 #1496, 0.83 #1565, 0.82 #3498), 018j2 (0.83 #1496, 0.83 #1565, 0.82 #3498), 0xzly (0.83 #1496, 0.83 #1565, 0.82 #3498), 011k_j (0.83 #1496, 0.83 #1565, 0.82 #3498), 03t22m (0.83 #1496, 0.83 #1565, 0.82 #3498), 016622 (0.83 #1496, 0.83 #1565, 0.82 #3498), 06rvn (0.83 #1496, 0.83 #1565, 0.82 #3498), 0l15bq (0.83 #1496, 0.83 #1565, 0.82 #3498) >> Best rule #2142 for best value: >> intensional similarity = 16 >> extensional distance = 12 >> proper extension: 03ndd; >> query: (?x1432, ?x2798) <- role(?x3161, ?x1432), role(?x2798, ?x1432), role(?x1750, ?x1432), role(?x432, ?x1432), role(?x5949, ?x1432), award(?x5949, ?x1079), music(?x1944, ?x5949), award_winner(?x669, ?x5949), type_of_union(?x5949, ?x566), ?x1750 = 02hnl, role(?x1432, ?x228), role(?x10574, ?x3161), role(?x645, ?x3161), ?x10574 = 02g40r, ?x432 = 042v_gx, ?x2798 = 03qjg >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1496 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 9 *> proper extension: 0342h; *> query: (?x1432, ?x74) <- role(?x4917, ?x1432), role(?x3161, ?x1432), role(?x1750, ?x1432), role(?x1495, ?x1432), role(?x868, ?x1432), role(?x74, ?x1432), role(?x5949, ?x1432), award(?x5949, ?x1079), music(?x1944, ?x5949), award_winner(?x669, ?x5949), type_of_union(?x5949, ?x566), ?x1750 = 02hnl, role(?x1432, ?x228), role(?x2306, ?x3161), award_nominee(?x5949, ?x6011), ?x1495 = 013y1f, role(?x3161, ?x645), ?x868 = 0dwvl, instrumentalists(?x4917, ?x1656) *> conf = 0.83 ranks of expected_values: 5 EVAL 0395lw role 0xzly CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 60.000 0.857 http://example.org/music/performance_role/track_performances./music/track_contribution/role #6061-03khn PRED entity: 03khn PRED relation: month PRED expected values: 06vkl 0lkm => 249 concepts (249 used for prediction) PRED predicted values (max 10 best out of 2): 06vkl (0.91 #226, 0.90 #177, 0.90 #93), 0lkm (0.91 #226, 0.88 #243, 0.88 #178) >> Best rule #226 for best value: >> intensional similarity = 8 >> extensional distance = 49 >> proper extension: 0177z; >> query: (?x11237, ?x1650) <- month(?x11237, ?x7298), month(?x11237, ?x3270), ?x3270 = 05cw8, month(?x3106, ?x7298), month(?x863, ?x7298), ?x3106 = 049d1, seasonal_months(?x1650, ?x7298), ?x863 = 0fhp9 >> conf = 0.91 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2 EVAL 03khn month 0lkm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 249.000 249.000 0.914 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 03khn month 06vkl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 249.000 249.000 0.914 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #6060-04411 PRED entity: 04411 PRED relation: people! PRED expected values: 07hwkr => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 50): 041rx (0.50 #312, 0.35 #1467, 0.29 #1544), 01qhm_ (0.33 #391, 0.17 #1854, 0.11 #1700), 013xrm (0.29 #1175, 0.29 #174, 0.27 #2176), 03lmx1 (0.29 #245, 0.12 #1092, 0.09 #1477), 07bch9 (0.22 #408, 0.17 #1871, 0.15 #1717), 02ctzb (0.20 #1863, 0.16 #3172, 0.15 #862), 013b6_ (0.17 #823, 0.14 #284, 0.14 #207), 063k3h (0.14 #3188, 0.11 #416, 0.07 #1725), 048z7l (0.12 #348, 0.10 #579, 0.06 #1965), 0g8_vp (0.12 #330, 0.02 #2871, 0.02 #3102) >> Best rule #312 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 06449; 01zwy; >> query: (?x920, 041rx) <- award(?x920, ?x921), influenced_by(?x920, ?x3941), student(?x2313, ?x920), ?x2313 = 07wrz >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4248 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 101 *> proper extension: 0z4s; 039bp; 04g865; 05fyss; 036jp8; 03l3ln; 0fn8jc; 02byfd; 01s7ns; 014vk4; *> query: (?x920, 07hwkr) <- award(?x920, ?x921), location(?x920, ?x7405), company(?x920, ?x1681), profession(?x920, ?x13369) *> conf = 0.09 ranks of expected_values: 17 EVAL 04411 people! 07hwkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 186.000 186.000 0.500 http://example.org/people/ethnicity/people #6059-07dzf PRED entity: 07dzf PRED relation: exported_to PRED expected values: 0345h => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 71): 09c7w0 (0.38 #282, 0.38 #567, 0.28 #736), 0d05w3 (0.32 #792, 0.20 #31, 0.12 #256), 088vb (0.32 #792, 0.06 #395, 0.04 #2327), 0hzlz (0.32 #792, 0.03 #239, 0.02 #295), 06qd3 (0.20 #21, 0.06 #78, 0.06 #246), 06tw8 (0.14 #323, 0.12 #608, 0.12 #777), 07ssc (0.13 #347, 0.12 #575, 0.12 #234), 0h3y (0.12 #231, 0.12 #458, 0.11 #629), 0345h (0.12 #243, 0.10 #299, 0.09 #584), 07dzf (0.12 #321, 0.09 #153, 0.07 #606) >> Best rule #282 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 047t_; >> query: (?x5360, 09c7w0) <- administrative_parent(?x5360, ?x551), countries_spoken_in(?x254, ?x5360), exported_to(?x5360, ?x252) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #243 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 07ytt; *> query: (?x5360, 0345h) <- countries_within(?x2467, ?x5360), religion(?x5360, ?x492), religion(?x111, ?x492) *> conf = 0.12 ranks of expected_values: 9 EVAL 07dzf exported_to 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 105.000 105.000 0.381 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #6058-068cn PRED entity: 068cn PRED relation: partially_contains PRED expected values: 0fcgd => 158 concepts (80 used for prediction) PRED predicted values (max 10 best out of 38): 0lm0n (0.39 #471, 0.14 #1875, 0.13 #2164), 0lcd (0.33 #16, 0.29 #97, 0.27 #3027), 065ky (0.27 #3027, 0.27 #2982, 0.02 #600), 05g56 (0.27 #3027, 0.27 #2982, 0.02 #598), 02cgp8 (0.16 #469, 0.05 #1873, 0.04 #1373), 026zt (0.14 #105, 0.11 #592, 0.11 #188), 09glw (0.11 #748, 0.06 #1326, 0.06 #1409), 05lx3 (0.10 #473, 0.05 #1877, 0.04 #1377), 04yf_ (0.09 #1029, 0.09 #1360, 0.07 #1860), 0f8l9c (0.08 #406, 0.08 #776, 0.07 #442) >> Best rule #471 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 01ly8d; >> query: (?x9230, 0lm0n) <- adjoins(?x9230, ?x789), administrative_division(?x10537, ?x9230), film_release_region(?x66, ?x789), contains(?x789, ?x790) >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 068cn partially_contains 0fcgd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 80.000 0.387 http://example.org/location/location/partially_contains #6057-0260bz PRED entity: 0260bz PRED relation: nominated_for! PRED expected values: 0l8z1 02x2gy0 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 199): 026mg3 (0.80 #475, 0.03 #3724), 02x258x (0.66 #11140, 0.14 #1022, 0.13 #1254), 0l8z1 (0.47 #1212, 0.25 #3946, 0.23 #1677), 054krc (0.40 #1227, 0.24 #1924, 0.22 #2156), 0p9sw (0.40 #1180, 0.23 #4430, 0.20 #2341), 02qyntr (0.36 #1102, 0.33 #1334, 0.29 #2031), 0gq9h (0.34 #1685, 0.34 #1917, 0.34 #4470), 0gs9p (0.34 #1687, 0.34 #1919, 0.30 #758), 04dn09n (0.34 #1892, 0.31 #1660, 0.31 #2124), 040njc (0.33 #1167, 0.23 #9747, 0.21 #935) >> Best rule #475 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01p9hgt; 01kv4mb; 0ggjt; 0bhvtc; 03cfjg; 0p_47; 0pmw9; >> query: (?x2107, 026mg3) <- nominated_for(?x5123, ?x2107), award(?x3374, ?x5123), ?x3374 = 01vsy95 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1212 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 025x1t; *> query: (?x2107, 0l8z1) <- award_winner(?x2107, ?x3186), award_winner(?x2107, ?x2135), ?x2135 = 06pj8, film(?x3186, ?x146) *> conf = 0.47 ranks of expected_values: 3, 80 EVAL 0260bz nominated_for! 02x2gy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 83.000 83.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0260bz nominated_for! 0l8z1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6056-0187x8 PRED entity: 0187x8 PRED relation: artists! PRED expected values: 0xhtw 0781g => 77 concepts (40 used for prediction) PRED predicted values (max 10 best out of 219): 064t9 (0.65 #1234, 0.55 #8255, 0.55 #8560), 0xhtw (0.45 #321, 0.44 #16, 0.38 #932), 03lty (0.33 #26, 0.25 #942, 0.24 #331), 08jyyk (0.33 #65, 0.14 #676, 0.09 #7086), 06j6l (0.28 #1572, 0.28 #4013, 0.27 #5538), 02k_kn (0.25 #1285, 0.14 #2810, 0.14 #1590), 0glt670 (0.25 #4006, 0.23 #5531, 0.23 #3701), 025sc50 (0.24 #4015, 0.23 #3710, 0.23 #1574), 01lyv (0.23 #1559, 0.20 #2169, 0.20 #4000), 03_d0 (0.23 #7643, 0.18 #10085, 0.18 #1843) >> Best rule #1234 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 01pbxb; 01vw87c; 0152cw; 01w61th; 09qr6; 01r9fv; 01sbf2; 02zmh5; 01vsnff; 0136pk; ... >> query: (?x7810, 064t9) <- award(?x7810, ?x2634), artists(?x3061, ?x7810), ?x3061 = 05bt6j >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #321 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 07qnf; 04r1t; 02r1tx7; 05563d; 03xhj6; 0394y; 018gm9; 01j59b0; 02mq_y; 0k1bs; ... *> query: (?x7810, 0xhtw) <- artists(?x1572, ?x7810), ?x1572 = 06by7, group(?x227, ?x7810) *> conf = 0.45 ranks of expected_values: 2, 61 EVAL 0187x8 artists! 0781g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 77.000 40.000 0.651 http://example.org/music/genre/artists EVAL 0187x8 artists! 0xhtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 40.000 0.651 http://example.org/music/genre/artists #6055-03d_zl4 PRED entity: 03d_zl4 PRED relation: award PRED expected values: 0gkvb7 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 267): 0gkvb7 (0.40 #1242, 0.33 #2052, 0.33 #27), 05zr6wv (0.40 #1232, 0.33 #2042, 0.33 #17), 05pcn59 (0.40 #1296, 0.33 #2106, 0.17 #1701), 0f4x7 (0.40 #3271, 0.30 #3676, 0.22 #4081), 05p09zm (0.33 #2149, 0.33 #124, 0.20 #1339), 09sb52 (0.33 #41, 0.26 #9761, 0.25 #9356), 0gqy2 (0.33 #165, 0.23 #8265, 0.22 #9075), 05zvj3m (0.33 #93, 0.20 #1308, 0.17 #2118), 09qvc0 (0.33 #1660, 0.20 #1255, 0.17 #2065), 07cbcy (0.33 #78, 0.20 #1293, 0.17 #2103) >> Best rule #1242 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 018grr; >> query: (?x6707, 0gkvb7) <- celebrities_impersonated(?x6707, ?x3017), gender(?x6707, ?x231), people(?x4322, ?x3017), place_of_birth(?x6707, ?x13959) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03d_zl4 award 0gkvb7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #6054-04svwx PRED entity: 04svwx PRED relation: genre PRED expected values: 01htzx 01z4y => 92 concepts (54 used for prediction) PRED predicted values (max 10 best out of 188): 07s9rl0 (0.89 #3165, 0.87 #3494, 0.76 #3246), 01z4y (0.65 #1809, 0.64 #2212, 0.63 #2456), 06n90 (0.59 #2288, 0.55 #737, 0.50 #3410), 02kdv5l (0.55 #737, 0.50 #3410, 0.40 #740), 01htzx (0.55 #737, 0.50 #3410, 0.38 #2374), 02l7c8 (0.55 #737, 0.50 #3410, 0.33 #264), 01jfsb (0.55 #737, 0.50 #3410, 0.23 #2287), 0c4xc (0.44 #1509, 0.42 #2155, 0.42 #2235), 0pr6f (0.39 #1437, 0.33 #458, 0.25 #703), 01t_vv (0.36 #1986, 0.31 #1823, 0.30 #2226) >> Best rule #3165 for best value: >> intensional similarity = 7 >> extensional distance = 129 >> proper extension: 03j63k; >> query: (?x12093, 07s9rl0) <- actor(?x12093, ?x13780), genre(?x12093, ?x1510), gender(?x13780, ?x514), titles(?x1510, ?x83), genre(?x5732, ?x1510), ?x5732 = 05pyrb, ?x83 = 014_x2 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1809 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 46 *> proper extension: 0d68qy; 01bv8b; 03y3bp7; 07zhjj; 0sw0q; *> query: (?x12093, 01z4y) <- actor(?x12093, ?x13780), actor(?x12093, ?x11175), genre(?x12093, ?x258), nationality(?x13780, ?x252), ?x258 = 05p553, category(?x11175, ?x134), film_release_region(?x9902, ?x252), ?x9902 = 0j8f09z *> conf = 0.65 ranks of expected_values: 2, 5 EVAL 04svwx genre 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 54.000 0.885 http://example.org/tv/tv_program/genre EVAL 04svwx genre 01htzx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 92.000 54.000 0.885 http://example.org/tv/tv_program/genre #6053-02bqmq PRED entity: 02bqmq PRED relation: district_represented PRED expected values: 0g0syc => 32 concepts (30 used for prediction) PRED predicted values (max 10 best out of 511): 0g0syc (0.86 #82, 0.85 #627, 0.79 #667), 06btq (0.86 #82, 0.74 #1106, 0.71 #805), 06yxd (0.86 #82, 0.71 #1118, 0.71 #805), 026mj (0.86 #82, 0.71 #1121, 0.71 #805), 0d0x8 (0.86 #82, 0.71 #1109, 0.71 #805), 03v0t (0.86 #82, 0.71 #805, 0.71 #910), 04ly1 (0.86 #82, 0.71 #805, 0.67 #1093), 05kkh (0.86 #82, 0.71 #805, 0.67 #1093), 0vbk (0.86 #82, 0.71 #805, 0.67 #1093), 07h34 (0.86 #82, 0.71 #805, 0.67 #1093) >> Best rule #82 for best value: >> intensional similarity = 50 >> extensional distance = 1 >> proper extension: 06f0dc; >> query: (?x3463, ?x177) <- legislative_sessions(?x3463, ?x4821), legislative_sessions(?x3463, ?x3766), legislative_sessions(?x3463, ?x3540), legislative_sessions(?x3463, ?x1137), legislative_sessions(?x3463, ?x845), legislative_sessions(?x3463, ?x605), legislative_sessions(?x3463, ?x356), legislative_sessions(?x3463, ?x355), district_represented(?x3463, ?x7405), district_represented(?x3463, ?x6226), district_represented(?x3463, ?x4600), district_represented(?x3463, ?x4198), district_represented(?x3463, ?x3670), district_represented(?x3463, ?x3634), district_represented(?x3463, ?x2977), district_represented(?x3463, ?x2020), district_represented(?x3463, ?x938), ?x6226 = 03gh4, ?x938 = 0vmt, ?x4600 = 081yw, ?x3766 = 02gkzs, ?x356 = 05l2z4, district_represented(?x3540, ?x4776), district_represented(?x3540, ?x4758), district_represented(?x3540, ?x3086), district_represented(?x3540, ?x1782), district_represented(?x3540, ?x177), legislative_sessions(?x3540, ?x4730), ?x605 = 077g7n, legislative_sessions(?x8607, ?x3540), legislative_sessions(?x6742, ?x3540), legislative_sessions(?x5266, ?x3540), ?x1137 = 02bqn1, ?x4821 = 02bqm0, ?x8607 = 0226cw, ?x355 = 0495ys, ?x4776 = 06yxd, ?x845 = 07p__7, ?x2020 = 05k7sb, ?x3670 = 05tbn, ?x3634 = 07b_l, ?x5266 = 016lh0, ?x1782 = 0488g, ?x4730 = 02cg7g, ?x7405 = 07_f2, ?x3086 = 0846v, ?x2977 = 081mh, ?x4758 = 0vbk, ?x4198 = 05fky, ?x6742 = 06bss >> conf = 0.86 => this is the best rule for 25 predicted values ranks of expected_values: 1 EVAL 02bqmq district_represented 0g0syc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 30.000 0.860 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #6052-0xpp5 PRED entity: 0xpp5 PRED relation: place_of_birth! PRED expected values: 06yj20 => 89 concepts (41 used for prediction) PRED predicted values (max 10 best out of 992): 0zjpz (0.33 #351, 0.02 #8190), 0kjgl (0.32 #36591, 0.30 #20907, 0.29 #52276), 013pp3 (0.09 #28749, 0.08 #3703, 0.06 #6316), 015wfg (0.09 #28749, 0.02 #8714), 01jqr_5 (0.09 #28749, 0.02 #8307), 01wb8bs (0.09 #28749, 0.01 #11230), 06n7h7 (0.09 #28749, 0.01 #10553), 01pcdn (0.09 #28749, 0.01 #44435, 0.01 #44434), 022s1m (0.09 #28749), 075npt (0.09 #28749) >> Best rule #351 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0xpq9; >> query: (?x6142, 0zjpz) <- contains(?x6895, ?x6142), contains(?x6143, ?x6142), category(?x6142, ?x134), ?x6143 = 0n5df, ?x6895 = 05fjf >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0xpp5 place_of_birth! 06yj20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 41.000 0.333 http://example.org/people/person/place_of_birth #6051-019l3m PRED entity: 019l3m PRED relation: gender PRED expected values: 02zsn => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.89 #12, 0.83 #8, 0.51 #26), 05zppz (0.75 #9, 0.72 #19, 0.72 #141) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 01j5ts; 01p7yb; 0159h6; 0c4f4; 03zqc1; 03f2_rc; 01gvr1; 01csvq; 07lt7b; 01tspc6; ... >> query: (?x8946, 02zsn) <- film(?x8946, ?x1746), nominated_for(?x8946, ?x8769), award(?x8946, ?x1245), ?x1245 = 0gqwc >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019l3m gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.894 http://example.org/people/person/gender #6050-09k56b7 PRED entity: 09k56b7 PRED relation: executive_produced_by PRED expected values: 02rchht => 81 concepts (30 used for prediction) PRED predicted values (max 10 best out of 52): 05hj_k (0.17 #98, 0.09 #350, 0.08 #1360), 06q8hf (0.09 #419, 0.08 #167, 0.08 #1429), 04jspq (0.05 #1666, 0.05 #2423, 0.04 #151), 016dmx (0.04 #186, 0.02 #438, 0.02 #2205), 076_74 (0.04 #93, 0.02 #345, 0.01 #850), 0343h (0.04 #42, 0.01 #4840, 0.01 #4585), 059x0w (0.04 #204, 0.01 #961), 02pq9yv (0.04 #85, 0.01 #842), 02mt4k (0.04 #119), 048lv (0.04 #41) >> Best rule #98 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 064lsn; >> query: (?x1988, 05hj_k) <- nominated_for(?x198, ?x1988), ?x198 = 040njc, film_release_region(?x1988, ?x456), ?x456 = 05qhw >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09k56b7 executive_produced_by 02rchht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 30.000 0.167 http://example.org/film/film/executive_produced_by #6049-0453t PRED entity: 0453t PRED relation: profession PRED expected values: 016fly => 183 concepts (122 used for prediction) PRED predicted values (max 10 best out of 117): 02hrh1q (0.81 #9789, 0.78 #14533, 0.62 #5937), 09jwl (0.54 #1796, 0.50 #2092, 0.38 #4904), 0dxtg (0.48 #7268, 0.46 #13348, 0.45 #10827), 0nbcg (0.46 #1808, 0.44 #2104, 0.38 #2844), 01d_h8 (0.46 #1783, 0.43 #1931, 0.38 #2079), 016fly (0.45 #1702, 0.29 #962, 0.28 #5403), 02jknp (0.43 #1932, 0.38 #1784, 0.31 #2080), 01c72t (0.38 #1801, 0.31 #2097, 0.29 #2837), 05z96 (0.33 #2559, 0.30 #3003, 0.26 #6113), 02hv44_ (0.33 #797, 0.21 #3758, 0.20 #1389) >> Best rule #9789 for best value: >> intensional similarity = 4 >> extensional distance = 178 >> proper extension: 01v_pj6; 08m4c8; 0c6qh; 072bb1; 01dy7j; 03jjzf; 02wxvtv; 01pkhw; 036px; 03_l8m; ... >> query: (?x2239, 02hrh1q) <- student(?x3995, ?x2239), profession(?x2239, ?x2225), profession(?x9493, ?x2225), ?x9493 = 01j6mff >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1702 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01nbq4; *> query: (?x2239, 016fly) <- student(?x3995, ?x2239), profession(?x2239, ?x2225), ?x2225 = 0kyk, company(?x2239, ?x263) *> conf = 0.45 ranks of expected_values: 6 EVAL 0453t profession 016fly CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 183.000 122.000 0.806 http://example.org/people/person/profession #6048-0294mx PRED entity: 0294mx PRED relation: currency PRED expected values: 09nqf => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.79 #204, 0.78 #288, 0.77 #274), 01nv4h (0.05 #44, 0.03 #16, 0.03 #93), 02l6h (0.02 #46, 0.02 #18, 0.01 #249), 088n7 (0.01 #91), 02gsvk (0.01 #286, 0.01 #307) >> Best rule #204 for best value: >> intensional similarity = 4 >> extensional distance = 484 >> proper extension: 07kb7vh; >> query: (?x7283, 09nqf) <- country(?x7283, ?x94), film_crew_role(?x7283, ?x1171), ?x1171 = 09vw2b7, nominated_for(?x618, ?x7283) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0294mx currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.788 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #6047-0147jt PRED entity: 0147jt PRED relation: profession PRED expected values: 02hrh1q 0nbcg => 75 concepts (74 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.97 #8495, 0.85 #8787, 0.70 #4550), 0nbcg (0.54 #759, 0.50 #2370, 0.49 #3250), 0dz3r (0.50 #586, 0.40 #3223, 0.40 #732), 039v1 (0.42 #34, 0.30 #2375, 0.29 #2521), 01d_h8 (0.31 #2054, 0.27 #5274, 0.26 #5420), 0dxtg (0.27 #2061, 0.27 #6157, 0.27 #5281), 03gjzk (0.22 #2063, 0.20 #6159, 0.19 #6305), 02jknp (0.19 #2056, 0.18 #9219, 0.18 #5276), 0fnpj (0.18 #642, 0.14 #1228, 0.12 #1668), 025352 (0.16 #641, 0.11 #1227, 0.08 #934) >> Best rule #8495 for best value: >> intensional similarity = 3 >> extensional distance = 2583 >> proper extension: 06v8s0; 07nznf; 079vf; 05vsxz; 05d7rk; 01k7d9; 05bp8g; 0byfz; 03x3qv; 01tvz5j; ... >> query: (?x9103, 02hrh1q) <- profession(?x9103, ?x955), profession(?x11335, ?x955), ?x11335 = 0bqch >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0147jt profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 74.000 0.966 http://example.org/people/person/profession EVAL 0147jt profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 74.000 0.966 http://example.org/people/person/profession #6046-04q24zv PRED entity: 04q24zv PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.81 #63, 0.81 #234, 0.80 #166), 07c52 (0.06 #28, 0.06 #43, 0.05 #33), 07z4p (0.05 #72, 0.04 #30, 0.04 #103), 02nxhr (0.04 #74, 0.04 #122, 0.04 #89), 0735l (0.02 #24, 0.02 #29, 0.02 #34) >> Best rule #63 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 02d44q; >> query: (?x2797, 029j_) <- category(?x2797, ?x134), film_crew_role(?x2797, ?x468), ?x468 = 02r96rf, titles(?x53, ?x2797) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04q24zv film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.813 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #6045-01xhh5 PRED entity: 01xhh5 PRED relation: people PRED expected values: 01_njt => 38 concepts (21 used for prediction) PRED predicted values (max 10 best out of 3340): 0311wg (0.50 #3742, 0.43 #10646, 0.33 #15826), 0g824 (0.50 #4350, 0.43 #12981, 0.31 #18161), 02cg2v (0.50 #5163, 0.29 #12067, 0.22 #17247), 078jnn (0.50 #4592, 0.29 #11496, 0.22 #16676), 04xrx (0.50 #3795, 0.29 #10699, 0.22 #15879), 07vfqj (0.45 #17261, 0.02 #24162, 0.02 #25888), 02zbjhq (0.45 #17261, 0.02 #24162, 0.02 #25888), 02zbjwr (0.45 #17261, 0.02 #24162, 0.02 #25888), 0487c3 (0.45 #17261), 016z2j (0.33 #305, 0.31 #17566, 0.29 #12386) >> Best rule #3742 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 033tf_; >> query: (?x8088, 0311wg) <- languages_spoken(?x8088, ?x7926), people(?x8088, ?x10401), people(?x8088, ?x6152), team(?x6152, ?x6153), nationality(?x6152, ?x1453), profession(?x6152, ?x7623), student(?x7075, ?x10401), award_nominee(?x2141, ?x10401), ?x2141 = 03_wj_ >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01xhh5 people 01_njt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 38.000 21.000 0.500 http://example.org/people/ethnicity/people #6044-027r7k PRED entity: 027r7k PRED relation: film_release_region PRED expected values: 09c7w0 0d060g 082fr => 78 concepts (77 used for prediction) PRED predicted values (max 10 best out of 161): 09c7w0 (0.93 #697, 0.93 #1565, 0.92 #6946), 06mkj (0.79 #2676, 0.27 #9439, 0.26 #7706), 05r4w (0.76 #2607, 0.25 #9370, 0.25 #7637), 0k6nt (0.74 #2638, 0.25 #9401, 0.25 #7668), 03rjj (0.74 #2613, 0.25 #9376, 0.24 #8683), 0chghy (0.72 #2621, 0.25 #9384, 0.24 #7651), 0jgd (0.70 #2610, 0.23 #9373, 0.23 #8680), 03_3d (0.69 #2615, 0.27 #877, 0.24 #9378), 0345h (0.68 #2648, 0.24 #9411, 0.23 #8718), 03gj2 (0.68 #2639, 0.22 #555, 0.22 #9402) >> Best rule #697 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 01br2w; >> query: (?x11324, 09c7w0) <- genre(?x11324, ?x4088), nominated_for(?x2599, ?x11324), film_release_region(?x11324, ?x304), ?x4088 = 04xvh5 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14, 47 EVAL 027r7k film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 78.000 77.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 027r7k film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 78.000 77.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 027r7k film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 77.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #6043-03mfqm PRED entity: 03mfqm PRED relation: award_winner! PRED expected values: 02hn5v => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 134): 02wzl1d (0.29 #11, 0.25 #150, 0.17 #6952), 09p30_ (0.27 #361, 0.20 #500, 0.17 #6952), 0d__c3 (0.20 #680, 0.17 #1097, 0.16 #819), 02hn5v (0.18 #318, 0.17 #6952, 0.17 #6953), 0275n3y (0.18 #351, 0.13 #490, 0.05 #1881), 050yyb (0.17 #6952, 0.17 #6953, 0.13 #557), 02yw5r (0.17 #6952, 0.17 #6953, 0.13 #557), 09gkdln (0.17 #6952, 0.17 #6953, 0.13 #557), 02cg41 (0.17 #6952, 0.17 #6953, 0.13 #557), 0h98b3k (0.17 #6952, 0.17 #6953, 0.13 #557) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0jfx1; 0kszw; 02bh9; 07rd7; 03v1w7; >> query: (?x6327, 02wzl1d) <- nominated_for(?x6327, ?x8570), ?x8570 = 04jpg2p, profession(?x6327, ?x7630), award_winner(?x6679, ?x6327) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #318 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 021yc7p; 011zd3; 01438g; 013knm; 05hj_k; 02mt4k; 06q8hf; 04xn2m; *> query: (?x6327, 02hn5v) <- nominated_for(?x6327, ?x8570), nominated_for(?x6327, ?x6048), ?x6048 = 01cmp9, produced_by(?x8570, ?x4314), nominated_for(?x298, ?x8570) *> conf = 0.18 ranks of expected_values: 4 EVAL 03mfqm award_winner! 02hn5v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 88.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #6042-0f25y PRED entity: 0f25y PRED relation: featured_film_locations! PRED expected values: 04j14qc => 139 concepts (124 used for prediction) PRED predicted values (max 10 best out of 663): 08nhfc1 (0.17 #1297, 0.06 #4982, 0.03 #13826), 043tvp3 (0.17 #1247, 0.03 #4195, 0.03 #4932), 0192hw (0.13 #1707, 0.12 #3181, 0.12 #2444), 04dsnp (0.13 #3751, 0.10 #5962, 0.08 #5225), 0473rc (0.10 #4139, 0.10 #6350, 0.09 #7824), 047csmy (0.10 #4081, 0.10 #6292, 0.08 #5555), 072x7s (0.10 #3798, 0.07 #6009, 0.07 #1587), 0btpm6 (0.10 #4234, 0.07 #6445, 0.05 #5708), 01lsl (0.08 #5793, 0.07 #6530, 0.07 #4319), 09fc83 (0.08 #5540, 0.07 #4066, 0.06 #4803) >> Best rule #1297 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0xtz9; >> query: (?x9341, 08nhfc1) <- source(?x9341, ?x958), contains(?x5575, ?x9341), ?x958 = 0jbk9, ?x5575 = 05fjy >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #6497 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 012wgb; *> query: (?x9341, 04j14qc) <- place_of_death(?x1287, ?x9341), contains(?x94, ?x9341), location_of_ceremony(?x5034, ?x9341) *> conf = 0.07 ranks of expected_values: 11 EVAL 0f25y featured_film_locations! 04j14qc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 139.000 124.000 0.167 http://example.org/film/film/featured_film_locations #6041-09f07 PRED entity: 09f07 PRED relation: administrative_parent! PRED expected values: 0dlv0 => 240 concepts (212 used for prediction) PRED predicted values (max 10 best out of 515): 09f07 (0.14 #34510, 0.05 #10545, 0.05 #11736), 03rk0 (0.14 #34510, 0.01 #52441), 0dlv0 (0.14 #34510), 0cc56 (0.11 #636, 0.08 #2421, 0.06 #4800), 0b24sf (0.11 #1754, 0.07 #4133, 0.04 #12467), 050xpd (0.09 #67822, 0.09 #42243, 0.08 #8925), 058z2d (0.09 #67822, 0.09 #42243, 0.08 #8925), 0fplg (0.08 #2926, 0.06 #7687, 0.05 #10662), 0fplv (0.08 #2890, 0.06 #7651, 0.05 #10626), 0dj7p (0.08 #2852, 0.06 #7613, 0.05 #10588) >> Best rule #34510 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 03ryn; 034cm; 07c5l; 062qg; 04wsz; >> query: (?x11801, ?x2146) <- contains(?x11801, ?x11975), service_location(?x10867, ?x11801), contains(?x2146, ?x11975) >> conf = 0.14 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 09f07 administrative_parent! 0dlv0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 240.000 212.000 0.140 http://example.org/base/aareas/schema/administrative_area/administrative_parent #6040-020bv3 PRED entity: 020bv3 PRED relation: nominated_for! PRED expected values: 04rsd2 06mmb => 74 concepts (43 used for prediction) PRED predicted values (max 10 best out of 686): 01qrbf (0.79 #74311, 0.79 #60380, 0.78 #60379), 01l2fn (0.47 #4645, 0.47 #2636, 0.35 #9289), 0b13g7 (0.39 #55732, 0.13 #3056, 0.06 #69667), 01yhvv (0.35 #9289, 0.27 #4644, 0.24 #53410), 01fh9 (0.35 #9289, 0.27 #4644, 0.24 #53410), 03v1jf (0.35 #9289, 0.27 #4644, 0.24 #53410), 04rsd2 (0.35 #9289, 0.27 #4644, 0.24 #81277), 05y7hc (0.35 #69668), 0jfx1 (0.20 #2816, 0.02 #44615, 0.02 #46937), 0g9zcgx (0.20 #3733, 0.02 #31602, 0.01 #43209) >> Best rule #74311 for best value: >> intensional similarity = 3 >> extensional distance = 835 >> proper extension: 02nf2c; 0m123; >> query: (?x2029, ?x488) <- nominated_for(?x112, ?x2029), award_winner(?x2029, ?x488), titles(?x512, ?x2029) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #9289 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 02bg8v; 085bd1; 02psgq; 02z0f6l; 01qbg5; *> query: (?x2029, ?x100) <- film(?x100, ?x2029), film_release_region(?x2029, ?x279), award(?x2029, ?x112) *> conf = 0.35 ranks of expected_values: 7, 68 EVAL 020bv3 nominated_for! 06mmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 74.000 43.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 020bv3 nominated_for! 04rsd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 74.000 43.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #6039-04kxsb PRED entity: 04kxsb PRED relation: nominated_for PRED expected values: 05jzt3 04vr_f 0c9k8 02vqsll 0bmhvpr 011yfd 01242_ 0194zl 049xgc 064lsn 01chpn 0bdjd 02wk7b 0gvt53w 07l50_1 => 46 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1498): 09gq0x5 (0.80 #5881, 0.75 #2941, 0.74 #1470), 0209hj (0.80 #5881, 0.75 #2941, 0.74 #1470), 07l50_1 (0.80 #5881, 0.75 #2941, 0.74 #1470), 0qmfz (0.80 #5881, 0.75 #2941, 0.74 #1470), 03hj5lq (0.80 #5881, 0.75 #2941, 0.74 #1470), 0j90s (0.80 #5881, 0.75 #2941, 0.74 #1470), 01sxly (0.80 #5881, 0.75 #2941, 0.74 #1470), 02k1pr (0.80 #5881, 0.75 #2941, 0.74 #1470), 0b6tzs (0.67 #10407, 0.29 #5995, 0.28 #7351), 04vr_f (0.57 #10429, 0.38 #4546, 0.29 #6017) >> Best rule #5881 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 03hkv_r; 09sb52; 05pcn59; 02w9sd7; >> query: (?x2375, ?x253) <- award(?x1208, ?x2375), award(?x253, ?x2375), nominated_for(?x2375, ?x89), ?x1208 = 0sz28 >> conf = 0.80 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 10, 13, 16, 19, 22, 96, 98, 99, 100, 194, 331, 385, 446, 561 EVAL 04kxsb nominated_for 07l50_1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 0gvt53w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 02wk7b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 0bdjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 01chpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 049xgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 0194zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 01242_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 011yfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 0bmhvpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 02vqsll CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 0c9k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 04vr_f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04kxsb nominated_for 05jzt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 18.000 0.796 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6038-09cdxn PRED entity: 09cdxn PRED relation: gender PRED expected values: 05zppz => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #23, 0.90 #27, 0.89 #25), 02zsn (0.25 #70, 0.25 #94, 0.24 #90) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 0280mv7; 03hltjb; 04cw0n4; 026sb55; >> query: (?x6115, 05zppz) <- cinematography(?x3294, ?x6115), film(?x2416, ?x3294), language(?x3294, ?x254) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09cdxn gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.903 http://example.org/people/person/gender #6037-02qvdc PRED entity: 02qvdc PRED relation: position! PRED expected values: 0j2zj => 27 concepts (19 used for prediction) PRED predicted values (max 10 best out of 244): 04l5f2 (0.73 #35, 0.69 #47, 0.68 #46), 06x6s (0.73 #35, 0.69 #47, 0.68 #46), 0j6tr (0.73 #35, 0.69 #47, 0.68 #46), 04l58n (0.73 #35, 0.69 #47, 0.68 #46), 0j8js (0.73 #35, 0.69 #47, 0.68 #46), 0j2zj (0.73 #35, 0.69 #47, 0.68 #46), 0jnm2 (0.73 #35, 0.69 #47, 0.67 #45), 0c1gj5 (0.33 #38, 0.33 #28, 0.33 #14), 03dkx (0.01 #94), 0h3c3g (0.01 #94) >> Best rule #35 for best value: >> intensional similarity = 39 >> extensional distance = 1 >> proper extension: 02qvzf; >> query: (?x5234, ?x5380) <- team(?x5234, ?x14124), team(?x5234, ?x13661), team(?x5234, ?x13326), team(?x5234, ?x13166), team(?x5234, ?x12977), team(?x5234, ?x11368), team(?x5234, ?x10941), team(?x5234, ?x10755), team(?x5234, ?x8892), team(?x5234, ?x8270), team(?x5234, ?x5380), team(?x5234, ?x5233), team(?x5234, ?x3723), team(?x5234, ?x2919), position(?x14183, ?x5234), position(?x14015, ?x5234), position(?x10142, ?x5234), position(?x9547, ?x5234), ?x13661 = 0jnr3, ?x5233 = 0j5m6, ?x9547 = 04l5d0, ?x14183 = 0j8cb, ?x12977 = 0jnkr, teams(?x1860, ?x14015), ?x13166 = 0j6tr, ?x2919 = 0c41y70, ?x3723 = 0hn6d, ?x10142 = 02r7lqg, ?x13326 = 0hm2b, ?x14124 = 04l590, colors(?x14015, ?x1101), colors(?x14015, ?x663), ?x8892 = 02fp3, ?x10755 = 0jbqf, ?x1101 = 06fvc, ?x8270 = 0j8js, ?x663 = 083jv, ?x10941 = 030ykh, ?x11368 = 032yps >> conf = 0.73 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 02qvdc position! 0j2zj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 27.000 19.000 0.730 http://example.org/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position #6036-027f7dj PRED entity: 027f7dj PRED relation: place_of_birth PRED expected values: 0fttg => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 51): 0gyh (0.28 #6337, 0.28 #70421, 0.27 #49291), 030qb3t (0.15 #1462, 0.12 #54, 0.10 #758), 01cx_ (0.12 #109, 0.10 #813, 0.08 #1517), 0f94t (0.12 #28, 0.08 #1436, 0.05 #2140), 0dclg (0.10 #2190, 0.10 #2894, 0.02 #9232), 0qpqn (0.10 #1049, 0.08 #1753, 0.01 #35207), 0d9y6 (0.10 #897, 0.08 #1601, 0.01 #35207), 02_286 (0.07 #20438, 0.07 #70441, 0.07 #11286), 01531 (0.05 #2217, 0.05 #2921, 0.02 #5737), 0f2s6 (0.05 #2479, 0.05 #3183, 0.01 #35207) >> Best rule #6337 for best value: >> intensional similarity = 3 >> extensional distance = 412 >> proper extension: 01qvgl; 03qjlz; 0flpy; 0135xb; 016s0m; 0147jt; 019fnv; 026m0; 01p0w_; >> query: (?x1559, ?x2831) <- award_winner(?x873, ?x1559), student(?x2830, ?x1559), location(?x1559, ?x2831) >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027f7dj place_of_birth 0fttg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 113.000 0.277 http://example.org/people/person/place_of_birth #6035-0l6mp PRED entity: 0l6mp PRED relation: olympics! PRED expected values: 01rhrd => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 437): 0154j (0.71 #2760, 0.60 #2563, 0.59 #2547), 06mkj (0.70 #2551, 0.68 #3947, 0.65 #3554), 05qhw (0.70 #2551, 0.68 #2754, 0.67 #2954), 0k6nt (0.70 #2551, 0.68 #2754, 0.67 #2954), 01mk6 (0.70 #2551, 0.68 #2754, 0.67 #2954), 03_3d (0.70 #2551, 0.67 #2962, 0.64 #3150), 0h7x (0.70 #2551, 0.64 #3392, 0.64 #3150), 015fr (0.70 #2551, 0.64 #3150, 0.60 #3152), 05r4w (0.70 #2551, 0.64 #3150, 0.57 #5114), 035qy (0.70 #2551, 0.59 #2547, 0.58 #1567) >> Best rule #2760 for best value: >> intensional similarity = 51 >> extensional distance = 5 >> proper extension: 0jdk_; >> query: (?x2233, 0154j) <- sports(?x2233, ?x4310), sports(?x2233, ?x4045), sports(?x2233, ?x2867), sports(?x2233, ?x2315), olympics(?x766, ?x2233), olympics(?x11872, ?x2233), olympics(?x7747, ?x2233), olympics(?x7430, ?x2233), olympics(?x2513, ?x2233), olympics(?x1353, ?x2233), olympics(?x456, ?x2233), olympics(?x421, ?x2233), ?x2315 = 06wrt, ?x421 = 03_r3, ?x456 = 05qhw, olympics(?x11872, ?x775), ?x775 = 0l998, ?x2867 = 02y8z, sports(?x358, ?x766), ?x4310 = 064vjs, participating_countries(?x1741, ?x11872), ?x4045 = 06z6r, countries_within(?x6956, ?x7747), film_release_region(?x9652, ?x7747), film_release_region(?x6587, ?x7747), film_release_region(?x6394, ?x7747), film_release_region(?x5016, ?x7747), film_release_region(?x2512, ?x7747), film_release_region(?x1932, ?x7747), film_release_region(?x1392, ?x7747), ?x6587 = 07s3m4g, ?x1392 = 017gm7, combatants(?x7747, ?x1790), ?x2512 = 07x4qr, film_release_region(?x1916, ?x1353), film_release_region(?x1452, ?x1353), ?x1916 = 0ch26b_, ?x1932 = 0btyf5z, organization(?x7430, ?x312), country(?x766, ?x47), adjoins(?x1122, ?x7747), ?x6394 = 0cmdwwg, organization(?x1790, ?x127), ?x5016 = 062zm5h, ?x1452 = 0jqn5, countries_within(?x455, ?x2513), ?x9652 = 0ddbjy4, country(?x11096, ?x1353), contains(?x1353, ?x7575), nationality(?x2610, ?x2513), participating_countries(?x418, ?x1353) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1564 for first EXPECTED value: *> intensional similarity = 46 *> extensional distance = 2 *> proper extension: 06sks6; *> query: (?x2233, ?x142) <- sports(?x2233, ?x4310), sports(?x2233, ?x3659), sports(?x2233, ?x2315), olympics(?x766, ?x2233), olympics(?x11872, ?x2233), olympics(?x2843, ?x2233), olympics(?x2152, ?x2233), olympics(?x1497, ?x2233), olympics(?x1353, ?x2233), olympics(?x456, ?x2233), olympics(?x421, ?x2233), ?x2315 = 06wrt, ?x421 = 03_r3, ?x456 = 05qhw, olympics(?x11872, ?x775), olympics(?x11872, ?x391), ?x775 = 0l998, ?x766 = 01hp22, film_release_region(?x204, ?x11872), medal(?x11872, ?x422), ?x3659 = 0dwxr, country(?x3757, ?x11872), ?x391 = 0l6vl, ?x1497 = 015qh, combatants(?x94, ?x1353), film_release_region(?x3748, ?x2843), film_release_region(?x1956, ?x2843), film_release_region(?x1370, ?x2843), film_release_region(?x1219, ?x2843), film_release_region(?x10346, ?x1353), film_release_region(?x6603, ?x1353), film_release_region(?x1456, ?x1353), ?x3748 = 05zlld0, country(?x4310, ?x142), ?x1456 = 0cz8mkh, participating_countries(?x418, ?x1353), ?x1219 = 03bx2lk, countries_spoken_in(?x732, ?x11872), ?x2152 = 06mkj, ?x6603 = 094g2z, country(?x668, ?x1353), ?x10346 = 0dw4b0, olympics(?x4310, ?x2966), ?x1370 = 0gmcwlb, combatants(?x326, ?x1353), ?x1956 = 05qbckf *> conf = 0.48 ranks of expected_values: 189 EVAL 0l6mp olympics! 01rhrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 33.000 33.000 0.714 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #6034-01cblr PRED entity: 01cblr PRED relation: group! PRED expected values: 02k84w => 95 concepts (72 used for prediction) PRED predicted values (max 10 best out of 116): 0l14md (0.64 #1385, 0.64 #1304, 0.63 #1548), 0l14qv (0.40 #330, 0.40 #248, 0.27 #1302), 07y_7 (0.40 #327, 0.40 #245, 0.12 #651), 0l14j_ (0.40 #370, 0.20 #288, 0.12 #2406), 03qjg (0.32 #1338, 0.27 #2402, 0.25 #1828), 01vj9c (0.28 #2453, 0.28 #2537, 0.28 #3027), 06ncr (0.20 #358, 0.20 #276, 0.15 #2394), 02fsn (0.20 #367, 0.20 #285, 0.12 #691), 07_l6 (0.20 #379, 0.20 #297, 0.12 #703), 07brj (0.20 #342, 0.20 #260, 0.12 #747) >> Best rule #1385 for best value: >> intensional similarity = 7 >> extensional distance = 74 >> proper extension: 089tm; 01pfr3; 04rcr; 0150jk; 02r3zy; 067mj; 01vsxdm; 03g5jw; 05crg7; 05k79; ... >> query: (?x4909, 0l14md) <- group(?x2460, ?x4909), group(?x716, ?x4909), category(?x4909, ?x134), award(?x4909, ?x1389), ?x716 = 018vs, role(?x922, ?x2460), ?x922 = 050rj >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #325 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 047cx; 02vgh; 07n3s; *> query: (?x4909, ?x74) <- group(?x2460, ?x4909), group(?x645, ?x4909), category(?x4909, ?x134), ?x2460 = 01wy6, artists(?x302, ?x4909), role(?x645, ?x74), performance_role(?x8323, ?x645) *> conf = 0.11 ranks of expected_values: 33 EVAL 01cblr group! 02k84w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 95.000 72.000 0.645 http://example.org/music/performance_role/regular_performances./music/group_membership/group #6033-02hrb2 PRED entity: 02hrb2 PRED relation: major_field_of_study PRED expected values: 05qjt => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 110): 02j62 (0.50 #914, 0.41 #1292, 0.41 #1166), 03g3w (0.48 #1288, 0.48 #1162, 0.45 #1919), 04rjg (0.48 #903, 0.43 #1281, 0.43 #1155), 01mkq (0.46 #898, 0.43 #1276, 0.43 #1150), 02lp1 (0.38 #1272, 0.38 #1146, 0.33 #1903), 062z7 (0.34 #1289, 0.34 #1163, 0.34 #1541), 05qjt (0.33 #1016, 0.31 #1520, 0.30 #1268), 01lj9 (0.32 #1302, 0.32 #1176, 0.29 #1050), 037mh8 (0.28 #953, 0.27 #1639, 0.25 #1331), 0fdys (0.27 #1553, 0.27 #1301, 0.27 #1639) >> Best rule #914 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 02l9wl; >> query: (?x9078, 02j62) <- student(?x9078, ?x8404), influenced_by(?x8404, ?x3993), people(?x1050, ?x8404), student(?x3437, ?x3993) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1016 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 0d5fb; *> query: (?x9078, 05qjt) <- student(?x9078, ?x8404), influenced_by(?x8404, ?x3712), people(?x1050, ?x8404), company(?x8404, ?x122) *> conf = 0.33 ranks of expected_values: 7 EVAL 02hrb2 major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 157.000 157.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #6032-0c0yh4 PRED entity: 0c0yh4 PRED relation: country PRED expected values: 03gj2 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 16): 09c7w0 (0.86 #61, 0.84 #1585, 0.83 #2052), 07ssc (0.43 #2517, 0.40 #2634, 0.40 #2284), 02jx1 (0.43 #2517, 0.40 #2634, 0.40 #2284), 0f8l9c (0.43 #2517, 0.40 #2634, 0.40 #2284), 0chghy (0.33 #12, 0.03 #2062, 0.03 #1944), 03_gx (0.07 #412, 0.06 #236, 0.06 #2049), 03mqtr (0.07 #412, 0.06 #236, 0.06 #2049), 04xvlr (0.07 #412, 0.06 #236, 0.06 #2049), 03_3d (0.04 #3170, 0.04 #3229, 0.04 #2760), 03rjj (0.03 #360, 0.03 #772, 0.03 #302) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 257 >> proper extension: 05f67hw; >> query: (?x278, 09c7w0) <- produced_by(?x278, ?x3828), country(?x278, ?x279), films(?x14144, ?x278) >> conf = 0.86 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c0yh4 country 03gj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 62.000 0.861 http://example.org/film/film/country #6031-01nr36 PRED entity: 01nr36 PRED relation: profession PRED expected values: 02jknp 02hrh1q => 128 concepts (127 used for prediction) PRED predicted values (max 10 best out of 74): 02hrh1q (0.91 #12215, 0.90 #9569, 0.89 #1336), 02jknp (0.57 #7, 0.54 #2212, 0.53 #2359), 03gjzk (0.45 #14, 0.42 #602, 0.39 #3689), 0nbcg (0.28 #4146, 0.28 #324, 0.26 #7969), 016z4k (0.27 #4120, 0.27 #298, 0.24 #5590), 0dz3r (0.24 #4118, 0.23 #5588, 0.22 #5882), 018gz8 (0.20 #3250, 0.15 #604, 0.14 #8396), 0np9r (0.17 #8546, 0.15 #3253, 0.14 #14573), 0cbd2 (0.16 #7651, 0.16 #6033, 0.15 #2799), 02krf9 (0.15 #613, 0.14 #2965, 0.14 #2230) >> Best rule #12215 for best value: >> intensional similarity = 3 >> extensional distance = 1652 >> proper extension: 05d7rk; 04yywz; 01l1b90; 01vw87c; 02g8h; 0d_84; 01yznp; 02nb2s; 0151ns; 0kr5_; ... >> query: (?x8491, 02hrh1q) <- award(?x8491, ?x102), film(?x8491, ?x155), profession(?x8491, ?x319) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01nr36 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 127.000 0.908 http://example.org/people/person/profession EVAL 01nr36 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 127.000 0.908 http://example.org/people/person/profession #6030-09c7w0 PRED entity: 09c7w0 PRED relation: combatants! PRED expected values: 03gqgt3 => 197 concepts (197 used for prediction) PRED predicted values (max 10 best out of 47): 02cnqk (0.61 #2169, 0.59 #5071, 0.58 #1799), 01hwkn (0.61 #2169, 0.59 #5071, 0.58 #1799), 08821 (0.61 #2169, 0.59 #5071, 0.58 #1799), 03gqgt3 (0.43 #1885, 0.33 #2392, 0.33 #1329), 01w1sx (0.32 #1800, 0.26 #2538, 0.14 #712), 05t2fh4 (0.32 #1800, 0.26 #2538, 0.11 #5164), 024jvz (0.32 #1800, 0.26 #2538, 0.11 #5164), 07j9n (0.29 #708, 0.27 #1769, 0.23 #2139), 0c3mz (0.29 #1038, 0.25 #992, 0.22 #1314), 0bqtx (0.29 #1042, 0.25 #996, 0.21 #1595) >> Best rule #2169 for best value: >> intensional similarity = 2 >> extensional distance = 29 >> proper extension: 01gpzx; >> query: (?x94, ?x1140) <- adjoins(?x94, ?x279), entity_involved(?x1140, ?x94) >> conf = 0.61 => this is the best rule for 3 predicted values *> Best rule #1885 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 02vzc; 087vz; 04g61; *> query: (?x94, 03gqgt3) <- contains(?x94, ?x95), country(?x54, ?x94), combatants(?x151, ?x94) *> conf = 0.43 ranks of expected_values: 4 EVAL 09c7w0 combatants! 03gqgt3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 197.000 197.000 0.612 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #6029-06bnz PRED entity: 06bnz PRED relation: country! PRED expected values: 01gqfm => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 8): 01gqfm (0.70 #78, 0.62 #222, 0.59 #70), 0d1t3 (0.65 #74, 0.55 #66, 0.51 #218), 02vx4 (0.55 #65, 0.53 #49, 0.52 #73), 02y74 (0.44 #173, 0.42 #109, 0.41 #45), 018jz (0.18 #43, 0.16 #51, 0.12 #107), 06br8 (0.12 #44, 0.11 #52, 0.09 #4), 037hz (0.09 #8, 0.07 #24, 0.07 #16), 09xp_ (0.09 #7, 0.05 #55, 0.04 #79) >> Best rule #78 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 05r4w; 09c7w0; 0jgd; 0154j; 03rjj; 0d060g; 0chghy; 03rt9; 07ssc; 015fr; ... >> query: (?x1603, 01gqfm) <- film_release_region(?x3603, ?x1603), adjoins(?x344, ?x1603), ?x3603 = 09gkx35 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06bnz country! 01gqfm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 171.000 0.696 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #6028-039fgy PRED entity: 039fgy PRED relation: nominated_for! PRED expected values: 0bp_b2 0fbtbt => 91 concepts (85 used for prediction) PRED predicted values (max 10 best out of 184): 0fbtbt (0.78 #9022, 0.78 #8544, 0.78 #9262), 02xcb6n (0.78 #9022, 0.78 #8544, 0.78 #9262), 05b4l5x (0.76 #721, 0.20 #19723, 0.19 #19011), 0bdw6t (0.70 #322, 0.23 #561, 0.20 #1272), 0m7yy (0.69 #2138, 0.67 #9021, 0.67 #8782), 0gq_v (0.50 #20, 0.28 #8325, 0.27 #9282), 0p9sw (0.50 #21, 0.23 #8326, 0.21 #9283), 0bp_b2 (0.50 #256, 0.19 #732, 0.18 #1917), 054krc (0.50 #69, 0.18 #8374, 0.16 #9091), 05b1610 (0.42 #747, 0.07 #12146, 0.07 #12385) >> Best rule #9022 for best value: >> intensional similarity = 4 >> extensional distance = 591 >> proper extension: 05wp1p; >> query: (?x687, ?x8660) <- award_winner(?x687, ?x4634), award(?x687, ?x8660), ceremony(?x8660, ?x1265), nominated_for(?x4634, ?x144) >> conf = 0.78 => this is the best rule for 2 predicted values ranks of expected_values: 1, 8 EVAL 039fgy nominated_for! 0fbtbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 85.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 039fgy nominated_for! 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 91.000 85.000 0.780 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6027-03gh4 PRED entity: 03gh4 PRED relation: location! PRED expected values: 018p4y => 198 concepts (160 used for prediction) PRED predicted values (max 10 best out of 2210): 01yzhn (0.40 #9687, 0.20 #27303, 0.14 #24785), 0fp_v1x (0.33 #2581, 0.04 #40333, 0.02 #85638), 019_1h (0.33 #2692, 0.02 #85749, 0.01 #178859), 02l6dy (0.21 #23880, 0.21 #21363, 0.20 #8782), 09yrh (0.21 #23567, 0.21 #21050, 0.13 #48735), 03nb5v (0.21 #18942, 0.20 #29011, 0.20 #8878), 03d_w3h (0.21 #22804, 0.20 #7706, 0.14 #20287), 0dn3n (0.21 #20724, 0.20 #8143, 0.14 #23241), 0738b8 (0.21 #20581, 0.20 #8000, 0.14 #23098), 0h7pj (0.21 #21948, 0.10 #49633, 0.08 #79839) >> Best rule #9687 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 081mh; >> query: (?x6226, 01yzhn) <- district_represented(?x6139, ?x6226), ?x6139 = 060ny2, contains(?x6226, ?x3704) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #47625 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 03gyl; *> query: (?x6226, 018p4y) <- vacationer(?x6226, ?x1093), contains(?x94, ?x6226), contains(?x6226, ?x3704), artists(?x671, ?x1093) *> conf = 0.06 ranks of expected_values: 1020 EVAL 03gh4 location! 018p4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 198.000 160.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #6026-01vyp_ PRED entity: 01vyp_ PRED relation: gender PRED expected values: 05zppz => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #19, 0.88 #13, 0.88 #21), 02zsn (0.28 #72, 0.27 #32, 0.26 #26) >> Best rule #19 for best value: >> intensional similarity = 2 >> extensional distance = 109 >> proper extension: 032t2z; 0hnlx; 016h9b; 0p3sf; 01wbz9; 04bgy; 0484q; 03_f0; 0k1wz; 011k4g; ... >> query: (?x2027, 05zppz) <- artists(?x3597, ?x2027), place_of_death(?x2027, ?x12756) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vyp_ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.892 http://example.org/people/person/gender #6025-02s62q PRED entity: 02s62q PRED relation: school_type PRED expected values: 05pcjw => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.54 #172, 0.42 #2093, 0.41 #2237), 01rs41 (0.54 #605, 0.54 #461, 0.53 #509), 05pcjw (0.48 #217, 0.47 #73, 0.46 #385), 07tf8 (0.19 #105, 0.16 #201, 0.16 #1185), 01_srz (0.12 #603, 0.12 #411, 0.11 #531), 01_9fk (0.12 #2478, 0.12 #2042, 0.12 #1874), 01y64 (0.05 #780, 0.05 #468, 0.05 #276), 04qbv (0.05 #544, 0.05 #520, 0.05 #616), 06cs1 (0.05 #342, 0.04 #414, 0.04 #846), 02p0qmm (0.04 #2147, 0.04 #1186, 0.04 #106) >> Best rule #172 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 013719; >> query: (?x2056, 05jxkf) <- major_field_of_study(?x2056, ?x2540), genre(?x3102, ?x2540), genre(?x802, ?x2540), producer_type(?x3102, ?x632), ?x802 = 0cwrr >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #217 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 05nrkb; *> query: (?x2056, 05pcjw) <- student(?x2056, ?x3673), currency(?x2056, ?x170), country(?x2056, ?x94), nominated_for(?x3673, ?x1395) *> conf = 0.48 ranks of expected_values: 3 EVAL 02s62q school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 190.000 190.000 0.543 http://example.org/education/educational_institution/school_type #6024-02x1z2s PRED entity: 02x1z2s PRED relation: ceremony PRED expected values: 0275n3y => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 136): 0gmdkyy (0.57 #434, 0.47 #569, 0.33 #29), 050yyb (0.57 #441, 0.47 #576, 0.33 #36), 05qb8vx (0.57 #461, 0.47 #596, 0.33 #56), 0bvhz9 (0.57 #529, 0.47 #664, 0.33 #124), 02pgky2 (0.57 #491, 0.47 #626, 0.33 #86), 02glmx (0.57 #483, 0.47 #618, 0.33 #78), 0n8_m93 (0.57 #519, 0.47 #654, 0.33 #114), 02jp5r (0.57 #471, 0.47 #606, 0.33 #66), 0bzm81 (0.57 #426, 0.47 #561, 0.33 #21), 02yxh9 (0.57 #502, 0.47 #637, 0.33 #97) >> Best rule #434 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x3911, 0gmdkyy) <- award_winner(?x3911, ?x541), award(?x8345, ?x3911), award(?x382, ?x3911), ?x382 = 086k8, produced_by(?x1685, ?x8345) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #5403 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 348 *> proper extension: 0257yf; 027x4ws; *> query: (?x3911, ?x5592) <- award(?x5959, ?x3911), award_nominee(?x541, ?x5959), award_winner(?x5592, ?x5959) *> conf = 0.21 ranks of expected_values: 87 EVAL 02x1z2s ceremony 0275n3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 49.000 49.000 0.571 http://example.org/award/award_category/winners./award/award_honor/ceremony #6023-0214km PRED entity: 0214km PRED relation: role! PRED expected values: 05r5c => 64 concepts (46 used for prediction) PRED predicted values (max 10 best out of 86): 07c6l (0.86 #1624, 0.86 #1551, 0.85 #644), 0gghm (0.85 #644, 0.84 #562, 0.84 #405), 02pprs (0.85 #644, 0.84 #562, 0.84 #405), 023r2x (0.85 #644, 0.84 #562, 0.84 #405), 05r5c (0.81 #3456, 0.81 #1961, 0.81 #2874), 02fsn (0.81 #2000, 0.79 #326, 0.79 #244), 0395lw (0.81 #1975, 0.77 #1481, 0.75 #1399), 0dwtp (0.79 #326, 0.79 #244, 0.78 #1146), 05148p4 (0.79 #326, 0.79 #244, 0.77 #2138), 04rzd (0.79 #326, 0.79 #244, 0.76 #3231) >> Best rule #1624 for best value: >> intensional similarity = 21 >> extensional distance = 12 >> proper extension: 05842k; >> query: (?x8014, ?x569) <- role(?x8014, ?x3991), role(?x8014, ?x614), role(?x8014, ?x569), role(?x8014, ?x314), ?x614 = 0mkg, role(?x8921, ?x3991), role(?x4712, ?x3991), role(?x3991, ?x2944), role(?x3991, ?x2377), role(?x3991, ?x885), ?x569 = 07c6l, ?x2377 = 01bns_, ?x8921 = 016s0m, role(?x487, ?x8014), role(?x1482, ?x3991), ?x885 = 0dwtp, ?x2944 = 0l14j_, ?x314 = 02sgy, instrumentalists(?x1482, ?x7581), location(?x4712, ?x739), nationality(?x4712, ?x94) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #3456 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 57 *> proper extension: 016622; *> query: (?x8014, 05r5c) <- role(?x8014, ?x614), instrumentalists(?x614, ?x2765), role(?x2698, ?x8014), ?x2765 = 01w724, artists(?x505, ?x2698), group(?x614, ?x11929), role(?x745, ?x614), award_nominee(?x1206, ?x2698), role(?x614, ?x2158), role(?x780, ?x614), ?x11929 = 07n3s, ?x745 = 01vj9c, award_winner(?x1206, ?x3481) *> conf = 0.81 ranks of expected_values: 5 EVAL 0214km role! 05r5c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 64.000 46.000 0.857 http://example.org/music/performance_role/track_performances./music/track_contribution/role #6022-01npw8 PRED entity: 01npw8 PRED relation: industry PRED expected values: 02jjt => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 44): 02jjt (0.88 #514, 0.50 #100, 0.33 #54), 01mw1 (0.29 #4143, 0.29 #3085, 0.25 #4051), 020mfr (0.23 #753, 0.22 #3101, 0.22 #4159), 02vxn (0.23 #738, 0.22 #2810, 0.21 #5573), 04rlf (0.21 #520, 0.08 #2316, 0.08 #3098), 03qh03g (0.17 #511, 0.17 #51, 0.14 #695), 011s0 (0.17 #102, 0.13 #286, 0.09 #148), 05jnl (0.17 #68, 0.09 #989, 0.09 #160), 023907r (0.17 #70, 0.07 #254, 0.05 #484), 0191_7 (0.13 #315, 0.12 #868, 0.10 #1190) >> Best rule #514 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 039cpd; >> query: (?x12471, 02jjt) <- industry(?x12471, ?x12380), industry(?x9517, ?x12380), industry(?x3367, ?x12380), ?x3367 = 02r5dz, service_location(?x9517, ?x279) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01npw8 industry 02jjt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 188.000 0.875 http://example.org/business/business_operation/industry #6021-0ctw_b PRED entity: 0ctw_b PRED relation: country! PRED expected values: 02fwfb => 235 concepts (139 used for prediction) PRED predicted values (max 10 best out of 1822): 0ddbjy4 (0.43 #11596, 0.20 #9910, 0.18 #23401), 01m13b (0.36 #22067, 0.34 #57479, 0.32 #60855), 063fh9 (0.31 #16862, 0.29 #11226, 0.27 #20235), 0_b9f (0.31 #16862, 0.27 #20235, 0.21 #15175), 01h7bb (0.31 #16862, 0.27 #20235, 0.21 #15175), 0dscrwf (0.29 #16930, 0.29 #10184, 0.27 #21989), 049mql (0.29 #17498, 0.29 #10752, 0.27 #22557), 0fjyzt (0.29 #17742, 0.29 #10996, 0.23 #24487), 06_sc3 (0.29 #18191, 0.29 #11445, 0.23 #24936), 016z5x (0.29 #16931, 0.29 #10185, 0.18 #21990) >> Best rule #11596 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 088vb; >> query: (?x1023, 0ddbjy4) <- religion(?x1023, ?x8967), olympics(?x1023, ?x452), ?x8967 = 03j6c >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #14680 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01bkb; *> query: (?x1023, 02fwfb) <- religion(?x1023, ?x109), featured_film_locations(?x522, ?x1023), vacationer(?x1023, ?x1897) *> conf = 0.10 ranks of expected_values: 1632 EVAL 0ctw_b country! 02fwfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 235.000 139.000 0.429 http://example.org/film/film/country #6020-02g2wv PRED entity: 02g2wv PRED relation: nominated_for PRED expected values: 01hv3t => 56 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1454): 07cyl (0.75 #3175, 0.72 #1587, 0.71 #14290), 016ky6 (0.75 #3175, 0.72 #1587, 0.71 #14290), 05zy2cy (0.75 #3175, 0.72 #1587, 0.71 #14290), 03fts (0.75 #3175, 0.72 #1587, 0.71 #14290), 0gfsq9 (0.75 #3175, 0.72 #1587, 0.71 #14290), 02yvct (0.56 #8260, 0.53 #5086, 0.50 #320), 0m313 (0.56 #7952, 0.44 #1600, 0.33 #4778), 019vhk (0.56 #2003, 0.39 #8355, 0.33 #415), 09gq0x5 (0.56 #8196, 0.33 #1844, 0.33 #256), 011yl_ (0.56 #8469, 0.33 #2117, 0.21 #11644) >> Best rule #3175 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02r22gf; 025m8l; 04kxsb; 02qvyrt; 02qyntr; 02g3gw; >> query: (?x5734, ?x1474) <- award(?x1474, ?x5734), nominated_for(?x5734, ?x3693), award(?x450, ?x5734), ?x3693 = 03r0g9 >> conf = 0.75 => this is the best rule for 5 predicted values *> Best rule #1130 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 02hsq3m; 0gr0m; 0gr51; 02g2yr; *> query: (?x5734, 01hv3t) <- award(?x1474, ?x5734), nominated_for(?x5734, ?x641), award(?x450, ?x5734), ?x641 = 08720 *> conf = 0.33 ranks of expected_values: 124 EVAL 02g2wv nominated_for 01hv3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 56.000 20.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6019-06j0md PRED entity: 06j0md PRED relation: profession PRED expected values: 03gjzk => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 48): 03gjzk (0.85 #1347, 0.84 #459, 0.84 #1051), 02hrh1q (0.71 #2382, 0.71 #2530, 0.69 #2826), 01d_h8 (0.53 #302, 0.50 #746, 0.48 #894), 02jknp (0.52 #1636, 0.28 #304, 0.28 #600), 018gz8 (0.30 #17, 0.19 #313, 0.16 #1645), 09jwl (0.25 #1943, 0.17 #4459, 0.17 #4311), 0dz3r (0.20 #1926, 0.10 #4294, 0.10 #4442), 0np9r (0.20 #21, 0.15 #1649, 0.13 #909), 0cbd2 (0.20 #1339, 0.19 #451, 0.19 #1191), 0nbcg (0.19 #1955, 0.12 #4471, 0.12 #4323) >> Best rule #1347 for best value: >> intensional similarity = 2 >> extensional distance = 249 >> proper extension: 0f1vrl; 02k76g; >> query: (?x201, 03gjzk) <- program(?x201, ?x2293), profession(?x201, ?x987) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06j0md profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.849 http://example.org/people/person/profession #6018-0170th PRED entity: 0170th PRED relation: nominated_for! PRED expected values: 063y_ky 09qv_s => 75 concepts (67 used for prediction) PRED predicted values (max 10 best out of 209): 019f4v (0.68 #8234, 0.67 #7057, 0.66 #8233), 0gqy2 (0.55 #354, 0.26 #6000, 0.25 #3762), 0gq9h (0.46 #5940, 0.32 #4998, 0.32 #4761), 0gq_v (0.37 #5901, 0.23 #4959, 0.23 #4722), 03hl6lc (0.36 #362, 0.32 #1537, 0.32 #1772), 0k611 (0.35 #5950, 0.27 #304, 0.24 #5008), 0gr42 (0.31 #555, 0.18 #790, 0.18 #1260), 0gr0m (0.30 #5937, 0.20 #4758, 0.20 #4995), 0gr4k (0.29 #5907, 0.22 #15764, 0.22 #4965), 040njc (0.29 #5888, 0.21 #4946, 0.20 #4709) >> Best rule #8234 for best value: >> intensional similarity = 3 >> extensional distance = 986 >> proper extension: 06mmr; >> query: (?x2757, ?x746) <- award(?x2757, ?x746), nominated_for(?x746, ?x69), award(?x276, ?x746) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #15764 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1584 *> proper extension: 06g60w; 0c3xpwy; *> query: (?x2757, ?x3066) <- nominated_for(?x6157, ?x2757), award(?x6157, ?x3066), nominated_for(?x3066, ?x144) *> conf = 0.22 ranks of expected_values: 32, 36 EVAL 0170th nominated_for! 09qv_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 75.000 67.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0170th nominated_for! 063y_ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 75.000 67.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6017-0jkvj PRED entity: 0jkvj PRED relation: sports PRED expected values: 03fyrh => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 36): 01cgz (0.83 #196, 0.82 #686, 0.82 #130), 01sgl (0.72 #197, 0.70 #653, 0.67 #231), 03fyrh (0.67 #112, 0.52 #535, 0.51 #633), 01gqfm (0.67 #92, 0.27 #652, 0.13 #191), 06z68 (0.50 #81, 0.27 #652, 0.21 #537), 019w9j (0.50 #80, 0.27 #652, 0.20 #179), 07jbh (0.50 #82, 0.27 #652, 0.20 #181), 0194d (0.50 #90, 0.27 #652, 0.13 #189), 035d1m (0.50 #78, 0.27 #652, 0.13 #177), 019tzd (0.50 #85, 0.27 #652, 0.13 #184) >> Best rule #196 for best value: >> intensional similarity = 8 >> extensional distance = 13 >> proper extension: 0kbvb; 0l6m5; 0swbd; 09x3r; 0lv1x; 0swff; 0kbvv; 0jhn7; 018qb4; 0124ld; >> query: (?x7688, ?x171) <- sports(?x7688, ?x171), olympics(?x5114, ?x7688), olympics(?x1264, ?x7688), olympics(?x2984, ?x7688), olympics(?x766, ?x7688), combatants(?x5114, ?x792), ?x1264 = 0345h, participating_countries(?x4255, ?x5114) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #112 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 0l6vl; 0l98s; 0l6mp; 0lbbj; 0lgxj; 0lbd9; 0ldqf; *> query: (?x7688, 03fyrh) <- sports(?x7688, ?x171), olympics(?x5114, ?x7688), olympics(?x1536, ?x7688), olympics(?x2984, ?x7688), olympics(?x766, ?x7688), ?x5114 = 05vz3zq, participating_countries(?x7688, ?x512), ?x1536 = 06c1y *> conf = 0.67 ranks of expected_values: 3 EVAL 0jkvj sports 03fyrh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 48.000 48.000 0.831 http://example.org/olympics/olympic_games/sports #6016-0ds11z PRED entity: 0ds11z PRED relation: film! PRED expected values: 09y20 => 141 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1191): 07rd7 (0.70 #54106, 0.48 #104057, 0.48 #6242), 05prs8 (0.49 #66596, 0.48 #104057, 0.48 #6242), 03mfqm (0.49 #66596, 0.48 #6242, 0.46 #70758), 05183k (0.49 #66596, 0.48 #6242, 0.46 #70758), 0bytkq (0.49 #66596, 0.46 #70758, 0.42 #93650), 0f0kz (0.29 #2594, 0.14 #4675, 0.08 #23401), 02cllz (0.21 #4570, 0.21 #2489, 0.04 #17053), 01tsbmv (0.21 #3977, 0.14 #6058, 0.04 #16461), 09wj5 (0.21 #2181, 0.14 #4262, 0.04 #16745), 01l2fn (0.21 #2343, 0.14 #4424, 0.04 #35634) >> Best rule #54106 for best value: >> intensional similarity = 4 >> extensional distance = 155 >> proper extension: 0170z3; 0d90m; 011yxg; 0bvn25; 0ds3t5x; 07xtqq; 01k1k4; 0ds33; 03h_yy; 01cssf; ... >> query: (?x485, ?x2444) <- film_crew_role(?x485, ?x137), nominated_for(?x2444, ?x485), participant(?x117, ?x2444), crewmember(?x485, ?x3782) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #249 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0353xq; *> query: (?x485, 09y20) <- film_crew_role(?x485, ?x137), genre(?x485, ?x53), film(?x6227, ?x485), ?x6227 = 05kwx2 *> conf = 0.15 ranks of expected_values: 13 EVAL 0ds11z film! 09y20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 141.000 69.000 0.703 http://example.org/film/actor/film./film/performance/film #6015-040b5k PRED entity: 040b5k PRED relation: genre PRED expected values: 02kdv5l => 69 concepts (67 used for prediction) PRED predicted values (max 10 best out of 94): 0653m (0.52 #5073, 0.51 #2880, 0.50 #3687), 0d05w3 (0.52 #5073, 0.51 #2880, 0.50 #3687), 05p553 (0.49 #5192, 0.36 #2998, 0.35 #2652), 02kdv5l (0.48 #1615, 0.48 #1845, 0.44 #691), 01jfsb (0.40 #701, 0.36 #356, 0.31 #1508), 06n90 (0.33 #357, 0.31 #702, 0.25 #1856), 082gq (0.24 #142, 0.14 #1062, 0.12 #2101), 0lsxr (0.21 #1274, 0.21 #1505, 0.20 #583), 02n4kr (0.20 #7, 0.16 #122, 0.15 #1273), 0219x_ (0.20 #253, 0.10 #2442, 0.10 #2787) >> Best rule #5073 for best value: >> intensional similarity = 4 >> extensional distance = 1101 >> proper extension: 0d8w2n; >> query: (?x2889, ?x2890) <- country(?x2889, ?x2346), titles(?x2890, ?x2889), film(?x382, ?x2889), genre(?x2889, ?x53) >> conf = 0.52 => this is the best rule for 2 predicted values *> Best rule #1615 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 319 *> proper extension: 07ng9k; *> query: (?x2889, 02kdv5l) <- film(?x1864, ?x2889), country(?x2889, ?x2346), genre(?x2889, ?x811), ?x811 = 03k9fj *> conf = 0.48 ranks of expected_values: 4 EVAL 040b5k genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 67.000 0.516 http://example.org/film/film/genre #6014-0235l PRED entity: 0235l PRED relation: currency PRED expected values: 09nqf => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.83 #70, 0.83 #65, 0.82 #69) >> Best rule #70 for best value: >> intensional similarity = 3 >> extensional distance = 339 >> proper extension: 0mlxt; >> query: (?x7697, 09nqf) <- second_level_divisions(?x94, ?x7697), ?x94 = 09c7w0, contains(?x1138, ?x7697) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0235l currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.833 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #6013-06s0l PRED entity: 06s0l PRED relation: contains! PRED expected values: 0261m => 76 concepts (65 used for prediction) PRED predicted values (max 10 best out of 127): 059g4 (0.69 #17886, 0.26 #3144, 0.26 #9402), 02j71 (0.61 #22357, 0.60 #51907, 0.59 #52805), 04pnx (0.57 #3106, 0.42 #9364, 0.40 #10258), 02qkt (0.53 #17338, 0.43 #29860, 0.42 #26281), 09c7w0 (0.52 #53705, 0.50 #54601, 0.47 #55497), 06n3y (0.39 #3406, 0.35 #9664, 0.33 #11452), 07ssc (0.37 #52838, 0.15 #21494, 0.14 #54630), 0dg3n1 (0.27 #20723, 0.26 #23406, 0.26 #24300), 04_1l0v (0.26 #2238, 0.25 #5814, 0.22 #13860), 02j9z (0.24 #17019, 0.21 #51908, 0.21 #12544) >> Best rule #17886 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 04fh3; >> query: (?x7096, ?x8483) <- countries_within(?x8483, ?x7096), jurisdiction_of_office(?x182, ?x7096), ?x182 = 060bp >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #51908 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 586 *> proper extension: 0nm8n; *> query: (?x7096, ?x2467) <- administrative_parent(?x7096, ?x551), administrative_parent(?x8948, ?x551), contains(?x2467, ?x8948) *> conf = 0.21 ranks of expected_values: 15 EVAL 06s0l contains! 0261m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 76.000 65.000 0.687 http://example.org/location/location/contains #6012-063y_ky PRED entity: 063y_ky PRED relation: nominated_for PRED expected values: 0bvn25 0170th 0fz3b1 0258dh => 47 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1377): 09q5w2 (0.46 #4818, 0.15 #14178, 0.15 #17300), 0gmgwnv (0.38 #5613, 0.21 #11853, 0.20 #10293), 017gl1 (0.38 #4800, 0.18 #17282, 0.18 #14160), 02c638 (0.38 #4975, 0.17 #6535, 0.16 #9655), 0b6tzs (0.33 #125, 0.31 #4797, 0.15 #17279), 0661ql3 (0.33 #340, 0.25 #3454, 0.13 #14372), 0j43swk (0.33 #439, 0.25 #3553, 0.11 #8231), 01g03q (0.33 #2900, 0.25 #4457, 0.08 #7575), 02mmwk (0.33 #1079, 0.25 #4193, 0.08 #5751), 0kfv9 (0.33 #1812, 0.25 #3369, 0.08 #6487) >> Best rule #4818 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 09sb52; 0cqh46; 02x73k6; 07cbcy; 05p09zm; 04kxsb; 09qv_s; 057xs89; 0gqy2; 0bdwqv; ... >> query: (?x2456, 09q5w2) <- award(?x7804, ?x2456), award(?x2280, ?x2456), nominated_for(?x2456, ?x86), ?x2280 = 0170qf, award_nominee(?x7804, ?x890) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #17153 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 180 *> proper extension: 0gqng; 02r0csl; 027c924; 0gq_v; 0p9sw; 02r22gf; 02hsq3m; 02z13jg; 047byns; 0gr42; ... *> query: (?x2456, ?x186) <- award(?x2280, ?x2456), nominated_for(?x2456, ?x86), award_nominee(?x2280, ?x57), film(?x2280, ?x186) *> conf = 0.08 ranks of expected_values: 387, 404, 641, 893 EVAL 063y_ky nominated_for 0258dh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 12.000 0.462 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 063y_ky nominated_for 0fz3b1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 12.000 0.462 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 063y_ky nominated_for 0170th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 12.000 0.462 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 063y_ky nominated_for 0bvn25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 12.000 0.462 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #6011-01k_0fp PRED entity: 01k_0fp PRED relation: executive_produced_by! PRED expected values: 0353xq => 122 concepts (101 used for prediction) PRED predicted values (max 10 best out of 40): 0353xq (0.22 #839, 0.18 #1903, 0.14 #2435), 02847m9 (0.18 #1147, 0.07 #4340, 0.07 #2743), 09gdh6k (0.07 #3072, 0.02 #7330), 01f7jt (0.07 #3175), 034b6k (0.07 #3171), 03n0cd (0.07 #3133), 01bn3l (0.07 #3091), 01xq8v (0.07 #3087), 0bt4g (0.07 #3084), 09hy79 (0.07 #3054) >> Best rule #839 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 09lwrt; >> query: (?x10243, 0353xq) <- artists(?x8386, ?x10243), artists(?x3061, ?x10243), ?x3061 = 05bt6j, instrumentalists(?x227, ?x10243), ?x8386 = 016ybr >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k_0fp executive_produced_by! 0353xq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 101.000 0.222 http://example.org/film/film/executive_produced_by #6010-05bkf PRED entity: 05bkf PRED relation: location_of_ceremony! PRED expected values: 04ztj => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.54 #93, 0.53 #89, 0.53 #81), 0jgjn (0.14 #509, 0.02 #100), 01g63y (0.03 #102, 0.02 #126, 0.02 #166) >> Best rule #93 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 0105y2; 013n0n; 0g3cw; >> query: (?x12638, 04ztj) <- contains(?x1679, ?x12638), jurisdiction_of_office(?x1195, ?x12638), category(?x1679, ?x134) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05bkf location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.542 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #6009-04gmp_z PRED entity: 04gmp_z PRED relation: nominated_for PRED expected values: 05sbv3 => 104 concepts (36 used for prediction) PRED predicted values (max 10 best out of 240): 04v8x9 (0.78 #32396, 0.78 #22678, 0.77 #35639), 05sbv3 (0.78 #32396, 0.78 #22678, 0.77 #35639), 025scjj (0.29 #3034, 0.20 #1415, 0.15 #6477), 0bbgvp (0.29 #3207, 0.20 #1588, 0.09 #40500), 0jvt9 (0.20 #492, 0.15 #6477, 0.14 #2111), 0p9rz (0.20 #1386, 0.14 #3005, 0.10 #6243), 03bxp5 (0.20 #982, 0.14 #2601, 0.10 #5839), 014kkm (0.20 #802, 0.14 #2421, 0.09 #40500), 0k5g9 (0.20 #399, 0.10 #8496, 0.07 #6876), 0bcndz (0.20 #5104, 0.07 #6724, 0.07 #8344) >> Best rule #32396 for best value: >> intensional similarity = 2 >> extensional distance = 981 >> proper extension: 04dyqk; >> query: (?x2801, ?x499) <- award_winner(?x499, ?x2801), place_of_birth(?x2801, ?x1025) >> conf = 0.78 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 04gmp_z nominated_for 05sbv3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 36.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #6008-0821j PRED entity: 0821j PRED relation: nationality PRED expected values: 0d060g => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 42): 0d060g (0.52 #496, 0.45 #2285, 0.28 #5165), 04_1l0v (0.27 #10237), 015jr (0.27 #10237), 07ssc (0.25 #4667, 0.18 #1206, 0.17 #1005), 06q1r (0.25 #4667, 0.03 #1067, 0.02 #3352), 0j5g9 (0.25 #4667), 02jx1 (0.18 #1224, 0.18 #1125, 0.16 #627), 0345h (0.12 #1222, 0.08 #4198, 0.07 #2811), 06m_5 (0.08 #82, 0.02 #379, 0.02 #578), 03rk0 (0.08 #144, 0.06 #9582, 0.06 #10381) >> Best rule #496 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 02633g; 02lj6p; 0dbb3; >> query: (?x8718, ?x94) <- influenced_by(?x10578, ?x8718), award_nominee(?x8718, ?x1752), category(?x10578, ?x134), nationality(?x10578, ?x94) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0821j nationality 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.523 http://example.org/people/person/nationality #6007-0l6m5 PRED entity: 0l6m5 PRED relation: olympics! PRED expected values: 0160w 05v8c 0ctw_b => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 329): 0h7x (0.77 #5318, 0.76 #5135, 0.76 #3421), 03_3d (0.76 #3409, 0.73 #2141, 0.67 #1785), 05qhw (0.76 #3412, 0.73 #2144, 0.67 #1788), 0ctw_b (0.67 #1078, 0.64 #2148, 0.60 #630), 06f32 (0.67 #1099, 0.60 #651, 0.50 #1012), 015qh (0.67 #1086, 0.53 #1156, 0.45 #2156), 07fj_ (0.67 #1116, 0.44 #1830, 0.40 #668), 06bnz (0.67 #1088, 0.44 #438, 0.42 #1423), 04w4s (0.67 #1104, 0.44 #438, 0.40 #656), 0jhd (0.67 #1141, 0.44 #438, 0.40 #693) >> Best rule #5318 for best value: >> intensional similarity = 16 >> extensional distance = 42 >> proper extension: 01f1jy; 015pkt; >> query: (?x1081, 0h7x) <- olympics(?x1558, ?x1081), olympics(?x429, ?x1081), olympics(?x390, ?x1081), film_release_region(?x1108, ?x429), olympics(?x390, ?x2432), country(?x901, ?x390), country(?x148, ?x429), ?x2432 = 0nbjq, ?x1108 = 0jjy0, countries_spoken_in(?x254, ?x390), currency(?x1558, ?x170), nationality(?x72, ?x390), nationality(?x294, ?x429), service_location(?x9968, ?x429), country(?x308, ?x390), religion(?x390, ?x492) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1078 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 4 *> proper extension: 0kbws; *> query: (?x1081, 0ctw_b) <- olympics(?x9458, ?x1081), olympics(?x7747, ?x1081), olympics(?x2843, ?x1081), olympics(?x1003, ?x1081), olympics(?x429, ?x1081), olympics(?x87, ?x1081), ?x429 = 03rt9, sports(?x1081, ?x359), ?x1003 = 03gj2, sports(?x391, ?x359), olympics(?x252, ?x1081), ?x87 = 05r4w, film_release_region(?x2868, ?x2843), film_release_region(?x1080, ?x2843), ?x2868 = 0dr3sl, capital(?x2843, ?x11232), olympics(?x2867, ?x1081), ?x7747 = 07f1x, ?x1080 = 01c22t, administrative_area_type(?x9458, ?x2792) *> conf = 0.67 ranks of expected_values: 4, 25, 71 EVAL 0l6m5 olympics! 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 64.000 64.000 0.773 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l6m5 olympics! 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 64.000 64.000 0.773 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l6m5 olympics! 0160w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 64.000 64.000 0.773 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #6006-0d9xq PRED entity: 0d9xq PRED relation: people! PRED expected values: 0x67 => 166 concepts (122 used for prediction) PRED predicted values (max 10 best out of 57): 0x67 (0.30 #1319, 0.27 #1935, 0.27 #626), 041rx (0.20 #466, 0.18 #2006, 0.18 #3781), 07hwkr (0.18 #166, 0.09 #551, 0.07 #936), 033tf_ (0.11 #5328, 0.11 #84, 0.11 #3784), 07bch9 (0.11 #100, 0.09 #177, 0.09 #2102), 0dryh9k (0.11 #93, 0.06 #2944, 0.05 #5337), 063k3h (0.11 #108, 0.05 #339, 0.04 #493), 02g7sp (0.10 #326, 0.08 #634, 0.06 #711), 01qhm_ (0.09 #160, 0.08 #468, 0.06 #237), 02w7gg (0.09 #156, 0.07 #3470, 0.05 #8717) >> Best rule #1319 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 0f0y8; 032t2z; 01ky2h; 0lgm5; 01lcxbb; 01gx5f; 01w8n89; 017yfz; 024zq; 0k1bs; ... >> query: (?x5101, 0x67) <- artist(?x3240, ?x5101), artists(?x505, ?x5101), ?x505 = 03_d0 >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d9xq people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 122.000 0.302 http://example.org/people/ethnicity/people #6005-01p7x7 PRED entity: 01p7x7 PRED relation: category PRED expected values: 08mbj5d => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.93 #32, 0.92 #21, 0.91 #95) >> Best rule #32 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 07lx1s; 04344j; 0172jm; 01hnb; 04p_hy; 03cz83; 02nvg1; 01wv24; 06l32y; 016sd3; ... >> query: (?x11349, 08mbj5d) <- colors(?x11349, ?x332), school_type(?x11349, ?x3205), ?x3205 = 01rs41, contains(?x94, ?x11349) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p7x7 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 181.000 0.926 http://example.org/common/topic/webpage./common/webpage/category #6004-09lxv9 PRED entity: 09lxv9 PRED relation: genre PRED expected values: 01hmnh => 93 concepts (69 used for prediction) PRED predicted values (max 10 best out of 102): 07s9rl0 (0.71 #237, 0.66 #4383, 0.63 #2248), 01z4y (0.61 #7590, 0.51 #3434, 0.51 #7114), 03k9fj (0.40 #1312, 0.40 #484, 0.36 #5816), 0lsxr (0.34 #1783, 0.21 #126, 0.21 #1191), 01hmnh (0.33 #16, 0.28 #1317, 0.25 #5821), 02n4kr (0.32 #125, 0.25 #1782, 0.15 #1190), 02l7c8 (0.29 #4396, 0.27 #3328, 0.27 #7008), 04xvlr (0.19 #947, 0.18 #238, 0.18 #1066), 060__y (0.18 #251, 0.16 #4397, 0.15 #2026), 03mqtr (0.18 #146, 0.11 #264, 0.05 #8184) >> Best rule #237 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 05dy7p; >> query: (?x8906, 07s9rl0) <- language(?x8906, ?x254), film_crew_role(?x8906, ?x137), costume_design_by(?x8906, ?x6327), crewmember(?x8906, ?x929) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 085bd1; *> query: (?x8906, 01hmnh) <- language(?x8906, ?x254), film(?x4233, ?x8906), genre(?x8906, ?x225), ?x4233 = 01trf3 *> conf = 0.33 ranks of expected_values: 5 EVAL 09lxv9 genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 69.000 0.711 http://example.org/film/film/genre #6003-06crk PRED entity: 06crk PRED relation: student! PRED expected values: 04rwx => 191 concepts (179 used for prediction) PRED predicted values (max 10 best out of 280): 065y4w7 (0.50 #14, 0.11 #17406, 0.08 #81156), 03ksy (0.27 #44383, 0.21 #3264, 0.14 #1158), 01w5m (0.22 #44382, 0.15 #8533, 0.14 #1157), 07wrz (0.20 #588, 0.14 #1114, 0.11 #1640), 015nl4 (0.20 #593, 0.06 #79098, 0.06 #81209), 02bzh0 (0.20 #937, 0.03 #10421, 0.02 #18857), 08815 (0.18 #44279, 0.13 #14755, 0.08 #36903), 02sjgpq (0.17 #11590, 0.16 #17392, 0.16 #12645), 01w3v (0.17 #11590, 0.16 #17392, 0.16 #12645), 02bbyw (0.17 #2863, 0.14 #3917, 0.11 #4970) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03gyh_z; 04z_x4v; >> query: (?x6342, 065y4w7) <- place_of_death(?x6342, ?x1523), ?x1523 = 030qb3t, profession(?x6342, ?x3802), student(?x3437, ?x6342) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #14791 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 05fg2; 05nn4k; *> query: (?x6342, 04rwx) <- company(?x6342, ?x741), profession(?x6342, ?x3802), student(?x6056, ?x6342), student(?x3437, ?x6342) *> conf = 0.02 ranks of expected_values: 190 EVAL 06crk student! 04rwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 191.000 179.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/student #6002-0ggx5q PRED entity: 0ggx5q PRED relation: artists PRED expected values: 01vrt_c 0840vq 01wj18h 01vsykc 01gg59 01dwrc => 48 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1013): 03f5spx (0.75 #5027, 0.67 #4033, 0.60 #6021), 01wj18h (0.67 #4221, 0.60 #6209, 0.50 #5215), 01323p (0.67 #4609, 0.50 #6597, 0.50 #2620), 011z3g (0.62 #5520, 0.60 #6514, 0.50 #4526), 01dwrc (0.62 #5450, 0.50 #4456, 0.50 #2467), 0x3n (0.62 #5489, 0.50 #4495, 0.50 #1512), 013w7j (0.62 #5476, 0.50 #4482, 0.50 #1499), 01vrt_c (0.62 #5046, 0.50 #2063, 0.40 #6040), 0ffgh (0.62 #5554, 0.50 #1577, 0.33 #4560), 016ppr (0.62 #5852, 0.50 #1875, 0.33 #4858) >> Best rule #5027 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 016clz; 0glt670; 06j6l; >> query: (?x5876, 03f5spx) <- artists(?x5876, ?x4740), artists(?x5876, ?x3256), artists(?x5876, ?x1896), ?x1896 = 0j1yf, award(?x4740, ?x2634), location(?x4740, ?x957), ?x2634 = 02f72n, artist(?x2149, ?x3256) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #4221 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 06by7; 0y3_8; 02lnbg; *> query: (?x5876, 01wj18h) <- artists(?x5876, ?x11026), artists(?x5876, ?x9184), artists(?x5876, ?x7634), artists(?x5876, ?x4740), ?x4740 = 03y82t6, artist(?x3265, ?x7634), ?x11026 = 01s7ns, profession(?x9184, ?x220), award_winner(?x1389, ?x9184) *> conf = 0.67 ranks of expected_values: 2, 5, 8, 88, 160, 197 EVAL 0ggx5q artists 01dwrc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 48.000 13.000 0.750 http://example.org/music/genre/artists EVAL 0ggx5q artists 01gg59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 48.000 13.000 0.750 http://example.org/music/genre/artists EVAL 0ggx5q artists 01vsykc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 48.000 13.000 0.750 http://example.org/music/genre/artists EVAL 0ggx5q artists 01wj18h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 48.000 13.000 0.750 http://example.org/music/genre/artists EVAL 0ggx5q artists 0840vq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 48.000 13.000 0.750 http://example.org/music/genre/artists EVAL 0ggx5q artists 01vrt_c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 48.000 13.000 0.750 http://example.org/music/genre/artists #6001-016_nr PRED entity: 016_nr PRED relation: artists PRED expected values: 03f4xvm 01vvyd8 => 45 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1041): 01vsgrn (0.67 #2638, 0.57 #3710, 0.38 #4781), 01vw917 (0.67 #2726, 0.57 #3798, 0.38 #4869), 047sxrj (0.62 #4461, 0.33 #2318, 0.33 #1248), 03sww (0.50 #2577, 0.43 #3649, 0.38 #4720), 01vw_dv (0.50 #2735, 0.43 #3807, 0.38 #4878), 01vzx45 (0.50 #2823, 0.43 #3895, 0.38 #4966), 02l840 (0.50 #4332, 0.33 #1119, 0.33 #49), 05mt_q (0.50 #4377, 0.33 #1164, 0.33 #94), 01vrt_c (0.50 #4360, 0.33 #1147, 0.33 #77), 01dwrc (0.50 #4802, 0.33 #1589, 0.33 #519) >> Best rule #2638 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 036jv; 0339z0; >> query: (?x5630, 01vsgrn) <- artists(?x5630, ?x5340), artists(?x5630, ?x3893), ?x5340 = 01vvzb1, parent_genre(?x5630, ?x1127), award_nominee(?x827, ?x3893), ?x827 = 02l840 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1459 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 0glt670; *> query: (?x5630, 03f4xvm) <- artists(?x5630, ?x4476), artists(?x5630, ?x2732), parent_genre(?x2937, ?x5630), parent_genre(?x5630, ?x1127), ?x4476 = 01vw20h, ?x2732 = 01wgxtl *> conf = 0.33 ranks of expected_values: 120, 155 EVAL 016_nr artists 01vvyd8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 45.000 27.000 0.667 http://example.org/music/genre/artists EVAL 016_nr artists 03f4xvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 45.000 27.000 0.667 http://example.org/music/genre/artists #6000-015y3j PRED entity: 015y3j PRED relation: organization! PRED expected values: 060c4 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 18): 060c4 (0.87 #275, 0.87 #210, 0.86 #197), 0dq_5 (0.34 #724, 0.34 #711, 0.34 #698), 07xl34 (0.29 #63, 0.24 #24, 0.23 #323), 05k17c (0.16 #501, 0.12 #423, 0.08 #33), 0hm4q (0.07 #632, 0.05 #801, 0.05 #1204), 05c0jwl (0.06 #629, 0.05 #837, 0.04 #18), 01t7n9 (0.03 #1275, 0.02 #1549), 0fkzq (0.03 #1275, 0.02 #1549), 09n5b9 (0.03 #1275, 0.02 #1549), 02079p (0.03 #1275, 0.02 #1549) >> Best rule #275 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 0kz2w; >> query: (?x7816, 060c4) <- registering_agency(?x7816, ?x1982), colors(?x7816, ?x663), institution(?x865, ?x7816), major_field_of_study(?x7816, ?x2314) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015y3j organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.868 http://example.org/organization/role/leaders./organization/leadership/organization #5999-0q5hw PRED entity: 0q5hw PRED relation: influenced_by PRED expected values: 02_p8v => 155 concepts (112 used for prediction) PRED predicted values (max 10 best out of 354): 0p_47 (0.26 #6110, 0.21 #3965, 0.17 #964), 081lh (0.25 #3022, 0.25 #20, 0.22 #6024), 01wp_jm (0.22 #6339, 0.17 #1193, 0.14 #4194), 0f7hc (0.21 #3996, 0.17 #995, 0.13 #6141), 01j7rd (0.20 #2196, 0.12 #4339, 0.07 #3911), 029_3 (0.17 #6121, 0.17 #3119, 0.06 #12129), 02633g (0.17 #1113, 0.14 #4114, 0.09 #6259), 052hl (0.17 #1064, 0.10 #2350, 0.08 #3208), 012gq6 (0.17 #3097, 0.09 #6099, 0.07 #12535), 02lj6p (0.17 #1143, 0.08 #2858, 0.08 #3715) >> Best rule #6110 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 010p3; 01xwqn; >> query: (?x2817, 0p_47) <- gender(?x2817, ?x231), influenced_by(?x2817, ?x4112), ?x4112 = 014z8v >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #3162 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 01s7qqw; 05rx__; 01wp_jm; *> query: (?x2817, 02_p8v) <- gender(?x2817, ?x231), influenced_by(?x2817, ?x4066), ?x4066 = 0ph2w *> conf = 0.08 ranks of expected_values: 62 EVAL 0q5hw influenced_by 02_p8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 155.000 112.000 0.261 http://example.org/influence/influence_node/influenced_by #5998-04913k PRED entity: 04913k PRED relation: team! PRED expected values: 06s27s => 62 concepts (60 used for prediction) PRED predicted values (max 10 best out of 105): 03n69x (0.40 #247, 0.29 #474, 0.20 #340), 0hcs3 (0.20 #1682, 0.20 #340, 0.18 #1116), 040j2_ (0.20 #340, 0.19 #1973, 0.19 #1860), 019g65 (0.20 #306, 0.14 #533, 0.08 #1552), 0f2zc (0.20 #290, 0.14 #517, 0.07 #567), 06s27s (0.20 #340, 0.12 #2027, 0.12 #1914), 01f492 (0.20 #340, 0.12 #1862, 0.10 #2317), 01g0jn (0.20 #340, 0.07 #567, 0.07 #3517), 054c1 (0.14 #547, 0.04 #1452, 0.04 #1566), 04g9sq (0.08 #3513, 0.03 #3285, 0.03 #3399) >> Best rule #247 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 07l24; 0wsr; >> query: (?x2011, 03n69x) <- colors(?x2011, ?x4557), colors(?x2011, ?x1101), colors(?x2011, ?x663), team(?x2010, ?x2011), ?x663 = 083jv, ?x4557 = 019sc, ?x1101 = 06fvc, team(?x2010, ?x1632), team(?x2010, ?x1160), draft(?x1632, ?x1161), team(?x5412, ?x1632), school(?x1632, ?x2959), school(?x1160, ?x581), ?x581 = 06pwq, ?x2959 = 01swxv, teams(?x739, ?x1632) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #340 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 3 *> proper extension: 07l24; 0wsr; *> query: (?x2011, ?x5412) <- colors(?x2011, ?x4557), colors(?x2011, ?x1101), colors(?x2011, ?x663), team(?x2010, ?x2011), ?x663 = 083jv, ?x4557 = 019sc, ?x1101 = 06fvc, team(?x2010, ?x1632), team(?x2010, ?x1160), draft(?x1632, ?x1161), team(?x5412, ?x1632), school(?x1632, ?x2959), school(?x1160, ?x581), ?x581 = 06pwq, ?x2959 = 01swxv, teams(?x739, ?x1632) *> conf = 0.20 ranks of expected_values: 6 EVAL 04913k team! 06s27s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 62.000 60.000 0.400 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #5997-0l99s PRED entity: 0l99s PRED relation: influenced_by! PRED expected values: 014dq7 040_9 => 158 concepts (66 used for prediction) PRED predicted values (max 10 best out of 402): 03_87 (0.60 #2781, 0.43 #5309, 0.40 #2276), 040db (0.57 #5633, 0.50 #4624, 0.25 #8668), 07g2b (0.57 #5070, 0.40 #2542, 0.40 #2037), 0683n (0.50 #4882, 0.50 #1849, 0.38 #8926), 03f47xl (0.50 #4806, 0.29 #5815, 0.25 #8850), 073v6 (0.43 #5673, 0.33 #4664, 0.31 #8708), 0d4jl (0.40 #2639, 0.29 #5672, 0.29 #5167), 06whf (0.40 #2686, 0.29 #5214, 0.25 #1677), 03f70xs (0.40 #2621, 0.29 #5149, 0.20 #2116), 04jwp (0.40 #2764, 0.29 #5292, 0.20 #2259) >> Best rule #2781 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0448r; >> query: (?x7334, 03_87) <- influenced_by(?x9508, ?x7334), ?x9508 = 0c1jh, place_of_birth(?x7334, ?x739) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2588 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0448r; *> query: (?x7334, 014dq7) <- influenced_by(?x9508, ?x7334), ?x9508 = 0c1jh, place_of_birth(?x7334, ?x739) *> conf = 0.20 ranks of expected_values: 55, 161 EVAL 0l99s influenced_by! 040_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 158.000 66.000 0.600 http://example.org/influence/influence_node/influenced_by EVAL 0l99s influenced_by! 014dq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 158.000 66.000 0.600 http://example.org/influence/influence_node/influenced_by #5996-015grj PRED entity: 015grj PRED relation: award PRED expected values: 02w9sd7 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 227): 0ck27z (0.50 #86, 0.16 #8710, 0.15 #2438), 05zr6wv (0.25 #408, 0.13 #30580, 0.10 #2760), 05pcn59 (0.21 #467, 0.13 #30580, 0.13 #2819), 09qv3c (0.21 #45, 0.13 #30580, 0.07 #437), 02x4w6g (0.21 #499, 0.05 #1283, 0.05 #8731), 0bdw6t (0.18 #495, 0.13 #22346, 0.13 #30580), 05ztrmj (0.18 #566, 0.06 #3310, 0.06 #8798), 09qv_s (0.18 #535, 0.05 #3279, 0.05 #4455), 0cqh46 (0.18 #438, 0.05 #3182, 0.05 #4358), 0gqwc (0.17 #852, 0.11 #1244, 0.07 #12612) >> Best rule #86 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0htlr; 021b_; 02js_6; >> query: (?x968, 0ck27z) <- nominated_for(?x968, ?x5594), profession(?x968, ?x353), ?x5594 = 01fx1l >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #24307 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1743 *> proper extension: 01nzs7; 03czrpj; 09mfvx; 05d6q1; 0fvppk; 0kcdl; *> query: (?x968, ?x68) <- nominated_for(?x968, ?x9701), genre(?x9701, ?x258), nominated_for(?x68, ?x9701) *> conf = 0.12 ranks of expected_values: 62 EVAL 015grj award 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 94.000 94.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5995-0c9d9 PRED entity: 0c9d9 PRED relation: artist! PRED expected values: 011k1h => 115 concepts (101 used for prediction) PRED predicted values (max 10 best out of 127): 015_1q (0.75 #3692, 0.53 #4916, 0.48 #971), 0181dw (0.65 #2622, 0.22 #854, 0.21 #1534), 011k1h (0.57 #2731, 0.29 #4908, 0.22 #827), 03mp8k (0.52 #1015, 0.45 #1423, 0.44 #1559), 01cl0d (0.30 #186, 0.27 #322, 0.24 #458), 017l96 (0.25 #4915, 0.15 #2738, 0.11 #1650), 041n43 (0.22 #108, 0.05 #652, 0.04 #3238), 0g768 (0.22 #3026, 0.16 #3979, 0.15 #2210), 01cl2y (0.21 #2069, 0.18 #2885, 0.11 #3021), 02p11jq (0.20 #149, 0.18 #285, 0.16 #2054) >> Best rule #3692 for best value: >> intensional similarity = 5 >> extensional distance = 152 >> proper extension: 0147dk; 03f2_rc; 0cg9y; 04xrx; 01dw9z; 01vx5w7; 02_fj; 0m_v0; 01m3x5p; 0f7hc; ... >> query: (?x317, 015_1q) <- profession(?x317, ?x131), artist(?x5666, ?x317), artist(?x5666, ?x7937), artists(?x497, ?x317), ?x7937 = 018phr >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2731 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 107 *> proper extension: 01x1cn2; 01w806h; 0840vq; 01vwyqp; 014488; 049qx; 01vw8mh; 03sww; 01vvzb1; 01wgfp6; ... *> query: (?x317, 011k1h) <- profession(?x317, ?x131), artist(?x5666, ?x317), artist(?x5666, ?x4420), artists(?x497, ?x317), ?x4420 = 01pfkw *> conf = 0.57 ranks of expected_values: 3 EVAL 0c9d9 artist! 011k1h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 101.000 0.747 http://example.org/music/record_label/artist #5994-04f73rc PRED entity: 04f73rc PRED relation: artists PRED expected values: 016lj_ 03j_hq => 74 concepts (29 used for prediction) PRED predicted values (max 10 best out of 985): 017j6 (0.60 #4631, 0.50 #7880, 0.50 #2460), 0b1zz (0.60 #4882, 0.50 #2711, 0.38 #9757), 01w8n89 (0.55 #10076, 0.50 #12247, 0.39 #8671), 0285c (0.50 #2309, 0.50 #1225, 0.40 #7729), 03j_hq (0.50 #3176, 0.50 #2092, 0.40 #8596), 01wt4wc (0.50 #2898, 0.50 #1814, 0.40 #5069), 02y7sr (0.50 #2961, 0.50 #1877, 0.40 #5132), 015196 (0.50 #3136, 0.50 #2052, 0.40 #5307), 03lgg (0.50 #2614, 0.50 #1530, 0.40 #4785), 01w03jv (0.50 #3220, 0.50 #2136, 0.40 #5391) >> Best rule #4631 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 06by7; >> query: (?x13938, 017j6) <- artists(?x13938, ?x4877), parent_genre(?x13938, ?x2249), ?x4877 = 03sww, parent_genre(?x10676, ?x13938), parent_genre(?x3642, ?x13938), ?x3642 = 0dls3, parent_genre(?x10676, ?x13359), ?x13359 = 02mscn >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3176 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 0jmwg; *> query: (?x13938, 03j_hq) <- artists(?x13938, ?x7125), artists(?x13938, ?x4877), parent_genre(?x13938, ?x2249), ?x4877 = 03sww, parent_genre(?x8639, ?x13938), parent_genre(?x3642, ?x13938), ?x3642 = 0dls3, ?x7125 = 01jcxwp, artists(?x8639, ?x646) *> conf = 0.50 ranks of expected_values: 5, 86 EVAL 04f73rc artists 03j_hq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 74.000 29.000 0.600 http://example.org/music/genre/artists EVAL 04f73rc artists 016lj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 74.000 29.000 0.600 http://example.org/music/genre/artists #5993-02ckl3 PRED entity: 02ckl3 PRED relation: school_type PRED expected values: 05pcjw => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 21): 05jxkf (0.46 #799, 0.46 #510, 0.43 #583), 01rs41 (0.44 #101, 0.33 #173, 0.31 #608), 05pcjw (0.38 #97, 0.35 #73, 0.33 #169), 07tf8 (0.17 #564, 0.16 #297, 0.15 #924), 01_9fk (0.15 #363, 0.15 #242, 0.15 #677), 01_srz (0.08 #2457, 0.07 #606, 0.07 #485), 0bwd5 (0.08 #2457, 0.06 #115, 0.03 #139), 02p0qmm (0.08 #2457, 0.06 #516, 0.05 #805), 02dk5q (0.08 #2457, 0.05 #223, 0.04 #295), 04qbv (0.08 #2457, 0.04 #595, 0.03 #450) >> Best rule #799 for best value: >> intensional similarity = 5 >> extensional distance = 189 >> proper extension: 0ymbl; 037s9x; 0ymb6; 015g1w; 05p7tx; 017rbx; 04gd8j; 01nn7r; 01x5fb; >> query: (?x11680, 05jxkf) <- major_field_of_study(?x11680, ?x2981), contains(?x108, ?x11680), location(?x236, ?x108), featured_film_locations(?x103, ?x108), currency(?x108, ?x170) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 09krm_; *> query: (?x11680, 05pcjw) <- organization(?x3484, ?x11680), contains(?x94, ?x11680), ?x3484 = 05k17c, currency(?x11680, ?x170) *> conf = 0.38 ranks of expected_values: 3 EVAL 02ckl3 school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 149.000 149.000 0.461 http://example.org/education/educational_institution/school_type #5992-0l15bq PRED entity: 0l15bq PRED relation: role! PRED expected values: 0770cd 014cw2 => 86 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1119): 050z2 (0.77 #17816, 0.75 #19632, 0.75 #11024), 04bpm6 (0.75 #10915, 0.73 #14534, 0.70 #11821), 01wxdn3 (0.75 #11242, 0.69 #18034, 0.67 #8530), 023l9y (0.70 #13316, 0.70 #11955, 0.67 #11501), 0161sp (0.70 #11873, 0.67 #8255, 0.64 #15038), 01vs4ff (0.70 #12045, 0.67 #11591, 0.62 #11139), 05qhnq (0.67 #8885, 0.64 #15217, 0.60 #12960), 01w806h (0.67 #11432, 0.60 #6915, 0.50 #15502), 0lzkm (0.64 #14628, 0.62 #11009, 0.60 #11915), 082brv (0.64 #14722, 0.62 #11103, 0.55 #15174) >> Best rule #17816 for best value: >> intensional similarity = 18 >> extensional distance = 11 >> proper extension: 0jtg0; >> query: (?x1574, 050z2) <- role(?x1574, ?x3296), role(?x1574, ?x1969), role(?x1574, ?x868), role(?x1574, ?x75), ?x1969 = 04rzd, role(?x3175, ?x1574), role(?x2799, ?x1574), role(?x7033, ?x1574), role(?x645, ?x3296), role(?x3296, ?x74), ?x7033 = 0gkd1, ?x75 = 07y_7, role(?x1715, ?x868), category(?x2799, ?x134), artists(?x671, ?x2799), celebrity(?x3020, ?x3175), peers(?x9826, ?x2799), award_nominee(?x3175, ?x827) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #6855 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 3 *> proper extension: 018vs; *> query: (?x1574, 0770cd) <- role(?x1574, ?x2785), role(?x1574, ?x1969), role(?x1574, ?x1437), role(?x1574, ?x1436), role(?x1574, ?x868), role(?x1574, ?x433), ?x1969 = 04rzd, role(?x7972, ?x1574), role(?x2944, ?x1574), ?x433 = 025cbm, role(?x2944, ?x5990), instrumentalists(?x2944, ?x2492), ?x2492 = 01tp5bj, ?x1436 = 0xzly, ?x868 = 0dwvl, ?x1437 = 01vdm0, ?x5990 = 0192l, ?x2785 = 0jtg0, role(?x2944, ?x74), ?x7972 = 0326tc *> conf = 0.60 ranks of expected_values: 16, 95 EVAL 0l15bq role! 014cw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 86.000 50.000 0.769 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0l15bq role! 0770cd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 86.000 50.000 0.769 http://example.org/music/artist/track_contributions./music/track_contribution/role #5991-0ddd9 PRED entity: 0ddd9 PRED relation: award_winner PRED expected values: 073v6 02kz_ => 53 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1857): 07w21 (0.43 #73, 0.27 #7458, 0.24 #4997), 0c5tl (0.38 #22151, 0.36 #12305, 0.36 #7384), 03hnd (0.38 #22151, 0.36 #12305, 0.36 #7384), 01tz6vs (0.38 #22151, 0.36 #12305, 0.36 #7384), 0l99s (0.38 #22151, 0.36 #12305, 0.36 #7384), 03cdg (0.38 #22151, 0.36 #12305, 0.36 #7384), 0hcvy (0.38 #22151, 0.36 #12305, 0.36 #7384), 0hky (0.38 #22151, 0.36 #12305, 0.36 #7384), 037jz (0.38 #22151, 0.36 #12305, 0.36 #7384), 02kz_ (0.38 #22151, 0.36 #12305, 0.36 #7384) >> Best rule #73 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 0grw_; >> query: (?x921, 07w21) <- award(?x5434, ?x921), gender(?x5434, ?x231), influenced_by(?x5434, ?x5435), influenced_by(?x2485, ?x5434), ?x5435 = 01v9724 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #22151 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 57 *> proper extension: 0l8z1; 05q8pss; 0262s1; 058bzgm; *> query: (?x921, ?x11554) <- award_winner(?x921, ?x2080), award(?x11554, ?x921), influenced_by(?x2080, ?x2240), place_of_death(?x2080, ?x4627), influenced_by(?x11554, ?x6204) *> conf = 0.38 ranks of expected_values: 10, 124 EVAL 0ddd9 award_winner 02kz_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 53.000 24.000 0.429 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0ddd9 award_winner 073v6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 53.000 24.000 0.429 http://example.org/award/award_category/winners./award/award_honor/award_winner #5990-027gs1_ PRED entity: 027gs1_ PRED relation: nominated_for PRED expected values: 02czd5 07zhjj => 50 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1338): 0l76z (0.80 #3130, 0.79 #3129, 0.78 #12522), 0330r (0.80 #3130, 0.79 #3129, 0.78 #12522), 01fszq (0.80 #3130, 0.79 #3129, 0.78 #12522), 01ft14 (0.80 #3130, 0.79 #3129, 0.78 #12522), 02czd5 (0.50 #1249, 0.14 #2814, 0.12 #4380), 02r5qtm (0.38 #613, 0.17 #23481, 0.17 #29744), 0557yqh (0.38 #536, 0.08 #2101, 0.07 #3667), 0ddd0gc (0.29 #1759, 0.24 #3325, 0.22 #4890), 026p4q7 (0.29 #6611, 0.28 #11308, 0.28 #8177), 09gq0x5 (0.26 #6512, 0.26 #11209, 0.26 #8078) >> Best rule #3130 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 02py_sj; >> query: (?x7510, ?x2436) <- ceremony(?x7510, ?x1265), award(?x2436, ?x7510), nominated_for(?x7510, ?x631), program(?x1039, ?x2436) >> conf = 0.80 => this is the best rule for 4 predicted values *> Best rule #1249 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 09v7wsg; *> query: (?x7510, 02czd5) <- ceremony(?x7510, ?x1265), award_winner(?x7510, ?x201), award(?x4588, ?x7510), ?x4588 = 0l76z *> conf = 0.50 ranks of expected_values: 5, 96 EVAL 027gs1_ nominated_for 07zhjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 50.000 23.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 027gs1_ nominated_for 02czd5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 50.000 23.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5989-05x_5 PRED entity: 05x_5 PRED relation: institution! PRED expected values: 014mlp => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 18): 014mlp (0.75 #22, 0.75 #136, 0.69 #1229), 0bkj86 (0.69 #139, 0.59 #44, 0.54 #101), 04zx3q1 (0.54 #134, 0.45 #39, 0.44 #96), 013zdg (0.50 #62, 0.36 #138, 0.34 #81), 027f2w (0.49 #140, 0.41 #102, 0.37 #83), 03mkk4 (0.31 #47, 0.31 #142, 0.27 #104), 0bjrnt (0.31 #42, 0.24 #99, 0.24 #137), 022h5x (0.28 #73, 0.21 #35, 0.20 #208), 01rr_d (0.21 #51, 0.20 #108, 0.17 #146), 028dcg (0.19 #72, 0.17 #34, 0.15 #148) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 01jssp; 05krk; 01pl14; 065y4w7; 01t8sr; 07szy; 0bx8pn; 01jswq; 0j_sncb; 01swxv; ... >> query: (?x6973, 014mlp) <- school(?x1883, ?x6973), ?x1883 = 02qw1zx, institution(?x620, ?x6973) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05x_5 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5988-03kxj2 PRED entity: 03kxj2 PRED relation: genre PRED expected values: 0vgkd => 80 concepts (79 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.70 #243, 0.68 #727, 0.67 #1), 01z4y (0.61 #6182, 0.61 #2788, 0.53 #2424), 03k9fj (0.49 #617, 0.23 #4738, 0.23 #8622), 02l7c8 (0.47 #259, 0.39 #380, 0.38 #501), 02kdv5l (0.33 #608, 0.29 #8613, 0.28 #1819), 01jfsb (0.33 #1829, 0.33 #2072, 0.32 #3650), 06cvj (0.25 #125, 0.17 #4, 0.11 #367), 0hn10 (0.25 #131, 0.06 #8247, 0.05 #858), 01g6gs (0.23 #263, 0.20 #384, 0.19 #505), 0hcr (0.22 #629, 0.08 #8634, 0.07 #5599) >> Best rule #243 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 016kz1; >> query: (?x2231, 07s9rl0) <- award(?x2231, ?x4317), produced_by(?x2231, ?x163), nominated_for(?x143, ?x2231), film_sets_designed(?x2230, ?x2231) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #132 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 02qr3k8; *> query: (?x2231, 0vgkd) <- film(?x10588, ?x2231), ?x10588 = 021b_, genre(?x2231, ?x258) *> conf = 0.12 ranks of expected_values: 25 EVAL 03kxj2 genre 0vgkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 80.000 79.000 0.698 http://example.org/film/film/genre #5987-04jpl PRED entity: 04jpl PRED relation: film_release_region! PRED expected values: 03wh49y => 216 concepts (198 used for prediction) PRED predicted values (max 10 best out of 1367): 0cmc26r (0.79 #11092, 0.72 #9771, 0.42 #79801), 03cw411 (0.79 #11045, 0.67 #9724, 0.42 #79754), 0dtfn (0.78 #9412, 0.74 #10733, 0.55 #79442), 08hmch (0.78 #9370, 0.68 #10691, 0.57 #79400), 05z7c (0.78 #9503, 0.63 #10824, 0.30 #79533), 01fmys (0.74 #10820, 0.72 #9499, 0.57 #79529), 017gm7 (0.74 #10734, 0.72 #9413, 0.49 #79443), 03nm_fh (0.74 #11178, 0.72 #9857, 0.49 #79887), 05p1tzf (0.74 #10631, 0.72 #9310, 0.49 #79340), 03qnc6q (0.74 #10894, 0.72 #9573, 0.46 #79603) >> Best rule #11092 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 0154j; 01mk6; >> query: (?x362, 0cmc26r) <- contains(?x362, ?x639), film_release_region(?x2958, ?x362), ?x2958 = 0b_5d >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #11298 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0154j; 01mk6; *> query: (?x362, 03wh49y) <- contains(?x362, ?x639), film_release_region(?x2958, ?x362), ?x2958 = 0b_5d *> conf = 0.32 ranks of expected_values: 400 EVAL 04jpl film_release_region! 03wh49y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 216.000 198.000 0.789 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5986-0ggl02 PRED entity: 0ggl02 PRED relation: award PRED expected values: 02681xs => 78 concepts (63 used for prediction) PRED predicted values (max 10 best out of 257): 03tcnt (0.79 #1207, 0.79 #3219, 0.74 #8447), 03qbnj (0.79 #1207, 0.79 #3219, 0.74 #8447), 03t5kl (0.50 #225, 0.33 #1433, 0.28 #2639), 01ckcd (0.50 #1138, 0.17 #9654, 0.17 #736), 02f77l (0.43 #1057, 0.18 #18507, 0.17 #9654), 02f5qb (0.42 #153, 0.36 #957, 0.28 #1361), 02v1m7 (0.36 #914, 0.25 #110, 0.19 #2926), 02f6yz (0.36 #1121, 0.17 #9654, 0.17 #719), 0c4z8 (0.33 #1279, 0.33 #71, 0.32 #2887), 01c9dd (0.33 #1519, 0.33 #311, 0.18 #18507) >> Best rule #1207 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 02r3zy; 05crg7; 0dvqq; 05vzw3; 0kr_t; 01kd57; 0dw4g; 016732; 0187x8; 06mj4; ... >> query: (?x1566, ?x1237) <- award_nominee(?x1282, ?x1566), award_winner(?x3103, ?x1566), award_winner(?x1237, ?x1566), ?x3103 = 03tcnt >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #8044 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 657 *> proper extension: 01pbxb; 0jz9f; 05ty4m; 0l8v5; 04wqr; 01wl38s; 0168cl; 06n7h7; 03ldxq; 03qd_; ... *> query: (?x1566, ?x724) <- award_nominee(?x2335, ?x1566), category(?x1566, ?x134), award_nominee(?x527, ?x2335), award(?x2335, ?x724) *> conf = 0.16 ranks of expected_values: 51 EVAL 0ggl02 award 02681xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 78.000 63.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5985-01h72l PRED entity: 01h72l PRED relation: producer_type PRED expected values: 0ckd1 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.76 #11, 0.75 #19, 0.74 #16) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0gxsh4; >> query: (?x2555, 0ckd1) <- genre(?x2555, ?x7685), tv_program(?x1483, ?x2555), genre(?x11377, ?x7685), ?x11377 = 025x1t >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01h72l producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.756 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #5984-01nn3m PRED entity: 01nn3m PRED relation: artist! PRED expected values: 011k11 => 114 concepts (83 used for prediction) PRED predicted values (max 10 best out of 112): 015_1q (0.34 #866, 0.34 #1007, 0.30 #1289), 011k1h (0.25 #10, 0.21 #151, 0.17 #433), 043g7l (0.25 #32, 0.17 #455, 0.14 #314), 017l96 (0.25 #19, 0.10 #3685, 0.09 #4672), 03rhqg (0.23 #1285, 0.22 #1426, 0.21 #862), 01clyr (0.21 #175, 0.11 #457, 0.10 #3700), 01cszh (0.17 #857, 0.14 #293, 0.12 #998), 0n85g (0.17 #487, 0.14 #346, 0.14 #205), 0181dw (0.17 #466, 0.14 #325, 0.14 #7093), 03mp8k (0.17 #491, 0.14 #350, 0.10 #1055) >> Best rule #866 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 02z4b_8; >> query: (?x12623, 015_1q) <- artists(?x671, ?x12623), artists(?x505, ?x12623), ?x505 = 03_d0, profession(?x12623, ?x220), role(?x12623, ?x315), ?x671 = 064t9 >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #459 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 01wp8w7; 02fn5r; 018phr; 01wvxw1; *> query: (?x12623, 011k11) <- artists(?x2823, ?x12623), gender(?x12623, ?x231), role(?x12623, ?x315), ?x2823 = 02qdgx *> conf = 0.11 ranks of expected_values: 18 EVAL 01nn3m artist! 011k11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 114.000 83.000 0.345 http://example.org/music/record_label/artist #5983-01vnt4 PRED entity: 01vnt4 PRED relation: role! PRED expected values: 03q5t => 52 concepts (40 used for prediction) PRED predicted values (max 10 best out of 115): 05r5c (0.88 #2898, 0.84 #3973, 0.83 #3851), 01v1d8 (0.88 #584, 0.86 #466, 0.86 #3594), 03qlv7 (0.88 #584, 0.86 #466, 0.85 #1671), 03q5t (0.88 #584, 0.86 #466, 0.85 #1671), 0239kh (0.88 #584, 0.86 #466, 0.85 #1671), 018vs (0.88 #2784, 0.86 #2544, 0.83 #3139), 02sgy (0.83 #2050, 0.82 #2417, 0.81 #2536), 0bxl5 (0.83 #2116, 0.81 #112, 0.78 #1872), 01vj9c (0.82 #3734, 0.77 #3614, 0.75 #1696), 01vdm0 (0.82 #1954, 0.80 #2922, 0.79 #3750) >> Best rule #2898 for best value: >> intensional similarity = 30 >> extensional distance = 23 >> proper extension: 03q5t; 0xzly; 02k84w; 01s0ps; 02fsn; 0bxl5; 0dwt5; 03ndd; 01w4c9; 0gkd1; >> query: (?x7449, 05r5c) <- role(?x2944, ?x7449), role(?x1267, ?x7449), role(?x227, ?x7449), role(?x7449, ?x1332), role(?x7449, ?x1750), ?x227 = 0342h, role(?x9413, ?x1332), role(?x214, ?x1332), performance_role(?x120, ?x1332), role(?x1332, ?x569), ?x9413 = 07m2y, ?x1267 = 07brj, role(?x11186, ?x7449), role(?x2944, ?x3296), role(?x2944, ?x2725), role(?x2944, ?x2206), role(?x2944, ?x1473), role(?x2944, ?x960), instrumentalists(?x2944, ?x7972), ?x2725 = 0l1589, group(?x2944, ?x1751), ?x2206 = 07gql, ?x1473 = 0g2dz, ?x960 = 04q7r, ?x3296 = 07_l6, ?x7972 = 0326tc, role(?x2620, ?x2944), ?x214 = 02pprs, ?x1750 = 02hnl, ?x2620 = 01kcd >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #584 for first EXPECTED value: *> intensional similarity = 35 *> extensional distance = 2 *> proper extension: 026t6; 01vj9c; *> query: (?x7449, ?x1433) <- role(?x3112, ?x7449), role(?x2944, ?x7449), role(?x1432, ?x7449), role(?x227, ?x7449), role(?x7449, ?x3161), role(?x7449, ?x1433), role(?x7449, ?x1332), role(?x7449, ?x74), role(?x7449, ?x315), ?x227 = 0342h, ?x1332 = 03qlv7, ?x1432 = 0395lw, role(?x4425, ?x74), role(?x1473, ?x74), role(?x868, ?x74), role(?x4913, ?x74), role(?x1166, ?x74), ?x3112 = 0mbct, ?x1473 = 0g2dz, ?x4425 = 0979zs, role(?x74, ?x432), ?x868 = 0dwvl, ?x2944 = 0l14j_, ?x3161 = 01v1d8, role(?x6626, ?x74), role(?x6399, ?x74), role(?x4873, ?x74), role(?x1818, ?x74), ?x1818 = 0770cd, ?x1166 = 05148p4, ?x6626 = 0b_j2, ?x4913 = 03ndd, ?x6399 = 0bvzp, languages(?x4873, ?x254), role(?x74, ?x4311) *> conf = 0.88 ranks of expected_values: 4 EVAL 01vnt4 role! 03q5t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 52.000 40.000 0.880 http://example.org/music/performance_role/track_performances./music/track_contribution/role #5982-01f5q5 PRED entity: 01f5q5 PRED relation: participant! PRED expected values: 09889g => 135 concepts (82 used for prediction) PRED predicted values (max 10 best out of 256): 09889g (0.81 #15326, 0.80 #16606, 0.80 #17245), 019n7x (0.20 #606, 0.03 #1244, 0.01 #15293), 026c1 (0.20 #150, 0.02 #788, 0.02 #16116), 022q32 (0.20 #602, 0.02 #1240, 0.01 #15289), 0c1j_ (0.20 #600, 0.02 #1238, 0.01 #4430), 067sqt (0.20 #621, 0.02 #1259), 0c7xjb (0.20 #343, 0.02 #981), 01svw8n (0.20 #278, 0.02 #916), 01p85y (0.20 #536, 0.01 #3728), 01pcdn (0.20 #341, 0.01 #3533) >> Best rule #15326 for best value: >> intensional similarity = 3 >> extensional distance = 349 >> proper extension: 02lnhv; 06w2sn5; 019g40; 01z0rcq; 026_dq6; >> query: (?x11851, ?x4960) <- nationality(?x11851, ?x94), participant(?x11851, ?x4960), place_of_birth(?x11851, ?x1131) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f5q5 participant! 09889g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 82.000 0.809 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #5981-0dvqq PRED entity: 0dvqq PRED relation: award PRED expected values: 02wh75 => 95 concepts (73 used for prediction) PRED predicted values (max 10 best out of 251): 01bgqh (0.86 #7010, 0.86 #7009, 0.86 #5060), 02f73p (0.86 #7010, 0.86 #7009, 0.86 #5060), 03qbh5 (0.54 #4867, 0.41 #5258, 0.38 #4478), 0c4z8 (0.50 #71, 0.46 #4741, 0.34 #6690), 01c9jp (0.47 #1739, 0.45 #2906, 0.40 #2517), 02wh75 (0.40 #1567, 0.30 #5061, 0.28 #2734), 01c99j (0.38 #4499, 0.38 #218, 0.30 #6448), 03qbnj (0.38 #223, 0.36 #4893, 0.31 #6842), 01c92g (0.36 #4765, 0.29 #6714, 0.27 #5156), 02f6ym (0.31 #4528, 0.25 #247, 0.23 #6477) >> Best rule #7010 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 0152cw; 0161c2; 03f0vvr; 01wf86y; 063t3j; >> query: (?x2395, ?x1565) <- award_winner(?x1565, ?x2395), artist(?x6672, ?x2395), category(?x2395, ?x134) >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #1567 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 016fmf; 0d193h; 013w8y; 016l09; 0134pk; *> query: (?x2395, 02wh75) <- group(?x227, ?x2395), award(?x2395, ?x8994), ?x8994 = 02f6yz, award_winner(?x139, ?x2395) *> conf = 0.40 ranks of expected_values: 6 EVAL 0dvqq award 02wh75 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 95.000 73.000 0.861 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5980-0b_6s7 PRED entity: 0b_6s7 PRED relation: team PRED expected values: 02pyyld 026dqjm => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 18): 03by7wc (0.86 #130, 0.82 #166, 0.80 #148), 02py8_w (0.78 #111, 0.71 #129, 0.65 #165), 03y9p40 (0.71 #141, 0.71 #132, 0.68 #177), 02plv57 (0.67 #100, 0.65 #154, 0.64 #136), 026dqjm (0.60 #72, 0.60 #63, 0.56 #117), 02pyyld (0.60 #80, 0.60 #71, 0.56 #116), 03d555l (0.56 #101, 0.54 #201, 0.47 #146), 03d5m8w (0.56 #106, 0.54 #201, 0.43 #265), 02r2qt7 (0.54 #201, 0.44 #104, 0.43 #265), 01jvgt (0.05 #549, 0.04 #322, 0.03 #191) >> Best rule #130 for best value: >> intensional similarity = 12 >> extensional distance = 12 >> proper extension: 0b_6v_; >> query: (?x8992, 03by7wc) <- team(?x8992, ?x9147), team(?x8992, ?x8528), locations(?x8992, ?x3786), source(?x3786, ?x958), colors(?x9147, ?x663), team(?x13209, ?x9147), team(?x9146, ?x9147), ?x13209 = 0b_734, ?x8528 = 091tgz, location(?x1852, ?x3786), ?x9146 = 0b_6qj, ?x958 = 0jbk9 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #72 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 3 *> proper extension: 0b_6qj; *> query: (?x8992, 026dqjm) <- team(?x8992, ?x9576), team(?x8992, ?x9147), team(?x8992, ?x5551), team(?x8992, ?x3798), ?x9147 = 0263cyj, locations(?x8992, ?x5267), position(?x3798, ?x1579), team(?x12451, ?x3798), ?x9576 = 02qk2d5, ?x12451 = 0b_6xf, team(?x10441, ?x5551), ?x10441 = 0b_71r, ?x5267 = 0d9jr *> conf = 0.60 ranks of expected_values: 5, 6 EVAL 0b_6s7 team 026dqjm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 70.000 70.000 0.857 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_6s7 team 02pyyld CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 70.000 70.000 0.857 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #5979-081jbk PRED entity: 081jbk PRED relation: location PRED expected values: 0f2w0 => 149 concepts (72 used for prediction) PRED predicted values (max 10 best out of 224): 030qb3t (0.26 #24192, 0.25 #48304, 0.24 #32228), 01cx_ (0.25 #163, 0.20 #966, 0.11 #1770), 01531 (0.25 #158, 0.20 #961, 0.11 #1765), 02_286 (0.24 #24146, 0.22 #48258, 0.20 #49063), 059rby (0.17 #7245, 0.11 #15282, 0.08 #2426), 07b_l (0.11 #1794, 0.09 #8219, 0.08 #2597), 05tbn (0.11 #1795, 0.08 #2598, 0.03 #11433), 04jpl (0.09 #7246, 0.09 #48238, 0.08 #2427), 0v9qg (0.09 #7439, 0.08 #2620, 0.07 #3424), 0r00l (0.09 #9441, 0.07 #11047, 0.07 #3819) >> Best rule #24192 for best value: >> intensional similarity = 4 >> extensional distance = 112 >> proper extension: 0c8hct; >> query: (?x5350, 030qb3t) <- profession(?x5350, ?x1383), ?x1383 = 0np9r, location(?x5350, ?x4733), locations(?x4803, ?x4733) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #15360 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 02vntj; *> query: (?x5350, 0f2w0) <- profession(?x5350, ?x1383), language(?x5350, ?x254), type_of_union(?x5350, ?x566), location(?x5350, ?x4733) *> conf = 0.02 ranks of expected_values: 103 EVAL 081jbk location 0f2w0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 149.000 72.000 0.263 http://example.org/people/person/places_lived./people/place_lived/location #5978-0c9t0y PRED entity: 0c9t0y PRED relation: language PRED expected values: 02h40lc => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 31): 02h40lc (0.91 #474, 0.91 #356, 0.90 #297), 064_8sq (0.19 #140, 0.15 #851, 0.15 #612), 06nm1 (0.14 #129, 0.11 #542, 0.11 #661), 04h9h (0.14 #161, 0.06 #220, 0.03 #397), 03_9r (0.12 #187, 0.06 #1077, 0.05 #1732), 02bjrlw (0.10 #119, 0.07 #355, 0.06 #473), 05zjd (0.10 #144, 0.02 #380, 0.01 #1630), 04306rv (0.09 #300, 0.09 #241, 0.08 #1369), 06b_j (0.06 #613, 0.06 #1149, 0.06 #734), 0653m (0.05 #130, 0.04 #662, 0.03 #1079) >> Best rule #474 for best value: >> intensional similarity = 4 >> extensional distance = 283 >> proper extension: 03m8y5; 02gqm3; >> query: (?x7187, 02h40lc) <- country(?x7187, ?x94), ?x94 = 09c7w0, genre(?x7187, ?x571), cinematography(?x7187, ?x7740) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c9t0y language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.912 http://example.org/film/film/language #5977-0f2wj PRED entity: 0f2wj PRED relation: film_release_region! PRED expected values: 06fcqw => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1454): 08hmch (0.46 #70199, 0.37 #80775, 0.37 #90029), 0bpm4yw (0.44 #70626, 0.38 #81202, 0.37 #90456), 02vxq9m (0.43 #70097, 0.35 #80673, 0.34 #89927), 0fpgp26 (0.43 #71222, 0.38 #81798, 0.37 #91052), 047vnkj (0.42 #70779, 0.34 #81355, 0.33 #90609), 04f52jw (0.41 #70412, 0.32 #90242, 0.32 #80988), 0gd0c7x (0.40 #70321, 0.35 #80897, 0.33 #90151), 017jd9 (0.40 #70672, 0.34 #81248, 0.34 #90502), 01fmys (0.40 #70326, 0.33 #55779, 0.32 #43878), 0gkz15s (0.40 #70168, 0.32 #80744, 0.31 #89998) >> Best rule #70199 for best value: >> intensional similarity = 2 >> extensional distance = 132 >> proper extension: 0jgd; 0154j; 0d0vqn; 0j1z8; 04gzd; 03_r3; 03rt9; 05qhw; 06npd; 03gj2; ... >> query: (?x682, 08hmch) <- film_release_region(?x1219, ?x682), contains(?x682, ?x5522) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #70915 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 132 *> proper extension: 0jgd; 0154j; 0d0vqn; 0j1z8; 04gzd; 03_r3; 03rt9; 05qhw; 06npd; 03gj2; ... *> query: (?x682, 06fcqw) <- film_release_region(?x1219, ?x682), contains(?x682, ?x5522) *> conf = 0.33 ranks of expected_values: 50 EVAL 0f2wj film_release_region! 06fcqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 126.000 126.000 0.455 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5976-0csdzz PRED entity: 0csdzz PRED relation: award PRED expected values: 0fhpv4 => 127 concepts (86 used for prediction) PRED predicted values (max 10 best out of 316): 0gqz2 (0.46 #6899, 0.43 #2486, 0.38 #4492), 054ks3 (0.42 #6958, 0.29 #4551, 0.28 #2545), 0c4z8 (0.41 #6890, 0.18 #4483, 0.17 #5285), 0f_nbyh (0.40 #10, 0.33 #411, 0.16 #28479), 040njc (0.40 #8, 0.17 #409, 0.16 #1612), 0gq9h (0.40 #77, 0.17 #478, 0.16 #1681), 09sb52 (0.37 #2046, 0.34 #9266, 0.33 #1645), 0drtkx (0.33 #696, 0.14 #19653, 0.13 #32093), 01l29r (0.33 #566, 0.04 #1769, 0.03 #4978), 01lj_c (0.33 #698, 0.02 #9121, 0.01 #9522) >> Best rule #6899 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 01vw87c; 06cc_1; 01vvycq; 02r4qs; 01sbf2; 015_30; 09mq4m; 052gzr; 05pq9; 018pj3; ... >> query: (?x10634, 0gqz2) <- award_winner(?x2379, ?x10634), award(?x6011, ?x2379), ?x6011 = 02zft0 >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #2599 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 025vry; 01vsl3_; 03bnv; 018gqj; 02mz_6; 01vttb9; 02rf51g; *> query: (?x10634, 0fhpv4) <- award_winner(?x1854, ?x10634), award_winner(?x4050, ?x10634), music(?x1318, ?x10634) *> conf = 0.23 ranks of expected_values: 14 EVAL 0csdzz award 0fhpv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 127.000 86.000 0.465 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5975-07_m9_ PRED entity: 07_m9_ PRED relation: entity_involved! PRED expected values: 02kxg_ => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 76): 0chhs (0.60 #445, 0.57 #701, 0.44 #829), 02h2z_ (0.40 #434, 0.33 #242, 0.29 #690), 03w6sj (0.40 #426, 0.33 #234, 0.29 #682), 07_nf (0.33 #80, 0.11 #848, 0.11 #2385), 01h6pn (0.29 #651, 0.22 #779, 0.08 #3789), 048n7 (0.25 #1558, 0.22 #789, 0.16 #2390), 0cm2xh (0.22 #842, 0.20 #522, 0.14 #650), 0cwt70 (0.22 #873, 0.20 #553, 0.14 #681), 01y998 (0.22 #850, 0.20 #1170, 0.11 #978), 03jqfx (0.22 #3805, 0.19 #4189, 0.14 #731) >> Best rule #445 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 018q7; >> query: (?x4736, 0chhs) <- profession(?x4736, ?x353), nationality(?x4736, ?x1264), entity_involved(?x12031, ?x4736), ?x12031 = 02kxjx >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #546 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 01llxp; *> query: (?x4736, 02kxg_) <- gender(?x4736, ?x231), nationality(?x4736, ?x1264), entity_involved(?x5352, ?x4736), ?x1264 = 0345h *> conf = 0.20 ranks of expected_values: 14 EVAL 07_m9_ entity_involved! 02kxg_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 189.000 189.000 0.600 http://example.org/base/culturalevent/event/entity_involved #5974-044_7j PRED entity: 044_7j PRED relation: actor! PRED expected values: 03gyvwg => 88 concepts (52 used for prediction) PRED predicted values (max 10 best out of 29): 0b60sq (0.29 #2, 0.26 #118, 0.20 #31), 02q3fdr (0.29 #130, 0.21 #101, 0.20 #43), 016ztl (0.27 #73, 0.22 #189, 0.21 #102), 02gs6r (0.26 #127, 0.23 #69, 0.18 #156), 0dh8v4 (0.20 #41, 0.14 #12, 0.08 #215), 076xkdz (0.20 #50, 0.14 #21, 0.06 #224), 02z9hqn (0.20 #32, 0.14 #3, 0.06 #206), 07ghv5 (0.14 #16, 0.10 #45, 0.08 #219), 02r9p0c (0.14 #19, 0.10 #48, 0.06 #222), 0cks1m (0.14 #13, 0.03 #158, 0.02 #216) >> Best rule #2 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 07cn2c; >> query: (?x8629, 0b60sq) <- profession(?x8629, ?x1032), profession(?x8629, ?x987), nationality(?x8629, ?x94), ?x1032 = 02hrh1q, actor(?x2508, ?x8629), ?x987 = 0dxtg >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #57 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 02hblj; *> query: (?x8629, 03gyvwg) <- profession(?x8629, ?x987), actor(?x2508, ?x8629), place_of_birth(?x8629, ?x242), ?x987 = 0dxtg *> conf = 0.10 ranks of expected_values: 16 EVAL 044_7j actor! 03gyvwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 88.000 52.000 0.286 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #5973-05hks PRED entity: 05hks PRED relation: jurisdiction_of_office PRED expected values: 0cdbq => 220 concepts (140 used for prediction) PRED predicted values (max 10 best out of 44): 06bnz (0.47 #865, 0.44 #1177, 0.41 #1021), 09c7w0 (0.36 #355, 0.33 #971, 0.33 #305), 05vz3zq (0.25 #35, 0.08 #745, 0.07 #389), 07ssc (0.17 #515, 0.14 #160, 0.12 #1187), 05fjf (0.14 #193, 0.11 #346, 0.11 #293), 02xry (0.11 #329, 0.07 #379, 0.04 #685), 07t21 (0.10 #813, 0.06 #2427, 0.04 #4187), 03v0t (0.07 #385, 0.06 #845, 0.05 #1157), 059rby (0.07 #411, 0.06 #976, 0.03 #1447), 0d05w3 (0.07 #378, 0.04 #684, 0.04 #734) >> Best rule #865 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 05wh0sh; 0424m; 012bk; 08_hns; >> query: (?x10328, ?x1603) <- type_of_union(?x10328, ?x566), nationality(?x10328, ?x1603), profession(?x10328, ?x5805), entity_involved(?x5503, ?x10328) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #2323 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 120 *> proper extension: 09c7w0; 0jgd; 07ssc; 06mzp; 03gj2; 01z215; 02vzc; 06mkj; 03pn9; 03bxbql; ... *> query: (?x10328, ?x87) <- entity_involved(?x5503, ?x10328), combatants(?x5503, ?x1023), combatants(?x5503, ?x87), film_release_region(?x66, ?x1023) *> conf = 0.01 ranks of expected_values: 41 EVAL 05hks jurisdiction_of_office 0cdbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 220.000 140.000 0.474 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #5972-01yfj PRED entity: 01yfj PRED relation: country PRED expected values: 0f8l9c 05b4w => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 318): 0f8l9c (0.90 #1881, 0.89 #2290, 0.88 #2694), 03rjj (0.89 #2275, 0.88 #2679, 0.88 #2478), 07t21 (0.81 #1290, 0.78 #664, 0.75 #1081), 0b90_r (0.81 #1255, 0.75 #1046, 0.75 #412), 03_3d (0.81 #2276, 0.80 #825, 0.77 #3097), 05qhw (0.81 #2285, 0.77 #3106, 0.77 #2689), 0h7x (0.80 #825, 0.78 #1491, 0.78 #662), 0chghy (0.79 #829, 0.77 #3101, 0.76 #3505), 0163v (0.78 #683, 0.75 #1100, 0.73 #607), 05b4w (0.77 #1721, 0.75 #1316, 0.73 #607) >> Best rule #1881 for best value: >> intensional similarity = 80 >> extensional distance = 27 >> proper extension: 0d1tm; 064vjs; 07jbh; 06zgc; >> query: (?x12943, 0f8l9c) <- country(?x12943, ?x1603), country(?x12943, ?x1264), country(?x12943, ?x774), country(?x12943, ?x304), country(?x12943, ?x279), ?x1264 = 0345h, ?x279 = 0d060g, ?x1603 = 06bnz, olympics(?x774, ?x4255), olympics(?x774, ?x1277), olympics(?x774, ?x391), film_release_region(?x9902, ?x774), film_release_region(?x7700, ?x774), film_release_region(?x7393, ?x774), film_release_region(?x6556, ?x774), film_release_region(?x6376, ?x774), film_release_region(?x6321, ?x774), film_release_region(?x6216, ?x774), film_release_region(?x6078, ?x774), film_release_region(?x5992, ?x774), film_release_region(?x5578, ?x774), film_release_region(?x5271, ?x774), film_release_region(?x4707, ?x774), film_release_region(?x4024, ?x774), film_release_region(?x3088, ?x774), film_release_region(?x2746, ?x774), film_release_region(?x2655, ?x774), film_release_region(?x1463, ?x774), film_release_region(?x1386, ?x774), film_release_region(?x1370, ?x774), film_release_region(?x1364, ?x774), film_release_region(?x1108, ?x774), film_release_region(?x385, ?x774), film_release_region(?x141, ?x774), ?x1370 = 0gmcwlb, organization(?x774, ?x127), ?x6216 = 06fcqw, ?x3088 = 06w839_, ?x6078 = 04pk1f, ?x141 = 0gtsx8c, ?x2655 = 0fpmrm3, ?x1386 = 0dtfn, ?x6556 = 05dss7, ?x7700 = 0cp08zg, ?x1108 = 0jjy0, country(?x779, ?x774), ?x4255 = 0lgxj, ?x5992 = 0g5q34q, ?x9902 = 0j8f09z, nationality(?x9308, ?x774), ?x5578 = 0ddj0x, film_release_distribution_medium(?x6321, ?x81), partially_contains(?x774, ?x8154), teams(?x774, ?x11564), ?x5271 = 047vnkj, film_production_design_by(?x4024, ?x9067), nominated_for(?x1008, ?x6321), language(?x6321, ?x254), ?x7393 = 02vz6dn, ?x779 = 096f8, ?x1277 = 0swbd, ?x385 = 0ds3t5x, ?x2746 = 04f52jw, film(?x2353, ?x6321), film_release_region(?x6321, ?x2152), film_release_region(?x6321, ?x1558), ?x2152 = 06mkj, genre(?x6321, ?x53), ?x1008 = 05zvq6g, ?x6376 = 01f85k, ?x304 = 0d0vqn, ?x4707 = 02xbyr, countries_spoken_in(?x90, ?x774), ?x1463 = 0gtvrv3, ?x1364 = 047msdk, influenced_by(?x9308, ?x2240), ?x53 = 07s9rl0, ?x391 = 0l6vl, currency(?x6321, ?x170), ?x1558 = 01mjq >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 01yfj country 05b4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 20.000 20.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01yfj country 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 20.000 20.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #5971-0c58k PRED entity: 0c58k PRED relation: people PRED expected values: 04093 01kkx2 => 87 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1535): 0gyy0 (0.40 #8567, 0.33 #1743, 0.25 #22893), 024qwq (0.33 #11358, 0.33 #1800, 0.29 #13405), 01938t (0.33 #11885, 0.33 #10521, 0.29 #12568), 053yx (0.33 #10337, 0.29 #13066, 0.29 #12384), 0b22w (0.33 #1856, 0.29 #13461, 0.29 #12779), 0cgbf (0.33 #1658, 0.29 #13263, 0.22 #10922), 02dth1 (0.33 #10385, 0.25 #4233, 0.23 #26753), 03f3_p3 (0.33 #340, 0.25 #4428, 0.22 #15357), 01fdc0 (0.33 #116, 0.25 #4204, 0.18 #29336), 0bdt8 (0.33 #270, 0.25 #4358, 0.13 #11604) >> Best rule #8567 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0hg45; >> query: (?x8523, 0gyy0) <- symptom_of(?x4905, ?x8523), ?x4905 = 01j6t0, risk_factors(?x8523, ?x13662), risk_factors(?x8523, ?x8524), risk_factors(?x8523, ?x8023), ?x8023 = 0jpmt, ?x8524 = 01hbgs, risk_factors(?x13891, ?x13662), risk_factors(?x6197, ?x13662), ?x13891 = 0146bp, ?x6197 = 05mdx >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1966 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 0gk4g; *> query: (?x8523, 01kkx2) <- people(?x8523, ?x6061), people(?x8523, ?x5048), risk_factors(?x14376, ?x8523), artists(?x505, ?x5048), instrumentalists(?x227, ?x5048), ?x6061 = 0432b, risk_factors(?x8523, ?x8023), symptom_of(?x10717, ?x14376), people(?x2510, ?x5048) *> conf = 0.33 ranks of expected_values: 66, 762 EVAL 0c58k people 01kkx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 87.000 61.000 0.400 http://example.org/people/cause_of_death/people EVAL 0c58k people 04093 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 87.000 61.000 0.400 http://example.org/people/cause_of_death/people #5970-02vqsll PRED entity: 02vqsll PRED relation: production_companies PRED expected values: 03sb38 => 66 concepts (44 used for prediction) PRED predicted values (max 10 best out of 99): 03sb38 (0.55 #289, 0.16 #527, 0.16 #448), 016tw3 (0.48 #715, 0.41 #878, 0.38 #330), 025hwq (0.17 #214, 0.04 #373, 0.02 #1652), 04wvhz (0.13 #717, 0.11 #879, 0.10 #714), 0g2lq (0.13 #717, 0.11 #879, 0.10 #714), 02q42j_ (0.13 #717, 0.11 #879, 0.10 #714), 0b13g7 (0.13 #717, 0.11 #879, 0.10 #714), 04954 (0.13 #717, 0.11 #879, 0.10 #714), 0150t6 (0.13 #717, 0.11 #879, 0.10 #714), 0ksrf8 (0.13 #717, 0.11 #879, 0.10 #714) >> Best rule #289 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 027tbrc; >> query: (?x2989, 03sb38) <- nominated_for(?x5973, ?x2989), nominated_for(?x143, ?x2989), ?x5973 = 02q42j_ >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vqsll production_companies 03sb38 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 44.000 0.545 http://example.org/film/film/production_companies #5969-07_jd PRED entity: 07_jd PRED relation: diet! PRED expected values: 01w60_p 0lx2l 016ypb 01x53m 01wttr1 => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1863): 01w7nww (0.33 #7, 0.05 #25, 0.03 #20), 01hgwkr (0.33 #17, 0.05 #27, 0.03 #29), 01gbbz (0.33 #6, 0.05 #27, 0.02 #26), 012ycy (0.33 #18, 0.05 #27), 03d_w3h (0.33 #5, 0.03 #20, 0.02 #24), 01x0yrt (0.33 #15, 0.03 #29, 0.02 #35), 01vvycq (0.33 #2, 0.03 #29, 0.02 #35), 0fpj9pm (0.33 #13, 0.03 #29), 09l3p (0.33 #8, 0.02 #24, 0.02 #26), 04x1_w (0.33 #14, 0.02 #24) >> Best rule #7 for best value: >> intensional similarity = 87 >> extensional distance = 1 >> proper extension: 07_hy; >> query: (?x3130, 01w7nww) <- diet(?x12102, ?x3130), diet(?x9545, ?x3130), diet(?x9105, ?x3130), diet(?x8020, ?x3130), diet(?x6916, ?x3130), diet(?x4960, ?x3130), diet(?x4645, ?x3130), diet(?x4247, ?x3130), diet(?x3868, ?x3130), diet(?x3633, ?x3130), diet(?x3503, ?x3130), diet(?x3321, ?x3130), diet(?x2817, ?x3130), diet(?x1004, ?x3130), nationality(?x3633, ?x94), award_nominee(?x1660, ?x12102), type_of_union(?x3633, ?x566), peers(?x702, ?x4960), award(?x4960, ?x1827), award_nominee(?x4960, ?x3997), influenced_by(?x4960, ?x2426), gender(?x12102, ?x514), instrumentalists(?x227, ?x4960), participant(?x513, ?x9545), ?x227 = 0342h, award(?x3503, ?x247), gender(?x3633, ?x231), film(?x6916, ?x6450), film(?x6916, ?x2893), location(?x4645, ?x9305), award(?x9545, ?x757), artist(?x12467, ?x8020), award_nominee(?x6916, ?x57), category(?x4645, ?x134), origin(?x12102, ?x2277), ceremony(?x1827, ?x5766), people(?x4659, ?x3321), ?x5766 = 013b2h, location_of_ceremony(?x4960, ?x47), currency(?x3503, ?x170), group(?x3503, ?x5493), participant(?x2499, ?x9545), artists(?x2480, ?x3868), profession(?x3321, ?x131), origin(?x5476, ?x9305), award_winner(?x1827, ?x4574), religion(?x9105, ?x2694), film_release_region(?x2893, ?x1453), film_release_region(?x2893, ?x550), film_release_region(?x2893, ?x87), participant(?x8020, ?x1093), actor(?x293, ?x3633), ?x87 = 05r4w, currency(?x122, ?x170), award_winner(?x247, ?x3403), artist(?x3240, ?x3503), currency(?x54, ?x170), category_of(?x1827, ?x2421), currency(?x99, ?x170), currency(?x126, ?x170), award(?x6380, ?x247), artists(?x671, ?x8020), award_winner(?x4247, ?x262), award_nominee(?x123, ?x4247), location(?x3633, ?x13739), currency(?x266, ?x170), ?x1004 = 01vv7sc, ?x4574 = 02dbp7, ?x3403 = 02qwg, film(?x3633, ?x9646), ?x550 = 05v8c, ?x6380 = 02s2wq, currency(?x1675, ?x170), instrumentalists(?x228, ?x3321), award_winner(?x2319, ?x4960), award_winner(?x10618, ?x3868), ?x1660 = 012x4t, artists(?x3928, ?x12102), ?x1453 = 06qd3, award_winner(?x4517, ?x3633), profession(?x8020, ?x1383), people(?x4195, ?x4247), award(?x4247, ?x704), award(?x2817, ?x678), ?x6450 = 0bz3jx, place_of_birth(?x2817, ?x2850), celebrity(?x2817, ?x6443) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 88 *> extensional distance = 1 *> proper extension: 07_hy; *> query: (?x3130, ?x65) <- diet(?x12102, ?x3130), diet(?x9545, ?x3130), diet(?x9105, ?x3130), diet(?x8020, ?x3130), diet(?x6916, ?x3130), diet(?x4960, ?x3130), diet(?x4645, ?x3130), diet(?x4247, ?x3130), diet(?x3868, ?x3130), diet(?x3633, ?x3130), diet(?x3503, ?x3130), diet(?x3321, ?x3130), diet(?x2817, ?x3130), diet(?x1004, ?x3130), nationality(?x3633, ?x94), award_nominee(?x1660, ?x12102), type_of_union(?x3633, ?x566), peers(?x702, ?x4960), award(?x4960, ?x1827), award_nominee(?x4960, ?x3997), influenced_by(?x4960, ?x2426), gender(?x12102, ?x514), instrumentalists(?x227, ?x4960), participant(?x513, ?x9545), ?x227 = 0342h, award(?x3503, ?x247), gender(?x3633, ?x231), film(?x6916, ?x6450), film(?x6916, ?x2893), location(?x4645, ?x9305), award(?x9545, ?x757), artist(?x12467, ?x8020), award_nominee(?x6916, ?x57), category(?x4645, ?x134), origin(?x12102, ?x2277), ceremony(?x1827, ?x5766), people(?x4659, ?x3321), ?x5766 = 013b2h, location_of_ceremony(?x4960, ?x47), currency(?x3503, ?x170), group(?x3503, ?x5493), participant(?x2499, ?x9545), artists(?x2480, ?x3868), profession(?x3321, ?x131), origin(?x5476, ?x9305), award_winner(?x1827, ?x4574), religion(?x9105, ?x2694), film_release_region(?x2893, ?x1453), film_release_region(?x2893, ?x550), film_release_region(?x2893, ?x87), participant(?x8020, ?x1093), actor(?x293, ?x3633), ?x87 = 05r4w, currency(?x122, ?x170), award_winner(?x247, ?x3403), artist(?x3240, ?x3503), currency(?x54, ?x170), category_of(?x1827, ?x2421), currency(?x65, ?x170), currency(?x99, ?x170), currency(?x126, ?x170), award(?x6380, ?x247), artists(?x671, ?x8020), award_winner(?x4247, ?x262), award_nominee(?x123, ?x4247), location(?x3633, ?x13739), currency(?x266, ?x170), ?x1004 = 01vv7sc, ?x4574 = 02dbp7, ?x3403 = 02qwg, film(?x3633, ?x9646), ?x550 = 05v8c, ?x6380 = 02s2wq, currency(?x1675, ?x170), instrumentalists(?x228, ?x3321), award_winner(?x2319, ?x4960), award_winner(?x10618, ?x3868), ?x1660 = 012x4t, artists(?x3928, ?x12102), ?x1453 = 06qd3, award_winner(?x4517, ?x3633), profession(?x8020, ?x1383), people(?x4195, ?x4247), award(?x4247, ?x704), award(?x2817, ?x678), ?x6450 = 0bz3jx, place_of_birth(?x2817, ?x2850), celebrity(?x2817, ?x6443) *> conf = 0.05 ranks of expected_values: 230, 796 EVAL 07_jd diet! 01wttr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 28.000 28.000 0.333 http://example.org/base/eating/practicer_of_diet/diet EVAL 07_jd diet! 01x53m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 28.000 28.000 0.333 http://example.org/base/eating/practicer_of_diet/diet EVAL 07_jd diet! 016ypb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 28.000 28.000 0.333 http://example.org/base/eating/practicer_of_diet/diet EVAL 07_jd diet! 0lx2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 28.000 28.000 0.333 http://example.org/base/eating/practicer_of_diet/diet EVAL 07_jd diet! 01w60_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 28.000 28.000 0.333 http://example.org/base/eating/practicer_of_diet/diet #5968-01v3vp PRED entity: 01v3vp PRED relation: award PRED expected values: 09qv3c => 99 concepts (90 used for prediction) PRED predicted values (max 10 best out of 256): 05pcn59 (0.40 #82, 0.29 #487, 0.15 #2107), 05p09zm (0.40 #124, 0.29 #529, 0.06 #19971), 05b4l5x (0.40 #6, 0.29 #411, 0.05 #12563), 02ppm4q (0.33 #967, 0.30 #1372, 0.09 #2182), 0bdwft (0.33 #879, 0.30 #1284, 0.09 #2094), 0ck27z (0.29 #7788, 0.28 #8194, 0.26 #8599), 09sb52 (0.26 #12598, 0.25 #19888, 0.24 #13003), 0gqyl (0.22 #916, 0.20 #1321, 0.09 #2131), 09td7p (0.22 #931, 0.20 #1336, 0.09 #2146), 0bfvw2 (0.22 #825, 0.20 #1230, 0.05 #8927) >> Best rule #82 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 019pm_; 01x0sy; 01gbn6; >> query: (?x4109, 05pcn59) <- profession(?x4109, ?x563), film(?x4109, ?x6140), film(?x4109, ?x5353), ?x6140 = 0241y7, featured_film_locations(?x5353, ?x739) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #8101 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 372 *> proper extension: 06gh0t; 01wk7ql; 03rgvr; *> query: (?x4109, ?x870) <- profession(?x4109, ?x563), actor(?x7551, ?x4109), place_of_birth(?x4109, ?x3964), award(?x7551, ?x870) *> conf = 0.14 ranks of expected_values: 29 EVAL 01v3vp award 09qv3c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 99.000 90.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5967-05y5fw PRED entity: 05y5fw PRED relation: profession PRED expected values: 0dxtg => 106 concepts (72 used for prediction) PRED predicted values (max 10 best out of 75): 0dxtg (0.84 #2365, 0.83 #895, 0.83 #1042), 02hrh1q (0.82 #2513, 0.82 #2660, 0.79 #2954), 01d_h8 (0.66 #2358, 0.65 #1917, 0.65 #1035), 02jknp (0.60 #1036, 0.57 #2359, 0.56 #1918), 02krf9 (0.30 #1496, 0.30 #1790, 0.29 #761), 09jwl (0.23 #165, 0.21 #6487, 0.16 #3253), 018gz8 (0.19 #3398, 0.18 #898, 0.17 #1339), 02hv44_ (0.15 #56, 0.12 #1232, 0.10 #2261), 0nbcg (0.15 #6499, 0.12 #7088, 0.12 #6941), 0dz3r (0.15 #6471, 0.12 #149, 0.11 #3237) >> Best rule #2365 for best value: >> intensional similarity = 3 >> extensional distance = 279 >> proper extension: 05drq5; 03kpvp; 0884hk; >> query: (?x5033, 0dxtg) <- nominated_for(?x5033, ?x4534), written_by(?x1035, ?x5033), profession(?x5033, ?x353) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05y5fw profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 72.000 0.840 http://example.org/people/person/profession #5966-01hbq0 PRED entity: 01hbq0 PRED relation: nationality PRED expected values: 09c7w0 => 106 concepts (104 used for prediction) PRED predicted values (max 10 best out of 39): 09c7w0 (0.89 #701, 0.88 #201, 0.87 #501), 02jx1 (0.40 #9136, 0.14 #1434, 0.14 #1735), 07ssc (0.40 #9136, 0.13 #1416, 0.12 #1516), 03_3d (0.40 #9136, 0.07 #1407, 0.03 #4015), 0d060g (0.40 #9136, 0.07 #307, 0.05 #807), 0f8l9c (0.40 #9136, 0.03 #7324, 0.03 #4512), 03rt9 (0.40 #9136, 0.02 #2017, 0.02 #1414), 03rjj (0.40 #9136, 0.02 #305, 0.02 #3212), 0345h (0.40 #9136, 0.02 #1835, 0.02 #4241), 0m2fr (0.27 #9034) >> Best rule #701 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 026_dq6; >> query: (?x13084, 09c7w0) <- profession(?x13084, ?x1032), student(?x735, ?x13084), ?x735 = 065y4w7 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hbq0 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 104.000 0.887 http://example.org/people/person/nationality #5965-03902 PRED entity: 03902 PRED relation: month PRED expected values: 06vkl 05cw8 => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 2): 05cw8 (0.89 #22, 0.88 #42, 0.88 #18), 06vkl (0.83 #13, 0.82 #33, 0.82 #29) >> Best rule #22 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 05ywg; 06t2t; 03hrz; 0cv3w; 0f04v; >> query: (?x10610, 05cw8) <- month(?x10610, ?x9905), month(?x10610, ?x7298), contains(?x10610, ?x4031), ?x7298 = 04wzr, ?x9905 = 028kb >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03902 month 05cw8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.886 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 03902 month 06vkl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.886 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #5964-0kf9p PRED entity: 0kf9p PRED relation: state PRED expected values: 081yw => 123 concepts (116 used for prediction) PRED predicted values (max 10 best out of 62): 081yw (0.33 #3163, 0.31 #3077, 0.29 #5222), 0mlvc (0.26 #5991, 0.21 #4363, 0.21 #6247), 01n7q (0.22 #354, 0.18 #14, 0.16 #184), 07h34 (0.12 #41, 0.03 #723, 0.03 #2091), 059rby (0.10 #87, 0.10 #342, 0.08 #513), 07b_l (0.10 #124, 0.08 #209, 0.07 #464), 02xry (0.06 #27, 0.05 #2675, 0.05 #879), 04ych (0.06 #12, 0.03 #779, 0.02 #2660), 081mh (0.06 #31, 0.02 #1483, 0.01 #3622), 0j95 (0.06 #68, 0.02 #493, 0.02 #664) >> Best rule #3163 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 031sn; >> query: (?x11367, ?x4600) <- county_seat(?x11366, ?x11367), source(?x11366, ?x958), contains(?x4600, ?x11366) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kf9p state 081yw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 116.000 0.326 http://example.org/base/biblioness/bibs_location/state #5963-0f4_l PRED entity: 0f4_l PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #79, 0.81 #174, 0.81 #190), 07c52 (0.05 #23, 0.03 #208, 0.03 #108), 07z4p (0.05 #25, 0.03 #50, 0.03 #45), 02nxhr (0.03 #207, 0.03 #212, 0.03 #196) >> Best rule #79 for best value: >> intensional similarity = 3 >> extensional distance = 561 >> proper extension: 03s6l2; 02z3r8t; 03ckwzc; 04kkz8; 03t97y; 07sc6nw; 07g_0c; 03twd6; 02847m9; 0c8tkt; ... >> query: (?x2177, 029j_) <- film(?x368, ?x2177), titles(?x53, ?x2177), featured_film_locations(?x2177, ?x1523) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f4_l film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.842 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #5962-0d9jr PRED entity: 0d9jr PRED relation: teams PRED expected values: 06wpc 0jmnl => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 237): 0cqt41 (0.08 #746, 0.06 #1104, 0.05 #1820), 0hmtk (0.08 #1032, 0.06 #1390, 0.05 #2106), 05g76 (0.08 #751, 0.06 #1109, 0.05 #1825), 0jm3v (0.08 #729, 0.06 #1087, 0.05 #1803), 023fb (0.08 #845, 0.02 #10511, 0.01 #12659), 0ckf6 (0.06 #1392, 0.05 #2108, 0.04 #2466), 01z1r (0.06 #1224, 0.05 #1940, 0.04 #2298), 02_lt (0.06 #1197, 0.05 #1913, 0.04 #2271), 0jmk7 (0.06 #1376, 0.05 #2092, 0.04 #2450), 0jnq8 (0.06 #1302, 0.05 #2018, 0.04 #2376) >> Best rule #746 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0bvqq; >> query: (?x5267, 0cqt41) <- contains(?x4600, ?x5267), location_of_ceremony(?x2012, ?x5267), citytown(?x9309, ?x4600) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d9jr teams 0jmnl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.077 http://example.org/sports/sports_team_location/teams EVAL 0d9jr teams 06wpc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.077 http://example.org/sports/sports_team_location/teams #5961-0b_j2 PRED entity: 0b_j2 PRED relation: type_of_union PRED expected values: 04ztj => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.74 #9, 0.70 #185, 0.69 #213), 01g63y (0.42 #249, 0.18 #6, 0.13 #54), 0jgjn (0.05 #4, 0.01 #12, 0.01 #16) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 0443c; >> query: (?x6626, 04ztj) <- award_winner(?x1232, ?x6626), nationality(?x6626, ?x94), inductee(?x1091, ?x6626) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_j2 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.744 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5960-04vjh PRED entity: 04vjh PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 20): 060bp (0.74 #23, 0.70 #45, 0.70 #67), 0f6c3 (0.37 #183, 0.30 #601, 0.29 #667), 0pqc5 (0.36 #1413, 0.36 #1457, 0.14 #1236), 0fkvn (0.35 #179, 0.34 #1125, 0.28 #663), 09n5b9 (0.34 #187, 0.26 #605, 0.25 #671), 04syw (0.17 #72, 0.16 #512, 0.16 #622), 0dq3c (0.15 #200, 0.15 #552, 0.14 #156), 01zq91 (0.13 #36, 0.11 #124, 0.10 #168), 0p5vf (0.13 #56, 0.12 #122, 0.11 #210), 0377k9 (0.11 #59, 0.08 #169, 0.07 #125) >> Best rule #23 for best value: >> intensional similarity = 2 >> extensional distance = 21 >> proper extension: 0h44w; >> query: (?x10451, 060bp) <- countries_spoken_in(?x5359, ?x10451), ?x5359 = 0jzc >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04vjh jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.739 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #5959-0l_n1 PRED entity: 0l_n1 PRED relation: currency PRED expected values: 09nqf => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.84 #2, 0.83 #17, 0.82 #14) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0f4y_; 0nj1c; 0n5_g; 0nm8n; 0n4z2; >> query: (?x13425, 09nqf) <- administrative_parent(?x13425, ?x953), second_level_divisions(?x94, ?x13425), district_represented(?x605, ?x953) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l_n1 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.837 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #5958-0dyb1 PRED entity: 0dyb1 PRED relation: film! PRED expected values: 085q5 => 64 concepts (47 used for prediction) PRED predicted values (max 10 best out of 932): 04jspq (0.72 #91479, 0.58 #72768, 0.45 #91478), 05jcn8 (0.72 #91479, 0.58 #72768, 0.45 #91478), 05_k56 (0.72 #91479, 0.58 #72768, 0.45 #91478), 016szr (0.45 #91478, 0.42 #70688, 0.41 #89399), 01vz80y (0.20 #43661, 0.19 #43660, 0.18 #54059), 03ym1 (0.09 #1011, 0.05 #19723, 0.04 #28041), 09y20 (0.09 #247, 0.05 #16881, 0.04 #18959), 0f0kz (0.07 #515, 0.06 #19227, 0.05 #27545), 06ltr (0.07 #945, 0.04 #19657, 0.03 #27975), 0l6px (0.07 #387, 0.03 #19099, 0.03 #17021) >> Best rule #91479 for best value: >> intensional similarity = 3 >> extensional distance = 1229 >> proper extension: 015qsq; 0c0yh4; 090s_0; 05jf85; 0209xj; 02py4c8; 0416y94; 01kff7; 0sxfd; 02bg8v; ... >> query: (?x3053, ?x3853) <- genre(?x3053, ?x258), nominated_for(?x3853, ?x3053), film(?x3853, ?x1259) >> conf = 0.72 => this is the best rule for 3 predicted values *> Best rule #5876 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 106 *> proper extension: 07bz5; *> query: (?x3053, 085q5) <- nominated_for(?x4850, ?x3053), list(?x3053, ?x3004), award(?x4850, ?x1079) *> conf = 0.03 ranks of expected_values: 152 EVAL 0dyb1 film! 085q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 64.000 47.000 0.722 http://example.org/film/actor/film./film/performance/film #5957-09n48 PRED entity: 09n48 PRED relation: participating_countries PRED expected values: 0ctw_b 02wt0 03spz 07f1x 0jt3tjf 0168t => 58 concepts (55 used for prediction) PRED predicted values (max 10 best out of 185): 02vzc (0.91 #377, 0.71 #125, 0.69 #376), 0d0vqn (0.91 #377, 0.71 #125, 0.69 #376), 0d060g (0.91 #377, 0.71 #125, 0.69 #376), 03_3d (0.91 #377, 0.71 #125, 0.69 #376), 09c7w0 (0.71 #125, 0.69 #376, 0.68 #124), 0h7x (0.71 #125, 0.69 #376, 0.68 #124), 06mzp (0.71 #125, 0.69 #376, 0.68 #124), 03gj2 (0.71 #125, 0.69 #376, 0.68 #124), 0163v (0.71 #125, 0.69 #376, 0.68 #124), 0jhd (0.71 #125, 0.69 #376, 0.68 #124) >> Best rule #377 for best value: >> intensional similarity = 15 >> extensional distance = 2 >> proper extension: 018ctl; 0kbws; >> query: (?x418, ?x1203) <- olympics(?x5482, ?x418), olympics(?x1203, ?x418), participating_countries(?x418, ?x3855), participating_countries(?x418, ?x151), sports(?x418, ?x520), ?x3855 = 0jgx, olympics(?x1203, ?x391), film_release_region(?x9501, ?x1203), film_release_region(?x3252, ?x1203), film_release_region(?x2189, ?x1203), ?x9501 = 0g5qmbz, ?x5482 = 04g5k, ?x2189 = 02yvct, ?x3252 = 0gh8zks, ?x151 = 0b90_r >> conf = 0.91 => this is the best rule for 4 predicted values *> Best rule #268 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 2 *> proper extension: 018ctl; 0kbws; *> query: (?x418, 0ctw_b) <- olympics(?x5482, ?x418), olympics(?x1203, ?x418), participating_countries(?x418, ?x3855), participating_countries(?x418, ?x151), sports(?x418, ?x520), ?x3855 = 0jgx, olympics(?x1203, ?x391), film_release_region(?x9501, ?x1203), film_release_region(?x3252, ?x1203), film_release_region(?x2189, ?x1203), ?x9501 = 0g5qmbz, ?x5482 = 04g5k, ?x2189 = 02yvct, ?x3252 = 0gh8zks, ?x151 = 0b90_r *> conf = 0.50 ranks of expected_values: 11, 30, 64, 65, 102, 147 EVAL 09n48 participating_countries 0168t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 58.000 55.000 0.913 http://example.org/olympics/olympic_games/participating_countries EVAL 09n48 participating_countries 0jt3tjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 58.000 55.000 0.913 http://example.org/olympics/olympic_games/participating_countries EVAL 09n48 participating_countries 07f1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 58.000 55.000 0.913 http://example.org/olympics/olympic_games/participating_countries EVAL 09n48 participating_countries 03spz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 58.000 55.000 0.913 http://example.org/olympics/olympic_games/participating_countries EVAL 09n48 participating_countries 02wt0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 58.000 55.000 0.913 http://example.org/olympics/olympic_games/participating_countries EVAL 09n48 participating_countries 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 58.000 55.000 0.913 http://example.org/olympics/olympic_games/participating_countries #5956-01vs4ff PRED entity: 01vs4ff PRED relation: profession PRED expected values: 09jwl => 185 concepts (111 used for prediction) PRED predicted values (max 10 best out of 94): 09jwl (0.95 #12913, 0.83 #894, 0.83 #4116), 02hrh1q (0.76 #15259, 0.76 #15551, 0.74 #12322), 0nbcg (0.67 #907, 0.64 #13072, 0.64 #3103), 01c72t (0.57 #169, 0.33 #899, 0.33 #753), 01d_h8 (0.54 #15982, 0.34 #10992, 0.33 #12313), 0n1h (0.43 #156, 0.31 #1032, 0.30 #1471), 0dxtg (0.42 #1619, 0.37 #2498, 0.32 #15990), 02jknp (0.32 #15984, 0.23 #1613, 0.20 #15398), 0cbd2 (0.31 #1612, 0.29 #2491, 0.18 #15397), 0kyk (0.30 #2515, 0.30 #1636, 0.15 #4274) >> Best rule #12913 for best value: >> intensional similarity = 6 >> extensional distance = 487 >> proper extension: 04bs3j; 0fsm8c; 02g0mx; 02bfxb; 01vvdm; 05b_7n; 0dpqk; 03pp73; 091yn0; 06q5t7; ... >> query: (?x7084, 09jwl) <- award(?x7084, ?x4912), profession(?x7084, ?x2659), profession(?x9693, ?x2659), profession(?x7233, ?x2659), ?x7233 = 01lz4tf, ?x9693 = 02pt27 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vs4ff profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 185.000 111.000 0.953 http://example.org/people/person/profession #5955-047jhq PRED entity: 047jhq PRED relation: languages PRED expected values: 0688f => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 16): 02h40lc (0.91 #458, 0.87 #534, 0.87 #496), 02hxcvy (0.20 #25, 0.05 #2015), 07c9s (0.20 #12, 0.03 #278, 0.02 #468), 064_8sq (0.11 #470, 0.09 #546, 0.08 #508), 09bnf (0.10 #38), 02bjrlw (0.05 #533, 0.05 #495, 0.04 #457), 0688f (0.05 #2015), 06nm1 (0.03 #499, 0.03 #537, 0.03 #461), 04306rv (0.03 #535, 0.03 #497, 0.02 #459), 03_9r (0.03 #194, 0.02 #460, 0.01 #498) >> Best rule #458 for best value: >> intensional similarity = 3 >> extensional distance = 320 >> proper extension: 04shbh; 022769; 094xh; 03h40_7; 0b5x23; >> query: (?x12616, 02h40lc) <- place_of_birth(?x12616, ?x12524), languages(?x12616, ?x1882), location(?x12616, ?x7412) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #2015 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1100 *> proper extension: 06jzh; *> query: (?x12616, ?x1882) <- location(?x12616, ?x7412), people(?x5025, ?x12616), languages_spoken(?x5025, ?x1882) *> conf = 0.05 ranks of expected_values: 7 EVAL 047jhq languages 0688f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 96.000 96.000 0.907 http://example.org/people/person/languages #5954-02dth1 PRED entity: 02dth1 PRED relation: gender PRED expected values: 05zppz => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #49, 0.85 #61, 0.84 #39), 02zsn (0.52 #181, 0.48 #18, 0.47 #22) >> Best rule #49 for best value: >> intensional similarity = 2 >> extensional distance = 481 >> proper extension: 075wq; >> query: (?x4204, 05zppz) <- place_of_death(?x4204, ?x739), contains(?x739, ?x1005) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02dth1 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.847 http://example.org/people/person/gender #5953-04zqmj PRED entity: 04zqmj PRED relation: award_winner! PRED expected values: 05p09zm => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 180): 05zr6wv (0.37 #13830, 0.31 #11237, 0.31 #24209), 09sb52 (0.30 #41, 0.15 #4364, 0.12 #5229), 05b4l5x (0.15 #16424, 0.15 #15991, 0.08 #18155), 05p09zm (0.15 #16424, 0.15 #15991, 0.08 #18155), 03c7tr1 (0.15 #16424, 0.15 #15991, 0.08 #18155), 0ck27z (0.10 #7009, 0.09 #4416, 0.08 #10033), 0cqhk0 (0.09 #4360, 0.07 #6953, 0.06 #9977), 02x8n1n (0.09 #121, 0.02 #4876, 0.01 #5309), 05zrvfd (0.09 #17289, 0.05 #27669), 01by1l (0.08 #6165, 0.05 #7029, 0.05 #10917) >> Best rule #13830 for best value: >> intensional similarity = 2 >> extensional distance = 1462 >> proper extension: 01wp8w7; 01t2h2; 01vb403; 0h1p; 02645b; 02t_v1; 0kvqv; 0pmw9; 0khth; 049gc; ... >> query: (?x11381, ?x401) <- award_winner(?x8793, ?x11381), award(?x11381, ?x401) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #16424 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1531 *> proper extension: 0gsgr; *> query: (?x11381, ?x154) <- award_winner(?x8793, ?x11381), award_winner(?x154, ?x8793) *> conf = 0.15 ranks of expected_values: 4 EVAL 04zqmj award_winner! 05p09zm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 85.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #5952-0bs1yy PRED entity: 0bs1yy PRED relation: music! PRED expected values: 060v34 => 100 concepts (66 used for prediction) PRED predicted values (max 10 best out of 69): 02rrfzf (0.07 #1327, 0.06 #3336, 0.05 #4341), 04tqtl (0.07 #1311, 0.06 #3320, 0.05 #4325), 084qpk (0.07 #1076, 0.06 #3085, 0.05 #4090), 025s1wg (0.07 #1969, 0.06 #3978, 0.05 #4983), 0gyv0b4 (0.07 #1938, 0.06 #3947, 0.05 #4952), 01s7w3 (0.05 #7899, 0.05 #9907, 0.04 #10911), 03mh_tp (0.04 #4020, 0.03 #2011, 0.02 #5025), 02ht1k (0.03 #7400, 0.03 #10412, 0.02 #11416), 09d3b7 (0.03 #7871, 0.03 #10883, 0.02 #11887), 07bzz7 (0.03 #10569, 0.02 #14585, 0.02 #9565) >> Best rule #1327 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0dky9n; 06t8b; >> query: (?x3042, 02rrfzf) <- place_of_birth(?x3042, ?x6960), edited_by(?x394, ?x3042), film_release_distribution_medium(?x394, ?x81) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bs1yy music! 060v34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 66.000 0.067 http://example.org/film/film/music #5951-03t79f PRED entity: 03t79f PRED relation: nominated_for! PRED expected values: 07bdd_ => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 161): 07bdd_ (0.72 #762, 0.33 #288, 0.08 #6924), 04ljl_l (0.44 #714, 0.17 #240, 0.10 #477), 07cbcy (0.42 #773, 0.09 #6935, 0.08 #8831), 099c8n (0.40 #55, 0.26 #1003, 0.20 #1714), 0gq9h (0.40 #61, 0.22 #5986, 0.22 #6460), 019f4v (0.40 #52, 0.21 #5977, 0.21 #1948), 0gs9p (0.40 #63, 0.19 #5988, 0.19 #6462), 0p9sw (0.40 #20, 0.19 #1442, 0.18 #1679), 0k611 (0.40 #72, 0.17 #4812, 0.17 #5997), 04dn09n (0.40 #34, 0.17 #271, 0.17 #5959) >> Best rule #762 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 06wzvr; >> query: (?x5372, 07bdd_) <- film_crew_role(?x5372, ?x137), nominated_for(?x350, ?x5372), ?x350 = 05f4m9q >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03t79f nominated_for! 07bdd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.720 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5950-0xnvg PRED entity: 0xnvg PRED relation: people PRED expected values: 01wbg84 0htlr 02whj 02lfns 01vvb4m 0f502 026l37 04ls53 0gn30 => 27 concepts (15 used for prediction) PRED predicted values (max 10 best out of 3312): 01gy7r (0.50 #548, 0.18 #2182, 0.08 #7076), 01pk3z (0.27 #2370, 0.25 #736, 0.16 #7264), 0127m7 (0.27 #1932, 0.25 #298, 0.12 #3563), 046zh (0.27 #2332, 0.25 #698, 0.12 #7226), 0lkr7 (0.25 #663, 0.18 #2297, 0.14 #8825), 016kkx (0.25 #860, 0.18 #2494, 0.12 #4125), 0k9j_ (0.25 #1194, 0.18 #2828, 0.11 #6091), 0169dl (0.25 #291, 0.18 #1925, 0.08 #6819), 01tfck (0.25 #263, 0.18 #1897, 0.08 #6791), 01_ztw (0.25 #739, 0.18 #2373, 0.08 #7267) >> Best rule #548 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 09vc4s; 07hwkr; >> query: (?x3591, 01gy7r) <- people(?x3591, ?x8793), people(?x3591, ?x6182), people(?x3591, ?x3210), geographic_distribution(?x3591, ?x94), ?x3210 = 01vwllw, award(?x6182, ?x678), participant(?x338, ?x8793), participant(?x8793, ?x5589), participant(?x8793, ?x2352) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2022 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 9 *> proper extension: 01qhm_; 013xrm; 0g6ff; 07bch9; 03ts0c; 0dbxy; 01p7s6; *> query: (?x3591, 01vvb4m) <- people(?x3591, ?x6182), people(?x3591, ?x3662), people(?x3591, ?x3210), geographic_distribution(?x3591, ?x94), award_winner(?x1313, ?x3662), film(?x3210, ?x670), ?x1313 = 0gs9p, religion(?x3210, ?x1985), type_of_union(?x6182, ?x566) *> conf = 0.09 ranks of expected_values: 235, 465, 523, 654, 1082, 1502, 1811, 2664 EVAL 0xnvg people 0gn30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 15.000 0.500 http://example.org/people/ethnicity/people EVAL 0xnvg people 04ls53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 15.000 0.500 http://example.org/people/ethnicity/people EVAL 0xnvg people 026l37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 27.000 15.000 0.500 http://example.org/people/ethnicity/people EVAL 0xnvg people 0f502 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 15.000 0.500 http://example.org/people/ethnicity/people EVAL 0xnvg people 01vvb4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 27.000 15.000 0.500 http://example.org/people/ethnicity/people EVAL 0xnvg people 02lfns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 15.000 0.500 http://example.org/people/ethnicity/people EVAL 0xnvg people 02whj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 27.000 15.000 0.500 http://example.org/people/ethnicity/people EVAL 0xnvg people 0htlr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 15.000 0.500 http://example.org/people/ethnicity/people EVAL 0xnvg people 01wbg84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 15.000 0.500 http://example.org/people/ethnicity/people #5949-0557q PRED entity: 0557q PRED relation: artists PRED expected values: 01jqr_5 03n0q5 03f4k => 62 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1373): 01wd9lv (0.39 #6961, 0.37 #8025, 0.33 #1637), 09hnb (0.37 #7665, 0.31 #8730, 0.25 #2342), 02ck1 (0.33 #1275, 0.31 #4469, 0.25 #2340), 012z8_ (0.33 #1462, 0.25 #6786, 0.25 #2527), 06449 (0.33 #1306, 0.25 #4500, 0.25 #2371), 0146pg (0.33 #1106, 0.25 #4300, 0.25 #2171), 0kvrb (0.33 #1245, 0.25 #4439, 0.25 #2310), 02z81h (0.33 #1613, 0.25 #4807, 0.25 #2678), 0h6sv (0.33 #2118, 0.25 #5312, 0.25 #3183), 0kn3g (0.33 #1945, 0.25 #5139, 0.25 #3010) >> Best rule #6961 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 07sbbz2; 0fd3y; 0ggq0m; 064t9; 01wtlq; 02x8m; 061fhg; 06by7; 0glt670; 06j6l; ... >> query: (?x10332, 01wd9lv) <- artists(?x10332, ?x7955), student(?x2909, ?x7955), award(?x7955, ?x1079), ?x1079 = 0l8z1, music(?x1454, ?x7955) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1935 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 017_qw; *> query: (?x10332, 03f4k) <- artists(?x10332, ?x12947), artists(?x10332, ?x7955), artists(?x10332, ?x1894), ?x7955 = 01l3mk3, award_winner(?x6943, ?x12947), ?x1894 = 02fgpf, award(?x12947, ?x1323) *> conf = 0.33 ranks of expected_values: 16, 281, 1344 EVAL 0557q artists 03f4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 62.000 32.000 0.393 http://example.org/music/genre/artists EVAL 0557q artists 03n0q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 32.000 0.393 http://example.org/music/genre/artists EVAL 0557q artists 01jqr_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 32.000 0.393 http://example.org/music/genre/artists #5948-0fxmbn PRED entity: 0fxmbn PRED relation: titles! PRED expected values: 07ssc => 88 concepts (62 used for prediction) PRED predicted values (max 10 best out of 54): 07ssc (0.43 #530, 0.33 #625, 0.33 #624), 014tss (0.33 #625, 0.33 #624, 0.25 #207), 01jfsb (0.30 #963, 0.27 #2635, 0.27 #2550), 07s9rl0 (0.27 #2954, 0.26 #2848, 0.26 #1155), 03h64 (0.25 #664, 0.25 #141, 0.17 #453), 04xvlr (0.25 #107, 0.24 #2851, 0.23 #2957), 04t2t (0.23 #804, 0.19 #1434, 0.19 #1121), 02kdv5l (0.21 #5909, 0.20 #3059, 0.19 #5171), 01hmnh (0.19 #1285, 0.19 #2344, 0.19 #2236), 024qqx (0.18 #2076, 0.18 #2506, 0.17 #1866) >> Best rule #530 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0bz6sq; >> query: (?x7713, 07ssc) <- language(?x7713, ?x254), country(?x7713, ?x6371), film(?x9406, ?x7713), ?x254 = 02h40lc, currency(?x7713, ?x170), ?x9406 = 017lqp, countries_spoken_in(?x9617, ?x6371) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fxmbn titles! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 62.000 0.429 http://example.org/media_common/netflix_genre/titles #5947-02t__3 PRED entity: 02t__3 PRED relation: type_of_union PRED expected values: 04ztj => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #82, 0.71 #78, 0.71 #246), 01g63y (0.56 #29, 0.33 #18, 0.30 #26) >> Best rule #82 for best value: >> intensional similarity = 4 >> extensional distance = 346 >> proper extension: 033hqf; 02d9k; 02bh9; 01v3bn; 03n93; 036jb; 0lkr7; 0gmtm; 01c6l; 03x400; ... >> query: (?x5979, 04ztj) <- participant(?x5979, ?x10491), participant(?x5979, ?x8134), spouse(?x10491, ?x1815), film(?x8134, ?x1444) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02t__3 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.721 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5946-02wt0 PRED entity: 02wt0 PRED relation: countries_spoken_in! PRED expected values: 07c9s => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 54): 064_8sq (0.29 #3082, 0.26 #3539, 0.21 #2036), 06nm1 (0.23 #1657, 0.23 #1877, 0.21 #3529), 02bjrlw (0.17 #166, 0.16 #221, 0.15 #331), 02hxcvy (0.17 #195, 0.16 #250, 0.13 #580), 0121sr (0.17 #207, 0.16 #262, 0.10 #482), 03k50 (0.17 #171, 0.11 #281, 0.11 #226), 09s02 (0.17 #210, 0.11 #265, 0.07 #595), 0jzc (0.16 #1885, 0.16 #290, 0.16 #235), 05zjd (0.16 #241, 0.12 #1396, 0.10 #461), 04306rv (0.14 #1654, 0.11 #224, 0.10 #554) >> Best rule #3082 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 0j3b; 05rgl; 075mb; 0f1_p; 04_1l0v; 068cn; 02j7k; 070zc; 052gtg; 06k5_; ... >> query: (?x2290, ?x254) <- contains(?x2290, ?x13607), adjoins(?x2290, ?x2291), official_language(?x2291, ?x254) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #234 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0d060g; 0ctw_b; 06mkj; *> query: (?x2290, 07c9s) <- organization(?x2290, ?x127), vacationer(?x2290, ?x6187), exported_to(?x2290, ?x4164) *> conf = 0.11 ranks of expected_values: 15 EVAL 02wt0 countries_spoken_in! 07c9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 127.000 127.000 0.290 http://example.org/language/human_language/countries_spoken_in #5945-0418ft PRED entity: 0418ft PRED relation: award PRED expected values: 09qwmm => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 273): 09sb52 (0.38 #853, 0.33 #18718, 0.31 #13034), 040njc (0.27 #3256, 0.24 #2038, 0.23 #1226), 0ck27z (0.27 #25266, 0.25 #905, 0.21 #28514), 05pcn59 (0.25 #894, 0.25 #82, 0.17 #488), 0fbtbt (0.25 #1046, 0.25 #234, 0.17 #640), 05zr6wv (0.25 #17, 0.21 #2047, 0.19 #1235), 0gqwc (0.25 #75, 0.17 #481, 0.15 #26872), 03c7tr1 (0.25 #59, 0.17 #465, 0.15 #1683), 0gqyl (0.25 #106, 0.17 #512, 0.14 #26903), 04kxsb (0.25 #127, 0.17 #533, 0.14 #11902) >> Best rule #853 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0blbxk; 02lhm2; >> query: (?x8183, 09sb52) <- profession(?x8183, ?x1032), gender(?x8183, ?x514), film(?x8183, ?x1702), ?x1702 = 0c00zd0 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0bxtg; *> query: (?x8183, 09qwmm) <- languages(?x8183, ?x254), film(?x8183, ?x1702), film(?x8183, ?x887), honored_for(?x886, ?x887), ?x1702 = 0c00zd0 *> conf = 0.25 ranks of expected_values: 11 EVAL 0418ft award 09qwmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 121.000 121.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5944-027cyf7 PRED entity: 027cyf7 PRED relation: award! PRED expected values: 0ds3t5x 03cvvlg => 38 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1077): 05c46y6 (0.57 #266, 0.10 #2306, 0.06 #3326), 0bmhvpr (0.43 #371, 0.07 #2411, 0.05 #3431), 0170xl (0.43 #976, 0.05 #3016, 0.05 #4036), 0h1x5f (0.33 #1935, 0.11 #2955, 0.07 #3975), 04b2qn (0.29 #791, 0.14 #2831, 0.11 #3851), 07s846j (0.29 #398, 0.12 #3458, 0.10 #4478), 0pv3x (0.29 #107, 0.10 #3167, 0.08 #2147), 011yqc (0.29 #142, 0.09 #3202, 0.08 #2182), 0421ng (0.29 #503, 0.03 #3563, 0.02 #2543), 0c0zq (0.25 #1923, 0.16 #2943, 0.12 #3963) >> Best rule #266 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0gr4k; 05zvq6g; 02z0dfh; 02x17s4; 03qgjwc; >> query: (?x4135, 05c46y6) <- award(?x3096, ?x4135), ?x3096 = 02s5v5, award(?x945, ?x4135), award_winner(?x4135, ?x2422), participant(?x1890, ?x2422) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1050 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 027dtxw; 0789_m; 0f4x7; 09sb52; 099tbz; 05pcn59; 0bs0bh; 04kxsb; 0279c15; 0gqy2; *> query: (?x4135, 0ds3t5x) <- award(?x4294, ?x4135), award(?x3096, ?x4135), profession(?x3096, ?x524), religion(?x3096, ?x2694), ?x4294 = 01r93l *> conf = 0.17 ranks of expected_values: 20, 560 EVAL 027cyf7 award! 03cvvlg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 38.000 5.000 0.571 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 027cyf7 award! 0ds3t5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 38.000 5.000 0.571 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #5943-02c638 PRED entity: 02c638 PRED relation: executive_produced_by PRED expected values: 0glyyw => 79 concepts (71 used for prediction) PRED predicted values (max 10 best out of 54): 0343h (0.08 #42, 0.03 #799, 0.03 #294), 0237jb (0.07 #3788, 0.04 #2020, 0.02 #8843), 06q8hf (0.06 #419, 0.05 #924, 0.04 #3701), 0sz28 (0.04 #2020, 0.02 #10869, 0.02 #8843), 01nr36 (0.04 #2020, 0.02 #8843, 0.02 #5807), 05hj_k (0.04 #3632, 0.04 #2875, 0.04 #855), 0bwh6 (0.03 #4545, 0.03 #6313, 0.02 #10869), 0glyyw (0.03 #2209, 0.03 #2462, 0.02 #7512), 06pj8 (0.03 #4347, 0.02 #55, 0.02 #8898), 02hy9p (0.03 #433, 0.01 #938, 0.01 #1190) >> Best rule #42 for best value: >> intensional similarity = 2 >> extensional distance = 47 >> proper extension: 02fn5r; >> query: (?x2116, 0343h) <- category(?x2116, ?x134), nominated_for(?x2116, ?x4067) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #2209 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 340 *> proper extension: 0ckr7s; 02847m9; 02qyv3h; 04z_3pm; 0bs8ndx; 0353tm; 04180vy; *> query: (?x2116, 0glyyw) <- country(?x2116, ?x94), film(?x1208, ?x2116), category(?x2116, ?x134) *> conf = 0.03 ranks of expected_values: 8 EVAL 02c638 executive_produced_by 0glyyw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 79.000 71.000 0.082 http://example.org/film/film/executive_produced_by #5942-01r2c7 PRED entity: 01r2c7 PRED relation: student! PRED expected values: 0bwfn => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 175): 01k2wn (0.25 #24, 0.04 #8441, 0.03 #14762), 06kknt (0.20 #992, 0.04 #2570, 0.04 #3622), 02sjgpq (0.20 #789, 0.01 #9206), 017z88 (0.15 #1660, 0.14 #1134, 0.04 #2186), 013807 (0.14 #1462, 0.08 #3566, 0.08 #3040), 0234_c (0.14 #1468, 0.04 #2520, 0.04 #3572), 03hdz8 (0.14 #1312, 0.04 #2364, 0.04 #3416), 065y4w7 (0.13 #3696, 0.10 #4748, 0.10 #5274), 0bwfn (0.12 #4482, 0.12 #7639, 0.12 #25011), 04s934 (0.09 #2320, 0.08 #3372, 0.08 #2846) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03c9pqt; >> query: (?x9354, 01k2wn) <- executive_produced_by(?x136, ?x9354), nominated_for(?x9354, ?x385), produced_by(?x2128, ?x9354), ?x2128 = 035s95 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4482 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 02qggqc; *> query: (?x9354, 0bwfn) <- executive_produced_by(?x4235, ?x9354), nominated_for(?x9354, ?x385), film_crew_role(?x4235, ?x137), nominated_for(?x2006, ?x4235) *> conf = 0.12 ranks of expected_values: 9 EVAL 01r2c7 student! 0bwfn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 168.000 168.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #5941-06rhz7 PRED entity: 06rhz7 PRED relation: film! PRED expected values: 0glmv => 96 concepts (70 used for prediction) PRED predicted values (max 10 best out of 710): 01vsykc (0.54 #4169, 0.44 #39587, 0.43 #93762), 027kmrb (0.44 #39587, 0.43 #93762, 0.43 #129180), 0lx2l (0.06 #2504, 0.02 #6673, 0.02 #8758), 0jfx1 (0.06 #6659, 0.04 #4575, 0.03 #21243), 01kwsg (0.06 #66674, 0.03 #2923, 0.03 #7092), 01j7z7 (0.06 #66674, 0.02 #5494, 0.02 #11746), 01gbn6 (0.06 #66674, 0.02 #1629, 0.01 #18300), 02633g (0.06 #66674), 03rwz3 (0.06 #66674), 02ts3h (0.06 #66674) >> Best rule #4169 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 0d6b7; 09rfpk; >> query: (?x6254, ?x3290) <- titles(?x53, ?x6254), nominated_for(?x3290, ?x6254), notable_people_with_this_condition(?x13560, ?x3290) >> conf = 0.54 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06rhz7 film! 0glmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 70.000 0.543 http://example.org/film/actor/film./film/performance/film #5940-01mh8zn PRED entity: 01mh8zn PRED relation: type_of_union PRED expected values: 01g63y => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.77 #13, 0.71 #105, 0.71 #25), 01g63y (0.19 #301, 0.13 #86, 0.12 #62), 0jgjn (0.19 #301, 0.02 #16, 0.01 #24), 01bl8s (0.19 #301) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 62 >> proper extension: 081wh1; >> query: (?x8013, 04ztj) <- award(?x8013, ?x4481), ?x4481 = 02x17c2 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #301 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4243 *> proper extension: 05_6_y; 04411; 0bn9sc; 0487c3; 05fg2; 080dyk; 09ntbc; 083qy7; 0n00; 07nv3_; ... *> query: (?x8013, ?x566) <- profession(?x8013, ?x6476), profession(?x647, ?x6476), type_of_union(?x647, ?x566) *> conf = 0.19 ranks of expected_values: 2 EVAL 01mh8zn type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 77.000 0.766 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5939-0p76z PRED entity: 0p76z PRED relation: group! PRED expected values: 018vs 0l14j_ => 83 concepts (70 used for prediction) PRED predicted values (max 10 best out of 123): 0l14md (0.69 #1774, 0.66 #1950, 0.50 #1420), 018vs (0.58 #1869, 0.58 #1781, 0.56 #1427), 03qjg (0.50 #1461, 0.47 #1549, 0.45 #1903), 04rzd (0.43 #739, 0.23 #1887, 0.21 #1711), 01vj9c (0.38 #1782, 0.37 #1958, 0.23 #1870), 028tv0 (0.38 #1780, 0.35 #1514, 0.33 #1692), 0l14qv (0.35 #1772, 0.31 #1948, 0.26 #1860), 07y_7 (0.31 #1769, 0.26 #1945, 0.19 #1327), 05r5c (0.29 #1863, 0.29 #715, 0.26 #1951), 013y1f (0.29 #735, 0.26 #1883, 0.21 #1707) >> Best rule #1774 for best value: >> intensional similarity = 8 >> extensional distance = 24 >> proper extension: 05crg7; >> query: (?x10145, 0l14md) <- artists(?x12618, ?x10145), artists(?x2809, ?x10145), parent_genre(?x12618, ?x5138), parent_genre(?x10128, ?x12618), ?x2809 = 05w3f, group(?x1166, ?x10145), ?x1166 = 05148p4, artists(?x5138, ?x248) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1869 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 29 *> proper extension: 01v0sxx; *> query: (?x10145, 018vs) <- artist(?x1543, ?x10145), artists(?x7083, ?x10145), artists(?x1572, ?x10145), ?x1572 = 06by7, artists(?x7083, ?x8579), artists(?x7083, ?x2876), ?x8579 = 01vs4f3, ?x2876 = 01vn35l, group(?x227, ?x10145) *> conf = 0.58 ranks of expected_values: 2, 11 EVAL 0p76z group! 0l14j_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 83.000 70.000 0.692 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0p76z group! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 70.000 0.692 http://example.org/music/performance_role/regular_performances./music/group_membership/group #5938-03nk3t PRED entity: 03nk3t PRED relation: people! PRED expected values: 041rx => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 35): 02w7gg (0.26 #79, 0.21 #156, 0.12 #2), 041rx (0.22 #312, 0.21 #235, 0.20 #620), 0d7wh (0.21 #94, 0.06 #171, 0.02 #3490), 048z7l (0.12 #40, 0.06 #271, 0.06 #502), 0xnvg (0.12 #244, 0.06 #1707, 0.06 #1630), 033tf_ (0.11 #1701, 0.09 #1779, 0.08 #1935), 02ctzb (0.11 #92, 0.04 #1787, 0.03 #2561), 0x67 (0.10 #4255, 0.10 #4332, 0.09 #5025), 03bkbh (0.06 #186, 0.03 #1726, 0.03 #263), 0222qb (0.06 #275, 0.05 #352, 0.04 #583) >> Best rule #79 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 0n00; 0kh6b; 04hcw; 0xnc3; 0kn3g; 030dr; >> query: (?x4472, 02w7gg) <- religion(?x4472, ?x4641), student(?x2999, ?x4472), ?x2999 = 07tg4 >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #312 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 022_lg; 0mm1q; 0522wp; 030vmc; 06y0xx; *> query: (?x4472, 041rx) <- award(?x4472, ?x350), ?x350 = 05f4m9q, film(?x4472, ?x4751) *> conf = 0.22 ranks of expected_values: 2 EVAL 03nk3t people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.263 http://example.org/people/ethnicity/people #5937-03339m PRED entity: 03339m PRED relation: artists PRED expected values: 01wt4wc 01516r 03j_hq => 61 concepts (23 used for prediction) PRED predicted values (max 10 best out of 982): 07yg2 (0.58 #4684, 0.54 #5764, 0.50 #6847), 0191h5 (0.56 #9291, 0.55 #10369, 0.42 #4965), 016wvy (0.50 #7393, 0.50 #5230, 0.46 #6310), 02ndj5 (0.50 #1976, 0.50 #897, 0.43 #3056), 0p76z (0.50 #1993, 0.50 #914, 0.43 #3073), 067mj (0.50 #1178, 0.50 #99, 0.43 #2258), 016ntp (0.50 #1343, 0.50 #264, 0.43 #2423), 0m2l9 (0.50 #1105, 0.50 #26, 0.43 #2185), 012zng (0.50 #133, 0.42 #4450, 0.38 #5530), 01vng3b (0.50 #560, 0.33 #4877, 0.33 #1639) >> Best rule #4684 for best value: >> intensional similarity = 11 >> extensional distance = 10 >> proper extension: 05w3f; 0cx7f; 02qm5j; 09jw2; 052smk; >> query: (?x10471, 07yg2) <- artists(?x10471, ?x10106), artists(?x10471, ?x4712), artists(?x10471, ?x3657), ?x4712 = 03f0fnk, artists(?x10930, ?x3657), artists(?x2249, ?x3657), artists(?x10930, ?x11635), group(?x227, ?x10106), gender(?x3657, ?x231), ?x2249 = 03lty, ?x11635 = 01nrz4 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #8289 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 19 *> proper extension: 02yw1c; 066d03; 012xsy; *> query: (?x10471, 01wt4wc) <- artists(?x10471, ?x9463), artists(?x10471, ?x3657), ?x9463 = 01shhf, artists(?x5379, ?x3657), artists(?x3108, ?x3657), ?x5379 = 08jyyk, artists(?x3108, ?x4062), artists(?x3108, ?x1997), ?x1997 = 01wsl7c, ?x4062 = 0bqsy *> conf = 0.43 ranks of expected_values: 21, 79, 264 EVAL 03339m artists 03j_hq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 23.000 0.583 http://example.org/music/genre/artists EVAL 03339m artists 01516r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 61.000 23.000 0.583 http://example.org/music/genre/artists EVAL 03339m artists 01wt4wc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 61.000 23.000 0.583 http://example.org/music/genre/artists #5936-02sfnv PRED entity: 02sfnv PRED relation: nominated_for! PRED expected values: 04ljl_l => 69 concepts (61 used for prediction) PRED predicted values (max 10 best out of 177): 05p09zm (0.40 #325, 0.33 #90, 0.30 #560), 04ljl_l (0.33 #473, 0.20 #2590, 0.20 #238), 05q8pss (0.33 #147, 0.20 #382, 0.09 #617), 0641kkh (0.33 #207, 0.20 #442, 0.04 #677), 0gq9h (0.28 #1941, 0.28 #2176, 0.21 #1235), 0gq_v (0.25 #1900, 0.25 #2135, 0.19 #1194), 05p1dby (0.23 #548, 0.20 #313, 0.14 #2665), 05ztjjw (0.23 #2596, 0.08 #5651, 0.08 #2361), 0gs9p (0.22 #1942, 0.21 #2177, 0.17 #7819), 0k611 (0.21 #1951, 0.20 #2186, 0.17 #1245) >> Best rule #325 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 033f8n; 02b6n9; >> query: (?x5187, 05p09zm) <- film(?x703, ?x5187), ?x703 = 0187y5, nominated_for(?x154, ?x5187) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #473 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 024mxd; *> query: (?x5187, 04ljl_l) <- film(?x703, ?x5187), participant(?x703, ?x6331), nominated_for(?x154, ?x5187), ?x154 = 05b4l5x *> conf = 0.33 ranks of expected_values: 2 EVAL 02sfnv nominated_for! 04ljl_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 61.000 0.400 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5935-014g9y PRED entity: 014g9y PRED relation: nationality PRED expected values: 0f8l9c => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 21): 02jx1 (0.16 #230, 0.15 #329, 0.13 #1023), 07ssc (0.12 #311, 0.12 #608, 0.10 #906), 03rk0 (0.07 #2929, 0.07 #2830, 0.06 #4121), 0d060g (0.07 #501, 0.06 #1496, 0.06 #1597), 0f8l9c (0.05 #219, 0.02 #2806, 0.02 #1511), 0chghy (0.05 #207, 0.02 #1998, 0.02 #1499), 06q1r (0.04 #175, 0.03 #472, 0.01 #2265), 03rt9 (0.03 #12, 0.02 #1502, 0.02 #309), 0hzlz (0.03 #22, 0.02 #220, 0.02 #319), 0345h (0.03 #4106, 0.02 #4404, 0.02 #3907) >> Best rule #230 for best value: >> intensional similarity = 2 >> extensional distance = 62 >> proper extension: 0n6f8; 01k5zk; 057hz; 0h32q; 0hwbd; 02g0rb; 01skmp; 0lfbm; 02jr26; 0421st; ... >> query: (?x10675, 02jx1) <- award(?x10675, ?x1716), ?x1716 = 02y_rq5 >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #219 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 62 *> proper extension: 0n6f8; 01k5zk; 057hz; 0h32q; 0hwbd; 02g0rb; 01skmp; 0lfbm; 02jr26; 0421st; ... *> query: (?x10675, 0f8l9c) <- award(?x10675, ?x1716), ?x1716 = 02y_rq5 *> conf = 0.05 ranks of expected_values: 5 EVAL 014g9y nationality 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 79.000 0.156 http://example.org/people/person/nationality #5934-018dyl PRED entity: 018dyl PRED relation: artists! PRED expected values: 06j6l => 128 concepts (127 used for prediction) PRED predicted values (max 10 best out of 239): 06j6l (0.62 #362, 0.50 #989, 0.43 #1929), 064t9 (0.52 #3147, 0.46 #13818, 0.45 #10679), 0155w (0.50 #1049, 0.43 #1676, 0.36 #1989), 02yv6b (0.42 #1041, 0.36 #1981, 0.36 #1668), 0ggq0m (0.31 #1265, 0.09 #14758, 0.09 #3459), 016clz (0.29 #10357, 0.27 #6274, 0.25 #5960), 03_d0 (0.29 #11, 0.20 #6908, 0.20 #9106), 05bt6j (0.28 #2238, 0.26 #3178, 0.25 #4745), 0glt670 (0.25 #10707, 0.25 #13846, 0.21 #8510), 01lyv (0.25 #974, 0.25 #347, 0.25 #6617) >> Best rule #362 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01vrncs; 01kv4mb; 01wwvc5; 01w724; 01vrnsk; 01z9_x; >> query: (?x4288, 06j6l) <- award_winner(?x4288, ?x3403), ?x3403 = 02qwg, role(?x4288, ?x745) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018dyl artists! 06j6l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 127.000 0.625 http://example.org/music/genre/artists #5933-0lvng PRED entity: 0lvng PRED relation: contains! PRED expected values: 059j2 => 193 concepts (131 used for prediction) PRED predicted values (max 10 best out of 464): 02jx1 (0.92 #43982, 0.76 #17106, 0.43 #3668), 07371 (0.81 #93198, 0.79 #102156, 0.77 #115609), 0cl8c (0.78 #66305, 0.78 #77960, 0.77 #89612), 09c7w0 (0.71 #103952, 0.69 #67205, 0.69 #23293), 02qkt (0.70 #53202, 0.49 #32597, 0.33 #68444), 059j2 (0.63 #86027, 0.43 #114713, 0.41 #111127), 049nq (0.63 #86027, 0.41 #111127, 0.38 #104846), 0345h (0.52 #43079, 0.26 #64593, 0.25 #1872), 01qh7 (0.50 #1083, 0.06 #6456, 0.05 #25271), 03rjj (0.47 #106639, 0.33 #10, 0.24 #4487) >> Best rule #43982 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 0144wg; 0133ch; 025r_t; 029spt; >> query: (?x7363, 02jx1) <- contains(?x455, ?x7363), category(?x7363, ?x134), contains(?x455, ?x11740), ?x11740 = 07wtc >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #86027 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 271 *> proper extension: 08l_c1; 03x1s8; *> query: (?x7363, ?x1229) <- colors(?x7363, ?x3189), citytown(?x7363, ?x13675), contains(?x455, ?x7363), contains(?x1229, ?x13675) *> conf = 0.63 ranks of expected_values: 6 EVAL 0lvng contains! 059j2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 193.000 131.000 0.920 http://example.org/location/location/contains #5932-0345h PRED entity: 0345h PRED relation: teams PRED expected values: 01l3wr => 248 concepts (248 used for prediction) PRED predicted values (max 10 best out of 223): 038zh6 (0.14 #1070, 0.10 #4311, 0.10 #2150), 02pp1 (0.14 #991, 0.10 #2071, 0.10 #1711), 03_3z4 (0.11 #1411, 0.10 #2131, 0.10 #1771), 086x3 (0.11 #1440, 0.10 #1800, 0.06 #2880), 03z1c5 (0.10 #2125, 0.10 #1765, 0.06 #2845), 02ryyk (0.10 #2148, 0.10 #1788, 0.03 #10430), 01l3vx (0.07 #2204, 0.06 #2924, 0.05 #3645), 0329nn (0.07 #2258, 0.06 #2978, 0.05 #4059), 03dj48 (0.07 #2407, 0.05 #4208, 0.05 #5648), 0356gk (0.07 #2396, 0.05 #4557, 0.05 #5277) >> Best rule #1070 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 022dp5; 012f86; >> query: (?x1264, 038zh6) <- split_to(?x5540, ?x1264), people(?x5540, ?x380) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0345h teams 01l3wr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 248.000 248.000 0.143 http://example.org/sports/sports_team_location/teams #5931-0d99m PRED entity: 0d99m PRED relation: contains! PRED expected values: 03rjj => 88 concepts (35 used for prediction) PRED predicted values (max 10 best out of 251): 03rjj (0.94 #7185, 0.85 #20631, 0.84 #7175), 09c7w0 (0.58 #10767, 0.56 #13458, 0.56 #17043), 05k7sb (0.45 #18968, 0.44 #12691, 0.17 #3717), 02j71 (0.41 #23327, 0.24 #29605), 068cn (0.36 #3189, 0.16 #4983, 0.12 #6780), 0bzty (0.32 #5086, 0.25 #6883, 0.25 #5985), 0345h (0.30 #9948, 0.20 #19816, 0.16 #21612), 0d060g (0.28 #19747, 0.23 #21543, 0.23 #12571), 05kr_ (0.26 #12684, 0.23 #18961, 0.22 #19860), 01n7q (0.22 #10842, 0.21 #18015, 0.19 #20709) >> Best rule #7185 for best value: >> intensional similarity = 6 >> extensional distance = 33 >> proper extension: 01lfvj; 01n1pp; 057bxr; 04jr87; 0cht6; 02bbyw; 0bwfn; 0g7yx; 02bd_f; 031y2; ... >> query: (?x14366, 03rjj) <- category(?x14366, ?x134), ?x134 = 08mbj5d, contains(?x10495, ?x14366), contains(?x205, ?x10495), contains(?x10495, ?x12684), ?x12684 = 0prxp >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d99m contains! 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 35.000 0.943 http://example.org/location/location/contains #5930-0xhtw PRED entity: 0xhtw PRED relation: parent_genre! PRED expected values: 0y2tr => 52 concepts (29 used for prediction) PRED predicted values (max 10 best out of 292): 0g_bh (0.57 #1636, 0.50 #1891, 0.50 #869), 0y3_8 (0.50 #1060, 0.50 #549, 0.33 #1316), 059kh (0.50 #1062, 0.50 #551, 0.33 #41), 0xjl2 (0.50 #1314, 0.50 #803, 0.33 #37), 01243b (0.50 #1312, 0.50 #801, 0.33 #35), 0grjmv (0.50 #624, 0.33 #1135, 0.33 #114), 01gbcf (0.50 #770, 0.33 #1281, 0.33 #4), 0bt7w (0.50 #851, 0.33 #1362, 0.33 #85), 016jhr (0.50 #266, 0.33 #11, 0.27 #2825), 0781g (0.43 #1681, 0.38 #1936, 0.33 #2192) >> Best rule #1636 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 0dl5d; 05w3f; 08jyyk; 0cx7f; >> query: (?x1000, 0g_bh) <- artists(?x1000, ?x12449), artists(?x1000, ?x9791), artists(?x1000, ?x8029), artists(?x1000, ?x7653), ?x7653 = 0b_xm, award(?x9791, ?x2139), parent_genre(?x1380, ?x1000), group(?x227, ?x8029), artist(?x2149, ?x12449) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1505 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 011j5x; 0xjl2; *> query: (?x1000, 0y2tr) <- artists(?x1000, ?x10671), artists(?x1000, ?x7966), artists(?x1000, ?x646), ?x7966 = 013rfk, origin(?x10671, ?x362), award(?x646, ?x2634) *> conf = 0.33 ranks of expected_values: 20 EVAL 0xhtw parent_genre! 0y2tr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 52.000 29.000 0.571 http://example.org/music/genre/parent_genre #5929-01vyv9 PRED entity: 01vyv9 PRED relation: gender PRED expected values: 05zppz => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #61, 0.85 #79, 0.84 #77), 02zsn (0.44 #12, 0.43 #16, 0.33 #84) >> Best rule #61 for best value: >> intensional similarity = 2 >> extensional distance = 481 >> proper extension: 075wq; >> query: (?x4553, 05zppz) <- place_of_death(?x4553, ?x5719), contains(?x5719, ?x3387) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vyv9 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.847 http://example.org/people/person/gender #5928-013qvn PRED entity: 013qvn PRED relation: influenced_by! PRED expected values: 01svq8 => 157 concepts (81 used for prediction) PRED predicted values (max 10 best out of 461): 049fgvm (0.40 #3350, 0.29 #4378, 0.25 #1294), 016_mj (0.40 #3138, 0.29 #4166, 0.25 #1082), 03g5_y (0.40 #3397, 0.29 #4425, 0.25 #1341), 0q5hw (0.36 #8328, 0.23 #14502, 0.20 #3186), 01j7rd (0.32 #8297, 0.29 #4183, 0.25 #1099), 04bs3j (0.29 #4126, 0.25 #1042, 0.20 #3098), 0pz7h (0.29 #4135, 0.25 #1051, 0.20 #3107), 01s7qqw (0.25 #1238, 0.24 #8436, 0.20 #3294), 01xwqn (0.25 #1471, 0.24 #8669, 0.20 #3527), 0126rp (0.25 #1098, 0.20 #8296, 0.20 #3154) >> Best rule #3350 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 014zfs; >> query: (?x7414, 049fgvm) <- influenced_by(?x236, ?x7414), profession(?x7414, ?x1032), ?x236 = 01xdf5, ?x1032 = 02hrh1q >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #31382 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 133 *> proper extension: 03j90; 0459z; *> query: (?x7414, ?x1145) <- influenced_by(?x236, ?x7414), place_of_death(?x7414, ?x191), influenced_by(?x236, ?x1145), people(?x1446, ?x236) *> conf = 0.10 ranks of expected_values: 68 EVAL 013qvn influenced_by! 01svq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 157.000 81.000 0.400 http://example.org/influence/influence_node/influenced_by #5927-01pj_5 PRED entity: 01pj_5 PRED relation: film_crew_role PRED expected values: 01pvkk => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.75 #1140, 0.74 #880, 0.74 #1287), 01vx2h (0.40 #300, 0.38 #448, 0.36 #743), 0dxtw (0.39 #1149, 0.38 #742, 0.38 #889), 01pvkk (0.33 #12, 0.29 #1113, 0.28 #744), 02rh1dz (0.32 #190, 0.16 #695, 0.14 #446), 02ynfr (0.23 #564, 0.19 #748, 0.18 #453), 089g0h (0.20 #128, 0.16 #695, 0.15 #457), 01xy5l_ (0.17 #303, 0.16 #695, 0.15 #451), 0d2b38 (0.17 #463, 0.16 #695, 0.15 #315), 0215hd (0.16 #456, 0.16 #695, 0.15 #308) >> Best rule #1140 for best value: >> intensional similarity = 4 >> extensional distance = 436 >> proper extension: 02d44q; >> query: (?x4500, 09zzb8) <- nominated_for(?x5541, ?x4500), film_crew_role(?x4500, ?x468), film_release_distribution_medium(?x4500, ?x81), produced_by(?x4500, ?x8071) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0m491; 01l2b3; 01k0xy; *> query: (?x4500, 01pvkk) <- genre(?x4500, ?x6452), film_format(?x4500, ?x909), film(?x932, ?x4500), ?x6452 = 02b5_l *> conf = 0.33 ranks of expected_values: 4 EVAL 01pj_5 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 77.000 0.747 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5926-01_d4 PRED entity: 01_d4 PRED relation: citytown! PRED expected values: 07y2s => 147 concepts (137 used for prediction) PRED predicted values (max 10 best out of 662): 065r8g (0.51 #66209, 0.27 #77378, 0.21 #85362), 0jpn8 (0.51 #66209, 0.27 #77378, 0.21 #85362), 02ktt7 (0.38 #10368, 0.36 #18344, 0.36 #18343), 0181hw (0.38 #10368, 0.36 #18344, 0.36 #18343), 0537b (0.38 #10368, 0.36 #18344, 0.36 #18343), 04htfd (0.38 #10368, 0.36 #18344, 0.36 #18343), 01jzyx (0.33 #1024, 0.02 #12987, 0.02 #15378), 01dtcb (0.29 #1972, 0.07 #9947, 0.06 #5162), 0146mv (0.29 #2169, 0.06 #5359, 0.06 #4561), 06182p (0.29 #1982, 0.06 #5172, 0.06 #4374) >> Best rule #66209 for best value: >> intensional similarity = 2 >> extensional distance = 236 >> proper extension: 0fngy; 07sb1; >> query: (?x1860, ?x2838) <- citytown(?x1924, ?x1860), contains(?x1860, ?x2838) >> conf = 0.51 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01_d4 citytown! 07y2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 137.000 0.513 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #5925-03hkch7 PRED entity: 03hkch7 PRED relation: film_crew_role PRED expected values: 09zzb8 0ch6mp2 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.80 #713, 0.77 #377, 0.77 #1718), 09zzb8 (0.75 #707, 0.73 #1712, 0.72 #1638), 0dxtw (0.45 #85, 0.37 #1722, 0.36 #233), 01vx2h (0.40 #12, 0.38 #1723, 0.30 #2656), 0215hd (0.40 #20, 0.32 #168, 0.27 #94), 01pvkk (0.33 #309, 0.32 #383, 0.29 #2061), 089g0h (0.27 #95, 0.23 #169, 0.18 #391), 0d2b38 (0.20 #27, 0.19 #175, 0.18 #101), 01xy5l_ (0.20 #15, 0.18 #89, 0.17 #385), 0263ycg (0.20 #19, 0.13 #167, 0.12 #56) >> Best rule #713 for best value: >> intensional similarity = 3 >> extensional distance = 288 >> proper extension: 03kg2v; 02prwdh; >> query: (?x3124, 0ch6mp2) <- titles(?x53, ?x3124), film_crew_role(?x3124, ?x468), ?x53 = 07s9rl0 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03hkch7 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.803 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 03hkch7 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.803 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5924-047s_cr PRED entity: 047s_cr PRED relation: profession PRED expected values: 02hrh1q => 94 concepts (38 used for prediction) PRED predicted values (max 10 best out of 67): 02hrh1q (0.98 #2252, 0.95 #5083, 0.92 #611), 09jwl (0.70 #4342, 0.19 #5236, 0.18 #5087), 0dxtg (0.65 #909, 0.58 #2698, 0.57 #2847), 0np9r (0.61 #3003, 0.31 #1960, 0.31 #1811), 01d_h8 (0.59 #4627, 0.40 #901, 0.39 #1050), 02krf9 (0.49 #3456, 0.18 #2711, 0.17 #2860), 03gjzk (0.48 #3445, 0.45 #1806, 0.44 #911), 02jknp (0.33 #4629, 0.31 #3437, 0.27 #2096), 0nbcg (0.29 #4355, 0.11 #5249, 0.11 #3908), 0cbd2 (0.25 #902, 0.19 #1797, 0.17 #3138) >> Best rule #2252 for best value: >> intensional similarity = 5 >> extensional distance = 160 >> proper extension: 087z12; 05zdk2; 0bxy67; 070c93; 03z_g7; 03d63lb; >> query: (?x12681, 02hrh1q) <- nationality(?x12681, ?x2146), ?x2146 = 03rk0, profession(?x12681, ?x1146), profession(?x3868, ?x1146), ?x3868 = 01fs_4 >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047s_cr profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 38.000 0.981 http://example.org/people/person/profession #5923-04306rv PRED entity: 04306rv PRED relation: languages! PRED expected values: 04k15 0hr3g 03f1zhf => 76 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1988): 01j5sv (0.56 #6920, 0.50 #8193, 0.50 #3745), 01ps2h8 (0.50 #3469, 0.50 #2834, 0.44 #7281), 02lf70 (0.50 #3269, 0.50 #2634, 0.44 #7081), 0dqcm (0.50 #3657, 0.33 #7469, 0.33 #6832), 028pzq (0.50 #3658, 0.33 #7470, 0.33 #6833), 02f2p7 (0.50 #3471, 0.33 #7283, 0.33 #6646), 015q43 (0.50 #3462, 0.33 #7274, 0.33 #6637), 01h4rj (0.50 #3694, 0.33 #7506, 0.33 #6869), 01syr4 (0.50 #3692, 0.33 #7504, 0.33 #6867), 0g7k2g (0.50 #3625, 0.33 #7437, 0.33 #6800) >> Best rule #6920 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 03k50; >> query: (?x732, 01j5sv) <- language(?x8373, ?x732), language(?x6489, ?x732), language(?x3517, ?x732), service_language(?x555, ?x732), film_release_region(?x3517, ?x94), person(?x8373, ?x10245), countries_spoken_in(?x732, ?x172), produced_by(?x6489, ?x6187) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #520 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x732, 03f1zhf) <- language(?x8217, ?x732), language(?x5122, ?x732), language(?x3517, ?x732), language(?x2928, ?x732), ?x3517 = 09rsjpv, ?x2928 = 07024, languages(?x147, ?x732), ?x5122 = 07z6xs, ?x8217 = 04v89z *> conf = 0.33 ranks of expected_values: 49 EVAL 04306rv languages! 03f1zhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 76.000 37.000 0.556 http://example.org/people/person/languages EVAL 04306rv languages! 0hr3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 37.000 0.556 http://example.org/people/person/languages EVAL 04306rv languages! 04k15 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 37.000 0.556 http://example.org/people/person/languages #5922-016fjj PRED entity: 016fjj PRED relation: award PRED expected values: 0gqy2 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 244): 05pcn59 (0.26 #2062, 0.25 #77, 0.23 #4841), 0bs0bh (0.25 #99, 0.05 #6451, 0.04 #893), 05zr6wv (0.17 #412, 0.17 #2000, 0.15 #3588), 0gqwc (0.17 #3643, 0.16 #467, 0.16 #4437), 05p09zm (0.16 #2105, 0.16 #517, 0.14 #4884), 0gqy2 (0.16 #24616, 0.14 #2145, 0.13 #954), 02z0dfh (0.16 #24616, 0.12 #71, 0.08 #6423), 0gq9h (0.16 #24616, 0.12 #73, 0.08 #27396), 0gs9p (0.16 #24616, 0.08 #27396, 0.07 #472), 019f4v (0.16 #24616, 0.08 #27396, 0.07 #459) >> Best rule #2062 for best value: >> intensional similarity = 3 >> extensional distance = 245 >> proper extension: 01sl1q; 04bdxl; 01j5ts; 06dv3; 014zcr; 01qscs; 01q_ph; 09fb5; 0l8v5; 01dw4q; ... >> query: (?x3701, 05pcn59) <- participant(?x3701, ?x3210), award_winner(?x5129, ?x3701), award_nominee(?x1871, ?x3701) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #24616 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1462 *> proper extension: 0hm0k; *> query: (?x3701, ?x500) <- award_winner(?x5129, ?x3701), award(?x5129, ?x500) *> conf = 0.16 ranks of expected_values: 6 EVAL 016fjj award 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 109.000 109.000 0.259 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5921-013q07 PRED entity: 013q07 PRED relation: film_format PRED expected values: 07fb8_ => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.43 #16, 0.33 #21, 0.25 #53), 0cj16 (0.18 #29, 0.14 #34, 0.14 #156), 017fx5 (0.11 #45, 0.07 #66, 0.06 #40) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 03nx8mj; 032sl_; 056xkh; >> query: (?x2218, 07fb8_) <- film(?x1986, ?x2218), ?x1986 = 0gz5hs, titles(?x2480, ?x2218) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013q07 film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.429 http://example.org/film/film/film_format #5920-09hnb PRED entity: 09hnb PRED relation: role PRED expected values: 025cbm 01wy6 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 111): 0342h (0.48 #1966, 0.47 #1279, 0.42 #2260), 05r5c (0.44 #1775, 0.40 #2655, 0.39 #3143), 05148p4 (0.33 #1569, 0.32 #1766, 0.32 #1765), 0l14j_ (0.32 #1570, 0.32 #491, 0.30 #2550), 03f5mt (0.32 #1570, 0.32 #491, 0.30 #2550), 06ch55 (0.32 #1570, 0.32 #491, 0.30 #2550), 02sgy (0.28 #1280, 0.28 #1870, 0.27 #2261), 042v_gx (0.28 #1873, 0.26 #2264, 0.24 #2559), 01vj9c (0.23 #1486, 0.22 #310, 0.21 #408), 018vs (0.23 #1975, 0.20 #1681, 0.20 #1484) >> Best rule #1966 for best value: >> intensional similarity = 3 >> extensional distance = 205 >> proper extension: 09g0h; >> query: (?x2698, 0342h) <- role(?x2698, ?x228), role(?x2698, ?x316), role(?x316, ?x75) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1767 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 02zrv7; *> query: (?x2698, ?x227) <- performance_role(?x2698, ?x2764), gender(?x2698, ?x231), role(?x2764, ?x227) *> conf = 0.05 ranks of expected_values: 50, 88 EVAL 09hnb role 01wy6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 105.000 105.000 0.478 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 09hnb role 025cbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 105.000 105.000 0.478 http://example.org/music/artist/track_contributions./music/track_contribution/role #5919-0jpn8 PRED entity: 0jpn8 PRED relation: category PRED expected values: 08mbj5d => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #43, 0.90 #42, 0.89 #47) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 291 >> proper extension: 054lpb6; 03pmfw; 0gsgr; >> query: (?x9071, 08mbj5d) <- state_province_region(?x9071, ?x3818), organization(?x346, ?x9071), ?x346 = 060c4 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jpn8 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.901 http://example.org/common/topic/webpage./common/webpage/category #5918-09p5mwg PRED entity: 09p5mwg PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 6): 029j_ (0.84 #125, 0.83 #135, 0.83 #145), 02nxhr (0.21 #332, 0.19 #417, 0.18 #259), 07c52 (0.21 #332, 0.05 #53, 0.03 #313), 07z4p (0.21 #332, 0.03 #315, 0.03 #238), 0735l (0.19 #417, 0.19 #82, 0.18 #259), 0dq6p (0.19 #417, 0.18 #259) >> Best rule #125 for best value: >> intensional similarity = 6 >> extensional distance = 467 >> proper extension: 03_wm6; >> query: (?x9752, 029j_) <- currency(?x9752, ?x170), genre(?x9752, ?x604), genre(?x12693, ?x604), genre(?x2954, ?x604), ?x12693 = 04jn6y7, ?x2954 = 0crh5_f >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09p5mwg film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.838 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #5917-0trv PRED entity: 0trv PRED relation: student PRED expected values: 0lkr7 => 169 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1668): 02t_w8 (0.33 #3008, 0.29 #5096, 0.12 #9272), 01zfmm (0.33 #2528, 0.29 #4616, 0.12 #8792), 05bnp0 (0.29 #6275, 0.12 #8363, 0.11 #10451), 0f13b (0.29 #7735, 0.08 #16088, 0.08 #18176), 05xd_v (0.25 #1823, 0.14 #8087, 0.06 #10175), 0cbgl (0.25 #2082, 0.06 #10434, 0.05 #62635), 083chw (0.25 #26, 0.04 #45963, 0.04 #14643), 02nwxc (0.25 #994, 0.04 #15611, 0.03 #23963), 06y9c2 (0.25 #87, 0.04 #14704, 0.03 #23056), 01gv_f (0.25 #622, 0.04 #17327, 0.04 #19415) >> Best rule #3008 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 05krk; 06pwq; 065y4w7; 0bx8pn; >> query: (?x8706, 02t_w8) <- institution(?x1519, ?x8706), ?x1519 = 013zdg, school(?x2820, ?x8706), school(?x580, ?x8706), ?x2820 = 0jmj7, ?x580 = 05m_8, student(?x8706, ?x1817) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #114847 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 117 *> proper extension: 04s934; 0fr9jp; *> query: (?x8706, ?x8863) <- category(?x8706, ?x134), student(?x8706, ?x1817), citytown(?x8706, ?x7408), contains(?x94, ?x7408), location(?x8863, ?x7408), ?x94 = 09c7w0 *> conf = 0.02 ranks of expected_values: 1336 EVAL 0trv student 0lkr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 169.000 68.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #5916-09s5q8 PRED entity: 09s5q8 PRED relation: school! PRED expected values: 01slc => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 92): 0jmj7 (0.68 #303, 0.66 #1223, 0.65 #763), 05g49 (0.21 #44, 0.11 #320, 0.08 #596), 01y49 (0.21 #20, 0.09 #112, 0.07 #204), 051vz (0.21 #297, 0.14 #21, 0.14 #573), 07l4z (0.19 #345, 0.14 #69, 0.11 #161), 07l8x (0.19 #341, 0.11 #157, 0.10 #617), 049n7 (0.15 #286, 0.09 #470, 0.08 #654), 06x68 (0.14 #5, 0.11 #649, 0.11 #557), 04wmvz (0.14 #78, 0.11 #170, 0.11 #630), 0bwjj (0.14 #74, 0.11 #534, 0.10 #718) >> Best rule #303 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 02zkz7; >> query: (?x6083, 0jmj7) <- fraternities_and_sororities(?x6083, ?x3697), school(?x1883, ?x6083), currency(?x6083, ?x170) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #609 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 06pwq; 02gr81; 017j69; 09f2j; 027mdh; 0ks67; 08qnnv; 0trv; *> query: (?x6083, 01slc) <- major_field_of_study(?x6083, ?x1682), category(?x6083, ?x134), institution(?x1368, ?x6083), school(?x1883, ?x6083) *> conf = 0.14 ranks of expected_values: 23 EVAL 09s5q8 school! 01slc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 122.000 122.000 0.681 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #5915-0lbd9 PRED entity: 0lbd9 PRED relation: olympics! PRED expected values: 0345h => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 265): 0f8l9c (0.67 #2977, 0.67 #1594, 0.65 #1768), 059j2 (0.67 #1602, 0.65 #1768, 0.54 #1767), 0chghy (0.67 #1584, 0.56 #588, 0.54 #1767), 0345h (0.67 #1604, 0.51 #9480, 0.50 #422), 03_3d (0.65 #1768, 0.56 #1578, 0.54 #1767), 05qhw (0.65 #1768, 0.54 #1767, 0.54 #585), 0d0vqn (0.65 #1768, 0.54 #1767, 0.54 #585), 0154j (0.65 #1768, 0.54 #1767, 0.54 #585), 015fr (0.65 #1768, 0.54 #1767, 0.54 #585), 02vzc (0.65 #1768, 0.54 #1767, 0.54 #585) >> Best rule #2977 for best value: >> intensional similarity = 14 >> extensional distance = 10 >> proper extension: 0sxrz; 0lgxj; >> query: (?x6464, 0f8l9c) <- sports(?x6464, ?x2044), sports(?x6464, ?x1967), sports(?x6464, ?x1121), sports(?x6464, ?x4876), olympics(?x1229, ?x6464), olympics(?x390, ?x6464), ?x1121 = 0bynt, medal(?x6464, ?x422), ?x4876 = 0d1t3, ?x390 = 0chghy, ?x2044 = 06f41, second_level_divisions(?x1229, ?x3408), country(?x1967, ?x47), country(?x1009, ?x1229) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1604 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 7 *> proper extension: 06sks6; *> query: (?x6464, 0345h) <- olympics(?x151, ?x6464), olympics(?x1497, ?x6464), olympics(?x456, ?x6464), olympics(?x304, ?x6464), ?x1497 = 015qh, sports(?x6464, ?x171), ?x151 = 0b90_r, ?x456 = 05qhw, participating_countries(?x784, ?x304), film_release_region(?x5400, ?x304), film_release_region(?x3784, ?x304), film_release_region(?x633, ?x304), ?x5400 = 0bhwhj, ?x3784 = 0bmhvpr, ?x633 = 0c40vxk *> conf = 0.67 ranks of expected_values: 4 EVAL 0lbd9 olympics! 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 67.000 67.000 0.667 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #5914-02m501 PRED entity: 02m501 PRED relation: film PRED expected values: 05qbckf 01b195 05nyqk => 121 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1123): 01r97z (0.20 #110, 0.10 #1895, 0.09 #3680), 02b6n9 (0.20 #1570, 0.05 #3355, 0.04 #5140), 033f8n (0.20 #822, 0.05 #2607, 0.04 #4392), 02sfnv (0.20 #896, 0.05 #2681, 0.04 #4466), 032016 (0.10 #2286, 0.09 #4071, 0.03 #11211), 07gp9 (0.10 #1828, 0.09 #3613, 0.03 #10753), 026wlxw (0.09 #4985, 0.05 #3200, 0.04 #6770), 0gffmn8 (0.09 #4090, 0.05 #2305, 0.02 #13015), 01shy7 (0.07 #23629, 0.07 #25414, 0.05 #36124), 02qr3k8 (0.06 #20923, 0.02 #69121, 0.02 #90541) >> Best rule #110 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02qjj7; >> query: (?x9886, 01r97z) <- gender(?x9886, ?x231), athlete(?x1967, ?x9886), participant(?x4930, ?x9886) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #19945 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 141 *> proper extension: 0cj2w; *> query: (?x9886, 05qbckf) <- award(?x9886, ?x591), ?x591 = 0f4x7 *> conf = 0.01 ranks of expected_values: 706, 730, 967 EVAL 02m501 film 05nyqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 121.000 70.000 0.200 http://example.org/film/actor/film./film/performance/film EVAL 02m501 film 01b195 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 121.000 70.000 0.200 http://example.org/film/actor/film./film/performance/film EVAL 02m501 film 05qbckf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 121.000 70.000 0.200 http://example.org/film/actor/film./film/performance/film #5913-0ply0 PRED entity: 0ply0 PRED relation: mode_of_transportation PRED expected values: 025t3bg 01bjv => 253 concepts (253 used for prediction) PRED predicted values (max 10 best out of 4): 01bjv (0.89 #30, 0.86 #54, 0.85 #74), 025t3bg (0.82 #53, 0.81 #281, 0.81 #81), 0k4j (0.07 #187, 0.06 #35, 0.05 #47), 06d_3 (0.04 #336, 0.04 #76, 0.03 #132) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0cv3w; 01cx_; 0d6lp; 071vr; 02frhbc; >> query: (?x3373, 01bjv) <- citytown(?x5844, ?x3373), month(?x3373, ?x1459), contains(?x2623, ?x3373), dog_breed(?x3373, ?x1706) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0ply0 mode_of_transportation 01bjv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 253.000 253.000 0.889 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 0ply0 mode_of_transportation 025t3bg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 253.000 253.000 0.889 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #5912-029_3 PRED entity: 029_3 PRED relation: award PRED expected values: 0gkvb7 => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 316): 0cjyzs (0.33 #6166, 0.33 #22730, 0.32 #23134), 09sb52 (0.33 #24281, 0.31 #25493, 0.27 #31553), 0gkvb7 (0.31 #4471, 0.27 #6491, 0.25 #4875), 0fbtbt (0.30 #13160, 0.30 #21644, 0.28 #11948), 0ck27z (0.27 #33220, 0.23 #36856, 0.20 #42917), 05zr6wv (0.25 #1633, 0.20 #2845, 0.17 #10521), 05zvj3m (0.25 #1305, 0.18 #901, 0.16 #10597), 05pcn59 (0.23 #24321, 0.22 #25533, 0.18 #31593), 02q1tc5 (0.22 #17117, 0.20 #17521, 0.17 #18733), 019bnn (0.22 #5116, 0.19 #4712, 0.17 #7540) >> Best rule #6166 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 07nznf; 0grwj; 0c4f4; 0bxtg; 01pw2f1; 06x58; 0gz5hs; 06chf; 01jbx1; 0gy6z9; ... >> query: (?x4065, 0cjyzs) <- participant(?x4066, ?x4065), profession(?x4065, ?x1032), program(?x4065, ?x2710) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4471 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 086qd; 0gcs9; *> query: (?x4065, 0gkvb7) <- influenced_by(?x692, ?x4065), influenced_by(?x4065, ?x4066), participant(?x4065, ?x1145) *> conf = 0.31 ranks of expected_values: 3 EVAL 029_3 award 0gkvb7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 154.000 154.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5911-060d2 PRED entity: 060d2 PRED relation: entity_involved! PRED expected values: 0gfhg1y => 2 concepts (2 used for prediction) No prediction ranks of expected_values: EVAL 060d2 entity_involved! 0gfhg1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 2.000 2.000 0.000 http://example.org/base/culturalevent/event/entity_involved #5910-02tz9z PRED entity: 02tz9z PRED relation: student PRED expected values: 03jm6c => 137 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1425): 018_lb (0.11 #1871, 0.08 #3964), 01nm3s (0.11 #661, 0.08 #2754), 073v6 (0.11 #4715, 0.05 #8901, 0.03 #10994), 0306ds (0.11 #4596, 0.04 #31805, 0.03 #35991), 01_xtx (0.11 #4818, 0.03 #17376, 0.03 #19469), 015v3r (0.11 #4688, 0.03 #21432, 0.02 #31897), 0ff3y (0.09 #10444, 0.06 #16723, 0.06 #6258), 0fpzt5 (0.08 #7818, 0.06 #5725, 0.05 #14097), 09v6tz (0.07 #9716, 0.05 #15995, 0.02 #70413), 013pp3 (0.06 #5112, 0.05 #9298, 0.03 #11391) >> Best rule #1871 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 06wxw; 013gxt; 013gwb; 013h1c; 0fvwz; 01z1c; >> query: (?x12127, 018_lb) <- category(?x12127, ?x134), contains(?x1025, ?x12127), ?x134 = 08mbj5d, ?x1025 = 04ych >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02tz9z student 03jm6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 75.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #5909-0ftqr PRED entity: 0ftqr PRED relation: profession PRED expected values: 0fnpj => 115 concepts (51 used for prediction) PRED predicted values (max 10 best out of 59): 02hrh1q (0.59 #4023, 0.59 #4770, 0.57 #3874), 0dz3r (0.58 #2, 0.47 #595, 0.47 #891), 0nbcg (0.57 #1364, 0.55 #5680, 0.54 #1956), 016z4k (0.52 #1041, 0.50 #3863, 0.50 #449), 039v1 (0.47 #481, 0.35 #1369, 0.35 #1665), 0dxtg (0.38 #311, 0.15 #6258, 0.13 #1199), 0fnpj (0.27 #949, 0.18 #1393, 0.16 #1689), 01d_h8 (0.25 #303, 0.17 #6697, 0.17 #6250), 0n1h (0.21 #5213, 0.20 #1049, 0.19 #3573), 02jknp (0.19 #305, 0.11 #6252, 0.10 #1193) >> Best rule #4023 for best value: >> intensional similarity = 4 >> extensional distance = 383 >> proper extension: 02bfxb; 01rzxl; >> query: (?x10039, 02hrh1q) <- profession(?x10039, ?x1183), ?x1183 = 09jwl, type_of_union(?x10039, ?x566), ?x566 = 04ztj >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #949 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 134 *> proper extension: 03ds3; 0bg539; 0ph2w; 0pmw9; 02qfhb; 09g0h; *> query: (?x10039, 0fnpj) <- gender(?x10039, ?x231), instrumentalists(?x1495, ?x10039), ?x231 = 05zppz, performance_role(?x1574, ?x1495), ?x1574 = 0l15bq, role(?x74, ?x1495) *> conf = 0.27 ranks of expected_values: 7 EVAL 0ftqr profession 0fnpj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 115.000 51.000 0.590 http://example.org/people/person/profession #5908-04wgh PRED entity: 04wgh PRED relation: country! PRED expected values: 07gyv => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 45): 064vjs (0.74 #463, 0.62 #1211, 0.62 #1475), 06f41 (0.73 #101, 0.71 #453, 0.65 #321), 01z27 (0.71 #455, 0.58 #1203, 0.56 #1467), 06wrt (0.68 #102, 0.68 #454, 0.59 #234), 0194d (0.68 #125, 0.59 #477, 0.52 #345), 07bs0 (0.65 #452, 0.55 #100, 0.52 #1200), 07gyv (0.65 #314, 0.64 #94, 0.63 #226), 019tzd (0.62 #470, 0.52 #250, 0.46 #778), 01hp22 (0.62 #447, 0.51 #1459, 0.46 #1195), 03rbzn (0.62 #460, 0.50 #108, 0.49 #1472) >> Best rule #463 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 09c7w0; 0jgd; 03_3d; 04gzd; 01ls2; 03rt9; 05qhw; 07ssc; 06npd; 06mzp; ... >> query: (?x1273, 064vjs) <- country(?x3309, ?x1273), country(?x2266, ?x1273), ?x3309 = 09w1n, ?x2266 = 01lb14 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #314 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 0160w; *> query: (?x1273, 07gyv) <- country(?x471, ?x1273), vacationer(?x1273, ?x2626), olympics(?x1273, ?x778) *> conf = 0.65 ranks of expected_values: 7 EVAL 04wgh country! 07gyv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 179.000 179.000 0.735 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #5907-01_c4 PRED entity: 01_c4 PRED relation: country PRED expected values: 07ssc => 114 concepts (54 used for prediction) PRED predicted values (max 10 best out of 34): 07ssc (0.67 #375, 0.62 #620, 0.55 #1217), 036wy (0.63 #1094, 0.61 #607, 0.51 #2452), 09c7w0 (0.56 #548, 0.37 #731, 0.33 #915), 02jx1 (0.34 #3313, 0.27 #2574, 0.26 #729), 04jpl (0.27 #2574, 0.19 #3314, 0.17 #667), 048kw (0.27 #2574, 0.19 #3314, 0.16 #1838), 01_c4 (0.17 #667, 0.11 #1220, 0.11 #1219), 03_3d (0.10 #1352, 0.06 #2095, 0.02 #2399), 0345h (0.10 #1058, 0.08 #1369, 0.06 #1863), 05qhw (0.08 #1046, 0.06 #1357, 0.03 #2100) >> Best rule #375 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01w0v; >> query: (?x9491, 07ssc) <- contains(?x9491, ?x9844), second_level_divisions(?x1310, ?x9491), ?x1310 = 02jx1, administrative_parent(?x9491, ?x12774) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_c4 country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 54.000 0.667 http://example.org/location/administrative_division/country #5906-01pxcf PRED entity: 01pxcf PRED relation: institution! PRED expected values: 016t_3 014mlp 01rr_d => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 20): 02h4rq6 (0.86 #2, 0.69 #142, 0.69 #118), 014mlp (0.77 #121, 0.76 #145, 0.70 #263), 019v9k (0.70 #9, 0.68 #125, 0.65 #149), 03bwzr4 (0.63 #14, 0.40 #154, 0.39 #272), 0bkj86 (0.51 #8, 0.41 #148, 0.38 #266), 016t_3 (0.49 #3, 0.43 #143, 0.42 #119), 07s6fsf (0.41 #1, 0.31 #141, 0.28 #117), 013zdg (0.37 #7, 0.28 #778, 0.18 #147), 027f2w (0.29 #10, 0.28 #778, 0.23 #34), 0bjrnt (0.28 #778, 0.17 #6, 0.13 #122) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 08815; 06pwq; 065y4w7; 01w3v; 07tgn; 07w0v; 04rwx; 07szy; 01s0_f; 07wrz; ... >> query: (?x12051, 02h4rq6) <- major_field_of_study(?x12051, ?x4100), student(?x12051, ?x11399), ?x4100 = 01lj9 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #121 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 143 *> proper extension: 03v6t; 07vht; 01q460; 018m5q; 01y888; 02897w; 037njl; 09hgk; 0172jm; 01xrlm; ... *> query: (?x12051, 014mlp) <- major_field_of_study(?x12051, ?x2981), ?x2981 = 02j62, contains(?x390, ?x12051) *> conf = 0.77 ranks of expected_values: 2, 6, 11 EVAL 01pxcf institution! 01rr_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 57.000 57.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01pxcf institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 57.000 57.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01pxcf institution! 016t_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 57.000 57.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5905-03yk8z PRED entity: 03yk8z PRED relation: people! PRED expected values: 09vc4s => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 23): 02w7gg (0.22 #2, 0.21 #156, 0.06 #2081), 0x67 (0.20 #87, 0.10 #1165, 0.09 #2551), 041rx (0.16 #158, 0.14 #235, 0.14 #620), 033tf_ (0.11 #7, 0.07 #700, 0.07 #2317), 07bch9 (0.11 #23, 0.05 #177, 0.03 #562), 03bkbh (0.10 #109, 0.02 #648, 0.02 #1495), 065b6q (0.10 #80, 0.01 #1235, 0.01 #2313), 0dbxy (0.10 #124), 0d7wh (0.05 #171, 0.02 #2096, 0.02 #1095), 013xrm (0.05 #174, 0.02 #1252, 0.01 #4948) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 016gr2; 03y_46; >> query: (?x9924, 02w7gg) <- student(?x7021, ?x9924), award_winner(?x9924, ?x1738), ?x1738 = 0170pk >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1164 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 835 *> proper extension: 01wz_ml; *> query: (?x9924, 09vc4s) <- award_winner(?x9924, ?x72), location(?x9924, ?x760) *> conf = 0.02 ranks of expected_values: 15 EVAL 03yk8z people! 09vc4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 77.000 77.000 0.222 http://example.org/people/ethnicity/people #5904-0c5tl PRED entity: 0c5tl PRED relation: type_of_union PRED expected values: 04ztj => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.92 #109, 0.89 #113, 0.87 #317), 01g63y (0.37 #485, 0.25 #14, 0.20 #22), 0jgjn (0.37 #485, 0.19 #638, 0.05 #84), 01bl8s (0.19 #638, 0.02 #131, 0.01 #143) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 09xvf7; >> query: (?x5091, 04ztj) <- student(?x8223, ?x5091), award(?x5091, ?x921), place_of_burial(?x5091, ?x4435), profession(?x5091, ?x353) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c5tl type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.921 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5903-09k2t1 PRED entity: 09k2t1 PRED relation: award PRED expected values: 03qbh5 => 116 concepts (96 used for prediction) PRED predicted values (max 10 best out of 284): 01by1l (0.50 #113, 0.40 #2537, 0.32 #4557), 02g3gj (0.50 #25, 0.24 #1237, 0.11 #2449), 054ks3 (0.50 #143, 0.19 #7819, 0.19 #4587), 03qbh5 (0.38 #1418, 0.28 #2630, 0.23 #9499), 026mfs (0.35 #5382, 0.14 #6594, 0.14 #10231), 01cky2 (0.34 #2619, 0.29 #1407, 0.13 #9488), 01bgqh (0.33 #2063, 0.29 #1255, 0.28 #9336), 02f6xy (0.30 #2625, 0.29 #1413, 0.25 #201), 01c427 (0.29 #2105, 0.20 #489, 0.18 #1701), 02f5qb (0.29 #1368, 0.19 #2580, 0.15 #964) >> Best rule #113 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 016vqk; >> query: (?x2226, 01by1l) <- instrumentalists(?x227, ?x2226), artists(?x3562, ?x2226), artists(?x2664, ?x2226), ?x3562 = 025sc50, ?x2664 = 01lyv >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1418 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 03f5spx; 09qr6; 0j1yf; 019g40; 0136p1; 03bxwtd; 01wj18h; 02wb6yq; 01wzlxj; 0gbwp; ... *> query: (?x2226, 03qbh5) <- instrumentalists(?x227, ?x2226), artists(?x3996, ?x2226), artists(?x3562, ?x2226), ?x3562 = 025sc50, ?x3996 = 02lnbg *> conf = 0.38 ranks of expected_values: 4 EVAL 09k2t1 award 03qbh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 96.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5902-03qd_ PRED entity: 03qd_ PRED relation: film PRED expected values: 01hv3t => 93 concepts (84 used for prediction) PRED predicted values (max 10 best out of 507): 02stbw (0.70 #381, 0.44 #2162, 0.04 #23158), 0pdp8 (0.59 #74817, 0.58 #80163, 0.43 #65911), 07c72 (0.59 #74817, 0.58 #80163, 0.40 #65910), 02825cv (0.20 #1137, 0.12 #2918, 0.04 #15388), 048tv9 (0.20 #1395, 0.12 #3176, 0.03 #101543), 0b6m5fy (0.20 #1121, 0.12 #2902, 0.03 #101543), 01k0xy (0.20 #1277, 0.06 #3058, 0.03 #101543), 04k9y6 (0.20 #1039, 0.06 #2820, 0.03 #101543), 07y9w5 (0.20 #226, 0.03 #101543, 0.03 #58782), 04gv3db (0.12 #2531, 0.10 #750, 0.05 #16782) >> Best rule #381 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 07y8l9; >> query: (?x806, 02stbw) <- profession(?x806, ?x319), award_nominee(?x4107, ?x806), ?x4107 = 073749 >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03qd_ film 01hv3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 84.000 0.700 http://example.org/film/actor/film./film/performance/film #5901-0kbhf PRED entity: 0kbhf PRED relation: genre PRED expected values: 01g6gs => 71 concepts (67 used for prediction) PRED predicted values (max 10 best out of 112): 01jfsb (0.61 #2437, 0.37 #4373, 0.37 #4494), 02kdv5l (0.58 #2426, 0.53 #244, 0.43 #2), 03k9fj (0.48 #254, 0.27 #2436, 0.26 #5219), 05p553 (0.35 #1215, 0.34 #5332, 0.33 #4122), 06n90 (0.32 #256, 0.20 #2438, 0.19 #1940), 02l7c8 (0.32 #2562, 0.31 #1957, 0.31 #865), 01hmnh (0.27 #261, 0.14 #7042, 0.14 #2322), 0lsxr (0.24 #2433, 0.22 #4611, 0.20 #493), 04xvlr (0.24 #364, 0.23 #606, 0.23 #727), 060__y (0.20 #987, 0.19 #744, 0.19 #1940) >> Best rule #2437 for best value: >> intensional similarity = 4 >> extensional distance = 766 >> proper extension: 0c40vxk; 026p_bs; 0401sg; 035xwd; 02sg5v; 03t97y; 0jjy0; 07sc6nw; 026q3s3; 02vw1w2; ... >> query: (?x5843, 01jfsb) <- film(?x2417, ?x5843), genre(?x5843, ?x3515), genre(?x6918, ?x3515), ?x6918 = 02scbv >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1940 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 524 *> proper extension: 01cgz; *> query: (?x5843, ?x53) <- films(?x326, ?x5843), films(?x326, ?x4810), country(?x4810, ?x390), genre(?x4810, ?x53) *> conf = 0.19 ranks of expected_values: 20 EVAL 0kbhf genre 01g6gs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 71.000 67.000 0.607 http://example.org/film/film/genre #5900-023mdt PRED entity: 023mdt PRED relation: film PRED expected values: 0bm2nq => 152 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1046): 03nfnx (0.29 #3180, 0.03 #19227, 0.03 #31708), 0640m69 (0.20 #1755, 0.14 #3538, 0.09 #5321), 02x2jl_ (0.20 #1748, 0.14 #3531, 0.04 #7097), 0pc62 (0.20 #93, 0.07 #7225, 0.04 #9008), 07kdkfj (0.20 #1335, 0.07 #8467, 0.04 #10250), 04jwly (0.20 #455, 0.04 #64189, 0.02 #9370), 04ghz4m (0.20 #1238, 0.04 #64189, 0.02 #29766), 011x_4 (0.20 #1322, 0.04 #8454, 0.02 #10237), 09cr8 (0.20 #283, 0.02 #37726, 0.02 #30594), 0f40w (0.20 #360, 0.02 #25322, 0.02 #21756) >> Best rule #3180 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 028d4v; 0f7h2v; 0372kf; >> query: (?x9207, 03nfnx) <- location(?x9207, ?x739), film(?x9207, ?x2788), ?x2788 = 05q4y12 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #6977 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 016cff; *> query: (?x9207, 0bm2nq) <- location(?x9207, ?x739), participant(?x9207, ?x5097), sibling(?x2626, ?x9207) *> conf = 0.04 ranks of expected_values: 205 EVAL 023mdt film 0bm2nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 152.000 104.000 0.286 http://example.org/film/actor/film./film/performance/film #5899-0kk9v PRED entity: 0kk9v PRED relation: artist PRED expected values: 016szr => 127 concepts (110 used for prediction) PRED predicted values (max 10 best out of 760): 04jspq (0.19 #14301, 0.15 #34503, 0.14 #24401), 04gcd1 (0.19 #14301, 0.15 #34503, 0.14 #24401), 03xhj6 (0.14 #16294, 0.10 #26394, 0.10 #28077), 06y3r (0.13 #42075, 0.12 #16827, 0.11 #32819), 016s_5 (0.11 #32369, 0.10 #26477, 0.10 #28160), 01xzb6 (0.10 #26464, 0.10 #28147, 0.09 #16364), 016376 (0.10 #26840, 0.10 #28523, 0.09 #16740), 01kph_c (0.10 #26426, 0.10 #28109, 0.09 #16326), 019g40 (0.10 #13562, 0.09 #30397, 0.07 #33764), 01wx756 (0.10 #14257, 0.08 #32775, 0.08 #39506) >> Best rule #14301 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 02bh8z; 01dtcb; >> query: (?x3945, ?x2295) <- child(?x3920, ?x3945), company(?x2295, ?x3945), industry(?x3945, ?x373) >> conf = 0.19 => this is the best rule for 2 predicted values *> Best rule #26432 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 04gvyp; *> query: (?x3945, 016szr) <- organizations_founded(?x9105, ?x3945), profession(?x9105, ?x967), child(?x3920, ?x3945) *> conf = 0.07 ranks of expected_values: 111 EVAL 0kk9v artist 016szr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 127.000 110.000 0.192 http://example.org/music/record_label/artist #5898-06jrhz PRED entity: 06jrhz PRED relation: award_winner! PRED expected values: 0884hk 0brkwj => 92 concepts (41 used for prediction) PRED predicted values (max 10 best out of 639): 0h53p1 (0.82 #51215, 0.82 #62419, 0.82 #65618), 0d7hg4 (0.82 #51215, 0.82 #62419, 0.82 #65618), 06jrhz (0.27 #51216, 0.26 #19206, 0.24 #8003), 0415svh (0.27 #51216, 0.24 #8003, 0.16 #60819), 0bxtyq (0.27 #51216, 0.24 #8003, 0.16 #60819), 016dmx (0.27 #51216, 0.24 #8003, 0.16 #60819), 05m9f9 (0.27 #51216, 0.24 #8003, 0.16 #60819), 02qjpv5 (0.27 #51216, 0.24 #8003, 0.02 #1406), 0k9j_ (0.27 #51216, 0.24 #8003, 0.01 #1374), 0884hk (0.27 #51216, 0.16 #60819, 0.06 #5479) >> Best rule #51215 for best value: >> intensional similarity = 3 >> extensional distance = 1270 >> proper extension: 018_q8; >> query: (?x5832, ?x4034) <- award_winner(?x5832, ?x4034), award_winner(?x4034, ?x4022), award_winner(?x3263, ?x5832) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #51216 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1270 *> proper extension: 018_q8; *> query: (?x5832, ?x4022) <- award_winner(?x5832, ?x4034), award_winner(?x4034, ?x4022), award_winner(?x3263, ?x5832) *> conf = 0.27 ranks of expected_values: 10, 11 EVAL 06jrhz award_winner! 0brkwj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 92.000 41.000 0.821 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner EVAL 06jrhz award_winner! 0884hk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 92.000 41.000 0.821 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #5897-01f8gz PRED entity: 01f8gz PRED relation: prequel PRED expected values: 027m67 => 80 concepts (42 used for prediction) PRED predicted values (max 10 best out of 45): 01f85k (0.12 #109, 0.01 #834, 0.01 #1015), 027m67 (0.12 #129, 0.01 #1035), 0198b6 (0.12 #68, 0.01 #974), 06bc59 (0.02 #344, 0.01 #706, 0.01 #1250), 033qdy (0.02 #296, 0.01 #658, 0.01 #1202), 06_sc3 (0.02 #333, 0.01 #695), 08fbnx (0.02 #274, 0.01 #636), 013q0p (0.02 #272, 0.01 #634), 02d478 (0.02 #254, 0.01 #616), 02_sr1 (0.02 #253, 0.01 #615) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0198b6; 027m67; >> query: (?x1625, 01f85k) <- film(?x11657, ?x1625), ?x11657 = 01f873, film(?x4169, ?x1625), titles(?x2645, ?x1625) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #129 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0198b6; 027m67; *> query: (?x1625, 027m67) <- film(?x11657, ?x1625), ?x11657 = 01f873, film(?x4169, ?x1625), titles(?x2645, ?x1625) *> conf = 0.12 ranks of expected_values: 2 EVAL 01f8gz prequel 027m67 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 42.000 0.125 http://example.org/film/film/prequel #5896-0432mrk PRED entity: 0432mrk PRED relation: people PRED expected values: 01xwqn => 83 concepts (37 used for prediction) PRED predicted values (max 10 best out of 2234): 0x3n (0.40 #12967, 0.33 #18143, 0.29 #19869), 02ts3h (0.40 #13074, 0.33 #18250, 0.29 #19976), 031zkw (0.33 #17438, 0.33 #181, 0.25 #5360), 09h4b5 (0.33 #18370, 0.25 #6292, 0.20 #13194), 03l3ln (0.33 #926, 0.25 #6105, 0.20 #13007), 08x5c_ (0.33 #1624, 0.25 #6803, 0.20 #13705), 04bdlg (0.33 #1607, 0.25 #6786, 0.20 #13688), 0mbw0 (0.33 #1177, 0.25 #6356, 0.20 #13258), 011zd3 (0.33 #292, 0.25 #5471, 0.20 #12373), 01ypsj (0.33 #1382, 0.25 #6561, 0.20 #13463) >> Best rule #12967 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0x67; >> query: (?x13662, 0x3n) <- people(?x13662, ?x1128), risk_factors(?x13891, ?x13662), risk_factors(?x8523, ?x13662), geographic_distribution(?x13662, ?x8483), people(?x8523, ?x2807), risk_factors(?x1158, ?x8523), symptom_of(?x4905, ?x13891) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #41201 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 18 *> proper extension: 02gx2x; *> query: (?x13662, 01xwqn) <- people(?x13662, ?x3930), artists(?x671, ?x3930), award_nominee(?x748, ?x3930), award(?x3930, ?x704), geographic_distribution(?x13662, ?x8483), gender(?x3930, ?x514) *> conf = 0.10 ranks of expected_values: 464 EVAL 0432mrk people 01xwqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 83.000 37.000 0.400 http://example.org/people/ethnicity/people #5895-01f39b PRED entity: 01f39b PRED relation: actor PRED expected values: 01y8cr => 123 concepts (84 used for prediction) PRED predicted values (max 10 best out of 344): 02v2jy (0.75 #7384, 0.71 #15688, 0.71 #7383), 01v90t (0.75 #7384, 0.71 #15688, 0.71 #7383), 01vrnsk (0.71 #15688, 0.71 #7383, 0.69 #11076), 03xkps (0.71 #15688, 0.71 #7383, 0.69 #11076), 02wr6r (0.71 #15688, 0.71 #7383, 0.69 #11076), 01wk7b7 (0.71 #15688, 0.71 #7383, 0.69 #11076), 01ggc9 (0.33 #763, 0.25 #7224, 0.20 #8147), 01x0sy (0.33 #716, 0.25 #7177, 0.20 #8100), 01vh18t (0.33 #709, 0.25 #7170, 0.20 #8093), 04qsdh (0.33 #622, 0.25 #7083, 0.20 #8006) >> Best rule #7384 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 063zky; >> query: (?x5684, ?x11423) <- film(?x11423, ?x5684), film(?x4554, ?x5684), actor(?x5684, ?x2378), genre(?x5684, ?x811), influenced_by(?x1726, ?x4554), place_of_death(?x11423, ?x3026) >> conf = 0.75 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01f39b actor 01y8cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 84.000 0.750 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #5894-07z6xs PRED entity: 07z6xs PRED relation: film! PRED expected values: 0mz73 => 61 concepts (32 used for prediction) PRED predicted values (max 10 best out of 568): 05br10 (0.46 #37450, 0.45 #20805, 0.44 #41611), 0bytkq (0.46 #37450, 0.45 #20805, 0.44 #41611), 01c6l (0.15 #22886), 0dqmt0 (0.10 #14563), 0j_c (0.09 #411, 0.06 #2491, 0.02 #10813), 044qx (0.09 #733, 0.02 #2813, 0.01 #21538), 044bn (0.09 #1847, 0.02 #3927, 0.01 #6007), 0161h5 (0.09 #1826, 0.01 #3906), 01q_ph (0.08 #8379, 0.02 #14620, 0.02 #18781), 01vsn38 (0.06 #10175, 0.02 #8095, 0.02 #18496) >> Best rule #37450 for best value: >> intensional similarity = 4 >> extensional distance = 899 >> proper extension: 0gtvrv3; >> query: (?x5122, ?x3080) <- film(?x3705, ?x5122), film_crew_role(?x5122, ?x137), award_nominee(?x1582, ?x3705), nominated_for(?x3080, ?x5122) >> conf = 0.46 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 07z6xs film! 0mz73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 32.000 0.457 http://example.org/film/actor/film./film/performance/film #5893-09306z PRED entity: 09306z PRED relation: ceremony! PRED expected values: 0gq_v 0gr4k 0gq9h => 40 concepts (36 used for prediction) PRED predicted values (max 10 best out of 341): 0gq9h (0.89 #2473, 0.88 #5605, 0.86 #5365), 0gq_v (0.87 #4126, 0.85 #3630, 0.83 #1938), 0gr4k (0.85 #2442, 0.84 #4132, 0.82 #2925), 0gqzz (0.74 #7967, 0.73 #7723, 0.36 #282), 0czp_ (0.74 #7967, 0.73 #7723, 0.16 #5025), 02x201b (0.74 #7967, 0.73 #7723, 0.14 #3077), 0cjyzs (0.41 #5796, 0.17 #3212, 0.14 #6343), 0fbtbt (0.41 #5796, 0.12 #6423, 0.10 #3292), 040njc (0.38 #2181, 0.38 #2182, 0.37 #1939), 02qyntr (0.38 #2181, 0.38 #2182, 0.37 #1939) >> Best rule #2473 for best value: >> intensional similarity = 24 >> extensional distance = 25 >> proper extension: 073hmq; 02yvhx; 0fk0xk; 0c4hx0; >> query: (?x7884, 0gq9h) <- ceremony(?x6860, ?x7884), ceremony(?x5409, ?x7884), ceremony(?x4573, ?x7884), ceremony(?x3617, ?x7884), ceremony(?x1972, ?x7884), ceremony(?x4573, ?x11428), ceremony(?x4573, ?x7940), ceremony(?x4573, ?x7038), ceremony(?x4573, ?x6344), ceremony(?x4573, ?x3579), ceremony(?x4573, ?x1819), ?x3579 = 0bc773, ?x7940 = 0bzjvm, ?x7038 = 073hgx, award(?x10011, ?x4573), ?x6860 = 018wdw, ?x1972 = 0gqyl, ?x3617 = 0gvx_, ?x1819 = 02yv_b, ?x5409 = 0gr07, ?x11428 = 0dznvw, award_winner(?x7884, ?x241), ?x6344 = 0bzm__, award_winner(?x2016, ?x10011) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 09306z ceremony! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 36.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09306z ceremony! 0gr4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 36.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 09306z ceremony! 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 36.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony #5892-01wyy_ PRED entity: 01wyy_ PRED relation: profession PRED expected values: 02krf9 => 113 concepts (87 used for prediction) PRED predicted values (max 10 best out of 64): 02hrh1q (0.88 #5999, 0.86 #6437, 0.85 #5561), 0cbd2 (0.50 #2488, 0.50 #2342, 0.49 #2634), 02krf9 (0.33 #754, 0.32 #1484, 0.32 #608), 018gz8 (0.31 #306, 0.22 #6585, 0.19 #452), 09jwl (0.27 #5857, 0.21 #7171, 0.21 #7317), 0kyk (0.27 #2801, 0.23 #2363, 0.23 #2509), 0np9r (0.20 #310, 0.14 #6589, 0.13 #3376), 015cjr (0.18 #339, 0.07 #3405, 0.07 #1653), 02hv44_ (0.15 #2829, 0.14 #2391, 0.13 #2537), 0nbcg (0.14 #7330, 0.13 #5870, 0.13 #7184) >> Best rule #5999 for best value: >> intensional similarity = 3 >> extensional distance = 380 >> proper extension: 01r42_g; 01csvq; 018db8; 01wmxfs; 049g_xj; 01wxyx1; 01wk7b7; 02wb6yq; 05mkhs; 01svw8n; ... >> query: (?x3405, 02hrh1q) <- participant(?x5604, ?x3405), gender(?x3405, ?x231), nominated_for(?x3405, ?x810) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #754 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 0br1w; *> query: (?x3405, 02krf9) <- program_creator(?x11818, ?x3405), country_of_origin(?x11818, ?x94), nominated_for(?x3405, ?x810) *> conf = 0.33 ranks of expected_values: 3 EVAL 01wyy_ profession 02krf9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 87.000 0.880 http://example.org/people/person/profession #5891-01jft4 PRED entity: 01jft4 PRED relation: music PRED expected values: 01mkn_d => 124 concepts (93 used for prediction) PRED predicted values (max 10 best out of 97): 02bh9 (0.17 #472, 0.14 #894, 0.13 #1105), 03c_8t (0.14 #210, 0.02 #2108, 0.02 #843), 0gv07g (0.14 #132, 0.01 #1609, 0.01 #8179), 0150t6 (0.08 #257, 0.07 #889, 0.06 #1100), 06fxnf (0.08 #280, 0.05 #2391, 0.04 #1546), 023361 (0.08 #361, 0.04 #2048, 0.04 #783), 01hw6wq (0.07 #38, 0.03 #2994, 0.02 #881), 01m5m5b (0.07 #188, 0.02 #3780, 0.02 #3992), 03975z (0.07 #166, 0.01 #2911), 0146pg (0.07 #1908, 0.06 #12286, 0.06 #643) >> Best rule #472 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 02qrv7; 02rrfzf; 01_1hw; 06bc59; >> query: (?x7248, 02bh9) <- produced_by(?x7248, ?x2534), prequel(?x2207, ?x7248), film(?x5636, ?x7248), award_winner(?x709, ?x2534) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #542 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 02qrv7; 02rrfzf; 01_1hw; 06bc59; *> query: (?x7248, 01mkn_d) <- produced_by(?x7248, ?x2534), prequel(?x2207, ?x7248), film(?x5636, ?x7248), award_winner(?x709, ?x2534) *> conf = 0.02 ranks of expected_values: 46 EVAL 01jft4 music 01mkn_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 124.000 93.000 0.170 http://example.org/film/film/music #5890-018wdw PRED entity: 018wdw PRED relation: ceremony PRED expected values: 073h9x 02pgky2 => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 104): 073h9x (0.86 #662, 0.86 #558, 0.84 #454), 0bzjgq (0.82 #710, 0.81 #606, 0.79 #502), 0bz6l9 (0.82 #663, 0.81 #559, 0.79 #455), 02pgky2 (0.82 #692, 0.76 #588, 0.74 #484), 0bz6sb (0.81 #569, 0.79 #465, 0.77 #673), 0fzrtf (0.81 #567, 0.79 #463, 0.77 #671), 0bzlrh (0.77 #699, 0.76 #595, 0.74 #491), 0bzkgg (0.77 #656, 0.76 #552, 0.74 #448), 0c6vcj (0.77 #697, 0.76 #593, 0.74 #489), 0bzkvd (0.77 #706, 0.68 #498, 0.67 #602) >> Best rule #662 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x6860, 073h9x) <- ceremony(?x6860, ?x5349), ceremony(?x6860, ?x3579), ?x5349 = 02jp5r, ?x3579 = 0bc773, award_winner(?x6860, ?x1933) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 018wdw ceremony 02pgky2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 47.000 47.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 018wdw ceremony 073h9x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony #5889-04l_pt PRED entity: 04l_pt PRED relation: languages_spoken PRED expected values: 0653m => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 65): 02h40lc (0.93 #977, 0.86 #1133, 0.83 #1237), 0t_2 (0.50 #726, 0.45 #830, 0.45 #369), 0653m (0.50 #61, 0.33 #10, 0.29 #205), 064_8sq (0.40 #324, 0.33 #784, 0.30 #1563), 03_9r (0.40 #161, 0.15 #1079, 0.12 #1131), 06nm1 (0.36 #367, 0.31 #571, 0.31 #520), 06b_j (0.31 #580, 0.30 #325, 0.25 #427), 07qv_ (0.25 #80, 0.20 #182, 0.12 #285), 01jb8r (0.25 #94, 0.12 #299, 0.12 #248), 0999q (0.25 #127, 0.12 #281, 0.12 #230) >> Best rule #977 for best value: >> intensional similarity = 32 >> extensional distance = 25 >> proper extension: 02w7gg; 033tf_; 09v5bdn; 03lmx1; 0d7wh; 0g8_vp; 078vc; 0bbz66j; 03w9bjf; 078ds; ... >> query: (?x9979, 02h40lc) <- languages_spoken(?x9979, ?x10296), languages_spoken(?x9979, ?x9980), languages_spoken(?x9979, ?x3271), language(?x5502, ?x10296), ?x5502 = 01bl7g, language(?x10446, ?x3271), language(?x7713, ?x3271), language(?x7541, ?x3271), language(?x7502, ?x3271), language(?x7293, ?x3271), language(?x5826, ?x3271), language(?x4038, ?x3271), language(?x3897, ?x3271), language(?x1135, ?x3271), ?x7541 = 02gpkt, service_language(?x1492, ?x9980), ?x7502 = 0233bn, ?x5826 = 0gl02yg, ?x10446 = 0gyv0b4, film_release_region(?x3897, ?x1353), film_release_region(?x3897, ?x1264), film_release_region(?x3897, ?x205), ?x1264 = 0345h, ?x7293 = 027m67, ?x1135 = 04vr_f, ?x7713 = 0fxmbn, film_crew_role(?x3897, ?x137), ?x1353 = 035qy, ?x205 = 03rjj, ?x137 = 09zzb8, ?x4038 = 02_sr1, film(?x8587, ?x3897) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #61 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 2 *> proper extension: 04czx7; *> query: (?x9979, 0653m) <- languages_spoken(?x9979, ?x10296), languages_spoken(?x9979, ?x5974), languages_spoken(?x9979, ?x4605), languages_spoken(?x9979, ?x3271), language(?x5502, ?x10296), language(?x1625, ?x10296), ?x5502 = 01bl7g, ?x3271 = 012w70, ?x5974 = 01r2l, service_language(?x11188, ?x10296), language(?x7502, ?x4605), language(?x6069, ?x4605), language(?x4604, ?x4605), language(?x3076, ?x4605), language(?x1745, ?x4605), ?x7502 = 0233bn, languages(?x7835, ?x10296), ?x1625 = 01f8gz, ?x6069 = 0bs4r, ?x4604 = 0432_5, service_language(?x11303, ?x4605), ?x1745 = 0bcndz, service_location(?x11303, ?x252), industry(?x11303, ?x245), ?x252 = 03_3d, countries_spoken_in(?x10296, ?x2629), contact_category(?x11303, ?x897), ?x3076 = 0g5838s *> conf = 0.50 ranks of expected_values: 3 EVAL 04l_pt languages_spoken 0653m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 41.000 0.926 http://example.org/people/ethnicity/languages_spoken #5888-0np9r PRED entity: 0np9r PRED relation: profession! PRED expected values: 01q_ph 04l3_z 0pgjm 02wrhj 0gz5hs 03pmzt 01trf3 016yr0 05txrz 0dzf_ 01twdk 03y82t6 01nzz8 04s430 06tp4h 0436kgz 040696 06gb2q 05zjx 04zkj5 01w1ywm 0582cf 01rmnp 0fthdk 05mlqj 05z775 0dn44 0ckm4x 047jhq 013rds => 30 concepts (17 used for prediction) PRED predicted values (max 10 best out of 3966): 015pxr (0.67 #16030, 0.60 #12161, 0.50 #8292), 04fcx7 (0.67 #16965, 0.60 #13096, 0.50 #9227), 02v49c (0.67 #18088, 0.60 #14219, 0.50 #10350), 01jgpsh (0.67 #17385, 0.60 #13516, 0.50 #9647), 05zjx (0.67 #17756, 0.60 #13887, 0.50 #10018), 0gkydb (0.67 #16260, 0.60 #12391, 0.50 #8522), 083chw (0.67 #15529, 0.50 #7791, 0.50 #3922), 0dpqk (0.67 #16957, 0.50 #9219, 0.50 #5350), 028k57 (0.67 #16791, 0.50 #9053, 0.50 #5184), 01h1b (0.67 #17530, 0.50 #9792, 0.50 #5923) >> Best rule #16030 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01d_h8; >> query: (?x1383, 015pxr) <- profession(?x11624, ?x1383), film(?x11624, ?x886), gender(?x11624, ?x231), ?x886 = 0kv2hv >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #17756 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 01d_h8; *> query: (?x1383, 05zjx) <- profession(?x11624, ?x1383), film(?x11624, ?x886), gender(?x11624, ?x231), ?x886 = 0kv2hv *> conf = 0.67 ranks of expected_values: 5, 53, 125, 126, 128, 177, 256, 324, 459, 461, 462, 605, 657, 709, 837, 906, 1210, 1223, 1375, 1776, 1856, 1859, 2204, 2307, 2538, 2574, 2727, 2952 EVAL 0np9r profession! 013rds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 047jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 0ckm4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 0dn44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 05z775 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 05mlqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 0fthdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 01rmnp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 0582cf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 01w1ywm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 04zkj5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 05zjx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 06gb2q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 040696 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 0436kgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 06tp4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 04s430 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 01nzz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 03y82t6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 01twdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 0dzf_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 05txrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 016yr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 01trf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 03pmzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 0gz5hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 02wrhj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 0pgjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 04l3_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 17.000 0.667 http://example.org/people/person/profession EVAL 0np9r profession! 01q_ph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 17.000 0.667 http://example.org/people/person/profession #5887-01lxd4 PRED entity: 01lxd4 PRED relation: artists PRED expected values: 0p3sf => 56 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1388): 09hnb (0.50 #1294, 0.43 #3459, 0.42 #5624), 0132k4 (0.50 #1710, 0.29 #3875, 0.23 #7121), 046p9 (0.50 #1831, 0.29 #3996, 0.21 #21327), 03f3_p3 (0.50 #6127, 0.18 #15872, 0.14 #21293), 01vsy95 (0.45 #6493, 0.40 #2449, 0.36 #21659), 017yfz (0.45 #6493, 0.36 #21659, 0.34 #16238), 07s3vqk (0.42 #5422, 0.30 #15167, 0.24 #20588), 012vd6 (0.42 #5892, 0.24 #15637, 0.21 #21058), 0197tq (0.40 #2175, 0.33 #5423, 0.30 #15168), 032nwy (0.40 #2190, 0.33 #26, 0.29 #3273) >> Best rule #1294 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 07ym47; >> query: (?x119, 09hnb) <- parent_genre(?x119, ?x10207), parent_genre(?x119, ?x505), ?x505 = 03_d0, artists(?x119, ?x9134), artists(?x119, ?x1563), ?x1563 = 0fpjd_g, parent_genre(?x3753, ?x10207), student(?x1151, ?x9134), artists(?x3753, ?x3754), ?x3754 = 0p3r8 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4593 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 0161rf; *> query: (?x119, 0p3sf) <- artists(?x119, ?x7706), artists(?x119, ?x120), nationality(?x120, ?x94), influenced_by(?x3374, ?x120), ?x94 = 09c7w0, profession(?x120, ?x1183), ?x7706 = 0lsw9 *> conf = 0.33 ranks of expected_values: 29 EVAL 01lxd4 artists 0p3sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 56.000 42.000 0.500 http://example.org/music/genre/artists #5886-03r00m PRED entity: 03r00m PRED relation: award! PRED expected values: 01dwrc => 44 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2214): 01w9wwg (0.79 #10116, 0.79 #43842, 0.79 #57329), 01mskc3 (0.79 #10116, 0.79 #43842, 0.79 #57329), 01vw20h (0.70 #8026, 0.50 #1282, 0.33 #4654), 01vsgrn (0.60 #8371, 0.50 #4999, 0.50 #1627), 0gbwp (0.60 #7854, 0.25 #1110, 0.17 #31460), 016pns (0.50 #4180, 0.50 #808, 0.40 #7552), 01vs_v8 (0.50 #7329, 0.50 #585, 0.19 #30935), 0gdh5 (0.50 #7504, 0.50 #760, 0.12 #31110), 02z4b_8 (0.50 #8810, 0.50 #2066, 0.12 #32416), 0478__m (0.50 #8066, 0.50 #1322, 0.12 #31672) >> Best rule #10116 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 02nhxf; 02v1m7; 02f5qb; 02f716; 02f76h; >> query: (?x12835, ?x6162) <- award(?x6715, ?x12835), award(?x3894, ?x12835), award_winner(?x12835, ?x6162), place_of_birth(?x3894, ?x739), ?x6715 = 011z3g >> conf = 0.79 => this is the best rule for 2 predicted values *> Best rule #1693 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 01bgqh; 01by1l; *> query: (?x12835, 01dwrc) <- award(?x6715, ?x12835), award(?x5405, ?x12835), award(?x3894, ?x12835), award_winner(?x12835, ?x6162), place_of_birth(?x3894, ?x739), ?x6715 = 011z3g, ?x5405 = 01vvlyt *> conf = 0.50 ranks of expected_values: 20 EVAL 03r00m award! 01dwrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 44.000 23.000 0.795 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5885-03bnv PRED entity: 03bnv PRED relation: group PRED expected values: 07c0j => 154 concepts (94 used for prediction) PRED predicted values (max 10 best out of 69): 0123r4 (0.18 #150, 0.11 #43, 0.09 #257), 07mvp (0.18 #152, 0.06 #259, 0.03 #2841), 01v0sxx (0.18 #191, 0.02 #621, 0.02 #2880), 0g_g2 (0.11 #31, 0.06 #245, 0.02 #1425), 01wv9xn (0.11 #8, 0.04 #545, 0.02 #2804), 0bk1p (0.11 #73, 0.02 #2869, 0.01 #2221), 02r1tx7 (0.09 #123, 0.04 #2164, 0.03 #2812), 07c0j (0.09 #111, 0.03 #218, 0.02 #326), 0frsw (0.09 #122, 0.03 #229, 0.02 #552), 016l09 (0.09 #188, 0.02 #618, 0.01 #1368) >> Best rule #150 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01mwsnc; 01k_0fp; >> query: (?x3321, 0123r4) <- role(?x3321, ?x314), instrumentalists(?x228, ?x3321), type_of_appearance(?x3321, ?x3429) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #111 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 01mwsnc; 01k_0fp; *> query: (?x3321, 07c0j) <- role(?x3321, ?x314), instrumentalists(?x228, ?x3321), type_of_appearance(?x3321, ?x3429) *> conf = 0.09 ranks of expected_values: 8 EVAL 03bnv group 07c0j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 154.000 94.000 0.182 http://example.org/music/group_member/membership./music/group_membership/group #5884-01mjq PRED entity: 01mjq PRED relation: film_release_region! PRED expected values: 028_yv 02x3lt7 07qg8v 04zyhx 0jqn5 0gvrws1 05z7c 02yvct 0661m4p 06w839_ 0192hw 02mt51 02rmd_2 06zn2v2 043sct5 05pdh86 03nm_fh 0dc_ms 07pd_j 0280061 035zr0 0gh6j94 03nsm5x 03lfd_ 049w1q 072hx4 => 162 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1656): 0bpm4yw (0.82 #3865, 0.81 #7288, 0.74 #24409), 01fmys (0.82 #3619, 0.76 #10465, 0.73 #11606), 0bwfwpj (0.82 #3519, 0.68 #11506, 0.67 #2378), 0j43swk (0.82 #3726, 0.62 #11713, 0.60 #20845), 02mt51 (0.82 #3831, 0.61 #2690, 0.57 #24375), 0gd0c7x (0.81 #7037, 0.73 #3614, 0.68 #11601), 05zlld0 (0.78 #2663, 0.77 #3804, 0.74 #7227), 05pdh86 (0.77 #7309, 0.73 #3886, 0.71 #10732), 0407yj_ (0.77 #7139, 0.73 #3716, 0.57 #24260), 0dzlbx (0.77 #3962, 0.74 #7385, 0.70 #24506) >> Best rule #3865 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 095w_; >> query: (?x1558, 0bpm4yw) <- film_release_region(?x11809, ?x1558), film_release_region(?x7554, ?x1558), ?x11809 = 0b85mm, film(?x6211, ?x7554) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #3831 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 095w_; *> query: (?x1558, 02mt51) <- film_release_region(?x11809, ?x1558), film_release_region(?x7554, ?x1558), ?x11809 = 0b85mm, film(?x6211, ?x7554) *> conf = 0.82 ranks of expected_values: 5, 8, 21, 22, 26, 33, 34, 37, 51, 52, 58, 60, 64, 65, 77, 83, 86, 103, 106, 108, 117, 152, 168, 174, 190, 220 EVAL 01mjq film_release_region! 072hx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 049w1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 03lfd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 03nsm5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 0gh6j94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 035zr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 0280061 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 07pd_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 03nm_fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 05pdh86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 043sct5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 06zn2v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 02rmd_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 02mt51 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 0192hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 06w839_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 0661m4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 05z7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 0gvrws1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 0jqn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 04zyhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 07qg8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 02x3lt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01mjq film_release_region! 028_yv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 162.000 22.000 0.818 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5883-02n9bh PRED entity: 02n9bh PRED relation: nominated_for! PRED expected values: 02r22gf => 79 concepts (46 used for prediction) PRED predicted values (max 10 best out of 250): 02pqp12 (0.61 #292, 0.29 #5641, 0.28 #3347), 019f4v (0.58 #288, 0.43 #3343, 0.33 #7104), 04dn09n (0.56 #270, 0.30 #3325, 0.26 #7086), 04kxsb (0.56 #330, 0.20 #3385, 0.16 #7146), 0gq9h (0.53 #296, 0.47 #3351, 0.41 #7112), 0gs9p (0.53 #298, 0.41 #3353, 0.36 #7114), 0gr0m (0.50 #293, 0.32 #3348, 0.29 #5641), 0k611 (0.43 #3362, 0.36 #307, 0.31 #7123), 0gqyl (0.42 #314, 0.23 #784, 0.23 #7130), 0gq_v (0.39 #254, 0.36 #3309, 0.33 #7070) >> Best rule #292 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 09p7fh; 05hjnw; 016ks5; >> query: (?x3398, 02pqp12) <- genre(?x3398, ?x1403), language(?x3398, ?x254), ?x1403 = 02l7c8, nominated_for(?x6909, ?x3398), ?x6909 = 02qyntr >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #263 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 09p7fh; 05hjnw; 016ks5; *> query: (?x3398, 02r22gf) <- genre(?x3398, ?x1403), language(?x3398, ?x254), ?x1403 = 02l7c8, nominated_for(?x6909, ?x3398), ?x6909 = 02qyntr *> conf = 0.39 ranks of expected_values: 11 EVAL 02n9bh nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 79.000 46.000 0.611 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5882-0k89p PRED entity: 0k89p PRED relation: inductee PRED expected values: 095nx => 99 concepts (44 used for prediction) PRED predicted values (max 10 best out of 154): 015cbq (0.50 #741, 0.33 #587, 0.25 #1049), 029b9k (0.50 #729, 0.33 #575, 0.25 #1037), 01zlh5 (0.33 #566, 0.33 #412, 0.29 #874), 0127xk (0.33 #600, 0.29 #908, 0.25 #1062), 0grwj (0.33 #463, 0.29 #771, 0.25 #925), 028qyn (0.33 #440, 0.29 #902, 0.25 #1056), 03h_fk5 (0.33 #338, 0.29 #800, 0.25 #954), 02v2jy (0.33 #612, 0.25 #766, 0.14 #920), 01m4kpp (0.33 #609, 0.25 #763, 0.14 #917), 0488g9 (0.33 #604, 0.25 #758, 0.14 #912) >> Best rule #741 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 04dm2n; >> query: (?x12338, 015cbq) <- inductee(?x12338, ?x12339), inductee(?x12338, ?x11924), athlete(?x4833, ?x12339), location(?x12339, ?x1131), gender(?x11924, ?x231), religion(?x12339, ?x492), profession(?x11924, ?x1032), award_winner(?x10746, ?x12339) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k89p inductee 095nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 44.000 0.500 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #5881-0rj0z PRED entity: 0rj0z PRED relation: location_of_ceremony! PRED expected values: 04ztj => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.85 #49, 0.75 #13, 0.70 #57), 0jgjn (0.07 #44, 0.06 #16, 0.05 #28), 01g63y (0.06 #14, 0.05 #26, 0.05 #22), 01bl8s (0.01 #79, 0.01 #75, 0.01 #87) >> Best rule #49 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0h7h6; 01_d4; 0dclg; 02h6_6p; 03h64; 012wgb; 0c8tk; 06y57; 02hrh0_; 09bkv; ... >> query: (?x3892, 04ztj) <- contains(?x94, ?x3892), place_of_death(?x6996, ?x3892), location_of_ceremony(?x12334, ?x3892), profession(?x6996, ?x131) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rj0z location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.854 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #5880-08y2fn PRED entity: 08y2fn PRED relation: nominated_for! PRED expected values: 0bdwqv => 91 concepts (85 used for prediction) PRED predicted values (max 10 best out of 246): 0gqwc (0.84 #6937, 0.42 #10730, 0.20 #11204), 0m7yy (0.67 #1660, 0.66 #15650, 0.65 #14463), 0gqy2 (0.59 #11266, 0.22 #10792, 0.21 #6999), 0bdwqv (0.56 #1077, 0.48 #3449, 0.44 #2264), 0bfvw2 (0.52 #3334, 0.47 #1436, 0.38 #2149), 0gq9h (0.49 #11206, 0.38 #6939, 0.35 #10732), 0fbvqf (0.46 #4070, 0.41 #4307, 0.20 #39), 0gqyl (0.45 #10750, 0.26 #6957, 0.26 #11224), 0gq_v (0.45 #4525, 0.37 #4999, 0.30 #11163), 0fbtbt (0.44 #4429, 0.43 #4192, 0.26 #5614) >> Best rule #6937 for best value: >> intensional similarity = 4 >> extensional distance = 144 >> proper extension: 02wk7b; 0cvkv5; >> query: (?x7424, 0gqwc) <- titles(?x53, ?x7424), nominated_for(?x1132, ?x7424), award(?x4349, ?x1132), ?x4349 = 01dvms >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1077 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 03kq98; 05jyb2; *> query: (?x7424, 0bdwqv) <- titles(?x53, ?x7424), actor(?x7424, ?x940), nominated_for(?x1132, ?x7424), ?x53 = 07s9rl0 *> conf = 0.56 ranks of expected_values: 4 EVAL 08y2fn nominated_for! 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 91.000 85.000 0.836 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5879-04xbq3 PRED entity: 04xbq3 PRED relation: award PRED expected values: 0m7yy => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 176): 0m7yy (0.42 #1302, 0.40 #600, 0.36 #2238), 07kjk7c (0.25 #184, 0.12 #1120, 0.07 #8193), 0bdwqv (0.25 #128, 0.12 #1064, 0.07 #8193), 0fc9js (0.20 #617, 0.08 #1319, 0.07 #8193), 047byns (0.20 #511, 0.07 #8193, 0.07 #8662), 027gs1_ (0.14 #2286, 0.13 #3457, 0.11 #3223), 0cjyzs (0.14 #2188, 0.13 #3359, 0.11 #3125), 0gkr9q (0.13 #2307, 0.11 #2775, 0.11 #2541), 0ck27z (0.13 #2178, 0.11 #2646, 0.09 #2880), 09qs08 (0.13 #2214, 0.10 #3385, 0.09 #3619) >> Best rule #1302 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 02rlj20; >> query: (?x9188, 0m7yy) <- honored_for(?x4760, ?x9188), award(?x9188, ?x4728), ?x4760 = 02q690_, nominated_for(?x1416, ?x9188) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04xbq3 award 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.417 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #5878-050t68 PRED entity: 050t68 PRED relation: award_nominee PRED expected values: 02p7_k => 66 concepts (27 used for prediction) PRED predicted values (max 10 best out of 790): 050t68 (0.85 #5543, 0.78 #7867, 0.77 #3219), 015vq_ (0.81 #11622, 0.81 #6973, 0.81 #4649), 01k7d9 (0.81 #11622, 0.81 #6973, 0.81 #4649), 0z4s (0.81 #11622, 0.81 #6973, 0.81 #4649), 02p7_k (0.77 #3143, 0.62 #5467, 0.61 #7791), 05kfs (0.18 #46477, 0.08 #48801), 0146pg (0.18 #46477), 02bkdn (0.16 #53449, 0.15 #5046, 0.15 #2722), 0hvb2 (0.16 #53449, 0.15 #60421, 0.14 #51125), 043kzcr (0.16 #53449, 0.15 #60421, 0.14 #51125) >> Best rule #5543 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0z4s; 0410cp; 02ch1w; >> query: (?x3932, 050t68) <- award_nominee(?x7923, ?x3932), award_nominee(?x4103, ?x3932), ?x4103 = 02jsgf, ?x7923 = 02t_vx >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #3143 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 017149; 03q1vd; 015vq_; 014g22; 016ks_; 03q95r; 01vyv9; 042z_g; 034zc0; 02ct_k; ... *> query: (?x3932, 02p7_k) <- award_nominee(?x5840, ?x3932), award_nominee(?x4103, ?x3932), ?x4103 = 02jsgf, ?x5840 = 02ch1w *> conf = 0.77 ranks of expected_values: 5 EVAL 050t68 award_nominee 02p7_k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 66.000 27.000 0.846 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5877-03x23q PRED entity: 03x23q PRED relation: educational_institution! PRED expected values: 03x23q => 90 concepts (65 used for prediction) PRED predicted values (max 10 best out of 147): 03x33n (0.07 #113, 0.02 #1730, 0.01 #28592), 01pl14 (0.07 #8, 0.01 #28592, 0.01 #2164), 019tfm (0.07 #539, 0.01 #28592, 0.01 #30753), 02vkzcx (0.07 #535, 0.01 #28592, 0.01 #30753), 0160nk (0.07 #394, 0.01 #28592, 0.01 #30753), 01t0dy (0.03 #741, 0.02 #1280, 0.02 #1819), 05zl0 (0.03 #728, 0.02 #1267, 0.02 #1806), 02301 (0.03 #607, 0.02 #1146, 0.02 #1685), 02gnmp (0.03 #955, 0.02 #1494, 0.01 #2572), 0177sq (0.03 #911, 0.02 #1450, 0.01 #2528) >> Best rule #113 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01pl14; 0fvvz; 03x33n; 0tln7; 013d7t; 035ktt; 0160nk; 0tk02; 0tn9j; 02vkzcx; ... >> query: (?x12732, 03x33n) <- category(?x12732, ?x134), ?x134 = 08mbj5d, contains(?x3908, ?x12732), ?x3908 = 04ly1 >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #28592 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 599 *> proper extension: 01nn79; 01hpf6; *> query: (?x12732, ?x466) <- category(?x12732, ?x134), ?x134 = 08mbj5d, state_province_region(?x12732, ?x3908), state_province_region(?x466, ?x3908) *> conf = 0.01 ranks of expected_values: 73 EVAL 03x23q educational_institution! 03x23q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 90.000 65.000 0.071 http://example.org/education/educational_institution_campus/educational_institution #5876-09nhvw PRED entity: 09nhvw PRED relation: location PRED expected values: 030qb3t => 151 concepts (143 used for prediction) PRED predicted values (max 10 best out of 221): 030qb3t (0.31 #884, 0.24 #15322, 0.23 #40995), 05jbn (0.18 #251, 0.15 #1053, 0.07 #1855), 0cc56 (0.17 #4868, 0.12 #3264, 0.11 #9680), 0ccvx (0.16 #5834, 0.12 #3428, 0.11 #9844), 01n7q (0.09 #62, 0.08 #864, 0.07 #1666), 0h7h6 (0.09 #89, 0.08 #891, 0.07 #1693), 0_xdd (0.09 #247, 0.08 #1049, 0.07 #1851), 0ply0 (0.09 #175, 0.08 #977, 0.03 #6591), 0r62v (0.09 #46, 0.08 #848, 0.03 #6462), 0xhmb (0.09 #514, 0.08 #1316, 0.03 #6930) >> Best rule #884 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 0lk90; 03y82t6; >> query: (?x9395, 030qb3t) <- artists(?x8878, ?x9395), artists(?x3996, ?x9395), participant(?x5058, ?x9395), ?x3996 = 02lnbg, ?x8878 = 02ny8t >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09nhvw location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 143.000 0.308 http://example.org/people/person/places_lived./people/place_lived/location #5875-03k1vm PRED entity: 03k1vm PRED relation: nationality PRED expected values: 09c7w0 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 41): 09c7w0 (0.82 #502, 0.80 #1204, 0.78 #1506), 0kpys (0.32 #4218, 0.32 #4320, 0.32 #6632), 01n7q (0.32 #4218, 0.32 #4320, 0.32 #6632), 02jx1 (0.26 #5426, 0.13 #1939, 0.13 #1135), 07ssc (0.26 #5426, 0.11 #1117, 0.10 #4739), 0d060g (0.22 #207, 0.19 #6735, 0.10 #1009), 0b90_r (0.19 #6735, 0.02 #1005, 0.01 #2512), 03rk0 (0.15 #1852, 0.08 #1752, 0.08 #2955), 012m_ (0.10 #391, 0.09 #491, 0.02 #1193), 03_3d (0.07 #2815, 0.06 #3217, 0.04 #501) >> Best rule #502 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 012vf6; 0hwqg; >> query: (?x11326, 09c7w0) <- place_of_death(?x11326, ?x5895), type_of_union(?x11326, ?x566), special_performance_type(?x11326, ?x9609), film(?x11326, ?x2425), gender(?x11326, ?x231) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03k1vm nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.818 http://example.org/people/person/nationality #5874-06www PRED entity: 06www PRED relation: genre! PRED expected values: 04w7rn => 63 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2020): 0fqt1ns (0.71 #10144, 0.67 #6411, 0.60 #4546), 0cd2vh9 (0.71 #9589, 0.67 #5856, 0.60 #3991), 0340hj (0.71 #9572, 0.67 #5839, 0.60 #3974), 02wgk1 (0.71 #10109, 0.67 #6376, 0.60 #4511), 01hqk (0.71 #10073, 0.67 #6340, 0.60 #4475), 06gb1w (0.71 #10084, 0.67 #6351, 0.60 #4486), 01qb5d (0.71 #9470, 0.67 #5737, 0.60 #3872), 09sh8k (0.71 #9339, 0.67 #5606, 0.60 #3741), 0d90m (0.71 #9336, 0.67 #5603, 0.60 #3738), 01hw5kk (0.71 #13769, 0.33 #8163, 0.33 #6295) >> Best rule #10144 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 04pbhw; >> query: (?x13368, 0fqt1ns) <- genre(?x7480, ?x13368), genre(?x7239, ?x13368), ?x7239 = 0bl3nn, genre(?x7480, ?x162), category(?x7480, ?x134), genre(?x7566, ?x13368), titles(?x162, ?x7844), titles(?x162, ?x5152), genre(?x3310, ?x162), ?x5152 = 08sfxj, ?x7844 = 0g0x9c >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #13314 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 5 *> proper extension: 07s9rl0; 05p553; *> query: (?x13368, 04w7rn) <- genre(?x7239, ?x13368), genre(?x1842, ?x13368), genre(?x708, ?x13368), currency(?x708, ?x170), edited_by(?x708, ?x707), ?x1842 = 015x74, award(?x708, ?x2393), nominated_for(?x2444, ?x708), film_crew_role(?x7239, ?x137), story_by(?x7239, ?x8753), film(?x1469, ?x708), film(?x4314, ?x708) *> conf = 0.29 ranks of expected_values: 846 EVAL 06www genre! 04w7rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 63.000 16.000 0.714 http://example.org/film/film/genre #5873-02d44q PRED entity: 02d44q PRED relation: country PRED expected values: 09c7w0 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 139): 09c7w0 (0.84 #4864, 0.84 #3963, 0.84 #4440), 02jx1 (0.45 #4139, 0.03 #1356, 0.02 #933), 0164v (0.45 #4139), 0f8l9c (0.43 #18, 0.19 #1347, 0.17 #924), 0345h (0.22 #88, 0.20 #932, 0.18 #1355), 0d060g (0.16 #122, 0.14 #9, 0.13 #1747), 03rjj (0.16 #122, 0.13 #1747, 0.11 #3725), 059j2 (0.16 #122, 0.13 #1747, 0.11 #3725), 035qy (0.16 #122, 0.13 #1747, 0.11 #3725), 03gj2 (0.16 #122, 0.13 #1747, 0.11 #3725) >> Best rule #4864 for best value: >> intensional similarity = 4 >> extensional distance = 1360 >> proper extension: 02sg5v; 02qrv7; 0g5pv3; 05cj_j; 018nnz; 04kzqz; 03l6q0; 0d_wms; 015g28; 05sw5b; ... >> query: (?x1071, 09c7w0) <- country(?x1071, ?x429), film(?x10866, ?x1071), film(?x617, ?x1071), award(?x10866, ?x451) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d44q country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.844 http://example.org/film/film/country #5872-02nd_ PRED entity: 02nd_ PRED relation: featured_film_locations! PRED expected values: 07cw4 => 79 concepts (52 used for prediction) PRED predicted values (max 10 best out of 361): 01_1hw (0.33 #605, 0.25 #1330, 0.20 #2055), 04dsnp (0.33 #64, 0.25 #789, 0.08 #5865), 08g_jw (0.33 #683, 0.25 #1408, 0.07 #2858), 047csmy (0.33 #390, 0.25 #1115, 0.05 #6191), 061681 (0.33 #45, 0.25 #770, 0.04 #5846), 0bl1_ (0.33 #337, 0.25 #1062, 0.04 #6138), 072x7s (0.33 #109, 0.25 #834, 0.04 #5910), 035yn8 (0.33 #116, 0.25 #841, 0.04 #3016), 02rjv2w (0.33 #194, 0.25 #919, 0.04 #3094), 011ycb (0.33 #362, 0.25 #1087, 0.04 #3262) >> Best rule #605 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02_286; >> query: (?x6310, 01_1hw) <- featured_film_locations(?x6309, ?x6310), featured_film_locations(?x1130, ?x6310), contains(?x335, ?x6310), ?x6309 = 0jqd3, ?x1130 = 0jyx6 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #429 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 02_286; *> query: (?x6310, 07cw4) <- featured_film_locations(?x6309, ?x6310), featured_film_locations(?x1130, ?x6310), contains(?x335, ?x6310), ?x6309 = 0jqd3, ?x1130 = 0jyx6 *> conf = 0.33 ranks of expected_values: 129 EVAL 02nd_ featured_film_locations! 07cw4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 79.000 52.000 0.333 http://example.org/film/film/featured_film_locations #5871-09pj68 PRED entity: 09pj68 PRED relation: ceremony! PRED expected values: 027s4dn => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 318): 0gqy2 (0.75 #3953, 0.65 #5995, 0.64 #5486), 0k611 (0.75 #3904, 0.63 #5437, 0.63 #5946), 018wng (0.73 #3868, 0.62 #4891, 0.62 #5401), 0gq_d (0.71 #3991, 0.62 #5014, 0.62 #5524), 0gqyl (0.71 #3912, 0.62 #5445, 0.60 #4935), 0gvx_ (0.71 #3968, 0.61 #6010, 0.60 #4991), 0gq9h (0.69 #3893, 0.62 #4916, 0.62 #5426), 0gqwc (0.69 #3892, 0.61 #5934, 0.60 #5425), 0gs9p (0.69 #3894, 0.60 #5427, 0.59 #5936), 0p9sw (0.68 #3853, 0.60 #5895, 0.59 #5386) >> Best rule #3953 for best value: >> intensional similarity = 15 >> extensional distance = 57 >> proper extension: 0bzm81; >> query: (?x7573, 0gqy2) <- honored_for(?x7573, ?x861), award_winner(?x7573, ?x10491), award_winner(?x7573, ?x8167), award_winner(?x7573, ?x669), ceremony(?x7965, ?x7573), award_winner(?x5593, ?x8167), award_winner(?x3624, ?x5593), nominated_for(?x669, ?x670), music(?x796, ?x669), award(?x534, ?x7965), nominated_for(?x7965, ?x6270), award_nominee(?x10491, ?x336), award_winner(?x995, ?x8167), film(?x10491, ?x4643), ?x6270 = 0g9zljd >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3002 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 32 *> proper extension: 073hmq; *> query: (?x7573, 027s4dn) <- honored_for(?x7573, ?x861), award_winner(?x7573, ?x8167), award_winner(?x7573, ?x669), ceremony(?x746, ?x7573), award_winner(?x5593, ?x8167), award_winner(?x3624, ?x5593), nominated_for(?x669, ?x670), vacationer(?x151, ?x5593), location(?x669, ?x739), award(?x5593, ?x1007), music(?x796, ?x669), award_winner(?x1079, ?x669), location(?x5593, ?x108) *> conf = 0.24 ranks of expected_values: 227 EVAL 09pj68 ceremony! 027s4dn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 32.000 32.000 0.746 http://example.org/award/award_category/winners./award/award_honor/ceremony #5870-0dl567 PRED entity: 0dl567 PRED relation: profession PRED expected values: 0nbcg => 123 concepts (121 used for prediction) PRED predicted values (max 10 best out of 64): 0nbcg (0.59 #1189, 0.53 #1044, 0.49 #1479), 0dz3r (0.50 #437, 0.48 #1017, 0.46 #292), 01d_h8 (0.49 #2472, 0.40 #2327, 0.40 #2037), 0d1pc (0.43 #47, 0.34 #1741, 0.27 #192), 03gjzk (0.35 #2480, 0.30 #2190, 0.26 #3931), 0dxtg (0.35 #2479, 0.29 #9015, 0.29 #2189), 012t_z (0.34 #1741, 0.10 #301, 0.10 #1026), 025352 (0.34 #1741, 0.10 #636, 0.07 #491), 01c72t (0.29 #5682, 0.28 #2633, 0.28 #2778), 02jknp (0.24 #2474, 0.21 #7703, 0.21 #12201) >> Best rule #1189 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 01q7cb_; 09prnq; 01vv126; 02r3cn; 01ydzx; 01vsyjy; 04_jsg; 01w9mnm; 012ycy; 020_4z; ... >> query: (?x4080, 0nbcg) <- nationality(?x4080, ?x94), artists(?x302, ?x4080), ?x302 = 016clz >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dl567 profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 121.000 0.585 http://example.org/people/person/profession #5869-0hzlz PRED entity: 0hzlz PRED relation: film_release_region! PRED expected values: 0gkz15s 017gl1 03qnvdl 0407yj_ 05zlld0 0gjcrrw 01rwpj 01mgw 0j8f09z => 229 concepts (195 used for prediction) PRED predicted values (max 10 best out of 1751): 0gvs1kt (0.90 #20478, 0.89 #15454, 0.88 #10430), 06wbm8q (0.89 #15364, 0.87 #20388, 0.84 #10340), 0879bpq (0.88 #10364, 0.87 #20412, 0.86 #15388), 0g9wdmc (0.88 #10242, 0.87 #20290, 0.86 #15266), 0dt8xq (0.88 #10673, 0.87 #20721, 0.82 #15697), 062zm5h (0.88 #10661, 0.83 #20709, 0.82 #15685), 03qnc6q (0.88 #10344, 0.83 #20392, 0.82 #15368), 0gmcwlb (0.88 #10192, 0.83 #20240, 0.82 #15216), 02vr3gz (0.88 #10491, 0.83 #20539, 0.79 #15515), 0gx9rvq (0.88 #10111, 0.82 #15135, 0.80 #20159) >> Best rule #20478 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 03rt9; 01p1v; 06mkj; >> query: (?x792, 0gvs1kt) <- film_release_region(?x5588, ?x792), ?x5588 = 0gtt5fb, organization(?x792, ?x127) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #20537 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 03rt9; 01p1v; 06mkj; *> query: (?x792, 05zlld0) <- film_release_region(?x5588, ?x792), ?x5588 = 0gtt5fb, organization(?x792, ?x127) *> conf = 0.87 ranks of expected_values: 18, 39, 41, 58, 59, 80, 87, 114, 258 EVAL 0hzlz film_release_region! 0j8f09z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 229.000 195.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hzlz film_release_region! 01mgw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 229.000 195.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hzlz film_release_region! 01rwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 229.000 195.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hzlz film_release_region! 0gjcrrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 229.000 195.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hzlz film_release_region! 05zlld0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 229.000 195.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hzlz film_release_region! 0407yj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 229.000 195.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hzlz film_release_region! 03qnvdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 229.000 195.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hzlz film_release_region! 017gl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 229.000 195.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0hzlz film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 229.000 195.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5868-04q24zv PRED entity: 04q24zv PRED relation: nominated_for! PRED expected values: 0fm3kw => 58 concepts (51 used for prediction) PRED predicted values (max 10 best out of 227): 054knh (0.43 #195, 0.21 #2338, 0.16 #5002), 019f4v (0.41 #6008, 0.30 #4579, 0.28 #4817), 0gq9h (0.35 #6017, 0.33 #301, 0.32 #4588), 0gs9p (0.34 #6019, 0.33 #4590, 0.32 #4828), 0k611 (0.33 #312, 0.28 #2455, 0.27 #6028), 099c8n (0.33 #771, 0.27 #1009, 0.25 #3629), 0l8z1 (0.33 #290, 0.20 #766, 0.20 #2433), 0p9sw (0.33 #259, 0.20 #4546, 0.19 #5975), 0gr42 (0.33 #328, 0.09 #3662, 0.08 #1995), 02x2gy0 (0.31 #579, 0.14 #1293, 0.14 #103) >> Best rule #195 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 03_wm6; >> query: (?x2797, 054knh) <- genre(?x2797, ?x1626), film(?x1414, ?x2797), ?x1414 = 024rbz, ?x1626 = 03q4nz >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #5002 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 331 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x2797, ?x4226) <- award(?x2797, ?x533), award(?x4696, ?x533), disciplines_or_subjects(?x533, ?x373), award(?x4696, ?x4226) *> conf = 0.16 ranks of expected_values: 45 EVAL 04q24zv nominated_for! 0fm3kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 58.000 51.000 0.429 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5867-01rc6f PRED entity: 01rc6f PRED relation: institution! PRED expected values: 013zdg => 208 concepts (151 used for prediction) PRED predicted values (max 10 best out of 19): 014mlp (0.81 #145, 0.77 #487, 0.76 #166), 02_xgp2 (0.72 #192, 0.67 #493, 0.65 #172), 016t_3 (0.69 #143, 0.67 #184, 0.65 #485), 0bkj86 (0.59 #209, 0.58 #490, 0.56 #189), 04zx3q1 (0.50 #203, 0.50 #142, 0.47 #163), 027f2w (0.42 #350, 0.42 #491, 0.42 #410), 013zdg (0.42 #107, 0.33 #86, 0.31 #147), 0bjrnt (0.39 #187, 0.29 #755, 0.29 #488), 01rr_d (0.38 #155, 0.35 #176, 0.29 #755), 022h5x (0.29 #755, 0.27 #460, 0.27 #219) >> Best rule #145 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 07tgn; >> query: (?x8120, 014mlp) <- school_type(?x8120, ?x4994), company(?x1620, ?x8120), institution(?x620, ?x8120), ?x4994 = 07tf8, major_field_of_study(?x8120, ?x2014) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #107 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 02bh8z; 01rs59; *> query: (?x8120, 013zdg) <- company(?x1620, ?x8120), company(?x5510, ?x8120), state_province_region(?x8120, ?x4758), category(?x1620, ?x134) *> conf = 0.42 ranks of expected_values: 7 EVAL 01rc6f institution! 013zdg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 208.000 151.000 0.812 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5866-0sq2v PRED entity: 0sq2v PRED relation: location! PRED expected values: 029m83 => 147 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2245): 02ts3h (0.82 #20158, 0.76 #22679, 0.72 #153773), 0171lb (0.53 #2520, 0.49 #20157, 0.49 #128563), 029_3 (0.25 #795, 0.22 #3315, 0.12 #8353), 0prfz (0.25 #49, 0.19 #7607, 0.11 #2569), 023kzp (0.25 #1217, 0.14 #23896, 0.13 #16332), 02sjf5 (0.25 #202, 0.14 #35484, 0.11 #2722), 0227tr (0.25 #480, 0.13 #28202, 0.11 #5519), 0pyww (0.25 #982, 0.13 #16097, 0.12 #33742), 01s21dg (0.25 #965, 0.13 #16080, 0.11 #3485), 05myd2 (0.25 #1929, 0.13 #17044, 0.11 #4449) >> Best rule #20158 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 029kpy; 04bz2f; >> query: (?x11848, ?x7140) <- place_of_birth(?x7140, ?x11848), service_location(?x6315, ?x11848), film(?x7140, ?x4331), location(?x7140, ?x1227) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1619 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 02_286; *> query: (?x11848, 029m83) <- place_of_birth(?x4180, ?x11848), source(?x11848, ?x958), locations(?x11210, ?x11848), service_location(?x6315, ?x11848) *> conf = 0.25 ranks of expected_values: 129 EVAL 0sq2v location! 029m83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 147.000 83.000 0.816 http://example.org/people/person/places_lived./people/place_lived/location #5865-083jv PRED entity: 083jv PRED relation: colors! PRED expected values: 05g3b 049n7 0hvjr 061xq 051q5 084l5 0frm7n 04mp9q 0jm4b 01x4wq 0k_l4 01slc 01cwm1 07147 04v9wn 0138mv 03z8bw 03m6zs 0jmh7 059nf5 048ldh 049n3s 04zxrt 0230rx 04l59s => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 764): 01fjz9 (0.50 #1455, 0.33 #1169, 0.33 #740), 0cgwt8 (0.50 #1485, 0.33 #1342, 0.33 #770), 0329gm (0.40 #1823, 0.38 #1968, 0.33 #2113), 061xq (0.40 #1752, 0.33 #1322, 0.33 #28), 0289q (0.40 #1616, 0.33 #1187, 0.33 #901), 05g76 (0.40 #1589, 0.33 #1160, 0.33 #874), 04l57x (0.40 #1852, 0.33 #1422, 0.33 #128), 01fwqn (0.40 #1835, 0.33 #1405, 0.33 #111), 0hn6d (0.40 #1747, 0.33 #1317, 0.33 #23), 02q4ntp (0.40 #1662, 0.33 #1233, 0.33 #947) >> Best rule #1455 for best value: >> intensional similarity = 31 >> extensional distance = 2 >> proper extension: 036k5h; >> query: (?x663, 01fjz9) <- colors(?x7439, ?x663), colors(?x6545, ?x663), colors(?x4692, ?x663), colors(?x3394, ?x663), colors(?x2196, ?x663), colors(?x1772, ?x663), colors(?x1153, ?x663), ?x4692 = 0345gh, colors(?x10085, ?x663), colors(?x9172, ?x663), colors(?x7485, ?x663), colors(?x6537, ?x663), colors(?x5850, ?x663), major_field_of_study(?x1772, ?x947), team(?x13559, ?x10085), institution(?x1771, ?x1153), student(?x2196, ?x2609), major_field_of_study(?x2196, ?x2014), category(?x1153, ?x134), currency(?x6545, ?x170), position_s(?x9172, ?x180), institution(?x865, ?x1772), citytown(?x3394, ?x13062), team(?x60, ?x6537), fraternities_and_sororities(?x7439, ?x3697), team(?x7484, ?x7485), institution(?x734, ?x6545), teams(?x13262, ?x5850), ?x734 = 04zx3q1, ?x2014 = 04rjg, contains(?x94, ?x6545) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1752 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 3 *> proper extension: 09ggk; *> query: (?x663, 061xq) <- colors(?x10994, ?x663), colors(?x10038, ?x663), colors(?x4692, ?x663), major_field_of_study(?x4692, ?x3490), colors(?x6645, ?x663), colors(?x5686, ?x663), colors(?x2398, ?x663), colors(?x1599, ?x663), colors(?x684, ?x663), position(?x1599, ?x60), currency(?x10994, ?x1099), current_club(?x676, ?x5686), team(?x982, ?x5686), ?x684 = 01ct6, contains(?x362, ?x4692), team(?x8324, ?x1599), position(?x6645, ?x180), institution(?x4981, ?x4692), currency(?x10038, ?x170), sport(?x6645, ?x1083), ?x3490 = 05qfh, school(?x2398, ?x4980), student(?x4692, ?x4693) *> conf = 0.40 ranks of expected_values: 4, 13, 51, 64, 65, 69, 72, 77, 88, 98, 99, 101, 102, 109, 114, 124, 130, 136, 139, 145, 157, 359, 539, 587, 672 EVAL 083jv colors! 04l59s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 0230rx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 04zxrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 049n3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 048ldh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 059nf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 0jmh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 03m6zs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 03z8bw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 0138mv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 04v9wn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 07147 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 01cwm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 01slc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 0k_l4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 01x4wq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 0jm4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 04mp9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 0frm7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 084l5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 051q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 061xq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 0hvjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 049n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 22.000 22.000 0.500 http://example.org/sports/sports_team/colors EVAL 083jv colors! 05g3b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 22.000 22.000 0.500 http://example.org/sports/sports_team/colors #5864-0167_s PRED entity: 0167_s PRED relation: artists! PRED expected values: 05bt6j => 103 concepts (73 used for prediction) PRED predicted values (max 10 best out of 282): 06by7 (0.76 #8442, 0.75 #16250, 0.75 #21266), 016clz (0.74 #20937, 0.72 #21563, 0.57 #13104), 05w3f (0.67 #4710, 0.54 #2216, 0.53 #2841), 064t9 (0.64 #22509, 0.61 #11553, 0.55 #11865), 05bt6j (0.62 #9711, 0.60 #9088, 0.57 #5027), 02yv6b (0.50 #6334, 0.33 #3215, 0.30 #15076), 08jyyk (0.41 #7866, 0.40 #2872, 0.38 #4741), 018lg0 (0.40 #338, 0.28 #6546, 0.20 #1583), 01fh36 (0.38 #1022, 0.28 #12564, 0.27 #13500), 016jny (0.33 #729, 0.28 #6546, 0.27 #3221) >> Best rule #8442 for best value: >> intensional similarity = 7 >> extensional distance = 36 >> proper extension: 03sww; 01vn0t_; >> query: (?x2250, 06by7) <- artists(?x9063, ?x2250), artists(?x2249, ?x2250), origin(?x2250, ?x1310), ?x2249 = 03lty, artist(?x1543, ?x2250), artists(?x9063, ?x1817), ?x1817 = 015882 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #9711 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 45 *> proper extension: 018y81; 095x_; *> query: (?x2250, 05bt6j) <- artists(?x9063, ?x2250), artists(?x1380, ?x2250), ?x1380 = 0dl5d, artists(?x9063, ?x7237), artists(?x9063, ?x6469), artists(?x9063, ?x5858), artists(?x9063, ?x5057), ?x7237 = 0473q, ?x5858 = 013w2r, ?x6469 = 04bgy, ?x5057 = 01w3lzq *> conf = 0.62 ranks of expected_values: 5 EVAL 0167_s artists! 05bt6j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 103.000 73.000 0.763 http://example.org/music/genre/artists #5863-07qg8v PRED entity: 07qg8v PRED relation: genre PRED expected values: 05p553 03q4nz => 81 concepts (80 used for prediction) PRED predicted values (max 10 best out of 92): 03q4nz (0.58 #17, 0.15 #968, 0.12 #1086), 01jfsb (0.52 #6524, 0.52 #3793, 0.52 #3792), 064_8sq (0.52 #6524, 0.52 #3793, 0.52 #3792), 0f8l9c (0.52 #6524, 0.52 #3793, 0.52 #3792), 03k9fj (0.43 #248, 0.32 #485, 0.28 #1198), 05p553 (0.41 #7358, 0.36 #3915, 0.35 #4390), 02l7c8 (0.31 #3807, 0.30 #4636, 0.28 #3687), 01hmnh (0.30 #255, 0.23 #136, 0.20 #492), 02kdv5l (0.28 #4269, 0.27 #478, 0.26 #5455), 06n90 (0.21 #487, 0.17 #1436, 0.17 #1318) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0cvkv5; >> query: (?x1421, 03q4nz) <- nominated_for(?x8888, ?x1421), titles(?x5607, ?x1421), major_field_of_study(?x90, ?x5607), language(?x80, ?x5607) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 07qg8v genre 03q4nz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 80.000 0.579 http://example.org/film/film/genre EVAL 07qg8v genre 05p553 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 81.000 80.000 0.579 http://example.org/film/film/genre #5862-09z2b7 PRED entity: 09z2b7 PRED relation: executive_produced_by PRED expected values: 05hj_k => 79 concepts (52 used for prediction) PRED predicted values (max 10 best out of 97): 05hj_k (0.12 #602, 0.12 #4637, 0.10 #3628), 03hbzj (0.11 #102), 0l99s (0.08 #2772, 0.08 #2268, 0.08 #1007), 02z6l5f (0.08 #622, 0.03 #6424, 0.02 #3648), 029m83 (0.08 #680, 0.02 #1688, 0.02 #1940), 079vf (0.08 #757, 0.07 #2018, 0.06 #2522), 06pj8 (0.06 #4594, 0.05 #6361, 0.03 #3585), 03v1xb (0.05 #4791, 0.04 #703), 0b7xl8 (0.05 #4791), 052hl (0.05 #4791) >> Best rule #602 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 0j8f09z; >> query: (?x1508, 05hj_k) <- nominated_for(?x1245, ?x1508), nominated_for(?x1180, ?x1508), genre(?x1508, ?x53), ?x1245 = 0gqwc, ?x1180 = 02n9nmz >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09z2b7 executive_produced_by 05hj_k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 52.000 0.125 http://example.org/film/film/executive_produced_by #5861-027mdh PRED entity: 027mdh PRED relation: institution! PRED expected values: 014mlp => 212 concepts (156 used for prediction) PRED predicted values (max 10 best out of 15): 014mlp (0.96 #2353, 0.83 #1467, 0.82 #988), 019v9k (0.79 #505, 0.78 #2021, 0.76 #1469), 02_xgp2 (0.69 #509, 0.68 #977, 0.67 #58), 04zx3q1 (0.55 #153, 0.50 #17, 0.47 #987), 027f2w (0.55 #157, 0.41 #506, 0.40 #1703), 03mkk4 (0.50 #192, 0.46 #225, 0.45 #125), 01rr_d (0.50 #28, 0.44 #362, 0.41 #496), 071tyz (0.40 #1703, 0.40 #1921, 0.33 #22), 0bjrnt (0.40 #1703, 0.40 #1921, 0.30 #2350), 02m4yg (0.40 #1703, 0.40 #1921, 0.30 #2350) >> Best rule #2353 for best value: >> intensional similarity = 6 >> extensional distance = 289 >> proper extension: 01v3ht; >> query: (?x5651, 014mlp) <- institution(?x8398, ?x5651), colors(?x5651, ?x332), student(?x8398, ?x8587), institution(?x8398, ?x6501), ?x6501 = 01ljpm, award_nominee(?x3013, ?x8587) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027mdh institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 156.000 0.955 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5860-042v2 PRED entity: 042v2 PRED relation: influenced_by! PRED expected values: 01hc9_ => 140 concepts (37 used for prediction) PRED predicted values (max 10 best out of 327): 02yl42 (0.12 #1167, 0.08 #9950, 0.08 #11501), 040rjq (0.08 #1003, 0.06 #4101, 0.04 #6686), 0p8jf (0.08 #1144, 0.06 #9927, 0.05 #6311), 01hc9_ (0.08 #1396, 0.05 #10179, 0.05 #11886), 040db (0.07 #9891, 0.07 #11442, 0.05 #6275), 0683n (0.07 #10155, 0.07 #6539, 0.06 #11706), 01hb6v (0.07 #1643, 0.06 #6293, 0.06 #13530), 047g6 (0.07 #2031, 0.03 #4613, 0.03 #13918), 05jm7 (0.07 #9956, 0.06 #6340, 0.06 #11507), 0c00lh (0.06 #2293, 0.03 #9007, 0.03 #743) >> Best rule #1167 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 03j90; >> query: (?x8656, 02yl42) <- place_of_birth(?x8656, ?x7600), influenced_by(?x8656, ?x5091), story_by(?x167, ?x8656), student(?x331, ?x8656) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #1396 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 03j90; *> query: (?x8656, 01hc9_) <- place_of_birth(?x8656, ?x7600), influenced_by(?x8656, ?x5091), story_by(?x167, ?x8656), student(?x331, ?x8656) *> conf = 0.08 ranks of expected_values: 4 EVAL 042v2 influenced_by! 01hc9_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 140.000 37.000 0.120 http://example.org/influence/influence_node/influenced_by #5859-01gtcq PRED entity: 01gtcq PRED relation: district_represented PRED expected values: 04ych 07z1m 07h34 04ly1 0498y 0vbk => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 355): 04ych (0.92 #201, 0.88 #864, 0.87 #965), 0498y (0.92 #201, 0.86 #652, 0.86 #752), 07h34 (0.92 #201, 0.86 #652, 0.86 #752), 04ly1 (0.92 #201, 0.86 #652, 0.86 #752), 07z1m (0.92 #201, 0.86 #652, 0.86 #752), 07b_l (0.86 #652, 0.86 #752, 0.86 #750), 0vbk (0.86 #652, 0.86 #752, 0.86 #750), 02xry (0.86 #652, 0.86 #752, 0.86 #750), 03s0w (0.86 #652, 0.86 #752, 0.86 #750), 0824r (0.86 #652, 0.86 #752, 0.86 #750) >> Best rule #201 for best value: >> intensional similarity = 62 >> extensional distance = 1 >> proper extension: 01gt99; >> query: (?x5252, ?x1025) <- district_represented(?x5252, ?x7518), district_represented(?x5252, ?x7405), district_represented(?x5252, ?x7058), district_represented(?x5252, ?x4776), district_represented(?x5252, ?x4622), district_represented(?x5252, ?x3818), district_represented(?x5252, ?x3670), district_represented(?x5252, ?x3038), district_represented(?x5252, ?x2713), district_represented(?x5252, ?x2020), district_represented(?x5252, ?x1767), district_represented(?x5252, ?x1755), district_represented(?x5252, ?x760), district_represented(?x5252, ?x728), district_represented(?x5252, ?x448), district_represented(?x5252, ?x335), ?x2020 = 05k7sb, ?x3670 = 05tbn, legislative_sessions(?x5252, ?x10803), legislative_sessions(?x5252, ?x7973), legislative_sessions(?x5252, ?x6021), legislative_sessions(?x5252, ?x5006), legislative_sessions(?x5252, ?x5005), legislative_sessions(?x5252, ?x759), ?x759 = 043djx, ?x760 = 05fkf, ?x1755 = 01x73, ?x3818 = 03v0t, legislative_sessions(?x10291, ?x5252), legislative_sessions(?x7944, ?x5252), ?x7058 = 050ks, ?x7973 = 01gsvb, ?x335 = 059rby, legislative_sessions(?x9765, ?x5006), legislative_sessions(?x5401, ?x5006), district_represented(?x10803, ?x4061), district_represented(?x10803, ?x1426), district_represented(?x10803, ?x1025), legislative_sessions(?x6712, ?x10803), ?x2713 = 06btq, ?x4622 = 04tgp, ?x728 = 059f4, ?x7518 = 026mj, legislative_sessions(?x11956, ?x10803), ?x4061 = 0498y, ?x10291 = 01gtdd, ?x1767 = 04rrd, district_represented(?x7944, ?x2623), ?x5005 = 01gstn, ?x2623 = 02xry, legislative_sessions(?x2860, ?x5006), ?x5401 = 0dq2k, ?x1426 = 07z1m, ?x2860 = 0b3wk, ?x6021 = 01gsvp, ?x3038 = 0d0x8, ?x4776 = 06yxd, politician(?x8714, ?x9765), ?x448 = 03v1s, ?x6712 = 01gst9, ?x7405 = 07_f2, type_of_union(?x9765, ?x566) >> conf = 0.92 => this is the best rule for 5 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 7 EVAL 01gtcq district_represented 0vbk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 29.000 29.000 0.920 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtcq district_represented 0498y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.920 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtcq district_represented 04ly1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.920 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtcq district_represented 07h34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.920 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtcq district_represented 07z1m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.920 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtcq district_represented 04ych CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.920 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #5858-01nd9f PRED entity: 01nd9f PRED relation: parent_genre PRED expected values: 05r6t 0jmwg => 51 concepts (36 used for prediction) PRED predicted values (max 10 best out of 202): 06by7 (0.76 #3841, 0.57 #4008, 0.40 #844), 016clz (0.52 #1662, 0.50 #1996, 0.50 #666), 05r6t (0.50 #716, 0.48 #1050, 0.46 #1548), 059kh (0.50 #696, 0.33 #364, 0.17 #328), 0133_p (0.50 #592, 0.30 #996, 0.28 #828), 03p7rp (0.40 #936, 0.13 #3155, 0.11 #1823), 05w3f (0.35 #2352, 0.29 #3016, 0.22 #3185), 09jw2 (0.33 #432, 0.25 #764, 0.17 #328), 01h0kx (0.33 #425, 0.25 #757, 0.17 #328), 03_d0 (0.33 #9, 0.22 #4001, 0.13 #4499) >> Best rule #3841 for best value: >> intensional similarity = 14 >> extensional distance = 76 >> proper extension: 016clz; 0m0jc; 0xhtw; 061fhg; 0mhfr; 03lty; 05bt6j; 059kh; 01qzt1; 02k_kn; ... >> query: (?x14408, 06by7) <- parent_genre(?x14408, ?x2996), artists(?x2996, ?x7612), artists(?x2996, ?x6049), artists(?x2996, ?x4387), artists(?x2996, ?x3206), parent_genre(?x9935, ?x2996), origin(?x6049, ?x8451), group(?x227, ?x7612), award_nominee(?x9262, ?x7612), ?x3206 = 01m65sp, instrumentalists(?x316, ?x4387), artists(?x9935, ?x211), artists(?x474, ?x7612), ?x474 = 0m0jc >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #716 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 03xnwz; *> query: (?x14408, 05r6t) <- parent_genre(?x14408, ?x10366), parent_genre(?x14408, ?x2996), ?x2996 = 01243b, artists(?x10366, ?x9791), parent_genre(?x10366, ?x9853), parent_genre(?x10366, ?x1572), artists(?x9853, ?x9179), artists(?x9853, ?x8035), artists(?x9853, ?x8029), artists(?x9853, ?x6469), ?x9179 = 01vsqvs, ?x6469 = 04bgy, ?x1572 = 06by7, ?x8029 = 08w4pm, artist(?x382, ?x9791), award_winner(?x486, ?x9791), ?x8035 = 095x_ *> conf = 0.50 ranks of expected_values: 3, 22 EVAL 01nd9f parent_genre 0jmwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 51.000 36.000 0.756 http://example.org/music/genre/parent_genre EVAL 01nd9f parent_genre 05r6t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 36.000 0.756 http://example.org/music/genre/parent_genre #5857-0k4y6 PRED entity: 0k4y6 PRED relation: combatants PRED expected values: 05r4w => 93 concepts (52 used for prediction) PRED predicted values (max 10 best out of 460): 09c7w0 (0.91 #2806, 0.83 #3052, 0.60 #483), 07ssc (0.81 #3169, 0.80 #2923, 0.63 #5126), 0chghy (0.60 #491, 0.45 #2814, 0.39 #3060), 05vz3zq (0.60 #540, 0.36 #1633, 0.33 #421), 0d05w3 (0.60 #522, 0.14 #2845, 0.13 #3091), 040vgd (0.57 #1087, 0.57 #4388, 0.41 #965), 01k6y1 (0.57 #901, 0.33 #56, 0.31 #2114), 0q307 (0.57 #4388, 0.41 #965, 0.39 #1086), 05qhw (0.53 #1328, 0.33 #5721, 0.33 #374), 017v_ (0.53 #1328, 0.33 #5721, 0.33 #274) >> Best rule #2806 for best value: >> intensional similarity = 8 >> extensional distance = 20 >> proper extension: 0d06vc; 0gfq9; 06k75; 022840; 01y998; 053_7s; 075k5; 01_3rn; 02kxg_; 018w0j; ... >> query: (?x9532, 09c7w0) <- combatants(?x9532, ?x10524), combatants(?x9532, ?x6371), contains(?x10524, ?x5560), adjoins(?x456, ?x10524), nationality(?x1328, ?x6371), split_to(?x6371, ?x512), locations(?x12777, ?x10524), country(?x7713, ?x6371) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #1089 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0cm2xh; 07_nf; 086m1; 01gqg3; *> query: (?x9532, 05r4w) <- combatants(?x9532, ?x10524), contains(?x10524, ?x5560), adjoins(?x1558, ?x10524), taxonomy(?x9532, ?x939), film_release_region(?x9349, ?x1558), ?x9349 = 0jdr0, nationality(?x4008, ?x1558), country(?x103, ?x1558) *> conf = 0.12 ranks of expected_values: 101 EVAL 0k4y6 combatants 05r4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 93.000 52.000 0.909 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #5856-01pr6q7 PRED entity: 01pr6q7 PRED relation: music! PRED expected values: 048rn => 106 concepts (75 used for prediction) PRED predicted values (max 10 best out of 550): 0kb1g (0.59 #11089, 0.07 #43354, 0.07 #29235), 0gvvm6l (0.06 #802, 0.04 #5842, 0.03 #13907), 042y1c (0.06 #235, 0.02 #3259, 0.01 #5275), 02rrfzf (0.04 #14438, 0.04 #9397, 0.03 #4357), 01s7w3 (0.04 #7923, 0.04 #21028, 0.04 #22036), 09d3b7 (0.04 #7894, 0.04 #8902, 0.04 #9910), 07bzz7 (0.04 #8591, 0.03 #7583, 0.03 #9599), 02ht1k (0.04 #9439, 0.03 #4399, 0.03 #7423), 0h3k3f (0.03 #4873, 0.03 #7897, 0.03 #8905), 0pdp8 (0.03 #4257, 0.03 #7281, 0.03 #8289) >> Best rule #11089 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 06k02; 02nfjp; 02wk4d; 02cj_f; 01z0lb; 07rzf; >> query: (?x3811, ?x3137) <- profession(?x3811, ?x1614), ?x1614 = 01c72t, nominated_for(?x3811, ?x3137) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pr6q7 music! 048rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 75.000 0.593 http://example.org/film/film/music #5855-01mvpv PRED entity: 01mvpv PRED relation: place_of_death PRED expected values: 02dtg => 102 concepts (69 used for prediction) PRED predicted values (max 10 best out of 42): 05jbn (0.17 #71, 0.06 #266, 0.06 #461), 0rh6k (0.12 #1564, 0.12 #392, 0.10 #1175), 030qb3t (0.06 #217, 0.06 #607, 0.06 #412), 0t_gg (0.06 #268, 0.06 #463, 0.05 #1441), 0r3w7 (0.06 #372, 0.06 #957, 0.05 #1545), 03pcgf (0.06 #774, 0.06 #579, 0.06 #1166), 0dq16 (0.06 #654, 0.06 #459, 0.06 #1046), 0dclg (0.06 #619, 0.03 #2182, 0.03 #2376), 0bxbr (0.06 #869, 0.05 #3015, 0.04 #1651), 02_286 (0.06 #990, 0.05 #1381, 0.04 #6061) >> Best rule #71 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 02yy8; >> query: (?x13086, 05jbn) <- profession(?x13086, ?x5805), profession(?x13086, ?x3342), ?x5805 = 0fj9f, ?x3342 = 04gc2, jurisdiction_of_office(?x13086, ?x94), basic_title(?x13086, ?x900), ?x900 = 0fkvn >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01mvpv place_of_death 02dtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 69.000 0.167 http://example.org/people/deceased_person/place_of_death #5854-04ls53 PRED entity: 04ls53 PRED relation: nationality PRED expected values: 03rjj => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 70): 02jx1 (0.18 #2015, 0.15 #2114, 0.14 #2807), 07ssc (0.12 #410, 0.10 #2096, 0.09 #2294), 0345h (0.06 #822, 0.06 #1021, 0.05 #1219), 03rk0 (0.06 #8962, 0.05 #9160, 0.05 #9061), 0d060g (0.05 #402, 0.05 #105, 0.05 #303), 06qd3 (0.05 #134, 0.03 #1389, 0.03 #1289), 0f8l9c (0.04 #318, 0.03 #417, 0.03 #1389), 06q1r (0.04 #373, 0.03 #472, 0.03 #769), 0chghy (0.03 #1794, 0.03 #1389, 0.03 #1289), 03rjj (0.03 #1389, 0.03 #1289, 0.03 #892) >> Best rule #2015 for best value: >> intensional similarity = 2 >> extensional distance = 300 >> proper extension: 0c8hct; >> query: (?x4727, 02jx1) <- profession(?x4727, ?x1614), ?x1614 = 01c72t >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #1389 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 176 *> proper extension: 06zd1c; *> query: (?x4727, ?x87) <- music(?x6536, ?x4727), genre(?x6536, ?x53), film_release_region(?x6536, ?x87) *> conf = 0.03 ranks of expected_values: 10 EVAL 04ls53 nationality 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 101.000 101.000 0.175 http://example.org/people/person/nationality #5853-01mszz PRED entity: 01mszz PRED relation: honored_for PRED expected values: 02scbv => 102 concepts (50 used for prediction) PRED predicted values (max 10 best out of 366): 01mszz (0.84 #1344, 0.82 #878, 0.75 #1189), 06ybb1 (0.84 #2479, 0.84 #2478, 0.83 #1081), 02ny6g (0.84 #2479, 0.84 #2478, 0.83 #1081), 02scbv (0.75 #1049, 0.73 #894, 0.68 #1360), 03s6l2 (0.11 #317, 0.10 #625), 0jsf6 (0.10 #1656, 0.03 #2741, 0.03 #3723), 06_wqk4 (0.07 #1563, 0.02 #2648, 0.02 #2802), 02q7yfq (0.05 #1669, 0.03 #3723, 0.03 #2444), 0y_yw (0.05 #1651, 0.03 #3723, 0.02 #2426), 09g8vhw (0.05 #1596, 0.02 #2681, 0.02 #2835) >> Best rule #1344 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 01771z; 0946bb; 0q9sg; 07sgdw; 06c0ns; >> query: (?x6205, 01mszz) <- honored_for(?x6963, ?x6205), film(?x5338, ?x6963), nominated_for(?x102, ?x6205), ?x5338 = 0gn30 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1049 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 069q4f; 06ybb1; 02ny6g; 03phtz; *> query: (?x6205, 02scbv) <- honored_for(?x6963, ?x6205), ?x6963 = 06c0ns, film(?x8796, ?x6205) *> conf = 0.75 ranks of expected_values: 4 EVAL 01mszz honored_for 02scbv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 50.000 0.842 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #5852-013dy7 PRED entity: 013dy7 PRED relation: place PRED expected values: 013dy7 => 96 concepts (34 used for prediction) PRED predicted values (max 10 best out of 18): 0vm39 (0.08 #753, 0.08 #238, 0.07 #1268), 0xckc (0.08 #703, 0.08 #188, 0.07 #1218), 0v9qg (0.08 #606, 0.08 #91, 0.07 #1121), 02dtg (0.08 #524, 0.08 #9, 0.07 #1039), 013d_f (0.08 #957, 0.08 #442, 0.07 #1472), 01fq7 (0.08 #519, 0.08 #4, 0.07 #1034), 0vrmb (0.08 #402, 0.07 #1432, 0.04 #3493), 0vg8x (0.08 #260, 0.07 #1290, 0.04 #3351), 0f67f (0.08 #697, 0.07 #1728, 0.05 #2758), 0v1xg (0.08 #744, 0.07 #1775, 0.05 #2805) >> Best rule #753 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 0f67f; 0v1xg; >> query: (?x11900, 0vm39) <- contains(?x1906, ?x11900), contains(?x94, ?x11900), category(?x11900, ?x134), ?x1906 = 04rrx, ?x94 = 09c7w0, source(?x11900, ?x958) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 013dy7 place 013dy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 34.000 0.083 http://example.org/location/hud_county_place/place #5851-02tr7d PRED entity: 02tr7d PRED relation: nationality PRED expected values: 06q1r => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 16): 07ssc (0.80 #401, 0.78 #4329, 0.43 #15), 09c7w0 (0.75 #502, 0.73 #101, 0.73 #3622), 06q1r (0.57 #77, 0.05 #377, 0.01 #4304), 02jx1 (0.33 #9140, 0.16 #233, 0.12 #1139), 0f8l9c (0.33 #9140, 0.05 #322, 0.03 #222), 04xn_ (0.33 #9140, 0.03 #274), 06mkj (0.33 #9140), 0d060g (0.09 #307, 0.06 #207, 0.05 #811), 03rk0 (0.06 #9990, 0.06 #7083, 0.05 #3164), 03rt9 (0.03 #213, 0.02 #414, 0.02 #2529) >> Best rule #401 for best value: >> intensional similarity = 3 >> extensional distance = 349 >> proper extension: 034rd; >> query: (?x1669, ?x512) <- location(?x1669, ?x4030), type_of_union(?x1669, ?x566), second_level_divisions(?x512, ?x4030) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #77 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 01wdcxk; *> query: (?x1669, 06q1r) <- location(?x1669, ?x4030), type_of_union(?x1669, ?x566), ?x4030 = 0hyxv *> conf = 0.57 ranks of expected_values: 3 EVAL 02tr7d nationality 06q1r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 111.000 0.797 http://example.org/people/person/nationality #5850-02_33l PRED entity: 02_33l PRED relation: student! PRED expected values: 065y4w7 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 156): 01w5m (0.25 #105, 0.11 #1686, 0.05 #9064), 052nd (0.25 #9, 0.11 #1590, 0.02 #12130), 05mv4 (0.25 #130, 0.11 #1711, 0.01 #13305), 0187nd (0.22 #1947, 0.01 #10906, 0.01 #11433), 02bzh0 (0.20 #939, 0.14 #1466, 0.02 #6736), 017z88 (0.15 #3244, 0.15 #2190, 0.15 #3771), 01jq34 (0.11 #1638), 015zyd (0.11 #1582), 017rbx (0.10 #2450, 0.08 #3504, 0.04 #7720), 02_gzx (0.10 #2492, 0.08 #7762, 0.04 #8816) >> Best rule #105 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 018gqj; 01c7p_; >> query: (?x11281, 01w5m) <- place_of_birth(?x11281, ?x2017), nationality(?x11281, ?x94), music(?x3433, ?x11281), ?x2017 = 04f_d >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3176 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 0hl3d; *> query: (?x11281, 065y4w7) <- artists(?x4910, ?x11281), category(?x11281, ?x134), profession(?x11281, ?x563), ?x563 = 01c8w0 *> conf = 0.08 ranks of expected_values: 13 EVAL 02_33l student! 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 143.000 143.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #5849-0drnwh PRED entity: 0drnwh PRED relation: nominated_for! PRED expected values: 094tsh6 => 110 concepts (37 used for prediction) PRED predicted values (max 10 best out of 596): 03mfqm (0.81 #35080, 0.80 #49109, 0.78 #23386), 06pj8 (0.56 #67822, 0.22 #2771, 0.09 #7448), 0bn3jg (0.40 #4676, 0.37 #25724, 0.32 #28063), 0284n42 (0.34 #11694, 0.29 #46770, 0.22 #51449), 03cglm (0.34 #79514, 0.34 #32740, 0.33 #72498), 08x5c_ (0.34 #79514, 0.34 #32740, 0.33 #72498), 04bdlg (0.34 #79514, 0.34 #32740, 0.33 #72498), 012ykt (0.34 #79514, 0.34 #32740, 0.33 #72498), 02q_cc (0.22 #2498, 0.06 #9514, 0.05 #30562), 0jz9f (0.22 #2358, 0.04 #30422, 0.03 #67842) >> Best rule #35080 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 05h95s; >> query: (?x6679, ?x669) <- award(?x6679, ?x1243), titles(?x53, ?x6679), award_winner(?x6679, ?x669), category(?x6679, ?x134) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #46371 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 222 *> proper extension: 09g8vhw; 047p7fr; 03nsm5x; 09fqgj; *> query: (?x6679, 094tsh6) <- film(?x5888, ?x6679), language(?x6679, ?x2164), nominated_for(?x143, ?x6679), crewmember(?x6679, ?x666) *> conf = 0.02 ranks of expected_values: 327 EVAL 0drnwh nominated_for! 094tsh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 110.000 37.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5848-02kfzz PRED entity: 02kfzz PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 24): 0ch6mp2 (0.84 #272, 0.80 #338, 0.79 #372), 0dxtw (0.47 #275, 0.44 #341, 0.43 #375), 01vx2h (0.43 #276, 0.39 #342, 0.39 #376), 01pvkk (0.29 #377, 0.29 #10, 0.29 #343), 0215hd (0.15 #283, 0.12 #782, 0.12 #1607), 01xy5l_ (0.14 #12, 0.13 #279, 0.12 #1607), 04pyp5 (0.14 #14, 0.12 #1607, 0.06 #448), 02vs3x5 (0.14 #21, 0.12 #1607, 0.05 #153), 0d2b38 (0.12 #290, 0.12 #1607, 0.10 #356), 089g0h (0.12 #1607, 0.12 #284, 0.11 #783) >> Best rule #272 for best value: >> intensional similarity = 4 >> extensional distance = 254 >> proper extension: 0cnztc4; 0crh5_f; 0h95zbp; 02h22; 0581vn8; 0dmn0x; 02yy9r; >> query: (?x4089, 0ch6mp2) <- genre(?x4089, ?x812), film_crew_role(?x4089, ?x1171), ?x812 = 01jfsb, ?x1171 = 09vw2b7 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02kfzz film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.840 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5847-0gkts9 PRED entity: 0gkts9 PRED relation: award! PRED expected values: 01fx2g 09zmys 02js9p 03x16f => 42 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2701): 04g4n (0.86 #13331, 0.80 #16664, 0.79 #26669), 01dbgw (0.67 #9840, 0.31 #16506, 0.20 #3175), 02jsgf (0.60 #1140, 0.38 #14471, 0.33 #7805), 01gvr1 (0.60 #133, 0.38 #10131, 0.33 #6798), 01hkhq (0.60 #657, 0.33 #7322, 0.25 #10655), 01pcq3 (0.60 #188, 0.33 #6853, 0.15 #13519), 01dbk6 (0.60 #1569, 0.25 #11567, 0.23 #14900), 0l6px (0.50 #10613, 0.40 #3947, 0.40 #615), 01kp66 (0.50 #7841, 0.38 #14507, 0.38 #11174), 01gq0b (0.40 #3814, 0.40 #482, 0.25 #10480) >> Best rule #13331 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0bb57s; >> query: (?x3184, ?x1343) <- award_winner(?x3184, ?x1343), award(?x3267, ?x3184), ?x3267 = 011_3s, ceremony(?x3184, ?x1265) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1609 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0bfvw2; 09sb52; 0cqgl9; *> query: (?x3184, 09zmys) <- nominated_for(?x3184, ?x687), ceremony(?x3184, ?x1265), award(?x1995, ?x3184), ?x1995 = 0509bl *> conf = 0.40 ranks of expected_values: 22, 647, 776, 2326 EVAL 0gkts9 award! 03x16f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 17.000 0.862 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gkts9 award! 02js9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 17.000 0.862 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gkts9 award! 09zmys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 42.000 17.000 0.862 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gkts9 award! 01fx2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 17.000 0.862 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5846-0m31m PRED entity: 0m31m PRED relation: nationality PRED expected values: 07ssc => 100 concepts (92 used for prediction) PRED predicted values (max 10 best out of 84): 02jx1 (0.88 #901, 0.80 #8031, 0.46 #33), 07ssc (0.88 #901, 0.80 #8031, 0.40 #8936), 09c7w0 (0.75 #1603, 0.74 #5624, 0.73 #4718), 0dj0x (0.40 #8936, 0.39 #8032, 0.33 #8937), 03rk0 (0.11 #546, 0.10 #846, 0.08 #746), 0hzlz (0.08 #23, 0.05 #123, 0.03 #223), 0d060g (0.07 #607, 0.06 #407, 0.06 #908), 0j5g9 (0.05 #262, 0.04 #462, 0.04 #362), 03rjj (0.05 #1406, 0.05 #105, 0.04 #1106), 0chghy (0.05 #110, 0.04 #1211, 0.04 #1311) >> Best rule #901 for best value: >> intensional similarity = 2 >> extensional distance = 112 >> proper extension: 0dv1hh; 09m465; >> query: (?x2654, ?x512) <- sibling(?x2654, ?x2280), nationality(?x2280, ?x512) >> conf = 0.88 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0m31m nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 92.000 0.878 http://example.org/people/person/nationality #5845-03whyr PRED entity: 03whyr PRED relation: genre PRED expected values: 03k9fj => 105 concepts (78 used for prediction) PRED predicted values (max 10 best out of 106): 07s9rl0 (0.68 #6382, 0.63 #121, 0.58 #4212), 01jfsb (0.64 #1339, 0.53 #374, 0.53 #735), 03k9fj (0.46 #614, 0.42 #734, 0.38 #373), 05p553 (0.38 #3492, 0.38 #2532, 0.38 #2412), 060__y (0.33 #17, 0.19 #1584, 0.18 #3145), 03npn (0.33 #7, 0.16 #127, 0.12 #969), 09blyk (0.33 #31, 0.05 #4604, 0.05 #151), 01585b (0.33 #52, 0.05 #172, 0.04 #9398), 02l7c8 (0.26 #1583, 0.26 #136, 0.26 #3504), 04pbhw (0.22 #658, 0.16 #176, 0.14 #1382) >> Best rule #6382 for best value: >> intensional similarity = 5 >> extensional distance = 533 >> proper extension: 0dckvs; >> query: (?x9524, 07s9rl0) <- produced_by(?x9524, ?x8345), film_crew_role(?x9524, ?x137), genre(?x9524, ?x225), genre(?x9250, ?x225), ?x9250 = 0581vn8 >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #614 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 0b60sq; *> query: (?x9524, 03k9fj) <- language(?x9524, ?x254), category(?x9524, ?x134), production_companies(?x9524, ?x2156), story_by(?x9524, ?x8210) *> conf = 0.46 ranks of expected_values: 3 EVAL 03whyr genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 78.000 0.677 http://example.org/film/film/genre #5844-015wc0 PRED entity: 015wc0 PRED relation: people! PRED expected values: 0gk4g => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 40): 0gk4g (0.19 #868, 0.15 #10, 0.15 #1066), 0dq9p (0.10 #677, 0.09 #875, 0.09 #1073), 04p3w (0.10 #77, 0.05 #1991, 0.05 #209), 02y0js (0.09 #332, 0.06 #662, 0.05 #1850), 0qcr0 (0.08 #859, 0.08 #199, 0.07 #2179), 01mtqf (0.08 #4, 0.04 #664, 0.04 #862), 02knxx (0.08 #32, 0.04 #890, 0.03 #2738), 01_qc_ (0.07 #358, 0.03 #688, 0.03 #1084), 07s4l (0.05 #124, 0.05 #190, 0.02 #520), 074m2 (0.05 #95, 0.05 #161, 0.02 #491) >> Best rule #868 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 0520r2x; 041h0; 0chsq; 0jf1b; 012t1; 02lkcc; 01gzm2; 0gl88b; 0c6g29; 018swb; ... >> query: (?x9946, 0gk4g) <- people(?x1050, ?x9946), place_of_death(?x9946, ?x1358), nominated_for(?x9946, ?x9185) >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015wc0 people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.188 http://example.org/people/cause_of_death/people #5843-06tw8 PRED entity: 06tw8 PRED relation: taxonomy PRED expected values: 04n6k => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.83 #15, 0.82 #16, 0.81 #21) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 01bkb; >> query: (?x5457, 04n6k) <- religion(?x5457, ?x109), ?x109 = 01lp8, contains(?x2467, ?x5457) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06tw8 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.825 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #5842-04wx2v PRED entity: 04wx2v PRED relation: award PRED expected values: 0bfvw2 099cng => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 272): 099t8j (0.73 #32978, 0.70 #32579, 0.70 #18268), 094qd5 (0.52 #440, 0.51 #1234, 0.31 #1631), 02ppm4q (0.46 #1737, 0.38 #546, 0.31 #1340), 0ck27z (0.34 #4060, 0.33 #7636, 0.31 #7239), 099cng (0.33 #480, 0.25 #1274, 0.16 #1671), 0bdwft (0.30 #1655, 0.29 #1258, 0.19 #464), 0bfvw2 (0.27 #1603, 0.24 #412, 0.23 #1206), 0cqgl9 (0.24 #1376, 0.24 #582, 0.21 #1773), 01by1l (0.22 #9242, 0.09 #2092, 0.09 #3283), 05ztrmj (0.21 #177, 0.13 #31385, 0.12 #31384) >> Best rule #32978 for best value: >> intensional similarity = 3 >> extensional distance = 2278 >> proper extension: 06lxn; >> query: (?x9437, ?x2478) <- award_winner(?x2478, ?x9437), award(?x6772, ?x2478), award_nominee(?x221, ?x6772) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #480 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 01wk51; *> query: (?x9437, 099cng) <- award(?x9437, ?x2478), languages(?x9437, ?x254), ?x2478 = 02x4x18 *> conf = 0.33 ranks of expected_values: 5, 7 EVAL 04wx2v award 099cng CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 96.000 96.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04wx2v award 0bfvw2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 96.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5841-04rcr PRED entity: 04rcr PRED relation: award PRED expected values: 02wh75 => 98 concepts (73 used for prediction) PRED predicted values (max 10 best out of 298): 01ckrr (0.79 #1593, 0.78 #25497, 0.78 #21514), 01by1l (0.54 #12861, 0.40 #18038, 0.37 #2104), 0c4z8 (0.39 #12820, 0.22 #14014, 0.21 #15608), 01bgqh (0.33 #17968, 0.33 #12791, 0.29 #12393), 01ckcd (0.29 #1128, 0.18 #4313, 0.17 #1526), 02f72_ (0.29 #1422, 0.21 #1024, 0.18 #12180), 03qbh5 (0.26 #12554, 0.23 #15740, 0.23 #18129), 054ks3 (0.25 #12891, 0.23 #10500, 0.20 #14085), 0gqz2 (0.22 #14023, 0.20 #10438, 0.18 #12829), 01ck6h (0.21 #521, 0.11 #3706, 0.11 #1317) >> Best rule #1593 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 01vsy3q; >> query: (?x646, ?x4912) <- artists(?x2249, ?x646), ?x2249 = 03lty, award_winner(?x4912, ?x646), artist(?x2931, ?x646) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #11961 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 209 *> proper extension: 016qtt; 0197tq; 01pfr3; 01cv3n; 01vvycq; 025xt8y; 03f5spx; 01vv7sc; 02r3zy; 018y2s; ... *> query: (?x646, 02wh75) <- artists(?x2249, ?x646), artists(?x2249, ?x10427), award(?x646, ?x2634), ?x10427 = 04qzm *> conf = 0.17 ranks of expected_values: 19 EVAL 04rcr award 02wh75 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 98.000 73.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5840-07cjqy PRED entity: 07cjqy PRED relation: location PRED expected values: 0cc56 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 262): 030qb3t (0.36 #12115, 0.35 #6498, 0.33 #20137), 02_286 (0.35 #52183, 0.27 #8056, 0.27 #10463), 0f2wj (0.22 #3241, 0.17 #33, 0.14 #2439), 059rby (0.17 #1619, 0.17 #15, 0.14 #2421), 01_d4 (0.17 #903, 0.17 #101, 0.11 #3309), 03v0t (0.17 #999, 0.17 #197, 0.11 #3405), 0sb1r (0.17 #1007, 0.17 #205, 0.11 #3413), 0r111 (0.17 #1447, 0.17 #645, 0.11 #3853), 05mph (0.17 #1119, 0.17 #317, 0.11 #3525), 01n7q (0.17 #62, 0.11 #7280, 0.11 #3270) >> Best rule #12115 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 02jg92; 01lz4tf; >> query: (?x3536, 030qb3t) <- place_of_birth(?x3536, ?x3450), location(?x3536, ?x191), participant(?x3536, ?x2221) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #10483 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 02v3yy; 01l3mk3; *> query: (?x3536, 0cc56) <- place_of_birth(?x3536, ?x3450), participant(?x2221, ?x3536), award(?x3536, ?x401) *> conf = 0.10 ranks of expected_values: 25 EVAL 07cjqy location 0cc56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 143.000 143.000 0.364 http://example.org/people/person/places_lived./people/place_lived/location #5839-04hgpt PRED entity: 04hgpt PRED relation: major_field_of_study PRED expected values: 04rjg 0_jm => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 113): 02j62 (0.65 #705, 0.50 #27, 0.45 #1044), 04rjg (0.58 #696, 0.42 #1035, 0.39 #1827), 04x_3 (0.50 #24, 0.45 #702, 0.33 #1041), 05qjt (0.45 #686, 0.37 #1025, 0.33 #1817), 0g26h (0.41 #2752, 0.40 #1961, 0.39 #2978), 0fdys (0.38 #262, 0.32 #714, 0.29 #149), 05qfh (0.34 #1050, 0.32 #711, 0.23 #598), 0dc_v (0.33 #40, 0.32 #718, 0.17 #1057), 0_jm (0.33 #55, 0.32 #1638, 0.31 #2768), 02h40lc (0.33 #4, 0.29 #682, 0.29 #117) >> Best rule #705 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 09c7w0; 059j2; 03rj0; 04hzj; 05c74; >> query: (?x4750, 02j62) <- contains(?x94, ?x4750), company(?x5652, ?x4750), organization(?x4750, ?x5487) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #696 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 09c7w0; 059j2; 03rj0; 04hzj; 05c74; *> query: (?x4750, 04rjg) <- contains(?x94, ?x4750), company(?x5652, ?x4750), organization(?x4750, ?x5487) *> conf = 0.58 ranks of expected_values: 2, 9 EVAL 04hgpt major_field_of_study 0_jm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 101.000 101.000 0.645 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04hgpt major_field_of_study 04rjg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.645 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #5838-0c3ybss PRED entity: 0c3ybss PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.82 #110, 0.82 #140, 0.81 #115), 0735l (0.16 #44, 0.10 #22, 0.05 #33), 07c52 (0.13 #57, 0.10 #25, 0.08 #47), 07z4p (0.09 #59, 0.07 #38, 0.06 #27), 02nxhr (0.08 #81, 0.08 #61, 0.08 #46) >> Best rule #110 for best value: >> intensional similarity = 3 >> extensional distance = 465 >> proper extension: 02vw1w2; >> query: (?x249, 029j_) <- film(?x8626, ?x249), genre(?x249, ?x812), ?x812 = 01jfsb >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c3ybss film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.822 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #5837-0d060g PRED entity: 0d060g PRED relation: jurisdiction_of_office! PRED expected values: 0d1_f => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 358): 0d1_f (0.29 #386, 0.13 #1944, 0.13 #534), 083pr (0.14 #306, 0.13 #528, 0.10 #602), 0lzcs (0.14 #429, 0.11 #503, 0.07 #577), 0948xk (0.14 #416, 0.11 #490, 0.07 #564), 03f77 (0.14 #394, 0.11 #468, 0.07 #542), 03f5vvx (0.14 #389, 0.11 #463, 0.07 #537), 02c4s (0.14 #378, 0.11 #452, 0.07 #526), 0kn4c (0.14 #376, 0.11 #450, 0.07 #524), 081t6 (0.14 #367, 0.10 #663, 0.07 #589), 083p7 (0.14 #300, 0.10 #596, 0.07 #522) >> Best rule #386 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 018lkp; >> query: (?x279, 0d1_f) <- combatants(?x11802, ?x279), ?x11802 = 0bqtx >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d060g jurisdiction_of_office! 0d1_f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 194.000 0.286 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #5836-0160w PRED entity: 0160w PRED relation: participating_countries! PRED expected values: 0lgxj => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 40): 0lgxj (0.67 #105, 0.65 #1704, 0.60 #1236), 09x3r (0.67 #89, 0.62 #1688, 0.60 #1220), 09n48 (0.67 #81, 0.60 #1212, 0.60 #1680), 0blfl (0.53 #613, 0.50 #106, 0.40 #1705), 06sks6 (0.50 #101, 0.33 #140, 0.31 #218), 0c_tl (0.50 #100, 0.27 #607, 0.25 #139), 0sx8l (0.47 #598, 0.40 #1378, 0.40 #1690), 016r9z (0.40 #605, 0.37 #1229, 0.36 #1385), 0jdk_ (0.33 #103, 0.20 #64, 0.20 #1717), 0l6ny (0.20 #47, 0.17 #86, 0.09 #398) >> Best rule #105 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 07ssc; >> query: (?x126, 0lgxj) <- location_of_ceremony(?x872, ?x126), organization(?x126, ?x127), time_zones(?x126, ?x2674) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0160w participating_countries! 0lgxj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.667 http://example.org/olympics/olympic_games/participating_countries #5835-048yqf PRED entity: 048yqf PRED relation: titles! PRED expected values: 04t2t => 91 concepts (62 used for prediction) PRED predicted values (max 10 best out of 67): 01hmnh (0.43 #2686, 0.43 #2609, 0.42 #2373), 07s9rl0 (0.40 #1, 0.28 #824, 0.27 #926), 04t2t (0.33 #3513, 0.30 #3204, 0.29 #206), 01jfsb (0.33 #3513, 0.30 #3204, 0.18 #3931), 024qqx (0.22 #698, 0.17 #1210, 0.16 #1936), 04xvlr (0.22 #1133, 0.20 #4, 0.20 #2066), 01z4y (0.21 #5005, 0.20 #3550, 0.20 #447), 03mqtr (0.20 #46, 0.10 #457, 0.06 #3353), 02kdv5l (0.18 #3931, 0.18 #4968, 0.18 #4967), 0lsxr (0.18 #3931, 0.18 #4968, 0.18 #4967) >> Best rule #2686 for best value: >> intensional similarity = 3 >> extensional distance = 225 >> proper extension: 04svwx; >> query: (?x9914, ?x1510) <- genre(?x9914, ?x1510), country(?x9914, ?x94), ?x1510 = 01hmnh >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3513 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 442 *> proper extension: 0hz6mv2; *> query: (?x9914, ?x1510) <- genre(?x9914, ?x1510), country(?x9914, ?x94), executive_produced_by(?x9914, ?x96), titles(?x1510, ?x83) *> conf = 0.33 ranks of expected_values: 3 EVAL 048yqf titles! 04t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 62.000 0.432 http://example.org/media_common/netflix_genre/titles #5834-01ddbl PRED entity: 01ddbl PRED relation: team PRED expected values: 04l59s => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 195): 038_0z (0.33 #161), 023fxp (0.33 #160), 024nj1 (0.33 #159), 038_3y (0.33 #157), 020wyp (0.33 #154), 098knd (0.33 #146), 02fbb5 (0.33 #116), 02ryyk (0.12 #963), 03ym73 (0.12 #961), 049m_l (0.12 #960) >> Best rule #161 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 021q23; >> query: (?x13270, 038_0z) <- team(?x13270, ?x14124), team(?x13270, ?x12541), sport(?x14124, ?x453), colors(?x12541, ?x663), country(?x13270, ?x94), teams(?x9445, ?x12541) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #967 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 0356lc; 03zv9; 0355pl; 07y9k; 059yj; 0h69c; *> query: (?x13270, ?x3298) <- team(?x13270, ?x10142), team(?x13270, ?x2919), team(?x3724, ?x2919), colors(?x10142, ?x663), ?x663 = 083jv, team(?x3724, ?x3298) *> conf = 0.05 ranks of expected_values: 165 EVAL 01ddbl team 04l59s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 138.000 138.000 0.333 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #5833-02jxmr PRED entity: 02jxmr PRED relation: music! PRED expected values: 0jjy0 0d_2fb 0kvbl6 08mg_b 05k4my 04180vy => 115 concepts (77 used for prediction) PRED predicted values (max 10 best out of 366): 03f7nt (0.75 #9872, 0.74 #8884, 0.73 #12835), 0gfsq9 (0.75 #9872, 0.74 #8884, 0.73 #12835), 02rrfzf (0.09 #1304, 0.04 #8213, 0.04 #9201), 07bzz7 (0.09 #1498, 0.04 #3472, 0.04 #5446), 09d3b7 (0.09 #1806, 0.04 #5754, 0.04 #7728), 04tqtl (0.09 #1289, 0.03 #7211, 0.03 #8198), 08rr3p (0.09 #1251, 0.02 #4212, 0.02 #5199), 084qpk (0.09 #1056, 0.02 #4017, 0.02 #5004), 013q0p (0.09 #1455, 0.02 #4416, 0.02 #5403), 05qm9f (0.09 #1646, 0.02 #4607, 0.02 #5594) >> Best rule #9872 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 02qfhb; >> query: (?x4428, ?x2772) <- music(?x781, ?x4428), nominated_for(?x4428, ?x2772), film_release_region(?x781, ?x87) >> conf = 0.75 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02jxmr music! 04180vy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 77.000 0.746 http://example.org/film/film/music EVAL 02jxmr music! 05k4my CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 77.000 0.746 http://example.org/film/film/music EVAL 02jxmr music! 08mg_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 77.000 0.746 http://example.org/film/film/music EVAL 02jxmr music! 0kvbl6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 77.000 0.746 http://example.org/film/film/music EVAL 02jxmr music! 0d_2fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 77.000 0.746 http://example.org/film/film/music EVAL 02jxmr music! 0jjy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 77.000 0.746 http://example.org/film/film/music #5832-05g76 PRED entity: 05g76 PRED relation: school PRED expected values: 01jq0j 0trv => 101 concepts (73 used for prediction) PRED predicted values (max 10 best out of 316): 0trv (0.89 #184, 0.67 #684, 0.33 #318), 065y4w7 (0.89 #184, 0.57 #742, 0.50 #1657), 01dzg0 (0.89 #184, 0.57 #892, 0.40 #526), 07w0v (0.89 #184, 0.56 #928, 0.42 #3132), 06pwq (0.89 #184, 0.43 #740, 0.42 #2391), 03tw2s (0.89 #184, 0.43 #839, 0.33 #656), 02pptm (0.89 #184, 0.43 #873, 0.33 #140), 01q0kg (0.89 #184, 0.40 #426, 0.33 #975), 021w0_ (0.89 #184, 0.40 #505, 0.33 #138), 01tx9m (0.89 #184, 0.40 #464, 0.29 #830) >> Best rule #184 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0jmj7; >> query: (?x2067, ?x466) <- team(?x2010, ?x2067), draft(?x2067, ?x1161), colors(?x2067, ?x3189), school(?x2067, ?x5750), team(?x2010, ?x8894), school(?x8894, ?x466), ?x5750 = 01nnsv >> conf = 0.89 => this is the best rule for 103 predicted values ranks of expected_values: 1, 12 EVAL 05g76 school 0trv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 73.000 0.887 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 05g76 school 01jq0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 101.000 73.000 0.887 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #5831-05xd_v PRED entity: 05xd_v PRED relation: film PRED expected values: 016kv6 => 104 concepts (60 used for prediction) PRED predicted values (max 10 best out of 684): 02b6n9 (0.44 #5138, 0.02 #21204, 0.01 #64051), 0jzw (0.25 #119, 0.21 #26777, 0.20 #5474), 027m5wv (0.25 #1053, 0.21 #26777, 0.11 #4623), 03m5y9p (0.25 #1416, 0.21 #26777, 0.10 #6771), 07cyl (0.25 #559, 0.21 #26777, 0.10 #5914), 01718w (0.25 #1395, 0.21 #26777, 0.10 #6750), 0sxgv (0.25 #1043, 0.21 #26777, 0.10 #6398), 01j5ql (0.25 #1197, 0.21 #26777, 0.10 #6552), 065z3_x (0.25 #383, 0.21 #26777, 0.10 #5738), 09ps01 (0.25 #815, 0.21 #26777, 0.10 #6170) >> Best rule #5138 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02qgqt; 01tcf7; 04t7ts; 0408np; 01kb2j; 04d2yp; >> query: (?x10855, 02b6n9) <- gender(?x10855, ?x231), film(?x10855, ?x2812), film(?x10855, ?x2090), ?x2090 = 01hqhm, award(?x2812, ?x618) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05xd_v film 016kv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 60.000 0.444 http://example.org/film/actor/film./film/performance/film #5830-02l3_5 PRED entity: 02l3_5 PRED relation: profession PRED expected values: 018gz8 => 144 concepts (139 used for prediction) PRED predicted values (max 10 best out of 82): 03gjzk (0.51 #2536, 0.50 #1944, 0.48 #2240), 018gz8 (0.50 #17, 0.41 #1055, 0.34 #1946), 01d_h8 (0.49 #3860, 0.48 #3564, 0.47 #3416), 0dxtg (0.39 #3572, 0.38 #1052, 0.37 #4164), 0dz3r (0.31 #742, 0.21 #3412, 0.19 #1486), 09jwl (0.29 #2984, 0.27 #2836, 0.26 #3281), 015cjr (0.28 #2570, 0.27 #789, 0.24 #1978), 0cbd2 (0.25 #155, 0.25 #7, 0.19 #1936), 02jknp (0.25 #8, 0.25 #7118, 0.25 #9338), 0kyk (0.25 #177, 0.22 #2550, 0.20 #2254) >> Best rule #2536 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 0c7ct; 03ldxq; 01k70_; 0fwy0h; 01yg9y; 0261x8t; 05sj55; 010p3; 0163t3; 02_wxh; ... >> query: (?x8081, 03gjzk) <- type_of_union(?x8081, ?x566), ?x566 = 04ztj, program(?x8081, ?x2583) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 02ch1w; *> query: (?x8081, 018gz8) <- film(?x8081, ?x8279), award(?x8081, ?x537), ?x8279 = 0291hr *> conf = 0.50 ranks of expected_values: 2 EVAL 02l3_5 profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 139.000 0.507 http://example.org/people/person/profession #5829-0bqch PRED entity: 0bqch PRED relation: gender PRED expected values: 05zppz => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #31, 0.90 #37, 0.90 #33), 02zsn (0.46 #124, 0.26 #132, 0.25 #136) >> Best rule #31 for best value: >> intensional similarity = 6 >> extensional distance = 122 >> proper extension: 099bk; 03sbs; 015k7; 0131kb; >> query: (?x11335, 05zppz) <- nationality(?x11335, ?x789), influenced_by(?x10075, ?x11335), film_release_region(?x2627, ?x789), film_release_region(?x1999, ?x789), ?x2627 = 0gz6b6g, ?x1999 = 0gd0c7x >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bqch gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.903 http://example.org/people/person/gender #5828-02t_st PRED entity: 02t_st PRED relation: film PRED expected values: 0gbfn9 => 93 concepts (60 used for prediction) PRED predicted values (max 10 best out of 311): 0h03fhx (0.50 #776, 0.06 #67728, 0.04 #44555), 05zr0xl (0.47 #19603, 0.41 #48121, 0.40 #87333), 02825cv (0.19 #2921, 0.02 #45695, 0.01 #33216), 07p62k (0.17 #353, 0.04 #2135, 0.03 #37425), 04gv3db (0.12 #2532, 0.02 #32827, 0.02 #15006), 034qzw (0.08 #333, 0.08 #2115, 0.03 #37425), 0bvn25 (0.08 #50, 0.08 #1832, 0.03 #37425), 06fpsx (0.08 #1335, 0.06 #67728, 0.04 #3117), 0prrm (0.08 #858, 0.04 #2640, 0.04 #4422), 026wlxw (0.08 #1413, 0.04 #3195, 0.04 #44555) >> Best rule #776 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 04t2l2; 03pmty; 0151w_; 05fnl9; 034g2b; 01y665; 01y9xg; 01nm3s; 069nzr; 01_p6t; >> query: (?x7381, 0h03fhx) <- film(?x7381, ?x463), award_winner(?x7381, ?x968), ?x968 = 015grj >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #37425 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1044 *> proper extension: 02645b; 0kvqv; 03d1y3; 024y6w; 02v49c; 03cd1q; 01p0w_; *> query: (?x7381, ?x1120) <- location(?x7381, ?x461), award_nominee(?x7381, ?x968), film(?x968, ?x1120) *> conf = 0.03 ranks of expected_values: 131 EVAL 02t_st film 0gbfn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 93.000 60.000 0.500 http://example.org/film/actor/film./film/performance/film #5827-01fx4k PRED entity: 01fx4k PRED relation: honored_for! PRED expected values: 0bz6sb => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 112): 09p30_ (0.11 #72, 0.10 #193, 0.03 #2371), 02hn5v (0.11 #33, 0.07 #154, 0.03 #2332), 04n2r9h (0.07 #278, 0.07 #157, 0.06 #883), 0bzmt8 (0.07 #83, 0.03 #204, 0.02 #3229), 09k5jh7 (0.07 #192, 0.03 #2370, 0.03 #5396), 0418154 (0.07 #213, 0.02 #5901, 0.02 #5417), 05c1t6z (0.05 #5820, 0.02 #8120, 0.02 #8241), 02q690_ (0.05 #5863, 0.02 #8163, 0.02 #8284), 0275n3y (0.04 #5873, 0.04 #3210, 0.03 #185), 0gvstc3 (0.04 #5836, 0.02 #8741, 0.02 #10314) >> Best rule #72 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 0sxfd; 0f4vx; 0j90s; 01gvts; >> query: (?x10049, 09p30_) <- films(?x2286, ?x10049), award(?x10049, ?x1245), nominated_for(?x749, ?x10049), ?x749 = 094qd5 >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #1263 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 145 *> proper extension: 0j43swk; 0h0wd9; *> query: (?x10049, 0bz6sb) <- genre(?x10049, ?x53), music(?x10049, ?x3690), nominated_for(?x1107, ?x10049), ?x1107 = 019f4v *> conf = 0.01 ranks of expected_values: 92 EVAL 01fx4k honored_for! 0bz6sb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 108.000 108.000 0.107 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #5826-0789_m PRED entity: 0789_m PRED relation: award! PRED expected values: 015rkw 03q1vd 0b_dy 02xv8m 01z7_f 06dn58 => 50 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2201): 03mg35 (0.80 #23150, 0.80 #23149, 0.78 #29770), 0gr36 (0.80 #23149, 0.78 #29770, 0.76 #46315), 025h4z (0.80 #23149, 0.78 #29770, 0.76 #46315), 0g8st4 (0.80 #23149, 0.78 #29770, 0.76 #46315), 0h1q6 (0.80 #23149, 0.78 #29770, 0.76 #46315), 016gr2 (0.80 #23149, 0.78 #29770, 0.76 #46315), 048q6x (0.80 #23149, 0.78 #29770, 0.76 #46315), 0hwd8 (0.80 #23149, 0.78 #29770, 0.76 #46315), 0c6qh (0.50 #13867, 0.50 #7253, 0.33 #639), 0237fw (0.50 #13850, 0.33 #622, 0.25 #7236) >> Best rule #23150 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 0m57f; >> query: (?x458, ?x2280) <- award_winner(?x458, ?x2280), award(?x8002, ?x458), participant(?x2280, ?x9944), celebrities_impersonated(?x3649, ?x8002) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #7047 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09sb52; 099tbz; *> query: (?x458, 015rkw) <- award_winner(?x458, ?x5661), award(?x4294, ?x458), ?x4294 = 01r93l, ?x5661 = 03ym1 *> conf = 0.50 ranks of expected_values: 20, 44, 212, 382, 407, 482 EVAL 0789_m award! 06dn58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 50.000 19.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0789_m award! 01z7_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 19.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0789_m award! 02xv8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 50.000 19.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0789_m award! 0b_dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 50.000 19.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0789_m award! 03q1vd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 19.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0789_m award! 015rkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 50.000 19.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5825-01hpf6 PRED entity: 01hpf6 PRED relation: place_founded PRED expected values: 0dqyw => 194 concepts (106 used for prediction) PRED predicted values (max 10 best out of 103): 07dfk (0.50 #49, 0.29 #181, 0.26 #1321), 018jcq (0.50 #66, 0.29 #198, 0.05 #2475), 0dqyw (0.31 #1673, 0.31 #1404, 0.30 #1338), 080h2 (0.25 #78, 0.12 #210, 0.10 #277), 04jpl (0.25 #71, 0.10 #270, 0.07 #1342), 0vzm (0.12 #223, 0.10 #290, 0.10 #1097), 071vr (0.12 #243, 0.10 #310, 0.04 #1316), 05qtj (0.12 #232, 0.10 #299, 0.04 #1305), 02_286 (0.12 #2016, 0.08 #411, 0.08 #3835), 0gp5l6 (0.12 #257, 0.03 #5319, 0.02 #3008) >> Best rule #49 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 03pmfw; >> query: (?x14087, 07dfk) <- state_province_region(?x14087, ?x8889), category(?x14087, ?x134), ?x134 = 08mbj5d, citytown(?x14087, ?x10980), location_of_ceremony(?x566, ?x8889), contains(?x252, ?x8889), ?x252 = 03_3d, jurisdiction_of_office(?x900, ?x8889) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1673 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 30 *> proper extension: 019rl6; *> query: (?x14087, ?x10980) <- industry(?x14087, ?x245), category(?x14087, ?x134), citytown(?x14087, ?x10980), industry(?x12493, ?x245), industry(?x12074, ?x245), industry(?x3253, ?x245), state_province_region(?x12074, ?x760), ?x12493 = 0317zz, organization(?x4682, ?x3253) *> conf = 0.31 ranks of expected_values: 3 EVAL 01hpf6 place_founded 0dqyw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 194.000 106.000 0.500 http://example.org/organization/organization/place_founded #5824-035qy PRED entity: 035qy PRED relation: service_location! PRED expected values: 018mxj => 214 concepts (209 used for prediction) PRED predicted values (max 10 best out of 138): 018mxj (0.41 #558, 0.29 #969, 0.29 #832), 01c6k4 (0.36 #2198, 0.30 #691, 0.29 #554), 07zl6m (0.29 #681, 0.21 #2736, 0.20 #1229), 0cv9b (0.28 #1107, 0.21 #2203, 0.21 #3299), 0p4wb (0.24 #1105, 0.24 #557, 0.21 #2201), 077w0b (0.24 #1025, 0.24 #888, 0.21 #1984), 06p8m (0.24 #655, 0.19 #1066, 0.19 #929), 05b5c (0.22 #1635, 0.21 #2183, 0.20 #1224), 064f29 (0.21 #1978, 0.19 #1019, 0.19 #882), 069b85 (0.21 #2321, 0.19 #1088, 0.19 #951) >> Best rule #558 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 047lj; 047yc; 06t2t; >> query: (?x1353, 018mxj) <- film_release_region(?x6536, ?x1353), film_release_region(?x1421, ?x1353), ?x6536 = 09gmmt6, ?x1421 = 07qg8v >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035qy service_location! 018mxj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 214.000 209.000 0.412 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #5823-07147 PRED entity: 07147 PRED relation: teams! PRED expected values: 071vr => 76 concepts (65 used for prediction) PRED predicted values (max 10 best out of 130): 0h7h6 (0.33 #596, 0.25 #1681, 0.24 #7313), 02_286 (0.33 #834, 0.24 #7313, 0.19 #9482), 01531 (0.33 #902, 0.24 #7313, 0.19 #9482), 0nqph (0.33 #259, 0.20 #2696, 0.20 #2425), 01_d4 (0.29 #3037, 0.29 #2767, 0.25 #1956), 0n1rj (0.25 #1768, 0.14 #2849, 0.07 #4743), 0d9jr (0.25 #2029, 0.14 #3110, 0.07 #4734), 01cx_ (0.24 #7313, 0.19 #9482, 0.08 #7136), 0dc95 (0.24 #7313, 0.19 #9482, 0.05 #12746), 0fpzwf (0.20 #2304, 0.13 #4739, 0.13 #4469) >> Best rule #596 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 07l4z; >> query: (?x8111, 0h7h6) <- school(?x8111, ?x10838), school(?x8111, ?x6602), school(?x8111, ?x5621), ?x5621 = 01vs5c, season(?x8111, ?x9498), season(?x8111, ?x2406), ?x2406 = 03c6sl9, team(?x2010, ?x8111), draft(?x8111, ?x1161), team(?x8110, ?x8111), ?x9498 = 027pwzc, school_type(?x10838, ?x3205), student(?x6602, ?x3025), contains(?x94, ?x6602), currency(?x10838, ?x170) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7198 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 24 *> proper extension: 026xxv_; *> query: (?x8111, 071vr) <- sport(?x8111, ?x5063), team(?x12323, ?x8111), place_of_birth(?x12323, ?x1860), team(?x12323, ?x8901), team(?x12323, ?x1632), school(?x8901, ?x466), draft(?x8901, ?x1161), colors(?x1632, ?x663), team(?x2010, ?x1632), teams(?x1658, ?x8901), school(?x1632, ?x735), profession(?x12323, ?x14261), team(?x4244, ?x8901) *> conf = 0.04 ranks of expected_values: 47 EVAL 07147 teams! 071vr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 76.000 65.000 0.333 http://example.org/sports/sports_team_location/teams #5822-07bx6 PRED entity: 07bx6 PRED relation: film_release_region PRED expected values: 09c7w0 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 100): 09c7w0 (0.74 #6993, 0.74 #2153, 0.73 #11120), 06mkj (0.50 #255, 0.29 #1868, 0.26 #792), 07ssc (0.50 #203, 0.28 #740, 0.27 #2174), 0d0vqn (0.50 #191, 0.27 #1804, 0.27 #2162), 059j2 (0.50 #224, 0.27 #761, 0.26 #1837), 015fr (0.50 #205, 0.26 #1818, 0.24 #742), 0chghy (0.50 #196, 0.26 #733, 0.25 #1809), 0345h (0.50 #226, 0.25 #2197, 0.24 #1839), 03spz (0.50 #298, 0.24 #1911, 0.21 #835), 01znc_ (0.50 #237, 0.24 #1850, 0.22 #774) >> Best rule #6993 for best value: >> intensional similarity = 3 >> extensional distance = 447 >> proper extension: 0bby9p5; >> query: (?x7482, 09c7w0) <- film_crew_role(?x7482, ?x468), ?x468 = 02r96rf, produced_by(?x7482, ?x1285) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07bx6 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.739 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5821-07f1x PRED entity: 07f1x PRED relation: participating_countries! PRED expected values: 09n48 => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 40): 018ctl (0.78 #907, 0.73 #516, 0.72 #1180), 09x3r (0.63 #520, 0.63 #130, 0.62 #51), 09n48 (0.63 #511, 0.58 #902, 0.58 #121), 0blfl (0.62 #67, 0.40 #536, 0.37 #146), 016r9z (0.47 #529, 0.44 #920, 0.40 #1193), 0sx8l (0.46 #53, 0.39 #171, 0.39 #913), 06sks6 (0.37 #142, 0.31 #103, 0.31 #923), 0c_tl (0.37 #141, 0.26 #180, 0.25 #922), 0sxrz (0.23 #59, 0.10 #2151, 0.09 #177), 0l6ny (0.21 #1290, 0.21 #1330, 0.21 #1760) >> Best rule #907 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 0154j; 0j1z8; 0chghy; 0hzlz; 059j2; 07t21; 06c1y; 0d0kn; 03rk0; 06mkj; ... >> query: (?x7747, 018ctl) <- film_release_region(?x4610, ?x7747), country(?x6054, ?x7747), ?x4610 = 017jd9 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #511 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 0jhd; *> query: (?x7747, 09n48) <- film_release_region(?x3377, ?x7747), country(?x6054, ?x7747), ?x3377 = 0gj8nq2 *> conf = 0.63 ranks of expected_values: 3 EVAL 07f1x participating_countries! 09n48 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 178.000 178.000 0.778 http://example.org/olympics/olympic_games/participating_countries #5820-026zt PRED entity: 026zt PRED relation: contains! PRED expected values: 02j9z => 24 concepts (20 used for prediction) PRED predicted values (max 10 best out of 78): 02j9z (0.56 #15390, 0.40 #17212, 0.33 #8129), 09c7w0 (0.50 #14490, 0.42 #17225, 0.40 #12676), 02j71 (0.35 #13587, 0.19 #17220), 02qkt (0.33 #8129, 0.33 #7216, 0.33 #7214), 09b69 (0.33 #8129, 0.33 #7216, 0.33 #7214), 087vz (0.33 #8129, 0.33 #7216, 0.33 #7214), 059g4 (0.33 #1363, 0.31 #9509, 0.29 #4073), 07c5l (0.22 #6710, 0.17 #8533, 0.17 #7621), 0h7x (0.19 #17220, 0.12 #10855, 0.09 #10858), 049nq (0.19 #17220) >> Best rule #15390 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 0fv_t; >> query: (?x10517, ?x455) <- partially_contains(?x1497, ?x10517), contains(?x455, ?x1497), location(?x6558, ?x455), time_zones(?x1497, ?x10735), adjoins(?x2467, ?x455), partially_contains(?x455, ?x404), taxonomy(?x455, ?x939) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026zt contains! 02j9z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 20.000 0.562 http://example.org/location/location/contains #5819-087wc7n PRED entity: 087wc7n PRED relation: story_by PRED expected values: 02g3w => 53 concepts (35 used for prediction) PRED predicted values (max 10 best out of 52): 04l3_z (0.14 #11, 0.03 #661), 04hw4b (0.07 #341, 0.06 #557, 0.04 #991), 0184dt (0.07 #254, 0.06 #470, 0.04 #904), 079vf (0.07 #1086, 0.04 #869, 0.03 #1302), 04jspq (0.06 #549, 0.04 #983, 0.04 #2282), 041h0 (0.06 #438, 0.04 #222, 0.03 #1305), 079ws (0.05 #1215, 0.01 #3594), 042xh (0.05 #1299), 046_v (0.04 #1040, 0.03 #1473, 0.03 #606), 05jcn8 (0.04 #2220, 0.03 #2653, 0.03 #487) >> Best rule #11 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 026f__m; >> query: (?x791, 04l3_z) <- film(?x7663, ?x791), language(?x791, ?x254), ?x254 = 02h40lc, genre(?x791, ?x258), ?x7663 = 04zkj5 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1272 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 55 *> proper extension: 04nlb94; *> query: (?x791, 02g3w) <- genre(?x791, ?x2540), genre(?x3008, ?x2540), genre(?x1628, ?x2540), ?x3008 = 05wp1p, ?x1628 = 0436yk, film_format(?x791, ?x10390) *> conf = 0.02 ranks of expected_values: 32 EVAL 087wc7n story_by 02g3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 53.000 35.000 0.143 http://example.org/film/film/story_by #5818-02jxrw PRED entity: 02jxrw PRED relation: nominated_for! PRED expected values: 0gq_v 09qwmm 0l8z1 02x2gy0 => 88 concepts (80 used for prediction) PRED predicted values (max 10 best out of 218): 0l8z1 (0.77 #4857, 0.77 #8325, 0.77 #9022), 0gq_v (0.57 #710, 0.33 #17, 0.31 #3948), 0gq9h (0.42 #3990, 0.41 #1676, 0.41 #1907), 019f4v (0.41 #744, 0.37 #3982, 0.37 #1668), 0gr0m (0.41 #750, 0.29 #57, 0.26 #1674), 0gs9p (0.37 #754, 0.37 #1909, 0.37 #3992), 0k611 (0.37 #761, 0.37 #1685, 0.35 #1916), 099c8n (0.37 #747, 0.33 #1671, 0.26 #3754), 02x2gy0 (0.36 #789, 0.13 #96, 0.12 #12031), 040njc (0.32 #1622, 0.32 #3936, 0.28 #698) >> Best rule #4857 for best value: >> intensional similarity = 4 >> extensional distance = 474 >> proper extension: 06mmr; >> query: (?x10060, ?x3458) <- award(?x10060, ?x3458), ceremony(?x3458, ?x78), nominated_for(?x3458, ?x69), honored_for(?x6238, ?x10060) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 9, 30 EVAL 02jxrw nominated_for! 02x2gy0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 88.000 80.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02jxrw nominated_for! 0l8z1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 80.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02jxrw nominated_for! 09qwmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 88.000 80.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02jxrw nominated_for! 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 80.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5817-0gvstc3 PRED entity: 0gvstc3 PRED relation: award_winner PRED expected values: 05bnq3j 02xc1w4 0dvld 02624g 04sry 0cp9f9 05bnx3j => 37 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2175): 0gcs9 (0.60 #14053, 0.44 #21629, 0.43 #11025), 01vw20h (0.54 #20371, 0.42 #18858, 0.40 #14307), 01lmj3q (0.50 #16687, 0.50 #13653, 0.44 #21229), 02773nt (0.50 #7667, 0.37 #3025, 0.33 #9180), 02773m2 (0.50 #7668, 0.37 #3025, 0.33 #9181), 0p_2r (0.50 #7755, 0.37 #3025, 0.33 #9268), 05bnq3j (0.50 #8280, 0.33 #9793, 0.33 #3743), 02xs0q (0.50 #8101, 0.33 #9614, 0.33 #2051), 0cp9f9 (0.50 #8736, 0.33 #10249, 0.33 #2686), 01_x6d (0.50 #8245, 0.33 #9758, 0.33 #2195) >> Best rule #14053 for best value: >> intensional similarity = 14 >> extensional distance = 8 >> proper extension: 019bk0; >> query: (?x2213, 0gcs9) <- award_winner(?x2213, ?x10011), award_winner(?x2213, ?x1896), award(?x1896, ?x704), award_winner(?x10011, ?x201), participant(?x1896, ?x4397), celebrity(?x1896, ?x3397), ceremony(?x375, ?x2213), artists(?x671, ?x3397), award_winner(?x1896, ?x959), participant(?x2108, ?x3397), participant(?x556, ?x3397), award_nominee(?x10011, ?x636), artists(?x302, ?x1896), award_nominee(?x4062, ?x1896) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #8280 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 2 *> proper extension: 02q690_; *> query: (?x2213, 05bnq3j) <- award_winner(?x2213, ?x10011), award_winner(?x2213, ?x9871), award_winner(?x2213, ?x8933), award_winner(?x2213, ?x2942), award_winner(?x2213, ?x1896), ?x1896 = 0j1yf, ceremony(?x6724, ?x2213), ceremony(?x1132, ?x2213), award_winner(?x201, ?x10011), nationality(?x9871, ?x94), award_nominee(?x636, ?x10011), ?x1132 = 0bdwft, honored_for(?x2213, ?x9788), film(?x2942, ?x463), participant(?x12255, ?x2942), ?x9788 = 01b7h8, award_nominee(?x8933, ?x3789), ?x6724 = 09v7wsg *> conf = 0.50 ranks of expected_values: 7, 9, 71, 82, 99, 105, 1893 EVAL 0gvstc3 award_winner 05bnx3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 37.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gvstc3 award_winner 0cp9f9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 37.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gvstc3 award_winner 04sry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 37.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gvstc3 award_winner 02624g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 37.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gvstc3 award_winner 0dvld CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 37.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gvstc3 award_winner 02xc1w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0gvstc3 award_winner 05bnq3j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 37.000 23.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5816-0cd2vh9 PRED entity: 0cd2vh9 PRED relation: language PRED expected values: 064_8sq => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 35): 064_8sq (0.32 #129, 0.20 #184, 0.20 #74), 02bjrlw (0.24 #111, 0.12 #1, 0.10 #56), 03_9r (0.12 #8, 0.10 #118, 0.07 #63), 04h9h (0.12 #39, 0.05 #149, 0.03 #754), 01r2l (0.12 #21, 0.03 #186, 0.02 #76), 0jzc (0.07 #72, 0.06 #127, 0.05 #622), 012w70 (0.06 #120, 0.03 #615, 0.03 #560), 05zjd (0.06 #132, 0.03 #572, 0.02 #737), 0653m (0.05 #559, 0.04 #1224, 0.04 #1279), 06mp7 (0.05 #123, 0.03 #343, 0.03 #233) >> Best rule #129 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 0g5qmbz; 03xj05; >> query: (?x1640, 064_8sq) <- film_crew_role(?x1640, ?x137), nominated_for(?x2771, ?x1640), language(?x1640, ?x732), ?x732 = 04306rv >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cd2vh9 language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.323 http://example.org/film/film/language #5815-0jhn7 PRED entity: 0jhn7 PRED relation: olympics! PRED expected values: 035dk => 58 concepts (54 used for prediction) PRED predicted values (max 10 best out of 213): 015qh (0.84 #178, 0.82 #179, 0.69 #543), 03_3d (0.84 #178, 0.82 #179, 0.69 #543), 06c1y (0.84 #178, 0.82 #179, 0.69 #543), 0345h (0.84 #178, 0.82 #179, 0.69 #543), 06bnz (0.84 #178, 0.82 #179, 0.69 #543), 06mkj (0.84 #178, 0.82 #179, 0.69 #543), 03spz (0.84 #178, 0.82 #179, 0.69 #543), 05qhw (0.84 #178, 0.82 #179, 0.69 #543), 07t21 (0.84 #178, 0.82 #179, 0.69 #543), 07f1x (0.84 #178, 0.82 #179, 0.69 #543) >> Best rule #178 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 06sks6; >> query: (?x3971, ?x279) <- sports(?x3971, ?x4310), sports(?x3971, ?x2315), sports(?x3971, ?x766), ?x766 = 01hp22, ?x2315 = 06wrt, olympics(?x4521, ?x3971), olympics(?x2188, ?x3971), olympics(?x291, ?x3971), ?x2188 = 0163v, ?x291 = 0h3y, ?x4521 = 07fj_, country(?x4310, ?x279) >> conf = 0.84 => this is the best rule for 182 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 62 EVAL 0jhn7 olympics! 035dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 58.000 54.000 0.842 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #5814-095zvfg PRED entity: 095zvfg PRED relation: nominated_for PRED expected values: 07cyl => 92 concepts (43 used for prediction) PRED predicted values (max 10 best out of 610): 05sy0cv (0.78 #17782, 0.77 #33944, 0.77 #25863), 02fttd (0.36 #4847, 0.28 #9699, 0.26 #6466), 011yrp (0.36 #4847, 0.28 #9699, 0.26 #6466), 0ndsl1x (0.36 #4847, 0.28 #9699, 0.26 #6466), 0cbv4g (0.36 #4847, 0.28 #9699, 0.26 #6466), 01k60v (0.36 #4847, 0.28 #9699, 0.26 #6466), 05dy7p (0.36 #4847, 0.28 #9699, 0.25 #4846), 05v38p (0.36 #4847, 0.28 #9699, 0.25 #4846), 0pv54 (0.36 #4847, 0.28 #9699, 0.25 #4846), 04vr_f (0.33 #158, 0.02 #14707, 0.02 #19555) >> Best rule #17782 for best value: >> intensional similarity = 3 >> extensional distance = 572 >> proper extension: 087yty; 04pp9s; 07zhd7; 03wdsbz; >> query: (?x9151, ?x8837) <- award_winner(?x2082, ?x9151), award_winner(?x8837, ?x9151), place_of_birth(?x9151, ?x739) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0jz9f; 0bytkq; 0dvmd; 02pq9yv; 01tc9r; 0jmj; 016fnb; 06r_by; 018ygt; 08h79x; *> query: (?x9151, 07cyl) <- award_winner(?x2082, ?x9151), nominated_for(?x9151, ?x5648), ?x5648 = 049xgc *> conf = 0.08 ranks of expected_values: 51 EVAL 095zvfg nominated_for 07cyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 92.000 43.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5813-022411 PRED entity: 022411 PRED relation: film PRED expected values: 047p798 => 94 concepts (59 used for prediction) PRED predicted values (max 10 best out of 464): 01b_lz (0.42 #69748, 0.38 #60805, 0.38 #84056), 03hkch7 (0.17 #512, 0.04 #55440, 0.03 #62594), 0gmd3k7 (0.17 #1107, 0.03 #62594), 08r4x3 (0.08 #154, 0.04 #3730, 0.04 #55440), 040_lv (0.08 #1046, 0.04 #2834, 0.01 #4622), 03p2xc (0.08 #1243, 0.04 #55440, 0.03 #62594), 0g9yrw (0.08 #664, 0.04 #55440, 0.03 #62594), 07cz2 (0.08 #445, 0.04 #55440, 0.03 #62594), 01dyvs (0.08 #280, 0.04 #55440, 0.03 #62594), 02d478 (0.08 #673, 0.04 #55440) >> Best rule #69748 for best value: >> intensional similarity = 4 >> extensional distance = 1554 >> proper extension: 04b19t; >> query: (?x9890, ?x3326) <- nominated_for(?x9890, ?x3326), nominated_for(?x9890, ?x1866), gender(?x9890, ?x514), genre(?x1866, ?x53) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 022411 film 047p798 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 59.000 0.420 http://example.org/film/actor/film./film/performance/film #5812-06kl78 PRED entity: 06kl78 PRED relation: genre PRED expected values: 0hn10 => 108 concepts (80 used for prediction) PRED predicted values (max 10 best out of 94): 07ssc (0.63 #3263, 0.63 #2447, 0.62 #4547), 02n4kr (0.58 #8, 0.49 #358, 0.16 #2571), 05p553 (0.43 #8755, 0.37 #3732, 0.33 #9223), 02l7c8 (0.38 #7362, 0.38 #715, 0.34 #250), 0lsxr (0.36 #359, 0.25 #243, 0.25 #9), 02kdv5l (0.35 #3265, 0.27 #468, 0.26 #9221), 04xvlr (0.31 #4196, 0.30 #2331, 0.29 #3147), 03npn (0.30 #357, 0.25 #7, 0.09 #589), 03k9fj (0.27 #478, 0.25 #3275, 0.22 #944), 03g3w (0.25 #141, 0.11 #2354, 0.11 #3170) >> Best rule #3263 for best value: >> intensional similarity = 4 >> extensional distance = 385 >> proper extension: 027pfb2; >> query: (?x4772, ?x512) <- titles(?x512, ?x4772), titles(?x53, ?x4772), film(?x2657, ?x4772), ?x53 = 07s9rl0 >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 02fqxm; *> query: (?x4772, 0hn10) <- titles(?x3250, ?x4772), film(?x2657, ?x4772), nominated_for(?x3828, ?x4772), ?x3250 = 0glj9q *> conf = 0.17 ranks of expected_values: 20 EVAL 06kl78 genre 0hn10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 108.000 80.000 0.634 http://example.org/film/film/genre #5811-01_6dw PRED entity: 01_6dw PRED relation: award PRED expected values: 0gr4k => 112 concepts (105 used for prediction) PRED predicted values (max 10 best out of 307): 01pqx6 (0.72 #2389, 0.71 #20307, 0.71 #20706), 02gdjb (0.47 #3003, 0.11 #216, 0.07 #8976), 026mfs (0.46 #3310, 0.06 #8885, 0.05 #2912), 01l29r (0.44 #163, 0.13 #35448, 0.12 #31067), 01lk0l (0.44 #275, 0.13 #35448, 0.12 #31067), 0c4z8 (0.44 #8829, 0.24 #3254, 0.22 #2856), 054ks3 (0.44 #8898, 0.22 #3323, 0.20 #2925), 0gqz2 (0.39 #8838, 0.33 #2865, 0.23 #3263), 0gr4k (0.36 #4809, 0.34 #6401, 0.31 #4013), 01lj_c (0.33 #294, 0.13 #35448, 0.12 #31067) >> Best rule #2389 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0d9_96; 0h3mrc; 0g28b1; 01trf3; 06msq2; 045w_4; 08n__5; 0bqs56; 03q45x; 04glr5h; ... >> query: (?x6534, ?x7606) <- profession(?x6534, ?x353), award_winner(?x7606, ?x6534), tv_program(?x6534, ?x715) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #4809 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 157 *> proper extension: 019z7q; 0prjs; 04gcd1; 034bgm; 01q4qv; 0jrny; 0jw67; 0br1w; 01f7v_; 0171lb; ... *> query: (?x6534, 0gr4k) <- written_by(?x1685, ?x6534), type_of_union(?x6534, ?x566), award_winner(?x715, ?x6534) *> conf = 0.36 ranks of expected_values: 9 EVAL 01_6dw award 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 112.000 105.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5810-0m313 PRED entity: 0m313 PRED relation: nominated_for! PRED expected values: 094qd5 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 160): 0gq9h (0.68 #9076, 0.67 #5156, 0.67 #208), 09sb52 (0.68 #9076, 0.67 #5156, 0.67 #208), 099t8j (0.50 #287, 0.40 #494, 0.33 #79), 099ck7 (0.50 #354, 0.40 #561, 0.24 #8662), 054krc (0.50 #260, 0.40 #467, 0.21 #1291), 094qd5 (0.49 #2296, 0.33 #27, 0.25 #4331), 099jhq (0.45 #1252, 0.25 #221, 0.20 #428), 0gr4k (0.37 #2289, 0.33 #20, 0.33 #6206), 03hkv_r (0.33 #10, 0.27 #1249, 0.20 #425), 02x17s4 (0.33 #73, 0.22 #1312, 0.19 #16502) >> Best rule #9076 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 06w7mlh; >> query: (?x144, ?x1972) <- award_winner(?x144, ?x166), award(?x144, ?x1972), award(?x91, ?x1972) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #2296 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 132 *> proper extension: 01jc6q; *> query: (?x144, 094qd5) <- award_winner(?x144, ?x166), nominated_for(?x1245, ?x144), ?x1245 = 0gqwc *> conf = 0.49 ranks of expected_values: 6 EVAL 0m313 nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 87.000 87.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5809-03s9kp PRED entity: 03s9kp PRED relation: story_by PRED expected values: 0jt90f5 => 54 concepts (40 used for prediction) PRED predicted values (max 10 best out of 23): 0jt90f5 (0.04 #464), 02lkcc (0.02 #3041, 0.02 #4129, 0.02 #3477), 03cdg (0.02 #623), 01lc5 (0.02 #615), 01vl17 (0.02 #573), 052hl (0.02 #549), 0br1w (0.02 #495), 05ldnp (0.02 #482), 0bxtg (0.02 #4129, 0.02 #3477, 0.02 #5647), 06bzwt (0.02 #4129, 0.02 #5647, 0.01 #6955) >> Best rule #464 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 03g90h; 0ds33; 016z5x; 01sxly; 0963mq; 01pgp6; 0cz_ym; 011yth; 0kvgxk; 02qhqz4; ... >> query: (?x11996, 0jt90f5) <- film(?x8835, ?x11996), genre(?x11996, ?x162), award(?x8835, ?x7788), ?x7788 = 09lvl1 >> conf = 0.04 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s9kp story_by 0jt90f5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 40.000 0.038 http://example.org/film/film/story_by #5808-02pqp12 PRED entity: 02pqp12 PRED relation: award! PRED expected values: 02_kd 08vd2q => 61 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1646): 0cq806 (0.57 #3829, 0.50 #4827, 0.33 #835), 0ywrc (0.57 #3294, 0.45 #8285, 0.42 #4292), 017jd9 (0.50 #8435, 0.43 #3444, 0.42 #4442), 05jf85 (0.50 #1022, 0.43 #3018, 0.25 #4016), 0pv3x (0.50 #8086, 0.42 #4093, 0.33 #5091), 0f4_l (0.50 #1207, 0.29 #3203, 0.25 #4201), 0gwjw0c (0.50 #1678, 0.29 #3674, 0.22 #2992), 05y0cr (0.50 #2862, 0.14 #8852, 0.08 #6855), 0k20s (0.50 #2946, 0.10 #7937, 0.09 #8936), 09gq0x5 (0.43 #3992, 0.43 #3160, 0.33 #4158) >> Best rule #3829 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 040njc; 019f4v; 0gs9p; >> query: (?x1198, 0cq806) <- award(?x407, ?x1198), award(?x8042, ?x1198), ?x8042 = 02hfp_, nominated_for(?x1198, ?x1813), ?x1813 = 09gq0x5 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2992 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 054knh; *> query: (?x1198, ?x89) <- award(?x7554, ?x1198), ?x7554 = 01mgw, nominated_for(?x1198, ?x8188), nominated_for(?x1198, ?x7009), nominated_for(?x1198, ?x89), ?x8188 = 01qz5, country(?x7009, ?x94) *> conf = 0.22 ranks of expected_values: 168, 170 EVAL 02pqp12 award! 08vd2q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 61.000 17.000 0.571 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02pqp12 award! 02_kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 61.000 17.000 0.571 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #5807-037xlx PRED entity: 037xlx PRED relation: honored_for PRED expected values: 059lwy => 82 concepts (37 used for prediction) PRED predicted values (max 10 best out of 73): 07b1gq (0.85 #1561, 0.85 #1560, 0.85 #1718), 06c0ns (0.85 #1561, 0.85 #1560, 0.85 #1717), 02ny6g (0.85 #1561, 0.85 #1560, 0.85 #1717), 059lwy (0.75 #276, 0.64 #432, 0.45 #1248), 037xlx (0.58 #248, 0.57 #404, 0.14 #562), 08984j (0.45 #1248), 01srq2 (0.05 #597, 0.03 #907, 0.02 #1689), 01vfqh (0.05 #497, 0.03 #807, 0.02 #1589), 05pxnmb (0.05 #1223, 0.04 #1694, 0.04 #1537), 0bxxzb (0.05 #1208, 0.04 #1679, 0.04 #1522) >> Best rule #1561 for best value: >> intensional similarity = 4 >> extensional distance = 131 >> proper extension: 0m313; 011yxg; 0ds3t5x; 0g5qs2k; 0ds33; 0dqytn; 0hmr4; 0jzw; 0pv2t; 06_wqk4; ... >> query: (?x5731, ?x4749) <- honored_for(?x4749, ?x5731), nominated_for(?x154, ?x5731), honored_for(?x5731, ?x188), film(?x806, ?x4749) >> conf = 0.85 => this is the best rule for 3 predicted values *> Best rule #276 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0140g4; 0946bb; 07b1gq; 04cbbz; 074rg9; 01mszz; 0cwfgz; 059lwy; 02scbv; 06c0ns; *> query: (?x5731, 059lwy) <- honored_for(?x4749, ?x5731), ?x4749 = 07sgdw, nominated_for(?x154, ?x5731), honored_for(?x5731, ?x188) *> conf = 0.75 ranks of expected_values: 4 EVAL 037xlx honored_for 059lwy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 37.000 0.852 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #5806-0d05q4 PRED entity: 0d05q4 PRED relation: contains PRED expected values: 0ft0s 01cgxp => 124 concepts (90 used for prediction) PRED predicted values (max 10 best out of 2569): 0bwfn (0.10 #12831, 0.10 #15776, 0.07 #9884), 021q2j (0.10 #13045, 0.10 #15990, 0.04 #45444), 03bmmc (0.10 #12561, 0.10 #15506, 0.04 #44960), 04ftdq (0.10 #13029, 0.10 #15974, 0.04 #45428), 02lwv5 (0.10 #13528, 0.10 #16473, 0.04 #45927), 09k9d0 (0.10 #13757, 0.10 #16702, 0.04 #46156), 01p7x7 (0.10 #13621, 0.10 #16566, 0.04 #46020), 026ssfj (0.10 #12987, 0.10 #15932, 0.04 #45386), 02lv2v (0.10 #12972, 0.10 #15917, 0.04 #45371), 06182p (0.10 #12919, 0.10 #15864, 0.04 #45318) >> Best rule #12831 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 0n5gq; >> query: (?x4092, 0bwfn) <- contains(?x6304, ?x4092), contains(?x4092, ?x13482), jurisdiction_of_office(?x12920, ?x4092) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d05q4 contains 01cgxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 90.000 0.103 http://example.org/location/location/contains EVAL 0d05q4 contains 0ft0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 90.000 0.103 http://example.org/location/location/contains #5805-05zr6wv PRED entity: 05zr6wv PRED relation: award! PRED expected values: 012d40 014zcr 06cgy 0127m7 055c8 035rnz 042ly5 02633g 059j1m 01yf85 => 46 concepts (23 used for prediction) PRED predicted values (max 10 best out of 2462): 0p__8 (0.79 #3282, 0.79 #9848, 0.70 #75544), 0bksh (0.79 #3282, 0.79 #9848, 0.70 #75544), 012d40 (0.79 #3282, 0.79 #9848, 0.70 #75544), 02qgqt (0.60 #3302, 0.12 #9868, 0.10 #13151), 0237fw (0.40 #3900, 0.33 #618, 0.25 #7184), 0170qf (0.40 #3849, 0.33 #567, 0.25 #7133), 03ym1 (0.40 #4897, 0.33 #1615, 0.15 #19701), 0blq0z (0.40 #3967, 0.33 #685, 0.12 #7251), 01wmxfs (0.40 #3454, 0.33 #172, 0.12 #6738), 02m501 (0.40 #5999, 0.25 #12565, 0.12 #9283) >> Best rule #3282 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 05ztrmj; >> query: (?x401, ?x147) <- award(?x408, ?x401), award_winner(?x401, ?x147), nominated_for(?x401, ?x5502), ?x5502 = 01bl7g >> conf = 0.79 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 11, 64, 84, 93, 618, 741, 1075, 2263 EVAL 05zr6wv award! 01yf85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 23.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zr6wv award! 059j1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 23.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zr6wv award! 02633g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 23.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zr6wv award! 042ly5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 23.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zr6wv award! 035rnz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 46.000 23.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zr6wv award! 055c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 23.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zr6wv award! 0127m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 46.000 23.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zr6wv award! 06cgy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 46.000 23.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zr6wv award! 014zcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 46.000 23.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zr6wv award! 012d40 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 46.000 23.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5804-03f6fl0 PRED entity: 03f6fl0 PRED relation: student! PRED expected values: 0lyjf => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 140): 02g839 (0.20 #552, 0.11 #1079, 0.11 #2660), 0lyjf (0.20 #684, 0.11 #1211, 0.02 #11225), 01r3y2 (0.20 #616, 0.11 #1143, 0.02 #7467), 02237m (0.11 #1451, 0.03 #5140, 0.03 #4613), 01t0dy (0.08 #3906, 0.06 #4960, 0.06 #4433), 0288zy (0.08 #1597, 0.04 #3705, 0.02 #8448), 04sylm (0.08 #1657, 0.04 #3765, 0.02 #16942), 03fgm (0.08 #1962, 0.04 #4070), 033x5p (0.08 #1723, 0.04 #3831), 07wjk (0.08 #1644, 0.04 #3752) >> Best rule #552 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 011z3g; >> query: (?x4977, 02g839) <- artists(?x5300, ?x4977), artists(?x1572, ?x4977), artists(?x302, ?x4977), ?x5300 = 02k_kn, ?x302 = 016clz, ?x1572 = 06by7 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #684 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 011z3g; *> query: (?x4977, 0lyjf) <- artists(?x5300, ?x4977), artists(?x1572, ?x4977), artists(?x302, ?x4977), ?x5300 = 02k_kn, ?x302 = 016clz, ?x1572 = 06by7 *> conf = 0.20 ranks of expected_values: 2 EVAL 03f6fl0 student! 0lyjf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 115.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #5803-08052t3 PRED entity: 08052t3 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.88 #16, 0.83 #121, 0.83 #126), 07c52 (0.33 #8, 0.27 #3, 0.09 #138), 02nxhr (0.27 #12, 0.11 #27, 0.09 #22), 07z4p (0.20 #10, 0.18 #5, 0.07 #100) >> Best rule #16 for best value: >> intensional similarity = 8 >> extensional distance = 32 >> proper extension: 03t79f; 0bl3nn; 0dp7wt; 03tbg6; >> query: (?x2471, 029j_) <- genre(?x2471, ?x225), language(?x2471, ?x254), film_crew_role(?x2471, ?x2095), film_crew_role(?x2471, ?x1966), film_crew_role(?x2471, ?x468), ?x468 = 02r96rf, ?x2095 = 0dxtw, ?x1966 = 015h31 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08052t3 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #5802-0d04z6 PRED entity: 0d04z6 PRED relation: country! PRED expected values: 0w0d 01lb14 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 41): 01lb14 (0.73 #914, 0.69 #1283, 0.65 #504), 06wrt (0.69 #1284, 0.66 #915, 0.65 #505), 0w0d (0.68 #912, 0.62 #502, 0.61 #584), 0194d (0.66 #937, 0.58 #1306, 0.55 #322), 07bs0 (0.62 #1282, 0.59 #913, 0.55 #298), 01hp22 (0.62 #1277, 0.54 #498, 0.50 #580), 01z27 (0.62 #1285, 0.54 #916, 0.45 #301), 01sgl (0.60 #319, 0.54 #934, 0.50 #524), 03rbzn (0.60 #1291, 0.54 #922, 0.50 #512), 09w1n (0.60 #1288, 0.54 #919, 0.50 #304) >> Best rule #914 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 049nq; >> query: (?x5147, 01lb14) <- nationality(?x4258, ?x5147), country(?x10708, ?x5147), profession(?x4258, ?x131) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0d04z6 country! 01lb14 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.732 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d04z6 country! 0w0d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 150.000 150.000 0.732 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #5801-0415ggl PRED entity: 0415ggl PRED relation: film_crew_role PRED expected values: 02r96rf => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 22): 02r96rf (0.77 #119, 0.77 #1465, 0.74 #1026), 01vx2h (0.69 #38, 0.62 #125, 0.41 #504), 02_n3z (0.35 #1, 0.34 #117, 0.14 #30), 01pvkk (0.32 #1443, 0.32 #1238, 0.31 #1793), 02rh1dz (0.26 #37, 0.21 #124, 0.18 #473), 033smt (0.25 #137, 0.24 #50, 0.14 #21), 0263ycg (0.24 #14, 0.12 #3340, 0.10 #43), 020xn5 (0.19 #123, 0.17 #36, 0.12 #3340), 089fss (0.15 #122, 0.12 #3340, 0.11 #6), 02vs3x5 (0.14 #222, 0.12 #3340, 0.09 #76) >> Best rule #119 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 0cmf0m0; >> query: (?x5724, 02r96rf) <- genre(?x5724, ?x53), film_crew_role(?x5724, ?x7591), executive_produced_by(?x5724, ?x7831), ?x7591 = 0d2b38 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0415ggl film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.774 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5800-0kyk PRED entity: 0kyk PRED relation: split_to PRED expected values: 0kyk => 47 concepts (21 used for prediction) PRED predicted values (max 10 best out of 8): 02hrh1q (0.33 #117, 0.25 #309, 0.20 #606), 03gjzk (0.10 #996, 0.09 #1097, 0.02 #1585), 05148p4 (0.02 #1487, 0.02 #1587, 0.02 #1686), 0342h (0.02 #1468, 0.02 #1568, 0.02 #1667), 02hnl (0.02 #1495, 0.02 #1595, 0.02 #1694), 09lbv (0.02 #1490, 0.02 #1590, 0.02 #1689), 026t6 (0.02 #1467, 0.02 #1567, 0.02 #1666), 018vs (0.02 #1479, 0.02 #1678, 0.02 #1781) >> Best rule #117 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 02hrh1q; >> query: (?x2225, 02hrh1q) <- profession(?x10909, ?x2225), profession(?x9493, ?x2225), profession(?x7334, ?x2225), profession(?x4877, ?x2225), ?x9493 = 01j6mff, ?x10909 = 029k55, nationality(?x7334, ?x94), ?x4877 = 03sww >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0kyk split_to 0kyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 21.000 0.333 http://example.org/dataworld/gardening_hint/split_to #5799-0gy7bj4 PRED entity: 0gy7bj4 PRED relation: nominated_for! PRED expected values: 02qvyrt => 73 concepts (66 used for prediction) PRED predicted values (max 10 best out of 205): 0gq9h (0.57 #3309, 0.44 #2381, 0.42 #3773), 099c8n (0.50 #287, 0.27 #2376, 0.21 #3768), 0gs9p (0.49 #3311, 0.38 #2383, 0.37 #3775), 019f4v (0.47 #3301, 0.38 #2373, 0.37 #3765), 0k611 (0.43 #3319, 0.33 #2391, 0.32 #3783), 040njc (0.38 #3255, 0.33 #2327, 0.30 #3719), 0gr4k (0.35 #3274, 0.23 #3738, 0.22 #2346), 0p9sw (0.34 #251, 0.30 #3268, 0.27 #2340), 04dn09n (0.33 #3283, 0.26 #2355, 0.26 #3747), 0gqy2 (0.33 #3365, 0.30 #2437, 0.27 #3829) >> Best rule #3309 for best value: >> intensional similarity = 3 >> extensional distance = 346 >> proper extension: 06mmr; >> query: (?x9839, 0gq9h) <- award(?x9839, ?x2222), nominated_for(?x2222, ?x3882), ?x3882 = 0mcl0 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #3341 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 346 *> proper extension: 06mmr; *> query: (?x9839, 02qvyrt) <- award(?x9839, ?x2222), nominated_for(?x2222, ?x3882), ?x3882 = 0mcl0 *> conf = 0.26 ranks of expected_values: 16 EVAL 0gy7bj4 nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 73.000 66.000 0.572 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5798-016l09 PRED entity: 016l09 PRED relation: artists! PRED expected values: 01243b 05r6t => 92 concepts (55 used for prediction) PRED predicted values (max 10 best out of 261): 064t9 (0.57 #11093, 0.52 #10167, 0.50 #9860), 0155w (0.41 #4411, 0.38 #8717, 0.25 #8919), 03lty (0.41 #4639, 0.32 #4332, 0.27 #8638), 05r6t (0.38 #1925, 0.29 #3466, 0.28 #4080), 0dl5d (0.38 #19, 0.37 #4631, 0.27 #5862), 05bt6j (0.37 #10197, 0.37 #8964, 0.36 #9581), 05w3f (0.36 #8648, 0.34 #4342, 0.31 #959), 016jny (0.32 #4409, 0.25 #8919, 0.25 #8715), 02lnbg (0.30 #4979, 0.23 #6824, 0.14 #10213), 06j6l (0.30 #9895, 0.29 #11128, 0.29 #10202) >> Best rule #11093 for best value: >> intensional similarity = 5 >> extensional distance = 188 >> proper extension: 04lgymt; 0146pg; 0jdhp; 01k5t_3; 0ggl02; 04bpm6; 0770cd; 0gt_k; 02zmh5; 02qlg7s; ... >> query: (?x9791, 064t9) <- award(?x9791, ?x2877), award(?x9791, ?x2139), ?x2139 = 01by1l, award(?x7115, ?x2877), ?x7115 = 02z4b_8 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1925 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 0frsw; 0hvbj; 07mvp; 081wh1; 07hgm; 017959; 04qzm; 0ycfj; *> query: (?x9791, 05r6t) <- award(?x9791, ?x8994), group(?x2187, ?x9791), artist(?x382, ?x9791), group(?x227, ?x9791), ?x8994 = 02f6yz *> conf = 0.38 ranks of expected_values: 4, 17 EVAL 016l09 artists! 05r6t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 92.000 55.000 0.574 http://example.org/music/genre/artists EVAL 016l09 artists! 01243b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 92.000 55.000 0.574 http://example.org/music/genre/artists #5797-04ly1 PRED entity: 04ly1 PRED relation: location! PRED expected values: 0d810y 044mvs 0mb0 => 206 concepts (160 used for prediction) PRED predicted values (max 10 best out of 2030): 03csqj4 (0.49 #376248, 0.47 #125418, 0.46 #308518), 03nb5v (0.21 #6337, 0.20 #8845, 0.20 #3828), 094xh (0.21 #6092, 0.13 #8600, 0.11 #13616), 016z2j (0.20 #2937, 0.14 #5446, 0.07 #12970), 01wy5m (0.20 #3489, 0.14 #5998, 0.07 #13522), 02ghq (0.14 #7200, 0.13 #9708, 0.10 #4691), 06jw0s (0.11 #16192, 0.09 #48801, 0.09 #56326), 023s8 (0.10 #4611, 0.09 #24677, 0.08 #17152), 0cgbf (0.10 #3899, 0.08 #11424, 0.08 #18949), 0c01c (0.10 #2981, 0.07 #13014, 0.07 #5490) >> Best rule #376248 for best value: >> intensional similarity = 3 >> extensional distance = 312 >> proper extension: 0k049; 06_kh; 01tlmw; 0ydpd; 02cl1; 0yc84; 0xkq4; 0fvxz; 095w_; 0r7fy; ... >> query: (?x3908, ?x12010) <- place_of_birth(?x12010, ?x3908), category(?x3908, ?x134), location(?x1299, ?x3908) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #4633 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0d6hn; *> query: (?x3908, 0mb0) <- place_of_birth(?x12010, ?x3908), first_level_division_of(?x3908, ?x94), featured_film_locations(?x5725, ?x3908) *> conf = 0.10 ranks of expected_values: 61, 278 EVAL 04ly1 location! 0mb0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 206.000 160.000 0.492 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04ly1 location! 044mvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 206.000 160.000 0.492 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04ly1 location! 0d810y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 206.000 160.000 0.492 http://example.org/people/person/places_lived./people/place_lived/location #5796-0c1pj PRED entity: 0c1pj PRED relation: people! PRED expected values: 033tf_ => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 48): 033tf_ (0.29 #539, 0.24 #919, 0.20 #995), 063k3h (0.25 #258, 0.18 #942, 0.17 #1018), 06v41q (0.25 #28, 0.06 #7610, 0.04 #940), 0x67 (0.23 #3432, 0.21 #4576, 0.20 #3736), 041rx (0.22 #4188, 0.21 #4570, 0.21 #3730), 07hwkr (0.20 #88, 0.12 #164, 0.07 #2596), 0xnvg (0.17 #773, 0.11 #545, 0.10 #3055), 09vc4s (0.14 #541, 0.12 #237, 0.10 #921), 02w7gg (0.13 #3576, 0.13 #4186, 0.12 #3044), 02ctzb (0.12 #851, 0.12 #243, 0.09 #1155) >> Best rule #539 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 0hnp7; >> query: (?x556, 033tf_) <- people(?x5741, ?x556), ?x5741 = 07bch9, participant(?x262, ?x556) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c1pj people! 033tf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.286 http://example.org/people/ethnicity/people #5795-02z0dfh PRED entity: 02z0dfh PRED relation: award! PRED expected values: 0h32q 0c3p7 01qqtr => 41 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2295): 01hkhq (0.83 #6644, 0.81 #13289, 0.79 #3322), 04znsy (0.83 #6644, 0.81 #13289, 0.79 #3322), 03lvyj (0.83 #6644, 0.81 #13289, 0.79 #3322), 039x1k (0.83 #6644, 0.81 #13289, 0.79 #3322), 0159h6 (0.67 #95, 0.58 #3417, 0.43 #6740), 0dvld (0.67 #1718, 0.57 #8363, 0.47 #11685), 03mp9s (0.67 #1989, 0.43 #8634, 0.42 #5311), 0c4f4 (0.67 #99, 0.43 #6744, 0.27 #10066), 09l3p (0.67 #1189, 0.42 #4511, 0.36 #7834), 0mz73 (0.58 #5557, 0.33 #2235, 0.29 #8880) >> Best rule #6644 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0bfvw2; 09qwmm; 094qd5; 02x4x18; 026m9w; >> query: (?x1254, ?x1641) <- award_winner(?x1254, ?x1641), award(?x8612, ?x1254), award(?x1244, ?x1254), ?x1244 = 0h1nt, film(?x8612, ?x4664) >> conf = 0.83 => this is the best rule for 4 predicted values *> Best rule #1227 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 09sb52; 0gqwc; 0gqyl; 099t8j; *> query: (?x1254, 0h32q) <- award_winner(?x1254, ?x1641), award(?x8612, ?x1254), award(?x4247, ?x1254), ?x8612 = 01jw4r, ?x4247 = 02vntj *> conf = 0.50 ranks of expected_values: 17, 49, 263 EVAL 02z0dfh award! 01qqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 41.000 14.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02z0dfh award! 0c3p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 41.000 14.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02z0dfh award! 0h32q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 41.000 14.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5794-02sgy PRED entity: 02sgy PRED relation: role! PRED expected values: 03c7ln 0qf3p 02qwg 06gd4 0326tc 01q_wyj 01m7pwq 017f4y => 87 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1901): 01vn35l (0.60 #3789, 0.50 #8951, 0.50 #5633), 01gx5f (0.60 #3442, 0.50 #2335, 0.50 #1967), 06k02 (0.60 #3396, 0.38 #6348, 0.33 #8927), 0326tc (0.50 #9124, 0.50 #6545, 0.50 #5068), 01wgjj5 (0.50 #4998, 0.50 #2786, 0.50 #2416), 05qhnq (0.50 #5036, 0.50 #2824, 0.50 #2454), 01tp5bj (0.50 #4879, 0.50 #2667, 0.50 #2297), 04kjrv (0.50 #9088, 0.50 #6509, 0.50 #2450), 01w9wwg (0.50 #12013, 0.50 #6482, 0.50 #2055), 017f4y (0.50 #2558, 0.50 #2190, 0.50 #1821) >> Best rule #3789 for best value: >> intensional similarity = 13 >> extensional distance = 3 >> proper extension: 026t6; >> query: (?x314, 01vn35l) <- role(?x9128, ?x314), performance_role(?x314, ?x212), award(?x9128, ?x2379), role(?x2460, ?x314), role(?x1750, ?x314), role(?x1662, ?x314), role(?x922, ?x314), profession(?x9128, ?x131), ?x2460 = 01wy6, ?x922 = 050rj, ?x1750 = 02hnl, ?x1662 = 02bxd, role(?x736, ?x314) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #9124 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 10 *> proper extension: 013y1f; 02dlh2; 03gvt; *> query: (?x314, 0326tc) <- role(?x9128, ?x314), performance_role(?x314, ?x212), award(?x9128, ?x10316), role(?x2460, ?x314), role(?x2377, ?x314), profession(?x9128, ?x131), ?x2460 = 01wy6, group(?x314, ?x442), family(?x2377, ?x7256), instrumentalists(?x314, ?x133), ?x10316 = 02ddq4 *> conf = 0.50 ranks of expected_values: 4, 10, 28, 49, 60, 79, 147, 312 EVAL 02sgy role! 017f4y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 54.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 02sgy role! 01m7pwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 87.000 54.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 02sgy role! 01q_wyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 87.000 54.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 02sgy role! 0326tc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 54.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 02sgy role! 06gd4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 87.000 54.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 02sgy role! 02qwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 87.000 54.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 02sgy role! 0qf3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 87.000 54.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 02sgy role! 03c7ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 87.000 54.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role #5793-022g44 PRED entity: 022g44 PRED relation: film PRED expected values: 09q5w2 019vhk => 88 concepts (55 used for prediction) PRED predicted values (max 10 best out of 886): 04vr_f (0.40 #5538, 0.05 #53671, 0.03 #10905), 02cbhg (0.33 #1405, 0.04 #21084, 0.03 #22873), 02qhqz4 (0.33 #344, 0.03 #11078, 0.03 #12867), 03pc89 (0.33 #1460, 0.03 #12194, 0.03 #13983), 02_qt (0.33 #634, 0.03 #11368, 0.03 #13157), 072zl1 (0.33 #1281, 0.03 #20960, 0.02 #22749), 09sr0 (0.33 #1521, 0.02 #17622, 0.02 #26567), 09p4w8 (0.33 #832, 0.01 #59870), 0crd8q6 (0.33 #1634), 0cwfgz (0.33 #1089) >> Best rule #5538 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0gy6z9; 015vq_; 042xrr; >> query: (?x4961, 04vr_f) <- people(?x743, ?x4961), place_of_birth(?x4961, ?x11794), award_nominee(?x4961, ?x4282), ?x4282 = 02yxwd >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #7619 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 02hfp_; 040dv; *> query: (?x4961, 019vhk) <- people(?x743, ?x4961), ?x743 = 02w7gg, profession(?x4961, ?x220), place_of_death(?x4961, ?x4962) *> conf = 0.09 ranks of expected_values: 83, 90 EVAL 022g44 film 019vhk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 88.000 55.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 022g44 film 09q5w2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 88.000 55.000 0.400 http://example.org/film/actor/film./film/performance/film #5792-0gr69 PRED entity: 0gr69 PRED relation: award PRED expected values: 02f5qb => 127 concepts (103 used for prediction) PRED predicted values (max 10 best out of 289): 01bgqh (0.57 #1639, 0.50 #2437, 0.35 #17600), 01by1l (0.52 #2108, 0.50 #2507, 0.50 #1709), 01c9jp (0.50 #985, 0.40 #586, 0.32 #6173), 02f5qb (0.50 #1752, 0.36 #6142, 0.32 #2550), 02v1m7 (0.50 #1710, 0.33 #912, 0.29 #4902), 02f73b (0.50 #1879, 0.27 #2677, 0.25 #6269), 01ck6h (0.39 #9700, 0.21 #1719, 0.18 #2517), 02f77l (0.37 #4640, 0.36 #2645, 0.34 #5039), 02wh75 (0.36 #2403, 0.21 #1605, 0.21 #3600), 03qbh5 (0.36 #1798, 0.32 #2596, 0.25 #3793) >> Best rule #1639 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 02r3zy; 07c0j; 01vs_v8; 0gdh5; 0gcs9; 0gbwp; 03f0fnk; 09889g; 01wgfp6; 07bzp; ... >> query: (?x7188, 01bgqh) <- artists(?x114, ?x7188), origin(?x7188, ?x1523), award_winner(?x3045, ?x7188), ?x3045 = 02sp_v >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1752 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 02r3zy; 07c0j; 01vs_v8; 0gdh5; 0gcs9; 0gbwp; 03f0fnk; 09889g; 01wgfp6; 07bzp; ... *> query: (?x7188, 02f5qb) <- artists(?x114, ?x7188), origin(?x7188, ?x1523), award_winner(?x3045, ?x7188), ?x3045 = 02sp_v *> conf = 0.50 ranks of expected_values: 4 EVAL 0gr69 award 02f5qb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 127.000 103.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5791-043djx PRED entity: 043djx PRED relation: district_represented PRED expected values: 01n7q 03v0t 0g0syc => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 874): 03v0t (0.88 #576, 0.86 #538, 0.84 #350), 01n7q (0.88 #576, 0.86 #538, 0.84 #350), 0g0syc (0.88 #576, 0.86 #538, 0.84 #350), 050l8 (0.71 #1021, 0.69 #944, 0.69 #906), 05fky (0.71 #1030, 0.69 #953, 0.69 #915), 059_c (0.69 #900, 0.67 #782, 0.65 #1015), 07srw (0.68 #1102, 0.67 #789, 0.65 #1022), 05mph (0.67 #617, 0.67 #802, 0.62 #958), 05fjy (0.67 #617, 0.65 #1033, 0.62 #956), 05kr_ (0.67 #617, 0.60 #1170, 0.59 #578) >> Best rule #576 for best value: >> intensional similarity = 33 >> extensional distance = 6 >> proper extension: 070mff; >> query: (?x759, ?x3818) <- legislative_sessions(?x759, ?x10291), legislative_sessions(?x759, ?x3669), legislative_sessions(?x5401, ?x759), district_represented(?x759, ?x6895), district_represented(?x759, ?x3908), district_represented(?x759, ?x3778), district_represented(?x759, ?x2831), district_represented(?x759, ?x1906), district_represented(?x759, ?x1426), district_represented(?x759, ?x177), ?x1906 = 04rrx, ?x3778 = 07h34, ?x3908 = 04ly1, legislative_sessions(?x3669, ?x5005), ?x2831 = 0gyh, legislative_sessions(?x2860, ?x3669), contains(?x1426, ?x12946), contains(?x1426, ?x5486), ?x6895 = 05fjf, state_province_region(?x4077, ?x1426), ?x2860 = 0b3wk, film_release_region(?x11701, ?x1426), location(?x5880, ?x1426), institution(?x620, ?x5486), film(?x5880, ?x3222), major_field_of_study(?x5486, ?x254), legislative_sessions(?x13098, ?x10291), district_represented(?x10291, ?x3818), contains(?x94, ?x177), adjoins(?x177, ?x1905), contains(?x177, ?x388), source(?x12946, ?x958), student(?x5486, ?x118) >> conf = 0.88 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2, 3 EVAL 043djx district_represented 0g0syc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.879 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 043djx district_represented 03v0t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.879 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 043djx district_represented 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.879 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #5790-01vswx5 PRED entity: 01vswx5 PRED relation: role PRED expected values: 05842k => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 117): 018vs (0.29 #401, 0.25 #112, 0.24 #4409), 05148p4 (0.29 #401, 0.24 #4409, 0.24 #5319), 042v_gx (0.27 #2110, 0.25 #1509, 0.24 #2410), 02sgy (0.25 #2108, 0.25 #1507, 0.23 #2909), 026t6 (0.25 #103, 0.20 #303, 0.17 #3), 01s0ps (0.18 #159, 0.12 #359, 0.09 #1060), 05842k (0.17 #4484, 0.17 #4685, 0.17 #75), 0dwsp (0.17 #11, 0.09 #5014, 0.05 #311), 01399x (0.17 #98, 0.04 #4711, 0.04 #198), 01vj9c (0.16 #2117, 0.16 #4423, 0.16 #4624) >> Best rule #401 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0gs6vr; >> query: (?x5170, ?x227) <- person(?x10796, ?x5170), instrumentalists(?x227, ?x5170), profession(?x5170, ?x1183) >> conf = 0.29 => this is the best rule for 2 predicted values *> Best rule #4484 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 422 *> proper extension: 03j0br4; 01wz_ml; 023l9y; 01l4g5; 04m2zj; 01wxdn3; 06p03s; 023slg; *> query: (?x5170, 05842k) <- role(?x5170, ?x227), artists(?x302, ?x5170), profession(?x5170, ?x1183) *> conf = 0.17 ranks of expected_values: 7 EVAL 01vswx5 role 05842k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 117.000 117.000 0.286 http://example.org/music/artist/track_contributions./music/track_contribution/role #5789-03khn PRED entity: 03khn PRED relation: time_zones PRED expected values: 03plfd => 260 concepts (260 used for prediction) PRED predicted values (max 10 best out of 13): 03plfd (0.83 #1565, 0.80 #1353, 0.79 #1272), 02lcqs (0.56 #385, 0.56 #477, 0.53 #555), 02hcv8 (0.55 #225, 0.50 #369, 0.50 #81), 02llzg (0.52 #831, 0.38 #265, 0.38 #632), 02fqwt (0.29 #53, 0.28 #1830, 0.26 #1750), 03bdv (0.21 #1702, 0.21 #1809, 0.15 #2098), 052vwh (0.20 #12, 0.14 #77, 0.12 #130), 02hczc (0.14 #54, 0.13 #1910, 0.13 #2751), 042g7t (0.14 #63, 0.12 #115, 0.10 #168), 05jphn (0.14 #65, 0.12 #117, 0.10 #170) >> Best rule #1565 for best value: >> intensional similarity = 5 >> extensional distance = 49 >> proper extension: 0vm5t; >> query: (?x11237, ?x10735) <- adjoins(?x11237, ?x11236), category(?x11236, ?x134), time_zones(?x11236, ?x10735), citytown(?x13349, ?x11236), contains(?x1892, ?x11236) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03khn time_zones 03plfd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 260.000 260.000 0.827 http://example.org/location/location/time_zones #5788-01w02sy PRED entity: 01w02sy PRED relation: nationality PRED expected values: 09c7w0 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 42): 09c7w0 (0.83 #2610, 0.82 #2107, 0.81 #10144), 02jx1 (0.27 #2039, 0.25 #435, 0.24 #3245), 03rt9 (0.25 #4016, 0.05 #2019, 0.02 #5837), 02cft (0.25 #4016, 0.02 #201, 0.01 #11847), 012wgb (0.25 #4016), 07ssc (0.24 #2021, 0.14 #217, 0.12 #917), 0d060g (0.14 #2013, 0.05 #5831, 0.05 #9247), 03rk0 (0.08 #10389, 0.08 #1650, 0.08 #11291), 0j5g9 (0.07 #162, 0.05 #264, 0.02 #1566), 0chghy (0.07 #110, 0.03 #1815, 0.02 #6737) >> Best rule #2610 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 04t2l2; 046lt; 034ls; >> query: (?x3118, 09c7w0) <- participant(?x3118, ?x2221), location(?x3118, ?x252), administrative_parent(?x536, ?x252) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w02sy nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.834 http://example.org/people/person/nationality #5787-0l9k1 PRED entity: 0l9k1 PRED relation: place_of_death PRED expected values: 0f2wj => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 32): 030qb3t (0.15 #1966, 0.13 #4495, 0.11 #4300), 02_286 (0.11 #1957, 0.07 #4095, 0.06 #4291), 0k049 (0.08 #1947, 0.06 #4476, 0.05 #1753), 0156q (0.07 #4473, 0.07 #4278, 0.04 #995), 0f2wj (0.07 #12, 0.06 #207, 0.06 #1179), 0r3tq (0.07 #149, 0.06 #344, 0.06 #538), 04jpl (0.05 #2923, 0.05 #3117, 0.04 #979), 02h6_6p (0.05 #1009, 0.05 #814), 06_kh (0.05 #588, 0.04 #4283, 0.04 #4087), 05qtj (0.03 #4146, 0.03 #4342, 0.02 #3174) >> Best rule #1966 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 01d6jf; 015zql; 0969fd; >> query: (?x11305, 030qb3t) <- nationality(?x11305, ?x94), award_winner(?x902, ?x11305), people(?x4322, ?x11305) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 01b9ck; 0343h; 032v0v; 08hp53; 0794g; 0jrqq; 05nn4k; 03ktjq; 04flrx; 0d6484; ... *> query: (?x11305, 0f2wj) <- award_nominee(?x574, ?x11305), nationality(?x11305, ?x94), ?x574 = 016tt2 *> conf = 0.07 ranks of expected_values: 5 EVAL 0l9k1 place_of_death 0f2wj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 92.000 0.153 http://example.org/people/deceased_person/place_of_death #5786-02vjp3 PRED entity: 02vjp3 PRED relation: produced_by PRED expected values: 02rchht => 82 concepts (55 used for prediction) PRED predicted values (max 10 best out of 152): 0bwh6 (0.06 #3525, 0.03 #7780, 0.02 #2366), 05hj_k (0.05 #1301, 0.04 #527, 0.02 #3232), 03_gd (0.05 #1189, 0.04 #415, 0.02 #3120), 04pqqb (0.05 #1337, 0.02 #3268, 0.02 #563), 03ktjq (0.05 #3678, 0.03 #7933, 0.03 #9479), 0b13g7 (0.05 #6302, 0.04 #5915, 0.03 #5142), 06pj8 (0.04 #3931, 0.03 #6637, 0.03 #2386), 0f4vbz (0.04 #10053, 0.02 #773, 0.02 #10052), 02q_cc (0.04 #2352, 0.04 #3897, 0.04 #3511), 06chf (0.04 #485, 0.03 #1259, 0.02 #2032) >> Best rule #3525 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 0gx9rvq; 035xwd; 0cz8mkh; 085ccd; 0gjc4d3; 0gtt5fb; 0mbql; >> query: (?x7480, 0bwh6) <- film(?x382, ?x7480), film_crew_role(?x7480, ?x137), ?x382 = 086k8 >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02vjp3 produced_by 02rchht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 55.000 0.059 http://example.org/film/film/produced_by #5785-04shbh PRED entity: 04shbh PRED relation: actor! PRED expected values: 02zv4b => 130 concepts (97 used for prediction) PRED predicted values (max 10 best out of 87): 03ctqqf (0.17 #505), 03r0g9 (0.09 #16987, 0.09 #18052, 0.09 #23108), 0clpml (0.09 #18052, 0.09 #7427, 0.08 #16721), 08gsvw (0.09 #18052, 0.09 #7427, 0.08 #16721), 0vjr (0.04 #893, 0.04 #627, 0.02 #4339), 02zv4b (0.04 #557, 0.04 #1618, 0.03 #823), 0ddd0gc (0.03 #3999, 0.02 #9039, 0.01 #16741), 05631 (0.03 #1585, 0.02 #789, 0.02 #3176), 01p4wv (0.03 #1156, 0.02 #625, 0.01 #6193), 02py4c8 (0.03 #1075, 0.02 #1605, 0.02 #3461) >> Best rule #505 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 03y1mlp; >> query: (?x1018, 03ctqqf) <- nominated_for(?x1018, ?x3693), award(?x1018, ?x2375), ?x3693 = 03r0g9 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #557 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 0162c8; *> query: (?x1018, 02zv4b) <- participant(?x1017, ?x1018), nominated_for(?x1018, ?x787), celebrity(?x548, ?x1018) *> conf = 0.04 ranks of expected_values: 6 EVAL 04shbh actor! 02zv4b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 130.000 97.000 0.167 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #5784-027jk PRED entity: 027jk PRED relation: exported_to! PRED expected values: 0d05w3 => 129 concepts (124 used for prediction) PRED predicted values (max 10 best out of 60): 06tgw (0.36 #516, 0.33 #1034, 0.33 #1033), 01z88t (0.36 #516, 0.33 #1034, 0.33 #1033), 09c7w0 (0.31 #806, 0.31 #288, 0.30 #460), 0d05w3 (0.31 #316, 0.21 #30, 0.19 #661), 05r4w (0.26 #116, 0.20 #805, 0.17 #459), 0f8l9c (0.23 #300, 0.14 #14, 0.12 #645), 06q1r (0.20 #386, 0.17 #501, 0.16 #559), 047t_ (0.15 #840, 0.12 #494, 0.12 #322), 0j1z8 (0.14 #8, 0.12 #66, 0.09 #123), 04sj3 (0.12 #512, 0.12 #340, 0.11 #397) >> Best rule #516 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 06qd3; >> query: (?x8558, ?x1780) <- olympics(?x8558, ?x2233), administrative_area_type(?x8558, ?x2792), exported_to(?x8558, ?x1780) >> conf = 0.36 => this is the best rule for 2 predicted values *> Best rule #316 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0n3g; *> query: (?x8558, 0d05w3) <- currency(?x8558, ?x170), exported_to(?x1781, ?x8558), religion(?x8558, ?x109) *> conf = 0.31 ranks of expected_values: 4 EVAL 027jk exported_to! 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 129.000 124.000 0.364 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #5783-02qgqt PRED entity: 02qgqt PRED relation: nominated_for PRED expected values: 027r9t => 85 concepts (38 used for prediction) PRED predicted values (max 10 best out of 252): 01hqhm (0.78 #22587, 0.77 #11292, 0.30 #4838), 0h03fhx (0.50 #711, 0.02 #3935, 0.02 #5549), 07w8fz (0.50 #471), 03cvvlg (0.30 #4838, 0.28 #8065, 0.27 #25812), 07bwr (0.30 #4838, 0.28 #8065, 0.27 #25812), 06fqlk (0.30 #4838, 0.28 #8065, 0.27 #25812), 02cbhg (0.30 #4838, 0.28 #8065, 0.27 #25812), 01s7w3 (0.30 #4838, 0.28 #8065, 0.27 #25812), 03ydlnj (0.30 #4838, 0.28 #8065, 0.27 #25812), 04z_3pm (0.30 #4838, 0.28 #8065, 0.27 #25812) >> Best rule #22587 for best value: >> intensional similarity = 2 >> extensional distance = 1088 >> proper extension: 07zhd7; >> query: (?x157, ?x1064) <- type_of_union(?x157, ?x1873), award_winner(?x1064, ?x157) >> conf = 0.78 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02qgqt nominated_for 027r9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 38.000 0.783 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5782-02b9g4 PRED entity: 02b9g4 PRED relation: producer_type PRED expected values: 0ckd1 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.32 #33, 0.30 #1, 0.19 #8) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 573 >> proper extension: 079vf; 012c6x; 01pr_j6; 04n7njg; 01c58j; 01vb403; 036c_0; 0c_mvb; 0309jm; 03m_k0; ... >> query: (?x7040, 0ckd1) <- profession(?x7040, ?x1041), nationality(?x7040, ?x94), ?x1041 = 03gjzk >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02b9g4 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.322 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #5781-04ch23 PRED entity: 04ch23 PRED relation: student! PRED expected values: 07tgn => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 122): 03w1lf (0.26 #877, 0.10 #2455, 0.05 #4033), 03ksy (0.10 #2736, 0.09 #1158, 0.09 #5892), 088gzp (0.10 #1039, 0.08 #2617, 0.02 #4195), 050xpd (0.10 #987, 0.03 #2565, 0.02 #3617), 02hwww (0.10 #967, 0.02 #3597, 0.02 #4123), 07tgn (0.09 #1069, 0.08 #1595, 0.07 #17), 01w5m (0.09 #5891, 0.09 #6943, 0.08 #1683), 0bwfn (0.07 #19212, 0.07 #21316, 0.07 #22368), 0h6rm (0.07 #144, 0.06 #1196, 0.06 #1722), 015nl4 (0.07 #67, 0.05 #15847, 0.04 #18477) >> Best rule #877 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 02756j; 081hvm; >> query: (?x12071, 03w1lf) <- type_of_union(?x12071, ?x566), nationality(?x12071, ?x2146), ?x2146 = 03rk0, student(?x13856, ?x12071), ?x566 = 04ztj >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #1069 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 30 *> proper extension: 05qw5; 03pm9; 03h502k; 03jht; 03j0d; 03jxw; 084nh; *> query: (?x12071, 07tgn) <- type_of_union(?x12071, ?x566), profession(?x12071, ?x3746), profession(?x12071, ?x353), ?x353 = 0cbd2, ?x3746 = 05z96 *> conf = 0.09 ranks of expected_values: 6 EVAL 04ch23 student! 07tgn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 86.000 86.000 0.258 http://example.org/education/educational_institution/students_graduates./education/education/student #5780-0407yj_ PRED entity: 0407yj_ PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #71, 0.83 #66, 0.83 #81), 02nxhr (0.16 #12, 0.14 #17, 0.12 #7), 07c52 (0.09 #93, 0.06 #48, 0.05 #53), 07z4p (0.07 #95, 0.07 #5, 0.04 #50) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 249 >> proper extension: 035s95; >> query: (?x2933, 029j_) <- country(?x2933, ?x94), film(?x5642, ?x2933), producer_type(?x5642, ?x632), gender(?x5642, ?x231) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0407yj_ film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.837 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #5779-02qlp4 PRED entity: 02qlp4 PRED relation: genre PRED expected values: 02kdv5l => 80 concepts (57 used for prediction) PRED predicted values (max 10 best out of 87): 02kdv5l (0.70 #3, 0.63 #363, 0.53 #483), 07s9rl0 (0.66 #2646, 0.63 #1322, 0.61 #3731), 01jfsb (0.55 #3502, 0.51 #373, 0.45 #13), 024qqx (0.52 #5539, 0.52 #3851, 0.51 #6501), 05p553 (0.39 #1807, 0.37 #2047, 0.36 #1686), 01hmnh (0.37 #138, 0.34 #18, 0.28 #258), 02l7c8 (0.28 #3987, 0.28 #3746, 0.28 #5434), 04pbhw (0.22 #57, 0.18 #417, 0.15 #297), 0lsxr (0.22 #3499, 0.19 #1812, 0.18 #970), 04xvlr (0.20 #842, 0.19 #2647, 0.18 #3732) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 0fvr1; >> query: (?x10902, 02kdv5l) <- genre(?x10902, ?x1013), executive_produced_by(?x10902, ?x3223), film(?x851, ?x10902), ?x1013 = 06n90 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qlp4 genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 57.000 0.701 http://example.org/film/film/genre #5778-01rr9f PRED entity: 01rr9f PRED relation: award PRED expected values: 05b4l5x => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 247): 09sb52 (0.36 #25089, 0.35 #19433, 0.35 #19029), 05pcn59 (0.28 #2505, 0.25 #4525, 0.24 #1697), 0ck27z (0.19 #19889, 0.16 #25141, 0.16 #21505), 03c7tr1 (0.19 #1674, 0.17 #2482, 0.17 #462), 05zr6wv (0.18 #1229, 0.18 #4461, 0.17 #5269), 0gqyl (0.18 #105, 0.15 #3741, 0.13 #1317), 09qj50 (0.18 #34343, 0.15 #35960, 0.13 #43637), 0cqhmg (0.18 #34343, 0.15 #35960, 0.13 #43637), 09qs08 (0.18 #34343, 0.15 #35960, 0.13 #43637), 057xs89 (0.18 #34343, 0.15 #35960, 0.13 #4604) >> Best rule #25089 for best value: >> intensional similarity = 3 >> extensional distance = 1045 >> proper extension: 016qtt; 01k7d9; 03x3qv; 07lmxq; 01v3s2_; 01yb09; 01v42g; 0bg539; 02wcx8c; 05tk7y; ... >> query: (?x513, 09sb52) <- film(?x513, ?x1184), nominated_for(?x513, ?x4535), award_nominee(?x917, ?x513) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1622 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 121 *> proper extension: 04d_mtq; *> query: (?x513, 05b4l5x) <- type_of_union(?x513, ?x566), vacationer(?x2856, ?x513), profession(?x513, ?x1032) *> conf = 0.17 ranks of expected_values: 11 EVAL 01rr9f award 05b4l5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 121.000 121.000 0.361 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5777-0233bn PRED entity: 0233bn PRED relation: film_release_region PRED expected values: 07ssc 02vzc 06mkj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 130): 06mkj (0.86 #994, 0.86 #1462, 0.84 #2399), 07ssc (0.86 #3450, 0.78 #951, 0.78 #2044), 02vzc (0.85 #988, 0.85 #1456, 0.82 #2549), 015fr (0.82 #2045, 0.75 #171, 0.71 #3451), 01znc_ (0.75 #2070, 0.74 #977, 0.69 #1445), 0d060g (0.75 #161, 0.73 #2035, 0.67 #3441), 04gzd (0.75 #164, 0.49 #2038, 0.40 #3756), 0b90_r (0.74 #2033, 0.63 #3439, 0.62 #159), 06bnz (0.70 #2074, 0.62 #200, 0.62 #3480), 06t2t (0.69 #2092, 0.62 #218, 0.58 #3498) >> Best rule #994 for best value: >> intensional similarity = 5 >> extensional distance = 99 >> proper extension: 0bh8yn3; >> query: (?x7502, 06mkj) <- film_release_region(?x7502, ?x985), film_release_region(?x7502, ?x142), ?x985 = 0k6nt, award_winner(?x7502, ?x1864), ?x142 = 0jgd >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0233bn film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0233bn film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0233bn film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.861 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5776-01gb54 PRED entity: 01gb54 PRED relation: industry PRED expected values: 02vxn => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 47): 02vxn (0.59 #1299, 0.54 #1059, 0.53 #1107), 01mw1 (0.33 #97, 0.32 #577, 0.27 #2407), 020mfr (0.27 #593, 0.26 #2279, 0.25 #2423), 03qh03g (0.26 #629, 0.20 #293, 0.15 #1350), 04rlf (0.25 #350, 0.25 #158, 0.23 #542), 02jjt (0.23 #536, 0.19 #632, 0.17 #488), 029g_vk (0.20 #299, 0.07 #635, 0.07 #2225), 0hz28 (0.11 #990, 0.11 #654, 0.10 #318), 0sydc (0.11 #657, 0.10 #321, 0.09 #993), 01mf0 (0.10 #1472, 0.08 #2245, 0.08 #463) >> Best rule #1299 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 07k2x; >> query: (?x4564, 02vxn) <- production_companies(?x1904, ?x4564), film(?x4564, ?x385), film_release_region(?x1904, ?x94) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gb54 industry 02vxn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.587 http://example.org/business/business_operation/industry #5775-0czkbt PRED entity: 0czkbt PRED relation: languages PRED expected values: 02h40lc => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 12): 02h40lc (0.42 #158, 0.34 #1055, 0.33 #392), 064_8sq (0.10 #132, 0.06 #795, 0.05 #2706), 0t_2 (0.05 #594, 0.04 #1179, 0.03 #477), 06b_j (0.04 #874, 0.02 #757, 0.02 #796), 03_9r (0.03 #356, 0.02 #824, 0.02 #980), 02bjrlw (0.03 #2692, 0.02 #3394, 0.02 #3433), 04306rv (0.03 #549, 0.02 #783, 0.02 #861), 06nm1 (0.02 #630, 0.02 #1371, 0.02 #1449), 05zjd (0.02 #642), 07c9s (0.02 #1846, 0.02 #2704, 0.01 #2782) >> Best rule #158 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 06pj8; 02kz_; >> query: (?x4713, 02h40lc) <- participant(?x4713, ?x1634), peers(?x4713, ?x5225), gender(?x4713, ?x514), location(?x4713, ?x362) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0czkbt languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.417 http://example.org/people/person/languages #5774-018vbf PRED entity: 018vbf PRED relation: locations PRED expected values: 04hqz => 69 concepts (67 used for prediction) PRED predicted values (max 10 best out of 119): 04wsz (0.54 #3779, 0.49 #4530, 0.45 #4343), 04hqz (0.54 #3779, 0.49 #4530, 0.45 #4343), 03spz (0.54 #3779, 0.49 #4530, 0.45 #4343), 082pc (0.54 #3779, 0.49 #4530, 0.45 #4343), 0d05q4 (0.33 #272, 0.29 #1127, 0.20 #1212), 0d05w3 (0.33 #54, 0.20 #1184, 0.11 #4774), 0jdd (0.29 #1127, 0.09 #7743, 0.07 #3396), 02j9z (0.25 #4920, 0.25 #1703, 0.21 #4543), 05qhw (0.22 #2461, 0.18 #3217, 0.14 #3601), 0j0k (0.18 #934, 0.15 #4717, 0.07 #3583) >> Best rule #3779 for best value: >> intensional similarity = 10 >> extensional distance = 12 >> proper extension: 02tvsn; >> query: (?x14182, ?x4743) <- entity_involved(?x14182, ?x14077), entity_involved(?x14182, ?x8437), entity_involved(?x14182, ?x4302), combatants(?x13022, ?x14077), entity_involved(?x11814, ?x8437), gender(?x8437, ?x231), combatants(?x4302, ?x608), combatants(?x11814, ?x14265), contains(?x4302, ?x13593), locations(?x11814, ?x4743) >> conf = 0.54 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 018vbf locations 04hqz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 67.000 0.542 http://example.org/time/event/locations #5773-0gy6z9 PRED entity: 0gy6z9 PRED relation: nominated_for PRED expected values: 0gj9tn5 => 146 concepts (94 used for prediction) PRED predicted values (max 10 best out of 784): 047d21r (0.56 #80838, 0.43 #79220, 0.40 #61431), 0glqh5_ (0.43 #79220, 0.40 #61431, 0.40 #50116), 08r4x3 (0.42 #4992, 0.35 #16304, 0.08 #143), 087vnr5 (0.34 #24245, 0.33 #27480, 0.30 #37180), 09hy79 (0.34 #24245, 0.33 #27480, 0.30 #37180), 0g7pm1 (0.34 #24245, 0.33 #27480, 0.30 #37180), 040_lv (0.34 #24245, 0.33 #27480, 0.30 #37180), 047wh1 (0.34 #24245, 0.33 #27480, 0.30 #37180), 02pxmgz (0.34 #24245, 0.33 #27480, 0.30 #37180), 05lfwd (0.20 #18688, 0.08 #910, 0.03 #97915) >> Best rule #80838 for best value: >> intensional similarity = 2 >> extensional distance = 278 >> proper extension: 024c1b; >> query: (?x3293, ?x3743) <- produced_by(?x3743, ?x3293), award(?x3743, ?x451) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #8332 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 30 *> proper extension: 025504; *> query: (?x3293, 0gj9tn5) <- category(?x3293, ?x134), program(?x3293, ?x493) *> conf = 0.03 ranks of expected_values: 261 EVAL 0gy6z9 nominated_for 0gj9tn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 146.000 94.000 0.561 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5772-09gq0x5 PRED entity: 09gq0x5 PRED relation: nominated_for! PRED expected values: 09td7p 09sdmz 09ly2r6 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 184): 04kxsb (0.67 #7962, 0.66 #6329, 0.66 #13067), 0gq9h (0.67 #7962, 0.66 #6329, 0.66 #13067), 02x1dht (0.67 #7962, 0.66 #6329, 0.66 #13067), 02qt02v (0.67 #7962, 0.66 #6329, 0.66 #13067), 09cm54 (0.67 #7962, 0.66 #6329, 0.66 #13067), 09sdmz (0.64 #726, 0.41 #318, 0.20 #1543), 09td7p (0.55 #274, 0.28 #1499, 0.16 #682), 02x17s4 (0.45 #276, 0.25 #684, 0.20 #1501), 0gr4k (0.36 #222, 0.34 #7980, 0.29 #1447), 03hkv_r (0.36 #214, 0.25 #622, 0.25 #1439) >> Best rule #7962 for best value: >> intensional similarity = 3 >> extensional distance = 535 >> proper extension: 05m_jsg; >> query: (?x1813, ?x68) <- nominated_for(?x72, ?x1813), award(?x1813, ?x68), film_crew_role(?x1813, ?x137) >> conf = 0.67 => this is the best rule for 5 predicted values *> Best rule #726 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 53 *> proper extension: 01719t; 02qmsr; 02ryz24; 02d478; 01rwpj; 0h63gl9; 02jxrw; *> query: (?x1813, 09sdmz) <- nominated_for(?x451, ?x1813), ?x451 = 099jhq *> conf = 0.64 ranks of expected_values: 6, 7, 25 EVAL 09gq0x5 nominated_for! 09ly2r6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 94.000 94.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09gq0x5 nominated_for! 09sdmz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 94.000 94.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09gq0x5 nominated_for! 09td7p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 94.000 94.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5771-059_w PRED entity: 059_w PRED relation: people PRED expected values: 01wyz92 => 91 concepts (67 used for prediction) PRED predicted values (max 10 best out of 2830): 01pk3z (0.50 #5953, 0.23 #40378, 0.07 #105777), 0lkr7 (0.50 #5876, 0.20 #11039, 0.14 #42023), 06cgy (0.50 #5361, 0.13 #77653, 0.12 #105185), 032_jg (0.50 #5274, 0.08 #39699, 0.06 #105098), 0132k4 (0.50 #6130, 0.07 #42277, 0.04 #105954), 01tpl1p (0.40 #13515, 0.25 #27283, 0.20 #11795), 0311wg (0.38 #39881, 0.20 #12339, 0.20 #10619), 0g824 (0.31 #40489, 0.20 #12947, 0.20 #11227), 05p92jn (0.25 #6085, 0.16 #32701, 0.06 #104988), 01fwj8 (0.25 #5379, 0.10 #77671, 0.09 #72509) >> Best rule #5953 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 013xrm; >> query: (?x7139, 01pk3z) <- people(?x7139, ?x7140), people(?x7139, ?x5910), people(?x7139, ?x4014), ?x4014 = 06gh0t, award_winner(?x5910, ?x1672), award(?x5910, ?x1336), participant(?x7140, ?x4819) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #12525 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 0g8_vp; 03x1x; *> query: (?x7139, 01wyz92) <- languages_spoken(?x7139, ?x2502), languages_spoken(?x7139, ?x254), language(?x10192, ?x2502), ?x10192 = 01sbv9, ?x254 = 02h40lc, official_language(?x47, ?x2502), people(?x7139, ?x4014) *> conf = 0.20 ranks of expected_values: 279 EVAL 059_w people 01wyz92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 91.000 67.000 0.500 http://example.org/people/ethnicity/people #5770-026gyn_ PRED entity: 026gyn_ PRED relation: films! PRED expected values: 01vq3 => 65 concepts (14 used for prediction) PRED predicted values (max 10 best out of 49): 081pw (0.12 #3, 0.08 #161, 0.06 #1108), 0kbq (0.10 #105, 0.08 #263, 0.04 #421), 05489 (0.07 #368, 0.04 #1157, 0.04 #842), 015j7 (0.06 #298, 0.05 #140), 0fx2s (0.06 #389, 0.04 #231, 0.04 #1178), 06d4h (0.05 #43, 0.04 #201, 0.04 #1305), 0hkt6 (0.05 #120, 0.04 #278), 0fzyg (0.05 #54, 0.02 #686, 0.02 #844), 018h2 (0.04 #1444, 0.02 #1284, 0.02 #338), 02_h0 (0.04 #416, 0.03 #1362, 0.03 #574) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 01jwxx; >> query: (?x1903, 081pw) <- genre(?x1903, ?x4757), film(?x294, ?x1903), ?x4757 = 06l3bl, film(?x13579, ?x1903) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026gyn_ films! 01vq3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 14.000 0.125 http://example.org/film/film_subject/films #5769-0sw6g PRED entity: 0sw6g PRED relation: student! PRED expected values: 06182p => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 130): 017z88 (0.18 #81, 0.06 #607, 0.05 #16387), 09f2j (0.12 #684, 0.09 #158, 0.07 #15938), 07w0v (0.12 #546, 0.09 #20, 0.03 #15800), 065y4w7 (0.10 #15794, 0.05 #24736, 0.04 #33153), 0bwfn (0.09 #3956, 0.09 #22892, 0.08 #3430), 03ksy (0.09 #15885, 0.05 #10625, 0.04 #18515), 03hdz8 (0.09 #260, 0.06 #786, 0.01 #1312), 06182p (0.09 #297, 0.04 #1349, 0.03 #1875), 01w5m (0.08 #15884, 0.04 #18514, 0.04 #13254), 08815 (0.07 #15782, 0.04 #3684, 0.04 #10522) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0q9kd; 01j5ts; 0p_pd; 01q_ph; 0mdqp; 0f502; 0dzf_; 02__7n; 01nxzv; >> query: (?x8061, 017z88) <- award_nominee(?x8061, ?x1119), ?x1119 = 039bp, nominated_for(?x8061, ?x3180) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #297 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0q9kd; 01j5ts; 0p_pd; 01q_ph; 0mdqp; 0f502; 0dzf_; 02__7n; 01nxzv; *> query: (?x8061, 06182p) <- award_nominee(?x8061, ?x1119), ?x1119 = 039bp, nominated_for(?x8061, ?x3180) *> conf = 0.09 ranks of expected_values: 8 EVAL 0sw6g student! 06182p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 123.000 123.000 0.182 http://example.org/education/educational_institution/students_graduates./education/education/student #5768-0660b9b PRED entity: 0660b9b PRED relation: executive_produced_by PRED expected values: 02z6l5f => 108 concepts (82 used for prediction) PRED predicted values (max 10 best out of 110): 02z6l5f (0.17 #1637, 0.12 #3150, 0.12 #2646), 06pj8 (0.16 #2330, 0.08 #3592, 0.07 #3844), 02qzjj (0.11 #2511, 0.06 #3773, 0.05 #4025), 0glyyw (0.11 #2464, 0.04 #3726, 0.04 #3978), 06q8hf (0.10 #421, 0.10 #167, 0.09 #1434), 04w1j9 (0.10 #629, 0.10 #377), 03c9pqt (0.10 #247, 0.09 #1260, 0.09 #1006), 01zfmm (0.10 #71, 0.09 #1084, 0.09 #830), 02z2xdf (0.10 #664, 0.09 #3190, 0.08 #1677), 05hj_k (0.10 #352, 0.09 #1365, 0.08 #1617) >> Best rule #1637 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 05zvzf3; >> query: (?x5747, 02z6l5f) <- film_festivals(?x5747, ?x11147), film_crew_role(?x5747, ?x4305), ?x4305 = 0215hd, genre(?x5747, ?x258), ?x11147 = 04_m9gk >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0660b9b executive_produced_by 02z6l5f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 82.000 0.167 http://example.org/film/film/executive_produced_by #5767-024zq PRED entity: 024zq PRED relation: nationality PRED expected values: 09c7w0 => 188 concepts (172 used for prediction) PRED predicted values (max 10 best out of 47): 09c7w0 (0.87 #7632, 0.87 #7531, 0.87 #7431), 03rk0 (0.70 #1649, 0.09 #12701, 0.09 #12801), 04_1l0v (0.43 #8935, 0.41 #9237), 02jx1 (0.32 #1135, 0.32 #733, 0.31 #1335), 07ssc (0.17 #1017, 0.16 #1117, 0.16 #3228), 05vz3zq (0.15 #670, 0.02 #1976, 0.02 #2077), 0h7x (0.15 #936, 0.10 #1537, 0.09 #1840), 0345h (0.11 #932, 0.11 #331, 0.08 #831), 02k1b (0.11 #384, 0.01 #2996), 0f8l9c (0.10 #1524, 0.08 #822, 0.07 #3235) >> Best rule #7632 for best value: >> intensional similarity = 4 >> extensional distance = 361 >> proper extension: 0blbxk; 05x2t7; 02xv8m; 0c2dl; 0pyww; 05gp3x; 013t9y; 0879xc; 03h40_7; 06cl2w; >> query: (?x5718, ?x94) <- location(?x5718, ?x938), religion(?x938, ?x1985), country(?x938, ?x94), ?x1985 = 0c8wxp >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024zq nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 172.000 0.874 http://example.org/people/person/nationality #5766-013807 PRED entity: 013807 PRED relation: student PRED expected values: 02r34n 03jldb 04ktcgn 01wg6y => 144 concepts (113 used for prediction) PRED predicted values (max 10 best out of 1267): 0cbgl (0.14 #6252, 0.04 #22940, 0.04 #25026), 0fpzt5 (0.11 #3618, 0.03 #20306, 0.03 #36996), 022411 (0.09 #5855, 0.06 #3769, 0.03 #16285), 0ff3y (0.09 #6235, 0.05 #16665, 0.05 #10407), 030hcs (0.09 #4444, 0.03 #14874, 0.03 #19046), 01zfmm (0.09 #4610, 0.03 #15040, 0.03 #21298), 049gc (0.09 #5095, 0.03 #15525, 0.03 #21783), 0c4y8 (0.09 #5820, 0.03 #16250, 0.03 #22508), 02t_w8 (0.09 #5088, 0.03 #15518, 0.03 #21776), 012t1 (0.09 #4316, 0.03 #14746, 0.03 #21004) >> Best rule #6252 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 03ksy; 02zd460; >> query: (?x10910, 0cbgl) <- major_field_of_study(?x10910, ?x4321), major_field_of_study(?x10910, ?x2014), citytown(?x10910, ?x3125), ?x4321 = 0g26h, ?x2014 = 04rjg >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #127255 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 363 *> proper extension: 01hhvg; 0473m9; 071_8; 01314k; 02txdf; 03l78j; 01v3k2; 02pptm; 03kmyy; 01xysf; ... *> query: (?x10910, ?x744) <- major_field_of_study(?x10910, ?x4321), citytown(?x10910, ?x3125), student(?x4321, ?x744) *> conf = 0.01 ranks of expected_values: 1062 EVAL 013807 student 01wg6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 113.000 0.136 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 013807 student 04ktcgn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 113.000 0.136 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 013807 student 03jldb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 113.000 0.136 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 013807 student 02r34n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 144.000 113.000 0.136 http://example.org/education/educational_institution/students_graduates./education/education/student #5765-0hfzr PRED entity: 0hfzr PRED relation: film! PRED expected values: 06pj8 => 69 concepts (59 used for prediction) PRED predicted values (max 10 best out of 129): 06pj8 (0.53 #276, 0.53 #48, 0.25 #325), 07s93v (0.41 #4400, 0.41 #5775, 0.41 #827), 03q8ch (0.16 #275, 0.15 #5776, 0.15 #2201), 0146pg (0.16 #275, 0.15 #5776, 0.12 #10447), 016k6x (0.11 #7700, 0.11 #277, 0.09 #1650), 0h5g_ (0.11 #7700, 0.11 #277, 0.09 #1650), 0b79gfg (0.11 #7700, 0.11 #277, 0.09 #1650), 02hfp_ (0.10 #468, 0.02 #1292, 0.02 #3492), 081lh (0.07 #578, 0.02 #5526, 0.02 #8274), 0j_c (0.07 #615, 0.02 #2539, 0.02 #9136) >> Best rule #276 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 025x1t; >> query: (?x4216, ?x2135) <- award_winner(?x4216, ?x2135), nominated_for(?x489, ?x4216), ?x2135 = 06pj8 >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hfzr film! 06pj8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 59.000 0.533 http://example.org/film/director/film #5764-01_d4 PRED entity: 01_d4 PRED relation: mode_of_transportation PRED expected values: 07jdr => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 3): 07jdr (0.83 #22, 0.82 #40, 0.78 #28), 0k4j (0.05 #8, 0.04 #131, 0.03 #38), 06d_3 (0.04 #132, 0.03 #39, 0.02 #75) >> Best rule #22 for best value: >> intensional similarity = 2 >> extensional distance = 27 >> proper extension: 03khn; >> query: (?x1860, 07jdr) <- locations(?x6583, ?x1860), month(?x1860, ?x1459) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_d4 mode_of_transportation 07jdr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 178.000 0.828 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #5763-0164w8 PRED entity: 0164w8 PRED relation: award_winner! PRED expected values: 02yw5r => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 133): 02yw5r (0.18 #9456, 0.10 #12935, 0.04 #14882), 09pnw5 (0.18 #9456, 0.10 #12935, 0.03 #3855), 092_25 (0.18 #9456, 0.10 #12935, 0.02 #8690), 0fk0xk (0.17 #354, 0.06 #493, 0.06 #632), 0bzjgq (0.11 #117, 0.10 #12935, 0.04 #14882), 02pgky2 (0.11 #88, 0.09 #227, 0.04 #14882), 050yyb (0.11 #38, 0.09 #177, 0.04 #14882), 073h9x (0.11 #49, 0.09 #188, 0.04 #14882), 0dth6b (0.11 #24, 0.04 #14882, 0.04 #11961), 09qvms (0.10 #12935, 0.06 #4462, 0.06 #4184) >> Best rule #9456 for best value: >> intensional similarity = 3 >> extensional distance = 1138 >> proper extension: 02v0ff; 02bwc7; 01pctb; 01my_c; >> query: (?x8288, ?x2707) <- nominated_for(?x8288, ?x7738), type_of_union(?x8288, ?x566), honored_for(?x2707, ?x7738) >> conf = 0.18 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0164w8 award_winner! 02yw5r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.180 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5762-0b_j2 PRED entity: 0b_j2 PRED relation: award PRED expected values: 02v1m7 02f6xy 02f72_ => 118 concepts (106 used for prediction) PRED predicted values (max 10 best out of 275): 04njml (0.34 #4442, 0.12 #15898, 0.07 #10367), 01c9jp (0.29 #2551, 0.25 #4131, 0.17 #5316), 054ks3 (0.29 #5666, 0.28 #1321, 0.27 #2111), 03qbnj (0.29 #5754, 0.23 #2594, 0.23 #1409), 01c99j (0.29 #5747, 0.19 #217, 0.15 #1007), 09sb52 (0.25 #24531, 0.22 #24926, 0.20 #32039), 01ckcd (0.25 #3882, 0.24 #3487, 0.23 #5462), 0gqz2 (0.25 #4423, 0.23 #15879, 0.18 #868), 01c427 (0.21 #5612, 0.17 #2452, 0.16 #4032), 04mqgr (0.21 #4493, 0.06 #148, 0.05 #37136) >> Best rule #4442 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 025tdwc; 081k8; 0h0p_; 04g_wd; >> query: (?x6626, 04njml) <- nationality(?x6626, ?x94), profession(?x6626, ?x6476), gender(?x6626, ?x231), ?x6476 = 025352 >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #5722 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 92 *> proper extension: 0288fyj; *> query: (?x6626, 02f6xy) <- nationality(?x6626, ?x94), award(?x6626, ?x724), category(?x6626, ?x134), ?x724 = 01bgqh *> conf = 0.20 ranks of expected_values: 11, 27, 30 EVAL 0b_j2 award 02f72_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 118.000 106.000 0.338 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0b_j2 award 02f6xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 118.000 106.000 0.338 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0b_j2 award 02v1m7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 118.000 106.000 0.338 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5761-01309x PRED entity: 01309x PRED relation: artist! PRED expected values: 03rhqg 015_1q 043g7l => 134 concepts (90 used for prediction) PRED predicted values (max 10 best out of 94): 015_1q (0.23 #2662, 0.19 #2383, 0.19 #4336), 03rhqg (0.18 #15, 0.14 #4472, 0.14 #4333), 033hn8 (0.14 #13, 0.13 #4331, 0.12 #4609), 01clyr (0.14 #31, 0.09 #867, 0.08 #4488), 0g768 (0.13 #4353, 0.12 #4631, 0.12 #3376), 0181dw (0.11 #4497, 0.11 #2684, 0.10 #459), 02p11jq (0.10 #2656, 0.09 #12, 0.08 #2238), 01w40h (0.09 #584, 0.09 #862, 0.09 #165), 043g7l (0.09 #29, 0.09 #2673, 0.09 #4347), 03mp8k (0.09 #65, 0.08 #2709, 0.08 #4383) >> Best rule #2662 for best value: >> intensional similarity = 3 >> extensional distance = 338 >> proper extension: 0565cz; 01vw917; 05qhnq; 01w5gg6; >> query: (?x3632, 015_1q) <- gender(?x3632, ?x231), award_nominee(?x3632, ?x158), artist(?x2149, ?x3632) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 9 EVAL 01309x artist! 043g7l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 134.000 90.000 0.226 http://example.org/music/record_label/artist EVAL 01309x artist! 015_1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 90.000 0.226 http://example.org/music/record_label/artist EVAL 01309x artist! 03rhqg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 90.000 0.226 http://example.org/music/record_label/artist #5760-03ntbmw PRED entity: 03ntbmw PRED relation: film! PRED expected values: 05drq5 => 80 concepts (39 used for prediction) PRED predicted values (max 10 best out of 75): 013pp3 (0.30 #276, 0.28 #553, 0.22 #1383), 059x0w (0.26 #1934, 0.22 #8816, 0.19 #1659), 0633p0 (0.13 #275, 0.12 #552, 0.12 #7439), 06pj8 (0.05 #878, 0.05 #602, 0.04 #1155), 081lh (0.05 #26, 0.05 #303, 0.03 #856), 06q8hf (0.04 #5511, 0.04 #5512, 0.03 #3580), 05hj_k (0.04 #5511, 0.04 #5512, 0.03 #3580), 02kxbx3 (0.04 #641, 0.01 #4219, 0.01 #1471), 02vyw (0.04 #90, 0.03 #367, 0.03 #920), 026dx (0.04 #118, 0.03 #395, 0.02 #1225) >> Best rule #276 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 0sw0q; >> query: (?x12403, ?x5335) <- award_winner(?x12403, ?x5335), titles(?x53, ?x12403), influenced_by(?x2609, ?x5335), gender(?x5335, ?x231) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03ntbmw film! 05drq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 39.000 0.299 http://example.org/film/director/film #5759-04ldyx1 PRED entity: 04ldyx1 PRED relation: nominated_for PRED expected values: 0828jw => 74 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1461): 01flv_ (0.67 #952, 0.57 #2545, 0.09 #8923), 0gmgwnv (0.50 #964, 0.43 #2557, 0.37 #21687), 09gq0x5 (0.50 #255, 0.43 #1848, 0.36 #20978), 026p4q7 (0.50 #358, 0.43 #1951, 0.33 #21081), 05hjnw (0.50 #767, 0.43 #2360, 0.33 #21490), 095zlp (0.50 #53, 0.43 #1646, 0.30 #20776), 03hmt9b (0.50 #596, 0.43 #2189, 0.30 #21319), 07024 (0.50 #433, 0.43 #2026, 0.29 #8404), 017gl1 (0.50 #133, 0.43 #1726, 0.27 #8104), 02mpyh (0.50 #1287, 0.43 #2880, 0.24 #30290) >> Best rule #952 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0l8z1; 025m8y; 02qvyrt; 0fhpv4; >> query: (?x4728, 01flv_) <- award(?x4727, ?x4728), award(?x1434, ?x4728), ceremony(?x4728, ?x1265), nominated_for(?x4728, ?x1395), ?x4727 = 04ls53 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #30290 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 119 *> proper extension: 0gqng; 02r0csl; 02r22gf; 02hsq3m; 0gqzz; 0k611; 02x2gy0; 02qyxs5; 0gqxm; 018wdw; ... *> query: (?x4728, ?x1012) <- award(?x4727, ?x4728), award(?x1434, ?x4728), ceremony(?x4728, ?x1265), nominated_for(?x4728, ?x1395), nominated_for(?x4727, ?x1012) *> conf = 0.24 ranks of expected_values: 108 EVAL 04ldyx1 nominated_for 0828jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 74.000 23.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5758-0l6qt PRED entity: 0l6qt PRED relation: award_winner! PRED expected values: 02qkk9_ => 114 concepts (106 used for prediction) PRED predicted values (max 10 best out of 272): 0gr4k (0.37 #21992, 0.37 #24150, 0.37 #10351), 02n9nmz (0.37 #21992, 0.37 #24150, 0.37 #10351), 03hkv_r (0.37 #21992, 0.37 #24150, 0.37 #10351), 02qyp19 (0.37 #21992, 0.37 #24150, 0.37 #10351), 0d085 (0.17 #250, 0.13 #1112, 0.06 #681), 0gs9p (0.17 #80, 0.10 #1374, 0.08 #1805), 03hl6lc (0.17 #177, 0.07 #1902, 0.07 #1471), 027b9ly (0.17 #244, 0.04 #4988, 0.04 #1969), 02x4sn8 (0.17 #157, 0.04 #4901, 0.04 #6195), 0gq9h (0.15 #28462, 0.15 #22424, 0.11 #78) >> Best rule #21992 for best value: >> intensional similarity = 3 >> extensional distance = 1402 >> proper extension: 01vvydl; 07s3vqk; 0411q; 01lmj3q; 01dw4q; 04lgymt; 06cc_1; 04rcr; 01vvycq; 02l840; ... >> query: (?x164, ?x68) <- award_winner(?x163, ?x164), award_winner(?x164, ?x264), award(?x164, ?x68) >> conf = 0.37 => this is the best rule for 4 predicted values *> Best rule #28462 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1531 *> proper extension: 0l56b; *> query: (?x164, ?x3105) <- award_winner(?x3170, ?x164), award_winner(?x3105, ?x3170) *> conf = 0.15 ranks of expected_values: 12 EVAL 0l6qt award_winner! 02qkk9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 114.000 106.000 0.371 http://example.org/award/award_category/winners./award/award_honor/award_winner #5757-04954r PRED entity: 04954r PRED relation: genre PRED expected values: 05p553 => 85 concepts (67 used for prediction) PRED predicted values (max 10 best out of 95): 05p553 (0.51 #963, 0.51 #2524, 0.46 #1324), 02l7c8 (0.33 #2416, 0.33 #3018, 0.33 #3138), 06l3bl (0.33 #38, 0.29 #398, 0.25 #518), 03g3w (0.33 #24, 0.25 #504, 0.20 #264), 04xvh5 (0.33 #34, 0.17 #1115, 0.16 #634), 0jdm8 (0.33 #82, 0.10 #7925, 0.02 #1523), 01jfsb (0.33 #1692, 0.29 #3254, 0.29 #1212), 01hmnh (0.31 #1818, 0.26 #1698, 0.26 #1218), 060__y (0.27 #616, 0.25 #496, 0.22 #1577), 082gq (0.26 #630, 0.20 #150, 0.15 #6513) >> Best rule #963 for best value: >> intensional similarity = 3 >> extensional distance = 182 >> proper extension: 027qgy; 0g5qs2k; 0jyx6; 09146g; 014kq6; 02xtxw; 024lff; 05m_jsg; 0dr_9t7; 047wh1; ... >> query: (?x3755, 05p553) <- film(?x3017, ?x3755), award_winner(?x3755, ?x5206), artists(?x505, ?x3017) >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04954r genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 67.000 0.511 http://example.org/film/film/genre #5756-083pr PRED entity: 083pr PRED relation: basic_title PRED expected values: 01t7n9 => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 15): 0dq3c (0.43 #182, 0.43 #167, 0.38 #212), 0789n (0.33 #83, 0.29 #188, 0.29 #173), 01gkgk (0.33 #4, 0.20 #439, 0.18 #889), 060bp (0.22 #376, 0.14 #871, 0.13 #796), 0fkzq (0.17 #102, 0.12 #207, 0.06 #372), 01q24l (0.11 #386, 0.07 #806, 0.06 #821), 0p5vf (0.08 #1090, 0.08 #895, 0.07 #1075), 0f6c3 (0.08 #276, 0.06 #291, 0.06 #321), 02079p (0.06 #309, 0.06 #324, 0.06 #384), 0pqc5 (0.06 #858, 0.06 #918, 0.05 #948) >> Best rule #182 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 09bg4l; >> query: (?x1913, 0dq3c) <- organization(?x1913, ?x10530), profession(?x1913, ?x3342), religion(?x1913, ?x13061), politician(?x1912, ?x1913), basic_title(?x1913, ?x346) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #733 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 012cph; 07_m9_; *> query: (?x1913, 01t7n9) <- profession(?x1913, ?x3342), politician(?x1912, ?x1913), place_of_death(?x1913, ?x108) *> conf = 0.03 ranks of expected_values: 13 EVAL 083pr basic_title 01t7n9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 182.000 182.000 0.429 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #5755-035482 PRED entity: 035482 PRED relation: symptom_of! PRED expected values: 01j6t0 04kllm9 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 61): 0cjf0 (0.78 #416, 0.63 #657, 0.53 #580), 01j6t0 (0.73 #982, 0.71 #283, 0.71 #264), 01cdt5 (0.60 #136, 0.57 #277, 0.57 #258), 0brgy (0.57 #308, 0.53 #577, 0.44 #413), 0hgxh (0.43 #120, 0.40 #119, 0.40 #105), 0f3kl (0.43 #120, 0.36 #118, 0.33 #421), 02tfl8 (0.33 #572, 0.33 #408, 0.33 #205), 02y0js (0.33 #19, 0.26 #589, 0.25 #37), 04kllm9 (0.26 #589, 0.20 #114, 0.20 #76), 098s1 (0.26 #589, 0.15 #525, 0.11 #661) >> Best rule #416 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 087z2; >> query: (?x6781, 0cjf0) <- symptom_of(?x13099, ?x6781), symptom_of(?x6780, ?x6781), people(?x13099, ?x8858), symptom_of(?x13099, ?x10199), ?x6780 = 0j5fv, ?x10199 = 02k6hp >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #982 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 42 *> proper extension: 01g2q; *> query: (?x6781, 01j6t0) <- symptom_of(?x9438, ?x6781), symptom_of(?x9438, ?x13131), symptom_of(?x9438, ?x11307), symptom_of(?x9438, ?x10480), symptom_of(?x9438, ?x8675), ?x13131 = 0d19y2, people(?x11307, ?x6745), ?x8675 = 01gkcc, ?x10480 = 0h1n9 *> conf = 0.73 ranks of expected_values: 2, 9 EVAL 035482 symptom_of! 04kllm9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 58.000 58.000 0.778 http://example.org/medicine/symptom/symptom_of EVAL 035482 symptom_of! 01j6t0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 58.000 0.778 http://example.org/medicine/symptom/symptom_of #5754-0161sp PRED entity: 0161sp PRED relation: artists! PRED expected values: 02x8m 06by7 => 117 concepts (51 used for prediction) PRED predicted values (max 10 best out of 204): 064t9 (0.82 #8483, 0.65 #3339, 0.64 #3037), 06by7 (0.64 #3347, 0.62 #3954, 0.61 #10311), 016clz (0.33 #1214, 0.33 #10294, 0.31 #3330), 025sc50 (0.25 #8517, 0.22 #3071, 0.22 #652), 02k_kn (0.25 #3388, 0.22 #3995, 0.22 #3086), 01lyv (0.24 #1243, 0.16 #2452, 0.15 #3057), 017_qw (0.23 #2478, 0.17 #1572, 0.13 #11859), 0glt670 (0.23 #6394, 0.21 #10329, 0.19 #644), 03_d0 (0.22 #3639, 0.20 #4247, 0.19 #8783), 02lnbg (0.21 #3079, 0.19 #3381, 0.19 #8525) >> Best rule #8483 for best value: >> intensional similarity = 3 >> extensional distance = 425 >> proper extension: 03c7ln; 07s3vqk; 01l1b90; 0fp_v1x; 01pfr3; 0147dk; 02mslq; 06cc_1; 0c7ct; 0kzy0; ... >> query: (?x2908, 064t9) <- artists(?x3061, ?x2908), artists(?x3061, ?x10989), ?x10989 = 02s6sh >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #3347 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 147 *> proper extension: 01pbxb; 0197tq; 0lbj1; 01vw87c; 01vvycq; 0150jk; 0152cw; 01w61th; 03f5spx; 01gf5h; ... *> query: (?x2908, 06by7) <- artists(?x3061, ?x2908), ?x3061 = 05bt6j, award(?x2908, ?x2322) *> conf = 0.64 ranks of expected_values: 2, 23 EVAL 0161sp artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 51.000 0.824 http://example.org/music/genre/artists EVAL 0161sp artists! 02x8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 117.000 51.000 0.824 http://example.org/music/genre/artists #5753-0123qq PRED entity: 0123qq PRED relation: genre PRED expected values: 0fdjb => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 68): 06n90 (0.52 #242, 0.45 #164, 0.29 #1479), 0hcr (0.50 #90, 0.36 #940, 0.31 #245), 0c4xc (0.45 #345, 0.44 #499, 0.35 #577), 0pr6f (0.40 #121, 0.24 #276, 0.20 #198), 095bb (0.30 #108, 0.17 #263, 0.15 #185), 025s89p (0.30 #123, 0.15 #200, 0.10 #509), 01jfsb (0.25 #9, 0.21 #241, 0.20 #163), 02n4kr (0.25 #6, 0.17 #238, 0.15 #160), 0fdjb (0.25 #27, 0.15 #181, 0.10 #104), 0c031k6 (0.25 #48, 0.10 #125, 0.07 #589) >> Best rule #242 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 09g_31; >> query: (?x11203, 06n90) <- genre(?x11203, ?x1844), country_of_origin(?x11203, ?x94), ?x94 = 09c7w0, ?x1844 = 01htzx >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0cskb; 07g9f; *> query: (?x11203, 0fdjb) <- program(?x12969, ?x11203), program(?x9842, ?x11203), ?x9842 = 0bbxd3, actor(?x11203, ?x5505), nationality(?x12969, ?x94) *> conf = 0.25 ranks of expected_values: 9 EVAL 0123qq genre 0fdjb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 66.000 66.000 0.517 http://example.org/tv/tv_program/genre #5752-01vtj38 PRED entity: 01vtj38 PRED relation: artists! PRED expected values: 026z9 => 134 concepts (92 used for prediction) PRED predicted values (max 10 best out of 280): 0dl5d (0.41 #319, 0.18 #5119, 0.17 #619), 0xhtw (0.35 #316, 0.21 #5116, 0.20 #15318), 017_qw (0.31 #12056, 0.16 #19258, 0.12 #24661), 05w3f (0.29 #336, 0.17 #636, 0.15 #2436), 0gywn (0.27 #5451, 0.26 #15653, 0.23 #651), 016clz (0.24 #15307, 0.23 #23710, 0.23 #605), 03lty (0.24 #327, 0.16 #5127, 0.15 #12328), 0cx7f (0.24 #429, 0.14 #5229, 0.13 #12430), 08jyyk (0.18 #360, 0.17 #5160, 0.14 #3960), 02yv6b (0.18 #391, 0.13 #15393, 0.13 #5191) >> Best rule #319 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 032t2z; 01nn6c; 020_4z; 01vzz1c; >> query: (?x7331, 0dl5d) <- artist(?x3265, ?x7331), artist(?x2149, ?x7331), ?x2149 = 011k1h, ?x3265 = 015_1q >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #670 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 06c44; 01sxd1; 01k3qj; *> query: (?x7331, 026z9) <- artist(?x2149, ?x7331), ?x2149 = 011k1h, people(?x1816, ?x7331) *> conf = 0.09 ranks of expected_values: 31 EVAL 01vtj38 artists! 026z9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 134.000 92.000 0.412 http://example.org/music/genre/artists #5751-02t_vx PRED entity: 02t_vx PRED relation: film PRED expected values: 0170_p => 76 concepts (37 used for prediction) PRED predicted values (max 10 best out of 389): 05sw5b (0.14 #810, 0.06 #2593, 0.03 #48144), 0prrm (0.14 #855, 0.01 #34734, 0.01 #18686), 032sl_ (0.14 #1553, 0.01 #8685), 0ckt6 (0.14 #1774), 03wjm2 (0.14 #1753), 0h7t36 (0.14 #1678), 07tlfx (0.14 #1602), 02q5bx2 (0.14 #1462), 0cp0t91 (0.14 #1445), 0gd92 (0.14 #1299) >> Best rule #810 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01h4rj; >> query: (?x7923, 05sw5b) <- film(?x7923, ?x4032), film(?x7923, ?x3859), ?x3859 = 0c57yj, executive_produced_by(?x4032, ?x1387) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02t_vx film 0170_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 37.000 0.143 http://example.org/film/actor/film./film/performance/film #5750-02x4w6g PRED entity: 02x4w6g PRED relation: award! PRED expected values: 02p65p 0h5g_ 05zbm4 04hpck 04t7ts 01_xtx 033w9g 01tt43d 04954 022yb4 => 36 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2440): 0f5xn (0.71 #33179, 0.70 #39821, 0.70 #39819), 0171cm (0.70 #39821, 0.70 #39819, 0.69 #43142), 0sz28 (0.70 #39821, 0.70 #39819, 0.69 #43142), 0205dx (0.70 #39821, 0.70 #39819, 0.69 #43142), 02qgqt (0.70 #39821, 0.70 #39819, 0.69 #43142), 03m6_z (0.70 #39821, 0.70 #39819, 0.69 #43142), 03ym1 (0.70 #39821, 0.70 #39819, 0.69 #43142), 0170pk (0.62 #7070, 0.59 #10388, 0.25 #434), 0h0jz (0.56 #6687, 0.53 #10005, 0.50 #51), 0d6d2 (0.56 #8951, 0.53 #12269, 0.50 #2315) >> Best rule #33179 for best value: >> intensional similarity = 4 >> extensional distance = 170 >> proper extension: 073y53; 03dkh6; >> query: (?x2183, ?x11259) <- award_winner(?x2183, ?x11259), award_winner(?x2183, ?x123), profession(?x123, ?x319), diet(?x11259, ?x11141) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0f4x7; 0bfvd4; *> query: (?x2183, 02p65p) <- award(?x4969, ?x2183), award(?x4327, ?x2183), ?x4969 = 016k6x, nominated_for(?x2183, ?x696), ?x4327 = 016yr0 *> conf = 0.50 ranks of expected_values: 21, 130, 138, 146, 253, 291, 523, 702, 828, 2011 EVAL 02x4w6g award! 022yb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 36.000 21.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4w6g award! 04954 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 36.000 21.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4w6g award! 01tt43d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 36.000 21.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4w6g award! 033w9g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 21.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4w6g award! 01_xtx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 36.000 21.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4w6g award! 04t7ts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 21.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4w6g award! 04hpck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 36.000 21.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4w6g award! 05zbm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 21.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4w6g award! 0h5g_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 36.000 21.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x4w6g award! 02p65p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 36.000 21.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5749-049yf PRED entity: 049yf PRED relation: contains PRED expected values: 049wm => 118 concepts (34 used for prediction) PRED predicted values (max 10 best out of 2672): 049wm (0.74 #17651, 0.45 #38247, 0.42 #85336), 03_3d (0.46 #23536, 0.32 #82394, 0.32 #20594), 049yf (0.33 #101, 0.32 #82394, 0.31 #67675), 018jcq (0.33 #1272, 0.20 #4213, 0.14 #7155), 018qt8 (0.33 #2670, 0.20 #5611, 0.14 #8553), 018jn4 (0.33 #2617, 0.20 #5558, 0.14 #8500), 018qd6 (0.33 #2546, 0.20 #5487, 0.14 #8429), 018jkl (0.33 #1794, 0.20 #4735, 0.14 #7677), 01f1ps (0.33 #1508, 0.20 #4449, 0.14 #7391), 018txg (0.33 #1481, 0.20 #4422, 0.14 #7364) >> Best rule #17651 for best value: >> intensional similarity = 6 >> extensional distance = 53 >> proper extension: 05qhw; >> query: (?x1054, ?x13343) <- contains(?x1054, ?x12984), contains(?x1054, ?x4163), contains(?x252, ?x1054), location_of_ceremony(?x566, ?x4163), place_of_birth(?x4162, ?x4163), administrative_division(?x13343, ?x12984) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049yf contains 049wm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 34.000 0.740 http://example.org/location/location/contains #5748-07_fj54 PRED entity: 07_fj54 PRED relation: film! PRED expected values: 09yhzs => 85 concepts (62 used for prediction) PRED predicted values (max 10 best out of 875): 0mdqp (0.33 #118, 0.25 #2193, 0.17 #8418), 018ygt (0.33 #1114, 0.25 #3189, 0.07 #9414), 016_mj (0.33 #294, 0.25 #2369, 0.03 #8594), 01w9wwg (0.33 #1082, 0.25 #3157, 0.03 #126603), 020ffd (0.33 #1083, 0.25 #3158, 0.02 #11458), 01j7z7 (0.33 #1319, 0.25 #3394, 0.02 #17920), 02lj6p (0.33 #1490, 0.25 #3565, 0.01 #24320), 02lhm2 (0.33 #962, 0.25 #3037, 0.01 #65307), 0c9c0 (0.33 #472, 0.25 #2547, 0.01 #33682), 03k48_ (0.33 #1782, 0.25 #3857, 0.01 #66127) >> Best rule #118 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0b3n61; >> query: (?x4953, 0mdqp) <- film(?x3930, ?x4953), nominated_for(?x102, ?x4953), ?x3930 = 01svw8n, film_crew_role(?x4953, ?x137) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #17114 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 197 *> proper extension: 0g60z; 0180mw; *> query: (?x4953, 09yhzs) <- nominated_for(?x4953, ?x857), nominated_for(?x7560, ?x4953), nominated_for(?x102, ?x4953), award(?x123, ?x102) *> conf = 0.01 ranks of expected_values: 859 EVAL 07_fj54 film! 09yhzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 85.000 62.000 0.333 http://example.org/film/actor/film./film/performance/film #5747-05tfm PRED entity: 05tfm PRED relation: school PRED expected values: 05krk => 103 concepts (72 used for prediction) PRED predicted values (max 10 best out of 471): 0lyjf (0.42 #7394, 0.42 #7017, 0.37 #4010), 01pl14 (0.40 #751, 0.29 #1314, 0.25 #2628), 01qgr3 (0.33 #118, 0.16 #4055, 0.13 #5178), 025v3k (0.33 #52, 0.15 #1171, 0.12 #610), 02pptm (0.33 #143, 0.15 #1262, 0.12 #701), 0trv (0.31 #1256, 0.19 #2194, 0.12 #12423), 05krk (0.30 #750, 0.25 #562, 0.25 #190), 01vs5c (0.26 #4024, 0.25 #2710, 0.25 #2523), 07w0v (0.25 #383, 0.25 #197, 0.23 #12436), 01rc6f (0.25 #688, 0.25 #316, 0.21 #2001) >> Best rule #7394 for best value: >> intensional similarity = 12 >> extensional distance = 31 >> proper extension: 06x76; >> query: (?x1576, 0lyjf) <- position(?x1576, ?x2312), school(?x1576, ?x6315), position(?x9115, ?x2312), position(?x4924, ?x2312), position(?x4170, ?x2312), ?x9115 = 0g0z58, ?x4924 = 025_64l, team(?x2312, ?x5472), ?x5472 = 02wvfxl, position(?x2312, ?x706), student(?x6315, ?x1400), ?x4170 = 05l71 >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #750 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 8 *> proper extension: 02896; 07l24; 03b3j; 084l5; 05g49; 06rny; 0487_; 06rpd; *> query: (?x1576, 05krk) <- position(?x1576, ?x2247), position(?x1576, ?x1240), school(?x1576, ?x3090), team(?x11282, ?x1576), colors(?x1576, ?x332), sport(?x1576, ?x1083), student(?x3090, ?x3547), ?x1240 = 023wyl, major_field_of_study(?x3090, ?x254), draft(?x1576, ?x465), ?x2247 = 01_9c1 *> conf = 0.30 ranks of expected_values: 7 EVAL 05tfm school 05krk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 103.000 72.000 0.424 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #5746-018qb4 PRED entity: 018qb4 PRED relation: olympics! PRED expected values: 0hzlz 0h7x => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 224): 0h7x (0.86 #1159, 0.78 #2678, 0.77 #3061), 06mzp (0.83 #898, 0.82 #1276, 0.81 #1148), 03_3d (0.83 #884, 0.68 #1262, 0.68 #1764), 059j2 (0.78 #1380, 0.78 #905, 0.77 #1283), 0154j (0.78 #1380, 0.76 #503, 0.74 #1003), 02jx1 (0.78 #1380, 0.76 #503, 0.73 #3280), 0chghy (0.78 #888, 0.77 #1266, 0.71 #1768), 015fr (0.73 #396, 0.67 #895, 0.57 #1775), 05qhw (0.72 #893, 0.68 #1271, 0.68 #1773), 0b90_r (0.72 #882, 0.64 #383, 0.55 #1260) >> Best rule #1159 for best value: >> intensional similarity = 9 >> extensional distance = 19 >> proper extension: 0swbd; >> query: (?x5395, 0h7x) <- olympics(?x1264, ?x5395), sports(?x5395, ?x2315), country(?x2315, ?x4743), country(?x2315, ?x2629), ?x4743 = 03spz, ?x1264 = 0345h, film_release_region(?x3850, ?x2629), ?x3850 = 047fjjr, sports(?x391, ?x2315) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 20 EVAL 018qb4 olympics! 0h7x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.857 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 018qb4 olympics! 0hzlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 54.000 54.000 0.857 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #5745-026spg PRED entity: 026spg PRED relation: award_winner! PRED expected values: 0466p0j => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 104): 01c6qp (0.33 #19, 0.18 #158, 0.15 #436), 019bk0 (0.20 #989, 0.17 #433, 0.17 #13208), 013b2h (0.18 #1191, 0.17 #13208, 0.15 #496), 02rjjll (0.18 #978, 0.17 #422, 0.17 #1117), 01s695 (0.17 #420, 0.17 #3, 0.12 #1671), 02cg41 (0.17 #541, 0.15 #1236, 0.12 #1792), 01xqqp (0.17 #511, 0.11 #372, 0.08 #3430), 056878 (0.17 #13208, 0.16 #1005, 0.15 #727), 0gpjbt (0.17 #13208, 0.14 #1141, 0.13 #724), 0466p0j (0.17 #13208, 0.13 #770, 0.12 #492) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 06688p; >> query: (?x4675, 01c6qp) <- artists(?x671, ?x4675), film(?x4675, ?x8664), profession(?x4675, ?x220), ?x8664 = 03hfmm >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13208 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1342 *> proper extension: 035_2h; 0hm0k; 039cq4; 01j53q; *> query: (?x4675, ?x1362) <- award_winner(?x1238, ?x4675), award_winner(?x724, ?x1238), award_winner(?x1362, ?x1238) *> conf = 0.17 ranks of expected_values: 10 EVAL 026spg award_winner! 0466p0j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 140.000 140.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5744-044lyq PRED entity: 044lyq PRED relation: type_of_union PRED expected values: 04ztj => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.94 #197, 0.94 #206, 0.94 #16), 0jgjn (0.02 #9) >> Best rule #197 for best value: >> intensional similarity = 2 >> extensional distance = 2753 >> proper extension: 0qkj7; >> query: (?x7242, 04ztj) <- type_of_union(?x7242, ?x1873), gender(?x7242, ?x231) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044lyq type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.943 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5743-098n5 PRED entity: 098n5 PRED relation: place_of_birth PRED expected values: 01_d4 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 100): 02_286 (0.14 #1428, 0.12 #19, 0.10 #4950), 01_d4 (0.07 #1475, 0.07 #4293, 0.06 #6405), 0k049 (0.07 #14092, 0.06 #7044, 0.06 #13387), 01531 (0.06 #105, 0.05 #2922, 0.05 #2218), 0cr3d (0.05 #7138, 0.04 #21938, 0.04 #1503), 030qb3t (0.04 #3576, 0.04 #12736, 0.04 #10621), 0rh6k (0.04 #2, 0.04 #2115, 0.03 #2819), 0dclg (0.04 #5713, 0.03 #1487, 0.02 #3600), 0d6lp (0.03 #6453, 0.02 #12796, 0.02 #13501), 01sn3 (0.03 #1558, 0.02 #3671, 0.02 #853) >> Best rule #1428 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 0f1vrl; >> query: (?x3555, 02_286) <- type_of_union(?x3555, ?x566), program_creator(?x7551, ?x3555), profession(?x3555, ?x319) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1475 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 0f1vrl; *> query: (?x3555, 01_d4) <- type_of_union(?x3555, ?x566), program_creator(?x7551, ?x3555), profession(?x3555, ?x319) *> conf = 0.07 ranks of expected_values: 2 EVAL 098n5 place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.143 http://example.org/people/person/place_of_birth #5742-01z2sn PRED entity: 01z2sn PRED relation: contains! PRED expected values: 07ssc => 65 concepts (40 used for prediction) PRED predicted values (max 10 best out of 170): 07ssc (0.93 #4508, 0.91 #5403, 0.73 #23291), 09c7w0 (0.73 #23294, 0.67 #25088, 0.67 #25983), 03rt9 (0.64 #3605, 0.04 #19729, 0.02 #24212), 02jx1 (0.56 #5458, 0.55 #4563, 0.34 #7249), 059rby (0.20 #19724, 0.10 #34963, 0.07 #22415), 0d060g (0.20 #7175, 0.19 #8070, 0.13 #15236), 05bcl (0.20 #1144, 0.08 #2934, 0.07 #32253), 0yl27 (0.20 #1143, 0.08 #2933, 0.07 #32253), 0345h (0.19 #7244, 0.18 #8139, 0.12 #15305), 01n7q (0.19 #21576, 0.17 #18886, 0.15 #20680) >> Best rule #4508 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 04jpl; 0fm2_; 022_6; 02jx1; 0dbdy; 05l5n; 09tlh; 0hyxv; 0nccd; 04p3c; ... >> query: (?x14586, 07ssc) <- location_of_ceremony(?x566, ?x14586), contains(?x4221, ?x14586), ?x566 = 04ztj, nationality(?x3708, ?x4221), ?x3708 = 013knm >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01z2sn contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 40.000 0.932 http://example.org/location/location/contains #5741-0drnwh PRED entity: 0drnwh PRED relation: language PRED expected values: 02h40lc => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 54): 02h40lc (0.98 #5538, 0.96 #2408, 0.95 #1701), 06b_j (0.27 #80, 0.11 #315, 0.10 #373), 064_8sq (0.22 #489, 0.19 #547, 0.18 #79), 04306rv (0.21 #298, 0.18 #63, 0.15 #531), 06nm1 (0.21 #536, 0.13 #830, 0.11 #1006), 02bjrlw (0.09 #703, 0.08 #879, 0.08 #469), 012w70 (0.09 #70, 0.04 #246, 0.04 #714), 02hwyss (0.09 #99, 0.04 #567, 0.04 #4587), 04h9h (0.08 #568, 0.04 #1038, 0.04 #4587), 06mp7 (0.07 #308, 0.04 #541, 0.04 #4587) >> Best rule #5538 for best value: >> intensional similarity = 4 >> extensional distance = 1236 >> proper extension: 05f67hw; >> query: (?x6679, 02h40lc) <- country(?x6679, ?x94), language(?x6679, ?x2164), ?x94 = 09c7w0, languages(?x5314, ?x2164) >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0drnwh language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.977 http://example.org/film/film/language #5740-01sby_ PRED entity: 01sby_ PRED relation: film_release_region PRED expected values: 03h64 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 212): 05r4w (0.96 #461, 0.95 #155, 0.86 #2607), 03h64 (0.90 #523, 0.89 #217, 0.82 #2669), 05qhw (0.88 #473, 0.84 #167, 0.79 #3231), 03rjj (0.86 #1383, 0.84 #3374, 0.84 #3221), 015fr (0.84 #170, 0.83 #476, 0.78 #3387), 03spz (0.77 #552, 0.76 #246, 0.76 #859), 03rt9 (0.71 #472, 0.68 #166, 0.67 #1392), 04gzd (0.68 #161, 0.67 #467, 0.60 #774), 01ls2 (0.66 #164, 0.65 #470, 0.47 #1390), 015qh (0.63 #194, 0.63 #807, 0.58 #500) >> Best rule #461 for best value: >> intensional similarity = 8 >> extensional distance = 46 >> proper extension: 0b76d_m; 0g5qs2k; 02d44q; 0cc7hmk; 0j43swk; 0bmhvpr; 07s846j; 0bhwhj; 026lgs; 087pfc; >> query: (?x5255, 05r4w) <- film_release_region(?x5255, ?x2513), film_release_region(?x5255, ?x1917), film_release_region(?x5255, ?x304), ?x304 = 0d0vqn, produced_by(?x5255, ?x6146), nominated_for(?x5497, ?x5255), olympics(?x2513, ?x418), ?x1917 = 01p1v >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #523 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 46 *> proper extension: 0b76d_m; 0g5qs2k; 02d44q; 0cc7hmk; 0j43swk; 0bmhvpr; 07s846j; 0bhwhj; 026lgs; 087pfc; *> query: (?x5255, 03h64) <- film_release_region(?x5255, ?x2513), film_release_region(?x5255, ?x1917), film_release_region(?x5255, ?x304), ?x304 = 0d0vqn, produced_by(?x5255, ?x6146), nominated_for(?x5497, ?x5255), olympics(?x2513, ?x418), ?x1917 = 01p1v *> conf = 0.90 ranks of expected_values: 2 EVAL 01sby_ film_release_region 03h64 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.958 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5739-01gz9n PRED entity: 01gz9n PRED relation: location PRED expected values: 06yxd => 120 concepts (115 used for prediction) PRED predicted values (max 10 best out of 114): 0gkgp (0.47 #20118, 0.45 #50702, 0.44 #61159), 02_286 (0.22 #37, 0.13 #51545, 0.13 #7279), 030qb3t (0.12 #24145, 0.10 #14484, 0.10 #30669), 0cr3d (0.06 #18653, 0.05 #16239, 0.05 #50042), 01cx_ (0.04 #19476, 0.02 #163, 0.02 #18671), 04jpl (0.04 #30603, 0.04 #8868, 0.04 #61176), 0cc56 (0.04 #57, 0.03 #16957, 0.03 #9714), 03pzf (0.03 #1330, 0.03 #2939, 0.02 #6963), 0hptm (0.03 #19616, 0.01 #1108), 0mw1j (0.03 #20119) >> Best rule #20118 for best value: >> intensional similarity = 3 >> extensional distance = 439 >> proper extension: 05fh2; >> query: (?x9964, ?x9394) <- place_of_birth(?x9964, ?x9394), category(?x9394, ?x134), administrative_division(?x9394, ?x13621) >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01gz9n location 06yxd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 115.000 0.474 http://example.org/people/person/places_lived./people/place_lived/location #5738-01p896 PRED entity: 01p896 PRED relation: major_field_of_study PRED expected values: 01mkq 062z7 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 117): 02j62 (0.50 #32, 0.48 #2911, 0.46 #2658), 01mkq (0.50 #16, 0.39 #2895, 0.38 #2642), 02lp1 (0.50 #12, 0.38 #2638, 0.37 #3769), 0g26h (0.50 #45, 0.31 #7191, 0.27 #1795), 03qsdpk (0.50 #50, 0.19 #1675, 0.17 #2300), 062z7 (0.39 #7175, 0.34 #2279, 0.33 #29), 03g3w (0.37 #2907, 0.34 #1278, 0.33 #28), 04rjg (0.35 #2647, 0.35 #2900, 0.33 #21), 05qjt (0.33 #8, 0.32 #7154, 0.29 #2509), 05qfh (0.33 #38, 0.27 #2539, 0.25 #1788) >> Best rule #32 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02w2bc; 01w5m; 07vyf; 01bm_; >> query: (?x9912, 02j62) <- colors(?x9912, ?x663), student(?x9912, ?x8813), school_type(?x9912, ?x3092), award_nominee(?x8813, ?x6979), ?x6979 = 057176, institution(?x865, ?x9912) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 02w2bc; 01w5m; 07vyf; 01bm_; *> query: (?x9912, 01mkq) <- colors(?x9912, ?x663), student(?x9912, ?x8813), school_type(?x9912, ?x3092), award_nominee(?x8813, ?x6979), ?x6979 = 057176, institution(?x865, ?x9912) *> conf = 0.50 ranks of expected_values: 2, 6 EVAL 01p896 major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 141.000 141.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01p896 major_field_of_study 01mkq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 141.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #5737-0ch26b_ PRED entity: 0ch26b_ PRED relation: film! PRED expected values: 01r93l => 87 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1229): 0bytkq (0.45 #14541, 0.44 #51944, 0.44 #54024), 06r_by (0.45 #14541, 0.44 #51944, 0.44 #54024), 0jfx1 (0.45 #14541, 0.44 #76890, 0.43 #108067), 05183k (0.45 #14541, 0.44 #76890, 0.43 #108067), 02pq9yv (0.45 #14541, 0.44 #76890, 0.43 #108067), 01tc9r (0.45 #14541, 0.44 #76890, 0.43 #108067), 095zvfg (0.45 #14541, 0.44 #76890, 0.43 #108067), 08h79x (0.45 #14541, 0.44 #76890, 0.43 #108067), 02mxbd (0.45 #14541, 0.44 #76890, 0.43 #108067), 05qd_ (0.45 #14541, 0.44 #76890, 0.43 #108067) >> Best rule #14541 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 011yrp; 0ds3t5x; 04969y; 01vksx; 02d44q; 0_92w; 05z_kps; 0fpkhkz; 04jkpgv; 0g9wdmc; ... >> query: (?x1916, ?x902) <- film_release_region(?x1916, ?x1558), ?x1558 = 01mjq, award(?x1916, ?x289), nominated_for(?x902, ?x1916) >> conf = 0.45 => this is the best rule for 10 predicted values *> Best rule #74811 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 490 *> proper extension: 01cgz; *> query: (?x1916, ?x436) <- films(?x10705, ?x1916), films(?x10705, ?x2886), film(?x436, ?x2886), award(?x2886, ?x3190) *> conf = 0.07 ranks of expected_values: 132 EVAL 0ch26b_ film! 01r93l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 87.000 52.000 0.455 http://example.org/film/actor/film./film/performance/film #5736-010xjr PRED entity: 010xjr PRED relation: people! PRED expected values: 0jdk0 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 10): 0gk4g (0.04 #5158, 0.04 #472, 0.04 #1132), 0dq9p (0.03 #1139, 0.02 #281, 0.02 #479), 0qcr0 (0.02 #463, 0.02 #1717, 0.02 #4225), 04p3w (0.02 #77, 0.02 #1727, 0.01 #2519), 02k6hp (0.02 #1225, 0.01 #1621, 0.01 #2545), 02y0js (0.01 #1124, 0.01 #5084, 0.01 #2510), 01psyx (0.01 #507), 01l2m3 (0.01 #280), 02knxx (0.01 #1154), 01_qc_ (0.01 #94) >> Best rule #5158 for best value: >> intensional similarity = 2 >> extensional distance = 2862 >> proper extension: 09jrf; 0443c; 0cfywh; >> query: (?x9797, 0gk4g) <- type_of_union(?x9797, ?x566), ?x566 = 04ztj >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 010xjr people! 0jdk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 88.000 0.039 http://example.org/people/cause_of_death/people #5735-04228s PRED entity: 04228s PRED relation: genre! PRED expected values: 026390q 03kx49 => 25 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1866): 03s6l2 (0.67 #9336, 0.50 #14890, 0.50 #7485), 021pqy (0.67 #8187, 0.50 #6338, 0.33 #11890), 03kg2v (0.67 #9739, 0.42 #15293, 0.33 #17144), 029k4p (0.67 #15658, 0.38 #13807, 0.33 #10104), 0258dh (0.67 #10560, 0.33 #16114, 0.33 #8709), 07nt8p (0.67 #9616, 0.33 #15170, 0.33 #2217), 09p0ct (0.67 #9470, 0.33 #15024, 0.33 #2071), 02mpyh (0.67 #10747, 0.33 #16301, 0.33 #3348), 03hmt9b (0.67 #9927, 0.33 #15481, 0.33 #2528), 01kff7 (0.50 #12951, 0.50 #3915, 0.38 #13168) >> Best rule #9336 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0lsxr; 01jfsb; 02l7c8; >> query: (?x10182, 03s6l2) <- genre(?x4355, ?x10182), genre(?x3854, ?x10182), genre(?x2815, ?x10182), ?x2815 = 059rc, film_release_region(?x4355, ?x87), country(?x4355, ?x205), film_regional_debut_venue(?x4355, ?x6601), film(?x2156, ?x3854), nominated_for(?x3632, ?x3854), film(?x400, ?x3854), award(?x3632, ?x159) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7592 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 04t36; 01t_vv; *> query: (?x10182, 026390q) <- genre(?x7141, ?x10182), genre(?x4355, ?x10182), genre(?x2815, ?x10182), ?x7141 = 027r9t, country(?x4355, ?x205), film_release_region(?x2815, ?x94), production_companies(?x2815, ?x382), film_release_region(?x4355, ?x1603), ?x1603 = 06bnz, nationality(?x101, ?x205), contains(?x205, ?x1356), film_release_region(?x66, ?x205) *> conf = 0.50 ranks of expected_values: 81, 126 EVAL 04228s genre! 03kx49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 25.000 14.000 0.667 http://example.org/film/film/genre EVAL 04228s genre! 026390q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 25.000 14.000 0.667 http://example.org/film/film/genre #5734-084l5 PRED entity: 084l5 PRED relation: organization! PRED expected values: 0dq_5 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 30): 0dq_5 (0.80 #256, 0.78 #191, 0.78 #282), 060c4 (0.21 #575, 0.16 #735, 0.16 #550), 0dq3c (0.21 #575, 0.05 #157, 0.05 #170), 07xl34 (0.04 #744, 0.04 #678, 0.04 #704), 028fjr (0.03 #654), 0g686w (0.03 #654), 04192r (0.03 #654), 06hpx2 (0.03 #654), 02h53vq (0.03 #654), 09lq2c (0.03 #654) >> Best rule #256 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 01s73z; >> query: (?x4519, 0dq_5) <- service_language(?x4519, ?x254), ?x254 = 02h40lc, contact_category(?x4519, ?x3231), ?x3231 = 014dgf >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 084l5 organization! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.800 http://example.org/organization/role/leaders./organization/leadership/organization #5733-047wh1 PRED entity: 047wh1 PRED relation: nominated_for! PRED expected values: 02r0csl 05b4l5x 057xs89 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 190): 05b4l5x (0.68 #11390, 0.67 #7593, 0.67 #8779), 0gq9h (0.50 #2432, 0.49 #3383, 0.44 #2670), 0gs9p (0.41 #3385, 0.39 #2434, 0.35 #3623), 0gq_v (0.41 #2389, 0.38 #2627, 0.38 #3340), 019f4v (0.37 #3374, 0.35 #2423, 0.31 #3612), 0k611 (0.33 #2443, 0.32 #3394, 0.30 #3632), 0gr4k (0.33 #3346, 0.30 #2395, 0.29 #3584), 0f4x7 (0.31 #3345, 0.29 #3583, 0.29 #2394), 040njc (0.30 #3327, 0.27 #3565, 0.26 #2376), 0gqy2 (0.28 #3442, 0.26 #3680, 0.25 #4154) >> Best rule #11390 for best value: >> intensional similarity = 3 >> extensional distance = 989 >> proper extension: 03j63k; 097h2; 019g8j; 0147w8; 0300ml; 02rq7nd; >> query: (?x5135, ?x2022) <- award(?x5135, ?x2022), nominated_for(?x507, ?x5135), nominated_for(?x2022, ?x148) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1, 26, 27 EVAL 047wh1 nominated_for! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 88.000 88.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 047wh1 nominated_for! 05b4l5x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 047wh1 nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 88.000 88.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5732-06yrj6 PRED entity: 06yrj6 PRED relation: gender PRED expected values: 05zppz => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.82 #11, 0.81 #25, 0.81 #19), 02zsn (0.46 #173, 0.30 #40, 0.30 #34) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 0f1vrl; >> query: (?x8295, 05zppz) <- place_of_birth(?x8295, ?x4499), program(?x8295, ?x631), location(?x396, ?x4499) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06yrj6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.819 http://example.org/people/person/gender #5731-011s9r PRED entity: 011s9r PRED relation: written_by! PRED expected values: 0d_wms => 132 concepts (116 used for prediction) PRED predicted values (max 10 best out of 159): 0d_wms (0.36 #3313, 0.33 #250, 0.29 #2899), 01_mdl (0.36 #3313, 0.31 #3312, 0.29 #7943), 044g_k (0.36 #3313, 0.31 #3312, 0.29 #7943), 042fgh (0.36 #3313, 0.31 #3312, 0.29 #7943), 042g97 (0.36 #3313, 0.31 #3312, 0.29 #7943), 024mxd (0.36 #3313, 0.04 #23815, 0.04 #26463), 0gjc4d3 (0.29 #7943, 0.29 #7942, 0.28 #11252), 063y9fp (0.29 #7943, 0.29 #7942, 0.28 #11252), 03d8jd1 (0.25 #1309, 0.02 #11235, 0.02 #10572), 0dgq80b (0.25 #1283, 0.02 #11209, 0.02 #10546) >> Best rule #3313 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 079vf; 0343h; 05pq9; 0kb3n; >> query: (?x11928, ?x3672) <- people(?x1050, ?x11928), award_winner(?x11928, ?x8209), story_by(?x7425, ?x11928), honored_for(?x7425, ?x3672) >> conf = 0.36 => this is the best rule for 6 predicted values ranks of expected_values: 1 EVAL 011s9r written_by! 0d_wms CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 116.000 0.360 http://example.org/film/film/written_by #5730-03qk20 PRED entity: 03qk20 PRED relation: artist PRED expected values: 01czx 01wyz92 01ww_vs => 46 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1005): 013rfk (0.90 #1677, 0.55 #8129, 0.51 #5026), 03f0fnk (0.90 #1677, 0.53 #6704, 0.51 #5026), 070b4 (0.90 #1677, 0.53 #6704, 0.51 #5026), 01k_yf (0.90 #1677, 0.53 #6704, 0.51 #5026), 048xh (0.90 #1677, 0.51 #5026, 0.42 #10059), 018dyl (0.90 #1677, 0.51 #5026, 0.42 #10059), 016lmg (0.90 #1677, 0.51 #5026, 0.42 #10059), 02vnpv (0.90 #1677, 0.51 #5026, 0.42 #10059), 07_3qd (0.90 #1677, 0.51 #5026, 0.42 #10059), 01vsyg9 (0.90 #1677, 0.51 #5026, 0.42 #10059) >> Best rule #1677 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 017l96; >> query: (?x9419, ?x4712) <- artist(?x9419, ?x10043), artist(?x9419, ?x9096), artists(?x5934, ?x9096), artist(?x14315, ?x9096), group(?x2048, ?x9096), group(?x716, ?x9096), group(?x227, ?x9096), group(?x75, ?x9096), artist(?x14315, ?x4712), ?x227 = 0342h, ?x2048 = 018j2, ?x10043 = 0fb2l, ?x716 = 018vs, ?x75 = 07y_7, artists(?x5934, ?x13511), ?x13511 = 06lxn, parent_genre(?x2407, ?x5934) >> conf = 0.90 => this is the best rule for 16 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 15, 288, 533 EVAL 03qk20 artist 01ww_vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 46.000 20.000 0.897 http://example.org/music/record_label/artist EVAL 03qk20 artist 01wyz92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 20.000 0.897 http://example.org/music/record_label/artist EVAL 03qk20 artist 01czx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 20.000 0.897 http://example.org/music/record_label/artist #5729-01l03w2 PRED entity: 01l03w2 PRED relation: award_winner! PRED expected values: 019bk0 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 101): 01bx35 (0.22 #7, 0.18 #6862, 0.17 #7423), 01mh_q (0.20 #228, 0.12 #368, 0.08 #1628), 01s695 (0.18 #6862, 0.17 #7423, 0.12 #143), 01c6qp (0.18 #6862, 0.17 #7423, 0.11 #19), 0466p0j (0.18 #6862, 0.17 #7423, 0.11 #76), 01mhwk (0.18 #6862, 0.17 #7423, 0.11 #41), 09n4nb (0.18 #6862, 0.17 #7423, 0.11 #48), 01xqqp (0.18 #6862, 0.17 #7423, 0.11 #95), 09pj68 (0.18 #6862, 0.17 #7423, 0.11 #104), 02cg41 (0.18 #6862, 0.17 #7423, 0.11 #125) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0jdhp; >> query: (?x4635, 01bx35) <- award_nominee(?x4101, ?x4635), award(?x4635, ?x2139), ?x4101 = 01vd7hn >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #156 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 012gq6; *> query: (?x4635, 019bk0) <- award_winner(?x8076, ?x4635), award_winner(?x2139, ?x4635), ?x2139 = 01by1l, award(?x2906, ?x8076) *> conf = 0.11 ranks of expected_values: 21 EVAL 01l03w2 award_winner! 019bk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 89.000 89.000 0.222 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5728-02b29 PRED entity: 02b29 PRED relation: place_of_birth PRED expected values: 01_d4 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 108): 07_f2 (0.28 #47898, 0.27 #55646, 0.27 #33105), 02_286 (0.20 #19, 0.13 #14104, 0.12 #3539), 04jpl (0.13 #8, 0.06 #712, 0.02 #28181), 01cx_ (0.07 #109, 0.03 #813, 0.02 #26871), 0_vn7 (0.07 #156, 0.03 #860, 0.02 #1564), 01jr6 (0.07 #143, 0.03 #847, 0.01 #5072), 01yj2 (0.07 #317, 0.03 #1021), 05fjf (0.07 #253, 0.03 #957), 0cr3d (0.06 #26856, 0.06 #14179, 0.06 #1502), 030qb3t (0.06 #758, 0.05 #14139, 0.05 #40907) >> Best rule #47898 for best value: >> intensional similarity = 3 >> extensional distance = 1657 >> proper extension: 07m69t; 02x8kk; 069d71; >> query: (?x6914, ?x7405) <- nationality(?x6914, ?x94), ?x94 = 09c7w0, location(?x6914, ?x7405) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #4995 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 142 *> proper extension: 01c58j; 01wyzyl; 0c8hct; 015zql; 068g3p; 01bbwp; 0bbxd3; 027jq2; 01p8r8; *> query: (?x6914, 01_d4) <- profession(?x6914, ?x1943), profession(?x6914, ?x987), ?x987 = 0dxtg, ?x1943 = 02krf9 *> conf = 0.04 ranks of expected_values: 13 EVAL 02b29 place_of_birth 01_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 101.000 101.000 0.277 http://example.org/people/person/place_of_birth #5727-0b_6_l PRED entity: 0b_6_l PRED relation: locations PRED expected values: 0fr0t => 67 concepts (60 used for prediction) PRED predicted values (max 10 best out of 330): 013yq (0.62 #3011, 0.60 #3882, 0.60 #1270), 0f2r6 (0.60 #1064, 0.50 #2979, 0.50 #1934), 0d9y6 (0.60 #1314, 0.50 #791, 0.45 #5596), 029cr (0.50 #3015, 0.50 #926, 0.35 #4239), 0f2rq (0.50 #795, 0.45 #5596, 0.40 #3930), 0d9jr (0.45 #5596, 0.40 #1141, 0.39 #5775), 0fr0t (0.45 #5596, 0.40 #1120, 0.39 #5775), 0f2tj (0.45 #5596, 0.40 #1155, 0.39 #5775), 0djd3 (0.45 #5596, 0.40 #1328, 0.39 #5775), 0vzm (0.45 #5596, 0.39 #5775, 0.33 #2155) >> Best rule #3011 for best value: >> intensional similarity = 14 >> extensional distance = 6 >> proper extension: 0b_6mr; >> query: (?x12162, 013yq) <- locations(?x12162, ?x1719), team(?x12162, ?x6003), team(?x12162, ?x5551), team(?x12162, ?x4804), team(?x5258, ?x4804), team(?x3797, ?x4804), team(?x2302, ?x4804), ?x2302 = 0b_77q, ?x6003 = 02py8_w, colors(?x4804, ?x332), ?x3797 = 0b_6zk, ?x5258 = 0b_6h7, ?x5551 = 02pjzvh, position(?x4804, ?x6848) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #5596 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 22 *> proper extension: 01y998; *> query: (?x12162, ?x674) <- locations(?x12162, ?x8993), locations(?x12162, ?x4499), locations(?x12162, ?x2879), locations(?x12162, ?x2017), locations(?x9974, ?x2879), contains(?x94, ?x2879), contains(?x760, ?x8993), featured_film_locations(?x1015, ?x2017), service_location(?x6315, ?x2879), time_zones(?x8993, ?x2674), locations(?x9974, ?x674), partially_contains(?x760, ?x10710), contains(?x4499, ?x331) *> conf = 0.45 ranks of expected_values: 7 EVAL 0b_6_l locations 0fr0t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 67.000 60.000 0.625 http://example.org/time/event/locations #5726-0274ck PRED entity: 0274ck PRED relation: instrumentalists! PRED expected values: 0342h => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 115): 0342h (0.80 #1463, 0.67 #176, 0.66 #1031), 05r5c (0.57 #265, 0.53 #1552, 0.50 #2152), 02hnl (0.50 #205, 0.21 #718, 0.20 #1747), 026t6 (0.42 #174, 0.33 #87, 0.22 #514), 0l14qv (0.33 #172, 0.33 #90, 0.31 #1027), 0l14md (0.33 #92, 0.17 #692, 0.14 #1034), 0mkg (0.33 #11, 0.07 #268, 0.06 #438), 03ndd (0.33 #69, 0.04 #1289, 0.03 #3775), 03bx0bm (0.31 #1027, 0.30 #1288, 0.30 #1200), 01v1d8 (0.31 #1027, 0.30 #1288, 0.30 #1200) >> Best rule #1463 for best value: >> intensional similarity = 3 >> extensional distance = 164 >> proper extension: 016qtt; 01p9hgt; 01wwvc5; 01w02sy; 03bnv; 01wz_ml; 01309x; 01s21dg; 0ddkf; 01lz4tf; ... >> query: (?x764, 0342h) <- artists(?x497, ?x764), profession(?x764, ?x2659), ?x2659 = 039v1 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0274ck instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.795 http://example.org/music/instrument/instrumentalists #5725-02ny8t PRED entity: 02ny8t PRED relation: artists PRED expected values: 06w2sn5 0j1yf 0127s7 09h4b5 016ppr => 37 concepts (11 used for prediction) PRED predicted values (max 10 best out of 993): 03t9sp (0.67 #2247, 0.62 #4372, 0.50 #5436), 01vtj38 (0.67 #2774, 0.60 #1712, 0.57 #3837), 01vwyqp (0.67 #2395, 0.50 #4520, 0.43 #5584), 02zmh5 (0.67 #2278, 0.50 #4403, 0.40 #1216), 03f5spx (0.60 #1120, 0.57 #3245, 0.50 #4307), 0bqsy (0.60 #1415, 0.57 #3540, 0.50 #4602), 06mt91 (0.60 #1661, 0.57 #3786, 0.50 #2723), 0127s7 (0.60 #1591, 0.57 #3716, 0.50 #2653), 0gbwp (0.60 #1407, 0.57 #3532, 0.50 #2469), 0j1yf (0.60 #1197, 0.57 #3322, 0.38 #4384) >> Best rule #2247 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 06by7; 05bt6j; 0ggx5q; >> query: (?x8878, 03t9sp) <- artists(?x8878, ?x8693), artists(?x8878, ?x6577), artists(?x8878, ?x5566), artists(?x8878, ?x5514), ?x6577 = 0gs6vr, ?x8693 = 0bdxs5, ?x5566 = 01_ztw, artists(?x671, ?x5514), ?x671 = 064t9 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1591 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 064t9; 06j6l; *> query: (?x8878, 0127s7) <- artists(?x8878, ?x8365), artists(?x8878, ?x6577), artists(?x8878, ?x5878), artists(?x8878, ?x3244), instrumentalists(?x212, ?x6577), ?x8365 = 05w6cw, ?x212 = 026t6, ?x3244 = 02wb6yq, ?x5878 = 01jfr3y *> conf = 0.60 ranks of expected_values: 8, 10, 19, 94, 551 EVAL 02ny8t artists 016ppr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 37.000 11.000 0.667 http://example.org/music/genre/artists EVAL 02ny8t artists 09h4b5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 37.000 11.000 0.667 http://example.org/music/genre/artists EVAL 02ny8t artists 0127s7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 37.000 11.000 0.667 http://example.org/music/genre/artists EVAL 02ny8t artists 0j1yf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 37.000 11.000 0.667 http://example.org/music/genre/artists EVAL 02ny8t artists 06w2sn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 11.000 0.667 http://example.org/music/genre/artists #5724-05cj4r PRED entity: 05cj4r PRED relation: award_winner! PRED expected values: 03nnm4t => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 100): 02q690_ (0.19 #341, 0.18 #480, 0.17 #619), 03nnm4t (0.19 #350, 0.16 #489, 0.12 #628), 0g55tzk (0.18 #135, 0.15 #274, 0.11 #8342), 05c1t6z (0.16 #292, 0.14 #431, 0.10 #570), 0gx_st (0.12 #314, 0.12 #453, 0.12 #36), 027n06w (0.12 #349, 0.12 #488, 0.12 #627), 02wzl1d (0.12 #288, 0.10 #427, 0.10 #566), 0gvstc3 (0.11 #8342, 0.11 #8760, 0.10 #9178), 0hn821n (0.11 #8342, 0.11 #8760, 0.10 #9178), 09p2r9 (0.11 #8342, 0.11 #8760, 0.10 #9178) >> Best rule #341 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 01y8d4; 023jq1; >> query: (?x374, 02q690_) <- award_winner(?x1950, ?x374), program_creator(?x7254, ?x374), people(?x743, ?x1950) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #350 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 01y8d4; 023jq1; *> query: (?x374, 03nnm4t) <- award_winner(?x1950, ?x374), program_creator(?x7254, ?x374), people(?x743, ?x1950) *> conf = 0.19 ranks of expected_values: 2 EVAL 05cj4r award_winner! 03nnm4t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.188 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5723-016t0h PRED entity: 016t0h PRED relation: award PRED expected values: 02f73b => 76 concepts (49 used for prediction) PRED predicted values (max 10 best out of 245): 02f72n (0.81 #4794, 0.81 #13580, 0.80 #6391), 01bgqh (0.60 #3239, 0.50 #7232, 0.44 #7632), 03c7tr1 (0.59 #2057, 0.18 #8846, 0.12 #3654), 02v1m7 (0.51 #2910, 0.26 #3309, 0.21 #4506), 01by1l (0.50 #4505, 0.46 #3308, 0.43 #6103), 054ks3 (0.46 #3737, 0.28 #6932, 0.24 #3338), 01d38t (0.33 #325, 0.25 #726, 0.20 #1125), 02f73b (0.29 #3878, 0.27 #5875, 0.26 #3479), 05p09zm (0.27 #7713, 0.23 #8911, 0.22 #7313), 0c4z8 (0.27 #6063, 0.26 #3667, 0.24 #3268) >> Best rule #4794 for best value: >> intensional similarity = 6 >> extensional distance = 200 >> proper extension: 0l56b; >> query: (?x11749, ?x2634) <- award_winner(?x2634, ?x11749), award(?x2784, ?x2634), award(?x2395, ?x2634), role(?x2784, ?x212), ?x2395 = 0dvqq, profession(?x2784, ?x131) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #3878 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 175 *> proper extension: 08f3b1; 01s3kv; 01j7z7; *> query: (?x11749, 02f73b) <- award(?x11749, ?x2855), category(?x11749, ?x134), award(?x8693, ?x2855), ?x8693 = 0bdxs5 *> conf = 0.29 ranks of expected_values: 8 EVAL 016t0h award 02f73b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 76.000 49.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5722-053yx PRED entity: 053yx PRED relation: nationality PRED expected values: 09c7w0 => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 51): 09c7w0 (0.81 #4704, 0.78 #6208, 0.76 #2401), 02jx1 (0.43 #1733, 0.29 #1633, 0.28 #3033), 0nv99 (0.33 #16732), 03v0t (0.33 #16732), 07ssc (0.21 #3015, 0.20 #1015, 0.19 #2715), 03rk0 (0.17 #46, 0.12 #246, 0.08 #646), 06m_5 (0.17 #83, 0.12 #283, 0.08 #683), 0345h (0.12 #1131, 0.12 #5537, 0.08 #731), 0h7x (0.12 #135, 0.11 #3535, 0.10 #535), 0d060g (0.12 #107, 0.10 #507, 0.06 #5914) >> Best rule #4704 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 07_grx; >> query: (?x2835, 09c7w0) <- people(?x1158, ?x2835), award_nominee(?x6382, ?x2835), place_of_birth(?x2835, ?x13979) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 053yx nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.812 http://example.org/people/person/nationality #5721-01w5n51 PRED entity: 01w5n51 PRED relation: origin PRED expected values: 01p726 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 98): 0d6lp (0.17 #1709, 0.07 #2414, 0.07 #4295), 0r3tb (0.15 #138, 0.02 #3193, 0.01 #4369), 04jpl (0.14 #1651, 0.13 #241, 0.12 #2356), 030qb3t (0.14 #9200, 0.13 #9670, 0.11 #1679), 02_286 (0.13 #9182, 0.12 #9652, 0.09 #1661), 052bw (0.08 #145, 0.04 #4376, 0.03 #1790), 0nbwf (0.08 #140, 0.04 #1550, 0.03 #1785), 0fvzg (0.08 #58, 0.04 #1468, 0.03 #1703), 09c7w0 (0.08 #1, 0.03 #1646, 0.02 #2351), 01sn3 (0.08 #77, 0.02 #2897, 0.02 #7128) >> Best rule #1709 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 05crg7; 06br6t; >> query: (?x7612, 0d6lp) <- artists(?x2809, ?x7612), ?x2809 = 05w3f, group(?x227, ?x7612) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w5n51 origin 01p726 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 86.000 0.171 http://example.org/music/artist/origin #5720-0bz3jx PRED entity: 0bz3jx PRED relation: country PRED expected values: 0345h => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 74): 09c7w0 (0.87 #5183, 0.86 #1001, 0.86 #4180), 07ssc (0.52 #897, 0.47 #425, 0.43 #585), 02jx1 (0.43 #585, 0.37 #3358, 0.37 #5593), 03spz (0.43 #585, 0.37 #3358, 0.37 #5593), 0345h (0.34 #2911, 0.29 #200, 0.15 #552), 064_8sq (0.31 #939, 0.30 #467, 0.10 #292), 03_3d (0.25 #66, 0.16 #534, 0.15 #2893), 03rjj (0.17 #123, 0.12 #240, 0.11 #415), 06c1y (0.17 #149, 0.02 #501, 0.02 #559), 06mkj (0.16 #447, 0.07 #468, 0.05 #940) >> Best rule #5183 for best value: >> intensional similarity = 4 >> extensional distance = 1566 >> proper extension: 05hd32; >> query: (?x6450, 09c7w0) <- country(?x6450, ?x3040), country(?x1557, ?x3040), taxonomy(?x3040, ?x939), contains(?x455, ?x3040) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #2911 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 459 *> proper extension: 02vl9ln; *> query: (?x6450, 0345h) <- country(?x6450, ?x3040), country(?x1557, ?x3040), taxonomy(?x3040, ?x939), administrative_parent(?x3040, ?x551) *> conf = 0.34 ranks of expected_values: 5 EVAL 0bz3jx country 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 99.000 99.000 0.874 http://example.org/film/film/country #5719-02y_rq5 PRED entity: 02y_rq5 PRED relation: nominated_for PRED expected values: 01jc6q 047n8xt 0_b9f 01jw67 01fwzk => 45 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1410): 0hv4t (0.78 #13337, 0.78 #5631, 0.70 #7172), 011yhm (0.73 #10242, 0.70 #7160, 0.67 #5619), 0gmcwlb (0.72 #12508, 0.67 #4802, 0.60 #6343), 0f4_l (0.72 #12637, 0.56 #4931, 0.50 #6472), 0m313 (0.69 #10800, 0.67 #4636, 0.64 #9259), 09gq0x5 (0.67 #12576, 0.62 #11034, 0.60 #6411), 011yqc (0.67 #4825, 0.61 #12531, 0.60 #6366), 03hj3b3 (0.67 #4891, 0.61 #12597, 0.60 #6432), 07xtqq (0.67 #4674, 0.61 #12380, 0.50 #10838), 0pv3x (0.67 #4785, 0.61 #12491, 0.50 #10949) >> Best rule #13337 for best value: >> intensional similarity = 7 >> extensional distance = 16 >> proper extension: 027dtxw; 0f4x7; 0gq9h; 0gqyl; 02w9sd7; >> query: (?x1716, 0hv4t) <- nominated_for(?x1716, ?x10049), nominated_for(?x1716, ?x6013), nominated_for(?x1716, ?x1230), ?x6013 = 04j13sx, award(?x241, ?x1716), genre(?x10049, ?x53), film(?x1678, ?x1230) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #11495 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 02qvyrt; 02qyntr; *> query: (?x1716, 0_b9f) <- nominated_for(?x1716, ?x6013), nominated_for(?x1716, ?x1230), award(?x6958, ?x1716), type_of_union(?x6958, ?x566), ?x1230 = 026390q, genre(?x6013, ?x53) *> conf = 0.56 ranks of expected_values: 31, 50, 84, 98, 623 EVAL 02y_rq5 nominated_for 01fwzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 45.000 18.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02y_rq5 nominated_for 01jw67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 45.000 18.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02y_rq5 nominated_for 0_b9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 45.000 18.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02y_rq5 nominated_for 047n8xt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 18.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02y_rq5 nominated_for 01jc6q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 45.000 18.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5718-03rt9 PRED entity: 03rt9 PRED relation: nationality! PRED expected values: 065jlv 010xjr => 140 concepts (115 used for prediction) PRED predicted values (max 10 best out of 4102): 02c4s (0.43 #157076, 0.10 #4432, 0.09 #16513), 0170qf (0.41 #306095, 0.36 #52358, 0.35 #136937), 010xjr (0.41 #306095), 065jlv (0.41 #306095), 09d5d5 (0.36 #52358, 0.35 #136937, 0.10 #10752), 01ps2h8 (0.36 #52358, 0.35 #136937, 0.10 #9666), 026fd (0.36 #52358, 0.35 #136937, 0.05 #25982), 0l12d (0.36 #52358, 0.35 #136937, 0.05 #338315), 024rbz (0.36 #52358, 0.35 #136937), 02d42t (0.35 #136937, 0.10 #9534, 0.10 #5507) >> Best rule #157076 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 0dv0z; 01fvhp; 01s47p; 0gtzp; >> query: (?x429, ?x585) <- capital(?x429, ?x6357), place_of_birth(?x585, ?x6357), location(?x489, ?x6357) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #306095 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 115 *> proper extension: 07cfx; 0jcg8; 0chgr2; 075_t2; 0msyb; 036wy; 06mtq; *> query: (?x429, ?x6424) <- contains(?x429, ?x3198), jurisdiction_of_office(?x346, ?x429), location(?x6424, ?x3198) *> conf = 0.41 ranks of expected_values: 3, 4 EVAL 03rt9 nationality! 010xjr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 115.000 0.434 http://example.org/people/person/nationality EVAL 03rt9 nationality! 065jlv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 115.000 0.434 http://example.org/people/person/nationality #5717-06mz5 PRED entity: 06mz5 PRED relation: contains PRED expected values: 0_rwf => 167 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2735): 01s7pm (0.12 #1992, 0.06 #19656, 0.04 #34375), 0bwfn (0.12 #3990, 0.11 #6934, 0.06 #65814), 021q2j (0.12 #4204, 0.11 #7148, 0.06 #18925), 03bmmc (0.12 #3720, 0.11 #6664, 0.06 #18441), 02lwv5 (0.12 #4687, 0.11 #7631, 0.06 #19408), 0ccvx (0.12 #3488, 0.11 #6432, 0.06 #18209), 04ftdq (0.12 #4188, 0.11 #7132, 0.06 #18909), 01t0dy (0.12 #3789, 0.11 #6733, 0.06 #18510), 09k9d0 (0.12 #4915, 0.11 #7859, 0.06 #19636), 01p7x7 (0.12 #4779, 0.11 #7723, 0.06 #19500) >> Best rule #1992 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0fr0t; 0qpn9; 0qpjt; 0nlh7; 0qplq; >> query: (?x1351, 01s7pm) <- jurisdiction_of_office(?x900, ?x1351), time_zones(?x1351, ?x2088), ?x2088 = 02hczc >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #70655 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 0978r; *> query: (?x1351, ?x94) <- time_zones(?x1351, ?x2088), state_province_region(?x1350, ?x1351), time_zones(?x94, ?x2088) *> conf = 0.04 ranks of expected_values: 727 EVAL 06mz5 contains 0_rwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 167.000 105.000 0.125 http://example.org/location/location/contains #5716-01p8s PRED entity: 01p8s PRED relation: organization PRED expected values: 02vk52z 04k4l => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 51): 02vk52z (0.89 #1452, 0.86 #1590, 0.86 #1405), 04k4l (0.61 #185, 0.58 #2004, 0.45 #120), 0b6css (0.46 #11, 0.43 #380, 0.42 #196), 0_2v (0.46 #4, 0.40 #442, 0.40 #27), 041288 (0.45 #524, 0.39 #1306, 0.39 #1260), 01rz1 (0.45 #371, 0.41 #440, 0.41 #647), 018cqq (0.42 #173, 0.38 #12, 0.35 #127), 0j7v_ (0.38 #6, 0.32 #2398, 0.29 #214), 0gkjy (0.38 #515, 0.32 #2398, 0.30 #1205), 02jxk (0.32 #2398, 0.28 #372, 0.27 #441) >> Best rule #1452 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 01z88t; 088xp; 05qkp; 04w58; 04gqr; 01p1b; 04tr1; 036b_; 04xn_; 01699; ... >> query: (?x9730, 02vk52z) <- adjoins(?x9730, ?x1475), country(?x1121, ?x9730), ?x1121 = 0bynt >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01p8s organization 04k4l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.890 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 01p8s organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.890 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #5715-04qt29 PRED entity: 04qt29 PRED relation: location PRED expected values: 02_286 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 286): 02_286 (0.33 #20091, 0.28 #23301, 0.28 #24103), 059rby (0.15 #16, 0.06 #4830, 0.06 #5632), 04jpl (0.12 #20072, 0.10 #23282, 0.10 #24084), 0cr3d (0.10 #945, 0.08 #28220, 0.08 #29023), 01n7q (0.08 #62, 0.06 #864, 0.06 #2468), 05qtj (0.08 #239, 0.05 #36100, 0.04 #20294), 0cv3w (0.08 #157, 0.05 #36100, 0.03 #28880), 0n95v (0.08 #592, 0.03 #1394, 0.02 #56960), 0d0x8 (0.08 #159, 0.02 #56960, 0.02 #2565), 0v9qg (0.08 #208, 0.02 #56960, 0.02 #3416) >> Best rule #20091 for best value: >> intensional similarity = 3 >> extensional distance = 1053 >> proper extension: 0184jc; 032xhg; 0h5g_; 02nb2s; 01lbp; 04jzj; 03f1zdw; 0170pk; 04zwjd; 0136p1; ... >> query: (?x9085, 02_286) <- location(?x9085, ?x1523), featured_film_locations(?x83, ?x1523), film_release_region(?x204, ?x1523) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04qt29 location 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.332 http://example.org/people/person/places_lived./people/place_lived/location #5714-0bqsy PRED entity: 0bqsy PRED relation: artists! PRED expected values: 02qdgx 05bt6j 0gywn 02k_kn 07d2d => 136 concepts (62 used for prediction) PRED predicted values (max 10 best out of 224): 0gywn (0.52 #5782, 0.45 #6083, 0.44 #3975), 05bt6j (0.40 #2455, 0.33 #3059, 0.33 #7879), 01lyv (0.34 #3353, 0.31 #3654, 0.28 #7267), 02vjzr (0.31 #3449, 0.29 #3750, 0.20 #2543), 05w3f (0.29 #338, 0.18 #9685, 0.11 #14813), 08jyyk (0.29 #363, 0.15 #9409, 0.15 #9710), 0ggq0m (0.29 #313, 0.09 #18408, 0.07 #16296), 0xhtw (0.28 #9664, 0.23 #14491, 0.22 #12981), 0y3_8 (0.24 #2458, 0.21 #9391, 0.16 #6074), 03_d0 (0.24 #15691, 0.21 #14184, 0.19 #3331) >> Best rule #5782 for best value: >> intensional similarity = 4 >> extensional distance = 106 >> proper extension: 05mt_q; 07ss8_; 047sxrj; 01trhmt; 01vx5w7; 016pns; 01w806h; 01svw8n; 025ldg; 049qx; ... >> query: (?x4062, 0gywn) <- artists(?x3562, ?x4062), award(?x4062, ?x1232), artist(?x648, ?x4062), ?x3562 = 025sc50 >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 19, 35, 81 EVAL 0bqsy artists! 07d2d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 136.000 62.000 0.519 http://example.org/music/genre/artists EVAL 0bqsy artists! 02k_kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 136.000 62.000 0.519 http://example.org/music/genre/artists EVAL 0bqsy artists! 0gywn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 62.000 0.519 http://example.org/music/genre/artists EVAL 0bqsy artists! 05bt6j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 62.000 0.519 http://example.org/music/genre/artists EVAL 0bqsy artists! 02qdgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 136.000 62.000 0.519 http://example.org/music/genre/artists #5713-02mpb PRED entity: 02mpb PRED relation: location PRED expected values: 0s5cg => 155 concepts (141 used for prediction) PRED predicted values (max 10 best out of 254): 0d6lp (0.25 #168, 0.20 #973, 0.03 #25942), 01x73 (0.25 #96, 0.20 #901, 0.02 #15403), 09c7w0 (0.20 #2416, 0.14 #6442, 0.13 #4029), 0281rb (0.20 #3223, 0.10 #4028, 0.05 #6443), 01cx_ (0.20 #968, 0.06 #4996, 0.04 #13861), 0281y0 (0.20 #25773, 0.20 #32214, 0.19 #33827), 02_286 (0.18 #34668, 0.16 #40302, 0.16 #11317), 07ssc (0.14 #1637, 0.10 #3249, 0.05 #5663), 06m_5 (0.14 #2014, 0.10 #3626, 0.05 #6040), 0sbbq (0.14 #2021, 0.10 #3633, 0.03 #43487) >> Best rule #168 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03d_zl4; 021r6w; >> query: (?x8210, 0d6lp) <- place_of_death(?x8210, ?x7769), student(?x13219, ?x8210), people(?x4322, ?x8210), ?x7769 = 0281y0 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #39717 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 184 *> proper extension: 0k4gf; 02n9k; 034ks; *> query: (?x8210, 0s5cg) <- influenced_by(?x5334, ?x8210), influenced_by(?x8210, ?x3542), nationality(?x8210, ?x94), type_of_union(?x8210, ?x566) *> conf = 0.01 ranks of expected_values: 252 EVAL 02mpb location 0s5cg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 155.000 141.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #5712-04cf09 PRED entity: 04cf09 PRED relation: location PRED expected values: 02jx1 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 66): 030qb3t (0.23 #1686, 0.22 #7302, 0.21 #4896), 0k049 (0.17 #8, 0.03 #5624, 0.03 #3218), 094jv (0.17 #92, 0.02 #37721, 0.01 #15338), 0d6lp (0.14 #968, 0.03 #1770, 0.03 #2573), 0vp5f (0.14 #1487), 0gyh (0.14 #943), 0r0m6 (0.07 #1820, 0.05 #2623, 0.04 #3426), 0cr3d (0.06 #15389, 0.06 #1747, 0.05 #51507), 04jpl (0.05 #50578, 0.05 #45764, 0.05 #56995), 0cc56 (0.05 #4068, 0.05 #1660, 0.05 #6474) >> Best rule #1686 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 01sb5r; 01817f; 094xh; 07jrjb; 01vxqyl; 0d3k14; 01q8fxx; >> query: (?x1205, 030qb3t) <- nationality(?x1205, ?x94), celebrity(?x4126, ?x1205), location(?x1205, ?x739) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #6488 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 388 *> proper extension: 01xyt7; *> query: (?x1205, 02jx1) <- participant(?x1205, ?x4126), place_of_birth(?x1205, ?x2680) *> conf = 0.02 ranks of expected_values: 35 EVAL 04cf09 location 02jx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 88.000 88.000 0.228 http://example.org/people/person/places_lived./people/place_lived/location #5711-0c1j_ PRED entity: 0c1j_ PRED relation: award PRED expected values: 063y_ky => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 260): 09sb52 (0.34 #14839, 0.33 #15639, 0.27 #18039), 026mg3 (0.33 #411, 0.02 #10011, 0.02 #10411), 05pcn59 (0.31 #880, 0.19 #2080, 0.18 #5680), 03c7tr1 (0.29 #1657, 0.19 #857, 0.14 #1257), 0fbtbt (0.27 #2629, 0.15 #4229, 0.13 #4629), 0cjyzs (0.23 #2505, 0.17 #4105, 0.14 #4505), 05ztrmj (0.21 #983, 0.12 #2183, 0.10 #6183), 0gqwc (0.20 #1673, 0.12 #10873, 0.09 #2073), 05zr6wv (0.19 #816, 0.16 #2016, 0.15 #6016), 01by1l (0.18 #10111, 0.15 #12111, 0.15 #10511) >> Best rule #14839 for best value: >> intensional similarity = 3 >> extensional distance = 1166 >> proper extension: 02xb2bt; 0bl60p; 012g92; >> query: (?x10754, 09sb52) <- award(?x10754, ?x154), award_nominee(?x10754, ?x4259), film(?x10754, ?x7800) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #930 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 02qjj7; *> query: (?x10754, 063y_ky) <- profession(?x10754, ?x1041), ?x1041 = 03gjzk, vacationer(?x126, ?x10754) *> conf = 0.07 ranks of expected_values: 82 EVAL 0c1j_ award 063y_ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 98.000 98.000 0.341 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5710-0b6m5fy PRED entity: 0b6m5fy PRED relation: film! PRED expected values: 03n52j => 126 concepts (70 used for prediction) PRED predicted values (max 10 best out of 648): 02kxwk (0.42 #14562, 0.42 #116524, 0.41 #141495), 0171cm (0.25 #425, 0.12 #6666, 0.12 #8746), 02fx3c (0.25 #610, 0.07 #2690, 0.06 #6851), 08_438 (0.25 #2048, 0.06 #20771, 0.06 #8289), 03ym1 (0.25 #1011, 0.06 #7252, 0.06 #5172), 0l6px (0.25 #388, 0.06 #6629, 0.06 #4549), 01tspc6 (0.25 #163, 0.06 #6404, 0.06 #4324), 015vq_ (0.25 #714, 0.06 #6955, 0.06 #4875), 01jw4r (0.25 #1494, 0.06 #7735, 0.06 #5655), 01tzm9 (0.25 #1284, 0.06 #7525, 0.06 #5445) >> Best rule #14562 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 09fc83; 04sskp; >> query: (?x6375, ?x1031) <- nominated_for(?x1031, ?x6375), country_of_origin(?x6375, ?x94), film(?x3649, ?x6375), nominated_for(?x375, ?x6375) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0b6m5fy film! 03n52j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 70.000 0.424 http://example.org/film/actor/film./film/performance/film #5709-02x8mt PRED entity: 02x8mt PRED relation: people! PRED expected values: 0qcr0 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 27): 02knxx (0.12 #32, 0.03 #98, 0.03 #164), 0gk4g (0.07 #1330, 0.07 #1396, 0.05 #1198), 012hw (0.05 #184, 0.04 #316, 0.04 #448), 0qcr0 (0.04 #925, 0.04 #265, 0.04 #397), 02y0js (0.04 #332, 0.03 #68, 0.03 #530), 0dq9p (0.04 #1337, 0.03 #1403, 0.03 #1205), 0148xv (0.03 #132, 0.03 #198, 0.02 #330), 0gg4h (0.03 #1026, 0.02 #1158, 0.02 #1290), 01psyx (0.03 #1365, 0.03 #1431, 0.02 #243), 02k6hp (0.03 #169, 0.02 #1555, 0.02 #301) >> Best rule #32 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01wg982; 06g2d1; 0gs1_; 01gbb4; 01mqnr; 0488g9; >> query: (?x9353, 02knxx) <- student(?x5981, ?x9353), ?x5981 = 03bmmc, gender(?x9353, ?x231), ?x231 = 05zppz >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #925 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 01j5ts; 018pj3; 01w1kyf; *> query: (?x9353, 0qcr0) <- sibling(?x9352, ?x9353), nationality(?x9353, ?x94), gender(?x9353, ?x231), ?x94 = 09c7w0 *> conf = 0.04 ranks of expected_values: 4 EVAL 02x8mt people! 0qcr0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 84.000 84.000 0.125 http://example.org/people/cause_of_death/people #5708-05c26ss PRED entity: 05c26ss PRED relation: film_regional_debut_venue PRED expected values: 0prpt => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 19): 04jpl (0.12 #103, 0.10 #138, 0.09 #173), 018cvf (0.10 #874, 0.10 #945, 0.10 #981), 0prpt (0.08 #957, 0.08 #993, 0.08 #1028), 015hr (0.07 #872, 0.07 #943, 0.07 #979), 01ly5m (0.06 #146, 0.06 #181, 0.06 #111), 030qb3t (0.06 #106, 0.03 #141, 0.03 #176), 0fhzf (0.06 #129, 0.03 #164, 0.03 #199), 056_y (0.06 #115, 0.03 #150, 0.03 #185), 07751 (0.03 #867, 0.03 #938, 0.03 #147), 0kfhjq0 (0.03 #873, 0.03 #944, 0.03 #980) >> Best rule #103 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 08hmch; 053rxgm; 05qbckf; 0gd0c7x; 0407yj_; 0gffmn8; 09g7vfw; 0bpm4yw; 017jd9; 0dlngsd; ... >> query: (?x3839, 04jpl) <- film_release_region(?x3839, ?x2000), film_release_region(?x3839, ?x1790), film_release_region(?x3839, ?x1471), ?x1471 = 07t21, ?x1790 = 01pj7, ?x2000 = 0d0kn >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #957 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 493 *> proper extension: 03cvwkr; 02qpt1w; 0h7t36; *> query: (?x3839, 0prpt) <- film_release_region(?x3839, ?x1471), administrative_parent(?x1471, ?x551), nationality(?x558, ?x1471) *> conf = 0.08 ranks of expected_values: 3 EVAL 05c26ss film_regional_debut_venue 0prpt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 82.000 0.125 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #5707-058m5m4 PRED entity: 058m5m4 PRED relation: ceremony! PRED expected values: 0cqhk0 09td7p 02py7pj => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 285): 09td7p (0.71 #1335, 0.44 #2585, 0.33 #3338), 02py7pj (0.71 #1457, 0.44 #2707, 0.33 #3460), 0cqhk0 (0.71 #1276, 0.33 #1026, 0.33 #524), 0gqy2 (0.49 #5372, 0.43 #6627, 0.43 #5873), 0gq_d (0.47 #5408, 0.42 #5909, 0.42 #6913), 0k611 (0.47 #5323, 0.42 #6578, 0.41 #5824), 0gqwc (0.47 #5311, 0.42 #6566, 0.41 #5812), 0gvx_ (0.47 #5387, 0.42 #6642, 0.41 #5888), 018wng (0.47 #5287, 0.41 #5788, 0.41 #5538), 0f4x7 (0.47 #5279, 0.41 #6534, 0.41 #5780) >> Best rule #1335 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 09qvms; >> query: (?x3609, 09td7p) <- award_winner(?x3609, ?x11447), award_winner(?x3609, ?x4508), award_winner(?x3609, ?x906), ceremony(?x618, ?x3609), award(?x4508, ?x2041), award_winner(?x4508, ?x5041), gender(?x5041, ?x231), nominated_for(?x906, ?x2528), profession(?x4508, ?x1032), location(?x4508, ?x9605), language(?x906, ?x254), award_winner(?x906, ?x829), ?x618 = 09qwmm, nationality(?x11447, ?x2146) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 058m5m4 ceremony! 02py7pj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 058m5m4 ceremony! 09td7p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 058m5m4 ceremony! 0cqhk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony #5706-05l4yg PRED entity: 05l4yg PRED relation: actor! PRED expected values: 0g60z => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 79): 0g60z (0.55 #269, 0.04 #1331, 0.02 #14332), 02gjrc (0.10 #227, 0.08 #757, 0.04 #1554), 06dfz1 (0.10 #167, 0.08 #697, 0.02 #2554), 032xky (0.10 #232, 0.08 #762), 0275kr (0.10 #214, 0.08 #744), 0330r (0.10 #189, 0.08 #719), 02py4c8 (0.09 #277, 0.04 #1339, 0.02 #1869), 0124k9 (0.09 #286, 0.03 #1878, 0.02 #2408), 02qkq0 (0.09 #390, 0.02 #14332, 0.02 #14863), 014gjp (0.09 #408, 0.02 #14332, 0.02 #14863) >> Best rule #269 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 040t74; >> query: (?x6791, 0g60z) <- award_nominee(?x6791, ?x820), ?x820 = 04bd8y, student(?x7271, ?x6791) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05l4yg actor! 0g60z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.545 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #5705-02gpkt PRED entity: 02gpkt PRED relation: film! PRED expected values: 0k525 => 95 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1057): 014v6f (0.17 #966, 0.07 #3045, 0.03 #13444), 01kwsg (0.17 #836, 0.06 #7076, 0.02 #32025), 05vsxz (0.17 #9, 0.03 #2088, 0.03 #81111), 05mc99 (0.17 #1317, 0.03 #7557, 0.03 #9637), 023kzp (0.17 #1054, 0.03 #5214, 0.03 #81111), 027bs_2 (0.17 #1276, 0.03 #7516, 0.03 #81111), 043js (0.17 #451, 0.03 #4611, 0.01 #31640), 06dv3 (0.17 #32, 0.03 #81111, 0.01 #54105), 04v7kt (0.17 #1984, 0.03 #81111, 0.01 #14462), 04qsdh (0.17 #1401, 0.03 #81111, 0.01 #13879) >> Best rule #966 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0cz_ym; 093dqjy; >> query: (?x7541, 014v6f) <- nominated_for(?x401, ?x7541), film(?x818, ?x7541), ?x818 = 0785v8, genre(?x7541, ?x225) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #6003 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 04kzqz; *> query: (?x7541, 0k525) <- music(?x7541, ?x1940), film(?x2549, ?x7541), genre(?x7541, ?x225), ?x2549 = 024rgt *> conf = 0.03 ranks of expected_values: 349 EVAL 02gpkt film! 0k525 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 95.000 46.000 0.167 http://example.org/film/actor/film./film/performance/film #5704-0416y94 PRED entity: 0416y94 PRED relation: featured_film_locations PRED expected values: 02_286 => 92 concepts (83 used for prediction) PRED predicted values (max 10 best out of 61): 02_286 (0.30 #2652, 0.19 #498, 0.17 #4091), 030qb3t (0.12 #2671, 0.10 #4829, 0.10 #995), 04jpl (0.10 #2641, 0.09 #726, 0.08 #248), 01_d4 (0.07 #764, 0.05 #2679, 0.03 #286), 06y57 (0.06 #1537, 0.05 #2496, 0.04 #2256), 03rjj (0.05 #245, 0.05 #723, 0.02 #2638), 0rh6k (0.05 #2633, 0.04 #5272, 0.04 #5991), 080h2 (0.04 #5295, 0.03 #4814, 0.03 #10816), 03gh4 (0.03 #593, 0.01 #9949, 0.01 #4425), 0ctw_b (0.03 #501, 0.01 #2655, 0.01 #5533) >> Best rule #2652 for best value: >> intensional similarity = 4 >> extensional distance = 174 >> proper extension: 0bm2g; 0ptxj; >> query: (?x1318, 02_286) <- nominated_for(?x1774, ?x1318), honored_for(?x6108, ?x1318), film(?x2736, ?x1318), featured_film_locations(?x1318, ?x12738) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0416y94 featured_film_locations 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 83.000 0.295 http://example.org/film/film/featured_film_locations #5703-02yy_j PRED entity: 02yy_j PRED relation: award PRED expected values: 02x4wr9 => 114 concepts (99 used for prediction) PRED predicted values (max 10 best out of 352): 0gr0m (0.42 #3315, 0.42 #4125, 0.30 #1695), 0gkvb7 (0.39 #27, 0.28 #2458, 0.18 #2863), 019bnn (0.28 #269, 0.19 #2700, 0.17 #5535), 02rdyk7 (0.26 #1713, 0.17 #12561, 0.14 #1621), 0gs9p (0.22 #1701, 0.20 #15881, 0.17 #12561), 02x258x (0.22 #1750, 0.09 #4180, 0.08 #3370), 0gr51 (0.19 #15902, 0.17 #1316, 0.14 #5773), 0gq9h (0.18 #15879, 0.17 #12561, 0.14 #1621), 040njc (0.18 #15809, 0.17 #12561, 0.14 #1621), 019f4v (0.17 #15868, 0.17 #12561, 0.14 #1621) >> Best rule #3315 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 04qvl7; 0gp9mp; 0dqzkv; 02rgz97; 07xr3w; 06g60w; 0bqytm; 0627sn; 087yty; 087v17; ... >> query: (?x9468, 0gr0m) <- place_of_birth(?x9468, ?x1705), gender(?x9468, ?x231), profession(?x9468, ?x2265), ?x2265 = 0dgd_ >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #1758 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 02kxbx3; 06r_by; 0164w8; 01qg7c; 0405l; *> query: (?x9468, 02x4wr9) <- award(?x9468, ?x2902), student(?x7545, ?x9468), profession(?x9468, ?x2265), ?x2265 = 0dgd_ *> conf = 0.13 ranks of expected_values: 30 EVAL 02yy_j award 02x4wr9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 114.000 99.000 0.420 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5702-0cbgl PRED entity: 0cbgl PRED relation: profession PRED expected values: 016fly => 170 concepts (141 used for prediction) PRED predicted values (max 10 best out of 101): 01d_h8 (0.72 #19245, 0.48 #5820, 0.46 #6566), 02hrh1q (0.69 #17167, 0.69 #16569, 0.68 #5828), 0kyk (0.56 #3608, 0.53 #1819, 0.50 #1521), 0dxtg (0.50 #7468, 0.50 #8066, 0.49 #9111), 02jknp (0.40 #7606, 0.39 #6710, 0.35 #19246), 03gjzk (0.40 #7606, 0.39 #6710, 0.33 #612), 09jwl (0.40 #7606, 0.39 #6710, 0.33 #11336), 02krf9 (0.40 #7606, 0.39 #6710, 0.33 #11336), 01c72t (0.40 #7606, 0.39 #6710, 0.33 #11336), 0dgd_ (0.40 #7606, 0.39 #6710, 0.33 #11336) >> Best rule #19245 for best value: >> intensional similarity = 3 >> extensional distance = 1356 >> proper extension: 021wpb; >> query: (?x14008, 01d_h8) <- profession(?x14008, ?x353), profession(?x13348, ?x353), ?x13348 = 027hq5f >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #3057 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 0klh7; 01xcr4; *> query: (?x14008, 016fly) <- company(?x14008, ?x8525), award_winner(?x12729, ?x14008), student(?x3437, ?x14008), location(?x14008, ?x3818) *> conf = 0.25 ranks of expected_values: 18 EVAL 0cbgl profession 016fly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 170.000 141.000 0.718 http://example.org/people/person/profession #5701-0bq3x PRED entity: 0bq3x PRED relation: films PRED expected values: 033qdy => 50 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1461): 011yxg (0.18 #15, 0.16 #1036, 0.14 #3077), 0hfzr (0.15 #2240, 0.14 #2750, 0.12 #199), 03s9kp (0.15 #2539, 0.11 #1519, 0.10 #2030), 02yvct (0.14 #4692, 0.11 #612, 0.10 #8262), 09g8vhw (0.14 #3061, 0.06 #1532, 0.01 #13262), 06_wqk4 (0.14 #3061, 0.06 #1532, 0.01 #13262), 02q7yfq (0.14 #3061, 0.06 #1532), 01s9vc (0.14 #3061, 0.03 #15814, 0.03 #15304), 05css_ (0.14 #3061, 0.03 #15814, 0.03 #15304), 0k0rf (0.14 #3061, 0.03 #15814, 0.03 #15304) >> Best rule #15 for best value: >> intensional similarity = 12 >> extensional distance = 15 >> proper extension: 038_l; 02w1b8; >> query: (?x3530, 011yxg) <- films(?x3530, ?x7283), films(?x3530, ?x4799), films(?x3530, ?x2098), film(?x6618, ?x4799), film(?x2549, ?x4799), region(?x2098, ?x512), honored_for(?x2245, ?x2098), nominated_for(?x1441, ?x7283), film_crew_role(?x2098, ?x468), titles(?x812, ?x2098), award_winner(?x1441, ?x396), award(?x516, ?x1441) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #15304 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 66 *> proper extension: 0jrg; *> query: (?x3530, ?x1048) <- films(?x3530, ?x7012), films(?x3530, ?x6005), films(?x3530, ?x4799), films(?x3530, ?x2098), film(?x7585, ?x4799), film(?x2549, ?x4799), film_crew_role(?x2098, ?x468), nominated_for(?x749, ?x2098), film(?x609, ?x2098), film(?x875, ?x6005), film(?x7585, ?x1048), music(?x4799, ?x3910), film_crew_role(?x6005, ?x1171), film_release_region(?x7012, ?x94) *> conf = 0.03 ranks of expected_values: 675 EVAL 0bq3x films 033qdy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 32.000 0.176 http://example.org/film/film_subject/films #5700-03t8v3 PRED entity: 03t8v3 PRED relation: award PRED expected values: 03rbj2 => 81 concepts (79 used for prediction) PRED predicted values (max 10 best out of 315): 03r8v_ (0.39 #2374, 0.32 #1968, 0.30 #1156), 03rbj2 (0.38 #2662, 0.30 #1038, 0.29 #1444), 03r8tl (0.35 #918, 0.33 #1324, 0.32 #1730), 09sb52 (0.30 #9786, 0.27 #6132, 0.25 #7756), 05pcn59 (0.24 #6173, 0.16 #8203, 0.16 #9015), 05b4l5x (0.23 #3661, 0.22 #413, 0.19 #4067), 03c7tr1 (0.22 #466, 0.20 #3714, 0.20 #4120), 05p09zm (0.19 #3780, 0.15 #6216, 0.15 #4186), 05zr6wv (0.18 #6514, 0.18 #6108, 0.13 #8950), 0gqy2 (0.15 #9911, 0.12 #13161, 0.10 #6663) >> Best rule #2374 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 03fwln; >> query: (?x13784, 03r8v_) <- film(?x13784, ?x5247), nationality(?x13784, ?x2146), profession(?x13784, ?x319), ?x5247 = 0f42nz >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #2662 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 51 *> proper extension: 0265z9l; 081hvm; *> query: (?x13784, 03rbj2) <- film(?x13784, ?x5247), nationality(?x13784, ?x2146), ?x2146 = 03rk0, gender(?x13784, ?x231), titles(?x1882, ?x5247) *> conf = 0.38 ranks of expected_values: 2 EVAL 03t8v3 award 03rbj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 79.000 0.393 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5699-01_6dw PRED entity: 01_6dw PRED relation: religion PRED expected values: 03_gx => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 19): 03_gx (0.25 #104, 0.13 #1005, 0.13 #959), 0kpl (0.18 #1001, 0.16 #955, 0.16 #1046), 0c8wxp (0.13 #1987, 0.13 #1357, 0.13 #1537), 0kq2 (0.04 #1009, 0.04 #963, 0.04 #153), 04pk9 (0.04 #155, 0.01 #1011, 0.01 #650), 03j6c (0.03 #201, 0.03 #291, 0.03 #381), 05sfs (0.03 #183, 0.01 #498), 0n2g (0.03 #1004, 0.03 #1049, 0.03 #1139), 01lp8 (0.03 #496, 0.03 #316, 0.03 #271), 092bf5 (0.03 #331, 0.03 #286, 0.03 #376) >> Best rule #104 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 07h1q; >> query: (?x6534, 03_gx) <- influenced_by(?x6534, ?x1645), ?x1645 = 017r2, place_of_birth(?x6534, ?x1131) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_6dw religion 03_gx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.250 http://example.org/people/person/religion #5698-02bkdn PRED entity: 02bkdn PRED relation: award_nominee! PRED expected values: 07ddz9 01cwkq => 76 concepts (35 used for prediction) PRED predicted values (max 10 best out of 611): 048lv (0.81 #80271, 0.81 #22933, 0.81 #57336), 01ggc9 (0.81 #80271, 0.81 #22933, 0.81 #57336), 040t74 (0.81 #80271, 0.81 #22933, 0.81 #57336), 02s2ft (0.81 #80271, 0.81 #22933, 0.81 #57336), 01cwkq (0.81 #80271, 0.81 #22933, 0.81 #57336), 01kb2j (0.81 #80271, 0.81 #22933, 0.81 #57336), 011_3s (0.81 #80271, 0.81 #22933, 0.81 #57336), 01gq0b (0.81 #80271, 0.81 #22933, 0.81 #57336), 01jz6x (0.81 #80271, 0.81 #22933, 0.81 #57336), 07ddz9 (0.81 #80271, 0.81 #22933, 0.81 #57336) >> Best rule #80271 for best value: >> intensional similarity = 3 >> extensional distance = 1434 >> proper extension: 01sl1q; 044mz_; 0184jc; 04bdxl; 02s2ft; 05vsxz; 0dbpyd; 01vvydl; 07fq1y; 02qgqt; ... >> query: (?x1871, ?x92) <- award_winner(?x1871, ?x820), award_nominee(?x1871, ?x92), award_nominee(?x624, ?x1871) >> conf = 0.81 => this is the best rule for 12 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 10 EVAL 02bkdn award_nominee! 01cwkq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 76.000 35.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 02bkdn award_nominee! 07ddz9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 76.000 35.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5697-010bxh PRED entity: 010bxh PRED relation: time_zones PRED expected values: 02fqwt => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 9): 02fqwt (0.79 #53, 0.69 #14, 0.68 #27), 02hcv8 (0.44 #797, 0.44 #810, 0.43 #120), 02lcqs (0.38 #447, 0.32 #538, 0.29 #135), 02hczc (0.33 #2, 0.25 #834, 0.20 #716), 02llzg (0.08 #134, 0.07 #160, 0.06 #147), 03bdv (0.04 #957, 0.04 #84, 0.04 #918), 042g7t (0.02 #76, 0.01 #583, 0.01 #596), 02lcrv (0.02 #72), 03plfd (0.01 #153, 0.01 #465, 0.01 #712) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0mrs1; 0d1xh; 0mq17; 0mqs0; 0fxwx; 0mrhq; 0mpzm; 0mskq; 0ms1n; 0mr_8; >> query: (?x7282, 02fqwt) <- source(?x7282, ?x958), ?x958 = 0jbk9, contains(?x3634, ?x7282), ?x3634 = 07b_l >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 010bxh time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.788 http://example.org/location/location/time_zones #5696-019v9k PRED entity: 019v9k PRED relation: institution PRED expected values: 01pl14 06pwq 01w3v 0kz2w 0bx8pn 0dplh 01jq34 07w4j 07wrz 01wdj_ 01r3y2 086xm 02ccqg 02qvvv 02183k 0bqxw 02zcnq 0hd7j 01jpyb 01y20v 0cwx_ 07x4c 02j04_ 012mzw 01n_g9 01q7q2 0c5x_ 01nm8w 0187nd 0vkl2 02mzg9 0ghvb 02ckl3 02jztz 01_f90 01c57n 02jx_v 0ym20 => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 491): 0bx8pn (0.78 #6081, 0.75 #5724, 0.71 #5366), 01w3v (0.78 #6065, 0.71 #5350, 0.62 #5708), 06pwq (0.78 #6063, 0.67 #3924, 0.63 #6421), 0g2jl (0.71 #5591, 0.67 #6306, 0.62 #5949), 07wrz (0.67 #6415, 0.67 #6090, 0.67 #4307), 012mzw (0.67 #6216, 0.62 #5859, 0.60 #3364), 01jq34 (0.67 #6087, 0.60 #3235, 0.57 #2137), 02mzg9 (0.67 #6311, 0.57 #2137, 0.57 #4884), 01dbns (0.67 #4086, 0.57 #2137, 0.49 #2494), 0bqxw (0.62 #5778, 0.60 #3283, 0.57 #2137) >> Best rule #6081 for best value: >> intensional similarity = 23 >> extensional distance = 7 >> proper extension: 04zx3q1; >> query: (?x1771, 0bx8pn) <- institution(?x1771, ?x7707), institution(?x1771, ?x6925), institution(?x1771, ?x6315), institution(?x1771, ?x5941), institution(?x1771, ?x5158), institution(?x1771, ?x4599), institution(?x1771, ?x3576), ?x6925 = 01bm_, ?x6315 = 08qnnv, major_field_of_study(?x4599, ?x12363), student(?x4599, ?x5586), ?x7707 = 01jt2w, student(?x1771, ?x744), colors(?x5941, ?x663), category(?x5158, ?x134), major_field_of_study(?x1771, ?x8925), ?x12363 = 02cm61, school(?x1639, ?x4599), major_field_of_study(?x5941, ?x2172), major_field_of_study(?x2313, ?x8925), ?x2313 = 07wrz, state_province_region(?x3576, ?x3670), film(?x5586, ?x4041) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 6, 7, 8, 10, 11, 14, 17, 19, 25, 28, 29, 32, 34, 37, 38, 50, 66, 68, 91, 103, 104, 112, 117, 164, 179, 185, 225, 231, 241, 244, 252, 305, 317 EVAL 019v9k institution 0ym20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 02jx_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01c57n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01_f90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 02jztz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 02ckl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 0ghvb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 02mzg9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 0vkl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 0187nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01nm8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 0c5x_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01q7q2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01n_g9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 012mzw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 02j04_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 07x4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 0cwx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01y20v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01jpyb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 0hd7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 02zcnq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 0bqxw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 02183k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 02qvvv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 02ccqg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 086xm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01r3y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01wdj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 07wrz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 07w4j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01jq34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 0dplh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 0bx8pn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 0kz2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01w3v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 06pwq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 019v9k institution 01pl14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5695-0mmpz PRED entity: 0mmpz PRED relation: source PRED expected values: 0jbk9 => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #27, 0.92 #21, 0.91 #49) >> Best rule #27 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 0nv99; >> query: (?x11525, 0jbk9) <- contains(?x4600, ?x11525), contains(?x11525, ?x5267), currency(?x11525, ?x170), place_of_birth(?x275, ?x5267) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mmpz source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.921 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #5694-01zfzb PRED entity: 01zfzb PRED relation: genre PRED expected values: 0556j8 => 74 concepts (67 used for prediction) PRED predicted values (max 10 best out of 84): 01z4y (0.61 #5758, 0.61 #2701, 0.59 #1995), 01jfsb (0.50 #948, 0.37 #831, 0.33 #1065), 02l7c8 (0.33 #1421, 0.32 #365, 0.31 #483), 06n90 (0.29 #949, 0.13 #1066, 0.13 #2006), 0lsxr (0.21 #828, 0.21 #945, 0.19 #476), 06cvj (0.21 #1409, 0.11 #2, 0.10 #353), 04xvlr (0.20 #352, 0.19 #1877, 0.19 #3054), 01hmnh (0.19 #2011, 0.18 #954, 0.17 #2717), 060__y (0.17 #3068, 0.17 #602, 0.17 #1305), 02n4kr (0.14 #123, 0.13 #357, 0.13 #475) >> Best rule #5758 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 09g_31; 03y317; 02xhwm; >> query: (?x5320, ?x811) <- titles(?x811, ?x5320), genre(?x50, ?x811) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1448 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 564 *> proper extension: 087wc7n; 03wh49y; 063zky; *> query: (?x5320, 0556j8) <- film(?x665, ?x5320), genre(?x5320, ?x258), ?x258 = 05p553 *> conf = 0.06 ranks of expected_values: 29 EVAL 01zfzb genre 0556j8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 74.000 67.000 0.612 http://example.org/film/film/genre #5693-0g3bw PRED entity: 0g3bw PRED relation: contains PRED expected values: 02lf_x => 122 concepts (30 used for prediction) PRED predicted values (max 10 best out of 2770): 0g3cw (0.73 #82194, 0.71 #70452, 0.70 #85131), 0dqyw (0.73 #82194, 0.71 #70452, 0.70 #85131), 03_3d (0.58 #82195, 0.57 #88069, 0.55 #38161), 0g3bw (0.58 #82195, 0.55 #38161, 0.50 #70453), 09d4_ (0.35 #44031, 0.33 #839, 0.31 #85130), 01fv4z (0.33 #8204, 0.33 #2335, 0.25 #5269), 018q42 (0.33 #7134, 0.33 #1265, 0.25 #4199), 0gqkd (0.33 #6399, 0.33 #530, 0.25 #3464), 05gqf (0.33 #5970, 0.33 #101, 0.25 #3035), 019q50 (0.33 #7339, 0.33 #1470, 0.25 #4404) >> Best rule #82194 for best value: >> intensional similarity = 5 >> extensional distance = 73 >> proper extension: 05kkh; 03v1s; 05fkf; 03s0w; 04ych; 059_c; 04ykg; 01x73; 04rrd; 0488g; ... >> query: (?x2651, ?x10980) <- contains(?x2651, ?x8889), contains(?x2651, ?x536), category(?x536, ?x134), ?x134 = 08mbj5d, administrative_division(?x10980, ?x8889) >> conf = 0.73 => this is the best rule for 2 predicted values *> Best rule #2481 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 03_3d; *> query: (?x2651, 02lf_x) <- contains(?x2651, ?x13893), contains(?x2651, ?x11211), contains(?x2651, ?x9870), contains(?x2651, ?x536), ?x536 = 018jk2, ?x13893 = 018qt8, ?x9870 = 018txg, ?x11211 = 018jkl *> conf = 0.33 ranks of expected_values: 20 EVAL 0g3bw contains 02lf_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 122.000 30.000 0.727 http://example.org/location/location/contains #5692-03gt0c5 PRED entity: 03gt0c5 PRED relation: award PRED expected values: 0gs96 => 101 concepts (77 used for prediction) PRED predicted values (max 10 best out of 296): 0gs96 (0.79 #924, 0.78 #521, 0.73 #118), 05pcn59 (0.70 #4111, 0.17 #6126, 0.10 #10962), 09sb52 (0.47 #11324, 0.38 #4070, 0.31 #6085), 0gqy2 (0.46 #6612, 0.19 #6209, 0.11 #2985), 05ztrmj (0.40 #4214, 0.13 #6229, 0.07 #9050), 0f4x7 (0.38 #6075, 0.16 #6478, 0.14 #4060), 05zr6wv (0.30 #6062, 0.21 #4047, 0.12 #11301), 04kxsb (0.26 #6171, 0.14 #4156, 0.13 #6574), 027h4yd (0.26 #778, 0.25 #1181, 0.24 #1584), 027dtxw (0.24 #6452, 0.14 #2422, 0.13 #2019) >> Best rule #924 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 02vkvcz; >> query: (?x13091, 0gs96) <- award(?x13091, ?x507), nominated_for(?x13091, ?x2380), costume_design_by(?x3904, ?x13091) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03gt0c5 award 0gs96 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 77.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5691-0pj9t PRED entity: 0pj9t PRED relation: artist! PRED expected values: 03qy3l => 117 concepts (87 used for prediction) PRED predicted values (max 10 best out of 90): 015_1q (0.41 #295, 0.38 #19, 0.27 #157), 03rhqg (0.33 #430, 0.21 #1258, 0.20 #1396), 01w40h (0.20 #166, 0.14 #994, 0.12 #442), 0mzkr (0.20 #163, 0.10 #577, 0.08 #439), 01xyqk (0.18 #354, 0.08 #492, 0.08 #78), 0181dw (0.17 #454, 0.15 #40, 0.12 #1006), 02p11jq (0.17 #427, 0.13 #151, 0.10 #1255), 01clyr (0.17 #447, 0.11 #1275, 0.11 #1413), 0190vc (0.17 #497, 0.08 #83, 0.06 #1049), 0k_kr (0.15 #41, 0.07 #1283, 0.07 #1421) >> Best rule #295 for best value: >> intensional similarity = 2 >> extensional distance = 20 >> proper extension: 01fl3; >> query: (?x3241, 015_1q) <- artists(?x9427, ?x3241), ?x9427 = 0m40d >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #475 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 0411q; 0m2l9; 01vvycq; 01vrncs; 01wp8w7; 01w60_p; 0407f; 01wz_ml; 02qwg; 0p7h7; ... *> query: (?x3241, 03qy3l) <- inductee(?x1091, ?x3241), artists(?x7440, ?x3241), ?x7440 = 0155w *> conf = 0.12 ranks of expected_values: 16 EVAL 0pj9t artist! 03qy3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 117.000 87.000 0.409 http://example.org/music/record_label/artist #5690-02xc1w4 PRED entity: 02xc1w4 PRED relation: crewmember! PRED expected values: 0f4_l 03hkch7 024mpp 065_cjc => 99 concepts (38 used for prediction) PRED predicted values (max 10 best out of 308): 0c3xpwy (0.35 #1529, 0.23 #307, 0.23 #306), 03d34x8 (0.35 #1529, 0.23 #307, 0.23 #306), 02qjv1p (0.35 #1529, 0.23 #307, 0.23 #306), 0dtfn (0.33 #50, 0.13 #1273, 0.10 #662), 085wqm (0.33 #292, 0.04 #1515, 0.03 #904), 04j14qc (0.33 #260, 0.02 #1483), 01_0f7 (0.33 #218, 0.02 #1441), 01cmp9 (0.33 #199, 0.02 #1422), 0gtxj2q (0.33 #136, 0.02 #1359), 04g9gd (0.33 #85, 0.02 #1308) >> Best rule #1529 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 04cy8rb; 06cv1; 0284n42; 076lxv; 027rwmr; 03h26tm; 09rp4r_; 09pjnd; 0c94fn; 04ktcgn; ... >> query: (?x5664, ?x2009) <- crewmember(?x4375, ?x5664), nominated_for(?x5664, ?x2009), language(?x4375, ?x254), film(?x1678, ?x4375) >> conf = 0.35 => this is the best rule for 3 predicted values *> Best rule #739 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 01yznp; *> query: (?x5664, 024mpp) <- crewmember(?x2331, ?x5664), profession(?x5664, ?x319), production_companies(?x2331, ?x1478), genre(?x2331, ?x53) *> conf = 0.10 ranks of expected_values: 15 EVAL 02xc1w4 crewmember! 065_cjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 38.000 0.351 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember EVAL 02xc1w4 crewmember! 024mpp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 99.000 38.000 0.351 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember EVAL 02xc1w4 crewmember! 03hkch7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 38.000 0.351 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember EVAL 02xc1w4 crewmember! 0f4_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 38.000 0.351 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #5689-05jyb2 PRED entity: 05jyb2 PRED relation: program! PRED expected values: 05gnf => 103 concepts (89 used for prediction) PRED predicted values (max 10 best out of 50): 0gsg7 (0.40 #2, 0.22 #59, 0.22 #1621), 03mdt (0.29 #754, 0.24 #467, 0.21 #180), 05gnf (0.27 #646, 0.27 #704, 0.24 #878), 0g5lhl7 (0.22 #753, 0.14 #466, 0.14 #237), 01z77k (0.19 #806, 0.15 #805, 0.12 #922), 03mqtr (0.19 #806, 0.15 #805, 0.12 #922), 017fp (0.19 #806, 0.15 #805, 0.12 #922), 07s9rl0 (0.19 #806, 0.15 #805, 0.12 #922), 09d5h (0.17 #635, 0.13 #1622, 0.13 #1738), 0cjdk (0.14 #869, 0.11 #1450, 0.11 #1797) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 027pfb2; >> query: (?x3725, 0gsg7) <- actor(?x3725, ?x11380), titles(?x53, ?x3725), genre(?x3725, ?x1014), ?x11380 = 015qq1 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 016tvq; *> query: (?x3725, 05gnf) <- nominated_for(?x375, ?x3725), titles(?x53, ?x3725), actor(?x3725, ?x8081), diet(?x8081, ?x3130) *> conf = 0.27 ranks of expected_values: 3 EVAL 05jyb2 program! 05gnf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 89.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #5688-02zk08 PRED entity: 02zk08 PRED relation: genre PRED expected values: 0hn10 => 92 concepts (77 used for prediction) PRED predicted values (max 10 best out of 106): 05p553 (0.82 #2526, 0.45 #245, 0.42 #365), 02l7c8 (0.45 #257, 0.43 #377, 0.32 #1938), 02kdv5l (0.39 #2, 0.35 #8533, 0.28 #3485), 03bxz7 (0.37 #1137, 0.19 #1257, 0.15 #176), 01jfsb (0.33 #12, 0.32 #1454, 0.31 #2774), 04xvlr (0.29 #602, 0.28 #1203, 0.23 #722), 03k9fj (0.28 #11, 0.27 #972, 0.25 #3494), 0lsxr (0.24 #729, 0.23 #2770, 0.22 #849), 082gq (0.22 #31, 0.15 #1082, 0.15 #632), 03g3w (0.22 #146, 0.15 #506, 0.11 #25) >> Best rule #2526 for best value: >> intensional similarity = 4 >> extensional distance = 645 >> proper extension: 03z9585; 0199wf; >> query: (?x8701, 05p553) <- language(?x8701, ?x254), genre(?x8701, ?x6452), genre(?x8072, ?x6452), ?x8072 = 02mc5v >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1211 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 250 *> proper extension: 05n6sq; *> query: (?x8701, 0hn10) <- language(?x8701, ?x254), award_winner(?x8701, ?x6232), titles(?x53, ?x8701), ?x53 = 07s9rl0 *> conf = 0.10 ranks of expected_values: 31 EVAL 02zk08 genre 0hn10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 92.000 77.000 0.822 http://example.org/film/film/genre #5687-056_y PRED entity: 056_y PRED relation: location_of_ceremony! PRED expected values: 0436kgz => 201 concepts (77 used for prediction) PRED predicted values (max 10 best out of 205): 02m30v (0.33 #254, 0.25 #2548, 0.25 #1017), 01rwcgb (0.33 #228, 0.25 #991, 0.17 #1499), 03j24kf (0.33 #112, 0.25 #875, 0.17 #1383), 014v1q (0.33 #246, 0.25 #1009, 0.17 #1517), 0436kgz (0.33 #162, 0.25 #925, 0.17 #1433), 02g0rb (0.33 #157, 0.25 #920, 0.17 #1428), 01vsy7t (0.33 #110, 0.25 #873, 0.17 #1381), 02_j7t (0.33 #47, 0.25 #810, 0.17 #1318), 01vsl3_ (0.25 #1084, 0.02 #15897, 0.01 #18199), 01nz1q6 (0.25 #1248) >> Best rule #254 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 04jpl; >> query: (?x4698, 02m30v) <- taxonomy(?x4698, ?x939), place_of_death(?x2162, ?x4698), film_regional_debut_venue(?x3217, ?x4698), month(?x4698, ?x1459) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #162 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 04jpl; *> query: (?x4698, 0436kgz) <- taxonomy(?x4698, ?x939), place_of_death(?x2162, ?x4698), film_regional_debut_venue(?x3217, ?x4698), month(?x4698, ?x1459) *> conf = 0.33 ranks of expected_values: 5 EVAL 056_y location_of_ceremony! 0436kgz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 201.000 77.000 0.333 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #5686-06x43v PRED entity: 06x43v PRED relation: titles! PRED expected values: 01z4y => 81 concepts (49 used for prediction) PRED predicted values (max 10 best out of 55): 01z4y (0.34 #342, 0.32 #444, 0.24 #1060), 01jfsb (0.33 #20, 0.22 #3498, 0.20 #102), 04xvlr (0.33 #4, 0.21 #3294, 0.20 #3398), 01hmnh (0.33 #130, 0.16 #231, 0.13 #333), 02n4kr (0.33 #14, 0.05 #3408, 0.05 #1860), 02xh1 (0.33 #86, 0.01 #2037), 07s9rl0 (0.31 #3395, 0.31 #3291, 0.30 #2983), 0556j8 (0.22 #3498, 0.20 #102, 0.18 #3913), 0lsxr (0.22 #3498, 0.20 #102, 0.18 #3913), 05p553 (0.22 #3498, 0.20 #102, 0.18 #3913) >> Best rule #342 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 0bmpm; >> query: (?x7514, 01z4y) <- film(?x4462, ?x7514), genre(?x7514, ?x225), executive_produced_by(?x7514, ?x12790), influenced_by(?x5450, ?x4462) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06x43v titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 49.000 0.339 http://example.org/media_common/netflix_genre/titles #5685-02pzy52 PRED entity: 02pzy52 PRED relation: team! PRED expected values: 0b_72t 0bzrsh 0b_770 0b_734 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 17): 0b_770 (0.78 #362, 0.73 #323, 0.67 #260), 0cc8q3 (0.75 #435, 0.73 #323, 0.71 #303), 0b_72t (0.73 #323, 0.71 #294, 0.70 #436), 0b_6mr (0.73 #323, 0.71 #319, 0.67 #258), 0bzrsh (0.73 #323, 0.71 #317, 0.61 #251), 0b_756 (0.73 #323, 0.67 #359, 0.61 #251), 0f9rw9 (0.73 #323, 0.61 #251, 0.60 #218), 05g_nr (0.73 #323, 0.61 #251, 0.57 #316), 0b_734 (0.73 #323, 0.61 #251, 0.50 #100), 0br1xn (0.73 #323, 0.61 #251, 0.40 #201) >> Best rule #362 for best value: >> intensional similarity = 19 >> extensional distance = 7 >> proper extension: 02ptzz0; >> query: (?x10846, 0b_770) <- team(?x9146, ?x10846), team(?x7378, ?x10846), team(?x2302, ?x10846), instance_of_recurring_event(?x9146, ?x10863), team(?x5755, ?x10846), team(?x9146, ?x4938), ?x2302 = 0b_77q, locations(?x9146, ?x5771), locations(?x9146, ?x5267), locations(?x9146, ?x2277), locations(?x9146, ?x2087), ?x10863 = 02jp2w, ?x7378 = 0bzrxn, ?x5267 = 0d9jr, ?x4938 = 027yf83, ?x2087 = 099ty, place_of_birth(?x849, ?x5771), contains(?x94, ?x5771), month(?x2277, ?x1459) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 5, 9 EVAL 02pzy52 team! 0b_734 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 71.000 71.000 0.778 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02pzy52 team! 0b_770 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.778 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02pzy52 team! 0bzrsh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 71.000 0.778 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 02pzy52 team! 0b_72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.778 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #5684-0c8tk PRED entity: 0c8tk PRED relation: place_of_birth! PRED expected values: 06wvfq => 178 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1981): 02wmbg (0.33 #5211, 0.33 #4375, 0.27 #7816), 08bqy9 (0.33 #3870, 0.11 #101611, 0.07 #6477), 084z0w (0.27 #7816, 0.26 #114637, 0.26 #78164), 06wvfq (0.27 #7816, 0.26 #114637, 0.26 #78164), 06zmg7m (0.27 #7816, 0.26 #114637, 0.26 #78164), 02xfrd (0.27 #7816, 0.26 #114637, 0.26 #78164), 0dfjb8 (0.27 #7816, 0.26 #114637, 0.26 #78164), 09ld6g (0.12 #7818, 0.10 #18237, 0.10 #18238), 09_2gj (0.12 #7818, 0.10 #18237, 0.10 #18238), 0f2c8g (0.12 #7818, 0.10 #18237, 0.10 #18238) >> Best rule #5211 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 09c17; >> query: (?x4335, ?x8530) <- location(?x10579, ?x4335), location(?x8530, ?x4335), ?x8530 = 02wmbg, service_location(?x10867, ?x4335), nationality(?x10579, ?x2146) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7816 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 06mkj; *> query: (?x4335, ?x3890) <- location(?x8530, ?x4335), location(?x3890, ?x4335), languages(?x8530, ?x254), service_location(?x10867, ?x4335), place_of_death(?x6249, ?x4335) *> conf = 0.27 ranks of expected_values: 4 EVAL 0c8tk place_of_birth! 06wvfq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 178.000 57.000 0.333 http://example.org/people/person/place_of_birth #5683-049qx PRED entity: 049qx PRED relation: profession PRED expected values: 0dz3r 0cbd2 01c72t => 140 concepts (139 used for prediction) PRED predicted values (max 10 best out of 74): 09jwl (0.67 #5493, 0.64 #7366, 0.63 #2610), 016z4k (0.51 #2598, 0.49 #4182, 0.49 #3894), 0dz3r (0.50 #1299, 0.49 #3028, 0.45 #3892), 01c72t (0.35 #4488, 0.34 #11539, 0.33 #11829), 0d1pc (0.35 #1487, 0.34 #11539, 0.33 #11829), 03gjzk (0.34 #4624, 0.29 #3182, 0.29 #7651), 039v1 (0.34 #11539, 0.33 #11829, 0.33 #11828), 012t_z (0.34 #11539, 0.33 #11829, 0.33 #11828), 0fnpj (0.34 #11539, 0.33 #11829, 0.33 #11828), 05vyk (0.34 #11539, 0.33 #11829, 0.33 #11828) >> Best rule #5493 for best value: >> intensional similarity = 3 >> extensional distance = 260 >> proper extension: 01p9hgt; 01dw9z; 0163m1; 01wy61y; 02pt7h_; 0gr69; 021r7r; 0134wr; 01nhkxp; 06p03s; ... >> query: (?x4394, 09jwl) <- gender(?x4394, ?x514), artists(?x1572, ?x4394), ?x1572 = 06by7 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1299 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 01v27pl; *> query: (?x4394, 0dz3r) <- artists(?x5876, ?x4394), artists(?x3319, ?x4394), ?x5876 = 0ggx5q, ?x3319 = 06j6l *> conf = 0.50 ranks of expected_values: 3, 4, 20 EVAL 049qx profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 139.000 0.672 http://example.org/people/person/profession EVAL 049qx profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 140.000 139.000 0.672 http://example.org/people/person/profession EVAL 049qx profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 139.000 0.672 http://example.org/people/person/profession #5682-01y9st PRED entity: 01y9st PRED relation: organization! PRED expected values: 060c4 => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 12): 060c4 (0.76 #654, 0.71 #381, 0.70 #54), 07xl34 (0.50 #63, 0.50 #37, 0.42 #89), 0dq_5 (0.29 #804, 0.29 #139, 0.29 #778), 05k17c (0.20 #254, 0.18 #293, 0.13 #360), 05c0jwl (0.19 #226, 0.18 #187, 0.15 #239), 09d6p2 (0.18 #339, 0.07 #127, 0.02 #231), 0hm4q (0.16 #353, 0.14 #112, 0.09 #242), 08jcfy (0.05 #246, 0.04 #351, 0.04 #194), 04n1q6 (0.03 #305, 0.03 #227, 0.03 #331), 0fj45 (0.03 #1875, 0.03 #2058, 0.03 #2137) >> Best rule #654 for best value: >> intensional similarity = 4 >> extensional distance = 252 >> proper extension: 03v6t; 0cchk3; 01wdj_; 01lnyf; 017v71; 01qgr3; 01hx2t; 03k7dn; 0ghvb; 01xysf; ... >> query: (?x5221, 060c4) <- currency(?x5221, ?x2244), contains(?x1905, ?x5221), colors(?x5221, ?x5325), state(?x12755, ?x1905) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y9st organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.756 http://example.org/organization/role/leaders./organization/leadership/organization #5681-04cnp4 PRED entity: 04cnp4 PRED relation: registering_agency PRED expected values: 03z19 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.84 #22, 0.84 #19, 0.84 #18) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 02htv6; >> query: (?x8463, 03z19) <- contains(?x94, ?x8463), currency(?x8463, ?x170), ?x170 = 09nqf >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cnp4 registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.845 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #5680-033hqf PRED entity: 033hqf PRED relation: film PRED expected values: 0k4f3 => 134 concepts (127 used for prediction) PRED predicted values (max 10 best out of 782): 0gzy02 (0.12 #44, 0.07 #5414, 0.04 #14364), 01lsl (0.12 #1534, 0.06 #3324, 0.04 #17644), 0cq806 (0.12 #1494, 0.06 #3284, 0.03 #8654), 0bbgly (0.12 #1738, 0.04 #5318, 0.04 #25008), 0k54q (0.12 #936, 0.04 #17046, 0.04 #18836), 0bx0l (0.12 #349, 0.04 #3929, 0.04 #5719), 03kx49 (0.12 #1343, 0.03 #56833, 0.03 #47883), 0jdr0 (0.12 #1553, 0.03 #37353, 0.03 #40933), 02dwj (0.12 #906, 0.03 #11646, 0.03 #24176), 06lpmt (0.12 #685, 0.03 #23955, 0.02 #32905) >> Best rule #44 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0c12h; >> query: (?x544, 0gzy02) <- profession(?x544, ?x1032), type_of_union(?x544, ?x566), film(?x544, ?x8461), ?x8461 = 01lbcqx >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #11189 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 02lkcc; 04264n; 069z_5; 06f_qn; 06nd8c; *> query: (?x544, 0k4f3) <- place_of_death(?x544, ?x1523), type_of_union(?x544, ?x566), film(?x544, ?x3755), ?x1523 = 030qb3t *> conf = 0.03 ranks of expected_values: 144 EVAL 033hqf film 0k4f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 134.000 127.000 0.125 http://example.org/film/actor/film./film/performance/film #5679-010nlt PRED entity: 010nlt PRED relation: location! PRED expected values: 09n70c => 83 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1429): 05d1y (0.40 #9247, 0.33 #24372, 0.27 #26892), 01rh0w (0.40 #7817, 0.25 #2773, 0.22 #22942), 0dszr0 (0.40 #10037, 0.22 #25162, 0.20 #15081), 01qn8k (0.40 #9452, 0.22 #24577, 0.20 #14496), 0pyww (0.40 #8547, 0.22 #23672, 0.20 #13591), 05myd2 (0.40 #9494, 0.22 #24619, 0.20 #14538), 0blt6 (0.40 #8255, 0.22 #23380, 0.20 #13299), 0sx5w (0.40 #9708, 0.22 #24833, 0.20 #14752), 02sjf5 (0.40 #7767, 0.22 #22892, 0.20 #12811), 07663r (0.40 #10009, 0.22 #25134, 0.20 #15053) >> Best rule #9247 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0cc56; >> query: (?x13858, 05d1y) <- vacationer(?x13858, ?x848), administrative_parent(?x13858, ?x3912), vacationer(?x3912, ?x11992), administrative_parent(?x3912, ?x551), location_of_ceremony(?x566, ?x3912), participant(?x4741, ?x11992), participant(?x11992, ?x9257) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 010nlt location! 09n70c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 51.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #5678-014_x2 PRED entity: 014_x2 PRED relation: genre PRED expected values: 01jfsb => 87 concepts (86 used for prediction) PRED predicted values (max 10 best out of 113): 01hmnh (0.79 #1324, 0.77 #604, 0.76 #242), 0djd22 (0.62 #1323, 0.61 #4445, 0.59 #2761), 05p553 (0.50 #4, 0.42 #1806, 0.42 #125), 03k9fj (0.50 #133, 0.39 #616, 0.37 #494), 01jfsb (0.46 #1215, 0.44 #975, 0.37 #2293), 02kdv5l (0.41 #964, 0.37 #606, 0.36 #1085), 01t_vv (0.38 #54, 0.10 #1856, 0.09 #2936), 04gm78f (0.38 #69, 0.05 #3605), 02n4kr (0.26 #1210, 0.18 #250, 0.14 #370), 0lsxr (0.25 #1211, 0.21 #971, 0.19 #251) >> Best rule #1324 for best value: >> intensional similarity = 4 >> extensional distance = 261 >> proper extension: 0cz8mkh; 078mm1; 09v42sf; >> query: (?x83, ?x1510) <- film_crew_role(?x83, ?x137), titles(?x1510, ?x83), genre(?x573, ?x1510), ?x573 = 0bth54 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1215 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 261 *> proper extension: 0cz8mkh; 078mm1; 09v42sf; *> query: (?x83, 01jfsb) <- film_crew_role(?x83, ?x137), titles(?x1510, ?x83), genre(?x573, ?x1510), ?x573 = 0bth54 *> conf = 0.46 ranks of expected_values: 5 EVAL 014_x2 genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 86.000 0.786 http://example.org/film/film/genre #5677-02gqm3 PRED entity: 02gqm3 PRED relation: music PRED expected values: 012ky3 => 89 concepts (58 used for prediction) PRED predicted values (max 10 best out of 129): 01cbt3 (0.25 #512, 0.20 #932, 0.11 #2195), 0146pg (0.19 #1061, 0.13 #3378, 0.11 #2534), 02cyfz (0.17 #245, 0.17 #34, 0.07 #875), 0bxtyq (0.17 #389, 0.17 #178), 02bh9 (0.12 #472, 0.08 #682, 0.06 #5743), 0150t6 (0.12 #467, 0.07 #887, 0.05 #3202), 0drc1 (0.12 #1200, 0.04 #2884, 0.03 #4150), 04ls53 (0.12 #500, 0.03 #1972, 0.02 #7465), 018gqj (0.12 #532), 02sj1x (0.10 #4057, 0.09 #2791, 0.06 #1528) >> Best rule #512 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0407yj_; >> query: (?x10047, 01cbt3) <- country(?x10047, ?x94), film(?x1802, ?x10047), story_by(?x10047, ?x3686), ?x94 = 09c7w0, genre(?x10047, ?x5104), ?x5104 = 0bkbm >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2176 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 35 *> proper extension: 0cq8nx; *> query: (?x10047, 012ky3) <- country(?x10047, ?x512), film_release_distribution_medium(?x10047, ?x81), ?x81 = 029j_, film(?x382, ?x10047), ?x512 = 07ssc, cinematography(?x10047, ?x11915) *> conf = 0.03 ranks of expected_values: 75 EVAL 02gqm3 music 012ky3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 89.000 58.000 0.250 http://example.org/film/film/music #5676-03x23q PRED entity: 03x23q PRED relation: organization! PRED expected values: 060c4 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 16): 060c4 (0.82 #132, 0.81 #67, 0.78 #145), 07xl34 (0.36 #128, 0.30 #180, 0.27 #206), 0dq_5 (0.17 #1153, 0.17 #1205, 0.16 #1244), 05k17c (0.10 #1060, 0.10 #813, 0.09 #670), 0hm4q (0.08 #177, 0.05 #1165, 0.05 #957), 05c0jwl (0.05 #681, 0.04 #863, 0.04 #759), 01t7n9 (0.04 #1379, 0.03 #1471), 02079p (0.04 #1379, 0.03 #1471), 0789n (0.04 #1379, 0.03 #1471), 0f6c3 (0.04 #1379, 0.03 #1471) >> Best rule #132 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 0gy3w; >> query: (?x12732, 060c4) <- currency(?x12732, ?x170), school_type(?x12732, ?x3092), ?x170 = 09nqf, school(?x2820, ?x12732) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x23q organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.817 http://example.org/organization/role/leaders./organization/leadership/organization #5675-0134s5 PRED entity: 0134s5 PRED relation: award PRED expected values: 03tcnt 01ckcd => 126 concepts (100 used for prediction) PRED predicted values (max 10 best out of 275): 054krc (0.48 #13016, 0.43 #7360, 0.39 #18675), 01by1l (0.46 #26783, 0.37 #11425, 0.35 #25571), 0c4z8 (0.40 #880, 0.27 #11384, 0.22 #7748), 03qbh5 (0.40 #1015, 0.25 #7883, 0.25 #9903), 01d38g (0.40 #836, 0.25 #432, 0.15 #1240), 01c92g (0.40 #906, 0.24 #3330, 0.23 #11410), 01cw51 (0.40 #948, 0.24 #3372, 0.12 #9836), 0l8z1 (0.39 #12992, 0.34 #7336, 0.31 #18651), 0gqz2 (0.38 #13009, 0.29 #7353, 0.28 #19881), 01bgqh (0.38 #7719, 0.34 #9739, 0.33 #43) >> Best rule #13016 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 01p7b6b; >> query: (?x3420, 054krc) <- award(?x3420, ?x6126), award_winner(?x139, ?x3420), music(?x2642, ?x3420), film(?x3917, ?x2642) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #12052 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 85 *> proper extension: 05crg7; 015cxv; *> query: (?x3420, 01ckcd) <- award_winner(?x9462, ?x3420), artists(?x378, ?x3420), group(?x227, ?x3420) *> conf = 0.36 ranks of expected_values: 12, 33 EVAL 0134s5 award 01ckcd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 126.000 100.000 0.481 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0134s5 award 03tcnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 126.000 100.000 0.481 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5674-06mfvc PRED entity: 06mfvc PRED relation: participant PRED expected values: 04fzk => 115 concepts (71 used for prediction) PRED predicted values (max 10 best out of 143): 0gyx4 (0.04 #5495, 0.03 #25594, 0.03 #21706), 046zh (0.03 #5546, 0.02 #21757, 0.02 #8139), 01pcrw (0.03 #216, 0.03 #865, 0.02 #6050), 0dvmd (0.03 #8645, 0.02 #7997, 0.02 #6701), 0bq2g (0.03 #901, 0.03 #1549, 0.02 #2845), 04fzk (0.03 #938, 0.02 #2882, 0.02 #6772), 019pm_ (0.02 #6673, 0.02 #7969, 0.02 #8617), 0d_84 (0.02 #3252, 0.02 #6494, 0.02 #660), 0227vl (0.02 #3131, 0.02 #3779, 0.02 #8317), 0f4vbz (0.02 #5331, 0.02 #6628, 0.02 #145) >> Best rule #5495 for best value: >> intensional similarity = 3 >> extensional distance = 269 >> proper extension: 0htlr; 03_vx9; 04shbh; 0prjs; 01t07j; 034bgm; 04cbtrw; 01k5zk; 01ft2l; 057hz; ... >> query: (?x1987, 0gyx4) <- nationality(?x1987, ?x94), award_winner(?x9350, ?x1987), participant(?x1987, ?x1530) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #938 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 155 *> proper extension: 0d05fv; 015lhm; 06tp4h; *> query: (?x1987, 04fzk) <- participant(?x1987, ?x1530), currency(?x1987, ?x170), film(?x1987, ?x4021) *> conf = 0.03 ranks of expected_values: 6 EVAL 06mfvc participant 04fzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 115.000 71.000 0.041 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #5673-09h_q PRED entity: 09h_q PRED relation: type_of_union PRED expected values: 04ztj => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.83 #73, 0.81 #29, 0.79 #181), 01g63y (0.25 #6, 0.14 #234, 0.14 #358), 01bl8s (0.04 #19, 0.04 #51, 0.02 #43) >> Best rule #73 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 040db; 013sg6; >> query: (?x8080, 04ztj) <- place_of_death(?x8080, ?x739), influenced_by(?x1092, ?x8080), award(?x8080, ?x2324), profession(?x8080, ?x563) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09h_q type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.829 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5672-02vnmc9 PRED entity: 02vnmc9 PRED relation: titles! PRED expected values: 04xvlr => 90 concepts (40 used for prediction) PRED predicted values (max 10 best out of 78): 07c52 (0.83 #742, 0.75 #1350, 0.59 #640), 07s9rl0 (0.49 #410, 0.36 #512, 0.33 #2851), 04xvlr (0.28 #413, 0.27 #1634, 0.24 #2854), 024qqx (0.28 #79, 0.17 #996, 0.16 #590), 02l7c8 (0.24 #2748, 0.23 #510, 0.23 #1121), 05p553 (0.23 #510, 0.23 #1121, 0.23 #1322), 01z4y (0.22 #1970, 0.22 #136, 0.21 #3189), 07ssc (0.20 #418, 0.11 #3061, 0.10 #3164), 01jfsb (0.17 #936, 0.14 #1853, 0.13 #1751), 09b3v (0.12 #1068, 0.12 #1269, 0.03 #2388) >> Best rule #742 for best value: >> intensional similarity = 4 >> extensional distance = 85 >> proper extension: 06qwh; 023ny6; 06qv_; 0300ml; >> query: (?x7750, 07c52) <- award(?x7750, ?x1245), nominated_for(?x749, ?x7750), titles(?x307, ?x7750), country_of_origin(?x7750, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #413 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 0hmr4; 064n1pz; *> query: (?x7750, 04xvlr) <- genre(?x7750, ?x53), nominated_for(?x749, ?x7750), film_release_region(?x7750, ?x94), ?x749 = 094qd5 *> conf = 0.28 ranks of expected_values: 3 EVAL 02vnmc9 titles! 04xvlr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 40.000 0.828 http://example.org/media_common/netflix_genre/titles #5671-01wg982 PRED entity: 01wg982 PRED relation: film PRED expected values: 084qpk => 121 concepts (31 used for prediction) PRED predicted values (max 10 best out of 76): 09qljs (0.45 #4981, 0.26 #14942, 0.23 #19923), 043n1r5 (0.03 #4089, 0.02 #5750, 0.01 #7410), 08rr3p (0.03 #3549, 0.02 #5210, 0.01 #6870), 0bz3jx (0.02 #4714, 0.01 #25467), 07bxqz (0.02 #4966), 05k4my (0.02 #4929), 0bs5vty (0.02 #4924), 02ptczs (0.02 #4910), 0g5qmbz (0.02 #4898), 0m3gy (0.02 #4884) >> Best rule #4981 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 027l0b; 0fx02; 03xp8d5; 06b_0; >> query: (?x2408, ?x10651) <- type_of_union(?x2408, ?x566), category(?x2408, ?x134), written_by(?x10651, ?x2408) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #4202 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 027l0b; 0fx02; 03xp8d5; 06b_0; *> query: (?x2408, 084qpk) <- type_of_union(?x2408, ?x566), category(?x2408, ?x134), written_by(?x10651, ?x2408) *> conf = 0.02 ranks of expected_values: 73 EVAL 01wg982 film 084qpk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 121.000 31.000 0.450 http://example.org/film/director/film #5670-0298n7 PRED entity: 0298n7 PRED relation: language PRED expected values: 02h40lc => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 32): 02h40lc (0.91 #415, 0.91 #592, 0.91 #1072), 04h9h (0.20 #43, 0.18 #4705, 0.04 #456), 064_8sq (0.18 #4705, 0.17 #435, 0.15 #199), 04306rv (0.18 #4705, 0.11 #1015, 0.11 #775), 06nm1 (0.18 #4705, 0.11 #247, 0.11 #601), 06b_j (0.18 #4705, 0.07 #200, 0.07 #673), 0jzc (0.18 #4705, 0.06 #197, 0.05 #138), 03_9r (0.18 #4705, 0.06 #305, 0.05 #187), 0349s (0.18 #4705, 0.02 #222, 0.02 #399), 03hkp (0.18 #4705, 0.02 #1025, 0.02 #546) >> Best rule #415 for best value: >> intensional similarity = 3 >> extensional distance = 282 >> proper extension: 0cbl95; >> query: (?x7755, 02h40lc) <- nominated_for(?x1307, ?x7755), nominated_for(?x92, ?x7755), ?x1307 = 0gq9h >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0298n7 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.908 http://example.org/film/film/language #5669-0dtfn PRED entity: 0dtfn PRED relation: film! PRED expected values: 016tt2 => 84 concepts (75 used for prediction) PRED predicted values (max 10 best out of 63): 03xq0f (0.61 #1638, 0.57 #1935, 0.57 #1787), 05qd_ (0.29 #231, 0.27 #157, 0.26 #528), 016tt2 (0.25 #226, 0.25 #4, 0.24 #374), 086k8 (0.25 #76, 0.19 #1487, 0.19 #1413), 04mkft (0.17 #110, 0.10 #1818, 0.09 #1966), 01795t (0.16 #612, 0.15 #834, 0.10 #908), 01gb54 (0.15 #548, 0.12 #399, 0.12 #325), 017s11 (0.14 #1191, 0.13 #2304, 0.12 #3358), 016tw3 (0.14 #2015, 0.14 #2536, 0.13 #159), 054g1r (0.13 #554, 0.11 #851, 0.11 #1073) >> Best rule #1638 for best value: >> intensional similarity = 2 >> extensional distance = 163 >> proper extension: 04nlb94; >> query: (?x1386, 03xq0f) <- film_crew_role(?x1386, ?x137), film_distribution_medium(?x1386, ?x81) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #226 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 0jzw; *> query: (?x1386, 016tt2) <- crewmember(?x1386, ?x1585), film_release_region(?x1386, ?x87), nominated_for(?x198, ?x1386), honored_for(?x1386, ?x2366) *> conf = 0.25 ranks of expected_values: 3 EVAL 0dtfn film! 016tt2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 75.000 0.612 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5668-0c1fs PRED entity: 0c1fs PRED relation: influenced_by! PRED expected values: 0m77m 034bs => 118 concepts (31 used for prediction) PRED predicted values (max 10 best out of 460): 07h1q (0.44 #1436, 0.21 #2463, 0.20 #5552), 040db (0.38 #1619, 0.27 #2646, 0.23 #4189), 0683n (0.36 #2394, 0.27 #4452, 0.27 #2909), 07dnx (0.33 #1390, 0.20 #2932, 0.16 #3446), 041jlr (0.33 #1389, 0.16 #5505, 0.14 #2416), 049gc (0.33 #225, 0.15 #1768, 0.13 #2795), 016dmx (0.33 #333, 0.11 #1361, 0.08 #5477), 06jcc (0.33 #313, 0.11 #1341, 0.08 #13689), 0jcx (0.33 #118, 0.11 #1146, 0.07 #2173), 03f0324 (0.33 #711, 0.08 #13573, 0.08 #1740) >> Best rule #1436 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 03f0324; 0bk5r; 04hcw; 07dnx; 03jht; 06myp; >> query: (?x8441, 07h1q) <- influenced_by(?x8441, ?x11097), influenced_by(?x8441, ?x2240), ?x11097 = 02wh0, influenced_by(?x3279, ?x8441), profession(?x8441, ?x353), ?x2240 = 0j3v >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2724 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 099bk; *> query: (?x8441, 034bs) <- influenced_by(?x8441, ?x11097), ?x11097 = 02wh0, influenced_by(?x3279, ?x8441), type_of_union(?x8441, ?x566), gender(?x8441, ?x231) *> conf = 0.13 ranks of expected_values: 68, 150 EVAL 0c1fs influenced_by! 034bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 118.000 31.000 0.444 http://example.org/influence/influence_node/influenced_by EVAL 0c1fs influenced_by! 0m77m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 118.000 31.000 0.444 http://example.org/influence/influence_node/influenced_by #5667-01mxnvc PRED entity: 01mxnvc PRED relation: origin PRED expected values: 0k33p => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 113): 0n95v (0.25 #191, 0.04 #1136, 0.01 #2316), 02jx1 (0.15 #505, 0.04 #2157, 0.02 #5666), 030qb3t (0.09 #271, 0.09 #1451, 0.08 #1687), 04jpl (0.09 #1423, 0.08 #1659, 0.08 #2131), 0cr3d (0.08 #529, 0.06 #765, 0.03 #1473), 0nbwf (0.08 #614, 0.03 #1794, 0.01 #2266), 02_286 (0.07 #1197, 0.06 #2849, 0.06 #725), 0k33p (0.07 #1344, 0.06 #1580, 0.06 #872), 01sn3 (0.06 #787, 0.03 #1259, 0.02 #3383), 0s3y5 (0.06 #716, 0.03 #1188) >> Best rule #191 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0cm03; >> query: (?x10802, 0n95v) <- instrumentalists(?x5990, ?x10802), instrumentalists(?x3156, ?x10802), nationality(?x10802, ?x1310), group(?x3156, ?x2901), role(?x1332, ?x3156), ?x5990 = 0192l >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1344 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 27 *> proper extension: 03c7ln; 0c9d9; 0kzy0; 01vvycq; 02l840; 01x66d; 09qr6; 0ftps; 0l12d; 09hnb; ... *> query: (?x10802, 0k33p) <- profession(?x10802, ?x131), gender(?x10802, ?x231), category(?x10802, ?x134), artist(?x2149, ?x10802), instrumentalists(?x228, ?x10802), ?x228 = 0l14qv *> conf = 0.07 ranks of expected_values: 8 EVAL 01mxnvc origin 0k33p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 117.000 117.000 0.250 http://example.org/music/artist/origin #5666-0170vn PRED entity: 0170vn PRED relation: award PRED expected values: 0f4x7 0cqh46 => 119 concepts (113 used for prediction) PRED predicted values (max 10 best out of 286): 09cm54 (0.70 #20005, 0.70 #34009, 0.68 #14803), 0gqy2 (0.40 #2161, 0.16 #32807, 0.15 #33608), 0gq9h (0.33 #6477, 0.30 #7677, 0.30 #5277), 0f4x7 (0.31 #2031, 0.20 #31, 0.16 #32807), 09sdmz (0.27 #2202, 0.16 #32807, 0.15 #33608), 040njc (0.26 #6408, 0.23 #3608, 0.23 #7608), 0gr4k (0.25 #5633, 0.21 #4433, 0.13 #5233), 04dn09n (0.23 #5643, 0.20 #4443, 0.14 #3643), 04kxsb (0.23 #2123, 0.20 #123, 0.16 #32807), 099ck7 (0.23 #2263, 0.16 #32807, 0.15 #33608) >> Best rule #20005 for best value: >> intensional similarity = 2 >> extensional distance = 1324 >> proper extension: 0gsg7; 0cjdk; 027_tg; 05gnf; >> query: (?x1070, ?x1770) <- award_winner(?x1770, ?x1070), award_winner(?x7452, ?x1070) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2031 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 02nb2s; 07nx9j; 05myd2; 01hmb_; *> query: (?x1070, 0f4x7) <- film(?x1070, ?x1069), award(?x1070, ?x2183), ?x2183 = 02x4w6g *> conf = 0.31 ranks of expected_values: 4, 12 EVAL 0170vn award 0cqh46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 119.000 113.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0170vn award 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 119.000 113.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5665-0fhp9 PRED entity: 0fhp9 PRED relation: capital! PRED expected values: 012m_ => 244 concepts (202 used for prediction) PRED predicted values (max 10 best out of 200): 0345h (0.14 #163, 0.12 #430, 0.11 #563), 084n_ (0.14 #251, 0.12 #518, 0.11 #651), 059z0 (0.14 #238, 0.12 #505, 0.11 #638), 01k6y1 (0.14 #205, 0.12 #472, 0.11 #605), 06q1r (0.14 #222, 0.10 #755, 0.07 #1420), 0dv0z (0.12 #508, 0.11 #2372, 0.09 #907), 0gtzp (0.12 #533, 0.10 #799, 0.09 #932), 0f8l9c (0.12 #421, 0.10 #687, 0.09 #820), 049nq (0.12 #520, 0.09 #919, 0.06 #1985), 059j2 (0.12 #429, 0.09 #828, 0.06 #1894) >> Best rule #163 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0yl27; >> query: (?x863, 0345h) <- place_of_death(?x8938, ?x863), gender(?x8938, ?x231), first_level_division_of(?x863, ?x1355) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2641 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 0fs29; *> query: (?x863, 012m_) <- country(?x863, ?x1355), capital(?x13265, ?x863), location_of_ceremony(?x566, ?x863) *> conf = 0.05 ranks of expected_values: 62 EVAL 0fhp9 capital! 012m_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 244.000 202.000 0.143 http://example.org/location/country/capital #5664-02cft PRED entity: 02cft PRED relation: location_of_ceremony! PRED expected values: 01kkx2 => 232 concepts (91 used for prediction) PRED predicted values (max 10 best out of 227): 03j24kf (0.25 #875, 0.10 #1893, 0.10 #1637), 04vmqg (0.25 #728, 0.03 #6843, 0.03 #8118), 037s5h (0.25 #722, 0.03 #6837, 0.03 #8112), 01x72k (0.25 #610, 0.03 #6725, 0.03 #8000), 01f7j9 (0.25 #558, 0.03 #6673, 0.03 #7948), 02m30v (0.12 #3309, 0.10 #1779, 0.08 #5603), 01rwcgb (0.10 #1753, 0.08 #2773, 0.08 #2517), 014v1q (0.10 #1771, 0.08 #2791, 0.08 #2535), 0436kgz (0.10 #1687, 0.08 #2707, 0.08 #2451), 02g0rb (0.10 #1682, 0.08 #2702, 0.08 #2446) >> Best rule #875 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 03rt9; >> query: (?x6357, 03j24kf) <- contains(?x6357, ?x8694), film_release_region(?x6394, ?x6357), ?x8694 = 011xy1 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02cft location_of_ceremony! 01kkx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 232.000 91.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #5663-03kx49 PRED entity: 03kx49 PRED relation: currency PRED expected values: 09nqf => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.81 #106, 0.80 #113, 0.80 #99), 01nv4h (0.02 #135, 0.02 #156, 0.02 #219), 02l6h (0.01 #158, 0.01 #165, 0.01 #200) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 193 >> proper extension: 047msdk; 0cz8mkh; 01hw5kk; 0243cq; 0h21v2; 016ky6; 0b6l1st; 032clf; 09p5mwg; >> query: (?x7723, 09nqf) <- film(?x5245, ?x7723), film_distribution_medium(?x7723, ?x81), genre(?x7723, ?x53), gender(?x5245, ?x231) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03kx49 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.805 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #5662-02jqjm PRED entity: 02jqjm PRED relation: group! PRED expected values: 05148p4 => 91 concepts (65 used for prediction) PRED predicted values (max 10 best out of 122): 05148p4 (0.85 #279, 0.80 #540, 0.79 #1326), 03bx0bm (0.65 #1506, 0.64 #1942, 0.62 #1593), 028tv0 (0.45 #359, 0.44 #1493, 0.43 #1319), 03qjg (0.40 #308, 0.36 #395, 0.29 #569), 07y_7 (0.40 #263, 0.13 #1571, 0.12 #1484), 06ncr (0.35 #299, 0.17 #1346, 0.17 #560), 01vj9c (0.28 #2544, 0.28 #2722, 0.27 #2810), 05r5c (0.25 #2538, 0.24 #2627, 0.24 #2363), 07c6l (0.25 #8, 0.18 #356, 0.17 #530), 0l14j_ (0.25 #312, 0.18 #399, 0.14 #1620) >> Best rule #279 for best value: >> intensional similarity = 8 >> extensional distance = 18 >> proper extension: 0123r4; >> query: (?x5512, 05148p4) <- group(?x716, ?x5512), group(?x315, ?x5512), group(?x228, ?x5512), group(?x227, ?x5512), ?x227 = 0342h, ?x315 = 0l14md, ?x716 = 018vs, ?x228 = 0l14qv >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jqjm group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 65.000 0.850 http://example.org/music/performance_role/regular_performances./music/group_membership/group #5661-0crh5_f PRED entity: 0crh5_f PRED relation: genre PRED expected values: 02kdv5l => 138 concepts (124 used for prediction) PRED predicted values (max 10 best out of 170): 05zjd (0.67 #6254, 0.66 #3609, 0.65 #9626), 05p553 (0.56 #9026, 0.53 #12537, 0.42 #5055), 02n4kr (0.51 #6141, 0.50 #3496, 0.31 #3375), 02kdv5l (0.50 #2, 0.44 #11685, 0.43 #3369), 03k9fj (0.50 #613, 0.42 #5062, 0.39 #3740), 02l7c8 (0.44 #11698, 0.39 #14004, 0.36 #13032), 0hcr (0.40 #263, 0.25 #143, 0.18 #865), 01hmnh (0.33 #378, 0.30 #619, 0.29 #6029), 09blyk (0.29 #6164, 0.23 #3519, 0.19 #3398), 060__y (0.25 #2181, 0.25 #16, 0.22 #3383) >> Best rule #6254 for best value: >> intensional similarity = 7 >> extensional distance = 137 >> proper extension: 02qzmz6; 0yx7h; >> query: (?x2954, ?x6753) <- film_release_distribution_medium(?x2954, ?x81), titles(?x6753, ?x2954), titles(?x812, ?x2954), genre(?x2954, ?x604), ?x812 = 01jfsb, genre(?x3471, ?x604), ?x3471 = 07cyl >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 0cnztc4; 0dgrwqr; *> query: (?x2954, 02kdv5l) <- film_crew_role(?x2954, ?x468), film_festivals(?x2954, ?x6828), film(?x609, ?x2954), ?x468 = 02r96rf, ?x6828 = 0fpkxfd, film_release_region(?x2954, ?x94) *> conf = 0.50 ranks of expected_values: 4 EVAL 0crh5_f genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 138.000 124.000 0.674 http://example.org/film/film/genre #5660-0k6yt1 PRED entity: 0k6yt1 PRED relation: profession PRED expected values: 0dz3r => 109 concepts (83 used for prediction) PRED predicted values (max 10 best out of 61): 09jwl (0.64 #760, 0.60 #1797, 0.56 #1649), 0dz3r (0.62 #2, 0.49 #150, 0.48 #298), 016z4k (0.42 #1634, 0.42 #597, 0.41 #893), 01d_h8 (0.34 #2821, 0.33 #2525, 0.31 #3564), 01c72t (0.27 #4026, 0.27 #5067, 0.24 #3135), 0n1h (0.27 #160, 0.26 #605, 0.25 #308), 0dxtg (0.25 #11729, 0.25 #10100, 0.25 #12174), 039v1 (0.22 #1814, 0.21 #777, 0.19 #1666), 03gjzk (0.22 #10101, 0.21 #11730, 0.21 #11582), 02jknp (0.20 #2823, 0.20 #2527, 0.19 #2675) >> Best rule #760 for best value: >> intensional similarity = 4 >> extensional distance = 194 >> proper extension: 0c7ct; 01q7cb_; 01p45_v; 012zng; 0285c; 025tdwc; 02jg92; 013v5j; 01tp5bj; 03xl77; ... >> query: (?x11123, 09jwl) <- profession(?x11123, ?x2348), profession(?x11123, ?x1032), ?x1032 = 02hrh1q, ?x2348 = 0nbcg >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 01wgfp6; *> query: (?x11123, 0dz3r) <- award(?x11123, ?x6287), origin(?x11123, ?x2254), ?x6287 = 02f75t *> conf = 0.62 ranks of expected_values: 2 EVAL 0k6yt1 profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 83.000 0.638 http://example.org/people/person/profession #5659-0gghm PRED entity: 0gghm PRED relation: role! PRED expected values: 01m1dzc => 72 concepts (42 used for prediction) PRED predicted values (max 10 best out of 747): 050z2 (0.71 #2529, 0.67 #9096, 0.67 #1591), 04bpm6 (0.67 #1008, 0.53 #11328, 0.50 #9917), 016ntp (0.67 #1080, 0.50 #612, 0.43 #2015), 0137g1 (0.61 #9027, 0.44 #5270, 0.43 #6207), 023l9y (0.57 #2082, 0.56 #9120, 0.50 #3962), 01wxdn3 (0.57 #2753, 0.50 #9320, 0.50 #5096), 02s6sh (0.57 #2779, 0.50 #5122, 0.50 #1841), 045zr (0.57 #2452, 0.50 #4795, 0.50 #1514), 03ryks (0.56 #5452, 0.50 #6389, 0.50 #1236), 0565cz (0.50 #4820, 0.50 #3886, 0.50 #1539) >> Best rule #2529 for best value: >> intensional similarity = 19 >> extensional distance = 5 >> proper extension: 0l14md; >> query: (?x2310, 050z2) <- role(?x2310, ?x2923), role(?x2310, ?x2048), role(?x2310, ?x645), role(?x2310, ?x75), role(?x1495, ?x2310), ?x75 = 07y_7, role(?x2575, ?x2310), performance_role(?x736, ?x2310), role(?x6449, ?x2310), ?x2048 = 018j2, ?x1495 = 013y1f, ?x2923 = 02k856, role(?x2310, ?x1436), ?x645 = 028tv0, role(?x3321, ?x2310), group(?x2310, ?x3109), award_nominee(?x483, ?x3321), award(?x3321, ?x4892), ?x4892 = 02f72_ >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #657 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 2 *> proper extension: 0342h; *> query: (?x2310, 01m1dzc) <- role(?x2310, ?x2923), role(?x2310, ?x2309), role(?x2310, ?x2048), role(?x2310, ?x645), role(?x2310, ?x75), role(?x8014, ?x2310), role(?x1495, ?x2310), ?x75 = 07y_7, role(?x2575, ?x2310), performance_role(?x736, ?x2310), role(?x6449, ?x2310), ?x2048 = 018j2, ?x1495 = 013y1f, ?x2923 = 02k856, role(?x2310, ?x1436), ?x645 = 028tv0, role(?x3321, ?x2310), group(?x2310, ?x3109), award_nominee(?x4288, ?x3321), ?x8014 = 0214km, ?x4288 = 018dyl, ?x2309 = 06ncr *> conf = 0.25 ranks of expected_values: 226 EVAL 0gghm role! 01m1dzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 72.000 42.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #5658-05pd94v PRED entity: 05pd94v PRED relation: ceremony! PRED expected values: 025m8l 01dpdh 01dk00 0257w4 026mff 025m98 024_dt => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 225): 024_dt (0.86 #3069, 0.85 #2705, 0.82 #2524), 01dk00 (0.86 #2973, 0.85 #2609, 0.82 #2428), 0257w4 (0.86 #2977, 0.85 #2613, 0.82 #2432), 01ckrr (0.85 #2652, 0.80 #902, 0.80 #2290), 025m8l (0.80 #902, 0.80 #2055, 0.80 #2893), 025m98 (0.80 #902, 0.80 #2893, 0.79 #3018), 026mff (0.80 #902, 0.80 #2893, 0.79 #2988), 03nl5k (0.80 #902, 0.80 #2893, 0.77 #3255), 03t5b6 (0.80 #902, 0.80 #2893, 0.77 #3255), 02flpq (0.80 #902, 0.80 #2893, 0.77 #3255) >> Best rule #3069 for best value: >> intensional similarity = 19 >> extensional distance = 12 >> proper extension: 0jzphpx; 01mhwk; >> query: (?x139, 024_dt) <- ceremony(?x8409, ?x139), ceremony(?x528, ?x139), award_winner(?x139, ?x6835), award_winner(?x139, ?x3632), award_winner(?x139, ?x1720), ?x8409 = 03ncb2, award(?x5364, ?x528), award(?x2925, ?x528), award_nominee(?x5364, ?x286), category(?x3632, ?x134), participant(?x5364, ?x1117), participant(?x6835, ?x2227), artist(?x2299, ?x5364), ?x2925 = 01vx5w7, profession(?x3632, ?x220), award_nominee(?x3632, ?x158), location_of_ceremony(?x5364, ?x1523), role(?x3632, ?x227), artists(?x505, ?x1720) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 6, 7, 11 EVAL 05pd94v ceremony! 024_dt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 05pd94v ceremony! 025m98 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 38.000 38.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 05pd94v ceremony! 026mff CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 38.000 38.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 05pd94v ceremony! 0257w4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 05pd94v ceremony! 01dk00 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 05pd94v ceremony! 01dpdh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 38.000 38.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 05pd94v ceremony! 025m8l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 38.000 38.000 0.857 http://example.org/award/award_category/winners./award/award_honor/ceremony #5657-077qn PRED entity: 077qn PRED relation: administrative_parent PRED expected values: 02j71 => 183 concepts (97 used for prediction) PRED predicted values (max 10 best out of 34): 02j71 (0.86 #8705, 0.85 #8981, 0.85 #10501), 087vz (0.75 #547, 0.56 #546, 0.22 #6761), 03rjj (0.60 #413, 0.08 #7040, 0.08 #7732), 02j9z (0.56 #546, 0.22 #6761, 0.19 #4271), 02qkt (0.56 #546, 0.22 #6761, 0.19 #4271), 09c7w0 (0.45 #3033, 0.39 #4134, 0.38 #3997), 09b69 (0.17 #6346, 0.16 #6759, 0.13 #5929), 0d05w3 (0.10 #456, 0.03 #5977, 0.02 #11505), 059rby (0.08 #6214, 0.08 #4003, 0.07 #6627), 07ssc (0.07 #559, 0.05 #1665, 0.05 #1802) >> Best rule #8705 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 035hm; >> query: (?x4059, 02j71) <- adjoins(?x2979, ?x4059), country(?x1121, ?x2979), currency(?x4059, ?x170), countries_spoken_in(?x8650, ?x4059) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 077qn administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 97.000 0.860 http://example.org/base/aareas/schema/administrative_area/administrative_parent #5656-01wmjkb PRED entity: 01wmjkb PRED relation: artist! PRED expected values: 015_1q => 136 concepts (108 used for prediction) PRED predicted values (max 10 best out of 133): 0n85g (0.31 #1050, 0.31 #345, 0.21 #627), 033hn8 (0.31 #295, 0.21 #859, 0.16 #1282), 0181dw (0.29 #606, 0.22 #747, 0.17 #888), 0k_kr (0.29 #608, 0.17 #890, 0.08 #2300), 023rwm (0.25 #143, 0.15 #284, 0.14 #1130), 01cszh (0.22 #715, 0.15 #292, 0.12 #1420), 01dtcb (0.22 #752, 0.11 #1457, 0.09 #2303), 01w40h (0.21 #592, 0.21 #874, 0.12 #1438), 01clyr (0.21 #597, 0.17 #879, 0.11 #1302), 03rhqg (0.21 #1002, 0.17 #720, 0.17 #156) >> Best rule #1050 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 01vwyqp; 01svw8n; >> query: (?x8341, 0n85g) <- film(?x8341, ?x9858), artists(?x2249, ?x8341), artists(?x2249, ?x2073), ?x2073 = 01czx >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #6929 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 444 *> proper extension: 089tm; 01t_xp_; 01pfr3; 0m19t; 07qnf; 02r3zy; 07c0j; 01wv9xn; 03t9sp; 04r1t; ... *> query: (?x8341, 015_1q) <- artist(?x1954, ?x8341), origin(?x8341, ?x11843), artists(?x671, ?x8341) *> conf = 0.19 ranks of expected_values: 11 EVAL 01wmjkb artist! 015_1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 136.000 108.000 0.308 http://example.org/music/record_label/artist #5655-0mmr1 PRED entity: 0mmr1 PRED relation: contains! PRED expected values: 081yw => 82 concepts (32 used for prediction) PRED predicted values (max 10 best out of 71): 01n7q (0.60 #3670, 0.59 #7269, 0.57 #5470), 081yw (0.53 #26067, 0.40 #1175, 0.15 #2973), 09c7w0 (0.47 #8090, 0.47 #7194, 0.34 #6293), 05kj_ (0.23 #1838, 0.22 #2736, 0.17 #3633), 06pvr (0.21 #5558, 0.20 #3758, 0.19 #4657), 059rby (0.15 #9003, 0.15 #9902, 0.14 #11701), 04_1l0v (0.12 #6741, 0.07 #4942, 0.05 #18421), 041_3z (0.09 #2639, 0.09 #3537, 0.07 #4434), 05tbn (0.09 #11006, 0.09 #15498, 0.09 #25393), 05fjf (0.09 #12952, 0.09 #13850, 0.08 #17446) >> Best rule #3670 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 0f04c; 0r679; 0r5wt; 0135g; 0f04v; 0b2ds; 02frhbc; 0r6cx; 0ckhc; 0r6c4; ... >> query: (?x8546, 01n7q) <- time_zones(?x8546, ?x2950), adjoins(?x8546, ?x8547), source(?x8546, ?x958), ?x2950 = 02lcqs, adjoins(?x8547, ?x11366) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #26067 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 280 *> proper extension: 0cr3d; 04kbn; *> query: (?x8546, ?x4600) <- adjoins(?x8546, ?x8547), source(?x8547, ?x958), ?x958 = 0jbk9, adjoins(?x8547, ?x11366), currency(?x8547, ?x170), contains(?x4600, ?x11366) *> conf = 0.53 ranks of expected_values: 2 EVAL 0mmr1 contains! 081yw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 32.000 0.600 http://example.org/location/location/contains #5654-02vmzp PRED entity: 02vmzp PRED relation: people! PRED expected values: 0dryh9k => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 54): 0dryh9k (0.48 #478, 0.42 #16, 0.39 #863), 033tf_ (0.22 #315, 0.18 #238, 0.17 #84), 041rx (0.18 #1237, 0.14 #3548, 0.14 #3704), 02sch9 (0.17 #35, 0.11 #420, 0.10 #497), 01qhm_ (0.13 #314, 0.06 #545, 0.06 #622), 07bch9 (0.12 #562, 0.11 #639, 0.11 #100), 0x67 (0.11 #87, 0.10 #780, 0.09 #2475), 03bkbh (0.11 #109, 0.09 #263, 0.09 #340), 065b6q (0.11 #80, 0.09 #234, 0.09 #311), 07hwkr (0.09 #243, 0.09 #551, 0.09 #320) >> Best rule #478 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 02xfrd; 061zc_; 02tq2r; 038b_x; 03m2fg; 01s0l0; 05_zc7; 07t3x8; 023sng; 03f02ct; ... >> query: (?x2145, 0dryh9k) <- profession(?x2145, ?x1032), ?x1032 = 02hrh1q, award(?x2145, ?x1937), ?x1937 = 03r8tl >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vmzp people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.483 http://example.org/people/ethnicity/people #5653-05k7sb PRED entity: 05k7sb PRED relation: district_represented! PRED expected values: 070mff 024tkd 01grr2 01grrf 01gsry => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 10): 070mff (0.75 #92, 0.74 #122, 0.70 #142), 024tkd (0.62 #93, 0.61 #123, 0.57 #143), 01grr2 (0.54 #201, 0.33 #14, 0.29 #94), 01gsry (0.54 #201, 0.33 #17, 0.27 #97), 01grrf (0.54 #201, 0.33 #15, 0.25 #95), 04fhps (0.19 #462, 0.16 #199, 0.16 #149), 01gvxh (0.18 #196, 0.16 #146, 0.16 #116), 04lgybj (0.18 #191, 0.16 #141, 0.16 #111), 03h_f4 (0.13 #198, 0.12 #118, 0.11 #128), 034_7s (0.10 #200, 0.10 #130, 0.09 #100) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 05kr_; >> query: (?x2020, 070mff) <- district_represented(?x6743, ?x2020), state(?x3007, ?x2020), legislative_sessions(?x652, ?x6743) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 05k7sb district_represented! 01gsry CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.750 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05k7sb district_represented! 01grrf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.750 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05k7sb district_represented! 01grr2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.750 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05k7sb district_represented! 024tkd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.750 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05k7sb district_represented! 070mff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.750 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #5652-0lbj1 PRED entity: 0lbj1 PRED relation: award PRED expected values: 02f6xy 02x17c2 => 133 concepts (131 used for prediction) PRED predicted values (max 10 best out of 280): 02f716 (0.56 #1710, 0.20 #2868, 0.13 #40533), 02f5qb (0.55 #1690, 0.25 #2848, 0.14 #6708), 02f72n (0.52 #1680, 0.18 #2838, 0.13 #40533), 02f73b (0.48 #1813, 0.20 #2971, 0.13 #40533), 02v1m7 (0.35 #1650, 0.14 #2808, 0.13 #106), 09sb52 (0.32 #30146, 0.32 #28602, 0.31 #22812), 054krc (0.29 #4714, 0.28 #3170, 0.14 #10504), 01ckcd (0.27 #1862, 0.26 #3020, 0.13 #40533), 0l8z1 (0.26 #4692, 0.24 #3148, 0.12 #42464), 02f705 (0.26 #1687, 0.15 #2845, 0.12 #6705) >> Best rule #1710 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 089tm; 01pfr3; 01vsxdm; 01r9fv; 0dtd6; 016fmf; 014_lq; 01jfr3y; 0b1zz; 0178kd; ... >> query: (?x248, 02f716) <- artists(?x505, ?x248), award(?x248, ?x4892), ?x4892 = 02f72_ >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1732 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 089tm; 01pfr3; 01vsxdm; 01r9fv; 0dtd6; 016fmf; 014_lq; 01jfr3y; 0b1zz; 0178kd; ... *> query: (?x248, 02f6xy) <- artists(?x505, ?x248), award(?x248, ?x4892), ?x4892 = 02f72_ *> conf = 0.23 ranks of expected_values: 13, 18 EVAL 0lbj1 award 02x17c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 133.000 131.000 0.561 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0lbj1 award 02f6xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 133.000 131.000 0.561 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5651-02v8kmz PRED entity: 02v8kmz PRED relation: film! PRED expected values: 016tt2 => 64 concepts (53 used for prediction) PRED predicted values (max 10 best out of 57): 086k8 (0.25 #2, 0.21 #756, 0.21 #832), 016tt2 (0.25 #4, 0.17 #683, 0.12 #834), 05qd_ (0.22 #235, 0.20 #688, 0.18 #84), 03xq0f (0.17 #5, 0.11 #156, 0.10 #306), 0g1rw (0.16 #687, 0.07 #385, 0.07 #838), 017s11 (0.16 #607, 0.14 #154, 0.14 #304), 016tw3 (0.14 #1145, 0.13 #388, 0.13 #2662), 01795t (0.14 #169, 0.09 #622, 0.06 #1302), 0gyx4 (0.12 #151, 0.06 #2956, 0.06 #1057), 017jv5 (0.10 #694, 0.07 #392, 0.07 #769) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0gfzfj; >> query: (?x240, 086k8) <- film(?x1522, ?x240), genre(?x240, ?x53), ?x1522 = 02lkcc >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0gfzfj; *> query: (?x240, 016tt2) <- film(?x1522, ?x240), genre(?x240, ?x53), ?x1522 = 02lkcc *> conf = 0.25 ranks of expected_values: 2 EVAL 02v8kmz film! 016tt2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 53.000 0.250 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5650-012gx2 PRED entity: 012gx2 PRED relation: student! PRED expected values: 062z7 => 182 concepts (181 used for prediction) PRED predicted values (max 10 best out of 61): 062z7 (0.50 #21, 0.36 #83, 0.27 #328), 04gb7 (0.38 #33, 0.15 #401, 0.14 #95), 0g26h (0.21 #93, 0.08 #644, 0.08 #338), 02822 (0.19 #1748, 0.19 #3157, 0.18 #3096), 02j62 (0.14 #85, 0.08 #2842, 0.07 #2353), 03qsdpk (0.12 #3470, 0.12 #3961, 0.10 #4392), 0fdys (0.12 #1990, 0.09 #2358, 0.09 #2420), 0w7c (0.10 #4029, 0.09 #2860, 0.09 #2371), 01zc2w (0.08 #3482, 0.07 #3113, 0.06 #2315), 05qjt (0.08 #1723, 0.05 #1967, 0.05 #2947) >> Best rule #21 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 04xfb; 0c_md_; >> query: (?x5804, 062z7) <- type_of_union(?x5804, ?x566), student(?x2605, ?x5804), location(?x5804, ?x9544), profession(?x5804, ?x3342), ?x3342 = 04gc2 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012gx2 student! 062z7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 181.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/student #5649-02h48 PRED entity: 02h48 PRED relation: type_of_union PRED expected values: 04ztj => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.87 #93, 0.87 #29, 0.86 #85), 01g63y (0.20 #385, 0.19 #6, 0.14 #238), 01bl8s (0.20 #385, 0.03 #15, 0.03 #19), 0jgjn (0.20 #385, 0.01 #88) >> Best rule #93 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 0d1_f; >> query: (?x12334, 04ztj) <- location_of_ceremony(?x12334, ?x3892), location(?x12334, ?x2850), place_of_birth(?x217, ?x2850) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02h48 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.866 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5648-02zft0 PRED entity: 02zft0 PRED relation: award_winner! PRED expected values: 0bzk8w => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 124): 0bzk8w (0.28 #3615, 0.17 #5423, 0.10 #8065), 09gkdln (0.28 #3615, 0.04 #2205, 0.03 #3595), 0bzknt (0.17 #5423, 0.14 #82, 0.10 #8065), 013b2h (0.14 #80, 0.10 #914, 0.07 #358), 0bzmt8 (0.14 #98, 0.10 #8065, 0.04 #515), 026kqs9 (0.14 #90, 0.10 #8065, 0.03 #507), 0bzjgq (0.14 #117, 0.10 #8065, 0.03 #534), 09pnw5 (0.14 #102, 0.10 #8065, 0.02 #2187), 01bx35 (0.14 #7, 0.10 #285, 0.07 #841), 0466p0j (0.14 #76, 0.10 #354, 0.07 #910) >> Best rule #3615 for best value: >> intensional similarity = 2 >> extensional distance = 1177 >> proper extension: 06jntd; >> query: (?x6011, ?x602) <- award_winner(?x10948, ?x6011), honored_for(?x602, ?x10948) >> conf = 0.28 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 02zft0 award_winner! 0bzk8w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.280 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5647-0739y PRED entity: 0739y PRED relation: profession PRED expected values: 02hrh1q => 83 concepts (60 used for prediction) PRED predicted values (max 10 best out of 122): 02hrh1q (0.98 #7603, 0.94 #4593, 0.87 #8175), 01d_h8 (0.47 #7310, 0.36 #864, 0.34 #3721), 0kyk (0.46 #1457, 0.45 #6185, 0.37 #1314), 0nbcg (0.46 #6903, 0.42 #6617, 0.38 #6760), 03gjzk (0.41 #2589, 0.40 #3018, 0.38 #1158), 01c72t (0.38 #6752, 0.23 #6609, 0.20 #6895), 016z4k (0.35 #6878, 0.35 #6592, 0.32 #4584), 0np9r (0.34 #3721, 0.33 #18, 0.32 #5584), 02jknp (0.34 #3721, 0.32 #5584, 0.30 #6015), 0n1h (0.34 #3721, 0.32 #5584, 0.30 #6015) >> Best rule #7603 for best value: >> intensional similarity = 6 >> extensional distance = 2287 >> proper extension: 06v8s0; 01sl1q; 044mz_; 07nznf; 0184jc; 04bdxl; 02s2ft; 079vf; 05vsxz; 06qgvf; ... >> query: (?x7679, 02hrh1q) <- nationality(?x7679, ?x429), profession(?x7679, ?x1146), profession(?x12334, ?x1146), profession(?x4107, ?x1146), ?x4107 = 073749, ?x12334 = 02h48 >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0739y profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 60.000 0.983 http://example.org/people/person/profession #5646-06rqw PRED entity: 06rqw PRED relation: artists PRED expected values: 01tv3x2 01dw_f 01w5gg6 => 59 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1099): 03t9sp (0.67 #7632, 0.60 #4414, 0.56 #5486), 03fbc (0.67 #7712, 0.60 #4494, 0.33 #2348), 01dwrc (0.67 #8027, 0.40 #4809, 0.33 #1591), 01323p (0.60 #4981, 0.50 #3908, 0.44 #6053), 06p03s (0.60 #5294, 0.50 #8512, 0.33 #3148), 03f5spx (0.60 #4348, 0.50 #7566, 0.33 #2202), 01vvycq (0.60 #4337, 0.44 #5409, 0.42 #7555), 01x1cn2 (0.60 #4486, 0.42 #7704, 0.33 #2340), 049qx (0.60 #4671, 0.42 #7889, 0.33 #2525), 019x62 (0.60 #4920, 0.42 #8138, 0.33 #2774) >> Best rule #7632 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 0m0jc; 029h7y; 08cyft; 0gywn; 03mb9; 012yc; >> query: (?x6101, 03t9sp) <- artists(?x6101, ?x4628), artists(?x6101, ?x3894), parent_genre(?x283, ?x6101), ?x3894 = 01vxlbm, award_winner(?x3488, ?x4628), award(?x4628, ?x704) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3894 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 011j5x; 059kh; *> query: (?x6101, 01dw_f) <- artists(?x6101, ?x8058), artists(?x6101, ?x3894), artists(?x6101, ?x2521), parent_genre(?x283, ?x6101), artist(?x1954, ?x3894), ?x8058 = 014pg1, ?x2521 = 0frsw *> conf = 0.50 ranks of expected_values: 30, 232, 338 EVAL 06rqw artists 01w5gg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 59.000 23.000 0.667 http://example.org/music/genre/artists EVAL 06rqw artists 01dw_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 59.000 23.000 0.667 http://example.org/music/genre/artists EVAL 06rqw artists 01tv3x2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 59.000 23.000 0.667 http://example.org/music/genre/artists #5645-0gy2y8r PRED entity: 0gy2y8r PRED relation: film! PRED expected values: 02jr26 => 90 concepts (48 used for prediction) PRED predicted values (max 10 best out of 874): 0738b8 (0.25 #404, 0.14 #2485, 0.03 #14973), 0f5xn (0.17 #969, 0.14 #3050, 0.05 #15538), 0147dk (0.17 #81, 0.14 #2162, 0.03 #54119), 02js_6 (0.17 #1973, 0.14 #4054, 0.02 #16542), 027kmrb (0.15 #16651, 0.13 #47874, 0.12 #54120), 0170s4 (0.14 #2479, 0.08 #398, 0.07 #35385), 01hmb_ (0.14 #3791, 0.08 #1710, 0.02 #7953), 0d_84 (0.14 #2124, 0.08 #43, 0.02 #29182), 0jfx1 (0.12 #6649, 0.07 #8730, 0.07 #12894), 0b25vg (0.11 #5934, 0.07 #35385, 0.04 #10096) >> Best rule #404 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 04ynx7; >> query: (?x4041, 0738b8) <- film(?x4702, ?x4041), film_crew_role(?x4041, ?x137), genre(?x4041, ?x53), ?x4702 = 01kwsg >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #15799 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 117 *> proper extension: 04fzfj; 02hxhz; 0b73_1d; 02qm_f; 048scx; 0k2sk; 07y9w5; 0340hj; 0fdv3; 0cz_ym; ... *> query: (?x4041, 02jr26) <- film(?x237, ?x4041), film_crew_role(?x4041, ?x137), executive_produced_by(?x4041, ?x5647), crewmember(?x4041, ?x9151) *> conf = 0.02 ranks of expected_values: 580 EVAL 0gy2y8r film! 02jr26 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 48.000 0.250 http://example.org/film/actor/film./film/performance/film #5644-03qcfvw PRED entity: 03qcfvw PRED relation: language PRED expected values: 02h40lc => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 28): 02h40lc (0.95 #1171, 0.95 #467, 0.95 #1582), 06nm1 (0.11 #185, 0.10 #302, 0.10 #947), 04306rv (0.10 #179, 0.09 #63, 0.09 #529), 0653m (0.08 #186, 0.05 #594, 0.05 #653), 06b_j (0.07 #604, 0.07 #663, 0.07 #546), 03_9r (0.07 #651, 0.06 #1709, 0.06 #592), 02bjrlw (0.07 #1463, 0.06 #59, 0.06 #642), 012w70 (0.04 #187, 0.04 #537, 0.03 #595), 0jzc (0.04 #602, 0.04 #544, 0.04 #661), 0459q4 (0.03 #210, 0.02 #618, 0.02 #677) >> Best rule #1171 for best value: >> intensional similarity = 4 >> extensional distance = 665 >> proper extension: 03g90h; 09xbpt; 0dq626; 0dnvn3; 03h_yy; 02_1sj; 0gx9rvq; 026mfbr; 035xwd; 03ckwzc; ... >> query: (?x103, 02h40lc) <- film_crew_role(?x103, ?x1171), genre(?x103, ?x225), ?x1171 = 09vw2b7, language(?x103, ?x5607) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qcfvw language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.949 http://example.org/film/film/language #5643-0gr69 PRED entity: 0gr69 PRED relation: artists! PRED expected values: 01qzt1 => 107 concepts (74 used for prediction) PRED predicted values (max 10 best out of 284): 064t9 (0.66 #17909, 0.52 #5874, 0.48 #13901), 0155w (0.50 #2882, 0.39 #6894, 0.29 #4732), 0xhtw (0.48 #6805, 0.45 #6187, 0.43 #10817), 05bt6j (0.48 #5905, 0.35 #7757, 0.33 #17940), 03lty (0.40 #955, 0.33 #1571, 0.25 #3420), 01_bkd (0.40 #982, 0.33 #1598, 0.20 #1290), 02t8gf (0.40 #1069, 0.33 #1685, 0.20 #1377), 06j6l (0.38 #2825, 0.36 #17945, 0.32 #19489), 0gywn (0.38 #2834, 0.29 #5611, 0.29 #4684), 01lyv (0.38 #2810, 0.24 #10214, 0.24 #13305) >> Best rule #17909 for best value: >> intensional similarity = 4 >> extensional distance = 446 >> proper extension: 01pbxb; 07s3vqk; 0197tq; 01l1b90; 01vw87c; 01vrx3g; 032nwy; 0147dk; 02mslq; 06cc_1; ... >> query: (?x7188, 064t9) <- award(?x7188, ?x2634), artists(?x7083, ?x7188), artists(?x7083, ?x3244), ?x3244 = 02wb6yq >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #7099 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 54 *> proper extension: 089tm; 01vrncs; 0lgsq; 018y2s; 067mj; 05crg7; 01czx; 01kv4mb; 016h9b; 01vn35l; ... *> query: (?x7188, ?x2407) <- award(?x7188, ?x2634), artists(?x7083, ?x7188), artists(?x5934, ?x7188), ?x7083 = 02yv6b, parent_genre(?x2407, ?x5934) *> conf = 0.06 ranks of expected_values: 110 EVAL 0gr69 artists! 01qzt1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 107.000 74.000 0.658 http://example.org/music/genre/artists #5642-0785v8 PRED entity: 0785v8 PRED relation: award_nominee PRED expected values: 0pmhf => 88 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1052): 05mc99 (0.83 #6973, 0.81 #18596, 0.81 #16270), 02p7_k (0.83 #6973, 0.81 #18596, 0.81 #16270), 05vsxz (0.83 #6973, 0.81 #18596, 0.81 #16270), 014v6f (0.83 #6973, 0.81 #18596, 0.81 #16270), 0785v8 (0.56 #2475, 0.33 #4799, 0.30 #62778), 02ck7w (0.54 #5889, 0.03 #19837, 0.02 #25571), 0241jw (0.54 #5039, 0.03 #18987, 0.02 #32935), 0pmhf (0.50 #2887, 0.29 #83701, 0.25 #55800), 09wj5 (0.50 #4764, 0.03 #18712, 0.02 #25571), 01jw4r (0.50 #1877, 0.03 #8850, 0.02 #25571) >> Best rule #6973 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 05wqr1; >> query: (?x818, ?x100) <- nominated_for(?x818, ?x1877), award_nominee(?x230, ?x818), award_nominee(?x100, ?x818), ?x230 = 02bfmn >> conf = 0.83 => this is the best rule for 4 predicted values *> Best rule #2887 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 05vsxz; 06qgvf; 06dv3; 04wvhz; 07s93v; 03n_7k; 0pmhf; 06chf; 014v6f; 04mg6l; ... *> query: (?x818, 0pmhf) <- nominated_for(?x818, ?x1877), award(?x818, ?x704), ?x1877 = 0cz_ym *> conf = 0.50 ranks of expected_values: 8 EVAL 0785v8 award_nominee 0pmhf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 88.000 36.000 0.828 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5641-048tv9 PRED entity: 048tv9 PRED relation: genre PRED expected values: 02kdv5l => 134 concepts (79 used for prediction) PRED predicted values (max 10 best out of 100): 02kdv5l (0.88 #1056, 0.86 #705, 0.86 #1173), 06n90 (0.66 #1182, 0.64 #1065, 0.59 #714), 05p553 (0.62 #2345, 0.60 #5, 0.56 #824), 07s9rl0 (0.61 #5158, 0.57 #8562, 0.56 #8444), 0gf28 (0.60 #62, 0.10 #3692, 0.10 #3223), 07yjb (0.56 #6213, 0.53 #7503, 0.51 #6095), 0l4h_ (0.40 #71, 0.02 #3701, 0.02 #4760), 0lsxr (0.35 #8922, 0.31 #1530, 0.28 #1062), 02l7c8 (0.34 #4821, 0.33 #6580, 0.33 #3882), 09blyk (0.29 #264, 0.20 #147, 0.08 #8943) >> Best rule #1056 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 07f_t4; >> query: (?x8068, 02kdv5l) <- film_release_distribution_medium(?x8068, ?x81), story_by(?x8068, ?x8582), genre(?x8068, ?x6888), ?x6888 = 04pbhw, currency(?x8068, ?x170) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 048tv9 genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 79.000 0.880 http://example.org/film/film/genre #5640-07vyf PRED entity: 07vyf PRED relation: institution! PRED expected values: 02h4rq6 014mlp => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.82 #310, 0.80 #410, 0.79 #430), 014mlp (0.74 #313, 0.74 #107, 0.69 #148), 016t_3 (0.71 #64, 0.71 #44, 0.69 #146), 0bkj86 (0.68 #69, 0.64 #151, 0.61 #49), 04zx3q1 (0.54 #62, 0.50 #144, 0.48 #184), 07s6fsf (0.54 #41, 0.49 #286, 0.48 #143), 013zdg (0.39 #68, 0.36 #48, 0.33 #150), 03mkk4 (0.29 #71, 0.28 #153, 0.27 #133), 0bjrnt (0.29 #67, 0.24 #129, 0.22 #149), 01rr_d (0.25 #197, 0.20 #137, 0.18 #75) >> Best rule #310 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 02zc7f; >> query: (?x4296, 02h4rq6) <- student(?x4296, ?x3927), school_type(?x4296, ?x1507), school(?x700, ?x4296) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07vyf institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.816 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07vyf institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.816 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5639-05fjy PRED entity: 05fjy PRED relation: vacationer PRED expected values: 01k5zk => 170 concepts (166 used for prediction) PRED predicted values (max 10 best out of 177): 03lt8g (0.11 #2695, 0.08 #5903, 0.08 #3941), 016fnb (0.10 #4023, 0.10 #7951, 0.08 #4559), 0bksh (0.10 #4027, 0.10 #4563, 0.09 #5276), 0bbf1f (0.10 #3981, 0.08 #5943, 0.06 #10227), 0320jz (0.10 #3952, 0.07 #7880, 0.07 #2706), 0261x8t (0.10 #4597, 0.09 #5310, 0.09 #7989), 0k8y7 (0.09 #4097), 05r5w (0.08 #3994, 0.07 #7922, 0.07 #5243), 01dw4q (0.08 #3921, 0.07 #2318, 0.07 #5883), 01pgzn_ (0.08 #3963, 0.07 #5925, 0.07 #2717) >> Best rule #2695 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0261m; >> query: (?x5575, 03lt8g) <- contains(?x5575, ?x6683), vacationer(?x5575, ?x4284), taxonomy(?x5575, ?x939) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #4003 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 01mc11; *> query: (?x5575, 01k5zk) <- contains(?x5575, ?x6683), vacationer(?x5575, ?x4284), participant(?x4285, ?x4284) *> conf = 0.08 ranks of expected_values: 12 EVAL 05fjy vacationer 01k5zk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 170.000 166.000 0.109 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #5638-0qlrh PRED entity: 0qlrh PRED relation: place! PRED expected values: 0qlrh => 87 concepts (74 used for prediction) PRED predicted values (max 10 best out of 171): 0167q3 (0.12 #173, 0.09 #688, 0.06 #1203), 0rh6k (0.12 #2, 0.04 #2577, 0.04 #2062), 01m1zk (0.09 #607, 0.06 #1122, 0.04 #1637), 0rqf1 (0.09 #837, 0.06 #1352, 0.04 #1867), 0rnmy (0.09 #571, 0.04 #2116, 0.04 #1601), 01cx_ (0.09 #579, 0.04 #2124, 0.04 #3154), 0n5kc (0.07 #17000, 0.07 #21644, 0.06 #20093), 02dtg (0.06 #1039, 0.04 #2069, 0.04 #1554), 0h6l4 (0.06 #1406, 0.04 #2436, 0.04 #1921), 01tlmw (0.06 #1040, 0.04 #2070, 0.04 #1555) >> Best rule #173 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0h7h6; >> query: (?x13665, 0167q3) <- place_of_death(?x4447, ?x13665), time_zones(?x13665, ?x2674), ?x2674 = 02hcv8, profession(?x4447, ?x524), award_winner(?x3510, ?x4447), award(?x4447, ?x1703) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qlrh place! 0qlrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 74.000 0.125 http://example.org/location/hud_county_place/place #5637-030hbp PRED entity: 030hbp PRED relation: film PRED expected values: 03lvwp 08sk8l => 107 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1086): 02py4c8 (0.59 #58997, 0.58 #116202, 0.58 #10725), 0431v3 (0.59 #58997, 0.58 #116202, 0.58 #10725), 0g60z (0.58 #116202, 0.58 #10725, 0.49 #46480), 08r4x3 (0.44 #7304, 0.05 #12667, 0.04 #16242), 04vr_f (0.13 #7321, 0.05 #132292, 0.03 #3745), 0c0zq (0.11 #1562, 0.07 #3349, 0.05 #132292), 058kh7 (0.11 #1578, 0.07 #3365, 0.04 #15878), 08s6mr (0.11 #1318, 0.07 #3105, 0.03 #13831), 03bzyn4 (0.11 #1567, 0.07 #3354, 0.03 #15867), 0prrm (0.11 #860, 0.07 #2647, 0.03 #4434) >> Best rule #58997 for best value: >> intensional similarity = 3 >> extensional distance = 643 >> proper extension: 025p38; >> query: (?x10491, ?x715) <- location(?x10491, ?x4776), film(?x10491, ?x4643), award_winner(?x715, ?x10491) >> conf = 0.59 => this is the best rule for 2 predicted values *> Best rule #132292 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1761 *> proper extension: 0flpy; 01p0w_; *> query: (?x10491, ?x337) <- award_nominee(?x10491, ?x820), award(?x10491, ?x678), award_winner(?x337, ?x820) *> conf = 0.05 ranks of expected_values: 78, 188 EVAL 030hbp film 08sk8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 107.000 94.000 0.592 http://example.org/film/actor/film./film/performance/film EVAL 030hbp film 03lvwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 107.000 94.000 0.592 http://example.org/film/actor/film./film/performance/film #5636-01bpc9 PRED entity: 01bpc9 PRED relation: type_of_union PRED expected values: 04ztj => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.72 #29, 0.72 #33, 0.71 #117), 01g63y (0.22 #14, 0.15 #98, 0.14 #138), 0jgjn (0.01 #60) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 03qd_; 0bg539; 01wk7b7; 03llf8; 020jqv; >> query: (?x1654, 04ztj) <- actor(?x1653, ?x1654), instrumentalists(?x2798, ?x1654), role(?x2798, ?x2459), ?x2459 = 021bmf >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bpc9 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.724 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5635-042tq PRED entity: 042tq PRED relation: place PRED expected values: 042tq => 91 concepts (38 used for prediction) PRED predicted values (max 10 best out of 5): 042tq (0.09 #4642, 0.08 #11349, 0.07 #12381), 02cft (0.09 #4642, 0.08 #11349, 0.07 #12381), 01n4w (0.09 #4642, 0.08 #11349, 0.07 #12381), 059rby (0.09 #4642, 0.08 #11349, 0.07 #12381), 043z0 (0.08 #1032, 0.07 #2063, 0.05 #516) >> Best rule #4642 for best value: >> intensional similarity = 4 >> extensional distance = 302 >> proper extension: 0f2wj; 03s0w; 0f94t; 0284jb; 013jz2; 05k7sb; 050l8; 0cr3d; 04n3l; 0tj4y; ... >> query: (?x8911, ?x335) <- contains(?x94, ?x8911), ?x94 = 09c7w0, location(?x3651, ?x8911), location(?x3651, ?x335) >> conf = 0.09 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 042tq place 042tq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 38.000 0.088 http://example.org/location/hud_county_place/place #5634-05m63c PRED entity: 05m63c PRED relation: student! PRED expected values: 07w0v => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 132): 07vhb (0.20 #168, 0.17 #694, 0.08 #3326), 0m9_5 (0.20 #126, 0.03 #3810, 0.03 #4336), 0bwfn (0.18 #1852, 0.08 #20264, 0.08 #6588), 01w5m (0.17 #630, 0.04 #32718, 0.04 #41668), 02nq10 (0.17 #871, 0.04 #3503, 0.03 #4029), 011xy1 (0.17 #843, 0.04 #3475, 0.03 #4001), 09f2j (0.12 #1210, 0.09 #1736, 0.05 #6472), 03qdm (0.12 #1460, 0.05 #3039, 0.05 #2512), 01d34b (0.12 #1307, 0.05 #2886, 0.05 #2359), 065y4w7 (0.12 #1066, 0.05 #41578, 0.04 #47892) >> Best rule #168 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0794g; >> query: (?x287, 07vhb) <- participant(?x287, ?x989), ?x989 = 0151w_, nationality(?x287, ?x94), ?x94 = 09c7w0 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #8438 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 123 *> proper extension: 033wx9; 06hgym; 022q32; *> query: (?x287, 07w0v) <- participant(?x287, ?x989), participant(?x545, ?x989), currency(?x989, ?x170) *> conf = 0.02 ranks of expected_values: 72 EVAL 05m63c student! 07w0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 117.000 117.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #5633-06rpd PRED entity: 06rpd PRED relation: school PRED expected values: 0225bv => 90 concepts (68 used for prediction) PRED predicted values (max 10 best out of 565): 0lyjf (0.52 #4871, 0.50 #436, 0.45 #6167), 01vs5c (0.50 #1003, 0.43 #1556, 0.30 #2296), 06pwq (0.50 #189, 0.33 #6, 0.31 #3695), 065y4w7 (0.40 #2219, 0.35 #7772, 0.33 #6475), 012vwb (0.33 #969, 0.29 #1522, 0.20 #2262), 0j_sncb (0.33 #1140, 0.25 #221, 0.20 #2249), 0bx8pn (0.31 #3712, 0.27 #2418, 0.25 #206), 01rc6f (0.30 #2339, 0.25 #677, 0.17 #4929), 07w0v (0.29 #7775, 0.29 #1482, 0.26 #8522), 03tw2s (0.29 #1393, 0.25 #655, 0.25 #289) >> Best rule #4871 for best value: >> intensional similarity = 13 >> extensional distance = 21 >> proper extension: 01ct6; 01y3c; 01xvb; 070xg; 03b3j; 05tg3; 03lsq; 0289q; 07l2m; 04vn5; ... >> query: (?x9172, 0lyjf) <- position(?x9172, ?x2247), position(?x9172, ?x935), draft(?x9172, ?x465), ?x935 = 06b1q, sport(?x9172, ?x1083), ?x1083 = 0jm_, position(?x9172, ?x1792), position_s(?x6976, ?x2247), position_s(?x3674, ?x2247), ?x6976 = 04vn5, position(?x729, ?x2247), ?x3674 = 05tg3, team(?x3072, ?x9172) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #355 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 0jmj7; *> query: (?x9172, 0225bv) <- team(?x1177, ?x9172), school(?x9172, ?x9847), school(?x9172, ?x6953), school(?x9172, ?x4603), ?x9847 = 0187nd, major_field_of_study(?x4603, ?x1154), category(?x4603, ?x134), state_province_region(?x4603, ?x4622), school(?x465, ?x6953), institution(?x865, ?x6953), student(?x6953, ?x117) *> conf = 0.25 ranks of expected_values: 35 EVAL 06rpd school 0225bv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 90.000 68.000 0.522 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #5632-04shbh PRED entity: 04shbh PRED relation: award PRED expected values: 02x8n1n => 139 concepts (125 used for prediction) PRED predicted values (max 10 best out of 289): 09sb52 (0.46 #25432, 0.44 #41, 0.42 #2862), 05pcn59 (0.30 #888, 0.30 #3306, 0.29 #2903), 05p09zm (0.23 #931, 0.23 #3349, 0.22 #2946), 0gqy2 (0.22 #165, 0.15 #1374, 0.14 #25556), 027dtxw (0.22 #4, 0.14 #1213, 0.12 #3631), 02x73k6 (0.22 #61, 0.12 #7658, 0.08 #1270), 09qv_s (0.22 #152, 0.11 #1361, 0.10 #11437), 0bdwqv (0.22 #173, 0.10 #25564, 0.08 #27177), 02w9sd7 (0.22 #171, 0.09 #2992, 0.08 #25562), 0789_m (0.22 #20, 0.07 #25411, 0.07 #25814) >> Best rule #25432 for best value: >> intensional similarity = 3 >> extensional distance = 868 >> proper extension: 0cg9y; 0dvqq; 016fmf; 01vrwfv; 018ndc; 01rm8b; 0fcsd; 01cblr; 01fmz6; 02jqjm; ... >> query: (?x1018, 09sb52) <- award(?x1018, ?x2375), award(?x4563, ?x2375), ?x4563 = 0dzf_ >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #26722 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 953 *> proper extension: 03_wpf; *> query: (?x1018, 02x8n1n) <- film(?x1018, ?x1518), film_release_region(?x1518, ?x87), ?x87 = 05r4w *> conf = 0.05 ranks of expected_values: 142 EVAL 04shbh award 02x8n1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 139.000 125.000 0.462 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5631-05sxr_ PRED entity: 05sxr_ PRED relation: film! PRED expected values: 042z_g => 80 concepts (44 used for prediction) PRED predicted values (max 10 best out of 922): 02_p5w (0.41 #4806, 0.03 #8967, 0.03 #13128), 02gf_l (0.35 #5429, 0.04 #13751, 0.03 #9590), 046lt (0.33 #505, 0.14 #2585, 0.01 #10906), 01nm3s (0.33 #690, 0.06 #4850, 0.01 #54777), 03knl (0.33 #158, 0.03 #10559, 0.02 #20960), 0bmh4 (0.33 #418, 0.02 #8739), 01x6jd (0.33 #1935, 0.01 #14417, 0.01 #12336), 048hf (0.33 #1368), 0154qm (0.23 #6802, 0.04 #21364, 0.04 #23444), 015pkc (0.20 #6518, 0.03 #21080, 0.03 #16920) >> Best rule #4806 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 0crfwmx; 0k2sk; 0jnwx; 0k4d7; 023p7l; 0fgrm; 02xbyr; 0d4htf; 039zft; 0241y7; ... >> query: (?x10684, 02_p5w) <- film(?x1690, ?x10684), genre(?x10684, ?x53), production_companies(?x10684, ?x10685), language(?x10684, ?x254), ?x10685 = 04rcl7 >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #9233 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 57 *> proper extension: 09rfpk; *> query: (?x10684, 042z_g) <- film_crew_role(?x10684, ?x468), language(?x10684, ?x254), genre(?x10684, ?x4088), ?x4088 = 04xvh5, ?x254 = 02h40lc *> conf = 0.02 ranks of expected_values: 627 EVAL 05sxr_ film! 042z_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 80.000 44.000 0.412 http://example.org/film/actor/film./film/performance/film #5630-02lk95 PRED entity: 02lk95 PRED relation: location PRED expected values: 0ggh3 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 74): 030qb3t (0.24 #8920, 0.21 #8117, 0.20 #7314), 02_286 (0.20 #3250, 0.20 #8874, 0.18 #1644), 04jpl (0.08 #45008, 0.06 #3230, 0.06 #36173), 0cr3d (0.07 #49955, 0.06 #39513, 0.06 #948), 059rby (0.05 #7247, 0.05 #8853, 0.04 #10460), 0cc56 (0.05 #8894, 0.05 #1664, 0.04 #45048), 05jbn (0.04 #252, 0.04 #1055, 0.04 #5072), 05fkf (0.04 #38, 0.02 #4858, 0.01 #9678), 01n7q (0.04 #3276, 0.04 #8900, 0.04 #12917), 0k049 (0.04 #1615, 0.03 #2418, 0.03 #7239) >> Best rule #8920 for best value: >> intensional similarity = 3 >> extensional distance = 483 >> proper extension: 02r3cn; 01kgg9; >> query: (?x4560, 030qb3t) <- participant(?x3122, ?x4560), profession(?x4560, ?x955), location(?x4560, ?x3908) >> conf = 0.24 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02lk95 location 0ggh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 92.000 0.237 http://example.org/people/person/places_lived./people/place_lived/location #5629-03l6q0 PRED entity: 03l6q0 PRED relation: film! PRED expected values: 014z8v => 76 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1107): 01pfkw (0.71 #33118, 0.65 #20698, 0.55 #66247), 0f5xn (0.21 #21661, 0.17 #29941, 0.03 #34081), 0h5g_ (0.20 #2143, 0.06 #49683, 0.06 #53827), 0159h6 (0.20 #2142, 0.06 #49683, 0.06 #53827), 02fz3w (0.20 #3640, 0.06 #49683, 0.06 #53827), 07hbxm (0.20 #2440, 0.06 #49683, 0.06 #53827), 02zq43 (0.20 #2120, 0.06 #49683, 0.06 #53827), 0175wg (0.20 #3081, 0.06 #49683, 0.06 #53827), 09y20 (0.20 #2318, 0.06 #49683, 0.06 #53827), 01l2fn (0.20 #2332, 0.06 #49683, 0.06 #53827) >> Best rule #33118 for best value: >> intensional similarity = 3 >> extensional distance = 193 >> proper extension: 02hct1; 07c72; 01rf57; 05lfwd; 02h2vv; 0gvsh7l; 07zhjj; >> query: (?x3317, ?x4420) <- award_winner(?x3317, ?x4420), vacationer(?x1957, ?x4420), award_nominee(?x527, ?x4420) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #8984 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 05gnf; *> query: (?x3317, 014z8v) <- nominated_for(?x4420, ?x3317), program(?x4420, ?x7433), profession(?x4420, ?x131) *> conf = 0.01 ranks of expected_values: 813 EVAL 03l6q0 film! 014z8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 45.000 0.714 http://example.org/film/actor/film./film/performance/film #5628-01l87db PRED entity: 01l87db PRED relation: people! PRED expected values: 03bkbh => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 48): 03bkbh (0.44 #725, 0.36 #1341, 0.31 #494), 0x67 (0.26 #4016, 0.25 #4863, 0.25 #4940), 0xnvg (0.22 #167, 0.09 #1014, 0.09 #1476), 041rx (0.18 #2083, 0.16 #6089, 0.15 #8565), 02w7gg (0.17 #2, 0.15 #387, 0.14 #79), 02g7sp (0.14 #95, 0.09 #1019, 0.08 #403), 0d7wh (0.14 #94, 0.08 #402, 0.07 #556), 01rv7x (0.14 #116, 0.03 #1117, 0.03 #1194), 033tf_ (0.13 #5322, 0.09 #3550, 0.09 #6092), 07bch9 (0.09 #1794, 0.08 #1871, 0.08 #2333) >> Best rule #725 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 02pkpfs; 0dfrq; >> query: (?x5745, 03bkbh) <- gender(?x5745, ?x231), nationality(?x5745, ?x429), religion(?x5745, ?x2694), profession(?x5745, ?x220), ?x429 = 03rt9 >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l87db people! 03bkbh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.444 http://example.org/people/ethnicity/people #5627-047cqr PRED entity: 047cqr PRED relation: award_nominee! PRED expected values: 0697kh => 90 concepts (50 used for prediction) PRED predicted values (max 10 best out of 793): 0d7hg4 (0.85 #4662, 0.82 #13984, 0.81 #114211), 0884hk (0.85 #4662, 0.82 #13984, 0.81 #114211), 0697kh (0.85 #4662, 0.82 #13984, 0.81 #114211), 047cqr (0.47 #11653, 0.42 #4504, 0.38 #2173), 09hd6f (0.47 #11653, 0.38 #2130, 0.25 #4461), 09_99w (0.47 #11653, 0.38 #1917, 0.25 #4248), 0h53p1 (0.47 #11653, 0.33 #2955, 0.25 #624), 04snp2 (0.47 #11653, 0.01 #44283), 0brkwj (0.33 #4124, 0.25 #1793, 0.22 #30297), 044mm6 (0.22 #30297, 0.01 #70235) >> Best rule #4662 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 09hd6f; >> query: (?x10667, ?x2650) <- award_nominee(?x10667, ?x4023), award_nominee(?x10667, ?x2650), nominated_for(?x10667, ?x5810), ?x4023 = 09hd16 >> conf = 0.85 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 047cqr award_nominee! 0697kh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 50.000 0.849 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5626-05xbx PRED entity: 05xbx PRED relation: nominated_for PRED expected values: 03ctqqf => 164 concepts (130 used for prediction) PRED predicted values (max 10 best out of 791): 0dl6fv (0.78 #181549, 0.71 #9722, 0.65 #43777), 03cv_gy (0.50 #7328, 0.25 #12194, 0.25 #10570), 011ywj (0.39 #74242, 0.02 #199044, 0.02 #200665), 047csmy (0.33 #838, 0.25 #12184, 0.25 #5698), 03qcfvw (0.33 #9, 0.25 #4869, 0.14 #24328), 04xbq3 (0.31 #59987, 0.25 #24318, 0.23 #22696), 04glx0 (0.31 #59987, 0.24 #17831, 0.23 #22696), 03ctqqf (0.26 #163719, 0.25 #8073, 0.17 #42155), 03ffcz (0.26 #163719, 0.17 #42155, 0.02 #11345), 021gzd (0.25 #24318, 0.23 #22696, 0.20 #30807) >> Best rule #181549 for best value: >> intensional similarity = 2 >> extensional distance = 545 >> proper extension: 0glmv; 03kxp7; 03fnyk; >> query: (?x5007, ?x8733) <- award_winner(?x8733, ?x5007), actor(?x8733, ?x988) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #163719 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 286 *> proper extension: 098n5; 04pz5c; 07bzp; 07g7h2; *> query: (?x5007, ?x6597) <- award_winner(?x5007, ?x2776), program(?x2776, ?x10234), award_winner(?x6597, ?x2776) *> conf = 0.26 ranks of expected_values: 8 EVAL 05xbx nominated_for 03ctqqf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 164.000 130.000 0.783 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5625-0fq8f PRED entity: 0fq8f PRED relation: location_of_ceremony! PRED expected values: 04ztj => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.81 #133, 0.80 #69, 0.79 #157), 0jgjn (0.13 #52, 0.12 #60, 0.09 #92), 01g63y (0.08 #38, 0.07 #46, 0.07 #50), 01bl8s (0.03 #163, 0.02 #175, 0.02 #195) >> Best rule #133 for best value: >> intensional similarity = 8 >> extensional distance = 30 >> proper extension: 0dclg; 0d6lp; >> query: (?x1464, 04ztj) <- mode_of_transportation(?x1464, ?x6665), film_release_region(?x1463, ?x1464), mode_of_transportation(?x13811, ?x6665), mode_of_transportation(?x2985, ?x6665), mode_of_transportation(?x1646, ?x6665), ?x13811 = 0jpkg, ?x2985 = 03hrz, ?x1646 = 0156q >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fq8f location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.812 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #5624-0g83dv PRED entity: 0g83dv PRED relation: language PRED expected values: 06x8y => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 35): 04306rv (0.25 #4, 0.16 #179, 0.13 #237), 06nm1 (0.25 #68, 0.16 #126, 0.16 #301), 04h9h (0.25 #42, 0.06 #217, 0.05 #449), 064_8sq (0.16 #196, 0.16 #781, 0.14 #545), 0653m (0.12 #127, 0.05 #418, 0.04 #771), 012w70 (0.12 #128, 0.03 #772, 0.03 #2769), 02bjrlw (0.09 #761, 0.09 #408, 0.08 #117), 03_9r (0.08 #242, 0.07 #2766, 0.06 #184), 0c_v2 (0.08 #132, 0.02 #191, 0.01 #659), 0459q4 (0.08 #152, 0.01 #1146, 0.01 #1204) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03cw411; 02cbhg; >> query: (?x4158, 04306rv) <- film(?x166, ?x4158), produced_by(?x4158, ?x8041), category(?x4158, ?x134), ?x8041 = 029m83 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g83dv language 06x8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.250 http://example.org/film/film/language #5623-01ps2h8 PRED entity: 01ps2h8 PRED relation: award PRED expected values: 04kxsb => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 277): 04kxsb (0.46 #525, 0.14 #26004, 0.12 #32806), 0gqy2 (0.35 #563, 0.18 #5763, 0.14 #10563), 02w9sd7 (0.33 #569, 0.09 #5769, 0.07 #10569), 0bdwqv (0.25 #571, 0.11 #5771, 0.09 #10571), 05pcn59 (0.23 #80, 0.18 #1280, 0.17 #2480), 09sdmz (0.23 #604, 0.19 #18803, 0.15 #32005), 0ck27z (0.21 #4491, 0.20 #10091, 0.19 #18803), 027dtxw (0.20 #404, 0.19 #18803, 0.15 #32005), 03c7tr1 (0.20 #57, 0.15 #32005, 0.12 #2457), 0789_m (0.19 #18803, 0.18 #420, 0.15 #32005) >> Best rule #525 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 01200d; >> query: (?x5283, 04kxsb) <- award_winner(?x704, ?x5283), award(?x5283, ?x591), ?x591 = 0f4x7 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ps2h8 award 04kxsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.457 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5622-0d6_s PRED entity: 0d6_s PRED relation: produced_by PRED expected values: 08d9z7 => 91 concepts (45 used for prediction) PRED predicted values (max 10 best out of 211): 04fyhv (0.33 #282, 0.04 #1828, 0.04 #2214), 07f8wg (0.33 #18, 0.02 #1950, 0.02 #2336), 0fvf9q (0.13 #779, 0.08 #4642, 0.07 #3483), 01t6b4 (0.13 #816, 0.06 #3907, 0.05 #3134), 0mdqp (0.13 #800), 0f4vbz (0.10 #13929, 0.10 #16253, 0.08 #12768), 03kpvp (0.10 #2057, 0.09 #2443, 0.06 #3216), 02xnjd (0.10 #2205, 0.09 #3364, 0.08 #4137), 08d9z7 (0.09 #653, 0.04 #3744, 0.04 #4131), 04wvhz (0.09 #422, 0.04 #2354, 0.03 #6606) >> Best rule #282 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01gwk3; >> query: (?x10405, 04fyhv) <- produced_by(?x10405, ?x8345), prequel(?x7263, ?x10405), genre(?x10405, ?x225), ?x8345 = 016dmx >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #653 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 02qr69m; *> query: (?x10405, 08d9z7) <- nominated_for(?x1007, ?x10405), currency(?x10405, ?x170), film(?x2258, ?x10405), ?x2258 = 0f4vbz *> conf = 0.09 ranks of expected_values: 9 EVAL 0d6_s produced_by 08d9z7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 91.000 45.000 0.333 http://example.org/film/film/produced_by #5621-03mdt PRED entity: 03mdt PRED relation: program PRED expected values: 0hr41p6 => 145 concepts (141 used for prediction) PRED predicted values (max 10 best out of 243): 01g03q (0.75 #2744, 0.71 #2285, 0.60 #229), 01qbg5 (0.75 #2744, 0.71 #2285, 0.60 #229), 0qmk5 (0.75 #2744, 0.03 #22863, 0.03 #23093), 05h43ls (0.75 #2744, 0.02 #23094, 0.02 #23092), 06k176 (0.71 #2285, 0.60 #229, 0.33 #195), 05z43v (0.71 #2285, 0.60 #229, 0.33 #114), 0b6m5fy (0.71 #2285, 0.60 #229, 0.14 #916), 03cffvv (0.71 #2285, 0.60 #229, 0.14 #916), 02rlj20 (0.71 #2285, 0.60 #229, 0.14 #916), 0f4k49 (0.71 #2285, 0.60 #229, 0.14 #916) >> Best rule #2744 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0kcd5; >> query: (?x3381, ?x1542) <- nominated_for(?x3381, ?x1542), program(?x3381, ?x493), citytown(?x3381, ?x739) >> conf = 0.75 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 03mdt program 0hr41p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 145.000 141.000 0.750 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #5620-0gpprt PRED entity: 0gpprt PRED relation: award PRED expected values: 09sb52 02x4sn8 => 105 concepts (88 used for prediction) PRED predicted values (max 10 best out of 275): 02x4wr9 (0.73 #34925, 0.72 #22073, 0.71 #30506), 0gs9p (0.39 #1282, 0.18 #2485, 0.17 #2084), 09sb52 (0.37 #9670, 0.36 #6459, 0.34 #14888), 019f4v (0.35 #1269, 0.17 #65, 0.17 #2472), 040njc (0.34 #1211, 0.17 #2414, 0.16 #20866), 0gr4k (0.33 #32, 0.25 #2038, 0.25 #2439), 04dn09n (0.33 #43, 0.25 #2049, 0.24 #2450), 03hkv_r (0.33 #15, 0.20 #2021, 0.19 #2422), 02n9nmz (0.33 #68, 0.17 #2074, 0.17 #2475), 02x17s4 (0.33 #123, 0.16 #2129, 0.15 #2530) >> Best rule #34925 for best value: >> intensional similarity = 3 >> extensional distance = 2274 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01vsxdm; 01wv9xn; 0c3kw; 0frsw; 016fmf; 01vrwfv; 0134s5; ... >> query: (?x8783, ?x9343) <- award_winner(?x9343, ?x8783), award(?x3961, ?x9343), award_nominee(?x3961, ?x163) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #9670 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 818 *> proper extension: 0157m; 06k02; 01dw9z; 03sww; 0c12h; 02_0d2; 03f7jfh; 0pksh; *> query: (?x8783, 09sb52) <- award_nominee(?x969, ?x8783), award_winner(?x68, ?x8783), film(?x8783, ?x964) *> conf = 0.37 ranks of expected_values: 3, 65 EVAL 0gpprt award 02x4sn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 105.000 88.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gpprt award 09sb52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 88.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5619-0_7w6 PRED entity: 0_7w6 PRED relation: genre PRED expected values: 0hcr => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 108): 07s9rl0 (0.71 #9559, 0.66 #5733, 0.65 #3224), 09b3v (0.71 #599, 0.63 #2149, 0.61 #7645), 03k9fj (0.65 #131, 0.53 #490, 0.47 #250), 05p553 (0.61 #124, 0.55 #243, 0.50 #5), 01jfsb (0.52 #2042, 0.34 #850, 0.31 #5984), 0hcr (0.42 #142, 0.20 #501, 0.18 #261), 02kdv5l (0.33 #9561, 0.33 #602, 0.32 #840), 02n4kr (0.32 #2037, 0.14 #7533, 0.13 #5740), 0lsxr (0.25 #2038, 0.19 #1560, 0.18 #846), 01zhp (0.25 #76, 0.23 #195, 0.05 #314) >> Best rule #9559 for best value: >> intensional similarity = 4 >> extensional distance = 1322 >> proper extension: 0dr1c2; >> query: (?x1919, 07s9rl0) <- genre(?x1919, ?x4088), film(?x4109, ?x1919), genre(?x9993, ?x4088), ?x9993 = 0kb1g >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #142 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 03whyr; *> query: (?x1919, 0hcr) <- country(?x1919, ?x94), production_companies(?x1919, ?x2156), film(?x5636, ?x1919), ?x2156 = 01795t *> conf = 0.42 ranks of expected_values: 6 EVAL 0_7w6 genre 0hcr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 102.000 102.000 0.711 http://example.org/film/film/genre #5618-0fb7sd PRED entity: 0fb7sd PRED relation: film_format PRED expected values: 07fb8_ => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 3): 0cj16 (0.33 #3, 0.25 #13, 0.18 #18), 07fb8_ (0.26 #87, 0.25 #38, 0.25 #82), 017fx5 (0.07 #41, 0.07 #160, 0.06 #74) >> Best rule #3 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 02ryz24; >> query: (?x4967, 0cj16) <- nominated_for(?x2771, ?x4967), film(?x436, ?x4967), film_crew_role(?x4967, ?x468), ?x436 = 032xhg, language(?x4967, ?x254), crewmember(?x4967, ?x9769) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 177 *> proper extension: 02sg5v; 035s95; 0661m4p; 04g9gd; 04tz52; 03kg2v; 08k40m; 025n07; 014nq4; 0gjc4d3; ... *> query: (?x4967, 07fb8_) <- genre(?x4967, ?x225), ?x225 = 02kdv5l, production_companies(?x4967, ?x1104), produced_by(?x4967, ?x2332), film(?x92, ?x4967) *> conf = 0.26 ranks of expected_values: 2 EVAL 0fb7sd film_format 07fb8_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.333 http://example.org/film/film/film_format #5617-034fl9 PRED entity: 034fl9 PRED relation: languages PRED expected values: 02h40lc => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.92 #112, 0.90 #134, 0.90 #156), 03_9r (0.15 #48, 0.13 #103, 0.04 #378), 0t_2 (0.06 #83, 0.05 #39, 0.05 #149), 06nm1 (0.03 #214, 0.03 #181, 0.03 #357), 064_8sq (0.02 #304, 0.02 #117, 0.01 #216), 02bv9 (0.02 #119, 0.01 #141, 0.01 #306), 04306rv (0.02 #113, 0.01 #135, 0.01 #300), 02bjrlw (0.02 #111, 0.01 #133, 0.01 #298) >> Best rule #112 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 07s8z_l; >> query: (?x9029, 02h40lc) <- genre(?x9029, ?x8805), program_creator(?x9029, ?x2176), program(?x2062, ?x9029), award_winner(?x9029, ?x912) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034fl9 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.921 http://example.org/tv/tv_program/languages #5616-07tds PRED entity: 07tds PRED relation: student PRED expected values: 03xds => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1298): 0d3k14 (0.17 #12248, 0.12 #18490, 0.12 #16409), 0f7h2g (0.17 #12022, 0.04 #28668, 0.02 #26587), 06hx2 (0.15 #13547, 0.08 #11467, 0.05 #21869), 0194xc (0.15 #14116, 0.08 #12036, 0.05 #22438), 02lt8 (0.15 #13156, 0.04 #29802, 0.02 #42288), 03l3ln (0.14 #35372, 0.08 #27049, 0.08 #11552), 02bn75 (0.12 #7595, 0.02 #34645, 0.02 #36726), 012201 (0.12 #7706, 0.02 #34756), 01vw_dv (0.12 #7385), 0bvzp (0.12 #7352) >> Best rule #12248 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 03_c8p; 0cv_2; >> query: (?x4672, 0d3k14) <- organization(?x4672, ?x5487), service_location(?x4672, ?x94) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #12415 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 10 *> proper extension: 03_c8p; 0cv_2; *> query: (?x4672, 03xds) <- organization(?x4672, ?x5487), service_location(?x4672, ?x94) *> conf = 0.08 ranks of expected_values: 117 EVAL 07tds student 03xds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 85.000 85.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #5615-01q_ph PRED entity: 01q_ph PRED relation: award PRED expected values: 02qyp19 0gr51 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 307): 01by1l (0.33 #11679, 0.27 #6492, 0.26 #6093), 01bgqh (0.32 #6425, 0.27 #11612, 0.26 #2036), 0gr4k (0.26 #12401, 0.25 #12800, 0.10 #3224), 094qd5 (0.24 #43, 0.22 #442, 0.13 #42296), 02ppm4q (0.24 #152, 0.22 #551, 0.13 #42296), 03qbh5 (0.24 #6185, 0.23 #11771, 0.20 #7382), 054ks3 (0.23 #6521, 0.17 #6122, 0.17 #11708), 0gr51 (0.23 #12465, 0.22 #12864, 0.13 #42296), 04dn09n (0.22 #12810, 0.22 #12411, 0.11 #3234), 0gq9h (0.22 #8454, 0.19 #3267, 0.18 #10848) >> Best rule #11679 for best value: >> intensional similarity = 2 >> extensional distance = 280 >> proper extension: 06lxn; >> query: (?x400, 01by1l) <- artist(?x9224, ?x400), award_winner(?x4631, ?x400) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #12465 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 304 *> proper extension: 0fx02; 032md; *> query: (?x400, 0gr51) <- written_by(?x9941, ?x400), nationality(?x400, ?x94) *> conf = 0.23 ranks of expected_values: 8, 48 EVAL 01q_ph award 0gr51 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 132.000 132.000 0.326 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01q_ph award 02qyp19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 132.000 132.000 0.326 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5614-01v_0b PRED entity: 01v_0b PRED relation: influenced_by PRED expected values: 084nh => 137 concepts (51 used for prediction) PRED predicted values (max 10 best out of 335): 03f0324 (0.57 #149, 0.43 #578, 0.29 #4010), 0l99s (0.57 #221, 0.43 #650, 0.12 #4512), 073v6 (0.29 #516, 0.29 #87, 0.21 #1374), 041_y (0.29 #642, 0.29 #213, 0.16 #1500), 058vp (0.29 #609, 0.26 #1467, 0.18 #4291), 03f47xl (0.29 #628, 0.16 #1486, 0.14 #199), 040db (0.29 #484, 0.16 #1342, 0.14 #3916), 0379s (0.29 #507, 0.14 #3939, 0.14 #78), 07lp1 (0.29 #772, 0.14 #343, 0.05 #13754), 041xl (0.29 #649, 0.14 #220, 0.05 #13754) >> Best rule #149 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 019z7q; 01vdrw; >> query: (?x12382, 03f0324) <- award(?x12382, ?x7111), gender(?x12382, ?x231), ?x7111 = 0c_dx, people(?x3584, ?x12382) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #12894 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 226 *> proper extension: 01c58j; 0453t; 03f70xs; 0379s; 032l1; 052h3; 0372p; 04h07s; 0hgqq; 01s7qqw; ... *> query: (?x12382, ?x587) <- place_of_birth(?x12382, ?x8263), influenced_by(?x12382, ?x118), influenced_by(?x118, ?x587), nationality(?x12382, ?x94) *> conf = 0.12 ranks of expected_values: 78 EVAL 01v_0b influenced_by 084nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 137.000 51.000 0.571 http://example.org/influence/influence_node/influenced_by #5613-0pmhf PRED entity: 0pmhf PRED relation: religion PRED expected values: 01lp8 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 19): 0c8wxp (0.23 #141, 0.20 #771, 0.18 #51), 0kpl (0.15 #640, 0.15 #730, 0.14 #505), 03_gx (0.12 #509, 0.12 #644, 0.11 #734), 0kq2 (0.04 #648, 0.04 #738, 0.03 #513), 01lp8 (0.04 #46, 0.03 #91, 0.03 #676), 05sfs (0.04 #48, 0.03 #93, 0.02 #408), 07y1z (0.04 #88, 0.02 #223, 0.02 #133), 092bf5 (0.04 #286, 0.03 #241, 0.03 #331), 03j6c (0.03 #921, 0.03 #201, 0.03 #1371), 04pk9 (0.03 #200, 0.03 #290, 0.03 #335) >> Best rule #141 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 013cr; 03fvqg; 0gr36; 01rnxn; 016k6x; 04l19_; 02zyq6; 01v90t; 02661h; 020x5r; ... >> query: (?x2596, 0c8wxp) <- award(?x2596, ?x1033), film(?x2596, ?x675), ?x1033 = 02x73k6 >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 0prfz; 05gml8; 031zkw; 011zd3; 0164nb; 09b6zr; 01trf3; 0gn30; 0f5zj6; 01zh29; ... *> query: (?x2596, 01lp8) <- award(?x2596, ?x401), student(?x1368, ?x2596), participant(?x2596, ?x2908) *> conf = 0.04 ranks of expected_values: 5 EVAL 0pmhf religion 01lp8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 81.000 81.000 0.228 http://example.org/people/person/religion #5612-01520h PRED entity: 01520h PRED relation: type_of_union PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.73 #37, 0.72 #101, 0.72 #9), 01g63y (0.17 #2, 0.15 #54, 0.15 #38) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 785 >> proper extension: 0d05fv; 01twdk; 0hfml; 03d9v8; 05g7q; 054c1; 049sb; >> query: (?x6755, 04ztj) <- award_winner(?x591, ?x6755), film(?x6755, ?x430), location(?x6755, ?x1227) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01520h type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.726 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5611-0n839 PRED entity: 0n839 PRED relation: profession PRED expected values: 01d_h8 012t_z => 55 concepts (54 used for prediction) PRED predicted values (max 10 best out of 99): 01d_h8 (0.64 #447, 0.59 #594, 0.48 #2801), 0dxtg (0.55 #5312, 0.45 #602, 0.44 #896), 02jknp (0.45 #596, 0.32 #449, 0.26 #5306), 012t_z (0.36 #454, 0.23 #1926, 0.20 #2514), 0q04f (0.33 #98, 0.06 #392, 0.05 #833), 0fj9f (0.32 #789, 0.31 #1230, 0.29 #1377), 0np9r (0.29 #4435, 0.28 #3697, 0.28 #3845), 02krf9 (0.26 #5324, 0.25 #320, 0.18 #761), 0cbd2 (0.25 #3537, 0.25 #301, 0.23 #742), 0nbcg (0.23 #1060, 0.19 #2385, 0.18 #3267) >> Best rule #447 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 05qd_; >> query: (?x11949, 01d_h8) <- organizations_founded(?x11949, ?x9224), child(?x7793, ?x9224), industry(?x7793, ?x3368) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0n839 profession 012t_z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 55.000 54.000 0.636 http://example.org/people/person/profession EVAL 0n839 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 54.000 0.636 http://example.org/people/person/profession #5610-0845v PRED entity: 0845v PRED relation: entity_involved PRED expected values: 04pwg => 60 concepts (52 used for prediction) PRED predicted values (max 10 best out of 223): 0j5b8 (0.60 #871, 0.50 #1034, 0.33 #386), 024pcx (0.50 #1619, 0.47 #2109, 0.44 #1454), 014tss (0.50 #1619, 0.47 #2109, 0.44 #1454), 03f4n1 (0.47 #2109, 0.44 #1454, 0.40 #2111), 040vgd (0.47 #2109, 0.44 #1454, 0.39 #1457), 01k6y1 (0.44 #1454, 0.40 #2111, 0.39 #1457), 0cn_tpv (0.44 #1454, 0.39 #1457, 0.36 #2106), 03gk2 (0.44 #1454, 0.35 #1616, 0.33 #1450), 0dv0z (0.40 #2111, 0.36 #2106, 0.35 #1616), 01m41_ (0.40 #926, 0.36 #2721, 0.33 #3374) >> Best rule #871 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 0py8j; 01_3rn; >> query: (?x1777, 0j5b8) <- combatants(?x1777, ?x9602), combatants(?x1777, ?x8949), combatants(?x1777, ?x6371), ?x9602 = 0285m87, jurisdiction_of_office(?x182, ?x6371), nationality(?x1328, ?x6371), entity_involved(?x7734, ?x6371), combatants(?x8949, ?x789), locations(?x1777, ?x455), countries_spoken_in(?x9617, ?x6371) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0845v entity_involved 04pwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 52.000 0.600 http://example.org/base/culturalevent/event/entity_involved #5609-0gnjh PRED entity: 0gnjh PRED relation: genre PRED expected values: 05p553 02l7c8 => 69 concepts (38 used for prediction) PRED predicted values (max 10 best out of 90): 07s9rl0 (0.80 #1191, 0.74 #120, 0.70 #953), 05p553 (0.61 #1789, 0.37 #361, 0.34 #3575), 02l7c8 (0.41 #373, 0.37 #1801, 0.33 #968), 06l3bl (0.33 #37, 0.13 #156, 0.11 #989), 01jfsb (0.33 #1916, 0.32 #2750, 0.32 #2631), 02kdv5l (0.32 #836, 0.30 #1074, 0.29 #717), 03k9fj (0.28 #1677, 0.26 #844, 0.26 #3106), 04xvlr (0.27 #954, 0.17 #1192, 0.17 #4051), 0lsxr (0.21 #722, 0.19 #1555, 0.18 #1079), 01hmnh (0.20 #1684, 0.17 #3113, 0.17 #375) >> Best rule #1191 for best value: >> intensional similarity = 5 >> extensional distance = 286 >> proper extension: 011yfd; 0j8f09z; 01c9d; >> query: (?x6604, 07s9rl0) <- nominated_for(?x1323, ?x6604), nominated_for(?x1307, ?x6604), ?x1307 = 0gq9h, award(?x115, ?x1323), ceremony(?x1323, ?x78) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1789 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 430 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x6604, 05p553) <- titles(?x307, ?x6604), titles(?x307, ?x9016), ?x9016 = 0bz6sq *> conf = 0.61 ranks of expected_values: 2, 3 EVAL 0gnjh genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 38.000 0.802 http://example.org/film/film/genre EVAL 0gnjh genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 38.000 0.802 http://example.org/film/film/genre #5608-016jll PRED entity: 016jll PRED relation: award_winner! PRED expected values: 0dnw1 => 103 concepts (69 used for prediction) PRED predicted values (max 10 best out of 187): 0ccd3x (0.38 #499, 0.05 #52265, 0.02 #18675), 0jqn5 (0.12 #152, 0.05 #3560, 0.05 #4696), 0dnw1 (0.12 #689, 0.05 #52265, 0.02 #5233), 0283_zv (0.12 #196, 0.05 #52265, 0.01 #7012), 01k5y0 (0.12 #1060, 0.05 #5604), 0bcndz (0.12 #183, 0.02 #20631, 0.02 #30858), 0286gm1 (0.12 #718, 0.01 #21166), 0hv81 (0.12 #668, 0.01 #7484), 01gvsn (0.10 #5615, 0.04 #78418, 0.02 #6751), 0hv8w (0.08 #2888, 0.05 #4024, 0.03 #8568) >> Best rule #499 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 03csqj4; >> query: (?x10412, 0ccd3x) <- award_nominee(?x10412, ?x7168), award_winner(?x1821, ?x10412), ?x1821 = 0ftlkg >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #689 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 03csqj4; *> query: (?x10412, 0dnw1) <- award_nominee(?x10412, ?x7168), award_winner(?x1821, ?x10412), ?x1821 = 0ftlkg *> conf = 0.12 ranks of expected_values: 3 EVAL 016jll award_winner! 0dnw1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 69.000 0.375 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #5607-02rn_bj PRED entity: 02rn_bj PRED relation: artists! PRED expected values: 016clz => 170 concepts (88 used for prediction) PRED predicted values (max 10 best out of 269): 025sc50 (0.71 #672, 0.31 #24626, 0.31 #361), 0glt670 (0.71 #662, 0.28 #16215, 0.28 #5016), 0gywn (0.71 #680, 0.27 #12191, 0.24 #24634), 06by7 (0.63 #8419, 0.62 #1576, 0.57 #11217), 06j6l (0.57 #670, 0.33 #24624, 0.33 #24935), 02x8m (0.50 #640, 0.17 #2196, 0.16 #4994), 0xhtw (0.44 #1571, 0.33 #4370, 0.29 #5613), 05lwjc (0.43 #822, 0.06 #12333, 0.04 #24776), 016clz (0.40 #6847, 0.39 #1871, 0.37 #11200), 017_qw (0.38 #11885, 0.31 #14373, 0.30 #13441) >> Best rule #672 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 011z3g; 046p9; 016ppr; 01f2q5; >> query: (?x8328, 025sc50) <- origin(?x8328, ?x739), category(?x8328, ?x134), artists(?x8327, ?x8328), ?x8327 = 01fm07 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6847 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 03m6_z; *> query: (?x8328, 016clz) <- role(?x8328, ?x716), ?x716 = 018vs, profession(?x8328, ?x131) *> conf = 0.40 ranks of expected_values: 9 EVAL 02rn_bj artists! 016clz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 170.000 88.000 0.714 http://example.org/music/genre/artists #5606-026t6 PRED entity: 026t6 PRED relation: role! PRED expected values: 0dwsp 01s0ps => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 48): 018vs (0.85 #121, 0.83 #1326, 0.83 #1203), 0l14md (0.85 #121, 0.83 #712, 0.82 #555), 0l14qv (0.85 #121, 0.83 #712, 0.82 #555), 026g73 (0.85 #121, 0.83 #712, 0.82 #555), 0dwr4 (0.85 #121, 0.81 #40, 0.71 #1235), 02snj9 (0.85 #121, 0.81 #40, 0.71 #1235), 0d8lm (0.85 #121, 0.81 #40, 0.71 #1235), 085jw (0.85 #121, 0.81 #40, 0.71 #1235), 018j2 (0.83 #712, 0.82 #555, 0.82 #554), 023r2x (0.83 #712, 0.82 #555, 0.82 #554) >> Best rule #121 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 0342h; >> query: (?x212, ?x1225) <- instrumentalists(?x212, ?x8344), instrumentalists(?x212, ?x568), role(?x227, ?x212), role(?x212, ?x3328), ?x568 = 06cc_1, performance_role(?x1225, ?x212), ?x8344 = 01jfnvd, role(?x1147, ?x212), ?x3328 = 016622 >> conf = 0.85 => this is the best rule for 8 predicted values *> Best rule #1080 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 14 *> proper extension: 01p970; *> query: (?x212, 0dwsp) <- instrumentalists(?x212, ?x226), role(?x227, ?x212), role(?x212, ?x8172), role(?x212, ?x3991), role(?x212, ?x2460), ?x8172 = 06rvn, performance_role(?x212, ?x1147), ?x3991 = 05842k, instrumentalists(?x2460, ?x680) *> conf = 0.75 ranks of expected_values: 17, 20 EVAL 026t6 role! 01s0ps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 69.000 69.000 0.848 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 026t6 role! 0dwsp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 69.000 69.000 0.848 http://example.org/music/performance_role/track_performances./music/track_contribution/role #5605-04ly1 PRED entity: 04ly1 PRED relation: contains PRED expected values: 0tlq9 => 140 concepts (100 used for prediction) PRED predicted values (max 10 best out of 2732): 0f2tj (0.81 #82013, 0.81 #55650, 0.80 #58579), 02zkz7 (0.48 #114235, 0.47 #134739, 0.47 #125953), 02cvcd (0.48 #114235, 0.47 #134739, 0.47 #125953), 02zccd (0.48 #114235, 0.47 #134739, 0.47 #125953), 0s69k (0.25 #236, 0.08 #6094, 0.08 #50026), 0ftvg (0.25 #1626, 0.08 #7484, 0.08 #10412), 0f2s6 (0.25 #1443, 0.08 #7301, 0.07 #19015), 0s6g4 (0.25 #2152, 0.08 #8010, 0.07 #19724), 02fgdx (0.25 #422, 0.08 #6280, 0.07 #26780), 0jpn8 (0.25 #1313, 0.08 #7171, 0.07 #27671) >> Best rule #82013 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 0d9rp; >> query: (?x3908, ?x6769) <- contains(?x94, ?x3908), contains(?x3908, ?x466), state(?x6769, ?x3908) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #2737 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0tlq9; *> query: (?x3908, 0tlq9) <- contains(?x94, ?x3908), contains(?x3908, ?x6186), ?x6186 = 02fs_d *> conf = 0.25 ranks of expected_values: 795 EVAL 04ly1 contains 0tlq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 140.000 100.000 0.812 http://example.org/location/location/contains #5604-0725ny PRED entity: 0725ny PRED relation: profession PRED expected values: 02hrh1q => 137 concepts (86 used for prediction) PRED predicted values (max 10 best out of 64): 02hrh1q (0.92 #7615, 0.92 #4933, 0.92 #5231), 0dxtg (0.50 #312, 0.37 #3740, 0.33 #163), 018gz8 (0.50 #316, 0.33 #167, 0.33 #18), 03gjzk (0.50 #314, 0.33 #165, 0.33 #16), 09jwl (0.37 #10154, 0.37 #5683, 0.37 #10601), 0cbd2 (0.33 #7, 0.25 #305, 0.19 #2540), 01d_h8 (0.31 #5520, 0.30 #6414, 0.30 #6861), 0dz3r (0.29 #896, 0.23 #8199, 0.22 #10136), 0nbcg (0.26 #10166, 0.26 #11060, 0.26 #8229), 02jknp (0.25 #306, 0.19 #3883, 0.19 #12528) >> Best rule #7615 for best value: >> intensional similarity = 3 >> extensional distance = 626 >> proper extension: 05tk7y; 0m32_; 01520h; 02dlfh; 0bbvr84; >> query: (?x8273, 02hrh1q) <- actor(?x3102, ?x8273), profession(?x8273, ?x1383), titles(?x2008, ?x3102) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0725ny profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 86.000 0.920 http://example.org/people/person/profession #5603-027r7k PRED entity: 027r7k PRED relation: titles! PRED expected values: 01jfsb => 89 concepts (52 used for prediction) PRED predicted values (max 10 best out of 72): 07s9rl0 (0.50 #1, 0.41 #817, 0.37 #2665), 04xvlr (0.47 #206, 0.41 #412, 0.39 #514), 01jfsb (0.34 #630, 0.25 #120, 0.18 #938), 02n4kr (0.30 #4212, 0.28 #1839, 0.24 #2664), 0lsxr (0.30 #4212, 0.24 #2664, 0.22 #4316), 0d060g (0.25 #510, 0.12 #2046, 0.11 #3179), 09c7w0 (0.25 #510, 0.03 #1225), 0j5nm (0.24 #2664, 0.22 #4316, 0.22 #4624), 0vjs6 (0.24 #2664, 0.22 #4316, 0.22 #4624), 028v3 (0.24 #2664, 0.22 #4316, 0.22 #4624) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 04glx0; >> query: (?x11324, 07s9rl0) <- nominated_for(?x2033, ?x11324), award_winner(?x11324, ?x11573), ?x2033 = 01ycbq >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #630 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 125 *> proper extension: 02rb607; *> query: (?x11324, 01jfsb) <- genre(?x11324, ?x604), genre(?x11324, ?x53), ?x53 = 07s9rl0, ?x604 = 0lsxr, film_release_region(?x11324, ?x304) *> conf = 0.34 ranks of expected_values: 3 EVAL 027r7k titles! 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 52.000 0.500 http://example.org/media_common/netflix_genre/titles #5602-072hx4 PRED entity: 072hx4 PRED relation: film! PRED expected values: 03xsby => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 61): 02slt7 (0.50 #2685, 0.45 #4507, 0.42 #1887), 01795t (0.33 #593, 0.30 #377, 0.29 #161), 03xq0f (0.29 #4, 0.27 #76, 0.21 #148), 020h2v (0.26 #403, 0.23 #331, 0.22 #619), 016tw3 (0.21 #154, 0.18 #1020, 0.18 #658), 016tt2 (0.19 #219, 0.15 #1526, 0.12 #2614), 024rbz (0.19 #227, 0.11 #1021, 0.07 #155), 0jz9f (0.19 #217, 0.09 #289, 0.09 #361), 017s11 (0.18 #1525, 0.14 #2324, 0.13 #2396), 05qd_ (0.17 #368, 0.16 #296, 0.15 #1531) >> Best rule #2685 for best value: >> intensional similarity = 4 >> extensional distance = 378 >> proper extension: 08cfr1; >> query: (?x11839, ?x3331) <- language(?x11839, ?x5607), titles(?x1014, ?x11839), production_companies(?x11839, ?x3331), award(?x11839, ?x3617) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #663 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 117 *> proper extension: 0djb3vw; 053tj7; 0h95zbp; 064lsn; 0cp08zg; 0g4pl7z; 0267wwv; *> query: (?x11839, 03xsby) <- film_release_region(?x11839, ?x1603), film_release_region(?x11839, ?x1229), ?x1603 = 06bnz, production_companies(?x11839, ?x3331), ?x1229 = 059j2 *> conf = 0.06 ranks of expected_values: 26 EVAL 072hx4 film! 03xsby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 97.000 97.000 0.502 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5601-034q3l PRED entity: 034q3l PRED relation: student! PRED expected values: 09r4xx => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 90): 015zyd (0.33 #1, 0.03 #528, 0.03 #1055), 01d34b (0.08 #1310, 0.08 #1837, 0.06 #783), 0bwfn (0.06 #7654, 0.05 #14505, 0.05 #22410), 01w5m (0.06 #4321, 0.05 #7484, 0.04 #2740), 05nrkb (0.06 #876, 0.05 #1403, 0.05 #1930), 06kknt (0.06 #994, 0.05 #1521, 0.03 #2048), 03ksy (0.05 #4322, 0.03 #6430, 0.02 #8539), 06182p (0.04 #2933, 0.04 #3460, 0.03 #5568), 065y4w7 (0.04 #10555, 0.03 #18987, 0.03 #13717), 015nl4 (0.04 #15351, 0.04 #14297, 0.03 #3756) >> Best rule #1 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0bl2g; >> query: (?x8802, 015zyd) <- profession(?x8802, ?x1032), film(?x8802, ?x8000), film(?x8802, ?x7415), ?x7415 = 02qr3k8, ?x8000 = 0b4lkx >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2758 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 123 *> proper extension: 05hjmd; *> query: (?x8802, 09r4xx) <- place_of_birth(?x8802, ?x3014), nominated_for(?x8802, ?x8000), people(?x4322, ?x8802), film(?x398, ?x8000) *> conf = 0.02 ranks of expected_values: 48 EVAL 034q3l student! 09r4xx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 113.000 113.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #5600-07wtc PRED entity: 07wtc PRED relation: institution! PRED expected values: 014mlp => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 21): 02_xgp2 (0.86 #100, 0.84 #78, 0.79 #188), 02h4rq6 (0.78 #90, 0.78 #178, 0.76 #68), 014mlp (0.72 #248, 0.67 #447, 0.65 #181), 0bkj86 (0.61 #96, 0.59 #184, 0.58 #74), 016t_3 (0.60 #179, 0.58 #91, 0.57 #157), 04zx3q1 (0.53 #177, 0.49 #89, 0.49 #45), 027f2w (0.46 #97, 0.43 #185, 0.42 #75), 013zdg (0.32 #95, 0.31 #29, 0.30 #51), 0bjrnt (0.26 #50, 0.23 #28, 0.21 #205), 01rr_d (0.25 #104, 0.25 #192, 0.22 #170) >> Best rule #100 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 0jksm; >> query: (?x11740, 02_xgp2) <- list(?x11740, ?x2197), citytown(?x11740, ?x12491), ?x2197 = 09g7thr >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #248 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 100 *> proper extension: 0ylvj; 0yl_3; *> query: (?x11740, 014mlp) <- student(?x11740, ?x473), major_field_of_study(?x11740, ?x2014), ?x2014 = 04rjg *> conf = 0.72 ranks of expected_values: 3 EVAL 07wtc institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 50.000 50.000 0.864 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5599-01sgl PRED entity: 01sgl PRED relation: country PRED expected values: 035qy 0h7x 06c1y 0345_ => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 197): 03_3d (0.81 #4413, 0.76 #4941, 0.73 #4236), 07t21 (0.74 #4434, 0.67 #4962, 0.64 #4257), 06qd3 (0.71 #729, 0.58 #4256, 0.57 #4433), 0163v (0.71 #746, 0.55 #4450, 0.50 #4978), 03gj2 (0.71 #720, 0.47 #4424, 0.42 #4247), 0h7x (0.66 #4432, 0.59 #4960, 0.56 #4255), 06c1y (0.57 #4438, 0.57 #734, 0.53 #4261), 01ls2 (0.57 #713, 0.50 #361, 0.38 #4240), 035qy (0.57 #727, 0.47 #4254, 0.45 #4431), 02k54 (0.57 #717, 0.43 #4421, 0.42 #4244) >> Best rule #4413 for best value: >> intensional similarity = 8 >> extensional distance = 45 >> proper extension: 07rlg; 0d1tm; 01dys; 07gyv; 01hp22; 096f8; 09_bl; 02_5h; 07bs0; 01cgz; ... >> query: (?x6733, 03_3d) <- olympics(?x6733, ?x358), country(?x6733, ?x2984), country(?x6733, ?x1310), language(?x3276, ?x1310), film_release_region(?x204, ?x2984), nationality(?x57, ?x1310), teams(?x1310, ?x11309), contains(?x1310, ?x892) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #4432 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 45 *> proper extension: 07rlg; 0d1tm; 01dys; 07gyv; 01hp22; 096f8; 09_bl; 02_5h; 07bs0; 01cgz; ... *> query: (?x6733, 0h7x) <- olympics(?x6733, ?x358), country(?x6733, ?x2984), country(?x6733, ?x1310), language(?x3276, ?x1310), film_release_region(?x204, ?x2984), nationality(?x57, ?x1310), teams(?x1310, ?x11309), contains(?x1310, ?x892) *> conf = 0.66 ranks of expected_values: 6, 7, 9, 192 EVAL 01sgl country 0345_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 57.000 57.000 0.809 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01sgl country 06c1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 57.000 57.000 0.809 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01sgl country 0h7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 57.000 57.000 0.809 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01sgl country 035qy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 57.000 57.000 0.809 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #5598-01flv_ PRED entity: 01flv_ PRED relation: film! PRED expected values: 01zg98 => 84 concepts (48 used for prediction) PRED predicted values (max 10 best out of 772): 0146pg (0.48 #2078, 0.45 #6234, 0.45 #45711), 06pj8 (0.48 #2078, 0.45 #6234, 0.45 #45711), 02ldv0 (0.20 #5298, 0.03 #24935, 0.03 #74808), 019vgs (0.13 #4813, 0.03 #24935, 0.03 #74808), 01d0b1 (0.13 #5685, 0.03 #24935, 0.03 #74808), 015pvh (0.13 #5255, 0.03 #24935, 0.03 #74808), 0p8r1 (0.13 #4739, 0.02 #19283, 0.02 #6817), 0c0k1 (0.12 #3584, 0.12 #1506, 0.08 #7740), 0169dl (0.12 #2478, 0.12 #400, 0.07 #4556), 01mylz (0.12 #4021, 0.12 #1943, 0.07 #6099) >> Best rule #2078 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0g22z; 0jzw; 04vr_f; 02prw4h; 05pxnmb; 09sr0; >> query: (?x6114, ?x669) <- film(?x1870, ?x6114), award_winner(?x6114, ?x669), ?x1870 = 0hvb2, nominated_for(?x1033, ?x6114) >> conf = 0.48 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01flv_ film! 01zg98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 48.000 0.484 http://example.org/film/actor/film./film/performance/film #5597-0sx7r PRED entity: 0sx7r PRED relation: medal PRED expected values: 02lq5w => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1): 02lq5w (0.91 #20, 0.88 #18, 0.88 #16) >> Best rule #20 for best value: >> intensional similarity = 83 >> extensional distance = 7 >> proper extension: 0kbws; >> query: (?x452, ?x422) <- olympics(?x520, ?x452), olympics(?x453, ?x452), olympics(?x1558, ?x452), olympics(?x1229, ?x452), ?x1558 = 01mjq, sport(?x12757, ?x453), film_release_region(?x11839, ?x1229), film_release_region(?x10475, ?x1229), film_release_region(?x10048, ?x1229), film_release_region(?x7016, ?x1229), film_release_region(?x6932, ?x1229), film_release_region(?x6621, ?x1229), film_release_region(?x6168, ?x1229), film_release_region(?x5735, ?x1229), film_release_region(?x5688, ?x1229), film_release_region(?x5255, ?x1229), film_release_region(?x4446, ?x1229), film_release_region(?x4352, ?x1229), film_release_region(?x3252, ?x1229), film_release_region(?x3157, ?x1229), film_release_region(?x3151, ?x1229), film_release_region(?x3076, ?x1229), film_release_region(?x2714, ?x1229), film_release_region(?x2709, ?x1229), film_release_region(?x2676, ?x1229), film_release_region(?x1552, ?x1229), film_release_region(?x1392, ?x1229), film_release_region(?x1170, ?x1229), film_release_region(?x664, ?x1229), film_release_region(?x428, ?x1229), film_release_region(?x186, ?x1229), ?x2714 = 0kv238, country(?x3407, ?x1229), sports(?x2496, ?x453), country(?x779, ?x1229), ?x6932 = 027pfg, ?x3076 = 0g5838s, combatants(?x1229, ?x172), ?x186 = 02vxq9m, sports(?x784, ?x453), ?x5255 = 01sby_, organization(?x1229, ?x127), ?x1552 = 0gj9qxr, time_zones(?x1229, ?x2864), ?x6168 = 0gj96ln, olympics(?x1229, ?x775), ?x1170 = 09gdm7q, nominated_for(?x112, ?x3157), ?x3252 = 0gh8zks, service_location(?x896, ?x1229), sports(?x452, ?x1175), ?x428 = 0h1cdwq, ?x2676 = 0f4m2z, ?x3151 = 0gtsxr4, ?x779 = 096f8, ?x6621 = 0h63gl9, ?x172 = 0154j, ?x4352 = 09v71cj, combatants(?x326, ?x1229), ?x896 = 018mxj, ?x11839 = 072hx4, ?x1392 = 017gm7, ?x5688 = 0dr89x, ?x4446 = 0db94w, nationality(?x731, ?x1229), ?x7016 = 07g1sm, country(?x1009, ?x1229), ?x2496 = 0sxrz, film(?x541, ?x3157), ?x10475 = 047p798, film_crew_role(?x3157, ?x137), participating_countries(?x1931, ?x1229), country(?x3408, ?x1229), ?x2709 = 06ztvyx, language(?x10048, ?x254), award_winner(?x3157, ?x1656), music(?x5735, ?x8374), film(?x902, ?x10048), ?x664 = 0401sg, team(?x2918, ?x12757), ?x902 = 05qd_, country(?x520, ?x756), medal(?x775, ?x422) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sx7r medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 20.000 20.000 0.913 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #5596-019n9w PRED entity: 019n9w PRED relation: student PRED expected values: 05m883 => 195 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1458): 0335fp (0.20 #3465, 0.13 #5555, 0.11 #9735), 0306ds (0.20 #2497, 0.10 #12947, 0.10 #10857), 015wc0 (0.20 #3781, 0.10 #14231, 0.10 #12141), 01l1rw (0.20 #3085, 0.10 #13535, 0.10 #11445), 03rs8y (0.20 #2135, 0.10 #12585, 0.10 #10495), 044mrh (0.20 #2948, 0.10 #13398, 0.10 #11308), 0d6484 (0.20 #3757, 0.10 #14207, 0.10 #12117), 06dkzt (0.20 #3599, 0.10 #14049, 0.10 #11959), 04vq3h (0.20 #1701, 0.04 #28869, 0.03 #33048), 0cbgl (0.17 #31347, 0.17 #52246, 0.16 #43886) >> Best rule #3465 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 07wrz; >> query: (?x8525, 0335fp) <- company(?x14008, ?x8525), currency(?x8525, ?x170), registering_agency(?x8525, ?x1982), contains(?x94, ?x8525) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2252 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 07wrz; *> query: (?x8525, 05m883) <- company(?x14008, ?x8525), currency(?x8525, ?x170), registering_agency(?x8525, ?x1982), contains(?x94, ?x8525) *> conf = 0.10 ranks of expected_values: 197 EVAL 019n9w student 05m883 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 195.000 75.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #5595-05567m PRED entity: 05567m PRED relation: executive_produced_by PRED expected values: 04q5zw => 80 concepts (62 used for prediction) PRED predicted values (max 10 best out of 69): 02mt4k (0.18 #119, 0.13 #371, 0.08 #1127), 0fz27v (0.11 #722, 0.08 #1226, 0.04 #1731), 0gg9_5q (0.09 #90, 0.07 #342, 0.05 #594), 04fcx7 (0.09 #126, 0.07 #378, 0.05 #630), 0343h (0.08 #1302, 0.06 #2563, 0.05 #2816), 04pqqb (0.08 #873, 0.05 #621, 0.04 #1125), 06pj8 (0.06 #1315, 0.06 #2829, 0.06 #2072), 079vf (0.06 #5299, 0.05 #2271, 0.04 #3785), 0415svh (0.05 #531, 0.04 #1035, 0.01 #1540), 0bxtg (0.05 #521, 0.04 #1025, 0.01 #1530) >> Best rule #119 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 02mt51; 05fm6m; >> query: (?x9303, 02mt4k) <- titles(?x2480, ?x9303), titles(?x1510, ?x9303), ?x2480 = 01z4y, ?x1510 = 01hmnh, award(?x9303, ?x1691) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #1594 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 79 *> proper extension: 01sxly; 02_1sj; 0kv2hv; 03m4mj; 06rmdr; 0pvms; 04t6fk; 02_kd; 02ht1k; 07tw_b; ... *> query: (?x9303, 04q5zw) <- titles(?x2480, ?x9303), titles(?x1510, ?x9303), ?x2480 = 01z4y, genre(?x97, ?x1510) *> conf = 0.02 ranks of expected_values: 33 EVAL 05567m executive_produced_by 04q5zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 80.000 62.000 0.182 http://example.org/film/film/executive_produced_by #5594-0299hs PRED entity: 0299hs PRED relation: award PRED expected values: 018wdw => 115 concepts (91 used for prediction) PRED predicted values (max 10 best out of 192): 0p9sw (0.73 #235, 0.33 #2114, 0.33 #1897), 0k611 (0.73 #235, 0.33 #73, 0.29 #13854), 02hsq3m (0.73 #235, 0.29 #13854, 0.29 #2112), 02r0csl (0.73 #235, 0.29 #13854, 0.29 #2112), 0gq9h (0.33 #63, 0.29 #1940, 0.21 #8983), 0gs9p (0.33 #65, 0.29 #1942, 0.19 #8985), 0gq_v (0.33 #19, 0.25 #1896, 0.13 #2602), 0gr42 (0.33 #88, 0.22 #1027, 0.18 #1261), 0gs96 (0.33 #1966, 0.18 #1730, 0.11 #2672), 019f4v (0.33 #54, 0.17 #1931, 0.16 #8974) >> Best rule #235 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 04v8x9; >> query: (?x3433, ?x143) <- film_release_region(?x3433, ?x94), nominated_for(?x143, ?x3433), film(?x788, ?x3433), genre(?x3433, ?x225), ?x788 = 0g1rw >> conf = 0.73 => this is the best rule for 4 predicted values *> Best rule #2049 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 22 *> proper extension: 017gm7; 0y_9q; 0gmgwnv; *> query: (?x3433, 018wdw) <- genre(?x3433, ?x225), nominated_for(?x500, ?x3433), nominated_for(?x143, ?x3433), music(?x3433, ?x11281), ?x500 = 0p9sw, ?x143 = 02r0csl *> conf = 0.17 ranks of expected_values: 24 EVAL 0299hs award 018wdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 115.000 91.000 0.727 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #5593-04wlh PRED entity: 04wlh PRED relation: olympics PRED expected values: 06sks6 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 41): 06sks6 (0.88 #2328, 0.88 #1918, 0.88 #1257), 0kbws (0.66 #55, 0.57 #343, 0.55 #1575), 0kbvb (0.49 #419, 0.47 #460, 0.43 #336), 0kbvv (0.49 #67, 0.43 #355, 0.43 #232), 018ctl (0.49 #49, 0.39 #584, 0.38 #214), 0jdk_ (0.44 #371, 0.43 #124, 0.40 #68), 0jhn7 (0.44 #371, 0.43 #124, 0.38 #1811), 0swbd (0.40 #52, 0.31 #423, 0.30 #710), 09n48 (0.38 #415, 0.37 #44, 0.33 #456), 0lgxj (0.27 #2838, 0.27 #2880, 0.24 #441) >> Best rule #2328 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 06sff; >> query: (?x8742, 06sks6) <- country(?x1967, ?x8742), organization(?x8742, ?x127), administrative_area_type(?x8742, ?x2792) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wlh olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.882 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #5592-011yl_ PRED entity: 011yl_ PRED relation: nominated_for! PRED expected values: 0l8z1 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 195): 02r22gf (0.77 #9044, 0.77 #9261, 0.67 #6457), 027b9j5 (0.67 #6457, 0.67 #9043, 0.67 #9260), 027986c (0.67 #6457, 0.67 #9043, 0.67 #9260), 09cm54 (0.67 #6457, 0.67 #9043, 0.67 #9260), 0l8z1 (0.46 #906, 0.36 #4780, 0.30 #1766), 0gr0m (0.40 #911, 0.33 #3277, 0.33 #1771), 0gr4k (0.40 #3249, 0.29 #883, 0.27 #8635), 0p9sw (0.39 #1093, 0.31 #878, 0.28 #4752), 02hsq3m (0.39 #1101, 0.19 #886, 0.13 #4975), 0gq_v (0.37 #877, 0.36 #3243, 0.34 #1092) >> Best rule #9044 for best value: >> intensional similarity = 4 >> extensional distance = 680 >> proper extension: 06w7mlh; 06mmr; >> query: (?x3573, ?x591) <- award(?x3573, ?x591), ceremony(?x591, ?x78), award(?x123, ?x591), nominated_for(?x591, ?x54) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #906 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 02zk08; *> query: (?x3573, 0l8z1) <- nominated_for(?x2379, ?x3573), nominated_for(?x1738, ?x3573), ?x2379 = 02qvyrt, featured_film_locations(?x3573, ?x362) *> conf = 0.46 ranks of expected_values: 5 EVAL 011yl_ nominated_for! 0l8z1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 91.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5591-0pc62 PRED entity: 0pc62 PRED relation: featured_film_locations PRED expected values: 03gh4 => 73 concepts (58 used for prediction) PRED predicted values (max 10 best out of 40): 02_286 (0.20 #260, 0.18 #20, 0.14 #2430), 030qb3t (0.13 #279, 0.09 #39, 0.07 #1001), 01_d4 (0.09 #47, 0.07 #287, 0.03 #2214), 02dtg (0.09 #12, 0.02 #493), 0f2tj (0.09 #123), 04jpl (0.07 #249, 0.06 #1212, 0.06 #6765), 06c62 (0.07 #370, 0.01 #1092), 02301 (0.07 #304), 0rh6k (0.05 #482, 0.04 #3136, 0.03 #1445), 06y57 (0.03 #825, 0.02 #3478, 0.02 #2513) >> Best rule #260 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0gtvpkw; >> query: (?x667, 02_286) <- film(?x6324, ?x667), film(?x5636, ?x667), ?x6324 = 018ygt >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #596 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 191 *> proper extension: 02qjv1p; *> query: (?x667, 03gh4) <- nominated_for(?x1983, ?x667), genre(?x667, ?x53), crewmember(?x508, ?x1983) *> conf = 0.02 ranks of expected_values: 21 EVAL 0pc62 featured_film_locations 03gh4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 73.000 58.000 0.200 http://example.org/film/film/featured_film_locations #5590-02zrv7 PRED entity: 02zrv7 PRED relation: location PRED expected values: 0gkgp => 95 concepts (59 used for prediction) PRED predicted values (max 10 best out of 194): 030qb3t (0.26 #16952, 0.26 #24988, 0.24 #3295), 0l3kx (0.25 #22492, 0.02 #25709), 02_286 (0.21 #3249, 0.21 #16906, 0.18 #32172), 0cr3d (0.17 #145, 0.09 #2554, 0.09 #3357), 04jpl (0.17 #17, 0.07 #29742, 0.06 #3229), 05fkf (0.17 #38, 0.03 #3250, 0.02 #17710), 01531 (0.09 #21846, 0.03 #8995, 0.03 #31490), 059rby (0.09 #3228, 0.05 #15278, 0.05 #4031), 0f2wj (0.09 #3246, 0.02 #16903, 0.02 #22526), 0cc56 (0.08 #21745, 0.06 #3269, 0.04 #12910) >> Best rule #16952 for best value: >> intensional similarity = 4 >> extensional distance = 319 >> proper extension: 036hf4; >> query: (?x6328, 030qb3t) <- gender(?x6328, ?x231), nominated_for(?x6328, ?x6516), participant(?x6328, ?x2221), location(?x6328, ?x13208) >> conf = 0.26 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02zrv7 location 0gkgp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 59.000 0.262 http://example.org/people/person/places_lived./people/place_lived/location #5589-0c8hct PRED entity: 0c8hct PRED relation: gender PRED expected values: 05zppz => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #11, 0.88 #5, 0.87 #9), 02zsn (0.50 #128, 0.46 #151, 0.46 #162) >> Best rule #11 for best value: >> intensional similarity = 9 >> extensional distance = 48 >> proper extension: 083chw; 03f2_rc; 015grj; 015pxr; 098n5; 07d370; 06jnvs; 0bzyh; 016yzz; 04m_zp; ... >> query: (?x5681, 05zppz) <- profession(?x5681, ?x1943), profession(?x5681, ?x1383), profession(?x5681, ?x987), ?x987 = 0dxtg, profession(?x10998, ?x1383), profession(?x3975, ?x1383), ?x1943 = 02krf9, ?x3975 = 02v0ff, ?x10998 = 0223g8 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c8hct gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.900 http://example.org/people/person/gender #5588-07y9ts PRED entity: 07y9ts PRED relation: award_winner PRED expected values: 09hd16 0697kh => 27 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2275): 04ns3gy (0.57 #7448, 0.50 #8978, 0.42 #15105), 0697kh (0.50 #4258, 0.43 #4599, 0.33 #3063), 0cp9f9 (0.50 #8840, 0.43 #7310, 0.40 #11900), 03wbzp (0.50 #5660, 0.33 #2595, 0.30 #10249), 01d8yn (0.50 #5164, 0.33 #2099, 0.30 #9753), 02rhfsc (0.50 #5812, 0.33 #2747, 0.20 #11931), 01q415 (0.50 #3383, 0.33 #319, 0.10 #18709), 0884hk (0.43 #4599, 0.33 #3063, 0.33 #12246), 0brkwj (0.43 #4599, 0.33 #3063, 0.33 #12246), 0h584v (0.43 #4599, 0.33 #3063, 0.33 #12246) >> Best rule #7448 for best value: >> intensional similarity = 22 >> extensional distance = 5 >> proper extension: 05c1t6z; 0gx_st; 02q690_; 03nnm4t; >> query: (?x5296, 04ns3gy) <- ceremony(?x4225, ?x5296), ceremony(?x686, ?x5296), award_winner(?x5296, ?x7301), award_winner(?x5296, ?x6190), award_winner(?x5296, ?x4919), award_winner(?x5296, ?x4035), award_nominee(?x2650, ?x7301), ?x686 = 0bdw1g, profession(?x7301, ?x987), honored_for(?x5296, ?x2078), place_of_birth(?x6190, ?x4253), ?x4225 = 09qvf4, profession(?x6190, ?x1146), participant(?x6190, ?x4065), award_winner(?x6190, ?x3446), people(?x3584, ?x4919), award_winner(?x3989, ?x4919), gender(?x4919, ?x514), student(?x263, ?x4035), location(?x4035, ?x739), film(?x4035, ?x1012), program(?x4035, ?x4084) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4258 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 2 *> proper extension: 0bq_mx; *> query: (?x5296, 0697kh) <- ceremony(?x686, ?x5296), award_winner(?x5296, ?x7301), award_winner(?x5296, ?x6190), award_winner(?x5296, ?x5387), award_nominee(?x4022, ?x7301), nationality(?x7301, ?x94), award(?x5065, ?x686), award(?x1871, ?x686), profession(?x6190, ?x987), profession(?x5065, ?x1032), award_nominee(?x1871, ?x92), award_winner(?x426, ?x5065), student(?x263, ?x5065), award(?x1871, ?x1670), ?x1670 = 0ck27z, award_winner(?x337, ?x1871), award_winner(?x678, ?x6190), award_nominee(?x5387, ?x8337), nominated_for(?x5065, ?x3787), ?x4022 = 0884hk *> conf = 0.50 ranks of expected_values: 2, 11 EVAL 07y9ts award_winner 0697kh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 27.000 19.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 07y9ts award_winner 09hd16 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 27.000 19.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5587-03bxwtd PRED entity: 03bxwtd PRED relation: artists! PRED expected values: 07gxw => 111 concepts (52 used for prediction) PRED predicted values (max 10 best out of 278): 064t9 (0.65 #320, 0.56 #1245, 0.55 #2168), 06by7 (0.57 #1869, 0.55 #3406, 0.54 #4022), 0ggx5q (0.45 #383, 0.29 #1308, 0.27 #2231), 06j6l (0.41 #1280, 0.39 #2203, 0.29 #48), 0gywn (0.32 #1288, 0.29 #2211, 0.21 #7442), 0y3_8 (0.30 #354, 0.09 #1279, 0.08 #3124), 02w4v (0.29 #44, 0.15 #4660, 0.14 #3121), 0xhtw (0.27 #4941, 0.27 #5557, 0.24 #3709), 03_d0 (0.26 #2473, 0.23 #1550, 0.19 #4627), 0dn16 (0.25 #323, 0.03 #15992, 0.02 #7402) >> Best rule #320 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 02wb6yq; 04cr6qv; 04f7c55; 0gs6vr; 06tp4h; 09nhvw; 04d_mtq; >> query: (?x3062, 064t9) <- artists(?x8878, ?x3062), nationality(?x3062, ?x94), ?x8878 = 02ny8t >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #15992 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1085 *> proper extension: 03_gx; *> query: (?x3062, ?x671) <- artists(?x9007, ?x3062), artists(?x9007, ?x9008), artists(?x671, ?x9008) *> conf = 0.03 ranks of expected_values: 122 EVAL 03bxwtd artists! 07gxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 111.000 52.000 0.650 http://example.org/music/genre/artists #5586-0k9j_ PRED entity: 0k9j_ PRED relation: award_winner! PRED expected values: 02py7pj => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 270): 0bfvd4 (0.37 #30498, 0.37 #30497, 0.37 #25770), 0gq9h (0.20 #77, 0.17 #19330, 0.08 #15465), 02x73k6 (0.20 #60, 0.17 #19330, 0.08 #15465), 05p1dby (0.20 #107, 0.03 #7839, 0.03 #16431), 07bdd_ (0.20 #65, 0.03 #16389, 0.03 #18107), 09sb52 (0.18 #4763, 0.17 #19330, 0.16 #7772), 0gqwc (0.17 #19330, 0.14 #503, 0.11 #39520), 02w9sd7 (0.17 #19330, 0.14 #595, 0.08 #15465), 02z13jg (0.17 #19330, 0.14 #478, 0.06 #1766), 09cm54 (0.17 #19330, 0.14 #525, 0.06 #1813) >> Best rule #30498 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 07bzp; >> query: (?x9000, ?x2192) <- award(?x9000, ?x2192), award_winner(?x9000, ?x1357) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #734 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 02knnd; 0hwqg; *> query: (?x9000, 02py7pj) <- award_winner(?x6331, ?x9000), ?x6331 = 029ql, award_winner(?x9000, ?x1357) *> conf = 0.14 ranks of expected_values: 27 EVAL 0k9j_ award_winner! 02py7pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 117.000 117.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #5585-0hqzm6r PRED entity: 0hqzm6r PRED relation: current_club! PRED expected values: 035qgm => 74 concepts (65 used for prediction) PRED predicted values (max 10 best out of 35): 02s9vc (0.44 #295, 0.33 #82, 0.33 #52), 03dj48 (0.33 #53, 0.16 #153, 0.14 #206), 02pp1 (0.33 #56, 0.16 #153, 0.14 #209), 03_qrp (0.16 #153, 0.13 #625, 0.12 #728), 03y_f8 (0.16 #153, 0.13 #625, 0.10 #309), 03_qj1 (0.16 #153, 0.13 #625, 0.10 #661), 03zrhb (0.16 #153, 0.13 #625, 0.09 #950), 03_44z (0.15 #367, 0.14 #212, 0.12 #398), 02s2lg (0.14 #189, 0.10 #312, 0.08 #344), 0cnk2q (0.14 #184, 0.10 #307, 0.08 #339) >> Best rule #295 for best value: >> intensional similarity = 17 >> extensional distance = 7 >> proper extension: 01k2yr; 011v3; 02b13y; 027ffq; >> query: (?x12539, 02s9vc) <- position(?x12539, ?x530), position(?x12539, ?x203), position(?x12539, ?x63), position(?x12539, ?x60), ?x60 = 02nzb8, ?x530 = 02_j1w, team(?x13846, ?x12539), ?x203 = 0dgrmp, team(?x13846, ?x8338), team(?x13846, ?x5710), nationality(?x13846, ?x512), ?x63 = 02sdk9v, gender(?x13846, ?x231), ?x512 = 07ssc, ?x5710 = 050fh, teams(?x11117, ?x8338), team(?x927, ?x8338) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #715 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 72 *> proper extension: 01k2xy; *> query: (?x12539, 035qgm) <- position(?x12539, ?x530), position(?x12539, ?x203), position(?x12539, ?x60), ?x60 = 02nzb8, ?x530 = 02_j1w, team(?x13846, ?x12539), ?x203 = 0dgrmp, team(?x13846, ?x8537), team(?x13846, ?x5710), teams(?x14446, ?x8537), team(?x13846, ?x13306), colors(?x8537, ?x663), current_club(?x8102, ?x8537), colors(?x5710, ?x1101), sport(?x5710, ?x471), ?x1101 = 06fvc, team(?x208, ?x5710) *> conf = 0.04 ranks of expected_values: 21 EVAL 0hqzm6r current_club! 035qgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 74.000 65.000 0.444 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #5584-0gmgwnv PRED entity: 0gmgwnv PRED relation: film! PRED expected values: 016tt2 => 102 concepts (48 used for prediction) PRED predicted values (max 10 best out of 63): 04cygb3 (0.55 #1909, 0.51 #2279, 0.48 #2131), 030_1_ (0.55 #1909, 0.51 #2279, 0.48 #2131), 016tw3 (0.37 #376, 0.18 #816, 0.17 #11), 016tt2 (0.33 #4, 0.29 #77, 0.19 #1104), 017s11 (0.29 #149, 0.14 #1472, 0.13 #1399), 086k8 (0.24 #441, 0.19 #1176, 0.18 #734), 05qd_ (0.23 #1992, 0.23 #1257, 0.22 #1330), 03xq0f (0.18 #1032, 0.15 #884, 0.13 #2433), 0jz9f (0.17 #514, 0.14 #660, 0.14 #954), 01f_mw (0.17 #47, 0.14 #120, 0.02 #2030) >> Best rule #1909 for best value: >> intensional similarity = 3 >> extensional distance = 137 >> proper extension: 0sxg4; 0gzy02; 04v8x9; 01sxly; 0c5dd; 020fcn; 0sxfd; 083skw; 02rjv2w; 0bmpm; ... >> query: (?x6176, ?x1686) <- nominated_for(?x1313, ?x6176), ?x1313 = 0gs9p, production_companies(?x6176, ?x1686) >> conf = 0.55 => this is the best rule for 2 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 016y_f; *> query: (?x6176, 016tt2) <- edited_by(?x6176, ?x4215), nominated_for(?x1254, ?x6176), ?x1254 = 02z0dfh, film(?x818, ?x6176) *> conf = 0.33 ranks of expected_values: 4 EVAL 0gmgwnv film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 48.000 0.551 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5583-0ddfwj1 PRED entity: 0ddfwj1 PRED relation: language PRED expected values: 02h40lc => 103 concepts (99 used for prediction) PRED predicted values (max 10 best out of 59): 02h40lc (0.90 #2551, 0.90 #1515, 0.90 #1634), 064_8sq (0.20 #22, 0.18 #384, 0.17 #683), 06nm1 (0.19 #133, 0.16 #796, 0.13 #11), 04306rv (0.14 #127, 0.10 #968, 0.10 #1397), 02bjrlw (0.08 #1333, 0.08 #3392, 0.07 #3454), 0653m (0.07 #253, 0.06 #975, 0.04 #1768), 0t_2 (0.07 #195, 0.05 #435, 0.05 #136), 0880p (0.07 #227, 0.05 #168, 0.04 #770), 06b_j (0.07 #3538, 0.07 #505, 0.06 #927), 03_9r (0.07 #492, 0.07 #10, 0.06 #3951) >> Best rule #2551 for best value: >> intensional similarity = 6 >> extensional distance = 254 >> proper extension: 02v63m; 048rn; 02r858_; 01_1hw; 04sh80; >> query: (?x370, 02h40lc) <- genre(?x370, ?x53), executive_produced_by(?x370, ?x794), titles(?x512, ?x370), award_winner(?x794, ?x364), award_nominee(?x794, ?x241), film(?x4468, ?x370) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ddfwj1 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 99.000 0.898 http://example.org/film/film/language #5582-0969fd PRED entity: 0969fd PRED relation: student! PRED expected values: 0bkj86 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 15): 0bkj86 (0.33 #8, 0.20 #44, 0.16 #224), 019v9k (0.27 #135, 0.24 #117, 0.24 #189), 02h4rq6 (0.19 #147, 0.11 #543, 0.11 #255), 028dcg (0.17 #70, 0.16 #268, 0.11 #538), 016t_3 (0.17 #58, 0.10 #94, 0.10 #148), 013zdg (0.16 #151, 0.10 #79, 0.07 #133), 03mkk4 (0.12 #174, 0.09 #264, 0.08 #516), 04zx3q1 (0.12 #218, 0.10 #110, 0.07 #128), 03bwzr4 (0.10 #85, 0.05 #121, 0.05 #229), 027f2w (0.10 #82, 0.05 #226, 0.03 #280) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01d494; >> query: (?x10677, 0bkj86) <- influenced_by(?x10677, ?x8430), ?x8430 = 0ct9_, student(?x6056, ?x10677), student(?x1368, ?x10677) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0969fd student! 0bkj86 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.333 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #5581-02hrlh PRED entity: 02hrlh PRED relation: role! PRED expected values: 01vj9c => 42 concepts (39 used for prediction) PRED predicted values (max 10 best out of 130): 0l14md (0.82 #1885, 0.78 #2017, 0.77 #4034), 0342h (0.80 #2696, 0.78 #2014, 0.78 #1083), 03bx0bm (0.79 #1655, 0.75 #1518, 0.73 #2320), 05148p4 (0.78 #1104, 0.77 #2310, 0.76 #1903), 05r5c (0.78 #2018, 0.76 #1886, 0.75 #1491), 042v_gx (0.78 #1088, 0.75 #1492, 0.74 #2157), 018j2 (0.78 #1128, 0.72 #2059, 0.71 #1927), 0l14j_ (0.78 #1150, 0.70 #1281, 0.67 #1830), 07_l6 (0.78 #1160, 0.70 #1291, 0.67 #1027), 07y_7 (0.73 #1760, 0.70 #2944, 0.68 #2693) >> Best rule #1885 for best value: >> intensional similarity = 34 >> extensional distance = 15 >> proper extension: 07y_7; >> query: (?x11978, 0l14md) <- role(?x11978, ?x8014), role(?x11978, ?x3991), role(?x11978, ?x314), role(?x11978, ?x75), role(?x1969, ?x11978), ?x314 = 02sgy, ?x3991 = 05842k, group(?x11978, ?x1945), instrumentalists(?x1969, ?x10744), instrumentalists(?x1969, ?x6461), instrumentalists(?x1969, ?x4741), instrumentalists(?x1969, ?x2170), instrumentalists(?x1969, ?x1654), instrumentalists(?x1969, ?x1413), role(?x2923, ?x1969), role(?x2253, ?x1969), role(?x1432, ?x1969), role(?x1166, ?x1969), ?x2170 = 0144l1, ?x2253 = 01679d, ?x1432 = 0395lw, role(?x228, ?x1969), role(?x1969, ?x569), ?x6461 = 01t110, group(?x1969, ?x5279), ?x4741 = 01s21dg, ?x8014 = 0214km, ?x2923 = 02k856, ?x10744 = 01t8399, ?x1654 = 01bpc9, ?x5279 = 06nv27, ?x1413 = 01p9hgt, group(?x75, ?x1751), instrumentalists(?x1166, ?x130) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #677 for first EXPECTED value: *> intensional similarity = 29 *> extensional distance = 4 *> proper extension: 02fsn; *> query: (?x11978, ?x2785) <- role(?x11978, ?x3991), role(?x11978, ?x1437), role(?x11978, ?x314), role(?x11978, ?x227), role(?x11978, ?x75), role(?x5417, ?x11978), role(?x1969, ?x11978), ?x314 = 02sgy, ?x3991 = 05842k, group(?x11978, ?x1945), ?x1969 = 04rzd, ?x5417 = 02w3w, ?x1437 = 01vdm0, group(?x2945, ?x1945), group(?x2785, ?x1945), group(?x315, ?x1945), group(?x228, ?x1945), ?x227 = 0342h, role(?x2945, ?x2620), ?x75 = 07y_7, ?x228 = 0l14qv, nominated_for(?x2945, ?x4047), award(?x2945, ?x247), ?x315 = 0l14md, artist(?x7793, ?x2945), role(?x2785, ?x1495), profession(?x2945, ?x131), role(?x1165, ?x2785), ?x1495 = 013y1f *> conf = 0.73 ranks of expected_values: 13 EVAL 02hrlh role! 01vj9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 42.000 39.000 0.824 http://example.org/music/performance_role/regular_performances./music/group_membership/role #5580-07cbs PRED entity: 07cbs PRED relation: influenced_by PRED expected values: 043s3 => 195 concepts (95 used for prediction) PRED predicted values (max 10 best out of 369): 026lj (0.53 #29131, 0.27 #5259, 0.16 #13078), 07ym0 (0.53 #29131, 0.18 #4189, 0.09 #5058), 09gnn (0.50 #354, 0.22 #2526, 0.07 #6436), 03sbs (0.45 #4134, 0.26 #17170, 0.25 #19780), 081k8 (0.36 #4067, 0.17 #25805, 0.16 #15364), 05qmj (0.36 #4104, 0.14 #17140, 0.13 #33671), 0gz_ (0.36 #4014, 0.13 #33581, 0.12 #17050), 048cl (0.29 #9357, 0.27 #5014, 0.25 #6751), 015n8 (0.27 #4320, 0.25 #410, 0.19 #15181), 042q3 (0.27 #4276, 0.25 #366, 0.11 #2538) >> Best rule #29131 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 03d9d6; >> query: (?x5254, ?x1857) <- peers(?x8991, ?x5254), peers(?x12258, ?x8991), influenced_by(?x12258, ?x1857) >> conf = 0.53 => this is the best rule for 2 predicted values *> Best rule #4027 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01tz6vs; *> query: (?x5254, 043s3) <- profession(?x5254, ?x353), influenced_by(?x5254, ?x11830), ?x11830 = 0420y, influenced_by(?x2608, ?x5254) *> conf = 0.18 ranks of expected_values: 36 EVAL 07cbs influenced_by 043s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 195.000 95.000 0.526 http://example.org/influence/influence_node/influenced_by #5579-011xg5 PRED entity: 011xg5 PRED relation: country PRED expected values: 09c7w0 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 25): 09c7w0 (0.83 #676, 0.82 #63, 0.81 #1540), 07ssc (0.25 #384, 0.21 #200, 0.21 #937), 03_3d (0.24 #313, 0.11 #252, 0.06 #1670), 0345h (0.14 #517, 0.13 #1132, 0.13 #1317), 0d060g (0.09 #9, 0.06 #498, 0.06 #70), 0f8l9c (0.09 #1124, 0.08 #2907, 0.08 #1433), 0chghy (0.06 #135, 0.06 #196, 0.06 #441), 0ctw_b (0.06 #207, 0.04 #24, 0.03 #85), 03rt9 (0.04 #15, 0.03 #76, 0.01 #1863), 03h64 (0.04 #230, 0.03 #1151, 0.03 #1336) >> Best rule #676 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 0fq27fp; >> query: (?x8349, 09c7w0) <- genre(?x8349, ?x53), crewmember(?x8349, ?x929), currency(?x8349, ?x170) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011xg5 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.829 http://example.org/film/film/country #5578-02dtg PRED entity: 02dtg PRED relation: place_of_birth! PRED expected values: 025j1t 01jfnvd 01pj3h 01mxqyk => 204 concepts (167 used for prediction) PRED predicted values (max 10 best out of 2179): 012vd6 (0.40 #193480, 0.38 #216697, 0.37 #216696), 012x4t (0.40 #193480, 0.37 #294102, 0.30 #193481), 0ql36 (0.40 #193480, 0.37 #294102, 0.29 #188318), 0x3n (0.38 #216697, 0.37 #216696, 0.37 #294102), 01vs_v8 (0.38 #216697, 0.37 #216696, 0.36 #15477), 04qt29 (0.38 #216697, 0.37 #216696, 0.36 #15477), 019vgs (0.38 #216697, 0.37 #216696, 0.36 #15477), 01pbxb (0.38 #216697, 0.37 #216696, 0.36 #15477), 02hy5d (0.38 #216697, 0.37 #216696, 0.36 #15477), 01mxqyk (0.37 #294102, 0.30 #193481, 0.29 #219278) >> Best rule #193480 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 0r3tb; >> query: (?x479, ?x1660) <- origin(?x9791, ?x479), origin(?x1660, ?x479), award_winner(?x1565, ?x9791), instrumentalists(?x212, ?x1660) >> conf = 0.40 => this is the best rule for 3 predicted values *> Best rule #294102 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 188 *> proper extension: 01zk9d; *> query: (?x479, ?x1660) <- origin(?x9791, ?x479), origin(?x1660, ?x479), profession(?x1660, ?x220), artists(?x302, ?x9791) *> conf = 0.37 ranks of expected_values: 10 EVAL 02dtg place_of_birth! 01mxqyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 204.000 167.000 0.395 http://example.org/people/person/place_of_birth EVAL 02dtg place_of_birth! 01pj3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 167.000 0.395 http://example.org/people/person/place_of_birth EVAL 02dtg place_of_birth! 01jfnvd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 167.000 0.395 http://example.org/people/person/place_of_birth EVAL 02dtg place_of_birth! 025j1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 167.000 0.395 http://example.org/people/person/place_of_birth #5577-05q4y12 PRED entity: 05q4y12 PRED relation: film_release_region PRED expected values: 015fr 01znc_ => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 208): 06bnz (0.85 #634, 0.83 #1677, 0.83 #2123), 015fr (0.83 #1651, 0.83 #457, 0.83 #2097), 01znc_ (0.78 #1526, 0.77 #1972, 0.76 #2120), 05v8c (0.74 #456, 0.68 #2096, 0.68 #1650), 03rj0 (0.71 #797, 0.68 #1990, 0.68 #2138), 016wzw (0.64 #504, 0.55 #1996, 0.54 #1550), 015qh (0.62 #479, 0.55 #1673, 0.55 #1971), 04gzd (0.61 #1644, 0.60 #2090, 0.57 #2685), 06mzp (0.60 #462, 0.56 #613, 0.54 #912), 047yc (0.58 #619, 0.57 #1960, 0.56 #1514) >> Best rule #634 for best value: >> intensional similarity = 5 >> extensional distance = 46 >> proper extension: 0gkz15s; 087wc7n; 0gxtknx; 0bq8tmw; 0gd0c7x; 06ztvyx; 0gyfp9c; 0bpm4yw; 06tpmy; 017jd9; ... >> query: (?x2788, 06bnz) <- film_release_region(?x2788, ?x4743), film(?x1871, ?x2788), ?x4743 = 03spz, film_format(?x2788, ?x6392), award_nominee(?x336, ?x1871) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1651 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 137 *> proper extension: 014lc_; 0g56t9t; 0gtsx8c; 0g5qs2k; 0dscrwf; 0c40vxk; 0401sg; 053rxgm; 0dgst_d; 0gtvrv3; ... *> query: (?x2788, 015fr) <- film_release_region(?x2788, ?x4743), film_release_region(?x2788, ?x1264), film_release_region(?x2788, ?x172), film(?x1871, ?x2788), ?x4743 = 03spz, ?x172 = 0154j, ?x1264 = 0345h *> conf = 0.83 ranks of expected_values: 2, 3 EVAL 05q4y12 film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.854 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05q4y12 film_release_region 015fr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 94.000 0.854 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5576-01wttr1 PRED entity: 01wttr1 PRED relation: languages PRED expected values: 055qm => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 19): 02h40lc (0.90 #1142, 0.89 #1028, 0.87 #838), 07c9s (0.26 #202, 0.12 #354, 0.08 #278), 09s02 (0.19 #225, 0.08 #73, 0.06 #1445), 02hxcvy (0.17 #63, 0.10 #139, 0.10 #215), 0999q (0.16 #212, 0.06 #364, 0.06 #1445), 055qm (0.13 #213, 0.04 #3462, 0.04 #2967), 064_8sq (0.10 #850, 0.09 #546, 0.09 #736), 0688f (0.08 #66, 0.06 #1445, 0.06 #104), 01c7y (0.06 #220, 0.06 #258, 0.06 #296), 0121sr (0.06 #222, 0.04 #3462, 0.04 #2967) >> Best rule #1142 for best value: >> intensional similarity = 3 >> extensional distance = 505 >> proper extension: 03j0br4; 01jbx1; >> query: (?x14044, 02h40lc) <- languages(?x14044, ?x1882), award(?x14044, ?x10156), countries_spoken_in(?x1882, ?x792) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #213 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 02r99xw; *> query: (?x14044, 055qm) <- languages(?x14044, ?x1882), people(?x5025, ?x14044), ?x5025 = 0dryh9k *> conf = 0.13 ranks of expected_values: 6 EVAL 01wttr1 languages 055qm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 121.000 121.000 0.899 http://example.org/people/person/languages #5575-0gry51 PRED entity: 0gry51 PRED relation: nationality PRED expected values: 09c7w0 => 51 concepts (47 used for prediction) PRED predicted values (max 10 best out of 67): 09c7w0 (0.68 #1, 0.66 #1701, 0.66 #1101), 02jx1 (0.10 #633, 0.10 #33, 0.09 #1333), 03rk0 (0.09 #346, 0.08 #146, 0.08 #446), 07ssc (0.09 #615, 0.08 #1315, 0.08 #915), 0345h (0.05 #31, 0.04 #931, 0.03 #1231), 0d060g (0.05 #107, 0.05 #207, 0.04 #307), 0f8l9c (0.03 #822, 0.03 #922, 0.03 #622), 0d05w3 (0.02 #250, 0.02 #450, 0.02 #350), 03rjj (0.02 #305, 0.02 #405, 0.02 #1005), 03_3d (0.02 #1606, 0.02 #1706, 0.02 #4002) >> Best rule #1 for best value: >> intensional similarity = 5 >> extensional distance = 77 >> proper extension: 012vct; 01vsy9_; 0l9k1; 04dyqk; >> query: (?x13488, 09c7w0) <- profession(?x13488, ?x1032), profession(?x13488, ?x319), ?x1032 = 02hrh1q, ?x319 = 01d_h8, people(?x5801, ?x13488) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gry51 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 47.000 0.684 http://example.org/people/person/nationality #5574-0m0fw PRED entity: 0m0fw PRED relation: parent_genre PRED expected values: 05r6t => 62 concepts (42 used for prediction) PRED predicted values (max 10 best out of 181): 05r6t (0.65 #2702, 0.39 #1373, 0.35 #1873), 01243b (0.61 #1347, 0.41 #1847, 0.33 #29), 016jny (0.45 #1223, 0.11 #893, 0.08 #3307), 03_d0 (0.44 #1493, 0.42 #1660, 0.27 #340), 06by7 (0.43 #1334, 0.41 #1834, 0.37 #1003), 016clz (0.39 #1322, 0.33 #4, 0.26 #1822), 08jyyk (0.33 #45, 0.14 #210, 0.09 #2812), 05fx6 (0.33 #156, 0.14 #321, 0.09 #1648), 03lty (0.32 #1006, 0.27 #2333, 0.18 #350), 0y3_8 (0.29 #2018, 0.16 #2680, 0.09 #1851) >> Best rule #2702 for best value: >> intensional similarity = 7 >> extensional distance = 61 >> proper extension: 0133k0; 028cl7; 088vmr; >> query: (?x4711, 05r6t) <- parent_genre(?x4711, ?x7220), artists(?x7220, ?x11633), artists(?x7220, ?x7221), ?x11633 = 01ww_vs, artists(?x3916, ?x7221), ?x3916 = 08cyft, profession(?x7221, ?x131) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m0fw parent_genre 05r6t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 42.000 0.651 http://example.org/music/genre/parent_genre #5573-06yxd PRED entity: 06yxd PRED relation: contains PRED expected values: 0mvxt => 144 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2777): 06yxd (0.38 #316221, 0.37 #313293, 0.25 #3537), 09c7w0 (0.38 #316221, 0.37 #313293, 0.02 #225446), 017z88 (0.25 #3270, 0.25 #343, 0.17 #6198), 02lwv5 (0.25 #4663, 0.25 #1736, 0.17 #7591), 021q2j (0.25 #4182, 0.25 #1255, 0.17 #7110), 03bmmc (0.25 #3703, 0.25 #776, 0.17 #6631), 04ftdq (0.25 #4166, 0.25 #1239, 0.17 #7094), 0f94t (0.25 #3022, 0.25 #95, 0.17 #5950), 0ccvx (0.25 #3471, 0.25 #544, 0.17 #6399), 01t0dy (0.25 #3772, 0.25 #845, 0.17 #6700) >> Best rule #316221 for best value: >> intensional similarity = 4 >> extensional distance = 379 >> proper extension: 0jhwd; 0h9vh; >> query: (?x4776, ?x94) <- contains(?x4776, ?x9394), contains(?x4776, ?x7777), place_of_birth(?x9964, ?x9394), contains(?x94, ?x7777) >> conf = 0.38 => this is the best rule for 2 predicted values *> Best rule #225446 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 210 *> proper extension: 017236; *> query: (?x4776, ?x108) <- administrative_parent(?x4776, ?x94), time_zones(?x4776, ?x2674), time_zones(?x108, ?x2674) *> conf = 0.02 ranks of expected_values: 2016 EVAL 06yxd contains 0mvxt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 108.000 0.375 http://example.org/location/location/contains #5572-0kctd PRED entity: 0kctd PRED relation: titles PRED expected values: 09kn9 => 72 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1574): 02py4c8 (0.33 #91, 0.25 #17234, 0.25 #15676), 06zsk51 (0.33 #1314, 0.17 #18457, 0.17 #16899), 027pfb2 (0.33 #586, 0.17 #17729, 0.17 #16171), 06f0k (0.33 #1529, 0.14 #6203, 0.12 #18672), 0jq2r (0.33 #1192, 0.14 #5866, 0.12 #18335), 03j63k (0.33 #1073, 0.14 #5747, 0.12 #18216), 03ffcz (0.33 #987, 0.14 #5661, 0.12 #18130), 08cx5g (0.33 #569, 0.14 #5243, 0.12 #17712), 01cjhz (0.33 #386, 0.14 #5060, 0.12 #17529), 0h95b81 (0.33 #1352, 0.14 #6026, 0.12 #16937) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 07c52; >> query: (?x11493, 02py4c8) <- titles(?x11493, ?x8870), titles(?x11493, ?x2447), ?x2447 = 027tbrc, ?x8870 = 0fhzwl >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #244 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 07c52; *> query: (?x11493, 09kn9) <- titles(?x11493, ?x8870), titles(?x11493, ?x2447), ?x2447 = 027tbrc, ?x8870 = 0fhzwl *> conf = 0.33 ranks of expected_values: 129 EVAL 0kctd titles 09kn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 72.000 31.000 0.333 http://example.org/media_common/netflix_genre/titles #5571-01p8s PRED entity: 01p8s PRED relation: country! PRED expected values: 07rlg => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 50): 071t0 (0.77 #1770, 0.77 #2321, 0.77 #2271), 06f41 (0.67 #263, 0.63 #113, 0.62 #1013), 07jbh (0.62 #1030, 0.54 #2281, 0.53 #280), 06wrt (0.61 #364, 0.60 #264, 0.59 #114), 064vjs (0.60 #278, 0.59 #128, 0.58 #1028), 09w1n (0.57 #271, 0.53 #421, 0.48 #121), 0194d (0.56 #143, 0.51 #1043, 0.50 #443), 02y8z (0.55 #1017, 0.47 #267, 0.44 #117), 07bs0 (0.53 #262, 0.53 #412, 0.48 #112), 0486tv (0.53 #286, 0.48 #136, 0.47 #1036) >> Best rule #1770 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 05c17; >> query: (?x9730, 071t0) <- film_release_region(?x280, ?x9730), contains(?x7273, ?x9730), form_of_government(?x9730, ?x48) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #251 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 035hm; *> query: (?x9730, 07rlg) <- location_of_ceremony(?x566, ?x9730), adjoins(?x9730, ?x1475), form_of_government(?x9730, ?x48) *> conf = 0.43 ranks of expected_values: 19 EVAL 01p8s country! 07rlg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 135.000 135.000 0.771 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #5570-02qhlwd PRED entity: 02qhlwd PRED relation: films! PRED expected values: 081pw => 107 concepts (68 used for prediction) PRED predicted values (max 10 best out of 71): 081pw (0.26 #477, 0.18 #160, 0.07 #634), 0ddct (0.22 #88, 0.02 #719, 0.01 #3564), 0kbq (0.07 #579, 0.07 #262, 0.03 #419), 01w1sx (0.07 #405, 0.05 #248, 0.04 #565), 07_nf (0.05 #224, 0.05 #381, 0.05 #541), 0fzyg (0.05 #528, 0.04 #844, 0.04 #211), 07jq_ (0.05 #556, 0.04 #239, 0.03 #2136), 06d4h (0.05 #3839, 0.04 #1622, 0.03 #5901), 0fx2s (0.04 #863, 0.04 #230, 0.03 #1021), 0cm2xh (0.04 #521, 0.04 #204, 0.02 #4001) >> Best rule #477 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0cq8nx; >> query: (?x4188, 081pw) <- genre(?x4188, ?x3515), music(?x4188, ?x3042), ?x3515 = 082gq, language(?x4188, ?x254) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qhlwd films! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 68.000 0.256 http://example.org/film/film_subject/films #5569-02pg45 PRED entity: 02pg45 PRED relation: film! PRED expected values: 02p21g => 104 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1151): 06cv1 (0.40 #31165, 0.27 #54031, 0.21 #14542), 0h5g_ (0.33 #4228, 0.33 #2151, 0.06 #20849), 01q_ph (0.33 #4211, 0.18 #10442, 0.14 #16677), 013knm (0.33 #4789, 0.10 #8943, 0.04 #18698), 0bksh (0.33 #2929, 0.09 #11237, 0.07 #17472), 014g22 (0.33 #2793, 0.09 #11101, 0.07 #17336), 016sp_ (0.33 #2492, 0.09 #10800, 0.07 #17035), 015v3r (0.33 #2608, 0.09 #12995, 0.04 #18698), 01xsc9 (0.33 #1935, 0.07 #18555, 0.01 #43494), 01xpxv (0.33 #1870, 0.07 #18490, 0.01 #24723) >> Best rule #31165 for best value: >> intensional similarity = 6 >> extensional distance = 100 >> proper extension: 047svrl; >> query: (?x5358, ?x523) <- film(?x3117, ?x5358), film(?x523, ?x5358), film(?x3117, ?x2907), influenced_by(?x3117, ?x1947), award(?x3117, ?x68), film_crew_role(?x2907, ?x137) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #41558 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 141 *> proper extension: 0qm8b; *> query: (?x5358, ?x1593) <- edited_by(?x5358, ?x523), film(?x3117, ?x5358), award(?x3117, ?x1587), nominated_for(?x1587, ?x696), participant(?x3117, ?x1593) *> conf = 0.02 ranks of expected_values: 436 EVAL 02pg45 film! 02p21g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 104.000 70.000 0.398 http://example.org/film/actor/film./film/performance/film #5568-06frc PRED entity: 06frc PRED relation: contains! PRED expected values: 05g2v => 131 concepts (85 used for prediction) PRED predicted values (max 10 best out of 116): 02qkt (0.91 #41623, 0.88 #45212, 0.86 #18279), 09c7w0 (0.90 #72713, 0.86 #73611, 0.75 #74510), 0j0k (0.82 #7547, 0.77 #8442, 0.76 #14723), 0dg3n1 (0.78 #49507, 0.43 #38742, 0.40 #39637), 02j9z (0.58 #20651, 0.42 #41304, 0.35 #26036), 07ssc (0.46 #69148, 0.45 #70046, 0.43 #70944), 04swx (0.40 #4480, 0.07 #58334, 0.04 #21385), 06w92 (0.40 #4480, 0.02 #61028, 0.02 #62829), 0hkt6 (0.40 #4480, 0.02 #61028, 0.01 #35884), 07c5l (0.33 #20122, 0.32 #56936, 0.30 #58731) >> Best rule #41623 for best value: >> intensional similarity = 6 >> extensional distance = 53 >> proper extension: 04gzd; 0h7x; 0bjv6; 06t8v; 04g5k; >> query: (?x8845, 02qkt) <- contains(?x9122, ?x8845), capital(?x8845, ?x6959), contains(?x9122, ?x10569), contains(?x9122, ?x3016), currency(?x10569, ?x170), ?x3016 = 0697s >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #332 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 06cmp; *> query: (?x8845, 05g2v) <- official_language(?x8845, ?x11590), official_language(?x8845, ?x11038), contains(?x9122, ?x8845), ?x9122 = 04wsz, language(?x238, ?x11590), ?x11038 = 04h9h *> conf = 0.33 ranks of expected_values: 11 EVAL 06frc contains! 05g2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 131.000 85.000 0.909 http://example.org/location/location/contains #5567-01d_4t PRED entity: 01d_4t PRED relation: film PRED expected values: 03h3x5 => 96 concepts (63 used for prediction) PRED predicted values (max 10 best out of 787): 0p9lw (0.15 #1937, 0.01 #18058), 01719t (0.15 #2022), 016dj8 (0.11 #4697, 0.02 #6488, 0.01 #8279), 0k_9j (0.11 #4989, 0.01 #17528, 0.01 #19319), 05nlzq (0.11 #8956, 0.09 #34039, 0.08 #39416), 0ctzf1 (0.11 #8956, 0.09 #34039, 0.08 #39416), 01mszz (0.09 #1087, 0.06 #4669, 0.01 #29750), 01hvjx (0.09 #375, 0.04 #7539, 0.02 #20078), 099bhp (0.09 #1620, 0.04 #21323, 0.03 #8784), 047csmy (0.09 #915, 0.04 #20618, 0.03 #8079) >> Best rule #1937 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01wmxfs; 048wrb; 0gd9k; 01w5gg6; 0341n5; 03h8_g; >> query: (?x8876, 0p9lw) <- profession(?x8876, ?x8709), film(?x8876, ?x5378), ?x8709 = 08z956, nationality(?x8876, ?x94) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #5796 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 01vv7sc; 0fpj4lx; 06c44; 01386_; 03s9v; 06jkm; *> query: (?x8876, 03h3x5) <- profession(?x8876, ?x319), diet(?x8876, ?x11141), student(?x2228, ?x8876), nationality(?x8876, ?x94) *> conf = 0.02 ranks of expected_values: 250 EVAL 01d_4t film 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 96.000 63.000 0.154 http://example.org/film/actor/film./film/performance/film #5566-0gl02yg PRED entity: 0gl02yg PRED relation: film! PRED expected values: 04jb97 => 90 concepts (40 used for prediction) PRED predicted values (max 10 best out of 822): 01f873 (0.40 #3979, 0.37 #12304, 0.33 #10223), 0jlv5 (0.25 #1182, 0.11 #7425, 0.08 #5344), 02p65p (0.25 #21, 0.06 #14589, 0.02 #45807), 016z2j (0.25 #389, 0.03 #12876, 0.02 #29525), 079vf (0.25 #8, 0.03 #45794, 0.03 #16657), 0bq2g (0.25 #606, 0.03 #17255, 0.02 #46392), 01twdk (0.25 #845, 0.03 #17494, 0.01 #32062), 0184jc (0.25 #5, 0.02 #20816, 0.02 #29141), 016k6x (0.25 #892, 0.02 #23784, 0.01 #32109), 0ksrf8 (0.25 #994, 0.02 #19724) >> Best rule #3979 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0dckvs; 01f8gz; 0198b6; 0432_5; 01f85k; 0233bn; 08j7lh; 065ym0c; >> query: (?x5826, 01f873) <- film_release_region(?x5826, ?x1229), nominated_for(?x9217, ?x5826), language(?x5826, ?x254), ?x9217 = 09v51c2, ?x1229 = 059j2 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3498 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 0dckvs; 01f8gz; 0198b6; 0432_5; 01f85k; 0233bn; 08j7lh; 065ym0c; *> query: (?x5826, 04jb97) <- film_release_region(?x5826, ?x1229), nominated_for(?x9217, ?x5826), language(?x5826, ?x254), ?x9217 = 09v51c2, ?x1229 = 059j2 *> conf = 0.20 ranks of expected_values: 12 EVAL 0gl02yg film! 04jb97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 90.000 40.000 0.400 http://example.org/film/actor/film./film/performance/film #5565-0ycp3 PRED entity: 0ycp3 PRED relation: group! PRED expected values: 03bx0bm 02hnl => 95 concepts (71 used for prediction) PRED predicted values (max 10 best out of 118): 02hnl (0.83 #1146, 0.81 #2267, 0.80 #1577), 05148p4 (0.78 #532, 0.75 #274, 0.74 #1738), 03bx0bm (0.72 #1141, 0.67 #538, 0.65 #1572), 03qjg (0.52 #992, 0.44 #1165, 0.44 #562), 013y1f (0.42 #971, 0.22 #541, 0.19 #1144), 0l14qv (0.35 #951, 0.33 #521, 0.33 #349), 01vj9c (0.35 #956, 0.33 #526, 0.31 #2250), 06ncr (0.33 #381, 0.23 #983, 0.17 #897), 04rzd (0.33 #546, 0.19 #976, 0.15 #1580), 02sgy (0.33 #522, 0.11 #350, 0.09 #1033) >> Best rule #1146 for best value: >> intensional similarity = 6 >> extensional distance = 34 >> proper extension: 01qqwp9; >> query: (?x6876, 02hnl) <- group(?x2392, ?x6876), group(?x645, ?x6876), group(?x227, ?x6876), artists(?x302, ?x6876), ?x645 = 028tv0, ?x227 = 0342h >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 0ycp3 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 71.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0ycp3 group! 03bx0bm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 71.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group #5564-063y_ky PRED entity: 063y_ky PRED relation: award! PRED expected values: 0blbxk 02nfjp 0c1j_ => 43 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2731): 01vw20h (0.62 #7972, 0.12 #18023, 0.09 #20102), 01vsgrn (0.54 #8313, 0.15 #18364, 0.12 #33508), 01vvydl (0.54 #6719, 0.08 #16770, 0.06 #26823), 02kxbwx (0.50 #3529, 0.25 #179, 0.13 #13581), 08vr94 (0.50 #1085, 0.17 #4435, 0.12 #56968), 0151w_ (0.50 #3583, 0.15 #13635, 0.14 #10284), 01wgxtl (0.46 #7434, 0.09 #17485, 0.09 #20102), 05mt_q (0.46 #7040, 0.09 #20102, 0.08 #17091), 01vzx45 (0.46 #8880, 0.09 #20102, 0.07 #18931), 016kjs (0.46 #6968, 0.09 #20102, 0.04 #17019) >> Best rule #7972 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01bgqh; 01by1l; 02f76h; 02f6xy; 03t5b6; 02f764; 03t5kl; 02f75t; 023vrq; 02f79n; >> query: (?x2456, 01vw20h) <- award(?x3853, ?x2456), award(?x2926, ?x2456), ?x2926 = 016pns, nominated_for(?x3853, ?x1474) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #10362 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 04ljl_l; 05b4l5x; 026mg3; 05f4m9q; 03hkv_r; 05zr6wv; 05zkcn5; 0gq6s3; 0gkvb7; 02p_7cr; ... *> query: (?x2456, 0blbxk) <- nominated_for(?x2456, ?x86), award(?x3118, ?x2456), instrumentalists(?x227, ?x3118) *> conf = 0.06 ranks of expected_values: 1165, 1214, 2564 EVAL 063y_ky award! 0c1j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 20.000 0.615 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 063y_ky award! 02nfjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 43.000 20.000 0.615 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 063y_ky award! 0blbxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 20.000 0.615 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5563-01c57n PRED entity: 01c57n PRED relation: major_field_of_study PRED expected values: 041y2 => 191 concepts (183 used for prediction) PRED predicted values (max 10 best out of 118): 01mkq (0.70 #2250, 0.60 #3244, 0.59 #3865), 03g3w (0.67 #1269, 0.67 #1145, 0.65 #10477), 04rjg (0.67 #518, 0.65 #2255, 0.64 #3870), 01lj9 (0.52 #2276, 0.50 #166, 0.43 #1779), 05qjt (0.51 #3857, 0.50 #1249, 0.50 #132), 02j62 (0.50 #3260, 0.50 #1521, 0.50 #1025), 037mh8 (0.50 #1807, 0.50 #1063, 0.50 #194), 0193x (0.50 #1030, 0.50 #534, 0.50 #161), 062z7 (0.50 #1270, 0.50 #1022, 0.43 #2263), 01lhy (0.50 #1006, 0.40 #262, 0.34 #4470) >> Best rule #2250 for best value: >> intensional similarity = 8 >> extensional distance = 21 >> proper extension: 09kvv; 0bx8pn; 02t4yc; 0bqxw; 02zd460; 0bwfn; >> query: (?x12489, 01mkq) <- citytown(?x12489, ?x11731), company(?x3970, ?x12489), school_type(?x12489, ?x3092), organization(?x5510, ?x12489), institution(?x1526, ?x12489), institution(?x1368, ?x12489), ?x1368 = 014mlp, ?x1526 = 0bkj86 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #249 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 06pwq; *> query: (?x12489, ?x2605) <- currency(?x12489, ?x7888), company(?x3970, ?x12489), ?x3970 = 01___w, major_field_of_study(?x12489, ?x6364), school_type(?x12489, ?x3092), institution(?x1368, ?x12489), major_field_of_study(?x2605, ?x6364) *> conf = 0.38 ranks of expected_values: 21 EVAL 01c57n major_field_of_study 041y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 191.000 183.000 0.696 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #5562-02l3_5 PRED entity: 02l3_5 PRED relation: student! PRED expected values: 017j69 => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 182): 015zyd (0.20 #527, 0.02 #5261, 0.02 #6839), 01vc5m (0.20 #619, 0.02 #5353, 0.02 #6931), 0bwfn (0.10 #4482, 0.09 #17633, 0.08 #20264), 026gvfj (0.08 #1688, 0.04 #4844, 0.04 #5896), 04b_46 (0.08 #6538, 0.07 #7590, 0.05 #4434), 09f2j (0.07 #4366, 0.06 #6470, 0.05 #7522), 05nrkb (0.07 #2452, 0.06 #4030, 0.04 #1926), 017j69 (0.07 #2248, 0.06 #3826, 0.03 #3300), 0ks67 (0.07 #2292, 0.06 #3870, 0.02 #11760), 03k7dn (0.06 #1484, 0.03 #3062, 0.03 #3588) >> Best rule #527 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 03f1zhf; >> query: (?x8081, 015zyd) <- person(?x3480, ?x8081), student(?x4268, ?x8081), ?x3480 = 043q4d >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2248 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 04nw9; 03c5bz; *> query: (?x8081, 017j69) <- award(?x8081, ?x2603), award_winner(?x2880, ?x8081), ?x2603 = 09qs08 *> conf = 0.07 ranks of expected_values: 8 EVAL 02l3_5 student! 017j69 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 148.000 148.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #5561-018fmr PRED entity: 018fmr PRED relation: type_of_union PRED expected values: 04ztj => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.85 #9, 0.83 #37, 0.83 #13), 01g63y (0.31 #102, 0.31 #134, 0.28 #70), 01bl8s (0.01 #59) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0mbhr; >> query: (?x5079, 04ztj) <- gender(?x5079, ?x514), special_performance_type(?x5079, ?x4832), film(?x5079, ?x5080), film_sets_designed(?x786, ?x5080) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018fmr type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.850 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5560-0bsnm PRED entity: 0bsnm PRED relation: state_province_region PRED expected values: 0694j => 163 concepts (122 used for prediction) PRED predicted values (max 10 best out of 118): 0694j (0.73 #4320, 0.69 #2591, 0.62 #3208), 052p7 (0.28 #5806, 0.28 #5311, 0.27 #4816), 059rby (0.20 #2595, 0.20 #1236, 0.19 #3088), 01n7q (0.18 #2485, 0.16 #4338, 0.13 #2362), 05k7sb (0.16 #1015, 0.09 #2498, 0.09 #892), 03v0t (0.12 #1037, 0.06 #545, 0.06 #1780), 04rrx (0.12 #153, 0.09 #276, 0.08 #399), 05tbn (0.10 #1035, 0.08 #4743, 0.07 #5238), 0jt5zcn (0.08 #403, 0.06 #157, 0.05 #772), 05kr_ (0.07 #2126, 0.05 #1756, 0.05 #3730) >> Best rule #4320 for best value: >> intensional similarity = 5 >> extensional distance = 178 >> proper extension: 0qkcb; 01lxw6; >> query: (?x8191, ?x6842) <- contains(?x2474, ?x8191), state(?x2474, ?x6842), time_zones(?x2474, ?x2674), adjoins(?x335, ?x6842), state_province_region(?x481, ?x6842) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bsnm state_province_region 0694j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 122.000 0.728 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #5559-03g3w PRED entity: 03g3w PRED relation: major_field_of_study! PRED expected values: 05qjt => 93 concepts (88 used for prediction) PRED predicted values (max 10 best out of 127): 02j62 (0.84 #2998, 0.83 #1196, 0.82 #5103), 01zc2w (0.84 #2998, 0.83 #1196, 0.82 #5103), 01lhy (0.84 #2998, 0.83 #1196, 0.82 #5103), 04g51 (0.84 #2998, 0.83 #1196, 0.82 #5103), 03g3w (0.58 #2266, 0.57 #1365, 0.50 #1139), 05qfh (0.50 #1071, 0.40 #847, 0.38 #2572), 06ms6 (0.50 #1133, 0.38 #2485, 0.36 #2186), 0_jm (0.43 #1462, 0.33 #113, 0.23 #2512), 03qsdpk (0.33 #2280, 0.33 #1153, 0.33 #32), 01mkq (0.33 #2258, 0.33 #159, 0.29 #1357) >> Best rule #2998 for best value: >> intensional similarity = 7 >> extensional distance = 33 >> proper extension: 01r4k; >> query: (?x2605, ?x254) <- major_field_of_study(?x6912, ?x2605), major_field_of_study(?x581, ?x2605), major_field_of_study(?x734, ?x2605), category(?x6912, ?x134), student(?x6912, ?x1564), major_field_of_study(?x2605, ?x254), ?x581 = 06pwq >> conf = 0.84 => this is the best rule for 4 predicted values *> Best rule #1049 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 06mq7; *> query: (?x2605, 05qjt) <- major_field_of_study(?x13316, ?x2605), major_field_of_study(?x2396, ?x2605), major_field_of_study(?x4100, ?x2605), company(?x2240, ?x13316), student(?x13316, ?x1211), ?x2396 = 07xpm, major_field_of_study(?x196, ?x4100) *> conf = 0.33 ranks of expected_values: 12 EVAL 03g3w major_field_of_study! 05qjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 93.000 88.000 0.845 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #5558-03_gx PRED entity: 03_gx PRED relation: religion! PRED expected values: 025vry 0lccn 04kj2v 01y665 05hj_k 06mn7 018z_c 022p06 0b0pf 01_6dw 02b29 01gbb4 0q9zc 01t94_1 0427y 041b4j 01sbhvd 076689 => 72 concepts (56 used for prediction) PRED predicted values (max 10 best out of 3743): 0315q3 (0.33 #7574, 0.33 #3044, 0.29 #9384), 041mt (0.33 #2847, 0.33 #1942, 0.17 #7377), 032r1 (0.33 #4448, 0.33 #3543, 0.17 #8073), 03_87 (0.33 #4097, 0.33 #3192, 0.17 #7722), 02xyl (0.33 #3606, 0.33 #2701, 0.17 #8136), 0jmj (0.33 #3924, 0.33 #3019, 0.17 #7549), 0kjrx (0.33 #4205, 0.33 #2395, 0.08 #23229), 03xnq9_ (0.33 #3124, 0.29 #9464, 0.25 #11282), 04rfq (0.33 #3607, 0.29 #9947, 0.25 #11765), 049m19 (0.33 #3539, 0.29 #9879, 0.25 #11697) >> Best rule #7574 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 01fgks; 01gr6h; >> query: (?x7131, 0315q3) <- religion(?x4743, ?x7131), religion(?x2517, ?x7131), ?x2517 = 03pn9, adjoins(?x608, ?x4743), jurisdiction_of_office(?x182, ?x4743), film_release_region(?x141, ?x608) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3926 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 0kq2; *> query: (?x7131, 06mn7) <- religion(?x12216, ?x7131), religion(?x8129, ?x7131), religion(?x3999, ?x7131), ?x12216 = 047g6, award_winner(?x5766, ?x8129), ?x5766 = 013b2h, award_nominee(?x221, ?x3999) *> conf = 0.33 ranks of expected_values: 372, 415, 673, 711, 808, 1037, 1062, 1381, 1408, 1498, 1500, 2618, 2853, 2954, 3053, 3231, 3655 EVAL 03_gx religion! 076689 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 01sbhvd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 041b4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 0427y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 01t94_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 0q9zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 01gbb4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 02b29 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 01_6dw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 0b0pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 022p06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 018z_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 06mn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 05hj_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 01y665 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 04kj2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 0lccn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 72.000 56.000 0.333 http://example.org/people/person/religion EVAL 03_gx religion! 025vry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 56.000 0.333 http://example.org/people/person/religion #5557-0jcx PRED entity: 0jcx PRED relation: people! PRED expected values: 013xrm 013b6_ => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 58): 013xrm (0.47 #1743, 0.38 #918, 0.32 #693), 0x67 (0.23 #9173, 0.20 #7062, 0.19 #383), 019lrz (0.20 #36, 0.05 #936, 0.03 #1461), 09zyn5 (0.20 #71, 0.02 #2471, 0.01 #2921), 07bch9 (0.19 #1446, 0.16 #771, 0.15 #321), 0xnvg (0.15 #311, 0.11 #611, 0.09 #2336), 033tf_ (0.15 #7059, 0.15 #9170, 0.14 #7889), 02ctzb (0.15 #2038, 0.15 #2413, 0.13 #2263), 063k3h (0.15 #2054, 0.13 #2429, 0.11 #1454), 07hwkr (0.14 #1435, 0.13 #2785, 0.09 #3535) >> Best rule #1743 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 0136pk; 0841zn; 07mz77; 01nc3rh; 0bhtzw; >> query: (?x3335, 013xrm) <- nationality(?x3335, ?x1355), nationality(?x3335, ?x1264), place_of_birth(?x3335, ?x14568), ?x1264 = 0345h, film_release_region(?x66, ?x1355) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12 EVAL 0jcx people! 013b6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 200.000 200.000 0.475 http://example.org/people/ethnicity/people EVAL 0jcx people! 013xrm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 200.000 200.000 0.475 http://example.org/people/ethnicity/people #5556-09py7 PRED entity: 09py7 PRED relation: entity_involved! PRED expected values: 05nqz => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 72): 02kxjx (0.53 #1413, 0.38 #1218, 0.29 #1283), 06k75 (0.40 #405, 0.09 #1512, 0.08 #1578), 01h6pn (0.38 #1184, 0.29 #662, 0.26 #1379), 0cm2xh (0.33 #466, 0.25 #141, 0.15 #1835), 0cwt70 (0.33 #496, 0.25 #171, 0.15 #1865), 0chhs (0.31 #1234, 0.29 #712, 0.26 #1429), 02h2z_ (0.31 #1223, 0.21 #1418, 0.14 #4374), 07_nf (0.25 #147, 0.25 #82, 0.25 #17), 01y998 (0.25 #734, 0.25 #19, 0.24 #3974), 048n7 (0.25 #737, 0.25 #22, 0.20 #412) >> Best rule #1413 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 0c4b8; 059z0; 088q1s; >> query: (?x10218, 02kxjx) <- entity_involved(?x10764, ?x10218), combatants(?x10764, ?x8687), combatants(?x10764, ?x512), ?x8687 = 059z0, participating_countries(?x358, ?x512) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 07_m9_; 06c97; *> query: (?x10218, 05nqz) <- politician(?x14092, ?x10218), entity_involved(?x10764, ?x10218), place_of_death(?x10218, ?x8745), gender(?x10218, ?x231), contains(?x1603, ?x8745), location(?x8659, ?x8745) *> conf = 0.25 ranks of expected_values: 12 EVAL 09py7 entity_involved! 05nqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 136.000 136.000 0.526 http://example.org/base/culturalevent/event/entity_involved #5555-06s0l PRED entity: 06s0l PRED relation: country! PRED expected values: 06z6r => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 56): 06z6r (0.78 #1600, 0.77 #2104, 0.77 #872), 03_8r (0.68 #1142, 0.67 #862, 0.66 #1758), 01cgz (0.62 #1246, 0.62 #1078, 0.62 #854), 071t0 (0.60 #79, 0.57 #1591, 0.57 #1927), 064vjs (0.60 #89, 0.47 #369, 0.43 #145), 01lb14 (0.60 #72, 0.45 #1584, 0.44 #352), 07bs0 (0.60 #69, 0.41 #349, 0.39 #125), 01sgl (0.60 #102, 0.36 #270, 0.35 #326), 06f41 (0.53 #71, 0.50 #351, 0.39 #1583), 09w1n (0.53 #81, 0.41 #361, 0.35 #137) >> Best rule #1600 for best value: >> intensional similarity = 2 >> extensional distance = 129 >> proper extension: 02jxk; >> query: (?x7096, 06z6r) <- member_states(?x7695, ?x7096), jurisdiction_of_office(?x182, ?x7096) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06s0l country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.779 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #5554-0h1q6 PRED entity: 0h1q6 PRED relation: gender PRED expected values: 05zppz => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.93 #9, 0.90 #27, 0.89 #41), 02zsn (0.55 #157, 0.46 #200, 0.30 #66) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0hwd8; >> query: (?x12298, 05zppz) <- nationality(?x12298, ?x94), award_winner(?x458, ?x12298), ?x458 = 0789_m >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h1q6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.933 http://example.org/people/person/gender #5553-046rfv PRED entity: 046rfv PRED relation: people! PRED expected values: 0dryh9k 01rv7x => 170 concepts (151 used for prediction) PRED predicted values (max 10 best out of 70): 0dryh9k (0.66 #1918, 0.50 #320, 0.38 #549), 041rx (0.34 #3504, 0.28 #5178, 0.25 #7234), 0x67 (0.27 #5336, 0.23 #4728, 0.18 #7544), 01rv7x (0.21 #876, 0.17 #343, 0.14 #648), 033tf_ (0.19 #1072, 0.16 #2137, 0.15 #2517), 07hwkr (0.17 #1381, 0.16 #1533, 0.15 #2142), 0bpjh3 (0.15 #558, 0.10 #938, 0.09 #1014), 02w7gg (0.12 #763, 0.12 #382, 0.09 #3426), 07bch9 (0.12 #784, 0.11 #2153, 0.10 #1620), 0xnvg (0.12 #393, 0.10 #1306, 0.08 #1078) >> Best rule #1918 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 0cfywh; >> query: (?x8097, 0dryh9k) <- nationality(?x8097, ?x2146), people(?x13008, ?x8097), place_of_birth(?x8097, ?x4335), ?x2146 = 03rk0 >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 046rfv people! 01rv7x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 170.000 151.000 0.661 http://example.org/people/ethnicity/people EVAL 046rfv people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 151.000 0.661 http://example.org/people/ethnicity/people #5552-05fky PRED entity: 05fky PRED relation: contains PRED expected values: 0ynfz => 224 concepts (88 used for prediction) PRED predicted values (max 10 best out of 3131): 0fvyz (0.86 #241199, 0.84 #150004, 0.83 #105883), 0ynfz (0.82 #91176, 0.79 #41173, 0.74 #147063), 09c7w0 (0.62 #147062, 0.05 #214723, 0.05 #164712), 05fky (0.56 #161770, 0.49 #241198, 0.05 #214723), 01s7pm (0.17 #4932, 0.08 #7872, 0.08 #1992), 0s69k (0.15 #238, 0.09 #79650, 0.08 #6118), 0s6g4 (0.15 #2158, 0.08 #8038, 0.08 #10978), 0jpn8 (0.15 #1320, 0.08 #7200, 0.08 #10140), 01y17m (0.15 #401, 0.08 #6281, 0.08 #9221), 065r8g (0.15 #336, 0.08 #6216, 0.08 #9156) >> Best rule #241199 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 0djgt; 01l_9d; 01tmtg; 050tt8; >> query: (?x4198, ?x9371) <- contains(?x4198, ?x12132), category(?x12132, ?x134), capital(?x4198, ?x9371) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #91176 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 54 *> proper extension: 07c5l; *> query: (?x4198, ?x9333) <- contains(?x4198, ?x13475), contains(?x4198, ?x7067), taxonomy(?x4198, ?x939), time_zones(?x7067, ?x1638), administrative_division(?x9333, ?x13475) *> conf = 0.82 ranks of expected_values: 2 EVAL 05fky contains 0ynfz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 224.000 88.000 0.859 http://example.org/location/location/contains #5551-05gp3x PRED entity: 05gp3x PRED relation: type_of_union PRED expected values: 04ztj => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.78 #21, 0.70 #161, 0.70 #165), 01g63y (0.15 #90, 0.14 #86, 0.14 #118) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 09pl3s; 03b78r; >> query: (?x6072, 04ztj) <- program(?x6072, ?x1653), award_nominee(?x6072, ?x6071), location(?x6072, ?x108) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05gp3x type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.779 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5550-0jhjl PRED entity: 0jhjl PRED relation: institution! PRED expected values: 014mlp => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 22): 014mlp (0.74 #235, 0.64 #396, 0.60 #534), 019v9k (0.59 #399, 0.58 #238, 0.50 #630), 02_xgp2 (0.54 #242, 0.45 #81, 0.43 #173), 03bwzr4 (0.48 #244, 0.42 #405, 0.40 #83), 016t_3 (0.41 #233, 0.38 #394, 0.34 #672), 07s6fsf (0.38 #231, 0.32 #392, 0.28 #47), 04zx3q1 (0.30 #232, 0.26 #71, 0.26 #48), 027f2w (0.25 #78, 0.25 #239, 0.22 #55), 013zdg (0.23 #99, 0.21 #7, 0.21 #214), 01rr_d (0.18 #247, 0.16 #1375, 0.13 #86) >> Best rule #235 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 01w_sh; >> query: (?x9409, 014mlp) <- institution(?x1526, ?x9409), category(?x9409, ?x134), ?x1526 = 0bkj86 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jhjl institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.738 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5549-04n3l PRED entity: 04n3l PRED relation: location_of_ceremony! PRED expected values: 04ztj => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.90 #53, 0.88 #37, 0.86 #45), 01g63y (0.33 #285, 0.32 #310, 0.05 #38), 0jgjn (0.04 #56, 0.03 #40, 0.03 #48) >> Best rule #53 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 0nqv1; 01c1nm; 0nbfm; 0kc40; >> query: (?x3415, 04ztj) <- place_of_birth(?x666, ?x3415), location_of_ceremony(?x6231, ?x3415), contains(?x94, ?x3415) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04n3l location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.897 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #5548-01snm PRED entity: 01snm PRED relation: teams PRED expected values: 01ypc => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 231): 0cqt41 (0.03 #389, 0.03 #2544, 0.03 #5775), 0jmgb (0.03 #660, 0.03 #1019, 0.02 #1379), 02r2qt7 (0.03 #497, 0.03 #856, 0.02 #1216), 051q5 (0.03 #435, 0.03 #794, 0.02 #1154), 0512p (0.03 #385, 0.03 #744, 0.02 #1104), 0jnlm (0.03 #710, 0.03 #1069, 0.02 #1429), 0jm74 (0.03 #505, 0.03 #864, 0.02 #1224), 01slc (0.03 #501, 0.03 #860, 0.02 #1220), 01yjl (0.03 #415, 0.03 #774, 0.02 #1134), 01y3v (0.03 #406, 0.03 #765, 0.02 #1125) >> Best rule #389 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 0r6c4; >> query: (?x6555, 0cqt41) <- citytown(?x9675, ?x6555), county(?x6555, ?x12764), service_location(?x9675, ?x94) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01snm teams 01ypc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 177.000 177.000 0.034 http://example.org/sports/sports_team_location/teams #5547-02x2t07 PRED entity: 02x2t07 PRED relation: film_production_design_by! PRED expected values: 03tn80 => 128 concepts (67 used for prediction) PRED predicted values (max 10 best out of 155): 077q8x (0.20 #99, 0.10 #715, 0.08 #1332), 0286hyp (0.20 #154, 0.10 #770, 0.08 #1387), 0by17xn (0.11 #611, 0.10 #765, 0.09 #1074), 0q9b0 (0.11 #580, 0.10 #734, 0.09 #1043), 047myg9 (0.11 #569, 0.10 #723, 0.09 #1032), 0yyn5 (0.11 #549, 0.10 #703, 0.09 #1012), 01k60v (0.11 #533, 0.10 #687, 0.09 #996), 04cv9m (0.11 #530, 0.10 #684, 0.09 #993), 04y5j64 (0.11 #528, 0.10 #682, 0.09 #991), 0170th (0.11 #507, 0.10 #661, 0.09 #970) >> Best rule #99 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0520r2x; >> query: (?x9062, 077q8x) <- place_of_birth(?x9062, ?x1131), profession(?x9062, ?x2450), film_art_direction_by(?x1454, ?x9062), place_of_death(?x9062, ?x6769) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02x2t07 film_production_design_by! 03tn80 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 67.000 0.200 http://example.org/film/film/film_production_design_by #5546-014g22 PRED entity: 014g22 PRED relation: film PRED expected values: 0421v9q => 80 concepts (75 used for prediction) PRED predicted values (max 10 best out of 556): 016z9n (0.58 #58916, 0.38 #94632, 0.36 #92846), 01f69m (0.58 #58916, 0.38 #94632, 0.36 #92846), 0fpmrm3 (0.42 #2204, 0.03 #110701, 0.03 #39273), 026lgs (0.19 #4503, 0.03 #39273, 0.03 #89275), 0b2km_ (0.17 #3393, 0.14 #1608, 0.03 #37487), 03hp2y1 (0.17 #3390, 0.03 #37487, 0.03 #39273), 04ghz4m (0.17 #3022), 02_1sj (0.14 #77, 0.12 #3647, 0.03 #37487), 0c0zq (0.14 #1558, 0.08 #3343, 0.07 #42844), 08052t3 (0.14 #389, 0.08 #2174, 0.06 #3959) >> Best rule #58916 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 049tjg; 025p38; 01wjrn; 02wrhj; 02lq10; 05wjnt; 01nrq5; 039crh; 02zrv7; 0n8bn; ... >> query: (?x4154, ?x2336) <- film(?x4154, ?x69), nominated_for(?x4154, ?x2336) >> conf = 0.58 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 014g22 film 0421v9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 75.000 0.585 http://example.org/film/actor/film./film/performance/film #5545-0fk0xk PRED entity: 0fk0xk PRED relation: ceremony! PRED expected values: 0gq_d => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 360): 0gq_d (0.94 #3275, 0.93 #2793, 0.93 #3998), 0gqz2 (0.88 #2701, 0.88 #3183, 0.87 #2219), 0l8z1 (0.82 #2209, 0.76 #4137, 0.75 #3414), 0czp_ (0.68 #1929, 0.45 #915, 0.40 #674), 0gqxm (0.68 #1929, 0.45 #2286, 0.44 #3009), 0gqzz (0.68 #1929, 0.19 #2689, 0.18 #2207), 02x201b (0.68 #1929, 0.13 #2342, 0.12 #2824), 019f4v (0.33 #41, 0.25 #5791, 0.24 #5548), 054ks3 (0.33 #90, 0.25 #5791, 0.14 #5549), 054krc (0.33 #54, 0.14 #5845, 0.14 #5360) >> Best rule #3275 for best value: >> intensional similarity = 20 >> extensional distance = 47 >> proper extension: 0fzrhn; >> query: (?x5723, 0gq_d) <- honored_for(?x5723, ?x3294), award_winner(?x5723, ?x8401), award_winner(?x5723, ?x4423), award_winner(?x5723, ?x3237), award_winner(?x5723, ?x2068), ceremony(?x3617, ?x5723), ceremony(?x1862, ?x5723), ceremony(?x591, ?x5723), language(?x3294, ?x254), ?x3617 = 0gvx_, nominated_for(?x3237, ?x951), award_nominee(?x199, ?x8401), film(?x2416, ?x3294), film(?x4926, ?x3294), nationality(?x4423, ?x94), location(?x2068, ?x739), award_nominee(?x2068, ?x2069), award_winner(?x1745, ?x4423), ?x591 = 0f4x7, nominated_for(?x1862, ?x69) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fk0xk ceremony! 0gq_d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.939 http://example.org/award/award_category/winners./award/award_honor/ceremony #5544-03s9b PRED entity: 03s9b PRED relation: people! PRED expected values: 06mvq => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 64): 041rx (0.26 #928, 0.22 #1005, 0.22 #466), 02g7sp (0.25 #95, 0.17 #172, 0.04 #403), 065b6q (0.25 #3, 0.05 #311, 0.04 #1928), 02w7gg (0.17 #156, 0.11 #772, 0.11 #310), 07hwkr (0.17 #166, 0.08 #1937, 0.07 #3092), 048z7l (0.17 #194, 0.07 #810, 0.06 #271), 0g96wd (0.17 #218, 0.02 #4625, 0.02 #603), 0xnvg (0.15 #398, 0.11 #706, 0.10 #783), 0x67 (0.13 #1088, 0.10 #1627, 0.10 #7947), 033tf_ (0.12 #1932, 0.12 #2394, 0.11 #2856) >> Best rule #928 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 081_zm; 04511f; 0cm89v; 0hky; 0hcvy; >> query: (?x6957, 041rx) <- profession(?x6957, ?x319), written_by(?x6345, ?x6957), religion(?x6957, ?x109) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #4625 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 652 *> proper extension: 02r99xw; *> query: (?x6957, ?x743) <- languages(?x6957, ?x5607), languages(?x7045, ?x5607), people(?x743, ?x7045) *> conf = 0.02 ranks of expected_values: 46 EVAL 03s9b people! 06mvq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 137.000 137.000 0.263 http://example.org/people/ethnicity/people #5543-08966 PRED entity: 08966 PRED relation: mode_of_transportation PRED expected values: 07jdr => 258 concepts (258 used for prediction) PRED predicted values (max 10 best out of 3): 07jdr (0.81 #160, 0.80 #196, 0.79 #151), 06d_3 (0.06 #252, 0.05 #261, 0.05 #276), 0k4j (0.05 #260, 0.05 #275, 0.05 #230) >> Best rule #160 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0fq8f; >> query: (?x6458, 07jdr) <- mode_of_transportation(?x6458, ?x6665), ?x6665 = 025t3bg, film_release_region(?x3392, ?x6458), award(?x3392, ?x640) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08966 mode_of_transportation 07jdr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 258.000 258.000 0.808 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #5542-02mjf2 PRED entity: 02mjf2 PRED relation: gender PRED expected values: 05zppz => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #139, 0.72 #165, 0.72 #231), 02zsn (0.57 #4, 0.56 #2, 0.53 #26) >> Best rule #139 for best value: >> intensional similarity = 2 >> extensional distance = 814 >> proper extension: 02vptk_; 03c_8t; >> query: (?x4400, 05zppz) <- student(?x1011, ?x4400), fraternities_and_sororities(?x1011, ?x3697) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02mjf2 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.724 http://example.org/people/person/gender #5541-02z9rr PRED entity: 02z9rr PRED relation: featured_film_locations PRED expected values: 05rgl => 83 concepts (54 used for prediction) PRED predicted values (max 10 best out of 46): 030qb3t (0.20 #39, 0.16 #279, 0.10 #2202), 02_286 (0.19 #500, 0.17 #2183, 0.15 #8196), 0rh6k (0.16 #241, 0.08 #1201, 0.07 #1442), 06y57 (0.12 #823, 0.04 #2025, 0.03 #2506), 04jpl (0.08 #2172, 0.06 #5054, 0.06 #6980), 0cv3w (0.07 #70, 0.05 #310, 0.03 #550), 07b_l (0.07 #77, 0.05 #317, 0.02 #797), 080h2 (0.06 #504, 0.03 #3868, 0.02 #2187), 05kj_ (0.06 #498, 0.02 #738), 0gkgp (0.05 #401, 0.01 #3525) >> Best rule #39 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 09lxv9; >> query: (?x7849, 030qb3t) <- nominated_for(?x102, ?x7849), film(?x11233, ?x7849), genre(?x7849, ?x225), ?x11233 = 01vsn38 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02z9rr featured_film_locations 05rgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 54.000 0.200 http://example.org/film/film/featured_film_locations #5540-0g57wgv PRED entity: 0g57wgv PRED relation: film_release_region PRED expected values: 0jgd 0b90_r 05v8c 059j2 0345h 02vzc 06f32 03spz => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 94): 05qhw (0.90 #158, 0.88 #12, 0.73 #742), 03gj2 (0.89 #169, 0.82 #23, 0.77 #753), 03rjj (0.88 #5, 0.87 #151, 0.84 #735), 0b90_r (0.88 #4, 0.86 #150, 0.70 #734), 02vzc (0.88 #53, 0.81 #783, 0.78 #1221), 0345h (0.87 #179, 0.84 #763, 0.75 #1201), 059j2 (0.86 #177, 0.85 #761, 0.82 #1199), 0jgd (0.84 #149, 0.80 #733, 0.75 #1171), 01znc_ (0.84 #188, 0.76 #42, 0.73 #772), 0ctw_b (0.82 #24, 0.66 #170, 0.49 #754) >> Best rule #158 for best value: >> intensional similarity = 6 >> extensional distance = 112 >> proper extension: 0gtsx8c; 0c3ybss; 0gx1bnj; 087wc7n; 0crfwmx; 0jjy0; 0cz8mkh; 0661m4p; 07x4qr; 0gffmn8; ... >> query: (?x9859, 05qhw) <- film_release_region(?x9859, ?x8449), film_release_region(?x9859, ?x1917), film_release_region(?x9859, ?x512), ?x512 = 07ssc, ?x1917 = 01p1v, countries_spoken_in(?x2502, ?x8449) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 08hmch; 03bx2lk; 043tvp3; 09v3jyg; *> query: (?x9859, 0b90_r) <- film_release_region(?x9859, ?x8449), film_release_region(?x9859, ?x1917), film_release_region(?x9859, ?x512), ?x512 = 07ssc, ?x1917 = 01p1v, ?x8449 = 02k1b *> conf = 0.88 ranks of expected_values: 4, 5, 6, 7, 8, 12, 15, 28 EVAL 0g57wgv film_release_region 03spz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 57.000 57.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g57wgv film_release_region 06f32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 57.000 57.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g57wgv film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 57.000 57.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g57wgv film_release_region 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 57.000 57.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g57wgv film_release_region 059j2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 57.000 57.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g57wgv film_release_region 05v8c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 57.000 57.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g57wgv film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 57.000 57.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g57wgv film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 57.000 57.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5539-0kryqm PRED entity: 0kryqm PRED relation: location_of_ceremony PRED expected values: 0r0m6 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 13): 0cv3w (0.03 #511, 0.03 #154, 0.03 #273), 0f25y (0.03 #80, 0.01 #199, 0.01 #318), 0k049 (0.02 #480, 0.02 #599, 0.02 #4), 0b90_r (0.02 #3, 0.01 #241, 0.01 #479), 030qb3t (0.02 #19, 0.01 #614, 0.01 #973), 059rby (0.01 #365), 0r0m6 (0.01 #169, 0.01 #288, 0.01 #526), 0gx1l (0.01 #212), 0ggyr (0.01 #211), 07fr_ (0.01 #192) >> Best rule #511 for best value: >> intensional similarity = 2 >> extensional distance = 275 >> proper extension: 022_lg; >> query: (?x6889, 0cv3w) <- spouse(?x6889, ?x843), nominated_for(?x6889, ?x3822) >> conf = 0.03 => this is the best rule for 1 predicted values *> Best rule #169 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 147 *> proper extension: 03f0fnk; *> query: (?x6889, 0r0m6) <- spouse(?x6889, ?x843), award_winner(?x8964, ?x6889) *> conf = 0.01 ranks of expected_values: 7 EVAL 0kryqm location_of_ceremony 0r0m6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 80.000 80.000 0.029 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #5538-0j_sncb PRED entity: 0j_sncb PRED relation: school! PRED expected values: 06439y => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 15): 05vsb7 (0.26 #61, 0.25 #1, 0.19 #181), 047dpm0 (0.25 #14, 0.14 #74, 0.08 #314), 04f4z1k (0.25 #13, 0.11 #193, 0.11 #208), 03nt7j (0.23 #66, 0.14 #156, 0.14 #306), 09l0x9 (0.21 #69, 0.17 #159, 0.16 #189), 0g3zpp (0.21 #62, 0.14 #17, 0.13 #332), 02pq_x5 (0.19 #72, 0.16 #207, 0.16 #342), 02z6872 (0.16 #68, 0.14 #188, 0.14 #203), 09th87 (0.16 #70, 0.12 #295, 0.12 #340), 02pq_rp (0.16 #67, 0.12 #292, 0.11 #187) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 01lnyf; 01jpyb; 03tw2s; 0225v9; >> query: (?x2948, 05vsb7) <- major_field_of_study(?x2948, ?x1154), school(?x8499, ?x2948), ?x1154 = 02lp1, draft(?x260, ?x8499) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 05kj_; *> query: (?x2948, 06439y) <- contains(?x94, ?x2948), school(?x1883, ?x2948), category(?x2948, ?x134), ?x94 = 09c7w0 *> conf = 0.14 ranks of expected_values: 12 EVAL 0j_sncb school! 06439y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 117.000 117.000 0.256 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #5537-01_4lx PRED entity: 01_4lx PRED relation: child PRED expected values: 07zl6m => 191 concepts (150 used for prediction) PRED predicted values (max 10 best out of 337): 0dwcl (0.27 #1503, 0.12 #4916, 0.12 #4746), 01jx9 (0.27 #1407, 0.12 #4820, 0.12 #4650), 07y2b (0.20 #1159, 0.17 #819, 0.14 #989), 01scmq (0.20 #492, 0.12 #2706, 0.12 #4756), 01qszl (0.20 #506, 0.08 #4940, 0.07 #2378), 06nfl (0.20 #505, 0.07 #2377, 0.04 #5279), 031rq5 (0.18 #1413, 0.17 #562, 0.12 #2434), 05s_k6 (0.18 #1316, 0.14 #976, 0.12 #2507), 016tw3 (0.18 #1372, 0.13 #2053, 0.13 #1883), 03sb38 (0.18 #1429, 0.13 #2110, 0.13 #1940) >> Best rule #1503 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0kx4m; >> query: (?x10377, 0dwcl) <- child(?x10377, ?x13872), industry(?x10377, ?x245), state_province_region(?x13872, ?x335), place_founded(?x13872, ?x739) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #8021 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 09tlc8; *> query: (?x10377, ?x244) <- child(?x10377, ?x13872), industry(?x10377, ?x245), industry(?x13890, ?x245), industry(?x244, ?x245), ?x13890 = 02b07b *> conf = 0.02 ranks of expected_values: 245 EVAL 01_4lx child 07zl6m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 191.000 150.000 0.273 http://example.org/organization/organization/child./organization/organization_relationship/child #5536-06x4l_ PRED entity: 06x4l_ PRED relation: place_of_birth PRED expected values: 0fpzwf => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 94): 013kcv (0.20 #23, 0.01 #44374, 0.01 #10590), 04n3l (0.20 #123), 0cr3d (0.12 #798, 0.03 #50104, 0.03 #55737), 03lrc (0.12 #1186), 03v0t (0.12 #841), 02_286 (0.09 #2132, 0.08 #9177, 0.07 #55662), 01sn3 (0.04 #1558, 0.03 #2262, 0.01 #3672), 01cx_ (0.04 #1518, 0.01 #2222), 09ctj (0.04 #2063), 0b_yz (0.04 #1842) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 023p29; >> query: (?x2862, 013kcv) <- award_nominee(?x2862, ?x7115), award_winner(?x1232, ?x2862), ?x7115 = 02z4b_8 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06x4l_ place_of_birth 0fpzwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 97.000 0.200 http://example.org/people/person/place_of_birth #5535-01hdht PRED entity: 01hdht PRED relation: profession PRED expected values: 02hrh1q => 115 concepts (58 used for prediction) PRED predicted values (max 10 best out of 105): 02hrh1q (0.83 #4537, 0.82 #4683, 0.81 #5413), 0cbd2 (0.60 #3218, 0.56 #3364, 0.44 #4094), 0kyk (0.37 #3239, 0.35 #3385, 0.33 #4115), 0fj9f (0.37 #198, 0.32 #490, 0.29 #52), 03gjzk (0.34 #2640, 0.34 #1034, 0.32 #596), 018gz8 (0.31 #3518, 0.30 #3080, 0.20 #1620), 09jwl (0.25 #1768, 0.19 #1914, 0.17 #3812), 02krf9 (0.23 #6157, 0.21 #7765, 0.21 #6303), 05z96 (0.20 #3252, 0.19 #3398, 0.16 #186), 02hv44_ (0.19 #3121, 0.17 #3267, 0.12 #3705) >> Best rule #4537 for best value: >> intensional similarity = 4 >> extensional distance = 241 >> proper extension: 01gvr1; 030h95; 045zr; 0253b6; 0f502; 02v60l; 01d0fp; 016yvw; 02t__3; 01jfrg; ... >> query: (?x11626, 02hrh1q) <- gender(?x11626, ?x231), profession(?x11626, ?x319), spouse(?x13144, ?x11626), location(?x11626, ?x659) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hdht profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 58.000 0.831 http://example.org/people/person/profession #5534-0bvhz9 PRED entity: 0bvhz9 PRED relation: award_winner PRED expected values: 015grj 02ktrs => 42 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1080): 0bytkq (0.33 #458, 0.17 #3532, 0.14 #1996), 02mxbd (0.33 #876, 0.17 #3950, 0.11 #7023), 04gcd1 (0.33 #322, 0.17 #3396, 0.08 #11080), 02pv_d (0.33 #1168, 0.14 #2706, 0.08 #4242), 0bwh6 (0.33 #182, 0.11 #6329, 0.08 #3256), 0bbxx9b (0.33 #585, 0.08 #3659, 0.08 #11343), 0mz73 (0.33 #1138, 0.08 #4212, 0.07 #13433), 06r_by (0.33 #931, 0.08 #4005, 0.06 #19377), 02mt4k (0.33 #764, 0.08 #3838, 0.06 #6911), 05m883 (0.33 #156, 0.08 #3230, 0.06 #6303) >> Best rule #458 for best value: >> intensional similarity = 18 >> extensional distance = 1 >> proper extension: 050yyb; >> query: (?x9921, 0bytkq) <- ceremony(?x4573, ?x9921), ceremony(?x2222, ?x9921), ceremony(?x1972, ?x9921), ceremony(?x601, ?x9921), award_winner(?x9921, ?x7333), award_winner(?x9921, ?x6629), award_winner(?x9921, ?x3528), profession(?x6629, ?x319), ?x2222 = 0gs96, ?x601 = 0gr4k, written_by(?x3219, ?x6629), instance_of_recurring_event(?x9921, ?x3459), ?x4573 = 0gq_d, honored_for(?x9921, ?x1135), ?x7333 = 08h79x, location(?x6629, ?x5036), ?x1972 = 0gqyl, award_nominee(?x2135, ?x3528) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #16908 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 29 *> proper extension: 0c53zb; *> query: (?x9921, ?x1872) <- ceremony(?x2222, ?x9921), ceremony(?x601, ?x9921), ceremony(?x77, ?x9921), award_winner(?x9921, ?x6629), profession(?x6629, ?x987), profession(?x6629, ?x524), profession(?x6629, ?x319), ?x2222 = 0gs96, ?x601 = 0gr4k, written_by(?x3219, ?x6629), award(?x1872, ?x77), ?x987 = 0dxtg, ?x319 = 01d_h8, profession(?x11813, ?x524), profession(?x1855, ?x524), ?x1855 = 01c58j, ?x11813 = 0716t2, nominated_for(?x77, ?x303) *> conf = 0.01 ranks of expected_values: 519, 1017 EVAL 0bvhz9 award_winner 02ktrs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 13.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bvhz9 award_winner 015grj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 13.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5533-0bdw1g PRED entity: 0bdw1g PRED relation: ceremony PRED expected values: 0gvstc3 => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 130): 0gvstc3 (0.77 #422, 0.47 #552, 0.39 #682), 0gpjbt (0.55 #1978, 0.54 #1068, 0.52 #2238), 09n4nb (0.53 #1995, 0.52 #1085, 0.51 #2255), 0466p0j (0.53 #2020, 0.51 #1110, 0.51 #2150), 05pd94v (0.53 #1952, 0.51 #1042, 0.51 #2082), 056878 (0.53 #1980, 0.51 #1070, 0.50 #2240), 02rjjll (0.52 #1955, 0.51 #1045, 0.50 #2215), 02cg41 (0.52 #2067, 0.50 #2327, 0.50 #1157), 01c6qp (0.51 #1968, 0.50 #1058, 0.49 #2228), 01bx35 (0.49 #1957, 0.47 #2217, 0.46 #2087) >> Best rule #422 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 02_3zj; 09v82c0; >> query: (?x686, 0gvstc3) <- award(?x376, ?x686), ceremony(?x686, ?x1265), nominated_for(?x686, ?x337), ?x1265 = 05c1t6z >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bdw1g ceremony 0gvstc3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.774 http://example.org/award/award_category/winners./award/award_honor/ceremony #5532-05sxzwc PRED entity: 05sxzwc PRED relation: film_crew_role PRED expected values: 089fss 01vx2h 0d2b38 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 21): 01vx2h (0.48 #159, 0.36 #432, 0.34 #372), 0d2b38 (0.46 #170, 0.12 #262, 0.11 #443), 01pvkk (0.38 #10, 0.30 #1741, 0.28 #556), 02_n3z (0.27 #151, 0.12 #1, 0.09 #243), 02rh1dz (0.17 #158, 0.14 #431, 0.12 #8), 015h31 (0.17 #157, 0.12 #613, 0.10 #797), 033smt (0.15 #172, 0.06 #628, 0.05 #264), 020xn5 (0.14 #156, 0.06 #66, 0.04 #248), 04pyp5 (0.12 #13, 0.07 #468, 0.07 #559), 02vs3x5 (0.12 #18, 0.05 #321, 0.05 #351) >> Best rule #159 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 02hfk5; >> query: (?x1487, 01vx2h) <- film_crew_role(?x1487, ?x2472), genre(?x1487, ?x53), ?x2472 = 01xy5l_ >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 12 EVAL 05sxzwc film_crew_role 0d2b38 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.482 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05sxzwc film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.482 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05sxzwc film_crew_role 089fss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 83.000 83.000 0.482 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5531-059z0 PRED entity: 059z0 PRED relation: nationality! PRED expected values: 0jcx => 151 concepts (70 used for prediction) PRED predicted values (max 10 best out of 4079): 0jcx (0.71 #17213, 0.50 #13147, 0.23 #41609), 0l9k1 (0.62 #146382, 0.47 #89455, 0.45 #207377), 01h2_6 (0.62 #146382, 0.47 #89455, 0.45 #252107), 012gbb (0.62 #146382, 0.36 #166713, 0.35 #89454), 08c7cz (0.62 #146382, 0.36 #166713, 0.35 #89454), 026rm_y (0.62 #146382, 0.36 #166713, 0.35 #89454), 05hdf (0.62 #146382, 0.36 #166713, 0.35 #89454), 0k4gf (0.62 #146382, 0.36 #166713, 0.35 #89454), 02my3z (0.62 #146382, 0.36 #166713, 0.35 #89454), 01dhpj (0.62 #146382, 0.36 #166713, 0.35 #89454) >> Best rule #17213 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0h7x; >> query: (?x8687, 0jcx) <- nationality(?x9178, ?x8687), nationality(?x9178, ?x10003), adjoins(?x2517, ?x8687), ?x10003 = 084n_ >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059z0 nationality! 0jcx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 70.000 0.714 http://example.org/people/person/nationality #5530-05kfs PRED entity: 05kfs PRED relation: award PRED expected values: 05f4m9q => 119 concepts (100 used for prediction) PRED predicted values (max 10 best out of 291): 02qt02v (0.69 #21962, 0.69 #39228, 0.68 #21963), 040njc (0.69 #1968, 0.56 #3536, 0.45 #5888), 09sb52 (0.67 #38, 0.32 #22786, 0.28 #15333), 05pcn59 (0.32 #857, 0.22 #10586, 0.20 #1641), 05zr6wv (0.27 #800, 0.22 #10586, 0.19 #3936), 0f_nbyh (0.23 #3538, 0.22 #5890, 0.17 #1970), 0f4x7 (0.22 #30, 0.22 #10586, 0.15 #3950), 0gqy2 (0.22 #153, 0.22 #10586, 0.14 #29418), 0bfvd4 (0.22 #105, 0.07 #15400, 0.07 #24031), 05f4m9q (0.22 #10586, 0.17 #2757, 0.17 #8245) >> Best rule #21962 for best value: >> intensional similarity = 4 >> extensional distance = 1181 >> proper extension: 01w92; 04glx0; 026v1z; >> query: (?x777, ?x601) <- award_nominee(?x2967, ?x777), award_winner(?x1107, ?x777), award_winner(?x601, ?x777), award(?x299, ?x1107) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #10586 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 278 *> proper extension: 054187; *> query: (?x777, ?x384) <- profession(?x777, ?x319), written_by(?x9060, ?x777), nominated_for(?x384, ?x9060) *> conf = 0.22 ranks of expected_values: 10 EVAL 05kfs award 05f4m9q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 119.000 100.000 0.695 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5529-0jm_ PRED entity: 0jm_ PRED relation: sport! PRED expected values: 05g3b 01y49 07kbp5 06x76 => 60 concepts (17 used for prediction) PRED predicted values (max 10 best out of 531): 0fsb_6 (0.63 #1306, 0.61 #2612, 0.41 #6095), 01jv_6 (0.63 #1306, 0.61 #2612, 0.41 #6095), 01lpwh (0.63 #1306, 0.61 #2612, 0.41 #6095), 0bs09lb (0.63 #1306, 0.61 #2612, 0.41 #6095), 0fbtm7 (0.63 #1306, 0.61 #2612, 0.41 #6095), 025v26c (0.63 #1306, 0.61 #2612, 0.41 #6095), 057xlyq (0.63 #1306, 0.61 #2612, 0.41 #6095), 07kcvl (0.63 #1306, 0.61 #2612, 0.41 #6095), 02wvfxl (0.63 #1306, 0.61 #2612, 0.41 #6095), 05g3b (0.63 #1306, 0.61 #2612, 0.41 #6095) >> Best rule #1306 for best value: >> intensional similarity = 13 >> extensional distance = 1 >> proper extension: 018w8; >> query: (?x1083, ?x729) <- sport(?x4856, ?x1083), sport(?x4519, ?x1083), draft(?x4856, ?x685), films(?x1083, ?x3081), athlete(?x1083, ?x9180), athlete(?x1083, ?x7064), athlete(?x1083, ?x1177), gender(?x1177, ?x231), team(?x180, ?x4519), type_of_union(?x1177, ?x566), ?x9180 = 0f2zc, team(?x1177, ?x729), location(?x7064, ?x5090) >> conf = 0.63 => this is the best rule for 17 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 10, 20, 22, 38 EVAL 0jm_ sport! 06x76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 60.000 17.000 0.630 http://example.org/sports/sports_team/sport EVAL 0jm_ sport! 07kbp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 60.000 17.000 0.630 http://example.org/sports/sports_team/sport EVAL 0jm_ sport! 01y49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 60.000 17.000 0.630 http://example.org/sports/sports_team/sport EVAL 0jm_ sport! 05g3b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 60.000 17.000 0.630 http://example.org/sports/sports_team/sport #5528-05br2 PRED entity: 05br2 PRED relation: contains! PRED expected values: 05nrg => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 64): 02j71 (0.60 #45732, 0.59 #46632, 0.56 #44832), 02qkt (0.43 #20063, 0.42 #29027, 0.42 #27235), 0dg3n1 (0.41 #3740, 0.39 #2844, 0.37 #7324), 09c7w0 (0.30 #43041, 0.16 #36753, 0.14 #40345), 07c5l (0.26 #9357, 0.24 #2188, 0.23 #17422), 0j0k (0.24 #12029, 0.23 #17405, 0.23 #26369), 05nrg (0.21 #45733, 0.21 #5048, 0.20 #6840), 02j9z (0.21 #45733, 0.20 #31396, 0.19 #27812), 04pnx (0.21 #45733, 0.15 #3114, 0.13 #8490), 0157g9 (0.21 #45733, 0.10 #1377, 0.10 #481) >> Best rule #45732 for best value: >> intensional similarity = 3 >> extensional distance = 586 >> proper extension: 0crjn65; 016v46; 0l9rg; 03pbf; 0l_q9; 0d331; 02sn34; 0g_wn2; 05vw7; 0p828; ... >> query: (?x9613, ?x551) <- administrative_parent(?x9613, ?x551), administrative_parent(?x9251, ?x551), contains(?x2467, ?x9251) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #45733 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 586 *> proper extension: 0crjn65; 016v46; 0l9rg; 03pbf; 0l_q9; 0d331; 02sn34; 0g_wn2; 05vw7; 0p828; ... *> query: (?x9613, ?x2467) <- administrative_parent(?x9613, ?x551), administrative_parent(?x9251, ?x551), contains(?x2467, ?x9251) *> conf = 0.21 ranks of expected_values: 7 EVAL 05br2 contains! 05nrg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 53.000 53.000 0.602 http://example.org/location/location/contains #5527-09q5w2 PRED entity: 09q5w2 PRED relation: genre PRED expected values: 03k9fj 06l3bl => 106 concepts (99 used for prediction) PRED predicted values (max 10 best out of 102): 01jfsb (0.48 #2161, 0.46 #7307, 0.46 #250), 03k9fj (0.41 #2160, 0.34 #7306, 0.27 #4434), 05p553 (0.39 #3110, 0.37 #361, 0.36 #2752), 02l7c8 (0.30 #6114, 0.29 #6953, 0.29 #4319), 06n90 (0.27 #2162, 0.20 #7308, 0.17 #1087), 0lsxr (0.23 #366, 0.23 #843, 0.21 #127), 04xvlr (0.23 #955, 0.22 #836, 0.21 #2269), 02n4kr (0.20 #7, 0.17 #7302, 0.17 #245), 03g3w (0.20 #25, 0.16 #979, 0.10 #2414), 02p0szs (0.20 #29, 0.08 #983, 0.06 #11712) >> Best rule #2161 for best value: >> intensional similarity = 4 >> extensional distance = 282 >> proper extension: 018js4; 02z9hqn; 01dyvs; 01kf3_9; 01kf4tt; 0407yj_; 03n785; 02ny6g; 07b1gq; 01hw5kk; ... >> query: (?x1077, 01jfsb) <- country(?x1077, ?x94), genre(?x1077, ?x225), nominated_for(?x112, ?x1077), ?x225 = 02kdv5l >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2160 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 282 *> proper extension: 018js4; 02z9hqn; 01dyvs; 01kf3_9; 01kf4tt; 0407yj_; 03n785; 02ny6g; 07b1gq; 01hw5kk; ... *> query: (?x1077, 03k9fj) <- country(?x1077, ?x94), genre(?x1077, ?x225), nominated_for(?x112, ?x1077), ?x225 = 02kdv5l *> conf = 0.41 ranks of expected_values: 2, 13 EVAL 09q5w2 genre 06l3bl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 106.000 99.000 0.479 http://example.org/film/film/genre EVAL 09q5w2 genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 99.000 0.479 http://example.org/film/film/genre #5526-0kn3g PRED entity: 0kn3g PRED relation: company PRED expected values: 0345gh => 158 concepts (104 used for prediction) PRED predicted values (max 10 best out of 66): 05p7tx (0.12 #1291, 0.07 #2263, 0.06 #2650), 0c0cs (0.12 #1356, 0.07 #2328, 0.06 #2715), 07vsl (0.12 #1351, 0.07 #2323, 0.06 #2710), 06pwq (0.12 #1175, 0.07 #2147, 0.06 #2534), 07tg4 (0.10 #1596, 0.08 #1791, 0.06 #2953), 02hcxm (0.10 #1635, 0.08 #1830, 0.06 #2992), 016ckq (0.10 #1472, 0.08 #2054, 0.02 #6134), 03ksy (0.07 #5096, 0.06 #3348, 0.04 #7042), 02_gzx (0.07 #2474, 0.06 #2860, 0.06 #3248), 01w3v (0.06 #4670, 0.06 #3313, 0.03 #4281) >> Best rule #1291 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0pcc0; 0k4gf; 0g7k2g; 0hr3g; 014vk4; >> query: (?x9728, 05p7tx) <- artists(?x10853, ?x9728), ?x10853 = 0l8gh, gender(?x9728, ?x231), student(?x2999, ?x9728) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0kn3g company 0345gh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 104.000 0.125 http://example.org/people/person/employment_history./business/employment_tenure/company #5525-01hgwkr PRED entity: 01hgwkr PRED relation: type_of_union PRED expected values: 01g63y => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.75 #129, 0.75 #49, 0.74 #213), 01g63y (0.24 #10, 0.18 #18, 0.17 #34), 0jgjn (0.01 #52, 0.01 #56) >> Best rule #129 for best value: >> intensional similarity = 3 >> extensional distance = 331 >> proper extension: 04smkr; 0fqyzz; 0drdv; >> query: (?x9442, 04ztj) <- award_nominee(?x2518, ?x9442), award_winner(?x2420, ?x9442), religion(?x9442, ?x7422) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 0j3v; 015k7; 01cqz5; *> query: (?x9442, 01g63y) <- religion(?x9442, ?x7422), gender(?x9442, ?x514), ?x7422 = 092bf5 *> conf = 0.24 ranks of expected_values: 2 EVAL 01hgwkr type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 120.000 0.754 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5524-04rsd2 PRED entity: 04rsd2 PRED relation: nominated_for PRED expected values: 020bv3 => 65 concepts (37 used for prediction) PRED predicted values (max 10 best out of 249): 020bv3 (0.65 #295, 0.36 #1623, 0.26 #34103), 0c3xpwy (0.47 #17864, 0.39 #9742, 0.34 #16240), 09rvcvl (0.36 #1623, 0.26 #34103, 0.25 #37351), 011yg9 (0.13 #51966, 0.11 #55214, 0.10 #60086), 02_kd (0.13 #51966, 0.11 #55214, 0.10 #60086), 0_9l_ (0.13 #51966, 0.11 #55214, 0.10 #60086), 07pd_j (0.11 #55214, 0.10 #60086, 0.05 #8118), 0164qt (0.11 #55214, 0.10 #60086, 0.05 #8118), 0ctb4g (0.11 #55214, 0.10 #60086, 0.05 #8118), 046f3p (0.11 #55214, 0.10 #60086, 0.05 #8118) >> Best rule #295 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 03f1zdw; >> query: (?x2487, 020bv3) <- film(?x2487, ?x2029), award_nominee(?x1634, ?x2487), ?x1634 = 01l2fn >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04rsd2 nominated_for 020bv3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 37.000 0.650 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5523-049g_xj PRED entity: 049g_xj PRED relation: award_winner! PRED expected values: 05zrvfd => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 237): 05pcn59 (0.34 #19401, 0.31 #39669, 0.31 #40102), 0gqwc (0.34 #19401, 0.31 #39669, 0.31 #40102), 0c422z4 (0.34 #19401, 0.31 #39669, 0.31 #40102), 094qd5 (0.34 #19401, 0.31 #39669, 0.31 #40102), 09qwmm (0.34 #19401, 0.31 #39669, 0.31 #40102), 099cng (0.34 #19401, 0.31 #39669, 0.31 #40102), 09sb52 (0.12 #13837, 0.11 #3920, 0.11 #16854), 099tbz (0.09 #58, 0.08 #920, 0.07 #21126), 02g3gj (0.09 #26, 0.08 #888, 0.04 #1319), 0ck27z (0.09 #13889, 0.09 #12164, 0.08 #20787) >> Best rule #19401 for best value: >> intensional similarity = 3 >> extensional distance = 1175 >> proper extension: 0jz9f; 0cb77r; 086k8; 03ckxdg; 017s11; 016tt2; 025jfl; 0d4fqn; 0g1rw; 0kx4m; ... >> query: (?x1530, ?x618) <- award_nominee(?x5944, ?x1530), award_winner(?x1531, ?x1530), award(?x1530, ?x618) >> conf = 0.34 => this is the best rule for 6 predicted values *> Best rule #21126 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1209 *> proper extension: 03jvmp; 0g5lhl7; 031rx9; 05v1sb; 05xbx; 08_83x; *> query: (?x1530, ?x68) <- award_nominee(?x5944, ?x1530), award_winner(?x2394, ?x1530), nominated_for(?x68, ?x2394) *> conf = 0.07 ranks of expected_values: 35 EVAL 049g_xj award_winner! 05zrvfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 110.000 110.000 0.336 http://example.org/award/award_category/winners./award/award_honor/award_winner #5522-01c7y PRED entity: 01c7y PRED relation: languages! PRED expected values: 0pj8m 03x31g => 42 concepts (26 used for prediction) PRED predicted values (max 10 best out of 762): 040nwr (0.75 #3879, 0.71 #3230, 0.67 #4528), 0448r (0.60 #4983, 0.50 #5632, 0.50 #436), 09r_wb (0.57 #3052, 0.57 #2401, 0.57 #1751), 046rfv (0.57 #3036, 0.57 #2385, 0.57 #1735), 03x31g (0.57 #3192, 0.57 #2541, 0.50 #3841), 05vzql (0.57 #2515, 0.57 #1865, 0.50 #1216), 03wpmd (0.50 #769, 0.50 #120, 0.43 #2068), 01ps2h8 (0.50 #4846, 0.50 #299, 0.40 #5495), 03vrnh (0.50 #1053, 0.44 #4301, 0.43 #3003), 04cmrt (0.50 #1258, 0.43 #3208, 0.43 #2557) >> Best rule #3879 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 0121sr; >> query: (?x11341, 040nwr) <- languages(?x12204, ?x11341), languages(?x9253, ?x11341), languages_spoken(?x11665, ?x11341), type_of_union(?x12204, ?x566), ?x11665 = 03w9bjf, film(?x12204, ?x5247), special_performance_type(?x9253, ?x4832), profession(?x12204, ?x319), location(?x12204, ?x11801), nationality(?x9253, ?x2146) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3192 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 09s02; *> query: (?x11341, 03x31g) <- languages(?x12204, ?x11341), languages(?x9253, ?x11341), languages_spoken(?x11665, ?x11341), type_of_union(?x12204, ?x566), ?x11665 = 03w9bjf, film(?x12204, ?x5247), languages(?x9253, ?x5121), religion(?x9253, ?x2694), people(?x5025, ?x12204), profession(?x12204, ?x319), ?x5121 = 07c9s *> conf = 0.57 ranks of expected_values: 5, 42 EVAL 01c7y languages! 03x31g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 42.000 26.000 0.750 http://example.org/people/person/languages EVAL 01c7y languages! 0pj8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 42.000 26.000 0.750 http://example.org/people/person/languages #5521-0hfzr PRED entity: 0hfzr PRED relation: nominated_for! PRED expected values: 06pj8 => 82 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1041): 07s93v (0.78 #60722, 0.72 #11677, 0.59 #35030), 06pj8 (0.78 #60722, 0.58 #63058, 0.42 #49044), 0170qf (0.54 #2335, 0.38 #42038, 0.35 #37366), 02q_cc (0.16 #35031, 0.06 #2494, 0.05 #11836), 05bm4sm (0.14 #1258, 0.11 #3593, 0.05 #12935), 02cyfz (0.14 #446, 0.11 #2781, 0.04 #16794), 017s11 (0.11 #2434, 0.07 #99, 0.07 #4769), 0bxtg (0.11 #2418, 0.07 #83, 0.06 #9340), 0dvmd (0.11 #2993, 0.07 #658, 0.04 #17006), 048lv (0.11 #2608, 0.07 #273, 0.04 #16621) >> Best rule #60722 for best value: >> intensional similarity = 4 >> extensional distance = 438 >> proper extension: 0bh8yn3; >> query: (?x4216, ?x1616) <- currency(?x4216, ?x170), music(?x4216, ?x669), film(?x489, ?x4216), award_winner(?x4216, ?x1616) >> conf = 0.78 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0hfzr nominated_for! 06pj8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 27.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5520-02nhxf PRED entity: 02nhxf PRED relation: award_winner PRED expected values: 02jqjm => 46 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1574): 05crg7 (0.50 #359, 0.27 #2821, 0.11 #5285), 0dw4g (0.50 #1249, 0.20 #3711, 0.19 #54167), 012x4t (0.44 #29542, 0.40 #46776, 0.40 #4926), 01vvycq (0.44 #29542, 0.40 #46776, 0.39 #19695), 0b_j2 (0.44 #29542, 0.40 #46776, 0.39 #19695), 01kd57 (0.40 #46776, 0.39 #19695, 0.39 #64017), 02r4qs (0.40 #46776, 0.39 #19695, 0.39 #64017), 02qtywd (0.40 #46776, 0.39 #19695, 0.39 #64017), 03f5spx (0.40 #46776, 0.39 #19695, 0.39 #64017), 03bxwtd (0.40 #46776, 0.39 #64017, 0.38 #51704) >> Best rule #359 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01bgqh; 01c427; 01by1l; 01c9jp; >> query: (?x1827, 05crg7) <- award(?x5512, ?x1827), category_of(?x1827, ?x2421), ceremony(?x1827, ?x139), ?x5512 = 02jqjm, award_winner(?x1827, ?x3442) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #24619 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 111 *> proper extension: 04hddx; *> query: (?x1827, ?x5544) <- award(?x6162, ?x1827), award(?x5544, ?x1827), category_of(?x1827, ?x2421), film(?x6162, ?x6798), award_winner(?x528, ?x5544) *> conf = 0.38 ranks of expected_values: 18 EVAL 02nhxf award_winner 02jqjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 46.000 27.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #5519-0mgcr PRED entity: 0mgcr PRED relation: artists! PRED expected values: 016clz 06by7 05jg58 => 74 concepts (28 used for prediction) PRED predicted values (max 10 best out of 252): 016clz (0.86 #2161, 0.83 #7100, 0.67 #7716), 06by7 (0.83 #1562, 0.66 #3412, 0.64 #4338), 05bt6j (0.61 #1583, 0.55 #2815, 0.50 #657), 03lty (0.58 #5890, 0.50 #3109, 0.45 #1877), 064t9 (0.50 #321, 0.50 #6184, 0.49 #4950), 059kh (0.36 #971, 0.30 #663, 0.29 #1279), 05jg58 (0.33 #2274, 0.25 #425, 0.12 #3508), 02yv6b (0.30 #3488, 0.25 #3795, 0.25 #98), 0pm85 (0.30 #768, 0.24 #2926, 0.21 #1384), 0jrv_ (0.29 #2639, 0.20 #2024, 0.12 #3256) >> Best rule #2161 for best value: >> intensional similarity = 8 >> extensional distance = 19 >> proper extension: 01pfr3; 0m2l9; 01gf5h; 02r3zy; 0285c; 01w02sy; 016ntp; 01wy61y; 0d193h; 05xq9; ... >> query: (?x3875, 016clz) <- artists(?x3642, ?x3875), artists(?x2995, ?x3875), artist(?x3265, ?x3875), artists(?x2995, ?x7781), artists(?x2995, ?x6699), ?x6699 = 09lwrt, ?x3642 = 0dls3, ?x7781 = 089pg7 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 7 EVAL 0mgcr artists! 05jg58 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 74.000 28.000 0.857 http://example.org/music/genre/artists EVAL 0mgcr artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 28.000 0.857 http://example.org/music/genre/artists EVAL 0mgcr artists! 016clz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 28.000 0.857 http://example.org/music/genre/artists #5518-03_3d PRED entity: 03_3d PRED relation: location_of_ceremony! PRED expected values: 0f14q => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 232): 02m30v (0.12 #1012, 0.09 #1520, 0.05 #1773), 01nglk (0.12 #999, 0.01 #12909), 01f9mq (0.12 #998, 0.01 #12908), 01lc5 (0.12 #991, 0.01 #12901), 0168dy (0.12 #987, 0.01 #12897), 01k53x (0.12 #973, 0.01 #12883), 06lbp (0.12 #914, 0.01 #12824), 043zg (0.12 #890, 0.01 #12800), 02778yp (0.12 #887, 0.01 #12797), 01sb5r (0.12 #857, 0.01 #12767) >> Best rule #1012 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 0c4kv; >> query: (?x252, 02m30v) <- location(?x3118, ?x252), ?x3118 = 01w02sy >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_3d location_of_ceremony! 0f14q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 130.000 0.125 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #5517-04l19_ PRED entity: 04l19_ PRED relation: nationality PRED expected values: 09c7w0 => 133 concepts (116 used for prediction) PRED predicted values (max 10 best out of 85): 09c7w0 (0.84 #901, 0.80 #1902, 0.78 #1501), 02jx1 (0.50 #33, 0.20 #1133, 0.19 #1433), 07z1m (0.34 #7522, 0.33 #11048), 0345h (0.17 #231, 0.08 #631, 0.08 #2232), 07ssc (0.15 #2216, 0.14 #3320, 0.13 #3720), 03rk0 (0.12 #3951, 0.09 #6156, 0.09 #6056), 0d060g (0.12 #507, 0.09 #807, 0.08 #607), 03_3d (0.06 #4112, 0.04 #4712, 0.04 #606), 0d04z6 (0.06 #571, 0.06 #471, 0.04 #671), 03rt9 (0.06 #813, 0.04 #6813, 0.04 #2214) >> Best rule #901 for best value: >> intensional similarity = 6 >> extensional distance = 36 >> proper extension: 0168ql; >> query: (?x6692, 09c7w0) <- profession(?x6692, ?x1383), profession(?x6692, ?x1041), profession(?x6692, ?x319), ?x1383 = 0np9r, ?x1041 = 03gjzk, ?x319 = 01d_h8 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04l19_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 116.000 0.842 http://example.org/people/person/nationality #5516-01mk6 PRED entity: 01mk6 PRED relation: combatants! PRED expected values: 015fr => 178 concepts (116 used for prediction) PRED predicted values (max 10 best out of 252): 035qy (0.83 #580, 0.82 #1106, 0.82 #3903), 05vz3zq (0.83 #580, 0.82 #1106, 0.82 #3903), 015qh (0.60 #399, 0.52 #463, 0.50 #1054), 0345h (0.55 #525, 0.45 #396, 0.44 #266), 01mk6 (0.50 #560, 0.44 #301, 0.43 #495), 06bnz (0.45 #530, 0.43 #465, 0.35 #401), 0bq0p9 (0.43 #197, 0.38 #69, 0.35 #391), 015fr (0.41 #518, 0.35 #389, 0.35 #847), 07f1x (0.40 #433, 0.33 #1153, 0.32 #759), 0dv0z (0.34 #3246, 0.30 #1625, 0.30 #3181) >> Best rule #580 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0bq0p9; >> query: (?x7430, ?x1353) <- combatants(?x1023, ?x7430), organization(?x7430, ?x312), combatants(?x7430, ?x1353), ?x1023 = 0ctw_b >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #518 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 0bq0p9; *> query: (?x7430, 015fr) <- combatants(?x1023, ?x7430), organization(?x7430, ?x312), combatants(?x7430, ?x1353), ?x1023 = 0ctw_b *> conf = 0.41 ranks of expected_values: 8 EVAL 01mk6 combatants! 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 178.000 116.000 0.834 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #5515-065y4w7 PRED entity: 065y4w7 PRED relation: colors PRED expected values: 01l849 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 19): 01l849 (0.36 #161, 0.26 #541, 0.24 #621), 083jv (0.35 #482, 0.34 #242, 0.34 #622), 01g5v (0.28 #164, 0.25 #24, 0.24 #104), 0jc_p (0.20 #65, 0.11 #525, 0.10 #485), 019sc (0.17 #548, 0.16 #88, 0.14 #528), 01jnf1 (0.13 #72, 0.11 #92, 0.07 #532), 067z2v (0.13 #70, 0.08 #210, 0.06 #550), 036k5h (0.12 #26, 0.11 #86, 0.10 #586), 02rnmb (0.12 #34, 0.10 #114, 0.08 #174), 09ggk (0.12 #36, 0.08 #216, 0.07 #356) >> Best rule #161 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 030nwm; >> query: (?x735, 01l849) <- school_type(?x735, ?x1044), contains(?x94, ?x735), time_zones(?x735, ?x2950) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065y4w7 colors 01l849 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.361 http://example.org/education/educational_institution/colors #5514-0h7h6 PRED entity: 0h7h6 PRED relation: film_release_region! PRED expected values: 047msdk => 168 concepts (155 used for prediction) PRED predicted values (max 10 best out of 1361): 08hmch (0.57 #62456, 0.42 #69087, 0.37 #86331), 04f52jw (0.55 #62670, 0.39 #69301, 0.36 #86545), 017jd9 (0.55 #62932, 0.36 #86807, 0.35 #69563), 05zlld0 (0.54 #62811, 0.35 #86686, 0.34 #69442), 017gm7 (0.52 #62499, 0.34 #86374, 0.34 #69130), 01fmys (0.52 #62584, 0.34 #86459, 0.32 #16163), 0bpm4yw (0.51 #62885, 0.45 #69516, 0.37 #135843), 02vxq9m (0.51 #62354, 0.38 #68985, 0.34 #135312), 047vnkj (0.51 #63039, 0.38 #69670, 0.33 #86914), 043tvp3 (0.51 #63261, 0.37 #69892, 0.33 #87136) >> Best rule #62456 for best value: >> intensional similarity = 2 >> extensional distance = 63 >> proper extension: 06bnz; 03ryn; 034cm; 07c5l; 07tp2; >> query: (?x1658, 08hmch) <- contains(?x1658, ?x1306), service_location(?x6717, ?x1658) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #62495 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 63 *> proper extension: 06bnz; 03ryn; 034cm; 07c5l; 07tp2; *> query: (?x1658, 047msdk) <- contains(?x1658, ?x1306), service_location(?x6717, ?x1658) *> conf = 0.48 ranks of expected_values: 31 EVAL 0h7h6 film_release_region! 047msdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 168.000 155.000 0.569 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5513-02q3fdr PRED entity: 02q3fdr PRED relation: executive_produced_by PRED expected values: 04jspq => 115 concepts (104 used for prediction) PRED predicted values (max 10 best out of 97): 04jspq (0.29 #1415, 0.29 #910, 0.22 #1918), 079vf (0.16 #6305, 0.05 #12593, 0.04 #16619), 0343h (0.15 #4835, 0.12 #5339, 0.09 #6345), 03c9pqt (0.12 #1762, 0.05 #6549, 0.04 #16612), 02qggqc (0.12 #1540, 0.02 #6327, 0.02 #6830), 05vtbl (0.11 #1983, 0.08 #2742, 0.05 #3497), 05hj_k (0.10 #12941, 0.10 #16715, 0.09 #16464), 06q8hf (0.10 #13009, 0.10 #16783, 0.09 #16532), 02xnjd (0.07 #6478, 0.02 #8994, 0.02 #16792), 030_3z (0.07 #4901, 0.05 #5405, 0.04 #6914) >> Best rule #1415 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0j6b5; >> query: (?x5936, 04jspq) <- written_by(?x5936, ?x5287), genre(?x5936, ?x53), ?x5287 = 0534v, film(?x2156, ?x5936) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q3fdr executive_produced_by 04jspq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 104.000 0.286 http://example.org/film/film/executive_produced_by #5512-01vsn38 PRED entity: 01vsn38 PRED relation: film PRED expected values: 0gjk1d 0830vk => 101 concepts (86 used for prediction) PRED predicted values (max 10 best out of 573): 025rxjq (0.45 #74475, 0.44 #88664, 0.43 #106401), 0bvn25 (0.33 #50, 0.17 #3598, 0.01 #76299), 03bx2lk (0.33 #185, 0.09 #9053, 0.02 #49836), 027pfg (0.33 #1215, 0.09 #10083, 0.01 #15403), 01shy7 (0.27 #7516, 0.09 #9289, 0.04 #18155), 047vnkj (0.25 #2677, 0.17 #4452, 0.09 #7999), 038bh3 (0.25 #2549, 0.17 #6097), 03b1sb (0.25 #3263, 0.09 #10358), 01chpn (0.25 #2876, 0.01 #50754, 0.01 #59619), 01pvxl (0.25 #2673, 0.01 #23954) >> Best rule #74475 for best value: >> intensional similarity = 3 >> extensional distance = 1155 >> proper extension: 0kk9v; >> query: (?x11233, ?x4453) <- nominated_for(?x11233, ?x4453), award_nominee(?x11233, ?x2275), film_release_distribution_medium(?x4453, ?x81) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #5908 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0150t6; 06fxnf; 056ws9; *> query: (?x11233, 0830vk) <- nominated_for(?x11233, ?x1904), award(?x11233, ?x401), ?x1904 = 09146g *> conf = 0.17 ranks of expected_values: 30 EVAL 01vsn38 film 0830vk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 101.000 86.000 0.450 http://example.org/film/actor/film./film/performance/film EVAL 01vsn38 film 0gjk1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 86.000 0.450 http://example.org/film/actor/film./film/performance/film #5511-0hz_1 PRED entity: 0hz_1 PRED relation: people! PRED expected values: 0g8_vp => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 40): 0xnvg (0.17 #244, 0.14 #90, 0.08 #398), 041rx (0.14 #1390, 0.14 #2699, 0.14 #1544), 033tf_ (0.12 #315, 0.12 #469, 0.11 #546), 0x67 (0.12 #857, 0.11 #2089, 0.11 #395), 07bch9 (0.11 #408, 0.09 #331, 0.08 #716), 09vc4s (0.11 #394, 0.09 #317, 0.08 #471), 06v41q (0.09 #337, 0.06 #491, 0.05 #799), 02ctzb (0.09 #246, 0.06 #323, 0.04 #631), 0g8_vp (0.08 #22, 0.06 #330, 0.06 #176), 02w7gg (0.08 #387, 0.07 #79, 0.07 #849) >> Best rule #244 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 02tn0_; >> query: (?x8596, 0xnvg) <- award_winner(?x7452, ?x8596), ?x7452 = 09pnw5, award_nominee(?x2661, ?x8596) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #22 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 03_6y; *> query: (?x8596, 0g8_vp) <- location(?x8596, ?x1036), award(?x8596, ?x594), ?x1036 = 080h2 *> conf = 0.08 ranks of expected_values: 9 EVAL 0hz_1 people! 0g8_vp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 122.000 122.000 0.174 http://example.org/people/ethnicity/people #5510-02q56mk PRED entity: 02q56mk PRED relation: currency PRED expected values: 09nqf => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.83 #50, 0.82 #176, 0.82 #29), 01nv4h (0.06 #184, 0.02 #170, 0.02 #212), 02l6h (0.01 #508) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 05m_jsg; >> query: (?x2613, 09nqf) <- film(?x2857, ?x2613), honored_for(?x7141, ?x2613), vacationer(?x362, ?x2857), citytown(?x752, ?x362) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q56mk currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.833 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #5509-0bdg5 PRED entity: 0bdg5 PRED relation: location! PRED expected values: 0fhxv => 106 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1846): 016lv3 (0.39 #50326, 0.17 #2517, 0.17 #7550), 01pcql (0.33 #704, 0.25 #5737, 0.25 #3221), 09yrh (0.33 #914, 0.25 #5947, 0.20 #10980), 0c6qh (0.33 #461, 0.25 #5494, 0.20 #10527), 09fb5 (0.33 #51, 0.25 #5084, 0.20 #10117), 03rl84 (0.33 #362, 0.25 #5395, 0.20 #10428), 0pyww (0.33 #982, 0.25 #6015, 0.20 #11048), 01w02sy (0.33 #596, 0.25 #5629, 0.20 #10662), 01797x (0.33 #2094, 0.25 #7127, 0.20 #12160), 03d9v8 (0.33 #1856, 0.25 #6889, 0.20 #11922) >> Best rule #50326 for best value: >> intensional similarity = 4 >> extensional distance = 178 >> proper extension: 01qjct; >> query: (?x9502, ?x12252) <- place_of_birth(?x12252, ?x9502), gender(?x12252, ?x231), profession(?x12252, ?x524), ?x524 = 02jknp >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bdg5 location! 0fhxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 35.000 0.389 http://example.org/people/person/places_lived./people/place_lived/location #5508-011vx3 PRED entity: 011vx3 PRED relation: religion PRED expected values: 07w8f => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 31): 03_gx (0.21 #1365, 0.20 #1592, 0.19 #1863), 0c8wxp (0.19 #276, 0.16 #3342, 0.16 #3658), 0kpl (0.16 #2085, 0.14 #1859, 0.14 #1588), 01lp8 (0.11 #1, 0.06 #1081, 0.06 #766), 03j6c (0.09 #561, 0.06 #246, 0.06 #786), 092bf5 (0.07 #871, 0.06 #241, 0.06 #331), 051kv (0.07 #140, 0.04 #590, 0.02 #1401), 0kq2 (0.07 #1369, 0.06 #2093, 0.04 #1279), 0n2g (0.07 #1364, 0.06 #2088, 0.05 #418), 0flw86 (0.05 #1534, 0.04 #2573, 0.04 #1761) >> Best rule #1365 for best value: >> intensional similarity = 4 >> extensional distance = 119 >> proper extension: 0j3v; 02ln1; 03j90; 047g6; 01h2_6; >> query: (?x7398, 03_gx) <- student(?x3439, ?x7398), influenced_by(?x1089, ?x7398), institution(?x620, ?x3439), company(?x5796, ?x3439) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #1613 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 148 *> proper extension: 02wh0; *> query: (?x7398, 07w8f) <- location(?x7398, ?x739), influenced_by(?x1089, ?x7398), people(?x2510, ?x7398) *> conf = 0.01 ranks of expected_values: 27 EVAL 011vx3 religion 07w8f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 177.000 177.000 0.215 http://example.org/people/person/religion #5507-027s39y PRED entity: 027s39y PRED relation: featured_film_locations PRED expected values: 02_286 => 95 concepts (75 used for prediction) PRED predicted values (max 10 best out of 95): 02_286 (0.22 #3144, 0.21 #3624, 0.20 #1941), 030qb3t (0.11 #279, 0.08 #1960, 0.08 #3163), 04jpl (0.10 #5296, 0.09 #6259, 0.07 #4574), 0rh6k (0.09 #2403, 0.07 #2884, 0.05 #11556), 052p7 (0.06 #2460, 0.05 #2941, 0.04 #1018), 06y57 (0.06 #583, 0.05 #3467, 0.05 #4187), 0d6lp (0.06 #312, 0.01 #11627, 0.01 #10903), 0k_q_ (0.06 #299, 0.01 #2702), 01q1j (0.06 #427), 0nbwf (0.06 #388) >> Best rule #3144 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 027qgy; 0209xj; 0hmr4; 0kv2hv; 0c5dd; 03m4mj; 0sxfd; 048qrd; 0gxfz; 0k4f3; ... >> query: (?x3946, 02_286) <- nominated_for(?x500, ?x3946), genre(?x3946, ?x239), ?x239 = 06cvj, film_release_distribution_medium(?x3946, ?x81) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027s39y featured_film_locations 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 75.000 0.216 http://example.org/film/film/featured_film_locations #5506-01qb5d PRED entity: 01qb5d PRED relation: executive_produced_by PRED expected values: 079vf => 78 concepts (46 used for prediction) PRED predicted values (max 10 best out of 75): 05hj_k (0.21 #98, 0.06 #602, 0.06 #3373), 079vf (0.14 #2, 0.08 #254, 0.07 #1513), 06q8hf (0.14 #167, 0.08 #671, 0.07 #2938), 07nznf (0.09 #2770, 0.09 #3275, 0.05 #4030), 03c9pqt (0.07 #246, 0.05 #3773, 0.04 #750), 02465 (0.07 #226), 0glyyw (0.07 #3715, 0.05 #692, 0.05 #1196), 06pj8 (0.06 #3582, 0.05 #1315, 0.05 #1566), 0gg9_5q (0.05 #3617, 0.03 #4372, 0.02 #4624), 02q_cc (0.04 #1036, 0.04 #2294, 0.03 #1539) >> Best rule #98 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0dl6fv; 0170xl; >> query: (?x936, 05hj_k) <- genre(?x936, ?x225), film(?x5661, ?x936), film(?x574, ?x936), ?x5661 = 03ym1 >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0dl6fv; 0170xl; *> query: (?x936, 079vf) <- genre(?x936, ?x225), film(?x5661, ?x936), film(?x574, ?x936), ?x5661 = 03ym1 *> conf = 0.14 ranks of expected_values: 2 EVAL 01qb5d executive_produced_by 079vf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 46.000 0.214 http://example.org/film/film/executive_produced_by #5505-026670 PRED entity: 026670 PRED relation: student! PRED expected values: 023znp => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 163): 03ksy (0.11 #106, 0.08 #11680, 0.08 #7470), 07tgn (0.11 #17, 0.07 #6329, 0.07 #543), 015nl4 (0.11 #67, 0.07 #593, 0.06 #1119), 04b_46 (0.11 #227, 0.07 #753, 0.06 #1279), 0yjf0 (0.11 #48, 0.07 #574, 0.06 #1100), 02bzh0 (0.11 #411, 0.07 #937, 0.06 #1463), 0187nd (0.11 #365, 0.06 #1417, 0.06 #1943), 09f2j (0.10 #2789, 0.04 #12785, 0.04 #22780), 065y4w7 (0.07 #540, 0.06 #12640, 0.06 #3696), 02301 (0.07 #600, 0.06 #1652, 0.05 #2178) >> Best rule #106 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0bzyh; 02f93t; >> query: (?x9754, 03ksy) <- award_winner(?x10597, ?x9754), award_winner(?x1587, ?x9754), gender(?x9754, ?x231), ?x1587 = 02rdyk7, ?x10597 = 02wypbh >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #7483 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 158 *> proper extension: 03qcq; 03f70xs; 02lt8; 0zm1; 01s7qqw; 080r3; 04jwp; 0h0p_; 02zjd; 06y8v; ... *> query: (?x9754, 023znp) <- influenced_by(?x5351, ?x9754), type_of_union(?x9754, ?x1873), student(?x7545, ?x9754) *> conf = 0.01 ranks of expected_values: 140 EVAL 026670 student! 023znp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 125.000 125.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #5504-0498y PRED entity: 0498y PRED relation: state! PRED expected values: 0f__1 => 145 concepts (120 used for prediction) PRED predicted values (max 10 best out of 303): 0bx9y (0.12 #798, 0.10 #1105, 0.04 #1412), 0fvwg (0.12 #742, 0.10 #1049, 0.04 #1356), 0d8jf (0.12 #709, 0.10 #1016, 0.04 #1323), 094jv (0.12 #637, 0.10 #944, 0.04 #1251), 0yfvf (0.12 #897, 0.10 #1204, 0.04 #1511), 0yj9v (0.12 #846, 0.10 #1153, 0.04 #1460), 0jfqp (0.12 #763, 0.10 #1070, 0.04 #1377), 013hxv (0.12 #724, 0.10 #1031, 0.04 #1338), 0ygbf (0.12 #715, 0.10 #1022, 0.04 #1329), 0ydpd (0.12 #614, 0.10 #921, 0.04 #1228) >> Best rule #798 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0f8x_r; >> query: (?x4061, 0bx9y) <- adjoins(?x4061, ?x1426), ?x1426 = 07z1m, adjoins(?x177, ?x4061) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #33406 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 443 *> proper extension: 0msck; *> query: (?x4061, ?x14493) <- contains(?x4061, ?x10845), contains(?x14493, ?x10845), adjoins(?x4061, ?x448) *> conf = 0.05 ranks of expected_values: 27 EVAL 0498y state! 0f__1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 145.000 120.000 0.125 http://example.org/base/biblioness/bibs_location/state #5503-02pq_rp PRED entity: 02pq_rp PRED relation: draft! PRED expected values: 02d02 => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 313): 05m_8 (0.77 #923, 0.67 #995, 0.60 #784), 02d02 (0.77 #923, 0.67 #1044, 0.60 #833), 01yhm (0.77 #923, 0.60 #795, 0.58 #216), 05g76 (0.77 #923, 0.53 #1202, 0.50 #1007), 051wf (0.77 #923, 0.50 #563, 0.34 #777), 06rpd (0.67 #1116, 0.67 #908, 0.58 #216), 043vc (0.67 #1089, 0.67 #881, 0.58 #216), 05g3b (0.67 #1067, 0.67 #859, 0.47 #496), 06x76 (0.67 #1123, 0.67 #915, 0.42 #567), 02c_4 (0.67 #1110, 0.67 #902, 0.42 #567) >> Best rule #923 for best value: >> intensional similarity = 61 >> extensional distance = 4 >> proper extension: 0g3zpp; 02qw1zx; 09l0x9; >> query: (?x3334, ?x580) <- draft(?x11361, ?x3334), draft(?x8995, ?x3334), draft(?x3333, ?x3334), draft(?x1632, ?x3334), draft(?x8995, ?x1633), school(?x11361, ?x10297), school(?x11361, ?x7350), school(?x11361, ?x735), teams(?x739, ?x1632), school(?x3334, ?x9847), school(?x3334, ?x7439), school(?x3334, ?x4599), team(?x5412, ?x1632), organization(?x5510, ?x9847), institution(?x620, ?x9847), school(?x1632, ?x2171), team(?x2010, ?x11361), category(?x8995, ?x134), institution(?x1771, ?x4599), ?x735 = 065y4w7, school(?x2820, ?x9847), major_field_of_study(?x4599, ?x6859), major_field_of_study(?x4599, ?x4268), major_field_of_study(?x4599, ?x2606), colors(?x8995, ?x3315), fraternities_and_sororities(?x9847, ?x3697), teams(?x1705, ?x8995), ?x1771 = 019v9k, state_province_region(?x7439, ?x1025), colors(?x3333, ?x3189), colors(?x3333, ?x1101), student(?x9847, ?x3789), school(?x3089, ?x4599), major_field_of_study(?x10297, ?x1527), award_winner(?x3789, ?x989), award_winner(?x944, ?x3789), school(?x3333, ?x2150), ?x1101 = 06fvc, award(?x3789, ?x1670), ?x5412 = 03n69x, sport(?x8995, ?x5063), participant(?x3789, ?x545), student(?x4599, ?x3273), ?x4268 = 02822, currency(?x10297, ?x170), award_winner(?x2670, ?x3789), colors(?x4599, ?x332), school(?x1639, ?x4599), ?x3089 = 03nt7j, contains(?x94, ?x7350), major_field_of_study(?x3509, ?x2606), major_field_of_study(?x2313, ?x2606), student(?x2606, ?x677), nominated_for(?x3789, ?x86), ?x3509 = 03fmfs, film(?x3789, ?x1192), ?x6859 = 01tbp, ?x3189 = 01g5v, institution(?x1526, ?x10297), ?x2313 = 07wrz, draft(?x580, ?x1633) >> conf = 0.77 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02pq_rp draft! 02d02 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 18.000 18.000 0.769 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #5502-0p_tz PRED entity: 0p_tz PRED relation: nominated_for! PRED expected values: 02z0dfh 0gq9h => 71 concepts (66 used for prediction) PRED predicted values (max 10 best out of 213): 0gq9h (0.83 #759, 0.70 #992, 0.68 #3555), 02pqp12 (0.59 #755, 0.50 #988, 0.41 #1687), 0k611 (0.54 #1700, 0.52 #768, 0.52 #3564), 040njc (0.52 #706, 0.51 #1638, 0.48 #3502), 02qyntr (0.52 #874, 0.42 #1806, 0.40 #1107), 0gr0m (0.50 #989, 0.45 #756, 0.37 #3552), 0p9sw (0.48 #720, 0.43 #953, 0.33 #3516), 0gqy2 (0.45 #816, 0.43 #1049, 0.40 #3612), 0f4x7 (0.42 #1657, 0.39 #3521, 0.37 #958), 0gq_v (0.42 #3515, 0.38 #1651, 0.34 #719) >> Best rule #759 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 0_92w; 07024; 0j43swk; 011ywj; 03pc89; >> query: (?x6740, 0gq9h) <- nominated_for(?x1107, ?x6740), nominated_for(?x1063, ?x6740), nominated_for(?x746, ?x6740), ?x1107 = 019f4v, ?x1063 = 02rdxsh, ?x746 = 04dn09n >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 27 EVAL 0p_tz nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 66.000 0.828 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0p_tz nominated_for! 02z0dfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 71.000 66.000 0.828 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5501-01jrbb PRED entity: 01jrbb PRED relation: film! PRED expected values: 018009 => 104 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1162): 01x6v6 (0.47 #4151, 0.46 #120349, 0.46 #118274), 0c94fn (0.42 #78847, 0.41 #87148, 0.39 #2076), 0kk9v (0.42 #78847, 0.41 #87148, 0.39 #2076), 0f6_x (0.22 #622, 0.03 #8921, 0.02 #23446), 0gr36 (0.22 #494, 0.02 #4645, 0.02 #33695), 01tnbn (0.22 #1070, 0.02 #5221, 0.02 #28043), 0154d7 (0.22 #1498, 0.02 #5649, 0.02 #7723), 02ck7w (0.11 #936, 0.07 #3012, 0.07 #5087), 03hzl42 (0.11 #784, 0.07 #2860, 0.03 #122426), 0f5xn (0.11 #966, 0.07 #13414, 0.06 #27939) >> Best rule #4151 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 02n9bh; 06zn1c; 05dl1s; >> query: (?x2893, ?x1052) <- award_winner(?x2893, ?x1052), country(?x2893, ?x390), nominated_for(?x68, ?x2893), ?x390 = 0chghy >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #58847 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 306 *> proper extension: 0963mq; 0bscw; 0c00zd0; 0g3zrd; 014zwb; 0c57yj; 05_5rjx; 0bs5k8r; 07bwr; 01q2nx; ... *> query: (?x2893, 018009) <- crewmember(?x2893, ?x1933), film_crew_role(?x2893, ?x3197), genre(?x2893, ?x258) *> conf = 0.01 ranks of expected_values: 817 EVAL 01jrbb film! 018009 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 104.000 60.000 0.471 http://example.org/film/actor/film./film/performance/film #5500-0bmhvpr PRED entity: 0bmhvpr PRED relation: nominated_for! PRED expected values: 019f4v 04kxsb 099flj => 94 concepts (93 used for prediction) PRED predicted values (max 10 best out of 188): 027c95y (0.68 #1534, 0.67 #7669, 0.67 #6792), 019f4v (0.63 #266, 0.50 #47, 0.45 #4428), 02pqp12 (0.56 #269, 0.25 #5527, 0.24 #4431), 0gqyl (0.50 #68, 0.30 #2259, 0.27 #4449), 09td7p (0.50 #81, 0.22 #300, 0.22 #2272), 02qyntr (0.44 #380, 0.27 #4542, 0.26 #6733), 0gr0m (0.41 #270, 0.30 #4432, 0.28 #6623), 0l8z1 (0.41 #264, 0.25 #6617, 0.24 #4426), 054krc (0.41 #277, 0.25 #58, 0.23 #2249), 0gq_v (0.34 #4398, 0.33 #236, 0.33 #6589) >> Best rule #1534 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 085bd1; >> query: (?x3784, ?x384) <- film_release_region(?x3784, ?x94), award(?x3784, ?x384), nominated_for(?x198, ?x3784) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #266 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 0dtfn; 09k56b7; 0yzvw; 01jrbb; 0ddj0x; 0cq86w; 0gmgwnv; 027pfg; 0pd64; 05ldxl; ... *> query: (?x3784, 019f4v) <- film_release_region(?x3784, ?x1558), nominated_for(?x198, ?x3784), ?x1558 = 01mjq, ?x198 = 040njc *> conf = 0.63 ranks of expected_values: 2, 13, 63 EVAL 0bmhvpr nominated_for! 099flj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 94.000 93.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bmhvpr nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 94.000 93.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bmhvpr nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 93.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5499-0gr36 PRED entity: 0gr36 PRED relation: religion PRED expected values: 0c8wxp => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 21): 0c8wxp (0.19 #231, 0.18 #366, 0.17 #1086), 03_gx (0.15 #284, 0.12 #194, 0.12 #239), 0kpl (0.14 #10, 0.12 #100, 0.11 #145), 0flw86 (0.14 #2, 0.12 #92, 0.02 #362), 0kq2 (0.06 #648, 0.04 #198, 0.04 #243), 0631_ (0.05 #638, 0.04 #188, 0.02 #1223), 03j6c (0.04 #201, 0.03 #1461, 0.03 #1281), 06nzl (0.04 #240, 0.03 #330, 0.02 #375), 092bf5 (0.03 #376, 0.02 #646, 0.02 #1456), 0n2g (0.03 #283, 0.03 #1228, 0.03 #1633) >> Best rule #231 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 01rw116; >> query: (?x2916, 0c8wxp) <- actor(?x10492, ?x2916), award_winner(?x1386, ?x2916), people(?x268, ?x2916) >> conf = 0.19 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gr36 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.192 http://example.org/people/person/religion #5498-08g_jw PRED entity: 08g_jw PRED relation: executive_produced_by PRED expected values: 02x20c9 => 93 concepts (69 used for prediction) PRED predicted values (max 10 best out of 40): 06q8hf (0.05 #673, 0.04 #11061, 0.04 #4216), 05hj_k (0.04 #604, 0.04 #351, 0.04 #2123), 0kvsb (0.04 #1519, 0.04 #3541, 0.04 #2278), 06pj8 (0.04 #561, 0.03 #1321, 0.03 #2080), 03c9pqt (0.03 #1766, 0.03 #2525, 0.02 #2778), 079vf (0.02 #2533, 0.02 #5574, 0.02 #5321), 0glyyw (0.02 #8043, 0.02 #5508, 0.02 #9564), 0gg9_5q (0.02 #2873, 0.02 #5409, 0.02 #2621), 03m3nzf (0.02 #12665, 0.02 #10385, 0.02 #10892), 02z6l5f (0.02 #6703, 0.02 #1890, 0.02 #4422) >> Best rule #673 for best value: >> intensional similarity = 4 >> extensional distance = 198 >> proper extension: 0d90m; 02vxq9m; 09m6kg; 011yrp; 011yxg; 01k1k4; 01h7bb; 0fg04; 04fzfj; 0dsvzh; ... >> query: (?x10842, 06q8hf) <- film(?x10061, ?x10842), film_crew_role(?x10842, ?x137), nominated_for(?x2252, ?x10842), featured_film_locations(?x10842, ?x739) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08g_jw executive_produced_by 02x20c9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 69.000 0.050 http://example.org/film/film/executive_produced_by #5497-0pkyh PRED entity: 0pkyh PRED relation: place_of_birth PRED expected values: 01n244 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 103): 0sbv7 (0.14 #622, 0.01 #2738), 0z2gq (0.14 #342, 0.01 #2458), 0jfqp (0.14 #302), 0pzpz (0.14 #72), 04jpl (0.12 #5648, 0.12 #2828, 0.11 #15517), 02_286 (0.07 #22573, 0.07 #14824, 0.06 #29621), 030qb3t (0.05 #28246, 0.05 #27539, 0.05 #29656), 0b_yz (0.04 #1138, 0.03 #3253, 0.02 #6073), 0n9r8 (0.04 #952, 0.01 #5887), 01_d4 (0.04 #5002, 0.03 #35303, 0.03 #43052) >> Best rule #622 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01lmj3q; 03qmj9; 0bhvtc; 01m1dzc; 02cx90; >> query: (?x2930, 0sbv7) <- artist(?x3006, ?x2930), ?x3006 = 043ljr, nationality(?x2930, ?x512) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pkyh place_of_birth 01n244 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 107.000 0.143 http://example.org/people/person/place_of_birth #5496-01lqnff PRED entity: 01lqnff PRED relation: film PRED expected values: 06gb1w => 138 concepts (86 used for prediction) PRED predicted values (max 10 best out of 822): 01dvbd (0.59 #146807, 0.59 #148598, 0.56 #89515), 03whyr (0.09 #3360, 0.03 #19471, 0.03 #6940), 065dc4 (0.09 #2441, 0.03 #18552, 0.02 #22132), 031hcx (0.07 #46028, 0.03 #15595, 0.03 #19176), 026wlxw (0.06 #4999, 0.04 #8579, 0.04 #12159), 0c0yh4 (0.06 #17935, 0.05 #1824, 0.03 #25095), 03177r (0.05 #45218, 0.02 #55959, 0.02 #59541), 0ndwt2w (0.05 #6371, 0.05 #2791, 0.04 #18902), 03wjm2 (0.05 #7130, 0.03 #19661, 0.03 #21451), 011ywj (0.05 #46190, 0.03 #15757, 0.03 #19338) >> Best rule #146807 for best value: >> intensional similarity = 3 >> extensional distance = 1270 >> proper extension: 05tk7y; 0m32_; 073749; 0436kgz; 02dlfh; 09gb9xh; 06r3p2; >> query: (?x7870, ?x3048) <- nominated_for(?x7870, ?x3048), film(?x7870, ?x3839), gender(?x7870, ?x231) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #2524 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 07f3xb; 0350l7; 023jq1; *> query: (?x7870, 06gb1w) <- place_of_birth(?x7870, ?x12597), nationality(?x7870, ?x512), ?x512 = 07ssc, languages(?x7870, ?x254) *> conf = 0.05 ranks of expected_values: 46 EVAL 01lqnff film 06gb1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 138.000 86.000 0.594 http://example.org/film/actor/film./film/performance/film #5495-04btyz PRED entity: 04btyz PRED relation: titles PRED expected values: 0dnvn3 => 53 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1557): 0296rz (0.50 #4514, 0.41 #13846, 0.33 #1407), 07z6xs (0.50 #3861, 0.33 #754, 0.29 #13193), 03h_yy (0.50 #3170, 0.33 #63, 0.29 #12502), 0k0rf (0.50 #3863, 0.33 #756, 0.29 #13195), 0jymd (0.50 #3655, 0.33 #548, 0.25 #9874), 01q7h2 (0.50 #4446, 0.33 #1339, 0.25 #10665), 0191n (0.50 #3841, 0.33 #734, 0.25 #10060), 04nnpw (0.50 #3785, 0.33 #678, 0.25 #10004), 0f4m2z (0.50 #3473, 0.33 #366, 0.25 #11250), 047fjjr (0.50 #3639, 0.33 #532, 0.25 #11416) >> Best rule #4514 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 02n4kr; >> query: (?x9360, 0296rz) <- titles(?x9360, ?x9744), titles(?x9360, ?x3790), titles(?x9360, ?x2500), genre(?x582, ?x9360), film_crew_role(?x2500, ?x137), cinematography(?x2500, ?x7903), ?x9744 = 0gm2_0, nominated_for(?x112, ?x3790) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04btyz titles 0dnvn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 13.000 0.500 http://example.org/media_common/netflix_genre/titles #5494-011_vz PRED entity: 011_vz PRED relation: artists! PRED expected values: 08jyyk => 97 concepts (50 used for prediction) PRED predicted values (max 10 best out of 256): 06by7 (0.72 #14965, 0.72 #6122, 0.72 #4592), 0cx7f (0.57 #1046, 0.38 #1656, 0.33 #2265), 02x8m (0.56 #3368, 0.48 #3979, 0.43 #1236), 064t9 (0.51 #11605, 0.48 #5809, 0.48 #13434), 0dl5d (0.50 #1541, 0.44 #2150, 0.27 #6731), 08jyyk (0.40 #674, 0.38 #1588, 0.33 #2197), 0jrv_ (0.40 #2610, 0.35 #2915, 0.33 #3219), 02yv6b (0.38 #12297, 0.34 #6809, 0.33 #6198), 01fh36 (0.38 #1608, 0.33 #2217, 0.29 #998), 05bt6j (0.34 #5838, 0.33 #42, 0.31 #6143) >> Best rule #14965 for best value: >> intensional similarity = 4 >> extensional distance = 410 >> proper extension: 0lk90; 01x1cn2; 07g2v; 01whg97; 01j590z; 01vsn38; >> query: (?x9155, 06by7) <- award(?x9155, ?x2634), artists(?x9013, ?x9155), artists(?x9013, ?x10625), ?x10625 = 01y_rz >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #674 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 01w8n89; *> query: (?x9155, 08jyyk) <- artists(?x8031, ?x9155), artists(?x1000, ?x9155), ?x1000 = 0xhtw, role(?x9155, ?x1466), category(?x9155, ?x134), ?x8031 = 01738f *> conf = 0.40 ranks of expected_values: 6 EVAL 011_vz artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 97.000 50.000 0.721 http://example.org/music/genre/artists #5493-0cskb PRED entity: 0cskb PRED relation: genre PRED expected values: 03k9fj 0fdjb => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 75): 03k9fj (0.60 #9, 0.57 #477, 0.33 #165), 06n90 (0.46 #479, 0.44 #401, 0.33 #323), 0hcr (0.44 #404, 0.40 #482, 0.39 #1502), 01jfsb (0.40 #10, 0.19 #400, 0.15 #478), 0pr6f (0.33 #201, 0.33 #123, 0.19 #435), 0c4xc (0.32 #1525, 0.26 #1839, 0.26 #1682), 06q7n (0.23 #273, 0.22 #195, 0.18 #585), 02n4kr (0.22 #162, 0.22 #84, 0.20 #6), 095bb (0.22 #188, 0.22 #110, 0.12 #3135), 0fdjb (0.22 #184, 0.22 #106, 0.12 #3135) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0hz55; 0828jw; >> query: (?x9843, 03k9fj) <- program(?x11291, ?x9843), genre(?x9843, ?x53), tv_program(?x8785, ?x9843), ?x8785 = 09_99w >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 0cskb genre 0fdjb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 97.000 97.000 0.600 http://example.org/tv/tv_program/genre EVAL 0cskb genre 03k9fj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.600 http://example.org/tv/tv_program/genre #5492-0dx_q PRED entity: 0dx_q PRED relation: type_of_union PRED expected values: 01g63y => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 2): 01g63y (0.60 #61, 0.51 #25, 0.40 #16), 0jgjn (0.01 #33, 0.01 #142, 0.01 #39) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 01xzb6; >> query: (?x7605, ?x566) <- celebrity(?x4397, ?x7605), award(?x7605, ?x154), type_of_union(?x4397, ?x566) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dx_q type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.596 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5491-087wc7n PRED entity: 087wc7n PRED relation: music PRED expected values: 06fxnf => 62 concepts (21 used for prediction) PRED predicted values (max 10 best out of 73): 03c_8t (0.20 #844, 0.14 #210, 0.13 #421), 02jxkw (0.14 #142, 0.07 #353, 0.05 #776), 01mkn_d (0.14 #121, 0.07 #332, 0.05 #755), 0dl567 (0.13 #634, 0.04 #633, 0.03 #1481), 0gv07g (0.10 #766, 0.07 #343, 0.03 #1824), 0150t6 (0.09 #1102, 0.06 #1738, 0.05 #2800), 02bh9 (0.08 #896, 0.07 #473, 0.04 #2173), 01tc9r (0.08 #910, 0.07 #487, 0.03 #3454), 04ls53 (0.08 #924, 0.02 #2623, 0.02 #2201), 089kpp (0.07 #415, 0.06 #1260, 0.04 #1473) >> Best rule #844 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 0h1x5f; >> query: (?x791, 03c_8t) <- film(?x7663, ?x791), award_nominee(?x4719, ?x7663), film(?x7663, ?x2207), ?x2207 = 07p62k, ?x4719 = 08hsww >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2823 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 201 *> proper extension: 02_fm2; 0dnvn3; 04fzfj; 020fcn; 0bscw; 0340hj; 0qm8b; 050gkf; 07yk1xz; 048htn; ... *> query: (?x791, 06fxnf) <- film(?x4080, ?x791), film_format(?x791, ?x10390), genre(?x791, ?x258), participant(?x2626, ?x4080), award_winner(?x139, ?x4080) *> conf = 0.04 ranks of expected_values: 27 EVAL 087wc7n music 06fxnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 62.000 21.000 0.200 http://example.org/film/film/music #5490-01vw37m PRED entity: 01vw37m PRED relation: location PRED expected values: 0s5cg => 99 concepts (97 used for prediction) PRED predicted values (max 10 best out of 94): 0cr3d (0.18 #948, 0.08 #6569, 0.07 #33076), 02_286 (0.16 #19312, 0.15 #16903, 0.15 #32968), 0h7h6 (0.14 #90, 0.02 #6514, 0.02 #7317), 0162v (0.14 #104, 0.01 #1710), 030qb3t (0.14 #16949, 0.14 #6507, 0.14 #33014), 0ccvx (0.12 #1025, 0.04 #1828, 0.04 #2631), 01531 (0.12 #961, 0.03 #2567, 0.03 #3370), 0xl08 (0.12 #1125, 0.02 #11243, 0.02 #2731), 013yq (0.08 #1725, 0.06 #2528, 0.05 #3331), 0n6dc (0.06 #1405, 0.03 #15260, 0.02 #11243) >> Best rule #948 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 03sww; 01wj5hp; >> query: (?x6264, 0cr3d) <- artists(?x8123, ?x6264), ?x8123 = 01flzq, people(?x2510, ?x6264) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01vw37m location 0s5cg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 97.000 0.176 http://example.org/people/person/places_lived./people/place_lived/location #5489-03z8bw PRED entity: 03z8bw PRED relation: current_club PRED expected values: 02029f => 73 concepts (55 used for prediction) PRED predicted values (max 10 best out of 768): 0138mv (0.40 #77, 0.29 #223, 0.17 #1540), 080_y (0.40 #106, 0.29 #252, 0.12 #1129), 0xbm (0.33 #312, 0.25 #750, 0.24 #1042), 04ltf (0.29 #217, 0.25 #802, 0.25 #364), 023fb (0.29 #196, 0.20 #50, 0.12 #1073), 050fh (0.29 #187, 0.20 #41, 0.12 #1064), 02k9k9 (0.29 #264, 0.20 #118, 0.11 #703), 03x726 (0.29 #275, 0.20 #129, 0.11 #714), 0y9j (0.21 #634, 0.20 #780, 0.16 #1072), 049f05 (0.21 #1573, 0.20 #1133, 0.20 #841) >> Best rule #77 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 03y_f8; 03ys48; 02w64f; >> query: (?x9799, 0138mv) <- position(?x9799, ?x60), colors(?x9799, ?x3189), current_club(?x9799, ?x1833), sport(?x9799, ?x471), ?x3189 = 01g5v, ?x60 = 02nzb8, position(?x9799, ?x530) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #655 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 17 *> proper extension: 03zrhb; *> query: (?x9799, 02029f) <- position(?x9799, ?x203), current_club(?x9799, ?x8807), sport(?x9799, ?x471), teams(?x3720, ?x9799), ?x203 = 0dgrmp, organization(?x3720, ?x127), position(?x8807, ?x530), countries_spoken_in(?x254, ?x3720) *> conf = 0.05 ranks of expected_values: 81 EVAL 03z8bw current_club 02029f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 73.000 55.000 0.400 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #5488-01tpvt PRED entity: 01tpvt PRED relation: citytown PRED expected values: 08966 => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 101): 08966 (0.23 #1474, 0.22 #3689, 0.22 #3688), 06mzp (0.23 #1474, 0.22 #3689, 0.22 #3688), 02_286 (0.17 #4809, 0.16 #5177, 0.15 #5915), 01qh7 (0.07 #431, 0.03 #1537, 0.03 #2277), 01k4f (0.06 #133, 0.02 #1238, 0.02 #1608), 030qb3t (0.05 #6664, 0.05 #4822, 0.05 #5190), 04jpl (0.05 #4065, 0.04 #744, 0.03 #1482), 05l5n (0.05 #7041, 0.04 #9249, 0.04 #8881), 019xz9 (0.04 #5900), 0978r (0.03 #444, 0.03 #9287, 0.03 #7079) >> Best rule #1474 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 0bqxw; 09hgk; 02x9g_; 023zl; 019_6d; 02185j; 0p7tb; >> query: (?x6811, ?x6458) <- company(?x3335, ?x6811), contains(?x6458, ?x6811), contains(?x774, ?x6811), film_release_region(?x66, ?x774) >> conf = 0.23 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01tpvt citytown 08966 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.229 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #5487-02z5x7l PRED entity: 02z5x7l PRED relation: genre PRED expected values: 07s9rl0 => 103 concepts (54 used for prediction) PRED predicted values (max 10 best out of 103): 07s9rl0 (0.95 #3434, 0.74 #2841, 0.70 #3314), 03q4nz (0.91 #3451, 0.40 #1672, 0.35 #2147), 03k9fj (0.80 #3682, 0.73 #1311, 0.65 #2378), 02kdv5l (0.70 #2725, 0.67 #1776, 0.66 #2013), 01jfsb (0.61 #2735, 0.51 #5341, 0.50 #1075), 05p553 (0.57 #5925, 0.55 #1304, 0.51 #3557), 01zhp (0.36 #1374, 0.26 #4809, 0.26 #4454), 03g3w (0.36 #6180, 0.34 #6299, 0.33 #5825), 02l7c8 (0.33 #16, 0.33 #3449, 0.32 #3329), 0bj8m2 (0.33 #47, 0.20 #755, 0.20 #637) >> Best rule #3434 for best value: >> intensional similarity = 6 >> extensional distance = 74 >> proper extension: 0bs8hvm; 05y0cr; 0k20s; >> query: (?x6840, 07s9rl0) <- genre(?x6840, ?x5937), genre(?x6840, ?x714), genre(?x5430, ?x5937), titles(?x714, ?x7664), ?x7664 = 046f3p, ?x5430 = 0dh8v4 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02z5x7l genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 54.000 0.947 http://example.org/film/film/genre #5486-03v0t PRED entity: 03v0t PRED relation: state_province_region! PRED expected values: 0206k5 027lf1 => 190 concepts (142 used for prediction) PRED predicted values (max 10 best out of 764): 065r8g (0.50 #30561, 0.29 #66969, 0.25 #87353), 03cz83 (0.50 #30561, 0.29 #66969, 0.25 #87353), 02zd2b (0.50 #30561, 0.29 #66969, 0.25 #87353), 0sgxg (0.23 #16004, 0.23 #18913, 0.23 #26192), 0s69k (0.23 #16004, 0.23 #18913, 0.23 #26192), 0sg4x (0.23 #16004, 0.23 #18913, 0.23 #26192), 0s4sj (0.23 #16004, 0.23 #18913, 0.23 #26192), 0sc6p (0.23 #16004, 0.23 #18913, 0.23 #26192), 0sbv7 (0.23 #16004, 0.23 #18913, 0.23 #26192), 0s9b_ (0.23 #16004, 0.23 #18913, 0.23 #26192) >> Best rule #30561 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 0jgd; 0d060g; 06qd3; 07t21; 0156q; 06t2t; 06f32; 03hrz; >> query: (?x3818, ?x13753) <- contains(?x3818, ?x13753), currency(?x3818, ?x170), major_field_of_study(?x13753, ?x2606) >> conf = 0.50 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 03v0t state_province_region! 027lf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 142.000 0.504 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 03v0t state_province_region! 0206k5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 142.000 0.504 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #5485-0gy0l_ PRED entity: 0gy0l_ PRED relation: genre PRED expected values: 02kdv5l 082gq => 115 concepts (102 used for prediction) PRED predicted values (max 10 best out of 114): 02kdv5l (0.55 #1312, 0.46 #1431, 0.45 #1550), 03_9r (0.54 #10982, 0.51 #5487, 0.51 #4650), 03k9fj (0.48 #1322, 0.45 #132, 0.37 #3946), 0jxy (0.46 #1236, 0.46 #1474, 0.45 #1593), 02l7c8 (0.45 #137, 0.38 #10520, 0.35 #7774), 01jfsb (0.45 #1323, 0.40 #8368, 0.34 #6817), 0hcr (0.44 #1453, 0.43 #1572, 0.39 #382), 05p553 (0.43 #8240, 0.35 #4414, 0.34 #5370), 04xvlr (0.38 #954, 0.35 #1073, 0.29 #240), 06n90 (0.31 #1443, 0.30 #1562, 0.28 #1324) >> Best rule #1312 for best value: >> intensional similarity = 5 >> extensional distance = 63 >> proper extension: 0p3_y; 02h22; 05r3qc; 015bpl; 0m5s5; >> query: (?x9133, 02kdv5l) <- nominated_for(?x6860, ?x9133), nominated_for(?x1307, ?x9133), ?x6860 = 018wdw, currency(?x9133, ?x170), award(?x71, ?x1307) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12 EVAL 0gy0l_ genre 082gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 115.000 102.000 0.554 http://example.org/film/film/genre EVAL 0gy0l_ genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 102.000 0.554 http://example.org/film/film/genre #5484-0gtv7pk PRED entity: 0gtv7pk PRED relation: nominated_for! PRED expected values: 0151ns => 116 concepts (22 used for prediction) PRED predicted values (max 10 best out of 842): 0151ns (0.16 #51498, 0.15 #37452, 0.12 #28088), 01sl1q (0.16 #51498, 0.15 #37452, 0.12 #28088), 04ktcgn (0.14 #399, 0.08 #19122, 0.08 #2738), 016ypb (0.14 #624, 0.05 #17006, 0.04 #19347), 01kwld (0.14 #114, 0.04 #18837, 0.04 #2453), 024rgt (0.14 #531, 0.04 #19254, 0.03 #23934), 0154qm (0.14 #695, 0.04 #19418, 0.03 #10056), 0js9s (0.14 #1434, 0.04 #20157, 0.03 #10795), 02h1rt (0.14 #1046, 0.04 #19769, 0.03 #10407), 01ps2h8 (0.14 #1169, 0.04 #19892, 0.03 #33942) >> Best rule #51498 for best value: >> intensional similarity = 7 >> extensional distance = 126 >> proper extension: 02wgbb; 025s1wg; >> query: (?x409, ?x56) <- country(?x409, ?x94), genre(?x409, ?x812), film(?x56, ?x409), prequel(?x409, ?x10590), ?x94 = 09c7w0, genre(?x7502, ?x812), film_release_region(?x7502, ?x87) >> conf = 0.16 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0gtv7pk nominated_for! 0151ns CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 22.000 0.155 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5483-034b6k PRED entity: 034b6k PRED relation: genre PRED expected values: 03k9fj => 82 concepts (73 used for prediction) PRED predicted values (max 10 best out of 91): 05p553 (0.94 #2250, 0.39 #4, 0.39 #714), 07s9rl0 (0.64 #2129, 0.62 #4140, 0.61 #3312), 03k9fj (0.62 #6154, 0.61 #6391, 0.49 #6035), 01jfsb (0.43 #5456, 0.37 #1314, 0.34 #1904), 01hmnh (0.29 #728, 0.29 #846, 0.27 #964), 06n90 (0.24 #724, 0.20 #14, 0.19 #960), 0lsxr (0.20 #5452, 0.19 #1900, 0.18 #1782), 06cvj (0.19 #2249, 0.09 #4024, 0.08 #6038), 04xvlr (0.19 #5445, 0.17 #6037, 0.16 #6274), 060__y (0.19 #5460, 0.15 #2145, 0.14 #6052) >> Best rule #2250 for best value: >> intensional similarity = 3 >> extensional distance = 617 >> proper extension: 0ddfwj1; 07g_0c; 05q4y12; 02rmd_2; 06cgf; >> query: (?x10742, 05p553) <- genre(?x10742, ?x8280), genre(?x1210, ?x8280), ?x1210 = 018f8 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #6154 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1203 *> proper extension: 01qn7n; 05r1_t; 03y317; 02xhwm; *> query: (?x10742, ?x811) <- titles(?x811, ?x10742), genre(?x8870, ?x811), honored_for(?x762, ?x8870) *> conf = 0.62 ranks of expected_values: 3 EVAL 034b6k genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 73.000 0.942 http://example.org/film/film/genre #5482-02pbzv PRED entity: 02pbzv PRED relation: contains! PRED expected values: 06c1y => 99 concepts (46 used for prediction) PRED predicted values (max 10 best out of 323): 09c7w0 (0.69 #8058, 0.63 #23283, 0.62 #30450), 07ssc (0.68 #29581, 0.55 #32268, 0.33 #926), 02qkt (0.56 #18242, 0.49 #12870, 0.48 #13764), 0j0k (0.44 #18273, 0.31 #20065, 0.31 #20964), 02jx1 (0.43 #5456, 0.39 #29636, 0.38 #4562), 0f8l9c (0.41 #17047, 0.17 #1837, 0.08 #4522), 06c1y (0.33 #7160, 0.33 #4476, 0.29 #6265), 01rdm0 (0.33 #4476, 0.25 #2686), 06bnz (0.33 #1895, 0.18 #3685, 0.14 #5474), 015qh (0.33 #98, 0.06 #6363, 0.04 #1790) >> Best rule #8058 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 065y4w7; 01hhvg; 02zd460; 0k__z; 019n9w; >> query: (?x8820, 09c7w0) <- contains(?x4962, ?x8820), major_field_of_study(?x8820, ?x10518), major_field_of_study(?x893, ?x10518), ?x893 = 0ymbl, place_of_death(?x4961, ?x4962) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #7160 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 01g_k3; 0ftns; *> query: (?x8820, ?x1536) <- contains(?x4962, ?x8820), contains(?x455, ?x8820), ?x455 = 02j9z, time_zones(?x4962, ?x10735), contains(?x1536, ?x4962) *> conf = 0.33 ranks of expected_values: 7 EVAL 02pbzv contains! 06c1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 99.000 46.000 0.692 http://example.org/location/location/contains #5481-04m064 PRED entity: 04m064 PRED relation: film PRED expected values: 08fn5b => 102 concepts (20 used for prediction) PRED predicted values (max 10 best out of 286): 07024 (0.57 #33954, 0.57 #1787, 0.57 #30380), 03nx8mj (0.50 #697, 0.14 #7148, 0.13 #32167), 0fphgb (0.25 #599, 0.14 #7148, 0.13 #32167), 013q07 (0.25 #356, 0.02 #5715, 0.02 #3929), 02q7fl9 (0.25 #1032, 0.02 #13539, 0.01 #31412), 04x4vj (0.25 #773, 0.01 #13280, 0.01 #4346), 0f4_l (0.25 #349, 0.01 #12856, 0.01 #30729), 011ysn (0.25 #566, 0.01 #2353, 0.01 #30946), 029k4p (0.25 #836, 0.01 #13343), 07ykkx5 (0.25 #1781) >> Best rule #33954 for best value: >> intensional similarity = 3 >> extensional distance = 902 >> proper extension: 03z509; 01r7t9; >> query: (?x12425, ?x2928) <- award_winner(?x2928, ?x12425), profession(?x12425, ?x1032), film(?x12425, ?x825) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04m064 film 08fn5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 20.000 0.572 http://example.org/film/actor/film./film/performance/film #5480-02vzc PRED entity: 02vzc PRED relation: film_release_region! PRED expected values: 0b76d_m 0h1cdwq 0m2kd 0dckvs 026p_bs 04969y 01vksx 0m_mm 0crfwmx 0h3xztt 0c0nhgv 0c8tkt 0gj9tn5 0fq7dv_ 0_7w6 07j8r 01shy7 047p7fr 0ywrc 0crc2cp 0gffmn8 02fqrf 0cp0ph6 05c26ss 0blpg 07s846j 0hgnl3t 0h03fhx 02xbyr 0cqnss 0bh8tgs 0gt1k 0cc97st 0992d9 02h22 0gj96ln 0cmdwwg 04cppj 0gfh84d 0h63gl9 0g4vmj8 032clf 02vz6dn 0233bn 0h95927 0gvvf4j 0bs8hvm 078mm1 0g57wgv 072hx4 => 171 concepts (161 used for prediction) PRED predicted values (max 10 best out of 994): 01vksx (0.91 #6005, 0.82 #16874, 0.80 #20826), 07s846j (0.87 #6277, 0.79 #17146, 0.78 #21098), 0h63gl9 (0.87 #6571, 0.74 #17440, 0.72 #21392), 0g4vmj8 (0.87 #6616, 0.72 #17485, 0.70 #21437), 0dscrwf (0.83 #5968, 0.79 #16837, 0.78 #20789), 0gj9tn5 (0.83 #6071, 0.77 #16940, 0.75 #20892), 0c0nhgv (0.83 #6026, 0.74 #16895, 0.72 #20847), 05c26ss (0.83 #6261, 0.72 #17130, 0.70 #21082), 047svrl (0.83 #6151, 0.72 #17020, 0.70 #20972), 0hgnl3t (0.83 #6331, 0.69 #17200, 0.68 #21152) >> Best rule #6005 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 05r4w; 09c7w0; 0jgd; 03rjj; 03_3d; 0d060g; 0d0vqn; 0chghy; 05qhw; 015fr; ... >> query: (?x1892, 01vksx) <- film_release_region(?x3886, ?x1892), film_release_region(?x1724, ?x1892), ?x1724 = 02r8hh_, ?x3886 = 0198b6 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 24, 25, 28, 29, 30, 31, 34, 36, 38, 39, 48, 50, 51, 53, 56, 57, 59, 62, 63, 66, 67, 72, 73, 74, 81, 84, 97, 101, 103, 105, 107 EVAL 02vzc film_release_region! 072hx4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0g57wgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 078mm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0bs8hvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0gvvf4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0h95927 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0233bn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 02vz6dn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 032clf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0g4vmj8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0h63gl9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0gfh84d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 04cppj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0cmdwwg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0gj96ln CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 02h22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0992d9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0cc97st CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0gt1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0bh8tgs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0cqnss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 02xbyr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0h03fhx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0hgnl3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 07s846j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0blpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 05c26ss CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0cp0ph6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 02fqrf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0gffmn8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0crc2cp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0ywrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 047p7fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 01shy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 07j8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0_7w6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0fq7dv_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0gj9tn5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0c8tkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0c0nhgv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0h3xztt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0crfwmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0m_mm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 01vksx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 04969y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 026p_bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0dckvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0m2kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0h1cdwq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02vzc film_release_region! 0b76d_m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 171.000 161.000 0.913 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5479-0j7v_ PRED entity: 0j7v_ PRED relation: organization! PRED expected values: 0ctw_b 06t2t 0167v 06v36 07fb6 06m_5 034m8 => 124 concepts (67 used for prediction) PRED predicted values (max 10 best out of 297): 02vzc (0.73 #2242, 0.67 #2513, 0.62 #3058), 0k6nt (0.73 #2209, 0.67 #2480, 0.62 #3025), 0d0vqn (0.73 #2188, 0.67 #2459, 0.62 #3004), 03rjj (0.73 #2185, 0.67 #2456, 0.62 #3001), 059j2 (0.73 #2216, 0.67 #2487, 0.62 #3032), 03188 (0.68 #1361, 0.67 #1889, 0.58 #3538), 02wt0 (0.68 #1361, 0.67 #1705, 0.58 #3538), 02lx0 (0.68 #1361, 0.67 #1739, 0.50 #649), 0f8l9c (0.68 #1361, 0.64 #2207, 0.60 #1119), 09c7w0 (0.68 #1361, 0.60 #1092, 0.58 #3538) >> Best rule #2242 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 01rz1; 02jxk; 04k4l; 018cqq; 059dn; >> query: (?x4403, 02vzc) <- organization(?x279, ?x4403), film_release_region(?x66, ?x279), country(?x136, ?x279), country(?x6354, ?x279), ?x6354 = 09_b4 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1361 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0b6css; *> query: (?x4403, ?x11593) <- organization(?x9654, ?x4403), organization(?x1061, ?x4403), organization(?x279, ?x4403), ?x279 = 0d060g, adjoins(?x11593, ?x9654), film_release_region(?x80, ?x1061) *> conf = 0.68 ranks of expected_values: 24, 28, 35, 86, 88, 97, 160 EVAL 0j7v_ organization! 034m8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 124.000 67.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0j7v_ organization! 06m_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 124.000 67.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0j7v_ organization! 07fb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 124.000 67.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0j7v_ organization! 06v36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 124.000 67.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0j7v_ organization! 0167v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 124.000 67.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0j7v_ organization! 06t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 124.000 67.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0j7v_ organization! 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 124.000 67.000 0.727 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #5478-0bl8l PRED entity: 0bl8l PRED relation: team! PRED expected values: 04bsx1 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 76): 07m69t (0.85 #1038, 0.85 #1037, 0.85 #1036), 0fw2d3 (0.85 #1038, 0.85 #1037, 0.85 #1036), 071h5c (0.50 #140, 0.50 #65, 0.40 #290), 0djvzd (0.38 #183, 0.33 #108, 0.33 #33), 05s_c38 (0.33 #95, 0.33 #20, 0.30 #245), 0f1pyf (0.33 #93, 0.33 #18, 0.26 #907), 0487c3 (0.33 #78, 0.33 #3, 0.25 #153), 09lhln (0.33 #14, 0.25 #164, 0.20 #239), 02d9k (0.24 #599, 0.20 #372, 0.19 #524), 08gwzt (0.24 #642, 0.14 #788, 0.12 #1305) >> Best rule #1038 for best value: >> intensional similarity = 7 >> extensional distance = 33 >> proper extension: 025txtg; 0ljbg; 049dzz; >> query: (?x5207, ?x8598) <- team(?x530, ?x5207), team(?x8598, ?x5207), ?x530 = 02_j1w, team(?x8598, ?x348), colors(?x5207, ?x3189), position(?x348, ?x60), ?x3189 = 01g5v >> conf = 0.85 => this is the best rule for 2 predicted values *> Best rule #1169 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 37 *> proper extension: 02gys2; 01j95f; 0212mp; 0196bp; 02b1k5; 02b10w; 02rh_0; 0fvly; 0j13b; 014nzp; *> query: (?x5207, 04bsx1) <- team(?x530, ?x5207), team(?x8598, ?x5207), ?x530 = 02_j1w, team(?x8598, ?x3162), nationality(?x8598, ?x94), ?x3162 = 027pwl, position(?x5207, ?x60) *> conf = 0.21 ranks of expected_values: 12 EVAL 0bl8l team! 04bsx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 87.000 87.000 0.849 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #5477-01vw_dv PRED entity: 01vw_dv PRED relation: nationality PRED expected values: 09c7w0 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.80 #1002, 0.80 #801, 0.80 #901), 05tbn (0.25 #9511), 0dclg (0.25 #9511), 02jx1 (0.15 #4035, 0.15 #5037, 0.12 #1134), 07ssc (0.10 #2117, 0.10 #4017, 0.09 #1917), 03spz (0.06 #267, 0.03 #467, 0.02 #967), 0d060g (0.05 #1709, 0.05 #5911, 0.05 #4710), 03rk0 (0.05 #11363, 0.05 #11263, 0.05 #11463), 0f8l9c (0.04 #322, 0.03 #422, 0.03 #1424), 013yq (0.03 #4904) >> Best rule #1002 for best value: >> intensional similarity = 2 >> extensional distance = 84 >> proper extension: 0frmb1; >> query: (?x6659, 09c7w0) <- person(?x3480, ?x6659), gender(?x6659, ?x231) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vw_dv nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.802 http://example.org/people/person/nationality #5476-0c57yj PRED entity: 0c57yj PRED relation: costume_design_by PRED expected values: 03mfqm => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 11): 03mfqm (0.06 #46, 0.03 #496, 0.02 #694), 0bytfv (0.04 #11, 0.02 #151, 0.02 #39), 02mxbd (0.04 #45, 0.03 #73, 0.02 #325), 02w0dc0 (0.02 #1, 0.02 #85, 0.02 #508), 03gt0c5 (0.02 #27, 0.02 #167, 0.01 #111), 03y1mlp (0.02 #114, 0.02 #58, 0.02 #1018), 02pqgt8 (0.02 #659, 0.02 #716, 0.02 #376), 0c6g29 (0.01 #287, 0.01 #259), 02cqbx (0.01 #1284, 0.01 #663, 0.01 #296), 06w33f8 (0.01 #227) >> Best rule #46 for best value: >> intensional similarity = 5 >> extensional distance = 125 >> proper extension: 03xj05; >> query: (?x3859, 03mfqm) <- country(?x3859, ?x94), film_crew_role(?x3859, ?x3197), film_crew_role(?x3859, ?x1171), ?x1171 = 09vw2b7, ?x3197 = 02ynfr >> conf = 0.06 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c57yj costume_design_by 03mfqm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.055 http://example.org/film/film/costume_design_by #5475-09gwd PRED entity: 09gwd PRED relation: nutrient! PRED expected values: 0fj52s 014j1m 01645p 05z55 => 56 concepts (53 used for prediction) PRED predicted values (max 10 best out of 14): 0fj52s (0.94 #733, 0.94 #721, 0.92 #536), 014j1m (0.91 #68, 0.90 #126, 0.90 #124), 0dcfv (0.91 #68, 0.90 #126, 0.90 #124), 01645p (0.91 #510, 0.91 #461, 0.90 #438), 05z55 (0.90 #126, 0.90 #124, 0.90 #120), 06x4c (0.90 #126, 0.90 #124, 0.90 #120), 01sh2 (0.03 #176, 0.02 #161, 0.02 #565), 025rw19 (0.03 #176, 0.02 #46, 0.02 #59), 025tkqy (0.03 #176, 0.02 #46, 0.02 #59), 06jry (0.03 #176, 0.02 #46, 0.02 #59) >> Best rule #733 for best value: >> intensional similarity = 115 >> extensional distance = 45 >> proper extension: 02kb_jm; >> query: (?x7431, ?x1303) <- nutrient(?x10612, ?x7431), nutrient(?x7719, ?x7431), nutrient(?x5009, ?x7431), nutrient(?x2701, ?x7431), nutrient(?x1257, ?x7431), nutrient(?x5009, ?x13944), nutrient(?x5009, ?x13498), nutrient(?x5009, ?x12868), nutrient(?x5009, ?x12454), nutrient(?x5009, ?x12083), nutrient(?x5009, ?x11758), nutrient(?x5009, ?x11592), nutrient(?x5009, ?x11409), nutrient(?x5009, ?x11270), nutrient(?x5009, ?x10891), nutrient(?x5009, ?x10195), nutrient(?x5009, ?x10098), nutrient(?x5009, ?x9949), nutrient(?x5009, ?x9855), nutrient(?x5009, ?x9733), nutrient(?x5009, ?x9619), nutrient(?x5009, ?x9490), nutrient(?x5009, ?x9436), nutrient(?x5009, ?x9426), nutrient(?x5009, ?x9365), nutrient(?x5009, ?x8487), nutrient(?x5009, ?x8442), nutrient(?x5009, ?x8413), nutrient(?x5009, ?x7894), nutrient(?x5009, ?x7720), nutrient(?x5009, ?x7652), nutrient(?x5009, ?x7364), nutrient(?x5009, ?x7362), nutrient(?x5009, ?x7219), nutrient(?x5009, ?x7135), nutrient(?x5009, ?x6586), nutrient(?x5009, ?x6192), nutrient(?x5009, ?x6033), nutrient(?x5009, ?x6026), nutrient(?x5009, ?x5549), nutrient(?x5009, ?x5526), nutrient(?x5009, ?x5451), nutrient(?x5009, ?x5374), nutrient(?x5009, ?x5337), nutrient(?x5009, ?x4069), nutrient(?x5009, ?x3203), nutrient(?x5009, ?x2702), nutrient(?x5009, ?x2018), nutrient(?x5009, ?x1960), nutrient(?x5009, ?x1258), ?x7652 = 025s0s0, ?x9855 = 0d9t0, ?x7362 = 02kc5rj, ?x5451 = 05wvs, ?x9619 = 0h1tg, nutrient(?x2701, ?x9915), ?x8413 = 02kc4sf, ?x9490 = 0h1sg, ?x3203 = 04kl74p, ?x7135 = 025rsfk, ?x12868 = 03d49, nutrient(?x10612, ?x14210), nutrient(?x10612, ?x13545), nutrient(?x10612, ?x12336), nutrient(?x10612, ?x6517), ?x6517 = 02kd8zw, ?x1258 = 0h1wg, ?x9915 = 025tkqy, ?x2702 = 0838f, ?x5374 = 025s0zp, ?x6192 = 06jry, ?x1960 = 07hnp, ?x5549 = 025s7j4, ?x10891 = 0g5gq, ?x6586 = 05gh50, ?x10098 = 0h1_c, ?x11270 = 02kc008, nutrient(?x9732, ?x9949), nutrient(?x6285, ?x9949), nutrient(?x6191, ?x9949), nutrient(?x1303, ?x9949), ?x1303 = 0fj52s, nutrient(?x7719, ?x8243), ?x9732 = 05z55, ?x11592 = 025sf0_, ?x10195 = 0hkwr, ?x13545 = 01w_3, ?x11409 = 0h1yf, ?x9436 = 025sqz8, ?x12083 = 01n78x, ?x4069 = 0hqw8p_, ?x9733 = 0h1tz, ?x7219 = 0h1vg, ?x9426 = 0h1yy, ?x7894 = 0f4hc, ?x8243 = 014d7f, ?x12454 = 025rw19, ?x6033 = 04zjxcz, ?x13944 = 0f4kp, ?x6026 = 025sf8g, ?x8442 = 02kcv4x, ?x5337 = 06x4c, ?x11758 = 0q01m, ?x12336 = 0f4l5, ?x6285 = 01645p, ?x14210 = 0f4k5, ?x13498 = 07q0m, ?x7364 = 09gvd, ?x5526 = 09pbb, ?x2018 = 01sh2, ?x9365 = 04k8n, ?x1257 = 09728, ?x8487 = 014yzm, ?x7720 = 025s7x6, ?x6191 = 014j1m >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5 EVAL 09gwd nutrient! 05z55 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 53.000 0.936 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 09gwd nutrient! 01645p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 53.000 0.936 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 09gwd nutrient! 014j1m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 53.000 0.936 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 09gwd nutrient! 0fj52s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 53.000 0.936 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #5474-07zl1 PRED entity: 07zl1 PRED relation: award_winner! PRED expected values: 01bb1c => 142 concepts (93 used for prediction) PRED predicted values (max 10 best out of 320): 0265vt (0.54 #747, 0.48 #1286, 0.46 #857), 0262yt (0.48 #1286, 0.46 #857, 0.36 #39397), 0265wl (0.48 #1286, 0.46 #857, 0.36 #39397), 01tgwv (0.48 #1286, 0.46 #857, 0.36 #39397), 0262zm (0.43 #940, 0.31 #511, 0.23 #4365), 01bb1c (0.36 #1269, 0.20 #4694, 0.15 #840), 040_9s0 (0.29 #1169, 0.23 #740, 0.18 #4594), 02662b (0.29 #933, 0.15 #504, 0.13 #4358), 039yzf (0.23 #775, 0.14 #1204, 0.08 #4629), 02664f (0.21 #1071, 0.10 #4496, 0.08 #642) >> Best rule #747 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 02g75; 04mhl; >> query: (?x10438, 0265vt) <- award_winner(?x575, ?x10438), nationality(?x10438, ?x94), award(?x10438, ?x5050), ?x575 = 040vk98 >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1269 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0p8jf; 0210f1; 02y49; 033cw; *> query: (?x10438, 01bb1c) <- award_winner(?x3337, ?x10438), type_of_union(?x10438, ?x566), ?x3337 = 01yz0x, award(?x10438, ?x5050) *> conf = 0.36 ranks of expected_values: 6 EVAL 07zl1 award_winner! 01bb1c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 142.000 93.000 0.538 http://example.org/award/award_category/winners./award/award_honor/award_winner #5473-0ygbf PRED entity: 0ygbf PRED relation: country PRED expected values: 09c7w0 => 216 concepts (200 used for prediction) PRED predicted values (max 10 best out of 51): 09c7w0 (0.81 #10313, 0.81 #6144, 0.77 #4589), 07ssc (0.33 #17, 0.09 #5727, 0.08 #2175), 05fkf (0.31 #3719, 0.27 #7442, 0.26 #4586), 02jx1 (0.27 #10483, 0.26 #14039, 0.26 #15598), 0ygbf (0.23 #9614, 0.15 #11874, 0.10 #9180), 04_1l0v (0.11 #16813), 0d060g (0.09 #1995, 0.06 #1560, 0.06 #4855), 03rk0 (0.07 #4200, 0.05 #5930, 0.05 #5324), 06bnz (0.05 #559, 0.05 #731, 0.03 #2114), 0chghy (0.05 #2865, 0.05 #1564, 0.04 #874) >> Best rule #10313 for best value: >> intensional similarity = 3 >> extensional distance = 201 >> proper extension: 0s6g4; 0_kfv; 0s4sj; >> query: (?x5837, 09c7w0) <- source(?x5837, ?x958), state(?x5837, ?x760), location(?x120, ?x760) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ygbf country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 216.000 200.000 0.813 http://example.org/base/biblioness/bibs_location/country #5472-013zdg PRED entity: 013zdg PRED relation: institution PRED expected values: 01j_cy 0j_sncb 01y17m 01w5m 03ksy 01rc6f 02rky4 => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 585): 01w5m (0.80 #10195, 0.78 #7948, 0.75 #10759), 03ksy (0.78 #7949, 0.75 #10760, 0.73 #10196), 01bm_ (0.78 #8092, 0.73 #9774, 0.67 #10339), 05zl0 (0.78 #8047, 0.73 #9729, 0.60 #10294), 025v3k (0.71 #7404, 0.69 #10775, 0.67 #10211), 012mzw (0.71 #7561, 0.60 #9242, 0.60 #7001), 08qnnv (0.67 #8621, 0.67 #8060, 0.64 #9742), 07tg4 (0.67 #10171, 0.67 #7924, 0.62 #10735), 07wlf (0.67 #7913, 0.64 #9595, 0.60 #10160), 09kvv (0.67 #7881, 0.64 #9563, 0.60 #10128) >> Best rule #10195 for best value: >> intensional similarity = 21 >> extensional distance = 13 >> proper extension: 071tyz; >> query: (?x1519, 01w5m) <- institution(?x1519, ?x10507), institution(?x1519, ?x5807), institution(?x1519, ?x5651), institution(?x1519, ?x1201), major_field_of_study(?x1519, ?x2981), school(?x2820, ?x5651), category(?x5651, ?x134), state_province_region(?x5807, ?x6895), contains(?x94, ?x10507), student(?x1519, ?x1620), organization(?x346, ?x5651), major_field_of_study(?x5807, ?x3489), major_field_of_study(?x7092, ?x2981), major_field_of_study(?x5281, ?x2981), major_field_of_study(?x2760, ?x2981), contains(?x1458, ?x5281), ?x7092 = 01g7_r, ?x3489 = 0193x, fraternities_and_sororities(?x1201, ?x4348), student(?x5807, ?x690), currency(?x2760, ?x170) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 21, 72, 111, 129, 438 EVAL 013zdg institution 02rky4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 25.000 25.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 013zdg institution 01rc6f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 25.000 25.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 013zdg institution 03ksy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 013zdg institution 01w5m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 013zdg institution 01y17m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 25.000 25.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 013zdg institution 0j_sncb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 25.000 25.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 013zdg institution 01j_cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 25.000 25.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5471-0lw_s PRED entity: 0lw_s PRED relation: contains! PRED expected values: 0gsl0 => 118 concepts (34 used for prediction) PRED predicted values (max 10 best out of 136): 09c7w0 (0.90 #8071, 0.82 #10758, 0.81 #12550), 01n7q (0.43 #4557, 0.42 #5454, 0.38 #6351), 0d060g (0.38 #1803, 0.31 #3595, 0.30 #28704), 0gslw (0.33 #895, 0.12 #1790, 0.04 #3581), 0f8l9c (0.29 #2733, 0.09 #6321, 0.05 #7218), 07ssc (0.29 #2718, 0.07 #5409, 0.05 #7203), 02j9z (0.29 #7199, 0.24 #8992, 0.13 #29616), 0345h (0.26 #28772, 0.17 #2767, 0.17 #1871), 02qkt (0.25 #7517, 0.23 #29934, 0.21 #9310), 0chghy (0.25 #1813, 0.21 #3605, 0.12 #28714) >> Best rule #8071 for best value: >> intensional similarity = 7 >> extensional distance = 154 >> proper extension: 0ftjx; >> query: (?x14345, 09c7w0) <- jurisdiction_of_office(?x1195, ?x14345), contains(?x1023, ?x14345), film_release_region(?x2868, ?x1023), film_release_region(?x1219, ?x1023), combatants(?x1023, ?x94), ?x1219 = 03bx2lk, ?x2868 = 0dr3sl >> conf = 0.90 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0lw_s contains! 0gsl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 34.000 0.897 http://example.org/location/location/contains #5470-07vyf PRED entity: 07vyf PRED relation: student PRED expected values: 01t6b4 022q32 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1317): 09v6tz (0.08 #11759, 0.06 #20096, 0.05 #22180), 0p8jf (0.08 #10898, 0.06 #19235, 0.05 #21319), 0d3k14 (0.07 #14354, 0.07 #10185, 0.05 #12269), 0ff3y (0.07 #16651, 0.07 #18735, 0.05 #29155), 0405l (0.07 #16437, 0.07 #18521, 0.03 #26857), 03h40_7 (0.07 #8061, 0.07 #10145, 0.05 #12229), 04hw4b (0.07 #7483, 0.07 #9567, 0.05 #15820), 02779r4 (0.07 #7413, 0.06 #1161, 0.05 #13666), 016lh0 (0.07 #7162, 0.05 #11330, 0.04 #19667), 026m0 (0.07 #8071, 0.05 #12239, 0.04 #20576) >> Best rule #11759 for best value: >> intensional similarity = 2 >> extensional distance = 37 >> proper extension: 0d06m5; 0d05fv; >> query: (?x4296, 09v6tz) <- list(?x4296, ?x2197), organization(?x4296, ?x5487) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #145892 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 558 *> proper extension: 01fpvz; 05f7s1; 01k7xz; 015nl4; 0dy04; 04sylm; 017z88; 0820xz; 01vc5m; 0373qg; ... *> query: (?x4296, ?x690) <- major_field_of_study(?x4296, ?x10046), student(?x10046, ?x690) *> conf = 0.01 ranks of expected_values: 1046 EVAL 07vyf student 022q32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 91.000 0.077 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07vyf student 01t6b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 91.000 91.000 0.077 http://example.org/education/educational_institution/students_graduates./education/education/student #5469-0d02km PRED entity: 0d02km PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #50, 0.81 #106, 0.73 #40), 02zsn (0.53 #158, 0.43 #10, 0.42 #6) >> Best rule #50 for best value: >> intensional similarity = 2 >> extensional distance = 361 >> proper extension: 024c1b; >> query: (?x5999, 05zppz) <- produced_by(?x1868, ?x5999), film(?x1914, ?x1868) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d02km gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.840 http://example.org/people/person/gender #5468-0ptdz PRED entity: 0ptdz PRED relation: film! PRED expected values: 09_gdc => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 4): 01pb34 (0.14 #8, 0.10 #3, 0.08 #33), 014kbl (0.05 #5, 0.05 #10, 0.01 #20), 01kyvx (0.03 #163, 0.02 #153, 0.02 #168), 09_gdc (0.02 #134, 0.02 #179, 0.02 #12) >> Best rule #8 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 0c00zd0; 03mh_tp; 01cz7r; 01bjbk; >> query: (?x11909, 01pb34) <- film(?x1104, ?x11909), ?x1104 = 016tw3, genre(?x11909, ?x239), ?x239 = 06cvj, film(?x2726, ?x11909), film_crew_role(?x11909, ?x137) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #134 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 680 *> proper extension: 0b85mm; *> query: (?x11909, 09_gdc) <- language(?x11909, ?x90), genre(?x11909, ?x1403), film(?x3056, ?x11909), titles(?x1403, ?x144), participant(?x6259, ?x3056) *> conf = 0.02 ranks of expected_values: 4 EVAL 0ptdz film! 09_gdc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 81.000 0.143 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #5467-05qd_ PRED entity: 05qd_ PRED relation: film PRED expected values: 0k4kk 0f40w 06wbm8q 02ryz24 07z6xs 0k54q => 137 concepts (120 used for prediction) PRED predicted values (max 10 best out of 1463): 0b1y_2 (0.74 #26757, 0.67 #4227, 0.62 #47885), 0j_t1 (0.74 #26757, 0.67 #4227, 0.62 #47885), 072192 (0.74 #26757, 0.67 #4227, 0.62 #47885), 0llcx (0.74 #26757, 0.67 #4227, 0.62 #47885), 0y_yw (0.74 #26757, 0.67 #4227, 0.62 #47885), 047d21r (0.74 #26757, 0.67 #4227, 0.62 #47885), 0d87hc (0.74 #26757, 0.67 #4227, 0.62 #47885), 0hv4t (0.74 #26757, 0.67 #4227, 0.62 #47885), 015gm8 (0.74 #26757, 0.67 #4227, 0.62 #47885), 01cssf (0.74 #26757, 0.67 #4227, 0.62 #47885) >> Best rule #26757 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0jz9f; 086k8; 017s11; 016tt2; 025jfl; 0338lq; 0g1rw; 016tw3; 030_1m; 017jv5; ... >> query: (?x902, ?x66) <- production_companies(?x66, ?x902), award_nominee(?x163, ?x902), film(?x902, ?x103) >> conf = 0.74 => this is the best rule for 31 predicted values *> Best rule #3185 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0hpt3; *> query: (?x902, 02ryz24) <- production_companies(?x66, ?x902), award_nominee(?x7094, ?x902), ?x7094 = 05mvd62 *> conf = 0.25 ranks of expected_values: 89, 248, 873, 1365 EVAL 05qd_ film 0k54q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 137.000 120.000 0.738 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 05qd_ film 07z6xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 137.000 120.000 0.738 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 05qd_ film 02ryz24 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 137.000 120.000 0.738 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 05qd_ film 06wbm8q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 120.000 0.738 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 05qd_ film 0f40w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 137.000 120.000 0.738 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 05qd_ film 0k4kk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 120.000 0.738 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5466-04tng0 PRED entity: 04tng0 PRED relation: film_release_region PRED expected values: 0f8l9c => 119 concepts (101 used for prediction) PRED predicted values (max 10 best out of 236): 09c7w0 (0.94 #1973, 0.93 #9537, 0.92 #16289), 0f8l9c (0.91 #1340, 0.90 #8900, 0.89 #1012), 0345h (0.89 #1025, 0.87 #1517, 0.86 #1353), 03gj2 (0.88 #4959, 0.86 #4466, 0.85 #5288), 06mkj (0.86 #5323, 0.86 #5652, 0.86 #4994), 035qy (0.86 #4477, 0.85 #5299, 0.84 #4970), 0chghy (0.86 #4943, 0.86 #4121, 0.85 #5601), 07ssc (0.83 #4950, 0.82 #5279, 0.82 #7581), 05r4w (0.83 #7562, 0.82 #166, 0.81 #8876), 0jgd (0.82 #1319, 0.81 #7565, 0.80 #5263) >> Best rule #1973 for best value: >> intensional similarity = 6 >> extensional distance = 33 >> proper extension: 0hv81; >> query: (?x7265, 09c7w0) <- film_release_region(?x7265, ?x205), nominated_for(?x1822, ?x7265), genre(?x7265, ?x3515), written_by(?x7265, ?x10819), genre(?x5835, ?x3515), ?x5835 = 01znj1 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #1340 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 20 *> proper extension: 0284b56; 067ghz; 0pd64; *> query: (?x7265, 0f8l9c) <- film_release_region(?x7265, ?x252), film_release_region(?x7265, ?x205), film(?x8151, ?x7265), ?x205 = 03rjj, nominated_for(?x1822, ?x7265), film_crew_role(?x7265, ?x137), nationality(?x256, ?x252) *> conf = 0.91 ranks of expected_values: 2 EVAL 04tng0 film_release_region 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 101.000 0.943 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5465-04vjh PRED entity: 04vjh PRED relation: participating_countries! PRED expected values: 0kbws => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 41): 0kbws (0.72 #795, 0.72 #467, 0.71 #836), 018ctl (0.35 #131, 0.33 #254, 0.32 #336), 0lgxj (0.34 #152, 0.28 #1260, 0.27 #768), 09x3r (0.33 #135, 0.23 #1243, 0.22 #464), 09n48 (0.31 #126, 0.30 #331, 0.30 #1234), 06sks6 (0.24 #1273, 0.23 #1315, 0.08 #353), 0sx8l (0.18 #137, 0.15 #1287, 0.15 #1245), 0blfl (0.16 #153, 0.15 #317, 0.14 #194), 016r9z (0.13 #145, 0.12 #474, 0.11 #433), 0c_tl (0.11 #147, 0.08 #393, 0.07 #311) >> Best rule #795 for best value: >> intensional similarity = 3 >> extensional distance = 165 >> proper extension: 027rn; 05r4w; 0160w; 0b90_r; 0154j; 03rjj; 03_3d; 0h3y; 0d0vqn; 0j1z8; ... >> query: (?x10451, 0kbws) <- administrative_parent(?x10451, ?x551), organization(?x10451, ?x127), country(?x1121, ?x10451) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04vjh participating_countries! 0kbws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.719 http://example.org/olympics/olympic_games/participating_countries #5464-0vkl2 PRED entity: 0vkl2 PRED relation: institution! PRED expected values: 019v9k => 173 concepts (115 used for prediction) PRED predicted values (max 10 best out of 21): 02h4rq6 (0.81 #583, 0.69 #267, 0.69 #739), 019v9k (0.78 #588, 0.69 #96, 0.67 #476), 02_xgp2 (0.58 #276, 0.56 #592, 0.54 #748), 0bkj86 (0.57 #587, 0.47 #407, 0.46 #743), 07s6fsf (0.43 #581, 0.41 #737, 0.41 #921), 027f2w (0.38 #97, 0.31 #477, 0.31 #745), 01rr_d (0.38 #104, 0.30 #1568, 0.30 #1771), 04zx3q1 (0.35 #738, 0.35 #582, 0.31 #90), 013zdg (0.31 #94, 0.30 #1568, 0.30 #1771), 03mkk4 (0.31 #99, 0.21 #591, 0.18 #747) >> Best rule #583 for best value: >> intensional similarity = 6 >> extensional distance = 66 >> proper extension: 014b4h; >> query: (?x10240, 02h4rq6) <- category(?x10240, ?x134), institution(?x4981, ?x10240), institution(?x1368, ?x10240), currency(?x10240, ?x1099), ?x1368 = 014mlp, ?x4981 = 03bwzr4 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #588 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 66 *> proper extension: 014b4h; *> query: (?x10240, 019v9k) <- category(?x10240, ?x134), institution(?x4981, ?x10240), institution(?x1368, ?x10240), currency(?x10240, ?x1099), ?x1368 = 014mlp, ?x4981 = 03bwzr4 *> conf = 0.78 ranks of expected_values: 2 EVAL 0vkl2 institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 173.000 115.000 0.809 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5463-0prjs PRED entity: 0prjs PRED relation: film PRED expected values: 062zjtt => 99 concepts (64 used for prediction) PRED predicted values (max 10 best out of 30): 01kff7 (0.15 #42263, 0.09 #49724, 0.09 #42262), 0dgst_d (0.09 #49724, 0.09 #42262, 0.09 #41433), 02q_x_l (0.09 #49724, 0.09 #42262, 0.09 #41432), 0gxtknx (0.07 #1780, 0.07 #952, 0.06 #2608), 02pjc1h (0.07 #1760, 0.07 #932, 0.06 #2588), 03xf_m (0.07 #2206, 0.07 #1378, 0.05 #3863), 04ltlj (0.03 #17396, 0.02 #11598, 0.02 #30656), 01qz5 (0.03 #17396, 0.02 #11598, 0.02 #30656), 03ydlnj (0.03 #17396, 0.02 #11598, 0.02 #30656), 02qhlwd (0.03 #17396, 0.02 #11598, 0.02 #30656) >> Best rule #42263 for best value: >> intensional similarity = 3 >> extensional distance = 1299 >> proper extension: 0fvppk; >> query: (?x1371, ?x1372) <- nominated_for(?x1371, ?x1372), genre(?x1372, ?x225), film(?x9681, ?x1372) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0prjs film 062zjtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 64.000 0.153 http://example.org/film/director/film #5462-018kcp PRED entity: 018kcp PRED relation: program PRED expected values: 01_2n => 34 concepts (28 used for prediction) PRED predicted values (max 10 best out of 247): 01hn_t (0.58 #4246, 0.58 #3262, 0.44 #2522), 027tbrc (0.50 #1509, 0.33 #1754, 0.25 #1999), 026bfsh (0.47 #6165, 0.29 #6416), 05631 (0.40 #727, 0.25 #2450, 0.18 #3190), 01_2n (0.33 #174, 0.25 #419, 0.20 #911), 03bww6 (0.33 #1099, 0.20 #2823, 0.17 #3562), 08cx5g (0.33 #3748, 0.12 #5727, 0.11 #2516), 0cpz4k (0.29 #6416, 0.20 #789, 0.17 #1774), 043qqt5 (0.26 #6123, 0.20 #5132, 0.20 #4884), 04glx0 (0.25 #4043, 0.25 #2318, 0.25 #2071) >> Best rule #4246 for best value: >> intensional similarity = 16 >> extensional distance = 10 >> proper extension: 0g4c1t; 013fn; 09bv45; >> query: (?x14550, 01hn_t) <- program(?x14550, ?x7433), genre(?x7433, ?x5728), actor(?x7433, ?x9639), actor(?x7433, ?x2925), award_winner(?x2926, ?x2925), profession(?x2925, ?x1032), profession(?x2925, ?x220), group(?x2925, ?x10740), artists(?x671, ?x2925), artist(?x1124, ?x2925), gender(?x2925, ?x514), artist(?x5744, ?x9639), ?x220 = 016z4k, ?x1032 = 02hrh1q, currency(?x9639, ?x170), languages(?x7433, ?x254) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #174 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 02kx91; *> query: (?x14550, 01_2n) <- program(?x14550, ?x7433), category(?x14550, ?x134), ?x7433 = 03gvm3t, ?x134 = 08mbj5d *> conf = 0.33 ranks of expected_values: 5 EVAL 018kcp program 01_2n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 34.000 28.000 0.583 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #5461-015wfg PRED entity: 015wfg PRED relation: profession PRED expected values: 02hrh1q => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 85): 02hrh1q (0.89 #11867, 0.89 #10817, 0.89 #6766), 01d_h8 (0.40 #1506, 0.39 #4506, 0.36 #10208), 09jwl (0.38 #8121, 0.37 #8571, 0.37 #9471), 0dxtg (0.34 #7515, 0.31 #10216, 0.30 #12616), 03gjzk (0.33 #7367, 0.31 #7517, 0.24 #4516), 0nbcg (0.33 #783, 0.33 #1383, 0.32 #933), 026sdt1 (0.29 #70, 0.05 #2170, 0.05 #2620), 02jknp (0.28 #1508, 0.25 #10210, 0.24 #12310), 01c72t (0.26 #1075, 0.26 #925, 0.20 #1375), 016z4k (0.26 #8555, 0.25 #8105, 0.24 #10356) >> Best rule #11867 for best value: >> intensional similarity = 4 >> extensional distance = 1118 >> proper extension: 016qtt; 05cj4r; 0436f4; 03f2_rc; 01gvr1; 03qd_; 05ml_s; 04bd8y; 02lk1s; 03gm48; ... >> query: (?x4370, 02hrh1q) <- nominated_for(?x4370, ?x802), award(?x4370, ?x591), film(?x4370, ?x7246), award(?x7246, ?x1587) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015wfg profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.892 http://example.org/people/person/profession #5460-0g_rs_ PRED entity: 0g_rs_ PRED relation: people! PRED expected values: 018s6c => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 36): 041rx (0.42 #4, 0.22 #698, 0.19 #1622), 018s6c (0.25 #66, 0.03 #221, 0.03 #298), 0d7wh (0.12 #95, 0.06 #249, 0.05 #326), 0x67 (0.10 #3245, 0.09 #3707, 0.09 #3476), 02w7gg (0.09 #542, 0.08 #850, 0.08 #927), 0xnvg (0.08 #245, 0.08 #322, 0.07 #168), 048z7l (0.07 #195, 0.06 #272, 0.05 #349), 0222qb (0.07 #199, 0.06 #276, 0.05 #353), 06v41q (0.07 #184, 0.06 #261, 0.05 #338), 01qhm_ (0.06 #84, 0.05 #392, 0.03 #238) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 070m12; >> query: (?x14126, 041rx) <- nationality(?x14126, ?x4743), gender(?x14126, ?x231), ?x4743 = 03spz >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #66 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 070m12; *> query: (?x14126, 018s6c) <- nationality(?x14126, ?x4743), gender(?x14126, ?x231), ?x4743 = 03spz *> conf = 0.25 ranks of expected_values: 2 EVAL 0g_rs_ people! 018s6c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.417 http://example.org/people/ethnicity/people #5459-05r3qc PRED entity: 05r3qc PRED relation: film! PRED expected values: 01kwsg => 94 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1196): 05cl2w (0.40 #1499, 0.33 #3577), 0dzf_ (0.20 #809, 0.17 #2887, 0.11 #4965), 0zcbl (0.20 #1219, 0.17 #3297, 0.11 #5375), 03qd_ (0.20 #123, 0.17 #2201, 0.11 #4279), 04xhwn (0.20 #1987, 0.17 #4065, 0.07 #10300), 01mqnr (0.20 #1432, 0.17 #3510, 0.07 #7666), 0c3p7 (0.20 #1116, 0.17 #3194, 0.07 #9429), 0170s4 (0.20 #398, 0.17 #2476, 0.05 #27414), 02mjf2 (0.20 #774, 0.17 #2852, 0.05 #13243), 06jz0 (0.20 #1759, 0.17 #3837, 0.05 #14228) >> Best rule #1499 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 03mh94; 02_1sj; 01633c; >> query: (?x6167, 05cl2w) <- film_crew_role(?x6167, ?x2091), genre(?x6167, ?x53), film(?x2942, ?x6167), ?x2942 = 046lt, film_crew_role(?x10918, ?x2091), ?x10918 = 09rvwmy >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #11228 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 16 *> proper extension: 0p9tm; *> query: (?x6167, 01kwsg) <- film_crew_role(?x6167, ?x137), genre(?x6167, ?x5231), genre(?x6167, ?x225), film(?x1365, ?x6167), ?x225 = 02kdv5l, ?x5231 = 0556j8 *> conf = 0.11 ranks of expected_values: 74 EVAL 05r3qc film! 01kwsg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 94.000 44.000 0.400 http://example.org/film/actor/film./film/performance/film #5458-0ddcbd5 PRED entity: 0ddcbd5 PRED relation: film! PRED expected values: 016tw3 => 67 concepts (61 used for prediction) PRED predicted values (max 10 best out of 80): 03sb38 (0.58 #76, 0.44 #3563, 0.44 #3337), 02j_j0 (0.58 #76, 0.44 #3563, 0.44 #3337), 054lpb6 (0.58 #76, 0.44 #3563, 0.44 #3337), 016tw3 (0.33 #11, 0.23 #87, 0.22 #237), 0g1rw (0.22 #8, 0.17 #84, 0.16 #234), 017jv5 (0.22 #241, 0.20 #91, 0.04 #1529), 086k8 (0.21 #680, 0.18 #1365, 0.17 #1591), 07k2x (0.20 #418, 0.18 #493, 0.17 #568), 017s11 (0.17 #3, 0.15 #681, 0.14 #229), 0jz9f (0.15 #602, 0.11 #1, 0.07 #909) >> Best rule #76 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 08gsvw; 01lbcqx; >> query: (?x4048, ?x1478) <- country(?x4048, ?x252), genre(?x4048, ?x5104), film_release_distribution_medium(?x4048, ?x81), film(?x820, ?x4048), ?x5104 = 0bkbm, production_companies(?x4048, ?x1478) >> conf = 0.58 => this is the best rule for 3 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 16 *> proper extension: 08gsvw; 01lbcqx; *> query: (?x4048, 016tw3) <- country(?x4048, ?x252), genre(?x4048, ?x5104), film_release_distribution_medium(?x4048, ?x81), film(?x820, ?x4048), ?x5104 = 0bkbm, production_companies(?x4048, ?x1478) *> conf = 0.33 ranks of expected_values: 4 EVAL 0ddcbd5 film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 67.000 61.000 0.577 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5457-04v8h1 PRED entity: 04v8h1 PRED relation: language PRED expected values: 02h40lc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 43): 02h40lc (0.97 #2716, 0.97 #2597, 0.95 #2950), 04306rv (0.20 #121, 0.17 #5, 0.13 #239), 064_8sq (0.18 #490, 0.17 #372, 0.17 #431), 03x42 (0.17 #49, 0.05 #2714, 0.01 #459), 06b_j (0.13 #197, 0.07 #491, 0.06 #80), 02bjrlw (0.09 #176, 0.08 #293, 0.07 #470), 04h9h (0.06 #100, 0.05 #2714, 0.04 #276), 0653m (0.06 #69, 0.05 #2714, 0.04 #127), 0880p (0.06 #103, 0.05 #2714), 03hkp (0.06 #130, 0.05 #2714, 0.02 #306) >> Best rule #2716 for best value: >> intensional similarity = 4 >> extensional distance = 1241 >> proper extension: 0192hw; 0h95zbp; 0hz6mv2; 0bx_hnp; 05f67hw; >> query: (?x4648, 02h40lc) <- country(?x4648, ?x94), ?x94 = 09c7w0, language(?x4648, ?x2502), service_language(?x234, ?x2502) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04v8h1 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.973 http://example.org/film/film/language #5456-029qzx PRED entity: 029qzx PRED relation: student PRED expected values: 01yg9y => 136 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1436): 06pwf6 (0.25 #2556, 0.10 #6742, 0.06 #4649), 053y4h (0.20 #890, 0.02 #73262), 017149 (0.20 #67, 0.02 #81636, 0.01 #16811), 01963w (0.20 #204, 0.02 #81636, 0.01 #19042), 047c9l (0.20 #886, 0.02 #38562, 0.01 #21818), 016kkx (0.20 #1149, 0.01 #17893), 03bggl (0.20 #1874), 05b__vr (0.20 #106), 03h40_7 (0.12 #3907, 0.09 #12279, 0.06 #14372), 01hbq0 (0.12 #4152, 0.06 #6245, 0.05 #10431) >> Best rule #2556 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 07szy; >> query: (?x10824, 06pwf6) <- organization(?x346, ?x10824), currency(?x10824, ?x170), time_zones(?x10824, ?x1638), school(?x7399, ?x10824) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 029qzx student 01yg9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 60.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #5455-090gpr PRED entity: 090gpr PRED relation: profession PRED expected values: 03gjzk => 86 concepts (44 used for prediction) PRED predicted values (max 10 best out of 56): 03gjzk (0.86 #4607, 0.62 #1793, 0.61 #2089), 0dxtg (0.57 #1792, 0.54 #2088, 0.53 #4606), 01d_h8 (0.53 #5932, 0.47 #1043, 0.47 #4598), 02jknp (0.52 #1786, 0.52 #2082, 0.36 #5934), 0d1pc (0.20 #50, 0.17 #1680, 0.16 #790), 0fj9f (0.20 #202, 0.13 #350, 0.10 #54), 018gz8 (0.19 #4609, 0.16 #2091, 0.15 #1795), 0cbd2 (0.18 #2229, 0.16 #5636, 0.15 #2969), 09jwl (0.16 #6241, 0.14 #6389, 0.14 #3870), 0np9r (0.15 #2095, 0.14 #465, 0.14 #5947) >> Best rule #4607 for best value: >> intensional similarity = 7 >> extensional distance = 730 >> proper extension: 0f1vrl; >> query: (?x13519, 03gjzk) <- profession(?x13519, ?x1943), profession(?x9164, ?x1943), profession(?x4553, ?x1943), profession(?x2389, ?x1943), ?x9164 = 030vmc, ?x4553 = 01vyv9, ?x2389 = 0bgrsl >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 090gpr profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 44.000 0.861 http://example.org/people/person/profession #5454-03cxsvl PRED entity: 03cxsvl PRED relation: film PRED expected values: 0h1fktn => 120 concepts (56 used for prediction) PRED predicted values (max 10 best out of 401): 0h1fktn (0.57 #4556, 0.46 #2763, 0.30 #971), 05f4vxd (0.47 #96800, 0.35 #71704, 0.35 #60947), 02825cv (0.10 #1144, 0.08 #2936, 0.07 #4729), 06fpsx (0.10 #1340, 0.08 #3132, 0.07 #4925), 0888c3 (0.10 #1417, 0.08 #3209, 0.07 #5002), 0cc97st (0.10 #989, 0.08 #2781, 0.07 #4574), 02ht1k (0.10 #631, 0.08 #2423, 0.07 #4216), 0fphf3v (0.10 #1364, 0.08 #3156, 0.07 #4949), 05znxx (0.10 #880, 0.08 #2672, 0.07 #4465), 065_cjc (0.10 #1198, 0.08 #2990, 0.07 #4783) >> Best rule #4556 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 064nh4k; 07sgfvl; 0806vbn; 0783m_; 080knyg; 07sgfsl; 077yk0; 0fn8jc; 02qw2xb; 07k51gd; ... >> query: (?x5571, 0h1fktn) <- award_winner(?x1902, ?x5571), actor(?x5060, ?x5571), ?x1902 = 07s6prs >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cxsvl film 0h1fktn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 56.000 0.571 http://example.org/film/actor/film./film/performance/film #5453-024t0y PRED entity: 024t0y PRED relation: produced_by! PRED expected values: 03nqnnk => 165 concepts (162 used for prediction) PRED predicted values (max 10 best out of 623): 05ch98 (0.10 #6412, 0.06 #27437, 0.06 #29331), 0h03fhx (0.10 #6100, 0.06 #15561, 0.05 #8939), 0dqcs3 (0.10 #6123, 0.05 #8962, 0.04 #9908), 04ghz4m (0.10 #6341, 0.05 #9180, 0.04 #10126), 06g77c (0.10 #5898, 0.04 #9683, 0.03 #15359), 0gg5qcw (0.10 #6153, 0.03 #15614, 0.03 #19398), 07tlfx (0.10 #6537, 0.03 #19782, 0.02 #21675), 04vr_f (0.10 #5780, 0.03 #19025, 0.02 #20918), 0c0nhgv (0.10 #5781, 0.02 #20919, 0.01 #53103), 01vw8k (0.10 #6025, 0.02 #21163, 0.01 #35362) >> Best rule #6412 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 012d40; 01t2h2; 0c6qh; 026dx; >> query: (?x12254, 05ch98) <- type_of_union(?x12254, ?x566), executive_produced_by(?x3133, ?x12254), profession(?x12254, ?x1032), ?x1032 = 02hrh1q, location_of_ceremony(?x12254, ?x1957) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 024t0y produced_by! 03nqnnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 162.000 0.100 http://example.org/film/film/produced_by #5452-016jfw PRED entity: 016jfw PRED relation: award PRED expected values: 054krc => 91 concepts (89 used for prediction) PRED predicted values (max 10 best out of 287): 01by1l (0.37 #4142, 0.36 #112, 0.36 #515), 026mfs (0.35 #2144, 0.21 #935, 0.20 #5368), 01bgqh (0.31 #4073, 0.28 #849, 0.28 #446), 03qbh5 (0.28 #1010, 0.23 #204, 0.22 #607), 01d38g (0.28 #431, 0.09 #11312, 0.09 #4058), 03qbnj (0.25 #232, 0.17 #4262, 0.14 #635), 09sb52 (0.22 #16565, 0.21 #14549, 0.20 #21403), 01c92g (0.20 #97, 0.19 #903, 0.17 #4127), 01ck6h (0.20 #122, 0.13 #3346, 0.13 #2540), 01ckrr (0.19 #1036, 0.09 #7081, 0.09 #2648) >> Best rule #4142 for best value: >> intensional similarity = 3 >> extensional distance = 181 >> proper extension: 02r3zy; 07c0j; 03g5jw; 03fbc; 018ndc; 04qmr; 0kr_t; 0dw4g; 03d9d6; 07bzp; ... >> query: (?x6129, 01by1l) <- artists(?x1572, ?x6129), award_nominee(?x538, ?x6129), ?x1572 = 06by7 >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #11371 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 523 *> proper extension: 04rcr; 0dvqq; 014hr0; 0249kn; 0hvbj; 09bx1k; 01w5n51; 06mj4; 016lmg; 01jkqfz; ... *> query: (?x6129, 054krc) <- artists(?x671, ?x6129), award_nominee(?x538, ?x6129) *> conf = 0.08 ranks of expected_values: 69 EVAL 016jfw award 054krc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 91.000 89.000 0.372 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5451-02yvct PRED entity: 02yvct PRED relation: film! PRED expected values: 016tw3 => 96 concepts (80 used for prediction) PRED predicted values (max 10 best out of 65): 016tw3 (0.96 #1037, 0.19 #84, 0.18 #230), 017s11 (0.27 #295, 0.26 #76, 0.21 #588), 086k8 (0.23 #2, 0.17 #1843, 0.17 #1101), 03xq0f (0.21 #370, 0.15 #811, 0.13 #884), 05qd_ (0.19 #448, 0.19 #82, 0.18 #301), 016tt2 (0.16 #223, 0.16 #810, 0.12 #735), 01795t (0.15 #457, 0.15 #164, 0.14 #530), 017jv5 (0.13 #234, 0.10 #968, 0.09 #1114), 01gb54 (0.12 #759, 0.06 #2163, 0.06 #1351), 07k2x (0.12 #699, 0.09 #626, 0.08 #480) >> Best rule #1037 for best value: >> intensional similarity = 3 >> extensional distance = 213 >> proper extension: 02qr3k8; 01jnc_; >> query: (?x2189, 016tw3) <- film(?x10958, ?x2189), film(?x10958, ?x1452), ?x1452 = 0jqn5 >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02yvct film! 016tw3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 80.000 0.958 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5450-06v36 PRED entity: 06v36 PRED relation: nationality! PRED expected values: 01sp81 => 154 concepts (119 used for prediction) PRED predicted values (max 10 best out of 4051): 0466k4 (0.20 #65095, 0.20 #36617, 0.18 #28480), 0784v1 (0.17 #507, 0.12 #4576, 0.04 #16781), 016ypb (0.17 #829, 0.12 #4898, 0.03 #29309), 059xvg (0.14 #17326, 0.11 #37669, 0.10 #58010), 06b_0 (0.14 #10525, 0.06 #43072, 0.05 #59345), 0dzkq (0.12 #5048, 0.07 #9117, 0.06 #41664), 0w6w (0.12 #8092, 0.04 #20297, 0.03 #24366), 08h79x (0.12 #6350, 0.04 #18555, 0.03 #22624), 0p__8 (0.11 #18123, 0.09 #34397, 0.09 #38466), 07m69t (0.11 #18980, 0.09 #39323, 0.08 #59664) >> Best rule #65095 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 034tl; >> query: (?x6437, ?x12279) <- participating_countries(?x1931, ?x6437), form_of_government(?x6437, ?x1926), country(?x1121, ?x6437), location(?x12279, ?x6437) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #16496 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 052p7; 04sqj; *> query: (?x6437, 01sp81) <- contains(?x2467, ?x6437), location(?x12279, ?x6437), adjoins(?x792, ?x6437), locations(?x11802, ?x6437) *> conf = 0.04 ranks of expected_values: 3805 EVAL 06v36 nationality! 01sp81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 119.000 0.202 http://example.org/people/person/nationality #5449-0cwy47 PRED entity: 0cwy47 PRED relation: honored_for! PRED expected values: 0fk0xk => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 118): 0dznvw (0.25 #118, 0.14 #240, 0.11 #484), 0fy59t (0.25 #101, 0.05 #1077, 0.03 #1565), 0bz6l9 (0.18 #285, 0.09 #7443, 0.06 #407), 0ftlxj (0.14 #181, 0.06 #425, 0.03 #669), 09gkdln (0.11 #472, 0.05 #960, 0.03 #2058), 0fy6bh (0.09 #282, 0.09 #7443, 0.05 #1014), 0c4hnm (0.09 #357, 0.03 #1577, 0.03 #1699), 0bz6sb (0.09 #297, 0.02 #907, 0.02 #1029), 0fv89q (0.09 #351, 0.02 #1083, 0.01 #1571), 0d__c3 (0.09 #7443, 0.05 #963, 0.05 #1085) >> Best rule #118 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0gxfz; >> query: (?x951, 0dznvw) <- film_release_region(?x951, ?x142), nominated_for(?x484, ?x951), nominated_for(?x8225, ?x951), ?x8225 = 027vps >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #7443 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 932 *> proper extension: 0n2bh; 03y3bp7; 08cx5g; 025x1t; 03cf9ly; 03czz87; *> query: (?x951, ?x5723) <- titles(?x162, ?x951), nominated_for(?x8401, ?x951), award_nominee(?x199, ?x8401), award_winner(?x5723, ?x8401) *> conf = 0.09 ranks of expected_values: 12 EVAL 0cwy47 honored_for! 0fk0xk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 103.000 103.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #5448-03l3ln PRED entity: 03l3ln PRED relation: religion PRED expected values: 03j6c => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 34): 0kpl (0.20 #10, 0.16 #190, 0.11 #506), 019cr (0.18 #146, 0.10 #281, 0.07 #372), 0c8wxp (0.17 #1042, 0.17 #952, 0.16 #997), 0631_ (0.14 #278, 0.11 #143, 0.11 #369), 0v53x (0.11 #164, 0.06 #299, 0.06 #209), 05sfs (0.11 #138, 0.06 #273, 0.04 #183), 03_gx (0.10 #194, 0.08 #1366, 0.08 #1998), 0kq2 (0.10 #198, 0.04 #514, 0.04 #829), 051kv (0.08 #275, 0.07 #366, 0.04 #185), 02rsw (0.08 #294, 0.07 #159, 0.05 #385) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02wcx8c; 013pk3; >> query: (?x6677, 0kpl) <- nominated_for(?x6677, ?x6439), award_nominee(?x6677, ?x11782), ?x11782 = 067sqt >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #382 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 93 *> proper extension: 099bk; *> query: (?x6677, 03j6c) <- politician(?x8714, ?x6677), gender(?x6677, ?x231) *> conf = 0.07 ranks of expected_values: 11 EVAL 03l3ln religion 03j6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 93.000 93.000 0.200 http://example.org/people/person/religion #5447-0162v PRED entity: 0162v PRED relation: vacationer PRED expected values: 04xrx => 127 concepts (55 used for prediction) PRED predicted values (max 10 best out of 218): 05r5w (0.27 #774, 0.23 #2172, 0.13 #2870), 016fnb (0.27 #803, 0.16 #4644, 0.15 #5341), 0bksh (0.23 #2204, 0.20 #806, 0.13 #2902), 0lk90 (0.23 #2118, 0.13 #720, 0.11 #4561), 0261x8t (0.21 #489, 0.20 #1189, 0.20 #1014), 09yrh (0.20 #800, 0.14 #2198, 0.13 #2896), 01vw20_ (0.20 #762, 0.14 #2160, 0.10 #2858), 02mjf2 (0.18 #2197, 0.13 #2895, 0.13 #799), 026c1 (0.18 #2135, 0.13 #737, 0.10 #2833), 01xyt7 (0.18 #2223, 0.13 #825, 0.09 #4316) >> Best rule #774 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 01x73; 0f25y; 0d1y7; >> query: (?x1957, 05r5w) <- location_of_ceremony(?x12254, ?x1957), contains(?x8882, ?x1957), executive_produced_by(?x3133, ?x12254) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #754 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 01x73; 0f25y; 0d1y7; *> query: (?x1957, 04xrx) <- location_of_ceremony(?x12254, ?x1957), contains(?x8882, ?x1957), executive_produced_by(?x3133, ?x12254) *> conf = 0.13 ranks of expected_values: 27 EVAL 0162v vacationer 04xrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 127.000 55.000 0.267 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #5446-0h1cdwq PRED entity: 0h1cdwq PRED relation: genre PRED expected values: 04rlf => 73 concepts (72 used for prediction) PRED predicted values (max 10 best out of 117): 03k9fj (0.70 #132, 0.51 #1572, 0.41 #252), 07s9rl0 (0.68 #5536, 0.57 #6977, 0.56 #2761), 02kdv5l (0.59 #963, 0.41 #483, 0.36 #1083), 01jfsb (0.51 #973, 0.37 #373, 0.36 #1333), 01zhp (0.35 #197, 0.11 #437, 0.10 #1277), 02l7c8 (0.33 #3018, 0.31 #3620, 0.28 #5552), 06n90 (0.32 #494, 0.27 #614, 0.26 #1094), 082gq (0.21 #3633, 0.09 #5565, 0.08 #7006), 0lsxr (0.21 #969, 0.20 #9, 0.18 #1929), 06cvj (0.20 #3005, 0.20 #4, 0.16 #3607) >> Best rule #132 for best value: >> intensional similarity = 7 >> extensional distance = 21 >> proper extension: 04svwx; >> query: (?x428, 03k9fj) <- genre(?x428, ?x2540), genre(?x428, ?x1510), genre(?x428, ?x258), ?x258 = 05p553, ?x1510 = 01hmnh, country(?x428, ?x94), ?x2540 = 0hcr >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #185 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 21 *> proper extension: 04svwx; *> query: (?x428, 04rlf) <- genre(?x428, ?x2540), genre(?x428, ?x1510), genre(?x428, ?x258), ?x258 = 05p553, ?x1510 = 01hmnh, country(?x428, ?x94), ?x2540 = 0hcr *> conf = 0.04 ranks of expected_values: 41 EVAL 0h1cdwq genre 04rlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 73.000 72.000 0.696 http://example.org/film/film/genre #5445-03b1sb PRED entity: 03b1sb PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.84 #66, 0.83 #21, 0.81 #116), 07c52 (0.10 #18, 0.09 #33, 0.09 #58), 02nxhr (0.10 #17, 0.04 #42, 0.04 #52), 07z4p (0.08 #55, 0.06 #60, 0.06 #40) >> Best rule #66 for best value: >> intensional similarity = 4 >> extensional distance = 285 >> proper extension: 0gx9rvq; 026p_bs; 035xwd; 03twd6; 05p3738; 03sxd2; 035s95; 06v9_x; 04g9gd; 03kg2v; ... >> query: (?x8890, 029j_) <- genre(?x8890, ?x5722), genre(?x8890, ?x604), genre(?x12739, ?x5722), ?x604 = 0lsxr >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03b1sb film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.836 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #5444-014cw2 PRED entity: 014cw2 PRED relation: role PRED expected values: 0l15bq 018j2 => 117 concepts (47 used for prediction) PRED predicted values (max 10 best out of 120): 05r5c (0.57 #7, 0.50 #304, 0.48 #205), 05842k (0.50 #76, 0.33 #373, 0.30 #274), 01vj9c (0.43 #13, 0.40 #410, 0.36 #310), 026t6 (0.36 #3, 0.33 #300, 0.33 #400), 013y1f (0.36 #35, 0.29 #134, 0.28 #332), 01vdm0 (0.33 #427, 0.29 #30, 0.28 #3808), 0l14qv (0.31 #302, 0.29 #5, 0.22 #203), 018j2 (0.29 #44, 0.19 #341, 0.19 #143), 07brj (0.21 #24, 0.17 #222, 0.16 #497), 03qjg (0.21 #61, 0.16 #497, 0.14 #358) >> Best rule #7 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 023l9y; >> query: (?x13413, 05r5c) <- profession(?x13413, ?x220), role(?x13413, ?x4917), role(?x13413, ?x432), artists(?x302, ?x13413), ?x4917 = 06w7v, ?x432 = 042v_gx >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #44 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 023l9y; *> query: (?x13413, 018j2) <- profession(?x13413, ?x220), role(?x13413, ?x4917), role(?x13413, ?x432), artists(?x302, ?x13413), ?x4917 = 06w7v, ?x432 = 042v_gx *> conf = 0.29 ranks of expected_values: 8, 11 EVAL 014cw2 role 018j2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 117.000 47.000 0.571 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 014cw2 role 0l15bq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 117.000 47.000 0.571 http://example.org/music/artist/track_contributions./music/track_contribution/role #5443-0f1sm PRED entity: 0f1sm PRED relation: location! PRED expected values: 014635 => 171 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1884): 014635 (0.33 #770, 0.08 #158580, 0.05 #30975), 0738b8 (0.33 #444, 0.08 #158580, 0.05 #10512), 02l6dy (0.33 #1227, 0.08 #158580, 0.03 #3744), 021yw7 (0.33 #708, 0.08 #158580, 0.03 #5742), 025b5y (0.33 #1148, 0.08 #158580, 0.02 #61557), 01wz01 (0.33 #815, 0.03 #33537, 0.02 #71292), 0320jz (0.33 #332, 0.03 #7883, 0.02 #83396), 0d__g (0.33 #2203, 0.02 #12271, 0.02 #14788), 0hnjt (0.33 #962, 0.02 #11030, 0.02 #13547), 03_l8m (0.33 #1038, 0.02 #16140, 0.02 #33760) >> Best rule #770 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01x73; >> query: (?x9445, 014635) <- contains(?x9445, ?x7271), category(?x9445, ?x134), ?x7271 = 02j04_ >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f1sm location! 014635 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 117.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #5442-06cm5 PRED entity: 06cm5 PRED relation: award PRED expected values: 027c95y => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 194): 02z0dfh (0.32 #918, 0.32 #746, 0.26 #2066), 0gq9h (0.28 #518, 0.26 #2066, 0.25 #917), 0gs9p (0.27 #520, 0.26 #2066, 0.25 #917), 0p9sw (0.26 #2066, 0.25 #917, 0.25 #1835), 0gqyl (0.26 #2066, 0.25 #917, 0.25 #1835), 019f4v (0.26 #2066, 0.25 #917, 0.25 #1835), 0gr0m (0.26 #2066, 0.25 #917, 0.25 #1835), 04dn09n (0.26 #2066, 0.25 #917, 0.25 #1835), 02rdyk7 (0.26 #2066, 0.25 #917, 0.25 #1835), 04kxsb (0.26 #2066, 0.25 #917, 0.25 #1835) >> Best rule #918 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 0m313; 0ds3t5x; 0dsvzh; 0fh694; 092vkg; 0pv3x; 03m4mj; 0p_th; 0jym0; 01hqhm; ... >> query: (?x6137, ?x1254) <- nominated_for(?x1254, ?x6137), titles(?x53, ?x6137), ?x1254 = 02z0dfh >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1949 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 257 *> proper extension: 0gzy02; 0bth54; 0jzw; 09q5w2; 04vr_f; 0c0nhgv; 0sxfd; 035yn8; 0ch26b_; 03hj3b3; ... *> query: (?x6137, 027c95y) <- nominated_for(?x1307, ?x6137), ?x1307 = 0gq9h, award(?x6137, ?x591) *> conf = 0.12 ranks of expected_values: 25 EVAL 06cm5 award 027c95y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 63.000 63.000 0.323 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #5441-0qpn9 PRED entity: 0qpn9 PRED relation: contains PRED expected values: 0qpqn => 158 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1846): 0trv (0.70 #94273, 0.68 #212123, 0.68 #197392), 07wlf (0.33 #327, 0.11 #12107, 0.03 #15053), 0qpqn (0.25 #7261, 0.25 #4316, 0.14 #10206), 0288zy (0.14 #8908, 0.03 #106134, 0.03 #17744), 02z6fs (0.14 #10221, 0.02 #27895, 0.02 #39679), 02kxx1 (0.14 #10826, 0.02 #40284, 0.02 #46178), 03gdf1 (0.14 #10799, 0.02 #40257, 0.02 #46151), 031vy_ (0.14 #9891, 0.02 #39349, 0.02 #45243), 01s7pm (0.11 #13774, 0.03 #16720, 0.03 #108055), 0l2tk (0.11 #12123, 0.02 #23905, 0.02 #38636) >> Best rule #94273 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 013kcv; 03v_5; 099ty; 0mp3l; 029cr; 0ftvz; 01qh7; 019k6n; 0ply0; 0tbql; ... >> query: (?x7408, ?x8706) <- location(?x8863, ?x7408), citytown(?x8706, ?x7408), school(?x580, ?x8706) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #7261 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0qplq; *> query: (?x7408, 0qpqn) <- location(?x8863, ?x7408), citytown(?x8706, ?x7408), ?x8706 = 0trv, category(?x7408, ?x134) *> conf = 0.25 ranks of expected_values: 3 EVAL 0qpn9 contains 0qpqn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 158.000 106.000 0.703 http://example.org/location/location/contains #5440-0pyg6 PRED entity: 0pyg6 PRED relation: award PRED expected values: 0cqhk0 => 106 concepts (104 used for prediction) PRED predicted values (max 10 best out of 278): 01by1l (0.53 #111, 0.48 #905, 0.42 #2096), 054ks3 (0.41 #141, 0.27 #935, 0.18 #2920), 01c92g (0.41 #96, 0.24 #890, 0.15 #2081), 0c4z8 (0.35 #71, 0.33 #865, 0.21 #7614), 03qbh5 (0.33 #997, 0.24 #203, 0.22 #9731), 09sb52 (0.33 #22273, 0.32 #21479, 0.30 #9966), 01ckcd (0.32 #2315, 0.18 #6682, 0.12 #1124), 0ck27z (0.32 #14780, 0.31 #15177, 0.28 #11604), 0fbtbt (0.30 #4993, 0.26 #5787, 0.11 #11742), 0cjyzs (0.28 #4869, 0.25 #5663, 0.14 #11618) >> Best rule #111 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 03f0vvr; >> query: (?x2194, 01by1l) <- award(?x2194, ?x3647), ?x3647 = 01c9jp, nationality(?x2194, ?x94) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #15123 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 565 *> proper extension: 07sgfsl; *> query: (?x2194, 0cqhk0) <- award_nominee(?x3522, ?x2194), profession(?x2194, ?x131), actor(?x10731, ?x2194) *> conf = 0.18 ranks of expected_values: 26 EVAL 0pyg6 award 0cqhk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 106.000 104.000 0.529 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5439-04qftx PRED entity: 04qftx PRED relation: artists PRED expected values: 02t3ln 01pny5 => 58 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1285): 04r1t (0.67 #1222, 0.57 #2308, 0.50 #3391), 01vw20_ (0.67 #1333, 0.50 #248, 0.43 #2419), 0zjpz (0.67 #1228, 0.50 #143, 0.43 #2314), 0qf11 (0.57 #2554, 0.54 #6896, 0.50 #3637), 01pny5 (0.57 #3231, 0.50 #4314, 0.50 #2145), 067mj (0.57 #2271, 0.50 #3354, 0.50 #100), 01l_w0 (0.57 #2963, 0.50 #4046, 0.50 #792), 05563d (0.57 #2482, 0.50 #3565, 0.50 #311), 0p76z (0.57 #3091, 0.50 #4174, 0.50 #920), 01vs4ff (0.57 #2807, 0.50 #3890, 0.50 #636) >> Best rule #1222 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0mhfr; 0155w; >> query: (?x12178, 04r1t) <- artists(?x12178, ?x10670), artists(?x12178, ?x4029), ?x4029 = 01c8v0, artist(?x2931, ?x10670), influenced_by(?x10670, ?x115), influenced_by(?x5547, ?x10670), award(?x5547, ?x2877), ?x2877 = 02f5qb >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3231 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 0xhtw; 0dl5d; 016zgj; *> query: (?x12178, 01pny5) <- artists(?x12178, ?x9638), artists(?x12178, ?x4029), instrumentalists(?x227, ?x4029), student(?x11963, ?x4029), ?x9638 = 017959, award_winner(?x341, ?x4029), role(?x4029, ?x645) *> conf = 0.57 ranks of expected_values: 5, 439 EVAL 04qftx artists 01pny5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 58.000 19.000 0.667 http://example.org/music/genre/artists EVAL 04qftx artists 02t3ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 58.000 19.000 0.667 http://example.org/music/genre/artists #5438-07k5l PRED entity: 07k5l PRED relation: politician PRED expected values: 01w03jv => 124 concepts (83 used for prediction) PRED predicted values (max 10 best out of 64): 0bwh6 (0.10 #314, 0.08 #416, 0.08 #520), 0d06m5 (0.10 #326, 0.08 #428, 0.08 #532), 0203v (0.10 #316, 0.08 #418, 0.08 #522), 0sx5w (0.07 #83, 0.05 #288, 0.05 #185), 0gzh (0.05 #410, 0.04 #512, 0.04 #616), 08959 (0.05 #409, 0.04 #511, 0.04 #615), 042fk (0.05 #408, 0.04 #510, 0.04 #614), 01mvpv (0.05 #407, 0.04 #509, 0.04 #613), 01s7z0 (0.05 #406, 0.04 #508, 0.04 #612), 02yy8 (0.05 #405, 0.04 #507, 0.04 #611) >> Best rule #314 for best value: >> intensional similarity = 2 >> extensional distance = 18 >> proper extension: 07wbk; 0d075m; 01kcmr; 0c0sl; 01z_jj; >> query: (?x13630, 0bwh6) <- citytown(?x13630, ?x108), ?x108 = 0rh6k >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07k5l politician 01w03jv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 83.000 0.100 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #5437-02prwdh PRED entity: 02prwdh PRED relation: titles! PRED expected values: 03mqtr => 83 concepts (66 used for prediction) PRED predicted values (max 10 best out of 88): 01z4y (0.49 #2644, 0.40 #1538, 0.22 #5458), 018h2 (0.33 #31, 0.24 #130, 0.23 #229), 07ssc (0.31 #1000, 0.31 #907, 0.15 #999), 05p553 (0.27 #3012, 0.24 #6135, 0.21 #6134), 01jfsb (0.26 #3331, 0.26 #3632, 0.17 #17), 04jjy (0.17 #18, 0.14 #117, 0.12 #216), 02n4kr (0.17 #12, 0.11 #3326, 0.11 #3627), 017fp (0.16 #2533, 0.15 #219, 0.14 #319), 03rt9 (0.15 #999, 0.14 #499, 0.09 #1605), 01hmnh (0.13 #2635, 0.13 #523, 0.13 #4946) >> Best rule #2644 for best value: >> intensional similarity = 4 >> extensional distance = 406 >> proper extension: 04cf_l; >> query: (?x5425, 01z4y) <- country(?x5425, ?x429), genre(?x5425, ?x258), titles(?x162, ?x5425), ?x258 = 05p553 >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #2555 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 401 *> proper extension: 03kq98; 02qjv1p; *> query: (?x5425, 03mqtr) <- titles(?x714, ?x5425), titles(?x53, ?x5425), ?x53 = 07s9rl0, genre(?x161, ?x714), ?x161 = 0sxg4 *> conf = 0.11 ranks of expected_values: 14 EVAL 02prwdh titles! 03mqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 83.000 66.000 0.490 http://example.org/media_common/netflix_genre/titles #5436-01r42_g PRED entity: 01r42_g PRED relation: location PRED expected values: 04n3l => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 68): 02_286 (0.29 #37, 0.20 #840, 0.15 #20113), 0cr3d (0.25 #3357, 0.20 #2554, 0.20 #948), 030qb3t (0.24 #5704, 0.22 #4901, 0.22 #8113), 01531 (0.14 #1764, 0.14 #158, 0.13 #2567), 05fjf (0.14 #1937, 0.14 #331, 0.12 #3543), 0yc7f (0.14 #374, 0.10 #1177, 0.07 #1980), 019fh (0.14 #191, 0.07 #1797), 0cv3w (0.10 #962, 0.07 #1765, 0.07 #2568), 0f8l9c (0.10 #843, 0.07 #2449, 0.06 #3252), 0yc84 (0.07 #1662, 0.07 #2465, 0.06 #3268) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02lfcm; 021_rm; 02lfns; 01l1sq; 03zyvw; >> query: (?x369, 02_286) <- award_winner(?x1991, ?x369), award_winner(?x446, ?x369), ?x1991 = 02lf70, ?x446 = 0436f4 >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01r42_g location 04n3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 84.000 0.286 http://example.org/people/person/places_lived./people/place_lived/location #5435-04bbv7 PRED entity: 04bbv7 PRED relation: nationality PRED expected values: 09c7w0 => 159 concepts (132 used for prediction) PRED predicted values (max 10 best out of 34): 09c7w0 (0.88 #9952, 0.86 #8442, 0.85 #11262), 0ms6_ (0.31 #7438, 0.28 #6026, 0.28 #9048), 0ms1n (0.31 #7438, 0.28 #6026, 0.28 #9048), 03_3d (0.25 #401, 0.11 #2215, 0.05 #206), 02jx1 (0.17 #1138, 0.16 #3044, 0.15 #5457), 07ssc (0.15 #1120, 0.13 #616, 0.11 #1220), 0d060g (0.12 #2717, 0.09 #1615, 0.08 #3319), 03rk0 (0.07 #9998, 0.07 #10906, 0.07 #12017), 0j5g9 (0.04 #1267, 0.03 #563, 0.03 #1368), 03rt9 (0.03 #514, 0.03 #614, 0.03 #816) >> Best rule #9952 for best value: >> intensional similarity = 3 >> extensional distance = 1008 >> proper extension: 0784v1; >> query: (?x9269, ?x94) <- place_of_birth(?x9269, ?x2017), country(?x2017, ?x94), time_zones(?x2017, ?x1638) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bbv7 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 132.000 0.878 http://example.org/people/person/nationality #5434-02qrv7 PRED entity: 02qrv7 PRED relation: film! PRED expected values: 017jv5 => 108 concepts (92 used for prediction) PRED predicted values (max 10 best out of 97): 017jv5 (0.50 #458, 0.50 #14, 0.43 #162), 016tt2 (0.43 #226, 0.43 #78, 0.28 #1041), 086k8 (0.42 #1113, 0.41 #1335, 0.29 #1261), 016tw3 (0.21 #602, 0.19 #1935, 0.17 #2084), 05qd_ (0.20 #822, 0.19 #1193, 0.18 #896), 05s_k6 (0.20 #803, 0.16 #1100, 0.11 #1470), 03xq0f (0.17 #1338, 0.17 #2675, 0.15 #1116), 017s11 (0.17 #2595, 0.15 #1558, 0.15 #817), 03rwz3 (0.17 #2595, 0.14 #191, 0.10 #413), 0jz9f (0.15 #1186, 0.09 #889, 0.08 #2223) >> Best rule #458 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 0g5pv3; 0g5pvv; 02n72k; >> query: (?x1261, 017jv5) <- nominated_for(?x6077, ?x1261), nominated_for(?x3643, ?x1261), country(?x1261, ?x512), story_by(?x6077, ?x3686), language(?x1261, ?x254), prequel(?x10088, ?x1261), ?x3643 = 0d1qmz >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qrv7 film! 017jv5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 92.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5433-0rs6x PRED entity: 0rs6x PRED relation: time_zones PRED expected values: 02hcv8 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 8): 02hcv8 (0.68 #42, 0.55 #55, 0.50 #3), 02fqwt (0.47 #222, 0.19 #66, 0.18 #79), 02lcqs (0.18 #96, 0.18 #83, 0.18 #70), 02hczc (0.17 #661, 0.17 #634, 0.17 #620), 02lcrv (0.17 #661, 0.17 #634, 0.17 #620), 042g7t (0.16 #593, 0.15 #579, 0.15 #458), 02llzg (0.05 #501, 0.05 #370, 0.05 #396), 03bdv (0.03 #294, 0.03 #503, 0.03 #640) >> Best rule #42 for best value: >> intensional similarity = 6 >> extensional distance = 36 >> proper extension: 0ply0; 0jxgx; 0rn8q; 0rjg8; 0jrtv; 0jhz_; 0jgld; 0jxh9; 0jgj7; 0rqf1; ... >> query: (?x95, 02hcv8) <- contains(?x2623, ?x95), contains(?x94, ?x95), ?x2623 = 02xry, source(?x95, ?x958), ?x958 = 0jbk9, origin(?x1333, ?x94) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rs6x time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.684 http://example.org/location/location/time_zones #5432-069q4f PRED entity: 069q4f PRED relation: genre PRED expected values: 05p553 => 91 concepts (71 used for prediction) PRED predicted values (max 10 best out of 104): 01jfsb (0.93 #2193, 0.49 #4503, 0.39 #7170), 05p553 (0.76 #609, 0.76 #488, 0.61 #367), 07s9rl0 (0.69 #6190, 0.66 #3275, 0.64 #3397), 01z4y (0.61 #7522, 0.61 #5825, 0.51 #2545), 0lsxr (0.44 #372, 0.37 #614, 0.36 #493), 03k9fj (0.35 #5230, 0.29 #1938, 0.29 #4502), 02l7c8 (0.32 #2925, 0.32 #2318, 0.32 #6206), 082gq (0.29 #1938, 0.27 #1090, 0.25 #32), 06n90 (0.29 #1938, 0.27 #1090, 0.21 #5960), 01drsx (0.29 #1938, 0.27 #1090, 0.11 #165) >> Best rule #2193 for best value: >> intensional similarity = 4 >> extensional distance = 499 >> proper extension: 052_mn; 03z9585; 09v42sf; 04jn6y7; >> query: (?x1311, 01jfsb) <- genre(?x1311, ?x5231), film(?x4667, ?x1311), genre(?x3048, ?x5231), ?x3048 = 01dvbd >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #609 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 06v9_x; 0661m4p; 04z257; 0f8j13; 01jnc_; 0640m69; *> query: (?x1311, 05p553) <- genre(?x1311, ?x5231), film(?x1104, ?x1311), ?x5231 = 0556j8, film_release_distribution_medium(?x1311, ?x81) *> conf = 0.76 ranks of expected_values: 2 EVAL 069q4f genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 71.000 0.930 http://example.org/film/film/genre #5431-015t56 PRED entity: 015t56 PRED relation: award_nominee! PRED expected values: 03_6y 01mqc_ => 89 concepts (35 used for prediction) PRED predicted values (max 10 best out of 680): 0154qm (0.82 #9218, 0.81 #62229, 0.81 #64534), 015t7v (0.82 #9218, 0.81 #62229, 0.81 #64534), 01kwld (0.82 #9218, 0.81 #62229, 0.81 #64534), 01v9l67 (0.82 #9218, 0.81 #62229, 0.81 #64534), 016zp5 (0.82 #9218, 0.81 #62229, 0.81 #64534), 0btpx (0.82 #9218, 0.81 #62229, 0.81 #64534), 0278x6s (0.82 #9218, 0.81 #62229, 0.81 #64534), 073x6y (0.82 #9218, 0.81 #62229, 0.81 #64534), 03_6y (0.82 #9218, 0.81 #62229, 0.81 #64534), 02fgm7 (0.77 #80673, 0.76 #39178, 0.76 #50706) >> Best rule #9218 for best value: >> intensional similarity = 3 >> extensional distance = 354 >> proper extension: 0d02km; >> query: (?x2762, ?x221) <- award_nominee(?x2762, ?x221), nominated_for(?x2762, ?x972), religion(?x2762, ?x1985) >> conf = 0.82 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 9, 198 EVAL 015t56 award_nominee! 01mqc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 89.000 35.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 015t56 award_nominee! 03_6y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 89.000 35.000 0.817 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5430-0277990 PRED entity: 0277990 PRED relation: award_nominee PRED expected values: 02773nt => 82 concepts (27 used for prediction) PRED predicted values (max 10 best out of 886): 018ygt (0.81 #46681, 0.81 #51350, 0.81 #46680), 04tnqn (0.81 #51350, 0.81 #46680, 0.80 #18674), 0863x_ (0.81 #51350, 0.81 #46680, 0.80 #18674), 02pb53 (0.81 #51350, 0.81 #46680, 0.80 #18674), 02773nt (0.81 #51350, 0.81 #46680, 0.80 #18674), 0284gcb (0.71 #307, 0.28 #37343, 0.27 #4976), 02778pf (0.64 #164, 0.28 #37343, 0.23 #4833), 026w_gk (0.64 #1240, 0.28 #37343, 0.23 #5909), 02773m2 (0.64 #163, 0.27 #4832, 0.18 #63016), 02778qt (0.64 #690, 0.27 #5359, 0.18 #63016) >> Best rule #46681 for best value: >> intensional similarity = 3 >> extensional distance = 981 >> proper extension: 02k6rq; 09dv0sz; 050_qx; >> query: (?x2445, ?x5620) <- award_nominee(?x5620, ?x2445), nominated_for(?x2445, ?x2528), participant(?x8146, ?x5620) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #51350 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1384 *> proper extension: 01vvydl; 07s3vqk; 0197tq; 01vrx3g; 01dw4q; 09fqtq; 0m2l9; 02pp_q_; 044rvb; 01vvycq; ... *> query: (?x2445, ?x829) <- award_nominee(?x829, ?x2445), profession(?x2445, ?x1032), ?x1032 = 02hrh1q *> conf = 0.81 ranks of expected_values: 5 EVAL 0277990 award_nominee 02773nt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 82.000 27.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5429-0d_2fb PRED entity: 0d_2fb PRED relation: film_crew_role PRED expected values: 02r96rf => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 31): 02r96rf (0.83 #622, 0.83 #2247, 0.82 #794), 01pvkk (0.38 #44, 0.34 #629, 0.33 #421), 02ynfr (0.33 #14, 0.30 #184, 0.25 #2258), 02rh1dz (0.32 #800, 0.31 #972, 0.26 #628), 0d2b38 (0.27 #643, 0.25 #58, 0.22 #815), 015h31 (0.27 #144, 0.24 #349, 0.23 #627), 094hwz (0.27 #149, 0.19 #424, 0.18 #115), 01xy5l_ (0.25 #319, 0.18 #631, 0.18 #597), 0215hd (0.20 #739, 0.20 #808, 0.19 #671), 033smt (0.19 #645, 0.16 #1479, 0.15 #4089) >> Best rule #622 for best value: >> intensional similarity = 7 >> extensional distance = 75 >> proper extension: 03t97y; 01kff7; >> query: (?x2339, 02r96rf) <- film_crew_role(?x2339, ?x2154), country(?x2339, ?x94), genre(?x2339, ?x811), film_release_distribution_medium(?x2339, ?x81), ?x2154 = 01vx2h, ?x94 = 09c7w0, ?x811 = 03k9fj >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d_2fb film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.831 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5428-078jt5 PRED entity: 078jt5 PRED relation: type_of_union PRED expected values: 04ztj => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.77 #29, 0.74 #25, 0.67 #93), 01g63y (0.11 #106, 0.11 #66, 0.10 #58) >> Best rule #29 for best value: >> intensional similarity = 2 >> extensional distance = 270 >> proper extension: 0cm89v; 013zyw; 0454s1; 032md; 0jpdn; 0dr5y; >> query: (?x3018, 04ztj) <- film(?x3018, ?x13027), profession(?x3018, ?x1041) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 078jt5 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.768 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5427-0p_pd PRED entity: 0p_pd PRED relation: film PRED expected values: 02x8fs => 111 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1081): 024lt6 (0.62 #65726, 0.59 #79942, 0.58 #120811), 0gkz3nz (0.62 #65726, 0.59 #79942, 0.58 #120811), 027r9t (0.33 #1239, 0.05 #24867, 0.03 #122588), 0170z3 (0.17 #1, 0.05 #24867, 0.03 #122588), 0blpg (0.17 #653, 0.03 #2429, 0.03 #122588), 05zpghd (0.17 #950, 0.03 #13383, 0.03 #122588), 033qdy (0.17 #1167, 0.03 #122588, 0.02 #29587), 06fqlk (0.17 #1136), 03nfnx (0.12 #4945, 0.06 #12049, 0.05 #20930), 04gv3db (0.10 #4302, 0.07 #11406, 0.05 #20287) >> Best rule #65726 for best value: >> intensional similarity = 3 >> extensional distance = 696 >> proper extension: 0q1lp; >> query: (?x397, ?x4690) <- film(?x397, ?x696), nominated_for(?x397, ?x4690), people(?x1446, ?x397) >> conf = 0.62 => this is the best rule for 2 predicted values *> Best rule #18621 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 01m42d0; 06d6y; 013sg6; 0gthm; *> query: (?x397, 02x8fs) <- film(?x397, ?x7656), influenced_by(?x1814, ?x397), genre(?x7656, ?x239) *> conf = 0.02 ranks of expected_values: 654 EVAL 0p_pd film 02x8fs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 111.000 75.000 0.616 http://example.org/film/actor/film./film/performance/film #5426-05hj_k PRED entity: 05hj_k PRED relation: award_winner! PRED expected values: 02ywhz => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 124): 01mhwk (0.18 #41, 0.05 #601, 0.04 #1581), 09q_6t (0.17 #10221, 0.14 #288, 0.11 #148), 013b2h (0.17 #10221, 0.09 #80, 0.05 #640), 02ywhz (0.17 #10221, 0.05 #219, 0.05 #359), 01c6qp (0.17 #10221, 0.05 #159, 0.05 #579), 0bzkvd (0.17 #10221, 0.05 #253, 0.02 #673), 0h_9252 (0.17 #10221, 0.04 #478, 0.02 #758), 0466p0j (0.16 #216, 0.10 #636, 0.04 #1616), 01s695 (0.16 #143, 0.05 #563, 0.03 #11484), 02q690_ (0.11 #3005, 0.04 #485, 0.04 #1605) >> Best rule #41 for best value: >> intensional similarity = 2 >> extensional distance = 9 >> proper extension: 036jp8; >> query: (?x4060, 01mhwk) <- company(?x4060, ?x166), sibling(?x4060, ?x7324) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #10221 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1354 *> proper extension: 0lzkm; *> query: (?x4060, ?x747) <- award_winner(?x4634, ?x4060), award(?x4634, ?x198), award_winner(?x747, ?x4634) *> conf = 0.17 ranks of expected_values: 4 EVAL 05hj_k award_winner! 02ywhz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 115.000 0.182 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5425-02kxwk PRED entity: 02kxwk PRED relation: nominated_for PRED expected values: 01lv85 => 120 concepts (47 used for prediction) PRED predicted values (max 10 best out of 484): 02q7fl9 (0.82 #45244, 0.80 #21003, 0.80 #38782), 01j7mr (0.43 #3779), 05zy3sc (0.30 #45245, 0.29 #33934, 0.28 #8078), 034qmv (0.30 #45245, 0.29 #33934, 0.28 #8078), 06_wqk4 (0.30 #45245, 0.29 #33934, 0.28 #8078), 035_2h (0.30 #45245, 0.29 #33934, 0.28 #8078), 02qk3fk (0.30 #45245, 0.29 #33934, 0.28 #8078), 04x4vj (0.30 #45245, 0.29 #33934, 0.28 #8078), 02xtxw (0.22 #2149), 011yd2 (0.12 #328, 0.10 #6790, 0.02 #50096) >> Best rule #45244 for best value: >> intensional similarity = 3 >> extensional distance = 882 >> proper extension: 0c01c; 06_bq1; >> query: (?x4367, ?x1597) <- award_winner(?x1597, ?x4367), nominated_for(?x4367, ?x1120), film(?x4367, ?x148) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #20547 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 470 *> proper extension: 03fnyk; *> query: (?x4367, 01lv85) <- award_winner(?x5236, ?x4367), nationality(?x4367, ?x94), actor(?x5236, ?x2588) *> conf = 0.01 ranks of expected_values: 479 EVAL 02kxwk nominated_for 01lv85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 120.000 47.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5424-03ytc PRED entity: 03ytc PRED relation: major_field_of_study! PRED expected values: 016t_3 => 90 concepts (70 used for prediction) PRED predicted values (max 10 best out of 19): 016t_3 (0.86 #918, 0.86 #899, 0.86 #428), 014mlp (0.81 #1097, 0.81 #820, 0.80 #1076), 04zx3q1 (0.80 #323, 0.64 #405, 0.58 #583), 02_xgp2 (0.76 #1143, 0.76 #886, 0.75 #827), 03mkk4 (0.72 #344, 0.70 #1005, 0.69 #1299), 028dcg (0.72 #344, 0.64 #100, 0.62 #425), 0bkj86 (0.65 #1079, 0.64 #1100, 0.64 #823), 022h5x (0.57 #834, 0.50 #1092, 0.50 #96), 07s6fsf (0.51 #963, 0.50 #80, 0.46 #1027), 0bjrnt (0.51 #963, 0.46 #1027, 0.46 #984) >> Best rule #918 for best value: >> intensional similarity = 11 >> extensional distance = 40 >> proper extension: 064_8sq; >> query: (?x8855, ?x1200) <- major_field_of_study(?x4981, ?x8855), major_field_of_study(?x865, ?x8855), major_field_of_study(?x11502, ?x8855), institution(?x3386, ?x11502), institution(?x1200, ?x11502), ?x3386 = 03mkk4, major_field_of_study(?x6756, ?x8855), contains(?x94, ?x11502), ?x865 = 02h4rq6, ?x1200 = 016t_3, institution(?x4981, ?x122) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03ytc major_field_of_study! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 70.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #5423-06sfk6 PRED entity: 06sfk6 PRED relation: costume_design_by PRED expected values: 0bytfv => 94 concepts (63 used for prediction) PRED predicted values (max 10 best out of 15): 02mxbd (0.12 #17, 0.05 #101, 0.04 #158), 0bytfv (0.10 #39, 0.06 #11, 0.05 #520), 02pqgt8 (0.06 #12, 0.02 #96, 0.02 #153), 03mfqm (0.06 #300, 0.03 #470, 0.03 #46), 0gl88b (0.03 #33, 0.03 #61, 0.02 #89), 02w0dc0 (0.03 #29, 0.02 #283, 0.02 #707), 03y1mlp (0.03 #993, 0.02 #86, 0.02 #256), 02cqbx (0.02 #638, 0.02 #326, 0.02 #354), 0c6g29 (0.02 #345, 0.02 #374, 0.01 #459), 0h7pj (0.02 #735, 0.01 #1480, 0.01 #1307) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0m313; 09q5w2; 04vr_f; 0c0nhgv; 075wx7_; 011yd2; 03177r; 05zlld0; 02q7fl9; 051ys82; ... >> query: (?x4525, 02mxbd) <- film_format(?x4525, ?x909), film_release_distribution_medium(?x4525, ?x81), nominated_for(?x508, ?x4525), films(?x9677, ?x4525) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #39 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 0hv81; *> query: (?x4525, 0bytfv) <- honored_for(?x4525, ?x508), cinematography(?x4525, ?x185), film_crew_role(?x4525, ?x137) *> conf = 0.10 ranks of expected_values: 2 EVAL 06sfk6 costume_design_by 0bytfv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 63.000 0.125 http://example.org/film/film/costume_design_by #5422-0jhd PRED entity: 0jhd PRED relation: nationality! PRED expected values: 0149xx => 123 concepts (106 used for prediction) PRED predicted values (max 10 best out of 4070): 0149xx (0.44 #93577, 0.33 #386511, 0.05 #5643), 03_lf (0.37 #44753, 0.35 #93576, 0.02 #52064), 059xvg (0.16 #5120, 0.13 #9188, 0.09 #13257), 0p__8 (0.16 #5917, 0.13 #9985, 0.09 #14054), 0jcx (0.13 #9084, 0.12 #17222, 0.11 #5016), 01vsps (0.13 #9451, 0.11 #5383, 0.09 #13520), 040_9 (0.12 #1013, 0.09 #9149, 0.06 #13218), 06b_0 (0.12 #2387, 0.09 #10523, 0.06 #14592), 04jvt (0.12 #3118, 0.05 #7186, 0.05 #92625), 0m77m (0.12 #307, 0.05 #4375, 0.05 #40991) >> Best rule #93577 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 0h44w; 0dv0z; 01fvhp; 01s47p; 0gtzp; >> query: (?x8588, ?x5125) <- capital(?x8588, ?x11419), place_of_birth(?x5125, ?x11419), location(?x10293, ?x11419) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jhd nationality! 0149xx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 106.000 0.443 http://example.org/people/person/nationality #5421-01jgpsh PRED entity: 01jgpsh PRED relation: award PRED expected values: 02ppm4q => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 269): 01by1l (0.31 #3328, 0.24 #4534, 0.17 #7348), 09sb52 (0.25 #12502, 0.25 #12904, 0.23 #6070), 01bgqh (0.24 #3258, 0.19 #4464, 0.14 #7278), 0ck27z (0.23 #4916, 0.12 #12956, 0.12 #12554), 0c4z8 (0.21 #3287, 0.17 #71, 0.14 #4493), 03qbh5 (0.19 #3422, 0.12 #4628, 0.10 #7442), 02n9nmz (0.19 #20504, 0.16 #14875, 0.16 #22115), 0fbtbt (0.19 #20504, 0.16 #22115, 0.15 #20907), 0gkr9q (0.19 #20504, 0.16 #22115, 0.15 #20907), 027gs1_ (0.19 #20504, 0.06 #1087, 0.05 #1489) >> Best rule #3328 for best value: >> intensional similarity = 3 >> extensional distance = 341 >> proper extension: 06lxn; >> query: (?x6363, 01by1l) <- award_winner(?x635, ?x6363), artists(?x2480, ?x6363), award_winner(?x537, ?x6363) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #7795 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 874 *> proper extension: 0jgd; 058j2; 02sch9; 02bh_v; 015c1b; 01nd9f; 0513yzt; *> query: (?x6363, 02ppm4q) <- gender(?x6363, ?x514), ?x514 = 02zsn *> conf = 0.08 ranks of expected_values: 81 EVAL 01jgpsh award 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 97.000 97.000 0.312 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5420-04h9h PRED entity: 04h9h PRED relation: language! PRED expected values: 01gc7 04vh83 0cq806 => 64 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1782): 0443v1 (0.76 #13602, 0.65 #18703, 0.33 #1652), 03n0cd (0.76 #13602, 0.65 #18703, 0.33 #1405), 0yyn5 (0.76 #13602, 0.65 #18703, 0.33 #908), 01shy7 (0.76 #13602, 0.65 #18703, 0.33 #404), 016z5x (0.76 #13602, 0.65 #18703), 034xyf (0.50 #11562, 0.50 #6462, 0.50 #4762), 03z9585 (0.50 #11532, 0.50 #6432, 0.50 #4732), 024l2y (0.50 #10445, 0.50 #5345, 0.50 #3645), 08nvyr (0.50 #10926, 0.50 #5826, 0.50 #4126), 061681 (0.50 #10298, 0.50 #5198, 0.50 #3498) >> Best rule #13602 for best value: >> intensional similarity = 8 >> extensional distance = 11 >> proper extension: 055qm; >> query: (?x11038, ?x518) <- languages(?x3583, ?x11038), film(?x3583, ?x518), place_of_birth(?x3583, ?x739), profession(?x3583, ?x1041), profession(?x5034, ?x1041), profession(?x1996, ?x1041), ?x1996 = 0g51l1, ?x5034 = 03772 >> conf = 0.76 => this is the best rule for 5 predicted values *> Best rule #6503 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 064_8sq; *> query: (?x11038, 0cq806) <- language(?x9941, ?x11038), language(?x4159, ?x11038), language(?x3953, ?x11038), written_by(?x3953, ?x8692), ?x9941 = 024lt6, nominated_for(?x3889, ?x3953), ?x4159 = 011yr9, ?x3889 = 0fhpv4, music(?x3953, ?x3069) *> conf = 0.50 ranks of expected_values: 26, 202, 1443 EVAL 04h9h language! 0cq806 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 64.000 27.000 0.763 http://example.org/film/film/language EVAL 04h9h language! 04vh83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 27.000 0.763 http://example.org/film/film/language EVAL 04h9h language! 01gc7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 64.000 27.000 0.763 http://example.org/film/film/language #5419-04hwbq PRED entity: 04hwbq PRED relation: currency PRED expected values: 09nqf => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.84 #169, 0.84 #162, 0.83 #120), 02l6h (0.03 #270, 0.02 #291, 0.02 #221), 01nv4h (0.03 #289, 0.03 #373, 0.03 #443), 088n7 (0.02 #105, 0.01 #126, 0.01 #252), 02gsvk (0.01 #405) >> Best rule #169 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 0h1x5f; >> query: (?x1259, 09nqf) <- nominated_for(?x3053, ?x1259), film_release_distribution_medium(?x1259, ?x81), film_crew_role(?x1259, ?x1966) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04hwbq currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.837 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #5418-07ssc PRED entity: 07ssc PRED relation: form_of_government PRED expected values: 01fpfn => 195 concepts (195 used for prediction) PRED predicted values (max 10 best out of 4): 01fpfn (0.42 #306, 0.40 #206, 0.39 #222), 06cx9 (0.39 #601, 0.35 #613, 0.32 #621), 01d9r3 (0.36 #291, 0.33 #3, 0.33 #315), 026wp (0.12 #188, 0.12 #180, 0.12 #116) >> Best rule #306 for best value: >> intensional similarity = 2 >> extensional distance = 55 >> proper extension: 01c4pv; >> query: (?x512, 01fpfn) <- country(?x150, ?x512), geographic_distribution(?x1571, ?x512) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ssc form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 195.000 0.421 http://example.org/location/country/form_of_government #5417-063tn PRED entity: 063tn PRED relation: place_of_death PRED expected values: 06pr6 => 192 concepts (120 used for prediction) PRED predicted values (max 10 best out of 47): 04swd (0.33 #120, 0.29 #1091, 0.25 #2841), 06pr6 (0.33 #688, 0.25 #1465, 0.20 #494), 0k049 (0.20 #392, 0.14 #1168, 0.14 #780), 02_286 (0.14 #984, 0.14 #790, 0.12 #1373), 030qb3t (0.14 #799, 0.12 #1770, 0.11 #3132), 05qtj (0.14 #1035, 0.12 #1812, 0.04 #3952), 0284jb (0.12 #1575, 0.10 #1964, 0.05 #3325), 0fhp9 (0.12 #1762, 0.09 #4874, 0.06 #4680), 06c62 (0.10 #2044, 0.03 #4961, 0.02 #6128), 0cc56 (0.10 #1960, 0.03 #4877, 0.02 #6044) >> Best rule #120 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 06wvj; >> query: (?x9480, 04swd) <- people(?x5590, ?x9480), student(?x10223, ?x9480), nationality(?x9480, ?x1603), languages(?x9480, ?x5671), ?x5590 = 0g6ff, artists(?x597, ?x9480) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #688 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 032l1; *> query: (?x9480, 06pr6) <- people(?x5590, ?x9480), ?x5590 = 0g6ff, nationality(?x9480, ?x1603), profession(?x9480, ?x1614), ?x1603 = 06bnz, type_of_union(?x9480, ?x566) *> conf = 0.33 ranks of expected_values: 2 EVAL 063tn place_of_death 06pr6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 192.000 120.000 0.333 http://example.org/people/deceased_person/place_of_death #5416-031rq5 PRED entity: 031rq5 PRED relation: award_winner! PRED expected values: 0m7yy => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 191): 05p1dby (0.57 #1400, 0.50 #3124, 0.43 #6142), 0m7yy (0.52 #6214, 0.42 #3196, 0.35 #7939), 02x1z2s (0.50 #197, 0.33 #3214, 0.32 #5369), 0gq9h (0.31 #18109, 0.25 #3094, 0.20 #5604), 01lk0l (0.31 #18109, 0.15 #29749, 0.15 #31476), 01l29r (0.29 #1027, 0.17 #596, 0.14 #1889), 0p9sw (0.25 #24, 0.08 #3041, 0.07 #9054), 018wng (0.20 #5604, 0.15 #9917, 0.11 #7760), 07cbcy (0.17 #6544, 0.14 #9995, 0.08 #19480), 05f4m9q (0.16 #30613, 0.11 #36653, 0.09 #9931) >> Best rule #1400 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 030_1_; 025hwq; >> query: (?x5908, 05p1dby) <- production_companies(?x770, ?x5908), country(?x5908, ?x94), award_winner(?x5908, ?x541) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #6214 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 09d5h; 0cjdk; 01_8w2; 05s34b; *> query: (?x5908, 0m7yy) <- child(?x7008, ?x5908), company(?x265, ?x7008), award_winner(?x541, ?x5908) *> conf = 0.52 ranks of expected_values: 2 EVAL 031rq5 award_winner! 0m7yy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner #5415-01rtm4 PRED entity: 01rtm4 PRED relation: student PRED expected values: 01xcr4 => 110 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1467): 0453t (0.12 #12471, 0.08 #39488, 0.08 #29097), 015wc0 (0.08 #1684, 0.07 #3763, 0.06 #7920), 0306ds (0.08 #406, 0.07 #2485, 0.04 #12877), 03rs8y (0.08 #45, 0.07 #2124, 0.04 #4203), 01l1rw (0.08 #992, 0.07 #3071, 0.04 #5150), 0ff3y (0.08 #2055, 0.04 #12447, 0.04 #26994), 0405l (0.08 #1840, 0.04 #8076, 0.04 #3919), 01c7qd (0.08 #1671, 0.04 #7907, 0.04 #3750), 041_y (0.08 #1210, 0.04 #7446, 0.04 #3289), 01_6dw (0.08 #1121, 0.04 #7357, 0.04 #3200) >> Best rule #12471 for best value: >> intensional similarity = 2 >> extensional distance = 70 >> proper extension: 09c7w0; >> query: (?x263, ?x2239) <- contains(?x94, ?x263), company(?x2239, ?x263) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01rtm4 student 01xcr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 96.000 0.120 http://example.org/education/educational_institution/students_graduates./education/education/student #5414-0fkhz PRED entity: 0fkhz PRED relation: source PRED expected values: 0jbk9 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #47, 0.92 #46, 0.92 #52) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 260 >> proper extension: 0nv2x; >> query: (?x12027, ?x958) <- second_level_divisions(?x94, ?x12027), adjoins(?x8055, ?x12027), source(?x8055, ?x958), time_zones(?x8055, ?x2674) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fkhz source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.924 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #5413-02784z PRED entity: 02784z PRED relation: type_of_union PRED expected values: 04ztj => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.85 #17, 0.78 #53, 0.78 #45), 01g63y (0.44 #213, 0.37 #390, 0.20 #10), 01bl8s (0.37 #390, 0.01 #35, 0.01 #43), 0jgjn (0.02 #24, 0.02 #28, 0.01 #40) >> Best rule #17 for best value: >> intensional similarity = 5 >> extensional distance = 44 >> proper extension: 0h1_w; 04nw9; 036jb; 05kh_; 013qvn; 03xx3m; 0mb5x; 063_t; 01k6nm; 02l0xc; ... >> query: (?x10454, 04ztj) <- nationality(?x10454, ?x512), gender(?x10454, ?x231), religion(?x10454, ?x7131), place_of_death(?x10454, ?x362), film(?x10454, ?x1547) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02784z type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.848 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5412-040_9 PRED entity: 040_9 PRED relation: influenced_by! PRED expected values: 04cbtrw => 134 concepts (61 used for prediction) PRED predicted values (max 10 best out of 813): 0683n (0.67 #1335, 0.60 #833, 0.47 #3349), 0lrh (0.60 #602, 0.50 #1104, 0.24 #2109), 040_t (0.60 #750, 0.50 #1252, 0.21 #3266), 0mb5x (0.60 #830, 0.50 #1332, 0.18 #2337), 06whf (0.43 #1664, 0.24 #2167, 0.20 #660), 01vdrw (0.40 #936, 0.33 #1438, 0.32 #3954), 0n6kf (0.40 #687, 0.33 #1189, 0.16 #3705), 041xl (0.40 #784, 0.33 #1286, 0.14 #1788), 080r3 (0.40 #711, 0.33 #1213, 0.14 #1715), 03_87 (0.33 #1257, 0.29 #2262, 0.29 #1759) >> Best rule #1335 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 04xjp; >> query: (?x3541, 0683n) <- influenced_by(?x3325, ?x3541), influenced_by(?x2161, ?x3541), ?x2161 = 040db, ?x3325 = 073v6, nationality(?x3541, ?x456), film_release_region(?x66, ?x456) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #605 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 03f0324; 081k8; 03_87; *> query: (?x3541, 04cbtrw) <- influenced_by(?x3325, ?x3541), influenced_by(?x2161, ?x3541), ?x2161 = 040db, ?x3325 = 073v6, nationality(?x3541, ?x456), religion(?x3541, ?x1985) *> conf = 0.20 ranks of expected_values: 56 EVAL 040_9 influenced_by! 04cbtrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 134.000 61.000 0.667 http://example.org/influence/influence_node/influenced_by #5411-0z05l PRED entity: 0z05l PRED relation: award PRED expected values: 09sb52 => 83 concepts (62 used for prediction) PRED predicted values (max 10 best out of 247): 09sb52 (0.67 #1246, 0.67 #40, 0.58 #442), 0ck27z (0.34 #2504, 0.21 #3710, 0.17 #494), 02x8n1n (0.33 #119, 0.25 #521, 0.20 #1325), 0bfvd4 (0.33 #114, 0.17 #516, 0.13 #24131), 05zr6wv (0.25 #419, 0.17 #17, 0.15 #13271), 0gs9p (0.21 #2089, 0.08 #5227, 0.07 #16892), 09qvc0 (0.21 #843, 0.04 #2451, 0.04 #4863), 0gq9h (0.21 #2087, 0.17 #479, 0.08 #5227), 040njc (0.21 #2018, 0.17 #410, 0.07 #19707), 019f4v (0.21 #2076, 0.08 #468, 0.07 #19707) >> Best rule #1246 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 02lkcc; 02d4ct; 04wp3s; 015pvh; 01d0b1; >> query: (?x7069, 09sb52) <- film(?x7069, ?x1120), award_nominee(?x9449, ?x7069), ?x9449 = 06bzwt >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0z05l award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 62.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5410-02lj6p PRED entity: 02lj6p PRED relation: award_nominee! PRED expected values: 0c6qh => 86 concepts (45 used for prediction) PRED predicted values (max 10 best out of 794): 013knm (0.82 #13974, 0.81 #102480, 0.81 #93166), 0c6qh (0.25 #5194, 0.15 #102481, 0.12 #16303), 078mgh (0.25 #6460), 025j1t (0.25 #6064), 0gy6z9 (0.21 #5400, 0.15 #102481, 0.12 #16303), 02qgyv (0.21 #5152, 0.15 #102481, 0.07 #494), 0dvmd (0.21 #5352, 0.12 #16303, 0.07 #694), 02yxwd (0.21 #5648, 0.12 #16303, 0.07 #990), 09fb5 (0.21 #4725, 0.12 #16303, 0.07 #67), 04m064 (0.21 #6956, 0.01 #95464, 0.01 #97792) >> Best rule #13974 for best value: >> intensional similarity = 3 >> extensional distance = 151 >> proper extension: 01wp8w7; 06449; 0gcs9; 03b78r; >> query: (?x8619, ?x192) <- influenced_by(?x8619, ?x1145), profession(?x8619, ?x987), award_nominee(?x8619, ?x192) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #5194 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 02p65p; 0bxtg; 032_jg; 0151w_; 01fwj8; 025n3p; 03jqw5; 0dvmd; 0gy6z9; 013knm; ... *> query: (?x8619, 0c6qh) <- award_nominee(?x2422, ?x8619), ?x2422 = 0169dl, film(?x8619, ?x349) *> conf = 0.25 ranks of expected_values: 2 EVAL 02lj6p award_nominee! 0c6qh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 45.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5409-026670 PRED entity: 026670 PRED relation: award PRED expected values: 040njc 0gq9h 0gr51 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 281): 04dn09n (0.71 #39, 0.58 #821, 0.57 #1212), 05h5nb8 (0.68 #19566, 0.68 #25437, 0.67 #21916), 019f4v (0.50 #61, 0.37 #6320, 0.36 #4755), 02x17s4 (0.46 #116, 0.46 #898, 0.45 #1289), 040njc (0.43 #7, 0.35 #6266, 0.35 #4701), 0gq9h (0.43 #70, 0.34 #8286, 0.28 #6329), 0gr51 (0.42 #7133, 0.32 #4393, 0.31 #873), 09sb52 (0.41 #8643, 0.28 #14513, 0.28 #2773), 02x4wr9 (0.36 #127, 0.17 #909, 0.15 #1300), 02x4sn8 (0.32 #149, 0.23 #1322, 0.22 #931) >> Best rule #39 for best value: >> intensional similarity = 6 >> extensional distance = 26 >> proper extension: 0qf43; 0159h6; 05kfs; 02kxbwx; 0151w_; 05m883; 01q415; 0184dt; 05ldnp; 085pr; ... >> query: (?x9754, 04dn09n) <- award(?x9754, ?x10597), award(?x9754, ?x384), award(?x9754, ?x372), ?x384 = 03hkv_r, disciplines_or_subjects(?x10597, ?x373), nominated_for(?x372, ?x303) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 26 *> proper extension: 0qf43; 0159h6; 05kfs; 02kxbwx; 0151w_; 05m883; 01q415; 0184dt; 05ldnp; 085pr; ... *> query: (?x9754, 040njc) <- award(?x9754, ?x10597), award(?x9754, ?x384), award(?x9754, ?x372), ?x384 = 03hkv_r, disciplines_or_subjects(?x10597, ?x373), nominated_for(?x372, ?x303) *> conf = 0.43 ranks of expected_values: 5, 6, 7 EVAL 026670 award 0gr51 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 111.000 111.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 026670 award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 111.000 111.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 026670 award 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 111.000 111.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5408-02qd04y PRED entity: 02qd04y PRED relation: film_crew_role PRED expected values: 02r96rf => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 29): 02r96rf (0.62 #2586, 0.60 #3, 0.59 #2660), 09vw2b7 (0.59 #2590, 0.58 #674, 0.58 #2738), 0dxtw (0.36 #790, 0.36 #2594, 0.35 #752), 01vx2h (0.36 #791, 0.34 #753, 0.33 #866), 01pvkk (0.30 #420, 0.29 #346, 0.29 #2744), 02rh1dz (0.29 #84, 0.20 #10, 0.15 #789), 02ynfr (0.20 #17, 0.16 #871, 0.15 #758), 094hwz (0.20 #16, 0.14 #90, 0.06 #795), 0d2b38 (0.14 #881, 0.11 #1594, 0.10 #434), 0215hd (0.13 #1063, 0.12 #2603, 0.12 #2751) >> Best rule #2586 for best value: >> intensional similarity = 4 >> extensional distance = 903 >> proper extension: 0gtsx8c; 0gtvrv3; 07kb7vh; >> query: (?x9175, 02r96rf) <- film(?x7610, ?x9175), film_crew_role(?x9175, ?x137), film_release_distribution_medium(?x9175, ?x81), language(?x9175, ?x2890) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qd04y film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.621 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5407-0gh8zks PRED entity: 0gh8zks PRED relation: genre PRED expected values: 07s9rl0 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 79): 07s9rl0 (0.72 #616, 0.70 #124, 0.70 #1), 05p553 (0.34 #497, 0.34 #1852, 0.34 #2591), 02l7c8 (0.34 #18, 0.31 #264, 0.31 #141), 01jfsb (0.30 #1000, 0.30 #2600, 0.29 #506), 060__y (0.30 #19, 0.18 #265, 0.14 #3099), 03k9fj (0.29 #382, 0.26 #876, 0.23 #1245), 02kdv5l (0.26 #2589, 0.26 #2466, 0.26 #5546), 0lsxr (0.23 #863, 0.19 #133, 0.17 #4690), 06n90 (0.23 #863, 0.14 #1247, 0.13 #1001), 03bxz7 (0.23 #863, 0.14 #58, 0.11 #304) >> Best rule #616 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 03mh_tp; >> query: (?x3252, 07s9rl0) <- language(?x3252, ?x254), film_festivals(?x3252, ?x2686), country(?x3252, ?x512) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gh8zks genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.720 http://example.org/film/film/genre #5406-0g5q34q PRED entity: 0g5q34q PRED relation: film_release_region PRED expected values: 01znc_ => 94 concepts (72 used for prediction) PRED predicted values (max 10 best out of 439): 03h64 (0.90 #2716, 0.85 #4585, 0.83 #4272), 03rjj (0.90 #5300, 0.89 #5927, 0.88 #6083), 06bnz (0.86 #3939, 0.85 #4250, 0.85 #4563), 01znc_ (0.86 #3935, 0.85 #4246, 0.81 #2690), 0b90_r (0.83 #4988, 0.81 #3743, 0.80 #4833), 03_3d (0.80 #4212, 0.79 #3901, 0.79 #6556), 03spz (0.78 #6018, 0.78 #4301, 0.78 #5391), 06t2t (0.76 #2710, 0.74 #4266, 0.72 #3955), 05b4w (0.75 #5986, 0.75 #5359, 0.75 #4893), 03rj0 (0.75 #5981, 0.75 #5354, 0.75 #4888) >> Best rule #2716 for best value: >> intensional similarity = 14 >> extensional distance = 19 >> proper extension: 02vxq9m; 01vksx; 0bwfwpj; 0c0nhgv; 011yqc; 0gd0c7x; 08052t3; 040rmy; 0gh65c5; 05zlld0; ... >> query: (?x5992, 03h64) <- film_release_region(?x5992, ?x985), film_release_region(?x5992, ?x774), film_release_region(?x5992, ?x583), film_release_region(?x5992, ?x279), film_release_region(?x5992, ?x172), ?x774 = 06mzp, ?x172 = 0154j, ?x985 = 0k6nt, ?x279 = 0d060g, genre(?x5992, ?x2753), ?x583 = 015fr, film_format(?x5992, ?x6392), genre(?x8267, ?x2753), ?x8267 = 0234j5 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #3935 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 41 *> proper extension: 011yrp; 017gl1; 08hmch; 0jjy0; 053rxgm; 0gmcwlb; 02r8hh_; 01fmys; 06wbm8q; 03qnc6q; ... *> query: (?x5992, 01znc_) <- film_release_region(?x5992, ?x1003), film_release_region(?x5992, ?x985), film_release_region(?x5992, ?x774), film_release_region(?x5992, ?x583), film_release_region(?x5992, ?x304), film_release_region(?x5992, ?x279), film_release_region(?x5992, ?x172), film_release_region(?x5992, ?x142), ?x774 = 06mzp, ?x172 = 0154j, ?x985 = 0k6nt, ?x279 = 0d060g, genre(?x5992, ?x53), ?x583 = 015fr, ?x142 = 0jgd, ?x1003 = 03gj2, ?x304 = 0d0vqn *> conf = 0.86 ranks of expected_values: 4 EVAL 0g5q34q film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 72.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5405-06fvc PRED entity: 06fvc PRED relation: split_to PRED expected values: 019sc => 22 concepts (19 used for prediction) No prediction ranks of expected_values: EVAL 06fvc split_to 019sc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 22.000 19.000 0.000 http://example.org/dataworld/gardening_hint/split_to #5404-01271h PRED entity: 01271h PRED relation: role PRED expected values: 01vdm0 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 98): 0342h (0.51 #899, 0.49 #800, 0.40 #3594), 0l14qv (0.33 #1893, 0.32 #2891, 0.31 #398), 0dwsp (0.33 #1893, 0.32 #2891, 0.31 #398), 07brj (0.33 #1893, 0.32 #2891, 0.31 #398), 02hnl (0.33 #1893, 0.32 #2891, 0.31 #398), 02snj9 (0.33 #1893, 0.32 #2891, 0.31 #398), 06ncr (0.33 #1893, 0.32 #2891, 0.31 #398), 07m2y (0.33 #1893, 0.32 #2891, 0.31 #398), 048j4l (0.33 #1893, 0.32 #2891, 0.31 #398), 01vdm0 (0.32 #925, 0.30 #826, 0.28 #2224) >> Best rule #899 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 06br6t; >> query: (?x2945, 0342h) <- artists(?x302, ?x2945), role(?x2945, ?x314), ?x302 = 016clz >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #925 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 06br6t; *> query: (?x2945, 01vdm0) <- artists(?x302, ?x2945), role(?x2945, ?x314), ?x302 = 016clz *> conf = 0.32 ranks of expected_values: 10 EVAL 01271h role 01vdm0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 140.000 140.000 0.505 http://example.org/music/artist/track_contributions./music/track_contribution/role #5403-01540 PRED entity: 01540 PRED relation: major_field_of_study PRED expected values: 01mkq => 66 concepts (55 used for prediction) PRED predicted values (max 10 best out of 118): 05qfh (0.59 #1618, 0.50 #645, 0.38 #1352), 02j62 (0.50 #375, 0.44 #993, 0.40 #1260), 06ms6 (0.50 #628, 0.44 #892, 0.40 #1248), 0fdys (0.50 #647, 0.33 #911, 0.33 #295), 0_jm (0.44 #924, 0.40 #1102, 0.33 #660), 037mh8 (0.44 #1022, 0.38 #846, 0.33 #1463), 01mkq (0.44 #979, 0.38 #803, 0.33 #1420), 02lp1 (0.40 #533, 0.33 #95, 0.25 #709), 03g3w (0.38 #1344, 0.35 #1701, 0.33 #637), 04rjg (0.38 #808, 0.35 #1604, 0.35 #1695) >> Best rule #1618 for best value: >> intensional similarity = 13 >> extensional distance = 15 >> proper extension: 06ntj; >> query: (?x6870, 05qfh) <- taxonomy(?x6870, ?x939), major_field_of_study(?x4100, ?x6870), student(?x4100, ?x1328), major_field_of_study(?x10071, ?x4100), major_field_of_study(?x3351, ?x4100), major_field_of_study(?x2228, ?x4100), major_field_of_study(?x122, ?x4100), major_field_of_study(?x9829, ?x4100), ?x122 = 08815, institution(?x865, ?x10071), major_field_of_study(?x8221, ?x9829), ?x2228 = 01s0_f, contains(?x94, ?x3351) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #979 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 7 *> proper extension: 04rjg; 03g3w; 041y2; *> query: (?x6870, 01mkq) <- major_field_of_study(?x6271, ?x6870), major_field_of_study(?x5280, ?x6870), major_field_of_study(?x4293, ?x6870), major_field_of_study(?x3439, ?x6870), major_field_of_study(?x2711, ?x6870), colors(?x4293, ?x663), state_province_region(?x4293, ?x3670), institution(?x865, ?x2711), ?x5280 = 07vhb, contains(?x94, ?x4293), ?x3439 = 03ksy, school(?x1883, ?x6271), school(?x1578, ?x6271) *> conf = 0.44 ranks of expected_values: 7 EVAL 01540 major_field_of_study 01mkq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 66.000 55.000 0.588 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #5402-01qgr3 PRED entity: 01qgr3 PRED relation: school! PRED expected values: 01ync => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 83): 05m_8 (0.33 #418, 0.33 #169, 0.31 #584), 04wmvz (0.33 #236, 0.31 #651, 0.11 #2245), 0512p (0.33 #179, 0.23 #594, 0.17 #428), 0jmk7 (0.33 #246, 0.23 #661, 0.08 #495), 061xq (0.27 #364, 0.25 #447, 0.22 #198), 01y3v (0.27 #358, 0.25 #441, 0.12 #856), 01yhm (0.27 #350, 0.25 #433, 0.11 #1513), 07147 (0.27 #392, 0.25 #475, 0.11 #2245), 05g49 (0.27 #374, 0.25 #457, 0.11 #2245), 051vz (0.25 #436, 0.23 #602, 0.22 #187) >> Best rule #418 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 025v3k; >> query: (?x7338, 05m_8) <- organization(?x5510, ?x7338), school(?x6462, ?x7338), ?x6462 = 09l0x9, school(?x729, ?x7338) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2245 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 109 *> proper extension: 0frm7n; *> query: (?x7338, ?x580) <- school(?x11905, ?x7338), school(?x729, ?x7338), draft(?x580, ?x11905) *> conf = 0.11 ranks of expected_values: 32 EVAL 01qgr3 school! 01ync CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 160.000 160.000 0.333 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #5401-01xdf5 PRED entity: 01xdf5 PRED relation: influenced_by PRED expected values: 014z8v => 134 concepts (115 used for prediction) PRED predicted values (max 10 best out of 323): 014z8v (0.17 #982, 0.16 #2278, 0.11 #4868), 01hmk9 (0.14 #1082, 0.14 #2378, 0.11 #5830), 013tjc (0.14 #1236, 0.08 #2532, 0.08 #5122), 081lh (0.13 #4769, 0.12 #2179, 0.11 #22876), 01k9lpl (0.12 #2467, 0.11 #22876, 0.08 #2898), 0ph2w (0.11 #980, 0.10 #4866, 0.09 #2276), 01s7qqw (0.11 #22876, 0.09 #1025, 0.08 #4911), 029_3 (0.11 #22876, 0.09 #979, 0.05 #4865), 01wj9y9 (0.11 #22876, 0.06 #2219, 0.04 #1788), 0l5yl (0.11 #22876, 0.06 #1130, 0.06 #2857) >> Best rule #982 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0lrh; 0ph2w; 02kz_; 0gd9k; 063_t; 022q4j; >> query: (?x236, 014z8v) <- nationality(?x236, ?x94), participant(?x236, ?x237), influenced_by(?x10512, ?x236) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xdf5 influenced_by 014z8v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 115.000 0.171 http://example.org/influence/influence_node/influenced_by #5400-0jgm8 PRED entity: 0jgm8 PRED relation: time_zones PRED expected values: 02hcv8 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.86 #636, 0.85 #515, 0.85 #542), 02lcqs (0.30 #18, 0.24 #84, 0.22 #97), 02fqwt (0.23 #80, 0.19 #237, 0.19 #106), 02hczc (0.16 #1332, 0.14 #28, 0.12 #81), 02lcrv (0.16 #1332, 0.01 #401, 0.01 #86), 02llzg (0.11 #586, 0.07 #772, 0.07 #786), 03bdv (0.04 #588, 0.04 #1129, 0.03 #1155), 03plfd (0.04 #592, 0.03 #751, 0.03 #792), 0gsrz4 (0.03 #590, 0.02 #843, 0.02 #896), 042g7t (0.02 #647, 0.01 #405, 0.01 #952) >> Best rule #636 for best value: >> intensional similarity = 3 >> extensional distance = 280 >> proper extension: 0m25p; 0n6nl; 0l2nd; 035p3; 0mk59; >> query: (?x10379, ?x2674) <- adjoins(?x11986, ?x10379), time_zones(?x11986, ?x2674), currency(?x11986, ?x170) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgm8 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.857 http://example.org/location/location/time_zones #5399-0154j PRED entity: 0154j PRED relation: film_release_region! PRED expected values: 0ds3t5x 01c22t 0h3xztt 04hwbq 04zyhx 02r1c18 0gj9qxr 05qbckf 0gydcp7 02yvct 0cc5mcj 06ztvyx 040b5k 01jrbb 047p7fr 0gffmn8 0dgpwnk 02fqrf 06r2_ 0gjcrrw 05c26ss 080nwsb 01rwpj 0glqh5_ 0gl02yg 0ggbfwf 03rg2b 0cmdwwg 0421v9q 07s3m4g 027pfg 02825nf 04z_3pm 0ndsl1x 0fpgp26 0gvt53w 0j8f09z 0dw4b0 0640m69 => 182 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1059): 0fpgp26 (0.88 #14625, 0.86 #4064, 0.80 #30469), 04hwbq (0.86 #3273, 0.78 #29678, 0.78 #13834), 0gffmn8 (0.86 #3452, 0.70 #29857, 0.70 #14013), 0cc5mcj (0.85 #13946, 0.72 #29790, 0.71 #3385), 06ztvyx (0.81 #3398, 0.80 #13959, 0.74 #29803), 05qbckf (0.81 #3335, 0.76 #29740, 0.72 #9671), 0glqh5_ (0.81 #3704, 0.72 #10040, 0.69 #30109), 01c22t (0.81 #3262, 0.70 #13823, 0.66 #28609), 02fqrf (0.81 #3481, 0.69 #29886, 0.62 #14042), 02r1c18 (0.81 #3293, 0.52 #9629, 0.51 #28640) >> Best rule #14625 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 03rjj; 03_3d; 0d060g; 0d0vqn; 04gzd; 0chghy; ... >> query: (?x172, 0fpgp26) <- film_release_region(?x8292, ?x172), member_states(?x2106, ?x172), ?x8292 = 0cmf0m0 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 23, 24, 25, 26, 27, 30, 31, 41, 42, 43, 50, 55, 56, 81, 99, 117, 120, 155, 240 EVAL 0154j film_release_region! 0640m69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0dw4b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0j8f09z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0gvt53w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0fpgp26 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0ndsl1x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 04z_3pm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 02825nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 027pfg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 07s3m4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0421v9q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0cmdwwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 03rg2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0ggbfwf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0gl02yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0glqh5_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 01rwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 080nwsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 05c26ss CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0gjcrrw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 06r2_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 02fqrf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0dgpwnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0gffmn8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 047p7fr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 01jrbb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 040b5k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 06ztvyx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0cc5mcj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 02yvct CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0gydcp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 05qbckf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0gj9qxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 02r1c18 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 04zyhx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 04hwbq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0h3xztt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 01c22t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0154j film_release_region! 0ds3t5x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 34.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5398-0kvgnq PRED entity: 0kvgnq PRED relation: award_winner PRED expected values: 05dppk => 76 concepts (36 used for prediction) PRED predicted values (max 10 best out of 397): 01y64_ (0.44 #13156, 0.38 #54278, 0.38 #59214), 02lp3c (0.18 #18093, 0.17 #21384, 0.10 #9866), 0bytfv (0.14 #16448), 05dppk (0.14 #19738, 0.12 #23029), 05m883 (0.14 #188, 0.12 #1832, 0.11 #5121), 0237jb (0.14 #1223, 0.12 #2867, 0.05 #6156), 03mfqm (0.11 #4323, 0.04 #9256, 0.04 #15836), 04sry (0.08 #6102, 0.06 #2813, 0.05 #11035), 09fb5 (0.08 #4984, 0.03 #9917, 0.03 #11561), 06pj8 (0.08 #16786, 0.07 #338, 0.06 #8559) >> Best rule #13156 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 0fy34l; 0kvgxk; 0bpx1k; 02xtxw; 07kh6f3; 0qf2t; 0dt8xq; 02q7fl9; 06__m6; 08nhfc1; ... >> query: (?x5752, ?x4440) <- nominated_for(?x384, ?x5752), ?x384 = 03hkv_r, nominated_for(?x4440, ?x5752) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #19738 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 115 *> proper extension: 047gn4y; 08hmch; 0c00zd0; 0m491; 025n07; 023gxx; 0bbw2z6; 07bwr; 047vnkj; 0267wwv; ... *> query: (?x5752, ?x2530) <- film_crew_role(?x5752, ?x137), ?x137 = 09zzb8, cinematography(?x5752, ?x2530), nominated_for(?x4440, ?x5752) *> conf = 0.14 ranks of expected_values: 4 EVAL 0kvgnq award_winner 05dppk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 36.000 0.439 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #5397-0248jb PRED entity: 0248jb PRED relation: category_of PRED expected values: 0c4ys => 56 concepts (50 used for prediction) PRED predicted values (max 10 best out of 3): 0c4ys (0.92 #253, 0.90 #295, 0.88 #106), 0gcf2r (0.22 #318, 0.13 #488, 0.12 #726), 0g_w (0.19 #876, 0.09 #684, 0.09 #706) >> Best rule #253 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 02flpc; >> query: (?x6623, 0c4ys) <- award(?x3403, ?x6623), ceremony(?x6623, ?x139), role(?x3403, ?x227), ?x139 = 05pd94v >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0248jb category_of 0c4ys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 50.000 0.922 http://example.org/award/award_category/category_of #5396-087wc7n PRED entity: 087wc7n PRED relation: crewmember PRED expected values: 0bbxx9b => 67 concepts (47 used for prediction) PRED predicted values (max 10 best out of 29): 0bbxx9b (0.12 #164, 0.06 #449, 0.03 #1310), 0c94fn (0.08 #154, 0.05 #392, 0.04 #583), 092ys_y (0.07 #116, 0.07 #307, 0.07 #20), 04ktcgn (0.07 #12, 0.06 #346, 0.05 #204), 0b6mgp_ (0.07 #22, 0.05 #118, 0.03 #403), 0g9zcgx (0.07 #32, 0.03 #80, 0.02 #793), 051z6rz (0.05 #221, 0.05 #125, 0.05 #269), 06rnl9 (0.05 #112, 0.04 #397, 0.02 #588), 04wp63 (0.05 #138, 0.03 #423, 0.02 #519), 0284n42 (0.04 #765, 0.03 #1391, 0.02 #1829) >> Best rule #164 for best value: >> intensional similarity = 7 >> extensional distance = 50 >> proper extension: 023cjg; >> query: (?x791, 0bbxx9b) <- film_release_distribution_medium(?x791, ?x81), genre(?x791, ?x2540), country(?x791, ?x94), ?x94 = 09c7w0, film(?x9140, ?x791), ?x2540 = 0hcr, award(?x9140, ?x594) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087wc7n crewmember 0bbxx9b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 47.000 0.115 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #5395-02h9_l PRED entity: 02h9_l PRED relation: award_winner! PRED expected values: 01bx35 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 120): 013b2h (0.29 #635, 0.15 #2720, 0.14 #3138), 0jt3qpk (0.24 #2266, 0.09 #1710, 0.08 #320), 0gkxgfq (0.22 #2329, 0.09 #1773, 0.08 #383), 02rjjll (0.21 #561, 0.18 #3342, 0.18 #3064), 02cg41 (0.21 #680, 0.17 #2765, 0.12 #8187), 0gpjbt (0.21 #585, 0.17 #307, 0.12 #3783), 05pd94v (0.21 #558, 0.13 #8065, 0.12 #9178), 09n4nb (0.18 #881, 0.15 #464, 0.14 #603), 01s695 (0.17 #2644, 0.16 #3062, 0.14 #559), 019bk0 (0.17 #2657, 0.14 #3770, 0.11 #8079) >> Best rule #635 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 01dwrc; >> query: (?x10148, 013b2h) <- artists(?x2937, ?x10148), award(?x10148, ?x4958), ?x4958 = 03qbnj, ?x2937 = 0glt670 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2648 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 05pdbs; *> query: (?x10148, 01bx35) <- award_winner(?x4796, ?x10148), award(?x10148, ?x1565), award_winner(?x6487, ?x10148), ?x6487 = 01mh_q *> conf = 0.12 ranks of expected_values: 18 EVAL 02h9_l award_winner! 01bx35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 162.000 162.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5394-0420y PRED entity: 0420y PRED relation: influenced_by PRED expected values: 043s3 => 139 concepts (63 used for prediction) PRED predicted values (max 10 best out of 414): 081k8 (0.60 #1905, 0.38 #2776, 0.25 #1468), 026lj (0.40 #9586, 0.36 #3492, 0.33 #45), 042q3 (0.40 #2114, 0.33 #436, 0.25 #1677), 07ym0 (0.40 #2027, 0.25 #2898, 0.25 #1590), 05qmj (0.33 #1067, 0.33 #629, 0.25 #2812), 03sbs (0.33 #436, 0.33 #222, 0.24 #9373), 02wh0 (0.33 #820, 0.33 #436, 0.22 #435), 039n1 (0.33 #763, 0.33 #436, 0.22 #435), 01tz6vs (0.33 #436, 0.22 #435, 0.18 #3234), 048cl (0.33 #436, 0.22 #435, 0.13 #9385) >> Best rule #1905 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0dw6b; 01vh096; >> query: (?x11830, 081k8) <- influenced_by(?x5434, ?x11830), nationality(?x11830, ?x774), ?x5434 = 01tz6vs, location(?x11830, ?x4627), influenced_by(?x11830, ?x3712) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #116 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 032r1; *> query: (?x11830, 043s3) <- religion(?x11830, ?x1985), ?x1985 = 0c8wxp, influenced_by(?x7250, ?x11830), influenced_by(?x5254, ?x11830), basic_title(?x5254, ?x265), influenced_by(?x587, ?x7250) *> conf = 0.33 ranks of expected_values: 18 EVAL 0420y influenced_by 043s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 139.000 63.000 0.600 http://example.org/influence/influence_node/influenced_by #5393-03_xj PRED entity: 03_xj PRED relation: contains! PRED expected values: 02qkt => 173 concepts (103 used for prediction) PRED predicted values (max 10 best out of 291): 02qkt (0.91 #52273, 0.86 #22728, 0.76 #30777), 07ssc (0.88 #53717, 0.88 #52853, 0.86 #55511), 03rjj (0.86 #53728, 0.20 #46559, 0.13 #64476), 01n7q (0.84 #77989, 0.83 #42147, 0.12 #84256), 09c7w0 (0.79 #37593, 0.79 #25067, 0.76 #36698), 02jx1 (0.77 #51118, 0.55 #52908, 0.54 #43051), 04_1l0v (0.76 #38040, 0.70 #37145, 0.68 #45206), 059j2 (0.71 #40354, 0.13 #64476, 0.09 #8052), 06bnz (0.66 #42965, 0.09 #42068, 0.07 #33221), 049nq (0.51 #40852, 0.11 #13105, 0.10 #16686) >> Best rule #52273 for best value: >> intensional similarity = 6 >> extensional distance = 65 >> proper extension: 01z88t; 07bxhl; 0jgx; 01c4pv; 0jt3tjf; 04ty8; >> query: (?x5498, 02qkt) <- contains(?x455, ?x5498), form_of_government(?x5498, ?x1926), contains(?x455, ?x11754), contains(?x455, ?x2756), ?x11754 = 0366c, ?x2756 = 0hg5 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_xj contains! 02qkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 103.000 0.910 http://example.org/location/location/contains #5392-0ck27z PRED entity: 0ck27z PRED relation: nominated_for PRED expected values: 030k94 030p35 => 44 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1599): 0ddd0gc (0.68 #15813, 0.68 #20558, 0.66 #25309), 015ppk (0.68 #15813, 0.68 #20558, 0.66 #25309), 080dwhx (0.68 #15813, 0.68 #20558, 0.66 #25309), 05c46y6 (0.60 #9879, 0.20 #3553, 0.15 #13041), 049xgc (0.50 #10356, 0.24 #13518, 0.20 #4030), 05hjnw (0.50 #10248, 0.21 #13410, 0.20 #3922), 011ywj (0.50 #10734, 0.20 #4408, 0.15 #12317), 0b1y_2 (0.50 #9916, 0.20 #3590, 0.12 #13078), 027r9t (0.50 #10570, 0.20 #4244, 0.12 #13732), 092vkg (0.50 #9630, 0.20 #3304, 0.12 #14373) >> Best rule #15813 for best value: >> intensional similarity = 5 >> extensional distance = 182 >> proper extension: 0fqnzts; >> query: (?x1670, ?x337) <- award(?x6867, ?x1670), award(?x275, ?x1670), award(?x337, ?x1670), film(?x6867, ?x4399), location(?x275, ?x1523) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #9486 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 09qj50; 0bdwft; 0cqhmg; *> query: (?x1670, ?x5116) <- award_winner(?x1670, ?x56), award(?x10491, ?x1670), award(?x190, ?x1670), actor(?x5116, ?x190), ?x10491 = 030hbp *> conf = 0.31 ranks of expected_values: 54, 55 EVAL 0ck27z nominated_for 030p35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 44.000 18.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0ck27z nominated_for 030k94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 44.000 18.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5391-013hvr PRED entity: 013hvr PRED relation: category PRED expected values: 08mbj5d => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #5, 0.75 #7, 0.61 #24) >> Best rule #5 for best value: >> intensional similarity = 1 >> extensional distance = 514 >> proper extension: 0mn0v; 0f04v; 0f2tj; 0_rwf; 0x335; 0_wm_; 010bnr; 0104lr; >> query: (?x12646, 08mbj5d) <- place(?x12646, ?x12646) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013hvr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.777 http://example.org/common/topic/webpage./common/webpage/category #5390-023tp8 PRED entity: 023tp8 PRED relation: spouse PRED expected values: 0408np => 110 concepts (88 used for prediction) PRED predicted values (max 10 best out of 119): 0408np (0.79 #14437, 0.78 #10537), 02mjf2 (0.18 #780, 0.13 #7023, 0.12 #1952), 01mqc_ (0.18 #780, 0.12 #1952, 0.11 #8193), 019pm_ (0.11 #97, 0.08 #486, 0.06 #877), 0kjrx (0.11 #281), 01jfrg (0.08 #606, 0.06 #997, 0.05 #1778), 039bpc (0.08 #517, 0.06 #908, 0.05 #1689), 05szp (0.08 #625, 0.06 #1016, 0.05 #1797), 0993r (0.08 #498, 0.06 #889, 0.05 #1670), 06y9c2 (0.08 #411, 0.06 #802, 0.05 #1583) >> Best rule #14437 for best value: >> intensional similarity = 3 >> extensional distance = 355 >> proper extension: 0c9d9; 04bs3j; 0151ns; 06y9c2; 0456xp; 03ft8; 02d9k; 0zjpz; 03rl84; 02fb1n; ... >> query: (?x376, ?x2692) <- type_of_union(?x376, ?x566), profession(?x376, ?x1032), spouse(?x2692, ?x376) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023tp8 spouse 0408np CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 88.000 0.791 http://example.org/people/person/spouse_s./people/marriage/spouse #5389-01mjq PRED entity: 01mjq PRED relation: countries_spoken_in! PRED expected values: 0k0sv => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 83): 02h40lc (0.43 #2731, 0.37 #3390, 0.35 #284), 06nm1 (0.22 #1700, 0.22 #1465, 0.21 #1794), 064_8sq (0.22 #2745, 0.19 #3404, 0.19 #2509), 02bjrlw (0.18 #424, 0.14 #189, 0.14 #565), 0jzc (0.16 #296, 0.16 #1614, 0.15 #1003), 02hwyss (0.14 #221, 0.06 #691, 0.06 #362), 02bv9 (0.12 #256, 0.11 #397, 0.09 #632), 02hwhyv (0.11 #399, 0.10 #540, 0.10 #305), 02hxcvy (0.11 #403, 0.10 #309, 0.08 #779), 07c9s (0.10 #295, 0.10 #201, 0.09 #624) >> Best rule #2731 for best value: >> intensional similarity = 3 >> extensional distance = 151 >> proper extension: 0g8bw; 0h44w; >> query: (?x1558, 02h40lc) <- countries_spoken_in(?x732, ?x1558), service_language(?x555, ?x732), official_language(?x774, ?x732) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #205 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 0366c; *> query: (?x1558, 0k0sv) <- contains(?x6304, ?x1558), location(?x6849, ?x1558), ?x6304 = 02qkt *> conf = 0.05 ranks of expected_values: 35 EVAL 01mjq countries_spoken_in! 0k0sv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 141.000 141.000 0.431 http://example.org/language/human_language/countries_spoken_in #5388-0dgrwqr PRED entity: 0dgrwqr PRED relation: film_release_region PRED expected values: 03rt9 06bnz => 74 concepts (70 used for prediction) PRED predicted values (max 10 best out of 211): 09c7w0 (0.92 #7266, 0.92 #7428, 0.92 #7105), 03rjj (0.87 #1135, 0.87 #1620, 0.86 #1781), 05r4w (0.86 #1615, 0.86 #1130, 0.85 #1776), 0154j (0.85 #1134, 0.84 #1619, 0.82 #1780), 03gj2 (0.83 #1155, 0.83 #1640, 0.81 #1801), 03h64 (0.82 #1198, 0.81 #1683, 0.80 #1844), 06bnz (0.82 #1175, 0.79 #1660, 0.77 #1821), 0b90_r (0.80 #1133, 0.79 #1618, 0.78 #1779), 0k6nt (0.79 #1800, 0.78 #1639, 0.78 #1961), 03_3d (0.75 #1137, 0.75 #1783, 0.74 #1622) >> Best rule #7266 for best value: >> intensional similarity = 5 >> extensional distance = 1325 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 0g56t9t; 09sh8k; 0m313; 034qmv; 0g22z; ... >> query: (?x7494, 09c7w0) <- film_release_region(?x7494, ?x583), service_location(?x127, ?x583), combatants(?x94, ?x583), film_release_region(?x80, ?x583), ?x80 = 0b76d_m >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1175 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 163 *> proper extension: 014lc_; 0b76d_m; 0ds35l9; 0gtsx8c; 02vxq9m; 0c3ybss; 0ddfwj1; 0ds3t5x; 0gtv7pk; 0h1cdwq; ... *> query: (?x7494, 06bnz) <- film_release_region(?x7494, ?x2152), film_release_region(?x7494, ?x1353), film_release_region(?x7494, ?x789), film_release_region(?x7494, ?x583), ?x583 = 015fr, ?x1353 = 035qy, ?x789 = 0f8l9c, ?x2152 = 06mkj *> conf = 0.82 ranks of expected_values: 7, 14 EVAL 0dgrwqr film_release_region 06bnz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 74.000 70.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dgrwqr film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 74.000 70.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5387-03_c8p PRED entity: 03_c8p PRED relation: citytown PRED expected values: 04jpl => 229 concepts (229 used for prediction) PRED predicted values (max 10 best out of 223): 02_286 (0.74 #18184, 0.57 #43646, 0.51 #58197), 030qb3t (0.72 #27293, 0.26 #61818, 0.25 #9836), 071vr (0.53 #15258, 0.49 #23628, 0.48 #42178), 052p7 (0.37 #16714, 0.29 #16713, 0.07 #29811), 0hn4h (0.33 #720, 0.26 #61818, 0.24 #82907), 0r6c4 (0.33 #1397, 0.25 #2851, 0.09 #7575), 01qh7 (0.33 #61, 0.18 #7328, 0.17 #8417), 0snty (0.33 #1040, 0.02 #28307, 0.02 #31581), 04jpl (0.29 #12721, 0.26 #61818, 0.24 #82907), 0r6cx (0.26 #61818, 0.25 #2426, 0.24 #82907) >> Best rule #18184 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 01xdn1; 0178g; 0hsb3; >> query: (?x11303, 02_286) <- service_location(?x11303, ?x94), citytown(?x11303, ?x1658), company(?x346, ?x11303), film_regional_debut_venue(?x1283, ?x1658), location(?x483, ?x1658) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #12721 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 0c0sl; *> query: (?x11303, 04jpl) <- service_language(?x11303, ?x2164), citytown(?x11303, ?x9559), organization(?x4682, ?x11303), place_founded(?x3636, ?x9559), capital(?x252, ?x9559) *> conf = 0.29 ranks of expected_values: 9 EVAL 03_c8p citytown 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 229.000 229.000 0.739 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #5386-077q8x PRED entity: 077q8x PRED relation: film_release_region PRED expected values: 0chghy 082fr => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 136): 0chghy (0.85 #1028, 0.79 #859, 0.79 #1538), 0345h (0.81 #1055, 0.75 #209, 0.73 #1565), 03rjj (0.80 #174, 0.80 #1020, 0.78 #851), 03gj2 (0.77 #1046, 0.68 #2063, 0.68 #1556), 03_3d (0.76 #1022, 0.76 #853, 0.75 #176), 07ssc (0.75 #1035, 0.72 #2052, 0.72 #1545), 03h64 (0.75 #1093, 0.72 #247, 0.70 #1603), 015fr (0.73 #1037, 0.66 #1547, 0.65 #2054), 05qhw (0.71 #1033, 0.64 #1543, 0.64 #2050), 05b4w (0.71 #1090, 0.68 #1600, 0.66 #2107) >> Best rule #1028 for best value: >> intensional similarity = 6 >> extensional distance = 219 >> proper extension: 0gtsx8c; >> query: (?x6169, 0chghy) <- film_release_region(?x6169, ?x985), film_release_region(?x6169, ?x304), film_release_region(?x6169, ?x142), ?x985 = 0k6nt, ?x304 = 0d0vqn, ?x142 = 0jgd >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 45 EVAL 077q8x film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 99.000 99.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 077q8x film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.851 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5385-01438g PRED entity: 01438g PRED relation: film PRED expected values: 01rnly => 113 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1097): 05hjnw (0.50 #840, 0.06 #67756, 0.01 #2623), 0bpm4yw (0.17 #721, 0.01 #2504, 0.01 #23900), 02qr3k8 (0.08 #8416, 0.08 #10199, 0.02 #54774), 017gl1 (0.08 #143, 0.06 #67756, 0.04 #9058), 0cqr0q (0.08 #1493, 0.06 #67756, 0.01 #3276), 049xgc (0.08 #970, 0.06 #67756, 0.01 #2753), 0btpm6 (0.08 #1299, 0.06 #67756), 027m5wv (0.08 #1052, 0.06 #67756), 08nvyr (0.08 #764, 0.06 #67756), 04jkpgv (0.08 #235, 0.06 #67756) >> Best rule #840 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 02js6_; 02pjvc; 01yfm8; >> query: (?x3078, 05hjnw) <- film(?x3078, ?x2571), award_nominee(?x2443, ?x3078), ?x2443 = 0237fw >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5132 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 012_53; *> query: (?x3078, 01rnly) <- film(?x3078, ?x2571), type_of_union(?x3078, ?x566), friend(?x3078, ?x6187) *> conf = 0.02 ranks of expected_values: 229 EVAL 01438g film 01rnly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 113.000 69.000 0.500 http://example.org/film/actor/film./film/performance/film #5384-05rfst PRED entity: 05rfst PRED relation: category PRED expected values: 08mbj5d => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #21, 0.27 #47, 0.27 #4) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 610 >> proper extension: 0gx1bnj; 0dq626; 0dtw1x; 0h1cdwq; 0c40vxk; 0gx9rvq; 026mfbr; 07g_0c; 04zyhx; 0cz8mkh; ... >> query: (?x5674, 08mbj5d) <- genre(?x5674, ?x812), film_release_region(?x5674, ?x94), production_companies(?x5674, ?x2549), film_crew_role(?x5674, ?x468) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05rfst category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.275 http://example.org/common/topic/webpage./common/webpage/category #5383-02n72k PRED entity: 02n72k PRED relation: nominated_for PRED expected values: 014kq6 => 122 concepts (46 used for prediction) PRED predicted values (max 10 best out of 201): 014kq6 (0.87 #4349, 0.84 #10884, 0.84 #10642), 02n72k (0.60 #1384, 0.57 #661, 0.47 #10399), 05b_gq (0.47 #10399, 0.44 #2897, 0.12 #892), 0jsf6 (0.22 #1131, 0.05 #3307, 0.04 #3548), 02r_pp (0.17 #4244, 0.11 #2308, 0.09 #3035), 05css_ (0.16 #4258, 0.11 #2322, 0.09 #3049), 01s9vc (0.16 #4338, 0.07 #6754, 0.07 #2402), 05cj_j (0.16 #4148, 0.07 #2212, 0.06 #7048), 03176f (0.15 #2288, 0.09 #3741, 0.09 #3015), 031hcx (0.15 #2367, 0.09 #3094, 0.08 #3820) >> Best rule #4349 for best value: >> intensional similarity = 5 >> extensional distance = 56 >> proper extension: 0gzlb9; >> query: (?x6533, ?x2160) <- genre(?x6533, ?x225), country(?x6533, ?x94), film(?x3692, ?x6533), nominated_for(?x2160, ?x6533), costume_design_by(?x2160, ?x1500) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02n72k nominated_for 014kq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 46.000 0.867 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #5382-0n08r PRED entity: 0n08r PRED relation: film_release_region PRED expected values: 05r4w 059j2 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 134): 06mkj (0.85 #1403, 0.84 #2072, 0.84 #3239), 059j2 (0.84 #1375, 0.83 #3211, 0.80 #2044), 03_3d (0.83 #1344, 0.77 #2013, 0.77 #1678), 03h64 (0.83 #1415, 0.72 #3251, 0.69 #245), 05r4w (0.80 #1339, 0.80 #3175, 0.77 #1673), 0k6nt (0.80 #1366, 0.79 #3202, 0.78 #2035), 07ssc (0.80 #1356, 0.78 #2025, 0.77 #1690), 03gj2 (0.77 #1367, 0.76 #3203, 0.75 #2036), 015fr (0.71 #3194, 0.68 #1358, 0.62 #2027), 0154j (0.69 #1342, 0.67 #3178, 0.63 #172) >> Best rule #1403 for best value: >> intensional similarity = 5 >> extensional distance = 156 >> proper extension: 07s3m4g; >> query: (?x11065, 06mkj) <- titles(?x53, ?x11065), film_release_region(?x11065, ?x5036), film_release_region(?x11065, ?x142), ?x142 = 0jgd, month(?x5036, ?x1459) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1375 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 156 *> proper extension: 07s3m4g; *> query: (?x11065, 059j2) <- titles(?x53, ?x11065), film_release_region(?x11065, ?x5036), film_release_region(?x11065, ?x142), ?x142 = 0jgd, month(?x5036, ?x1459) *> conf = 0.84 ranks of expected_values: 2, 5 EVAL 0n08r film_release_region 059j2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.848 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0n08r film_release_region 05r4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 86.000 0.848 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5381-02czd5 PRED entity: 02czd5 PRED relation: nominated_for! PRED expected values: 027gs1_ => 87 concepts (77 used for prediction) PRED predicted values (max 10 best out of 225): 0m7yy (0.70 #3750, 0.69 #3046, 0.69 #4454), 027gs1_ (0.49 #2059, 0.48 #1355, 0.39 #2293), 0fbtbt (0.38 #1094, 0.33 #2969, 0.31 #2501), 09qs08 (0.37 #1982, 0.32 #2216, 0.30 #1278), 0bdx29 (0.36 #1017, 0.26 #2892, 0.25 #1720), 03ccq3s (0.36 #2016, 0.32 #1312, 0.27 #2250), 09qv3c (0.36 #1914, 0.27 #2148, 0.27 #1210), 0gq9h (0.33 #61, 0.32 #12711, 0.28 #12945), 0gr51 (0.33 #77, 0.25 #5391, 0.19 #15468), 03hl6lc (0.33 #129, 0.25 #5391, 0.19 #15468) >> Best rule #3750 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 02_1q9; >> query: (?x8484, ?x693) <- actor(?x8484, ?x1709), award(?x8484, ?x693), award_nominee(?x1709, ?x495), award_winner(?x1709, ?x2851) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2059 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 01d8yn; *> query: (?x8484, 027gs1_) <- nominated_for(?x693, ?x8484), nominated_for(?x693, ?x4535), ?x4535 = 030cx, award(?x71, ?x693) *> conf = 0.49 ranks of expected_values: 2 EVAL 02czd5 nominated_for! 027gs1_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 77.000 0.698 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5380-030h95 PRED entity: 030h95 PRED relation: film PRED expected values: 06lpmt => 115 concepts (76 used for prediction) PRED predicted values (max 10 best out of 652): 058kh7 (0.24 #3574, 0.16 #14293, 0.15 #16080), 0dj0m5 (0.14 #97, 0.03 #135756, 0.03 #133969), 0h1fktn (0.14 #2754, 0.05 #13474, 0.04 #15261), 02q56mk (0.07 #415, 0.06 #3573, 0.06 #89309), 01cmp9 (0.07 #1047, 0.06 #3573, 0.06 #89309), 046488 (0.07 #849, 0.06 #3573, 0.06 #89309), 02d478 (0.07 #672, 0.06 #3573, 0.06 #89309), 016fyc (0.07 #56, 0.06 #3573, 0.06 #89309), 0g22z (0.07 #16, 0.06 #3573, 0.06 #89309), 08phg9 (0.07 #883, 0.06 #3573, 0.06 #89309) >> Best rule #3574 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 01kph_c; 07mvp; 01nz1q6; >> query: (?x1802, ?x9646) <- award_winner(?x3139, ?x1802), person(?x9646, ?x1802), award_winner(?x1820, ?x3139) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #2469 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 01kph_c; 07mvp; 01nz1q6; *> query: (?x1802, 06lpmt) <- award_winner(?x3139, ?x1802), person(?x9646, ?x1802), award_winner(?x1820, ?x3139) *> conf = 0.03 ranks of expected_values: 91 EVAL 030h95 film 06lpmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 115.000 76.000 0.239 http://example.org/film/actor/film./film/performance/film #5379-0dwsp PRED entity: 0dwsp PRED relation: role! PRED expected values: 050z2 => 72 concepts (44 used for prediction) PRED predicted values (max 10 best out of 703): 050z2 (0.78 #9890, 0.67 #11281, 0.61 #14982), 0137g1 (0.67 #11674, 0.60 #5660, 0.57 #8895), 082brv (0.67 #9507, 0.56 #15061, 0.56 #11360), 0161sp (0.60 #5671, 0.57 #8906, 0.56 #11685), 05qhnq (0.60 #5853, 0.54 #13254, 0.50 #7244), 01wl38s (0.60 #5563, 0.46 #12964, 0.44 #11577), 01nhkxp (0.57 #8263, 0.56 #9645, 0.50 #3638), 01wxdn3 (0.56 #11967, 0.56 #10112, 0.56 #9650), 0lzkm (0.56 #9874, 0.56 #9412, 0.50 #7106), 02s6sh (0.56 #10137, 0.56 #9675, 0.50 #4130) >> Best rule #9890 for best value: >> intensional similarity = 20 >> extensional distance = 7 >> proper extension: 01vdm0; >> query: (?x615, 050z2) <- role(?x615, ?x1969), role(?x615, ?x315), ?x315 = 0l14md, role(?x614, ?x615), role(?x615, ?x4616), role(?x615, ?x1433), role(?x615, ?x745), ?x4616 = 01rhl, role(?x10239, ?x615), group(?x1969, ?x7544), group(?x1969, ?x5279), ?x7544 = 07m4c, ?x1433 = 0239kh, gender(?x10239, ?x231), instrumentalists(?x1969, ?x2731), instrumentalists(?x1969, ?x1654), ?x745 = 01vj9c, ?x5279 = 06nv27, ?x1654 = 01bpc9, award(?x2731, ?x567) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dwsp role! 050z2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 44.000 0.778 http://example.org/music/artist/track_contributions./music/track_contribution/role #5378-0gzy02 PRED entity: 0gzy02 PRED relation: film! PRED expected values: 0g1rw => 78 concepts (52 used for prediction) PRED predicted values (max 10 best out of 41): 0g1rw (0.25 #7, 0.20 #79, 0.13 #223), 086k8 (0.25 #2, 0.20 #723, 0.20 #435), 016tt2 (0.20 #75, 0.15 #147, 0.15 #868), 05qd_ (0.19 #224, 0.17 #801, 0.16 #873), 03xq0f (0.15 #76, 0.15 #869, 0.14 #797), 01795t (0.13 #664, 0.13 #808, 0.13 #880), 024rgt (0.12 #161, 0.05 #305, 0.04 #1607), 054g1r (0.11 #681, 0.10 #897, 0.09 #825), 01gb54 (0.07 #603, 0.06 #170, 0.06 #675), 0jz9f (0.07 #1663, 0.07 #1011, 0.07 #1591) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04954r; 0bbgly; >> query: (?x327, 0g1rw) <- film(?x1567, ?x327), genre(?x327, ?x53), nominated_for(?x500, ?x327), ?x1567 = 0f2df >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gzy02 film! 0g1rw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 52.000 0.250 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5377-050ks PRED entity: 050ks PRED relation: state_province_region! PRED expected values: 01f99l => 180 concepts (125 used for prediction) PRED predicted values (max 10 best out of 744): 01c333 (0.44 #72643, 0.29 #57661, 0.25 #82386), 0c4kv (0.25 #82386, 0.23 #21709, 0.23 #26198), 0cf_n (0.25 #82386, 0.23 #21709, 0.23 #26198), 0tr3p (0.25 #82386, 0.23 #21709, 0.23 #26198), 0nm9y (0.23 #21709, 0.23 #26198, 0.19 #65149), 0tnkg (0.23 #21709, 0.23 #26198, 0.19 #65149), 0nm87 (0.23 #21709, 0.23 #26198, 0.19 #65149), 0nm9h (0.23 #21709, 0.23 #26198, 0.19 #65149), 0nm3n (0.23 #21709, 0.23 #26198, 0.19 #65149), 0nm6k (0.23 #21709, 0.23 #26198, 0.19 #65149) >> Best rule #72643 for best value: >> intensional similarity = 3 >> extensional distance = 222 >> proper extension: 037n3; >> query: (?x7058, ?x8822) <- contains(?x7058, ?x8822), country(?x7058, ?x94), school_type(?x8822, ?x3092) >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 050ks state_province_region! 01f99l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 125.000 0.440 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #5376-07m69t PRED entity: 07m69t PRED relation: profession PRED expected values: 0gl2ny2 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 132): 0gl2ny2 (0.76 #1419, 0.67 #3070, 0.65 #2920), 02hrh1q (0.75 #915, 0.69 #5716, 0.69 #6466), 02y5kn (0.44 #2101, 0.20 #437, 0.09 #2838), 01445t (0.29 #1524, 0.21 #1974, 0.20 #2425), 01d_h8 (0.25 #7508, 0.25 #4057, 0.25 #6607), 0dxtg (0.24 #6165, 0.23 #6615, 0.22 #7516), 03gjzk (0.20 #4067, 0.18 #6617, 0.18 #5417), 09jwl (0.19 #4071, 0.19 #4521, 0.18 #5871), 02jknp (0.18 #4059, 0.17 #908, 0.16 #6309), 0cbd2 (0.17 #907, 0.16 #4208, 0.15 #4358) >> Best rule #1419 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 0bn9sc; 0487c3; 080dyk; 02d9k; 083qy7; 054kmq; >> query: (?x8598, 0gl2ny2) <- team(?x8598, ?x6831), location(?x8598, ?x1406), team(?x5763, ?x6831), nationality(?x8598, ?x94), team(?x5763, ?x2355) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07m69t profession 0gl2ny2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.765 http://example.org/people/person/profession #5375-030qb3t PRED entity: 030qb3t PRED relation: locations! PRED expected values: 0c4hgj => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 110): 01w1sx (0.20 #448, 0.13 #569, 0.07 #5774), 0b_6pv (0.18 #4311, 0.17 #2133, 0.15 #4190), 0b_6rk (0.17 #4158, 0.17 #3553, 0.16 #4279), 0b_75k (0.17 #2103, 0.16 #4281, 0.13 #5249), 0b_6x2 (0.16 #4267, 0.13 #4146, 0.12 #758), 0bzrsh (0.15 #4189, 0.14 #5278, 0.12 #4310), 0b_6jz (0.14 #5236, 0.14 #4268, 0.11 #4147), 0b_6zk (0.14 #5232, 0.14 #2086, 0.12 #4264), 0b_6mr (0.14 #5287, 0.14 #2141, 0.12 #6136), 0b_6_l (0.14 #4335, 0.13 #4214, 0.13 #5303) >> Best rule #448 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 022b_; >> query: (?x1523, 01w1sx) <- films(?x1523, ?x6103), jurisdiction_of_office(?x1195, ?x1523) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 030qb3t locations! 0c4hgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 166.000 166.000 0.200 http://example.org/time/event/locations #5374-09tcg4 PRED entity: 09tcg4 PRED relation: film_release_region PRED expected values: 03rjj 0ctw_b 0345h 01znc_ => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 180): 01znc_ (0.91 #185, 0.74 #1068, 0.67 #1510), 03rjj (0.90 #151, 0.85 #1034, 0.78 #1476), 035qy (0.89 #178, 0.81 #1061, 0.68 #1503), 0345h (0.87 #176, 0.85 #1059, 0.75 #1501), 0b90_r (0.87 #150, 0.73 #1033, 0.61 #1475), 03h64 (0.85 #207, 0.81 #1090, 0.72 #1532), 06bnz (0.79 #188, 0.72 #1071, 0.60 #1513), 05v8c (0.76 #160, 0.56 #1043, 0.46 #1485), 04gzd (0.68 #155, 0.52 #1038, 0.40 #1480), 047yc (0.63 #171, 0.46 #1054, 0.37 #1496) >> Best rule #185 for best value: >> intensional similarity = 6 >> extensional distance = 80 >> proper extension: 0ds35l9; 0c3ybss; 0h1cdwq; 05p1tzf; 087wc7n; 01vksx; 0bwfwpj; 02d44q; 0jjy0; 0h3xztt; ... >> query: (?x10048, 01znc_) <- film_release_region(?x10048, ?x1475), film_release_region(?x10048, ?x583), film_release_region(?x10048, ?x87), ?x87 = 05r4w, ?x583 = 015fr, ?x1475 = 05qx1 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 14 EVAL 09tcg4 film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.915 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09tcg4 film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 78.000 0.915 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09tcg4 film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 78.000 78.000 0.915 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09tcg4 film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.915 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5373-01vh096 PRED entity: 01vh096 PRED relation: profession PRED expected values: 05z96 => 161 concepts (122 used for prediction) PRED predicted values (max 10 best out of 94): 02hrh1q (0.82 #14866, 0.75 #15160, 0.75 #14425), 0kyk (0.61 #1793, 0.57 #1940, 0.50 #470), 02jknp (0.59 #8976, 0.47 #10448, 0.47 #1183), 01d_h8 (0.52 #10447, 0.49 #6033, 0.47 #6327), 05z96 (0.43 #1806, 0.42 #1512, 0.33 #2541), 03gjzk (0.33 #1190, 0.31 #9130, 0.29 #3983), 0d8qb (0.28 #9559, 0.28 #10883, 0.26 #7939), 04cvn_ (0.28 #9559, 0.28 #10883, 0.26 #7939), 015btn (0.28 #10883, 0.26 #7939, 0.20 #1130), 0n1h (0.28 #10883, 0.26 #7939, 0.14 #3686) >> Best rule #14866 for best value: >> intensional similarity = 6 >> extensional distance = 388 >> proper extension: 01vrx3g; 01ty7ll; 0f0p0; 07z1_q; 03bpn6; 0d9xq; 01l1rw; 0bdlj; 01d5vk; 03h_yfh; ... >> query: (?x8700, 02hrh1q) <- people(?x6260, ?x8700), profession(?x8700, ?x987), profession(?x9796, ?x987), profession(?x2092, ?x987), ?x2092 = 02_j7t, ?x9796 = 03c5f7l >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1806 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 26 *> proper extension: 019z7q; 081lh; 045bg; 016hvl; 017r2; 04xjp; 03pm9; 085pr; 0p_47; 06whf; ... *> query: (?x8700, 05z96) <- influenced_by(?x2161, ?x8700), profession(?x8700, ?x6421), location(?x8700, ?x5498), gender(?x8700, ?x231), ?x6421 = 02hv44_ *> conf = 0.43 ranks of expected_values: 5 EVAL 01vh096 profession 05z96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 161.000 122.000 0.821 http://example.org/people/person/profession #5372-023n39 PRED entity: 023n39 PRED relation: nationality PRED expected values: 09c7w0 => 143 concepts (49 used for prediction) PRED predicted values (max 10 best out of 52): 09c7w0 (0.89 #2237, 0.87 #2843, 0.87 #3459), 01mjq (0.27 #1112, 0.27 #1010, 0.26 #1314), 03rt9 (0.25 #13, 0.12 #113, 0.02 #4485), 02qkt (0.23 #4984, 0.23 #4983, 0.08 #2236), 02j9z (0.23 #4984, 0.23 #4983, 0.08 #2236), 059rby (0.23 #4984, 0.23 #4983), 0345h (0.17 #838, 0.15 #636, 0.13 #1143), 02jx1 (0.17 #1245, 0.17 #941, 0.14 #1043), 07ssc (0.17 #1227, 0.16 #1432, 0.14 #1738), 02_286 (0.14 #1111, 0.14 #1009, 0.13 #1313) >> Best rule #2237 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 03bw6; >> query: (?x6849, 09c7w0) <- place_of_birth(?x6849, ?x739), people(?x913, ?x6849), ?x739 = 02_286 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023n39 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 49.000 0.888 http://example.org/people/person/nationality #5371-01jft4 PRED entity: 01jft4 PRED relation: titles! PRED expected values: 01z4y => 120 concepts (86 used for prediction) PRED predicted values (max 10 best out of 67): 07s9rl0 (0.41 #1323, 0.39 #6953, 0.36 #1222), 024qqx (0.32 #486, 0.30 #587, 0.17 #282), 07c52 (0.29 #914, 0.19 #4316, 0.08 #8416), 04xvlr (0.29 #714, 0.26 #6956, 0.25 #1326), 01z4y (0.29 #35, 0.24 #2581, 0.23 #4219), 09b3v (0.20 #353, 0.07 #3005, 0.04 #5976), 03k9fj (0.17 #221, 0.17 #120, 0.07 #2975), 01jfsb (0.16 #1138, 0.14 #1445, 0.14 #6972), 017fp (0.15 #836, 0.10 #1449, 0.10 #6976), 07ssc (0.12 #4914, 0.12 #5425, 0.11 #5119) >> Best rule #1323 for best value: >> intensional similarity = 4 >> extensional distance = 135 >> proper extension: 0bhwhj; 016ztl; 012jfb; >> query: (?x7248, 07s9rl0) <- titles(?x1510, ?x7248), written_by(?x7248, ?x975), film(?x5636, ?x7248), films(?x2008, ?x7248) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #35 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 032016; *> query: (?x7248, 01z4y) <- cinematography(?x7248, ?x7249), executive_produced_by(?x7248, ?x975), film(?x237, ?x7248), ?x7249 = 027t8fw *> conf = 0.29 ranks of expected_values: 5 EVAL 01jft4 titles! 01z4y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 120.000 86.000 0.409 http://example.org/media_common/netflix_genre/titles #5370-0dc_ms PRED entity: 0dc_ms PRED relation: genre PRED expected values: 03k9fj => 85 concepts (45 used for prediction) PRED predicted values (max 10 best out of 155): 07s9rl0 (0.74 #3787, 0.68 #3669, 0.68 #4143), 01jfsb (0.53 #13, 0.50 #3443, 0.49 #1785), 05p553 (0.51 #3198, 0.42 #831, 0.39 #2012), 03k9fj (0.47 #1666, 0.46 #1784, 0.46 #485), 02l7c8 (0.29 #725, 0.28 #843, 0.27 #607), 0btmb (0.24 #559, 0.09 #1740, 0.09 #1858), 02n4kr (0.24 #8, 0.16 #244, 0.15 #3320), 04xvlr (0.22 #3670, 0.19 #120, 0.15 #2956), 0lsxr (0.22 #600, 0.22 #3439, 0.21 #1426), 060__y (0.19 #135, 0.19 #3685, 0.17 #3803) >> Best rule #3787 for best value: >> intensional similarity = 5 >> extensional distance = 570 >> proper extension: 03t79f; >> query: (?x6528, 07s9rl0) <- film(?x541, ?x6528), film(?x12856, ?x6528), genre(?x6528, ?x225), genre(?x10158, ?x225), ?x10158 = 026hh0m >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1666 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 157 *> proper extension: 08cfr1; *> query: (?x6528, 03k9fj) <- film(?x9655, ?x6528), country(?x6528, ?x94), genre(?x6528, ?x1013), ?x1013 = 06n90, award_nominee(?x450, ?x9655) *> conf = 0.47 ranks of expected_values: 4 EVAL 0dc_ms genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 45.000 0.738 http://example.org/film/film/genre #5369-082gq PRED entity: 082gq PRED relation: genre! PRED expected values: 011yrp 02__34 02yvct 0m9p3 09rsjpv 04nm0n0 0bh8tgs 048xyn 016ywb 07xvf 0gy0l_ 014knw 07l450 06bc59 09qycb 0gys2jp => 41 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1728): 09q23x (0.71 #21436, 0.60 #17994, 0.50 #19715), 0jqd3 (0.71 #23411, 0.60 #16527, 0.50 #13086), 07w8fz (0.71 #21135, 0.50 #19414, 0.50 #14251), 05znbh7 (0.62 #25111, 0.60 #16505, 0.50 #14784), 03_wm6 (0.62 #26894, 0.40 #16567, 0.33 #7967), 0gtv7pk (0.62 #25859, 0.40 #15532, 0.33 #6932), 0gd0c7x (0.62 #26109, 0.40 #15782, 0.33 #7182), 02vw1w2 (0.62 #26011, 0.40 #15684, 0.33 #7084), 0401sg (0.62 #25896, 0.40 #15569, 0.33 #6969), 03kg2v (0.60 #15939, 0.57 #22823, 0.50 #14218) >> Best rule #21436 for best value: >> intensional similarity = 17 >> extensional distance = 5 >> proper extension: 03mqtr; 01f9r0; >> query: (?x3515, 09q23x) <- genre(?x7114, ?x3515), genre(?x6446, ?x3515), genre(?x5570, ?x3515), genre(?x4993, ?x3515), genre(?x4118, ?x3515), genre(?x3638, ?x3515), genre(?x810, ?x3515), nominated_for(?x154, ?x5570), film_release_region(?x6446, ?x87), nominated_for(?x2626, ?x4118), film(?x541, ?x3638), ?x4993 = 046488, film_crew_role(?x7114, ?x137), language(?x7114, ?x254), film(?x92, ?x4118), film_crew_role(?x4118, ?x468), nominated_for(?x810, ?x6222) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #24636 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 6 *> proper extension: 02p0szs; *> query: (?x3515, 09rsjpv) <- genre(?x6446, ?x3515), genre(?x5570, ?x3515), genre(?x5169, ?x3515), genre(?x4118, ?x3515), genre(?x3638, ?x3515), genre(?x2402, ?x3515), genre(?x1048, ?x3515), nominated_for(?x154, ?x5570), film_release_region(?x6446, ?x1475), nominated_for(?x2626, ?x4118), film(?x541, ?x3638), film_format(?x3638, ?x6392), ?x1475 = 05qx1, film(?x92, ?x4118), titles(?x53, ?x5169), ?x1048 = 048scx, production_companies(?x2402, ?x7980) *> conf = 0.50 ranks of expected_values: 83, 119, 138, 288, 325, 335, 406, 422, 651, 739, 762, 1191, 1271, 1303, 1543, 1546 EVAL 082gq genre! 0gys2jp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 09qycb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 06bc59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 07l450 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 014knw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 0gy0l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 07xvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 016ywb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 048xyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 0bh8tgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 04nm0n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 09rsjpv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 0m9p3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 02__34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 20.000 0.714 http://example.org/film/film/genre EVAL 082gq genre! 011yrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 41.000 20.000 0.714 http://example.org/film/film/genre #5368-03x6m PRED entity: 03x6m PRED relation: colors PRED expected values: 01g5v => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 16): 06fvc (0.81 #747, 0.58 #461, 0.50 #938), 083jv (0.75 #937, 0.67 #784, 0.64 #632), 01g5v (0.41 #500, 0.39 #1110, 0.38 #996), 04mkbj (0.33 #67, 0.16 #1993, 0.15 #1509), 01l849 (0.31 #1164, 0.30 #305, 0.22 #363), 02rnmb (0.30 #305, 0.22 #363, 0.20 #146), 038hg (0.30 #305, 0.22 #363, 0.19 #897), 088fh (0.30 #305, 0.22 #363, 0.19 #897), 06kqt3 (0.30 #305, 0.22 #363, 0.19 #897), 036k5h (0.30 #305, 0.22 #363, 0.19 #897) >> Best rule #747 for best value: >> intensional similarity = 9 >> extensional distance = 57 >> proper extension: 04czcb; >> query: (?x8750, 06fvc) <- team(?x203, ?x8750), team(?x63, ?x8750), team(?x60, ?x8750), colors(?x8750, ?x4557), ?x60 = 02nzb8, ?x63 = 02sdk9v, ?x203 = 0dgrmp, colors(?x3416, ?x4557), ?x3416 = 02183k >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #500 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 25 *> proper extension: 01xn5th; 035l_9; *> query: (?x8750, 01g5v) <- team(?x530, ?x8750), team(?x203, ?x8750), team(?x60, ?x8750), colors(?x8750, ?x4557), ?x60 = 02nzb8, teams(?x8956, ?x8750), ?x203 = 0dgrmp, ?x530 = 02_j1w, sport(?x8750, ?x471), location(?x8476, ?x8956) *> conf = 0.41 ranks of expected_values: 3 EVAL 03x6m colors 01g5v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 118.000 118.000 0.814 http://example.org/sports/sports_team/colors #5367-0p3sf PRED entity: 0p3sf PRED relation: profession PRED expected values: 09jwl => 76 concepts (31 used for prediction) PRED predicted values (max 10 best out of 72): 09jwl (0.74 #882, 0.73 #2327, 0.71 #1172), 0dz3r (0.63 #866, 0.49 #2311, 0.43 #2746), 0nbcg (0.54 #894, 0.54 #2339, 0.50 #30), 039v1 (0.50 #35, 0.32 #2344, 0.30 #2489), 016z4k (0.42 #2025, 0.42 #4199, 0.40 #2313), 01c8w0 (0.30 #584, 0.20 #728, 0.18 #1017), 0gbbt (0.25 #9, 0.09 #873, 0.08 #2463), 09j9h (0.25 #73, 0.01 #937), 0dxtg (0.23 #445, 0.22 #3485, 0.22 #3340), 01d_h8 (0.21 #294, 0.21 #1449, 0.19 #3478) >> Best rule #882 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 053y0s; 07q1v4; 01vrz41; 09qr6; 0l12d; 01v_pj6; 06k02; 09hnb; 01vn35l; 0m_v0; ... >> query: (?x3171, 09jwl) <- role(?x3171, ?x3112), role(?x3171, ?x228), performance_role(?x3112, ?x315), role(?x3112, ?x212), ?x228 = 0l14qv >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p3sf profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 31.000 0.743 http://example.org/people/person/profession #5366-03115z PRED entity: 03115z PRED relation: languages_spoken! PRED expected values: 0d2by => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 71): 07hwkr (0.55 #935, 0.55 #864, 0.53 #2710), 04czx7 (0.50 #352, 0.50 #281, 0.50 #210), 0d2by (0.50 #314, 0.50 #243, 0.50 #172), 071x0k (0.50 #79, 0.33 #1215, 0.27 #1002), 059_w (0.50 #98, 0.29 #524, 0.27 #1021), 0x67 (0.50 #81, 0.25 #223, 0.20 #365), 02vsw1 (0.44 #826, 0.37 #1607, 0.36 #968), 03w9bjf (0.40 #403, 0.33 #474, 0.29 #687), 078vc (0.40 #397, 0.33 #468, 0.29 #681), 04gfy7 (0.33 #1905, 0.29 #556, 0.27 #2615) >> Best rule #935 for best value: >> intensional similarity = 16 >> extensional distance = 9 >> proper extension: 06mp7; >> query: (?x10296, 07hwkr) <- language(?x1625, ?x10296), countries_spoken_in(?x10296, ?x2629), languages_spoken(?x9979, ?x10296), languages(?x7835, ?x10296), costume_design_by(?x1625, ?x9086), genre(?x1625, ?x53), award_winner(?x1625, ?x4169), film_release_region(?x8682, ?x2629), film_release_region(?x7554, ?x2629), film_release_region(?x5162, ?x2629), film_release_region(?x2501, ?x2629), ?x5162 = 0j3d9tn, combatants(?x2629, ?x94), ?x7554 = 01mgw, ?x2501 = 040rmy, production_companies(?x8682, ?x9518) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #314 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 01r2l; *> query: (?x10296, 0d2by) <- language(?x5795, ?x10296), language(?x1625, ?x10296), countries_spoken_in(?x10296, ?x2629), languages_spoken(?x9979, ?x10296), languages(?x8801, ?x10296), costume_design_by(?x1625, ?x9086), ?x9979 = 04l_pt, film(?x794, ?x5795), genre(?x5795, ?x53), profession(?x8801, ?x1032), award_winner(?x1972, ?x8801), award(?x8801, ?x1132), ?x53 = 07s9rl0 *> conf = 0.50 ranks of expected_values: 3 EVAL 03115z languages_spoken! 0d2by CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 51.000 0.545 http://example.org/people/ethnicity/languages_spoken #5365-0h1fktn PRED entity: 0h1fktn PRED relation: film! PRED expected values: 03cxsvl 02qw2xb => 56 concepts (35 used for prediction) PRED predicted values (max 10 best out of 860): 07sgfsl (0.33 #6227, 0.14 #12454, 0.12 #14531), 018ygt (0.33 #1115, 0.08 #21874, 0.06 #32253), 0205dx (0.33 #847, 0.06 #21606, 0.04 #31985), 01q_ph (0.33 #57, 0.05 #20816, 0.04 #35349), 0p_pd (0.33 #54, 0.04 #35346, 0.03 #39501), 039bp (0.33 #180, 0.04 #41704, 0.04 #45855), 01j5ts (0.33 #29, 0.02 #16636, 0.02 #35321), 04yyhw (0.33 #2074, 0.01 #37366), 07y8l9 (0.31 #7197, 0.29 #9272, 0.23 #17577), 051wwp (0.26 #17479, 0.23 #7099, 0.21 #9174) >> Best rule #6227 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04969y; >> query: (?x5639, ?x823) <- titles(?x12316, ?x5639), ?x12316 = 01j28z, genre(?x5639, ?x307), person(?x5639, ?x823) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2076 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 01gkp1; *> query: (?x5639, ?x806) <- film(?x8896, ?x5639), film(?x3553, ?x5639), award_nominee(?x8896, ?x806), student(?x1771, ?x8896), actor(?x5060, ?x8896), ?x3553 = 0bq2g *> conf = 0.10 ranks of expected_values: 57, 59 EVAL 0h1fktn film! 02qw2xb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 56.000 35.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 0h1fktn film! 03cxsvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 56.000 35.000 0.333 http://example.org/film/actor/film./film/performance/film #5364-015pvh PRED entity: 015pvh PRED relation: profession PRED expected values: 03gjzk 015cjr => 82 concepts (67 used for prediction) PRED predicted values (max 10 best out of 56): 01d_h8 (0.67 #1734, 0.36 #582, 0.34 #2743), 03gjzk (0.26 #1741, 0.25 #8788, 0.24 #4911), 09jwl (0.25 #8788, 0.25 #16, 0.18 #304), 0cbd2 (0.16 #1015, 0.15 #2888, 0.15 #871), 0nbcg (0.14 #315, 0.11 #891, 0.11 #8094), 0n1h (0.14 #299, 0.06 #1451, 0.06 #2027), 0d1pc (0.13 #2062, 0.12 #1630, 0.12 #1486), 0kyk (0.12 #2906, 0.12 #1033, 0.11 #889), 016z4k (0.11 #1444, 0.10 #2309, 0.10 #2020), 0dz3r (0.10 #8069, 0.10 #6628, 0.10 #7349) >> Best rule #1734 for best value: >> intensional similarity = 3 >> extensional distance = 594 >> proper extension: 04qr6d; 03p01x; 0jnb0; 0894_x; 01qnfc; 06101p; >> query: (?x6255, 01d_h8) <- nationality(?x6255, ?x94), profession(?x6255, ?x524), ?x524 = 02jknp >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1741 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 594 *> proper extension: 04qr6d; 03p01x; 0jnb0; 0894_x; 01qnfc; 06101p; *> query: (?x6255, 03gjzk) <- nationality(?x6255, ?x94), profession(?x6255, ?x524), ?x524 = 02jknp *> conf = 0.26 ranks of expected_values: 2, 24 EVAL 015pvh profession 015cjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 82.000 67.000 0.666 http://example.org/people/person/profession EVAL 015pvh profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 67.000 0.666 http://example.org/people/person/profession #5363-0crc2cp PRED entity: 0crc2cp PRED relation: film! PRED expected values: 0151ns => 76 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1006): 0154qm (0.19 #6809, 0.04 #64550, 0.04 #72884), 016zp5 (0.19 #7225, 0.04 #64550, 0.04 #72884), 03ym1 (0.16 #7260, 0.04 #64550, 0.04 #9344), 016ypb (0.15 #6746, 0.04 #64550, 0.04 #72884), 07swvb (0.14 #698, 0.03 #2780, 0.03 #4862), 062dn7 (0.14 #662, 0.03 #2744, 0.02 #8993), 07ldhs (0.14 #887, 0.03 #2969, 0.02 #9218), 0fthdk (0.14 #1594, 0.03 #3676, 0.01 #45321), 02w29z (0.14 #3496, 0.05 #9745, 0.05 #15991), 01ps2h8 (0.13 #7188, 0.04 #64550, 0.04 #72884) >> Best rule #6809 for best value: >> intensional similarity = 5 >> extensional distance = 92 >> proper extension: 05jf85; 02qrv7; 0dnqr; 032016; 017kct; 0pd6l; 0prhz; 016ywb; 02qr3k8; 0dl6fv; ... >> query: (?x3191, 0154qm) <- film(?x629, ?x3191), award_winner(?x2762, ?x629), student(?x9844, ?x629), ?x2762 = 015t56, award_winner(?x704, ?x629) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #4258 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 014nq4; 01hqk; 01s3vk; 063hp4; 0dp7wt; *> query: (?x3191, 0151ns) <- film_crew_role(?x3191, ?x2178), ?x2178 = 01pvkk, country(?x3191, ?x789), production_companies(?x3191, ?x9997), story_by(?x3191, ?x6420) *> conf = 0.03 ranks of expected_values: 376 EVAL 0crc2cp film! 0151ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 45.000 0.191 http://example.org/film/actor/film./film/performance/film #5362-09hy79 PRED entity: 09hy79 PRED relation: nominated_for! PRED expected values: 094qd5 => 73 concepts (63 used for prediction) PRED predicted values (max 10 best out of 187): 099c8n (0.38 #56, 0.30 #526, 0.22 #996), 0gq9h (0.36 #7817, 0.35 #6877, 0.30 #8287), 0gs9p (0.32 #6879, 0.30 #7819, 0.27 #8289), 019f4v (0.29 #7808, 0.28 #6868, 0.26 #2168), 0fhpv4 (0.29 #139, 0.12 #12694, 0.11 #11283), 0k611 (0.28 #7827, 0.25 #6887, 0.25 #2187), 0gr4k (0.26 #1200, 0.25 #7780, 0.24 #1670), 0gq_v (0.25 #6833, 0.25 #1663, 0.24 #7773), 02n9nmz (0.25 #997, 0.22 #1232, 0.19 #57), 040njc (0.24 #6821, 0.23 #7761, 0.20 #8231) >> Best rule #56 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0g5838s; 093dqjy; 03hmt9b; 05f4_n0; 02d49z; 09gmmt6; 0bs8s1p; 0466s8n; >> query: (?x7012, 099c8n) <- nominated_for(?x11466, ?x7012), film(?x447, ?x7012), film_crew_role(?x7012, ?x281), ?x11466 = 099flj >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1210 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 192 *> proper extension: 06bc59; *> query: (?x7012, 094qd5) <- genre(?x7012, ?x1509), ?x1509 = 060__y, country(?x7012, ?x94), film(?x447, ?x7012) *> conf = 0.14 ranks of expected_values: 39 EVAL 09hy79 nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 73.000 63.000 0.381 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5361-01mhwk PRED entity: 01mhwk PRED relation: award_winner PRED expected values: 01wp8w7 01vsykc 017vkx 01jkqfz 01kp_1t => 43 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1742): 0gcs9 (0.70 #15472, 0.62 #10962, 0.62 #9456), 01vw20h (0.62 #9703, 0.60 #15719, 0.50 #11209), 01w60_p (0.57 #7818, 0.40 #13835, 0.37 #18348), 0lbj1 (0.57 #7542, 0.33 #3029, 0.33 #23), 01lmj3q (0.50 #10562, 0.50 #9056, 0.43 #7551), 0g824 (0.50 #15987, 0.50 #6964, 0.38 #11477), 0dw4g (0.50 #14376, 0.44 #12873, 0.43 #8359), 0fpjd_g (0.50 #9229, 0.43 #7724, 0.42 #18254), 05pdbs (0.50 #6178, 0.40 #16705, 0.40 #15201), 02l840 (0.50 #15139, 0.40 #16643, 0.38 #10629) >> Best rule #15472 for best value: >> intensional similarity = 20 >> extensional distance = 8 >> proper extension: 05pd94v; 02rjjll; 056878; 0466p0j; >> query: (?x2704, 0gcs9) <- ceremony(?x11456, ?x2704), ceremony(?x8141, ?x2704), ceremony(?x2139, ?x2704), ceremony(?x1361, ?x2704), award_winner(?x2704, ?x6939), award_winner(?x2704, ?x5815), award_winner(?x2704, ?x1660), award_winner(?x2704, ?x1504), ?x8141 = 024_41, role(?x5815, ?x1332), ?x1361 = 01c9f2, artist(?x2241, ?x6939), role(?x1504, ?x316), ?x11456 = 03q27t, person(?x6125, ?x5815), ?x2139 = 01by1l, currency(?x1660, ?x170), award_winner(?x6104, ?x1504), nationality(?x1504, ?x94), instance_of_recurring_event(?x2704, ?x2421) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2802 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 1 *> proper extension: 09n4nb; *> query: (?x2704, 01kp_1t) <- ceremony(?x12833, ?x2704), ceremony(?x10556, ?x2704), ceremony(?x8141, ?x2704), ceremony(?x4796, ?x2704), ceremony(?x3647, ?x2704), award_winner(?x2704, ?x7601), award_winner(?x2704, ?x1504), award_winner(?x2704, ?x1092), ?x8141 = 024_41, ?x1504 = 02r4qs, ?x3647 = 01c9jp, ?x10556 = 02flq1, ?x12833 = 0257pw, award_nominee(?x1128, ?x7601), location(?x1092, ?x1705), artists(?x505, ?x1092), award(?x7601, ?x462), ?x4796 = 01c99j, ?x505 = 03_d0 *> conf = 0.33 ranks of expected_values: 87, 133, 150, 459, 536 EVAL 01mhwk award_winner 01kp_1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 43.000 16.000 0.700 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01mhwk award_winner 01jkqfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 43.000 16.000 0.700 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01mhwk award_winner 017vkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 16.000 0.700 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01mhwk award_winner 01vsykc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 16.000 0.700 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01mhwk award_winner 01wp8w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 43.000 16.000 0.700 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5360-02v63m PRED entity: 02v63m PRED relation: genre PRED expected values: 0219x_ => 67 concepts (50 used for prediction) PRED predicted values (max 10 best out of 84): 07s9rl0 (0.74 #5351, 0.70 #4069, 0.57 #2905), 03k9fj (0.38 #2682, 0.25 #3148, 0.25 #938), 02kdv5l (0.35 #931, 0.28 #583, 0.28 #1048), 02l7c8 (0.33 #130, 0.32 #4082, 0.30 #246), 0lsxr (0.33 #123, 0.21 #5357, 0.17 #2911), 01j1n2 (0.26 #289, 0.04 #753, 0.04 #1801), 09blyk (0.24 #378, 0.06 #1890, 0.05 #1309), 09q17 (0.22 #174, 0.04 #522, 0.03 #406), 01t_vv (0.22 #283, 0.14 #515, 0.13 #1213), 06n90 (0.21 #359, 0.19 #1173, 0.15 #939) >> Best rule #5351 for best value: >> intensional similarity = 3 >> extensional distance = 1360 >> proper extension: 04svwx; >> query: (?x1184, 07s9rl0) <- genre(?x1184, ?x6625), genre(?x6624, ?x6625), ?x6624 = 033qdy >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #25 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 02v5_g; 0prrm; 0yzbg; *> query: (?x1184, 0219x_) <- production_companies(?x1184, ?x10884), titles(?x571, ?x1184), film(?x10212, ?x1184), ?x10212 = 02pzck *> conf = 0.17 ranks of expected_values: 14 EVAL 02v63m genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 67.000 50.000 0.739 http://example.org/film/film/genre #5359-0c4z8 PRED entity: 0c4z8 PRED relation: award! PRED expected values: 02lz1s 01c8v0 01817f 0478__m 01vvlyt 01vsgrn 018gqj 01vrlr4 06rrzn 06rgq 02fgp0 01w5jwb 01f9zw 063t3j => 46 concepts (29 used for prediction) PRED predicted values (max 10 best out of 2236): 018gqj (0.80 #45736, 0.80 #39201, 0.80 #62071), 016vqk (0.80 #45736, 0.80 #39201, 0.80 #62071), 016jll (0.80 #45736, 0.80 #39201, 0.80 #62071), 037lyl (0.80 #45736, 0.80 #39201, 0.80 #62071), 0412f5y (0.80 #45736, 0.80 #39201, 0.80 #62071), 01x6v6 (0.67 #37826, 0.50 #14960, 0.33 #1892), 0146pg (0.67 #36074, 0.33 #140, 0.29 #23006), 01vs_v8 (0.62 #29966, 0.57 #23432, 0.56 #33233), 026spg (0.60 #20908, 0.50 #30708, 0.44 #33975), 01dw9z (0.60 #20300, 0.50 #30100, 0.44 #33367) >> Best rule #45736 for best value: >> intensional similarity = 5 >> extensional distance = 99 >> proper extension: 02581q; 02wh75; 0bfvw2; 02g3gj; 0gkvb7; 01d38g; 0f4x7; 02grdc; 09qvc0; 0bdwft; ... >> query: (?x1232, ?x3607) <- award(?x6418, ?x1232), award_winner(?x1232, ?x3607), category_of(?x1232, ?x2421), ceremony(?x1232, ?x139), artist(?x2149, ?x6418) >> conf = 0.80 => this is the best rule for 5 predicted values ranks of expected_values: 1, 17, 19, 20, 25, 40, 82, 115, 127, 148, 170, 181, 239, 312 EVAL 0c4z8 award! 063t3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 01f9zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 01w5jwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 02fgp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 06rgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 06rrzn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 01vrlr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 018gqj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 01vsgrn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 01vvlyt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 0478__m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 01817f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 01c8v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0c4z8 award! 02lz1s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 46.000 29.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5358-04sh3 PRED entity: 04sh3 PRED relation: major_field_of_study! PRED expected values: 027f2w => 57 concepts (43 used for prediction) PRED predicted values (max 10 best out of 20): 02h4rq6 (0.83 #440, 0.80 #343, 0.79 #304), 019v9k (0.74 #445, 0.74 #612, 0.73 #589), 0bkj86 (0.70 #228, 0.67 #187, 0.64 #425), 03bwzr4 (0.61 #430, 0.60 #449, 0.60 #111), 0bjrnt (0.60 #83, 0.58 #40, 0.50 #185), 01ysy9 (0.60 #119, 0.58 #40, 0.50 #180), 02m4yg (0.58 #40, 0.50 #153, 0.49 #121), 01rr_d (0.58 #40, 0.49 #121, 0.49 #100), 027f2w (0.58 #40, 0.49 #121, 0.49 #100), 071tyz (0.58 #40, 0.49 #121, 0.49 #100) >> Best rule #440 for best value: >> intensional similarity = 12 >> extensional distance = 33 >> proper extension: 036hv; 04_tv; 01jzxy; 0h5k; 04x_3; 0jjw; 0193x; 05qfh; 06mnr; 01zc2w; ... >> query: (?x9111, 02h4rq6) <- major_field_of_study(?x9447, ?x9111), major_field_of_study(?x7818, ?x9111), major_field_of_study(?x1768, ?x9111), major_field_of_study(?x581, ?x9111), major_field_of_study(?x1768, ?x2606), major_field_of_study(?x9111, ?x1468), student(?x1768, ?x920), category(?x7818, ?x134), ?x2606 = 062z7, ?x581 = 06pwq, institution(?x734, ?x1768), citytown(?x9447, ?x9336) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 1 *> proper extension: 02j62; *> query: (?x9111, ?x1200) <- major_field_of_study(?x13316, ?x9111), major_field_of_study(?x7818, ?x9111), major_field_of_study(?x7545, ?x9111), major_field_of_study(?x6925, ?x9111), major_field_of_study(?x2142, ?x9111), major_field_of_study(?x1768, ?x9111), major_field_of_study(?x735, ?x9111), major_field_of_study(?x122, ?x9111), ?x1768 = 09kvv, ?x7545 = 0bwfn, ?x735 = 065y4w7, ?x6925 = 01bm_, ?x122 = 08815, institution(?x1200, ?x7818), ?x2142 = 0dplh, ?x13316 = 01stzp, student(?x9111, ?x10394) *> conf = 0.58 ranks of expected_values: 9 EVAL 04sh3 major_field_of_study! 027f2w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 57.000 43.000 0.829 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #5357-0bz60q PRED entity: 0bz60q PRED relation: location PRED expected values: 0cr3d => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 76): 02cl1 (0.50 #32, 0.17 #836, 0.03 #8042), 07_fl (0.25 #567, 0.17 #1371, 0.02 #16083), 01cx_ (0.25 #163, 0.02 #16083, 0.02 #9009), 02_286 (0.17 #841, 0.13 #1645, 0.10 #42654), 030qb3t (0.17 #887, 0.13 #9733, 0.12 #12145), 013kcv (0.17 #846, 0.03 #8042, 0.02 #1650), 0pmq2 (0.17 #873, 0.02 #16083, 0.01 #1677), 0cr3d (0.05 #9795, 0.04 #33917, 0.04 #42762), 059rby (0.05 #2428, 0.03 #9666, 0.03 #10470), 04jpl (0.04 #8059, 0.04 #33789, 0.04 #9667) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01_x6v; 01_x6d; >> query: (?x7000, 02cl1) <- award_winner(?x3972, ?x7000), ?x3972 = 02cm2m, profession(?x7000, ?x353) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9795 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 574 *> proper extension: 02f8lw; *> query: (?x7000, 0cr3d) <- award_winner(?x1835, ?x7000), award(?x1835, ?x537), people(?x2510, ?x7000) *> conf = 0.05 ranks of expected_values: 8 EVAL 0bz60q location 0cr3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 66.000 66.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #5356-0g6ff PRED entity: 0g6ff PRED relation: people PRED expected values: 0pcc0 01y665 0zm1 03f47xl 05hks => 36 concepts (28 used for prediction) PRED predicted values (max 10 best out of 4224): 0ck91 (0.50 #5000, 0.33 #10160, 0.33 #8438), 0hwqz (0.40 #5989, 0.17 #12865, 0.12 #18015), 0159h6 (0.33 #10312, 0.33 #8652, 0.33 #8592), 014x77 (0.33 #8663, 0.33 #6941, 0.33 #70), 0h0yt (0.33 #9662, 0.33 #7940, 0.33 #1069), 0c9c0 (0.33 #8961, 0.33 #7239, 0.33 #368), 01j2xj (0.33 #9287, 0.33 #7565, 0.33 #694), 025t9b (0.33 #9122, 0.33 #7400, 0.33 #529), 013_vh (0.33 #9118, 0.33 #7396, 0.33 #525), 0kszw (0.33 #8918, 0.33 #7196, 0.33 #325) >> Best rule #5000 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 0d7wh; >> query: (?x5590, 0ck91) <- people(?x5590, ?x5434), people(?x5590, ?x2733), people(?x5590, ?x2536), languages_spoken(?x5590, ?x5671), location(?x2536, ?x3125), nominated_for(?x2733, ?x715), award_nominee(?x2733, ?x488), ?x488 = 0159h6, influenced_by(?x8494, ?x5434), peers(?x7513, ?x8494), student(?x1695, ?x8494) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 041rx; *> query: (?x5590, 01y665) <- people(?x5590, ?x5591), people(?x5590, ?x2733), people(?x5590, ?x2536), languages_spoken(?x5590, ?x5671), location(?x2536, ?x3125), ?x2733 = 0hskw, language(?x8063, ?x5671), language(?x5271, ?x5671), official_language(?x404, ?x5671), ?x5591 = 022_q8, award_winner(?x8063, ?x2596), film_release_region(?x5271, ?x142) *> conf = 0.33 ranks of expected_values: 338, 477, 486, 1432, 1545 EVAL 0g6ff people 05hks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 28.000 0.500 http://example.org/people/ethnicity/people EVAL 0g6ff people 03f47xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 28.000 0.500 http://example.org/people/ethnicity/people EVAL 0g6ff people 0zm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 28.000 0.500 http://example.org/people/ethnicity/people EVAL 0g6ff people 01y665 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 36.000 28.000 0.500 http://example.org/people/ethnicity/people EVAL 0g6ff people 0pcc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 28.000 0.500 http://example.org/people/ethnicity/people #5355-02qyp19 PRED entity: 02qyp19 PRED relation: award! PRED expected values: 01q_ph 0jrqq 01_f_5 0p50v 06dkzt => 56 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2703): 0237fw (0.70 #24025, 0.50 #7315, 0.44 #40735), 06cgy (0.60 #23771, 0.50 #7061, 0.43 #10401), 07r1h (0.60 #25173, 0.43 #11803, 0.33 #8463), 0f502 (0.60 #24608, 0.43 #11238, 0.33 #7898), 0pmhf (0.57 #10702, 0.50 #7362, 0.33 #34097), 05kfs (0.55 #30237, 0.50 #3500, 0.31 #36918), 01713c (0.50 #23780, 0.50 #7070, 0.44 #40490), 022wxh (0.50 #4542, 0.45 #31279, 0.43 #14565), 0170pk (0.50 #7118, 0.43 #10458, 0.40 #23828), 02m501 (0.50 #9451, 0.43 #12791, 0.40 #26161) >> Best rule #24025 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 02x4w6g; 05p09zm; 05ztrmj; >> query: (?x68, 0237fw) <- nominated_for(?x68, ?x4993), nominated_for(?x68, ?x4040), award(?x5316, ?x68), titles(?x53, ?x4993), country(?x4040, ?x94), ?x5316 = 01f6zc >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4398 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0gr51; 03hl6lc; *> query: (?x68, 0jrqq) <- nominated_for(?x68, ?x11417), nominated_for(?x68, ?x4993), ?x4993 = 046488, ?x11417 = 07bxqz, award(?x164, ?x68) *> conf = 0.50 ranks of expected_values: 12, 32, 95, 213, 375 EVAL 02qyp19 award! 06dkzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 56.000 26.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qyp19 award! 0p50v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 26.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qyp19 award! 01_f_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 56.000 26.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qyp19 award! 0jrqq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 56.000 26.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02qyp19 award! 01q_ph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 56.000 26.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5354-0kzy0 PRED entity: 0kzy0 PRED relation: role PRED expected values: 01s0ps 03qjg 0680x0 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 118): 0342h (0.67 #405, 0.59 #1405, 0.58 #305), 042v_gx (0.50 #409, 0.40 #109, 0.35 #909), 05842k (0.50 #675, 0.29 #1875, 0.26 #1775), 018vs (0.45 #1714, 0.32 #2904, 0.32 #8614), 05148p4 (0.44 #23, 0.38 #523, 0.21 #623), 02sgy (0.42 #407, 0.33 #307, 0.32 #1707), 0l14qv (0.38 #506, 0.33 #6, 0.32 #2904), 03qjg (0.33 #461, 0.25 #961, 0.22 #61), 026t6 (0.26 #1703, 0.24 #1803, 0.21 #6109), 0l15bq (0.25 #435, 0.25 #335, 0.20 #135) >> Best rule #405 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0m2l9; 02whj; 01wp8w7; 053yx; >> query: (?x654, 0342h) <- influenced_by(?x654, ?x1930), role(?x654, ?x745), role(?x654, ?x316), ?x316 = 05r5c >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #461 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0m2l9; 02whj; 01wp8w7; 053yx; *> query: (?x654, 03qjg) <- influenced_by(?x654, ?x1930), role(?x654, ?x745), role(?x654, ?x316), ?x316 = 05r5c *> conf = 0.33 ranks of expected_values: 8, 14, 24 EVAL 0kzy0 role 0680x0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 153.000 153.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0kzy0 role 03qjg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 153.000 153.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0kzy0 role 01s0ps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 153.000 153.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #5353-05lfwd PRED entity: 05lfwd PRED relation: category PRED expected values: 08mbj5d => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.46 #12, 0.45 #18, 0.45 #9) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 01h72l; 02kk_c; 07s8z_l; 03czz87; >> query: (?x5808, 08mbj5d) <- honored_for(?x762, ?x5808), program(?x9011, ?x5808), award_winner(?x5808, ?x848), award_nominee(?x9011, ?x1630) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05lfwd category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.465 http://example.org/common/topic/webpage./common/webpage/category #5352-027dtv3 PRED entity: 027dtv3 PRED relation: award_winner! PRED expected values: 0hr3c8y => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 96): 0hr3c8y (0.79 #10, 0.65 #292, 0.47 #151), 0g55tzk (0.36 #137, 0.29 #419, 0.20 #278), 0gx_st (0.17 #3667, 0.13 #178, 0.07 #37), 03gyp30 (0.17 #3667, 0.12 #2091, 0.08 #2937), 092t4b (0.17 #3667, 0.07 #52, 0.07 #193), 09gkdln (0.17 #3667, 0.07 #122, 0.07 #263), 0clfdj (0.17 #3667, 0.07 #4, 0.07 #145), 04n2r9h (0.17 #3667, 0.07 #45, 0.06 #327), 0g5b0q5 (0.17 #3667, 0.07 #161, 0.03 #443), 0hndn2q (0.13 #181, 0.02 #745, 0.02 #886) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0f830f; 08w7vj; 02lfns; 02tr7d; 0fx0mw; 03yj_0n; 07s8hms; 0cjsxp; 0bx0lc; 026v437; ... >> query: (?x561, 0hr3c8y) <- award_winner(?x561, ?x494), actor(?x493, ?x561), ?x494 = 03w1v2 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027dtv3 award_winner! 0hr3c8y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.786 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5351-012z8_ PRED entity: 012z8_ PRED relation: artists! PRED expected values: 06cqb 026z9 0509cr => 111 concepts (71 used for prediction) PRED predicted values (max 10 best out of 250): 025sc50 (0.50 #48, 0.38 #2188, 0.37 #10435), 026z9 (0.50 #73, 0.31 #2213, 0.29 #3131), 07sbbz2 (0.50 #8, 0.23 #1228, 0.21 #2148), 016cjb (0.50 #71, 0.18 #2211, 0.13 #1598), 0glt670 (0.47 #5236, 0.31 #4013, 0.31 #3097), 0155w (0.37 #1323, 0.31 #4382, 0.29 #3466), 0xhtw (0.31 #4295, 0.31 #3379, 0.30 #1850), 016clz (0.29 #19857, 0.28 #20163, 0.27 #4896), 05bt6j (0.26 #19894, 0.24 #10429, 0.23 #20200), 09nwwf (0.25 #133, 0.23 #5024, 0.21 #2885) >> Best rule #48 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 019f9z; 016376; >> query: (?x4576, 025sc50) <- artists(?x9789, ?x4576), artists(?x3928, ?x4576), artists(?x1127, ?x4576), ?x1127 = 02x8m, ?x3928 = 0gywn, ?x9789 = 02b71x >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #73 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 019f9z; 016376; *> query: (?x4576, 026z9) <- artists(?x9789, ?x4576), artists(?x3928, ?x4576), artists(?x1127, ?x4576), ?x1127 = 02x8m, ?x3928 = 0gywn, ?x9789 = 02b71x *> conf = 0.50 ranks of expected_values: 2, 13, 67 EVAL 012z8_ artists! 0509cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 111.000 71.000 0.500 http://example.org/music/genre/artists EVAL 012z8_ artists! 026z9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 71.000 0.500 http://example.org/music/genre/artists EVAL 012z8_ artists! 06cqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 111.000 71.000 0.500 http://example.org/music/genre/artists #5350-02p65p PRED entity: 02p65p PRED relation: award PRED expected values: 02x4w6g 02x8n1n 09qv_s 09sdmz 09qrn4 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 230): 09sb52 (0.83 #36, 0.72 #20153, 0.70 #5926), 0gqy2 (0.25 #157, 0.13 #19361, 0.12 #18965), 0gqwc (0.21 #68, 0.13 #19361, 0.07 #7970), 094qd5 (0.21 #40, 0.13 #19361, 0.06 #7942), 09qwmm (0.21 #29, 0.13 #19361, 0.05 #10668), 0gqyl (0.17 #98, 0.13 #19361, 0.12 #18965), 02ppm4q (0.17 #149, 0.13 #19361, 0.12 #18965), 02z0dfh (0.17 #69, 0.13 #19361, 0.12 #18965), 0789_m (0.17 #16, 0.13 #19361, 0.05 #7918), 0fq9zdn (0.17 #52, 0.02 #1237, 0.02 #5187) >> Best rule #36 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0408np; 0bqdvt; >> query: (?x192, 09sb52) <- award(?x192, ?x112), award_nominee(?x1958, ?x192), ?x1958 = 02wgln >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #19361 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2309 *> proper extension: 02xnjd; *> query: (?x192, ?x451) <- award_nominee(?x4992, ?x192), award(?x4992, ?x451) *> conf = 0.13 ranks of expected_values: 11, 21, 23, 34, 63 EVAL 02p65p award 09qrn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 70.000 70.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02p65p award 09sdmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 70.000 70.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02p65p award 09qv_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 70.000 70.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02p65p award 02x8n1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 70.000 70.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02p65p award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 70.000 70.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5349-0p9tm PRED entity: 0p9tm PRED relation: nominated_for! PRED expected values: 0ptxj => 97 concepts (41 used for prediction) PRED predicted values (max 10 best out of 183): 06fpsx (0.14 #209, 0.03 #963, 0.01 #1465), 03f7nt (0.14 #137, 0.03 #891, 0.01 #1393), 05k2xy (0.14 #69, 0.03 #823, 0.01 #1325), 026gyn_ (0.14 #53, 0.03 #807, 0.01 #1309), 02k1pr (0.14 #221, 0.03 #975, 0.01 #1979), 05j82v (0.14 #41, 0.03 #795), 02jr6k (0.07 #1626, 0.05 #3388, 0.04 #4897), 05cj_j (0.07 #1551, 0.05 #3313, 0.04 #4822), 0k5g9 (0.05 #3347, 0.05 #1585, 0.04 #4856), 075cph (0.05 #3341, 0.05 #1579, 0.04 #4850) >> Best rule #209 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 05cvgl; 0pd64; >> query: (?x7846, 06fpsx) <- award(?x7846, ?x2375), film(?x6558, ?x7846), nominated_for(?x1822, ?x7846), ?x2375 = 04kxsb >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1402 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 0m313; 0ds3t5x; 016fyc; 0pc62; 0209xj; 0jzw; 0dsvzh; 092vkg; 0bshwmp; 09q5w2; ... *> query: (?x7846, 0ptxj) <- award(?x7846, ?x198), film(?x6558, ?x7846), nominated_for(?x1822, ?x7846), honored_for(?x3332, ?x7846) *> conf = 0.01 ranks of expected_values: 126 EVAL 0p9tm nominated_for! 0ptxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 97.000 41.000 0.143 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #5348-026fd PRED entity: 026fd PRED relation: influenced_by PRED expected values: 08433 => 147 concepts (40 used for prediction) PRED predicted values (max 10 best out of 276): 01k9lpl (0.13 #1183, 0.11 #2055, 0.09 #2493), 014z8v (0.13 #994, 0.11 #2304, 0.09 #1866), 01hmk9 (0.11 #1093, 0.09 #2403, 0.08 #3275), 081lh (0.11 #892, 0.09 #2202, 0.08 #3074), 014zfs (0.11 #897, 0.09 #2207, 0.08 #25), 0p_47 (0.11 #980, 0.09 #2290, 0.07 #1852), 081k8 (0.11 #14118, 0.09 #14554, 0.08 #15427), 032l1 (0.10 #14051, 0.08 #14487, 0.08 #13179), 03_87 (0.09 #14165, 0.08 #14601, 0.08 #11111), 05qmj (0.09 #14155, 0.08 #15464, 0.07 #14591) >> Best rule #1183 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 01vvyfh; >> query: (?x5898, 01k9lpl) <- award_winner(?x5898, ?x3828), influenced_by(?x8718, ?x5898), influenced_by(?x5898, ?x6504) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #893 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 01vvyfh; *> query: (?x5898, 08433) <- award_winner(?x5898, ?x3828), influenced_by(?x8718, ?x5898), influenced_by(?x5898, ?x6504) *> conf = 0.04 ranks of expected_values: 45 EVAL 026fd influenced_by 08433 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 147.000 40.000 0.133 http://example.org/influence/influence_node/influenced_by #5347-062zjtt PRED entity: 062zjtt PRED relation: film! PRED expected values: 0blq0z 09l3p 04v7kt => 105 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1157): 079vf (0.42 #14570, 0.27 #52043, 0.25 #8), 046_v (0.42 #14570, 0.10 #31221, 0.10 #35387), 0br1w (0.42 #14570, 0.10 #31221, 0.10 #35387), 0k269 (0.25 #611, 0.18 #2692, 0.14 #4773), 01vy_v8 (0.25 #734, 0.18 #2815, 0.14 #4896), 09l3p (0.25 #750, 0.18 #2831, 0.14 #4912), 01chc7 (0.25 #560, 0.09 #4722, 0.07 #8884), 0f276 (0.18 #3750, 0.14 #5831, 0.12 #1669), 01kwsg (0.18 #2921, 0.12 #840, 0.09 #5002), 0jbp0 (0.18 #8001, 0.09 #12164, 0.06 #14246) >> Best rule #14570 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 09fc83; >> query: (?x4273, ?x96) <- film(?x5470, ?x4273), story_by(?x4273, ?x96), genre(?x4273, ?x225), story_by(?x6293, ?x5470) >> conf = 0.42 => this is the best rule for 3 predicted values *> Best rule #750 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 025n07; *> query: (?x4273, 09l3p) <- film_crew_role(?x4273, ?x2095), film_crew_role(?x4273, ?x468), film(?x5462, ?x4273), ?x5462 = 0f5xn, ?x2095 = 0dxtw, ?x468 = 02r96rf *> conf = 0.25 ranks of expected_values: 6, 468, 1155 EVAL 062zjtt film! 04v7kt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 105.000 38.000 0.420 http://example.org/film/actor/film./film/performance/film EVAL 062zjtt film! 09l3p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 105.000 38.000 0.420 http://example.org/film/actor/film./film/performance/film EVAL 062zjtt film! 0blq0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 105.000 38.000 0.420 http://example.org/film/actor/film./film/performance/film #5346-04jn6y7 PRED entity: 04jn6y7 PRED relation: film_crew_role PRED expected values: 0dxtw => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 29): 0d2b38 (0.55 #520, 0.55 #552, 0.15 #3018), 01vx2h (0.48 #509, 0.46 #541, 0.39 #1741), 0dxtw (0.45 #2369, 0.45 #540, 0.43 #1644), 02_n3z (0.43 #532, 0.36 #500, 0.33 #1730), 02ynfr (0.33 #1730, 0.33 #323, 0.31 #1191), 04pyp5 (0.33 #1730, 0.31 #1191, 0.30 #1319), 02vs3x5 (0.33 #1730, 0.31 #1191, 0.30 #1319), 0263ycg (0.33 #1730, 0.30 #1319, 0.15 #3018), 0ckd1 (0.33 #1730, 0.30 #1319, 0.15 #3018), 015h31 (0.22 #538, 0.19 #506, 0.10 #1515) >> Best rule #520 for best value: >> intensional similarity = 5 >> extensional distance = 102 >> proper extension: 0b76t12; 04q00lw; 065z3_x; 05zy2cy; 0b1y_2; 093dqjy; 07kh6f3; 05szq8z; 05t0_2v; 051ys82; ... >> query: (?x12693, 0d2b38) <- film_crew_role(?x12693, ?x5136), language(?x12693, ?x254), film(?x1286, ?x12693), genre(?x12693, ?x600), ?x5136 = 089g0h >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2369 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 889 *> proper extension: 0d6b7; *> query: (?x12693, 0dxtw) <- film_crew_role(?x12693, ?x137), language(?x12693, ?x254), film_crew_role(?x6076, ?x137), film_crew_role(?x5509, ?x137), ?x6076 = 03hj5lq, ?x5509 = 0cy__l *> conf = 0.45 ranks of expected_values: 3 EVAL 04jn6y7 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 130.000 0.548 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5345-01gkp1 PRED entity: 01gkp1 PRED relation: nominated_for! PRED expected values: 099tbz => 82 concepts (75 used for prediction) PRED predicted values (max 10 best out of 248): 02w9sd7 (0.70 #713, 0.68 #9737, 0.67 #7362), 0gq9h (0.38 #4101, 0.37 #3150, 0.37 #3626), 0gs9p (0.35 #4103, 0.33 #3152, 0.33 #3390), 019f4v (0.33 #1479, 0.33 #2903, 0.33 #3141), 02rdyk7 (0.29 #71, 0.27 #951, 0.23 #8788), 040njc (0.29 #6, 0.26 #3569, 0.25 #1431), 0f4x7 (0.29 #25, 0.24 #4063, 0.23 #3588), 04kxsb (0.29 #96, 0.23 #8788, 0.20 #12822), 02pqp12 (0.29 #59, 0.20 #1484, 0.20 #2196), 02rdxsh (0.29 #51, 0.12 #10450, 0.12 #3376) >> Best rule #713 for best value: >> intensional similarity = 4 >> extensional distance = 221 >> proper extension: 0464pz; >> query: (?x4768, ?x3209) <- nominated_for(?x68, ?x4768), award(?x4768, ?x3209), nominated_for(?x7980, ?x4768), film(?x7980, ?x385) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #8788 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 949 *> proper extension: 01b64v; 0phrl; 01b65l; 01b9w3; 02_1kl; 06k176; *> query: (?x4768, ?x591) <- nominated_for(?x68, ?x4768), award(?x4768, ?x3209), nominated_for(?x397, ?x4768), award(?x397, ?x591) *> conf = 0.23 ranks of expected_values: 51 EVAL 01gkp1 nominated_for! 099tbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 82.000 75.000 0.700 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5344-082db PRED entity: 082db PRED relation: influenced_by PRED expected values: 03bxh => 196 concepts (109 used for prediction) PRED predicted values (max 10 best out of 367): 03sbs (0.45 #5019, 0.33 #224, 0.27 #5455), 0j3v (0.36 #4856, 0.33 #61, 0.29 #2240), 02wh0 (0.36 #5178, 0.33 #383, 0.27 #5614), 026lj (0.36 #4840, 0.27 #4404, 0.18 #5711), 032l1 (0.33 #90, 0.29 #2269, 0.20 #962), 042q3 (0.33 #365, 0.18 #5596, 0.18 #5160), 06myp (0.33 #375, 0.14 #2554, 0.09 #5606), 0420y (0.29 #4328, 0.29 #3457, 0.21 #8682), 015k7 (0.29 #2455, 0.20 #1148, 0.05 #14648), 015n8 (0.27 #5642, 0.27 #5206, 0.21 #11301) >> Best rule #5019 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 032r1; >> query: (?x7386, 03sbs) <- influenced_by(?x3774, ?x7386), nationality(?x7386, ?x1355), ?x1355 = 0h7x, influenced_by(?x7386, ?x7559) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2802 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 043d4; 0hqgp; *> query: (?x7386, 03bxh) <- artists(?x11193, ?x7386), artists(?x10853, ?x7386), influenced_by(?x3774, ?x7386), ?x11193 = 06q6jz, ?x10853 = 0l8gh *> conf = 0.14 ranks of expected_values: 39 EVAL 082db influenced_by 03bxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 196.000 109.000 0.455 http://example.org/influence/influence_node/influenced_by #5343-0fqjks PRED entity: 0fqjks PRED relation: film_production_design_by! PRED expected values: 01d2v1 => 120 concepts (103 used for prediction) PRED predicted values (max 10 best out of 189): 0bcndz (0.13 #1525, 0.05 #1696, 0.03 #2038), 03wy8t (0.07 #1000, 0.02 #1507, 0.02 #1678), 02vnmc9 (0.04 #1698, 0.03 #985, 0.01 #4921), 027rpym (0.04 #1698, 0.01 #4921), 0cq7tx (0.04 #1698, 0.01 #4921), 09qycb (0.03 #1006, 0.02 #1513, 0.02 #1526), 0286gm1 (0.03 #960, 0.02 #1467, 0.02 #1526), 0gy0l_ (0.03 #997, 0.02 #1504, 0.02 #1675), 01jr4j (0.03 #975, 0.02 #1482, 0.02 #1653), 08cfr1 (0.03 #972, 0.02 #1479, 0.02 #1650) >> Best rule #1525 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 04kj2v; 05728w1; 03gyh_z; 057dxsg; 07hhnl; 0584j4n; 0cdf37; 0fmqp6; 051x52f; 0523v5y; ... >> query: (?x7528, ?x951) <- nominated_for(?x7528, ?x951), award_nominee(?x200, ?x7528), film_sets_designed(?x200, ?x3986) >> conf = 0.13 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fqjks film_production_design_by! 01d2v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 103.000 0.134 http://example.org/film/film/film_production_design_by #5342-09xrxq PRED entity: 09xrxq PRED relation: film PRED expected values: 0k2cb => 97 concepts (51 used for prediction) PRED predicted values (max 10 best out of 172): 05c5z8j (0.53 #66325, 0.48 #28678, 0.38 #26885), 0407yfx (0.11 #345, 0.09 #2137), 07tj4c (0.07 #1703, 0.06 #3495), 01qb5d (0.07 #138, 0.06 #1930), 01gc7 (0.07 #39, 0.06 #1831), 031786 (0.06 #3069, 0.04 #1277), 0qf2t (0.06 #2626, 0.04 #834), 0bh8x1y (0.06 #2587, 0.04 #795), 01242_ (0.06 #2496, 0.04 #704), 085bd1 (0.06 #2244, 0.04 #452) >> Best rule #66325 for best value: >> intensional similarity = 3 >> extensional distance = 1458 >> proper extension: 033071; >> query: (?x10464, ?x4329) <- profession(?x10464, ?x1032), ?x1032 = 02hrh1q, nominated_for(?x10464, ?x4329) >> conf = 0.53 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09xrxq film 0k2cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 51.000 0.526 http://example.org/film/actor/film./film/performance/film #5341-02g1jh PRED entity: 02g1jh PRED relation: award_winner! PRED expected values: 01mh_q => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 124): 0275n3y (0.17 #10153, 0.03 #6984, 0.03 #4869), 0fzrhn (0.17 #138, 0.12 #561, 0.10 #702), 0466p0j (0.14 #358, 0.14 #217, 0.12 #499), 019bk0 (0.14 #721, 0.08 #3823, 0.08 #4669), 01s695 (0.13 #1272, 0.12 #1131, 0.10 #1554), 013b2h (0.12 #1208, 0.12 #3887, 0.12 #3605), 02rjjll (0.11 #3107, 0.10 #3530, 0.10 #3671), 01bx35 (0.11 #1135, 0.09 #712, 0.09 #3814), 0gpjbt (0.11 #1157, 0.08 #3695, 0.07 #3554), 05pd94v (0.10 #3668, 0.10 #3527, 0.09 #4655) >> Best rule #10153 for best value: >> intensional similarity = 2 >> extensional distance = 1379 >> proper extension: 01j53q; >> query: (?x7027, ?x5592) <- award_winner(?x1001, ?x7027), award_winner(?x5592, ?x1001) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #794 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 03h610; 0bk1p; *> query: (?x7027, 01mh_q) <- music(?x6588, ?x7027), category(?x7027, ?x134), costume_design_by(?x6588, ?x3685) *> conf = 0.09 ranks of expected_values: 13 EVAL 02g1jh award_winner! 01mh_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 127.000 127.000 0.172 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5340-02tj96 PRED entity: 02tj96 PRED relation: award! PRED expected values: 0f7hc => 41 concepts (13 used for prediction) PRED predicted values (max 10 best out of 2393): 03d2k (0.79 #33723, 0.79 #30350, 0.79 #23603), 012x4t (0.67 #3791, 0.67 #420, 0.18 #7163), 03j24kf (0.53 #4732, 0.33 #1361, 0.26 #8104), 01vrz41 (0.53 #3665, 0.33 #294, 0.20 #7037), 01vvycq (0.53 #3519, 0.33 #148, 0.19 #17007), 09889g (0.53 #4816, 0.33 #1445, 0.16 #18304), 01vvyvk (0.50 #1280, 0.47 #4651, 0.16 #8023), 0ffgh (0.50 #2083, 0.47 #5454, 0.14 #8826), 01vvlyt (0.50 #1584, 0.40 #4955, 0.20 #33725), 086qd (0.50 #559, 0.40 #3930, 0.14 #7302) >> Best rule #33723 for best value: >> intensional similarity = 4 >> extensional distance = 154 >> proper extension: 05qck; 0bqsk5; 02q3s; >> query: (?x11478, ?x4010) <- award_winner(?x11478, ?x4010), award_nominee(?x4010, ?x3384), artist(?x382, ?x4010), category(?x4010, ?x134) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #18207 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 133 *> proper extension: 04ljl_l; 07bdd_; 02z0dfh; 05pcn59; 03qgjwc; 0h53c_5; 02g2yr; 02f6yz; *> query: (?x11478, 0f7hc) <- award(?x12593, ?x11478), artists(?x3319, ?x12593), category(?x12593, ?x134), ?x3319 = 06j6l *> conf = 0.11 ranks of expected_values: 332 EVAL 02tj96 award! 0f7hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 41.000 13.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5339-05z43v PRED entity: 05z43v PRED relation: nominated_for! PRED expected values: 0cqgl9 => 105 concepts (99 used for prediction) PRED predicted values (max 10 best out of 261): 0m7yy (0.71 #7297, 0.70 #6589, 0.70 #7061), 019f4v (0.45 #3112, 0.43 #2877, 0.42 #3347), 09v7wsg (0.43 #410, 0.33 #175, 0.18 #3703), 0gq_v (0.39 #8257, 0.25 #13667, 0.25 #12959), 0cqgl9 (0.38 #607, 0.36 #843, 0.23 #1785), 0gq9h (0.38 #13237, 0.38 #13001, 0.38 #13473), 04dn09n (0.36 #2859, 0.34 #3094, 0.32 #3329), 0gs9p (0.35 #13239, 0.35 #13003, 0.35 #13475), 0bfvw2 (0.33 #1427, 0.32 #1662, 0.31 #2132), 0fbtbt (0.33 #159, 0.28 #3687, 0.28 #6276) >> Best rule #7297 for best value: >> intensional similarity = 4 >> extensional distance = 115 >> proper extension: 097h2; >> query: (?x7783, ?x3247) <- languages(?x7783, ?x254), genre(?x7783, ?x53), award(?x7783, ?x3247), nominated_for(?x880, ?x7783) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #607 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 090s_0; 02py4c8; 02bg8v; 015g28; 02ppg1r; 03cv_gy; 064r97z; 0bbm7r; 08y2fn; 021gzd; ... *> query: (?x7783, 0cqgl9) <- genre(?x7783, ?x53), award(?x7783, ?x3247), program(?x2776, ?x7783), nominated_for(?x2246, ?x7783) *> conf = 0.38 ranks of expected_values: 5 EVAL 05z43v nominated_for! 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 105.000 99.000 0.705 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5338-033f8n PRED entity: 033f8n PRED relation: film_crew_role PRED expected values: 0ch6mp2 0dxtw 0215hd 01tkqy => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.82 #130, 0.81 #566, 0.76 #379), 0215hd (0.72 #141, 0.15 #577, 0.14 #484), 0dxtw (0.41 #570, 0.35 #912, 0.35 #943), 01pvkk (0.28 #913, 0.28 #944, 0.28 #976), 02ynfr (0.20 #138, 0.19 #387, 0.19 #574), 0263ycg (0.16 #140, 0.11 #1439, 0.11 #1814), 02rh1dz (0.14 #288, 0.14 #569, 0.13 #70), 015h31 (0.14 #132, 0.11 #1439, 0.11 #1814), 089fss (0.13 #129, 0.11 #1439, 0.11 #1814), 033smt (0.13 #148, 0.11 #1439, 0.11 #1814) >> Best rule #130 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 047gn4y; 047svrl; 0415ggl; >> query: (?x4820, 0ch6mp2) <- titles(?x2480, ?x4820), film(?x703, ?x4820), film_crew_role(?x4820, ?x5136), ?x5136 = 089g0h >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 18 EVAL 033f8n film_crew_role 01tkqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 81.000 81.000 0.822 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 033f8n film_crew_role 0215hd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.822 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 033f8n film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.822 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 033f8n film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.822 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5337-03cvwkr PRED entity: 03cvwkr PRED relation: film! PRED expected values: 037bm2 => 81 concepts (65 used for prediction) PRED predicted values (max 10 best out of 58): 03xq0f (0.57 #1202, 0.10 #676, 0.09 #2705), 086k8 (0.33 #2, 0.17 #375, 0.17 #1423), 037bm2 (0.33 #46, 0.05 #1197), 016tw3 (0.22 #158, 0.15 #1356, 0.15 #829), 05qd_ (0.17 #82, 0.15 #1354, 0.14 #1205), 017jv5 (0.17 #88, 0.07 #1435, 0.07 #1509), 032dg7 (0.17 #121, 0.04 #195, 0.02 #718), 017s11 (0.16 #822, 0.16 #376, 0.13 #973), 025jfl (0.15 #228, 0.13 #303, 0.10 #529), 024rbz (0.13 #308, 0.12 #233, 0.04 #534) >> Best rule #1202 for best value: >> intensional similarity = 2 >> extensional distance = 207 >> proper extension: 0522wp; >> query: (?x915, 03xq0f) <- film(?x788, ?x915), film_distribution_medium(?x915, ?x81) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 02tqm5; *> query: (?x915, 037bm2) <- nominated_for(?x7036, ?x915), nominated_for(?x2252, ?x915), award(?x123, ?x2252), ?x7036 = 0f5mdz *> conf = 0.33 ranks of expected_values: 3 EVAL 03cvwkr film! 037bm2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 81.000 65.000 0.574 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5336-014zfs PRED entity: 014zfs PRED relation: influenced_by! PRED expected values: 019vgs => 110 concepts (68 used for prediction) PRED predicted values (max 10 best out of 361): 015pxr (0.27 #574, 0.08 #1075, 0.05 #2077), 05ty4m (0.25 #1008, 0.11 #2010, 0.09 #507), 0bqs56 (0.25 #246, 0.09 #8766, 0.09 #2250), 014zfs (0.25 #32, 0.09 #533, 0.07 #16542), 014z8v (0.25 #155, 0.09 #656, 0.07 #16542), 07ymr5 (0.25 #61, 0.08 #1063, 0.05 #21060), 0dzf_ (0.25 #178, 0.07 #2182, 0.05 #21060), 0p_47 (0.25 #139, 0.07 #16542, 0.05 #21060), 086nl7 (0.25 #172, 0.05 #21060, 0.02 #2176), 0126rp (0.18 #570, 0.05 #21060, 0.04 #6084) >> Best rule #574 for best value: >> intensional similarity = 2 >> extensional distance = 9 >> proper extension: 01wj9y9; >> query: (?x1145, 015pxr) <- artists(?x2480, ?x1145), ?x2480 = 01z4y >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 014zfs influenced_by! 019vgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 68.000 0.273 http://example.org/influence/influence_node/influenced_by #5335-06p03s PRED entity: 06p03s PRED relation: artist! PRED expected values: 03mp8k => 109 concepts (75 used for prediction) PRED predicted values (max 10 best out of 111): 03rhqg (0.33 #16, 0.19 #1709, 0.17 #298), 01trtc (0.25 #919, 0.20 #214, 0.17 #1201), 011k1h (0.24 #574, 0.22 #1421, 0.21 #856), 015_1q (0.22 #1995, 0.21 #3123, 0.19 #2136), 0n85g (0.20 #204, 0.19 #627, 0.17 #1474), 0k_kr (0.20 #185, 0.17 #890, 0.17 #326), 06wcbk7 (0.17 #286, 0.05 #1556, 0.03 #1132), 03mp8k (0.14 #490, 0.14 #772, 0.11 #1478), 043g7l (0.14 #455, 0.12 #1019, 0.11 #1725), 02bh8z (0.14 #586, 0.12 #868, 0.09 #727) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0l8g0; >> query: (?x11689, 03rhqg) <- artists(?x11242, ?x11689), award_winner(?x139, ?x11689), ?x11242 = 0175zz >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #490 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 01v_pj6; 01vwbts; *> query: (?x11689, 03mp8k) <- artists(?x6714, ?x11689), profession(?x11689, ?x220), ?x6714 = 07d2d, instrumentalists(?x227, ?x11689) *> conf = 0.14 ranks of expected_values: 8 EVAL 06p03s artist! 03mp8k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 109.000 75.000 0.333 http://example.org/music/record_label/artist #5334-03rk0 PRED entity: 03rk0 PRED relation: film_release_region! PRED expected values: 014lc_ 0bq8tmw 0gtvpkw 02xbyr 0g9zljd 0cmf0m0 => 261 concepts (192 used for prediction) PRED predicted values (max 10 best out of 1210): 06fcqw (0.76 #71539, 0.71 #47538, 0.68 #22338), 03bx2lk (0.76 #70924, 0.64 #46923, 0.62 #21723), 02xbyr (0.75 #71331, 0.71 #22130, 0.67 #47330), 0421v9q (0.75 #71582, 0.66 #21181, 0.66 #18781), 01jrbb (0.75 #71105, 0.64 #47104, 0.62 #21904), 0jjy0 (0.74 #21715, 0.73 #46915, 0.71 #70916), 024mpp (0.73 #71224, 0.67 #47223, 0.66 #20823), 0ndsl1x (0.73 #71814, 0.67 #47813, 0.63 #25013), 03z9585 (0.73 #71748, 0.65 #22547, 0.64 #47747), 01c22t (0.71 #70915, 0.69 #20514, 0.67 #46914) >> Best rule #71539 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 047lj; 047yc; 05qx1; 015qh; 07twz; >> query: (?x2146, 06fcqw) <- film_release_region(?x972, ?x2146), country(?x1352, ?x2146), ?x972 = 017gl1 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #71331 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 047lj; 047yc; 05qx1; 015qh; 07twz; *> query: (?x2146, 02xbyr) <- film_release_region(?x972, ?x2146), country(?x1352, ?x2146), ?x972 = 017gl1 *> conf = 0.75 ranks of expected_values: 3, 13, 19, 31, 43, 74 EVAL 03rk0 film_release_region! 0cmf0m0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 261.000 192.000 0.765 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rk0 film_release_region! 0g9zljd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 261.000 192.000 0.765 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rk0 film_release_region! 02xbyr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 261.000 192.000 0.765 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rk0 film_release_region! 0gtvpkw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 261.000 192.000 0.765 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rk0 film_release_region! 0bq8tmw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 261.000 192.000 0.765 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rk0 film_release_region! 014lc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 261.000 192.000 0.765 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5333-0mj1l PRED entity: 0mj1l PRED relation: film PRED expected values: 01rwyq 0421ng => 94 concepts (66 used for prediction) PRED predicted values (max 10 best out of 683): 03nt59 (0.63 #37534, 0.48 #85794, 0.38 #110820), 03bzyn4 (0.11 #3353, 0.04 #15865, 0.03 #17652), 05h43ls (0.11 #2200, 0.04 #14712, 0.03 #16499), 05fm6m (0.09 #1318, 0.06 #4892, 0.05 #6679), 02x3lt7 (0.09 #83, 0.03 #5444, 0.02 #12594), 02qkq0 (0.08 #12511, 0.07 #71495, 0.07 #69707), 0bvn25 (0.07 #5410, 0.06 #1836, 0.05 #8985), 0gfzfj (0.06 #12416, 0.02 #28503, 0.02 #26716), 03l6q0 (0.06 #13053, 0.05 #9478, 0.05 #5903), 0b3n61 (0.06 #4931, 0.06 #3144, 0.05 #10293) >> Best rule #37534 for best value: >> intensional similarity = 3 >> extensional distance = 415 >> proper extension: 025hzx; >> query: (?x1909, ?x6070) <- profession(?x1909, ?x1032), participant(?x1909, ?x5996), nominated_for(?x1909, ?x6070) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #16943 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 151 *> proper extension: 0q9kd; 05fnl9; 0170s4; 0p8r1; 015qt5; 016z51; 01nrgq; 02tkzn; 03d_zl4; 0347db; ... *> query: (?x1909, 0421ng) <- award(?x1909, ?x678), profession(?x1909, ?x1032), award(?x10086, ?x678), ?x10086 = 096lf_ *> conf = 0.02 ranks of expected_values: 302, 454 EVAL 0mj1l film 0421ng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 94.000 66.000 0.631 http://example.org/film/actor/film./film/performance/film EVAL 0mj1l film 01rwyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 94.000 66.000 0.631 http://example.org/film/actor/film./film/performance/film #5332-02dr9j PRED entity: 02dr9j PRED relation: nominated_for! PRED expected values: 019f4v => 105 concepts (68 used for prediction) PRED predicted values (max 10 best out of 200): 0gr42 (0.67 #5574, 0.66 #4645, 0.66 #12310), 02g3ft (0.67 #5574, 0.66 #4645, 0.66 #12310), 027b9k6 (0.67 #5574, 0.66 #4645, 0.66 #12310), 0gq9h (0.54 #2381, 0.49 #3310, 0.45 #4470), 019f4v (0.47 #2373, 0.42 #3302, 0.39 #4462), 0gs9p (0.45 #2383, 0.41 #3312, 0.40 #4472), 0gs96 (0.42 #2408, 0.40 #3337, 0.24 #15565), 054knh (0.41 #188, 0.05 #6458, 0.04 #10175), 0gr0m (0.41 #2378, 0.37 #3307, 0.33 #7486), 0l8z1 (0.41 #744, 0.30 #2371, 0.30 #3300) >> Best rule #5574 for best value: >> intensional similarity = 4 >> extensional distance = 429 >> proper extension: 064q5v; >> query: (?x7214, ?x500) <- award_winner(?x7214, ?x1983), film_crew_role(?x7214, ?x137), country(?x7214, ?x94), award(?x7214, ?x500) >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #2373 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 190 *> proper extension: 0k4f3; 084302; 07nxvj; 0pd57; 0295sy; 0kbhf; 0gl3hr; 01gvpz; 01rnly; 08xvpn; ... *> query: (?x7214, 019f4v) <- award_winner(?x7214, ?x1983), genre(?x7214, ?x53), nominated_for(?x484, ?x7214), ?x484 = 0gq_v *> conf = 0.47 ranks of expected_values: 5 EVAL 02dr9j nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 105.000 68.000 0.674 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5331-01f6zc PRED entity: 01f6zc PRED relation: film PRED expected values: 02fqrf 031hcx => 99 concepts (63 used for prediction) PRED predicted values (max 10 best out of 555): 06ztvyx (0.64 #23106, 0.63 #12441, 0.59 #81764), 0ggbhy7 (0.64 #23106, 0.63 #12441, 0.59 #81764), 030cx (0.64 #23106, 0.63 #12441, 0.59 #81764), 02qr3k8 (0.20 #1282, 0.03 #3059, 0.02 #6613), 0418wg (0.20 #400, 0.03 #3954, 0.02 #9286), 017kct (0.20 #579, 0.02 #18353), 02pg45 (0.20 #926, 0.02 #4480, 0.02 #2703), 084qpk (0.20 #120, 0.02 #1897, 0.02 #3674), 017gl1 (0.20 #142, 0.02 #33913, 0.02 #35690), 04tqtl (0.20 #508, 0.02 #21836, 0.01 #11171) >> Best rule #23106 for best value: >> intensional similarity = 3 >> extensional distance = 431 >> proper extension: 05hdf; 01pnn3; 02wb6yq; 039crh; 02zrv7; 0bkmf; 01p47r; 01gc7h; 01507p; >> query: (?x5316, ?x351) <- profession(?x5316, ?x1032), nominated_for(?x5316, ?x351), participant(?x5316, ?x914) >> conf = 0.64 => this is the best rule for 3 predicted values *> Best rule #19042 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 394 *> proper extension: 0784v1; *> query: (?x5316, 031hcx) <- nationality(?x5316, ?x1310), ?x1310 = 02jx1 *> conf = 0.03 ranks of expected_values: 70, 465 EVAL 01f6zc film 031hcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 99.000 63.000 0.638 http://example.org/film/actor/film./film/performance/film EVAL 01f6zc film 02fqrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 99.000 63.000 0.638 http://example.org/film/actor/film./film/performance/film #5330-04tgp PRED entity: 04tgp PRED relation: location! PRED expected values: 0cv72h 091n7z => 106 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1136): 06jw0s (0.18 #1141, 0.09 #18690, 0.08 #23704), 01yzhn (0.18 #2123, 0.08 #4630, 0.06 #7137), 0gs1_ (0.18 #1318, 0.06 #6332, 0.04 #38924), 049gc (0.18 #1086, 0.05 #3593, 0.04 #6100), 03nb5v (0.10 #3824, 0.10 #8838, 0.09 #16359), 023s8 (0.10 #4609, 0.09 #2102, 0.08 #7116), 0x3b7 (0.09 #827, 0.08 #3334, 0.06 #5841), 0p_pd (0.09 #47, 0.08 #2554, 0.06 #5061), 094xh (0.09 #1074, 0.08 #16116, 0.08 #13609), 0738b8 (0.09 #444, 0.06 #5458, 0.05 #2951) >> Best rule #1141 for best value: >> intensional similarity = 2 >> extensional distance = 9 >> proper extension: 0g0syc; >> query: (?x4622, 06jw0s) <- district_represented(?x606, ?x4622), ?x606 = 03ww_x >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #32592 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 03_3d; 0hzlz; 02jx1; 03hrz; 06q1r; *> query: (?x4622, ?x672) <- contains(?x4622, ?x2821), currency(?x4622, ?x170), student(?x2821, ?x672) *> conf = 0.04 ranks of expected_values: 122 EVAL 04tgp location! 091n7z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 101.000 0.182 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04tgp location! 0cv72h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 106.000 101.000 0.182 http://example.org/people/person/places_lived./people/place_lived/location #5329-02ll45 PRED entity: 02ll45 PRED relation: nominated_for! PRED expected values: 02r22gf => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 184): 054krc (0.70 #2028, 0.68 #14199, 0.68 #14426), 02wkmx (0.70 #2028, 0.68 #14199, 0.68 #14426), 0gr4k (0.50 #248, 0.44 #4076, 0.37 #1373), 0p9sw (0.50 #242, 0.37 #1367, 0.31 #1819), 04kxsb (0.49 #2787, 0.30 #4137, 0.21 #1886), 0f4x7 (0.41 #4075, 0.33 #2725, 0.32 #1824), 099c8n (0.37 #2753, 0.27 #4103, 0.24 #1852), 0gqy2 (0.36 #4163, 0.34 #1460, 0.30 #335), 02r22gf (0.34 #2728, 0.30 #250, 0.25 #1601), 0gr51 (0.34 #2770, 0.27 #4120, 0.21 #1417) >> Best rule #2028 for best value: >> intensional similarity = 4 >> extensional distance = 115 >> proper extension: 05y0cr; >> query: (?x5028, ?x112) <- award(?x5028, ?x112), nominated_for(?x2222, ?x5028), language(?x5028, ?x254), ?x2222 = 0gs96 >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #2728 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 144 *> proper extension: 02qr69m; 0581vn8; *> query: (?x5028, 02r22gf) <- nominated_for(?x1198, ?x5028), nominated_for(?x112, ?x5028), ?x1198 = 02pqp12, award(?x92, ?x112) *> conf = 0.34 ranks of expected_values: 9 EVAL 02ll45 nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 95.000 95.000 0.700 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5328-0147jt PRED entity: 0147jt PRED relation: type_of_union PRED expected values: 04ztj => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.73 #121, 0.72 #173, 0.70 #245), 01g63y (0.14 #22, 0.12 #18, 0.12 #122), 0jgjn (0.01 #20) >> Best rule #121 for best value: >> intensional similarity = 4 >> extensional distance = 361 >> proper extension: 02k6rq; 0p51w; 01438g; 01wb8bs; 01z7_f; 01bcq; 03ym1; 05lb30; 031k24; 026rm_y; ... >> query: (?x9103, 04ztj) <- award_winner(?x10556, ?x9103), award_winner(?x2704, ?x9103), student(?x13141, ?x9103), location(?x9103, ?x3097) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0147jt type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.727 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5327-0888c3 PRED entity: 0888c3 PRED relation: film! PRED expected values: 073749 => 83 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1003): 028d4v (0.73 #89045, 0.73 #97332, 0.73 #76620), 01pcbg (0.73 #89045, 0.73 #97332, 0.73 #76620), 073749 (0.73 #89045, 0.73 #97332, 0.73 #76620), 0169dl (0.50 #8686, 0.07 #6616, 0.06 #12829), 015wnl (0.29 #4792, 0.05 #97333, 0.04 #15147), 0127m7 (0.29 #10763, 0.05 #97333, 0.02 #19045), 07ddz9 (0.25 #1719, 0.20 #3791, 0.07 #7937), 02114t (0.22 #8920, 0.14 #6850, 0.05 #97333), 031k24 (0.20 #3470, 0.06 #9686, 0.05 #11757), 01gq0b (0.20 #2374, 0.06 #8590, 0.05 #10661) >> Best rule #89045 for best value: >> intensional similarity = 5 >> extensional distance = 1150 >> proper extension: 034qmv; 02vp1f_; 011yrp; 09xbpt; 04nl83; 01hp5; 0p9lw; 06krf3; 02qm_f; 0jqp3; ... >> query: (?x8182, ?x5488) <- nominated_for(?x5488, ?x8182), nominated_for(?x2383, ?x8182), film(?x2143, ?x8182), film(?x5488, ?x2207), type_of_union(?x2383, ?x566) >> conf = 0.73 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0888c3 film! 073749 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 47.000 0.733 http://example.org/film/actor/film./film/performance/film #5326-014zfs PRED entity: 014zfs PRED relation: influenced_by PRED expected values: 01gn36 => 135 concepts (82 used for prediction) PRED predicted values (max 10 best out of 338): 01hmk9 (0.50 #1083, 0.14 #1949, 0.12 #3247), 014zfs (0.40 #889, 0.11 #1755, 0.11 #5220), 01s7qqw (0.30 #1027, 0.07 #16457, 0.07 #27292), 01gn36 (0.20 #999, 0.07 #16457, 0.07 #27292), 0427y (0.20 #1185, 0.07 #16457, 0.07 #27292), 03_87 (0.14 #14489, 0.12 #17959, 0.12 #18392), 081k8 (0.14 #17912, 0.14 #12274, 0.12 #18345), 032l1 (0.12 #17846, 0.12 #18279, 0.10 #19578), 013tjc (0.11 #2103, 0.07 #16457, 0.07 #27292), 05qmj (0.11 #17949, 0.10 #18382, 0.09 #19681) >> Best rule #1083 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02633g; 02lj6p; 01wp_jm; >> query: (?x1145, 01hmk9) <- influenced_by(?x1835, ?x1145), ?x1835 = 016_mj, influenced_by(?x1145, ?x2283) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #999 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 02633g; 02lj6p; 01wp_jm; *> query: (?x1145, 01gn36) <- influenced_by(?x1835, ?x1145), ?x1835 = 016_mj, influenced_by(?x1145, ?x2283) *> conf = 0.20 ranks of expected_values: 4 EVAL 014zfs influenced_by 01gn36 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 135.000 82.000 0.500 http://example.org/influence/influence_node/influenced_by #5325-02fgm7 PRED entity: 02fgm7 PRED relation: nationality PRED expected values: 0chghy => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 66): 09c7w0 (0.75 #302, 0.69 #4505, 0.69 #4304), 02jx1 (0.34 #2003, 0.22 #133, 0.19 #33), 0chghy (0.34 #2003, 0.19 #10, 0.11 #110), 07ssc (0.34 #2003, 0.12 #15, 0.11 #115), 0345h (0.34 #2003, 0.06 #31, 0.06 #131), 06q1r (0.34 #2003, 0.06 #77, 0.06 #177), 0j5g9 (0.34 #2003, 0.06 #62, 0.06 #162), 05cgv (0.34 #2003, 0.06 #30, 0.06 #130), 0ctw_b (0.34 #2003, 0.06 #27, 0.05 #602), 03rk0 (0.06 #5751, 0.05 #5951, 0.05 #5851) >> Best rule #302 for best value: >> intensional similarity = 2 >> extensional distance = 916 >> proper extension: 0kcdl; 0kcd5; >> query: (?x7505, 09c7w0) <- nominated_for(?x7505, ?x11895), genre(?x11895, ?x53) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2003 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1289 *> proper extension: 0c9l1; *> query: (?x7505, ?x1310) <- award_winner(?x7505, ?x5283), award_winner(?x7505, ?x1194), award_winner(?x762, ?x5283), nationality(?x1194, ?x1310) *> conf = 0.34 ranks of expected_values: 3 EVAL 02fgm7 nationality 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 65.000 0.755 http://example.org/people/person/nationality #5324-07gghl PRED entity: 07gghl PRED relation: film_format PRED expected values: 07fb8_ => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 4): 07fb8_ (0.22 #41, 0.19 #56, 0.18 #51), 0cj16 (0.15 #43, 0.14 #58, 0.12 #302), 017fx5 (0.06 #34, 0.05 #54, 0.05 #44), 01dc60 (0.01 #60) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 044g_k; 09g8vhw; >> query: (?x6627, 07fb8_) <- film_crew_role(?x6627, ?x137), currency(?x6627, ?x170), honored_for(?x6627, ?x886) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07gghl film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.224 http://example.org/film/film/film_format #5323-0ctb4g PRED entity: 0ctb4g PRED relation: film_crew_role PRED expected values: 02r96rf => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 36): 02r96rf (0.78 #147, 0.77 #111, 0.72 #39), 0dxtw (0.41 #46, 0.36 #1641, 0.35 #118), 01vx2h (0.30 #1642, 0.27 #155, 0.26 #915), 01pvkk (0.25 #699, 0.25 #1643, 0.25 #372), 0d2b38 (0.21 #62, 0.18 #170, 0.17 #134), 01xy5l_ (0.21 #50, 0.17 #122, 0.16 #158), 02ynfr (0.19 #124, 0.18 #160, 0.17 #52), 0215hd (0.19 #127, 0.18 #163, 0.17 #55), 04pyp5 (0.17 #17, 0.08 #2503, 0.08 #704), 02_n3z (0.15 #109, 0.14 #145, 0.14 #73) >> Best rule #147 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 064n1pz; >> query: (?x3430, 02r96rf) <- nominated_for(?x2341, ?x3430), honored_for(?x762, ?x3430), ?x2341 = 02x17s4 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ctb4g film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.776 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5322-01dzq6 PRED entity: 01dzq6 PRED relation: contains! PRED expected values: 0cxgc => 97 concepts (59 used for prediction) PRED predicted values (max 10 best out of 176): 09c7w0 (0.90 #41104, 0.81 #42892, 0.81 #43785), 059rby (0.46 #12527, 0.45 #13420, 0.31 #17886), 01n7q (0.33 #23301, 0.31 #25087, 0.27 #28665), 036wy (0.25 #3442, 0.23 #4335, 0.21 #27693), 0cxgc (0.21 #27693, 0.12 #3330, 0.09 #4223), 012wyq (0.21 #27693, 0.09 #4379, 0.08 #2593), 02j9z (0.21 #27693, 0.09 #37552, 0.06 #47384), 09bkv (0.21 #27693, 0.08 #2342, 0.08 #1449), 0f485 (0.21 #27693, 0.06 #3500, 0.05 #5286), 01_c4 (0.21 #27693, 0.06 #3199, 0.05 #4092) >> Best rule #41104 for best value: >> intensional similarity = 4 >> extensional distance = 984 >> proper extension: 03ksy; >> query: (?x11634, 09c7w0) <- contains(?x362, ?x11634), place_of_birth(?x4319, ?x362), award_winner(?x1596, ?x4319), place_founded(?x2776, ?x362) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #27693 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 582 *> proper extension: 06klyh; 01fy2s; 05hf_5; *> query: (?x11634, ?x11933) <- contains(?x362, ?x11634), citytown(?x752, ?x362), contains(?x362, ?x11049), contains(?x11933, ?x11049) *> conf = 0.21 ranks of expected_values: 5 EVAL 01dzq6 contains! 0cxgc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 59.000 0.905 http://example.org/location/location/contains #5321-01wmgrf PRED entity: 01wmgrf PRED relation: artist! PRED expected values: 03gfvsz => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 4): 03gfvsz (0.09 #126, 0.09 #132, 0.08 #1), 01fjfv (0.03 #8, 0.03 #220, 0.03 #281), 04rqd (0.03 #223, 0.03 #284, 0.03 #11), 04y652m (0.02 #10, 0.02 #247, 0.02 #296) >> Best rule #126 for best value: >> intensional similarity = 3 >> extensional distance = 395 >> proper extension: 04rcr; 01wmxfs; 02r3zy; 07c0j; 01vrt_c; 04dqdk; 03g5jw; 01wbl_r; 05d8vw; 0pyg6; ... >> query: (?x3122, 03gfvsz) <- award_nominee(?x3122, ?x2638), award(?x3122, ?x1361), artist(?x2931, ?x3122) >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wmgrf artist! 03gfvsz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.091 http://example.org/broadcast/content/artist #5320-09v3jyg PRED entity: 09v3jyg PRED relation: film_release_region PRED expected values: 0jgd 01ls2 0f8l9c 0k6nt 05qx1 05b4w 06t8v 07twz => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 224): 0jgd (0.90 #1012, 0.89 #435, 0.88 #723), 05b4w (0.86 #775, 0.85 #343, 0.84 #1064), 0f8l9c (0.85 #592, 0.85 #304, 0.85 #2181), 03h64 (0.84 #1067, 0.84 #634, 0.82 #490), 0k6nt (0.82 #452, 0.82 #1029, 0.82 #1318), 06t2t (0.79 #628, 0.76 #1061, 0.76 #484), 03rj0 (0.65 #1348, 0.65 #338, 0.64 #1637), 01ls2 (0.63 #296, 0.62 #728, 0.60 #584), 06f32 (0.62 #1355, 0.62 #1066, 0.62 #345), 03rk0 (0.56 #622, 0.55 #478, 0.53 #766) >> Best rule #1012 for best value: >> intensional similarity = 9 >> extensional distance = 99 >> proper extension: 02vxq9m; 0h63gl9; >> query: (?x6931, 0jgd) <- film_release_region(?x6931, ?x1023), film_release_region(?x6931, ?x512), film_release_region(?x6931, ?x429), film_release_region(?x6931, ?x151), ?x429 = 03rt9, ?x1023 = 0ctw_b, ?x151 = 0b90_r, region(?x54, ?x512), contains(?x512, ?x362) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 8, 12, 16, 21 EVAL 09v3jyg film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 66.000 66.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v3jyg film_release_region 06t8v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 66.000 66.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v3jyg film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v3jyg film_release_region 05qx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 66.000 66.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v3jyg film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v3jyg film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v3jyg film_release_region 01ls2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 66.000 66.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v3jyg film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.901 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5319-0jym0 PRED entity: 0jym0 PRED relation: nominated_for! PRED expected values: 0gr4k 0gs9p => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 164): 0gs9p (0.51 #290, 0.48 #522, 0.43 #59), 0k611 (0.49 #531, 0.45 #299, 0.38 #68), 0gq_v (0.46 #481, 0.39 #249, 0.38 #18), 0p9sw (0.40 #482, 0.31 #250, 0.28 #19), 0gr4k (0.36 #256, 0.34 #25, 0.31 #488), 0f4x7 (0.36 #255, 0.30 #24, 0.27 #463), 0l8z1 (0.35 #514, 0.26 #1207, 0.21 #1669), 04dn09n (0.34 #497, 0.29 #265, 0.27 #463), 0gs96 (0.32 #547, 0.24 #778, 0.23 #1240), 02qyntr (0.31 #636, 0.22 #404, 0.22 #1791) >> Best rule #290 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 07bz5; >> query: (?x2057, 0gs9p) <- nominated_for(?x2248, ?x2057), award_winner(?x384, ?x2248), list(?x2057, ?x3004) >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 0jym0 nominated_for! 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.514 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0jym0 nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 58.000 58.000 0.514 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5318-09bw4_ PRED entity: 09bw4_ PRED relation: production_companies PRED expected values: 0c41qv => 76 concepts (67 used for prediction) PRED predicted values (max 10 best out of 53): 04f525m (0.22 #92, 0.10 #174, 0.02 #338), 054lpb6 (0.16 #342, 0.09 #998, 0.08 #1490), 086k8 (0.13 #2053, 0.12 #330, 0.12 #3121), 016tw3 (0.12 #339, 0.12 #1487, 0.11 #995), 05qd_ (0.12 #337, 0.11 #3128, 0.10 #3375), 02jd_7 (0.11 #151, 0.05 #233, 0.04 #315), 02slt7 (0.11 #111, 0.05 #193, 0.02 #1505), 08wjc1 (0.10 #191, 0.02 #1011, 0.02 #1503), 017s11 (0.09 #1479, 0.08 #987, 0.08 #413), 03sb38 (0.09 #628, 0.07 #464, 0.03 #1530) >> Best rule #92 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0g5qs2k; 02rx2m5; 05pdh86; 03nm_fh; 05q7874; 0cp0t91; 03c7twt; >> query: (?x8658, 04f525m) <- film(?x5462, ?x8658), film(?x4046, ?x8658), ?x4046 = 07swvb, film_crew_role(?x8658, ?x468), gender(?x5462, ?x231) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #957 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 297 *> proper extension: 0522wp; *> query: (?x8658, 0c41qv) <- film(?x574, ?x8658), film(?x574, ?x9059), film(?x574, ?x1035), film(?x7091, ?x9059), ?x1035 = 08hmch *> conf = 0.08 ranks of expected_values: 13 EVAL 09bw4_ production_companies 0c41qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 76.000 67.000 0.222 http://example.org/film/film/production_companies #5317-0mb0 PRED entity: 0mb0 PRED relation: influenced_by PRED expected values: 045bg 02kz_ => 211 concepts (70 used for prediction) PRED predicted values (max 10 best out of 349): 03f0324 (0.67 #579, 0.31 #5290, 0.29 #1435), 03_87 (0.50 #1055, 0.43 #1483, 0.35 #5338), 084w8 (0.50 #431, 0.29 #1287, 0.27 #5142), 028p0 (0.43 #3457, 0.24 #4742, 0.19 #3886), 01tz6vs (0.35 #5313, 0.33 #602, 0.29 #1458), 02kz_ (0.33 #596, 0.29 #1452, 0.20 #2308), 06whf (0.33 #552, 0.29 #1408, 0.20 #2264), 058vp (0.33 #1037, 0.19 #4036, 0.16 #3855), 02lt8 (0.30 #2689, 0.29 #1403, 0.23 #5258), 05gpy (0.30 #2765, 0.25 #4050, 0.19 #4906) >> Best rule #579 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0g72r; >> query: (?x10598, 03f0324) <- influenced_by(?x10598, ?x4028), nationality(?x10598, ?x94), student(?x216, ?x10598), ?x4028 = 0lcx >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #596 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0g72r; *> query: (?x10598, 02kz_) <- influenced_by(?x10598, ?x4028), nationality(?x10598, ?x94), student(?x216, ?x10598), ?x4028 = 0lcx *> conf = 0.33 ranks of expected_values: 6, 50 EVAL 0mb0 influenced_by 02kz_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 211.000 70.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 0mb0 influenced_by 045bg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 211.000 70.000 0.667 http://example.org/influence/influence_node/influenced_by #5316-0yt73 PRED entity: 0yt73 PRED relation: place PRED expected values: 0yt73 => 87 concepts (42 used for prediction) PRED predicted values (max 10 best out of 15): 0n2sh (0.08 #4125, 0.07 #6192, 0.07 #5160), 0z1l8 (0.07 #514, 0.06 #1029, 0.04 #1544), 0yw93 (0.07 #485, 0.06 #1000, 0.04 #1515), 0yzyn (0.07 #340, 0.06 #855, 0.04 #1370), 0z1vw (0.07 #331, 0.06 #846, 0.04 #1361), 0z20d (0.07 #203, 0.06 #718, 0.04 #1233), 0yvjx (0.07 #466, 0.06 #981, 0.03 #2011), 0z18v (0.07 #455, 0.06 #970, 0.03 #2000), 0z2gq (0.07 #244, 0.06 #759, 0.03 #1789), 0yshw (0.07 #116, 0.06 #631, 0.03 #1661) >> Best rule #4125 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 0s3y5; 02dtg; 0ydpd; 02cl1; 0f2r6; 0r62v; 0cc56; 0mnzd; 0mp3l; 0dc95; ... >> query: (?x11029, ?x11028) <- county_seat(?x11028, ?x11029), source(?x11029, ?x958), category(?x11029, ?x134), ?x958 = 0jbk9 >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0yt73 place 0yt73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 42.000 0.076 http://example.org/location/hud_county_place/place #5315-0bkf72 PRED entity: 0bkf72 PRED relation: gender PRED expected values: 05zppz => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #15, 0.84 #29, 0.84 #39), 02zsn (0.28 #50, 0.25 #62, 0.24 #104) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 196 >> proper extension: 079vf; >> query: (?x8590, 05zppz) <- award_winner(?x8590, ?x2179), produced_by(?x7243, ?x8590), nominated_for(?x2179, ?x1224) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bkf72 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.848 http://example.org/people/person/gender #5314-016zp5 PRED entity: 016zp5 PRED relation: award PRED expected values: 0bs0bh => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 245): 099tbz (0.70 #29076, 0.70 #12347, 0.69 #6769), 05zr6wv (0.24 #16, 0.18 #19118, 0.18 #21112), 0ck27z (0.20 #6062, 0.18 #19118, 0.18 #21112), 09sdmz (0.18 #19118, 0.18 #21112, 0.18 #16329), 099ck7 (0.18 #19118, 0.18 #21112, 0.18 #16329), 0789_m (0.18 #19118, 0.18 #21112, 0.18 #16329), 02x4w6g (0.18 #19118, 0.18 #21112, 0.18 #16329), 027c95y (0.18 #19118, 0.18 #21112, 0.18 #16329), 027986c (0.18 #19118, 0.18 #21112, 0.18 #16329), 05ztrmj (0.15 #16728, 0.15 #18321, 0.15 #19119) >> Best rule #29076 for best value: >> intensional similarity = 2 >> extensional distance = 2276 >> proper extension: 01vw87c; 089tm; 01pfr3; 0kzy0; 042rnl; 03ds3; 0152cw; 07q1v4; 02whj; 0m77m; ... >> query: (?x5495, ?x112) <- award_winner(?x112, ?x5495), award(?x5495, ?x1033) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #897 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 187 *> proper extension: 0c9d9; 04bs3j; 014x77; 06y9c2; 0htlr; 0456xp; 04shbh; 0prjs; 01mqz0; 03ft8; ... *> query: (?x5495, 0bs0bh) <- student(?x2486, ?x5495), spouse(?x5144, ?x5495) *> conf = 0.06 ranks of expected_values: 86 EVAL 016zp5 award 0bs0bh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 87.000 87.000 0.701 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5313-0139q5 PRED entity: 0139q5 PRED relation: languages PRED expected values: 03115z => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 25): 02bjrlw (0.27 #145, 0.19 #109, 0.08 #2993), 04306rv (0.12 #146, 0.11 #110, 0.08 #2993), 06nm1 (0.11 #113, 0.10 #149, 0.04 #1014), 0653m (0.08 #78, 0.08 #2993, 0.01 #186), 03_9r (0.08 #76, 0.01 #1121, 0.01 #1265), 03k50 (0.08 #1012, 0.08 #1264, 0.08 #1300), 06b_j (0.06 #157, 0.04 #121), 07c9s (0.04 #119, 0.04 #1128, 0.04 #1020), 0349s (0.04 #137, 0.04 #173), 04h9h (0.04 #135, 0.02 #171) >> Best rule #145 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 028pzq; 01syr4; >> query: (?x9809, 02bjrlw) <- languages(?x9809, ?x5607), ?x5607 = 064_8sq, gender(?x9809, ?x514) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #133 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 0m2wm; 06wvj; 02pk6x; 02n9k; 0448r; 0g7k2g; 0dw6b; 01vsqvs; 05vzql; 01j5sv; *> query: (?x9809, 03115z) <- languages(?x9809, ?x5607), ?x5607 = 064_8sq, location(?x9809, ?x206) *> conf = 0.02 ranks of expected_values: 16 EVAL 0139q5 languages 03115z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 143.000 143.000 0.275 http://example.org/people/person/languages #5312-026lg0s PRED entity: 026lg0s PRED relation: category PRED expected values: 08mbj5d => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.55 #43, 0.55 #42, 0.55 #58) >> Best rule #43 for best value: >> intensional similarity = 34 >> extensional distance = 36 >> proper extension: 02px_23; >> query: (?x4189, ?x134) <- position_s(?x4189, ?x2147), position(?x4189, ?x7079), position(?x4189, ?x1114), team(?x2147, ?x11061), team(?x2147, ?x7019), team(?x2147, ?x6976), team(?x2147, ?x6843), team(?x2147, ?x6379), team(?x2147, ?x5603), team(?x2147, ?x4546), team(?x2147, ?x3674), team(?x2147, ?x1718), team(?x2147, ?x705), team(?x2147, ?x387), ?x705 = 07k53y, ?x1114 = 047g8h, ?x1718 = 0fgg8c, position_s(?x2312, ?x2147), position(?x6645, ?x2147), ?x3674 = 05tg3, ?x6645 = 0wsr, team(?x7079, ?x10253), ?x6379 = 0bjkk9, ?x6976 = 04vn5, ?x10253 = 0bs09lb, position_s(?x10280, ?x2147), ?x4546 = 05gg4, ?x11061 = 06x76, ?x6843 = 026cmdc, ?x7019 = 026ldz7, position(?x4494, ?x7079), position_s(?x5603, ?x8329), ?x387 = 02896, category(?x10280, ?x134) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026lg0s category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.553 http://example.org/common/topic/webpage./common/webpage/category #5311-0gh8zks PRED entity: 0gh8zks PRED relation: film_release_region PRED expected values: 09c7w0 05qhw 07t21 01znc_ 05b4w 03spz 07f1x => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 143): 09c7w0 (0.93 #2712, 0.92 #5872, 0.92 #3463), 05qhw (0.91 #463, 0.79 #1368, 0.78 #1969), 01znc_ (0.85 #487, 0.74 #1392, 0.73 #1993), 06t2t (0.85 #507, 0.69 #1412, 0.66 #1862), 05b4w (0.81 #510, 0.75 #1415, 0.74 #1565), 03spz (0.80 #541, 0.69 #390, 0.69 #1446), 07ssc (0.79 #1370, 0.79 #1820, 0.79 #465), 05v8c (0.79 #466, 0.59 #1371, 0.57 #1821), 03rj0 (0.68 #505, 0.64 #1410, 0.63 #1860), 04gzd (0.67 #458, 0.52 #1363, 0.50 #1813) >> Best rule #2712 for best value: >> intensional similarity = 4 >> extensional distance = 507 >> proper extension: 0170z3; 014lc_; 02d413; 0b76d_m; 0ds35l9; 03qcfvw; 0g56t9t; 09sh8k; 0m313; 034qmv; ... >> query: (?x3252, 09c7w0) <- film_release_distribution_medium(?x3252, ?x81), film_release_region(?x3252, ?x87), nominated_for(?x899, ?x3252), production_companies(?x3252, ?x9041) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 6, 25, 27 EVAL 0gh8zks film_release_region 07f1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 85.000 85.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh8zks film_release_region 03spz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh8zks film_release_region 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh8zks film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh8zks film_release_region 07t21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 85.000 85.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh8zks film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh8zks film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5310-07l8x PRED entity: 07l8x PRED relation: season PRED expected values: 026fmqm => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 7): 026fmqm (0.88 #375, 0.88 #354, 0.86 #298), 05kcgsf (0.60 #50, 0.54 #554, 0.52 #463), 04110b0 (0.40 #52, 0.39 #388, 0.36 #297), 02h7s73 (0.40 #54, 0.35 #509, 0.33 #467), 03c6s24 (0.40 #55, 0.29 #468, 0.27 #559), 03c74_8 (0.40 #51, 0.24 #464, 0.23 #555), 04n36qk (0.08 #560, 0.06 #658, 0.06 #392) >> Best rule #375 for best value: >> intensional similarity = 8 >> extensional distance = 15 >> proper extension: 03m1n; >> query: (?x7725, 026fmqm) <- category(?x7725, ?x134), draft(?x7725, ?x4779), ?x4779 = 02z6872, school(?x7725, ?x331), school(?x7725, ?x122), institution(?x620, ?x331), season(?x7725, ?x2406), major_field_of_study(?x122, ?x254) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07l8x season 026fmqm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.882 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #5309-01386_ PRED entity: 01386_ PRED relation: profession PRED expected values: 02hrh1q => 147 concepts (68 used for prediction) PRED predicted values (max 10 best out of 89): 02hrh1q (0.72 #3285, 0.69 #5997, 0.68 #8857), 01c72t (0.46 #1017, 0.38 #590, 0.33 #1303), 01d_h8 (0.44 #5988, 0.38 #572, 0.37 #8848), 0n1h (0.38 #578, 0.28 #1433, 0.25 #2286), 0dxtg (0.33 #154, 0.31 #5996, 0.31 #6708), 02jknp (0.33 #148, 0.21 #6702, 0.20 #5990), 0kyk (0.33 #170, 0.17 #1167, 0.14 #7009), 018gz8 (0.33 #158, 0.15 #6855, 0.14 #1723), 0np9r (0.33 #161, 0.13 #3291, 0.13 #5144), 03gjzk (0.27 #8858, 0.25 #5998, 0.24 #6710) >> Best rule #3285 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 032_jg; 05hdf; 01xcfy; 05r5w; 057hz; 084z0w; 01wy5m; 01tz6vs; 0mdyn; 07ym0; ... >> query: (?x6406, 02hrh1q) <- profession(?x6406, ?x6565), profession(?x7112, ?x6565), award(?x7112, ?x1479), diet(?x6406, ?x3130) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01386_ profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 68.000 0.716 http://example.org/people/person/profession #5308-04g5k PRED entity: 04g5k PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.87 #35, 0.86 #13, 0.86 #34) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 05rznz; >> query: (?x5482, 0hzc9wc) <- administrative_parent(?x5482, ?x551), organization(?x5482, ?x127), adjoins(?x344, ?x5482) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04g5k administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.866 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #5307-0m2l9 PRED entity: 0m2l9 PRED relation: music! PRED expected values: 0symg => 119 concepts (109 used for prediction) PRED predicted values (max 10 best out of 24): 01hp5 (0.20 #1076, 0.14 #2092, 0.07 #4124), 0jzw (0.20 #70, 0.05 #5150, 0.04 #6166), 033g4d (0.07 #4175, 0.03 #10271, 0.01 #15351), 06pyc2 (0.03 #8079, 0.01 #14175), 0djlxb (0.03 #7434, 0.01 #13530), 09qycb (0.03 #8051), 0gvvm6l (0.03 #7921), 04gcyg (0.03 #7905), 05ch98 (0.03 #7896), 08l0x2 (0.03 #7867) >> Best rule #1076 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0jmj; >> query: (?x483, 01hp5) <- notable_people_with_this_condition(?x5784, ?x483), award_winner(?x139, ?x483), inductee(?x1091, ?x483) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0m2l9 music! 0symg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 109.000 0.200 http://example.org/film/film/music #5306-01d38g PRED entity: 01d38g PRED relation: award! PRED expected values: 01w60_p 07ss8_ 03q2t9 015bwt 01f2q5 => 42 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2536): 01trhmt (0.81 #23334, 0.78 #83359, 0.78 #46670), 07s3vqk (0.81 #23334, 0.78 #83359, 0.78 #46670), 0g824 (0.81 #23334, 0.78 #83359, 0.78 #46670), 01jgkj2 (0.81 #23334, 0.78 #83359, 0.78 #46670), 012x03 (0.81 #23334, 0.78 #83359, 0.78 #46670), 01wmxfs (0.81 #23334, 0.78 #83359, 0.78 #46670), 0x3n (0.62 #18482, 0.50 #11816, 0.33 #8483), 03q2t9 (0.56 #21581, 0.50 #14914, 0.33 #1581), 0136p1 (0.50 #17168, 0.33 #7169, 0.33 #3835), 030155 (0.50 #10903, 0.33 #20903, 0.33 #7570) >> Best rule #23334 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0c4z8; 01c427; 03qbh5; >> query: (?x567, ?x215) <- award(?x5904, ?x567), ceremony(?x567, ?x139), ?x5904 = 01k_mc, award_winner(?x567, ?x215) >> conf = 0.81 => this is the best rule for 6 predicted values *> Best rule #21581 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0c4z8; 01c427; 03qbh5; *> query: (?x567, 03q2t9) <- award(?x5904, ?x567), ceremony(?x567, ?x139), ?x5904 = 01k_mc, award_winner(?x567, ?x215) *> conf = 0.56 ranks of expected_values: 8, 16, 45, 127, 330 EVAL 01d38g award! 01f2q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 42.000 25.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01d38g award! 015bwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 42.000 25.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01d38g award! 03q2t9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 42.000 25.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01d38g award! 07ss8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 42.000 25.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01d38g award! 01w60_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 25.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5305-01hgwkr PRED entity: 01hgwkr PRED relation: music! PRED expected values: 0kvgtf => 125 concepts (108 used for prediction) PRED predicted values (max 10 best out of 57): 04j4tx (0.07 #419, 0.01 #9564), 035yn8 (0.07 #169, 0.01 #9314), 035zr0 (0.06 #1763, 0.02 #3795, 0.02 #4811), 0dgpwnk (0.06 #1352, 0.02 #3384, 0.02 #4400), 01hv3t (0.06 #1757, 0.01 #11918), 021y7yw (0.06 #1257, 0.01 #11418), 05dy7p (0.06 #1252, 0.01 #11413), 07nt8p (0.06 #1232, 0.01 #11393), 01f69m (0.06 #2006), 01xdxy (0.06 #1908) >> Best rule #419 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 082brv; 04m2zj; >> query: (?x9442, 04j4tx) <- role(?x9442, ?x1969), role(?x9442, ?x432), ?x432 = 042v_gx, ?x1969 = 04rzd, artists(?x671, ?x9442) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01hgwkr music! 0kvgtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 108.000 0.071 http://example.org/film/film/music #5304-01fwk3 PRED entity: 01fwk3 PRED relation: profession PRED expected values: 0d1pc => 112 concepts (79 used for prediction) PRED predicted values (max 10 best out of 50): 02jknp (0.48 #3342, 0.47 #4502, 0.39 #3487), 0dxtg (0.47 #3348, 0.46 #157, 0.46 #4508), 02krf9 (0.21 #168, 0.14 #3359, 0.14 #4519), 09jwl (0.19 #6542, 0.18 #6687, 0.18 #9008), 018gz8 (0.18 #449, 0.17 #3495, 0.15 #739), 0np9r (0.18 #453, 0.13 #10751, 0.13 #8429), 0d1pc (0.15 #2077, 0.15 #2367, 0.15 #3818), 0kyk (0.14 #26, 0.10 #11339, 0.10 #171), 0g_qdz (0.14 #134), 0dz3r (0.14 #6528, 0.13 #6673, 0.12 #7978) >> Best rule #3342 for best value: >> intensional similarity = 3 >> extensional distance = 478 >> proper extension: 037q1z; 01nc3rh; >> query: (?x2715, 02jknp) <- award_winner(?x1230, ?x2715), profession(?x2715, ?x319), ?x319 = 01d_h8 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #2077 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 287 *> proper extension: 0gm34; *> query: (?x2715, 0d1pc) <- award_winner(?x1230, ?x2715), film(?x2715, ?x4489), participant(?x2715, ?x2499) *> conf = 0.15 ranks of expected_values: 7 EVAL 01fwk3 profession 0d1pc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 112.000 79.000 0.477 http://example.org/people/person/profession #5303-02_1q9 PRED entity: 02_1q9 PRED relation: producer_type PRED expected values: 0ckd1 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.82 #2, 0.73 #8, 0.72 #10) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0358x_; 01b64v; 01b66d; 0phrl; 0gj50; 01b65l; 01b66t; 01y6dz; 02_1kl; >> query: (?x416, 0ckd1) <- nominated_for(?x415, ?x416), nominated_for(?x2773, ?x416), program(?x6678, ?x416), ?x2773 = 02pzz3p >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_1q9 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.818 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #5302-018gkb PRED entity: 018gkb PRED relation: instrumentalists! PRED expected values: 02hnl => 104 concepts (35 used for prediction) PRED predicted values (max 10 best out of 120): 02hnl (0.50 #270, 0.44 #510, 0.33 #591), 03bx0bm (0.40 #1995, 0.39 #1596, 0.37 #1915), 0dwsp (0.34 #2317, 0.33 #2650, 0.33 #2484), 02sgy (0.34 #2317, 0.33 #2650, 0.33 #2484), 042v_gx (0.34 #2317, 0.33 #2650, 0.33 #2484), 06ncr (0.33 #122, 0.25 #441, 0.25 #361), 01wy6 (0.33 #125, 0.05 #1161, 0.05 #1559), 02qjv (0.33 #2400, 0.29 #2565, 0.28 #2733), 013y1f (0.25 #267, 0.25 #27, 0.22 #507), 0l14qv (0.25 #405, 0.25 #325, 0.15 #1122) >> Best rule #270 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 01vs14j; 0fpjd_g; 01vsnff; 06k02; 03h_fqv; 03ryks; >> query: (?x11161, 02hnl) <- gender(?x11161, ?x231), role(?x11161, ?x615), ?x615 = 0dwsp, award(?x11161, ?x2634), instrumentalists(?x212, ?x11161) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018gkb instrumentalists! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 35.000 0.500 http://example.org/music/instrument/instrumentalists #5301-04m064 PRED entity: 04m064 PRED relation: award PRED expected values: 0cqh46 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 268): 01by1l (0.22 #1719, 0.20 #3730, 0.20 #5338), 0ck27z (0.18 #13760, 0.16 #4916, 0.15 #13358), 01bgqh (0.18 #5268, 0.18 #1649, 0.17 #8886), 0f4x7 (0.18 #20505, 0.15 #14474, 0.14 #22114), 09qv_s (0.18 #20505, 0.15 #14474, 0.14 #22114), 05zr6wv (0.18 #20505, 0.15 #14474, 0.13 #30560), 0279c15 (0.18 #20505, 0.15 #14474, 0.13 #30560), 02g2wv (0.18 #20505, 0.15 #14474, 0.13 #30560), 027c95y (0.18 #20505), 09cm54 (0.18 #20505) >> Best rule #1719 for best value: >> intensional similarity = 3 >> extensional distance = 397 >> proper extension: 01wp8w7; 09d5h; 0gcs9; 017vkx; 01wn718; 01wgfp6; 01m3b1t; 01dhpj; 0knjh; >> query: (?x12425, 01by1l) <- award_nominee(?x12425, ?x496), award_winner(?x12425, ?x2927), category(?x12425, ?x134) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 01vz80y; *> query: (?x12425, 0cqh46) <- award_nominee(?x6255, ?x12425), film(?x6255, ?x6256), ?x6256 = 02c7k4 *> conf = 0.07 ranks of expected_values: 115 EVAL 04m064 award 0cqh46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 89.000 89.000 0.218 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5300-02r2j8 PRED entity: 02r2j8 PRED relation: program! PRED expected values: 01nzs7 => 99 concepts (92 used for prediction) PRED predicted values (max 10 best out of 43): 05gnf (0.33 #14, 0.22 #242, 0.19 #1386), 09d5h (0.33 #3, 0.14 #117, 0.12 #174), 0gsg7 (0.29 #116, 0.25 #173, 0.17 #2060), 0g5lhl7 (0.25 #177, 0.14 #120, 0.13 #405), 03mdt (0.17 #406, 0.14 #121, 0.13 #349), 01nzs7 (0.14 #629, 0.11 #229, 0.04 #400), 0cjdk (0.11 #1147, 0.11 #633, 0.10 #1262), 01z77k (0.11 #1200, 0.11 #1315, 0.06 #686), 07c52 (0.11 #1200, 0.11 #1315, 0.06 #686), 07y2b (0.11 #668, 0.04 #1125, 0.03 #1297) >> Best rule #14 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 034fl9; >> query: (?x7928, 05gnf) <- actor(?x7928, ?x10663), actor(?x7928, ?x3210), genre(?x7928, ?x1013), ?x3210 = 01vwllw, film(?x10663, ?x4786) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #629 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 06qxh; 01j95; *> query: (?x7928, 01nzs7) <- titles(?x2008, ?x7928), genre(?x7928, ?x1013), ?x1013 = 06n90 *> conf = 0.14 ranks of expected_values: 6 EVAL 02r2j8 program! 01nzs7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 99.000 92.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #5299-01vzz1c PRED entity: 01vzz1c PRED relation: gender PRED expected values: 05zppz => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #59, 0.86 #43, 0.86 #17), 02zsn (0.50 #95, 0.49 #190, 0.46 #239) >> Best rule #59 for best value: >> intensional similarity = 4 >> extensional distance = 182 >> proper extension: 02ldv0; 01r4zfk; >> query: (?x11442, 05zppz) <- role(?x11442, ?x227), category(?x11442, ?x134), profession(?x11442, ?x220), nationality(?x11442, ?x2146) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vzz1c gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.859 http://example.org/people/person/gender #5298-03bx0bm PRED entity: 03bx0bm PRED relation: role! PRED expected values: 07c6l 01xqw => 71 concepts (56 used for prediction) PRED predicted values (max 10 best out of 70): 03bx0bm (0.88 #2238, 0.88 #1457, 0.86 #1140), 01vj9c (0.86 #1129, 0.85 #244, 0.85 #684), 0mkg (0.85 #244, 0.85 #684, 0.85 #492), 018vs (0.85 #244, 0.85 #684, 0.85 #492), 02fsn (0.85 #244, 0.85 #684, 0.85 #492), 026t6 (0.85 #244, 0.85 #684, 0.85 #492), 07c6l (0.85 #244, 0.85 #684, 0.85 #492), 01wy6 (0.85 #244, 0.85 #684, 0.85 #492), 014zz1 (0.85 #244, 0.85 #684, 0.85 #492), 01p970 (0.85 #244, 0.85 #684, 0.85 #492) >> Best rule #2238 for best value: >> intensional similarity = 11 >> extensional distance = 23 >> proper extension: 07c6l; 0jtg0; >> query: (?x1466, 03bx0bm) <- group(?x1466, ?x442), role(?x7252, ?x1466), role(?x702, ?x1466), role(?x1466, ?x4975), role(?x1466, ?x227), instrumentalists(?x7938, ?x7252), participant(?x932, ?x702), role(?x922, ?x4975), role(?x4975, ?x1662), group(?x227, ?x11107), ?x11107 = 0pqp3 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #244 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 05148p4; *> query: (?x1466, ?x74) <- performance_role(?x1128, ?x1466), role(?x9241, ?x1466), role(?x5691, ?x1466), role(?x3062, ?x1466), role(?x2862, ?x1466), role(?x1466, ?x74), role(?x314, ?x1466), ?x5691 = 04f7c55, artists(?x505, ?x1128), ?x3062 = 03bxwtd, group(?x1466, ?x442), award_winner(?x1232, ?x2862), ?x9241 = 01w5gg6 *> conf = 0.85 ranks of expected_values: 7, 20 EVAL 03bx0bm role! 01xqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 71.000 56.000 0.880 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 03bx0bm role! 07c6l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 71.000 56.000 0.880 http://example.org/music/performance_role/regular_performances./music/group_membership/role #5297-0147dk PRED entity: 0147dk PRED relation: award PRED expected values: 0279c15 02f72n 02f71y 02f75t 099ck7 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 309): 02f6xy (0.78 #12841, 0.77 #7782, 0.70 #31518), 03qbnj (0.38 #1388, 0.33 #1777, 0.26 #610), 01bgqh (0.36 #1207, 0.36 #3541, 0.35 #3152), 01by1l (0.35 #7500, 0.35 #7890, 0.34 #8279), 01c99j (0.33 #1381, 0.29 #1770, 0.26 #603), 02f6ym (0.33 #1412, 0.29 #1801, 0.26 #634), 03t5kl (0.33 #215, 0.18 #1382, 0.16 #1771), 0gqy2 (0.33 #157, 0.15 #35412, 0.15 #41252), 01cky2 (0.33 #184, 0.15 #35412, 0.15 #41252), 01cw51 (0.33 #133, 0.15 #35412, 0.15 #41252) >> Best rule #12841 for best value: >> intensional similarity = 3 >> extensional distance = 373 >> proper extension: 05cljf; 0m2l9; 0kzy0; 06w2sn5; 01r9fv; 01bpc9; 015_30; 01ky2h; 0cg9y; 01vvpjj; ... >> query: (?x521, ?x462) <- artist(?x3265, ?x521), profession(?x521, ?x106), award_winner(?x462, ?x521) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #254 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 01wmxfs; *> query: (?x521, 099ck7) <- artist(?x3265, ?x521), award_winner(?x1488, ?x521), ?x1488 = 01719t *> conf = 0.33 ranks of expected_values: 15, 37, 46, 68, 77 EVAL 0147dk award 099ck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 121.000 121.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0147dk award 02f75t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 121.000 121.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0147dk award 02f71y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 121.000 121.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0147dk award 02f72n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 121.000 121.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0147dk award 0279c15 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 121.000 121.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5296-01_xtx PRED entity: 01_xtx PRED relation: award_nominee PRED expected values: 06cgy => 126 concepts (54 used for prediction) PRED predicted values (max 10 best out of 949): 03lq43 (0.18 #7012, 0.15 #14027, 0.12 #21042), 0bksh (0.12 #1131, 0.12 #49097, 0.11 #16366), 0lx2l (0.12 #49097, 0.11 #16366, 0.11 #46759), 01q_ph (0.10 #69, 0.03 #2406, 0.02 #46828), 039bp (0.10 #229, 0.03 #2566, 0.02 #14257), 0205dx (0.10 #1128, 0.01 #47887, 0.01 #40874), 06cgy (0.10 #327, 0.01 #9678, 0.01 #42411), 030vnj (0.10 #1838, 0.01 #11189, 0.01 #15866), 01vw37m (0.09 #14028, 0.08 #7013, 0.03 #1455), 0227tr (0.09 #14028, 0.08 #7013, 0.03 #563) >> Best rule #7012 for best value: >> intensional similarity = 3 >> extensional distance = 166 >> proper extension: 01pl9g; 02jg92; 01sxd1; 01lz4tf; >> query: (?x3865, ?x4042) <- profession(?x3865, ?x319), spouse(?x3865, ?x4042), award_winner(?x192, ?x4042) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #327 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 036hf4; *> query: (?x3865, 06cgy) <- award(?x3865, ?x1691), award_nominee(?x3865, ?x1445), ?x1691 = 05zvj3m *> conf = 0.10 ranks of expected_values: 7 EVAL 01_xtx award_nominee 06cgy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 126.000 54.000 0.185 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5295-0jdr0 PRED entity: 0jdr0 PRED relation: titles! PRED expected values: 01jfsb => 102 concepts (38 used for prediction) PRED predicted values (max 10 best out of 74): 01jfsb (0.49 #1538, 0.48 #2450, 0.29 #121), 07s9rl0 (0.44 #1824, 0.42 #2230, 0.32 #3451), 04xvlr (0.36 #1827, 0.32 #307, 0.30 #2233), 07ssc (0.27 #2025, 0.27 #1933, 0.15 #2239), 01g6gs (0.25 #1619, 0.25 #2532, 0.22 #1923), 060__y (0.25 #1619, 0.25 #2532, 0.22 #1923), 09blyk (0.21 #1565, 0.20 #2477, 0.19 #148), 024qqx (0.20 #1394, 0.17 #2409, 0.17 #79), 01hmnh (0.19 #735, 0.17 #1645, 0.17 #633), 01z4y (0.18 #338, 0.16 #1249, 0.16 #1149) >> Best rule #1538 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 0cvkv5; >> query: (?x9349, 01jfsb) <- nominated_for(?x10758, ?x9349), genre(?x9349, ?x600), ?x600 = 02n4kr, film_crew_role(?x9349, ?x2178) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jdr0 titles! 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 38.000 0.489 http://example.org/media_common/netflix_genre/titles #5294-0894_x PRED entity: 0894_x PRED relation: nationality PRED expected values: 0d060g => 137 concepts (122 used for prediction) PRED predicted values (max 10 best out of 115): 09c7w0 (0.87 #9352, 0.78 #10337, 0.77 #9451), 07ssc (0.11 #6411, 0.11 #7297, 0.10 #6806), 02jx1 (0.11 #8597, 0.10 #6429, 0.10 #4065), 03rjj (0.07 #2166, 0.04 #1871, 0.03 #2855), 03shp (0.07 #1035, 0.05 #839), 0d060g (0.07 #2070, 0.07 #1873, 0.07 #2462), 0f8l9c (0.04 #2183, 0.04 #2872, 0.03 #7304), 0d05w3 (0.04 #2700, 0.04 #2898, 0.03 #3194), 0345h (0.03 #619, 0.03 #4852, 0.03 #2192), 03_3d (0.03 #2658, 0.03 #1276, 0.02 #3152) >> Best rule #9352 for best value: >> intensional similarity = 4 >> extensional distance = 1903 >> proper extension: 0785v8; 04sx9_; 011zf2; 03yf3z; 04n_g; 07_grx; 015wfg; 03q95r; 0grrq8; 05typm; ... >> query: (?x12309, 09c7w0) <- location(?x12309, ?x7412), nationality(?x12309, ?x2146), film_release_region(?x8657, ?x2146), ?x8657 = 030z4z >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #2070 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 130 *> proper extension: 02lk1s; 0bz5v2; 02dlfh; 03mv0b; 07ddz9; 0f87jy; 02hblj; 0pgm3; *> query: (?x12309, 0d060g) <- profession(?x12309, ?x1146), profession(?x12309, ?x1032), profession(?x12309, ?x987), gender(?x12309, ?x231), ?x987 = 0dxtg, ?x1146 = 018gz8, ?x1032 = 02hrh1q *> conf = 0.07 ranks of expected_values: 6 EVAL 0894_x nationality 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 137.000 122.000 0.870 http://example.org/people/person/nationality #5293-02qyxs5 PRED entity: 02qyxs5 PRED relation: nominated_for PRED expected values: 050f0s => 55 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1786): 0fs9vc (0.67 #2671, 0.57 #4254, 0.40 #1088), 0cmf0m0 (0.67 #2824, 0.57 #4407, 0.40 #1241), 06fcqw (0.67 #2549, 0.57 #4132, 0.40 #966), 0cc97st (0.67 #2464, 0.57 #4047, 0.40 #881), 0dr3sl (0.57 #3577, 0.50 #1994, 0.38 #6745), 017gl1 (0.54 #6465, 0.50 #4880, 0.40 #131), 01jrbb (0.50 #2002, 0.46 #6753, 0.43 #3585), 09gq0x5 (0.50 #5002, 0.46 #6587, 0.28 #11344), 03hmt9b (0.50 #5335, 0.46 #6920, 0.25 #11677), 0cc5qkt (0.50 #5274, 0.46 #6859, 0.24 #11091) >> Best rule #2671 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0gqzz; 09tqxt; 02x1z2s; 0drtkx; >> query: (?x2706, 0fs9vc) <- nominated_for(?x2706, ?x5839), nominated_for(?x2706, ?x3457), ?x5839 = 05650n, ceremony(?x2706, ?x2032), ?x3457 = 03x7hd >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1860 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0gqzz; 09tqxt; 02x1z2s; 0drtkx; *> query: (?x2706, 050f0s) <- nominated_for(?x2706, ?x5839), nominated_for(?x2706, ?x3457), ?x5839 = 05650n, ceremony(?x2706, ?x2032), ?x3457 = 03x7hd *> conf = 0.50 ranks of expected_values: 12 EVAL 02qyxs5 nominated_for 050f0s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 55.000 23.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5292-0vhm PRED entity: 0vhm PRED relation: award_winner PRED expected values: 03yxwq => 108 concepts (55 used for prediction) PRED predicted values (max 10 best out of 703): 06pj8 (0.60 #3286, 0.53 #52567, 0.50 #1643), 01ccr8 (0.42 #26281, 0.38 #32850, 0.38 #68998), 031c2r (0.42 #26281, 0.38 #32850, 0.38 #41066), 083wr9 (0.42 #26281, 0.38 #32850, 0.38 #41066), 0sw6y (0.42 #26281, 0.38 #32850, 0.38 #41066), 0sw62 (0.42 #26281, 0.38 #32850, 0.38 #41066), 02wrhj (0.42 #26281, 0.38 #32850, 0.38 #41066), 02fp82 (0.27 #42710, 0.25 #34492, 0.22 #50923), 0187wh (0.27 #42710, 0.25 #34492, 0.22 #50923), 06jrhz (0.26 #24639, 0.25 #961, 0.24 #32849) >> Best rule #3286 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01h72l; >> query: (?x5219, ?x2135) <- actor(?x5219, ?x12054), nominated_for(?x2135, ?x5219), ?x12054 = 0sw6y, program(?x11291, ?x5219) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #24639 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 86 *> proper extension: 07s8z_l; *> query: (?x5219, ?x6948) <- award_winner(?x5219, ?x12505), producer_type(?x5219, ?x632), award_winner(?x6948, ?x12505) *> conf = 0.26 ranks of expected_values: 16 EVAL 0vhm award_winner 03yxwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 108.000 55.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #5291-03__y PRED entity: 03__y PRED relation: organization PRED expected values: 07t65 => 99 concepts (97 used for prediction) PRED predicted values (max 10 best out of 51): 07t65 (0.92 #508, 0.91 #24, 0.91 #255), 01rz1 (0.54 #25, 0.42 #440, 0.37 #279), 0b6css (0.49 #34, 0.42 #265, 0.39 #518), 04k4l (0.48 #305, 0.40 #28, 0.37 #98), 0_2v (0.46 #74, 0.46 #258, 0.43 #442), 018cqq (0.46 #35, 0.39 #289, 0.36 #312), 02jxk (0.34 #26, 0.32 #1693, 0.25 #303), 041288 (0.32 #874, 0.32 #1693, 0.31 #1453), 0j7v_ (0.32 #1693, 0.30 #6, 0.26 #122), 0gkjy (0.32 #1693, 0.26 #1029, 0.25 #935) >> Best rule #508 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 01mk6; >> query: (?x3951, 07t65) <- adjoins(?x3951, ?x1781), participating_countries(?x1931, ?x3951), taxonomy(?x3951, ?x939) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03__y organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 97.000 0.918 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #5290-07b_l PRED entity: 07b_l PRED relation: district_represented! PRED expected values: 070mff 024tkd 01h7xx => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 48): 070mff (0.84 #558, 0.83 #846, 0.81 #1183), 024tkd (0.69 #848, 0.68 #1185, 0.67 #656), 01gt99 (0.55 #865, 0.53 #572, 0.50 #668), 01gtdd (0.55 #865, 0.53 #569, 0.50 #665), 01gst_ (0.55 #865, 0.50 #537, 0.48 #633), 01gtbb (0.55 #865, 0.50 #536, 0.48 #632), 01gtc0 (0.55 #865, 0.47 #550, 0.47 #358), 01gtcc (0.55 #865, 0.47 #541, 0.45 #637), 02bqn1 (0.55 #865, 0.46 #918, 0.46 #821), 01gsvb (0.55 #865, 0.45 #565, 0.44 #373) >> Best rule #558 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 0vmt; 059_c; 0gyh; >> query: (?x3634, 070mff) <- location(?x56, ?x3634), district_represented(?x176, ?x3634), time_zones(?x3634, ?x1638), religion(?x3634, ?x109) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 26 EVAL 07b_l district_represented! 01h7xx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 199.000 199.000 0.842 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07b_l district_represented! 024tkd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.842 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 07b_l district_represented! 070mff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.842 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #5289-025sppp PRED entity: 025sppp PRED relation: profession! PRED expected values: 024t0y => 43 concepts (17 used for prediction) PRED predicted values (max 10 best out of 4200): 02f_k_ (0.87 #16955, 0.40 #19022, 0.33 #2067), 05mc99 (0.87 #16955, 0.33 #2478, 0.25 #6715), 046m59 (0.87 #16955, 0.33 #1778, 0.25 #6015), 05dtsb (0.87 #16955), 014g_s (0.83 #8475, 0.81 #16952, 0.37 #67818), 09b6zr (0.83 #8475, 0.81 #16952, 0.37 #67818), 01ggc9 (0.83 #8475, 0.81 #16952, 0.37 #67818), 01vw87c (0.83 #8475, 0.35 #50863, 0.33 #63), 0gn30 (0.83 #8475, 0.35 #50863, 0.29 #22922), 03h_0_z (0.82 #16954, 0.81 #16952, 0.53 #25430) >> Best rule #16955 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 01445t; >> query: (?x7841, ?x5460) <- profession(?x11924, ?x7841), profession(?x5798, ?x7841), profession(?x4058, ?x7841), profession(?x2387, ?x7841), ?x11924 = 054c1, award_winner(?x324, ?x2387), award(?x2387, ?x102), film(?x5460, ?x324), participant(?x4058, ?x1149), participant(?x5798, ?x338) >> conf = 0.87 => this is the best rule for 4 predicted values *> Best rule #20868 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 01d_h8; 012t_z; *> query: (?x7841, 024t0y) <- profession(?x11949, ?x7841), profession(?x11924, ?x7841), profession(?x5798, ?x7841), profession(?x3884, ?x7841), nationality(?x11924, ?x94), ?x3884 = 0cqt90, place_of_birth(?x11924, ?x2850), location(?x11924, ?x11639), ?x5798 = 01vvyc_, inductee(?x12338, ?x11924), type_of_union(?x11949, ?x566) *> conf = 0.60 ranks of expected_values: 37 EVAL 025sppp profession! 024t0y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 43.000 17.000 0.872 http://example.org/people/person/profession #5288-01xcr4 PRED entity: 01xcr4 PRED relation: inductee! PRED expected values: 06szd3 => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 5): 06szd3 (0.10 #272, 0.09 #218, 0.09 #20), 04045y (0.05 #24, 0.03 #69, 0.03 #96), 0g2c8 (0.03 #586, 0.03 #631, 0.03 #487), 0qjfl (0.03 #48, 0.03 #66, 0.03 #75), 04dm2n (0.01 #233) >> Best rule #272 for best value: >> intensional similarity = 2 >> extensional distance = 104 >> proper extension: 03jl0_; 0d_rw; >> query: (?x4259, 06szd3) <- program_creator(?x4891, ?x4259), genre(?x4891, ?x14160) >> conf = 0.10 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xcr4 inductee! 06szd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.104 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #5287-01vvyfh PRED entity: 01vvyfh PRED relation: artist! PRED expected values: 043g7l => 102 concepts (78 used for prediction) PRED predicted values (max 10 best out of 101): 03rhqg (0.21 #1935, 0.18 #15, 0.18 #837), 0g768 (0.18 #2777, 0.14 #1955, 0.13 #3188), 017l96 (0.18 #18, 0.17 #155, 0.16 #840), 01w40h (0.18 #27, 0.13 #164, 0.12 #849), 0mzkr (0.14 #435, 0.09 #1670, 0.08 #3177), 0181dw (0.13 #1686, 0.12 #451, 0.12 #40), 011k1h (0.13 #284, 0.13 #147, 0.12 #832), 03mp8k (0.12 #1709, 0.10 #474, 0.09 #2531), 0k_kr (0.12 #42, 0.11 #316, 0.09 #864), 02y21l (0.12 #92, 0.09 #914, 0.09 #229) >> Best rule #1935 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 05563d; >> query: (?x3929, 03rhqg) <- artists(?x505, ?x3929), artist(?x2299, ?x3929), ?x505 = 03_d0 >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #1676 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 110 *> proper extension: 04lgymt; 05pdbs; *> query: (?x3929, 043g7l) <- award(?x3929, ?x8458), award(?x11026, ?x8458), ?x11026 = 01s7ns *> conf = 0.12 ranks of expected_values: 13 EVAL 01vvyfh artist! 043g7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 102.000 78.000 0.207 http://example.org/music/record_label/artist #5286-0f2r6 PRED entity: 0f2r6 PRED relation: location! PRED expected values: 02nb2s => 225 concepts (145 used for prediction) PRED predicted values (max 10 best out of 2204): 01mbwlb (0.25 #4876, 0.08 #14924, 0.08 #12412), 0gs1_ (0.25 #3834, 0.08 #13882, 0.06 #16394), 01q_ph (0.22 #7586, 0.07 #27683, 0.06 #30195), 02p5hf (0.21 #19684, 0.07 #74949, 0.06 #95048), 06s6hs (0.20 #296428, 0.04 #31330, 0.04 #43890), 044mjy (0.20 #296428, 0.02 #31941, 0.02 #44501), 0hnp7 (0.17 #18822, 0.07 #97973, 0.05 #94186), 09889g (0.17 #18593, 0.06 #73858, 0.05 #93957), 02lt8 (0.12 #18379, 0.11 #25916, 0.11 #8331), 023kzp (0.12 #18798, 0.11 #26335, 0.08 #33871) >> Best rule #4876 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0l39b; >> query: (?x674, 01mbwlb) <- contains(?x2256, ?x674), time_zones(?x674, ?x2088), ?x2256 = 07srw, location(?x436, ?x674) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #17661 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 07cfx; *> query: (?x674, 02nb2s) <- contains(?x673, ?x674), location(?x8543, ?x674), place_of_burial(?x8543, ?x11327), participant(?x8543, ?x3627) *> conf = 0.04 ranks of expected_values: 887 EVAL 0f2r6 location! 02nb2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 225.000 145.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #5285-03v1jf PRED entity: 03v1jf PRED relation: film PRED expected values: 0cd2vh9 => 87 concepts (58 used for prediction) PRED predicted values (max 10 best out of 553): 07pd_j (0.75 #1187, 0.03 #13697, 0.01 #36931), 0gtsx8c (0.62 #12, 0.44 #14298, 0.03 #12522), 01k0vq (0.50 #1314, 0.02 #13824, 0.01 #37058), 02rzdcp (0.45 #57194, 0.34 #62557, 0.33 #71494), 03n0cd (0.25 #1493), 04gv3db (0.12 #753, 0.03 #13263, 0.02 #11476), 0888c3 (0.12 #1412, 0.03 #13922, 0.01 #37156), 0prrm (0.12 #860, 0.02 #9795, 0.02 #13370), 02qzh2 (0.12 #693, 0.02 #13203, 0.01 #36437), 02ht1k (0.12 #630, 0.02 #13140, 0.01 #36374) >> Best rule #1187 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02g87m; 03n08b; 049k07; 0c01c; 03l3jy; 028k57; >> query: (?x5216, 07pd_j) <- award_winner(?x5216, ?x1342), film(?x5216, ?x7348), ?x7348 = 01k0xy >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #25274 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 467 *> proper extension: 01sl1q; 044mz_; 04bdxl; 02s2ft; 079vf; 01vvydl; 02p65p; 0337vz; 07s3vqk; 06151l; ... *> query: (?x5216, 0cd2vh9) <- award_winner(?x5216, ?x1342), film(?x5216, ?x2029), place_of_birth(?x5216, ?x11163) *> conf = 0.01 ranks of expected_values: 449 EVAL 03v1jf film 0cd2vh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 87.000 58.000 0.750 http://example.org/film/actor/film./film/performance/film #5284-0_z91 PRED entity: 0_z91 PRED relation: place PRED expected values: 0_z91 => 118 concepts (84 used for prediction) PRED predicted values (max 10 best out of 140): 0f2w0 (0.09 #37, 0.06 #1067, 0.03 #2613), 0f2rq (0.09 #139, 0.04 #1685, 0.03 #2715), 0f2sq (0.09 #351, 0.04 #1897, 0.03 #2927), 013m_x (0.09 #138, 0.03 #2714, 0.03 #2199), 013n2h (0.09 #225, 0.03 #2801, 0.02 #4863), 013mtx (0.09 #400, 0.03 #2976, 0.02 #5038), 0105y2 (0.09 #263, 0.03 #2839, 0.02 #4901), 010bxh (0.09 #185, 0.03 #2761, 0.02 #4823), 030qb3t (0.08 #545, 0.03 #2091, 0.02 #3638), 0f2tj (0.08 #686, 0.02 #3264, 0.02 #4294) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 010bxh; 0105y2; >> query: (?x10465, 0f2w0) <- category(?x10465, ?x134), contains(?x3634, ?x10465), ?x3634 = 07b_l, location(?x3495, ?x10465) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0_z91 place 0_z91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 84.000 0.091 http://example.org/location/hud_county_place/place #5283-073hgx PRED entity: 073hgx PRED relation: award_winner PRED expected values: 02fgpf => 29 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1414): 0b6mgp_ (0.33 #677, 0.28 #1539, 0.27 #1540), 02fgpf (0.33 #265, 0.28 #1539, 0.27 #1540), 06r_by (0.33 #933, 0.28 #1539, 0.06 #4013), 0c94fn (0.33 #269, 0.27 #1540, 0.10 #3349), 092ys_y (0.33 #576, 0.27 #1540, 0.06 #3656), 03r1pr (0.33 #422, 0.10 #3502, 0.10 #1962), 0bn3jg (0.33 #1465, 0.06 #4545, 0.06 #3005), 0chw_ (0.33 #1281, 0.05 #7440, 0.04 #8979), 02t_w8 (0.33 #832, 0.03 #3912, 0.03 #2372), 02w0dc0 (0.33 #91, 0.03 #3171, 0.03 #1631) >> Best rule #677 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 073h9x; >> query: (?x7038, 0b6mgp_) <- honored_for(?x7038, ?x9060), ceremony(?x5409, ?x7038), ceremony(?x1862, ?x7038), ceremony(?x1307, ?x7038), ceremony(?x1243, ?x7038), ceremony(?x720, ?x7038), ?x720 = 018wng, ?x5409 = 0gr07, ?x1307 = 0gq9h, award_winner(?x7038, ?x10262), award_winner(?x7038, ?x8661), ?x1243 = 0gr0m, ?x8661 = 02fgp0, nominated_for(?x669, ?x9060), award(?x9060, ?x746), ?x1862 = 0gr51, award_nominee(?x1933, ?x10262) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #265 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 1 *> proper extension: 073h9x; *> query: (?x7038, 02fgpf) <- honored_for(?x7038, ?x9060), ceremony(?x5409, ?x7038), ceremony(?x1862, ?x7038), ceremony(?x1307, ?x7038), ceremony(?x1243, ?x7038), ceremony(?x720, ?x7038), ?x720 = 018wng, ?x5409 = 0gr07, ?x1307 = 0gq9h, award_winner(?x7038, ?x10262), award_winner(?x7038, ?x8661), ?x1243 = 0gr0m, ?x8661 = 02fgp0, nominated_for(?x669, ?x9060), award(?x9060, ?x746), ?x1862 = 0gr51, award_nominee(?x1933, ?x10262) *> conf = 0.33 ranks of expected_values: 2 EVAL 073hgx award_winner 02fgpf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 29.000 15.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5282-09qr6 PRED entity: 09qr6 PRED relation: instrumentalists! PRED expected values: 026t6 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 114): 05148p4 (0.39 #3217, 0.38 #16, 0.35 #920), 042v_gx (0.26 #576, 0.26 #4352, 0.10 #247), 02sgy (0.26 #576, 0.26 #4352, 0.10 #247), 02hnl (0.26 #523, 0.24 #934, 0.20 #1508), 01v1d8 (0.25 #53, 0.11 #300, 0.11 #135), 0l14md (0.20 #169, 0.19 #498, 0.15 #909), 026t6 (0.17 #1480, 0.12 #2, 0.12 #5097), 06ncr (0.12 #40, 0.11 #122, 0.08 #1518), 048j4l (0.11 #158, 0.03 #487, 0.03 #980), 07brj (0.11 #101, 0.03 #5178, 0.03 #923) >> Best rule #3217 for best value: >> intensional similarity = 3 >> extensional distance = 312 >> proper extension: 0f0y8; 0c9d9; 01w923; 012zng; 09prnq; 02jg92; 0lgm5; 0gkg6; 01vv6_6; 0bkg4; ... >> query: (?x1338, 05148p4) <- type_of_union(?x1338, ?x566), instrumentalists(?x75, ?x1338), artist(?x1543, ?x1338) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1480 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 01tszq; 01mwsnc; 03llf8; 04bgy; 04_jsg; 01vtg4q; 01vsqvs; 01rmnp; 01k_0fp; 09g0h; ... *> query: (?x1338, 026t6) <- type_of_union(?x1338, ?x566), instrumentalists(?x75, ?x1338), film(?x1338, ?x8664) *> conf = 0.17 ranks of expected_values: 7 EVAL 09qr6 instrumentalists! 026t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 120.000 120.000 0.385 http://example.org/music/instrument/instrumentalists #5281-04gb7 PRED entity: 04gb7 PRED relation: major_field_of_study! PRED expected values: 01pq4w 020923 017cy9 0gjv_ 09vzz => 93 concepts (84 used for prediction) PRED predicted values (max 10 best out of 591): 01bm_ (0.77 #6569, 0.75 #3936, 0.71 #2883), 07szy (0.75 #3724, 0.71 #2671, 0.69 #6357), 07wrz (0.75 #3745, 0.71 #2692, 0.62 #6378), 03ksy (0.74 #10648, 0.71 #2740, 0.67 #12230), 07tds (0.71 #2784, 0.62 #3837, 0.60 #8582), 01j_9c (0.71 #2641, 0.62 #3694, 0.54 #6327), 01mpwj (0.71 #2741, 0.62 #3794, 0.47 #8539), 01w3v (0.67 #10553, 0.67 #10023, 0.67 #8443), 09f2j (0.67 #11756, 0.63 #10701, 0.59 #10171), 017j69 (0.62 #3831, 0.62 #6464, 0.62 #5937) >> Best rule #6569 for best value: >> intensional similarity = 8 >> extensional distance = 11 >> proper extension: 01mkq; >> query: (?x5179, 01bm_) <- major_field_of_study(?x9200, ?x5179), major_field_of_study(?x5288, ?x5179), major_field_of_study(?x865, ?x5179), major_field_of_study(?x5179, ?x2606), student(?x5288, ?x460), ?x9200 = 0dzst, ?x865 = 02h4rq6, list(?x5288, ?x2197) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #6472 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 11 *> proper extension: 01mkq; *> query: (?x5179, 017cy9) <- major_field_of_study(?x9200, ?x5179), major_field_of_study(?x5288, ?x5179), major_field_of_study(?x865, ?x5179), major_field_of_study(?x5179, ?x2606), student(?x5288, ?x460), ?x9200 = 0dzst, ?x865 = 02h4rq6, list(?x5288, ?x2197) *> conf = 0.46 ranks of expected_values: 40, 43, 53, 307, 579 EVAL 04gb7 major_field_of_study! 09vzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 93.000 84.000 0.769 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04gb7 major_field_of_study! 0gjv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 93.000 84.000 0.769 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04gb7 major_field_of_study! 017cy9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 93.000 84.000 0.769 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04gb7 major_field_of_study! 020923 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 93.000 84.000 0.769 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04gb7 major_field_of_study! 01pq4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 93.000 84.000 0.769 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #5280-07p12s PRED entity: 07p12s PRED relation: genre PRED expected values: 01jfsb => 72 concepts (54 used for prediction) PRED predicted values (max 10 best out of 115): 01jfsb (0.69 #3017, 0.58 #2416, 0.58 #973), 05p553 (0.62 #3733, 0.53 #4334, 0.39 #1325), 03k9fj (0.46 #3258, 0.43 #611, 0.39 #2535), 02n4kr (0.46 #2412, 0.23 #3255, 0.23 #1089), 082gq (0.35 #390, 0.12 #3156, 0.11 #270), 06n90 (0.34 #613, 0.32 #974, 0.28 #2297), 02l7c8 (0.30 #3141, 0.28 #495, 0.27 #5554), 01hmnh (0.26 #4107, 0.25 #618, 0.21 #3265), 04xvlr (0.26 #3611, 0.22 #1, 0.18 #3127), 060__y (0.25 #3626, 0.22 #496, 0.18 #376) >> Best rule #3017 for best value: >> intensional similarity = 6 >> extensional distance = 657 >> proper extension: 0bmc4cm; 0413cff; 0k20s; 02pcq92; >> query: (?x10722, 01jfsb) <- genre(?x10722, ?x225), language(?x10722, ?x254), genre(?x7225, ?x225), genre(?x2749, ?x225), ?x2749 = 01771z, ?x7225 = 02mmwk >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07p12s genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 54.000 0.687 http://example.org/film/film/genre #5279-0bk1p PRED entity: 0bk1p PRED relation: award PRED expected values: 02f72_ => 131 concepts (114 used for prediction) PRED predicted values (max 10 best out of 307): 01by1l (0.60 #515, 0.42 #1319, 0.35 #32688), 01bgqh (0.60 #445, 0.37 #6877, 0.33 #4063), 054krc (0.53 #8933, 0.44 #18989, 0.44 #19391), 0l8z1 (0.42 #8909, 0.34 #21377, 0.33 #22583), 01c92g (0.42 #1304, 0.30 #500, 0.24 #4118), 03qbnj (0.40 #635, 0.25 #1439, 0.19 #4253), 0gqz2 (0.39 #8926, 0.38 #21394, 0.36 #22600), 025m8y (0.37 #8945, 0.30 #6130, 0.28 #19001), 054ks3 (0.36 #6172, 0.36 #8987, 0.30 #544), 02f6yz (0.35 #9970, 0.31 #14795, 0.31 #15197) >> Best rule #515 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01pbxb; >> query: (?x8999, 01by1l) <- artist(?x3240, ?x8999), ?x3240 = 017l96, inductee(?x1091, ?x8999), award(?x8999, ?x2379) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #14705 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 82 *> proper extension: 089tm; 01pfr3; 04rcr; 02r3zy; 03g5jw; 0dvqq; 0frsw; 016fmf; 01vrwfv; 0249kn; ... *> query: (?x8999, 02f72_) <- award_winner(?x10169, ?x8999), award(?x8999, ?x2379), artist(?x2149, ?x8999), group(?x227, ?x8999) *> conf = 0.33 ranks of expected_values: 14 EVAL 0bk1p award 02f72_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 131.000 114.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5278-04nnpw PRED entity: 04nnpw PRED relation: currency PRED expected values: 09nqf => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.82 #78, 0.79 #57, 0.79 #50), 01nv4h (0.17 #2, 0.09 #9, 0.06 #23), 02l6h (0.09 #11, 0.06 #25, 0.06 #32), 02gsvk (0.03 #90, 0.02 #139, 0.01 #174) >> Best rule #78 for best value: >> intensional similarity = 4 >> extensional distance = 173 >> proper extension: 0c3ybss; 09gdm7q; 053rxgm; 07sc6nw; 0fq7dv_; 01fmys; 03177r; 02fqrf; 0243cq; 09v71cj; ... >> query: (?x4696, 09nqf) <- country(?x4696, ?x94), film(?x2275, ?x4696), film_distribution_medium(?x4696, ?x2099), genre(?x4696, ?x162) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04nnpw currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.817 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #5277-0hwd8 PRED entity: 0hwd8 PRED relation: profession PRED expected values: 02hrh1q => 164 concepts (154 used for prediction) PRED predicted values (max 10 best out of 108): 02hrh1q (0.93 #1971, 0.92 #3173, 0.89 #1368), 01d_h8 (0.57 #1811, 0.52 #2413, 0.48 #2262), 09jwl (0.50 #20, 0.19 #4079, 0.19 #15490), 0nbcg (0.50 #33, 0.18 #4811, 0.14 #6198), 039v1 (0.50 #38, 0.18 #4811, 0.11 #3947), 0cbd2 (0.41 #910, 0.39 #1060, 0.25 #4066), 02jknp (0.36 #1813, 0.31 #2415, 0.31 #2264), 0dxtg (0.33 #4523, 0.32 #5126, 0.31 #17134), 0kyk (0.32 #934, 0.30 #1084, 0.23 #2589), 0np9r (0.28 #5134, 0.28 #1676, 0.27 #6637) >> Best rule #1971 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 01m42d0; >> query: (?x2514, 02hrh1q) <- people(?x4322, ?x2514), award_winner(?x458, ?x2514), award(?x2514, ?x3066), ?x3066 = 0gqy2 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hwd8 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 154.000 0.930 http://example.org/people/person/profession #5276-02g8mp PRED entity: 02g8mp PRED relation: ceremony PRED expected values: 0466p0j 013b2h 02cg41 => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 122): 0466p0j (0.87 #569, 0.87 #443, 0.84 #695), 013b2h (0.85 #69, 0.85 #195, 0.83 #321), 02cg41 (0.85 #111, 0.84 #615, 0.83 #489), 04n2r9h (0.46 #1387, 0.41 #1514, 0.41 #1641), 08pc1x (0.41 #1514, 0.41 #1641, 0.28 #2528), 0bzm81 (0.16 #1024, 0.11 #1783, 0.09 #1909), 0n8_m93 (0.16 #1112, 0.10 #1871, 0.09 #1997), 02yxh9 (0.15 #1095, 0.10 #1854, 0.09 #1980), 0bc773 (0.15 #1051, 0.10 #1810, 0.09 #1936), 02yw5r (0.15 #1016, 0.10 #1775, 0.09 #1901) >> Best rule #569 for best value: >> intensional similarity = 6 >> extensional distance = 91 >> proper extension: 0257yf; 03t5n3; 03nc9d; 0257__; >> query: (?x1237, 0466p0j) <- ceremony(?x1237, ?x2431), award_winner(?x2431, ?x215), ceremony(?x9462, ?x2431), ceremony(?x7005, ?x2431), ?x9462 = 01d38t, ?x7005 = 01ck6v >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 02g8mp ceremony 02cg41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.871 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02g8mp ceremony 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.871 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02g8mp ceremony 0466p0j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.871 http://example.org/award/award_category/winners./award/award_honor/ceremony #5275-01z3bz PRED entity: 01z3bz PRED relation: major_field_of_study PRED expected values: 04x_3 => 213 concepts (213 used for prediction) PRED predicted values (max 10 best out of 122): 01mkq (0.67 #1016, 0.52 #1641, 0.52 #1516), 02lp1 (0.64 #1012, 0.54 #1637, 0.49 #1887), 03g3w (0.61 #1028, 0.50 #1653, 0.36 #3280), 02j62 (0.55 #1032, 0.50 #1532, 0.45 #3284), 04rjg (0.55 #1021, 0.44 #1521, 0.41 #1896), 0g26h (0.52 #1545, 0.45 #1045, 0.45 #2421), 062z7 (0.52 #1029, 0.42 #1654, 0.38 #1529), 04sh3 (0.48 #1078, 0.48 #1703, 0.23 #1578), 0fdys (0.48 #1041, 0.33 #1666, 0.29 #1541), 05qfh (0.48 #1038, 0.31 #1663, 0.26 #663) >> Best rule #1016 for best value: >> intensional similarity = 6 >> extensional distance = 31 >> proper extension: 08qnnv; >> query: (?x11717, 01mkq) <- institution(?x2636, ?x11717), institution(?x1200, ?x11717), currency(?x11717, ?x5696), student(?x11717, ?x8768), ?x2636 = 027f2w, ?x1200 = 016t_3 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1027 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 31 *> proper extension: 08qnnv; *> query: (?x11717, 04x_3) <- institution(?x2636, ?x11717), institution(?x1200, ?x11717), currency(?x11717, ?x5696), student(?x11717, ?x8768), ?x2636 = 027f2w, ?x1200 = 016t_3 *> conf = 0.39 ranks of expected_values: 13 EVAL 01z3bz major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 213.000 213.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #5274-0dc7hc PRED entity: 0dc7hc PRED relation: nominated_for! PRED expected values: 07cbcy 05p1dby => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 214): 0gq9h (0.31 #7055, 0.23 #4884, 0.21 #7778), 019f4v (0.27 #7046, 0.22 #5599, 0.19 #7528), 0gs9p (0.25 #7057, 0.21 #1753, 0.20 #4886), 02hsq3m (0.22 #512, 0.20 #2440, 0.19 #3404), 040njc (0.22 #1694, 0.19 #6998, 0.18 #4827), 03m73lj (0.22 #597, 0.10 #2284, 0.09 #2525), 04dn09n (0.22 #7027, 0.17 #5580, 0.16 #4856), 0k611 (0.22 #7066, 0.19 #1762, 0.19 #5619), 099c8n (0.20 #7049, 0.18 #6567, 0.17 #7772), 0gqwc (0.20 #62, 0.15 #1749, 0.12 #7776) >> Best rule #7055 for best value: >> intensional similarity = 3 >> extensional distance = 440 >> proper extension: 0kfpm; 0ddd0gc; 05lfwd; 02qkq0; 0gvsh7l; 03d17dg; >> query: (?x9774, 0gq9h) <- nominated_for(?x3917, ?x9774), award_winner(?x139, ?x3917), written_by(?x10268, ?x3917) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #18810 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1588 *> proper extension: 01tspc6; 06g60w; *> query: (?x9774, ?x384) <- nominated_for(?x3917, ?x9774), award(?x3917, ?x384) *> conf = 0.19 ranks of expected_values: 17, 23 EVAL 0dc7hc nominated_for! 05p1dby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 87.000 87.000 0.314 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0dc7hc nominated_for! 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 87.000 87.000 0.314 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5273-09bxq9 PRED entity: 09bxq9 PRED relation: award PRED expected values: 0gr0m => 96 concepts (88 used for prediction) PRED predicted values (max 10 best out of 286): 0gr0m (0.76 #2505, 0.73 #2100, 0.66 #2911), 0gq9h (0.36 #483, 0.14 #8103, 0.14 #11344), 09sb52 (0.27 #446, 0.27 #5713, 0.25 #7333), 040njc (0.27 #413, 0.14 #8103, 0.14 #11344), 05f4m9q (0.20 #13, 0.17 #823, 0.14 #8103), 04dn09n (0.18 #449, 0.14 #8103, 0.14 #11344), 0gr4k (0.18 #438, 0.14 #8103, 0.14 #11344), 03hkv_r (0.18 #421, 0.14 #8103, 0.14 #11344), 02x17s4 (0.18 #531, 0.14 #8103, 0.14 #11344), 02n9nmz (0.18 #475, 0.14 #8103, 0.14 #11344) >> Best rule #2505 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 09cdxn; 02rybfn; 04dz_y7; >> query: (?x7782, 0gr0m) <- cinematography(?x6425, ?x7782), award(?x7782, ?x2393), nominated_for(?x350, ?x6425) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09bxq9 award 0gr0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 88.000 0.759 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5272-0gm34 PRED entity: 0gm34 PRED relation: place_of_burial PRED expected values: 018mmw => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 10): 018mm4 (0.08 #136, 0.08 #105, 0.08 #167), 018mmj (0.08 #200, 0.06 #617, 0.06 #107), 0lbp_ (0.04 #143, 0.04 #174, 0.02 #79), 018mmw (0.04 #80, 0.03 #302, 0.03 #334), 018mlg (0.03 #309, 0.03 #373, 0.02 #341), 01n7q (0.02 #193, 0.02 #162, 0.01 #546), 0nb1s (0.02 #347, 0.01 #315, 0.01 #636), 01f38z (0.01 #282, 0.01 #314, 0.01 #346), 018mrd (0.01 #629, 0.01 #533), 0r04p (0.01 #140) >> Best rule #136 for best value: >> intensional similarity = 2 >> extensional distance = 94 >> proper extension: 0d9kl; 057ph; 0dng4; >> query: (?x7458, 018mm4) <- celebrities_impersonated(?x3649, ?x7458), ?x3649 = 03m6t5 >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #80 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 01vq3nl; *> query: (?x7458, 018mmw) <- nationality(?x7458, ?x94), place_of_death(?x7458, ?x1523), actor(?x11482, ?x7458) *> conf = 0.04 ranks of expected_values: 4 EVAL 0gm34 place_of_burial 018mmw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 82.000 0.083 http://example.org/people/deceased_person/place_of_burial #5271-04t36 PRED entity: 04t36 PRED relation: artists PRED expected values: 03n0q5 => 57 concepts (40 used for prediction) PRED predicted values (max 10 best out of 510): 017lb_ (0.40 #9445, 0.03 #39854, 0.03 #43113), 06mj4 (0.40 #9425, 0.03 #26800, 0.02 #38747), 0lbj1 (0.40 #8698, 0.03 #26073, 0.02 #38020), 01mbwlb (0.33 #5378, 0.20 #10805, 0.07 #14063), 01tl50z (0.33 #5153, 0.01 #30129, 0.01 #32300), 01ldw4 (0.33 #4923, 0.01 #29899, 0.01 #32070), 02fgpf (0.33 #4477, 0.01 #29453, 0.01 #31624), 0f3nn (0.33 #5409), 0164y7 (0.33 #5408), 03n0pv (0.33 #5363) >> Best rule #9445 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 01qzt1; 05r6t; >> query: (?x307, 017lb_) <- titles(?x307, ?x11039), titles(?x307, ?x6222), titles(?x307, ?x6103), ?x6103 = 05q7874, nominated_for(?x591, ?x11039), nominated_for(?x2683, ?x6222), country(?x6222, ?x94) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04t36 artists 03n0q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 40.000 0.400 http://example.org/music/genre/artists #5270-01vsy9_ PRED entity: 01vsy9_ PRED relation: gender PRED expected values: 05zppz => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #63, 0.88 #65, 0.88 #55), 02zsn (0.38 #24, 0.35 #68, 0.34 #58) >> Best rule #63 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 014dq7; >> query: (?x8803, 05zppz) <- celebrities_impersonated(?x3649, ?x8803), profession(?x8803, ?x220), nationality(?x8803, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vsy9_ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 160.000 0.894 http://example.org/people/person/gender #5269-0n5j_ PRED entity: 0n5j_ PRED relation: contains! PRED expected values: 05fjf => 111 concepts (50 used for prediction) PRED predicted values (max 10 best out of 113): 05fjf (0.87 #4491, 0.64 #44009, 0.25 #36828), 059rby (0.64 #44009, 0.15 #25175, 0.15 #22481), 04_1l0v (0.46 #3144, 0.25 #11227, 0.24 #10328), 01n7q (0.35 #974, 0.27 #1872, 0.16 #2771), 01x73 (0.33 #114, 0.05 #21681, 0.03 #4605), 0n5j_ (0.25 #36828, 0.25 #44908, 0.23 #39523), 06pvr (0.23 #1062, 0.18 #1960, 0.09 #7351), 02qkt (0.20 #13820, 0.15 #40764, 0.14 #10224), 05tbn (0.11 #18191, 0.10 #19090, 0.10 #5613), 05k7sb (0.11 #21699, 0.08 #4623, 0.06 #9113) >> Best rule #4491 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 0g_wn2; >> query: (?x321, ?x6895) <- second_level_divisions(?x94, ?x321), county(?x1189, ?x321), state(?x1189, ?x6895) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n5j_ contains! 05fjf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 50.000 0.866 http://example.org/location/location/contains #5268-01c6k4 PRED entity: 01c6k4 PRED relation: service_language PRED expected values: 0295r => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 119): 01jb8r (0.25 #40, 0.25 #16, 0.20 #56), 097kp (0.25 #15, 0.20 #55, 0.20 #47), 0459q4 (0.25 #13, 0.20 #53, 0.20 #45), 03k50 (0.25 #9, 0.20 #49, 0.20 #41), 02bv9 (0.20 #538, 0.14 #68, 0.12 #77), 03115z (0.03 #885, 0.03 #884, 0.03 #875), 0c_v2 (0.03 #885, 0.03 #884, 0.03 #875), 0jzc (0.03 #156, 0.02 #221, 0.02 #237), 01d3n8 (0.01 #473, 0.01 #389, 0.01 #398), 0277g (0.01 #473, 0.01 #389, 0.01 #398) >> Best rule #40 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0xwj; >> query: (?x555, 01jb8r) <- currency(?x555, ?x170), industry(?x555, ?x5078), ?x170 = 09nqf, category(?x555, ?x134), ?x134 = 08mbj5d, ?x5078 = 019z7b, currency(?x555, ?x170) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01c6k4 service_language 0295r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 111.000 0.250 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #5267-01vswwx PRED entity: 01vswwx PRED relation: role PRED expected values: 0l14qv => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 113): 026t6 (0.44 #1029, 0.40 #1847, 0.30 #1540), 02sgy (0.42 #1032, 0.41 #2156, 0.36 #1543), 042v_gx (0.36 #1034, 0.32 #1545, 0.31 #1852), 01vj9c (0.36 #1041, 0.30 #1552, 0.27 #1859), 018vs (0.33 #630, 0.31 #1039, 0.27 #1550), 03bx0bm (0.32 #5628, 0.32 #4501, 0.26 #5834), 0l14qv (0.27 #2155, 0.25 #1542, 0.25 #1031), 03qjg (0.26 #6552, 0.23 #6039, 0.17 #1907), 0l14md (0.25 #2157, 0.25 #1851, 0.25 #1033), 013y1f (0.25 #2186, 0.25 #1062, 0.23 #1573) >> Best rule #1029 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 016lj_; >> query: (?x5301, 026t6) <- role(?x5301, ?x3991), ?x3991 = 05842k, origin(?x5301, ?x6357) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2155 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 01l4g5; *> query: (?x5301, 0l14qv) <- role(?x5301, ?x3991), ?x3991 = 05842k, award(?x5301, ?x567) *> conf = 0.27 ranks of expected_values: 7 EVAL 01vswwx role 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 137.000 137.000 0.444 http://example.org/music/artist/track_contributions./music/track_contribution/role #5266-05m0h PRED entity: 05m0h PRED relation: entity_involved! PRED expected values: 02n5d => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 36): 02kxjx (0.07 #377, 0.04 #575, 0.03 #1302), 0chhs (0.07 #393, 0.04 #591, 0.02 #789), 02h2z_ (0.07 #382, 0.04 #580, 0.02 #778), 018w0j (0.07 #366, 0.02 #1291, 0.02 #1357), 07_nf (0.07 #347, 0.01 #1272), 0d06vc (0.06 #1259, 0.03 #1325, 0.02 #1193), 0cwt70 (0.04 #570, 0.03 #1033, 0.02 #768), 0cm2xh (0.04 #539, 0.03 #1002, 0.02 #737), 086m1 (0.03 #1275, 0.01 #1143), 0cbvg (0.02 #1218, 0.02 #822, 0.01 #1284) >> Best rule #377 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 06c97; >> query: (?x10818, 02kxjx) <- company(?x10818, ?x892), profession(?x10818, ?x12647), nationality(?x10818, ?x9328), contains(?x892, ?x893), contains(?x1310, ?x892) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05m0h entity_involved! 02n5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 121.000 0.071 http://example.org/base/culturalevent/event/entity_involved #5265-01y49 PRED entity: 01y49 PRED relation: sport PRED expected values: 0jm_ => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 8): 0jm_ (0.87 #320, 0.86 #311, 0.84 #602), 02vx4 (0.63 #911, 0.63 #950, 0.63 #790), 018jz (0.50 #5, 0.38 #569, 0.38 #241), 018w8 (0.36 #643, 0.28 #735, 0.28 #440), 03tmr (0.33 #612, 0.27 #92, 0.18 #183), 039yzs (0.18 #98, 0.12 #173, 0.10 #997), 09xp_ (0.12 #173, 0.10 #997, 0.09 #592), 0z74 (0.12 #173, 0.10 #997, 0.09 #592) >> Best rule #320 for best value: >> intensional similarity = 14 >> extensional distance = 28 >> proper extension: 01y3c; 07l2m; 04vn5; >> query: (?x2114, 0jm_) <- position(?x2114, ?x1792), team(?x11323, ?x2114), position_s(?x2114, ?x1240), position(?x6645, ?x1792), position(?x4856, ?x1792), position(?x4469, ?x1792), position(?x705, ?x1792), ?x6645 = 0wsr, ?x705 = 07k53y, position(?x684, ?x1792), ?x4856 = 0289q, position_s(?x2247, ?x1792), ?x4469 = 043vc, draft(?x2114, ?x685) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y49 sport 0jm_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.867 http://example.org/sports/sports_team/sport #5264-01p85y PRED entity: 01p85y PRED relation: award PRED expected values: 09qs08 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 289): 09sb52 (0.36 #28179, 0.36 #28581, 0.35 #20943), 0cjyzs (0.25 #104, 0.16 #34574, 0.13 #42216), 09qs08 (0.25 #143, 0.16 #34574, 0.13 #29347), 05p1dby (0.25 #105, 0.13 #42216, 0.13 #29347), 07bdd_ (0.25 #63, 0.13 #42216, 0.12 #45835), 05b1610 (0.25 #37, 0.13 #42216, 0.12 #45835), 0ck27z (0.23 #20190, 0.22 #24612, 0.20 #25014), 05pcn59 (0.19 #14953, 0.19 #12943, 0.18 #10933), 05p09zm (0.18 #2132, 0.18 #2534, 0.15 #5750), 0gqwc (0.16 #7710, 0.15 #474, 0.15 #6102) >> Best rule #28179 for best value: >> intensional similarity = 3 >> extensional distance = 1045 >> proper extension: 01p0vf; >> query: (?x8741, 09sb52) <- nominated_for(?x8741, ?x2586), film(?x8741, ?x2084), award_nominee(?x5889, ?x8741) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #143 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 070j61; *> query: (?x8741, 09qs08) <- nominated_for(?x8741, ?x4588), award_winner(?x1193, ?x8741), ?x4588 = 0l76z *> conf = 0.25 ranks of expected_values: 3 EVAL 01p85y award 09qs08 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 130.000 0.361 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5263-08z39v PRED entity: 08z39v PRED relation: cinematography! PRED expected values: 0prhz => 131 concepts (68 used for prediction) PRED predicted values (max 10 best out of 344): 03cw411 (0.08 #122, 0.05 #1154, 0.05 #1498), 04hk0w (0.08 #342, 0.03 #686, 0.03 #1030), 02b6n9 (0.08 #305, 0.03 #649, 0.03 #993), 087pfc (0.08 #299, 0.03 #643, 0.03 #987), 02p76f9 (0.08 #279, 0.03 #623, 0.03 #967), 026wlxw (0.08 #278, 0.03 #622, 0.03 #966), 0f2sx4 (0.08 #272, 0.03 #616, 0.03 #960), 072r5v (0.08 #268, 0.03 #612, 0.03 #956), 08zrbl (0.08 #267, 0.03 #611, 0.03 #955), 02z9rr (0.08 #265, 0.03 #609, 0.03 #953) >> Best rule #122 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0854hr; >> query: (?x10078, 03cw411) <- award_winner(?x3882, ?x10078), cinematography(?x4460, ?x10078), genre(?x4460, ?x239), ?x239 = 06cvj >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08z39v cinematography! 0prhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 68.000 0.083 http://example.org/film/film/cinematography #5262-01vsy95 PRED entity: 01vsy95 PRED relation: artist! PRED expected values: 0fb0v 01cf93 => 103 concepts (84 used for prediction) PRED predicted values (max 10 best out of 107): 015_1q (0.25 #157, 0.23 #18, 0.21 #3495), 043g7l (0.23 #30, 0.14 #169, 0.13 #308), 011k1h (0.18 #844, 0.15 #10, 0.13 #427), 0n85g (0.15 #62, 0.14 #201, 0.10 #1035), 0mzkr (0.15 #24, 0.13 #302, 0.09 #719), 03mp8k (0.15 #66, 0.11 #205, 0.10 #344), 01gfq4 (0.15 #21, 0.10 #299, 0.08 #438), 0g768 (0.15 #1009, 0.14 #592, 0.14 #175), 01w40h (0.14 #166, 0.10 #1000, 0.10 #1556), 0fb0v (0.14 #146, 0.08 #1536, 0.07 #702) >> Best rule #157 for best value: >> intensional similarity = 2 >> extensional distance = 26 >> proper extension: 02dw1_; >> query: (?x3374, 015_1q) <- artists(?x284, ?x3374), ?x284 = 0827d >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #146 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 26 *> proper extension: 02dw1_; *> query: (?x3374, 0fb0v) <- artists(?x284, ?x3374), ?x284 = 0827d *> conf = 0.14 ranks of expected_values: 10, 20 EVAL 01vsy95 artist! 01cf93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 103.000 84.000 0.250 http://example.org/music/record_label/artist EVAL 01vsy95 artist! 0fb0v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 103.000 84.000 0.250 http://example.org/music/record_label/artist #5261-0820xz PRED entity: 0820xz PRED relation: institution! PRED expected values: 027f2w 03bwzr4 01rr_d => 173 concepts (101 used for prediction) PRED predicted values (max 10 best out of 22): 019v9k (0.78 #103, 0.71 #593, 0.67 #809), 04zx3q1 (0.71 #48, 0.60 #25, 0.44 #96), 014mlp (0.69 #520, 0.68 #1701, 0.67 #805), 03bwzr4 (0.62 #598, 0.57 #814, 0.55 #960), 0bkj86 (0.60 #31, 0.56 #102, 0.53 #242), 071tyz (0.60 #34, 0.29 #57, 0.28 #678), 016t_3 (0.58 #587, 0.57 #49, 0.51 #681), 0bjrnt (0.57 #52, 0.40 #1532, 0.40 #1531), 013zdg (0.43 #53, 0.40 #1532, 0.40 #1531), 01rr_d (0.40 #1532, 0.40 #1531, 0.40 #974) >> Best rule #103 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 05mv4; 01q0kg; 02z6fs; >> query: (?x3132, 019v9k) <- contains(?x13481, ?x3132), major_field_of_study(?x3132, ?x866), category(?x3132, ?x134), ?x866 = 088tb, place_of_birth(?x11018, ?x13481), school_type(?x3132, ?x3092) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #598 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 74 *> proper extension: 08815; 07wrz; 0f1nl; 04hgpt; 02dq8f; 01t38b; 02y9bj; 01qgr3; 0jpkw; 0gl6x; ... *> query: (?x3132, 03bwzr4) <- contains(?x13481, ?x3132), major_field_of_study(?x3132, ?x866), company(?x3131, ?x3132), institution(?x865, ?x3132), ?x865 = 02h4rq6, location(?x11018, ?x13481) *> conf = 0.62 ranks of expected_values: 4, 10, 15 EVAL 0820xz institution! 01rr_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 173.000 101.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0820xz institution! 03bwzr4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 173.000 101.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0820xz institution! 027f2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 173.000 101.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5260-015fr PRED entity: 015fr PRED relation: taxonomy PRED expected values: 04n6k => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.85 #13, 0.80 #47, 0.79 #20) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 04w58; 06dfg; 0164b; 04ty8; >> query: (?x583, 04n6k) <- form_of_government(?x583, ?x6377), country(?x150, ?x583), vacationer(?x583, ?x1735) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015fr taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.848 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #5259-01pbxb PRED entity: 01pbxb PRED relation: gender PRED expected values: 05zppz => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #99, 0.87 #65, 0.87 #123), 02zsn (0.38 #8, 0.29 #112, 0.27 #6) >> Best rule #99 for best value: >> intensional similarity = 3 >> extensional distance = 210 >> proper extension: 03f70xs; 0379s; 032l1; 052h3; 0372p; 080r3; 0h0p_; 05qmj; 08304; 043tg; ... >> query: (?x115, 05zppz) <- influenced_by(?x10670, ?x115), profession(?x115, ?x220), place_of_birth(?x115, ?x12794) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pbxb gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.873 http://example.org/people/person/gender #5258-0k4kk PRED entity: 0k4kk PRED relation: award PRED expected values: 0gr42 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 177): 0f4x7 (0.28 #4216, 0.20 #12185, 0.19 #7966), 09lvl1 (0.28 #4216, 0.20 #12185, 0.19 #7966), 02qkk9_ (0.28 #4216, 0.20 #12185, 0.19 #7966), 0gq_v (0.27 #13126, 0.27 #13125, 0.27 #15001), 0gq9h (0.27 #13126, 0.27 #13125, 0.27 #15001), 0k611 (0.27 #13126, 0.27 #13125, 0.27 #15001), 0gs96 (0.27 #13126, 0.27 #13125, 0.27 #15001), 0p9sw (0.27 #13126, 0.27 #13125, 0.27 #15001), 0gqwc (0.25 #61, 0.18 #529, 0.16 #763), 0gs9p (0.20 #2171, 0.19 #1937, 0.14 #767) >> Best rule #4216 for best value: >> intensional similarity = 4 >> extensional distance = 374 >> proper extension: 07s8z_l; >> query: (?x1746, ?x591) <- award_winner(?x1746, ?x10914), titles(?x4757, ?x1746), honored_for(?x3029, ?x1746), award_winner(?x591, ?x10914) >> conf = 0.28 => this is the best rule for 3 predicted values *> Best rule #323 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 0bmc4cm; 01jwxx; *> query: (?x1746, 0gr42) <- film_release_region(?x1746, ?x94), genre(?x1746, ?x4757), ?x4757 = 06l3bl *> conf = 0.16 ranks of expected_values: 12 EVAL 0k4kk award 0gr42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 97.000 97.000 0.276 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #5257-0dbb3 PRED entity: 0dbb3 PRED relation: inductee! PRED expected values: 0g2c8 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 4): 0g2c8 (0.36 #55, 0.16 #127, 0.15 #118), 06szd3 (0.07 #110, 0.06 #92, 0.06 #83), 04045y (0.05 #15), 0qjfl (0.03 #30, 0.02 #39, 0.02 #318) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 04r1t; 01czx; 07yg2; 0d193h; 05xq9; 0134tg; 07mvp; 01kcms4; 048xh; 07m4c; ... >> query: (?x10559, 0g2c8) <- influenced_by(?x4142, ?x10559), artist(?x3240, ?x10559), artists(?x505, ?x10559) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dbb3 inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.365 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #5256-02d6cy PRED entity: 02d6cy PRED relation: place_of_death PRED expected values: 030qb3t => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 8): 02_286 (0.06 #13, 0.02 #12246, 0.02 #15937), 030qb3t (0.05 #22, 0.04 #16141, 0.04 #216), 0k049 (0.05 #3, 0.03 #391, 0.02 #1169), 0f2wj (0.02 #1178, 0.01 #206, 0.01 #983), 04jpl (0.01 #12629, 0.01 #12240, 0.01 #12045), 06_kh (0.01 #1171, 0.01 #393), 0r3tq (0.01 #537), 027l4q (0.01 #526) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 106 >> proper extension: 04107; >> query: (?x4948, 02_286) <- award(?x4948, ?x1862), ?x1862 = 0gr51 >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #22 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 106 *> proper extension: 04107; *> query: (?x4948, 030qb3t) <- award(?x4948, ?x1862), ?x1862 = 0gr51 *> conf = 0.05 ranks of expected_values: 2 EVAL 02d6cy place_of_death 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.065 http://example.org/people/deceased_person/place_of_death #5255-02qwg PRED entity: 02qwg PRED relation: person! PRED expected values: 0bx_hnp => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 33): 0bx_hnp (0.18 #64, 0.17 #204, 0.11 #555), 0dtw1x (0.12 #1125, 0.06 #774, 0.06 #2813), 0g9lm2 (0.09 #23, 0.08 #163, 0.03 #1355), 02847m9 (0.08 #79, 0.05 #290, 0.04 #1411), 037q31 (0.08 #115, 0.05 #326, 0.03 #466), 04cf_l (0.05 #408, 0.01 #1459), 05_5_22 (0.04 #2273, 0.04 #938, 0.03 #1923), 0dtzkt (0.04 #1048, 0.03 #1611, 0.02 #628), 03nqnnk (0.03 #2210, 0.02 #4666, 0.01 #5577), 03mnn0 (0.03 #459, 0.02 #2074, 0.02 #3059) >> Best rule #64 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0lrh; >> query: (?x3403, 0bx_hnp) <- participant(?x3403, ?x3321), peers(?x3403, ?x4873), award_winner(?x1930, ?x3321) >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qwg person! 0bx_hnp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.182 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #5254-0l6vl PRED entity: 0l6vl PRED relation: sports PRED expected values: 06f41 => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 40): 06z6r (0.88 #786, 0.86 #862, 0.86 #667), 01hp22 (0.85 #190, 0.85 #38, 0.84 #1034), 07_53 (0.85 #190, 0.85 #38, 0.84 #1034), 0486tv (0.85 #190, 0.85 #38, 0.84 #1034), 0w0d (0.85 #190, 0.85 #38, 0.84 #1034), 06f41 (0.84 #457, 0.83 #544, 0.82 #467), 01sgl (0.76 #266, 0.75 #996, 0.74 #534), 07jbh (0.66 #265, 0.65 #76, 0.63 #303), 01gqfm (0.66 #265, 0.65 #76, 0.63 #303), 019tzd (0.66 #265, 0.65 #76, 0.63 #303) >> Best rule #786 for best value: >> intensional similarity = 52 >> extensional distance = 14 >> proper extension: 018qb4; >> query: (?x391, 06z6r) <- sports(?x391, ?x7687), sports(?x391, ?x3659), sports(?x391, ?x3641), sports(?x391, ?x471), ?x471 = 02vx4, olympics(?x5114, ?x391), olympics(?x1229, ?x391), olympics(?x304, ?x391), olympics(?x172, ?x391), ?x3641 = 03fyrh, ?x304 = 0d0vqn, olympics(?x94, ?x391), sports(?x2966, ?x3659), sports(?x2043, ?x3659), countries_spoken_in(?x403, ?x5114), olympics(?x5114, ?x1741), combatants(?x326, ?x5114), film_release_region(?x5873, ?x1229), film_release_region(?x5400, ?x1229), film_release_region(?x5347, ?x1229), film_release_region(?x4950, ?x1229), film_release_region(?x4446, ?x1229), film_release_region(?x3745, ?x1229), film_release_region(?x1080, ?x1229), film_release_region(?x504, ?x1229), film_release_region(?x428, ?x1229), country(?x1037, ?x172), ?x428 = 0h1cdwq, ?x5347 = 02ylg6, ?x1037 = 09_bl, combatants(?x5114, ?x792), country(?x7687, ?x142), film_release_region(?x10860, ?x172), film_release_region(?x1999, ?x172), administrative_parent(?x3407, ?x1229), ?x2043 = 0lv1x, ?x5400 = 0bhwhj, country(?x3408, ?x1229), ?x1999 = 0gd0c7x, ?x3745 = 03cw411, ?x504 = 0g5qs2k, ?x4446 = 0db94w, ?x10860 = 049w1q, ?x1080 = 01c22t, ?x5873 = 0cq86w, contains(?x172, ?x4826), countries_spoken_in(?x7658, ?x1229), olympics(?x7687, ?x1931), ?x2966 = 06sks6, ?x4950 = 07k2mq, ?x1741 = 0sx8l, second_level_divisions(?x1229, ?x10728) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #457 for first EXPECTED value: *> intensional similarity = 56 *> extensional distance = 7 *> proper extension: 0l6m5; *> query: (?x391, ?x766) <- sports(?x391, ?x3659), sports(?x391, ?x2978), sports(?x391, ?x779), sports(?x391, ?x171), ?x3659 = 0dwxr, olympics(?x5114, ?x391), olympics(?x1536, ?x391), olympics(?x1203, ?x391), olympics(?x789, ?x391), olympics(?x390, ?x391), olympics(?x304, ?x391), ?x304 = 0d0vqn, organization(?x1203, ?x127), olympics(?x766, ?x391), ?x789 = 0f8l9c, film_release_region(?x9652, ?x1203), film_release_region(?x9174, ?x1203), film_release_region(?x6528, ?x1203), film_release_region(?x5496, ?x1203), film_release_region(?x3377, ?x1203), film_release_region(?x3276, ?x1203), film_release_region(?x2050, ?x1203), film_release_region(?x1868, ?x1203), film_release_region(?x781, ?x1203), country(?x4673, ?x1203), ?x3276 = 0gjc4d3, ?x9174 = 087pfc, ?x9652 = 0ddbjy4, adjoins(?x410, ?x1203), ?x1868 = 0cc7hmk, ?x390 = 0chghy, location(?x5283, ?x1203), ?x779 = 096f8, ?x5496 = 07l50vn, ?x1536 = 06c1y, countries_spoken_in(?x2502, ?x1203), ?x2050 = 01fmys, ?x171 = 0d1tm, participating_countries(?x418, ?x1203), country(?x2978, ?x9035), country(?x2978, ?x2843), country(?x2978, ?x1497), country(?x2978, ?x756), contains(?x5114, ?x8745), ?x756 = 06npd, ?x1497 = 015qh, ?x6528 = 0dc_ms, ?x4673 = 07jbh, ?x3377 = 0gj8nq2, sports(?x584, ?x2978), ?x2843 = 016wzw, jurisdiction_of_office(?x3341, ?x5114), ?x9035 = 04v09, ?x781 = 0gkz15s, sports(?x358, ?x766), combatants(?x279, ?x5114) *> conf = 0.84 ranks of expected_values: 6 EVAL 0l6vl sports 06f41 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 29.000 29.000 0.875 http://example.org/olympics/olympic_games/sports #5253-03m8y5 PRED entity: 03m8y5 PRED relation: film_crew_role PRED expected values: 09zzb8 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 26): 09zzb8 (0.80 #425, 0.80 #354, 0.76 #604), 01vx2h (0.42 #364, 0.41 #435, 0.38 #829), 0dxtw (0.42 #150, 0.41 #363, 0.40 #434), 02ynfr (0.24 #439, 0.23 #368, 0.19 #833), 0215hd (0.16 #158, 0.14 #442, 0.14 #836), 02rh1dz (0.16 #362, 0.16 #433, 0.13 #827), 015h31 (0.15 #148, 0.14 #8, 0.11 #113), 094hwz (0.14 #14, 0.06 #119, 0.05 #367), 0n1h (0.14 #5, 0.03 #40, 0.03 #75), 0d2b38 (0.14 #165, 0.12 #485, 0.11 #378) >> Best rule #425 for best value: >> intensional similarity = 4 >> extensional distance = 345 >> proper extension: 0gh6j94; >> query: (?x2529, 09zzb8) <- featured_film_locations(?x2529, ?x739), film_crew_role(?x2529, ?x468), ?x468 = 02r96rf, genre(?x2529, ?x53) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03m8y5 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.804 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5252-028qdb PRED entity: 028qdb PRED relation: role PRED expected values: 026t6 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 116): 0342h (0.40 #489, 0.38 #1370, 0.37 #3333), 0mkg (0.34 #878, 0.33 #10, 0.33 #1464), 05842k (0.33 #73, 0.28 #558, 0.25 #364), 042v_gx (0.33 #104, 0.22 #1373, 0.22 #885), 018vs (0.33 #12, 0.21 #303, 0.20 #400), 01vj9c (0.33 #14, 0.21 #305, 0.20 #402), 026t6 (0.33 #2, 0.19 #880, 0.17 #293), 0dwt5 (0.33 #80, 0.17 #371, 0.16 #468), 03gvt (0.33 #72, 0.12 #363, 0.12 #460), 0l14md (0.33 #6, 0.12 #297, 0.12 #394) >> Best rule #489 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 09g0h; >> query: (?x4206, 0342h) <- role(?x4206, ?x1437), ?x1437 = 01vdm0, instrumentalists(?x614, ?x4206) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 05qhnq; *> query: (?x4206, 026t6) <- role(?x4206, ?x885), award_nominee(?x4206, ?x1381), ?x885 = 0dwtp *> conf = 0.33 ranks of expected_values: 7 EVAL 028qdb role 026t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 86.000 86.000 0.404 http://example.org/music/artist/track_contributions./music/track_contribution/role #5251-0725ny PRED entity: 0725ny PRED relation: film PRED expected values: 018nnz => 143 concepts (60 used for prediction) PRED predicted values (max 10 best out of 804): 05zlld0 (0.37 #46468, 0.20 #5975, 0.04 #38146), 02qhqz4 (0.33 #7491, 0.25 #2130, 0.11 #9278), 0dfw0 (0.33 #7987, 0.02 #29434, 0.01 #45519), 0fdv3 (0.33 #7430, 0.02 #28877, 0.01 #44962), 05c26ss (0.25 #4205, 0.20 #5992, 0.11 #9566), 03bx2lk (0.25 #1971, 0.17 #7332, 0.11 #9119), 0b3n61 (0.25 #4932, 0.11 #10293, 0.07 #29953), 02mc5v (0.25 #4974, 0.10 #12122, 0.03 #33570), 0gldyz (0.25 #5229, 0.05 #33825, 0.05 #35612), 0gfzfj (0.25 #1694, 0.05 #30289, 0.04 #19564) >> Best rule #46468 for best value: >> intensional similarity = 3 >> extensional distance = 192 >> proper extension: 01t2h2; 047sxrj; 01v9l67; 02v406; 0klw; 013zyw; 01wbsdz; 01w9wwg; 0x3n; 02b9g4; ... >> query: (?x8273, ?x3748) <- category(?x8273, ?x134), film(?x8273, ?x5277), prequel(?x5277, ?x3748) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #11000 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 062hgx; 024my5; *> query: (?x8273, 018nnz) <- actor(?x3102, ?x8273), nationality(?x8273, ?x94), film(?x8273, ?x4766), ?x4766 = 05sw5b *> conf = 0.10 ranks of expected_values: 99 EVAL 0725ny film 018nnz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 143.000 60.000 0.373 http://example.org/film/actor/film./film/performance/film #5250-011ywj PRED entity: 011ywj PRED relation: language PRED expected values: 02h40lc => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 29): 02h40lc (0.90 #240, 0.89 #299, 0.89 #2737), 064_8sq (0.16 #438, 0.15 #556, 0.14 #319), 02bjrlw (0.14 #1, 0.08 #60, 0.08 #417), 06b_j (0.14 #23, 0.08 #82, 0.07 #261), 04306rv (0.11 #302, 0.10 #243, 0.10 #421), 06nm1 (0.10 #486, 0.10 #545, 0.10 #427), 03_9r (0.04 #3519, 0.04 #3281, 0.04 #2983), 0653m (0.04 #901, 0.04 #1495, 0.03 #487), 04h9h (0.04 #221, 0.03 #161, 0.03 #459), 0jzc (0.03 #258, 0.03 #317, 0.03 #377) >> Best rule #240 for best value: >> intensional similarity = 3 >> extensional distance = 241 >> proper extension: 0k20s; 0c5qvw; >> query: (?x8367, 02h40lc) <- nominated_for(?x1313, ?x8367), ?x1313 = 0gs9p, nominated_for(?x926, ?x8367) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011ywj language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.897 http://example.org/film/film/language #5249-0kbq PRED entity: 0kbq PRED relation: locations PRED expected values: 09c7w0 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 264): 02j9z (0.50 #1685, 0.40 #941, 0.36 #5967), 04wsz (0.45 #4616, 0.36 #5967, 0.36 #5966), 09c7w0 (0.43 #1859, 0.31 #5782, 0.22 #3355), 059g4 (0.40 #871, 0.36 #5967, 0.36 #5966), 05rgl (0.40 #782, 0.36 #5967, 0.36 #5966), 0j0k (0.40 #858, 0.36 #5967, 0.36 #5966), 0dg3n1 (0.36 #5967, 0.36 #5966, 0.36 #5965), 073q1 (0.36 #5967, 0.36 #5966, 0.36 #5965), 04swx (0.36 #5967, 0.36 #5966, 0.36 #5965), 02qkt (0.36 #5967, 0.36 #5966, 0.36 #5965) >> Best rule #1685 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0dr7s; >> query: (?x11988, 02j9z) <- entity_involved(?x11988, ?x8866), combatants(?x11988, ?x14711), taxonomy(?x11988, ?x939), capital(?x8866, ?x9846), combatants(?x7734, ?x8866), entity_involved(?x7734, ?x6371), locations(?x7734, ?x94), films(?x7734, ?x5304) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1859 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 0dr7s; *> query: (?x11988, ?x94) <- entity_involved(?x11988, ?x8866), combatants(?x11988, ?x14711), taxonomy(?x11988, ?x939), capital(?x8866, ?x9846), combatants(?x7734, ?x8866), entity_involved(?x7734, ?x6371), locations(?x7734, ?x94), films(?x7734, ?x5304) *> conf = 0.43 ranks of expected_values: 3 EVAL 0kbq locations 09c7w0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 98.000 0.500 http://example.org/time/event/locations #5248-03_nq PRED entity: 03_nq PRED relation: profession PRED expected values: 04gc2 016m9h => 188 concepts (135 used for prediction) PRED predicted values (max 10 best out of 132): 02hrh1q (0.68 #15560, 0.68 #19859, 0.67 #17932), 04gc2 (0.58 #3743, 0.55 #2559, 0.54 #3891), 0cbd2 (0.54 #1043, 0.44 #3411, 0.40 #451), 0dxtg (0.36 #10523, 0.32 #16596, 0.32 #11263), 099md (0.33 #221, 0.25 #813, 0.20 #517), 0kyk (0.31 #1067, 0.29 #1363, 0.28 #4323), 01d_h8 (0.30 #15551, 0.29 #17033, 0.29 #19257), 03gjzk (0.26 #10525, 0.23 #11265, 0.18 #16598), 0g0vx (0.25 #404, 0.19 #1884, 0.14 #1440), 01d30f (0.25 #367, 0.14 #17472, 0.14 #2735) >> Best rule #15560 for best value: >> intensional similarity = 4 >> extensional distance = 437 >> proper extension: 04bdxl; 01r42_g; 0m2wm; 0p_pd; 0l8v5; 04wqr; 0147dk; 012cj0; 01w61th; 03_vx9; ... >> query: (?x9046, 02hrh1q) <- people(?x4195, ?x9046), religion(?x9046, ?x14017), profession(?x9046, ?x5805), location(?x9046, ?x3052) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #3743 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 06bss; *> query: (?x9046, 04gc2) <- legislative_sessions(?x9046, ?x4437), profession(?x9046, ?x5805), student(?x3439, ?x9046) *> conf = 0.58 ranks of expected_values: 2, 29 EVAL 03_nq profession 016m9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 188.000 135.000 0.683 http://example.org/people/person/profession EVAL 03_nq profession 04gc2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 188.000 135.000 0.683 http://example.org/people/person/profession #5247-02vyw PRED entity: 02vyw PRED relation: currency PRED expected values: 09nqf => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.46 #10, 0.37 #1, 0.36 #19), 01nv4h (0.03 #80, 0.02 #56, 0.01 #92) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 016kjs; 013w7j; 016732; 023p29; >> query: (?x3662, 09nqf) <- award_nominee(?x3405, ?x3662), organizations_founded(?x3662, ?x10503), student(?x3394, ?x3662) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vyw currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.458 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #5246-09qv3c PRED entity: 09qv3c PRED relation: nominated_for PRED expected values: 03nt59 => 57 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1394): 072kp (0.80 #1574, 0.79 #4721, 0.79 #4722), 015pnb (0.80 #1574, 0.79 #4721, 0.79 #4722), 01b9w3 (0.80 #1574, 0.79 #4721, 0.77 #12602), 01ft14 (0.60 #1428, 0.50 #3002, 0.23 #9453), 02czd5 (0.60 #1256, 0.50 #2830, 0.17 #5978), 01lv85 (0.60 #1132, 0.50 #2706, 0.17 #5854), 02r5qtm (0.60 #615, 0.50 #2189, 0.12 #5337), 05p9_ql (0.40 #1109, 0.33 #2683, 0.19 #4256), 0cs134 (0.40 #1465, 0.33 #3039, 0.17 #6187), 0l76z (0.40 #696, 0.33 #2270, 0.17 #5418) >> Best rule #1574 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0cqhk0; 0cjyzs; 09qrn4; >> query: (?x870, ?x631) <- ceremony(?x870, ?x1265), award(?x4676, ?x870), ?x4676 = 04cl1, award(?x631, ?x870) >> conf = 0.80 => this is the best rule for 3 predicted values *> Best rule #931 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0cqhk0; 0cjyzs; 09qrn4; *> query: (?x870, 03nt59) <- ceremony(?x870, ?x1265), award(?x4676, ?x870), ?x4676 = 04cl1, award(?x631, ?x870) *> conf = 0.40 ranks of expected_values: 12 EVAL 09qv3c nominated_for 03nt59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 57.000 24.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5245-0_j_z PRED entity: 0_j_z PRED relation: place! PRED expected values: 0_j_z => 94 concepts (57 used for prediction) PRED predicted values (max 10 best out of 153): 0c1d0 (0.27 #12374, 0.25 #732, 0.12 #1247), 0_jws (0.27 #12374, 0.25 #1006, 0.12 #1521), 0_j_z (0.27 #12374, 0.05 #2062), 030qb3t (0.25 #30, 0.04 #2092, 0.04 #2607), 0r04p (0.25 #113, 0.04 #2175, 0.04 #2690), 0mw5x (0.18 #5153, 0.05 #2062, 0.05 #10309), 0_jsl (0.12 #1536, 0.10 #2051, 0.05 #2062), 0jpy_ (0.10 #1929, 0.05 #2062, 0.01 #22714), 01bm_ (0.05 #2062), 01jszm (0.05 #2062) >> Best rule #12374 for best value: >> intensional similarity = 4 >> extensional distance = 248 >> proper extension: 01mc11; 0wh3; 010dft; 0r4xt; 0rjg8; 0rvty; 0vm4s; 0yc7f; 0_kq3; 01zlwg6; ... >> query: (?x13019, ?x8263) <- time_zones(?x13019, ?x2674), county(?x13019, ?x10067), contains(?x2713, ?x13019), county(?x8263, ?x10067) >> conf = 0.27 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0_j_z place! 0_j_z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 57.000 0.267 http://example.org/location/hud_county_place/place #5244-0h0wc PRED entity: 0h0wc PRED relation: award PRED expected values: 0fq9zdn => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 259): 09qwmm (0.71 #20052, 0.70 #14153, 0.70 #15333), 07h0cl (0.71 #20052, 0.70 #14153, 0.70 #15333), 027b9k6 (0.71 #20052, 0.70 #14153, 0.70 #15333), 02y_rq5 (0.71 #20052, 0.70 #14153, 0.70 #15333), 09cn0c (0.71 #20052, 0.70 #14153, 0.70 #15333), 02y_j8g (0.71 #20052, 0.70 #14153, 0.70 #15333), 02z1nbg (0.71 #20052, 0.70 #14153, 0.70 #15333), 0ck27z (0.26 #6770, 0.21 #7949, 0.20 #8735), 01by1l (0.16 #2857, 0.11 #2071, 0.11 #499), 05pcn59 (0.16 #4008, 0.16 #3614, 0.15 #4794) >> Best rule #20052 for best value: >> intensional similarity = 3 >> extensional distance = 1566 >> proper extension: 0l56b; 024y6w; >> query: (?x2551, ?x618) <- award_nominee(?x2551, ?x9526), award_winner(?x618, ?x2551), award(?x9526, ?x154) >> conf = 0.71 => this is the best rule for 7 predicted values *> Best rule #9096 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 874 *> proper extension: 0jgd; 058j2; 02sch9; 02bh_v; 015c1b; 01nd9f; 0513yzt; *> query: (?x2551, 0fq9zdn) <- gender(?x2551, ?x514), ?x514 = 02zsn *> conf = 0.03 ranks of expected_values: 190 EVAL 0h0wc award 0fq9zdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 91.000 91.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5243-07l50vn PRED entity: 07l50vn PRED relation: nominated_for! PRED expected values: 0fms83 => 115 concepts (85 used for prediction) PRED predicted values (max 10 best out of 224): 0gqng (0.77 #18833, 0.77 #18355, 0.77 #16208), 0gq6s3 (0.71 #23, 0.09 #6433, 0.09 #18834), 0fm3kw (0.71 #192, 0.09 #6433, 0.09 #18834), 0gq9h (0.48 #5781, 0.48 #11026, 0.41 #14842), 0gs9p (0.48 #5783, 0.44 #11028, 0.37 #14844), 0fm3nb (0.43 #219, 0.09 #6433, 0.09 #18834), 019f4v (0.43 #11017, 0.41 #15071, 0.40 #5772), 0k611 (0.42 #2458, 0.40 #3172, 0.38 #5792), 099c8n (0.39 #3394, 0.37 #3155, 0.36 #2441), 0f_nbyh (0.36 #484, 0.18 #3344, 0.18 #2391) >> Best rule #18833 for best value: >> intensional similarity = 4 >> extensional distance = 680 >> proper extension: 06mmr; >> query: (?x5496, ?x77) <- award(?x5496, ?x77), ceremony(?x77, ?x78), nominated_for(?x77, ?x303), award(?x1872, ?x77) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #220 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 04nnpw; *> query: (?x5496, 0fms83) <- nominated_for(?x12686, ?x5496), nominated_for(?x4695, ?x5496), film_release_distribution_medium(?x5496, ?x81), ?x81 = 029j_, award_winner(?x12686, ?x395), ?x4695 = 0fm3b5 *> conf = 0.29 ranks of expected_values: 36 EVAL 07l50vn nominated_for! 0fms83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 115.000 85.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5242-06r_by PRED entity: 06r_by PRED relation: award_winner! PRED expected values: 0gmdkyy => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 133): 09k5jh7 (0.38 #221, 0.28 #4032, 0.12 #8205), 0hn821n (0.33 #129, 0.02 #2075, 0.02 #2353), 09g90vz (0.31 #261, 0.28 #4032, 0.12 #8205), 0bc773 (0.28 #4032, 0.06 #330, 0.04 #9457), 09gkdln (0.28 #4032, 0.04 #2066, 0.04 #5264), 05qb8vx (0.28 #4032, 0.04 #9457, 0.02 #6119), 0gmdkyy (0.28 #4032, 0.04 #9457, 0.02 #6119), 0hndn2q (0.28 #4032, 0.03 #1985, 0.02 #1012), 05zksls (0.28 #4032, 0.02 #1008, 0.02 #1981), 0hhtgcw (0.28 #4032, 0.01 #5228, 0.01 #3698) >> Best rule #221 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 05qd_; 0c6qh; 07m9cm; 01q6bg; 0f7hc; 0bksh; 08qxx9; >> query: (?x6062, 09k5jh7) <- award_winner(?x1916, ?x6062), award_nominee(?x6062, ?x5940), ?x5940 = 0p__8 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #4032 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1177 *> proper extension: 0f721s; 06jntd; 03kxp7; *> query: (?x6062, ?x1442) <- award_winner(?x1916, ?x6062), honored_for(?x1442, ?x1916) *> conf = 0.28 ranks of expected_values: 7 EVAL 06r_by award_winner! 0gmdkyy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 99.000 99.000 0.385 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5241-09jcj6 PRED entity: 09jcj6 PRED relation: country PRED expected values: 07ssc => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 139): 07ssc (0.39 #904, 0.38 #1040, 0.33 #16), 0chghy (0.39 #904, 0.06 #5197, 0.06 #552), 02jx1 (0.39 #904, 0.02 #1052, 0.01 #448), 01jfsb (0.23 #1086, 0.13 #1085, 0.06 #4470), 0345h (0.19 #87, 0.15 #749, 0.14 #1113), 0f8l9c (0.14 #379, 0.12 #1043, 0.11 #1767), 01z4y (0.13 #1085, 0.06 #4470, 0.06 #4469), 03_3d (0.09 #127, 0.07 #427, 0.06 #67), 0d060g (0.09 #128, 0.06 #730, 0.06 #1877), 06mkj (0.09 #160, 0.06 #5197, 0.03 #701) >> Best rule #904 for best value: >> intensional similarity = 5 >> extensional distance = 303 >> proper extension: 023p33; >> query: (?x4688, ?x1310) <- film(?x9152, ?x4688), genre(?x4688, ?x1403), ?x1403 = 02l7c8, nationality(?x9152, ?x1310), film_release_region(?x4688, ?x94) >> conf = 0.39 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 09jcj6 country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.391 http://example.org/film/film/country #5240-02d44q PRED entity: 02d44q PRED relation: nominated_for! PRED expected values: 0gqy2 => 89 concepts (83 used for prediction) PRED predicted values (max 10 best out of 204): 02z0dfh (0.52 #514, 0.48 #286, 0.45 #742), 0gq9h (0.52 #2796, 0.49 #2340, 0.42 #3252), 0gs9p (0.48 #2342, 0.45 #2798, 0.42 #61), 02x4sn8 (0.48 #566, 0.41 #794, 0.38 #338), 094qd5 (0.47 #3228, 0.47 #4597, 0.17 #2772), 0k611 (0.44 #2806, 0.38 #2350, 0.38 #1437), 0gqyl (0.43 #531, 0.41 #2356, 0.38 #303), 019f4v (0.41 #1420, 0.40 #2789, 0.37 #2333), 02pqp12 (0.39 #2793, 0.33 #2337, 0.29 #3249), 040njc (0.39 #2744, 0.33 #2288, 0.31 #3200) >> Best rule #514 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0cfhfz; 0170xl; >> query: (?x1071, 02z0dfh) <- nominated_for(?x3499, ?x1071), ?x3499 = 03qgjwc, language(?x1071, ?x254), film_release_distribution_medium(?x1071, ?x81) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1483 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 06g60w; *> query: (?x1071, 0gqy2) <- nominated_for(?x6356, ?x1071), award(?x6356, ?x198), nominated_for(?x1002, ?x6356) *> conf = 0.31 ranks of expected_values: 22 EVAL 02d44q nominated_for! 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 89.000 83.000 0.524 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5239-0fpzt5 PRED entity: 0fpzt5 PRED relation: profession PRED expected values: 05z96 => 113 concepts (44 used for prediction) PRED predicted values (max 10 best out of 78): 0cbd2 (0.96 #883, 0.86 #445, 0.85 #299), 02hrh1q (0.73 #1914, 0.70 #4253, 0.70 #2206), 01d_h8 (0.55 #3514, 0.55 #4099, 0.55 #3368), 02jknp (0.48 #1176, 0.43 #3516, 0.43 #4101), 03gjzk (0.41 #3815, 0.38 #3377, 0.37 #6301), 018gz8 (0.21 #3525, 0.21 #1185, 0.20 #4110), 09jwl (0.20 #2503, 0.14 #2211, 0.14 #4258), 05z96 (0.20 #42, 0.15 #918, 0.11 #1064), 0d8qb (0.20 #78, 0.07 #1100, 0.06 #954), 015cjr (0.20 #49, 0.05 #3557, 0.05 #3411) >> Best rule #883 for best value: >> intensional similarity = 4 >> extensional distance = 168 >> proper extension: 07kb5; 0ct9_; >> query: (?x8863, 0cbd2) <- profession(?x8863, ?x11999), religion(?x8863, ?x1363), profession(?x5612, ?x11999), ?x5612 = 058vp >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 058vp; *> query: (?x8863, 05z96) <- profession(?x8863, ?x11999), religion(?x8863, ?x1363), ?x11999 = 015btn, nationality(?x8863, ?x94) *> conf = 0.20 ranks of expected_values: 8 EVAL 0fpzt5 profession 05z96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 113.000 44.000 0.965 http://example.org/people/person/profession #5238-0ddj0x PRED entity: 0ddj0x PRED relation: film! PRED expected values: 016tt2 => 76 concepts (58 used for prediction) PRED predicted values (max 10 best out of 56): 086k8 (0.20 #303, 0.16 #152, 0.16 #379), 01795t (0.16 #848, 0.10 #18, 0.07 #2664), 05qd_ (0.16 #613, 0.14 #2272, 0.13 #310), 03xq0f (0.16 #5, 0.14 #80, 0.13 #685), 017s11 (0.15 #229, 0.13 #78, 0.12 #380), 016tw3 (0.14 #1218, 0.14 #388, 0.14 #1068), 016tt2 (0.12 #985, 0.11 #1361, 0.11 #1737), 0jz9f (0.12 #302, 0.09 #529, 0.08 #906), 054g1r (0.11 #865, 0.08 #1467, 0.08 #35), 0g1rw (0.09 #385, 0.07 #1215, 0.06 #1666) >> Best rule #303 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 0qm8b; 026p4q7; 02q56mk; 0yx7h; 0mcl0; 03hmt9b; 03bxp5; 0sxlb; >> query: (?x5578, 086k8) <- nominated_for(?x4056, ?x5578), nominated_for(?x384, ?x5578), genre(?x5578, ?x53), ?x384 = 03hkv_r >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #985 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 315 *> proper extension: 011yxg; 0dnvn3; 0ds11z; 0ds33; 04fzfj; 0dsvzh; 02hxhz; 0b73_1d; 02qm_f; 0jyx6; ... *> query: (?x5578, 016tt2) <- nominated_for(?x4056, ?x5578), written_by(?x5578, ?x7943), language(?x5578, ?x254), film_crew_role(?x5578, ?x1171) *> conf = 0.12 ranks of expected_values: 7 EVAL 0ddj0x film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 76.000 58.000 0.202 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5237-04znsy PRED entity: 04znsy PRED relation: award_winner! PRED expected values: 09k5jh7 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 95): 013b2h (0.11 #1585, 0.10 #1722, 0.07 #2955), 05pd94v (0.10 #1509, 0.09 #1646, 0.06 #2879), 02rjjll (0.10 #1512, 0.09 #1649, 0.06 #2608), 01s695 (0.10 #1510, 0.09 #1647, 0.06 #277), 0466p0j (0.10 #1581, 0.09 #1718, 0.06 #2677), 01c6qp (0.09 #1526, 0.09 #1663, 0.06 #2896), 02cg41 (0.09 #1629, 0.08 #1766, 0.06 #2725), 01bx35 (0.09 #1514, 0.08 #1651, 0.06 #2610), 019bk0 (0.09 #1523, 0.08 #1660, 0.06 #2619), 09n4nb (0.09 #1554, 0.08 #1691, 0.05 #2924) >> Best rule #1585 for best value: >> intensional similarity = 3 >> extensional distance = 440 >> proper extension: 0gsg7; 0cjdk; 0kk9v; 05xbx; 05gnf; >> query: (?x9211, 013b2h) <- category(?x9211, ?x134), award_winner(?x1254, ?x9211), award_winner(?x2220, ?x9211) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #1178 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 420 *> proper extension: 0k9ctht; *> query: (?x9211, 09k5jh7) <- award(?x9211, ?x704), award(?x919, ?x704), ?x919 = 04sx9_ *> conf = 0.02 ranks of expected_values: 55 EVAL 04znsy award_winner! 09k5jh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 82.000 82.000 0.106 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5236-019389 PRED entity: 019389 PRED relation: artist! PRED expected values: 0n85g => 108 concepts (85 used for prediction) PRED predicted values (max 10 best out of 115): 01w40h (0.44 #1834, 0.13 #1556, 0.10 #1139), 015_1q (0.24 #1547, 0.20 #5027, 0.19 #1130), 02p11jq (0.24 #151, 0.22 #568, 0.21 #290), 016ckq (0.20 #1848, 0.09 #1431, 0.08 #2543), 02zn1b (0.18 #147, 0.17 #564, 0.16 #286), 0g768 (0.18 #174, 0.16 #313, 0.13 #2120), 0n85g (0.17 #617, 0.16 #895, 0.12 #1173), 02y21l (0.17 #650, 0.14 #789, 0.12 #233), 01dtcb (0.17 #601, 0.10 #4310, 0.09 #1852), 01clyr (0.16 #309, 0.14 #448, 0.12 #170) >> Best rule #1834 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 01t_xp_; 0m19t; 0167_s; 07yg2; 0g_g2; 0dw4g; 016890; 015srx; 0178kd; 048xh; ... >> query: (?x7874, 01w40h) <- artist(?x5634, ?x7874), artists(?x302, ?x7874), artist(?x5634, ?x9407), ?x9407 = 024qwq >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #617 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 06gcn; 0153nq; *> query: (?x7874, 0n85g) <- artist(?x5634, ?x7874), artist(?x2931, ?x7874), artists(?x302, ?x7874), ?x5634 = 01cl2y, ?x2931 = 03rhqg *> conf = 0.17 ranks of expected_values: 7 EVAL 019389 artist! 0n85g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 108.000 85.000 0.435 http://example.org/music/record_label/artist #5235-02gs6r PRED entity: 02gs6r PRED relation: actor PRED expected values: 01mmslz => 136 concepts (72 used for prediction) PRED predicted values (max 10 best out of 65): 0ckm4x (0.50 #309, 0.44 #625, 0.40 #1195), 05j0wc (0.50 #362, 0.33 #45, 0.29 #489), 0814k3 (0.43 #433, 0.33 #623, 0.33 #53), 091n7z (0.33 #628, 0.33 #312, 0.29 #438), 066l3y (0.33 #589, 0.33 #273, 0.27 #1159), 08141d (0.33 #373, 0.33 #56, 0.25 #183), 0chrwb (0.33 #9, 0.29 #389, 0.27 #1149), 0cpjgj (0.33 #276, 0.27 #1162, 0.22 #656), 044_7j (0.33 #33, 0.25 #1237, 0.25 #160), 05v954 (0.33 #655, 0.25 #1544, 0.20 #781) >> Best rule #309 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0dh8v4; >> query: (?x5286, 0ckm4x) <- actor(?x5286, ?x1382), country(?x5286, ?x252), genre(?x5286, ?x1626), film(?x9753, ?x5286), ?x1626 = 03q4nz, film(?x5636, ?x5286) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02gs6r actor 01mmslz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 72.000 0.500 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #5234-03hkch7 PRED entity: 03hkch7 PRED relation: person PRED expected values: 06c0j => 103 concepts (73 used for prediction) PRED predicted values (max 10 best out of 196): 09b6zr (0.26 #1206, 0.15 #1768, 0.14 #2143), 0157m (0.21 #1157, 0.21 #970, 0.14 #2468), 06c97 (0.21 #1230, 0.21 #1043, 0.11 #2541), 0d3k14 (0.11 #1297, 0.11 #1110, 0.05 #2608), 01n4f8 (0.11 #1158, 0.08 #2469, 0.06 #1720), 0jw67 (0.11 #1199, 0.07 #3073, 0.06 #1761), 0127s7 (0.11 #1048, 0.05 #2546, 0.05 #1235), 046lt (0.09 #1936, 0.09 #2123, 0.07 #2684), 0sz28 (0.08 #2812, 0.07 #3188, 0.07 #3000), 05bnp0 (0.08 #2812, 0.07 #3188, 0.07 #3000) >> Best rule #1206 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 0dtw1x; >> query: (?x3124, 09b6zr) <- person(?x3124, ?x11290), titles(?x53, ?x3124), category(?x3124, ?x134) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #1307 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 0dtw1x; *> query: (?x3124, 06c0j) <- person(?x3124, ?x11290), titles(?x53, ?x3124), category(?x3124, ?x134) *> conf = 0.05 ranks of expected_values: 25 EVAL 03hkch7 person 06c0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 103.000 73.000 0.263 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #5233-08d6bd PRED entity: 08d6bd PRED relation: award PRED expected values: 03r8tl => 152 concepts (123 used for prediction) PRED predicted values (max 10 best out of 278): 03r8tl (0.34 #2535, 0.31 #4965, 0.28 #11040), 03r8v_ (0.21 #5202, 0.20 #11277, 0.09 #7632), 09sb52 (0.20 #13406, 0.19 #23531, 0.18 #38112), 0b6k___ (0.20 #11155, 0.14 #2650, 0.12 #12370), 0f4x7 (0.15 #13396, 0.15 #11371, 0.11 #12991), 05zr6wv (0.15 #13382, 0.14 #11357, 0.14 #10142), 0gq9h (0.15 #19923, 0.14 #11418, 0.13 #22353), 0gs9p (0.14 #11420, 0.14 #3320, 0.10 #14255), 05p09zm (0.14 #11465, 0.13 #13490, 0.10 #14300), 019f4v (0.14 #3307, 0.12 #13432, 0.11 #11407) >> Best rule #2535 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 03wpmd; 01n8_g; 016vg8; 08hhm6; 0239zv; 021j72; 03j367r; 01k6nm; 03f22dp; 04qp06; ... >> query: (?x6442, 03r8tl) <- profession(?x6442, ?x319), gender(?x6442, ?x231), religion(?x6442, ?x8967), award_winner(?x4687, ?x6442), ?x8967 = 03j6c >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08d6bd award 03r8tl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 123.000 0.343 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5232-01d4cb PRED entity: 01d4cb PRED relation: category PRED expected values: 08mbj5d => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.81 #7, 0.79 #19, 0.79 #20) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 0cg9y; 01vx5w7; 01rm8b; 0163m1; 0hvbj; 016890; 01dwrc; 015srx; 0flpy; 019f9z; ... >> query: (?x9128, 08mbj5d) <- award(?x9128, ?x2379), artists(?x3928, ?x9128), ?x3928 = 0gywn >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01d4cb category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.810 http://example.org/common/topic/webpage./common/webpage/category #5231-02hzz PRED entity: 02hzz PRED relation: artist! PRED expected values: 03rhqg 0n85g => 76 concepts (68 used for prediction) PRED predicted values (max 10 best out of 135): 015_1q (0.57 #6463, 0.45 #7025, 0.25 #1139), 03rhqg (0.50 #1136, 0.42 #2116, 0.33 #1416), 0181dw (0.50 #1021, 0.33 #1441, 0.31 #6485), 01cf93 (0.50 #1177, 0.33 #197, 0.25 #1037), 0n85g (0.33 #1462, 0.33 #622, 0.25 #1742), 01cl2y (0.33 #1429, 0.29 #2269, 0.25 #1709), 02bh8z (0.33 #441, 0.25 #861, 0.21 #2821), 0k_kr (0.33 #603, 0.25 #883, 0.17 #2143), 015mlw (0.33 #506, 0.25 #926, 0.17 #2186), 011k1h (0.33 #570, 0.23 #2530, 0.18 #4630) >> Best rule #6463 for best value: >> intensional similarity = 10 >> extensional distance = 260 >> proper extension: 01wmxfs; 03f1r6t; >> query: (?x8131, 015_1q) <- artist(?x3888, ?x8131), artist(?x3888, ?x10924), artist(?x3888, ?x9144), artist(?x3888, ?x4620), ?x4620 = 01vsy7t, category(?x3888, ?x134), award_nominee(?x4343, ?x9144), ?x4343 = 02cx90, instrumentalists(?x227, ?x9144), location_of_ceremony(?x10924, ?x9283) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1136 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 03xhj6; 016lmg; *> query: (?x8131, 03rhqg) <- artists(?x9853, ?x8131), artists(?x5911, ?x8131), artists(?x9853, ?x7221), artists(?x9853, ?x2120), ?x2120 = 05qw5, ?x5911 = 01_sz1, group(?x1750, ?x8131), ?x1750 = 02hnl, ?x7221 = 0191h5 *> conf = 0.50 ranks of expected_values: 2, 5 EVAL 02hzz artist! 0n85g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 68.000 0.569 http://example.org/music/record_label/artist EVAL 02hzz artist! 03rhqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 68.000 0.569 http://example.org/music/record_label/artist #5230-064_8sq PRED entity: 064_8sq PRED relation: countries_spoken_in PRED expected values: 01n6c 0164v => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 363): 0d060g (0.73 #3413, 0.65 #890, 0.62 #3113), 06dfg (0.73 #3413, 0.65 #890, 0.62 #3113), 027jk (0.73 #3413, 0.65 #890, 0.62 #3113), 088xp (0.73 #3413, 0.65 #890, 0.62 #3113), 01n6c (0.73 #3413, 0.65 #890, 0.62 #3113), 07f5x (0.73 #3413, 0.65 #890, 0.62 #3113), 0fv4v (0.73 #3413, 0.65 #890, 0.62 #3113), 01p1b (0.73 #3413, 0.65 #890, 0.62 #3113), 05c17 (0.65 #890, 0.62 #3113, 0.61 #3263), 0366c (0.65 #890, 0.62 #3113, 0.61 #3263) >> Best rule #3413 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 0swlx; >> query: (?x5607, ?x5680) <- official_language(?x5680, ?x5607), official_language(?x172, ?x5607), country(?x1121, ?x5680), countries_spoken_in(?x5607, ?x183), participating_countries(?x1608, ?x172), countries_within(?x455, ?x172), ?x1608 = 09x3r >> conf = 0.73 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5 EVAL 064_8sq countries_spoken_in 0164v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 91.000 0.728 http://example.org/language/human_language/countries_spoken_in EVAL 064_8sq countries_spoken_in 01n6c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 91.000 0.728 http://example.org/language/human_language/countries_spoken_in #5229-01k_mc PRED entity: 01k_mc PRED relation: artist! PRED expected values: 03gfvsz => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 4): 03gfvsz (0.11 #81, 0.10 #44, 0.09 #56), 01fjfv (0.05 #2, 0.03 #82, 0.03 #88), 04rqd (0.02 #91, 0.02 #5, 0.02 #85), 04y652m (0.01 #229) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 388 >> proper extension: 012wg; >> query: (?x5904, 03gfvsz) <- award(?x5904, ?x3835), award(?x7162, ?x3835), ?x7162 = 0ffgh >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k_mc artist! 03gfvsz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.113 http://example.org/broadcast/content/artist #5228-09f2j PRED entity: 09f2j PRED relation: student PRED expected values: 0gg9_5q 03fg0r 04z542 01w9ph_ 017m2y 035wq7 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1206): 01gq0b (0.33 #277, 0.03 #4338, 0.01 #71067), 0f4vbz (0.33 #333, 0.03 #4394, 0.01 #10488), 01wbg84 (0.33 #37, 0.01 #71067, 0.01 #75129), 0219q (0.33 #682, 0.01 #69718, 0.01 #73780), 026rm_y (0.33 #1471), 02q3bb (0.33 #1361), 02t_vx (0.33 #1328), 026r8q (0.33 #1235), 03lq43 (0.33 #654), 02114t (0.33 #582) >> Best rule #277 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 026gvfj; >> query: (?x4955, 01gq0b) <- student(?x4955, ?x5521), student(?x4955, ?x1417), award_nominee(?x1417, ?x541), ?x5521 = 09zmys >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09f2j student 035wq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 62.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 09f2j student 017m2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 62.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 09f2j student 01w9ph_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 62.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 09f2j student 04z542 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 62.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 09f2j student 03fg0r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 62.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 09f2j student 0gg9_5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 62.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #5227-0lhql PRED entity: 0lhql PRED relation: location! PRED expected values: 02jt1k => 177 concepts (136 used for prediction) PRED predicted values (max 10 best out of 2105): 023kzp (0.43 #6252, 0.25 #8770, 0.20 #13806), 01s21dg (0.43 #6000, 0.17 #41252, 0.16 #43770), 02mjmr (0.30 #13091, 0.29 #5537, 0.25 #23163), 01p7yb (0.29 #5083, 0.25 #7601, 0.20 #12637), 0405l (0.29 #7241, 0.25 #9759, 0.20 #14795), 0151ns (0.29 #5120, 0.20 #2602, 0.18 #15192), 0pyww (0.29 #6017, 0.20 #13571, 0.17 #23643), 0jsg0m (0.29 #6532, 0.20 #14086, 0.17 #24158), 014g9y (0.29 #7182, 0.16 #44952, 0.12 #9700), 09yrh (0.29 #5949, 0.14 #28611, 0.12 #8467) >> Best rule #6252 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02cl1; >> query: (?x4144, 023kzp) <- location_of_ceremony(?x566, ?x4144), county(?x4144, ?x4143), locations(?x6583, ?x4144), place_founded(?x10133, ?x4144) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #27997 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 026mj; *> query: (?x4144, 02jt1k) <- place_founded(?x10133, ?x4144), adjoins(?x6084, ?x4144), jurisdiction_of_office(?x1195, ?x4144) *> conf = 0.14 ranks of expected_values: 88 EVAL 0lhql location! 02jt1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 177.000 136.000 0.429 http://example.org/people/person/places_lived./people/place_lived/location #5226-0g476 PRED entity: 0g476 PRED relation: award_winner! PRED expected values: 0gqwc 02qkk9_ => 161 concepts (135 used for prediction) PRED predicted values (max 10 best out of 288): 02y_rq5 (0.42 #1293, 0.38 #2153, 0.38 #2152), 0gkvb7 (0.42 #1293, 0.38 #2153, 0.38 #2152), 05b4l5x (0.42 #1293, 0.38 #2153, 0.38 #2152), 03tk6z (0.42 #1293, 0.38 #2153, 0.38 #2152), 01bgqh (0.33 #43, 0.22 #474, 0.08 #5631), 05p09zm (0.33 #986, 0.17 #1845, 0.11 #9143), 0gqwc (0.30 #4371, 0.17 #1795, 0.11 #936), 027571b (0.27 #4571, 0.13 #1995, 0.11 #1136), 09cn0c (0.27 #4614, 0.11 #1179, 0.07 #7620), 03x3wf (0.22 #494, 0.17 #63, 0.03 #5651) >> Best rule #1293 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01z0rcq; 032wdd; >> query: (?x9963, ?x154) <- award(?x9963, ?x1007), award(?x9963, ?x154), student(?x7075, ?x9963), ?x1007 = 03c7tr1, participant(?x10001, ?x9963) >> conf = 0.42 => this is the best rule for 4 predicted values *> Best rule #4371 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 01j5ts; 0159h6; 01gvr1; 07lt7b; 03d_w3h; 01tspc6; 0h1nt; 0n6f8; 01l2fn; 01l9p; ... *> query: (?x9963, 0gqwc) <- award(?x9963, ?x1716), nominated_for(?x9963, ?x197), ?x1716 = 02y_rq5 *> conf = 0.30 ranks of expected_values: 7, 24 EVAL 0g476 award_winner! 02qkk9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 161.000 135.000 0.416 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0g476 award_winner! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 161.000 135.000 0.416 http://example.org/award/award_category/winners./award/award_honor/award_winner #5225-0g8bw PRED entity: 0g8bw PRED relation: contains! PRED expected values: 073q1 => 112 concepts (67 used for prediction) PRED predicted values (max 10 best out of 49): 0dg3n1 (0.60 #10035, 0.48 #12727, 0.43 #21720), 02qkt (0.57 #23708, 0.50 #20115, 0.47 #46189), 02j9z (0.38 #3621, 0.31 #8113, 0.27 #54876), 0j0k (0.33 #7567, 0.32 #23739, 0.31 #8463), 05nrg (0.33 #2364, 0.25 #567, 0.20 #5955), 0157g9 (0.25 #3178, 0.09 #13053, 0.06 #22046), 07c5l (0.23 #36347, 0.22 #4885, 0.20 #5783), 04pnx (0.22 #4915, 0.15 #36377, 0.15 #39972), 073q1 (0.16 #58446, 0.10 #5798, 0.09 #6699), 048fz (0.16 #58446, 0.09 #6842, 0.08 #7742) >> Best rule #10035 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0169t; 03676; >> query: (?x5776, 0dg3n1) <- countries_spoken_in(?x5607, ?x5776), ?x5607 = 064_8sq, form_of_government(?x5776, ?x48), ?x48 = 06cx9 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #58446 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 66 *> proper extension: 0212ny; *> query: (?x5776, ?x10150) <- combatants(?x5776, ?x550), combatants(?x5776, ?x390), contains(?x550, ?x4845), combatants(?x2629, ?x550), contains(?x10150, ?x390), combatants(?x390, ?x151), combatants(?x326, ?x2629), combatants(?x172, ?x2629) *> conf = 0.16 ranks of expected_values: 9 EVAL 0g8bw contains! 073q1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 112.000 67.000 0.600 http://example.org/location/location/contains #5224-073tm9 PRED entity: 073tm9 PRED relation: artist PRED expected values: 01w7nww 01f2q5 => 183 concepts (123 used for prediction) PRED predicted values (max 10 best out of 967): 01q99h (0.45 #11083, 0.42 #12722, 0.36 #23375), 01k23t (0.43 #3828, 0.33 #12020, 0.27 #11201), 09889g (0.43 #3628, 0.27 #11001, 0.25 #6905), 03xhj6 (0.42 #11770, 0.27 #25704, 0.25 #28983), 02vr7 (0.42 #12067, 0.27 #26001, 0.19 #29280), 01vxlbm (0.40 #1901, 0.29 #3539, 0.27 #10912), 01vw8mh (0.40 #1979, 0.25 #29022, 0.17 #47056), 0k6yt1 (0.40 #2388, 0.19 #29431, 0.18 #11399), 01w60_p (0.40 #1754, 0.19 #28797, 0.14 #26222), 070b4 (0.40 #2278, 0.06 #29321, 0.05 #41614) >> Best rule #11083 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 013x0b; >> query: (?x6230, 01q99h) <- artist(?x6230, ?x10427), industry(?x6230, ?x245), artist(?x12476, ?x10427), artists(?x302, ?x10427) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2415 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0fb0v; 03d96s; 01trtc; *> query: (?x6230, 01f2q5) <- artist(?x6230, ?x10427), artist(?x6230, ?x5364), ?x10427 = 04qzm, profession(?x5364, ?x131) *> conf = 0.20 ranks of expected_values: 97, 736 EVAL 073tm9 artist 01f2q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 183.000 123.000 0.455 http://example.org/music/record_label/artist EVAL 073tm9 artist 01w7nww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 183.000 123.000 0.455 http://example.org/music/record_label/artist #5223-02jqjm PRED entity: 02jqjm PRED relation: artists! PRED expected values: 06by7 02k_kn => 107 concepts (72 used for prediction) PRED predicted values (max 10 best out of 278): 06by7 (0.82 #8700, 0.77 #9009, 0.71 #19222), 064t9 (0.68 #5591, 0.66 #5900, 0.65 #8693), 017_qw (0.61 #6258, 0.53 #7813, 0.50 #2231), 02x8m (0.60 #19, 0.50 #329, 0.27 #1878), 016clz (0.50 #2482, 0.49 #4653, 0.47 #6510), 06j6l (0.40 #11202, 0.40 #48, 0.34 #5625), 05w3f (0.40 #1897, 0.36 #3135, 0.33 #970), 026z9 (0.40 #78, 0.33 #388, 0.12 #2865), 03lty (0.39 #16448, 0.28 #3125, 0.28 #7158), 0gywn (0.37 #11212, 0.35 #5635, 0.34 #5944) >> Best rule #8700 for best value: >> intensional similarity = 5 >> extensional distance = 101 >> proper extension: 0fq117k; 01nhkxp; 01wwnh2; >> query: (?x5512, 06by7) <- award(?x5512, ?x2139), artists(?x1380, ?x5512), ?x2139 = 01by1l, artists(?x1380, ?x4461), ?x4461 = 0fcsd >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 21 EVAL 02jqjm artists! 02k_kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 107.000 72.000 0.816 http://example.org/music/genre/artists EVAL 02jqjm artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 72.000 0.816 http://example.org/music/genre/artists #5222-01jzyx PRED entity: 01jzyx PRED relation: student PRED expected values: 0c1ps1 => 183 concepts (157 used for prediction) PRED predicted values (max 10 best out of 1437): 02mjmr (0.20 #2513, 0.05 #46446, 0.04 #61090), 073v6 (0.17 #6803, 0.12 #13079, 0.11 #527), 08k1lz (0.12 #16382, 0.11 #1738, 0.10 #24750), 0d6484 (0.12 #14224, 0.11 #1672, 0.08 #39328), 06dkzt (0.12 #14066, 0.11 #1514, 0.08 #39170), 0d3k14 (0.12 #16499, 0.10 #24867, 0.07 #43695), 03ktjq (0.11 #1006, 0.11 #19834, 0.09 #32386), 0c9xjl (0.11 #951, 0.11 #19779, 0.09 #32331), 037lyl (0.11 #17398, 0.10 #25766, 0.09 #29950), 030vnj (0.11 #1442, 0.10 #3534, 0.08 #7718) >> Best rule #2513 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01cyd5; >> query: (?x5426, 02mjmr) <- currency(?x5426, ?x170), student(?x5426, ?x11605), ?x170 = 09nqf, legislative_sessions(?x11605, ?x605) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jzyx student 0c1ps1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 183.000 157.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #5221-07wkd PRED entity: 07wkd PRED relation: school_type PRED expected values: 05jxkf => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 22): 05jxkf (0.66 #508, 0.62 #364, 0.60 #652), 05pcjw (0.37 #337, 0.33 #457, 0.31 #121), 01rs41 (0.30 #2577, 0.28 #2817, 0.26 #1326), 01_9fk (0.30 #338, 0.29 #26, 0.27 #458), 07tf8 (0.30 #345, 0.27 #465, 0.25 #129), 02p0qmm (0.17 #178, 0.14 #418, 0.12 #610), 01_srz (0.10 #769, 0.09 #964, 0.08 #1300), 06cs1 (0.10 #769, 0.07 #342, 0.07 #318), 04399 (0.10 #769, 0.05 #1215, 0.04 #1455), 04qbv (0.10 #769, 0.04 #905, 0.03 #2153) >> Best rule #508 for best value: >> intensional similarity = 6 >> extensional distance = 30 >> proper extension: 018m5q; 01xrlm; 01y9qr; 01314k; 01g6l8; 06b19; 01v3k2; 0jpkw; 01g4yw; >> query: (?x12356, 05jxkf) <- citytown(?x12356, ?x12135), major_field_of_study(?x12356, ?x1682), colors(?x12356, ?x1101), currency(?x12356, ?x2244), major_field_of_study(?x4955, ?x1682), ?x4955 = 09f2j >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07wkd school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 191.000 0.656 http://example.org/education/educational_institution/school_type #5220-0gtsxr4 PRED entity: 0gtsxr4 PRED relation: genre PRED expected values: 0hcr => 50 concepts (45 used for prediction) PRED predicted values (max 10 best out of 106): 02kdv5l (0.62 #2127, 0.42 #1655, 0.41 #2482), 07s9rl0 (0.58 #3782, 0.57 #3309, 0.57 #3545), 02l7c8 (0.40 #2494, 0.27 #3323, 0.26 #2968), 01jfsb (0.36 #2135, 0.30 #1899, 0.30 #2254), 06n90 (0.29 #2136, 0.25 #1664, 0.20 #1074), 0hcr (0.25 #376, 0.23 #494, 0.21 #730), 04xvlr (0.21 #238, 0.15 #474, 0.15 #356), 0lsxr (0.19 #1896, 0.18 #2014, 0.18 #2251), 0219x_ (0.17 #143, 0.17 #25, 0.16 #261), 0556j8 (0.17 #41, 0.11 #159, 0.08 #513) >> Best rule #2127 for best value: >> intensional similarity = 4 >> extensional distance = 741 >> proper extension: 06n90; >> query: (?x3151, 02kdv5l) <- genre(?x3151, ?x811), genre(?x5378, ?x811), ?x5378 = 0k54q, genre(?x50, ?x811) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #376 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 09fc83; *> query: (?x3151, 0hcr) <- film(?x4004, ?x3151), film(?x4004, ?x1370), ?x1370 = 0gmcwlb, genre(?x3151, ?x258), nominated_for(?x1053, ?x3151) *> conf = 0.25 ranks of expected_values: 6 EVAL 0gtsxr4 genre 0hcr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 50.000 45.000 0.618 http://example.org/film/film/genre #5219-0c0zq PRED entity: 0c0zq PRED relation: award PRED expected values: 0gs9p 027b9k6 => 88 concepts (78 used for prediction) PRED predicted values (max 10 best out of 253): 0m7yy (0.32 #555, 0.12 #13692, 0.11 #8910), 05ztjjw (0.28 #218, 0.27 #9562, 0.27 #9998), 0k611 (0.28 #218, 0.27 #9562, 0.27 #9998), 02x258x (0.28 #218, 0.27 #9562, 0.27 #9998), 094qd5 (0.28 #218, 0.27 #9562, 0.27 #9998), 0l8z1 (0.28 #218, 0.27 #9562, 0.27 #9998), 054krc (0.28 #218, 0.27 #9562, 0.27 #9998), 0gqwc (0.28 #218, 0.27 #9562, 0.27 #9998), 02rdyk7 (0.28 #218, 0.27 #9562, 0.27 #9998), 09sdmz (0.28 #218, 0.27 #9562, 0.27 #9998) >> Best rule #555 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 01h72l; 02kk_c; 0bx_hnp; 07s8z_l; >> query: (?x9452, 0m7yy) <- award_winner(?x9452, ?x3058), honored_for(?x1084, ?x9452), program(?x3058, ?x3822) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #13692 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1261 *> proper extension: 0h95b81; 04bp0l; *> query: (?x9452, ?x1107) <- nominated_for(?x4922, ?x9452), nominated_for(?x1871, ?x9452), award_winner(?x624, ?x1871), award_winner(?x1107, ?x4922) *> conf = 0.12 ranks of expected_values: 62, 70 EVAL 0c0zq award 027b9k6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 88.000 78.000 0.322 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0c0zq award 0gs9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 88.000 78.000 0.322 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #5218-0244r8 PRED entity: 0244r8 PRED relation: artist! PRED expected values: 026k4d => 62 concepts (40 used for prediction) PRED predicted values (max 10 best out of 84): 015_1q (0.18 #1441, 0.17 #2295, 0.17 #2010), 03rhqg (0.14 #2149, 0.13 #726, 0.12 #2861), 0g768 (0.10 #1175, 0.10 #2741, 0.09 #2883), 033hn8 (0.10 #1151, 0.09 #2717, 0.09 #2859), 0181dw (0.10 #2176, 0.10 #1749, 0.09 #1180), 011k1h (0.09 #2855, 0.09 #1147, 0.09 #2713), 03mp8k (0.09 #1774, 0.08 #1205, 0.07 #2771), 01w40h (0.09 #597, 0.08 #2162, 0.06 #1450), 01clyr (0.08 #744, 0.07 #2879, 0.07 #2737), 043g7l (0.08 #1169, 0.07 #1738, 0.06 #2307) >> Best rule #1441 for best value: >> intensional similarity = 2 >> extensional distance = 203 >> proper extension: 01kx_81; 02x8z_; 02lk95; 01yg9y; 01w9wwg; 05szp; 02yygk; 0dzlk; >> query: (?x1489, 015_1q) <- nominated_for(?x1489, ?x1077), artists(?x497, ?x1489) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0244r8 artist! 026k4d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 40.000 0.176 http://example.org/music/record_label/artist #5217-06nrt PRED entity: 06nrt PRED relation: district_represented! PRED expected values: 04fhps => 222 concepts (222 used for prediction) PRED predicted values (max 10 best out of 55): 077g7n (0.87 #1269, 0.86 #1214, 0.82 #1104), 06f0dc (0.80 #1274, 0.80 #1219, 0.77 #1109), 070m6c (0.80 #1271, 0.80 #1216, 0.74 #1106), 07p__7 (0.80 #1218, 0.78 #1273, 0.74 #1108), 070mff (0.79 #1140, 0.76 #1305, 0.75 #1250), 024tcq (0.73 #1286, 0.73 #1231, 0.72 #1121), 04fhps (0.67 #109, 0.62 #274, 0.62 #219), 02bn_p (0.64 #1220, 0.62 #1275, 0.59 #1110), 024tkd (0.62 #1307, 0.62 #1142, 0.61 #1252), 02bp37 (0.60 #1279, 0.59 #1224, 0.54 #1665) >> Best rule #1269 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 03gh4; >> query: (?x9311, 077g7n) <- country(?x9311, ?x279), district_represented(?x10543, ?x9311), location(?x10607, ?x9311), legislative_sessions(?x8776, ?x10543) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #109 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0h5qxv; *> query: (?x9311, 04fhps) <- adjoins(?x9311, ?x10544), district_represented(?x3473, ?x9311), ?x10544 = 059ts, country(?x9311, ?x279) *> conf = 0.67 ranks of expected_values: 7 EVAL 06nrt district_represented! 04fhps CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 222.000 222.000 0.867 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #5216-01qcx_ PRED entity: 01qcx_ PRED relation: featured_film_locations! PRED expected values: 03wbqc4 => 102 concepts (61 used for prediction) PRED predicted values (max 10 best out of 643): 0872p_c (0.50 #78, 0.15 #814, 0.08 #1550), 0m491 (0.50 #125, 0.15 #861, 0.05 #1597), 0btpm6 (0.50 #548, 0.15 #1284, 0.05 #2020), 0gw7p (0.50 #440, 0.15 #1176, 0.05 #1912), 05p1qyh (0.25 #166, 0.15 #902, 0.05 #2374), 04dsnp (0.25 #66, 0.13 #1538, 0.11 #3010), 0473rc (0.25 #453, 0.08 #1925, 0.08 #1189), 047csmy (0.25 #395, 0.08 #1867, 0.08 #1131), 0hmr4 (0.25 #44, 0.08 #780, 0.07 #2252), 0g3zrd (0.25 #163, 0.08 #899, 0.07 #3107) >> Best rule #78 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 030qb3t; 01_d4; >> query: (?x12738, 0872p_c) <- location(?x10259, ?x12738), ?x10259 = 016tbr, place_of_birth(?x3701, ?x12738), contains(?x94, ?x12738) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1784 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 04f_d; 0978r; 03gh4; *> query: (?x12738, 03wbqc4) <- location(?x10259, ?x12738), student(?x4410, ?x10259), friend(?x917, ?x10259), religion(?x10259, ?x7131) *> conf = 0.03 ranks of expected_values: 320 EVAL 01qcx_ featured_film_locations! 03wbqc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 102.000 61.000 0.500 http://example.org/film/film/featured_film_locations #5215-09myny PRED entity: 09myny PRED relation: gender PRED expected values: 05zppz => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #21, 0.90 #15, 0.89 #7), 02zsn (0.55 #105, 0.46 #136, 0.24 #70) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 025tdwc; 01qnfc; >> query: (?x10720, 05zppz) <- profession(?x10720, ?x2265), ?x2265 = 0dgd_, nationality(?x10720, ?x94), film_release_region(?x54, ?x94) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09myny gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.920 http://example.org/people/person/gender #5214-02glmx PRED entity: 02glmx PRED relation: ceremony! PRED expected values: 0gqng 0gq_v 0l8z1 0gqyl => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 372): 0gqng (0.89 #1694, 0.83 #1935, 0.77 #3624), 0gqyl (0.87 #3687, 0.86 #3204, 0.86 #2963), 0l8z1 (0.87 #1973, 0.85 #1732, 0.76 #3662), 0gq_v (0.82 #2188, 0.82 #741, 0.82 #2671), 054krc (0.38 #1503, 0.33 #3863, 0.24 #727), 04dn09n (0.38 #1477, 0.33 #3863, 0.24 #727), 054knh (0.38 #1635, 0.33 #3863, 0.22 #242), 019f4v (0.33 #3863, 0.33 #1491, 0.24 #727), 054ks3 (0.33 #3863, 0.33 #1540, 0.24 #727), 02x2gy0 (0.33 #3863, 0.31 #1052, 0.25 #1293) >> Best rule #1694 for best value: >> intensional similarity = 19 >> extensional distance = 25 >> proper extension: 02yv_b; 0bzmt8; 09306z; >> query: (?x5902, 0gqng) <- honored_for(?x5902, ?x696), ceremony(?x6860, ?x5902), ceremony(?x3617, ?x5902), ceremony(?x1313, ?x5902), ceremony(?x1243, ?x5902), ceremony(?x1053, ?x5902), award(?x5287, ?x1053), nominated_for(?x1053, ?x4656), nominated_for(?x1053, ?x3854), ?x3617 = 0gvx_, film(?x1594, ?x4656), production_companies(?x4656, ?x4564), edited_by(?x5936, ?x5287), ?x1243 = 0gr0m, profession(?x5287, ?x353), ?x6860 = 018wdw, ?x1313 = 0gs9p, film_release_region(?x3854, ?x2629), ?x2629 = 06f32 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 02glmx ceremony! 0gqyl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02glmx ceremony! 0l8z1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02glmx ceremony! 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02glmx ceremony! 0gqng CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony #5213-02p86pb PRED entity: 02p86pb PRED relation: nominated_for! PRED expected values: 0f4x7 0gr0m 0gq9h => 77 concepts (65 used for prediction) PRED predicted values (max 10 best out of 212): 0gq9h (0.66 #2118, 0.51 #1889, 0.50 #2347), 02qyntr (0.44 #2232, 0.32 #2003, 0.29 #629), 040njc (0.42 #2068, 0.34 #2297, 0.32 #1839), 02pqp12 (0.42 #2114, 0.27 #10082, 0.27 #1885), 0gq_v (0.41 #1851, 0.33 #2767, 0.32 #2309), 0gr0m (0.39 #1886, 0.35 #2115, 0.28 #2344), 0f4x7 (0.38 #2085, 0.37 #482, 0.35 #2314), 0gqy2 (0.37 #2175, 0.35 #2404, 0.29 #2633), 099c8n (0.34 #2113, 0.28 #281, 0.25 #1884), 02qvyrt (0.34 #1920, 0.29 #546, 0.24 #2149) >> Best rule #2118 for best value: >> intensional similarity = 4 >> extensional distance = 175 >> proper extension: 0sxg4; 083shs; 02v8kmz; 01gc7; 0pv2t; 0b6tzs; 0_92w; 0gmcwlb; 0jqn5; 011yqc; ... >> query: (?x9060, 0gq9h) <- genre(?x9060, ?x53), nominated_for(?x669, ?x9060), nominated_for(?x746, ?x9060), ?x746 = 04dn09n >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 7 EVAL 02p86pb nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 65.000 0.661 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02p86pb nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 65.000 0.661 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02p86pb nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 65.000 0.661 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5212-059z0 PRED entity: 059z0 PRED relation: jurisdiction_of_office! PRED expected values: 07_m9_ => 152 concepts (129 used for prediction) PRED predicted values (max 10 best out of 95): 0d1_f (0.15 #4144, 0.11 #858, 0.08 #4296), 083pr (0.10 #1002, 0.08 #544, 0.07 #2376), 0dj5q (0.08 #569, 0.06 #722, 0.06 #799), 06c0j (0.08 #604, 0.05 #910, 0.05 #1062), 0fd_1 (0.08 #574, 0.05 #880, 0.05 #1032), 081t6 (0.08 #607, 0.05 #913, 0.05 #1065), 083p7 (0.08 #538, 0.05 #844, 0.05 #996), 0gzh (0.08 #610, 0.05 #916, 0.05 #1068), 02yy8 (0.08 #605, 0.05 #911, 0.05 #1063), 07hyk (0.08 #590, 0.05 #896, 0.05 #1048) >> Best rule #4144 for best value: >> intensional similarity = 5 >> extensional distance = 78 >> proper extension: 027nb; 06s6l; 0h8d; 047t_; 06ryl; 03_xj; 05tr7; 07fsv; 06s0l; 07fb6; ... >> query: (?x8687, 0d1_f) <- official_language(?x8687, ?x732), language(?x6578, ?x732), language(?x5331, ?x732), ?x6578 = 01y9jr, ?x5331 = 09r94m >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #1298 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 06cmp; *> query: (?x8687, ?x1645) <- nationality(?x9178, ?x8687), capital(?x8687, ?x1646), place_of_death(?x1645, ?x1646) *> conf = 0.02 ranks of expected_values: 69 EVAL 059z0 jurisdiction_of_office! 07_m9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 152.000 129.000 0.150 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #5211-04pyp5 PRED entity: 04pyp5 PRED relation: film_crew_role! PRED expected values: 072x7s 0qf2t 03whyr => 56 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1210): 01gwk3 (0.78 #11656, 0.71 #10449, 0.50 #5618), 0fdv3 (0.78 #11079, 0.71 #9872, 0.50 #5041), 024l2y (0.78 #11061, 0.71 #9854, 0.50 #5023), 047wh1 (0.78 #11498, 0.71 #10291, 0.50 #5460), 06fqlk (0.78 #11663, 0.71 #10456, 0.50 #5625), 03z20c (0.78 #11221, 0.71 #10014, 0.50 #5183), 0dp7wt (0.78 #11818, 0.71 #10611, 0.50 #5780), 020fcn (0.78 #11004, 0.71 #9797, 0.50 #4966), 02b61v (0.78 #11579, 0.71 #10372, 0.50 #5541), 0pc62 (0.78 #10939, 0.71 #9732, 0.50 #4901) >> Best rule #11656 for best value: >> intensional similarity = 10 >> extensional distance = 7 >> proper extension: 02ynfr; >> query: (?x3305, 01gwk3) <- film_crew_role(?x6181, ?x3305), film_crew_role(?x2168, ?x3305), film_crew_role(?x1889, ?x3305), film_crew_role(?x1035, ?x3305), award(?x2168, ?x198), ?x1889 = 028cg00, film_release_region(?x1035, ?x87), produced_by(?x2168, ?x8645), nominated_for(?x5348, ?x6181), film(?x574, ?x1035) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #11946 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 7 *> proper extension: 02ynfr; *> query: (?x3305, 03whyr) <- film_crew_role(?x6181, ?x3305), film_crew_role(?x2168, ?x3305), film_crew_role(?x1889, ?x3305), film_crew_role(?x1035, ?x3305), award(?x2168, ?x198), ?x1889 = 028cg00, film_release_region(?x1035, ?x87), produced_by(?x2168, ?x8645), nominated_for(?x5348, ?x6181), film(?x574, ?x1035) *> conf = 0.78 ranks of expected_values: 16, 728, 933 EVAL 04pyp5 film_crew_role! 03whyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 56.000 40.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 04pyp5 film_crew_role! 0qf2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 40.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 04pyp5 film_crew_role! 072x7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 40.000 0.778 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5210-02pw_n PRED entity: 02pw_n PRED relation: film! PRED expected values: 0jz9f => 83 concepts (57 used for prediction) PRED predicted values (max 10 best out of 43): 016tt2 (0.20 #4, 0.14 #78, 0.11 #3635), 03rwz3 (0.20 #42, 0.05 #410, 0.04 #707), 05s_k6 (0.20 #62, 0.02 #284, 0.02 #801), 086k8 (0.16 #3633, 0.16 #3111, 0.15 #2962), 03xq0f (0.14 #79, 0.14 #1261, 0.12 #596), 024rdh (0.14 #109, 0.04 #848, 0.03 #2845), 0gfmc_ (0.14 #112, 0.01 #2256), 027jw0c (0.14 #127), 0283xx2 (0.14 #124), 016tw3 (0.14 #823, 0.13 #4086, 0.13 #1708) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 09k56b7; 04cj79; 0dfw0; >> query: (?x6619, 016tt2) <- nominated_for(?x4295, ?x6619), film_crew_role(?x6619, ?x137), genre(?x6619, ?x53), ?x4295 = 09l3p >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #814 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 314 *> proper extension: 0fq27fp; 0g5q34q; *> query: (?x6619, 0jz9f) <- genre(?x6619, ?x1403), ?x1403 = 02l7c8, film_crew_role(?x6619, ?x137) *> conf = 0.09 ranks of expected_values: 13 EVAL 02pw_n film! 0jz9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 83.000 57.000 0.200 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5209-09d5h PRED entity: 09d5h PRED relation: artist PRED expected values: 01wg6y => 177 concepts (153 used for prediction) PRED predicted values (max 10 best out of 720): 01k3qj (0.20 #14014, 0.08 #46830, 0.05 #60291), 01w_10 (0.15 #38708, 0.15 #38707, 0.12 #53852), 0frmb1 (0.15 #38708, 0.15 #38707, 0.12 #29453), 020_4z (0.13 #14206, 0.10 #29353, 0.08 #26829), 01vw20h (0.13 #13772, 0.07 #28919, 0.05 #38173), 0bk1p (0.13 #14131, 0.05 #46947, 0.04 #53676), 09qr6 (0.13 #13531, 0.03 #28678, 0.03 #46347), 0kr_t (0.13 #13860, 0.03 #29007, 0.03 #46676), 01f2q5 (0.13 #14263, 0.03 #29410, 0.03 #47079), 01wx756 (0.12 #6686, 0.08 #26885, 0.07 #29409) >> Best rule #14014 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 02qdyj; 02rky4; >> query: (?x2062, 01k3qj) <- child(?x9923, ?x2062), service_location(?x2062, ?x94), citytown(?x2062, ?x739) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #57884 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 89 *> proper extension: 04gmlt; 03qx_f; 0gyr_7; 086h6p; *> query: (?x2062, 01wg6y) <- child(?x9923, ?x2062), company(?x1491, ?x9923) *> conf = 0.03 ranks of expected_values: 410 EVAL 09d5h artist 01wg6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 177.000 153.000 0.200 http://example.org/music/record_label/artist #5208-04wsz PRED entity: 04wsz PRED relation: geographic_distribution! PRED expected values: 01rv7x => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 37): 0d29z (0.43 #141, 0.39 #863, 0.38 #301), 071x0k (0.29 #123, 0.28 #845, 0.26 #1930), 0g6ff (0.25 #290, 0.15 #491, 0.14 #130), 0g48m4 (0.21 #1968, 0.15 #2329, 0.11 #2731), 01xhh5 (0.20 #340, 0.14 #140, 0.13 #1947), 0432mrk (0.17 #479, 0.17 #438, 0.14 #559), 04mvp8 (0.17 #1841, 0.15 #1276, 0.15 #1316), 06mvq (0.17 #860, 0.08 #1945, 0.07 #2065), 013b6_ (0.14 #147, 0.12 #307, 0.12 #227), 04gfy7 (0.14 #153, 0.12 #313, 0.12 #233) >> Best rule #141 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 06mkj; >> query: (?x9122, 0d29z) <- locations(?x326, ?x9122), contains(?x9122, ?x4302), partially_contains(?x5903, ?x9122), administrative_parent(?x13593, ?x4302) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #262 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 07ssc; *> query: (?x9122, 01rv7x) <- locations(?x326, ?x9122), contains(?x9122, ?x8845), contains(?x9122, ?x6841), form_of_government(?x8845, ?x48), jurisdiction_of_office(?x11169, ?x8845), adjoins(?x6841, ?x311) *> conf = 0.12 ranks of expected_values: 23 EVAL 04wsz geographic_distribution! 01rv7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 99.000 99.000 0.429 http://example.org/people/ethnicity/geographic_distribution #5207-055c8 PRED entity: 055c8 PRED relation: award PRED expected values: 05zr6wv => 111 concepts (89 used for prediction) PRED predicted values (max 10 best out of 271): 0gqy2 (0.70 #28075, 0.70 #23325, 0.69 #4746), 027c95y (0.70 #28075, 0.70 #23325, 0.69 #4746), 027b9j5 (0.70 #28075, 0.70 #23325, 0.69 #4746), 07bdd_ (0.68 #1644, 0.65 #1248, 0.60 #2039), 05p1dby (0.60 #1685, 0.52 #1289, 0.50 #2080), 02x1z2s (0.30 #1378, 0.30 #2169, 0.28 #1774), 0gq9h (0.28 #1656, 0.23 #2051, 0.23 #5610), 04kxsb (0.28 #7236, 0.13 #34008, 0.12 #6446), 0ck27z (0.27 #8390, 0.24 #8785, 0.21 #17481), 0gq_d (0.26 #1400, 0.24 #1796, 0.20 #2191) >> Best rule #28075 for best value: >> intensional similarity = 3 >> extensional distance = 1528 >> proper extension: 04cy8rb; 01r42_g; 02pp_q_; 08wq0g; 01qkqwg; 08m4c8; 03jvmp; 0275_pj; 0g5lhl7; 06rnl9; ... >> query: (?x3186, ?x1033) <- award_nominee(?x7831, ?x3186), award_winner(?x1033, ?x3186), award_winner(?x618, ?x7831) >> conf = 0.70 => this is the best rule for 3 predicted values *> Best rule #18579 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 916 *> proper extension: 02wb6yq; 02f9wb; 0f3zsq; 07zhd7; *> query: (?x3186, ?x68) <- award_winner(?x8474, ?x3186), nominated_for(?x68, ?x8474), award_winner(?x2515, ?x3186) *> conf = 0.14 ranks of expected_values: 44 EVAL 055c8 award 05zr6wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 111.000 89.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5206-040p3y PRED entity: 040p3y PRED relation: position PRED expected values: 02sdk9v => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 8): 02sdk9v (0.86 #282, 0.86 #278, 0.86 #312), 03f0fp (0.50 #450, 0.44 #437, 0.25 #394), 02md_2 (0.50 #450, 0.44 #437, 0.25 #394), 02qvgy (0.44 #437, 0.02 #139, 0.02 #181), 05b3ts (0.25 #394, 0.25 #393, 0.21 #367), 04nfpk (0.25 #394, 0.25 #393, 0.21 #367), 02g_6x (0.25 #394, 0.25 #393, 0.21 #367), 01r3hr (0.25 #394, 0.25 #393, 0.21 #367) >> Best rule #282 for best value: >> intensional similarity = 12 >> extensional distance = 108 >> proper extension: 0y54; >> query: (?x11451, ?x63) <- position(?x11451, ?x530), position(?x11451, ?x203), position(?x11451, ?x63), position(?x11451, ?x60), ?x60 = 02nzb8, ?x203 = 0dgrmp, ?x63 = 02sdk9v, ?x530 = 02_j1w, team(?x60, ?x11451), position(?x11451, ?x203), team(?x530, ?x11451), position(?x11451, ?x60) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 040p3y position 02sdk9v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.864 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #5205-01vh096 PRED entity: 01vh096 PRED relation: story_by! PRED expected values: 0241y7 => 131 concepts (118 used for prediction) PRED predicted values (max 10 best out of 112): 07jqjx (0.11 #648, 0.02 #2368, 0.01 #2713), 0dl6fv (0.11 #627, 0.02 #2347, 0.01 #2692), 0cq86w (0.11 #555, 0.02 #2275, 0.01 #2620), 03lrqw (0.11 #423, 0.02 #2143, 0.01 #2488), 0gydcp7 (0.11 #412, 0.02 #2132, 0.01 #2477), 02vqhv0 (0.11 #408, 0.02 #2128, 0.01 #2473), 0d61px (0.06 #829, 0.02 #1517, 0.02 #1861), 0crc2cp (0.06 #793, 0.02 #1481, 0.02 #1825), 03kg2v (0.06 #787, 0.02 #1475, 0.02 #1819), 0125xq (0.06 #841, 0.02 #1529, 0.02 #1873) >> Best rule #648 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 032l1; 0gz_; 01v9724; 05qmj; 0dw6b; 0113sg; 0420y; >> query: (?x8700, 07jqjx) <- influenced_by(?x9673, ?x8700), influenced_by(?x5434, ?x8700), profession(?x8700, ?x353), ?x5434 = 01tz6vs, nationality(?x8700, ?x789), type_of_union(?x9673, ?x1873) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01vh096 story_by! 0241y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 118.000 0.111 http://example.org/film/film/story_by #5204-047vnkj PRED entity: 047vnkj PRED relation: film_crew_role PRED expected values: 02_n3z 0ckd1 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 20): 0dxtw (0.49 #462, 0.39 #1002, 0.38 #433), 01pvkk (0.32 #463, 0.31 #177, 0.31 #1463), 02_n3z (0.22 #85, 0.20 #1, 0.18 #29), 02ynfr (0.22 #466, 0.17 #1006, 0.17 #209), 02rh1dz (0.21 #461, 0.21 #204, 0.21 #233), 02vs3x5 (0.20 #18, 0.06 #842, 0.06 #186), 0263ycg (0.14 #42, 0.09 #98, 0.04 #468), 094hwz (0.11 #179, 0.10 #208, 0.09 #151), 089fss (0.09 #459, 0.08 #317, 0.07 #117), 05smlt (0.08 #212, 0.07 #127, 0.07 #155) >> Best rule #462 for best value: >> intensional similarity = 4 >> extensional distance = 325 >> proper extension: 03t97y; 01kff7; 05p3738; 01s3vk; 08sk8l; 02nx2k; 05ch98; 01gglm; 07p12s; 0h63q6t; >> query: (?x5271, 0dxtw) <- film_crew_role(?x5271, ?x2154), ?x2154 = 01vx2h, language(?x5271, ?x90), film(?x100, ?x5271) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 08bytj; *> query: (?x5271, 02_n3z) <- honored_for(?x8964, ?x5271), award_winner(?x8964, ?x8740), ?x8740 = 026rm_y *> conf = 0.22 ranks of expected_values: 3, 13 EVAL 047vnkj film_crew_role 0ckd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 76.000 76.000 0.492 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 047vnkj film_crew_role 02_n3z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 76.000 76.000 0.492 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5203-07rzf PRED entity: 07rzf PRED relation: film PRED expected values: 046f3p => 128 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1013): 048vhl (0.37 #6836, 0.14 #1490, 0.10 #5054), 016017 (0.29 #3488, 0.10 #5270, 0.05 #7052), 023p7l (0.29 #2400, 0.10 #4182, 0.05 #5964), 0jnwx (0.29 #2079, 0.10 #3861, 0.05 #5643), 04mcw4 (0.21 #7896, 0.09 #90890, 0.01 #39973), 0mbql (0.21 #6515, 0.14 #1169, 0.10 #4733), 0315rp (0.18 #8564), 0298n7 (0.18 #8473), 03tbg6 (0.14 #1651, 0.11 #8779, 0.11 #6997), 03t95n (0.14 #1166, 0.11 #6512, 0.10 #4730) >> Best rule #6836 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0mdyn; 0mbs8; >> query: (?x11465, 048vhl) <- film(?x11465, ?x5002), executive_produced_by(?x5002, ?x4552), film(?x9647, ?x5002), ?x9647 = 01b9z4 >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #10235 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 05j0wc; 075npt; 0814k3; *> query: (?x11465, 046f3p) <- student(?x6908, ?x11465), language(?x11465, ?x254), profession(?x11465, ?x1383) *> conf = 0.02 ranks of expected_values: 545 EVAL 07rzf film 046f3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 128.000 84.000 0.368 http://example.org/film/actor/film./film/performance/film #5202-01vsgrn PRED entity: 01vsgrn PRED relation: award PRED expected values: 0c4z8 02f72_ => 121 concepts (109 used for prediction) PRED predicted values (max 10 best out of 289): 02f6xy (0.79 #1935, 0.79 #4638, 0.79 #387), 02f79n (0.79 #1935, 0.79 #4638, 0.79 #387), 0gqz2 (0.79 #1935, 0.79 #4638, 0.79 #387), 02f76h (0.79 #1935, 0.79 #4638, 0.79 #387), 02f71y (0.50 #175, 0.29 #1723, 0.21 #562), 02f72_ (0.43 #216, 0.29 #603, 0.24 #1764), 02f6ym (0.43 #244, 0.26 #1792, 0.20 #4495), 09sb52 (0.36 #21675, 0.34 #9699, 0.27 #8927), 0c4z8 (0.36 #458, 0.35 #1619, 0.30 #2778), 02f777 (0.36 #294, 0.29 #681, 0.26 #1842) >> Best rule #1935 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 01wrcxr; >> query: (?x5536, ?x1323) <- award_winner(?x1323, ?x5536), artists(?x2937, ?x5536), friend(?x4476, ?x5536) >> conf = 0.79 => this is the best rule for 4 predicted values *> Best rule #216 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 0d193h; 011z3g; 0134pk; *> query: (?x5536, 02f72_) <- award_winner(?x2877, ?x5536), award_winner(?x139, ?x5536), ?x2877 = 02f5qb *> conf = 0.43 ranks of expected_values: 6, 9 EVAL 01vsgrn award 02f72_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 121.000 109.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vsgrn award 0c4z8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 121.000 109.000 0.793 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5201-02j62 PRED entity: 02j62 PRED relation: major_field_of_study! PRED expected values: 071tyz 01kxxq => 82 concepts (78 used for prediction) PRED predicted values (max 10 best out of 10): 01gkg3 (0.64 #362, 0.43 #624, 0.40 #183), 07s6fsf (0.64 #362, 0.43 #624, 0.36 #103), 02m4yg (0.43 #624, 0.40 #184, 0.38 #266), 071tyz (0.43 #624, 0.36 #103, 0.35 #260), 03mkk4 (0.43 #624, 0.36 #103, 0.35 #260), 02cq61 (0.43 #624, 0.36 #103, 0.35 #260), 02mjs7 (0.43 #624, 0.36 #103, 0.35 #260), 01kxxq (0.43 #624, 0.36 #103, 0.34 #198), 028dcg (0.36 #103, 0.35 #260, 0.35 #141), 0g26h (0.33 #270, 0.20 #776, 0.20 #755) >> Best rule #362 for best value: >> intensional similarity = 7 >> extensional distance = 27 >> proper extension: 06ntj; >> query: (?x2981, ?x734) <- major_field_of_study(?x4100, ?x2981), major_field_of_study(?x5638, ?x4100), major_field_of_study(?x2228, ?x4100), student(?x5638, ?x2239), major_field_of_study(?x734, ?x4100), contains(?x94, ?x5638), ?x2228 = 01s0_f >> conf = 0.64 => this is the best rule for 2 predicted values *> Best rule #624 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 85 *> proper extension: 03ll3; *> query: (?x2981, ?x734) <- major_field_of_study(?x4100, ?x2981), major_field_of_study(?x5638, ?x4100), student(?x5638, ?x2239), major_field_of_study(?x734, ?x4100), contains(?x94, ?x5638) *> conf = 0.43 ranks of expected_values: 4, 8 EVAL 02j62 major_field_of_study! 01kxxq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 82.000 78.000 0.636 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 02j62 major_field_of_study! 071tyz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 78.000 0.636 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #5200-07j87 PRED entity: 07j87 PRED relation: nutrient PRED expected values: 07hnp 06jry 02kcv4x 04k8n 025sqz8 => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 45): 06x4c (0.88 #448, 0.84 #66, 0.75 #27), 06jry (0.88 #448, 0.84 #66, 0.75 #27), 04k8n (0.88 #448, 0.84 #66, 0.75 #27), 025sqz8 (0.88 #448, 0.84 #66, 0.75 #27), 0hqw8p_ (0.88 #448, 0.84 #66, 0.75 #27), 07hnp (0.88 #448, 0.84 #66, 0.75 #27), 02kcv4x (0.88 #448, 0.84 #66, 0.75 #27), 04kl74p (0.88 #448, 0.84 #66, 0.75 #27), 014yzm (0.88 #448, 0.84 #66, 0.75 #27), 05v_8y (0.88 #448, 0.84 #66, 0.75 #27) >> Best rule #448 for best value: >> intensional similarity = 126 >> extensional distance = 2 >> proper extension: 0fjfh; >> query: (?x9489, ?x1960) <- nutrient(?x9489, ?x13944), nutrient(?x9489, ?x12902), nutrient(?x9489, ?x12454), nutrient(?x9489, ?x12083), nutrient(?x9489, ?x11784), nutrient(?x9489, ?x11758), nutrient(?x9489, ?x11592), nutrient(?x9489, ?x11409), nutrient(?x9489, ?x11270), nutrient(?x9489, ?x10891), nutrient(?x9489, ?x10709), nutrient(?x9489, ?x10195), nutrient(?x9489, ?x10098), nutrient(?x9489, ?x9949), nutrient(?x9489, ?x9915), nutrient(?x9489, ?x9733), nutrient(?x9489, ?x9708), nutrient(?x9489, ?x9619), nutrient(?x9489, ?x9490), nutrient(?x9489, ?x9426), nutrient(?x9489, ?x8413), nutrient(?x9489, ?x7894), nutrient(?x9489, ?x7720), nutrient(?x9489, ?x7652), nutrient(?x9489, ?x7431), nutrient(?x9489, ?x7364), nutrient(?x9489, ?x7362), nutrient(?x9489, ?x7219), nutrient(?x9489, ?x7135), nutrient(?x9489, ?x6586), nutrient(?x9489, ?x6160), nutrient(?x9489, ?x6033), nutrient(?x9489, ?x6026), nutrient(?x9489, ?x5549), nutrient(?x9489, ?x5526), nutrient(?x9489, ?x5451), nutrient(?x9489, ?x5374), nutrient(?x9489, ?x5010), nutrient(?x9489, ?x3469), nutrient(?x9489, ?x2702), nutrient(?x9489, ?x2018), nutrient(?x9489, ?x1304), nutrient(?x9489, ?x1258), ?x7362 = 02kc5rj, ?x7894 = 0f4hc, ?x5451 = 05wvs, ?x2702 = 0838f, ?x11409 = 0h1yf, ?x2018 = 01sh2, ?x1258 = 0h1wg, ?x10195 = 0hkwr, ?x9490 = 0h1sg, ?x11270 = 02kc008, ?x11784 = 07zqy, ?x9949 = 02kd0rh, ?x5549 = 025s7j4, nutrient(?x9732, ?x9708), nutrient(?x8298, ?x9708), nutrient(?x7719, ?x9708), nutrient(?x7057, ?x9708), nutrient(?x6191, ?x9708), nutrient(?x4068, ?x9708), nutrient(?x3900, ?x9708), ?x9732 = 05z55, ?x8413 = 02kc4sf, ?x7719 = 0dj75, ?x8298 = 037ls6, ?x4068 = 0fbw6, ?x7720 = 025s7x6, ?x10709 = 0h1sz, nutrient(?x10612, ?x9915), nutrient(?x9005, ?x9915), nutrient(?x6285, ?x9915), nutrient(?x6159, ?x9915), nutrient(?x6032, ?x9915), nutrient(?x5373, ?x9915), nutrient(?x5337, ?x9915), nutrient(?x3468, ?x9915), nutrient(?x3264, ?x9915), nutrient(?x2701, ?x9915), nutrient(?x1303, ?x9915), nutrient(?x1257, ?x9915), ?x12902 = 0fzjh, ?x6191 = 014j1m, ?x5337 = 06x4c, ?x6033 = 04zjxcz, ?x6032 = 01nkt, ?x7057 = 0fbdb, ?x7652 = 025s0s0, ?x10098 = 0h1_c, ?x7219 = 0h1vg, ?x9426 = 0h1yy, ?x13944 = 0f4kp, ?x7364 = 09gvd, ?x12083 = 01n78x, ?x5010 = 0h1vz, ?x6586 = 05gh50, ?x6159 = 033cnk, ?x9619 = 0h1tg, ?x11592 = 025sf0_, ?x2701 = 0hkxq, ?x5374 = 025s0zp, ?x3900 = 061_f, ?x1304 = 08lb68, ?x7431 = 09gwd, ?x11758 = 0q01m, ?x7135 = 025rsfk, ?x3468 = 0cxn2, ?x10612 = 0frq6, ?x3264 = 0dcfv, ?x5373 = 0971v, ?x5526 = 09pbb, ?x10891 = 0g5gq, ?x6026 = 025sf8g, ?x6160 = 041r51, ?x3469 = 0h1zw, ?x1303 = 0fj52s, ?x1257 = 09728, ?x12454 = 025rw19, ?x9005 = 04zpv, nutrient(?x6285, ?x9855), nutrient(?x6285, ?x6286), nutrient(?x6285, ?x1960), ?x9855 = 0d9t0, ?x6286 = 02y_3rf, ?x9733 = 0h1tz >> conf = 0.88 => this is the best rule for 19 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3, 4, 6, 7 EVAL 07j87 nutrient 025sqz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 22.000 22.000 0.877 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 07j87 nutrient 04k8n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 22.000 22.000 0.877 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 07j87 nutrient 02kcv4x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 22.000 0.877 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 07j87 nutrient 06jry CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 22.000 22.000 0.877 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 07j87 nutrient 07hnp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 22.000 0.877 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #5199-02x0dzw PRED entity: 02x0dzw PRED relation: award PRED expected values: 099cng => 99 concepts (84 used for prediction) PRED predicted values (max 10 best out of 267): 0gqwc (0.80 #399, 0.80 #71, 0.76 #26236), 05ztrmj (0.72 #14702, 0.72 #15897, 0.71 #23453), 0gqyl (0.39 #100, 0.12 #31405, 0.11 #5661), 0bdwft (0.37 #65, 0.09 #5626, 0.06 #464), 0cqgl9 (0.34 #186, 0.09 #585, 0.06 #5747), 02ppm4q (0.32 #150, 0.08 #5711, 0.08 #549), 0bb57s (0.27 #238, 0.05 #5799, 0.04 #4607), 02z0dfh (0.24 #72, 0.07 #5633, 0.06 #471), 0bfvw2 (0.22 #15, 0.08 #5576, 0.04 #4384), 01by1l (0.22 #902, 0.20 #1696, 0.11 #7257) >> Best rule #399 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 0hwbd; 0h7dd; 0bw87; 0lfbm; 015nhn; 0hsn_; 0chw_; 0dqcm; 01bj6y; 01dbhb; >> query: (?x8739, ?x1245) <- award_winner(?x1245, ?x8739), award_winner(?x8407, ?x8739), ?x1245 = 0gqwc >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #82 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 0hwbd; 0h7dd; 0bw87; 0lfbm; 015nhn; 0hsn_; 0chw_; 0dqcm; 01bj6y; 01dbhb; *> query: (?x8739, 099cng) <- award_winner(?x1245, ?x8739), award_winner(?x8407, ?x8739), ?x1245 = 0gqwc *> conf = 0.17 ranks of expected_values: 15 EVAL 02x0dzw award 099cng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 99.000 84.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5198-060__y PRED entity: 060__y PRED relation: genre! PRED expected values: 01jc6q 026gyn_ 04kzqz 02725hs 0c_j9x 07s846j 0prhz 0b2qtl 03lvwp 03cp4cn 0286gm1 01k7b0 02q0k7v 01xq8v 0cbn7c 08xvpn 06t2t2 0170xl => 30 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1664): 011ywj (0.75 #26338, 0.50 #12997, 0.50 #9667), 0209xj (0.67 #15089, 0.53 #8329, 0.50 #6755), 0yx1m (0.67 #16329, 0.50 #7995, 0.50 #13326), 01hqk (0.67 #27335, 0.50 #10662, 0.40 #13995), 07b1gq (0.67 #27230, 0.50 #10557, 0.40 #13890), 0gd92 (0.67 #16207, 0.50 #7873, 0.38 #26212), 02x0fs9 (0.67 #16533, 0.50 #8199, 0.38 #21535), 03c7twt (0.67 #16554, 0.50 #8220, 0.38 #21556), 0ptdz (0.67 #16630, 0.50 #8296, 0.38 #21632), 0bpx1k (0.67 #15427, 0.50 #7093, 0.38 #20429) >> Best rule #26338 for best value: >> intensional similarity = 12 >> extensional distance = 6 >> proper extension: 0hn10; >> query: (?x1509, 011ywj) <- genre(?x7671, ?x1509), genre(?x3919, ?x1509), genre(?x3790, ?x1509), genre(?x2772, ?x1509), nominated_for(?x521, ?x2772), award_winner(?x3790, ?x92), country(?x3790, ?x94), award_winner(?x7671, ?x2045), film(?x1690, ?x7671), ?x3919 = 05_5rjx, nominated_for(?x2090, ?x7671), film_crew_role(?x7671, ?x137) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #8329 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 05p553; *> query: (?x1509, ?x1708) <- genre(?x9761, ?x1509), genre(?x5976, ?x1509), genre(?x4874, ?x1509), genre(?x4136, ?x1509), genre(?x3745, ?x1509), genre(?x2772, ?x1509), nominated_for(?x521, ?x2772), ?x5976 = 02q7fl9, nominated_for(?x4136, ?x1708), production_companies(?x3745, ?x3462), film_crew_role(?x2772, ?x137), film_release_region(?x3745, ?x87), award(?x3745, ?x749), film(?x1104, ?x4136), ?x9761 = 0sxlb, film(?x156, ?x4874) *> conf = 0.53 ranks of expected_values: 106, 128, 162, 261, 379, 441, 657, 758, 765, 923, 933, 960, 969, 1025, 1060, 1236, 1240, 1634 EVAL 060__y genre! 0170xl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 06t2t2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 08xvpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 0cbn7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 01xq8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 02q0k7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 01k7b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 0286gm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 03cp4cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 03lvwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 0b2qtl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 0prhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 07s846j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 0c_j9x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 02725hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 04kzqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 026gyn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre EVAL 060__y genre! 01jc6q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 19.000 0.750 http://example.org/film/film/genre #5197-07k2mq PRED entity: 07k2mq PRED relation: film_release_region PRED expected values: 05b4w => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 200): 01znc_ (0.88 #611, 0.81 #1480, 0.80 #179), 05r4w (0.84 #2457, 0.82 #1448, 0.80 #723), 015fr (0.83 #734, 0.80 #1459, 0.75 #590), 05b4w (0.80 #1500, 0.78 #631, 0.73 #775), 05v8c (0.75 #589, 0.68 #1458, 0.62 #1747), 047yc (0.75 #599, 0.60 #1468, 0.41 #743), 03rt9 (0.70 #587, 0.66 #1456, 0.62 #1745), 03rk0 (0.70 #624, 0.60 #1493, 0.41 #768), 01p1v (0.70 #620, 0.59 #1489, 0.56 #764), 04gzd (0.70 #584, 0.59 #1453, 0.44 #728) >> Best rule #611 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 064lsn; >> query: (?x4950, 01znc_) <- nominated_for(?x1245, ?x4950), film_release_region(?x4950, ?x2843), film_release_region(?x4950, ?x404), ?x2843 = 016wzw, ?x404 = 047lj >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1500 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 02h22; *> query: (?x4950, 05b4w) <- nominated_for(?x1245, ?x4950), film_release_region(?x4950, ?x2843), ?x2843 = 016wzw *> conf = 0.80 ranks of expected_values: 4 EVAL 07k2mq film_release_region 05b4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 79.000 79.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5196-02prwdh PRED entity: 02prwdh PRED relation: film_release_region PRED expected values: 0jgd 05qhw 0345h 06mkj => 108 concepts (105 used for prediction) PRED predicted values (max 10 best out of 145): 06mkj (0.88 #1119, 0.88 #967, 0.85 #2648), 05qhw (0.87 #926, 0.80 #1078, 0.76 #2607), 07ssc (0.86 #1219, 0.84 #1080, 0.82 #928), 0345h (0.85 #1096, 0.85 #944, 0.80 #3388), 0jgd (0.83 #1069, 0.82 #917, 0.77 #2446), 06t2t (0.75 #973, 0.65 #1125, 0.64 #2502), 01znc_ (0.74 #953, 0.73 #1105, 0.70 #2634), 03rj0 (0.64 #971, 0.59 #1123, 0.54 #3415), 047yc (0.60 #939, 0.48 #1091, 0.48 #2468), 04gzd (0.57 #921, 0.47 #2450, 0.47 #3365) >> Best rule #1119 for best value: >> intensional similarity = 7 >> extensional distance = 145 >> proper extension: 0gtsx8c; 0gtvrv3; 047svrl; 0hgnl3t; 07k2mq; >> query: (?x5425, 06mkj) <- film_release_region(?x5425, ?x1003), film_release_region(?x5425, ?x279), film_release_region(?x5425, ?x94), ?x279 = 0d060g, ?x94 = 09c7w0, ?x1003 = 03gj2, country(?x5425, ?x512) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5 EVAL 02prwdh film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 105.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02prwdh film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 105.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02prwdh film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 105.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02prwdh film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 105.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5195-02t_tp PRED entity: 02t_tp PRED relation: category PRED expected values: 08mbj5d => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.32 #29, 0.31 #28, 0.30 #59) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 1248 >> proper extension: 0dbb3; >> query: (?x2587, 08mbj5d) <- award_nominee(?x286, ?x2587), location(?x2587, ?x11086), profession(?x2587, ?x1032) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02t_tp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.318 http://example.org/common/topic/webpage./common/webpage/category #5194-0gqxm PRED entity: 0gqxm PRED relation: award! PRED expected values: 01qxc7 => 47 concepts (20 used for prediction) PRED predicted values (max 10 best out of 804): 01pgp6 (0.50 #168, 0.03 #9231, 0.03 #13260), 017jd9 (0.45 #3471, 0.28 #1461, 0.23 #12084), 0209hj (0.33 #1068, 0.24 #3078, 0.23 #12084), 04v8x9 (0.33 #1043, 0.24 #3053, 0.22 #2048), 0hfzr (0.33 #3424, 0.23 #12084, 0.23 #8055), 01jc6q (0.33 #1020, 0.19 #2025, 0.15 #3030), 03hmt9b (0.30 #3403, 0.28 #1393, 0.17 #4411), 0dr_4 (0.28 #1158, 0.24 #6041, 0.23 #12084), 0cq806 (0.28 #1854, 0.19 #2859, 0.18 #3864), 0ccd3x (0.28 #1455, 0.19 #2460, 0.18 #3465) >> Best rule #168 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 057xs89; 05ztrmj; >> query: (?x3458, 01pgp6) <- nominated_for(?x3458, ?x1812), nominated_for(?x3458, ?x299), ?x1812 = 0fdv3, titles(?x53, ?x299), genre(?x299, ?x811) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #12084 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 127 *> proper extension: 04fgkf_; *> query: (?x3458, ?x5570) <- nominated_for(?x3458, ?x5570), nominated_for(?x3458, ?x1812), nominated_for(?x3580, ?x1812), award_winner(?x5570, ?x5246), ceremony(?x3458, ?x78) *> conf = 0.23 ranks of expected_values: 52 EVAL 0gqxm award! 01qxc7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 47.000 20.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #5193-0gl88b PRED entity: 0gl88b PRED relation: gender PRED expected values: 02zsn => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #63, 0.83 #57, 0.83 #45), 02zsn (0.47 #10, 0.30 #36, 0.30 #50) >> Best rule #63 for best value: >> intensional similarity = 1 >> extensional distance = 755 >> proper extension: 03c7ln; 032t2z; 021sv1; 0pcc0; 04zd4m; 0kn4c; 01d494; 01c59k; 01c58j; 0177s6; ... >> query: (?x2068, 05zppz) <- place_of_death(?x2068, ?x739) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0dck27; 08gyz_; 09x8ms; *> query: (?x2068, 02zsn) <- costume_design_by(?x1006, ?x2068), nationality(?x2068, ?x94), place_of_death(?x2068, ?x739) *> conf = 0.47 ranks of expected_values: 2 EVAL 0gl88b gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.845 http://example.org/people/person/gender #5192-08c6k9 PRED entity: 08c6k9 PRED relation: genre PRED expected values: 0ltv => 97 concepts (62 used for prediction) PRED predicted values (max 10 best out of 204): 02n4kr (0.56 #483, 0.33 #126, 0.28 #6713), 01hmnh (0.51 #3352, 0.34 #1801, 0.33 #7322), 03k9fj (0.48 #7316, 0.41 #6954, 0.39 #1795), 060__y (0.44 #729, 0.40 #848, 0.33 #1205), 05p553 (0.41 #3820, 0.35 #955, 0.34 #6468), 06n90 (0.38 #4069, 0.34 #3708, 0.29 #1796), 0219x_ (0.35 #5532, 0.33 #25, 0.11 #4808), 02l7c8 (0.33 #14, 0.30 #847, 0.27 #4555), 06cvj (0.33 #2, 0.11 #1668, 0.09 #6227), 0bkbm (0.33 #157, 0.11 #514, 0.07 #6706) >> Best rule #483 for best value: >> intensional similarity = 12 >> extensional distance = 7 >> proper extension: 03cp4cn; 02mpyh; >> query: (?x8979, 02n4kr) <- genre(?x8979, ?x604), film_crew_role(?x8979, ?x2154), film_crew_role(?x8979, ?x1966), ?x2154 = 01vx2h, ?x604 = 0lsxr, executive_produced_by(?x8979, ?x3744), film_crew_role(?x5458, ?x1966), film_crew_role(?x5081, ?x1966), film_crew_role(?x1259, ?x1966), ?x5458 = 05szq8z, ?x5081 = 0642xf3, ?x1259 = 04hwbq >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #317 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 0ct2tf5; *> query: (?x8979, 0ltv) <- genre(?x8979, ?x604), film_crew_role(?x8979, ?x13719), film_crew_role(?x8979, ?x2154), film_crew_role(?x8979, ?x1284), ?x2154 = 01vx2h, ?x604 = 0lsxr, ?x1284 = 0ch6mp2, film(?x2858, ?x8979), profession(?x147, ?x13719), production_companies(?x8979, ?x1104), ?x1104 = 016tw3 *> conf = 0.20 ranks of expected_values: 13 EVAL 08c6k9 genre 0ltv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 97.000 62.000 0.556 http://example.org/film/film/genre #5191-053rxgm PRED entity: 053rxgm PRED relation: genre PRED expected values: 02kdv5l => 101 concepts (100 used for prediction) PRED predicted values (max 10 best out of 109): 02kdv5l (0.67 #491, 0.59 #1101, 0.55 #125), 07s9rl0 (0.64 #5014, 0.64 #6117, 0.63 #2322), 03k9fj (0.52 #623, 0.50 #745, 0.50 #135), 05p553 (0.46 #8933, 0.43 #371, 0.39 #249), 0lsxr (0.40 #10, 0.24 #498, 0.19 #1108), 01hmnh (0.35 #141, 0.32 #1606, 0.31 #385), 02l7c8 (0.29 #2093, 0.28 #1848, 0.28 #1971), 06n90 (0.28 #380, 0.24 #2213, 0.24 #258), 01t_vv (0.20 #56, 0.09 #2377, 0.09 #6172), 0vgkd (0.20 #12, 0.07 #1843, 0.06 #2088) >> Best rule #491 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 04cf_l; >> query: (?x1178, 02kdv5l) <- prequel(?x3217, ?x1178), music(?x1178, ?x562), currency(?x1178, ?x170), produced_by(?x1178, ?x7146) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 053rxgm genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 100.000 0.667 http://example.org/film/film/genre #5190-053rxgm PRED entity: 053rxgm PRED relation: film_release_region PRED expected values: 05r4w 06qd3 06t2t 05b4w 03h64 016wzw 04hqz => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 102): 03h64 (0.93 #288, 0.87 #654, 0.86 #776), 05r4w (0.87 #246, 0.87 #734, 0.87 #612), 06t2t (0.87 #284, 0.84 #650, 0.80 #772), 05b4w (0.81 #774, 0.80 #286, 0.77 #652), 016wzw (0.65 #289, 0.64 #777, 0.61 #655), 01p1v (0.64 #766, 0.59 #278, 0.59 #644), 01ls2 (0.63 #251, 0.57 #617, 0.52 #739), 06qd3 (0.61 #756, 0.57 #268, 0.51 #1855), 0ctw_b (0.60 #625, 0.59 #747, 0.52 #259), 06c1y (0.53 #759, 0.52 #271, 0.43 #637) >> Best rule #288 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 0bhwhj; >> query: (?x1178, 03h64) <- genre(?x1178, ?x812), film_release_region(?x1178, ?x1475), produced_by(?x1178, ?x7146), ?x1475 = 05qx1 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 8, 14 EVAL 053rxgm film_release_region 04hqz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 79.000 79.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 053rxgm film_release_region 016wzw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 053rxgm film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 053rxgm film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 053rxgm film_release_region 06t2t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 053rxgm film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 79.000 79.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 053rxgm film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5189-037q31 PRED entity: 037q31 PRED relation: honored_for! PRED expected values: 02yxh9 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 121): 02ywhz (0.10 #67, 0.02 #1287, 0.02 #3972), 0275n3y (0.09 #308, 0.07 #796, 0.05 #918), 0bvhz9 (0.09 #358, 0.07 #846, 0.05 #968), 09bymc (0.08 #227, 0.03 #2668, 0.02 #3278), 09p2r9 (0.08 #201, 0.03 #2642, 0.02 #6916), 0fqpc7d (0.08 #151, 0.02 #1249, 0.02 #2348), 050yyb (0.08 #153, 0.02 #6962, 0.02 #6961), 05hmp6 (0.08 #196, 0.02 #6962, 0.02 #6961), 0bzjgq (0.08 #226, 0.02 #6962, 0.02 #6961), 0g5b0q5 (0.08 #136, 0.02 #2577, 0.02 #4288) >> Best rule #67 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 035xwd; 01dvbd; 032zq6; 0qf2t; 03lvwp; 05dss7; 03s9kp; >> query: (?x6864, 02ywhz) <- film(?x2169, ?x6864), film_crew_role(?x6864, ?x1284), genre(?x6864, ?x1014), film(?x752, ?x6864), ?x752 = 0338lq >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #6962 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 691 *> proper extension: 02_1q9; 02_1rq; 0kfpm; 0358x_; 0ddd0gc; 02hct1; 01b64v; 01b66d; 0phrl; 01j7mr; ... *> query: (?x6864, ?x602) <- award(?x6864, ?x3617), ceremony(?x3617, ?x7940), ceremony(?x3617, ?x602), award_winner(?x7940, ?x669) *> conf = 0.02 ranks of expected_values: 69 EVAL 037q31 honored_for! 02yxh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 116.000 116.000 0.100 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #5188-011yn5 PRED entity: 011yn5 PRED relation: genre PRED expected values: 07s9rl0 0hn10 => 95 concepts (94 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.77 #474, 0.72 #238, 0.71 #1894), 01z4y (0.61 #8052, 0.48 #9710, 0.47 #2250), 01jfsb (0.35 #131, 0.32 #367, 0.32 #3803), 02kdv5l (0.33 #122, 0.27 #3794, 0.27 #3676), 04xvlr (0.27 #239, 0.25 #475, 0.19 #2252), 03k9fj (0.25 #7469, 0.22 #958, 0.21 #3684), 060__y (0.24 #16, 0.20 #489, 0.19 #371), 01hmnh (0.22 #7475, 0.17 #17, 0.17 #608), 03bxz7 (0.21 #290, 0.14 #526, 0.13 #1946), 017fp (0.21 #252, 0.11 #2265, 0.11 #2502) >> Best rule #474 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 0bth54; 04vr_f; 0c0nhgv; 0ch26b_; 0fpv_3_; 02q56mk; 01jrbb; 02vqsll; 0j43swk; 0h03fhx; ... >> query: (?x5323, 07s9rl0) <- award(?x5323, ?x591), award_winner(?x5323, ?x406), nominated_for(?x1162, ?x5323), ?x1162 = 099c8n >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 21 EVAL 011yn5 genre 0hn10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 95.000 94.000 0.773 http://example.org/film/film/genre EVAL 011yn5 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 94.000 0.773 http://example.org/film/film/genre #5187-02k856 PRED entity: 02k856 PRED relation: role! PRED expected values: 0dq630k => 70 concepts (51 used for prediction) PRED predicted values (max 10 best out of 106): 05r5c (0.84 #3426, 0.83 #2125, 0.83 #2029), 018vs (0.83 #3208, 0.83 #3113, 0.83 #1482), 05148p4 (0.83 #1482, 0.83 #1592, 0.83 #211), 0dwtp (0.83 #1482, 0.83 #1592, 0.83 #211), 0gghm (0.83 #1482, 0.83 #1592, 0.83 #211), 0dq630k (0.83 #1482, 0.83 #1592, 0.83 #211), 07y_7 (0.79 #3203, 0.75 #4592, 0.74 #1589), 0l15bq (0.77 #2163, 0.76 #3023, 0.76 #2917), 013y1f (0.76 #3450, 0.73 #4408, 0.72 #3131), 07brj (0.75 #1938, 0.75 #3523, 0.71 #317) >> Best rule #3426 for best value: >> intensional similarity = 19 >> extensional distance = 23 >> proper extension: 01bns_; >> query: (?x2923, 05r5c) <- instrumentalists(?x2923, ?x7272), role(?x2964, ?x2923), role(?x2923, ?x2158), role(?x2923, ?x780), role(?x2923, ?x432), role(?x2923, ?x228), role(?x2923, ?x212), role(?x2158, ?x4616), role(?x2158, ?x3161), ?x212 = 026t6, role(?x74, ?x2158), ?x4616 = 01rhl, ?x432 = 042v_gx, instrumentalists(?x2785, ?x7272), ?x228 = 0l14qv, role(?x219, ?x780), role(?x6449, ?x2785), ?x3161 = 01v1d8, ?x6449 = 014zz1 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1482 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 7 *> proper extension: 0l14md; *> query: (?x2923, ?x212) <- role(?x2923, ?x2310), role(?x2923, ?x716), role(?x2923, ?x614), role(?x2923, ?x75), ?x716 = 018vs, role(?x2923, ?x1212), role(?x2923, ?x227), role(?x2923, ?x212), group(?x2923, ?x6475), ?x75 = 07y_7, ?x6475 = 07mvp, instrumentalists(?x2923, ?x2242), role(?x1267, ?x2310), role(?x1212, ?x2059), role(?x1720, ?x1212), ?x614 = 0mkg, ?x2059 = 0dwr4, instrumentalists(?x2310, ?x2575), ?x227 = 0342h, role(?x2964, ?x2923) *> conf = 0.83 ranks of expected_values: 6 EVAL 02k856 role! 0dq630k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 70.000 51.000 0.840 http://example.org/music/performance_role/track_performances./music/track_contribution/role #5186-02zfg3 PRED entity: 02zfg3 PRED relation: nominated_for PRED expected values: 024hbv => 98 concepts (49 used for prediction) PRED predicted values (max 10 best out of 293): 02_1q9 (0.51 #34048, 0.49 #30805, 0.34 #43777), 0gw7p (0.34 #22699, 0.27 #56753, 0.27 #71348), 0k4p0 (0.34 #22699, 0.27 #56753, 0.27 #71348), 03kx49 (0.27 #56753, 0.27 #71348, 0.27 #69726), 035_2h (0.27 #56753, 0.27 #71348, 0.27 #69726), 097zcz (0.06 #652, 0.02 #12002, 0.02 #20107), 0bj25 (0.04 #2956, 0.03 #20790, 0.03 #11064), 0hv27 (0.04 #2601, 0.03 #980, 0.02 #9088), 0kb1g (0.04 #3078, 0.03 #11186, 0.02 #9565), 04vvh9 (0.04 #2173, 0.02 #20007, 0.02 #8660) >> Best rule #34048 for best value: >> intensional similarity = 3 >> extensional distance = 412 >> proper extension: 02k6rq; >> query: (?x13194, ?x416) <- actor(?x416, ?x13194), award_winner(?x1921, ?x13194), nominated_for(?x13194, ?x6111) >> conf = 0.51 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02zfg3 nominated_for 024hbv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 49.000 0.515 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5185-01vrt_c PRED entity: 01vrt_c PRED relation: award PRED expected values: 02f72_ 02f77y => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 293): 09sb52 (0.39 #25131, 0.38 #18074, 0.32 #7882), 05zr6wv (0.33 #1193, 0.20 #3545, 0.15 #7858), 01bgqh (0.30 #4356, 0.28 #13372, 0.27 #7100), 05p09zm (0.29 #1299, 0.21 #1691, 0.20 #5612), 0f4x7 (0.29 #1207, 0.21 #1599, 0.16 #3559), 054ks3 (0.25 #533, 0.25 #141, 0.20 #4454), 0c4z8 (0.25 #464, 0.22 #13401, 0.22 #12225), 04kxsb (0.25 #1301, 0.21 #1693, 0.13 #3653), 02f76h (0.25 #174, 0.18 #35286, 0.17 #29796), 01c92g (0.25 #489, 0.18 #35286, 0.17 #29796) >> Best rule #25131 for best value: >> intensional similarity = 2 >> extensional distance = 850 >> proper extension: 03mz9r; 025t9b; 03h2d4; 02qfhb; 0g2mbn; 05dtwm; 0djywgn; 03k48_; >> query: (?x1206, 09sb52) <- award_nominee(?x1206, ?x5906), participant(?x5906, ?x1896) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #222 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 01gf5h; 01w5n51; *> query: (?x1206, 02f72_) <- award_nominee(?x1206, ?x140), artists(?x9342, ?x1206), ?x9342 = 0grjmv *> conf = 0.25 ranks of expected_values: 11, 48 EVAL 01vrt_c award 02f77y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 116.000 116.000 0.393 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vrt_c award 02f72_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 116.000 116.000 0.393 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5184-07q1v4 PRED entity: 07q1v4 PRED relation: nationality PRED expected values: 09c7w0 => 146 concepts (144 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.87 #2007, 0.87 #2308, 0.81 #3310), 07ssc (0.40 #12758, 0.24 #2207, 0.14 #15), 0345h (0.40 #12758, 0.24 #2207, 0.14 #31), 0d060g (0.40 #12758, 0.24 #2207, 0.07 #7), 03_3d (0.40 #12758, 0.24 #2207, 0.04 #7123), 02jx1 (0.40 #12758, 0.18 #3442, 0.16 #4646), 0f8l9c (0.40 #12758, 0.06 #422, 0.04 #7123), 03rjj (0.40 #12758, 0.04 #7123, 0.04 #907), 03rt9 (0.40 #12758, 0.04 #7123, 0.02 #5128), 03rk0 (0.29 #46, 0.14 #1048, 0.13 #1148) >> Best rule #2007 for best value: >> intensional similarity = 4 >> extensional distance = 193 >> proper extension: 07nznf; 079vf; 0fvf9q; 03m8lq; 05kfs; 0mdqp; 02lk1s; 03pmty; 01jrz5j; 0456xp; ... >> query: (?x925, 09c7w0) <- profession(?x925, ?x1183), place_of_birth(?x925, ?x739), gender(?x925, ?x231), ?x739 = 02_286 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07q1v4 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 144.000 0.867 http://example.org/people/person/nationality #5183-06mj4 PRED entity: 06mj4 PRED relation: artist! PRED expected values: 011k1h => 111 concepts (69 used for prediction) PRED predicted values (max 10 best out of 119): 015_1q (0.26 #2980, 0.25 #2698, 0.25 #1288), 033hn8 (0.23 #1283, 0.21 #860, 0.20 #1565), 0n85g (0.21 #909, 0.17 #1614, 0.13 #2460), 01dtcb (0.20 #611, 0.17 #1034, 0.14 #2585), 01trtc (0.20 #214, 0.16 #637, 0.15 #778), 02p11jq (0.20 #154, 0.11 #1846, 0.11 #1141), 017l96 (0.19 #1146, 0.19 #723, 0.17 #1005), 011k1h (0.19 #715, 0.16 #1984, 0.15 #1279), 0229rs (0.19 #722, 0.12 #2696, 0.11 #2978), 01clyr (0.16 #2853, 0.14 #1020, 0.13 #1866) >> Best rule #2980 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 01vsxdm; 0mgcr; >> query: (?x8060, 015_1q) <- award(?x8060, ?x2139), group(?x315, ?x8060), ?x315 = 0l14md >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #715 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 0dtd6; 0ycfj; *> query: (?x8060, 011k1h) <- award(?x8060, ?x9828), group(?x315, ?x8060), ?x315 = 0l14md, ?x9828 = 01ckcd *> conf = 0.19 ranks of expected_values: 8 EVAL 06mj4 artist! 011k1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 111.000 69.000 0.263 http://example.org/music/record_label/artist #5182-018gz8 PRED entity: 018gz8 PRED relation: profession! PRED expected values: 0p_pd 01wmxfs 04l3_z 049dyj 0pz91 049g_xj 02wrhj 016_mj 04fhxp 05wjnt 0lx2l 01dw9z 01y0y6 01wgcvn 0h3mrc 01gn36 01nzz8 01pk3z 01nrgq 01fyzy 02238b 02l3_5 059j1m 04mhbh 05myd2 01twmp 01g4bk 02p7xc 03k48_ 0sx5w 06pcz0 07rn0z 016lv3 06b3g4 01svq8 0qkj7 => 37 concepts (16 used for prediction) PRED predicted values (max 10 best out of 3830): 01vw8mh (0.71 #36871, 0.56 #40805, 0.55 #44742), 0q5hw (0.69 #7870, 0.67 #28327, 0.60 #31479), 027cxsm (0.69 #7870, 0.67 #27961, 0.57 #35832), 01gzm2 (0.69 #7870, 0.67 #28001, 0.50 #31937), 03h8_g (0.69 #7870, 0.67 #34869, 0.50 #30933), 046zh (0.69 #7870, 0.63 #11806, 0.46 #35415), 0bxtg (0.69 #7870, 0.60 #23714, 0.60 #31479), 0187y5 (0.69 #7870, 0.60 #23768, 0.60 #31479), 01_x6v (0.69 #7870, 0.60 #24243, 0.57 #36050), 05fnl9 (0.69 #7870, 0.60 #24040, 0.50 #12238) >> Best rule #36871 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0n1h; >> query: (?x1146, 01vw8mh) <- profession(?x10963, ?x1146), profession(?x10560, ?x1146), profession(?x7745, ?x1146), film(?x7745, ?x3088), ?x10560 = 01xwv7, nationality(?x7745, ?x94), people(?x5540, ?x10963) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #7870 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 04pyp5; *> query: (?x1146, ?x364) <- profession(?x13005, ?x1146), profession(?x6443, ?x1146), profession(?x2733, ?x1146), ?x13005 = 070px, nationality(?x2733, ?x94), award_nominee(?x364, ?x6443), award(?x2733, ?x1107) *> conf = 0.69 ranks of expected_values: 13, 16, 49, 50, 60, 69, 281, 332, 521, 533, 647, 702, 728, 731, 755, 761, 816, 824, 840, 858, 960, 987, 1020, 1062, 1181, 1185, 1709, 1724, 1745, 1958, 2160, 2404, 2405, 2559 EVAL 018gz8 profession! 0qkj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01svq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 06b3g4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 016lv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 07rn0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 06pcz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 0sx5w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 03k48_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 02p7xc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01g4bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01twmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 05myd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 04mhbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 059j1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 02l3_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 02238b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01fyzy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01nrgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01pk3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01nzz8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01gn36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 0h3mrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01wgcvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01y0y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01dw9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 0lx2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 05wjnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 04fhxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 016_mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 02wrhj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 049g_xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 0pz91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 049dyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 04l3_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 01wmxfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession EVAL 018gz8 profession! 0p_pd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 16.000 0.714 http://example.org/people/person/profession #5181-02404v PRED entity: 02404v PRED relation: cinematography! PRED expected values: 0198b6 => 98 concepts (25 used for prediction) PRED predicted values (max 10 best out of 342): 0233bn (0.75 #2740, 0.75 #2739, 0.02 #2642), 03wy8t (0.05 #2018, 0.04 #2702, 0.04 #2360), 03cw411 (0.05 #1833, 0.04 #2517, 0.04 #2175), 084qpk (0.04 #2420, 0.04 #2078, 0.03 #1736), 0kbhf (0.04 #2595, 0.04 #2253, 0.03 #1911), 083skw (0.04 #2478, 0.04 #2136, 0.03 #1794), 0jvt9 (0.04 #2504, 0.03 #1820, 0.02 #2162), 0jymd (0.04 #2185, 0.03 #1843, 0.02 #2527), 02yy9r (0.03 #2054, 0.02 #2738, 0.02 #2396), 0422v0 (0.03 #2053, 0.02 #2737, 0.02 #2395) >> Best rule #2740 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 04qvl7; 06cv1; 0f3zf_; 0gp9mp; 079hvk; 05dppk; 04g865; 0693l; 0dqzkv; 07xr3w; ... >> query: (?x7740, ?x6788) <- nominated_for(?x7740, ?x6788), nominated_for(?x372, ?x6788), cinematography(?x1625, ?x7740) >> conf = 0.75 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02404v cinematography! 0198b6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 25.000 0.754 http://example.org/film/film/cinematography #5180-01bbwp PRED entity: 01bbwp PRED relation: nationality PRED expected values: 07ssc => 56 concepts (52 used for prediction) PRED predicted values (max 10 best out of 31): 09c7w0 (0.76 #4169, 0.68 #1889, 0.68 #4268), 07ssc (0.52 #213, 0.25 #15, 0.23 #1387), 02jx1 (0.29 #132, 0.23 #528, 0.20 #330), 06q1r (0.25 #76, 0.03 #571, 0.01 #967), 03rjj (0.23 #1387, 0.04 #896, 0.03 #797), 06mkj (0.23 #1387, 0.02 #542), 03rk0 (0.09 #442, 0.08 #1433, 0.06 #2332), 0345h (0.05 #526, 0.05 #328, 0.03 #427), 0f8l9c (0.05 #814, 0.03 #913, 0.03 #517), 0d060g (0.04 #799, 0.04 #4175, 0.04 #1493) >> Best rule #4169 for best value: >> intensional similarity = 3 >> extensional distance = 3635 >> proper extension: 04cy8rb; 02qggqc; 0dky9n; 07qnf; 0784v1; 05218gr; 01yzl2; 031x_3; 01ly8d; 06s27s; ... >> query: (?x9685, 09c7w0) <- nationality(?x9685, ?x4221), time_zones(?x4221, ?x5327), form_of_government(?x4221, ?x6065) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #213 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 19 *> proper extension: 07m69t; *> query: (?x9685, 07ssc) <- nationality(?x9685, ?x4221), ?x4221 = 0j5g9 *> conf = 0.52 ranks of expected_values: 2 EVAL 01bbwp nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 52.000 0.756 http://example.org/people/person/nationality #5179-0fqpc7d PRED entity: 0fqpc7d PRED relation: award_winner PRED expected values: 0pyg6 018ygt => 33 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2111): 016gr2 (0.50 #3215, 0.34 #4586, 0.33 #1686), 02tr7d (0.50 #3284, 0.33 #1755, 0.25 #15510), 0cp9f9 (0.50 #5766, 0.22 #11880, 0.14 #7296), 018ygt (0.47 #6114, 0.44 #11656, 0.43 #7072), 0p_2r (0.47 #6114, 0.33 #4775, 0.22 #10889), 02778pf (0.47 #6114, 0.33 #4690, 0.22 #10804), 02778qt (0.47 #6114, 0.33 #5038, 0.22 #11152), 0277470 (0.47 #6114, 0.19 #9172, 0.17 #30580), 0284gcb (0.47 #6114, 0.19 #9172, 0.17 #30580), 026w_gk (0.47 #6114, 0.19 #9172, 0.17 #30580) >> Best rule #3215 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 0clfdj; 092t4b; >> query: (?x2245, 016gr2) <- award_winner(?x2245, ?x1222), honored_for(?x2245, ?x3743), honored_for(?x2245, ?x2394), honored_for(?x2245, ?x1813), film_release_region(?x2394, ?x1536), film_release_region(?x2394, ?x1353), genre(?x2394, ?x225), genre(?x3743, ?x53), film(?x1222, ?x695), nominated_for(?x68, ?x1813), award_nominee(?x2531, ?x1222), award_nominee(?x1738, ?x1222), ?x1353 = 035qy, award(?x3743, ?x2257), ?x1738 = 0170pk, ?x2531 = 0kszw, ?x1536 = 06c1y >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6114 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 4 *> proper extension: 07z31v; *> query: (?x2245, ?x829) <- award_winner(?x2245, ?x5944), award_winner(?x2245, ?x5454), honored_for(?x2245, ?x2528), honored_for(?x2245, ?x2394), ?x5454 = 020_95, nominated_for(?x198, ?x2394), genre(?x2528, ?x258), nominated_for(?x5944, ?x2436), award(?x2528, ?x693), nominated_for(?x748, ?x2394), nationality(?x5944, ?x279), ?x258 = 05p553, program(?x829, ?x2528), nominated_for(?x368, ?x2528), award_winner(?x198, ?x269) *> conf = 0.47 ranks of expected_values: 4, 561 EVAL 0fqpc7d award_winner 018ygt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 33.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0fqpc7d award_winner 0pyg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 33.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5178-0bwh6 PRED entity: 0bwh6 PRED relation: people! PRED expected values: 09vc4s => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 43): 041rx (0.40 #596, 0.24 #3778, 0.23 #3704), 02ctzb (0.25 #14, 0.25 #310, 0.24 #458), 0x67 (0.25 #9, 0.17 #3265, 0.17 #4893), 019lrz (0.25 #35, 0.02 #257, 0.02 #331), 063k3h (0.21 #250, 0.13 #768, 0.12 #398), 02w7gg (0.12 #594, 0.12 #3702, 0.12 #3258), 0xnvg (0.09 #604, 0.08 #3712, 0.08 #3268), 048z7l (0.08 #629, 0.07 #111, 0.06 #999), 0222qb (0.08 #633, 0.04 #1299, 0.04 #1595), 0dryh9k (0.07 #533, 0.06 #755, 0.05 #5195) >> Best rule #596 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 019z7q; 0prjs; 05whq_9; 0p51w; 0jw67; 01twdk; 054bt3; 022_q8; 01_f_5; 03hy3g; ... >> query: (?x1365, 041rx) <- film(?x1365, ?x1118), award_winner(?x289, ?x1365), people(?x1446, ?x1365) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #230 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 51 *> proper extension: 0d1_f; *> query: (?x1365, 09vc4s) <- basic_title(?x1365, ?x1195), location(?x1365, ?x2552) *> conf = 0.04 ranks of expected_values: 19 EVAL 0bwh6 people! 09vc4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 126.000 126.000 0.404 http://example.org/people/ethnicity/people #5177-0btpm6 PRED entity: 0btpm6 PRED relation: film_format PRED expected values: 017fx5 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 3): 017fx5 (0.28 #20, 0.24 #28, 0.19 #11), 07fb8_ (0.21 #47, 0.19 #22, 0.17 #78), 01dc60 (0.01 #50) >> Best rule #20 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 0661m4p; 07x4qr; 0gffmn8; 0gj8nq2; 09g7vfw; 0dll_t2; 0bq6ntw; 0hhggmy; 0ddbjy4; >> query: (?x7493, 017fx5) <- film_release_region(?x7493, ?x2236), film_release_region(?x7493, ?x410), ?x2236 = 05sb1, ?x410 = 01ls2, film(?x2443, ?x7493) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0btpm6 film_format 017fx5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.281 http://example.org/film/film/film_format #5176-08bqy9 PRED entity: 08bqy9 PRED relation: languages PRED expected values: 03k50 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 19): 03k50 (0.23 #521, 0.21 #743, 0.14 #225), 07c9s (0.13 #530, 0.12 #752, 0.05 #86), 064_8sq (0.09 #1938, 0.08 #199, 0.08 #2012), 0999q (0.06 #762, 0.06 #540, 0.05 #96), 02bjrlw (0.05 #186, 0.05 #223, 0.04 #1925), 04306rv (0.05 #187, 0.05 #224, 0.03 #1926), 09s02 (0.05 #552, 0.04 #774, 0.03 #108), 09bnf (0.04 #777, 0.03 #555, 0.02 #222), 055qm (0.04 #541, 0.02 #763), 06nm1 (0.03 #2003, 0.03 #1929, 0.02 #190) >> Best rule #521 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 0cfywh; >> query: (?x6189, 03k50) <- nationality(?x6189, ?x2146), type_of_union(?x6189, ?x566), ?x2146 = 03rk0 >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08bqy9 languages 03k50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.225 http://example.org/people/person/languages #5175-01hhvg PRED entity: 01hhvg PRED relation: school! PRED expected values: 05m_8 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 93): 0713r (0.25 #127, 0.14 #311, 0.14 #495), 05m_8 (0.19 #95, 0.14 #831, 0.13 #1659), 04mjl (0.19 #154, 0.10 #522, 0.08 #246), 04wmvz (0.14 #78, 0.13 #906, 0.12 #170), 051vz (0.14 #850, 0.12 #666, 0.11 #1678), 0cqt41 (0.14 #18, 0.12 #662, 0.10 #846), 07l8x (0.14 #893, 0.12 #1261, 0.11 #1353), 07147 (0.14 #66, 0.09 #1262, 0.09 #1354), 06wpc (0.14 #63, 0.07 #2486, 0.06 #155), 07l4z (0.13 #897, 0.10 #1909, 0.10 #1725) >> Best rule #127 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 02zd460; 04p_hy; 021w0_; 02pptm; >> query: (?x946, 0713r) <- contains(?x1227, ?x946), ?x1227 = 01n7q, currency(?x946, ?x170), school_type(?x946, ?x1507), school(?x1823, ?x946) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #95 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 02zd460; 04p_hy; 021w0_; 02pptm; *> query: (?x946, 05m_8) <- contains(?x1227, ?x946), ?x1227 = 01n7q, currency(?x946, ?x170), school_type(?x946, ?x1507), school(?x1823, ?x946) *> conf = 0.19 ranks of expected_values: 2 EVAL 01hhvg school! 05m_8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 139.000 139.000 0.250 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #5174-014_lq PRED entity: 014_lq PRED relation: artists! PRED expected values: 05w3f => 113 concepts (86 used for prediction) PRED predicted values (max 10 best out of 291): 05w3f (0.64 #12265, 0.50 #3089, 0.50 #645), 0dl5d (0.61 #3379, 0.58 #1544, 0.47 #6127), 064t9 (0.55 #24766, 0.53 #25376, 0.49 #20489), 01fh36 (0.51 #12619, 0.33 #389, 0.18 #3138), 025sc50 (0.41 #2797, 0.31 #4019, 0.30 #2492), 06j6l (0.37 #2795, 0.31 #4017, 0.30 #2490), 05bt6j (0.37 #1566, 0.35 #3401, 0.34 #6149), 05r6t (0.33 #1910, 0.33 #79, 0.25 #4661), 01243b (0.33 #650, 0.33 #345, 0.25 #955), 0mmp3 (0.33 #403, 0.33 #98, 0.23 #9170) >> Best rule #12265 for best value: >> intensional similarity = 5 >> extensional distance = 127 >> proper extension: 01wwvt2; 011hdn; 01mwsnc; 01bpnd; 01lz4tf; 017g21; 06mj4; 07sbk; >> query: (?x5329, 05w3f) <- artists(?x9063, ?x5329), artists(?x9063, ?x1838), artists(?x9063, ?x1749), ?x1838 = 012zng, ?x1749 = 01fl3 >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014_lq artists! 05w3f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 86.000 0.643 http://example.org/music/genre/artists #5173-0bwh6 PRED entity: 0bwh6 PRED relation: award PRED expected values: 0f_nbyh 02g3ft 02w9sd7 => 158 concepts (136 used for prediction) PRED predicted values (max 10 best out of 317): 0gs9p (0.75 #8693, 0.73 #46254, 0.72 #37149), 0gq9h (0.75 #8693, 0.73 #46254, 0.72 #37149), 027c95y (0.75 #8693, 0.73 #46254, 0.72 #37149), 027b9ly (0.75 #8693, 0.73 #46254, 0.72 #37149), 027c924 (0.75 #8693, 0.73 #46254, 0.72 #37149), 02w_6xj (0.75 #8693, 0.73 #46254, 0.72 #37149), 054ky1 (0.75 #8693, 0.73 #46254, 0.72 #37149), 02qt02v (0.75 #8693, 0.72 #37149, 0.71 #36753), 02py7pj (0.75 #8693, 0.72 #37149, 0.71 #36753), 02qvyrt (0.45 #2887, 0.38 #9208, 0.36 #8417) >> Best rule #8693 for best value: >> intensional similarity = 3 >> extensional distance = 138 >> proper extension: 01vsxdm; 0134s5; 02jqjm; 015cxv; 0bk1p; >> query: (?x1365, ?x289) <- award(?x1365, ?x198), music(?x2116, ?x1365), award_winner(?x289, ?x1365) >> conf = 0.75 => this is the best rule for 9 predicted values *> Best rule #10678 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 163 *> proper extension: 0n6f8; 01j5sd; 06dkzt; 02hh8j; 01gz9n; 06s1qy; *> query: (?x1365, 0f_nbyh) <- nominated_for(?x1365, ?x1118), produced_by(?x2203, ?x1365), award_winner(?x762, ?x1365) *> conf = 0.18 ranks of expected_values: 59, 62, 109 EVAL 0bwh6 award 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 158.000 136.000 0.745 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bwh6 award 02g3ft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 158.000 136.000 0.745 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bwh6 award 0f_nbyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 158.000 136.000 0.745 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5172-01vz80y PRED entity: 01vz80y PRED relation: profession PRED expected values: 03gjzk 01c72t => 97 concepts (73 used for prediction) PRED predicted values (max 10 best out of 75): 03gjzk (0.86 #2013, 0.86 #440, 0.86 #2299), 09jwl (0.68 #3876, 0.67 #4020, 0.65 #1731), 0nbcg (0.46 #3888, 0.46 #1600, 0.45 #4032), 0dz3r (0.42 #1718, 0.40 #3863, 0.40 #4007), 01c72t (0.42 #1593, 0.35 #878, 0.34 #1307), 016z4k (0.40 #862, 0.38 #1720, 0.37 #3007), 0kyk (0.28 #740, 0.16 #2456, 0.15 #3600), 039v1 (0.27 #890, 0.27 #3893, 0.26 #4037), 018gz8 (0.25 #156, 0.20 #442, 0.17 #728), 0fnpj (0.23 #914, 0.21 #1629, 0.15 #1772) >> Best rule #2013 for best value: >> intensional similarity = 3 >> extensional distance = 213 >> proper extension: 08xz51; >> query: (?x7587, 03gjzk) <- program(?x7587, ?x9843), award_nominee(?x3456, ?x7587), profession(?x7587, ?x319) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 01vz80y profession 01c72t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 73.000 0.860 http://example.org/people/person/profession EVAL 01vz80y profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 73.000 0.860 http://example.org/people/person/profession #5171-084l5 PRED entity: 084l5 PRED relation: colors PRED expected values: 01l849 083jv => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 19): 019sc (0.72 #597, 0.45 #293, 0.38 #445), 01g5v (0.70 #61, 0.32 #921, 0.32 #669), 083jv (0.64 #287, 0.62 #154, 0.62 #1515), 01l849 (0.50 #115, 0.45 #96, 0.44 #39), 06fvc (0.36 #1516, 0.35 #1538, 0.35 #1558), 03vtbc (0.32 #669, 0.25 #803, 0.21 #618), 0jc_p (0.32 #669, 0.25 #803, 0.20 #1533), 036k5h (0.32 #669, 0.25 #803, 0.20 #1533), 02rnmb (0.32 #669, 0.25 #803, 0.20 #629), 09ggk (0.32 #669, 0.25 #803, 0.20 #629) >> Best rule #597 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 038_0z; >> query: (?x4519, 019sc) <- colors(?x4519, ?x13863), team(?x11323, ?x4519), colors(?x7447, ?x13863), colors(?x12734, ?x13863), ?x12734 = 04l57x >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #287 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 20 *> proper extension: 01y3c; 05g3v; 0289q; 03wnh; 0ws7; *> query: (?x4519, 083jv) <- position(?x4519, ?x3346), position(?x4519, ?x2573), position(?x4519, ?x2247), team(?x1717, ?x4519), ?x3346 = 02g_7z, position_s(?x4519, ?x11424), draft(?x4519, ?x465), ?x2573 = 05b3ts, position(?x387, ?x2247) *> conf = 0.64 ranks of expected_values: 3, 4 EVAL 084l5 colors 083jv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 90.000 0.721 http://example.org/sports/sports_team/colors EVAL 084l5 colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 90.000 0.721 http://example.org/sports/sports_team/colors #5170-01npcy7 PRED entity: 01npcy7 PRED relation: nationality PRED expected values: 09c7w0 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 49): 09c7w0 (0.84 #9131, 0.78 #9233, 0.77 #1004), 030qb3t (0.33 #10747, 0.31 #6719), 01n7q (0.33 #10747, 0.31 #6719), 02jx1 (0.15 #33, 0.15 #2843, 0.15 #2037), 07ssc (0.15 #2019, 0.14 #2721, 0.13 #115), 0d060g (0.07 #7, 0.06 #508, 0.05 #207), 014wxc (0.07 #2808), 0lmgy (0.07 #2808), 03gh4 (0.07 #2808), 02hrh0_ (0.07 #2808) >> Best rule #9131 for best value: >> intensional similarity = 2 >> extensional distance = 1519 >> proper extension: 02784z; 01cqz5; 0bhtzw; >> query: (?x9482, ?x94) <- place_of_birth(?x9482, ?x7886), country(?x7886, ?x94) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01npcy7 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.837 http://example.org/people/person/nationality #5169-04rwx PRED entity: 04rwx PRED relation: student PRED expected values: 02v406 06crk => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 1687): 0b78hw (0.15 #52242, 0.14 #54332, 0.12 #43885), 0x3r3 (0.15 #52242, 0.14 #54332, 0.12 #43885), 073v6 (0.14 #4706, 0.12 #8886, 0.12 #10976), 0d3k14 (0.14 #6031, 0.11 #1852, 0.10 #31111), 063vn (0.14 #4476, 0.05 #23288, 0.05 #19108), 020_95 (0.12 #9305, 0.12 #11395, 0.11 #15576), 0335fp (0.12 #9736, 0.12 #11826, 0.11 #16007), 09v6tz (0.12 #26421, 0.08 #36867, 0.07 #28510), 01d494 (0.11 #2354, 0.11 #264, 0.08 #25345), 049gc (0.11 #3017, 0.11 #927, 0.08 #26008) >> Best rule #52242 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 053mhx; 02_gzx; >> query: (?x1665, ?x4308) <- student(?x1665, ?x4463), contains(?x94, ?x1665), company(?x4308, ?x1665) >> conf = 0.15 => this is the best rule for 2 predicted values *> Best rule #30363 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 0cv_2; 02z_b; *> query: (?x1665, 06crk) <- organization(?x1665, ?x5487), company(?x346, ?x1665), category(?x1665, ?x134) *> conf = 0.03 ranks of expected_values: 848, 981 EVAL 04rwx student 06crk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 143.000 143.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 04rwx student 02v406 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 143.000 143.000 0.153 http://example.org/education/educational_institution/students_graduates./education/education/student #5168-0hvvf PRED entity: 0hvvf PRED relation: nominated_for! PRED expected values: 07qy0b => 66 concepts (31 used for prediction) PRED predicted values (max 10 best out of 546): 07mkj0 (0.80 #56081, 0.78 #37387, 0.78 #58417), 0hw1j (0.68 #28039, 0.67 #30376, 0.66 #25702), 027l0b (0.31 #53744, 0.30 #7008, 0.22 #32715), 086k8 (0.15 #18692, 0.14 #23366, 0.13 #49071), 02q4mt (0.11 #63090, 0.11 #65427, 0.08 #60753), 0sw6g (0.11 #63090, 0.11 #65427, 0.08 #60753), 0dzf_ (0.11 #63090, 0.11 #65427, 0.08 #60753), 01v80y (0.11 #63090, 0.11 #65427, 0.08 #60753), 02779r4 (0.11 #63090, 0.11 #65427, 0.08 #60753), 028knk (0.11 #63090, 0.11 #65427, 0.08 #60753) >> Best rule #56081 for best value: >> intensional similarity = 3 >> extensional distance = 856 >> proper extension: 048scx; 05t0_2v; 026f__m; 05pxnmb; 0g4pl7z; 02bj22; 03ntbmw; 0322yj; >> query: (?x7765, ?x10084) <- nominated_for(?x1119, ?x7765), titles(?x1316, ?x7765), award_winner(?x7765, ?x10084) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #60753 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 891 *> proper extension: 0cwrr; 075cph; 05m_jsg; 0fsw_7; 01kf5lf; 04glx0; 0k7tq; 05fgr_; 05sy0cv; 06w7mlh; *> query: (?x7765, ?x3371) <- nominated_for(?x1314, ?x7765), award(?x7765, ?x1033), award_nominee(?x3371, ?x1314) *> conf = 0.08 ranks of expected_values: 26 EVAL 0hvvf nominated_for! 07qy0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 66.000 31.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5167-013yq PRED entity: 013yq PRED relation: category PRED expected values: 08mbj5d => 185 concepts (185 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #15, 0.85 #17, 0.82 #3) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0fvyz; 0fvvg; 0fvzz; 0fw1y; >> query: (?x2277, 08mbj5d) <- capital(?x3038, ?x2277), partially_contains(?x3038, ?x10954), state_province_region(?x2276, ?x3038) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013yq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 185.000 185.000 0.875 http://example.org/common/topic/webpage./common/webpage/category #5166-09xbpt PRED entity: 09xbpt PRED relation: genre PRED expected values: 0lsxr => 65 concepts (59 used for prediction) PRED predicted values (max 10 best out of 82): 07s9rl0 (0.64 #2408, 0.58 #2769, 0.58 #3250), 04btyz (0.50 #5536, 0.50 #69, 0.50 #2528), 02kdv5l (0.50 #3, 0.35 #724, 0.32 #1206), 0lsxr (0.36 #129, 0.25 #9, 0.22 #369), 02l7c8 (0.29 #2423, 0.27 #616, 0.27 #2784), 0jdm8 (0.25 #83, 0.10 #7102, 0.02 #1527), 03k9fj (0.24 #1335, 0.24 #853, 0.23 #2660), 01f9r0 (0.21 #196, 0.10 #7102, 0.04 #556), 04xvlr (0.18 #602, 0.18 #2409, 0.16 #1686), 01hmnh (0.17 #1341, 0.16 #2666, 0.16 #859) >> Best rule #2408 for best value: >> intensional similarity = 3 >> extensional distance = 792 >> proper extension: 04969y; 04m1bm; 05dy7p; 01h72l; 016kz1; 02n9bh; 02phtzk; 0bhwhj; 027ct7c; 02q3fdr; ... >> query: (?x349, 07s9rl0) <- award_winner(?x349, ?x286), titles(?x9360, ?x349), genre(?x349, ?x258) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #129 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 015qsq; 02py4c8; 011yth; 01qvz8; 0ch3qr1; 0y_yw; 0ptx_; 0jsf6; 0bxsk; 0_9wr; ... *> query: (?x349, 0lsxr) <- film(?x2035, ?x349), film(?x286, ?x349), ?x2035 = 0bj9k, award_winner(?x286, ?x426), participant(?x287, ?x286) *> conf = 0.36 ranks of expected_values: 4 EVAL 09xbpt genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 65.000 59.000 0.640 http://example.org/film/film/genre #5165-06t8b PRED entity: 06t8b PRED relation: profession PRED expected values: 02jknp 0dgd_ => 101 concepts (81 used for prediction) PRED predicted values (max 10 best out of 62): 0dgd_ (0.89 #759, 0.87 #1635, 0.67 #320), 02jknp (0.89 #1760, 0.89 #1468, 0.88 #3951), 02hrh1q (0.84 #4980, 0.84 #4541, 0.83 #451), 0cbd2 (0.38 #152, 0.33 #298, 0.28 #4680), 018gz8 (0.34 #1330, 0.24 #1914, 0.24 #2060), 09jwl (0.25 #163, 0.22 #309, 0.20 #4545), 0d8qb (0.25 #1246, 0.12 #223, 0.11 #369), 0kyk (0.22 #4701, 0.19 #4263, 0.13 #5432), 0np9r (0.18 #2210, 0.18 #2502, 0.18 #1334), 015cjr (0.14 #1362, 0.12 #1216, 0.12 #1946) >> Best rule #759 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 04qvl7; 079hvk; 05dppk; 0dqzkv; 06nz46; 06g60w; 03cx282; 0627sn; 03ctv8m; 087yty; ... >> query: (?x7903, 0dgd_) <- cinematography(?x876, ?x7903), genre(?x876, ?x225), award_winner(?x289, ?x7903) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 06t8b profession 0dgd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 81.000 0.892 http://example.org/people/person/profession EVAL 06t8b profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 81.000 0.892 http://example.org/people/person/profession #5164-01rp13 PRED entity: 01rp13 PRED relation: nominated_for! PRED expected values: 01fs_4 02sh8y => 118 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1240): 0cjdk (0.81 #37414, 0.81 #51451, 0.78 #70163), 05fnl9 (0.62 #39754, 0.60 #25721, 0.60 #25720), 03vpf_ (0.60 #25721, 0.60 #25720, 0.57 #4675), 05gnf (0.27 #1454, 0.12 #20158, 0.11 #36529), 02778pf (0.18 #156, 0.07 #2493, 0.07 #4831), 0p_2r (0.18 #284, 0.07 #2621, 0.07 #4959), 0pz7h (0.18 #176, 0.05 #65485, 0.04 #2513), 0hvb2 (0.18 #373, 0.05 #19077, 0.04 #14401), 07m77x (0.18 #1869, 0.04 #4206, 0.04 #6544), 01qr1_ (0.18 #743, 0.04 #3080, 0.04 #5418) >> Best rule #37414 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 0yyn5; 02ndy4; >> query: (?x6341, ?x2554) <- titles(?x2008, ?x6341), country_of_origin(?x6341, ?x94), nominated_for(?x693, ?x6341), award_winner(?x6341, ?x2554) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #123963 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 191 *> proper extension: 04bp0l; *> query: (?x6341, ?x436) <- genre(?x6341, ?x258), nominated_for(?x3210, ?x6341), genre(?x2436, ?x258), award_winner(?x2436, ?x436) *> conf = 0.01 ranks of expected_values: 998 EVAL 01rp13 nominated_for! 02sh8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 55.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 01rp13 nominated_for! 01fs_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 118.000 55.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5163-0jdx PRED entity: 0jdx PRED relation: country! PRED expected values: 08tq4x => 142 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1708): 01m13b (0.23 #6979, 0.22 #13812, 0.22 #3563), 0bmch_x (0.19 #4206, 0.17 #790, 0.16 #9330), 0cp08zg (0.19 #4686, 0.17 #1270, 0.14 #11518), 0401sg (0.19 #3509, 0.17 #93, 0.14 #10341), 0dscrwf (0.19 #3484, 0.16 #8608, 0.15 #17149), 04z4j2 (0.19 #4966, 0.16 #10090, 0.14 #11798), 06_sc3 (0.19 #4764, 0.14 #11596, 0.14 #15013), 0fjyzt (0.19 #4308, 0.14 #11140, 0.14 #14557), 0cmc26r (0.19 #4058, 0.14 #10890, 0.14 #14307), 02vxq9m (0.17 #11957, 0.09 #1729, 0.09 #78571) >> Best rule #6979 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 0bq0p9; >> query: (?x7833, 01m13b) <- nationality(?x2259, ?x7833), organization(?x7833, ?x4230), ?x4230 = 04k4l >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #687 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 088q1s; *> query: (?x7833, 08tq4x) <- official_language(?x7833, ?x12283), countries_spoken_in(?x11590, ?x7833), ?x11590 = 0349s *> conf = 0.17 ranks of expected_values: 19 EVAL 0jdx country! 08tq4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 142.000 49.000 0.226 http://example.org/film/film/country #5162-01r0t_j PRED entity: 01r0t_j PRED relation: influenced_by PRED expected values: 02vgh => 110 concepts (43 used for prediction) PRED predicted values (max 10 best out of 602): 053yx (0.33 #72, 0.25 #938, 0.25 #505), 0f0y8 (0.33 #4, 0.25 #870, 0.25 #437), 012vd6 (0.21 #3632, 0.15 #4499, 0.15 #6664), 014z8v (0.14 #11819, 0.13 #12252, 0.13 #11386), 08433 (0.14 #1754, 0.14 #4786, 0.13 #7385), 01hmk9 (0.14 #14090, 0.13 #12352, 0.12 #11486), 0f7hc (0.14 #3604, 0.08 #8802, 0.07 #9670), 014zfs (0.13 #12156, 0.12 #11290, 0.11 #13894), 081lh (0.12 #12152, 0.11 #13890, 0.10 #11286), 0p_47 (0.12 #13976, 0.12 #12238, 0.10 #11372) >> Best rule #72 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01vsy95; >> query: (?x8323, 053yx) <- role(?x8323, ?x780), performance_role(?x8323, ?x432), ?x780 = 01qzyz, ?x432 = 042v_gx >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01r0t_j influenced_by 02vgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 43.000 0.333 http://example.org/influence/influence_node/influenced_by #5161-04glx0 PRED entity: 04glx0 PRED relation: genre PRED expected values: 07s9rl0 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 77): 07s9rl0 (0.53 #2909, 0.51 #1163, 0.50 #1745), 05p553 (0.45 #918, 0.45 #1832, 0.43 #2082), 0hcr (0.43 #1764, 0.22 #2928, 0.18 #3097), 06n90 (0.40 #346, 0.38 #1758, 0.19 #2922), 03npn (0.40 #340, 0.10 #1752, 0.05 #2916), 01z4y (0.34 #932, 0.33 #102, 0.32 #1430), 06nbt (0.33 #105, 0.25 #271, 0.09 #1599), 01w613 (0.33 #132, 0.25 #298, 0.07 #796), 0dm00 (0.33 #155, 0.25 #321, 0.04 #736), 0q00t (0.33 #158, 0.25 #324, 0.04 #739) >> Best rule #2909 for best value: >> intensional similarity = 2 >> extensional distance = 244 >> proper extension: 027pfb2; 07qht4; >> query: (?x6590, 07s9rl0) <- genre(?x6590, ?x9083), genre(?x6450, ?x9083) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04glx0 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.533 http://example.org/tv/tv_program/genre #5160-0hsqf PRED entity: 0hsqf PRED relation: locations! PRED expected values: 0l6mp => 168 concepts (159 used for prediction) PRED predicted values (max 10 best out of 95): 0b_6x2 (0.18 #809, 0.16 #3787, 0.15 #3267), 0b_6_l (0.18 #882, 0.13 #3860, 0.12 #3340), 0b_6pv (0.18 #857, 0.12 #3315, 0.11 #2406), 0bzrsh (0.18 #856, 0.11 #2405, 0.11 #3834), 0b_6qj (0.18 #844, 0.11 #1231, 0.10 #4210), 0b_6rk (0.14 #822, 0.12 #3280, 0.10 #5224), 0b_6q5 (0.14 #872, 0.11 #1259, 0.11 #1646), 0b_6s7 (0.14 #842, 0.11 #1616, 0.10 #3820), 0bzrxn (0.14 #831, 0.09 #2380, 0.08 #3289), 0b_6zk (0.14 #806, 0.09 #4172, 0.08 #3264) >> Best rule #809 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0fsb8; >> query: (?x9310, 0b_6x2) <- citytown(?x8121, ?x9310), category(?x9310, ?x134), teams(?x9310, ?x4006), industry(?x8121, ?x5078) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hsqf locations! 0l6mp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 168.000 159.000 0.182 http://example.org/time/event/locations #5159-06y57 PRED entity: 06y57 PRED relation: place_of_birth! PRED expected values: 0c3ns => 182 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2164): 02r6c_ (0.42 #271380, 0.41 #88715, 0.37 #88714), 013t9y (0.42 #271380, 0.41 #88715, 0.37 #88714), 0154qm (0.42 #271380, 0.41 #88715, 0.37 #88714), 05dbf (0.42 #271380, 0.41 #88715, 0.37 #88714), 06dv3 (0.42 #271380, 0.41 #88715, 0.37 #88714), 02c6pq (0.42 #271380, 0.41 #88715, 0.37 #88714), 02v406 (0.42 #271380, 0.37 #88714, 0.35 #287040), 0hnp7 (0.42 #271380, 0.37 #88714, 0.35 #287040), 0c3ns (0.41 #88715, 0.37 #88714, 0.36 #146117), 02r5w9 (0.41 #88715, 0.37 #88714, 0.36 #146117) >> Best rule #271380 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 0ht8h; >> query: (?x5036, ?x8812) <- location(?x8812, ?x5036), place_of_birth(?x8812, ?x11743), featured_film_locations(?x308, ?x5036) >> conf = 0.42 => this is the best rule for 8 predicted values *> Best rule #88715 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 02j3w; 07bcn; 07mgr; 049kw; 0dp90; *> query: (?x5036, ?x4544) <- location(?x4544, ?x5036), citytown(?x10312, ?x5036), nominated_for(?x4544, ?x2189), capital(?x8506, ?x5036) *> conf = 0.41 ranks of expected_values: 9 EVAL 06y57 place_of_birth! 0c3ns CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 182.000 115.000 0.416 http://example.org/people/person/place_of_birth #5158-01r216 PRED entity: 01r216 PRED relation: profession PRED expected values: 0dxtg 03gjzk => 102 concepts (101 used for prediction) PRED predicted values (max 10 best out of 54): 0dxtg (0.86 #614, 0.85 #2114, 0.84 #1814), 03gjzk (0.84 #1366, 0.83 #1666, 0.83 #1966), 02hrh1q (0.70 #5719, 0.70 #4517, 0.69 #4067), 01d_h8 (0.68 #456, 0.67 #2106, 0.66 #1806), 02jknp (0.59 #2108, 0.57 #458, 0.54 #2409), 0cbd2 (0.50 #7, 0.29 #2709, 0.29 #2408), 02krf9 (0.32 #1528, 0.30 #1978, 0.29 #1678), 018gz8 (0.28 #8558, 0.28 #7957, 0.25 #18), 0dgd_ (0.28 #8558, 0.28 #7957, 0.25 #32), 0kyk (0.25 #31, 0.17 #13509, 0.12 #2733) >> Best rule #614 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 02k76g; >> query: (?x2320, 0dxtg) <- tv_program(?x2320, ?x10234), nationality(?x2320, ?x6401), student(?x12726, ?x2320) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01r216 profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 101.000 0.856 http://example.org/people/person/profession EVAL 01r216 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 101.000 0.856 http://example.org/people/person/profession #5157-01cwm1 PRED entity: 01cwm1 PRED relation: colors PRED expected values: 083jv 06fvc => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 16): 083jv (0.45 #482, 0.44 #882, 0.44 #302), 06fvc (0.26 #783, 0.26 #483, 0.25 #423), 01g5v (0.25 #204, 0.24 #784, 0.23 #484), 02rnmb (0.25 #34, 0.20 #54, 0.17 #1082), 09ggk (0.20 #76, 0.17 #96, 0.12 #116), 019sc (0.19 #909, 0.19 #888, 0.17 #1082), 088fh (0.17 #1082, 0.17 #1103, 0.10 #901), 01l849 (0.17 #1082, 0.17 #1103, 0.10 #901), 04mkbj (0.17 #1082, 0.17 #1103, 0.10 #901), 038hg (0.10 #901, 0.10 #1315, 0.10 #1314) >> Best rule #482 for best value: >> intensional similarity = 7 >> extensional distance = 130 >> proper extension: 0jnmj; >> query: (?x7798, 083jv) <- team(?x203, ?x7798), team(?x5685, ?x7798), gender(?x5685, ?x231), team(?x203, ?x9358), position(?x470, ?x203), sport(?x7798, ?x471), team(?x8594, ?x9358) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01cwm1 colors 06fvc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.455 http://example.org/sports/sports_team/colors EVAL 01cwm1 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.455 http://example.org/sports/sports_team/colors #5156-03xl77 PRED entity: 03xl77 PRED relation: artists! PRED expected values: 0dl5d => 200 concepts (130 used for prediction) PRED predicted values (max 10 best out of 253): 064t9 (0.73 #1821, 0.65 #16295, 0.65 #6949), 06by7 (0.63 #35900, 0.58 #5449, 0.58 #6656), 025sc50 (0.50 #6985, 0.45 #1857, 0.43 #4874), 05bt6j (0.50 #644, 0.35 #6978, 0.33 #5168), 0ggx5q (0.45 #1886, 0.35 #7014, 0.33 #4903), 02lnbg (0.43 #4883, 0.41 #8203, 0.40 #6994), 06j6l (0.42 #6983, 0.39 #8192, 0.34 #16028), 0xhtw (0.37 #6350, 0.29 #7254, 0.28 #24739), 01flzq (0.36 #2222, 0.13 #4335, 0.10 #9162), 036jv (0.36 #2294, 0.10 #9234, 0.08 #16166) >> Best rule #1821 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 04xrx; 0gy6z9; 0127s7; 013w7j; 0227vl; >> query: (?x2946, 064t9) <- religion(?x2946, ?x1985), currency(?x2946, ?x170), artists(?x302, ?x2946), participant(?x2946, ?x8793) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #3337 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 02whj; 01vs_v8; 0137g1; 01wj18h; 01nn6c; 01gg59; 050z2; 094xh; 04f7c55; 01vt5c_; ... *> query: (?x2946, 0dl5d) <- religion(?x2946, ?x1985), instrumentalists(?x315, ?x2946), artists(?x302, ?x2946), ?x315 = 0l14md *> conf = 0.19 ranks of expected_values: 43 EVAL 03xl77 artists! 0dl5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 200.000 130.000 0.727 http://example.org/music/genre/artists #5155-024lt6 PRED entity: 024lt6 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.81 #161, 0.80 #84, 0.75 #122), 02r96rf (0.70 #613, 0.69 #232, 0.69 #575), 09vw2b7 (0.69 #160, 0.64 #693, 0.64 #732), 0dxtw (0.45 #432, 0.44 #622, 0.44 #737), 01vx2h (0.41 #280, 0.39 #623, 0.37 #242), 01pvkk (0.33 #52, 0.30 #281, 0.29 #739), 0215hd (0.33 #59, 0.13 #212, 0.13 #288), 01xy5l_ (0.33 #54, 0.09 #702, 0.09 #741), 02ynfr (0.25 #171, 0.25 #132, 0.23 #209), 02rh1dz (0.22 #621, 0.22 #240, 0.19 #697) >> Best rule #161 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0d_2fb; >> query: (?x9941, 0ch6mp2) <- film(?x8064, ?x9941), ?x8064 = 02xs5v, film_crew_role(?x9941, ?x137), country(?x9941, ?x94) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024lt6 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.812 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5154-0jyb4 PRED entity: 0jyb4 PRED relation: film! PRED expected values: 015wfg => 73 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1182): 03v1xb (0.46 #12503, 0.46 #33337, 0.46 #31253), 03v1w7 (0.46 #12503, 0.46 #33337, 0.46 #31253), 0146pg (0.46 #12503, 0.46 #33337, 0.46 #31253), 01520h (0.46 #12503, 0.46 #33337, 0.46 #31253), 0pz91 (0.27 #4380, 0.02 #62720, 0.02 #23131), 01wyy_ (0.19 #8335, 0.19 #10419, 0.17 #60424), 0c1pj (0.17 #4261, 0.04 #93, 0.04 #39680), 0gz5hs (0.13 #4487), 01fyzy (0.11 #5231, 0.02 #63571), 06rq2l (0.10 #5746) >> Best rule #12503 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 0dnvn3; >> query: (?x6215, ?x669) <- film_crew_role(?x6215, ?x137), honored_for(?x6215, ?x5212), nominated_for(?x669, ?x6215) >> conf = 0.46 => this is the best rule for 4 predicted values *> Best rule #11185 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 0dnvn3; *> query: (?x6215, 015wfg) <- film_crew_role(?x6215, ?x137), honored_for(?x6215, ?x5212), nominated_for(?x669, ?x6215) *> conf = 0.02 ranks of expected_values: 508 EVAL 0jyb4 film! 015wfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 54.000 0.460 http://example.org/film/actor/film./film/performance/film #5153-03__y PRED entity: 03__y PRED relation: form_of_government PRED expected values: 01fpfn => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 4): 01fpfn (0.51 #34, 0.47 #142, 0.46 #54), 06cx9 (0.43 #141, 0.41 #177, 0.39 #281), 01d9r3 (0.36 #131, 0.34 #179, 0.32 #143), 026wp (0.12 #36, 0.09 #12, 0.09 #108) >> Best rule #34 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0160w; 05v8c; 034m8; >> query: (?x3951, 01fpfn) <- country(?x13896, ?x3951), form_of_government(?x3951, ?x1926), adjustment_currency(?x3951, ?x170) >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03__y form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.510 http://example.org/location/country/form_of_government #5152-0dclg PRED entity: 0dclg PRED relation: mode_of_transportation PRED expected values: 07jdr 025t3bg => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 4): 025t3bg (0.84 #42, 0.79 #122, 0.79 #82), 07jdr (0.80 #69, 0.79 #45, 0.79 #81), 0k4j (0.04 #175, 0.04 #131, 0.03 #43), 06d_3 (0.04 #176, 0.02 #136, 0.02 #140) >> Best rule #42 for best value: >> intensional similarity = 2 >> extensional distance = 30 >> proper extension: 0fq8f; >> query: (?x2254, 025t3bg) <- film_release_region(?x1108, ?x2254), mode_of_transportation(?x2254, ?x8731) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0dclg mode_of_transportation 025t3bg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.844 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation EVAL 0dclg mode_of_transportation 07jdr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.844 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #5151-09sdmz PRED entity: 09sdmz PRED relation: nominated_for PRED expected values: 09q5w2 09gq0x5 01hqhm 01rwpj 011yn5 => 54 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1545): 0gmgwnv (0.77 #25922, 0.77 #27448, 0.77 #24396), 083shs (0.77 #25922, 0.77 #27448, 0.77 #24396), 09gq0x5 (0.75 #20067, 0.56 #15491, 0.50 #13966), 09q5w2 (0.67 #10815, 0.62 #13864, 0.56 #15389), 0drnwh (0.67 #20815, 0.33 #16239, 0.21 #23865), 049xgc (0.62 #14554, 0.60 #19129, 0.58 #20655), 019vhk (0.62 #14115, 0.50 #18690, 0.42 #20216), 026p4q7 (0.58 #20160, 0.47 #23210, 0.44 #15584), 020fcn (0.58 #19979, 0.44 #15403, 0.38 #13878), 02mpyh (0.58 #21045, 0.44 #16469, 0.33 #11895) >> Best rule #25922 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 09v7wsg; >> query: (?x4091, ?x167) <- award(?x167, ?x4091), ceremony(?x4091, ?x873), award_winner(?x4091, ?x525), nominated_for(?x4091, ?x144) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #20067 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 099c8n; *> query: (?x4091, 09gq0x5) <- award(?x167, ?x4091), ceremony(?x4091, ?x873), nominated_for(?x4091, ?x522), ?x522 = 01h7bb *> conf = 0.75 ranks of expected_values: 3, 4, 21, 108, 235 EVAL 09sdmz nominated_for 011yn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 54.000 29.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sdmz nominated_for 01rwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 54.000 29.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sdmz nominated_for 01hqhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 54.000 29.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sdmz nominated_for 09gq0x5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 54.000 29.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09sdmz nominated_for 09q5w2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 54.000 29.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5150-0ckt6 PRED entity: 0ckt6 PRED relation: featured_film_locations PRED expected values: 030qb3t => 110 concepts (95 used for prediction) PRED predicted values (max 10 best out of 65): 02_286 (0.30 #9370, 0.30 #259, 0.30 #9609), 080h2 (0.30 #263, 0.04 #1458, 0.04 #9374), 0rh6k (0.29 #479, 0.10 #240, 0.09 #1435), 030qb3t (0.13 #9389, 0.13 #9628, 0.10 #2912), 04jpl (0.11 #9598, 0.11 #9359, 0.11 #2882), 02nd_ (0.10 #355, 0.03 #2989, 0.03 #2030), 01_d4 (0.07 #525, 0.04 #2920, 0.03 #9397), 052p7 (0.07 #536, 0.03 #2931, 0.02 #2211), 0160w (0.07 #480), 0b90_r (0.06 #721, 0.03 #960, 0.03 #1199) >> Best rule #9370 for best value: >> intensional similarity = 4 >> extensional distance = 601 >> proper extension: 0czyxs; 0gtv7pk; 03ckwzc; 03t97y; 0jjy0; 07sc6nw; 07g_0c; 03twd6; 03qnvdl; 02847m9; ... >> query: (?x12899, 02_286) <- film_release_distribution_medium(?x12899, ?x81), film(?x5913, ?x12899), award(?x5913, ?x693), featured_film_locations(?x12899, ?x10982) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #9389 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 601 *> proper extension: 0czyxs; 0gtv7pk; 03ckwzc; 03t97y; 0jjy0; 07sc6nw; 07g_0c; 03twd6; 03qnvdl; 02847m9; ... *> query: (?x12899, 030qb3t) <- film_release_distribution_medium(?x12899, ?x81), film(?x5913, ?x12899), award(?x5913, ?x693), featured_film_locations(?x12899, ?x10982) *> conf = 0.13 ranks of expected_values: 4 EVAL 0ckt6 featured_film_locations 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 95.000 0.300 http://example.org/film/film/featured_film_locations #5149-0k9wp PRED entity: 0k9wp PRED relation: contains! PRED expected values: 0p07l => 101 concepts (41 used for prediction) PRED predicted values (max 10 best out of 195): 0p07l (0.27 #25905, 0.14 #5361, 0.12 #3573), 01cx_ (0.24 #2874, 0.15 #4662, 0.08 #5556), 05k7sb (0.18 #2811, 0.14 #4599, 0.08 #5493), 02jx1 (0.17 #6341, 0.12 #9914, 0.10 #28670), 01n7q (0.16 #32236, 0.11 #1863, 0.10 #17942), 07z1m (0.14 #984, 0.07 #5452, 0.07 #1877), 059rby (0.13 #17884, 0.11 #12526, 0.11 #10740), 0ncj8 (0.10 #240, 0.07 #1133, 0.02 #2026), 031sn (0.10 #850, 0.01 #6211), 07b_l (0.10 #2899, 0.08 #5581, 0.07 #4687) >> Best rule #25905 for best value: >> intensional similarity = 3 >> extensional distance = 424 >> proper extension: 0dwl2; 05qd_; 030_1m; 06rq1k; 06jntd; 045c7b; 081bls; 03rwz3; 01rs59; 01qxs3; ... >> query: (?x5983, ?x94) <- citytown(?x5983, ?x7770), source(?x7770, ?x958), contains(?x94, ?x7770) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k9wp contains! 0p07l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 41.000 0.270 http://example.org/location/location/contains #5148-018lg0 PRED entity: 018lg0 PRED relation: artists PRED expected values: 01516r => 42 concepts (13 used for prediction) PRED predicted values (max 10 best out of 981): 02ndj5 (0.45 #1981, 0.26 #4152, 0.24 #6319), 01w8n89 (0.39 #10081, 0.36 #11164, 0.36 #13330), 01w5n51 (0.37 #3943, 0.36 #1772, 0.18 #6110), 0l8g0 (0.36 #2163, 0.36 #1644, 0.21 #3815), 06br6t (0.36 #1975, 0.26 #4146, 0.19 #5232), 0326tc (0.33 #725, 0.17 #5063, 0.17 #8317), 07r1_ (0.33 #634, 0.11 #2165, 0.11 #2799), 0191h5 (0.27 #1730, 0.23 #4987, 0.22 #10413), 06gd4 (0.27 #1418, 0.21 #3589, 0.15 #5756), 012vm6 (0.27 #1868, 0.21 #4039, 0.11 #2165) >> Best rule #1981 for best value: >> intensional similarity = 9 >> extensional distance = 9 >> proper extension: 0fd3y; 0dl5d; 0190_q; 06cp5; 0cx7f; 0grjmv; 0781g; 0509cr; 0b_6yv; >> query: (?x2072, 02ndj5) <- parent_genre(?x2072, ?x13553), parent_genre(?x2072, ?x2809), artists(?x2072, ?x1955), parent_genre(?x13553, ?x1000), artists(?x13553, ?x8332), artists(?x13553, ?x6234), ?x2809 = 05w3f, ?x6234 = 0l8g0, ?x8332 = 07sbk >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #13013 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 76 *> proper extension: 02ny8t; *> query: (?x2072, ?x475) <- artists(?x2072, ?x6225), instrumentalists(?x316, ?x6225), artist(?x441, ?x6225), artists(?x7083, ?x6225), artists(?x3642, ?x6225), artists(?x3642, ?x475), ?x7083 = 02yv6b, profession(?x6225, ?x131), category(?x6225, ?x134) *> conf = 0.08 ranks of expected_values: 532 EVAL 018lg0 artists 01516r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 13.000 0.455 http://example.org/music/genre/artists #5147-032w8h PRED entity: 032w8h PRED relation: film PRED expected values: 035s95 0bmssv => 106 concepts (75 used for prediction) PRED predicted values (max 10 best out of 831): 06fpsx (0.61 #14258, 0.56 #26734, 0.55 #33863), 08952r (0.33 #714, 0.30 #2496, 0.19 #4278), 01shy7 (0.33 #420, 0.12 #3984, 0.10 #2202), 0gldyz (0.33 #1649, 0.10 #3431, 0.06 #5213), 01k1k4 (0.33 #56, 0.10 #1838, 0.06 #3620), 013q0p (0.33 #804, 0.06 #4368, 0.03 #96243), 04yc76 (0.33 #439, 0.06 #4003, 0.03 #96243), 0prrm (0.33 #857, 0.06 #7986, 0.03 #6203), 07tw_b (0.33 #678, 0.05 #78420, 0.03 #96243), 0bm2nq (0.33 #1627, 0.05 #78420, 0.03 #96243) >> Best rule #14258 for best value: >> intensional similarity = 2 >> extensional distance = 147 >> proper extension: 04cbtrw; 05_2h8; >> query: (?x1736, ?x167) <- nominated_for(?x1736, ?x167), location_of_ceremony(?x1736, ?x739) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #3901 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0p__8; *> query: (?x1736, 035s95) <- award_nominee(?x1736, ?x2101), film(?x1736, ?x167), celebrities_impersonated(?x2101, ?x4196) *> conf = 0.06 ranks of expected_values: 88, 102 EVAL 032w8h film 0bmssv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 106.000 75.000 0.609 http://example.org/film/actor/film./film/performance/film EVAL 032w8h film 035s95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 106.000 75.000 0.609 http://example.org/film/actor/film./film/performance/film #5146-01x4sb PRED entity: 01x4sb PRED relation: languages PRED expected values: 02h40lc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 7): 02h40lc (0.38 #470, 0.30 #353, 0.29 #431), 064_8sq (0.05 #444, 0.04 #327, 0.03 #366), 03k50 (0.02 #1135, 0.01 #2890, 0.01 #3358), 02bjrlw (0.01 #352, 0.01 #1132, 0.01 #742), 04306rv (0.01 #354), 06nm1 (0.01 #474, 0.01 #435, 0.01 #747), 07c9s (0.01 #1144) >> Best rule #470 for best value: >> intensional similarity = 2 >> extensional distance = 271 >> proper extension: 02d9k; 01386_; 0jsg0m; 010p3; 02rn_bj; 037s5h; 0cymln; 09zw90; >> query: (?x6259, 02h40lc) <- location(?x6259, ?x1523), ?x1523 = 030qb3t >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x4sb languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.381 http://example.org/people/person/languages #5145-0n2bh PRED entity: 0n2bh PRED relation: actor PRED expected values: 0mz73 01npcy7 => 69 concepts (35 used for prediction) PRED predicted values (max 10 best out of 812): 03fg0r (0.37 #18572, 0.35 #20430, 0.35 #19501), 01pcmd (0.37 #18572, 0.35 #20430, 0.35 #19501), 0443y3 (0.30 #2953, 0.07 #3882, 0.06 #4810), 04qt29 (0.20 #2550, 0.14 #1621, 0.04 #6263), 0k2mxq (0.20 #3278, 0.06 #5135, 0.04 #6063), 08m4c8 (0.20 #2936, 0.06 #4793, 0.02 #10364), 069nzr (0.14 #1344, 0.10 #3201, 0.10 #2273), 044mvs (0.14 #1699, 0.10 #2628, 0.03 #9127), 03pmty (0.14 #1010, 0.10 #1939, 0.02 #7510), 07m77x (0.14 #1613, 0.10 #2542, 0.02 #9970) >> Best rule #18572 for best value: >> intensional similarity = 4 >> extensional distance = 168 >> proper extension: 0304nh; 0170k0; 023ny6; >> query: (?x2137, ?x2136) <- actor(?x2137, ?x10423), nominated_for(?x2136, ?x2137), film(?x10423, ?x7757), type_of_union(?x10423, ?x566) >> conf = 0.37 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0n2bh actor 01npcy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 35.000 0.366 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 0n2bh actor 0mz73 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 35.000 0.366 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #5144-0bs8ndx PRED entity: 0bs8ndx PRED relation: film_crew_role PRED expected values: 089fss => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 33): 01pvkk (0.44 #10, 0.36 #44, 0.31 #629), 01vx2h (0.36 #524, 0.34 #111, 0.30 #1674), 015h31 (0.22 #7, 0.18 #41, 0.12 #1526), 02ynfr (0.22 #14, 0.16 #529, 0.16 #739), 089fss (0.18 #2323, 0.12 #1526, 0.09 #2671), 0215hd (0.16 #85, 0.14 #223, 0.13 #742), 02rh1dz (0.14 #523, 0.12 #1526, 0.11 #110), 0d2b38 (0.12 #1526, 0.11 #24, 0.10 #749), 02_n3z (0.12 #1526, 0.11 #1, 0.09 #35), 01xy5l_ (0.12 #1526, 0.10 #737, 0.09 #981) >> Best rule #10 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0kb57; >> query: (?x8162, 01pvkk) <- language(?x8162, ?x3592), language(?x8162, ?x254), country(?x8162, ?x94), ?x254 = 02h40lc, film(?x275, ?x8162), ?x3592 = 0t_2 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2323 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1544 *> proper extension: 0522wp; *> query: (?x8162, ?x137) <- film(?x7303, ?x8162), film(?x7303, ?x3012), films(?x10849, ?x3012), award_winner(?x3012, ?x3568), film_crew_role(?x3012, ?x137) *> conf = 0.18 ranks of expected_values: 5 EVAL 0bs8ndx film_crew_role 089fss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 79.000 0.444 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5143-02bn_p PRED entity: 02bn_p PRED relation: legislative_sessions PRED expected values: 04gp1d => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 39): 03tcbx (0.88 #922, 0.87 #236, 0.87 #551), 077g7n (0.87 #236, 0.87 #551, 0.86 #670), 070m6c (0.87 #236, 0.87 #551, 0.86 #670), 02bqn1 (0.87 #236, 0.87 #551, 0.86 #670), 02bn_p (0.84 #353, 0.83 #830, 0.83 #872), 04gp1d (0.84 #353, 0.83 #830, 0.83 #872), 043djx (0.50 #477, 0.45 #1031, 0.45 #912), 01gsvb (0.45 #1031, 0.45 #912, 0.44 #590), 01gstn (0.45 #1031, 0.45 #912, 0.44 #590), 01gtcc (0.45 #1031, 0.45 #912, 0.44 #590) >> Best rule #922 for best value: >> intensional similarity = 31 >> extensional distance = 14 >> proper extension: 04gp1d; 060ny2; >> query: (?x1027, 03tcbx) <- legislative_sessions(?x1027, ?x1028), legislative_sessions(?x1027, ?x952), legislative_sessions(?x1027, ?x356), district_represented(?x1027, ?x1767), district_represented(?x1027, ?x1227), district_represented(?x1027, ?x726), ?x952 = 06f0dc, state_province_region(?x4582, ?x726), contains(?x726, ?x727), location(?x5217, ?x726), location(?x820, ?x1767), featured_film_locations(?x2754, ?x1767), ?x1028 = 032ft5, contains(?x1767, ?x1396), legislative_sessions(?x652, ?x1027), religion(?x726, ?x10107), ?x356 = 05l2z4, state_province_region(?x3379, ?x1767), film(?x400, ?x2754), legislative_sessions(?x2860, ?x1027), state_province_region(?x7526, ?x1227), contains(?x1227, ?x5288), student(?x11215, ?x5217), film(?x5217, ?x392), ?x10107 = 05w5d, religion(?x1227, ?x109), legislative_sessions(?x605, ?x1027), ?x109 = 01lp8, location(?x397, ?x1227), student(?x5288, ?x460), industry(?x7526, ?x373) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #353 for first EXPECTED value: *> intensional similarity = 35 *> extensional distance = 3 *> proper extension: 06f0dc; *> query: (?x1027, ?x653) <- legislative_sessions(?x1027, ?x5977), legislative_sessions(?x1027, ?x952), district_represented(?x1027, ?x6895), district_represented(?x1027, ?x4622), district_represented(?x1027, ?x2977), district_represented(?x1027, ?x1351), district_represented(?x1027, ?x728), district_represented(?x1027, ?x177), legislative_sessions(?x5932, ?x952), legislative_sessions(?x3445, ?x952), district_represented(?x176, ?x4622), ?x5977 = 06r713, contains(?x94, ?x4622), legislative_sessions(?x952, ?x653), ?x177 = 05kkh, location(?x118, ?x4622), ?x6895 = 05fjf, jurisdiction_of_office(?x900, ?x4622), ?x176 = 03rl1g, ?x2977 = 081mh, adjoins(?x3778, ?x4622), ?x1351 = 06mz5, religion(?x4622, ?x109), district_represented(?x952, ?x1024), adjoins(?x728, ?x279), contains(?x4622, ?x10666), ?x3445 = 0d06m5, ?x1024 = 05fhy, legislative_sessions(?x2860, ?x952), contains(?x3448, ?x728), school(?x4243, ?x10666), ?x5932 = 012v1t, category(?x4622, ?x134), major_field_of_study(?x10666, ?x1668), ?x4243 = 0713r *> conf = 0.84 ranks of expected_values: 6 EVAL 02bn_p legislative_sessions 04gp1d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 34.000 34.000 0.875 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #5142-084l5 PRED entity: 084l5 PRED relation: school PRED expected values: 01wdj_ => 72 concepts (64 used for prediction) PRED predicted values (max 10 best out of 195): 065y4w7 (0.40 #1534, 0.33 #4597, 0.32 #5558), 07t90 (0.33 #831, 0.33 #68, 0.20 #1594), 01rc6f (0.33 #134, 0.27 #2044, 0.23 #2235), 0g8rj (0.33 #1040, 0.25 #1422, 0.25 #276), 0j_sncb (0.33 #39, 0.20 #1565, 0.18 #1949), 09f2j (0.33 #76, 0.19 #2370, 0.18 #6321), 02q253 (0.33 #186, 0.17 #1331, 0.17 #949), 02qvvv (0.33 #45, 0.17 #808, 0.10 #1571), 07w0v (0.28 #5371, 0.27 #1921, 0.24 #5561), 01jq0j (0.25 #6631, 0.23 #4131, 0.20 #5862) >> Best rule #1534 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 05tfm; 01y3v; 05l71; 05gg4; >> query: (?x4519, 065y4w7) <- position(?x4519, ?x2247), position(?x4519, ?x1517), position(?x4519, ?x935), draft(?x4519, ?x3089), ?x2247 = 01_9c1, team(?x11323, ?x4519), ?x3089 = 03nt7j, position(?x5229, ?x1517), position_s(?x387, ?x1517), ?x5229 = 07l2m >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1563 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 8 *> proper extension: 05tfm; 01y3v; 05l71; 05gg4; *> query: (?x4519, 01wdj_) <- position(?x4519, ?x2247), position(?x4519, ?x1517), position(?x4519, ?x935), draft(?x4519, ?x3089), ?x2247 = 01_9c1, team(?x11323, ?x4519), ?x3089 = 03nt7j, position(?x5229, ?x1517), position_s(?x387, ?x1517), ?x5229 = 07l2m *> conf = 0.20 ranks of expected_values: 21 EVAL 084l5 school 01wdj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 72.000 64.000 0.400 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #5141-06jw0s PRED entity: 06jw0s PRED relation: award_winner! PRED expected values: 0jt3qpk => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 110): 0jt3qpk (0.52 #43, 0.26 #701, 0.23 #183), 09n4nb (0.13 #6723, 0.10 #11064, 0.10 #11205), 073hd1 (0.13 #6723, 0.10 #11064, 0.10 #11205), 05c1t6z (0.13 #575, 0.12 #716, 0.09 #1136), 03nnm4t (0.09 #634, 0.09 #775, 0.08 #1195), 02q690_ (0.09 #625, 0.09 #766, 0.07 #1186), 027hjff (0.09 #617, 0.08 #758, 0.06 #1038), 013b2h (0.09 #1621, 0.09 #2041, 0.08 #2181), 03gyp30 (0.09 #676, 0.07 #817, 0.07 #1097), 01s695 (0.08 #1544, 0.08 #2104, 0.07 #1964) >> Best rule #43 for best value: >> intensional similarity = 2 >> extensional distance = 19 >> proper extension: 0_b3d; 04qw17; 0ccd3x; 06x77g; >> query: (?x5574, 0jt3qpk) <- nominated_for(?x5574, ?x6678), award_winner(?x631, ?x6678) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06jw0s award_winner! 0jt3qpk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.524 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5140-0c9c0 PRED entity: 0c9c0 PRED relation: place_of_birth PRED expected values: 01b8w_ => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 118): 030qb3t (0.28 #8450, 0.27 #78167, 0.27 #50705), 0b1t1 (0.28 #8450, 0.27 #78167, 0.27 #50705), 0pqz3 (0.20 #673), 0ccvx (0.17 #857, 0.05 #5082, 0.04 #2969), 0nbwf (0.17 #1010, 0.01 #12980), 02_286 (0.12 #7060, 0.10 #7764, 0.09 #28185), 01_d4 (0.08 #1474, 0.07 #6403, 0.05 #14852), 01nl79 (0.08 #1949, 0.06 #2653, 0.01 #13215), 02cl1 (0.08 #1424, 0.03 #4240, 0.02 #4945), 094jv (0.08 #1469, 0.02 #12031, 0.02 #23297) >> Best rule #8450 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 02z6l5f; 0hqly; >> query: (?x2790, ?x1523) <- producer_type(?x2790, ?x632), award_nominee(?x262, ?x2790), location(?x2790, ?x1523) >> conf = 0.28 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0c9c0 place_of_birth 01b8w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 125.000 0.278 http://example.org/people/person/place_of_birth #5139-01j2xj PRED entity: 01j2xj PRED relation: award_winner! PRED expected values: 0gs9p => 148 concepts (138 used for prediction) PRED predicted values (max 10 best out of 281): 02pqp12 (0.40 #31345, 0.39 #32634, 0.37 #31775), 02rdyk7 (0.40 #31345, 0.39 #32634, 0.37 #31775), 0bm70b (0.40 #31345, 0.39 #32634, 0.37 #31775), 01l78d (0.38 #284, 0.05 #54108, 0.04 #1143), 0gs9p (0.37 #2658, 0.26 #1797, 0.19 #8238), 09d28z (0.33 #2019, 0.22 #2880, 0.12 #8460), 027c924 (0.26 #2590, 0.23 #1729, 0.19 #1299), 05b1610 (0.25 #39, 0.04 #6052, 0.04 #3477), 02wkmx (0.23 #1733, 0.10 #2594, 0.08 #1303), 02wypbh (0.21 #2065, 0.07 #2926, 0.05 #8936) >> Best rule #31345 for best value: >> intensional similarity = 3 >> extensional distance = 1219 >> proper extension: 07bzp; >> query: (?x4922, ?x1107) <- award(?x4922, ?x1107), award_winner(?x5398, ?x4922), award_winner(?x4922, ?x1532) >> conf = 0.40 => this is the best rule for 3 predicted values *> Best rule #2658 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 02r5w9; 0343h; 022_lg; 0c3ns; 02645b; 036jb; 01p1z_; 07h5d; 0flddp; 0d6d2; ... *> query: (?x4922, 0gs9p) <- award(?x4922, ?x1107), type_of_union(?x4922, ?x566), ?x1107 = 019f4v *> conf = 0.37 ranks of expected_values: 5 EVAL 01j2xj award_winner! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 148.000 138.000 0.398 http://example.org/award/award_category/winners./award/award_honor/award_winner #5138-074tb5 PRED entity: 074tb5 PRED relation: film PRED expected values: 02704ff => 92 concepts (47 used for prediction) PRED predicted values (max 10 best out of 595): 027r9t (0.08 #3036, 0.05 #10188, 0.05 #8400), 05fm6m (0.08 #1320, 0.04 #6684, 0.04 #8472), 02pg45 (0.08 #932, 0.04 #4508, 0.03 #2720), 02825cv (0.08 #1143, 0.04 #10083, 0.04 #8295), 05650n (0.08 #1013, 0.03 #2801, 0.02 #8165), 06t2t2 (0.08 #1657, 0.02 #8809, 0.02 #5233), 0gkz3nz (0.08 #800, 0.02 #7952, 0.02 #4376), 03bzjpm (0.07 #6679, 0.06 #8467, 0.06 #4891), 01shy7 (0.07 #5788, 0.06 #7576, 0.06 #2212), 06_wqk4 (0.07 #5491, 0.06 #7279, 0.05 #9067) >> Best rule #3036 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 05m63c; 01q_ph; 05gml8; 01pcq3; 0lk90; 0j1yf; 03rl84; 07ss8_; 01vs_v8; 05dbf; ... >> query: (?x5880, 027r9t) <- participant(?x5880, ?x91), languages(?x5880, ?x254), profession(?x5880, ?x524) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #8135 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 83 *> proper extension: 01n5309; 018db8; 01lbp; 01ztgm; 01rh0w; 015pkc; 026c1; 0136pk; 0237fw; 0jfx1; ... *> query: (?x5880, 02704ff) <- participant(?x5880, ?x91), profession(?x5880, ?x524), award_nominee(?x5880, ?x4126) *> conf = 0.02 ranks of expected_values: 282 EVAL 074tb5 film 02704ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 92.000 47.000 0.083 http://example.org/film/actor/film./film/performance/film #5137-06bnz PRED entity: 06bnz PRED relation: contains PRED expected values: 01v15f => 198 concepts (87 used for prediction) PRED predicted values (max 10 best out of 2885): 01mzwp (0.46 #105700, 0.45 #44040, 0.06 #25942), 06bnz (0.46 #105700, 0.45 #44040, 0.02 #102944), 02j9z (0.46 #105700, 0.45 #44040, 0.01 #126255), 05vz3zq (0.46 #105700, 0.45 #44040), 05cwl_ (0.17 #15418, 0.17 #3674, 0.17 #737), 01bzw5 (0.17 #14814, 0.17 #3070, 0.17 #133), 02zd460 (0.17 #15365, 0.17 #3621, 0.17 #684), 0135g (0.17 #15356, 0.17 #3612, 0.17 #675), 01jr6 (0.17 #15180, 0.17 #3436, 0.17 #499), 03b8c4 (0.17 #16915, 0.17 #5171, 0.17 #2234) >> Best rule #105700 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 01l3lx; >> query: (?x1603, ?x455) <- contains(?x1603, ?x10223), contains(?x1603, ?x8745), contains(?x455, ?x10223), taxonomy(?x8745, ?x939) >> conf = 0.46 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 06bnz contains 01v15f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 198.000 87.000 0.459 http://example.org/location/location/contains #5136-042ly5 PRED entity: 042ly5 PRED relation: actor! PRED expected values: 0vjr => 75 concepts (52 used for prediction) PRED predicted values (max 10 best out of 57): 0bt3j9 (0.11 #5586, 0.09 #6651, 0.09 #11173), 0cs134 (0.10 #478, 0.09 #744, 0.07 #1011), 06qv_ (0.10 #476, 0.09 #742, 0.07 #1009), 01_2n (0.10 #461, 0.09 #727, 0.07 #994), 03q4hl (0.07 #1063), 02q5bx2 (0.07 #961), 063ykwt (0.07 #857), 026bfsh (0.02 #2754, 0.02 #3019, 0.02 #4352), 0828jw (0.02 #1170, 0.02 #4625, 0.01 #5691), 0kfv9 (0.02 #4282, 0.01 #5613, 0.01 #6412) >> Best rule #5586 for best value: >> intensional similarity = 3 >> extensional distance = 1045 >> proper extension: 0134w7; 02_hj4; 06k02; 01dw9z; 029_3; 02lymt; 0c12h; 02_0d2; 01p0vf; 01hmk9; ... >> query: (?x7255, ?x5142) <- film(?x7255, ?x97), award_nominee(?x843, ?x7255), nominated_for(?x7255, ?x5142) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #3017 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 443 *> proper extension: 01l_vgt; *> query: (?x7255, 0vjr) <- participant(?x7255, ?x2857), type_of_union(?x7255, ?x566), award(?x2857, ?x154) *> conf = 0.01 ranks of expected_values: 25 EVAL 042ly5 actor! 0vjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 75.000 52.000 0.110 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #5135-041738 PRED entity: 041738 PRED relation: artists PRED expected values: 03t9sp 089pg7 016lmg => 63 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1072): 03t9sp (0.73 #15242, 0.72 #16322, 0.67 #10920), 016lmg (0.67 #14793, 0.60 #5072, 0.50 #2913), 01vv7sc (0.60 #4382, 0.60 #3302, 0.50 #10860), 01vxlbm (0.60 #5738, 0.60 #4658, 0.50 #2499), 01w806h (0.60 #7818, 0.60 #4578, 0.50 #2419), 06k02 (0.60 #6652, 0.60 #3413, 0.50 #2334), 01dwrc (0.60 #5922, 0.60 #3762, 0.50 #2683), 0m19t (0.60 #7586, 0.56 #8665, 0.43 #11905), 01yzl2 (0.60 #8054, 0.50 #12373, 0.47 #13454), 06p03s (0.60 #5328, 0.50 #3169, 0.44 #9647) >> Best rule #15242 for best value: >> intensional similarity = 10 >> extensional distance = 24 >> proper extension: 02dsz1; >> query: (?x5909, 03t9sp) <- artists(?x5909, ?x8332), artists(?x5909, ?x7578), artists(?x5909, ?x2005), artists(?x7267, ?x2005), group(?x1166, ?x2005), ?x1166 = 05148p4, languages(?x7578, ?x254), role(?x7578, ?x1437), ?x7267 = 03mb9, category(?x8332, ?x134) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 165 EVAL 041738 artists 016lmg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 27.000 0.731 http://example.org/music/genre/artists EVAL 041738 artists 089pg7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 63.000 27.000 0.731 http://example.org/music/genre/artists EVAL 041738 artists 03t9sp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 27.000 0.731 http://example.org/music/genre/artists #5134-02s2ft PRED entity: 02s2ft PRED relation: award_winner! PRED expected values: 06_bq1 => 97 concepts (39 used for prediction) PRED predicted values (max 10 best out of 551): 028knk (0.82 #56106, 0.82 #56105, 0.82 #17631), 0blq0z (0.82 #56106, 0.82 #56105, 0.82 #17631), 015pkc (0.53 #62518, 0.53 #62517, 0.52 #41676), 0flw6 (0.53 #62518, 0.53 #62517, 0.52 #41676), 0h0wc (0.53 #62518, 0.53 #62517, 0.52 #41676), 0dlglj (0.53 #62518, 0.53 #62517, 0.52 #41676), 02qgqt (0.53 #62518, 0.53 #62517, 0.52 #41676), 04bdxl (0.53 #62518, 0.53 #62517, 0.52 #33661), 01yfm8 (0.53 #62518, 0.53 #62517, 0.52 #33661), 02xs5v (0.53 #62518, 0.53 #62517, 0.52 #33661) >> Best rule #56106 for best value: >> intensional similarity = 3 >> extensional distance = 1323 >> proper extension: 0c_mvb; 0lzkm; 01p5yn; 05s34b; >> query: (?x92, ?x2028) <- award_winner(?x92, ?x2028), award_winner(?x989, ?x92), award_winner(?x1553, ?x2028) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #36866 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1037 *> proper extension: 0hm0k; *> query: (?x92, ?x1867) <- award_winner(?x92, ?x1384), award_winner(?x3790, ?x92), award_winner(?x1867, ?x1384) *> conf = 0.28 ranks of expected_values: 46 EVAL 02s2ft award_winner! 06_bq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 97.000 39.000 0.820 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #5133-031x_3 PRED entity: 031x_3 PRED relation: profession PRED expected values: 09jwl => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 97): 01c72t (0.83 #2275, 0.60 #175, 0.50 #475), 09jwl (0.77 #4071, 0.75 #1370, 0.75 #5422), 02hrh1q (0.71 #12796, 0.71 #13850, 0.70 #13700), 0nbcg (0.53 #933, 0.53 #2734, 0.50 #1383), 016z4k (0.50 #1504, 0.46 #1804, 0.46 #11879), 0dz3r (0.47 #4053, 0.47 #902, 0.43 #7807), 05vyk (0.40 #246, 0.37 #2346, 0.33 #546), 039v1 (0.38 #1838, 0.36 #1538, 0.36 #3489), 0kyk (0.35 #1681, 0.32 #2432, 0.27 #2131), 0cbd2 (0.33 #3158, 0.32 #2408, 0.30 #1657) >> Best rule #2275 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 043d4; 03d6q; 0c73g; >> query: (?x8583, 01c72t) <- artists(?x888, ?x8583), nationality(?x8583, ?x94), ?x888 = 05lls >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #4071 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 053y0s; 0274ck; 0326tc; *> query: (?x8583, 09jwl) <- artists(?x888, ?x8583), performance_role(?x8583, ?x14713), location(?x8583, ?x2410) *> conf = 0.77 ranks of expected_values: 2 EVAL 031x_3 profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.833 http://example.org/people/person/profession #5132-043q4d PRED entity: 043q4d PRED relation: person PRED expected values: 02w5q6 0163t3 0gps0z 0ck91 => 7 concepts (7 used for prediction) PRED predicted values (max 10 best out of 3296): 01yg9y (0.33 #181, 0.33 #105, 0.33 #20), 054fvj (0.33 #192, 0.33 #31, 0.14 #236), 0q5hw (0.33 #96, 0.20 #133, 0.17 #172), 01jb26 (0.33 #60, 0.20 #141, 0.17 #180), 01lbp (0.33 #44, 0.20 #125, 0.17 #164), 0347db (0.33 #110, 0.20 #147, 0.17 #186), 012gq6 (0.33 #98, 0.20 #135, 0.17 #174), 0pyg6 (0.33 #49, 0.20 #130, 0.17 #169), 046lt (0.33 #97, 0.20 #134, 0.17 #173), 014zfs (0.33 #90, 0.20 #127, 0.17 #166) >> Best rule #181 for best value: >> intensional similarity = 39 >> extensional distance = 4 >> proper extension: 05ll37; >> query: (?x3480, 01yg9y) <- person(?x3480, ?x12254), person(?x3480, ?x8342), person(?x3480, ?x8100), person(?x3480, ?x4407), person(?x3480, ?x3817), person(?x3480, ?x3421), person(?x3480, ?x2274), person(?x3480, ?x2226), participant(?x7613, ?x4407), profession(?x4407, ?x319), type_of_union(?x8100, ?x1873), place_of_birth(?x8100, ?x9336), profession(?x2274, ?x220), location(?x3817, ?x3818), location(?x3817, ?x1705), location(?x2226, ?x11863), award(?x3817, ?x102), jurisdiction_of_office(?x1195, ?x1705), category(?x2274, ?x134), place_of_birth(?x1092, ?x1705), nominated_for(?x102, ?x103), country(?x1705, ?x94), award_winner(?x102, ?x269), source(?x11863, ?x958), participant(?x3421, ?x2790), student(?x9525, ?x12254), profession(?x8342, ?x987), contains(?x321, ?x11863), featured_film_locations(?x7726, ?x1705), ?x94 = 09c7w0, type_of_union(?x3817, ?x566), profession(?x2226, ?x955), contains(?x3818, ?x405), influenced_by(?x8342, ?x10101), people(?x8088, ?x8342), ?x987 = 0dxtg, major_field_of_study(?x9525, ?x4100), award(?x3421, ?x1007), award(?x8100, ?x1801) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #69 for first EXPECTED value: *> intensional similarity = 48 *> extensional distance = 1 *> proper extension: 0c5lg; *> query: (?x3480, 0163t3) <- person(?x3480, ?x9184), person(?x3480, ?x8100), person(?x3480, ?x8081), person(?x3480, ?x7909), person(?x3480, ?x4611), person(?x3480, ?x3034), person(?x3480, ?x1794), person(?x3480, ?x858), artists(?x2664, ?x9184), nationality(?x9184, ?x94), profession(?x8100, ?x220), ?x220 = 016z4k, film(?x858, ?x5721), participant(?x3034, ?x7346), artists(?x2664, ?x6475), artists(?x2664, ?x4609), artists(?x2664, ?x4191), artists(?x2664, ?x3442), artists(?x2664, ?x2987), artists(?x2664, ?x2807), artists(?x2664, ?x2269), artists(?x2664, ?x1800), ?x6475 = 07mvp, award_nominee(?x100, ?x3034), ?x4609 = 0p7h7, location(?x8081, ?x4362), ?x2269 = 02jg92, ?x1800 = 015_30, award(?x8081, ?x537), film(?x8081, ?x1728), participant(?x1503, ?x4611), award_winner(?x2251, ?x7346), ?x94 = 09c7w0, featured_film_locations(?x1728, ?x1036), film(?x1794, ?x2558), film(?x5394, ?x1728), participant(?x858, ?x3422), ?x7909 = 03f3yfj, film(?x4611, ?x1184), ?x2987 = 01vw20_, artist(?x2190, ?x1794), ?x2807 = 03h_fk5, profession(?x8081, ?x1383), participant(?x7346, ?x3070), ?x4191 = 036px, participant(?x7346, ?x2237), participant(?x5443, ?x3034), ?x3442 = 0m_v0 *> conf = 0.33 ranks of expected_values: 19, 1368, 2295, 2622 EVAL 043q4d person 0ck91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 7.000 7.000 0.333 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person EVAL 043q4d person 0gps0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 7.000 7.000 0.333 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person EVAL 043q4d person 0163t3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 7.000 7.000 0.333 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person EVAL 043q4d person 02w5q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 7.000 7.000 0.333 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #5131-04kwbt PRED entity: 04kwbt PRED relation: type_of_union PRED expected values: 04ztj => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.80 #53, 0.80 #65, 0.80 #29), 01g63y (0.20 #6, 0.17 #10, 0.13 #130) >> Best rule #53 for best value: >> intensional similarity = 5 >> extensional distance = 256 >> proper extension: 0dfjb8; 01d5vk; >> query: (?x12741, 04ztj) <- profession(?x12741, ?x1032), profession(?x12741, ?x524), ?x524 = 02jknp, ?x1032 = 02hrh1q, film(?x12741, ?x7415) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04kwbt type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.802 http://example.org/people/person/spouse_s./people/marriage/type_of_union #5130-01j_cy PRED entity: 01j_cy PRED relation: school! PRED expected values: 02d02 0jmjr => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 89): 07l8x (0.33 #64, 0.16 #420, 0.12 #1132), 051vz (0.19 #378, 0.12 #1090, 0.11 #200), 07l4z (0.14 #423, 0.13 #1135, 0.09 #1313), 07147 (0.14 #421, 0.12 #1133, 0.09 #243), 01d5z (0.14 #365, 0.11 #1077, 0.09 #276), 01slc (0.14 #1125, 0.11 #1303, 0.11 #413), 0713r (0.13 #1103, 0.09 #1281, 0.08 #391), 01yhm (0.13 #197, 0.13 #375, 0.12 #1087), 01yjl (0.13 #385, 0.12 #1097, 0.09 #1275), 049n7 (0.13 #367, 0.11 #1079, 0.09 #278) >> Best rule #64 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 08815; >> query: (?x1675, 07l8x) <- student(?x1675, ?x2967), ?x2967 = 02l5rm, school_type(?x1675, ?x1507) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1134 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 111 *> proper extension: 0frm7n; *> query: (?x1675, 02d02) <- school(?x580, ?x1675), position(?x580, ?x261), team(?x5727, ?x580) *> conf = 0.11 ranks of expected_values: 19, 66 EVAL 01j_cy school! 0jmjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 101.000 101.000 0.333 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01j_cy school! 02d02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 101.000 101.000 0.333 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #5129-01tj34 PRED entity: 01tj34 PRED relation: award PRED expected values: 0gqyl => 124 concepts (97 used for prediction) PRED predicted values (max 10 best out of 272): 09sb52 (0.46 #5226, 0.40 #6423, 0.32 #23581), 0fbtbt (0.40 #8208, 0.35 #10203, 0.32 #11002), 0cjyzs (0.38 #901, 0.36 #10877, 0.35 #10078), 05pcn59 (0.36 #4468, 0.31 #6862, 0.30 #5266), 05p09zm (0.22 #5307, 0.21 #6903, 0.20 #4908), 05zr6wv (0.20 #4405, 0.17 #6001, 0.17 #6799), 05ztrmj (0.20 #4569, 0.15 #6963, 0.14 #6165), 03c7tr1 (0.19 #4844, 0.17 #4445, 0.17 #5243), 09qvf4 (0.18 #1403, 0.16 #10375, 0.16 #28731), 027gs1_ (0.16 #10375, 0.16 #28731, 0.14 #30727) >> Best rule #5226 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 023tp8; 01qscs; 01q_ph; 0159h6; 04wqr; 01rr9f; 06cv1; 01kwld; 09wj5; 03m8lq; ... >> query: (?x4119, 09sb52) <- award_nominee(?x1290, ?x4119), celebrity(?x4397, ?x4119), nominated_for(?x4119, ?x697) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #13669 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 318 *> proper extension: 02vkvcz; *> query: (?x4119, 0gqyl) <- nationality(?x4119, ?x94), spouse(?x1872, ?x4119), award(?x4119, ?x154) *> conf = 0.12 ranks of expected_values: 31 EVAL 01tj34 award 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 124.000 97.000 0.455 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5128-0kbf1 PRED entity: 0kbf1 PRED relation: film_release_region PRED expected values: 03rjj => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 142): 06mkj (0.85 #1384, 0.82 #888, 0.82 #1714), 03rjj (0.80 #1326, 0.75 #1491, 0.75 #830), 0jgd (0.79 #1324, 0.77 #828, 0.74 #1489), 07ssc (0.78 #843, 0.75 #1339, 0.72 #1504), 03gj2 (0.75 #853, 0.74 #1349, 0.68 #1514), 035qy (0.72 #1359, 0.68 #863, 0.66 #1524), 01znc_ (0.70 #1367, 0.69 #871, 0.65 #1532), 05qhw (0.67 #841, 0.65 #1337, 0.60 #1502), 0d060g (0.67 #1328, 0.65 #832, 0.63 #1493), 05b4w (0.65 #897, 0.64 #1393, 0.60 #1558) >> Best rule #1384 for best value: >> intensional similarity = 5 >> extensional distance = 203 >> proper extension: 047svrl; >> query: (?x5220, 06mkj) <- nominated_for(?x200, ?x5220), film_release_region(?x5220, ?x1229), film_release_region(?x5220, ?x304), ?x304 = 0d0vqn, ?x1229 = 059j2 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1326 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 203 *> proper extension: 047svrl; *> query: (?x5220, 03rjj) <- nominated_for(?x200, ?x5220), film_release_region(?x5220, ?x1229), film_release_region(?x5220, ?x304), ?x304 = 0d0vqn, ?x1229 = 059j2 *> conf = 0.80 ranks of expected_values: 2 EVAL 0kbf1 film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.854 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5127-015p37 PRED entity: 015p37 PRED relation: award PRED expected values: 05zvj3m => 102 concepts (85 used for prediction) PRED predicted values (max 10 best out of 310): 09sb52 (0.57 #3274, 0.32 #19838, 0.31 #9334), 0fbvqf (0.43 #48, 0.15 #2425, 0.07 #34343), 0cqhk0 (0.21 #37, 0.16 #12562, 0.16 #5694), 05pcn59 (0.20 #9375, 0.18 #4931, 0.17 #10183), 05zr6wv (0.20 #1229, 0.19 #2037, 0.14 #10118), 01by1l (0.19 #13445, 0.18 #13041, 0.15 #17485), 0f4x7 (0.17 #1243, 0.15 #2051, 0.14 #3668), 05p09zm (0.17 #1336, 0.15 #4973, 0.14 #2144), 01bgqh (0.16 #13376, 0.15 #12972, 0.14 #2468), 04kxsb (0.16 #1338, 0.15 #2425, 0.13 #2146) >> Best rule #3274 for best value: >> intensional similarity = 3 >> extensional distance = 92 >> proper extension: 03zqc1; >> query: (?x10919, 09sb52) <- award_nominee(?x10919, ?x4586), award_nominee(?x4586, ?x10161), ?x10161 = 01ggc9 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1305 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 0q9kd; 0p_pd; 0bl2g; 0bxtg; 0mdqp; 0151w_; 04t7ts; 0bwh6; 01k8rb; 0292l3; ... *> query: (?x10919, 05zvj3m) <- award_nominee(?x10919, ?x336), special_performance_type(?x10919, ?x296), nominated_for(?x10919, ?x337) *> conf = 0.14 ranks of expected_values: 38 EVAL 015p37 award 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 102.000 85.000 0.574 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5126-0bk25 PRED entity: 0bk25 PRED relation: entity_involved! PRED expected values: 0727h => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 36): 0dl4z (0.11 #335, 0.11 #5, 0.10 #71), 03jv8d (0.11 #51, 0.10 #117, 0.09 #315), 02cnqk (0.11 #53, 0.10 #119, 0.07 #185), 08821 (0.11 #16, 0.10 #82, 0.07 #148), 031x2 (0.11 #9, 0.10 #75, 0.07 #141), 01hwkn (0.11 #50, 0.10 #116, 0.07 #182), 01gjd0 (0.11 #2, 0.10 #68, 0.07 #134), 01h6pn (0.11 #12, 0.05 #210, 0.04 #276), 03jqfx (0.10 #94, 0.10 #226, 0.09 #292), 048n7 (0.07 #154, 0.07 #352, 0.05 #220) >> Best rule #335 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 03_3d; 0d060g; 0chghy; 03_r3; 0bq0p9; 019rg5; 0k6nt; 03gj2; 07t21; 01mjq; ... >> query: (?x14703, 0dl4z) <- nationality(?x12167, ?x14703), gender(?x12167, ?x231), influenced_by(?x12167, ?x3712), nationality(?x3712, ?x1353) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #255 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 0h3y; 0d0vqn; 06mzp; 035qy; 03gk2; 03b79; 084n_; *> query: (?x14703, 0727h) <- nationality(?x12167, ?x14703), gender(?x12167, ?x231), influenced_by(?x12167, ?x3712), influenced_by(?x7250, ?x3712), ?x7250 = 03sbs *> conf = 0.05 ranks of expected_values: 16 EVAL 0bk25 entity_involved! 0727h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 14.000 14.000 0.111 http://example.org/base/culturalevent/event/entity_involved #5125-024dgj PRED entity: 024dgj PRED relation: artists! PRED expected values: 07v64s => 191 concepts (128 used for prediction) PRED predicted values (max 10 best out of 273): 025sc50 (0.62 #972, 0.46 #10836, 0.35 #13610), 02lnbg (0.50 #981, 0.47 #1907, 0.45 #3759), 05bt6j (0.48 #3126, 0.48 #31175, 0.47 #1892), 0ggx5q (0.47 #1927, 0.38 #1001, 0.35 #3779), 0dl5d (0.47 #8959, 0.46 #7418, 0.45 #11115), 0xhtw (0.45 #7415, 0.38 #8956, 0.36 #11112), 08jyyk (0.41 #7465, 0.39 #11162, 0.34 #9006), 0y3_8 (0.38 #1278, 0.27 #1896, 0.21 #8061), 01243b (0.38 #1273, 0.23 #8324, 0.18 #7440), 0gywn (0.34 #10844, 0.25 #13618, 0.23 #8324) >> Best rule #972 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01q7cb_; >> query: (?x3503, 025sc50) <- artists(?x2937, ?x3503), participant(?x7121, ?x3503), ?x2937 = 0glt670, group(?x3503, ?x5493) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #8991 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 0m19t; 01fl3; 0dtd6; 0167_s; 01qqwp9; 0fcsd; 047cx; 014_lq; 013w2r; 0ycp3; ... *> query: (?x3503, 07v64s) <- artists(?x9063, ?x3503), category(?x3503, ?x134), ?x134 = 08mbj5d, ?x9063 = 0cx7f *> conf = 0.10 ranks of expected_values: 72 EVAL 024dgj artists! 07v64s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 191.000 128.000 0.625 http://example.org/music/genre/artists #5124-03q3sy PRED entity: 03q3sy PRED relation: award_nominee PRED expected values: 0738b8 => 80 concepts (30 used for prediction) PRED predicted values (max 10 best out of 651): 073x6y (0.13 #56172, 0.02 #57722, 0.01 #69422), 0dlglj (0.13 #56172, 0.02 #56508, 0.01 #58848), 05drr9 (0.13 #56172, 0.02 #1335, 0.01 #3677), 07ymr5 (0.13 #56172, 0.02 #414, 0.01 #23821), 03q3sy (0.13 #56172, 0.01 #6078), 09yhzs (0.13 #56172, 0.01 #45151, 0.01 #47491), 013vdl (0.13 #56172), 058s44 (0.13 #56172), 02qfhb (0.13 #56172), 086nl7 (0.13 #56172) >> Best rule #56172 for best value: >> intensional similarity = 3 >> extensional distance = 811 >> proper extension: 03bw6; 05d6q1; >> query: (?x5944, ?x1596) <- nominated_for(?x5944, ?x5945), award_winner(?x2245, ?x5944), film(?x1596, ?x5945) >> conf = 0.13 => this is the best rule for 17 predicted values No rule for expected values ranks of expected_values: EVAL 03q3sy award_nominee 0738b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 30.000 0.131 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5123-0yx74 PRED entity: 0yx74 PRED relation: place! PRED expected values: 0yx74 => 153 concepts (95 used for prediction) PRED predicted values (max 10 best out of 203): 0yx74 (0.22 #9790, 0.21 #13400, 0.18 #15979), 04tgp (0.22 #9790, 0.21 #13400, 0.18 #15979), 0wq3z (0.15 #43332, 0.13 #44884, 0.12 #48498), 0c_m3 (0.14 #132, 0.03 #1677, 0.02 #3222), 013kcv (0.14 #16, 0.03 #1561, 0.02 #3621), 013yq (0.14 #45, 0.02 #2620, 0.02 #3135), 0qkcb (0.14 #214, 0.01 #6395), 0rh7t (0.14 #145, 0.01 #6326), 043yj (0.12 #15463, 0.10 #19590, 0.10 #20623), 0wqwj (0.09 #990, 0.07 #1505, 0.02 #4080) >> Best rule #9790 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 0fm2_; >> query: (?x12883, ?x4622) <- time_zones(?x12883, ?x1638), citytown(?x2821, ?x12883), contains(?x4622, ?x2821), currency(?x2821, ?x170) >> conf = 0.22 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0yx74 place! 0yx74 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 95.000 0.219 http://example.org/location/hud_county_place/place #5122-02_7t PRED entity: 02_7t PRED relation: taxonomy PRED expected values: 04n6k => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.71 #19, 0.70 #38, 0.63 #42) >> Best rule #19 for best value: >> intensional similarity = 11 >> extensional distance = 43 >> proper extension: 01ftz; >> query: (?x7134, 04n6k) <- major_field_of_study(?x4981, ?x7134), major_field_of_study(?x865, ?x7134), ?x4981 = 03bwzr4, institution(?x865, ?x7912), institution(?x865, ?x5983), institution(?x865, ?x4341), institution(?x865, ?x1675), ?x1675 = 01j_cy, colors(?x7912, ?x332), contains(?x94, ?x5983), fraternities_and_sororities(?x4341, ?x3697) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_7t taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.711 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #5121-094tsh6 PRED entity: 094tsh6 PRED relation: gender PRED expected values: 05zppz => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #7, 0.86 #21, 0.84 #13), 02zsn (0.25 #52, 0.25 #89, 0.25 #48) >> Best rule #7 for best value: >> intensional similarity = 2 >> extensional distance = 16 >> proper extension: 03c7ln; 09prnq; 0f0qfz; >> query: (?x9391, 05zppz) <- profession(?x9391, ?x5654), ?x5654 = 02tx6q >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 094tsh6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.889 http://example.org/people/person/gender #5120-02ddq4 PRED entity: 02ddq4 PRED relation: award_winner PRED expected values: 02cx90 => 40 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1325): 0140t7 (0.43 #4447, 0.29 #1979, 0.08 #6916), 02dbp7 (0.43 #1024, 0.29 #3492, 0.07 #5961), 01htxr (0.43 #1373, 0.21 #3841, 0.07 #6310), 02_fj (0.43 #648, 0.21 #3116, 0.06 #4937), 05pdbs (0.43 #241, 0.21 #2709, 0.06 #4937), 026spg (0.43 #1058, 0.21 #3526, 0.06 #4937), 016s0m (0.39 #24695, 0.39 #32102, 0.32 #27164), 01d4cb (0.39 #24695, 0.39 #32102, 0.32 #27164), 01x15dc (0.39 #32102, 0.32 #27164, 0.32 #24694), 0khth (0.39 #32102, 0.32 #27164, 0.32 #24694) >> Best rule #4447 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0gqz2; 02nhxf; 02qvyrt; 054ks3; 099vwn; 02x17c2; 026mmy; >> query: (?x10316, 0140t7) <- award_winner(?x10316, ?x3442), ?x3442 = 0m_v0, ceremony(?x10316, ?x6869), award_winner(?x6869, ?x1128) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #960 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 01bgqh; 01by1l; 025m8l; 03tk6z; 0248jb; *> query: (?x10316, 02cx90) <- award_winner(?x10316, ?x3442), ?x3442 = 0m_v0, ceremony(?x10316, ?x6869), ?x6869 = 01xqqp *> conf = 0.29 ranks of expected_values: 20 EVAL 02ddq4 award_winner 02cx90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 40.000 13.000 0.429 http://example.org/award/award_category/winners./award/award_honor/award_winner #5119-0565cz PRED entity: 0565cz PRED relation: profession PRED expected values: 09jwl 039v1 => 95 concepts (57 used for prediction) PRED predicted values (max 10 best out of 58): 09jwl (0.87 #914, 0.78 #616, 0.77 #3454), 016z4k (0.67 #4, 0.58 #451, 0.52 #749), 02hrh1q (0.66 #4351, 0.64 #6147, 0.64 #5998), 0dz3r (0.54 #598, 0.53 #896, 0.45 #2539), 039v1 (0.54 #633, 0.41 #931, 0.35 #484), 01c72t (0.31 #3759, 0.31 #2264, 0.31 #4211), 0n1h (0.23 #2400, 0.22 #608, 0.21 #757), 01d_h8 (0.23 #6138, 0.23 #5989, 0.22 #7332), 0fnpj (0.20 #3045, 0.19 #2598, 0.19 #2747), 0dxtg (0.18 #7340, 0.16 #6295, 0.16 #6892) >> Best rule #914 for best value: >> intensional similarity = 5 >> extensional distance = 141 >> proper extension: 01zmpg; 01vsykc; 04_jsg; >> query: (?x2964, 09jwl) <- artist(?x2299, ?x2964), instrumentalists(?x2888, ?x2964), instrumentalists(?x716, ?x2964), ?x716 = 018vs, role(?x2888, ?x74) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 0565cz profession 039v1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 95.000 57.000 0.867 http://example.org/people/person/profession EVAL 0565cz profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 57.000 0.867 http://example.org/people/person/profession #5118-03g3w PRED entity: 03g3w PRED relation: major_field_of_study PRED expected values: 05qjt 04rjg => 84 concepts (75 used for prediction) PRED predicted values (max 10 best out of 110): 04rjg (0.82 #1799, 0.82 #1150, 0.81 #573), 04sh3 (0.82 #1799, 0.82 #1150, 0.81 #573), 02822 (0.82 #1799, 0.82 #1150, 0.81 #573), 05qfh (0.50 #670, 0.50 #525, 0.43 #1317), 06ms6 (0.50 #657, 0.43 #1304, 0.33 #1088), 03g3w (0.50 #519, 0.38 #2105, 0.36 #2178), 03qsdpk (0.50 #677, 0.33 #31, 0.25 #604), 064_8sq (0.50 #531, 0.33 #245, 0.20 #1755), 0_jm (0.40 #1761, 0.33 #1113, 0.25 #682), 01mkq (0.33 #1952, 0.33 #1086, 0.33 #80) >> Best rule #1799 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 01540; >> query: (?x2605, ?x254) <- major_field_of_study(?x7545, ?x2605), major_field_of_study(?x6912, ?x2605), ?x7545 = 0bwfn, ?x6912 = 0gl5_, major_field_of_study(?x254, ?x2605), major_field_of_study(?x734, ?x2605) >> conf = 0.82 => this is the best rule for 3 predicted values ranks of expected_values: 1, 12 EVAL 03g3w major_field_of_study 04rjg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 75.000 0.818 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study EVAL 03g3w major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 84.000 75.000 0.818 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #5117-05zl0 PRED entity: 05zl0 PRED relation: student PRED expected values: 02kxbwx 02645b 038w8 => 160 concepts (78 used for prediction) PRED predicted values (max 10 best out of 1670): 01d494 (0.17 #64418, 0.13 #45716, 0.13 #47795), 0nk72 (0.17 #64418, 0.13 #45716, 0.13 #47795), 0bv7t (0.17 #64418, 0.13 #47795, 0.12 #51951), 01hc9_ (0.17 #64418, 0.13 #47795, 0.12 #51951), 01vdrw (0.17 #64418, 0.13 #47795, 0.12 #51951), 0d4jl (0.17 #64418, 0.13 #47795, 0.12 #51951), 083q7 (0.17 #64418, 0.13 #47795, 0.12 #51951), 0gt3p (0.17 #3403, 0.12 #7561, 0.12 #5482), 0crqcc (0.17 #3286, 0.12 #7444, 0.12 #5365), 0ff3y (0.17 #4131, 0.12 #33224, 0.12 #8289) >> Best rule #64418 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 0194_r; >> query: (?x6056, ?x1159) <- student(?x6056, ?x4240), company(?x1159, ?x6056), people(?x5741, ?x4240) >> conf = 0.17 => this is the best rule for 7 predicted values No rule for expected values ranks of expected_values: EVAL 05zl0 student 038w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 78.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 05zl0 student 02645b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 78.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 05zl0 student 02kxbwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 78.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #5116-06xpp7 PRED entity: 06xpp7 PRED relation: state_province_region PRED expected values: 01n7q => 95 concepts (82 used for prediction) PRED predicted values (max 10 best out of 72): 01n7q (0.59 #387, 0.55 #510, 0.33 #141), 059rby (0.57 #620, 0.38 #993, 0.33 #1243), 0rh6k (0.33 #1, 0.08 #247, 0.03 #866), 0f2wj (0.24 #3099, 0.23 #3725, 0.23 #3474), 09c7w0 (0.24 #3099, 0.23 #3725, 0.23 #3474), 030qb3t (0.16 #4599, 0.15 #3476, 0.13 #5844), 07c5l (0.16 #4599, 0.15 #3476, 0.13 #5844), 0kpys (0.16 #4599, 0.15 #3476, 0.13 #5844), 05k7sb (0.15 #277, 0.06 #1394, 0.06 #1518), 05tbn (0.08 #297, 0.04 #2279, 0.04 #3026) >> Best rule #387 for best value: >> intensional similarity = 2 >> extensional distance = 32 >> proper extension: 0kc6x; 016tt2; 0338lq; 046b0s; 024rgt; 0cjdk; 0k9ctht; 01w5gp; 06nzl; 0c41qv; ... >> query: (?x5522, 01n7q) <- citytown(?x5522, ?x1523), ?x1523 = 030qb3t >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06xpp7 state_province_region 01n7q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 82.000 0.588 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #5115-03q3x5 PRED entity: 03q3x5 PRED relation: award PRED expected values: 0cqhk0 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 267): 0ck27z (0.32 #8133, 0.30 #4112, 0.29 #5318), 09sb52 (0.31 #16122, 0.28 #19338, 0.28 #16524), 0cqhk0 (0.29 #37, 0.18 #5263, 0.18 #8078), 0c_dx (0.28 #677, 0.02 #3089, 0.02 #3491), 0grw_ (0.22 #715), 04hddx (0.17 #768, 0.02 #3180, 0.02 #3582), 0ddd9 (0.17 #458, 0.02 #10911, 0.01 #14127), 0cjyzs (0.14 #106, 0.14 #5629, 0.14 #22112), 09qrn4 (0.14 #238, 0.14 #22112, 0.13 #33373), 09qv3c (0.14 #51, 0.14 #22112, 0.13 #33373) >> Best rule #8133 for best value: >> intensional similarity = 3 >> extensional distance = 540 >> proper extension: 01r42_g; 02zq43; 0f830f; 06n7h7; 08w7vj; 01v3s2_; 0bz5v2; 04cf09; 08m4c8; 07ymr5; ... >> query: (?x10127, 0ck27z) <- award_nominee(?x436, ?x10127), gender(?x10127, ?x514), actor(?x2436, ?x10127) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 0cjdk; *> query: (?x10127, 0cqhk0) <- award_winner(?x2436, ?x10127), ?x2436 = 02hct1 *> conf = 0.29 ranks of expected_values: 3 EVAL 03q3x5 award 0cqhk0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 107.000 107.000 0.325 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5114-049xgc PRED entity: 049xgc PRED relation: film! PRED expected values: 054g1r => 117 concepts (61 used for prediction) PRED predicted values (max 10 best out of 72): 0jz9f (0.68 #2509, 0.50 #3633, 0.49 #2138), 017s11 (0.25 #2, 0.20 #883, 0.17 #223), 05qd_ (0.23 #154, 0.22 #448, 0.19 #374), 07k2x (0.20 #628, 0.18 #775, 0.17 #848), 016tw3 (0.18 #1554, 0.17 #2965, 0.16 #1407), 03xq0f (0.15 #77, 0.15 #1179, 0.14 #150), 0g1rw (0.15 #80, 0.09 #887, 0.08 #300), 04mkft (0.15 #108, 0.07 #475, 0.07 #988), 01gb54 (0.14 #248, 0.09 #1572, 0.09 #174), 04y8r (0.14 #2508, 0.05 #221, 0.05 #3030) >> Best rule #2509 for best value: >> intensional similarity = 2 >> extensional distance = 341 >> proper extension: 04bp0l; >> query: (?x5648, ?x166) <- nominated_for(?x166, ?x5648), film(?x166, ?x167) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2840 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 376 *> proper extension: 043tvp3; 09v3jyg; *> query: (?x5648, 054g1r) <- film_crew_role(?x5648, ?x137), executive_produced_by(?x5648, ?x3101), film(?x382, ?x5648) *> conf = 0.08 ranks of expected_values: 29 EVAL 049xgc film! 054g1r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 117.000 61.000 0.682 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5113-02xlf PRED entity: 02xlf PRED relation: disciplines_or_subjects! PRED expected values: 0265vt 01tgwv 01bb1c => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 97): 0265vt (0.71 #1190, 0.50 #1101, 0.50 #572), 01bb1c (0.57 #1226, 0.50 #1137, 0.50 #608), 058bzgm (0.50 #588, 0.43 #1206, 0.39 #1146), 0262s1 (0.40 #754, 0.39 #1146, 0.33 #1107), 047xyn (0.40 #817, 0.39 #1146, 0.33 #1082), 04zngls (0.40 #741, 0.33 #1094, 0.33 #125), 02x0gk1 (0.40 #685, 0.33 #1038, 0.25 #509), 01tgwv (0.39 #1146, 0.33 #232, 0.29 #1202), 02r0d0 (0.39 #1146, 0.33 #253, 0.20 #869), 0j6j8 (0.39 #1146, 0.33 #223, 0.20 #839) >> Best rule #1190 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 014dfn; >> query: (?x6647, 0265vt) <- disciplines_or_subjects(?x6687, ?x6647), disciplines_or_subjects(?x575, ?x6647), award(?x9982, ?x6687), award(?x4417, ?x6687), ?x4417 = 04mhl, award(?x4895, ?x575), ?x9982 = 05qzv, ?x4895 = 0klw >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 8 EVAL 02xlf disciplines_or_subjects! 01bb1c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.714 http://example.org/award/award_category/disciplines_or_subjects EVAL 02xlf disciplines_or_subjects! 01tgwv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 74.000 74.000 0.714 http://example.org/award/award_category/disciplines_or_subjects EVAL 02xlf disciplines_or_subjects! 0265vt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.714 http://example.org/award/award_category/disciplines_or_subjects #5112-04s5_s PRED entity: 04s5_s PRED relation: profession PRED expected values: 09jwl => 92 concepts (74 used for prediction) PRED predicted values (max 10 best out of 121): 09jwl (0.84 #1316, 0.82 #2329, 0.81 #1461), 02hrh1q (0.60 #7236, 0.60 #8248, 0.60 #8104), 039v1 (0.56 #1332, 0.40 #2345, 0.40 #1477), 016z4k (0.45 #2895, 0.45 #1301, 0.45 #5204), 09lbv (0.40 #451, 0.11 #2475, 0.10 #1462), 0dxtg (0.32 #156, 0.29 #8391, 0.29 #6658), 01d_h8 (0.27 #8384, 0.27 #6651, 0.27 #7228), 03gjzk (0.21 #158, 0.21 #5504, 0.21 #7237), 02jknp (0.21 #151, 0.19 #8386, 0.18 #6653), 05vyk (0.21 #90, 0.15 #1678, 0.11 #954) >> Best rule #1316 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 015196; >> query: (?x12557, 09jwl) <- profession(?x12557, ?x131), role(?x12557, ?x227), artists(?x302, ?x12557), ?x227 = 0342h >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04s5_s profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 74.000 0.844 http://example.org/people/person/profession #5111-01w_10 PRED entity: 01w_10 PRED relation: place_of_birth PRED expected values: 0mpbx => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 193): 0mpbx (0.39 #45808, 0.34 #51448, 0.33 #48629), 059rby (0.32 #71896, 0.28 #71895, 0.27 #55674), 0f94t (0.25 #732, 0.14 #2140, 0.02 #17636), 02_286 (0.17 #57105, 0.16 #59925, 0.15 #12696), 0d6lp (0.14 #2932, 0.07 #45216, 0.06 #8566), 01snm (0.14 #3057, 0.06 #8691, 0.05 #9395), 071cn (0.14 #2953, 0.06 #8587, 0.05 #9291), 03b12 (0.14 #3225, 0.03 #25064, 0.02 #29993), 031y2 (0.14 #2476, 0.02 #19383, 0.01 #24316), 01cx_ (0.12 #4335, 0.10 #5744, 0.10 #5039) >> Best rule #45808 for best value: >> intensional similarity = 3 >> extensional distance = 182 >> proper extension: 01mvpv; >> query: (?x8122, ?x11240) <- location(?x8122, ?x11240), currency(?x11240, ?x170), country(?x11240, ?x94) >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w_10 place_of_birth 0mpbx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.389 http://example.org/people/person/place_of_birth #5110-02ghq PRED entity: 02ghq PRED relation: influenced_by PRED expected values: 037jz => 126 concepts (44 used for prediction) PRED predicted values (max 10 best out of 322): 081k8 (0.56 #3588, 0.35 #2300, 0.26 #3159), 041h0 (0.36 #1727, 0.25 #439, 0.22 #868), 037jz (0.33 #206, 0.27 #1923, 0.25 #635), 09dt7 (0.33 #889, 0.27 #1748, 0.25 #460), 040db (0.27 #1772, 0.25 #484, 0.12 #2630), 03j0d (0.25 #760, 0.22 #1189, 0.19 #2906), 03_87 (0.25 #2345, 0.21 #4062, 0.18 #3204), 02lt8 (0.25 #547, 0.20 #2264, 0.14 #8282), 03j2gxx (0.25 #807, 0.18 #2095, 0.11 #1236), 03hpr (0.25 #770, 0.18 #2058, 0.11 #1199) >> Best rule #3588 for best value: >> intensional similarity = 5 >> extensional distance = 46 >> proper extension: 03j90; >> query: (?x10978, 081k8) <- gender(?x10978, ?x231), influenced_by(?x10978, ?x587), profession(?x587, ?x6476), ?x6476 = 025352, location(?x10978, ?x1227) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #206 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 042xh; *> query: (?x10978, 037jz) <- influenced_by(?x10978, ?x6796), ?x6796 = 01wd02c, location(?x10978, ?x1227), category(?x10978, ?x134) *> conf = 0.33 ranks of expected_values: 3 EVAL 02ghq influenced_by 037jz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 44.000 0.562 http://example.org/influence/influence_node/influenced_by #5109-02k1pr PRED entity: 02k1pr PRED relation: nominated_for! PRED expected values: 054krc 0k611 02ppm4q => 85 concepts (64 used for prediction) PRED predicted values (max 10 best out of 203): 04kxsb (0.70 #1176, 0.66 #10350, 0.66 #10587), 0gq9h (0.64 #530, 0.52 #2411, 0.49 #1471), 019f4v (0.62 #522, 0.46 #1463, 0.46 #2403), 0gs9p (0.56 #532, 0.49 #2413, 0.48 #1473), 0gqy2 (0.49 #589, 0.36 #2470, 0.35 #1530), 0gr4k (0.47 #495, 0.41 #1201, 0.41 #1436), 0k611 (0.45 #541, 0.37 #776, 0.35 #2422), 02ppm4q (0.44 #2934, 0.35 #1289, 0.35 #2464), 0f4x7 (0.37 #2375, 0.37 #1435, 0.36 #1200), 040njc (0.36 #477, 0.36 #2828, 0.36 #1183) >> Best rule #1176 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 019kyn; >> query: (?x8456, ?x2375) <- genre(?x8456, ?x811), award_winner(?x8456, ?x1119), ?x811 = 03k9fj, award(?x8456, ?x2375) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #541 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 53 *> proper extension: 0m_mm; 011yqc; 0b1y_2; 0ctb4g; 0pd57; 0cq7tx; 049xgc; 011ywj; 01fwzk; 0kt_4; ... *> query: (?x8456, 0k611) <- nominated_for(?x1972, ?x8456), nominated_for(?x484, ?x8456), ?x1972 = 0gqyl, film(?x2378, ?x8456), ?x484 = 0gq_v *> conf = 0.45 ranks of expected_values: 7, 8, 21 EVAL 02k1pr nominated_for! 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 64.000 0.702 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02k1pr nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 64.000 0.702 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02k1pr nominated_for! 054krc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 85.000 64.000 0.702 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5108-03q45x PRED entity: 03q45x PRED relation: student! PRED expected values: 03ksy => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 162): 08815 (0.20 #2, 0.06 #28408, 0.04 #26304), 033gn8 (0.20 #378, 0.02 #11424, 0.02 #12476), 01fpvz (0.20 #11), 07vhb (0.15 #3325, 0.14 #695, 0.11 #1221), 07vyf (0.14 #664, 0.11 #1190, 0.10 #2242), 01hb1t (0.14 #617, 0.11 #1143, 0.10 #1669), 0g8rj (0.14 #702, 0.11 #1228, 0.08 #3332), 0bwfn (0.12 #28681, 0.09 #4483, 0.09 #11321), 03ksy (0.11 #8522, 0.10 #10100, 0.09 #2736), 065y4w7 (0.10 #2118, 0.09 #2644, 0.07 #28420) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 019y64; >> query: (?x7795, 08815) <- student(?x10945, ?x7795), award_winner(?x2597, ?x7795), ?x10945 = 01jsk6 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #8522 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 149 *> proper extension: 04pg29; 023qfd; 01z0lb; *> query: (?x7795, 03ksy) <- profession(?x7795, ?x987), tv_program(?x7795, ?x3630), award(?x7795, ?x757) *> conf = 0.11 ranks of expected_values: 9 EVAL 03q45x student! 03ksy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 133.000 133.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #5107-01y9pk PRED entity: 01y9pk PRED relation: colors PRED expected values: 01g5v => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 20): 01g5v (0.40 #3, 0.35 #243, 0.31 #343), 083jv (0.39 #621, 0.37 #801, 0.36 #1901), 06fvc (0.31 #162, 0.16 #622, 0.16 #1802), 019sc (0.29 #87, 0.23 #187, 0.23 #167), 036k5h (0.22 #145, 0.09 #365, 0.08 #1805), 02rnmb (0.20 #13, 0.14 #93, 0.12 #253), 03vtbc (0.15 #188, 0.11 #148, 0.08 #2501), 088fh (0.14 #106, 0.14 #86, 0.07 #426), 04mkbj (0.14 #110, 0.11 #130, 0.10 #630), 038hg (0.12 #652, 0.12 #252, 0.11 #632) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01jssp; 07vht; 07vjm; >> query: (?x2243, 01g5v) <- contains(?x279, ?x2243), adjoins(?x2243, ?x11993), organization(?x5510, ?x2243), ?x5510 = 07xl34 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y9pk colors 01g5v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.400 http://example.org/education/educational_institution/colors #5106-0cbkc PRED entity: 0cbkc PRED relation: gender PRED expected values: 02zsn => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #267, 0.71 #263, 0.71 #223), 02zsn (0.60 #4, 0.52 #14, 0.46 #54) >> Best rule #267 for best value: >> intensional similarity = 3 >> extensional distance = 2584 >> proper extension: 0c11mj; 071pf2; 0cm03; 0457w0; 02rnns; 0frmb1; 04mx7s; 07zr66; 019g65; 0jrg; ... >> query: (?x8888, 05zppz) <- type_of_union(?x8888, ?x566), ?x566 = 04ztj, nationality(?x8888, ?x789) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 0hskw; *> query: (?x8888, 02zsn) <- award_winner(?x1421, ?x8888), languages(?x8888, ?x90), spouse(?x8888, ?x7261) *> conf = 0.60 ranks of expected_values: 2 EVAL 0cbkc gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 141.000 0.718 http://example.org/people/person/gender #5105-0m2gk PRED entity: 0m2gk PRED relation: contains! PRED expected values: 01x73 => 71 concepts (12 used for prediction) PRED predicted values (max 10 best out of 53): 09c7w0 (0.66 #3597, 0.55 #4497, 0.39 #8094), 04_1l0v (0.61 #4045, 0.45 #4945, 0.28 #5845), 01x73 (0.50 #1797, 0.50 #1013, 0.39 #8094), 0m2gk (0.39 #8094, 0.18 #5393, 0.16 #1798), 07z1m (0.29 #1890, 0.08 #7287, 0.02 #9087), 05k7sb (0.26 #1931, 0.20 #2829, 0.09 #4627), 07c5l (0.25 #395, 0.17 #1293, 0.07 #3091), 029jpy (0.25 #216, 0.12 #2912, 0.08 #1114), 01n7q (0.15 #7273, 0.13 #9073, 0.13 #6373), 07ssc (0.10 #9926) >> Best rule #3597 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 027rqbx; 02v3m7; 0k_s5; 08xpv_; 041_3z; >> query: (?x3164, 09c7w0) <- contains(?x3164, ?x9445), contains(?x3164, ?x3163), time_zones(?x3163, ?x2674), currency(?x9445, ?x170), contains(?x9445, ?x7271) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #1797 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0f2nf; 0f1sm; 01p726; *> query: (?x3164, ?x1755) <- contains(?x3164, ?x3163), contains(?x1755, ?x3163), ?x1755 = 01x73 *> conf = 0.50 ranks of expected_values: 3 EVAL 0m2gk contains! 01x73 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 12.000 0.661 http://example.org/location/location/contains #5104-0g28b1 PRED entity: 0g28b1 PRED relation: profession PRED expected values: 02hrh1q => 83 concepts (82 used for prediction) PRED predicted values (max 10 best out of 47): 02hrh1q (0.79 #163, 0.69 #6870, 0.68 #2399), 03gjzk (0.72 #462, 0.72 #313, 0.69 #611), 01d_h8 (0.52 #1347, 0.34 #304, 0.33 #453), 02jknp (0.44 #1349, 0.29 #2087, 0.21 #157), 0cbd2 (0.23 #1050, 0.23 #603, 0.21 #156), 02krf9 (0.23 #474, 0.22 #325, 0.22 #623), 09jwl (0.21 #168, 0.18 #3448, 0.17 #4193), 0nbcg (0.21 #181, 0.12 #3461, 0.11 #7037), 0kyk (0.21 #179, 0.12 #1371, 0.09 #7184), 018gz8 (0.19 #613, 0.18 #1060, 0.17 #911) >> Best rule #163 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 04dyqk; 05wm88; >> query: (?x4146, 02hrh1q) <- place_of_birth(?x4146, ?x6769), award(?x4146, ?x2720), ?x6769 = 0f2tj >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g28b1 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 82.000 0.786 http://example.org/people/person/profession #5103-01m_zd PRED entity: 01m_zd PRED relation: service_language PRED expected values: 02h40lc => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 70): 02h40lc (0.97 #403, 0.97 #526, 0.96 #607), 064_8sq (0.60 #69, 0.38 #290, 0.38 #250), 06nm1 (0.50 #125, 0.43 #105, 0.40 #65), 01r2l (0.40 #71, 0.24 #171, 0.19 #131), 05zjd (0.30 #72, 0.19 #132, 0.18 #92), 02bjrlw (0.23 #565, 0.21 #502, 0.20 #646), 03_9r (0.23 #565, 0.21 #502, 0.20 #646), 03115z (0.23 #565, 0.21 #502, 0.20 #646), 02bv9 (0.23 #565, 0.21 #502, 0.20 #646), 06b_j (0.20 #70, 0.12 #170, 0.09 #251) >> Best rule #403 for best value: >> intensional similarity = 9 >> extensional distance = 60 >> proper extension: 0g5lhl7; >> query: (?x11892, 02h40lc) <- service_language(?x11892, ?x732), industry(?x11892, ?x1605), contact_category(?x11892, ?x897), major_field_of_study(?x735, ?x732), countries_spoken_in(?x732, ?x172), language(?x5960, ?x732), language(?x2506, ?x732), ?x5960 = 0f4_2k, ?x2506 = 01kf4tt >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m_zd service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.968 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #5102-028k57 PRED entity: 028k57 PRED relation: languages PRED expected values: 02h40lc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.26 #470, 0.26 #431, 0.26 #509), 064_8sq (0.04 #93, 0.03 #327, 0.03 #561), 03k50 (0.03 #394, 0.03 #355, 0.02 #823), 02bjrlw (0.02 #430, 0.01 #547, 0.01 #1015), 07c9s (0.01 #403, 0.01 #832, 0.01 #13), 04306rv (0.01 #471, 0.01 #510), 0999q (0.01 #23), 06mp7 (0.01 #11), 06nm1 (0.01 #6), 03_9r (0.01 #356) >> Best rule #470 for best value: >> intensional similarity = 3 >> extensional distance = 530 >> proper extension: 034ls; 01npcy7; 017f4y; 01xwqn; 01g0jn; >> query: (?x4478, 02h40lc) <- profession(?x4478, ?x1032), participant(?x4478, ?x3917), ?x1032 = 02hrh1q >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028k57 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.261 http://example.org/people/person/languages #5101-04fyhv PRED entity: 04fyhv PRED relation: gender PRED expected values: 05zppz => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #21, 0.88 #13, 0.86 #27), 02zsn (0.25 #94, 0.24 #96, 0.24 #86) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 152 >> proper extension: 0g_rs_; >> query: (?x8208, 05zppz) <- produced_by(?x6429, ?x8208), executive_produced_by(?x3640, ?x8208), country(?x6429, ?x94) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04fyhv gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.877 http://example.org/people/person/gender #5100-06lpmt PRED entity: 06lpmt PRED relation: film! PRED expected values: 030h95 019pm_ 0zcbl 02jr26 => 65 concepts (33 used for prediction) PRED predicted values (max 10 best out of 618): 031rx9 (0.48 #10375, 0.45 #31123, 0.45 #20750), 01wbg84 (0.12 #47, 0.06 #2122, 0.02 #4196), 01p7yb (0.12 #53, 0.03 #2128, 0.01 #6277), 01swck (0.12 #799, 0.03 #2874, 0.01 #60971), 01nm3s (0.12 #688, 0.03 #6912, 0.01 #2763), 0170pk (0.12 #279, 0.02 #6503, 0.02 #16879), 016zp5 (0.12 #974, 0.02 #11349, 0.02 #15499), 02js_6 (0.12 #1966, 0.01 #4041, 0.01 #12341), 02qfhb (0.12 #873, 0.01 #2948, 0.01 #7097), 02661h (0.12 #1390, 0.01 #3465, 0.01 #5539) >> Best rule #10375 for best value: >> intensional similarity = 4 >> extensional distance = 297 >> proper extension: 08cx5g; >> query: (?x4130, ?x986) <- nominated_for(?x986, ?x4130), titles(?x2480, ?x4130), titles(?x2480, ?x370), ?x370 = 0ddfwj1 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #7439 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 282 *> proper extension: 0ddd0gc; 01fx1l; 0qmk5; 0ph24; 03ctqqf; *> query: (?x4130, 0zcbl) <- nominated_for(?x4129, ?x4130), nominated_for(?x1862, ?x4130), film(?x4129, ?x1009), award_nominee(?x4129, ?x2246) *> conf = 0.02 ranks of expected_values: 151, 420 EVAL 06lpmt film! 02jr26 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 33.000 0.476 http://example.org/film/actor/film./film/performance/film EVAL 06lpmt film! 0zcbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 65.000 33.000 0.476 http://example.org/film/actor/film./film/performance/film EVAL 06lpmt film! 019pm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 33.000 0.476 http://example.org/film/actor/film./film/performance/film EVAL 06lpmt film! 030h95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 65.000 33.000 0.476 http://example.org/film/actor/film./film/performance/film #5099-04hwbq PRED entity: 04hwbq PRED relation: nominated_for! PRED expected values: 0f_nbyh => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 185): 09tqxt (0.72 #917, 0.67 #4357, 0.67 #3898), 0gr51 (0.55 #73, 0.25 #2064, 0.25 #2063), 0l8z1 (0.45 #49, 0.28 #736, 0.23 #3258), 09sb52 (0.38 #491, 0.13 #3242, 0.12 #3471), 0f_nbyh (0.38 #466, 0.11 #3217, 0.09 #3676), 019f4v (0.37 #3260, 0.34 #3719, 0.33 #3489), 0gs9p (0.37 #3268, 0.35 #3497, 0.35 #3727), 0p9sw (0.36 #21, 0.27 #250, 0.26 #3230), 0k611 (0.32 #3277, 0.31 #526, 0.29 #3736), 040njc (0.32 #3216, 0.28 #3675, 0.27 #4134) >> Best rule #917 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 04lqvly; >> query: (?x1259, ?x1723) <- nominated_for(?x3911, ?x1259), award(?x1259, ?x1723), ?x3911 = 02x1z2s >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #466 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 02r5qtm; 04f6hhm; *> query: (?x1259, 0f_nbyh) <- nominated_for(?x601, ?x1259), honored_for(?x1553, ?x1259), ?x1553 = 0g5b0q5 *> conf = 0.38 ranks of expected_values: 5 EVAL 04hwbq nominated_for! 0f_nbyh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 77.000 0.724 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5098-0cjdk PRED entity: 0cjdk PRED relation: program PRED expected values: 01qn7n 0n2bh 03y3bp7 0vhm => 172 concepts (156 used for prediction) PRED predicted values (max 10 best out of 224): 04glx0 (0.40 #534, 0.27 #979, 0.25 #3866), 097h2 (0.30 #584, 0.27 #1029, 0.25 #1252), 017dbx (0.25 #1318, 0.23 #2206, 0.20 #650), 043qqt5 (0.23 #2183, 0.19 #8631, 0.19 #3959), 01hn_t (0.23 #10281, 0.13 #13172, 0.12 #3831), 0q9jk (0.20 #565, 0.18 #1010, 0.17 #1233), 01fs__ (0.20 #549, 0.18 #994, 0.17 #1217), 034fl9 (0.20 #582, 0.18 #1027, 0.17 #1250), 01b66d (0.20 #479, 0.18 #924, 0.17 #1147), 0bx_hnp (0.17 #1938, 0.15 #2160, 0.14 #2604) >> Best rule #534 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06jntd; >> query: (?x2554, 04glx0) <- state_province_region(?x2554, ?x1227), award_winner(?x10089, ?x2554), genre(?x10089, ?x53) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #6960 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 18 *> proper extension: 05xbx; 0hm10; *> query: (?x2554, 0vhm) <- program(?x2554, ?x8396), program(?x2554, ?x4761), program(?x2554, ?x3180), award_winner(?x3180, ?x1394), actor(?x4761, ?x11603), producer_type(?x8396, ?x632) *> conf = 0.15 ranks of expected_values: 19 EVAL 0cjdk program 0vhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 172.000 156.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program EVAL 0cjdk program 03y3bp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 156.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program EVAL 0cjdk program 0n2bh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 156.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program EVAL 0cjdk program 01qn7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 172.000 156.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #5097-0gxtknx PRED entity: 0gxtknx PRED relation: film_release_region PRED expected values: 04gzd 03rj0 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 144): 03h64 (0.91 #355, 0.90 #1092, 0.77 #1239), 01znc_ (0.88 #1070, 0.81 #333, 0.72 #1217), 07ssc (0.84 #309, 0.80 #1046, 0.78 #1193), 03_3d (0.75 #300, 0.75 #1037, 0.75 #1184), 03rj0 (0.73 #1087, 0.66 #350, 0.63 #1234), 05v8c (0.71 #1047, 0.69 #310, 0.57 #1194), 04gzd (0.70 #1040, 0.69 #303, 0.50 #1187), 01mjq (0.69 #336, 0.63 #1073, 0.57 #1220), 01ls2 (0.69 #306, 0.54 #1043, 0.46 #1190), 03rk0 (0.62 #346, 0.62 #1083, 0.44 #1230) >> Best rule #355 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0407yfx; >> query: (?x1602, 03h64) <- film_release_region(?x1602, ?x1917), film(?x494, ?x1602), crewmember(?x1602, ?x5664), ?x1917 = 01p1v >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #1087 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 121 *> proper extension: 0fq27fp; *> query: (?x1602, 03rj0) <- film_release_region(?x1602, ?x1497), film_release_region(?x1602, ?x789), ?x789 = 0f8l9c, genre(?x1602, ?x258), ?x1497 = 015qh *> conf = 0.73 ranks of expected_values: 5, 7 EVAL 0gxtknx film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 75.000 75.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gxtknx film_release_region 04gzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 75.000 75.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5096-0g2c8 PRED entity: 0g2c8 PRED relation: organization! PRED expected values: 0dq_5 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 10): 060c4 (0.77 #171, 0.77 #158, 0.76 #470), 0dq_5 (0.49 #243, 0.41 #659, 0.41 #685), 07xl34 (0.14 #1559, 0.14 #1130, 0.13 #1117), 05k17c (0.10 #839, 0.09 #878, 0.08 #852), 05c0jwl (0.05 #850, 0.04 #889, 0.04 #968), 0hm4q (0.04 #1075, 0.04 #1556, 0.04 #1114), 0dq3c (0.02 #235, 0.02 #300, 0.02 #391), 08jcfy (0.02 #1131, 0.02 #1118, 0.02 #1170), 0krdk (0.01 #445), 09d6p2 (0.01 #478, 0.01 #517, 0.01 #530) >> Best rule #171 for best value: >> intensional similarity = 6 >> extensional distance = 29 >> proper extension: 08qs09; >> query: (?x1091, 060c4) <- citytown(?x1091, ?x4090), registering_agency(?x1091, ?x1982), location(?x971, ?x4090), profession(?x971, ?x1032), state_province_region(?x1091, ?x177), county_seat(?x6689, ?x4090) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #243 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 43 *> proper extension: 05b5c; *> query: (?x1091, 0dq_5) <- citytown(?x1091, ?x4090), currency(?x1091, ?x170), ?x170 = 09nqf, place_founded(?x10926, ?x4090), time_zones(?x4090, ?x2674) *> conf = 0.49 ranks of expected_values: 2 EVAL 0g2c8 organization! 0dq_5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 130.000 130.000 0.774 http://example.org/organization/role/leaders./organization/leadership/organization #5095-0341n5 PRED entity: 0341n5 PRED relation: award PRED expected values: 0gqy2 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 235): 0gqy2 (0.67 #1353, 0.14 #21496, 0.13 #5731), 027dtxw (0.48 #1198, 0.10 #800, 0.08 #5576), 0f4x7 (0.44 #1224, 0.12 #4408, 0.10 #6000), 0ck27z (0.33 #88, 0.16 #29458, 0.15 #10039), 0bdwqv (0.33 #1361, 0.18 #19903, 0.17 #167), 02x4w6g (0.27 #1304, 0.18 #19903, 0.16 #29458), 04kxsb (0.27 #1314, 0.17 #120, 0.09 #4498), 09qv_s (0.27 #1340, 0.14 #21496, 0.13 #31449), 02w9sd7 (0.27 #1359, 0.14 #21496, 0.12 #32644), 05pcn59 (0.25 #873, 0.19 #1271, 0.11 #1669) >> Best rule #1353 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 05bnp0; 02qgqt; 014zcr; 01qscs; 09fb5; 0z4s; 018db8; 032_jg; 015grj; 0151w_; ... >> query: (?x10317, 0gqy2) <- award(?x10317, ?x451), film(?x10317, ?x1511), ?x451 = 099jhq >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0341n5 award 0gqy2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.673 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5094-040981l PRED entity: 040981l PRED relation: award_winner! PRED expected values: 0g55tzk => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 98): 0g55tzk (0.47 #276, 0.42 #136, 0.38 #416), 04n2r9h (0.17 #4063, 0.17 #4484, 0.17 #1402), 09gkdln (0.17 #4063, 0.17 #4484, 0.17 #1402), 0g5b0q5 (0.13 #981, 0.08 #19, 0.07 #159), 0gvstc3 (0.13 #981, 0.03 #2975, 0.03 #2555), 0hn821n (0.13 #981, 0.02 #550, 0.02 #2652), 0lp_cd3 (0.13 #981, 0.01 #2544, 0.01 #862), 03gyp30 (0.08 #116, 0.07 #256, 0.07 #676), 092t4b (0.08 #51, 0.07 #191, 0.06 #331), 0clfdj (0.08 #4, 0.07 #144, 0.06 #284) >> Best rule #276 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 03yj_0n; 07s8hms; 02sb1w; >> query: (?x7959, 0g55tzk) <- award_winner(?x7959, ?x561), ?x561 = 027dtv3, award_nominee(?x1669, ?x7959) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 040981l award_winner! 0g55tzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.467 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5093-02mj7c PRED entity: 02mj7c PRED relation: student PRED expected values: 057d89 0lzkm => 181 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1978): 073v6 (0.22 #4688, 0.22 #2605, 0.05 #17188), 013pp3 (0.17 #917, 0.11 #5083, 0.11 #3000), 0ff3y (0.17 #2060, 0.09 #14558, 0.05 #16641), 03ft8 (0.17 #254, 0.09 #12752, 0.05 #14835), 04ld94 (0.17 #1011, 0.07 #9343, 0.03 #13509), 0dbpyd (0.17 #12, 0.07 #8344, 0.03 #12510), 01pqy_ (0.17 #891, 0.06 #13389, 0.05 #15472), 02cyfz (0.17 #331, 0.06 #12829, 0.05 #14912), 01wwvt2 (0.17 #361, 0.06 #12859, 0.05 #14942), 04hw4b (0.17 #1225, 0.06 #13723, 0.05 #15806) >> Best rule #4688 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0178g; 0537b; 02ktt7; >> query: (?x5149, 073v6) <- state_province_region(?x5149, ?x3818), currency(?x5149, ?x170), organization(?x346, ?x5149), ?x3818 = 03v0t >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02mj7c student 0lzkm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 107.000 0.222 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 02mj7c student 057d89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 107.000 0.222 http://example.org/education/educational_institution/students_graduates./education/education/student #5092-0trv PRED entity: 0trv PRED relation: school! PRED expected values: 06x68 05g76 0x2p 03m1n => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 85): 0jmk7 (0.40 #167, 0.25 #422, 0.20 #592), 01slc (0.27 #308, 0.20 #138, 0.18 #648), 01yjl (0.27 #536, 0.25 #366, 0.17 #876), 01d5z (0.27 #518, 0.25 #348, 0.17 #858), 07147 (0.25 #401, 0.21 #486, 0.20 #571), 0cqt41 (0.25 #355, 0.21 #440, 0.20 #525), 02c_4 (0.25 #400, 0.20 #570, 0.14 #485), 07l2m (0.25 #381, 0.20 #551, 0.12 #2128), 0jmnl (0.21 #509, 0.20 #169, 0.18 #339), 06wpc (0.21 #484, 0.17 #399, 0.13 #569) >> Best rule #167 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 08815; 06pwq; 09f2j; >> query: (?x8706, 0jmk7) <- major_field_of_study(?x8706, ?x3490), school(?x7725, ?x8706), ?x7725 = 07l8x, student(?x8706, ?x1817), ?x3490 = 05qfh >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #103 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 08815; 06pwq; 09f2j; *> query: (?x8706, 05g76) <- major_field_of_study(?x8706, ?x3490), school(?x7725, ?x8706), ?x7725 = 07l8x, student(?x8706, ?x1817), ?x3490 = 05qfh *> conf = 0.20 ranks of expected_values: 12, 34, 72, 81 EVAL 0trv school! 03m1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 199.000 199.000 0.400 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0trv school! 0x2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 199.000 199.000 0.400 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0trv school! 05g76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 199.000 199.000 0.400 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0trv school! 06x68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 199.000 199.000 0.400 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #5091-01pl9g PRED entity: 01pl9g PRED relation: spouse! PRED expected values: 05kh_ => 191 concepts (110 used for prediction) PRED predicted values (max 10 best out of 216): 05kh_ (0.84 #16475, 0.83 #21304, 0.83 #20497), 01933d (0.33 #293, 0.20 #1499, 0.06 #4312), 03mp9s (0.25 #659, 0.12 #2267, 0.11 #2668), 02_fj (0.25 #915, 0.07 #2925, 0.04 #6942), 0m0nq (0.25 #1137, 0.07 #3147, 0.04 #7164), 0b80__ (0.25 #986, 0.05 #5004, 0.02 #10227), 02n9k (0.20 #1495, 0.04 #7119, 0.02 #8726), 0f2df (0.20 #14868, 0.19 #14063, 0.19 #21306), 0b_fw (0.19 #21306, 0.18 #21305, 0.18 #20499), 01d5vk (0.17 #1881, 0.07 #7103, 0.07 #3086) >> Best rule #16475 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 036hf4; >> query: (?x1568, ?x5601) <- spouse(?x1568, ?x5601), participant(?x1567, ?x1568), nationality(?x1567, ?x512), participant(?x1567, ?x4057) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pl9g spouse! 05kh_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 110.000 0.836 http://example.org/people/person/spouse_s./people/marriage/spouse #5090-01wgxtl PRED entity: 01wgxtl PRED relation: award_nominee! PRED expected values: 03f19q4 => 108 concepts (48 used for prediction) PRED predicted values (max 10 best out of 935): 0837ql (0.81 #65041, 0.81 #72014, 0.80 #67366), 04mn81 (0.81 #65041, 0.81 #72014, 0.80 #67366), 04xrx (0.17 #9289, 0.15 #76660, 0.14 #78986), 0bqvs2 (0.17 #9289, 0.15 #76660, 0.14 #78986), 01fyzy (0.17 #9289, 0.15 #76660, 0.14 #78986), 04qmr (0.17 #9289, 0.14 #78986, 0.12 #826), 01wgxtl (0.14 #2921, 0.10 #7565, 0.07 #599), 01vsgrn (0.12 #1298, 0.12 #8264, 0.07 #10587), 0478__m (0.12 #1083, 0.07 #10372, 0.07 #3405), 03g5jw (0.12 #328, 0.07 #9617, 0.06 #78987) >> Best rule #65041 for best value: >> intensional similarity = 3 >> extensional distance = 394 >> proper extension: 01k5zk; 02t_99; 01g969; >> query: (?x2732, ?x827) <- award_nominee(?x2732, ?x827), participant(?x2732, ?x10777), award(?x2732, ?x2139) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #3543 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 0jpdn; *> query: (?x2732, 03f19q4) <- profession(?x2732, ?x955), ?x955 = 0n1h, currency(?x2732, ?x170) *> conf = 0.07 ranks of expected_values: 34 EVAL 01wgxtl award_nominee! 03f19q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 108.000 48.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5089-0vmt PRED entity: 0vmt PRED relation: currency PRED expected values: 09nqf => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.89 #83, 0.89 #80, 0.88 #40), 02l6h (0.06 #6, 0.03 #97, 0.03 #21), 0ptk_ (0.03 #20, 0.03 #117, 0.03 #123) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 011hq1; >> query: (?x938, 09nqf) <- location(?x1285, ?x938), religion(?x938, ?x109), jurisdiction_of_office(?x900, ?x938) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0vmt currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.885 http://example.org/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency #5088-02pqs8l PRED entity: 02pqs8l PRED relation: genre PRED expected values: 01hmnh => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 74): 05p553 (0.48 #564, 0.45 #724, 0.44 #1124), 01z4y (0.34 #575, 0.33 #15, 0.32 #1135), 01t_vv (0.33 #30, 0.20 #190, 0.20 #350), 06q7n (0.33 #41, 0.15 #601, 0.14 #121), 02n4kr (0.33 #7, 0.10 #327, 0.09 #487), 02l7c8 (0.33 #12, 0.04 #412, 0.03 #1614), 0c4xc (0.27 #599, 0.23 #1240, 0.22 #1159), 06n90 (0.23 #650, 0.18 #1612, 0.16 #1932), 0hcr (0.19 #1778, 0.19 #176, 0.18 #1938), 01htzx (0.19 #1134, 0.19 #654, 0.17 #1215) >> Best rule #564 for best value: >> intensional similarity = 2 >> extensional distance = 113 >> proper extension: 0300ml; 02rq7nd; >> query: (?x3822, 05p553) <- nominated_for(?x1670, ?x3822), producer_type(?x3822, ?x632) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1615 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 239 *> proper extension: 0q9nj; *> query: (?x3822, 01hmnh) <- genre(?x3822, ?x53), titles(?x53, ?x54) *> conf = 0.15 ranks of expected_values: 12 EVAL 02pqs8l genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 66.000 66.000 0.478 http://example.org/tv/tv_program/genre #5087-02z1nbg PRED entity: 02z1nbg PRED relation: award_winner PRED expected values: 0chw_ => 38 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1563): 01l9p (0.71 #348, 0.20 #5222, 0.19 #7660), 0mz73 (0.57 #1679, 0.36 #4116, 0.25 #6553), 09l3p (0.57 #935, 0.20 #5809, 0.19 #8247), 0h1mt (0.55 #2645, 0.30 #5082, 0.29 #7520), 0bw87 (0.45 #3886, 0.25 #6323, 0.24 #8761), 0chw_ (0.43 #1904, 0.20 #6778, 0.18 #4341), 02x0dzw (0.43 #1838, 0.10 #6712, 0.10 #9150), 01tspc6 (0.36 #2621, 0.25 #5058, 0.24 #7496), 07lt7b (0.36 #2559, 0.20 #4996, 0.19 #7434), 05dbf (0.29 #458, 0.27 #2895, 0.20 #5332) >> Best rule #348 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 09qwmm; 099cng; 02x4x18; >> query: (?x3902, 01l9p) <- award_winner(?x3902, ?x5043), award_winner(?x3902, ?x4234), ?x5043 = 015q43, award(?x898, ?x3902), award(?x4234, ?x375) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1904 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 09qwmm; 099cng; 02x4x18; *> query: (?x3902, 0chw_) <- award_winner(?x3902, ?x5043), award_winner(?x3902, ?x4234), ?x5043 = 015q43, award(?x898, ?x3902), award(?x4234, ?x375) *> conf = 0.43 ranks of expected_values: 6 EVAL 02z1nbg award_winner 0chw_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 38.000 16.000 0.714 http://example.org/award/award_category/winners./award/award_honor/award_winner #5086-07ymr5 PRED entity: 07ymr5 PRED relation: cast_members PRED expected values: 02k21g => 96 concepts (53 used for prediction) PRED predicted values (max 10 best out of 6): 030wkp (0.09 #28, 0.09 #21, 0.07 #34), 02k21g (0.08 #26, 0.07 #19, 0.06 #32), 07ymr5 (0.08 #25, 0.07 #18, 0.06 #31), 01v3s2_ (0.06 #23, 0.05 #16, 0.05 #29), 04s430 (0.05 #20, 0.05 #27, 0.04 #33), 0pz7h (0.03 #17, 0.03 #24, 0.03 #30) >> Best rule #28 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 01jbx1; >> query: (?x1942, 030wkp) <- profession(?x1942, ?x319), program(?x1942, ?x6884), film(?x1942, ?x3812) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 01jbx1; *> query: (?x1942, 02k21g) <- profession(?x1942, ?x319), program(?x1942, ?x6884), film(?x1942, ?x3812) *> conf = 0.08 ranks of expected_values: 2 EVAL 07ymr5 cast_members 02k21g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 53.000 0.092 http://example.org/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members #5085-03nkts PRED entity: 03nkts PRED relation: film PRED expected values: 043h78 => 79 concepts (71 used for prediction) PRED predicted values (max 10 best out of 500): 02wgk1 (0.10 #757, 0.04 #2546, 0.04 #4335), 03whyr (0.07 #1569, 0.04 #3358, 0.02 #5147), 0p9lw (0.07 #146, 0.04 #1935, 0.02 #3724), 0340hj (0.07 #237, 0.03 #3815, 0.03 #2026), 012s1d (0.07 #919, 0.03 #4497, 0.03 #2708), 0872p_c (0.06 #3753, 0.02 #175, 0.01 #1964), 09xbpt (0.05 #47, 0.05 #71565, 0.03 #55463), 03q0r1 (0.05 #636, 0.04 #2425, 0.03 #4214), 0prrm (0.05 #859, 0.04 #2648, 0.02 #4437), 0bpm4yw (0.05 #723, 0.04 #2512, 0.02 #4301) >> Best rule #757 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 03yrkt; 03dbww; >> query: (?x6397, 02wgk1) <- award_nominee(?x6397, ?x286), language(?x6397, ?x254), gender(?x6397, ?x231) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03nkts film 043h78 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 71.000 0.095 http://example.org/film/actor/film./film/performance/film #5084-01d4cb PRED entity: 01d4cb PRED relation: role PRED expected values: 042v_gx => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 113): 01vdm0 (0.41 #132, 0.27 #1651, 0.26 #1550), 05148p4 (0.29 #123, 0.25 #22, 0.12 #1541), 026t6 (0.29 #104, 0.16 #1623, 0.14 #1522), 01s0ps (0.28 #262, 0.07 #363, 0.07 #1071), 042v_gx (0.25 #7, 0.24 #108, 0.23 #1018), 05842k (0.25 #77, 0.24 #178, 0.17 #1697), 0214km (0.25 #97, 0.14 #299, 0.06 #1108), 02dlh2 (0.25 #75, 0.06 #176, 0.03 #378), 01rhl (0.25 #83, 0.06 #184, 0.02 #1926), 01vnt4 (0.25 #95) >> Best rule #132 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 06k02; 0lccn; 045zr; 0pkyh; 0565cz; 03bnv; 01vsy95; 0phx4; 01gg59; 050z2; ... >> query: (?x9128, 01vdm0) <- artists(?x284, ?x9128), role(?x9128, ?x227), ?x284 = 0827d >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 015cxv; *> query: (?x9128, 042v_gx) <- artists(?x14558, ?x9128), ?x14558 = 013rxq, award(?x9128, ?x2379) *> conf = 0.25 ranks of expected_values: 5 EVAL 01d4cb role 042v_gx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 80.000 80.000 0.412 http://example.org/music/artist/track_contributions./music/track_contribution/role #5083-02zq43 PRED entity: 02zq43 PRED relation: award_nominee PRED expected values: 0175wg 02fz3w => 82 concepts (34 used for prediction) PRED predicted values (max 10 best out of 785): 01l2fn (0.92 #2330, 0.85 #4661, 0.85 #6993), 03f1zdw (0.92 #2330, 0.85 #4661, 0.85 #6993), 02cllz (0.92 #2330, 0.85 #4661, 0.85 #6993), 0175wg (0.81 #3678, 0.70 #6009, 0.62 #1347), 02fz3w (0.75 #4304, 0.70 #6635, 0.62 #1973), 02zq43 (0.62 #2391, 0.60 #4722, 0.50 #60), 0dvld (0.21 #6994, 0.20 #6051, 0.19 #3720), 0blbxk (0.21 #6994, 0.18 #74557, 0.15 #79219), 02lhm2 (0.21 #6994, 0.18 #74557, 0.15 #79219), 05yh_t (0.21 #6994, 0.18 #74557, 0.15 #79219) >> Best rule #2330 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0159h6; 05tk7y; 07hbxm; 04rsd2; 01qrbf; 02fz3w; >> query: (?x381, ?x100) <- award_nominee(?x1634, ?x381), award_nominee(?x100, ?x381), ?x1634 = 01l2fn, actor(?x8554, ?x381) >> conf = 0.92 => this is the best rule for 3 predicted values *> Best rule #3678 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 05vsxz; 01yhvv; 09y20; 02cllz; 06mmb; 0993r; 016xh5; 0djywgn; *> query: (?x381, 0175wg) <- award_nominee(?x1634, ?x381), award_nominee(?x380, ?x381), ?x1634 = 01l2fn, ?x380 = 0m2wm *> conf = 0.81 ranks of expected_values: 4, 5 EVAL 02zq43 award_nominee 02fz3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 34.000 0.918 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 02zq43 award_nominee 0175wg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 34.000 0.918 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5082-05hf_5 PRED entity: 05hf_5 PRED relation: major_field_of_study PRED expected values: 03g3w => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 118): 02lp1 (0.67 #887, 0.63 #1012, 0.56 #762), 01mkq (0.61 #766, 0.54 #1266, 0.53 #1016), 04rjg (0.61 #771, 0.47 #1021, 0.44 #1271), 03g3w (0.57 #1028, 0.56 #1278, 0.50 #903), 062z7 (0.56 #779, 0.53 #1029, 0.44 #904), 05qfh (0.56 #912, 0.44 #787, 0.43 #1037), 04sh3 (0.50 #1078, 0.48 #1328, 0.44 #953), 05qjt (0.50 #508, 0.43 #383, 0.38 #1383), 0fdys (0.44 #665, 0.43 #415, 0.37 #1040), 02_7t (0.44 #817, 0.28 #942, 0.23 #1067) >> Best rule #887 for best value: >> intensional similarity = 7 >> extensional distance = 16 >> proper extension: 08qnnv; >> query: (?x12330, 02lp1) <- school_type(?x12330, ?x3092), institution(?x2636, ?x12330), institution(?x1771, ?x12330), ?x1771 = 019v9k, ?x2636 = 027f2w, category(?x12330, ?x134), ?x3092 = 05jxkf >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1028 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 28 *> proper extension: 07szy; 01bvw5; 07vfj; 07t90; 01hc1j; 02tz9z; *> query: (?x12330, 03g3w) <- school_type(?x12330, ?x3092), institution(?x2636, ?x12330), institution(?x1771, ?x12330), ?x1771 = 019v9k, ?x2636 = 027f2w, category(?x12330, ?x134), contains(?x2051, ?x12330) *> conf = 0.57 ranks of expected_values: 4 EVAL 05hf_5 major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 154.000 154.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #5081-03lt8g PRED entity: 03lt8g PRED relation: award_nominee PRED expected values: 04vmqg => 125 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1185): 04psyp (0.83 #2325, 0.83 #806, 0.81 #9296), 05683p (0.81 #9296, 0.81 #118532, 0.81 #55777), 04vmqg (0.81 #9296, 0.81 #118532, 0.81 #162685), 030znt (0.80 #58102, 0.77 #113882, 0.77 #174302), 038g2x (0.77 #113882, 0.77 #174302, 0.76 #106908), 03lt8g (0.54 #223, 0.16 #113883, 0.03 #32757), 043zg (0.18 #46478, 0.17 #34858, 0.17 #48804), 0kjgl (0.18 #46478, 0.17 #34858, 0.17 #48804), 01pk8v (0.18 #46478, 0.17 #34858, 0.17 #48804), 01yf85 (0.18 #46478, 0.17 #34858, 0.17 #48804) >> Best rule #2325 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 01rs5p; >> query: (?x1117, ?x3602) <- award_nominee(?x3602, ?x1117), award_nominee(?x1117, ?x1116), ?x3602 = 04psyp >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #9296 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 53 *> proper extension: 063472; 015dcj; 01qbjg; 01vn0t_; 07d3x; 016nvh; *> query: (?x1117, ?x444) <- award_nominee(?x444, ?x1117), award_nominee(?x1117, ?x1116), notable_people_with_this_condition(?x8318, ?x1117) *> conf = 0.81 ranks of expected_values: 3 EVAL 03lt8g award_nominee 04vmqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 75.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5080-03_gz8 PRED entity: 03_gz8 PRED relation: honored_for! PRED expected values: 03gwpw2 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 115): 03gwpw2 (0.83 #5, 0.05 #1578, 0.05 #1699), 02wzl1d (0.20 #7, 0.03 #1580, 0.03 #1701), 03gyp30 (0.13 #101, 0.02 #585, 0.02 #1795), 02q690_ (0.10 #54, 0.05 #2112, 0.05 #1627), 0275n3y (0.10 #64, 0.05 #2122, 0.04 #1637), 04n2r9h (0.07 #36, 0.04 #1609, 0.04 #1730), 03nnm4t (0.07 #63, 0.04 #2121, 0.04 #1878), 0drtv8 (0.07 #55, 0.03 #1628, 0.02 #2113), 027hjff (0.07 #47, 0.02 #1620, 0.02 #168), 05c1t6z (0.06 #1826, 0.05 #1584, 0.05 #1705) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0cnjm0; >> query: (?x6362, 03gwpw2) <- honored_for(?x6594, ?x6362), honored_for(?x6594, ?x1531), ?x1531 = 02rv_dz >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_gz8 honored_for! 03gwpw2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.833 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #5079-0ymb6 PRED entity: 0ymb6 PRED relation: organization! PRED expected values: 07xl34 => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 15): 060c4 (0.71 #132, 0.68 #145, 0.68 #1055), 05c0jwl (0.57 #702, 0.48 #356, 0.47 #661), 07xl34 (0.46 #419, 0.43 #275, 0.41 #500), 0dq_5 (0.45 #351, 0.38 #445, 0.37 #325), 08jcfy (0.35 #1132, 0.33 #1446, 0.25 #1930), 04n1q6 (0.35 #1132, 0.33 #1446, 0.25 #1930), 05k17c (0.35 #1132, 0.33 #1446, 0.25 #1930), 0hm4q (0.35 #1132, 0.33 #1446, 0.25 #1930), 02wlwtm (0.22 #1250, 0.10 #2258, 0.04 #2100), 01___w (0.10 #450, 0.07 #170, 0.04 #2205) >> Best rule #132 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 033q4k; 01j_5k; 0k__z; >> query: (?x7971, 060c4) <- state_province_region(?x7971, ?x2235), student(?x7971, ?x11797), contains(?x1310, ?x7971), child(?x892, ?x7971) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #419 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 01w_sh; *> query: (?x7971, 07xl34) <- currency(?x7971, ?x1099), citytown(?x7971, ?x1841), student(?x7971, ?x11797), ?x1099 = 01nv4h *> conf = 0.46 ranks of expected_values: 3 EVAL 0ymb6 organization! 07xl34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 203.000 203.000 0.706 http://example.org/organization/role/leaders./organization/leadership/organization #5078-01w1ywm PRED entity: 01w1ywm PRED relation: profession PRED expected values: 0np9r => 106 concepts (87 used for prediction) PRED predicted values (max 10 best out of 66): 02hrh1q (0.89 #1505, 0.89 #2697, 0.89 #2101), 03gjzk (0.33 #6128, 0.32 #4026, 0.31 #761), 01d_h8 (0.33 #453, 0.30 #9099, 0.29 #12080), 0dxtg (0.32 #4026, 0.30 #6126, 0.29 #759), 02jknp (0.32 #4026, 0.28 #6858, 0.28 #9541), 0np9r (0.32 #4026, 0.28 #6858, 0.28 #9541), 02krf9 (0.32 #4026, 0.28 #6858, 0.28 #9541), 09jwl (0.29 #318, 0.19 #5536, 0.19 #7474), 0kyk (0.24 #329, 0.16 #627, 0.15 #478), 0cbd2 (0.21 #2242, 0.17 #4927, 0.17 #4480) >> Best rule #1505 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 02js_6; >> query: (?x7997, 02hrh1q) <- actor(?x802, ?x7997), award_winner(?x7997, ?x803), award_winner(?x4386, ?x7997) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #4026 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 671 *> proper extension: 029_3; 0d_skg; 01hmk9; 06vqdf; 060pl5; *> query: (?x7997, ?x1032) <- award_nominee(?x803, ?x7997), profession(?x803, ?x1383), profession(?x803, ?x1032), ?x1383 = 0np9r *> conf = 0.32 ranks of expected_values: 6 EVAL 01w1ywm profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 106.000 87.000 0.894 http://example.org/people/person/profession #5077-0cqh57 PRED entity: 0cqh57 PRED relation: nominated_for PRED expected values: 01cssf => 82 concepts (41 used for prediction) PRED predicted values (max 10 best out of 341): 09zf_q (0.46 #16202, 0.41 #14581, 0.41 #6477), 08hmch (0.41 #14581, 0.41 #6477, 0.40 #12961), 02d003 (0.41 #14581, 0.41 #6477, 0.40 #12961), 037cr1 (0.41 #14581, 0.41 #6477, 0.40 #12961), 01rxyb (0.25 #2281, 0.03 #5519, 0.03 #7140), 05dl1s (0.25 #1532, 0.02 #9633, 0.01 #6478), 0bs5k8r (0.25 #649, 0.02 #8750), 0yyts (0.25 #350, 0.02 #8451), 01qdmh (0.25 #3159), 02qjv1p (0.25 #2941) >> Best rule #16202 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 04cw0n4; >> query: (?x7427, ?x3845) <- cinematography(?x3845, ?x7427), nationality(?x7427, ?x390), film(?x489, ?x3845), award(?x3845, ?x143) >> conf = 0.46 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cqh57 nominated_for 01cssf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 41.000 0.462 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #5076-05v8c PRED entity: 05v8c PRED relation: contains! PRED expected values: 0j0k => 164 concepts (132 used for prediction) PRED predicted values (max 10 best out of 197): 0j0k (0.78 #59089, 0.72 #88637, 0.53 #81851), 02j71 (0.73 #3579, 0.65 #87742, 0.64 #89535), 09c7w0 (0.61 #15214, 0.55 #63569, 0.53 #48344), 0jhwd (0.38 #116396, 0.13 #111914, 0.06 #102960), 0195pd (0.38 #116396), 04_1l0v (0.37 #69392, 0.34 #13870, 0.27 #83713), 0dg3n1 (0.36 #37753, 0.34 #58349, 0.34 #71782), 02j9z (0.33 #4501, 0.33 #81502, 0.31 #25083), 03rk0 (0.29 #68186, 0.25 #42210, 0.09 #39525), 03_3d (0.27 #2696, 0.06 #48353, 0.06 #116408) >> Best rule #59089 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 07ytt; >> query: (?x550, ?x6956) <- contains(?x6304, ?x550), countries_within(?x6956, ?x550), taxonomy(?x550, ?x939) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v8c contains! 0j0k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 132.000 0.781 http://example.org/location/location/contains #5075-02hgz PRED entity: 02hgz PRED relation: contains! PRED expected values: 05bcl => 132 concepts (61 used for prediction) PRED predicted values (max 10 best out of 239): 09c7w0 (0.73 #42989, 0.72 #43885, 0.71 #44780), 02jx1 (0.63 #1876, 0.60 #14409, 0.59 #3667), 03rt9 (0.52 #25067, 0.47 #35816, 0.41 #41193), 02qkt (0.33 #42985, 0.32 #39399, 0.11 #28994), 04_1l0v (0.26 #13878, 0.21 #20146, 0.20 #19251), 02j9z (0.22 #28676, 0.05 #2713, 0.04 #15246), 04jpl (0.21 #8973, 0.19 #8078, 0.19 #5393), 03_3d (0.16 #9859, 0.08 #18813, 0.07 #37619), 0345h (0.15 #37688, 0.13 #9928, 0.10 #39482), 0j5g9 (0.14 #34919, 0.14 #3841, 0.12 #1155) >> Best rule #42989 for best value: >> intensional similarity = 4 >> extensional distance = 924 >> proper extension: 014b4h; 02mg7n; >> query: (?x12744, 09c7w0) <- contains(?x512, ?x12744), category(?x12744, ?x134), ?x134 = 08mbj5d, country_of_origin(?x293, ?x512) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #34919 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 375 *> proper extension: 014tss; 03t1s; *> query: (?x12744, ?x1310) <- contains(?x512, ?x12744), contains(?x512, ?x1310), contains(?x512, ?x362), contains(?x6304, ?x512), featured_film_locations(?x136, ?x362), nationality(?x57, ?x1310) *> conf = 0.14 ranks of expected_values: 11 EVAL 02hgz contains! 05bcl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 132.000 61.000 0.733 http://example.org/location/location/contains #5074-0kvbl6 PRED entity: 0kvbl6 PRED relation: nominated_for! PRED expected values: 08mg_b => 86 concepts (41 used for prediction) PRED predicted values (max 10 best out of 212): 08mg_b (0.81 #5042, 0.80 #3527, 0.80 #754), 0kvbl6 (0.52 #5294, 0.06 #3022, 0.03 #5548), 0dtfn (0.12 #540, 0.05 #289, 0.04 #2808), 0h1x5f (0.11 #234, 0.03 #2752, 0.02 #3004), 08nvyr (0.11 #130, 0.02 #632, 0.02 #2900), 0g9lm2 (0.11 #126, 0.02 #628, 0.02 #2896), 0p9lw (0.11 #23, 0.02 #525, 0.02 #1028), 0ddt_ (0.10 #588, 0.04 #2856, 0.03 #2604), 0fdv3 (0.10 #552, 0.03 #2568, 0.03 #2820), 059lwy (0.08 #1195, 0.04 #3969, 0.04 #1446) >> Best rule #5042 for best value: >> intensional similarity = 3 >> extensional distance = 240 >> proper extension: 026p_bs; 02sg5v; 02qrv7; 018nnz; 0d_wms; 0k0rf; 05css_; 0g5pvv; 02n72k; 01jr4j; ... >> query: (?x6334, ?x4623) <- genre(?x6334, ?x53), nominated_for(?x6334, ?x4623), film(?x193, ?x6334) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kvbl6 nominated_for! 08mg_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 41.000 0.814 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #5073-050r1z PRED entity: 050r1z PRED relation: film! PRED expected values: 018swb => 114 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1174): 09bxq9 (0.42 #102081, 0.40 #108332, 0.40 #79161), 04y8r (0.18 #95829, 0.18 #70826, 0.18 #74993), 03qncl3 (0.13 #10415, 0.12 #43740, 0.12 #131246), 0c6qh (0.10 #2497, 0.04 #12912, 0.04 #414), 0h0wc (0.08 #424, 0.07 #2507, 0.06 #17088), 0h5g_ (0.08 #74, 0.05 #10489, 0.03 #8406), 0k269 (0.08 #611, 0.03 #2694, 0.03 #11026), 01l2fn (0.08 #262, 0.03 #8594, 0.02 #46087), 01s7zw (0.08 #426, 0.03 #12924, 0.03 #4592), 0170qf (0.08 #6616, 0.06 #14948, 0.05 #8699) >> Best rule #102081 for best value: >> intensional similarity = 4 >> extensional distance = 584 >> proper extension: 025n07; >> query: (?x586, ?x7782) <- produced_by(?x586, ?x2332), film(?x585, ?x586), nominated_for(?x7782, ?x586), currency(?x586, ?x170) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #8674 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 016z5x; 07w8fz; *> query: (?x586, 018swb) <- produced_by(?x586, ?x2332), film(?x585, ?x586), nominated_for(?x2375, ?x586), ?x2375 = 04kxsb *> conf = 0.05 ranks of expected_values: 57 EVAL 050r1z film! 018swb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 114.000 64.000 0.418 http://example.org/film/actor/film./film/performance/film #5072-0cbv4g PRED entity: 0cbv4g PRED relation: film! PRED expected values: 06fc0b => 95 concepts (39 used for prediction) PRED predicted values (max 10 best out of 765): 0gdh5 (0.46 #2079, 0.46 #10394, 0.43 #14551), 016tt2 (0.46 #2079, 0.46 #10394, 0.43 #14551), 0gv07g (0.42 #72767, 0.36 #74847, 0.32 #74846), 01kwsg (0.17 #839, 0.11 #4158, 0.02 #25790), 01pgzn_ (0.17 #384, 0.11 #4158, 0.02 #6621), 02k21g (0.17 #794, 0.11 #4158, 0.02 #7031), 02qgyv (0.17 #385, 0.11 #4158, 0.02 #21177), 01csvq (0.17 #110, 0.06 #2189, 0.03 #10504), 081lh (0.17 #162, 0.05 #2241, 0.02 #29270), 0bl2g (0.17 #55, 0.03 #2134, 0.02 #18767) >> Best rule #2079 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0jyx6; 0p_th; 06q8qh; 01chpn; >> query: (?x5293, ?x574) <- honored_for(?x5293, ?x3684), nominated_for(?x2551, ?x5293), nominated_for(?x574, ?x5293), nominated_for(?x143, ?x5293), ?x2551 = 0h0wc >> conf = 0.46 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0cbv4g film! 06fc0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 39.000 0.464 http://example.org/film/actor/film./film/performance/film #5071-015x74 PRED entity: 015x74 PRED relation: film_production_design_by PRED expected values: 0bytkq => 82 concepts (42 used for prediction) PRED predicted values (max 10 best out of 13): 02vxyl5 (0.09 #28, 0.02 #501, 0.02 #279), 0bytkq (0.04 #37, 0.02 #68, 0.02 #130), 0cdf37 (0.03 #47, 0.01 #455, 0.01 #361), 03wd5tk (0.03 #46, 0.01 #970), 03mdw3c (0.02 #85, 0.01 #147, 0.01 #116), 02x2t07 (0.02 #786, 0.02 #117, 0.02 #463), 0d5wn3 (0.02 #41, 0.02 #72, 0.02 #103), 04z_x4v (0.02 #56), 0fqjks (0.02 #52), 04_1nk (0.02 #327, 0.01 #453, 0.01 #359) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 04kkz8; >> query: (?x1842, 02vxyl5) <- film(?x541, ?x1842), written_by(?x1842, ?x7352), film(?x4563, ?x1842), ?x4563 = 0dzf_ >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 110 *> proper extension: 0k4kk; 097zcz; 02gd6x; 0hv4t; 0hvvf; 09tkzy; 01fwzk; 072192; *> query: (?x1842, 0bytkq) <- award(?x1842, ?x143), nominated_for(?x2222, ?x1842), ?x2222 = 0gs96, country(?x1842, ?x94) *> conf = 0.04 ranks of expected_values: 2 EVAL 015x74 film_production_design_by 0bytkq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 42.000 0.091 http://example.org/film/film/film_production_design_by #5070-0dthsy PRED entity: 0dthsy PRED relation: ceremony! PRED expected values: 0gr0m => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 342): 0gs9p (0.88 #4691, 0.87 #2981, 0.86 #2737), 0gq9h (0.87 #2980, 0.87 #5425, 0.86 #2736), 0gr51 (0.83 #5685, 0.83 #3729, 0.82 #5440), 0gr4k (0.83 #2948, 0.82 #2704, 0.81 #2460), 0gr0m (0.82 #4689, 0.82 #5669, 0.81 #4201), 018wdw (0.81 #2612, 0.78 #3100, 0.77 #2856), 0gqxm (0.76 #6851, 0.76 #6850, 0.76 #7586), 0gqzz (0.76 #6851, 0.76 #6850, 0.76 #7586), 02x201b (0.76 #6851, 0.76 #6850, 0.76 #7586), 0czp_ (0.76 #6851, 0.76 #6850, 0.76 #7586) >> Best rule #4691 for best value: >> intensional similarity = 26 >> extensional distance = 47 >> proper extension: 0fz20l; >> query: (?x5053, 0gs9p) <- ceremony(?x5409, ?x5053), ceremony(?x2209, ?x5053), award_winner(?x5053, ?x9127), gender(?x9127, ?x231), nominated_for(?x9127, ?x4970), ?x2209 = 0gr42, award_nominee(?x11535, ?x9127), ceremony(?x5409, ?x9899), ceremony(?x5409, ?x6344), ceremony(?x5409, ?x3579), ceremony(?x5409, ?x3173), ceremony(?x5409, ?x2294), ceremony(?x5409, ?x2082), award(?x10184, ?x5409), award(?x1853, ?x5409), award(?x1371, ?x5409), ?x3579 = 0bc773, ?x1371 = 0prjs, ?x2082 = 0gmdkyy, ?x6344 = 0bzm__, profession(?x10184, ?x319), ?x1853 = 052gzr, ?x2294 = 050yyb, ?x9899 = 0c4hnm, ?x3173 = 0bzk2h, type_of_union(?x11535, ?x566) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #4689 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 47 *> proper extension: 0fz20l; *> query: (?x5053, 0gr0m) <- ceremony(?x5409, ?x5053), ceremony(?x2209, ?x5053), award_winner(?x5053, ?x9127), gender(?x9127, ?x231), nominated_for(?x9127, ?x4970), ?x2209 = 0gr42, award_nominee(?x11535, ?x9127), ceremony(?x5409, ?x9899), ceremony(?x5409, ?x6344), ceremony(?x5409, ?x3579), ceremony(?x5409, ?x3173), ceremony(?x5409, ?x2294), ceremony(?x5409, ?x2082), award(?x10184, ?x5409), award(?x1853, ?x5409), award(?x1371, ?x5409), ?x3579 = 0bc773, ?x1371 = 0prjs, ?x2082 = 0gmdkyy, ?x6344 = 0bzm__, profession(?x10184, ?x319), ?x1853 = 052gzr, ?x2294 = 050yyb, ?x9899 = 0c4hnm, ?x3173 = 0bzk2h, type_of_union(?x11535, ?x566) *> conf = 0.82 ranks of expected_values: 5 EVAL 0dthsy ceremony! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 34.000 34.000 0.878 http://example.org/award/award_category/winners./award/award_honor/ceremony #5069-02lf1j PRED entity: 02lf1j PRED relation: category PRED expected values: 08mbj5d => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.44 #14, 0.43 #15, 0.43 #31) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 030pr; >> query: (?x2564, 08mbj5d) <- type_of_union(?x2564, ?x566), person(?x424, ?x2564), nationality(?x2564, ?x94), country(?x108, ?x94) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lf1j category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.442 http://example.org/common/topic/webpage./common/webpage/category #5068-03jldb PRED entity: 03jldb PRED relation: film PRED expected values: 0gtsxr4 => 105 concepts (82 used for prediction) PRED predicted values (max 10 best out of 537): 019nnl (0.58 #107119, 0.50 #19639, 0.48 #73199), 0gj9tn5 (0.33 #275, 0.20 #2060, 0.11 #5630), 0dcz8_ (0.33 #1581, 0.20 #3366, 0.11 #6936), 0fphf3v (0.22 #4929, 0.03 #8499, 0.02 #10285), 0bvn25 (0.11 #3620, 0.05 #17903, 0.04 #10762), 02ht1k (0.11 #4200, 0.05 #18483, 0.03 #23839), 02stbw (0.11 #3954, 0.04 #18237, 0.03 #23593), 03bx2lk (0.11 #3755, 0.04 #10897, 0.03 #18038), 04k9y6 (0.11 #4610, 0.03 #18893, 0.02 #24249), 05q4y12 (0.11 #4021, 0.03 #110690, 0.01 #18304) >> Best rule #107119 for best value: >> intensional similarity = 3 >> extensional distance = 1401 >> proper extension: 04yywz; 049tjg; 02g8h; 0d_84; 0h1_w; 02nb2s; 04bs3j; 014x77; 0151ns; 0lzb8; ... >> query: (?x1537, ?x1395) <- nominated_for(?x1537, ?x1395), film(?x1537, ?x5388), genre(?x5388, ?x225) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03jldb film 0gtsxr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 82.000 0.585 http://example.org/film/actor/film./film/performance/film #5067-01vn35l PRED entity: 01vn35l PRED relation: artists! PRED expected values: 0dl5d 02qdgx => 143 concepts (85 used for prediction) PRED predicted values (max 10 best out of 234): 064t9 (0.60 #20836, 0.51 #8407, 0.49 #24563), 0xhtw (0.55 #3435, 0.50 #2192, 0.35 #16), 0155w (0.48 #1037, 0.42 #726, 0.34 #3525), 016clz (0.36 #2181, 0.32 #6227, 0.30 #7469), 06j6l (0.34 #1288, 0.33 #978, 0.31 #11238), 01lyv (0.33 #964, 0.30 #33, 0.29 #653), 016jny (0.33 #1035, 0.29 #724, 0.25 #3523), 05bt6j (0.31 #8437, 0.31 #1283, 0.30 #4705), 02k_kn (0.30 #65, 0.26 #996, 0.17 #685), 025sc50 (0.27 #11240, 0.22 #17457, 0.20 #19320) >> Best rule #20836 for best value: >> intensional similarity = 3 >> extensional distance = 580 >> proper extension: 0123r4; >> query: (?x2876, 064t9) <- artists(?x1572, ?x2876), artists(?x1572, ?x1398), ?x1398 = 01j4ls >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3438 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 02mq_y; *> query: (?x2876, 0dl5d) <- artists(?x7083, ?x2876), artists(?x1572, ?x2876), ?x1572 = 06by7, ?x7083 = 02yv6b *> conf = 0.21 ranks of expected_values: 15, 20 EVAL 01vn35l artists! 02qdgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 143.000 85.000 0.605 http://example.org/music/genre/artists EVAL 01vn35l artists! 0dl5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 143.000 85.000 0.605 http://example.org/music/genre/artists #5066-036nz PRED entity: 036nz PRED relation: major_field_of_study! PRED expected values: 016t_3 0bkj86 => 59 concepts (35 used for prediction) PRED predicted values (max 10 best out of 19): 016t_3 (0.88 #183, 0.86 #80, 0.83 #142), 02h4rq6 (0.86 #96, 0.86 #79, 0.84 #200), 0bkj86 (0.79 #64, 0.71 #125, 0.71 #104), 07s6fsf (0.67 #405, 0.52 #283, 0.51 #466), 0bjrnt (0.56 #714, 0.52 #283, 0.50 #592), 071tyz (0.52 #283, 0.50 #592, 0.47 #118), 01kxxq (0.49 #57, 0.47 #425, 0.46 #77), 01gkg3 (0.49 #57, 0.47 #425, 0.39 #569), 028dcg (0.47 #425, 0.47 #118, 0.45 #179), 03mkk4 (0.47 #425, 0.47 #118, 0.45 #179) >> Best rule #183 for best value: >> intensional similarity = 13 >> extensional distance = 30 >> proper extension: 036hv; 02lp1; 0g4gr; 0fdys; 0g26h; 0db86; 04rlf; 01zc2w; 041y2; >> query: (?x7979, 016t_3) <- major_field_of_study(?x5486, ?x7979), major_field_of_study(?x4187, ?x7979), major_field_of_study(?x2605, ?x7979), major_field_of_study(?x4187, ?x2014), major_field_of_study(?x4187, ?x866), ?x866 = 088tb, institution(?x865, ?x4187), ?x865 = 02h4rq6, student(?x4187, ?x201), colors(?x4187, ?x9464), ?x2014 = 04rjg, currency(?x4187, ?x170), company(?x346, ?x5486) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 036nz major_field_of_study! 0bkj86 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 59.000 35.000 0.875 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 036nz major_field_of_study! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 35.000 0.875 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #5065-034cm PRED entity: 034cm PRED relation: origin! PRED expected values: 0b68vs => 157 concepts (142 used for prediction) PRED predicted values (max 10 best out of 191): 06nv27 (0.33 #2284, 0.29 #3832, 0.25 #4348), 0153nq (0.17 #3613, 0.17 #2581, 0.17 #2064), 015bwt (0.17 #3573, 0.17 #2541, 0.17 #2024), 01tpl1p (0.17 #3554, 0.17 #2522, 0.17 #2005), 0cbm64 (0.17 #3508, 0.17 #2476, 0.17 #1959), 01jgkj2 (0.17 #3504, 0.17 #2472, 0.17 #1955), 01d_h (0.17 #3482, 0.17 #2450, 0.17 #1933), 0qmny (0.17 #3477, 0.17 #2445, 0.17 #1928), 0f_y9 (0.17 #3417, 0.17 #2385, 0.17 #1868), 03zz8b (0.17 #3416, 0.17 #2384, 0.17 #1867) >> Best rule #2284 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03_gx; >> query: (?x5073, 06nv27) <- taxonomy(?x5073, ?x939), ?x939 = 04n6k, split_to(?x5073, ?x6371), combatants(?x1777, ?x6371) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 034cm origin! 0b68vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 157.000 142.000 0.333 http://example.org/music/artist/origin #5064-01dvbd PRED entity: 01dvbd PRED relation: film! PRED expected values: 024rbz 032dg7 => 96 concepts (81 used for prediction) PRED predicted values (max 10 best out of 53): 03xq0f (0.41 #1215, 0.26 #2095, 0.11 #1283), 016tt2 (0.40 #1687, 0.14 #946, 0.14 #407), 05qd_ (0.37 #2098, 0.15 #2909, 0.14 #5014), 017s11 (0.25 #3, 0.20 #137, 0.20 #272), 03rwz3 (0.25 #38, 0.05 #307, 0.04 #1856), 025jfl (0.25 #6, 0.04 #342, 0.04 #2636), 081bls (0.25 #35, 0.02 #371, 0.01 #1111), 086k8 (0.21 #405, 0.19 #944, 0.18 #1078), 0jz9f (0.16 #1684, 0.10 #270, 0.09 #808), 024rbz (0.09 #480, 0.09 #615, 0.09 #749) >> Best rule #1215 for best value: >> intensional similarity = 5 >> extensional distance = 330 >> proper extension: 0522wp; >> query: (?x3048, 03xq0f) <- film(?x1104, ?x3048), film(?x1104, ?x10274), film(?x1104, ?x2441), ?x2441 = 0cc5mcj, nominated_for(?x4657, ?x10274) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #480 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 156 *> proper extension: 0bvn25; 01ln5z; 02_1sj; 0170_p; 05jzt3; 0jqn5; 03qnvdl; 028cg00; 09g8vhw; 05z7c; ... *> query: (?x3048, 024rbz) <- film(?x1104, ?x3048), genre(?x3048, ?x812), ?x1104 = 016tw3, film_crew_role(?x3048, ?x137) *> conf = 0.09 ranks of expected_values: 10, 35 EVAL 01dvbd film! 032dg7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 96.000 81.000 0.410 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 01dvbd film! 024rbz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 96.000 81.000 0.410 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #5063-02_1kl PRED entity: 02_1kl PRED relation: award_winner PRED expected values: 026n9h3 => 71 concepts (54 used for prediction) PRED predicted values (max 10 best out of 567): 026n9h3 (0.53 #32938, 0.46 #27999, 0.44 #83996), 050_qx (0.47 #23057, 0.41 #21408, 0.41 #9877), 02rmfm (0.47 #23057, 0.41 #21408, 0.41 #9877), 026dcvf (0.44 #4996, 0.40 #6642, 0.21 #8289), 02760sl (0.44 #6438, 0.40 #8084, 0.21 #9731), 03x16f (0.41 #21408, 0.41 #9877, 0.39 #18112), 055c8 (0.41 #21408, 0.41 #9877, 0.39 #18112), 02__ww (0.41 #21408, 0.41 #9877, 0.39 #18112), 02jt1k (0.41 #9877, 0.39 #18112, 0.37 #21407), 041b4j (0.41 #9877, 0.39 #18112, 0.37 #21407) >> Best rule #32938 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 0bx_hnp; >> query: (?x7175, ?x6970) <- nominated_for(?x6970, ?x7175), languages(?x7175, ?x254), program(?x1762, ?x7175), award_winner(?x438, ?x6970) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_1kl award_winner 026n9h3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 54.000 0.535 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #5062-05l0j5 PRED entity: 05l0j5 PRED relation: award_nominee PRED expected values: 0cnl1c => 69 concepts (27 used for prediction) PRED predicted values (max 10 best out of 617): 0bt4r4 (0.87 #9325, 0.87 #7647, 0.87 #2985), 0cnl1c (0.70 #5669, 0.60 #3338, 0.50 #8000), 05xpms (0.67 #4313, 0.65 #6644, 0.60 #1982), 05l0j5 (0.67 #4047, 0.65 #6378, 0.43 #8709), 043js (0.60 #5244, 0.60 #2913, 0.60 #582), 0cl0bk (0.60 #6175, 0.60 #1513, 0.53 #3844), 04t2l2 (0.60 #7033, 0.55 #4702, 0.53 #2371), 0cj2nl (0.40 #885, 0.37 #7878, 0.20 #5547), 027cxsm (0.40 #341, 0.33 #7334, 0.20 #5003), 048wrb (0.40 #1682, 0.30 #8675, 0.20 #6344) >> Best rule #9325 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 027cxsm; 0cj2t3; 06jnvs; 0cj2nl; 0b7gxq; 03qmfzx; >> query: (?x7752, ?x2912) <- award_nominee(?x7663, ?x7752), award_nominee(?x2912, ?x7752), ?x2912 = 0bt4r4, film(?x7663, ?x559) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #5669 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 083chw; 072bb1; 08hsww; 05xpms; *> query: (?x7752, 0cnl1c) <- award_nominee(?x7663, ?x7752), award_nominee(?x2602, ?x7752), ?x7663 = 04zkj5, film(?x2602, ?x6480) *> conf = 0.70 ranks of expected_values: 2 EVAL 05l0j5 award_nominee 0cnl1c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 27.000 0.867 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5061-0vmt PRED entity: 0vmt PRED relation: location! PRED expected values: 0bymv => 194 concepts (185 used for prediction) PRED predicted values (max 10 best out of 1983): 05b2gsm (0.50 #113054, 0.49 #55275, 0.48 #178372), 03nb5v (0.19 #39011, 0.18 #8860, 0.17 #13886), 01yzhn (0.18 #4642, 0.18 #2129, 0.13 #32282), 0p_pd (0.18 #2561, 0.18 #48, 0.12 #7587), 016z2j (0.18 #2941, 0.18 #428, 0.11 #10480), 01wy5m (0.18 #3496, 0.18 #983, 0.11 #11035), 01wp8w7 (0.18 #2772, 0.18 #259, 0.11 #15337), 01vtmw6 (0.18 #3873, 0.18 #1360, 0.11 #16438), 02yl42 (0.18 #3217, 0.18 #704, 0.11 #15782), 016yzz (0.18 #3286, 0.18 #773, 0.11 #15851) >> Best rule #113054 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 0fg6k; 025569; 01v8c; >> query: (?x938, ?x6388) <- place_of_birth(?x6388, ?x938), country(?x938, ?x94), nominated_for(?x6388, ?x195) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0vmt location! 0bymv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 194.000 185.000 0.497 http://example.org/people/person/places_lived./people/place_lived/location #5060-0c3351 PRED entity: 0c3351 PRED relation: titles PRED expected values: 0404j37 04ynx7 => 63 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1651): 0hmm7 (0.64 #1501, 0.62 #1502, 0.61 #1503), 02yy9r (0.64 #1501, 0.62 #1502, 0.61 #1503), 0gm2_0 (0.64 #1501, 0.62 #1502, 0.61 #1503), 04s1zr (0.64 #1501, 0.62 #1502, 0.61 #1503), 0cf8qb (0.64 #1501, 0.62 #1502, 0.61 #1503), 0bw20 (0.64 #1501, 0.62 #1502, 0.61 #1503), 02yxbc (0.64 #1501, 0.62 #1502, 0.61 #1503), 020y73 (0.64 #1501, 0.62 #1502, 0.61 #1503), 02pxmgz (0.64 #1501, 0.62 #1502, 0.61 #1503), 02c6d (0.64 #1501, 0.62 #1502, 0.56 #6010) >> Best rule #1501 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 09blyk; >> query: (?x4205, ?x599) <- genre(?x10831, ?x4205), genre(?x5791, ?x4205), genre(?x5134, ?x4205), genre(?x2081, ?x4205), genre(?x599, ?x4205), ?x5134 = 0k0rf, ?x5791 = 03mgx6z, titles(?x4205, ?x641), film(?x902, ?x10831), film_crew_role(?x2081, ?x137) >> conf = 0.64 => this is the best rule for 12 predicted values *> Best rule #2425 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 01jfsb; *> query: (?x4205, 0404j37) <- genre(?x8890, ?x4205), genre(?x5791, ?x4205), genre(?x5134, ?x4205), genre(?x4530, ?x4205), genre(?x1988, ?x4205), ?x4530 = 07j94, film_regional_debut_venue(?x1988, ?x10083), ?x5791 = 03mgx6z, ?x8890 = 03b1sb, country(?x5134, ?x94) *> conf = 0.33 ranks of expected_values: 133, 366 EVAL 0c3351 titles 04ynx7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 63.000 38.000 0.644 http://example.org/media_common/netflix_genre/titles EVAL 0c3351 titles 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 63.000 38.000 0.644 http://example.org/media_common/netflix_genre/titles #5059-0cc5mcj PRED entity: 0cc5mcj PRED relation: film! PRED expected values: 04x1_w => 84 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1190): 09pl3f (0.21 #79032, 0.20 #54075, 0.13 #39514), 09pl3s (0.21 #79032, 0.20 #54075, 0.13 #39514), 0697kh (0.21 #79032, 0.20 #54075, 0.13 #39514), 0h5g_ (0.15 #74, 0.12 #2153, 0.07 #6313), 030vnj (0.15 #1449, 0.12 #3528, 0.07 #7688), 01twdk (0.14 #43674, 0.12 #12477, 0.11 #76952), 06pj8 (0.14 #43674, 0.12 #12477, 0.11 #76952), 079vf (0.13 #4166, 0.10 #12485, 0.10 #14565), 03h_9lg (0.13 #4291, 0.10 #14690, 0.08 #12610), 0jbp0 (0.13 #5914, 0.10 #16313, 0.08 #14233) >> Best rule #79032 for best value: >> intensional similarity = 4 >> extensional distance = 438 >> proper extension: 01gglm; >> query: (?x2441, ?x2442) <- film(?x7372, ?x2441), titles(?x8280, ?x2441), written_by(?x2441, ?x2442), student(?x4410, ?x7372) >> conf = 0.21 => this is the best rule for 3 predicted values *> Best rule #17930 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 41 *> proper extension: 0ds35l9; 0c3ybss; 03g90h; 02x3lt7; 017gl1; 01c22t; 0872p_c; 03bx2lk; 04hwbq; 047msdk; ... *> query: (?x2441, 04x1_w) <- film_release_region(?x2441, ?x1122), film_release_region(?x2441, ?x151), titles(?x8280, ?x2441), ?x1122 = 09pmkv, ?x151 = 0b90_r *> conf = 0.02 ranks of expected_values: 697 EVAL 0cc5mcj film! 04x1_w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 73.000 0.207 http://example.org/film/actor/film./film/performance/film #5058-01_qc_ PRED entity: 01_qc_ PRED relation: people PRED expected values: 018swb 04264n 02xyl => 76 concepts (41 used for prediction) PRED predicted values (max 10 best out of 672): 0gyy0 (0.38 #4404, 0.27 #8435, 0.25 #9783), 0chsq (0.30 #6729, 0.23 #11437, 0.21 #12782), 09ld6g (0.29 #3339, 0.18 #8715, 0.13 #6716), 0jrny (0.25 #4136, 0.23 #11531, 0.23 #10859), 05v45k (0.25 #4634, 0.22 #5978, 0.20 #6649), 016gkf (0.25 #4241, 0.22 #5585, 0.20 #6256), 0b22w (0.25 #4512, 0.20 #7199, 0.18 #8543), 02hg53 (0.25 #4620, 0.20 #7307, 0.18 #8651), 03lpd0 (0.25 #4604, 0.20 #7291, 0.18 #8635), 01rw116 (0.25 #4542, 0.20 #7229, 0.18 #8573) >> Best rule #4404 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0gk4g; 04p3w; 01dcqj; 01bcp7; 0dq9p; 02k6hp; >> query: (?x7260, 0gyy0) <- people(?x7260, ?x11011), symptom_of(?x3679, ?x7260), type_of_union(?x11011, ?x566), award(?x11011, ?x458), place_of_death(?x11011, ?x739), location(?x11011, ?x9026), award_winner(?x2915, ?x11011), award_winner(?x951, ?x11011) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #12836 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 12 *> proper extension: 08g5q7; *> query: (?x7260, 018swb) <- people(?x7260, ?x11011), people(?x7260, ?x9977), people(?x7260, ?x6934), nationality(?x11011, ?x512), celebrities_impersonated(?x3649, ?x11011), award(?x11011, ?x458), type_of_union(?x6934, ?x566), film(?x9977, ?x4024), participant(?x6934, ?x9355) *> conf = 0.07 ranks of expected_values: 557 EVAL 01_qc_ people 02xyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 41.000 0.375 http://example.org/people/cause_of_death/people EVAL 01_qc_ people 04264n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 41.000 0.375 http://example.org/people/cause_of_death/people EVAL 01_qc_ people 018swb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 41.000 0.375 http://example.org/people/cause_of_death/people #5057-03xx9l PRED entity: 03xx9l PRED relation: program PRED expected values: 06hwzy => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 16): 06hwzy (0.56 #110, 0.54 #136, 0.37 #240), 0cpz4k (0.29 #9, 0.08 #217, 0.07 #165), 01j7mr (0.16 #86, 0.10 #242, 0.07 #164), 0275kr (0.14 #21, 0.05 #99, 0.04 #177), 026bfsh (0.11 #141, 0.07 #115, 0.06 #245), 01h1bf (0.11 #163, 0.07 #293, 0.04 #346), 01b7h8 (0.08 #227, 0.07 #123, 0.07 #149), 070ltt (0.05 #98, 0.04 #176, 0.02 #306), 05_z42 (0.05 #90, 0.02 #220, 0.02 #246), 0304nh (0.05 #296, 0.04 #218, 0.04 #244) >> Best rule #110 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 02v60l; >> query: (?x7625, 06hwzy) <- film(?x7625, ?x2153), person(?x3480, ?x7625), ?x3480 = 043q4d, people(?x2510, ?x7625) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xx9l program 06hwzy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.556 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #5056-0symg PRED entity: 0symg PRED relation: genre PRED expected values: 01hmnh 04xvh5 => 95 concepts (76 used for prediction) PRED predicted values (max 10 best out of 100): 05p553 (0.43 #6160, 0.43 #1743, 0.42 #930), 03k9fj (0.43 #589, 0.40 #821, 0.26 #3380), 01jfsb (0.42 #2101, 0.40 #822, 0.40 #474), 01hmnh (0.40 #1176, 0.33 #132, 0.18 #1525), 06n90 (0.40 #823, 0.29 #591, 0.20 #475), 02l7c8 (0.34 #2337, 0.33 #246, 0.32 #4546), 04xvh5 (0.33 #31, 0.29 #727, 0.25 #379), 060__y (0.33 #15, 0.29 #711, 0.20 #2338), 0vgkd (0.33 #124, 0.13 #1052, 0.11 #1749), 06nbt (0.33 #139, 0.08 #951, 0.07 #1067) >> Best rule #6160 for best value: >> intensional similarity = 4 >> extensional distance = 966 >> proper extension: 0fq27fp; >> query: (?x11027, 05p553) <- genre(?x11027, ?x1805), currency(?x11027, ?x170), genre(?x5429, ?x1805), ?x5429 = 02psgq >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1176 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 0bq8tmw; 05nlx4; *> query: (?x11027, 01hmnh) <- nominated_for(?x1063, ?x11027), film(?x2444, ?x11027), genre(?x11027, ?x53), ?x2444 = 0jfx1 *> conf = 0.40 ranks of expected_values: 4, 7 EVAL 0symg genre 04xvh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 95.000 76.000 0.434 http://example.org/film/film/genre EVAL 0symg genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 95.000 76.000 0.434 http://example.org/film/film/genre #5055-0127gn PRED entity: 0127gn PRED relation: nationality PRED expected values: 09c7w0 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 40): 07t21 (0.84 #9242, 0.02 #1139), 09c7w0 (0.79 #2913, 0.77 #9846, 0.76 #1505), 05vz3zq (0.36 #8940, 0.11 #12456, 0.04 #7435), 06bnz (0.29 #9343, 0.11 #12456, 0.07 #1344), 02jx1 (0.16 #1235, 0.15 #4050, 0.14 #5554), 07ssc (0.16 #716, 0.15 #1822, 0.14 #415), 0345h (0.11 #12456, 0.09 #832, 0.08 #1032), 0f8l9c (0.11 #12456, 0.08 #923, 0.07 #622), 0h7x (0.11 #12456, 0.07 #1137, 0.06 #1641), 05r7t (0.11 #12456, 0.07 #178, 0.07 #278) >> Best rule #9242 for best value: >> intensional similarity = 2 >> extensional distance = 1519 >> proper extension: 0784v1; >> query: (?x5132, ?x1471) <- place_of_birth(?x5132, ?x6494), country(?x6494, ?x1471) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2913 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 191 *> proper extension: 05218gr; *> query: (?x5132, 09c7w0) <- award_nominee(?x5150, ?x5132), place_of_death(?x5132, ?x739), award_winner(?x2324, ?x5150) *> conf = 0.79 ranks of expected_values: 2 EVAL 0127gn nationality 09c7w0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 134.000 134.000 0.837 http://example.org/people/person/nationality #5054-02tqm5 PRED entity: 02tqm5 PRED relation: country PRED expected values: 0chghy => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 20): 07ssc (0.48 #946, 0.37 #4198, 0.31 #16), 02jx1 (0.48 #946, 0.37 #4198), 0chghy (0.35 #189, 0.06 #425, 0.04 #366), 0f8l9c (0.14 #373, 0.10 #314, 0.08 #4038), 0345h (0.10 #262, 0.09 #4045, 0.09 #4104), 07s9rl0 (0.06 #709, 0.06 #3665, 0.06 #1656), 03h64 (0.05 #399, 0.02 #281, 0.02 #635), 03rjj (0.05 #124, 0.04 #360, 0.03 #596), 0d05w3 (0.04 #396, 0.02 #632, 0.02 #1048), 0d060g (0.04 #185, 0.04 #421, 0.04 #4027) >> Best rule #946 for best value: >> intensional similarity = 3 >> extensional distance = 823 >> proper extension: 07s8z_l; 01j95; >> query: (?x3246, ?x512) <- award_winner(?x3246, ?x12651), place_of_birth(?x12651, ?x362), nationality(?x12651, ?x512) >> conf = 0.48 => this is the best rule for 2 predicted values *> Best rule #189 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0d_2fb; 04z_3pm; *> query: (?x3246, 0chghy) <- country(?x3246, ?x1023), genre(?x3246, ?x53), ?x1023 = 0ctw_b *> conf = 0.35 ranks of expected_values: 3 EVAL 02tqm5 country 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 73.000 73.000 0.481 http://example.org/film/film/country #5053-05sb1 PRED entity: 05sb1 PRED relation: teams PRED expected values: 038_0z => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 179): 046fz5 (0.14 #852, 0.02 #7674, 0.02 #9828), 024nj1 (0.07 #1430, 0.06 #1789, 0.04 #2507), 035l_9 (0.07 #1391, 0.06 #1750, 0.04 #2468), 086x3 (0.07 #1436, 0.06 #1795, 0.04 #2513), 02ltg3 (0.07 #1160, 0.06 #1519, 0.04 #2237), 023fxp (0.07 #1435, 0.06 #1794, 0.02 #6103), 04nrcg (0.07 #1079, 0.02 #11850, 0.01 #19389), 01l3vx (0.06 #1480, 0.06 #1839, 0.04 #2198), 023zd7 (0.06 #1598, 0.06 #1957, 0.04 #2316), 02bh_v (0.06 #1651, 0.06 #2010, 0.04 #2369) >> Best rule #852 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01jssp; >> query: (?x2236, 046fz5) <- adjoins(?x2236, ?x3411), organization(?x2236, ?x127), category(?x3411, ?x134) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05sb1 teams 038_0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 171.000 0.143 http://example.org/sports/sports_team_location/teams #5052-025sc50 PRED entity: 025sc50 PRED relation: artists PRED expected values: 0b68vs 0pyg6 05w6cw => 56 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1006): 012vd6 (0.75 #14842, 0.71 #12920, 0.70 #17728), 024qwq (0.71 #14227, 0.71 #13266, 0.67 #7491), 0407f (0.71 #12746, 0.62 #14668, 0.60 #17554), 012z8_ (0.71 #12846, 0.57 #13807, 0.50 #17654), 015srx (0.71 #13919, 0.57 #12958, 0.50 #9108), 01vzxld (0.67 #8498, 0.67 #7536, 0.67 #6573), 0134wr (0.67 #8333, 0.67 #7371, 0.57 #14107), 02p68d (0.67 #8335, 0.67 #7373, 0.50 #3526), 016jfw (0.67 #8166, 0.67 #7204, 0.43 #13940), 0137hn (0.67 #8206, 0.67 #7244, 0.43 #13980) >> Best rule #14842 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 03_d0; 0155w; >> query: (?x3562, 012vd6) <- artists(?x3562, ?x10712), artists(?x3562, ?x6715), artists(?x3562, ?x5906), artists(?x3562, ?x2138), artists(?x3562, ?x702), ?x6715 = 011z3g, ?x702 = 01vvycq, group(?x4873, ?x10712), ?x5906 = 0127s7, award(?x2138, ?x401) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #4518 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 02ny8t; *> query: (?x3562, 05w6cw) <- artists(?x3562, ?x5514), artists(?x3562, ?x2226), artists(?x3562, ?x1378), artists(?x3562, ?x1338), ?x2226 = 09k2t1, award(?x1378, ?x567), ?x5514 = 04cr6qv, participant(?x1338, ?x2352), category(?x1378, ?x134) *> conf = 0.60 ranks of expected_values: 34, 72, 73 EVAL 025sc50 artists 05w6cw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 56.000 31.000 0.750 http://example.org/music/genre/artists EVAL 025sc50 artists 0pyg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 56.000 31.000 0.750 http://example.org/music/genre/artists EVAL 025sc50 artists 0b68vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 56.000 31.000 0.750 http://example.org/music/genre/artists #5051-07w4j PRED entity: 07w4j PRED relation: institution! PRED expected values: 019v9k => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 18): 02_xgp2 (0.85 #51, 0.65 #151, 0.63 #10), 019v9k (0.76 #47, 0.63 #6, 0.62 #147), 04zx3q1 (0.50 #43, 0.44 #2, 0.34 #143), 07s6fsf (0.50 #42, 0.40 #142, 0.30 #347), 027f2w (0.42 #48, 0.37 #7, 0.26 #148), 013zdg (0.34 #46, 0.33 #5, 0.24 #189), 0bjrnt (0.30 #4, 0.21 #45, 0.12 #145), 01rr_d (0.27 #55, 0.22 #14, 0.15 #155), 03mkk4 (0.19 #50, 0.19 #9, 0.14 #150), 02cq61 (0.19 #15, 0.13 #56, 0.06 #280) >> Best rule #51 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 019q50; >> query: (?x2196, 02_xgp2) <- currency(?x2196, ?x1099), institution(?x865, ?x2196), list(?x2196, ?x2197) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 019q50; *> query: (?x2196, 019v9k) <- currency(?x2196, ?x1099), institution(?x865, ?x2196), list(?x2196, ?x2197) *> conf = 0.76 ranks of expected_values: 2 EVAL 07w4j institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.855 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #5050-0n1s0 PRED entity: 0n1s0 PRED relation: film_crew_role PRED expected values: 09zzb8 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.69 #1492, 0.69 #1081, 0.69 #1118), 02r96rf (0.62 #230, 0.61 #1495, 0.59 #1084), 0dxtw (0.34 #1502, 0.33 #274, 0.32 #1091), 01vx2h (0.29 #1503, 0.29 #87, 0.27 #1092), 089g0h (0.29 #96, 0.20 #134, 0.15 #247), 015h31 (0.29 #84, 0.20 #122, 0.10 #161), 01pvkk (0.27 #1504, 0.26 #1130, 0.26 #1093), 01xy5l_ (0.25 #52, 0.15 #241, 0.14 #90), 05smlt (0.25 #22, 0.02 #396, 0.02 #1028), 0215hd (0.23 #246, 0.17 #283, 0.11 #1511) >> Best rule #1492 for best value: >> intensional similarity = 2 >> extensional distance = 1267 >> proper extension: 0fq27fp; >> query: (?x5984, 09zzb8) <- genre(?x5984, ?x53), film_crew_role(?x5984, ?x1171) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n1s0 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.693 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #5049-0f502 PRED entity: 0f502 PRED relation: award_nominee! PRED expected values: 042ly5 => 109 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1011): 0kjrx (0.81 #55631, 0.81 #143697, 0.81 #141379), 02bkdn (0.81 #55631, 0.81 #143697, 0.81 #141379), 07ddz9 (0.81 #55631, 0.81 #143697, 0.81 #141379), 01d1st (0.78 #8517, 0.75 #1565, 0.14 #76491), 06151l (0.75 #32, 0.74 #6984, 0.14 #76491), 015t56 (0.75 #606, 0.74 #7558, 0.06 #56237), 0278x6s (0.75 #1188, 0.70 #8140, 0.14 #76491), 08swgx (0.75 #637, 0.65 #7589, 0.03 #51630), 03_6y (0.75 #778, 0.57 #7730, 0.02 #51771), 0btpx (0.74 #8801, 0.62 #1849, 0.14 #76491) >> Best rule #55631 for best value: >> intensional similarity = 2 >> extensional distance = 417 >> proper extension: 0knjh; >> query: (?x4360, ?x71) <- participant(?x5625, ?x4360), award_nominee(?x4360, ?x71) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #76491 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 514 *> proper extension: 01w5gg6; *> query: (?x4360, ?x221) <- award_nominee(?x7525, ?x4360), artists(?x671, ?x4360), award_nominee(?x221, ?x7525) *> conf = 0.14 ranks of expected_values: 67 EVAL 0f502 award_nominee! 042ly5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 109.000 65.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5048-02q0k7v PRED entity: 02q0k7v PRED relation: language PRED expected values: 0jzc => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 49): 04306rv (0.55 #2450, 0.13 #586, 0.12 #237), 064_8sq (0.16 #370, 0.15 #254, 0.14 #2588), 06nm1 (0.15 #243, 0.14 #533, 0.14 #301), 06b_j (0.11 #80, 0.09 #371, 0.09 #1012), 0jzc (0.11 #77, 0.08 #193, 0.08 #252), 02bjrlw (0.11 #234, 0.09 #117, 0.09 #408), 03_9r (0.09 #9, 0.08 #300, 0.07 #416), 0295r (0.09 #28), 0653m (0.07 #826, 0.06 #710, 0.06 #943), 012w70 (0.06 #535, 0.04 #827, 0.04 #419) >> Best rule #2450 for best value: >> intensional similarity = 4 >> extensional distance = 604 >> proper extension: 016zfm; 0fpxp; >> query: (?x7694, ?x254) <- nominated_for(?x4286, ?x7694), film(?x4286, ?x278), nationality(?x4286, ?x512), languages(?x4286, ?x254) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #77 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 0yx7h; *> query: (?x7694, 0jzc) <- genre(?x7694, ?x4205), genre(?x7694, ?x53), ?x53 = 07s9rl0, ?x4205 = 0c3351, production_companies(?x7694, ?x382) *> conf = 0.11 ranks of expected_values: 5 EVAL 02q0k7v language 0jzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 92.000 92.000 0.547 http://example.org/film/film/language #5047-04wgh PRED entity: 04wgh PRED relation: featured_film_locations! PRED expected values: 05dy7p => 142 concepts (104 used for prediction) PRED predicted values (max 10 best out of 681): 0413cff (0.18 #2573, 0.15 #4776, 0.12 #12853), 047csmy (0.18 #3331, 0.10 #9205, 0.09 #11408), 04dsnp (0.14 #3002, 0.13 #64, 0.10 #8876), 0872p_c (0.14 #3014, 0.10 #8888, 0.09 #11091), 072x7s (0.14 #3049, 0.09 #11126, 0.07 #8923), 024l2y (0.14 #3050, 0.08 #3785, 0.07 #8924), 0ds2n (0.14 #3166, 0.08 #3901, 0.07 #9040), 033srr (0.14 #3215, 0.08 #3950, 0.07 #9089), 02vz6dn (0.14 #3474, 0.07 #9348, 0.07 #10083), 09fc83 (0.13 #378, 0.05 #9925, 0.05 #3316) >> Best rule #2573 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 04v09; >> query: (?x1273, 0413cff) <- organization(?x1273, ?x127), country(?x471, ?x1273), featured_film_locations(?x549, ?x1273) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04wgh featured_film_locations! 05dy7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 104.000 0.182 http://example.org/film/film/featured_film_locations #5046-02d45s PRED entity: 02d45s PRED relation: award PRED expected values: 09sb52 => 73 concepts (55 used for prediction) PRED predicted values (max 10 best out of 261): 09sb52 (0.60 #442, 0.34 #6874, 0.34 #2050), 027dtxw (0.48 #406, 0.23 #4, 0.13 #21716), 0f4x7 (0.44 #432, 0.23 #30, 0.13 #21716), 02x73k6 (0.38 #462, 0.09 #22119, 0.06 #3678), 0bdwqv (0.33 #573, 0.07 #11427, 0.07 #9015), 0cqh46 (0.33 #453, 0.05 #3669, 0.04 #2061), 04kxsb (0.27 #527, 0.15 #125, 0.13 #21716), 02w9sd7 (0.27 #571, 0.15 #169, 0.13 #21716), 09qv_s (0.27 #553, 0.13 #21716, 0.13 #19302), 02x4w6g (0.27 #516, 0.06 #3330, 0.05 #2124) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 04l19_; >> query: (?x10866, 09sb52) <- film(?x10866, ?x1071), award(?x10866, ?x451), ?x451 = 099jhq >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d45s award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 55.000 0.596 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5045-046p9 PRED entity: 046p9 PRED relation: group! PRED expected values: 02bxd 02hnl => 79 concepts (52 used for prediction) PRED predicted values (max 10 best out of 114): 02hnl (0.83 #1523, 0.81 #692, 0.80 #1273), 018vs (0.63 #1674, 0.62 #1923, 0.61 #2006), 028tv0 (0.46 #1673, 0.37 #2088, 0.36 #1922), 03qjg (0.37 #1540, 0.28 #1789, 0.27 #1955), 0l14qv (0.33 #1501, 0.30 #1251, 0.30 #586), 02snj9 (0.25 #218, 0.09 #665, 0.08 #467), 07y_7 (0.24 #1498, 0.24 #667, 0.23 #1248), 05r5c (0.23 #2001, 0.23 #2168, 0.23 #1669), 03qlv7 (0.18 #933, 0.17 #1265, 0.11 #517), 013y1f (0.15 #1521, 0.15 #1936, 0.15 #2019) >> Best rule #1523 for best value: >> intensional similarity = 6 >> extensional distance = 44 >> proper extension: 01fl3; 07yg2; 07r1_; 06mj4; 06br6t; >> query: (?x8156, 02hnl) <- group(?x227, ?x8156), artists(?x6210, ?x8156), artists(?x6210, ?x8429), artists(?x6210, ?x7193), ?x8429 = 01lf293, ?x7193 = 018d6l >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 111 EVAL 046p9 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 52.000 0.826 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 046p9 group! 02bxd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 79.000 52.000 0.826 http://example.org/music/performance_role/regular_performances./music/group_membership/group #5044-02dh86 PRED entity: 02dh86 PRED relation: student! PRED expected values: 04gb7 => 132 concepts (129 used for prediction) PRED predicted values (max 10 best out of 40): 01lhf (0.33 #116, 0.06 #360, 0.04 #543), 02822 (0.23 #1983, 0.19 #1495, 0.16 #1129), 03qsdpk (0.14 #1988, 0.07 #2110, 0.07 #2172), 01zc2w (0.11 #1146, 0.05 #2000, 0.05 #1512), 0w7c (0.10 #1994, 0.05 #1140, 0.05 #1506), 03g3w (0.08 #1119, 0.07 #1973, 0.05 #2157), 0fdys (0.06 #2103, 0.06 #1981, 0.06 #2165), 02vxn (0.05 #1102, 0.05 #1956, 0.02 #2078), 06n6p (0.05 #388, 0.04 #449, 0.03 #754), 05qfh (0.05 #1125, 0.04 #1369, 0.04 #1979) >> Best rule #116 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0sx5w; >> query: (?x2739, 01lhf) <- person(?x3775, ?x2739), nationality(?x2739, ?x94), actor(?x10827, ?x2739), student(?x1771, ?x2739) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #643 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 012x1l; *> query: (?x2739, 04gb7) <- inductee(?x9953, ?x2739), gender(?x2739, ?x514), ?x514 = 02zsn *> conf = 0.03 ranks of expected_values: 15 EVAL 02dh86 student! 04gb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 132.000 129.000 0.333 http://example.org/education/field_of_study/students_majoring./education/education/student #5043-05qgd9 PRED entity: 05qgd9 PRED relation: educational_institution PRED expected values: 05qgd9 => 162 concepts (113 used for prediction) PRED predicted values (max 10 best out of 210): 0g8rj (0.33 #163, 0.23 #4314, 0.20 #1079), 07x4c (0.23 #4314, 0.14 #2394, 0.12 #11324), 05qgd9 (0.23 #4314, 0.12 #11324, 0.11 #20498), 07wf9 (0.23 #4314, 0.12 #11324, 0.11 #20498), 0d075m (0.23 #4314, 0.12 #11324, 0.11 #20498), 07wbk (0.23 #4314, 0.12 #11324, 0.11 #20498), 07wrz (0.14 #1675, 0.14 #1136, 0.11 #4371), 0hsb3 (0.14 #1812, 0.14 #1273, 0.08 #6664), 0g8fs (0.11 #4664, 0.11 #4124, 0.08 #6820), 015zyd (0.11 #4315, 0.11 #3775, 0.08 #6471) >> Best rule #163 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0g8rj; >> query: (?x12026, 0g8rj) <- organizations_founded(?x5254, ?x12026), currency(?x12026, ?x170), ?x5254 = 07cbs, school_type(?x12026, ?x3092) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4314 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 04gdr; *> query: (?x12026, ?x1912) <- organizations_founded(?x5254, ?x12026), contains(?x1426, ?x12026), organizations_founded(?x5254, ?x1912), people(?x5741, ?x5254) *> conf = 0.23 ranks of expected_values: 3 EVAL 05qgd9 educational_institution 05qgd9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 162.000 113.000 0.333 http://example.org/education/educational_institution_campus/educational_institution #5042-047jhq PRED entity: 047jhq PRED relation: gender PRED expected values: 02zsn => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #19, 0.80 #27, 0.79 #107), 02zsn (0.49 #175, 0.39 #30, 0.39 #44) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 04qr6d; >> query: (?x12616, 05zppz) <- religion(?x12616, ?x8967), profession(?x12616, ?x319), ?x8967 = 03j6c, ?x319 = 01d_h8 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #175 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 879 *> proper extension: 06lht1; *> query: (?x12616, ?x231) <- actor(?x12165, ?x12616), actor(?x12165, ?x9488), genre(?x12165, ?x5728), gender(?x9488, ?x231) *> conf = 0.49 ranks of expected_values: 2 EVAL 047jhq gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 170.000 170.000 0.879 http://example.org/people/person/gender #5041-01hn_t PRED entity: 01hn_t PRED relation: genre PRED expected values: 0hcr 0pr6f => 78 concepts (73 used for prediction) PRED predicted values (max 10 best out of 97): 05p553 (0.92 #1564, 0.85 #1154, 0.81 #3461), 0hcr (0.86 #759, 0.86 #595, 0.82 #677), 01z4y (0.65 #1578, 0.62 #1168, 0.54 #3475), 07s9rl0 (0.62 #329, 0.58 #3290, 0.58 #5360), 095bb (0.57 #614, 0.53 #696, 0.50 #285), 06nbt (0.56 #432, 0.44 #514, 0.36 #597), 0c4xc (0.53 #1603, 0.44 #1193, 0.39 #3500), 0pr6f (0.53 #709, 0.50 #298, 0.43 #627), 01htzx (0.49 #2482, 0.28 #2236, 0.23 #4880), 01hmnh (0.48 #2235, 0.44 #509, 0.29 #674) >> Best rule #1564 for best value: >> intensional similarity = 8 >> extensional distance = 47 >> proper extension: 01cjhz; >> query: (?x4275, 05p553) <- genre(?x4275, ?x10159), program(?x5007, ?x4275), genre(?x9340, ?x10159), genre(?x5583, ?x10159), ?x5583 = 099pks, country_of_origin(?x4275, ?x94), ?x9340 = 05nlzq, award_winner(?x2988, ?x5007) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #759 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 19 *> proper extension: 0vhm; 031kyy; 04mx8h4; *> query: (?x4275, 0hcr) <- genre(?x4275, ?x10159), program(?x5007, ?x4275), genre(?x8628, ?x10159), genre(?x5583, ?x10159), genre(?x3144, ?x10159), ?x5583 = 099pks, award_winner(?x5007, ?x2776), ?x8628 = 09g_31, ?x3144 = 015w8_ *> conf = 0.86 ranks of expected_values: 2, 8 EVAL 01hn_t genre 0pr6f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 78.000 73.000 0.918 http://example.org/tv/tv_program/genre EVAL 01hn_t genre 0hcr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 73.000 0.918 http://example.org/tv/tv_program/genre #5040-05w6cw PRED entity: 05w6cw PRED relation: artists! PRED expected values: 05bt6j 025sc50 => 67 concepts (22 used for prediction) PRED predicted values (max 10 best out of 179): 01lyv (0.81 #344, 0.19 #4364, 0.15 #4674), 025sc50 (0.72 #979, 0.71 #1288, 0.37 #669), 0ggx5q (0.63 #1316, 0.62 #1007, 0.24 #697), 06by7 (0.57 #3733, 0.46 #4351, 0.43 #951), 05bt6j (0.40 #1282, 0.36 #973, 0.31 #4373), 0mhfr (0.38 #334, 0.08 #4354, 0.07 #3736), 0gywn (0.31 #1296, 0.30 #987, 0.24 #677), 0y3_8 (0.30 #977, 0.26 #1286, 0.15 #667), 016clz (0.26 #624, 0.23 #314, 0.21 #3716), 03_d0 (0.22 #4341, 0.16 #4651, 0.15 #321) >> Best rule #344 for best value: >> intensional similarity = 2 >> extensional distance = 24 >> proper extension: 016qtt; 0249kn; 018ndc; 01wmgrf; 0mjn2; >> query: (?x8365, 01lyv) <- artists(?x10833, ?x8365), ?x10833 = 06924p >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #979 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 59 *> proper extension: 01trhmt; 015f7; 0gbwp; 0c7xjb; 04cr6qv; 01vzxld; *> query: (?x8365, 025sc50) <- nationality(?x8365, ?x94), artists(?x3996, ?x8365), artists(?x671, ?x8365), ?x3996 = 02lnbg, ?x671 = 064t9 *> conf = 0.72 ranks of expected_values: 2, 5 EVAL 05w6cw artists! 025sc50 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 22.000 0.808 http://example.org/music/genre/artists EVAL 05w6cw artists! 05bt6j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 67.000 22.000 0.808 http://example.org/music/genre/artists #5039-05g3ss PRED entity: 05g3ss PRED relation: profession PRED expected values: 02hrh1q => 175 concepts (144 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.91 #7858, 0.91 #2678, 0.91 #13778), 0dxtg (0.53 #7265, 0.41 #8153, 0.31 #10225), 02jknp (0.48 #7259, 0.39 #8147, 0.26 #451), 0nbcg (0.41 #8911, 0.23 #3139, 0.15 #2399), 03gjzk (0.38 #7267, 0.31 #8155, 0.30 #1495), 016z4k (0.35 #8884, 0.23 #3112, 0.14 #1928), 0dz3r (0.34 #8882, 0.22 #3110, 0.14 #2370), 0d1pc (0.30 #346, 0.20 #50, 0.15 #2270), 039v1 (0.24 #8916, 0.12 #3144, 0.07 #2256), 01c72t (0.23 #3131, 0.22 #8903, 0.08 #16601) >> Best rule #7858 for best value: >> intensional similarity = 4 >> extensional distance = 477 >> proper extension: 01yznp; >> query: (?x12204, 02hrh1q) <- nationality(?x12204, ?x2146), film(?x12204, ?x5247), religion(?x12204, ?x492), profession(?x12204, ?x319) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05g3ss profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 144.000 0.910 http://example.org/people/person/profession #5038-01817f PRED entity: 01817f PRED relation: award PRED expected values: 0c4z8 03tk6z => 164 concepts (138 used for prediction) PRED predicted values (max 10 best out of 315): 02x17c2 (0.59 #1806, 0.52 #2204, 0.33 #4194), 0c4z8 (0.48 #4051, 0.40 #2061, 0.38 #1265), 054krc (0.46 #9637, 0.45 #1677, 0.44 #2075), 01by1l (0.44 #14835, 0.42 #3293, 0.41 #9263), 09sb52 (0.44 #2827, 0.34 #7603, 0.30 #15563), 025m8y (0.38 #9648, 0.26 #4076, 0.24 #2086), 02gdjb (0.37 #10961, 0.14 #1807, 0.12 #9767), 099vwn (0.36 #1803, 0.36 #2201, 0.25 #10161), 0l8z1 (0.35 #9615, 0.31 #4043, 0.27 #1655), 02f73b (0.33 #2669, 0.26 #9435, 0.21 #7445) >> Best rule #1806 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0pgjm; 02r4qs; 02cyfz; 02cx72; 01x6v6; 02jxkw; 01ycfv; 05q9g1; >> query: (?x4537, 02x17c2) <- profession(?x4537, ?x220), award_winner(?x2238, ?x4537), ?x2238 = 025m8l, type_of_union(?x4537, ?x566) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #4051 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 40 *> proper extension: 025cn2; *> query: (?x4537, 0c4z8) <- award_winner(?x1389, ?x4537), award_winner(?x1323, ?x4537), award(?x4909, ?x1389), group(?x227, ?x4909), ?x1323 = 0gqz2 *> conf = 0.48 ranks of expected_values: 2, 93 EVAL 01817f award 03tk6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 164.000 138.000 0.591 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01817f award 0c4z8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 164.000 138.000 0.591 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #5037-03rx9 PRED entity: 03rx9 PRED relation: story_by! PRED expected values: 02qkwl => 165 concepts (134 used for prediction) PRED predicted values (max 10 best out of 141): 01cycq (0.33 #257, 0.03 #4717, 0.01 #7461), 07tw_b (0.08 #1852, 0.02 #4940, 0.01 #8027), 065zlr (0.08 #1797, 0.01 #10030), 0kvbl6 (0.07 #2285, 0.06 #2971, 0.04 #3658), 011wtv (0.07 #2218, 0.06 #2904, 0.04 #3591), 0jdgr (0.07 #2138, 0.06 #2824, 0.04 #3511), 0gvrws1 (0.07 #2124, 0.06 #2810, 0.04 #3497), 08ct6 (0.06 #2914, 0.04 #3601, 0.03 #4630), 04gcyg (0.06 #3005, 0.04 #3692, 0.03 #4721), 031hcx (0.06 #2989, 0.04 #3676, 0.03 #4705) >> Best rule #257 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 06bng; >> query: (?x9738, 01cycq) <- award_winner(?x575, ?x9738), nationality(?x9738, ?x94), influenced_by(?x1727, ?x9738), ?x1727 = 0c3kw >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03rx9 story_by! 02qkwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 134.000 0.333 http://example.org/film/film/story_by #5036-07vhb PRED entity: 07vhb PRED relation: company! PRED expected values: 01n1gc => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 104): 06y3r (0.12 #420, 0.06 #1640, 0.04 #2128), 0d05fv (0.08 #333, 0.06 #1553, 0.03 #577), 07n39 (0.06 #677, 0.05 #1409, 0.03 #1897), 06y7d (0.06 #720, 0.05 #1452, 0.03 #964), 03gkn5 (0.04 #2990, 0.04 #7388, 0.03 #6407), 01bpn (0.04 #326, 0.03 #570, 0.03 #814), 01mr2g6 (0.04 #406, 0.03 #650, 0.03 #894), 01g6bk (0.04 #473, 0.03 #717, 0.02 #1205), 03v40v (0.04 #351, 0.03 #595, 0.02 #1083), 099p5 (0.04 #427, 0.02 #1159, 0.02 #1891) >> Best rule #420 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 01n073; >> query: (?x5280, 06y3r) <- state_province_region(?x5280, ?x1227), ?x1227 = 01n7q, list(?x5280, ?x2197) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07vhb company! 01n1gc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 102.000 0.120 http://example.org/people/person/employment_history./business/employment_tenure/company #5035-01nyl PRED entity: 01nyl PRED relation: adjoins PRED expected values: 01nln => 72 concepts (71 used for prediction) PRED predicted values (max 10 best out of 361): 01nln (0.83 #40044, 0.82 #17703, 0.82 #15393), 06tw8 (0.83 #40044, 0.82 #17703, 0.82 #15393), 05rznz (0.83 #40044, 0.82 #16163, 0.81 #17702), 01nyl (0.22 #49303, 0.22 #44675, 0.22 #25403), 07tp2 (0.22 #49303, 0.22 #44675, 0.22 #25403), 04gqr (0.22 #49303, 0.22 #44675, 0.22 #25403), 019pcs (0.22 #49303, 0.22 #44675, 0.22 #53162), 02k54 (0.22 #49303, 0.22 #44675, 0.22 #53162), 088vb (0.22 #44675, 0.22 #25403, 0.22 #53162), 07dzf (0.22 #44675, 0.22 #25403, 0.22 #53162) >> Best rule #40044 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 0cc56; 0glb5; 0vh3; 0kn68; >> query: (?x7871, ?x5457) <- adjoins(?x7871, ?x2804), administrative_parent(?x7871, ?x551), adjoins(?x5457, ?x7871) >> conf = 0.83 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 01nyl adjoins 01nln CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 71.000 0.827 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #5034-02vyw PRED entity: 02vyw PRED relation: award_winner! PRED expected values: 0bzkgg => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 134): 0hndn2q (0.14 #40, 0.12 #316, 0.11 #454), 0drtv8 (0.13 #201, 0.09 #753, 0.05 #1581), 05c1t6z (0.10 #1533, 0.07 #2637, 0.06 #1119), 0466p0j (0.10 #2833, 0.09 #2281, 0.06 #3247), 02rjjll (0.09 #2213, 0.09 #2765, 0.07 #5), 09k5jh7 (0.09 #1047, 0.07 #219, 0.06 #1185), 013b2h (0.09 #2837, 0.08 #2285, 0.07 #215), 09q_6t (0.07 #8, 0.07 #1526, 0.07 #146), 02wzl1d (0.07 #11, 0.07 #149, 0.06 #1115), 01c6qp (0.07 #19, 0.07 #157, 0.06 #295) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02yy8; >> query: (?x3662, 0hndn2q) <- influenced_by(?x3662, ?x5669), organizations_founded(?x3662, ?x10503), student(?x3394, ?x3662) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #320 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 09bg4l; 07cbs; 04xzm; 03_lf; 05g7q; 0d3k14; 01lc5; 019fz; *> query: (?x3662, 0bzkgg) <- influenced_by(?x3662, ?x5669), organizations_founded(?x3662, ?x10503), location(?x3662, ?x2632) *> conf = 0.06 ranks of expected_values: 24 EVAL 02vyw award_winner! 0bzkgg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 118.000 118.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #5033-026hxwx PRED entity: 026hxwx PRED relation: film! PRED expected values: 073749 0dq9wx => 109 concepts (58 used for prediction) PRED predicted values (max 10 best out of 843): 016tt2 (0.50 #35372, 0.47 #97811, 0.46 #45779), 073749 (0.33 #708, 0.05 #2788, 0.05 #4868), 028k57 (0.33 #791, 0.03 #2871, 0.02 #4951), 028d4v (0.33 #392, 0.03 #2472, 0.02 #4552), 021bk (0.22 #378, 0.03 #2458, 0.02 #4538), 07m77x (0.22 #1541, 0.03 #3621, 0.02 #5701), 07y8l9 (0.22 #973, 0.02 #88377, 0.02 #80054), 051wwp (0.22 #876, 0.02 #79957, 0.02 #86200), 03qd_ (0.22 #123, 0.01 #79204, 0.01 #97935), 0pgjm (0.22 #215) >> Best rule #35372 for best value: >> intensional similarity = 5 >> extensional distance = 246 >> proper extension: 0bq8tmw; 0b76kw1; 011ypx; 05nyqk; >> query: (?x6500, ?x574) <- executive_produced_by(?x6500, ?x4854), nominated_for(?x932, ?x6500), nominated_for(?x574, ?x6500), participant(?x702, ?x932), genre(?x6500, ?x258) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #708 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 07y9w5; 02stbw; 02ht1k; 027s39y; 01k0xy; 04g73n; *> query: (?x6500, 073749) <- currency(?x6500, ?x170), film(?x3402, ?x6500), film(?x574, ?x6500), ?x3402 = 01pcbg *> conf = 0.33 ranks of expected_values: 2 EVAL 026hxwx film! 0dq9wx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 58.000 0.498 http://example.org/film/actor/film./film/performance/film EVAL 026hxwx film! 073749 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 58.000 0.498 http://example.org/film/actor/film./film/performance/film #5032-0456zg PRED entity: 0456zg PRED relation: titles! PRED expected values: 02l7c8 => 57 concepts (23 used for prediction) PRED predicted values (max 10 best out of 62): 01z4y (0.38 #345, 0.33 #450, 0.21 #138), 07s9rl0 (0.36 #1865, 0.36 #1762, 0.34 #2283), 04xvlr (0.29 #936, 0.23 #1765, 0.22 #2182), 02l7c8 (0.22 #2282, 0.22 #2281, 0.22 #1864), 0gsy3b (0.22 #2282, 0.22 #2281, 0.22 #1864), 01t_vv (0.22 #2282, 0.22 #2281, 0.22 #1864), 05p553 (0.22 #2282, 0.22 #2281, 0.22 #1864), 06cvj (0.22 #2282, 0.22 #2281, 0.22 #1864), 024qqx (0.16 #81, 0.12 #703, 0.11 #600), 07c52 (0.13 #132, 0.12 #1170, 0.11 #1274) >> Best rule #345 for best value: >> intensional similarity = 5 >> extensional distance = 80 >> proper extension: 0dq626; 09p35z; 0963mq; 0fvr1; 03m8y5; 04grkmd; 0gbfn9; 05r3qc; 058kh7; 03wy8t; ... >> query: (?x8358, 01z4y) <- genre(?x8358, ?x6674), genre(?x8358, ?x53), ?x53 = 07s9rl0, ?x6674 = 01t_vv, film(?x902, ?x8358) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2282 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 818 *> proper extension: 02d413; 0g22z; 0140g4; 0b2v79; 01jc6q; 0c0yh4; 0yyg4; 0gzy02; 0n0bp; 0dj0m5; ... *> query: (?x8358, ?x1403) <- genre(?x8358, ?x1403), genre(?x8358, ?x53), ?x53 = 07s9rl0, nominated_for(?x1291, ?x8358) *> conf = 0.22 ranks of expected_values: 4 EVAL 0456zg titles! 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 57.000 23.000 0.378 http://example.org/media_common/netflix_genre/titles #5031-044zvm PRED entity: 044zvm PRED relation: people! PRED expected values: 038723 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 41): 0x67 (0.29 #87, 0.09 #4322, 0.09 #5015), 033tf_ (0.25 #315, 0.21 #392, 0.16 #469), 041rx (0.21 #543, 0.21 #389, 0.19 #312), 01qhm_ (0.20 #237, 0.14 #83, 0.12 #314), 048z7l (0.14 #194, 0.12 #348, 0.11 #502), 07bch9 (0.14 #100, 0.06 #331, 0.06 #793), 09vc4s (0.14 #86, 0.06 #317, 0.05 #471), 065b6q (0.14 #80, 0.04 #773, 0.04 #1389), 0xnvg (0.12 #321, 0.11 #552, 0.11 #398), 09kr66 (0.10 #274, 0.03 #582, 0.02 #1583) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 05fnl9; 02lhm2; >> query: (?x12041, 0x67) <- film(?x12041, ?x1702), award_nominee(?x12041, ?x496), ?x1702 = 0c00zd0 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #839 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 148 *> proper extension: 02wb6yq; *> query: (?x12041, 038723) <- participant(?x496, ?x12041), nominated_for(?x12041, ?x4881), genre(?x4881, ?x53) *> conf = 0.02 ranks of expected_values: 29 EVAL 044zvm people! 038723 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 115.000 115.000 0.286 http://example.org/people/ethnicity/people #5030-0gq_v PRED entity: 0gq_v PRED relation: nominated_for PRED expected values: 0b2v79 0jzw 0b73_1d 0147sh 04w7rn 0cz_ym 0661ql3 0kb57 0ndwt2w 0dnw1 0jsf6 0hv4t 02z0f6l 0bdjd 0298n7 025scjj 02jxrw 043n1r5 01gvsn => 50 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1139): 0gmgwnv (0.80 #9046, 0.78 #12335, 0.77 #16450), 0ch26b_ (0.78 #12335, 0.77 #16450, 0.77 #12334), 04v8x9 (0.78 #12335, 0.77 #16450, 0.77 #12334), 0pv3x (0.78 #12335, 0.77 #16450, 0.77 #12334), 01jc6q (0.78 #12335, 0.77 #16450, 0.77 #12334), 0qmfk (0.78 #12335, 0.77 #16450, 0.77 #12334), 05sbv3 (0.78 #12335, 0.77 #16450, 0.77 #12334), 0hfzr (0.78 #12335, 0.77 #16450, 0.77 #12334), 0286gm1 (0.78 #12335, 0.77 #16450, 0.77 #12334), 029jt9 (0.78 #12335, 0.77 #16450, 0.77 #12334) >> Best rule #9046 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0p9sw; 0l8z1; >> query: (?x484, 0gmgwnv) <- ceremony(?x484, ?x78), nominated_for(?x484, ?x11483), nominated_for(?x484, ?x5183), ?x5183 = 0cq8qq, film_release_region(?x11483, ?x94) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5079 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0gqy2; *> query: (?x484, 0bdjd) <- ceremony(?x484, ?x78), nominated_for(?x484, ?x11483), nominated_for(?x484, ?x5533), ?x11483 = 01f69m, ?x5533 = 027ct7c *> conf = 0.60 ranks of expected_values: 50, 52, 54, 56, 57, 66, 99, 168, 198, 247, 251, 252, 261, 263, 342, 401, 430, 440, 525 EVAL 0gq_v nominated_for 01gvsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 043n1r5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 02jxrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 025scjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0298n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0bdjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 02z0f6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0hv4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0jsf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0dnw1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0ndwt2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0kb57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0cz_ym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 04w7rn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0147sh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0b73_1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0jzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gq_v nominated_for 0b2v79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 50.000 26.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5029-011ysn PRED entity: 011ysn PRED relation: film! PRED expected values: 0408np 01cj6y => 68 concepts (31 used for prediction) PRED predicted values (max 10 best out of 804): 0150t6 (0.43 #49769, 0.41 #62217, 0.41 #55996), 0kvqv (0.40 #62218, 0.34 #45620, 0.31 #45621), 016tt2 (0.40 #62218, 0.34 #45620, 0.31 #51848), 04sry (0.18 #3342, 0.03 #5415, 0.02 #11634), 01yb09 (0.11 #197, 0.03 #10562, 0.01 #29226), 01wbg84 (0.11 #46, 0.03 #4192, 0.03 #20780), 01swck (0.11 #793, 0.03 #4939, 0.02 #38117), 03m3nzf (0.11 #1559, 0.03 #5705, 0.01 #16070), 0bvls5 (0.11 #2061, 0.03 #6207), 03j367r (0.11 #1870, 0.03 #6016) >> Best rule #49769 for best value: >> intensional similarity = 4 >> extensional distance = 675 >> proper extension: 0cwrr; 01h72l; 01h1bf; 02kk_c; 05sy0cv; 07s8z_l; 03d17dg; 06r1k; 025x1t; 0gxsh4; >> query: (?x3496, ?x3069) <- award_winner(?x3496, ?x3069), award_nominee(?x9719, ?x3069), award_nominee(?x3069, ?x1489), category(?x9719, ?x134) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #11117 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 03kwtb; 05whq_9; 081l_; *> query: (?x3496, 01cj6y) <- film_festivals(?x3496, ?x9080), category(?x3496, ?x134), ?x134 = 08mbj5d *> conf = 0.02 ranks of expected_values: 552, 559 EVAL 011ysn film! 01cj6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 31.000 0.426 http://example.org/film/actor/film./film/performance/film EVAL 011ysn film! 0408np CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 68.000 31.000 0.426 http://example.org/film/actor/film./film/performance/film #5028-01ksr1 PRED entity: 01ksr1 PRED relation: nationality PRED expected values: 09c7w0 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.75 #2805, 0.75 #1403, 0.75 #2504), 02jx1 (0.50 #33, 0.40 #133, 0.34 #5410), 07ssc (0.34 #5410, 0.33 #9517, 0.21 #15), 04xn_ (0.34 #5410, 0.33 #9517, 0.07 #174), 0d060g (0.34 #5410, 0.33 #9517, 0.05 #1209), 0f8l9c (0.34 #5410, 0.07 #122, 0.03 #323), 03rt9 (0.34 #5410, 0.02 #514, 0.02 #614), 026mj (0.27 #10321), 03rk0 (0.08 #3652, 0.08 #4253, 0.08 #4653), 0chghy (0.03 #411, 0.03 #711, 0.03 #1812) >> Best rule #2805 for best value: >> intensional similarity = 2 >> extensional distance = 606 >> proper extension: 06688p; 018dnt; 09byk; 01nczg; 01bpc9; 02jt1k; 0jt90f5; 01tszq; 03pmzt; 02tqkf; ... >> query: (?x3307, 09c7w0) <- location(?x3307, ?x2832), actor(?x5047, ?x3307) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ksr1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.752 http://example.org/people/person/nationality #5027-02xry PRED entity: 02xry PRED relation: geographic_distribution! PRED expected values: 0g48m4 => 214 concepts (214 used for prediction) PRED predicted values (max 10 best out of 39): 0d29z (0.33 #21, 0.25 #221, 0.25 #181), 071x0k (0.33 #3, 0.25 #203, 0.25 #163), 04mvp8 (0.33 #34, 0.25 #234, 0.25 #194), 01xhh5 (0.33 #20, 0.25 #220, 0.25 #180), 0g6ff (0.33 #10, 0.25 #210, 0.25 #170), 013b6_ (0.33 #27, 0.25 #227, 0.25 #187), 04gfy7 (0.33 #33, 0.25 #233, 0.25 #193), 0ffjqy (0.33 #31, 0.25 #231, 0.25 #191), 0cn68 (0.33 #29, 0.25 #229, 0.25 #189), 0dbxy (0.33 #26, 0.25 #226, 0.25 #186) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09c7w0; >> query: (?x2623, 0d29z) <- contains(?x2623, ?x95), ?x95 = 0rs6x, location(?x91, ?x2623) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #481 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 0162v; 0hptm; *> query: (?x2623, 0g48m4) <- location(?x7162, ?x2623), participant(?x7162, ?x1522), jurisdiction_of_office(?x5742, ?x2623) *> conf = 0.21 ranks of expected_values: 19 EVAL 02xry geographic_distribution! 0g48m4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 214.000 214.000 0.333 http://example.org/people/ethnicity/geographic_distribution #5026-0g768 PRED entity: 0g768 PRED relation: artist PRED expected values: 07s3vqk 0h1nt 0137n0 0136p1 030155 094xh 02qsjt 01pgk0 => 121 concepts (103 used for prediction) PRED predicted values (max 10 best out of 985): 02f1c (0.50 #741, 0.38 #5757, 0.25 #1312), 0g824 (0.50 #741, 0.29 #4108, 0.25 #5588), 03g5jw (0.50 #741, 0.29 #3783, 0.15 #14146), 0ffgh (0.50 #741, 0.29 #4157, 0.15 #14520), 01cwhp (0.50 #741, 0.29 #3836, 0.15 #14199), 01wcp_g (0.50 #741, 0.29 #3768, 0.15 #14131), 0m_v0 (0.50 #741, 0.25 #7618, 0.22 #9099), 01bczm (0.50 #741, 0.25 #1099, 0.20 #2581), 02b25y (0.50 #741, 0.25 #879, 0.20 #2361), 03q2t9 (0.50 #741, 0.25 #5535, 0.13 #66673) >> Best rule #741 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0gh4g0; >> query: (?x6474, ?x2443) <- artist(?x6474, ?x7172), artist(?x6474, ?x3481), artist(?x6474, ?x3280), participant(?x3481, ?x2926), award_nominee(?x3280, ?x2443), ?x7172 = 02l_7y, profession(?x3481, ?x1032) >> conf = 0.50 => this is the best rule for 151 predicted values *> Best rule #3803 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 04qhdf; *> query: (?x6474, 0136p1) <- child(?x6474, ?x14234), company(?x6151, ?x14234), category(?x14234, ?x134), artist(?x2190, ?x6151) *> conf = 0.14 ranks of expected_values: 287, 385, 419, 447, 574, 719, 756 EVAL 0g768 artist 01pgk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 121.000 103.000 0.500 http://example.org/music/record_label/artist EVAL 0g768 artist 02qsjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 121.000 103.000 0.500 http://example.org/music/record_label/artist EVAL 0g768 artist 094xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 121.000 103.000 0.500 http://example.org/music/record_label/artist EVAL 0g768 artist 030155 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 121.000 103.000 0.500 http://example.org/music/record_label/artist EVAL 0g768 artist 0136p1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 121.000 103.000 0.500 http://example.org/music/record_label/artist EVAL 0g768 artist 0137n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 121.000 103.000 0.500 http://example.org/music/record_label/artist EVAL 0g768 artist 0h1nt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 103.000 0.500 http://example.org/music/record_label/artist EVAL 0g768 artist 07s3vqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 121.000 103.000 0.500 http://example.org/music/record_label/artist #5025-0t_3w PRED entity: 0t_3w PRED relation: category PRED expected values: 08mbj5d => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #4, 0.86 #8, 0.84 #9) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0v0d9; 0d739; 01m8dg; 01m2n1; >> query: (?x10431, 08mbj5d) <- time_zones(?x10431, ?x2674), currency(?x10431, ?x170), contains(?x2020, ?x10431), ?x2020 = 05k7sb >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0t_3w category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.885 http://example.org/common/topic/webpage./common/webpage/category #5024-04gc2 PRED entity: 04gc2 PRED relation: profession! PRED expected values: 03f0324 07cbs 03_nq 0948xk => 83 concepts (36 used for prediction) PRED predicted values (max 10 best out of 4009): 048lv (0.83 #25199, 0.68 #37806, 0.67 #12600), 01pcrw (0.83 #25199, 0.48 #134417, 0.47 #134416), 09b6zr (0.83 #25199, 0.47 #134416, 0.33 #13895), 014zcr (0.68 #37806, 0.67 #12600, 0.48 #134417), 0d05fv (0.68 #37806, 0.48 #134417, 0.47 #134416), 03f77 (0.68 #37806, 0.48 #134417, 0.47 #134416), 01l9p (0.68 #37806, 0.48 #134417, 0.47 #134416), 03nb5v (0.60 #35715, 0.33 #77715, 0.33 #73516), 04x4s2 (0.60 #34739, 0.33 #9535, 0.25 #22135), 01lct6 (0.56 #79286, 0.56 #75087, 0.50 #54087) >> Best rule #25199 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 02hrh1q; >> query: (?x3342, ?x1384) <- profession(?x9686, ?x3342), profession(?x9334, ?x3342), profession(?x1620, ?x3342), specialization_of(?x10204, ?x3342), ?x9334 = 02hy5d, participant(?x1384, ?x1620), award_winner(?x594, ?x1620), film(?x1620, ?x1619), ?x9686 = 02p8v8 >> conf = 0.83 => this is the best rule for 3 predicted values *> Best rule #96607 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 15 *> proper extension: 01c8w0; 04s2z; 01c979; *> query: (?x3342, ?x5254) <- profession(?x9334, ?x3342), profession(?x8991, ?x3342), profession(?x8462, ?x3342), profession(?x1620, ?x3342), specialization_of(?x10204, ?x3342), category(?x9334, ?x134), organizations_founded(?x8462, ?x11548), peers(?x5254, ?x8991), company(?x1620, ?x94) *> conf = 0.46 ranks of expected_values: 88, 118, 749, 3186 EVAL 04gc2 profession! 0948xk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 83.000 36.000 0.829 http://example.org/people/person/profession EVAL 04gc2 profession! 03_nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 83.000 36.000 0.829 http://example.org/people/person/profession EVAL 04gc2 profession! 07cbs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 83.000 36.000 0.829 http://example.org/people/person/profession EVAL 04gc2 profession! 03f0324 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 36.000 0.829 http://example.org/people/person/profession #5023-0147dk PRED entity: 0147dk PRED relation: award_winner! PRED expected values: 02f75t => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 271): 02g2wv (0.36 #36986, 0.36 #28907, 0.36 #28055), 02v1m7 (0.36 #36986, 0.36 #28907, 0.36 #28055), 0f4x7 (0.36 #36986, 0.36 #28907, 0.36 #28055), 05zr6wv (0.36 #36986, 0.36 #28907, 0.36 #28055), 05zvj3m (0.36 #36986, 0.36 #28907, 0.36 #28055), 09qv_s (0.36 #36986, 0.36 #28907, 0.36 #28055), 02f777 (0.36 #36986, 0.36 #28907, 0.36 #28055), 02f5qb (0.36 #36986, 0.36 #28907, 0.36 #28055), 03t5b6 (0.36 #36986, 0.36 #28907, 0.36 #28055), 02f73p (0.36 #36986, 0.36 #28907, 0.36 #28055) >> Best rule #36986 for best value: >> intensional similarity = 2 >> extensional distance = 2276 >> proper extension: 05d7rk; 084w8; 089tm; 01pfr3; 07w21; 02mslq; 0kzy0; 042rnl; 07q1v4; 02whj; ... >> query: (?x521, ?x401) <- award_winner(?x462, ?x521), award(?x521, ?x401) >> conf = 0.36 => this is the best rule for 11 predicted values *> Best rule #678 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 96 *> proper extension: 01j4ls; 0161c2; 024dgj; 0kxbc; 0m0hw; 03f7m4h; 016j2t; *> query: (?x521, 02f75t) <- award_winner(?x462, ?x521), participant(?x521, ?x1735), artist(?x3265, ?x521) *> conf = 0.03 ranks of expected_values: 124 EVAL 0147dk award_winner! 02f75t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 111.000 111.000 0.364 http://example.org/award/award_category/winners./award/award_honor/award_winner #5022-02_kd PRED entity: 02_kd PRED relation: nominated_for! PRED expected values: 0gr51 02qvyrt => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 193): 0gr51 (0.62 #304, 0.25 #11502, 0.25 #10581), 0f4x7 (0.54 #943, 0.23 #253, 0.22 #3703), 019f4v (0.51 #971, 0.31 #281, 0.29 #3731), 0gs9p (0.50 #980, 0.44 #290, 0.28 #6730), 0k611 (0.49 #989, 0.29 #299, 0.25 #6739), 099c8n (0.42 #284, 0.31 #974, 0.25 #2124), 0gr4k (0.40 #944, 0.22 #1404, 0.21 #6694), 02qvyrt (0.35 #1012, 0.25 #322, 0.19 #25537), 02n9nmz (0.32 #975, 0.25 #11502, 0.25 #10581), 02x1dht (0.32 #271, 0.12 #15643, 0.10 #961) >> Best rule #304 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 07w8fz; >> query: (?x3567, 0gr51) <- award_winner(?x3567, ?x1367), genre(?x3567, ?x239), nominated_for(?x68, ?x3567), ?x68 = 02qyp19 >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 02_kd nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 117.000 117.000 0.619 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02_kd nominated_for! 0gr51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.619 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #5021-02jtjz PRED entity: 02jtjz PRED relation: award_winner! PRED expected values: 05pcn59 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 175): 05zr6wv (0.33 #18, 0.05 #23771, 0.03 #1746), 05ztrmj (0.33 #183, 0.02 #1911, 0.02 #2775), 0gkts9 (0.31 #25933, 0.31 #25932, 0.31 #22473), 05pcn59 (0.31 #25933, 0.31 #25932, 0.31 #22473), 0gqwc (0.17 #75, 0.11 #22040, 0.05 #4827), 099tbz (0.17 #58, 0.11 #22040, 0.05 #23771), 09qwmm (0.17 #34, 0.11 #22040, 0.05 #23771), 02x4x18 (0.17 #134, 0.11 #22040, 0.02 #2294), 09sb52 (0.17 #41, 0.10 #13437, 0.09 #9548), 05p09zm (0.17 #125, 0.04 #1853, 0.04 #1421) >> Best rule #18 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0292l3; 02x0dzw; >> query: (?x3866, 05zr6wv) <- award_nominee(?x3865, ?x3866), location(?x3866, ?x739), ?x3865 = 01_xtx >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #25933 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2153 *> proper extension: 0gfmc_; *> query: (?x3866, ?x3184) <- award_nominee(?x3866, ?x890), award(?x3866, ?x3184) *> conf = 0.31 ranks of expected_values: 4 EVAL 02jtjz award_winner! 05pcn59 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 78.000 78.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #5020-01n951 PRED entity: 01n951 PRED relation: student PRED expected values: 05pq9 02yy8 => 147 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1453): 037lyl (0.17 #661, 0.13 #2753, 0.12 #4845), 015wc0 (0.13 #3788, 0.12 #5880, 0.08 #1696), 01l1rw (0.13 #3091, 0.12 #5183, 0.08 #999), 0306ds (0.13 #2499, 0.12 #4591, 0.08 #407), 03rs8y (0.13 #2138, 0.12 #4230, 0.08 #46), 04ls53 (0.13 #2910, 0.12 #5002, 0.08 #818), 02vntj (0.13 #2795, 0.12 #4887, 0.08 #13255), 03kts (0.13 #3457, 0.12 #5549, 0.08 #1365), 01p6xx (0.13 #3638, 0.12 #5730, 0.05 #9914), 0cms7f (0.13 #3182, 0.12 #5274, 0.05 #9458) >> Best rule #661 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 026ssfj; 04ftdq; 021q2j; 0hpv3; >> query: (?x7787, 037lyl) <- contains(?x739, ?x7787), school_type(?x7787, ?x3205), ?x3205 = 01rs41, ?x739 = 02_286 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #2473 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 05nrkb; 02lwv5; *> query: (?x7787, 05pq9) <- citytown(?x7787, ?x739), category(?x7787, ?x134), student(?x7787, ?x2511), ?x739 = 02_286 *> conf = 0.07 ranks of expected_values: 278, 832 EVAL 01n951 student 02yy8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 147.000 103.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01n951 student 05pq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 147.000 103.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #5019-02hkw6 PRED entity: 02hkw6 PRED relation: people! PRED expected values: 04mvp8 => 67 concepts (34 used for prediction) PRED predicted values (max 10 best out of 25): 0dryh9k (0.38 #478, 0.36 #16, 0.35 #247), 041rx (0.09 #2164, 0.09 #2241, 0.09 #1856), 0x67 (0.08 #2324, 0.08 #1398, 0.08 #2478), 02w7gg (0.07 #619, 0.04 #1235, 0.04 #1544), 01rv7x (0.06 #116, 0.06 #270, 0.06 #347), 02sch9 (0.06 #35, 0.06 #112, 0.05 #266), 033tf_ (0.06 #1317, 0.06 #1704, 0.06 #1395), 04mvp8 (0.04 #144, 0.04 #221, 0.04 #375), 013xrm (0.03 #637, 0.01 #1872, 0.01 #2180), 0222qb (0.03 #661, 0.01 #1896) >> Best rule #478 for best value: >> intensional similarity = 4 >> extensional distance = 158 >> proper extension: 05vzql; 03fwln; 01vzz1c; 0276g40; 03d63lb; >> query: (?x13759, 0dryh9k) <- nationality(?x13759, ?x2146), ?x2146 = 03rk0, profession(?x13759, ?x1032), ?x1032 = 02hrh1q >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 141 *> proper extension: 0cc63l; *> query: (?x13759, 04mvp8) <- nationality(?x13759, ?x2146), type_of_union(?x13759, ?x566), ?x2146 = 03rk0, profession(?x13759, ?x1032) *> conf = 0.04 ranks of expected_values: 8 EVAL 02hkw6 people! 04mvp8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 67.000 34.000 0.381 http://example.org/people/ethnicity/people #5018-03yj_0n PRED entity: 03yj_0n PRED relation: award_nominee! PRED expected values: 027dtv3 => 100 concepts (38 used for prediction) PRED predicted values (max 10 best out of 801): 06cgy (0.85 #6943, 0.81 #71729, 0.81 #83300), 043kzcr (0.85 #6943, 0.81 #71729, 0.81 #83300), 0292l3 (0.85 #6943, 0.81 #71729, 0.81 #83300), 027dtv3 (0.85 #6943, 0.81 #71729, 0.81 #83300), 0f830f (0.85 #6943, 0.81 #71729, 0.81 #83300), 02x0dzw (0.85 #6943, 0.81 #71729, 0.81 #83300), 03yj_0n (0.67 #808, 0.44 #5437, 0.40 #16199), 01r42_g (0.52 #4685, 0.18 #60159, 0.16 #16198), 021_rm (0.52 #4840, 0.18 #60159, 0.16 #16198), 01dy7j (0.48 #5292, 0.26 #64788, 0.18 #60159) >> Best rule #6943 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 0f830f; 08w7vj; 02lgfh; >> query: (?x3594, ?x368) <- award_nominee(?x3594, ?x1169), award_nominee(?x3594, ?x368), film(?x3594, ?x2655), ?x1169 = 02lfns >> conf = 0.85 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 03yj_0n award_nominee! 027dtv3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 100.000 38.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #5017-05r4w PRED entity: 05r4w PRED relation: olympics PRED expected values: 0l6ny => 232 concepts (232 used for prediction) PRED predicted values (max 10 best out of 35): 0l6ny (0.79 #428, 0.61 #780, 0.58 #463), 0lgxj (0.73 #795, 0.67 #443, 0.63 #900), 0l998 (0.67 #778, 0.67 #426, 0.57 #286), 0lv1x (0.67 #293, 0.54 #890, 0.54 #433), 018ctl (0.65 #3798, 0.60 #4536, 0.58 #5625), 0kbvv (0.65 #3798, 0.60 #4536, 0.58 #5625), 0l98s (0.58 #425, 0.58 #777, 0.49 #882), 0ldqf (0.58 #451, 0.55 #803, 0.48 #311), 0lbd9 (0.58 #799, 0.50 #447, 0.48 #307), 0nbjq (0.57 #297, 0.50 #437, 0.49 #894) >> Best rule #428 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 03_3d; 03rj0; 06f32; 03h64; 082fr; >> query: (?x87, 0l6ny) <- olympics(?x87, ?x778), film_release_region(?x11209, ?x87), ?x11209 = 04fjzv >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05r4w olympics 0l6ny CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 232.000 232.000 0.792 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #5016-027yjnv PRED entity: 027yjnv PRED relation: instance_of_recurring_event PRED expected values: 07hn5 => 1 concepts (1 used for prediction) No prediction ranks of expected_values: EVAL 027yjnv instance_of_recurring_event 07hn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.000 http://example.org/time/event/instance_of_recurring_event #5015-05r7t PRED entity: 05r7t PRED relation: exported_to PRED expected values: 0n3g => 224 concepts (186 used for prediction) PRED predicted values (max 10 best out of 171): 09c7w0 (0.36 #369, 0.33 #1228, 0.31 #738), 0h3y (0.27 #375, 0.25 #191, 0.23 #744), 0j4b (0.27 #417, 0.24 #1337, 0.24 #1276), 06s_2 (0.27 #428, 0.23 #797, 0.21 #858), 07fsv (0.19 #1338, 0.19 #1277, 0.14 #1582), 06tw8 (0.19 #1335, 0.18 #415, 0.15 #784), 07ssc (0.18 #379, 0.15 #748, 0.14 #1238), 0jdd (0.18 #406, 0.15 #775, 0.14 #836), 01f08r (0.18 #408, 0.15 #777, 0.14 #838), 047t_ (0.14 #1331, 0.14 #1270, 0.12 #1453) >> Best rule #369 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 030qb3t; >> query: (?x6559, 09c7w0) <- origin(?x7547, ?x6559), film_release_region(?x9902, ?x6559), ?x9902 = 0j8f09z >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #414 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 030qb3t; *> query: (?x6559, 0n3g) <- origin(?x7547, ?x6559), film_release_region(?x9902, ?x6559), ?x9902 = 0j8f09z *> conf = 0.09 ranks of expected_values: 30 EVAL 05r7t exported_to 0n3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 224.000 186.000 0.364 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #5014-06fcqw PRED entity: 06fcqw PRED relation: production_companies PRED expected values: 04rcl7 => 84 concepts (71 used for prediction) PRED predicted values (max 10 best out of 56): 04rcl7 (0.33 #558, 0.25 #70, 0.25 #884), 0kk9v (0.25 #33, 0.20 #440, 0.18 #196), 056ws9 (0.20 #451, 0.16 #858, 0.15 #777), 05qd_ (0.14 #336, 0.13 #3267, 0.11 #3429), 086k8 (0.13 #3259, 0.12 #3421, 0.10 #1871), 04jspq (0.13 #245, 0.09 #2521, 0.03 #5379), 01gb54 (0.12 #850, 0.11 #443, 0.10 #769), 016tw3 (0.11 #3269, 0.11 #3431, 0.09 #3839), 054lpb6 (0.10 #910, 0.09 #1153, 0.09 #991), 017s11 (0.10 #248, 0.09 #3260, 0.07 #3422) >> Best rule #558 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x6216, 04rcl7) <- titles(?x3920, ?x6216), award_nominee(?x1285, ?x3920), industry(?x3920, ?x2271) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06fcqw production_companies 04rcl7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 71.000 0.326 http://example.org/film/film/production_companies #5013-03wjm2 PRED entity: 03wjm2 PRED relation: film! PRED expected values: 02qfhb => 78 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1293): 0jfx1 (0.33 #404, 0.25 #2480, 0.10 #17011), 0f4vbz (0.33 #360, 0.25 #2436, 0.10 #16967), 0143wl (0.33 #1065, 0.25 #3141, 0.10 #17672), 01chc7 (0.33 #556, 0.25 #2632, 0.10 #17163), 01r9c_ (0.33 #1788, 0.25 #3864, 0.10 #18395), 01q_ph (0.29 #8360, 0.25 #12513, 0.22 #14588), 0mdqp (0.29 #8422, 0.07 #60223, 0.07 #103862), 06cgy (0.29 #8552, 0.07 #60223, 0.07 #103862), 01bcq (0.29 #9173, 0.07 #60223, 0.07 #103862), 04yqlk (0.29 #9078, 0.07 #60223, 0.07 #103862) >> Best rule #404 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0bbw2z6; >> query: (?x11945, 0jfx1) <- produced_by(?x11945, ?x10430), produced_by(?x11945, ?x2789), ?x10430 = 027z0pl, written_by(?x11945, ?x5345), film(?x5636, ?x11945), student(?x581, ?x2789) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #70611 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 556 *> proper extension: 02d413; 014_x2; 0d90m; 03qcfvw; 083shs; 02vxq9m; 02_fm2; 06w99h3; 0gzy02; 0gtv7pk; ... *> query: (?x11945, ?x4929) <- genre(?x11945, ?x258), film(?x2012, ?x11945), film(?x5636, ?x11945), actor(?x3180, ?x2012), participant(?x4929, ?x2012) *> conf = 0.04 ranks of expected_values: 205 EVAL 03wjm2 film! 02qfhb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 78.000 56.000 0.333 http://example.org/film/actor/film./film/performance/film #5012-01bqnc PRED entity: 01bqnc PRED relation: category PRED expected values: 08mbj5d => 7 concepts (7 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.71 #8, 0.71 #7, 0.68 #4) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 151 >> proper extension: 086k8; 016tt2; 05qd_; 04f525m; 011k1h; 0cjdk; 03rhqg; 0229rs; 01t7jy; 03mdt; ... >> query: (?x12703, ?x134) <- child(?x12007, ?x12703), child(?x12007, ?x14326), category(?x14326, ?x134), ?x134 = 08mbj5d >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bqnc category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 7.000 7.000 0.712 http://example.org/common/topic/webpage./common/webpage/category #5011-01v0sxx PRED entity: 01v0sxx PRED relation: artists! PRED expected values: 064t9 => 82 concepts (31 used for prediction) PRED predicted values (max 10 best out of 298): 017_qw (0.56 #4356, 0.55 #8048, 0.55 #7129), 02yv6b (0.55 #3777, 0.49 #2550, 0.40 #5006), 064t9 (0.46 #7695, 0.34 #3081, 0.33 #5536), 03_d0 (0.40 #626, 0.29 #2159, 0.29 #1547), 0155w (0.40 #719, 0.26 #5627, 0.26 #5937), 016clz (0.39 #9219, 0.36 #8912, 0.33 #5), 01lyv (0.36 #3100, 0.29 #2180, 0.28 #2794), 08jyyk (0.35 #1907, 0.28 #4975, 0.27 #3746), 05r6t (0.33 #81, 0.19 #6758, 0.18 #8988), 059kh (0.33 #47, 0.17 #967, 0.17 #8954) >> Best rule #4356 for best value: >> intensional similarity = 5 >> extensional distance = 62 >> proper extension: 0b6yp2; 01jpmpv; 02sj1x; 01pr6q7; 02w670; 02ryx0; 02bn75; 01mh8zn; 02fgp0; 03975z; ... >> query: (?x10257, 017_qw) <- music(?x3566, ?x10257), award(?x10257, ?x3045), film_release_region(?x3566, ?x985), genre(?x3566, ?x53), ?x985 = 0k6nt >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #7695 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 128 *> proper extension: 01l1b90; 018y2s; 01vrt_c; 07_3qd; 011zf2; 0j1yf; 09k2t1; 07ss8_; 01trhmt; 0161c2; ... *> query: (?x10257, 064t9) <- artist(?x2241, ?x10257), artists(?x1000, ?x10257), artist(?x2241, ?x4718), artist(?x2241, ?x3957), profession(?x4718, ?x1032), ?x3957 = 09r8l *> conf = 0.46 ranks of expected_values: 3 EVAL 01v0sxx artists! 064t9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 31.000 0.562 http://example.org/music/genre/artists #5010-0z4s PRED entity: 0z4s PRED relation: location_of_ceremony PRED expected values: 0r0m6 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 28): 0cv3w (0.05 #154, 0.02 #392, 0.02 #2298), 030qb3t (0.05 #138, 0.01 #614, 0.01 #733), 0gx1l (0.05 #212), 059rby (0.05 #127), 0pswc (0.03 #697, 0.02 #816), 05qtj (0.02 #769, 0.01 #650), 0k049 (0.02 #956, 0.02 #1553, 0.01 #1791), 0b90_r (0.02 #360, 0.02 #479, 0.01 #598), 0f0sbl (0.02 #445, 0.02 #564), 03rk0 (0.02 #383, 0.02 #502) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 06151l; 0c4f4; 0hvb2; 01pgzn_; 015t56; 019pm_; 03_6y; 04w391; 016vg8; 0bksh; ... >> query: (?x450, 0cv3w) <- award(?x450, ?x112), award_nominee(?x7830, ?x450), ?x7830 = 01p4vl >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #407 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 0d05fv; 0hfml; *> query: (?x450, 0r0m6) <- film(?x450, ?x518), award_winner(?x591, ?x450), company(?x450, ?x13471) *> conf = 0.02 ranks of expected_values: 12 EVAL 0z4s location_of_ceremony 0r0m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 106.000 106.000 0.048 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #5009-0b76d_m PRED entity: 0b76d_m PRED relation: film_release_region PRED expected values: 07t21 02vzc 01crd5 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 137): 0chghy (0.82 #592, 0.79 #1615, 0.76 #1176), 02vzc (0.79 #631, 0.79 #1654, 0.77 #923), 06t2t (0.79 #640, 0.61 #1663, 0.56 #1224), 01znc_ (0.78 #620, 0.68 #1643, 0.67 #1204), 0k6nt (0.77 #1188, 0.77 #1627, 0.77 #604), 05b4w (0.74 #643, 0.67 #1666, 0.65 #1373), 03rt9 (0.72 #595, 0.57 #1618, 0.53 #1179), 03rj0 (0.67 #638, 0.53 #1661, 0.50 #1222), 0ctw_b (0.61 #605, 0.43 #1628, 0.41 #1189), 01mjq (0.59 #623, 0.49 #1646, 0.43 #1938) >> Best rule #592 for best value: >> intensional similarity = 6 >> extensional distance = 162 >> proper extension: 087wc7n; 0fq7dv_; 045j3w; 01f85k; 07s3m4g; 0hz6mv2; >> query: (?x80, 0chghy) <- film_release_region(?x80, ?x4743), film_release_region(?x80, ?x1353), film_release_region(?x80, ?x789), ?x789 = 0f8l9c, ?x4743 = 03spz, ?x1353 = 035qy >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #631 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 162 *> proper extension: 087wc7n; 0fq7dv_; 045j3w; 01f85k; 07s3m4g; 0hz6mv2; *> query: (?x80, 02vzc) <- film_release_region(?x80, ?x4743), film_release_region(?x80, ?x1353), film_release_region(?x80, ?x789), ?x789 = 0f8l9c, ?x4743 = 03spz, ?x1353 = 035qy *> conf = 0.79 ranks of expected_values: 2, 26, 52 EVAL 0b76d_m film_release_region 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 64.000 64.000 0.823 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b76d_m film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.823 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0b76d_m film_release_region 07t21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 64.000 64.000 0.823 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5008-07n3s PRED entity: 07n3s PRED relation: group! PRED expected values: 05148p4 => 101 concepts (68 used for prediction) PRED predicted values (max 10 best out of 118): 05148p4 (0.71 #273, 0.70 #1466, 0.69 #1811), 03bx0bm (0.65 #1217, 0.65 #1472, 0.60 #1644), 0l14md (0.63 #1200, 0.60 #1455, 0.58 #1800), 07y_7 (0.57 #257, 0.26 #768, 0.17 #87), 028tv0 (0.43 #267, 0.39 #1460, 0.38 #1205), 05r5c (0.43 #263, 0.37 #852, 0.37 #774), 06ncr (0.43 #293, 0.32 #804, 0.25 #378), 0l14qv (0.43 #260, 0.24 #1798, 0.23 #1198), 0l14j_ (0.43 #304, 0.17 #134, 0.12 #853), 0dwt5 (0.43 #324, 0.12 #853, 0.11 #835) >> Best rule #273 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 05563d; >> query: (?x11929, 05148p4) <- artist(?x8721, ?x11929), group(?x2460, ?x11929), group(?x1750, ?x11929), artists(?x302, ?x11929), ?x1750 = 02hnl, ?x2460 = 01wy6 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07n3s group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 68.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #5007-06wpc PRED entity: 06wpc PRED relation: season PRED expected values: 027pwzc => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 7): 027pwzc (0.77 #53, 0.75 #74, 0.74 #95), 05kcgsf (0.58 #92, 0.52 #113, 0.50 #36), 04110b0 (0.34 #115, 0.33 #17, 0.32 #101), 02h7s73 (0.34 #117, 0.33 #19, 0.32 #103), 03c6s24 (0.33 #20, 0.28 #118, 0.26 #104), 03c74_8 (0.33 #16, 0.24 #114, 0.21 #100), 04n36qk (0.14 #70, 0.07 #119, 0.05 #161) >> Best rule #53 for best value: >> intensional similarity = 11 >> extensional distance = 11 >> proper extension: 04wmvz; >> query: (?x7399, 027pwzc) <- team(?x261, ?x7399), team(?x8206, ?x7399), season(?x7399, ?x2406), school(?x7399, ?x8822), school(?x7399, ?x8202), company(?x346, ?x8822), major_field_of_study(?x8202, ?x1154), school(?x2067, ?x8202), ?x2067 = 05g76, currency(?x8822, ?x170), colors(?x8822, ?x663) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06wpc season 027pwzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.769 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #5006-0f2sx4 PRED entity: 0f2sx4 PRED relation: nominated_for PRED expected values: 074w86 => 81 concepts (30 used for prediction) PRED predicted values (max 10 best out of 166): 074w86 (0.82 #3521, 0.81 #3522, 0.81 #3268), 06sfk6 (0.33 #128, 0.17 #631, 0.14 #1133), 04fzfj (0.33 #16, 0.17 #519, 0.14 #1021), 09q5w2 (0.17 #782, 0.01 #2540), 01kf3_9 (0.06 #2566, 0.05 #3323, 0.01 #3070), 0fsw_7 (0.06 #2664, 0.04 #3421, 0.01 #3168), 0g5pvv (0.06 #2680, 0.04 #3437, 0.01 #3184), 014kq6 (0.06 #2576, 0.04 #3333, 0.01 #3080), 01s9vc (0.05 #3509, 0.04 #2752, 0.02 #3256), 01kf4tt (0.05 #2587, 0.04 #3344, 0.01 #3091) >> Best rule #3521 for best value: >> intensional similarity = 4 >> extensional distance = 245 >> proper extension: 02fn5r; >> query: (?x7967, ?x2907) <- nominated_for(?x4054, ?x7967), nominated_for(?x2907, ?x7967), nominated_for(?x2325, ?x4054), award(?x286, ?x2325) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2sx4 nominated_for 074w86 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 30.000 0.819 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #5005-02qzh2 PRED entity: 02qzh2 PRED relation: genre PRED expected values: 05p553 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 85): 05p553 (0.60 #123, 0.54 #243, 0.42 #2047), 04228s (0.40 #196, 0.20 #76, 0.15 #316), 01jfsb (0.39 #973, 0.39 #1574, 0.38 #1093), 02kdv5l (0.37 #602, 0.34 #1083, 0.33 #963), 03k9fj (0.28 #611, 0.27 #732, 0.26 #491), 0lsxr (0.22 #1329, 0.20 #1812, 0.20 #1089), 06n90 (0.21 #974, 0.21 #1094, 0.17 #734), 01hmnh (0.20 #137, 0.20 #17, 0.19 #617), 01t_vv (0.20 #174, 0.20 #54, 0.15 #294), 0gf28 (0.20 #184, 0.20 #64, 0.08 #304) >> Best rule #123 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0p9lw; 0prrm; 0gd92; >> query: (?x4160, 05p553) <- film(?x1814, ?x4160), film(?x989, ?x4160), ?x989 = 0151w_, ?x1814 = 034np8 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qzh2 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.600 http://example.org/film/film/genre #5004-016y_f PRED entity: 016y_f PRED relation: executive_produced_by PRED expected values: 030_3z => 93 concepts (65 used for prediction) PRED predicted values (max 10 best out of 89): 04sry (0.12 #3522, 0.05 #1007, 0.03 #920), 05hj_k (0.10 #3365, 0.10 #599, 0.09 #852), 06q8hf (0.10 #668, 0.09 #3434, 0.09 #921), 03h40_7 (0.07 #222, 0.04 #473, 0.02 #1229), 0g_rs_ (0.07 #250, 0.03 #1006, 0.01 #3519), 0kjgl (0.07 #173), 02q42j_ (0.06 #638, 0.04 #1394, 0.03 #2148), 0b13g7 (0.06 #587, 0.02 #2600, 0.02 #1343), 06t8b (0.06 #1431, 0.03 #2436, 0.02 #3441), 06cgy (0.05 #2264, 0.03 #502, 0.02 #2515) >> Best rule #3522 for best value: >> intensional similarity = 5 >> extensional distance = 173 >> proper extension: 047svrl; 01gglm; >> query: (?x4454, ?x7310) <- country(?x4454, ?x94), nominated_for(?x3100, ?x4454), executive_produced_by(?x4454, ?x846), film(?x1554, ?x4454), film(?x7310, ?x4454) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #3375 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 173 *> proper extension: 047svrl; 01gglm; *> query: (?x4454, 030_3z) <- country(?x4454, ?x94), nominated_for(?x3100, ?x4454), executive_produced_by(?x4454, ?x846), film(?x1554, ?x4454), film(?x7310, ?x4454) *> conf = 0.03 ranks of expected_values: 22 EVAL 016y_f executive_produced_by 030_3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 93.000 65.000 0.119 http://example.org/film/film/executive_produced_by #5003-01vsy7t PRED entity: 01vsy7t PRED relation: award_winner! PRED expected values: 031x_3 => 148 concepts (87 used for prediction) PRED predicted values (max 10 best out of 710): 03cfjg (0.69 #13411, 0.67 #8592, 0.18 #89983), 016sp_ (0.69 #13247, 0.67 #8428, 0.04 #88772), 05sq0m (0.67 #9214, 0.62 #14033, 0.04 #71876), 0x3b7 (0.62 #13566, 0.58 #8747, 0.18 #89983), 01l47f5 (0.62 #13923, 0.58 #9104, 0.18 #89983), 051m56 (0.62 #14215, 0.58 #9396, 0.18 #89983), 02cx90 (0.54 #13591, 0.50 #8772, 0.18 #89983), 01k_r5b (0.54 #13751, 0.50 #8932, 0.18 #89983), 016srn (0.54 #13369, 0.50 #8550, 0.18 #89983), 01lmj3q (0.54 #12889, 0.50 #8070, 0.18 #89983) >> Best rule #13411 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 05sq20; >> query: (?x4620, 03cfjg) <- award_winner(?x4620, ?x2300), ?x2300 = 01ww2fs, profession(?x4620, ?x131) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #14187 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 05sq20; *> query: (?x4620, 031x_3) <- award_winner(?x4620, ?x2300), ?x2300 = 01ww2fs, profession(?x4620, ?x131) *> conf = 0.08 ranks of expected_values: 74 EVAL 01vsy7t award_winner! 031x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 148.000 87.000 0.692 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #5002-0jgx PRED entity: 0jgx PRED relation: film_release_region! PRED expected values: 0ch26b_ 0gffmn8 05c26ss 0btpm6 0fphf3v 0bmfnjs => 133 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1292): 017jd9 (0.90 #17369, 0.78 #22537, 0.76 #9617), 043tvp3 (0.88 #17692, 0.78 #22860, 0.68 #9940), 053rxgm (0.86 #16925, 0.78 #22093, 0.65 #9173), 09k56b7 (0.86 #17027, 0.67 #22195, 0.62 #9275), 05zlld0 (0.83 #17252, 0.72 #22420, 0.65 #9500), 04hwbq (0.81 #16937, 0.73 #22105, 0.71 #9185), 0bq6ntw (0.81 #17583, 0.67 #22751, 0.59 #9831), 0by1wkq (0.81 #17020, 0.65 #22188, 0.62 #9268), 04w7rn (0.81 #16968, 0.62 #9216, 0.62 #22136), 0407yj_ (0.81 #17149, 0.62 #22317, 0.58 #14565) >> Best rule #17369 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0d0vqn; 04gzd; 0chghy; 0k6nt; 01znc_; 06t2t; 0d05w3; 05b4w; 06f32; 016wzw; >> query: (?x3855, 017jd9) <- film_release_region(?x5271, ?x3855), film_release_region(?x1173, ?x3855), ?x5271 = 047vnkj, ?x1173 = 0872p_c >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #17179 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 0d0vqn; 04gzd; 0chghy; 0k6nt; 01znc_; 06t2t; 0d05w3; 05b4w; 06f32; 016wzw; *> query: (?x3855, 0gffmn8) <- film_release_region(?x5271, ?x3855), film_release_region(?x1173, ?x3855), ?x5271 = 047vnkj, ?x1173 = 0872p_c *> conf = 0.79 ranks of expected_values: 20, 34, 38, 46, 106, 155 EVAL 0jgx film_release_region! 0bmfnjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 133.000 124.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgx film_release_region! 0fphf3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 133.000 124.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgx film_release_region! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 133.000 124.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgx film_release_region! 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 133.000 124.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgx film_release_region! 0gffmn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 133.000 124.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0jgx film_release_region! 0ch26b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 133.000 124.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #5001-0150jk PRED entity: 0150jk PRED relation: group! PRED expected values: 028tv0 => 93 concepts (68 used for prediction) PRED predicted values (max 10 best out of 123): 0l14md (0.74 #1748, 0.67 #2272, 0.67 #355), 028tv0 (0.67 #361, 0.45 #1580, 0.45 #1754), 0l14qv (0.29 #2183, 0.24 #1746, 0.24 #2270), 01vj9c (0.28 #2279, 0.28 #2627, 0.27 #3241), 03qjg (0.28 #2486, 0.28 #2660, 0.27 #2312), 05r5c (0.27 #1575, 0.26 #1227, 0.25 #8), 04rzd (0.25 #31, 0.17 #379, 0.16 #1598), 05842k (0.25 #66, 0.07 #3051, 0.06 #4013), 0l14j_ (0.20 #225, 0.17 #399, 0.17 #312), 013y1f (0.20 #201, 0.17 #288, 0.14 #2640) >> Best rule #1748 for best value: >> intensional similarity = 7 >> extensional distance = 72 >> proper extension: 05crg7; 02t3ln; >> query: (?x717, 0l14md) <- group(?x227, ?x717), artists(?x2249, ?x717), artists(?x1000, ?x717), parent_genre(?x2072, ?x2249), artists(?x2249, ?x8708), ?x1000 = 0xhtw, ?x8708 = 01vn0t_ >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #361 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 089tm; 02whj; 0g_g2; 013w2r; 01q99h; 0ycp3; 0134pk; 0ycfj; 027kwc; *> query: (?x717, 028tv0) <- artist(?x441, ?x717), award(?x717, ?x9828), ?x9828 = 01ckcd, artists(?x2249, ?x717), ?x2249 = 03lty, category(?x717, ?x134) *> conf = 0.67 ranks of expected_values: 2 EVAL 0150jk group! 028tv0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 68.000 0.743 http://example.org/music/performance_role/regular_performances./music/group_membership/group #5000-01k_0fp PRED entity: 01k_0fp PRED relation: instrumentalists! PRED expected values: 0l14md => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 123): 018vs (0.54 #442, 0.46 #615, 0.46 #1999), 05r5c (0.52 #2344, 0.51 #2258, 0.51 #4510), 05148p4 (0.46 #623, 0.43 #1488, 0.43 #450), 03bx0bm (0.43 #603, 0.42 #2597, 0.42 #2686), 02hnl (0.36 #292, 0.31 #983, 0.28 #637), 0l14md (0.25 #7, 0.20 #956, 0.18 #610), 01wy6 (0.25 #47, 0.13 #650, 0.07 #305), 018j2 (0.23 #987, 0.18 #641, 0.17 #554), 026t6 (0.22 #778, 0.21 #433, 0.21 #952), 0l14qv (0.21 #435, 0.20 #521, 0.12 #867) >> Best rule #442 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 05crg7; >> query: (?x10243, 018vs) <- origin(?x10243, ?x9042), artists(?x1000, ?x10243), ?x1000 = 0xhtw, role(?x10243, ?x569) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 016vn3; *> query: (?x10243, 0l14md) <- origin(?x10243, ?x9042), artists(?x8386, ?x10243), artists(?x1000, ?x10243), ?x1000 = 0xhtw, ?x8386 = 016ybr *> conf = 0.25 ranks of expected_values: 6 EVAL 01k_0fp instrumentalists! 0l14md CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 170.000 170.000 0.536 http://example.org/music/instrument/instrumentalists #4999-05typm PRED entity: 05typm PRED relation: profession PRED expected values: 02hrh1q => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 52): 02hrh1q (0.89 #2265, 0.89 #1965, 0.89 #4515), 02jknp (0.50 #158, 0.42 #1058, 0.38 #308), 01d_h8 (0.38 #156, 0.36 #906, 0.33 #756), 0dxtg (0.38 #164, 0.32 #2564, 0.31 #5114), 03gjzk (0.34 #2566, 0.29 #1366, 0.26 #2416), 018gz8 (0.26 #1368, 0.20 #1518, 0.18 #918), 0np9r (0.25 #472, 0.20 #1522, 0.20 #3622), 09jwl (0.22 #770, 0.18 #920, 0.16 #10374), 0cbd2 (0.22 #757, 0.15 #7351, 0.15 #5407), 0d1pc (0.18 #952, 0.17 #1102, 0.15 #7351) >> Best rule #2265 for best value: >> intensional similarity = 3 >> extensional distance = 448 >> proper extension: 06gh0t; >> query: (?x4630, 02hrh1q) <- place_of_birth(?x4630, ?x8451), actor(?x6726, ?x4630), award(?x4630, ?x375) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05typm profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.893 http://example.org/people/person/profession #4998-0415ggl PRED entity: 0415ggl PRED relation: language PRED expected values: 02h40lc => 99 concepts (89 used for prediction) PRED predicted values (max 10 best out of 43): 02h40lc (0.91 #711, 0.91 #2209, 0.91 #2449), 064_8sq (0.22 #81, 0.20 #495, 0.20 #317), 02bjrlw (0.15 #887, 0.14 #1, 0.13 #415), 06b_j (0.14 #23, 0.12 #673, 0.11 #82), 0295r (0.14 #29, 0.11 #88, 0.02 #443), 03_9r (0.14 #10, 0.09 #542, 0.07 #246), 06nm1 (0.14 #1135, 0.13 #897, 0.13 #1254), 04306rv (0.13 #891, 0.12 #655, 0.12 #2333), 0jzc (0.11 #256, 0.07 #670, 0.07 #788), 04h9h (0.08 #457, 0.05 #634, 0.04 #398) >> Best rule #711 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 0b60sq; 016kz1; >> query: (?x5724, 02h40lc) <- production_companies(?x5724, ?x617), genre(?x5724, ?x1403), executive_produced_by(?x5724, ?x7831), ?x1403 = 02l7c8 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0415ggl language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 89.000 0.910 http://example.org/film/film/language #4997-01gbcf PRED entity: 01gbcf PRED relation: parent_genre PRED expected values: 08jyyk => 52 concepts (39 used for prediction) PRED predicted values (max 10 best out of 296): 05r6t (0.68 #4553, 0.57 #849, 0.54 #1809), 05w3f (0.43 #1141, 0.33 #1462, 0.23 #3553), 05bt6j (0.40 #2587, 0.33 #26, 0.32 #3556), 0xhtw (0.35 #3542, 0.27 #2573, 0.20 #2413), 08jyyk (0.33 #203, 0.29 #839, 0.29 #679), 0jmwg (0.33 #73, 0.29 #1032, 0.29 #871), 0cx7f (0.33 #246, 0.25 #404, 0.20 #563), 03_d0 (0.33 #170, 0.25 #328, 0.20 #487), 0126t5 (0.33 #215, 0.25 #373, 0.20 #532), 01pfpt (0.33 #219, 0.25 #377, 0.20 #536) >> Best rule #4553 for best value: >> intensional similarity = 9 >> extensional distance = 58 >> proper extension: 020ngt; 011j5x; 01g888; 07d2d; 02srgf; 0jmwg; 01738f; 07ffjc; 02z7f3; 0148nj; ... >> query: (?x301, 05r6t) <- parent_genre(?x301, ?x2996), artists(?x2996, ?x5618), artists(?x2996, ?x4942), artists(?x2996, ?x4715), artists(?x2996, ?x3735), role(?x3735, ?x227), ?x4942 = 05xq9, award_winner(?x2186, ?x4715), group(?x315, ?x5618) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #203 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 1 *> proper extension: 0g_bh; *> query: (?x301, 08jyyk) <- parent_genre(?x301, ?x10933), parent_genre(?x301, ?x10290), parent_genre(?x301, ?x2996), parent_genre(?x301, ?x1380), parent_genre(?x301, ?x302), ?x2996 = 01243b, ?x1380 = 0dl5d, artists(?x10933, ?x9603), artists(?x10933, ?x1060), ?x302 = 016clz, ?x1060 = 02r3zy, artists(?x10290, ?x7410), artists(?x10290, ?x6406), ?x7410 = 011lvx, ?x9603 = 012ycy, ?x6406 = 01386_ *> conf = 0.33 ranks of expected_values: 5 EVAL 01gbcf parent_genre 08jyyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 52.000 39.000 0.683 http://example.org/music/genre/parent_genre #4996-0xgpv PRED entity: 0xgpv PRED relation: time_zones PRED expected values: 02fqwt => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 8): 02fqwt (0.82 #27, 0.78 #14, 0.78 #1), 02hcv8 (0.49 #237, 0.44 #289, 0.43 #315), 02lcqs (0.18 #148, 0.18 #135, 0.17 #304), 02hczc (0.08 #132, 0.07 #67, 0.06 #301), 02llzg (0.06 #655, 0.05 #225, 0.05 #212), 03bdv (0.03 #670, 0.03 #709, 0.03 #918), 042g7t (0.01 #180, 0.01 #206, 0.01 #219), 03plfd (0.01 #192, 0.01 #270, 0.01 #1053) >> Best rule #27 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0wr_s; >> query: (?x13861, 02fqwt) <- source(?x13861, ?x958), ?x958 = 0jbk9, contains(?x4622, ?x13861), ?x4622 = 04tgp >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xgpv time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.818 http://example.org/location/location/time_zones #4995-04mzf8 PRED entity: 04mzf8 PRED relation: honored_for! PRED expected values: 0fy59t => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 111): 0bzkvd (0.14 #99, 0.12 #222, 0.02 #1320), 0ftlxj (0.12 #182, 0.03 #426, 0.02 #670), 0bzknt (0.12 #192, 0.01 #3541, 0.01 #3664), 09gkdln (0.07 #595, 0.05 #351, 0.04 #839), 0hr6lkl (0.06 #257, 0.06 #501, 0.04 #867), 0418154 (0.06 #338, 0.06 #582, 0.04 #826), 0gmdkyy (0.06 #269, 0.05 #513, 0.03 #879), 0hndn2q (0.05 #277, 0.04 #521, 0.03 #887), 0h_cssd (0.05 #267, 0.04 #511, 0.03 #877), 0c53zb (0.05 #417, 0.02 #1393, 0.01 #1759) >> Best rule #99 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 02qr3k8; >> query: (?x1308, 0bzkvd) <- film(?x9477, ?x1308), film(?x269, ?x1308), ?x269 = 0byfz, genre(?x1308, ?x53), award_winner(?x11428, ?x9477) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #468 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 78 *> proper extension: 0m9p3; 0jymd; 02q_4ph; 014kkm; 0p_tz; 04tng0; 034xyf; 0f3m1; 05ypj5; 04vq33; *> query: (?x1308, 0fy59t) <- film(?x269, ?x1308), nominated_for(?x484, ?x1308), film_art_direction_by(?x1308, ?x4251) *> conf = 0.03 ranks of expected_values: 35 EVAL 04mzf8 honored_for! 0fy59t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 85.000 85.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #4994-0bz6l9 PRED entity: 0bz6l9 PRED relation: ceremony! PRED expected values: 0gr0m => 31 concepts (28 used for prediction) PRED predicted values (max 10 best out of 356): 0gr0m (0.84 #3885, 0.83 #1964, 0.83 #1007), 018wdw (0.76 #6237, 0.73 #1365, 0.71 #647), 0gqxm (0.76 #6237, 0.57 #596, 0.56 #356), 0gqzz (0.76 #6237, 0.31 #278, 0.24 #1236), 0czp_ (0.76 #6237, 0.19 #431, 0.18 #910), 02pqp12 (0.37 #3356, 0.36 #3598, 0.29 #5754), 040njc (0.37 #3356, 0.36 #3598, 0.29 #5754), 03nqnk3 (0.37 #3356, 0.36 #3598, 0.29 #5754), 03hkv_r (0.37 #3356, 0.36 #3598, 0.20 #5755), 02rdyk7 (0.37 #3356, 0.36 #3598, 0.13 #4555) >> Best rule #3885 for best value: >> intensional similarity = 17 >> extensional distance = 59 >> proper extension: 073hgx; 0c4hx0; >> query: (?x3332, 0gr0m) <- award_winner(?x3332, ?x2372), ceremony(?x3066, ?x3332), ceremony(?x2209, ?x3332), ceremony(?x1245, ?x3332), nominated_for(?x2209, ?x9611), nominated_for(?x2209, ?x8217), nominated_for(?x2209, ?x2699), award(?x241, ?x1245), nominated_for(?x1245, ?x1944), ceremony(?x1245, ?x1601), award_winner(?x2209, ?x788), ?x1944 = 03hj3b3, ?x2699 = 04t6fk, ?x3066 = 0gqy2, ?x8217 = 04v89z, ?x9611 = 0cq8nx, ?x1601 = 073hmq >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bz6l9 ceremony! 0gr0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 28.000 0.836 http://example.org/award/award_category/winners./award/award_honor/ceremony #4993-016z9n PRED entity: 016z9n PRED relation: nominated_for! PRED expected values: 09qwmm => 58 concepts (39 used for prediction) PRED predicted values (max 10 best out of 230): 099c8n (0.66 #754, 0.23 #1453, 0.18 #288), 0gq9h (0.59 #759, 0.41 #1458, 0.31 #1691), 0p9sw (0.52 #254, 0.27 #720, 0.25 #1652), 0k611 (0.50 #770, 0.33 #304, 0.29 #1469), 0gs9p (0.49 #761, 0.36 #1460, 0.28 #1693), 040njc (0.45 #706, 0.27 #1405, 0.23 #1638), 019f4v (0.43 #751, 0.33 #1450, 0.32 #1683), 04dn09n (0.43 #733, 0.27 #1432, 0.22 #1199), 02pqp12 (0.41 #756, 0.22 #1455, 0.19 #9094), 02qyntr (0.38 #874, 0.22 #1573, 0.21 #408) >> Best rule #754 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 07w8fz; 0bnzd; 0bs5vty; 04b_jc; >> query: (?x2336, 099c8n) <- nominated_for(?x704, ?x2336), nominated_for(?x450, ?x2336), film(?x525, ?x2336), ?x704 = 09sb52 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #9094 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1378 *> proper extension: 0m491; 01j8wk; 0c3xw46; 027j9wd; 05n6sq; 032clf; *> query: (?x2336, ?x1079) <- nominated_for(?x669, ?x2336), film(?x525, ?x2336), award(?x669, ?x1079) *> conf = 0.19 ranks of expected_values: 59 EVAL 016z9n nominated_for! 09qwmm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 58.000 39.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4992-01x53m PRED entity: 01x53m PRED relation: place_of_birth PRED expected values: 01yj2 => 136 concepts (114 used for prediction) PRED predicted values (max 10 best out of 158): 01yj2 (0.29 #4544, 0.22 #5250, 0.17 #6660), 0g284 (0.22 #5009, 0.17 #6419), 05ksh (0.17 #2151, 0.14 #2855, 0.03 #9199), 04vmp (0.17 #2382, 0.14 #3086, 0.03 #10137), 06wjf (0.17 #2268, 0.14 #2972, 0.03 #10023), 0hptm (0.15 #7274, 0.14 #3748, 0.10 #5864), 0c499 (0.14 #4837, 0.11 #5543, 0.08 #6953), 02_286 (0.14 #3542, 0.10 #5658, 0.10 #12003), 0cr3d (0.14 #3617, 0.10 #5733, 0.08 #7143), 0n920 (0.14 #3969, 0.07 #8200, 0.03 #10315) >> Best rule #4544 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 03t0k1; 053ksp; >> query: (?x9173, 01yj2) <- nationality(?x9173, ?x792), profession(?x9173, ?x353), ?x792 = 0hzlz, student(?x1011, ?x9173) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x53m place_of_birth 01yj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 114.000 0.286 http://example.org/people/person/place_of_birth #4991-072x7s PRED entity: 072x7s PRED relation: language PRED expected values: 02bjrlw 02h40lc 03hkp => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 53): 02h40lc (0.98 #2026, 0.97 #6986, 0.97 #1589), 02bjrlw (0.25 #109, 0.21 #3567, 0.15 #492), 06nm1 (0.24 #391, 0.21 #3567, 0.20 #501), 05qqm (0.21 #3567, 0.14 #6487, 0.05 #91), 01r2l (0.21 #3567, 0.14 #6487, 0.05 #129), 02hxcvy (0.21 #3567, 0.14 #6487, 0.04 #246), 04h9h (0.21 #3567, 0.11 #39, 0.10 #147), 02ztjwg (0.21 #3567, 0.08 #244, 0.02 #353), 03hkp (0.21 #3567, 0.06 #285, 0.05 #68), 01wgr (0.21 #3567, 0.02 #307, 0.02 #361) >> Best rule #2026 for best value: >> intensional similarity = 3 >> extensional distance = 292 >> proper extension: 0gtsx8c; >> query: (?x1685, 02h40lc) <- crewmember(?x1685, ?x1622), language(?x1685, ?x732), film(?x1018, ?x1685) >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 9 EVAL 072x7s language 03hkp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 133.000 133.000 0.976 http://example.org/film/film/language EVAL 072x7s language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.976 http://example.org/film/film/language EVAL 072x7s language 02bjrlw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.976 http://example.org/film/film/language #4990-01zzy3 PRED entity: 01zzy3 PRED relation: institution! PRED expected values: 02_xgp2 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 24): 03bwzr4 (0.71 #91, 0.43 #216, 0.42 #191), 02_xgp2 (0.64 #89, 0.53 #189, 0.52 #214), 014mlp (0.62 #431, 0.61 #556, 0.61 #581), 02h4rq6 (0.58 #578, 0.58 #428, 0.57 #78), 019v9k (0.57 #85, 0.55 #285, 0.53 #585), 0bkj86 (0.50 #84, 0.46 #209, 0.46 #184), 027f2w (0.50 #86, 0.26 #186, 0.25 #211), 016t_3 (0.38 #279, 0.38 #304, 0.37 #204), 04zx3q1 (0.36 #77, 0.31 #202, 0.30 #177), 0bjrnt (0.36 #82, 0.18 #207, 0.17 #182) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 03ksy; 07tds; >> query: (?x12343, 03bwzr4) <- contains(?x14507, ?x12343), student(?x12343, ?x3994), category(?x14507, ?x134), interests(?x3994, ?x2014) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #89 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 03ksy; 07tds; *> query: (?x12343, 02_xgp2) <- contains(?x14507, ?x12343), student(?x12343, ?x3994), category(?x14507, ?x134), interests(?x3994, ?x2014) *> conf = 0.64 ranks of expected_values: 2 EVAL 01zzy3 institution! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 126.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4989-0h5k PRED entity: 0h5k PRED relation: major_field_of_study! PRED expected values: 0bkj86 => 64 concepts (29 used for prediction) PRED predicted values (max 10 best out of 16): 0bkj86 (0.69 #277, 0.68 #363, 0.64 #310), 01gkg3 (0.60 #178, 0.58 #322, 0.57 #232), 0bjrnt (0.58 #322, 0.55 #134, 0.50 #50), 028dcg (0.55 #134, 0.50 #50, 0.47 #100), 03mkk4 (0.55 #134, 0.50 #50, 0.44 #205), 01rr_d (0.50 #50, 0.47 #100, 0.44 #205), 027f2w (0.50 #50, 0.47 #100, 0.44 #205), 07s6fsf (0.50 #50, 0.47 #100, 0.44 #205), 02cq61 (0.50 #50, 0.47 #100, 0.44 #205), 013zdg (0.50 #50, 0.44 #205, 0.43 #394) >> Best rule #277 for best value: >> intensional similarity = 14 >> extensional distance = 11 >> proper extension: 0l5mz; >> query: (?x2314, 0bkj86) <- major_field_of_study(?x8825, ?x2314), major_field_of_study(?x7816, ?x2314), major_field_of_study(?x5778, ?x2314), major_field_of_study(?x4955, ?x2314), major_field_of_study(?x2313, ?x2314), major_field_of_study(?x2013, ?x2314), contains(?x11743, ?x5778), currency(?x8825, ?x170), ?x4955 = 09f2j, school_type(?x7816, ?x1044), major_field_of_study(?x2013, ?x6859), ?x2313 = 07wrz, major_field_of_study(?x254, ?x2314), ?x6859 = 01tbp >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h5k major_field_of_study! 0bkj86 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 29.000 0.692 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #4988-09jrf PRED entity: 09jrf PRED relation: gender PRED expected values: 05zppz => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 28): 05zppz (0.88 #145, 0.88 #113, 0.88 #35), 02zsn (0.23 #83, 0.21 #184, 0.21 #182), 05jqy (0.09 #67, 0.06 #96), 0cycc (0.09 #67, 0.06 #96), 0d19y2 (0.09 #67, 0.06 #96), 0g0vx (0.09 #67, 0.06 #96), 0dcp_ (0.09 #67, 0.06 #96), 0fltx (0.09 #67, 0.06 #96), 01psyx (0.09 #67, 0.06 #96), 012jc (0.09 #67, 0.06 #96) >> Best rule #145 for best value: >> intensional similarity = 6 >> extensional distance = 162 >> proper extension: 033hqf; 0lgm5; >> query: (?x13355, 05zppz) <- student(?x10355, ?x13355), type_of_union(?x13355, ?x566), ?x566 = 04ztj, school_type(?x10355, ?x5931), people(?x10717, ?x13355), state_province_region(?x10355, ?x3302) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09jrf gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.884 http://example.org/people/person/gender #4987-0f42nz PRED entity: 0f42nz PRED relation: nominated_for! PRED expected values: 03r8v_ => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 194): 03r8v_ (0.46 #448, 0.33 #211, 0.15 #11146), 019f4v (0.41 #5512, 0.24 #3136, 0.22 #2662), 0gs9p (0.34 #5523, 0.23 #1488, 0.21 #1725), 0gq9h (0.33 #5521, 0.27 #1249, 0.25 #1486), 0k611 (0.27 #5532, 0.21 #1260, 0.20 #3393), 054krc (0.27 #5528, 0.14 #782, 0.14 #1019), 04dn09n (0.26 #5493, 0.22 #3117, 0.21 #2643), 040njc (0.25 #5464, 0.18 #1429, 0.17 #1666), 0gq_v (0.22 #5477, 0.18 #7136, 0.17 #7610), 02qyntr (0.21 #5636, 0.17 #3497, 0.16 #3260) >> Best rule #448 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 04jwjq; 01p3ty; 09fn1w; 021pqy; 052_mn; 030z4z; 07vfy4; 09yxcz; 08g_jw; 02tcgh; >> query: (?x5247, 03r8v_) <- film(?x6312, ?x5247), language(?x5247, ?x1882), genre(?x5247, ?x3741), location(?x6312, ?x94), ?x3741 = 01chg >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f42nz nominated_for! 03r8v_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.462 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4986-02nfjp PRED entity: 02nfjp PRED relation: award PRED expected values: 063y_ky => 107 concepts (91 used for prediction) PRED predicted values (max 10 best out of 329): 09v1lrz (0.72 #31207, 0.71 #17024, 0.70 #30801), 05pcn59 (0.40 #892, 0.11 #3322, 0.11 #9402), 01by1l (0.31 #1328, 0.24 #1733, 0.21 #2543), 01bgqh (0.31 #1258, 0.16 #1663, 0.14 #448), 054ks3 (0.31 #1358, 0.13 #2573, 0.12 #2978), 05ztrmj (0.30 #996, 0.07 #9506, 0.06 #3426), 0bdwft (0.30 #879, 0.06 #4119, 0.05 #4525), 0cqgl9 (0.30 #1004, 0.05 #9514, 0.04 #4244), 05b4l5x (0.30 #816, 0.04 #17436, 0.04 #17841), 03c7tr1 (0.30 #869, 0.04 #19514, 0.04 #10189) >> Best rule #31207 for best value: >> intensional similarity = 3 >> extensional distance = 2323 >> proper extension: 06lxn; >> query: (?x5106, ?x11702) <- award_winner(?x11702, ?x5106), award(?x5922, ?x11702), profession(?x5922, ?x524) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #12969 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1125 *> proper extension: 01nzs7; 031rx9; *> query: (?x5106, ?x2456) <- award_winner(?x5782, ?x5106), nominated_for(?x5106, ?x4427), nominated_for(?x2456, ?x4427), film_release_distribution_medium(?x4427, ?x81) *> conf = 0.13 ranks of expected_values: 67 EVAL 02nfjp award 063y_ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 107.000 91.000 0.717 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4985-04jpg2p PRED entity: 04jpg2p PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #91, 0.85 #36, 0.85 #61), 02nxhr (0.07 #12, 0.06 #2, 0.05 #67), 07c52 (0.04 #48, 0.03 #78, 0.03 #287), 07z4p (0.03 #120, 0.03 #125, 0.02 #304) >> Best rule #91 for best value: >> intensional similarity = 3 >> extensional distance = 195 >> proper extension: 0d1qmz; >> query: (?x8570, 029j_) <- language(?x8570, ?x254), nominated_for(?x8570, ?x857), nominated_for(?x2444, ?x8570) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04jpg2p film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.853 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #4984-04f6df0 PRED entity: 04f6df0 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.81 #96, 0.81 #117, 0.80 #154), 07c52 (0.09 #29, 0.06 #18, 0.05 #23), 02nxhr (0.03 #145, 0.03 #28, 0.03 #118), 07z4p (0.02 #221, 0.02 #282, 0.02 #121) >> Best rule #96 for best value: >> intensional similarity = 4 >> extensional distance = 472 >> proper extension: 014lc_; 014_x2; 0d90m; 03qcfvw; 09sh8k; 02y_lrp; 034qmv; 07gp9; 09xbpt; 0bvn25; ... >> query: (?x8030, 029j_) <- film(?x3717, ?x8030), genre(?x8030, ?x162), executive_produced_by(?x8030, ?x2803), titles(?x162, ?x144) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04f6df0 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.808 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #4983-02js_6 PRED entity: 02js_6 PRED relation: gender PRED expected values: 05zppz => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #11, 0.88 #68, 0.87 #13), 02zsn (0.64 #10, 0.49 #16, 0.48 #24) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 01m4kpp; >> query: (?x12359, 05zppz) <- award(?x12359, ?x1921), award(?x12359, ?x870), ?x1921 = 0bs0bh, ceremony(?x870, ?x1265) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02js_6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.889 http://example.org/people/person/gender #4982-015882 PRED entity: 015882 PRED relation: gender PRED expected values: 02zsn => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #61, 0.80 #101, 0.77 #43), 02zsn (0.45 #68, 0.44 #94, 0.42 #70) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 363 >> proper extension: 01rrwf6; 09g0h; >> query: (?x1817, 05zppz) <- profession(?x1817, ?x1183), ?x1183 = 09jwl, place_of_birth(?x1817, ?x3983) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #68 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 380 *> proper extension: 01n7qlf; 0hhqw; 01npcy7; 0202p_; *> query: (?x1817, 02zsn) <- profession(?x1817, ?x131), participant(?x1817, ?x1401), place_of_birth(?x1817, ?x3983) *> conf = 0.45 ranks of expected_values: 2 EVAL 015882 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.797 http://example.org/people/person/gender #4981-011vx3 PRED entity: 011vx3 PRED relation: nationality PRED expected values: 09c7w0 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 89): 09c7w0 (0.84 #902, 0.80 #1403, 0.78 #12742), 02jx1 (0.22 #233, 0.22 #834, 0.20 #133), 0cc56 (0.20 #7720, 0.06 #401, 0.03 #13344), 059rby (0.20 #7720), 07ssc (0.19 #816, 0.14 #4326, 0.14 #2217), 0d060g (0.11 #307, 0.11 #207, 0.08 #2409), 0h7x (0.09 #736, 0.05 #2538, 0.03 #6551), 0345h (0.07 #2333, 0.06 #732, 0.06 #6547), 03rk0 (0.06 #747, 0.06 #11381, 0.06 #9068), 01p1v (0.06 #344) >> Best rule #902 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0443c; >> query: (?x7398, 09c7w0) <- type_of_union(?x7398, ?x566), location(?x7398, ?x739), inductee(?x1091, ?x7398), student(?x3439, ?x7398) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011vx3 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.838 http://example.org/people/person/nationality #4980-043mk4y PRED entity: 043mk4y PRED relation: nominated_for! PRED expected values: 0bdwqv 0cqgl9 07kjk7c => 86 concepts (79 used for prediction) PRED predicted values (max 10 best out of 228): 04g2jz2 (0.79 #2148, 0.70 #2626, 0.69 #1909), 07kjk7c (0.70 #191, 0.64 #429, 0.59 #906), 0bdwft (0.41 #770, 0.40 #55, 0.36 #293), 0bdwqv (0.40 #128, 0.36 #366, 0.29 #843), 0gq9h (0.37 #5072, 0.36 #1016, 0.35 #3881), 0cqgl9 (0.35 #853, 0.30 #138, 0.29 #614), 09v82c0 (0.35 #901, 0.29 #662, 0.20 #186), 0gs9p (0.33 #5074, 0.31 #3644, 0.30 #3168), 019f4v (0.32 #5063, 0.32 #1007, 0.32 #3633), 099c8n (0.30 #1965, 0.29 #1487, 0.29 #1726) >> Best rule #2148 for best value: >> intensional similarity = 4 >> extensional distance = 133 >> proper extension: 019g8j; >> query: (?x7768, ?x4838) <- award(?x7768, ?x4838), category(?x7768, ?x134), nominated_for(?x375, ?x7768), ceremony(?x4838, ?x1265) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #191 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 0f4k49; 02q_x_l; *> query: (?x7768, 07kjk7c) <- titles(?x3652, ?x7768), titles(?x53, ?x7768), ?x53 = 07s9rl0, ?x3652 = 015w9s, nominated_for(?x318, ?x7768) *> conf = 0.70 ranks of expected_values: 2, 4, 6 EVAL 043mk4y nominated_for! 07kjk7c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 79.000 0.789 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 043mk4y nominated_for! 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 79.000 0.789 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 043mk4y nominated_for! 0bdwqv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 79.000 0.789 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4979-09jm8 PRED entity: 09jm8 PRED relation: group! PRED expected values: 05r5c => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 120): 05r5c (0.35 #432, 0.29 #1963, 0.25 #1707), 03qjg (0.32 #2001, 0.29 #1745, 0.27 #470), 01vj9c (0.29 #2819, 0.28 #1968, 0.28 #3244), 0l14qv (0.27 #1620, 0.26 #2046, 0.24 #855), 02k856 (0.20 #133, 0.07 #3318, 0.06 #1956), 013y1f (0.18 #1981, 0.15 #2832, 0.15 #450), 06ncr (0.16 #2077, 0.16 #2843, 0.15 #1651), 04rzd (0.15 #2070, 0.15 #454, 0.15 #1644), 042v_gx (0.15 #858, 0.11 #2049, 0.11 #1623), 03_vpw (0.12 #302, 0.06 #1956, 0.04 #557) >> Best rule #432 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 01yzl2; 01dwrc; >> query: (?x10561, 05r5c) <- group(?x7053, ?x10561), award_nominee(?x8669, ?x10561), award_winner(?x1565, ?x10561), award(?x10561, ?x2180) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09jm8 group! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.346 http://example.org/music/performance_role/regular_performances./music/group_membership/group #4978-098sx PRED entity: 098sx PRED relation: award_winner! PRED expected values: 02r0d0 => 183 concepts (135 used for prediction) PRED predicted values (max 10 best out of 355): 040_9s0 (0.43 #3022, 0.36 #3887, 0.35 #25884), 0208wk (0.43 #3022, 0.36 #3887, 0.35 #25884), 02tzwd (0.43 #3022, 0.36 #3887, 0.35 #25884), 01yz0x (0.30 #3629, 0.27 #4062, 0.25 #3196), 02v1m7 (0.30 #2273, 0.20 #2704, 0.17 #547), 02sp_v (0.30 #2320, 0.20 #2751, 0.06 #9224), 0262x6 (0.25 #3769, 0.23 #4202, 0.19 #3336), 0262zm (0.25 #3539, 0.23 #3972, 0.14 #13458), 02662b (0.25 #3532, 0.23 #3965, 0.12 #3099), 0m7yy (0.22 #23045, 0.13 #47641, 0.04 #10537) >> Best rule #3022 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 0kzy0; >> query: (?x10536, ?x8909) <- category(?x10536, ?x134), languages(?x10536, ?x254), award_winner(?x9629, ?x10536), influenced_by(?x10536, ?x2625), award(?x10536, ?x8909) >> conf = 0.43 => this is the best rule for 3 predicted values *> Best rule #2137 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 09jd9; *> query: (?x10536, 02r0d0) <- award(?x10536, ?x8909), student(?x892, ?x10536), ?x8909 = 040_9s0, story_by(?x1518, ?x10536) *> conf = 0.10 ranks of expected_values: 80 EVAL 098sx award_winner! 02r0d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 183.000 135.000 0.435 http://example.org/award/award_category/winners./award/award_honor/award_winner #4977-01w7nww PRED entity: 01w7nww PRED relation: artists! PRED expected values: 0m0jc 06j6l => 124 concepts (79 used for prediction) PRED predicted values (max 10 best out of 221): 06j6l (0.69 #4929, 0.51 #11336, 0.44 #658), 064t9 (0.67 #4894, 0.56 #623, 0.55 #11301), 06by7 (0.52 #2156, 0.50 #936, 0.44 #12531), 016clz (0.34 #2140, 0.23 #18626, 0.22 #12515), 0xhtw (0.34 #2152, 0.19 #18638, 0.19 #5508), 0ggx5q (0.33 #687, 0.24 #4958, 0.20 #2212), 05bt6j (0.31 #2178, 0.24 #958, 0.22 #12553), 017_qw (0.30 #7382, 0.18 #7077, 0.11 #11655), 0155w (0.26 #1020, 0.23 #4986, 0.19 #8341), 02lnbg (0.25 #4938, 0.19 #7683, 0.19 #11345) >> Best rule #4929 for best value: >> intensional similarity = 3 >> extensional distance = 116 >> proper extension: 0qmny; >> query: (?x3176, 06j6l) <- artists(?x3928, ?x3176), artist(?x5021, ?x3176), ?x3928 = 0gywn >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 21 EVAL 01w7nww artists! 06j6l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 79.000 0.695 http://example.org/music/genre/artists EVAL 01w7nww artists! 0m0jc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 124.000 79.000 0.695 http://example.org/music/genre/artists #4976-016kv6 PRED entity: 016kv6 PRED relation: film! PRED expected values: 016tt2 => 102 concepts (59 used for prediction) PRED predicted values (max 10 best out of 56): 05qd_ (0.19 #235, 0.17 #386, 0.15 #159), 086k8 (0.18 #680, 0.17 #77, 0.17 #830), 016tt2 (0.16 #79, 0.14 #1437, 0.13 #1816), 016tw3 (0.15 #237, 0.14 #1294, 0.13 #86), 0g1rw (0.12 #536, 0.12 #8, 0.09 #234), 017s11 (0.12 #1059, 0.12 #1286, 0.12 #3327), 017jv5 (0.11 #467, 0.06 #165, 0.06 #316), 0jz9f (0.10 #754, 0.09 #76, 0.07 #1133), 03xq0f (0.09 #1061, 0.09 #683, 0.09 #382), 025jfl (0.09 #609, 0.08 #759, 0.05 #534) >> Best rule #235 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 0jzw; 01hqhm; 02r_pp; 02nt3d; 011xg5; 09v8clw; >> query: (?x3523, 05qd_) <- music(?x3523, ?x3690), genre(?x3523, ?x53), produced_by(?x3523, ?x777), nominated_for(?x3523, ?x4734) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #79 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 0m313; 0209xj; 034qzw; 06ybb1; 03qnc6q; 01771z; 0946bb; 074w86; 0h6r5; 02qzh2; ... *> query: (?x3523, 016tt2) <- film(?x3522, ?x3523), nominated_for(?x591, ?x3523), nominated_for(?x3523, ?x4734), written_by(?x3523, ?x777) *> conf = 0.16 ranks of expected_values: 3 EVAL 016kv6 film! 016tt2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 59.000 0.187 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #4975-04yg13l PRED entity: 04yg13l PRED relation: film_release_region PRED expected values: 05r4w 01znc_ 03ryn => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 248): 035qy (0.94 #690, 0.89 #1501, 0.85 #2311), 03rjj (0.90 #1956, 0.86 #1794, 0.86 #2280), 03gj2 (0.89 #1329, 0.86 #1653, 0.84 #2301), 0345h (0.88 #1823, 0.88 #1661, 0.87 #2309), 01znc_ (0.88 #699, 0.80 #1510, 0.78 #1834), 02vzc (0.86 #1845, 0.84 #2655, 0.83 #1521), 05r4w (0.86 #2276, 0.86 #2600, 0.85 #1628), 03h64 (0.86 #2347, 0.84 #1861, 0.84 #1699), 0chghy (0.86 #1962, 0.85 #1638, 0.84 #2124), 015fr (0.85 #1481, 0.84 #2291, 0.83 #2129) >> Best rule #690 for best value: >> intensional similarity = 8 >> extensional distance = 46 >> proper extension: 0c40vxk; 0ndsl1x; >> query: (?x5052, 035qy) <- film_release_region(?x5052, ?x1229), film_release_region(?x5052, ?x512), film_release_region(?x5052, ?x404), ?x512 = 07ssc, currency(?x5052, ?x170), film(?x1223, ?x5052), ?x404 = 047lj, ?x1229 = 059j2 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #699 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 46 *> proper extension: 0c40vxk; 0ndsl1x; *> query: (?x5052, 01znc_) <- film_release_region(?x5052, ?x1229), film_release_region(?x5052, ?x512), film_release_region(?x5052, ?x404), ?x512 = 07ssc, currency(?x5052, ?x170), film(?x1223, ?x5052), ?x404 = 047lj, ?x1229 = 059j2 *> conf = 0.88 ranks of expected_values: 5, 7, 29 EVAL 04yg13l film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 90.000 90.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04yg13l film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 90.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04yg13l film_release_region 05r4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 90.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4974-0cfgd PRED entity: 0cfgd PRED relation: artist! PRED expected values: 011k1h => 116 concepts (103 used for prediction) PRED predicted values (max 10 best out of 128): 033hn8 (0.53 #1960, 0.22 #1126, 0.19 #2794), 017l96 (0.43 #713, 0.25 #991, 0.23 #1686), 01cf93 (0.38 #890, 0.23 #1724, 0.19 #2280), 0229rs (0.33 #17, 0.25 #295, 0.17 #2380), 03y5g8 (0.33 #246, 0.14 #802, 0.14 #663), 03qy3l (0.33 #201, 0.14 #757, 0.14 #618), 015_1q (0.32 #4884, 0.25 #2660, 0.25 #2243), 01w40h (0.31 #2113, 0.14 #3781, 0.14 #723), 0g768 (0.29 #592, 0.25 #2121, 0.20 #2677), 011k1h (0.29 #705, 0.23 #3207, 0.21 #3763) >> Best rule #1960 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 014_xj; >> query: (?x11551, 033hn8) <- artists(?x1000, ?x11551), group(?x745, ?x11551), ?x745 = 01vj9c, artist(?x5744, ?x11551), artist(?x5744, ?x2274), ?x2274 = 013v5j >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #705 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 01vs4ff; *> query: (?x11551, 011k1h) <- origin(?x11551, ?x11971), artists(?x7083, ?x11551), artists(?x2809, ?x11551), ?x7083 = 02yv6b, artist(?x5744, ?x11551), ?x2809 = 05w3f, artist(?x5744, ?x1136), ?x1136 = 07c0j *> conf = 0.29 ranks of expected_values: 10 EVAL 0cfgd artist! 011k1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 116.000 103.000 0.533 http://example.org/music/record_label/artist #4973-04mlmx PRED entity: 04mlmx PRED relation: nationality PRED expected values: 0d060g => 118 concepts (115 used for prediction) PRED predicted values (max 10 best out of 67): 07ssc (0.38 #7329, 0.29 #8320, 0.24 #1202), 02jx1 (0.33 #32, 0.25 #1913, 0.22 #1220), 0f8l9c (0.20 #120, 0.17 #219, 0.11 #318), 0345h (0.11 #327, 0.05 #2802, 0.05 #1119), 03rk0 (0.11 #837, 0.10 #6085, 0.09 #4006), 0d060g (0.08 #2778, 0.06 #3471, 0.06 #1986), 03_3d (0.06 #3470, 0.02 #8523, 0.02 #1985), 0chghy (0.04 #504, 0.04 #1098, 0.03 #1197), 05vz3zq (0.04 #1158, 0.03 #1257, 0.02 #1950), 03rt9 (0.04 #1893, 0.03 #1200, 0.03 #1002) >> Best rule #7329 for best value: >> intensional similarity = 4 >> extensional distance = 988 >> proper extension: 06lgq8; >> query: (?x8222, ?x512) <- film(?x8222, ?x12214), genre(?x12214, ?x811), ?x811 = 03k9fj, country(?x12214, ?x512) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2778 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 314 *> proper extension: 01w3v; 0mcf4; *> query: (?x8222, 0d060g) <- religion(?x8222, ?x2694), religion(?x2794, ?x2694), religion(?x2499, ?x2694), ?x2794 = 027l0b, participant(?x286, ?x2499) *> conf = 0.08 ranks of expected_values: 6 EVAL 04mlmx nationality 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 118.000 115.000 0.381 http://example.org/people/person/nationality #4972-02t_st PRED entity: 02t_st PRED relation: award_winner! PRED expected values: 027hjff => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 93): 0gvstc3 (0.30 #34, 0.17 #10153, 0.07 #316), 0g55tzk (0.30 #137, 0.17 #10153, 0.07 #419), 0hr3c8y (0.30 #10, 0.17 #10153, 0.05 #292), 0hn821n (0.30 #131, 0.03 #413, 0.03 #1118), 09v0p2c (0.20 #83, 0.04 #788, 0.04 #1070), 050yyb (0.18 #179), 0hndn2q (0.17 #10153, 0.10 #40, 0.07 #322), 027hjff (0.17 #10153, 0.04 #1608, 0.04 #2031), 09qftb (0.17 #10153, 0.03 #1100, 0.03 #1382), 0lp_cd3 (0.17 #10153, 0.03 #728, 0.02 #1010) >> Best rule #34 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0crx5w; 0gsg7; 026_dcw; 06msq2; >> query: (?x7381, 0gvstc3) <- award_winner(?x237, ?x7381), nominated_for(?x7381, ?x8533), ?x8533 = 05zr0xl >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #10153 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1699 *> proper extension: 09mfvx; 0kcdl; 0kc9f; *> query: (?x7381, ?x3624) <- nominated_for(?x7381, ?x9701), honored_for(?x3624, ?x9701) *> conf = 0.17 ranks of expected_values: 8 EVAL 02t_st award_winner! 027hjff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 115.000 115.000 0.300 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4971-05_5_22 PRED entity: 05_5_22 PRED relation: film_crew_role PRED expected values: 089g0h => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 22): 01pvkk (0.39 #9, 0.32 #552, 0.31 #401), 01vx2h (0.36 #460, 0.34 #551, 0.34 #793), 02ynfr (0.21 #797, 0.20 #192, 0.19 #434), 02rh1dz (0.16 #459, 0.15 #67, 0.15 #429), 089fss (0.15 #184, 0.11 #1791, 0.09 #789), 033smt (0.12 #202, 0.11 #1791, 0.10 #414), 015h31 (0.11 #1791, 0.11 #428, 0.10 #186), 089g0h (0.11 #1791, 0.10 #1683, 0.10 #1652), 04pyp5 (0.11 #1791, 0.07 #405, 0.07 #616), 02vs3x5 (0.11 #1791, 0.06 #411, 0.06 #562) >> Best rule #9 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 0hz6mv2; >> query: (?x5201, 01pvkk) <- person(?x5201, ?x6171), person(?x5201, ?x5906), artists(?x474, ?x5906), award_winner(?x342, ?x5906), award_nominee(?x6171, ?x690) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1791 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1133 *> proper extension: 03j63k; *> query: (?x5201, ?x137) <- titles(?x2480, ?x5201), nominated_for(?x1691, ?x5201), titles(?x2480, ?x146), film_crew_role(?x146, ?x137) *> conf = 0.11 ranks of expected_values: 8 EVAL 05_5_22 film_crew_role 089g0h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 91.000 91.000 0.389 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4970-0fnb4 PRED entity: 0fnb4 PRED relation: category PRED expected values: 08mbj5d => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.87 #39, 0.83 #58, 0.81 #70) >> Best rule #39 for best value: >> intensional similarity = 6 >> extensional distance = 21 >> proper extension: 0fvwz; >> query: (?x13165, 08mbj5d) <- capital(?x10457, ?x13165), administrative_parent(?x10457, ?x551), jurisdiction_of_office(?x182, ?x10457), adjoins(?x10457, ?x2146), taxonomy(?x10457, ?x939), place_of_birth(?x1806, ?x2146) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fnb4 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.870 http://example.org/common/topic/webpage./common/webpage/category #4969-018mrd PRED entity: 018mrd PRED relation: place_of_burial! PRED expected values: 0gry51 => 95 concepts (50 used for prediction) PRED predicted values (max 10 best out of 798): 0l786 (0.40 #201, 0.20 #63, 0.12 #618), 022p06 (0.25 #322, 0.20 #461, 0.20 #45), 03bw6 (0.25 #342, 0.20 #65, 0.13 #481), 01t94_1 (0.25 #360, 0.20 #83, 0.13 #499), 0cf2h (0.25 #332, 0.20 #55, 0.13 #471), 0bkmf (0.25 #364, 0.20 #87, 0.13 #503), 0hnp7 (0.25 #330, 0.20 #53, 0.13 #469), 0c921 (0.20 #224, 0.20 #86, 0.12 #363), 01ynzf (0.20 #223, 0.20 #85, 0.12 #362), 05xpv (0.20 #220, 0.20 #82, 0.12 #359) >> Best rule #201 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0lbp_; >> query: (?x11261, 0l786) <- place_of_burial(?x6239, ?x11261), people(?x6260, ?x6239), award(?x6239, ?x601), ?x601 = 0gr4k >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018mrd place_of_burial! 0gry51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 50.000 0.400 http://example.org/people/deceased_person/place_of_burial #4968-02v_r7d PRED entity: 02v_r7d PRED relation: titles! PRED expected values: 03mqtr => 88 concepts (64 used for prediction) PRED predicted values (max 10 best out of 67): 03bxz7 (0.33 #5328, 0.29 #3694, 0.25 #5839), 017fp (0.33 #5328, 0.27 #123, 0.25 #5839), 01z4y (0.32 #335, 0.20 #3930, 0.20 #3728), 04xvh5 (0.25 #5839, 0.23 #6352, 0.22 #5838), 07ssc (0.23 #3194, 0.12 #511, 0.11 #2782), 01jfsb (0.20 #18, 0.17 #5348, 0.17 #1135), 01hmnh (0.20 #25, 0.14 #731, 0.14 #629), 02n4kr (0.20 #12, 0.09 #113, 0.07 #2070), 0c3351 (0.20 #50, 0.08 #2108, 0.08 #2209), 015w9s (0.20 #46, 0.03 #2820, 0.02 #2720) >> Best rule #5328 for best value: >> intensional similarity = 6 >> extensional distance = 990 >> proper extension: 05jyb2; >> query: (?x6178, ?x6887) <- genre(?x6178, ?x6887), country(?x6178, ?x94), genre(?x9565, ?x6887), genre(?x6169, ?x6887), ?x6169 = 077q8x, film_release_region(?x9565, ?x87) >> conf = 0.33 => this is the best rule for 2 predicted values *> Best rule #2718 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 401 *> proper extension: 03kq98; *> query: (?x6178, 03mqtr) <- titles(?x162, ?x6178), titles(?x53, ?x6178), ?x53 = 07s9rl0, genre(?x144, ?x162), titles(?x162, ?x5425), ?x5425 = 02prwdh *> conf = 0.11 ranks of expected_values: 13 EVAL 02v_r7d titles! 03mqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 88.000 64.000 0.328 http://example.org/media_common/netflix_genre/titles #4967-02y5kn PRED entity: 02y5kn PRED relation: specialization_of PRED expected values: 04_tv => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 23): 0cbd2 (0.25 #98, 0.12 #130, 0.11 #358), 09jwl (0.20 #496, 0.16 #561, 0.14 #528), 0n1h (0.18 #231, 0.14 #296, 0.07 #426), 02hrh1q (0.14 #266, 0.12 #167, 0.12 #135), 015cjr (0.12 #145, 0.11 #210, 0.07 #276), 01445t (0.11 #204, 0.07 #270, 0.06 #335), 06q2q (0.08 #766, 0.08 #601, 0.07 #799), 01c979 (0.07 #284, 0.06 #349, 0.06 #381), 04_tv (0.04 #929, 0.03 #894, 0.02 #794), 0kyk (0.03 #227, 0.02 #752, 0.02 #785) >> Best rule #98 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0kyk; >> query: (?x14261, 0cbd2) <- profession(?x12339, ?x14261), profession(?x12323, ?x14261), profession(?x9586, ?x14261), ?x12339 = 049sb, team(?x12323, ?x10279), draft(?x10279, ?x1161), gender(?x9586, ?x231) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #929 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 138 *> proper extension: 01c6nk; 03_vpw; 02ynfr; 04pyp5; 0mch7; 05kb8h; 01pn0r; 018rn4; 01nxfc; 028sgq; *> query: (?x14261, 04_tv) <- profession(?x12339, ?x14261), gender(?x12339, ?x231), nationality(?x12339, ?x94), ?x231 = 05zppz *> conf = 0.04 ranks of expected_values: 9 EVAL 02y5kn specialization_of 04_tv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 30.000 30.000 0.250 http://example.org/people/profession/specialization_of #4966-0g_rs_ PRED entity: 0g_rs_ PRED relation: executive_produced_by! PRED expected values: 053rxgm 09rvwmy => 101 concepts (15 used for prediction) PRED predicted values (max 10 best out of 425): 02fj8n (0.05 #1984, 0.05 #3038, 0.05 #930), 048tv9 (0.04 #1493, 0.03 #2020, 0.03 #3074), 0gwjw0c (0.04 #1440, 0.03 #4076, 0.02 #4603), 049xgc (0.04 #1376, 0.02 #4539, 0.02 #7175), 0bs8s1p (0.04 #1444, 0.02 #4607, 0.02 #5134), 03s6l2 (0.04 #1077, 0.02 #4240, 0.02 #5294), 0bt4g (0.04 #1473, 0.02 #4109, 0.02 #7799), 0mbql (0.04 #1431, 0.02 #4067, 0.02 #7757), 01f7kl (0.04 #1189, 0.02 #3825, 0.02 #7515), 0fsd9t (0.04 #1519, 0.02 #4155, 0.02 #7318) >> Best rule #1984 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 02hy9p; >> query: (?x14126, 02fj8n) <- place_of_birth(?x14126, ?x10519), executive_produced_by(?x7107, ?x14126), profession(?x14126, ?x987), ?x987 = 0dxtg, film(?x1914, ?x7107) >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g_rs_ executive_produced_by! 09rvwmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 15.000 0.055 http://example.org/film/film/executive_produced_by EVAL 0g_rs_ executive_produced_by! 053rxgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 15.000 0.055 http://example.org/film/film/executive_produced_by #4965-06f32 PRED entity: 06f32 PRED relation: participating_countries! PRED expected values: 09x3r => 202 concepts (202 used for prediction) PRED predicted values (max 10 best out of 39): 09x3r (0.75 #788, 0.71 #751, 0.68 #602), 0sx8l (0.54 #790, 0.50 #753, 0.50 #123), 0blfl (0.52 #842, 0.50 #767, 0.46 #804), 016r9z (0.48 #611, 0.47 #426, 0.45 #1058), 06sks6 (0.40 #614, 0.38 #133, 0.36 #800), 0c_tl (0.38 #132, 0.32 #613, 0.32 #428), 0jdk_ (0.38 #135, 0.22 #816, 0.21 #1817), 0kbvv (0.28 #1893, 0.26 #2900, 0.16 #891), 0l6ny (0.25 #118, 0.22 #816, 0.21 #1817), 019n8z (0.25 #141, 0.16 #891, 0.14 #2638) >> Best rule #788 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 09c7w0; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 0chghy; 05qhw; 07ssc; ... >> query: (?x2629, 09x3r) <- film_release_region(?x6882, ?x2629), film_release_region(?x66, ?x2629), ?x6882 = 043tvp3, olympics(?x2629, ?x775), ?x66 = 014lc_ >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06f32 participating_countries! 09x3r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.750 http://example.org/olympics/olympic_games/participating_countries #4964-0bsb4j PRED entity: 0bsb4j PRED relation: film PRED expected values: 0hwpz => 77 concepts (42 used for prediction) PRED predicted values (max 10 best out of 555): 0gg5qcw (0.48 #39351, 0.44 #35773, 0.42 #25043), 0h03fhx (0.48 #39351, 0.44 #35773, 0.42 #25043), 0660b9b (0.13 #5365, 0.12 #8944, 0.11 #7154), 09xbpt (0.05 #46507, 0.02 #47, 0.01 #1835), 0gmgwnv (0.05 #46507, 0.02 #1078, 0.01 #11813), 029k4p (0.05 #46507, 0.02 #836), 01qvz8 (0.05 #46507, 0.02 #805), 03s9kp (0.05 #46507, 0.02 #3549, 0.01 #1761), 0fh694 (0.05 #46507, 0.01 #1930, 0.01 #142), 06z8s_ (0.05 #46507, 0.01 #1918, 0.01 #130) >> Best rule #39351 for best value: >> intensional similarity = 3 >> extensional distance = 969 >> proper extension: 049tjg; 02wrhj; >> query: (?x2590, ?x3133) <- type_of_union(?x2590, ?x566), location(?x2590, ?x3670), nominated_for(?x2590, ?x3133) >> conf = 0.48 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0bsb4j film 0hwpz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 42.000 0.484 http://example.org/film/actor/film./film/performance/film #4963-02r6c_ PRED entity: 02r6c_ PRED relation: profession PRED expected values: 01d_h8 => 117 concepts (113 used for prediction) PRED predicted values (max 10 best out of 78): 01d_h8 (0.81 #2381, 0.78 #450, 0.77 #3567), 02hrh1q (0.81 #4758, 0.80 #5942, 0.79 #754), 03gjzk (0.42 #3279, 0.39 #3723, 0.38 #310), 0cbd2 (0.35 #155, 0.30 #3272, 0.29 #3716), 01c72t (0.25 #3436, 0.14 #7855, 0.10 #10990), 02krf9 (0.24 #3587, 0.23 #26, 0.22 #4623), 02hv44_ (0.22 #205, 0.15 #353, 0.15 #947), 0kyk (0.19 #325, 0.18 #2849, 0.18 #2997), 09jwl (0.18 #16316, 0.18 #10985, 0.17 #11725), 018gz8 (0.15 #312, 0.14 #3281, 0.14 #3873) >> Best rule #2381 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 0cm89v; 013zyw; 03ys2f; 03ysmg; 0dr5y; >> query: (?x8812, 01d_h8) <- gender(?x8812, ?x514), written_by(?x2121, ?x8812), film(?x8812, ?x4742) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r6c_ profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 113.000 0.808 http://example.org/people/person/profession #4962-01z77k PRED entity: 01z77k PRED relation: genre! PRED expected values: 0gbtbm 02kk_c => 51 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1394): 0gxr1c (0.60 #2181, 0.50 #2455, 0.38 #6043), 0123qq (0.60 #2147, 0.50 #2421, 0.36 #3245), 020qr4 (0.60 #1932, 0.50 #2206, 0.35 #5244), 03g9xj (0.60 #2114, 0.50 #2388, 0.33 #3764), 0fhzwl (0.60 #2093, 0.33 #2367, 0.33 #719), 06r1k (0.60 #2144, 0.33 #2418, 0.33 #770), 02py9yf (0.60 #2139, 0.33 #2413, 0.33 #765), 06qw_ (0.60 #2183, 0.33 #2457, 0.33 #809), 06qv_ (0.60 #2138, 0.33 #2412, 0.33 #764), 06qxh (0.60 #2124, 0.33 #2398, 0.33 #750) >> Best rule #2181 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 01htzx; >> query: (?x5518, 0gxr1c) <- genre(?x9222, ?x5518), genre(?x4138, ?x5518), film(?x4767, ?x9222), nominated_for(?x375, ?x9222), actor(?x4138, ?x5586), ?x5586 = 03rwng, profession(?x4767, ?x319) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #634 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 07s9rl0; *> query: (?x5518, 02kk_c) <- genre(?x8733, ?x5518), genre(?x6415, ?x5518), genre(?x623, ?x5518), genre(?x531, ?x5518), titles(?x5518, ?x1763), ?x8733 = 0dl6fv, ?x623 = 03kq98, titles(?x53, ?x6415), film_crew_role(?x6415, ?x9094), ?x53 = 07s9rl0, ?x531 = 06cs95 *> conf = 0.33 ranks of expected_values: 81, 135 EVAL 01z77k genre! 02kk_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 51.000 32.000 0.600 http://example.org/tv/tv_program/genre EVAL 01z77k genre! 0gbtbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 51.000 32.000 0.600 http://example.org/tv/tv_program/genre #4961-016y_f PRED entity: 016y_f PRED relation: music PRED expected values: 015wc0 => 95 concepts (53 used for prediction) PRED predicted values (max 10 best out of 102): 01tc9r (0.13 #697, 0.13 #1751, 0.09 #275), 0146pg (0.13 #10, 0.07 #1908, 0.07 #2542), 023361 (0.12 #1415, 0.11 #571, 0.10 #1205), 02g0mx (0.07 #5067, 0.07 #10783, 0.07 #10782), 08h79x (0.07 #5067, 0.07 #10783, 0.07 #10782), 02jxmr (0.07 #495, 0.06 #74, 0.06 #1129), 02cyfz (0.06 #34, 0.06 #244, 0.04 #666), 05y7hc (0.06 #126, 0.03 #2024, 0.02 #3081), 015wc0 (0.06 #1231, 0.06 #1441, 0.04 #597), 07hgkd (0.06 #292, 0.04 #714, 0.04 #1768) >> Best rule #697 for best value: >> intensional similarity = 5 >> extensional distance = 44 >> proper extension: 026n4h6; 0fq7dv_; 0hmm7; 06g77c; 0315w4; 06bd5j; 098s2w; 02cbg0; 07nnp_; >> query: (?x4454, 01tc9r) <- nominated_for(?x591, ?x4454), titles(?x3613, ?x4454), film(?x1554, ?x4454), film(?x1104, ?x4454), ?x3613 = 09blyk >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #1231 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 47 *> proper extension: 07s3m4g; *> query: (?x4454, 015wc0) <- genre(?x4454, ?x4205), genre(?x4454, ?x812), ?x4205 = 0c3351, genre(?x12720, ?x812), ?x12720 = 02fqxm *> conf = 0.06 ranks of expected_values: 9 EVAL 016y_f music 015wc0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 95.000 53.000 0.130 http://example.org/film/film/music #4960-0bpx1k PRED entity: 0bpx1k PRED relation: nominated_for! PRED expected values: 06cgy => 98 concepts (38 used for prediction) PRED predicted values (max 10 best out of 572): 0b13g7 (0.44 #51401, 0.42 #39717, 0.42 #44392), 014x77 (0.29 #67758, 0.28 #9344, 0.27 #86447), 01_p6t (0.28 #9344, 0.27 #86447, 0.27 #44391), 02j_j0 (0.12 #81774, 0.12 #39716, 0.11 #35042), 03sb38 (0.12 #81774, 0.12 #39716, 0.11 #2336), 06cgy (0.11 #35042, 0.11 #60746, 0.04 #14325), 04wvhz (0.11 #35042, 0.11 #60746, 0.04 #209), 0g2lq (0.11 #35042, 0.11 #60746, 0.02 #8680), 027cxsm (0.11 #35042, 0.11 #60746), 07h07 (0.11 #35042, 0.04 #853, 0.01 #5524) >> Best rule #51401 for best value: >> intensional similarity = 4 >> extensional distance = 572 >> proper extension: 0c0yh4; 0yyg4; 05jf85; 08lr6s; 034qrh; 0n0bp; 04969y; 04mzf8; 05j82v; 02s4l6; ... >> query: (?x2881, ?x3568) <- language(?x2881, ?x254), film_release_distribution_medium(?x2881, ?x81), nominated_for(?x1104, ?x2881), produced_by(?x2881, ?x3568) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #35042 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 425 *> proper extension: 06mr2s; 01b7h8; 03d17dg; *> query: (?x2881, ?x647) <- nominated_for(?x5973, ?x2881), place_of_birth(?x5973, ?x1310), award_nominee(?x5973, ?x647), executive_produced_by(?x1209, ?x5973) *> conf = 0.11 ranks of expected_values: 6 EVAL 0bpx1k nominated_for! 06cgy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 98.000 38.000 0.438 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4959-03m73lj PRED entity: 03m73lj PRED relation: ceremony PRED expected values: 0g55tzk => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 135): 09qvms (0.87 #147, 0.29 #552, 0.25 #687), 092_25 (0.80 #204, 0.26 #609, 0.23 #744), 0g55tzk (0.73 #266, 0.28 #947, 0.24 #671), 092t4b (0.73 #186, 0.26 #591, 0.23 #726), 092c5f (0.73 #148, 0.24 #553, 0.20 #688), 02q690_ (0.70 #737, 0.22 #873, 0.20 #1009), 0n8_m93 (0.65 #653, 0.28 #947, 0.26 #383), 0bzm81 (0.65 #561, 0.26 #291, 0.22 #426), 02yxh9 (0.65 #637, 0.26 #367, 0.22 #502), 0bc773 (0.65 #593, 0.26 #323, 0.22 #458) >> Best rule #147 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 02py7pj; >> query: (?x2771, 09qvms) <- ceremony(?x2771, ?x3609), honored_for(?x3609, ?x3610), award_winner(?x3609, ?x8424), ?x8424 = 027n4zv, program(?x1762, ?x3610) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #266 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 02py7pj; *> query: (?x2771, 0g55tzk) <- ceremony(?x2771, ?x3609), honored_for(?x3609, ?x3610), award_winner(?x3609, ?x8424), ?x8424 = 027n4zv, program(?x1762, ?x3610) *> conf = 0.73 ranks of expected_values: 3 EVAL 03m73lj ceremony 0g55tzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 28.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony #4958-03hp2y1 PRED entity: 03hp2y1 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #16, 0.82 #119, 0.82 #54), 02nxhr (0.04 #70, 0.04 #22, 0.03 #55), 07c52 (0.03 #3, 0.03 #34, 0.03 #201), 07z4p (0.03 #15, 0.02 #30, 0.02 #154) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 186 >> proper extension: 0c_j9x; 04wddl; 01q7h2; >> query: (?x9981, 029j_) <- film(?x902, ?x9981), genre(?x9981, ?x53), film(?x91, ?x9981), ?x902 = 05qd_ >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hp2y1 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.830 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #4957-02g3ft PRED entity: 02g3ft PRED relation: award! PRED expected values: 0661ql3 07cz2 0f4yh => 41 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1344): 0jqn5 (0.43 #129, 0.21 #1131, 0.17 #1002), 0f4yh (0.36 #1343, 0.17 #1002, 0.14 #341), 061681 (0.36 #1066, 0.04 #5074, 0.04 #6074), 07cz2 (0.29 #2004, 0.29 #1265, 0.22 #18030), 0ch26b_ (0.29 #1180, 0.29 #178, 0.06 #2183), 0jqj5 (0.29 #507, 0.21 #1509, 0.09 #2512), 04v8x9 (0.29 #36, 0.21 #1038, 0.08 #2041), 09sr0 (0.29 #853, 0.21 #1855, 0.04 #2858), 0661ql3 (0.29 #1231, 0.17 #1002, 0.14 #229), 0bdjd (0.29 #725, 0.17 #1002, 0.14 #1727) >> Best rule #129 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 02qt02v; >> query: (?x1429, 0jqn5) <- award(?x573, ?x1429), nominated_for(?x1429, ?x1430), award_winner(?x1429, ?x12894), ?x573 = 0bth54, film(?x12894, ?x1511) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1343 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 05zr6wv; 0p9sw; 02g3v6; 02r22gf; 0k611; 0gr42; 02g2wv; 02g2yr; 018wdw; 02qyntr; ... *> query: (?x1429, 0f4yh) <- award(?x97, ?x1429), nominated_for(?x1429, ?x2770), award_winner(?x1429, ?x65), award(?x276, ?x1429), ?x2770 = 07cz2 *> conf = 0.36 ranks of expected_values: 2, 4, 9 EVAL 02g3ft award! 0f4yh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 41.000 19.000 0.429 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02g3ft award! 07cz2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 19.000 0.429 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02g3ft award! 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 41.000 19.000 0.429 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4956-01xzb6 PRED entity: 01xzb6 PRED relation: award PRED expected values: 026mfs => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 249): 026mfs (0.34 #517, 0.15 #37633, 0.14 #4829), 01dk00 (0.34 #528, 0.06 #11504, 0.06 #10328), 09sb52 (0.34 #22385, 0.25 #23953, 0.24 #24345), 01c99j (0.28 #609, 0.20 #9625, 0.15 #4529), 054ks3 (0.21 #6802, 0.21 #7194, 0.20 #6410), 01c9f2 (0.21 #473, 0.07 #38418, 0.05 #15761), 02wh75 (0.20 #3145, 0.17 #2753, 0.14 #3537), 0gqz2 (0.19 #7527, 0.18 #6743, 0.18 #7135), 01ckcd (0.19 #324, 0.14 #1892, 0.14 #2284), 054krc (0.18 #7534, 0.14 #10670, 0.12 #8710) >> Best rule #517 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 03yf3z; >> query: (?x5285, 026mfs) <- student(?x216, ?x5285), artists(?x2664, ?x5285), award(?x5285, ?x724), ?x2664 = 01lyv >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xzb6 award 026mfs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.345 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4955-04sntd PRED entity: 04sntd PRED relation: language PRED expected values: 02h40lc => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.97 #3201, 0.96 #2312, 0.95 #1725), 02bjrlw (0.25 #59, 0.14 #117, 0.11 #234), 064_8sq (0.22 #196, 0.19 #255, 0.16 #550), 04306rv (0.21 #121, 0.14 #297, 0.11 #533), 06nm1 (0.14 #127, 0.14 #185, 0.13 #481), 03_9r (0.12 #68, 0.07 #480, 0.06 #184), 0295r (0.12 #86), 0jzc (0.09 #253, 0.08 #194, 0.06 #490), 0653m (0.07 #128, 0.06 #482, 0.04 #659), 02bv9 (0.07 #143, 0.02 #260, 0.01 #319) >> Best rule #3201 for best value: >> intensional similarity = 3 >> extensional distance = 1250 >> proper extension: 05f67hw; >> query: (?x2960, 02h40lc) <- country(?x2960, ?x94), ?x94 = 09c7w0, language(?x2960, ?x5671) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sntd language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.966 http://example.org/film/film/language #4954-01s47p PRED entity: 01s47p PRED relation: official_language PRED expected values: 06nm1 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.33 #752, 0.32 #2387, 0.29 #1104), 04306rv (0.33 #49, 0.23 #843, 0.20 #138), 04h9h (0.33 #76, 0.20 #253, 0.20 #165), 02bjrlw (0.33 #45, 0.20 #134, 0.17 #354), 064_8sq (0.25 #104, 0.17 #369, 0.17 #325), 06nm1 (0.21 #1154, 0.21 #1110, 0.20 #229), 02ztjwg (0.20 #202, 0.13 #1216, 0.12 #1348), 0jzc (0.17 #323, 0.13 #2341, 0.09 #2795), 0349s (0.13 #1225, 0.08 #828, 0.08 #872), 06b_j (0.13 #2341, 0.10 #1765, 0.04 #3284) >> Best rule #752 for best value: >> intensional similarity = 8 >> extensional distance = 10 >> proper extension: 019rg5; >> query: (?x13906, 02h40lc) <- capital(?x13906, ?x4698), contains(?x4698, ?x7377), category(?x4698, ?x134), location(?x2161, ?x4698), influenced_by(?x476, ?x2161), influenced_by(?x2161, ?x118), type_of_union(?x2161, ?x566), ?x566 = 04ztj >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1154 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 12 *> proper extension: 05r7t; *> query: (?x13906, 06nm1) <- capital(?x13906, ?x4698), location(?x2162, ?x4698), featured_film_locations(?x763, ?x4698), nationality(?x2162, ?x2152), profession(?x2162, ?x2225), languages(?x2162, ?x2502), honored_for(?x762, ?x763) *> conf = 0.21 ranks of expected_values: 6 EVAL 01s47p official_language 06nm1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 126.000 126.000 0.333 http://example.org/location/country/official_language #4953-020bv3 PRED entity: 020bv3 PRED relation: film! PRED expected values: 06mmb => 80 concepts (50 used for prediction) PRED predicted values (max 10 best out of 866): 03f1zdw (0.59 #101236, 0.56 #2066, 0.52 #26852), 02cllz (0.56 #2066, 0.52 #26852, 0.47 #90904), 0136g9 (0.56 #2066, 0.52 #26852, 0.47 #90904), 02q42j_ (0.56 #2066, 0.52 #26852, 0.47 #90904), 0zcbl (0.50 #3273, 0.01 #81777, 0.01 #38391), 0l6px (0.25 #6576, 0.05 #68175, 0.05 #72309), 02p65p (0.25 #20, 0.03 #82636, 0.02 #24806), 06l9n8 (0.25 #1678, 0.03 #82636, 0.01 #18201), 02lhm2 (0.25 #954, 0.03 #82636, 0.01 #9217), 05yh_t (0.25 #1007, 0.03 #82636, 0.01 #19595) >> Best rule #101236 for best value: >> intensional similarity = 3 >> extensional distance = 1240 >> proper extension: 03cf9ly; >> query: (?x2029, ?x488) <- nominated_for(?x488, ?x2029), film(?x488, ?x218), award_winner(?x384, ?x488) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #68175 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 947 *> proper extension: 0m123; *> query: (?x2029, ?x100) <- award_winner(?x2029, ?x2457), award_nominee(?x2457, ?x100) *> conf = 0.05 ranks of expected_values: 116 EVAL 020bv3 film! 06mmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 80.000 50.000 0.595 http://example.org/film/actor/film./film/performance/film #4952-01lp8 PRED entity: 01lp8 PRED relation: religion! PRED expected values: 059rby 05kj_ 03s5t 06yxd 026mj => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 554): 059rby (0.73 #237, 0.67 #284, 0.64 #190), 06yxd (0.73 #259, 0.67 #306, 0.55 #212), 03s5t (0.67 #301, 0.64 #254, 0.64 #207), 05kj_ (0.64 #241, 0.64 #194, 0.58 #288), 026mj (0.64 #264, 0.58 #311, 0.55 #217), 07ssc (0.50 #100, 0.38 #147, 0.35 #431), 016zwt (0.50 #83, 0.38 #176, 0.34 #565), 0chghy (0.50 #52, 0.34 #565, 0.33 #6), 0156q (0.50 #109, 0.33 #17, 0.27 #249), 07f1x (0.50 #79, 0.33 #33, 0.25 #172) >> Best rule #237 for best value: >> intensional similarity = 9 >> extensional distance = 9 >> proper extension: 0631_; 01y0s9; 019cr; 04pk9; 05w5d; >> query: (?x109, 059rby) <- religion(?x1274, ?x109), religion(?x1023, ?x109), ?x1274 = 04ykg, film_release_region(?x6882, ?x1023), film_release_region(?x1219, ?x1023), religion(?x521, ?x109), country(?x6882, ?x205), film(?x123, ?x1219), jurisdiction_of_office(?x3444, ?x1023) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 01lp8 religion! 026mj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 28.000 0.727 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01lp8 religion! 06yxd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 28.000 0.727 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01lp8 religion! 03s5t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 28.000 0.727 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01lp8 religion! 05kj_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 28.000 0.727 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01lp8 religion! 059rby CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 28.000 0.727 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #4951-0lyjf PRED entity: 0lyjf PRED relation: contains! PRED expected values: 09c7w0 => 161 concepts (100 used for prediction) PRED predicted values (max 10 best out of 255): 09c7w0 (0.90 #5361, 0.83 #15186, 0.83 #23224), 0d060g (0.48 #60739, 0.07 #35738, 0.06 #38417), 0d04z6 (0.48 #60739), 0jrxx (0.30 #3182, 0.18 #4968, 0.06 #9434), 0ftvz (0.25 #1949, 0.20 #2842, 0.12 #4628), 07h34 (0.20 #229, 0.08 #3801, 0.07 #7373), 06yxd (0.20 #1180, 0.08 #3859, 0.04 #8324), 04rrx (0.20 #127, 0.06 #8164, 0.06 #9951), 0498y (0.20 #1138, 0.04 #8282, 0.03 #10069), 081mh (0.20 #1075, 0.03 #10006, 0.03 #10899) >> Best rule #5361 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 0rs6x; 0ftvz; 0rnmy; 0f2v0; 0rj0z; 0rkkv; 0rh7t; 0n1rj; 0rsjf; 0jrxx; ... >> query: (?x4904, 09c7w0) <- contains(?x3501, ?x4904), contains(?x2623, ?x4904), ?x2623 = 02xry, place_of_birth(?x5925, ?x3501), award_nominee(?x92, ?x5925) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lyjf contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 100.000 0.897 http://example.org/location/location/contains #4950-02jxk PRED entity: 02jxk PRED relation: member_states PRED expected values: 03rjj 02vzc => 58 concepts (15 used for prediction) PRED predicted values (max 10 best out of 195): 03rjj (0.21 #1036, 0.20 #1151, 0.18 #1271), 05b4w (0.21 #1070, 0.20 #1185, 0.18 #1305), 01znc_ (0.21 #1055, 0.20 #1170, 0.18 #1290), 0d060g (0.21 #1038, 0.20 #1153, 0.18 #1273), 09c7w0 (0.21 #1033, 0.20 #1148, 0.18 #1268), 04g61 (0.17 #518, 0.15 #860, 0.14 #1096), 0ctw_b (0.14 #1145, 0.14 #1049, 0.13 #1164), 06mzp (0.14 #1045, 0.13 #1160, 0.12 #353), 06qd3 (0.14 #1053, 0.13 #1168, 0.12 #361), 0chghy (0.14 #1040, 0.13 #1155, 0.12 #348) >> Best rule #1036 for best value: >> intensional similarity = 7 >> extensional distance = 12 >> proper extension: 085h1; >> query: (?x2106, 03rjj) <- organization(?x5274, ?x2106), adjustment_currency(?x5274, ?x170), organization(?x5274, ?x4230), taxonomy(?x5274, ?x939), organization(?x1023, ?x4230), ?x1023 = 0ctw_b, jurisdiction_of_office(?x182, ?x5274) >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14 EVAL 02jxk member_states 02vzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 58.000 15.000 0.214 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 02jxk member_states 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 15.000 0.214 http://example.org/user/ktrueman/default_domain/international_organization/member_states #4949-0nm9y PRED entity: 0nm9y PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 169 concepts (47 used for prediction) PRED predicted values (max 10 best out of 6): 09c7w0 (0.87 #212, 0.87 #201, 0.87 #91), 050ks (0.14 #213, 0.11 #571, 0.11 #456), 03rt9 (0.03 #160, 0.02 #561, 0.02 #391), 03rjj (0.02 #158, 0.02 #144), 02jx1 (0.02 #580), 059j2 (0.01 #120, 0.01 #136) >> Best rule #212 for best value: >> intensional similarity = 5 >> extensional distance = 154 >> proper extension: 0235l; >> query: (?x13871, ?x94) <- adjoins(?x13871, ?x7417), contains(?x7058, ?x13871), second_level_divisions(?x94, ?x7417), administrative_parent(?x7954, ?x7058), source(?x7417, ?x958) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nm9y second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 47.000 0.872 http://example.org/location/country/second_level_divisions #4948-0d060g PRED entity: 0d060g PRED relation: geographic_distribution! PRED expected values: 04mvp8 => 246 concepts (246 used for prediction) PRED predicted values (max 10 best out of 36): 04mvp8 (0.33 #354, 0.29 #426, 0.25 #3199), 0g48m4 (0.28 #505, 0.26 #1801, 0.25 #217), 01xhh5 (0.18 #1099, 0.18 #415, 0.16 #991), 0g6ff (0.17 #873, 0.16 #3178, 0.16 #3142), 013b6_ (0.17 #456, 0.13 #312, 0.12 #132), 06mvq (0.13 #305, 0.11 #485, 0.10 #1205), 06gbnc (0.12 #121, 0.11 #193, 0.07 #337), 04gfy7 (0.12 #137, 0.11 #173, 0.07 #353), 0ffjqy (0.12 #136, 0.11 #172, 0.07 #352), 0cn68 (0.12 #134, 0.11 #170, 0.07 #350) >> Best rule #354 for best value: >> intensional similarity = 2 >> extensional distance = 13 >> proper extension: 09nm_; >> query: (?x279, 04mvp8) <- region(?x280, ?x279), film_crew_role(?x280, ?x281) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d060g geographic_distribution! 04mvp8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 246.000 246.000 0.333 http://example.org/people/ethnicity/geographic_distribution #4947-01lz4tf PRED entity: 01lz4tf PRED relation: artist! PRED expected values: 017l96 => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 129): 015_1q (0.50 #19, 0.21 #2275, 0.21 #10880), 01w40h (0.25 #28, 0.21 #451, 0.12 #874), 01clyr (0.25 #315, 0.20 #174, 0.13 #8073), 011k11 (0.25 #317, 0.20 #176, 0.06 #881), 017l96 (0.24 #1992, 0.15 #1428, 0.15 #4390), 01trtc (0.24 #1624, 0.21 #1201, 0.20 #1483), 033hn8 (0.22 #1000, 0.16 #1282, 0.16 #1141), 01cl2y (0.21 #594, 0.16 #1299, 0.14 #1581), 0n85g (0.21 #1191, 0.17 #1050, 0.14 #486), 011k1h (0.21 #1137, 0.17 #996, 0.13 #11717) >> Best rule #19 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03f2_rc; >> query: (?x7233, 015_1q) <- artists(?x302, ?x7233), place_of_birth(?x7233, ?x242), artist(?x382, ?x7233), music(?x9154, ?x7233), spouse(?x7233, ?x932) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1992 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 05cljf; 03y82t6; 01s1zk; *> query: (?x7233, 017l96) <- artists(?x302, ?x7233), profession(?x7233, ?x2659), type_of_union(?x7233, ?x566), participant(?x3754, ?x7233), ?x2659 = 039v1 *> conf = 0.24 ranks of expected_values: 5 EVAL 01lz4tf artist! 017l96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 168.000 168.000 0.500 http://example.org/music/record_label/artist #4946-07m2y PRED entity: 07m2y PRED relation: family PRED expected values: 01kcd => 76 concepts (64 used for prediction) PRED predicted values (max 10 best out of 123): 0d8lm (0.50 #111, 0.13 #892, 0.13 #1747), 0fx80y (0.40 #236, 0.32 #1071, 0.30 #1166), 0l14md (0.33 #476, 0.25 #120, 0.20 #183), 05148p4 (0.30 #602, 0.30 #542, 0.29 #340), 0342h (0.25 #89, 0.21 #779, 0.20 #1146), 085jw (0.25 #168, 0.20 #581, 0.18 #673), 026t6 (0.25 #117, 0.20 #180, 0.13 #1238), 0l14_3 (0.25 #110, 0.12 #437, 0.12 #409), 01kcd (0.21 #851, 0.20 #194, 0.12 #1380), 02fsn (0.20 #113, 0.13 #1206, 0.10 #894) >> Best rule #111 for best value: >> intensional similarity = 20 >> extensional distance = 2 >> proper extension: 02fsn; >> query: (?x9413, 0d8lm) <- role(?x9413, ?x2888), instrumentalists(?x9413, ?x11916), instrumentalists(?x9413, ?x6469), artists(?x671, ?x6469), role(?x6949, ?x2888), role(?x4568, ?x2888), performance_role(?x2888, ?x316), ?x4568 = 02j3d4, group(?x6469, ?x3207), group(?x2888, ?x2521), role(?x1148, ?x2888), role(?x745, ?x2888), role(?x432, ?x2888), ?x11916 = 023slg, instrumentalists(?x2888, ?x425), ?x1148 = 02qjv, ?x432 = 042v_gx, ?x745 = 01vj9c, role(?x248, ?x2888), artist(?x2149, ?x6949) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #851 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 12 *> proper extension: 0dq630k; *> query: (?x9413, 01kcd) <- role(?x9413, ?x3418), role(?x9413, ?x2253), role(?x9413, ?x1225), role(?x9413, ?x569), role(?x9413, ?x228), ?x3418 = 02w4b, role(?x569, ?x9987), role(?x569, ?x885), role(?x9413, ?x316), role(?x569, ?x2059), role(?x1225, ?x4913), role(?x1225, ?x432), instrumentalists(?x569, ?x642), ?x432 = 042v_gx, ?x2059 = 0dwr4, role(?x2253, ?x1436), ?x9987 = 037c9s, ?x4913 = 03ndd, role(?x1997, ?x2253), ?x885 = 0dwtp, ?x228 = 0l14qv, group(?x569, ?x1751) *> conf = 0.21 ranks of expected_values: 9 EVAL 07m2y family 01kcd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 76.000 64.000 0.500 http://example.org/music/instrument/family #4945-02hvkf PRED entity: 02hvkf PRED relation: location_of_ceremony! PRED expected values: 04ztj => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.41 #81, 0.40 #103, 0.40 #30), 01g63y (0.07 #18, 0.05 #43, 0.05 #22), 0jgjn (0.07 #20, 0.05 #24, 0.04 #28) >> Best rule #81 for best value: >> intensional similarity = 5 >> extensional distance = 130 >> proper extension: 0fn2g; >> query: (?x13499, 04ztj) <- country(?x13499, ?x512), olympics(?x512, ?x358), titles(?x512, ?x144), organization(?x512, ?x127), film_release_region(?x66, ?x512) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hvkf location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.409 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #4944-0ym69 PRED entity: 0ym69 PRED relation: institution! PRED expected values: 014mlp => 175 concepts (168 used for prediction) PRED predicted values (max 10 best out of 24): 014mlp (0.69 #1656, 0.68 #1986, 0.66 #3518), 02h4rq6 (0.63 #1246, 0.61 #2547, 0.61 #2907), 019v9k (0.59 #1660, 0.58 #1253, 0.56 #1990), 013zdg (0.50 #486, 0.50 #436, 0.48 #461), 02_xgp2 (0.50 #1547, 0.48 #2635, 0.45 #1358), 03bwzr4 (0.50 #1547, 0.44 #1853, 0.43 #1259), 016t_3 (0.50 #1547, 0.44 #1853, 0.42 #993), 0bjrnt (0.50 #1547, 0.44 #1853, 0.38 #3874), 0bkj86 (0.45 #134, 0.45 #385, 0.44 #1853), 01rr_d (0.44 #1853, 0.43 #472, 0.35 #447) >> Best rule #1656 for best value: >> intensional similarity = 8 >> extensional distance = 174 >> proper extension: 01q460; 01mpwj; >> query: (?x14287, 014mlp) <- major_field_of_study(?x14287, ?x5179), organization(?x2361, ?x14287), major_field_of_study(?x11459, ?x5179), major_field_of_study(?x7918, ?x5179), major_field_of_study(?x4692, ?x5179), ?x4692 = 0345gh, ?x7918 = 0gl6f, institution(?x734, ?x11459) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ym69 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 168.000 0.688 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4943-09tkzy PRED entity: 09tkzy PRED relation: nominated_for! PRED expected values: 02r22gf 0gr0m 0gqyl => 83 concepts (73 used for prediction) PRED predicted values (max 10 best out of 192): 0gr4k (0.66 #5230, 0.66 #4091, 0.66 #2954), 02ppm4q (0.66 #5230, 0.66 #4091, 0.66 #2954), 03hkv_r (0.66 #5230, 0.66 #4091, 0.66 #2954), 0k611 (0.34 #1427, 0.26 #2564, 0.26 #1881), 0gr0m (0.30 #53, 0.27 #1415, 0.26 #507), 0f4x7 (0.28 #1385, 0.23 #2522, 0.22 #14775), 04dn09n (0.28 #1395, 0.24 #9779, 0.23 #2532), 0gqyl (0.27 #5229, 0.25 #1434, 0.24 #5913), 09sb52 (0.27 #5229, 0.24 #5913, 0.24 #9779), 07t_l23 (0.27 #5229, 0.24 #5913, 0.24 #9779) >> Best rule #5230 for best value: >> intensional similarity = 3 >> extensional distance = 843 >> proper extension: 04glx0; 06w7mlh; 07bz5; >> query: (?x8595, ?x198) <- award_winner(?x8595, ?x2805), award_winner(?x704, ?x2805), award(?x8595, ?x198) >> conf = 0.66 => this is the best rule for 3 predicted values *> Best rule #53 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 011yfd; *> query: (?x8595, 0gr0m) <- genre(?x8595, ?x162), language(?x8595, ?x90), ?x162 = 04xvlr, ?x90 = 02bjrlw *> conf = 0.30 ranks of expected_values: 5, 8, 47 EVAL 09tkzy nominated_for! 0gqyl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 83.000 73.000 0.661 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09tkzy nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 83.000 73.000 0.661 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09tkzy nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 83.000 73.000 0.661 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4942-0wq36 PRED entity: 0wq36 PRED relation: location! PRED expected values: 01yf85 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 353): 091n7z (0.45 #25184, 0.44 #32740, 0.44 #17629), 0cgbf (0.05 #1395, 0.02 #6431, 0.02 #13986), 02sjf5 (0.04 #2720, 0.03 #7756, 0.02 #15312), 02lt8 (0.03 #3315, 0.02 #18426, 0.02 #8351), 0pyww (0.03 #3500, 0.02 #8536, 0.02 #16092), 0c6qh (0.03 #461, 0.02 #18090, 0.01 #10533), 01963w (0.03 #237, 0.01 #5273, 0.01 #12828), 01fkxr (0.03 #1863, 0.01 #6899), 01364q (0.03 #399, 0.01 #5435), 023kzp (0.02 #3735, 0.02 #18846, 0.02 #8771) >> Best rule #25184 for best value: >> intensional similarity = 3 >> extensional distance = 333 >> proper extension: 01sn04; 01c40n; 036k0s; 0281rb; 062qg; 0p9z5; 02s838; 0kdqw; 01glqw; 0t6sb; ... >> query: (?x13556, ?x8749) <- contains(?x4622, ?x13556), place_of_birth(?x8749, ?x13556), district_represented(?x176, ?x4622) >> conf = 0.45 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0wq36 location! 01yf85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 66.000 0.446 http://example.org/people/person/places_lived./people/place_lived/location #4941-021bk PRED entity: 021bk PRED relation: tv_program PRED expected values: 039cq4 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 18): 039cq4 (0.10 #744, 0.03 #1179, 0.03 #3020), 0pdp8 (0.05 #3500, 0.04 #2532, 0.04 #6831), 0888c3 (0.04 #3854, 0.04 #3323, 0.03 #3765), 02stbw (0.04 #3854, 0.04 #3323, 0.03 #3765), 02ht1k (0.04 #3323, 0.03 #3765, 0.03 #4379), 01j7mr (0.02 #722), 01b66d (0.02 #2991, 0.01 #3430, 0.01 #3253), 02rkkn1 (0.01 #693, 0.01 #84, 0.01 #345), 07zhjj (0.01 #667), 019nnl (0.01 #6, 0.01 #702) >> Best rule #744 for best value: >> intensional similarity = 4 >> extensional distance = 164 >> proper extension: 01wj9y9; 0739y; 010p3; 02wd48; 02v49c; 01t94_1; 01k9lpl; 02_wxh; 03mv0b; 01wp_jm; ... >> query: (?x2328, 039cq4) <- profession(?x2328, ?x1146), profession(?x2328, ?x987), ?x987 = 0dxtg, ?x1146 = 018gz8 >> conf = 0.10 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021bk tv_program 039cq4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.102 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #4940-08pn_9 PRED entity: 08pn_9 PRED relation: artist PRED expected values: 01w7nwm => 46 concepts (20 used for prediction) PRED predicted values (max 10 best out of 930): 04rcr (0.50 #1708, 0.33 #867, 0.15 #5898), 01vtj38 (0.46 #6397, 0.45 #5561, 0.44 #8076), 01w7nwm (0.45 #5241, 0.36 #6916, 0.33 #1046), 01vrnsk (0.43 #4688, 0.43 #3849, 0.40 #3011), 07zft (0.43 #4845, 0.33 #1488, 0.33 #651), 08w4pm (0.43 #4778, 0.29 #3939, 0.23 #6452), 01vw917 (0.43 #4659, 0.23 #6333, 0.20 #2982), 01vw8mh (0.38 #6216, 0.33 #348, 0.31 #7895), 0677ng (0.33 #9746, 0.33 #526, 0.31 #8073), 03xhj6 (0.33 #1146, 0.33 #309, 0.31 #7545) >> Best rule #1708 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 07gqbk; >> query: (?x14457, 04rcr) <- artist(?x14457, ?x8754), artist(?x14457, ?x7547), ?x8754 = 01wg3q, artists(?x2937, ?x7547), award_nominee(?x1125, ?x7547), award(?x7547, ?x4837), gender(?x7547, ?x231), profession(?x1125, ?x131), category(?x1125, ?x134), ?x131 = 0dz3r >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5241 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 9 *> proper extension: 086k8; 0fb0v; 02p11jq; 073tm9; 0181hw; *> query: (?x14457, 01w7nwm) <- artist(?x14457, ?x8754), artist(?x14457, ?x7547), artists(?x5630, ?x7547), ?x5630 = 016_nr, place_of_birth(?x8754, ?x8755), profession(?x8754, ?x1183), film(?x7547, ?x3317), origin(?x7547, ?x3014), profession(?x7547, ?x955), currency(?x7547, ?x170), ?x1183 = 09jwl *> conf = 0.45 ranks of expected_values: 3 EVAL 08pn_9 artist 01w7nwm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 46.000 20.000 0.500 http://example.org/music/record_label/artist #4939-09ly2r6 PRED entity: 09ly2r6 PRED relation: award! PRED expected values: 0cvkv5 => 45 concepts (12 used for prediction) PRED predicted values (max 10 best out of 825): 0k20s (0.50 #975, 0.04 #11202, 0.03 #7112), 0gvvm6l (0.50 #816, 0.04 #11043, 0.03 #6953), 0fgpvf (0.25 #1024, 0.25 #1023, 0.25 #1022), 040rmy (0.25 #1024, 0.25 #1023, 0.25 #1022), 07l450 (0.25 #1024, 0.25 #1023, 0.25 #1022), 0ctb4g (0.25 #1024, 0.25 #1023, 0.25 #1022), 0dmn0x (0.25 #1024, 0.25 #1023, 0.25 #1022), 0bh8drv (0.25 #1024, 0.25 #1023, 0.25 #1022), 072zl1 (0.25 #1024, 0.25 #1023, 0.25 #1022), 046488 (0.25 #1024, 0.25 #1023, 0.25 #1022) >> Best rule #975 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 02rdxsh; 054knh; >> query: (?x6165, 0k20s) <- award(?x3965, ?x6165), nominated_for(?x6165, ?x3757), nominated_for(?x6165, ?x2501), ?x3965 = 04lqvly, film_release_region(?x2501, ?x87), genre(?x3757, ?x53) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1023 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 02rdxsh; 054knh; *> query: (?x6165, ?x3757) <- award(?x3965, ?x6165), nominated_for(?x6165, ?x3757), nominated_for(?x6165, ?x2501), ?x3965 = 04lqvly, film_release_region(?x2501, ?x87), genre(?x3757, ?x53) *> conf = 0.25 ranks of expected_values: 14 EVAL 09ly2r6 award! 0cvkv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 45.000 12.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4938-02847m9 PRED entity: 02847m9 PRED relation: film! PRED expected values: 016tt2 => 124 concepts (110 used for prediction) PRED predicted values (max 10 best out of 54): 01795t (0.43 #1128, 0.20 #1499, 0.19 #2315), 05qd_ (0.33 #453, 0.26 #897, 0.23 #1416), 086k8 (0.30 #298, 0.26 #668, 0.19 #2966), 016tw3 (0.30 #307, 0.17 #1863, 0.15 #2530), 054g1r (0.28 #1144, 0.11 #1515, 0.11 #2331), 03xsby (0.25 #90, 0.22 #904, 0.20 #164), 034f0d (0.25 #106, 0.20 #180, 0.05 #846), 03xq0f (0.21 #671, 0.17 #893, 0.17 #1264), 0g1rw (0.20 #156, 0.13 #1489, 0.12 #1563), 020h2v (0.20 #414, 0.08 #2415, 0.07 #2193) >> Best rule #1128 for best value: >> intensional similarity = 5 >> extensional distance = 44 >> proper extension: 0564x; >> query: (?x1619, 01795t) <- music(?x1619, ?x6475), country(?x1619, ?x94), titles(?x5138, ?x1619), genre(?x1619, ?x8681), industry(?x648, ?x8681) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2227 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 141 *> proper extension: 06g77c; 011ycb; 0y_yw; 011x_4; 0sxlb; 09d38d; *> query: (?x1619, 016tt2) <- music(?x1619, ?x6475), country(?x1619, ?x94), titles(?x5138, ?x1619), genre(?x1619, ?x307), film_format(?x1619, ?x6392) *> conf = 0.15 ranks of expected_values: 13 EVAL 02847m9 film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 124.000 110.000 0.435 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #4937-01p4r3 PRED entity: 01p4r3 PRED relation: film PRED expected values: 04x4nv => 94 concepts (77 used for prediction) PRED predicted values (max 10 best out of 557): 072kp (0.59 #75151, 0.41 #84099, 0.40 #78730), 0jvt9 (0.20 #540, 0.05 #18431, 0.04 #9485), 0kb1g (0.20 #1611, 0.01 #8767, 0.01 #10556), 0gndh (0.20 #1333, 0.01 #8489, 0.01 #10278), 03rtz1 (0.12 #1957, 0.11 #3746), 03kq98 (0.06 #53676, 0.06 #64414, 0.06 #62624), 04954r (0.06 #9561, 0.05 #7772, 0.04 #20296), 06lpmt (0.06 #2474, 0.06 #4263, 0.02 #7841), 01shy7 (0.06 #2213, 0.06 #4002, 0.02 #14736), 014l6_ (0.06 #2316, 0.06 #4105, 0.02 #11261) >> Best rule #75151 for best value: >> intensional similarity = 3 >> extensional distance = 1273 >> proper extension: 0m2wm; 04wqr; 07lmxq; 0f830f; 08w7vj; 02wrhj; 049k07; 04smkr; 05wjnt; 05hdf; ... >> query: (?x5913, ?x631) <- nationality(?x5913, ?x94), film(?x5913, ?x3369), nominated_for(?x5913, ?x631) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01p4r3 film 04x4nv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 77.000 0.586 http://example.org/film/actor/film./film/performance/film #4936-0b13g7 PRED entity: 0b13g7 PRED relation: produced_by! PRED expected values: 03_gz8 => 133 concepts (130 used for prediction) PRED predicted values (max 10 best out of 484): 0ctb4g (0.48 #6464, 0.39 #39721, 0.34 #8311), 027tbrc (0.48 #6464, 0.39 #39721, 0.34 #8311), 03_gz8 (0.33 #602, 0.12 #22168, 0.10 #1526), 0g54xkt (0.13 #3051, 0.12 #22168, 0.07 #2128), 02qr69m (0.13 #2983, 0.12 #22168, 0.07 #2060), 0cc5mcj (0.13 #2979, 0.12 #22168, 0.07 #2056), 0djb3vw (0.13 #2817, 0.12 #22168, 0.07 #1894), 04ynx7 (0.13 #3603, 0.12 #22168, 0.07 #2680), 0f4_2k (0.13 #3322, 0.12 #22168, 0.07 #2399), 050gkf (0.13 #2938, 0.12 #22168, 0.07 #2015) >> Best rule #6464 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 02g8h; 04b19t; 04g865; 01vsps; 03fw4y; >> query: (?x3568, ?x2447) <- company(?x3568, ?x6554), nominated_for(?x3568, ?x2447), produced_by(?x2029, ?x3568) >> conf = 0.48 => this is the best rule for 2 predicted values *> Best rule #602 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 02q42j_; *> query: (?x3568, 03_gz8) <- executive_produced_by(?x11958, ?x3568), ?x11958 = 02t_h3, award_nominee(?x647, ?x3568) *> conf = 0.33 ranks of expected_values: 3 EVAL 0b13g7 produced_by! 03_gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 133.000 130.000 0.476 http://example.org/film/film/produced_by #4935-023p29 PRED entity: 023p29 PRED relation: award_winner! PRED expected values: 03tcnt => 116 concepts (95 used for prediction) PRED predicted values (max 10 best out of 295): 01by1l (0.49 #1719, 0.39 #10300, 0.38 #11159), 03t5n3 (0.49 #1719, 0.39 #10300, 0.38 #11159), 02v1m7 (0.49 #1719, 0.39 #10300, 0.38 #11159), 0gq9h (0.24 #1366, 0.11 #2654, 0.09 #2225), 01c9dd (0.15 #1167, 0.11 #34345, 0.04 #28756), 02f6xy (0.15 #1054, 0.09 #32195, 0.08 #30475), 02f76h (0.15 #1031, 0.03 #4895, 0.02 #9614), 054ks3 (0.15 #33915, 0.15 #33914, 0.15 #29186), 0c4z8 (0.15 #33915, 0.15 #33914, 0.15 #29186), 0gqz2 (0.15 #33915, 0.15 #33914, 0.15 #29186) >> Best rule #1719 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0c1pj; 02q_cc; 04411; 04wvhz; 016kjs; 0pz91; 0343h; 0gz5hs; 06pj8; 01vs_v8; ... >> query: (?x10209, ?x1361) <- gender(?x10209, ?x231), award(?x10209, ?x1361), organizations_founded(?x10209, ?x6230) >> conf = 0.49 => this is the best rule for 3 predicted values *> Best rule #33915 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1519 *> proper extension: 018_q8; *> query: (?x10209, ?x3103) <- award_winner(?x10209, ?x8060), award_winner(?x3103, ?x8060) *> conf = 0.15 ranks of expected_values: 14 EVAL 023p29 award_winner! 03tcnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 116.000 95.000 0.485 http://example.org/award/award_category/winners./award/award_honor/award_winner #4934-01vsksr PRED entity: 01vsksr PRED relation: profession PRED expected values: 0lgw7 => 149 concepts (89 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.94 #2429, 0.83 #580, 0.80 #864), 0nbcg (0.78 #5438, 0.69 #9857, 0.67 #12000), 016z4k (0.55 #9833, 0.50 #4413, 0.48 #3701), 02jknp (0.45 #716, 0.43 #574, 0.39 #858), 0dxtg (0.44 #863, 0.42 #721, 0.41 #4279), 03gjzk (0.44 #865, 0.34 #581, 0.33 #13), 0cbd2 (0.40 #10974, 0.36 #1711, 0.33 #5), 0n1h (0.40 #10974, 0.23 #4991, 0.22 #5562), 06q2q (0.40 #10974, 0.11 #182, 0.01 #1746), 039v1 (0.40 #3303, 0.39 #1168, 0.38 #4584) >> Best rule #2429 for best value: >> intensional similarity = 5 >> extensional distance = 103 >> proper extension: 06q5t7; 02cj_f; 01pbwwl; >> query: (?x6351, 02hrh1q) <- profession(?x6351, ?x1614), profession(?x6351, ?x319), ?x1614 = 01c72t, profession(?x12123, ?x319), ?x12123 = 021npv >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #611 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 09b0xs; 01z0lb; *> query: (?x6351, 0lgw7) <- profession(?x6351, ?x1614), profession(?x6351, ?x319), ?x1614 = 01c72t, ?x319 = 01d_h8 *> conf = 0.03 ranks of expected_values: 43 EVAL 01vsksr profession 0lgw7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 149.000 89.000 0.943 http://example.org/people/person/profession #4933-02pjxr PRED entity: 02pjxr PRED relation: profession! PRED expected values: 0g51l1 03wd5tk 0fqjks 01qnfc => 48 concepts (18 used for prediction) PRED predicted values (max 10 best out of 4148): 03wpmd (0.62 #21747, 0.46 #30181, 0.43 #34395), 0dpqk (0.57 #35324, 0.50 #22676, 0.46 #31110), 06cv1 (0.57 #33844, 0.50 #21196, 0.38 #29630), 026dx (0.57 #35225, 0.38 #31011, 0.38 #22577), 02tn0_ (0.53 #59021, 0.53 #59018, 0.44 #25289), 03wd5tk (0.53 #59021, 0.53 #59018), 05wm88 (0.50 #24872, 0.46 #33306, 0.43 #37520), 015pxr (0.50 #21676, 0.46 #30110, 0.43 #34324), 02b29 (0.50 #23302, 0.46 #31736, 0.43 #35950), 083chw (0.50 #21131, 0.46 #29565, 0.43 #33779) >> Best rule #21747 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 01d_h8; 0cbd2; 0dxtg; 026sdt1; >> query: (?x2450, 03wpmd) <- profession(?x12933, ?x2450), profession(?x6096, ?x2450), type_of_union(?x12933, ?x566), film_production_design_by(?x407, ?x12933), film_production_design_by(?x240, ?x6096), award_winner(?x6096, ?x7438) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #59021 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 012wxt; *> query: (?x2450, ?x5894) <- profession(?x12933, ?x2450), profession(?x6514, ?x2450), type_of_union(?x12933, ?x566), nationality(?x6514, ?x1310), sibling(?x5894, ?x12933) *> conf = 0.53 ranks of expected_values: 6, 66, 662, 1154 EVAL 02pjxr profession! 01qnfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 18.000 0.625 http://example.org/people/person/profession EVAL 02pjxr profession! 0fqjks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 48.000 18.000 0.625 http://example.org/people/person/profession EVAL 02pjxr profession! 03wd5tk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 48.000 18.000 0.625 http://example.org/people/person/profession EVAL 02pjxr profession! 0g51l1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 18.000 0.625 http://example.org/people/person/profession #4932-04306rv PRED entity: 04306rv PRED relation: languages_spoken! PRED expected values: 013xrm => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 68): 059_w (0.40 #365, 0.33 #501, 0.33 #433), 071x0k (0.40 #347, 0.33 #483, 0.33 #415), 0x67 (0.40 #349, 0.33 #485, 0.33 #417), 013b6_ (0.40 #384, 0.33 #452, 0.33 #44), 0bbz66j (0.33 #518, 0.33 #42, 0.25 #314), 03w9bjf (0.33 #45, 0.25 #317, 0.23 #1949), 04czx7 (0.33 #65, 0.25 #337, 0.20 #1289), 09zyn5 (0.33 #63, 0.25 #335, 0.20 #403), 0c41n (0.33 #68, 0.25 #340, 0.20 #408), 0fk3s (0.33 #61, 0.25 #333, 0.20 #401) >> Best rule #365 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 06nm1; 06b_j; >> query: (?x732, 059_w) <- language(?x8631, ?x732), language(?x6533, ?x732), service_language(?x555, ?x732), official_language(?x774, ?x732), languages(?x147, ?x732), ?x6533 = 02n72k, produced_by(?x8631, ?x8208) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04306rv languages_spoken! 013xrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 83.000 0.400 http://example.org/people/ethnicity/languages_spoken #4931-02j4sk PRED entity: 02j4sk PRED relation: film PRED expected values: 03kxj2 => 135 concepts (107 used for prediction) PRED predicted values (max 10 best out of 789): 0k54q (0.33 #935, 0.04 #22379, 0.03 #27741), 05sw5b (0.33 #814, 0.01 #54426), 0c57yj (0.33 #638, 0.01 #54250), 01f39b (0.20 #2765, 0.08 #33145, 0.07 #15274), 0jswp (0.20 #2333, 0.04 #21990, 0.04 #32713), 02k1pr (0.20 #3235, 0.02 #62208, 0.02 #37189), 03l6q0 (0.20 #2329, 0.02 #29135, 0.02 #30922), 07h9gp (0.20 #2052, 0.02 #39580), 04954r (0.13 #14911, 0.08 #22059, 0.07 #48865), 02qr3k8 (0.13 #15585, 0.08 #22733, 0.05 #28095) >> Best rule #935 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01h4rj; >> query: (?x10219, 0k54q) <- film(?x10219, ?x12899), film(?x10219, ?x1673), ?x12899 = 0ckt6, film_format(?x1673, ?x6392), type_of_union(?x10219, ?x566) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #23589 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 05h7tk; *> query: (?x10219, 03kxj2) <- sibling(?x6336, ?x10219), religion(?x6336, ?x1624), nationality(?x10219, ?x94), ?x94 = 09c7w0 *> conf = 0.03 ranks of expected_values: 242 EVAL 02j4sk film 03kxj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 135.000 107.000 0.333 http://example.org/film/actor/film./film/performance/film #4930-035hm PRED entity: 035hm PRED relation: form_of_government PRED expected values: 01q20 => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 5): 01q20 (0.73 #84, 0.71 #199, 0.64 #154), 01fpfn (0.43 #298, 0.40 #188, 0.36 #323), 06cx9 (0.42 #297, 0.39 #377, 0.37 #322), 01d9r3 (0.34 #255, 0.34 #300, 0.33 #325), 026wp (0.10 #86, 0.10 #41, 0.09 #91) >> Best rule #84 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 06jnv; 049nq; >> query: (?x9283, 01q20) <- official_language(?x9283, ?x254), form_of_government(?x9283, ?x1926), ?x1926 = 018wl5, ?x254 = 02h40lc >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035hm form_of_government 01q20 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.733 http://example.org/location/country/form_of_government #4929-06gb1w PRED entity: 06gb1w PRED relation: currency PRED expected values: 09nqf => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.86 #22, 0.86 #36, 0.84 #141), 01nv4h (0.07 #79, 0.05 #9, 0.03 #16), 02l6h (0.02 #32, 0.01 #333, 0.01 #207) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 04dsnp; 02phtzk; >> query: (?x4392, 09nqf) <- country(?x4392, ?x94), executive_produced_by(?x4392, ?x96), produced_by(?x4392, ?x7976), featured_film_locations(?x4392, ?x739) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06gb1w currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.864 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #4928-01jvxb PRED entity: 01jvxb PRED relation: student PRED expected values: 042xh => 140 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1593): 0tfc (0.22 #4097, 0.13 #8277, 0.08 #6187), 05p92jn (0.22 #3233, 0.13 #7413, 0.02 #13683), 01w8sf (0.20 #407, 0.10 #8767, 0.03 #10857), 0kh6b (0.15 #4796, 0.10 #8976, 0.08 #13156), 02g3w (0.13 #8169, 0.11 #3989, 0.02 #14439), 01wd3l (0.11 #3238, 0.08 #5328, 0.07 #7418), 0ff3y (0.11 #4157, 0.08 #6247, 0.07 #8337), 0c6g1l (0.11 #2468, 0.08 #4558, 0.07 #6648), 07g2b (0.11 #2165, 0.08 #4255, 0.07 #6345), 01tdnyh (0.11 #2979, 0.07 #7159, 0.06 #13429) >> Best rule #4097 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01c57n; >> query: (?x7097, 0tfc) <- major_field_of_study(?x7097, ?x6364), ?x6364 = 05qt0, institution(?x734, ?x7097), company(?x5510, ?x7097) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01jvxb student 042xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 140.000 77.000 0.222 http://example.org/education/educational_institution/students_graduates./education/education/student #4927-0404wqb PRED entity: 0404wqb PRED relation: award_winner! PRED expected values: 0g55tzk => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 112): 09bymc (0.29 #120, 0.02 #5440, 0.02 #820), 0g55tzk (0.17 #7982, 0.11 #276, 0.11 #10224), 0gvstc3 (0.17 #7982, 0.11 #10224, 0.10 #9803), 0hn821n (0.17 #7982, 0.11 #10224, 0.10 #9803), 09g90vz (0.15 #543, 0.08 #1383, 0.08 #1663), 09qvms (0.14 #12, 0.10 #712, 0.09 #1272), 02q690_ (0.14 #64, 0.08 #204, 0.06 #344), 05q7cj (0.14 #94, 0.01 #234, 0.01 #1634), 092t4b (0.13 #191, 0.12 #331, 0.06 #751), 027hjff (0.11 #196, 0.11 #336, 0.08 #756) >> Best rule #120 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 05mkhs; >> query: (?x10814, 09bymc) <- award_nominee(?x10814, ?x1784), film(?x10814, ?x6510), ?x6510 = 027gy0k >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #7982 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1364 *> proper extension: 0f721s; 01p5yn; 035_2h; 0hm0k; 0283xx2; 01j53q; 01zcrv; 05s34b; *> query: (?x10814, ?x873) <- award_winner(?x10814, ?x10086), award_winner(?x873, ?x10086) *> conf = 0.17 ranks of expected_values: 2 EVAL 0404wqb award_winner! 0g55tzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4926-0dkv90 PRED entity: 0dkv90 PRED relation: film_crew_role PRED expected values: 0ckd1 01vx2h => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 35): 0ch6mp2 (0.78 #1106, 0.77 #1694, 0.76 #1069), 09zzb8 (0.76 #1099, 0.75 #1, 0.74 #732), 09vw2b7 (0.69 #1105, 0.68 #1693, 0.66 #1068), 01vx2h (0.38 #1698, 0.37 #1073, 0.33 #1662), 0dxtw (0.37 #1697, 0.36 #1072, 0.35 #779), 01pvkk (0.34 #121, 0.29 #744, 0.29 #157), 0215hd (0.23 #163, 0.19 #272, 0.18 #382), 02ynfr (0.19 #1114, 0.18 #1077, 0.18 #1702), 089g0h (0.15 #494, 0.14 #383, 0.14 #128), 02rh1dz (0.14 #10, 0.13 #521, 0.13 #1696) >> Best rule #1106 for best value: >> intensional similarity = 4 >> extensional distance = 562 >> proper extension: 0c3ybss; 02v63m; 0cz8mkh; 0blpg; 02ph9tm; 05n6sq; 0bwhdbl; 03m5y9p; 02bj22; 09p5mwg; ... >> query: (?x7789, 0ch6mp2) <- film_crew_role(?x7789, ?x468), genre(?x7789, ?x53), ?x468 = 02r96rf, titles(?x2346, ?x7789) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1698 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 769 *> proper extension: 0c40vxk; 0gydcp7; 03m8y5; 03mh_tp; 0gjc4d3; 0dgpwnk; 09lcsj; 035w2k; 0bc1yhb; 0gtt5fb; ... *> query: (?x7789, 01vx2h) <- film_crew_role(?x7789, ?x468), genre(?x7789, ?x53), ?x468 = 02r96rf *> conf = 0.38 ranks of expected_values: 4, 21 EVAL 0dkv90 film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 107.000 107.000 0.777 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dkv90 film_crew_role 0ckd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 107.000 107.000 0.777 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4925-06x76 PRED entity: 06x76 PRED relation: school PRED expected values: 04hgpt 01jq0j => 75 concepts (66 used for prediction) PRED predicted values (max 10 best out of 266): 0lyjf (0.50 #1602, 0.44 #5744, 0.44 #5629), 065y4w7 (0.44 #2680, 0.44 #583, 0.42 #1919), 01rc6f (0.33 #708, 0.25 #2044, 0.20 #134), 07szy (0.33 #1547, 0.22 #2691, 0.21 #3077), 01lnyf (0.33 #1787, 0.18 #1404, 0.12 #2548), 07w0v (0.29 #10389, 0.26 #11926, 0.26 #11748), 01vs5c (0.29 #2188, 0.25 #1809, 0.24 #8346), 06pwq (0.29 #391, 0.20 #11928, 0.20 #11743), 0bx8pn (0.29 #408, 0.20 #11760, 0.18 #12154), 01ptt7 (0.29 #412, 0.14 #11929, 0.14 #11930) >> Best rule #1602 for best value: >> intensional similarity = 17 >> extensional distance = 10 >> proper extension: 01y3v; >> query: (?x11061, 0lyjf) <- draft(?x11061, ?x3089), draft(?x11061, ?x1883), draft(?x11061, ?x685), position(?x11061, ?x1114), school(?x1883, ?x6083), school(?x1883, ?x4296), school(?x1883, ?x2948), ?x685 = 0g3zpp, state_province_region(?x6083, ?x2623), position(?x11061, ?x1717), school(?x11061, ?x3777), ?x3089 = 03nt7j, school(?x1823, ?x4296), ?x1717 = 02g_6x, ?x2948 = 0j_sncb, ?x1823 = 01yhm, ?x1114 = 047g8h >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1454 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 9 *> proper extension: 086x3; *> query: (?x11061, 01jq0j) <- teams(?x4356, ?x11061), contains(?x4356, ?x12127), location(?x9587, ?x4356), location(?x3539, ?x4356), film(?x9587, ?x1746), locations(?x358, ?x4356), jurisdiction_of_office(?x1195, ?x4356), place_of_birth(?x587, ?x4356), award_winner(?x1247, ?x3539), notable_people_with_this_condition(?x9933, ?x9587), award_nominee(?x3539, ?x4101), gender(?x3539, ?x231), adjoins(?x4356, ?x10877), profession(?x3539, ?x2348) *> conf = 0.27 ranks of expected_values: 12, 39 EVAL 06x76 school 01jq0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 75.000 66.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 06x76 school 04hgpt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 75.000 66.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #4924-01frpd PRED entity: 01frpd PRED relation: list PRED expected values: 01ptsx => 231 concepts (231 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.89 #274, 0.88 #154, 0.87 #114), 09g7thr (0.53 #713, 0.49 #661, 0.49 #788), 05glt (0.53 #871, 0.38 #922, 0.16 #616), 026cl_m (0.37 #576, 0.15 #571, 0.13 #617) >> Best rule #274 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 01n073; 0py9b; >> query: (?x14236, 01ptsx) <- place_founded(?x14236, ?x659), list(?x14236, ?x5997), company(?x265, ?x14236), basic_title(?x5254, ?x265), ?x5254 = 07cbs >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01frpd list 01ptsx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 231.000 231.000 0.893 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #4923-02qx5h PRED entity: 02qx5h PRED relation: student! PRED expected values: 01w5m => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 151): 015nl4 (0.14 #1645, 0.12 #593, 0.05 #12692), 0bwfn (0.14 #274, 0.08 #13952, 0.08 #17634), 02sdwt (0.14 #401, 0.01 #2505), 02237m (0.14 #396), 01w5m (0.08 #2209, 0.05 #3787, 0.05 #3261), 03ksy (0.07 #2210, 0.04 #2736, 0.04 #3788), 065y4w7 (0.05 #2118, 0.04 #2644, 0.04 #23686), 0dy04 (0.05 #2175, 0.03 #3753, 0.03 #3227), 0m4yg (0.04 #890, 0.04 #1942, 0.02 #11411), 07wrz (0.04 #2166, 0.03 #3218, 0.03 #2692) >> Best rule #1645 for best value: >> intensional similarity = 5 >> extensional distance = 88 >> proper extension: 021yzs; 01vh3r; >> query: (?x12788, 015nl4) <- award(?x12788, ?x3209), award(?x12788, ?x112), film(?x12788, ?x1692), ?x112 = 027dtxw, award_winner(?x3209, ?x157) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2209 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 0dzkq; 02ln1; 047g6; 01h2_6; *> query: (?x12788, 01w5m) <- religion(?x12788, ?x7131), ?x7131 = 03_gx, student(?x6787, ?x12788) *> conf = 0.08 ranks of expected_values: 5 EVAL 02qx5h student! 01w5m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 108.000 0.144 http://example.org/education/educational_institution/students_graduates./education/education/student #4922-04rkkv PRED entity: 04rkkv PRED relation: contains! PRED expected values: 0chghy => 58 concepts (56 used for prediction) PRED predicted values (max 10 best out of 174): 09c7w0 (0.62 #11652, 0.61 #10756, 0.61 #2691), 0chghy (0.56 #15235, 0.44 #42129, 0.42 #40335), 05cgv (0.44 #42129, 0.42 #40335, 0.40 #23301), 02jx1 (0.40 #983, 0.16 #3671, 0.16 #31370), 0dp90 (0.33 #742, 0.20 #1638), 0vh3 (0.33 #725, 0.20 #1621), 059rby (0.25 #1812, 0.22 #2708, 0.14 #3604), 07ssc (0.20 #928, 0.16 #31370, 0.07 #39470), 04jpl (0.20 #918, 0.08 #3606, 0.08 #4502), 01_c4 (0.20 #1419, 0.05 #2315, 0.02 #4107) >> Best rule #11652 for best value: >> intensional similarity = 5 >> extensional distance = 177 >> proper extension: 03zj9; 09s5q8; >> query: (?x8357, 09c7w0) <- category(?x8357, ?x134), student(?x8357, ?x8674), ?x134 = 08mbj5d, major_field_of_study(?x8357, ?x5740), actor(?x4581, ?x8674) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #15235 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 199 *> proper extension: 02t4yc; 0778_3; *> query: (?x8357, ?x390) <- category(?x8357, ?x134), student(?x8357, ?x4153), ?x134 = 08mbj5d, actor(?x4581, ?x4153), nationality(?x4153, ?x390) *> conf = 0.56 ranks of expected_values: 2 EVAL 04rkkv contains! 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 56.000 0.620 http://example.org/location/location/contains #4921-099ck7 PRED entity: 099ck7 PRED relation: award_winner PRED expected values: 0flw6 => 54 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1773): 0zcbl (0.50 #4007, 0.35 #17278, 0.33 #1539), 01713c (0.50 #2780, 0.35 #17278, 0.33 #312), 048lv (0.50 #2737, 0.33 #269, 0.23 #7676), 026rm_y (0.50 #4328, 0.33 #1860, 0.22 #6798), 018db8 (0.35 #17278, 0.33 #131, 0.29 #34555), 0dzf_ (0.35 #17278, 0.30 #22216, 0.29 #34555), 0jfx1 (0.35 #17278, 0.30 #22216, 0.29 #34555), 01_xtx (0.35 #17278, 0.30 #22216, 0.29 #34555), 0c6qh (0.35 #17278, 0.29 #34555, 0.28 #46898), 01wmxfs (0.35 #17278, 0.29 #34555, 0.28 #46898) >> Best rule #4007 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 09sb52; >> query: (?x6729, 0zcbl) <- nominated_for(?x6729, ?x1077), award(?x6085, ?x6729), award(?x286, ?x6729), ?x1077 = 09q5w2, ?x6085 = 06g2d1, nominated_for(?x286, ?x349) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #34555 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 124 *> proper extension: 02qyp19; 0gqng; 02r0csl; 0bfvw2; 03hkv_r; 0bp_b2; 0gkvb7; 02p_7cr; 0gr4k; 09qwmm; ... *> query: (?x6729, ?x123) <- nominated_for(?x6729, ?x1077), award(?x123, ?x6729), award(?x1077, ?x451), ceremony(?x6729, ?x1442) *> conf = 0.29 ranks of expected_values: 37 EVAL 099ck7 award_winner 0flw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 54.000 24.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #4920-01l1hr PRED entity: 01l1hr PRED relation: languages PRED expected values: 02h40lc => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 19): 02h40lc (0.34 #1055, 0.32 #665, 0.32 #587), 064_8sq (0.08 #171, 0.07 #249, 0.07 #3045), 04h9h (0.07 #3045, 0.06 #2264, 0.02 #108), 032f6 (0.07 #3045, 0.06 #2264), 02hxcvy (0.07 #3045, 0.06 #2264), 0jzc (0.07 #3045, 0.06 #2264), 0653m (0.07 #3045, 0.06 #2264), 03_9r (0.07 #3045, 0.06 #2264), 02bjrlw (0.05 #352, 0.05 #430, 0.04 #391), 06nm1 (0.03 #201, 0.03 #45, 0.03 #162) >> Best rule #1055 for best value: >> intensional similarity = 3 >> extensional distance = 352 >> proper extension: 06c0j; >> query: (?x3581, 02h40lc) <- participant(?x3581, ?x400), people(?x1050, ?x3581), gender(?x3581, ?x514) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l1hr languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.342 http://example.org/people/person/languages #4919-02lq67 PRED entity: 02lq67 PRED relation: medal! PRED expected values: 0sx8l 0l6mp 019n8z 015l4k => 3 concepts (3 used for prediction) PRED predicted values (max 10 best out of 4): 019n8z (0.81 #5, 0.33 #9, 0.33 #3), 0sx8l (0.81 #5, 0.33 #1), 0l6mp (0.60 #6, 0.33 #8, 0.33 #2), 015l4k (0.33 #10, 0.33 #4) >> Best rule #5 for best value: >> intensional similarity = 348 >> extensional distance = 1 >> proper extension: 02lpp7; >> query: (?x422, ?x1741) <- medal(?x11872, ?x422), medal(?x9459, ?x422), medal(?x9455, ?x422), medal(?x9072, ?x422), medal(?x9051, ?x422), medal(?x7413, ?x422), medal(?x5482, ?x422), medal(?x5453, ?x422), medal(?x4569, ?x422), medal(?x4302, ?x422), medal(?x3749, ?x422), medal(?x2267, ?x422), medal(?x2236, ?x422), medal(?x2188, ?x422), medal(?x2152, ?x422), medal(?x1892, ?x422), medal(?x1558, ?x422), medal(?x1499, ?x422), medal(?x1471, ?x422), medal(?x1355, ?x422), medal(?x1264, ?x422), medal(?x1241, ?x422), medal(?x1229, ?x422), medal(?x910, ?x422), medal(?x792, ?x422), medal(?x756, ?x422), medal(?x608, ?x422), medal(?x583, ?x422), medal(?x512, ?x422), medal(?x456, ?x422), medal(?x429, ?x422), medal(?x410, ?x422), medal(?x404, ?x422), medal(?x151, ?x422), medal(?x47, ?x422), ?x1499 = 01znc_, medal(?x7429, ?x422), medal(?x2630, ?x422), medal(?x1608, ?x422), medal(?x1081, ?x422), medal(?x584, ?x422), medal(?x452, ?x422), medal(?x418, ?x422), medal(?x391, ?x422), ?x1241 = 05cgv, ?x512 = 07ssc, ?x2188 = 0163v, ?x1558 = 01mjq, ?x3749 = 03ryn, ?x1471 = 07t21, film_release_region(?x11839, ?x2267), film_release_region(?x11209, ?x2267), film_release_region(?x10048, ?x2267), film_release_region(?x9941, ?x2267), film_release_region(?x9839, ?x2267), film_release_region(?x9565, ?x2267), film_release_region(?x9432, ?x2267), film_release_region(?x9002, ?x2267), film_release_region(?x8891, ?x2267), film_release_region(?x8682, ?x2267), film_release_region(?x8258, ?x2267), film_release_region(?x8176, ?x2267), film_release_region(?x7693, ?x2267), film_release_region(?x7629, ?x2267), film_release_region(?x7502, ?x2267), film_release_region(?x7493, ?x2267), film_release_region(?x7009, ?x2267), film_release_region(?x6886, ?x2267), film_release_region(?x6882, ?x2267), film_release_region(?x6215, ?x2267), film_release_region(?x6175, ?x2267), film_release_region(?x5877, ?x2267), film_release_region(?x5791, ?x2267), film_release_region(?x5576, ?x2267), film_release_region(?x5315, ?x2267), film_release_region(?x5271, ?x2267), film_release_region(?x5255, ?x2267), film_release_region(?x5162, ?x2267), film_release_region(?x5067, ?x2267), film_release_region(?x5052, ?x2267), film_release_region(?x5016, ?x2267), film_release_region(?x4690, ?x2267), film_release_region(?x4607, ?x2267), film_release_region(?x4441, ?x2267), film_release_region(?x4352, ?x2267), film_release_region(?x4040, ?x2267), film_release_region(?x3606, ?x2267), film_release_region(?x3423, ?x2267), film_release_region(?x3287, ?x2267), film_release_region(?x3201, ?x2267), film_release_region(?x3088, ?x2267), film_release_region(?x2896, ?x2267), film_release_region(?x2889, ?x2267), film_release_region(?x2709, ?x2267), film_release_region(?x2598, ?x2267), film_release_region(?x2441, ?x2267), film_release_region(?x2189, ?x2267), film_release_region(?x1988, ?x2267), film_release_region(?x1744, ?x2267), film_release_region(?x1743, ?x2267), film_release_region(?x1421, ?x2267), film_release_region(?x1386, ?x2267), film_release_region(?x1315, ?x2267), film_release_region(?x1263, ?x2267), film_release_region(?x504, ?x2267), film_release_region(?x385, ?x2267), film_release_region(?x66, ?x2267), ?x6175 = 0gg5kmg, ?x7493 = 0btpm6, ?x10048 = 09tcg4, country(?x4045, ?x2267), country(?x3015, ?x2267), country(?x1121, ?x2267), ?x1081 = 0l6m5, ?x1263 = 0dgst_d, jurisdiction_of_office(?x265, ?x410), ?x4690 = 0gkz3nz, ?x583 = 015fr, ?x6215 = 0jyb4, currency(?x2236, ?x170), ?x8176 = 0gvvm6l, ?x3015 = 071t0, jurisdiction_of_office(?x11622, ?x2267), ?x5255 = 01sby_, ?x7693 = 0m63c, ?x5315 = 0glqh5_, film_release_region(?x633, ?x2236), film_release_region(?x11218, ?x151), film_release_region(?x9961, ?x151), film_release_region(?x9832, ?x151), film_release_region(?x7864, ?x151), film_release_region(?x7628, ?x151), film_release_region(?x6931, ?x151), film_release_region(?x6321, ?x151), film_release_region(?x5588, ?x151), film_release_region(?x5496, ?x151), film_release_region(?x3830, ?x151), film_release_region(?x3453, ?x151), film_release_region(?x3252, ?x151), film_release_region(?x3053, ?x151), film_release_region(?x2655, ?x151), film_release_region(?x1904, ?x151), film_release_region(?x785, ?x151), film_release_region(?x409, ?x151), film_release_region(?x80, ?x151), ?x3252 = 0gh8zks, ?x456 = 05qhw, contains(?x6956, ?x2236), contains(?x6304, ?x2236), ?x6304 = 02qkt, ?x1386 = 0dtfn, ?x11622 = 0377k9, organization(?x2236, ?x4403), form_of_government(?x2236, ?x48), ?x6886 = 0gwjw0c, contains(?x9459, ?x13946), ?x1743 = 0c8tkt, ?x391 = 0l6vl, ?x1608 = 09x3r, ?x2152 = 06mkj, ?x608 = 02k54, adjoins(?x2236, ?x2146), contains(?x455, ?x2267), ?x47 = 027rn, country(?x1967, ?x151), combatants(?x9203, ?x410), ?x7009 = 0bs8s1p, administrative_parent(?x2364, ?x2236), geographic_distribution(?x1571, ?x2236), organization(?x151, ?x4230), ?x4352 = 09v71cj, nationality(?x7622, ?x9459), ?x5162 = 0j3d9tn, ?x5877 = 02qyv3h, ?x11839 = 072hx4, ?x5052 = 04yg13l, ?x4569 = 09lxtg, ?x5791 = 03mgx6z, ?x5576 = 0gbfn9, featured_film_locations(?x11355, ?x151), featured_film_locations(?x1820, ?x151), ?x785 = 03hjv97, ?x504 = 0g5qs2k, ?x4040 = 02mt51, crewmember(?x9961, ?x9151), ?x3088 = 06w839_, contains(?x12315, ?x410), contains(?x7273, ?x410), ?x7502 = 0233bn, member_states(?x7695, ?x9072), ?x792 = 0hzlz, contains(?x2467, ?x5453), genre(?x8682, ?x1403), ?x11209 = 04fjzv, ?x5067 = 01rwpj, ?x4607 = 0h03fhx, taxonomy(?x9459, ?x939), film_release_distribution_medium(?x9941, ?x81), official_language(?x9455, ?x5671), vacationer(?x151, ?x7963), vacationer(?x151, ?x6835), vacationer(?x151, ?x2443), ?x2896 = 0645k5, ?x8258 = 05ldxl, ?x2189 = 02yvct, contains(?x7413, ?x8082), ?x385 = 0ds3t5x, ?x4045 = 06z6r, official_language(?x2236, ?x254), nominated_for(?x397, ?x9941), ?x1892 = 02vzc, contains(?x7708, ?x151), nationality(?x84, ?x7413), contains(?x151, ?x3285), ?x8891 = 0gwlfnb, category(?x8082, ?x134), ?x9051 = 06nnj, ?x1403 = 02l7c8, ?x6321 = 0gg8z1f, ?x2709 = 06ztvyx, contains(?x2236, ?x4344), ?x3287 = 026njb5, organization(?x9072, ?x5701), ?x756 = 06npd, ?x5701 = 0b6css, ?x1315 = 053tj7, ?x12315 = 06n3y, adjoins(?x5453, ?x2804), nominated_for(?x941, ?x8682), ?x5016 = 062zm5h, ?x3053 = 0dyb1, genre(?x1421, ?x307), film_regional_debut_venue(?x3453, ?x3288), ?x2889 = 040b5k, ?x1744 = 035yn8, award_winner(?x1820, ?x192), production_companies(?x9941, ?x7980), ?x7429 = 0124ld, ?x584 = 0l98s, member_states(?x2106, ?x5482), ?x7273 = 07c5l, film_crew_role(?x7628, ?x137), award_nominee(?x7963, ?x2028), film_crew_role(?x9961, ?x2154), ?x5271 = 047vnkj, participant(?x2891, ?x2443), film(?x2443, ?x898), genre(?x1904, ?x225), ?x941 = 0fq9zdn, film(?x2258, ?x1904), film(?x1253, ?x1820), location(?x7963, ?x2713), official_language(?x7413, ?x5359), country(?x1967, ?x9874), country(?x1967, ?x9251), country(?x1967, ?x8620), country(?x1967, ?x8420), ?x4441 = 0125xq, film_release_region(?x9859, ?x5482), film_release_region(?x1463, ?x5482), participant(?x262, ?x2443), award(?x2443, ?x112), ?x939 = 04n6k, ?x11872 = 03f2w, ?x404 = 047lj, ?x6956 = 0j0k, film(?x7310, ?x9961), profession(?x7963, ?x1032), award(?x7963, ?x678), genre(?x11355, ?x1805), language(?x1820, ?x2502), ?x80 = 0b76d_m, ?x8420 = 06m_5, people(?x11067, ?x2443), film(?x6658, ?x633), religion(?x5453, ?x109), person(?x9961, ?x1291), film_crew_role(?x8682, ?x1171), ?x265 = 0dq3c, ?x307 = 04t36, nominated_for(?x4168, ?x11218), ?x8620 = 016zwt, ?x1171 = 09vw2b7, ?x3201 = 01ffx4, citytown(?x4403, ?x362), ?x1229 = 059j2, ?x3830 = 0gjcrrw, ?x3423 = 09g7vfw, ?x2441 = 0cc5mcj, award(?x7628, ?x1245), ?x6882 = 043tvp3, film(?x4988, ?x6931), ?x9565 = 0hz6mv2, nominated_for(?x198, ?x11218), ?x1032 = 02hrh1q, ?x1463 = 0gtvrv3, ?x5588 = 0gtt5fb, participant(?x1958, ?x7963), ?x4302 = 06vbd, ?x5671 = 06b_j, award(?x1820, ?x289), ?x66 = 014lc_, ?x9874 = 01nty, ?x1121 = 0bynt, ?x6658 = 0436kgz, ?x2258 = 0f4vbz, ?x9432 = 0gvt53w, films(?x942, ?x1421), adjoins(?x5482, ?x2517), ?x429 = 03rt9, form_of_government(?x9459, ?x4763), award_winner(?x757, ?x7963), music(?x83, ?x84), ?x2598 = 07f_7h, ?x9839 = 0gy7bj4, ?x4988 = 041c4, ?x9859 = 0g57wgv, administrative_parent(?x5482, ?x551), ?x1355 = 0h7x, film(?x2279, ?x9941), award_nominee(?x6835, ?x140), film(?x382, ?x11355), ?x9002 = 0ndsl1x, film(?x396, ?x3453), country(?x453, ?x5482), films(?x13252, ?x1820), music(?x1904, ?x3069), ?x7864 = 0cbn7c, ?x418 = 09n48, artists(?x283, ?x6835), participating_countries(?x1741, ?x7413), ?x409 = 0gtv7pk, ?x7629 = 02825nf, award(?x84, ?x1443), ?x9251 = 07tp2, ?x3606 = 0gh65c5, administrative_area_type(?x5453, ?x2792), ?x9832 = 01xlqd, ?x1264 = 0345h, award_winner(?x3078, ?x2443), olympics(?x151, ?x2233), ?x5496 = 07l50vn, ?x1988 = 09k56b7, ?x452 = 0sx7r, nominated_for(?x1008, ?x1421), ?x2655 = 0fpmrm3, ?x910 = 019rg5, ?x2630 = 0swff >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 02lq67 medal! 015l4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.811 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 019n8z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.811 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 0l6mp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.811 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal EVAL 02lq67 medal! 0sx8l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 3.000 3.000 0.811 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #4918-07y9ts PRED entity: 07y9ts PRED relation: honored_for PRED expected values: 01j7mr => 25 concepts (19 used for prediction) PRED predicted values (max 10 best out of 742): 01j7mr (0.62 #1988, 0.62 #3769, 0.56 #2580), 07s8z_l (0.50 #1737, 0.38 #4109, 0.38 #2328), 03nt59 (0.50 #1546, 0.33 #2729, 0.33 #953), 08jgk1 (0.50 #1277, 0.33 #2460, 0.33 #684), 039cq4 (0.46 #3965, 0.44 #2776, 0.38 #2184), 07zhjj (0.44 #2862, 0.38 #4051, 0.38 #2270), 06mr2s (0.44 #2650, 0.38 #3839, 0.38 #2058), 06hwzy (0.38 #3711, 0.38 #1930, 0.33 #2522), 01vnbh (0.38 #3873, 0.33 #2684, 0.33 #908), 0d68qy (0.38 #4301, 0.35 #4896, 0.33 #2519) >> Best rule #1988 for best value: >> intensional similarity = 18 >> extensional distance = 6 >> proper extension: 0gvstc3; >> query: (?x5296, 01j7mr) <- ceremony(?x7850, ?x5296), ceremony(?x5235, ?x5296), ceremony(?x2041, ?x5296), ceremony(?x2016, ?x5296), ceremony(?x686, ?x5296), award_winner(?x5296, ?x8151), award(?x8081, ?x686), nominated_for(?x686, ?x337), honored_for(?x5296, ?x2078), ?x8081 = 02l3_5, ?x7850 = 07kjk7c, ?x2041 = 0bdx29, award(?x3610, ?x686), award_winner(?x591, ?x8151), award(?x8151, ?x112), ?x2016 = 0cjyzs, ?x5235 = 09qrn4, award_nominee(?x8151, ?x3931) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07y9ts honored_for 01j7mr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 19.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #4917-0298n7 PRED entity: 0298n7 PRED relation: executive_produced_by PRED expected values: 03h304l => 84 concepts (47 used for prediction) PRED predicted values (max 10 best out of 104): 05hj_k (0.17 #598, 0.12 #3103, 0.11 #3355), 06q8hf (0.12 #667, 0.12 #917, 0.12 #3172), 06pj8 (0.11 #305, 0.06 #3312, 0.06 #3060), 05prs8 (0.07 #296, 0.04 #2254, 0.02 #796), 0glyyw (0.06 #1690, 0.05 #2943, 0.04 #4699), 079vf (0.06 #1504, 0.04 #4513, 0.03 #3511), 02s2ft (0.06 #3258, 0.05 #3006, 0.05 #3760), 023kzp (0.06 #3258, 0.05 #3006, 0.05 #3760), 0716t2 (0.06 #3258, 0.05 #3006, 0.05 #3760), 0flw6 (0.06 #3258, 0.05 #3006, 0.05 #3760) >> Best rule #598 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 0ds35l9; 083shs; 027qgy; 09m6kg; 011yrp; 011yxg; 0ds3t5x; 011yph; 0b6tzs; 017gl1; ... >> query: (?x7755, 05hj_k) <- nominated_for(?x704, ?x7755), film(?x1104, ?x7755), country(?x7755, ?x94), ?x704 = 09sb52 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #3257 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 293 *> proper extension: 0c0nhgv; 0dgst_d; 0jdgr; 06wbm8q; 0j_t1; 0cw3yd; 0gt1k; 0gl02yg; 016dj8; 0gvvf4j; ... *> query: (?x7755, ?x902) <- nominated_for(?x384, ?x7755), executive_produced_by(?x7755, ?x10430), award_nominee(?x10430, ?x902), nominated_for(?x92, ?x7755) *> conf = 0.05 ranks of expected_values: 16 EVAL 0298n7 executive_produced_by 03h304l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 84.000 47.000 0.172 http://example.org/film/film/executive_produced_by #4916-025twgt PRED entity: 025twgt PRED relation: film_release_region PRED expected values: 09c7w0 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 206): 09c7w0 (0.75 #3769, 0.70 #6278, 0.70 #10941), 07ssc (0.48 #11656, 0.48 #9504, 0.47 #9324), 0f8l9c (0.39 #16316, 0.38 #16497, 0.38 #16496), 0d0vqn (0.22 #17229, 0.21 #6466, 0.21 #21720), 03h64 (0.21 #6542, 0.20 #9412, 0.19 #14252), 03gj2 (0.20 #6491, 0.20 #14201, 0.19 #13843), 03_3d (0.20 #13995, 0.19 #13816, 0.19 #14354), 05r4w (0.19 #17219, 0.18 #13808, 0.18 #22427), 06mkj (0.19 #18908, 0.19 #12986, 0.19 #16932), 059j2 (0.19 #17262, 0.19 #16901, 0.18 #23013) >> Best rule #3769 for best value: >> intensional similarity = 6 >> extensional distance = 38 >> proper extension: 07b1gq; >> query: (?x11362, 09c7w0) <- nominated_for(?x11362, ?x835), genre(?x835, ?x812), produced_by(?x835, ?x3692), film(?x11965, ?x835), ?x812 = 01jfsb, production_companies(?x835, ?x788) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025twgt film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.750 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4915-01fs_4 PRED entity: 01fs_4 PRED relation: participant PRED expected values: 0161h5 => 106 concepts (57 used for prediction) PRED predicted values (max 10 best out of 134): 0161h5 (0.80 #14150, 0.80 #14795), 09yrh (0.04 #1603, 0.04 #14467, 0.03 #13822), 014zcr (0.04 #13523, 0.03 #14168, 0.02 #21886), 01rr9f (0.03 #13539, 0.03 #14184, 0.03 #1320), 0c6qh (0.03 #14316, 0.02 #13671, 0.01 #12384), 0237fw (0.03 #14311, 0.03 #13666, 0.03 #1447), 07r1h (0.03 #14565, 0.03 #1701, 0.02 #13920), 044qx (0.03 #2216, 0.02 #4788, 0.02 #6074), 0m66w (0.03 #1683, 0.02 #13902, 0.02 #14547), 019pm_ (0.03 #1474, 0.02 #14338, 0.02 #13693) >> Best rule #14150 for best value: >> intensional similarity = 3 >> extensional distance = 378 >> proper extension: 0184jc; 06dv3; 0bl2g; 0prfz; 032xhg; 0bxtg; 03zqc1; 06cv1; 01kwld; 01lbp; ... >> query: (?x3868, ?x10929) <- nationality(?x3868, ?x94), nominated_for(?x3868, ?x10618), participant(?x10929, ?x3868) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fs_4 participant 0161h5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 57.000 0.801 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #4914-02md2d PRED entity: 02md2d PRED relation: titles! PRED expected values: 07c52 => 82 concepts (31 used for prediction) PRED predicted values (max 10 best out of 49): 07c52 (0.67 #652, 0.65 #442, 0.63 #1493), 01z4y (0.15 #345, 0.05 #764, 0.03 #2539), 03mdt (0.12 #147, 0.10 #1297, 0.10 #457), 05gnf (0.12 #727, 0.12 #937, 0.08 #307), 07s9rl0 (0.10 #729, 0.08 #2504, 0.08 #1147), 01z77k (0.10 #473, 0.08 #1629, 0.08 #578), 01hmnh (0.09 #336, 0.03 #1595, 0.03 #755), 0kctd (0.09 #294, 0.05 #607, 0.04 #502), 0djd22 (0.08 #1251, 0.04 #516, 0.04 #621), 04xvlr (0.08 #313, 0.07 #732, 0.04 #1572) >> Best rule #652 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 01b7h8; >> query: (?x4223, 07c52) <- nominated_for(?x11334, ?x4223), actor(?x5684, ?x11334), program(?x6678, ?x4223), program(?x8231, ?x4223) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02md2d titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 31.000 0.670 http://example.org/media_common/netflix_genre/titles #4913-04wp3s PRED entity: 04wp3s PRED relation: award PRED expected values: 099jhq 09sb52 => 93 concepts (77 used for prediction) PRED predicted values (max 10 best out of 234): 09sb52 (0.73 #41, 0.35 #851, 0.35 #11787), 05pcn59 (0.28 #892, 0.22 #487, 0.22 #2918), 05p09zm (0.26 #529, 0.24 #934, 0.20 #2960), 0gqy2 (0.23 #165, 0.14 #14177, 0.13 #29167), 02x4w6g (0.23 #115, 0.12 #19849, 0.12 #19038), 03c7tr1 (0.20 #464, 0.15 #869, 0.14 #3705), 0ck27z (0.18 #93, 0.16 #11839, 0.15 #13459), 0f4x7 (0.18 #31, 0.14 #14177, 0.12 #19849), 09qv_s (0.18 #152, 0.14 #14177, 0.12 #19849), 05zr6wv (0.16 #422, 0.14 #827, 0.14 #17) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 014zcr; 0bxtg; 03pmty; 02lkcc; 03jldb; 02wcx8c; 03mg35; 016z2j; 0bsb4j; 024bbl; ... >> query: (?x5492, 09sb52) <- award_nominee(?x2374, ?x5492), film(?x5492, ?x603), ?x2374 = 02d4ct >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 15 EVAL 04wp3s award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 77.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04wp3s award 099jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 93.000 77.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4912-0l6ny PRED entity: 0l6ny PRED relation: olympics! PRED expected values: 05r4w 04wgh 0fv4v => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 169): 03rjj (0.84 #2638, 0.80 #2343, 0.79 #2047), 03gj2 (0.82 #590, 0.80 #979, 0.79 #882), 03shp (0.73 #1014, 0.73 #625, 0.71 #917), 05qhw (0.73 #975, 0.73 #586, 0.71 #878), 0d04z6 (0.71 #255, 0.53 #1029, 0.50 #932), 019pcs (0.71 #238, 0.53 #1012, 0.50 #915), 015qh (0.64 #890, 0.64 #598, 0.60 #987), 0hzlz (0.57 #881, 0.57 #204, 0.55 #589), 05b7q (0.57 #265, 0.50 #942, 0.47 #1039), 04wgh (0.57 #209, 0.45 #594, 0.43 #886) >> Best rule #2638 for best value: >> intensional similarity = 10 >> extensional distance = 41 >> proper extension: 018ctl; 0kbws; 06sks6; >> query: (?x867, 03rjj) <- olympics(?x2984, ?x867), sports(?x867, ?x171), film_release_region(?x9345, ?x2984), film_release_region(?x6215, ?x2984), film_release_region(?x5849, ?x2984), medal(?x867, ?x422), ?x5849 = 02h22, nationality(?x12564, ?x2984), ?x6215 = 0jyb4, ?x9345 = 014knw >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #209 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 5 *> proper extension: 0lbbj; *> query: (?x867, 04wgh) <- sports(?x867, ?x359), olympics(?x1241, ?x867), olympics(?x583, ?x867), participating_countries(?x418, ?x583), film_release_region(?x3252, ?x583), film_release_region(?x2340, ?x583), olympics(?x779, ?x867), ?x1241 = 05cgv, ?x3252 = 0gh8zks, ?x2340 = 0fpv_3_ *> conf = 0.57 ranks of expected_values: 10, 11 EVAL 0l6ny olympics! 0fv4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 54.000 0.837 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l6ny olympics! 04wgh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 54.000 54.000 0.837 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l6ny olympics! 05r4w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 54.000 54.000 0.837 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #4911-0n3g PRED entity: 0n3g PRED relation: form_of_government PRED expected values: 018wl5 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 5): 018wl5 (0.79 #102, 0.56 #152, 0.54 #147), 01fpfn (0.64 #268, 0.55 #63, 0.40 #58), 01d9r3 (0.50 #99, 0.44 #119, 0.43 #24), 06cx9 (0.45 #331, 0.39 #381, 0.38 #146), 026wp (0.33 #10, 0.25 #15, 0.20 #60) >> Best rule #102 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 0bq0p9; 06jnv; 0c4b8; >> query: (?x5411, 018wl5) <- official_language(?x5411, ?x254), ?x254 = 02h40lc, form_of_government(?x5411, ?x6065), ?x6065 = 01q20 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n3g form_of_government 018wl5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.786 http://example.org/location/country/form_of_government #4910-01j_06 PRED entity: 01j_06 PRED relation: colors PRED expected values: 01g5v => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.30 #343, 0.28 #703, 0.28 #763), 01l849 (0.27 #341, 0.26 #361, 0.25 #401), 0jc_p (0.25 #4, 0.11 #144, 0.10 #184), 019sc (0.19 #707, 0.18 #367, 0.18 #107), 06fvc (0.15 #702, 0.15 #662, 0.15 #762), 04mkbj (0.12 #70, 0.12 #130, 0.09 #370), 036k5h (0.10 #125, 0.10 #485, 0.09 #285), 038hg (0.09 #112, 0.09 #692, 0.09 #712), 03wkwg (0.08 #15, 0.07 #115, 0.07 #1081), 06kqt3 (0.08 #17, 0.07 #1081, 0.04 #117) >> Best rule #343 for best value: >> intensional similarity = 4 >> extensional distance = 253 >> proper extension: 01v3ht; 0ylsr; 02hp6p; 0yl_w; >> query: (?x1428, 01g5v) <- contains(?x94, ?x1428), colors(?x1428, ?x663), institution(?x1368, ?x1428), ?x1368 = 014mlp >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j_06 colors 01g5v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.298 http://example.org/education/educational_institution/colors #4909-06r_by PRED entity: 06r_by PRED relation: award_nominee! PRED expected values: 026rm_y => 91 concepts (41 used for prediction) PRED predicted values (max 10 best out of 765): 0c6qh (0.81 #95571, 0.81 #60600, 0.81 #72256), 07m9cm (0.81 #95571, 0.81 #60600, 0.81 #72256), 06r_by (0.55 #6069, 0.26 #39623, 0.23 #79252), 026rm_y (0.36 #6573, 0.26 #39623, 0.23 #79252), 05qd_ (0.26 #39623, 0.23 #79252, 0.17 #93239), 0bksh (0.26 #39623, 0.17 #93239, 0.16 #95572), 04q5zw (0.26 #39623, 0.17 #93239, 0.16 #95572), 016vg8 (0.26 #39623, 0.17 #93239, 0.16 #95572), 0pz04 (0.26 #39623, 0.17 #93239, 0.16 #95572), 0f7hc (0.26 #39623, 0.17 #93239, 0.09 #3437) >> Best rule #95571 for best value: >> intensional similarity = 3 >> extensional distance = 1233 >> proper extension: 0gv2r; 0grmhb; >> query: (?x6062, ?x2499) <- award_winner(?x1916, ?x6062), award_nominee(?x6062, ?x2499), award_nominee(?x2499, ?x192) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #6573 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 058kqy; 05qd_; 0c6qh; 04q5zw; 07m9cm; 01q6bg; 0f7hc; 0336mc; 08qxx9; *> query: (?x6062, 026rm_y) <- award_winner(?x2294, ?x6062), award_nominee(?x6062, ?x5940), ?x5940 = 0p__8 *> conf = 0.36 ranks of expected_values: 4 EVAL 06r_by award_nominee! 026rm_y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 91.000 41.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4908-02lfwp PRED entity: 02lfwp PRED relation: people! PRED expected values: 02w7gg => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 41): 041rx (0.27 #312, 0.24 #235, 0.20 #4), 02w7gg (0.24 #1927, 0.10 #233, 0.09 #1080), 048z7l (0.20 #40, 0.05 #579, 0.05 #194), 0xnvg (0.17 #90, 0.07 #1091, 0.07 #1014), 0x67 (0.11 #164, 0.10 #395, 0.10 #5400), 033tf_ (0.07 #623, 0.07 #1085, 0.06 #700), 0d7wh (0.06 #1942, 0.05 #402, 0.02 #479), 0cn68 (0.05 #212, 0.05 #443, 0.02 #520), 01qhm_ (0.05 #160, 0.05 #237, 0.05 #314), 019kn7 (0.05 #200, 0.03 #431, 0.02 #508) >> Best rule #312 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 06y0xx; >> query: (?x11965, 041rx) <- film(?x11965, ?x835), program(?x11965, ?x11818), place_of_birth(?x11965, ?x14306) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1927 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 346 *> proper extension: 0fv6dr; 0dv1hh; 09m465; *> query: (?x11965, 02w7gg) <- gender(?x11965, ?x231), nationality(?x11965, ?x1310), ?x1310 = 02jx1 *> conf = 0.24 ranks of expected_values: 2 EVAL 02lfwp people! 02w7gg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.273 http://example.org/people/ethnicity/people #4907-0l8gh PRED entity: 0l8gh PRED relation: artists PRED expected values: 09h_q => 53 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1797): 01w03jv (0.55 #5328, 0.12 #7471, 0.10 #6399), 06wvj (0.50 #2336, 0.33 #193, 0.11 #7693), 0bvzp (0.50 #2722, 0.33 #1651, 0.10 #3794), 01vvy (0.50 #2180, 0.33 #37, 0.10 #3252), 01vyp_ (0.50 #2290, 0.07 #3215, 0.06 #7647), 050z2 (0.42 #7856, 0.33 #356, 0.24 #5713), 020_4z (0.38 #6291, 0.26 #7363, 0.18 #5220), 0qf11 (0.38 #5737, 0.26 #6809, 0.17 #7880), 01kcms4 (0.38 #6008, 0.24 #7080, 0.13 #13519), 01wt4wc (0.36 #5013, 0.22 #10373, 0.15 #7156) >> Best rule #5328 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 06j6l; 02t8gf; 0xv2x; >> query: (?x10853, 01w03jv) <- artists(?x10853, ?x12453), artists(?x10853, ?x11497), nationality(?x11497, ?x1355), music(?x4347, ?x11497), instrumentalists(?x316, ?x11497), profession(?x12453, ?x5805), profession(?x11497, ?x1614), ?x5805 = 0fj9f >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1811 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 05lls; *> query: (?x10853, 09h_q) <- artists(?x10853, ?x12453), artists(?x10853, ?x11497), artists(?x10853, ?x9728), artists(?x10853, ?x1211), ?x11497 = 0c73z, origin(?x12453, ?x6952), company(?x12453, ?x581), ?x1211 = 0k4gf, student(?x7596, ?x12453), ?x9728 = 0kn3g *> conf = 0.33 ranks of expected_values: 18 EVAL 0l8gh artists 09h_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 53.000 19.000 0.545 http://example.org/music/genre/artists #4906-022769 PRED entity: 022769 PRED relation: profession PRED expected values: 02hrh1q => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 79): 02hrh1q (0.92 #2865, 0.91 #2565, 0.90 #5565), 01d_h8 (0.55 #306, 0.37 #1806, 0.35 #606), 0np9r (0.46 #22, 0.19 #322, 0.14 #14723), 0dxtg (0.34 #314, 0.31 #3764, 0.31 #7515), 02jknp (0.30 #308, 0.28 #158, 0.23 #8), 03gjzk (0.30 #316, 0.25 #1816, 0.25 #1366), 09jwl (0.25 #320, 0.23 #20, 0.18 #1970), 018gz8 (0.25 #318, 0.15 #18, 0.15 #3768), 02krf9 (0.23 #28, 0.11 #178, 0.10 #7529), 0cbd2 (0.18 #2107, 0.16 #3757, 0.16 #11108) >> Best rule #2865 for best value: >> intensional similarity = 3 >> extensional distance = 306 >> proper extension: 0h0jz; 02zq43; 0p_pd; 0z4s; 09byk; 032_jg; 01tspc6; 02g87m; 03jldb; 09y20; ... >> query: (?x2100, 02hrh1q) <- languages(?x2100, ?x254), film(?x2100, ?x5024), people(?x1050, ?x2100) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022769 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.916 http://example.org/people/person/profession #4905-0fqyzz PRED entity: 0fqyzz PRED relation: student! PRED expected values: 052nd => 122 concepts (71 used for prediction) PRED predicted values (max 10 best out of 109): 07tg4 (0.14 #86, 0.05 #613, 0.05 #2194), 015nl4 (0.14 #67, 0.03 #12188, 0.03 #21674), 017z88 (0.14 #82, 0.03 #31704, 0.03 #24324), 02l9wl (0.14 #252, 0.02 #779, 0.02 #16589), 02cw8s (0.14 #70, 0.01 #12191), 0lfgr (0.14 #43, 0.01 #16380, 0.01 #10583), 0138t4 (0.14 #403), 02ldmw (0.14 #285), 07wjk (0.12 #1644, 0.09 #2698, 0.04 #1117), 0bwfn (0.10 #6599, 0.09 #5018, 0.09 #3437) >> Best rule #86 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 014x77; >> query: (?x3828, 07tg4) <- nationality(?x3828, ?x279), nominated_for(?x3828, ?x278), ?x278 = 0c0yh4 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1590 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 99 *> proper extension: 069z_5; *> query: (?x3828, 052nd) <- nationality(?x3828, ?x279), ?x279 = 0d060g, type_of_union(?x3828, ?x566) *> conf = 0.06 ranks of expected_values: 12 EVAL 0fqyzz student! 052nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 122.000 71.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #4904-016y_f PRED entity: 016y_f PRED relation: film! PRED expected values: 0lkr7 0m0hw 0k9j_ 01pg1d => 86 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1158): 08h79x (0.42 #104042, 0.41 #72836, 0.40 #45781), 0j_c (0.18 #4573, 0.09 #6653, 0.05 #21217), 04sry (0.17 #22888, 0.16 #35375, 0.16 #24969), 06pj8 (0.12 #31212, 0.10 #52025, 0.09 #47863), 02q_cc (0.12 #31212, 0.10 #52025, 0.09 #47863), 015c4g (0.10 #780, 0.06 #2860, 0.03 #19506), 0klh7 (0.10 #489, 0.03 #8812, 0.02 #21296), 02qgqt (0.08 #8341, 0.03 #10422, 0.03 #39556), 01kb2j (0.08 #9233, 0.02 #21717, 0.02 #23798), 014gf8 (0.08 #3089, 0.03 #1009, 0.02 #7252) >> Best rule #104042 for best value: >> intensional similarity = 4 >> extensional distance = 1025 >> proper extension: 09rfpk; >> query: (?x4454, ?x3100) <- country(?x4454, ?x94), titles(?x53, ?x4454), nominated_for(?x3100, ?x4454), genre(?x4454, ?x604) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #3249 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 0d1qmz; *> query: (?x4454, 0m0hw) <- films(?x5179, ?x4454), nominated_for(?x3100, ?x4454), film_production_design_by(?x4454, ?x4168) *> conf = 0.02 ranks of expected_values: 493, 527, 912, 929 EVAL 016y_f film! 01pg1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 56.000 0.418 http://example.org/film/actor/film./film/performance/film EVAL 016y_f film! 0k9j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 86.000 56.000 0.418 http://example.org/film/actor/film./film/performance/film EVAL 016y_f film! 0m0hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 86.000 56.000 0.418 http://example.org/film/actor/film./film/performance/film EVAL 016y_f film! 0lkr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 56.000 0.418 http://example.org/film/actor/film./film/performance/film #4903-030dx5 PRED entity: 030dx5 PRED relation: sibling! PRED expected values: 02cvp8 => 102 concepts (54 used for prediction) PRED predicted values (max 10 best out of 85): 02cvp8 (0.25 #98, 0.19 #2168), 030dx5 (0.25 #77, 0.03 #1332), 01zmpg (0.14 #131, 0.09 #473, 0.06 #1500), 0gbwp (0.14 #150, 0.09 #492, 0.05 #835), 03f4k (0.14 #201, 0.09 #543, 0.05 #886), 013v5j (0.14 #132, 0.09 #474, 0.05 #817), 03n0pv (0.14 #215, 0.09 #557, 0.05 #900), 0cfz_z (0.14 #220, 0.09 #562, 0.03 #1475), 03wdsbz (0.14 #224, 0.09 #566), 0194xc (0.12 #314, 0.10 #885, 0.03 #1455) >> Best rule #98 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 045g4l; 02cvp8; >> query: (?x9015, 02cvp8) <- profession(?x9015, ?x1146), ?x1146 = 018gz8, people(?x4322, ?x9015), sibling(?x10901, ?x9015) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030dx5 sibling! 02cvp8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 54.000 0.250 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #4902-0kv9d3 PRED entity: 0kv9d3 PRED relation: film_crew_role PRED expected values: 0dxtw => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.80 #3111, 0.79 #1506, 0.78 #451), 09vw2b7 (0.68 #1795, 0.67 #450, 0.67 #1505), 0dxtw (0.46 #158, 0.41 #492, 0.38 #1800), 01vx2h (0.41 #85, 0.38 #122, 0.36 #456), 01pvkk (0.31 #160, 0.29 #3264, 0.28 #1585), 02ynfr (0.22 #164, 0.19 #1516, 0.19 #1589), 02vs3x5 (0.19 #171, 0.08 #468, 0.07 #939), 0215hd (0.16 #646, 0.16 #19, 0.14 #1518), 02rh1dz (0.16 #491, 0.13 #454, 0.12 #962), 0d2b38 (0.15 #99, 0.13 #63, 0.12 #136) >> Best rule #3111 for best value: >> intensional similarity = 4 >> extensional distance = 1105 >> proper extension: 0fq27fp; 0cnztc4; 0gj9qxr; 040rmy; 0h95zbp; 02h22; 03_wm6; 0bs8hvm; 09rfpk; >> query: (?x4050, 0ch6mp2) <- genre(?x4050, ?x162), film_crew_role(?x4050, ?x3305), film_crew_role(?x7563, ?x3305), ?x7563 = 03bzjpm >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #158 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 0crh5_f; *> query: (?x4050, 0dxtw) <- genre(?x4050, ?x162), film_crew_role(?x4050, ?x3305), ?x3305 = 04pyp5 *> conf = 0.46 ranks of expected_values: 3 EVAL 0kv9d3 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 115.000 0.803 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4901-01zzy3 PRED entity: 01zzy3 PRED relation: student PRED expected values: 0j3v => 145 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1114): 02ln1 (0.33 #1462, 0.10 #5646, 0.07 #9830), 042q3 (0.33 #1803, 0.10 #5987, 0.07 #10171), 06c44 (0.33 #1079, 0.10 #5263, 0.07 #9447), 01lwx (0.17 #14534, 0.07 #10349, 0.07 #8257), 0ff3y (0.14 #10437, 0.14 #8345, 0.06 #20898), 0tfc (0.14 #8285, 0.07 #10377, 0.06 #20838), 043s3 (0.14 #6941, 0.07 #9033, 0.06 #19494), 03j2gxx (0.14 #8133, 0.07 #10225, 0.06 #20686), 082_p (0.14 #7822, 0.07 #9914, 0.06 #20375), 0136g9 (0.14 #6478, 0.07 #8570, 0.06 #19031) >> Best rule #1462 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01y06y; >> query: (?x12343, 02ln1) <- contains(?x10766, ?x12343), student(?x12343, ?x3994), adjoins(?x10766, ?x10765), organization(?x4095, ?x12343), ?x10765 = 09hrc >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #25444 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 0ym8f; 0yjf0; 01nnsv; 0ylvj; 0ymcz; 0yldt; 0ym1n; *> query: (?x12343, 0j3v) <- citytown(?x12343, ?x14507), student(?x12343, ?x3994), administrative_parent(?x14507, ?x10766), category(?x12343, ?x134) *> conf = 0.02 ranks of expected_values: 600 EVAL 01zzy3 student 0j3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 145.000 39.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #4900-05xb7q PRED entity: 05xb7q PRED relation: educational_institution! PRED expected values: 05xb7q => 147 concepts (52 used for prediction) PRED predicted values (max 10 best out of 109): 05ftw3 (0.14 #324), 03x83_ (0.14 #127), 07wlf (0.04 #608, 0.03 #1686, 0.03 #1147), 01j_cy (0.04 #573, 0.03 #1651, 0.03 #1112), 01dyk8 (0.04 #868, 0.03 #1946, 0.03 #2485), 0g8rj (0.04 #702, 0.03 #1780, 0.03 #2319), 01f1r4 (0.04 #649, 0.03 #1727, 0.03 #2266), 025v3k (0.04 #644, 0.03 #1722, 0.03 #2261), 09f2j (0.04 #683, 0.03 #1761, 0.02 #3378), 02482c (0.04 #850, 0.03 #1389, 0.02 #4085) >> Best rule #324 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 0hj6h; >> query: (?x5968, 05ftw3) <- contains(?x5967, ?x5968), category(?x5968, ?x134), ?x134 = 08mbj5d, contains(?x2365, ?x5967), contains(?x2236, ?x5967), ?x2236 = 05sb1, adjoins(?x2365, ?x2146) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #6474 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 48 *> proper extension: 0ymbl; 01nn7r; *> query: (?x5968, ?x466) <- citytown(?x5968, ?x5967), school_type(?x5968, ?x4994), category(?x5968, ?x134), organization(?x5510, ?x5968), school_type(?x6548, ?x4994), school_type(?x466, ?x4994), ?x6548 = 0yls9 *> conf = 0.01 ranks of expected_values: 91 EVAL 05xb7q educational_institution! 05xb7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 147.000 52.000 0.143 http://example.org/education/educational_institution_campus/educational_institution #4899-01d0b1 PRED entity: 01d0b1 PRED relation: award PRED expected values: 0bp_b2 => 103 concepts (82 used for prediction) PRED predicted values (max 10 best out of 247): 0ck27z (0.24 #6136, 0.24 #7345, 0.23 #5330), 0gqy2 (0.21 #1373, 0.14 #22974, 0.13 #11448), 0f4x7 (0.19 #1240, 0.15 #33056, 0.14 #28621), 099jhq (0.15 #33056, 0.14 #28621, 0.14 #22974), 0bdwqv (0.15 #33056, 0.14 #28621, 0.14 #22974), 02z0dfh (0.15 #33056, 0.14 #28621, 0.14 #22974), 0gkts9 (0.15 #33056, 0.14 #28621, 0.14 #22974), 0279c15 (0.15 #33056, 0.14 #28621, 0.14 #22974), 024fz9 (0.15 #33056, 0.14 #28621, 0.14 #22974), 09qv_s (0.15 #33056, 0.14 #28621, 0.07 #151) >> Best rule #6136 for best value: >> intensional similarity = 3 >> extensional distance = 747 >> proper extension: 04n7njg; 02wb6yq; 0f3zsq; 03yf4d; >> query: (?x8835, 0ck27z) <- nominated_for(?x8835, ?x4639), actor(?x4639, ?x192), producer_type(?x4639, ?x632) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #22974 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1566 *> proper extension: 018ndc; 0187x8; 06mj4; 016lmg; 026v1z; *> query: (?x8835, ?x451) <- award_winner(?x870, ?x8835), award_nominee(?x8835, ?x1522), award(?x1522, ?x451) *> conf = 0.14 ranks of expected_values: 18 EVAL 01d0b1 award 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 103.000 82.000 0.243 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4898-03zz8b PRED entity: 03zz8b PRED relation: award_nominee PRED expected values: 0306ds => 131 concepts (49 used for prediction) PRED predicted values (max 10 best out of 961): 0h10vt (0.81 #111584, 0.81 #111583, 0.81 #69740), 05th8t (0.81 #111584, 0.81 #111583, 0.81 #69740), 0347xl (0.81 #111584, 0.81 #111583, 0.81 #69740), 0306ds (0.81 #111584, 0.81 #111583, 0.81 #69740), 025t9b (0.81 #111584, 0.81 #111583, 0.81 #69740), 01kp66 (0.81 #111584, 0.81 #111583, 0.81 #69740), 03zz8b (0.42 #13266, 0.20 #8618, 0.18 #111585), 01vw37m (0.29 #13064, 0.18 #111585, 0.15 #13947), 0227tr (0.29 #12175, 0.18 #111585, 0.15 #13947), 03mp9s (0.27 #8545, 0.02 #24817, 0.01 #29466) >> Best rule #111584 for best value: >> intensional similarity = 2 >> extensional distance = 594 >> proper extension: 0q9kd; 06qgvf; 0grwj; 01k7d9; 0byfz; 03x3qv; 02zq43; 0p_pd; 032xhg; 0159h6; ... >> query: (?x7337, ?x4043) <- award_nominee(?x4043, ?x7337), actor(?x7756, ?x7337) >> conf = 0.81 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 03zz8b award_nominee 0306ds CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 131.000 49.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4897-01q0kg PRED entity: 01q0kg PRED relation: institution! PRED expected values: 014mlp => 217 concepts (217 used for prediction) PRED predicted values (max 10 best out of 20): 014mlp (0.76 #195, 0.76 #492, 0.71 #2126), 016t_3 (0.70 #214, 0.64 #490, 0.61 #425), 02_xgp2 (0.67 #138, 0.67 #116, 0.65 #306), 07s6fsf (0.55 #423, 0.50 #488, 0.48 #467), 027f2w (0.50 #113, 0.42 #303, 0.42 #135), 04zx3q1 (0.50 #107, 0.40 #297, 0.37 #318), 028dcg (0.50 #122, 0.36 #872, 0.36 #919), 013zdg (0.50 #112, 0.36 #429, 0.32 #218), 01rr_d (0.50 #120, 0.26 #331, 0.26 #310), 03mkk4 (0.36 #872, 0.36 #919, 0.33 #115) >> Best rule #195 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 017z88; 019_6d; >> query: (?x4257, 014mlp) <- school_type(?x4257, ?x3092), citytown(?x4257, ?x6960), company(?x7749, ?x4257), currency(?x4257, ?x170) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q0kg institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 217.000 217.000 0.765 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4896-01719t PRED entity: 01719t PRED relation: film_crew_role PRED expected values: 09vw2b7 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 23): 09vw2b7 (0.77 #73, 0.74 #279, 0.71 #1411), 0dxtw (0.44 #179, 0.42 #1415, 0.40 #283), 01vx2h (0.44 #78, 0.42 #284, 0.36 #1484), 01pvkk (0.31 #559, 0.31 #696, 0.30 #2376), 02rh1dz (0.19 #76, 0.15 #282, 0.12 #1482), 0215hd (0.15 #429, 0.15 #1423, 0.14 #497), 01xy5l_ (0.13 #425, 0.13 #287, 0.12 #81), 089g0h (0.13 #430, 0.12 #292, 0.12 #498), 0d2b38 (0.12 #504, 0.11 #436, 0.11 #298), 02_n3z (0.11 #413, 0.09 #481, 0.09 #1407) >> Best rule #73 for best value: >> intensional similarity = 5 >> extensional distance = 55 >> proper extension: 0c57yj; 02prwdh; 05b6rdt; 02ph9tm; 06fqlk; 065_cjc; 03z9585; 03whyr; >> query: (?x1488, 09vw2b7) <- film_crew_role(?x1488, ?x3197), film_crew_role(?x1488, ?x137), written_by(?x1488, ?x2332), ?x137 = 09zzb8, ?x3197 = 02ynfr >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01719t film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.772 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4895-03x400 PRED entity: 03x400 PRED relation: award PRED expected values: 09sb52 => 107 concepts (101 used for prediction) PRED predicted values (max 10 best out of 269): 0gqy2 (0.61 #3387, 0.54 #12656, 0.14 #20313), 09sb52 (0.39 #3264, 0.36 #21802, 0.34 #22608), 05p09zm (0.39 #928, 0.24 #4958, 0.24 #6167), 05pcn59 (0.35 #4110, 0.30 #886, 0.30 #6528), 027dtxw (0.34 #3228, 0.19 #12497, 0.13 #407), 09sdmz (0.34 #3429, 0.19 #12698, 0.10 #205), 03c7tr1 (0.30 #864, 0.21 #1670, 0.21 #2073), 0f4x7 (0.28 #3254, 0.19 #12523, 0.15 #9702), 0bdwqv (0.24 #3395, 0.19 #12664, 0.09 #20321), 02w9sd7 (0.23 #3393, 0.11 #12662, 0.09 #4602) >> Best rule #3387 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 01v90t; >> query: (?x6618, 0gqy2) <- award(?x6618, ?x1033), film(?x6618, ?x1209), ?x1033 = 02x73k6 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #3264 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 77 *> proper extension: 01v90t; *> query: (?x6618, 09sb52) <- award(?x6618, ?x1033), film(?x6618, ?x1209), ?x1033 = 02x73k6 *> conf = 0.39 ranks of expected_values: 2 EVAL 03x400 award 09sb52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 101.000 0.608 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4894-03bx2lk PRED entity: 03bx2lk PRED relation: featured_film_locations PRED expected values: 030qb3t => 50 concepts (41 used for prediction) PRED predicted values (max 10 best out of 56): 02_286 (0.18 #20, 0.16 #3632, 0.16 #3391), 04jpl (0.12 #970, 0.06 #3863, 0.06 #9), 030qb3t (0.10 #279, 0.09 #1480, 0.08 #1963), 01_d4 (0.07 #287, 0.04 #528, 0.02 #3612), 0h7h6 (0.06 #43, 0.03 #283, 0.02 #524), 0r0m6 (0.06 #89, 0.03 #3854, 0.03 #6264), 0gkgp (0.06 #161), 081m_ (0.06 #155), 0cc56 (0.06 #27), 03gh4 (0.04 #596, 0.02 #1797) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 06g77c; 03f7xg; 09zf_q; 0bbm7r; 021gzd; 01_1hw; 04xg2f; >> query: (?x1219, 02_286) <- film(?x9656, ?x1219), film(?x1867, ?x1219), award_winner(?x9656, ?x906), ?x1867 = 016ywr >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #279 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 0g56t9t; 0g5qs2k; 05p1tzf; 0401sg; 0bwfwpj; 08hmch; 053rxgm; 0gtvrv3; 03twd6; 04w7rn; ... *> query: (?x1219, 030qb3t) <- film_release_region(?x1219, ?x1892), film_release_region(?x1219, ?x1471), ?x1892 = 02vzc, music(?x1219, ?x2363), ?x1471 = 07t21 *> conf = 0.10 ranks of expected_values: 3 EVAL 03bx2lk featured_film_locations 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 50.000 41.000 0.176 http://example.org/film/film/featured_film_locations #4893-02sjp PRED entity: 02sjp PRED relation: profession PRED expected values: 028kk_ => 123 concepts (108 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.82 #4257, 0.82 #3672, 0.81 #5429), 0dxtg (0.76 #10404, 0.29 #10989, 0.29 #11866), 09jwl (0.72 #3823, 0.70 #4555, 0.70 #3970), 0nbcg (0.49 #3835, 0.47 #4859, 0.46 #4567), 016z4k (0.46 #149, 0.42 #3516, 0.41 #4393), 01d_h8 (0.39 #10397, 0.38 #7179, 0.36 #8203), 02jknp (0.33 #10399, 0.31 #299, 0.26 #7181), 039v1 (0.30 #3840, 0.28 #4864, 0.27 #4425), 03gjzk (0.28 #10406, 0.23 #8650, 0.23 #11868), 0cbd2 (0.19 #10398, 0.14 #298, 0.13 #883) >> Best rule #4257 for best value: >> intensional similarity = 3 >> extensional distance = 339 >> proper extension: 03j90; >> query: (?x9163, 02hrh1q) <- award_winner(?x1443, ?x9163), languages(?x9163, ?x90), gender(?x9163, ?x231) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1243 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 0hnlx; 0pcc0; 06wvj; 02ck1; 06c44; 05n19y; 02r38; 05f2jk; 0k1wz; 0561xh; ... *> query: (?x9163, 028kk_) <- artists(?x505, ?x9163), profession(?x9163, ?x563), ?x563 = 01c8w0 *> conf = 0.11 ranks of expected_values: 17 EVAL 02sjp profession 028kk_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 123.000 108.000 0.821 http://example.org/people/person/profession #4892-09lvl1 PRED entity: 09lvl1 PRED relation: ceremony PRED expected values: 0h_9252 => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 139): 0h_9252 (0.90 #197, 0.14 #58, 0.07 #336), 0gpjbt (0.46 #585, 0.45 #446, 0.34 #2254), 09n4nb (0.45 #604, 0.45 #465, 0.33 #2273), 056878 (0.44 #588, 0.44 #449, 0.32 #2257), 02rjjll (0.44 #561, 0.43 #422, 0.33 #2230), 0466p0j (0.44 #632, 0.43 #493, 0.33 #2301), 02cg41 (0.43 #681, 0.42 #542, 0.32 #2350), 05pd94v (0.43 #558, 0.42 #419, 0.32 #2227), 05c1t6z (0.43 #15, 0.16 #293, 0.14 #988), 02q690_ (0.43 #65, 0.15 #343, 0.13 #1038) >> Best rule #197 for best value: >> intensional similarity = 2 >> extensional distance = 18 >> proper extension: 06bwtj; >> query: (?x7788, 0h_9252) <- ceremony(?x7788, ?x10337), ?x10337 = 0ds460j >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09lvl1 ceremony 0h_9252 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.900 http://example.org/award/award_category/winners./award/award_honor/ceremony #4891-068g3p PRED entity: 068g3p PRED relation: profession PRED expected values: 02jknp => 79 concepts (51 used for prediction) PRED predicted values (max 10 best out of 43): 02jknp (0.66 #152, 0.46 #4213, 0.45 #1457), 018gz8 (0.33 #1029, 0.32 #304, 0.31 #449), 0cbd2 (0.29 #5663, 0.19 #586, 0.17 #4212), 0kyk (0.25 #26, 0.14 #5683, 0.12 #6118), 0np9r (0.21 #598, 0.20 #1903, 0.20 #308), 09jwl (0.21 #5238, 0.20 #6108, 0.17 #5963), 0nbcg (0.14 #5250, 0.13 #6120, 0.11 #6990), 016z4k (0.11 #5226, 0.10 #6096, 0.10 #2179), 0dz3r (0.10 #5224, 0.09 #6094, 0.09 #6964), 015cjr (0.09 #481, 0.07 #1061, 0.07 #626) >> Best rule #152 for best value: >> intensional similarity = 6 >> extensional distance = 83 >> proper extension: 06w33f8; 01c58j; 01f7j9; 0721cy; 0bgrsl; 0c_mvb; 06chf; 03m_k0; 09b0xs; 098n5; ... >> query: (?x9291, 02jknp) <- profession(?x9291, ?x1943), profession(?x9291, ?x1041), profession(?x9291, ?x319), ?x1041 = 03gjzk, ?x319 = 01d_h8, ?x1943 = 02krf9 >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 068g3p profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 51.000 0.659 http://example.org/people/person/profession #4890-01fh0q PRED entity: 01fh0q PRED relation: artist! PRED expected values: 01clyr => 109 concepts (76 used for prediction) PRED predicted values (max 10 best out of 108): 03mp8k (0.43 #343, 0.14 #204, 0.09 #1596), 03rhqg (0.19 #15, 0.19 #711, 0.17 #154), 0181dw (0.19 #318, 0.14 #179, 0.12 #736), 011k1h (0.16 #288, 0.15 #10, 0.11 #2236), 02p11jq (0.14 #152, 0.10 #1544, 0.08 #4048), 01cl0d (0.14 #192, 0.07 #4227, 0.06 #3949), 01f_3w (0.14 #171, 0.05 #310, 0.04 #3650), 0g768 (0.12 #5182, 0.12 #6294, 0.12 #3653), 01cszh (0.12 #289, 0.07 #150, 0.07 #1124), 01trtc (0.12 #71, 0.09 #2297, 0.09 #1602) >> Best rule #343 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 08w4pm; 01s560x; >> query: (?x8972, 03mp8k) <- artist(?x5666, ?x8972), artists(?x505, ?x8972), ?x5666 = 043g7l >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4205 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 470 *> proper extension: 02ht0ln; *> query: (?x8972, 01clyr) <- artist(?x2299, ?x8972), artists(?x505, ?x8972), instrumentalists(?x315, ?x8972) *> conf = 0.08 ranks of expected_values: 18 EVAL 01fh0q artist! 01clyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 109.000 76.000 0.431 http://example.org/music/record_label/artist #4889-0ml25 PRED entity: 0ml25 PRED relation: contains! PRED expected values: 0824r => 150 concepts (126 used for prediction) PRED predicted values (max 10 best out of 107): 0824r (0.91 #55708, 0.89 #54809, 0.89 #92566), 09c7w0 (0.77 #83572, 0.77 #82672, 0.77 #70089), 04_1l0v (0.77 #83572, 0.77 #82672, 0.77 #70089), 07b_l (0.25 #11901, 0.22 #8309, 0.21 #10106), 01n7q (0.21 #20745, 0.20 #12654, 0.17 #17148), 0ml25 (0.19 #28749, 0.18 #18868, 0.17 #33245), 0chghy (0.18 #919, 0.14 #16194, 0.11 #38657), 03v0t (0.17 #8320, 0.15 #55942, 0.12 #45158), 0f8l9c (0.16 #29695, 0.12 #18915, 0.09 #83621), 03rjj (0.16 #8995, 0.14 #40442, 0.12 #30558) >> Best rule #55708 for best value: >> intensional similarity = 6 >> extensional distance = 44 >> proper extension: 0l3n4; 0jgm8; 0mpbx; >> query: (?x2679, ?x4105) <- county_seat(?x2679, ?x2680), source(?x2679, ?x958), second_level_divisions(?x94, ?x2679), currency(?x2679, ?x170), ?x94 = 09c7w0, state(?x2680, ?x4105) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ml25 contains! 0824r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 126.000 0.913 http://example.org/location/location/contains #4888-0b_dh PRED entity: 0b_dh PRED relation: profession PRED expected values: 01d_h8 03gjzk => 128 concepts (56 used for prediction) PRED predicted values (max 10 best out of 86): 02hrh1q (0.89 #605, 0.85 #1049, 0.85 #1641), 01d_h8 (0.80 #4002, 0.80 #4446, 0.79 #2818), 03gjzk (0.44 #2382, 0.44 #2234, 0.43 #7267), 0cbd2 (0.34 #1487, 0.25 #747, 0.22 #5632), 02krf9 (0.33 #26, 0.30 #2394, 0.28 #2246), 0kyk (0.26 #1509, 0.21 #177, 0.19 #621), 018gz8 (0.25 #1496, 0.18 #7862, 0.17 #7269), 02hv44_ (0.23 #1537, 0.08 #7903, 0.08 #5386), 09jwl (0.22 #2534, 0.20 #4162, 0.17 #4310), 0fj9f (0.22 #794, 0.11 #942, 0.09 #1978) >> Best rule #605 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 0z4s; 0m2l9; 0chsq; 0147dk; 09wj5; 034x61; 01rh0w; 049g_xj; 06x58; 0j1yf; ... >> query: (?x11239, 02hrh1q) <- profession(?x11239, ?x524), nationality(?x11239, ?x512), notable_people_with_this_condition(?x6656, ?x11239), award_winner(?x5723, ?x11239) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #4002 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 182 *> proper extension: 04b19t; 01ycck; 03nk3t; 0522wp; *> query: (?x11239, 01d_h8) <- profession(?x11239, ?x524), film(?x11239, ?x2898), award_winner(?x1307, ?x11239), ?x524 = 02jknp *> conf = 0.80 ranks of expected_values: 2, 3 EVAL 0b_dh profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 56.000 0.889 http://example.org/people/person/profession EVAL 0b_dh profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 56.000 0.889 http://example.org/people/person/profession #4887-014kbl PRED entity: 014kbl PRED relation: film_crew_role! PRED expected values: 09txzv 01gwk3 => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1271): 0bth54 (0.75 #12777, 0.71 #10234, 0.67 #7691), 06znpjr (0.75 #13726, 0.71 #11183, 0.67 #8640), 0ct2tf5 (0.75 #13846, 0.71 #11303, 0.67 #8760), 09sh8k (0.75 #12726, 0.71 #10183, 0.67 #7640), 01gwk3 (0.75 #13550, 0.71 #11007, 0.67 #8464), 047wh1 (0.75 #13380, 0.71 #10837, 0.67 #8294), 024l2y (0.75 #12914, 0.71 #10371, 0.67 #7828), 057lbk (0.75 #13269, 0.71 #10726, 0.67 #8183), 05qbckf (0.75 #12955, 0.71 #10412, 0.67 #7869), 047csmy (0.75 #13396, 0.71 #10853, 0.67 #8310) >> Best rule #12777 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 01vx2h; >> query: (?x13327, 0bth54) <- film_crew_role(?x11416, ?x13327), film_crew_role(?x6099, ?x13327), film_crew_role(?x1511, ?x13327), ?x6099 = 0473rc, ?x1511 = 0340hj, crewmember(?x11416, ?x7675) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #13550 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 01vx2h; *> query: (?x13327, 01gwk3) <- film_crew_role(?x11416, ?x13327), film_crew_role(?x6099, ?x13327), film_crew_role(?x1511, ?x13327), ?x6099 = 0473rc, ?x1511 = 0340hj, crewmember(?x11416, ?x7675) *> conf = 0.75 ranks of expected_values: 5, 461 EVAL 014kbl film_crew_role! 01gwk3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 44.000 44.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 014kbl film_crew_role! 09txzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 44.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4886-083jv PRED entity: 083jv PRED relation: colors! PRED expected values: 04wlz2 04344j 07ccs 0lvng 02482c 02f4s3 013nky 02rky4 0234_c 037q2p 0558_1 01cf5 024cg8 => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 755): 03b8c4 (0.50 #4281, 0.33 #5207, 0.33 #3048), 0hpv3 (0.50 #4208, 0.33 #2975, 0.33 #520), 06fq2 (0.50 #4178, 0.33 #2945, 0.25 #4487), 0pspl (0.50 #4050, 0.33 #2817, 0.25 #4359), 01j_9c (0.50 #3385, 0.33 #315, 0.25 #3694), 01jq4b (0.50 #3491, 0.33 #421, 0.25 #3800), 01pq4w (0.50 #3436, 0.33 #366, 0.25 #3745), 01lnyf (0.50 #3763, 0.33 #384, 0.25 #3454), 05x_5 (0.50 #3532, 0.33 #462, 0.25 #3841), 01bzw5 (0.50 #4009, 0.33 #2776, 0.25 #4318) >> Best rule #4281 for best value: >> intensional similarity = 31 >> extensional distance = 2 >> proper extension: 036k5h; >> query: (?x663, 03b8c4) <- colors(?x11587, ?x663), colors(?x2174, ?x663), colors(?x1010, ?x663), colors(?x8354, ?x663), colors(?x7618, ?x663), colors(?x6127, ?x663), colors(?x4199, ?x663), colors(?x1440, ?x663), institution(?x7636, ?x6127), institution(?x734, ?x6127), position(?x2174, ?x2010), ?x734 = 04zx3q1, currency(?x4199, ?x2244), student(?x4199, ?x2033), major_field_of_study(?x7618, ?x742), institution(?x7636, ?x11975), institution(?x7636, ?x735), registering_agency(?x7618, ?x1982), team(?x7533, ?x1010), school(?x1010, ?x3948), season(?x1010, ?x8529), company(?x346, ?x7618), ?x8529 = 025ygws, ?x8354 = 01hjy5, school_type(?x1440, ?x3092), ?x11975 = 050xpd, student(?x7636, ?x1984), institution(?x620, ?x4199), ?x7533 = 01yvvn, team(?x1696, ?x11587), ?x735 = 065y4w7 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5226 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 7 *> proper extension: 01jnf1; *> query: (?x663, 024cg8) <- colors(?x8228, ?x663), colors(?x5551, ?x663), colors(?x11854, ?x663), colors(?x9724, ?x663), colors(?x6127, ?x663), colors(?x4794, ?x663), colors(?x1428, ?x663), colors(?x1011, ?x663), institution(?x1526, ?x6127), ?x1526 = 0bkj86, currency(?x9724, ?x170), student(?x9724, ?x3051), student(?x1011, ?x400), sport(?x8228, ?x4833), citytown(?x11854, ?x3450), major_field_of_study(?x6127, ?x742), school_type(?x9724, ?x4994), organization(?x5510, ?x6127), contains(?x94, ?x1428), school(?x465, ?x1011), student(?x6127, ?x1515), team(?x3797, ?x5551), featured_film_locations(?x253, ?x4794), team(?x4570, ?x8228) *> conf = 0.33 ranks of expected_values: 26, 83, 178, 180, 197, 227, 246, 247, 258, 284, 314, 337, 417 EVAL 083jv colors! 024cg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 01cf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 0558_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 037q2p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 0234_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 02rky4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 013nky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 02f4s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 02482c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 0lvng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 07ccs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 04344j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 22.000 22.000 0.500 http://example.org/education/educational_institution/colors EVAL 083jv colors! 04wlz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 22.000 22.000 0.500 http://example.org/education/educational_institution/colors #4885-0kc9f PRED entity: 0kc9f PRED relation: category PRED expected values: 08mbj5d => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #69, 0.80 #70, 0.79 #66) >> Best rule #69 for best value: >> intensional similarity = 3 >> extensional distance = 734 >> proper extension: 036hnm; >> query: (?x13952, 08mbj5d) <- state_province_region(?x13952, ?x1227), state_province_region(?x10178, ?x1227), major_field_of_study(?x10178, ?x1527) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kc9f category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.799 http://example.org/common/topic/webpage./common/webpage/category #4884-045xh PRED entity: 045xh PRED relation: category_of! PRED expected values: 045xh => 51 concepts (47 used for prediction) PRED predicted values (max 10 best out of 33): 0grw_ (0.25 #603, 0.03 #647, 0.03 #1734), 01b8bn (0.09 #1265, 0.03 #1427, 0.03 #1588), 04jhhng (0.03 #1450, 0.03 #1611, 0.03 #1936), 05x2s (0.03 #647, 0.03 #1603, 0.03 #1928), 01tgwv (0.03 #647, 0.03 #1759, 0.03 #2082), 04hddx (0.03 #647, 0.03 #1762, 0.02 #2247), 027x4ws (0.03 #647, 0.03 #1736, 0.02 #2221), 01ppdy (0.03 #1425, 0.03 #1911, 0.03 #2072), 045xh (0.03 #647), 058bzgm (0.03 #647) >> Best rule #603 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0grw_; >> query: (?x12418, 0grw_) <- award(?x10667, ?x12418), award(?x2934, ?x12418), award_nominee(?x4023, ?x10667), nominated_for(?x10667, ?x5810), ?x2934 = 04cbtrw >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #647 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0grw_; *> query: (?x12418, ?x575) <- award(?x10667, ?x12418), award(?x9284, ?x12418), award(?x2934, ?x12418), award_nominee(?x4023, ?x10667), nominated_for(?x10667, ?x5810), ?x2934 = 04cbtrw, award(?x9284, ?x575) *> conf = 0.03 ranks of expected_values: 9 EVAL 045xh category_of! 045xh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 51.000 47.000 0.250 http://example.org/award/award_category/category_of #4883-0pd57 PRED entity: 0pd57 PRED relation: genre PRED expected values: 01jfsb => 121 concepts (89 used for prediction) PRED predicted values (max 10 best out of 108): 07s9rl0 (0.82 #1578, 0.81 #1457, 0.80 #485), 01jfsb (0.75 #4377, 0.66 #4619, 0.60 #2438), 05p553 (0.53 #125, 0.44 #7155, 0.36 #4489), 02l7c8 (0.43 #380, 0.43 #988, 0.41 #2563), 03k9fj (0.40 #2437, 0.38 #5466, 0.37 #3164), 04xvlr (0.37 #1094, 0.30 #486, 0.30 #608), 06n90 (0.36 #2439, 0.33 #3166, 0.28 #5468), 060__y (0.35 #381, 0.26 #624, 0.25 #502), 01hmnh (0.33 #19, 0.22 #4747, 0.19 #2444), 01g6gs (0.29 #750, 0.19 #993, 0.18 #7030) >> Best rule #1578 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 011yfd; >> query: (?x4179, 07s9rl0) <- featured_film_locations(?x4179, ?x1860), nominated_for(?x1307, ?x4179), ?x1307 = 0gq9h, genre(?x4179, ?x225) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #4377 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 316 *> proper extension: 035xwd; 038bh3; 0413cff; 05b6rdt; 02bg55; 048tv9; 02pcq92; *> query: (?x4179, 01jfsb) <- featured_film_locations(?x4179, ?x1860), genre(?x4179, ?x225), genre(?x5008, ?x225), ?x5008 = 035w2k *> conf = 0.75 ranks of expected_values: 2 EVAL 0pd57 genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 89.000 0.818 http://example.org/film/film/genre #4882-01q3_2 PRED entity: 01q3_2 PRED relation: award PRED expected values: 054ks3 => 79 concepts (77 used for prediction) PRED predicted values (max 10 best out of 259): 01c92g (0.79 #6805, 0.79 #1601, 0.78 #5203), 054krc (0.58 #886, 0.18 #14009, 0.14 #4087), 01bgqh (0.49 #2044, 0.32 #1243, 0.28 #2444), 01by1l (0.48 #2111, 0.38 #1310, 0.33 #4912), 0l8z1 (0.44 #864, 0.09 #4065, 0.08 #5667), 054ks3 (0.37 #938, 0.33 #2139, 0.27 #538), 025m8y (0.37 #898, 0.13 #22427, 0.12 #21226), 04mqgr (0.27 #551, 0.18 #1752, 0.16 #151), 09sb52 (0.25 #12847, 0.23 #16455, 0.23 #17659), 03qbh5 (0.24 #2203, 0.20 #4603, 0.20 #5004) >> Best rule #6805 for best value: >> intensional similarity = 3 >> extensional distance = 466 >> proper extension: 01ky2h; 01wz_ml; 0lsw9; 0f6lx; 013rds; 06lxn; >> query: (?x9731, ?x1232) <- artists(?x671, ?x9731), award_winner(?x1232, ?x9731), category(?x9731, ?x134) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #938 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 03n0q5; 02vyw; 02ryx0; 02zft0; 01mh8zn; 05yzt_; 02fgp0; 03975z; 020jqv; 0csdzz; *> query: (?x9731, 054ks3) <- award(?x9731, ?x2379), ?x2379 = 02qvyrt, nominated_for(?x9731, ?x8677) *> conf = 0.37 ranks of expected_values: 6 EVAL 01q3_2 award 054ks3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 77.000 0.789 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4881-030vmc PRED entity: 030vmc PRED relation: profession PRED expected values: 0dxtg => 117 concepts (78 used for prediction) PRED predicted values (max 10 best out of 60): 0dxtg (0.82 #4217, 0.74 #8425, 0.71 #3202), 0cbd2 (0.31 #9724, 0.28 #4211, 0.24 #151), 09jwl (0.24 #4511, 0.22 #3786, 0.22 #2481), 018gz8 (0.23 #594, 0.20 #5961, 0.18 #739), 0nbcg (0.20 #3798, 0.20 #4523, 0.16 #2493), 0np9r (0.20 #598, 0.19 #743, 0.17 #1903), 0dz3r (0.19 #4497, 0.18 #3772, 0.16 #2467), 0kyk (0.15 #9744, 0.13 #4231, 0.11 #4521), 016z4k (0.13 #4499, 0.13 #3774, 0.12 #6822), 012t_z (0.12 #301, 0.10 #2041, 0.08 #2331) >> Best rule #4217 for best value: >> intensional similarity = 3 >> extensional distance = 307 >> proper extension: 08433; 0p8jf; 0hky; 06z4wj; 0dbb3; 0hcvy; 01t_wfl; 02xyl; >> query: (?x9164, 0dxtg) <- award(?x9164, ?x350), profession(?x9164, ?x319), written_by(?x1743, ?x9164) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030vmc profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 78.000 0.819 http://example.org/people/person/profession #4880-03ctqqf PRED entity: 03ctqqf PRED relation: nominated_for! PRED expected values: 05xbx => 94 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1157): 0g5lhl7 (0.82 #14015, 0.82 #16352, 0.81 #7008), 01tspc6 (0.56 #86452, 0.54 #49068, 0.52 #53740), 01f2w0 (0.32 #21027, 0.26 #30374, 0.26 #25700), 0dgskx (0.26 #18691, 0.23 #7009, 0.22 #32713), 02l4pj (0.26 #18691, 0.23 #7009, 0.22 #32713), 051wwp (0.26 #18691, 0.23 #7009, 0.22 #32713), 0171cm (0.26 #18691, 0.23 #7009, 0.22 #32713), 0h0yt (0.26 #18691, 0.23 #7009, 0.22 #32713), 016xk5 (0.26 #18691, 0.23 #7009, 0.22 #32713), 02l4rh (0.26 #18691, 0.23 #7009, 0.22 #32713) >> Best rule #14015 for best value: >> intensional similarity = 5 >> extensional distance = 85 >> proper extension: 0fkwzs; >> query: (?x12117, ?x2776) <- award_winner(?x12117, ?x2776), award_winner(?x12117, ?x374), nominated_for(?x375, ?x12117), languages(?x12117, ?x254), actor(?x7254, ?x374) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #18691 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 94 *> proper extension: 0g60z; 080dwhx; 02_1rq; 072kp; 039fgy; 02py4c8; 02k_4g; 02nf2c; 0358x_; 0ddd0gc; ... *> query: (?x12117, ?x926) <- award_winner(?x12117, ?x374), nominated_for(?x375, ?x12117), languages(?x12117, ?x254), award_winner(?x374, ?x926) *> conf = 0.26 ranks of expected_values: 24 EVAL 03ctqqf nominated_for! 05xbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 94.000 55.000 0.822 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4879-015pkc PRED entity: 015pkc PRED relation: award_nominee PRED expected values: 029_l => 120 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1252): 0flw6 (0.82 #21004, 0.81 #35013, 0.81 #144711), 029_l (0.82 #21004, 0.81 #35013, 0.81 #144711), 0716t2 (0.82 #21004, 0.81 #35013, 0.81 #144711), 0306ds (0.46 #19239, 0.14 #144712, 0.07 #14572), 030znt (0.43 #280, 0.27 #14283, 0.20 #11949), 026l37 (0.42 #19749, 0.01 #131783, 0.01 #143455), 015pkc (0.40 #14370, 0.40 #12036, 0.29 #367), 01bh6y (0.40 #16038, 0.29 #2035, 0.27 #13704), 01q9b9 (0.40 #13345, 0.29 #1676, 0.27 #15679), 0c3p7 (0.38 #20126, 0.14 #144712, 0.07 #15459) >> Best rule #21004 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 043kzcr; 03c5f7l; >> query: (?x1733, ?x92) <- award_nominee(?x1641, ?x1733), award_nominee(?x92, ?x1733), award(?x1733, ?x704), ?x1641 = 07s8r0 >> conf = 0.82 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 015pkc award_nominee 029_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 69.000 0.820 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4878-040njc PRED entity: 040njc PRED relation: award! PRED expected values: 07cdz => 45 concepts (26 used for prediction) PRED predicted values (max 10 best out of 949): 0209hj (0.50 #58, 0.42 #3982, 0.29 #2942), 07xtqq (0.50 #29, 0.29 #2942, 0.27 #15701), 042y1c (0.50 #223, 0.29 #2942, 0.27 #15701), 09q5w2 (0.50 #97, 0.29 #2942, 0.27 #15701), 0bmhn (0.50 #895, 0.29 #2942, 0.27 #15701), 0bdjd (0.50 #705, 0.29 #2942, 0.27 #15701), 0pd64 (0.50 #742, 0.29 #2942, 0.27 #15701), 05qm9f (0.50 #645, 0.29 #2942, 0.27 #15701), 07g1sm (0.50 #678, 0.26 #4602, 0.25 #1659), 0bm2x (0.50 #509, 0.25 #1490, 0.16 #4433) >> Best rule #58 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0f4x7; 0gq9h; >> query: (?x198, 0209hj) <- award(?x144, ?x198), award(?x71, ?x198), nominated_for(?x198, ?x9572), ?x9572 = 025scjj >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3275 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 027dtxw; 03hkv_r; 02r22gf; 02n9nmz; 02pqp12; 0k611; 02qyntr; *> query: (?x198, 07cdz) <- award(?x144, ?x198), award(?x71, ?x198), nominated_for(?x198, ?x5578), ?x5578 = 0ddj0x *> conf = 0.11 ranks of expected_values: 342 EVAL 040njc award! 07cdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 45.000 26.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4877-09nwwf PRED entity: 09nwwf PRED relation: parent_genre! PRED expected values: 01738f => 66 concepts (41 used for prediction) PRED predicted values (max 10 best out of 281): 01ym9b (0.50 #838, 0.44 #1901, 0.40 #2432), 059kh (0.50 #841, 0.33 #1638, 0.27 #2701), 0g_bh (0.40 #1438, 0.40 #1172, 0.36 #2766), 0bt7w (0.40 #1420, 0.38 #4343, 0.33 #622), 016jhr (0.40 #1341, 0.33 #11, 0.27 #2935), 0xv2x (0.40 #1457, 0.33 #659, 0.23 #4380), 01gbcf (0.40 #1334, 0.33 #536, 0.23 #3992), 0xjl2 (0.40 #1369, 0.33 #571, 0.23 #4292), 01243b (0.40 #1367, 0.33 #569, 0.20 #1101), 01b4p4 (0.38 #4419, 0.16 #4953, 0.12 #5220) >> Best rule #838 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 064t9; 02x8m; >> query: (?x9013, 01ym9b) <- artists(?x9013, ?x9210), artists(?x9013, ?x7865), artists(?x9013, ?x6651), artists(?x9013, ?x5512), artists(?x9013, ?x5478), ?x5478 = 01yzl2, ?x7865 = 02k5sc, ?x6651 = 019f9z, ?x9210 = 03d2k, award(?x5512, ?x724) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #893 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 064t9; 02x8m; *> query: (?x9013, 01738f) <- artists(?x9013, ?x9210), artists(?x9013, ?x7865), artists(?x9013, ?x6651), artists(?x9013, ?x5512), artists(?x9013, ?x5478), ?x5478 = 01yzl2, ?x7865 = 02k5sc, ?x6651 = 019f9z, ?x9210 = 03d2k, award(?x5512, ?x724) *> conf = 0.25 ranks of expected_values: 39 EVAL 09nwwf parent_genre! 01738f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 66.000 41.000 0.500 http://example.org/music/genre/parent_genre #4876-08j7lh PRED entity: 08j7lh PRED relation: film_release_region PRED expected values: 09c7w0 0d0vqn 035qy 06mkj => 136 concepts (101 used for prediction) PRED predicted values (max 10 best out of 161): 0d0vqn (0.94 #2498, 0.92 #6644, 0.89 #4983), 09c7w0 (0.94 #10777, 0.94 #10444, 0.93 #15762), 03gj2 (0.92 #1689, 0.89 #2352, 0.89 #2850), 035qy (0.92 #3026, 0.91 #2861, 0.90 #2696), 03h64 (0.91 #3394, 0.89 #2067, 0.89 #1736), 0345h (0.91 #1367, 0.88 #2694, 0.87 #3189), 05r4w (0.90 #4978, 0.90 #2989, 0.89 #2824), 06mkj (0.89 #5040, 0.88 #3713, 0.87 #3216), 0chghy (0.88 #2669, 0.87 #4988, 0.87 #3164), 06bnz (0.84 #2709, 0.78 #3039, 0.78 #2874) >> Best rule #2498 for best value: >> intensional similarity = 7 >> extensional distance = 47 >> proper extension: 02d44q; 07k2mq; >> query: (?x9216, 0d0vqn) <- film_release_region(?x9216, ?x279), film_release_region(?x9216, ?x142), nominated_for(?x5923, ?x9216), award_winner(?x9216, ?x8262), titles(?x2645, ?x9216), ?x279 = 0d060g, ?x142 = 0jgd >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 8 EVAL 08j7lh film_release_region 06mkj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 136.000 101.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08j7lh film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 101.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08j7lh film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 101.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 08j7lh film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 101.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4875-05d1dy PRED entity: 05d1dy PRED relation: profession PRED expected values: 02krf9 => 122 concepts (81 used for prediction) PRED predicted values (max 10 best out of 54): 01d_h8 (0.74 #3656, 0.73 #590, 0.68 #2342), 025352 (0.67 #203, 0.40 #57, 0.17 #1079), 018gz8 (0.54 #2934, 0.48 #744, 0.33 #160), 01c72t (0.50 #167, 0.43 #1043, 0.20 #21), 02krf9 (0.49 #608, 0.37 #2798, 0.33 #170), 0np9r (0.40 #18, 0.33 #164, 0.26 #748), 09jwl (0.40 #1038, 0.19 #11706, 0.18 #3958), 0nbcg (0.36 #1051, 0.17 #175, 0.16 #3971), 0cbd2 (0.33 #153, 0.29 #883, 0.29 #1613), 016z4k (0.25 #1026, 0.17 #150, 0.11 #11694) >> Best rule #3656 for best value: >> intensional similarity = 4 >> extensional distance = 661 >> proper extension: 04wqr; 09byk; 01q7cb_; 02knnd; 01wjrn; 02zyy4; 049k07; 01pnn3; 02v406; 039crh; ... >> query: (?x6794, 01d_h8) <- film(?x6794, ?x3201), profession(?x6794, ?x1041), profession(?x7324, ?x1041), ?x7324 = 06q8hf >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #608 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 129 *> proper extension: 06w58f; *> query: (?x6794, 02krf9) <- nominated_for(?x6794, ?x3201), profession(?x6794, ?x1041), profession(?x6794, ?x524), ?x524 = 02jknp, ?x1041 = 03gjzk *> conf = 0.49 ranks of expected_values: 5 EVAL 05d1dy profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 122.000 81.000 0.741 http://example.org/people/person/profession #4874-03_6y PRED entity: 03_6y PRED relation: nationality PRED expected values: 09c7w0 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.86 #498, 0.83 #4072, 0.83 #1394), 02jx1 (0.14 #2317, 0.11 #2914, 0.11 #5693), 03rk0 (0.11 #841, 0.08 #5508, 0.08 #5805), 07ssc (0.10 #611, 0.09 #2299, 0.09 #6072), 03h64 (0.06 #52), 03rt9 (0.06 #111, 0.04 #210, 0.02 #5375), 0j5g9 (0.06 #160, 0.02 #1256, 0.01 #458), 0b90_r (0.04 #201), 0f8l9c (0.03 #2306, 0.02 #6079, 0.02 #4391), 0345h (0.03 #627, 0.03 #1522, 0.03 #1324) >> Best rule #498 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 05m63c; 03rl84; 02jg92; 01vhb0; 02t__3; 0gs6vr; 0l786; 017yxq; 01ccr8; 031sg0; ... >> query: (?x3466, 09c7w0) <- location(?x3466, ?x1036), nationality(?x3466, ?x279), participant(?x3466, ?x1672) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_6y nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.862 http://example.org/people/person/nationality #4873-052q4j PRED entity: 052q4j PRED relation: school_type! PRED expected values: 027kp3 => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 700): 04jr87 (0.60 #2362, 0.19 #1760, 0.16 #1761), 037njl (0.57 #1947, 0.51 #1178, 0.38 #1779), 027xx3 (0.56 #4180, 0.56 #4179, 0.51 #1176), 01wv24 (0.56 #4180, 0.56 #4179, 0.51 #1176), 02sjgpq (0.51 #1176, 0.51 #1178, 0.43 #2363), 012mzw (0.51 #1176, 0.51 #1178, 0.43 #2363), 02bhj4 (0.51 #1176, 0.51 #1178, 0.43 #2363), 02nvg1 (0.51 #1176, 0.51 #1178, 0.43 #2363), 026ssfj (0.51 #1176, 0.51 #1178, 0.43 #2363), 04sylm (0.51 #1176, 0.51 #1178, 0.43 #2363) >> Best rule #2362 for best value: >> intensional similarity = 71 >> extensional distance = 5 >> proper extension: 06cs1; >> query: (?x11094, ?x5994) <- school_type(?x11093, ?x11094), school_type(?x11093, ?x3205), institution(?x3437, ?x11093), contains(?x6959, ?x11093), school_type(?x14216, ?x3205), school_type(?x13148, ?x3205), school_type(?x12692, ?x3205), school_type(?x12260, ?x3205), school_type(?x11502, ?x3205), school_type(?x11349, ?x3205), school_type(?x11102, ?x3205), school_type(?x10576, ?x3205), school_type(?x10421, ?x3205), school_type(?x10175, ?x3205), school_type(?x9827, ?x3205), school_type(?x9803, ?x3205), school_type(?x8565, ?x3205), school_type(?x8216, ?x3205), school_type(?x7816, ?x3205), school_type(?x7596, ?x3205), school_type(?x7278, ?x3205), school_type(?x7065, ?x3205), school_type(?x6912, ?x3205), school_type(?x5733, ?x3205), school_type(?x5702, ?x3205), school_type(?x5178, ?x3205), school_type(?x4293, ?x3205), school_type(?x3576, ?x3205), school_type(?x2980, ?x3205), school_type(?x2767, ?x3205), school_type(?x1520, ?x3205), school_type(?x1513, ?x3205), ?x14216 = 022r38, location(?x914, ?x6959), vacationer(?x6959, ?x444), ?x13148 = 03hvk2, place_of_birth(?x9099, ?x6959), ?x2767 = 04sylm, ?x8565 = 05q2c, ?x7596 = 012mzw, ?x10576 = 0g2jl, contains(?x6959, ?x5994), ?x2980 = 02q636, ?x9803 = 02h659, ?x11102 = 03_fmr, contains(?x11886, ?x6959), ?x1513 = 017d77, ?x5702 = 05cwl_, category(?x6959, ?x134), ?x9827 = 05bnq8, ?x4293 = 02rg_4, ?x7816 = 015y3j, ?x134 = 08mbj5d, ?x5733 = 03zj9, location_of_ceremony(?x566, ?x6959), ?x5178 = 02bq1j, ?x8216 = 01rgn3, locations(?x6464, ?x6959), ?x7278 = 02sjgpq, ?x10175 = 03fgm, ?x12692 = 032d52, ?x11502 = 0l0wv, ?x7065 = 04ycjk, ?x3437 = 02_xgp2, ?x1520 = 07lx1s, ?x12260 = 030w19, ?x10421 = 02qw_v, organization(?x5510, ?x11093), ?x6912 = 0gl5_, ?x3576 = 012fvq, ?x11349 = 01p7x7 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3154 for first EXPECTED value: *> intensional similarity = 69 *> extensional distance = 7 *> proper extension: 01jlsn; *> query: (?x11094, 027kp3) <- school_type(?x11093, ?x11094), school_type(?x11093, ?x3205), institution(?x3437, ?x11093), contains(?x6959, ?x11093), contains(?x205, ?x11093), school_type(?x14216, ?x3205), school_type(?x13089, ?x3205), school_type(?x11349, ?x3205), school_type(?x10576, ?x3205), school_type(?x10421, ?x3205), school_type(?x9911, ?x3205), school_type(?x9827, ?x3205), school_type(?x9803, ?x3205), school_type(?x9718, ?x3205), school_type(?x8216, ?x3205), school_type(?x7900, ?x3205), school_type(?x7787, ?x3205), school_type(?x7596, ?x3205), school_type(?x7418, ?x3205), school_type(?x7278, ?x3205), school_type(?x7202, ?x3205), school_type(?x5178, ?x3205), school_type(?x3576, ?x3205), school_type(?x3182, ?x3205), school_type(?x3178, ?x3205), school_type(?x2767, ?x3205), school_type(?x1520, ?x3205), school_type(?x1513, ?x3205), ?x14216 = 022r38, location(?x914, ?x6959), vacationer(?x6959, ?x444), month(?x6959, ?x4925), month(?x6959, ?x3107), month(?x6959, ?x1650), ?x1520 = 07lx1s, major_field_of_study(?x11093, ?x8221), ?x9718 = 06l32y, ?x7787 = 01n951, ?x7278 = 02sjgpq, place_of_birth(?x9099, ?x6959), featured_film_locations(?x11686, ?x6959), ?x3107 = 05lf_, ?x1513 = 017d77, ?x11686 = 04180vy, ?x13089 = 043q2z, ?x10576 = 0g2jl, ?x1650 = 06vkl, organization(?x5510, ?x11093), ?x7202 = 02bhj4, ?x9803 = 02h659, ?x10421 = 02qw_v, ?x11349 = 01p7x7, location_of_ceremony(?x8556, ?x6959), ?x9827 = 05bnq8, ?x7596 = 012mzw, ?x5178 = 02bq1j, ?x4925 = 0ll3, ?x3182 = 02ccqg, ?x7418 = 03cz83, ?x9911 = 02yr1q, ?x2767 = 04sylm, ?x8216 = 01rgn3, ?x3576 = 012fvq, film_release_region(?x8682, ?x205), ?x3178 = 01vc5m, adjoins(?x774, ?x205), featured_film_locations(?x787, ?x205), ?x8682 = 0bmfnjs, ?x7900 = 02nvg1 *> conf = 0.22 ranks of expected_values: 202 EVAL 052q4j school_type! 027kp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 12.000 12.000 0.600 http://example.org/education/educational_institution/school_type #4872-04glx0 PRED entity: 04glx0 PRED relation: nominated_for! PRED expected values: 0bdwqv => 95 concepts (94 used for prediction) PRED predicted values (max 10 best out of 180): 0m7yy (0.70 #6757, 0.68 #6273, 0.68 #6515), 0gqy2 (0.38 #1331, 0.23 #10498, 0.23 #10741), 0gq9h (0.37 #10437, 0.36 #10680, 0.33 #10922), 027gs1_ (0.35 #1879, 0.33 #431, 0.26 #6463), 0cjyzs (0.35 #1772, 0.25 #3220, 0.25 #1289), 09qs08 (0.33 #352, 0.30 #1800, 0.25 #1317), 09qvf4 (0.33 #391, 0.30 #1839, 0.20 #3287), 09qv3c (0.33 #283, 0.25 #1731, 0.25 #1248), 047byns (0.33 #285, 0.14 #526, 0.12 #13277), 0gkvb7 (0.33 #265, 0.14 #506, 0.12 #13277) >> Best rule #6757 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 01b9w3; 0dl6fv; 05sy0cv; 0sw0q; 06zsk51; 06qv_; 024hbv; >> query: (?x6590, ?x3486) <- genre(?x6590, ?x9083), nominated_for(?x2033, ?x6590), award(?x6590, ?x3486) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #18826 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1588 *> proper extension: 0dckvs; 05c26ss; 02dpl9; *> query: (?x6590, ?x451) <- nominated_for(?x2033, ?x6590), award(?x2033, ?x451) *> conf = 0.19 ranks of expected_values: 37 EVAL 04glx0 nominated_for! 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 95.000 94.000 0.697 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4871-04gxp2 PRED entity: 04gxp2 PRED relation: campuses PRED expected values: 04gxp2 => 78 concepts (46 used for prediction) PRED predicted values (max 10 best out of 53): 015fs3 (0.12 #417, 0.03 #963), 02w2bc (0.12 #10, 0.03 #556), 04gxp2 (0.10 #2185, 0.07 #7104, 0.06 #6556), 07szy (0.10 #2185, 0.07 #7104, 0.06 #6556), 02vkzcx (0.01 #1635), 02bf58 (0.01 #1632), 0ch280 (0.01 #1628), 01nhgd (0.01 #1621), 07wm6 (0.01 #1591), 03b8c4 (0.01 #1587) >> Best rule #417 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02grjf; >> query: (?x13215, 015fs3) <- contains(?x1906, ?x13215), school_type(?x13215, ?x3092), institution(?x1519, ?x13215), ?x1906 = 04rrx >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #2185 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 102 *> proper extension: 02lwv5; *> query: (?x13215, ?x1681) <- contains(?x1106, ?x13215), county(?x1106, ?x9751), contains(?x1106, ?x1681), currency(?x13215, ?x170) *> conf = 0.10 ranks of expected_values: 3 EVAL 04gxp2 campuses 04gxp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 46.000 0.125 http://example.org/education/educational_institution/campuses #4870-04sry PRED entity: 04sry PRED relation: influenced_by PRED expected values: 016bx2 => 125 concepts (81 used for prediction) PRED predicted values (max 10 best out of 78): 014z8v (0.07 #3182, 0.06 #559, 0.05 #996), 0p_47 (0.06 #545, 0.05 #982, 0.04 #1419), 063_t (0.06 #724, 0.05 #1161, 0.04 #1598), 012gq6 (0.06 #534, 0.05 #971, 0.04 #1408), 01svq8 (0.06 #864, 0.05 #1301, 0.04 #1738), 013tjc (0.06 #815, 0.05 #1252, 0.04 #1689), 029_3 (0.06 #556, 0.05 #993, 0.04 #1430), 02pb53 (0.06 #478, 0.05 #915, 0.04 #1352), 01hmk9 (0.05 #2845, 0.05 #4157, 0.04 #3282), 01wp_jm (0.04 #4278, 0.04 #2966, 0.03 #3403) >> Best rule #3182 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 05r5w; 02v2jy; >> query: (?x7310, 014z8v) <- award_winner(?x198, ?x7310), gender(?x7310, ?x231), producer_type(?x7310, ?x632) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04sry influenced_by 016bx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 81.000 0.066 http://example.org/influence/influence_node/influenced_by #4869-026cl_m PRED entity: 026cl_m PRED relation: list! PRED expected values: 0grwj 0j1yf => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 365): 02ktt7 (0.60 #547, 0.50 #1096, 0.50 #273), 07gyp7 (0.60 #544, 0.50 #1093, 0.50 #270), 0dq23 (0.60 #536, 0.50 #1085, 0.50 #262), 0hkqn (0.60 #532, 0.50 #1081, 0.50 #258), 0lwkh (0.60 #530, 0.50 #1079, 0.50 #256), 03s7h (0.60 #518, 0.50 #1067, 0.50 #244), 0k9ts (0.60 #493, 0.50 #1042, 0.50 #219), 01dfb6 (0.60 #492, 0.50 #1041, 0.50 #218), 035nm (0.60 #487, 0.50 #1036, 0.50 #213), 04sv4 (0.60 #486, 0.50 #1035, 0.50 #212) >> Best rule #547 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 04k4rt; >> query: (?x5160, 02ktt7) <- list(?x3281, ?x5160), award_nominee(?x3281, ?x230) >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026cl_m list! 0j1yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 026cl_m list! 0grwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.600 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #4868-064nh4k PRED entity: 064nh4k PRED relation: gender PRED expected values: 02zsn => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.71 #7, 0.71 #155, 0.71 #163), 02zsn (0.53 #104, 0.53 #93, 0.43 #2) >> Best rule #7 for best value: >> intensional similarity = 1 >> extensional distance = 187 >> proper extension: 03pvt; 034ls; 0466k4; 0qkj7; >> query: (?x823, 05zppz) <- person(?x5639, ?x823) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #104 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1503 *> proper extension: 0khth; 02c0mv; 05g7q; *> query: (?x823, ?x231) <- award_winner(?x823, ?x2359), gender(?x2359, ?x231) *> conf = 0.53 ranks of expected_values: 2 EVAL 064nh4k gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.714 http://example.org/people/person/gender #4867-02ppm4q PRED entity: 02ppm4q PRED relation: award! PRED expected values: 0y_pg => 49 concepts (23 used for prediction) PRED predicted values (max 10 best out of 896): 0_b9f (0.40 #2479, 0.33 #471, 0.08 #8511), 09p3_s (0.40 #2559, 0.33 #551, 0.06 #5574), 0286gm1 (0.40 #2641, 0.33 #633, 0.04 #10685), 0m313 (0.33 #6, 0.27 #21118, 0.27 #21119), 01cmp9 (0.33 #603, 0.27 #21118, 0.27 #21119), 0b1y_2 (0.33 #284, 0.27 #21118, 0.27 #21119), 0y_pg (0.33 #784, 0.27 #21118, 0.27 #21119), 016z43 (0.33 #993, 0.27 #21118, 0.27 #21119), 0sxns (0.33 #620, 0.27 #21118, 0.27 #21119), 04t9c0 (0.33 #538, 0.27 #21118, 0.27 #21119) >> Best rule #2479 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 02z1nbg; 04fgkf_; >> query: (?x2880, 0_b9f) <- award_winner(?x2880, ?x8081), award_winner(?x2880, ?x3183), nominated_for(?x2880, ?x86), ?x3183 = 0fb1q, award(?x156, ?x2880), award(?x8081, ?x537) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #784 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 0gqyl; *> query: (?x2880, 0y_pg) <- award_winner(?x2880, ?x8081), award_winner(?x2880, ?x3183), nominated_for(?x2880, ?x86), ?x3183 = 0fb1q, award(?x156, ?x2880), ?x8081 = 02l3_5 *> conf = 0.33 ranks of expected_values: 7 EVAL 02ppm4q award! 0y_pg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 49.000 23.000 0.400 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4866-0f4k5 PRED entity: 0f4k5 PRED relation: nutrient! PRED expected values: 0f25w9 => 30 concepts (23 used for prediction) PRED predicted values (max 10 best out of 22): 0fjfh (0.89 #563, 0.89 #549, 0.89 #662), 0fbdb (0.89 #567, 0.89 #553, 0.89 #660), 0f25w9 (0.89 #514, 0.88 #339, 0.88 #323), 0fj52s (0.89 #665, 0.89 #636, 0.88 #418), 014j1m (0.89 #644, 0.88 #444, 0.88 #426), 061_f (0.88 #595, 0.88 #576, 0.86 #656), 0hkxq (0.88 #435, 0.88 #420, 0.87 #317), 037ls6 (0.87 #314, 0.87 #304, 0.86 #165), 07j87 (0.84 #658, 0.84 #649, 0.84 #320), 09728 (0.84 #320, 0.84 #594, 0.84 #571) >> Best rule #563 for best value: >> intensional similarity = 135 >> extensional distance = 36 >> proper extension: 0hqw8p_; >> query: (?x14210, ?x5009) <- nutrient(?x10612, ?x14210), nutrient(?x9732, ?x14210), nutrient(?x9005, ?x14210), nutrient(?x6159, ?x14210), nutrient(?x6032, ?x14210), nutrient(?x5373, ?x14210), nutrient(?x3468, ?x14210), ?x6159 = 033cnk, nutrient(?x10612, ?x13545), nutrient(?x10612, ?x13126), nutrient(?x10612, ?x12902), nutrient(?x10612, ?x12454), nutrient(?x10612, ?x12336), nutrient(?x10612, ?x12083), nutrient(?x10612, ?x11758), nutrient(?x10612, ?x11592), nutrient(?x10612, ?x10891), nutrient(?x10612, ?x10098), nutrient(?x10612, ?x9949), nutrient(?x10612, ?x9840), nutrient(?x10612, ?x9795), nutrient(?x10612, ?x9733), nutrient(?x10612, ?x9619), nutrient(?x10612, ?x9490), nutrient(?x10612, ?x9436), nutrient(?x10612, ?x9426), nutrient(?x10612, ?x9365), nutrient(?x10612, ?x8487), nutrient(?x10612, ?x8442), nutrient(?x10612, ?x8413), nutrient(?x10612, ?x7894), nutrient(?x10612, ?x7720), nutrient(?x10612, ?x7652), nutrient(?x10612, ?x7431), nutrient(?x10612, ?x7364), nutrient(?x10612, ?x7219), nutrient(?x10612, ?x7135), nutrient(?x10612, ?x6586), nutrient(?x10612, ?x6517), nutrient(?x10612, ?x6192), nutrient(?x10612, ?x6160), nutrient(?x10612, ?x6026), nutrient(?x10612, ?x5549), nutrient(?x10612, ?x5526), nutrient(?x10612, ?x5451), nutrient(?x10612, ?x5010), nutrient(?x10612, ?x3469), nutrient(?x10612, ?x3203), nutrient(?x10612, ?x2702), nutrient(?x10612, ?x1960), nutrient(?x10612, ?x1304), nutrient(?x10612, ?x1258), ?x7894 = 0f4hc, ?x5010 = 0h1vz, ?x9365 = 04k8n, ?x13126 = 02kc_w5, ?x7431 = 09gwd, ?x6032 = 01nkt, ?x5451 = 05wvs, ?x12336 = 0f4l5, ?x13545 = 01w_3, ?x9733 = 0h1tz, ?x8413 = 02kc4sf, ?x11592 = 025sf0_, ?x1258 = 0h1wg, ?x9840 = 02p0tjr, nutrient(?x9732, ?x14698), nutrient(?x9732, ?x11409), nutrient(?x9732, ?x6033), nutrient(?x9732, ?x5374), nutrient(?x9732, ?x2018), ?x2018 = 01sh2, ?x3469 = 0h1zw, ?x7219 = 0h1vg, ?x6026 = 025sf8g, ?x5549 = 025s7j4, ?x8487 = 014yzm, ?x9490 = 0h1sg, ?x8442 = 02kcv4x, ?x9005 = 04zpv, ?x9436 = 025sqz8, ?x14698 = 02kb_jm, nutrient(?x9489, ?x10891), nutrient(?x8298, ?x10891), nutrient(?x7057, ?x10891), nutrient(?x6285, ?x10891), nutrient(?x6191, ?x10891), nutrient(?x5009, ?x10891), nutrient(?x4068, ?x10891), nutrient(?x3900, ?x10891), nutrient(?x2701, ?x10891), nutrient(?x1303, ?x10891), nutrient(?x1257, ?x10891), ?x5374 = 025s0zp, ?x11758 = 0q01m, ?x7135 = 025rsfk, ?x6033 = 04zjxcz, ?x9795 = 05v_8y, ?x2702 = 0838f, ?x12083 = 01n78x, ?x3900 = 061_f, ?x7057 = 0fbdb, ?x5526 = 09pbb, ?x6160 = 041r51, ?x3468 = 0cxn2, ?x2701 = 0hkxq, ?x8298 = 037ls6, ?x10098 = 0h1_c, ?x1257 = 09728, ?x5009 = 0fjfh, ?x1303 = 0fj52s, ?x6586 = 05gh50, ?x6285 = 01645p, nutrient(?x7719, ?x12902), ?x6517 = 02kd8zw, ?x1960 = 07hnp, ?x1304 = 08lb68, ?x6191 = 014j1m, nutrient(?x5373, ?x10453), nutrient(?x5373, ?x8243), ?x7720 = 025s7x6, ?x12454 = 025rw19, ?x7364 = 09gvd, ?x3203 = 04kl74p, ?x9949 = 02kd0rh, ?x11409 = 0h1yf, ?x7652 = 025s0s0, ?x9619 = 0h1tg, ?x9426 = 0h1yy, ?x9489 = 07j87, ?x10453 = 075pwf, ?x6192 = 06jry, ?x7719 = 0dj75, ?x8243 = 014d7f, ?x4068 = 0fbw6 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #514 for first EXPECTED value: *> intensional similarity = 138 *> extensional distance = 35 *> proper extension: 04kl74p; 02p0tjr; *> query: (?x14210, 0f25w9) <- nutrient(?x10612, ?x14210), nutrient(?x9732, ?x14210), nutrient(?x9005, ?x14210), nutrient(?x6159, ?x14210), nutrient(?x6032, ?x14210), nutrient(?x5373, ?x14210), nutrient(?x3468, ?x14210), ?x6159 = 033cnk, ?x10612 = 0frq6, ?x3468 = 0cxn2, ?x9005 = 04zpv, nutrient(?x5373, ?x13944), nutrient(?x5373, ?x13545), nutrient(?x5373, ?x13126), nutrient(?x5373, ?x12454), nutrient(?x5373, ?x12083), nutrient(?x5373, ?x11758), nutrient(?x5373, ?x11592), nutrient(?x5373, ?x11409), nutrient(?x5373, ?x11270), nutrient(?x5373, ?x10709), nutrient(?x5373, ?x10098), nutrient(?x5373, ?x9795), nutrient(?x5373, ?x9733), nutrient(?x5373, ?x9619), nutrient(?x5373, ?x9490), nutrient(?x5373, ?x9436), nutrient(?x5373, ?x9426), nutrient(?x5373, ?x9365), nutrient(?x5373, ?x8487), nutrient(?x5373, ?x8442), nutrient(?x5373, ?x8413), nutrient(?x5373, ?x8243), nutrient(?x5373, ?x7894), nutrient(?x5373, ?x7720), nutrient(?x5373, ?x7652), nutrient(?x5373, ?x7431), nutrient(?x5373, ?x7364), nutrient(?x5373, ?x7362), nutrient(?x5373, ?x7219), nutrient(?x5373, ?x7135), nutrient(?x5373, ?x6517), nutrient(?x5373, ?x6192), nutrient(?x5373, ?x6160), nutrient(?x5373, ?x6033), nutrient(?x5373, ?x6026), nutrient(?x5373, ?x5549), nutrient(?x5373, ?x5526), nutrient(?x5373, ?x5451), nutrient(?x5373, ?x5374), nutrient(?x5373, ?x5010), nutrient(?x5373, ?x3469), nutrient(?x5373, ?x1960), nutrient(?x5373, ?x1304), nutrient(?x5373, ?x1258), ?x8413 = 02kc4sf, ?x11409 = 0h1yf, ?x7894 = 0f4hc, ?x9436 = 025sqz8, ?x8442 = 02kcv4x, ?x5526 = 09pbb, ?x12083 = 01n78x, ?x6517 = 02kd8zw, ?x9733 = 0h1tz, ?x12454 = 025rw19, ?x6160 = 041r51, nutrient(?x9489, ?x13944), nutrient(?x8298, ?x13944), nutrient(?x7719, ?x13944), nutrient(?x7057, ?x13944), nutrient(?x6285, ?x13944), nutrient(?x6191, ?x13944), nutrient(?x5009, ?x13944), nutrient(?x4068, ?x13944), nutrient(?x3900, ?x13944), nutrient(?x2701, ?x13944), nutrient(?x1303, ?x13944), nutrient(?x1257, ?x13944), ?x7719 = 0dj75, ?x9795 = 05v_8y, ?x7219 = 0h1vg, ?x7057 = 0fbdb, ?x6032 = 01nkt, ?x7720 = 025s7x6, ?x5010 = 0h1vz, ?x4068 = 0fbw6, ?x7364 = 09gvd, ?x11758 = 0q01m, ?x13126 = 02kc_w5, ?x6191 = 014j1m, ?x6026 = 025sf8g, ?x7362 = 02kc5rj, ?x8487 = 014yzm, ?x5374 = 025s0zp, ?x6192 = 06jry, ?x1257 = 09728, ?x7431 = 09gwd, nutrient(?x9732, ?x12336), nutrient(?x9732, ?x10891), nutrient(?x9732, ?x9949), nutrient(?x9732, ?x6586), nutrient(?x9732, ?x2702), nutrient(?x9732, ?x2018), ?x3469 = 0h1zw, ?x6285 = 01645p, ?x3900 = 061_f, ?x8298 = 037ls6, ?x1303 = 0fj52s, ?x9490 = 0h1sg, ?x9619 = 0h1tg, ?x12336 = 0f4l5, ?x2702 = 0838f, ?x6586 = 05gh50, ?x10891 = 0g5gq, ?x5451 = 05wvs, ?x1304 = 08lb68, ?x2018 = 01sh2, ?x11592 = 025sf0_, ?x11270 = 02kc008, ?x6033 = 04zjxcz, ?x13545 = 01w_3, ?x1258 = 0h1wg, ?x7652 = 025s0s0, nutrient(?x5337, ?x8243), nutrient(?x3264, ?x8243), ?x9365 = 04k8n, ?x10709 = 0h1sz, ?x5549 = 025s7j4, ?x9426 = 0h1yy, ?x5009 = 0fjfh, ?x1960 = 07hnp, ?x5337 = 06x4c, ?x3264 = 0dcfv, ?x7135 = 025rsfk, ?x2701 = 0hkxq, ?x9489 = 07j87, ?x9949 = 02kd0rh, ?x10098 = 0h1_c *> conf = 0.89 ranks of expected_values: 3 EVAL 0f4k5 nutrient! 0f25w9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 30.000 23.000 0.895 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #4865-02h2z_ PRED entity: 02h2z_ PRED relation: entity_involved PRED expected values: 06v9sf => 56 concepts (35 used for prediction) PRED predicted values (max 10 best out of 205): 09b6zr (0.40 #353, 0.33 #672, 0.29 #832), 079dy (0.40 #434, 0.33 #753, 0.29 #913), 0285m87 (0.40 #240, 0.24 #1356, 0.23 #1197), 09c7w0 (0.36 #1588, 0.36 #1587, 0.35 #2225), 0chghy (0.36 #1588, 0.36 #1587, 0.35 #2225), 0c4b8 (0.36 #1588, 0.36 #1587, 0.35 #2225), 088q1s (0.36 #1588, 0.36 #1587, 0.35 #2225), 059z0 (0.36 #1588, 0.36 #1587, 0.35 #2225), 07ssc (0.36 #1588, 0.36 #1587, 0.35 #2225), 0ctw_b (0.36 #1588, 0.36 #1587, 0.35 #2225) >> Best rule #353 for best value: >> intensional similarity = 10 >> extensional distance = 3 >> proper extension: 0d06vc; 018w0j; 01cpp0; >> query: (?x12789, 09b6zr) <- combatants(?x12789, ?x512), combatants(?x12789, ?x390), combatants(?x12789, ?x94), ?x512 = 07ssc, entity_involved(?x12789, ?x2663), ?x390 = 0chghy, ?x94 = 09c7w0, locations(?x12789, ?x1273), olympics(?x1273, ?x778), country(?x471, ?x1273) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #4945 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 36 *> proper extension: 022840; 01y998; 053_7s; 075k5; 03w6sj; 0j5ym; 0chhs; *> query: (?x12789, 06v9sf) <- combatants(?x12789, ?x512), film_release_region(?x2394, ?x512), film_release_region(?x424, ?x512), nationality(?x111, ?x512), ?x424 = 0dtw1x, first_level_division_of(?x1310, ?x512), country(?x136, ?x512), ?x2394 = 0661ql3, country(?x150, ?x512) *> conf = 0.11 ranks of expected_values: 67 EVAL 02h2z_ entity_involved 06v9sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 56.000 35.000 0.400 http://example.org/base/culturalevent/event/entity_involved #4864-03qcfvw PRED entity: 03qcfvw PRED relation: prequel! PRED expected values: 0gwlfnb => 95 concepts (45 used for prediction) PRED predicted values (max 10 best out of 75): 0661m4p (0.03 #950, 0.02 #48, 0.02 #588), 05qbckf (0.02 #43, 0.02 #223, 0.02 #403), 0bpm4yw (0.02 #74, 0.02 #434, 0.02 #614), 03r0g9 (0.02 #67, 0.02 #607, 0.01 #789), 03177r (0.02 #54, 0.02 #594, 0.01 #776), 048yqf (0.02 #160, 0.02 #700), 06x43v (0.02 #128, 0.02 #668), 031786 (0.02 #125, 0.02 #665), 02wgk1 (0.02 #79, 0.02 #619), 0btyf5z (0.02 #42, 0.02 #582) >> Best rule #950 for best value: >> intensional similarity = 4 >> extensional distance = 75 >> proper extension: 01dyvs; 01kf3_9; 0fvr1; 0jdgr; 07b1gq; 0g9yrw; 070g7; 02x8fs; 09fc83; 063y9fp; ... >> query: (?x103, 0661m4p) <- genre(?x103, ?x1013), film(?x1017, ?x103), ?x1013 = 06n90, executive_produced_by(?x103, ?x4946) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03qcfvw prequel! 0gwlfnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 45.000 0.026 http://example.org/film/film/prequel #4863-08xvpn PRED entity: 08xvpn PRED relation: nominated_for! PRED expected values: 019f4v 0gs96 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 204): 0gs9p (0.68 #1438, 0.32 #748, 0.26 #3278), 019f4v (0.59 #1431, 0.42 #741, 0.29 #971), 0k611 (0.55 #1446, 0.32 #756, 0.27 #3286), 040njc (0.45 #1387, 0.28 #697, 0.21 #467), 04dn09n (0.44 #1413, 0.21 #493, 0.21 #3253), 0f4x7 (0.42 #1404, 0.34 #714, 0.22 #2094), 0gqy2 (0.39 #1495, 0.23 #2185, 0.23 #2415), 0gs96 (0.39 #772, 0.26 #542, 0.25 #1462), 02pqp12 (0.35 #1435, 0.20 #14726, 0.20 #16337), 02qyntr (0.33 #1552, 0.24 #862, 0.21 #632) >> Best rule #1438 for best value: >> intensional similarity = 3 >> extensional distance = 253 >> proper extension: 02rjv2w; 01c9d; >> query: (?x9801, 0gs9p) <- nominated_for(?x1307, ?x9801), award_winner(?x9801, ?x3183), ?x1307 = 0gq9h >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #1431 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 253 *> proper extension: 02rjv2w; 01c9d; *> query: (?x9801, 019f4v) <- nominated_for(?x1307, ?x9801), award_winner(?x9801, ?x3183), ?x1307 = 0gq9h *> conf = 0.59 ranks of expected_values: 2, 8 EVAL 08xvpn nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 85.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 08xvpn nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4862-0r4h3 PRED entity: 0r4h3 PRED relation: place! PRED expected values: 0r4h3 => 87 concepts (53 used for prediction) PRED predicted values (max 10 best out of 131): 0r03f (0.07 #360, 0.05 #875, 0.04 #1391), 0k9p4 (0.07 #254, 0.05 #769, 0.04 #1285), 0qymv (0.07 #293, 0.05 #808, 0.04 #1324), 0q_0z (0.07 #408, 0.05 #923, 0.04 #1439), 071vr (0.07 #176, 0.05 #691, 0.03 #2239), 0nbwf (0.07 #224, 0.05 #739, 0.03 #2287), 0r172 (0.07 #468, 0.02 #3047, 0.02 #4077), 0r62v (0.07 #17, 0.02 #2596, 0.02 #3626), 0kvt9 (0.06 #2579), 0r066 (0.05 #878, 0.04 #1394, 0.04 #1910) >> Best rule #360 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0gx1l; >> query: (?x12025, 0r03f) <- contains(?x1227, ?x12025), contains(?x94, ?x12025), ?x94 = 09c7w0, ?x1227 = 01n7q, origin(?x2946, ?x12025) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r4h3 place! 0r4h3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 53.000 0.071 http://example.org/location/hud_county_place/place #4861-017_pb PRED entity: 017_pb PRED relation: influenced_by PRED expected values: 053yx => 110 concepts (45 used for prediction) PRED predicted values (max 10 best out of 402): 03_87 (0.41 #1075, 0.18 #2382, 0.17 #6750), 032l1 (0.36 #962, 0.24 #2269, 0.18 #4894), 081k8 (0.36 #5833, 0.34 #6704, 0.18 #1029), 03f0324 (0.27 #1025, 0.13 #16590, 0.12 #12814), 084w8 (0.27 #875, 0.13 #16590, 0.08 #15722), 051cc (0.27 #9607, 0.25 #9606, 0.23 #6547), 03f3_p3 (0.25 #9606, 0.23 #6547, 0.22 #3490), 014z8v (0.24 #8419, 0.21 #7544, 0.20 #10606), 01hmk9 (0.23 #10705, 0.18 #7643, 0.14 #8518), 01tz6vs (0.23 #1049, 0.21 #9608, 0.13 #16590) >> Best rule #1075 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 043hg; >> query: (?x7513, 03_87) <- profession(?x7513, ?x353), award(?x7513, ?x11471), ?x11471 = 0g9wd99 >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #11428 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 126 *> proper extension: 0lhn5; 014_lq; 07r1_; 016ppr; *> query: (?x7513, 053yx) <- influenced_by(?x7513, ?x5366), award_winner(?x1107, ?x5366), award_winner(?x8015, ?x5366), award(?x5366, ?x198) *> conf = 0.02 ranks of expected_values: 288 EVAL 017_pb influenced_by 053yx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 110.000 45.000 0.409 http://example.org/influence/influence_node/influenced_by #4860-0m_v0 PRED entity: 0m_v0 PRED relation: artists! PRED expected values: 09n5t_ => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 257): 017_qw (0.51 #2239, 0.41 #3169, 0.41 #1928), 064t9 (0.50 #1256, 0.48 #4984, 0.42 #9646), 06j6l (0.47 #670, 0.29 #5019, 0.24 #9681), 0155w (0.37 #730, 0.21 #108, 0.19 #10254), 016clz (0.34 #1559, 0.27 #3733, 0.22 #7770), 02yv6b (0.32 #722, 0.19 #10254, 0.13 #100), 0glt670 (0.30 #5011, 0.22 #9673, 0.19 #6254), 08jyyk (0.29 #1622, 0.14 #1000, 0.12 #3796), 025sc50 (0.27 #5021, 0.21 #9683, 0.19 #6264), 03_d0 (0.25 #3739, 0.25 #943, 0.24 #11) >> Best rule #2239 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 012ljv; 07s3vqk; 03f2_rc; 01wl38s; 0146pg; 01vvycq; 01vrncs; 07c0j; 01kx_81; 03kwtb; ... >> query: (?x3442, 017_qw) <- award_winner(?x3442, ?x84), music(?x861, ?x3442), artists(?x378, ?x3442) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #836 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 60 *> proper extension: 05563d; *> query: (?x3442, 09n5t_) <- artists(?x378, ?x3442), ?x378 = 07sbbz2 *> conf = 0.08 ranks of expected_values: 51 EVAL 0m_v0 artists! 09n5t_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 130.000 130.000 0.506 http://example.org/music/genre/artists #4859-0mbf4 PRED entity: 0mbf4 PRED relation: adjoins PRED expected values: 0jpkg => 142 concepts (57 used for prediction) PRED predicted values (max 10 best out of 524): 0jpkg (0.85 #19358, 0.81 #41826, 0.81 #20132), 02dtg (0.25 #27, 0.07 #19386, 0.05 #18612), 0mbf4 (0.20 #1486, 0.17 #3811, 0.17 #3035), 05kr_ (0.20 #1653, 0.09 #3978, 0.09 #20133), 059rby (0.20 #1566, 0.09 #3891, 0.07 #18602), 02gt5s (0.20 #2162, 0.09 #4487, 0.06 #6811), 059f4 (0.20 #1583, 0.09 #3908, 0.05 #18619), 059t8 (0.20 #1976, 0.09 #4301, 0.04 #10492), 050ks (0.20 #1860, 0.09 #4185, 0.04 #10376), 07_f2 (0.20 #1875, 0.09 #4200, 0.04 #10391) >> Best rule #19358 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 017wh; 018f94; >> query: (?x13190, ?x13811) <- category(?x13190, ?x134), ?x134 = 08mbj5d, adjoins(?x13811, ?x13190), citytown(?x9880, ?x13811) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mbf4 adjoins 0jpkg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 57.000 0.852 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #4858-01qd_r PRED entity: 01qd_r PRED relation: school! PRED expected values: 092j54 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 19): 0f4vx0 (0.29 #68, 0.26 #201, 0.23 #106), 02qw1zx (0.22 #195, 0.16 #100, 0.16 #214), 03nt7j (0.19 #45, 0.14 #197, 0.14 #457), 09l0x9 (0.18 #69, 0.16 #202, 0.14 #457), 05vsb7 (0.17 #191, 0.16 #39, 0.14 #457), 09th87 (0.16 #53, 0.14 #457, 0.12 #205), 02pq_rp (0.16 #46, 0.14 #457, 0.10 #217), 02pq_x5 (0.16 #73, 0.15 #225, 0.14 #206), 025tn92 (0.15 #203, 0.14 #457, 0.14 #51), 092j54 (0.15 #199, 0.14 #457, 0.14 #47) >> Best rule #68 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0cv_2; 02z_b; >> query: (?x7660, 0f4vx0) <- state_province_region(?x7660, ?x2982), citytown(?x7660, ?x3987), organization(?x7660, ?x5487) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #199 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 102 *> proper extension: 06mkj; 0d05w3; *> query: (?x7660, 092j54) <- contains(?x94, ?x7660), school(?x8586, ?x7660) *> conf = 0.15 ranks of expected_values: 10 EVAL 01qd_r school! 092j54 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 102.000 102.000 0.289 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #4857-02zd2b PRED entity: 02zd2b PRED relation: institution! PRED expected values: 02h4rq6 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 20): 02h4rq6 (0.81 #152, 0.77 #110, 0.75 #66), 03bwzr4 (0.75 #121, 0.73 #163, 0.62 #548), 02_xgp2 (0.72 #161, 0.71 #546, 0.70 #119), 0bkj86 (0.60 #28, 0.52 #115, 0.47 #157), 016t_3 (0.59 #111, 0.53 #67, 0.52 #153), 04zx3q1 (0.32 #109, 0.31 #151, 0.30 #22), 013zdg (0.30 #114, 0.29 #70, 0.20 #156), 027f2w (0.30 #29, 0.28 #116, 0.24 #158), 022h5x (0.28 #1210, 0.24 #83, 0.22 #127), 01kxxq (0.28 #1210, 0.17 #1254, 0.16 #1163) >> Best rule #152 for best value: >> intensional similarity = 5 >> extensional distance = 158 >> proper extension: 02cttt; 019dwp; 08qnnv; 02z6fs; 019q50; 0lk0l; >> query: (?x5737, 02h4rq6) <- institution(?x11690, ?x5737), institution(?x1771, ?x5737), institution(?x11690, ?x7508), ?x7508 = 0m7yh, ?x1771 = 019v9k >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zd2b institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.806 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4856-0ksf29 PRED entity: 0ksf29 PRED relation: executive_produced_by! PRED expected values: 035gnh => 154 concepts (41 used for prediction) PRED predicted values (max 10 best out of 424): 0cw3yd (0.12 #154, 0.08 #684, 0.03 #1215), 0b6m5fy (0.12 #363, 0.05 #1061, 0.04 #5309), 025ts_z (0.12 #470, 0.03 #7372, 0.03 #8434), 0h1v19 (0.12 #149, 0.02 #4396, 0.02 #4927), 047svrl (0.12 #145, 0.02 #5454, 0.02 #5985), 0b76kw1 (0.12 #107, 0.01 #8071), 03ynwqj (0.12 #464), 0ft18 (0.12 #449), 04cppj (0.12 #372), 0cy__l (0.12 #315) >> Best rule #154 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01rzqj; >> query: (?x1714, 0cw3yd) <- executive_produced_by(?x8562, ?x1714), award(?x1714, ?x11230), nationality(?x1714, ?x279), ?x279 = 0d060g >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ksf29 executive_produced_by! 035gnh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 41.000 0.125 http://example.org/film/film/executive_produced_by #4855-010xjr PRED entity: 010xjr PRED relation: profession PRED expected values: 0cbd2 => 84 concepts (69 used for prediction) PRED predicted values (max 10 best out of 51): 01d_h8 (0.50 #4, 0.37 #588, 0.36 #734), 03gjzk (0.38 #12, 0.28 #596, 0.27 #304), 0dxtg (0.33 #11, 0.28 #8334, 0.28 #6728), 0dz3r (0.25 #1, 0.13 #731, 0.12 #2338), 0nbcg (0.21 #29, 0.13 #5432, 0.12 #5578), 01c72t (0.21 #21, 0.11 #1189, 0.09 #5424), 025352 (0.21 #57, 0.05 #1225, 0.03 #5606), 09jwl (0.20 #746, 0.18 #5419, 0.18 #5565), 0cbd2 (0.17 #5, 0.11 #2926, 0.11 #6284), 012t_z (0.17 #10, 0.04 #448, 0.03 #1324) >> Best rule #4 for best value: >> intensional similarity = 2 >> extensional distance = 22 >> proper extension: 03f7m4h; >> query: (?x9797, 01d_h8) <- profession(?x9797, ?x106), ?x106 = 05sxg2 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 22 *> proper extension: 03f7m4h; *> query: (?x9797, 0cbd2) <- profession(?x9797, ?x106), ?x106 = 05sxg2 *> conf = 0.17 ranks of expected_values: 9 EVAL 010xjr profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 84.000 69.000 0.500 http://example.org/people/person/profession #4854-09c7w0 PRED entity: 09c7w0 PRED relation: service_location! PRED expected values: 0p4wb 049dk 0l8sx 0cjdk 03mnk 01s73z 059yj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 371): 06_9lg (0.49 #758, 0.30 #1382, 0.09 #956), 03s7h (0.33 #23, 0.25 #279, 0.13 #647), 0p4wb (0.25 #314, 0.17 #286, 0.16 #569), 018mxj (0.24 #626, 0.23 #682, 0.23 #400), 04fv0k (0.17 #330, 0.17 #302, 0.15 #415), 08qnnv (0.10 #748, 0.08 #1372, 0.03 #946), 01m_zd (0.08 #337, 0.08 #422, 0.07 #450), 017vb_ (0.08 #755, 0.05 #1379, 0.05 #614), 01y81r (0.06 #743, 0.04 #1367, 0.03 #941), 02slt7 (0.04 #318, 0.04 #290, 0.03 #374) >> Best rule #758 for best value: >> intensional similarity = 2 >> extensional distance = 47 >> proper extension: 0ftxw; 01d88c; 09c6w; 02_n7; 027wvb; 04vmp; 029kpy; 01j922; 01_yvy; 02cb1j; ... >> query: (?x94, 06_9lg) <- place_of_birth(?x129, ?x94), service_location(?x127, ?x94) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #314 for first EXPECTED value: *> intensional similarity = 1 *> extensional distance = 22 *> proper extension: 09nm_; *> query: (?x94, 0p4wb) <- region(?x280, ?x94) *> conf = 0.25 ranks of expected_values: 3, 275, 312, 326, 345 EVAL 09c7w0 service_location! 059yj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 105.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 09c7w0 service_location! 01s73z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 105.000 105.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 09c7w0 service_location! 03mnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 105.000 105.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 09c7w0 service_location! 0cjdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 105.000 105.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 09c7w0 service_location! 0l8sx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 105.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 09c7w0 service_location! 049dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 105.000 105.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 09c7w0 service_location! 0p4wb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 105.000 0.490 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #4853-02xcb6n PRED entity: 02xcb6n PRED relation: award! PRED expected values: 04x4s2 09hd16 05y5fw 0697kh => 55 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2838): 04x4s2 (0.86 #3379, 0.85 #6757, 0.85 #3378), 05ldnp (0.86 #3379, 0.85 #6757, 0.85 #3378), 019pkm (0.86 #3379, 0.85 #6757, 0.85 #3378), 05strv (0.86 #3379, 0.85 #6757, 0.85 #3378), 04mx__ (0.86 #3379, 0.85 #6757, 0.85 #3378), 05cqhl (0.86 #3379, 0.85 #6757, 0.85 #3378), 03gm48 (0.50 #3605, 0.40 #6982, 0.19 #3377), 02tkzn (0.50 #5024, 0.40 #8401, 0.18 #27027), 01wbg84 (0.50 #3444, 0.40 #6821, 0.17 #30404), 0f6_x (0.50 #4389, 0.40 #7766, 0.17 #14522) >> Best rule #3379 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 03nqnk3; >> query: (?x8660, ?x3762) <- ceremony(?x8660, ?x2292), award_winner(?x8660, ?x9335), award_winner(?x8660, ?x3762), ?x9335 = 019pkm, profession(?x3762, ?x987), award_winner(?x2292, ?x965) >> conf = 0.86 => this is the best rule for 6 predicted values ranks of expected_values: 1, 170, 173, 2385 EVAL 02xcb6n award! 0697kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 25.000 0.855 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02xcb6n award! 05y5fw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 25.000 0.855 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02xcb6n award! 09hd16 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 25.000 0.855 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02xcb6n award! 04x4s2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 25.000 0.855 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4852-09_94 PRED entity: 09_94 PRED relation: sports! PRED expected values: 018ctl => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 30): 0blfl (0.88 #101, 0.86 #169, 0.85 #236), 0kbvv (0.77 #442, 0.77 #174, 0.77 #311), 0jdk_ (0.69 #599, 0.68 #668, 0.61 #968), 06sks6 (0.68 #901, 0.63 #666, 0.63 #966), 0kbvb (0.62 #582, 0.59 #951, 0.58 #651), 0sxrz (0.60 #863, 0.59 #747, 0.55 #1046), 0jhn7 (0.59 #969, 0.56 #1002, 0.56 #1069), 018ctl (0.56 #382, 0.51 #1148, 0.50 #350), 0l6m5 (0.55 #788, 0.53 #822, 0.53 #1019), 0nbjq (0.51 #862, 0.50 #292, 0.47 #309) >> Best rule #101 for best value: >> intensional similarity = 53 >> extensional distance = 3 >> proper extension: 09_b4; >> query: (?x2752, ?x418) <- sports(?x4424, ?x2752), sports(?x418, ?x2752), country(?x2752, ?x3277), country(?x2752, ?x1558), country(?x2752, ?x1471), country(?x2752, ?x774), country(?x2752, ?x279), sports(?x8584, ?x2752), ?x3277 = 06t8v, ?x1471 = 07t21, olympics(?x2752, ?x3110), ?x279 = 0d060g, ?x1558 = 01mjq, ?x774 = 06mzp, sports(?x8584, ?x520), participating_countries(?x4424, ?x7430), participating_countries(?x4424, ?x304), olympics(?x5114, ?x8584), ?x5114 = 05vz3zq, sports(?x4424, ?x453), combatants(?x9814, ?x7430), country(?x1557, ?x7430), ?x453 = 03tmr, ?x1557 = 07bs0, film_release_region(?x7204, ?x304), film_release_region(?x7126, ?x304), film_release_region(?x6556, ?x304), film_release_region(?x5721, ?x304), film_release_region(?x5425, ?x304), film_release_region(?x4643, ?x304), film_release_region(?x3287, ?x304), film_release_region(?x2961, ?x304), film_release_region(?x251, ?x304), film_release_region(?x86, ?x304), film_release_region(?x66, ?x304), ?x5425 = 02prwdh, ?x251 = 02vp1f_, ?x86 = 0ds35l9, ?x66 = 014lc_, ?x3287 = 026njb5, medal(?x7430, ?x1242), combatants(?x151, ?x7430), ?x6556 = 05dss7, ?x7126 = 0ds1glg, nationality(?x2083, ?x304), ?x7204 = 0280061, country(?x5168, ?x304), ?x2961 = 047p7fr, country(?x1009, ?x304), ?x5721 = 01d259, country(?x150, ?x304), ?x4643 = 080lkt7, ?x9814 = 025rzfc >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #382 for first EXPECTED value: *> intensional similarity = 62 *> extensional distance = 7 *> proper extension: 03tmr; *> query: (?x2752, 018ctl) <- sports(?x418, ?x2752), country(?x2752, ?x3277), country(?x2752, ?x1603), country(?x2752, ?x1355), sports(?x8584, ?x2752), medal(?x3277, ?x422), film_release_region(?x6168, ?x3277), film_release_region(?x5496, ?x3277), film_release_region(?x5162, ?x3277), film_release_region(?x5109, ?x3277), film_release_region(?x4615, ?x3277), film_release_region(?x4610, ?x3277), film_release_region(?x4464, ?x3277), film_release_region(?x4422, ?x3277), film_release_region(?x3226, ?x3277), film_release_region(?x2714, ?x3277), film_release_region(?x1988, ?x3277), film_release_region(?x1785, ?x3277), film_release_region(?x1496, ?x3277), film_release_region(?x1173, ?x3277), film_release_region(?x1163, ?x3277), film_release_region(?x781, ?x3277), film_release_region(?x633, ?x3277), film_release_region(?x124, ?x3277), ?x4422 = 06zn2v2, ?x633 = 0c40vxk, ?x422 = 02lq67, ?x5109 = 0b44shh, ?x781 = 0gkz15s, ?x1163 = 0c0nhgv, adjustment_currency(?x3277, ?x170), ?x5496 = 07l50vn, ?x6168 = 0gj96ln, ?x5162 = 0j3d9tn, ?x4610 = 017jd9, ?x4464 = 05pdh86, administrative_parent(?x3277, ?x551), ?x8584 = 01f1jf, country(?x6564, ?x3277), ?x4615 = 0dlngsd, ?x1988 = 09k56b7, jurisdiction_of_office(?x346, ?x1355), ?x3226 = 0gyfp9c, film_release_region(?x6216, ?x1355), film_release_region(?x5825, ?x1355), film_release_region(?x4514, ?x1355), film_release_region(?x3757, ?x1355), film_release_region(?x2783, ?x1355), film_release_region(?x972, ?x1355), ?x5825 = 067ghz, ?x2714 = 0kv238, ?x6564 = 0152n0, ?x1173 = 0872p_c, ?x1785 = 0gj9tn5, ?x6216 = 06fcqw, ?x972 = 017gl1, ?x4514 = 06tpmy, ?x1603 = 06bnz, ?x1496 = 011yqc, ?x124 = 0g56t9t, ?x2783 = 0879bpq, ?x3757 = 02vr3gz *> conf = 0.56 ranks of expected_values: 8 EVAL 09_94 sports! 018ctl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 35.000 35.000 0.885 http://example.org/olympics/olympic_games/sports #4851-03fnmd PRED entity: 03fnmd PRED relation: position PRED expected values: 02sdk9v => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 4): 02sdk9v (0.93 #204, 0.93 #200, 0.90 #196), 03f0fp (0.46 #72, 0.32 #241, 0.31 #247), 02qvgy (0.46 #72), 02md_2 (0.32 #241, 0.31 #247) >> Best rule #204 for best value: >> intensional similarity = 18 >> extensional distance = 328 >> proper extension: 03fn8k; 02v4vl; 02psgvg; >> query: (?x5552, ?x63) <- position(?x5552, ?x530), position(?x5552, ?x203), position(?x5552, ?x60), ?x530 = 02_j1w, ?x203 = 0dgrmp, ?x60 = 02nzb8, team(?x63, ?x5552), position(?x10896, ?x63), position(?x7389, ?x63), position(?x6964, ?x63), position(?x6153, ?x63), position(?x5524, ?x63), ?x10896 = 03lygq, ?x6153 = 016gp5, ?x7389 = 01xn6mc, position(?x209, ?x63), ?x6964 = 047fwlg, ?x5524 = 04mp9q >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03fnmd position 02sdk9v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.933 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #4850-0yyts PRED entity: 0yyts PRED relation: language PRED expected values: 02h40lc => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.96 #1700, 0.96 #1992, 0.96 #1523), 064_8sq (0.19 #720, 0.17 #79, 0.17 #896), 04306rv (0.13 #354, 0.12 #1348, 0.11 #2170), 02bjrlw (0.12 #1, 0.09 #1344, 0.08 #2049), 06nm1 (0.12 #69, 0.11 #476, 0.10 #1473), 06b_j (0.07 #605, 0.07 #546, 0.07 #80), 03k50 (0.05 #67, 0.04 #125, 0.02 #9), 05qqm (0.05 #98, 0.01 #1383, 0.01 #2321), 03_9r (0.05 #4636, 0.05 #651, 0.05 #5688), 0jzc (0.05 #1362, 0.04 #2184, 0.04 #2300) >> Best rule #1700 for best value: >> intensional similarity = 4 >> extensional distance = 414 >> proper extension: 085ccd; >> query: (?x2370, 02h40lc) <- executive_produced_by(?x2370, ?x9044), language(?x2370, ?x3966), film(?x3186, ?x2370), film(?x382, ?x2370) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0yyts language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.964 http://example.org/film/film/language #4849-0rydq PRED entity: 0rydq PRED relation: country PRED expected values: 09c7w0 => 95 concepts (48 used for prediction) PRED predicted values (max 10 best out of 19): 09c7w0 (0.78 #2159, 0.77 #1901, 0.72 #2333), 04_1l0v (0.41 #2678, 0.41 #2418, 0.40 #2505), 0d0x8 (0.23 #4064, 0.17 #3543, 0.15 #1640), 07ssc (0.07 #1224, 0.07 #1138, 0.06 #1743), 0f8l9c (0.06 #109, 0.01 #886, 0.01 #1058), 0d060g (0.05 #1130, 0.04 #1735, 0.04 #1216), 0chghy (0.03 #962, 0.03 #790, 0.03 #1220), 059j2 (0.03 #2622, 0.03 #2361, 0.03 #2449), 0345h (0.03 #1499, 0.02 #1068, 0.02 #1413), 03rk0 (0.03 #4111, 0.01 #3069, 0.01 #3155) >> Best rule #2159 for best value: >> intensional similarity = 3 >> extensional distance = 251 >> proper extension: 03fb3t; 0qf5p; 012q8y; >> query: (?x14277, 09c7w0) <- state(?x14277, ?x3038), contains(?x8260, ?x3038), district_represented(?x176, ?x3038) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rydq country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 48.000 0.779 http://example.org/base/biblioness/bibs_location/country #4848-0pz04 PRED entity: 0pz04 PRED relation: company PRED expected values: 06rq1k => 128 concepts (87 used for prediction) PRED predicted values (max 10 best out of 49): 09c7w0 (0.04 #1356, 0.03 #775, 0.03 #1163), 01w3v (0.03 #789, 0.03 #401, 0.03 #1177), 013807 (0.03 #968, 0.02 #1162, 0.02 #2321), 07wrz (0.02 #1391, 0.02 #810, 0.02 #1004), 01w5m (0.02 #823, 0.02 #2176, 0.02 #1017), 03ksy (0.02 #824, 0.02 #1018, 0.02 #1212), 01rs59 (0.02 #909, 0.02 #1103, 0.02 #1297), 07vsl (0.02 #961, 0.02 #1349, 0.02 #1542), 09f2j (0.02 #851, 0.01 #2011, 0.01 #1818), 07tg4 (0.02 #818, 0.01 #2171) >> Best rule #1356 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 0n00; 09bg4l; 0kh6b; 01dvtx; 017yfz; 0b78hw; 03f77; 016lh0; 06c97; 012gx2; ... >> query: (?x8145, 09c7w0) <- student(?x1368, ?x8145), people(?x1446, ?x8145), profession(?x8145, ?x987) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pz04 company 06rq1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 87.000 0.039 http://example.org/people/person/employment_history./business/employment_tenure/company #4847-02k1b PRED entity: 02k1b PRED relation: form_of_government PRED expected values: 01fpfn => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 5): 01fpfn (0.43 #78, 0.42 #88, 0.42 #293), 06cx9 (0.43 #291, 0.43 #296, 0.40 #181), 018wl5 (0.38 #77, 0.33 #297, 0.33 #252), 01q20 (0.34 #79, 0.32 #369, 0.31 #64), 026wp (0.08 #90, 0.08 #50, 0.08 #10) >> Best rule #78 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 01k6y1; 012m_; >> query: (?x8449, 01fpfn) <- nationality(?x5906, ?x8449), form_of_government(?x8449, ?x6377), official_language(?x8449, ?x2502) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02k1b form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.431 http://example.org/location/country/form_of_government #4846-0c53vt PRED entity: 0c53vt PRED relation: ceremony! PRED expected values: 0gqz2 => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 333): 0gqz2 (0.81 #2715, 0.81 #1989, 0.80 #3685), 0gqng (0.81 #1941, 0.76 #2183, 0.75 #8003), 0gr07 (0.80 #4273, 0.75 #8003, 0.74 #4516), 0l8z1 (0.75 #8003, 0.75 #4159, 0.73 #7031), 018wdw (0.75 #8003, 0.73 #7031, 0.68 #2108), 0gqxm (0.75 #8003, 0.73 #7031, 0.48 #2056), 0gqzz (0.75 #8003, 0.73 #7031, 0.20 #3431), 02x201b (0.75 #8003, 0.73 #7031, 0.18 #4362), 040njc (0.74 #728, 0.73 #971, 0.67 #485), 04dn09n (0.74 #728, 0.67 #485, 0.22 #2934) >> Best rule #2715 for best value: >> intensional similarity = 19 >> extensional distance = 40 >> proper extension: 050yyb; 0bzn6_; 0bzmt8; >> query: (?x8015, 0gqz2) <- ceremony(?x3617, ?x8015), ceremony(?x2209, ?x8015), ceremony(?x1703, ?x8015), ceremony(?x1245, ?x8015), ?x2209 = 0gr42, honored_for(?x8015, ?x3909), ?x1703 = 0k611, ?x3617 = 0gvx_, award(?x197, ?x1245), award(?x11264, ?x1245), award_winner(?x1245, ?x971), ceremony(?x1245, ?x6323), ceremony(?x1245, ?x4598), nominated_for(?x1245, ?x4680), place_of_birth(?x11264, ?x9375), ?x4680 = 01f8hf, ?x6323 = 05hmp6, ?x4598 = 0fzrtf, award_winner(?x9761, ?x11264) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c53vt ceremony! 0gqz2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony #4845-012hw PRED entity: 012hw PRED relation: films PRED expected values: 02yvct => 52 concepts (35 used for prediction) PRED predicted values (max 10 best out of 14): 09ps01 (0.14 #6085, 0.12 #9272, 0.06 #14584), 0qm98 (0.14 #5909, 0.12 #9096, 0.06 #14408), 04jn6y7 (0.14 #5839, 0.07 #13277, 0.06 #14871), 03s9kp (0.14 #7958), 0294mx (0.14 #7811), 0k7tq (0.14 #7782), 01cmp9 (0.14 #7744), 09p4w8 (0.14 #7682), 055td_ (0.14 #7659), 01jzyf (0.14 #7621) >> Best rule #6085 for best value: >> intensional similarity = 10 >> extensional distance = 5 >> proper extension: 06z5s; >> query: (?x12781, 09ps01) <- people(?x12781, ?x11088), student(?x3439, ?x11088), location(?x11088, ?x2020), place_of_death(?x11088, ?x5719), influenced_by(?x11088, ?x7893), taxonomy(?x2020, ?x939), contains(?x2020, ?x1151), state_province_region(?x1520, ?x2020), profession(?x11088, ?x353), major_field_of_study(?x3439, ?x254) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 012hw films 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 35.000 0.143 http://example.org/film/film_subject/films #4844-02v8kmz PRED entity: 02v8kmz PRED relation: nominated_for! PRED expected values: 0gr51 => 46 concepts (36 used for prediction) PRED predicted values (max 10 best out of 209): 02x1z2s (0.40 #144, 0.25 #383, 0.06 #4784), 0gqzz (0.40 #50, 0.25 #289, 0.04 #528), 019f4v (0.28 #771, 0.20 #1490, 0.20 #6459), 099c8n (0.26 #774, 0.20 #57, 0.17 #535), 0gr4k (0.25 #266, 0.23 #744, 0.20 #27), 0gq9h (0.25 #302, 0.23 #3411, 0.21 #1499), 02hsq3m (0.25 #269, 0.20 #30, 0.09 #1705), 05zr6wv (0.25 #255, 0.20 #16, 0.06 #8375), 0f4x7 (0.23 #743, 0.19 #8615, 0.17 #5741), 04kxsb (0.23 #814, 0.19 #8615, 0.17 #5741) >> Best rule #144 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02_fm2; 04hwbq; 01xdxy; >> query: (?x240, 02x1z2s) <- film(?x10660, ?x240), ?x10660 = 01rs5p, film_crew_role(?x240, ?x137), nominated_for(?x746, ?x240) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #8615 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1588 *> proper extension: 06g60w; *> query: (?x240, ?x350) <- nominated_for(?x4397, ?x240), award(?x4397, ?x350) *> conf = 0.19 ranks of expected_values: 29 EVAL 02v8kmz nominated_for! 0gr51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 46.000 36.000 0.400 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4843-041c4 PRED entity: 041c4 PRED relation: group PRED expected values: 04sd0 => 129 concepts (120 used for prediction) PRED predicted values (max 10 best out of 16): 01v0sx2 (0.06 #1742, 0.03 #1199, 0.03 #3596), 0123r4 (0.05 #695, 0.03 #1781, 0.02 #2543), 04sd0 (0.04 #859, 0.03 #2165, 0.03 #3040), 01qqwp9 (0.03 #1106, 0.02 #1868, 0.01 #2411), 011_vz (0.02 #726), 0frsw (0.02 #666), 01v0sxx (0.02 #844), 02mq_y (0.02 #793), 06nv27 (0.02 #2098, 0.01 #3408, 0.01 #2973), 015srx (0.01 #1017, 0.01 #1126) >> Best rule #1742 for best value: >> intensional similarity = 2 >> extensional distance = 106 >> proper extension: 04rcr; >> query: (?x4988, 01v0sx2) <- influenced_by(?x5940, ?x4988), award_nominee(?x4988, ?x4297) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #859 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 081l_; *> query: (?x4988, 04sd0) <- type_of_union(?x4988, ?x566), written_by(?x582, ?x4988), category(?x4988, ?x134) *> conf = 0.04 ranks of expected_values: 3 EVAL 041c4 group 04sd0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 120.000 0.056 http://example.org/music/group_member/membership./music/group_membership/group #4842-041mt PRED entity: 041mt PRED relation: influenced_by! PRED expected values: 03qcq 01vsl3_ 01w524f 03h_fqv => 217 concepts (104 used for prediction) PRED predicted values (max 10 best out of 564): 01wp_jm (0.50 #912, 0.07 #26811, 0.06 #9036), 0d4jl (0.33 #115, 0.20 #18398, 0.12 #2147), 01vdrw (0.33 #437, 0.20 #18720, 0.11 #2977), 0g72r (0.33 #493, 0.12 #18776, 0.07 #35048), 0c4y8 (0.33 #388, 0.05 #52830, 0.03 #18671), 08433 (0.30 #10665, 0.26 #21331, 0.25 #1554), 01vsl3_ (0.25 #2133, 0.25 #1626, 0.11 #11176), 0683n (0.25 #18617, 0.22 #2874, 0.12 #21668), 03qcq (0.25 #1526, 0.13 #18284, 0.12 #2033), 05qw5 (0.25 #1594, 0.12 #2101, 0.11 #11176) >> Best rule #912 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01k9lpl; >> query: (?x2208, 01wp_jm) <- influenced_by(?x7527, ?x2208), influenced_by(?x6456, ?x2208), artists(?x505, ?x6456), ?x7527 = 06crng, nationality(?x6456, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2133 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02jq1; 01wg25j; *> query: (?x2208, 01vsl3_) <- influenced_by(?x1089, ?x2208), location(?x2208, ?x739), ?x1089 = 01vrncs, place_of_birth(?x65, ?x739) *> conf = 0.25 ranks of expected_values: 7, 9, 96, 125 EVAL 041mt influenced_by! 03h_fqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 217.000 104.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 041mt influenced_by! 01w524f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 217.000 104.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 041mt influenced_by! 01vsl3_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 217.000 104.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 041mt influenced_by! 03qcq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 217.000 104.000 0.500 http://example.org/influence/influence_node/influenced_by #4841-0jbk9 PRED entity: 0jbk9 PRED relation: source! PRED expected values: 0cb4j 0_3cs 01mc11 0xkq4 013jz2 0nvrd 0s69k 013yq 01cx_ 0d6lp 01m1_t 0xrzh 01jr6 03l2n 0tct_ 0tln7 0n5yh 0tj4y 02_n7 01smm 0f2tj 0lphb 0fr61 0mnm2 0mkdm 0mn8t 04gxf 0vfs8 0jgk3 0mb2b 0l30v 0f2nf 013nws 0mwxl 02hyt 0fc_9 0l2q3 0mrq3 010m55 07l5z 013h1c 0mmpz 0l3kx 0r785 0hz35 0mlxt 0fkhz 0l39b 0m24v 0_565 0r111 0nm87 0mkv3 0mx5p 0nzw2 0n2vl 013hvr 0fvvg 0mkp7 0ms6_ 0kwmc 0mww2 0135p7 0_j_z 0c5v2 0j_1v 0x1jc 0r3w7 0rw2x 0th3k 0t_48 0txhf 0l2nd 0mw_q 0fsv2 0jj6k 031sn 0dzs0 0nm9y 0nqph 0vm5t 0p07l 0ghtf 0mkc3 => 95 concepts (95 used for prediction) No prediction ranks of expected_values: EVAL 0jbk9 source! 0mkc3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0ghtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0p07l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0vm5t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0nqph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0nm9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0dzs0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 031sn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0jj6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0fsv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mw_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0l2nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0txhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0t_48 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0th3k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0rw2x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0r3w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0x1jc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0j_1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0c5v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0_j_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0135p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mww2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0kwmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0ms6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mkp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0fvvg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 013hvr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0n2vl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0nzw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mx5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mkv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0nm87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0r111 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0_565 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0m24v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0l39b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0fkhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mlxt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0hz35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0r785 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0l3kx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mmpz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 013h1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 07l5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 010m55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mrq3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0l2q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0fc_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 02hyt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mwxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 013nws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0f2nf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0l30v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mb2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0jgk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0vfs8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 04gxf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mn8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mkdm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0mnm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0fr61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0lphb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0f2tj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 01smm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 02_n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0tj4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0n5yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0tln7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0tct_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 03l2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 01jr6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0xrzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 01m1_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0d6lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 01cx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 013yq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0s69k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0nvrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 013jz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0xkq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 01mc11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0_3cs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source EVAL 0jbk9 source! 0cb4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 95.000 0.000 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #4840-03vrv9 PRED entity: 03vrv9 PRED relation: film PRED expected values: 029k4p 02ntb8 => 84 concepts (78 used for prediction) PRED predicted values (max 10 best out of 418): 03l6q0 (0.25 #544, 0.04 #34575, 0.02 #36366), 01r97z (0.25 #110, 0.03 #21603, 0.03 #23394), 02b6n9 (0.25 #1574, 0.02 #23067, 0.01 #24858), 02sfnv (0.25 #900, 0.02 #22393, 0.01 #24184), 033f8n (0.25 #826, 0.02 #22319, 0.01 #24110), 026wlxw (0.20 #3211, 0.15 #5002, 0.12 #8584), 01gglm (0.10 #3198, 0.08 #4989, 0.07 #6780), 01718w (0.10 #3192, 0.08 #4983, 0.07 #6774), 0963mq (0.10 #1930, 0.08 #3721, 0.07 #5512), 0kv2hv (0.10 #1923, 0.08 #3714, 0.07 #5505) >> Best rule #544 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0187y5; 016ksk; >> query: (?x11282, 03l6q0) <- type_of_union(?x11282, ?x566), profession(?x11282, ?x524), athlete(?x1083, ?x11282), ?x524 = 02jknp >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2631 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 037gjc; 03jg5t; 0234pg; 02bf2s; 014g_s; 063g7l; 054c1; 049sb; *> query: (?x11282, 02ntb8) <- team(?x11282, ?x1576), profession(?x11282, ?x1032), ?x1032 = 02hrh1q, film(?x11282, ?x8551) *> conf = 0.10 ranks of expected_values: 16, 398 EVAL 03vrv9 film 02ntb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 84.000 78.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 03vrv9 film 029k4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 78.000 0.250 http://example.org/film/actor/film./film/performance/film #4839-03gyp30 PRED entity: 03gyp30 PRED relation: honored_for PRED expected values: 0d68qy => 28 concepts (19 used for prediction) PRED predicted values (max 10 best out of 912): 0d68qy (0.62 #4288, 0.40 #5472, 0.40 #1920), 02rzdcp (0.50 #4334, 0.14 #6700, 0.13 #7294), 05zr0xl (0.40 #2256, 0.33 #3439, 0.29 #4032), 0330r (0.40 #1701, 0.33 #1110, 0.20 #2885), 030cx (0.40 #1447, 0.33 #856, 0.20 #2631), 0g60z (0.40 #1198, 0.24 #6524, 0.20 #2382), 02k_4g (0.40 #1223, 0.19 #6549, 0.10 #7143), 01j7mr (0.38 #6717, 0.20 #1391, 0.17 #3166), 01q_y0 (0.33 #4868, 0.33 #3090, 0.30 #5459), 05lfwd (0.33 #342, 0.22 #5076, 0.20 #5667) >> Best rule #4288 for best value: >> intensional similarity = 17 >> extensional distance = 6 >> proper extension: 09v0p2c; >> query: (?x8347, 0d68qy) <- award_winner(?x8347, ?x4702), award_winner(?x8347, ?x3852), award_winner(?x8347, ?x906), award_winner(?x8347, ?x237), award_nominee(?x3852, ?x2657), ceremony(?x11179, ?x8347), honored_for(?x8347, ?x7911), award_winner(?x968, ?x237), nominated_for(?x112, ?x7911), award_nominee(?x364, ?x237), award(?x4411, ?x11179), ?x906 = 0pz7h, film_release_region(?x7911, ?x94), film(?x4702, ?x339), nominated_for(?x11179, ?x631), film_crew_role(?x7911, ?x137), award_winner(?x4411, ?x9815) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03gyp30 honored_for 0d68qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 19.000 0.625 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #4838-0hw1j PRED entity: 0hw1j PRED relation: profession PRED expected values: 02jknp => 119 concepts (107 used for prediction) PRED predicted values (max 10 best out of 51): 01d_h8 (0.80 #6, 0.67 #154, 0.66 #894), 02jknp (0.58 #748, 0.55 #1341, 0.55 #1489), 03gjzk (0.46 #902, 0.40 #1051, 0.39 #1199), 0cbd2 (0.43 #1037, 0.38 #6817, 0.30 #7), 09jwl (0.22 #9937, 0.21 #4756, 0.20 #4016), 05sxg2 (0.20 #149, 0.10 #1, 0.07 #445), 02krf9 (0.20 #914, 0.17 #1063, 0.17 #1359), 018gz8 (0.17 #3866, 0.16 #904, 0.15 #6826), 0nbcg (0.17 #9950, 0.15 #4769, 0.13 #4029), 0kyk (0.17 #6839, 0.14 #769, 0.13 #1362) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 04cw0j; >> query: (?x3736, 01d_h8) <- award_nominee(?x902, ?x3736), ?x902 = 05qd_, student(?x8525, ?x3736) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #748 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 218 *> proper extension: 016hvl; 04b19t; 0d05fv; 06n9lt; 0k_mt; 0pqzh; 072vj; *> query: (?x3736, 02jknp) <- written_by(?x5008, ?x3736), gender(?x3736, ?x231), award_winner(?x746, ?x3736) *> conf = 0.58 ranks of expected_values: 2 EVAL 0hw1j profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 107.000 0.800 http://example.org/people/person/profession #4837-05148p4 PRED entity: 05148p4 PRED relation: instrumentalists PRED expected values: 01vvydl 01x66d 07_3qd 0ftps 07g2v 01lvcs1 0bkg4 012z8_ 01l47f5 02p2zq 02vr7 0232lm 024qwq => 71 concepts (59 used for prediction) PRED predicted values (max 10 best out of 984): 01wzlxj (0.72 #3474, 0.71 #3472, 0.68 #1736), 045zr (0.72 #3474, 0.71 #3472, 0.68 #1736), 0133x7 (0.72 #3474, 0.71 #3472, 0.68 #1736), 0161sp (0.72 #3474, 0.71 #3472, 0.67 #2717), 04mn81 (0.72 #3474, 0.71 #3472, 0.65 #3475), 01z9_x (0.72 #3474, 0.71 #3472, 0.65 #3475), 0ddkf (0.72 #3474, 0.71 #3472, 0.65 #3475), 019x62 (0.72 #3474, 0.71 #3472, 0.65 #3475), 02p2zq (0.72 #3474, 0.71 #3472, 0.65 #3475), 023p29 (0.72 #3474, 0.65 #3475, 0.14 #5210) >> Best rule #3474 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 09lbv; >> query: (?x1166, ?x4960) <- split_to(?x6565, ?x1166), profession(?x4960, ?x6565), profession(?x2945, ?x6565), instrumentalists(?x228, ?x2945), award(?x4960, ?x462), specialization_of(?x6565, ?x1183), artists(?x671, ?x4960), award_nominee(?x4960, ?x3997) >> conf = 0.72 => this is the best rule for 11 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 9, 12, 16, 18, 49, 53, 62, 107, 109, 134, 188, 226, 481 EVAL 05148p4 instrumentalists 024qwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 0232lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 02vr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 02p2zq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 01l47f5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 012z8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 0bkg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 01lvcs1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 07g2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 0ftps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 07_3qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 01x66d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists EVAL 05148p4 instrumentalists 01vvydl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 59.000 0.723 http://example.org/music/instrument/instrumentalists #4836-01d_s5 PRED entity: 01d_s5 PRED relation: artists PRED expected values: 03f0qd7 => 69 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1077): 011z3g (0.71 #2767, 0.60 #603, 0.56 #3849), 03fbc (0.71 #2368, 0.60 #204, 0.56 #3450), 01vw37m (0.67 #1649, 0.44 #4896, 0.27 #23824), 03t9sp (0.60 #123, 0.57 #2287, 0.44 #3369), 01vxlbm (0.60 #341, 0.57 #2505, 0.44 #3587), 06p03s (0.60 #1012, 0.57 #3176, 0.44 #4258), 01pfr3 (0.60 #25, 0.57 #2189, 0.44 #3271), 0191h5 (0.60 #649, 0.44 #3895, 0.43 #2813), 01w8n89 (0.60 #317, 0.43 #2481, 0.33 #3563), 01wy61y (0.60 #367, 0.43 #2531, 0.33 #3613) >> Best rule #2767 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 07d2d; 012yc; >> query: (?x7673, 011z3g) <- artists(?x7673, ?x9262), artists(?x7673, ?x6659), artists(?x7673, ?x959), ?x9262 = 04n2vgk, ?x959 = 03f5spx, instrumentalists(?x1437, ?x6659), type_of_union(?x6659, ?x566), role(?x130, ?x1437) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5343 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 06cp5; 036jv; 0233qs; *> query: (?x7673, 03f0qd7) <- artists(?x7673, ?x9262), artists(?x7673, ?x6659), ?x6659 = 01vw_dv, student(?x10220, ?x9262), award_winner(?x9262, ?x3930), award_nominee(?x827, ?x9262), location(?x9262, ?x2850) *> conf = 0.33 ranks of expected_values: 152 EVAL 01d_s5 artists 03f0qd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 69.000 28.000 0.714 http://example.org/music/genre/artists #4835-02s58t PRED entity: 02s58t PRED relation: type_of_union PRED expected values: 01g63y => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 3): 01g63y (0.29 #79, 0.28 #187, 0.27 #151), 01bl8s (0.22 #346, 0.02 #107, 0.01 #161), 0jgjn (0.22 #346) >> Best rule #79 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 0bxtg; 06pj8; 013knm; 073w14; 01twdk; 037gjc; 015q43; 02jq1; 0dx_q; 01vsqvs; ... >> query: (?x8900, 01g63y) <- location(?x8900, ?x6555), participant(?x8900, ?x543), languages(?x8900, ?x254), type_of_union(?x8900, ?x566) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02s58t type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 178.000 0.290 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4834-0gtvpkw PRED entity: 0gtvpkw PRED relation: film_release_region PRED expected values: 0chghy 0k6nt 03rk0 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 178): 09c7w0 (0.94 #1011, 0.93 #8095, 0.92 #5927), 0chghy (0.90 #1162, 0.89 #1451, 0.89 #874), 0k6nt (0.85 #1176, 0.84 #311, 0.84 #744), 03_3d (0.83 #293, 0.79 #2315, 0.79 #582), 05v8c (0.78 #735, 0.78 #158, 0.69 #302), 047yc (0.64 #170, 0.61 #747, 0.60 #26), 03rk0 (0.64 #194, 0.58 #771, 0.55 #483), 01ls2 (0.63 #444, 0.59 #299, 0.58 #155), 06t8v (0.62 #355, 0.57 #932, 0.56 #211), 06qd3 (0.60 #323, 0.55 #756, 0.50 #2201) >> Best rule #1011 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 0m2kd; 0209hj; 026mfbr; 01r97z; 0p_sc; 08gsvw; 0bshwmp; 0hv1t; 05pbl56; 031t2d; ... >> query: (?x3491, 09c7w0) <- film_release_region(?x3491, ?x2645), film(?x541, ?x3491), ?x541 = 017s11, country(?x11304, ?x2645) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #1162 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 126 *> proper extension: 011yrp; 03bx2lk; 0gj9tn5; 0k5g9; 0g5838s; 0gyfp9c; 0gvs1kt; 02rmd_2; 0125xq; 03mgx6z; ... *> query: (?x3491, 0chghy) <- film_release_region(?x3491, ?x2645), film_release_region(?x3491, ?x1023), film_release_region(?x3491, ?x789), film(?x541, ?x3491), ?x2645 = 03h64, ?x1023 = 0ctw_b, medal(?x789, ?x422), combatants(?x94, ?x789) *> conf = 0.90 ranks of expected_values: 2, 3, 7 EVAL 0gtvpkw film_release_region 03rk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 76.000 76.000 0.943 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gtvpkw film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.943 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gtvpkw film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.943 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4833-07r4c PRED entity: 07r4c PRED relation: profession PRED expected values: 016z4k 0dxtg => 143 concepts (78 used for prediction) PRED predicted values (max 10 best out of 63): 01d_h8 (0.71 #5731, 0.68 #7309, 0.67 #8026), 0dxtg (0.63 #5738, 0.60 #7316, 0.59 #8033), 01c72t (0.55 #450, 0.50 #21, 0.38 #6177), 01b30l (0.50 #52, 0.22 #1625, 0.21 #1768), 016z4k (0.49 #6734, 0.48 #4152, 0.48 #4438), 05vyk (0.45 #519, 0.25 #90, 0.20 #1663), 039v1 (0.32 #3465, 0.32 #2035, 0.32 #2321), 0n1h (0.27 #4445, 0.26 #5305, 0.25 #2727), 03gjzk (0.26 #7317, 0.26 #8034, 0.25 #5739), 0gbbt (0.25 #8, 0.12 #1009, 0.11 #2296) >> Best rule #5731 for best value: >> intensional similarity = 3 >> extensional distance = 354 >> proper extension: 016hvl; 0g51l1; 06qgjh; 0q1lp; 03z0l6; 0kc6; 03p01x; 04g_wd; 0d0l91; 0jnb0; ... >> query: (?x6208, 01d_h8) <- profession(?x6208, ?x524), location(?x6208, ?x362), ?x524 = 02jknp >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5738 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 354 *> proper extension: 016hvl; 0g51l1; 06qgjh; 0q1lp; 03z0l6; 0kc6; 03p01x; 04g_wd; 0d0l91; 0jnb0; ... *> query: (?x6208, 0dxtg) <- profession(?x6208, ?x524), location(?x6208, ?x362), ?x524 = 02jknp *> conf = 0.63 ranks of expected_values: 2, 5 EVAL 07r4c profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 78.000 0.708 http://example.org/people/person/profession EVAL 07r4c profession 016z4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 143.000 78.000 0.708 http://example.org/people/person/profession #4832-043n0v_ PRED entity: 043n0v_ PRED relation: nominated_for! PRED expected values: 0dgr5xp => 90 concepts (81 used for prediction) PRED predicted values (max 10 best out of 196): 07kfzsg (0.50 #698, 0.12 #12040, 0.12 #12039), 0l8z1 (0.44 #53, 0.18 #289, 0.18 #6899), 019f4v (0.43 #6901, 0.27 #291, 0.23 #8553), 0gq9h (0.36 #6910, 0.33 #64, 0.27 #7618), 0gs9p (0.34 #6912, 0.25 #7620, 0.24 #8564), 0f4x7 (0.33 #26, 0.21 #6872, 0.20 #2150), 040njc (0.28 #1895, 0.25 #6853, 0.23 #3075), 0k611 (0.28 #6921, 0.23 #2199, 0.22 #75), 054krc (0.28 #6917, 0.22 #71, 0.18 #307), 0p9sw (0.27 #257, 0.25 #2381, 0.22 #21) >> Best rule #698 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 0mb8c; 08j7lh; 065ym0c; >> query: (?x5038, 07kfzsg) <- genre(?x5038, ?x162), nominated_for(?x7215, ?x5038), titles(?x2164, ?x5038), language(?x5038, ?x2890), ?x7215 = 09v92_x >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #18654 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1584 *> proper extension: 06g60w; *> query: (?x5038, ?x5039) <- nominated_for(?x10695, ?x5038), award(?x10695, ?x5039), nominated_for(?x5039, ?x3376) *> conf = 0.22 ranks of expected_values: 22 EVAL 043n0v_ nominated_for! 0dgr5xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 90.000 81.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4831-0m2lt PRED entity: 0m2lt PRED relation: jurisdiction_of_office! PRED expected values: 01q24l => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 22): 0f6c3 (0.56 #239, 0.52 #100, 0.50 #77), 09n5b9 (0.56 #243, 0.47 #266, 0.42 #381), 0fkvn (0.55 #73, 0.52 #235, 0.52 #96), 060bp (0.35 #139, 0.20 #116, 0.18 #70), 0789n (0.33 #33, 0.23 #79, 0.22 #102), 060c4 (0.29 #141, 0.18 #2054, 0.18 #72), 01t7n9 (0.23 #88, 0.22 #111, 0.17 #42), 0pqc5 (0.22 #1065, 0.21 #1527, 0.20 #1665), 04syw (0.16 #145, 0.14 #76, 0.09 #99), 0fj45 (0.16 #158, 0.09 #89, 0.09 #112) >> Best rule #239 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 03v1s; 05kj_; 059f4; 05fkf; 0vmt; 0hjy; 03s0w; 05fhy; 04ych; 059_c; ... >> query: (?x2832, 0f6c3) <- adjoins(?x1396, ?x2832), contains(?x1767, ?x1396), administrative_division(?x10211, ?x2832), location(?x3307, ?x2832) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #866 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 172 *> proper extension: 0pspl; 0kw4j; 02vnp2; 02ckl3; *> query: (?x2832, 01q24l) <- contains(?x7518, ?x2832), religion(?x7518, ?x11552), ?x11552 = 072w0, place_founded(?x11273, ?x7518) *> conf = 0.10 ranks of expected_values: 13 EVAL 0m2lt jurisdiction_of_office! 01q24l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 141.000 141.000 0.556 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #4830-02x2jl_ PRED entity: 02x2jl_ PRED relation: film_crew_role PRED expected values: 09zzb8 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 25): 09zzb8 (0.77 #1692, 0.73 #457, 0.72 #949), 01vx2h (0.37 #255, 0.30 #45, 0.30 #1418), 01pvkk (0.30 #1702, 0.28 #256, 0.28 #959), 02ynfr (0.25 #15, 0.17 #260, 0.16 #120), 01xy5l_ (0.25 #13, 0.12 #2045, 0.09 #1421), 089fss (0.20 #41, 0.12 #6, 0.12 #2045), 02rh1dz (0.18 #254, 0.13 #184, 0.12 #2045), 0215hd (0.12 #18, 0.12 #1709, 0.12 #2045), 0ckd1 (0.12 #4, 0.12 #2045, 0.02 #285), 089g0h (0.12 #2045, 0.12 #264, 0.10 #1427) >> Best rule #1692 for best value: >> intensional similarity = 4 >> extensional distance = 1147 >> proper extension: 0fq27fp; >> query: (?x11735, 09zzb8) <- genre(?x11735, ?x53), film_crew_role(?x11735, ?x1284), film_crew_role(?x7501, ?x1284), ?x7501 = 0gd92 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02x2jl_ film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.765 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4829-06btq PRED entity: 06btq PRED relation: taxonomy PRED expected values: 04n6k => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.90 #8, 0.90 #16, 0.89 #6) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0rh6k; 07ssc; 0694j; >> query: (?x2713, 04n6k) <- state_province_region(?x2021, ?x2713), adjoins(?x1755, ?x2713), religion(?x2713, ?x109) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06btq taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 180.000 180.000 0.902 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #4828-0kvf3b PRED entity: 0kvf3b PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #1, 0.81 #96, 0.81 #51), 07c52 (0.06 #3, 0.03 #23, 0.03 #271), 02nxhr (0.03 #108, 0.03 #205, 0.03 #149), 07z4p (0.02 #95, 0.02 #85, 0.02 #273) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 02gqm3; >> query: (?x10549, 029j_) <- genre(?x10549, ?x162), film_art_direction_by(?x10549, ?x11330), titles(?x162, ?x144) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kvf3b film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.816 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #4827-02vcp0 PRED entity: 02vcp0 PRED relation: instrumentalists! PRED expected values: 0342h => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 92): 0342h (0.61 #779, 0.60 #607, 0.60 #2163), 05r5c (0.60 #95, 0.48 #2167, 0.45 #2254), 018vs (0.33 #787, 0.30 #615, 0.27 #2258), 0l14qv (0.25 #6, 0.14 #1464, 0.11 #780), 06ncr (0.25 #130, 0.08 #818, 0.07 #2202), 02hnl (0.20 #808, 0.16 #636, 0.16 #2192), 026t6 (0.15 #777, 0.14 #1464, 0.12 #2248), 018j2 (0.15 #124, 0.09 #2196, 0.08 #812), 04rzd (0.15 #123, 0.08 #811, 0.08 #2195), 03gvt (0.15 #150, 0.08 #666, 0.07 #838) >> Best rule #779 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 0d608; >> query: (?x8049, 0342h) <- profession(?x8049, ?x220), award(?x8049, ?x724), group(?x8049, ?x1271) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vcp0 instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.606 http://example.org/music/instrument/instrumentalists #4826-01b_lz PRED entity: 01b_lz PRED relation: honored_for! PRED expected values: 03nnm4t => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 87): 0drtv8 (0.33 #284, 0.30 #400, 0.06 #632), 0bvhz9 (0.33 #340, 0.30 #456, 0.02 #4400), 027hjff (0.25 #277, 0.22 #393, 0.06 #741), 0gvstc3 (0.24 #954, 0.23 #722, 0.22 #1302), 03nnm4t (0.22 #755, 0.21 #639, 0.21 #987), 0lp_cd3 (0.18 #944, 0.16 #712, 0.16 #1292), 0bx6zs (0.13 #105, 0.11 #221, 0.08 #917), 09qftb (0.13 #92, 0.11 #208, 0.05 #788), 07y_p6 (0.13 #77, 0.10 #657, 0.08 #889), 07z31v (0.13 #24, 0.09 #604, 0.07 #836) >> Best rule #284 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 024mxd; >> query: (?x3326, 0drtv8) <- honored_for(?x5592, ?x3326), nominated_for(?x435, ?x3326), ?x5592 = 0275n3y >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #755 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 01b7h8; *> query: (?x3326, 03nnm4t) <- honored_for(?x1265, ?x3326), program(?x8831, ?x3326), country_of_origin(?x3326, ?x94) *> conf = 0.22 ranks of expected_values: 5 EVAL 01b_lz honored_for! 03nnm4t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 69.000 69.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #4825-0clz7 PRED entity: 0clz7 PRED relation: location! PRED expected values: 01tw31 => 174 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1709): 010xjr (0.33 #4508, 0.14 #22134, 0.12 #24652), 0170qf (0.20 #13001, 0.17 #18037, 0.14 #20555), 016z68 (0.20 #27430, 0.02 #45058, 0.01 #105763), 01wp8w7 (0.10 #30476, 0.10 #25440, 0.08 #35512), 0sx5w (0.10 #32358, 0.08 #42430, 0.07 #39912), 06jw0s (0.10 #31363, 0.08 #36399, 0.07 #38917), 0prfz (0.10 #25229, 0.07 #30265, 0.05 #37819), 04x1_w (0.10 #26674, 0.07 #31710, 0.05 #39264), 0c6g1l (0.10 #25633, 0.07 #30669, 0.05 #38223), 01_p6t (0.10 #26355, 0.06 #128428, 0.05 #100726) >> Best rule #4508 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0p54z; >> query: (?x3198, 010xjr) <- contains(?x3699, ?x3198), adjoins(?x3198, ?x7986), location(?x6424, ?x3198), ?x3699 = 012wgb, contains(?x3198, ?x3199) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #29878 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 22 *> proper extension: 0n03f; 0121h7; 0m_zm; 08314; 02496r; 01rng; 0ng8v; *> query: (?x3198, 01tw31) <- contains(?x7985, ?x3198), contains(?x429, ?x3198), adjoins(?x7985, ?x7986), ?x429 = 03rt9, adjoins(?x7986, ?x1788), contains(?x7986, ?x6617) *> conf = 0.04 ranks of expected_values: 180 EVAL 0clz7 location! 01tw31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 174.000 55.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #4824-05lb30 PRED entity: 05lb30 PRED relation: category PRED expected values: 08mbj5d => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.32 #43, 0.32 #54, 0.31 #8) >> Best rule #43 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 01czx; 016fmf; 0134s5; 02lbrd; 0d193h; 0g_g2; 0134tg; 0b1zz; 07h76; 0l8g0; ... >> query: (?x6632, 08mbj5d) <- award(?x6632, ?x678), award_winner(?x1112, ?x6632) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05lb30 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.319 http://example.org/common/topic/webpage./common/webpage/category #4823-0144l1 PRED entity: 0144l1 PRED relation: role PRED expected values: 05r5c 042v_gx => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 90): 05r5c (0.40 #2158, 0.39 #3496, 0.38 #3701), 05842k (0.40 #179, 0.40 #77, 0.33 #281), 0l14qv (0.34 #1227, 0.32 #2462, 0.30 #1944), 028tv0 (0.34 #1227, 0.32 #2462, 0.30 #1944), 01vdm0 (0.32 #1154, 0.30 #2389, 0.27 #1052), 05148p4 (0.31 #2463, 0.26 #1433, 0.26 #2567), 03qjg (0.31 #2463, 0.26 #1433, 0.26 #2567), 04rzd (0.31 #2463, 0.26 #1433, 0.26 #2567), 018j2 (0.31 #2463, 0.26 #1433, 0.26 #2567), 0mkg (0.31 #2463, 0.26 #1433, 0.26 #2567) >> Best rule #2158 for best value: >> intensional similarity = 3 >> extensional distance = 165 >> proper extension: 01yzl2; 089pg7; >> query: (?x2170, 05r5c) <- artist(?x3888, ?x2170), instrumentalists(?x1166, ?x2170), ?x1166 = 05148p4 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12 EVAL 0144l1 role 042v_gx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 118.000 118.000 0.401 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0144l1 role 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.401 http://example.org/music/artist/track_contributions./music/track_contribution/role #4822-025hwq PRED entity: 025hwq PRED relation: production_companies! PRED expected values: 0qm8b 02d003 08984j 037cr1 => 109 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1665): 04t6fk (0.22 #1436, 0.18 #4860, 0.17 #7142), 0drnwh (0.22 #1896, 0.18 #5320, 0.17 #7602), 07y9w5 (0.22 #1298, 0.18 #4722, 0.17 #7004), 0184tc (0.22 #1581, 0.18 #5005, 0.17 #7287), 03459x (0.22 #1528, 0.18 #4952, 0.17 #7234), 0bz6sq (0.18 #5540, 0.17 #6681, 0.15 #8963), 01xq8v (0.18 #5423, 0.17 #6564, 0.15 #8846), 0hv8w (0.18 #5177, 0.17 #6318, 0.15 #8600), 04ltlj (0.18 #5661, 0.17 #6802, 0.15 #9084), 05zlld0 (0.17 #9543, 0.14 #14108, 0.12 #17531) >> Best rule #1436 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 018p5f; >> query: (?x7935, 04t6fk) <- country(?x7935, ?x94), ?x94 = 09c7w0, award_nominee(?x541, ?x7935) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #9915 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 086k8; 017s11; 016tt2; 0g1rw; 0kx4m; 05qd_; 016tw3; 030_1m; 030_1_; 017jv5; ... *> query: (?x7935, 08984j) <- award_winner(?x1561, ?x7935), award(?x7935, ?x1105), production_companies(?x2339, ?x7935) *> conf = 0.13 ranks of expected_values: 17, 139, 797, 1308 EVAL 025hwq production_companies! 037cr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 109.000 31.000 0.222 http://example.org/film/film/production_companies EVAL 025hwq production_companies! 08984j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 109.000 31.000 0.222 http://example.org/film/film/production_companies EVAL 025hwq production_companies! 02d003 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 109.000 31.000 0.222 http://example.org/film/film/production_companies EVAL 025hwq production_companies! 0qm8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 109.000 31.000 0.222 http://example.org/film/film/production_companies #4821-01vsnff PRED entity: 01vsnff PRED relation: role PRED expected values: 04rzd => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 112): 06w7v (0.27 #172, 0.15 #3522, 0.14 #3585), 03qjg (0.27 #158, 0.11 #1548, 0.11 #1182), 028tv0 (0.18 #131, 0.13 #2130, 0.13 #1521), 042v_gx (0.18 #127, 0.12 #181, 0.11 #1325), 02sgy (0.18 #125, 0.12 #181, 0.11 #1325), 0l14qv (0.18 #124, 0.08 #1148, 0.08 #546), 06ncr (0.18 #152, 0.05 #334, 0.05 #574), 02hnl (0.16 #1535, 0.16 #2144, 0.16 #1169), 0dwsp (0.15 #3522, 0.14 #3585, 0.14 #3211), 013y1f (0.15 #3522, 0.14 #3585, 0.14 #3211) >> Best rule #172 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01mwsnc; 01k_0fp; >> query: (?x2187, 06w7v) <- role(?x2187, ?x212), profession(?x2187, ?x131), type_of_appearance(?x2187, ?x3429) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1537 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 201 *> proper extension: 01r4zfk; *> query: (?x2187, 04rzd) <- type_of_union(?x2187, ?x566), role(?x2187, ?x212), role(?x228, ?x212) *> conf = 0.05 ranks of expected_values: 21 EVAL 01vsnff role 04rzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 138.000 138.000 0.273 http://example.org/music/group_member/membership./music/group_membership/role #4820-05x_5 PRED entity: 05x_5 PRED relation: school! PRED expected values: 01y3v => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 90): 05m_8 (0.27 #2, 0.20 #813, 0.20 #723), 04wmvz (0.27 #77, 0.17 #348, 0.17 #618), 06wpc (0.27 #62, 0.14 #513, 0.14 #423), 0cqt41 (0.27 #17, 0.14 #468, 0.13 #288), 07l8x (0.27 #64, 0.13 #605, 0.13 #335), 07147 (0.27 #65, 0.13 #336, 0.12 #876), 01yhm (0.18 #19, 0.17 #830, 0.17 #470), 051vz (0.18 #22, 0.17 #473, 0.15 #833), 01slc (0.18 #56, 0.16 #1407, 0.14 #1587), 07l4z (0.18 #68, 0.14 #519, 0.13 #609) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 06pwq; 01w3v; 09f2j; >> query: (?x6973, 05m_8) <- school(?x387, ?x6973), major_field_of_study(?x6973, ?x947), organization(?x6973, ?x5487), ?x947 = 036hv >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 06pwq; 01w3v; 09f2j; *> query: (?x6973, 01y3v) <- school(?x387, ?x6973), major_field_of_study(?x6973, ?x947), organization(?x6973, ?x5487), ?x947 = 036hv *> conf = 0.09 ranks of expected_values: 38 EVAL 05x_5 school! 01y3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 114.000 114.000 0.273 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #4819-0582cf PRED entity: 0582cf PRED relation: profession PRED expected values: 0np9r => 121 concepts (95 used for prediction) PRED predicted values (max 10 best out of 52): 0np9r (0.79 #1512, 0.75 #1065, 0.75 #618), 018gz8 (0.50 #166, 0.33 #17, 0.29 #464), 09jwl (0.37 #7622, 0.37 #6877, 0.37 #5685), 01d_h8 (0.35 #1646, 0.33 #752, 0.33 #6), 0dxtg (0.33 #14, 0.30 #8512, 0.29 #7170), 03gjzk (0.33 #15, 0.21 #8963, 0.21 #7171), 02jknp (0.33 #8, 0.19 #5227, 0.19 #9254), 01c72t (0.33 #24, 0.15 #4051, 0.15 #5690), 02krf9 (0.33 #27, 0.14 #474, 0.12 #1667), 015h31 (0.33 #28, 0.01 #12999, 0.01 #7482) >> Best rule #1512 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 05dxl5; 05v954; 05q_mg; 09ykwk; >> query: (?x9238, 0np9r) <- nationality(?x9238, ?x94), actor(?x5955, ?x9238), place_of_birth(?x9238, ?x12953), ?x94 = 09c7w0 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0582cf profession 0np9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 95.000 0.789 http://example.org/people/person/profession #4818-012gx2 PRED entity: 012gx2 PRED relation: student! PRED expected values: 013zdg => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 18): 01rr_d (0.25 #73, 0.18 #396, 0.14 #282), 019v9k (0.24 #218, 0.21 #180, 0.19 #237), 02h4rq6 (0.24 #212, 0.21 #174, 0.19 #231), 013zdg (0.24 #216, 0.15 #159, 0.14 #235), 07s6fsf (0.12 #96, 0.12 #210, 0.11 #115), 04zx3q1 (0.12 #97, 0.11 #116, 0.08 #154), 027f2w (0.12 #105, 0.11 #124, 0.07 #181), 0bkj86 (0.12 #103, 0.10 #1110, 0.10 #901), 02_xgp2 (0.12 #1020, 0.11 #127, 0.10 #241), 016t_3 (0.11 #118, 0.10 #232, 0.07 #384) >> Best rule #73 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0444x; >> query: (?x5804, 01rr_d) <- politician(?x8714, ?x5804), profession(?x5804, ?x3342), gender(?x5804, ?x231), place_of_birth(?x5804, ?x9544), athlete(?x1083, ?x5804) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #216 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 081t6; *> query: (?x5804, 013zdg) <- profession(?x5804, ?x5805), people(?x1446, ?x5804), student(?x1368, ?x5804), type_of_union(?x5804, ?x566), ?x5805 = 0fj9f *> conf = 0.24 ranks of expected_values: 4 EVAL 012gx2 student! 013zdg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 152.000 152.000 0.250 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #4817-05nlx4 PRED entity: 05nlx4 PRED relation: nominated_for! PRED expected values: 0gqxm => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 196): 0p9sw (0.49 #3529, 0.33 #19, 0.24 #3295), 057xs89 (0.40 #115, 0.31 #349, 0.26 #1051), 0gq9h (0.39 #3570, 0.30 #8954, 0.29 #8018), 0gq_v (0.38 #3528, 0.29 #3294, 0.21 #8912), 02r22gf (0.38 #3536, 0.25 #260, 0.21 #3302), 0k611 (0.38 #3580, 0.22 #8964, 0.22 #8028), 019f4v (0.36 #3561, 0.26 #8009, 0.25 #8945), 02qyntr (0.32 #3686, 0.18 #8134, 0.18 #9070), 02x1z2s (0.32 #2713, 0.17 #4819, 0.17 #5053), 0gr0m (0.31 #3567, 0.18 #8951, 0.17 #8015) >> Best rule #3529 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 080dwhx; 03d34x8; >> query: (?x7199, 0p9sw) <- titles(?x811, ?x7199), award(?x7199, ?x1691), nominated_for(?x6546, ?x7199), crewmember(?x781, ?x6546) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #16624 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1588 *> proper extension: 06g60w; 0c3xpwy; *> query: (?x7199, ?x749) <- nominated_for(?x1634, ?x7199), award(?x1634, ?x749) *> conf = 0.19 ranks of expected_values: 28 EVAL 05nlx4 nominated_for! 0gqxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 79.000 79.000 0.486 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4816-0f_y9 PRED entity: 0f_y9 PRED relation: artists! PRED expected values: 01lyv => 125 concepts (74 used for prediction) PRED predicted values (max 10 best out of 269): 01lyv (0.47 #5947, 0.26 #1278, 0.19 #2522), 0155w (0.39 #6020, 0.17 #2907, 0.17 #2595), 025sc50 (0.33 #2227, 0.25 #4408, 0.24 #361), 016clz (0.32 #1249, 0.26 #5607, 0.25 #10275), 05bt6j (0.32 #2221, 0.27 #12497, 0.26 #1288), 06j6l (0.32 #2225, 0.28 #4406, 0.27 #5961), 0ggx5q (0.31 #390, 0.28 #701, 0.24 #2256), 0xhtw (0.31 #1261, 0.27 #950, 0.24 #17), 0glt670 (0.26 #4399, 0.24 #1907, 0.23 #2218), 0gywn (0.25 #1924, 0.25 #680, 0.24 #2235) >> Best rule #5947 for best value: >> intensional similarity = 5 >> extensional distance = 249 >> proper extension: 03_gx; >> query: (?x7345, 01lyv) <- artists(?x3108, ?x7345), artists(?x3108, ?x7937), artists(?x3108, ?x4473), ?x4473 = 0m_31, instrumentalists(?x75, ?x7937) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f_y9 artists! 01lyv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 74.000 0.474 http://example.org/music/genre/artists #4815-01trhmt PRED entity: 01trhmt PRED relation: award PRED expected values: 02f71y 01cky2 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 311): 01d38g (0.80 #7965, 0.79 #3983, 0.78 #15138), 09sb52 (0.37 #29124, 0.32 #17171, 0.30 #19563), 0c4z8 (0.36 #7637, 0.24 #71, 0.24 #2858), 03qbh5 (0.32 #7768, 0.32 #3786, 0.31 #600), 0ck27z (0.31 #16823, 0.26 #27978, 0.20 #29973), 01cky2 (0.28 #2979, 0.22 #988, 0.19 #34267), 031b3h (0.27 #994, 0.19 #34267, 0.18 #36660), 054ks3 (0.25 #538, 0.22 #7706, 0.22 #8105), 01c99j (0.25 #620, 0.22 #7788, 0.21 #3806), 03qbnj (0.25 #7794, 0.25 #3812, 0.24 #626) >> Best rule #7965 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 01bmlb; >> query: (?x2562, ?x567) <- award_winner(?x567, ?x2562), award(?x2562, ?x724), ?x724 = 01bgqh >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2979 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 07h76; *> query: (?x2562, 01cky2) <- award_winner(?x567, ?x2562), artists(?x2937, ?x2562), ?x2937 = 0glt670 *> conf = 0.28 ranks of expected_values: 6, 11 EVAL 01trhmt award 01cky2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 120.000 120.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01trhmt award 02f71y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 120.000 120.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4814-08720 PRED entity: 08720 PRED relation: nominated_for! PRED expected values: 0p9sw => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 218): 02r22gf (0.66 #14638, 0.66 #15112, 0.66 #15111), 02g3ft (0.66 #14638, 0.66 #15112, 0.66 #15111), 0gq9h (0.47 #2186, 0.45 #1242, 0.42 #6907), 0gs9p (0.44 #2188, 0.38 #7617, 0.38 #6909), 019f4v (0.41 #2178, 0.37 #7607, 0.36 #1234), 0k611 (0.39 #2197, 0.37 #1253, 0.33 #1489), 099c8n (0.36 #1473, 0.30 #1237, 0.28 #4069), 040njc (0.35 #1187, 0.33 #2131, 0.30 #7560), 04dn09n (0.33 #2159, 0.28 #7588, 0.27 #6880), 0gq_v (0.32 #2144, 0.30 #5449, 0.30 #4976) >> Best rule #14638 for best value: >> intensional similarity = 3 >> extensional distance = 1000 >> proper extension: 015qsq; 02y_lrp; 083shs; 06wzvr; 0c0yh4; 0yyg4; 090s_0; 05jf85; 011yxg; 0gzy02; ... >> query: (?x641, ?x637) <- nominated_for(?x5734, ?x641), award(?x641, ?x637), award(?x450, ?x5734) >> conf = 0.66 => this is the best rule for 2 predicted values *> Best rule #1201 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 84 *> proper extension: 0m_mm; 0j43swk; 0bhwhj; 0gmgwnv; 0m63c; 072hx4; *> query: (?x641, 0p9sw) <- film_release_region(?x641, ?x304), honored_for(?x2988, ?x641), nominated_for(?x640, ?x641), ?x304 = 0d0vqn *> conf = 0.28 ranks of expected_values: 18 EVAL 08720 nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 92.000 92.000 0.663 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4813-01w9ph_ PRED entity: 01w9ph_ PRED relation: artists! PRED expected values: 07sbbz2 => 192 concepts (155 used for prediction) PRED predicted values (max 10 best out of 289): 03lty (0.56 #10885, 0.27 #15229, 0.27 #39136), 064t9 (0.51 #45338, 0.49 #24225, 0.47 #34468), 0155w (0.50 #1657, 0.44 #8483, 0.38 #15307), 05bt6j (0.47 #2835, 0.35 #4076, 0.30 #12141), 0cx7f (0.44 #1378, 0.29 #3860, 0.20 #1999), 03_d0 (0.42 #2184, 0.33 #39120, 0.31 #6839), 08jyyk (0.35 #3790, 0.33 #4411, 0.33 #1308), 06j6l (0.33 #8115, 0.33 #2220, 0.28 #27984), 0ggx5q (0.33 #1009, 0.27 #2871, 0.21 #8146), 016clz (0.32 #10242, 0.29 #45951, 0.27 #31977) >> Best rule #10885 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 01hw6wq; 0qf11; 018d6l; 019389; 01k47c; 01t8399; 015196; >> query: (?x8004, 03lty) <- group(?x8004, ?x6241), role(?x8004, ?x1466), artists(?x1000, ?x8004), ?x1000 = 0xhtw >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #10555 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 50 *> proper extension: 02mq_y; *> query: (?x8004, 07sbbz2) <- artists(?x7083, ?x8004), artists(?x1572, ?x8004), ?x7083 = 02yv6b, ?x1572 = 06by7, category(?x8004, ?x134) *> conf = 0.31 ranks of expected_values: 12 EVAL 01w9ph_ artists! 07sbbz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 192.000 155.000 0.564 http://example.org/music/genre/artists #4812-016zfm PRED entity: 016zfm PRED relation: titles! PRED expected values: 07c52 => 82 concepts (63 used for prediction) PRED predicted values (max 10 best out of 65): 07c52 (0.81 #451, 0.76 #763, 0.76 #972), 01hmnh (0.33 #27, 0.05 #6455, 0.05 #5935), 015w9s (0.33 #48, 0.04 #3207, 0.04 #2157), 07s9rl0 (0.33 #6325, 0.27 #6118, 0.27 #5909), 04xvlr (0.23 #6328, 0.17 #6432, 0.16 #6121), 0215n (0.20 #914, 0.10 #1337, 0.09 #1443), 07ssc (0.20 #113, 0.14 #221, 0.10 #6334), 0g5lhl7 (0.20 #143, 0.14 #251, 0.06 #4315), 09c7w0 (0.20 #206, 0.14 #314, 0.04 #1574), 01z4y (0.15 #6464, 0.14 #5944, 0.13 #6049) >> Best rule #451 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 026wlxw; >> query: (?x6248, 07c52) <- nominated_for(?x10694, ?x6248), nominated_for(?x5235, ?x6248), ?x5235 = 09qrn4 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016zfm titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 63.000 0.808 http://example.org/media_common/netflix_genre/titles #4811-03f7xg PRED entity: 03f7xg PRED relation: nominated_for! PRED expected values: 02qrbbx => 125 concepts (85 used for prediction) PRED predicted values (max 10 best out of 296): 02qrwjt (0.67 #15660, 0.67 #16372, 0.66 #19460), 09cm54 (0.67 #15660, 0.67 #16372, 0.66 #19460), 02w_6xj (0.67 #15660, 0.67 #16372, 0.66 #19460), 019f4v (0.49 #528, 0.33 #2663, 0.28 #1239), 0gq9h (0.42 #537, 0.36 #2672, 0.32 #6941), 0gq_v (0.35 #494, 0.25 #1205, 0.25 #2629), 0l8z1 (0.33 #1237, 0.27 #9963, 0.27 #15659), 0gs9p (0.33 #2674, 0.30 #539, 0.27 #6943), 040njc (0.30 #481, 0.25 #2616, 0.22 #7), 054krc (0.29 #1255, 0.27 #9963, 0.27 #15659) >> Best rule #15660 for best value: >> intensional similarity = 4 >> extensional distance = 837 >> proper extension: 02_1rq; 0kfpm; 0358x_; 0ddd0gc; 02hct1; 01b64v; 01b66d; 0phrl; 01j7mr; 0gj50; ... >> query: (?x3306, ?x1770) <- nominated_for(?x507, ?x3306), award_winner(?x3306, ?x3910), award(?x3306, ?x1770), award(?x3910, ?x1079) >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #20175 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x3306, ?x834) <- award(?x3306, ?x8843), award(?x8575, ?x8843), award(?x8575, ?x834) *> conf = 0.12 ranks of expected_values: 83 EVAL 03f7xg nominated_for! 02qrbbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 125.000 85.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4810-02p72j PRED entity: 02p72j PRED relation: colors PRED expected values: 09ggk => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 20): 01g5v (0.44 #583, 0.28 #263, 0.27 #943), 01l849 (0.35 #161, 0.28 #241, 0.26 #261), 06fvc (0.25 #582, 0.16 #762, 0.16 #742), 019sc (0.19 #167, 0.18 #747, 0.18 #767), 038hg (0.13 #961, 0.12 #172, 0.12 #352), 036k5h (0.13 #961, 0.11 #265, 0.11 #245), 04mkbj (0.13 #961, 0.10 #170, 0.09 #250), 09ggk (0.13 #961, 0.07 #176, 0.06 #256), 0jc_p (0.13 #961, 0.07 #764, 0.07 #744), 03wkwg (0.13 #961, 0.06 #255, 0.06 #275) >> Best rule #583 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 02zkz7; 02xwzh; 019vv1; >> query: (?x13191, 01g5v) <- colors(?x13191, ?x663), colors(?x9254, ?x663), ?x9254 = 03ys48 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #961 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 485 *> proper extension: 0204jh; *> query: (?x13191, ?x3189) <- colors(?x13191, ?x663), colors(?x13166, ?x663), colors(?x13166, ?x3189) *> conf = 0.13 ranks of expected_values: 8 EVAL 02p72j colors 09ggk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 151.000 151.000 0.437 http://example.org/education/educational_institution/colors #4809-0ck91 PRED entity: 0ck91 PRED relation: program PRED expected values: 06hwzy => 130 concepts (110 used for prediction) PRED predicted values (max 10 best out of 6): 06hwzy (0.04 #137, 0.02 #554, 0.02 #866), 04xbq3 (0.03 #174, 0.03 #200, 0.03 #226), 01b7h8 (0.02 #541, 0.02 #567, 0.02 #879), 0304nh (0.01 #298, 0.01 #402, 0.01 #428), 0cpz4k (0.01 #323, 0.01 #401, 0.01 #427), 01h1bf (0.01 #399, 0.01 #425) >> Best rule #137 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 029cpw; >> query: (?x11601, 06hwzy) <- film(?x11601, ?x6077), nominated_for(?x6077, ?x836), ?x836 = 02sg5v >> conf = 0.04 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ck91 program 06hwzy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 110.000 0.038 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #4808-02t8gf PRED entity: 02t8gf PRED relation: artists PRED expected values: 0jg77 => 61 concepts (27 used for prediction) PRED predicted values (max 10 best out of 948): 0pkyh (0.71 #9850, 0.50 #6647, 0.50 #1306), 0fpj4lx (0.67 #6730, 0.57 #9933, 0.57 #8865), 014_lq (0.67 #6886, 0.57 #10089, 0.57 #9021), 0ycp3 (0.67 #7019, 0.57 #10222, 0.57 #9154), 0150jk (0.67 #6456, 0.57 #9659, 0.57 #8591), 01386_ (0.60 #5918, 0.57 #10188, 0.50 #12328), 011_vz (0.60 #6180, 0.50 #12590, 0.50 #8315), 04qzm (0.60 #6269, 0.50 #8404, 0.50 #3062), 01vw20_ (0.57 #9855, 0.50 #6652, 0.50 #2378), 01gx5f (0.57 #9902, 0.50 #7767, 0.50 #6699) >> Best rule #9850 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 0dl5d; >> query: (?x9248, 0pkyh) <- artists(?x9248, ?x5227), artists(?x9248, ?x3657), group(?x645, ?x5227), ?x645 = 028tv0, category(?x5227, ?x134), parent_genre(?x9248, ?x2249), artists(?x3642, ?x5227), ?x3642 = 0dls3, ?x3657 = 01w8n89 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #17091 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 20 *> proper extension: 018lg0; 01g888; 01cbwl; 0cx7f; *> query: (?x9248, ?x7013) <- artists(?x9248, ?x5227), artists(?x9248, ?x3657), group(?x645, ?x5227), ?x645 = 028tv0, category(?x5227, ?x134), parent_genre(?x9248, ?x2249), artists(?x3642, ?x5227), ?x3642 = 0dls3, group(?x3657, ?x7013) *> conf = 0.52 ranks of expected_values: 32 EVAL 02t8gf artists 0jg77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 61.000 27.000 0.714 http://example.org/music/genre/artists #4807-06929s PRED entity: 06929s PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 28): 0ch6mp2 (0.63 #1273, 0.62 #1194, 0.61 #1590), 09zzb8 (0.62 #1186, 0.62 #709, 0.61 #827), 02r96rf (0.62 #238, 0.61 #712, 0.61 #830), 09vw2b7 (0.57 #242, 0.56 #834, 0.55 #716), 01vx2h (0.32 #722, 0.31 #1119, 0.31 #840), 0dxtw (0.32 #443, 0.32 #561, 0.31 #839), 01pvkk (0.32 #93, 0.25 #367, 0.23 #524), 02ynfr (0.20 #19, 0.14 #646, 0.14 #1561), 01xy5l_ (0.18 #95, 0.14 #251, 0.13 #134), 02rh1dz (0.15 #207, 0.14 #246, 0.13 #442) >> Best rule #1273 for best value: >> intensional similarity = 4 >> extensional distance = 383 >> proper extension: 0gtsx8c; >> query: (?x4312, 0ch6mp2) <- film(?x2972, ?x4312), executive_produced_by(?x4312, ?x12254), language(?x4312, ?x254), country(?x4312, ?x94) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06929s film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.626 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4806-06ms6 PRED entity: 06ms6 PRED relation: major_field_of_study! PRED expected values: 0bjrnt 019v9k 071tyz => 77 concepts (70 used for prediction) PRED predicted values (max 10 best out of 16): 019v9k (0.82 #374, 0.81 #544, 0.80 #423), 071tyz (0.67 #34, 0.49 #758, 0.43 #218), 01ysy9 (0.50 #234, 0.50 #32, 0.49 #758), 02m4yg (0.50 #61, 0.43 #218, 0.40 #452), 0bjrnt (0.49 #758, 0.47 #338, 0.44 #203), 022h5x (0.49 #758, 0.43 #218, 0.40 #452), 027f2w (0.49 #758, 0.43 #218, 0.40 #452), 01rr_d (0.49 #758, 0.43 #218, 0.40 #452), 013zdg (0.49 #758, 0.43 #218, 0.40 #452), 028dcg (0.49 #758, 0.43 #218, 0.40 #452) >> Best rule #374 for best value: >> intensional similarity = 11 >> extensional distance = 15 >> proper extension: 02h40lc; 05qjt; 036hv; 02ky346; 04x_3; 05qfh; 04gb7; 04g51; 037mh8; 04rlf; ... >> query: (?x1695, 019v9k) <- major_field_of_study(?x5288, ?x1695), major_field_of_study(?x4410, ?x1695), major_field_of_study(?x2621, ?x1695), organization(?x2621, ?x5487), major_field_of_study(?x734, ?x1695), student(?x1695, ?x3806), organization(?x5510, ?x2621), ?x5288 = 02zd460, major_field_of_study(?x373, ?x1695), taxonomy(?x1695, ?x939), service_location(?x4410, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5 EVAL 06ms6 major_field_of_study! 071tyz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 70.000 0.824 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 06ms6 major_field_of_study! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 70.000 0.824 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 06ms6 major_field_of_study! 0bjrnt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 70.000 0.824 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #4805-09v8db5 PRED entity: 09v8db5 PRED relation: nominated_for PRED expected values: 0df92l 0gl02yg => 58 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1774): 0233bn (0.72 #4768, 0.68 #46131, 0.67 #44541), 0df92l (0.67 #4074, 0.60 #2485, 0.50 #896), 01f85k (0.60 #2593, 0.55 #5772, 0.50 #1004), 027m67 (0.60 #2705, 0.50 #1116, 0.45 #5884), 01f8gz (0.60 #1815, 0.50 #226, 0.44 #3404), 0gl02yg (0.60 #2493, 0.50 #904, 0.36 #5672), 02qd04y (0.50 #1353, 0.40 #2942, 0.36 #6121), 031ldd (0.40 #2518, 0.25 #929, 0.22 #4107), 01f8f7 (0.33 #4236, 0.25 #1058, 0.20 #2647), 09gq0x5 (0.26 #32065, 0.21 #40026, 0.21 #36841) >> Best rule #4768 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02x258x; 05ztrmj; >> query: (?x5923, ?x7502) <- nominated_for(?x5923, ?x3376), ?x3376 = 05g8pg, award(?x7502, ?x5923), award(?x754, ?x5923) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #4074 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 02x258x; 05ztrmj; *> query: (?x5923, 0df92l) <- nominated_for(?x5923, ?x3376), ?x3376 = 05g8pg, award(?x7502, ?x5923), award(?x754, ?x5923) *> conf = 0.67 ranks of expected_values: 2, 6 EVAL 09v8db5 nominated_for 0gl02yg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 58.000 31.000 0.717 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09v8db5 nominated_for 0df92l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 58.000 31.000 0.717 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4804-0d_wms PRED entity: 0d_wms PRED relation: genre PRED expected values: 02l7c8 => 144 concepts (68 used for prediction) PRED predicted values (max 10 best out of 97): 07s9rl0 (0.72 #2623, 0.72 #7511, 0.71 #2266), 01jfsb (0.62 #846, 0.60 #1203, 0.54 #4898), 01hmnh (0.62 #851, 0.60 #613, 0.53 #2265), 03k9fj (0.58 #1440, 0.57 #2515, 0.56 #1083), 05p553 (0.56 #2745, 0.46 #1551, 0.42 #1909), 0lsxr (0.43 #1675, 0.41 #3465, 0.33 #2274), 060__y (0.42 #1445, 0.25 #374, 0.24 #2639), 0bkbm (0.40 #1230, 0.20 #2185, 0.14 #2424), 02l7c8 (0.33 #7049, 0.33 #7526, 0.32 #5738), 03npn (0.33 #7, 0.25 #364, 0.12 #840) >> Best rule #2623 for best value: >> intensional similarity = 6 >> extensional distance = 23 >> proper extension: 017gl1; >> query: (?x3847, 07s9rl0) <- written_by(?x3847, ?x8209), genre(?x3847, ?x225), place_of_birth(?x8209, ?x4090), honored_for(?x3847, ?x7425), honored_for(?x5592, ?x3847), film(?x773, ?x7425) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #7049 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 106 *> proper extension: 0ds35l9; 0b6tzs; 02r1c18; 016z7s; 0k4f3; 0b_5d; 015g28; 0k4fz; 0421ng; 04t9c0; ... *> query: (?x3847, 02l7c8) <- written_by(?x3847, ?x8209), genre(?x3847, ?x225), film(?x773, ?x3847), honored_for(?x5592, ?x3847), award_winner(?x11928, ?x8209) *> conf = 0.33 ranks of expected_values: 9 EVAL 0d_wms genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 144.000 68.000 0.720 http://example.org/film/film/genre #4803-0j_sncb PRED entity: 0j_sncb PRED relation: school_type PRED expected values: 01_9fk 05jxkf => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.62 #196, 0.58 #292, 0.56 #220), 01_9fk (0.35 #194, 0.31 #290, 0.29 #146), 07tf8 (0.33 #33, 0.31 #81, 0.29 #153), 05pcjw (0.33 #25, 0.31 #73, 0.24 #241), 01rs41 (0.26 #989, 0.25 #269, 0.25 #965), 01_srz (0.07 #483, 0.07 #555, 0.06 #987), 06cs1 (0.06 #78, 0.03 #126, 0.03 #150), 04399 (0.04 #590, 0.04 #422, 0.04 #614), 02p0qmm (0.04 #370, 0.04 #442, 0.03 #730), 04qbv (0.03 #112, 0.03 #136, 0.03 #160) >> Best rule #196 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 0frm7n; >> query: (?x2948, 05jxkf) <- school(?x1883, ?x2948), school(?x4469, ?x2948), position(?x4469, ?x180) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0j_sncb school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.618 http://example.org/education/educational_institution/school_type EVAL 0j_sncb school_type 01_9fk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.618 http://example.org/education/educational_institution/school_type #4802-03q43g PRED entity: 03q43g PRED relation: nominated_for PRED expected values: 0d68qy => 83 concepts (40 used for prediction) PRED predicted values (max 10 best out of 253): 0vjr (0.46 #856, 0.03 #4100, 0.02 #47902), 02hct1 (0.45 #48671, 0.38 #361, 0.36 #4867), 0661m4p (0.25 #53545, 0.24 #61661, 0.22 #56791), 05sxzwc (0.25 #53545, 0.24 #61661, 0.22 #56791), 084qpk (0.25 #53545, 0.24 #61661, 0.22 #56791), 03bx2lk (0.25 #53545, 0.24 #61661, 0.22 #56791), 04180vy (0.25 #53545, 0.24 #61661, 0.22 #56791), 0gldyz (0.25 #53545, 0.24 #61661, 0.22 #56791), 0d68qy (0.08 #374, 0.05 #3618, 0.03 #8485), 05t0_2v (0.08 #933, 0.02 #64913) >> Best rule #856 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 032xhg; 016z2j; 08k881; 03q3sy; 05sdxx; >> query: (?x6569, 0vjr) <- award_nominee(?x6569, ?x5899), profession(?x6569, ?x987), ?x5899 = 01pcz9 >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #374 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 032xhg; 016z2j; 08k881; 03q3sy; 05sdxx; *> query: (?x6569, 0d68qy) <- award_nominee(?x6569, ?x5899), profession(?x6569, ?x987), ?x5899 = 01pcz9 *> conf = 0.08 ranks of expected_values: 9 EVAL 03q43g nominated_for 0d68qy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 83.000 40.000 0.462 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4801-05r5c PRED entity: 05r5c PRED relation: role! PRED expected values: 0214km => 75 concepts (64 used for prediction) PRED predicted values (max 10 best out of 48): 01vdm0 (0.83 #515, 0.81 #565, 0.80 #342), 05842k (0.83 #515, 0.81 #495, 0.80 #342), 0mkg (0.83 #515, 0.80 #342, 0.80 #1823), 0dwt5 (0.83 #515, 0.80 #342, 0.80 #1823), 042v_gx (0.83 #515, 0.80 #342, 0.80 #1823), 02dlh2 (0.83 #515, 0.80 #342, 0.80 #1823), 0l14qv (0.83 #515, 0.80 #342, 0.80 #1823), 06w7v (0.83 #515, 0.80 #342, 0.80 #1823), 0dq630k (0.83 #515, 0.80 #342, 0.80 #1823), 0l1589 (0.83 #515, 0.80 #342, 0.80 #1823) >> Best rule #515 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 01bns_; >> query: (?x316, ?x74) <- instrumentalists(?x316, ?x8490), instrumentalists(?x316, ?x1413), role(?x316, ?x74), role(?x483, ?x316), family(?x2253, ?x316), artists(?x302, ?x8490), profession(?x1413, ?x220) >> conf = 0.83 => this is the best rule for 15 predicted values *> Best rule #465 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 12 *> proper extension: 05842k; *> query: (?x316, 0214km) <- role(?x74, ?x316), role(?x316, ?x569), role(?x7084, ?x316), role(?x4701, ?x316), ?x7084 = 01vs4ff, group(?x316, ?x997), award_winner(?x1854, ?x4701) *> conf = 0.71 ranks of expected_values: 19 EVAL 05r5c role! 0214km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 75.000 64.000 0.831 http://example.org/music/performance_role/track_performances./music/track_contribution/role #4800-028kk_ PRED entity: 028kk_ PRED relation: profession! PRED expected values: 01pr_j6 0pmw9 0bvzp 02sjp => 51 concepts (12 used for prediction) PRED predicted values (max 10 best out of 4047): 0ddkf (0.82 #40196, 0.67 #27539, 0.64 #35977), 02cx90 (0.73 #39334, 0.67 #26677, 0.67 #22458), 01vsy7t (0.73 #39443, 0.67 #26786, 0.64 #35224), 0473q (0.67 #27670, 0.67 #23451, 0.64 #40327), 014q2g (0.67 #26127, 0.67 #21908, 0.60 #17690), 02fybl (0.67 #27645, 0.67 #23426, 0.60 #19208), 01ydzx (0.67 #27511, 0.67 #23292, 0.60 #19074), 03j24kf (0.67 #22600, 0.64 #39476, 0.64 #35257), 0161c2 (0.67 #22016, 0.64 #34673, 0.60 #17798), 01w02sy (0.67 #22014, 0.64 #34671, 0.60 #17796) >> Best rule #40196 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 029bkp; >> query: (?x8353, 0ddkf) <- profession(?x3890, ?x8353), profession(?x1413, ?x8353), nominated_for(?x2124, ?x1413), artists(?x5300, ?x3890), artists(?x284, ?x3890), instrumentalists(?x75, ?x3890), ?x5300 = 02k_kn, ?x284 = 0827d >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #32548 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 01d30f; 05vyk; *> query: (?x8353, 02sjp) <- profession(?x8978, ?x8353), profession(?x1413, ?x8353), ?x8978 = 01wg6y, artists(?x378, ?x1413), role(?x1413, ?x227), award_winner(?x341, ?x1413) *> conf = 0.38 ranks of expected_values: 504, 506, 509, 629 EVAL 028kk_ profession! 02sjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 12.000 0.818 http://example.org/people/person/profession EVAL 028kk_ profession! 0bvzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 12.000 0.818 http://example.org/people/person/profession EVAL 028kk_ profession! 0pmw9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 12.000 0.818 http://example.org/people/person/profession EVAL 028kk_ profession! 01pr_j6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 12.000 0.818 http://example.org/people/person/profession #4799-01vrx35 PRED entity: 01vrx35 PRED relation: award PRED expected values: 02qvyrt => 114 concepts (105 used for prediction) PRED predicted values (max 10 best out of 291): 01by1l (0.36 #6158, 0.33 #113, 0.32 #13412), 01bgqh (0.33 #43, 0.32 #2058, 0.28 #6088), 02g3gj (0.33 #25, 0.07 #831, 0.06 #2040), 054krc (0.27 #7745, 0.15 #36679, 0.14 #31436), 09sb52 (0.25 #17773, 0.25 #20997, 0.24 #20594), 0c4z8 (0.25 #1684, 0.22 #72, 0.22 #2893), 054ks3 (0.23 #7799, 0.23 #2157, 0.19 #1754), 02qvyrt (0.22 #7784, 0.15 #530, 0.14 #37487), 0gqz2 (0.22 #81, 0.20 #7738, 0.18 #2096), 03qbnj (0.22 #233, 0.14 #2248, 0.12 #6278) >> Best rule #6158 for best value: >> intensional similarity = 3 >> extensional distance = 182 >> proper extension: 016qtt; 05cljf; 01q_ph; 0147dk; 03f2_rc; 0168cl; 01wmxfs; 01w61th; 01kwlwp; 01vrt_c; ... >> query: (?x7668, 01by1l) <- award_nominee(?x3910, ?x7668), artist(?x382, ?x7668), people(?x1050, ?x7668) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #7784 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 203 *> proper extension: 01nqfh_; 01nc3rh; *> query: (?x7668, 02qvyrt) <- artists(?x1572, ?x7668), nominated_for(?x7668, ?x2852) *> conf = 0.22 ranks of expected_values: 8 EVAL 01vrx35 award 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 114.000 105.000 0.359 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4798-02qdymm PRED entity: 02qdymm PRED relation: student! PRED expected values: 01w3v => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 60): 0bwfn (0.09 #275, 0.06 #4491, 0.06 #1329), 03ksy (0.04 #633, 0.04 #18446, 0.04 #9065), 065y4w7 (0.04 #1068, 0.04 #6865, 0.04 #5284), 01w5m (0.04 #18446, 0.04 #1686, 0.03 #2740), 09f2j (0.04 #18446, 0.04 #1213, 0.03 #4375), 01w3v (0.04 #18446, 0.03 #15, 0.03 #542), 01d34b (0.04 #18446, 0.03 #256, 0.01 #1310), 03bmmc (0.04 #18446, 0.03 #196), 0fr9jp (0.04 #18446, 0.03 #1399, 0.02 #4034), 08815 (0.04 #18446, 0.03 #6853, 0.03 #3691) >> Best rule #275 for best value: >> intensional similarity = 4 >> extensional distance = 85 >> proper extension: 02x8kk; 02x8mt; >> query: (?x11389, 0bwfn) <- nationality(?x11389, ?x94), place_of_birth(?x11389, ?x2850), ?x2850 = 0cr3d, ?x94 = 09c7w0 >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #18446 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2040 *> proper extension: 0ct_yc; *> query: (?x11389, ?x735) <- nationality(?x11389, ?x94), place_of_birth(?x11389, ?x2850), place_of_birth(?x13298, ?x2850), student(?x735, ?x13298) *> conf = 0.04 ranks of expected_values: 6 EVAL 02qdymm student! 01w3v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 78.000 78.000 0.092 http://example.org/education/educational_institution/students_graduates./education/education/student #4797-07jjt PRED entity: 07jjt PRED relation: country PRED expected values: 047lj 01ls2 06mkj 09lxtg => 40 concepts (38 used for prediction) PRED predicted values (max 10 best out of 312): 06mkj (0.89 #3350, 0.85 #4644, 0.83 #3744), 015fr (0.89 #3350, 0.85 #4616, 0.82 #524), 0d0vqn (0.89 #3350, 0.83 #4053, 0.83 #3887), 0b90_r (0.89 #3350, 0.83 #3884, 0.82 #524), 03gj2 (0.89 #3350, 0.82 #524, 0.82 #173), 047lj (0.83 #3889, 0.82 #173, 0.78 #3360), 06c1y (0.82 #524, 0.82 #173, 0.79 #2459), 06mzp (0.82 #524, 0.82 #173, 0.79 #2459), 0h7x (0.82 #524, 0.82 #173, 0.79 #2459), 01znc_ (0.82 #524, 0.82 #173, 0.79 #2459) >> Best rule #3350 for best value: >> intensional similarity = 50 >> extensional distance = 7 >> proper extension: 0w0d; >> query: (?x2885, ?x792) <- sports(?x7688, ?x2885), sports(?x6464, ?x2885), sports(?x3729, ?x2885), sports(?x2553, ?x2885), sports(?x2043, ?x2885), sports(?x778, ?x2885), sports(?x358, ?x2885), ?x3729 = 0jdk_, olympics(?x3728, ?x2043), olympics(?x1264, ?x2043), olympics(?x1023, ?x2043), olympics(?x792, ?x2043), olympics(?x304, ?x2043), olympics(?x2885, ?x1608), country(?x2885, ?x5147), country(?x2885, ?x1203), ?x304 = 0d0vqn, sports(?x2043, ?x471), form_of_government(?x5147, ?x48), nationality(?x5769, ?x5147), nationality(?x4258, ?x5147), ?x3728 = 087vz, ?x4258 = 0dzc16, ?x471 = 02vx4, capital(?x5147, ?x10708), ?x1264 = 0345h, ?x1023 = 0ctw_b, ?x2553 = 016r9z, organization(?x5147, ?x312), ?x1203 = 07ylj, olympics(?x5147, ?x1931), entity_involved(?x7455, ?x5147), ?x5769 = 03_wvl, sports(?x778, ?x1352), film_release_region(?x6761, ?x792), film_release_region(?x4998, ?x792), film_release_region(?x3191, ?x792), olympics(?x1499, ?x778), olympics(?x47, ?x778), administrative_area_type(?x792, ?x2792), adjoins(?x792, ?x4421), ?x4998 = 0dzlbx, ?x47 = 027rn, medal(?x358, ?x422), ?x7688 = 0jkvj, ?x1499 = 01znc_, jurisdiction_of_office(?x182, ?x792), ?x3191 = 0crc2cp, ?x6761 = 05ft32, locations(?x6464, ?x6959) >> conf = 0.89 => this is the best rule for 5 predicted values ranks of expected_values: 1, 6, 30, 44 EVAL 07jjt country 09lxtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 40.000 38.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07jjt country 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 38.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07jjt country 01ls2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 40.000 38.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07jjt country 047lj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 40.000 38.000 0.886 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #4796-029m83 PRED entity: 029m83 PRED relation: location PRED expected values: 0sq2v => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 124): 04jpl (0.20 #17, 0.06 #1621, 0.06 #34514), 01m1_d (0.20 #674, 0.03 #2278, 0.01 #3882), 030qb3t (0.18 #11313, 0.16 #36986, 0.16 #34579), 027l4q (0.14 #11231, 0.10 #36102, 0.09 #1299), 0cr3d (0.13 #13782, 0.10 #11375, 0.09 #21807), 0fhp9 (0.09 #3250, 0.05 #4053, 0.04 #10470), 0cc56 (0.06 #13694, 0.06 #8078, 0.05 #24126), 01531 (0.06 #13795, 0.05 #8179, 0.04 #21820), 0ccvx (0.05 #13859, 0.05 #1023, 0.04 #21884), 013yq (0.05 #11349, 0.04 #4931, 0.03 #1722) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02z6l5f; >> query: (?x8041, 04jpl) <- profession(?x8041, ?x319), executive_produced_by(?x4963, ?x8041), ?x4963 = 0194zl >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 029m83 location 0sq2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 111.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location #4795-0g83dv PRED entity: 0g83dv PRED relation: film! PRED expected values: 01r93l => 86 concepts (48 used for prediction) PRED predicted values (max 10 best out of 814): 05th8t (0.25 #447, 0.13 #2081, 0.03 #2528), 0c3p7 (0.25 #1116, 0.13 #2081, 0.03 #64490), 07lmxq (0.25 #87, 0.13 #2081, 0.03 #64490), 0h1nt (0.25 #196, 0.13 #2081, 0.03 #64490), 07s8r0 (0.25 #263, 0.13 #2081, 0.03 #64490), 025t9b (0.25 #668, 0.13 #2081, 0.03 #64490), 06t74h (0.25 #696, 0.13 #2081, 0.03 #64490), 03zz8b (0.25 #1280, 0.13 #2081, 0.03 #64490), 053y4h (0.25 #917, 0.13 #2081, 0.03 #64490), 026l37 (0.25 #810, 0.13 #2081, 0.03 #64490) >> Best rule #447 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02_1rq; >> query: (?x4158, 05th8t) <- nominated_for(?x4254, ?x4158), nominated_for(?x540, ?x4158), ?x540 = 06jzh, award_nominee(?x4254, ?x57) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #17387 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 301 *> proper extension: 03xj05; *> query: (?x4158, 01r93l) <- film(?x166, ?x4158), film_crew_role(?x4158, ?x137), category(?x4158, ?x134) *> conf = 0.02 ranks of expected_values: 410 EVAL 0g83dv film! 01r93l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 86.000 48.000 0.250 http://example.org/film/actor/film./film/performance/film #4794-04t36 PRED entity: 04t36 PRED relation: genre! PRED expected values: 0fkwzs => 86 concepts (48 used for prediction) PRED predicted values (max 10 best out of 438): 03ln8b (0.50 #1796, 0.33 #3861, 0.33 #3565), 02v5xg (0.50 #1938, 0.33 #4003, 0.33 #3707), 06xkst (0.50 #1989, 0.33 #4054, 0.33 #3758), 099pks (0.47 #5407, 0.33 #1278, 0.33 #984), 01h72l (0.47 #5345, 0.33 #1216, 0.33 #922), 020qr4 (0.45 #5018, 0.41 #5312, 0.33 #889), 0gxr1c (0.45 #5286, 0.35 #5580, 0.33 #862), 03g9xj (0.45 #5216, 0.33 #204, 0.30 #4923), 05f7w84 (0.41 #5415, 0.33 #992, 0.33 #697), 02q_x_l (0.40 #4909, 0.36 #5202, 0.33 #190) >> Best rule #1796 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02l7c8; >> query: (?x307, 03ln8b) <- titles(?x307, ?x1009), genre(?x8575, ?x307), film_release_region(?x1009, ?x94), ?x8575 = 0qmfz, award_winner(?x1009, ?x2671) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5471 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 0c4xc; 025s89p; *> query: (?x307, 0fkwzs) <- genre(?x6597, ?x307), program(?x2776, ?x6597), actor(?x6597, ?x988), genre(?x6597, ?x53), titles(?x512, ?x6597) *> conf = 0.35 ranks of expected_values: 26 EVAL 04t36 genre! 0fkwzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 86.000 48.000 0.500 http://example.org/tv/tv_program/genre #4793-04jpl PRED entity: 04jpl PRED relation: month PRED expected values: 040fv => 208 concepts (208 used for prediction) PRED predicted values (max 10 best out of 1): 040fv (0.83 #46, 0.83 #35, 0.79 #26) >> Best rule #46 for best value: >> intensional similarity = 2 >> extensional distance = 52 >> proper extension: 03czqs; >> query: (?x362, 040fv) <- mode_of_transportation(?x362, ?x4272), month(?x362, ?x1459) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04jpl month 040fv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 208.000 208.000 0.833 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #4792-0136g9 PRED entity: 0136g9 PRED relation: nominated_for PRED expected values: 01b7h8 => 83 concepts (51 used for prediction) PRED predicted values (max 10 best out of 326): 03ydlnj (0.50 #16183, 0.36 #17802, 0.08 #8091), 02vqsll (0.38 #3687, 0.36 #6923, 0.33 #5305), 0ctb4g (0.33 #512, 0.27 #6984, 0.25 #3748), 027tbrc (0.27 #6834, 0.17 #362, 0.12 #3598), 0gy7bj4 (0.25 #4676, 0.22 #6294, 0.18 #7912), 011yd2 (0.25 #3565, 0.22 #5183, 0.18 #6801), 02hct1 (0.25 #3596, 0.22 #5214, 0.18 #6832), 072zl1 (0.18 #7608, 0.17 #1136, 0.12 #4372), 0bpx1k (0.17 #427, 0.12 #3663, 0.11 #5281), 0g4pl7z (0.17 #1348, 0.12 #4584, 0.11 #6202) >> Best rule #16183 for best value: >> intensional similarity = 2 >> extensional distance = 304 >> proper extension: 0fx02; 012x2b; 0dr5y; 0k_mt; 03p01x; >> query: (?x1367, ?x2029) <- nationality(?x1367, ?x512), written_by(?x2029, ?x1367) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #80943 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1906 *> proper extension: 0338lq; *> query: (?x1367, ?x2447) <- award_nominee(?x3568, ?x1367), award(?x1367, ?x68), nominated_for(?x3568, ?x2447) *> conf = 0.07 ranks of expected_values: 58 EVAL 0136g9 nominated_for 01b7h8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 83.000 51.000 0.496 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4791-02f76h PRED entity: 02f76h PRED relation: award! PRED expected values: 01wn718 0bqvs2 0k6yt1 => 41 concepts (13 used for prediction) PRED predicted values (max 10 best out of 2128): 01vsgrn (0.82 #16796, 0.79 #30235, 0.79 #13436), 018n6m (0.79 #30235, 0.79 #13436, 0.79 #23517), 0fhxv (0.50 #11413, 0.50 #8054, 0.44 #4695), 09889g (0.50 #11514, 0.50 #8155, 0.33 #4796), 02z4b_8 (0.50 #12134, 0.50 #8775, 0.33 #5416), 0g824 (0.50 #11930, 0.50 #8571, 0.22 #5212), 0dvqq (0.50 #10704, 0.44 #3986, 0.40 #7345), 017959 (0.50 #12807, 0.44 #6089, 0.40 #9448), 01xzb6 (0.50 #8249, 0.44 #11608, 0.33 #4890), 0gdh5 (0.50 #7472, 0.44 #10831, 0.22 #4113) >> Best rule #16796 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 02qkk9_; 02q3s; >> query: (?x3391, ?x5536) <- award_winner(?x3391, ?x5536), currency(?x5536, ?x170), award(?x5536, ?x4018), gender(?x5536, ?x231), ?x4018 = 03qbh5 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1104 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 01c9dd; 023vrq; *> query: (?x3391, 01wn718) <- award_winner(?x3391, ?x6715), award(?x2737, ?x3391), award(?x2031, ?x3391), ?x6715 = 011z3g, artist(?x5666, ?x2031), ?x2737 = 0126y2 *> conf = 0.25 ranks of expected_values: 89, 99, 100 EVAL 02f76h award! 0k6yt1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 41.000 13.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f76h award! 0bqvs2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 41.000 13.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f76h award! 01wn718 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 41.000 13.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4790-03qlv7 PRED entity: 03qlv7 PRED relation: role! PRED expected values: 0f6lx => 74 concepts (43 used for prediction) PRED predicted values (max 10 best out of 893): 050z2 (0.67 #11915, 0.67 #7227, 0.62 #10514), 04bpm6 (0.67 #7111, 0.60 #5699, 0.58 #13203), 0l12d (0.67 #7107, 0.60 #5695, 0.57 #8044), 023l9y (0.67 #7720, 0.60 #5840, 0.56 #11940), 01wxdn3 (0.67 #7920, 0.60 #6040, 0.56 #12140), 01vs4ff (0.67 #7344, 0.60 #5932, 0.50 #7812), 016ntp (0.67 #7182, 0.57 #8119, 0.56 #11870), 0j6cj (0.67 #7389, 0.56 #12077, 0.50 #10676), 045zr (0.67 #7149, 0.56 #11837, 0.50 #7617), 0m_v0 (0.67 #7203, 0.56 #11891, 0.50 #9544) >> Best rule #11915 for best value: >> intensional similarity = 19 >> extensional distance = 7 >> proper extension: 0l14qv; >> query: (?x1332, 050z2) <- role(?x1332, ?x5417), role(?x1332, ?x2675), role(?x1332, ?x569), role(?x1332, ?x214), role(?x1332, ?x75), instrumentalists(?x1332, ?x5815), ?x569 = 07c6l, ?x75 = 07y_7, role(?x432, ?x1332), gender(?x5815, ?x231), role(?x885, ?x1332), ?x214 = 02pprs, award_nominee(?x5815, ?x2662), profession(?x5815, ?x563), role(?x2675, ?x7869), ?x7869 = 0l14v3, role(?x2157, ?x5417), instrumentalists(?x5417, ?x367), ?x2157 = 011_6p >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2816 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 2 *> proper extension: 05r5c; *> query: (?x1332, ?x3146) <- role(?x1332, ?x3239), role(?x1332, ?x1750), role(?x1332, ?x716), role(?x1332, ?x569), instrumentalists(?x1332, ?x120), ?x569 = 07c6l, ?x3239 = 03qmg1, group(?x1332, ?x3516), role(?x716, ?x2764), role(?x716, ?x2048), role(?x716, ?x1662), role(?x9762, ?x716), ?x9762 = 03f1zhf, instrumentalists(?x716, ?x8556), role(?x677, ?x716), group(?x716, ?x379), ?x1662 = 02bxd, ?x8556 = 01wqflx, ?x2764 = 01s0ps, ?x1750 = 02hnl, role(?x3146, ?x2048) *> conf = 0.40 ranks of expected_values: 270 EVAL 03qlv7 role! 0f6lx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 74.000 43.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #4789-0ynfz PRED entity: 0ynfz PRED relation: contains! PRED expected values: 05fky => 136 concepts (66 used for prediction) PRED predicted values (max 10 best out of 253): 05fky (0.81 #43824, 0.79 #37561, 0.71 #36666), 01n7q (0.21 #37638, 0.20 #51956, 0.19 #56427), 04_1l0v (0.19 #53223, 0.18 #49645, 0.18 #18333), 07b_l (0.16 #18104, 0.14 #50311, 0.12 #3799), 059rby (0.14 #19, 0.11 #7173, 0.11 #4491), 07ssc (0.13 #34907, 0.12 #41171, 0.12 #42065), 0kpys (0.11 #37741, 0.10 #1969, 0.08 #53847), 02xry (0.11 #53829, 0.07 #37723, 0.06 #22517), 05kkh (0.10 #10738, 0.09 #16997, 0.08 #16103), 04tgp (0.09 #3857, 0.05 #25317, 0.04 #50369) >> Best rule #43824 for best value: >> intensional similarity = 3 >> extensional distance = 167 >> proper extension: 0mnm2; >> query: (?x9333, ?x4198) <- county_seat(?x13475, ?x9333), contains(?x94, ?x9333), contains(?x4198, ?x13475) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ynfz contains! 05fky CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 66.000 0.806 http://example.org/location/location/contains #4788-0bzknt PRED entity: 0bzknt PRED relation: ceremony! PRED expected values: 0gqng 0f4x7 0l8z1 0gr42 => 36 concepts (32 used for prediction) PRED predicted values (max 10 best out of 353): 0f4x7 (0.86 #3931, 0.86 #4911, 0.86 #3197), 0gr42 (0.84 #3496, 0.77 #4966, 0.76 #3986), 0l8z1 (0.81 #2240, 0.78 #3220, 0.76 #1995), 0gqng (0.81 #2201, 0.76 #3181, 0.76 #1956), 01by1l (0.73 #1287, 0.44 #2514, 0.42 #1462), 025mb9 (0.73 #1349, 0.44 #2576, 0.25 #5628), 02nbqh (0.73 #1291, 0.44 #2518, 0.25 #5628), 02v1m7 (0.73 #1288, 0.44 #2515, 0.11 #1045), 02wh75 (0.73 #1224, 0.44 #2451, 0.11 #981), 02hdky (0.73 #1421, 0.44 #2648, 0.11 #1178) >> Best rule #3931 for best value: >> intensional similarity = 13 >> extensional distance = 57 >> proper extension: 05qb8vx; 0c53zb; >> query: (?x5924, 0f4x7) <- award_winner(?x5924, ?x8081), award_winner(?x5924, ?x669), honored_for(?x5924, ?x3992), language(?x3992, ?x254), ceremony(?x3066, ?x5924), award(?x92, ?x3066), nominated_for(?x8081, ?x758), nominated_for(?x3066, ?x6222), award_winner(?x537, ?x8081), ?x6222 = 01jw67, award_winner(?x3066, ?x395), award(?x669, ?x1079), music(?x670, ?x669) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0bzknt ceremony! 0gr42 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 32.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzknt ceremony! 0l8z1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 32.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzknt ceremony! 0f4x7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 32.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0bzknt ceremony! 0gqng CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 32.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony #4787-07ym47 PRED entity: 07ym47 PRED relation: artists PRED expected values: 03c3yf 06mj4 => 55 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1059): 011z3g (0.67 #7060, 0.31 #11375, 0.26 #12454), 03t9sp (0.60 #2277, 0.55 #3354, 0.54 #5505), 01vxlbm (0.47 #6797, 0.21 #7874, 0.18 #3569), 02k5sc (0.47 #7166, 0.21 #11481, 0.20 #1783), 0197tq (0.40 #1088, 0.33 #6471, 0.33 #12), 0163m1 (0.40 #1424, 0.33 #6807, 0.33 #348), 02mslq (0.40 #1109, 0.33 #33, 0.27 #6492), 01wg6y (0.40 #1901, 0.33 #825, 0.20 #7284), 0135xb (0.40 #2798, 0.33 #644, 0.20 #1720), 01dhjz (0.40 #1913, 0.33 #837, 0.20 #2991) >> Best rule #7060 for best value: >> intensional similarity = 9 >> extensional distance = 13 >> proper extension: 02lnbg; >> query: (?x5424, 011z3g) <- artists(?x5424, ?x8156), artists(?x5424, ?x7906), artists(?x5424, ?x3390), artists(?x5424, ?x2698), ?x8156 = 046p9, origin(?x3390, ?x739), instrumentalists(?x316, ?x7906), gender(?x7906, ?x231), award_nominee(?x2698, ?x217) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7143 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 13 *> proper extension: 02lnbg; *> query: (?x5424, 03c3yf) <- artists(?x5424, ?x8156), artists(?x5424, ?x7906), artists(?x5424, ?x3390), artists(?x5424, ?x2698), ?x8156 = 046p9, origin(?x3390, ?x739), instrumentalists(?x316, ?x7906), gender(?x7906, ?x231), award_nominee(?x2698, ?x217) *> conf = 0.33 ranks of expected_values: 63, 344 EVAL 07ym47 artists 06mj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 23.000 0.667 http://example.org/music/genre/artists EVAL 07ym47 artists 03c3yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 55.000 23.000 0.667 http://example.org/music/genre/artists #4786-01kkx2 PRED entity: 01kkx2 PRED relation: film PRED expected values: 0cwy47 => 128 concepts (100 used for prediction) PRED predicted values (max 10 best out of 1007): 015pnb (0.57 #107485, 0.55 #84195, 0.52 #94944), 02z3r8t (0.12 #1899, 0.07 #5483, 0.02 #57429), 0b_5d (0.09 #489, 0.03 #4072, 0.03 #13028), 02q87z6 (0.08 #2823, 0.05 #6407, 0.01 #83435), 03q0r1 (0.07 #7804, 0.03 #34672, 0.03 #31088), 0k5fg (0.06 #4675, 0.05 #20795, 0.05 #1092), 09sr0 (0.06 #5104, 0.05 #1521, 0.03 #14060), 0k4f3 (0.06 #4032, 0.05 #449, 0.03 #12988), 04954r (0.06 #23902, 0.04 #29276, 0.03 #36442), 027rpym (0.06 #4418, 0.04 #11583, 0.03 #27702) >> Best rule #107485 for best value: >> intensional similarity = 3 >> extensional distance = 816 >> proper extension: 03zqc1; 04shbh; 01v42g; 02k6rq; 06lgq8; 0f6_dy; 0308kx; 015wfg; 03q95r; 05typm; ... >> query: (?x12037, ?x12533) <- nominated_for(?x12037, ?x12533), student(?x5907, ?x12037), film(?x12037, ?x6218) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #141 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 01h4rj; *> query: (?x12037, 0cwy47) <- place_of_death(?x12037, ?x4801), celebrities_impersonated(?x3649, ?x12037), place_of_burial(?x12037, ?x3153) *> conf = 0.05 ranks of expected_values: 27 EVAL 01kkx2 film 0cwy47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 128.000 100.000 0.567 http://example.org/film/actor/film./film/performance/film #4785-08s0m7 PRED entity: 08s0m7 PRED relation: languages PRED expected values: 03k50 => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 21): 02h40lc (0.98 #3388, 0.96 #2596, 0.93 #2200), 03k50 (0.67 #184, 0.60 #76, 0.52 #472), 09bnf (0.60 #108, 0.33 #216, 0.33 #144), 01c7y (0.18 #173, 0.13 #281, 0.11 #4827), 064_8sq (0.17 #194, 0.14 #518, 0.13 #1744), 055qm (0.17 #202, 0.11 #4827, 0.10 #634), 02hxcvy (0.08 #420, 0.08 #204, 0.07 #492), 04306rv (0.08 #219, 0.06 #615, 0.05 #795), 0121sr (0.08 #211, 0.06 #5189, 0.04 #355), 012w70 (0.08 #224, 0.04 #332, 0.04 #944) >> Best rule #3388 for best value: >> intensional similarity = 4 >> extensional distance = 568 >> proper extension: 02zq43; 02qjj7; 0z4s; 0kzy0; 01l2fn; 0806vbn; 07ss8_; 01vvpjj; 01wgxtl; 02w4fkq; ... >> query: (?x12595, 02h40lc) <- languages(?x12595, ?x5121), countries_spoken_in(?x5121, ?x279), languages_spoken(?x14336, ?x5121), ?x14336 = 0bhsnb >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #184 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 05nqq3; 04cmrt; 0b66qd; *> query: (?x12595, 03k50) <- place_of_birth(?x12595, ?x7412), nationality(?x12595, ?x2146), languages(?x12595, ?x5121), ?x5121 = 07c9s *> conf = 0.67 ranks of expected_values: 2 EVAL 08s0m7 languages 03k50 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 180.000 180.000 0.975 http://example.org/people/person/languages #4784-0295r PRED entity: 0295r PRED relation: language! PRED expected values: 01v1ln => 33 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1859): 017gl1 (0.85 #1735, 0.72 #8667, 0.72 #8666), 017jd9 (0.85 #1735, 0.72 #8667, 0.72 #8666), 0cmc26r (0.85 #1735, 0.72 #8667, 0.72 #8666), 017gm7 (0.85 #1735, 0.61 #8664, 0.33 #200), 047vnkj (0.85 #1735, 0.50 #4340, 0.35 #9541), 03z9585 (0.85 #1735, 0.50 #4825, 0.33 #3094), 02yvct (0.85 #1735, 0.43 #22528, 0.42 #10399), 0c0nhgv (0.85 #1735, 0.43 #22528, 0.42 #10399), 0gtsxr4 (0.85 #1735, 0.43 #22528, 0.42 #10399), 07pd_j (0.85 #1735, 0.43 #22528, 0.42 #10399) >> Best rule #1735 for best value: >> intensional similarity = 14 >> extensional distance = 1 >> proper extension: 02h40lc; >> query: (?x7791, ?x66) <- language(?x7819, ?x7791), languages(?x5283, ?x7791), ?x7819 = 025rxjq, official_language(?x985, ?x7791), ?x5283 = 01ps2h8, taxonomy(?x985, ?x939), film_release_region(?x9432, ?x985), film_release_region(?x2441, ?x985), film_release_region(?x2189, ?x985), film_release_region(?x66, ?x985), ?x2189 = 02yvct, ?x2441 = 0cc5mcj, nationality(?x2671, ?x985), ?x9432 = 0gvt53w >> conf = 0.85 => this is the best rule for 334 predicted values *> Best rule #1184 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x7791, 01v1ln) <- language(?x7819, ?x7791), languages(?x5283, ?x7791), ?x7819 = 025rxjq, official_language(?x985, ?x7791), ?x5283 = 01ps2h8, taxonomy(?x985, ?x939), film_release_region(?x9432, ?x985), film_release_region(?x2441, ?x985), film_release_region(?x2189, ?x985), ?x2189 = 02yvct, ?x2441 = 0cc5mcj, nationality(?x2671, ?x985), ?x9432 = 0gvt53w *> conf = 0.33 ranks of expected_values: 728 EVAL 0295r language! 01v1ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 15.000 0.853 http://example.org/film/film/language #4783-03qhyn8 PRED entity: 03qhyn8 PRED relation: award PRED expected values: 02qyntr => 122 concepts (92 used for prediction) PRED predicted values (max 10 best out of 273): 0gq_v (0.82 #2452, 0.74 #3262, 0.62 #3667), 0gs96 (0.80 #1738, 0.79 #29183, 0.78 #36076), 0k611 (0.44 #4144, 0.17 #904, 0.14 #28777), 02qyntr (0.40 #4323, 0.13 #35264, 0.05 #4861), 09sb52 (0.34 #17868, 0.30 #17463, 0.29 #12601), 02x2gy0 (0.26 #2969, 0.21 #4589, 0.20 #4995), 027h4yd (0.23 #5238, 0.22 #1592, 0.21 #4832), 0f4x7 (0.23 #5701, 0.18 #6918, 0.18 #8134), 0gs9p (0.20 #4129, 0.18 #8183, 0.17 #6967), 019f4v (0.20 #4116, 0.17 #6954, 0.15 #8170) >> Best rule #2452 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 053j4w4; >> query: (?x12848, 0gq_v) <- award_winner(?x303, ?x12848), film_sets_designed(?x12848, ?x6029), nationality(?x12848, ?x205), film_release_region(?x66, ?x205) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #4323 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 027rfxc; 04_m9gk; *> query: (?x12848, 02qyntr) <- edited_by(?x9261, ?x12848), award_winner(?x9261, ?x1742), genre(?x9261, ?x1403) *> conf = 0.40 ranks of expected_values: 4 EVAL 03qhyn8 award 02qyntr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 122.000 92.000 0.824 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4782-0c73g PRED entity: 0c73g PRED relation: people! PRED expected values: 074m2 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 46): 0qcr0 (0.43 #456, 0.11 #1756, 0.10 #5854), 0gk4g (0.25 #1505, 0.22 #6318, 0.21 #5863), 02vrr (0.25 #79, 0.03 #1379, 0.02 #1899), 06z5s (0.18 #934, 0.14 #219, 0.07 #1649), 02y0js (0.18 #912, 0.13 #1822, 0.12 #1497), 04p3w (0.17 #141, 0.13 #1766, 0.12 #271), 01dcqj (0.17 #142, 0.12 #272, 0.08 #2222), 02k6hp (0.17 #166, 0.12 #296, 0.07 #491), 07jwr (0.14 #854, 0.14 #204, 0.09 #1244), 0m32h (0.14 #217, 0.05 #1647, 0.05 #867) >> Best rule #456 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 0459z; >> query: (?x13248, 0qcr0) <- instrumentalists(?x316, ?x13248), people(?x6260, ?x13248), nationality(?x13248, ?x1264), people(?x6260, ?x6745), ?x6745 = 01938t >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #613 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 06449; 04k15; 01m3x5p; 0hgqq; 06c44; 0c73z; *> query: (?x13248, 074m2) <- artists(?x11193, ?x13248), influenced_by(?x13248, ?x5912), profession(?x13248, ?x1614), artists(?x11193, ?x598), ?x598 = 01vvy *> conf = 0.06 ranks of expected_values: 19 EVAL 0c73g people! 074m2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 145.000 145.000 0.429 http://example.org/people/cause_of_death/people #4781-030qb3t PRED entity: 030qb3t PRED relation: film_release_region! PRED expected values: 0g9wdmc 0b_5d 0n04r 042zrm => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 1326): 017jd9 (0.85 #29309, 0.40 #90662, 0.34 #121987), 08hmch (0.82 #28838, 0.46 #90191, 0.37 #121516), 0bpm4yw (0.80 #29263, 0.44 #90616, 0.38 #121941), 0fpgp26 (0.80 #29854, 0.43 #91207, 0.38 #122532), 047vnkj (0.80 #29413, 0.42 #90766, 0.34 #122091), 04f52jw (0.78 #29050, 0.41 #90403, 0.32 #126948), 017gm7 (0.78 #28880, 0.40 #90233, 0.33 #126778), 062zm5h (0.78 #29370, 0.39 #90723, 0.35 #122048), 087wc7n (0.78 #28810, 0.34 #90163, 0.27 #121488), 0421v9q (0.78 #29596, 0.33 #90949, 0.27 #122274) >> Best rule #29309 for best value: >> intensional similarity = 2 >> extensional distance = 38 >> proper extension: 0bjv6; 0hv7l; >> query: (?x1523, 017jd9) <- film_release_region(?x9902, ?x1523), ?x9902 = 0j8f09z >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #28933 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 38 *> proper extension: 0bjv6; 0hv7l; *> query: (?x1523, 0g9wdmc) <- film_release_region(?x9902, ?x1523), ?x9902 = 0j8f09z *> conf = 0.72 ranks of expected_values: 36, 343, 367, 397 EVAL 030qb3t film_release_region! 042zrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 154.000 154.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 030qb3t film_release_region! 0n04r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 154.000 154.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 030qb3t film_release_region! 0b_5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 154.000 154.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 030qb3t film_release_region! 0g9wdmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 154.000 154.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4780-01dq0z PRED entity: 01dq0z PRED relation: major_field_of_study PRED expected values: 01x3g => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 116): 062z7 (0.62 #3355, 0.36 #396, 0.35 #273), 0g26h (0.60 #289, 0.55 #412, 0.50 #782), 02j62 (0.50 #399, 0.50 #276, 0.37 #1141), 01mkq (0.46 #5191, 0.41 #385, 0.40 #262), 04rjg (0.45 #266, 0.41 #389, 0.29 #5195), 02lp1 (0.41 #381, 0.40 #258, 0.40 #5187), 02_7t (0.41 #435, 0.35 #312, 0.32 #805), 01tbp (0.40 #307, 0.36 #430, 0.21 #3389), 05qjt (0.36 #377, 0.35 #254, 0.24 #3336), 01540 (0.36 #431, 0.30 #308, 0.22 #5237) >> Best rule #3355 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 026gvfj; >> query: (?x13670, 062z7) <- major_field_of_study(?x13670, ?x9444), specialization_of(?x9444, ?x8498), major_field_of_study(?x865, ?x9444) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #9363 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 524 *> proper extension: 015g1w; 03gn1x; 02bzh0; *> query: (?x13670, ?x373) <- major_field_of_study(?x13670, ?x1695), contains(?x279, ?x13670), major_field_of_study(?x373, ?x1695) *> conf = 0.13 ranks of expected_values: 39 EVAL 01dq0z major_field_of_study 01x3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 148.000 148.000 0.622 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #4779-0gzlb9 PRED entity: 0gzlb9 PRED relation: language PRED expected values: 02h40lc => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.92 #887, 0.92 #1714, 0.91 #828), 064_8sq (0.50 #494, 0.44 #553, 0.43 #140), 06b_j (0.20 #318, 0.18 #377, 0.17 #495), 03_9r (0.17 #482, 0.17 #423, 0.17 #69), 06nm1 (0.17 #70, 0.16 #837, 0.15 #896), 0jzc (0.17 #79, 0.14 #138, 0.10 #315), 04h9h (0.12 #279, 0.07 #751, 0.04 #987), 0cjk9 (0.12 #240), 01bkv (0.10 #354, 0.09 #413, 0.08 #531), 0653m (0.10 #307, 0.08 #484, 0.06 #543) >> Best rule #887 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 014_x2; 0bwfwpj; 04dsnp; 0872p_c; 0gj8t_b; 018f8; 0m491; 06v9_x; 05p1qyh; 02qr69m; ... >> query: (?x8562, 02h40lc) <- featured_film_locations(?x8562, ?x2474), featured_film_locations(?x603, ?x2474), ?x603 = 03s6l2, place_of_birth(?x1984, ?x2474) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gzlb9 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.918 http://example.org/film/film/language #4778-0dbbz PRED entity: 0dbbz PRED relation: award PRED expected values: 03hl6lc => 112 concepts (92 used for prediction) PRED predicted values (max 10 best out of 273): 02qt02v (0.71 #19158, 0.70 #36340, 0.70 #36339), 03ybrwc (0.71 #19158, 0.70 #36340, 0.70 #36339), 0gq9h (0.31 #2467, 0.29 #2068, 0.27 #1669), 09sb52 (0.28 #5227, 0.25 #9617, 0.25 #4030), 0gr51 (0.27 #1690, 0.25 #2089, 0.25 #892), 03hkv_r (0.23 #414, 0.22 #2809, 0.20 #3208), 03hl6lc (0.20 #572, 0.19 #2967, 0.17 #1769), 02n9nmz (0.19 #465, 0.17 #2860, 0.15 #3259), 05f4m9q (0.18 #810, 0.16 #2406, 0.15 #1209), 0fdtd7 (0.18 #27947, 0.13 #29548, 0.13 #36740) >> Best rule #19158 for best value: >> intensional similarity = 3 >> extensional distance = 1229 >> proper extension: 0l6qt; 0411q; 0hl3d; 01lmj3q; 026ps1; 04lgymt; 06cc_1; 04rcr; 01vvycq; 05gml8; ... >> query: (?x9606, ?x746) <- award_winner(?x746, ?x9606), award(?x9606, ?x198), award_winner(?x8652, ?x9606) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #572 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 0gs5q; *> query: (?x9606, 03hl6lc) <- people(?x10035, ?x9606), award_nominee(?x9606, ?x8652), written_by(?x3157, ?x9606) *> conf = 0.20 ranks of expected_values: 7 EVAL 0dbbz award 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 112.000 92.000 0.715 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4777-0f0qfz PRED entity: 0f0qfz PRED relation: role PRED expected values: 01399x => 154 concepts (97 used for prediction) PRED predicted values (max 10 best out of 121): 05r5c (0.60 #110, 0.57 #415, 0.55 #822), 0342h (0.60 #107, 0.50 #5, 0.48 #412), 0l1589 (0.60 #204, 0.50 #102, 0.42 #509), 042v_gx (0.60 #204, 0.50 #102, 0.40 #111), 01vj9c (0.60 #118, 0.50 #16, 0.38 #423), 018vs (0.60 #204, 0.50 #102, 0.34 #1530), 0l14j_ (0.60 #204, 0.50 #102, 0.34 #1530), 013y1f (0.50 #35, 0.48 #442, 0.40 #137), 05842k (0.43 #484, 0.31 #586, 0.30 #1505), 02snj9 (0.42 #509, 0.39 #2142, 0.36 #1837) >> Best rule #110 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 01wxdn3; >> query: (?x4186, 05r5c) <- role(?x4186, ?x432), profession(?x4186, ?x131), performance_role(?x4186, ?x3214), performance_role(?x4186, ?x228), ?x3214 = 02snj9, role(?x130, ?x228) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3163 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 199 *> proper extension: 0zjpz; 01vv6_6; 04d_mtq; *> query: (?x4186, ?x212) <- role(?x4186, ?x2725), type_of_union(?x4186, ?x566), role(?x75, ?x2725), role(?x2725, ?x212), instrumentalists(?x212, ?x226) *> conf = 0.04 ranks of expected_values: 106 EVAL 0f0qfz role 01399x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 154.000 97.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role #4776-02624g PRED entity: 02624g PRED relation: award_winner! PRED expected values: 0gvstc3 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 106): 02pgky2 (0.14 #89), 09g90vz (0.08 #263, 0.05 #1103, 0.05 #1663), 03gyp30 (0.08 #256, 0.04 #1656, 0.04 #1096), 07y9ts (0.08 #207, 0.02 #1467, 0.02 #767), 0bxs_d (0.08 #254, 0.02 #954, 0.02 #1514), 07y_p6 (0.08 #237, 0.01 #1497, 0.01 #1077), 09qvms (0.06 #993, 0.05 #1553, 0.05 #1273), 013b2h (0.05 #2599, 0.05 #2739, 0.05 #1899), 092c5f (0.05 #994, 0.04 #294, 0.04 #1274), 092t4b (0.05 #1031, 0.04 #751, 0.04 #1591) >> Best rule #89 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02_j7t; >> query: (?x7048, 02pgky2) <- student(?x10497, ?x7048), film(?x7048, ?x6642), ?x6642 = 063fh9 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1294 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 875 *> proper extension: 01sl1q; 044mz_; 0184jc; 02s2ft; 05bnp0; 02qgqt; 0fvf9q; 0jz9f; 02p65p; 0337vz; ... *> query: (?x7048, 0gvstc3) <- award_winner(?x2515, ?x7048), award_nominee(?x7048, ?x2374), nominated_for(?x7048, ?x7119) *> conf = 0.03 ranks of expected_values: 30 EVAL 02624g award_winner! 0gvstc3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 81.000 81.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4775-039cpd PRED entity: 039cpd PRED relation: contains! PRED expected values: 0b90_r => 158 concepts (107 used for prediction) PRED predicted values (max 10 best out of 447): 0b90_r (0.82 #44781, 0.81 #61804, 0.75 #88685), 09c7w0 (0.81 #89583, 0.78 #90479, 0.77 #91374), 02jx1 (0.60 #3669, 0.59 #61891, 0.44 #71748), 04jpl (0.57 #37634, 0.24 #53761, 0.20 #3604), 07ssc (0.53 #69005, 0.41 #75277, 0.39 #61836), 059rby (0.52 #58239, 0.32 #63617, 0.29 #54655), 0d060g (0.45 #34937, 0.38 #53752, 0.22 #61817), 0j0k (0.33 #378, 0.03 #35302, 0.02 #77415), 06t2t (0.33 #149, 0.01 #85101, 0.01 #81519), 05nrg (0.29 #35490, 0.02 #44450, 0.02 #53410) >> Best rule #44781 for best value: >> intensional similarity = 5 >> extensional distance = 150 >> proper extension: 05j49; 02bm8; >> query: (?x11461, ?x151) <- contains(?x8181, ?x11461), contains(?x151, ?x8181), adjoins(?x13910, ?x8181), country(?x150, ?x151), vacationer(?x151, ?x286) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039cpd contains! 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 107.000 0.822 http://example.org/location/location/contains #4774-0n04r PRED entity: 0n04r PRED relation: nominated_for! PRED expected values: 019f4v => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 259): 0gs9p (0.65 #1933, 0.61 #295, 0.57 #2401), 019f4v (0.62 #286, 0.54 #1924, 0.52 #2392), 0gq_v (0.50 #253, 0.47 #955, 0.39 #2827), 04dn09n (0.50 #267, 0.46 #1905, 0.40 #2373), 0p9sw (0.47 #254, 0.40 #956, 0.32 #1892), 02pqp12 (0.45 #291, 0.44 #1929, 0.32 #993), 099c8n (0.45 #1927, 0.32 #289, 0.28 #991), 04kxsb (0.41 #2432, 0.37 #1964, 0.27 #326), 0l8z1 (0.41 #284, 0.35 #986, 0.30 #1922), 02qyntr (0.38 #2048, 0.38 #410, 0.35 #1112) >> Best rule #1933 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 0b4lkx; >> query: (?x4024, 0gs9p) <- genre(?x4024, ?x600), film_crew_role(?x4024, ?x137), nominated_for(?x1307, ?x4024), ?x1307 = 0gq9h >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #286 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 64 *> proper extension: 07w8fz; *> query: (?x4024, 019f4v) <- produced_by(?x4024, ?x3637), nominated_for(?x1307, ?x4024), nominated_for(?x1243, ?x4024), ?x1243 = 0gr0m, ?x1307 = 0gq9h *> conf = 0.62 ranks of expected_values: 2 EVAL 0n04r nominated_for! 019f4v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.653 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4773-02qyv3h PRED entity: 02qyv3h PRED relation: film_release_region PRED expected values: 01pj7 077qn => 114 concepts (91 used for prediction) PRED predicted values (max 10 best out of 226): 0b90_r (0.94 #2030, 0.87 #4339, 0.85 #4628), 015fr (0.92 #4638, 0.91 #4349, 0.90 #1174), 035qy (0.91 #2344, 0.90 #1190, 0.88 #4365), 05r4w (0.91 #2027, 0.89 #4625, 0.88 #4336), 05v8c (0.90 #1173, 0.86 #2327, 0.77 #1317), 01znc_ (0.89 #2352, 0.81 #1198, 0.77 #4662), 03gj2 (0.84 #2336, 0.83 #4357, 0.82 #4646), 0jgd (0.82 #2317, 0.82 #4772, 0.80 #2173), 03spz (0.78 #4420, 0.76 #1245, 0.76 #4709), 03rt9 (0.77 #1315, 0.77 #2181, 0.76 #2037) >> Best rule #2030 for best value: >> intensional similarity = 10 >> extensional distance = 31 >> proper extension: 0g56t9t; 0gx1bnj; 0h1cdwq; 05p1tzf; 0gj9tn5; 0661m4p; 08052t3; 06ztvyx; 04f52jw; 06w839_; ... >> query: (?x5877, 0b90_r) <- country(?x5877, ?x390), genre(?x5877, ?x258), film_release_region(?x5877, ?x1229), film_release_region(?x5877, ?x344), film_release_region(?x5877, ?x304), film_crew_role(?x5877, ?x137), ?x304 = 0d0vqn, ?x344 = 04gzd, ?x1229 = 059j2, ?x258 = 05p553 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #190 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 3 *> proper extension: 0bpm4yw; 0gvvm6l; *> query: (?x5877, 01pj7) <- film_regional_debut_venue(?x5877, ?x5416), film_release_region(?x5877, ?x3749), film_release_region(?x5877, ?x3277), film_release_region(?x5877, ?x1892), film_release_region(?x5877, ?x311), ?x3749 = 03ryn, ?x3277 = 06t8v, film(?x8116, ?x5877), ?x311 = 0j1z8, ?x1892 = 02vzc *> conf = 0.60 ranks of expected_values: 19, 28 EVAL 02qyv3h film_release_region 077qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 114.000 91.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02qyv3h film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 114.000 91.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4772-05m_8 PRED entity: 05m_8 PRED relation: sport PRED expected values: 018jz => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 9): 018jz (0.83 #204, 0.82 #249, 0.80 #294), 02vx4 (0.75 #101, 0.54 #507, 0.45 #390), 018w8 (0.40 #131, 0.39 #149, 0.33 #4), 09xp_ (0.33 #33, 0.25 #69, 0.03 #223), 0jm_ (0.31 #427, 0.31 #112, 0.25 #346), 03tmr (0.19 #849, 0.16 #281, 0.14 #254), 06f3l (0.08 #118), 0z74 (0.07 #216, 0.01 #378), 039yzs (0.06 #684, 0.06 #323, 0.05 #855) >> Best rule #204 for best value: >> intensional similarity = 7 >> extensional distance = 27 >> proper extension: 04913k; 01v3x8; 02hfgl; 03qrh9; >> query: (?x580, 018jz) <- team(?x5727, ?x580), team(?x2010, ?x580), ?x2010 = 02lyr4, position(?x580, ?x261), colors(?x580, ?x332), ?x5727 = 02wszf, colors(?x331, ?x332) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05m_8 sport 018jz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.828 http://example.org/sports/sports_team/sport #4771-0jvtp PRED entity: 0jvtp PRED relation: film PRED expected values: 0jqd3 => 118 concepts (85 used for prediction) PRED predicted values (max 10 best out of 627): 0ptxj (0.19 #50041, 0.14 #904), 0283_zv (0.19 #50041, 0.14 #286), 03q0r1 (0.14 #638, 0.02 #2425, 0.01 #88216), 0gw7p (0.14 #1038, 0.01 #18911, 0.01 #24272), 03shpq (0.14 #1447, 0.01 #49700, 0.01 #47912), 01y9r2 (0.14 #1347, 0.01 #40664), 015gm8 (0.14 #1740), 04tng0 (0.14 #1269), 026p_bs (0.14 #91), 02gjrc (0.07 #67913, 0.07 #66125, 0.07 #48253) >> Best rule #50041 for best value: >> intensional similarity = 3 >> extensional distance = 339 >> proper extension: 0gs5q; 03h40_7; >> query: (?x8254, ?x1822) <- nominated_for(?x8254, ?x6915), location(?x8254, ?x12237), nominated_for(?x1822, ?x6915) >> conf = 0.19 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0jvtp film 0jqd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 85.000 0.188 http://example.org/film/actor/film./film/performance/film #4770-030nwm PRED entity: 030nwm PRED relation: contains! PRED expected values: 06q1r => 175 concepts (95 used for prediction) PRED predicted values (max 10 best out of 392): 02jx1 (0.96 #42138, 0.70 #18878, 0.67 #13510), 06q1r (0.95 #42947, 0.72 #43842, 0.70 #39366), 09c7w0 (0.78 #70698, 0.78 #71592, 0.72 #73383), 04jpl (0.68 #32231, 0.57 #18814, 0.27 #48338), 0hyxv (0.33 #2032, 0.17 #2926, 0.06 #6507), 07z1m (0.33 #91, 0.04 #63627, 0.04 #44828), 0mp3l (0.33 #147, 0.03 #15360, 0.03 #16255), 059rby (0.30 #36703, 0.20 #51918, 0.17 #35808), 0978r (0.23 #11839, 0.17 #8260, 0.17 #28839), 05l5n (0.19 #11754, 0.15 #13545, 0.14 #7279) >> Best rule #42138 for best value: >> intensional similarity = 6 >> extensional distance = 146 >> proper extension: 0dhdp; 0fm2_; 022_6; 0crjn65; 0121c1; 09tlh; 0nccd; 04p3c; 0fgj2; 013bqg; ... >> query: (?x13770, 02jx1) <- contains(?x6885, ?x13770), contains(?x512, ?x13770), teams(?x6885, ?x4148), place_of_birth(?x1857, ?x6885), administrative_parent(?x6885, ?x6401), ?x512 = 07ssc >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #42947 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 146 *> proper extension: 0dhdp; 0fm2_; 022_6; 0crjn65; 0121c1; 09tlh; 0nccd; 04p3c; 0fgj2; 013bqg; ... *> query: (?x13770, ?x6401) <- contains(?x6885, ?x13770), contains(?x512, ?x13770), teams(?x6885, ?x4148), place_of_birth(?x1857, ?x6885), administrative_parent(?x6885, ?x6401), ?x512 = 07ssc *> conf = 0.95 ranks of expected_values: 2 EVAL 030nwm contains! 06q1r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 175.000 95.000 0.959 http://example.org/location/location/contains #4769-07g_0c PRED entity: 07g_0c PRED relation: produced_by PRED expected values: 027kmrb => 89 concepts (44 used for prediction) PRED predicted values (max 10 best out of 135): 07rd7 (0.33 #149, 0.02 #2476, 0.02 #2864), 0184dt (0.15 #469, 0.09 #857, 0.02 #3185), 030_3z (0.06 #938, 0.05 #550, 0.02 #6368), 03kpvp (0.06 #900, 0.01 #7882), 01vvb4m (0.06 #12806, 0.06 #7756, 0.03 #4654), 02kxbwx (0.05 #2746, 0.05 #1582, 0.05 #2358), 01qg7c (0.05 #1875, 0.03 #2263, 0.03 #2651), 019pm_ (0.05 #482, 0.03 #870, 0.02 #1646), 03tf_h (0.05 #491, 0.03 #879), 092kgw (0.05 #583, 0.02 #3299, 0.01 #4074) >> Best rule #149 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01s3vk; >> query: (?x1293, 07rd7) <- crewmember(?x1293, ?x1983), genre(?x1293, ?x258), ?x258 = 05p553, film_crew_role(?x1293, ?x3305), ?x3305 = 04pyp5 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8733 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 398 *> proper extension: 02y_lrp; 01ln5z; 02_1sj; 02z3r8t; 0dsvzh; 0jyx6; 02prw4h; 02pxmgz; 0416y94; 09z2b7; ... *> query: (?x1293, 027kmrb) <- film_release_distribution_medium(?x1293, ?x81), film_crew_role(?x1293, ?x137), featured_film_locations(?x1293, ?x1860), film(?x3056, ?x1293), country(?x1293, ?x94) *> conf = 0.01 ranks of expected_values: 106 EVAL 07g_0c produced_by 027kmrb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 89.000 44.000 0.333 http://example.org/film/film/produced_by #4768-012x4t PRED entity: 012x4t PRED relation: profession PRED expected values: 0dz3r 0n1h 01c72t 04f2zj => 117 concepts (101 used for prediction) PRED predicted values (max 10 best out of 77): 0nbcg (0.73 #172, 0.58 #456, 0.51 #5713), 0dxtg (0.61 #12810, 0.33 #3281, 0.29 #11247), 01c72t (0.60 #1158, 0.59 #1443, 0.50 #21), 0dz3r (0.55 #145, 0.49 #1282, 0.47 #5686), 01d_h8 (0.50 #5, 0.37 #6971, 0.33 #12802), 03gjzk (0.34 #2856, 0.34 #3566, 0.27 #6980), 039v1 (0.33 #5718, 0.31 #603, 0.31 #1598), 0n1h (0.30 #3421, 0.26 #2569, 0.25 #3990), 02jknp (0.27 #12804, 0.23 #11241, 0.20 #12662), 01c8w0 (0.26 #1145, 0.26 #1430, 0.07 #8115) >> Best rule #172 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01wy61y; 0kvnn; 03f6fl0; 01386_; >> query: (?x1660, 0nbcg) <- instrumentalists(?x1473, ?x1660), ?x1473 = 0g2dz, profession(?x1660, ?x220) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1158 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 012pd4; *> query: (?x1660, 01c72t) <- profession(?x1660, ?x11127), ?x11127 = 05vyk, artists(?x505, ?x1660) *> conf = 0.60 ranks of expected_values: 3, 4, 8, 14 EVAL 012x4t profession 04f2zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 117.000 101.000 0.727 http://example.org/people/person/profession EVAL 012x4t profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 101.000 0.727 http://example.org/people/person/profession EVAL 012x4t profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 117.000 101.000 0.727 http://example.org/people/person/profession EVAL 012x4t profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 101.000 0.727 http://example.org/people/person/profession #4767-02w_6xj PRED entity: 02w_6xj PRED relation: award! PRED expected values: 07024 05hjnw => 57 concepts (32 used for prediction) PRED predicted values (max 10 best out of 992): 0hfzr (0.50 #1394, 0.43 #3379, 0.42 #9339), 07s846j (0.50 #388, 0.43 #3365, 0.33 #2373), 0gmcwlb (0.50 #1116, 0.43 #3101, 0.31 #4094), 0hmr4 (0.50 #1058, 0.43 #3043, 0.31 #4036), 0j_t1 (0.50 #256, 0.33 #2241, 0.29 #3233), 05hjnw (0.43 #3461, 0.35 #10415, 0.33 #1476), 064lsn (0.43 #3586, 0.33 #2594, 0.31 #4579), 0209hj (0.33 #1055, 0.32 #9000, 0.29 #3040), 0ywrc (0.33 #1292, 0.32 #9237, 0.23 #4270), 011yhm (0.33 #2639, 0.29 #3631, 0.27 #6610) >> Best rule #1394 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 040njc; 0gq9h; 02g3ft; 09d28z; >> query: (?x5398, 0hfzr) <- award_winner(?x5398, ?x9153), award_winner(?x5398, ?x2135), ?x2135 = 06pj8, award(?x4610, ?x5398), ?x4610 = 017jd9, film(?x9153, ?x1941) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3461 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 03nqnk3; *> query: (?x5398, 05hjnw) <- award_winner(?x5398, ?x7310), award_winner(?x5398, ?x2135), award_winner(?x5398, ?x1365), ?x2135 = 06pj8, ?x7310 = 04sry, award_winner(?x1118, ?x1365), award_nominee(?x538, ?x1365) *> conf = 0.43 ranks of expected_values: 6, 36 EVAL 02w_6xj award! 05hjnw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 57.000 32.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02w_6xj award! 07024 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 57.000 32.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4766-0677ng PRED entity: 0677ng PRED relation: origin PRED expected values: 0cr3d => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 71): 02_286 (0.30 #5411, 0.29 #7058, 0.09 #251), 0cr3d (0.22 #56, 0.09 #291, 0.04 #1701), 043yj (0.11 #209, 0.04 #679, 0.01 #2089), 0f__1 (0.11 #55, 0.04 #760), 0ftxw (0.11 #57), 030qb3t (0.10 #974, 0.09 #10148, 0.09 #5916), 06wxw (0.09 #317, 0.04 #552, 0.02 #1022), 0f94t (0.09 #257, 0.02 #962, 0.02 #1667), 01sn3 (0.09 #312, 0.01 #1957, 0.01 #5959), 04jpl (0.08 #476, 0.07 #10120, 0.07 #946) >> Best rule #5411 for best value: >> intensional similarity = 3 >> extensional distance = 336 >> proper extension: 01nqfh_; 018y81; 03wjb7; >> query: (?x7259, ?x739) <- profession(?x7259, ?x131), instrumentalists(?x1166, ?x7259), place_of_birth(?x7259, ?x739) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 026yqrr; *> query: (?x7259, 0cr3d) <- award(?x7259, ?x2139), award_nominee(?x5203, ?x7259), ?x5203 = 03f19q4 *> conf = 0.22 ranks of expected_values: 2 EVAL 0677ng origin 0cr3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 120.000 0.299 http://example.org/music/artist/origin #4765-0dgpwnk PRED entity: 0dgpwnk PRED relation: film_release_region PRED expected values: 0154j 05qhw 06npd 0345h 01znc_ => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 163): 03h64 (0.89 #834, 0.79 #373, 0.79 #1755), 0f8l9c (0.88 #1862, 0.88 #1709, 0.88 #481), 05qhw (0.85 #781, 0.82 #935, 0.81 #474), 0345h (0.84 #1721, 0.84 #493, 0.81 #800), 03gj2 (0.84 #791, 0.84 #330, 0.82 #945), 05b4w (0.84 #370, 0.77 #831, 0.77 #985), 06bnz (0.84 #352, 0.77 #813, 0.73 #1734), 0154j (0.84 #465, 0.83 #311, 0.81 #772), 01znc_ (0.82 #809, 0.78 #348, 0.76 #40), 0d060g (0.81 #313, 0.77 #774, 0.75 #1695) >> Best rule #834 for best value: >> intensional similarity = 5 >> extensional distance = 99 >> proper extension: 087wc7n; 03twd6; >> query: (?x3453, 03h64) <- country(?x3453, ?x429), film_release_region(?x3453, ?x410), film_release_region(?x3453, ?x390), ?x410 = 01ls2, ?x390 = 0chghy >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #781 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 99 *> proper extension: 087wc7n; 03twd6; *> query: (?x3453, 05qhw) <- country(?x3453, ?x429), film_release_region(?x3453, ?x410), film_release_region(?x3453, ?x390), ?x410 = 01ls2, ?x390 = 0chghy *> conf = 0.85 ranks of expected_values: 3, 4, 8, 9, 31 EVAL 0dgpwnk film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 81.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dgpwnk film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 81.000 81.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dgpwnk film_release_region 06npd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 81.000 81.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dgpwnk film_release_region 05qhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 81.000 81.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dgpwnk film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 81.000 0.891 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4764-02mq_y PRED entity: 02mq_y PRED relation: artists! PRED expected values: 01_qp_ => 69 concepts (37 used for prediction) PRED predicted values (max 10 best out of 277): 0xhtw (0.78 #7187, 0.61 #8126, 0.52 #5935), 059kh (0.62 #5654, 0.60 #1598, 0.58 #3466), 05r6t (0.51 #8420, 0.50 #3500, 0.42 #4748), 05w3f (0.50 #2209, 0.38 #2520, 0.25 #7207), 08jyyk (0.50 #996, 0.33 #2239, 0.33 #377), 01243b (0.50 #971, 0.33 #352, 0.21 #8463), 0mhfr (0.44 #4377, 0.12 #2507, 0.10 #10317), 05bt6j (0.42 #10647, 0.39 #6272, 0.39 #10960), 064t9 (0.42 #9370, 0.41 #9682, 0.39 #8747), 03lty (0.38 #10914, 0.37 #7198, 0.33 #4664) >> Best rule #7187 for best value: >> intensional similarity = 11 >> extensional distance = 49 >> proper extension: 0167xy; >> query: (?x5303, 0xhtw) <- artists(?x3167, ?x5303), artists(?x3167, ?x11633), artists(?x3167, ?x7966), artists(?x3167, ?x565), parent_genre(?x3167, ?x5934), ?x565 = 01wl38s, ?x7966 = 013rfk, artists(?x5934, ?x248), parent_genre(?x5934, ?x1000), group(?x7210, ?x5303), profession(?x11633, ?x131) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3941 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 11 *> proper extension: 01ww_vs; *> query: (?x5303, 01_qp_) <- artists(?x10318, ?x5303), artists(?x3167, ?x5303), artists(?x302, ?x5303), ?x3167 = 0xjl2, artists(?x10318, ?x7238), parent_genre(?x301, ?x302), artists(?x302, ?x9757), artists(?x302, ?x7221), artists(?x302, ?x3206), ?x3206 = 01m65sp, ?x7221 = 0191h5, ?x9757 = 06br6t, award(?x7238, ?x247) *> conf = 0.08 ranks of expected_values: 143 EVAL 02mq_y artists! 01_qp_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 69.000 37.000 0.784 http://example.org/music/genre/artists #4763-06_sc3 PRED entity: 06_sc3 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 6): 029j_ (0.91 #42, 0.83 #98, 0.83 #52), 02nxhr (0.21 #87, 0.15 #344, 0.07 #104), 07z4p (0.15 #344, 0.06 #56, 0.06 #61), 0735l (0.15 #344), 0dq6p (0.15 #344), 07c52 (0.02 #326, 0.02 #352, 0.02 #291) >> Best rule #42 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 0ckrgs; >> query: (?x8234, 029j_) <- film(?x9930, ?x8234), prequel(?x8234, ?x9996), nationality(?x9930, ?x94), category(?x8234, ?x134), ?x134 = 08mbj5d >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06_sc3 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #4762-0283d PRED entity: 0283d PRED relation: parent_genre! PRED expected values: 03ckfl9 => 62 concepts (19 used for prediction) PRED predicted values (max 10 best out of 234): 0193f (0.40 #1147, 0.33 #96, 0.22 #1411), 0y3_8 (0.39 #2940, 0.33 #299, 0.33 #39), 0283d (0.34 #4564, 0.33 #82, 0.22 #2983), 03xnwz (0.33 #551, 0.33 #287, 0.27 #1871), 0xjl2 (0.33 #562, 0.33 #298, 0.18 #1882), 01243b (0.33 #560, 0.33 #296, 0.18 #1880), 0g_bh (0.33 #629, 0.27 #1949, 0.22 #3006), 029fbr (0.33 #674, 0.27 #1994, 0.16 #4745), 01h0kx (0.33 #386, 0.22 #3027, 0.16 #4608), 0xv2x (0.33 #648, 0.22 #4606, 0.18 #1968) >> Best rule #1147 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0m0jc; 0hh2s; 01z9l_; >> query: (?x7280, 0193f) <- parent_genre(?x7280, ?x3915), parent_genre(?x7280, ?x1127), artists(?x7280, ?x8199), ?x8199 = 016lmg, ?x3915 = 07gxw, parent_genre(?x3232, ?x7280), artists(?x1127, ?x130) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2372 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 9 *> proper extension: 012x7b; *> query: (?x7280, ?x302) <- parent_genre(?x7280, ?x3915), parent_genre(?x3915, ?x2936), artists(?x3915, ?x10624), artists(?x3915, ?x8636), artists(?x3915, ?x1732), ?x8636 = 0k60, ?x10624 = 016nvh, artists(?x302, ?x1732) *> conf = 0.01 ranks of expected_values: 219 EVAL 0283d parent_genre! 03ckfl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 19.000 0.400 http://example.org/music/genre/parent_genre #4761-0h7x PRED entity: 0h7x PRED relation: country! PRED expected values: 019w9j 0152n0 01sgl 09_9n => 182 concepts (182 used for prediction) PRED predicted values (max 10 best out of 30): 06f41 (0.80 #151, 0.76 #463, 0.75 #439), 01sgl (0.75 #161, 0.62 #113, 0.62 #89), 07gyv (0.71 #459, 0.70 #147, 0.69 #99), 01cgz (0.70 #967, 0.70 #895, 0.69 #438), 0w0d (0.69 #101, 0.69 #77, 0.67 #293), 02vx4 (0.69 #98, 0.69 #74, 0.60 #146), 07jjt (0.65 #152, 0.62 #104, 0.62 #80), 03rbzn (0.62 #105, 0.62 #81, 0.61 #465), 0486tv (0.62 #110, 0.62 #86, 0.56 #134), 019w9j (0.62 #106, 0.62 #82, 0.50 #130) >> Best rule #151 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 05v8c; >> query: (?x1355, 06f41) <- film_release_region(?x324, ?x1355), ?x324 = 07gp9, contains(?x1355, ?x863) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 05v8c; *> query: (?x1355, 01sgl) <- film_release_region(?x324, ?x1355), ?x324 = 07gp9, contains(?x1355, ?x863) *> conf = 0.75 ranks of expected_values: 2, 10, 11, 15 EVAL 0h7x country! 09_9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 182.000 182.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0h7x country! 01sgl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 182.000 182.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0h7x country! 0152n0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 182.000 182.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0h7x country! 019w9j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 182.000 182.000 0.800 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #4760-02h2vv PRED entity: 02h2vv PRED relation: nominated_for! PRED expected values: 03xn3s2 05zjx 0745k7 => 50 concepts (18 used for prediction) PRED predicted values (max 10 best out of 604): 01tj34 (0.79 #18677, 0.47 #30354, 0.44 #11672), 09d5h (0.79 #18677, 0.17 #9337, 0.16 #25682), 011_3s (0.47 #30354, 0.44 #11672, 0.38 #30353), 06j0md (0.20 #28, 0.11 #2362, 0.06 #7030), 0gsg7 (0.13 #352, 0.11 #2686, 0.06 #14360), 03mdt (0.13 #710, 0.08 #3044, 0.07 #19387), 041c4 (0.13 #1115, 0.08 #3449, 0.03 #8117), 0crx5w (0.13 #306, 0.05 #2640, 0.04 #4974), 0b7t3p (0.13 #1407, 0.05 #3741, 0.02 #6075), 01vwllw (0.13 #678, 0.05 #3012, 0.02 #5346) >> Best rule #18677 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 0bx_hnp; >> query: (?x6339, ?x2062) <- award_winner(?x6339, ?x2062), languages(?x6339, ?x254), nominated_for(?x1870, ?x6339) >> conf = 0.79 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02h2vv nominated_for! 0745k7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 18.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 02h2vv nominated_for! 05zjx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 18.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 02h2vv nominated_for! 03xn3s2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 18.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4759-0fttg PRED entity: 0fttg PRED relation: place_of_birth! PRED expected values: 027f7dj => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 320): 02qtywd (0.33 #2327), 0h5jg5 (0.33 #1516), 054187 (0.33 #1500), 08q3s0 (0.33 #1100), 09v6gc9 (0.33 #1040), 0h584v (0.33 #795), 0884hk (0.33 #792), 05b4rcb (0.33 #407), 06w33f8 (0.33 #302), 05cv94 (0.33 #229) >> Best rule #2327 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09c7w0; >> query: (?x12384, 02qtywd) <- contains(?x94, ?x12384), contains(?x12384, ?x12126), ?x12126 = 03x1s8 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fttg place_of_birth! 027f7dj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 63.000 0.333 http://example.org/people/person/place_of_birth #4758-03pmzt PRED entity: 03pmzt PRED relation: film PRED expected values: 03cmsqb => 143 concepts (91 used for prediction) PRED predicted values (max 10 best out of 914): 02p86pb (0.38 #35604, 0.02 #10413, 0.01 #22874), 031f_m (0.38 #35604), 01shy7 (0.25 #422, 0.14 #2202, 0.11 #3982), 034qzw (0.25 #332, 0.14 #2112, 0.11 #3892), 02ntb8 (0.25 #834, 0.14 #2614, 0.11 #4394), 034qrh (0.25 #63, 0.14 #1843, 0.11 #3623), 02rx2m5 (0.25 #291, 0.11 #3851, 0.09 #5631), 08phg9 (0.25 #879, 0.11 #4439, 0.01 #34702), 0315rp (0.25 #1432, 0.11 #4992), 01ry_x (0.18 #7038, 0.04 #10598, 0.03 #14159) >> Best rule #35604 for best value: >> intensional similarity = 4 >> extensional distance = 249 >> proper extension: 04f62k; >> query: (?x2910, ?x9060) <- film(?x2910, ?x4734), actor(?x4339, ?x2910), country(?x4734, ?x512), prequel(?x9060, ?x4734) >> conf = 0.38 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 03pmzt film 03cmsqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 91.000 0.375 http://example.org/film/actor/film./film/performance/film #4757-05ypj5 PRED entity: 05ypj5 PRED relation: film! PRED expected values: 02h0f3 => 94 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1340): 012v9y (0.25 #3329, 0.17 #11657, 0.11 #17904), 01v5h (0.25 #3630, 0.17 #11958, 0.05 #18205), 011lpr (0.25 #4149, 0.17 #12477, 0.05 #18724), 0btj0 (0.25 #4085, 0.17 #12413, 0.05 #18660), 01l3j (0.25 #4142, 0.17 #12470, 0.02 #35379), 015gy7 (0.25 #3188, 0.17 #11516, 0.02 #34425), 0428bc (0.20 #7948, 0.20 #5866, 0.03 #43352), 0c1pj (0.20 #4257, 0.10 #12586, 0.02 #58400), 0chsq (0.20 #8407, 0.07 #20901, 0.06 #25067), 039bp (0.20 #8509, 0.07 #21003, 0.06 #25169) >> Best rule #3329 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0k4fz; 0gt1k; >> query: (?x11395, 012v9y) <- film_art_direction_by(?x11395, ?x6766), film_release_region(?x11395, ?x1603), ?x1603 = 06bnz, cinematography(?x11395, ?x7118) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #24219 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 06mmr; *> query: (?x11395, 02h0f3) <- award(?x11395, ?x500), ?x500 = 0p9sw, honored_for(?x8015, ?x11395), award_winner(?x8015, ?x199) *> conf = 0.02 ranks of expected_values: 572 EVAL 05ypj5 film! 02h0f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 94.000 65.000 0.250 http://example.org/film/actor/film./film/performance/film #4756-0f4_l PRED entity: 0f4_l PRED relation: film_distribution_medium PRED expected values: 0735l => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.87 #71, 0.86 #59, 0.84 #77), 029j_ (0.16 #61, 0.13 #73, 0.12 #49), 02nxhr (0.12 #74, 0.12 #68, 0.11 #50), 0dq6p (0.10 #51, 0.08 #75, 0.08 #15), 07z4p (0.01 #54) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 0cnztc4; 0crh5_f; >> query: (?x2177, 0735l) <- film(?x609, ?x2177), genre(?x2177, ?x604), film_crew_role(?x2177, ?x1171), ?x609 = 03xq0f >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f4_l film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.866 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #4755-07s9rl0 PRED entity: 07s9rl0 PRED relation: genre! PRED expected values: 015qsq 02vp1f_ 0czyxs 0dscrwf 02x3lt7 04jwjq 0gkz15s 02hxhz 0147sh 0b6tzs 0bwfwpj 05q96q6 06fvc 03rtz1 02c6d 0dgst_d 0gtvrv3 024l2y 09gq0x5 0gd0c7x 04g9gd 085ccd 09p7fh 0pvms 047svrl 0kv238 0j_t1 0p7qm 0g54xkt 0gh8zks 0djlxb 0jwmp 09g7vfw 0dgpwnk 09lcsj 0kxf1 0fy66 01sxdy 03r0g9 0kvgtf 0n04r 05c9zr 062zjtt 0hgnl3t 0432_5 0h03fhx 017jd9 04nnpw 0bl1_ 043n0v_ 09zf_q 0b2qtl 011yn5 0cy__l 09p3_s 0c3zjn7 0gtt5fb 05rfst 040_lv 026zlh9 095z4q 02z5x7l 06c0ns 07g1sm 01jr4j 0294mx 011x_4 0p9tm 03np63f 01qz5 02p76f9 0ds2l81 0170yd 09d3b7 0bj25 02bqvs 0ndsl1x 063y9fp 087pfc 02qd04y 0p9rz 0f8j13 0h1x5f 06bc59 06y611 0ccck7 03k8th 03s9kp 0bbgvp 04svwx 04jn6y7 => 62 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1018): 048qrd (0.77 #10449, 0.72 #7836, 0.70 #32231), 06_x996 (0.77 #10449, 0.72 #7836, 0.70 #32231), 04xg2f (0.77 #10449, 0.72 #7836, 0.70 #32231), 0f4_l (0.77 #10449, 0.72 #7836, 0.70 #32231), 07s846j (0.77 #10449, 0.72 #7836, 0.70 #32231), 09ps01 (0.77 #10449, 0.72 #7836, 0.70 #32231), 092vkg (0.77 #10449, 0.72 #7836, 0.70 #32231), 07k2mq (0.77 #10449, 0.72 #7836, 0.70 #32231), 08zrbl (0.77 #10449, 0.72 #7836, 0.70 #32231), 07l50_1 (0.77 #10449, 0.72 #7836, 0.70 #32231) >> Best rule #10449 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 01j1n2; >> query: (?x53, ?x54) <- titles(?x53, ?x54), genre(?x6832, ?x53), genre(?x1916, ?x53), genre(?x1803, ?x53), ?x6832 = 03cyslc, film_crew_role(?x1916, ?x137), film_release_region(?x1803, ?x87) >> conf = 0.77 => this is the best rule for 80 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 12, 36, 63, 81, 95, 100, 141, 159, 170, 173, 254, 262, 293, 298, 301, 307, 308, 309, 326, 327, 338, 369, 372, 374, 375, 394, 413, 418, 420, 427, 457, 461, 462, 477, 483, 484, 485, 510, 515, 516, 522, 527, 529, 536, 537, 540, 561, 577, 578, 579, 583, 584, 585, 602, 606, 607, 618, 647, 657, 660, 666, 682, 684, 698, 706, 712, 726, 731, 734, 763, 774, 790, 796, 802, 915, 916, 917, 918, 919, 931, 946, 956, 959, 961, 964 EVAL 07s9rl0 genre! 04jn6y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 04svwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0bbgvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 03s9kp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 03k8th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0ccck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 06y611 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 06bc59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0h1x5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0f8j13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0p9rz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 02qd04y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 087pfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 063y9fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0ndsl1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 02bqvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0bj25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 09d3b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0170yd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0ds2l81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 02p76f9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 01qz5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 03np63f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0p9tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 011x_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0294mx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 01jr4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 07g1sm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 06c0ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 02z5x7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 095z4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 026zlh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 040_lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 05rfst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0gtt5fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0c3zjn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 09p3_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0cy__l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 011yn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0b2qtl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 09zf_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 043n0v_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0bl1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 04nnpw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 017jd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0h03fhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0432_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0hgnl3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 062zjtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 05c9zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0n04r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0kvgtf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 03r0g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 01sxdy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0fy66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0kxf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 09lcsj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0dgpwnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 09g7vfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0jwmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0djlxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0gh8zks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0g54xkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0p7qm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0j_t1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0kv238 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 047svrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0pvms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 09p7fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 085ccd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 04g9gd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0gd0c7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 09gq0x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 024l2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0gtvrv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0dgst_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 02c6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 03rtz1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 06fvc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 05q96q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0bwfwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0b6tzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0147sh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 02hxhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 04jwjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 02x3lt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0dscrwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 0czyxs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 02vp1f_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre EVAL 07s9rl0 genre! 015qsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 48.000 0.766 http://example.org/film/film/genre #4754-01xqw PRED entity: 01xqw PRED relation: instrumentalists PRED expected values: 032t2z 04f7c55 01t110 01vsyjy => 66 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1004): 016j2t (0.67 #2943, 0.38 #7809, 0.36 #12059), 01sb5r (0.58 #11178, 0.50 #11784, 0.45 #6320), 04m2zj (0.55 #6534, 0.50 #4712, 0.33 #2882), 01wmjkb (0.54 #7761, 0.44 #10192, 0.42 #13831), 01vw20_ (0.50 #5639, 0.50 #5032, 0.50 #2594), 01vrnsk (0.50 #5855, 0.50 #5248, 0.38 #7676), 01vrncs (0.50 #4307, 0.50 #2477, 0.33 #48), 01lvcs1 (0.50 #8103, 0.50 #2630, 0.33 #201), 0161sp (0.50 #2588, 0.46 #7454, 0.37 #11098), 032t2z (0.50 #11572, 0.42 #10966, 0.38 #7322) >> Best rule #2943 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 018vs; 03qjg; 02fsn; >> query: (?x4311, 016j2t) <- instrumentalists(?x4311, ?x7193), instrumentalists(?x4311, ?x6383), group(?x4311, ?x1945), role(?x960, ?x4311), role(?x314, ?x4311), ?x960 = 04q7r, role(?x74, ?x4311), ?x314 = 02sgy, role(?x4311, ?x614), ?x7193 = 018d6l, people(?x1446, ?x6383) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #11572 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 20 *> proper extension: 06ch55; *> query: (?x4311, 032t2z) <- instrumentalists(?x4311, ?x7237), instrumentalists(?x4311, ?x5356), instrumentalists(?x4311, ?x5125), nationality(?x5125, ?x94), award_nominee(?x5125, ?x5132), nationality(?x7237, ?x1310), profession(?x7237, ?x1032), ?x1032 = 02hrh1q, people(?x4959, ?x5125), award_winner(?x2324, ?x5125), student(?x2767, ?x5356) *> conf = 0.50 ranks of expected_values: 10, 66, 191, 203 EVAL 01xqw instrumentalists 01vsyjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 66.000 40.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 01xqw instrumentalists 01t110 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 66.000 40.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 01xqw instrumentalists 04f7c55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 66.000 40.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 01xqw instrumentalists 032t2z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 66.000 40.000 0.667 http://example.org/music/instrument/instrumentalists #4753-01vx5w7 PRED entity: 01vx5w7 PRED relation: award_winner! PRED expected values: 019bk0 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 112): 019bk0 (0.24 #15, 0.18 #8962, 0.17 #6301), 02rjjll (0.24 #5, 0.12 #1545, 0.11 #1965), 02cg41 (0.18 #8962, 0.17 #6301, 0.12 #125), 05pd94v (0.18 #2, 0.10 #11343, 0.10 #1542), 056878 (0.18 #31, 0.09 #1571, 0.08 #1851), 0466p0j (0.15 #75, 0.10 #11343, 0.09 #1615), 09n4nb (0.15 #47, 0.09 #1587, 0.08 #1867), 0jzphpx (0.15 #38, 0.08 #1578, 0.08 #1858), 013b2h (0.13 #1619, 0.12 #1899, 0.12 #2039), 0gpjbt (0.12 #28, 0.09 #1428, 0.09 #1568) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 0770cd; 0412f5y; 01wwvd2; >> query: (?x2925, 019bk0) <- award_winner(?x2926, ?x2925), award(?x2925, ?x3835), ?x3835 = 01cky2 >> conf = 0.24 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vx5w7 award_winner! 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.235 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4752-01rwpj PRED entity: 01rwpj PRED relation: film_release_region PRED expected values: 0jgd 0154j 0hzlz 03gj2 0345h 03h64 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 140): 03gj2 (0.90 #340, 0.82 #655, 0.80 #1758), 03_3d (0.86 #322, 0.78 #1110, 0.77 #637), 0345h (0.84 #349, 0.83 #664, 0.82 #1137), 01znc_ (0.84 #358, 0.77 #673, 0.72 #1146), 05r4w (0.82 #317, 0.81 #632, 0.79 #1735), 0jgd (0.82 #319, 0.79 #634, 0.78 #1107), 03h64 (0.82 #382, 0.79 #1800, 0.77 #697), 015fr (0.81 #648, 0.79 #333, 0.78 #1121), 0154j (0.80 #320, 0.77 #1738, 0.73 #635), 06bnz (0.79 #361, 0.71 #1779, 0.70 #676) >> Best rule #340 for best value: >> intensional similarity = 5 >> extensional distance = 78 >> proper extension: 02vxq9m; 0ds3t5x; 0g5qs2k; 05p1tzf; 02x3lt7; 0gkz15s; 04969y; 017gl1; 02d44q; 053rxgm; ... >> query: (?x5067, 03gj2) <- award_winner(?x5067, ?x4969), film_release_region(?x5067, ?x4743), film_release_region(?x5067, ?x789), ?x789 = 0f8l9c, ?x4743 = 03spz >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 6, 7, 9, 37 EVAL 01rwpj film_release_region 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 111.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01rwpj film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01rwpj film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01rwpj film_release_region 0hzlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 111.000 111.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01rwpj film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 111.000 111.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01rwpj film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 111.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4751-01jzyx PRED entity: 01jzyx PRED relation: registering_agency PRED expected values: 03z19 => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.89 #5, 0.82 #20, 0.82 #17) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 07wrz; 03bmmc; 08qs09; 02lv2v; 03bnd9; 01qdhx; >> query: (?x5426, 03z19) <- institution(?x620, ?x5426), student(?x5426, ?x9156), citytown(?x5426, ?x4253), currency(?x5426, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jzyx registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.886 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #4750-016sqs PRED entity: 016sqs PRED relation: profession PRED expected values: 02hrh1q => 89 concepts (68 used for prediction) PRED predicted values (max 10 best out of 50): 02hrh1q (0.76 #5649, 0.73 #5797, 0.73 #5501), 09jwl (0.69 #462, 0.64 #1797, 0.64 #2539), 016z4k (0.57 #3, 0.54 #151, 0.52 #299), 0nbcg (0.52 #475, 0.49 #2552, 0.48 #1810), 0dxtg (0.42 #9794, 0.25 #8165, 0.25 #7869), 01d_h8 (0.31 #9787, 0.30 #4158, 0.28 #6529), 02jknp (0.29 #9789, 0.20 #6828, 0.20 #6680), 01c72t (0.28 #1209, 0.28 #3581, 0.27 #6673), 039v1 (0.27 #184, 0.27 #6673, 0.27 #480), 0cbd2 (0.27 #6673, 0.26 #8894, 0.26 #10081) >> Best rule #5649 for best value: >> intensional similarity = 3 >> extensional distance = 1243 >> proper extension: 05ty4m; 05cj4r; 02zq43; 0436f4; 01rr9f; 01gvr1; 01n5309; 05ml_s; 04bd8y; 066m4g; ... >> query: (?x6592, 02hrh1q) <- profession(?x6592, ?x131), location(?x6592, ?x1719), award_nominee(?x6592, ?x672) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016sqs profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 68.000 0.757 http://example.org/people/person/profession #4749-01nkxvx PRED entity: 01nkxvx PRED relation: type_of_union PRED expected values: 04ztj => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.70 #197, 0.68 #313, 0.68 #309), 01g63y (0.25 #381, 0.20 #10, 0.19 #398), 01bl8s (0.25 #381, 0.19 #398), 0jgjn (0.19 #398) >> Best rule #197 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 019y64; 0dj5q; 01gct2; 0443c; >> query: (?x8599, 04ztj) <- award_winner(?x11010, ?x8599), nationality(?x8599, ?x94), people(?x11321, ?x8599) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nkxvx type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.695 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4748-03nfmq PRED entity: 03nfmq PRED relation: major_field_of_study! PRED expected values: 065y4w7 0217m9 => 58 concepts (33 used for prediction) PRED predicted values (max 10 best out of 635): 07szy (0.83 #7972, 0.78 #6839, 0.78 #5705), 04hgpt (0.67 #8095, 0.56 #5828, 0.50 #2996), 07wrz (0.64 #9126, 0.52 #11967, 0.50 #3462), 01mpwj (0.64 #9179, 0.52 #12020, 0.46 #13726), 09f2j (0.59 #10376, 0.58 #8103, 0.56 #6970), 065y4w7 (0.59 #10218, 0.58 #7945, 0.53 #11351), 06pwq (0.59 #10216, 0.58 #11349, 0.57 #13622), 01bk1y (0.58 #8228, 0.57 #5394, 0.56 #7095), 01bm_ (0.57 #9330, 0.57 #12171, 0.56 #6498), 03v6t (0.57 #5136, 0.56 #6837, 0.56 #5703) >> Best rule #7972 for best value: >> intensional similarity = 11 >> extensional distance = 10 >> proper extension: 02_7t; 0w7s; >> query: (?x3878, 07szy) <- major_field_of_study(?x6056, ?x3878), major_field_of_study(?x3948, ?x3878), major_field_of_study(?x1665, ?x3878), ?x1665 = 04rwx, currency(?x3948, ?x170), school(?x260, ?x3948), major_field_of_study(?x3948, ?x1154), ?x1154 = 02lp1, contains(?x1274, ?x3948), company(?x1159, ?x6056), school(?x1633, ?x3948) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #10218 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 15 *> proper extension: 0mkz; 04g7x; *> query: (?x3878, 065y4w7) <- major_field_of_study(?x7596, ?x3878), major_field_of_study(?x4599, ?x3878), major_field_of_study(?x3948, ?x3878), major_field_of_study(?x1665, ?x3878), ?x1665 = 04rwx, currency(?x3948, ?x170), school(?x3089, ?x4599), major_field_of_study(?x865, ?x3878), state_province_region(?x7596, ?x448), contains(?x94, ?x4599) *> conf = 0.59 ranks of expected_values: 6, 154 EVAL 03nfmq major_field_of_study! 0217m9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 58.000 33.000 0.833 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 03nfmq major_field_of_study! 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 58.000 33.000 0.833 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #4747-018swb PRED entity: 018swb PRED relation: film PRED expected values: 050r1z => 114 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1038): 04jpg2p (0.11 #1452, 0.04 #6789, 0.03 #10347), 0m9p3 (0.07 #9277, 0.06 #5719, 0.02 #87553), 04v89z (0.06 #4967, 0.03 #33431, 0.03 #29873), 04954r (0.06 #32631, 0.05 #29073, 0.03 #4167), 09q5w2 (0.06 #161, 0.05 #5498, 0.04 #9056), 03177r (0.06 #457, 0.04 #34258, 0.04 #5794), 031hcx (0.06 #35065, 0.04 #6601, 0.03 #58192), 04cv9m (0.06 #694, 0.04 #6031, 0.03 #9589), 0prh7 (0.06 #827, 0.04 #6164, 0.03 #9722), 09gq0x5 (0.06 #280, 0.04 #5617, 0.03 #9175) >> Best rule #1452 for best value: >> intensional similarity = 2 >> extensional distance = 16 >> proper extension: 021wpb; >> query: (?x2122, 04jpg2p) <- profession(?x2122, ?x5716), ?x5716 = 021wpb >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018swb film 050r1z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 99.000 0.111 http://example.org/film/actor/film./film/performance/film #4746-0157g9 PRED entity: 0157g9 PRED relation: partially_contains PRED expected values: 059j2 049nq => 156 concepts (68 used for prediction) PRED predicted values (max 10 best out of 45): 0lcd (0.33 #16, 0.25 #632, 0.22 #884), 065ky (0.33 #32, 0.08 #773, 0.06 #857), 05g56 (0.33 #30, 0.08 #771, 0.06 #855), 0lm0n (0.32 #1991, 0.29 #2867, 0.27 #2603), 0261m (0.25 #184, 0.20 #263, 0.20 #223), 09glw (0.25 #181, 0.20 #220, 0.17 #300), 0k3nk (0.22 #419, 0.20 #255, 0.20 #215), 0f8l9c (0.22 #1118, 0.21 #994, 0.19 #1374), 06c6l (0.20 #272, 0.20 #232, 0.17 #690), 026zt (0.17 #305, 0.12 #1684, 0.08 #640) >> Best rule #16 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0f8l9c; >> query: (?x8882, 0lcd) <- contains(?x8882, ?x8883), contains(?x8882, ?x1925), jurisdiction_of_office(?x182, ?x1925), time_zones(?x8882, ?x11506), ?x8883 = 04vg8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2352 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 05v8c; 0jhwd; *> query: (?x8882, ?x789) <- contains(?x8882, ?x8883), contains(?x8882, ?x4569), film_release_region(?x4336, ?x4569), country(?x8883, ?x789) *> conf = 0.06 ranks of expected_values: 30 EVAL 0157g9 partially_contains 049nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 156.000 68.000 0.333 http://example.org/location/location/partially_contains EVAL 0157g9 partially_contains 059j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 156.000 68.000 0.333 http://example.org/location/location/partially_contains #4745-03g5jw PRED entity: 03g5jw PRED relation: artists! PRED expected values: 016clz 0xhtw 01243b 02yv6b => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 230): 016clz (0.57 #627, 0.44 #8103, 0.43 #8415), 064t9 (0.47 #7489, 0.46 #11229, 0.45 #11851), 0xhtw (0.41 #8115, 0.40 #328, 0.40 #9363), 0glt670 (0.38 #7517, 0.25 #8765, 0.25 #11879), 05bt6j (0.35 #2180, 0.33 #1557, 0.32 #978), 011j5x (0.35 #2180, 0.33 #1557, 0.24 #654), 01243b (0.35 #2180, 0.33 #1557, 0.19 #665), 0y3_8 (0.35 #2180, 0.33 #1557, 0.16 #18069), 01cbwl (0.35 #2180, 0.33 #1557, 0.16 #18069), 05hs4r (0.35 #2180, 0.33 #1557, 0.16 #18069) >> Best rule #627 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 05xq9; 0k1bs; 01kcms4; 070b4; >> query: (?x1573, 016clz) <- influenced_by(?x1573, ?x483), artists(?x482, ?x1573), group(?x227, ?x1573) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 7, 18 EVAL 03g5jw artists! 02yv6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 82.000 82.000 0.571 http://example.org/music/genre/artists EVAL 03g5jw artists! 01243b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 82.000 82.000 0.571 http://example.org/music/genre/artists EVAL 03g5jw artists! 0xhtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.571 http://example.org/music/genre/artists EVAL 03g5jw artists! 016clz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.571 http://example.org/music/genre/artists #4744-0d6_s PRED entity: 0d6_s PRED relation: film! PRED expected values: 07fpm3 => 101 concepts (69 used for prediction) PRED predicted values (max 10 best out of 923): 01fh9 (0.20 #317, 0.08 #4479, 0.07 #6560), 03cglm (0.20 #3126, 0.04 #5207, 0.03 #7288), 0dt645q (0.16 #10087, 0.09 #14250, 0.07 #8006), 016dmx (0.14 #12487, 0.14 #20812, 0.12 #52037), 01q_ph (0.13 #2138, 0.03 #14625, 0.03 #10462), 09l3p (0.13 #2829, 0.03 #15316, 0.03 #17397), 01xllf (0.13 #3803, 0.03 #26697, 0.02 #35022), 059_gf (0.13 #3079, 0.03 #19728, 0.02 #15566), 02tv80 (0.13 #3212, 0.02 #21943, 0.01 #28188), 014v6f (0.13 #3048, 0.01 #23860, 0.01 #36348) >> Best rule #317 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0db94w; >> query: (?x10405, 01fh9) <- country(?x10405, ?x252), genre(?x10405, ?x225), ?x252 = 03_3d, film_format(?x10405, ?x909) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #15207 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 09146g; 0ckrgs; 0b3n61; *> query: (?x10405, 07fpm3) <- production_companies(?x10405, ?x902), film(?x1018, ?x10405), prequel(?x7263, ?x10405), film_crew_role(?x10405, ?x468) *> conf = 0.02 ranks of expected_values: 492 EVAL 0d6_s film! 07fpm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 101.000 69.000 0.200 http://example.org/film/actor/film./film/performance/film #4743-07l5z PRED entity: 07l5z PRED relation: source PRED expected values: 0jbk9 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #45, 0.92 #44, 0.91 #81) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 210 >> proper extension: 0qlrh; >> query: (?x11058, ?x958) <- county(?x11058, ?x8178), category(?x11058, ?x134), source(?x8178, ?x958) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07l5z source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.920 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #4742-02w7gg PRED entity: 02w7gg PRED relation: people PRED expected values: 0qf43 0m31m 015wnl 043s3 021yzs 015t7v 081k8 01v9724 07c37 05kwx2 02tn0_ 07rzf 02lfwp => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 2254): 02h761 (0.65 #6344, 0.39 #6343, 0.25 #14758), 04kj2v (0.65 #6344, 0.39 #6343, 0.25 #14556), 01hb6v (0.65 #6344, 0.39 #6343, 0.25 #8230), 03p9hl (0.65 #6344, 0.39 #6343, 0.25 #9497), 02_j8x (0.65 #6344, 0.39 #6343, 0.25 #9000), 03ftmg (0.65 #6344, 0.39 #6343, 0.25 #8839), 03dpqd (0.65 #6344, 0.39 #6343, 0.25 #8523), 0134w7 (0.65 #6344, 0.39 #6343, 0.12 #15857), 03s9v (0.65 #6344, 0.39 #6343, 0.12 #15857), 0465_ (0.65 #6344, 0.39 #6343, 0.12 #15857) >> Best rule #6344 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02k54; >> query: (?x743, ?x1231) <- split_to(?x743, ?x1310), featured_film_locations(?x708, ?x1310), contains(?x1310, ?x6130), nationality(?x57, ?x1310), student(?x6130, ?x1231) >> conf = 0.65 => this is the best rule for 175 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 24, 41, 46, 67, 93, 166, 226, 233, 307, 378, 424, 834 EVAL 02w7gg people 02lfwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 07rzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 02tn0_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 05kwx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 07c37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 01v9724 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 081k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 015t7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 021yzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 043s3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 015wnl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 0m31m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 44.000 44.000 0.652 http://example.org/people/ethnicity/people EVAL 02w7gg people 0qf43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 44.000 0.652 http://example.org/people/ethnicity/people #4741-013xrm PRED entity: 013xrm PRED relation: people PRED expected values: 0jcx 036jp8 0h336 => 40 concepts (37 used for prediction) PRED predicted values (max 10 best out of 2074): 022_q8 (0.50 #14173, 0.50 #4127, 0.33 #2452), 06crk (0.50 #4221, 0.33 #14267, 0.33 #2546), 041b4j (0.50 #4671, 0.33 #14717, 0.33 #2996), 0fb1q (0.34 #15073, 0.33 #2095, 0.25 #3770), 01pcmd (0.34 #15073, 0.33 #1941, 0.25 #3616), 0159h6 (0.34 #15073, 0.17 #13455, 0.11 #21829), 03_80b (0.34 #15073, 0.12 #15072, 0.10 #3351), 01kt17 (0.34 #15073, 0.10 #3351, 0.06 #53590), 04ld94 (0.34 #15073, 0.10 #3351, 0.06 #51915), 05zh9c (0.34 #15073, 0.10 #3351, 0.06 #51915) >> Best rule #14173 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 02w7gg; 018s6c; >> query: (?x5540, 022_q8) <- people(?x5540, ?x11305), people(?x5540, ?x2733), people(?x5540, ?x380), award_nominee(?x488, ?x2733), nominated_for(?x2733, ?x3605), type_of_union(?x11305, ?x566), ?x3605 = 04cj79, religion(?x380, ?x1985), award_winner(?x902, ?x11305) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2115 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 041rx; *> query: (?x5540, 0jcx) <- people(?x5540, ?x11305), people(?x5540, ?x5898), people(?x5540, ?x5593), people(?x5540, ?x3994), ?x11305 = 0l9k1, ?x5898 = 026fd, interests(?x3994, ?x2014), profession(?x3994, ?x3342), nationality(?x3994, ?x1264), award_nominee(?x848, ?x5593) *> conf = 0.33 ranks of expected_values: 16, 419, 1911 EVAL 013xrm people 0h336 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 40.000 37.000 0.500 http://example.org/people/ethnicity/people EVAL 013xrm people 036jp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 40.000 37.000 0.500 http://example.org/people/ethnicity/people EVAL 013xrm people 0jcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 40.000 37.000 0.500 http://example.org/people/ethnicity/people #4740-03nt59 PRED entity: 03nt59 PRED relation: nominated_for! PRED expected values: 09qv3c => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 198): 0m7yy (0.66 #8026, 0.65 #16762, 0.51 #13219), 09qrn4 (0.58 #399, 0.58 #635, 0.37 #871), 09qs08 (0.55 #580, 0.48 #344, 0.43 #816), 09qv3c (0.52 #512, 0.45 #276, 0.37 #748), 09qvf4 (0.42 #382, 0.39 #618, 0.30 #854), 0fbtbt (0.37 #1103, 0.33 #2283, 0.29 #3227), 0gq9h (0.36 #13989, 0.36 #13753, 0.35 #14461), 09qj50 (0.35 #272, 0.33 #508, 0.28 #744), 0gkr9q (0.35 #1151, 0.23 #2331, 0.21 #3748), 0gs9p (0.33 #13755, 0.33 #14463, 0.33 #13991) >> Best rule #8026 for best value: >> intensional similarity = 3 >> extensional distance = 161 >> proper extension: 08cfr1; >> query: (?x6070, ?x3486) <- titles(?x2008, ?x6070), award(?x6070, ?x3486), films(?x2008, ?x590) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #512 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 072kp; 019nnl; 0124k9; 08jgk1; 0584r4; 02xhpl; 01q_y0; 02hct1; 0d68qy; 01h72l; ... *> query: (?x6070, 09qv3c) <- genre(?x6070, ?x8534), nominated_for(?x1909, ?x6070), award_winner(?x6070, ?x5410), ?x8534 = 0c4xc *> conf = 0.52 ranks of expected_values: 4 EVAL 03nt59 nominated_for! 09qv3c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 93.000 0.662 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4739-05f4m9q PRED entity: 05f4m9q PRED relation: award_winner PRED expected values: 02rk45 => 47 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1313): 0pz91 (0.50 #260, 0.40 #2722, 0.20 #5185), 0p__8 (0.50 #1329, 0.40 #3791, 0.20 #6254), 014zfs (0.50 #217, 0.40 #2679, 0.20 #5142), 02t_99 (0.50 #1048, 0.40 #3510, 0.20 #5973), 0bx_q (0.40 #6199, 0.20 #3736, 0.03 #8661), 0343h (0.33 #32026, 0.29 #36957, 0.29 #34490), 0gn30 (0.33 #32026, 0.29 #36957, 0.29 #34490), 06jz0 (0.33 #32026, 0.29 #36957, 0.29 #34490), 06mn7 (0.33 #32026, 0.29 #36957, 0.29 #34490), 0g2lq (0.33 #32026, 0.29 #36957, 0.29 #34490) >> Best rule #260 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 05b1610; 07bdd_; >> query: (?x350, 0pz91) <- nominated_for(?x350, ?x6053), nominated_for(?x350, ?x5089), nominated_for(?x350, ?x2084), ?x5089 = 0bh8tgs, ?x6053 = 05qbbfb, ?x2084 = 048qrd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #32026 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 199 *> proper extension: 06196; *> query: (?x350, ?x5338) <- award(?x5338, ?x350), award_winner(?x350, ?x916), award(?x770, ?x350), award_winner(?x1311, ?x5338) *> conf = 0.33 ranks of expected_values: 20 EVAL 05f4m9q award_winner 02rk45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 47.000 16.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #4738-03_lf PRED entity: 03_lf PRED relation: entity_involved! PRED expected values: 05nqz 06k75 02kxg_ => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 77): 06k75 (0.50 #210, 0.34 #1564, 0.34 #1563), 0f6rc (0.44 #3258, 0.34 #1564, 0.34 #1563), 0j5ym (0.44 #3258, 0.34 #1564, 0.34 #1563), 01y998 (0.44 #3258, 0.22 #3850, 0.10 #409), 0flry (0.37 #1209, 0.09 #2839, 0.09 #3035), 02kxjx (0.22 #3850, 0.20 #436, 0.12 #1086), 0dl4z (0.22 #3850, 0.20 #395, 0.11 #1175), 07_nf (0.22 #3850, 0.14 #2817, 0.14 #3013), 0d06vc (0.19 #1435, 0.19 #1369, 0.10 #1304), 0cm2xh (0.17 #271, 0.15 #726, 0.14 #1442) >> Best rule #210 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 05wh0sh; >> query: (?x10293, 06k75) <- politician(?x14092, ?x10293), type_of_union(?x10293, ?x566), organizations_founded(?x10293, ?x12362), ?x14092 = 02245, influenced_by(?x10293, ?x7509) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 31, 34 EVAL 03_lf entity_involved! 02kxg_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 175.000 175.000 0.500 http://example.org/base/culturalevent/event/entity_involved EVAL 03_lf entity_involved! 06k75 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.500 http://example.org/base/culturalevent/event/entity_involved EVAL 03_lf entity_involved! 05nqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 175.000 175.000 0.500 http://example.org/base/culturalevent/event/entity_involved #4737-01w524f PRED entity: 01w524f PRED relation: profession PRED expected values: 016z4k => 145 concepts (104 used for prediction) PRED predicted values (max 10 best out of 94): 01c72t (0.86 #9079, 0.71 #2798, 0.56 #2506), 02hrh1q (0.83 #5271, 0.75 #4103, 0.74 #12287), 0cbd2 (0.70 #4387, 0.53 #6723, 0.53 #6139), 016z4k (0.51 #3070, 0.51 #1610, 0.50 #2194), 0dxtg (0.51 #4540, 0.46 #6146, 0.44 #5708), 0dz3r (0.49 #5550, 0.49 #1608, 0.46 #878), 039v1 (0.46 #912, 0.40 #3832, 0.40 #328), 0kyk (0.42 #4410, 0.36 #4264, 0.35 #6162), 0n1h (0.38 #888, 0.33 #1910, 0.28 #1764), 01d_h8 (0.36 #1028, 0.35 #5262, 0.33 #2342) >> Best rule #9079 for best value: >> intensional similarity = 5 >> extensional distance = 346 >> proper extension: 02lfp4; >> query: (?x4237, 01c72t) <- profession(?x4237, ?x6476), profession(?x7398, ?x6476), profession(?x1073, ?x6476), ?x1073 = 01pr_j6, ?x7398 = 011vx3 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #3070 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 058s57; 01zmpg; *> query: (?x4237, 016z4k) <- artist(?x7089, ?x4237), instrumentalists(?x316, ?x4237), artist(?x7089, ?x8352), ?x8352 = 03f7m4h *> conf = 0.51 ranks of expected_values: 4 EVAL 01w524f profession 016z4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 145.000 104.000 0.865 http://example.org/people/person/profession #4736-02qny_ PRED entity: 02qny_ PRED relation: nationality PRED expected values: 09c7w0 => 53 concepts (39 used for prediction) PRED predicted values (max 10 best out of 103): 09c7w0 (0.77 #2314, 0.75 #2816, 0.75 #2615), 02jx1 (0.33 #133, 0.29 #834, 0.27 #934), 07ssc (0.33 #115, 0.25 #315, 0.21 #516), 0chghy (0.33 #10, 0.11 #711, 0.11 #511), 0f8l9c (0.33 #222, 0.06 #1426, 0.06 #823), 0345_ (0.25 #369, 0.05 #570, 0.04 #770), 02h6_6p (0.17 #401, 0.12 #2213, 0.12 #2212), 0f04v (0.17 #401, 0.12 #2213, 0.12 #2212), 015fr (0.16 #518, 0.11 #718, 0.05 #918), 04lh6 (0.12 #2213, 0.12 #2212, 0.05 #1002) >> Best rule #2314 for best value: >> intensional similarity = 6 >> extensional distance = 213 >> proper extension: 01z7_f; 06lht1; 036hf4; >> query: (?x11941, 09c7w0) <- gender(?x11941, ?x231), currency(?x11941, ?x170), ?x231 = 05zppz, type_of_union(?x11941, ?x566), ?x170 = 09nqf, ?x566 = 04ztj >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qny_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 39.000 0.767 http://example.org/people/person/nationality #4735-023kzp PRED entity: 023kzp PRED relation: award PRED expected values: 0gqy2 09sdmz => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 268): 0bdwqv (0.72 #43405, 0.71 #32254, 0.70 #33449), 027cyf7 (0.72 #43405, 0.71 #32254, 0.70 #33449), 0ck27z (0.25 #89, 0.18 #27163, 0.14 #27960), 0f4x7 (0.25 #30, 0.14 #428, 0.14 #2418), 05pcn59 (0.25 #2466, 0.22 #4058, 0.19 #6846), 0gqy2 (0.21 #557, 0.13 #41811, 0.13 #41014), 09sdmz (0.21 #599, 0.13 #41811, 0.13 #41014), 01by1l (0.19 #16433, 0.09 #31964, 0.09 #33159), 0gq9h (0.18 #1268, 0.13 #41811, 0.13 #41014), 05zr6wv (0.18 #415, 0.18 #2405, 0.15 #2007) >> Best rule #43405 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 01t265; >> query: (?x5925, ?x3247) <- award_winner(?x3247, ?x5925), award(?x192, ?x3247) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #557 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 02qgqt; 02p65p; 09fb5; 0187y5; 01yb09; 04t7ts; 0hvb2; 02wgln; 02qgyv; 0169dl; ... *> query: (?x5925, 0gqy2) <- award_nominee(?x3293, ?x5925), award_nominee(?x5925, ?x157), ?x3293 = 0gy6z9 *> conf = 0.21 ranks of expected_values: 6, 7 EVAL 023kzp award 09sdmz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 122.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 023kzp award 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 122.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4734-0bvzp PRED entity: 0bvzp PRED relation: gender PRED expected values: 05zppz => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #37, 0.90 #41, 0.90 #39), 02zsn (0.46 #245, 0.41 #68, 0.37 #90) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 92 >> proper extension: 02sj1x; 01r6jt2; 026dx; 0c_drn; 020jqv; >> query: (?x6399, 05zppz) <- award_winner(?x6399, ?x2897), music(?x4559, ?x6399), award(?x6399, ?x594) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bvzp gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.904 http://example.org/people/person/gender #4733-0cqh46 PRED entity: 0cqh46 PRED relation: ceremony PRED expected values: 092c5f => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 131): 092c5f (0.77 #536, 0.69 #667, 0.08 #929), 05c1t6z (0.67 #144, 0.33 #13, 0.25 #275), 0gvstc3 (0.67 #163, 0.33 #32, 0.25 #294), 03nnm4t (0.67 #199, 0.33 #68, 0.25 #330), 0gx_st (0.67 #166, 0.33 #35, 0.18 #428), 02q690_ (0.50 #191, 0.33 #60, 0.25 #322), 0hn821n (0.50 #253, 0.33 #122, 0.18 #515), 0gpjbt (0.36 #3171, 0.34 #3434, 0.34 #3565), 09n4nb (0.35 #3190, 0.34 #3453, 0.33 #3584), 0466p0j (0.35 #3214, 0.33 #3608, 0.33 #3477) >> Best rule #536 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 02py7pj; >> query: (?x880, 092c5f) <- award_winner(?x880, ?x1549), ceremony(?x880, ?x3624), ?x3624 = 027hjff >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cqh46 ceremony 092c5f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.769 http://example.org/award/award_category/winners./award/award_honor/ceremony #4732-07nt8p PRED entity: 07nt8p PRED relation: language PRED expected values: 02h40lc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 40): 02h40lc (0.89 #1914, 0.89 #1674, 0.89 #1554), 064_8sq (0.25 #22, 0.20 #200, 0.14 #556), 04306rv (0.25 #5, 0.10 #539, 0.10 #183), 06nm1 (0.13 #307, 0.12 #784, 0.12 #902), 02bjrlw (0.10 #119, 0.06 #1492, 0.06 #1311), 06b_j (0.10 #201, 0.08 #498, 0.08 #319), 03_9r (0.06 #128, 0.06 #69, 0.06 #603), 012w70 (0.06 #131, 0.03 #904, 0.03 #786), 03hkp (0.05 #549, 0.03 #252, 0.03 #311), 0jzc (0.04 #793, 0.04 #1210, 0.04 #1872) >> Best rule #1914 for best value: >> intensional similarity = 4 >> extensional distance = 704 >> proper extension: 01jc6q; 03g90h; 03hjv97; 0c5dd; 01fmys; 0k4d7; 083skw; 0kcn7; 082scv; 014nq4; ... >> query: (?x2211, 02h40lc) <- film(?x1251, ?x2211), produced_by(?x2211, ?x12159), genre(?x2211, ?x53), currency(?x2211, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07nt8p language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.894 http://example.org/film/film/language #4731-0chghy PRED entity: 0chghy PRED relation: combatants! PRED expected values: 05vz3zq => 190 concepts (139 used for prediction) PRED predicted values (max 10 best out of 316): 05vz3zq (0.82 #4773, 0.80 #1170, 0.56 #1140), 015fr (0.82 #4773, 0.80 #1170, 0.33 #500), 03rjj (0.82 #4773, 0.80 #1170, 0.26 #886), 0chghy (0.56 #1114, 0.54 #1059, 0.49 #2473), 059z0 (0.30 #1603, 0.30 #2791, 0.29 #2847), 02psqkz (0.30 #2774, 0.26 #886, 0.25 #5398), 04g61 (0.26 #886, 0.25 #5398, 0.25 #5845), 0d05q4 (0.26 #886, 0.25 #5398, 0.25 #5845), 01pj7 (0.26 #886, 0.25 #5398, 0.25 #5845), 06c1y (0.26 #886, 0.25 #5398, 0.25 #5845) >> Best rule #4773 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 0bq0p9; 025ndl; 0g78xc; 0j06n; 06v9sf; 03bxbql; 03b79; 02psqkz; 05kyr; 07l75; ... >> query: (?x390, ?x205) <- combatants(?x94, ?x390), combatants(?x326, ?x390), combatants(?x390, ?x205) >> conf = 0.82 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0chghy combatants! 05vz3zq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 190.000 139.000 0.819 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #4730-0b85mm PRED entity: 0b85mm PRED relation: film! PRED expected values: 01j5sv => 65 concepts (53 used for prediction) PRED predicted values (max 10 best out of 874): 01vsqvs (0.33 #1578, 0.05 #5746, 0.03 #7830), 015gjr (0.33 #1270, 0.05 #5438, 0.03 #7522), 05nzw6 (0.15 #3279, 0.03 #17868, 0.03 #19953), 04yywz (0.15 #2103, 0.02 #14608, 0.02 #16692), 0gn30 (0.15 #3034, 0.01 #98912, 0.01 #109333), 01xcqc (0.15 #2344), 059_gf (0.11 #5170, 0.08 #3086, 0.03 #7254), 01yfm8 (0.11 #5464, 0.03 #17969, 0.03 #20054), 0154qm (0.11 #4731, 0.03 #17236, 0.03 #19321), 01ypsj (0.11 #5849, 0.02 #10017, 0.02 #12102) >> Best rule #1578 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02psgq; >> query: (?x11809, 01vsqvs) <- titles(?x205, ?x11809), film_format(?x11809, ?x6392), nominated_for(?x198, ?x11809), ?x205 = 03rjj, ?x198 = 040njc >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0b85mm film! 01j5sv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 53.000 0.333 http://example.org/film/actor/film./film/performance/film #4729-08z0wx PRED entity: 08z0wx PRED relation: parent_genre! PRED expected values: 04b675 => 40 concepts (24 used for prediction) PRED predicted values (max 10 best out of 233): 02vk5b6 (0.33 #237, 0.25 #507, 0.20 #778), 05jt_ (0.13 #1189, 0.13 #916, 0.10 #1457), 04b675 (0.11 #1164, 0.11 #891, 0.08 #1432), 0g_bh (0.09 #3617, 0.09 #2000, 0.09 #2270), 066d03 (0.09 #1339, 0.08 #1066, 0.07 #4047), 03fpx (0.09 #1279, 0.08 #1006, 0.07 #1815), 01_bkd (0.09 #1133, 0.07 #4316, 0.07 #4047), 04f73rc (0.09 #1314, 0.05 #1041, 0.04 #2119), 06cp5 (0.08 #3854, 0.07 #4123, 0.07 #1162), 08z0wx (0.07 #4316, 0.07 #4047, 0.07 #1159) >> Best rule #237 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 04b675; >> query: (?x6349, 02vk5b6) <- parent_genre(?x6349, ?x5580), parent_genre(?x6349, ?x2249), ?x2249 = 03lty, ?x5580 = 01jwt, artists(?x6349, ?x10265), origin(?x10265, ?x2997), group(?x227, ?x10265) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1164 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 43 *> proper extension: 05jg58; *> query: (?x6349, 04b675) <- parent_genre(?x6349, ?x2249), artists(?x2249, ?x10625), artists(?x2249, ?x10209), artists(?x2249, ?x9830), artists(?x2249, ?x4957), ?x4957 = 0g_g2, ?x10209 = 023p29, ?x10625 = 01y_rz, role(?x9830, ?x227), place_of_birth(?x9830, ?x2474) *> conf = 0.11 ranks of expected_values: 3 EVAL 08z0wx parent_genre! 04b675 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 40.000 24.000 0.333 http://example.org/music/genre/parent_genre #4728-0d6d2 PRED entity: 0d6d2 PRED relation: student! PRED expected values: 019c57 => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 199): 0bwfn (0.25 #273, 0.11 #12326, 0.10 #25952), 01w5m (0.13 #2199, 0.08 #12156, 0.06 #7963), 017j69 (0.08 #667, 0.03 #6431, 0.03 #25822), 01d34b (0.08 #778, 0.02 #7066, 0.02 #21216), 06thjt (0.07 #3015, 0.03 #10876, 0.03 #11400), 015nl4 (0.07 #4258, 0.04 #6878, 0.04 #25745), 03ksy (0.07 #2200, 0.05 #9012, 0.05 #18445), 0dy04 (0.07 #2166, 0.03 #8454, 0.03 #7406), 01stzp (0.07 #2604, 0.02 #8892, 0.02 #9416), 09f2j (0.06 #1205, 0.05 #3825, 0.05 #1729) >> Best rule #273 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 015np0; >> query: (?x8151, 0bwfn) <- award(?x8151, ?x112), film(?x8151, ?x7265), ?x7265 = 04tng0 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d6d2 student! 019c57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 141.000 141.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #4727-0143wl PRED entity: 0143wl PRED relation: film PRED expected values: 03h3x5 => 168 concepts (115 used for prediction) PRED predicted values (max 10 best out of 1379): 0_9l_ (0.50 #7066, 0.10 #21309, 0.04 #67592), 03cffvv (0.33 #1734, 0.25 #3514, 0.04 #65820), 06zsk51 (0.33 #1533, 0.14 #13993, 0.07 #67399), 01lbcqx (0.33 #1442, 0.14 #13902, 0.03 #44167), 01gkp1 (0.33 #810, 0.12 #27514, 0.05 #50655), 016ywb (0.33 #6571, 0.10 #20814, 0.05 #35056), 031hcx (0.33 #6607, 0.09 #67133, 0.09 #65353), 04180vy (0.33 #1742, 0.08 #39127, 0.04 #78289), 043t8t (0.33 #783, 0.06 #27487, 0.05 #34608), 04ltlj (0.33 #7050, 0.06 #67576, 0.06 #64016) >> Best rule #7066 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0kszw; >> query: (?x6028, 0_9l_) <- film(?x6028, ?x4138), celebrity(?x4398, ?x6028), participant(?x3183, ?x6028), genre(?x4138, ?x53) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #34244 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 031c2r; *> query: (?x6028, 03h3x5) <- film(?x6028, ?x4786), country(?x4786, ?x789), language(?x6028, ?x254), ?x789 = 0f8l9c *> conf = 0.05 ranks of expected_values: 527 EVAL 0143wl film 03h3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 168.000 115.000 0.500 http://example.org/film/actor/film./film/performance/film #4726-02lhm2 PRED entity: 02lhm2 PRED relation: profession PRED expected values: 0dxtg => 90 concepts (66 used for prediction) PRED predicted values (max 10 best out of 63): 0dxtg (0.77 #5416, 0.74 #5854, 0.71 #159), 0np9r (0.50 #18, 0.16 #2062, 0.15 #9221), 0cbd2 (0.45 #2781, 0.44 #2196, 0.44 #1466), 02jknp (0.43 #7457, 0.40 #3366, 0.37 #5848), 09jwl (0.37 #4397, 0.36 #4543, 0.34 #3667), 0kyk (0.31 #1487, 0.30 #2802, 0.29 #1341), 02krf9 (0.28 #3091, 0.27 #1192, 0.26 #2068), 0nbcg (0.28 #6573, 0.26 #4556, 0.26 #4410), 0d1pc (0.28 #6573, 0.07 #6035, 0.07 #3991), 012t_z (0.28 #6573, 0.06 #12, 0.05 #3371) >> Best rule #5416 for best value: >> intensional similarity = 3 >> extensional distance = 1237 >> proper extension: 04rs03; 042rnl; 019z7q; 01g4zr; 045bg; 022_lg; 01p45_v; 017r2; 064p92m; 0177s6; ... >> query: (?x5450, 0dxtg) <- profession(?x5450, ?x1041), profession(?x2828, ?x1041), ?x2828 = 026n6cs >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lhm2 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 66.000 0.772 http://example.org/people/person/profession #4725-0j5nm PRED entity: 0j5nm PRED relation: genre! PRED expected values: 03g90h => 33 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1890): 0c34mt (0.75 #4346, 0.67 #600, 0.60 #6220), 07l4zhn (0.71 #2879, 0.67 #1006, 0.50 #12250), 03h_yy (0.71 #1950, 0.67 #77, 0.50 #3823), 07z6xs (0.67 #913, 0.62 #4659, 0.57 #2786), 0y_yw (0.67 #1100, 0.62 #4846, 0.57 #2973), 0296rz (0.67 #1714, 0.57 #3587, 0.50 #5460), 03cp4cn (0.67 #1145, 0.57 #3018, 0.50 #4891), 02rb607 (0.67 #402, 0.57 #2275, 0.50 #4148), 07nt8p (0.62 #4115, 0.50 #5989, 0.50 #369), 03s6l2 (0.57 #1960, 0.55 #7581, 0.50 #13206) >> Best rule #4346 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 04xvh5; >> query: (?x14614, 0c34mt) <- genre(?x11324, ?x14614), genre(?x8367, ?x14614), film(?x1414, ?x8367), film(?x4928, ?x8367), film(?x2173, ?x8367), award_nominee(?x4928, ?x3258), student(?x7545, ?x4928), ?x11324 = 027r7k, titles(?x162, ?x8367), people(?x1158, ?x2173), ?x3258 = 02qx69 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #35 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 07s9rl0; 04xvlr; 02n4kr; 0lsxr; *> query: (?x14614, 03g90h) <- genre(?x11324, ?x14614), genre(?x8367, ?x14614), ?x8367 = 011ywj, ?x11324 = 027r7k *> conf = 0.33 ranks of expected_values: 244 EVAL 0j5nm genre! 03g90h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 24.000 0.750 http://example.org/film/film/genre #4724-0q9zc PRED entity: 0q9zc PRED relation: influenced_by PRED expected values: 0p_pd 052hl => 98 concepts (66 used for prediction) PRED predicted values (max 10 best out of 259): 014z8v (0.14 #121, 0.12 #4049, 0.08 #3613), 01hmk9 (0.14 #221, 0.12 #4149, 0.07 #11354), 014zfs (0.14 #24, 0.10 #3952, 0.07 #11354), 01k9lpl (0.14 #311, 0.08 #4239, 0.07 #11354), 0ph2w (0.14 #119, 0.07 #11354, 0.05 #3611), 0q9zc (0.14 #274, 0.07 #11354, 0.05 #13101), 013tjc (0.14 #377, 0.07 #11354, 0.04 #4305), 02pb53 (0.14 #40, 0.07 #11354, 0.02 #3968), 02dztn (0.14 #245, 0.07 #11354, 0.01 #1555), 01wj9y9 (0.11 #12228, 0.07 #11354, 0.04 #3989) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0fxky3; 06w58f; >> query: (?x8375, 014z8v) <- award_nominee(?x8375, ?x7002), award(?x8375, ?x678), ?x7002 = 0dbc1s >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #3936 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 154 *> proper extension: 01d5g; *> query: (?x8375, 0p_pd) <- influenced_by(?x8375, ?x986), nominated_for(?x986, ?x306) *> conf = 0.04 ranks of expected_values: 63, 77 EVAL 0q9zc influenced_by 052hl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 98.000 66.000 0.143 http://example.org/influence/influence_node/influenced_by EVAL 0q9zc influenced_by 0p_pd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 98.000 66.000 0.143 http://example.org/influence/influence_node/influenced_by #4723-0456xp PRED entity: 0456xp PRED relation: vacationer! PRED expected values: 0cp6w => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 97): 03gh4 (0.23 #3159, 0.21 #4023, 0.20 #695), 05qtj (0.16 #4014, 0.16 #3150, 0.15 #932), 0cv3w (0.14 #3136, 0.13 #918, 0.12 #4000), 0b90_r (0.13 #618, 0.12 #741, 0.12 #3946), 0f2v0 (0.13 #678, 0.12 #801, 0.12 #924), 04jpl (0.11 #624, 0.10 #747, 0.09 #3088), 07_pf (0.10 #354, 0.02 #3187, 0.02 #1339), 04ych (0.10 #270, 0.01 #1255, 0.01 #1747), 01w2v (0.10 #328), 078lk (0.09 #423, 0.04 #792, 0.04 #1285) >> Best rule #3159 for best value: >> intensional similarity = 3 >> extensional distance = 120 >> proper extension: 015z4j; 02v60l; 01xyt7; 01pgk0; 01g0jn; >> query: (?x1017, 03gh4) <- gender(?x1017, ?x514), participant(?x629, ?x1017), vacationer(?x3699, ?x1017) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #610 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 01j851; *> query: (?x1017, 0cp6w) <- award(?x1017, ?x154), participant(?x1017, ?x72), ?x154 = 05b4l5x *> conf = 0.02 ranks of expected_values: 47 EVAL 0456xp vacationer! 0cp6w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 133.000 133.000 0.230 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #4722-02v63m PRED entity: 02v63m PRED relation: film! PRED expected values: 013zyw => 73 concepts (49 used for prediction) PRED predicted values (max 10 best out of 102): 03p01x (0.22 #7713), 0j_c (0.20 #63, 0.05 #1437, 0.03 #1711), 0gd9k (0.19 #1924, 0.08 #737, 0.04 #1288), 01qg7c (0.14 #505, 0.02 #1879, 0.01 #3535), 021yw7 (0.14 #365), 02qx69 (0.12 #1100, 0.11 #8264, 0.09 #8813), 0l8v5 (0.12 #1100, 0.11 #8264, 0.09 #8813), 081lh (0.08 #575, 0.05 #1126, 0.05 #1674), 06pj8 (0.08 #597, 0.04 #873, 0.03 #5010), 0343h (0.08 #586, 0.02 #1685) >> Best rule #7713 for best value: >> intensional similarity = 3 >> extensional distance = 806 >> proper extension: 05f67hw; >> query: (?x1184, ?x10407) <- language(?x1184, ?x254), produced_by(?x1184, ?x10407), gender(?x10407, ?x231) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #965 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 03n785; 0yx7h; *> query: (?x1184, 013zyw) <- genre(?x1184, ?x571), production_companies(?x1184, ?x10884), ?x571 = 03npn, nominated_for(?x413, ?x1184) *> conf = 0.02 ranks of expected_values: 47 EVAL 02v63m film! 013zyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 73.000 49.000 0.220 http://example.org/film/director/film #4721-06dl_ PRED entity: 06dl_ PRED relation: people! PRED expected values: 0dq9p => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 34): 0gk4g (0.22 #76, 0.18 #142, 0.13 #3178), 07jwr (0.18 #141, 0.17 #9, 0.04 #999), 02vrr (0.17 #14, 0.02 #278, 0.02 #344), 0m32h (0.11 #89, 0.09 #155, 0.03 #3191), 02y0js (0.11 #68, 0.07 #332, 0.07 #266), 01_qc_ (0.09 #160, 0.03 #3328, 0.02 #3196), 0dq9p (0.09 #281, 0.08 #3185, 0.06 #1667), 0qcr0 (0.07 #265, 0.07 #3169, 0.06 #1651), 04psf (0.07 #271, 0.04 #667, 0.03 #601), 06z5s (0.06 #1213, 0.06 #355, 0.05 #223) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0g5ff; >> query: (?x1947, 0gk4g) <- student(?x5987, ?x1947), place_of_birth(?x1947, ?x1860), influenced_by(?x2993, ?x1947), ?x2993 = 0p8jf >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #281 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 43 *> proper extension: 01h320; *> query: (?x1947, 0dq9p) <- profession(?x1947, ?x2225), profession(?x1947, ?x353), ?x2225 = 0kyk, ?x353 = 0cbd2, place_of_death(?x1947, ?x1659) *> conf = 0.09 ranks of expected_values: 7 EVAL 06dl_ people! 0dq9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 117.000 117.000 0.222 http://example.org/people/cause_of_death/people #4720-04n7jdv PRED entity: 04n7jdv PRED relation: parent_genre PRED expected values: 0glt670 => 83 concepts (61 used for prediction) PRED predicted values (max 10 best out of 181): 06by7 (0.64 #5100, 0.57 #668, 0.54 #1485), 05r6t (0.55 #3825, 0.50 #380, 0.33 #1032), 0xhtw (0.50 #176, 0.33 #502, 0.33 #13), 02yv6b (0.33 #65, 0.25 #228, 0.14 #717), 0pm85 (0.33 #98, 0.25 #261, 0.14 #750), 011j5x (0.33 #510, 0.17 #999, 0.11 #3792), 07gxw (0.29 #690, 0.25 #853, 0.25 #364), 02x8m (0.25 #829, 0.25 #340, 0.25 #177), 05w3f (0.25 #351, 0.25 #188, 0.24 #2152), 016_nr (0.25 #373, 0.25 #210, 0.17 #1025) >> Best rule #5100 for best value: >> intensional similarity = 6 >> extensional distance = 90 >> proper extension: 01gbcf; 017ht; >> query: (?x14409, 06by7) <- parent_genre(?x14409, ?x2249), artists(?x2249, ?x10265), artists(?x2249, ?x7013), artist(?x441, ?x10265), ?x7013 = 081wh1, parent_genre(?x2249, ?x1000) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #353 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 01_bkd; *> query: (?x14409, 0glt670) <- artists(?x14409, ?x4877), artists(?x14409, ?x3422), artists(?x14409, ?x970), ?x3422 = 07g2v, people(?x2510, ?x4877), ?x970 = 01q7cb_, award(?x4877, ?x3978), award_winner(?x3978, ?x140) *> conf = 0.25 ranks of expected_values: 11 EVAL 04n7jdv parent_genre 0glt670 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 83.000 61.000 0.641 http://example.org/music/genre/parent_genre #4719-0jfx1 PRED entity: 0jfx1 PRED relation: people! PRED expected values: 01qhm_ => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 48): 041rx (0.23 #4129, 0.22 #3304, 0.21 #4429), 06v41q (0.22 #28, 0.02 #1078, 0.02 #178), 0x67 (0.21 #3309, 0.19 #1659, 0.19 #609), 02w7gg (0.12 #3527, 0.10 #3302, 0.08 #6002), 048z7l (0.11 #39, 0.05 #714, 0.04 #2064), 02ctzb (0.11 #14, 0.04 #314, 0.04 #6014), 03ts0c (0.11 #25, 0.02 #6025, 0.01 #4450), 059_w (0.11 #29, 0.01 #929, 0.01 #1829), 0xnvg (0.10 #687, 0.08 #3312, 0.08 #1212), 07bch9 (0.07 #322, 0.06 #697, 0.06 #1222) >> Best rule #4129 for best value: >> intensional similarity = 2 >> extensional distance = 1050 >> proper extension: 01h2_6; >> query: (?x2444, 041rx) <- place_of_birth(?x2444, ?x14081), people(?x1446, ?x2444) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #681 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 041h0; 02wb6yq; *> query: (?x2444, 01qhm_) <- profession(?x2444, ?x319), nominated_for(?x2444, ?x224), friend(?x2444, ?x2531) *> conf = 0.07 ranks of expected_values: 11 EVAL 0jfx1 people! 01qhm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 119.000 119.000 0.235 http://example.org/people/ethnicity/people #4718-01m_zd PRED entity: 01m_zd PRED relation: citytown PRED expected values: 02cft => 72 concepts (71 used for prediction) PRED predicted values (max 10 best out of 106): 013yq (0.33 #412, 0.25 #780, 0.20 #8877), 0fg6k (0.33 #214, 0.17 #5546, 0.11 #2057), 02_286 (0.29 #3326, 0.29 #2971, 0.25 #13331), 0f2rq (0.25 #863, 0.17 #7397, 0.17 #5546), 0f2v0 (0.24 #3325, 0.17 #5546, 0.12 #22582), 07dfk (0.18 #19833, 0.17 #16491, 0.14 #19464), 0f2s6 (0.17 #7397, 0.17 #5546, 0.11 #2061), 04jpl (0.17 #4443, 0.14 #5553, 0.11 #5926), 024bqj (0.13 #3525, 0.07 #19819, 0.06 #16477), 04f_d (0.12 #1145, 0.11 #1513, 0.09 #2625) >> Best rule #412 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 0k9ts; >> query: (?x11892, 013yq) <- currency(?x11892, ?x5696), service_location(?x11892, ?x512), industry(?x11892, ?x1605), ?x1605 = 0vg8, ?x512 = 07ssc, service_language(?x11892, ?x732), languages(?x731, ?x732), language(?x1420, ?x732), film(?x157, ?x1420), film(?x731, ?x3093) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #23324 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 230 *> proper extension: 02583l; 07lx1s; 01jtp7; 01k7xz; 02bb47; 02rff2; 01q2sk; 01mpwj; 04d5v9; 01ymvk; ... *> query: (?x11892, ?x4627) <- organization(?x4682, ?x11892), organization(?x4682, ?x11693), organization(?x4682, ?x11468), organization(?x4682, ?x5956), organization(?x4682, ?x1783), citytown(?x11468, ?x4627), company(?x4682, ?x555), ?x11693 = 02p8454, institution(?x734, ?x1783), company(?x346, ?x5956), currency(?x1783, ?x170), state_province_region(?x11468, ?x335) *> conf = 0.02 ranks of expected_values: 91 EVAL 01m_zd citytown 02cft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 72.000 71.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #4717-02725hs PRED entity: 02725hs PRED relation: genre PRED expected values: 060__y => 140 concepts (93 used for prediction) PRED predicted values (max 10 best out of 132): 05p553 (0.86 #7464, 0.68 #6041, 0.43 #4977), 02kdv5l (0.59 #831, 0.46 #356, 0.41 #1900), 01jfsb (0.41 #10441, 0.40 #10797, 0.40 #248), 03k9fj (0.39 #3447, 0.38 #366, 0.38 #2148), 060__y (0.37 #964, 0.30 #5345, 0.26 #490), 02n4kr (0.35 #481, 0.27 #2964, 0.20 #1550), 06n90 (0.34 #843, 0.23 #368, 0.22 #2740), 0lsxr (0.33 #8, 0.30 #244, 0.28 #1313), 03mqtr (0.30 #147, 0.11 #1809, 0.10 #265), 06cvj (0.28 #4976, 0.19 #7463, 0.19 #6040) >> Best rule #7464 for best value: >> intensional similarity = 5 >> extensional distance = 530 >> proper extension: 02pg45; >> query: (?x2289, 05p553) <- language(?x2289, ?x90), film(?x166, ?x2289), genre(?x2289, ?x8681), genre(?x8495, ?x8681), ?x8495 = 0ds5_72 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #964 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 33 *> proper extension: 0194zl; *> query: (?x2289, 060__y) <- film(?x166, ?x2289), country(?x2289, ?x94), film_crew_role(?x2289, ?x1171), ?x1171 = 09vw2b7, ?x166 = 0jz9f *> conf = 0.37 ranks of expected_values: 5 EVAL 02725hs genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 140.000 93.000 0.857 http://example.org/film/film/genre #4716-04vr_f PRED entity: 04vr_f PRED relation: cinematography PRED expected values: 0bqytm => 92 concepts (52 used for prediction) PRED predicted values (max 10 best out of 50): 0854hr (0.09 #148, 0.07 #19, 0.05 #464), 0jsw9l (0.07 #51, 0.04 #306, 0.02 #243), 04qvl7 (0.06 #957, 0.03 #130, 0.03 #1735), 05br10 (0.06 #183, 0.03 #118, 0.03 #499), 02vx4c2 (0.05 #800, 0.04 #226, 0.03 #479), 06r_by (0.04 #215, 0.03 #87, 0.03 #152), 06p0s1 (0.04 #250, 0.03 #122, 0.03 #696), 04sry (0.04 #572, 0.03 #637, 0.03 #573), 018ygt (0.04 #572, 0.03 #637, 0.03 #573), 042xrr (0.04 #572, 0.03 #637, 0.03 #573) >> Best rule #148 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 0209xj; 0hmr4; 0b6tzs; 017gl1; 0_92w; 0jqn5; 011yqc; 02c638; 0f4_l; 0j_t1; ... >> query: (?x1135, 0854hr) <- nominated_for(?x1587, ?x1135), nominated_for(?x1107, ?x1135), ?x1587 = 02rdyk7, currency(?x1135, ?x170), ?x1107 = 019f4v >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #81 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 025x1t; *> query: (?x1135, 0bqytm) <- award_winner(?x1135, ?x382), nominated_for(?x7333, ?x1135), edited_by(?x1916, ?x7333), spouse(?x5669, ?x7333) *> conf = 0.03 ranks of expected_values: 26 EVAL 04vr_f cinematography 0bqytm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 92.000 52.000 0.091 http://example.org/film/film/cinematography #4715-0jqp3 PRED entity: 0jqp3 PRED relation: currency PRED expected values: 09nqf => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.87 #22, 0.83 #57, 0.81 #113), 01nv4h (0.03 #86, 0.03 #163, 0.02 #142), 02l6h (0.03 #18, 0.02 #81, 0.02 #109), 0kz1h (0.01 #19), 02gsvk (0.01 #69) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 09fc83; 02nczh; >> query: (?x1069, 09nqf) <- featured_film_locations(?x1069, ?x3983), nominated_for(?x112, ?x1069), county_seat(?x8727, ?x3983) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jqp3 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.868 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #4714-03b78r PRED entity: 03b78r PRED relation: award_winner! PRED expected values: 0gkxgfq => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 116): 0gkxgfq (0.28 #7091, 0.02 #4971, 0.02 #5249), 03nnm4t (0.12 #72, 0.12 #906, 0.11 #211), 02q690_ (0.12 #63, 0.11 #202, 0.11 #341), 05c1t6z (0.12 #15, 0.11 #293, 0.10 #1962), 02rjjll (0.12 #700, 0.10 #978, 0.09 #1117), 0466p0j (0.10 #1186, 0.09 #1047, 0.08 #769), 0gx_st (0.09 #870, 0.08 #1287, 0.08 #36), 013b2h (0.09 #1190, 0.09 #217, 0.08 #1051), 0gvstc3 (0.09 #1563, 0.08 #311, 0.07 #1702), 07y9ts (0.08 #66, 0.07 #1735, 0.06 #2013) >> Best rule #7091 for best value: >> intensional similarity = 2 >> extensional distance = 1177 >> proper extension: 024rbz; >> query: (?x7395, ?x1764) <- award_winner(?x802, ?x7395), honored_for(?x1764, ?x802) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03b78r award_winner! 0gkxgfq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.280 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4713-013rfk PRED entity: 013rfk PRED relation: artists! PRED expected values: 03lty => 110 concepts (69 used for prediction) PRED predicted values (max 10 best out of 303): 03lty (0.71 #6236, 0.67 #1577, 0.62 #4995), 064t9 (0.62 #2804, 0.60 #3114, 0.58 #4049), 059kh (0.52 #7187, 0.49 #9053, 0.40 #1287), 05r6t (0.43 #10960, 0.43 #9087, 0.40 #1321), 05bt6j (0.40 #3143, 0.40 #1282, 0.38 #2833), 02yv6b (0.38 #9419, 0.33 #11288, 0.30 #12224), 0ggx5q (0.38 #2868, 0.30 #3178, 0.29 #2247), 0dl5d (0.37 #13078, 0.37 #13388, 0.36 #3431), 05w3f (0.36 #5934, 0.32 #7798, 0.30 #8731), 02x8m (0.34 #14320, 0.20 #19295, 0.20 #7780) >> Best rule #6236 for best value: >> intensional similarity = 9 >> extensional distance = 15 >> proper extension: 01wt4wc; >> query: (?x7966, 03lty) <- artists(?x302, ?x7966), category(?x7966, ?x134), artist(?x12666, ?x7966), artist(?x441, ?x7966), artist(?x12666, ?x5916), artist(?x12666, ?x4712), ?x441 = 023rwm, place_of_birth(?x4712, ?x2850), award(?x5916, ?x1565) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013rfk artists! 03lty CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 69.000 0.706 http://example.org/music/genre/artists #4712-0mbs8 PRED entity: 0mbs8 PRED relation: student! PRED expected values: 05nrkb => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 96): 017z88 (0.20 #82, 0.08 #2190, 0.07 #40055), 01nrnm (0.20 #204, 0.07 #40055), 0bwfn (0.12 #802, 0.08 #1329, 0.08 #2383), 04b_46 (0.12 #754, 0.08 #1281, 0.08 #1808), 02fgdx (0.12 #629, 0.07 #40055, 0.02 #2737), 017v3q (0.12 #772, 0.07 #40055, 0.01 #3407), 03ksy (0.12 #633, 0.04 #2741, 0.03 #5376), 07w0v (0.08 #1074, 0.08 #1601, 0.07 #40055), 09f2j (0.08 #1213, 0.08 #1740, 0.07 #40055), 017j69 (0.08 #1199, 0.08 #1726, 0.07 #40055) >> Best rule #82 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 06chf; 02__7n; 0hz_1; >> query: (?x12148, 017z88) <- nominated_for(?x12148, ?x3787), award_winner(?x3184, ?x12148), place_of_birth(?x12148, ?x739), ?x3787 = 063ykwt >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2984 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 02rmxx; 01j851; 04pp9s; 01gw8b; *> query: (?x12148, 05nrkb) <- award(?x12148, ?x3184), ?x3184 = 0gkts9, profession(?x12148, ?x1032) *> conf = 0.04 ranks of expected_values: 19 EVAL 0mbs8 student! 05nrkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 97.000 97.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #4711-0cmd3zy PRED entity: 0cmd3zy PRED relation: film_festivals! PRED expected values: 093dqjy => 27 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1868): 047vp1n (0.40 #1111, 0.33 #406, 0.29 #1585), 02rb607 (0.40 #989, 0.33 #284, 0.29 #1463), 03cw411 (0.33 #556, 0.33 #86, 0.20 #1735), 0462hhb (0.33 #582, 0.29 #1525, 0.23 #2470), 0b76d_m (0.33 #1, 0.29 #1176, 0.20 #1650), 0cw3yd (0.33 #296, 0.20 #1711, 0.20 #1001), 04jplwp (0.33 #417, 0.20 #1122, 0.14 #1596), 03cp4cn (0.33 #384, 0.20 #1089, 0.14 #1563), 0d6b7 (0.33 #267, 0.20 #972, 0.14 #1446), 05mrf_p (0.33 #351, 0.20 #1056, 0.14 #1530) >> Best rule #1111 for best value: >> intensional similarity = 24 >> extensional distance = 3 >> proper extension: 04_m9gk; >> query: (?x13810, 047vp1n) <- film_regional_debut_venue(?x1861, ?x13810), film_festivals(?x6103, ?x13810), film_festivals(?x4643, ?x13810), titles(?x307, ?x6103), genre(?x6103, ?x53), nominated_for(?x6103, ?x1721), nominated_for(?x4046, ?x6103), genre(?x257, ?x307), film_release_region(?x4643, ?x1892), currency(?x6103, ?x170), film_distribution_medium(?x4643, ?x2099), country(?x6103, ?x94), film_release_region(?x9941, ?x1892), film_release_region(?x8236, ?x1892), film_release_region(?x7126, ?x1892), film_release_region(?x2094, ?x1892), film_release_region(?x1999, ?x1892), combatants(?x756, ?x1892), olympics(?x1892, ?x391), ?x1999 = 0gd0c7x, ?x7126 = 0ds1glg, ?x8236 = 042zrm, ?x2094 = 05z7c, ?x9941 = 024lt6 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #319 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 09rwjly; *> query: (?x13810, 093dqjy) <- film_regional_debut_venue(?x1861, ?x13810), film_festivals(?x6103, ?x13810), titles(?x307, ?x6103), genre(?x6103, ?x6887), genre(?x6103, ?x2753), nominated_for(?x6103, ?x1721), nominated_for(?x4046, ?x6103), featured_film_locations(?x6103, ?x1523), film_crew_role(?x6103, ?x137), currency(?x6103, ?x170), award_nominee(?x4046, ?x91), ?x1523 = 030qb3t, genre(?x6352, ?x6887), genre(?x3009, ?x6887), genre(?x2151, ?x6887), genre(?x4130, ?x2753), ?x2151 = 0yzvw, ?x6352 = 08mg_b, ?x3009 = 0kb57, titles(?x2753, ?x994), ?x4130 = 06lpmt *> conf = 0.33 ranks of expected_values: 11 EVAL 0cmd3zy film_festivals! 093dqjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 27.000 14.000 0.400 http://example.org/film/film/film_festivals #4710-027pfg PRED entity: 027pfg PRED relation: nominated_for! PRED expected values: 02pqp12 => 72 concepts (60 used for prediction) PRED predicted values (max 10 best out of 217): 0gq9h (0.35 #1217, 0.34 #1682, 0.33 #4469), 0k611 (0.35 #1227, 0.31 #1692, 0.29 #4479), 0gr0m (0.33 #1214, 0.31 #1679, 0.28 #54), 0p9sw (0.33 #1178, 0.30 #1643, 0.26 #946), 019f4v (0.32 #1210, 0.30 #1675, 0.29 #4462), 0gq_v (0.30 #4429, 0.28 #1642, 0.23 #1177), 03hl6lc (0.30 #821, 0.13 #13933, 0.12 #589), 0gs9p (0.29 #1219, 0.29 #1684, 0.26 #4471), 0gqy2 (0.28 #1277, 0.24 #1742, 0.20 #13466), 0f4x7 (0.26 #1183, 0.22 #1648, 0.20 #487) >> Best rule #1217 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 018js4; 01gc7; 01h7bb; 0pc62; 0jzw; 0cwy47; 09q5w2; 0pv3x; 0168ls; 02r8hh_; ... >> query: (?x6932, 0gq9h) <- genre(?x6932, ?x3515), nominated_for(?x112, ?x6932), ?x3515 = 082gq, film_release_region(?x6932, ?x87) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #981 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 048scx; 02prw4h; 020fcn; 0qm8b; 0cz_ym; 05dy7p; 04t6fk; 0dx8gj; 04lqvly; 0kv9d3; ... *> query: (?x6932, 02pqp12) <- genre(?x6932, ?x3515), nominated_for(?x112, ?x6932), ?x3515 = 082gq, film_crew_role(?x6932, ?x281) *> conf = 0.21 ranks of expected_values: 18 EVAL 027pfg nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 72.000 60.000 0.354 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4709-011zf2 PRED entity: 011zf2 PRED relation: people! PRED expected values: 0d2by => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 34): 03ts0c (0.28 #411), 041rx (0.27 #312, 0.24 #466, 0.23 #235), 0x67 (0.18 #2089, 0.18 #164, 0.18 #1858), 0cn68 (0.12 #58, 0.07 #2850, 0.07 #6933), 048z7l (0.12 #271, 0.10 #117, 0.09 #348), 013b6_ (0.12 #284, 0.09 #361, 0.08 #515), 033tf_ (0.10 #238, 0.08 #469, 0.07 #6933), 07hwkr (0.07 #6933, 0.05 #551, 0.04 #705), 0xnvg (0.07 #6933, 0.04 #2323, 0.04 #4557), 065b6q (0.07 #6933, 0.04 #234, 0.03 #311) >> Best rule #411 for best value: >> intensional similarity = 2 >> extensional distance = 70 >> proper extension: 0gct_; >> query: (?x1399, 03ts0c) <- nationality(?x1399, ?x789), ?x789 = 0f8l9c >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 011zf2 people! 0d2by CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 116.000 0.278 http://example.org/people/ethnicity/people #4708-087z12 PRED entity: 087z12 PRED relation: people! PRED expected values: 0dryh9k => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 45): 0dryh9k (0.47 #940, 0.46 #709, 0.45 #555), 04mvp8 (0.17 #298, 0.15 #375, 0.07 #760), 0bpjh3 (0.15 #333, 0.13 #410, 0.10 #564), 02w7gg (0.15 #1157, 0.10 #2390, 0.08 #2467), 02sch9 (0.14 #574, 0.11 #651, 0.11 #497), 041rx (0.13 #3317, 0.12 #1621, 0.12 #1698), 0x67 (0.10 #1935, 0.09 #3400, 0.09 #3169), 033tf_ (0.09 #1624, 0.09 #1701, 0.09 #1778), 03bkbh (0.07 #1187, 0.04 #1803, 0.04 #1880), 07hwkr (0.07 #2014, 0.07 #1629, 0.07 #1706) >> Best rule #940 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 04b19t; >> query: (?x7531, 0dryh9k) <- languages(?x7531, ?x1882), ?x1882 = 03k50, profession(?x7531, ?x1032) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087z12 people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.469 http://example.org/people/ethnicity/people #4707-02qyntr PRED entity: 02qyntr PRED relation: award! PRED expected values: 0pb33 => 57 concepts (31 used for prediction) PRED predicted values (max 10 best out of 762): 04v8x9 (0.67 #10020, 0.67 #6026, 0.50 #7025), 09gq0x5 (0.60 #4157, 0.50 #8152, 0.50 #3160), 0gmcwlb (0.60 #4109, 0.38 #9102, 0.35 #12099), 0209hj (0.56 #10045, 0.50 #7050, 0.50 #6051), 01jc6q (0.56 #9996, 0.33 #7001, 0.33 #6002), 0cq806 (0.50 #7825, 0.50 #6826, 0.50 #5826), 0bmhn (0.50 #7900, 0.50 #6901, 0.50 #3905), 07xtqq (0.50 #6020, 0.50 #5020, 0.50 #3024), 0_92w (0.50 #6089, 0.50 #5089, 0.50 #2095), 0pd64 (0.50 #5745, 0.50 #3749, 0.40 #4746) >> Best rule #10020 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0gq_v; >> query: (?x6909, 04v8x9) <- nominated_for(?x6909, ?x5533), nominated_for(?x6909, ?x3573), ?x5533 = 027ct7c, film(?x1738, ?x3573) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7118 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 0p9sw; 0gr0m; *> query: (?x6909, 0pb33) <- nominated_for(?x6909, ?x5533), nominated_for(?x6909, ?x4047), nominated_for(?x6909, ?x3573), ?x5533 = 027ct7c, film(?x1738, ?x3573), ?x4047 = 07s846j *> conf = 0.17 ranks of expected_values: 290 EVAL 02qyntr award! 0pb33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 57.000 31.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4706-09k56b7 PRED entity: 09k56b7 PRED relation: language PRED expected values: 02bjrlw => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 53): 06nm1 (0.15 #1519, 0.13 #2393, 0.11 #2569), 04306rv (0.15 #1572, 0.14 #1922, 0.13 #352), 02bjrlw (0.11 #1510, 0.09 #291, 0.09 #2501), 0653m (0.10 #11, 0.07 #474, 0.07 #1346), 06b_j (0.08 #196, 0.08 #835, 0.07 #3745), 04h9h (0.07 #99, 0.05 #331, 0.05 #7918), 03_9r (0.07 #6002, 0.06 #357, 0.06 #472), 05zjd (0.06 #372, 0.05 #2970, 0.05 #7918), 0jzc (0.05 #1644, 0.05 #2970, 0.05 #7918), 032f6 (0.05 #2970, 0.05 #7918, 0.05 #54) >> Best rule #1519 for best value: >> intensional similarity = 4 >> extensional distance = 149 >> proper extension: 01_1pv; 0dgq_kn; 0k4bc; 01y9r2; >> query: (?x1988, 06nm1) <- titles(?x812, ?x1988), nominated_for(?x4126, ?x1988), honored_for(?x1553, ?x1988), featured_film_locations(?x1988, ?x739) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #1510 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 149 *> proper extension: 01_1pv; 0dgq_kn; 0k4bc; 01y9r2; *> query: (?x1988, 02bjrlw) <- titles(?x812, ?x1988), nominated_for(?x4126, ?x1988), honored_for(?x1553, ?x1988), featured_film_locations(?x1988, ?x739) *> conf = 0.11 ranks of expected_values: 3 EVAL 09k56b7 language 02bjrlw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 145.000 0.152 http://example.org/film/film/language #4705-087z12 PRED entity: 087z12 PRED relation: gender PRED expected values: 05zppz => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #17, 0.87 #23, 0.81 #15), 02zsn (0.46 #195, 0.42 #12, 0.38 #46) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 02xfrd; 08hhm6; 023sng; 044pqn; >> query: (?x7531, 05zppz) <- award(?x7531, ?x1937), ?x1937 = 03r8tl, type_of_union(?x7531, ?x566), nationality(?x7531, ?x2146) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087z12 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.880 http://example.org/people/person/gender #4704-04gycf PRED entity: 04gycf PRED relation: origin PRED expected values: 0ply0 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 36): 0cr3d (0.09 #1418, 0.09 #1237, 0.06 #8035), 04jpl (0.06 #1662, 0.05 #3551, 0.04 #2135), 030qb3t (0.05 #3579, 0.04 #3107, 0.04 #8542), 02_286 (0.03 #4978, 0.03 #2617, 0.03 #8524), 09c7w0 (0.03 #3546, 0.02 #2602, 0.02 #2366), 02dtg (0.02 #246, 0.02 #3083, 0.01 #2611), 0k33p (0.02 #399, 0.01 #1819, 0.01 #2292), 04lh6 (0.02 #386, 0.01 #1806, 0.01 #2279), 01_d4 (0.02 #276, 0.01 #6893, 0.01 #8548), 01hvzr (0.02 #469) >> Best rule #1418 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 0197tq; 02rchht; 042l3v; 01wbg84; 03f2_rc; 019z7q; 015grj; 01r9fv; 01g257; 01pl9g; ... >> query: (?x3546, ?x2850) <- profession(?x3546, ?x1032), location(?x3546, ?x2850), ?x2850 = 0cr3d >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04gycf origin 0ply0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 93.000 0.087 http://example.org/music/artist/origin #4703-07k2p6 PRED entity: 07k2p6 PRED relation: award PRED expected values: 09qs08 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 244): 09sb52 (0.33 #41, 0.30 #10545, 0.29 #445), 0bp_b2 (0.33 #18, 0.16 #18181, 0.14 #422), 0gqwc (0.33 #1287, 0.12 #8963, 0.12 #1691), 0gqyl (0.33 #1318, 0.11 #8994, 0.09 #10610), 0bdwft (0.33 #1281, 0.09 #8957, 0.06 #10573), 0gkts9 (0.30 #1381, 0.16 #18181, 0.14 #20606), 0ck27z (0.27 #5749, 0.25 #6153, 0.23 #4133), 02x73k6 (0.25 #869, 0.17 #61, 0.14 #465), 09qs08 (0.22 #1357, 0.12 #28687, 0.04 #5397), 0cqgl9 (0.22 #1405, 0.06 #9081, 0.05 #10697) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 06sn8m; >> query: (?x11346, 09sb52) <- student(?x10497, ?x11346), ?x10497 = 02m0b0, place_of_birth(?x11346, ?x2254) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1357 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 01dbgw; *> query: (?x11346, 09qs08) <- award(?x11346, ?x4225), place_of_birth(?x11346, ?x2254), ?x4225 = 09qvf4 *> conf = 0.22 ranks of expected_values: 9 EVAL 07k2p6 award 09qs08 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 92.000 92.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4702-0vm5t PRED entity: 0vm5t PRED relation: source PRED expected values: 0jbk9 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #44, 0.91 #16, 0.89 #35) >> Best rule #44 for best value: >> intensional similarity = 1 >> extensional distance = 514 >> proper extension: 010bnr; >> query: (?x13998, 0jbk9) <- place(?x13998, ?x13998) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0vm5t source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #4701-03h64 PRED entity: 03h64 PRED relation: country! PRED expected values: 040b5k => 227 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1798): 01m13b (0.60 #10243, 0.55 #33807, 0.50 #32122), 0gl02yg (0.39 #40394, 0.37 #40393, 0.23 #10095), 0df92l (0.39 #40394, 0.37 #40393, 0.04 #39646), 01f8gz (0.39 #40394, 0.37 #40393, 0.03 #119466), 023g6w (0.33 #13165, 0.33 #11480, 0.32 #35044), 049mql (0.27 #34297, 0.27 #32612, 0.27 #12418), 04jkpgv (0.27 #12009, 0.27 #10324, 0.23 #33888), 0bmch_x (0.27 #12563, 0.25 #9193, 0.24 #39491), 04lqvly (0.27 #12389, 0.25 #9019, 0.24 #19120), 07f_7h (0.27 #12171, 0.25 #8801, 0.23 #10095) >> Best rule #10243 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0b90_r; >> query: (?x2645, 01m13b) <- film_release_region(?x7864, ?x2645), film_release_region(?x5827, ?x2645), ?x7864 = 0cbn7c, ?x5827 = 0ggbfwf >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #10095 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0h7x; *> query: (?x2645, ?x80) <- film_release_region(?x9859, ?x2645), film_release_region(?x7864, ?x2645), film_release_region(?x80, ?x2645), ?x7864 = 0cbn7c, ?x9859 = 0g57wgv *> conf = 0.23 ranks of expected_values: 291 EVAL 03h64 country! 040b5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 227.000 71.000 0.600 http://example.org/film/film/country #4700-0134tg PRED entity: 0134tg PRED relation: artists! PRED expected values: 02yv6b => 117 concepts (85 used for prediction) PRED predicted values (max 10 best out of 261): 064t9 (0.67 #24681, 0.50 #11280, 0.46 #6713), 016clz (0.58 #2137, 0.51 #21628, 0.47 #8834), 07sbbz2 (0.53 #2444, 0.29 #1532, 0.26 #2749), 02yv6b (0.50 #2228, 0.50 #96, 0.43 #1620), 01fh36 (0.50 #2216, 0.25 #12182, 0.21 #16446), 0dl5d (0.43 #1235, 0.36 #3064, 0.33 #2150), 03lty (0.42 #3377, 0.32 #2766, 0.28 #12816), 05w3f (0.42 #2166, 0.25 #12182, 0.25 #34), 03_d0 (0.40 #316, 0.25 #2143, 0.22 #8536), 0ggq0m (0.40 #317, 0.08 #2144, 0.08 #11888) >> Best rule #24681 for best value: >> intensional similarity = 5 >> extensional distance = 523 >> proper extension: 01jrz5j; 01pr_j6; 01qvgl; 02r4qs; 01p45_v; 01qkqwg; 0770cd; 02fgpf; 0gt_k; 02zmh5; ... >> query: (?x5385, 064t9) <- artists(?x2664, ?x5385), artists(?x2664, ?x7331), artists(?x2664, ?x217), ?x217 = 0197tq, ?x7331 = 01vtj38 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2228 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 025xt8y; *> query: (?x5385, 02yv6b) <- artist(?x2190, ?x5385), artists(?x837, ?x5385), ?x837 = 016jhr *> conf = 0.50 ranks of expected_values: 4 EVAL 0134tg artists! 02yv6b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 85.000 0.670 http://example.org/music/genre/artists #4699-01n30p PRED entity: 01n30p PRED relation: executive_produced_by PRED expected values: 0d_skg => 86 concepts (62 used for prediction) PRED predicted values (max 10 best out of 42): 01twdk (0.33 #113, 0.03 #366, 0.02 #875), 079vf (0.17 #2, 0.06 #255, 0.03 #3554), 02vyw (0.17 #88, 0.02 #341), 05hj_k (0.09 #607, 0.04 #6444, 0.04 #860), 06q8hf (0.06 #676, 0.04 #6005, 0.04 #6513), 02xnjd (0.05 #429, 0.02 #938, 0.01 #1192), 06pj8 (0.04 #1833, 0.04 #3607, 0.04 #817), 0glyyw (0.03 #1714, 0.03 #3234, 0.03 #3488), 03c9pqt (0.03 #500, 0.02 #5833, 0.02 #5580), 02q42j_ (0.03 #390, 0.02 #6231, 0.02 #1915) >> Best rule #113 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 05qbckf; >> query: (?x8158, 01twdk) <- nominated_for(?x3705, ?x8158), nominated_for(?x384, ?x8158), ?x3705 = 02114t, genre(?x8158, ?x809) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01n30p executive_produced_by 0d_skg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 62.000 0.333 http://example.org/film/film/executive_produced_by #4698-01xwqn PRED entity: 01xwqn PRED relation: people! PRED expected values: 0432mrk => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 60): 041rx (0.34 #1188, 0.33 #1262, 0.31 #1706), 01336l (0.33 #40, 0.25 #114, 0.14 #262), 033tf_ (0.29 #229, 0.14 #155, 0.13 #525), 07bch9 (0.29 #244, 0.14 #170, 0.10 #688), 048z7l (0.20 #335, 0.14 #261, 0.10 #705), 0x67 (0.19 #1268, 0.19 #4750, 0.18 #4380), 0xnvg (0.19 #753, 0.15 #679, 0.14 #161), 07hwkr (0.18 #382, 0.14 #234, 0.14 #160), 033qxt (0.14 #201, 0.09 #497, 0.04 #5111), 06v41q (0.14 #250, 0.04 #5111, 0.03 #2174) >> Best rule #1188 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 01xdf5; 04t2l2; 041h0; 01n5309; 0mdqp; 019z7q; 081lh; 014zfs; 016hvl; 01n4f8; ... >> query: (?x10963, 041rx) <- people(?x12951, ?x10963), languages_spoken(?x12951, ?x1882), influenced_by(?x10963, ?x986), religion(?x986, ?x2694) >> conf = 0.34 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01xwqn people! 0432mrk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 97.000 0.336 http://example.org/people/ethnicity/people #4697-034fl9 PRED entity: 034fl9 PRED relation: nominated_for! PRED expected values: 02pzz3p => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 177): 02pzxlw (0.42 #135, 0.34 #372, 0.12 #14943), 027qq9b (0.38 #145, 0.31 #382, 0.24 #15181), 02pzz3p (0.38 #114, 0.31 #351, 0.20 #17792), 0gq9h (0.32 #10025, 0.29 #9313, 0.29 #9550), 02q1tc5 (0.31 #111, 0.25 #348, 0.24 #15181), 0gs9p (0.31 #10027, 0.25 #10264, 0.25 #12160), 019f4v (0.28 #10016, 0.24 #10253, 0.23 #9304), 0gq_v (0.28 #9983, 0.22 #14488, 0.21 #9508), 027gs1_ (0.27 #1848, 0.26 #2322, 0.25 #2796), 02p_04b (0.27 #178, 0.22 #415, 0.12 #14943) >> Best rule #135 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0jq2r; 05397h; >> query: (?x9029, 02pzxlw) <- genre(?x9029, ?x8805), program(?x6678, ?x9029), ?x8805 = 06q7n, award_winner(?x631, ?x6678) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #114 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 0jq2r; 05397h; *> query: (?x9029, 02pzz3p) <- genre(?x9029, ?x8805), program(?x6678, ?x9029), ?x8805 = 06q7n, award_winner(?x631, ?x6678) *> conf = 0.38 ranks of expected_values: 3 EVAL 034fl9 nominated_for! 02pzz3p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 80.000 80.000 0.423 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4696-0212ny PRED entity: 0212ny PRED relation: contains PRED expected values: 02sn34 => 115 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1693): 04swd (0.33 #4182, 0.20 #15967, 0.10 #36593), 07l75 (0.33 #660, 0.14 #21282, 0.08 #38964), 01d8l (0.33 #1693, 0.14 #22315, 0.04 #48836), 0h7jp (0.25 #8250, 0.20 #14142, 0.17 #20034), 0c82s (0.25 #7509, 0.20 #13401, 0.17 #19293), 0342z_ (0.20 #16703, 0.08 #40277, 0.02 #63860), 020vx9 (0.20 #16174, 0.08 #39748, 0.02 #63331), 05ywg (0.12 #23729, 0.10 #32570, 0.07 #41414), 0m_1s (0.12 #25438, 0.02 #63758, 0.02 #96188), 011w4n (0.12 #26438, 0.02 #64758, 0.02 #70654) >> Best rule #4182 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01mzwp; >> query: (?x13909, 04swd) <- contains(?x5114, ?x13909), ?x5114 = 05vz3zq, combatants(?x13354, ?x13909), combatants(?x13909, ?x13354), contains(?x5114, ?x13354) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #62753 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 0xff; *> query: (?x13909, 02sn34) <- combatants(?x7241, ?x13909), locations(?x7241, ?x404), combatants(?x7241, ?x4815), ?x4815 = 05kyr *> conf = 0.02 ranks of expected_values: 1636 EVAL 0212ny contains 02sn34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 115.000 61.000 0.333 http://example.org/location/location/contains #4695-05w3f PRED entity: 05w3f PRED relation: artists PRED expected values: 01p45_v 01wv9xn 03fbc 014_lq 021r7r 0134wr 0qmny 0kj34 0qmpd 01wxdn3 01t8399 07n68 => 76 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1805): 0fhxv (0.67 #3399, 0.62 #7414, 0.50 #8418), 0qf11 (0.67 #3373, 0.62 #8392, 0.41 #17426), 0b_j2 (0.67 #3570, 0.62 #8589, 0.40 #16620), 033s6 (0.67 #3809, 0.62 #8828, 0.40 #10835), 0178_w (0.67 #3588, 0.62 #8607, 0.38 #7603), 02p68d (0.67 #3697, 0.62 #8716, 0.38 #7712), 011z3g (0.67 #3578, 0.59 #17631, 0.52 #20643), 0140t7 (0.67 #3812, 0.50 #8831, 0.41 #17865), 024qwq (0.67 #3815, 0.50 #8834, 0.41 #17868), 025ldg (0.67 #3363, 0.50 #8382, 0.38 #7378) >> Best rule #3399 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 02k_kn; >> query: (?x2809, 0fhxv) <- artists(?x2809, ?x9589), artists(?x2809, ?x4918), artists(?x2809, ?x2799), ?x2799 = 01vsl3_, parent_genre(?x2809, ?x505), ?x9589 = 02cw1m, role(?x4918, ?x227) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3695 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 4 *> proper extension: 02k_kn; *> query: (?x2809, 0134wr) <- artists(?x2809, ?x9589), artists(?x2809, ?x4918), artists(?x2809, ?x2799), ?x2799 = 01vsl3_, parent_genre(?x2809, ?x505), ?x9589 = 02cw1m, role(?x4918, ?x227) *> conf = 0.67 ranks of expected_values: 11, 24, 97, 102, 110, 119, 140, 317, 495, 518, 528, 849 EVAL 05w3f artists 07n68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 01t8399 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 01wxdn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 0qmpd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 0kj34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 0qmny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 0134wr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 021r7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 014_lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 03fbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 01wv9xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 76.000 41.000 0.667 http://example.org/music/genre/artists EVAL 05w3f artists 01p45_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 76.000 41.000 0.667 http://example.org/music/genre/artists #4694-01k7b0 PRED entity: 01k7b0 PRED relation: featured_film_locations PRED expected values: 0k3p => 75 concepts (43 used for prediction) PRED predicted values (max 10 best out of 30): 07b_l (0.33 #77, 0.02 #4184, 0.01 #8781), 02_286 (0.15 #260, 0.14 #981, 0.14 #5821), 030qb3t (0.07 #4146, 0.07 #6082, 0.07 #4631), 04jpl (0.07 #490, 0.05 #6052, 0.05 #9679), 01_d4 (0.05 #287, 0.04 #1249, 0.04 #1008), 0rh6k (0.04 #6044, 0.03 #722, 0.03 #4593), 0dc95 (0.03 #4591, 0.03 #10156, 0.03 #8462), 0d6lp (0.03 #312, 0.02 #1755, 0.02 #553), 01sn3 (0.03 #10156, 0.03 #8462), 03rjj (0.02 #1930, 0.02 #246, 0.01 #1449) >> Best rule #77 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0m_mm; >> query: (?x6680, 07b_l) <- nominated_for(?x7232, ?x6680), nominated_for(?x484, ?x6680), ?x484 = 0gq_v, ?x7232 = 012vct >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01k7b0 featured_film_locations 0k3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 43.000 0.333 http://example.org/film/film/featured_film_locations #4693-06pyc2 PRED entity: 06pyc2 PRED relation: genre PRED expected values: 03g3w => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 94): 03k9fj (0.41 #133, 0.29 #1223, 0.27 #1466), 05p553 (0.40 #851, 0.36 #6668, 0.34 #4002), 02kdv5l (0.38 #123, 0.32 #1213, 0.31 #971), 01jfsb (0.35 #376, 0.31 #1467, 0.30 #2679), 02l7c8 (0.31 #1955, 0.30 #2197, 0.30 #2318), 0lsxr (0.25 #9, 0.25 #493, 0.23 #372), 01hmnh (0.21 #988, 0.18 #1230, 0.17 #1836), 060__y (0.19 #502, 0.17 #1956, 0.16 #2198), 04xvlr (0.18 #1939, 0.18 #2181, 0.18 #2302), 06n90 (0.17 #14, 0.16 #377, 0.15 #983) >> Best rule #133 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 0jswp; 09lcsj; 0f4k49; 0y_9q; 0gwjw0c; 0p9tm; 02rlj20; 02q5bx2; 06zsk51; 07tlfx; ... >> query: (?x10931, 03k9fj) <- film(?x1089, ?x10931), genre(?x10931, ?x8280), ?x8280 = 0hfjk >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #147 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0jswp; 09lcsj; 0f4k49; 0y_9q; 0gwjw0c; 0p9tm; 02rlj20; 02q5bx2; 06zsk51; 07tlfx; ... *> query: (?x10931, 03g3w) <- film(?x1089, ?x10931), genre(?x10931, ?x8280), ?x8280 = 0hfjk *> conf = 0.13 ranks of expected_values: 17 EVAL 06pyc2 genre 03g3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 61.000 61.000 0.410 http://example.org/film/film/genre #4692-03f47xl PRED entity: 03f47xl PRED relation: influenced_by PRED expected values: 03f0324 01tz6vs => 145 concepts (55 used for prediction) PRED predicted values (max 10 best out of 328): 01v9724 (0.50 #604, 0.36 #1885, 0.33 #2314), 081k8 (0.29 #2292, 0.20 #3148, 0.20 #1009), 01tz6vs (0.28 #3169, 0.25 #1457, 0.18 #1884), 02wh0 (0.27 #2086, 0.17 #2515, 0.16 #3371), 040_9 (0.27 #1807, 0.12 #1380, 0.12 #3092), 0c1jh (0.27 #2024, 0.12 #1597, 0.12 #3309), 0gz_ (0.25 #531, 0.25 #101, 0.17 #3524), 05qmj (0.25 #618, 0.25 #188, 0.14 #3611), 0j3v (0.25 #490, 0.20 #3056, 0.18 #1771), 03hnd (0.25 #97, 0.20 #954, 0.18 #1808) >> Best rule #604 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0l99s; >> query: (?x6504, 01v9724) <- nationality(?x6504, ?x94), award(?x6504, ?x601), influenced_by(?x6504, ?x8659), ?x8659 = 0dw6b >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3169 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0kb3n; 0mb0; *> query: (?x6504, 01tz6vs) <- type_of_union(?x6504, ?x566), influenced_by(?x6504, ?x3336), ?x3336 = 032l1, nationality(?x6504, ?x94) *> conf = 0.28 ranks of expected_values: 3, 11 EVAL 03f47xl influenced_by 01tz6vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 55.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 03f47xl influenced_by 03f0324 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 145.000 55.000 0.500 http://example.org/influence/influence_node/influenced_by #4691-06z6r PRED entity: 06z6r PRED relation: sports! PRED expected values: 0ldqf => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 20): 016r9z (0.84 #358, 0.83 #700, 0.83 #791), 0l6vl (0.84 #358, 0.83 #700, 0.83 #791), 0kbws (0.80 #336, 0.80 #293, 0.76 #594), 0ldqf (0.67 #352, 0.62 #439, 0.60 #204), 018ctl (0.51 #62, 0.48 #209, 0.47 #21), 09n48 (0.51 #62, 0.48 #209, 0.47 #21), 0sx8l (0.51 #62, 0.48 #209, 0.47 #21), 0sx92 (0.51 #62, 0.48 #209, 0.47 #21), 01f1kd (0.51 #62, 0.48 #209, 0.47 #21), 0blfl (0.51 #62, 0.48 #209, 0.47 #21) >> Best rule #358 for best value: >> intensional similarity = 37 >> extensional distance = 7 >> proper extension: 07jjt; >> query: (?x4045, ?x391) <- country(?x4045, ?x3277), country(?x4045, ?x2152), country(?x4045, ?x1536), country(?x4045, ?x1453), country(?x4045, ?x1264), country(?x4045, ?x512), ?x1453 = 06qd3, currency(?x1536, ?x170), film_release_region(?x5992, ?x3277), film_release_region(?x1498, ?x3277), film_release_region(?x622, ?x3277), ?x1498 = 04jkpgv, ?x5992 = 0g5q34q, participating_countries(?x418, ?x3277), film_release_region(?x6480, ?x2152), film_release_region(?x6095, ?x2152), film_release_region(?x3981, ?x2152), film_release_region(?x3599, ?x2152), ?x1264 = 0345h, sports(?x7688, ?x4045), sports(?x4255, ?x4045), sports(?x1081, ?x4045), sports(?x391, ?x4045), ?x3599 = 0kxf1, ?x1081 = 0l6m5, capital(?x1536, ?x4962), combatants(?x1536, ?x1003), ?x622 = 0fq27fp, ?x4255 = 0lgxj, ?x3981 = 047tsx3, titles(?x2152, ?x534), administrative_area_type(?x2152, ?x2792), ?x7688 = 0jkvj, ?x6480 = 02825cv, ?x6095 = 0bq6ntw, administrative_parent(?x13958, ?x2152), ?x512 = 07ssc >> conf = 0.84 => this is the best rule for 2 predicted values *> Best rule #352 for first EXPECTED value: *> intensional similarity = 36 *> extensional distance = 7 *> proper extension: 07jjt; *> query: (?x4045, 0ldqf) <- country(?x4045, ?x3277), country(?x4045, ?x2152), country(?x4045, ?x1536), country(?x4045, ?x1453), country(?x4045, ?x1264), country(?x4045, ?x512), ?x1453 = 06qd3, currency(?x1536, ?x170), film_release_region(?x5992, ?x3277), film_release_region(?x1498, ?x3277), film_release_region(?x622, ?x3277), ?x1498 = 04jkpgv, ?x5992 = 0g5q34q, participating_countries(?x418, ?x3277), film_release_region(?x6480, ?x2152), film_release_region(?x6095, ?x2152), film_release_region(?x3981, ?x2152), film_release_region(?x3599, ?x2152), ?x1264 = 0345h, sports(?x7688, ?x4045), sports(?x4255, ?x4045), sports(?x1081, ?x4045), ?x3599 = 0kxf1, ?x1081 = 0l6m5, capital(?x1536, ?x4962), combatants(?x1536, ?x1003), ?x622 = 0fq27fp, ?x4255 = 0lgxj, ?x3981 = 047tsx3, titles(?x2152, ?x534), administrative_area_type(?x2152, ?x2792), ?x7688 = 0jkvj, ?x6480 = 02825cv, ?x6095 = 0bq6ntw, administrative_parent(?x13958, ?x2152), ?x512 = 07ssc *> conf = 0.67 ranks of expected_values: 4 EVAL 06z6r sports! 0ldqf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 42.000 42.000 0.836 http://example.org/olympics/olympic_games/sports #4690-02tn0_ PRED entity: 02tn0_ PRED relation: student! PRED expected values: 01nrnm => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 157): 06kknt (0.33 #467, 0.10 #994, 0.02 #6264), 0bwfn (0.16 #6072, 0.13 #2910, 0.10 #7653), 065y4w7 (0.14 #3176, 0.13 #2649, 0.12 #4230), 03ksy (0.10 #633, 0.09 #2214, 0.08 #12754), 01jq34 (0.10 #584, 0.08 #1111, 0.05 #1638), 0lyjf (0.10 #684, 0.04 #2792, 0.04 #3319), 01w5m (0.10 #632, 0.04 #38578, 0.04 #23295), 02sdwt (0.10 #929, 0.03 #4091, 0.02 #4618), 03bmmc (0.10 #723, 0.03 #3885, 0.02 #4412), 02fgdx (0.10 #629, 0.03 #3791, 0.01 #19076) >> Best rule #467 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0g72r; >> query: (?x9785, 06kknt) <- place_of_death(?x9785, ?x1839), gender(?x9785, ?x231), ?x1839 = 01c40n >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2839 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 015pxr; 078jt5; 0603qp; 07g7h2; 025y9fn; *> query: (?x9785, 01nrnm) <- producer_type(?x9785, ?x632), program(?x9785, ?x2026), film(?x9785, ?x155) *> conf = 0.04 ranks of expected_values: 44 EVAL 02tn0_ student! 01nrnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 137.000 137.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #4689-027gy0k PRED entity: 027gy0k PRED relation: language PRED expected values: 02h40lc => 94 concepts (91 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.91 #475, 0.91 #1489, 0.91 #2263), 064_8sq (0.21 #376, 0.17 #258, 0.15 #317), 0295r (0.17 #29, 0.02 #383), 06nm1 (0.16 #484, 0.13 #247, 0.13 #840), 04306rv (0.14 #597, 0.12 #478, 0.12 #537), 06b_j (0.10 #496, 0.08 #615, 0.06 #377), 03_9r (0.10 #128, 0.08 #187, 0.06 #2573), 02bjrlw (0.08 #2143, 0.07 #2022, 0.07 #2383), 07zrf (0.08 #180, 0.02 #416, 0.02 #655), 04h9h (0.07 #516, 0.05 #575, 0.04 #635) >> Best rule #475 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 0ds11z; 0ds33; 0pc62; 0fg04; 017gl1; 048scx; 01kff7; 0dtfn; 017gm7; 04w7rn; ... >> query: (?x6510, 02h40lc) <- crewmember(?x6510, ?x9391), film_crew_role(?x6510, ?x2091), ?x2091 = 02rh1dz >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027gy0k language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 91.000 0.914 http://example.org/film/film/language #4688-09jg8 PRED entity: 09jg8 PRED relation: risk_factors! PRED expected values: 0hgxh => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 90): 02bft (0.43 #819, 0.43 #758, 0.38 #946), 09d11 (0.33 #692, 0.33 #21, 0.25 #1009), 01gkcc (0.33 #31, 0.25 #1019, 0.25 #226), 011zdm (0.33 #94, 0.25 #157, 0.20 #457), 072hv (0.33 #45, 0.25 #240, 0.17 #716), 07jwr (0.33 #7, 0.25 #202, 0.17 #678), 0dcrb (0.25 #251, 0.17 #1509, 0.17 #727), 0167bx (0.25 #171, 0.17 #506, 0.08 #1450), 014w_8 (0.20 #370, 0.17 #1356, 0.14 #1617), 01n3bm (0.20 #372, 0.17 #1358, 0.14 #841) >> Best rule #819 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 0cd25; >> query: (?x9898, 02bft) <- people(?x9898, ?x12334), people(?x9898, ?x11649), people(?x9898, ?x215), profession(?x12334, ?x1032), award_nominee(?x217, ?x215), award(?x215, ?x2430), gender(?x11649, ?x231), artists(?x378, ?x215), award_winner(?x567, ?x215), award_winner(?x2430, ?x4191), ?x4191 = 036px, instrumentalists(?x214, ?x215) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #565 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 01_qc_; *> query: (?x9898, 0hgxh) <- symptom_of(?x9438, ?x9898), symptom_of(?x7007, ?x9898), symptom_of(?x4905, ?x9898), symptom_of(?x4905, ?x8523), symptom_of(?x4905, ?x3680), people(?x8523, ?x2807), risk_factors(?x7007, ?x514), risk_factors(?x1158, ?x8523), ?x9438 = 012qjw, ?x3680 = 025hl8, risk_factors(?x8523, ?x8023) *> conf = 0.17 ranks of expected_values: 27 EVAL 09jg8 risk_factors! 0hgxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 57.000 57.000 0.429 http://example.org/medicine/disease/risk_factors #4687-02x73k6 PRED entity: 02x73k6 PRED relation: award! PRED expected values: 02sjf5 0blq0z 01swck 016yvw 07ddz9 => 49 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2477): 09fb5 (0.68 #39719, 0.68 #46343, 0.68 #52964), 017r13 (0.68 #39719, 0.68 #46343, 0.68 #52964), 08664q (0.68 #39719, 0.68 #46343, 0.68 #52964), 01xsbh (0.68 #39719, 0.68 #52964, 0.67 #46341), 03ym1 (0.62 #1638, 0.53 #11565, 0.33 #8256), 0171cm (0.62 #658, 0.47 #10585, 0.25 #7276), 018db8 (0.62 #159, 0.40 #10086, 0.25 #6777), 01r93l (0.62 #1181, 0.33 #11108, 0.33 #7799), 014zcr (0.56 #3358, 0.38 #49, 0.37 #13284), 05kfs (0.56 #3465, 0.37 #13391, 0.12 #16700) >> Best rule #39719 for best value: >> intensional similarity = 5 >> extensional distance = 187 >> proper extension: 06196; >> query: (?x1033, ?x192) <- award_winner(?x1033, ?x3701), award_winner(?x1033, ?x192), award(?x92, ?x1033), award_winner(?x7515, ?x3701), award(?x197, ?x1033) >> conf = 0.68 => this is the best rule for 4 predicted values *> Best rule #693 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 027dtxw; 0f4x7; 09sb52; 0279c15; 0gqy2; 02w9sd7; *> query: (?x1033, 0blq0z) <- nominated_for(?x1033, ?x195), award(?x1119, ?x1033), ?x1119 = 039bp, award(?x197, ?x1033) *> conf = 0.50 ranks of expected_values: 20, 73, 266, 276, 686 EVAL 02x73k6 award! 07ddz9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 17.000 0.681 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x73k6 award! 016yvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 49.000 17.000 0.681 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x73k6 award! 01swck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 17.000 0.681 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x73k6 award! 0blq0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 49.000 17.000 0.681 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x73k6 award! 02sjf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 17.000 0.681 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4686-011_3s PRED entity: 011_3s PRED relation: nominated_for PRED expected values: 0g60z => 91 concepts (56 used for prediction) PRED predicted values (max 10 best out of 376): 02h2vv (0.53 #32350, 0.52 #40441, 0.49 #33969), 0g60z (0.50 #3276, 0.10 #72806, 0.03 #38864), 028kj0 (0.49 #1618, 0.27 #63100, 0.21 #88984), 0dc7hc (0.49 #1618, 0.27 #63100, 0.21 #88984), 040_lv (0.49 #1618, 0.27 #63100, 0.21 #88984), 0320fn (0.49 #1618, 0.27 #63100, 0.21 #88984), 039cq4 (0.27 #2701, 0.12 #7555, 0.05 #25346), 0d68qy (0.27 #1992, 0.04 #6846, 0.03 #32725), 0ds35l9 (0.22 #6, 0.10 #72806, 0.02 #17795), 0bcp9b (0.22 #1175, 0.09 #2793, 0.02 #17795) >> Best rule #32350 for best value: >> intensional similarity = 3 >> extensional distance = 364 >> proper extension: 032xhg; 02r_d4; 06n7h7; 05zbm4; 019_1h; 0bz5v2; 07vc_9; 04mz10g; 04y79_n; 0clvcx; ... >> query: (?x3267, ?x5047) <- actor(?x5047, ?x3267), award_nominee(?x3267, ?x336), award_winner(?x2719, ?x3267) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #3276 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 14 *> proper extension: 05l4yg; 05gnf9; *> query: (?x3267, 0g60z) <- award_nominee(?x336, ?x3267), ?x336 = 03x3qv *> conf = 0.50 ranks of expected_values: 2 EVAL 011_3s nominated_for 0g60z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 56.000 0.526 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4685-01xzb6 PRED entity: 01xzb6 PRED relation: currency PRED expected values: 09nqf => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.40 #13, 0.31 #7, 0.31 #112), 01nv4h (0.10 #2, 0.08 #32, 0.07 #44) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 01q7cb_; 02wb6yq; 01pgk0; >> query: (?x5285, 09nqf) <- artists(?x671, ?x5285), celebrity(?x5285, ?x5312), gender(?x5285, ?x231) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xzb6 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.396 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #4684-0dj5q PRED entity: 0dj5q PRED relation: profession PRED expected values: 099md => 169 concepts (161 used for prediction) PRED predicted values (max 10 best out of 104): 0fj9f (0.89 #3206, 0.88 #2756, 0.86 #3806), 02hrh1q (0.79 #8567, 0.73 #10067, 0.71 #10367), 04gc2 (0.56 #2893, 0.55 #1243, 0.50 #1843), 0dxtg (0.50 #6614, 0.33 #14, 0.30 #914), 099md (0.43 #824, 0.29 #2774, 0.28 #4424), 0kyk (0.42 #3031, 0.33 #3481, 0.33 #2431), 02jknp (0.40 #908, 0.32 #6608, 0.32 #8110), 0cbd2 (0.37 #6607, 0.33 #2857, 0.33 #7), 01d_h8 (0.36 #9908, 0.35 #6606, 0.34 #8108), 02hv44_ (0.33 #59, 0.25 #6659, 0.21 #17854) >> Best rule #3206 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 0d0vj4; >> query: (?x6735, 0fj9f) <- nationality(?x6735, ?x789), basic_title(?x6735, ?x346), type_of_union(?x6735, ?x566), entity_involved(?x12031, ?x6735), company(?x346, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #824 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 01kx1j; 018q7; *> query: (?x6735, 099md) <- nationality(?x6735, ?x789), entity_involved(?x12031, ?x6735), gender(?x6735, ?x231), ?x12031 = 02kxjx, combatants(?x789, ?x94) *> conf = 0.43 ranks of expected_values: 5 EVAL 0dj5q profession 099md CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 169.000 161.000 0.895 http://example.org/people/person/profession #4683-015nvj PRED entity: 015nvj PRED relation: award PRED expected values: 02pqp12 => 103 concepts (78 used for prediction) PRED predicted values (max 10 best out of 273): 0gs9p (0.81 #8095, 0.79 #14161, 0.79 #20629), 0gq9h (0.60 #1696, 0.59 #1291, 0.54 #2910), 02pqp12 (0.47 #1689, 0.41 #1284, 0.31 #2903), 0gr51 (0.36 #1719, 0.34 #1314, 0.27 #3741), 09sb52 (0.32 #19051, 0.26 #17433, 0.24 #17029), 02rdyk7 (0.30 #1710, 0.27 #1305, 0.23 #3732), 0gr4k (0.28 #1652, 0.27 #1247, 0.24 #842), 04dn09n (0.24 #1258, 0.23 #1663, 0.23 #3685), 0gqyl (0.24 #13456, 0.09 #7794, 0.09 #19115), 0f_nbyh (0.23 #2843, 0.22 #1224, 0.21 #1629) >> Best rule #8095 for best value: >> intensional similarity = 4 >> extensional distance = 265 >> proper extension: 04cy8rb; 07fzq3; >> query: (?x10758, ?x1313) <- award_winner(?x1313, ?x10758), ceremony(?x1313, ?x78), nominated_for(?x1313, ?x5183), ?x5183 = 0cq8qq >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1689 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 0qf43; 05drq5; 01f7j9; 0hskw; 02l5rm; 0gv2r; *> query: (?x10758, 02pqp12) <- award_winner(?x1313, ?x10758), award(?x10758, ?x198), ?x1313 = 0gs9p, film(?x10758, ?x5873) *> conf = 0.47 ranks of expected_values: 3 EVAL 015nvj award 02pqp12 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 78.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4682-01skqzw PRED entity: 01skqzw PRED relation: company! PRED expected values: 01fs_4 => 93 concepts (47 used for prediction) PRED predicted values (max 10 best out of 493): 03gkn5 (0.25 #4530, 0.24 #8301, 0.17 #293), 06y3r (0.25 #171, 0.17 #406, 0.14 #878), 081nh (0.25 #40, 0.17 #275, 0.14 #747), 03rx9 (0.25 #183, 0.17 #418, 0.14 #890), 0d05fv (0.25 #1263, 0.15 #2909, 0.14 #1028), 0hfml (0.17 #378, 0.14 #1086, 0.14 #850), 095b70 (0.17 #352, 0.14 #1060, 0.14 #824), 014z8v (0.17 #306, 0.14 #1014, 0.14 #778), 03h_fk5 (0.17 #285, 0.14 #993, 0.14 #757), 01p45_v (0.17 #256, 0.14 #964, 0.14 #728) >> Best rule #4530 for best value: >> intensional similarity = 7 >> extensional distance = 30 >> proper extension: 03hdz8; >> query: (?x13490, 03gkn5) <- company(?x12100, ?x13490), company(?x772, ?x13490), company(?x187, ?x13490), profession(?x772, ?x319), student(?x4268, ?x12100), nationality(?x187, ?x94), state_province_region(?x13490, ?x1426) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01skqzw company! 01fs_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 47.000 0.250 http://example.org/people/person/employment_history./business/employment_tenure/company #4681-0dzt9 PRED entity: 0dzt9 PRED relation: origin! PRED expected values: 03j_hq => 207 concepts (165 used for prediction) PRED predicted values (max 10 best out of 436): 0gdh5 (0.20 #617, 0.06 #1647, 0.06 #2166), 01w1kyf (0.12 #6190, 0.11 #2062, 0.11 #7224), 0gyx4 (0.12 #6190, 0.11 #2062, 0.11 #7224), 01wzlxj (0.12 #6190, 0.11 #7224, 0.08 #73716), 03y9ccy (0.12 #6190, 0.11 #7224, 0.08 #73716), 0f87jy (0.12 #6190, 0.07 #3610, 0.07 #12378), 06nv27 (0.08 #16720, 0.06 #1763, 0.06 #2282), 018zvb (0.07 #3610, 0.07 #6189, 0.06 #7223), 084x96 (0.07 #3610, 0.07 #6189, 0.06 #7223), 0840vq (0.07 #1152, 0.06 #1667, 0.06 #2186) >> Best rule #617 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 07g0_; >> query: (?x9846, 0gdh5) <- state(?x9846, ?x1426), contains(?x94, ?x9846), capital(?x8866, ?x9846) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dzt9 origin! 03j_hq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 207.000 165.000 0.200 http://example.org/music/artist/origin #4680-04gp58p PRED entity: 04gp58p PRED relation: category PRED expected values: 08mbj5d => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.32 #6, 0.29 #5, 0.29 #7) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 0413cff; >> query: (?x8283, 08mbj5d) <- titles(?x53, ?x8283), genre(?x8283, ?x2753), ?x2753 = 0219x_ >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gp58p category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.317 http://example.org/common/topic/webpage./common/webpage/category #4679-0kn3g PRED entity: 0kn3g PRED relation: artists! PRED expected values: 021dvj => 130 concepts (72 used for prediction) PRED predicted values (max 10 best out of 221): 06by7 (0.68 #15798, 0.44 #6206, 0.43 #20444), 03_d0 (0.52 #2794, 0.33 #4650, 0.31 #1557), 064t9 (0.38 #1249, 0.37 #13623, 0.36 #9292), 021dvj (0.36 #670, 0.22 #361, 0.19 #2525), 0ggx5q (0.36 #13688, 0.13 #9357, 0.13 #6263), 07sbbz2 (0.31 #1244, 0.10 #15785, 0.08 #935), 026z9 (0.26 #10285, 0.08 #1313, 0.07 #3787), 016clz (0.23 #14542, 0.21 #20428, 0.21 #12994), 05bt6j (0.23 #1280, 0.21 #15821, 0.21 #13033), 0155w (0.23 #1343, 0.19 #3817, 0.17 #15884) >> Best rule #15798 for best value: >> intensional similarity = 5 >> extensional distance = 311 >> proper extension: 06fxnf; 07hgkd; 09swkk; 02g1jh; 0417z2; 01rwcgb; 02_33l; >> query: (?x9728, 06by7) <- instrumentalists(?x75, ?x9728), nationality(?x9728, ?x512), artists(?x597, ?x9728), artists(?x597, ?x5757), ?x5757 = 01pbs9w >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #670 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 03d6q; *> query: (?x9728, 021dvj) <- artists(?x888, ?x9728), type_of_union(?x9728, ?x566), religion(?x9728, ?x8140), ?x888 = 05lls *> conf = 0.36 ranks of expected_values: 4 EVAL 0kn3g artists! 021dvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 130.000 72.000 0.677 http://example.org/music/genre/artists #4678-01kx_81 PRED entity: 01kx_81 PRED relation: artists! PRED expected values: 06cqb => 136 concepts (87 used for prediction) PRED predicted values (max 10 best out of 247): 064t9 (0.69 #8291, 0.66 #7985, 0.56 #10441), 017_qw (0.48 #6193, 0.26 #10793, 0.17 #979), 025sc50 (0.47 #8019, 0.42 #8325, 0.29 #10475), 05bt6j (0.45 #348, 0.33 #961, 0.30 #655), 016clz (0.42 #2463, 0.30 #5218, 0.30 #14425), 0glt670 (0.32 #10466, 0.32 #8010, 0.32 #11386), 01fh36 (0.28 #1620, 0.12 #14504, 0.11 #14196), 03_d0 (0.28 #2776, 0.27 #7983, 0.27 #8289), 02lnbg (0.27 #10483, 0.24 #8333, 0.23 #8027), 0xhtw (0.27 #12283, 0.27 #14437, 0.26 #11057) >> Best rule #8291 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 0163m1; 0hvbj; 01dwrc; 011z3g; 0178_w; 01dq9q; 016376; 012x03; >> query: (?x1291, 064t9) <- award_winner(?x2585, ?x1291), artists(?x3319, ?x1291), ?x3319 = 06j6l >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1846 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 01wl38s; 02l840; 01vrz41; 01vs14j; 01w60_p; 0144l1; 01vsnff; 0lccn; 01_x6v; 0k7pf; ... *> query: (?x1291, 06cqb) <- role(?x1291, ?x2764), role(?x2764, ?x228), person(?x1619, ?x1291) *> conf = 0.11 ranks of expected_values: 41 EVAL 01kx_81 artists! 06cqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 136.000 87.000 0.693 http://example.org/music/genre/artists #4677-0xnt5 PRED entity: 0xnt5 PRED relation: category PRED expected values: 08mbj5d => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.69 #35, 0.67 #85, 0.65 #39) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 240 >> proper extension: 0l0mk; 0xpp5; 01n4nd; 0l4vc; 0mgp; 010m55; 0tk02; 0xn7b; 018dk_; >> query: (?x7593, 08mbj5d) <- contains(?x2236, ?x7593), citytown(?x9150, ?x7593), location(?x7969, ?x7593) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xnt5 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.686 http://example.org/common/topic/webpage./common/webpage/category #4676-05p9_ql PRED entity: 05p9_ql PRED relation: award PRED expected values: 09qj50 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 188): 09qj50 (0.45 #37, 0.44 #6330, 0.43 #5626), 0cqhk0 (0.45 #30, 0.44 #6330, 0.43 #5626), 0m7yy (0.45 #5757, 0.42 #6226, 0.37 #5522), 09qs08 (0.44 #6330, 0.43 #5626, 0.42 #6095), 0cjyzs (0.44 #6330, 0.43 #5626, 0.42 #6095), 027gs1_ (0.44 #6330, 0.43 #5626, 0.42 #6095), 0cqhmg (0.44 #6330, 0.43 #5626, 0.42 #6095), 09qvc0 (0.44 #6330, 0.43 #5626, 0.42 #6095), 02_3zj (0.44 #6330, 0.43 #5626, 0.42 #6095), 09qvf4 (0.40 #145, 0.23 #849, 0.21 #381) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 01rp13; >> query: (?x7317, 09qj50) <- nominated_for(?x2965, ?x7317), nominated_for(?x11179, ?x7317), genre(?x7317, ?x53), ?x11179 = 0cqhmg >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05p9_ql award 09qj50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.450 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4675-0162b PRED entity: 0162b PRED relation: organization PRED expected values: 07t65 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 49): 07t65 (0.92 #971, 0.91 #1587, 0.91 #401), 0b6css (0.73 #72, 0.67 #51, 0.50 #157), 01rz1 (0.50 #171, 0.41 #87, 0.41 #613), 04k4l (0.48 #509, 0.47 #278, 0.41 #446), 018cqq (0.47 #73, 0.45 #137, 0.42 #284), 041288 (0.38 #1474, 0.38 #1132, 0.35 #1367), 085h1 (0.35 #85, 0.21 #1309, 0.07 #369), 02jxk (0.33 #109, 0.31 #277, 0.28 #445), 0gkjy (0.31 #1208, 0.28 #1102, 0.26 #1123), 059dn (0.27 #77, 0.27 #56, 0.25 #141) >> Best rule #971 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 03h2c; 0d05q4; >> query: (?x10457, 07t65) <- country(?x668, ?x10457), taxonomy(?x10457, ?x939), currency(?x10457, ?x170) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0162b organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.922 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #4674-0n3g PRED entity: 0n3g PRED relation: location_of_ceremony! PRED expected values: 04ztj => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.86 #270, 0.86 #49, 0.86 #266), 01g63y (0.33 #2, 0.12 #22, 0.09 #38), 0jgjn (0.07 #96, 0.05 #156, 0.03 #204), 01bl8s (0.02 #187, 0.02 #203, 0.01 #236) >> Best rule #270 for best value: >> intensional similarity = 2 >> extensional distance = 111 >> proper extension: 0bqyhk; >> query: (?x5411, 04ztj) <- contains(?x1144, ?x5411), location_of_ceremony(?x4922, ?x5411) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n3g location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.858 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #4673-04jpl PRED entity: 04jpl PRED relation: contains! PRED expected values: 02jx1 => 251 concepts (215 used for prediction) PRED predicted values (max 10 best out of 486): 02jx1 (0.80 #109255, 0.65 #109254, 0.61 #145089), 014tss (0.65 #109254, 0.59 #125383, 0.44 #30444), 09c7w0 (0.61 #145089, 0.61 #111048, 0.58 #25970), 0345h (0.61 #145089, 0.56 #152258, 0.45 #82387), 0d0vqn (0.61 #145089, 0.56 #152258, 0.45 #82387), 01znc_ (0.61 #145089, 0.56 #152258, 0.03 #27857), 03rk0 (0.61 #145089, 0.43 #161220, 0.37 #174659), 06q1r (0.61 #111048, 0.45 #82387, 0.43 #161220), 02qkt (0.52 #11988, 0.47 #96166, 0.46 #82733), 02j9z (0.52 #175555, 0.45 #172867, 0.42 #54623) >> Best rule #109255 for best value: >> intensional similarity = 2 >> extensional distance = 90 >> proper extension: 0494n; 0gclb; 0dlm_; >> query: (?x362, ?x512) <- capital(?x512, ?x362), olympics(?x512, ?x391) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04jpl contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 251.000 215.000 0.796 http://example.org/location/location/contains #4672-0xhj2 PRED entity: 0xhj2 PRED relation: place PRED expected values: 0xhj2 => 114 concepts (94 used for prediction) PRED predicted values (max 10 best out of 229): 01x96 (0.23 #16522, 0.10 #156, 0.08 #673), 0n5yh (0.22 #4641, 0.16 #4125, 0.14 #1549), 0vzm (0.17 #5159, 0.02 #1620, 0.02 #2650), 0cr3d (0.13 #41311, 0.10 #39250), 0mzvm (0.10 #79, 0.08 #596, 0.07 #1112), 0pc7r (0.10 #63, 0.08 #580, 0.07 #1096), 0f2nf (0.10 #247, 0.08 #764, 0.07 #1280), 01cx_ (0.10 #64, 0.08 #581, 0.07 #1097), 013h9 (0.10 #311, 0.08 #828, 0.07 #1344), 0xhj2 (0.10 #39250, 0.07 #26857, 0.01 #25303) >> Best rule #16522 for best value: >> intensional similarity = 4 >> extensional distance = 198 >> proper extension: 0xkq4; >> query: (?x11937, ?x6188) <- county(?x11937, ?x5088), contains(?x728, ?x11937), time_zones(?x11937, ?x2674), administrative_division(?x6188, ?x728) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #39250 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 451 *> proper extension: 01vskn; *> query: (?x11937, ?x2850) <- category(?x11937, ?x134), location(?x1335, ?x11937), ?x134 = 08mbj5d, location(?x1335, ?x2850) *> conf = 0.10 ranks of expected_values: 10 EVAL 0xhj2 place 0xhj2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 114.000 94.000 0.227 http://example.org/location/hud_county_place/place #4671-0286gm1 PRED entity: 0286gm1 PRED relation: nominated_for! PRED expected values: 0gs9p => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 219): 0gq_v (0.76 #4178, 0.75 #2786, 0.75 #2553), 0gr0m (0.76 #4178, 0.74 #3250, 0.72 #465), 02z1nbg (0.72 #465, 0.71 #232, 0.69 #10898), 0gs9p (0.62 #5164, 0.61 #4933, 0.61 #4701), 0k611 (0.52 #5173, 0.52 #3319, 0.50 #4942), 02qyntr (0.45 #5277, 0.44 #5046, 0.36 #3423), 0p9sw (0.43 #20, 0.43 #2340, 0.42 #253), 0gr4k (0.43 #4666, 0.41 #2578, 0.40 #3275), 02pqp12 (0.42 #5160, 0.42 #4929, 0.34 #3306), 04kxsb (0.38 #5194, 0.37 #4963, 0.29 #4731) >> Best rule #4178 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 0gjk1d; 070fnm; 02q56mk; 01jzyf; 03cw411; 03lv4x; 0kvgnq; 0yxf4; 03cvvlg; 02ptczs; >> query: (?x6269, ?x484) <- award(?x6269, ?x484), cinematography(?x6269, ?x2466), nominated_for(?x484, ?x144), ceremony(?x484, ?x78) >> conf = 0.76 => this is the best rule for 2 predicted values *> Best rule #5164 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 165 *> proper extension: 06gjk9; *> query: (?x6269, 0gs9p) <- nominated_for(?x746, ?x6269), award_winner(?x6269, ?x2068), ?x746 = 04dn09n *> conf = 0.62 ranks of expected_values: 4 EVAL 0286gm1 nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 99.000 0.762 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4670-019vhk PRED entity: 019vhk PRED relation: film! PRED expected values: 022g44 0c_gcr => 106 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1072): 08h79x (0.52 #47786, 0.49 #16621, 0.43 #27008), 0dvmd (0.49 #16621, 0.47 #72717, 0.43 #27008), 01tc9r (0.49 #16621, 0.47 #72717, 0.43 #27008), 01vrx35 (0.49 #16621, 0.47 #72717, 0.43 #27008), 07s93v (0.49 #16621, 0.43 #27008, 0.43 #43630), 05hj_k (0.49 #16621, 0.43 #27008, 0.43 #43630), 02mxbd (0.49 #16621, 0.43 #27008, 0.43 #43630), 095zvfg (0.49 #16621, 0.43 #27008, 0.43 #43630), 0bytkq (0.49 #16621, 0.43 #27008, 0.43 #43630), 01vswx5 (0.49 #16621, 0.43 #27008, 0.43 #43630) >> Best rule #47786 for best value: >> intensional similarity = 3 >> extensional distance = 417 >> proper extension: 05fgr_; >> query: (?x2852, ?x7333) <- award(?x2852, ?x834), nominated_for(?x7333, ?x2852), spouse(?x5669, ?x7333) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #28648 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 349 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x2852, 0c_gcr) <- titles(?x162, ?x2852), titles(?x162, ?x11385), ?x11385 = 01c9d *> conf = 0.02 ranks of expected_values: 563, 998 EVAL 019vhk film! 0c_gcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 106.000 47.000 0.524 http://example.org/film/actor/film./film/performance/film EVAL 019vhk film! 022g44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 106.000 47.000 0.524 http://example.org/film/actor/film./film/performance/film #4669-014_x2 PRED entity: 014_x2 PRED relation: film_crew_role PRED expected values: 02r96rf 09vw2b7 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 34): 0ch6mp2 (0.75 #1258, 0.73 #928, 0.72 #1074), 02r96rf (0.72 #3, 0.66 #40, 0.63 #1253), 01vx2h (0.71 #11, 0.40 #48, 0.31 #1261), 09vw2b7 (0.65 #7, 0.63 #1257, 0.58 #2652), 0d2b38 (0.37 #26, 0.13 #353, 0.12 #63), 01pvkk (0.30 #783, 0.30 #1078, 0.29 #932), 0215hd (0.29 #19, 0.14 #346, 0.14 #1269), 089g0h (0.26 #20, 0.12 #347, 0.11 #1270), 033smt (0.26 #28, 0.10 #65, 0.09 #3602), 02rh1dz (0.21 #47, 0.19 #10, 0.14 #155) >> Best rule #1258 for best value: >> intensional similarity = 4 >> extensional distance = 838 >> proper extension: 0gtvrv3; 01gglm; >> query: (?x83, 0ch6mp2) <- film(?x965, ?x83), language(?x83, ?x254), currency(?x83, ?x170), film_crew_role(?x83, ?x137) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 014zwb; *> query: (?x83, 02r96rf) <- genre(?x83, ?x53), film_crew_role(?x83, ?x1966), nominated_for(?x84, ?x83), ?x1966 = 015h31 *> conf = 0.72 ranks of expected_values: 2, 4 EVAL 014_x2 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 99.000 0.746 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 014_x2 film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.746 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4668-05zdk2 PRED entity: 05zdk2 PRED relation: gender PRED expected values: 02zsn => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.74 #3, 0.74 #11, 0.73 #5), 02zsn (0.68 #2, 0.48 #126, 0.46 #14) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 05d7rk; 04rs03; 0292l3; 040wdl; 015npr; 02vmzp; 04cbtrw; 0jrqq; 01gg59; 02xfrd; ... >> query: (?x8904, 05zppz) <- award(?x8904, ?x10156), type_of_union(?x8904, ?x566), nationality(?x8904, ?x2146), ?x2146 = 03rk0 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 01zp33; 04y0yc; 03fwln; 03x31g; 090gpr; *> query: (?x8904, 02zsn) <- award(?x8904, ?x10156), nationality(?x8904, ?x2146), ?x10156 = 03r8v_, profession(?x8904, ?x1032) *> conf = 0.68 ranks of expected_values: 2 EVAL 05zdk2 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.741 http://example.org/people/person/gender #4667-01pr_j6 PRED entity: 01pr_j6 PRED relation: profession PRED expected values: 028kk_ => 115 concepts (42 used for prediction) PRED predicted values (max 10 best out of 59): 09jwl (0.82 #3194, 0.77 #304, 0.76 #4788), 0nbcg (0.67 #1471, 0.67 #316, 0.56 #605), 016z4k (0.57 #1447, 0.45 #1591, 0.45 #3327), 02jknp (0.49 #5357, 0.43 #440, 0.32 #151), 0dz3r (0.43 #1445, 0.43 #3180, 0.40 #146), 039v1 (0.40 #1476, 0.34 #4805, 0.33 #3356), 0kyk (0.28 #170, 0.13 #314, 0.12 #1325), 0n1h (0.28 #3189, 0.24 #155, 0.23 #1454), 02krf9 (0.25 #23, 0.20 #456, 0.16 #5373), 0cbd2 (0.24 #150, 0.12 #5067, 0.11 #5356) >> Best rule #3194 for best value: >> intensional similarity = 5 >> extensional distance = 235 >> proper extension: 01wbgdv; 01k5t_3; 015f7; 0f7hc; 01wgfp6; 01nkxvx; >> query: (?x1073, 09jwl) <- profession(?x1073, ?x987), people(?x5025, ?x1073), artists(?x671, ?x1073), profession(?x10101, ?x987), ?x10101 = 01wp_jm >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #2092 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 161 *> proper extension: 02rgz4; 01kwlwp; 015rmq; 01p9hgt; 01gg59; 01dhpj; 01wg6y; 0czhv7; 01mz9lt; 0561xh; ... *> query: (?x1073, 028kk_) <- gender(?x1073, ?x231), artists(?x671, ?x1073), profession(?x1073, ?x6476), profession(?x8756, ?x6476), ?x8756 = 04qr6d *> conf = 0.12 ranks of expected_values: 16 EVAL 01pr_j6 profession 028kk_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 115.000 42.000 0.823 http://example.org/people/person/profession #4666-03ytc PRED entity: 03ytc PRED relation: major_field_of_study! PRED expected values: 02hwww => 59 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1286): 01j_cy (0.67 #5959, 0.50 #1823, 0.40 #4185), 06pwq (0.62 #13613, 0.60 #4155, 0.56 #15389), 01w5m (0.60 #4261, 0.60 #3670, 0.54 #19071), 07szy (0.60 #4186, 0.59 #13644, 0.56 #5960), 03ksy (0.60 #4262, 0.59 #15496, 0.57 #16682), 01w3v (0.60 #4158, 0.56 #13616, 0.56 #5932), 0dzst (0.60 #4524, 0.56 #6298, 0.50 #2162), 015cz0 (0.60 #4334, 0.56 #6108, 0.50 #1972), 017cy9 (0.60 #4315, 0.50 #1953, 0.44 #6089), 04rwx (0.60 #4183, 0.50 #1821, 0.44 #5957) >> Best rule #5959 for best value: >> intensional similarity = 15 >> extensional distance = 7 >> proper extension: 04rjg; 0_jm; 09s1f; >> query: (?x8855, 01j_cy) <- major_field_of_study(?x11690, ?x8855), major_field_of_study(?x4981, ?x8855), ?x4981 = 03bwzr4, major_field_of_study(?x2171, ?x8855), ?x2171 = 01jq34, institution(?x11690, ?x12257), institution(?x11690, ?x9847), institution(?x11690, ?x5581), institution(?x11690, ?x2166), ?x12257 = 041pnt, ?x9847 = 0187nd, major_field_of_study(?x11690, ?x11691), ?x5581 = 037fqp, state_province_region(?x2166, ?x3670), ?x11691 = 05wkw >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2271 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 2 *> proper extension: 02lp1; 01mkq; *> query: (?x8855, 02hwww) <- major_field_of_study(?x11690, ?x8855), major_field_of_study(?x4981, ?x8855), ?x4981 = 03bwzr4, major_field_of_study(?x11502, ?x8855), major_field_of_study(?x8538, ?x8855), major_field_of_study(?x6894, ?x8855), major_field_of_study(?x2171, ?x8855), ?x2171 = 01jq34, ?x11690 = 01ysy9, ?x6894 = 0cwx_, school_type(?x8538, ?x1044), contains(?x94, ?x11502), ?x94 = 09c7w0, organization(?x346, ?x11502), contains(?x739, ?x8538) *> conf = 0.25 ranks of expected_values: 213 EVAL 03ytc major_field_of_study! 02hwww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 59.000 34.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #4665-06m61 PRED entity: 06m61 PRED relation: award_winner! PRED expected values: 024fz9 => 92 concepts (73 used for prediction) PRED predicted values (max 10 best out of 275): 026mff (0.47 #1288, 0.44 #3434, 0.37 #23611), 03x3wf (0.38 #922, 0.30 #1781, 0.22 #1352), 024fz9 (0.38 #1063, 0.15 #24899, 0.11 #1493), 01by1l (0.27 #1828, 0.25 #3115, 0.20 #540), 02v1m7 (0.25 #970, 0.20 #1829, 0.20 #541), 024dzn (0.25 #1180, 0.20 #751, 0.11 #1610), 02nbqh (0.22 #1404, 0.20 #545, 0.12 #974), 02nhxf (0.20 #526, 0.12 #955, 0.11 #1385), 0c4z8 (0.20 #500, 0.12 #14164, 0.11 #1359), 02sp_v (0.20 #587, 0.12 #14164, 0.11 #1446) >> Best rule #1288 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 03lgg; >> query: (?x4840, ?x1801) <- artist(?x6946, ?x4840), award(?x4840, ?x1801), award(?x4840, ?x594), ?x594 = 02grdc >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1063 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 03lgg; *> query: (?x4840, 024fz9) <- artist(?x6946, ?x4840), award(?x4840, ?x594), ?x594 = 02grdc *> conf = 0.38 ranks of expected_values: 3 EVAL 06m61 award_winner! 024fz9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 73.000 0.467 http://example.org/award/award_category/winners./award/award_honor/award_winner #4664-01g1lp PRED entity: 01g1lp PRED relation: profession PRED expected values: 0dxtg => 116 concepts (77 used for prediction) PRED predicted values (max 10 best out of 65): 0dxtg (0.81 #2872, 0.74 #1871, 0.72 #2729), 0nbcg (0.42 #5319, 0.41 #1028, 0.40 #4318), 016z4k (0.39 #1005, 0.37 #4152, 0.35 #5296), 0dz3r (0.34 #4150, 0.34 #5294, 0.33 #4293), 02krf9 (0.29 #1596, 0.28 #4028, 0.26 #2168), 0cbd2 (0.26 #2866, 0.20 #1007, 0.19 #1865), 018gz8 (0.26 #1015, 0.18 #2016, 0.17 #4019), 0dgd_ (0.25 #169, 0.17 #455, 0.14 #598), 01c72t (0.24 #4168, 0.23 #5312, 0.22 #1021), 039v1 (0.23 #5324, 0.23 #4323, 0.16 #4180) >> Best rule #2872 for best value: >> intensional similarity = 3 >> extensional distance = 220 >> proper extension: 08433; 03ft8; 01wg982; 0c9xjl; 054187; 01y8d4; 06pcz0; 09zw90; 013km; 07db6x; >> query: (?x7855, 0dxtg) <- profession(?x7855, ?x319), place_of_birth(?x7855, ?x2740), written_by(?x3781, ?x7855) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01g1lp profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 77.000 0.811 http://example.org/people/person/profession #4663-0459z PRED entity: 0459z PRED relation: influenced_by PRED expected values: 0c73g => 188 concepts (69 used for prediction) PRED predicted values (max 10 best out of 349): 03_f0 (0.25 #265, 0.24 #4611, 0.21 #1569), 042q3 (0.25 #5578, 0.17 #15136, 0.14 #10791), 03bxh (0.25 #184, 0.12 #2793, 0.08 #1053), 03jxw (0.25 #339, 0.12 #2948, 0.07 #7291), 03f70xs (0.25 #70, 0.11 #505, 0.10 #7022), 0h336 (0.25 #362, 0.08 #1231, 0.07 #1666), 06c44 (0.24 #2806, 0.07 #7149, 0.05 #4543), 081lh (0.22 #17837, 0.19 #20011, 0.16 #19577), 012vd6 (0.21 #12332, 0.17 #15374, 0.14 #17985), 01vrncs (0.19 #4369, 0.14 #6106, 0.09 #12187) >> Best rule #265 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01pw9v; >> query: (?x11512, 03_f0) <- influenced_by(?x11512, ?x7386), ?x7386 = 082db, location(?x11512, ?x863), influenced_by(?x862, ?x11512) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2165 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 01w7nww; *> query: (?x11512, 0c73g) <- instrumentalists(?x316, ?x11512), gender(?x11512, ?x231), place_of_birth(?x11512, ?x2985), influenced_by(?x862, ?x11512), teams(?x2985, ?x3791) *> conf = 0.07 ranks of expected_values: 90 EVAL 0459z influenced_by 0c73g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 188.000 69.000 0.250 http://example.org/influence/influence_node/influenced_by #4662-01x0yrt PRED entity: 01x0yrt PRED relation: award PRED expected values: 01bgqh => 142 concepts (113 used for prediction) PRED predicted values (max 10 best out of 284): 01bgqh (0.73 #1635, 0.42 #2431, 0.40 #1237), 01c99j (0.58 #2610, 0.42 #1814, 0.28 #8182), 02f71y (0.42 #2566, 0.15 #13710, 0.14 #12516), 03qbh5 (0.42 #11743, 0.39 #15723, 0.36 #16917), 02sp_v (0.41 #6924, 0.15 #2546, 0.13 #4536), 01c92g (0.41 #891, 0.32 #1289, 0.30 #11637), 02f705 (0.39 #2536, 0.28 #8108, 0.23 #1740), 054ks3 (0.37 #16853, 0.35 #13669, 0.35 #17649), 01dpdh (0.36 #3310, 0.22 #4504, 0.21 #6892), 03qbnj (0.33 #2617, 0.31 #1821, 0.25 #15751) >> Best rule #1635 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 01vrwfv; 01fmz6; 02jqjm; 017959; >> query: (?x8839, 01bgqh) <- award(?x8839, ?x2139), award(?x8839, ?x1389), artists(?x671, ?x8839), ?x1389 = 01c427, ?x2139 = 01by1l >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x0yrt award 01bgqh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 113.000 0.731 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4661-0640m69 PRED entity: 0640m69 PRED relation: film_release_region PRED expected values: 0154j => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 96): 0f8l9c (0.27 #6417, 0.27 #6594, 0.26 #4286), 0d0vqn (0.27 #6397, 0.27 #6574, 0.26 #4266), 0345h (0.27 #576, 0.26 #399, 0.23 #4301), 07ssc (0.27 #553, 0.24 #6409, 0.24 #6586), 06mkj (0.26 #428, 0.25 #6461, 0.25 #6638), 03rjj (0.26 #361, 0.25 #538, 0.23 #6394), 0jgd (0.26 #358, 0.22 #6391, 0.22 #6568), 0d060g (0.26 #540, 0.19 #6396, 0.19 #6573), 059j2 (0.24 #6430, 0.24 #6607, 0.24 #574), 02vzc (0.24 #6455, 0.24 #6632, 0.23 #4324) >> Best rule #6417 for best value: >> intensional similarity = 3 >> extensional distance = 1266 >> proper extension: 01br2w; 0dckvs; 0fq27fp; 0cnztc4; 04m1bm; 0d6b7; 02rb607; 040rmy; 026njb5; 02n9bh; ... >> query: (?x11980, 0f8l9c) <- film_crew_role(?x11980, ?x2154), film_crew_role(?x6429, ?x2154), ?x6429 = 01gwk3 >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1422 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 337 *> proper extension: 0bx_hnp; 09rfpk; *> query: (?x11980, 0154j) <- film_crew_role(?x11980, ?x2154), ?x2154 = 01vx2h, language(?x11980, ?x254) *> conf = 0.19 ranks of expected_values: 27 EVAL 0640m69 film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 64.000 64.000 0.274 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4660-01fx2g PRED entity: 01fx2g PRED relation: film PRED expected values: 0f8j13 => 138 concepts (94 used for prediction) PRED predicted values (max 10 best out of 969): 05lfwd (0.62 #42745, 0.57 #73025, 0.57 #80151), 06_wqk4 (0.11 #125, 0.04 #19716, 0.03 #33964), 0b3n61 (0.08 #4914, 0.07 #8476, 0.06 #10257), 02v5_g (0.08 #4351, 0.05 #7913, 0.05 #16818), 0418wg (0.08 #3960, 0.05 #7522, 0.05 #9303), 05c26ss (0.08 #4190, 0.05 #7752, 0.05 #9533), 0n6ds (0.08 #5182, 0.05 #8744, 0.04 #12306), 01shy7 (0.08 #27135, 0.07 #28916, 0.07 #12887), 03bzjpm (0.08 #6651, 0.06 #17337, 0.05 #4870), 013q07 (0.06 #14602, 0.06 #16383, 0.06 #23507) >> Best rule #42745 for best value: >> intensional similarity = 3 >> extensional distance = 234 >> proper extension: 02g8h; 0d_84; 04bs3j; 0htlr; 0456xp; 04shbh; 0n6f8; 0prjs; 013cr; 01mqz0; ... >> query: (?x5240, ?x5808) <- student(?x9479, ?x5240), nominated_for(?x5240, ?x5808), participant(?x5240, ?x1004) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #35396 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 166 *> proper extension: 01vvycq; 02l840; 01vv126; 01v40wd; 01vvyc_; 02r3cn; 06mt91; 01934k; 07pzc; *> query: (?x5240, 0f8j13) <- participant(?x1004, ?x5240), location(?x5240, ?x242), participant(?x5240, ?x2927) *> conf = 0.02 ranks of expected_values: 545 EVAL 01fx2g film 0f8j13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 138.000 94.000 0.621 http://example.org/film/actor/film./film/performance/film #4659-01j_cy PRED entity: 01j_cy PRED relation: registering_agency PRED expected values: 03z19 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.75 #10, 0.74 #15, 0.72 #23) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 01cyd5; 05nrkb; >> query: (?x1675, 03z19) <- student(?x1675, ?x1875), country(?x1675, ?x94), citytown(?x1675, ?x13337) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j_cy registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.754 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #4658-01p4r3 PRED entity: 01p4r3 PRED relation: award PRED expected values: 0bdwqv => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 264): 0cjyzs (0.61 #5371, 0.38 #6181, 0.15 #25516), 0f4x7 (0.41 #6511, 0.33 #2866, 0.27 #9751), 01bgqh (0.38 #2067, 0.30 #3282, 0.23 #3687), 01c92g (0.38 #2122, 0.30 #3337, 0.23 #3742), 02w9sd7 (0.33 #3006, 0.25 #171, 0.21 #4221), 04kxsb (0.33 #2961, 0.25 #126, 0.20 #1341), 09sb52 (0.32 #9355, 0.31 #8545, 0.28 #12190), 0ck27z (0.28 #18317, 0.28 #20747, 0.27 #17507), 0cqhk0 (0.28 #5302, 0.17 #6112, 0.17 #17452), 0gqy2 (0.28 #7050, 0.25 #2595, 0.23 #8265) >> Best rule #5371 for best value: >> intensional similarity = 2 >> extensional distance = 16 >> proper extension: 05gnf; >> query: (?x5913, 0cjyzs) <- nominated_for(?x5913, ?x631), ?x631 = 072kp >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #4628 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0q5hw; *> query: (?x5913, 0bdwqv) <- actor(?x623, ?x5913), award(?x5913, ?x693), celebrities_impersonated(?x3649, ?x5913), type_of_union(?x5913, ?x566) *> conf = 0.27 ranks of expected_values: 12 EVAL 01p4r3 award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 130.000 130.000 0.611 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4657-0tr3p PRED entity: 0tr3p PRED relation: place! PRED expected values: 0tr3p => 115 concepts (71 used for prediction) PRED predicted values (max 10 best out of 190): 0nm6k (0.35 #1547, 0.30 #3096, 0.29 #2063), 0c4kv (0.20 #373, 0.18 #13939, 0.14 #9287), 0cf_n (0.20 #259, 0.12 #774, 0.04 #1290), 0rrwt (0.14 #9287, 0.12 #22717, 0.10 #28922), 0tr3p (0.14 #9287, 0.12 #22717, 0.10 #28922), 050ks (0.14 #9287, 0.12 #22717, 0.10 #28922), 013h9 (0.04 #1342, 0.04 #1858, 0.04 #2374), 0dzt9 (0.04 #1295, 0.04 #1811, 0.04 #2327), 0tygl (0.04 #1189, 0.04 #1705, 0.04 #2221), 0mzvm (0.04 #1110, 0.04 #1626, 0.04 #2142) >> Best rule #1547 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 0fw4v; >> query: (?x8907, ?x7059) <- category(?x8907, ?x134), currency(?x8907, ?x170), ?x170 = 09nqf, county_seat(?x7059, ?x8907), source(?x8907, ?x958) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #9287 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 111 *> proper extension: 07bcn; 0sbbq; 0qzhw; 0rgxp; 0mpbx; *> query: (?x8907, ?x7058) <- county_seat(?x7059, ?x8907), location(?x2343, ?x8907), award(?x2343, ?x350), location(?x2343, ?x7058) *> conf = 0.14 ranks of expected_values: 5 EVAL 0tr3p place! 0tr3p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 115.000 71.000 0.348 http://example.org/location/hud_county_place/place #4656-01wp8w7 PRED entity: 01wp8w7 PRED relation: profession PRED expected values: 0nbcg => 115 concepts (87 used for prediction) PRED predicted values (max 10 best out of 79): 0nbcg (0.57 #596, 0.57 #454, 0.57 #3586), 0cbd2 (0.50 #1998, 0.49 #1713, 0.49 #2995), 0dxtg (0.48 #2717, 0.41 #4569, 0.41 #6281), 01d_h8 (0.41 #716, 0.35 #146, 0.33 #1570), 018gz8 (0.38 #726, 0.27 #2720, 0.24 #1580), 03gjzk (0.34 #724, 0.26 #2718, 0.23 #1578), 0kyk (0.33 #3157, 0.32 #1733, 0.32 #2589), 0fnpj (0.22 #196, 0.18 #4327, 0.16 #2189), 02hv44_ (0.19 #1192, 0.14 #3042, 0.13 #3184), 02jknp (0.19 #2712, 0.15 #1146, 0.15 #11700) >> Best rule #596 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 0drc1; >> query: (?x1521, 0nbcg) <- artists(?x378, ?x1521), profession(?x1521, ?x6476), ?x6476 = 025352 >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wp8w7 profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 87.000 0.574 http://example.org/people/person/profession #4655-06s_2 PRED entity: 06s_2 PRED relation: exported_to! PRED expected values: 0d05w3 => 167 concepts (160 used for prediction) PRED predicted values (max 10 best out of 76): 0154j (0.33 #667, 0.33 #666), 0d05w3 (0.29 #638, 0.23 #417, 0.23 #917), 06q1r (0.29 #98, 0.26 #265, 0.23 #431), 04sj3 (0.29 #108, 0.20 #165, 0.14 #551), 03_3d (0.29 #60, 0.16 #614, 0.13 #117), 0h3y (0.29 #62, 0.13 #229, 0.11 #505), 0j4b (0.29 #99, 0.11 #653, 0.10 #266), 05r4w (0.25 #890, 0.25 #724, 0.24 #945), 0ctw_b (0.20 #126, 0.14 #69, 0.13 #623), 0jdd (0.20 #144, 0.14 #87, 0.10 #254) >> Best rule #667 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0853g; >> query: (?x10450, ?x1229) <- exported_to(?x10450, ?x1229), film_release_region(?x66, ?x1229), form_of_government(?x1229, ?x4763) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #638 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 0853g; *> query: (?x10450, 0d05w3) <- exported_to(?x10450, ?x1229), film_release_region(?x66, ?x1229), form_of_government(?x1229, ?x4763) *> conf = 0.29 ranks of expected_values: 2 EVAL 06s_2 exported_to! 0d05w3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 167.000 160.000 0.330 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #4654-07w21 PRED entity: 07w21 PRED relation: influenced_by PRED expected values: 01tz6vs => 141 concepts (50 used for prediction) PRED predicted values (max 10 best out of 338): 09dt7 (0.50 #31, 0.33 #893, 0.14 #2186), 02lt8 (0.50 #549, 0.30 #1844, 0.24 #3565), 03hnd (0.44 #1392, 0.29 #2253, 0.19 #2683), 03_87 (0.40 #1923, 0.33 #628, 0.22 #1492), 058vp (0.40 #1906, 0.17 #611, 0.14 #2336), 032l1 (0.33 #518, 0.30 #1813, 0.17 #3534), 0379s (0.33 #507, 0.30 #1802, 0.17 #77), 03f47xl (0.33 #630, 0.30 #1925, 0.11 #1494), 06lbp (0.33 #626, 0.10 #1921, 0.09 #3642), 07ym0 (0.33 #704, 0.10 #1999, 0.05 #10335) >> Best rule #31 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0210f1; >> query: (?x476, 09dt7) <- award_winner(?x4418, ?x476), profession(?x476, ?x353), gender(?x476, ?x514), ?x4418 = 02664f >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1467 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 03j90; *> query: (?x476, 01tz6vs) <- award_winner(?x10270, ?x476), influenced_by(?x2934, ?x476), influenced_by(?x476, ?x477), ?x10270 = 06196 *> conf = 0.11 ranks of expected_values: 64 EVAL 07w21 influenced_by 01tz6vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 141.000 50.000 0.500 http://example.org/influence/influence_node/influenced_by #4653-03dj48 PRED entity: 03dj48 PRED relation: current_club PRED expected values: 01kkfp => 107 concepts (77 used for prediction) PRED predicted values (max 10 best out of 853): 049dzz (0.50 #677, 0.14 #3464, 0.10 #3318), 04ltf (0.45 #2117, 0.35 #3001, 0.33 #2853), 023zd7 (0.33 #206, 0.33 #60, 0.14 #3285), 06l22 (0.33 #201, 0.27 #2102, 0.24 #2986), 0175rc (0.33 #257, 0.25 #695, 0.18 #2158), 01w_d6 (0.33 #20, 0.25 #604, 0.10 #3245), 01634x (0.33 #223, 0.24 #3008, 0.18 #3448), 049f05 (0.33 #110, 0.23 #3481, 0.22 #1280), 02rh_0 (0.33 #92, 0.18 #3463, 0.10 #3317), 02gys2 (0.33 #152, 0.13 #2789, 0.12 #2937) >> Best rule #677 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 01l3wr; >> query: (?x10501, 049dzz) <- position(?x10501, ?x60), current_club(?x10501, ?x2428), current_club(?x10501, ?x2096), ?x2096 = 0371rb, team(?x2666, ?x2428), team(?x9739, ?x2428), ?x60 = 02nzb8 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2930 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 13 *> proper extension: 033nzk; 01l3vx; 03zrhb; 03_44z; *> query: (?x10501, ?x733) <- position(?x10501, ?x530), position(?x10501, ?x203), team(?x2666, ?x10501), current_club(?x10501, ?x2096), ?x530 = 02_j1w, position(?x11530, ?x203), position(?x11268, ?x203), position(?x733, ?x203), ?x11530 = 051qvn, ?x11268 = 03b6j8 *> conf = 0.01 ranks of expected_values: 446 EVAL 03dj48 current_club 01kkfp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 107.000 77.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #4652-06y9c2 PRED entity: 06y9c2 PRED relation: category PRED expected values: 08mbj5d => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #12, 0.88 #10, 0.87 #25) >> Best rule #12 for best value: >> intensional similarity = 7 >> extensional distance = 15 >> proper extension: 01w5n51; >> query: (?x677, 08mbj5d) <- artists(?x3243, ?x677), artists(?x3061, ?x677), artists(?x302, ?x677), ?x302 = 016clz, ?x3243 = 0y3_8, artists(?x3061, ?x1749), ?x1749 = 01fl3 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06y9c2 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.882 http://example.org/common/topic/webpage./common/webpage/category #4651-0168nq PRED entity: 0168nq PRED relation: list PRED expected values: 01ptsx => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.91 #39, 0.84 #64, 0.83 #44), 09g7thr (0.49 #307, 0.31 #226, 0.26 #420), 05glt (0.38 #421, 0.38 #427), 026cl_m (0.09 #422, 0.09 #428) >> Best rule #39 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 0300cp; 019rl6; 01qygl; >> query: (?x4793, 01ptsx) <- list(?x4793, ?x5997), currency(?x4793, ?x170), company(?x4682, ?x4793), citytown(?x4793, ?x6142), ?x4682 = 0dq_5 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0168nq list 01ptsx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.906 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #4650-02rlj20 PRED entity: 02rlj20 PRED relation: nominated_for! PRED expected values: 03jvmp => 81 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1084): 03mdt (0.44 #28055, 0.43 #14028, 0.43 #12401), 045c66 (0.38 #2338, 0.32 #95882, 0.27 #51446), 01pcq3 (0.38 #2338, 0.32 #95882, 0.27 #51446), 02p8v8 (0.38 #2338, 0.32 #95882, 0.27 #51446), 030xr_ (0.38 #2338, 0.32 #95882, 0.27 #51446), 044rvb (0.38 #2338, 0.32 #95882, 0.27 #51446), 03jvmp (0.33 #9807, 0.24 #16821, 0.24 #12144), 01jmv8 (0.25 #1828, 0.06 #6505, 0.06 #11180), 01z5tr (0.25 #1700, 0.06 #6377, 0.06 #11052), 0kcdl (0.25 #2182, 0.06 #6859, 0.06 #11534) >> Best rule #28055 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 08cx5g; 01hvv0; >> query: (?x7895, ?x3381) <- titles(?x3381, ?x7895), nominated_for(?x7856, ?x7895), nominated_for(?x3381, ?x1542), program(?x3381, ?x493) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #9807 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 08y2fn; *> query: (?x7895, 03jvmp) <- award(?x7895, ?x4838), nominated_for(?x2192, ?x7895), genre(?x7895, ?x162), ?x2192 = 0bfvd4 *> conf = 0.33 ranks of expected_values: 7 EVAL 02rlj20 nominated_for! 03jvmp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 81.000 42.000 0.441 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4649-0j_t1 PRED entity: 0j_t1 PRED relation: nominated_for! PRED expected values: 01wmcbg => 78 concepts (33 used for prediction) PRED predicted values (max 10 best out of 798): 04d2yp (0.78 #77177, 0.78 #49108, 0.77 #65484), 01y_px (0.45 #9353, 0.38 #74838, 0.37 #63145), 02mxw0 (0.45 #9353, 0.38 #74838, 0.37 #63145), 037w7r (0.45 #9353, 0.38 #74838, 0.37 #63145), 0421st (0.45 #9353, 0.38 #74838, 0.37 #63145), 03lmzl (0.45 #9353, 0.37 #63143, 0.33 #4677), 01wmcbg (0.45 #9353, 0.37 #63143, 0.33 #4677), 04264n (0.45 #9353, 0.33 #4677, 0.33 #30404), 04gc65 (0.45 #9353, 0.33 #4677, 0.32 #32742), 05xd_v (0.45 #9353, 0.33 #4677, 0.32 #32742) >> Best rule #77177 for best value: >> intensional similarity = 3 >> extensional distance = 855 >> proper extension: 06mmr; >> query: (?x2719, ?x3267) <- award_winner(?x2719, ?x3267), award(?x2719, ?x1587), nominated_for(?x1587, ?x696) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #9353 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 71 *> proper extension: 0hmr4; 092vkg; 0jyx6; 032_wv; 0p_th; 09cr8; 01qncf; 012mrr; 0dzz6g; 027s39y; ... *> query: (?x2719, ?x2263) <- film(?x2263, ?x2719), nominated_for(?x1063, ?x2719), ?x1063 = 02rdxsh, genre(?x2719, ?x307) *> conf = 0.45 ranks of expected_values: 7 EVAL 0j_t1 nominated_for! 01wmcbg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 78.000 33.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4648-0bs5k8r PRED entity: 0bs5k8r PRED relation: language PRED expected values: 0653m => 104 concepts (99 used for prediction) PRED predicted values (max 10 best out of 46): 064_8sq (0.33 #21, 0.23 #255, 0.17 #1669), 04306rv (0.33 #4, 0.23 #357, 0.19 #592), 06nm1 (0.33 #10, 0.12 #303, 0.11 #363), 03k50 (0.18 #477, 0.04 #713, 0.03 #536), 0653m (0.17 #480, 0.06 #304, 0.06 #364), 03_9r (0.17 #478, 0.05 #2901, 0.05 #1301), 06b_j (0.14 #550, 0.14 #727, 0.11 #375), 02hxcvy (0.13 #502, 0.05 #149, 0.04 #738), 02bjrlw (0.12 #825, 0.11 #1000, 0.10 #883), 0jzc (0.11 #547, 0.09 #724, 0.07 #1548) >> Best rule #21 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 011yxg; >> query: (?x4276, 064_8sq) <- film_crew_role(?x4276, ?x137), films(?x2346, ?x4276), nominated_for(?x3574, ?x4276), ?x3574 = 094wz7q, nominated_for(?x13664, ?x4276) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #480 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 58 *> proper extension: 04jwjq; *> query: (?x4276, 0653m) <- film(?x4809, ?x4276), genre(?x4276, ?x1626), genre(?x4276, ?x162), titles(?x162, ?x251), ?x1626 = 03q4nz *> conf = 0.17 ranks of expected_values: 5 EVAL 0bs5k8r language 0653m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 104.000 99.000 0.333 http://example.org/film/film/language #4647-037q2p PRED entity: 037q2p PRED relation: colors PRED expected values: 083jv => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.47 #306, 0.40 #629, 0.39 #458), 01l849 (0.25 #1863, 0.25 #1711, 0.25 #1901), 019sc (0.18 #2078, 0.18 #1906, 0.18 #633), 07plts (0.17 #36, 0.10 #112, 0.08 #55), 04mkbj (0.14 #313, 0.12 #1092, 0.12 #370), 038hg (0.13 #163, 0.09 #1721, 0.09 #182), 03wkwg (0.13 #166, 0.07 #2358, 0.07 #2034), 036k5h (0.11 #764, 0.11 #840, 0.11 #859), 0jc_p (0.11 #231, 0.08 #1732, 0.08 #1580), 02rnmb (0.10 #107, 0.08 #50, 0.08 #31) >> Best rule #306 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 01j_cy; 07wrz; 03bmmc; 04cnp4; >> query: (?x11278, 083jv) <- country(?x11278, ?x94), major_field_of_study(?x11278, ?x1154), citytown(?x11278, ?x479), colors(?x11278, ?x1101) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 037q2p colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.471 http://example.org/education/educational_institution/colors #4646-06bc59 PRED entity: 06bc59 PRED relation: genre PRED expected values: 07s9rl0 01q03 082gq => 50 concepts (49 used for prediction) PRED predicted values (max 10 best out of 107): 07s9rl0 (0.87 #1, 0.73 #473, 0.70 #591), 0c3351 (0.61 #2135, 0.54 #2134, 0.53 #1658), 02kdv5l (0.55 #357, 0.47 #239, 0.27 #1780), 02l7c8 (0.53 #16, 0.32 #488, 0.31 #3575), 03k9fj (0.44 #366, 0.21 #1789, 0.20 #1907), 05p553 (0.41 #714, 0.35 #832, 0.34 #950), 01hmnh (0.30 #371, 0.15 #844, 0.15 #962), 04xvlr (0.27 #2, 0.25 #474, 0.19 #1303), 06n90 (0.21 #367, 0.21 #249, 0.12 #1908), 0lsxr (0.21 #363, 0.20 #1190, 0.20 #599) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0f4vx; 0ctb4g; 05vxdh; 06kl78; 0fjyzt; 03prz_; 02w9k1c; 02q7fl9; 072zl1; 0crs0b8; ... >> query: (?x9786, 07s9rl0) <- country(?x9786, ?x789), genre(?x9786, ?x1509), ?x789 = 0f8l9c, ?x1509 = 060__y >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 13, 51 EVAL 06bc59 genre 082gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 50.000 49.000 0.867 http://example.org/film/film/genre EVAL 06bc59 genre 01q03 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 50.000 49.000 0.867 http://example.org/film/film/genre EVAL 06bc59 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 49.000 0.867 http://example.org/film/film/genre #4645-047sxrj PRED entity: 047sxrj PRED relation: award PRED expected values: 02f705 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 259): 03t5kl (0.33 #226, 0.16 #23202, 0.15 #25205), 09sb52 (0.33 #14441, 0.30 #14841, 0.23 #12841), 01bgqh (0.29 #3243, 0.29 #2043, 0.26 #4443), 01d38g (0.29 #2028, 0.15 #828, 0.13 #24403), 0c4z8 (0.28 #72, 0.23 #2072, 0.22 #3272), 03qbh5 (0.27 #2204, 0.25 #1404, 0.23 #1804), 02f6xy (0.22 #199, 0.19 #2199, 0.16 #23202), 02f705 (0.22 #152, 0.17 #1352, 0.16 #23202), 01cky2 (0.22 #193, 0.16 #23202, 0.16 #2193), 02f5qb (0.22 #155, 0.16 #23202, 0.15 #25205) >> Best rule #226 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 04lgymt; >> query: (?x2334, 03t5kl) <- award_nominee(?x6835, ?x2334), award(?x2334, ?x1389), ?x6835 = 06mt91 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #152 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 04lgymt; *> query: (?x2334, 02f705) <- award_nominee(?x6835, ?x2334), award(?x2334, ?x1389), ?x6835 = 06mt91 *> conf = 0.22 ranks of expected_values: 8 EVAL 047sxrj award 02f705 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 82.000 82.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4644-031x_3 PRED entity: 031x_3 PRED relation: award_winner PRED expected values: 01vsy7t => 107 concepts (58 used for prediction) PRED predicted values (max 10 best out of 570): 031x_3 (0.40 #2945, 0.18 #9389, 0.11 #9668), 02f1c (0.33 #1359, 0.01 #30362), 0kftt (0.33 #1316, 0.01 #30319), 03_0p (0.29 #8937, 0.20 #2493, 0.04 #10550), 0149xx (0.24 #8934, 0.20 #2490, 0.04 #10547), 0127gn (0.20 #2491, 0.18 #8935), 014hr0 (0.20 #2091, 0.12 #8535), 01vrlr4 (0.20 #2662, 0.06 #9106), 0479b (0.20 #4354), 0bvzp (0.18 #9119, 0.11 #9668, 0.01 #30067) >> Best rule #2945 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0hl3d; 015rmq; >> query: (?x8583, 031x_3) <- award(?x8583, ?x5765), award_nominee(?x352, ?x8583), ?x5765 = 024_fw >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #28189 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 145 *> proper extension: 01vvydl; 0197tq; 01vvycq; 02l840; 03f5spx; 01gf5h; 01vv7sc; 01vrncs; 07c0j; 01vrt_c; ... *> query: (?x8583, 01vsy7t) <- artist(?x4797, ?x8583), origin(?x8583, ?x2410), award_winner(?x8583, ?x352) *> conf = 0.01 ranks of expected_values: 408 EVAL 031x_3 award_winner 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 107.000 58.000 0.400 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #4643-083shs PRED entity: 083shs PRED relation: film! PRED expected values: 026v437 => 86 concepts (21 used for prediction) PRED predicted values (max 10 best out of 979): 042v2 (0.61 #10386, 0.49 #10385, 0.49 #24931), 0716t2 (0.57 #1904, 0.03 #10212, 0.03 #12291), 01l9p (0.57 #2078, 0.49 #10385, 0.48 #2077), 01l79yc (0.49 #10385, 0.48 #2077, 0.47 #22852), 05bnp0 (0.43 #13, 0.07 #2079, 0.03 #10400), 04fzk (0.43 #704, 0.07 #2079, 0.02 #11091), 01f7dd (0.43 #1204, 0.02 #11591, 0.02 #15745), 01h8f (0.43 #926, 0.02 #11313, 0.02 #21700), 01r93l (0.29 #2823, 0.04 #4899, 0.03 #11131), 079vf (0.29 #8, 0.03 #10395, 0.02 #24939) >> Best rule #10386 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 01hr1; 0kv2hv; 04tc1g; 018f8; 02prw4h; 026n4h6; 0260bz; 065zlr; 01shy7; 04yc76; ... >> query: (?x167, ?x8656) <- nominated_for(?x8656, ?x167), language(?x167, ?x254), category(?x8656, ?x134), influenced_by(?x8656, ?x5091) >> conf = 0.61 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 083shs film! 026v437 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 21.000 0.614 http://example.org/film/actor/film./film/performance/film #4642-043gj PRED entity: 043gj PRED relation: athlete! PRED expected values: 0jm_ => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 4): 037hz (0.02 #120, 0.01 #310, 0.01 #230), 02vx4 (0.02 #1593, 0.02 #1623, 0.02 #1653), 0jm_ (0.02 #153, 0.01 #1283, 0.01 #303), 018w8 (0.01 #846, 0.01 #1106, 0.01 #1016) >> Best rule #120 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 0b_dh; 0cj2w; >> query: (?x4647, 037hz) <- type_of_union(?x4647, ?x566), people(?x2482, ?x4647), award_winner(?x4647, ?x6745) >> conf = 0.02 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 125 *> proper extension: 0j3v; 01h2_6; *> query: (?x4647, 0jm_) <- people(?x5741, ?x4647), people(?x2482, ?x4647), student(?x735, ?x4647) *> conf = 0.02 ranks of expected_values: 3 EVAL 043gj athlete! 0jm_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 168.000 168.000 0.021 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #4641-0gpx6 PRED entity: 0gpx6 PRED relation: honored_for! PRED expected values: 09pnw5 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 109): 05c1t6z (0.26 #252, 0.13 #857, 0.05 #1583), 02q690_ (0.24 #295, 0.12 #900, 0.05 #1626), 0gvstc3 (0.21 #268, 0.11 #873, 0.04 #3174), 03nnm4t (0.18 #304, 0.09 #909, 0.04 #3210), 0gx_st (0.14 #271, 0.07 #876, 0.03 #1602), 0275n3y (0.11 #305, 0.06 #1031, 0.05 #910), 0lp_cd3 (0.11 #258, 0.05 #863, 0.03 #3164), 0bxs_d (0.08 #341, 0.04 #946, 0.02 #1672), 07y9ts (0.08 #298, 0.04 #903, 0.02 #1750), 0hr6lkl (0.07 #979, 0.05 #9081, 0.03 #1827) >> Best rule #252 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 025ljp; 09v38qj; >> query: (?x7735, 05c1t6z) <- honored_for(?x1084, ?x7735), titles(?x2502, ?x7735), major_field_of_study(?x481, ?x2502) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #1055 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 198 *> proper extension: 06fqlk; *> query: (?x7735, 09pnw5) <- honored_for(?x1084, ?x7735), language(?x7735, ?x254), film_crew_role(?x7735, ?x1284), ?x1284 = 0ch6mp2 *> conf = 0.03 ranks of expected_values: 61 EVAL 0gpx6 honored_for! 09pnw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 90.000 90.000 0.262 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #4640-05_5rjx PRED entity: 05_5rjx PRED relation: film! PRED expected values: 05dbyt => 117 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1186): 0gn30 (0.33 #947, 0.02 #38382, 0.02 #117397), 01g969 (0.33 #1669, 0.02 #16227, 0.01 #43263), 04xhwn (0.33 #1988, 0.01 #83091, 0.01 #47741), 02t_w8 (0.33 #944), 01dw4q (0.33 #65), 0bwh6 (0.22 #14558, 0.20 #18718, 0.18 #31197), 0j_c (0.13 #10807, 0.12 #4570, 0.08 #19128), 02xs5v (0.09 #5565, 0.08 #7644, 0.08 #11802), 014gf8 (0.09 #3087, 0.06 #13484, 0.06 #9326), 015c4g (0.09 #2859, 0.06 #13256, 0.05 #17417) >> Best rule #947 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 06c0ns; >> query: (?x3919, 0gn30) <- language(?x3919, ?x254), film_production_design_by(?x3919, ?x4168), genre(?x3919, ?x53), film(?x6758, ?x3919), ?x6758 = 041rhq >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05_5rjx film! 05dbyt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 58.000 0.333 http://example.org/film/actor/film./film/performance/film #4639-01l9p PRED entity: 01l9p PRED relation: vacationer! PRED expected values: 095w_ => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 61): 05qtj (0.21 #316, 0.16 #683, 0.09 #806), 03gh4 (0.19 #692, 0.17 #325, 0.12 #815), 0cv3w (0.11 #301, 0.10 #668, 0.09 #545), 012wgb (0.09 #63, 0.02 #675, 0.02 #308), 0d0vqn (0.09 #7), 0b90_r (0.09 #615, 0.08 #248, 0.05 #738), 0f2v0 (0.09 #674, 0.07 #307, 0.07 #797), 0160w (0.08 #247, 0.06 #614, 0.04 #737), 04jpl (0.08 #254, 0.07 #621, 0.05 #744), 02_286 (0.08 #259, 0.05 #626, 0.03 #749) >> Best rule #316 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 01g0jn; >> query: (?x1735, 05qtj) <- award_winner(?x618, ?x1735), profession(?x1735, ?x319), vacationer(?x583, ?x1735) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01l9p vacationer! 095w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 70.000 0.208 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #4638-015gy7 PRED entity: 015gy7 PRED relation: type_of_union PRED expected values: 04ztj => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.87 #37, 0.87 #9, 0.86 #45), 01g63y (0.20 #6, 0.17 #106, 0.17 #34), 01bl8s (0.01 #79), 0jgjn (0.01 #56) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 138 >> proper extension: 0f2df; 0407f; 03bpn6; 061zc_; 0m0hw; 01p1z_; 01v90t; 03xx3m; 0gt3p; 017g2y; ... >> query: (?x6261, 04ztj) <- film(?x6261, ?x3943), award_winner(?x458, ?x6261), people(?x6260, ?x6261) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015gy7 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.871 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4637-05k7sb PRED entity: 05k7sb PRED relation: jurisdiction_of_office! PRED expected values: 01gkgk => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 18): 0pqc5 (0.67 #1445, 0.54 #1679, 0.51 #1769), 060c4 (0.50 #508, 0.49 #1534, 0.46 #1876), 060bp (0.48 #506, 0.46 #416, 0.44 #1532), 01t7n9 (0.25 #50, 0.25 #32, 0.25 #14), 0789n (0.25 #44, 0.25 #26, 0.25 #8), 0dq3c (0.25 #38, 0.25 #20, 0.25 #2), 01gkgk (0.25 #41, 0.25 #23, 0.25 #5), 01q24l (0.25 #28, 0.20 #82, 0.16 #1451), 0p5vf (0.19 #117, 0.15 #207, 0.12 #334), 04syw (0.16 #601, 0.16 #745, 0.12 #457) >> Best rule #1445 for best value: >> intensional similarity = 3 >> extensional distance = 139 >> proper extension: 0xy28; 0r4xt; 02_n7; 0g_wn2; 0q_xk; 0qpjt; 0r4wn; 0r4z7; 0qymv; 0r4h3; ... >> query: (?x2020, 0pqc5) <- contains(?x94, ?x2020), ?x94 = 09c7w0, jurisdiction_of_office(?x900, ?x2020) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #41 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0k3kv; *> query: (?x2020, 01gkgk) <- contains(?x2020, ?x5875), contains(?x2020, ?x4990), ?x5875 = 0t_hx, source(?x4990, ?x958) *> conf = 0.25 ranks of expected_values: 7 EVAL 05k7sb jurisdiction_of_office! 01gkgk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 164.000 164.000 0.674 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #4636-03zj9 PRED entity: 03zj9 PRED relation: institution! PRED expected values: 014mlp => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 24): 014mlp (0.79 #909, 0.77 #589, 0.76 #500), 02_xgp2 (0.73 #665, 0.68 #867, 0.65 #214), 03bwzr4 (0.65 #667, 0.59 #216, 0.58 #509), 016t_3 (0.61 #498, 0.55 #587, 0.51 #656), 0bkj86 (0.59 #210, 0.50 #503, 0.50 #300), 07s6fsf (0.53 #203, 0.45 #293, 0.45 #270), 04zx3q1 (0.44 #678, 0.42 #497, 0.41 #655), 03mkk4 (0.44 #678, 0.40 #280, 0.39 #881), 01gkg3 (0.44 #678, 0.39 #881, 0.31 #2857), 01ysy9 (0.44 #678, 0.39 #881, 0.31 #2857) >> Best rule #909 for best value: >> intensional similarity = 5 >> extensional distance = 78 >> proper extension: 02zy1z; >> query: (?x5733, 014mlp) <- institution(?x865, ?x5733), category(?x5733, ?x134), currency(?x5733, ?x170), institution(?x865, ?x4363), ?x4363 = 033x5p >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03zj9 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.787 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4635-0bkg87 PRED entity: 0bkg87 PRED relation: people! PRED expected values: 0dryh9k => 122 concepts (98 used for prediction) PRED predicted values (max 10 best out of 38): 0dryh9k (0.33 #401, 0.08 #3019, 0.07 #1864), 0x67 (0.24 #472, 0.22 #1011, 0.20 #1396), 041rx (0.20 #312, 0.14 #3392, 0.13 #928), 01rv7x (0.14 #39, 0.05 #424, 0.05 #193), 0bpjh3 (0.12 #102, 0.02 #410), 033tf_ (0.07 #3781, 0.07 #4706, 0.07 #3318), 02w7gg (0.06 #3005, 0.05 #541, 0.05 #1850), 013xrm (0.05 #251, 0.04 #328, 0.04 #1868), 0g6ff (0.05 #252, 0.02 #329, 0.01 #560), 0g5y6 (0.05 #191, 0.02 #576, 0.02 #345) >> Best rule #401 for best value: >> intensional similarity = 4 >> extensional distance = 152 >> proper extension: 0frpd5; 02qfk4j; >> query: (?x11647, 0dryh9k) <- gender(?x11647, ?x231), ?x231 = 05zppz, nationality(?x11647, ?x2146), ?x2146 = 03rk0 >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bkg87 people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 98.000 0.331 http://example.org/people/ethnicity/people #4634-048_p PRED entity: 048_p PRED relation: location PRED expected values: 02hyt => 83 concepts (69 used for prediction) PRED predicted values (max 10 best out of 138): 02_286 (0.77 #7256, 0.24 #26534, 0.21 #32161), 030qb3t (0.46 #12116, 0.19 #26579, 0.17 #32206), 0nvd8 (0.27 #11232, 0.25 #14445, 0.02 #10429), 01cx_ (0.23 #1766, 0.13 #25693, 0.12 #29713), 0cr3d (0.15 #1748, 0.13 #2550, 0.11 #4154), 0cc56 (0.15 #7276, 0.10 #10486, 0.10 #13698), 0vzm (0.14 #172, 0.09 #974, 0.06 #2578), 0f25y (0.14 #453, 0.09 #1255, 0.03 #2859), 0f2s6 (0.14 #472, 0.09 #1274, 0.03 #2878), 01531 (0.13 #7376, 0.09 #10586, 0.08 #13798) >> Best rule #7256 for best value: >> intensional similarity = 4 >> extensional distance = 356 >> proper extension: 02g8h; 054_mz; 0151ns; 02ndbd; 02lk1s; 069ld1; 0456xp; 02r34n; 01t6b4; 044ntk; ... >> query: (?x5506, 02_286) <- award(?x5506, ?x575), location(?x5506, ?x8553), location(?x1204, ?x8553), ?x1204 = 02sjf5 >> conf = 0.77 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 048_p location 02hyt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 69.000 0.768 http://example.org/people/person/places_lived./people/place_lived/location #4633-06xpp7 PRED entity: 06xpp7 PRED relation: student PRED expected values: 03lvyj => 77 concepts (58 used for prediction) PRED predicted values (max 10 best out of 827): 0blbxk (0.33 #186, 0.01 #52045, 0.01 #31230), 01vh18t (0.33 #1614, 0.01 #7857), 0bv7t (0.33 #906, 0.01 #7149), 02_l96 (0.33 #878, 0.01 #7121), 031x_3 (0.33 #1485), 013w7j (0.33 #1066), 0277c3 (0.33 #1063), 095b70 (0.33 #1041), 01hbq0 (0.27 #4128, 0.09 #6209, 0.02 #8290), 03ft8 (0.18 #2338, 0.06 #4419, 0.03 #6500) >> Best rule #186 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0217m9; >> query: (?x5522, 0blbxk) <- student(?x5522, ?x10973), student(?x5522, ?x4606), ?x4606 = 042xrr, contains(?x94, ?x5522), award(?x10973, ?x537) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06xpp7 student 03lvyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 58.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #4632-02s2ft PRED entity: 02s2ft PRED relation: film PRED expected values: 0gltv => 89 concepts (54 used for prediction) PRED predicted values (max 10 best out of 762): 07kh6f3 (0.44 #24909, 0.42 #24908, 0.39 #23128), 0340hj (0.40 #234, 0.03 #3792, 0.03 #92536), 02wgk1 (0.40 #753, 0.03 #92536, 0.01 #43458), 012s1d (0.40 #914, 0.03 #92536, 0.01 #4472), 0660b9b (0.20 #991, 0.14 #2770, 0.03 #92536), 02ryz24 (0.20 #464, 0.14 #2243, 0.03 #92536), 0n08r (0.20 #1695, 0.04 #5253, 0.04 #7032), 01flv_ (0.20 #1060, 0.03 #4618, 0.03 #6397), 083shs (0.20 #19, 0.03 #92536, 0.01 #74734), 03tbg6 (0.20 #1646, 0.03 #92536, 0.01 #5204) >> Best rule #24909 for best value: >> intensional similarity = 3 >> extensional distance = 826 >> proper extension: 0kk9v; 0gfmc_; >> query: (?x92, ?x3790) <- award_nominee(?x92, ?x91), award_winner(?x3790, ?x92), country(?x3790, ?x94) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #5039 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 70 *> proper extension: 02t__l; *> query: (?x92, 0gltv) <- award(?x92, ?x1033), ?x1033 = 02x73k6, nominated_for(?x92, ?x1064) *> conf = 0.01 ranks of expected_values: 440 EVAL 02s2ft film 0gltv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 89.000 54.000 0.443 http://example.org/film/actor/film./film/performance/film #4631-032wdd PRED entity: 032wdd PRED relation: location PRED expected values: 0f2wj => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 134): 030qb3t (0.47 #1686, 0.40 #4093, 0.37 #4895), 01n7q (0.17 #62, 0.06 #864, 0.06 #12895), 059rby (0.17 #16, 0.06 #15255, 0.05 #22474), 0106dv (0.17 #502, 0.03 #1304, 0.03 #2106), 0rh6k (0.17 #4, 0.03 #14441, 0.03 #1608), 07b_l (0.17 #186, 0.03 #2592, 0.02 #3394), 0d0x8 (0.17 #160), 0cv3w (0.17 #158), 0k049 (0.13 #810, 0.10 #3216, 0.06 #10435), 0cc56 (0.10 #3264, 0.08 #4067, 0.07 #6473) >> Best rule #1686 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 01ztgm; 01s21dg; >> query: (?x8691, 030qb3t) <- participant(?x10915, ?x8691), award_winner(?x8691, ?x1460), participant(?x10915, ?x5906) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #17679 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 205 *> proper extension: 01r42_g; 08m4c8; 023jq1; *> query: (?x8691, 0f2wj) <- award_winner(?x1460, ?x8691), languages(?x8691, ?x254), nationality(?x8691, ?x94) *> conf = 0.02 ranks of expected_values: 82 EVAL 032wdd location 0f2wj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 110.000 110.000 0.471 http://example.org/people/person/places_lived./people/place_lived/location #4630-0h3xztt PRED entity: 0h3xztt PRED relation: film_release_region PRED expected values: 0154j 01pj7 02vzc 06mkj 01p8s => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 73): 09c7w0 (0.92 #3273, 0.92 #2318, 0.81 #409), 0d060g (0.85 #277, 0.65 #413, 0.21 #3277), 06mkj (0.83 #454, 0.79 #318, 0.27 #3318), 07ssc (0.79 #284, 0.77 #420, 0.43 #6542), 02vzc (0.79 #314, 0.77 #450, 0.26 #3314), 0154j (0.74 #275, 0.68 #411, 0.21 #3275), 05v8c (0.74 #285, 0.47 #421, 0.15 #2330), 03rj0 (0.62 #322, 0.50 #458, 0.15 #3322), 01mjq (0.62 #306, 0.46 #442, 0.14 #3306), 06qd3 (0.59 #300, 0.49 #436, 0.14 #3300) >> Best rule #3273 for best value: >> intensional similarity = 3 >> extensional distance = 1327 >> proper extension: 01br2w; 0dtw1x; 0djb3vw; 0b60sq; 0fq27fp; 04969y; 04dsnp; 0cnztc4; 04m1bm; 091z_p; ... >> query: (?x1150, 09c7w0) <- film_release_region(?x1150, ?x142), film_release_region(?x2037, ?x142), ?x2037 = 0gvrws1 >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #454 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 315 *> proper extension: 0b76d_m; 0ds35l9; 0g56t9t; 0gtsx8c; 02vp1f_; 01gc7; 011yrp; 0gtv7pk; 0h1cdwq; 0m2kd; ... *> query: (?x1150, 06mkj) <- film_release_region(?x1150, ?x142), ?x142 = 0jgd *> conf = 0.83 ranks of expected_values: 3, 5, 6, 16, 69 EVAL 0h3xztt film_release_region 01p8s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 56.000 56.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h3xztt film_release_region 06mkj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 56.000 56.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h3xztt film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 56.000 56.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h3xztt film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 56.000 56.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h3xztt film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 56.000 56.000 0.918 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4629-0wsr PRED entity: 0wsr PRED relation: school PRED expected values: 01jswq => 88 concepts (62 used for prediction) PRED predicted values (max 10 best out of 303): 06pwq (0.45 #6475, 0.22 #1332, 0.22 #9531), 0lyjf (0.44 #5778, 0.40 #4250, 0.40 #643), 01jq0j (0.33 #1251, 0.33 #874, 0.31 #2770), 01vs5c (0.33 #1983, 0.32 #4643, 0.32 #4076), 0bx8pn (0.33 #1347, 0.32 #6490, 0.26 #5533), 0g8rj (0.33 #1790, 0.30 #2169, 0.29 #1031), 07szy (0.33 #19, 0.29 #965, 0.22 #1914), 02pptm (0.33 #146, 0.20 #1515, 0.16 #5318), 01qgr3 (0.33 #121, 0.20 #1515, 0.16 #5318), 025v3k (0.33 #52, 0.20 #1515, 0.14 #5895) >> Best rule #6475 for best value: >> intensional similarity = 9 >> extensional distance = 36 >> proper extension: 0512p; 01yhm; 01ync; 0jml5; 0jm64; 01slc; 07l8x; 07l4z; 0jm3b; 0x0d; ... >> query: (?x6645, 06pwq) <- team(?x180, ?x6645), draft(?x6645, ?x465), school(?x6645, ?x735), organization(?x735, ?x5487), student(?x735, ?x2789), ?x2789 = 01zfmm, school(?x2820, ?x735), school(?x3334, ?x735), ?x2820 = 0jmj7 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1515 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 7 *> proper extension: 06x68; 061xq; 01k8vh; *> query: (?x6645, ?x2711) <- team(?x180, ?x6645), draft(?x6645, ?x465), school(?x6645, ?x10945), school(?x6645, ?x735), draft(?x4256, ?x465), school(?x4256, ?x2711), organization(?x735, ?x5487), student(?x735, ?x65), major_field_of_study(?x735, ?x254), fraternities_and_sororities(?x735, ?x3697), ?x10945 = 01jsk6 *> conf = 0.20 ranks of expected_values: 58 EVAL 0wsr school 01jswq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 88.000 62.000 0.447 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #4628-07bty PRED entity: 07bty PRED relation: award_winner! PRED expected values: 06zrp44 => 123 concepts (77 used for prediction) PRED predicted values (max 10 best out of 267): 06_y0kx (0.33 #427, 0.20 #857, 0.04 #1717), 03j2ts (0.33 #424, 0.20 #854, 0.04 #1714), 06zrp44 (0.33 #412, 0.20 #842, 0.04 #1702), 0dt39 (0.33 #370, 0.20 #800, 0.04 #1660), 0gq9h (0.21 #3088, 0.20 #3518, 0.19 #1368), 023vrq (0.20 #753, 0.07 #4193, 0.06 #2473), 02nhxf (0.20 #529, 0.06 #959, 0.05 #3969), 03t5b6 (0.20 #631, 0.05 #3641, 0.05 #4071), 01c9dd (0.20 #742, 0.03 #2462, 0.03 #3752), 02f75t (0.20 #689, 0.03 #2409, 0.03 #3699) >> Best rule #427 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0jcx; >> query: (?x10251, 06_y0kx) <- gender(?x10251, ?x231), type_of_union(?x10251, ?x566), award_winner(?x13058, ?x10251), organizations_founded(?x10251, ?x10808), diet(?x10251, ?x3130) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #412 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 0jcx; *> query: (?x10251, 06zrp44) <- gender(?x10251, ?x231), type_of_union(?x10251, ?x566), award_winner(?x13058, ?x10251), organizations_founded(?x10251, ?x10808), diet(?x10251, ?x3130) *> conf = 0.33 ranks of expected_values: 3 EVAL 07bty award_winner! 06zrp44 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 77.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #4627-034hck PRED entity: 034hck PRED relation: nationality PRED expected values: 02vzc => 97 concepts (94 used for prediction) PRED predicted values (max 10 best out of 22): 03rjj (0.37 #3664, 0.37 #4458, 0.35 #4358), 0f8l9c (0.37 #3664, 0.37 #4458, 0.35 #4358), 02jx1 (0.12 #1616, 0.11 #3299, 0.11 #3596), 07ssc (0.09 #2192, 0.09 #1895, 0.09 #3479), 03rk0 (0.09 #2421, 0.07 #4998, 0.07 #2916), 0d060g (0.05 #501, 0.04 #303, 0.04 #3075), 0d05w3 (0.03 #1534, 0.03 #1336, 0.03 #1732), 0345h (0.03 #525, 0.03 #1515, 0.02 #1218), 03h64 (0.03 #547, 0.01 #1933, 0.01 #2032), 0chghy (0.02 #1098, 0.02 #1197, 0.02 #1494) >> Best rule #3664 for best value: >> intensional similarity = 4 >> extensional distance = 1224 >> proper extension: 02rgz4; 0lgsq; 05y5fw; 0342vg; >> query: (?x9403, ?x205) <- gender(?x9403, ?x231), nominated_for(?x9403, ?x5731), film_release_region(?x5731, ?x94), country(?x5731, ?x205) >> conf = 0.37 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 034hck nationality 02vzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 94.000 0.371 http://example.org/people/person/nationality #4626-04tgp PRED entity: 04tgp PRED relation: district_represented! PRED expected values: 070m6c 06f0dc 01gtc0 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 29): 070m6c (0.91 #119, 0.89 #177, 0.85 #235), 06f0dc (0.86 #178, 0.84 #120, 0.83 #236), 01gtc0 (0.47 #100, 0.45 #187, 0.45 #494), 01gssz (0.45 #494, 0.41 #110, 0.39 #197), 01grpc (0.45 #494, 0.41 #99, 0.34 #186), 01gssm (0.45 #494, 0.39 #183, 0.37 #154), 01gsrl (0.45 #494, 0.36 #184, 0.35 #97), 01grq1 (0.45 #494, 0.35 #113, 0.32 #200), 01grr2 (0.45 #494, 0.34 #192, 0.33 #250), 01gsry (0.45 #494, 0.32 #196, 0.30 #254) >> Best rule #119 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0488g; 026mj; >> query: (?x4622, 070m6c) <- religion(?x4622, ?x8249), ?x8249 = 021_0p, contains(?x4622, ?x1505), adjoins(?x2831, ?x4622) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 04tgp district_represented! 01gtc0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.906 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04tgp district_represented! 06f0dc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.906 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04tgp district_represented! 070m6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.906 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #4625-063ykwt PRED entity: 063ykwt PRED relation: nominated_for! PRED expected values: 0bp_b2 0fbvqf => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 181): 0gs9p (0.38 #64, 0.33 #4957, 0.32 #4724), 0k611 (0.38 #72, 0.27 #4965, 0.27 #4732), 0gr0m (0.38 #59, 0.23 #4719, 0.22 #4952), 02qyntr (0.38 #176, 0.21 #4836, 0.20 #5069), 02qvyrt (0.38 #95, 0.17 #4988, 0.16 #4755), 0gq9h (0.36 #4955, 0.36 #4722, 0.35 #5188), 019f4v (0.32 #4714, 0.32 #4947, 0.30 #5180), 040njc (0.27 #4667, 0.26 #4900, 0.25 #7), 0gq_v (0.26 #4680, 0.26 #4913, 0.26 #5146), 027gs1_ (0.25 #1115, 0.25 #882, 0.24 #1814) >> Best rule #64 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 09q5w2; 026390q; 0qm8b; 0cz_ym; 04f6df0; >> query: (?x3787, 0gs9p) <- award(?x3787, ?x686), nominated_for(?x2803, ?x3787), ?x2803 = 06chf >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #5360 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 552 *> proper extension: 09dv8h; *> query: (?x3787, ?x686) <- nominated_for(?x5065, ?x3787), honored_for(?x1764, ?x3787), award_winner(?x686, ?x5065) *> conf = 0.25 ranks of expected_values: 25, 41 EVAL 063ykwt nominated_for! 0fbvqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 54.000 54.000 0.375 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 063ykwt nominated_for! 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 54.000 54.000 0.375 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4624-0jgwf PRED entity: 0jgwf PRED relation: profession PRED expected values: 02jknp => 109 concepts (85 used for prediction) PRED predicted values (max 10 best out of 65): 02jknp (0.88 #155, 0.88 #1191, 0.88 #599), 02hrh1q (0.77 #4897, 0.77 #2825, 0.77 #3269), 03gjzk (0.43 #1642, 0.41 #2086, 0.41 #1494), 0dgd_ (0.26 #10361, 0.14 #30, 0.09 #622), 02krf9 (0.25 #618, 0.23 #1062, 0.19 #174), 09jwl (0.22 #314, 0.21 #2386, 0.20 #462), 0cbd2 (0.17 #2670, 0.17 #302, 0.16 #598), 0nbcg (0.16 #327, 0.14 #771, 0.14 #2399), 0kyk (0.14 #325, 0.13 #473, 0.11 #2693), 016z4k (0.13 #2964, 0.12 #3112, 0.12 #4000) >> Best rule #155 for best value: >> intensional similarity = 3 >> extensional distance = 92 >> proper extension: 0qf43; 05cgy8; 03bw6; >> query: (?x8645, 02jknp) <- nominated_for(?x8645, ?x2168), award(?x8645, ?x1107), ?x1107 = 019f4v >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgwf profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 85.000 0.883 http://example.org/people/person/profession #4623-042xrr PRED entity: 042xrr PRED relation: award_nominee! PRED expected values: 0163r3 => 88 concepts (29 used for prediction) PRED predicted values (max 10 best out of 686): 06g2d1 (0.81 #4655, 0.81 #58168, 0.81 #67483), 07yp0f (0.81 #4655, 0.81 #58168, 0.81 #67483), 0337vz (0.58 #2355, 0.55 #27, 0.15 #60500), 026r8q (0.50 #3977, 0.45 #1649, 0.14 #9312), 01ksr1 (0.42 #3070, 0.36 #742, 0.15 #58169), 06t74h (0.42 #3260, 0.36 #932, 0.15 #58169), 02cpb7 (0.42 #3433, 0.36 #1105, 0.15 #60500), 016fnb (0.40 #5746, 0.15 #58169, 0.15 #60500), 07vc_9 (0.40 #4915, 0.15 #58169, 0.15 #60500), 0154qm (0.40 #5392, 0.15 #58169, 0.15 #60500) >> Best rule #4655 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0794g; >> query: (?x4606, ?x1290) <- award_nominee(?x4606, ?x6085), award_nominee(?x4606, ?x1290), ?x6085 = 06g2d1, profession(?x4606, ?x1032) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #58169 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1191 *> proper extension: 054_mz; 044rvb; 02knnd; 07b2lv; 033wx9; 01fwk3; 03xpsrx; 02g3mn; 0565cz; 03jjzf; ... *> query: (?x4606, ?x100) <- award_nominee(?x4606, ?x6085), award_nominee(?x4606, ?x1290), participant(?x6085, ?x971), award_nominee(?x1290, ?x100) *> conf = 0.15 ranks of expected_values: 61 EVAL 042xrr award_nominee! 0163r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 88.000 29.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4622-0421v9q PRED entity: 0421v9q PRED relation: film! PRED expected values: 014g22 => 76 concepts (40 used for prediction) PRED predicted values (max 10 best out of 897): 036hf4 (0.64 #62456, 0.63 #52047, 0.60 #22894), 0794g (0.64 #62456, 0.60 #22894, 0.58 #62458), 01wy5m (0.22 #861, 0.11 #2942, 0.03 #46663), 02_0d2 (0.22 #1176, 0.11 #3257, 0.03 #9500), 09pl3f (0.13 #12487, 0.11 #39556, 0.10 #45802), 09pl3s (0.13 #12487, 0.11 #39556, 0.10 #45802), 01wbg84 (0.11 #47, 0.11 #2128, 0.04 #8371), 016z2j (0.11 #390, 0.11 #2471, 0.04 #8714), 0kszw (0.11 #420, 0.11 #2501, 0.03 #10825), 08qxx9 (0.11 #1520, 0.11 #3601, 0.03 #18169) >> Best rule #62456 for best value: >> intensional similarity = 4 >> extensional distance = 851 >> proper extension: 0clpml; >> query: (?x6543, ?x9084) <- nominated_for(?x9140, ?x6543), nominated_for(?x9084, ?x6543), film(?x9140, ?x791), participant(?x9084, ?x1897) >> conf = 0.64 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0421v9q film! 014g22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 40.000 0.643 http://example.org/film/actor/film./film/performance/film #4621-01swck PRED entity: 01swck PRED relation: award PRED expected values: 02x73k6 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 238): 02z13jg (0.70 #22692, 0.70 #2389, 0.69 #5574), 09sb52 (0.41 #2427, 0.40 #10388, 0.37 #11582), 0gqy2 (0.22 #159, 0.16 #4538, 0.15 #21099), 0gr4k (0.22 #31, 0.13 #31849, 0.07 #25877), 03hkv_r (0.22 #15, 0.07 #25877, 0.04 #13948), 0gqwc (0.20 #469, 0.15 #21099, 0.13 #31849), 02x4x18 (0.20 #526, 0.15 #21099, 0.13 #31849), 02x73k6 (0.20 #455, 0.15 #21099, 0.11 #57), 0bs0bh (0.20 #498, 0.15 #21099, 0.11 #100), 0bfvw2 (0.20 #412, 0.15 #21099, 0.06 #810) >> Best rule #22692 for best value: >> intensional similarity = 2 >> extensional distance = 1587 >> proper extension: 04qvl7; 05cljf; 0h5f5n; 0m2l9; 06cv1; 01zkxv; 025jfl; 0jf1b; 01w61th; 01kwlwp; ... >> query: (?x4520, ?x693) <- award_winner(?x693, ?x4520), award_nominee(?x286, ?x4520) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #455 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 04z542; *> query: (?x4520, 02x73k6) <- film(?x4520, ?x1331), award(?x4520, ?x372), ?x1331 = 01vfqh *> conf = 0.20 ranks of expected_values: 8 EVAL 01swck award 02x73k6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 97.000 97.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4620-06rmdr PRED entity: 06rmdr PRED relation: film! PRED expected values: 05gml8 => 78 concepts (39 used for prediction) PRED predicted values (max 10 best out of 944): 03ktjq (0.49 #8325, 0.47 #10407, 0.41 #60378), 016tt2 (0.41 #60378, 0.39 #27068, 0.38 #41638), 034hck (0.16 #43721, 0.15 #22902, 0.15 #45804), 0pz91 (0.12 #4374, 0.08 #12700, 0.08 #6455), 018grr (0.10 #2420, 0.04 #12827, 0.02 #31570), 059j1m (0.10 #3555, 0.03 #9799, 0.03 #13962), 07y8l9 (0.10 #3055, 0.02 #32205, 0.02 #13462), 0gn30 (0.10 #948, 0.09 #9273, 0.08 #5110), 01pqy_ (0.10 #926, 0.06 #5088, 0.04 #9251), 0btpx (0.10 #1475, 0.06 #9800, 0.04 #5637) >> Best rule #8325 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 03qcfvw; 02y_lrp; 034qmv; 0140g4; 08lr6s; 04ddm4; 02x3lt7; 0dj0m5; 06_wqk4; 0kv2hv; ... >> query: (?x1769, ?x5781) <- nominated_for(?x102, ?x1769), ?x102 = 04ljl_l, nominated_for(?x5781, ?x1769), titles(?x600, ?x1769) >> conf = 0.49 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06rmdr film! 05gml8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 39.000 0.489 http://example.org/film/actor/film./film/performance/film #4619-031y2 PRED entity: 031y2 PRED relation: vacationer PRED expected values: 0dxmyh => 174 concepts (126 used for prediction) PRED predicted values (max 10 best out of 217): 0261x8t (0.38 #2274, 0.30 #3163, 0.28 #3519), 05r5w (0.33 #608, 0.15 #3097, 0.14 #2208), 026c1 (0.33 #571, 0.10 #2171, 0.08 #1104), 02ts3h (0.33 #677, 0.08 #1210, 0.05 #4945), 033wx9 (0.29 #1657, 0.19 #2192, 0.15 #3081), 02d9k (0.22 #746, 0.20 #923, 0.20 #212), 01xyt7 (0.21 #1725, 0.20 #305, 0.20 #128), 016fnb (0.21 #1702, 0.19 #2237, 0.17 #1170), 01pllx (0.21 #1755, 0.14 #2290, 0.11 #3179), 0151w_ (0.21 #1617, 0.14 #2152, 0.11 #3041) >> Best rule #2274 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 04w58; >> query: (?x9660, 0261x8t) <- vacationer(?x9660, ?x5246), participant(?x286, ?x5246), sibling(?x5246, ?x8749), award(?x5246, ?x154) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #344 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 07kg3; *> query: (?x9660, 0dxmyh) <- location_of_ceremony(?x566, ?x9660), contains(?x205, ?x9660), ?x205 = 03rjj, location_of_ceremony(?x2582, ?x9660) *> conf = 0.20 ranks of expected_values: 17 EVAL 031y2 vacationer 0dxmyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 174.000 126.000 0.381 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #4618-0137n0 PRED entity: 0137n0 PRED relation: profession PRED expected values: 039v1 => 113 concepts (90 used for prediction) PRED predicted values (max 10 best out of 52): 0nbcg (0.56 #1464, 0.55 #170, 0.54 #743), 039v1 (0.37 #1469, 0.36 #175, 0.34 #462), 01c72t (0.32 #1312, 0.31 #3608, 0.30 #4038), 0dxtg (0.30 #6761, 0.29 #8334, 0.26 #11767), 03gjzk (0.24 #6762, 0.23 #8192, 0.22 #8049), 02jknp (0.20 #11762, 0.20 #11476, 0.20 #8615), 0fnpj (0.18 #2354, 0.17 #486, 0.17 #772), 018gz8 (0.17 #2455, 0.13 #7908, 0.13 #8480), 025352 (0.15 #55, 0.14 #485, 0.09 #1060), 0cbd2 (0.15 #8328, 0.12 #12763, 0.12 #9759) >> Best rule #1464 for best value: >> intensional similarity = 3 >> extensional distance = 198 >> proper extension: 02qfhb; 01r4zfk; 09g0h; >> query: (?x1270, 0nbcg) <- type_of_union(?x1270, ?x566), role(?x1270, ?x227), profession(?x1270, ?x131) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1469 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 198 *> proper extension: 02qfhb; 01r4zfk; 09g0h; *> query: (?x1270, 039v1) <- type_of_union(?x1270, ?x566), role(?x1270, ?x227), profession(?x1270, ?x131) *> conf = 0.37 ranks of expected_values: 2 EVAL 0137n0 profession 039v1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 90.000 0.555 http://example.org/people/person/profession #4617-0p3sf PRED entity: 0p3sf PRED relation: role PRED expected values: 01vdm0 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 116): 01vdm0 (0.48 #733, 0.27 #2943, 0.26 #3246), 0342h (0.39 #2612, 0.39 #3420, 0.38 #3219), 03gvt (0.35 #401, 0.32 #3012, 0.32 #3315), 05148p4 (0.35 #401, 0.32 #3012, 0.32 #3315), 0dwvl (0.35 #401, 0.32 #3012, 0.32 #3315), 06ch55 (0.35 #401, 0.32 #3012, 0.32 #3315), 02sgy (0.25 #2613, 0.24 #3220, 0.24 #2917), 06ncr (0.25 #253, 0.22 #353, 0.14 #454), 02pprs (0.25 #204, 0.22 #304, 0.14 #405), 05842k (0.25 #276, 0.19 #1278, 0.18 #1982) >> Best rule #733 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 032t2z; 01vrz41; 0ftps; 0fpjd_g; 01v_pj6; 0144l1; 01w724; 01271h; 06gd4; 012z8_; ... >> query: (?x3171, 01vdm0) <- profession(?x3171, ?x6565), artists(?x5355, ?x3171), role(?x3171, ?x228), ?x6565 = 0fnpj >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p3sf role 01vdm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.477 http://example.org/music/artist/track_contributions./music/track_contribution/role #4616-02825nf PRED entity: 02825nf PRED relation: film_release_region PRED expected values: 0154j 0f8l9c 03gj2 07ylj 06bnz => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 124): 09c7w0 (0.93 #6487, 0.93 #6797, 0.92 #9266), 0f8l9c (0.89 #946, 0.89 #1564, 0.87 #3567), 03gj2 (0.85 #950, 0.79 #1568, 0.79 #1414), 03h64 (0.82 #992, 0.80 #2072, 0.79 #1456), 06bnz (0.82 #971, 0.71 #1435, 0.69 #2051), 0154j (0.78 #932, 0.77 #1550, 0.74 #2012), 03spz (0.75 #1022, 0.69 #1486, 0.69 #1176), 06t2t (0.74 #987, 0.66 #1451, 0.64 #2067), 047yc (0.62 #953, 0.51 #1417, 0.46 #1107), 03rk0 (0.62 #982, 0.48 #1446, 0.42 #1136) >> Best rule #6487 for best value: >> intensional similarity = 3 >> extensional distance = 977 >> proper extension: 064n1pz; 016kz1; 04lqvlr; 0413cff; 02h22; 0k2m6; 09rfh9; 0564x; 02pcq92; >> query: (?x7629, 09c7w0) <- genre(?x7629, ?x225), film_release_region(?x7629, ?x87), titles(?x2480, ?x7629) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #946 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 131 *> proper extension: 0gtsx8c; *> query: (?x7629, 0f8l9c) <- film_release_region(?x7629, ?x550), film_release_region(?x7629, ?x279), ?x550 = 05v8c, ?x279 = 0d060g, film(?x3013, ?x7629) *> conf = 0.89 ranks of expected_values: 2, 3, 5, 6, 29 EVAL 02825nf film_release_region 06bnz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02825nf film_release_region 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 83.000 83.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02825nf film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02825nf film_release_region 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 83.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02825nf film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.931 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4615-012q4n PRED entity: 012q4n PRED relation: film PRED expected values: 0315rp => 78 concepts (41 used for prediction) PRED predicted values (max 10 best out of 348): 011yhm (0.53 #26736, 0.47 #16043, 0.41 #35648), 0m313 (0.33 #13, 0.05 #60604, 0.04 #51693), 0h3xztt (0.21 #170, 0.04 #51693, 0.03 #73083), 04jpg2p (0.12 #1456, 0.03 #40996, 0.03 #39213), 031hcx (0.12 #1267, 0.01 #19092, 0.01 #26220), 0dl6fv (0.12 #1481), 09tkzy (0.08 #1461, 0.05 #60604, 0.04 #51693), 03bx2lk (0.08 #183, 0.03 #40996, 0.03 #39213), 04jplwp (0.08 #1365, 0.03 #40996, 0.03 #39213), 03y0pn (0.08 #1251, 0.03 #40996, 0.03 #39213) >> Best rule #26736 for best value: >> intensional similarity = 3 >> extensional distance = 1458 >> proper extension: 025p38; 01wjrn; 05wjnt; 0c01c; 01pnn3; 02dh86; 02wb6yq; 039crh; 06n9lt; 02zrv7; ... >> query: (?x6444, ?x6445) <- profession(?x6444, ?x1032), nominated_for(?x6444, ?x6445), ?x1032 = 02hrh1q >> conf = 0.53 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 012q4n film 0315rp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 41.000 0.526 http://example.org/film/actor/film./film/performance/film #4614-01nn3m PRED entity: 01nn3m PRED relation: profession PRED expected values: 039v1 => 108 concepts (49 used for prediction) PRED predicted values (max 10 best out of 51): 0dz3r (0.62 #6411, 0.57 #2185, 0.56 #1310), 039v1 (0.49 #3525, 0.41 #1341, 0.41 #1633), 01c72t (0.35 #166, 0.33 #747, 0.32 #2931), 0n1h (0.33 #2194, 0.29 #1174, 0.28 #1903), 0dxtg (0.20 #2632, 0.20 #2487, 0.18 #5256), 025352 (0.19 #56, 0.14 #1219, 0.14 #346), 01d_h8 (0.18 #2624, 0.18 #2479, 0.16 #5248), 0fnpj (0.18 #1365, 0.17 #1657, 0.17 #1220), 02jknp (0.18 #2481, 0.17 #2626, 0.15 #5250), 04f2zj (0.17 #238, 0.14 #674, 0.10 #383) >> Best rule #6411 for best value: >> intensional similarity = 7 >> extensional distance = 523 >> proper extension: 05pdbs; 0136g9; 05pq9; 01pfkw; 01wwvd2; 04g3p5; 02gyl0; 06fmdb; 02_jkc; 016732; ... >> query: (?x12623, 0dz3r) <- profession(?x12623, ?x220), profession(?x5048, ?x220), profession(?x4343, ?x220), profession(?x4200, ?x220), ?x4200 = 025ldg, ?x5048 = 015x1f, ?x4343 = 02cx90 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3525 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 324 *> proper extension: 03ds3; 0948xk; *> query: (?x12623, 039v1) <- profession(?x12623, ?x220), profession(?x4200, ?x220), profession(?x226, ?x220), ?x4200 = 025ldg, instrumentalists(?x212, ?x12623), ?x226 = 05cljf *> conf = 0.49 ranks of expected_values: 2 EVAL 01nn3m profession 039v1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 49.000 0.623 http://example.org/people/person/profession #4613-01ffx4 PRED entity: 01ffx4 PRED relation: featured_film_locations PRED expected values: 052p7 => 84 concepts (75 used for prediction) PRED predicted values (max 10 best out of 50): 02_286 (0.37 #5063, 0.31 #4342, 0.28 #5303), 030qb3t (0.16 #5082, 0.14 #5322, 0.12 #38), 04jpl (0.15 #5052, 0.14 #4331, 0.13 #1684), 0h7h6 (0.12 #42, 0.09 #281, 0.08 #520), 06m_5 (0.09 #384, 0.08 #623), 0rh6k (0.07 #5045, 0.05 #5285, 0.05 #4324), 080h2 (0.05 #1459, 0.04 #5307, 0.03 #5067), 01_d4 (0.05 #5090, 0.04 #5330, 0.04 #4369), 03gh4 (0.05 #1071, 0.02 #5158, 0.02 #5398), 052p7 (0.03 #5101, 0.03 #5341, 0.02 #1493) >> Best rule #5063 for best value: >> intensional similarity = 4 >> extensional distance = 508 >> proper extension: 05css_; 0d8w2n; >> query: (?x3201, 02_286) <- film(?x1914, ?x3201), genre(?x3201, ?x53), featured_film_locations(?x3201, ?x205), film_release_region(?x66, ?x205) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #5101 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 508 *> proper extension: 05css_; 0d8w2n; *> query: (?x3201, 052p7) <- film(?x1914, ?x3201), genre(?x3201, ?x53), featured_film_locations(?x3201, ?x205), film_release_region(?x66, ?x205) *> conf = 0.03 ranks of expected_values: 10 EVAL 01ffx4 featured_film_locations 052p7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 84.000 75.000 0.371 http://example.org/film/film/featured_film_locations #4612-0h53p1 PRED entity: 0h53p1 PRED relation: award_winner PRED expected values: 0d7hg4 => 106 concepts (37 used for prediction) PRED predicted values (max 10 best out of 629): 09hd16 (0.81 #33775, 0.81 #41816, 0.81 #41815), 06jrhz (0.81 #33775, 0.81 #41816, 0.81 #41815), 06chf (0.52 #36990, 0.52 #6433, 0.52 #49860), 013pk3 (0.52 #36990, 0.52 #6433, 0.52 #49860), 02tn0_ (0.52 #36990, 0.52 #6433, 0.52 #49860), 08xwck (0.52 #36990, 0.52 #6433, 0.52 #49860), 07nznf (0.52 #36990, 0.52 #6433, 0.52 #49860), 0d7hg4 (0.50 #5253, 0.32 #17690, 0.29 #53078), 0h53p1 (0.50 #5285, 0.32 #17690, 0.29 #53078), 0bxtg (0.33 #1668, 0.04 #16143, 0.03 #27402) >> Best rule #33775 for best value: >> intensional similarity = 3 >> extensional distance = 604 >> proper extension: 07g2b; 0157m; 0h32q; 02tkzn; 015zql; 01nz1q6; >> query: (?x2802, ?x4022) <- student(?x11785, ?x2802), award_winner(?x4921, ?x2802), award_winner(?x4022, ?x2802) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #5253 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0d7hg4; 0884hk; 09hd16; 0h584v; 01xndd; 06jrhz; 0h5jg5; 0brkwj; *> query: (?x2802, 0d7hg4) <- award_winner(?x2802, ?x8337), ?x8337 = 0697kh, award_nominee(?x2802, ?x65) *> conf = 0.50 ranks of expected_values: 8 EVAL 0h53p1 award_winner 0d7hg4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 106.000 37.000 0.812 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #4611-0166b PRED entity: 0166b PRED relation: film_release_region! PRED expected values: 0c40vxk 0661ql3 => 126 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1314): 08hmch (0.90 #118, 0.72 #11944, 0.57 #22457), 017jd9 (0.90 #586, 0.70 #12412, 0.57 #21611), 06fcqw (0.90 #828, 0.60 #12654, 0.45 #25795), 01fmys (0.86 #242, 0.65 #12068, 0.55 #22581), 043tvp3 (0.86 #915, 0.65 #12741, 0.54 #21940), 047vnkj (0.86 #693, 0.63 #12519, 0.57 #8577), 017gm7 (0.86 #159, 0.62 #11985, 0.54 #21184), 0jjy0 (0.86 #128, 0.62 #11954, 0.45 #21153), 087wc7n (0.86 #90, 0.58 #11916, 0.49 #21115), 09k56b7 (0.86 #237, 0.58 #12063, 0.42 #21262) >> Best rule #118 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 06qd3; >> query: (?x6435, 08hmch) <- official_language(?x6435, ?x5814), film_release_region(?x1228, ?x6435), ?x1228 = 05z_kps >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #288 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 06qd3; *> query: (?x6435, 0661ql3) <- official_language(?x6435, ?x5814), film_release_region(?x1228, ?x6435), ?x1228 = 05z_kps *> conf = 0.81 ranks of expected_values: 18, 142 EVAL 0166b film_release_region! 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 126.000 96.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0166b film_release_region! 0c40vxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 126.000 96.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4610-0drtkx PRED entity: 0drtkx PRED relation: nominated_for PRED expected values: 0g56t9t => 56 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1326): 017gl1 (0.54 #3287, 0.50 #1709, 0.34 #6312), 0b6tzs (0.46 #3284, 0.42 #1706, 0.25 #129), 0dr3sl (0.46 #3564, 0.42 #1986, 0.25 #409), 0dr_4 (0.42 #1799, 0.38 #3377, 0.38 #222), 017jd9 (0.38 #3854, 0.34 #6312, 0.33 #2276), 026p4q7 (0.38 #3508, 0.33 #1930, 0.25 #14566), 03hmt9b (0.38 #3740, 0.33 #2162, 0.25 #585), 0ch26b_ (0.38 #3425, 0.33 #1847, 0.25 #270), 01mgw (0.38 #4293, 0.33 #2715, 0.19 #15351), 0fpv_3_ (0.38 #3486, 0.33 #1908, 0.16 #14544) >> Best rule #3287 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 099c8n; >> query: (?x8059, 017gl1) <- nominated_for(?x8059, ?x7171), nominated_for(?x8059, ?x6168), nominated_for(?x8059, ?x1259), ?x1259 = 04hwbq, film_release_region(?x6168, ?x87), film(?x105, ?x7171) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 02x1dht; 0gqzz; 0gq9h; 02qyxs5; 02x1z2s; 018wdw; *> query: (?x8059, 0g56t9t) <- nominated_for(?x8059, ?x3455), nominated_for(?x8059, ?x1259), ?x1259 = 04hwbq, award_winner(?x8059, ?x846), ?x3455 = 02rn00y *> conf = 0.38 ranks of expected_values: 17 EVAL 0drtkx nominated_for 0g56t9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 56.000 17.000 0.538 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4609-02wk7b PRED entity: 02wk7b PRED relation: nominated_for! PRED expected values: 04kxsb => 106 concepts (84 used for prediction) PRED predicted values (max 10 best out of 196): 02wkmx (0.68 #16080, 0.67 #9505, 0.67 #8371), 02w_6xj (0.68 #16080, 0.67 #9505, 0.67 #8371), 027571b (0.68 #16080, 0.67 #9505, 0.67 #8371), 02qyntr (0.67 #168, 0.49 #1524, 0.40 #620), 04dn09n (0.56 #33, 0.47 #937, 0.47 #485), 019f4v (0.51 #50, 0.42 #7968, 0.40 #1632), 0k611 (0.51 #65, 0.36 #7983, 0.33 #3682), 099c8n (0.46 #53, 0.40 #505, 0.39 #1409), 04kxsb (0.41 #85, 0.31 #1441, 0.25 #3702), 02x1dht (0.40 #493, 0.39 #945, 0.37 #719) >> Best rule #16080 for best value: >> intensional similarity = 2 >> extensional distance = 987 >> proper extension: 08cfr1; >> query: (?x8247, ?x749) <- award(?x8247, ?x749), award(?x396, ?x749) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #85 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 37 *> proper extension: 07w8fz; *> query: (?x8247, 04kxsb) <- nominated_for(?x1313, ?x8247), nominated_for(?x68, ?x8247), titles(?x53, ?x8247), ?x68 = 02qyp19, ?x1313 = 0gs9p *> conf = 0.41 ranks of expected_values: 9 EVAL 02wk7b nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 106.000 84.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4608-026mfs PRED entity: 026mfs PRED relation: award! PRED expected values: 01bpc9 016h4r 01xzb6 01h5f8 => 35 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2345): 02f1c (0.79 #36639, 0.78 #53295, 0.78 #46631), 051m56 (0.79 #36639, 0.78 #53295, 0.78 #46631), 01lmj3q (0.79 #36639, 0.78 #53295, 0.78 #46631), 01dpsv (0.79 #36639, 0.78 #53295, 0.78 #46631), 02qwg (0.60 #7575, 0.57 #10905, 0.33 #4245), 01vrz41 (0.60 #6950, 0.57 #10280, 0.33 #3620), 03j24kf (0.60 #7995, 0.52 #11325, 0.33 #4665), 01vs_v8 (0.60 #7235, 0.52 #10565, 0.33 #3905), 0dw4g (0.60 #8263, 0.43 #11593, 0.33 #4933), 01vsgrn (0.60 #8257, 0.33 #11587, 0.33 #4927) >> Best rule #36639 for best value: >> intensional similarity = 5 >> extensional distance = 197 >> proper extension: 0j298t8; >> query: (?x2420, ?x367) <- award(?x3632, ?x2420), ceremony(?x2420, ?x139), award_winner(?x2420, ?x367), award_winner(?x3854, ?x3632), award_winner(?x158, ?x3632) >> conf = 0.79 => this is the best rule for 4 predicted values *> Best rule #8169 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 01bgqh; 03qbh5; *> query: (?x2420, 01xzb6) <- award(?x4343, ?x2420), award(?x1794, ?x2420), award(?x248, ?x2420), ?x248 = 0lbj1, people(?x3584, ?x1794), ?x4343 = 02cx90, ceremony(?x2420, ?x139) *> conf = 0.40 ranks of expected_values: 23, 195, 223, 376 EVAL 026mfs award! 01h5f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 35.000 16.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 026mfs award! 01xzb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 35.000 16.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 026mfs award! 016h4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 35.000 16.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 026mfs award! 01bpc9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 35.000 16.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4607-0htww PRED entity: 0htww PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 3): 029j_ (0.99 #104, 0.99 #81, 0.98 #152), 07z4p (0.08 #4, 0.07 #98, 0.06 #80), 02nxhr (0.07 #55, 0.06 #38, 0.05 #96) >> Best rule #104 for best value: >> intensional similarity = 4 >> extensional distance = 362 >> proper extension: 0gfzfj; >> query: (?x3137, 029j_) <- films(?x326, ?x3137), language(?x3137, ?x254), film(?x305, ?x3137), film_release_distribution_medium(?x3137, ?x2008) >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0htww film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.989 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #4606-01vrncs PRED entity: 01vrncs PRED relation: artists! PRED expected values: 06by7 016cjb => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 218): 06by7 (0.60 #22, 0.55 #2157, 0.51 #4294), 064t9 (0.53 #2759, 0.45 #16497, 0.41 #19560), 0xhtw (0.40 #17, 0.36 #2152, 0.31 #4289), 02qdgx (0.40 #343, 0.09 #2173, 0.07 #13466), 017_qw (0.32 #13184, 0.10 #23578, 0.10 #13489), 016clz (0.31 #1530, 0.26 #2751, 0.23 #15571), 02lnbg (0.28 #2803, 0.13 #16541, 0.12 #13180), 0ggx5q (0.26 #2823, 0.14 #13200, 0.14 #16561), 0glt670 (0.25 #16524, 0.25 #2786, 0.22 #19587), 05bt6j (0.25 #2789, 0.25 #2178, 0.21 #22645) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0m2l9; 0gcs9; 012vd6; >> query: (?x1089, 06by7) <- influenced_by(?x1573, ?x1089), award_nominee(?x1089, ?x483), ?x1573 = 03g5jw >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 21 EVAL 01vrncs artists! 016cjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 128.000 128.000 0.600 http://example.org/music/genre/artists EVAL 01vrncs artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.600 http://example.org/music/genre/artists #4605-033db3 PRED entity: 033db3 PRED relation: profession PRED expected values: 02hrh1q => 111 concepts (18 used for prediction) PRED predicted values (max 10 best out of 38): 02hrh1q (0.93 #2366, 0.91 #747, 0.91 #2072), 03gjzk (0.44 #1189, 0.43 #1484, 0.30 #454), 02krf9 (0.23 #466, 0.22 #319, 0.21 #1348), 0np9r (0.23 #1048, 0.22 #1637, 0.19 #1784), 0cbd2 (0.19 #300, 0.18 #1182, 0.18 #1477), 018gz8 (0.17 #1486, 0.17 #1191, 0.14 #1044), 09jwl (0.16 #17, 0.11 #1046, 0.11 #1635), 0kyk (0.10 #322, 0.09 #1204, 0.09 #469), 0nbcg (0.09 #30, 0.04 #2090, 0.04 #1059), 016z4k (0.07 #4, 0.04 #2064, 0.03 #2505) >> Best rule #2366 for best value: >> intensional similarity = 4 >> extensional distance = 599 >> proper extension: 01gvr1; 01n5309; 05tk7y; 0m32_; 01vx5w7; 055c8; 01jbx1; 01v3vp; 026g801; 03x22w; ... >> query: (?x13962, 02hrh1q) <- award(?x13962, ?x2071), film(?x13962, ?x8859), profession(?x13962, ?x319), actor(?x6706, ?x13962) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033db3 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 18.000 0.927 http://example.org/people/person/profession #4604-02_1rq PRED entity: 02_1rq PRED relation: nominated_for! PRED expected values: 02pzz3p => 69 concepts (66 used for prediction) PRED predicted values (max 10 best out of 164): 02pzz3p (0.75 #816, 0.42 #1050, 0.40 #582), 09qj50 (0.33 #37, 0.20 #505, 0.17 #973), 0gq9h (0.29 #8721, 0.29 #9189, 0.29 #8955), 027gs1_ (0.27 #2758, 0.26 #3226, 0.23 #4630), 019f4v (0.25 #8712, 0.25 #8946, 0.24 #9180), 0fbtbt (0.25 #2732, 0.24 #3902, 0.24 #2498), 0gs9p (0.24 #9191, 0.24 #8957, 0.23 #8723), 0cjyzs (0.24 #2656, 0.23 #3124, 0.22 #4528), 0k611 (0.23 #8732, 0.22 #8966, 0.22 #9200), 09qwmm (0.22 #14981, 0.19 #14982, 0.19 #14746) >> Best rule #816 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02_1q9; 02_1kl; >> query: (?x589, 02pzz3p) <- nominated_for(?x6853, ?x589), nominated_for(?x438, ?x589), ?x6853 = 02p_04b >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_1rq nominated_for! 02pzz3p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 66.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4603-02vp1f_ PRED entity: 02vp1f_ PRED relation: genre PRED expected values: 07s9rl0 04xvh5 => 109 concepts (104 used for prediction) PRED predicted values (max 10 best out of 99): 07s9rl0 (0.77 #4364, 0.77 #1696, 0.76 #2180), 04xvlr (0.64 #1574, 0.59 #4485, 0.52 #7644), 02kdv5l (0.50 #3393, 0.49 #2666, 0.38 #1455), 02l7c8 (0.44 #1227, 0.43 #1712, 0.42 #2196), 06n90 (0.43 #3404, 0.26 #2677, 0.22 #1103), 01jfsb (0.39 #1102, 0.34 #4983, 0.33 #255), 05p553 (0.37 #12497, 0.34 #1821, 0.34 #8496), 04xvh5 (0.35 #519, 0.30 #398, 0.23 #1730), 02n4kr (0.33 #251, 0.25 #9, 0.15 #9712), 060__y (0.26 #1349, 0.24 #2197, 0.22 #260) >> Best rule #4364 for best value: >> intensional similarity = 5 >> extensional distance = 582 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x251, 07s9rl0) <- titles(?x162, ?x251), titles(?x162, ?x4756), titles(?x162, ?x2490), nominated_for(?x143, ?x2490), ?x4756 = 0462hhb >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 02vp1f_ genre 04xvh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 109.000 104.000 0.769 http://example.org/film/film/genre EVAL 02vp1f_ genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 104.000 0.769 http://example.org/film/film/genre #4602-039bp PRED entity: 039bp PRED relation: type_of_union PRED expected values: 04ztj => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.77 #29, 0.76 #25, 0.74 #101), 01g63y (0.44 #382, 0.19 #14, 0.15 #18) >> Best rule #29 for best value: >> intensional similarity = 2 >> extensional distance = 225 >> proper extension: 04m_kpx; 081t6; >> query: (?x1119, 04ztj) <- profession(?x1119, ?x1032), student(?x1368, ?x1119) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039bp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.767 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4601-02_5h PRED entity: 02_5h PRED relation: country PRED expected values: 06mzp 02vzc => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 302): 0ctw_b (0.83 #2222, 0.64 #2405, 0.64 #4240), 0chghy (0.80 #5138, 0.76 #5503, 0.74 #7693), 06mzp (0.80 #1665, 0.75 #2766, 0.75 #2217), 0b90_r (0.79 #2387, 0.68 #4222, 0.67 #2204), 047lj (0.79 #2393, 0.63 #3310, 0.62 #3496), 02vzc (0.75 #2241, 0.75 #1324, 0.74 #3341), 0jgd (0.75 #2203, 0.71 #2386, 0.68 #4221), 04g5k (0.75 #2305, 0.71 #1205, 0.67 #837), 0hzlz (0.75 #2218, 0.64 #2401, 0.62 #1833), 01pj7 (0.75 #2240, 0.64 #2423, 0.62 #1833) >> Best rule #2222 for best value: >> intensional similarity = 14 >> extensional distance = 10 >> proper extension: 01lb14; >> query: (?x1175, 0ctw_b) <- olympics(?x1175, ?x418), country(?x1175, ?x8588), country(?x1175, ?x6305), country(?x1175, ?x2513), country(?x1175, ?x756), country(?x1175, ?x304), ?x304 = 0d0vqn, ?x2513 = 05b4w, religion(?x8588, ?x492), jurisdiction_of_office(?x182, ?x8588), capital(?x6305, ?x13440), administrative_parent(?x8588, ?x551), ?x756 = 06npd, film_release_region(?x1701, ?x8588) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1665 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 8 *> proper extension: 03hr1p; 06z6r; *> query: (?x1175, 06mzp) <- olympics(?x1175, ?x784), country(?x1175, ?x3277), country(?x1175, ?x252), sports(?x12388, ?x1175), ?x3277 = 06t8v, ?x252 = 03_3d, sports(?x784, ?x1037), olympics(?x774, ?x12388), participating_countries(?x784, ?x1023), participating_countries(?x784, ?x1003), ?x1003 = 03gj2, ?x1023 = 0ctw_b *> conf = 0.80 ranks of expected_values: 3, 6 EVAL 02_5h country 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 75.000 75.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02_5h country 06mzp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 75.000 75.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #4600-01s9vc PRED entity: 01s9vc PRED relation: film_release_region PRED expected values: 05r4w => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 202): 059j2 (0.89 #9513, 0.88 #8043, 0.87 #10166), 05r4w (0.87 #8008, 0.86 #9478, 0.85 #10131), 05qhw (0.86 #8023, 0.84 #9493, 0.82 #10146), 0154j (0.86 #8011, 0.84 #10134, 0.83 #9481), 0chghy (0.85 #8018, 0.84 #9488, 0.84 #10141), 03gj2 (0.85 #8035, 0.84 #9505, 0.84 #10158), 015fr (0.83 #8026, 0.83 #9496, 0.80 #1326), 035qy (0.83 #8046, 0.81 #10169, 0.81 #9516), 0d060g (0.81 #8013, 0.81 #9483, 0.79 #10136), 0b90_r (0.80 #8010, 0.76 #9480, 0.76 #10133) >> Best rule #9513 for best value: >> intensional similarity = 5 >> extensional distance = 159 >> proper extension: 07s3m4g; 0g4pl7z; 0hz6mv2; >> query: (?x10404, 059j2) <- film_release_region(?x10404, ?x1603), film_release_region(?x10404, ?x142), ?x142 = 0jgd, ?x1603 = 06bnz, film(?x1104, ?x10404) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #8008 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 136 *> proper extension: 053tj7; 040rmy; 0h95zbp; 0j8f09z; *> query: (?x10404, 05r4w) <- film_release_region(?x10404, ?x2645), film_release_region(?x10404, ?x1603), film_release_region(?x10404, ?x142), ?x142 = 0jgd, ?x1603 = 06bnz, film(?x1104, ?x10404), ?x2645 = 03h64 *> conf = 0.87 ranks of expected_values: 2 EVAL 01s9vc film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.888 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4599-0cnztc4 PRED entity: 0cnztc4 PRED relation: film_release_region PRED expected values: 05r4w 0d060g 0345h => 147 concepts (123 used for prediction) PRED predicted values (max 10 best out of 260): 05r4w (0.89 #3582, 0.86 #7006, 0.85 #10097), 059j2 (0.87 #7039, 0.86 #10130, 0.85 #2149), 035qy (0.86 #3619, 0.83 #7043, 0.76 #5248), 07ssc (0.86 #2458, 0.82 #7022, 0.82 #3762), 0345h (0.85 #7041, 0.80 #10132, 0.80 #5410), 03gj2 (0.85 #2143, 0.85 #7033, 0.79 #4588), 0jgd (0.85 #2118, 0.83 #7008, 0.81 #3584), 05b4w (0.85 #2184, 0.78 #7074, 0.70 #10165), 03rjj (0.85 #7010, 0.84 #10101, 0.79 #4565), 02vzc (0.84 #7061, 0.83 #10152, 0.81 #9500) >> Best rule #3582 for best value: >> intensional similarity = 9 >> extensional distance = 35 >> proper extension: 047svrl; >> query: (?x1283, 05r4w) <- film_release_region(?x1283, ?x1499), film_release_region(?x1283, ?x789), film_release_region(?x1283, ?x304), country(?x1283, ?x172), ?x789 = 0f8l9c, film_regional_debut_venue(?x1283, ?x1658), film_festivals(?x1283, ?x6828), ?x1499 = 01znc_, ?x304 = 0d0vqn >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 13 EVAL 0cnztc4 film_release_region 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 147.000 123.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cnztc4 film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 147.000 123.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cnztc4 film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 123.000 0.892 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4598-0340hj PRED entity: 0340hj PRED relation: film! PRED expected values: 07fq1y 07nx9j 015gsv => 62 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1078): 07fq1y (0.40 #15, 0.09 #2089, 0.04 #87086), 01r9md (0.40 #2026, 0.09 #4100, 0.04 #87086), 015gsv (0.40 #1538, 0.09 #3612, 0.04 #87086), 0jbp0 (0.27 #3825, 0.04 #10045, 0.03 #16264), 03h_9lg (0.27 #2204, 0.04 #8424, 0.03 #16716), 02wgln (0.20 #312, 0.09 #2386, 0.04 #87086), 044mrh (0.20 #884, 0.09 #2958, 0.04 #87086), 0g2mbn (0.20 #917, 0.09 #2991, 0.04 #87086), 07nx9j (0.20 #1311, 0.09 #3385, 0.04 #87086), 03ym1 (0.18 #3081, 0.05 #9301, 0.02 #13447) >> Best rule #15 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 06ys2; >> query: (?x1511, 07fq1y) <- nominated_for(?x5467, ?x1511), nominated_for(?x4106, ?x1511), nominated_for(?x1733, ?x1511), ?x4106 = 04fzk, ?x1733 = 015pkc, award_nominee(?x395, ?x5467) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 9 EVAL 0340hj film! 015gsv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 62.000 42.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 0340hj film! 07nx9j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 62.000 42.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 0340hj film! 07fq1y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 42.000 0.400 http://example.org/film/actor/film./film/performance/film #4597-04dsnp PRED entity: 04dsnp PRED relation: film! PRED expected values: 061dn_ => 132 concepts (127 used for prediction) PRED predicted values (max 10 best out of 56): 05qd_ (0.54 #380, 0.38 #83, 0.30 #232), 03xq0f (0.25 #79, 0.23 #598, 0.19 #450), 086k8 (0.23 #373, 0.20 #225, 0.19 #8299), 01795t (0.22 #1279, 0.10 #1427, 0.08 #3222), 016tw3 (0.20 #234, 0.16 #1199, 0.16 #4784), 017s11 (0.18 #1413, 0.16 #2310, 0.15 #1042), 05d6kv (0.18 #317, 0.11 #168, 0.09 #687), 016tt2 (0.17 #4, 0.15 #2761, 0.14 #8301), 017jv5 (0.17 #15, 0.09 #756, 0.07 #4189), 04f525m (0.17 #10, 0.03 #2091, 0.02 #2241) >> Best rule #380 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 058kh7; >> query: (?x1015, 05qd_) <- film(?x1914, ?x1015), produced_by(?x1015, ?x3593), featured_film_locations(?x1015, ?x362), person(?x1015, ?x1620) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #764 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 09gq0x5; 04q24zv; 046488; 02w9k1c; 02h22; 0cp08zg; 03xj05; *> query: (?x1015, 061dn_) <- film(?x1914, ?x1015), category(?x1015, ?x134), genre(?x1015, ?x2605), ?x2605 = 03g3w *> conf = 0.09 ranks of expected_values: 25 EVAL 04dsnp film! 061dn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 132.000 127.000 0.538 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #4596-08m4c8 PRED entity: 08m4c8 PRED relation: nationality PRED expected values: 0345h => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 18): 02jx1 (0.12 #3907, 0.12 #429, 0.12 #3610), 03rk0 (0.10 #3722, 0.06 #9278, 0.06 #6202), 07ssc (0.09 #3889, 0.09 #3492, 0.09 #9446), 0d060g (0.06 #6559, 0.05 #3782, 0.05 #4576), 0chghy (0.03 #506, 0.02 #1498, 0.02 #1300), 03rjj (0.03 #301, 0.02 #202, 0.02 #3383), 0345h (0.03 #725, 0.02 #4401, 0.02 #6187), 03_3d (0.03 #4079, 0.01 #3185, 0.01 #9833), 06q1r (0.03 #76, 0.02 #175, 0.02 #771), 0j5g9 (0.03 #61, 0.02 #160, 0.01 #458) >> Best rule #3907 for best value: >> intensional similarity = 3 >> extensional distance = 813 >> proper extension: 05bxwh; 063472; 0hqcy; 04flrx; 03t852; 01vn0t_; 0qdwr; 06jz0; 0l9k1; 098sv2; ... >> query: (?x1918, 02jx1) <- award_nominee(?x1918, ?x848), nationality(?x1918, ?x94), people(?x1423, ?x1918) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #725 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 191 *> proper extension: 099bk; *> query: (?x1918, 0345h) <- type_of_union(?x1918, ?x566), student(?x4268, ?x1918) *> conf = 0.03 ranks of expected_values: 7 EVAL 08m4c8 nationality 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 104.000 104.000 0.118 http://example.org/people/person/nationality #4595-04cbbz PRED entity: 04cbbz PRED relation: film! PRED expected values: 030_1m => 94 concepts (80 used for prediction) PRED predicted values (max 10 best out of 73): 030_1m (0.63 #3936, 0.52 #530, 0.51 #1287), 086k8 (0.27 #152, 0.24 #456, 0.24 #228), 016tw3 (0.25 #11, 0.17 #86, 0.17 #1828), 032dg7 (0.25 #48, 0.17 #123, 0.13 #1969), 016tt2 (0.23 #306, 0.21 #3027, 0.17 #912), 037bm2 (0.21 #3027, 0.13 #1969, 0.09 #197), 05qd_ (0.19 #387, 0.19 #1674, 0.19 #539), 03xq0f (0.15 #1746, 0.10 #2656, 0.10 #3184), 0g1rw (0.13 #1969, 0.12 #1749, 0.08 #3111), 020h2v (0.13 #1969, 0.09 #195, 0.06 #499) >> Best rule #3936 for best value: >> intensional similarity = 3 >> extensional distance = 775 >> proper extension: 053tj7; 02zk08; >> query: (?x5441, ?x1561) <- film_release_distribution_medium(?x5441, ?x81), production_companies(?x5441, ?x1561), film(?x1561, ?x69) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cbbz film! 030_1m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 80.000 0.630 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #4594-018pj3 PRED entity: 018pj3 PRED relation: origin PRED expected values: 0tygl => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 79): 0tygl (0.31 #2834, 0.25 #5431, 0.07 #8503), 04jpl (0.07 #714, 0.07 #478, 0.06 #2840), 02_286 (0.07 #724, 0.04 #4738, 0.04 #4974), 030qb3t (0.05 #2631, 0.05 #506, 0.04 #8538), 03b12 (0.05 #1351, 0.03 #2295, 0.02 #1587), 0fw4v (0.05 #846), 0cc56 (0.05 #732), 0z4_0 (0.04 #226, 0.04 #462, 0.02 #698), 02dtg (0.04 #10, 0.02 #3552, 0.02 #5441), 0d6lp (0.04 #65, 0.01 #8095) >> Best rule #2834 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 07_3qd; 01p45_v; 012zng; 0285c; 0zjpz; 02jg92; 01tp5bj; 03xl77; 01m65sp; 01lcxbb; ... >> query: (?x2575, ?x6295) <- artists(?x1928, ?x2575), role(?x2575, ?x614), place_of_birth(?x2575, ?x6295) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018pj3 origin 0tygl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.311 http://example.org/music/artist/origin #4593-0l14j_ PRED entity: 0l14j_ PRED relation: role! PRED expected values: 01mxnvc => 82 concepts (56 used for prediction) PRED predicted values (max 10 best out of 755): 04mx7s (0.60 #1085, 0.57 #2807, 0.56 #3387), 016h9b (0.60 #935, 0.50 #74, 0.47 #5255), 0167v4 (0.60 #1103, 0.44 #3405, 0.40 #1390), 05qhnq (0.60 #480, 0.44 #3356, 0.40 #1341), 02jg92 (0.60 #340, 0.40 #1201, 0.33 #3216), 01lz4tf (0.60 #481, 0.29 #2777, 0.23 #4798), 0bg539 (0.50 #30, 0.44 #3193, 0.43 #2613), 08n__5 (0.50 #159, 0.43 #2742, 0.40 #446), 01vsyjy (0.50 #2207, 0.40 #1060, 0.33 #4226), 06p03s (0.50 #280, 0.25 #4018, 0.20 #5461) >> Best rule #1085 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 04rzd; >> query: (?x2944, 04mx7s) <- role(?x745, ?x2944), role(?x432, ?x2944), role(?x4429, ?x2944), role(?x1655, ?x2944), instrumentalists(?x2944, ?x8152), ?x745 = 01vj9c, ?x1655 = 01hww_, ?x8152 = 04m2zj, role(?x2944, ?x1663), ?x4429 = 0g33q, ?x432 = 042v_gx >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3430 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 7 *> proper extension: 02hnl; *> query: (?x2944, 01mxnvc) <- role(?x745, ?x2944), role(?x2725, ?x2944), role(?x2158, ?x2944), role(?x1655, ?x2944), instrumentalists(?x2944, ?x120), ?x745 = 01vj9c, ?x1655 = 01hww_, ?x2725 = 0l1589, role(?x2158, ?x780), role(?x2944, ?x1574), performance_role(?x2944, ?x2059) *> conf = 0.44 ranks of expected_values: 14 EVAL 0l14j_ role! 01mxnvc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 82.000 56.000 0.600 http://example.org/music/group_member/membership./music/group_membership/role #4592-0qcr0 PRED entity: 0qcr0 PRED relation: people PRED expected values: 02wb6d 01zwy 02x8mt 02drd3 06nsb9 02xyl => 62 concepts (24 used for prediction) PRED predicted values (max 10 best out of 879): 045g4l (0.40 #5467, 0.33 #1721, 0.29 #7340), 09qh1 (0.40 #5109, 0.33 #1363, 0.29 #6982), 0cbgl (0.33 #2494, 0.29 #7489, 0.25 #4368), 0484q (0.33 #2165, 0.25 #4039, 0.20 #5287), 06hmd (0.33 #2067, 0.25 #3941, 0.20 #5189), 07_m9_ (0.33 #2034, 0.25 #3908, 0.20 #5156), 0p9gg (0.33 #2468, 0.25 #4342, 0.20 #5590), 08gyz_ (0.33 #2447, 0.25 #4321, 0.20 #5569), 01h2_6 (0.33 #2442, 0.25 #4316, 0.20 #5564), 07_m2 (0.33 #2349, 0.25 #4223, 0.20 #5471) >> Best rule #5467 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 02y0js; >> query: (?x268, 045g4l) <- people(?x268, ?x9170), people(?x268, ?x6688), people(?x268, ?x6342), people(?x268, ?x5894), people(?x268, ?x366), nominated_for(?x5894, ?x2943), sibling(?x5894, ?x12933), gender(?x9170, ?x231), music(?x278, ?x9170), award_winner(?x1288, ?x6688), religion(?x6342, ?x2694), instrumentalists(?x227, ?x366) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qcr0 people 02xyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 24.000 0.400 http://example.org/people/cause_of_death/people EVAL 0qcr0 people 06nsb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 24.000 0.400 http://example.org/people/cause_of_death/people EVAL 0qcr0 people 02drd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 24.000 0.400 http://example.org/people/cause_of_death/people EVAL 0qcr0 people 02x8mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 24.000 0.400 http://example.org/people/cause_of_death/people EVAL 0qcr0 people 01zwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 24.000 0.400 http://example.org/people/cause_of_death/people EVAL 0qcr0 people 02wb6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 24.000 0.400 http://example.org/people/cause_of_death/people #4591-03xx3m PRED entity: 03xx3m PRED relation: student! PRED expected values: 02bqy => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 88): 01w5m (0.11 #2213, 0.05 #8537, 0.05 #6956), 065y4w7 (0.10 #14, 0.04 #4230, 0.03 #8973), 025v3k (0.10 #120, 0.02 #1701, 0.02 #4863), 016wyn (0.10 #233), 0ylvj (0.10 #201), 06thjt (0.08 #1979, 0.04 #2506, 0.04 #3560), 0bwfn (0.06 #4491, 0.06 #18720, 0.04 #41388), 015nl4 (0.04 #2702, 0.04 #3756, 0.04 #5864), 06182p (0.04 #825, 0.04 #1352, 0.04 #1879), 017j69 (0.04 #672, 0.04 #1199, 0.03 #2253) >> Best rule #2213 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 0cl_m; >> query: (?x7454, 01w5m) <- place_of_death(?x7454, ?x739), gender(?x7454, ?x231), ?x739 = 02_286 >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #2290 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 0cl_m; *> query: (?x7454, 02bqy) <- place_of_death(?x7454, ?x739), gender(?x7454, ?x231), ?x739 = 02_286 *> conf = 0.01 ranks of expected_values: 73 EVAL 03xx3m student! 02bqy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 110.000 110.000 0.107 http://example.org/education/educational_institution/students_graduates./education/education/student #4590-0fp_v1x PRED entity: 0fp_v1x PRED relation: currency PRED expected values: 09nqf => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.56 #4, 0.50 #7, 0.37 #193), 01nv4h (0.12 #20, 0.11 #38, 0.10 #62), 02l6h (0.03 #48, 0.01 #126) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 03f19q4; 0bqvs2; >> query: (?x460, 09nqf) <- artist(?x10882, ?x460), nationality(?x460, ?x94), ?x10882 = 02p4jf0, award(?x460, ?x1443) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fp_v1x currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 179.000 0.556 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #4589-09bjv PRED entity: 09bjv PRED relation: category PRED expected values: 08mbj5d => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.71 #16, 0.67 #29, 0.66 #33) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 01fy2s; >> query: (?x461, 08mbj5d) <- contains(?x7413, ?x461), time_zones(?x7413, ?x10735), ?x10735 = 03plfd >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09bjv category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.711 http://example.org/common/topic/webpage./common/webpage/category #4588-01v3rb PRED entity: 01v3rb PRED relation: citytown PRED expected values: 0f04v => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 232): 02_286 (0.61 #20979, 0.51 #24659, 0.46 #27239), 04jpl (0.45 #9927, 0.41 #13966, 0.39 #10661), 024bqj (0.35 #38257, 0.31 #26118, 0.28 #7182), 0hsqf (0.35 #38257, 0.31 #26118, 0.26 #54435), 0h7h6 (0.35 #38257, 0.31 #26118, 0.26 #54435), 0chgzm (0.35 #38257, 0.31 #26118, 0.26 #54435), 030qb3t (0.26 #54435, 0.26 #54434, 0.26 #54065), 01xhb_ (0.26 #54435, 0.26 #54434, 0.26 #54065), 0r6cx (0.26 #54435, 0.26 #54434, 0.26 #54065), 06y57 (0.26 #54435, 0.26 #54434, 0.26 #54065) >> Best rule #20979 for best value: >> intensional similarity = 7 >> extensional distance = 153 >> proper extension: 02301; 06xpp7; 027kmrb; 024rdh; 095kp; 04b_46; 03m9c8; 0dbpwb; 016ckq; 02vptk_; ... >> query: (?x14437, 02_286) <- citytown(?x14437, ?x9559), film_release_region(?x10860, ?x9559), film_release_region(?x4950, ?x9559), month(?x9559, ?x1459), location(?x2306, ?x9559), film_release_distribution_medium(?x10860, ?x81), ?x4950 = 07k2mq >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #28478 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 206 *> proper extension: 02jxk; 041288; *> query: (?x14437, 0f04v) <- citytown(?x14437, ?x9559), month(?x9559, ?x9905), month(?x9559, ?x3107), place_of_birth(?x256, ?x9559), ?x9905 = 028kb, ?x3107 = 05lf_, country(?x9559, ?x252) *> conf = 0.02 ranks of expected_values: 89 EVAL 01v3rb citytown 0f04v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 154.000 154.000 0.606 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #4587-047g98 PRED entity: 047g98 PRED relation: colors PRED expected values: 06fvc => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.82 #1262, 0.81 #1724, 0.80 #1743), 019sc (0.49 #1501, 0.42 #1248, 0.31 #1000), 06fvc (0.42 #1611, 0.42 #478, 0.39 #1687), 01l849 (0.33 #438, 0.27 #1185, 0.15 #917), 088fh (0.24 #842, 0.17 #1378, 0.17 #1377), 09ggk (0.17 #1378, 0.17 #1377, 0.17 #1376), 038hg (0.17 #69, 0.15 #917, 0.15 #916), 04mkbj (0.17 #67, 0.15 #917, 0.15 #916), 0jc_p (0.15 #917, 0.15 #916, 0.15 #915), 02rnmb (0.15 #917, 0.15 #916, 0.15 #915) >> Best rule #1262 for best value: >> intensional similarity = 10 >> extensional distance = 121 >> proper extension: 04088s0; 026xxv_; 01lpx8; >> query: (?x10444, 083jv) <- colors(?x10444, ?x3189), team(?x2201, ?x10444), colors(?x8822, ?x3189), colors(?x3394, ?x3189), colors(?x10026, ?x3189), colors(?x5292, ?x3189), ?x3394 = 02607j, ?x5292 = 04zw9hs, ?x8822 = 020ddc, team(?x60, ?x10026) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1611 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 188 *> proper extension: 019lty; 01xn5th; 04mvk7; 059nf5; 0329gm; 0dkb83; 02vpvk; 0346qt; 03j0ss; 07sqbl; ... *> query: (?x10444, 06fvc) <- colors(?x10444, ?x3189), position(?x10444, ?x60), colors(?x10838, ?x3189), colors(?x7596, ?x3189), ?x10838 = 016sd3, school_type(?x7596, ?x1044), student(?x7596, ?x5222) *> conf = 0.42 ranks of expected_values: 3 EVAL 047g98 colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 116.000 0.821 http://example.org/sports/sports_team/colors #4586-0427y PRED entity: 0427y PRED relation: languages PRED expected values: 02h40lc => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 18): 02h40lc (0.28 #158, 0.27 #275, 0.26 #2537), 064_8sq (0.05 #444, 0.04 #171, 0.04 #600), 06b_j (0.04 #3239, 0.03 #445, 0.01 #1108), 03hkp (0.04 #3239, 0.02 #244, 0.01 #439), 0t_2 (0.04 #3239, 0.02 #243), 032f6 (0.04 #3239), 0880p (0.04 #3239), 06nm1 (0.03 #435, 0.01 #474, 0.01 #1995), 06mp7 (0.02 #167, 0.02 #284, 0.01 #401), 03k50 (0.02 #2539, 0.02 #2071, 0.02 #3203) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 02p21g; >> query: (?x9596, 02h40lc) <- influenced_by(?x2534, ?x9596), award_winner(?x5592, ?x9596), award_winner(?x747, ?x2534) >> conf = 0.28 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0427y languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.277 http://example.org/people/person/languages #4585-076tq0z PRED entity: 076tq0z PRED relation: production_companies PRED expected values: 054lpb6 => 80 concepts (60 used for prediction) PRED predicted values (max 10 best out of 49): 016tw3 (0.34 #417, 0.32 #2594, 0.32 #3518), 054lpb6 (0.11 #181, 0.10 #98, 0.08 #348), 017s11 (0.10 #3, 0.09 #252, 0.09 #169), 05qd_ (0.09 #10, 0.09 #1181, 0.09 #427), 086k8 (0.09 #586, 0.08 #502, 0.08 #2512), 016tt2 (0.08 #588, 0.07 #1175, 0.07 #1509), 01gb54 (0.07 #789, 0.06 #705, 0.06 #38), 024rgt (0.06 #108, 0.06 #25, 0.06 #274), 02j_j0 (0.06 #48, 0.05 #131, 0.04 #214), 06rq1k (0.06 #101, 0.06 #184, 0.06 #351) >> Best rule #417 for best value: >> intensional similarity = 4 >> extensional distance = 214 >> proper extension: 0hmr4; 0prrm; 02mc5v; 0f8j13; 03wjm2; >> query: (?x2846, ?x1104) <- titles(?x2480, ?x2846), film(?x2551, ?x2846), ?x2480 = 01z4y, film(?x1104, ?x2846) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #181 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 187 *> proper extension: 0ddfwj1; 09tqkv2; 05q4y12; 02ht1k; 02rmd_2; 01qvz8; 0dln8jk; 06__m6; 05fm6m; 0888c3; ... *> query: (?x2846, 054lpb6) <- titles(?x2480, ?x2846), film(?x2551, ?x2846), film_crew_role(?x2846, ?x137), ?x2480 = 01z4y *> conf = 0.11 ranks of expected_values: 2 EVAL 076tq0z production_companies 054lpb6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 60.000 0.335 http://example.org/film/film/production_companies #4584-01mqz0 PRED entity: 01mqz0 PRED relation: gender PRED expected values: 02zsn => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.78 #4, 0.65 #28, 0.62 #2), 05zppz (0.72 #189, 0.72 #131, 0.72 #194) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 027f7dj; 02lxj_; 0f4vbz; 013knm; 02vntj; 02kxwk; 02x7vq; 04qsdh; 04znsy; 02ktrs; >> query: (?x1607, 02zsn) <- award_winner(?x2257, ?x1607), ?x2257 = 09td7p, award(?x1607, ?x686) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mqz0 gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.778 http://example.org/people/person/gender #4583-081t6 PRED entity: 081t6 PRED relation: politician! PRED expected values: 07wbk => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 22): 0d075m (0.56 #172, 0.53 #75, 0.52 #316), 07wbk (0.38 #97, 0.37 #266, 0.35 #578), 07wf9 (0.28 #175, 0.24 #319, 0.22 #223), 07wgm (0.17 #207, 0.14 #351, 0.11 #423), 0136kr (0.14 #34, 0.06 #130, 0.05 #299), 07wdw (0.10 #296, 0.07 #416, 0.07 #488), 049tb (0.07 #417, 0.07 #489, 0.06 #513), 01c9x (0.07 #461, 0.06 #124, 0.05 #269), 0135dr (0.06 #691, 0.05 #859, 0.03 #547), 05g9h (0.06 #161, 0.03 #546, 0.02 #570) >> Best rule #172 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 083p7; 083q7; 083pr; 09bg4l; 07cbs; 0dq2k; 06c97; 0rlz; 03txms; 03_nq; ... >> query: (?x12928, 0d075m) <- people(?x4322, ?x12928), basic_title(?x12928, ?x346), religion(?x12928, ?x2769), people(?x4195, ?x12928) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 028rk; *> query: (?x12928, 07wbk) <- people(?x4322, ?x12928), profession(?x12928, ?x5805), ?x5805 = 0fj9f, taxonomy(?x12928, ?x939) *> conf = 0.38 ranks of expected_values: 2 EVAL 081t6 politician! 07wbk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.556 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #4582-0661ql3 PRED entity: 0661ql3 PRED relation: language PRED expected values: 064_8sq => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 38): 064_8sq (0.22 #310, 0.20 #253, 0.18 #888), 06nm1 (0.13 #299, 0.12 #473, 0.10 #1281), 04306rv (0.12 #351, 0.12 #872, 0.10 #237), 02bjrlw (0.09 #291, 0.07 #348, 0.07 #1273), 06b_j (0.07 #600, 0.06 #889, 0.06 #1293), 0653m (0.06 #10, 0.05 #67, 0.05 #415), 03k50 (0.06 #8, 0.03 #123, 0.03 #241), 0t_2 (0.06 #12, 0.02 #127, 0.02 #186), 0jzc (0.05 #251, 0.04 #365, 0.04 #1864), 032f6 (0.04 #228, 0.03 #169, 0.03 #459) >> Best rule #310 for best value: >> intensional similarity = 5 >> extensional distance = 114 >> proper extension: 0c_j9x; 09p7fh; >> query: (?x2394, 064_8sq) <- nominated_for(?x2379, ?x2394), nominated_for(?x1307, ?x2394), award(?x2394, ?x500), ?x2379 = 02qvyrt, award(?x71, ?x1307) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0661ql3 language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.216 http://example.org/film/film/language #4581-01t_xp_ PRED entity: 01t_xp_ PRED relation: group! PRED expected values: 018vs => 103 concepts (77 used for prediction) PRED predicted values (max 10 best out of 121): 0342h (0.92 #3280, 0.91 #3108, 0.91 #2936), 018vs (0.74 #1132, 0.71 #1391, 0.70 #442), 013y1f (0.50 #370, 0.50 #112, 0.20 #456), 03qjg (0.50 #390, 0.40 #476, 0.29 #1425), 05r5c (0.50 #351, 0.40 #437, 0.25 #93), 0mkg (0.50 #353, 0.25 #95, 0.12 #3891), 028tv0 (0.48 #1217, 0.43 #3114, 0.43 #2942), 0l14qv (0.42 #1126, 0.39 #1385, 0.38 #696), 06ncr (0.38 #381, 0.25 #123, 0.20 #467), 02k84w (0.38 #373, 0.25 #115, 0.20 #459) >> Best rule #3280 for best value: >> intensional similarity = 7 >> extensional distance = 113 >> proper extension: 06br6t; >> query: (?x442, 0342h) <- group(?x314, ?x442), artists(?x1380, ?x442), artists(?x1380, ?x11551), artists(?x1380, ?x8849), artist(?x2931, ?x8849), instrumentalists(?x212, ?x8849), ?x11551 = 0cfgd >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1132 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 29 *> proper extension: 0qmpd; *> query: (?x442, 018vs) <- group(?x314, ?x442), artists(?x1572, ?x442), artists(?x1380, ?x442), ?x1380 = 0dl5d, artists(?x1572, ?x4162), artists(?x1572, ?x3516), ?x3516 = 05563d, ?x4162 = 01wy61y *> conf = 0.74 ranks of expected_values: 2 EVAL 01t_xp_ group! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 77.000 0.922 http://example.org/music/performance_role/regular_performances./music/group_membership/group #4580-05hmp6 PRED entity: 05hmp6 PRED relation: honored_for PRED expected values: 0jswp => 34 concepts (19 used for prediction) PRED predicted values (max 10 best out of 567): 0gxfz (0.33 #1195, 0.33 #161, 0.25 #1357), 0bykpk (0.33 #370, 0.27 #4187, 0.25 #1566), 019kyn (0.33 #280, 0.25 #1476, 0.20 #2072), 0n83s (0.33 #1195, 0.15 #8974, 0.13 #5985), 0cq8nx (0.33 #1195, 0.15 #8974, 0.13 #5985), 0bbgly (0.33 #1195, 0.15 #8974, 0.13 #5985), 070fnm (0.33 #1195, 0.15 #8974, 0.13 #5985), 083skw (0.33 #1195, 0.13 #5985, 0.13 #5386), 02q52q (0.33 #1195, 0.13 #5985, 0.13 #5386), 02x0fs9 (0.33 #1195, 0.13 #5985, 0.13 #5386) >> Best rule #1195 for best value: >> intensional similarity = 15 >> extensional distance = 2 >> proper extension: 0c4hgj; >> query: (?x6323, ?x1804) <- ceremony(?x1972, ?x6323), ceremony(?x720, ?x6323), ?x1972 = 0gqyl, award_winner(?x6323, ?x4926), award_winner(?x6323, ?x3519), award_winner(?x6323, ?x788), ?x720 = 018wng, award_nominee(?x788, ?x1850), nominated_for(?x788, ?x1804), category(?x788, ?x134), location(?x4926, ?x7058), award_nominee(?x1850, ?x269), award_winner(?x5183, ?x4926), nominated_for(?x1850, ?x1308), ?x3519 = 02sj1x >> conf = 0.33 => this is the best rule for 9 predicted values *> Best rule #4789 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 26 *> proper extension: 04110lv; *> query: (?x6323, ?x5183) <- ceremony(?x2209, ?x6323), ceremony(?x1972, ?x6323), ceremony(?x720, ?x6323), ?x1972 = 0gqyl, award_winner(?x6323, ?x4926), award_winner(?x6323, ?x788), award(?x382, ?x720), award_winner(?x5183, ?x4926), religion(?x4926, ?x1985), award_nominee(?x788, ?x1850), category(?x788, ?x134), award_winner(?x788, ?x9928), ?x2209 = 0gr42 *> conf = 0.21 ranks of expected_values: 26 EVAL 05hmp6 honored_for 0jswp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 34.000 19.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #4579-0fby2t PRED entity: 0fby2t PRED relation: participant! PRED expected values: 07cjqy => 111 concepts (70 used for prediction) PRED predicted values (max 10 best out of 326): 07cjqy (0.80 #27247, 0.80 #25979, 0.03 #4047), 0237fw (0.17 #1431, 0.05 #3963, 0.05 #7133), 01pcvn (0.17 #1645, 0.05 #4177, 0.03 #8616), 01vvb4m (0.17 #1481, 0.01 #16691), 0fthdk (0.17 #2453), 016j2t (0.17 #2452), 01yhvv (0.17 #1990), 07rd7 (0.17 #1562), 014q2g (0.17 #1459), 0kszw (0.17 #1437) >> Best rule #27247 for best value: >> intensional similarity = 3 >> extensional distance = 536 >> proper extension: 01hxs4; 01t6b4; 027cxsm; 012x4t; 086qd; 0gcs9; 0d06m5; 02bwc7; 012dr7; 0kxbc; ... >> query: (?x4325, ?x364) <- profession(?x4325, ?x319), award(?x4325, ?x112), participant(?x4325, ?x364) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fby2t participant! 07cjqy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 70.000 0.799 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #4578-02y_2y PRED entity: 02y_2y PRED relation: gender PRED expected values: 05zppz => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #37, 0.80 #31, 0.80 #33), 02zsn (0.44 #2, 0.41 #26, 0.41 #24) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 354 >> proper extension: 01q7cb_; 04l3_z; 01p45_v; 03h502k; 01gj8_; 0c8hct; 01z7s_; 07r4c; 01tv3x2; 01fxck; ... >> query: (?x4470, 05zppz) <- profession(?x4470, ?x524), location(?x4470, ?x1523), ?x524 = 02jknp >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y_2y gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.834 http://example.org/people/person/gender #4577-07b3r9 PRED entity: 07b3r9 PRED relation: producer_type PRED expected values: 0ckd1 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.72 #1, 0.72 #5, 0.70 #7) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 01pw2f1; 03ft8; 023jq1; >> query: (?x4383, 0ckd1) <- program(?x4383, ?x4384), place_of_birth(?x4383, ?x682), religion(?x4383, ?x1985) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07b3r9 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.724 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #4576-026dqjm PRED entity: 026dqjm PRED relation: team! PRED expected values: 05g_nr 0b_6s7 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 15): 0b_75k (0.82 #560, 0.77 #635, 0.71 #365), 0b_6xf (0.77 #659, 0.75 #419, 0.73 #569), 0b_6zk (0.75 #753, 0.73 #557, 0.70 #677), 0b_71r (0.75 #415, 0.70 #677, 0.67 #235), 0b_6_l (0.73 #568, 0.70 #677, 0.69 #673), 0b_770 (0.71 #375, 0.70 #677, 0.67 #285), 0b_756 (0.70 #677, 0.67 #281, 0.62 #762), 0b_6s7 (0.70 #677, 0.67 #234, 0.62 #639), 0br1x_ (0.70 #677, 0.64 #561, 0.62 #757), 0bzrxn (0.70 #677, 0.64 #562, 0.62 #637) >> Best rule #560 for best value: >> intensional similarity = 14 >> extensional distance = 9 >> proper extension: 02pqcfz; 02qk2d5; >> query: (?x12370, 0b_75k) <- team(?x9974, ?x12370), team(?x9908, ?x12370), ?x9974 = 0b_6pv, team(?x9908, ?x9983), team(?x9908, ?x9909), team(?x9908, ?x2303), locations(?x9908, ?x6952), locations(?x9908, ?x6088), position(?x12370, ?x4570), ?x9983 = 02q4ntp, ?x6088 = 0dyl9, ?x9909 = 026wlnm, ?x2303 = 02plv57, contains(?x94, ?x6952) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #677 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 11 *> proper extension: 02pjzvh; *> query: (?x12370, ?x4803) <- team(?x9974, ?x12370), team(?x9956, ?x12370), team(?x9974, ?x9983), team(?x9974, ?x9975), team(?x9974, ?x9576), team(?x9974, ?x9147), team(?x9974, ?x6803), ?x9983 = 02q4ntp, ?x9975 = 03d5m8w, ?x9956 = 0bzrsh, ?x6803 = 03by7wc, locations(?x9974, ?x674), sport(?x9147, ?x12913), ?x9576 = 02qk2d5, team(?x4803, ?x9147) *> conf = 0.70 ranks of expected_values: 8, 11 EVAL 026dqjm team! 0b_6s7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 111.000 111.000 0.818 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 026dqjm team! 05g_nr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 111.000 111.000 0.818 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #4575-09xwz PRED entity: 09xwz PRED relation: organizations_founded! PRED expected values: 04rfq => 165 concepts (155 used for prediction) PRED predicted values (max 10 best out of 251): 01y8cr (0.50 #2367, 0.48 #2908, 0.48 #2800), 081nh (0.44 #1316, 0.25 #243, 0.20 #564), 01vhrz (0.33 #73, 0.22 #4598, 0.20 #395), 04411 (0.33 #763, 0.20 #548, 0.20 #441), 06pj8 (0.33 #25, 0.20 #347, 0.15 #3687), 0343h (0.30 #1525, 0.30 #1416, 0.21 #2387), 07cbs (0.29 #3926, 0.23 #5223, 0.16 #6411), 04rfq (0.25 #319, 0.11 #1392, 0.10 #1609), 026ck (0.25 #314, 0.11 #1387, 0.10 #1604), 01lc5 (0.25 #308, 0.11 #1381, 0.10 #1598) >> Best rule #2367 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0gsg7; 06rq1k; 061dn_; 032j_n; 059x3p; >> query: (?x11706, ?x4279) <- organizations_founded(?x8942, ?x11706), company(?x4279, ?x11706), award_winner(?x5697, ?x8942), gender(?x4279, ?x231), award_winner(?x1193, ?x4279) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #319 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 017jv5; *> query: (?x11706, 04rfq) <- organizations_founded(?x8942, ?x11706), company(?x4279, ?x11706), place_of_death(?x8942, ?x682), profession(?x8942, ?x524), place_of_burial(?x8942, ?x11261) *> conf = 0.25 ranks of expected_values: 8 EVAL 09xwz organizations_founded! 04rfq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 165.000 155.000 0.500 http://example.org/organization/organization_founder/organizations_founded #4574-09lhln PRED entity: 09lhln PRED relation: profession PRED expected values: 0gl2ny2 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 134): 0gl2ny2 (0.67 #1419, 0.66 #969, 0.66 #1869), 02hrh1q (0.61 #3916, 0.59 #5866, 0.58 #10521), 0dxtg (0.27 #3615, 0.27 #5715, 0.26 #3765), 01d_h8 (0.27 #5707, 0.27 #7211, 0.27 #6457), 09jwl (0.23 #4371, 0.22 #3621, 0.22 #3771), 02jknp (0.21 #4509, 0.20 #4959, 0.20 #4809), 03gjzk (0.19 #6017, 0.18 #6317, 0.18 #6467), 01445t (0.18 #1524, 0.16 #3325, 0.15 #2874), 0cbd2 (0.16 #4058, 0.16 #3608, 0.15 #3758), 0kyk (0.16 #3632, 0.15 #3782, 0.14 #4082) >> Best rule #1419 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 05_6_y; 0bn9sc; 02d9k; 083qy7; 02vl_pz; 09l9xt; 02y9ln; 02v_4xv; 026n047; 09r1j5; ... >> query: (?x4172, 0gl2ny2) <- team(?x4172, ?x8195), gender(?x4172, ?x231), position(?x8195, ?x60), position(?x8195, ?x530) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09lhln profession 0gl2ny2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.672 http://example.org/people/person/profession #4573-06gn7r PRED entity: 06gn7r PRED relation: nationality PRED expected values: 03rk0 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 62): 03rk0 (0.83 #446, 0.77 #1146, 0.75 #46), 09c7w0 (0.74 #6023, 0.74 #4715, 0.74 #8635), 086g2 (0.31 #8031, 0.28 #12858, 0.28 #10643), 0cvw9 (0.25 #10041, 0.25 #9237, 0.17 #11245), 02jx1 (0.12 #633, 0.12 #1233, 0.11 #1634), 07ssc (0.11 #1215, 0.09 #8950, 0.09 #2920), 05sb1 (0.08 #148, 0.07 #248, 0.06 #6324), 0h7x (0.08 #1936, 0.05 #635, 0.05 #3542), 0f8l9c (0.07 #622, 0.05 #822, 0.04 #4434), 0345h (0.06 #3538, 0.05 #1932, 0.04 #3036) >> Best rule #446 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 04b19t; >> query: (?x8296, 03rk0) <- profession(?x8296, ?x524), languages(?x8296, ?x1882), ?x524 = 02jknp, ?x1882 = 03k50 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06gn7r nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.833 http://example.org/people/person/nationality #4572-02pprs PRED entity: 02pprs PRED relation: role PRED expected values: 02sgy => 77 concepts (44 used for prediction) PRED predicted values (max 10 best out of 104): 0214km (0.85 #3760, 0.85 #3745, 0.84 #416), 01vdm0 (0.85 #3681, 0.84 #3168, 0.84 #1870), 013y1f (0.84 #1870, 0.84 #416, 0.83 #1868), 02sgy (0.84 #1870, 0.84 #416, 0.83 #1868), 0l14qv (0.84 #1870, 0.84 #416, 0.83 #1868), 018vs (0.84 #1870, 0.84 #416, 0.83 #1868), 03qmg1 (0.84 #1870, 0.84 #416, 0.83 #1868), 03t22m (0.84 #1870, 0.84 #416, 0.83 #1868), 0dwvl (0.84 #1870, 0.84 #416, 0.83 #1868), 05kms (0.84 #416, 0.83 #1868, 0.83 #1456) >> Best rule #3760 for best value: >> intensional similarity = 15 >> extensional distance = 24 >> proper extension: 01c3q; >> query: (?x214, ?x8014) <- role(?x120, ?x214), role(?x10843, ?x214), role(?x8014, ?x214), role(?x316, ?x214), role(?x1524, ?x214), ?x8014 = 0214km, performance_role(?x1432, ?x10843), instrumentalists(?x316, ?x5312), role(?x483, ?x316), role(?x3168, ?x316), ?x3168 = 016ntp, group(?x316, ?x997), role(?x645, ?x316), ?x645 = 028tv0, gender(?x5312, ?x514) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1870 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 7 *> proper extension: 0l15bq; *> query: (?x214, ?x3239) <- role(?x3492, ?x214), role(?x547, ?x214), role(?x3991, ?x214), role(?x3239, ?x214), role(?x1437, ?x214), role(?x868, ?x214), role(?x316, ?x214), role(?x214, ?x3703), role(?x214, ?x1166), ?x3991 = 05842k, ?x316 = 05r5c, ?x1437 = 01vdm0, ?x1166 = 05148p4, role(?x3716, ?x3239), artists(?x505, ?x547), artist(?x382, ?x547), ?x868 = 0dwvl, ?x3716 = 03gvt, gender(?x3492, ?x231), role(?x885, ?x3239), performance_role(?x1260, ?x3703) *> conf = 0.84 ranks of expected_values: 4 EVAL 02pprs role 02sgy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 44.000 0.846 http://example.org/music/performance_role/track_performances./music/track_contribution/role #4571-0l2l_ PRED entity: 0l2l_ PRED relation: currency PRED expected values: 09nqf => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.85 #15, 0.85 #46, 0.84 #51) >> Best rule #15 for best value: >> intensional similarity = 5 >> extensional distance = 58 >> proper extension: 0cb4j; 0mlxt; >> query: (?x3262, 09nqf) <- second_level_divisions(?x94, ?x3262), ?x94 = 09c7w0, contains(?x1227, ?x3262), time_zones(?x3262, ?x2950), ?x2950 = 02lcqs >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l2l_ currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.850 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #4570-01vrwfv PRED entity: 01vrwfv PRED relation: group! PRED expected values: 01vj9c 07gql => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 110): 03bx0bm (0.60 #354, 0.59 #1765, 0.58 #1932), 01vj9c (0.40 #343, 0.30 #509, 0.28 #1837), 04rzd (0.40 #278, 0.14 #1855, 0.14 #1772), 028tv0 (0.38 #1753, 0.36 #2252, 0.36 #1836), 0l14j_ (0.35 #546, 0.11 #2290, 0.11 #712), 06ncr (0.30 #534, 0.20 #368, 0.20 #285), 07gql (0.25 #532, 0.11 #698, 0.10 #1279), 05r5c (0.24 #1251, 0.24 #1916, 0.23 #2248), 07y_7 (0.20 #500, 0.20 #334, 0.11 #2244), 013y1f (0.20 #274, 0.14 #1270, 0.14 #1851) >> Best rule #354 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 014kyy; >> query: (?x2901, 03bx0bm) <- artists(?x6210, ?x2901), artists(?x5300, ?x2901), group(?x227, ?x2901), ?x6210 = 01fh36, ?x5300 = 02k_kn >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #343 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 014kyy; *> query: (?x2901, 01vj9c) <- artists(?x6210, ?x2901), artists(?x5300, ?x2901), group(?x227, ?x2901), ?x6210 = 01fh36, ?x5300 = 02k_kn *> conf = 0.40 ranks of expected_values: 2, 7 EVAL 01vrwfv group! 07gql CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 99.000 99.000 0.600 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01vrwfv group! 01vj9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.600 http://example.org/music/performance_role/regular_performances./music/group_membership/group #4569-0jdr0 PRED entity: 0jdr0 PRED relation: film_crew_role PRED expected values: 09zzb8 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.75 #458, 0.74 #1151, 0.72 #1112), 0ch6mp2 (0.74 #1159, 0.71 #2389, 0.71 #466), 09vw2b7 (0.64 #46, 0.61 #1158, 0.59 #465), 02r96rf (0.62 #1154, 0.62 #2384, 0.57 #499), 0dxtw (0.44 #470, 0.42 #13, 0.36 #1163), 01vx2h (0.29 #1164, 0.29 #2394, 0.27 #1434), 02ynfr (0.24 #475, 0.17 #1129, 0.15 #1168), 02rh1dz (0.17 #12, 0.14 #50, 0.13 #126), 0d2b38 (0.17 #28, 0.14 #66, 0.10 #1178), 04pyp5 (0.17 #19, 0.14 #57, 0.09 #476) >> Best rule #458 for best value: >> intensional similarity = 5 >> extensional distance = 112 >> proper extension: 0415ggl; >> query: (?x9349, 09zzb8) <- genre(?x9349, ?x53), written_by(?x9349, ?x11271), nominated_for(?x10758, ?x9349), film_crew_role(?x9349, ?x2178), films(?x10849, ?x9349) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jdr0 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.754 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4568-0885n PRED entity: 0885n PRED relation: student PRED expected values: 067pl7 => 170 concepts (92 used for prediction) PRED predicted values (max 10 best out of 871): 012j5h (0.22 #1057, 0.18 #3150, 0.15 #5243), 0gd5z (0.11 #382, 0.09 #2475, 0.08 #4568), 031v3p (0.11 #2006, 0.09 #4099, 0.08 #6192), 0ywqc (0.11 #1798, 0.09 #3891, 0.08 #5984), 01k165 (0.11 #496, 0.09 #2589, 0.08 #4682), 05d1dy (0.11 #1176, 0.09 #3269, 0.08 #5362), 026fd (0.11 #1030, 0.09 #3123, 0.08 #5216), 012rng (0.11 #719, 0.09 #2812, 0.08 #4905), 01vqrm (0.11 #621, 0.09 #2714, 0.08 #4807), 0p_2r (0.11 #220, 0.09 #2313, 0.08 #4406) >> Best rule #1057 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05cl8y; 05frqx; >> query: (?x7066, 012j5h) <- state_province_region(?x7066, ?x1905), citytown(?x7066, ?x1658), ?x1658 = 0h7h6 >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0885n student 067pl7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 170.000 92.000 0.222 http://example.org/education/educational_institution/students_graduates./education/education/student #4567-0jrv_ PRED entity: 0jrv_ PRED relation: artists PRED expected values: 0274ck 01wt4wc 016m5c => 62 concepts (21 used for prediction) PRED predicted values (max 10 best out of 979): 01shhf (0.67 #6208, 0.44 #12622, 0.43 #8344), 01t_xp_ (0.67 #5366, 0.40 #4298, 0.33 #3230), 0jn38 (0.67 #6134, 0.40 #5066, 0.33 #3998), 0889x (0.60 #5299, 0.50 #6367, 0.43 #8503), 016m5c (0.60 #5298, 0.43 #8502, 0.33 #6366), 01j59b0 (0.56 #12225, 0.50 #10085, 0.43 #7947), 01386_ (0.50 #5920, 0.43 #8056, 0.40 #4852), 0p76z (0.50 #6251, 0.40 #5183, 0.35 #14805), 01wt4wc (0.50 #6067, 0.40 #4999, 0.33 #15691), 0pkyh (0.50 #5582, 0.40 #4514, 0.33 #3446) >> Best rule #6208 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 0dl5d; 0hdf8; >> query: (?x10930, 01shhf) <- artists(?x10930, ?x12246), artists(?x10930, ?x5437), parent_genre(?x10930, ?x2249), artist(?x4483, ?x12246), award(?x12246, ?x6126), group(?x1750, ?x12246), ?x1750 = 02hnl, origin(?x12246, ?x739), ?x5437 = 027dpx >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5298 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 0xhtw; 07bbw; *> query: (?x10930, 016m5c) <- artists(?x10930, ?x12246), artists(?x10930, ?x11635), artists(?x10930, ?x11627), artists(?x10930, ?x8640), parent_genre(?x10930, ?x2249), ?x12246 = 0bsj9, parent_genre(?x5580, ?x10930), ?x8640 = 020hh3, ?x11635 = 01nrz4, artists(?x5934, ?x11627), ?x5934 = 05r6t *> conf = 0.60 ranks of expected_values: 5, 9, 303 EVAL 0jrv_ artists 016m5c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 21.000 0.667 http://example.org/music/genre/artists EVAL 0jrv_ artists 01wt4wc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 62.000 21.000 0.667 http://example.org/music/genre/artists EVAL 0jrv_ artists 0274ck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 21.000 0.667 http://example.org/music/genre/artists #4566-0crvfq PRED entity: 0crvfq PRED relation: type_of_union PRED expected values: 04ztj => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.71 #65, 0.70 #69, 0.70 #202), 01g63y (0.47 #89, 0.14 #6, 0.14 #10) >> Best rule #65 for best value: >> intensional similarity = 2 >> extensional distance = 1155 >> proper extension: 04n_g; 05typm; 01bcq; 02756j; 0265z9l; 0gm34; 012gbb; 0jvtp; 01h4rj; 03k545; ... >> query: (?x7913, 04ztj) <- award_winner(?x704, ?x7913), film(?x7913, ?x1820) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0crvfq type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.714 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4565-022840 PRED entity: 022840 PRED relation: taxonomy PRED expected values: 04n6k => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.45 #10, 0.43 #7, 0.40 #15) >> Best rule #10 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 018w0j; >> query: (?x7734, 04n6k) <- films(?x7734, ?x5304), locations(?x7734, ?x94), combatants(?x7734, ?x8866), titles(?x53, ?x5304), film(?x1657, ?x5304), cinematography(?x5304, ?x7249) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022840 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.455 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #4564-0lrh PRED entity: 0lrh PRED relation: influenced_by PRED expected values: 032md 03jxw => 195 concepts (100 used for prediction) PRED predicted values (max 10 best out of 407): 040_9 (0.33 #2229, 0.33 #522, 0.33 #95), 084w8 (0.33 #2137, 0.33 #430, 0.25 #5976), 0lrh (0.33 #926, 0.33 #72, 0.25 #3061), 0zm1 (0.33 #978, 0.33 #551, 0.17 #2258), 014635 (0.33 #2241, 0.33 #534, 0.11 #20608), 037jz (0.33 #631, 0.20 #20705, 0.17 #3620), 06whf (0.33 #122, 0.20 #6095, 0.10 #20623), 01tz6vs (0.33 #599, 0.17 #2306, 0.16 #20673), 06kb_ (0.33 #580, 0.17 #2287, 0.08 #20654), 0gs7x (0.33 #822, 0.17 #2529, 0.08 #20499) >> Best rule #2229 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0j_c; >> query: (?x2845, 040_9) <- participant(?x1089, ?x2845), influenced_by(?x2845, ?x1029), influenced_by(?x117, ?x2845), people(?x9898, ?x2845) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6305 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 0683n; *> query: (?x2845, 03jxw) <- profession(?x2845, ?x353), influenced_by(?x117, ?x2845), influenced_by(?x2845, ?x4915), ?x4915 = 03f0324 *> conf = 0.15 ranks of expected_values: 51 EVAL 0lrh influenced_by 03jxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 195.000 100.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 0lrh influenced_by 032md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 195.000 100.000 0.333 http://example.org/influence/influence_node/influenced_by #4563-0jzw PRED entity: 0jzw PRED relation: honored_for! PRED expected values: 026kq4q => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 93): 02yxh9 (0.09 #86, 0.08 #207, 0.01 #1780), 03gwpw2 (0.06 #852, 0.05 #973, 0.05 #1094), 05c1t6z (0.05 #1826, 0.05 #1947, 0.05 #1705), 02q690_ (0.05 #1869, 0.05 #1990, 0.05 #1022), 03nnm4t (0.05 #1031, 0.04 #1152, 0.04 #1878), 0gvstc3 (0.04 #1842, 0.04 #1963, 0.04 #1721), 0275n3y (0.04 #1879, 0.04 #2000, 0.04 #306), 02pgky2 (0.04 #923, 0.03 #1165, 0.03 #1044), 0drtv8 (0.04 #1023, 0.04 #1144, 0.04 #902), 09pj68 (0.04 #1057, 0.04 #1178, 0.03 #1541) >> Best rule #86 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 018js4; 01hr1; 01hw5kk; 049mql; 04ghz4m; 063_j5; 04ynx7; >> query: (?x810, 02yxh9) <- film(?x338, ?x810), ?x338 = 0d_84, nominated_for(?x112, ?x810) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #400 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 120 *> proper extension: 02k_4g; 0q9jk; 0pc_l; *> query: (?x810, 026kq4q) <- award(?x810, ?x372), honored_for(?x3943, ?x810), nominated_for(?x112, ?x810) *> conf = 0.03 ranks of expected_values: 22 EVAL 0jzw honored_for! 026kq4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 58.000 58.000 0.091 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #4562-0knjh PRED entity: 0knjh PRED relation: people! PRED expected values: 0gk4g => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 34): 0gk4g (0.14 #2452, 0.14 #2650, 0.12 #3112), 02y0js (0.11 #68, 0.05 #3104, 0.05 #2444), 06z5s (0.11 #91, 0.03 #1081, 0.03 #1213), 0dq9p (0.08 #1073, 0.08 #1205, 0.07 #2459), 0qcr0 (0.07 #2443, 0.06 #859, 0.06 #2641), 032s66 (0.07 #379, 0.03 #1039, 0.03 #841), 02k6hp (0.06 #499, 0.04 #1225, 0.04 #1093), 02knxx (0.06 #98, 0.03 #890, 0.03 #2474), 04p3w (0.05 #2651, 0.05 #1199, 0.04 #1067), 01dcqj (0.04 #870, 0.03 #276, 0.02 #408) >> Best rule #2452 for best value: >> intensional similarity = 3 >> extensional distance = 488 >> proper extension: 0443c; >> query: (?x8887, 0gk4g) <- nationality(?x8887, ?x789), place_of_death(?x8887, ?x4627), type_of_union(?x8887, ?x566) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0knjh people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.141 http://example.org/people/cause_of_death/people #4561-028tv0 PRED entity: 028tv0 PRED relation: role PRED expected values: 0gghm 0j862 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 85): 05148p4 (0.86 #5103, 0.85 #5425, 0.84 #545), 028tv0 (0.86 #2068, 0.82 #2954, 0.81 #2550), 0mkg (0.85 #1828, 0.84 #545, 0.84 #700), 0l14md (0.85 #5425, 0.84 #545, 0.84 #700), 04rzd (0.84 #545, 0.84 #700, 0.83 #1013), 0bxl5 (0.84 #545, 0.84 #700, 0.83 #1013), 0g2dz (0.84 #545, 0.84 #700, 0.83 #1013), 0gghm (0.84 #545, 0.84 #700, 0.83 #1013), 0395lw (0.84 #545, 0.84 #700, 0.83 #1013), 0j862 (0.84 #545, 0.84 #700, 0.83 #1013) >> Best rule #5103 for best value: >> intensional similarity = 10 >> extensional distance = 100 >> proper extension: 07m2y; >> query: (?x645, ?x227) <- role(?x227, ?x645), role(?x3384, ?x227), group(?x227, ?x3682), group(?x227, ?x2901), ?x2901 = 01vrwfv, instrumentalists(?x227, ?x3869), role(?x212, ?x227), ?x3869 = 06gd4, ?x3384 = 01w272y, ?x3682 = 04qmr >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #545 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 03_vpw; *> query: (?x645, ?x74) <- group(?x645, ?x1684), group(?x645, ?x1136), role(?x2798, ?x645), role(?x2309, ?x645), role(?x74, ?x645), ?x1136 = 07c0j, role(?x645, ?x716), ?x2309 = 06ncr, role(?x679, ?x645), award(?x1684, ?x2139), ?x2798 = 03qjg, artists(?x1000, ?x1684) *> conf = 0.84 ranks of expected_values: 8, 10 EVAL 028tv0 role 0j862 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 71.000 71.000 0.857 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 028tv0 role 0gghm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 71.000 71.000 0.857 http://example.org/music/performance_role/regular_performances./music/group_membership/role #4560-02yy8 PRED entity: 02yy8 PRED relation: student! PRED expected values: 01n951 => 166 concepts (155 used for prediction) PRED predicted values (max 10 best out of 288): 08815 (0.43 #2622, 0.38 #4194, 0.30 #7862), 01n951 (0.33 #284, 0.10 #8668, 0.08 #11813), 02bq1j (0.25 #4881, 0.22 #6977, 0.15 #16410), 07w0v (0.25 #544, 0.03 #24130, 0.02 #44054), 06kknt (0.20 #1512, 0.14 #4132, 0.12 #5180), 017hnw (0.20 #1554, 0.14 #3126, 0.12 #4698), 01fpvz (0.20 #2107, 0.14 #2631, 0.12 #4203), 01jsk6 (0.20 #2507, 0.14 #3031, 0.12 #4603), 07vhb (0.20 #1215, 0.07 #12744, 0.04 #19560), 0bwfn (0.18 #10229, 0.14 #3417, 0.11 #45356) >> Best rule #2622 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0cm03; >> query: (?x12571, 08815) <- student(?x3439, ?x12571), jurisdiction_of_office(?x12571, ?x94), location_of_ceremony(?x12571, ?x739), major_field_of_study(?x3439, ?x254) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #284 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 07hyk; *> query: (?x12571, 01n951) <- people(?x5269, ?x12571), people(?x4195, ?x12571), student(?x3439, ?x12571), ?x5269 = 07mqps, ?x4195 = 02ctzb *> conf = 0.33 ranks of expected_values: 2 EVAL 02yy8 student! 01n951 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 166.000 155.000 0.429 http://example.org/education/educational_institution/students_graduates./education/education/student #4559-0hmyfsv PRED entity: 0hmyfsv PRED relation: organization PRED expected values: 03mbdx_ => 160 concepts (136 used for prediction) PRED predicted values (max 10 best out of 9): 034h1h (0.14 #1032, 0.12 #1453, 0.12 #1651), 03mbdx_ (0.05 #192, 0.03 #336, 0.03 #312), 07t65 (0.03 #1617, 0.03 #1693, 0.02 #1868), 02vk52z (0.02 #1616, 0.02 #1692, 0.02 #1867), 018cqq (0.02 #1628, 0.02 #1704, 0.01 #1879), 0b6css (0.02 #1627, 0.02 #1703, 0.01 #1878), 0_2v (0.01 #1620, 0.01 #1001, 0.01 #1696), 01rz1 (0.01 #1618, 0.01 #1694), 059dn (0.01 #1013) >> Best rule #1032 for best value: >> intensional similarity = 6 >> extensional distance = 94 >> proper extension: 05zjtn4; >> query: (?x11652, 034h1h) <- citytown(?x11652, ?x6357), citytown(?x11652, ?x2935), category(?x6357, ?x134), source(?x2935, ?x958), county(?x2935, ?x7964), contains(?x6357, ?x8694) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #192 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 04vgq5; *> query: (?x11652, 03mbdx_) <- place_founded(?x11652, ?x3007), organization(?x4682, ?x11652), contains(?x3007, ?x1665), category(?x3007, ?x134) *> conf = 0.05 ranks of expected_values: 2 EVAL 0hmyfsv organization 03mbdx_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 136.000 0.135 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #4558-03tdlh PRED entity: 03tdlh PRED relation: religion PRED expected values: 092bf5 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 16): 02rsw (0.20 #69), 0c8wxp (0.19 #861, 0.19 #411, 0.19 #321), 03_gx (0.14 #104, 0.09 #149, 0.07 #194), 0kpl (0.05 #1675, 0.05 #2395, 0.05 #2710), 01lp8 (0.03 #721, 0.03 #856, 0.02 #901), 03j6c (0.03 #786, 0.03 #246, 0.03 #381), 0kq2 (0.03 #423, 0.02 #1008, 0.02 #2403), 092bf5 (0.03 #691, 0.02 #826, 0.02 #466), 0n2g (0.02 #328, 0.02 #238, 0.02 #418), 06nzl (0.02 #330, 0.02 #420, 0.02 #465) >> Best rule #69 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02yy8; >> query: (?x9483, 02rsw) <- student(?x2484, ?x9483), student(?x2484, ?x2485), spouse(?x6914, ?x9483), ?x2485 = 0gd5z >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #691 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 237 *> proper extension: 01npcy7; *> query: (?x9483, 092bf5) <- spouse(?x9483, ?x6914), place_of_birth(?x9483, ?x739) *> conf = 0.03 ranks of expected_values: 8 EVAL 03tdlh religion 092bf5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 110.000 110.000 0.200 http://example.org/people/person/religion #4557-015m08 PRED entity: 015m08 PRED relation: location! PRED expected values: 033rq => 167 concepts (15 used for prediction) PRED predicted values (max 10 best out of 446): 0465_ (0.33 #1297, 0.05 #21451, 0.04 #23970), 015d3h (0.33 #928, 0.02 #28639, 0.02 #26120), 0gdhhy (0.33 #1736, 0.01 #37004), 01kmd4 (0.33 #1517, 0.01 #36785), 0272kv (0.10 #6937, 0.08 #11975, 0.07 #14494), 03nb5v (0.08 #21478, 0.07 #29035, 0.07 #23997), 094xh (0.08 #21233, 0.07 #23752, 0.06 #31309), 0320jz (0.05 #28044, 0.05 #25525, 0.05 #23006), 02fybl (0.05 #29157, 0.05 #26638, 0.05 #24119), 025b3k (0.05 #22107, 0.04 #19588, 0.04 #24626) >> Best rule #1297 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03rjj; >> query: (?x9274, 0465_) <- adjoins(?x9274, ?x10706), contains(?x10706, ?x1356), contains(?x9274, ?x13218), ?x13218 = 061k5 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015m08 location! 033rq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 15.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #4556-01x1cn2 PRED entity: 01x1cn2 PRED relation: artists! PRED expected values: 0190yn => 133 concepts (61 used for prediction) PRED predicted values (max 10 best out of 214): 05bt6j (0.44 #945, 0.40 #42, 0.33 #4260), 08cyft (0.44 #654, 0.12 #13004, 0.08 #3064), 016clz (0.40 #4, 0.29 #1208, 0.26 #12956), 0dl5d (0.40 #19, 0.27 #12971, 0.14 #1223), 0glt670 (0.40 #4257, 0.37 #4558, 0.36 #9076), 02qdgx (0.33 #940, 0.20 #37, 0.11 #6664), 0m0jc (0.33 #610, 0.17 #12960, 0.11 #911), 03_d0 (0.28 #15072, 0.25 #17483, 0.24 #6638), 02x8m (0.28 #6645, 0.27 #7247, 0.24 #6344), 0155w (0.24 #6424, 0.22 #1001, 0.22 #7327) >> Best rule #945 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01rm8b; >> query: (?x2538, 05bt6j) <- artist(?x2149, ?x2538), artists(?x3370, ?x2538), artists(?x3319, ?x2538), ?x3319 = 06j6l, ?x3370 = 059kh >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #1430 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0168cl; 06y9c2; 016kjs; 01v_pj6; 02b25y; 0bkg4; 017yfz; 036px; 01mr2g6; 02rn_bj; *> query: (?x2538, 0190yn) <- artist(?x2149, ?x2538), type_of_union(?x2538, ?x566), artists(?x283, ?x2538), student(?x5614, ?x2538) *> conf = 0.07 ranks of expected_values: 76 EVAL 01x1cn2 artists! 0190yn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 133.000 61.000 0.444 http://example.org/music/genre/artists #4555-02hmw9 PRED entity: 02hmw9 PRED relation: colors PRED expected values: 036k5h => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.40 #154, 0.39 #971, 0.39 #819), 019sc (0.29 #25, 0.28 #158, 0.20 #690), 01l849 (0.26 #685, 0.26 #628, 0.26 #1369), 06fvc (0.26 #155, 0.21 #22, 0.19 #307), 038hg (0.11 #87, 0.11 #144, 0.10 #258), 04mkbj (0.11 #712, 0.10 #769, 0.09 #978), 0jc_p (0.10 #460, 0.09 #80, 0.09 #4), 036k5h (0.10 #423, 0.10 #1658, 0.09 #1677), 03wkwg (0.09 #14, 0.07 #432, 0.07 #299), 09ggk (0.09 #15, 0.07 #1326, 0.06 #205) >> Best rule #154 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0q19t; 01y9st; >> query: (?x6837, 083jv) <- currency(?x6837, ?x1099), colors(?x6837, ?x3621), colors(?x179, ?x3621), citytown(?x6837, ?x3301) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #423 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 81 *> proper extension: 0ylsr; *> query: (?x6837, 036k5h) <- institution(?x1368, ?x6837), institution(?x1200, ?x6837), ?x1200 = 016t_3, ?x1368 = 014mlp, category(?x6837, ?x134), colors(?x6837, ?x3189) *> conf = 0.10 ranks of expected_values: 8 EVAL 02hmw9 colors 036k5h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 154.000 154.000 0.404 http://example.org/education/educational_institution/colors #4554-03x16f PRED entity: 03x16f PRED relation: award_nominee! PRED expected values: 05lb65 => 115 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1025): 035gjq (0.82 #69700, 0.82 #6970, 0.81 #4865), 048hf (0.82 #69700, 0.82 #6970, 0.81 #153352), 07z1_q (0.82 #69700, 0.82 #6970, 0.81 #153352), 03zqc1 (0.82 #69700, 0.82 #6970, 0.81 #111529), 03w4sh (0.82 #69700, 0.82 #6970, 0.81 #111529), 02s_qz (0.82 #69700, 0.82 #6970, 0.81 #111529), 03x16f (0.69 #6552, 0.28 #120825, 0.21 #51114), 06b0d2 (0.56 #4867, 0.28 #120825, 0.21 #51114), 05lb65 (0.56 #6193, 0.28 #120825, 0.21 #51114), 01rs5p (0.56 #6811, 0.21 #51114, 0.17 #90617) >> Best rule #69700 for best value: >> intensional similarity = 3 >> extensional distance = 504 >> proper extension: 04bdxl; 07fq1y; 0197tq; 01j5ts; 0cnl80; 0m2wm; 01p7yb; 0l8v5; 03ckxdg; 0c4f4; ... >> query: (?x8746, ?x444) <- award_nominee(?x8746, ?x444), gender(?x8746, ?x514), ?x514 = 02zsn >> conf = 0.82 => this is the best rule for 6 predicted values *> Best rule #6193 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 01dw4q; 03zqc1; 06b0d2; 030znt; 026zvx7; 07z1_q; 04psyp; 05683p; 03w4sh; 05lb65; ... *> query: (?x8746, 05lb65) <- award_nominee(?x8746, ?x2578), actor(?x2078, ?x8746), ?x2578 = 038g2x *> conf = 0.56 ranks of expected_values: 9 EVAL 03x16f award_nominee! 05lb65 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 115.000 69.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4553-02b14q PRED entity: 02b14q PRED relation: position PRED expected values: 0dgrmp => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 4): 0dgrmp (0.83 #132, 0.83 #127, 0.83 #90), 02_j1w (0.82 #167, 0.82 #166, 0.82 #191), 03f0fp (0.47 #229, 0.31 #236, 0.01 #136), 02md_2 (0.47 #229, 0.31 #236) >> Best rule #132 for best value: >> intensional similarity = 9 >> extensional distance = 204 >> proper extension: 01_1kk; >> query: (?x8698, ?x203) <- team(?x530, ?x8698), team(?x63, ?x8698), team(?x60, ?x8698), ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x60 = 02nzb8, position(?x8698, ?x203), ?x203 = 0dgrmp, position(?x8698, ?x63) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02b14q position 0dgrmp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.830 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #4552-01mszz PRED entity: 01mszz PRED relation: film! PRED expected values: 01pcrw => 74 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1084): 032dg7 (0.47 #8303, 0.44 #4152, 0.43 #29060), 079vf (0.25 #2084, 0.05 #14536, 0.02 #26992), 02lkcc (0.25 #2318, 0.03 #14770, 0.03 #27226), 02js_6 (0.22 #8194, 0.17 #10270, 0.12 #6119), 01fyzy (0.17 #11437, 0.14 #1059, 0.03 #23890), 030xr_ (0.17 #9890, 0.12 #5739, 0.11 #7814), 04yywz (0.17 #10396, 0.08 #43592, 0.03 #18697), 0f6_x (0.14 #626, 0.08 #11004, 0.04 #19305), 0k525 (0.14 #1840, 0.08 #12218, 0.03 #28824), 01ps2h8 (0.14 #939, 0.08 #11317, 0.02 #46607) >> Best rule #8303 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0yyg4; 011yth; 02krdz; 017z49; 011x_4; >> query: (?x6205, ?x8796) <- film(?x11529, ?x6205), ?x11529 = 05w1vf, award(?x6205, ?x688), nominated_for(?x8796, ?x6205) >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01mszz film! 01pcrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 35.000 0.469 http://example.org/film/actor/film./film/performance/film #4551-05ml_s PRED entity: 05ml_s PRED relation: nationality PRED expected values: 09c7w0 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.88 #501, 0.86 #3212, 0.81 #2610), 0d060g (0.34 #5122, 0.07 #1208, 0.05 #2212), 014wxc (0.32 #4819), 03gh4 (0.32 #4819), 03fb3t (0.32 #4819), 02jx1 (0.20 #233, 0.20 #33, 0.17 #433), 07ssc (0.20 #15, 0.08 #4834, 0.08 #2925), 0j5g9 (0.20 #62), 03rk0 (0.13 #1247, 0.05 #1147, 0.05 #8583), 0chghy (0.05 #210, 0.05 #110, 0.03 #611) >> Best rule #501 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 026_dq6; 03c_8t; >> query: (?x819, 09c7w0) <- student(?x735, ?x819), place_of_birth(?x819, ?x5193), ?x735 = 065y4w7 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05ml_s nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.879 http://example.org/people/person/nationality #4550-0t_2 PRED entity: 0t_2 PRED relation: countries_spoken_in PRED expected values: 09c7w0 => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 543): 0d060g (0.53 #2756, 0.43 #2942, 0.42 #3489), 07ytt (0.50 #1262, 0.45 #1445, 0.42 #1630), 0hzlz (0.50 #943, 0.40 #1126, 0.38 #1861), 03rk0 (0.44 #1894, 0.33 #59, 0.25 #4638), 0697s (0.40 #993, 0.33 #810, 0.33 #76), 01ppq (0.38 #709, 0.33 #891, 0.33 #157), 0162v (0.33 #56, 0.31 #1709, 0.30 #1156), 034m8 (0.33 #164, 0.30 #1264, 0.27 #1447), 0h44w (0.33 #147, 0.30 #1064, 0.25 #699), 04hhv (0.33 #146, 0.25 #698, 0.25 #328) >> Best rule #2756 for best value: >> intensional similarity = 9 >> extensional distance = 17 >> proper extension: 02hxc3j; >> query: (?x3592, 0d060g) <- language(?x6394, ?x3592), languages_spoken(?x12502, ?x3592), film_release_region(?x6394, ?x2843), film_release_region(?x6394, ?x1061), nominated_for(?x241, ?x6394), production_companies(?x6394, ?x1478), ?x2843 = 016wzw, people(?x12502, ?x3034), contains(?x455, ?x1061) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1283 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 8 *> proper extension: 02bv9; *> query: (?x3592, ?x94) <- language(?x10459, ?x3592), language(?x2555, ?x3592), languages(?x6399, ?x3592), languages_spoken(?x913, ?x3592), award_nominee(?x6399, ?x5125), languages(?x3848, ?x3592), award_winner(?x2555, ?x4459), nationality(?x6399, ?x94), film(?x2101, ?x10459) *> conf = 0.27 ranks of expected_values: 74 EVAL 0t_2 countries_spoken_in 09c7w0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 35.000 35.000 0.526 http://example.org/language/human_language/countries_spoken_in #4549-0bq8tmw PRED entity: 0bq8tmw PRED relation: language PRED expected values: 02h40lc => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.95 #1011, 0.95 #893, 0.94 #775), 06b_j (0.21 #141, 0.09 #496, 0.08 #914), 04306rv (0.20 #5, 0.11 #597, 0.09 #837), 06mp7 (0.20 #16, 0.04 #311, 0.02 #1202), 06nm1 (0.16 #425, 0.14 #70, 0.14 #188), 02bjrlw (0.14 #60, 0.10 #355, 0.09 #178), 012w70 (0.14 #72, 0.09 #190, 0.05 #427), 03_9r (0.14 #69, 0.08 #305, 0.08 #424), 064_8sq (0.14 #854, 0.14 #734, 0.14 #199), 0jzc (0.09 #197, 0.04 #256, 0.04 #315) >> Best rule #1011 for best value: >> intensional similarity = 4 >> extensional distance = 179 >> proper extension: 01f39b; >> query: (?x1642, 02h40lc) <- story_by(?x1642, ?x4325), film(?x2444, ?x1642), student(?x10478, ?x4325), participant(?x2444, ?x117) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bq8tmw language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.950 http://example.org/film/film/language #4548-0gywn PRED entity: 0gywn PRED relation: parent_genre PRED expected values: 016cjb 0155w => 53 concepts (48 used for prediction) PRED predicted values (max 10 best out of 235): 06by7 (0.59 #1981, 0.57 #2145, 0.49 #3126), 03_d0 (0.46 #1647, 0.23 #1974, 0.18 #1811), 0gywn (0.33 #204, 0.29 #1023, 0.26 #1678), 0827d (0.33 #2, 0.25 #493, 0.20 #657), 0ggq0m (0.33 #10, 0.25 #501, 0.20 #665), 0283d (0.33 #233, 0.14 #1052, 0.14 #4265), 05w3f (0.29 #1009, 0.14 #4265, 0.11 #4266), 02x8m (0.26 #1652, 0.14 #4265, 0.11 #4266), 0155w (0.25 #563, 0.14 #1055, 0.14 #4265), 02fhtq (0.25 #635, 0.03 #1946, 0.02 #2109) >> Best rule #1981 for best value: >> intensional similarity = 5 >> extensional distance = 98 >> proper extension: 028cl7; 017ht; >> query: (?x3928, 06by7) <- parent_genre(?x3928, ?x3319), artists(?x3319, ?x5140), artists(?x3319, ?x4640), award_nominee(?x140, ?x4640), ?x5140 = 015xp4 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #563 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 01lyv; *> query: (?x3928, 0155w) <- artists(?x3928, ?x11992), artists(?x3928, ?x5456), artists(?x3928, ?x5048), artists(?x3928, ?x2169), ?x11992 = 01pgk0, parent_genre(?x1127, ?x3928), artist(?x2931, ?x5048), award(?x5456, ?x1801), award_winner(?x3846, ?x2169) *> conf = 0.25 ranks of expected_values: 9, 36 EVAL 0gywn parent_genre 0155w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 53.000 48.000 0.590 http://example.org/music/genre/parent_genre EVAL 0gywn parent_genre 016cjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 53.000 48.000 0.590 http://example.org/music/genre/parent_genre #4547-01p5_g PRED entity: 01p5_g PRED relation: profession! PRED expected values: 0bw6y => 67 concepts (23 used for prediction) PRED predicted values (max 10 best out of 4197): 022q4j (0.60 #8468, 0.60 #8467, 0.40 #25408), 0bkmf (0.60 #8468, 0.60 #8467, 0.40 #25408), 0gyx4 (0.60 #8468, 0.40 #5621, 0.33 #97394), 0sz28 (0.60 #8468, 0.40 #4548, 0.33 #97394), 036jb (0.60 #8468, 0.40 #5669, 0.33 #1436), 0hnp7 (0.60 #8468, 0.35 #50813, 0.33 #97394), 01b9ck (0.60 #8468, 0.35 #50813, 0.33 #97394), 0161sp (0.60 #8468, 0.33 #97394, 0.33 #17800), 0151xv (0.60 #8468, 0.33 #3612, 0.32 #59281), 0c6qh (0.60 #4950, 0.60 #8467, 0.40 #25408) >> Best rule #8468 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 01d_h8; 0d1pc; >> query: (?x10649, ?x4512) <- profession(?x10973, ?x10649), profession(?x6934, ?x10649), profession(?x509, ?x10649), ?x509 = 04wqr, participant(?x10973, ?x4512), location(?x10973, ?x4984), nominated_for(?x10973, ?x592), participant(?x6934, ?x4240) >> conf = 0.60 => this is the best rule for 9 predicted values *> Best rule #8467 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 01d_h8; 0d1pc; *> query: (?x10649, ?x4240) <- profession(?x10973, ?x10649), profession(?x6934, ?x10649), profession(?x509, ?x10649), ?x509 = 04wqr, participant(?x10973, ?x4512), location(?x10973, ?x4984), nominated_for(?x10973, ?x592), participant(?x6934, ?x4240) *> conf = 0.60 ranks of expected_values: 49 EVAL 01p5_g profession! 0bw6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 67.000 23.000 0.602 http://example.org/people/person/profession #4546-01wz_ml PRED entity: 01wz_ml PRED relation: artist! PRED expected values: 01w40h => 125 concepts (103 used for prediction) PRED predicted values (max 10 best out of 115): 015_1q (0.35 #1280, 0.31 #1140, 0.31 #1560), 03rhqg (0.33 #856, 0.23 #1136, 0.22 #1276), 0g768 (0.30 #878, 0.15 #3119, 0.15 #3259), 01trtc (0.29 #353, 0.25 #73, 0.12 #773), 011k1h (0.25 #10, 0.15 #710, 0.14 #290), 033hn8 (0.25 #14, 0.15 #854, 0.13 #4777), 016ckq (0.22 #884, 0.10 #2845, 0.10 #3265), 0181dw (0.19 #883, 0.18 #2283, 0.15 #2844), 02p11jq (0.17 #433, 0.15 #853, 0.15 #2253), 01cl2y (0.17 #451, 0.14 #1151, 0.14 #311) >> Best rule #1280 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 01vrncs; 01lcxbb; 01w524f; 012z8_; 01vt9p3; 012vd6; 011vx3; 01wg25j; 01vs4f3; >> query: (?x3401, 015_1q) <- role(?x3401, ?x736), profession(?x3401, ?x319), influenced_by(?x7227, ?x3401), artist(?x9243, ?x3401) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1289 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 01vrncs; 01lcxbb; 01w524f; 012z8_; 01vt9p3; 012vd6; 011vx3; 01wg25j; 01vs4f3; *> query: (?x3401, 01w40h) <- role(?x3401, ?x736), profession(?x3401, ?x319), influenced_by(?x7227, ?x3401), artist(?x9243, ?x3401) *> conf = 0.16 ranks of expected_values: 11 EVAL 01wz_ml artist! 01w40h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 125.000 103.000 0.351 http://example.org/music/record_label/artist #4545-01wg982 PRED entity: 01wg982 PRED relation: artists! PRED expected values: 01_bkd => 108 concepts (49 used for prediction) PRED predicted values (max 10 best out of 253): 06by7 (0.68 #4272, 0.58 #1539, 0.56 #22), 064t9 (0.61 #5782, 0.48 #3961, 0.46 #4264), 016clz (0.56 #5, 0.45 #611, 0.27 #2433), 01_bkd (0.56 #55, 0.19 #2179, 0.07 #3396), 01lyv (0.53 #3374, 0.28 #4283, 0.21 #3980), 0jmwg (0.32 #717, 0.11 #111, 0.10 #2235), 05bt6j (0.30 #952, 0.26 #5811, 0.25 #4293), 0glt670 (0.29 #3987, 0.21 #3684, 0.20 #6719), 06j6l (0.28 #3995, 0.28 #5816, 0.25 #1869), 025sc50 (0.26 #3997, 0.22 #5818, 0.19 #6729) >> Best rule #4272 for best value: >> intensional similarity = 4 >> extensional distance = 255 >> proper extension: 01vzz1c; >> query: (?x2408, 06by7) <- location(?x2408, ?x3052), artists(?x1000, ?x2408), artists(?x1000, ?x10263), ?x10263 = 0mjn2 >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01vsxdm; 01j59b0; 014_lq; *> query: (?x2408, 01_bkd) <- artists(?x9831, ?x2408), artists(?x2249, ?x2408), ?x2249 = 03lty, ?x9831 = 0xv2x *> conf = 0.56 ranks of expected_values: 4 EVAL 01wg982 artists! 01_bkd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 49.000 0.677 http://example.org/music/genre/artists #4544-0f7hw PRED entity: 0f7hw PRED relation: film! PRED expected values: 030vmc => 66 concepts (51 used for prediction) PRED predicted values (max 10 best out of 52): 0162c8 (0.12 #38, 0.01 #1412), 04353 (0.12 #224), 06pjs (0.12 #219), 06chf (0.12 #78), 0cw67g (0.09 #5492, 0.09 #825, 0.09 #4119), 06pj8 (0.07 #323, 0.03 #873, 0.02 #4715), 01f7j9 (0.04 #326, 0.01 #1701, 0.01 #876), 07rd7 (0.04 #379, 0.03 #929, 0.02 #654), 04sry (0.03 #443, 0.01 #1542, 0.01 #4287), 0js9s (0.03 #430, 0.01 #980) >> Best rule #38 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 07cyl; 02xs6_; 033qdy; 07gghl; 0jqkh; 0270k40; >> query: (?x9424, 0162c8) <- film(?x9934, ?x9424), language(?x9424, ?x254), country(?x9424, ?x94), ?x9934 = 09nz_c >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f7hw film! 030vmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 51.000 0.125 http://example.org/film/director/film #4543-0gv2r PRED entity: 0gv2r PRED relation: type_of_union PRED expected values: 04ztj => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.89 #9, 0.82 #5, 0.79 #53), 01g63y (0.11 #134, 0.11 #122, 0.11 #10) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 076_74; 058nh2; >> query: (?x6713, 04ztj) <- written_by(?x12829, ?x6713), award(?x6713, ?x198), profession(?x6713, ?x319), ?x198 = 040njc >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gv2r type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.891 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4542-08g5q7 PRED entity: 08g5q7 PRED relation: people PRED expected values: 019l3m 08849 => 62 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1192): 053yx (0.50 #3468, 0.33 #6166, 0.33 #96), 09889g (0.50 #2886, 0.20 #5584, 0.17 #15713), 0gyy0 (0.43 #7801, 0.40 #5100, 0.29 #8476), 0b22w (0.40 #5211, 0.33 #489, 0.30 #13992), 0407f (0.40 #4832, 0.33 #110, 0.29 #8208), 0jrny (0.40 #4828, 0.30 #13609, 0.29 #8204), 016gkf (0.40 #4932, 0.29 #8308, 0.29 #7633), 05v45k (0.40 #5332, 0.29 #8708, 0.29 #8033), 015gy7 (0.33 #260, 0.29 #9032, 0.25 #9707), 0168dy (0.33 #507, 0.29 #9279, 0.25 #9954) >> Best rule #3468 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 02y0js; >> query: (?x10900, 053yx) <- people(?x10900, ?x12571), people(?x10900, ?x5206), award(?x5206, ?x1079), symptom_of(?x10900, ?x1158), notable_people_with_this_condition(?x13845, ?x12571), nominated_for(?x5206, ?x4927), student(?x3439, ?x12571), nominated_for(?x484, ?x4927), origin(?x5206, ?x1860), award_nominee(?x5206, ?x3771) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08g5q7 people 08849 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 37.000 0.500 http://example.org/people/cause_of_death/people EVAL 08g5q7 people 019l3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 37.000 0.500 http://example.org/people/cause_of_death/people #4541-050ks PRED entity: 050ks PRED relation: location! PRED expected values: 0f6_dy => 171 concepts (151 used for prediction) PRED predicted values (max 10 best out of 1987): 04t2l2 (0.33 #2539, 0.13 #5054, 0.12 #7569), 0227tr (0.25 #478, 0.07 #5507, 0.06 #13050), 0170s4 (0.25 #438, 0.07 #5467, 0.03 #13010), 0g2lq (0.25 #1568, 0.04 #122253, 0.03 #14140), 09b6zr (0.25 #818, 0.03 #13390, 0.02 #30989), 01s7z0 (0.25 #2427, 0.03 #25055, 0.02 #40141), 044f7 (0.25 #1137, 0.03 #121822, 0.03 #26280), 03kbb8 (0.25 #1429, 0.03 #122114, 0.02 #56742), 049_zz (0.25 #599, 0.02 #40827, 0.02 #68484), 01kwsg (0.25 #952, 0.01 #111580, 0.01 #121637) >> Best rule #2539 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 04ych; >> query: (?x7058, 04t2l2) <- district_represented(?x7944, ?x7058), vacationer(?x7058, ?x848), ?x7944 = 01h7xx >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 050ks location! 0f6_dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 171.000 151.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #4540-02ddq4 PRED entity: 02ddq4 PRED relation: ceremony PRED expected values: 0jzphpx 02cg41 => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 122): 02cg41 (0.88 #615, 0.86 #741, 0.86 #489), 0jzphpx (0.73 #409, 0.70 #787, 0.70 #661), 0gx1673 (0.51 #609, 0.50 #105, 0.49 #483), 05qb8vx (0.21 #4035, 0.21 #4034, 0.21 #3655), 073h1t (0.21 #4035, 0.21 #4034, 0.21 #3655), 02yv_b (0.21 #4035, 0.21 #4034, 0.21 #3655), 09k5jh7 (0.21 #4035, 0.21 #4034, 0.21 #3655), 05zksls (0.21 #4035, 0.21 #4034, 0.21 #3655), 09pj68 (0.21 #4035, 0.21 #4034, 0.21 #3655), 0418154 (0.21 #4035, 0.21 #4034, 0.21 #3655) >> Best rule #615 for best value: >> intensional similarity = 4 >> extensional distance = 71 >> proper extension: 02581q; 02wh75; 026mg3; 02g3gj; 01d38g; 01bgqh; 02g8mp; 01c9f2; 01ckbq; 02gx2k; ... >> query: (?x10316, 02cg41) <- award(?x4715, ?x10316), award_winner(?x4715, ?x3735), ceremony(?x10316, ?x1362), ?x1362 = 019bk0 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02ddq4 ceremony 02cg41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.877 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02ddq4 ceremony 0jzphpx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.877 http://example.org/award/award_category/winners./award/award_honor/ceremony #4539-0512p PRED entity: 0512p PRED relation: teams! PRED expected values: 04ykg => 86 concepts (70 used for prediction) PRED predicted values (max 10 best out of 137): 0d6lp (0.33 #364, 0.25 #904, 0.20 #2249), 0nqph (0.33 #258, 0.25 #1336, 0.20 #2681), 02_286 (0.31 #4330, 0.20 #2176, 0.13 #5677), 01_d4 (0.25 #6253, 0.25 #1138, 0.20 #2483), 013yq (0.25 #882, 0.20 #1958, 0.08 #10309), 0h7h6 (0.20 #2478, 0.17 #3286, 0.17 #3017), 0dyl9 (0.20 #1489, 0.14 #3642, 0.08 #4181), 01cx_ (0.20 #1441, 0.09 #3864, 0.07 #5210), 0dc95 (0.20 #1425, 0.07 #4656, 0.07 #5464), 0dclg (0.17 #3303, 0.09 #3842, 0.09 #8148) >> Best rule #364 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 0713r; >> query: (?x1438, 0d6lp) <- position(?x1438, ?x12238), position(?x1438, ?x2010), team(?x261, ?x1438), colors(?x1438, ?x663), ?x2010 = 02lyr4, school(?x1438, ?x9131), ?x261 = 02dwn9, ?x12238 = 02dwpf, season(?x1438, ?x9267), season(?x1438, ?x8517), ?x9267 = 0dx84s, ?x9131 = 02pptm, draft(?x1438, ?x10600), teams(?x5771, ?x1438), ?x8517 = 0285r5d, ?x10600 = 04f4z1k >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #14830 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 75 *> proper extension: 023fxp; *> query: (?x1438, ?x1274) <- teams(?x5771, ?x1438), contains(?x5771, ?x11648), location(?x10219, ?x5771), location(?x3985, ?x5771), sport(?x1438, ?x5063), category(?x11648, ?x134), gender(?x3985, ?x514), nationality(?x3985, ?x94), award(?x10219, ?x2071), profession(?x3985, ?x7998), people(?x3984, ?x3985), contains(?x1274, ?x5771), film(?x10219, ?x1673) *> conf = 0.01 ranks of expected_values: 91 EVAL 0512p teams! 04ykg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 86.000 70.000 0.333 http://example.org/sports/sports_team_location/teams #4538-01pkhw PRED entity: 01pkhw PRED relation: student! PRED expected values: 02822 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 50): 02822 (0.35 #149, 0.24 #689, 0.23 #209), 03qsdpk (0.13 #694, 0.13 #454, 0.11 #574), 0w7c (0.12 #160, 0.11 #700, 0.10 #460), 03g3w (0.10 #199, 0.10 #619, 0.10 #499), 01zc2w (0.10 #226, 0.06 #646, 0.05 #466), 0fdys (0.08 #747, 0.08 #147, 0.08 #807), 02vxn (0.08 #183, 0.08 #123, 0.05 #423), 05qfh (0.08 #205, 0.04 #565, 0.03 #505), 04gb7 (0.05 #512, 0.05 #752, 0.05 #632), 062z7 (0.05 #500, 0.05 #620, 0.04 #800) >> Best rule #149 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 023s8; >> query: (?x4053, 02822) <- student(?x254, ?x4053), film(?x4053, ?x1077), spouse(?x4053, ?x5485) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pkhw student! 02822 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.346 http://example.org/education/field_of_study/students_majoring./education/education/student #4537-04gnbv1 PRED entity: 04gnbv1 PRED relation: award_winner! PRED expected values: 03nnm4t => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 104): 03nnm4t (0.28 #5209, 0.25 #71, 0.18 #6032), 0418154 (0.28 #5209, 0.18 #6032, 0.17 #5071), 05zksls (0.28 #5209, 0.18 #6032, 0.17 #5071), 02wzl1d (0.28 #5209, 0.18 #6032, 0.17 #5071), 09g90vz (0.28 #5209, 0.17 #7815, 0.11 #412), 02q690_ (0.25 #62, 0.18 #6032, 0.17 #5071), 027n06w (0.18 #6032, 0.17 #5071, 0.17 #6444), 07z31v (0.18 #6032, 0.17 #5071, 0.17 #6444), 0bzjvm (0.18 #6032, 0.17 #5071, 0.17 #6444), 0hn821n (0.17 #127, 0.13 #264, 0.06 #539) >> Best rule #5209 for best value: >> intensional similarity = 3 >> extensional distance = 1147 >> proper extension: 01nzs7; >> query: (?x4618, ?x944) <- award_winner(?x3310, ?x4618), honored_for(?x944, ?x3310), nominated_for(?x435, ?x3310) >> conf = 0.28 => this is the best rule for 5 predicted values ranks of expected_values: 1 EVAL 04gnbv1 award_winner! 03nnm4t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.278 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4536-04pnx PRED entity: 04pnx PRED relation: contains PRED expected values: 05qx1 03gyl => 117 concepts (47 used for prediction) PRED predicted values (max 10 best out of 2875): 05qx1 (0.64 #73277, 0.63 #87931, 0.63 #117246), 015fr (0.64 #73277, 0.63 #87931, 0.63 #117246), 0jgd (0.64 #73277, 0.63 #87931, 0.63 #117246), 05rgl (0.64 #73277, 0.63 #87931, 0.63 #117246), 02k1b (0.64 #73277, 0.63 #87931, 0.63 #117246), 01ls2 (0.64 #73277, 0.63 #87931, 0.63 #117246), 034m8 (0.64 #73277, 0.63 #87931, 0.63 #117246), 02613 (0.64 #73277, 0.63 #117246), 09c7w0 (0.63 #87931, 0.53 #49825, 0.33 #1), 04_1l0v (0.63 #87931, 0.53 #49825, 0.25 #4085) >> Best rule #73277 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 015zxh; 068p2; >> query: (?x7708, ?x1879) <- contains(?x7708, ?x9730), contains(?x7708, ?x151), organization(?x9730, ?x312), adjoins(?x1879, ?x151) >> conf = 0.64 => this is the best rule for 8 predicted values ranks of expected_values: 1, 34 EVAL 04pnx contains 03gyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 117.000 47.000 0.639 http://example.org/location/location/contains EVAL 04pnx contains 05qx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 47.000 0.639 http://example.org/location/location/contains #4535-01wdj_ PRED entity: 01wdj_ PRED relation: institution! PRED expected values: 019v9k => 172 concepts (93 used for prediction) PRED predicted values (max 10 best out of 20): 03bwzr4 (0.86 #97, 0.82 #119, 0.74 #54), 019v9k (0.77 #91, 0.71 #113, 0.70 #417), 02_xgp2 (0.73 #95, 0.68 #117, 0.64 #10), 0bkj86 (0.55 #90, 0.50 #243, 0.47 #47), 04zx3q1 (0.55 #86, 0.44 #501, 0.43 #108), 027f2w (0.50 #92, 0.39 #114, 0.37 #49), 013zdg (0.45 #4, 0.32 #242, 0.32 #89), 02m4yg (0.44 #501, 0.32 #1373, 0.29 #1817), 01ysy9 (0.44 #501, 0.32 #1373, 0.29 #1817), 01gkg3 (0.44 #501, 0.32 #1373, 0.29 #1817) >> Best rule #97 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 03v6t; 07szy; 0j_sncb; 027xx3; 03ksy; 07tds; 02zd460; 05x_5; >> query: (?x2830, 03bwzr4) <- institution(?x1200, ?x2830), school(?x684, ?x2830), major_field_of_study(?x2830, ?x1682), ?x1682 = 02ky346, ?x1200 = 016t_3 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #91 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 03v6t; 07szy; 0j_sncb; 027xx3; 03ksy; 07tds; 02zd460; 05x_5; *> query: (?x2830, 019v9k) <- institution(?x1200, ?x2830), school(?x684, ?x2830), major_field_of_study(?x2830, ?x1682), ?x1682 = 02ky346, ?x1200 = 016t_3 *> conf = 0.77 ranks of expected_values: 2 EVAL 01wdj_ institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 172.000 93.000 0.864 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4534-01l_vgt PRED entity: 01l_vgt PRED relation: artists! PRED expected values: 03_d0 => 182 concepts (78 used for prediction) PRED predicted values (max 10 best out of 253): 025sc50 (0.76 #8018, 0.71 #13537, 0.70 #12001), 05bt6j (0.60 #656, 0.41 #12915, 0.41 #13531), 0xhtw (0.60 #629, 0.21 #17799, 0.18 #1242), 06j6l (0.51 #12000, 0.51 #13536, 0.49 #12920), 0glt670 (0.42 #11992, 0.41 #12912, 0.40 #8009), 0y3_8 (0.33 #48, 0.30 #12919, 0.29 #8016), 08cyft (0.33 #57, 0.25 #363, 0.21 #8025), 017_qw (0.33 #62, 0.18 #981, 0.15 #1901), 029h7y (0.33 #40, 0.10 #8008, 0.09 #1265), 02cqny (0.33 #168, 0.09 #1087, 0.08 #2007) >> Best rule #8018 for best value: >> intensional similarity = 7 >> extensional distance = 40 >> proper extension: 09qr6; 03t9sp; 0136p1; 02zmh5; 07ss8_; 01vs_v8; 013v5j; 047sxrj; 01trhmt; 01vx5w7; ... >> query: (?x3382, 025sc50) <- artists(?x5876, ?x3382), artists(?x3996, ?x3382), artists(?x671, ?x3382), category(?x3382, ?x134), ?x3996 = 02lnbg, ?x671 = 064t9, ?x5876 = 0ggx5q >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #10739 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 51 *> proper extension: 012wg; 025cn2; 01jrs46; *> query: (?x3382, 03_d0) <- place_of_birth(?x3382, ?x2645), origin(?x3382, ?x789), film_release_region(?x1331, ?x789), nominated_for(?x185, ?x1331), adjoins(?x789, ?x172) *> conf = 0.28 ranks of expected_values: 14 EVAL 01l_vgt artists! 03_d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 182.000 78.000 0.762 http://example.org/music/genre/artists #4533-0kz2w PRED entity: 0kz2w PRED relation: citytown PRED expected values: 0hz35 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 212): 02_286 (0.16 #6641, 0.15 #8113, 0.15 #5167), 013yq (0.12 #43, 0.05 #2251, 0.05 #1883), 01_d4 (0.08 #3350, 0.06 #38, 0.05 #406), 0rh6k (0.07 #4417, 0.06 #5890, 0.06 #12883), 0dclg (0.06 #30925, 0.05 #37182, 0.03 #8876), 03l2n (0.06 #30925, 0.03 #5624, 0.02 #18138), 015zxh (0.06 #30925, 0.01 #5552, 0.01 #7762), 0r02m (0.06 #333, 0.05 #2541, 0.05 #2909), 01zqy6t (0.06 #343, 0.05 #711, 0.03 #1447), 0c1d0 (0.06 #182, 0.05 #550, 0.03 #918) >> Best rule #6641 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 0c_j5d; 0gztl; 04qhdf; 0k8z; 07l1c; 077w0b; 01dfb6; 05th69; 02l48d; 02ktt7; >> query: (?x1043, 02_286) <- currency(?x1043, ?x170), company(?x346, ?x1043), ?x170 = 09nqf, organization(?x346, ?x99) >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0kz2w citytown 0hz35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 125.000 0.163 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #4532-030_3z PRED entity: 030_3z PRED relation: profession PRED expected values: 02krf9 => 111 concepts (85 used for prediction) PRED predicted values (max 10 best out of 81): 0dxtg (0.75 #2640, 0.72 #5122, 0.72 #3662), 0np9r (0.69 #2792, 0.69 #2500, 0.14 #894), 02krf9 (0.32 #1046, 0.32 #900, 0.31 #1776), 012t_z (0.30 #595, 0.18 #303, 0.17 #1325), 0dgd_ (0.27 #10516, 0.10 #2364, 0.09 #1050), 0fj9f (0.25 #1366, 0.19 #2242, 0.12 #4286), 0cbd2 (0.22 #4240, 0.22 #3218, 0.20 #2634), 0kyk (0.18 #3239, 0.18 #4261, 0.17 #5575), 09jwl (0.18 #9362, 0.17 #9800, 0.17 #8778), 0dz3r (0.17 #586, 0.11 #8764, 0.11 #9348) >> Best rule #2640 for best value: >> intensional similarity = 3 >> extensional distance = 120 >> proper extension: 03ysmg; >> query: (?x4552, 0dxtg) <- film(?x4552, ?x5002), award_nominee(?x4552, ?x361), student(?x4955, ?x4552) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1046 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 0jpdn; *> query: (?x4552, 02krf9) <- film(?x4552, ?x5002), executive_produced_by(?x1470, ?x4552), nationality(?x4552, ?x94) *> conf = 0.32 ranks of expected_values: 3 EVAL 030_3z profession 02krf9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 85.000 0.754 http://example.org/people/person/profession #4531-0hfml PRED entity: 0hfml PRED relation: currency PRED expected values: 09nqf => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.43 #7, 0.29 #13, 0.28 #1), 01nv4h (0.02 #17, 0.02 #41, 0.02 #38) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 014zfs; 01wdqrx; 01kvqc; 01wj9y9; 0q5hw; 025ldg; 018z_c; 01yg9y; 044mfr; 0b7t3p; ... >> query: (?x7474, 09nqf) <- profession(?x7474, ?x1032), ?x1032 = 02hrh1q, people(?x913, ?x7474), person(?x3480, ?x7474) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hfml currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.429 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #4530-01wyz92 PRED entity: 01wyz92 PRED relation: artist! PRED expected values: 03qk20 => 133 concepts (113 used for prediction) PRED predicted values (max 10 best out of 97): 033hn8 (0.20 #155, 0.19 #1283, 0.15 #2411), 01w40h (0.20 #170, 0.11 #593, 0.10 #1298), 02jjdr (0.20 #153, 0.11 #576, 0.10 #1281), 015_1q (0.19 #3828, 0.19 #6091, 0.19 #6375), 03rhqg (0.19 #1708, 0.14 #6371, 0.13 #7643), 03mp8k (0.16 #2464, 0.09 #3593, 0.08 #6422), 043g7l (0.16 #2429, 0.09 #6387, 0.08 #7659), 011k1h (0.14 #2407, 0.12 #1702, 0.10 #8344), 0181dw (0.14 #1734, 0.13 #2439, 0.11 #3568), 04fcjt (0.14 #1722, 0.05 #2427, 0.04 #3556) >> Best rule #155 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 02p68d; 01vvybv; >> query: (?x3481, 033hn8) <- person(?x3480, ?x3481), participant(?x828, ?x3481), artists(?x671, ?x3481) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wyz92 artist! 03qk20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 113.000 0.200 http://example.org/music/record_label/artist #4529-06fc0b PRED entity: 06fc0b PRED relation: student! PRED expected values: 01pcj4 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 85): 05nrkb (0.16 #874, 0.02 #12447, 0.02 #7186), 04gd8j (0.12 #367, 0.03 #1945, 0.01 #7205), 0bwfn (0.10 #12899, 0.09 #21843, 0.08 #17633), 017z88 (0.06 #82, 0.05 #7972, 0.05 #12181), 03ksy (0.06 #106, 0.04 #1684, 0.04 #28516), 08815 (0.06 #2, 0.04 #528, 0.04 #31042), 026gvfj (0.06 #111, 0.04 #637, 0.03 #1163), 05zl0 (0.06 #202, 0.04 #728, 0.01 #1780), 0lfgr (0.06 #43, 0.03 #1095, 0.01 #1621), 06182p (0.06 #297, 0.02 #8187, 0.02 #12396) >> Best rule #874 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 01pl9g; 01vxlbm; 0227vl; 01nglk; >> query: (?x7823, 05nrkb) <- participant(?x2352, ?x7823), location(?x7823, ?x1131), ?x1131 = 0cc56 >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #17727 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 723 *> proper extension: 05qsxy; *> query: (?x7823, 01pcj4) <- student(?x7075, ?x7823), award_nominee(?x1909, ?x7823), place_of_birth(?x7823, ?x9341) *> conf = 0.01 ranks of expected_values: 77 EVAL 06fc0b student! 01pcj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 101.000 101.000 0.160 http://example.org/education/educational_institution/students_graduates./education/education/student #4528-03f0fnk PRED entity: 03f0fnk PRED relation: instrumentalists! PRED expected values: 05r5c => 185 concepts (185 used for prediction) PRED predicted values (max 10 best out of 123): 05r5c (0.70 #1184, 0.56 #2192, 0.55 #1604), 0cfdd (0.40 #156, 0.03 #8668, 0.03 #9600), 05842k (0.30 #1009, 0.28 #6561, 0.26 #9176), 03gvt (0.25 #314, 0.10 #1071, 0.10 #1155), 018j2 (0.24 #792, 0.17 #2221, 0.17 #1045), 02hnl (0.20 #4823, 0.20 #1041, 0.20 #6001), 0l14md (0.20 #90, 0.18 #4797, 0.16 #1099), 06ncr (0.20 #126, 0.14 #3656, 0.12 #294), 02qjv (0.18 #8244, 0.03 #8668, 0.03 #9600), 01vj9c (0.18 #8244, 0.03 #8668, 0.03 #9600) >> Best rule #1184 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 02mslq; >> query: (?x4712, 05r5c) <- artist(?x5634, ?x4712), ?x5634 = 01cl2y, instrumentalists(?x227, ?x4712) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f0fnk instrumentalists! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 185.000 185.000 0.697 http://example.org/music/instrument/instrumentalists #4527-01gq0b PRED entity: 01gq0b PRED relation: participant! PRED expected values: 02qfhb => 101 concepts (51 used for prediction) PRED predicted values (max 10 best out of 354): 02qfhb (0.81 #8948, 0.80 #12142, 0.80 #15977), 030hcs (0.20 #3832, 0.12 #5752, 0.06 #17895), 030hbp (0.20 #3832), 0dvmd (0.15 #5112, 0.12 #5752, 0.06 #17895), 05dbf (0.15 #5112, 0.06 #6392, 0.06 #5111), 0h0wc (0.15 #5112, 0.06 #6392, 0.06 #5111), 01kb2j (0.15 #5112, 0.06 #6392, 0.06 #5111), 02tc5y (0.12 #5752, 0.11 #3833, 0.06 #17895), 0169dl (0.12 #5752, 0.06 #17895, 0.06 #13420), 01q_ph (0.09 #26, 0.03 #3859, 0.03 #4498) >> Best rule #8948 for best value: >> intensional similarity = 3 >> extensional distance = 279 >> proper extension: 02wb6yq; >> query: (?x1890, ?x4929) <- nationality(?x1890, ?x94), award_winner(?x638, ?x1890), participant(?x1890, ?x4929) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gq0b participant! 02qfhb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 51.000 0.812 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #4526-02dqdp PRED entity: 02dqdp PRED relation: citytown PRED expected values: 01zqy6t => 65 concepts (61 used for prediction) PRED predicted values (max 10 best out of 177): 02_286 (0.25 #4076, 0.20 #5933, 0.17 #3337), 030qb3t (0.16 #1872, 0.14 #764, 0.14 #28), 0d6lp (0.14 #69, 0.13 #5917, 0.10 #15951), 01jr6 (0.14 #86, 0.13 #5917, 0.10 #15951), 01zqy6t (0.14 #343, 0.10 #15951, 0.06 #14835), 0dc95 (0.13 #5917, 0.10 #15951, 0.08 #10000), 0f04v (0.13 #5917, 0.10 #15951, 0.08 #10000), 0k9p4 (0.13 #5917, 0.05 #13344, 0.04 #18914), 0nbwf (0.10 #15951, 0.08 #10000, 0.06 #14835), 0r62v (0.10 #15951, 0.08 #10000, 0.06 #14835) >> Best rule #4076 for best value: >> intensional similarity = 7 >> extensional distance = 284 >> proper extension: 0h6rm; 03f2fw; 01z_jj; 07xhy; >> query: (?x14526, 02_286) <- state_province_region(?x14526, ?x1227), state_province_region(?x4955, ?x1227), jurisdiction_of_office(?x900, ?x1227), location(?x397, ?x1227), contains(?x1227, ?x191), film_release_region(?x86, ?x1227), major_field_of_study(?x4955, ?x373) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #343 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 02bb47; 020923; 021s9n; 02jmst; 06b7s9; *> query: (?x14526, 01zqy6t) <- organization(?x3484, ?x14526), state_province_region(?x14526, ?x1227), ?x1227 = 01n7q, ?x3484 = 05k17c *> conf = 0.14 ranks of expected_values: 5 EVAL 02dqdp citytown 01zqy6t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 65.000 61.000 0.252 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #4525-03hkch7 PRED entity: 03hkch7 PRED relation: genre PRED expected values: 0219x_ 03mqtr => 96 concepts (94 used for prediction) PRED predicted values (max 10 best out of 112): 05p553 (0.50 #3, 0.47 #588, 0.43 #471), 01jfsb (0.49 #5986, 0.36 #245, 0.35 #3527), 02kdv5l (0.47 #5976, 0.33 #3517, 0.29 #3400), 02l7c8 (0.42 #365, 0.37 #1887, 0.35 #950), 01hmnh (0.33 #16, 0.19 #3532, 0.16 #4937), 060__y (0.29 #1888, 0.27 #717, 0.23 #1302), 03k9fj (0.25 #3526, 0.25 #1063, 0.24 #5399), 0lsxr (0.24 #3641, 0.22 #5983, 0.22 #944), 0jtdp (0.23 #481, 0.17 #598, 0.06 #832), 06n90 (0.22 #597, 0.22 #129, 0.17 #480) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 027pfg; 0h95927; 01bn3l; 0c0zq; >> query: (?x3124, 05p553) <- nominated_for(?x68, ?x3124), nominated_for(?x6086, ?x3124), genre(?x3124, ?x53), ?x6086 = 058frd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #492 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 03nqnnk; 02qr3k8; 058kh7; *> query: (?x3124, 0219x_) <- film(?x123, ?x3124), person(?x3124, ?x11290), award_winner(?x2054, ?x11290), award_winner(?x594, ?x11290) *> conf = 0.20 ranks of expected_values: 12, 42 EVAL 03hkch7 genre 03mqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 96.000 94.000 0.500 http://example.org/film/film/genre EVAL 03hkch7 genre 0219x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 96.000 94.000 0.500 http://example.org/film/film/genre #4524-04vs9 PRED entity: 04vs9 PRED relation: country! PRED expected values: 0bynt => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 51): 071t0 (0.94 #21, 0.89 #328, 0.60 #379), 0bynt (0.87 #317, 0.85 #674, 0.85 #419), 06f41 (0.84 #13, 0.71 #320, 0.49 #166), 01lb14 (0.81 #14, 0.73 #321, 0.47 #167), 07jbh (0.81 #31, 0.63 #338, 0.45 #184), 06wrt (0.75 #15, 0.67 #322, 0.45 #168), 064vjs (0.75 #29, 0.63 #336, 0.38 #387), 07gyv (0.72 #6, 0.57 #313, 0.53 #159), 03rbzn (0.72 #25, 0.54 #332, 0.31 #178), 02y8z (0.69 #18, 0.57 #325, 0.37 #171) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 0chghy; ... >> query: (?x9072, 071t0) <- country(?x10585, ?x9072), country(?x3127, ?x9072), ?x3127 = 03hr1p, ?x10585 = 01gqfm >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #317 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 61 *> proper extension: 04gzd; 01ls2; 03rt9; 06npd; 06mzp; 0hzlz; 0ctw_b; 09pmkv; 04wgh; 06qd3; ... *> query: (?x9072, 0bynt) <- country(?x3127, ?x9072), ?x3127 = 03hr1p *> conf = 0.87 ranks of expected_values: 2 EVAL 04vs9 country! 0bynt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 49.000 49.000 0.938 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #4523-02s2lg PRED entity: 02s2lg PRED relation: current_club PRED expected values: 03j70t => 100 concepts (72 used for prediction) PRED predicted values (max 10 best out of 737): 03x6m (0.60 #923, 0.50 #639, 0.38 #1350), 047fwlg (0.50 #480, 0.12 #1902, 0.08 #1760), 0kqbh (0.40 #985, 0.33 #274, 0.25 #1412), 06l22 (0.33 #1052, 0.33 #198, 0.25 #1905), 0mmd6 (0.33 #281, 0.33 #138, 0.25 #708), 011v3 (0.33 #184, 0.33 #41, 0.25 #611), 01634x (0.33 #216, 0.25 #1923, 0.25 #1781), 0y9j (0.33 #192, 0.25 #1899, 0.25 #619), 0175rc (0.33 #250, 0.25 #677, 0.20 #961), 02mplj (0.33 #164, 0.25 #591, 0.20 #875) >> Best rule #923 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 03y_f8; >> query: (?x3587, 03x6m) <- current_club(?x3587, ?x1187), position(?x3587, ?x203), position(?x3587, ?x63), position(?x3587, ?x60), ?x60 = 02nzb8, ?x63 = 02sdk9v, ?x1187 = 0y54, position(?x59, ?x203) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #997 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 03y_f8; *> query: (?x3587, ?x59) <- current_club(?x3587, ?x1187), position(?x3587, ?x203), position(?x3587, ?x63), position(?x3587, ?x60), ?x60 = 02nzb8, ?x63 = 02sdk9v, ?x1187 = 0y54, position(?x59, ?x203) *> conf = 0.02 ranks of expected_values: 347 EVAL 02s2lg current_club 03j70t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 100.000 72.000 0.600 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #4522-08720 PRED entity: 08720 PRED relation: film_crew_role PRED expected values: 02r96rf => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 31): 02r96rf (0.78 #183, 0.70 #147, 0.69 #653), 09vw2b7 (0.67 #151, 0.66 #187, 0.64 #440), 01vx2h (0.47 #191, 0.42 #263, 0.37 #444), 01pvkk (0.28 #445, 0.28 #1815, 0.28 #300), 02rh1dz (0.23 #190, 0.19 #443, 0.17 #262), 02ynfr (0.20 #160, 0.17 #449, 0.17 #196), 0d2b38 (0.15 #206, 0.12 #170, 0.11 #1469), 01xy5l_ (0.15 #194, 0.11 #1817, 0.11 #122), 0215hd (0.14 #1822, 0.13 #1462, 0.13 #1426), 089g0h (0.13 #200, 0.12 #453, 0.12 #1823) >> Best rule #183 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 01q2nx; 04pmnt; 02tktw; 04y9mm8; 047gpsd; 08984j; 0bw20; >> query: (?x641, 02r96rf) <- language(?x641, ?x254), film(?x788, ?x641), crewmember(?x641, ?x9769), executive_produced_by(?x641, ?x4784) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08720 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.776 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4521-07s8r0 PRED entity: 07s8r0 PRED relation: award_nominee PRED expected values: 03zz8b => 84 concepts (34 used for prediction) PRED predicted values (max 10 best out of 701): 045c66 (0.81 #46361, 0.81 #9273, 0.81 #27815), 03zz8b (0.81 #9273, 0.81 #27815, 0.81 #23179), 026l37 (0.81 #9273, 0.81 #27815, 0.81 #6955), 0g8st4 (0.81 #9273, 0.81 #27815, 0.81 #6955), 029q_y (0.81 #9273, 0.81 #27815, 0.81 #6955), 05slvm (0.81 #9273, 0.81 #27815, 0.81 #6955), 01fx5l (0.81 #9273, 0.81 #27815, 0.81 #6955), 01w7nww (0.81 #9273, 0.81 #27815, 0.81 #6955), 0gnbw (0.81 #9273, 0.81 #27815, 0.81 #6955), 07s8r0 (0.43 #340, 0.16 #46363, 0.16 #78812) >> Best rule #46361 for best value: >> intensional similarity = 2 >> extensional distance = 1332 >> proper extension: 0c9l1; >> query: (?x1641, ?x1486) <- award_nominee(?x1486, ?x1641), religion(?x1486, ?x1985) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #9273 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 590 *> proper extension: 01r42_g; 02zq43; 0f830f; 06n7h7; 08w7vj; 01j5x6; 01v3s2_; 0bz5v2; 04cf09; 08m4c8; ... *> query: (?x1641, ?x190) <- award_nominee(?x190, ?x1641), actor(?x4639, ?x1641), award_nominee(?x1641, ?x1244) *> conf = 0.81 ranks of expected_values: 2 EVAL 07s8r0 award_nominee 03zz8b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 34.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4520-02y9bj PRED entity: 02y9bj PRED relation: category PRED expected values: 08mbj5d => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.93 #13, 0.93 #14, 0.92 #41) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 029d_; 0438f; 01gwck; >> query: (?x7071, 08mbj5d) <- colors(?x7071, ?x3189), major_field_of_study(?x7071, ?x7134), institution(?x620, ?x7071), ?x7134 = 02_7t >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y9bj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.931 http://example.org/common/topic/webpage./common/webpage/category #4519-01yfp7 PRED entity: 01yfp7 PRED relation: service_language PRED expected values: 02h40lc => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 21): 02h40lc (0.94 #2354, 0.93 #1283, 0.93 #2795), 06nm1 (0.36 #153, 0.19 #1980, 0.18 #783), 05zjd (0.25 #55, 0.09 #97, 0.08 #496), 064_8sq (0.16 #703, 0.15 #1984, 0.15 #2761), 03_9r (0.14 #152, 0.07 #1559, 0.07 #1979), 04306rv (0.12 #486, 0.12 #45, 0.12 #1977), 01r2l (0.12 #54, 0.07 #1146, 0.07 #1566), 02bjrlw (0.07 #148, 0.05 #400, 0.04 #1975), 03115z (0.07 #166, 0.03 #3613, 0.02 #1573), 02bv9 (0.07 #183, 0.05 #309, 0.05 #456) >> Best rule #2354 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 018mxj; 07y2s; 011k1h; 025v3k; 064f29; 02dgq2; 017vb_; 06_9lg; 04fc6c; 02_l39; ... >> query: (?x5956, 02h40lc) <- category(?x5956, ?x134), ?x134 = 08mbj5d, organization(?x4682, ?x5956), service_location(?x5956, ?x94) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01yfp7 service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 201.000 201.000 0.941 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #4518-01lvcs1 PRED entity: 01lvcs1 PRED relation: role PRED expected values: 026t6 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 114): 05r5c (0.40 #5, 0.40 #1386, 0.39 #694), 01s0ps (0.32 #591, 0.32 #1480, 0.30 #58), 06ncr (0.32 #591, 0.32 #1480, 0.24 #2471), 03qlv7 (0.32 #591, 0.32 #1480, 0.24 #2471), 0dwt5 (0.32 #591, 0.32 #1480, 0.24 #2471), 01wy6 (0.32 #591, 0.32 #1480, 0.24 #2471), 02fsn (0.32 #591, 0.32 #1480, 0.24 #984), 01vdm0 (0.26 #1509, 0.26 #1409, 0.25 #520), 026t6 (0.22 #297, 0.16 #692, 0.16 #1384), 042v_gx (0.22 #597, 0.21 #498, 0.20 #1387) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0163m1; >> query: (?x3492, 05r5c) <- artists(?x13572, ?x3492), artists(?x6210, ?x3492), ?x6210 = 01fh36, ?x13572 = 037n97 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #297 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 160 *> proper extension: 03c7ln; 05cljf; 0lbj1; 0c9d9; 01vrx3g; 032t2z; 0kzy0; 01cv3n; 01vvycq; 0274ck; ... *> query: (?x3492, 026t6) <- instrumentalists(?x716, ?x3492), profession(?x3492, ?x131), ?x716 = 018vs *> conf = 0.22 ranks of expected_values: 9 EVAL 01lvcs1 role 026t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 99.000 99.000 0.400 http://example.org/music/artist/track_contributions./music/track_contribution/role #4517-04xbq3 PRED entity: 04xbq3 PRED relation: award_winner PRED expected values: 05xbx => 96 concepts (61 used for prediction) PRED predicted values (max 10 best out of 770): 0438pz (0.49 #64219, 0.49 #74105, 0.46 #37877), 04sry (0.49 #64219, 0.49 #74105, 0.46 #37877), 0162c8 (0.46 #37877, 0.46 #46109, 0.45 #44462), 02_340 (0.45 #44462, 0.42 #41169, 0.42 #36231), 016khd (0.40 #34585, 0.39 #60925, 0.36 #23059), 020_95 (0.40 #34585, 0.39 #60925, 0.36 #23059), 01z_g6 (0.40 #34585, 0.39 #60925, 0.36 #23059), 02d4ct (0.40 #34585, 0.36 #23059, 0.34 #47755), 05xbx (0.31 #39523, 0.26 #42816, 0.19 #70810), 02mqc4 (0.26 #100455, 0.21 #100454, 0.17 #95515) >> Best rule #64219 for best value: >> intensional similarity = 5 >> extensional distance = 151 >> proper extension: 02_1q9; 03kq98; 02py4c8; 02bg8v; 01xr2s; 0d66j2; 02ppg1r; 064r97z; 0bbm7r; 01rp13; ... >> query: (?x9188, ?x7310) <- genre(?x9188, ?x1014), nominated_for(?x7310, ?x9188), nominated_for(?x1416, ?x9188), award_nominee(?x1416, ?x1417), award_winner(?x198, ?x7310) >> conf = 0.49 => this is the best rule for 2 predicted values *> Best rule #39523 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 82 *> proper extension: 02nf2c; 0300ml; *> query: (?x9188, ?x5007) <- producer_type(?x9188, ?x632), award(?x9188, ?x4728), genre(?x9188, ?x1014), program(?x5007, ?x9188) *> conf = 0.31 ranks of expected_values: 9 EVAL 04xbq3 award_winner 05xbx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 96.000 61.000 0.491 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #4516-046zh PRED entity: 046zh PRED relation: profession PRED expected values: 02hrh1q => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 60): 02hrh1q (0.90 #2678, 0.90 #606, 0.90 #1642), 0dxtg (0.33 #7401, 0.29 #8302, 0.29 #8746), 0d1pc (0.33 #7401, 0.28 #11399, 0.25 #9770), 02jknp (0.33 #7401, 0.28 #11399, 0.25 #9770), 0cbd2 (0.33 #7401, 0.25 #9770, 0.15 #8591), 018gz8 (0.33 #7401, 0.25 #9770, 0.15 #2976), 0np9r (0.33 #7401, 0.25 #9770, 0.15 #13935), 02krf9 (0.33 #7401, 0.25 #9770, 0.10 #2986), 09jwl (0.33 #7401, 0.25 #166, 0.24 #758), 0nbcg (0.33 #7401, 0.18 #771, 0.18 #179) >> Best rule #2678 for best value: >> intensional similarity = 3 >> extensional distance = 304 >> proper extension: 02hhtj; >> query: (?x5246, 02hrh1q) <- participant(?x5246, ?x489), participant(?x105, ?x5246), film(?x5246, ?x603) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 046zh profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.902 http://example.org/people/person/profession #4515-041h0 PRED entity: 041h0 PRED relation: story_by! PRED expected values: 017gm7 => 170 concepts (146 used for prediction) PRED predicted values (max 10 best out of 310): 0dp7wt (0.33 #254, 0.25 #934, 0.20 #1614), 02mmwk (0.33 #240, 0.25 #920, 0.20 #1600), 07bz5 (0.26 #5101, 0.26 #15989, 0.23 #13605), 01cycq (0.20 #1955, 0.20 #1275, 0.14 #2295), 0bv8h2 (0.20 #1820, 0.09 #3180, 0.08 #4200), 032xky (0.09 #3396, 0.08 #3736, 0.08 #4756), 04fjzv (0.09 #3389, 0.08 #3729, 0.08 #4749), 07jnt (0.09 #3292, 0.08 #3632, 0.08 #4652), 09fc83 (0.09 #3244, 0.08 #3584, 0.08 #4604), 0ct5zc (0.09 #3132, 0.08 #3472, 0.08 #4492) >> Best rule #254 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 03hnd; >> query: (?x477, 0dp7wt) <- influenced_by(?x6796, ?x477), award(?x477, ?x8909), ?x6796 = 01wd02c, student(?x892, ?x477) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 041h0 story_by! 017gm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 170.000 146.000 0.333 http://example.org/film/film/story_by #4514-05rx__ PRED entity: 05rx__ PRED relation: influenced_by PRED expected values: 081lh 081nh 029_3 014z8v 05kh_ 0fb7c => 62 concepts (29 used for prediction) PRED predicted values (max 10 best out of 354): 014z8v (0.33 #118, 0.18 #1823, 0.15 #3100), 01k9lpl (0.33 #304, 0.11 #2009, 0.08 #3286), 01wj9y9 (0.33 #62, 0.05 #2191, 0.05 #487), 0l5yl (0.33 #263, 0.05 #688, 0.04 #2816), 052hl (0.33 #204, 0.04 #1909, 0.04 #3186), 081k8 (0.19 #577, 0.14 #3558, 0.12 #3983), 03_87 (0.19 #623, 0.14 #4029, 0.13 #3604), 01hmk9 (0.16 #1921, 0.12 #2345, 0.12 #3198), 032l1 (0.16 #512, 0.15 #3493, 0.13 #6472), 02lt8 (0.16 #542, 0.12 #3523, 0.09 #3948) >> Best rule #118 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 014zfs; >> query: (?x7717, 014z8v) <- influenced_by(?x7717, ?x2807), influenced_by(?x6883, ?x7717), ?x6883 = 01h1b >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14, 54, 181 EVAL 05rx__ influenced_by 0fb7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 29.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 05rx__ influenced_by 05kh_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 29.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 05rx__ influenced_by 014z8v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 29.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 05rx__ influenced_by 029_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 62.000 29.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 05rx__ influenced_by 081nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 62.000 29.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 05rx__ influenced_by 081lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 62.000 29.000 0.333 http://example.org/influence/influence_node/influenced_by #4513-01p8r8 PRED entity: 01p8r8 PRED relation: currency PRED expected values: 09nqf => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.25 #16, 0.25 #1, 0.22 #49) >> Best rule #16 for best value: >> intensional similarity = 6 >> extensional distance = 42 >> proper extension: 05bnp0; 01wbg84; 0bxtg; 03f2_rc; 0htlr; 05zbm4; 015grj; 01vrncs; 0prjs; 0343h; ... >> query: (?x10124, 09nqf) <- profession(?x10124, ?x987), profession(?x10124, ?x524), ?x524 = 02jknp, languages(?x10124, ?x2502), ?x987 = 0dxtg, film(?x10124, ?x2349) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p8r8 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.250 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #4512-069z_5 PRED entity: 069z_5 PRED relation: gender PRED expected values: 05zppz => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #53, 0.85 #55, 0.85 #61), 02zsn (0.46 #202, 0.45 #191, 0.33 #78) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 462 >> proper extension: 02sj1x; 025cn2; 07fzq3; 0fqjks; 0cl_m; 05683cn; 05hjmd; >> query: (?x10374, 05zppz) <- nationality(?x10374, ?x94), place_of_death(?x10374, ?x1523), ?x94 = 09c7w0, location(?x71, ?x1523) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 069z_5 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.847 http://example.org/people/person/gender #4511-0190xp PRED entity: 0190xp PRED relation: artists PRED expected values: 0153nq => 83 concepts (35 used for prediction) PRED predicted values (max 10 best out of 983): 0285c (0.56 #8812, 0.55 #9897, 0.50 #7727), 01shhf (0.55 #10630, 0.50 #8460, 0.50 #1084), 016lj_ (0.50 #4168, 0.50 #1084, 0.33 #9588), 0gkg6 (0.50 #1084, 0.38 #7834, 0.36 #10004), 01gx5f (0.50 #1084, 0.38 #7879, 0.33 #8964), 02y7sr (0.50 #1084, 0.36 #10549, 0.33 #9464), 015196 (0.50 #1084, 0.36 #10724, 0.33 #9639), 01whg97 (0.50 #1084, 0.36 #10505, 0.30 #4335), 01w8n89 (0.50 #1084, 0.33 #6819, 0.33 #318), 01jcxwp (0.50 #1084, 0.33 #9312, 0.33 #642) >> Best rule #8812 for best value: >> intensional similarity = 9 >> extensional distance = 7 >> proper extension: 0jmwg; >> query: (?x10128, 0285c) <- parent_genre(?x10128, ?x12618), parent_genre(?x6805, ?x10128), artists(?x10128, ?x8012), artists(?x12618, ?x9868), artists(?x12618, ?x2408), parent_genre(?x14226, ?x6805), ?x8012 = 01wt4wc, ?x2408 = 01wg982, group(?x227, ?x9868) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #10833 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 9 *> proper extension: 0xhtw; 09jw2; *> query: (?x10128, 0153nq) <- parent_genre(?x10128, ?x12618), parent_genre(?x6805, ?x10128), artists(?x10128, ?x8012), artists(?x12618, ?x2408), parent_genre(?x14226, ?x6805), ?x8012 = 01wt4wc, artists(?x6805, ?x562), origin(?x2408, ?x11721) *> conf = 0.09 ranks of expected_values: 606 EVAL 0190xp artists 0153nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 83.000 35.000 0.556 http://example.org/music/genre/artists #4510-03j63k PRED entity: 03j63k PRED relation: nominated_for! PRED expected values: 0bdw1g => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 246): 0gqyl (0.88 #10236, 0.41 #9528, 0.32 #5513), 09sb52 (0.78 #5467, 0.43 #9482, 0.16 #10190), 0m7yy (0.69 #5906, 0.69 #3308, 0.67 #5197), 0bfvd4 (0.69 #1267, 0.62 #1740, 0.60 #1031), 09v82c0 (0.62 #1365, 0.50 #185, 0.40 #1129), 02ppm4q (0.59 #9564, 0.31 #10272, 0.27 #5549), 099c8n (0.52 #5489, 0.38 #9504, 0.19 #10212), 07kjk7c (0.50 #1370, 0.48 #1843, 0.40 #1134), 0cqh6z (0.50 #55, 0.21 #2652, 0.18 #3126), 0gq9h (0.47 #5495, 0.47 #10218, 0.42 #9510) >> Best rule #10236 for best value: >> intensional similarity = 6 >> extensional distance = 180 >> proper extension: 0ds35l9; 0m313; 09m6kg; 0yyg4; 095zlp; 0n0bp; 0b73_1d; 0fh694; 0m_mm; 0_b3d; ... >> query: (?x7254, 0gqyl) <- nominated_for(?x375, ?x7254), award(?x12287, ?x375), award(?x8758, ?x375), ?x12287 = 039wsf, languages(?x8758, ?x254), artist(?x3240, ?x8758) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #2864 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 064q5v; *> query: (?x7254, 0bdw1g) <- award(?x7254, ?x4921), titles(?x512, ?x7254), award(?x782, ?x4921), ?x782 = 02k_4g *> conf = 0.27 ranks of expected_values: 38 EVAL 03j63k nominated_for! 0bdw1g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 74.000 74.000 0.885 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4509-01gkg3 PRED entity: 01gkg3 PRED relation: institution PRED expected values: 01ngz1 02dq8f => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 649): 09f2j (0.86 #9791, 0.80 #5950, 0.78 #5309), 08815 (0.86 #9602, 0.78 #12179, 0.78 #11536), 065y4w7 (0.85 #8976, 0.83 #7694, 0.83 #3836), 07szy (0.85 #9008, 0.83 #7726, 0.82 #10940), 01w5m (0.83 #7802, 0.82 #7162, 0.80 #6524), 01jq34 (0.83 #3836, 0.67 #3904, 0.63 #4477), 0l2tk (0.83 #3836, 0.64 #10889, 0.63 #4477), 02y9bj (0.83 #3836, 0.62 #4777, 0.60 #2856), 078bz (0.82 #7126, 0.80 #6488, 0.77 #9048), 03ksy (0.80 #5884, 0.79 #9725, 0.75 #7803) >> Best rule #9791 for best value: >> intensional similarity = 22 >> extensional distance = 12 >> proper extension: 0bjrnt; 027f2w; >> query: (?x5739, 09f2j) <- student(?x5739, ?x2639), major_field_of_study(?x5739, ?x6760), major_field_of_study(?x5739, ?x4321), major_field_of_study(?x8016, ?x6760), major_field_of_study(?x2327, ?x6760), disciplines_or_subjects(?x850, ?x6760), ?x2327 = 07wjk, major_field_of_study(?x6760, ?x10332), ?x8016 = 02yxjs, institution(?x5739, ?x5844), student(?x3416, ?x2639), student(?x6760, ?x665), major_field_of_study(?x13680, ?x4321), major_field_of_study(?x10576, ?x4321), major_field_of_study(?x5750, ?x4321), major_field_of_study(?x2388, ?x4321), major_field_of_study(?x1527, ?x4321), ?x10576 = 0g2jl, student(?x5750, ?x652), citytown(?x13680, ?x4090), organization(?x346, ?x5750), contains(?x94, ?x2388) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #3837 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 4 *> proper extension: 03mkk4; *> query: (?x5739, ?x122) <- student(?x5739, ?x2639), major_field_of_study(?x5739, ?x2601), award_nominee(?x2639, ?x5285), award_nominee(?x2639, ?x2638), award_nominee(?x2638, ?x1413), award_winner(?x341, ?x2638), award_winner(?x367, ?x2638), instrumentalists(?x227, ?x2638), major_field_of_study(?x2895, ?x2601), major_field_of_study(?x122, ?x2601), award(?x5285, ?x724), profession(?x2638, ?x220), institution(?x5739, ?x5844), award_winner(?x3486, ?x2895), institution(?x620, ?x2895), celebrity(?x5285, ?x5312), contains(?x2256, ?x2895), award_winner(?x486, ?x2638) *> conf = 0.59 ranks of expected_values: 230, 399 EVAL 01gkg3 institution 02dq8f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 23.000 23.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 01gkg3 institution 01ngz1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 23.000 23.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4508-060__7 PRED entity: 060__7 PRED relation: genre PRED expected values: 0lsxr => 81 concepts (46 used for prediction) PRED predicted values (max 10 best out of 112): 02kdv5l (0.50 #3646, 0.35 #1058, 0.34 #118), 05p553 (0.45 #1177, 0.35 #5179, 0.33 #2826), 0lsxr (0.33 #7, 0.22 #475, 0.22 #3652), 09blyk (0.33 #28, 0.07 #262, 0.07 #496), 03k9fj (0.32 #3655, 0.29 #714, 0.29 #832), 01hmnh (0.28 #3659, 0.22 #365, 0.22 #718), 06cvj (0.24 #1176, 0.11 #4240, 0.09 #2470), 06n90 (0.23 #3656, 0.18 #362, 0.18 #715), 082gq (0.19 #2259, 0.19 #1437, 0.19 #1555), 04xvh5 (0.18 #1794, 0.13 #265, 0.10 #1677) >> Best rule #3646 for best value: >> intensional similarity = 3 >> extensional distance = 907 >> proper extension: 06n90; >> query: (?x8557, 02kdv5l) <- genre(?x8557, ?x812), genre(?x4392, ?x812), ?x4392 = 06gb1w >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 02mpyh; *> query: (?x8557, 0lsxr) <- film_release_region(?x8557, ?x94), crewmember(?x8557, ?x9391), film(?x3952, ?x8557), ?x3952 = 04fhn_ *> conf = 0.33 ranks of expected_values: 3 EVAL 060__7 genre 0lsxr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 81.000 46.000 0.505 http://example.org/film/film/genre #4507-01w4c9 PRED entity: 01w4c9 PRED relation: role PRED expected values: 03qjg => 67 concepts (44 used for prediction) PRED predicted values (max 10 best out of 119): 0l14md (0.86 #2720, 0.85 #5445, 0.85 #5331), 0l14qv (0.83 #2108, 0.77 #4829, 0.76 #4594), 03qjg (0.81 #2729, 0.81 #5433, 0.76 #4646), 01dnws (0.81 #2729, 0.81 #5433, 0.74 #495), 07gql (0.77 #4868, 0.76 #4633, 0.72 #5116), 028tv0 (0.75 #2134, 0.73 #3365, 0.71 #4118), 0l14j_ (0.74 #495, 0.73 #867, 0.73 #4885), 02bxd (0.74 #495, 0.70 #498, 0.65 #501), 01kcd (0.74 #495, 0.70 #498, 0.64 #490), 013y1f (0.73 #867, 0.71 #622, 0.70 #498) >> Best rule #2720 for best value: >> intensional similarity = 24 >> extensional distance = 7 >> proper extension: 0j862; >> query: (?x5480, ?x315) <- performance_role(?x5480, ?x315), role(?x5480, ?x1750), role(?x5480, ?x1466), role(?x5480, ?x1166), ?x1466 = 03bx0bm, ?x1166 = 05148p4, ?x1750 = 02hnl, role(?x2798, ?x5480), performance_role(?x212, ?x5480), ?x2798 = 03qjg, split_to(?x615, ?x315), performance_role(?x315, ?x2944), performance_role(?x315, ?x1432), role(?x315, ?x74), group(?x315, ?x8488), group(?x315, ?x1467), role(?x460, ?x315), role(?x2662, ?x315), role(?x214, ?x315), artist(?x5021, ?x8488), role(?x120, ?x1432), ?x2944 = 0l14j_, ?x1467 = 01vsxdm, role(?x1432, ?x780) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2729 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 7 *> proper extension: 0j862; *> query: (?x5480, ?x2158) <- performance_role(?x5480, ?x315), role(?x5480, ?x1750), role(?x5480, ?x1466), role(?x5480, ?x1166), ?x1466 = 03bx0bm, ?x1166 = 05148p4, ?x1750 = 02hnl, role(?x2798, ?x5480), role(?x2158, ?x5480), performance_role(?x212, ?x5480), ?x2798 = 03qjg, split_to(?x615, ?x315), performance_role(?x315, ?x2944), performance_role(?x315, ?x1432), role(?x315, ?x74), group(?x315, ?x8488), group(?x315, ?x1467), role(?x460, ?x315), role(?x2662, ?x315), role(?x214, ?x315), artist(?x5021, ?x8488), role(?x120, ?x1432), ?x2944 = 0l14j_, ?x1467 = 01vsxdm, role(?x1432, ?x780) *> conf = 0.81 ranks of expected_values: 3 EVAL 01w4c9 role 03qjg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 67.000 44.000 0.857 http://example.org/music/performance_role/regular_performances./music/group_membership/role #4506-0d__c3 PRED entity: 0d__c3 PRED relation: ceremony! PRED expected values: 0gqyl => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 372): 018wng (0.93 #2702, 0.91 #2458, 0.89 #3679), 0gqyl (0.93 #3230, 0.91 #2986, 0.90 #2742), 0gr07 (0.76 #3808, 0.74 #4052, 0.72 #3562), 0gqng (0.75 #3655, 0.74 #3409, 0.72 #3166), 0l8z1 (0.73 #3694, 0.72 #2717, 0.70 #3448), 018wdw (0.70 #2602, 0.66 #2846, 0.64 #3090), 0gqxm (0.52 #2794, 0.48 #2550, 0.47 #3282), 054krc (0.40 #2432, 0.23 #3164, 0.19 #6147), 054ks3 (0.40 #2432, 0.23 #3164, 0.18 #6184), 054ky1 (0.40 #2432, 0.23 #3164, 0.18 #2676) >> Best rule #2702 for best value: >> intensional similarity = 18 >> extensional distance = 27 >> proper extension: 09306z; >> query: (?x9400, 018wng) <- ceremony(?x2209, ?x9400), ceremony(?x1862, ?x9400), ceremony(?x1307, ?x9400), ?x1862 = 0gr51, ?x2209 = 0gr42, award_winner(?x9400, ?x9964), award_winner(?x9400, ?x2426), currency(?x2426, ?x170), nationality(?x9964, ?x94), award_winner(?x720, ?x2426), nominated_for(?x1307, ?x4530), nominated_for(?x1307, ?x4231), nominated_for(?x1307, ?x2719), nominated_for(?x1307, ?x2112), ?x4231 = 04j4tx, ?x2719 = 0j_t1, ?x4530 = 07j94, ?x2112 = 0bm2g >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #3230 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 38 *> proper extension: 02pgky2; *> query: (?x9400, 0gqyl) <- ceremony(?x8153, ?x9400), ceremony(?x2209, ?x9400), ceremony(?x1862, ?x9400), ceremony(?x1703, ?x9400), ceremony(?x500, ?x9400), ?x1862 = 0gr51, ?x2209 = 0gr42, award_winner(?x9400, ?x2426), ?x500 = 0p9sw, type_of_union(?x2426, ?x566), nominated_for(?x1703, ?x10362), nominated_for(?x1703, ?x8330), nominated_for(?x1703, ?x1813), nominated_for(?x1703, ?x1402), nominated_for(?x1703, ?x167), award(?x1172, ?x8153), ?x1402 = 0sxfd, ?x10362 = 0h0wd9, ?x167 = 083shs, ?x1813 = 09gq0x5, genre(?x8330, ?x53), location(?x2426, ?x1860) *> conf = 0.93 ranks of expected_values: 2 EVAL 0d__c3 ceremony! 0gqyl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 36.000 36.000 0.931 http://example.org/award/award_category/winners./award/award_honor/ceremony #4505-03x16f PRED entity: 03x16f PRED relation: student! PRED expected values: 03qsdpk => 116 concepts (114 used for prediction) PRED predicted values (max 10 best out of 11): 03qsdpk (0.06 #160, 0.05 #222, 0.05 #284), 02822 (0.05 #777, 0.04 #652, 0.04 #965), 0w7c (0.02 #663, 0.02 #851, 0.02 #352), 03g3w (0.02 #767, 0.02 #331, 0.02 #393), 01zc2w (0.02 #794, 0.02 #1231, 0.02 #857), 0fdys (0.01 #775, 0.01 #463, 0.01 #1150), 02vxn (0.01 #750), 04rlf (0.01 #481, 0.01 #793), 05qfh (0.01 #648, 0.01 #773), 0mg1w (0.01 #666) >> Best rule #160 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 03lt8g; 05lb30; >> query: (?x8746, 03qsdpk) <- award_nominee(?x8376, ?x8746), award_nominee(?x3956, ?x8746), ?x3956 = 05dxl5, ?x8376 = 06hgym >> conf = 0.06 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x16f student! 03qsdpk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 114.000 0.062 http://example.org/education/field_of_study/students_majoring./education/education/student #4504-09gnn PRED entity: 09gnn PRED relation: place_of_death PRED expected values: 0ht8h => 212 concepts (211 used for prediction) PRED predicted values (max 10 best out of 106): 0mp3l (0.33 #230, 0.17 #1203, 0.12 #2175), 0978r (0.29 #2140, 0.10 #6227, 0.09 #17324), 0cpyv (0.25 #847, 0.17 #1430, 0.06 #4934), 02_286 (0.18 #3127, 0.11 #19090, 0.11 #2542), 02m77 (0.18 #3212, 0.08 #4185, 0.08 #3991), 04jpl (0.17 #1369, 0.14 #7792, 0.14 #5262), 06pr6 (0.17 #1467, 0.03 #5555, 0.02 #6137), 030qb3t (0.16 #17151, 0.14 #17929, 0.13 #19099), 03l2n (0.14 #1817, 0.11 #2595, 0.07 #3568), 0t_gg (0.14 #2018, 0.03 #4745, 0.03 #4939) >> Best rule #230 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 07cbs; >> query: (?x10499, 0mp3l) <- influenced_by(?x5790, ?x10499), profession(?x10499, ?x5805), politician(?x10498, ?x10499), peers(?x4309, ?x10499), gender(?x10499, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09gnn place_of_death 0ht8h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 212.000 211.000 0.333 http://example.org/people/deceased_person/place_of_death #4503-0db94w PRED entity: 0db94w PRED relation: category PRED expected values: 08mbj5d => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.46 #4, 0.38 #9, 0.35 #19) >> Best rule #4 for best value: >> intensional similarity = 12 >> extensional distance = 11 >> proper extension: 0cmc26r; >> query: (?x4446, 08mbj5d) <- film_release_region(?x4446, ?x1892), film_release_region(?x4446, ?x1264), film_release_region(?x4446, ?x410), film_release_region(?x4446, ?x252), film_release_region(?x4446, ?x87), ?x252 = 03_3d, ?x1892 = 02vzc, ?x87 = 05r4w, titles(?x7926, ?x4446), ?x410 = 01ls2, ?x1264 = 0345h, film_format(?x4446, ?x6392) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0db94w category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.462 http://example.org/common/topic/webpage./common/webpage/category #4502-015ynm PRED entity: 015ynm PRED relation: language PRED expected values: 02h40lc => 87 concepts (67 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.91 #720, 0.91 #839, 0.89 #661), 064_8sq (0.15 #81, 0.15 #260, 0.15 #562), 06nm1 (0.13 #610, 0.12 #551, 0.11 #3714), 03_9r (0.12 #129, 0.11 #3714, 0.07 #1087), 04306rv (0.11 #3714, 0.09 #545, 0.09 #723), 0653m (0.11 #3714, 0.07 #311, 0.07 #371), 0jzc (0.11 #3714, 0.05 #258, 0.05 #500), 012w70 (0.11 #3714, 0.04 #312, 0.04 #372), 01wgr (0.11 #3714), 02bjrlw (0.08 #300, 0.08 #360, 0.08 #420) >> Best rule #720 for best value: >> intensional similarity = 4 >> extensional distance = 230 >> proper extension: 0bx_hnp; >> query: (?x8359, 02h40lc) <- country(?x8359, ?x94), crewmember(?x8359, ?x1983), genre(?x8359, ?x258), film_release_distribution_medium(?x8359, ?x81) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015ynm language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 67.000 0.914 http://example.org/film/film/language #4501-01hc1j PRED entity: 01hc1j PRED relation: student PRED expected values: 012bk => 185 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1688): 03qd_ (0.25 #6386, 0.22 #10575, 0.09 #12668), 01nqfh_ (0.25 #6358, 0.22 #10547, 0.09 #12640), 03fykz (0.25 #7040, 0.11 #11229, 0.09 #13322), 024y6w (0.25 #7737, 0.11 #11926, 0.04 #39155), 01_rh4 (0.25 #6819, 0.11 #11008, 0.04 #38237), 0d3k14 (0.22 #12328, 0.12 #10232, 0.12 #8139), 0cbgl (0.22 #12560, 0.12 #8371, 0.11 #43977), 015qq1 (0.22 #12366, 0.12 #8177, 0.09 #39595), 02pv_d (0.22 #11868, 0.12 #7679, 0.09 #39097), 03l3ln (0.22 #11628, 0.12 #7439, 0.09 #38857) >> Best rule #6386 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 015q1n; >> query: (?x11768, 03qd_) <- institution(?x1771, ?x11768), institution(?x620, ?x11768), major_field_of_study(?x11768, ?x947), ?x1771 = 019v9k, ?x620 = 07s6fsf, ?x947 = 036hv >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01hc1j student 012bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 185.000 86.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #4500-04lqvly PRED entity: 04lqvly PRED relation: country PRED expected values: 0chghy => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 69): 03spz (0.77 #717, 0.30 #716, 0.08 #942), 07ssc (0.59 #675, 0.46 #180, 0.45 #510), 03mqtr (0.30 #716, 0.08 #942, 0.08 #941), 07s9rl0 (0.30 #716, 0.08 #942, 0.08 #941), 03rjj (0.17 #390, 0.17 #60, 0.16 #335), 0d05w3 (0.17 #38, 0.05 #698, 0.04 #1315), 0d060g (0.15 #1284, 0.10 #172, 0.09 #557), 0chghy (0.12 #1288, 0.06 #341, 0.06 #176), 06mkj (0.10 #35, 0.06 #365, 0.06 #695), 03h64 (0.10 #41, 0.06 #1318, 0.03 #701) >> Best rule #717 for best value: >> intensional similarity = 3 >> extensional distance = 172 >> proper extension: 01cjhz; 0jq2r; 06f0k; >> query: (?x3965, ?x4743) <- titles(?x4743, ?x3965), locations(?x11047, ?x4743), film_release_region(?x66, ?x4743) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1288 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 448 *> proper extension: 026zlh9; *> query: (?x3965, 0chghy) <- country(?x3965, ?x789), olympics(?x789, ?x452), vacationer(?x789, ?x444) *> conf = 0.12 ranks of expected_values: 8 EVAL 04lqvly country 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 70.000 70.000 0.766 http://example.org/film/film/country #4499-066yfh PRED entity: 066yfh PRED relation: award_nominee! PRED expected values: 027km64 => 97 concepts (27 used for prediction) PRED predicted values (max 10 best out of 818): 062cg6 (0.81 #41959, 0.81 #60611, 0.81 #39627), 066yfh (0.40 #6954, 0.33 #9286, 0.25 #4622), 03v1w7 (0.33 #1466, 0.01 #20113), 027km64 (0.25 #3556, 0.20 #5888, 0.17 #8220), 03m9c8 (0.25 #3888, 0.20 #6220, 0.17 #8552), 0b7xl8 (0.25 #4230, 0.20 #6562, 0.17 #8894), 025hzx (0.20 #6818, 0.17 #9150, 0.04 #11482), 09gb9xh (0.19 #11586, 0.01 #13916), 09b0xs (0.19 #10031, 0.01 #12361), 0dbpyd (0.15 #9341, 0.02 #11671) >> Best rule #41959 for best value: >> intensional similarity = 3 >> extensional distance = 913 >> proper extension: 03qcq; 01j5x6; 01v3s2_; 04cf09; 05cv94; 04rsd2; 07qy0b; 025t9b; 03h2d4; 037hgm; ... >> query: (?x12274, ?x2691) <- type_of_union(?x12274, ?x566), award_nominee(?x12274, ?x2691), place_of_birth(?x12274, ?x1310) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #3556 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 062cg6; 01vhrz; *> query: (?x12274, 027km64) <- award_winner(?x12274, ?x4314), award_winner(?x12274, ?x2691), award_winner(?x9373, ?x12274), ?x2691 = 067pl7, ?x4314 = 07rd7 *> conf = 0.25 ranks of expected_values: 4 EVAL 066yfh award_nominee! 027km64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 27.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4498-026p_bs PRED entity: 026p_bs PRED relation: genre PRED expected values: 02n4kr => 116 concepts (98 used for prediction) PRED predicted values (max 10 best out of 164): 02qfv5d (0.77 #8496, 0.71 #10658, 0.64 #8495), 02kdv5l (0.72 #8619, 0.50 #9818, 0.50 #7299), 05p553 (0.65 #9460, 0.54 #10302, 0.52 #9221), 02n4kr (0.62 #485, 0.30 #1202, 0.27 #5512), 0c3351 (0.62 #514, 0.19 #873, 0.14 #156), 02l7c8 (0.50 #373, 0.43 #134, 0.42 #2165), 03k9fj (0.48 #8747, 0.40 #9827, 0.38 #9348), 03npn (0.43 #246, 0.31 #5146, 0.31 #3588), 060__y (0.40 #1210, 0.26 #2644, 0.25 #3004), 01hmnh (0.40 #614, 0.26 #9354, 0.20 #8753) >> Best rule #8496 for best value: >> intensional similarity = 6 >> extensional distance = 495 >> proper extension: 03kq98; >> query: (?x650, ?x11405) <- titles(?x11405, ?x650), genre(?x3614, ?x11405), titles(?x11405, ?x6778), ?x6778 = 01j5ql, film(?x3017, ?x3614), nominated_for(?x198, ?x3614) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #485 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 05cj_j; 02r_pp; *> query: (?x650, 02n4kr) <- film(?x8002, ?x650), nominated_for(?x650, ?x10404), produced_by(?x650, ?x11962), titles(?x812, ?x650), language(?x650, ?x254), ?x10404 = 01s9vc *> conf = 0.62 ranks of expected_values: 4 EVAL 026p_bs genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 98.000 0.766 http://example.org/film/film/genre #4497-0225bv PRED entity: 0225bv PRED relation: organization! PRED expected values: 060c4 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 15): 060c4 (0.79 #106, 0.78 #67, 0.78 #93), 07xl34 (0.32 #245, 0.29 #232, 0.29 #193), 0dq_5 (0.25 #308, 0.20 #334, 0.20 #543), 05k17c (0.15 #59, 0.11 #450, 0.10 #163), 01t7n9 (0.09 #365, 0.04 #977, 0.03 #991), 02079p (0.09 #365, 0.04 #977, 0.03 #991), 0789n (0.09 #365, 0.04 #977, 0.03 #991), 0f6c3 (0.09 #365, 0.04 #977, 0.03 #991), 01gkgk (0.09 #365, 0.04 #977, 0.03 #991), 0pqc5 (0.09 #365, 0.04 #977, 0.03 #991) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 03wv2g; >> query: (?x12485, 060c4) <- contains(?x94, ?x12485), school(?x2574, ?x12485), currency(?x12485, ?x170), ?x170 = 09nqf >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0225bv organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.786 http://example.org/organization/role/leaders./organization/leadership/organization #4496-0l6mp PRED entity: 0l6mp PRED relation: medal PRED expected values: 02lq67 => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.95 #45, 0.95 #41, 0.93 #37) >> Best rule #45 for best value: >> intensional similarity = 70 >> extensional distance = 25 >> proper extension: 0sxrz; >> query: (?x2233, ?x422) <- sports(?x2233, ?x7687), olympics(?x1499, ?x2233), olympics(?x1353, ?x2233), olympics(?x1229, ?x2233), medal(?x2233, ?x1242), film_release_region(?x9900, ?x1499), film_release_region(?x8193, ?x1499), film_release_region(?x7832, ?x1499), film_release_region(?x7170, ?x1499), film_release_region(?x6751, ?x1499), film_release_region(?x6422, ?x1499), film_release_region(?x6168, ?x1499), film_release_region(?x5013, ?x1499), film_release_region(?x3850, ?x1499), film_release_region(?x2896, ?x1499), film_release_region(?x2695, ?x1499), film_release_region(?x2628, ?x1499), film_release_region(?x1744, ?x1499), film_release_region(?x1283, ?x1499), film_release_region(?x1259, ?x1499), film_release_region(?x204, ?x1499), film_release_region(?x141, ?x1499), ?x7832 = 0fphf3v, medal(?x1499, ?x422), ?x204 = 028_yv, ?x6751 = 0372j5, ?x7170 = 02pxst, film_release_region(?x11351, ?x1353), film_release_region(?x9432, ?x1353), film_release_region(?x3226, ?x1353), film_release_region(?x1956, ?x1353), film_release_region(?x542, ?x1353), countries_spoken_in(?x5359, ?x1499), adjoins(?x1499, ?x2000), ?x6422 = 02qk3fk, adjustment_currency(?x1353, ?x170), ?x141 = 0gtsx8c, ?x542 = 0djb3vw, country(?x668, ?x1499), olympics(?x2044, ?x2233), ?x9432 = 0gvt53w, ?x5013 = 011ycb, ?x1956 = 05qbckf, ?x3226 = 0gyfp9c, ?x1283 = 0cnztc4, participating_countries(?x418, ?x1499), ?x2896 = 0645k5, ?x8193 = 03z9585, ?x1259 = 04hwbq, ?x1744 = 035yn8, ?x11351 = 02wtp6, exported_to(?x94, ?x1229), ?x9900 = 0qmfk, ?x2628 = 06wbm8q, sports(?x391, ?x7687), film_release_region(?x8646, ?x1229), film_release_region(?x5849, ?x1229), film_release_region(?x5139, ?x1229), ?x2695 = 047svrl, ?x8646 = 05zvzf3, contains(?x1499, ?x8809), ?x3850 = 047fjjr, country(?x3554, ?x1353), ?x5849 = 02h22, member_states(?x2106, ?x1229), ?x6168 = 0gj96ln, country(?x7687, ?x142), participating_countries(?x1741, ?x1353), adjoins(?x1353, ?x3227), ?x5139 = 07bzz7 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l6mp medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.955 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #4495-04w7rn PRED entity: 04w7rn PRED relation: language PRED expected values: 064_8sq => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 42): 06nm1 (0.29 #10, 0.15 #124, 0.12 #643), 064_8sq (0.18 #1233, 0.16 #944, 0.16 #135), 04306rv (0.14 #4, 0.11 #1216, 0.10 #118), 0jzc (0.14 #19, 0.04 #709, 0.04 #652), 03hkp (0.14 #14, 0.03 #128, 0.02 #533), 01r2l (0.14 #23, 0.01 #253, 0.01 #1062), 02bjrlw (0.08 #58, 0.08 #1213, 0.07 #1329), 03_9r (0.07 #1625, 0.07 #471, 0.06 #817), 0653m (0.06 #701, 0.04 #2147, 0.04 #759), 012w70 (0.04 #702, 0.03 #183, 0.03 #531) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 06mmr; >> query: (?x1518, 06nm1) <- category(?x1518, ?x134), award(?x1518, ?x2209), ?x2209 = 0gr42 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1233 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 429 *> proper extension: 042g97; *> query: (?x1518, 064_8sq) <- genre(?x1518, ?x53), film(?x1018, ?x1518), honored_for(?x6594, ?x1518), language(?x1518, ?x254) *> conf = 0.18 ranks of expected_values: 2 EVAL 04w7rn language 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.286 http://example.org/film/film/language #4494-0_7w6 PRED entity: 0_7w6 PRED relation: production_companies PRED expected values: 04rcl7 => 116 concepts (83 used for prediction) PRED predicted values (max 10 best out of 67): 04rcl7 (0.57 #70, 0.14 #313, 0.05 #475), 054g1r (0.47 #3743, 0.46 #2435, 0.35 #2844), 09b3v (0.29 #32, 0.12 #437, 0.12 #275), 086k8 (0.14 #2, 0.13 #977, 0.13 #2519), 04rtpt (0.14 #47, 0.05 #128, 0.04 #290), 05qd_ (0.14 #1390, 0.13 #985, 0.13 #2854), 016tt2 (0.13 #409, 0.10 #1384, 0.10 #2848), 016tw3 (0.12 #255, 0.10 #2529, 0.10 #2611), 054lpb6 (0.11 #502, 0.10 #1152, 0.09 #339), 017s11 (0.09 #1140, 0.09 #246, 0.09 #978) >> Best rule #70 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0d4htf; >> query: (?x1919, 04rcl7) <- music(?x1919, ?x1894), nominated_for(?x500, ?x1919), film_crew_role(?x1919, ?x468), ?x1894 = 02fgpf >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_7w6 production_companies 04rcl7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 83.000 0.571 http://example.org/film/film/production_companies #4493-0dvmd PRED entity: 0dvmd PRED relation: place_of_birth PRED expected values: 0f2wj => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 129): 030qb3t (0.27 #56335, 0.27 #30982, 0.27 #54926), 0cr3d (0.20 #94, 0.05 #3614, 0.04 #9248), 0b1t1 (0.20 #366, 0.01 #5294, 0.01 #45067), 0xt3t (0.20 #484), 02_286 (0.10 #4947, 0.09 #1427, 0.09 #5651), 02hrh0_ (0.04 #894, 0.01 #9344, 0.01 #10048), 0rh6k (0.04 #706, 0.01 #30279, 0.01 #29575), 0lphb (0.04 #960, 0.01 #5184, 0.01 #5888), 0ccvx (0.04 #857, 0.01 #17756), 0nlh7 (0.04 #1118) >> Best rule #56335 for best value: >> intensional similarity = 2 >> extensional distance = 2301 >> proper extension: 0qkj7; >> query: (?x3101, ?x1523) <- gender(?x3101, ?x231), location(?x3101, ?x1523) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dvmd place_of_birth 0f2wj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 94.000 0.275 http://example.org/people/person/place_of_birth #4492-01v_pj6 PRED entity: 01v_pj6 PRED relation: student! PRED expected values: 014mlp => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 12): 014mlp (0.34 #226, 0.30 #366, 0.17 #106), 019v9k (0.17 #70, 0.11 #230, 0.10 #370), 016t_3 (0.12 #104, 0.05 #224, 0.05 #364), 02_xgp2 (0.08 #114, 0.05 #234, 0.04 #374), 03bwzr4 (0.08 #75), 028dcg (0.06 #238, 0.05 #378, 0.04 #118), 02h4rq6 (0.05 #223, 0.04 #103, 0.03 #363), 0bkj86 (0.05 #369, 0.04 #229), 04zx3q1 (0.04 #102, 0.04 #222, 0.03 #362), 03mkk4 (0.04 #233, 0.03 #373) >> Best rule #226 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 05fg2; 06whf; >> query: (?x1674, 014mlp) <- nationality(?x1674, ?x512), student(?x8925, ?x1674), award_winner(?x2180, ?x1674) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v_pj6 student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.336 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #4491-0154j PRED entity: 0154j PRED relation: contains PRED expected values: 01nm8w => 181 concepts (128 used for prediction) PRED predicted values (max 10 best out of 2815): 037n3 (0.75 #97081), 0bwfn (0.22 #3988, 0.10 #21641, 0.08 #39292), 0366c (0.15 #10774, 0.11 #4890, 0.09 #28425), 02bd_f (0.15 #10184, 0.11 #4300, 0.08 #39604), 03tm68 (0.15 #10571, 0.11 #4687, 0.08 #39991), 06w92 (0.15 #10802, 0.11 #4918, 0.08 #40222), 078lk (0.15 #9164, 0.11 #3280, 0.08 #38584), 03x3l (0.15 #10158, 0.11 #4274, 0.08 #39578), 07mgr (0.15 #10458, 0.08 #39878, 0.03 #160497), 01y9st (0.13 #27150, 0.12 #41863, 0.11 #3615) >> Best rule #97081 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0160w; 02jx1; >> query: (?x172, ?x4826) <- olympics(?x172, ?x391), country(?x1283, ?x172), country(?x4826, ?x172) >> conf = 0.75 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0154j contains 01nm8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 128.000 0.746 http://example.org/location/location/contains #4490-0fb7sd PRED entity: 0fb7sd PRED relation: currency PRED expected values: 09nqf => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.84 #36, 0.83 #71, 0.81 #50), 01nv4h (0.04 #44, 0.04 #247, 0.03 #268), 02l6h (0.02 #270, 0.02 #249, 0.02 #60), 02gsvk (0.01 #111) >> Best rule #36 for best value: >> intensional similarity = 5 >> extensional distance = 55 >> proper extension: 0jym0; >> query: (?x4967, 09nqf) <- film(?x9397, ?x4967), award_nominee(?x4247, ?x9397), place_of_birth(?x9397, ?x1705), nominated_for(?x9397, ?x8283), ?x4247 = 02vntj >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fb7sd currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.842 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #4489-0f42nz PRED entity: 0f42nz PRED relation: film! PRED expected values: 047jhq => 88 concepts (52 used for prediction) PRED predicted values (max 10 best out of 933): 03m3nzf (0.33 #1558, 0.04 #15949, 0.02 #30338), 0292l3 (0.33 #231, 0.03 #22845, 0.02 #14622), 09r_wb (0.33 #1455), 0f5zj6 (0.25 #4111, 0.24 #8222, 0.22 #10278), 01s0l0 (0.25 #4111, 0.24 #8222, 0.22 #10278), 03wpmd (0.25 #4111, 0.24 #8222, 0.22 #10278), 0127s7 (0.17 #3105, 0.14 #7216, 0.14 #5161), 01mwsnc (0.17 #2927, 0.14 #7038, 0.14 #4983), 0157m (0.17 #2316, 0.14 #6427, 0.14 #4372), 01kx_81 (0.17 #2260, 0.14 #6371, 0.14 #4316) >> Best rule #1558 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0h2zvzr; >> query: (?x5247, 03m3nzf) <- film_festivals(?x5247, ?x13003), film(?x9537, ?x5247), genre(?x5247, ?x53), ?x9537 = 02c_wc >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f42nz film! 047jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 52.000 0.333 http://example.org/film/actor/film./film/performance/film #4488-03g62 PRED entity: 03g62 PRED relation: award PRED expected values: 0gs9p => 118 concepts (108 used for prediction) PRED predicted values (max 10 best out of 258): 0gs9p (0.60 #1298, 0.60 #1704, 0.54 #892), 040njc (0.56 #1226, 0.56 #1632, 0.49 #820), 0gq9h (0.55 #5763, 0.36 #3326, 0.33 #78), 019f4v (0.46 #879, 0.44 #1691, 0.44 #1285), 09sb52 (0.29 #23590, 0.26 #23184, 0.24 #20748), 0gr51 (0.28 #5379, 0.25 #4973, 0.22 #1725), 0gr4k (0.26 #5311, 0.24 #4905, 0.20 #3281), 02pqp12 (0.25 #5349, 0.23 #4943, 0.21 #9004), 0gqy2 (0.24 #5685, 0.22 #166, 0.16 #6663), 0f4x7 (0.24 #5685, 0.15 #6528, 0.15 #3685) >> Best rule #1298 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0j_c; >> query: (?x11180, 0gs9p) <- profession(?x11180, ?x319), film(?x11180, ?x9993), people(?x1158, ?x11180), gender(?x11180, ?x231) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03g62 award 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 108.000 0.605 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4487-0160w PRED entity: 0160w PRED relation: jurisdiction_of_office! PRED expected values: 04syw => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.73 #1620, 0.72 #1557, 0.72 #2292), 0pqc5 (0.62 #677, 0.57 #1328, 0.51 #530), 0f6c3 (0.47 #1457, 0.41 #1247, 0.40 #385), 0fkvn (0.46 #1243, 0.43 #1453, 0.40 #381), 04syw (0.46 #616, 0.39 #574, 0.34 #595), 09n5b9 (0.40 #1461, 0.37 #1251, 0.35 #516), 0p5vf (0.37 #222, 0.30 #264, 0.29 #180), 0dq3c (0.29 #169, 0.20 #85, 0.19 #148), 01zq91 (0.21 #77, 0.16 #792, 0.15 #1065), 0789n (0.20 #387, 0.18 #51, 0.18 #429) >> Best rule #1620 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 04v3q; 03h2c; 01699; 0jdx; 01nyl; 04hhv; >> query: (?x126, 060c4) <- organization(?x126, ?x127), participating_countries(?x784, ?x126), taxonomy(?x126, ?x939) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #616 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 01nty; 0168t; *> query: (?x126, 04syw) <- form_of_government(?x126, ?x1926), ?x1926 = 018wl5, country(?x1121, ?x126) *> conf = 0.46 ranks of expected_values: 5 EVAL 0160w jurisdiction_of_office! 04syw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 160.000 160.000 0.731 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #4486-01lqf49 PRED entity: 01lqf49 PRED relation: profession PRED expected values: 02hrh1q => 127 concepts (50 used for prediction) PRED predicted values (max 10 best out of 74): 02hrh1q (0.91 #2349, 0.88 #2787, 0.87 #1765), 0g0vx (0.70 #399), 01d_h8 (0.64 #589, 0.58 #2341, 0.56 #2633), 03gjzk (0.55 #4103, 0.53 #4249, 0.48 #4983), 0dz3r (0.54 #2192, 0.53 #2046, 0.49 #1024), 0nbcg (0.52 #6757, 0.51 #6316, 0.51 #7052), 018gz8 (0.45 #3666, 0.37 #2352, 0.36 #2790), 02jknp (0.44 #1613, 0.44 #6587, 0.44 #6000), 0fj9f (0.40 #345, 0.02 #3411, 0.01 #5314), 039v1 (0.36 #911, 0.26 #2955, 0.24 #1787) >> Best rule #2349 for best value: >> intensional similarity = 6 >> extensional distance = 128 >> proper extension: 06cv1; 0jf1b; 04n7njg; 0p_2r; 01vqrm; 03xp8d5; 049gc; 0c00lh; 05fyss; 01q9b9; ... >> query: (?x8848, 02hrh1q) <- category(?x8848, ?x134), profession(?x8848, ?x987), profession(?x8848, ?x220), ?x987 = 0dxtg, profession(?x248, ?x220), ?x248 = 0lbj1 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lqf49 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 50.000 0.908 http://example.org/people/person/profession #4485-01fxfk PRED entity: 01fxfk PRED relation: gender PRED expected values: 05zppz => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #19, 0.86 #23, 0.85 #45), 02zsn (0.55 #155, 0.24 #80, 0.23 #134) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 0652ty; >> query: (?x12872, 05zppz) <- profession(?x12872, ?x6421), type_of_union(?x12872, ?x566), ?x6421 = 02hv44_ >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fxfk gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.885 http://example.org/people/person/gender #4484-03qnvdl PRED entity: 03qnvdl PRED relation: film_release_region PRED expected values: 03rt9 0hzlz 0ctw_b 035qy 01znc_ 0d05w3 06f32 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 151): 035qy (0.92 #1787, 0.87 #433, 0.85 #1922), 01znc_ (0.90 #1794, 0.83 #1929, 0.80 #305), 03rt9 (0.80 #280, 0.80 #1769, 0.76 #1092), 0ctw_b (0.79 #1102, 0.65 #425, 0.63 #1779), 03rj0 (0.71 #1131, 0.70 #590, 0.65 #454), 06c1y (0.70 #578, 0.68 #1119, 0.53 #1796), 09pmkv (0.70 #427, 0.61 #563, 0.61 #1104), 06f32 (0.70 #595, 0.61 #1136, 0.54 #1813), 05qx1 (0.65 #575, 0.58 #1116, 0.54 #1793), 06qd3 (0.65 #437, 0.55 #1655, 0.54 #2466) >> Best rule #1787 for best value: >> intensional similarity = 6 >> extensional distance = 87 >> proper extension: 0gtsx8c; 0h1cdwq; 0c40vxk; 0gx9rvq; 0bh8yn3; 0c3xw46; 0glqh5_; 0gtt5fb; 0bq6ntw; 0gg5kmg; ... >> query: (?x1525, 035qy) <- film_release_region(?x1525, ?x2146), film_release_region(?x1525, ?x151), film_release_region(?x1525, ?x94), ?x94 = 09c7w0, ?x2146 = 03rk0, ?x151 = 0b90_r >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 8, 20, 24 EVAL 03qnvdl film_release_region 06f32 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 71.000 71.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03qnvdl film_release_region 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 71.000 71.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03qnvdl film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03qnvdl film_release_region 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03qnvdl film_release_region 0ctw_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03qnvdl film_release_region 0hzlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 71.000 71.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03qnvdl film_release_region 03rt9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4483-016nvh PRED entity: 016nvh PRED relation: category PRED expected values: 08mbj5d => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #7, 0.85 #2, 0.84 #3) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 208 >> proper extension: 01q_ph; 01wmxfs; 03f1r6t; >> query: (?x10624, 08mbj5d) <- artist(?x9114, ?x10624), award_nominee(?x10624, ?x2237), profession(?x10624, ?x1032), ?x1032 = 02hrh1q >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016nvh category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.862 http://example.org/common/topic/webpage./common/webpage/category #4482-02g3v6 PRED entity: 02g3v6 PRED relation: nominated_for PRED expected values: 01hp5 03177r 02ny6g 02dpl9 04pk1f 0btpm6 027fwmt 034b6k => 48 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1664): 0dr_4 (0.75 #6249, 0.67 #4738, 0.60 #1716), 0ddjy (0.64 #19655, 0.64 #19654, 0.40 #1826), 04mcw4 (0.64 #19655, 0.64 #19654, 0.20 #2166), 0df92l (0.64 #19655, 0.64 #19654, 0.20 #2357), 06r2h (0.64 #19655, 0.64 #19654, 0.04 #11842), 019vhk (0.62 #6431, 0.60 #1898, 0.53 #9457), 09q5w2 (0.60 #9208, 0.50 #4671, 0.50 #3160), 049xgc (0.60 #2336, 0.50 #6869, 0.50 #5358), 020fcn (0.60 #1665, 0.50 #4687, 0.44 #7710), 09k56b7 (0.60 #1773, 0.50 #4795, 0.38 #6306) >> Best rule #6249 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 02r0csl; >> query: (?x507, 0dr_4) <- nominated_for(?x507, ?x7305), nominated_for(?x507, ?x7239), nominated_for(?x507, ?x1707), film_release_region(?x1707, ?x205), story_by(?x7239, ?x8753), ?x7305 = 031786, ?x205 = 03rjj >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #5616 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 05ztjjw; *> query: (?x507, 0btpm6) <- nominated_for(?x507, ?x7554), nominated_for(?x507, ?x7239), nominated_for(?x507, ?x2889), nominated_for(?x507, ?x908), ?x908 = 01vksx, ?x7554 = 01mgw, executive_produced_by(?x7239, ?x5869), film_release_region(?x2889, ?x94) *> conf = 0.50 ranks of expected_values: 28, 47, 146, 147, 253, 498, 611, 1437 EVAL 02g3v6 nominated_for 034b6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 15.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02g3v6 nominated_for 027fwmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 48.000 15.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02g3v6 nominated_for 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 48.000 15.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02g3v6 nominated_for 04pk1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 48.000 15.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02g3v6 nominated_for 02dpl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 15.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02g3v6 nominated_for 02ny6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 15.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02g3v6 nominated_for 03177r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 48.000 15.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02g3v6 nominated_for 01hp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 48.000 15.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4481-07vqnc PRED entity: 07vqnc PRED relation: genre PRED expected values: 0c4xc => 73 concepts (42 used for prediction) PRED predicted values (max 10 best out of 126): 01htzx (0.85 #168, 0.82 #631, 0.72 #475), 07s9rl0 (0.70 #1995, 0.67 #461, 0.61 #771), 06n90 (0.67 #1240, 0.62 #165, 0.53 #318), 05p553 (0.67 #235, 0.64 #1922, 0.62 #388), 0pr6f (0.53 #276, 0.50 #429, 0.40 #352), 095bb (0.53 #263, 0.50 #416, 0.27 #339), 0jxy (0.50 #565, 0.50 #105, 0.40 #334), 01z4y (0.40 #1933, 0.33 #246, 0.33 #1549), 01z77k (0.40 #1177, 0.14 #1558, 0.13 #1101), 02n4kr (0.31 #162, 0.22 #469, 0.20 #315) >> Best rule #168 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 09kn9; 0hz55; 0828jw; 06qwh; 028k2x; 0170k0; 06r4f; 03g9xj; 053x8hr; 06r1k; ... >> query: (?x11454, 01htzx) <- genre(?x11454, ?x811), ?x811 = 03k9fj, actor(?x11454, ?x3785), titles(?x7712, ?x11454), tv_program(?x1182, ?x11454) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1955 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 149 *> proper extension: 072kp; 039fgy; 0kfpm; 02nf2c; 0124k9; 08jgk1; 02xhpl; 01q_y0; 02hct1; 0d68qy; ... *> query: (?x11454, 0c4xc) <- genre(?x11454, ?x811), genre(?x11945, ?x811), genre(?x2649, ?x811), ?x11945 = 03wjm2, film_crew_role(?x2649, ?x137) *> conf = 0.28 ranks of expected_values: 12 EVAL 07vqnc genre 0c4xc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 73.000 42.000 0.846 http://example.org/tv/tv_program/genre #4480-04913k PRED entity: 04913k PRED relation: colors PRED expected values: 036k5h => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 18): 02rnmb (0.50 #49, 0.48 #110, 0.47 #140), 01l849 (0.48 #110, 0.44 #181, 0.38 #388), 0jc_p (0.48 #110, 0.44 #181, 0.38 #388), 038hg (0.48 #110, 0.44 #181, 0.38 #388), 01g5v (0.39 #859, 0.38 #754, 0.38 #734), 03vtbc (0.25 #21, 0.24 #75, 0.21 #112), 036k5h (0.25 #21, 0.24 #75, 0.21 #112), 06kqt3 (0.24 #75, 0.21 #112, 0.20 #302), 07plts (0.24 #75, 0.21 #112, 0.20 #302), 088fh (0.24 #75, 0.21 #112, 0.20 #518) >> Best rule #49 for best value: >> intensional similarity = 24 >> extensional distance = 8 >> proper extension: 03lpp_; 021f30; >> query: (?x2011, 02rnmb) <- sport(?x2011, ?x5063), team(?x5727, ?x2011), team(?x2010, ?x2011), colors(?x2011, ?x1101), colors(?x2011, ?x663), ?x663 = 083jv, ?x5727 = 02wszf, ?x5063 = 018jz, ?x1101 = 06fvc, team(?x2010, ?x13733), team(?x2010, ?x10939), team(?x2010, ?x7725), team(?x2010, ?x7499), team(?x2010, ?x6823), team(?x2010, ?x2067), team(?x2010, ?x700), ?x7499 = 0132_h, ?x700 = 06x68, ?x7725 = 07l8x, ?x2067 = 05g76, position(?x662, ?x2010), ?x6823 = 07l8f, ?x10939 = 0x0d, position(?x13733, ?x4244) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 4 *> proper extension: 0c41y70; 0hm2b; *> query: (?x2011, ?x3364) <- sport(?x2011, ?x5063), team(?x5727, ?x2011), colors(?x2011, ?x4557), colors(?x2011, ?x1101), colors(?x2011, ?x663), ?x663 = 083jv, team(?x5727, ?x6074), team(?x5727, ?x4487), team(?x5727, ?x2174), team(?x5727, ?x1632), teams(?x3501, ?x6074), ?x4557 = 019sc, colors(?x2174, ?x332), ?x332 = 01l849, ?x1101 = 06fvc, category(?x2174, ?x134), team(?x5412, ?x1632), colors(?x1632, ?x3364), company(?x4486, ?x4487) *> conf = 0.25 ranks of expected_values: 7 EVAL 04913k colors 036k5h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 58.000 58.000 0.500 http://example.org/sports/sports_team/colors #4479-0cdw6 PRED entity: 0cdw6 PRED relation: contains! PRED expected values: 07ssc 02jx1 => 77 concepts (22 used for prediction) PRED predicted values (max 10 best out of 137): 02jx1 (0.98 #9063, 0.88 #9962, 0.86 #10862), 07ssc (0.81 #3621, 0.80 #927, 0.77 #1793), 09c7w0 (0.38 #17076, 0.38 #17975, 0.38 #18875), 01n7q (0.18 #12652, 0.17 #17151, 0.17 #18050), 04jpl (0.18 #13495, 0.16 #8998, 0.14 #9897), 02j9z (0.17 #15300, 0.10 #7210, 0.09 #8109), 0345h (0.16 #16254, 0.10 #12656, 0.04 #17155), 0dg3n1 (0.15 #7337, 0.14 #8236, 0.03 #6438), 094vy (0.14 #557, 0.04 #12570, 0.04 #16168), 0d6br (0.13 #1321, 0.05 #2219, 0.04 #3117) >> Best rule #9063 for best value: >> intensional similarity = 6 >> extensional distance = 222 >> proper extension: 07tgn; 022_6; 07tl0; 0crjn65; 01k8q5; 0dplh; 0121c1; 0ymc8; 07tg4; 018m5q; ... >> query: (?x14505, 02jx1) <- contains(?x12381, ?x14505), administrative_parent(?x4510, ?x12381), contains(?x12381, ?x13278), contains(?x512, ?x12381), ?x13278 = 0138kk, ?x512 = 07ssc >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0cdw6 contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 22.000 0.982 http://example.org/location/location/contains EVAL 0cdw6 contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 22.000 0.982 http://example.org/location/location/contains #4478-07w8fz PRED entity: 07w8fz PRED relation: language PRED expected values: 02h40lc => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.90 #61, 0.90 #595, 0.90 #1073), 064_8sq (0.19 #260, 0.18 #140, 0.17 #200), 06nm1 (0.14 #11, 0.11 #368, 0.09 #1319), 06b_j (0.14 #23, 0.07 #201, 0.06 #974), 04306rv (0.13 #123, 0.12 #183, 0.11 #956), 02bjrlw (0.12 #119, 0.11 #179, 0.10 #239), 03hkp (0.07 #15, 0.02 #549, 0.02 #608), 03_9r (0.06 #662, 0.05 #307, 0.05 #782), 04h9h (0.05 #102, 0.05 #161, 0.05 #221), 0jzc (0.05 #554, 0.04 #971, 0.04 #138) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 0pd64; >> query: (?x3133, 02h40lc) <- nominated_for(?x2375, ?x3133), ?x2375 = 04kxsb, film_crew_role(?x3133, ?x137) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07w8fz language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.901 http://example.org/film/film/language #4477-05gp3x PRED entity: 05gp3x PRED relation: award_winner! PRED expected values: 0bx6zs => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 106): 02q690_ (0.16 #344, 0.10 #764, 0.10 #624), 0jt3qpk (0.15 #183, 0.05 #883, 0.03 #1303), 0gkxgfq (0.15 #246, 0.03 #106, 0.02 #3326), 0gvstc3 (0.14 #874, 0.09 #314, 0.09 #1294), 027n06w (0.13 #912, 0.10 #352, 0.07 #1332), 03nnm4t (0.12 #353, 0.08 #773, 0.08 #633), 05c1t6z (0.12 #855, 0.10 #295, 0.08 #1275), 0gx_st (0.10 #317, 0.06 #457, 0.06 #877), 09v0p2c (0.10 #922, 0.07 #362, 0.06 #1342), 0bq_mx (0.09 #972, 0.05 #1392, 0.04 #1812) >> Best rule #344 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 02j8nx; 07lwsz; 0988cp; >> query: (?x6072, 02q690_) <- profession(?x6072, ?x353), award_winner(?x6072, ?x6071), program_creator(?x1653, ?x6072) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 02j8nx; 07lwsz; 0988cp; *> query: (?x6072, 0bx6zs) <- profession(?x6072, ?x353), award_winner(?x6072, ?x6071), program_creator(?x1653, ?x6072) *> conf = 0.03 ranks of expected_values: 45 EVAL 05gp3x award_winner! 0bx6zs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 112.000 112.000 0.155 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4476-05r7t PRED entity: 05r7t PRED relation: nationality! PRED expected values: 01wbl_r => 199 concepts (95 used for prediction) PRED predicted values (max 10 best out of 4269): 0bqvs2 (0.47 #60965, 0.22 #24391, 0.09 #125987), 09z1lg (0.47 #60965, 0.22 #24391, 0.09 #125987), 09hd6f (0.44 #296668, 0.07 #56088, 0.06 #60152), 018db8 (0.37 #186950, 0.35 #296669, 0.07 #53015), 01wbl_r (0.37 #186950, 0.35 #296669, 0.05 #162567), 06dn58 (0.28 #12197, 0.23 #52837, 0.19 #199143), 01sl1q (0.28 #12197, 0.23 #52837, 0.19 #199143), 059xvg (0.25 #9183, 0.19 #57951, 0.17 #102652), 0p__8 (0.25 #9980, 0.19 #58748, 0.15 #22174), 0cmpn (0.25 #11265, 0.14 #31586, 0.14 #27523) >> Best rule #60965 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 06c62; >> query: (?x6559, ?x7547) <- origin(?x7547, ?x6559), olympics(?x6559, ?x2134), film_release_region(?x1392, ?x6559), olympics(?x94, ?x2134) >> conf = 0.47 => this is the best rule for 2 predicted values *> Best rule #186950 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0dv0z; 01fvhp; 01m41_; 03f4n1; 0gtzp; *> query: (?x6559, ?x793) <- capital(?x6559, ?x8428), location(?x793, ?x8428), time_zones(?x8428, ?x11506) *> conf = 0.37 ranks of expected_values: 5 EVAL 05r7t nationality! 01wbl_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 199.000 95.000 0.472 http://example.org/people/person/nationality #4475-0fd6qb PRED entity: 0fd6qb PRED relation: award_nominee! PRED expected values: 05728w1 => 99 concepts (48 used for prediction) PRED predicted values (max 10 best out of 891): 05728w1 (0.81 #56000, 0.81 #46667, 0.81 #109668), 0520r2x (0.25 #2361, 0.18 #4694, 0.03 #14026), 01vsn38 (0.25 #2220), 0gyy0 (0.25 #1863), 02mjf2 (0.25 #1034), 0fmqp6 (0.23 #6240, 0.19 #3907, 0.02 #17905), 07hhnl (0.19 #3493, 0.18 #5826, 0.03 #15158), 057bc6m (0.19 #4185, 0.14 #6518, 0.04 #18183), 0fqjks (0.19 #4014, 0.14 #6347, 0.02 #13346), 072twv (0.19 #2858, 0.14 #5191, 0.02 #16856) >> Best rule #56000 for best value: >> intensional similarity = 3 >> extensional distance = 1093 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 04bdxl; 02s2ft; 05vsxz; 0grwj; 05bnp0; ... >> query: (?x10835, ?x2778) <- gender(?x10835, ?x231), award_winner(?x2958, ?x10835), award_nominee(?x10835, ?x2778) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fd6qb award_nominee! 05728w1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 48.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4474-0d9xq PRED entity: 0d9xq PRED relation: category PRED expected values: 08mbj5d => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #53, 0.85 #15, 0.84 #27) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 463 >> proper extension: 0lsw9; >> query: (?x5101, 08mbj5d) <- award_winner(?x4796, ?x5101), artist(?x3240, ?x5101), award_winner(?x4796, ?x7536), location(?x7536, ?x3014) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d9xq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.852 http://example.org/common/topic/webpage./common/webpage/category #4473-02ly_ PRED entity: 02ly_ PRED relation: place_of_birth! PRED expected values: 02rsz0 => 141 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1947): 01hkhq (0.40 #28750, 0.33 #104558, 0.28 #151607), 03fbc (0.38 #23521, 0.24 #128082, 0.22 #154221), 07rhpg (0.33 #4263, 0.12 #19943, 0.09 #30400), 01vrnsk (0.33 #4050, 0.12 #19730, 0.09 #30187), 025t9b (0.33 #3375, 0.12 #19055, 0.09 #29512), 0fvt2 (0.33 #4893, 0.12 #20573, 0.09 #31030), 02465 (0.33 #4888, 0.12 #20568, 0.09 #31025), 03bnv (0.33 #3252, 0.12 #18932, 0.09 #29389), 026g801 (0.33 #1061, 0.03 #58568, 0.03 #66408), 0dx97 (0.25 #6303, 0.20 #8916, 0.09 #29826) >> Best rule #28750 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0g251; >> query: (?x5376, ?x2493) <- contains(?x1310, ?x5376), ?x1310 = 02jx1, location(?x2493, ?x5376), place_of_death(?x4033, ?x5376) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #31366 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 0hyxv; *> query: (?x5376, ?x57) <- contains(?x1310, ?x5376), contains(?x5376, ?x5375), featured_film_locations(?x1228, ?x5376), nationality(?x8382, ?x1310), nationality(?x57, ?x1310), ?x8382 = 0mb5x *> conf = 0.01 ranks of expected_values: 1691 EVAL 02ly_ place_of_birth! 02rsz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 141.000 67.000 0.400 http://example.org/people/person/place_of_birth #4472-01dbhb PRED entity: 01dbhb PRED relation: religion PRED expected values: 051kv => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 18): 02rsw (0.33 #24, 0.02 #294, 0.01 #384), 0c8wxp (0.21 #141, 0.20 #96, 0.20 #906), 03_gx (0.10 #59, 0.09 #1049, 0.08 #374), 0kpl (0.08 #280, 0.07 #145, 0.06 #190), 051kv (0.05 #95, 0.03 #140, 0.03 #185), 0kq2 (0.04 #288, 0.03 #153, 0.03 #198), 0631_ (0.03 #143, 0.03 #188, 0.02 #2033), 03j6c (0.03 #1641, 0.03 #1821, 0.03 #1911), 0n2g (0.03 #328, 0.02 #2219, 0.02 #1048), 019cr (0.03 #326, 0.02 #461, 0.01 #911) >> Best rule #24 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02yy8; >> query: (?x13307, 02rsw) <- spouse(?x12359, ?x13307), nationality(?x12359, ?x94), influenced_by(?x12359, ?x7513), people(?x4322, ?x13307) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #95 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 012vf6; *> query: (?x13307, 051kv) <- film(?x13307, ?x7524), gender(?x13307, ?x514), place_of_death(?x13307, ?x739), ?x739 = 02_286 *> conf = 0.05 ranks of expected_values: 5 EVAL 01dbhb religion 051kv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 141.000 141.000 0.333 http://example.org/people/person/religion #4471-019gz PRED entity: 019gz PRED relation: influenced_by! PRED expected values: 0jt90f5 => 182 concepts (100 used for prediction) PRED predicted values (max 10 best out of 386): 040db (0.40 #76, 0.33 #1106, 0.29 #1621), 03_87 (0.40 #262, 0.33 #1292, 0.29 #1807), 06whf (0.40 #165, 0.33 #1195, 0.29 #1710), 07h07 (0.40 #152, 0.29 #1697, 0.17 #6333), 08433 (0.40 #29, 0.29 #1574, 0.17 #1059), 06jcc (0.23 #3404, 0.21 #7010, 0.16 #13709), 014ps4 (0.22 #9584, 0.20 #7523, 0.19 #4947), 07h1q (0.20 #925, 0.20 #410, 0.15 #30402), 0683n (0.20 #340, 0.19 #4975, 0.14 #1885), 034bs (0.20 #155, 0.17 #1185, 0.15 #30402) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 06whf; >> query: (?x11410, 040db) <- student(?x8694, ?x11410), influenced_by(?x10598, ?x11410), ?x8694 = 011xy1, profession(?x11410, ?x353) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #19659 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 05gpy; *> query: (?x11410, 0jt90f5) <- story_by(?x7741, ?x11410), influenced_by(?x10598, ?x11410), profession(?x11410, ?x353), award_winner(?x7741, ?x2887) *> conf = 0.10 ranks of expected_values: 78 EVAL 019gz influenced_by! 0jt90f5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 182.000 100.000 0.400 http://example.org/influence/influence_node/influenced_by #4470-02ct_k PRED entity: 02ct_k PRED relation: award_winner! PRED expected values: 07z31v => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 86): 019bk0 (0.20 #16, 0.05 #2396, 0.05 #1556), 09g90vz (0.20 #123, 0.05 #963, 0.04 #3063), 09p2r9 (0.20 #92, 0.03 #932, 0.02 #1772), 07y9ts (0.20 #68, 0.02 #908, 0.02 #2308), 02jp5r (0.20 #69, 0.02 #909, 0.01 #2309), 09k5jh7 (0.20 #83, 0.02 #1763, 0.02 #923), 05qb8vx (0.20 #59), 0h_cssd (0.17 #168), 092t4b (0.08 #892, 0.06 #1032, 0.06 #1732), 09qvms (0.08 #993, 0.07 #853, 0.06 #1693) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 03z509; >> query: (?x9655, 019bk0) <- film(?x9655, ?x12401), ?x12401 = 016z43, award_winner(?x9541, ?x9655) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #4231 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1115 *> proper extension: 06449; 037hgm; *> query: (?x9655, 07z31v) <- award_nominee(?x9655, ?x5806), student(?x122, ?x9655), nationality(?x5806, ?x94) *> conf = 0.02 ranks of expected_values: 65 EVAL 02ct_k award_winner! 07z31v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 83.000 83.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4469-098n5 PRED entity: 098n5 PRED relation: profession PRED expected values: 02hv44_ => 105 concepts (80 used for prediction) PRED predicted values (max 10 best out of 67): 02jknp (0.76 #2036, 0.60 #1021, 0.56 #586), 02hrh1q (0.73 #9874, 0.73 #12, 0.70 #4798), 09jwl (0.35 #741, 0.31 #1176, 0.29 #1321), 0nbcg (0.26 #753, 0.21 #1188, 0.21 #1333), 018gz8 (0.24 #594, 0.19 #1899, 0.18 #1464), 016z4k (0.20 #729, 0.19 #1164, 0.18 #1309), 0kyk (0.18 #461, 0.13 #26, 0.11 #751), 0dz3r (0.17 #727, 0.15 #1162, 0.14 #1307), 0np9r (0.15 #598, 0.14 #1468, 0.13 #1903), 02hv44_ (0.14 #489, 0.07 #924, 0.06 #2519) >> Best rule #2036 for best value: >> intensional similarity = 4 >> extensional distance = 427 >> proper extension: 0162c8; 049k07; 01d1yr; 03ys2f; 03ysmg; 06qgjh; 0grmhb; 025hzx; 04g_wd; >> query: (?x3555, 02jknp) <- award_nominee(?x6911, ?x3555), profession(?x3555, ?x1943), profession(?x11358, ?x1943), ?x11358 = 026xt5c >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #489 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 104 *> proper extension: 03qcq; 01gp_x; 027hnjh; 0b478; 0gs5q; *> query: (?x3555, 02hv44_) <- award_nominee(?x6911, ?x3555), profession(?x3555, ?x319), story_by(?x5712, ?x3555) *> conf = 0.14 ranks of expected_values: 10 EVAL 098n5 profession 02hv44_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 105.000 80.000 0.765 http://example.org/people/person/profession #4468-04s9n PRED entity: 04s9n PRED relation: taxonomy PRED expected values: 04n6k => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.04 #25, 0.04 #27, 0.03 #31) >> Best rule #25 for best value: >> intensional similarity = 5 >> extensional distance = 178 >> proper extension: 01pp3p; 0lh0c; 01zwy; 042f1; 042d1; 03f22dp; 081t6; >> query: (?x10688, 04n6k) <- place_of_death(?x10688, ?x12397), type_of_union(?x10688, ?x566), ?x566 = 04ztj, people(?x12262, ?x10688), gender(?x10688, ?x231) >> conf = 0.04 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04s9n taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.039 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #4467-056_y PRED entity: 056_y PRED relation: month PRED expected values: 0lkm => 233 concepts (233 used for prediction) PRED predicted values (max 10 best out of 1): 0lkm (0.90 #46, 0.86 #52, 0.85 #55) >> Best rule #46 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 0h3tv; 0g6xq; >> query: (?x4698, 0lkm) <- contains(?x2152, ?x4698), month(?x4698, ?x7298), month(?x4698, ?x1650), ?x7298 = 04wzr, ?x1650 = 06vkl >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 056_y month 0lkm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 233.000 233.000 0.902 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #4466-078mm1 PRED entity: 078mm1 PRED relation: genre PRED expected values: 01drsx => 102 concepts (44 used for prediction) PRED predicted values (max 10 best out of 168): 05p553 (0.87 #3416, 0.39 #943, 0.32 #4593), 082gq (0.62 #2498, 0.33 #2260, 0.28 #2379), 02l7c8 (0.50 #131, 0.34 #1777, 0.34 #2129), 02n4kr (0.44 #594, 0.24 #2714, 0.23 #3654), 09blyk (0.33 #616, 0.12 #3676, 0.11 #2736), 06n90 (0.33 #2362, 0.28 #4953, 0.24 #2952), 0lsxr (0.33 #3655, 0.30 #2715, 0.28 #476), 0hcr (0.32 #725, 0.28 #842, 0.27 #1078), 01hmnh (0.30 #2603, 0.29 #720, 0.26 #956), 0jxy (0.29 #746, 0.25 #863, 0.23 #276) >> Best rule #3416 for best value: >> intensional similarity = 8 >> extensional distance = 453 >> proper extension: 027qgy; 0ckr7s; 034qrh; 0hmr4; 087wc7n; 018f8; 03bx2lk; 026390q; 03fts; 07h9gp; ... >> query: (?x8477, 05p553) <- film_release_region(?x8477, ?x94), genre(?x8477, ?x1626), genre(?x9175, ?x1626), genre(?x5429, ?x1626), genre(?x3376, ?x1626), award_winner(?x9175, ?x7610), ?x5429 = 02psgq, ?x3376 = 05g8pg >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #627 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 25 *> proper extension: 0yx7h; *> query: (?x8477, 01drsx) <- genre(?x8477, ?x4205), genre(?x8477, ?x812), film(?x1870, ?x8477), ?x812 = 01jfsb, ?x4205 = 0c3351, music(?x8477, ?x669), country(?x8477, ?x205) *> conf = 0.07 ranks of expected_values: 44 EVAL 078mm1 genre 01drsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 102.000 44.000 0.868 http://example.org/film/film/genre #4465-0dlngsd PRED entity: 0dlngsd PRED relation: film_release_region PRED expected values: 0d0vqn 05qhw 059j2 06c1y 05b4w 03spz => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 137): 0d0vqn (0.94 #922, 0.91 #2760, 0.91 #2629), 05qhw (0.88 #927, 0.88 #140, 0.77 #534), 059j2 (0.84 #2779, 0.84 #3566, 0.83 #2648), 05b4w (0.83 #965, 0.81 #178, 0.74 #572), 09pmkv (0.81 #150, 0.63 #937, 0.55 #544), 03spz (0.77 #992, 0.69 #205, 0.65 #599), 0ctw_b (0.69 #149, 0.67 #936, 0.65 #543), 06c1y (0.62 #162, 0.61 #556, 0.60 #949), 03rj0 (0.62 #174, 0.61 #568, 0.59 #2668), 06mzp (0.56 #1064, 0.55 #2771, 0.54 #2640) >> Best rule #922 for best value: >> intensional similarity = 6 >> extensional distance = 50 >> proper extension: 07s3m4g; >> query: (?x4615, 0d0vqn) <- film_release_region(?x4615, ?x2146), film_release_region(?x4615, ?x1453), film_release_region(?x4615, ?x390), ?x390 = 0chghy, ?x1453 = 06qd3, ?x2146 = 03rk0 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 8 EVAL 0dlngsd film_release_region 03spz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 78.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dlngsd film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dlngsd film_release_region 06c1y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dlngsd film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dlngsd film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0dlngsd film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4464-0f14q PRED entity: 0f14q PRED relation: people! PRED expected values: 04psf => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 20): 0dcsx (0.07 #411, 0.06 #279, 0.03 #477), 0gk4g (0.07 #406, 0.05 #1264, 0.04 #1792), 06z5s (0.06 #289, 0.03 #487, 0.02 #751), 01tf_6 (0.06 #295, 0.03 #493, 0.01 #1417), 04psf (0.04 #337, 0.03 #403, 0.02 #1261), 04p3w (0.03 #407, 0.03 #473, 0.02 #539), 0dq9p (0.03 #413, 0.02 #7278, 0.02 #2327), 02k6hp (0.03 #433, 0.02 #565, 0.02 #3865), 02knxx (0.03 #428, 0.01 #692), 08g5q7 (0.03 #438, 0.01 #1362) >> Best rule #411 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 0b80__; 01kgg9; 02m30v; >> query: (?x9957, 0dcsx) <- spouse(?x9957, ?x1683), place_of_death(?x1683, ?x242), gender(?x1683, ?x231), profession(?x9957, ?x319) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #337 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 0hcvy; *> query: (?x9957, 04psf) <- actor(?x7488, ?x9957), student(?x4904, ?x9957), profession(?x9957, ?x353), ?x353 = 0cbd2 *> conf = 0.04 ranks of expected_values: 5 EVAL 0f14q people! 04psf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 125.000 125.000 0.067 http://example.org/people/cause_of_death/people #4463-0b4lkx PRED entity: 0b4lkx PRED relation: film! PRED expected values: 016kb7 => 89 concepts (36 used for prediction) PRED predicted values (max 10 best out of 739): 01tcf7 (0.51 #39499, 0.49 #49897, 0.49 #2079), 01fx5l (0.49 #49897, 0.49 #2079, 0.48 #2080), 085pr (0.49 #2079, 0.48 #2080, 0.44 #31182), 03fqv5 (0.49 #2079, 0.48 #2080, 0.44 #31182), 0854hr (0.49 #2079, 0.48 #2080, 0.42 #64446), 045931 (0.08 #1898, 0.04 #3979, 0.03 #8135), 023kzp (0.08 #1056, 0.04 #49896, 0.01 #26000), 0h0wc (0.08 #423, 0.03 #19131, 0.03 #2504), 01dbk6 (0.08 #957, 0.03 #3038, 0.02 #5116), 0l6px (0.08 #387, 0.03 #17017, 0.02 #4546) >> Best rule #39499 for best value: >> intensional similarity = 4 >> extensional distance = 421 >> proper extension: 01h1bf; >> query: (?x8000, ?x8802) <- honored_for(?x8478, ?x8000), nominated_for(?x8802, ?x8000), film(?x8802, ?x2094), award_winner(?x9899, ?x8802) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #3451 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 011yqc; 0f4_l; 07j8r; 011yl_; 02mt51; 0h6r5; 0g9lm2; *> query: (?x8000, 016kb7) <- nominated_for(?x1198, ?x8000), ?x1198 = 02pqp12, award(?x8000, ?x289), featured_film_locations(?x8000, ?x108) *> conf = 0.01 ranks of expected_values: 449 EVAL 0b4lkx film! 016kb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 89.000 36.000 0.508 http://example.org/film/actor/film./film/performance/film #4462-01l9p PRED entity: 01l9p PRED relation: award PRED expected values: 09sb52 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 238): 09sb52 (0.78 #39, 0.36 #13541, 0.34 #15529), 099cng (0.72 #1192, 0.72 #33767, 0.70 #25423), 05zr6wv (0.33 #413, 0.17 #5179, 0.16 #1208), 05p09zm (0.24 #2501, 0.23 #3295, 0.22 #912), 0bdw1g (0.22 #36, 0.04 #12347, 0.03 #12744), 09sdmz (0.17 #596, 0.13 #17476, 0.12 #31381), 04kxsb (0.17 #517, 0.13 #3694, 0.11 #4488), 057xs89 (0.17 #551, 0.12 #948, 0.11 #3728), 02w9sd7 (0.17 #561, 0.11 #3738, 0.11 #164), 04ljl_l (0.17 #400, 0.11 #3, 0.09 #4371) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 07s8r0; 032w8h; 01w7nww; 05slvm; 029_l; 0gnbw; >> query: (?x1735, 09sb52) <- film(?x1735, ?x721), nominated_for(?x1735, ?x167), ?x167 = 083shs >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l9p award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4461-026r8q PRED entity: 026r8q PRED relation: participant! PRED expected values: 01jb26 => 104 concepts (65 used for prediction) PRED predicted values (max 10 best out of 293): 01skmp (0.84 #20764, 0.81 #14272, 0.81 #14921), 01jb26 (0.84 #20764, 0.81 #14272, 0.81 #14921), 016vg8 (0.34 #3241, 0.26 #5188, 0.05 #8109), 01vs_v8 (0.34 #3241, 0.26 #5188, 0.02 #3379), 02114t (0.29 #907, 0.25 #259, 0.03 #5447), 0227vl (0.29 #1182, 0.07 #3125, 0.05 #5722), 01pw2f1 (0.25 #93, 0.14 #741, 0.03 #2684), 015f7 (0.25 #232, 0.14 #880, 0.03 #2823), 01pgzn_ (0.14 #797, 0.07 #2740, 0.05 #5337), 09l3p (0.14 #943, 0.03 #2886, 0.02 #8079) >> Best rule #20764 for best value: >> intensional similarity = 3 >> extensional distance = 592 >> proper extension: 0hnp7; 01vsqvs; 044kwr; 0202p_; >> query: (?x7346, ?x4782) <- participant(?x7346, ?x4782), film(?x4782, ?x1811), participant(?x4782, ?x1896) >> conf = 0.84 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 026r8q participant! 01jb26 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 65.000 0.838 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #4460-0cwy47 PRED entity: 0cwy47 PRED relation: genre PRED expected values: 02l7c8 => 96 concepts (95 used for prediction) PRED predicted values (max 10 best out of 97): 060__y (0.44 #17, 0.26 #1070, 0.23 #368), 01jfsb (0.43 #1299, 0.41 #1533, 0.41 #1182), 02l7c8 (0.42 #133, 0.37 #484, 0.34 #718), 02kdv5l (0.39 #1288, 0.39 #1171, 0.36 #1054), 05p553 (0.38 #3747, 0.35 #237, 0.35 #1758), 03k9fj (0.35 #245, 0.29 #1181, 0.29 #1415), 0lsxr (0.33 #8, 0.28 #944, 0.25 #1295), 04t36 (0.33 #122, 0.19 #1994, 0.12 #1877), 01hmnh (0.23 #3411, 0.21 #1422, 0.19 #1656), 03g3w (0.22 #25, 0.13 #727, 0.11 #493) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0bw20; >> query: (?x951, 060__y) <- featured_film_locations(?x951, ?x608), edited_by(?x951, ?x8469), genre(?x951, ?x53), combatants(?x7419, ?x608) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #133 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 05dmmc; 0gw7p; *> query: (?x951, 02l7c8) <- featured_film_locations(?x951, ?x362), film_release_region(?x951, ?x142), award(?x951, ?x484), ?x484 = 0gq_v *> conf = 0.42 ranks of expected_values: 3 EVAL 0cwy47 genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 95.000 0.444 http://example.org/film/film/genre #4459-0klw PRED entity: 0klw PRED relation: student! PRED expected values: 014xf6 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 240): 02sdwt (0.33 #402, 0.07 #7780, 0.05 #7253), 0h6rm (0.33 #671, 0.07 #2252, 0.05 #4360), 02l9wl (0.22 #1306, 0.11 #1833, 0.05 #6576), 01w5m (0.16 #3794, 0.15 #6956, 0.11 #1686), 015nl4 (0.11 #1648, 0.11 #1121, 0.06 #31691), 0trv (0.11 #1900, 0.11 #1373, 0.06 #6116), 02xwzh (0.11 #1969, 0.11 #1442, 0.05 #4077), 07vyf (0.11 #1719, 0.11 #1192, 0.05 #3827), 0dy04 (0.11 #1652, 0.11 #1125, 0.04 #9557), 07tgn (0.10 #6868, 0.10 #7395, 0.09 #17937) >> Best rule #402 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01k56k; >> query: (?x4895, 02sdwt) <- story_by(?x4699, ?x4895), award(?x4895, ?x5050), ?x5050 = 0265wl >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4520 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 0177s6; 0379s; 034bs; 03_87; 03jxw; 01lwx; *> query: (?x4895, 014xf6) <- place_of_burial(?x4895, ?x11762), influenced_by(?x1683, ?x4895), influenced_by(?x4895, ?x3542) *> conf = 0.05 ranks of expected_values: 50 EVAL 0klw student! 014xf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 139.000 139.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #4458-0bxl5 PRED entity: 0bxl5 PRED relation: role! PRED expected values: 012x4t 0lzkm 01nkxvx => 83 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1273): 050z2 (0.78 #10982, 0.71 #7368, 0.71 #6021), 04bpm6 (0.78 #10874, 0.64 #13128, 0.64 #12224), 023l9y (0.71 #7390, 0.67 #15971, 0.67 #11004), 01wxdn3 (0.67 #10291, 0.62 #8035, 0.60 #11648), 0326tc (0.64 #13390, 0.62 #8876, 0.50 #13843), 0l12d (0.62 #8610, 0.57 #6356, 0.57 #5008), 0m_v0 (0.62 #8699, 0.44 #10959, 0.43 #7345), 02s6sh (0.60 #11674, 0.60 #4913, 0.60 #4464), 082brv (0.60 #11511, 0.60 #4301, 0.57 #7447), 01xzb6 (0.60 #4726, 0.50 #2475, 0.43 #5624) >> Best rule #10982 for best value: >> intensional similarity = 18 >> extensional distance = 7 >> proper extension: 042v_gx; >> query: (?x3215, 050z2) <- role(?x3215, ?x3214), role(?x3215, ?x316), role(?x3215, ?x314), role(?x3215, ?x3409), role(?x3215, ?x1148), role(?x3215, ?x75), ?x1148 = 02qjv, ?x316 = 05r5c, role(?x217, ?x3215), role(?x3214, ?x960), role(?x8921, ?x314), ?x3409 = 0680x0, group(?x3214, ?x498), role(?x2205, ?x314), ?x8921 = 016s0m, ?x2205 = 0dq630k, ?x75 = 07y_7, performance_role(?x3214, ?x4975) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #7353 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 5 *> proper extension: 026t6; 018vs; *> query: (?x3215, 0lzkm) <- role(?x3215, ?x3214), role(?x3215, ?x316), role(?x3215, ?x314), role(?x3215, ?x4769), role(?x3215, ?x1148), ?x1148 = 02qjv, ?x316 = 05r5c, role(?x4206, ?x3215), role(?x3214, ?x960), ?x314 = 02sgy, ?x4769 = 0dwt5, role(?x74, ?x3215), performance_role(?x4186, ?x3214), performance_role(?x3214, ?x3703), award_nominee(?x1381, ?x4206), performance_role(?x6208, ?x3215), ?x4186 = 0f0qfz *> conf = 0.57 ranks of expected_values: 12, 64, 136 EVAL 0bxl5 role! 01nkxvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 83.000 37.000 0.778 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0bxl5 role! 0lzkm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 83.000 37.000 0.778 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0bxl5 role! 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 83.000 37.000 0.778 http://example.org/music/artist/track_contributions./music/track_contribution/role #4457-0f2r6 PRED entity: 0f2r6 PRED relation: dog_breed PRED expected values: 01t032 => 212 concepts (212 used for prediction) PRED predicted values (max 10 best out of 2): 01k3tq (0.75 #20, 0.60 #10, 0.60 #6), 01t032 (0.67 #19, 0.60 #9, 0.60 #5) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0fvyg; >> query: (?x674, 01k3tq) <- county_seat(?x673, ?x674), location(?x436, ?x674), capital(?x2256, ?x674), dog_breed(?x674, ?x1706) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #19 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0fvyg; *> query: (?x674, 01t032) <- county_seat(?x673, ?x674), location(?x436, ?x674), capital(?x2256, ?x674), dog_breed(?x674, ?x1706) *> conf = 0.67 ranks of expected_values: 2 EVAL 0f2r6 dog_breed 01t032 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 212.000 212.000 0.750 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #4456-023p7l PRED entity: 023p7l PRED relation: genre PRED expected values: 03k9fj => 71 concepts (52 used for prediction) PRED predicted values (max 10 best out of 110): 082gq (0.71 #1178, 0.31 #718, 0.30 #948), 05p553 (0.53 #1384, 0.45 #1615, 0.44 #349), 09b3v (0.50 #3808, 0.50 #4039, 0.49 #5769), 03k9fj (0.49 #356, 0.37 #471, 0.36 #1276), 01hmnh (0.42 #362, 0.33 #17, 0.32 #477), 04xvlr (0.36 #921, 0.35 #806, 0.34 #576), 017fp (0.33 #590, 0.33 #935, 0.33 #820), 01jfsb (0.29 #4630, 0.28 #5781, 0.28 #3820), 02kdv5l (0.28 #1152, 0.26 #4620, 0.26 #5771), 06cvj (0.25 #1614, 0.24 #2540, 0.13 #1383) >> Best rule #1178 for best value: >> intensional similarity = 5 >> extensional distance = 217 >> proper extension: 0192hw; 0c0wvx; >> query: (?x3759, 082gq) <- genre(?x3759, ?x2605), genre(?x10619, ?x2605), genre(?x6767, ?x2605), ?x10619 = 03xj05, ?x6767 = 05_61y >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #356 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x3759, 03k9fj) <- titles(?x3920, ?x3759), place_founded(?x3920, ?x1523), titles(?x3920, ?x1080), film(?x368, ?x1080) *> conf = 0.49 ranks of expected_values: 4 EVAL 023p7l genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 71.000 52.000 0.712 http://example.org/film/film/genre #4455-01skmp PRED entity: 01skmp PRED relation: nationality PRED expected values: 09c7w0 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 47): 09c7w0 (0.87 #1511, 0.83 #1811, 0.82 #2113), 0345h (0.34 #8852, 0.30 #9957, 0.29 #8045), 0f8l9c (0.34 #8852, 0.30 #9957, 0.29 #8045), 07ssc (0.30 #9957, 0.29 #8045, 0.29 #9154), 0d060g (0.30 #9957, 0.29 #8045, 0.29 #9154), 03rjj (0.30 #9957, 0.29 #8045, 0.29 #9154), 02jx1 (0.15 #840, 0.13 #1442, 0.12 #133), 03rk0 (0.11 #11572, 0.06 #11415, 0.05 #11720), 03_3d (0.11 #11572, 0.06 #813, 0.02 #4826), 06q1r (0.11 #11572, 0.03 #781, 0.02 #1486) >> Best rule #1511 for best value: >> intensional similarity = 3 >> extensional distance = 206 >> proper extension: 0fpj4lx; 03hbzj; 01r0t_j; 07lz9l; 0f3nn; 089z0z; 05h7tk; >> query: (?x6702, 09c7w0) <- gender(?x6702, ?x514), place_of_birth(?x6702, ?x739), ?x739 = 02_286 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01skmp nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.870 http://example.org/people/person/nationality #4454-011x_4 PRED entity: 011x_4 PRED relation: film! PRED expected values: 0pgm3 => 65 concepts (37 used for prediction) PRED predicted values (max 10 best out of 974): 01q_ph (0.18 #56, 0.13 #8376, 0.05 #72817), 01f7dd (0.12 #1210, 0.05 #72817, 0.02 #19933), 01j5ts (0.12 #29, 0.05 #72817, 0.02 #2109), 0d608 (0.12 #1306, 0.04 #22111, 0.04 #34594), 018ygt (0.12 #1119, 0.02 #21924, 0.01 #34407), 04yyhw (0.12 #2079, 0.01 #10399), 041c4 (0.09 #9215, 0.04 #21700, 0.02 #5055), 015c4g (0.08 #2860, 0.06 #780, 0.02 #13261), 0mdqp (0.06 #8438, 0.05 #4278, 0.02 #33406), 02zfg3 (0.06 #4116, 0.06 #2036, 0.02 #8276) >> Best rule #56 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 05p1qyh; 0gyh2wm; 043t8t; 07l4zhn; 026wlxw; 01d2v1; >> query: (?x7656, 01q_ph) <- film(?x397, ?x7656), ?x397 = 0p_pd, genre(?x7656, ?x239) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #24890 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 353 *> proper extension: 015qsq; 0d90m; 09xbpt; 016fyc; 016z5x; 01h7bb; 0fg04; 02z3r8t; 0dsvzh; 0pv2t; ... *> query: (?x7656, 0pgm3) <- film(?x7624, ?x7656), featured_film_locations(?x7656, ?x1860), film(?x397, ?x7656) *> conf = 0.01 ranks of expected_values: 747 EVAL 011x_4 film! 0pgm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 65.000 37.000 0.176 http://example.org/film/actor/film./film/performance/film #4453-01y68z PRED entity: 01y68z PRED relation: category PRED expected values: 08mbj5d => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.73 #17, 0.71 #18, 0.71 #19) >> Best rule #17 for best value: >> intensional similarity = 6 >> extensional distance = 171 >> proper extension: 09c7w0; 08815; 02vk52z; 02zs4; 087c7; 01j_9c; 01c6k4; 06pwq; 01w3v; 0f8l9c; ... >> query: (?x14678, 08mbj5d) <- company(?x8314, ?x14678), company(?x8314, ?x2554), company(?x8314, ?x2062), ?x2062 = 09d5h, award_winner(?x2436, ?x2554), state_province_region(?x2554, ?x1227) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y68z category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.734 http://example.org/common/topic/webpage./common/webpage/category #4452-0146pg PRED entity: 0146pg PRED relation: category PRED expected values: 08mbj5d => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #12, 0.80 #33, 0.79 #28) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 01syr4; >> query: (?x669, 08mbj5d) <- people(?x3584, ?x669), type_of_union(?x669, ?x566), origin(?x669, ?x739) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0146pg category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.821 http://example.org/common/topic/webpage./common/webpage/category #4451-02_sr1 PRED entity: 02_sr1 PRED relation: language PRED expected values: 0653m => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 31): 03_9r (0.58 #898, 0.25 #65, 0.25 #9), 04306rv (0.58 #898, 0.13 #228, 0.11 #284), 0653m (0.58 #898, 0.05 #235, 0.05 #291), 02hwhyv (0.58 #898, 0.04 #196, 0.02 #364), 064_8sq (0.22 #188, 0.15 #1256, 0.15 #692), 02bjrlw (0.20 #113, 0.10 #393, 0.09 #281), 06nm1 (0.12 #682, 0.12 #402, 0.11 #1302), 04h9h (0.10 #152, 0.04 #488, 0.04 #544), 0c_v2 (0.10 #127, 0.02 #407), 03k50 (0.09 #176, 0.04 #288, 0.02 #1700) >> Best rule #898 for best value: >> intensional similarity = 3 >> extensional distance = 348 >> proper extension: 02kk_c; 025x1t; >> query: (?x4038, ?x254) <- award_winner(?x4038, ?x147), film(?x147, ?x148), languages(?x147, ?x254) >> conf = 0.58 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 02_sr1 language 0653m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 86.000 0.583 http://example.org/film/film/language #4450-04sry PRED entity: 04sry PRED relation: type_of_appearance PRED expected values: 01jdpf => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 1): 01jdpf (0.14 #1, 0.08 #3, 0.08 #4) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 01wbg84; 0cf_h9; 0fhxv; 01gw4f; 0lkr7; 01cpqk; 01dw_f; 029q_y; 0q9zc; 01pg1d; >> query: (?x7310, 01jdpf) <- profession(?x7310, ?x319), film(?x7310, ?x9755), ?x9755 = 03wy8t >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sry type_of_appearance 01jdpf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.143 http://example.org/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance #4449-03gkn5 PRED entity: 03gkn5 PRED relation: gender PRED expected values: 05zppz => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #67, 0.87 #119, 0.87 #101), 02zsn (0.46 #261, 0.46 #286, 0.38 #164) >> Best rule #67 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 02ck1; >> query: (?x3520, 05zppz) <- company(?x3520, ?x6333), place_of_birth(?x3520, ?x3521), nationality(?x3520, ?x94), contains(?x1569, ?x6333), school_type(?x6333, ?x4994) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03gkn5 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.879 http://example.org/people/person/gender #4448-014kq6 PRED entity: 014kq6 PRED relation: film_crew_role PRED expected values: 01xy5l_ => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 24): 0215hd (0.64 #201, 0.62 #174, 0.16 #608), 089g0h (0.56 #202, 0.55 #175, 0.13 #609), 01xy5l_ (0.49 #171, 0.49 #198, 0.13 #605), 01pvkk (0.34 #223, 0.33 #61, 0.28 #1420), 02vs3x5 (0.23 #71, 0.17 #17, 0.09 #44), 02ynfr (0.22 #227, 0.20 #607, 0.19 #65), 02rh1dz (0.17 #168, 0.17 #195, 0.15 #222), 089fss (0.17 #166, 0.15 #193, 0.10 #220), 020xn5 (0.12 #167, 0.12 #194, 0.04 #221), 0ckd1 (0.06 #191, 0.06 #164, 0.02 #598) >> Best rule #201 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 03ckwzc; 09g8vhw; 02qhqz4; 047p7fr; 06w839_; 062zjtt; 0642xf3; 05zpghd; 02qsqmq; 0660b9b; ... >> query: (?x2160, 0215hd) <- film_crew_role(?x2160, ?x7591), language(?x2160, ?x90), film(?x971, ?x2160), ?x7591 = 0d2b38 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 064n1pz; *> query: (?x2160, 01xy5l_) <- film_crew_role(?x2160, ?x7591), language(?x2160, ?x90), ?x7591 = 0d2b38, country(?x2160, ?x94) *> conf = 0.49 ranks of expected_values: 3 EVAL 014kq6 film_crew_role 01xy5l_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 75.000 75.000 0.644 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4447-03kts PRED entity: 03kts PRED relation: film PRED expected values: 02qk3fk => 167 concepts (116 used for prediction) PRED predicted values (max 10 best out of 921): 027fwmt (0.09 #1594, 0.04 #10549, 0.03 #14131), 02sfnv (0.09 #899, 0.02 #8063, 0.02 #61793), 04954r (0.09 #616, 0.02 #7780, 0.01 #14944), 0gl3hr (0.09 #1099, 0.02 #8263, 0.01 #15427), 0fy66 (0.09 #598, 0.02 #7762, 0.01 #14926), 0bm2g (0.09 #338, 0.02 #7502, 0.01 #14666), 01jnc_ (0.09 #21271, 0.05 #33808, 0.04 #53509), 01hvjx (0.08 #2166, 0.04 #9330, 0.02 #5748), 0m63c (0.08 #4919), 01shy7 (0.06 #9379, 0.04 #91766, 0.04 #52363) >> Best rule #1594 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0bvzp; >> query: (?x7906, 027fwmt) <- profession(?x7906, ?x563), ?x563 = 01c8w0, artist(?x3265, ?x7906) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #17251 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 71 *> proper extension: 03n93; 026_dq6; *> query: (?x7906, 02qk3fk) <- gender(?x7906, ?x231), student(?x2767, ?x7906), participant(?x7963, ?x7906), category(?x7906, ?x134) *> conf = 0.01 ranks of expected_values: 573 EVAL 03kts film 02qk3fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 167.000 116.000 0.091 http://example.org/film/actor/film./film/performance/film #4446-0168ls PRED entity: 0168ls PRED relation: film_release_region PRED expected values: 03_3d 03h64 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 136): 09c7w0 (0.93 #5195, 0.92 #5033, 0.92 #7632), 05r4w (0.84 #650, 0.83 #326, 0.83 #2272), 059j2 (0.84 #2307, 0.79 #1333, 0.78 #361), 03_3d (0.83 #332, 0.80 #170, 0.78 #1304), 03h64 (0.79 #2343, 0.77 #397, 0.73 #721), 03gj2 (0.77 #2299, 0.69 #1487, 0.69 #353), 0d060g (0.73 #2279, 0.66 #333, 0.62 #1305), 0154j (0.73 #2276, 0.69 #1302, 0.68 #654), 05qhw (0.71 #2288, 0.68 #342, 0.67 #1476), 0b90_r (0.71 #2275, 0.62 #329, 0.60 #1463) >> Best rule #5195 for best value: >> intensional similarity = 3 >> extensional distance = 713 >> proper extension: 03g90h; 0h1cdwq; 026p_bs; 0401sg; 026mfbr; 04gknr; 03bx2lk; 07g_0c; 044g_k; 0cz8mkh; ... >> query: (?x1547, 09c7w0) <- music(?x1547, ?x7857), film_release_region(?x1547, ?x142), genre(?x1547, ?x53) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #332 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 03nsm5x; 0g5qmbz; 072hx4; *> query: (?x1547, 03_3d) <- film_release_region(?x1547, ?x1355), ?x1355 = 0h7x, award_winner(?x1547, ?x1548) *> conf = 0.83 ranks of expected_values: 4, 5 EVAL 0168ls film_release_region 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 82.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0168ls film_release_region 03_3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 82.000 82.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4445-0nbwf PRED entity: 0nbwf PRED relation: place_of_birth! PRED expected values: 07bsj => 203 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2128): 0249kn (0.33 #143427, 0.30 #268614, 0.29 #117347), 02ndj5 (0.29 #177334, 0.29 #177333, 0.28 #18251), 07qy0b (0.12 #647, 0.10 #3254, 0.09 #5861), 01nd6v (0.12 #2603, 0.10 #5210, 0.09 #7817), 04zn7g (0.12 #2562, 0.10 #5169, 0.09 #7776), 01fxfk (0.12 #2505, 0.10 #5112, 0.09 #7719), 08141d (0.12 #2498, 0.10 #5105, 0.09 #7712), 02bc74 (0.12 #2490, 0.10 #5097, 0.09 #7704), 03j9ml (0.12 #2435, 0.10 #5042, 0.09 #7649), 044zvm (0.12 #2392, 0.10 #4999, 0.09 #7606) >> Best rule #143427 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 01n4nd; >> query: (?x8468, ?x2906) <- citytown(?x3360, ?x8468), origin(?x2906, ?x8468), award_nominee(?x2906, ?x366), award_nominee(?x4239, ?x2906) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0nbwf place_of_birth! 07bsj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 203.000 109.000 0.325 http://example.org/people/person/place_of_birth #4444-09sdmz PRED entity: 09sdmz PRED relation: award_winner PRED expected values: 026rm_y => 50 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1854): 02qgqt (0.56 #7397, 0.40 #2477, 0.32 #17221), 016yvw (0.56 #8588, 0.40 #3668, 0.19 #20890), 09fb5 (0.56 #7443, 0.23 #19745, 0.20 #2523), 039bp (0.44 #7591, 0.40 #2671, 0.40 #211), 0bxtg (0.44 #7462, 0.40 #2542, 0.14 #5002), 06cgy (0.44 #7688, 0.32 #17221, 0.29 #49223), 0pmhf (0.44 #7927, 0.20 #3007, 0.20 #547), 01vvb4m (0.44 #8041, 0.20 #3121, 0.14 #5581), 02kxbx3 (0.43 #5689, 0.25 #10609, 0.23 #20451), 02kxbwx (0.43 #5065, 0.25 #9985, 0.23 #19827) >> Best rule #7397 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 027986c; 057xs89; >> query: (?x4091, 02qgqt) <- nominated_for(?x4091, ?x8063), nominated_for(?x4091, ?x7012), award(?x92, ?x4091), ?x8063 = 01718w, film_release_region(?x7012, ?x94) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1853 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 09sb52; 0gqy2; *> query: (?x4091, 026rm_y) <- nominated_for(?x4091, ?x11996), nominated_for(?x4091, ?x3573), award(?x92, ?x4091), ?x3573 = 011yl_, ?x11996 = 03s9kp *> conf = 0.40 ranks of expected_values: 14 EVAL 09sdmz award_winner 026rm_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 50.000 20.000 0.556 http://example.org/award/award_category/winners./award/award_honor/award_winner #4443-02bj22 PRED entity: 02bj22 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.84 #57, 0.83 #149, 0.83 #77), 0735l (0.25 #6), 02nxhr (0.05 #23, 0.05 #2, 0.04 #114), 07z4p (0.05 #5, 0.03 #56, 0.03 #191), 07c52 (0.03 #69, 0.03 #9, 0.03 #364) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 115 >> proper extension: 0140g4; 02qrv7; 0g5pv3; 01kf3_9; 0c_j9x; 0p3_y; 01kf4tt; 0dnqr; 0d_wms; 011yr9; ... >> query: (?x9193, 029j_) <- prequel(?x5012, ?x9193), country(?x5012, ?x94), film(?x2564, ?x9193), music(?x5012, ?x7856) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bj22 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.838 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #4442-0f04v PRED entity: 0f04v PRED relation: dog_breed PRED expected values: 0km3f => 232 concepts (232 used for prediction) PRED predicted values (max 10 best out of 1): 0km3f (0.93 #26, 0.92 #22, 0.92 #21) >> Best rule #26 for best value: >> intensional similarity = 2 >> extensional distance = 39 >> proper extension: 02hrh0_; >> query: (?x6703, 0km3f) <- dog_breed(?x6703, ?x1706), contains(?x6703, ?x8427) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f04v dog_breed 0km3f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 232.000 232.000 0.927 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #4441-0_7w6 PRED entity: 0_7w6 PRED relation: film! PRED expected values: 02sjf5 0161h5 => 110 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1042): 02fgpf (0.53 #6244, 0.49 #27058, 0.47 #29141), 03hhd3 (0.33 #1491, 0.03 #13979, 0.02 #16061), 01mqc_ (0.33 #1306, 0.02 #22120, 0.01 #40857), 016kb7 (0.33 #1370), 06mr6 (0.33 #1041), 0p8r1 (0.18 #10994, 0.07 #8913, 0.06 #6831), 02fgp0 (0.16 #45796), 04jspq (0.12 #14570, 0.12 #33305, 0.11 #8326), 02_p5w (0.08 #11054, 0.04 #2728, 0.01 #13135), 019vgs (0.08 #11069, 0.04 #4824, 0.03 #8988) >> Best rule #6244 for best value: >> intensional similarity = 5 >> extensional distance = 47 >> proper extension: 0gh8zks; >> query: (?x1919, ?x1894) <- award_winner(?x1919, ?x1894), film_release_region(?x1919, ?x2152), film_release_region(?x1919, ?x1497), ?x2152 = 06mkj, ?x1497 = 015qh >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #12233 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 0cks1m; *> query: (?x1919, 0161h5) <- genre(?x1919, ?x307), film(?x2156, ?x1919), film(?x4109, ?x1919), ?x2156 = 01795t *> conf = 0.01 ranks of expected_values: 748 EVAL 0_7w6 film! 0161h5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 110.000 22.000 0.529 http://example.org/film/actor/film./film/performance/film EVAL 0_7w6 film! 02sjf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 22.000 0.529 http://example.org/film/actor/film./film/performance/film #4440-06f41 PRED entity: 06f41 PRED relation: sports! PRED expected values: 0l6vl => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 24): 0l6mp (0.88 #292, 0.86 #375, 0.85 #402), 018ljb (0.88 #292, 0.86 #375, 0.85 #402), 018qb4 (0.88 #292, 0.86 #375, 0.85 #402), 0lbbj (0.88 #292, 0.86 #375, 0.85 #402), 09x3r (0.88 #292, 0.86 #375, 0.85 #402), 0l6vl (0.78 #350, 0.78 #213, 0.77 #293), 0kbws (0.78 #213, 0.77 #293, 0.74 #566), 018ctl (0.62 #239, 0.59 #240, 0.53 #403), 09n48 (0.62 #239, 0.59 #240, 0.53 #403), 0kbvv (0.62 #239, 0.59 #240, 0.53 #403) >> Best rule #292 for best value: >> intensional similarity = 39 >> extensional distance = 3 >> proper extension: 02vx4; >> query: (?x2044, ?x358) <- country(?x2044, ?x7709), country(?x2044, ?x6305), country(?x2044, ?x456), country(?x2044, ?x404), country(?x2044, ?x304), country(?x2044, ?x291), country(?x2044, ?x252), country(?x2044, ?x205), ?x304 = 0d0vqn, sports(?x4255, ?x2044), sports(?x778, ?x2044), sports(?x358, ?x2044), ?x205 = 03rjj, currency(?x7709, ?x170), ?x252 = 03_3d, ?x778 = 0kbvb, olympics(?x2044, ?x391), ?x4255 = 0lgxj, film_release_region(?x4610, ?x404), film_release_region(?x3812, ?x404), film_release_region(?x2441, ?x404), film_release_region(?x2394, ?x404), administrative_parent(?x7709, ?x551), country(?x6354, ?x404), country(?x4673, ?x404), country(?x3015, ?x404), country(?x520, ?x404), ?x6354 = 09_b4, ?x2394 = 0661ql3, ?x4610 = 017jd9, ?x4673 = 07jbh, ?x520 = 01dys, ?x291 = 0h3y, ?x456 = 05qhw, ?x2441 = 0cc5mcj, ?x3015 = 071t0, ?x3812 = 0c3xw46, adjoins(?x1879, ?x7709), contains(?x6305, ?x13440) >> conf = 0.88 => this is the best rule for 5 predicted values *> Best rule #350 for first EXPECTED value: *> intensional similarity = 36 *> extensional distance = 7 *> proper extension: 02bkg; 06wrt; *> query: (?x2044, 0l6vl) <- country(?x2044, ?x7709), country(?x2044, ?x404), country(?x2044, ?x304), country(?x2044, ?x252), country(?x2044, ?x205), ?x304 = 0d0vqn, sports(?x4255, ?x2044), sports(?x778, ?x2044), ?x205 = 03rjj, currency(?x7709, ?x170), ?x252 = 03_3d, ?x778 = 0kbvb, olympics(?x2044, ?x391), ?x4255 = 0lgxj, film_release_region(?x4610, ?x404), film_release_region(?x3498, ?x404), film_release_region(?x2868, ?x404), film_release_region(?x2394, ?x404), administrative_parent(?x7709, ?x551), country(?x6354, ?x404), country(?x4673, ?x404), country(?x4503, ?x404), country(?x1967, ?x404), country(?x520, ?x404), ?x6354 = 09_b4, ?x2394 = 0661ql3, ?x4610 = 017jd9, ?x4673 = 07jbh, ?x520 = 01dys, participating_countries(?x418, ?x404), ?x3498 = 02fqrf, ?x2868 = 0dr3sl, form_of_government(?x404, ?x4763), ?x1967 = 01cgz, ?x4503 = 06z68, countries_spoken_in(?x403, ?x404) *> conf = 0.78 ranks of expected_values: 6 EVAL 06f41 sports! 0l6vl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 34.000 34.000 0.883 http://example.org/olympics/olympic_games/sports #4439-03115z PRED entity: 03115z PRED relation: language! PRED expected values: 028cg00 027m67 0gys2jp => 42 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1859): 01y9jr (0.67 #2838, 0.57 #4565, 0.25 #18404), 047vnkj (0.60 #870, 0.50 #18165, 0.40 #14710), 0g5qmbz (0.60 #1498, 0.47 #15338, 0.44 #18793), 020bv3 (0.60 #305, 0.33 #10684, 0.33 #5490), 043t8t (0.60 #754, 0.33 #11133, 0.28 #21508), 014kq6 (0.50 #2058, 0.44 #5514, 0.43 #3785), 01qdmh (0.50 #3376, 0.43 #5103, 0.36 #8564), 034qmv (0.50 #1741, 0.43 #3468, 0.33 #5197), 017n9 (0.50 #3413, 0.43 #5140, 0.27 #8601), 06fqlk (0.50 #2820, 0.43 #4547, 0.25 #18386) >> Best rule #2838 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 01gp_d; >> query: (?x10296, 01y9jr) <- language(?x4427, ?x10296), countries_spoken_in(?x10296, ?x2629), language(?x4427, ?x254), genre(?x4427, ?x7160), film_crew_role(?x4427, ?x2154), film_crew_role(?x4427, ?x2095), produced_by(?x4427, ?x1417), ?x2154 = 01vx2h, ?x254 = 02h40lc, ?x7160 = 04t2t, film(?x123, ?x4427), ?x2095 = 0dxtw, film_release_region(?x4427, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2947 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 01gp_d; *> query: (?x10296, 027m67) <- language(?x4427, ?x10296), countries_spoken_in(?x10296, ?x2629), language(?x4427, ?x254), genre(?x4427, ?x7160), film_crew_role(?x4427, ?x2154), film_crew_role(?x4427, ?x2095), produced_by(?x4427, ?x1417), ?x2154 = 01vx2h, ?x254 = 02h40lc, ?x7160 = 04t2t, film(?x123, ?x4427), ?x2095 = 0dxtw, film_release_region(?x4427, ?x94) *> conf = 0.33 ranks of expected_values: 112, 393, 543 EVAL 03115z language! 0gys2jp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 20.000 0.667 http://example.org/film/film/language EVAL 03115z language! 027m67 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 42.000 20.000 0.667 http://example.org/film/film/language EVAL 03115z language! 028cg00 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 20.000 0.667 http://example.org/film/film/language #4438-0dc_ms PRED entity: 0dc_ms PRED relation: produced_by PRED expected values: 08d9z7 => 109 concepts (80 used for prediction) PRED predicted values (max 10 best out of 153): 0py5b (0.33 #377, 0.01 #6577), 02xnjd (0.26 #4147, 0.19 #4535, 0.17 #2985), 02pq9yv (0.25 #504, 0.20 #891, 0.17 #1278), 03h304l (0.25 #573, 0.20 #960, 0.17 #1347), 03h40_7 (0.25 #735, 0.20 #1122, 0.17 #1509), 0dqmt0 (0.25 #633, 0.20 #1020, 0.17 #1407), 063b4k (0.24 #13564, 0.23 #19777, 0.22 #1936), 07rd7 (0.17 #2861, 0.09 #2085, 0.08 #2473), 076_74 (0.12 #1680, 0.09 #2068, 0.08 #2844), 03h_9lg (0.12 #1580, 0.09 #1968, 0.08 #2356) >> Best rule #377 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 01fmys; >> query: (?x6528, 0py5b) <- film_release_region(?x6528, ?x1355), film_release_region(?x6528, ?x1241), film_release_region(?x6528, ?x456), ?x456 = 05qhw, administrative_division(?x863, ?x1355), adjustment_currency(?x1355, ?x170), country(?x150, ?x1355), ?x1241 = 05cgv >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #8405 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 103 *> proper extension: 01gglm; *> query: (?x6528, 08d9z7) <- film_crew_role(?x6528, ?x2154), film_crew_role(?x6528, ?x2095), ?x2154 = 01vx2h, country(?x6528, ?x94), language(?x6528, ?x254), production_companies(?x6528, ?x7935), ?x2095 = 0dxtw, film(?x665, ?x6528) *> conf = 0.03 ranks of expected_values: 47 EVAL 0dc_ms produced_by 08d9z7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 109.000 80.000 0.333 http://example.org/film/film/produced_by #4437-0ctw_b PRED entity: 0ctw_b PRED relation: combatants PRED expected values: 0b90_r => 211 concepts (165 used for prediction) PRED predicted values (max 10 best out of 349): 0b90_r (0.84 #393, 0.83 #5396, 0.83 #3749), 07ssc (0.84 #393, 0.83 #5396, 0.83 #3749), 05b4w (0.84 #393, 0.83 #5396, 0.83 #3749), 0ctw_b (0.73 #860, 0.67 #74, 0.53 #336), 02psqkz (0.39 #419, 0.35 #1729, 0.35 #353), 059z0 (0.39 #438, 0.35 #1748, 0.32 #3332), 04g61 (0.35 #362, 0.28 #428, 0.26 #6127), 03rjj (0.33 #67, 0.26 #6127, 0.25 #4866), 03b79 (0.26 #6127, 0.25 #4866, 0.25 #6853), 027qpc (0.26 #6127, 0.25 #4866, 0.25 #6853) >> Best rule #393 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 059z0; >> query: (?x1023, ?x94) <- combatants(?x456, ?x1023), combatants(?x94, ?x1023), nationality(?x226, ?x1023), ?x456 = 05qhw >> conf = 0.84 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0ctw_b combatants 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 211.000 165.000 0.835 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #4436-02rnmb PRED entity: 02rnmb PRED relation: colors! PRED expected values: 04bfg 02mw6c => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1305): 016sd3 (0.60 #4179, 0.50 #7982, 0.50 #6556), 0gjv_ (0.60 #3996, 0.50 #1611, 0.43 #5425), 01tntf (0.60 #4158, 0.50 #1773, 0.43 #5587), 01b1mj (0.60 #3828, 0.50 #1443, 0.43 #5257), 0pz6q (0.60 #4152, 0.50 #1767, 0.43 #5581), 0bsnm (0.60 #3606, 0.50 #3129, 0.40 #4559), 01jq34 (0.50 #6235, 0.50 #2903, 0.50 #1473), 02vnp2 (0.50 #6517, 0.50 #3185, 0.50 #1755), 0gl6x (0.50 #3198, 0.50 #1289, 0.40 #4628), 0k__z (0.50 #3136, 0.50 #1706, 0.40 #4566) >> Best rule #4179 for best value: >> intensional similarity = 35 >> extensional distance = 3 >> proper extension: 01g5v; >> query: (?x8271, 016sd3) <- colors(?x10045, ?x8271), colors(?x9620, ?x8271), colors(?x6823, ?x8271), colors(?x3216, ?x8271), currency(?x9620, ?x170), team(?x8520, ?x6823), season(?x6823, ?x11501), season(?x6823, ?x2406), major_field_of_study(?x9620, ?x1327), school(?x6823, ?x8202), school(?x6823, ?x3779), ?x11501 = 027mvrc, ?x8202 = 06fq2, draft(?x6823, ?x1161), citytown(?x9620, ?x6555), ?x8520 = 01z9v6, contains(?x94, ?x3779), draft(?x2067, ?x1161), category(?x3779, ?x134), student(?x3779, ?x2409), contains(?x2982, ?x10045), team(?x11781, ?x3216), institution(?x620, ?x3779), major_field_of_study(?x10045, ?x3561), position(?x3216, ?x63), ?x11781 = 02y0dd, ?x2067 = 05g76, school(?x1161, ?x2522), state_province_region(?x9620, ?x177), ?x2522 = 022lly, ?x2406 = 03c6sl9, contact_category(?x3779, ?x897), school_type(?x10045, ?x3092), school(?x4571, ?x9620), fraternities_and_sororities(?x3779, ?x3697) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #387 for first EXPECTED value: *> intensional similarity = 38 *> extensional distance = 1 *> proper extension: 01l849; *> query: (?x8271, 02mw6c) <- colors(?x10899, ?x8271), colors(?x10045, ?x8271), colors(?x9620, ?x8271), colors(?x6280, ?x8271), colors(?x6823, ?x8271), colors(?x3216, ?x8271), colors(?x2405, ?x8271), currency(?x9620, ?x170), team(?x8520, ?x6823), team(?x5727, ?x6823), season(?x6823, ?x11501), major_field_of_study(?x9620, ?x1327), school(?x6823, ?x8202), school(?x6823, ?x3779), ?x11501 = 027mvrc, ?x8202 = 06fq2, draft(?x6823, ?x8499), draft(?x6823, ?x1161), citytown(?x9620, ?x6555), ?x8520 = 01z9v6, contains(?x94, ?x3779), category(?x3779, ?x134), ?x10045 = 01_k7f, teams(?x4144, ?x6823), school(?x1161, ?x4209), institution(?x620, ?x9620), ?x8499 = 02r6gw6, ?x4209 = 02gr81, time_zones(?x6555, ?x2674), institution(?x1526, ?x3779), registering_agency(?x9620, ?x1982), place_of_birth(?x1913, ?x6555), citytown(?x10899, ?x12941), team(?x2666, ?x3216), organization(?x346, ?x6280), ?x5727 = 02wszf, service_language(?x10899, ?x254), position(?x2405, ?x261) *> conf = 0.33 ranks of expected_values: 173, 310 EVAL 02rnmb colors! 02mw6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 02rnmb colors! 04bfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 21.000 21.000 0.600 http://example.org/education/educational_institution/colors #4435-01ycck PRED entity: 01ycck PRED relation: nationality PRED expected values: 02jx1 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 55): 09c7w0 (0.77 #5024, 0.75 #5125, 0.74 #7942), 07ssc (0.50 #15, 0.40 #215, 0.38 #4823), 03rt9 (0.38 #4823, 0.06 #713, 0.04 #3410), 02jx1 (0.33 #10056, 0.33 #10361, 0.33 #10158), 04jpl (0.33 #10056, 0.33 #10361, 0.33 #10158), 03rjj (0.20 #105, 0.06 #1107, 0.04 #705), 0f8l9c (0.17 #322, 0.12 #422, 0.09 #622), 035qy (0.17 #334, 0.06 #434, 0.04 #634), 0345h (0.16 #531, 0.13 #631, 0.12 #431), 0d05w3 (0.16 #550, 0.09 #650, 0.05 #850) >> Best rule #5024 for best value: >> intensional similarity = 3 >> extensional distance = 1214 >> proper extension: 01w5gg6; >> query: (?x4056, 09c7w0) <- place_of_birth(?x4056, ?x13032), location(?x413, ?x13032), award_nominee(?x777, ?x4056) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #10056 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2421 *> proper extension: 0c11mj; 01qx13; 0cfywh; *> query: (?x4056, ?x362) <- place_of_birth(?x4056, ?x13032), place_of_birth(?x5528, ?x13032), contains(?x362, ?x13032), type_of_union(?x5528, ?x566) *> conf = 0.33 ranks of expected_values: 4 EVAL 01ycck nationality 02jx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 117.000 0.767 http://example.org/people/person/nationality #4434-056k77g PRED entity: 056k77g PRED relation: genre PRED expected values: 04t2t => 80 concepts (37 used for prediction) PRED predicted values (max 10 best out of 89): 07s9rl0 (0.93 #2581, 0.84 #2229, 0.77 #4233), 05p553 (0.70 #3173, 0.56 #4002, 0.49 #3884), 03k9fj (0.69 #1653, 0.68 #2826, 0.60 #2709), 04t36 (0.59 #1764, 0.23 #2234, 0.09 #3886), 01jfsb (0.58 #3064, 0.55 #3299, 0.54 #2945), 02l7c8 (0.41 #2244, 0.32 #2127, 0.24 #2009), 0bj8m2 (0.40 #163, 0.33 #397, 0.29 #631), 01zhp (0.38 #1715, 0.12 #2771, 0.12 #1597), 0lsxr (0.30 #3060, 0.27 #3533, 0.25 #3770), 04pbhw (0.26 #2750, 0.17 #4051, 0.09 #3761) >> Best rule #2581 for best value: >> intensional similarity = 10 >> extensional distance = 79 >> proper extension: 02py4c8; 0340hj; 029zqn; 01_1pv; 06lpmt; 09fc83; 01s3vk; 0gs973; 03prz_; 02nx2k; ... >> query: (?x9201, 07s9rl0) <- genre(?x9201, ?x1626), genre(?x9201, ?x1510), film(?x256, ?x9201), genre(?x5429, ?x1626), genre(?x3573, ?x1626), genre(?x1786, ?x1626), ?x1510 = 01hmnh, ?x1786 = 091z_p, ?x5429 = 02psgq, ?x3573 = 011yl_ >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #172 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 0436yk; *> query: (?x9201, 04t2t) <- genre(?x9201, ?x1626), genre(?x9201, ?x225), film(?x10418, ?x9201), actor(?x9201, ?x7742), ?x10418 = 0dt645q, ?x1626 = 03q4nz, country(?x9201, ?x252), ?x225 = 02kdv5l *> conf = 0.20 ranks of expected_values: 13 EVAL 056k77g genre 04t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 80.000 37.000 0.926 http://example.org/film/film/genre #4433-0bksh PRED entity: 0bksh PRED relation: participant! PRED expected values: 01hcj2 => 119 concepts (84 used for prediction) PRED predicted values (max 10 best out of 422): 013w7j (0.81 #30408, 0.80 #33573, 0.80 #8869), 01hcj2 (0.81 #30408, 0.80 #33573, 0.80 #8869), 019pm_ (0.81 #30408, 0.80 #33573, 0.80 #8869), 026c1 (0.81 #30408, 0.80 #8869, 0.80 #33572), 01yf85 (0.51 #3800, 0.51 #3166, 0.48 #5701), 018db8 (0.20 #47, 0.05 #2580, 0.04 #3214), 07r1h (0.20 #408, 0.04 #12445, 0.04 #7377), 01q_ph (0.20 #26, 0.03 #1924, 0.03 #1290), 015p37 (0.20 #601, 0.01 #8203, 0.01 #8836), 026r8q (0.15 #13941, 0.14 #16476, 0.14 #8868) >> Best rule #30408 for best value: >> intensional similarity = 2 >> extensional distance = 420 >> proper extension: 012x2b; >> query: (?x4782, ?x2221) <- nominated_for(?x4782, ?x1811), participant(?x4782, ?x2221) >> conf = 0.81 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0bksh participant! 01hcj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 84.000 0.806 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #4432-01g4yw PRED entity: 01g4yw PRED relation: colors PRED expected values: 06fvc => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 20): 01g5v (0.46 #1123, 0.43 #283, 0.39 #263), 06fvc (0.32 #282, 0.29 #262, 0.25 #1122), 019sc (0.32 #267, 0.30 #287, 0.23 #247), 01l849 (0.28 #501, 0.27 #621, 0.26 #1201), 06kqt3 (0.25 #17, 0.07 #2061, 0.04 #137), 088fh (0.22 #286, 0.18 #66, 0.13 #266), 036k5h (0.17 #85, 0.17 #25, 0.12 #425), 03wkwg (0.17 #35, 0.09 #515, 0.07 #2061), 0jc_p (0.17 #24, 0.07 #1264, 0.07 #2061), 09q2t (0.17 #34, 0.07 #2061, 0.05 #314) >> Best rule #1123 for best value: >> intensional similarity = 4 >> extensional distance = 265 >> proper extension: 02d9nr; >> query: (?x13052, 01g5v) <- contains(?x1310, ?x13052), colors(?x13052, ?x663), colors(?x12780, ?x663), ?x12780 = 019mdt >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #282 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 07w4j; 01v3ht; 0345gh; 02f46y; 01t38b; 01xrlm; 0gjv_; 0hsb3; 0ny75; 01314k; ... *> query: (?x13052, 06fvc) <- contains(?x1310, ?x13052), colors(?x13052, ?x663), currency(?x13052, ?x1099), institution(?x1200, ?x13052), ?x1099 = 01nv4h *> conf = 0.32 ranks of expected_values: 2 EVAL 01g4yw colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 188.000 188.000 0.457 http://example.org/education/educational_institution/colors #4431-012x4t PRED entity: 012x4t PRED relation: award_winner! PRED expected values: 054ks3 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 284): 02nhxf (0.46 #1277, 0.46 #948, 0.40 #1276), 01bgqh (0.40 #1276, 0.39 #13608, 0.38 #9357), 02gdjb (0.40 #1276, 0.39 #13608, 0.38 #9357), 025mb9 (0.40 #1276, 0.39 #13608, 0.38 #9357), 0c4z8 (0.40 #1276, 0.39 #13608, 0.38 #9357), 01d38g (0.40 #1276, 0.39 #13608, 0.38 #9357), 0gqz2 (0.40 #1276, 0.39 #13608, 0.38 #9357), 031b3h (0.40 #1276, 0.39 #13608, 0.38 #9357), 09qvc0 (0.40 #1276, 0.39 #13608, 0.38 #9357), 026mfs (0.24 #3530, 0.10 #8634, 0.09 #9910) >> Best rule #1277 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 01w806h; 02jqjm; 0b_j2; 01wwnh2; >> query: (?x1660, ?x1827) <- artists(?x505, ?x1660), award(?x1660, ?x1827), ?x1827 = 02nhxf >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #139 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 0pmw9; *> query: (?x1660, 054ks3) <- instrumentalists(?x3716, ?x1660), ?x3716 = 03gvt, award_winner(?x1660, ?x521) *> conf = 0.15 ranks of expected_values: 17 EVAL 012x4t award_winner! 054ks3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 105.000 105.000 0.464 http://example.org/award/award_category/winners./award/award_honor/award_winner #4430-0jdr0 PRED entity: 0jdr0 PRED relation: film_release_region PRED expected values: 082fr => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 203): 07ssc (0.79 #668, 0.76 #2948, 0.75 #3275), 0345h (0.76 #3294, 0.75 #2967, 0.74 #2316), 02jx1 (0.74 #3094, 0.72 #3583, 0.71 #814), 03gj2 (0.72 #2958, 0.72 #3285, 0.71 #678), 015fr (0.71 #2950, 0.70 #3277, 0.69 #2299), 0154j (0.69 #3262, 0.67 #2935, 0.66 #2284), 05qhw (0.68 #3273, 0.66 #2946, 0.65 #666), 01znc_ (0.68 #2325, 0.67 #2976, 0.66 #696), 05b4w (0.67 #3327, 0.67 #557, 0.66 #2349), 0d060g (0.67 #3264, 0.64 #2286, 0.64 #2937) >> Best rule #668 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 0cp08zg; >> query: (?x9349, 07ssc) <- country(?x9349, ?x512), written_by(?x9349, ?x11271), film_release_region(?x9349, ?x1229), ?x1229 = 059j2 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0jymd; 0cy__l; 0ktpx; 0cbn7c; *> query: (?x9349, 082fr) <- film_crew_role(?x9349, ?x2178), titles(?x11108, ?x9349), ?x11108 = 02xh1, film_release_region(?x9349, ?x87), nominated_for(?x1313, ?x9349) *> conf = 0.33 ranks of expected_values: 30 EVAL 0jdr0 film_release_region 082fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 112.000 112.000 0.792 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4429-01f69m PRED entity: 01f69m PRED relation: language PRED expected values: 02h40lc => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 33): 02h40lc (0.90 #1371, 0.89 #775, 0.89 #596), 064_8sq (0.14 #199, 0.13 #318, 0.13 #140), 06nm1 (0.13 #188, 0.11 #129, 0.09 #307), 04306rv (0.13 #123, 0.12 #182, 0.11 #539), 06b_j (0.13 #141, 0.07 #557, 0.06 #497), 0jzc (0.11 #138, 0.04 #554, 0.04 #494), 02bjrlw (0.08 #178, 0.07 #535, 0.07 #475), 03_9r (0.05 #187, 0.05 #1201, 0.05 #306), 04h9h (0.05 #220, 0.04 #161, 0.03 #936), 0t_2 (0.04 #132, 0.01 #787) >> Best rule #1371 for best value: >> intensional similarity = 4 >> extensional distance = 783 >> proper extension: 02sg5v; 0j_tw; 03whyr; >> query: (?x11483, 02h40lc) <- film(?x6061, ?x11483), produced_by(?x11483, ?x9363), student(?x3424, ?x6061), award_winner(?x591, ?x6061) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f69m language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.896 http://example.org/film/film/language #4428-03vrp PRED entity: 03vrp PRED relation: profession PRED expected values: 0d8qb => 187 concepts (158 used for prediction) PRED predicted values (max 10 best out of 101): 02hrh1q (0.80 #18647, 0.79 #19990, 0.79 #18498), 0kyk (0.59 #3756, 0.59 #6589, 0.56 #3905), 0dxtg (0.59 #4186, 0.58 #5677, 0.54 #5975), 01d_h8 (0.45 #4179, 0.35 #19533, 0.34 #5670), 05z96 (0.38 #6410, 0.37 #5068, 0.33 #3769), 02hv44_ (0.38 #6410, 0.37 #5068, 0.33 #58), 0q04f (0.38 #6410, 0.37 #5068, 0.30 #17292), 016fly (0.38 #6410, 0.37 #5068, 0.30 #17292), 0d8qb (0.38 #6410, 0.30 #17292, 0.30 #16546), 015btn (0.38 #6410, 0.30 #17292, 0.30 #16546) >> Best rule #18647 for best value: >> intensional similarity = 6 >> extensional distance = 571 >> proper extension: 09byk; 0j582; 0350l7; 03d_zl4; 01p85y; 08k1lz; >> query: (?x4914, 02hrh1q) <- languages(?x4914, ?x90), language(?x11296, ?x90), language(?x1415, ?x90), ?x1415 = 09p0ct, languages_spoken(?x3584, ?x90), ?x11296 = 03k8th >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #6410 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 70 *> proper extension: 0738b8; *> query: (?x4914, ?x353) <- languages(?x4914, ?x90), influenced_by(?x4914, ?x6504), influenced_by(?x4914, ?x6400), gender(?x6400, ?x231), profession(?x6504, ?x353) *> conf = 0.38 ranks of expected_values: 9 EVAL 03vrp profession 0d8qb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 187.000 158.000 0.796 http://example.org/people/person/profession #4427-017j69 PRED entity: 017j69 PRED relation: school_type PRED expected values: 05pcjw => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 29): 05jxkf (0.55 #533, 0.54 #629, 0.52 #173), 01_9fk (0.48 #171, 0.46 #147, 0.39 #27), 01rs41 (0.42 #678, 0.41 #342, 0.41 #294), 05pcjw (0.36 #842, 0.35 #674, 0.31 #986), 07tf8 (0.29 #154, 0.28 #178, 0.22 #34), 0bpgx (0.25 #21, 0.06 #238, 0.02 #1054), 02dk5q (0.25 #7, 0.06 #224, 0.02 #1040), 028dcg (0.17 #25), 03bwzr4 (0.17 #25), 02_xgp2 (0.17 #25) >> Best rule #533 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 05kj_; >> query: (?x4410, 05jxkf) <- category(?x4410, ?x134), ?x134 = 08mbj5d, school(?x465, ?x4410) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #842 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 135 *> proper extension: 087c7; 0fvppk; 01npw8; 0c0sl; *> query: (?x4410, 05pcjw) <- country(?x4410, ?x94), organization(?x346, ?x4410), ?x94 = 09c7w0 *> conf = 0.36 ranks of expected_values: 4 EVAL 017j69 school_type 05pcjw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 81.000 0.550 http://example.org/education/educational_institution/school_type #4426-07f_t4 PRED entity: 07f_t4 PRED relation: film! PRED expected values: 0686zv => 95 concepts (49 used for prediction) PRED predicted values (max 10 best out of 813): 0jpdn (0.09 #27031), 0b_dy (0.08 #534, 0.04 #2613, 0.02 #10931), 032_jg (0.08 #140, 0.04 #2219, 0.02 #70697), 0f4dx2 (0.08 #558, 0.04 #2637, 0.01 #8876), 09yrh (0.07 #87331, 0.04 #62380), 0c6qh (0.06 #8732, 0.04 #414, 0.03 #17048), 0h0wc (0.06 #8742, 0.02 #79438, 0.02 #83596), 053xw6 (0.06 #5412, 0.06 #7491, 0.03 #13728), 0170qf (0.06 #4527, 0.06 #6606, 0.03 #12843), 016ggh (0.05 #12263, 0.02 #26817, 0.02 #30977) >> Best rule #27031 for best value: >> intensional similarity = 3 >> extensional distance = 335 >> proper extension: 01f39b; 0gfzfj; 032xky; >> query: (?x7672, ?x8862) <- genre(?x7672, ?x225), film(?x1550, ?x7672), story_by(?x7672, ?x8862) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #6763 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 88 *> proper extension: 0qm8b; 05p3738; 04vh83; 0kv9d3; 08tq4x; 034r25; 0gbtbm; 0c38gj; 0f4k49; 0hv27; ... *> query: (?x7672, 0686zv) <- film_crew_role(?x7672, ?x468), genre(?x7672, ?x3515), film(?x1550, ?x7672), ?x3515 = 082gq *> conf = 0.01 ranks of expected_values: 784 EVAL 07f_t4 film! 0686zv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 95.000 49.000 0.092 http://example.org/film/actor/film./film/performance/film #4425-035yg PRED entity: 035yg PRED relation: nationality! PRED expected values: 0487c3 => 52 concepts (48 used for prediction) PRED predicted values (max 10 best out of 4063): 0d1_f (0.58 #12200, 0.54 #28465, 0.45 #32532), 01c8v0 (0.14 #1185, 0.10 #9318, 0.06 #29650), 0drdv (0.14 #3729, 0.06 #11862, 0.06 #7796), 0184jc (0.14 #6, 0.06 #8139, 0.06 #4073), 03_2td (0.14 #2940, 0.06 #11073, 0.06 #7007), 06dv3 (0.14 #52, 0.06 #8185, 0.06 #4119), 06ns98 (0.14 #1934, 0.06 #10067, 0.04 #30399), 07zr66 (0.14 #3228, 0.06 #11361, 0.04 #31693), 059xvg (0.13 #9185, 0.09 #29517, 0.09 #25450), 055t01 (0.12 #7826, 0.07 #3759, 0.06 #11892) >> Best rule #12200 for best value: >> intensional similarity = 2 >> extensional distance = 29 >> proper extension: 06frc; >> query: (?x8884, ?x3444) <- jurisdiction_of_office(?x3444, ?x8884), form_of_government(?x8884, ?x1926) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #89459 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 130 *> proper extension: 0f04v; 0f2tj; 0k5p1; *> query: (?x8884, ?x982) <- teams(?x8884, ?x7396), team(?x982, ?x7396) *> conf = 0.11 ranks of expected_values: 11 EVAL 035yg nationality! 0487c3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 52.000 48.000 0.583 http://example.org/people/person/nationality #4424-01_vfy PRED entity: 01_vfy PRED relation: award PRED expected values: 02wkmx 0gs9p => 142 concepts (107 used for prediction) PRED predicted values (max 10 best out of 274): 0gs9p (0.73 #4086, 0.67 #6091, 0.62 #2883), 02w_6xj (0.70 #32093, 0.67 #29685, 0.67 #33298), 0gqy2 (0.60 #161, 0.40 #3369, 0.33 #1364), 0gq9h (0.56 #8497, 0.43 #1678, 0.38 #2881), 027dtxw (0.40 #4, 0.22 #12434, 0.20 #3212), 09sdmz (0.40 #203, 0.20 #3411, 0.17 #1406), 02ppm4q (0.33 #1356, 0.29 #2158, 0.22 #12434), 0bs0bh (0.33 #1302, 0.29 #2104, 0.20 #3307), 0gr51 (0.30 #8519, 0.25 #15340, 0.24 #9722), 0f4x7 (0.29 #1634, 0.25 #14472, 0.22 #12434) >> Best rule #4086 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0kft; >> query: (?x2344, 0gs9p) <- people(?x3538, ?x2344), award(?x2344, ?x1198), award(?x2344, ?x1107), ?x1198 = 02pqp12, nominated_for(?x1107, ?x144) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 40 EVAL 01_vfy award 0gs9p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 107.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01_vfy award 02wkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 142.000 107.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4423-07vhb PRED entity: 07vhb PRED relation: school_type PRED expected values: 05jxkf => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 17): 05jxkf (0.51 #773, 0.48 #685, 0.48 #927), 05pcjw (0.29 #23, 0.27 #89, 0.25 #67), 01rs41 (0.27 #642, 0.26 #488, 0.25 #818), 01_srz (0.07 #486, 0.06 #640, 0.06 #596), 01y64 (0.07 #208, 0.06 #318, 0.05 #186), 02dk5q (0.05 #182, 0.04 #204, 0.03 #314), 01jlsn (0.04 #213, 0.03 #323, 0.03 #389), 047951 (0.04 #95, 0.02 #359, 0.02 #249), 0bpgx (0.04 #195, 0.03 #217, 0.03 #327), 02p0qmm (0.04 #294, 0.03 #822, 0.03 #932) >> Best rule #773 for best value: >> intensional similarity = 2 >> extensional distance = 480 >> proper extension: 02583l; 07w5rq; 031n8c; 03p7gb; 05xb7q; 07b2yw; 03t4nx; 04_j5s; 036921; 06b7s9; ... >> query: (?x5280, 05jxkf) <- school_type(?x5280, ?x1507), organization(?x5510, ?x5280) >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07vhb school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.508 http://example.org/education/educational_institution/school_type #4422-05l8y PRED entity: 05l8y PRED relation: film_release_region! PRED expected values: 02vxq9m => 95 concepts (49 used for prediction) PRED predicted values (max 10 best out of 1325): 0bpm4yw (0.77 #549, 0.64 #9824, 0.63 #4524), 017jd9 (0.75 #596, 0.58 #9871, 0.58 #4571), 0gd0c7x (0.73 #242, 0.57 #9517, 0.56 #13492), 04f52jw (0.73 #333, 0.56 #9608, 0.56 #5633), 0661m4p (0.73 #285, 0.49 #9560, 0.49 #5585), 062zm5h (0.71 #659, 0.56 #9934, 0.55 #13909), 0dzlbx (0.71 #654, 0.51 #5954, 0.51 #9929), 0872p_c (0.71 #135, 0.50 #9410, 0.48 #5435), 0fpgp26 (0.69 #1145, 0.61 #10420, 0.61 #6445), 08hmch (0.69 #119, 0.60 #9394, 0.58 #5419) >> Best rule #549 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 0f8l9c; 0hzlz; >> query: (?x6841, 0bpm4yw) <- film_release_region(?x1178, ?x6841), organization(?x6841, ?x127), geographic_distribution(?x13008, ?x6841) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 0f8l9c; 0hzlz; *> query: (?x6841, 02vxq9m) <- film_release_region(?x1178, ?x6841), organization(?x6841, ?x127), geographic_distribution(?x13008, ?x6841) *> conf = 0.67 ranks of expected_values: 15 EVAL 05l8y film_release_region! 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 95.000 49.000 0.771 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4421-0c8hct PRED entity: 0c8hct PRED relation: nationality PRED expected values: 09c7w0 => 93 concepts (58 used for prediction) PRED predicted values (max 10 best out of 94): 09c7w0 (0.89 #1304, 0.85 #4951, 0.84 #5152), 05kkh (0.75 #1404, 0.35 #3216, 0.34 #3115), 0fxz4 (0.75 #1404, 0.35 #3216, 0.34 #3115), 03_3d (0.33 #6, 0.25 #307, 0.25 #207), 0345h (0.20 #432, 0.17 #632, 0.08 #1132), 02jx1 (0.16 #2643, 0.15 #1134, 0.13 #1537), 07ssc (0.10 #2625, 0.10 #1116, 0.08 #816), 03rk0 (0.08 #847, 0.05 #4696, 0.05 #5703), 0d060g (0.07 #2718, 0.07 #1411, 0.05 #3527), 0f8l9c (0.06 #1123, 0.03 #2531, 0.03 #2005) >> Best rule #1304 for best value: >> intensional similarity = 5 >> extensional distance = 85 >> proper extension: 02mslq; >> query: (?x5681, 09c7w0) <- place_of_birth(?x5681, ?x2504), locations(?x4803, ?x2504), contains(?x94, ?x2504), citytown(?x2388, ?x2504), ?x4803 = 0b_6jz >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c8hct nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 58.000 0.885 http://example.org/people/person/nationality #4420-016nvh PRED entity: 016nvh PRED relation: type_of_union PRED expected values: 04ztj => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.74 #53, 0.73 #45, 0.73 #41), 01g63y (0.50 #6, 0.33 #2, 0.17 #22), 0jgjn (0.02 #36, 0.01 #64, 0.01 #68) >> Best rule #53 for best value: >> intensional similarity = 2 >> extensional distance = 118 >> proper extension: 0ct9_; 06c0j; >> query: (?x10624, 04ztj) <- profession(?x10624, ?x1032), notable_people_with_this_condition(?x1502, ?x10624) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016nvh type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.742 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4419-030qb3t PRED entity: 030qb3t PRED relation: location_of_ceremony! PRED expected values: 058frd => 161 concepts (158 used for prediction) PRED predicted values (max 10 best out of 566): 0dvld (0.25 #626, 0.10 #870, 0.06 #1355), 03m2fg (0.25 #661, 0.10 #905, 0.06 #1390), 02yy8 (0.25 #721, 0.10 #965, 0.06 #1450), 03l26m (0.25 #712, 0.10 #956, 0.06 #1441), 0djywgn (0.25 #672, 0.10 #916, 0.06 #1401), 05cx7x (0.25 #656, 0.10 #900, 0.06 #1385), 01p4r3 (0.25 #624, 0.10 #868, 0.06 #1353), 0c_jc (0.25 #621, 0.10 #865, 0.06 #1350), 05y5fw (0.25 #604, 0.10 #848, 0.06 #1333), 01vw20h (0.25 #588, 0.10 #832, 0.06 #1317) >> Best rule #626 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0281s1; >> query: (?x1523, 0dvld) <- place_of_death(?x457, ?x1523), location(?x4536, ?x1523), ?x4536 = 09yrh >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 030qb3t location_of_ceremony! 058frd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 161.000 158.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #4418-02c7k4 PRED entity: 02c7k4 PRED relation: production_companies PRED expected values: 01795t => 73 concepts (38 used for prediction) PRED predicted values (max 10 best out of 48): 01795t (0.62 #22, 0.57 #104, 0.28 #186), 09b3v (0.22 #248, 0.22 #197, 0.21 #332), 04rcl7 (0.22 #235, 0.21 #319, 0.04 #569), 05qd_ (0.11 #1169, 0.10 #1335, 0.09 #837), 086k8 (0.10 #664, 0.10 #500, 0.09 #1244), 054lpb6 (0.10 #348, 0.06 #759, 0.06 #1257), 01hmnh (0.10 #247, 0.09 #331), 016tw3 (0.08 #345, 0.07 #1254, 0.07 #2747), 01gb54 (0.08 #617, 0.06 #1031, 0.05 #948), 016tt2 (0.08 #1163, 0.07 #1329, 0.07 #502) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0209hj; 02qm_f; 01c22t; 0407yfx; 01jrbb; 0dyb1; 02rn00y; 03x7hd; 03q0r1; 027s39y; ... >> query: (?x6256, 01795t) <- film(?x3417, ?x6256), currency(?x6256, ?x170), ?x3417 = 0p8r1, ?x170 = 09nqf >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02c7k4 production_companies 01795t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 38.000 0.615 http://example.org/film/film/production_companies #4417-03s0w PRED entity: 03s0w PRED relation: religion PRED expected values: 02t7t => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 24): 03_gx (0.50 #157, 0.45 #308, 0.45 #208), 058x5 (0.44 #152, 0.44 #1284, 0.42 #1310), 0flw86 (0.44 #1284, 0.42 #1310, 0.40 #882), 092bf5 (0.44 #1284, 0.42 #1310, 0.40 #1536), 02t7t (0.44 #1284, 0.42 #1310, 0.40 #1536), 072w0 (0.44 #1284, 0.42 #1310, 0.40 #1536), 025t7ly (0.44 #201, 0.10 #856), 03j6c (0.33 #11, 0.09 #917, 0.09 #892), 0kpl (0.33 #4, 0.02 #631, 0.02 #860), 07w8f (0.33 #19, 0.02 #320, 0.02 #370) >> Best rule #157 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 0rh6k; >> query: (?x961, 03_gx) <- location(?x1987, ?x961), contains(?x961, ?x310), religion(?x961, ?x109), country(?x961, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1284 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 131 *> proper extension: 019rg5; 0169t; 02khs; 056vv; 04w4s; 0bjv6; 01p1b; 04tr1; 03676; 0mhhw; ... *> query: (?x961, ?x109) <- adjoins(?x961, ?x3818), time_zones(?x3818, ?x1638), religion(?x3818, ?x109) *> conf = 0.44 ranks of expected_values: 5 EVAL 03s0w religion 02t7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 135.000 135.000 0.500 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #4416-0h5g_ PRED entity: 0h5g_ PRED relation: award PRED expected values: 02x4w6g => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 280): 09sb52 (0.79 #12069, 0.35 #439, 0.34 #18886), 027986c (0.70 #24465, 0.67 #35700, 0.66 #26872), 0ck27z (0.33 #14527, 0.15 #18938, 0.15 #18136), 0gqy2 (0.31 #13797, 0.17 #12193, 0.12 #33693), 040njc (0.25 #13642, 0.12 #33693, 0.09 #18454), 05pcn59 (0.21 #8100, 0.20 #9303, 0.20 #10105), 0bdwqv (0.21 #13805, 0.14 #28477, 0.13 #12201), 0gr51 (0.19 #13733, 0.07 #1301, 0.06 #18545), 0gq9h (0.19 #13710, 0.09 #18522, 0.08 #24941), 0cqhk0 (0.18 #14471, 0.10 #4045, 0.10 #18882) >> Best rule #12069 for best value: >> intensional similarity = 3 >> extensional distance = 507 >> proper extension: 01m4kpp; >> query: (?x489, 09sb52) <- award(?x489, ?x458), award(?x1815, ?x458), ?x1815 = 030hcs >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #112 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 02_j7t; 0126rp; *> query: (?x489, 02x4w6g) <- film(?x489, ?x6642), profession(?x489, ?x1032), ?x6642 = 063fh9 *> conf = 0.14 ranks of expected_values: 32 EVAL 0h5g_ award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 101.000 101.000 0.790 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4415-023g6w PRED entity: 023g6w PRED relation: film! PRED expected values: 078mgh => 56 concepts (42 used for prediction) PRED predicted values (max 10 best out of 964): 04k25 (0.49 #74790, 0.48 #66481, 0.47 #60248), 01nwwl (0.33 #500, 0.03 #12966, 0.03 #8309), 0525b (0.33 #1909, 0.03 #8309, 0.01 #12296), 03h2d4 (0.33 #743, 0.02 #6974, 0.02 #9052), 0356dp (0.33 #1741, 0.02 #14207, 0.02 #22516), 0psss (0.22 #2634, 0.09 #4711, 0.04 #8866), 07mz77 (0.22 #3491, 0.04 #9723, 0.02 #15957), 012q4n (0.22 #3211, 0.03 #17754, 0.02 #26063), 0gn30 (0.18 #5098, 0.05 #17564, 0.04 #7175), 01fh9 (0.18 #4470, 0.04 #8625, 0.03 #14859) >> Best rule #74790 for best value: >> intensional similarity = 4 >> extensional distance = 1203 >> proper extension: 0clpml; >> query: (?x8679, ?x2671) <- nominated_for(?x2671, ?x8679), award(?x2671, ?x372), location(?x2671, ?x8174), award_winner(?x1009, ?x2671) >> conf = 0.49 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 023g6w film! 078mgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 42.000 0.488 http://example.org/film/actor/film./film/performance/film #4414-0bwtj PRED entity: 0bwtj PRED relation: capital! PRED expected values: 024pcx => 91 concepts (41 used for prediction) PRED predicted values (max 10 best out of 38): 02jx1 (0.06 #2863, 0.04 #955, 0.02 #2180), 06q1r (0.06 #500, 0.01 #1592, 0.01 #1727), 09c7w0 (0.04 #5048, 0.03 #819, 0.02 #956), 0c4b8 (0.03 #903, 0.02 #1314), 0hzlz (0.03 #840, 0.02 #1251), 02vzc (0.03 #860), 0cdbq (0.03 #1709, 0.02 #1980, 0.02 #2799), 07ssc (0.02 #1092, 0.02 #2181, 0.01 #1517), 084n_ (0.02 #1211, 0.02 #1348, 0.01 #1621), 059z0 (0.02 #1198, 0.02 #1335, 0.01 #1608) >> Best rule #2863 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 0fn2g; 0qlrh; >> query: (?x13212, ?x1310) <- place_of_death(?x8981, ?x13212), people(?x4291, ?x8981), nationality(?x8981, ?x1310) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #1616 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 0f2tj; *> query: (?x13212, 024pcx) <- citytown(?x12669, ?x13212), place_of_birth(?x8924, ?x13212), place_of_death(?x8981, ?x13212), award_winner(?x1545, ?x8924) *> conf = 0.01 ranks of expected_values: 27 EVAL 0bwtj capital! 024pcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 91.000 41.000 0.062 http://example.org/location/country/capital #4413-01vn35l PRED entity: 01vn35l PRED relation: instrumentalists! PRED expected values: 02hnl 04rzd => 102 concepts (82 used for prediction) PRED predicted values (max 10 best out of 117): 03qjg (0.41 #1145, 0.35 #1227, 0.22 #293), 026t6 (0.38 #82, 0.32 #1144, 0.32 #246), 01vj9c (0.38 #82, 0.32 #246, 0.31 #245), 0l14md (0.32 #1144, 0.28 #87, 0.26 #252), 02dlh2 (0.32 #1144, 0.25 #1635, 0.25 #819), 03bx0bm (0.32 #1144, 0.25 #1635, 0.25 #819), 02snj9 (0.32 #1144, 0.25 #1635, 0.25 #819), 02hnl (0.25 #521, 0.25 #357, 0.23 #1746), 06w7v (0.14 #477, 0.13 #1374, 0.11 #66), 018j2 (0.14 #444, 0.11 #33, 0.11 #1095) >> Best rule #1145 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 02qfhb; >> query: (?x2876, ?x227) <- gender(?x2876, ?x231), nationality(?x2876, ?x1310), performance_role(?x2876, ?x212), role(?x2876, ?x227) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #521 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 053y0s; 01gx5f; 0f0qfz; 0dw3l; 01r0t_j; 048tgl; 023322; 01k_0fp; *> query: (?x2876, 02hnl) <- artists(?x1572, ?x2876), ?x1572 = 06by7, performance_role(?x2876, ?x212), gender(?x2876, ?x231) *> conf = 0.25 ranks of expected_values: 8, 11 EVAL 01vn35l instrumentalists! 04rzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 102.000 82.000 0.409 http://example.org/music/instrument/instrumentalists EVAL 01vn35l instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 102.000 82.000 0.409 http://example.org/music/instrument/instrumentalists #4412-030pr PRED entity: 030pr PRED relation: gender PRED expected values: 05zppz => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 5): 05zppz (0.88 #87, 0.88 #57, 0.87 #71), 02zsn (0.32 #28, 0.32 #86, 0.30 #135), 0fltx (0.12 #125), 01hbgs (0.12 #125), 0c58k (0.12 #125) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 249 >> proper extension: 05ty4m; 02ndbd; 0162c8; 0bgrsl; 021lby; 04g865; 0m32_; 09p06; 021yw7; 01vqrm; ... >> query: (?x1134, 05zppz) <- film(?x1134, ?x1133), genre(?x1133, ?x239), nominated_for(?x591, ?x1133) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030pr gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 157.000 0.880 http://example.org/people/person/gender #4411-04n2vgk PRED entity: 04n2vgk PRED relation: people! PRED expected values: 01g7zj => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 49): 041rx (0.25 #2929, 0.24 #2854, 0.23 #604), 07hwkr (0.14 #1811, 0.14 #236, 0.13 #86), 0xnvg (0.13 #87, 0.13 #1812, 0.11 #162), 02w7gg (0.13 #2, 0.10 #2327, 0.09 #2927), 033tf_ (0.12 #1357, 0.11 #2332, 0.11 #2857), 048z7l (0.11 #189, 0.09 #339, 0.09 #264), 07bch9 (0.10 #1822, 0.07 #97, 0.05 #2947), 063k3h (0.09 #255, 0.07 #105, 0.05 #180), 06v41q (0.07 #103, 0.05 #178, 0.05 #328), 0g8_vp (0.07 #96, 0.05 #171, 0.05 #321) >> Best rule #2929 for best value: >> intensional similarity = 3 >> extensional distance = 854 >> proper extension: 026lj; 017yfz; 07t2k; 01sg7_; 042f1; 085q5; 0fs9jn; 042d1; 042fk; 045gzq; ... >> query: (?x9262, 041rx) <- profession(?x9262, ?x131), student(?x10220, ?x9262), people(?x2510, ?x9262) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04n2vgk people! 01g7zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 102.000 0.245 http://example.org/people/ethnicity/people #4410-0cm2xh PRED entity: 0cm2xh PRED relation: films PRED expected values: 0bx0l => 68 concepts (61 used for prediction) PRED predicted values (max 10 best out of 663): 08hmch (0.33 #46, 0.20 #565, 0.15 #4717), 0fjyzt (0.33 #271, 0.20 #790, 0.14 #1828), 0czyxs (0.33 #20, 0.20 #539, 0.14 #1577), 02p86pb (0.33 #443, 0.20 #962, 0.14 #2000), 09sr0 (0.33 #441, 0.20 #960, 0.14 #1998), 0jqj5 (0.33 #257, 0.20 #776, 0.14 #1814), 0p_qr (0.33 #167, 0.20 #686, 0.14 #1724), 0sxmx (0.33 #233, 0.20 #752, 0.14 #1790), 04j4tx (0.33 #205, 0.20 #724, 0.14 #1762), 05_61y (0.33 #342, 0.20 #861, 0.14 #1899) >> Best rule #46 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 07_nf; >> query: (?x5503, 08hmch) <- combatants(?x5503, ?x1023), combatants(?x5503, ?x94), films(?x5503, ?x9993), films(?x5503, ?x2133), ?x94 = 09c7w0, entity_involved(?x5503, ?x1159), award(?x9993, ?x1703), nominated_for(?x484, ?x2133), ?x1023 = 0ctw_b >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cm2xh films 0bx0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 61.000 0.333 http://example.org/film/film_subject/films #4409-0by1wkq PRED entity: 0by1wkq PRED relation: film_crew_role PRED expected values: 09zzb8 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 34): 09zzb8 (0.70 #882, 0.70 #918, 0.69 #808), 09vw2b7 (0.60 #888, 0.60 #924, 0.59 #814), 0dxtw (0.43 #83, 0.36 #818, 0.35 #928), 01pvkk (0.38 #12, 0.29 #194, 0.29 #48), 0215hd (0.25 #19, 0.17 #164, 0.14 #55), 02rh1dz (0.23 #82, 0.14 #521, 0.14 #559), 02ynfr (0.19 #88, 0.16 #453, 0.16 #490), 0d2b38 (0.13 #98, 0.12 #26, 0.12 #537), 01xy5l_ (0.12 #14, 0.12 #86, 0.11 #159), 089g0h (0.12 #20, 0.12 #165, 0.11 #92) >> Best rule #882 for best value: >> intensional similarity = 4 >> extensional distance = 968 >> proper extension: 07kb7vh; >> query: (?x1927, 09zzb8) <- film(?x1561, ?x1927), country(?x1927, ?x94), film_crew_role(?x1927, ?x468), film(?x3273, ?x1927) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0by1wkq film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.701 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4408-02wwsh8 PRED entity: 02wwsh8 PRED relation: award! PRED expected values: 08cfr1 0k20s => 52 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1701): 01ffx4 (0.50 #3369, 0.40 #4388, 0.04 #19697), 09tqkv2 (0.50 #2235, 0.04 #11418, 0.04 #12437), 07s846j (0.43 #6515, 0.25 #3457, 0.20 #4476), 0cvkv5 (0.40 #5933, 0.08 #7972, 0.06 #18181), 02hfk5 (0.40 #5581, 0.08 #7620, 0.05 #8641), 0hfzr (0.33 #414, 0.29 #6530, 0.25 #9593), 0pv3x (0.33 #109, 0.29 #6225, 0.25 #3167), 0mcl0 (0.33 #380, 0.29 #6496, 0.25 #3438), 09cr8 (0.33 #174, 0.29 #6290, 0.18 #11395), 017jd9 (0.33 #461, 0.25 #3519, 0.20 #4538) >> Best rule #3369 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0l8z1; 02qrwjt; >> query: (?x8313, 01ffx4) <- award(?x4772, ?x8313), award_winner(?x8313, ?x647), film(?x13128, ?x4772), ?x13128 = 04g2mkf, film_crew_role(?x4772, ?x137), film_release_region(?x4772, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3058 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 02x4sn8; 09cn0c; *> query: (?x8313, ?x951) <- award(?x4772, ?x8313), award_winner(?x8313, ?x8225), film(?x13128, ?x4772), ?x13128 = 04g2mkf, film_crew_role(?x4772, ?x137), film(?x8225, ?x951) *> conf = 0.12 ranks of expected_values: 201, 1699 EVAL 02wwsh8 award! 0k20s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 52.000 25.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02wwsh8 award! 08cfr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 25.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4407-07ww5 PRED entity: 07ww5 PRED relation: taxonomy PRED expected values: 04n6k => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.77 #14, 0.77 #12, 0.77 #10) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 07c5l; 04wsz; >> query: (?x1317, 04n6k) <- service_location(?x9517, ?x1317), currency(?x9517, ?x170), company(?x265, ?x9517) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ww5 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.773 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #4406-02qrbbx PRED entity: 02qrbbx PRED relation: nominated_for PRED expected values: 03f7xg 0g5q34q => 42 concepts (8 used for prediction) PRED predicted values (max 10 best out of 1557): 01srq2 (0.68 #4780, 0.65 #11163, 0.64 #11161), 07vfy4 (0.68 #4780, 0.65 #11163, 0.64 #11161), 0cmc26r (0.50 #3806, 0.40 #5400, 0.33 #620), 080dfr7 (0.50 #4661, 0.40 #6255, 0.33 #1475), 02vp1f_ (0.50 #3215, 0.20 #4809, 0.05 #12760), 03f7xg (0.50 #3677, 0.20 #5271, 0.05 #12760), 03cwwl (0.50 #4615, 0.02 #10996), 014bpd (0.50 #4403, 0.02 #10784), 0c0yh4 (0.40 #4813, 0.33 #33, 0.25 #3219), 0cp0790 (0.40 #5861, 0.33 #1081, 0.25 #2674) >> Best rule #4780 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 02qysm0; >> query: (?x13042, ?x7246) <- nominated_for(?x13042, ?x11324), nominated_for(?x13042, ?x11037), ?x11324 = 027r7k, award(?x7246, ?x13042), genre(?x11037, ?x53), language(?x11037, ?x254) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #3677 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 02qysm0; *> query: (?x13042, 03f7xg) <- nominated_for(?x13042, ?x11324), nominated_for(?x13042, ?x11037), ?x11324 = 027r7k, award(?x7246, ?x13042), genre(?x11037, ?x53), language(?x11037, ?x254) *> conf = 0.50 ranks of expected_values: 6 EVAL 02qrbbx nominated_for 0g5q34q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 42.000 8.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qrbbx nominated_for 03f7xg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 42.000 8.000 0.684 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4405-0hsn_ PRED entity: 0hsn_ PRED relation: film PRED expected values: 027pfg => 113 concepts (55 used for prediction) PRED predicted values (max 10 best out of 976): 0h3mh3q (0.71 #8941, 0.69 #5364, 0.62 #23240), 02x2jl_ (0.25 #1752, 0.17 #3539, 0.02 #8903), 0bs5vty (0.25 #1634, 0.05 #5210, 0.02 #8785), 0g3zrd (0.25 #367, 0.05 #3943, 0.02 #7518), 04yc76 (0.25 #441, 0.01 #18319, 0.01 #21893), 03qcfvw (0.25 #9, 0.01 #8950), 0dlngsd (0.25 #779, 0.01 #22231), 04n52p6 (0.25 #259, 0.01 #21711), 0gwlfnb (0.25 #1502), 02mmwk (0.25 #1258) >> Best rule #8941 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 03zqc1; 01gvr1; 01csvq; 0htlr; 049g_xj; 07s8r0; 01l9p; 028knk; 07b2lv; 02d4ct; ... >> query: (?x8734, ?x5682) <- award_winner(?x7192, ?x8734), award_winner(?x5682, ?x8734), award(?x8890, ?x7192), ?x8890 = 03b1sb >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4796 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 01j5ts; 0159h6; 07lt7b; 01tspc6; 0h1mt; 0h1nt; 0kszw; 01xcfy; 03bxsw; 07yp0f; ... *> query: (?x8734, 027pfg) <- award_winner(?x7192, ?x8734), award_winner(?x5682, ?x8734), ?x7192 = 027571b, profession(?x8734, ?x319) *> conf = 0.09 ranks of expected_values: 55 EVAL 0hsn_ film 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 113.000 55.000 0.713 http://example.org/film/actor/film./film/performance/film #4404-0dvmd PRED entity: 0dvmd PRED relation: currency PRED expected values: 09nqf => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.45 #25, 0.43 #22, 0.42 #49), 01nv4h (0.02 #26, 0.02 #29, 0.01 #161) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 02lfcm; 0mz73; >> query: (?x3101, 09nqf) <- award_nominee(?x406, ?x3101), executive_produced_by(?x5092, ?x3101), participant(?x709, ?x3101) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dvmd currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.452 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #4403-07tg4 PRED entity: 07tg4 PRED relation: school_type PRED expected values: 07tf8 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 18): 01rs41 (0.25 #1338, 0.24 #1407, 0.22 #1085), 05pcjw (0.25 #24, 0.24 #438, 0.24 #783), 07tf8 (0.25 #31, 0.22 #54, 0.21 #813), 01_9fk (0.15 #853, 0.14 #807, 0.14 #669), 047951 (0.11 #76, 0.11 #53, 0.10 #99), 01jlsn (0.11 #660, 0.06 #407, 0.04 #959), 02p0qmm (0.10 #469, 0.06 #653, 0.05 #446), 0m4mb (0.07 #654, 0.06 #401, 0.04 #953), 01_srz (0.07 #785, 0.06 #555, 0.05 #854), 01y64 (0.07 #126, 0.04 #402, 0.04 #908) >> Best rule #1338 for best value: >> intensional similarity = 3 >> extensional distance = 426 >> proper extension: 01v3k2; 02jztz; >> query: (?x2999, 01rs41) <- major_field_of_study(?x2999, ?x742), school_type(?x2999, ?x3092), contains(?x455, ?x2999) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 039cpd; *> query: (?x2999, 07tf8) <- child(?x2999, ?x7306), category(?x2999, ?x134), contains(?x455, ?x2999) *> conf = 0.25 ranks of expected_values: 3 EVAL 07tg4 school_type 07tf8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 85.000 0.250 http://example.org/education/educational_institution/school_type #4402-01gsrl PRED entity: 01gsrl PRED relation: legislative_sessions! PRED expected values: 01grpc 01gsvb => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 49): 01gsvb (0.85 #731, 0.85 #729, 0.85 #728), 01gst9 (0.85 #731, 0.85 #729, 0.85 #728), 01gssz (0.85 #731, 0.85 #729, 0.85 #728), 01gtcc (0.74 #1420, 0.70 #1419, 0.69 #1304), 01gtc0 (0.74 #1420, 0.70 #1419, 0.69 #1304), 01gtcq (0.74 #1420, 0.70 #1419, 0.69 #1304), 043djx (0.74 #1420, 0.70 #1419, 0.69 #1304), 01h7xx (0.74 #1420, 0.70 #1419, 0.69 #1304), 01gsrl (0.71 #870, 0.70 #623, 0.68 #1421), 01grp0 (0.70 #623, 0.67 #716, 0.61 #1483) >> Best rule #731 for best value: >> intensional similarity = 36 >> extensional distance = 4 >> proper extension: 01grnp; 01grmk; >> query: (?x4437, ?x6712) <- legislative_sessions(?x4437, ?x11142), legislative_sessions(?x4437, ?x6712), legislative_sessions(?x4437, ?x6021), district_represented(?x4437, ?x7518), district_represented(?x4437, ?x4776), district_represented(?x4437, ?x1767), district_represented(?x4437, ?x760), district_represented(?x4437, ?x728), district_represented(?x4437, ?x177), legislative_sessions(?x4665, ?x4437), ?x7518 = 026mj, ?x4776 = 06yxd, ?x11142 = 01grq1, ?x728 = 059f4, ?x1767 = 04rrd, district_represented(?x6712, ?x448), legislative_sessions(?x9046, ?x4437), location(?x10251, ?x177), state_province_region(?x13680, ?x177), currency(?x177, ?x170), capital(?x177, ?x6453), contains(?x177, ?x13626), religion(?x177, ?x2672), religion(?x177, ?x1363), profession(?x10251, ?x319), legislative_sessions(?x6021, ?x759), major_field_of_study(?x13680, ?x4321), ?x1363 = 058x5, district_represented(?x6021, ?x1025), ?x760 = 05fkf, ?x4665 = 07t58, contains(?x94, ?x177), district_represented(?x176, ?x177), source(?x13626, ?x958), ?x176 = 03rl1g, ?x2672 = 01y0s9 >> conf = 0.85 => this is the best rule for 3 predicted values ranks of expected_values: 1, 11 EVAL 01gsrl legislative_sessions! 01gsvb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.846 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions EVAL 01gsrl legislative_sessions! 01grpc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 38.000 38.000 0.846 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #4401-01k60v PRED entity: 01k60v PRED relation: nominated_for! PRED expected values: 094qd5 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 230): 0gq9h (0.74 #4489, 0.63 #2391, 0.56 #6120), 04dn09n (0.46 #2366, 0.44 #4464, 0.35 #6095), 0gr0m (0.45 #2390, 0.35 #4488, 0.28 #6119), 040njc (0.45 #4435, 0.44 #2337, 0.33 #6066), 0gr4k (0.44 #4455, 0.39 #6086, 0.33 #2357), 02qyntr (0.42 #2505, 0.34 #4603, 0.27 #641), 0f4x7 (0.42 #4454, 0.35 #6085, 0.31 #2356), 0p9sw (0.41 #2351, 0.26 #4449, 0.25 #1885), 02pqp12 (0.39 #2389, 0.34 #4487, 0.25 #6118), 0gqy2 (0.38 #4545, 0.37 #6176, 0.32 #2447) >> Best rule #4489 for best value: >> intensional similarity = 4 >> extensional distance = 241 >> proper extension: 0p_tz; 03bdkd; >> query: (?x4448, 0gq9h) <- nominated_for(?x1313, ?x4448), ?x1313 = 0gs9p, genre(?x4448, ?x53), nominated_for(?x1774, ?x4448) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #10722 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 860 *> proper extension: 05h95s; *> query: (?x4448, ?x528) <- award_winner(?x4448, ?x7331), titles(?x53, ?x4448), award(?x7331, ?x528) *> conf = 0.24 ranks of expected_values: 23 EVAL 01k60v nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 86.000 86.000 0.745 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4400-0gtt5fb PRED entity: 0gtt5fb PRED relation: film! PRED expected values: 017s11 => 94 concepts (79 used for prediction) PRED predicted values (max 10 best out of 69): 046b0s (0.66 #2674, 0.66 #2823, 0.57 #3643), 03xq0f (0.39 #1265, 0.14 #672, 0.13 #152), 016tw3 (0.22 #380, 0.22 #603, 0.19 #901), 05qd_ (0.17 #82, 0.16 #2756, 0.15 #2607), 017s11 (0.17 #893, 0.16 #595, 0.14 #1633), 016tt2 (0.14 #1042, 0.14 #373, 0.13 #596), 01795t (0.09 #685, 0.08 #17, 0.08 #759), 01gb54 (0.09 #1067, 0.08 #102, 0.07 #2106), 0jz9f (0.09 #892, 0.08 #75, 0.08 #818), 0g1rw (0.08 #81, 0.08 #7, 0.08 #2755) >> Best rule #2674 for best value: >> intensional similarity = 3 >> extensional distance = 739 >> proper extension: 0d8w2n; >> query: (?x5588, ?x2548) <- production_companies(?x5588, ?x2548), nominated_for(?x2548, ?x570), citytown(?x2548, ?x1523) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #893 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 197 *> proper extension: 04fzfj; 0dsvzh; 0b73_1d; 03m8y5; 019vhk; 065dc4; 06lpmt; 032zq6; 043t8t; 0pv54; ... *> query: (?x5588, 017s11) <- film_crew_role(?x5588, ?x1171), written_by(?x5588, ?x9281), ?x1171 = 09vw2b7, film(?x382, ?x5588), country(?x5588, ?x94) *> conf = 0.17 ranks of expected_values: 5 EVAL 0gtt5fb film! 017s11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 79.000 0.665 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #4399-01w5m PRED entity: 01w5m PRED relation: institution! PRED expected values: 013zdg => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 8): 013zdg (0.43 #67, 0.38 #142, 0.34 #97), 022h5x (0.28 #773, 0.25 #72, 0.24 #102), 02m4yg (0.28 #773, 0.16 #51, 0.13 #42), 02cq61 (0.28 #773, 0.13 #43, 0.12 #14), 01ysy9 (0.28 #773, 0.12 #37, 0.08 #131), 01gkg3 (0.28 #773, 0.04 #135, 0.03 #162), 01kxxq (0.02 #649, 0.02 #229, 0.02 #509), 0g26h (0.02 #125, 0.02 #152, 0.01 #170) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 03_c8p; >> query: (?x3424, 013zdg) <- company(?x346, ?x3424), organization(?x3424, ?x5487), citytown(?x3424, ?x739) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w5m institution! 013zdg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.429 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4398-054ky1 PRED entity: 054ky1 PRED relation: award_winner PRED expected values: 06pj8 0gyx4 01t9qj_ => 49 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1684): 016ggh (0.60 #9541, 0.21 #19275, 0.14 #11975), 01kwsg (0.43 #10782, 0.40 #8348, 0.38 #13215), 01ycbq (0.40 #7707, 0.29 #17441, 0.29 #10141), 01qscs (0.40 #7356, 0.29 #9790, 0.25 #12223), 026rm_y (0.40 #9133, 0.29 #11567, 0.25 #14000), 0237fw (0.40 #7795, 0.29 #10229, 0.25 #12662), 016gkf (0.40 #8499, 0.29 #10933, 0.25 #13366), 015c4g (0.40 #8270, 0.25 #3404, 0.21 #18004), 015gy7 (0.40 #8670, 0.21 #18404, 0.14 #11104), 09y20 (0.40 #7599, 0.21 #17333, 0.03 #27066) >> Best rule #9541 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 027dtxw; 0gqy2; 0bdwqv; >> query: (?x2060, 016ggh) <- award(?x8450, ?x2060), award(?x269, ?x2060), award_winner(?x2060, ?x450), ?x8450 = 0h953, ?x269 = 0byfz >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #60832 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 230 *> proper extension: 0p9sw; 09qwmm; 02r22gf; 047byns; 02681vq; 02x1dht; 0gqzz; 03x3wf; 0cqh6z; 02g8mp; ... *> query: (?x2060, ?x269) <- award(?x269, ?x2060), award_winner(?x2060, ?x4563), award_nominee(?x989, ?x4563), ceremony(?x2060, ?x747) *> conf = 0.32 ranks of expected_values: 38, 269 EVAL 054ky1 award_winner 01t9qj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 29.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 054ky1 award_winner 0gyx4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 49.000 29.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 054ky1 award_winner 06pj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 29.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #4397-04kj2v PRED entity: 04kj2v PRED relation: gender PRED expected values: 05zppz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #61, 0.73 #127, 0.72 #73), 02zsn (0.30 #34, 0.30 #10, 0.29 #26) >> Best rule #61 for best value: >> intensional similarity = 2 >> extensional distance = 1200 >> proper extension: 045m1_; 042q3; 05m0h; 04n7gc6; 0ngg; >> query: (?x2507, 05zppz) <- nationality(?x2507, ?x1264), official_language(?x1264, ?x732) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04kj2v gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.731 http://example.org/people/person/gender #4396-05hj_k PRED entity: 05hj_k PRED relation: executive_produced_by! PRED expected values: 02v63m 09z2b7 02rrfzf 02qzh2 02v5_g 04nnpw 027m5wv 0bwhdbl => 124 concepts (51 used for prediction) PRED predicted values (max 10 best out of 277): 0m313 (0.33 #8, 0.17 #491, 0.10 #18858), 0dgst_d (0.33 #59, 0.17 #542, 0.10 #18858), 04jplwp (0.33 #394, 0.17 #877, 0.10 #18858), 019vhk (0.33 #145, 0.17 #628, 0.10 #18858), 04nnpw (0.33 #245, 0.17 #728, 0.06 #3631), 0bwhdbl (0.33 #408, 0.17 #891, 0.03 #3794), 02v5_g (0.33 #240, 0.17 #723, 0.03 #3626), 027m5wv (0.33 #319, 0.17 #802, 0.03 #3705), 02qzh2 (0.33 #208, 0.17 #691, 0.03 #3594), 09z2b7 (0.33 #69, 0.17 #552, 0.03 #3455) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 06q8hf; >> query: (?x4060, 0m313) <- executive_produced_by(?x11001, ?x4060), executive_produced_by(?x1392, ?x4060), ?x11001 = 07tj4c, ?x1392 = 017gm7 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #245 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 06q8hf; *> query: (?x4060, 04nnpw) <- executive_produced_by(?x11001, ?x4060), executive_produced_by(?x1392, ?x4060), ?x11001 = 07tj4c, ?x1392 = 017gm7 *> conf = 0.33 ranks of expected_values: 5, 6, 7, 8, 9, 10, 11 EVAL 05hj_k executive_produced_by! 0bwhdbl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 51.000 0.333 http://example.org/film/film/executive_produced_by EVAL 05hj_k executive_produced_by! 027m5wv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 51.000 0.333 http://example.org/film/film/executive_produced_by EVAL 05hj_k executive_produced_by! 04nnpw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 51.000 0.333 http://example.org/film/film/executive_produced_by EVAL 05hj_k executive_produced_by! 02v5_g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 51.000 0.333 http://example.org/film/film/executive_produced_by EVAL 05hj_k executive_produced_by! 02qzh2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 51.000 0.333 http://example.org/film/film/executive_produced_by EVAL 05hj_k executive_produced_by! 02rrfzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 51.000 0.333 http://example.org/film/film/executive_produced_by EVAL 05hj_k executive_produced_by! 09z2b7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 51.000 0.333 http://example.org/film/film/executive_produced_by EVAL 05hj_k executive_produced_by! 02v63m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 51.000 0.333 http://example.org/film/film/executive_produced_by #4395-0bpm4yw PRED entity: 0bpm4yw PRED relation: film_regional_debut_venue PRED expected values: 02_286 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 14): 018cvf (0.14 #149, 0.11 #16, 0.11 #115), 0kfhjq0 (0.11 #15, 0.06 #81, 0.02 #114), 0prpt (0.11 #28, 0.04 #127, 0.04 #431), 0j63cyr (0.11 #13, 0.01 #1209, 0.01 #1176), 0bmj62v (0.11 #23), 02_286 (0.09 #35, 0.03 #304, 0.03 #371), 01ly5m (0.09 #41, 0.02 #107, 0.02 #141), 015hr (0.08 #181, 0.07 #113, 0.06 #316), 07zmj (0.05 #299, 0.04 #130, 0.04 #400), 07751 (0.04 #378, 0.04 #277, 0.03 #412) >> Best rule #149 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 05p09dd; 0dlngsd; 067ghz; 0g57wgv; >> query: (?x4336, 018cvf) <- film_release_region(?x4336, ?x1229), film_release_region(?x4336, ?x792), ?x792 = 0hzlz, nominated_for(?x640, ?x4336), country(?x150, ?x1229) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #35 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 02jxbw; *> query: (?x4336, 02_286) <- film(?x2533, ?x4336), nominated_for(?x640, ?x4336), film(?x3186, ?x4336), ?x3186 = 055c8 *> conf = 0.09 ranks of expected_values: 6 EVAL 0bpm4yw film_regional_debut_venue 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 77.000 0.140 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #4394-06t2t PRED entity: 06t2t PRED relation: form_of_government PRED expected values: 01fpfn => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 6): 018wl5 (0.43 #278, 0.39 #332, 0.39 #14), 06cx9 (0.41 #517, 0.36 #403, 0.36 #355), 01fpfn (0.41 #195, 0.41 #273, 0.41 #357), 01q20 (0.38 #40, 0.36 #334, 0.33 #52), 01d9r3 (0.36 #521, 0.32 #401, 0.30 #407), 026wp (0.11 #18, 0.10 #90, 0.10 #162) >> Best rule #278 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 020d5; 024pcx; 02wmy; >> query: (?x2316, 018wl5) <- official_language(?x2316, ?x254), jurisdiction_of_office(?x182, ?x2316), ?x254 = 02h40lc >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #195 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 01z215; 04xn_; 0j4b; *> query: (?x2316, 01fpfn) <- member_states(?x7695, ?x2316), olympics(?x2316, ?x778), geographic_distribution(?x9148, ?x2316) *> conf = 0.41 ranks of expected_values: 3 EVAL 06t2t form_of_government 01fpfn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 123.000 0.426 http://example.org/location/country/form_of_government #4393-0h1yf PRED entity: 0h1yf PRED relation: nutrient! PRED expected values: 0dj75 0frq6 => 56 concepts (51 used for prediction) PRED predicted values (max 10 best out of 14): 0dj75 (0.90 #11, 0.88 #86, 0.88 #22), 0dcfv (0.90 #11, 0.88 #86, 0.88 #22), 0frq6 (0.89 #435, 0.89 #431, 0.88 #86), 06x4c (0.88 #86, 0.88 #22, 0.88 #19), 04k8n (0.02 #163, 0.02 #426, 0.02 #472), 05wvs (0.02 #163, 0.02 #426, 0.02 #472), 01sh2 (0.02 #426, 0.02 #472, 0.01 #479), 025rw19 (0.01 #479), 025tkqy (0.01 #479), 014d7f (0.01 #479) >> Best rule #11 for best value: >> intensional similarity = 118 >> extensional distance = 11 >> proper extension: 02kc008; >> query: (?x11409, ?x7719) <- nutrient(?x9732, ?x11409), nutrient(?x9489, ?x11409), nutrient(?x9005, ?x11409), nutrient(?x8298, ?x11409), nutrient(?x7057, ?x11409), nutrient(?x6285, ?x11409), nutrient(?x6191, ?x11409), nutrient(?x6159, ?x11409), nutrient(?x6032, ?x11409), nutrient(?x5373, ?x11409), nutrient(?x5009, ?x11409), nutrient(?x4068, ?x11409), nutrient(?x3900, ?x11409), nutrient(?x3468, ?x11409), nutrient(?x2701, ?x11409), nutrient(?x1959, ?x11409), nutrient(?x1303, ?x11409), nutrient(?x1257, ?x11409), ?x1959 = 0f25w9, ?x8298 = 037ls6, ?x1257 = 09728, ?x4068 = 0fbw6, ?x5373 = 0971v, ?x5009 = 0fjfh, ?x6032 = 01nkt, ?x2701 = 0hkxq, ?x9732 = 05z55, ?x3900 = 061_f, ?x6191 = 014j1m, ?x6285 = 01645p, ?x3468 = 0cxn2, ?x9005 = 04zpv, ?x1303 = 0fj52s, nutrient(?x6159, ?x12902), nutrient(?x6159, ?x12868), nutrient(?x6159, ?x12454), nutrient(?x6159, ?x11758), nutrient(?x6159, ?x10891), nutrient(?x6159, ?x10709), nutrient(?x6159, ?x10098), nutrient(?x6159, ?x9949), nutrient(?x6159, ?x9855), nutrient(?x6159, ?x9795), nutrient(?x6159, ?x9733), nutrient(?x6159, ?x9619), nutrient(?x6159, ?x9490), nutrient(?x6159, ?x9426), nutrient(?x6159, ?x9365), nutrient(?x6159, ?x8487), nutrient(?x6159, ?x8442), nutrient(?x6159, ?x8413), nutrient(?x6159, ?x7720), nutrient(?x6159, ?x7652), nutrient(?x6159, ?x7431), nutrient(?x6159, ?x7364), nutrient(?x6159, ?x7362), nutrient(?x6159, ?x7219), nutrient(?x6159, ?x7135), nutrient(?x6159, ?x6586), nutrient(?x6159, ?x6517), nutrient(?x6159, ?x6192), nutrient(?x6159, ?x6033), nutrient(?x6159, ?x6026), nutrient(?x6159, ?x5549), nutrient(?x6159, ?x5451), nutrient(?x6159, ?x5374), nutrient(?x6159, ?x5337), nutrient(?x6159, ?x5010), nutrient(?x6159, ?x3469), nutrient(?x6159, ?x3264), nutrient(?x6159, ?x3203), nutrient(?x6159, ?x2702), nutrient(?x6159, ?x1304), ?x11758 = 0q01m, ?x5451 = 05wvs, ?x8442 = 02kcv4x, ?x9795 = 05v_8y, ?x7135 = 025rsfk, ?x7652 = 025s0s0, ?x9619 = 0h1tg, ?x9733 = 0h1tz, ?x5549 = 025s7j4, ?x10891 = 0g5gq, ?x7431 = 09gwd, ?x12902 = 0fzjh, ?x7362 = 02kc5rj, ?x9489 = 07j87, ?x8487 = 014yzm, ?x5374 = 025s0zp, ?x12868 = 03d49, ?x9949 = 02kd0rh, ?x8413 = 02kc4sf, nutrient(?x10612, ?x6517), ?x7219 = 0h1vg, ?x12454 = 025rw19, ?x6586 = 05gh50, ?x9365 = 04k8n, ?x7364 = 09gvd, ?x10612 = 0frq6, ?x1304 = 08lb68, ?x9426 = 0h1yy, ?x6192 = 06jry, ?x2702 = 0838f, ?x10709 = 0h1sz, ?x10098 = 0h1_c, ?x6033 = 04zjxcz, ?x6026 = 025sf8g, ?x9855 = 0d9t0, ?x7720 = 025s7x6, ?x5337 = 06x4c, nutrient(?x7719, ?x3264), nutrient(?x3264, ?x8243), ?x3469 = 0h1zw, ?x7057 = 0fbdb, ?x8243 = 014d7f, ?x3203 = 04kl74p, ?x5010 = 0h1vz, ?x9490 = 0h1sg >> conf = 0.90 => this is the best rule for 2 predicted values ranks of expected_values: 1, 3 EVAL 0h1yf nutrient! 0frq6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 51.000 0.898 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0h1yf nutrient! 0dj75 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 51.000 0.898 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #4392-04vmp PRED entity: 04vmp PRED relation: location! PRED expected values: 0239zv 07f0tw 03x31g => 182 concepts (109 used for prediction) PRED predicted values (max 10 best out of 2082): 0241wg (0.61 #251624, 0.60 #254118, 0.58 #124567), 04rs03 (0.61 #251624, 0.60 #254118, 0.58 #124567), 04qp06 (0.61 #251624, 0.60 #254118, 0.58 #124567), 03wpmd (0.61 #251624, 0.60 #254118, 0.58 #124567), 04cmrt (0.61 #251624, 0.60 #254118, 0.58 #124567), 04b19t (0.61 #251624, 0.60 #254118, 0.58 #124567), 01zp33 (0.58 #124567, 0.56 #84706, 0.56 #84705), 0bvls5 (0.58 #124567, 0.56 #84706, 0.56 #84705), 0bxy67 (0.58 #124567, 0.56 #84706, 0.56 #84705), 04cbtrw (0.58 #124567, 0.56 #84706, 0.56 #84705) >> Best rule #251624 for best value: >> intensional similarity = 6 >> extensional distance = 174 >> proper extension: 01423b; 01sv6k; 025569; 018djs; >> query: (?x7412, ?x3129) <- place_of_birth(?x13506, ?x7412), place_of_birth(?x7504, ?x7412), place_of_birth(?x3129, ?x7412), film(?x7504, ?x657), languages(?x3129, ?x254), type_of_union(?x13506, ?x566) >> conf = 0.61 => this is the best rule for 6 predicted values *> Best rule #14653 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 01_q7h; *> query: (?x7412, 03x31g) <- category(?x7412, ?x134), service_location(?x10867, ?x7412), ?x10867 = 06_9lg *> conf = 0.08 ranks of expected_values: 211, 214 EVAL 04vmp location! 03x31g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 182.000 109.000 0.609 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04vmp location! 07f0tw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 182.000 109.000 0.609 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04vmp location! 0239zv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 182.000 109.000 0.609 http://example.org/people/person/places_lived./people/place_lived/location #4391-0879bpq PRED entity: 0879bpq PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.89 #6, 0.89 #1, 0.82 #112), 02nxhr (0.13 #32, 0.13 #37, 0.12 #17), 07c52 (0.08 #104, 0.06 #23, 0.05 #33), 07z4p (0.07 #106, 0.05 #15, 0.03 #25) >> Best rule #6 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 016ztl; >> query: (?x2783, 029j_) <- films(?x5011, ?x2783), genre(?x2783, ?x2540), film(?x541, ?x2783), music(?x2783, ?x4019), ?x2540 = 0hcr >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0879bpq film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #4390-0cfhfz PRED entity: 0cfhfz PRED relation: country PRED expected values: 09c7w0 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 52): 09c7w0 (0.76 #491, 0.76 #3628, 0.76 #3506), 02jx1 (0.46 #2274, 0.44 #2398, 0.41 #3073), 0d060g (0.44 #2398, 0.41 #3073, 0.41 #1718), 07ssc (0.31 #814, 0.29 #261, 0.24 #444), 0f8l9c (0.29 #264, 0.22 #325, 0.10 #817), 0345h (0.14 #272, 0.09 #2179, 0.09 #2978), 03_3d (0.14 #252, 0.04 #3940, 0.03 #3695), 07s9rl0 (0.11 #859, 0.06 #1287, 0.06 #1656), 0chghy (0.11 #318, 0.03 #2164, 0.03 #440), 03rjj (0.04 #804, 0.03 #1479, 0.03 #2957) >> Best rule #491 for best value: >> intensional similarity = 3 >> extensional distance = 461 >> proper extension: 05f67hw; >> query: (?x2973, 09c7w0) <- language(?x2973, ?x254), ?x254 = 02h40lc, films(?x3359, ?x2973) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cfhfz country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.765 http://example.org/film/film/country #4389-021w0_ PRED entity: 021w0_ PRED relation: school! PRED expected values: 02rl201 047dpm0 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 18): 0f4vx0 (0.50 #10, 0.42 #65, 0.40 #28), 04f4z1k (0.50 #16, 0.40 #34, 0.27 #37), 02rl201 (0.50 #4, 0.40 #22, 0.27 #37), 05vsb7 (0.50 #56, 0.17 #401, 0.13 #309), 03nt7j (0.42 #62, 0.14 #407, 0.13 #315), 09th87 (0.33 #69, 0.27 #37, 0.14 #564), 09l0x9 (0.33 #66, 0.25 #11, 0.20 #29), 047dpm0 (0.27 #37, 0.25 #72, 0.25 #17), 02x2khw (0.27 #37, 0.25 #3, 0.20 #21), 02r6gw6 (0.27 #37, 0.25 #13, 0.20 #31) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01pq4w; 06fq2; >> query: (?x8851, 0f4vx0) <- school(?x6823, ?x8851), ?x6823 = 07l8f, institution(?x1368, ?x8851), state_province_region(?x8851, ?x1227) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01pq4w; 06fq2; *> query: (?x8851, 02rl201) <- school(?x6823, ?x8851), ?x6823 = 07l8f, institution(?x1368, ?x8851), state_province_region(?x8851, ?x1227) *> conf = 0.50 ranks of expected_values: 3, 8 EVAL 021w0_ school! 047dpm0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 145.000 145.000 0.500 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 021w0_ school! 02rl201 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 145.000 145.000 0.500 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #4388-012jfb PRED entity: 012jfb PRED relation: film! PRED expected values: 016tw3 => 124 concepts (88 used for prediction) PRED predicted values (max 10 best out of 64): 05qd_ (0.63 #603, 0.27 #232, 0.24 #1052), 016tt2 (0.39 #749, 0.21 #1791, 0.16 #2466), 0jw67 (0.37 #821, 0.18 #669, 0.16 #2542), 04mkft (0.22 #183, 0.17 #332, 0.11 #406), 0g1rw (0.21 #1791, 0.18 #231, 0.16 #2466), 017s11 (0.21 #1718, 0.20 #3, 0.17 #1342), 016tw3 (0.20 #11, 0.17 #308, 0.16 #382), 03xsby (0.20 #15, 0.12 #89, 0.11 #460), 05s_k6 (0.20 #63, 0.12 #137, 0.11 #211), 03xq0f (0.20 #1122, 0.18 #228, 0.17 #302) >> Best rule #603 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 01hvjx; >> query: (?x6043, 05qd_) <- nominated_for(?x1850, ?x6043), award_winner(?x1850, ?x574), ?x574 = 016tt2, production_companies(?x188, ?x1850) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 04dsnp; 0cz_ym; 01chpn; *> query: (?x6043, 016tw3) <- honored_for(?x2707, ?x6043), film(?x3593, ?x6043), films(?x13930, ?x6043), person(?x6043, ?x4196) *> conf = 0.20 ranks of expected_values: 7 EVAL 012jfb film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 124.000 88.000 0.632 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #4387-04gb7 PRED entity: 04gb7 PRED relation: films PRED expected values: 0gjk1d 038bh3 0qmjd 0ccck7 => 111 concepts (49 used for prediction) PRED predicted values (max 10 best out of 809): 02j69w (0.29 #14708, 0.18 #10051, 0.12 #19887), 04q01mn (0.25 #6722, 0.20 #8273, 0.17 #4653), 0jqj5 (0.25 #6456, 0.20 #8007, 0.15 #13182), 0p_qr (0.25 #6369, 0.20 #7920, 0.15 #13095), 02p86pb (0.25 #6644, 0.20 #8195, 0.15 #13370), 015g28 (0.20 #3808, 0.20 #3291, 0.07 #15191), 0m9p3 (0.20 #3734, 0.20 #3217, 0.07 #15117), 0hfzr (0.18 #10028, 0.17 #4338, 0.15 #16238), 07xtqq (0.18 #9846, 0.17 #4156, 0.14 #14503), 03cw411 (0.18 #10005, 0.17 #4315, 0.14 #14662) >> Best rule #14708 for best value: >> intensional similarity = 8 >> extensional distance = 12 >> proper extension: 07b_l; 0bpgx; >> query: (?x5179, 02j69w) <- films(?x5179, ?x8039), films(?x5179, ?x4024), films(?x5179, ?x3534), film_release_region(?x4024, ?x774), film_festivals(?x4024, ?x13969), ?x774 = 06mzp, film(?x2053, ?x8039), genre(?x3534, ?x53) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #11873 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 10 *> proper extension: 018h2; 02rwmk; 0nk95; *> query: (?x5179, 0ccck7) <- films(?x5179, ?x4853), films(?x5179, ?x4024), film_release_region(?x4024, ?x87), film_festivals(?x4024, ?x13969), film_format(?x4024, ?x6392), genre(?x4024, ?x600), film_crew_role(?x4024, ?x137), nominated_for(?x382, ?x4853) *> conf = 0.08 ranks of expected_values: 210, 343, 506 EVAL 04gb7 films 0ccck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 111.000 49.000 0.286 http://example.org/film/film_subject/films EVAL 04gb7 films 0qmjd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 111.000 49.000 0.286 http://example.org/film/film_subject/films EVAL 04gb7 films 038bh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 49.000 0.286 http://example.org/film/film_subject/films EVAL 04gb7 films 0gjk1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 111.000 49.000 0.286 http://example.org/film/film_subject/films #4386-04n32 PRED entity: 04n32 PRED relation: type_of_union PRED expected values: 04ztj => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.83 #65, 0.83 #37, 0.83 #17), 01g63y (0.17 #6, 0.16 #22, 0.14 #262), 01bl8s (0.04 #19, 0.02 #47, 0.01 #79), 0jgjn (0.01 #56, 0.01 #60, 0.01 #64) >> Best rule #65 for best value: >> intensional similarity = 2 >> extensional distance = 94 >> proper extension: 0d9kl; 057ph; 0dng4; >> query: (?x9367, 04ztj) <- celebrities_impersonated(?x3649, ?x9367), ?x3649 = 03m6t5 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04n32 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.833 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4385-01l4g5 PRED entity: 01l4g5 PRED relation: role PRED expected values: 05148p4 => 95 concepts (51 used for prediction) PRED predicted values (max 10 best out of 113): 01vj9c (0.60 #12, 0.56 #198, 0.25 #756), 0342h (0.53 #2246, 0.50 #97, 0.46 #2058), 02sgy (0.44 #191, 0.40 #5, 0.38 #749), 05148p4 (0.44 #205, 0.33 #763, 0.33 #112), 042v_gx (0.40 #6, 0.33 #192, 0.33 #99), 018vs (0.40 #10, 0.33 #196, 0.33 #103), 03qjg (0.40 #56, 0.17 #149, 0.14 #932), 0214km (0.40 #90, 0.07 #1022, 0.06 #1957), 013y1f (0.33 #123, 0.26 #1335, 0.22 #216), 06w7v (0.33 #171, 0.20 #78, 0.11 #264) >> Best rule #12 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 012x4t; 0161sp; 023l9y; >> query: (?x4855, 01vj9c) <- role(?x4855, ?x1225), artists(?x284, ?x4855), artist(?x2931, ?x4855), profession(?x4855, ?x131), ?x1225 = 01qbl >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #205 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 023slg; *> query: (?x4855, 05148p4) <- role(?x4855, ?x316), role(?x4855, ?x315), role(?x4855, ?x228), artists(?x284, ?x4855), ?x315 = 0l14md, ?x316 = 05r5c, ?x228 = 0l14qv *> conf = 0.44 ranks of expected_values: 4 EVAL 01l4g5 role 05148p4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 95.000 51.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role #4384-09hd6f PRED entity: 09hd6f PRED relation: award_winner! PRED expected values: 0bq_mx => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 103): 02q690_ (0.12 #65, 0.12 #485, 0.11 #765), 05c1t6z (0.12 #15, 0.11 #435, 0.10 #715), 03nnm4t (0.10 #73, 0.10 #493, 0.10 #773), 0gvstc3 (0.10 #874, 0.10 #34, 0.09 #314), 027n06w (0.10 #72, 0.09 #492, 0.08 #772), 0gx_st (0.10 #37, 0.09 #457, 0.08 #737), 09v0p2c (0.08 #362, 0.07 #922, 0.07 #642), 0bq_mx (0.07 #132, 0.07 #272, 0.07 #412), 03gt46z (0.06 #903, 0.06 #343, 0.06 #623), 07z31v (0.06 #451, 0.06 #31, 0.06 #731) >> Best rule #65 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 01lct6; >> query: (?x10340, 02q690_) <- program(?x10340, ?x3413), award_winner(?x10340, ?x2650), type_of_union(?x2650, ?x566) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #132 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 142 *> proper extension: 01lct6; *> query: (?x10340, 0bq_mx) <- program(?x10340, ?x3413), award_winner(?x10340, ?x2650), type_of_union(?x2650, ?x566) *> conf = 0.07 ranks of expected_values: 8 EVAL 09hd6f award_winner! 0bq_mx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 95.000 95.000 0.125 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4383-0jgd PRED entity: 0jgd PRED relation: taxonomy PRED expected values: 04n6k => 238 concepts (238 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.85 #45, 0.81 #66, 0.81 #99) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 03pn9; >> query: (?x142, 04n6k) <- contains(?x7273, ?x142), organization(?x142, ?x4230), adjoins(?x142, ?x583), ?x4230 = 04k4l >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jgd taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 238.000 238.000 0.854 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #4382-01p47r PRED entity: 01p47r PRED relation: actor! PRED expected values: 016zfm => 133 concepts (125 used for prediction) PRED predicted values (max 10 best out of 144): 0gxsh4 (0.35 #21682, 0.24 #796, 0.21 #21681), 05sy0cv (0.35 #21682, 0.21 #21681, 0.13 #795), 02py4c8 (0.20 #12, 0.08 #1073, 0.04 #808), 026y3cf (0.18 #779, 0.07 #513, 0.02 #2366), 026bfsh (0.10 #13843, 0.10 #2214, 0.09 #1422), 02zv4b (0.09 #1350, 0.09 #821, 0.08 #1086), 01f3p_ (0.08 #1113, 0.06 #1377, 0.05 #2169), 0431v3 (0.08 #1159, 0.04 #2743, 0.04 #3008), 03ln8b (0.07 #2148, 0.03 #9551, 0.03 #1356), 02_1q9 (0.07 #269, 0.06 #535, 0.04 #801) >> Best rule #21682 for best value: >> intensional similarity = 2 >> extensional distance = 899 >> proper extension: 044mz_; 0q9kd; 0dbpyd; 01k7d9; 02p65p; 06j0md; 06gp3f; 02bfmn; 01xdf5; 02rchht; ... >> query: (?x10001, ?x11422) <- nominated_for(?x10001, ?x11422), actor(?x11422, ?x71) >> conf = 0.35 => this is the best rule for 2 predicted values *> Best rule #375 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0hvb2; 0f6_x; 03h304l; 036qs_; 02vx4c2; 03tdlh; 01z0lb; 024jwt; 035wq7; *> query: (?x10001, 016zfm) <- nominated_for(?x10001, ?x11422), nominated_for(?x10001, ?x8837), ?x11422 = 0gxsh4, award_winner(?x8837, ?x105) *> conf = 0.07 ranks of expected_values: 17 EVAL 01p47r actor! 016zfm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 133.000 125.000 0.352 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #4381-07cjqy PRED entity: 07cjqy PRED relation: friend! PRED expected values: 09yrh => 109 concepts (80 used for prediction) PRED predicted values (max 10 best out of 151): 09yrh (0.81 #1248, 0.81 #3755, 0.79 #3441), 07r1h (0.24 #572, 0.06 #884, 0.06 #3390), 04fzk (0.17 #2031, 0.17 #2189, 0.07 #3913), 01s21dg (0.17 #2031, 0.17 #2189, 0.07 #3913), 0fby2t (0.17 #2031, 0.17 #2189, 0.07 #3284), 01hxs4 (0.15 #479, 0.12 #1104, 0.08 #1249), 0grwj (0.12 #468, 0.06 #780, 0.05 #937), 0dvmd (0.09 #522, 0.05 #1147, 0.04 #834), 01rrd4 (0.08 #1249, 0.06 #574, 0.05 #3756), 016tbr (0.08 #1249, 0.06 #609, 0.05 #3756) >> Best rule #1248 for best value: >> intensional similarity = 2 >> extensional distance = 58 >> proper extension: 04d_mtq; >> query: (?x3536, ?x4536) <- friend(?x3536, ?x4536), actor(?x4535, ?x4536) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07cjqy friend! 09yrh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 80.000 0.808 http://example.org/celebrities/celebrity/celebrity_friends./celebrities/friendship/friend #4380-03zz8b PRED entity: 03zz8b PRED relation: award_nominee! PRED expected values: 07s8r0 => 79 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1004): 0c3p7 (0.86 #3758, 0.83 #1443, 0.81 #67160), 02zfdp (0.83 #1953, 0.81 #67160, 0.81 #30107), 02k4b2 (0.81 #67160, 0.81 #30107, 0.80 #81066), 02vntj (0.81 #67160, 0.81 #30107, 0.80 #81066), 07lmxq (0.81 #67160, 0.81 #30107, 0.80 #81066), 07s8r0 (0.81 #67160, 0.81 #30107, 0.80 #81066), 03zz8b (0.65 #6268, 0.64 #3953, 0.58 #1638), 03m6_z (0.38 #60212, 0.27 #78749, 0.27 #85700), 015rkw (0.27 #85700, 0.25 #18526, 0.18 #6946), 018ygt (0.27 #85700, 0.25 #18526, 0.05 #22285) >> Best rule #3758 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 06t74h; 0fthdk; >> query: (?x7337, 0c3p7) <- award_nominee(?x9561, ?x7337), award_nominee(?x3872, ?x7337), ?x3872 = 025t9b, film(?x9561, ?x1263) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #67160 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 981 *> proper extension: 01t2h2; 01vb403; 09d5h; 038rzr; 0kvqv; 02ts3h; 01933d; 07sbk; 024y6w; 0gyy0; ... *> query: (?x7337, ?x539) <- award_winner(?x7337, ?x5242), award_nominee(?x7337, ?x539), film(?x5242, ?x3559) *> conf = 0.81 ranks of expected_values: 6 EVAL 03zz8b award_nominee! 07s8r0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 79.000 37.000 0.857 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4379-07r1h PRED entity: 07r1h PRED relation: film PRED expected values: 018js4 0yx_w => 143 concepts (113 used for prediction) PRED predicted values (max 10 best out of 1246): 02ryz24 (0.72 #53234, 0.67 #95829, 0.67 #101152), 03f7nt (0.72 #53234, 0.67 #95829, 0.67 #101152), 02mmwk (0.72 #53234, 0.67 #95829, 0.67 #101152), 01q7h2 (0.72 #53234, 0.67 #95829, 0.67 #101152), 02b6n9 (0.69 #1558, 0.06 #30166, 0.04 #10646), 0g9lm2 (0.22 #15970, 0.19 #28391, 0.18 #17745), 095zlp (0.15 #60, 0.06 #30166, 0.04 #10646), 08r4x3 (0.15 #153, 0.05 #21447, 0.04 #30319), 02d003 (0.15 #1225, 0.04 #10646, 0.03 #179236), 01h7bb (0.15 #58558, 0.11 #78083, 0.11 #86958) >> Best rule #53234 for best value: >> intensional similarity = 3 >> extensional distance = 161 >> proper extension: 0d_84; 0456xp; 04shbh; 0h1m9; 0n6f8; 01vhb0; 01mmslz; 01gbbz; 0c2ry; 04205z; ... >> query: (?x6187, ?x2886) <- nominated_for(?x6187, ?x2886), award(?x6187, ?x401), participant(?x6187, ?x4670) >> conf = 0.72 => this is the best rule for 4 predicted values *> Best rule #3323 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 025ldg; *> query: (?x6187, 0yx_w) <- award_nominee(?x6187, ?x157), diet(?x6187, ?x3130), participant(?x398, ?x6187) *> conf = 0.03 ranks of expected_values: 370, 1036 EVAL 07r1h film 0yx_w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 143.000 113.000 0.715 http://example.org/film/actor/film./film/performance/film EVAL 07r1h film 018js4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 143.000 113.000 0.715 http://example.org/film/actor/film./film/performance/film #4378-039yzf PRED entity: 039yzf PRED relation: award! PRED expected values: 02yl42 => 62 concepts (29 used for prediction) PRED predicted values (max 10 best out of 837): 0gd_s (0.78 #6032, 0.73 #9405, 0.71 #2657), 01dzz7 (0.78 #10122, 0.78 #10121, 0.77 #6748), 01k56k (0.71 #3278, 0.69 #20146, 0.67 #13400), 05x8n (0.71 #1942, 0.69 #18810, 0.67 #12064), 09dt7 (0.71 #310, 0.67 #3685, 0.64 #7058), 0c3kw (0.71 #440, 0.64 #7188, 0.56 #3815), 01g6bk (0.71 #3196, 0.46 #20064, 0.45 #9944), 018fq (0.67 #4863, 0.64 #8236, 0.62 #18356), 04mhl (0.57 #1262, 0.56 #4637, 0.55 #8010), 0jt86 (0.57 #3079, 0.55 #9827, 0.46 #19947) >> Best rule #6032 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 040vk98; 02662b; 0262zm; 02664f; 0262yt; 040_9s0; 045xh; >> query: (?x10678, 0gd_s) <- award_winner(?x10678, ?x476), disciplines_or_subjects(?x10678, ?x6647), award(?x7055, ?x10678), ?x476 = 07w21, ?x6647 = 02xlf, ?x7055 = 0210f1 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4386 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 040vk98; 02662b; 0262zm; 02664f; 0262yt; 040_9s0; 045xh; *> query: (?x10678, 02yl42) <- award_winner(?x10678, ?x476), disciplines_or_subjects(?x10678, ?x6647), award(?x7055, ?x10678), ?x476 = 07w21, ?x6647 = 02xlf, ?x7055 = 0210f1 *> conf = 0.44 ranks of expected_values: 16 EVAL 039yzf award! 02yl42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 62.000 29.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4377-05q8pss PRED entity: 05q8pss PRED relation: award! PRED expected values: 086qd 04b7xr 01l3mk3 01whg97 0cgfb => 57 concepts (31 used for prediction) PRED predicted values (max 10 best out of 2687): 023361 (0.78 #36809, 0.78 #36808, 0.74 #46850), 03f7m4h (0.78 #36809, 0.78 #36808, 0.74 #46850), 0dl567 (0.67 #17869, 0.60 #7830, 0.43 #14523), 0lbj1 (0.60 #6735, 0.57 #13428, 0.50 #16774), 0fhxv (0.60 #8029, 0.57 #14722, 0.50 #18068), 0gcs9 (0.60 #7502, 0.57 #14195, 0.50 #17541), 02z4b_8 (0.60 #8743, 0.57 #15436, 0.50 #18782), 01vrz41 (0.60 #6984, 0.43 #13677, 0.42 #17023), 03j24kf (0.60 #8046, 0.43 #14739, 0.42 #18085), 0140t7 (0.60 #9337, 0.43 #16030, 0.42 #19376) >> Best rule #36809 for best value: >> intensional similarity = 5 >> extensional distance = 100 >> proper extension: 02q3s; >> query: (?x4317, ?x8352) <- award_winner(?x4317, ?x8352), award_winner(?x4317, ?x2083), profession(?x8352, ?x106), artists(?x3061, ?x2083), ?x3061 = 05bt6j >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #15666 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0gqz2; 025m8l; 099vwn; 01c99j; *> query: (?x4317, 01l3mk3) <- award(?x8799, ?x4317), ?x8799 = 02f1c, nominated_for(?x4317, ?x12648), film(?x3289, ?x12648) *> conf = 0.43 ranks of expected_values: 21, 46, 48, 1350, 1399 EVAL 05q8pss award! 0cgfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 31.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05q8pss award! 01whg97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 31.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05q8pss award! 01l3mk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 57.000 31.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05q8pss award! 04b7xr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 57.000 31.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05q8pss award! 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 57.000 31.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4376-02465 PRED entity: 02465 PRED relation: nationality PRED expected values: 07ssc => 136 concepts (135 used for prediction) PRED predicted values (max 10 best out of 81): 09c7w0 (0.80 #3105, 0.79 #4707, 0.78 #901), 07ssc (0.56 #11639, 0.39 #3004, 0.37 #11640), 013p59 (0.39 #7515, 0.37 #11640), 02jx1 (0.39 #3004, 0.37 #11640, 0.33 #3506), 0dbdy (0.37 #11640), 0hzlz (0.12 #5808, 0.06 #23, 0.06 #10127), 0jgd (0.12 #5808, 0.02 #13547, 0.02 #13546), 0345h (0.09 #2834, 0.08 #4237, 0.07 #4938), 0f8l9c (0.08 #422, 0.07 #2825, 0.07 #1424), 03rk0 (0.07 #5854, 0.07 #4852, 0.07 #8768) >> Best rule #3105 for best value: >> intensional similarity = 3 >> extensional distance = 187 >> proper extension: 03lh3v; >> query: (?x11214, 09c7w0) <- currency(?x11214, ?x170), people(?x743, ?x11214), place_of_birth(?x11214, ?x9026) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #11639 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2196 *> proper extension: 04cy8rb; 0dky9n; 0784v1; 05fh2; *> query: (?x11214, ?x512) <- place_of_birth(?x11214, ?x9026), contains(?x512, ?x9026), film_release_region(?x66, ?x512) *> conf = 0.56 ranks of expected_values: 2 EVAL 02465 nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 135.000 0.799 http://example.org/people/person/nationality #4375-046lt PRED entity: 046lt PRED relation: category PRED expected values: 08mbj5d => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.73 #26, 0.64 #18, 0.57 #6) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 178 >> proper extension: 08815; 01jssp; 05krk; 052nd; 06pwq; 07tgn; 0277jc; 04rwx; 07szy; 09kvv; ... >> query: (?x2942, 08mbj5d) <- list(?x2942, ?x5160), list(?x496, ?x5160), award_winner(?x591, ?x496) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 046lt category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.728 http://example.org/common/topic/webpage./common/webpage/category #4374-03x22w PRED entity: 03x22w PRED relation: type_of_union PRED expected values: 04ztj => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.71 #117, 0.71 #241, 0.71 #217), 01g63y (0.23 #10, 0.20 #14, 0.18 #6) >> Best rule #117 for best value: >> intensional similarity = 3 >> extensional distance = 1315 >> proper extension: 04yywz; 02g8h; 0d_84; 0h1_w; 04bs3j; 014x77; 0151ns; 0kr5_; 012c6x; 0htlr; ... >> query: (?x5748, 04ztj) <- award(?x5748, ?x1670), nominated_for(?x5748, ?x5810), film(?x5748, ?x2847) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x22w type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.708 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4373-03_2td PRED entity: 03_2td PRED relation: film PRED expected values: 04mcw4 => 87 concepts (51 used for prediction) PRED predicted values (max 10 best out of 272): 0cs134 (0.47 #33984, 0.38 #39349, 0.38 #76908), 0dfw0 (0.20 #839, 0.07 #4415, 0.07 #6203), 09gq0x5 (0.20 #283, 0.07 #3859, 0.07 #5647), 011yqc (0.20 #233, 0.07 #3809, 0.07 #5597), 026qnh6 (0.20 #822, 0.07 #6186, 0.04 #7974), 04s1zr (0.20 #1722, 0.04 #5298, 0.03 #7086), 0466s8n (0.20 #1634, 0.04 #5210, 0.03 #6998), 02qdrjx (0.20 #1560, 0.04 #5136, 0.03 #6924), 02fwfb (0.20 #1270, 0.04 #4846, 0.03 #6634), 0gfh84d (0.20 #1151, 0.04 #4727, 0.03 #6515) >> Best rule #33984 for best value: >> intensional similarity = 3 >> extensional distance = 1165 >> proper extension: 02rgz4; 02wb6yq; 0bxfmk; 0gv07g; 079ws; 0gdhhy; 01m7f5r; 03g62; 07z4fy; >> query: (?x9317, ?x10731) <- nationality(?x9317, ?x390), location(?x9317, ?x12606), nominated_for(?x9317, ?x10731) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #4344 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 0bn9sc; 0hnp7; 0879xc; *> query: (?x9317, 04mcw4) <- nationality(?x9317, ?x390), location(?x9317, ?x12606), ?x390 = 0chghy *> conf = 0.04 ranks of expected_values: 100 EVAL 03_2td film 04mcw4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 87.000 51.000 0.471 http://example.org/film/actor/film./film/performance/film #4372-023zl PRED entity: 023zl PRED relation: institution! PRED expected values: 02_xgp2 => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 20): 02_xgp2 (0.71 #10, 0.71 #250, 0.63 #450), 0bkj86 (0.71 #6, 0.62 #26, 0.58 #246), 04zx3q1 (0.71 #2, 0.55 #242, 0.50 #22), 07s6fsf (0.57 #1, 0.50 #21, 0.49 #906), 027f2w (0.48 #247, 0.43 #7, 0.37 #467), 01rr_d (0.39 #254, 0.29 #14, 0.27 #474), 0bjrnt (0.38 #24, 0.32 #244, 0.30 #2179), 013zdg (0.35 #245, 0.29 #5, 0.28 #910), 071tyz (0.30 #2179, 0.29 #8, 0.28 #2435), 02m4yg (0.30 #2179, 0.28 #2435, 0.18 #2283) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 07tg4; 03ksy; 08qnnv; >> query: (?x10759, 02_xgp2) <- institution(?x1771, ?x10759), company(?x2127, ?x10759), ?x1771 = 019v9k, child(?x10759, ?x2730) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023zl institution! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.714 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4371-048q6x PRED entity: 048q6x PRED relation: award PRED expected values: 0bp_b2 0bs0bh => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 208): 0789_m (0.78 #807, 0.72 #17748, 0.72 #10889), 09sb52 (0.32 #6089, 0.31 #1655, 0.30 #2864), 0bfvd4 (0.31 #518, 0.08 #2132, 0.07 #922), 0cqhk0 (0.23 #440, 0.20 #844, 0.18 #37), 0fbvqf (0.23 #451, 0.18 #48, 0.12 #22184), 0gqy2 (0.23 #568, 0.09 #972, 0.08 #15894), 0cqh46 (0.23 #455, 0.06 #859, 0.04 #1666), 04ljl_l (0.23 #406, 0.05 #2020, 0.04 #15732), 05zr6wv (0.15 #420, 0.08 #6065, 0.08 #2840), 0bp_b2 (0.15 #421, 0.06 #1229, 0.05 #2035) >> Best rule #807 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01k7d9; 01vlj1g; 01yk13; 01ycbq; 01trf3; 01cwcr; >> query: (?x5041, ?x458) <- award_nominee(?x3051, ?x5041), award_winner(?x6878, ?x5041), award_winner(?x458, ?x5041), ?x6878 = 08_vwq >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 01k7d9; 01vlj1g; 01yk13; 01ycbq; 01trf3; 01cwcr; *> query: (?x5041, 0bp_b2) <- award_nominee(?x3051, ?x5041), award_winner(?x6878, ?x5041), ?x6878 = 08_vwq *> conf = 0.15 ranks of expected_values: 10, 15 EVAL 048q6x award 0bs0bh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 89.000 89.000 0.781 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 048q6x award 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 89.000 89.000 0.781 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4370-03tcbx PRED entity: 03tcbx PRED relation: legislative_sessions! PRED expected values: 021sv1 06bss => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 784): 0bymv (0.82 #619, 0.82 #596, 0.80 #568), 021sv1 (0.80 #567, 0.77 #50, 0.75 #511), 0d06m5 (0.79 #303, 0.77 #50, 0.75 #74), 0d3qd0 (0.79 #303, 0.77 #50, 0.70 #572), 03txms (0.79 #303, 0.77 #50, 0.69 #176), 06bss (0.78 #551, 0.77 #50, 0.75 #74), 012v1t (0.77 #50, 0.75 #74, 0.73 #604), 02mjmr (0.77 #50, 0.69 #176, 0.69 #174), 01lct6 (0.75 #74, 0.69 #176, 0.69 #174), 06hx2 (0.66 #590, 0.61 #618, 0.59 #382) >> Best rule #619 for best value: >> intensional similarity = 49 >> extensional distance = 9 >> proper extension: 02bqn1; >> query: (?x2861, ?x2357) <- legislative_sessions(?x4821, ?x2861), legislative_sessions(?x3540, ?x2861), legislative_sessions(?x653, ?x2861), legislative_sessions(?x356, ?x2861), legislative_sessions(?x2861, ?x1027), district_represented(?x2861, ?x4754), district_represented(?x2861, ?x3670), ?x4754 = 0g0syc, district_represented(?x653, ?x3818), district_represented(?x653, ?x3634), district_represented(?x653, ?x2982), district_represented(?x653, ?x1274), district_represented(?x653, ?x1227), district_represented(?x653, ?x1138), district_represented(?x653, ?x728), district_represented(?x653, ?x726), district_represented(?x653, ?x177), ?x3670 = 05tbn, legislative_sessions(?x4665, ?x356), ?x3634 = 07b_l, ?x177 = 05kkh, ?x1138 = 059_c, ?x1274 = 04ykg, legislative_sessions(?x9569, ?x653), legislative_sessions(?x2357, ?x653), ?x4821 = 02bqm0, state(?x659, ?x2982), ?x726 = 05kj_, ?x3540 = 024tcq, first_level_division_of(?x2982, ?x94), ?x3818 = 03v0t, ?x4665 = 07t58, ?x2357 = 0bymv, district_represented(?x6712, ?x728), district_represented(?x5256, ?x728), district_represented(?x5006, ?x728), location(?x117, ?x2982), contains(?x2982, ?x2983), partially_contains(?x728, ?x10954), state(?x6188, ?x728), ?x1227 = 01n7q, ?x5006 = 01gtc0, religion(?x2982, ?x7422), religion(?x2982, ?x7131), ?x6712 = 01gst9, ?x7422 = 092bf5, ?x5256 = 01grqd, people(?x268, ?x9569), ?x7131 = 03_gx >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #567 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 8 *> proper extension: 024tkd; *> query: (?x2861, 021sv1) <- legislative_sessions(?x3540, ?x2861), legislative_sessions(?x1830, ?x2861), legislative_sessions(?x653, ?x2861), legislative_sessions(?x356, ?x2861), legislative_sessions(?x2861, ?x1027), district_represented(?x2861, ?x6895), district_represented(?x2861, ?x4754), district_represented(?x2861, ?x2977), district_represented(?x2861, ?x961), ?x653 = 070m6c, ?x4754 = 0g0syc, ?x961 = 03s0w, district_represented(?x1027, ?x1767), district_represented(?x1027, ?x1025), district_represented(?x1027, ?x938), legislative_sessions(?x2860, ?x356), ?x938 = 0vmt, place_of_birth(?x4509, ?x6895), state_province_region(?x4793, ?x6895), contains(?x6895, ?x6253), legislative_sessions(?x11605, ?x2861), ?x3540 = 024tcq, location(?x6550, ?x6895), ?x6550 = 03nb5v, legislative_sessions(?x652, ?x356), ?x11605 = 024_vw, country(?x2977, ?x94), religion(?x6895, ?x7131), religion(?x6895, ?x1985), religion(?x6895, ?x492), location(?x1172, ?x6253), location(?x6113, ?x2977), ?x1025 = 04ych, ?x1985 = 0c8wxp, time_zones(?x6895, ?x2674), ?x7131 = 03_gx, ?x1767 = 04rrd, district_represented(?x1830, ?x6226), industry(?x4793, ?x13047), religion(?x111, ?x492), place_of_birth(?x3756, ?x6253) *> conf = 0.80 ranks of expected_values: 2, 6 EVAL 03tcbx legislative_sessions! 06bss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 37.000 37.000 0.818 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 03tcbx legislative_sessions! 021sv1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 37.000 37.000 0.818 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #4369-05xb7q PRED entity: 05xb7q PRED relation: educational_institution PRED expected values: 05xb7q => 159 concepts (50 used for prediction) PRED predicted values (max 10 best out of 109): 05ftw3 (0.14 #324), 03x83_ (0.14 #127), 07wlf (0.05 #608, 0.03 #1147, 0.03 #1686), 01j_cy (0.05 #573, 0.03 #1112, 0.03 #1651), 07tgn (0.05 #554, 0.03 #2171, 0.02 #3249), 02482c (0.05 #850, 0.03 #2467, 0.02 #3006), 02nq10 (0.05 #874, 0.02 #4108, 0.01 #4648), 07vyf (0.05 #662, 0.02 #3896, 0.01 #4975), 02ldkf (0.05 #959, 0.02 #4193), 0lwyk (0.05 #813, 0.02 #4047) >> Best rule #324 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 04cjn; 0hj6h; 01yf40; >> query: (?x5968, 05ftw3) <- category(?x5968, ?x134), ?x134 = 08mbj5d, contains(?x5967, ?x5968), contains(?x2365, ?x5967), contains(?x2236, ?x5967), ?x2236 = 05sb1, adjoins(?x2365, ?x2146) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05xb7q educational_institution 05xb7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 50.000 0.143 http://example.org/education/educational_institution_campus/educational_institution #4368-02_lt PRED entity: 02_lt PRED relation: current_club! PRED expected values: 03_qrp => 104 concepts (65 used for prediction) PRED predicted values (max 10 best out of 75): 03zrhb (0.33 #152, 0.20 #505, 0.20 #70), 03y_f8 (0.25 #2, 0.23 #479, 0.20 #505), 03_qrp (0.20 #505, 0.20 #41, 0.18 #410), 03_qj1 (0.20 #505, 0.20 #37, 0.14 #234), 03d8m4 (0.20 #505, 0.20 #36, 0.12 #290), 03xh50 (0.20 #505, 0.17 #367, 0.10 #571), 03ylxn (0.20 #505, 0.14 #246, 0.12 #418), 032jlh (0.20 #505, 0.13 #694, 0.13 #530), 02pp1 (0.20 #505, 0.13 #500, 0.12 #304), 033nzk (0.20 #505, 0.11 #1224, 0.11 #1334) >> Best rule #152 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 042rlf; >> query: (?x6477, 03zrhb) <- position(?x6477, ?x530), team(?x6152, ?x6477), ?x6152 = 02y9ln, current_club(?x59, ?x6477), position(?x6477, ?x60), team(?x530, ?x202) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #505 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 29 *> proper extension: 03m6zs; *> query: (?x6477, ?x11564) <- position(?x6477, ?x203), position(?x6477, ?x60), team(?x12447, ?x6477), team(?x12447, ?x12043), teams(?x362, ?x6477), current_club(?x11564, ?x12043), current_club(?x59, ?x6477) *> conf = 0.20 ranks of expected_values: 3 EVAL 02_lt current_club! 03_qrp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 65.000 0.333 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #4367-05g_nr PRED entity: 05g_nr PRED relation: team PRED expected values: 02plv57 091tgz 026xxv_ 02q4ntp 026dqjm => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 252): 091tgz (0.83 #213, 0.79 #204, 0.78 #226), 02q4ntp (0.80 #190, 0.80 #185, 0.79 #207), 026xxv_ (0.78 #173, 0.75 #195, 0.74 #227), 02plv57 (0.73 #85, 0.71 #202, 0.69 #179), 026dqjm (0.73 #85, 0.70 #188, 0.69 #179), 02pzy52 (0.73 #85, 0.69 #179, 0.67 #218), 026w398 (0.73 #85, 0.69 #179, 0.67 #198), 03d5m8w (0.73 #85, 0.69 #179, 0.64 #222), 03d555l (0.73 #85, 0.69 #179, 0.64 #222), 0jmk7 (0.23 #201, 0.23 #234, 0.22 #221) >> Best rule #213 for best value: >> intensional similarity = 33 >> extensional distance = 16 >> proper extension: 0b_6x2; >> query: (?x8824, 091tgz) <- team(?x8824, ?x9833), team(?x8824, ?x9576), team(?x8824, ?x9147), team(?x8824, ?x6803), ?x9576 = 02qk2d5, team(?x13045, ?x9833), team(?x12162, ?x9833), team(?x9956, ?x9833), team(?x9908, ?x9833), team(?x8527, ?x9833), team(?x5897, ?x9833), team(?x4803, ?x9833), sport(?x9147, ?x12913), teams(?x11246, ?x9147), ?x12162 = 0b_6_l, ?x9908 = 0b_6lb, ?x4803 = 0b_6jz, team(?x4747, ?x9833), ?x13045 = 0bqthy, ?x8527 = 0b_6v_, colors(?x9147, ?x663), ?x6803 = 03by7wc, team(?x4747, ?x11420), team(?x4747, ?x5483), team(?x4747, ?x799), ?x799 = 0jm3v, ?x9956 = 0bzrsh, ?x11420 = 0jmhr, team(?x5897, ?x8728), ?x5483 = 0jml5, ?x8728 = 026xxv_, locations(?x5897, ?x659), position(?x2568, ?x4747) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 05g_nr team 026dqjm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 05g_nr team 02q4ntp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 05g_nr team 026xxv_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 05g_nr team 091tgz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 05g_nr team 02plv57 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.833 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #4366-01jvxb PRED entity: 01jvxb PRED relation: institution! PRED expected values: 02h4rq6 => 148 concepts (101 used for prediction) PRED predicted values (max 10 best out of 21): 02h4rq6 (0.72 #181, 0.68 #315, 0.67 #383), 02_xgp2 (0.71 #190, 0.57 #213, 0.57 #392), 019v9k (0.63 #186, 0.63 #365, 0.62 #411), 03bwzr4 (0.62 #192, 0.53 #461, 0.53 #126), 016t_3 (0.61 #182, 0.53 #384, 0.51 #116), 0bkj86 (0.57 #185, 0.44 #543, 0.41 #119), 07s6fsf (0.43 #180, 0.37 #382, 0.33 #604), 027f2w (0.38 #187, 0.26 #121, 0.25 #456), 013zdg (0.30 #184, 0.26 #118, 0.23 #318), 0bjrnt (0.28 #1745, 0.24 #117, 0.24 #183) >> Best rule #181 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 05zjtn4; 0gjv_; 012mzw; 0gl6f; 01pxcf; 0lk0l; >> query: (?x7097, 02h4rq6) <- institution(?x734, ?x7097), major_field_of_study(?x7097, ?x5607), student(?x7097, ?x4149), ?x734 = 04zx3q1 >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jvxb institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 101.000 0.722 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4365-07h1h5 PRED entity: 07h1h5 PRED relation: gender PRED expected values: 05zppz => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #42, 0.91 #40, 0.91 #36), 02zsn (0.46 #311, 0.40 #7, 0.31 #95) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 100 >> proper extension: 0f2zc; >> query: (?x3586, 05zppz) <- team(?x3586, ?x4511), colors(?x4511, ?x3189), colors(?x331, ?x3189), team(?x63, ?x4511) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07h1h5 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.912 http://example.org/people/person/gender #4364-07wrz PRED entity: 07wrz PRED relation: major_field_of_study PRED expected values: 06n6p 0193x 02_7t => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 105): 02lp1 (0.54 #3037, 0.53 #1416, 0.53 #5852), 01540 (0.50 #2968, 0.42 #1455, 0.41 #2644), 04x_3 (0.42 #1427, 0.41 #3048, 0.38 #237), 01tbp (0.42 #1454, 0.38 #589, 0.36 #697), 02ky346 (0.42 #1420, 0.38 #230, 0.34 #2933), 04sh3 (0.42 #1467, 0.38 #277, 0.31 #2980), 03nfmq (0.42 #1435, 0.25 #2948, 0.25 #245), 04g7x (0.38 #275, 0.33 #167, 0.26 #1465), 02h40lc (0.37 #1410, 0.31 #2923, 0.28 #2599), 0pf2 (0.37 #1431, 0.25 #241, 0.21 #5301) >> Best rule #3037 for best value: >> intensional similarity = 2 >> extensional distance = 39 >> proper extension: 0f8l9c; 059j2; 03rj0; 04hzj; 05c74; 03_c8p; >> query: (?x2313, 02lp1) <- company(?x3131, ?x2313), organization(?x2313, ?x5487) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1350 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 05xbx; 0hhjk; *> query: (?x2313, 02_7t) <- registering_agency(?x2313, ?x1982), service_language(?x2313, ?x254), currency(?x2313, ?x170) *> conf = 0.35 ranks of expected_values: 11, 12, 40 EVAL 07wrz major_field_of_study 02_7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 164.000 164.000 0.537 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07wrz major_field_of_study 0193x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 164.000 164.000 0.537 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07wrz major_field_of_study 06n6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 164.000 164.000 0.537 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #4363-09hd6f PRED entity: 09hd6f PRED relation: profession PRED expected values: 0dxtg 03gjzk 0196pc => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 45): 0dxtg (0.87 #314, 0.87 #614, 0.85 #464), 03gjzk (0.86 #166, 0.85 #1216, 0.84 #1516), 02hrh1q (0.70 #3766, 0.68 #3316, 0.66 #7968), 01d_h8 (0.42 #1056, 0.41 #1506, 0.40 #1656), 02krf9 (0.35 #2401, 0.30 #1228, 0.30 #1678), 02jknp (0.35 #2401, 0.29 #7503, 0.29 #158), 01c72t (0.35 #2401, 0.29 #7503, 0.28 #9154), 0cbd2 (0.35 #2401, 0.29 #7503, 0.28 #9154), 09jwl (0.18 #7523, 0.18 #4521, 0.17 #8123), 018gz8 (0.16 #768, 0.14 #468, 0.13 #618) >> Best rule #314 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 07g7h2; >> query: (?x10340, 0dxtg) <- award_winner(?x10340, ?x2650), award_nominee(?x10340, ?x2802), tv_program(?x10340, ?x3413) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 20 EVAL 09hd6f profession 0196pc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 97.000 97.000 0.874 http://example.org/people/person/profession EVAL 09hd6f profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.874 http://example.org/people/person/profession EVAL 09hd6f profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.874 http://example.org/people/person/profession #4362-05kkh PRED entity: 05kkh PRED relation: jurisdiction_of_office! PRED expected values: 09n5b9 => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 17): 09n5b9 (0.84 #208, 0.83 #280, 0.82 #135), 0pqc5 (0.69 #1248, 0.52 #1572, 0.49 #1734), 060c4 (0.62 #1607, 0.49 #1337, 0.49 #1553), 060bp (0.43 #884, 0.43 #145, 0.43 #1551), 04syw (0.33 #150, 0.14 #24, 0.12 #42), 0fj45 (0.31 #159, 0.14 #33, 0.12 #51), 0fkx3 (0.14 #34, 0.12 #52, 0.08 #557), 01q24l (0.13 #1255, 0.11 #1741, 0.09 #318), 01gkgk (0.12 #77, 0.08 #222, 0.08 #420), 0dq3c (0.11 #1606, 0.10 #1336, 0.09 #1750) >> Best rule #208 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0rh6k; 059rby; 03v1s; 07ssc; 05kj_; 059f4; 05fkf; 0vmt; 03s0w; 05fhy; ... >> query: (?x177, 09n5b9) <- state_province_region(?x388, ?x177), religion(?x177, ?x109), contains(?x177, ?x1629) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05kkh jurisdiction_of_office! 09n5b9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.843 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #4361-06z4wj PRED entity: 06z4wj PRED relation: type_of_union PRED expected values: 04ztj => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.73 #9, 0.73 #41, 0.72 #17), 01g63y (0.33 #101, 0.17 #14, 0.14 #2), 0jgjn (0.04 #16, 0.03 #20, 0.03 #24) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 027lfrs; >> query: (?x6943, 04ztj) <- profession(?x6943, ?x11804), ?x11804 = 0q04f, place_of_death(?x6943, ?x739) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06z4wj type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.733 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4360-0crfwmx PRED entity: 0crfwmx PRED relation: film_release_region PRED expected values: 03rjj 0d0vqn 035qy 02vzc => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 125): 0d0vqn (0.88 #467, 0.88 #1387, 0.88 #927), 03h64 (0.87 #523, 0.84 #983, 0.82 #829), 03rjj (0.86 #1383, 0.85 #769, 0.84 #463), 035qy (0.84 #1412, 0.80 #952, 0.77 #492), 02vzc (0.82 #814, 0.82 #1121, 0.81 #508), 0154j (0.80 #1382, 0.78 #922, 0.75 #462), 03_3d (0.79 #1385, 0.76 #771, 0.75 #465), 0d060g (0.77 #1386, 0.72 #926, 0.69 #466), 06t2t (0.74 #1438, 0.72 #518, 0.72 #978), 05v8c (0.69 #476, 0.66 #1396, 0.65 #782) >> Best rule #467 for best value: >> intensional similarity = 8 >> extensional distance = 109 >> proper extension: 0ds35l9; 02vxq9m; 028_yv; 01gc7; 0ds3t5x; 0h1cdwq; 0dscrwf; 05p1tzf; 0gx9rvq; 0401sg; ... >> query: (?x1022, 0d0vqn) <- country(?x1022, ?x94), ?x94 = 09c7w0, film_release_region(?x1022, ?x2513), film_release_region(?x1022, ?x1499), film_release_region(?x1022, ?x1264), ?x2513 = 05b4w, ?x1499 = 01znc_, ?x1264 = 0345h >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 5 EVAL 0crfwmx film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 52.000 0.883 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0crfwmx film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 52.000 0.883 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0crfwmx film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.883 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0crfwmx film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 52.000 52.000 0.883 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4359-03h502k PRED entity: 03h502k PRED relation: origin PRED expected values: 02xry => 180 concepts (137 used for prediction) PRED predicted values (max 10 best out of 131): 0z1vw (0.30 #19605, 0.28 #22440, 0.06 #26927), 0n90z (0.14 #940, 0.14 #704, 0.12 #1412), 0ply0 (0.14 #1013, 0.05 #2665, 0.03 #3846), 0c_m3 (0.14 #1045, 0.05 #2697, 0.03 #3878), 0tz14 (0.14 #1133, 0.05 #2785, 0.03 #5147), 0d6lp (0.14 #1009, 0.05 #2661, 0.03 #5259), 04jpl (0.10 #4255, 0.08 #3311, 0.07 #2130), 030qb3t (0.08 #1922, 0.08 #9006, 0.08 #6646), 03dm7 (0.08 #2075, 0.07 #2311, 0.06 #4909), 0k33p (0.08 #1815, 0.04 #3468, 0.03 #3940) >> Best rule #19605 for best value: >> intensional similarity = 3 >> extensional distance = 339 >> proper extension: 0459z; >> query: (?x5126, ?x11595) <- instrumentalists(?x1166, ?x5126), place_of_birth(?x5126, ?x11595), role(?x74, ?x1166) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #8787 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 86 *> proper extension: 0cbm64; *> query: (?x5126, 02xry) <- artists(?x474, ?x5126), instrumentalists(?x212, ?x5126), participant(?x2258, ?x5126) *> conf = 0.01 ranks of expected_values: 119 EVAL 03h502k origin 02xry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 180.000 137.000 0.305 http://example.org/music/artist/origin #4358-01y8zd PRED entity: 01y8zd PRED relation: organization! PRED expected values: 060c4 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 14): 0dq_5 (0.79 #57, 0.78 #153, 0.78 #45), 060c4 (0.70 #363, 0.69 #315, 0.68 #495), 0hm4q (0.21 #289, 0.06 #705, 0.06 #645), 08jcfy (0.21 #289, 0.02 #648, 0.02 #708), 04n1q6 (0.21 #289, 0.02 #343, 0.01 #307), 05k17c (0.12 #7, 0.11 #476, 0.11 #308), 09d6p2 (0.07 #34), 05c0jwl (0.06 #306, 0.04 #438, 0.04 #390), 0fkx3 (0.02 #892, 0.02 #917), 0fj45 (0.02 #892, 0.02 #917) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 0p4wb; 05w3y; 0k9ts; 05b5c; >> query: (?x3091, 0dq_5) <- service_location(?x3091, ?x279), ?x279 = 0d060g, list(?x3091, ?x2197) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #363 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 284 *> proper extension: 031n8c; 0q19t; 0ym69; *> query: (?x3091, 060c4) <- state_province_region(?x3091, ?x1905), currency(?x3091, ?x2244), currency(?x481, ?x2244) *> conf = 0.70 ranks of expected_values: 2 EVAL 01y8zd organization! 060c4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.789 http://example.org/organization/role/leaders./organization/leadership/organization #4357-06hhrs PRED entity: 06hhrs PRED relation: film PRED expected values: 0h03fhx => 108 concepts (39 used for prediction) PRED predicted values (max 10 best out of 450): 02czd5 (0.41 #64382, 0.25 #66171, 0.08 #41135), 03q0r1 (0.25 #637, 0.19 #4214, 0.01 #23888), 01jrbb (0.25 #471, 0.12 #4048, 0.10 #2260), 03x7hd (0.25 #561, 0.12 #4138, 0.10 #2350), 043mk4y (0.20 #3141, 0.12 #4929, 0.01 #24603), 0407yj_ (0.12 #4060, 0.12 #483, 0.01 #21945), 04hwbq (0.12 #3769, 0.12 #192), 01c22t (0.12 #3742, 0.12 #165), 06lpmt (0.12 #685, 0.10 #2474, 0.09 #6050), 03rtz1 (0.12 #168, 0.10 #1957, 0.06 #3745) >> Best rule #64382 for best value: >> intensional similarity = 3 >> extensional distance = 897 >> proper extension: 0bxfmk; 03g62; 03yf4d; >> query: (?x1709, ?x1535) <- student(?x4410, ?x1709), type_of_union(?x1709, ?x566), nominated_for(?x1709, ?x1535) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #11508 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 208 *> proper extension: 05_2h8; 02m501; 08z39v; *> query: (?x1709, 0h03fhx) <- award_winner(?x1535, ?x1709), gender(?x1709, ?x231), film_festivals(?x1535, ?x11147) *> conf = 0.03 ranks of expected_values: 142 EVAL 06hhrs film 0h03fhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 108.000 39.000 0.408 http://example.org/film/actor/film./film/performance/film #4356-0kfpm PRED entity: 0kfpm PRED relation: genre PRED expected values: 0c4xc => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 78): 07s9rl0 (0.65 #4322, 0.61 #1910, 0.58 #3740), 0c4xc (0.56 #291, 0.46 #540, 0.35 #208), 0hcr (0.35 #184, 0.23 #4589, 0.19 #4922), 01t_vv (0.34 #282, 0.30 #531, 0.21 #863), 06nbt (0.25 #186, 0.16 #518, 0.14 #435), 06q7n (0.24 #376, 0.17 #1206, 0.17 #1870), 0pr6f (0.20 #216, 0.10 #4788, 0.10 #4871), 06n90 (0.20 #4584, 0.18 #4418, 0.17 #4167), 03k9fj (0.18 #4416, 0.17 #4582, 0.16 #4165), 01htzx (0.17 #3673, 0.17 #3175, 0.17 #4171) >> Best rule #4322 for best value: >> intensional similarity = 3 >> extensional distance = 199 >> proper extension: 07qht4; >> query: (?x758, 07s9rl0) <- genre(?x758, ?x258), genre(?x4768, ?x258), ?x4768 = 01gkp1 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #291 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 02xhpl; *> query: (?x758, 0c4xc) <- program(?x2062, ?x758), nominated_for(?x7510, ?x758), nominated_for(?x4385, ?x758), ?x7510 = 027gs1_ *> conf = 0.56 ranks of expected_values: 2 EVAL 0kfpm genre 0c4xc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.652 http://example.org/tv/tv_program/genre #4355-03g5jw PRED entity: 03g5jw PRED relation: group! PRED expected values: 0l14md 02hnl => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 65): 02hnl (0.76 #3039, 0.76 #3469, 0.70 #801), 05148p4 (0.69 #3029, 0.69 #3459, 0.54 #791), 0l14md (0.59 #3018, 0.57 #3448, 0.51 #780), 01vj9c (0.27 #3453, 0.27 #785, 0.25 #269), 03qjg (0.22 #3488, 0.22 #3058, 0.22 #820), 03_vpw (0.17 #134, 0.03 #1598, 0.03 #736), 06ncr (0.14 #3479, 0.14 #209, 0.12 #295), 07c6l (0.14 #180, 0.12 #266, 0.11 #782), 07gql (0.14 #207, 0.12 #293, 0.11 #809), 042v_gx (0.14 #179, 0.12 #265, 0.10 #3449) >> Best rule #3039 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 015cxv; >> query: (?x1573, 02hnl) <- award(?x1573, ?x724), artists(?x482, ?x1573), group(?x227, ?x1573) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 03g5jw group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.759 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 03g5jw group! 0l14md CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.759 http://example.org/music/performance_role/regular_performances./music/group_membership/group #4354-02fgp0 PRED entity: 02fgp0 PRED relation: award PRED expected values: 0c4z8 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 292): 0gqz2 (0.72 #24816, 0.71 #14005, 0.70 #10403), 025m8l (0.72 #24816, 0.71 #14005, 0.70 #10403), 0gr4k (0.57 #3232, 0.26 #4432, 0.15 #2432), 0c4z8 (0.46 #471, 0.36 #871, 0.21 #1271), 054krc (0.44 #1687, 0.34 #3687, 0.34 #2887), 0gq9h (0.40 #2477, 0.32 #6077, 0.22 #77), 040njc (0.35 #2408, 0.24 #6008, 0.19 #3208), 04dn09n (0.34 #3243, 0.22 #4443, 0.19 #2443), 0l8z1 (0.33 #1663, 0.26 #3663, 0.25 #2863), 02h3d1 (0.33 #978, 0.31 #578, 0.17 #1378) >> Best rule #24816 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 099ks0; 06lxn; >> query: (?x8661, ?x2585) <- award_winner(?x2585, ?x8661), award(?x115, ?x2585) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #471 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 07hgkd; 01lw3kh; 01c7p_; 01q3_2; *> query: (?x8661, 0c4z8) <- award(?x8661, ?x2379), award(?x8661, ?x1869), ?x1869 = 04njml, ?x2379 = 02qvyrt *> conf = 0.46 ranks of expected_values: 4 EVAL 02fgp0 award 0c4z8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 77.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4353-02482c PRED entity: 02482c PRED relation: major_field_of_study PRED expected values: 0pf2 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 121): 05qjt (0.48 #4523, 0.35 #984, 0.33 #1350), 02j62 (0.48 #1372, 0.46 #1006, 0.43 #2592), 02lp1 (0.48 #988, 0.47 #1354, 0.41 #2696), 04rjg (0.42 #996, 0.41 #2704, 0.41 #386), 03g3w (0.42 #1002, 0.40 #1368, 0.38 #2955), 0g26h (0.41 #287, 0.41 #653, 0.39 #1751), 01540 (0.39 #183, 0.29 #1037, 0.27 #305), 062z7 (0.37 #393, 0.36 #1003, 0.36 #637), 01lj9 (0.34 #1016, 0.33 #1382, 0.27 #406), 05qfh (0.33 #158, 0.32 #402, 0.28 #1012) >> Best rule #4523 for best value: >> intensional similarity = 5 >> extensional distance = 198 >> proper extension: 020vx9; >> query: (?x8937, 05qjt) <- major_field_of_study(?x8937, ?x1668), major_field_of_study(?x5842, ?x1668), major_field_of_study(?x3172, ?x1668), ?x5842 = 01p5xy, organization(?x346, ?x3172) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1009 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 94 *> proper extension: 04jr87; 019q50; 03p2m1; *> query: (?x8937, 0pf2) <- institution(?x3437, ?x8937), institution(?x1771, ?x8937), ?x1771 = 019v9k, ?x3437 = 02_xgp2, school_type(?x8937, ?x3092) *> conf = 0.12 ranks of expected_values: 38 EVAL 02482c major_field_of_study 0pf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 144.000 144.000 0.485 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #4352-01mxt_ PRED entity: 01mxt_ PRED relation: artist! PRED expected values: 033hn8 => 163 concepts (140 used for prediction) PRED predicted values (max 10 best out of 114): 015_1q (0.29 #1148, 0.27 #584, 0.26 #1430), 033hn8 (0.27 #1847, 0.18 #1142, 0.15 #2693), 011k1h (0.25 #715, 0.15 #3958, 0.15 #4099), 01clyr (0.24 #316, 0.23 #598, 0.22 #457), 03rhqg (0.23 #1708, 0.20 #1849, 0.19 #2131), 023rwm (0.21 #2, 0.10 #6206, 0.09 #3809), 0n85g (0.18 #1756, 0.17 #2179, 0.15 #1897), 0g768 (0.17 #1871, 0.14 #1448, 0.14 #1166), 01w40h (0.17 #1862, 0.14 #1157, 0.13 #2708), 01cszh (0.14 #2126, 0.13 #2549, 0.11 #3113) >> Best rule #1148 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 014z8v; 01q9b9; 0dqcm; >> query: (?x5587, 015_1q) <- gender(?x5587, ?x231), category(?x5587, ?x134), award_winner(?x2431, ?x5587), ?x2431 = 0jzphpx >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1847 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 01czx; *> query: (?x5587, 033hn8) <- artists(?x1000, ?x5587), ?x1000 = 0xhtw, award_winner(?x2431, ?x5587) *> conf = 0.27 ranks of expected_values: 2 EVAL 01mxt_ artist! 033hn8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 163.000 140.000 0.286 http://example.org/music/record_label/artist #4351-0hv81 PRED entity: 0hv81 PRED relation: film_crew_role PRED expected values: 01pvkk => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 32): 02r96rf (0.68 #1194, 0.66 #1604, 0.64 #1305), 09vw2b7 (0.67 #1646, 0.65 #1608, 0.64 #1309), 0dxtw (0.42 #48, 0.40 #828, 0.39 #1612), 01pvkk (0.34 #87, 0.28 #2137, 0.28 #1652), 01vx2h (0.32 #1203, 0.32 #1314, 0.31 #1613), 02ynfr (0.18 #1319, 0.18 #1208, 0.17 #1656), 0215hd (0.15 #1211, 0.14 #1659, 0.14 #1322), 089g0h (0.12 #1660, 0.11 #1212, 0.10 #838), 01xy5l_ (0.11 #1654, 0.11 #1206, 0.10 #1616), 02rh1dz (0.11 #1312, 0.11 #1201, 0.11 #1611) >> Best rule #1194 for best value: >> intensional similarity = 3 >> extensional distance = 509 >> proper extension: 03g90h; 0gx1bnj; 047gn4y; 026mfbr; 0gydcp7; 04g9gd; 05fgt1; 03kg2v; 025n07; 023gxx; ... >> query: (?x5980, 02r96rf) <- film_crew_role(?x5980, ?x137), produced_by(?x5980, ?x5366), film_release_region(?x5980, ?x94) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 0c8tkt; 047svrl; 0192hw; 0blpg; 080lkt7; 02j69w; 04nm0n0; 0353xq; 0g5q34q; 09v9mks; ... *> query: (?x5980, 01pvkk) <- film_crew_role(?x5980, ?x137), featured_film_locations(?x5980, ?x739), film_regional_debut_venue(?x5980, ?x1649) *> conf = 0.34 ranks of expected_values: 4 EVAL 0hv81 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 95.000 95.000 0.685 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4350-04f52jw PRED entity: 04f52jw PRED relation: film_release_region PRED expected values: 05qhw 01znc_ 01pj7 03rj0 016wzw => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 95): 05qhw (0.92 #856, 0.86 #371, 0.85 #250), 01znc_ (0.88 #871, 0.84 #386, 0.84 #265), 03rj0 (0.71 #158, 0.69 #885, 0.64 #279), 016wzw (0.70 #404, 0.69 #283, 0.58 #889), 015qh (0.60 #870, 0.58 #264, 0.58 #143), 06t8v (0.51 #900, 0.46 #415, 0.45 #294), 01pj7 (0.45 #271, 0.45 #392, 0.42 #877), 06q1r (0.32 #4967, 0.30 #6301, 0.30 #6300), 02jx1 (0.32 #4967, 0.30 #6301, 0.30 #6300), 077qn (0.31 #304, 0.31 #910, 0.30 #425) >> Best rule #856 for best value: >> intensional similarity = 5 >> extensional distance = 102 >> proper extension: 0dtfn; 0fq7dv_; 05qbckf; 0gd0c7x; 02fqrf; 05zlld0; 02mt51; 02rmd_2; 02bg55; 0fphf3v; ... >> query: (?x2746, 05qhw) <- film_release_region(?x2746, ?x1475), film_release_region(?x2746, ?x151), film_release_region(?x1392, ?x151), ?x1392 = 017gm7, ?x1475 = 05qx1 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 7 EVAL 04f52jw film_release_region 016wzw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04f52jw film_release_region 03rj0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04f52jw film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 62.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04f52jw film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04f52jw film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4349-01stzp PRED entity: 01stzp PRED relation: student PRED expected values: 02ln1 => 207 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1806): 0hgqq (0.30 #2930, 0.11 #9196, 0.07 #11284), 07s8hms (0.20 #2710, 0.08 #6887, 0.07 #8976), 0146pg (0.20 #2173, 0.07 #8439, 0.04 #6350), 0282x (0.20 #3013, 0.04 #7190, 0.03 #17637), 016gr2 (0.20 #2263, 0.04 #6440, 0.03 #16887), 07h1q (0.19 #8354, 0.13 #27159, 0.05 #37602), 039n1 (0.15 #35513, 0.15 #22981, 0.15 #18801), 041mt (0.15 #6597, 0.14 #8686, 0.10 #2420), 0ff3y (0.15 #8330, 0.11 #10419, 0.08 #27135), 015wc0 (0.12 #7958, 0.10 #3781, 0.07 #10047) >> Best rule #2930 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 02bzh0; >> query: (?x13316, 0hgqq) <- student(?x13316, ?x1211), people(?x1158, ?x1211), influenced_by(?x1211, ?x8177), instrumentalists(?x316, ?x1211) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #26530 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 09r4xx; 01wqg8; *> query: (?x13316, 02ln1) <- institution(?x1526, ?x13316), student(?x13316, ?x12592), school_type(?x13316, ?x3092), peers(?x10110, ?x12592) *> conf = 0.04 ranks of expected_values: 338 EVAL 01stzp student 02ln1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 207.000 73.000 0.300 http://example.org/education/educational_institution/students_graduates./education/education/student #4348-02lxrv PRED entity: 02lxrv PRED relation: nominated_for! PRED expected values: 027dtxw 02r0csl => 88 concepts (72 used for prediction) PRED predicted values (max 10 best out of 233): 027b9j5 (0.68 #11587, 0.68 #12538, 0.67 #12061), 0gq9h (0.42 #2428, 0.42 #1717, 0.41 #4318), 0gs9p (0.38 #1719, 0.37 #2430, 0.37 #4320), 019f4v (0.37 #2419, 0.36 #1708, 0.35 #4309), 0gr51 (0.33 #316, 0.22 #2444, 0.21 #4334), 0k611 (0.32 #2439, 0.31 #1728, 0.31 #3384), 040njc (0.31 #2372, 0.30 #1661, 0.30 #4026), 0gq_v (0.31 #2385, 0.31 #4275, 0.30 #4039), 04dn09n (0.29 #273, 0.28 #745, 0.26 #2401), 0gr0m (0.29 #60, 0.26 #2425, 0.26 #1714) >> Best rule #11587 for best value: >> intensional similarity = 3 >> extensional distance = 938 >> proper extension: 06qwh; >> query: (?x5890, ?x3458) <- award(?x5890, ?x3458), nominated_for(?x2444, ?x5890), award(?x2871, ?x3458) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #7799 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 733 *> proper extension: 02wk7b; *> query: (?x5890, ?x143) <- genre(?x5890, ?x53), award_winner(?x5890, ?x10416), award(?x5890, ?x1033), award_winner(?x143, ?x10416) *> conf = 0.27 ranks of expected_values: 12, 21 EVAL 02lxrv nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 88.000 72.000 0.677 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02lxrv nominated_for! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 88.000 72.000 0.677 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4347-02x3lt7 PRED entity: 02x3lt7 PRED relation: film_release_region PRED expected values: 06mzp 0345h 01mjq => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 98): 0chghy (0.87 #8, 0.85 #1252, 0.76 #2497), 0345h (0.81 #1266, 0.79 #22, 0.73 #2511), 06bnz (0.74 #33, 0.69 #1277, 0.56 #2522), 0d060g (0.74 #1248, 0.64 #4, 0.63 #2493), 01mjq (0.62 #31, 0.56 #1275, 0.43 #2520), 06f32 (0.62 #51, 0.47 #1295, 0.32 #2540), 06mzp (0.60 #15, 0.44 #1259, 0.37 #2504), 03rk0 (0.60 #43, 0.44 #1287, 0.30 #2532), 06qd3 (0.53 #26, 0.50 #1270, 0.43 #2515), 07f1x (0.51 #102, 0.33 #1346, 0.24 #2591) >> Best rule #8 for best value: >> intensional similarity = 6 >> extensional distance = 45 >> proper extension: 02vxq9m; 05p1tzf; 0fq27fp; 0401sg; 087wc7n; 08hmch; 02d44q; 01c22t; 0jjy0; 0h3xztt; ... >> query: (?x607, 0chghy) <- film_release_region(?x607, ?x792), film_release_region(?x607, ?x429), film_release_region(?x607, ?x304), ?x304 = 0d0vqn, ?x429 = 03rt9, ?x792 = 0hzlz >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1266 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 203 *> proper extension: 0ds35l9; 0gtsx8c; 028_yv; 0c3ybss; 0ddfwj1; 0ds3t5x; 0gtv7pk; 0h1cdwq; 0dscrwf; 0gx9rvq; ... *> query: (?x607, 0345h) <- film_release_region(?x607, ?x792), film_release_region(?x607, ?x429), film_release_region(?x607, ?x304), ?x304 = 0d0vqn, ?x429 = 03rt9, contains(?x792, ?x841) *> conf = 0.81 ranks of expected_values: 2, 5, 7 EVAL 02x3lt7 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 97.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02x3lt7 film_release_region 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02x3lt7 film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 97.000 97.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4346-041n43 PRED entity: 041n43 PRED relation: artist PRED expected values: 0137g1 017yfz => 54 concepts (8 used for prediction) PRED predicted values (max 10 best out of 984): 01k23t (0.67 #2225, 0.62 #1394, 0.16 #3889), 03g5jw (0.50 #915, 0.44 #1746, 0.15 #2578), 01vwbts (0.50 #1162, 0.44 #1993, 0.10 #2825), 0892sx (0.50 #991, 0.44 #1822, 0.10 #2654), 01vxlbm (0.44 #1929, 0.38 #1098, 0.16 #3593), 0ffgh (0.44 #2168, 0.38 #1337, 0.10 #3000), 011z3g (0.44 #2139, 0.38 #1308, 0.07 #3803), 01q99h (0.38 #1274, 0.33 #2105, 0.20 #2937), 08w4pm (0.38 #1414, 0.33 #2245, 0.17 #582), 046p9 (0.38 #1427, 0.33 #2258, 0.17 #595) >> Best rule #2225 for best value: >> intensional similarity = 10 >> extensional distance = 7 >> proper extension: 01q940; >> query: (?x13767, 01k23t) <- artist(?x13767, ?x12422), artist(?x13767, ?x8199), artist(?x13767, ?x6383), instrumentalists(?x716, ?x12422), ?x6383 = 0g824, ?x716 = 018vs, artists(?x3916, ?x8199), award_nominee(?x8199, ?x5618), location(?x12422, ?x2235), parent_genre(?x2439, ?x3916) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1117 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 01cszh; 015_1q; 043g7l; 0181dw; 03mp8k; 05byxm; *> query: (?x13767, 017yfz) <- artist(?x13767, ?x12422), artist(?x13767, ?x6383), instrumentalists(?x716, ?x12422), ?x6383 = 0g824, ?x716 = 018vs, award_winner(?x5766, ?x12422), artists(?x5379, ?x12422), location(?x12422, ?x2235) *> conf = 0.25 ranks of expected_values: 86, 625 EVAL 041n43 artist 017yfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 54.000 8.000 0.667 http://example.org/music/record_label/artist EVAL 041n43 artist 0137g1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 8.000 0.667 http://example.org/music/record_label/artist #4345-01nv4h PRED entity: 01nv4h PRED relation: currency! PRED expected values: 027xq5 => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 592): 02f46y (0.83 #1665, 0.83 #1240, 0.83 #1661), 0fvd03 (0.83 #1665, 0.83 #1240, 0.83 #1661), 02ps55 (0.83 #1665, 0.83 #1240, 0.83 #1661), 01x5fb (0.83 #1665, 0.83 #1240, 0.83 #1659), 01d650 (0.83 #1665, 0.83 #1240, 0.83 #1659), 01s753 (0.83 #1240, 0.83 #1659, 0.82 #1660), 0ylvj (0.83 #1240, 0.83 #1659, 0.82 #1660), 0ylzs (0.83 #1659, 0.82 #1660, 0.82 #1658), 02mg7n (0.83 #1659, 0.82 #1658, 0.82 #1237), 01bzs9 (0.82 #1250, 0.78 #1667, 0.77 #418) >> Best rule #1665 for best value: >> intensional similarity = 51 >> extensional distance = 2 >> proper extension: 088n7; >> query: (?x1099, ?x11707) <- currency(?x11707, ?x1099), currency(?x6034, ?x1099), currency(?x1369, ?x1099), currency(?x12726, ?x1099), currency(?x11740, ?x1099), currency(?x7516, ?x1099), currency(?x6605, ?x1099), currency(?x2939, ?x1099), currency(?x622, ?x1099), organization(?x346, ?x11707), currency(?x8223, ?x1099), institution(?x1526, ?x1369), contains(?x362, ?x8223), school_type(?x1369, ?x5931), school_type(?x11707, ?x3092), institution(?x620, ?x11740), film_release_distribution_medium(?x2939, ?x81), contains(?x3301, ?x1369), category(?x11740, ?x134), genre(?x622, ?x53), institution(?x734, ?x12726), country(?x2939, ?x94), ?x81 = 029j_, student(?x6034, ?x164), film(?x2185, ?x2939), film_release_region(?x622, ?x4743), genre(?x2939, ?x225), production_companies(?x6605, ?x617), contains(?x455, ?x11740), major_field_of_study(?x11707, ?x1154), colors(?x12726, ?x332), film_crew_role(?x622, ?x137), major_field_of_study(?x1526, ?x6756), major_field_of_study(?x1526, ?x373), ?x6756 = 0_jm, major_field_of_study(?x12726, ?x6978), ?x373 = 02vxn, institution(?x1526, ?x9110), institution(?x1526, ?x6912), institution(?x1526, ?x6637), institution(?x1526, ?x3913), institution(?x1526, ?x331), ?x331 = 01jssp, contains(?x4030, ?x12726), ?x6637 = 07vjm, ?x9110 = 07tjf, ?x4743 = 03spz, student(?x1526, ?x476), ?x3913 = 01b1pf, nominated_for(?x941, ?x7516), ?x6912 = 0gl5_ >> conf = 0.83 => this is the best rule for 5 predicted values *> Best rule #2496 for first EXPECTED value: *> intensional similarity = 58 *> extensional distance = 3 *> proper extension: 0ptk_; *> query: (?x1099, ?x10859) <- currency(?x6836, ?x1099), currency(?x6034, ?x1099), currency(?x14116, ?x1099), currency(?x10859, ?x1099), currency(?x10348, ?x1099), currency(?x7097, ?x1099), currency(?x4864, ?x1099), currency(?x4359, ?x1099), currency(?x2550, ?x1099), currency(?x2151, ?x1099), currency(?x892, ?x1099), institution(?x1368, ?x7097), citytown(?x6836, ?x1156), company(?x5652, ?x6836), student(?x14116, ?x6122), contains(?x512, ?x6836), currency(?x13491, ?x1099), country(?x4359, ?x205), nominated_for(?x166, ?x4864), nominated_for(?x361, ?x4359), colors(?x10348, ?x3189), student(?x7097, ?x4149), award_nominee(?x100, ?x6122), nominated_for(?x13951, ?x4359), nominated_for(?x899, ?x4359), participant(?x4884, ?x6122), film_release_region(?x2151, ?x985), film_release_region(?x11395, ?x985), film_release_region(?x10029, ?x985), film_release_region(?x6283, ?x985), film_release_region(?x5735, ?x985), film_release_region(?x4430, ?x985), film_release_region(?x2889, ?x985), film_release_region(?x2738, ?x985), film_release_region(?x2037, ?x985), genre(?x4864, ?x258), ?x4430 = 043sct5, ?x11395 = 05ypj5, olympics(?x985, ?x391), currency(?x13491, ?x5696), major_field_of_study(?x10859, ?x1154), countries_spoken_in(?x732, ?x985), school_type(?x7097, ?x3092), ?x1368 = 014mlp, award(?x813, ?x13951), nominated_for(?x1033, ?x2550), state_province_region(?x14116, ?x2235), film_release_region(?x2550, ?x94), student(?x6034, ?x164), ?x10029 = 02vzpb, ?x6283 = 0gmd3k7, ?x2037 = 0gvrws1, location(?x6122, ?x362), member_states(?x2106, ?x985), ?x2889 = 040b5k, ?x2738 = 0h1v19, ?x5735 = 0h21v2, award(?x286, ?x899) *> conf = 0.74 ranks of expected_values: 34 EVAL 01nv4h currency! 027xq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 8.000 8.000 0.834 http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency #4344-04dqdk PRED entity: 04dqdk PRED relation: place_of_birth PRED expected values: 05qtj => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 100): 05qtj (0.42 #2114, 0.41 #4227, 0.33 #75366), 02_286 (0.11 #42277, 0.11 #42982, 0.11 #43687), 0cr3d (0.04 #38127, 0.04 #53624, 0.04 #24744), 030qb3t (0.04 #2872, 0.03 #67671, 0.03 #68376), 0h7h6 (0.04 #2876, 0.02 #4285, 0.01 #2172), 01_d4 (0.04 #4997, 0.03 #1475, 0.03 #57117), 095w_ (0.03 #2866, 0.03 #48, 0.01 #9206), 01v8c (0.03 #690), 0dj0x (0.03 #672), 0qt85 (0.03 #498) >> Best rule #2114 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 094xh; >> query: (?x1381, ?x4627) <- artists(?x671, ?x1381), religion(?x1381, ?x7131), origin(?x1381, ?x4627) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04dqdk place_of_birth 05qtj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.424 http://example.org/people/person/place_of_birth #4343-03m3vr6 PRED entity: 03m3vr6 PRED relation: people PRED expected values: 0lrh => 38 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1265): 0gyy0 (0.33 #381, 0.25 #1067, 0.20 #2440), 0blgl (0.33 #570, 0.25 #1256, 0.13 #2629), 040z9 (0.33 #320, 0.25 #1006, 0.11 #6185), 0121rx (0.33 #644, 0.25 #1330, 0.11 #6142), 02r38 (0.33 #481, 0.25 #1167, 0.10 #1854), 023361 (0.33 #369, 0.25 #1055, 0.10 #1742), 043tg (0.33 #359, 0.25 #1045, 0.10 #1732), 03txms (0.33 #345, 0.25 #1031, 0.10 #1718), 06h7l7 (0.33 #303, 0.25 #989, 0.10 #1676), 0149xx (0.33 #196, 0.25 #882, 0.10 #1569) >> Best rule #381 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 01dcqj; >> query: (?x11674, 0gyy0) <- people(?x11674, ?x6639), people(?x11674, ?x2069), award_winner(?x2222, ?x2069), ?x6639 = 0137hn, award_winner(?x4598, ?x2069), award_winner(?x2779, ?x2069), award_winner(?x2068, ?x2069), ?x2779 = 0k4f3, location(?x2069, ?x739) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2846 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 14 *> proper extension: 0qcr0; 02y0js; 0jdk0; 0gk4g; 04p3w; 0c58k; 09jg8; 0gg4h; 0x2fg; 051_y; ... *> query: (?x11674, 0lrh) <- people(?x11674, ?x6639), people(?x11674, ?x2069), award_winner(?x2222, ?x2069), award_winner(?x6639, ?x2865), award_winner(?x2068, ?x2069), profession(?x6639, ?x1032), location(?x2069, ?x739), artist(?x5744, ?x6639), artists(?x671, ?x6639) *> conf = 0.06 ranks of expected_values: 475 EVAL 03m3vr6 people 0lrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 38.000 13.000 0.333 http://example.org/people/cause_of_death/people #4342-03z106 PRED entity: 03z106 PRED relation: film_crew_role PRED expected values: 02r96rf => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 32): 02r96rf (0.69 #1199, 0.68 #1233, 0.68 #1442), 02ynfr (0.39 #218, 0.33 #14, 0.29 #320), 01vx2h (0.38 #78, 0.36 #112, 0.34 #1240), 0d2b38 (0.38 #92, 0.13 #468, 0.12 #365), 02_n3z (0.36 #103, 0.13 #205, 0.10 #307), 0215hd (0.27 #119, 0.24 #153, 0.22 #461), 089g0h (0.27 #120, 0.19 #359, 0.17 #188), 01xy5l_ (0.25 #80, 0.18 #114, 0.17 #353), 02rh1dz (0.25 #77, 0.13 #213, 0.12 #1205), 02vs3x5 (0.22 #192, 0.17 #56, 0.12 #363) >> Best rule #1199 for best value: >> intensional similarity = 4 >> extensional distance = 600 >> proper extension: 01br2w; 02rb607; 040rmy; 02hfk5; 07l50vn; 0g9zljd; 03_wm6; 072r5v; 0k2m6; 0cvkv5; ... >> query: (?x3857, 02r96rf) <- film_crew_role(?x3857, ?x137), film(?x902, ?x3857), ?x137 = 09zzb8, currency(?x3857, ?x170) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03z106 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.686 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4341-018gkb PRED entity: 018gkb PRED relation: role PRED expected values: 04rzd => 117 concepts (59 used for prediction) PRED predicted values (max 10 best out of 123): 01vdm0 (0.54 #127, 0.39 #328, 0.39 #527), 05r5c (0.48 #905, 0.45 #3727, 0.42 #1814), 0l14md (0.39 #199, 0.36 #1907, 0.33 #3619), 03bx0bm (0.39 #199, 0.36 #1907, 0.33 #2311), 05148p4 (0.33 #3619, 0.33 #19, 0.32 #3720), 026t6 (0.33 #3619, 0.32 #3720, 0.32 #3618), 03qjg (0.33 #3619, 0.32 #3720, 0.32 #3618), 0l14j_ (0.33 #3619, 0.32 #3720, 0.32 #3618), 02fsn (0.33 #3619, 0.32 #3720, 0.32 #3618), 01vj9c (0.33 #12, 0.31 #111, 0.21 #312) >> Best rule #127 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 03kwtb; 0fpjd_g; 04bpm6; 012zng; 01w8n89; 0lzkm; 017vkx; 0f0qfz; 03h502k; 01vsyg9; ... >> query: (?x11161, 01vdm0) <- role(?x11161, ?x315), role(?x11161, ?x1148), gender(?x11161, ?x231), ?x1148 = 02qjv >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #2716 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 250 *> proper extension: 01p45_v; 02qfhb; 0jn5l; 04d_mtq; *> query: (?x11161, ?x228) <- role(?x11161, ?x1466), instrumentalists(?x212, ?x11161), performance_role(?x228, ?x1466), group(?x1466, ?x442) *> conf = 0.08 ranks of expected_values: 31 EVAL 018gkb role 04rzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 117.000 59.000 0.538 http://example.org/music/artist/track_contributions./music/track_contribution/role #4340-0p__8 PRED entity: 0p__8 PRED relation: influenced_by PRED expected values: 063_t => 108 concepts (45 used for prediction) PRED predicted values (max 10 best out of 252): 09889g (0.15 #155, 0.12 #589, 0.04 #1024), 01hmk9 (0.12 #655, 0.10 #221, 0.04 #7596), 014zfs (0.12 #459, 0.10 #25, 0.04 #7833), 014z8v (0.10 #122, 0.08 #556, 0.06 #8797), 052hl (0.10 #209, 0.08 #643, 0.04 #1078), 04wqr (0.10 #12, 0.08 #446, 0.02 #3483), 01k9lpl (0.08 #744, 0.04 #8985, 0.03 #8118), 08304 (0.08 #640, 0.02 #3243, 0.02 #6281), 032l1 (0.08 #7465, 0.07 #13098, 0.06 #1826), 03_87 (0.08 #7578, 0.06 #13211, 0.05 #6278) >> Best rule #155 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 09qh1; 03h_fqv; >> query: (?x5940, 09889g) <- friend(?x8716, ?x5940), influenced_by(?x5940, ?x4988), film(?x5940, ?x146) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #285 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 09qh1; 03h_fqv; *> query: (?x5940, 063_t) <- friend(?x8716, ?x5940), influenced_by(?x5940, ?x4988), film(?x5940, ?x146) *> conf = 0.05 ranks of expected_values: 40 EVAL 0p__8 influenced_by 063_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 108.000 45.000 0.150 http://example.org/influence/influence_node/influenced_by #4339-02xs6_ PRED entity: 02xs6_ PRED relation: edited_by PRED expected values: 0bn3jg => 106 concepts (82 used for prediction) PRED predicted values (max 10 best out of 25): 0bn3jg (0.06 #206, 0.02 #353, 0.02 #235), 03cp7b3 (0.06 #83, 0.02 #143, 0.02 #379), 03q8ch (0.05 #102, 0.05 #368, 0.04 #132), 02qggqc (0.05 #92, 0.03 #656, 0.03 #507), 06cv1 (0.05 #91, 0.02 #357, 0.01 #387), 02b29 (0.05 #504, 0.05 #237, 0.04 #119), 07s93v (0.05 #504, 0.05 #237, 0.04 #119), 06chf (0.04 #473, 0.04 #1372, 0.04 #1220), 02kxbwx (0.04 #212, 0.03 #34, 0.03 #478), 0272kv (0.03 #503, 0.02 #1432, 0.02 #924) >> Best rule #206 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 03ckwzc; 020fcn; 03nx8mj; 026f__m; 09lxv9; >> query: (?x4991, 0bn3jg) <- film(?x788, ?x4991), titles(?x600, ?x4991), film_crew_role(?x4991, ?x1078), ?x1078 = 089fss >> conf = 0.06 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xs6_ edited_by 0bn3jg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 82.000 0.059 http://example.org/film/film/edited_by #4338-0k6nt PRED entity: 0k6nt PRED relation: member_states! PRED expected values: 059dn => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 9): 059dn (0.62 #1, 0.56 #4, 0.44 #9), 0b6css (0.15 #39, 0.15 #31, 0.14 #37), 04k4l (0.15 #39, 0.15 #31, 0.14 #37), 0_2v (0.15 #39, 0.15 #31, 0.14 #37), 01rz1 (0.15 #39, 0.15 #31, 0.14 #37), 07t65 (0.15 #39, 0.15 #31, 0.14 #37), 02vk52z (0.15 #39, 0.15 #31, 0.14 #37), 0j7v_ (0.15 #31, 0.13 #19, 0.03 #191), 041288 (0.03 #191) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0fhnf; >> query: (?x985, 059dn) <- contains(?x455, ?x985), adjoins(?x985, ?x1264), ?x1264 = 0345h >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k6nt member_states! 059dn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.625 http://example.org/user/ktrueman/default_domain/international_organization/member_states #4337-01hq1 PRED entity: 01hq1 PRED relation: executive_produced_by PRED expected values: 06s26c => 106 concepts (81 used for prediction) PRED predicted values (max 10 best out of 95): 02q42j_ (0.29 #896, 0.29 #644, 0.03 #3422), 079vf (0.25 #1518, 0.13 #1266, 0.09 #2277), 0b13g7 (0.24 #845, 0.21 #593, 0.02 #3371), 02nygk (0.20 #255, 0.09 #6073, 0.09 #7087), 03c9pqt (0.20 #247, 0.05 #1258, 0.04 #2522), 01qg7c (0.20 #213, 0.02 #2488, 0.02 #2740), 025b3k (0.20 #212, 0.01 #4511), 06s26c (0.14 #728, 0.12 #980), 06pj8 (0.10 #1319, 0.10 #310, 0.07 #2834), 0343h (0.10 #297, 0.08 #1306, 0.07 #2064) >> Best rule #896 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 035xwd; 07bwr; 05dss7; >> query: (?x7881, 02q42j_) <- production_companies(?x7881, ?x752), titles(?x8581, ?x7881), ?x752 = 0338lq, film(?x382, ?x7881) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #728 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 01hr1; 01hp5; 0gjk1d; 016kz1; 02_kd; 011yr9; 01hqk; 0qmhk; 011yhm; 037q31; ... *> query: (?x7881, 06s26c) <- production_companies(?x7881, ?x752), titles(?x8581, ?x7881), ?x752 = 0338lq, nominated_for(?x102, ?x7881) *> conf = 0.14 ranks of expected_values: 8 EVAL 01hq1 executive_produced_by 06s26c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 106.000 81.000 0.294 http://example.org/film/film/executive_produced_by #4336-03ytc PRED entity: 03ytc PRED relation: industry! PRED expected values: 045c7b 019rl6 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 393): 04sv4 (0.60 #5720, 0.43 #4345, 0.40 #2849), 045c7b (0.60 #5720, 0.25 #61, 0.21 #4288), 05b5c (0.33 #1198, 0.29 #1943, 0.29 #1446), 0k8z (0.30 #2775, 0.21 #4271, 0.17 #1036), 0lwkh (0.25 #186, 0.17 #1178, 0.14 #1923), 01qygl (0.25 #111, 0.17 #1103, 0.14 #1848), 087c7 (0.25 #4, 0.17 #996, 0.14 #1741), 018p5f (0.25 #94, 0.17 #838, 0.14 #1334), 061v5m (0.25 #78, 0.17 #822, 0.14 #1318), 0l8sx (0.22 #2504, 0.22 #4498, 0.20 #519) >> Best rule #5720 for best value: >> intensional similarity = 10 >> extensional distance = 29 >> proper extension: 01zhp; >> query: (?x8855, ?x5072) <- industry(?x7970, ?x8855), industry(?x7970, ?x14555), organization(?x4682, ?x7970), currency(?x7970, ?x170), company(?x1491, ?x7970), ?x4682 = 0dq_5, industry(?x5072, ?x14555), state_province_region(?x7970, ?x3634), ?x170 = 09nqf, ?x1491 = 0krdk >> conf = 0.60 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 44 EVAL 03ytc industry! 019rl6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 73.000 73.000 0.598 http://example.org/business/business_operation/industry EVAL 03ytc industry! 045c7b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 73.000 0.598 http://example.org/business/business_operation/industry #4335-0163kf PRED entity: 0163kf PRED relation: artists! PRED expected values: 06j6l => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 198): 064t9 (0.61 #3145, 0.51 #3458, 0.47 #3771), 03_d0 (0.54 #1265, 0.52 #952, 0.50 #12), 06by7 (0.46 #3154, 0.42 #10041, 0.42 #9101), 0glt670 (0.34 #3487, 0.29 #4426, 0.28 #3800), 01fh36 (0.33 #402, 0.29 #89, 0.24 #1029), 06j6l (0.33 #3495, 0.30 #3182, 0.29 #3808), 025sc50 (0.31 #3497, 0.29 #3184, 0.26 #3810), 05bt6j (0.29 #3177, 0.23 #4116, 0.22 #5055), 0dl5d (0.25 #647, 0.11 #10039, 0.09 #14109), 01lyv (0.24 #4106, 0.23 #5045, 0.21 #5984) >> Best rule #3145 for best value: >> intensional similarity = 3 >> extensional distance = 121 >> proper extension: 0ggl02; 01x15dc; 016732; 05mxw33; >> query: (?x12102, 064t9) <- award_nominee(?x215, ?x12102), award(?x12102, ?x724), ?x724 = 01bgqh >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #3495 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 224 *> proper extension: 03g5jw; 0dvqq; 03yf3z; 04qmr; 01yzl2; 0kr_t; 0dw4g; 01dwrc; 02pt7h_; 02k5sc; ... *> query: (?x12102, 06j6l) <- artist(?x2241, ?x12102), award_nominee(?x6651, ?x12102), participant(?x6651, ?x2562) *> conf = 0.33 ranks of expected_values: 6 EVAL 0163kf artists! 06j6l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 107.000 107.000 0.610 http://example.org/music/genre/artists #4334-023vrq PRED entity: 023vrq PRED relation: award! PRED expected values: 018n6m 03f19q4 02x_h0 013w7j 01vw37m => 45 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2983): 013w7j (0.83 #20112, 0.79 #26821, 0.78 #53643), 02l840 (0.83 #20112, 0.79 #26821, 0.78 #53643), 01dwrc (0.78 #53643, 0.78 #43582, 0.78 #6704), 01vw37m (0.57 #5171, 0.38 #8523, 0.33 #1819), 01vzx45 (0.46 #8888, 0.43 #5536, 0.33 #2184), 02vwckw (0.43 #5710, 0.38 #9062, 0.07 #19116), 018n6m (0.43 #4684, 0.33 #1332, 0.31 #8036), 01vvzb1 (0.43 #4903, 0.33 #1551, 0.31 #8255), 01vvyd8 (0.43 #5154, 0.33 #1802, 0.31 #8506), 0k6yt1 (0.43 #6384, 0.33 #3032, 0.23 #9736) >> Best rule #20112 for best value: >> intensional similarity = 5 >> extensional distance = 83 >> proper extension: 05qck; >> query: (?x9295, ?x6151) <- award_winner(?x9295, ?x6151), award_winner(?x9295, ?x2926), participant(?x1017, ?x6151), award(?x2926, ?x724), artist(?x2190, ?x6151) >> conf = 0.83 => this is the best rule for 2 predicted values ranks of expected_values: 1, 4, 7, 79, 204 EVAL 023vrq award! 01vw37m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 45.000 24.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 023vrq award! 013w7j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 24.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 023vrq award! 02x_h0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 45.000 24.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 023vrq award! 03f19q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 45.000 24.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 023vrq award! 018n6m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 45.000 24.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4333-0p_pd PRED entity: 0p_pd PRED relation: type_of_union PRED expected values: 04ztj => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.88 #49, 0.88 #13, 0.87 #97), 01g63y (0.16 #154, 0.16 #22, 0.15 #170), 0jgjn (0.01 #60), 01bl8s (0.01 #83) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 012d40; 0byfz; 014zcr; 0l8v5; 0c4f4; 04wqr; 03m8lq; 01pcq3; 032_jg; 01j5x6; ... >> query: (?x397, 04ztj) <- award_nominee(?x241, ?x397), nominated_for(?x397, ?x696), location_of_ceremony(?x397, ?x3026) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p_pd type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.885 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4332-01jzyx PRED entity: 01jzyx PRED relation: school! PRED expected values: 01d5z => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 92): 01slc (0.33 #241, 0.31 #149, 0.27 #333), 05m_8 (0.31 #462, 0.31 #94, 0.27 #278), 01ync (0.23 #130, 0.20 #314, 0.20 #222), 0512p (0.23 #106, 0.20 #290, 0.19 #474), 051vz (0.23 #114, 0.20 #298, 0.19 #482), 01yhm (0.20 #295, 0.20 #203, 0.19 #479), 06rpd (0.20 #349, 0.19 #533, 0.18 #809), 02896 (0.20 #277, 0.19 #461, 0.08 #93), 0jmm4 (0.15 #164, 0.13 #348, 0.12 #624), 049n7 (0.15 #103, 0.13 #287, 0.12 #471) >> Best rule #241 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0l0wv; >> query: (?x5426, 01slc) <- service_language(?x5426, ?x254), country(?x5426, ?x94), institution(?x620, ?x5426), organization(?x346, ?x5426) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #101 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 06pwq; *> query: (?x5426, 01d5z) <- currency(?x5426, ?x170), service_location(?x5426, ?x94), major_field_of_study(?x5426, ?x2981), school(?x260, ?x5426) *> conf = 0.15 ranks of expected_values: 14 EVAL 01jzyx school! 01d5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 156.000 156.000 0.333 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #4331-018j2 PRED entity: 018j2 PRED relation: role! PRED expected values: 02snj9 => 88 concepts (58 used for prediction) PRED predicted values (max 10 best out of 78): 01vj9c (0.85 #2190, 0.85 #219, 0.85 #220), 05r5c (0.85 #2190, 0.85 #219, 0.85 #220), 0dwtp (0.85 #2190, 0.85 #219, 0.85 #220), 01dnws (0.85 #2190, 0.85 #219, 0.85 #220), 018vs (0.85 #2190, 0.85 #219, 0.85 #220), 03qjg (0.85 #2190, 0.85 #219, 0.85 #220), 026t6 (0.85 #2190, 0.85 #219, 0.85 #220), 07y_7 (0.85 #2190, 0.85 #219, 0.85 #220), 0l14j_ (0.85 #2190, 0.85 #219, 0.85 #220), 03ndd (0.85 #2190, 0.85 #219, 0.85 #220) >> Best rule #2190 for best value: >> intensional similarity = 17 >> extensional distance = 8 >> proper extension: 0dwt5; >> query: (?x2048, ?x75) <- role(?x2048, ?x4311), role(?x2048, ?x1466), role(?x2048, ?x780), role(?x2048, ?x75), role(?x2048, ?x74), role(?x2048, ?x1432), ?x1432 = 0395lw, ?x4311 = 01xqw, group(?x2048, ?x997), instrumentalists(?x2048, ?x3867), role(?x2253, ?x2048), role(?x2297, ?x780), ?x74 = 03q5t, currency(?x3867, ?x1099), currency(?x1098, ?x1099), ?x1466 = 03bx0bm, currency(?x892, ?x1099) >> conf = 0.85 => this is the best rule for 11 predicted values *> Best rule #2313 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 9 *> proper extension: 01s0ps; 0bxl5; *> query: (?x2048, 02snj9) <- role(?x2048, ?x4311), role(?x2048, ?x2310), role(?x2048, ?x2206), role(?x2048, ?x3409), role(?x2048, ?x1574), role(?x2048, ?x1432), ?x1432 = 0395lw, ?x3409 = 0680x0, role(?x2253, ?x2048), ?x2206 = 07gql, group(?x2048, ?x997), instrumentalists(?x4311, ?x562), ?x1574 = 0l15bq, role(?x4311, ?x645), role(?x1260, ?x2310), role(?x1662, ?x4311) *> conf = 0.73 ranks of expected_values: 38 EVAL 018j2 role! 02snj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 88.000 58.000 0.854 http://example.org/music/performance_role/regular_performances./music/group_membership/role #4330-0kq2g PRED entity: 0kq2g PRED relation: adjoins PRED expected values: 0l2nd => 105 concepts (55 used for prediction) PRED predicted values (max 10 best out of 400): 0l2sr (0.25 #22410, 0.25 #22409, 0.24 #17769), 0kq1l (0.25 #22410, 0.25 #22409, 0.24 #17769), 0l2nd (0.25 #22410, 0.25 #22409, 0.24 #17769), 0kq2g (0.25 #22410, 0.25 #22409, 0.24 #17769), 0kpzy (0.20 #292, 0.19 #40960, 0.17 #41735), 0kq0q (0.20 #689, 0.19 #40960, 0.17 #41735), 0l34j (0.20 #216, 0.19 #40960, 0.17 #41735), 0l2mg (0.20 #641, 0.19 #40960, 0.17 #41735), 0l2hf (0.20 #178, 0.19 #40960, 0.02 #2495), 0n6mc (0.20 #518, 0.19 #40960, 0.01 #5152) >> Best rule #22410 for best value: >> intensional similarity = 5 >> extensional distance = 316 >> proper extension: 07z1m; 04rrd; 05kr_; 01jr6; 05fjf; 0nm9y; >> query: (?x12056, ?x7520) <- adjoins(?x5892, ?x12056), adjoins(?x5892, ?x9582), adjoins(?x5892, ?x7520), second_level_divisions(?x94, ?x9582), source(?x5892, ?x958) >> conf = 0.25 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0kq2g adjoins 0l2nd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 55.000 0.250 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #4329-0161sp PRED entity: 0161sp PRED relation: category PRED expected values: 08mbj5d => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #37, 0.87 #26, 0.85 #25) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 219 >> proper extension: 02r3zy; 0dvqq; 03fbc; 0249kn; 018ndc; 017j6; 0163m1; 0hvbj; 01fmz6; 01yzl2; ... >> query: (?x2908, 08mbj5d) <- award_nominee(?x2908, ?x2415), artist(?x441, ?x2908), origin(?x2908, ?x94) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0161sp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.882 http://example.org/common/topic/webpage./common/webpage/category #4328-013423 PRED entity: 013423 PRED relation: instrumentalists! PRED expected values: 0342h => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 86): 0342h (0.70 #269, 0.70 #447, 0.66 #2295), 05148p4 (0.43 #285, 0.40 #903, 0.37 #463), 018vs (0.31 #3898, 0.30 #4253, 0.30 #277), 03qjg (0.26 #404, 0.23 #494, 0.21 #934), 01vdm0 (0.26 #3975, 0.03 #292, 0.03 #380), 0l14qv (0.21 #94, 0.13 #270, 0.11 #2472), 0l14md (0.20 #272, 0.14 #2298, 0.13 #1946), 02hnl (0.18 #2501, 0.17 #2325, 0.17 #3920), 018j2 (0.18 #391, 0.17 #303, 0.09 #4368), 04rzd (0.17 #302, 0.12 #214, 0.11 #656) >> Best rule #269 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 01w8n89; 01wbsdz; 01w5gg6; >> query: (?x6418, 0342h) <- artist(?x5666, ?x6418), artists(?x505, ?x6418), instrumentalists(?x316, ?x6418), ?x5666 = 043g7l >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013423 instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.700 http://example.org/music/instrument/instrumentalists #4327-0qzhw PRED entity: 0qzhw PRED relation: category PRED expected values: 08mbj5d => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #3, 0.84 #7, 0.84 #18) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 0r22d; >> query: (?x9300, 08mbj5d) <- county(?x9300, ?x9299), contains(?x1227, ?x9300), location(?x2580, ?x9300), ?x1227 = 01n7q >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qzhw category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.875 http://example.org/common/topic/webpage./common/webpage/category #4326-0134pk PRED entity: 0134pk PRED relation: artist! PRED expected values: 0fb0v => 103 concepts (78 used for prediction) PRED predicted values (max 10 best out of 125): 01w40h (0.44 #310, 0.14 #169, 0.13 #1297), 03rhqg (0.32 #1003, 0.30 #1285, 0.27 #2698), 033hn8 (0.26 #1142, 0.23 #1001, 0.18 #578), 0mzkr (0.25 #448, 0.25 #25, 0.22 #307), 01dtcb (0.25 #47, 0.22 #329, 0.17 #1457), 017l96 (0.25 #19, 0.18 #1570, 0.15 #1852), 011k1h (0.25 #10, 0.17 #1138, 0.14 #151), 01cl2y (0.25 #30, 0.14 #1017, 0.09 #2430), 01clyr (0.25 #33, 0.13 #1161, 0.12 #3985), 02p3cr5 (0.25 #27, 0.09 #1296, 0.08 #450) >> Best rule #310 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0zjpz; >> query: (?x9868, 01w40h) <- artists(?x11040, ?x9868), artists(?x1572, ?x9868), artists(?x1000, ?x9868), ?x1572 = 06by7, ?x1000 = 0xhtw, ?x11040 = 0173b0 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #148 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 016ppr; *> query: (?x9868, 0fb0v) <- award(?x9868, ?x10169), award(?x9868, ?x3365), group(?x8341, ?x9868), award(?x7407, ?x3365), ?x10169 = 02f79n, ?x7407 = 01dq9q *> conf = 0.14 ranks of expected_values: 25 EVAL 0134pk artist! 0fb0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 103.000 78.000 0.444 http://example.org/music/record_label/artist #4325-06ztvyx PRED entity: 06ztvyx PRED relation: production_companies PRED expected values: 056ws9 => 58 concepts (53 used for prediction) PRED predicted values (max 10 best out of 60): 05qd_ (0.38 #1764, 0.35 #2188, 0.32 #1763), 03xq0f (0.32 #1763, 0.31 #2187, 0.30 #3196), 04rcl7 (0.18 #322, 0.07 #910, 0.02 #658), 01795t (0.16 #272, 0.08 #860, 0.06 #106), 09b3v (0.13 #283, 0.07 #871, 0.03 #450), 086k8 (0.12 #672, 0.12 #756, 0.12 #588), 016tt2 (0.12 #88, 0.07 #1345, 0.07 #590), 0kk9v (0.11 #285, 0.06 #119, 0.04 #202), 056ws9 (0.10 #296, 0.10 #46, 0.06 #130), 054lpb6 (0.10 #349, 0.10 #15, 0.08 #516) >> Best rule #1764 for best value: >> intensional similarity = 3 >> extensional distance = 1070 >> proper extension: 011yfd; 03_wm6; >> query: (?x2709, ?x902) <- film(?x902, ?x2709), country(?x2709, ?x94), award_winner(?x574, ?x902) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #296 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 96 *> proper extension: 01_1pv; 02pb2bp; 03h3x5; 0cks1m; 02c7k4; 02r9p0c; 031f_m; 0gfzfj; 023cjg; 03gyvwg; *> query: (?x2709, 056ws9) <- film(?x147, ?x2709), film(?x609, ?x2709), genre(?x2709, ?x2540), ?x2540 = 0hcr *> conf = 0.10 ranks of expected_values: 9 EVAL 06ztvyx production_companies 056ws9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 58.000 53.000 0.380 http://example.org/film/film/production_companies #4324-0f3zsq PRED entity: 0f3zsq PRED relation: people! PRED expected values: 0cn68 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 32): 041rx (0.16 #928, 0.14 #1236, 0.14 #2930), 033tf_ (0.12 #161, 0.11 #238, 0.06 #2009), 0x67 (0.11 #4399, 0.11 #1550, 0.10 #4708), 07bch9 (0.08 #177, 0.07 #254, 0.03 #2718), 02w7gg (0.07 #310, 0.07 #2004, 0.07 #3852), 0g6ff (0.07 #329), 02ctzb (0.06 #92, 0.02 #1632, 0.02 #4790), 01g7zj (0.06 #129), 0xnvg (0.05 #1707, 0.05 #1014, 0.04 #2862), 013xrm (0.05 #328, 0.02 #405, 0.02 #482) >> Best rule #928 for best value: >> intensional similarity = 4 >> extensional distance = 316 >> proper extension: 01vyv9; >> query: (?x10542, 041rx) <- place_of_birth(?x10542, ?x6960), nominated_for(?x10542, ?x3471), location_of_ceremony(?x3525, ?x6960), county(?x6960, ?x9472) >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f3zsq people! 0cn68 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 101.000 0.157 http://example.org/people/ethnicity/people #4323-02_hj4 PRED entity: 02_hj4 PRED relation: film PRED expected values: 02tjl3 => 118 concepts (88 used for prediction) PRED predicted values (max 10 best out of 551): 04j14qc (0.64 #28531, 0.62 #32099, 0.58 #65978), 03nsm5x (0.64 #28531, 0.62 #32099, 0.58 #65978), 0872p_c (0.15 #24964, 0.03 #128400, 0.03 #133753), 07gghl (0.15 #24964, 0.01 #10085, 0.01 #2952), 0bxxzb (0.15 #24964, 0.01 #24350), 05pxnmb (0.15 #24964), 026f__m (0.15 #24964), 0dln8jk (0.15 #24964), 01shy7 (0.06 #2204, 0.05 #11120, 0.05 #32520), 062zm5h (0.06 #124832, 0.03 #128400, 0.03 #133753) >> Best rule #28531 for best value: >> intensional similarity = 3 >> extensional distance = 287 >> proper extension: 01j5ts; 0d_84; 0h5g_; 0c4f4; 01csvq; 0htlr; 03_vx9; 03knl; 0456xp; 04shbh; ... >> query: (?x1672, ?x8025) <- participant(?x1672, ?x2108), film(?x1672, ?x814), award_winner(?x8025, ?x1672) >> conf = 0.64 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02_hj4 film 02tjl3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 88.000 0.639 http://example.org/film/actor/film./film/performance/film #4322-0c33pl PRED entity: 0c33pl PRED relation: award_nominee! PRED expected values: 04znsy => 96 concepts (62 used for prediction) PRED predicted values (max 10 best out of 899): 01xcr4 (0.81 #51347, 0.81 #119029, 0.81 #100359), 0161sp (0.81 #51347, 0.81 #119029, 0.81 #100359), 020ffd (0.35 #4667, 0.22 #3764, 0.21 #28003), 0c33pl (0.35 #4667, 0.21 #28003, 0.20 #95691), 0c1j_ (0.35 #4667, 0.19 #144706, 0.03 #4519), 01yg9y (0.35 #4667, 0.16 #3602, 0.14 #5936), 039crh (0.35 #4667), 05sj55 (0.22 #4067, 0.14 #6401, 0.02 #20399), 05yjhm (0.22 #4287, 0.12 #6621, 0.02 #20619), 04znsy (0.21 #28003, 0.17 #1978, 0.02 #6645) >> Best rule #51347 for best value: >> intensional similarity = 3 >> extensional distance = 636 >> proper extension: 0fqy4p; 0c41qv; 076df9; >> query: (?x7983, ?x2908) <- award_nominee(?x2614, ?x7983), award_nominee(?x7983, ?x2908), category(?x7983, ?x134) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #28003 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 432 *> proper extension: 024rbz; 01nzs7; 09mfvx; 05d6q1; 0kctd; 0kcd5; *> query: (?x7983, ?x3183) <- nominated_for(?x7983, ?x4891), category(?x7983, ?x134), nominated_for(?x3183, ?x4891) *> conf = 0.21 ranks of expected_values: 10 EVAL 0c33pl award_nominee! 04znsy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 96.000 62.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4321-0639bg PRED entity: 0639bg PRED relation: nominated_for! PRED expected values: 057xs89 => 127 concepts (126 used for prediction) PRED predicted values (max 10 best out of 210): 02x1z2s (0.71 #235, 0.71 #5856, 0.68 #20152), 0k611 (0.46 #5223, 0.37 #1945, 0.33 #70), 0gq_v (0.45 #1894, 0.41 #5172, 0.32 #8687), 0gq9h (0.44 #1935, 0.42 #8728, 0.40 #5213), 0gr0m (0.40 #5210, 0.34 #1932, 0.31 #526), 019f4v (0.38 #1926, 0.37 #8719, 0.36 #5204), 0gs9p (0.38 #8730, 0.35 #9198, 0.32 #1937), 0l8z1 (0.37 #5202, 0.27 #1924, 0.23 #8717), 02r22gf (0.37 #1901, 0.33 #261, 0.31 #495), 02qyntr (0.35 #2051, 0.33 #176, 0.24 #5329) >> Best rule #235 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 06w99h3; >> query: (?x3845, ?x143) <- film(?x574, ?x3845), award(?x3845, ?x143), film(?x4282, ?x3845), ?x4282 = 02yxwd >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1053 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 02w9k1c; *> query: (?x3845, 057xs89) <- film(?x574, ?x3845), award(?x3845, ?x143), region(?x3845, ?x512), music(?x3845, ?x4019) *> conf = 0.27 ranks of expected_values: 22 EVAL 0639bg nominated_for! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 127.000 126.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4320-0dvmd PRED entity: 0dvmd PRED relation: award_nominee PRED expected values: 015t7v 0f5xn => 152 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1155): 05xf75 (0.81 #227047, 0.81 #208511, 0.81 #180707), 05zbm4 (0.81 #227047, 0.81 #208511, 0.81 #180707), 049g_xj (0.81 #227047, 0.81 #208511, 0.81 #180707), 01csvq (0.81 #227047, 0.81 #208511, 0.81 #180707), 017khj (0.81 #227047, 0.81 #208511, 0.81 #180707), 0dvmd (0.28 #81094, 0.20 #64874, 0.18 #240950), 04sry (0.28 #81094, 0.20 #64874, 0.18 #240950), 0dvld (0.28 #81094, 0.20 #64874, 0.17 #67191), 026rm_y (0.28 #81094, 0.20 #64874, 0.16 #199243), 03_gd (0.28 #81094, 0.20 #64874, 0.16 #199243) >> Best rule #227047 for best value: >> intensional similarity = 4 >> extensional distance = 1439 >> proper extension: 012ljv; 0l6qt; 0520r2x; 0cb77r; 06j0md; 03ckxdg; 026dcvf; 01wl38s; 026dg51; 03h26tm; ... >> query: (?x3101, ?x406) <- nominated_for(?x3101, ?x638), award_nominee(?x949, ?x3101), award_nominee(?x406, ?x3101), location(?x949, ?x335) >> conf = 0.81 => this is the best rule for 5 predicted values *> Best rule #81094 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 355 *> proper extension: 0bkq_8; *> query: (?x3101, ?x406) <- nominated_for(?x3101, ?x1135), languages(?x3101, ?x254), nominated_for(?x406, ?x1135) *> conf = 0.28 ranks of expected_values: 11, 14 EVAL 0dvmd award_nominee 0f5xn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 152.000 104.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 0dvmd award_nominee 015t7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 152.000 104.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4319-06y57 PRED entity: 06y57 PRED relation: featured_film_locations! PRED expected values: 01dyvs 02fwfb => 193 concepts (128 used for prediction) PRED predicted values (max 10 best out of 784): 047csmy (0.29 #1113, 0.19 #17819, 0.17 #19997), 0ds2n (0.29 #950, 0.14 #17656, 0.13 #19834), 033srr (0.29 #999, 0.14 #17705, 0.13 #19883), 0hmr4 (0.29 #769, 0.11 #13118, 0.11 #2949), 0jyx6 (0.29 #800, 0.11 #13149, 0.11 #2980), 0jqd3 (0.29 #1191, 0.11 #3371, 0.11 #2645), 06gb1w (0.29 #1035, 0.11 #3215, 0.11 #2489), 07g1sm (0.29 #1235, 0.11 #3415, 0.11 #2689), 03wy8t (0.29 #1373, 0.11 #3553, 0.11 #2827), 05css_ (0.29 #1149, 0.11 #3329, 0.11 #2603) >> Best rule #1113 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 035p3; >> query: (?x5036, 047csmy) <- featured_film_locations(?x1812, ?x5036), prequel(?x1386, ?x1812), honored_for(?x1812, ?x2366) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #37770 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 016tw3; 04lyk; *> query: (?x5036, ?x1386) <- featured_film_locations(?x1812, ?x5036), prequel(?x1386, ?x1812), nominated_for(?x3019, ?x1812) *> conf = 0.23 ranks of expected_values: 15, 437 EVAL 06y57 featured_film_locations! 02fwfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 193.000 128.000 0.286 http://example.org/film/film/featured_film_locations EVAL 06y57 featured_film_locations! 01dyvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 193.000 128.000 0.286 http://example.org/film/film/featured_film_locations #4318-03twd6 PRED entity: 03twd6 PRED relation: film_release_region PRED expected values: 03rjj 07ssc 0f8l9c 047yc 01znc_ 03rj0 => 83 concepts (53 used for prediction) PRED predicted values (max 10 best out of 216): 0f8l9c (0.91 #1144, 0.91 #2133, 0.90 #1568), 03rjj (0.91 #1132, 0.88 #1556, 0.86 #1415), 01znc_ (0.86 #1159, 0.79 #1583, 0.77 #2148), 07ssc (0.84 #1140, 0.84 #1564, 0.81 #2129), 0d060g (0.83 #1134, 0.72 #1558, 0.69 #2405), 06t2t (0.83 #1179, 0.71 #1603, 0.70 #1462), 047yc (0.72 #1149, 0.53 #1432, 0.52 #1573), 03rj0 (0.68 #1601, 0.64 #1177, 0.62 #2166), 05qx1 (0.62 #1158, 0.51 #1582, 0.49 #1441), 03rk0 (0.59 #1173, 0.43 #1597, 0.41 #1456) >> Best rule #1144 for best value: >> intensional similarity = 7 >> extensional distance = 56 >> proper extension: 07qg8v; 06v9_x; 06ztvyx; 047fjjr; 0ds1glg; >> query: (?x1470, 0f8l9c) <- film_release_region(?x1470, ?x2645), film_release_region(?x1470, ?x1453), film_release_region(?x1470, ?x404), film(?x92, ?x1470), ?x1453 = 06qd3, ?x404 = 047lj, place_of_birth(?x3382, ?x2645) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 7, 8 EVAL 03twd6 film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 53.000 0.914 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03twd6 film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 53.000 0.914 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03twd6 film_release_region 047yc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 53.000 0.914 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03twd6 film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 53.000 0.914 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03twd6 film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 53.000 0.914 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03twd6 film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 53.000 0.914 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4317-02jx_v PRED entity: 02jx_v PRED relation: colors PRED expected values: 06fvc => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 19): 06fvc (0.56 #116, 0.44 #173, 0.38 #78), 01g5v (0.47 #782, 0.39 #193, 0.32 #174), 01l849 (0.33 #1882, 0.31 #381, 0.30 #134), 036k5h (0.25 #81, 0.18 #556, 0.16 #176), 038hg (0.22 #1930, 0.18 #163, 0.17 #505), 088fh (0.14 #63, 0.12 #82, 0.12 #177), 09ggk (0.14 #53, 0.11 #110, 0.09 #680), 04mkbj (0.14 #66, 0.10 #142, 0.09 #1681), 067z2v (0.12 #84, 0.10 #141, 0.08 #730), 03wkwg (0.09 #1686, 0.09 #1078, 0.07 #3592) >> Best rule #116 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 0345gh; 0gjv_; 031hxk; 0gl6x; 02mg5r; >> query: (?x13150, 06fvc) <- citytown(?x13150, ?x14362), colors(?x13150, ?x663), country(?x14362, ?x8593), currency(?x13150, ?x7888), capital(?x5776, ?x14362), ?x663 = 083jv, organization(?x5510, ?x13150) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jx_v colors 06fvc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 201.000 201.000 0.556 http://example.org/education/educational_institution/colors #4316-011yqc PRED entity: 011yqc PRED relation: film_release_region PRED expected values: 07ssc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 171): 09c7w0 (0.93 #5071, 0.93 #4637, 0.93 #4348), 07ssc (0.85 #593, 0.85 #448, 0.82 #14), 05b4w (0.79 #1646, 0.79 #922, 0.79 #778), 0b90_r (0.77 #1596, 0.76 #728, 0.74 #872), 0h7x (0.64 #28, 0.62 #607, 0.60 #462), 06f32 (0.62 #780, 0.60 #924, 0.53 #1648), 04gzd (0.61 #732, 0.56 #1600, 0.56 #876), 047yc (0.61 #746, 0.55 #601, 0.54 #456), 016wzw (0.58 #781, 0.57 #925, 0.50 #636), 01p1v (0.54 #1635, 0.49 #767, 0.46 #1345) >> Best rule #5071 for best value: >> intensional similarity = 4 >> extensional distance = 618 >> proper extension: 0m63c; 0cbl95; >> query: (?x1496, 09c7w0) <- film_release_region(?x1496, ?x774), award(?x1496, ?x289), film_release_region(?x2656, ?x774), ?x2656 = 03qnc6q >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 64 *> proper extension: 02qyv3h; 0bq6ntw; *> query: (?x1496, 07ssc) <- country(?x1496, ?x94), film_release_region(?x1496, ?x756), film_release_region(?x1496, ?x142), ?x756 = 06npd, participating_countries(?x418, ?x142) *> conf = 0.85 ranks of expected_values: 2 EVAL 011yqc film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4315-01_k0d PRED entity: 01_k0d PRED relation: gender PRED expected values: 05zppz => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #35, 0.90 #33, 0.90 #31), 02zsn (0.55 #199, 0.46 #244, 0.33 #2) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 01vsl3_; 0lrh; 02qwg; 0dzkq; 05jm7; 034bs; 03j24kf; 0bk5r; 01pq5j7; 06hmd; ... >> query: (?x6723, 05zppz) <- influenced_by(?x6723, ?x9982), nationality(?x6723, ?x512), peers(?x3858, ?x6723), award(?x9982, ?x575) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_k0d gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.909 http://example.org/people/person/gender #4314-09gq0x5 PRED entity: 09gq0x5 PRED relation: film! PRED expected values: 0kszw => 99 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1150): 061dn_ (0.47 #24948, 0.42 #29105, 0.42 #16631), 0csdzz (0.47 #24948, 0.42 #29105, 0.42 #16630), 026g801 (0.20 #5079, 0.17 #7157, 0.02 #13392), 0170qf (0.17 #363, 0.11 #2443, 0.10 #4521), 01y64_ (0.17 #780, 0.11 #2860, 0.08 #9094), 0dvld (0.17 #1058, 0.11 #3138, 0.06 #11450), 01nm3s (0.17 #686, 0.11 #2766, 0.05 #13157), 05p606 (0.17 #1911, 0.11 #3991, 0.04 #10225), 01x0sy (0.17 #1619, 0.11 #3699, 0.04 #9933), 02rrsz (0.17 #1609, 0.11 #3689, 0.04 #9923) >> Best rule #24948 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 0k7tq; >> query: (?x1813, ?x72) <- film_festivals(?x1813, ?x7988), award(?x1813, ?x2853), nominated_for(?x72, ?x1813), award_winner(?x2853, ?x157) >> conf = 0.47 => this is the best rule for 2 predicted values *> Best rule #16632 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 013q07; *> query: (?x1813, ?x374) <- nominated_for(?x1223, ?x1813), nominated_for(?x2489, ?x1813), ?x2489 = 02x2gy0, award_winner(?x1223, ?x374) *> conf = 0.06 ranks of expected_values: 89 EVAL 09gq0x5 film! 0kszw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 99.000 41.000 0.471 http://example.org/film/actor/film./film/performance/film #4313-0bmssv PRED entity: 0bmssv PRED relation: featured_film_locations PRED expected values: 080h2 => 66 concepts (53 used for prediction) PRED predicted values (max 10 best out of 41): 030qb3t (0.08 #277, 0.08 #38, 0.07 #1473), 04jpl (0.06 #1444, 0.06 #1684, 0.06 #5764), 080h2 (0.04 #23, 0.04 #262, 0.03 #501), 06y57 (0.04 #341, 0.02 #102, 0.02 #819), 035p3 (0.03 #232, 0.03 #471, 0.01 #949), 0cv3w (0.03 #69, 0.02 #308, 0.02 #1265), 03rjj (0.03 #6, 0.02 #245), 0rh6k (0.03 #3835, 0.03 #4075, 0.03 #1676), 01_d4 (0.03 #763, 0.02 #2201, 0.02 #2440), 03gh4 (0.02 #353, 0.02 #114, 0.02 #831) >> Best rule #277 for best value: >> intensional similarity = 3 >> extensional distance = 129 >> proper extension: 09rfh9; 01d2v1; >> query: (?x4178, 030qb3t) <- prequel(?x4479, ?x4178), nominated_for(?x1691, ?x4178), genre(?x4178, ?x225) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #23 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 88 *> proper extension: 0140g4; 02ht1k; 03176f; 013q0p; 05nlx4; 0bt4g; 03nfnx; *> query: (?x4178, 080h2) <- prequel(?x4479, ?x4178), nominated_for(?x1691, ?x4178), music(?x4178, ?x7701) *> conf = 0.04 ranks of expected_values: 3 EVAL 0bmssv featured_film_locations 080h2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 53.000 0.084 http://example.org/film/film/featured_film_locations #4312-0cqt90 PRED entity: 0cqt90 PRED relation: politician! PRED expected values: 07wbk => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 3): 07wbk (0.06 #49, 0.04 #673, 0.04 #97), 0d075m (0.06 #675, 0.04 #171, 0.03 #3), 07w42 (0.02 #61, 0.01 #109, 0.01 #133) >> Best rule #49 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 01w23w; >> query: (?x3884, 07wbk) <- location_of_ceremony(?x3884, ?x11345), place_of_birth(?x3884, ?x4253), award(?x3884, ?x102), student(?x4672, ?x3884) >> conf = 0.06 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cqt90 politician! 07wbk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.061 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #4311-04xjp PRED entity: 04xjp PRED relation: influenced_by! PRED expected values: 014635 01v9724 => 119 concepts (30 used for prediction) PRED predicted values (max 10 best out of 675): 06whf (0.40 #3686, 0.15 #9736, 0.13 #7214), 03f47xl (0.33 #257, 0.25 #1264, 0.17 #2271), 041c4 (0.33 #701, 0.25 #1708, 0.17 #2211), 03vrp (0.33 #192, 0.25 #1199, 0.13 #7246), 0p8jf (0.33 #110, 0.25 #1117, 0.12 #12100), 0126rp (0.33 #574, 0.25 #1581, 0.06 #11665), 01t_wfl (0.33 #963, 0.25 #1970, 0.05 #6502), 04sd0 (0.33 #982, 0.25 #1989, 0.05 #6521), 02wh0 (0.33 #2454, 0.20 #3966, 0.17 #10016), 0mb5x (0.33 #2344, 0.20 #3856, 0.12 #12100) >> Best rule #3686 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 02wh0; >> query: (?x2162, 06whf) <- gender(?x2162, ?x231), influenced_by(?x8389, ?x2162), influenced_by(?x6457, ?x2162), location(?x2162, ?x291), ?x6457 = 03_87, award(?x8389, ?x8842) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #504 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 040db; *> query: (?x2162, ?x3541) <- profession(?x2162, ?x2225), influenced_by(?x3325, ?x2162), location(?x2162, ?x4698), ?x4698 = 056_y, influenced_by(?x3325, ?x3541) *> conf = 0.10 ranks of expected_values: 146, 151 EVAL 04xjp influenced_by! 01v9724 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 119.000 30.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 04xjp influenced_by! 014635 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 119.000 30.000 0.400 http://example.org/influence/influence_node/influenced_by #4310-0q8sw PRED entity: 0q8sw PRED relation: place PRED expected values: 0q8sw => 103 concepts (42 used for prediction) PRED predicted values (max 10 best out of 83): 0kwgs (0.20 #1031, 0.06 #9289, 0.06 #9808), 0dc95 (0.10 #564, 0.02 #2110, 0.02 #3142), 0ftvg (0.10 #803, 0.02 #2349, 0.02 #2865), 0l_q9 (0.10 #643, 0.02 #2189, 0.02 #2705), 0g_wn2 (0.10 #680, 0.02 #2226, 0.02 #3775), 0d35y (0.10 #618, 0.02 #3196, 0.02 #2680), 0fttg (0.09 #1409, 0.08 #1924, 0.01 #4127), 0q6lr (0.09 #1392, 0.08 #1907, 0.01 #4127), 0q48z (0.09 #1347, 0.08 #1862, 0.01 #4127), 0q8jl (0.09 #1323, 0.08 #1838, 0.01 #4127) >> Best rule #1031 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0d35y; 0fw4v; >> query: (?x9556, ?x9555) <- country(?x9556, ?x94), county_seat(?x9555, ?x9556), source(?x9556, ?x958), administrative_parent(?x9555, ?x2831) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #4127 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 01vsl; 0lpk3; 0s6jm; 0cf_n; 0jbrr; 0fsv2; *> query: (?x9556, ?x1201) <- country(?x9556, ?x94), county_seat(?x9555, ?x9556), state(?x9556, ?x2831), contains(?x2831, ?x1201) *> conf = 0.01 ranks of expected_values: 79 EVAL 0q8sw place 0q8sw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 103.000 42.000 0.200 http://example.org/location/hud_county_place/place #4309-01wv24 PRED entity: 01wv24 PRED relation: institution! PRED expected values: 016t_3 => 140 concepts (83 used for prediction) PRED predicted values (max 10 best out of 19): 02_xgp2 (0.85 #110, 0.79 #131, 0.62 #152), 03bwzr4 (0.69 #112, 0.64 #133, 0.62 #71), 016t_3 (0.62 #62, 0.54 #145, 0.54 #103), 02cq61 (0.50 #75, 0.36 #892, 0.30 #955), 04zx3q1 (0.46 #102, 0.43 #123, 0.38 #61), 01rr_d (0.40 #34, 0.38 #74, 0.37 #424), 027f2w (0.38 #107, 0.38 #66, 0.37 #424), 013zdg (0.38 #65, 0.37 #424, 0.36 #127), 0bjrnt (0.37 #424, 0.35 #249, 0.30 #1294), 022h5x (0.37 #424, 0.35 #249, 0.30 #1294) >> Best rule #110 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 08815; 04rwx; 07wjk; 01mpwj; 015cz0; 02zd460; 02bqy; 07vjm; 01bm_; 0bwfn; ... >> query: (?x8715, 02_xgp2) <- major_field_of_study(?x8715, ?x3400), organization(?x346, ?x8715), ?x3400 = 0pf2, institution(?x620, ?x8715), contains(?x550, ?x8715) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #62 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 03ksy; *> query: (?x8715, 016t_3) <- major_field_of_study(?x8715, ?x7979), major_field_of_study(?x8715, ?x2014), ?x7979 = 036nz, school_type(?x8715, ?x3205), category(?x8715, ?x134), ?x2014 = 04rjg *> conf = 0.62 ranks of expected_values: 3 EVAL 01wv24 institution! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 83.000 0.846 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4308-03lty PRED entity: 03lty PRED relation: artists PRED expected values: 01lz4tf 013rfk 014_xj 0153nq => 78 concepts (48 used for prediction) PRED predicted values (max 10 best out of 981): 04mx7s (0.67 #15326, 0.60 #23124, 0.60 #13377), 04r1t (0.67 #16696, 0.60 #8897, 0.50 #18645), 05563d (0.60 #13931, 0.60 #11983, 0.50 #18806), 0gcs9 (0.60 #11924, 0.60 #8999, 0.50 #18747), 0qf11 (0.60 #14000, 0.57 #17900, 0.50 #16926), 01vsy7t (0.60 #14024, 0.57 #17924, 0.50 #2331), 01v0sxx (0.60 #14491, 0.57 #18391, 0.50 #2798), 0dtd6 (0.60 #13783, 0.57 #17683, 0.50 #2090), 0144l1 (0.60 #13790, 0.57 #17690, 0.50 #2097), 095x_ (0.60 #14323, 0.57 #18223, 0.50 #2630) >> Best rule #15326 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0163zw; >> query: (?x2249, 04mx7s) <- artists(?x2249, ?x5126), parent_genre(?x9248, ?x2249), parent_genre(?x6513, ?x2249), ?x9248 = 02t8gf, profession(?x5126, ?x220), artists(?x6513, ?x970), parent_genre(?x2249, ?x1000) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5546 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 016clz; *> query: (?x2249, 013rfk) <- artists(?x2249, ?x10737), artists(?x2249, ?x6067), artists(?x2249, ?x5329), parent_genre(?x302, ?x2249), ?x5329 = 014_lq, instrumentalists(?x212, ?x6067), ?x10737 = 0b1hw *> conf = 0.50 ranks of expected_values: 132, 425, 490, 491 EVAL 03lty artists 0153nq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 78.000 48.000 0.667 http://example.org/music/genre/artists EVAL 03lty artists 014_xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 78.000 48.000 0.667 http://example.org/music/genre/artists EVAL 03lty artists 013rfk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 78.000 48.000 0.667 http://example.org/music/genre/artists EVAL 03lty artists 01lz4tf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 78.000 48.000 0.667 http://example.org/music/genre/artists #4307-04xvh5 PRED entity: 04xvh5 PRED relation: genre! PRED expected values: 02vp1f_ 0260bz 085bd1 02ll45 05dptj 0symg 016017 => 53 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1749): 03_gz8 (0.69 #3533, 0.50 #13456, 0.40 #18754), 03nfnx (0.69 #3533, 0.50 #11968, 0.33 #20798), 0415ggl (0.60 #18621, 0.50 #20387, 0.50 #13323), 09gmmt6 (0.60 #17021, 0.50 #9956, 0.50 #8187), 047myg9 (0.60 #18758, 0.50 #13460, 0.50 #8158), 0mcl0 (0.60 #18284, 0.50 #12986, 0.50 #7684), 011yfd (0.60 #18304, 0.50 #13006, 0.50 #7704), 02jxrw (0.60 #19254, 0.50 #13956, 0.50 #8654), 0c9k8 (0.60 #18128, 0.50 #12830, 0.50 #7528), 05j82v (0.60 #17891, 0.50 #12593, 0.50 #7291) >> Best rule #3533 for best value: >> intensional similarity = 14 >> extensional distance = 1 >> proper extension: 03k9fj; >> query: (?x4088, ?x6362) <- genre(?x5502, ?x4088), genre(?x4159, ?x4088), genre(?x2917, ?x4088), genre(?x2898, ?x4088), genre(?x351, ?x4088), ?x5502 = 01bl7g, genre(?x351, ?x162), film_release_region(?x351, ?x94), ?x2898 = 0p4v_, ?x2917 = 03kg2v, prequel(?x6362, ?x4159), ?x162 = 04xvlr, honored_for(?x747, ?x4159), nominated_for(?x68, ?x4159) >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #8357 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 2 *> proper extension: 02l7c8; *> query: (?x4088, 05dptj) <- genre(?x11001, ?x4088), genre(?x10684, ?x4088), genre(?x8062, ?x4088), genre(?x5502, ?x4088), genre(?x2376, ?x4088), ?x10684 = 05sxr_, award_winner(?x2376, ?x1622), ?x11001 = 07tj4c, produced_by(?x5502, ?x4946), film(?x731, ?x8062), nominated_for(?x5427, ?x2376) *> conf = 0.50 ranks of expected_values: 135, 421, 440, 463, 493, 504, 871 EVAL 04xvh5 genre! 016017 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 12.000 0.688 http://example.org/film/film/genre EVAL 04xvh5 genre! 0symg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 12.000 0.688 http://example.org/film/film/genre EVAL 04xvh5 genre! 05dptj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 12.000 0.688 http://example.org/film/film/genre EVAL 04xvh5 genre! 02ll45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 12.000 0.688 http://example.org/film/film/genre EVAL 04xvh5 genre! 085bd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 12.000 0.688 http://example.org/film/film/genre EVAL 04xvh5 genre! 0260bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 12.000 0.688 http://example.org/film/film/genre EVAL 04xvh5 genre! 02vp1f_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 12.000 0.688 http://example.org/film/film/genre #4306-08c4yn PRED entity: 08c4yn PRED relation: film_crew_role PRED expected values: 02r96rf => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 30): 0ch6mp2 (0.72 #156, 0.72 #1130, 0.71 #1805), 02r96rf (0.63 #227, 0.62 #77, 0.62 #339), 01vx2h (0.36 #49, 0.29 #236, 0.29 #1921), 0dxtw (0.34 #122, 0.34 #1809, 0.34 #1920), 01pvkk (0.30 #1136, 0.29 #162, 0.28 #1811), 02ynfr (0.29 #17, 0.18 #54, 0.16 #241), 01xy5l_ (0.18 #52, 0.12 #2433, 0.09 #239), 0215hd (0.13 #169, 0.12 #2433, 0.11 #1929), 0d2b38 (0.12 #101, 0.12 #2433, 0.11 #251), 089g0h (0.12 #2433, 0.10 #170, 0.10 #95) >> Best rule #156 for best value: >> intensional similarity = 3 >> extensional distance = 330 >> proper extension: 0gj8t_b; 0gd0c7x; 014nq4; 0gtvpkw; 04grkmd; 0435vm; 024mpp; 0184tc; 0640y35; 0gj96ln; ... >> query: (?x11544, 0ch6mp2) <- written_by(?x11544, ?x565), film_release_distribution_medium(?x11544, ?x81), film_crew_role(?x11544, ?x137) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #227 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 348 *> proper extension: 087wc7n; 047qxs; 0gh65c5; 01q2nx; 0bq6ntw; 02qkwl; 08c6k9; 0353tm; *> query: (?x11544, 02r96rf) <- film_format(?x11544, ?x909), film(?x1286, ?x11544), genre(?x11544, ?x53) *> conf = 0.63 ranks of expected_values: 2 EVAL 08c4yn film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 81.000 0.720 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4305-03npn PRED entity: 03npn PRED relation: genre! PRED expected values: 0cz8mkh 02ptczs 09rfh9 027r7k 032xky => 49 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1770): 080dfr7 (0.79 #1757, 0.75 #7026, 0.75 #14053), 09rfh9 (0.79 #1757, 0.75 #7026, 0.75 #14053), 01xq8v (0.79 #1757, 0.75 #7026, 0.72 #31639), 0fg04 (0.79 #1757, 0.75 #7026, 0.72 #31639), 0ct5zc (0.79 #1757, 0.75 #7026, 0.72 #31639), 076xkps (0.79 #1757, 0.75 #7026, 0.72 #31639), 06zn2v2 (0.79 #1757, 0.75 #7026, 0.72 #31639), 07s3m4g (0.79 #1757, 0.75 #7026, 0.72 #31639), 01g3gq (0.79 #1757, 0.75 #7026, 0.72 #31639), 0kv238 (0.79 #1757, 0.75 #7026, 0.72 #31639) >> Best rule #1757 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 01jfsb; >> query: (?x571, ?x708) <- genre(?x9213, ?x571), genre(?x3507, ?x571), genre(?x3322, ?x571), ?x3322 = 03n785, titles(?x571, ?x708), ?x3507 = 03459x, film_distribution_medium(?x9213, ?x81) >> conf = 0.79 => this is the best rule for 10 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 492, 655, 677, 1234 EVAL 03npn genre! 032xky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 19.000 0.787 http://example.org/film/film/genre EVAL 03npn genre! 027r7k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 19.000 0.787 http://example.org/film/film/genre EVAL 03npn genre! 09rfh9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 49.000 19.000 0.787 http://example.org/film/film/genre EVAL 03npn genre! 02ptczs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 19.000 0.787 http://example.org/film/film/genre EVAL 03npn genre! 0cz8mkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 19.000 0.787 http://example.org/film/film/genre #4304-02x02kb PRED entity: 02x02kb PRED relation: people! PRED expected values: 019dmc => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 39): 0gk4g (0.45 #208, 0.40 #142, 0.19 #472), 0dq9p (0.12 #1139, 0.11 #1205, 0.10 #347), 0qcr0 (0.10 #331, 0.09 #1123, 0.07 #1255), 01l2m3 (0.10 #148, 0.09 #214, 0.05 #874), 0gg4h (0.10 #168, 0.09 #234, 0.04 #696), 035482 (0.10 #156, 0.09 #222, 0.03 #420), 04p3w (0.10 #1265, 0.09 #1133, 0.08 #1199), 051_y (0.08 #774, 0.07 #378, 0.07 #906), 0dcsx (0.07 #345, 0.06 #675, 0.06 #807), 06z5s (0.07 #355, 0.06 #685, 0.06 #817) >> Best rule #208 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0b5x23; >> query: (?x12608, 0gk4g) <- gender(?x12608, ?x514), languages(?x12608, ?x1882), place_of_death(?x12608, ?x8918), ?x1882 = 03k50, nationality(?x12608, ?x2146) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #710 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 48 *> proper extension: 0pcc0; 012t1; 073bb; 041mt; 081nh; 03h_fk5; 0lgm5; 0q59y; 0kvnn; 081k8; ... *> query: (?x12608, 019dmc) <- gender(?x12608, ?x514), languages(?x12608, ?x1882), place_of_death(?x12608, ?x8918), languages(?x7517, ?x1882), ?x7517 = 03vrnh *> conf = 0.04 ranks of expected_values: 20 EVAL 02x02kb people! 019dmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 119.000 119.000 0.455 http://example.org/people/cause_of_death/people #4303-0345h PRED entity: 0345h PRED relation: location! PRED expected values: 0bwgc_ => 227 concepts (160 used for prediction) PRED predicted values (max 10 best out of 1984): 0g72r (0.33 #7456, 0.03 #284030, 0.03 #113106), 0g7k2g (0.25 #346862, 0.23 #155843, 0.20 #118136), 01yzhn (0.17 #32287, 0.14 #44854, 0.09 #82557), 09yrh (0.17 #31070, 0.13 #23530, 0.13 #48664), 0gs1_ (0.17 #31481, 0.13 #49075, 0.10 #44048), 032r1 (0.15 #22416, 0.12 #29957, 0.12 #17390), 0prfz (0.15 #20154, 0.12 #15128, 0.11 #30208), 03rl84 (0.14 #12925, 0.13 #22979, 0.11 #35546), 0hnp7 (0.14 #13803, 0.12 #28884, 0.12 #16317), 0465_ (0.14 #13859, 0.12 #16373, 0.11 #36480) >> Best rule #7456 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 06jtd; >> query: (?x1264, 0g72r) <- contains(?x1264, ?x13724), location(?x1221, ?x1264), ?x13724 = 054y8 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0345h location! 0bwgc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 227.000 160.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #4302-038nv6 PRED entity: 038nv6 PRED relation: film PRED expected values: 0ds2n => 124 concepts (49 used for prediction) PRED predicted values (max 10 best out of 593): 035gnh (0.28 #13812, 0.11 #4872, 0.08 #6660), 032sl_ (0.22 #5139, 0.14 #8715, 0.09 #14079), 02jxbw (0.20 #1083, 0.12 #2873, 0.03 #13603), 08fn5b (0.20 #695, 0.12 #2485, 0.02 #31105), 04b_jc (0.20 #1675, 0.12 #3465, 0.01 #21348), 09cr8 (0.20 #284, 0.12 #2074, 0.01 #39642), 05qbckf (0.20 #308, 0.12 #2098, 0.01 #34298), 01s7w3 (0.20 #1528, 0.12 #3318, 0.01 #31938), 0h63q6t (0.20 #1739, 0.12 #3529), 09p5mwg (0.20 #1585, 0.12 #3375) >> Best rule #13812 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 01rr9f; 09y20; 07csf4; 02lf1j; 02fn5; 06mr6; 01mqc_; 01d0b1; 0341n5; 01vsn38; >> query: (?x14340, 035gnh) <- film(?x14340, ?x6352), nominated_for(?x2451, ?x6352), ?x2451 = 0127m7 >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #4104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 039bp; 025j1t; 01tnxc; *> query: (?x14340, 0ds2n) <- film(?x14340, ?x6352), ?x6352 = 08mg_b, people(?x1446, ?x14340), profession(?x14340, ?x1032) *> conf = 0.11 ranks of expected_values: 48 EVAL 038nv6 film 0ds2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 124.000 49.000 0.281 http://example.org/film/actor/film./film/performance/film #4301-02rgz4 PRED entity: 02rgz4 PRED relation: category PRED expected values: 08mbj5d => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #52, 0.82 #53, 0.81 #55) >> Best rule #52 for best value: >> intensional similarity = 4 >> extensional distance = 658 >> proper extension: 089tm; 01pfr3; 0m19t; 0150jk; 07qnf; 02r3zy; 01v0sx2; 03g5jw; 03t9sp; 01fl3; ... >> query: (?x535, 08mbj5d) <- artists(?x1380, ?x535), parent_genre(?x1380, ?x1000), artists(?x1380, ?x6986), ?x6986 = 02vgh >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rgz4 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.821 http://example.org/common/topic/webpage./common/webpage/category #4300-04twmk PRED entity: 04twmk PRED relation: profession PRED expected values: 01d_h8 => 113 concepts (112 used for prediction) PRED predicted values (max 10 best out of 124): 018gz8 (0.50 #164, 0.34 #312, 0.13 #1348), 03gjzk (0.44 #14, 0.39 #310, 0.36 #162), 01d_h8 (0.41 #302, 0.32 #5778, 0.32 #2522), 0dxtg (0.39 #309, 0.32 #753, 0.31 #1937), 0np9r (0.36 #168, 0.20 #316, 0.20 #2980), 0kyk (0.22 #29, 0.10 #4913, 0.09 #8317), 02krf9 (0.21 #174, 0.20 #322, 0.15 #766), 09jwl (0.20 #1794, 0.19 #4310, 0.18 #3274), 0cbd2 (0.14 #303, 0.12 #13180, 0.12 #2227), 0dz3r (0.12 #3258, 0.12 #4294, 0.11 #5330) >> Best rule #164 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 01nrq5; >> query: (?x9435, 018gz8) <- award_winner(?x870, ?x9435), nominated_for(?x9435, ?x782), ?x870 = 09qv3c >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #302 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 42 *> proper extension: 011lpr; *> query: (?x9435, 01d_h8) <- award(?x9435, ?x5235), ?x5235 = 09qrn4 *> conf = 0.41 ranks of expected_values: 3 EVAL 04twmk profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 112.000 0.500 http://example.org/people/person/profession #4299-0c01c PRED entity: 0c01c PRED relation: type_of_union PRED expected values: 04ztj => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.77 #13, 0.74 #241, 0.73 #301), 01g63y (0.25 #2, 0.23 #46, 0.23 #126) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 03n0q5; 02sj1x; 02tkzn; 06q8hf; >> query: (?x2560, 04ztj) <- award_winner(?x1461, ?x2560), religion(?x2560, ?x7131), ?x7131 = 03_gx >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c01c type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.768 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4298-0k60 PRED entity: 0k60 PRED relation: instrumentalists! PRED expected values: 01v1d8 => 96 concepts (45 used for prediction) PRED predicted values (max 10 best out of 121): 05148p4 (0.86 #1917, 0.62 #2523, 0.53 #794), 0342h (0.73 #521, 0.72 #2857, 0.66 #3036), 018vs (0.53 #2515, 0.43 #1044, 0.42 #958), 06ncr (0.29 #388, 0.10 #1941, 0.10 #904), 03qjg (0.27 #567, 0.25 #653, 0.19 #1948), 0l14md (0.25 #867, 0.24 #781, 0.23 #953), 013y1f (0.25 #117, 0.15 #1063, 0.15 #805), 026t6 (0.25 #89, 0.15 #2506, 0.14 #347), 018j2 (0.25 #124, 0.14 #382, 0.13 #554), 04rzd (0.25 #123, 0.14 #381, 0.12 #811) >> Best rule #1917 for best value: >> intensional similarity = 5 >> extensional distance = 218 >> proper extension: 03k0yw; 020jqv; >> query: (?x8636, 05148p4) <- instrumentalists(?x5926, ?x8636), role(?x4186, ?x5926), role(?x212, ?x5926), role(?x3667, ?x5926), ?x4186 = 0f0qfz >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #230 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 01vvydl; 01386_; *> query: (?x8636, 01v1d8) <- instrumentalists(?x5926, ?x8636), instrumentalists(?x228, ?x8636), artists(?x497, ?x8636), ?x228 = 0l14qv, location(?x8636, ?x10314), ?x5926 = 0cfdd *> conf = 0.25 ranks of expected_values: 11 EVAL 0k60 instrumentalists! 01v1d8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 96.000 45.000 0.859 http://example.org/music/instrument/instrumentalists #4297-07t65 PRED entity: 07t65 PRED relation: award_winner! PRED expected values: 05f3q => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 15): 02x1z2s (0.33 #630, 0.14 #13591, 0.14 #5383), 0m7yy (0.15 #23080, 0.13 #27401, 0.12 #7093), 07bdd_ (0.14 #13459, 0.13 #13891, 0.12 #14323), 0gr42 (0.14 #5302, 0.07 #11782, 0.05 #13510), 05p1dby (0.10 #13501, 0.09 #13933, 0.08 #14365), 0gq9h (0.08 #14335, 0.07 #11743, 0.05 #16495), 0b6jkkg (0.06 #12762, 0.05 #16650, 0.05 #17082), 01lj_c (0.06 #12825, 0.04 #14553, 0.04 #15417), 01l78d (0.06 #12816, 0.04 #14544, 0.04 #15408), 01lk0l (0.06 #12807, 0.04 #14535, 0.04 #15399) >> Best rule #630 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 056ws9; >> query: (?x312, 02x1z2s) <- organizations_founded(?x12571, ?x312), citytown(?x312, ?x739), people(?x4195, ?x12571), nationality(?x12571, ?x94), spouse(?x12571, ?x7893) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07t65 award_winner! 05f3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 115.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #4296-01cgxp PRED entity: 01cgxp PRED relation: contains! PRED expected values: 0d05q4 => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 221): 09c7w0 (0.50 #1796, 0.44 #1793, 0.44 #899), 059rby (0.40 #20, 0.22 #916, 0.17 #1813), 0kpys (0.25 #1974, 0.12 #15455, 0.11 #10061), 01n7q (0.23 #15352, 0.18 #18043, 0.17 #28810), 02_286 (0.20 #43, 0.17 #1836, 0.14 #4531), 04_1l0v (0.20 #451, 0.11 #1347, 0.10 #3144), 01531 (0.20 #190, 0.11 #1086, 0.08 #1983), 0f8l9c (0.16 #33215, 0.12 #47585, 0.12 #38605), 059j2 (0.16 #33215, 0.12 #47585, 0.12 #45787), 02vzc (0.16 #33215, 0.12 #38605, 0.10 #46686) >> Best rule #1796 for best value: >> intensional similarity = 12 >> extensional distance = 10 >> proper extension: 0k_p5; >> query: (?x14572, 09c7w0) <- featured_film_locations(?x1744, ?x14572), nominated_for(?x6232, ?x1744), genre(?x1744, ?x53), film_release_region(?x1744, ?x2984), film_release_region(?x1744, ?x1229), film_release_region(?x1744, ?x151), film_crew_role(?x1744, ?x137), ?x2984 = 082fr, ?x1229 = 059j2, ?x53 = 07s9rl0, award_nominee(?x1585, ?x6232), featured_film_locations(?x224, ?x151) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #21552 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 65 *> proper extension: 02ly_; 09f07; 09c17; *> query: (?x14572, ?x1497) <- featured_film_locations(?x1744, ?x14572), nominated_for(?x1431, ?x1744), genre(?x1744, ?x53), film_release_region(?x1744, ?x1499), nominated_for(?x500, ?x1744), award_winner(?x500, ?x902), adjoins(?x1499, ?x1497), film_release_region(?x4998, ?x1499), film_release_region(?x343, ?x1499), ?x343 = 0gx1bnj, ?x4998 = 0dzlbx *> conf = 0.01 ranks of expected_values: 176 EVAL 01cgxp contains! 0d05q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 54.000 54.000 0.500 http://example.org/location/location/contains #4295-0207wx PRED entity: 0207wx PRED relation: profession PRED expected values: 01d_h8 02hv44_ => 105 concepts (93 used for prediction) PRED predicted values (max 10 best out of 70): 01d_h8 (0.86 #1181, 0.85 #887, 0.85 #1475), 02hrh1q (0.79 #5011, 0.79 #160, 0.76 #4717), 02jknp (0.52 #1183, 0.50 #889, 0.49 #1771), 03gjzk (0.42 #1337, 0.39 #1484, 0.38 #1190), 0dz3r (0.29 #1, 0.11 #6910, 0.10 #8527), 0cbd2 (0.28 #153, 0.22 #1917, 0.22 #3828), 02hv44_ (0.24 #204, 0.15 #351, 0.14 #498), 01c72t (0.24 #23, 0.19 #464, 0.17 #317), 0nbcg (0.24 #31, 0.13 #472, 0.11 #6940), 025352 (0.24 #59, 0.11 #353, 0.10 #500) >> Best rule #1181 for best value: >> intensional similarity = 3 >> extensional distance = 277 >> proper extension: 0kr5_; 0ksf29; 0h1p; 01f8ld; 01f7v_; 0522wp; 01pjr7; 06b_0; 03y3dk; 06dkzt; ... >> query: (?x1172, 01d_h8) <- produced_by(?x3599, ?x1172), award(?x1172, ?x601), type_of_union(?x1172, ?x566) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 0207wx profession 02hv44_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 105.000 93.000 0.857 http://example.org/people/person/profession EVAL 0207wx profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 93.000 0.857 http://example.org/people/person/profession #4294-032f6 PRED entity: 032f6 PRED relation: languages_spoken! PRED expected values: 022fdt => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 70): 03w9bjf (0.57 #516, 0.40 #448, 0.26 #985), 013b6_ (0.50 #246, 0.50 #112, 0.40 #447), 041rx (0.45 #470, 0.40 #272, 0.25 #541), 04gfy7 (0.45 #470, 0.29 #526, 0.26 #995), 033tf_ (0.45 #470, 0.25 #544, 0.25 #208), 0x67 (0.45 #470, 0.25 #211, 0.20 #412), 01qhm_ (0.45 #470, 0.22 #1744, 0.20 #341), 0bpjh3 (0.45 #470, 0.17 #2416, 0.06 #625), 09kr66 (0.40 #373, 0.33 #38, 0.25 #239), 018s6c (0.40 #325, 0.25 #594, 0.25 #191) >> Best rule #516 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 0c_v2; >> query: (?x13310, 03w9bjf) <- languages_spoken(?x10322, ?x13310), languages_spoken(?x9428, ?x13310), language(?x4998, ?x13310), countries_spoken_in(?x13310, ?x279), ?x10322 = 078vc, languages_spoken(?x9428, ?x5671), language(?x11065, ?x5671), language(?x8063, ?x5671), ?x11065 = 0n08r, nominated_for(?x2209, ?x4998), ?x8063 = 01718w, ceremony(?x2209, ?x78) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #49 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 06b_j; *> query: (?x13310, 022fdt) <- languages_spoken(?x14168, ?x13310), languages_spoken(?x10322, ?x13310), languages_spoken(?x9428, ?x13310), ?x9428 = 048z7l, countries_spoken_in(?x13310, ?x279), language(?x1724, ?x13310), ?x279 = 0d060g, people(?x14168, ?x10452), religion(?x9039, ?x10322) *> conf = 0.33 ranks of expected_values: 12 EVAL 032f6 languages_spoken! 022fdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 37.000 37.000 0.571 http://example.org/people/ethnicity/languages_spoken #4293-035s95 PRED entity: 035s95 PRED relation: music PRED expected values: 06fxnf => 101 concepts (79 used for prediction) PRED predicted values (max 10 best out of 104): 01tc9r (0.12 #273, 0.09 #691, 0.04 #1949), 0146pg (0.11 #1686, 0.10 #10, 0.09 #845), 02cyfz (0.09 #869, 0.08 #1078, 0.07 #2545), 07v4dm (0.08 #400, 0.06 #818, 0.03 #1867), 04bpm6 (0.08 #235, 0.06 #653, 0.03 #1702), 03dbds (0.08 #1885, 0.02 #4184, 0.02 #9622), 03h610 (0.07 #1752, 0.04 #4050, 0.04 #2379), 02bh9 (0.07 #4025, 0.05 #5903, 0.05 #10298), 0150t6 (0.07 #5273, 0.06 #1090, 0.06 #1299), 04pf4r (0.06 #484, 0.05 #5294, 0.04 #276) >> Best rule #273 for best value: >> intensional similarity = 6 >> extensional distance = 24 >> proper extension: 091z_p; >> query: (?x2128, 01tc9r) <- genre(?x2128, ?x53), film_crew_role(?x2128, ?x468), production_companies(?x2128, ?x963), film(?x2549, ?x2128), ?x2549 = 024rgt, ?x468 = 02r96rf >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #2788 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 156 *> proper extension: 0436yk; 01f7kl; 02pb2bp; 01pv91; 03h3x5; 0407yj_; 0g9yrw; 048rn; 02c7k4; 063y9fp; ... *> query: (?x2128, 06fxnf) <- genre(?x2128, ?x2605), production_companies(?x2128, ?x963), major_field_of_study(?x122, ?x2605) *> conf = 0.04 ranks of expected_values: 26 EVAL 035s95 music 06fxnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 101.000 79.000 0.115 http://example.org/film/film/music #4292-0ddfwj1 PRED entity: 0ddfwj1 PRED relation: film! PRED expected values: 061dn_ => 94 concepts (66 used for prediction) PRED predicted values (max 10 best out of 64): 05qd_ (0.33 #9, 0.15 #159, 0.12 #4298), 020h2v (0.23 #195, 0.05 #495, 0.05 #3204), 086k8 (0.21 #902, 0.16 #1277, 0.16 #1428), 016tw3 (0.20 #761, 0.17 #1437, 0.16 #1362), 024rdh (0.16 #337, 0.12 #262, 0.08 #1237), 054g1r (0.16 #485, 0.15 #185, 0.08 #2819), 017s11 (0.16 #453, 0.15 #678, 0.14 #978), 01795t (0.16 #468, 0.12 #693, 0.11 #843), 03xq0f (0.15 #155, 0.15 #1130, 0.14 #1356), 024rbz (0.15 #162, 0.12 #762, 0.08 #1212) >> Best rule #9 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 05c26ss; >> query: (?x370, 05qd_) <- film_release_region(?x370, ?x583), film_release_region(?x370, ?x94), film(?x794, ?x370), ?x583 = 015fr, genre(?x370, ?x53), ?x794 = 0mdqp, ?x94 = 09c7w0 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #474 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 75 *> proper extension: 0hr41p6; *> query: (?x370, 061dn_) <- country(?x370, ?x94), genre(?x370, ?x258), executive_produced_by(?x370, ?x794), ?x258 = 05p553, ?x94 = 09c7w0, film(?x794, ?x437) *> conf = 0.08 ranks of expected_values: 19 EVAL 0ddfwj1 film! 061dn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 94.000 66.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #4291-033w9g PRED entity: 033w9g PRED relation: gender PRED expected values: 05zppz => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #159, 0.72 #189, 0.72 #43), 02zsn (0.36 #24, 0.33 #56, 0.33 #18) >> Best rule #159 for best value: >> intensional similarity = 3 >> extensional distance = 1440 >> proper extension: 0c9d9; 0fp_v1x; 01nqfh_; 06y9c2; 04411; 0d0vj4; 01x66d; 0kn4c; 0ftps; 03qmj9; ... >> query: (?x4527, 05zppz) <- nationality(?x4527, ?x94), student(?x621, ?x4527), organization(?x346, ?x621) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033w9g gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.727 http://example.org/people/person/gender #4290-0mzww PRED entity: 0mzww PRED relation: place_of_birth! PRED expected values: 0c0tzp => 157 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1782): 02xbw2 (0.64 #5220, 0.32 #187841, 0.32 #187842), 012v9y (0.64 #5220, 0.32 #187841, 0.32 #187842), 01j590z (0.64 #5220, 0.32 #187841, 0.32 #187842), 0443c (0.64 #5220, 0.32 #187841, 0.30 #99143), 02t__3 (0.33 #1229, 0.03 #11669, 0.03 #9059), 020jqv (0.33 #4781), 0c_md_ (0.33 #4621), 0h953 (0.33 #4373), 02pv_d (0.33 #4284), 04__f (0.33 #4259) >> Best rule #5220 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0chrx; >> query: (?x6987, ?x7034) <- location(?x7034, ?x6987), location(?x3054, ?x6987), location(?x526, ?x6987), award_winner(?x496, ?x526), ?x3054 = 02xbw2 >> conf = 0.64 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 0mzww place_of_birth! 0c0tzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 157.000 89.000 0.636 http://example.org/people/person/place_of_birth #4289-021vwt PRED entity: 021vwt PRED relation: student! PRED expected values: 0qlnr => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 53): 0bwfn (0.07 #13953, 0.07 #12375, 0.07 #8165), 017z88 (0.05 #1134, 0.05 #82, 0.03 #13760), 09f2j (0.05 #1211, 0.04 #8049, 0.03 #3841), 015nl4 (0.05 #3749, 0.05 #1119, 0.05 #67), 01w5m (0.05 #105, 0.04 #9048, 0.04 #8521), 08815 (0.05 #2, 0.03 #7892, 0.03 #3684), 026gvfj (0.05 #111, 0.01 #3793, 0.01 #1163), 015zyd (0.05 #1, 0.01 #3683, 0.01 #1053), 025v3k (0.05 #120, 0.01 #1172, 0.01 #3802), 02zkdz (0.05 #519) >> Best rule #13953 for best value: >> intensional similarity = 3 >> extensional distance = 1541 >> proper extension: 05d7rk; 084w8; 0fp_v1x; 07w21; 07g2b; 01cv3n; 08f3b1; 04411; 08433; 01x66d; ... >> query: (?x1677, 0bwfn) <- award(?x1677, ?x458), profession(?x1677, ?x1032), student(?x8021, ?x1677) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 021vwt student! 0qlnr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 98.000 0.071 http://example.org/education/educational_institution/students_graduates./education/education/student #4288-029m83 PRED entity: 029m83 PRED relation: film PRED expected values: 02qcr => 119 concepts (74 used for prediction) PRED predicted values (max 10 best out of 820): 0mcl0 (0.71 #87677, 0.67 #87676, 0.60 #64425), 043mk4y (0.67 #87676, 0.60 #64425, 0.58 #96619), 02cbhg (0.53 #1790, 0.48 #100197, 0.42 #114512), 03cw411 (0.53 #1790, 0.48 #100197, 0.42 #114512), 01fwzk (0.53 #1790, 0.48 #100197, 0.38 #114511), 02mpyh (0.38 #17894, 0.32 #21472, 0.17 #23261), 05k2xy (0.38 #17894, 0.30 #16105, 0.17 #23261), 011yg9 (0.38 #17894, 0.17 #1026, 0.17 #23261), 0194zl (0.38 #17894, 0.17 #23261, 0.15 #12525), 02704ff (0.33 #981, 0.04 #11717, 0.03 #17086) >> Best rule #87677 for best value: >> intensional similarity = 3 >> extensional distance = 603 >> proper extension: 05hdf; 0n8bn; 06_bq1; >> query: (?x8041, ?x3882) <- award_winner(?x3882, ?x8041), film(?x8041, ?x964), currency(?x3882, ?x170) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #12253 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 01yznp; 05wm88; *> query: (?x8041, 02qcr) <- executive_produced_by(?x2323, ?x8041), award(?x8041, ?x198), religion(?x8041, ?x7131) *> conf = 0.02 ranks of expected_values: 415 EVAL 029m83 film 02qcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 119.000 74.000 0.714 http://example.org/film/actor/film./film/performance/film #4287-09qr6 PRED entity: 09qr6 PRED relation: award_nominee! PRED expected values: 01vsgrn => 167 concepts (73 used for prediction) PRED predicted values (max 10 best out of 981): 01vw20h (0.85 #79384, 0.83 #23345, 0.82 #79383), 02l840 (0.83 #23345, 0.82 #79383, 0.82 #130737), 01vsgrn (0.83 #23345, 0.82 #79383, 0.82 #130737), 026yqrr (0.48 #20126, 0.17 #1451, 0.16 #130738), 016kjs (0.34 #18900, 0.17 #225, 0.16 #130738), 0837ql (0.34 #19818, 0.17 #1143, 0.16 #130738), 01ws9n6 (0.34 #19732, 0.17 #1057, 0.16 #130738), 01wwvc5 (0.31 #19276, 0.17 #601, 0.16 #130738), 01wlt3k (0.28 #20906, 0.17 #2231, 0.16 #130738), 01wgxtl (0.28 #19277, 0.16 #130738, 0.08 #35620) >> Best rule #79384 for best value: >> intensional similarity = 3 >> extensional distance = 127 >> proper extension: 08wq0g; 08n__5; >> query: (?x1338, ?x4476) <- instrumentalists(?x75, ?x1338), award_nominee(?x1338, ?x4476), languages(?x4476, ?x254) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #23345 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 058s57; 01w7nww; *> query: (?x1338, ?x827) <- artist(?x1543, ?x1338), award(?x1338, ?x2634), award_nominee(?x1338, ?x827), celebrity(?x2275, ?x1338) *> conf = 0.83 ranks of expected_values: 3 EVAL 09qr6 award_nominee! 01vsgrn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 167.000 73.000 0.855 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4286-06b_0 PRED entity: 06b_0 PRED relation: award PRED expected values: 0gqng => 124 concepts (95 used for prediction) PRED predicted values (max 10 best out of 306): 02wkmx (0.70 #25296, 0.69 #35976, 0.68 #35975), 02w_6xj (0.70 #25296, 0.69 #35976, 0.68 #35975), 02qt02v (0.68 #35975, 0.68 #24899, 0.68 #30835), 0gr51 (0.33 #93, 0.33 #2464, 0.31 #2859), 03hl6lc (0.33 #170, 0.21 #2146, 0.21 #2541), 02qyp19 (0.33 #1, 0.19 #2372, 0.18 #5139), 03nqnk3 (0.33 #127, 0.13 #917, 0.12 #1708), 09sb52 (0.31 #18217, 0.30 #16637, 0.29 #17032), 03hkv_r (0.22 #1991, 0.22 #5153, 0.21 #2781), 0f_nbyh (0.21 #799, 0.20 #1590, 0.17 #9) >> Best rule #25296 for best value: >> intensional similarity = 3 >> extensional distance = 1135 >> proper extension: 04smkr; 03kpvp; 06vsbt; 03_wpf; 03h3vtz; >> query: (?x7670, ?x1587) <- film(?x7670, ?x6653), award_winner(?x1587, ?x7670), award(?x276, ?x1587) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #36769 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1734 *> proper extension: 0kc6x; 065y4w7; 01y67v; 01jq34; 0l2tk; 01w92; 01_8w2; 01p5yn; 01gl9g; 04glx0; ... *> query: (?x7670, ?x601) <- award_winner(?x198, ?x7670), award(?x4870, ?x198), nominated_for(?x601, ?x4870) *> conf = 0.05 ranks of expected_values: 112 EVAL 06b_0 award 0gqng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 124.000 95.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4285-0crx5w PRED entity: 0crx5w PRED relation: profession PRED expected values: 02krf9 => 103 concepts (102 used for prediction) PRED predicted values (max 10 best out of 51): 02hrh1q (0.79 #5490, 0.75 #3714, 0.70 #2382), 01d_h8 (0.49 #302, 0.44 #6, 0.43 #598), 018gz8 (0.44 #16, 0.28 #7401, 0.27 #10955), 02krf9 (0.32 #1062, 0.31 #1950, 0.30 #1358), 0cbd2 (0.28 #7401, 0.27 #10955, 0.26 #10214), 0np9r (0.27 #10955, 0.15 #908, 0.12 #1648), 02jknp (0.26 #2820, 0.26 #2672, 0.25 #2524), 09jwl (0.22 #2238, 0.20 #4014, 0.19 #5346), 0nbcg (0.14 #2251, 0.13 #4027, 0.13 #5359), 0dz3r (0.14 #2222, 0.13 #3998, 0.13 #2962) >> Best rule #5490 for best value: >> intensional similarity = 3 >> extensional distance = 1232 >> proper extension: 07nznf; 0l8v5; 01wp8w7; 01pcmd; 0150t6; 0fb1q; 056rgc; 0pj9t; 01vsykc; 06mnps; ... >> query: (?x1541, 02hrh1q) <- award_nominee(?x1541, ?x364), participant(?x237, ?x364), profession(?x1541, ?x987) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1062 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 117 *> proper extension: 0f721s; *> query: (?x1541, 02krf9) <- award_winner(?x3698, ?x1541), program(?x1541, ?x1542), award_winner(?x2016, ?x1541) *> conf = 0.32 ranks of expected_values: 4 EVAL 0crx5w profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 102.000 0.788 http://example.org/people/person/profession #4284-01pcj4 PRED entity: 01pcj4 PRED relation: registering_agency PRED expected values: 03z19 => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.88 #16, 0.86 #9, 0.85 #21) >> Best rule #16 for best value: >> intensional similarity = 5 >> extensional distance = 57 >> proper extension: 01cyd5; >> query: (?x9879, 03z19) <- student(?x9879, ?x7549), student(?x9879, ?x4320), currency(?x9879, ?x170), nominated_for(?x4320, ?x2287), profession(?x7549, ?x1183) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pcj4 registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.881 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #4283-087c7 PRED entity: 087c7 PRED relation: service_location PRED expected values: 0d060g => 206 concepts (170 used for prediction) PRED predicted values (max 10 best out of 112): 0d060g (0.39 #2161, 0.39 #1377, 0.35 #2062), 02j71 (0.33 #1778, 0.33 #901, 0.32 #3844), 07ssc (0.28 #2562, 0.22 #1385, 0.20 #899), 0chghy (0.20 #4426, 0.20 #895, 0.19 #1090), 01x73 (0.20 #6584, 0.18 #7074, 0.14 #5400), 0m2fr (0.20 #6584, 0.18 #7074, 0.14 #5400), 03rk0 (0.19 #1115, 0.12 #1212, 0.10 #1797), 0b90_r (0.18 #591, 0.09 #492, 0.07 #887), 0345h (0.16 #3854, 0.14 #6611, 0.13 #6414), 04_1l0v (0.15 #8946) >> Best rule #2161 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 059yj; >> query: (?x502, 0d060g) <- organization(?x4682, ?x502), ?x4682 = 0dq_5, service_language(?x502, ?x254), place_founded(?x502, ?x11086) >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 087c7 service_location 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 206.000 170.000 0.391 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #4282-015_1q PRED entity: 015_1q PRED relation: place_founded PRED expected values: 0rh6k => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 54): 02_286 (0.25 #141, 0.17 #6263, 0.17 #7052), 030qb3t (0.10 #472, 0.10 #1130, 0.09 #1987), 0mgp (0.10 #506, 0.05 #1164, 0.04 #1361), 0k_q_ (0.09 #611, 0.06 #1004, 0.03 #2456), 0r00l (0.09 #2892, 0.07 #1571, 0.05 #1110), 06pwq (0.07 #729, 0.06 #862, 0.03 #1981), 0qcrj (0.07 #786, 0.03 #2038, 0.03 #2501), 07dfk (0.06 #6051, 0.06 #7035, 0.05 #7297), 071vr (0.05 #1095, 0.05 #1292, 0.03 #1820), 0f2wj (0.05 #1126, 0.04 #3104, 0.03 #1719) >> Best rule #141 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 043g7l; 03mp8k; >> query: (?x3265, 02_286) <- artist(?x3265, ?x3929), artist(?x3265, ?x2575), award(?x3929, ?x4317), ?x2575 = 018pj3, ?x4317 = 05q8pss >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #7774 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 192 *> proper extension: 02mjk5; 07svc3; *> query: (?x3265, ?x739) <- industry(?x3265, ?x2271), industry(?x7326, ?x2271), list(?x7326, ?x7472), citytown(?x7326, ?x739) *> conf = 0.02 ranks of expected_values: 45 EVAL 015_1q place_founded 0rh6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 164.000 164.000 0.250 http://example.org/organization/organization/place_founded #4281-0d193h PRED entity: 0d193h PRED relation: group! PRED expected values: 01wqflx => 111 concepts (68 used for prediction) PRED predicted values (max 10 best out of 147): 01vswwx (0.25 #99, 0.06 #1100, 0.02 #3508), 01vswx5 (0.25 #97, 0.06 #1098, 0.02 #3506), 01vs14j (0.25 #21, 0.06 #1022, 0.02 #3430), 0lbj1 (0.20 #204, 0.02 #3413, 0.02 #4016), 0fp_v1x (0.20 #207, 0.02 #3416, 0.02 #4019), 018phr (0.20 #351, 0.02 #4163, 0.01 #5369), 018pj3 (0.20 #243, 0.02 #4055, 0.01 #5261), 0285c (0.14 #428, 0.12 #1029, 0.11 #829), 01wmjkb (0.14 #559, 0.03 #2764, 0.02 #3568), 024dgj (0.14 #466, 0.03 #2671, 0.02 #3475) >> Best rule #99 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01cblr; >> query: (?x4261, 01vswwx) <- group(?x1466, ?x4261), ?x1466 = 03bx0bm, award(?x4261, ?x2585), ?x2585 = 054ks3 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0d193h group! 01wqflx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 68.000 0.250 http://example.org/music/group_member/membership./music/group_membership/group #4280-0c1sgd3 PRED entity: 0c1sgd3 PRED relation: language PRED expected values: 02h40lc => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 55): 02h40lc (0.91 #715, 0.90 #3586, 0.90 #2629), 06nm1 (0.20 #11, 0.12 #783, 0.12 #485), 06b_j (0.20 #23, 0.09 #4657, 0.09 #556), 064_8sq (0.17 #1152, 0.16 #1334, 0.16 #1273), 04306rv (0.10 #1674, 0.10 #361, 0.10 #1494), 02bjrlw (0.10 #119, 0.09 #4657, 0.09 #60), 03_9r (0.09 #4657, 0.05 #128, 0.05 #5027), 06mp7 (0.09 #4657, 0.03 #134, 0.03 #7054), 0jzc (0.06 #79, 0.04 #1689, 0.04 #1810), 0653m (0.04 #1621, 0.04 #964, 0.04 #1202) >> Best rule #715 for best value: >> intensional similarity = 4 >> extensional distance = 222 >> proper extension: 01bjbk; 0jz71; >> query: (?x4729, 02h40lc) <- film_crew_role(?x4729, ?x137), ?x137 = 09zzb8, crewmember(?x4729, ?x5664), genre(?x4729, ?x53) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c1sgd3 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.906 http://example.org/film/film/language #4279-04f0xq PRED entity: 04f0xq PRED relation: service_language PRED expected values: 02h40lc => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 21): 02h40lc (0.93 #1010, 0.92 #926, 0.92 #905), 064_8sq (0.22 #325, 0.18 #1219, 0.17 #430), 06nm1 (0.21 #6, 0.20 #321, 0.19 #699), 05zjd (0.21 #13, 0.11 #55, 0.09 #223), 04306rv (0.18 #1219, 0.15 #1410, 0.14 #3), 01r2l (0.14 #12, 0.11 #54, 0.09 #327), 03_9r (0.09 #446, 0.09 #320, 0.08 #425), 02bjrlw (0.07 #1, 0.07 #316, 0.06 #43), 06b_j (0.07 #11, 0.06 #221, 0.06 #53), 02hwhyv (0.07 #16, 0.06 #226, 0.06 #58) >> Best rule #1010 for best value: >> intensional similarity = 2 >> extensional distance = 108 >> proper extension: 0cchk3; 01tx9m; >> query: (?x7471, 02h40lc) <- service_location(?x7471, ?x94), ?x94 = 09c7w0 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04f0xq service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.927 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #4278-04hs7d PRED entity: 04hs7d PRED relation: country_of_origin PRED expected values: 09c7w0 => 47 concepts (40 used for prediction) PRED predicted values (max 10 best out of 146): 09c7w0 (0.84 #383, 0.82 #255, 0.76 #243), 07ssc (0.25 #359, 0.13 #358, 0.12 #390), 0d060g (0.25 #359, 0.13 #358, 0.10 #64), 03rjj (0.13 #358, 0.10 #64, 0.04 #253), 03rt9 (0.13 #358, 0.04 #253, 0.04 #265), 05v8c (0.13 #358, 0.02 #172, 0.02 #184), 0d05w3 (0.10 #64, 0.04 #253, 0.04 #265), 06mkj (0.10 #64, 0.04 #253, 0.04 #265), 0345h (0.10 #64, 0.04 #253, 0.04 #265), 0f8l9c (0.10 #64, 0.04 #253, 0.04 #265) >> Best rule #383 for best value: >> intensional similarity = 17 >> extensional distance = 220 >> proper extension: 02zv4b; 0b6m5fy; >> query: (?x13651, 09c7w0) <- languages(?x13651, ?x2164), language(?x11701, ?x2164), language(?x7741, ?x2164), language(?x6272, ?x2164), language(?x5936, ?x2164), language(?x2815, ?x2164), language(?x2699, ?x2164), country_of_origin(?x13651, ?x252), ?x2815 = 059rc, ?x11701 = 0gys2jp, ?x6272 = 041td_, written_by(?x5936, ?x5287), service_language(?x555, ?x2164), actor(?x5936, ?x489), ?x7741 = 01xq8v, genre(?x5936, ?x53), ?x2699 = 04t6fk >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04hs7d country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 40.000 0.838 http://example.org/tv/tv_program/country_of_origin #4277-047vnkj PRED entity: 047vnkj PRED relation: film! PRED expected values: 03rwz3 => 81 concepts (62 used for prediction) PRED predicted values (max 10 best out of 61): 054lpb6 (0.58 #297, 0.48 #372, 0.46 #969), 05rrtf (0.58 #297, 0.48 #372, 0.46 #969), 04rtpt (0.58 #297, 0.48 #372, 0.46 #969), 05qd_ (0.22 #1127, 0.16 #381, 0.15 #8), 03xq0f (0.20 #4, 0.18 #78, 0.14 #748), 086k8 (0.19 #150, 0.17 #671, 0.17 #1418), 016tw3 (0.18 #904, 0.17 #2553, 0.17 #1426), 016tt2 (0.17 #3, 0.16 #77, 0.14 #1197), 01795t (0.12 #17, 0.12 #91, 0.07 #761), 03rwz3 (0.12 #265, 0.08 #638, 0.06 #712) >> Best rule #297 for best value: >> intensional similarity = 5 >> extensional distance = 70 >> proper extension: 0c5dd; 0jym0; 032016; 0y_hb; 0j90s; 01lbcqx; 01gvsn; >> query: (?x5271, ?x1478) <- production_companies(?x5271, ?x1478), production_companies(?x5271, ?x541), language(?x5271, ?x90), film(?x100, ?x5271), ?x541 = 017s11 >> conf = 0.58 => this is the best rule for 3 predicted values *> Best rule #265 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 0c5dd; 0jym0; 032016; 0y_hb; 0j90s; 01lbcqx; 01gvsn; *> query: (?x5271, 03rwz3) <- production_companies(?x5271, ?x541), language(?x5271, ?x90), film(?x100, ?x5271), ?x541 = 017s11 *> conf = 0.12 ranks of expected_values: 10 EVAL 047vnkj film! 03rwz3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 81.000 62.000 0.580 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #4276-055c8 PRED entity: 055c8 PRED relation: film PRED expected values: 01q2nx 0287477 01jft4 => 117 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1101): 07yk1xz (0.74 #8887, 0.59 #110226, 0.57 #58668), 07jnt (0.74 #8887, 0.59 #110226, 0.53 #21335), 0cf8qb (0.74 #8887, 0.59 #110226, 0.53 #21335), 0661ql3 (0.40 #380, 0.03 #7489, 0.02 #9267), 0gmgwnv (0.20 #1072, 0.04 #8181, 0.03 #9959), 07yvsn (0.20 #553, 0.03 #5884), 0gfh84d (0.20 #1144, 0.03 #8253, 0.01 #10031), 014lc_ (0.20 #2, 0.01 #60449, 0.01 #58670), 03ckwzc (0.20 #117, 0.01 #7226), 02q8ms8 (0.20 #1088, 0.01 #9975) >> Best rule #8887 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 03k7bd; 02wgln; 0fby2t; 03hzl42; 01f6zc; 01f7dd; 01l7qw; >> query: (?x3186, ?x2107) <- award(?x3186, ?x4091), nominated_for(?x3186, ?x2107), ?x4091 = 09sdmz >> conf = 0.74 => this is the best rule for 3 predicted values *> Best rule #8014 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 67 *> proper extension: 03k7bd; 02wgln; 0fby2t; 03hzl42; 01f6zc; 01f7dd; 01l7qw; *> query: (?x3186, 01q2nx) <- award(?x3186, ?x4091), nominated_for(?x3186, ?x2107), ?x4091 = 09sdmz *> conf = 0.01 ranks of expected_values: 555 EVAL 055c8 film 01jft4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 72.000 0.743 http://example.org/film/actor/film./film/performance/film EVAL 055c8 film 0287477 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 72.000 0.743 http://example.org/film/actor/film./film/performance/film EVAL 055c8 film 01q2nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 117.000 72.000 0.743 http://example.org/film/actor/film./film/performance/film #4275-01xdf5 PRED entity: 01xdf5 PRED relation: influenced_by! PRED expected values: 0g2mbn 04zkj5 => 117 concepts (98 used for prediction) PRED predicted values (max 10 best out of 361): 01xdf5 (0.11 #1036, 0.11 #518, 0.08 #2067), 01x4r3 (0.11 #1416, 0.11 #898, 0.08 #2447), 04t2l2 (0.11 #1037, 0.11 #519, 0.08 #2068), 04zkj5 (0.11 #1341, 0.11 #823, 0.08 #2372), 01s7qqw (0.11 #726, 0.10 #3828, 0.09 #4344), 01xwv7 (0.11 #940, 0.09 #6623, 0.09 #4558), 02p21g (0.11 #1078, 0.08 #2109, 0.05 #5212), 015pxr (0.11 #590, 0.06 #2655, 0.06 #3173), 06q5t7 (0.11 #795, 0.06 #2860, 0.06 #3378), 05txrz (0.11 #688, 0.06 #2753, 0.06 #3271) >> Best rule #1036 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 04bs3j; 0pz7h; 02p21g; 01j7rd; 01_x6v; 02qwg; 01_x6d; >> query: (?x236, 01xdf5) <- award_winner(?x5585, ?x236), influenced_by(?x236, ?x1145), ?x5585 = 03nnm4t >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #1341 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 04bs3j; 0pz7h; 02p21g; 01j7rd; 01_x6v; 02qwg; 01_x6d; *> query: (?x236, 04zkj5) <- award_winner(?x5585, ?x236), influenced_by(?x236, ?x1145), ?x5585 = 03nnm4t *> conf = 0.11 ranks of expected_values: 4 EVAL 01xdf5 influenced_by! 04zkj5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 98.000 0.111 http://example.org/influence/influence_node/influenced_by EVAL 01xdf5 influenced_by! 0g2mbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 98.000 0.111 http://example.org/influence/influence_node/influenced_by #4274-02x0fs9 PRED entity: 02x0fs9 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #42, 0.83 #318, 0.83 #16), 02nxhr (0.04 #43, 0.04 #112, 0.04 #132), 07c52 (0.03 #387, 0.03 #335, 0.03 #315), 07z4p (0.03 #276, 0.02 #266, 0.02 #79) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 167 >> proper extension: 03t97y; 044g_k; 03twd6; 06gjk9; 08sfxj; 047gpsd; 04ghz4m; 01xbxn; 080dfr7; >> query: (?x10425, 029j_) <- film_crew_role(?x10425, ?x468), featured_film_locations(?x10425, ?x279), ?x468 = 02r96rf, award_winner(?x10425, ?x7156) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02x0fs9 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.834 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #4273-01x73 PRED entity: 01x73 PRED relation: state_province_region! PRED expected values: 0kc6x 03bnb => 154 concepts (100 used for prediction) PRED predicted values (max 10 best out of 765): 01k2wn (0.64 #34789, 0.31 #23686, 0.30 #52558), 03bnb (0.64 #34789, 0.26 #37750, 0.25 #40712), 01jsn5 (0.31 #23686, 0.30 #52558, 0.27 #42931), 06182p (0.29 #2601, 0.07 #5561, 0.07 #3341), 05njw (0.29 #2786, 0.07 #5746, 0.07 #3526), 021gt5 (0.26 #5178, 0.26 #4438, 0.26 #2959), 01m1_d (0.26 #5178, 0.26 #4438, 0.26 #2959), 01m20m (0.26 #5178, 0.26 #4438, 0.26 #2959), 0rd6b (0.26 #5178, 0.26 #4438, 0.26 #2959), 0f2nf (0.26 #5178, 0.26 #4438, 0.26 #2959) >> Best rule #34789 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 0gqm3; >> query: (?x1755, ?x122) <- contains(?x1755, ?x9336), citytown(?x122, ?x9336), country(?x1755, ?x94) >> conf = 0.64 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01x73 state_province_region! 03bnb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 154.000 100.000 0.642 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 01x73 state_province_region! 0kc6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 100.000 0.642 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #4272-01j95 PRED entity: 01j95 PRED relation: tv_program! PRED expected values: 067xw => 112 concepts (69 used for prediction) PRED predicted values (max 10 best out of 174): 01xndd (0.29 #262, 0.20 #451, 0.15 #640), 046mxj (0.25 #97, 0.10 #853, 0.08 #664), 023qfd (0.25 #142, 0.05 #898, 0.03 #2032), 09hd6f (0.15 #734, 0.14 #356, 0.10 #923), 09_99w (0.14 #339, 0.10 #528, 0.10 #1095), 0h53p1 (0.14 #235, 0.10 #424, 0.10 #991), 0h584v (0.14 #261, 0.10 #450, 0.08 #1584), 0284gcb (0.14 #214, 0.10 #403, 0.08 #1537), 02t_8z (0.14 #367, 0.10 #556, 0.08 #745), 04s04 (0.14 #325, 0.10 #514, 0.08 #703) >> Best rule #262 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 08cx5g; >> query: (?x13130, 01xndd) <- titles(?x2008, ?x13130), genre(?x13130, ?x1013), category(?x13130, ?x134), program(?x3806, ?x13130), ?x1013 = 06n90 >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01j95 tv_program! 067xw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 69.000 0.286 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #4271-03f4w4 PRED entity: 03f4w4 PRED relation: people! PRED expected values: 02ctzb => 115 concepts (93 used for prediction) PRED predicted values (max 10 best out of 50): 0d7wh (0.39 #540, 0.12 #1482, 0.09 #1559), 02w7gg (0.33 #79, 0.33 #310, 0.30 #2237), 033tf_ (0.23 #624, 0.19 #778, 0.17 #469), 06gbnc (0.17 #104, 0.02 #3418, 0.02 #1800), 0x67 (0.15 #472, 0.11 #704, 0.11 #935), 041rx (0.15 #2316, 0.13 #621, 0.13 #2393), 07hwkr (0.11 #166, 0.06 #1939, 0.05 #2170), 03lmx1 (0.11 #168, 0.05 #1787, 0.04 #399), 0dryh9k (0.09 #1249, 0.07 #401, 0.06 #247), 07bch9 (0.07 #408, 0.06 #563, 0.06 #640) >> Best rule #540 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 01m3x5p; 026_dq6; 0cfz_z; >> query: (?x12652, ?x5042) <- nationality(?x12652, ?x1310), sibling(?x9807, ?x12652), award(?x9807, ?x941), people(?x5042, ?x9807) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1480 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 102 *> proper extension: 0784v1; *> query: (?x12652, 02ctzb) <- nationality(?x12652, ?x1310), ?x1310 = 02jx1, place_of_birth(?x12652, ?x362), location_of_ceremony(?x566, ?x362) *> conf = 0.04 ranks of expected_values: 24 EVAL 03f4w4 people! 02ctzb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 115.000 93.000 0.386 http://example.org/people/ethnicity/people #4270-09b6zr PRED entity: 09b6zr PRED relation: basic_title PRED expected values: 060c4 => 214 concepts (214 used for prediction) PRED predicted values (max 10 best out of 18): 060c4 (0.62 #190, 0.50 #650, 0.50 #616), 0dq3c (0.44 #223, 0.38 #394, 0.27 #615), 060bp (0.25 #205, 0.24 #563, 0.21 #444), 0p5vf (0.25 #215, 0.14 #164, 0.12 #743), 0789n (0.23 #401, 0.20 #486, 0.20 #264), 01gkgk (0.19 #1128, 0.19 #1145, 0.17 #273), 02079p (0.17 #273, 0.08 #385, 0.07 #453), 0f6c3 (0.17 #273, 0.07 #467, 0.05 #620), 01t7n9 (0.17 #273, 0.03 #814, 0.02 #1137), 09n5b9 (0.17 #273) >> Best rule #190 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0f7fy; >> query: (?x4196, 060c4) <- person(?x6773, ?x4196), award_winner(?x2325, ?x4196), ?x6773 = 05t54s >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09b6zr basic_title 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 214.000 214.000 0.625 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #4269-0gl3hr PRED entity: 0gl3hr PRED relation: film! PRED expected values: 04__f => 102 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1155): 07z4fy (0.42 #135401, 0.42 #139566, 0.42 #141649), 07djnx (0.42 #135401, 0.42 #139566, 0.42 #141649), 051ysmf (0.42 #135401, 0.42 #139566, 0.42 #141649), 05218gr (0.42 #135401, 0.42 #139566, 0.42 #141649), 0pz91 (0.29 #212, 0.04 #18960, 0.03 #14794), 0c1pj (0.24 #93, 0.01 #60508, 0.01 #64673), 027vps (0.20 #74995, 0.20 #74996, 0.20 #87497), 06z4wj (0.20 #74995, 0.20 #74996, 0.20 #87497), 0gv5c (0.20 #74995, 0.20 #87497, 0.20 #87496), 0d608 (0.18 #1307, 0.04 #20055, 0.03 #15889) >> Best rule #135401 for best value: >> intensional similarity = 3 >> extensional distance = 1001 >> proper extension: 09rfpk; 0k20s; >> query: (?x6243, ?x2304) <- genre(?x6243, ?x258), nominated_for(?x2304, ?x6243), film_release_region(?x6243, ?x94) >> conf = 0.42 => this is the best rule for 4 predicted values *> Best rule #5550 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 04tng0; 04vq33; *> query: (?x6243, 04__f) <- film_art_direction_by(?x6243, ?x2304), film(?x9604, ?x6243), language(?x6243, ?x254), award_winner(?x9604, ?x5289) *> conf = 0.07 ranks of expected_values: 22 EVAL 0gl3hr film! 04__f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 102.000 69.000 0.421 http://example.org/film/actor/film./film/performance/film #4268-03x3qv PRED entity: 03x3qv PRED relation: student! PRED expected values: 08815 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 47): 017z88 (0.12 #82, 0.08 #608, 0.04 #1134), 01jszm (0.12 #172, 0.08 #698, 0.04 #1224), 027kp3 (0.12 #152, 0.08 #678, 0.04 #1204), 02301 (0.12 #74, 0.08 #600, 0.04 #1126), 01k2wn (0.12 #24, 0.08 #550, 0.04 #1076), 02j04_ (0.12 #262, 0.08 #788), 02ldmw (0.09 #1336), 03ksy (0.08 #632, 0.04 #6418, 0.04 #1158), 05nrkb (0.08 #874, 0.04 #1400, 0.01 #6660), 0234_c (0.08 #942, 0.04 #1468) >> Best rule #82 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 04bd8y; 0jmj; 04yqlk; 05l4yg; 0335fp; 015p37; >> query: (?x336, 017z88) <- award_nominee(?x336, ?x5105), award_nominee(?x336, ?x1870), ?x5105 = 047c9l, ?x1870 = 0hvb2 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #6314 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1434 *> proper extension: 019y64; 01d494; 0frmb1; 0cl_m; 01gct2; 02x8mt; 019g65; 02vptk_; 02_nkp; 0443c; ... *> query: (?x336, 08815) <- nationality(?x336, ?x94), ?x94 = 09c7w0, student(?x4076, ?x336) *> conf = 0.03 ranks of expected_values: 25 EVAL 03x3qv student! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 63.000 63.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #4267-02tvsn PRED entity: 02tvsn PRED relation: entity_involved PRED expected values: 0j5b8 => 35 concepts (24 used for prediction) PRED predicted values (max 10 best out of 201): 0j5b8 (0.71 #467, 0.60 #689, 0.50 #850), 02c4s (0.71 #467, 0.60 #643, 0.39 #2579), 0285m87 (0.71 #467, 0.40 #711, 0.39 #2579), 0kn4c (0.71 #467, 0.40 #482, 0.33 #801), 09c7w0 (0.71 #467, 0.33 #157, 0.33 #2241), 03_js (0.71 #467, 0.33 #235, 0.33 #2241), 01h3dj (0.39 #2579, 0.38 #1180, 0.33 #2241), 02psqkz (0.39 #2579, 0.33 #2241, 0.31 #2238), 0193qj (0.39 #2579, 0.33 #2241, 0.31 #2238), 01hnp (0.39 #2579, 0.33 #2241, 0.31 #2238) >> Best rule #467 for best value: >> intensional similarity = 25 >> extensional distance = 1 >> proper extension: 0cbvg; >> query: (?x13264, ?x94) <- combatants(?x13264, ?x12625), combatants(?x13264, ?x10009), combatants(?x13264, ?x4492), ?x4492 = 0cdbq, ?x12625 = 01m41_, entity_involved(?x13264, ?x13265), entity_involved(?x13264, ?x4493), ?x10009 = 01flgk, combatants(?x12673, ?x4493), combatants(?x10206, ?x4493), combatants(?x10008, ?x4493), combatants(?x1777, ?x4493), ?x10206 = 01_3rn, ?x10008 = 0cbvg, locations(?x1777, ?x455), entity_involved(?x12673, ?x6830), entity_involved(?x12673, ?x1328), entity_involved(?x12673, ?x94), ?x1328 = 0kn4c, entity_involved(?x12777, ?x4493), locations(?x12673, ?x608), ?x12777 = 03jv8d, ?x455 = 02j9z, ?x6830 = 0j5b8, capital(?x13265, ?x863) >> conf = 0.71 => this is the best rule for 6 predicted values ranks of expected_values: 1 EVAL 02tvsn entity_involved 0j5b8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 24.000 0.714 http://example.org/base/culturalevent/event/entity_involved #4266-06mm1x PRED entity: 06mm1x PRED relation: award PRED expected values: 0cjcbg => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 397): 0cjcbg (0.50 #367, 0.36 #1179, 0.30 #773), 0cjyzs (0.36 #919, 0.20 #513, 0.17 #1731), 09sb52 (0.24 #3289, 0.23 #9785, 0.23 #1259), 0ck27z (0.23 #2529, 0.21 #2935, 0.13 #3341), 03ccq3s (0.21 #1012, 0.17 #200, 0.10 #606), 0drtkx (0.21 #1112, 0.13 #21117, 0.13 #20710), 0fc9js (0.17 #217, 0.14 #1029, 0.13 #20710), 0fbtbt (0.16 #1858, 0.14 #1452, 0.13 #20710), 0cqhk0 (0.14 #2473, 0.13 #2879, 0.09 #3285), 0gq9h (0.13 #20710, 0.11 #1296, 0.07 #5356) >> Best rule #367 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0b7gr2; >> query: (?x12650, 0cjcbg) <- gender(?x12650, ?x231), award_nominee(?x2952, ?x12650), award_nominee(?x2951, ?x12650), ?x2951 = 03xpf_7, written_by(?x1965, ?x2952) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mm1x award 0cjcbg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4265-0svqs PRED entity: 0svqs PRED relation: film PRED expected values: 02v8kmz 0mbql => 94 concepts (68 used for prediction) PRED predicted values (max 10 best out of 464): 0ndwt2w (0.44 #4567, 0.38 #2782, 0.35 #6352), 02qzh2 (0.25 #691, 0.06 #4261, 0.06 #6046), 03k8th (0.25 #1716, 0.06 #5286, 0.01 #14211), 06z8s_ (0.25 #130, 0.03 #9055, 0.02 #12625), 0418wg (0.25 #399, 0.03 #9324, 0.02 #12894), 07024 (0.25 #479, 0.02 #12974, 0.02 #16545), 04vr_f (0.25 #170, 0.02 #12665, 0.02 #16236), 09xbpt (0.25 #47, 0.02 #8972, 0.01 #12542), 02mpyh (0.25 #1460, 0.02 #10385, 0.01 #13955), 0prrm (0.25 #858, 0.01 #27634, 0.01 #16924) >> Best rule #4567 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 09wj5; 02gvwz; 01rh0w; 0241jw; 024n3z; 01v9l67; 015t56; 016ypb; 01846t; 0154qm; ... >> query: (?x4923, 0ndwt2w) <- award_winner(?x4999, ?x4923), film(?x4923, ?x972), ?x4999 = 015t7v >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #8953 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 01mvth; 01vrncs; 02k6rq; 03t0k1; 03bnv; 04psyp; 0308kx; 02cgb8; 095b70; 091yn0; ... *> query: (?x4923, 02v8kmz) <- award_winner(?x1424, ?x4923), film(?x4923, ?x972), notable_people_with_this_condition(?x1502, ?x1424) *> conf = 0.03 ranks of expected_values: 219 EVAL 0svqs film 0mbql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 68.000 0.438 http://example.org/film/actor/film./film/performance/film EVAL 0svqs film 02v8kmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 94.000 68.000 0.438 http://example.org/film/actor/film./film/performance/film #4264-03v6t PRED entity: 03v6t PRED relation: contains! PRED expected values: 09c7w0 => 108 concepts (60 used for prediction) PRED predicted values (max 10 best out of 326): 09c7w0 (0.82 #23255, 0.77 #7156, 0.77 #20572), 04_1l0v (0.42 #15202, 0.27 #4471), 02qkt (0.34 #27175, 0.34 #28070, 0.31 #30757), 07c5l (0.22 #14700, 0.13 #27223, 0.13 #28118), 0dg3n1 (0.21 #26983, 0.21 #27878, 0.20 #29668), 0j0k (0.18 #27206, 0.18 #28101, 0.16 #30788), 0t015 (0.17 #16, 0.08 #910, 0.06 #30409), 0nrnz (0.17 #820, 0.08 #1714, 0.06 #4396), 0nrqh (0.17 #400, 0.08 #1294, 0.06 #3976), 0nr_q (0.17 #258, 0.06 #3834) >> Best rule #23255 for best value: >> intensional similarity = 4 >> extensional distance = 192 >> proper extension: 0194_r; >> query: (?x1667, 09c7w0) <- currency(?x1667, ?x170), citytown(?x1667, ?x10350), contains(?x961, ?x1667), ?x170 = 09nqf >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03v6t contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 60.000 0.820 http://example.org/location/location/contains #4263-0l2nd PRED entity: 0l2nd PRED relation: adjoins PRED expected values: 0kq2g => 91 concepts (30 used for prediction) PRED predicted values (max 10 best out of 375): 01n7q (0.85 #4625, 0.04 #21590, 0.03 #21593), 0l2sr (0.25 #13881, 0.25 #9249, 0.25 #13880), 0kq1l (0.25 #13881, 0.25 #9249, 0.25 #13880), 0l2nd (0.25 #13881, 0.25 #9249, 0.25 #13880), 0kq2g (0.25 #13881, 0.25 #9249, 0.25 #13880), 0235l (0.24 #16964, 0.24 #19277, 0.24 #17735), 0kpzy (0.20 #292, 0.07 #1063, 0.07 #1833), 0l2hf (0.20 #178, 0.05 #949, 0.05 #1719), 0kq0q (0.20 #687, 0.04 #1458, 0.03 #2228), 0l34j (0.20 #216, 0.02 #987, 0.02 #1757) >> Best rule #4625 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 0kn68; >> query: (?x13522, ?x1227) <- adjoins(?x13522, ?x5892), administrative_division(?x5893, ?x5892), capital(?x1227, ?x5893) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #13881 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 285 *> proper extension: 0dc95; 0gyh; 01qh7; 0d0x8; 0k696; 0hz35; 0ms1n; 01lxw6; *> query: (?x13522, ?x9582) <- adjoins(?x13522, ?x5892), currency(?x5892, ?x170), adjoins(?x9582, ?x5892), adjoins(?x7520, ?x5892), contains(?x1227, ?x5892), time_zones(?x7520, ?x2950) *> conf = 0.25 ranks of expected_values: 5 EVAL 0l2nd adjoins 0kq2g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 30.000 0.849 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #4262-0292l3 PRED entity: 0292l3 PRED relation: film PRED expected values: 0dx8gj => 105 concepts (70 used for prediction) PRED predicted values (max 10 best out of 396): 020bv3 (0.25 #318, 0.02 #7458, 0.02 #39590), 05nlx4 (0.25 #1255, 0.02 #4825), 01vksx (0.25 #134, 0.01 #7274, 0.01 #5489), 05ch98 (0.25 #1366, 0.01 #8506), 03y0pn (0.25 #1257, 0.01 #8397), 0cmc26r (0.25 #681, 0.01 #7821), 05zwrg0 (0.25 #1618), 0gy7bj4 (0.25 #1595), 04qk12 (0.25 #1457), 072zl1 (0.25 #1279) >> Best rule #318 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01vzxmq; >> query: (?x1445, 020bv3) <- film(?x1445, ?x6451), award(?x1445, ?x1937), ?x6451 = 01l2b3 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0292l3 film 0dx8gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 70.000 0.250 http://example.org/film/actor/film./film/performance/film #4261-03cyslc PRED entity: 03cyslc PRED relation: produced_by PRED expected values: 034bgm => 93 concepts (59 used for prediction) PRED predicted values (max 10 best out of 149): 06pj8 (0.25 #456, 0.03 #6269, 0.03 #7045), 02q_cc (0.25 #422, 0.03 #3133, 0.02 #3522), 07rd7 (0.17 #925, 0.10 #1313, 0.01 #6738), 0bkf72 (0.17 #1070, 0.10 #1458), 07b3r9 (0.17 #931, 0.10 #1319), 03q3sy (0.14 #1164, 0.11 #20948, 0.10 #20947), 017s11 (0.14 #1164, 0.10 #20947, 0.10 #21336), 05ty4m (0.09 #1563, 0.03 #6214, 0.02 #7380), 081l_ (0.09 #1825, 0.02 #2599), 0d6484 (0.09 #1877, 0.01 #3039, 0.01 #15509) >> Best rule #456 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0jqn5; 05c26ss; >> query: (?x6832, 06pj8) <- film_release_distribution_medium(?x6832, ?x627), film_release_region(?x6832, ?x94), film_crew_role(?x6832, ?x1171), ?x627 = 02nxhr >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03cyslc produced_by 034bgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 59.000 0.250 http://example.org/film/film/produced_by #4260-03g3w PRED entity: 03g3w PRED relation: student PRED expected values: 046rfv => 92 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1097): 04z0g (0.40 #2787, 0.29 #5462, 0.20 #7252), 03_l8m (0.33 #105, 0.25 #1883, 0.20 #2995), 01_x6v (0.33 #36, 0.25 #1814, 0.20 #2926), 03gr7w (0.33 #27, 0.25 #1805, 0.20 #2917), 01v3bn (0.33 #66, 0.25 #1844, 0.20 #2956), 02rn_bj (0.33 #155, 0.25 #1933, 0.20 #3045), 0j6cj (0.33 #146, 0.25 #1924, 0.20 #3036), 0478__m (0.33 #97, 0.25 #1875, 0.20 #2987), 0br1w (0.33 #4082, 0.25 #6314, 0.10 #8993), 031v3p (0.33 #883, 0.20 #3329, 0.17 #4445) >> Best rule #2787 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 02j62; 0fdys; >> query: (?x2605, 04z0g) <- major_field_of_study(?x254, ?x2605), major_field_of_study(?x2999, ?x2605), major_field_of_study(?x3437, ?x2605), major_field_of_study(?x1526, ?x2605), student(?x2605, ?x445), ?x2999 = 07tg4, ?x1526 = 0bkj86, ?x3437 = 02_xgp2, service_language(?x127, ?x254) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #7356 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 01lj9; 04gb7; *> query: (?x2605, ?x476) <- major_field_of_study(?x254, ?x2605), major_field_of_study(?x2999, ?x2605), major_field_of_study(?x1526, ?x2605), student(?x2605, ?x445), ?x2999 = 07tg4, student(?x1526, ?x476), institution(?x1526, ?x331) *> conf = 0.02 ranks of expected_values: 235 EVAL 03g3w student 046rfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 92.000 47.000 0.400 http://example.org/education/field_of_study/students_majoring./education/education/student #4259-06bng PRED entity: 06bng PRED relation: profession PRED expected values: 0cbd2 => 128 concepts (92 used for prediction) PRED predicted values (max 10 best out of 102): 0cbd2 (0.89 #1477, 0.86 #595, 0.83 #1330), 02hrh1q (0.83 #8695, 0.66 #13406, 0.65 #12965), 0dxtg (0.68 #2808, 0.62 #4868, 0.61 #2955), 01d_h8 (0.47 #2800, 0.39 #4860, 0.39 #2947), 02jknp (0.40 #2802, 0.34 #2949, 0.34 #3530), 03gjzk (0.34 #3530, 0.29 #1912, 0.27 #8680), 05z96 (0.34 #3530, 0.29 #1912, 0.27 #8680), 018gz8 (0.30 #3989, 0.29 #2518, 0.23 #7668), 04gc2 (0.26 #1954, 0.26 #777, 0.24 #924), 09jwl (0.20 #3844, 0.20 #3550, 0.18 #3402) >> Best rule #1477 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0gd5z; 0p8jf; 02yl42; 0j0pf; 0b0pf; 056wb; 01zwy; 01vdrw; 0fvt2; 01v_0b; >> query: (?x8433, 0cbd2) <- influenced_by(?x8433, ?x3542), award(?x8433, ?x3337), award_winner(?x3337, ?x2993), ?x2993 = 0p8jf >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06bng profession 0cbd2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 92.000 0.894 http://example.org/people/person/profession #4258-01tfck PRED entity: 01tfck PRED relation: profession PRED expected values: 02hrh1q => 104 concepts (103 used for prediction) PRED predicted values (max 10 best out of 59): 02hrh1q (0.93 #1206, 0.92 #1057, 0.92 #1504), 0dxtg (0.29 #9404, 0.29 #4632, 0.29 #4334), 03gjzk (0.29 #1207, 0.28 #8199, 0.28 #1058), 0np9r (0.28 #8199, 0.26 #7453, 0.21 #4789), 02krf9 (0.28 #8199, 0.26 #7453, 0.09 #9567), 0d1pc (0.26 #349, 0.26 #7453, 0.25 #498), 02jknp (0.26 #7453, 0.22 #5520, 0.21 #901), 09jwl (0.26 #7453, 0.22 #5085, 0.21 #913), 0nbcg (0.26 #7453, 0.16 #5098, 0.15 #479), 0kyk (0.26 #7453, 0.10 #6141, 0.10 #6886) >> Best rule #1206 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 01vw87c; 01pw2f1; 047hpm; 01m65sp; 01jbx1; 05r5w; 057hz; 01jb26; 044mfr; 0bx_q; ... >> query: (?x2200, 02hrh1q) <- actor(?x4898, ?x2200), people(?x1423, ?x2200), participant(?x2200, ?x3308) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tfck profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 103.000 0.926 http://example.org/people/person/profession #4257-04107 PRED entity: 04107 PRED relation: profession PRED expected values: 0cbd2 => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 97): 0dxtg (0.78 #914, 0.76 #6614, 0.70 #614), 02hrh1q (0.70 #20123, 0.69 #16671, 0.69 #16071), 0cbd2 (0.69 #3457, 0.68 #2707, 0.68 #3007), 02jknp (0.60 #608, 0.56 #908, 0.47 #6608), 01d_h8 (0.56 #906, 0.52 #3906, 0.51 #6606), 0kyk (0.44 #3031, 0.43 #2731, 0.41 #3481), 03gjzk (0.34 #6916, 0.33 #6016, 0.32 #6316), 018gz8 (0.30 #6318, 0.24 #7218, 0.24 #5868), 01c72t (0.26 #1075, 0.25 #1825, 0.19 #2875), 09jwl (0.26 #1070, 0.21 #3320, 0.19 #7220) >> Best rule #914 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0jf1b; 01gzm2; 01t07j; 098n5; 02fn5; 06mn7; 03thw4; 0bs8d; 016gkf; 05kh_; ... >> query: (?x4808, 0dxtg) <- nationality(?x4808, ?x94), award(?x4808, ?x1862), people(?x10199, ?x4808), ?x1862 = 0gr51 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3457 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 01gp_x; *> query: (?x4808, 0cbd2) <- nationality(?x4808, ?x94), story_by(?x6100, ?x4808), influenced_by(?x4808, ?x118), gender(?x4808, ?x231) *> conf = 0.69 ranks of expected_values: 3 EVAL 04107 profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 167.000 167.000 0.778 http://example.org/people/person/profession #4256-02qzh2 PRED entity: 02qzh2 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.87 #21, 0.86 #26, 0.86 #86), 02nxhr (0.06 #17, 0.05 #27, 0.04 #32), 07c52 (0.04 #168, 0.04 #158, 0.04 #183), 07z4p (0.03 #145, 0.03 #130, 0.03 #185) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 05css_; >> query: (?x4160, 029j_) <- nominated_for(?x667, ?x4160), film(?x521, ?x4160), featured_film_locations(?x4160, ?x739), language(?x4160, ?x254) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qzh2 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.867 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #4255-015rkw PRED entity: 015rkw PRED relation: type_of_union PRED expected values: 01g63y => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1): 01g63y (0.46 #154, 0.30 #1, 0.19 #43) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 1262 >> proper extension: 07mvp; 06z4wj; 09h_q; 04k05; 014g91; >> query: (?x1739, ?x566) <- award_winner(?x1739, ?x4928), award(?x1739, ?x704), type_of_union(?x4928, ?x566) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015rkw type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.462 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4254-01w724 PRED entity: 01w724 PRED relation: artist! PRED expected values: 017l96 => 140 concepts (113 used for prediction) PRED predicted values (max 10 best out of 107): 015_1q (0.29 #160, 0.28 #861, 0.24 #581), 03rhqg (0.19 #156, 0.17 #5201, 0.17 #2257), 017l96 (0.19 #860, 0.12 #720, 0.12 #580), 0g768 (0.15 #2279, 0.13 #178, 0.13 #5223), 033hn8 (0.15 #2255, 0.13 #154, 0.12 #5199), 03mp8k (0.15 #2307, 0.09 #1327, 0.09 #2447), 0n85g (0.15 #62, 0.11 #2303, 0.09 #2163), 0k_kr (0.15 #44, 0.08 #7153, 0.08 #6589), 0181dw (0.14 #2284, 0.12 #744, 0.12 #5788), 01xyqk (0.13 #220, 0.09 #781, 0.08 #641) >> Best rule #160 for best value: >> intensional similarity = 2 >> extensional distance = 29 >> proper extension: 01p7b6b; >> query: (?x2765, 015_1q) <- profession(?x2765, ?x4654), ?x4654 = 029bkp >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #860 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 0m0hw; *> query: (?x2765, 017l96) <- artist(?x7448, ?x2765), profession(?x2765, ?x220), place_of_death(?x2765, ?x9331) *> conf = 0.19 ranks of expected_values: 3 EVAL 01w724 artist! 017l96 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 113.000 0.290 http://example.org/music/record_label/artist #4253-01qncf PRED entity: 01qncf PRED relation: nominated_for! PRED expected values: 03qgjwc => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 178): 0gq9h (0.39 #63, 0.33 #5679, 0.33 #5913), 0gs9p (0.35 #65, 0.28 #5681, 0.28 #5915), 019f4v (0.29 #1926, 0.27 #5904, 0.27 #5670), 0k611 (0.26 #73, 0.24 #5689, 0.24 #5923), 0gr51 (0.26 #78, 0.17 #3822, 0.16 #1950), 0gq_v (0.23 #1892, 0.22 #5636, 0.22 #5870), 040njc (0.22 #3751, 0.22 #5623, 0.22 #5857), 04dn09n (0.22 #36, 0.21 #3780, 0.21 #5652), 03qgjwc (0.22 #129, 0.21 #11701, 0.20 #16852), 0f4x7 (0.22 #26, 0.20 #5642, 0.20 #5876) >> Best rule #63 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0353xq; 03nqnnk; >> query: (?x2251, 0gq9h) <- language(?x2251, ?x254), written_by(?x2251, ?x1029), featured_film_locations(?x2251, ?x726), film_regional_debut_venue(?x2251, ?x6601) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #129 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 0353xq; 03nqnnk; *> query: (?x2251, 03qgjwc) <- language(?x2251, ?x254), written_by(?x2251, ?x1029), featured_film_locations(?x2251, ?x726), film_regional_debut_venue(?x2251, ?x6601) *> conf = 0.22 ranks of expected_values: 9 EVAL 01qncf nominated_for! 03qgjwc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 85.000 85.000 0.391 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4252-06t2t PRED entity: 06t2t PRED relation: country! PRED expected values: 01dbns => 109 concepts (100 used for prediction) PRED predicted values (max 10 best out of 201): 01q460 (0.22 #769, 0.08 #17125, 0.07 #17511), 03_c8p (0.08 #963, 0.08 #1541, 0.06 #2506), 0dmtp (0.08 #963, 0.08 #1541, 0.06 #2506), 01c6k4 (0.08 #963, 0.08 #1541, 0.06 #2505), 02wbnv (0.07 #1542), 086h6p (0.07 #1542), 02qdyj (0.07 #1542), 055z7 (0.06 #2487, 0.05 #559, 0.05 #1138), 01nds (0.06 #2457, 0.05 #529, 0.05 #1108), 02rr_z4 (0.05 #552, 0.05 #1131, 0.05 #1902) >> Best rule #769 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 05kr_; >> query: (?x2316, ?x3354) <- contains(?x2316, ?x3354), film_release_region(?x7789, ?x2316), major_field_of_study(?x3354, ?x254) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06t2t country! 01dbns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 100.000 0.223 http://example.org/organization/organization/headquarters./location/mailing_address/country #4251-05q96q6 PRED entity: 05q96q6 PRED relation: film! PRED expected values: 0k525 => 92 concepts (60 used for prediction) PRED predicted values (max 10 best out of 909): 05dbf (0.40 #362, 0.03 #16995, 0.03 #8678), 015rkw (0.20 #279, 0.09 #6514, 0.03 #16912), 0h0yt (0.20 #1342, 0.09 #7577, 0.03 #5499), 0755wz (0.20 #1223, 0.07 #7458, 0.01 #30327), 02yxwd (0.20 #741, 0.07 #13216, 0.05 #15295), 03v3xp (0.20 #614, 0.06 #6849, 0.03 #78993), 051wwp (0.20 #872, 0.06 #7107, 0.03 #78993), 016gr2 (0.20 #192, 0.04 #8315, 0.03 #78993), 09fqtq (0.20 #69, 0.04 #6304, 0.03 #78993), 02tr7d (0.20 #264, 0.04 #6499, 0.03 #78993) >> Best rule #362 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 011ywj; >> query: (?x1038, 05dbf) <- film_release_region(?x1038, ?x94), film(?x374, ?x1038), produced_by(?x1038, ?x1039), film_crew_role(?x1038, ?x137), ?x374 = 05cj4r >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #39260 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 402 *> proper extension: 0436yk; 0cks1m; 05znbh7; 02r9p0c; 0gndh; 052_mn; 02k1pr; 031f_m; 05vc35; 03gyvwg; *> query: (?x1038, 0k525) <- genre(?x1038, ?x225), country(?x1038, ?x94), film(?x262, ?x1038), ?x225 = 02kdv5l *> conf = 0.01 ranks of expected_values: 555 EVAL 05q96q6 film! 0k525 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 92.000 60.000 0.400 http://example.org/film/actor/film./film/performance/film #4250-0fpj9pm PRED entity: 0fpj9pm PRED relation: role PRED expected values: 01vj9c => 153 concepts (133 used for prediction) PRED predicted values (max 10 best out of 122): 03bx0bm (0.59 #404, 0.44 #22, 0.40 #3335), 05148p4 (0.31 #336, 0.30 #2676, 0.28 #843), 0l14md (0.31 #326, 0.15 #833, 0.12 #3512), 018vs (0.25 #395, 0.25 #332, 0.23 #446), 028tv0 (0.22 #12, 0.15 #3132, 0.15 #2688), 026t6 (0.20 #2740, 0.18 #64, 0.16 #1588), 01s0ps (0.20 #2740, 0.18 #64, 0.15 #318), 01vdm0 (0.20 #2740, 0.18 #64, 0.15 #318), 03qjg (0.19 #360, 0.12 #423, 0.10 #2588), 04rzd (0.12 #349, 0.07 #856, 0.07 #1619) >> Best rule #404 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 03d9d6; >> query: (?x7236, 03bx0bm) <- artist(?x6474, ?x7236), award(?x7236, ?x247), instrumentalists(?x227, ?x7236), ?x247 = 02wh75 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #333 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 04mx7s; *> query: (?x7236, 01vj9c) <- artist(?x6474, ?x7236), role(?x7236, ?x1750), ?x1750 = 02hnl *> conf = 0.09 ranks of expected_values: 13 EVAL 0fpj9pm role 01vj9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 153.000 133.000 0.594 http://example.org/music/group_member/membership./music/group_membership/role #4249-059j2 PRED entity: 059j2 PRED relation: combatants! PRED expected values: 015fr => 218 concepts (146 used for prediction) PRED predicted values (max 10 best out of 294): 09c7w0 (0.84 #3341, 0.84 #3340, 0.83 #7019), 0d060g (0.84 #3341, 0.84 #3340, 0.83 #7019), 04g61 (0.84 #3341, 0.84 #3340, 0.83 #7019), 059j2 (0.48 #1768, 0.44 #2843, 0.42 #574), 015fr (0.32 #1762, 0.30 #1699, 0.30 #3342), 027qpc (0.30 #541, 0.30 #3342, 0.26 #7021), 07f1x (0.30 #3342, 0.28 #2876, 0.27 #1757), 05v8c (0.30 #3342, 0.27 #1757, 0.26 #5555), 06qd3 (0.30 #3342, 0.27 #1757, 0.26 #5555), 03rjj (0.30 #3342, 0.27 #1757, 0.26 #5555) >> Best rule #3341 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 05kyr; >> query: (?x1229, ?x151) <- combatants(?x1229, ?x151), combatants(?x151, ?x1497), nationality(?x731, ?x1229) >> conf = 0.84 => this is the best rule for 3 predicted values *> Best rule #1762 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 014tss; *> query: (?x1229, 015fr) <- combatants(?x1229, ?x94), country(?x1009, ?x1229), jurisdiction_of_office(?x182, ?x1229) *> conf = 0.32 ranks of expected_values: 5 EVAL 059j2 combatants! 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 218.000 146.000 0.842 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #4248-0ck27z PRED entity: 0ck27z PRED relation: award! PRED expected values: 01pcq3 01j5x6 02sjf5 04cf09 04mz10g 059t6d 027r8p 02jtjz 05dtwm 02nwxc 059xnf 02624g 05wqr1 02zfdp 0306bt 03061d => 44 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2106): 01z7_f (0.85 #6336, 0.69 #44371, 0.69 #50710), 0dgskx (0.85 #6336, 0.69 #44371, 0.69 #50710), 027ht3n (0.85 #6336, 0.69 #44371, 0.69 #50710), 02nwxc (0.85 #6336, 0.69 #44371, 0.69 #50710), 0382m4 (0.85 #6336, 0.69 #44371, 0.69 #50710), 02lfcm (0.85 #6336, 0.69 #44371, 0.69 #50710), 08m4c8 (0.85 #6336, 0.69 #44371, 0.69 #50710), 01r42_g (0.85 #6336, 0.69 #44371, 0.69 #50710), 01wbg84 (0.85 #6336, 0.69 #44371, 0.69 #50710), 0gd_b_ (0.85 #6336, 0.69 #44371, 0.69 #50710) >> Best rule #6336 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0bb57s; >> query: (?x1670, ?x368) <- award(?x8424, ?x1670), award(?x3688, ?x1670), ?x3688 = 03zyvw, award_winner(?x3609, ?x8424), award_winner(?x1670, ?x368) >> conf = 0.85 => this is the best rule for 19 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 46, 177, 178, 273, 274, 510, 512, 579, 580, 581, 613, 614, 615, 616, 617 EVAL 0ck27z award! 03061d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 0306bt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 02zfdp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 05wqr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 02624g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 059xnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 02nwxc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 05dtwm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 02jtjz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 027r8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 059t6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 04mz10g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 04cf09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 02sjf5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 01j5x6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0ck27z award! 01pcq3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 44.000 16.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4247-0345h PRED entity: 0345h PRED relation: service_location! PRED expected values: 05b5c => 234 concepts (233 used for prediction) PRED predicted values (max 10 best out of 139): 05b5c (0.38 #994, 0.27 #1621, 0.26 #1997), 0cv9b (0.31 #758, 0.23 #884, 0.23 #1511), 01zpmq (0.31 #922, 0.20 #296, 0.18 #1549), 05w3y (0.31 #932, 0.20 #306, 0.17 #2186), 06_9lg (0.30 #15011, 0.30 #9743, 0.24 #2720), 0k9ts (0.26 #1210, 0.23 #959, 0.23 #1586), 06p8m (0.23 #974, 0.20 #348, 0.18 #1852), 0z07 (0.23 #970, 0.20 #344, 0.14 #1597), 045c7b (0.23 #916, 0.18 #1543, 0.17 #3174), 01nn79 (0.23 #946, 0.18 #1573, 0.14 #3706) >> Best rule #994 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 06v9sf; 027qpc; >> query: (?x1264, 05b5c) <- combatants(?x613, ?x1264), ?x613 = 0bq0p9 >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0345h service_location! 05b5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 234.000 233.000 0.385 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #4246-09_99w PRED entity: 09_99w PRED relation: award_winner! PRED expected values: 0gx_st => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 108): 09pj68 (0.29 #104, 0.28 #5007, 0.18 #5842), 09qvms (0.28 #5007, 0.18 #5842, 0.16 #4867), 092c5f (0.28 #5007, 0.18 #5842, 0.16 #4867), 09p3h7 (0.28 #5007, 0.18 #5842, 0.16 #4867), 05c1t6z (0.28 #5007, 0.14 #835, 0.12 #1545), 02q690_ (0.28 #5007, 0.14 #835, 0.09 #1595), 03nnm4t (0.28 #5007, 0.14 #835, 0.09 #1603), 03gyp30 (0.28 #5007, 0.14 #835, 0.08 #1646), 09g90vz (0.28 #5007, 0.14 #835, 0.05 #2209), 09bymc (0.28 #5007, 0.14 #835, 0.02 #3040) >> Best rule #104 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0h53p1; 09hd16; 01xndd; 08q3s0; 09hd6f; >> query: (?x8785, 09pj68) <- award_winner(?x8785, ?x7301), ?x7301 = 0h5jg5, award_nominee(?x1039, ?x8785) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #5842 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1257 *> proper extension: 04bdxl; 05vsxz; 07fq1y; 028q6; 0l6qt; 06j0md; 02rchht; 01tvz5j; 03rs8y; 025h4z; ... *> query: (?x8785, ?x5296) <- award_winner(?x8785, ?x7301), nationality(?x7301, ?x94), award_winner(?x5296, ?x7301) *> conf = 0.18 ranks of expected_values: 11 EVAL 09_99w award_winner! 0gx_st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 95.000 95.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4245-06q8hf PRED entity: 06q8hf PRED relation: executive_produced_by! PRED expected values: 017gl1 02yvct 047d21r 03cw411 05v38p 01svry 0h95927 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 230): 0194zl (0.25 #254, 0.17 #737, 0.11 #1220), 01cmp9 (0.25 #316, 0.17 #799, 0.11 #1282), 02s4l6 (0.25 #111, 0.17 #594, 0.11 #1077), 047d21r (0.25 #186, 0.17 #669, 0.11 #1152), 0h95927 (0.25 #383, 0.17 #866, 0.11 #1349), 01svry (0.25 #353, 0.17 #836, 0.11 #1319), 05v38p (0.25 #335, 0.17 #818, 0.11 #1301), 047fjjr (0.25 #189, 0.17 #672, 0.11 #1155), 03cw411 (0.25 #187, 0.17 #670, 0.11 #1153), 02yvct (0.25 #106, 0.17 #589, 0.11 #1072) >> Best rule #254 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 025b3k; >> query: (?x7324, 0194zl) <- executive_produced_by(?x8267, ?x7324), type_of_union(?x7324, ?x566), ?x8267 = 0234j5 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #186 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 025b3k; *> query: (?x7324, 047d21r) <- executive_produced_by(?x8267, ?x7324), type_of_union(?x7324, ?x566), ?x8267 = 0234j5 *> conf = 0.25 ranks of expected_values: 4, 5, 6, 7, 9, 10, 11 EVAL 06q8hf executive_produced_by! 0h95927 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 115.000 0.250 http://example.org/film/film/executive_produced_by EVAL 06q8hf executive_produced_by! 01svry CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 115.000 0.250 http://example.org/film/film/executive_produced_by EVAL 06q8hf executive_produced_by! 05v38p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 115.000 0.250 http://example.org/film/film/executive_produced_by EVAL 06q8hf executive_produced_by! 03cw411 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 115.000 115.000 0.250 http://example.org/film/film/executive_produced_by EVAL 06q8hf executive_produced_by! 047d21r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 115.000 0.250 http://example.org/film/film/executive_produced_by EVAL 06q8hf executive_produced_by! 02yvct CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 115.000 115.000 0.250 http://example.org/film/film/executive_produced_by EVAL 06q8hf executive_produced_by! 017gl1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 115.000 115.000 0.250 http://example.org/film/film/executive_produced_by #4244-01w5jwb PRED entity: 01w5jwb PRED relation: artists! PRED expected values: 0gywn 012yc => 98 concepts (72 used for prediction) PRED predicted values (max 10 best out of 227): 06by7 (0.60 #11393, 0.53 #20, 0.45 #4015), 02lnbg (0.50 #671, 0.45 #1285, 0.31 #4359), 0gywn (0.49 #55, 0.44 #1284, 0.41 #670), 0ggx5q (0.48 #691, 0.45 #1305, 0.34 #4379), 016clz (0.37 #312, 0.23 #9534, 0.23 #5), 05bt6j (0.28 #657, 0.26 #42, 0.25 #11415), 026z9 (0.24 #75, 0.12 #690, 0.10 #4378), 01lyv (0.22 #2491, 0.18 #8641, 0.17 #3413), 0xhtw (0.21 #4011, 0.19 #9545, 0.18 #7391), 0155w (0.20 #1948, 0.17 #3485, 0.16 #4100) >> Best rule #11393 for best value: >> intensional similarity = 3 >> extensional distance = 641 >> proper extension: 018gm9; 01wphh2; 032nl2; 01516r; 0ftqr; 01wkmgb; 01t8399; 01518s; >> query: (?x8722, 06by7) <- artists(?x3562, ?x8722), artists(?x3562, ?x9639), ?x9639 = 0gps0z >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 01wbgdv; 01vwyqp; 015x1f; 07h76; 0flpy; 0137hn; 0134wr; 024qwq; 01wk7ql; *> query: (?x8722, 0gywn) <- artists(?x1127, ?x8722), award(?x8722, ?x2139), ?x1127 = 02x8m *> conf = 0.49 ranks of expected_values: 3, 25 EVAL 01w5jwb artists! 012yc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 98.000 72.000 0.605 http://example.org/music/genre/artists EVAL 01w5jwb artists! 0gywn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 72.000 0.605 http://example.org/music/genre/artists #4243-064vjs PRED entity: 064vjs PRED relation: country PRED expected values: 06mzp 06mkj 056vv => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 417): 01p1v (0.83 #2510, 0.82 #2356, 0.82 #2045), 06mkj (0.83 #2516, 0.82 #2051, 0.75 #3752), 07ylj (0.83 #2496, 0.79 #2650, 0.78 #1875), 0d0kn (0.78 #1892, 0.75 #1738, 0.75 #1428), 01ls2 (0.77 #1086, 0.67 #937, 0.62 #1713), 03ryn (0.77 #1086, 0.67 #1147, 0.62 #1768), 07twz (0.77 #1086, 0.67 #1005, 0.62 #1781), 04g61 (0.77 #1086, 0.64 #2097, 0.62 #1477), 03rk0 (0.77 #1086, 0.62 #1430, 0.57 #1396), 06mzp (0.77 #1086, 0.61 #3881, 0.60 #630) >> Best rule #2510 for best value: >> intensional similarity = 52 >> extensional distance = 10 >> proper extension: 01cgz; 02y8z; >> query: (?x4310, 01p1v) <- country(?x4310, ?x2346), country(?x4310, ?x1892), country(?x4310, ?x1603), country(?x4310, ?x792), country(?x4310, ?x404), country(?x4310, ?x205), ?x404 = 047lj, film_release_region(?x11351, ?x1892), film_release_region(?x11209, ?x1892), film_release_region(?x9839, ?x1892), film_release_region(?x6520, ?x1892), film_release_region(?x5162, ?x1892), film_release_region(?x4828, ?x1892), film_release_region(?x4643, ?x1892), film_release_region(?x4372, ?x1892), film_release_region(?x4290, ?x1892), film_release_region(?x3524, ?x1892), film_release_region(?x3076, ?x1892), film_release_region(?x2933, ?x1892), film_release_region(?x2738, ?x1892), film_release_region(?x2342, ?x1892), film_release_region(?x1463, ?x1892), film_release_region(?x664, ?x1892), country(?x2266, ?x1892), country(?x2044, ?x1892), country(?x1557, ?x1892), organization(?x1892, ?x127), ?x5162 = 0j3d9tn, ?x4372 = 02rmd_2, ?x2044 = 06f41, olympics(?x1892, ?x391), ?x664 = 0401sg, ?x3524 = 06r2_, ?x11209 = 04fjzv, ?x2342 = 0ct5zc, ?x3076 = 0g5838s, ?x1463 = 0gtvrv3, ?x2933 = 0407yj_, ?x4828 = 02fttd, ?x9839 = 0gy7bj4, ?x2266 = 01lb14, ?x4643 = 080lkt7, ?x205 = 03rjj, ?x6520 = 02bg55, ?x2346 = 0d05w3, ?x4290 = 0gtxj2q, ?x1557 = 07bs0, ?x792 = 0hzlz, ?x11351 = 02wtp6, combatants(?x326, ?x1892), ?x1603 = 06bnz, ?x2738 = 0h1v19 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #2516 for first EXPECTED value: *> intensional similarity = 52 *> extensional distance = 10 *> proper extension: 01cgz; 02y8z; *> query: (?x4310, 06mkj) <- country(?x4310, ?x2346), country(?x4310, ?x1892), country(?x4310, ?x1603), country(?x4310, ?x792), country(?x4310, ?x404), country(?x4310, ?x205), ?x404 = 047lj, film_release_region(?x11351, ?x1892), film_release_region(?x11209, ?x1892), film_release_region(?x9839, ?x1892), film_release_region(?x6520, ?x1892), film_release_region(?x5162, ?x1892), film_release_region(?x4828, ?x1892), film_release_region(?x4643, ?x1892), film_release_region(?x4372, ?x1892), film_release_region(?x4290, ?x1892), film_release_region(?x3524, ?x1892), film_release_region(?x3076, ?x1892), film_release_region(?x2933, ?x1892), film_release_region(?x2738, ?x1892), film_release_region(?x2342, ?x1892), film_release_region(?x1463, ?x1892), film_release_region(?x664, ?x1892), country(?x2266, ?x1892), country(?x2044, ?x1892), country(?x1557, ?x1892), organization(?x1892, ?x127), ?x5162 = 0j3d9tn, ?x4372 = 02rmd_2, ?x2044 = 06f41, olympics(?x1892, ?x391), ?x664 = 0401sg, ?x3524 = 06r2_, ?x11209 = 04fjzv, ?x2342 = 0ct5zc, ?x3076 = 0g5838s, ?x1463 = 0gtvrv3, ?x2933 = 0407yj_, ?x4828 = 02fttd, ?x9839 = 0gy7bj4, ?x2266 = 01lb14, ?x4643 = 080lkt7, ?x205 = 03rjj, ?x6520 = 02bg55, ?x2346 = 0d05w3, ?x4290 = 0gtxj2q, ?x1557 = 07bs0, ?x792 = 0hzlz, ?x11351 = 02wtp6, combatants(?x326, ?x1892), ?x1603 = 06bnz, ?x2738 = 0h1v19 *> conf = 0.83 ranks of expected_values: 2, 10, 49 EVAL 064vjs country 056vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 35.000 35.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 064vjs country 06mkj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 35.000 35.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 064vjs country 06mzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 35.000 35.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #4242-03rj0 PRED entity: 03rj0 PRED relation: film_release_region! PRED expected values: 0gtv7pk 017gl1 0c0nhgv 03bx2lk 017gm7 03twd6 0gxtknx 0bh8yn3 02r8hh_ 047svrl 04f52jw 0407yj_ 0gffmn8 03cw411 05zlld0 080nwsb 03q0r1 0dr_9t7 06tpmy 0f4k49 064lsn 0gmgwnv 03nsm5x 02wtp6 => 97 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1662): 02r8hh_ (0.89 #6913, 0.89 #5788, 0.85 #9163), 0bwfwpj (0.89 #6849, 0.89 #5724, 0.85 #9099), 04f52jw (0.89 #7015, 0.87 #10390, 0.85 #9265), 0fpkhkz (0.89 #5769, 0.83 #6894, 0.80 #9144), 017gm7 (0.87 #10257, 0.85 #9132, 0.85 #8007), 05zlld0 (0.87 #10510, 0.83 #7135, 0.83 #6010), 017gl1 (0.87 #10217, 0.80 #7967, 0.80 #4592), 07s3m4g (0.87 #10857, 0.78 #7482, 0.78 #6357), 01fmys (0.85 #9196, 0.83 #6946, 0.83 #5821), 087wc7n (0.85 #9076, 0.83 #6826, 0.83 #5701) >> Best rule #6913 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0jgd; 0b90_r; 03rjj; 03_3d; 0chghy; 07ssc; 015fr; 06mzp; 03gj2; 059j2; ... >> query: (?x2267, 02r8hh_) <- film_release_region(?x3981, ?x2267), film_release_region(?x3619, ?x2267), ?x3619 = 0fphgb, ?x3981 = 047tsx3 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 5, 6, 7, 11, 14, 16, 21, 28, 33, 34, 42, 48, 55, 60, 61, 64, 73, 76, 106, 157, 224, 229 EVAL 03rj0 film_release_region! 02wtp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 03nsm5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 0gmgwnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 0f4k49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 06tpmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 0dr_9t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 03q0r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 080nwsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 05zlld0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 03cw411 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 0gffmn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 0407yj_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 04f52jw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 047svrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 02r8hh_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 0bh8yn3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 0gxtknx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 03twd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 017gm7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 03bx2lk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 0c0nhgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 017gl1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03rj0 film_release_region! 0gtv7pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 97.000 91.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4241-01phtd PRED entity: 01phtd PRED relation: film PRED expected values: 03sxd2 => 118 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1012): 031hcx (0.12 #1274, 0.03 #3064, 0.01 #33495), 09tkzy (0.12 #1468, 0.03 #3258), 04jpg2p (0.12 #1463, 0.02 #3253, 0.02 #12203), 09g7vfw (0.12 #554, 0.02 #2344, 0.02 #5924), 03hxsv (0.12 #1117, 0.02 #2907, 0.01 #33338), 04sskp (0.12 #1398, 0.02 #3188), 043tvp3 (0.12 #1212, 0.02 #3002), 050xxm (0.12 #276, 0.02 #2066), 0blpg (0.12 #657, 0.02 #31088, 0.02 #13187), 032016 (0.12 #504, 0.02 #4084, 0.02 #11244) >> Best rule #1274 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01v3vp; 04t969; 02wr6r; >> query: (?x8801, 031hcx) <- location(?x8801, ?x739), film(?x8801, ?x5353), ?x5353 = 04t9c0 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #34317 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 338 *> proper extension: 01sl1q; 04bdxl; 01j5ts; 023tp8; 0m2wm; 01qscs; 01q_ph; 0l8v5; 0c4f4; 0bxtg; ... *> query: (?x8801, 03sxd2) <- participant(?x6916, ?x8801), award_nominee(?x8801, ?x3694), award_nominee(?x6916, ?x57) *> conf = 0.01 ranks of expected_values: 436 EVAL 01phtd film 03sxd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 118.000 38.000 0.125 http://example.org/film/actor/film./film/performance/film #4240-01n30p PRED entity: 01n30p PRED relation: film_crew_role PRED expected values: 09vw2b7 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.71 #453, 0.67 #231, 0.66 #342), 09zzb8 (0.71 #446, 0.62 #557, 0.57 #335), 09vw2b7 (0.64 #452, 0.56 #267, 0.55 #563), 01pvkk (0.40 #87, 0.29 #124, 0.29 #50), 01vx2h (0.36 #457, 0.30 #272, 0.30 #420), 02rh1dz (0.17 #456, 0.10 #1011, 0.10 #419), 02ynfr (0.17 #462, 0.15 #573, 0.15 #351), 0215hd (0.14 #57, 0.12 #131, 0.12 #576), 015h31 (0.13 #84, 0.10 #455, 0.09 #2717), 04pyp5 (0.13 #92, 0.09 #18, 0.09 #2717) >> Best rule #453 for best value: >> intensional similarity = 3 >> extensional distance = 284 >> proper extension: 0hgnl3t; >> query: (?x8158, 0ch6mp2) <- film(?x368, ?x8158), country(?x8158, ?x94), crewmember(?x8158, ?x1622) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #452 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 284 *> proper extension: 0hgnl3t; *> query: (?x8158, 09vw2b7) <- film(?x368, ?x8158), country(?x8158, ?x94), crewmember(?x8158, ?x1622) *> conf = 0.64 ranks of expected_values: 3 EVAL 01n30p film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 76.000 76.000 0.713 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4239-049mql PRED entity: 049mql PRED relation: currency PRED expected values: 09nqf => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.84 #57, 0.83 #29, 0.83 #113), 01nv4h (0.06 #86, 0.05 #37, 0.04 #51), 02l6h (0.06 #88, 0.02 #53, 0.02 #277), 088n7 (0.01 #126) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 06wzvr; >> query: (?x4127, 09nqf) <- film_crew_role(?x4127, ?x468), nominated_for(?x350, ?x4127), ?x350 = 05f4m9q >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049mql currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.840 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #4238-0gg5qcw PRED entity: 0gg5qcw PRED relation: nominated_for! PRED expected values: 0bmhvpr => 80 concepts (33 used for prediction) PRED predicted values (max 10 best out of 61): 0q9sg (0.08 #891, 0.07 #1146, 0.02 #3950), 0gg5qcw (0.06 #1017, 0.03 #1274, 0.02 #7401), 029k4p (0.06 #1017, 0.03 #1274, 0.02 #7401), 0h03fhx (0.06 #1017, 0.03 #1274, 0.02 #7401), 06_x996 (0.06 #1017, 0.03 #1274, 0.02 #7401), 07w8fz (0.06 #1017, 0.03 #1274, 0.02 #7401), 01bb9r (0.06 #1017, 0.03 #1274, 0.02 #7401), 03s6l2 (0.06 #1017, 0.03 #1274, 0.02 #7401), 09xbpt (0.06 #1017, 0.03 #1274, 0.02 #7401), 078sj4 (0.06 #1017, 0.02 #7401, 0.01 #845) >> Best rule #891 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 06ybb1; 016ks5; 011xg5; 07bxqz; >> query: (?x5092, 0q9sg) <- written_by(?x5092, ?x286), participant(?x287, ?x286), award_winner(?x349, ?x286) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gg5qcw nominated_for! 0bmhvpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 33.000 0.080 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #4237-026wlxw PRED entity: 026wlxw PRED relation: film_crew_role PRED expected values: 09vw2b7 01pvkk => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 29): 09vw2b7 (0.63 #1177, 0.62 #143, 0.62 #452), 01pvkk (0.29 #1595, 0.28 #975, 0.28 #1491), 02ynfr (0.21 #151, 0.17 #117, 0.16 #460), 02rh1dz (0.18 #146, 0.18 #352, 0.16 #627), 015h31 (0.17 #145, 0.14 #179, 0.14 #351), 0d2b38 (0.17 #161, 0.12 #93, 0.12 #642), 0215hd (0.13 #154, 0.13 #292, 0.12 #982), 01xy5l_ (0.13 #149, 0.12 #287, 0.11 #458), 089g0h (0.12 #155, 0.10 #1603, 0.10 #1499), 02_n3z (0.11 #276, 0.10 #447, 0.10 #310) >> Best rule #1177 for best value: >> intensional similarity = 4 >> extensional distance = 742 >> proper extension: 0dtw1x; 0gj9qxr; 0crh5_f; 0h95zbp; 03_wm6; >> query: (?x8214, 09vw2b7) <- genre(?x8214, ?x225), production_companies(?x8214, ?x382), film_crew_role(?x8214, ?x137), country(?x8214, ?x94) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 026wlxw film_crew_role 01pvkk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.634 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 026wlxw film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.634 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4236-06mmb PRED entity: 06mmb PRED relation: award_winner! PRED expected values: 02g87m 049k07 => 80 concepts (35 used for prediction) PRED predicted values (max 10 best out of 543): 020_95 (0.53 #49822, 0.53 #48215, 0.53 #53036), 02fz3w (0.53 #49822, 0.53 #48215, 0.53 #53036), 016xh5 (0.53 #49822, 0.53 #48215, 0.53 #53036), 0175wg (0.53 #49822, 0.53 #48215, 0.53 #53036), 0993r (0.53 #49822, 0.53 #48215, 0.53 #53036), 02cllz (0.53 #49822, 0.53 #48215, 0.53 #53036), 07hbxm (0.53 #49822, 0.53 #48215, 0.53 #53036), 01l2fn (0.53 #49822, 0.53 #48215, 0.53 #53036), 05tk7y (0.53 #49822, 0.53 #48215, 0.53 #53036), 09y20 (0.53 #49822, 0.53 #48215, 0.53 #53036) >> Best rule #49822 for best value: >> intensional similarity = 3 >> extensional distance = 1291 >> proper extension: 0l56b; 0qdwr; >> query: (?x2559, ?x380) <- award_winner(?x1461, ?x2559), award_nominee(?x2559, ?x380), nationality(?x2559, ?x94) >> conf = 0.53 => this is the best rule for 16 predicted values *> Best rule #45001 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1268 *> proper extension: 01jq34; 05b2f_k; *> query: (?x2559, ?x1460) <- award_winner(?x2559, ?x1461), award_winner(?x1461, ?x1460), award_winner(?x7579, ?x1461) *> conf = 0.28 ranks of expected_values: 20, 21 EVAL 06mmb award_winner! 049k07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 80.000 35.000 0.534 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner EVAL 06mmb award_winner! 02g87m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 80.000 35.000 0.534 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #4235-0c00zd0 PRED entity: 0c00zd0 PRED relation: film_distribution_medium PRED expected values: 0735l => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.83 #49, 0.14 #73, 0.12 #11), 029j_ (0.17 #1, 0.14 #13, 0.14 #45), 0dq6p (0.17 #3, 0.14 #15, 0.12 #9), 02nxhr (0.11 #46, 0.07 #70, 0.07 #39), 07z4p (0.02 #37) >> Best rule #49 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 0522wp; >> query: (?x1702, 0735l) <- region(?x1702, ?x512), film(?x609, ?x1702), ?x512 = 07ssc >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c00zd0 film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.830 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #4234-016fly PRED entity: 016fly PRED relation: profession! PRED expected values: 0453t 01dvtx 0132k4 01wj5hp 06y7d 0cbgl => 64 concepts (39 used for prediction) PRED predicted values (max 10 best out of 4174): 040db (0.67 #30173, 0.50 #13278, 0.40 #21723), 017_pb (0.64 #8452, 0.60 #23553, 0.52 #25345), 03jxw (0.64 #8452, 0.52 #25345, 0.50 #15988), 080r3 (0.64 #8452, 0.52 #25345, 0.40 #33795), 0m77m (0.64 #8452, 0.52 #25345, 0.40 #33795), 016hvl (0.60 #21452, 0.50 #29902, 0.50 #13007), 06whf (0.60 #22449, 0.50 #30899, 0.50 #14004), 0mb5x (0.60 #23870, 0.50 #32320, 0.50 #15425), 0d5_f (0.60 #22464, 0.50 #30914, 0.50 #14019), 03pm9 (0.60 #21876, 0.50 #30326, 0.50 #13431) >> Best rule #30173 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 015btn; >> query: (?x8340, 040db) <- profession(?x12441, ?x8340), profession(?x5261, ?x8340), profession(?x1620, ?x8340), influenced_by(?x5261, ?x1235), people(?x2510, ?x5261), ?x1235 = 0m77m, influenced_by(?x4308, ?x12441), person(?x1015, ?x1620), influenced_by(?x12441, ?x1857), student(?x741, ?x5261), currency(?x1620, ?x170) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #32470 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 015btn; *> query: (?x8340, 01wj5hp) <- profession(?x12441, ?x8340), profession(?x5261, ?x8340), profession(?x1620, ?x8340), influenced_by(?x5261, ?x1235), people(?x2510, ?x5261), ?x1235 = 0m77m, influenced_by(?x4308, ?x12441), person(?x1015, ?x1620), influenced_by(?x12441, ?x1857), student(?x741, ?x5261), currency(?x1620, ?x170) *> conf = 0.33 ranks of expected_values: 177, 197, 229, 356, 357, 1016 EVAL 016fly profession! 0cbgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 39.000 0.667 http://example.org/people/person/profession EVAL 016fly profession! 06y7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 64.000 39.000 0.667 http://example.org/people/person/profession EVAL 016fly profession! 01wj5hp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 64.000 39.000 0.667 http://example.org/people/person/profession EVAL 016fly profession! 0132k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 39.000 0.667 http://example.org/people/person/profession EVAL 016fly profession! 01dvtx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 39.000 0.667 http://example.org/people/person/profession EVAL 016fly profession! 0453t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 64.000 39.000 0.667 http://example.org/people/person/profession #4233-0ds2l81 PRED entity: 0ds2l81 PRED relation: film_crew_role PRED expected values: 09zzb8 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.79 #615, 0.77 #854, 0.76 #956), 01vx2h (0.41 #181, 0.38 #625, 0.38 #1240), 0dxtw (0.41 #146, 0.40 #897, 0.40 #931), 01xy5l_ (0.33 #14, 0.20 #48, 0.14 #82), 02n9jv (0.33 #34, 0.20 #68), 0215hd (0.29 #86, 0.16 #632, 0.15 #905), 01pvkk (0.28 #1448, 0.28 #1721, 0.27 #1379), 02rh1dz (0.15 #179, 0.13 #248, 0.13 #1238), 089g0h (0.14 #87, 0.13 #633, 0.11 #906), 0d2b38 (0.14 #92, 0.13 #638, 0.11 #160) >> Best rule #615 for best value: >> intensional similarity = 5 >> extensional distance = 287 >> proper extension: 021y7yw; 08952r; >> query: (?x8377, 09zzb8) <- film(?x2221, ?x8377), film_crew_role(?x8377, ?x468), produced_by(?x8377, ?x3568), film(?x541, ?x8377), vacationer(?x126, ?x2221) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ds2l81 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.789 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4232-04x4s2 PRED entity: 04x4s2 PRED relation: student! PRED expected values: 06pwq => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 164): 02x9cv (0.25 #320, 0.08 #36228), 03ksy (0.24 #11657, 0.06 #9032, 0.06 #5356), 065y4w7 (0.15 #11040, 0.08 #539, 0.08 #36228), 09f2j (0.11 #11185, 0.04 #19060, 0.03 #34286), 017j69 (0.09 #11696, 0.06 #11171, 0.02 #19046), 08815 (0.08 #527, 0.08 #36228, 0.03 #21003), 023znp (0.08 #644, 0.08 #36228, 0.03 #1169), 01jq34 (0.08 #582, 0.03 #3207, 0.03 #1632), 07wjk (0.08 #588, 0.02 #3213, 0.02 #3738), 02m0sc (0.08 #870, 0.01 #2970) >> Best rule #320 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01gct2; >> query: (?x3762, 02x9cv) <- student(?x5907, ?x3762), award_winner(?x8660, ?x3762), ?x5907 = 01jq4b >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #11563 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 338 *> proper extension: 0dzkq; 01ry0f; *> query: (?x3762, 06pwq) <- student(?x7545, ?x3762), service_location(?x7545, ?x94) *> conf = 0.07 ranks of expected_values: 53 EVAL 04x4s2 student! 06pwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 92.000 92.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #4231-027l4q PRED entity: 027l4q PRED relation: location! PRED expected values: 01nm3s => 114 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1880): 0ddkf (0.33 #1381, 0.20 #6404, 0.04 #13939), 0lrh (0.33 #547, 0.20 #5570, 0.04 #13105), 0br1w (0.33 #733, 0.08 #13291, 0.04 #33389), 02wr6r (0.33 #1982, 0.04 #24589, 0.03 #32126), 012xdf (0.33 #1839, 0.01 #54594), 01w0yrc (0.33 #2051), 0427y (0.33 #1946), 0dt1cm (0.33 #1655), 01mh8zn (0.33 #1609), 03txms (0.33 #1597) >> Best rule #1381 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0hptm; >> query: (?x10298, 0ddkf) <- location(?x10897, ?x10298), location(?x8375, ?x10298), contains(?x1227, ?x10298), ?x10897 = 02j490, people(?x1050, ?x8375) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #35947 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 0f2tj; *> query: (?x10298, 01nm3s) <- place_of_death(?x2001, ?x10298), location(?x8375, ?x10298), award_nominee(?x8375, ?x1711), state(?x10298, ?x1227) *> conf = 0.02 ranks of expected_values: 1442 EVAL 027l4q location! 01nm3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 114.000 25.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #4230-08d9z7 PRED entity: 08d9z7 PRED relation: profession PRED expected values: 03gjzk => 84 concepts (54 used for prediction) PRED predicted values (max 10 best out of 42): 02hrh1q (0.82 #2547, 0.69 #4485, 0.68 #7915), 0dxtg (0.56 #4931, 0.54 #1056, 0.53 #1652), 02jknp (0.50 #1050, 0.48 #1497, 0.48 #1199), 03gjzk (0.46 #611, 0.44 #760, 0.42 #909), 09jwl (0.28 #5664, 0.27 #7454, 0.16 #6578), 012t_z (0.28 #5664, 0.27 #7454, 0.15 #161), 0cbd2 (0.16 #2986, 0.15 #4924, 0.15 #3434), 02krf9 (0.15 #921, 0.15 #623, 0.15 #772), 0kyk (0.14 #30, 0.11 #3458, 0.10 #3010), 018gz8 (0.13 #4935, 0.11 #1656, 0.10 #1954) >> Best rule #2547 for best value: >> intensional similarity = 3 >> extensional distance = 1138 >> proper extension: 01sbf2; 01q415; 03sww; 01wj5hp; 0k9j_; 04f9r2; >> query: (?x7848, 02hrh1q) <- profession(?x7848, ?x319), award_nominee(?x2451, ?x7848), participant(?x513, ?x2451) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #611 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 228 *> proper extension: 01yznp; 04l3_z; 0j582; 03ft8; 01t2h2; 0jt90f5; 04cbtrw; 0bs1yy; 03hbzj; 01twdk; ... *> query: (?x7848, 03gjzk) <- profession(?x7848, ?x319), gender(?x7848, ?x231), executive_produced_by(?x9533, ?x7848) *> conf = 0.46 ranks of expected_values: 4 EVAL 08d9z7 profession 03gjzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 84.000 54.000 0.819 http://example.org/people/person/profession #4229-01wy61y PRED entity: 01wy61y PRED relation: instrumentalists! PRED expected values: 0342h 018vs => 142 concepts (72 used for prediction) PRED predicted values (max 10 best out of 123): 0342h (0.68 #2317, 0.68 #4076, 0.66 #1490), 018vs (0.55 #671, 0.54 #588, 0.52 #755), 02hnl (0.48 #775, 0.48 #608, 0.47 #691), 02sgy (0.43 #2398, 0.34 #3146, 0.33 #3651), 01399x (0.32 #3399, 0.32 #3231, 0.31 #4823), 05ljv7 (0.32 #3399, 0.32 #3231, 0.30 #4322), 021bmf (0.30 #4322, 0.30 #4405, 0.29 #1655), 0l14md (0.24 #751, 0.22 #584, 0.21 #667), 0l14qv (0.23 #500, 0.15 #1491, 0.15 #1575), 01v1d8 (0.17 #549, 0.06 #1293, 0.05 #1654) >> Best rule #2317 for best value: >> intensional similarity = 5 >> extensional distance = 207 >> proper extension: 02nfjp; 02qmncd; 02fybl; 05mxw33; >> query: (?x4162, 0342h) <- profession(?x4162, ?x131), role(?x4162, ?x314), role(?x1970, ?x314), role(?x314, ?x74), ?x1970 = 0zjpz >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01wy61y instrumentalists! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 72.000 0.684 http://example.org/music/instrument/instrumentalists EVAL 01wy61y instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 72.000 0.684 http://example.org/music/instrument/instrumentalists #4228-0gq9h PRED entity: 0gq9h PRED relation: ceremony PRED expected values: 073hkh 0bzk8w 02ywhz 05hmp6 09306z => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 84): 073hkh (0.86 #421, 0.38 #337, 0.30 #169), 02ywhz (0.86 #472, 0.38 #388, 0.30 #220), 09306z (0.82 #487, 0.31 #403, 0.26 #3614), 0bzk8w (0.73 #426, 0.31 #342, 0.30 #174), 05hmp6 (0.68 #477, 0.31 #393, 0.30 #225), 0dthsy (0.68 #463, 0.19 #379, 0.14 #631), 0gpjbt (0.61 #1450, 0.46 #1030, 0.34 #2879), 09n4nb (0.60 #1463, 0.45 #1043, 0.34 #2892), 0ftlxj (0.59 #465, 0.25 #381, 0.12 #633), 0466p0j (0.59 #1479, 0.45 #1059, 0.33 #2908) >> Best rule #421 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x1307, 073hkh) <- ceremony(?x1307, ?x3254), award(?x71, ?x1307), ?x3254 = 073h9x >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5 EVAL 0gq9h ceremony 09306z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq9h ceremony 05hmp6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq9h ceremony 02ywhz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq9h ceremony 0bzk8w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq9h ceremony 073hkh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.864 http://example.org/award/award_category/winners./award/award_honor/ceremony #4227-07r1h PRED entity: 07r1h PRED relation: award PRED expected values: 02g2wv => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 307): 027cyf7 (0.70 #41557, 0.68 #21561, 0.67 #53322), 027c95y (0.70 #41557, 0.68 #21561, 0.67 #53322), 05pcn59 (0.36 #2430, 0.30 #4390, 0.30 #7134), 0gq9h (0.35 #21636, 0.32 #23204, 0.31 #20067), 0bfvd4 (0.33 #111, 0.21 #4031, 0.09 #895), 0gqwc (0.33 #72, 0.14 #13792, 0.13 #20848), 094qd5 (0.33 #42, 0.14 #2786, 0.13 #13762), 099cng (0.33 #83, 0.12 #2827, 0.09 #867), 0bdwft (0.33 #66, 0.09 #12610, 0.09 #14570), 0cqgl9 (0.33 #181, 0.08 #2533, 0.06 #6845) >> Best rule #41557 for best value: >> intensional similarity = 2 >> extensional distance = 1294 >> proper extension: 01jq34; 0c_mvb; 0lzkm; 01_8w2; 01p5yn; 03yxwq; 018_q8; 0gsgr; 0kc8y; 04rqd; ... >> query: (?x6187, ?x2915) <- award_winner(?x6187, ?x157), award_winner(?x2915, ?x6187) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #53715 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2257 *> proper extension: 0kcd5; *> query: (?x6187, ?x500) <- nominated_for(?x6187, ?x7225), nominated_for(?x500, ?x7225) *> conf = 0.12 ranks of expected_values: 75 EVAL 07r1h award 02g2wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 156.000 156.000 0.699 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4226-02v60l PRED entity: 02v60l PRED relation: sibling PRED expected values: 023tp8 => 129 concepts (47 used for prediction) PRED predicted values (max 10 best out of 73): 04d_mtq (0.09 #443, 0.02 #2197), 04zn7g (0.07 #346, 0.04 #462, 0.02 #813), 033wx9 (0.07 #256, 0.04 #372, 0.02 #723), 06t61y (0.07 #246, 0.04 #362, 0.02 #830), 032_jg (0.07 #240, 0.04 #356, 0.02 #824), 01z7s_ (0.07 #283, 0.04 #399, 0.02 #867), 02js6_ (0.07 #254, 0.04 #370, 0.02 #838), 0c7xjb (0.07 #275, 0.03 #624, 0.02 #859), 01pllx (0.07 #309, 0.02 #776, 0.02 #893), 018yj6 (0.07 #307, 0.02 #891, 0.01 #1122) >> Best rule #443 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 01q_ph; 018db8; 032_jg; 0170vn; 02js6_; 09889g; 02_l96; 046zh; 04cr6qv; 04f7c55; ... >> query: (?x4611, 04d_mtq) <- profession(?x4611, ?x319), participant(?x4611, ?x4536), film(?x4611, ?x1184), sibling(?x4611, ?x7617) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #819 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 03wd5tk; 02x8kk; 02x8mt; 0b5x23; *> query: (?x4611, 023tp8) <- location(?x4611, ?x4612), sibling(?x7617, ?x4611), gender(?x4611, ?x231) *> conf = 0.02 ranks of expected_values: 73 EVAL 02v60l sibling 023tp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 129.000 47.000 0.087 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #4225-06jry PRED entity: 06jry PRED relation: nutrient! PRED expected values: 01645p 07j87 05z55 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 72): 05z55 (0.90 #333, 0.90 #329, 0.90 #293), 01645p (0.90 #510, 0.90 #503, 0.88 #554), 037ls6 (0.89 #437, 0.89 #431, 0.88 #197), 07j87 (0.88 #197, 0.88 #124, 0.88 #121), 0f4kp (0.04 #29, 0.04 #12, 0.03 #72), 0fzjh (0.04 #29, 0.04 #12, 0.03 #72), 01n78x (0.04 #29, 0.04 #12, 0.03 #72), 0q01m (0.04 #29, 0.04 #12, 0.03 #72), 0h1yf (0.04 #29, 0.04 #12, 0.03 #72), 02kc008 (0.04 #29, 0.04 #12, 0.03 #72) >> Best rule #333 for best value: >> intensional similarity = 118 >> extensional distance = 18 >> proper extension: 0h1sz; >> query: (?x6192, ?x9732) <- nutrient(?x10612, ?x6192), nutrient(?x7057, ?x6192), nutrient(?x6191, ?x6192), nutrient(?x6159, ?x6192), nutrient(?x5373, ?x6192), nutrient(?x5337, ?x6192), nutrient(?x5009, ?x6192), nutrient(?x4068, ?x6192), nutrient(?x3900, ?x6192), nutrient(?x3468, ?x6192), nutrient(?x1959, ?x6192), nutrient(?x1303, ?x6192), nutrient(?x1257, ?x6192), nutrient(?x10612, ?x14210), nutrient(?x10612, ?x13944), nutrient(?x10612, ?x13545), nutrient(?x10612, ?x13498), nutrient(?x10612, ?x12454), nutrient(?x10612, ?x12083), nutrient(?x10612, ?x11758), nutrient(?x10612, ?x11270), nutrient(?x10612, ?x10098), nutrient(?x10612, ?x9949), nutrient(?x10612, ?x9915), nutrient(?x10612, ?x9840), nutrient(?x10612, ?x9795), nutrient(?x10612, ?x9733), nutrient(?x10612, ?x9619), nutrient(?x10612, ?x9490), nutrient(?x10612, ?x9436), nutrient(?x10612, ?x9426), nutrient(?x10612, ?x9365), nutrient(?x10612, ?x8487), nutrient(?x10612, ?x8442), nutrient(?x10612, ?x8413), nutrient(?x10612, ?x7894), nutrient(?x10612, ?x7652), nutrient(?x10612, ?x7364), nutrient(?x10612, ?x7362), nutrient(?x10612, ?x7219), nutrient(?x10612, ?x7135), nutrient(?x10612, ?x6586), nutrient(?x10612, ?x6160), nutrient(?x10612, ?x6026), nutrient(?x10612, ?x5549), nutrient(?x10612, ?x5451), nutrient(?x10612, ?x5010), nutrient(?x10612, ?x3901), nutrient(?x10612, ?x3469), nutrient(?x10612, ?x3203), nutrient(?x10612, ?x2702), nutrient(?x10612, ?x1960), nutrient(?x10612, ?x1258), ?x5010 = 0h1vz, ?x7135 = 025rsfk, ?x1960 = 07hnp, ?x3901 = 0466p20, ?x13944 = 0f4kp, ?x7219 = 0h1vg, ?x9795 = 05v_8y, ?x3900 = 061_f, ?x8413 = 02kc4sf, ?x9840 = 02p0tjr, ?x1303 = 0fj52s, ?x10098 = 0h1_c, ?x14210 = 0f4k5, ?x11758 = 0q01m, ?x8487 = 014yzm, ?x13498 = 07q0m, ?x7652 = 025s0s0, ?x3469 = 0h1zw, nutrient(?x5337, ?x8243), nutrient(?x5337, ?x2018), ?x7362 = 02kc5rj, ?x7364 = 09gvd, ?x6586 = 05gh50, ?x6160 = 041r51, ?x9949 = 02kd0rh, ?x2018 = 01sh2, ?x8243 = 014d7f, nutrient(?x9732, ?x5549), nutrient(?x9489, ?x5549), nutrient(?x8298, ?x5549), nutrient(?x6285, ?x5549), ?x6026 = 025sf8g, ?x6159 = 033cnk, ?x9426 = 0h1yy, ?x8442 = 02kcv4x, ?x3468 = 0cxn2, ?x9436 = 025sqz8, ?x8298 = 037ls6, ?x6191 = 014j1m, ?x11270 = 02kc008, ?x9365 = 04k8n, ?x9732 = 05z55, ?x7894 = 0f4hc, ?x2702 = 0838f, ?x1258 = 0h1wg, ?x9733 = 0h1tz, ?x12083 = 01n78x, ?x5373 = 0971v, ?x5009 = 0fjfh, ?x9489 = 07j87, ?x5451 = 05wvs, nutrient(?x1257, ?x6033), nutrient(?x1257, ?x5374), ?x9490 = 0h1sg, ?x9915 = 025tkqy, ?x13545 = 01w_3, ?x7057 = 0fbdb, ?x6033 = 04zjxcz, ?x1959 = 0f25w9, ?x12454 = 025rw19, ?x5374 = 025s0zp, ?x6285 = 01645p, ?x9619 = 0h1tg, ?x3203 = 04kl74p, ?x4068 = 0fbw6 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 06jry nutrient! 05z55 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.900 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 06jry nutrient! 07j87 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 56.000 0.900 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 06jry nutrient! 01645p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.900 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #4224-01c4_6 PRED entity: 01c4_6 PRED relation: award_winner PRED expected values: 0137g1 => 34 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1314): 01vs_v8 (0.50 #2928, 0.50 #461, 0.24 #5396), 01v_pj6 (0.50 #340, 0.38 #9869, 0.38 #14804), 02z4b_8 (0.50 #1571, 0.33 #4038, 0.20 #6506), 01vw20h (0.50 #999, 0.33 #3466, 0.06 #13336), 0137g1 (0.38 #9869, 0.38 #14804, 0.37 #12336), 01vv7sc (0.38 #9869, 0.38 #14804, 0.37 #12336), 03j24kf (0.38 #9869, 0.38 #14804, 0.37 #12336), 01vsy7t (0.38 #9869, 0.38 #14804, 0.37 #12336), 018x3 (0.38 #9869, 0.38 #14804, 0.37 #12336), 0133x7 (0.38 #9869, 0.38 #14804, 0.37 #12336) >> Best rule #2928 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02f72n; >> query: (?x1565, 01vs_v8) <- award_winner(?x1565, ?x9791), award_winner(?x1565, ?x2395), ?x9791 = 016l09, artists(?x302, ?x2395), artist(?x6672, ?x2395), award(?x1004, ?x1565) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9869 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 61 *> proper extension: 02wh75; 026mg3; 01d38g; 02grdc; 01bgqh; 0c4z8; 02g8mp; 01c9f2; 01c427; 02gx2k; ... *> query: (?x1565, ?x1004) <- award_winner(?x1565, ?x1566), ceremony(?x1565, ?x6487), ceremony(?x1565, ?x139), award(?x1004, ?x1565), ?x139 = 05pd94v, ?x6487 = 01mh_q *> conf = 0.38 ranks of expected_values: 5 EVAL 01c4_6 award_winner 0137g1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 34.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #4223-04xvlr PRED entity: 04xvlr PRED relation: genre! PRED expected values: 095zlp 092vkg 04t6fk 01jzyf 057__d => 47 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1651): 07g1sm (0.80 #1657, 0.77 #8299, 0.77 #8298), 0hv8w (0.80 #1657, 0.77 #8299, 0.77 #8298), 0168ls (0.80 #1657, 0.77 #8299, 0.77 #8298), 0n08r (0.80 #1657, 0.77 #8299, 0.77 #8298), 011yqc (0.80 #1657, 0.77 #8299, 0.77 #8298), 0qmd5 (0.80 #1657, 0.77 #8299, 0.77 #8298), 0k2cb (0.80 #1657, 0.77 #8299, 0.77 #8298), 09sr0 (0.80 #1657, 0.77 #8299, 0.77 #8298), 049xgc (0.80 #1657, 0.77 #8299, 0.77 #8298), 01fx6y (0.80 #1657, 0.77 #8299, 0.77 #8298) >> Best rule #1657 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 07s9rl0; >> query: (?x162, ?x144) <- titles(?x162, ?x11351), titles(?x162, ?x4458), titles(?x162, ?x4197), titles(?x162, ?x144), ?x4458 = 03cfkrw, genre(?x603, ?x162), ?x4197 = 01242_, ?x11351 = 02wtp6 >> conf = 0.80 => this is the best rule for 110 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 12, 25, 206, 937, 1282 EVAL 04xvlr genre! 057__d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 47.000 21.000 0.797 http://example.org/film/film/genre EVAL 04xvlr genre! 01jzyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 47.000 21.000 0.797 http://example.org/film/film/genre EVAL 04xvlr genre! 04t6fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 21.000 0.797 http://example.org/film/film/genre EVAL 04xvlr genre! 092vkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 21.000 0.797 http://example.org/film/film/genre EVAL 04xvlr genre! 095zlp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 47.000 21.000 0.797 http://example.org/film/film/genre #4222-0170z3 PRED entity: 0170z3 PRED relation: executive_produced_by PRED expected values: 027kmrb => 110 concepts (76 used for prediction) PRED predicted values (max 10 best out of 95): 02z6l5f (0.11 #1383, 0.07 #2644, 0.06 #2897), 06q8hf (0.10 #3198, 0.09 #2693, 0.08 #419), 05hj_k (0.08 #350, 0.07 #1363, 0.07 #3129), 0grrq8 (0.08 #361), 06pj8 (0.08 #1824, 0.06 #3340, 0.06 #1572), 02qzjj (0.08 #2005, 0.04 #1248, 0.04 #3521), 079vf (0.08 #3287, 0.06 #4044, 0.05 #4297), 02q42j_ (0.07 #2663, 0.05 #3168, 0.04 #2916), 0b13g7 (0.07 #2612, 0.05 #3117, 0.04 #2865), 059x0w (0.06 #709, 0.01 #3994) >> Best rule #1383 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 03q8xj; >> query: (?x54, 02z6l5f) <- nominated_for(?x591, ?x54), genre(?x54, ?x7223), film_crew_role(?x54, ?x1284), ?x7223 = 01j1n2 >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0170z3 executive_produced_by 027kmrb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 76.000 0.111 http://example.org/film/film/executive_produced_by #4221-0fthdk PRED entity: 0fthdk PRED relation: award_nominee! PRED expected values: 02zfdp => 117 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1023): 045c66 (0.81 #48759, 0.81 #48760, 0.81 #46436), 06t74h (0.81 #48759, 0.81 #48760, 0.81 #46436), 05th8t (0.81 #48759, 0.81 #48760, 0.81 #46436), 02zfdp (0.81 #48759, 0.81 #48760, 0.81 #46436), 062dn7 (0.33 #873, 0.17 #130023, 0.12 #16249), 07ldhs (0.33 #1173, 0.17 #130023, 0.12 #16249), 04bdxl (0.33 #6, 0.17 #130023, 0.07 #60370), 03k7bd (0.33 #387, 0.17 #130023, 0.07 #60370), 022411 (0.33 #2070, 0.17 #130023, 0.07 #60370), 08vr94 (0.33 #890, 0.17 #130023, 0.07 #60370) >> Best rule #48759 for best value: >> intensional similarity = 4 >> extensional distance = 428 >> proper extension: 03qcq; 01v3bn; 06s6hs; 03k48_; 019n7x; >> query: (?x9314, ?x1244) <- award_nominee(?x9314, ?x9152), award_nominee(?x9314, ?x1244), participant(?x9314, ?x10103), profession(?x9152, ?x1032) >> conf = 0.81 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 0fthdk award_nominee! 02zfdp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 58.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4220-07wqr6 PRED entity: 07wqr6 PRED relation: country_of_origin PRED expected values: 09c7w0 => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 14): 09c7w0 (0.86 #67, 0.85 #101, 0.84 #123), 07ssc (0.11 #64, 0.10 #53, 0.10 #233), 03_3d (0.08 #58, 0.08 #238, 0.07 #216), 0d060g (0.04 #59, 0.03 #70, 0.03 #81), 0f4zv (0.02 #581), 0kpys (0.02 #581), 030qb3t (0.02 #581), 059rby (0.02 #581), 07c52 (0.02 #89), 02jx1 (0.01 #66, 0.01 #202, 0.01 #213) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 152 >> proper extension: 063zky; >> query: (?x9562, 09c7w0) <- actor(?x9562, ?x8667), program(?x1394, ?x9562), film(?x8667, ?x915) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07wqr6 country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.857 http://example.org/tv/tv_program/country_of_origin #4219-03s0w PRED entity: 03s0w PRED relation: district_represented! PRED expected values: 077g7n 02glc4 => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 41): 077g7n (0.90 #414, 0.82 #3, 0.82 #291), 02glc4 (0.82 #21, 0.75 #42, 0.58 #617), 03z5xd (0.75 #42, 0.59 #7, 0.58 #617), 03ww_x (0.75 #42, 0.59 #4, 0.58 #617), 01gt99 (0.75 #42, 0.58 #617, 0.56 #325), 01gtdd (0.75 #42, 0.58 #617, 0.54 #322), 01gst_ (0.75 #42, 0.58 #617, 0.54 #297), 01gtcc (0.75 #42, 0.58 #617, 0.53 #11), 01gtcq (0.75 #42, 0.58 #617, 0.53 #19), 01gtbb (0.75 #42, 0.58 #617, 0.51 #296) >> Best rule #414 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 03gh4; >> query: (?x961, 077g7n) <- location(?x1987, ?x961), jurisdiction_of_office(?x900, ?x961), district_represented(?x176, ?x961) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03s0w district_represented! 02glc4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.898 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 03s0w district_represented! 077g7n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.898 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #4218-014ps4 PRED entity: 014ps4 PRED relation: place_of_birth PRED expected values: 02_286 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 120): 0cr3d (0.59 #16206, 0.59 #15594, 0.51 #12680), 03l2n (0.41 #16205, 0.33 #76809, 0.33 #77515), 01_d4 (0.33 #66, 0.14 #2884, 0.12 #4292), 0zlgm (0.20 #2291, 0.14 #2995, 0.12 #4403), 0n96z (0.20 #2788, 0.14 #3492, 0.12 #4900), 06pr6 (0.14 #3081, 0.12 #4489, 0.04 #10829), 0k_q_ (0.14 #3605, 0.08 #8535, 0.05 #9240), 0fvzg (0.14 #3621, 0.08 #8551, 0.02 #11369), 0sbbq (0.14 #3831, 0.08 #8761, 0.02 #11579), 02cl1 (0.14 #3538, 0.05 #9173, 0.02 #11286) >> Best rule #16206 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 02x8kk; 076df9; >> query: (?x7828, ?x2850) <- gender(?x7828, ?x231), location(?x7828, ?x2850), ?x2850 = 0cr3d, ?x231 = 05zppz >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #64842 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1230 *> proper extension: 049tjg; 01qx13; 03xyp_; 02y8bn; 018fwv; 0ngg; *> query: (?x7828, 02_286) <- gender(?x7828, ?x231), type_of_union(?x7828, ?x566), location(?x7828, ?x2850), citytown(?x5981, ?x2850) *> conf = 0.08 ranks of expected_values: 22 EVAL 014ps4 place_of_birth 02_286 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 128.000 128.000 0.586 http://example.org/people/person/place_of_birth #4217-092c5f PRED entity: 092c5f PRED relation: ceremony! PRED expected values: 09sb52 0cqh46 => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 312): 0gqy2 (0.50 #5626, 0.48 #5374, 0.48 #4621), 0gq_d (0.48 #5662, 0.47 #5410, 0.46 #4657), 0k611 (0.48 #5577, 0.47 #5325, 0.46 #4825), 0gqwc (0.48 #5565, 0.47 #5313, 0.45 #4560), 0gvx_ (0.47 #5641, 0.46 #4636, 0.46 #5389), 018wng (0.46 #4788, 0.46 #5540, 0.46 #4535), 0gqyl (0.46 #5585, 0.46 #5333, 0.45 #4580), 0f4x7 (0.46 #5532, 0.45 #5280, 0.45 #4780), 0p9sw (0.46 #5527, 0.45 #5275, 0.44 #1014), 0gs96 (0.45 #5595, 0.44 #5343, 0.44 #4843) >> Best rule #5626 for best value: >> intensional similarity = 9 >> extensional distance = 119 >> proper extension: 0fz2y7; 0fzrtf; 0c6vcj; 0fzrhn; >> query: (?x1193, 0gqy2) <- award_winner(?x1193, ?x2035), award_winner(?x1193, ?x1735), honored_for(?x1193, ?x715), award_nominee(?x3176, ?x1735), award_winner(?x618, ?x1735), award_winner(?x1818, ?x3176), award(?x3176, ?x704), profession(?x1735, ?x319), award(?x2035, ?x102) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2749 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 72 *> proper extension: 0ds460j; *> query: (?x1193, ?x451) <- award_winner(?x1193, ?x8491), award_winner(?x1193, ?x5103), award_winner(?x1193, ?x2900), award_winner(?x1193, ?x1735), award_winner(?x1193, ?x221), participant(?x1735, ?x521), film(?x1735, ?x721), award_nominee(?x275, ?x221), participant(?x286, ?x1735), place_of_birth(?x5103, ?x9336), gender(?x2900, ?x514), award_winner(?x451, ?x8491) *> conf = 0.26 ranks of expected_values: 34, 38 EVAL 092c5f ceremony! 0cqh46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 25.000 25.000 0.496 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 092c5f ceremony! 09sb52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 25.000 25.000 0.496 http://example.org/award/award_category/winners./award/award_honor/ceremony #4216-02j9z PRED entity: 02j9z PRED relation: countries_within PRED expected values: 03rjj 03rt9 07ssc 06mkj => 158 concepts (128 used for prediction) PRED predicted values (max 10 best out of 194): 084n_ (0.54 #1008, 0.33 #914, 0.19 #913), 012m_ (0.54 #1008, 0.33 #914, 0.19 #913), 04g61 (0.54 #1008, 0.23 #2404, 0.21 #2683), 056vv (0.54 #1008, 0.23 #2404, 0.21 #2683), 04w58 (0.54 #1008, 0.21 #2683, 0.19 #913), 035hm (0.54 #1008, 0.19 #913, 0.17 #1751), 03t1s (0.54 #1008, 0.19 #913, 0.17 #1751), 03_xj (0.54 #1008, 0.19 #913, 0.17 #1751), 0jdx (0.33 #914, 0.23 #2404, 0.21 #2683), 082fr (0.33 #914, 0.23 #2404, 0.21 #2683) >> Best rule #1008 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 02j7k; >> query: (?x455, ?x1264) <- contains(?x455, ?x1264), adjoins(?x2467, ?x455), form_of_government(?x1264, ?x6441) >> conf = 0.54 => this is the best rule for 8 predicted values *> Best rule #3145 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 02vzc; 068p2; *> query: (?x455, ?x789) <- contains(?x455, ?x1264), locations(?x1777, ?x455), adjoins(?x1264, ?x789) *> conf = 0.12 ranks of expected_values: 45, 48, 154 EVAL 02j9z countries_within 06mkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 158.000 128.000 0.537 http://example.org/base/locations/continents/countries_within EVAL 02j9z countries_within 07ssc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 158.000 128.000 0.537 http://example.org/base/locations/continents/countries_within EVAL 02j9z countries_within 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 128.000 0.537 http://example.org/base/locations/continents/countries_within EVAL 02j9z countries_within 03rjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 158.000 128.000 0.537 http://example.org/base/locations/continents/countries_within #4215-05sbv3 PRED entity: 05sbv3 PRED relation: nominated_for! PRED expected values: 04gmp_z => 91 concepts (33 used for prediction) PRED predicted values (max 10 best out of 362): 025jbj (0.78 #30374, 0.78 #30373, 0.78 #42054), 04gmp_z (0.78 #30374, 0.78 #30373, 0.78 #42054), 076lxv (0.33 #138, 0.09 #46727), 09r9m7 (0.33 #1281), 04vzv4 (0.33 #1000), 036jb (0.33 #998), 04g4n (0.30 #72421, 0.22 #70084), 0g1rw (0.14 #67748, 0.14 #58404, 0.03 #4812), 072twv (0.09 #46727, 0.03 #7517, 0.02 #12190), 0fmqp6 (0.09 #46727, 0.02 #8505, 0.02 #13178) >> Best rule #30374 for best value: >> intensional similarity = 3 >> extensional distance = 477 >> proper extension: 01h1bf; 02kk_c; 0c3xpwy; 07bz5; 07s8z_l; 03czz87; >> query: (?x11348, ?x9964) <- award_winner(?x11348, ?x9964), honored_for(?x4388, ?x11348), nationality(?x9964, ?x94) >> conf = 0.78 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 05sbv3 nominated_for! 04gmp_z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 33.000 0.783 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4214-04mz10g PRED entity: 04mz10g PRED relation: place_of_birth PRED expected values: 0f2tj => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 43): 0f2tj (0.27 #34509, 0.27 #29578), 0cr3d (0.07 #94, 0.05 #798, 0.05 #1502), 0c_m3 (0.07 #197, 0.05 #901, 0.05 #1605), 0vzm (0.07 #117, 0.05 #821, 0.05 #1525), 0rt80 (0.07 #668, 0.05 #1372, 0.05 #2076), 06y57 (0.07 #180, 0.05 #1588, 0.03 #2292), 0cv3w (0.07 #106, 0.05 #1514, 0.03 #2218), 0ccvx (0.07 #153, 0.05 #1561), 019k6n (0.07 #107, 0.05 #1515), 02_286 (0.07 #38048, 0.07 #29597, 0.07 #6356) >> Best rule #34509 for best value: >> intensional similarity = 2 >> extensional distance = 2301 >> proper extension: 0qkj7; >> query: (?x1404, ?x6769) <- gender(?x1404, ?x231), location(?x1404, ?x6769) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mz10g place_of_birth 0f2tj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.275 http://example.org/people/person/place_of_birth #4213-01_f_5 PRED entity: 01_f_5 PRED relation: award PRED expected values: 02qyp19 => 138 concepts (107 used for prediction) PRED predicted values (max 10 best out of 294): 04dn09n (0.77 #22813, 0.71 #40921, 0.71 #28323), 03hl6lc (0.77 #22813, 0.71 #40921, 0.71 #28323), 0789r6 (0.71 #40921, 0.71 #28323, 0.71 #28718), 0gr4k (0.36 #5143, 0.35 #5537, 0.34 #6717), 09sb52 (0.31 #4364, 0.29 #18128, 0.29 #30333), 02qyp19 (0.31 #1574, 0.19 #5113, 0.18 #6687), 03hkv_r (0.25 #5126, 0.23 #6700, 0.23 #5520), 02n9nmz (0.20 #5178, 0.20 #5572, 0.20 #6752), 05zr6wv (0.20 #15, 0.18 #4340, 0.14 #12206), 0f4x7 (0.20 #29, 0.18 #4354, 0.14 #35018) >> Best rule #22813 for best value: >> intensional similarity = 4 >> extensional distance = 973 >> proper extension: 011zf2; 03yf3z; 0grrq8; 01l03w2; 03wd5tk; 0g5ff; 059x0w; 019fnv; 014g91; 03cd1q; >> query: (?x6275, ?x746) <- award_winner(?x746, ?x6275), award(?x6275, ?x198), location(?x6275, ?x739), ceremony(?x746, ?x747) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #1574 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 86 *> proper extension: 05prs8; 06dl_; 02d6cy; 058nh2; 01vz80y; 023w9s; 02qdymm; *> query: (?x6275, 02qyp19) <- type_of_union(?x6275, ?x566), award(?x6275, ?x1862), ?x1862 = 0gr51 *> conf = 0.31 ranks of expected_values: 6 EVAL 01_f_5 award 02qyp19 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 138.000 107.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4212-09y20 PRED entity: 09y20 PRED relation: award_nominee! PRED expected values: 05vsxz => 75 concepts (47 used for prediction) PRED predicted values (max 10 best out of 690): 0993r (0.84 #2318, 0.81 #23174, 0.81 #23173), 05vsxz (0.84 #2318, 0.81 #23173, 0.81 #78791), 020_95 (0.84 #2318, 0.81 #23173, 0.81 #78791), 09y20 (0.75 #316, 0.16 #85747, 0.03 #21171), 0151w_ (0.18 #108927, 0.16 #85747, 0.06 #199), 016gr2 (0.18 #108927, 0.16 #85747, 0.06 #242), 0184jc (0.18 #108927, 0.16 #85747, 0.06 #5), 0bq2g (0.18 #108927, 0.16 #85747, 0.06 #790), 03yk8z (0.18 #108927, 0.16 #85747, 0.06 #2068), 0lpjn (0.18 #108927, 0.16 #85747, 0.06 #614) >> Best rule #2318 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 07hbxm; 04rsd2; >> query: (?x1549, ?x100) <- award_nominee(?x1549, ?x7186), award_nominee(?x1549, ?x6122), award_nominee(?x1549, ?x100), ?x6122 = 016xh5, ?x7186 = 01qrbf >> conf = 0.84 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 09y20 award_nominee! 05vsxz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 47.000 0.839 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4211-0nm9y PRED entity: 0nm9y PRED relation: source PRED expected values: 0jbk9 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #9, 0.92 #8, 0.91 #21) >> Best rule #9 for best value: >> intensional similarity = 5 >> extensional distance = 153 >> proper extension: 0nj1c; >> query: (?x13871, 0jbk9) <- adjoins(?x13871, ?x7059), time_zones(?x13871, ?x2674), ?x2674 = 02hcv8, contains(?x7058, ?x7059), second_level_divisions(?x94, ?x7059) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nm9y source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.923 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #4210-06b_0 PRED entity: 06b_0 PRED relation: gender PRED expected values: 05zppz => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #73, 0.87 #37, 0.87 #49), 02zsn (0.49 #60, 0.47 #70, 0.45 #46) >> Best rule #73 for best value: >> intensional similarity = 3 >> extensional distance = 246 >> proper extension: 0dr5y; 0k_mt; >> query: (?x7670, 05zppz) <- film(?x7670, ?x5074), profession(?x7670, ?x319), nationality(?x7670, ?x456) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06b_0 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.875 http://example.org/people/person/gender #4209-0wsr PRED entity: 0wsr PRED relation: colors PRED expected values: 03vtbc => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 18): 01g5v (0.54 #1050, 0.39 #1171, 0.31 #155), 03vtbc (0.43 #74, 0.33 #142, 0.33 #40), 02rnmb (0.37 #266, 0.23 #711, 0.20 #838), 01l849 (0.36 #290, 0.33 #1, 0.30 #343), 04d18d (0.20 #838, 0.20 #1316, 0.18 #135), 09ggk (0.20 #838, 0.20 #1316, 0.17 #1117), 0jc_p (0.20 #838, 0.19 #1340, 0.17 #1117), 0680m7 (0.20 #838, 0.17 #1117, 0.16 #1169), 036k5h (0.20 #838, 0.17 #1117, 0.15 #547), 038hg (0.20 #838, 0.16 #1169, 0.15 #547) >> Best rule #1050 for best value: >> intensional similarity = 11 >> extensional distance = 170 >> proper extension: 02gys2; 0690dn; 049dzz; 01rly6; 03tc8d; 02k9k9; 0bg4f9; >> query: (?x6645, 01g5v) <- colors(?x6645, ?x1101), team(?x935, ?x6645), team(?x935, ?x4469), team(?x935, ?x2574), team(?x9586, ?x2574), position(?x8329, ?x935), sport(?x2574, ?x1083), teams(?x7930, ?x4469), colors(?x481, ?x1101), colors(?x9618, ?x1101), ?x9618 = 05hyzx >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #74 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 5 *> proper extension: 05l71; *> query: (?x6645, 03vtbc) <- sport(?x6645, ?x1083), position(?x6645, ?x7079), position(?x6645, ?x3346), position(?x6645, ?x2312), position(?x6645, ?x2147), position(?x6645, ?x1717), position(?x6645, ?x1517), position(?x6645, ?x1240), position(?x6645, ?x180), ?x180 = 01r3hr, ?x1717 = 02g_6x, ?x2312 = 02qpbqj, ?x3346 = 02g_7z, ?x1517 = 02g_6j, team(?x2247, ?x6645), team(?x1240, ?x179), ?x7079 = 08ns5s, ?x2147 = 04nfpk *> conf = 0.43 ranks of expected_values: 2 EVAL 0wsr colors 03vtbc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 79.000 0.541 http://example.org/sports/sports_team/colors #4208-04sx9_ PRED entity: 04sx9_ PRED relation: profession PRED expected values: 02hrh1q => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 49): 02hrh1q (0.88 #7666, 0.87 #4666, 0.87 #7816), 01d_h8 (0.37 #1051, 0.36 #1357, 0.33 #156), 0cbd2 (0.37 #1051, 0.15 #4058, 0.14 #8708), 016z4k (0.37 #1051, 0.11 #154, 0.10 #6905), 07s467s (0.37 #1051), 0dxtg (0.28 #1065, 0.27 #5715, 0.27 #4365), 03gjzk (0.23 #1067, 0.21 #3317, 0.21 #2567), 02jknp (0.21 #1359, 0.21 #4359, 0.21 #5709), 0np9r (0.20 #22, 0.14 #8273, 0.14 #7823), 0kyk (0.20 #31, 0.11 #4082, 0.10 #5582) >> Best rule #7666 for best value: >> intensional similarity = 2 >> extensional distance = 1902 >> proper extension: 01rrwf6; 01n8_g; 08b8vd; 09p06; 0c2ry; 02hhtj; 011xjd; 02p59ry; 09r_wb; 013bd1; ... >> query: (?x919, 02hrh1q) <- film(?x919, ?x4749), nominated_for(?x1564, ?x4749) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sx9_ profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.879 http://example.org/people/person/profession #4207-093l8p PRED entity: 093l8p PRED relation: award PRED expected values: 02z1nbg => 85 concepts (72 used for prediction) PRED predicted values (max 10 best out of 226): 05zr6wv (0.33 #15, 0.03 #4937, 0.03 #4234), 025m8l (0.28 #5626, 0.26 #6564, 0.25 #1642), 0gqwc (0.28 #5626, 0.26 #6564, 0.25 #1642), 09qwmm (0.28 #5626, 0.26 #6564, 0.25 #1642), 054ks3 (0.28 #5626, 0.26 #6564, 0.25 #1642), 02x4wr9 (0.28 #5626, 0.26 #6564, 0.25 #1642), 02xj3rw (0.28 #5626, 0.26 #6564, 0.25 #1642), 0l8z1 (0.27 #522, 0.16 #1224, 0.08 #6147), 03x3wf (0.22 #2579, 0.18 #8674, 0.13 #16196), 05q8pss (0.22 #2579, 0.18 #8674, 0.13 #16196) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0kbwb; >> query: (?x7584, 05zr6wv) <- featured_film_locations(?x7584, ?x9331), nominated_for(?x618, ?x7584), genre(?x7584, ?x53), ?x9331 = 0qpqn >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2579 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 260 *> proper extension: 0275kr; *> query: (?x7584, ?x1088) <- award_winner(?x7584, ?x8799), award_winner(?x7584, ?x516), award_winner(?x1088, ?x8799), actor(?x2078, ?x516), student(?x6611, ?x516) *> conf = 0.22 ranks of expected_values: 16 EVAL 093l8p award 02z1nbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 85.000 72.000 0.333 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4206-051kv PRED entity: 051kv PRED relation: religion! PRED expected values: 03f5vvx 07rzf 01dbhb => 40 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2426): 05kfs (0.50 #1093, 0.33 #3187, 0.25 #45), 0kjrx (0.50 #665, 0.25 #1713, 0.17 #3807), 01wj92r (0.40 #2300, 0.29 #5442, 0.22 #9637), 01vrncs (0.40 #2157, 0.29 #5299, 0.22 #8444), 02ln1 (0.40 #2788, 0.29 #5930, 0.22 #9075), 0cwtm (0.33 #3949, 0.25 #1855, 0.25 #807), 0lrh (0.33 #3350, 0.25 #1256, 0.25 #208), 01g23m (0.33 #3459, 0.25 #1365, 0.25 #317), 02r34n (0.33 #3214, 0.25 #1120, 0.25 #72), 04v7k2 (0.29 #7313, 0.29 #5218, 0.25 #8360) >> Best rule #1093 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 02rsw; >> query: (?x1624, 05kfs) <- religion(?x6231, ?x1624), religion(?x1387, ?x1624), religion(?x1387, ?x7422), award(?x1387, ?x1313), film(?x1387, ?x1812), profession(?x1387, ?x319), award_nominee(?x574, ?x1387), ?x1313 = 0gs9p, languages(?x6231, ?x254), ?x7422 = 092bf5 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #25173 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 18 *> proper extension: 078vc; *> query: (?x1624, ?x457) <- religion(?x1138, ?x1624), religion(?x1138, ?x2591), religion(?x3701, ?x1624), film(?x3701, ?x708), award(?x3701, ?x112), nominated_for(?x112, ?x144), religion(?x457, ?x2591) *> conf = 0.08 ranks of expected_values: 1040, 1600 EVAL 051kv religion! 01dbhb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 40.000 25.000 0.500 http://example.org/people/person/religion EVAL 051kv religion! 07rzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 40.000 25.000 0.500 http://example.org/people/person/religion EVAL 051kv religion! 03f5vvx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 40.000 25.000 0.500 http://example.org/people/person/religion #4205-04t969 PRED entity: 04t969 PRED relation: location PRED expected values: 0824r => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 67): 02_286 (0.27 #19296, 0.26 #1641, 0.18 #12874), 030qb3t (0.25 #83, 0.21 #2490, 0.21 #6500), 0cr3d (0.13 #1748, 0.10 #19403, 0.06 #144), 01531 (0.12 #157, 0.05 #959, 0.04 #1761), 071cn (0.09 #1800), 0cc56 (0.06 #57, 0.05 #19316, 0.05 #6474), 059rby (0.06 #16, 0.05 #19275, 0.04 #9643), 01cx_ (0.06 #162, 0.04 #1766, 0.03 #19421), 0y1rf (0.06 #550, 0.04 #2154), 02xry (0.06 #132, 0.02 #2539, 0.01 #19391) >> Best rule #19296 for best value: >> intensional similarity = 3 >> extensional distance = 1277 >> proper extension: 04bbv7; 0466k4; >> query: (?x7382, 02_286) <- location(?x7382, ?x11375), profession(?x7382, ?x1032), adjoins(?x11375, ?x13341) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04t969 location 0824r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 90.000 0.266 http://example.org/people/person/places_lived./people/place_lived/location #4204-017149 PRED entity: 017149 PRED relation: award_winner! PRED expected values: 099jhq => 87 concepts (70 used for prediction) PRED predicted values (max 10 best out of 191): 09sb52 (0.50 #41, 0.37 #12058, 0.37 #10336), 0bdwqv (0.37 #12058, 0.37 #10336, 0.37 #14212), 0f4x7 (0.37 #12058, 0.37 #10336, 0.37 #14212), 0gqy2 (0.37 #12058, 0.37 #10336, 0.37 #14212), 027dtxw (0.37 #12058, 0.37 #10336, 0.37 #14212), 0bfvd4 (0.37 #12058, 0.37 #10336, 0.37 #14212), 0789_m (0.37 #12058, 0.37 #10336, 0.37 #14212), 027b9k6 (0.33 #208, 0.09 #24544, 0.08 #18087), 099cng (0.25 #86, 0.09 #24544, 0.08 #18087), 0bdwft (0.17 #68, 0.11 #23683, 0.09 #24544) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 015grj; 0fsm8c; 02bkdn; 01gq0b; 05dbf; 02qgyv; 0h0wc; 01kb2j; 02t_st; 07r_dg; >> query: (?x525, 09sb52) <- award_nominee(?x5758, ?x525), ?x5758 = 01_p6t, nominated_for(?x525, ?x253) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #23683 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2024 *> proper extension: 027_tg; 01j7pt; 0kctd; *> query: (?x525, ?x384) <- nominated_for(?x525, ?x253), award(?x253, ?x384) *> conf = 0.11 ranks of expected_values: 23 EVAL 017149 award_winner! 099jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 87.000 70.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #4203-0bthb PRED entity: 0bthb PRED relation: registering_agency PRED expected values: 03z19 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.85 #10, 0.85 #15, 0.84 #12) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 017j69; >> query: (?x1772, 03z19) <- major_field_of_study(?x1772, ?x947), organization(?x346, ?x1772), currency(?x1772, ?x170), ?x346 = 060c4 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bthb registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.848 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #4202-01w58n3 PRED entity: 01w58n3 PRED relation: profession PRED expected values: 016z4k => 156 concepts (154 used for prediction) PRED predicted values (max 10 best out of 82): 09jwl (0.70 #7524, 0.70 #2079, 0.69 #5463), 0nbcg (0.56 #2092, 0.50 #325, 0.49 #1503), 016z4k (0.55 #298, 0.54 #886, 0.45 #2065), 0dz3r (0.45 #296, 0.44 #443, 0.41 #7802), 0dxtg (0.42 #3988, 0.27 #6931, 0.27 #160), 01d_h8 (0.36 #153, 0.36 #4569, 0.35 #5157), 03gjzk (0.36 #161, 0.22 #18699, 0.21 #5165), 0kyk (0.36 #4004, 0.14 #4151, 0.13 #1206), 0n1h (0.30 #893, 0.28 #1335, 0.28 #599), 039v1 (0.29 #2980, 0.29 #7542, 0.28 #5481) >> Best rule #7524 for best value: >> intensional similarity = 3 >> extensional distance = 459 >> proper extension: 053y0s; 01yznp; 01nqfh_; 0274ck; 01vs14j; 01qvgl; 0p5mw; 04zwjd; 01vyp_; 01wk7b7; ... >> query: (?x9418, 09jwl) <- category(?x9418, ?x134), ?x134 = 08mbj5d, instrumentalists(?x227, ?x9418) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #298 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 01vrncs; 018pj3; 01k23t; 0c9l1; *> query: (?x9418, 016z4k) <- artist(?x9492, ?x9418), award_winner(?x9418, ?x4258), ?x9492 = 03mp8k *> conf = 0.55 ranks of expected_values: 3 EVAL 01w58n3 profession 016z4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 156.000 154.000 0.703 http://example.org/people/person/profession #4201-07cz2 PRED entity: 07cz2 PRED relation: nominated_for! PRED expected values: 0x25q => 81 concepts (16 used for prediction) PRED predicted values (max 10 best out of 135): 0x25q (0.85 #2272, 0.84 #2273, 0.03 #846), 024mxd (0.15 #858, 0.03 #1868, 0.02 #2121), 042g97 (0.12 #1003, 0.02 #2266, 0.02 #2520), 0d_wms (0.12 #862, 0.02 #2125, 0.02 #2379), 042fgh (0.12 #963, 0.02 #1973, 0.02 #2226), 0dtfn (0.09 #794, 0.06 #1046, 0.04 #1299), 0fdv3 (0.09 #804, 0.06 #1056, 0.03 #1309), 0dfw0 (0.09 #896, 0.04 #1148, 0.02 #1401), 0ddjy (0.09 #826, 0.04 #1078, 0.02 #1331), 01_mdl (0.09 #782, 0.02 #2045, 0.02 #2299) >> Best rule #2272 for best value: >> intensional similarity = 4 >> extensional distance = 225 >> proper extension: 02fn5r; >> query: (?x2770, ?x1807) <- nominated_for(?x2770, ?x3055), nominated_for(?x2770, ?x1807), nominated_for(?x1808, ?x2770), nominated_for(?x350, ?x3055) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07cz2 nominated_for! 0x25q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 16.000 0.847 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #4200-0g824 PRED entity: 0g824 PRED relation: profession PRED expected values: 0n1h => 122 concepts (52 used for prediction) PRED predicted values (max 10 best out of 66): 01d_h8 (0.48 #704, 0.39 #2948, 0.37 #1404), 0n1h (0.37 #430, 0.36 #990, 0.31 #1550), 01c72t (0.32 #1982, 0.32 #2263, 0.31 #4090), 039v1 (0.30 #4525, 0.28 #4101, 0.27 #5369), 03gjzk (0.30 #713, 0.27 #13, 0.24 #2957), 0dxtg (0.28 #2956, 0.27 #12, 0.27 #712), 02jknp (0.23 #706, 0.19 #1406, 0.18 #6), 0d1pc (0.23 #605, 0.21 #185, 0.20 #1445), 0cbd2 (0.21 #1826, 0.20 #2528, 0.10 #2949), 0np9r (0.18 #18, 0.11 #5075, 0.11 #6344) >> Best rule #704 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 06jzh; >> query: (?x6383, 01d_h8) <- participant(?x6383, ?x827), currency(?x6383, ?x170), award(?x6383, ?x724) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #430 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 0lk90; 02bwjv; *> query: (?x6383, 0n1h) <- artists(?x505, ?x6383), participant(?x2562, ?x6383), award_winner(?x827, ?x6383) *> conf = 0.37 ranks of expected_values: 2 EVAL 0g824 profession 0n1h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 52.000 0.481 http://example.org/people/person/profession #4199-01_1hw PRED entity: 01_1hw PRED relation: film_release_region PRED expected values: 09c7w0 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 205): 09c7w0 (0.76 #1435, 0.73 #1077, 0.72 #1256), 03gj2 (0.55 #8067, 0.47 #12385, 0.46 #13108), 07ssc (0.27 #5377, 0.23 #4863, 0.22 #2354), 0d060g (0.27 #5377, 0.18 #3237, 0.18 #6821), 03rk0 (0.27 #5377, 0.15 #12566, 0.12 #6885), 02jx1 (0.27 #5377), 0d0vqn (0.24 #1444, 0.22 #7899, 0.21 #9335), 0345h (0.24 #1479, 0.20 #405, 0.19 #7934), 03_3d (0.21 #4849, 0.21 #7897, 0.21 #1442), 0jgd (0.21 #4844, 0.20 #3231, 0.19 #8251) >> Best rule #1435 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 06z8s_; 03twd6; 04n52p6; 027m67; >> query: (?x8631, 09c7w0) <- film_release_distribution_medium(?x8631, ?x81), film(?x1549, ?x8631), language(?x8631, ?x254), genre(?x8631, ?x604), prequel(?x6773, ?x8631), ?x604 = 0lsxr >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_1hw film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.759 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4198-09b3v PRED entity: 09b3v PRED relation: nominated_for PRED expected values: 0pc62 => 124 concepts (70 used for prediction) PRED predicted values (max 10 best out of 538): 04hwbq (0.39 #1624, 0.33 #1801, 0.32 #8121), 0fs9vc (0.39 #1624, 0.32 #8121, 0.17 #12482), 015ynm (0.39 #1624, 0.32 #8121, 0.17 #11026), 04g73n (0.39 #1624, 0.32 #8121, 0.17 #11001), 06fcqw (0.39 #1624, 0.32 #8121, 0.17 #10736), 0241y7 (0.39 #1624, 0.32 #8121, 0.17 #10720), 01c22t (0.39 #1624, 0.32 #8121, 0.17 #9898), 02xbyr (0.39 #1624, 0.32 #8121, 0.16 #21101), 023p7l (0.39 #1624, 0.32 #8121, 0.16 #21101), 0kcn7 (0.39 #1624, 0.32 #8121, 0.16 #21101) >> Best rule #1624 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 03mdt; >> query: (?x3920, ?x218) <- production_companies(?x148, ?x3920), titles(?x3920, ?x218), child(?x3920, ?x166) >> conf = 0.39 => this is the best rule for 34 predicted values *> Best rule #9834 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 038bht; 02qzjj; *> query: (?x3920, 0pc62) <- award_nominee(?x3920, ?x1285), award(?x3920, ?x1105), ?x1285 = 01t6b4 *> conf = 0.33 ranks of expected_values: 48 EVAL 09b3v nominated_for 0pc62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 124.000 70.000 0.391 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4197-01w8sf PRED entity: 01w8sf PRED relation: student! PRED expected values: 0bkj86 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 15): 019v9k (0.16 #484, 0.15 #237, 0.13 #199), 028dcg (0.10 #245, 0.10 #492, 0.09 #207), 02h4rq6 (0.10 #231, 0.08 #193, 0.08 #478), 02_xgp2 (0.10 #203, 0.09 #241, 0.08 #488), 0bkj86 (0.09 #483, 0.08 #198, 0.06 #236), 016t_3 (0.06 #232, 0.05 #194, 0.04 #479), 03mkk4 (0.06 #202, 0.06 #487, 0.06 #240), 04zx3q1 (0.04 #192, 0.04 #477, 0.03 #230), 013zdg (0.03 #197, 0.02 #482, 0.02 #235), 07s6fsf (0.03 #191, 0.02 #229, 0.02 #476) >> Best rule #484 for best value: >> intensional similarity = 3 >> extensional distance = 170 >> proper extension: 0d0vj4; 02r34n; 04jzj; 01wyzyl; 06pwf6; 02tqkf; 03gkn5; 0bkg4; 01gj8_; 05j12n; ... >> query: (?x2609, 019v9k) <- type_of_union(?x2609, ?x566), student(?x1368, ?x2609), nationality(?x2609, ?x1310) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #483 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 170 *> proper extension: 0d0vj4; 02r34n; 04jzj; 01wyzyl; 06pwf6; 02tqkf; 03gkn5; 0bkg4; 01gj8_; 05j12n; ... *> query: (?x2609, 0bkj86) <- type_of_union(?x2609, ?x566), student(?x1368, ?x2609), nationality(?x2609, ?x1310) *> conf = 0.09 ranks of expected_values: 5 EVAL 01w8sf student! 0bkj86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 132.000 132.000 0.163 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #4196-0hmr4 PRED entity: 0hmr4 PRED relation: award PRED expected values: 09cn0c => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 173): 02rdyk7 (0.41 #292, 0.29 #226, 0.28 #9246), 019f4v (0.29 #226, 0.28 #9246, 0.27 #3387), 094qd5 (0.29 #226, 0.28 #9246, 0.27 #3387), 04dn09n (0.29 #226, 0.28 #9246, 0.27 #3387), 0f4x7 (0.29 #226, 0.28 #9246, 0.27 #3387), 04kxsb (0.29 #226, 0.28 #9246, 0.27 #3387), 0gr0m (0.23 #57, 0.18 #2993, 0.17 #2314), 02z1nbg (0.23 #132, 0.15 #3745, 0.12 #584), 02w_6xj (0.22 #379, 0.18 #1055, 0.17 #1730), 0k611 (0.21 #2779, 0.19 #1421, 0.18 #3907) >> Best rule #292 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 0fpkhkz; 01qncf; 011ydl; 0571m; 0dzz6g; 0191n; 0p_tz; 0k2m6; 0gvvm6l; 02jkkv; >> query: (?x718, 02rdyk7) <- award(?x718, ?x1063), genre(?x718, ?x53), ?x1063 = 02rdxsh >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #3800 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 130 *> proper extension: 0g5879y; 0kb57; 014l6_; 04jpk2; 04y5j64; 01k60v; 0dr89x; 03bxp5; 047myg9; 089j8p; ... *> query: (?x718, 09cn0c) <- nominated_for(?x1245, ?x718), film(?x788, ?x718), ?x1245 = 0gqwc *> conf = 0.09 ranks of expected_values: 44 EVAL 0hmr4 award 09cn0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 89.000 89.000 0.407 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4195-05mc99 PRED entity: 05mc99 PRED relation: film PRED expected values: 01jnc_ => 98 concepts (73 used for prediction) PRED predicted values (max 10 best out of 329): 03shpq (0.20 #3231, 0.11 #1444, 0.03 #75064), 01rwyq (0.11 #547, 0.10 #2334, 0.05 #101870), 01718w (0.11 #1396, 0.10 #3183, 0.05 #101870), 02x2jl_ (0.11 #1752, 0.10 #3539, 0.05 #101870), 05sxzwc (0.11 #228, 0.10 #2015, 0.05 #101870), 09q5w2 (0.11 #163, 0.10 #1950, 0.05 #101870), 0170_p (0.11 #95, 0.10 #1882, 0.05 #101870), 09m6kg (0.11 #31, 0.10 #1818, 0.05 #101870), 03mz5b (0.11 #863, 0.10 #2650, 0.03 #33961), 04ydr95 (0.11 #575, 0.10 #2362, 0.03 #33961) >> Best rule #3231 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 06qgvf; 06dv3; 03n_7k; 0pmhf; 04mg6l; 03cglm; 027bs_2; 04v7kt; >> query: (?x7595, 03shpq) <- award_nominee(?x8045, ?x7595), ?x8045 = 04qsdh, profession(?x7595, ?x1032) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #6927 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 157 *> proper extension: 02wr2r; 01vtg4q; 03vrv9; 063g7l; 04bdpf; 054c1; 045gzq; *> query: (?x7595, 01jnc_) <- people(?x2510, ?x7595), film(?x7595, ?x324), ?x2510 = 0x67 *> conf = 0.04 ranks of expected_values: 63 EVAL 05mc99 film 01jnc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 98.000 73.000 0.200 http://example.org/film/actor/film./film/performance/film #4194-07jmgz PRED entity: 07jmgz PRED relation: profession PRED expected values: 02jknp => 109 concepts (50 used for prediction) PRED predicted values (max 10 best out of 85): 02jknp (0.63 #1772, 0.63 #4272, 0.50 #2949), 03gjzk (0.53 #5160, 0.41 #2220, 0.40 #3102), 0cbd2 (0.38 #3242, 0.37 #3536, 0.25 #594), 018gz8 (0.27 #4721, 0.26 #3839, 0.26 #3986), 0np9r (0.25 #6048, 0.13 #4725, 0.13 #3843), 021wpb (0.20 #736, 0.12 #2060, 0.05 #6177), 02krf9 (0.19 #1790, 0.19 #2232, 0.17 #4290), 0kyk (0.18 #3558, 0.18 #3264, 0.13 #7088), 09jwl (0.16 #6782, 0.15 #5605, 0.15 #5458), 02hv44_ (0.11 #7116, 0.09 #3586, 0.09 #350) >> Best rule #1772 for best value: >> intensional similarity = 7 >> extensional distance = 266 >> proper extension: 014zcr; 05ty4m; 09fb5; 02qjj7; 02nb2s; 02pp_q_; 025p38; 01vvycq; 042rnl; 04yj5z; ... >> query: (?x10602, 02jknp) <- profession(?x10602, ?x1032), profession(?x10602, ?x987), profession(?x10602, ?x319), ?x987 = 0dxtg, gender(?x10602, ?x231), ?x319 = 01d_h8, ?x1032 = 02hrh1q >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07jmgz profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 50.000 0.634 http://example.org/people/person/profession #4193-04jplwp PRED entity: 04jplwp PRED relation: executive_produced_by PRED expected values: 0gg9_5q => 65 concepts (52 used for prediction) PRED predicted values (max 10 best out of 55): 05hj_k (0.09 #2362, 0.09 #2613, 0.09 #1606), 06pj8 (0.05 #1563, 0.05 #2570, 0.04 #55), 0glyyw (0.05 #1696, 0.04 #2703, 0.04 #2452), 02q_cc (0.04 #28, 0.03 #1536, 0.02 #2543), 079vf (0.04 #2266, 0.04 #2517, 0.03 #1510), 03c9pqt (0.04 #2510, 0.03 #2761, 0.03 #1754), 02z6l5f (0.04 #2382, 0.03 #2633, 0.03 #1626), 030_3z (0.03 #108, 0.02 #2623, 0.02 #2372), 02z2xdf (0.03 #410, 0.03 #661, 0.02 #1666), 04jspq (0.03 #1659, 0.03 #2415, 0.03 #2666) >> Best rule #2362 for best value: >> intensional similarity = 3 >> extensional distance = 479 >> proper extension: 09xbpt; 0dtw1x; 0h1cdwq; 03s6l2; 026mfbr; 09p35z; 08hmch; 0jjy0; 0g5pv3; 03s5lz; ... >> query: (?x7880, 05hj_k) <- country(?x7880, ?x94), genre(?x7880, ?x53), executive_produced_by(?x7880, ?x7324) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #1598 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 390 *> proper extension: 014lc_; 014_x2; 0ds35l9; 0d90m; 03qcfvw; 09sh8k; 0m313; 02y_lrp; 034qmv; 083shs; ... *> query: (?x7880, 0gg9_5q) <- nominated_for(?x143, ?x7880), film(?x748, ?x7880), executive_produced_by(?x7880, ?x7324) *> conf = 0.03 ranks of expected_values: 11 EVAL 04jplwp executive_produced_by 0gg9_5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 65.000 52.000 0.094 http://example.org/film/film/executive_produced_by #4192-0209hj PRED entity: 0209hj PRED relation: award PRED expected values: 027986c => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 184): 02w9sd7 (0.30 #546, 0.27 #7645, 0.27 #7644), 09qv_s (0.27 #537, 0.25 #101, 0.08 #7425), 027986c (0.27 #472, 0.13 #9828, 0.12 #12453), 02r0csl (0.27 #7645, 0.27 #7644, 0.25 #5), 040njc (0.27 #7645, 0.27 #7644, 0.25 #1529), 027dtxw (0.27 #7645, 0.27 #7644, 0.25 #1529), 02qvyrt (0.27 #7645, 0.27 #7644, 0.25 #1529), 0l8z1 (0.27 #7645, 0.27 #7644, 0.25 #1529), 02qyntr (0.27 #7645, 0.27 #7644, 0.25 #1529), 0p9sw (0.27 #7645, 0.27 #7644, 0.25 #1529) >> Best rule #546 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 09m6kg; 011yrp; 07xtqq; 016z5x; 01sxly; 011yph; 0pv2t; 092vkg; 0gmcwlb; 04m1bm; ... >> query: (?x697, 02w9sd7) <- genre(?x697, ?x53), award(?x697, ?x2375), award(?x697, ?x484), ?x2375 = 04kxsb, nominated_for(?x484, ?x144) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #472 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 09m6kg; 011yrp; 07xtqq; 016z5x; 01sxly; 011yph; 0pv2t; 092vkg; 0gmcwlb; 04m1bm; ... *> query: (?x697, 027986c) <- genre(?x697, ?x53), award(?x697, ?x2375), award(?x697, ?x484), ?x2375 = 04kxsb, nominated_for(?x484, ?x144) *> conf = 0.27 ranks of expected_values: 3 EVAL 0209hj award 027986c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.297 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #4191-06nm1 PRED entity: 06nm1 PRED relation: official_language! PRED expected values: 01s47p => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 302): 0d060g (0.50 #3031, 0.50 #175, 0.47 #3201), 09c7w0 (0.50 #3031, 0.47 #3201, 0.47 #3370), 0162v (0.50 #3031, 0.47 #3201, 0.47 #3370), 034m8 (0.50 #3031, 0.47 #3201, 0.47 #3370), 02kcz (0.50 #3031, 0.47 #3201, 0.47 #3370), 0164b (0.50 #3031, 0.47 #3201, 0.47 #3370), 06s0l (0.50 #3031, 0.47 #3201, 0.47 #3370), 07ytt (0.50 #3031, 0.47 #3201, 0.47 #3370), 0hg5 (0.50 #3031, 0.47 #3201, 0.47 #3370), 01nln (0.50 #276, 0.40 #612, 0.25 #1286) >> Best rule #3031 for best value: >> intensional similarity = 9 >> extensional distance = 13 >> proper extension: 0h407; >> query: (?x2502, ?x47) <- countries_spoken_in(?x2502, ?x142), countries_spoken_in(?x2502, ?x47), film_release_region(?x7524, ?x142), film_release_region(?x6587, ?x142), film_release_region(?x2050, ?x142), ?x7524 = 01cm8w, ?x2050 = 01fmys, olympics(?x142, ?x778), ?x6587 = 07s3m4g >> conf = 0.50 => this is the best rule for 9 predicted values No rule for expected values ranks of expected_values: EVAL 06nm1 official_language! 01s47p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 80.000 0.500 http://example.org/location/country/official_language #4190-0bm2g PRED entity: 0bm2g PRED relation: language PRED expected values: 02h40lc => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 28): 02h40lc (0.90 #1377, 0.90 #179, 0.89 #1555), 064_8sq (0.33 #81, 0.18 #140, 0.14 #199), 0c_v2 (0.33 #17, 0.01 #135, 0.01 #491), 04306rv (0.17 #123, 0.17 #64, 0.12 #781), 02bjrlw (0.17 #60, 0.09 #534, 0.09 #595), 06mp7 (0.17 #75, 0.02 #732, 0.01 #1687), 06nm1 (0.12 #366, 0.10 #666, 0.10 #1207), 0jzc (0.10 #138, 0.05 #553, 0.05 #614), 06b_j (0.09 #141, 0.07 #556, 0.07 #617), 03_9r (0.06 #128, 0.05 #2453, 0.04 #543) >> Best rule #1377 for best value: >> intensional similarity = 3 >> extensional distance = 709 >> proper extension: 0c40vxk; 026p_bs; 03m8y5; 0gbfn9; 0gtt5fb; 06fqlk; 0mbql; 06rzwx; 03clwtw; 0bwhdbl; ... >> query: (?x2112, 02h40lc) <- film(?x8634, ?x2112), film(?x2800, ?x2112), award_winner(?x5369, ?x8634) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bm2g language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.903 http://example.org/film/film/language #4189-0f1nl PRED entity: 0f1nl PRED relation: category PRED expected values: 08mbj5d => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #15, 0.91 #17, 0.91 #25) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 01y9st; >> query: (?x2497, 08mbj5d) <- contains(?x2277, ?x2497), colors(?x2497, ?x332), administrative_division(?x2277, ?x13275) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f1nl category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.913 http://example.org/common/topic/webpage./common/webpage/category #4188-0gsl0 PRED entity: 0gsl0 PRED relation: contains PRED expected values: 0lw_s => 95 concepts (28 used for prediction) PRED predicted values (max 10 best out of 68): 01pj48 (0.12 #2119, 0.07 #5893, 0.07 #5065), 07xpm (0.07 #5895, 0.06 #290, 0.05 #3236), 080z7 (0.06 #748, 0.03 #3694), 01bcwk (0.05 #3590), 07vk2 (0.05 #3193), 01nh5h (0.03 #5875, 0.01 #5896), 0lw_s (0.03 #5772, 0.01 #5896), 0gsl0 (0.03 #5704, 0.01 #5896), 02yc5b (0.03 #5546, 0.01 #5896), 04lc0h (0.03 #5347, 0.01 #5896) >> Best rule #2119 for best value: >> intensional similarity = 2 >> extensional distance = 15 >> proper extension: 07xpm; 01tm2s; 0gs0g; 0g1w5; 0283sdr; 0853g; 0173kj; 01pj48; 01wx74; 01z9z1; ... >> query: (?x14149, 01pj48) <- contains(?x1023, ?x14149), ?x1023 = 0ctw_b >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #5772 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 38 *> proper extension: 0chghy; 0ctw_b; 02wt0; 07z5n; 05qkp; 02lx0; 03ryn; 05tr7; 07fsv; 07fb6; ... *> query: (?x14149, 0lw_s) <- contains(?x1023, ?x14149), contains(?x1023, ?x13715), contains(?x1023, ?x12293), contains(?x1023, ?x2396), ?x12293 = 01pj48, jurisdiction_of_office(?x1195, ?x13715), major_field_of_study(?x2396, ?x1668) *> conf = 0.03 ranks of expected_values: 7 EVAL 0gsl0 contains 0lw_s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 95.000 28.000 0.118 http://example.org/location/location/contains #4187-06w7mlh PRED entity: 06w7mlh PRED relation: country_of_origin PRED expected values: 09c7w0 0d060g => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 12): 09c7w0 (0.90 #68, 0.90 #114, 0.90 #149), 03_3d (0.17 #36, 0.13 #458, 0.12 #266), 0d060g (0.13 #458, 0.12 #4, 0.09 #15), 07ssc (0.13 #458, 0.12 #98, 0.11 #214), 02jx1 (0.13 #458, 0.02 #100, 0.02 #216), 05v8c (0.13 #458, 0.01 #422, 0.01 #445), 03rjj (0.13 #458, 0.01 #46), 03rt9 (0.13 #458, 0.01 #63), 04jpl (0.13 #458), 0d0vqn (0.13 #458) >> Best rule #68 for best value: >> intensional similarity = 4 >> extensional distance = 100 >> proper extension: 0cwrr; 0ddd0gc; 0c3xpwy; 02gl58; 0qmk5; >> query: (?x9082, 09c7w0) <- award_winner(?x9082, ?x4380), program(?x12138, ?x9082), genre(?x9082, ?x9083), genre(?x6450, ?x9083) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 06w7mlh country_of_origin 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 75.000 0.902 http://example.org/tv/tv_program/country_of_origin EVAL 06w7mlh country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.902 http://example.org/tv/tv_program/country_of_origin #4186-0zpfy PRED entity: 0zpfy PRED relation: category PRED expected values: 08mbj5d => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #11, 0.78 #16, 0.77 #6) >> Best rule #11 for best value: >> intensional similarity = 5 >> extensional distance = 292 >> proper extension: 01jssp; 04wlz2; 0288zy; 02g839; 01j_06; 07lx1s; 01j_cy; 09kvv; 0lfgr; 02hft3; ... >> query: (?x13092, 08mbj5d) <- contains(?x9948, ?x13092), contains(?x94, ?x13092), ?x94 = 09c7w0, adjoins(?x9948, ?x12845), source(?x12845, ?x958) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0zpfy category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.782 http://example.org/common/topic/webpage./common/webpage/category #4185-05j12n PRED entity: 05j12n PRED relation: location PRED expected values: 0fk98 => 120 concepts (65 used for prediction) PRED predicted values (max 10 best out of 238): 030qb3t (0.24 #11350, 0.20 #12155, 0.19 #37115), 04vmp (0.22 #1962, 0.21 #2766, 0.19 #13231), 02_286 (0.19 #11304, 0.17 #18549, 0.16 #19354), 06_kh (0.17 #11, 0.05 #3228, 0.04 #4033), 04ly1 (0.17 #203, 0.03 #5032, 0.01 #34018), 0rh6k (0.10 #4833, 0.06 #15296, 0.06 #18516), 0hptm (0.09 #3520, 0.08 #4325, 0.07 #5132), 04jpl (0.09 #10479, 0.09 #5651, 0.08 #13699), 0hyxv (0.09 #5845, 0.06 #6649, 0.04 #10673), 0cvw9 (0.09 #8445, 0.07 #7640, 0.06 #13275) >> Best rule #11350 for best value: >> intensional similarity = 5 >> extensional distance = 70 >> proper extension: 05m63c; 031zkw; 03gkn5; 01nbq4; 042xh; >> query: (?x6347, 030qb3t) <- type_of_union(?x6347, ?x566), languages(?x6347, ?x8098), profession(?x6347, ?x1032), nationality(?x6347, ?x2146), student(?x3995, ?x6347) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #13621 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 79 *> proper extension: 0g2mbn; 03_80b; 01s0l0; *> query: (?x6347, 0fk98) <- profession(?x6347, ?x1032), nationality(?x6347, ?x2146), religion(?x6347, ?x8967), ?x2146 = 03rk0 *> conf = 0.02 ranks of expected_values: 89 EVAL 05j12n location 0fk98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 120.000 65.000 0.236 http://example.org/people/person/places_lived./people/place_lived/location #4184-06npd PRED entity: 06npd PRED relation: taxonomy PRED expected values: 04n6k => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.82 #40, 0.81 #91, 0.79 #4) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 020d5; >> query: (?x756, 04n6k) <- capital(?x756, ?x8601), jurisdiction_of_office(?x182, ?x756), nationality(?x595, ?x756) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06npd taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.817 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #4183-01817f PRED entity: 01817f PRED relation: notable_people_with_this_condition! PRED expected values: 0j8hd => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 11): 0h99n (0.07 #32, 0.06 #120, 0.06 #98), 01g2q (0.04 #97, 0.04 #141, 0.04 #207), 029sk (0.04 #133, 0.03 #507, 0.03 #859), 04nz3 (0.02 #63, 0.02 #41, 0.02 #129), 02vrr (0.02 #179, 0.01 #267, 0.01 #773), 01rt5h (0.02 #234, 0.02 #256), 0j8hd (0.01 #279, 0.01 #345, 0.01 #609), 0d19y2 (0.01 #370), 0m32h (0.01 #359), 03p41 (0.01 #358) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 01sxd1; >> query: (?x4537, 0h99n) <- spouse(?x4537, ?x3632), artist(?x2190, ?x4537), instrumentalists(?x227, ?x4537) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #279 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 01vv126; 02wb6yq; 04cr6qv; 044mfr; 04f7c55; 01wbsdz; 0gs6vr; 0kj34; *> query: (?x4537, 0j8hd) <- instrumentalists(?x227, ?x4537), participant(?x4537, ?x1291), artist(?x2190, ?x4537) *> conf = 0.01 ranks of expected_values: 7 EVAL 01817f notable_people_with_this_condition! 0j8hd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 145.000 145.000 0.073 http://example.org/medicine/disease/notable_people_with_this_condition #4182-03_gd PRED entity: 03_gd PRED relation: award_winner! PRED expected values: 05zksls => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 137): 073h9x (0.50 #50, 0.20 #1669, 0.04 #189), 02ywhz (0.33 #78, 0.08 #217, 0.03 #634), 073h1t (0.33 #27, 0.08 #166, 0.03 #305), 05zksls (0.20 #1669, 0.03 #313, 0.02 #12095), 09k5jh7 (0.20 #1669, 0.02 #1612, 0.02 #1195), 05qb8vx (0.20 #1669, 0.02 #12095, 0.01 #4091), 0h98b3k (0.20 #1669), 02wzl1d (0.12 #150, 0.07 #567, 0.06 #289), 03gwpw2 (0.12 #148, 0.05 #426, 0.02 #565), 02yxh9 (0.12 #239, 0.05 #517, 0.02 #656) >> Best rule #50 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0c94fn; 092ys_y; 0b6mgp_; >> query: (?x800, 073h9x) <- award(?x800, ?x198), award_winner(?x324, ?x800), ?x324 = 07gp9 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1669 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 178 *> proper extension: 024c1b; *> query: (?x800, ?x2220) <- produced_by(?x573, ?x800), nominated_for(?x198, ?x573), honored_for(?x2220, ?x573) *> conf = 0.20 ranks of expected_values: 4 EVAL 03_gd award_winner! 05zksls CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 96.000 96.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4181-0ctb4g PRED entity: 0ctb4g PRED relation: nominated_for! PRED expected values: 02r22gf 02qvyrt => 98 concepts (92 used for prediction) PRED predicted values (max 10 best out of 211): 0k611 (0.62 #61, 0.58 #495, 0.57 #5486), 0gs9p (0.60 #487, 0.59 #53, 0.57 #1355), 02qvyrt (0.59 #81, 0.42 #515, 0.30 #1817), 0p9sw (0.51 #452, 0.50 #18, 0.42 #1537), 02r22gf (0.44 #25, 0.42 #459, 0.35 #1761), 03hkv_r (0.42 #230, 0.41 #13, 0.34 #3268), 09sb52 (0.42 #248, 0.29 #31, 0.22 #465), 0f4x7 (0.37 #1325, 0.37 #3278, 0.31 #457), 0gqy2 (0.37 #1406, 0.35 #3359, 0.32 #321), 09qv_s (0.37 #314, 0.24 #97, 0.22 #1399) >> Best rule #61 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 09gq0x5; 0dgq_kn; >> query: (?x3430, 0k611) <- nominated_for(?x1443, ?x3430), nominated_for(?x1243, ?x3430), ?x1243 = 0gr0m, film_crew_role(?x3430, ?x137), ?x1443 = 054krc >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #81 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 09gq0x5; 0dgq_kn; *> query: (?x3430, 02qvyrt) <- nominated_for(?x1443, ?x3430), nominated_for(?x1243, ?x3430), ?x1243 = 0gr0m, film_crew_role(?x3430, ?x137), ?x1443 = 054krc *> conf = 0.59 ranks of expected_values: 3, 5 EVAL 0ctb4g nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 92.000 0.618 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0ctb4g nominated_for! 02r22gf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 92.000 0.618 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4180-01w60_p PRED entity: 01w60_p PRED relation: diet PRED expected values: 07_jd => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2): 07_jd (0.17 #11, 0.15 #13, 0.14 #1), 07_hy (0.07 #12, 0.05 #8, 0.05 #14) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0f0y8; 01w524f; 06c44; 013qvn; 03f3_p3; 0j6cj; 01w9ph_; 01whg97; 01vs4f3; >> query: (?x2169, 07_jd) <- nationality(?x2169, ?x94), influenced_by(?x3403, ?x2169), artist(?x1954, ?x2169) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w60_p diet 07_jd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.167 http://example.org/base/eating/practicer_of_diet/diet #4179-05h43ls PRED entity: 05h43ls PRED relation: production_companies PRED expected values: 03jvmp => 79 concepts (69 used for prediction) PRED predicted values (max 10 best out of 64): 024rgt (0.44 #3761, 0.31 #2039, 0.30 #3107), 04mkft (0.44 #3761, 0.31 #2039, 0.30 #3107), 05qd_ (0.11 #3688, 0.09 #1396, 0.09 #1150), 016tw3 (0.09 #1805, 0.09 #747, 0.09 #1479), 054lpb6 (0.09 #1808, 0.07 #1482, 0.07 #750), 017s11 (0.09 #2, 0.07 #3681, 0.07 #738), 016tt2 (0.08 #3682, 0.07 #166, 0.06 #1063), 030_1m (0.07 #15, 0.02 #178, 0.02 #260), 030_1_ (0.06 #97, 0.05 #1484, 0.04 #508), 06rq1k (0.06 #17, 0.02 #1485, 0.02 #1811) >> Best rule #3761 for best value: >> intensional similarity = 3 >> extensional distance = 982 >> proper extension: 0d_2fb; 0gs973; >> query: (?x2586, ?x2549) <- film(?x1205, ?x2586), film(?x2549, ?x2586), production_companies(?x2586, ?x382) >> conf = 0.44 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 05h43ls production_companies 03jvmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 69.000 0.441 http://example.org/film/film/production_companies #4178-02bqy PRED entity: 02bqy PRED relation: student PRED expected values: 0646qh 03xx3m 06y9bd => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1081): 06hx2 (0.14 #1064, 0.06 #3149, 0.04 #5234), 0194xc (0.14 #1635, 0.06 #3720, 0.04 #5805), 02lt8 (0.14 #673, 0.06 #2758, 0.02 #4843), 0kh6b (0.07 #613, 0.05 #6868, 0.03 #2698), 0d3k14 (0.07 #1848, 0.04 #16440, 0.03 #8103), 01d494 (0.07 #264, 0.03 #6519, 0.03 #2349), 063vn (0.07 #297, 0.03 #6552, 0.03 #2382), 073v6 (0.07 #524, 0.03 #6779, 0.03 #2609), 04411 (0.07 #124, 0.03 #6379, 0.03 #2209), 0641g8 (0.07 #855, 0.03 #7110, 0.02 #5025) >> Best rule #1064 for best value: >> intensional similarity = 2 >> extensional distance = 12 >> proper extension: 04gdr; >> query: (?x5638, 06hx2) <- organizations_founded(?x6779, ?x5638), contains(?x94, ?x5638) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #7980 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 019q50; *> query: (?x5638, 06y9bd) <- institution(?x3437, ?x5638), ?x3437 = 02_xgp2, list(?x5638, ?x2197) *> conf = 0.02 ranks of expected_values: 676 EVAL 02bqy student 06y9bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 56.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 02bqy student 03xx3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 56.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 02bqy student 0646qh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 56.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #4177-01w61th PRED entity: 01w61th PRED relation: location PRED expected values: 0f2v0 => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 193): 030qb3t (0.72 #32131, 0.51 #14503, 0.45 #26522), 01cx_ (0.52 #4167, 0.05 #58660, 0.05 #63469), 056_y (0.22 #242, 0.20 #1043), 0cc56 (0.18 #30502, 0.13 #43327, 0.11 #48136), 059rby (0.16 #30462, 0.12 #43287, 0.09 #12033), 01n7q (0.15 #30508, 0.10 #43333, 0.10 #15285), 02cl1 (0.12 #4037, 0.02 #32081, 0.02 #26472), 0f2v0 (0.11 #182, 0.10 #983, 0.04 #6591), 0fhzf (0.11 #577, 0.10 #1378), 0rh6k (0.10 #30450, 0.08 #70524, 0.07 #43275) >> Best rule #32131 for best value: >> intensional similarity = 3 >> extensional distance = 374 >> proper extension: 0cymln; >> query: (?x883, 030qb3t) <- location(?x883, ?x8428), administrative_division(?x8428, ?x6559), place_of_birth(?x7805, ?x6559) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #182 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01wj18h; 01wv9p; 09z1lg; 03c602; 01s7ns; 03x82v; 01qmy04; *> query: (?x883, 0f2v0) <- award(?x883, ?x12982), award_nominee(?x5906, ?x883), award_winner(?x1480, ?x883), ?x12982 = 02681_5 *> conf = 0.11 ranks of expected_values: 8 EVAL 01w61th location 0f2v0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 156.000 156.000 0.723 http://example.org/people/person/places_lived./people/place_lived/location #4176-048z7l PRED entity: 048z7l PRED relation: people PRED expected values: 05bnp0 0p8jf 0hwqz 01_k71 0hqly => 24 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1798): 0127m7 (0.60 #7028, 0.09 #10390, 0.09 #12071), 015_30 (0.50 #5261, 0.50 #3578, 0.33 #1898), 01nvmd_ (0.50 #5186, 0.50 #3503, 0.33 #1823), 01qqtr (0.50 #4592, 0.33 #2912, 0.25 #6275), 023v4_ (0.50 #4040, 0.33 #2360, 0.25 #5723), 04f7c55 (0.40 #7513, 0.33 #787, 0.09 #10875), 01vwllw (0.40 #7145, 0.33 #419, 0.09 #10507), 01rrd4 (0.40 #7610, 0.33 #884, 0.09 #10972), 0693l (0.40 #7128, 0.33 #402, 0.07 #10490), 06qgvf (0.40 #6733, 0.33 #7, 0.07 #10095) >> Best rule #7028 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 033tf_; 09vc4s; 0dbxy; >> query: (?x9428, 0127m7) <- people(?x9428, ?x9944), people(?x9428, ?x5566), people(?x9428, ?x3961), nominated_for(?x3961, ?x2955), participant(?x286, ?x9944), award(?x9944, ?x1132), profession(?x3961, ?x319), award_nominee(?x163, ?x3961), ?x5566 = 01_ztw >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3372 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 09kr66; *> query: (?x9428, 05bnp0) <- people(?x9428, ?x9596), people(?x9428, ?x5058), people(?x9428, ?x1607), ?x9596 = 0427y, student(?x8056, ?x1607), nationality(?x1607, ?x94), participant(?x1548, ?x1607), award_nominee(?x1871, ?x5058), spouse(?x1250, ?x1607) *> conf = 0.25 ranks of expected_values: 447, 528, 605 EVAL 048z7l people 0hqly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 24.000 12.000 0.600 http://example.org/people/ethnicity/people EVAL 048z7l people 01_k71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 24.000 12.000 0.600 http://example.org/people/ethnicity/people EVAL 048z7l people 0hwqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 24.000 12.000 0.600 http://example.org/people/ethnicity/people EVAL 048z7l people 0p8jf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 24.000 12.000 0.600 http://example.org/people/ethnicity/people EVAL 048z7l people 05bnp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 24.000 12.000 0.600 http://example.org/people/ethnicity/people #4175-0n2vl PRED entity: 0n2vl PRED relation: source PRED expected values: 0jbk9 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #10, 0.91 #9, 0.90 #3) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 236 >> proper extension: 0f4y_; 0nj1c; 0mmr1; 0mm0p; 0nvd8; 0nh57; 0cc1v; 043z0; 0drr3; 09dfcj; ... >> query: (?x12554, 0jbk9) <- time_zones(?x12554, ?x2674), currency(?x12554, ?x170), ?x170 = 09nqf, adjoins(?x12554, ?x8086) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n2vl source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.912 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #4174-01xvb PRED entity: 01xvb PRED relation: draft PRED expected values: 09l0x9 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 16): 09l0x9 (0.86 #216, 0.85 #281, 0.85 #377), 02qw1zx (0.78 #660, 0.78 #742, 0.71 #562), 0f4vx0 (0.45 #408, 0.38 #225, 0.33 #650), 025tn92 (0.43 #410, 0.33 #652, 0.32 #588), 038981 (0.43 #413, 0.30 #60, 0.29 #557), 038c0q (0.38 #405, 0.30 #647, 0.29 #583), 04f4z1k (0.38 #225, 0.30 #303, 0.28 #335), 047dpm0 (0.38 #225, 0.26 #304, 0.26 #740), 09th87 (0.36 #412, 0.30 #654, 0.30 #59), 06439y (0.36 #417, 0.30 #659, 0.27 #595) >> Best rule #216 for best value: >> intensional similarity = 8 >> extensional distance = 19 >> proper extension: 02896; 05g3b; 01y3c; 03lsq; 07l2m; 06rny; 04vn5; 01c_d; >> query: (?x1239, 09l0x9) <- school(?x1239, ?x6953), position_s(?x1239, ?x3113), ?x3113 = 0b13yt, position(?x1239, ?x1240), position(?x1239, ?x1114), school(?x580, ?x6953), ?x580 = 05m_8, school(?x465, ?x6953) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xvb draft 09l0x9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.857 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #4173-02xb2bt PRED entity: 02xb2bt PRED relation: nationality PRED expected values: 02jx1 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 11): 09c7w0 (0.77 #2452, 0.75 #197, 0.74 #687), 02jx1 (0.43 #31, 0.31 #1863, 0.10 #521), 06q1r (0.31 #1863, 0.21 #75, 0.01 #271), 03rk0 (0.05 #5437, 0.03 #5241, 0.03 #3671), 0d060g (0.05 #693, 0.04 #1183, 0.04 #791), 03_3d (0.03 #1182, 0.01 #3633, 0.01 #5399), 0chghy (0.02 #598, 0.02 #500, 0.02 #892), 0345h (0.02 #127, 0.02 #5422, 0.01 #617), 0bq0p9 (0.02 #115), 03rjj (0.02 #201, 0.02 #5398, 0.02 #1475) >> Best rule #2452 for best value: >> intensional similarity = 3 >> extensional distance = 1519 >> proper extension: 042l3v; 079hvk; >> query: (?x2371, 09c7w0) <- award_nominee(?x2371, ?x931), nominated_for(?x2371, ?x1434), nationality(?x2371, ?x429) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 06ns98; *> query: (?x2371, 02jx1) <- award_nominee(?x4859, ?x2371), award_winner(?x2372, ?x2371), ?x4859 = 05y5kf *> conf = 0.43 ranks of expected_values: 2 EVAL 02xb2bt nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 57.000 57.000 0.768 http://example.org/people/person/nationality #4172-02bqy PRED entity: 02bqy PRED relation: major_field_of_study PRED expected values: 01jzxy => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 103): 0g26h (0.38 #2143, 0.37 #2366, 0.36 #2255), 05qfh (0.38 #694, 0.31 #1027, 0.30 #1582), 0fdys (0.36 #697, 0.28 #1807, 0.26 #1030), 02_7t (0.32 #2164, 0.28 #2387, 0.28 #1054), 0_jm (0.29 #2381, 0.27 #2936, 0.25 #2270), 04x_3 (0.29 #688, 0.27 #1576, 0.25 #1021), 0l5mz (0.29 #62, 0.13 #728, 0.12 #284), 06ms6 (0.24 #680, 0.24 #1013, 0.22 #1568), 04sh3 (0.24 #730, 0.19 #3173, 0.16 #1063), 01tbp (0.24 #2383, 0.23 #2160, 0.23 #1827) >> Best rule #2143 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 01fsv9; >> query: (?x5638, 0g26h) <- state_province_region(?x5638, ?x728), institution(?x620, ?x5638), ?x620 = 07s6fsf >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #684 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 02897w; 037njl; 027mdh; *> query: (?x5638, 01jzxy) <- major_field_of_study(?x5638, ?x2981), major_field_of_study(?x5638, ?x742), ?x742 = 05qjt, ?x2981 = 02j62 *> conf = 0.11 ranks of expected_values: 33 EVAL 02bqy major_field_of_study 01jzxy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 80.000 80.000 0.378 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #4171-0jt90f5 PRED entity: 0jt90f5 PRED relation: influenced_by! PRED expected values: 02465 => 163 concepts (99 used for prediction) PRED predicted values (max 10 best out of 427): 0j0pf (0.27 #8397, 0.25 #10446, 0.07 #34498), 05rx__ (0.25 #2872, 0.19 #12592, 0.19 #5941), 040rjq (0.25 #996, 0.17 #2532, 0.14 #14300), 07lp1 (0.25 #929, 0.17 #2465, 0.08 #10143), 014dq7 (0.25 #577, 0.08 #9791, 0.08 #2113), 0n6kf (0.25 #705, 0.08 #9919, 0.08 #2241), 0282x (0.25 #225, 0.08 #9952, 0.08 #2274), 01w9ph_ (0.25 #832, 0.08 #2368, 0.05 #15160), 018zvb (0.25 #951, 0.08 #2487, 0.04 #22964), 05ty4m (0.19 #5128, 0.17 #17410, 0.14 #11779) >> Best rule #8397 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 05x8n; 0210f1; 0fpzt5; >> query: (?x2343, 0j0pf) <- award(?x2343, ?x1288), ?x1288 = 02662b, gender(?x2343, ?x231) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #2499 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 05jm7; *> query: (?x2343, 02465) <- written_by(?x5116, ?x2343), influenced_by(?x7180, ?x2343), influenced_by(?x2343, ?x7334), peers(?x7334, ?x3941) *> conf = 0.08 ranks of expected_values: 91 EVAL 0jt90f5 influenced_by! 02465 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 163.000 99.000 0.273 http://example.org/influence/influence_node/influenced_by #4170-09zmys PRED entity: 09zmys PRED relation: award_winner! PRED expected values: 0hndn2q => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 118): 03nnm4t (0.29 #214, 0.07 #1054, 0.07 #494), 092c5f (0.25 #14, 0.05 #994, 0.04 #4074), 092_25 (0.14 #212, 0.05 #352, 0.03 #492), 092t4b (0.14 #192, 0.04 #4112, 0.03 #4952), 05q7cj (0.14 #235, 0.03 #515, 0.01 #2895), 0clfdj (0.14 #144, 0.03 #4064, 0.03 #4904), 03gwpw2 (0.14 #149, 0.03 #3929, 0.02 #4629), 0hn821n (0.14 #270, 0.02 #2930, 0.01 #5030), 09g90vz (0.11 #963, 0.10 #1243, 0.10 #543), 09qvms (0.11 #293, 0.06 #3933, 0.05 #4073) >> Best rule #214 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02g8h; 048lv; 0171cm; 01zfmm; 051wwp; >> query: (?x5521, 03nnm4t) <- nominated_for(?x5521, ?x7768), ?x7768 = 043mk4y, student(?x3564, ?x5521) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #600 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 0163t3; 02yy8; *> query: (?x5521, 0hndn2q) <- profession(?x5521, ?x524), spouse(?x5521, ?x8143), person(?x5929, ?x5521) *> conf = 0.06 ranks of expected_values: 23 EVAL 09zmys award_winner! 0hndn2q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 93.000 93.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4169-09x8ms PRED entity: 09x8ms PRED relation: people! PRED expected values: 041rx => 94 concepts (92 used for prediction) PRED predicted values (max 10 best out of 35): 02ctzb (0.25 #246, 0.20 #323, 0.19 #554), 041rx (0.18 #1698, 0.17 #2006, 0.16 #1621), 07bch9 (0.17 #485, 0.10 #870, 0.09 #793), 033tf_ (0.16 #777, 0.14 #854, 0.12 #1855), 06v41q (0.14 #183, 0.06 #799, 0.03 #1030), 0x67 (0.11 #2397, 0.11 #3244, 0.11 #2782), 0xnvg (0.08 #3247, 0.08 #2785, 0.07 #3170), 02w7gg (0.07 #310, 0.06 #387, 0.05 #2851), 07hwkr (0.06 #1860, 0.05 #1783, 0.05 #3246), 09vc4s (0.06 #471, 0.05 #2319, 0.05 #2396) >> Best rule #246 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0c6g29; 0dck27; 04vzv4; 02cqbx; 0fx0j2; 026lyl4; >> query: (?x13187, 02ctzb) <- costume_design_by(?x2721, ?x13187), place_of_death(?x13187, ?x682), genre(?x2721, ?x239), film_sets_designed(?x786, ?x2721) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1698 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 226 *> proper extension: 025cn2; 03bw6; 0cl_m; *> query: (?x13187, 041rx) <- nationality(?x13187, ?x94), ?x94 = 09c7w0, student(?x12869, ?x13187), place_of_death(?x13187, ?x682) *> conf = 0.18 ranks of expected_values: 2 EVAL 09x8ms people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 92.000 0.250 http://example.org/people/ethnicity/people #4168-04yc76 PRED entity: 04yc76 PRED relation: titles! PRED expected values: 024qqx => 60 concepts (34 used for prediction) PRED predicted values (max 10 best out of 57): 07s9rl0 (0.32 #1, 0.32 #2472, 0.32 #412), 04xvlr (0.24 #1134, 0.22 #1545, 0.21 #3096), 02l7c8 (0.18 #1850, 0.17 #1437, 0.17 #2263), 0gsy3b (0.18 #1850, 0.17 #1437, 0.17 #2263), 011ys5 (0.18 #1850, 0.17 #1437, 0.17 #2263), 05p553 (0.18 #1850, 0.17 #1437, 0.17 #2263), 06cvj (0.18 #1850, 0.17 #1437, 0.17 #2263), 07c52 (0.16 #441, 0.13 #1264, 0.11 #543), 01jfsb (0.12 #1355, 0.12 #1665, 0.12 #636), 07ssc (0.11 #1140, 0.10 #1551, 0.10 #2067) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 076xkdz; >> query: (?x2754, 07s9rl0) <- genre(?x2754, ?x1403), ?x1403 = 02l7c8, category(?x2754, ?x134) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #696 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 295 *> proper extension: 040rmy; 04lqvlr; 0gcrg; 04lqvly; 0g9zljd; 04nlb94; *> query: (?x2754, 024qqx) <- genre(?x2754, ?x239), film_format(?x2754, ?x909), nominated_for(?x401, ?x2754) *> conf = 0.09 ranks of expected_values: 12 EVAL 04yc76 titles! 024qqx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 60.000 34.000 0.323 http://example.org/media_common/netflix_genre/titles #4167-018js4 PRED entity: 018js4 PRED relation: film! PRED expected values: 07r1h 02184q => 80 concepts (46 used for prediction) PRED predicted values (max 10 best out of 696): 05mt6w (0.46 #2081, 0.46 #68689, 0.44 #22892), 0284n42 (0.46 #2081, 0.46 #68689, 0.44 #22892), 02qggqc (0.46 #2081, 0.46 #68689, 0.44 #22892), 094tsh6 (0.46 #2081, 0.46 #68689, 0.43 #85336), 04cl1 (0.20 #836, 0.08 #2917), 0bxtg (0.20 #76, 0.06 #4239, 0.03 #8402), 044rvb (0.20 #101, 0.04 #4264, 0.04 #6346), 01gkmx (0.20 #1586, 0.04 #68690, 0.03 #68691), 01vvb4m (0.20 #522, 0.02 #19252, 0.02 #13011), 04fzk (0.20 #707, 0.02 #4870, 0.02 #9033) >> Best rule #2081 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 014zwb; >> query: (?x155, ?x666) <- nominated_for(?x666, ?x155), film(?x3070, ?x155), film_release_distribution_medium(?x155, ?x81), ?x3070 = 0dn3n >> conf = 0.46 => this is the best rule for 4 predicted values *> Best rule #3170 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 01ln5z; 031778; 01jmyj; *> query: (?x155, 07r1h) <- nominated_for(?x9391, ?x155), nominated_for(?x7222, ?x155), nominated_for(?x500, ?x155), genre(?x155, ?x53), ?x9391 = 094tsh6, profession(?x7222, ?x1183) *> conf = 0.15 ranks of expected_values: 27, 570 EVAL 018js4 film! 02184q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 80.000 46.000 0.462 http://example.org/film/actor/film./film/performance/film EVAL 018js4 film! 07r1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 80.000 46.000 0.462 http://example.org/film/actor/film./film/performance/film #4166-0j862 PRED entity: 0j862 PRED relation: role! PRED expected values: 0j1yf => 36 concepts (26 used for prediction) PRED predicted values (max 10 best out of 999): 01kx_81 (0.50 #612, 0.43 #2084, 0.38 #2972), 016h9b (0.50 #2131, 0.40 #1246, 0.38 #3613), 01mxnvc (0.50 #855, 0.38 #2031, 0.33 #2917), 01r0t_j (0.50 #813, 0.36 #2285, 0.33 #3767), 03bxwtd (0.50 #666, 0.33 #373, 0.33 #82), 0473q (0.50 #783, 0.33 #490, 0.33 #199), 01bpnd (0.50 #747, 0.33 #454, 0.33 #163), 01vsnff (0.50 #636, 0.33 #343, 0.33 #52), 01vs4ff (0.50 #772, 0.33 #479, 0.33 #188), 01vng3b (0.50 #754, 0.33 #461, 0.33 #170) >> Best rule #612 for best value: >> intensional similarity = 25 >> extensional distance = 2 >> proper extension: 018vs; >> query: (?x7772, 01kx_81) <- role(?x2957, ?x7772), role(?x2798, ?x7772), role(?x2460, ?x7772), role(?x1750, ?x7772), role(?x432, ?x7772), role(?x227, ?x7772), ?x2798 = 03qjg, ?x2957 = 01v8y9, performance_role(?x2592, ?x7772), ?x432 = 042v_gx, ?x1750 = 02hnl, ?x227 = 0342h, role(?x2460, ?x1472), role(?x2460, ?x75), role(?x1495, ?x2460), role(?x3716, ?x2460), role(?x745, ?x2460), role(?x433, ?x2460), ?x75 = 07y_7, ?x1472 = 0319l, ?x3716 = 03gvt, ?x433 = 025cbm, ?x745 = 01vj9c, role(?x130, ?x1495), instrumentalists(?x2460, ?x680) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5905 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 41 *> proper extension: 02hrlh; *> query: (?x7772, ?x3546) <- group(?x7772, ?x11455), group(?x7772, ?x4842), artists(?x3928, ?x4842), origin(?x4842, ?x2623), origin(?x11455, ?x94), parent_genre(?x1127, ?x3928), contains(?x94, ?x10507), film_release_region(?x54, ?x94), locations(?x7734, ?x94), adjoins(?x151, ?x94), group(?x3546, ?x4842), location_of_ceremony(?x566, ?x94), ?x10507 = 0b5hj5 *> conf = 0.25 ranks of expected_values: 156 EVAL 0j862 role! 0j1yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 36.000 26.000 0.500 http://example.org/music/group_member/membership./music/group_membership/role #4165-03bx0bm PRED entity: 03bx0bm PRED relation: performance_role! PRED expected values: 0x3b7 0kxbc 01w9mnm 01c7qd => 73 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1375): 02rn_bj (0.60 #534, 0.50 #1322, 0.50 #378), 050z2 (0.43 #2086, 0.38 #1847, 0.35 #2404), 0167v4 (0.33 #67, 0.25 #383, 0.25 #305), 014q2g (0.33 #19, 0.25 #335, 0.25 #257), 01vsyg9 (0.33 #45, 0.25 #361, 0.25 #283), 03d2k (0.33 #68, 0.25 #384, 0.25 #306), 0qdyf (0.33 #23, 0.25 #339, 0.25 #261), 043c4j (0.29 #1236, 0.29 #1078, 0.29 #920), 01w9mnm (0.29 #851, 0.20 #1482, 0.20 #693), 01wxdn3 (0.29 #857, 0.20 #1488, 0.20 #621) >> Best rule #534 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 05148p4; >> query: (?x1466, 02rn_bj) <- performance_role(?x10094, ?x1466), group(?x1466, ?x11425), group(?x1466, ?x10145), role(?x74, ?x1466), ?x10145 = 0p76z, role(?x1466, ?x212), role(?x5635, ?x1466), ?x11425 = 02vnpv, participant(?x5635, ?x5507), artists(?x302, ?x5635), profession(?x10094, ?x131) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #851 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 5 *> proper extension: 02snj9; *> query: (?x1466, 01w9mnm) <- performance_role(?x248, ?x1466), group(?x1466, ?x10043), group(?x1466, ?x8497), group(?x1466, ?x8058), role(?x314, ?x1466), ?x10043 = 0fb2l, role(?x702, ?x1466), role(?x1466, ?x212), award(?x702, ?x350), ?x8058 = 014pg1, ?x314 = 02sgy, artists(?x671, ?x8497) *> conf = 0.29 ranks of expected_values: 9, 100, 188 EVAL 03bx0bm performance_role! 01c7qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 63.000 0.600 http://example.org/music/artist/contribution./music/recording_contribution/performance_role EVAL 03bx0bm performance_role! 01w9mnm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 73.000 63.000 0.600 http://example.org/music/artist/contribution./music/recording_contribution/performance_role EVAL 03bx0bm performance_role! 0kxbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 73.000 63.000 0.600 http://example.org/music/artist/contribution./music/recording_contribution/performance_role EVAL 03bx0bm performance_role! 0x3b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 73.000 63.000 0.600 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #4164-01v0sxx PRED entity: 01v0sxx PRED relation: artist! PRED expected values: 03qy3l => 89 concepts (76 used for prediction) PRED predicted values (max 10 best out of 128): 011k1h (0.57 #420, 0.34 #2612, 0.33 #831), 017l96 (0.43 #427, 0.33 #838, 0.23 #2482), 01cl2y (0.40 #165, 0.33 #302, 0.29 #1261), 0181dw (0.40 #175, 0.33 #1134, 0.25 #723), 0n85g (0.40 #196, 0.17 #1155, 0.17 #744), 01dtcb (0.37 #1413, 0.14 #454, 0.13 #3879), 015_1q (0.33 #839, 0.29 #428, 0.25 #3716), 04fcjt (0.33 #301, 0.25 #986, 0.20 #164), 086k8 (0.33 #1, 0.17 #275, 0.11 #1645), 015mlw (0.33 #83, 0.17 #357, 0.08 #1042) >> Best rule #420 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 07c0j; 02cpp; >> query: (?x10257, 011k1h) <- group(?x227, ?x10257), artist(?x2931, ?x10257), artists(?x1000, ?x10257), artist(?x2931, ?x654), music(?x3566, ?x10257), ?x654 = 0kzy0 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #608 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 01kd57; *> query: (?x10257, 03qy3l) <- artists(?x3061, ?x10257), artists(?x1380, ?x10257), ?x3061 = 05bt6j, music(?x3566, ?x10257), artists(?x1380, ?x10237), ?x10237 = 023322, film(?x406, ?x3566) *> conf = 0.30 ranks of expected_values: 11 EVAL 01v0sxx artist! 03qy3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 89.000 76.000 0.571 http://example.org/music/record_label/artist #4163-0c3zjn7 PRED entity: 0c3zjn7 PRED relation: film! PRED expected values: 04fhn_ => 95 concepts (69 used for prediction) PRED predicted values (max 10 best out of 787): 0f5xn (0.33 #968, 0.25 #3047, 0.04 #13443), 01wbg84 (0.33 #47, 0.25 #2126, 0.03 #20840), 0f502 (0.33 #760, 0.25 #2839, 0.03 #21553), 0h7pj (0.33 #1542, 0.25 #3621, 0.03 #18175), 0klh7 (0.33 #487, 0.25 #2566, 0.02 #21280), 03z509 (0.33 #766, 0.25 #2845, 0.02 #11161), 016fjj (0.33 #633, 0.25 #2712, 0.02 #17266), 0kjrx (0.33 #1420, 0.25 #3499, 0.02 #18053), 0693l (0.33 #527, 0.25 #2606, 0.02 #33797), 032zg9 (0.33 #831, 0.25 #2910, 0.01 #13306) >> Best rule #968 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0f4_l; >> query: (?x5553, 0f5xn) <- film(?x14135, ?x5553), ?x14135 = 01nd6v, currency(?x5553, ?x170), film_crew_role(?x5553, ?x137) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #17313 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 114 *> proper extension: 07nxvj; 0n83s; 03clwtw; 06yykb; 048tv9; 09qljs; 03wjm2; 02t_h3; *> query: (?x5553, 04fhn_) <- film(?x539, ?x5553), film(?x902, ?x5553), film_format(?x5553, ?x10390), executive_produced_by(?x5553, ?x2135) *> conf = 0.03 ranks of expected_values: 208 EVAL 0c3zjn7 film! 04fhn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 95.000 69.000 0.333 http://example.org/film/actor/film./film/performance/film #4162-01gstn PRED entity: 01gstn PRED relation: legislative_sessions! PRED expected values: 0b3wk => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 6): 0b3wk (0.94 #188, 0.92 #195, 0.91 #149), 0x2sv (0.08 #202, 0.07 #208), 0h6dy (0.06 #203, 0.05 #209), 0l_j_ (0.04 #204, 0.03 #210), 030p4s (0.02 #206, 0.02 #212), 0162kb (0.02 #211) >> Best rule #188 for best value: >> intensional similarity = 35 >> extensional distance = 31 >> proper extension: 02bqn1; 03tcbx; 03rtmz; 02bqmq; 02gkzs; 02cg7g; 02bqm0; 02glc4; >> query: (?x5005, ?x2860) <- district_represented(?x5005, ?x7518), district_represented(?x5005, ?x7405), district_represented(?x5005, ?x4776), district_represented(?x5005, ?x2020), district_represented(?x5005, ?x1755), district_represented(?x5005, ?x177), legislative_sessions(?x10291, ?x5005), ?x2020 = 05k7sb, legislative_sessions(?x5005, ?x3973), ?x1755 = 01x73, location(?x397, ?x4776), district_represented(?x7944, ?x4776), district_represented(?x7715, ?x4776), district_represented(?x5256, ?x4776), contains(?x4776, ?x2034), religion(?x4776, ?x962), ?x5256 = 01grqd, legislative_sessions(?x2860, ?x10291), ?x7405 = 07_f2, jurisdiction_of_office(?x10093, ?x4776), ?x7715 = 01grp0, adjoins(?x4776, ?x12573), country(?x4776, ?x94), state_province_region(?x388, ?x177), ?x10093 = 09n5b9, contains(?x177, ?x11796), partially_contains(?x4776, ?x10710), source(?x11796, ?x958), ?x7944 = 01h7xx, state(?x12558, ?x177), district_represented(?x6933, ?x7518), taxonomy(?x4776, ?x939), ?x6933 = 024tkd, jurisdiction_of_office(?x1157, ?x177), location(?x932, ?x177) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gstn legislative_sessions! 0b3wk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.938 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #4161-019pm_ PRED entity: 019pm_ PRED relation: film PRED expected values: 06lpmt => 97 concepts (88 used for prediction) PRED predicted values (max 10 best out of 864): 01r97z (0.62 #57129, 0.61 #33920, 0.53 #28563), 09cr8 (0.29 #283, 0.04 #9209, 0.03 #110693), 013q07 (0.19 #3571, 0.13 #19637, 0.12 #32134), 013q0p (0.19 #3571, 0.13 #19637, 0.12 #32134), 01k1k4 (0.19 #3571, 0.13 #19637, 0.12 #32134), 0jzw (0.10 #118, 0.03 #110693, 0.02 #3689), 01shy7 (0.07 #12917, 0.05 #16487, 0.05 #5777), 06z8s_ (0.06 #129, 0.05 #3700, 0.03 #5485), 04tqtl (0.06 #508, 0.04 #5864, 0.04 #4079), 0kvgxk (0.06 #326, 0.03 #110693, 0.02 #3897) >> Best rule #57129 for best value: >> intensional similarity = 2 >> extensional distance = 421 >> proper extension: 02wb6yq; >> query: (?x2763, ?x351) <- participant(?x248, ?x2763), nominated_for(?x2763, ?x351) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #18533 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 204 *> proper extension: 0gdhhy; *> query: (?x2763, 06lpmt) <- award_winner(?x1384, ?x2763), produced_by(?x408, ?x2763) *> conf = 0.01 ranks of expected_values: 612 EVAL 019pm_ film 06lpmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 97.000 88.000 0.623 http://example.org/film/actor/film./film/performance/film #4160-026y3cf PRED entity: 026y3cf PRED relation: country_of_origin PRED expected values: 09c7w0 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.89 #241, 0.88 #275, 0.88 #81), 07ssc (0.41 #182, 0.33 #9, 0.14 #32), 03_3d (0.11 #390, 0.10 #479, 0.09 #512), 0d060g (0.05 #38, 0.05 #391, 0.04 #27), 01z77k (0.03 #308, 0.02 #46, 0.02 #263), 07c52 (0.03 #308, 0.02 #46, 0.02 #263), 02jx1 (0.02 #137, 0.02 #181, 0.02 #420), 03rt9 (0.01 #31, 0.01 #42, 0.01 #77), 03rjj (0.01 #25, 0.01 #36, 0.01 #71), 0d0vqn (0.01 #28, 0.01 #62) >> Best rule #241 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 01dvry; 07zhjj; 043qqt5; >> query: (?x12830, 09c7w0) <- genre(?x12830, ?x53), award_winner(?x12830, ?x7958), award_nominee(?x7958, ?x1126), award(?x7958, ?x112) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026y3cf country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.888 http://example.org/tv/tv_program/country_of_origin #4159-07wdw PRED entity: 07wdw PRED relation: politician PRED expected values: 0gzh => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 313): 0203v (0.93 #720, 0.91 #1335, 0.89 #1230), 0d06m5 (0.93 #720, 0.91 #1335, 0.89 #1230), 0rlz (0.93 #720, 0.91 #1335, 0.66 #822), 0dq2k (0.93 #720, 0.91 #1335, 0.66 #822), 042fk (0.93 #720, 0.91 #1335, 0.66 #822), 06c0j (0.93 #720, 0.91 #1335, 0.66 #822), 042f1 (0.93 #720, 0.91 #1335, 0.66 #822), 02yy8 (0.93 #720, 0.91 #1335, 0.66 #822), 038w8 (0.93 #720, 0.91 #1335, 0.66 #822), 042kg (0.93 #720, 0.91 #1335, 0.66 #822) >> Best rule #720 for best value: >> intensional similarity = 23 >> extensional distance = 3 >> proper extension: 01fml; >> query: (?x10558, ?x1600) <- politician(?x10558, ?x13592), politician(?x10558, ?x11956), basic_title(?x13592, ?x346), profession(?x13592, ?x1359), gender(?x13592, ?x231), company(?x346, ?x94), politician(?x8714, ?x13592), jurisdiction_of_office(?x346, ?x3730), jurisdiction_of_office(?x346, ?x2051), jurisdiction_of_office(?x346, ?x1353), jurisdiction_of_office(?x346, ?x429), ?x2051 = 035dk, ?x429 = 03rt9, profession(?x6550, ?x1359), profession(?x3762, ?x1359), ?x3730 = 03shp, ?x1353 = 035qy, entity_involved(?x8416, ?x11956), ?x231 = 05zppz, ?x3762 = 04x4s2, politician(?x8714, ?x1600), profession(?x11956, ?x10014), ?x6550 = 03nb5v >> conf = 0.93 => this is the best rule for 33 predicted values *> Best rule #1230 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 4 *> proper extension: 02bb8j; *> query: (?x10558, ?x744) <- politician(?x10558, ?x13592), politician(?x10558, ?x11956), profession(?x13592, ?x5805), profession(?x13592, ?x1359), profession(?x6550, ?x1359), profession(?x3762, ?x1359), politician(?x1912, ?x11956), place_of_death(?x13592, ?x108), location(?x13592, ?x2740), type_of_union(?x11956, ?x566), people(?x10613, ?x11956), politician(?x1912, ?x744), ?x6550 = 03nb5v, profession(?x11169, ?x5805), profession(?x4736, ?x5805), profession(?x4480, ?x5805), profession(?x3563, ?x5805), profession(?x2669, ?x5805), profession(?x11956, ?x10014), ?x4736 = 07_m9_, symptom_of(?x10717, ?x10613), ?x3563 = 09bg4l, ?x11169 = 041wm, ?x4480 = 0d05fv, ?x3762 = 04x4s2, ?x2669 = 02mjmr *> conf = 0.89 ranks of expected_values: 37 EVAL 07wdw politician 0gzh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 22.000 22.000 0.930 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #4158-02fqxm PRED entity: 02fqxm PRED relation: featured_film_locations PRED expected values: 0r1jr => 79 concepts (66 used for prediction) PRED predicted values (max 10 best out of 62): 02_286 (0.39 #4768, 0.30 #4530, 0.30 #6197), 030qb3t (0.25 #38, 0.22 #275, 0.17 #4786), 04jpl (0.15 #4757, 0.14 #4519, 0.14 #3804), 0rh6k (0.08 #4749, 0.07 #1420, 0.07 #474), 094jv (0.07 #516, 0.02 #752), 01_d4 (0.06 #2178, 0.05 #1465, 0.04 #4794), 06y57 (0.05 #1281, 0.03 #1993, 0.03 #2946), 01cx_ (0.04 #778, 0.02 #1726, 0.02 #1962), 0mzww (0.04 #8797), 080h2 (0.04 #7152, 0.04 #4772, 0.04 #497) >> Best rule #4768 for best value: >> intensional similarity = 4 >> extensional distance = 445 >> proper extension: 02qr69m; 0j_t1; 0gl3hr; 016dj8; 025ts_z; >> query: (?x12720, 02_286) <- nominated_for(?x7946, ?x12720), featured_film_locations(?x12720, ?x2552), citytown(?x817, ?x2552), institution(?x865, ?x817) >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02fqxm featured_film_locations 0r1jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 66.000 0.387 http://example.org/film/film/featured_film_locations #4157-03v1w7 PRED entity: 03v1w7 PRED relation: award PRED expected values: 040njc => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 280): 05p1dby (0.72 #34445, 0.70 #25931, 0.70 #25525), 07bdd_ (0.65 #1686, 0.64 #471, 0.53 #876), 040njc (0.49 #2438, 0.40 #3248, 0.31 #2843), 019f4v (0.33 #2497, 0.28 #3307, 0.20 #9787), 0gs9p (0.28 #2509, 0.28 #3319, 0.20 #9799), 05b1610 (0.27 #444, 0.20 #849, 0.15 #1659), 05f4m9q (0.27 #418, 0.20 #823, 0.15 #1633), 0f_nbyh (0.27 #2440, 0.22 #2845, 0.21 #3250), 0gr51 (0.25 #100, 0.21 #3340, 0.20 #2530), 0gvx_ (0.25 #187, 0.06 #21067, 0.05 #16610) >> Best rule #34445 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01vsxdm; 01wv9xn; 0frsw; 01vrwfv; 014_lq; 02jqjm; 0g5ff; ... >> query: (?x6369, ?x2022) <- award_winner(?x2022, ?x6369), award(?x166, ?x2022) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #2438 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 111 *> proper extension: 0d05fv; *> query: (?x6369, 040njc) <- produced_by(?x8570, ?x6369), student(?x581, ?x6369), honored_for(?x2245, ?x8570) *> conf = 0.49 ranks of expected_values: 3 EVAL 03v1w7 award 040njc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 102.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4156-04fzfj PRED entity: 04fzfj PRED relation: film! PRED expected values: 057_yx => 112 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1431): 032v0v (0.46 #66595, 0.43 #64514, 0.39 #79081), 08hp53 (0.46 #66595, 0.43 #64514, 0.39 #79081), 030_1m (0.46 #66595, 0.43 #64514, 0.39 #79081), 01fh9 (0.09 #2396, 0.02 #8640, 0.02 #16964), 0p8r1 (0.08 #584, 0.07 #8908, 0.04 #38044), 08x5c_ (0.08 #1948, 0.03 #4028, 0.01 #8190), 012d40 (0.08 #16, 0.02 #33312, 0.02 #29151), 01900g (0.08 #783, 0.02 #29918, 0.02 #4943), 030vnj (0.08 #1448, 0.02 #18096, 0.02 #34744), 01gkmx (0.08 #1585, 0.02 #70262, 0.01 #36963) >> Best rule #66595 for best value: >> intensional similarity = 4 >> extensional distance = 384 >> proper extension: 04xbq3; >> query: (?x723, ?x1561) <- nominated_for(?x154, ?x723), film(?x5489, ?x723), nominated_for(?x1561, ?x723), student(?x1526, ?x5489) >> conf = 0.46 => this is the best rule for 3 predicted values *> Best rule #5999 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 09d38d; *> query: (?x723, 057_yx) <- nominated_for(?x154, ?x723), film_format(?x723, ?x909), award(?x4867, ?x154), ?x4867 = 01gw4f *> conf = 0.04 ranks of expected_values: 162 EVAL 04fzfj film! 057_yx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 112.000 48.000 0.461 http://example.org/film/actor/film./film/performance/film #4155-05smlt PRED entity: 05smlt PRED relation: film_crew_role! PRED expected values: 0bmfnjs 076xkps => 63 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1729): 05m_jsg (0.67 #11781, 0.67 #10525, 0.60 #8011), 057lbk (0.67 #11846, 0.67 #10590, 0.60 #8076), 08052t3 (0.67 #11604, 0.67 #10348, 0.60 #7834), 06znpjr (0.67 #12301, 0.67 #11045, 0.60 #8531), 08c6k9 (0.67 #12394, 0.67 #11138, 0.60 #8624), 0dp7wt (0.67 #12291, 0.67 #11035, 0.60 #8521), 05szq8z (0.67 #11990, 0.67 #10734, 0.60 #8220), 014kq6 (0.67 #11566, 0.67 #10310, 0.60 #7796), 02mmwk (0.67 #12218, 0.67 #10962, 0.60 #8448), 01l_pn (0.67 #12009, 0.67 #10753, 0.60 #8239) >> Best rule #11781 for best value: >> intensional similarity = 19 >> extensional distance = 4 >> proper extension: 09zzb8; >> query: (?x5928, 05m_jsg) <- film_crew_role(?x8377, ?x5928), film_crew_role(?x4502, ?x5928), film_crew_role(?x4269, ?x5928), film_crew_role(?x3498, ?x5928), film_crew_role(?x2886, ?x5928), film_crew_role(?x1688, ?x5928), film_crew_role(?x1644, ?x5928), ?x3498 = 02fqrf, ?x4502 = 02wgk1, ?x1688 = 024l2y, film(?x382, ?x1644), ?x2886 = 02ryz24, film_release_region(?x1644, ?x94), film_crew_role(?x1644, ?x1171), ?x4269 = 05sns6, production_companies(?x8377, ?x1104), currency(?x1644, ?x170), ?x1171 = 09vw2b7, film_release_region(?x8377, ?x87) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #12390 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 4 *> proper extension: 09zzb8; *> query: (?x5928, 076xkps) <- film_crew_role(?x8377, ?x5928), film_crew_role(?x4502, ?x5928), film_crew_role(?x4269, ?x5928), film_crew_role(?x3498, ?x5928), film_crew_role(?x2886, ?x5928), film_crew_role(?x1688, ?x5928), film_crew_role(?x1644, ?x5928), ?x3498 = 02fqrf, ?x4502 = 02wgk1, ?x1688 = 024l2y, film(?x382, ?x1644), ?x2886 = 02ryz24, film_release_region(?x1644, ?x94), film_crew_role(?x1644, ?x1171), ?x4269 = 05sns6, production_companies(?x8377, ?x1104), currency(?x1644, ?x170), ?x1171 = 09vw2b7, film_release_region(?x8377, ?x87) *> conf = 0.67 ranks of expected_values: 20, 504 EVAL 05smlt film_crew_role! 076xkps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 63.000 13.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05smlt film_crew_role! 0bmfnjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 63.000 13.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4154-0f4_l PRED entity: 0f4_l PRED relation: crewmember PRED expected values: 02xc1w4 => 74 concepts (50 used for prediction) PRED predicted values (max 10 best out of 34): 03m49ly (0.06 #83, 0.02 #565, 0.02 #177), 04ktcgn (0.04 #107, 0.03 #203, 0.03 #347), 02xc1w4 (0.04 #75, 0.02 #169, 0.02 #122), 09pjnd (0.04 #58, 0.01 #345), 092ys_y (0.04 #115, 0.02 #452, 0.02 #211), 0bbxx9b (0.04 #116, 0.02 #1903, 0.02 #2192), 051z6rz (0.04 #171, 0.02 #124, 0.02 #509), 0b79gfg (0.03 #160, 0.03 #209, 0.03 #691), 0cw67g (0.03 #186, 0.02 #379, 0.02 #235), 0284n42 (0.03 #1886, 0.02 #146, 0.02 #195) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0g5q34q; >> query: (?x2177, 03m49ly) <- film_release_region(?x2177, ?x94), genre(?x2177, ?x809), ?x809 = 0vgkd >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 0g5q34q; *> query: (?x2177, 02xc1w4) <- film_release_region(?x2177, ?x94), genre(?x2177, ?x809), ?x809 = 0vgkd *> conf = 0.04 ranks of expected_values: 3 EVAL 0f4_l crewmember 02xc1w4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 50.000 0.061 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #4153-015np0 PRED entity: 015np0 PRED relation: award_winner! PRED expected values: 027c95y => 92 concepts (77 used for prediction) PRED predicted values (max 10 best out of 210): 02x73k6 (0.45 #1728, 0.39 #1296, 0.39 #923), 0gqy2 (0.45 #1728, 0.37 #3885, 0.37 #6045), 027dtxw (0.45 #1728, 0.37 #3885, 0.37 #6045), 0789_m (0.45 #1728, 0.37 #3885, 0.37 #6045), 03nqnk3 (0.29 #565, 0.20 #134, 0.04 #4450), 09sb52 (0.22 #6948, 0.22 #8241, 0.21 #8672), 027c95y (0.20 #157, 0.14 #588, 0.13 #1020), 0bdwqv (0.20 #170, 0.14 #601, 0.09 #1033), 09cm54 (0.20 #96, 0.14 #527, 0.09 #959), 040njc (0.20 #8, 0.14 #439, 0.07 #863) >> Best rule #1728 for best value: >> intensional similarity = 3 >> extensional distance = 141 >> proper extension: 0520r2x; 0cb77r; 0dck27; 05218gr; 016h9b; 02q5xsx; 063472; 01ws9n6; 04vzv4; 02kmx6; ... >> query: (?x8772, ?x112) <- award(?x8772, ?x112), place_of_death(?x8772, ?x10852), award_winner(?x1250, ?x8772) >> conf = 0.45 => this is the best rule for 4 predicted values *> Best rule #157 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0byfz; 01fwf1; 01385g; *> query: (?x8772, 027c95y) <- award_winner(?x850, ?x8772), film(?x8772, ?x4971), film(?x8772, ?x3549), ?x4971 = 01jwxx, ?x3549 = 017kct *> conf = 0.20 ranks of expected_values: 7 EVAL 015np0 award_winner! 027c95y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 92.000 77.000 0.449 http://example.org/award/award_category/winners./award/award_honor/award_winner #4152-025j1t PRED entity: 025j1t PRED relation: profession PRED expected values: 02hrh1q => 74 concepts (73 used for prediction) PRED predicted values (max 10 best out of 53): 02hrh1q (0.90 #2846, 0.88 #2250, 0.88 #5379), 0dxtg (0.48 #1653, 0.31 #460, 0.31 #1491), 02jknp (0.45 #1647, 0.36 #7, 0.31 #1491), 03gjzk (0.31 #1655, 0.31 #1491, 0.27 #15), 09jwl (0.27 #6410, 0.26 #7008, 0.26 #9095), 0np9r (0.27 #6410, 0.26 #7008, 0.26 #9095), 02krf9 (0.27 #6410, 0.26 #7008, 0.26 #9095), 0d1pc (0.17 #647, 0.16 #349, 0.08 #1542), 0cbd2 (0.16 #4030, 0.14 #5967, 0.14 #7759), 018gz8 (0.14 #762, 0.12 #6128, 0.12 #7174) >> Best rule #2846 for best value: >> intensional similarity = 3 >> extensional distance = 1193 >> proper extension: 01vvydl; 06151l; 0lbj1; 01vrx3g; 023tp8; 09fqtq; 01kwld; 064nh4k; 034x61; 016khd; ... >> query: (?x6068, 02hrh1q) <- award_nominee(?x6068, ?x496), film(?x6068, ?x667), profession(?x6068, ?x319) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025j1t profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 73.000 0.901 http://example.org/people/person/profession #4151-0blbxk PRED entity: 0blbxk PRED relation: award PRED expected values: 0gqyl 063y_ky 099t8j => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 237): 02x8n1n (0.18 #3610, 0.13 #11230, 0.13 #21660), 099tbz (0.18 #3610, 0.13 #11230, 0.13 #21660), 02x73k6 (0.18 #3610, 0.13 #11230, 0.13 #21660), 09qvc0 (0.18 #3610, 0.13 #11230, 0.13 #21660), 0cqhb3 (0.18 #3610, 0.13 #11230, 0.13 #21660), 027cyf7 (0.18 #3610, 0.12 #12435, 0.08 #12033), 01by1l (0.13 #11230, 0.13 #21660, 0.13 #1312), 0gqyl (0.13 #11230, 0.13 #21660, 0.12 #21258), 04kxsb (0.13 #11230, 0.13 #21660, 0.12 #21258), 0gq9h (0.13 #11230, 0.13 #21660, 0.12 #21258) >> Best rule #3610 for best value: >> intensional similarity = 3 >> extensional distance = 1102 >> proper extension: 0cjdk; >> query: (?x1290, ?x704) <- award_winner(?x192, ?x1290), nominated_for(?x1290, ?x1218), award_winner(?x704, ?x192) >> conf = 0.18 => this is the best rule for 6 predicted values *> Best rule #11230 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1468 *> proper extension: 0l56b; 02v49c; 0knjh; *> query: (?x1290, ?x154) <- award_winner(?x100, ?x1290), award_nominee(?x1290, ?x1995), award(?x1995, ?x154) *> conf = 0.13 ranks of expected_values: 8, 14, 127 EVAL 0blbxk award 099t8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 67.000 67.000 0.177 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0blbxk award 063y_ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 67.000 67.000 0.177 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0blbxk award 0gqyl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 67.000 67.000 0.177 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4150-01nds PRED entity: 01nds PRED relation: split_to! PRED expected values: 01nds => 177 concepts (106 used for prediction) PRED predicted values (max 10 best out of 5): 08z129 (0.02 #2112, 0.01 #3015, 0.01 #3214), 04jr87 (0.02 #2428, 0.01 #2932), 09f2j (0.02 #2417, 0.01 #4850), 065y4w7 (0.02 #2378), 07k2d (0.01 #3058, 0.01 #3257, 0.01 #3361) >> Best rule #2112 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 0g1rw; 02d6ph; 05md3l; 01fb6d; >> query: (?x11304, 08z129) <- category(?x11304, ?x134), organization(?x4682, ?x11304), industry(?x11304, ?x10022), industry(?x11325, ?x10022), child(?x9558, ?x11325) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nds split_to! 01nds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 177.000 106.000 0.021 http://example.org/dataworld/gardening_hint/split_to #4149-0gs1_ PRED entity: 0gs1_ PRED relation: profession PRED expected values: 01d_h8 07s467s => 132 concepts (131 used for prediction) PRED predicted values (max 10 best out of 73): 01d_h8 (0.85 #4418, 0.84 #4124, 0.84 #5741), 0dxtg (0.71 #2072, 0.69 #3395, 0.69 #3836), 03gjzk (0.45 #2514, 0.44 #3102, 0.43 #1042), 018gz8 (0.27 #750, 0.15 #1044, 0.14 #9574), 02krf9 (0.24 #3408, 0.23 #25, 0.23 #3849), 09jwl (0.20 #2959, 0.19 #605, 0.18 #2665), 0cbd2 (0.19 #1625, 0.17 #3390, 0.16 #1036), 0np9r (0.19 #460, 0.18 #754, 0.14 #15167), 0d1pc (0.17 #4755, 0.16 #1962, 0.15 #3726), 0dgd_ (0.15 #29, 0.10 #17060, 0.09 #176) >> Best rule #4418 for best value: >> intensional similarity = 2 >> extensional distance = 301 >> proper extension: 0gg9_5q; >> query: (?x6558, 01d_h8) <- type_of_union(?x6558, ?x566), produced_by(?x4591, ?x6558) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 25 EVAL 0gs1_ profession 07s467s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 132.000 131.000 0.855 http://example.org/people/person/profession EVAL 0gs1_ profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 131.000 0.855 http://example.org/people/person/profession #4148-04xvlr PRED entity: 04xvlr PRED relation: titles PRED expected values: 028_yv 095zlp 0ywrc 047tsx3 04yg13l 02vnmc9 => 57 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1391): 0ywrc (0.50 #6880, 0.40 #14679, 0.40 #13377), 0m9p3 (0.50 #6789, 0.40 #14588, 0.35 #3907), 035zr0 (0.50 #7440, 0.40 #15239, 0.33 #19143), 0f4k49 (0.50 #3190, 0.40 #13596, 0.33 #584), 0pv54 (0.50 #7200, 0.40 #14999, 0.33 #685), 0g9lm2 (0.50 #7029, 0.40 #14828, 0.33 #514), 046488 (0.50 #7125, 0.40 #14924, 0.33 #610), 04j4tx (0.50 #7012, 0.40 #14811, 0.33 #497), 0qf2t (0.50 #7109, 0.40 #14908, 0.33 #594), 06kl78 (0.50 #7096, 0.40 #14895, 0.33 #581) >> Best rule #6880 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 07ssc; >> query: (?x162, 0ywrc) <- titles(?x162, ?x11544), titles(?x162, ?x8084), titles(?x162, ?x7204), film(?x1286, ?x11544), genre(?x11544, ?x53), ?x7204 = 0280061, nominated_for(?x143, ?x8084), film(?x157, ?x8084) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 285, 331, 403, 489, 828 EVAL 04xvlr titles 02vnmc9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 57.000 36.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 04xvlr titles 04yg13l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 57.000 36.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 04xvlr titles 047tsx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 57.000 36.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 04xvlr titles 0ywrc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 36.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 04xvlr titles 095zlp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 57.000 36.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 04xvlr titles 028_yv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 57.000 36.000 0.500 http://example.org/media_common/netflix_genre/titles #4147-0f5xn PRED entity: 0f5xn PRED relation: film PRED expected values: 062zm5h => 142 concepts (81 used for prediction) PRED predicted values (max 10 best out of 647): 09p4w8 (0.57 #74092, 0.43 #29989, 0.34 #59979), 01xbxn (0.30 #1376, 0.03 #4904, 0.02 #8432), 06cm5 (0.20 #1055), 056xkh (0.19 #3340, 0.10 #1576, 0.03 #6868), 02pg45 (0.19 #2680, 0.03 #4444, 0.02 #7972), 03wy8t (0.12 #3327, 0.05 #6855, 0.03 #10383), 03z20c (0.12 #2230, 0.03 #3994, 0.03 #16342), 0277j40 (0.12 #2971, 0.03 #6499, 0.02 #10027), 02_qt (0.12 #2385, 0.03 #5913, 0.02 #9441), 0gkz3nz (0.12 #2550, 0.02 #18426, 0.01 #16662) >> Best rule #74092 for best value: >> intensional similarity = 3 >> extensional distance = 816 >> proper extension: 025p38; 01wjrn; 02lq10; 0c01c; 0n8bn; 012x2b; 0q1lp; 03bdm4; 0p_jc; 01f9mq; ... >> query: (?x5462, ?x4853) <- student(?x10170, ?x5462), film(?x5462, ?x626), nominated_for(?x5462, ?x4853) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f5xn film 062zm5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 81.000 0.567 http://example.org/film/actor/film./film/performance/film #4146-0jpkg PRED entity: 0jpkg PRED relation: adjoins! PRED expected values: 0mbf4 => 176 concepts (83 used for prediction) PRED predicted values (max 10 best out of 616): 0mbf4 (0.82 #44061, 0.81 #55869, 0.81 #59014), 02dtg (0.33 #1600, 0.14 #8677, 0.05 #18907), 0jpkg (0.25 #3116, 0.17 #7048, 0.17 #5475), 0dc95 (0.25 #9566, 0.17 #6421, 0.17 #5635), 02gt5s (0.25 #3771, 0.06 #16355, 0.05 #19505), 081mh (0.20 #14306, 0.12 #15091, 0.07 #26103), 026mj (0.20 #14504, 0.12 #15289, 0.07 #26301), 0dclg (0.20 #14278, 0.10 #26075, 0.01 #52049), 0l2hf (0.17 #6475, 0.17 #5689, 0.14 #7260), 01qh7 (0.17 #6443, 0.17 #5657, 0.14 #7228) >> Best rule #44061 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 0vh3; >> query: (?x13811, ?x13190) <- adjoins(?x13811, ?x13190), jurisdiction_of_office(?x1195, ?x13190), jurisdiction_of_office(?x1195, ?x8745), ?x8745 = 04swd >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jpkg adjoins! 0mbf4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 83.000 0.818 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #4145-016tw3 PRED entity: 016tw3 PRED relation: nominated_for PRED expected values: 076tq0z => 153 concepts (104 used for prediction) PRED predicted values (max 10 best out of 743): 0cbl95 (0.75 #121921, 0.68 #24057, 0.16 #43305), 05jzt3 (0.33 #1605, 0.33 #119, 0.16 #56146), 020fcn (0.33 #169, 0.16 #56146, 0.15 #75395), 011ysn (0.33 #517, 0.08 #13351, 0.08 #14954), 03n785 (0.33 #496, 0.08 #13330, 0.07 #157218), 06t2t2 (0.33 #1480, 0.08 #14314, 0.07 #23933), 0dfw0 (0.33 #767, 0.08 #13601, 0.07 #23220), 01y9r2 (0.33 #1192, 0.08 #14026, 0.05 #36474), 0419kt (0.33 #1540, 0.08 #14374, 0.05 #17580), 0bj25 (0.33 #1318, 0.08 #14152, 0.05 #17358) >> Best rule #121921 for best value: >> intensional similarity = 2 >> extensional distance = 291 >> proper extension: 0d05fv; 01twdk; >> query: (?x1104, ?x3600) <- category(?x1104, ?x134), award_winner(?x3600, ?x1104) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #56146 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 0k9ctht; 0gfmc_; 081bls; 07k2x; 01f_mw; 099ks0; 04mwxk3; 05f260; *> query: (?x1104, ?x861) <- film(?x1104, ?x861), production_companies(?x253, ?x1104), nominated_for(?x500, ?x861) *> conf = 0.16 ranks of expected_values: 179 EVAL 016tw3 nominated_for 076tq0z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 153.000 104.000 0.749 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #4144-07f3xb PRED entity: 07f3xb PRED relation: languages PRED expected values: 02bjrlw => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 16): 064_8sq (0.09 #457, 0.09 #531, 0.08 #309), 03k50 (0.08 #521, 0.08 #447, 0.03 #410), 02bjrlw (0.07 #2369, 0.05 #297, 0.05 #186), 04306rv (0.07 #2369, 0.03 #298, 0.03 #446), 02bv9 (0.07 #2369), 0jzc (0.07 #2369), 06nm1 (0.05 #190, 0.04 #449, 0.03 #523), 07c9s (0.04 #455, 0.04 #529, 0.02 #307), 0t_2 (0.02 #193, 0.02 #304, 0.01 #452), 0999q (0.02 #539, 0.02 #465) >> Best rule #457 for best value: >> intensional similarity = 2 >> extensional distance = 607 >> proper extension: 084w8; 02qjj7; 0kzy0; 0pcc0; 02vmzp; 04xjp; 01vvpjj; 06wvj; 02wb6yq; 034bs; ... >> query: (?x1515, 064_8sq) <- languages(?x1515, ?x254), nationality(?x1515, ?x512) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #2369 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1985 *> proper extension: 070px; *> query: (?x1515, ?x254) <- film(?x1515, ?x5002), profession(?x1515, ?x1032), language(?x5002, ?x254) *> conf = 0.07 ranks of expected_values: 3 EVAL 07f3xb languages 02bjrlw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 93.000 0.087 http://example.org/people/person/languages #4143-02vntj PRED entity: 02vntj PRED relation: nationality PRED expected values: 09c7w0 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 37): 09c7w0 (0.84 #101, 0.83 #601, 0.79 #2002), 02jx1 (0.27 #433, 0.25 #233, 0.15 #1033), 0chghy (0.14 #10, 0.03 #4411, 0.03 #710), 0d060g (0.12 #507, 0.07 #1708, 0.07 #1407), 07ssc (0.10 #4717, 0.10 #415, 0.09 #5921), 03rk0 (0.08 #5051, 0.08 #5352, 0.08 #5952), 03rjj (0.08 #505, 0.03 #1405, 0.03 #1706), 03_3d (0.06 #206, 0.05 #406, 0.02 #1406), 0345h (0.05 #331, 0.04 #1231, 0.03 #1331), 0f8l9c (0.04 #1723, 0.03 #1422, 0.02 #422) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 06gb2q; 02xfj0; 0517bc; >> query: (?x4247, 09c7w0) <- film(?x4247, ?x857), ?x857 = 06_wqk4, profession(?x4247, ?x955) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vntj nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.842 http://example.org/people/person/nationality #4142-03vtbc PRED entity: 03vtbc PRED relation: colors! PRED expected values: 0wsr 0jnkr => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 425): 05xvj (0.50 #2365, 0.50 #1663, 0.46 #693), 04l5d0 (0.50 #2286, 0.50 #1584, 0.46 #1732), 03lpp_ (0.50 #1741, 0.50 #1393, 0.46 #1732), 05gg4 (0.50 #2164, 0.50 #1462, 0.42 #2081), 07l8x (0.50 #2236, 0.50 #1534, 0.42 #2081), 01yjl (0.50 #2141, 0.50 #1439, 0.42 #2081), 07l2m (0.50 #1826, 0.50 #1478, 0.42 #2081), 01yhm (0.50 #1764, 0.50 #1416, 0.42 #2081), 0512p (0.50 #1757, 0.50 #1409, 0.42 #2081), 06x68 (0.50 #1743, 0.50 #1395, 0.42 #2081) >> Best rule #2365 for best value: >> intensional similarity = 50 >> extensional distance = 2 >> proper extension: 01g5v; >> query: (?x5325, 05xvj) <- colors(?x10279, ?x5325), colors(?x9835, ?x5325), colors(?x7643, ?x5325), colors(?x4487, ?x5325), colors(?x3298, ?x5325), colors(?x1115, ?x5325), colors(?x12736, ?x5325), colors(?x7660, ?x5325), colors(?x5596, ?x5325), colors(?x5221, ?x5325), ?x1115 = 01y3c, fraternities_and_sororities(?x7660, ?x4348), institution(?x4981, ?x7660), institution(?x3437, ?x7660), position(?x10279, ?x2010), team(?x11844, ?x4487), position(?x9835, ?x2918), team(?x11825, ?x3298), state_province_region(?x7660, ?x2982), colors(?x10279, ?x663), school(?x2820, ?x7660), major_field_of_study(?x7660, ?x6870), school_type(?x5596, ?x3092), school(?x10279, ?x6814), school(?x10279, ?x1011), school(?x4487, ?x3779), draft(?x10279, ?x1161), religion(?x2982, ?x7131), position(?x7643, ?x180), school(?x7643, ?x1675), ?x6870 = 01540, country(?x2982, ?x94), contains(?x2982, ?x659), ?x7131 = 03_gx, student(?x7660, ?x2390), organization(?x346, ?x12736), ?x663 = 083jv, ?x3437 = 02_xgp2, ?x4981 = 03bwzr4, location(?x117, ?x2982), category(?x12736, ?x134), team(?x13270, ?x9835), ?x3779 = 01pq4w, ?x1011 = 07w0v, currency(?x5221, ?x2244), ?x6814 = 03tw2s, currency(?x2982, ?x170), ?x2918 = 02qvl7, adjoins(?x938, ?x2982), organization(?x7660, ?x5487) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1510 for first EXPECTED value: *> intensional similarity = 49 *> extensional distance = 2 *> proper extension: 06fvc; *> query: (?x5325, 0wsr) <- colors(?x9995, ?x5325), colors(?x6074, ?x5325), colors(?x4170, ?x5325), colors(?x3298, ?x5325), colors(?x1115, ?x5325), colors(?x387, ?x5325), colors(?x8825, ?x5325), colors(?x5324, ?x5325), colors(?x1609, ?x5325), position_s(?x1115, ?x3346), position_s(?x1115, ?x3113), position_s(?x1115, ?x180), ?x180 = 01r3hr, ?x3346 = 02g_7z, ?x3298 = 0jnmj, colors(?x9995, ?x332), draft(?x9995, ?x8133), team(?x1114, ?x1115), institution(?x8398, ?x1609), institution(?x865, ?x1609), state_province_region(?x8825, ?x2049), ?x3113 = 0b13yt, school_type(?x8825, ?x1962), student(?x5324, ?x4806), ?x8398 = 028dcg, currency(?x5324, ?x170), school(?x8995, ?x5324), organization(?x346, ?x5324), position(?x9995, ?x1348), draft(?x1115, ?x465), ?x865 = 02h4rq6, season(?x6074, ?x2406), ?x2406 = 03c6sl9, major_field_of_study(?x8825, ?x1858), school(?x1115, ?x331), team(?x10361, ?x4170), colors(?x12732, ?x332), state_province_region(?x5324, ?x2713), school(?x6074, ?x2948), position_s(?x4170, ?x2247), team(?x2010, ?x6074), contains(?x94, ?x8825), draft(?x8995, ?x1161), ?x2247 = 01_9c1, ?x12732 = 03x23q, category(?x387, ?x134), institution(?x1519, ?x8825), institution(?x1200, ?x331), team(?x5412, ?x387) *> conf = 0.50 ranks of expected_values: 32, 206 EVAL 03vtbc colors! 0jnkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 21.000 21.000 0.500 http://example.org/sports/sports_team/colors EVAL 03vtbc colors! 0wsr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 21.000 21.000 0.500 http://example.org/sports/sports_team/colors #4141-01v5h PRED entity: 01v5h PRED relation: people! PRED expected values: 02k6hp => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 38): 0gk4g (0.20 #10, 0.17 #142, 0.16 #1000), 01mtqf (0.20 #4, 0.15 #202, 0.07 #796), 0dq9p (0.15 #215, 0.15 #479, 0.12 #677), 0qcr0 (0.11 #463, 0.10 #991, 0.10 #1), 02y0js (0.10 #2, 0.07 #794, 0.07 #728), 0dcsx (0.10 #15, 0.05 #807, 0.05 #741), 0jdk0 (0.10 #5, 0.05 #797, 0.05 #731), 0d19y2 (0.08 #187, 0.08 #253, 0.02 #847), 03m3vr6 (0.08 #178, 0.04 #508), 08g5q7 (0.08 #372, 0.07 #702, 0.03 #1032) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01_vfy; 09p06; 036jb; 043gj; 029m83; 023w9s; 015nvj; 02drd3; >> query: (?x8942, 0gk4g) <- place_of_death(?x8942, ?x682), profession(?x8942, ?x524), film(?x8942, ?x1746), film(?x8942, ?x4841) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #697 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 0154j; 0d0vqn; 03rt9; 07ssc; 0k6nt; 059j2; 04g61; *> query: (?x8942, 02k6hp) <- organizations_founded(?x8942, ?x11706), organizations_founded(?x11626, ?x11706), organization(?x11626, ?x8603) *> conf = 0.05 ranks of expected_values: 19 EVAL 01v5h people! 02k6hp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 123.000 123.000 0.200 http://example.org/people/cause_of_death/people #4140-059t8 PRED entity: 059t8 PRED relation: district_represented! PRED expected values: 034_7s => 246 concepts (246 used for prediction) PRED predicted values (max 10 best out of 55): 077g7n (0.83 #1379, 0.77 #1654, 0.76 #2754), 070m6c (0.81 #1381, 0.75 #1656, 0.73 #2756), 06f0dc (0.79 #1384, 0.76 #1329, 0.73 #1659), 07p__7 (0.79 #1383, 0.73 #1658, 0.71 #2758), 070mff (0.72 #1415, 0.72 #1360, 0.70 #2790), 024tcq (0.72 #1396, 0.68 #1121, 0.67 #1671), 02bn_p (0.62 #560, 0.62 #1385, 0.57 #1110), 034_7s (0.62 #164, 0.55 #3136, 0.48 #3632), 02bp37 (0.62 #564, 0.53 #1389, 0.50 #1334), 024tkd (0.62 #1417, 0.58 #1692, 0.57 #1142) >> Best rule #1379 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 03v1s; 05kj_; 059f4; 05fkf; 05fhy; 04ych; 059_c; 06mz5; 07z1m; 01x73; ... >> query: (?x9370, 077g7n) <- district_represented(?x3473, ?x9370), adjoins(?x9370, ?x6842), first_level_division_of(?x9370, ?x279), state_province_region(?x5085, ?x9370) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #164 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 06nrt; *> query: (?x9370, 034_7s) <- district_represented(?x10543, ?x9370), adjoins(?x9370, ?x6842), contains(?x279, ?x9370), ?x10543 = 03h_f4 *> conf = 0.62 ranks of expected_values: 8 EVAL 059t8 district_represented! 034_7s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 246.000 246.000 0.830 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #4139-0250f PRED entity: 0250f PRED relation: profession PRED expected values: 02krf9 015h31 => 127 concepts (42 used for prediction) PRED predicted values (max 10 best out of 81): 02hrh1q (0.74 #4364, 0.67 #4944, 0.65 #4509), 09jwl (0.59 #1176, 0.53 #886, 0.50 #1031), 0cbd2 (0.50 #6, 0.46 #1601, 0.42 #441), 0nbcg (0.42 #899, 0.41 #1189, 0.40 #1044), 01c72t (0.41 #1181, 0.26 #891, 0.25 #1036), 0kyk (0.38 #1622, 0.24 #2347, 0.23 #2928), 02krf9 (0.38 #3482, 0.33 #459, 0.28 #5828), 0np9r (0.38 #3482, 0.25 #2901, 0.23 #1323), 014kbl (0.38 #3482, 0.04 #1563, 0.04 #1708), 015h31 (0.33 #170, 0.32 #1330, 0.25 #750) >> Best rule #4364 for best value: >> intensional similarity = 5 >> extensional distance = 321 >> proper extension: 01vrx3g; 0f0p0; 053yx; 07z1_q; 03bnv; 03bpn6; 0d9xq; 01l1rw; 0bdlj; 03h_yfh; ... >> query: (?x7650, 02hrh1q) <- award(?x7650, ?x4573), profession(?x7650, ?x987), people(?x5855, ?x7650), profession(?x12355, ?x987), ?x12355 = 026ck >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #3482 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 0f6lx; *> query: (?x7650, ?x1383) <- peers(?x1855, ?x7650), profession(?x7650, ?x319), profession(?x1855, ?x1383) *> conf = 0.38 ranks of expected_values: 7, 10 EVAL 0250f profession 015h31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 127.000 42.000 0.743 http://example.org/people/person/profession EVAL 0250f profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 127.000 42.000 0.743 http://example.org/people/person/profession #4138-01jpyb PRED entity: 01jpyb PRED relation: institution! PRED expected values: 019v9k => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 21): 02_xgp2 (0.83 #33, 0.60 #122, 0.60 #145), 019v9k (0.81 #119, 0.77 #142, 0.71 #30), 014mlp (0.75 #2032, 0.72 #138, 0.69 #93), 03bwzr4 (0.69 #124, 0.68 #35, 0.60 #147), 0bkj86 (0.59 #29, 0.42 #141, 0.41 #118), 07s6fsf (0.52 #90, 0.50 #135, 0.46 #267), 04zx3q1 (0.51 #24, 0.33 #113, 0.28 #136), 013zdg (0.34 #95, 0.33 #140, 0.29 #28), 02m4yg (0.28 #2257, 0.18 #2417, 0.16 #2370), 01ysy9 (0.28 #2257, 0.18 #2417, 0.16 #2370) >> Best rule #33 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 01z3bz; >> query: (?x6644, 02_xgp2) <- institution(?x2636, ?x6644), institution(?x865, ?x6644), ?x865 = 02h4rq6, contains(?x94, ?x6644), ?x2636 = 027f2w >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #119 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 02sjgpq; 0ym17; *> query: (?x6644, 019v9k) <- currency(?x6644, ?x170), major_field_of_study(?x6644, ?x1154), citytown(?x6644, ?x12655), ?x1154 = 02lp1 *> conf = 0.81 ranks of expected_values: 2 EVAL 01jpyb institution! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 157.000 157.000 0.829 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4137-048lv PRED entity: 048lv PRED relation: film PRED expected values: 0yyn5 => 102 concepts (72 used for prediction) PRED predicted values (max 10 best out of 634): 01dc0c (0.62 #16023, 0.60 #56977, 0.59 #53415), 0c0zq (0.62 #16023, 0.60 #56977, 0.59 #53415), 011yd2 (0.62 #16023, 0.60 #56977, 0.59 #90822), 011ywj (0.32 #3207, 0.03 #128216, 0.03 #24570), 07s846j (0.28 #5341), 02ht1k (0.24 #2406), 0cbv4g (0.22 #912, 0.01 #15154, 0.01 #9814), 02stbw (0.18 #2159), 0888c3 (0.15 #3186, 0.03 #128216, 0.01 #24549), 092vkg (0.12 #1936, 0.03 #128216, 0.01 #26860) >> Best rule #16023 for best value: >> intensional similarity = 2 >> extensional distance = 421 >> proper extension: 0dzlk; >> query: (?x1384, ?x394) <- nominated_for(?x1384, ?x394), participant(?x1620, ?x1384) >> conf = 0.62 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 048lv film 0yyn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 72.000 0.625 http://example.org/film/actor/film./film/performance/film #4136-03f7m4h PRED entity: 03f7m4h PRED relation: award PRED expected values: 03tk6z => 105 concepts (74 used for prediction) PRED predicted values (max 10 best out of 274): 05q8pss (0.83 #3631, 0.82 #4438, 0.80 #1211), 02qkk9_ (0.82 #4438, 0.80 #1210, 0.78 #3630), 01by1l (0.44 #4144, 0.39 #110, 0.38 #4951), 01c99j (0.31 #1031, 0.23 #4259, 0.22 #3451), 03c7tr1 (0.31 #863, 0.16 #3283, 0.12 #1268), 05p09zm (0.28 #928, 0.14 #3348, 0.13 #8594), 05b4l5x (0.28 #812, 0.14 #3232, 0.08 #1217), 02f5qb (0.28 #3380, 0.18 #4188, 0.17 #1365), 03qbh5 (0.28 #607, 0.26 #1415, 0.25 #204), 02f6xy (0.27 #199, 0.25 #602, 0.17 #1410) >> Best rule #3631 for best value: >> intensional similarity = 5 >> extensional distance = 177 >> proper extension: 05pdbs; 0ggl02; 012gq6; 016732; 03cd1q; 02qtywd; >> query: (?x8352, ?x4317) <- award_winner(?x4317, ?x8352), award(?x3929, ?x4317), award(?x3397, ?x4317), ?x3397 = 015f7, influenced_by(?x3929, ?x4576) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #29876 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1787 *> proper extension: 030hcs; 03mg35; 014z8v; 026l37; 02_p8v; 016bx2; 01cspq; 01x4r3; 0h10vt; 014kg4; ... *> query: (?x8352, ?x154) <- award_winner(?x4317, ?x8352), award(?x3397, ?x4317), artists(?x671, ?x3397), award(?x3397, ?x154) *> conf = 0.07 ranks of expected_values: 91 EVAL 03f7m4h award 03tk6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 105.000 74.000 0.834 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4135-04gycf PRED entity: 04gycf PRED relation: nationality PRED expected values: 09c7w0 03rjj => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 18): 09c7w0 (0.83 #401, 0.78 #201, 0.75 #1303), 02jx1 (0.28 #133, 0.22 #733, 0.19 #333), 07ssc (0.15 #115, 0.14 #315, 0.13 #715), 03rk0 (0.10 #3851, 0.08 #4052, 0.08 #4352), 0d060g (0.07 #1809, 0.05 #507, 0.05 #6623), 03_3d (0.06 #1808, 0.02 #7025, 0.02 #1508), 03rt9 (0.05 #113, 0.03 #1715, 0.02 #713), 03rjj (0.05 #1707, 0.02 #405, 0.02 #4411), 0f8l9c (0.03 #1724, 0.03 #1223, 0.02 #4028), 06q1r (0.03 #377, 0.03 #177, 0.02 #777) >> Best rule #401 for best value: >> intensional similarity = 2 >> extensional distance = 131 >> proper extension: 02x8kk; 02x8mt; >> query: (?x3546, 09c7w0) <- location(?x3546, ?x2850), ?x2850 = 0cr3d >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 04gycf nationality 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 97.000 97.000 0.835 http://example.org/people/person/nationality EVAL 04gycf nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.835 http://example.org/people/person/nationality #4134-0bkj86 PRED entity: 0bkj86 PRED relation: major_field_of_study PRED expected values: 0h5k 01lj9 0g26h 036nz 02jfc 01r4k => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 120): 02jfc (0.78 #1222, 0.71 #1071, 0.70 #1298), 0g26h (0.78 #1120, 0.71 #1046, 0.70 #1273), 04gb7 (0.71 #1047, 0.70 #1274, 0.67 #1198), 01lj9 (0.71 #1045, 0.70 #1272, 0.67 #1196), 0h5k (0.71 #1040, 0.70 #1267, 0.67 #1191), 01tbp (0.71 #1059, 0.67 #762, 0.64 #953), 02ky346 (0.67 #1111, 0.67 #740, 0.64 #953), 02lp1 (0.67 #1109, 0.64 #953, 0.62 #732), 01r4k (0.67 #775, 0.64 #953, 0.62 #732), 09s1f (0.67 #1158, 0.64 #953, 0.62 #732) >> Best rule #1222 for best value: >> intensional similarity = 19 >> extensional distance = 7 >> proper extension: 0bjrnt; >> query: (?x1526, 02jfc) <- institution(?x1526, ?x6953), institution(?x1526, ?x4980), institution(?x1526, ?x741), student(?x1526, ?x11879), student(?x1526, ?x8812), major_field_of_study(?x1526, ?x742), major_field_of_study(?x1526, ?x254), ?x254 = 02h40lc, student(?x741, ?x881), school(?x580, ?x6953), major_field_of_study(?x741, ?x1682), student(?x6953, ?x9232), contains(?x94, ?x4980), nationality(?x8812, ?x1023), gender(?x8812, ?x514), category(?x11879, ?x134), state_province_region(?x741, ?x335), ?x742 = 05qjt, profession(?x9232, ?x1032) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5, 9, 21 EVAL 0bkj86 major_field_of_study 01r4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 0bkj86 major_field_of_study 02jfc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 0bkj86 major_field_of_study 036nz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 0bkj86 major_field_of_study 0g26h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 0bkj86 major_field_of_study 01lj9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 0bkj86 major_field_of_study 0h5k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #4133-0gqy2 PRED entity: 0gqy2 PRED relation: nominated_for PRED expected values: 02d44q 0dgst_d 01vfqh 0qm98 09cr8 07yk1xz 07j8r 011ydl 051zy_b 04x4vj 0h03fhx 0c38gj 04j13sx 06cm5 011yhm 07tlfx 0glbqt => 46 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1313): 0hv4t (0.78 #5158, 0.50 #10806, 0.50 #7982), 0btpm6 (0.77 #14122, 0.77 #12708, 0.67 #29666), 015qqg (0.77 #14122, 0.77 #12708, 0.67 #29666), 0bm2g (0.77 #14122, 0.77 #12708, 0.67 #29666), 04v8x9 (0.77 #14122, 0.77 #12708, 0.67 #29666), 01fwzk (0.77 #14122, 0.77 #12708, 0.67 #29666), 0ccd3x (0.77 #14122, 0.77 #12708, 0.67 #29666), 09cr8 (0.77 #14122, 0.77 #12708, 0.67 #29666), 011ykb (0.77 #14122, 0.77 #12708, 0.67 #29666), 02jxbw (0.77 #14122, 0.77 #12708, 0.67 #29666) >> Best rule #5158 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 054krc; >> query: (?x3066, 0hv4t) <- award(?x92, ?x3066), nominated_for(?x3066, ?x2729), ceremony(?x3066, ?x78), ?x2729 = 02rjv2w >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #14122 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 121 *> proper extension: 0fqnzts; *> query: (?x3066, ?x167) <- award(?x5661, ?x3066), ceremony(?x3066, ?x78), award(?x167, ?x3066), gender(?x5661, ?x231) *> conf = 0.77 ranks of expected_values: 8, 21, 34, 46, 72, 116, 121, 122, 133, 149, 240, 242, 244, 258, 300, 351, 453 EVAL 0gqy2 nominated_for 0glbqt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 07tlfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 011yhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 06cm5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 04j13sx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 0c38gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 0h03fhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 04x4vj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 051zy_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 011ydl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 07j8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 07yk1xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 09cr8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 0qm98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 01vfqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 0dgst_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gqy2 nominated_for 02d44q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 26.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4132-02qydsh PRED entity: 02qydsh PRED relation: prequel PRED expected values: 0dr3sl => 88 concepts (52 used for prediction) PRED predicted values (max 10 best out of 37): 0dr3sl (0.20 #46, 0.18 #226, 0.15 #406), 02lk60 (0.10 #87, 0.09 #267, 0.08 #447), 0d87hc (0.10 #167, 0.09 #347, 0.03 #1262), 03176f (0.08 #437), 0164qt (0.08 #373), 0dyb1 (0.04 #591, 0.01 #1132, 0.01 #1313), 034b6k (0.03 #1262), 01b7h8 (0.03 #1262), 0888c3 (0.03 #1262), 05pxnmb (0.03 #1262) >> Best rule #46 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0bxxzb; 0f7hw; >> query: (?x8794, 0dr3sl) <- film_release_distribution_medium(?x8794, ?x81), film(?x4657, ?x8794), ?x4657 = 0f7hc, ?x81 = 029j_, country(?x8794, ?x94) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qydsh prequel 0dr3sl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 52.000 0.200 http://example.org/film/film/prequel #4131-04_1nk PRED entity: 04_1nk PRED relation: gender PRED expected values: 05zppz => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #37, 0.78 #77, 0.76 #208), 02zsn (0.50 #193, 0.28 #14, 0.25 #100) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 782 >> proper extension: 03pvt; 017yfz; 023l9y; 01_k1z; 0c8hct; 04l19_; 02p59ry; 021r7r; 02q6cv4; 01wxdn3; ... >> query: (?x5532, 05zppz) <- profession(?x5532, ?x3197), place_of_birth(?x5532, ?x1310), film_crew_role(?x365, ?x3197) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04_1nk gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.784 http://example.org/people/person/gender #4130-01pj48 PRED entity: 01pj48 PRED relation: major_field_of_study PRED expected values: 01mkq => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 122): 01mkq (0.75 #742, 0.65 #863, 0.56 #984), 02lp1 (0.71 #738, 0.68 #859, 0.61 #980), 02j62 (0.57 #756, 0.57 #513, 0.51 #998), 04rjg (0.57 #504, 0.50 #868, 0.50 #747), 04sh3 (0.50 #801, 0.47 #922, 0.46 #1043), 037mh8 (0.43 #793, 0.43 #550, 0.40 #914), 05qfh (0.43 #762, 0.43 #519, 0.40 #883), 0l5mz (0.43 #556, 0.36 #799, 0.30 #920), 0g26h (0.43 #767, 0.33 #888, 0.31 #2824), 0h5k (0.43 #507, 0.29 #750, 0.25 #871) >> Best rule #742 for best value: >> intensional similarity = 7 >> extensional distance = 26 >> proper extension: 01y17m; >> query: (?x12293, 01mkq) <- institution(?x2636, ?x12293), institution(?x1368, ?x12293), institution(?x865, ?x12293), ?x2636 = 027f2w, ?x865 = 02h4rq6, citytown(?x12293, ?x13174), ?x1368 = 014mlp >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pj48 major_field_of_study 01mkq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #4129-04110lv PRED entity: 04110lv PRED relation: award_winner PRED expected values: 0dvld => 41 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1250): 018ygt (0.41 #22588), 02mxbd (0.33 #878, 0.25 #3968, 0.22 #5514), 026rm_y (0.33 #2793, 0.25 #4338, 0.22 #8973), 06r_by (0.33 #933, 0.25 #4023, 0.12 #16381), 07mb57 (0.33 #9267, 0.23 #1545, 0.20 #24717), 0bytkq (0.33 #457, 0.22 #5093, 0.15 #9728), 04gcd1 (0.33 #321, 0.22 #4957, 0.15 #9592), 0mz73 (0.33 #1142, 0.22 #5778, 0.15 #10413), 0151w_ (0.33 #1677, 0.22 #7857, 0.14 #10946), 04qvl7 (0.33 #9267, 0.17 #3087) >> Best rule #22588 for best value: >> intensional similarity = 15 >> extensional distance = 25 >> proper extension: 0hhtgcw; >> query: (?x7936, 018ygt) <- award_winner(?x7936, ?x2443), award_winner(?x7936, ?x1208), award_winner(?x7936, ?x276), honored_for(?x7936, ?x2490), award(?x276, ?x1429), award(?x276, ?x746), nominated_for(?x276, ?x4864), participant(?x1208, ?x872), award(?x97, ?x1429), sibling(?x1208, ?x13442), award_winner(?x1429, ?x65), award_winner(?x746, ?x361), nominated_for(?x746, ?x69), film(?x891, ?x4864), award_winner(?x3078, ?x2443) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1545 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 050yyb; *> query: (?x7936, ?x986) <- ceremony(?x5409, ?x7936), ceremony(?x500, ?x7936), honored_for(?x7936, ?x5736), honored_for(?x7936, ?x3745), instance_of_recurring_event(?x7936, ?x3459), ?x500 = 0p9sw, nominated_for(?x986, ?x5736), production_companies(?x5736, ?x788), film_release_region(?x3745, ?x583), ?x5409 = 0gr07, ?x583 = 015fr, film(?x5951, ?x3745), ?x5951 = 0dvld, genre(?x3745, ?x53), nominated_for(?x198, ?x3745) *> conf = 0.23 ranks of expected_values: 26 EVAL 04110lv award_winner 0dvld CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 41.000 16.000 0.407 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4128-081nh PRED entity: 081nh PRED relation: influenced_by! PRED expected values: 05rx__ => 122 concepts (52 used for prediction) PRED predicted values (max 10 best out of 517): 03g5jw (0.23 #3111, 0.13 #6178, 0.05 #8735), 0167xy (0.19 #3497, 0.07 #9121, 0.04 #17814), 01w9ph_ (0.13 #3382, 0.08 #5427, 0.05 #9006), 04sd0 (0.12 #17896, 0.12 #9203, 0.11 #21990), 0126rp (0.12 #17896, 0.12 #9203, 0.11 #21990), 01t_wfl (0.12 #17896, 0.12 #9203, 0.11 #21990), 041c4 (0.12 #17896, 0.12 #9203, 0.11 #21990), 015f7 (0.12 #17896, 0.12 #9203, 0.11 #21990), 01trhmt (0.12 #17896, 0.12 #9203, 0.11 #21990), 0j1yf (0.12 #17896, 0.12 #9203, 0.11 #21990) >> Best rule #3111 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 01kcms4; 07m4c; 0167xy; >> query: (?x2426, 03g5jw) <- influenced_by(?x4960, ?x2426), artist(?x2299, ?x4960), peers(?x702, ?x4960) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #12575 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 181 *> proper extension: 01czx; 048xh; 03c3yf; 033s6; 0131kb; *> query: (?x2426, 05rx__) <- award(?x2426, ?x3617), influenced_by(?x1855, ?x2426), ceremony(?x3617, ?x602) *> conf = 0.05 ranks of expected_values: 81 EVAL 081nh influenced_by! 05rx__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 122.000 52.000 0.234 http://example.org/influence/influence_node/influenced_by #4127-06ryl PRED entity: 06ryl PRED relation: countries_within! PRED expected values: 059g4 => 114 concepts (94 used for prediction) PRED predicted values (max 10 best out of 30): 0261m (0.56 #196, 0.37 #127, 0.32 #195), 0dg3n1 (0.44 #124, 0.43 #97, 0.43 #114), 059g4 (0.41 #59, 0.37 #54, 0.36 #26), 07c5l (0.37 #127, 0.32 #195, 0.28 #181), 02j9z (0.29 #1, 0.27 #7, 0.26 #140), 0j0k (0.21 #169, 0.20 #72, 0.19 #138), 02613 (0.07 #40, 0.03 #326), 065ky (0.03 #326, 0.03 #238), 06n3y (0.03 #326, 0.03 #238), 03v9w (0.03 #326, 0.03 #238) >> Best rule #196 for best value: >> intensional similarity = 5 >> extensional distance = 97 >> proper extension: 0285m87; >> query: (?x4402, ?x9729) <- jurisdiction_of_office(?x182, ?x4402), ?x182 = 060bp, contains(?x9729, ?x4402), contains(?x9729, ?x3248), category(?x3248, ?x134) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #59 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 30 *> proper extension: 0168t; *> query: (?x4402, 059g4) <- form_of_government(?x4402, ?x1926), contains(?x7273, ?x4402), ?x7273 = 07c5l, olympics(?x4402, ?x2966), country(?x1121, ?x4402) *> conf = 0.41 ranks of expected_values: 3 EVAL 06ryl countries_within! 059g4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 94.000 0.559 http://example.org/base/locations/continents/countries_within #4126-03x31g PRED entity: 03x31g PRED relation: languages PRED expected values: 01c7y => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 14): 09bnf (0.31 #170, 0.30 #102, 0.27 #136), 055qm (0.17 #224, 0.10 #360, 0.10 #190), 01c7y (0.15 #163, 0.13 #231, 0.10 #197), 064_8sq (0.12 #46, 0.09 #692, 0.09 #658), 02hxcvy (0.10 #192, 0.09 #362, 0.09 #226), 0121sr (0.09 #233, 0.06 #301, 0.05 #199), 02bjrlw (0.05 #681, 0.05 #647, 0.04 #817), 0688f (0.04 #297, 0.03 #2280, 0.03 #365), 06nm1 (0.03 #820, 0.03 #684, 0.02 #650), 04306rv (0.03 #682, 0.03 #818, 0.02 #648) >> Best rule #170 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 05j12n; 02qy3py; 046rfv; 01x2tm8; 0738y5; 0kst7v; 02hkv5; 08s0m7; >> query: (?x11170, 09bnf) <- nationality(?x11170, ?x2146), ?x2146 = 03rk0, languages(?x11170, ?x8098), ?x8098 = 0999q >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #163 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 05j12n; 02qy3py; 046rfv; 01x2tm8; 0738y5; 0kst7v; 02hkv5; 08s0m7; *> query: (?x11170, 01c7y) <- nationality(?x11170, ?x2146), ?x2146 = 03rk0, languages(?x11170, ?x8098), ?x8098 = 0999q *> conf = 0.15 ranks of expected_values: 3 EVAL 03x31g languages 01c7y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.308 http://example.org/people/person/languages #4125-0d6nx PRED entity: 0d6nx PRED relation: location! PRED expected values: 0jcx => 153 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1072): 02wh0 (0.20 #4729, 0.17 #7248, 0.14 #9767), 0h326 (0.20 #5035, 0.17 #7554, 0.10 #12592), 01qq_lp (0.20 #762, 0.17 #5800, 0.10 #10838), 03bxh (0.20 #1152, 0.10 #11228, 0.08 #28861), 01j5sv (0.20 #2190, 0.10 #12266, 0.05 #29899), 09bxq9 (0.20 #1557, 0.10 #11633, 0.03 #24228), 0ngg (0.20 #2507, 0.10 #12583, 0.03 #25178), 041wm (0.20 #2219, 0.10 #12295, 0.03 #24890), 02sdx (0.20 #2203, 0.10 #12279, 0.03 #24874), 0c43g (0.20 #1904, 0.10 #11980, 0.03 #24575) >> Best rule #4729 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 04qdj; 01k4f; 08966; >> query: (?x13478, 02wh0) <- country(?x13478, ?x774), contains(?x5291, ?x13478), adjoins(?x5291, ?x11694), ?x774 = 06mzp, adjoins(?x5535, ?x5291), contains(?x11694, ?x4893) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #37786 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 39 *> proper extension: 01q0l; *> query: (?x13478, ?x1221) <- capital(?x774, ?x13478), contains(?x5291, ?x13478), nationality(?x1221, ?x774), official_language(?x774, ?x732), major_field_of_study(?x2014, ?x732), language(?x148, ?x732) *> conf = 0.11 ranks of expected_values: 37 EVAL 0d6nx location! 0jcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 153.000 79.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location #4124-0jnh PRED entity: 0jnh PRED relation: combatants PRED expected values: 04bbb8 => 69 concepts (59 used for prediction) PRED predicted values (max 10 best out of 248): 07ssc (0.68 #4045, 0.59 #2768, 0.58 #1374), 09c7w0 (0.56 #1488, 0.50 #1363, 0.48 #2757), 0cdbq (0.56 #2054, 0.50 #427, 0.43 #1804), 01jcjt (0.50 #1996, 0.50 #247, 0.48 #2120), 09jrf (0.50 #1996, 0.50 #247, 0.48 #2120), 034rd (0.50 #1996, 0.50 #247, 0.48 #2120), 01tdpv (0.50 #471, 0.38 #2098, 0.36 #1848), 01k6y1 (0.50 #428, 0.36 #1805, 0.33 #1930), 01m41_ (0.50 #465, 0.33 #219, 0.31 #2092), 0chghy (0.42 #1371, 0.36 #1243, 0.30 #2765) >> Best rule #4045 for best value: >> intensional similarity = 9 >> extensional distance = 36 >> proper extension: 01fc7p; 02kxg_; 0c3mz; >> query: (?x11109, 07ssc) <- combatants(?x11109, ?x13859), combatants(?x11109, ?x11053), combatants(?x12486, ?x13859), entity_involved(?x11109, ?x5609), combatants(?x12486, ?x12625), combatants(?x12486, ?x4492), ?x12625 = 01m41_, ?x4492 = 0cdbq, entity_involved(?x8416, ?x11053) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2119 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 14 *> proper extension: 02tvsn; *> query: (?x11109, ?x4493) <- combatants(?x11109, ?x13859), combatants(?x12486, ?x13859), combatants(?x9532, ?x13859), combatants(?x1777, ?x13859), ?x12486 = 0dr7s, combatants(?x1777, ?x4493), entity_involved(?x11109, ?x5609), ?x9532 = 0k4y6 *> conf = 0.21 ranks of expected_values: 50 EVAL 0jnh combatants 04bbb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 69.000 59.000 0.684 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #4123-0f13b PRED entity: 0f13b PRED relation: award PRED expected values: 09qv3c => 167 concepts (162 used for prediction) PRED predicted values (max 10 best out of 313): 09sb52 (0.38 #2465, 0.33 #2061, 0.33 #1657), 0gq9h (0.36 #12197, 0.35 #19469, 0.19 #10581), 0ck27z (0.32 #19889, 0.31 #2516, 0.27 #24333), 0gr4k (0.31 #10537, 0.30 #10133, 0.26 #13365), 04dn09n (0.29 #3680, 0.29 #10548, 0.27 #10144), 05pcn59 (0.29 #3717, 0.17 #1697, 0.17 #14221), 040njc (0.29 #12128, 0.27 #19400, 0.18 #10512), 0gr51 (0.27 #10604, 0.26 #3736, 0.25 #10200), 03hkv_r (0.27 #10520, 0.26 #10116, 0.20 #13348), 03hl6lc (0.26 #3814, 0.23 #10682, 0.22 #10278) >> Best rule #2465 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 05mlqj; >> query: (?x8485, 09sb52) <- film(?x8485, ?x5128), place_of_birth(?x8485, ?x3052), ?x3052 = 01cx_, nominated_for(?x1596, ?x5128) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #59798 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2255 *> proper extension: 09mfvx; 05d6q1; 0fvppk; *> query: (?x8485, ?x4921) <- nominated_for(?x8485, ?x7488), nominated_for(?x4921, ?x7488), award_winner(?x4921, ?x1039) *> conf = 0.13 ranks of expected_values: 53 EVAL 0f13b award 09qv3c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 167.000 162.000 0.375 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4122-08pc1x PRED entity: 08pc1x PRED relation: award_winner PRED expected values: 03c602 => 33 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1417): 0cc5tgk (0.67 #5923, 0.60 #4384, 0.46 #4619), 0dw4g (0.50 #5482, 0.40 #3943, 0.33 #2401), 01w58n3 (0.46 #4619, 0.38 #6159, 0.33 #7697), 0gcs9 (0.42 #6601, 0.31 #9679, 0.30 #8140), 01vw20h (0.42 #6857, 0.28 #9935, 0.20 #8396), 02qwg (0.40 #3592, 0.33 #5131, 0.33 #2050), 0x3b7 (0.40 #3727, 0.33 #5266, 0.33 #2185), 0161sp (0.40 #3508, 0.33 #5047, 0.33 #1966), 02j3d4 (0.40 #3795, 0.33 #5334, 0.33 #2253), 018x3 (0.40 #3933, 0.33 #5472, 0.33 #2391) >> Best rule #5923 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 056878; >> query: (?x12139, 0cc5tgk) <- award_winner(?x12139, ?x5172), award_winner(?x12139, ?x4258), award_nominee(?x4258, ?x9418), award(?x4258, ?x1827), profession(?x4258, ?x131), ?x9418 = 01w58n3, award_nominee(?x4184, ?x5172), award(?x5172, ?x1854), place_of_birth(?x4258, ?x14571), ceremony(?x884, ?x12139) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1414 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 092868; *> query: (?x12139, 03c602) <- award_winner(?x12139, ?x11186), award_winner(?x12139, ?x4940), award_winner(?x12139, ?x4258), award_winner(?x12139, ?x1399), ?x4258 = 0dzc16, award_nominee(?x1399, ?x158), ?x4940 = 09swkk, ceremony(?x884, ?x12139), award_winner(?x1480, ?x1399), artists(?x671, ?x11186), ?x1480 = 01c6qp, award(?x1399, ?x159) *> conf = 0.33 ranks of expected_values: 31 EVAL 08pc1x award_winner 03c602 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 33.000 14.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4121-0n5df PRED entity: 0n5df PRED relation: currency PRED expected values: 09nqf => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.85 #41, 0.85 #24, 0.84 #39) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 150 >> proper extension: 0l_q9; >> query: (?x6143, 09nqf) <- time_zones(?x6143, ?x2674), contains(?x6895, ?x6143), county_seat(?x6143, ?x6142) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n5df currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 171.000 0.849 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #4120-01zpmq PRED entity: 01zpmq PRED relation: industry PRED expected values: 01mf0 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 45): 01mw1 (0.31 #1681, 0.30 #1777, 0.27 #2785), 020mfr (0.27 #2801, 0.22 #2561, 0.22 #1697), 01mf0 (0.19 #31, 0.17 #463, 0.15 #223), 0vg8 (0.19 #51, 0.11 #963, 0.08 #1587), 015p1m (0.18 #124, 0.12 #28, 0.12 #316), 029g_vk (0.17 #875, 0.14 #1643, 0.12 #59), 02h400t (0.16 #314, 0.15 #170, 0.13 #1034), 019z7b (0.13 #969, 0.10 #537, 0.08 #921), 0191_7 (0.13 #568, 0.12 #40, 0.11 #1240), 02vxn (0.12 #578, 0.12 #3698, 0.11 #1346) >> Best rule #1681 for best value: >> intensional similarity = 5 >> extensional distance = 52 >> proper extension: 05w3y; 026wmz6; >> query: (?x6016, 01mw1) <- citytown(?x6016, ?x12691), category(?x6016, ?x134), ?x134 = 08mbj5d, place_founded(?x6016, ?x11315), contains(?x94, ?x12691) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 01n073; 0py9b; 02ktt7; *> query: (?x6016, 01mf0) <- service_location(?x6016, ?x94), category(?x6016, ?x134), ?x94 = 09c7w0, company(?x265, ?x6016), place_founded(?x6016, ?x11315) *> conf = 0.19 ranks of expected_values: 3 EVAL 01zpmq industry 01mf0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 146.000 146.000 0.315 http://example.org/business/business_operation/industry #4119-0kqb0 PRED entity: 0kqb0 PRED relation: contains! PRED expected values: 07ssc => 100 concepts (44 used for prediction) PRED predicted values (max 10 best out of 147): 07ssc (0.82 #5407, 0.80 #11658, 0.80 #10792), 09c7w0 (0.60 #9865, 0.54 #12560, 0.51 #11661), 04_1l0v (0.58 #10312, 0.55 #13007, 0.51 #12108), 0dbdy (0.37 #9862, 0.36 #34116, 0.36 #34115), 0kqb0 (0.37 #9862, 0.36 #34116, 0.36 #34115), 02j9z (0.25 #35043, 0.14 #8094, 0.10 #14382), 02qkt (0.23 #8412, 0.22 #14700, 0.20 #346), 052bw (0.23 #21544, 0.21 #13455, 0.20 #16152), 04jpl (0.22 #32342, 0.16 #30552, 0.16 #31447), 0d6br (0.17 #1321, 0.10 #4010, 0.07 #4905) >> Best rule #5407 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 0n9dn; 0nlc7; 01d8wq; 0fg6k; 0g8g6; 095l0; 025r_t; 0619_; 025569; 01hvzr; >> query: (?x14351, 07ssc) <- contains(?x1310, ?x14351), ?x1310 = 02jx1, second_level_divisions(?x1310, ?x14351) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kqb0 contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 44.000 0.824 http://example.org/location/location/contains #4118-0sxrz PRED entity: 0sxrz PRED relation: olympics! PRED expected values: 059j2 => 53 concepts (46 used for prediction) PRED predicted values (max 10 best out of 391): 0d060g (0.86 #3645, 0.84 #2206, 0.83 #919), 0h7x (0.85 #5092, 0.79 #4459, 0.77 #5228), 0d0vqn (0.84 #2206, 0.83 #4437, 0.82 #1303), 09c7w0 (0.84 #2206, 0.82 #1303, 0.81 #4023), 02jx1 (0.84 #2206, 0.82 #1303, 0.81 #4023), 01mk6 (0.83 #919, 0.67 #1277, 0.64 #3739), 03gj2 (0.82 #4313, 0.80 #3788, 0.79 #3659), 059j2 (0.82 #5086, 0.79 #4453, 0.78 #4959), 05qhw (0.75 #3910, 0.75 #2869, 0.71 #2092), 01znc_ (0.71 #2589, 0.71 #2498, 0.69 #3930) >> Best rule #3645 for best value: >> intensional similarity = 40 >> extensional distance = 12 >> proper extension: 018ljb; >> query: (?x2496, 0d060g) <- olympics(?x252, ?x2496), sports(?x2496, ?x3641), sports(?x2496, ?x171), locations(?x2496, ?x10099), olympics(?x94, ?x2496), country(?x171, ?x7747), country(?x171, ?x1603), ?x3641 = 03fyrh, ?x1603 = 06bnz, sports(?x4255, ?x171), sports(?x2432, ?x171), sports(?x2369, ?x171), sports(?x778, ?x171), film_release_region(?x10048, ?x252), film_release_region(?x9194, ?x252), film_release_region(?x8193, ?x252), film_release_region(?x7629, ?x252), film_release_region(?x6684, ?x252), film_release_region(?x3453, ?x252), film_release_region(?x3081, ?x252), film_release_region(?x634, ?x252), ?x7629 = 02825nf, contains(?x252, ?x536), country(?x11027, ?x252), ?x3081 = 023gxx, country_of_origin(?x419, ?x252), nationality(?x256, ?x252), ?x634 = 0gx9rvq, ?x6684 = 07pd_j, ?x7747 = 07f1x, ?x4255 = 0lgxj, ?x9194 = 0fpgp26, ?x2432 = 0nbjq, ?x8193 = 03z9585, ?x3453 = 0dgpwnk, ?x2369 = 0lbbj, region(?x1315, ?x252), film(?x166, ?x11027), ?x778 = 0kbvb, ?x10048 = 09tcg4 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #5086 for first EXPECTED value: *> intensional similarity = 33 *> extensional distance = 31 *> proper extension: 01f1jy; 015pkt; *> query: (?x2496, 059j2) <- olympics(?x774, ?x2496), olympics(?x583, ?x2496), sports(?x2496, ?x171), sports(?x2496, ?x3015), ?x774 = 06mzp, country(?x3015, ?x6841), country(?x3015, ?x2188), country(?x3015, ?x2152), country(?x3015, ?x2000), country(?x3015, ?x1781), country(?x3015, ?x1603), country(?x3015, ?x279), ?x2000 = 0d0kn, olympics(?x3015, ?x1931), ?x1603 = 06bnz, organization(?x1781, ?x127), adjustment_currency(?x1781, ?x170), countries_spoken_in(?x5359, ?x1781), country(?x14657, ?x1781), contains(?x6304, ?x1781), entity_involved(?x12976, ?x1781), administrative_area_type(?x1781, ?x2792), ?x2152 = 06mkj, contains(?x1781, ?x6581), olympics(?x2188, ?x418), medal(?x2496, ?x422), form_of_government(?x6841, ?x4763), combatants(?x1781, ?x1780), ?x279 = 0d060g, jurisdiction_of_office(?x182, ?x583), member_states(?x7695, ?x1781), film_release_region(?x6376, ?x583), ?x6376 = 01f85k *> conf = 0.82 ranks of expected_values: 8 EVAL 0sxrz olympics! 059j2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 53.000 46.000 0.857 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #4117-05wdgq PRED entity: 05wdgq PRED relation: religion PRED expected values: 03j6c => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 17): 03j6c (0.60 #21, 0.52 #66, 0.50 #111), 0flw86 (0.24 #47, 0.22 #92, 0.10 #137), 0c8wxp (0.17 #276, 0.17 #459, 0.17 #322), 06yyp (0.10 #22, 0.05 #67, 0.03 #112), 03_gx (0.08 #1420, 0.08 #1876, 0.08 #2012), 0kpl (0.06 #1236, 0.06 #372, 0.06 #555), 01lp8 (0.05 #136, 0.03 #683, 0.03 #592), 092bf5 (0.03 #378, 0.03 #332, 0.02 #653), 03kbr (0.03 #165), 0n2g (0.02 #421, 0.02 #329, 0.02 #466) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 061zc_; 0f5zj6; 02xgdv; 03vrnh; 03m2fg; 03f02ct; 021j72; 03f22dp; >> query: (?x12311, 03j6c) <- people(?x5025, ?x12311), award_winner(?x1937, ?x12311), ?x1937 = 03r8tl, type_of_union(?x12311, ?x566) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05wdgq religion 03j6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.600 http://example.org/people/person/religion #4116-055qm PRED entity: 055qm PRED relation: countries_spoken_in PRED expected values: 03rk0 => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 189): 03rk0 (0.60 #789, 0.58 #1340, 0.55 #973), 09pmkv (0.50 #396, 0.40 #762, 0.33 #32), 06m_5 (0.50 #513, 0.40 #879, 0.33 #149), 0hzlz (0.45 #940, 0.40 #2220, 0.40 #2036), 0697s (0.45 #990, 0.40 #622, 0.36 #1903), 07ytt (0.42 #1259, 0.36 #1989, 0.33 #2172), 0d060g (0.40 #2020, 0.38 #1655, 0.36 #4957), 0162v (0.40 #602, 0.33 #1153, 0.33 #238), 04xn_ (0.33 #119, 0.25 #483, 0.20 #849), 016zwt (0.33 #334, 0.20 #698, 0.09 #1066) >> Best rule #789 for best value: >> intensional similarity = 13 >> extensional distance = 3 >> proper extension: 09bnf; >> query: (?x8531, 03rk0) <- languages(?x12675, ?x8531), languages(?x10714, ?x8531), languages(?x8917, ?x8531), languages(?x8530, ?x8531), languages(?x7082, ?x8531), nationality(?x10714, ?x2146), location(?x7082, ?x12040), award_winner(?x4687, ?x7082), people(?x5025, ?x10714), ?x8530 = 02wmbg, profession(?x10714, ?x1032), film(?x12675, ?x5247), place_of_death(?x8917, ?x8918) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 055qm countries_spoken_in 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.600 http://example.org/language/human_language/countries_spoken_in #4115-0nm87 PRED entity: 0nm87 PRED relation: source PRED expected values: 0jbk9 => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #49, 0.94 #37, 0.93 #13) >> Best rule #49 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 0f4y_; 0nj1c; 0n5_g; 09dfcj; 0njcw; >> query: (?x12319, 0jbk9) <- time_zones(?x12319, ?x2674), ?x2674 = 02hcv8, second_level_divisions(?x94, ?x12319), ?x94 = 09c7w0 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nm87 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.940 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #4114-07l4z PRED entity: 07l4z PRED relation: school PRED expected values: 02pptm => 100 concepts (65 used for prediction) PRED predicted values (max 10 best out of 547): 065y4w7 (0.80 #356, 0.57 #2500, 0.50 #6273), 02pptm (0.80 #356, 0.50 #1025, 0.46 #534), 0lyjf (0.80 #356, 0.50 #426, 0.46 #534), 01lnyf (0.80 #356, 0.46 #534, 0.40 #1128), 07vyf (0.80 #356, 0.46 #534, 0.33 #7218), 07ccs (0.80 #356, 0.46 #534, 0.33 #355), 0jkhr (0.80 #356, 0.46 #534, 0.33 #282), 01jzyx (0.80 #356, 0.46 #534, 0.33 #257), 021w0_ (0.80 #356, 0.46 #534, 0.33 #311), 01hx2t (0.80 #356, 0.46 #534, 0.28 #6800) >> Best rule #356 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 0jmj7; >> query: (?x8901, ?x735) <- school(?x8901, ?x4955), school(?x8901, ?x2895), school(?x8901, ?x1884), school(?x8901, ?x466), ?x1884 = 0bx8pn, draft(?x8901, ?x8786), ?x466 = 01pl14, ?x4955 = 09f2j, team(?x12323, ?x8901), major_field_of_study(?x2895, ?x1682), citytown(?x2895, ?x12088), ?x1682 = 02ky346, school(?x8786, ?x6333), school(?x8786, ?x735), ?x6333 = 07ccs, institution(?x620, ?x2895) >> conf = 0.80 => this is the best rule for 14 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07l4z school 02pptm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 65.000 0.800 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #4113-0k269 PRED entity: 0k269 PRED relation: film PRED expected values: 03mh94 02s4l6 0435vm 027pfg => 100 concepts (85 used for prediction) PRED predicted values (max 10 best out of 700): 040_lv (0.25 #1034, 0.02 #15235, 0.02 #25885), 016z5x (0.25 #69, 0.01 #8945, 0.01 #12495), 04z4j2 (0.25 #1614, 0.01 #15815, 0.01 #19365), 0kv9d3 (0.25 #666, 0.01 #14867), 06g77c (0.25 #406, 0.01 #14607), 0gy0n (0.25 #1724), 0glbqt (0.25 #1649), 06823p (0.25 #1141), 0dl9_4 (0.25 #888), 02vr3gz (0.25 #611) >> Best rule #1034 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01xcfy; 037w7r; >> query: (?x3580, 040_lv) <- film(?x3580, ?x3076), film(?x3580, ?x633), ?x3076 = 0g5838s, film_release_region(?x633, ?x87) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #26627 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 393 *> proper extension: 07c0j; *> query: (?x3580, ?x308) <- award_nominee(?x3580, ?x2938), film(?x2938, ?x308), participant(?x3580, ?x891) *> conf = 0.04 ranks of expected_values: 114, 248 EVAL 0k269 film 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 100.000 85.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 0k269 film 0435vm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 85.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 0k269 film 02s4l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 85.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 0k269 film 03mh94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 100.000 85.000 0.250 http://example.org/film/actor/film./film/performance/film #4112-047951 PRED entity: 047951 PRED relation: school_type! PRED expected values: 0jhjl => 21 concepts (12 used for prediction) PRED predicted values (max 10 best out of 725): 01pl14 (0.57 #3472, 0.57 #2897, 0.50 #4057), 04rkkv (0.56 #3469), 07w0v (0.50 #2329, 0.50 #599, 0.43 #2910), 01j_cy (0.50 #2353, 0.50 #623, 0.43 #2934), 0f102 (0.50 #2387, 0.50 #657, 0.43 #2968), 049dk (0.50 #2358, 0.50 #628, 0.43 #2939), 0c5x_ (0.50 #2628, 0.50 #898, 0.43 #3209), 07vyf (0.50 #2458, 0.50 #728, 0.43 #3039), 02ldkf (0.50 #2768, 0.50 #1038, 0.43 #3349), 01rc6f (0.50 #2624, 0.50 #894, 0.43 #3205) >> Best rule #3472 for best value: >> intensional similarity = 29 >> extensional distance = 5 >> proper extension: 03ss47; >> query: (?x4722, ?x466) <- school_type(?x12051, ?x4722), school_type(?x11252, ?x4722), school_type(?x3913, ?x4722), school_type(?x12051, ?x3092), citytown(?x3913, ?x12585), state_province_region(?x12051, ?x12300), student(?x12051, ?x11399), citytown(?x12051, ?x12503), category(?x11252, ?x134), award_nominee(?x11399, ?x804), award_winner(?x13189, ?x11399), country(?x12585, ?x550), school_type(?x9525, ?x3092), school_type(?x8287, ?x3092), school_type(?x4603, ?x3092), school_type(?x1961, ?x3092), school_type(?x466, ?x3092), ?x9525 = 01qrb2, gender(?x11399, ?x231), film(?x11399, ?x308), ?x4603 = 0hd7j, ?x134 = 08mbj5d, ?x231 = 05zppz, ?x8287 = 02x9g_, nationality(?x11399, ?x390), contains(?x6956, ?x12585), ?x466 = 01pl14, award(?x11399, ?x14647), ?x1961 = 07w5rq >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #3470 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 5 *> proper extension: 03ss47; *> query: (?x4722, ?x216) <- school_type(?x12051, ?x4722), school_type(?x11252, ?x4722), school_type(?x3913, ?x4722), school_type(?x12051, ?x3092), citytown(?x3913, ?x12585), state_province_region(?x12051, ?x12300), student(?x12051, ?x11399), citytown(?x12051, ?x12503), category(?x11252, ?x134), award_nominee(?x11399, ?x804), award_winner(?x13189, ?x11399), country(?x12585, ?x550), school_type(?x9525, ?x3092), school_type(?x8287, ?x3092), school_type(?x4603, ?x3092), school_type(?x1961, ?x3092), school_type(?x466, ?x3092), school_type(?x216, ?x3092), ?x9525 = 01qrb2, gender(?x11399, ?x231), film(?x11399, ?x308), ?x4603 = 0hd7j, ?x134 = 08mbj5d, ?x231 = 05zppz, ?x8287 = 02x9g_, nationality(?x11399, ?x390), contains(?x6956, ?x12585), ?x466 = 01pl14, award(?x11399, ?x14647), ?x1961 = 07w5rq *> conf = 0.41 ranks of expected_values: 125 EVAL 047951 school_type! 0jhjl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 21.000 12.000 0.571 http://example.org/education/educational_institution/school_type #4111-02fqrf PRED entity: 02fqrf PRED relation: film! PRED expected values: 04mkft => 76 concepts (62 used for prediction) PRED predicted values (max 10 best out of 55): 05qd_ (0.21 #372, 0.21 #7, 0.18 #153), 017s11 (0.19 #75, 0.19 #294, 0.17 #221), 016tw3 (0.18 #447, 0.18 #155, 0.18 #9), 016tt2 (0.17 #76, 0.15 #1831, 0.15 #1244), 0jz9f (0.12 #585, 0.08 #804, 0.08 #74), 04mkft (0.11 #837, 0.09 #1496, 0.06 #1129), 054g1r (0.11 #106, 0.07 #836, 0.07 #1128), 025tlyv (0.11 #860, 0.06 #1519, 0.03 #1372), 030_1m (0.10 #742, 0.06 #85, 0.05 #1180), 01795t (0.09 #381, 0.08 #527, 0.07 #892) >> Best rule #372 for best value: >> intensional similarity = 5 >> extensional distance = 51 >> proper extension: 0bwfwpj; 0c0nhgv; 04hwbq; 0dgst_d; 0gmcwlb; 07qg8v; 0btyf5z; 0gd0c7x; 02yvct; 06ztvyx; ... >> query: (?x3498, 05qd_) <- film_release_region(?x3498, ?x2316), film_release_region(?x3498, ?x404), ?x404 = 047lj, nominated_for(?x154, ?x3498), ?x2316 = 06t2t >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #837 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 02mc5v; *> query: (?x3498, 04mkft) <- film(?x2533, ?x3498), country(?x3498, ?x94), nationality(?x2533, ?x1310), film_distribution_medium(?x3498, ?x81) *> conf = 0.11 ranks of expected_values: 6 EVAL 02fqrf film! 04mkft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 76.000 62.000 0.208 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #4110-0pz04 PRED entity: 0pz04 PRED relation: location PRED expected values: 06pvr => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 145): 0cr3d (0.40 #948, 0.29 #2555, 0.20 #1751), 030qb3t (0.28 #4099, 0.27 #18556, 0.25 #14539), 01x73 (0.20 #1702, 0.20 #899, 0.14 #2506), 02_286 (0.20 #1643, 0.19 #16100, 0.18 #4053), 04n3l (0.20 #983, 0.14 #2590, 0.04 #3393), 0d7k1z (0.20 #1891, 0.04 #3498), 0cb4j (0.20 #1636, 0.04 #3243), 0c_m3 (0.14 #2681, 0.04 #3484, 0.01 #8303), 05ksh (0.14 #2470, 0.01 #8092, 0.01 #13713), 0cv3w (0.14 #2569, 0.01 #4175, 0.01 #25863) >> Best rule #948 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01n5309; 0f7hc; >> query: (?x8145, 0cr3d) <- celebrities_impersonated(?x8145, ?x3929), award(?x8145, ?x401), artist(?x2299, ?x3929) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pz04 location 06pvr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 131.000 131.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #4109-02lt8 PRED entity: 02lt8 PRED relation: influenced_by! PRED expected values: 05np2 01rgr 03jxw => 133 concepts (42 used for prediction) PRED predicted values (max 10 best out of 347): 04hcw (0.67 #3215, 0.60 #2236, 0.50 #279), 0bk5r (0.67 #3139, 0.60 #2160, 0.50 #203), 03jht (0.67 #3300, 0.60 #2321, 0.50 #364), 06myp (0.60 #2378, 0.50 #3357, 0.50 #421), 03_87 (0.50 #742, 0.40 #3678, 0.33 #3189), 0j3v (0.50 #77, 0.40 #2034, 0.33 #3013), 06whf (0.50 #160, 0.40 #2117, 0.33 #3096), 06c44 (0.50 #248, 0.40 #2205, 0.33 #3184), 04xm_ (0.50 #392, 0.40 #2349, 0.33 #3328), 045bg (0.50 #524, 0.33 #2971, 0.30 #3460) >> Best rule #3215 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 032l1; >> query: (?x4072, 04hcw) <- influenced_by(?x8768, ?x4072), influenced_by(?x4915, ?x4072), ?x4915 = 03f0324, ?x8768 = 07dnx, nationality(?x4072, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #863 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0379s; *> query: (?x4072, 01rgr) <- influenced_by(?x5612, ?x4072), influenced_by(?x4915, ?x4072), ?x4915 = 03f0324, ?x5612 = 058vp, place_of_birth(?x4072, ?x3052) *> conf = 0.25 ranks of expected_values: 59, 88, 91 EVAL 02lt8 influenced_by! 03jxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 133.000 42.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 02lt8 influenced_by! 01rgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 133.000 42.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 02lt8 influenced_by! 05np2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 133.000 42.000 0.667 http://example.org/influence/influence_node/influenced_by #4108-086xm PRED entity: 086xm PRED relation: institution! PRED expected values: 019v9k => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 20): 019v9k (0.67 #521, 0.65 #840, 0.64 #727), 02_xgp2 (0.59 #301, 0.56 #844, 0.56 #614), 03bwzr4 (0.56 #661, 0.54 #527, 0.53 #616), 016t_3 (0.53 #651, 0.51 #205, 0.51 #293), 04zx3q1 (0.42 #48, 0.37 #292, 0.36 #673), 07s6fsf (0.38 #203, 0.38 #92, 0.37 #47), 027f2w (0.36 #673, 0.33 #298, 0.30 #841), 0bjrnt (0.36 #673, 0.29 #2111, 0.29 #295), 01rr_d (0.36 #673, 0.29 #2111, 0.29 #1992), 013zdg (0.36 #673, 0.29 #2111, 0.29 #1992) >> Best rule #521 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 01j_9c; 06pwq; 02w2bc; 065y4w7; 01w3v; 0288zy; 07w0v; 0kz2w; 04rwx; 01jsn5; ... >> query: (?x3136, 019v9k) <- colors(?x3136, ?x332), company(?x346, ?x3136), currency(?x3136, ?x170) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 086xm institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 146.000 0.667 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4107-081nh PRED entity: 081nh PRED relation: inductee! PRED expected values: 06szd3 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 6): 0g2c8 (0.25 #172, 0.24 #308, 0.20 #163), 06szd3 (0.25 #172, 0.09 #174, 0.08 #47), 04045y (0.06 #87, 0.06 #114, 0.05 #132), 0qjfl (0.05 #165, 0.04 #220, 0.03 #120), 04dm2n (0.03 #62, 0.03 #89, 0.03 #116), 01b3l (0.03 #59, 0.03 #86, 0.03 #131) >> Best rule #172 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 0167xy; >> query: (?x2426, ?x9953) <- influenced_by(?x5562, ?x2426), award_nominee(?x5562, ?x803), inductee(?x9953, ?x5562) >> conf = 0.25 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 081nh inductee! 06szd3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 134.000 134.000 0.250 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #4106-05byxm PRED entity: 05byxm PRED relation: organizations_founded! PRED expected values: 016732 => 42 concepts (11 used for prediction) PRED predicted values (max 10 best out of 19): 028qyn (0.33 #91, 0.25 #316, 0.02 #544), 01vhrz (0.06 #643, 0.05 #759, 0.04 #992), 0m593 (0.04 #516, 0.04 #747, 0.03 #865), 0n839 (0.04 #1251, 0.04 #670, 0.03 #904), 023p29 (0.03 #1000, 0.03 #1116, 0.03 #1232), 016jll (0.02 #541, 0.02 #656, 0.02 #772), 013qvn (0.02 #519, 0.02 #634, 0.02 #750), 016732 (0.02 #514, 0.02 #629, 0.02 #745), 02_fj (0.02 #484, 0.02 #599, 0.02 #715), 06pj8 (0.02 #593, 0.02 #709, 0.02 #827) >> Best rule #91 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 01cl0d; >> query: (?x10037, 028qyn) <- artist(?x10037, ?x8556), artist(?x10037, ?x2690), artist(?x10037, ?x250), ?x8556 = 01wqflx, category(?x10037, ?x134), artists(?x671, ?x250), profession(?x250, ?x319), participant(?x250, ?x193), profession(?x2690, ?x131), people(?x2510, ?x250), award(?x2690, ?x724) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #514 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 43 *> proper extension: 0181hw; *> query: (?x10037, 016732) <- artist(?x10037, ?x8556), artist(?x10037, ?x6819), artists(?x302, ?x8556), artists(?x2664, ?x6819), artists(?x1572, ?x6819), origin(?x8556, ?x6960), nationality(?x8556, ?x94), award_winner(?x342, ?x6819), ?x1572 = 06by7, award(?x8556, ?x247), ?x2664 = 01lyv *> conf = 0.02 ranks of expected_values: 8 EVAL 05byxm organizations_founded! 016732 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 42.000 11.000 0.333 http://example.org/organization/organization_founder/organizations_founded #4105-06yj20 PRED entity: 06yj20 PRED relation: gender PRED expected values: 02zsn => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #25, 0.90 #36, 0.88 #23), 02zsn (0.47 #33, 0.46 #124, 0.26 #85) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 01xyt7; >> query: (?x12478, 05zppz) <- type_of_union(?x12478, ?x566), ?x566 = 04ztj, athlete(?x471, ?x12478) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #33 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 103 *> proper extension: 051q39; *> query: (?x12478, ?x231) <- athlete(?x471, ?x12478), profession(?x12478, ?x7623), profession(?x8712, ?x7623), gender(?x8712, ?x231) *> conf = 0.47 ranks of expected_values: 2 EVAL 06yj20 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 62.000 62.000 0.901 http://example.org/people/person/gender #4104-0km3f PRED entity: 0km3f PRED relation: dog_breed! PRED expected values: 04f_d 019fh 01sn3 0f04v => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 243): 04f_d (0.48 #17, 0.33 #13, 0.33 #8), 0dzt9 (0.48 #17, 0.20 #6), 029cr (0.48 #17, 0.16 #20, 0.12 #19), 013kcv (0.48 #17, 0.16 #20), 0vm39 (0.48 #17, 0.02 #12), 0kcw2 (0.48 #17), 0qpsn (0.48 #17), 0sq2v (0.48 #17), 010h9y (0.48 #17), 0f1sm (0.48 #17) >> Best rule #17 for best value: >> intensional similarity = 160 >> extensional distance = 1 >> proper extension: 0km5c; >> query: (?x6596, ?x2017) <- dog_breed(?x9713, ?x6596), dog_breed(?x9605, ?x6596), dog_breed(?x8993, ?x6596), dog_breed(?x8468, ?x6596), dog_breed(?x8451, ?x6596), dog_breed(?x6960, ?x6596), dog_breed(?x6769, ?x6596), dog_breed(?x6555, ?x6596), dog_breed(?x6088, ?x6596), dog_breed(?x6084, ?x6596), dog_breed(?x5719, ?x6596), dog_breed(?x5267, ?x6596), dog_breed(?x4733, ?x6596), dog_breed(?x4499, ?x6596), dog_breed(?x4356, ?x6596), dog_breed(?x3983, ?x6596), dog_breed(?x3373, ?x6596), dog_breed(?x3269, ?x6596), dog_breed(?x3052, ?x6596), dog_breed(?x3026, ?x6596), dog_breed(?x2941, ?x6596), dog_breed(?x2740, ?x6596), dog_breed(?x2277, ?x6596), dog_breed(?x1860, ?x6596), dog_breed(?x1719, ?x6596), dog_breed(?x1705, ?x6596), dog_breed(?x1523, ?x6596), dog_breed(?x479, ?x6596), dog_breed(?x108, ?x6596), ?x9605 = 02frhbc, ?x4356 = 06wxw, ?x4733 = 03l2n, ?x8993 = 0fsb8, ?x2740 = 0f__1, ?x5267 = 0d9jr, ?x3373 = 0ply0, month(?x2277, ?x4827), month(?x2277, ?x3270), month(?x2277, ?x2255), month(?x2277, ?x1650), month(?x2277, ?x1459), origin(?x12102, ?x2277), origin(?x8722, ?x2277), location(?x10482, ?x2277), location(?x9153, ?x2277), location(?x8749, ?x2277), location(?x8494, ?x2277), location(?x5343, ?x2277), location(?x5246, ?x2277), location(?x2437, ?x2277), location(?x827, ?x2277), citytown(?x11954, ?x2277), citytown(?x10217, ?x2277), teams(?x2277, ?x6645), ?x10482 = 0b25vg, ?x2941 = 0fvzg, ?x4499 = 068p2, profession(?x5343, ?x1032), locations(?x8527, ?x2277), ?x6555 = 01snm, location_of_ceremony(?x566, ?x2277), ?x3983 = 0fr0t, ?x5719 = 0f2rq, ?x6960 = 071vr, location(?x3054, ?x8451), award_nominee(?x5246, ?x4294), award_nominee(?x5246, ?x2422), participant(?x5246, ?x105), film(?x5246, ?x603), place_of_birth(?x4332, ?x2277), place_of_birth(?x3058, ?x2277), contains(?x94, ?x10217), type_of_union(?x8749, ?x1873), award_winner(?x618, ?x5246), ?x1650 = 06vkl, ?x4827 = 03_ly, administrative_division(?x2277, ?x13275), ?x566 = 04ztj, gender(?x3054, ?x514), citytown(?x4846, ?x8451), vacationer(?x957, ?x5246), award_winner(?x3647, ?x12102), ?x1705 = 094jv, award_winner(?x4332, ?x6634), award_winner(?x4332, ?x6263), award_winner(?x4332, ?x4333), award_winner(?x4332, ?x3924), award_winner(?x4332, ?x237), film(?x5343, ?x2644), ?x6088 = 0dyl9, ?x1719 = 0f2w0, film(?x8749, ?x1178), program(?x11954, ?x1876), titles(?x11954, ?x808), participant(?x5246, ?x489), actor(?x416, ?x8749), ?x3052 = 01cx_, participant(?x3421, ?x5343), award(?x5246, ?x154), people(?x2510, ?x12102), ?x4294 = 01r93l, participant(?x3054, ?x6085), ?x1459 = 04w_7, artists(?x3928, ?x12102), participant(?x2025, ?x5343), award_winner(?x9350, ?x3058), ?x6634 = 0cj36c, ?x237 = 04t2l2, currency(?x5343, ?x170), award_nominee(?x215, ?x12102), ?x479 = 02dtg, nominated_for(?x3058, ?x3822), ?x3270 = 05cw8, artists(?x505, ?x8722), ?x2255 = 040fv, adjoins(?x13275, ?x9053), ?x3928 = 0gywn, religion(?x3054, ?x1985), ?x4333 = 0cnl09, award_nominee(?x4337, ?x3054), ?x1032 = 02hrh1q, produced_by(?x943, ?x9153), award_winner(?x8500, ?x8722), award_winner(?x5450, ?x3054), student(?x734, ?x8494), position_s(?x6645, ?x180), award_nominee(?x827, ?x527), team(?x8527, ?x6803), ?x1860 = 01_d4, ?x3269 = 0vzm, ?x6263 = 0cms7f, ?x6803 = 03by7wc, participant(?x2596, ?x9153), profession(?x3058, ?x1943), profession(?x3058, ?x524), ?x1523 = 030qb3t, ?x94 = 09c7w0, award(?x827, ?x1232), award(?x8722, ?x4481), position(?x6645, ?x1717), ?x3924 = 0h3mrc, locations(?x8527, ?x2017), category(?x8451, ?x134), participant(?x9153, ?x971), ?x9713 = 0f2s6, student(?x6611, ?x9153), ?x1943 = 02krf9, ?x524 = 02jknp, ?x8468 = 0nbwf, ?x2422 = 0169dl, ?x108 = 0rh6k, award_winner(?x139, ?x827), mode_of_transportation(?x2277, ?x4272), artist(?x6230, ?x827), award_winner(?x364, ?x2437), award_nominee(?x2437, ?x436), ?x6084 = 0n1rj, ?x6769 = 0f2tj, award_winner(?x594, ?x8494), ?x3026 = 0cv3w >> conf = 0.48 => this is the best rule for 25 predicted values ranks of expected_values: 1, 42, 43, 44 EVAL 0km3f dog_breed! 0f04v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 5.000 5.000 0.483 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 0km3f dog_breed! 01sn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 5.000 5.000 0.483 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 0km3f dog_breed! 019fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 5.000 5.000 0.483 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 0km3f dog_breed! 04f_d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 5.000 5.000 0.483 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #4103-0d9jr PRED entity: 0d9jr PRED relation: location! PRED expected values: 0zcbl => 219 concepts (152 used for prediction) PRED predicted values (max 10 best out of 2261): 01gf5h (0.51 #50251, 0.50 #37686, 0.50 #90458), 02y7sr (0.51 #50251, 0.50 #37686, 0.50 #90458), 083chw (0.51 #50251, 0.50 #37686, 0.50 #90458), 04smkr (0.51 #50251, 0.50 #37686, 0.50 #90458), 05m883 (0.51 #50251, 0.50 #37686, 0.50 #90458), 03rwng (0.51 #50251, 0.50 #37686, 0.50 #90458), 01pm0_ (0.51 #50251, 0.50 #37686, 0.50 #90458), 02r3zy (0.36 #238723, 0.35 #236208, 0.34 #206060), 01d1st (0.36 #238723, 0.35 #236208, 0.31 #288980), 0d193h (0.34 #206060, 0.31 #288980, 0.31 #180932) >> Best rule #50251 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 01zrs_; 019xz9; >> query: (?x5267, ?x275) <- place_of_birth(?x4873, ?x5267), place_of_birth(?x275, ?x5267), peers(?x4873, ?x3403), influenced_by(?x4873, ?x2169) >> conf = 0.51 => this is the best rule for 7 predicted values *> Best rule #36574 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0rhp6; 0yz30; *> query: (?x5267, 0zcbl) <- place_of_birth(?x4873, ?x5267), artists(?x1000, ?x4873), influenced_by(?x2835, ?x4873) *> conf = 0.03 ranks of expected_values: 1658 EVAL 0d9jr location! 0zcbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 219.000 152.000 0.505 http://example.org/people/person/places_lived./people/place_lived/location #4102-02qx5h PRED entity: 02qx5h PRED relation: award PRED expected values: 0gqy2 => 82 concepts (71 used for prediction) PRED predicted values (max 10 best out of 254): 0f4x7 (0.57 #1646, 0.13 #8517, 0.13 #7305), 04kxsb (0.54 #1742, 0.12 #4974, 0.11 #7401), 09sb52 (0.44 #1656, 0.39 #6505, 0.36 #4888), 05ztrmj (0.36 #992, 0.17 #1396, 0.13 #1800), 05zr6wv (0.36 #824, 0.17 #1632, 0.15 #1228), 05zvj3m (0.36 #901, 0.15 #1305, 0.07 #11722), 0gqy2 (0.35 #1781, 0.33 #165, 0.25 #569), 0789_m (0.33 #19, 0.25 #423, 0.22 #1635), 08_vwq (0.33 #271, 0.25 #675, 0.09 #1079), 024dzn (0.33 #328, 0.25 #732, 0.04 #28287) >> Best rule #1646 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 03f1zdw; 0gm34; >> query: (?x12788, 0f4x7) <- film(?x12788, ?x1692), type_of_union(?x12788, ?x566), award(?x12788, ?x3209), ?x3209 = 02w9sd7 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1781 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 03f1zdw; 0gm34; *> query: (?x12788, 0gqy2) <- film(?x12788, ?x1692), type_of_union(?x12788, ?x566), award(?x12788, ?x3209), ?x3209 = 02w9sd7 *> conf = 0.35 ranks of expected_values: 7 EVAL 02qx5h award 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 82.000 71.000 0.574 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4101-016cjb PRED entity: 016cjb PRED relation: parent_genre! PRED expected values: 0gywn => 36 concepts (29 used for prediction) PRED predicted values (max 10 best out of 283): 016_nr (0.60 #586, 0.50 #62, 0.43 #1110), 07ym47 (0.50 #57, 0.40 #581, 0.29 #1105), 0133_p (0.50 #130, 0.40 #654, 0.29 #1178), 01fm07 (0.50 #103, 0.40 #627, 0.29 #1151), 01ym9b (0.40 #564, 0.40 #302, 0.29 #1088), 016_rm (0.40 #721, 0.29 #1245, 0.25 #197), 059kh (0.29 #829, 0.20 #305, 0.18 #1354), 0y3_8 (0.29 #827, 0.20 #303, 0.14 #1615), 01h0kx (0.29 #915, 0.20 #391, 0.11 #1440), 0grjmv (0.29 #905, 0.20 #381, 0.11 #1430) >> Best rule #586 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 0glt670; >> query: (?x5717, 016_nr) <- artists(?x5717, ?x10740), artists(?x5717, ?x5452), artists(?x5717, ?x4474), artists(?x5717, ?x2409), ?x4474 = 01vvyvk, ?x10740 = 016ppr, parent_genre(?x5355, ?x5717), gender(?x5452, ?x231), type_of_union(?x2409, ?x566) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #49 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 06j6l; 0gywn; *> query: (?x5717, 0gywn) <- artists(?x5717, ?x10740), artists(?x5717, ?x5452), artists(?x5717, ?x4474), artists(?x5717, ?x2765), ?x4474 = 01vvyvk, ?x10740 = 016ppr, parent_genre(?x5355, ?x5717), gender(?x5452, ?x231), ?x2765 = 01w724 *> conf = 0.25 ranks of expected_values: 20 EVAL 016cjb parent_genre! 0gywn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 36.000 29.000 0.600 http://example.org/music/genre/parent_genre #4100-01wj9y9 PRED entity: 01wj9y9 PRED relation: nationality PRED expected values: 09c7w0 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 71): 09c7w0 (0.89 #1602, 0.85 #1101, 0.85 #1001), 02jx1 (0.23 #7948, 0.20 #1534, 0.16 #4238), 0345h (0.21 #731, 0.14 #831, 0.10 #331), 07ssc (0.18 #4220, 0.18 #4420, 0.18 #4821), 0d060g (0.14 #3509, 0.14 #807, 0.14 #707), 0f8l9c (0.14 #722, 0.10 #10324, 0.10 #10223), 0h7x (0.12 #2336, 0.10 #335, 0.10 #1836), 03rk0 (0.10 #8562, 0.09 #8962, 0.08 #9364), 03spz (0.10 #10324, 0.10 #10223, 0.10 #10122), 03gj2 (0.10 #10324, 0.10 #10223, 0.10 #10122) >> Best rule #1602 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 012v9y; 0h1q6; >> query: (?x2283, 09c7w0) <- celebrities_impersonated(?x3649, ?x2283), profession(?x2283, ?x987), location(?x2283, ?x739), place_of_death(?x340, ?x739) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wj9y9 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.886 http://example.org/people/person/nationality #4099-06hx2 PRED entity: 06hx2 PRED relation: place_of_birth PRED expected values: 0p9z5 => 209 concepts (205 used for prediction) PRED predicted values (max 10 best out of 217): 01cx_ (0.39 #23966, 0.39 #23368, 0.36 #25377), 059rby (0.33 #128326, 0.29 #128325, 0.28 #110704), 018djs (0.25 #665, 0.10 #7707), 030qb3t (0.24 #11326, 0.16 #17674, 0.14 #9211), 02_286 (0.20 #1427, 0.20 #723, 0.18 #90269), 052p7 (0.20 #2194, 0.20 #786, 0.05 #12059), 06c62 (0.20 #3073, 0.14 #5186, 0.01 #57361), 01jr6 (0.20 #1551, 0.04 #17763, 0.04 #17057), 0p9z5 (0.14 #4596, 0.12 #6709, 0.09 #8118), 06pr6 (0.14 #5192, 0.10 #7305, 0.02 #47495) >> Best rule #23966 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 05mlqj; >> query: (?x6138, ?x3052) <- nationality(?x6138, ?x94), location(?x6138, ?x3052), ?x3052 = 01cx_, ?x94 = 09c7w0 >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #4596 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0l786; 0c8br; *> query: (?x6138, 0p9z5) <- student(?x3178, ?x6138), place_of_burial(?x6138, ?x7496), ?x7496 = 0lbp_, profession(?x6138, ?x3342) *> conf = 0.14 ranks of expected_values: 9 EVAL 06hx2 place_of_birth 0p9z5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 209.000 205.000 0.395 http://example.org/people/person/place_of_birth #4098-06ltr PRED entity: 06ltr PRED relation: film PRED expected values: 0b9rdk 02q8ms8 016ywb => 107 concepts (56 used for prediction) PRED predicted values (max 10 best out of 699): 01xbxn (0.25 #1381, 0.03 #12043, 0.02 #13820), 0dgst_d (0.25 #3748), 04k9y6 (0.22 #2811, 0.03 #15250, 0.02 #27689), 014kq6 (0.13 #40875, 0.06 #3896, 0.01 #5673), 01flv_ (0.13 #40875, 0.01 #40155), 011wtv (0.13 #40875, 0.01 #39860), 025twgt (0.13 #40875), 02n72k (0.13 #40875), 0d1qmz (0.13 #40875), 01kf4tt (0.13 #40875) >> Best rule #1381 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0bg539; 0f4vbz; 03l3ln; 04sry; 06pjs; 012x2b; >> query: (?x5332, 01xbxn) <- student(?x373, ?x5332), student(?x4390, ?x5332), film(?x5332, ?x835), ?x373 = 02vxn >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3004 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0159h6; 02tr7d; 0p8r1; 08c9b0; 016nff; 04mhbh; 01l7qw; *> query: (?x5332, 016ywb) <- profession(?x5332, ?x1032), ?x1032 = 02hrh1q, film(?x5332, ?x2155), ?x2155 = 0407yfx *> conf = 0.11 ranks of expected_values: 62 EVAL 06ltr film 016ywb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 107.000 56.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 06ltr film 02q8ms8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 56.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 06ltr film 0b9rdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 56.000 0.250 http://example.org/film/actor/film./film/performance/film #4097-037cr1 PRED entity: 037cr1 PRED relation: production_companies PRED expected values: 025hwq => 91 concepts (70 used for prediction) PRED predicted values (max 10 best out of 95): 046b0s (0.50 #105, 0.42 #845, 0.40 #187), 086k8 (0.41 #658, 0.32 #824, 0.31 #494), 017s11 (0.41 #5280, 0.39 #4532, 0.38 #822), 01795t (0.33 #21, 0.09 #431, 0.07 #2903), 0kk9v (0.33 #34, 0.09 #444, 0.06 #526), 01gb54 (0.31 #529, 0.08 #3001, 0.07 #1681), 05qd_ (0.26 #2974, 0.25 #338, 0.21 #914), 016tt2 (0.20 #250, 0.15 #908, 0.12 #496), 03sb38 (0.20 #300, 0.12 #382, 0.11 #2279), 02j_j0 (0.20 #293, 0.12 #375, 0.10 #2272) >> Best rule #105 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 05fcbk7; >> query: (?x10260, 046b0s) <- country(?x10260, ?x390), country(?x10260, ?x94), ?x390 = 0chghy, film_crew_role(?x10260, ?x2848), film_crew_role(?x10260, ?x2154), ?x2154 = 01vx2h, ?x2848 = 094hwz, ?x94 = 09c7w0 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1044 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 37 *> proper extension: 0bx_hnp; *> query: (?x10260, 025hwq) <- country(?x10260, ?x512), country(?x10260, ?x94), crewmember(?x10260, ?x1933), ?x94 = 09c7w0, cinematography(?x10260, ?x7427), film_crew_role(?x10260, ?x1284), ?x1284 = 0ch6mp2, combatants(?x512, ?x151) *> conf = 0.05 ranks of expected_values: 31 EVAL 037cr1 production_companies 025hwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 91.000 70.000 0.500 http://example.org/film/film/production_companies #4096-06_bq1 PRED entity: 06_bq1 PRED relation: award_winner! PRED expected values: 02s2ft => 141 concepts (109 used for prediction) PRED predicted values (max 10 best out of 853): 0blq0z (0.83 #83549, 0.82 #98013, 0.82 #57840), 03y_46 (0.33 #970, 0.26 #154256, 0.25 #107658), 0lpjn (0.30 #458, 0.24 #70695, 0.16 #175146), 0dgskx (0.26 #154256, 0.26 #1093, 0.25 #107658), 034g2b (0.26 #154256, 0.26 #438, 0.25 #107658), 0bq2g (0.26 #154256, 0.26 #580, 0.25 #107658), 01y665 (0.26 #154256, 0.26 #498, 0.25 #107658), 01tspc6 (0.26 #154256, 0.26 #140, 0.25 #107658), 0m31m (0.26 #154256, 0.26 #424, 0.25 #107658), 03pmty (0.26 #154256, 0.26 #136, 0.25 #107658) >> Best rule #83549 for best value: >> intensional similarity = 3 >> extensional distance = 181 >> proper extension: 01t_wfl; >> query: (?x7046, ?x989) <- award_winner(?x7046, ?x989), participant(?x10053, ?x7046), place_of_birth(?x7046, ?x5670) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #154256 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 750 *> proper extension: 01sl1q; 0184jc; 079vf; 05vsxz; 01vvydl; 07fq1y; 02qgqt; 02p65p; 0337vz; 07s3vqk; ... *> query: (?x7046, ?x969) <- film(?x7046, ?x1701), award_winner(?x989, ?x7046), award_winner(?x969, ?x989) *> conf = 0.26 ranks of expected_values: 37 EVAL 06_bq1 award_winner! 02s2ft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 141.000 109.000 0.832 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #4095-07gyv PRED entity: 07gyv PRED relation: country PRED expected values: 09pmkv 04wgh 06qd3 05qkp 0jgx 07dvs 04gqr 07fj_ 07f5x => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 263): 07ylj (0.83 #2747, 0.80 #2196, 0.79 #2608), 0163v (0.83 #2763, 0.79 #2624, 0.78 #2068), 06c1y (0.79 #2180, 0.78 #2058, 0.75 #1920), 05b4w (0.79 #2180, 0.78 #2075, 0.75 #1801), 03spz (0.79 #2180, 0.73 #1355, 0.62 #1697), 03gj2 (0.79 #2180, 0.73 #1355, 0.62 #1910), 047yc (0.79 #2180, 0.67 #1493, 0.58 #2181), 03h64 (0.79 #2180, 0.62 #1938, 0.62 #1667), 05sb1 (0.79 #2180, 0.62 #1795, 0.62 #404), 03rk0 (0.79 #2180, 0.61 #403, 0.60 #1114) >> Best rule #2747 for best value: >> intensional similarity = 49 >> extensional distance = 16 >> proper extension: 0dwxr; >> query: (?x668, 07ylj) <- country(?x668, ?x8778), country(?x668, ?x8197), country(?x668, ?x5147), country(?x668, ?x3730), country(?x668, ?x3720), country(?x668, ?x252), ?x5147 = 0d04z6, administrative_parent(?x8778, ?x551), sports(?x778, ?x668), form_of_government(?x8778, ?x1926), adjoins(?x2236, ?x3730), olympics(?x3730, ?x1081), country(?x8465, ?x252), film_release_region(?x8495, ?x252), film_release_region(?x6751, ?x252), film_release_region(?x6587, ?x252), film_release_region(?x6422, ?x252), film_release_region(?x5873, ?x252), film_release_region(?x3897, ?x252), film_release_region(?x3742, ?x252), film_release_region(?x3524, ?x252), film_release_region(?x3276, ?x252), film_release_region(?x2676, ?x252), film_release_region(?x1785, ?x252), film_release_region(?x1228, ?x252), nationality(?x10418, ?x252), ?x3524 = 06r2_, country(?x536, ?x252), currency(?x8197, ?x170), ?x3742 = 02w86hz, ?x1228 = 05z_kps, ?x3897 = 02dpl9, location(?x3118, ?x252), ?x5873 = 0cq86w, actor(?x8465, ?x7742), ?x6422 = 02qk3fk, organization(?x3720, ?x127), contains(?x252, ?x1054), ?x8495 = 0ds5_72, ?x1785 = 0gj9tn5, ?x2676 = 0f4m2z, country_of_origin(?x419, ?x252), ?x3276 = 0gjc4d3, genre(?x8465, ?x225), ?x6587 = 07s3m4g, profession(?x10418, ?x1032), ?x6751 = 0372j5, ?x1081 = 0l6m5, countries_spoken_in(?x254, ?x3720) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #2752 for first EXPECTED value: *> intensional similarity = 49 *> extensional distance = 16 *> proper extension: 0dwxr; *> query: (?x668, 06qd3) <- country(?x668, ?x8778), country(?x668, ?x8197), country(?x668, ?x5147), country(?x668, ?x3730), country(?x668, ?x3720), country(?x668, ?x252), ?x5147 = 0d04z6, administrative_parent(?x8778, ?x551), sports(?x778, ?x668), form_of_government(?x8778, ?x1926), adjoins(?x2236, ?x3730), olympics(?x3730, ?x1081), country(?x8465, ?x252), film_release_region(?x8495, ?x252), film_release_region(?x6751, ?x252), film_release_region(?x6587, ?x252), film_release_region(?x6422, ?x252), film_release_region(?x5873, ?x252), film_release_region(?x3897, ?x252), film_release_region(?x3742, ?x252), film_release_region(?x3524, ?x252), film_release_region(?x3276, ?x252), film_release_region(?x2676, ?x252), film_release_region(?x1785, ?x252), film_release_region(?x1228, ?x252), nationality(?x10418, ?x252), ?x3524 = 06r2_, country(?x536, ?x252), currency(?x8197, ?x170), ?x3742 = 02w86hz, ?x1228 = 05z_kps, ?x3897 = 02dpl9, location(?x3118, ?x252), ?x5873 = 0cq86w, actor(?x8465, ?x7742), ?x6422 = 02qk3fk, organization(?x3720, ?x127), contains(?x252, ?x1054), ?x8495 = 0ds5_72, ?x1785 = 0gj9tn5, ?x2676 = 0f4m2z, country_of_origin(?x419, ?x252), ?x3276 = 0gjc4d3, genre(?x8465, ?x225), ?x6587 = 07s3m4g, profession(?x10418, ?x1032), ?x6751 = 0372j5, ?x1081 = 0l6m5, countries_spoken_in(?x254, ?x3720) *> conf = 0.78 ranks of expected_values: 13, 34, 43, 58, 62, 72, 74, 77, 138 EVAL 07gyv country 07f5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 32.000 32.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07gyv country 07fj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 32.000 32.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07gyv country 04gqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 32.000 32.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07gyv country 07dvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 32.000 32.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07gyv country 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 32.000 32.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07gyv country 05qkp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 32.000 32.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07gyv country 06qd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 32.000 32.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07gyv country 04wgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 32.000 32.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07gyv country 09pmkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 32.000 32.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #4094-0jhn7 PRED entity: 0jhn7 PRED relation: sports PRED expected values: 0w0d => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 26): 018w8 (0.88 #104, 0.88 #103, 0.85 #391), 0194d (0.88 #104, 0.88 #103, 0.85 #391), 0w0d (0.88 #104, 0.88 #103, 0.85 #391), 035d1m (0.69 #263, 0.67 #618, 0.66 #694), 02y74 (0.69 #263, 0.67 #618, 0.66 #694), 01sgl (0.69 #263, 0.67 #618, 0.66 #694), 07gyv (0.50 #55, 0.40 #159, 0.40 #134), 09_9n (0.34 #613, 0.33 #689, 0.33 #203), 09w1n (0.33 #193, 0.29 #603, 0.29 #679), 01z27 (0.33 #190, 0.29 #600, 0.29 #676) >> Best rule #104 for best value: >> intensional similarity = 14 >> extensional distance = 2 >> proper extension: 0lk8j; >> query: (?x3971, ?x10585) <- olympics(?x5482, ?x3971), olympics(?x2843, ?x3971), olympics(?x1536, ?x3971), olympics(?x456, ?x3971), olympics(?x429, ?x3971), ?x456 = 05qhw, sports(?x3971, ?x10585), ?x5482 = 04g5k, country(?x10585, ?x279), film_release_region(?x5704, ?x2843), adjoins(?x410, ?x2843), ?x5704 = 0h95zbp, religion(?x1536, ?x962), ?x429 = 03rt9 >> conf = 0.88 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0jhn7 sports 0w0d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 55.000 55.000 0.884 http://example.org/olympics/olympic_games/sports #4093-01hb1t PRED entity: 01hb1t PRED relation: campuses PRED expected values: 01hb1t => 127 concepts (75 used for prediction) PRED predicted values (max 10 best out of 252): 065y4w7 (0.05 #12, 0.03 #558, 0.03 #1104), 03bmmc (0.05 #191, 0.03 #1283, 0.03 #38242), 06thjt (0.05 #394, 0.03 #38242, 0.02 #34412), 0bwfn (0.05 #263, 0.02 #1901, 0.02 #34412), 09f2j (0.05 #151, 0.02 #1789, 0.01 #3973), 0lyjf (0.05 #149, 0.02 #1787, 0.01 #3971), 03ksy (0.05 #94, 0.02 #1732, 0.01 #3916), 01rtm4 (0.05 #5, 0.02 #34412, 0.02 #2735), 021w0_ (0.05 #314, 0.01 #4136, 0.01 #4682), 01qd_r (0.05 #270, 0.01 #4092, 0.01 #6822) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 02g839; >> query: (?x3123, 065y4w7) <- student(?x3123, ?x65), producer_type(?x65, ?x632), colors(?x3123, ?x332), story_by(?x936, ?x65) >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #34412 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 456 *> proper extension: 01dbns; 05bnq8; 01d650; 01_f90; 0301dp; *> query: (?x3123, ?x166) <- institution(?x3386, ?x3123), state_province_region(?x3123, ?x335), major_field_of_study(?x3386, ?x373), state_province_region(?x166, ?x335) *> conf = 0.02 ranks of expected_values: 168 EVAL 01hb1t campuses 01hb1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 127.000 75.000 0.045 http://example.org/education/educational_institution/campuses #4092-018ldw PRED entity: 018ldw PRED relation: category PRED expected values: 08mbj5d => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.77 #1, 0.77 #2, 0.69 #8) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 05ksh; 0h7h6; 01y9pk; 01y8zd; 0843m; 016ndm; 01y9st; 018dcy; 0154gx; 0885n; ... >> query: (?x14019, 08mbj5d) <- contains(?x1905, ?x14019), contains(?x279, ?x14019), ?x279 = 0d060g, ?x1905 = 05kr_ >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018ldw category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.774 http://example.org/common/topic/webpage./common/webpage/category #4091-01dq5z PRED entity: 01dq5z PRED relation: country PRED expected values: 09c7w0 => 139 concepts (114 used for prediction) PRED predicted values (max 10 best out of 7): 09c7w0 (0.87 #50, 0.87 #32, 0.86 #41), 04ykg (0.31 #53, 0.30 #57, 0.10 #127), 04_1l0v (0.11 #264), 0nhmw (0.01 #265), 0b2lw (0.01 #265), 0nh0f (0.01 #265), 0fpzwf (0.01 #265) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 02yr3z; 08qs09; 01b7lc; 01fsv9; 02zy1z; >> query: (?x3204, 09c7w0) <- currency(?x3204, ?x170), institution(?x865, ?x3204), contains(?x94, ?x3204), school_type(?x3204, ?x3205) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dq5z country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 114.000 0.872 http://example.org/organization/organization/headquarters./location/mailing_address/country #4090-07ykkx5 PRED entity: 07ykkx5 PRED relation: language PRED expected values: 02h40lc => 108 concepts (101 used for prediction) PRED predicted values (max 10 best out of 58): 02h40lc (0.90 #474, 0.90 #891, 0.88 #5805), 06nm1 (0.40 #70, 0.33 #247, 0.33 #188), 064_8sq (0.17 #1747, 0.17 #1447, 0.16 #2103), 02bjrlw (0.12 #414, 0.11 #178, 0.08 #1426), 04306rv (0.11 #182, 0.11 #1908, 0.10 #1849), 07zrf (0.11 #180, 0.05 #1186, 0.05 #714), 0295r (0.11 #324, 0.05 #1186, 0.03 #4118), 0349s (0.11 #222, 0.03 #4118, 0.03 #4481), 06b_j (0.07 #2524, 0.07 #2165, 0.07 #2283), 03_9r (0.06 #1496, 0.06 #3108, 0.05 #1186) >> Best rule #474 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 07h9gp; 0fvr1; 02v5_g; 09dv8h; 0yzbg; 01g3gq; 02zk08; >> query: (?x13178, 02h40lc) <- film(?x5636, ?x13178), genre(?x13178, ?x6452), ?x6452 = 02b5_l, film_release_region(?x13178, ?x94), ?x94 = 09c7w0, titles(?x11671, ?x13178) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07ykkx5 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 101.000 0.897 http://example.org/film/film/language #4089-0cjsxp PRED entity: 0cjsxp PRED relation: award_winner! PRED expected values: 0g55tzk => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 103): 0g55tzk (0.42 #274, 0.17 #1947, 0.08 #413), 0418154 (0.25 #106, 0.20 #4589, 0.17 #9733), 058m5m4 (0.25 #53, 0.05 #748, 0.05 #887), 01s695 (0.25 #3, 0.04 #4035, 0.04 #4870), 0hndn2q (0.25 #39, 0.03 #4349, 0.02 #1986), 0g5b0q5 (0.20 #4589, 0.17 #9733, 0.17 #1947), 03gwpw2 (0.20 #4589, 0.17 #9733, 0.17 #1947), 0gvstc3 (0.20 #4589, 0.17 #9733, 0.03 #4343), 0hn821n (0.20 #4589, 0.17 #9733, 0.02 #4439), 0lp_cd3 (0.20 #4589, 0.17 #9733, 0.01 #8503) >> Best rule #274 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 03yj_0n; 07s8hms; 02sb1w; >> query: (?x3842, 0g55tzk) <- award_winner(?x3842, ?x560), film(?x3842, ?x667), ?x560 = 0f830f >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cjsxp award_winner! 0g55tzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.417 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4088-09j_g PRED entity: 09j_g PRED relation: industry PRED expected values: 020mfr => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 46): 01mw1 (0.60 #1, 0.55 #625, 0.50 #577), 020mfr (0.55 #625, 0.50 #593, 0.40 #17), 02vxn (0.50 #146, 0.36 #867, 0.36 #771), 02jjt (0.33 #56, 0.26 #1782, 0.20 #440), 04rlf (0.26 #1782, 0.17 #110, 0.17 #62), 01zhp (0.17 #210, 0.12 #306, 0.11 #402), 03qh03g (0.16 #1304, 0.15 #678, 0.13 #918), 06mbny (0.13 #942, 0.10 #1328, 0.04 #2436), 029g_vk (0.13 #2504, 0.11 #1890, 0.09 #2130), 0hz28 (0.10 #1329, 0.09 #943, 0.07 #1909) >> Best rule #1 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 025txrl; 0dwcl; >> query: (?x5861, 01mw1) <- child(?x11468, ?x5861), child(?x4619, ?x5861), ?x4619 = 03d6fyn, ?x11468 = 049ql1, citytown(?x5861, ?x242), location(?x241, ?x242) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #625 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0225z1; 086h6p; *> query: (?x5861, ?x245) <- child(?x4619, ?x5861), place_founded(?x4619, ?x242), industry(?x4619, ?x245), service_language(?x4619, ?x254) *> conf = 0.55 ranks of expected_values: 2 EVAL 09j_g industry 020mfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 193.000 193.000 0.600 http://example.org/business/business_operation/industry #4087-02gn8s PRED entity: 02gn8s PRED relation: student PRED expected values: 0lkr7 01j590z => 109 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1176): 028r4y (0.15 #948, 0.03 #3038, 0.01 #15579), 01f7j9 (0.15 #330, 0.03 #2420, 0.01 #14961), 01hbq0 (0.08 #4147, 0.08 #2057, 0.04 #6237), 015qq1 (0.08 #3981, 0.06 #6071, 0.04 #8161), 03ft8 (0.08 #257, 0.05 #2347, 0.04 #4437), 01pqy_ (0.08 #898, 0.05 #2988, 0.03 #5078), 01zfmm (0.08 #441, 0.05 #2531, 0.03 #4621), 02cyfz (0.08 #334, 0.05 #2424, 0.03 #4514), 01pj3h (0.08 #1916, 0.05 #4006, 0.03 #6096), 01wwvt2 (0.08 #365, 0.05 #2455, 0.03 #4545) >> Best rule #948 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 02897w; 04p_hy; 02pptm; 0325dj; >> query: (?x6988, 028r4y) <- contains(?x94, ?x6988), ?x94 = 09c7w0, colors(?x6988, ?x9464), ?x9464 = 03wkwg >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02gn8s student 01j590z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 37.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 02gn8s student 0lkr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 37.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #4086-01b65l PRED entity: 01b65l PRED relation: nominated_for! PRED expected values: 02py_sj => 87 concepts (83 used for prediction) PRED predicted values (max 10 best out of 186): 02py_sj (0.75 #675, 0.70 #909, 0.40 #441), 0gq9h (0.40 #12231, 0.40 #12465, 0.38 #12934), 0gs9p (0.38 #12467, 0.38 #12233, 0.34 #12936), 019f4v (0.34 #12222, 0.34 #12456, 0.31 #12925), 02_3zj (0.33 #181, 0.14 #2053, 0.12 #1585), 0gq_v (0.31 #11720, 0.30 #12188, 0.29 #12422), 0fbtbt (0.31 #4603, 0.30 #2731, 0.30 #4135), 0k611 (0.29 #12242, 0.29 #12476, 0.28 #12945), 0cjyzs (0.28 #1954, 0.27 #4528, 0.26 #5230), 027gs1_ (0.28 #1119, 0.24 #6735, 0.23 #4161) >> Best rule #675 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 02_1kl; >> query: (?x4114, 02py_sj) <- award(?x4114, ?x4115), award(?x4114, ?x3545), award(?x4114, ?x2872), ?x4115 = 027qq9b, ?x3545 = 02pzxlw, ?x2872 = 02pz3j5 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01b65l nominated_for! 02py_sj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 83.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4085-0ccck7 PRED entity: 0ccck7 PRED relation: genre PRED expected values: 07s9rl0 => 87 concepts (86 used for prediction) PRED predicted values (max 10 best out of 108): 07s9rl0 (0.77 #8726, 0.76 #4366, 0.74 #4850), 01jfsb (0.65 #1343, 0.62 #1827, 0.60 #6071), 02kdv5l (0.57 #6062, 0.26 #1940, 0.26 #8849), 03k9fj (0.42 #11, 0.29 #1584, 0.26 #6070), 02l7c8 (0.40 #863, 0.39 #621, 0.38 #1105), 01g6gs (0.34 #626, 0.32 #747, 0.31 #989), 05p553 (0.32 #8365, 0.32 #7274, 0.32 #8243), 082gq (0.29 #152, 0.19 #4273, 0.19 #2331), 04xvlr (0.27 #2060, 0.22 #4367, 0.21 #2302), 060__y (0.23 #259, 0.21 #1348, 0.19 #4866) >> Best rule #8726 for best value: >> intensional similarity = 3 >> extensional distance = 1298 >> proper extension: 02sg5v; 02qrv7; 0g5pv3; 07ng9k; 02vw1w2; 018nnz; 0d1qmz; 016ztl; 0bz3jx; 02r9p0c; ... >> query: (?x11218, 07s9rl0) <- genre(?x11218, ?x600), genre(?x11735, ?x600), ?x11735 = 02x2jl_ >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ccck7 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 86.000 0.774 http://example.org/film/film/genre #4084-0270k40 PRED entity: 0270k40 PRED relation: film_crew_role PRED expected values: 09zzb8 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 22): 09zzb8 (0.81 #183, 0.80 #577, 0.77 #419), 01pvkk (0.38 #85, 0.34 #189, 0.33 #33), 02ynfr (0.23 #192, 0.19 #428, 0.19 #114), 02zdwq (0.20 #15, 0.17 #41, 0.14 #67), 014kbl (0.17 #49, 0.12 #101, 0.10 #1793), 089fss (0.14 #261, 0.11 #187, 0.10 #109), 0263ycg (0.14 #261, 0.10 #1793, 0.09 #116), 04pyp5 (0.14 #261, 0.10 #1793, 0.07 #377), 094hwz (0.14 #261, 0.10 #1793, 0.07 #191), 02vs3x5 (0.14 #261, 0.10 #1793, 0.06 #146) >> Best rule #183 for best value: >> intensional similarity = 6 >> extensional distance = 207 >> proper extension: 049mql; >> query: (?x11565, 09zzb8) <- film_crew_role(?x11565, ?x2154), film_crew_role(?x11565, ?x1171), film_crew_role(?x11565, ?x468), ?x468 = 02r96rf, ?x2154 = 01vx2h, ?x1171 = 09vw2b7 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0270k40 film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.813 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4083-01ww2fs PRED entity: 01ww2fs PRED relation: type_of_union PRED expected values: 04ztj => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.73 #13, 0.72 #17, 0.70 #289), 01g63y (0.13 #218, 0.12 #290, 0.12 #246), 0jgjn (0.02 #16) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 0flpy; >> query: (?x2300, 04ztj) <- artist(?x2241, ?x2300), profession(?x2300, ?x1032), ?x2241 = 02p11jq >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ww2fs type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.727 http://example.org/people/person/spouse_s./people/marriage/type_of_union #4082-01vvycq PRED entity: 01vvycq PRED relation: artist! PRED expected values: 015_1q => 117 concepts (78 used for prediction) PRED predicted values (max 10 best out of 104): 015_1q (0.33 #156, 0.24 #985, 0.24 #1953), 01w40h (0.33 #164, 0.17 #1131, 0.14 #1961), 01clyr (0.25 #168, 0.17 #306, 0.16 #721), 01cl0d (0.25 #190, 0.17 #328, 0.10 #1848), 0mzkr (0.20 #1129, 0.18 #438, 0.17 #300), 01trtc (0.19 #3527, 0.17 #208, 0.16 #3249), 01dtcb (0.18 #458, 0.17 #1979, 0.17 #182), 03mp8k (0.18 #478, 0.17 #340, 0.15 #1169), 0g768 (0.18 #448, 0.14 #5702, 0.13 #3491), 043g7l (0.18 #442, 0.12 #28, 0.12 #1133) >> Best rule #156 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0lbj1; 03h_fk5; 01wj18h; 01vsy7t; 01vsgrn; 0dw4g; 01bczm; >> query: (?x702, 015_1q) <- award(?x702, ?x3631), award(?x702, ?x2585), ?x2585 = 054ks3, ?x3631 = 02f73p, artist(?x2190, ?x702) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vvycq artist! 015_1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 78.000 0.333 http://example.org/music/record_label/artist #4081-0g26h PRED entity: 0g26h PRED relation: major_field_of_study! PRED expected values: 0bkj86 => 69 concepts (34 used for prediction) PRED predicted values (max 10 best out of 13): 0bkj86 (0.75 #186, 0.71 #167, 0.71 #156), 02m4yg (0.60 #221, 0.50 #75, 0.50 #61), 01ysy9 (0.50 #226, 0.50 #438, 0.44 #53), 071tyz (0.50 #45, 0.46 #168, 0.44 #53), 0bjrnt (0.50 #438, 0.49 #138, 0.46 #168), 01rr_d (0.50 #438, 0.49 #138, 0.46 #168), 013zdg (0.50 #438, 0.49 #138, 0.46 #168), 027f2w (0.50 #438, 0.49 #138, 0.46 #168), 028dcg (0.49 #138, 0.46 #168, 0.44 #53), 03mkk4 (0.49 #138, 0.46 #168, 0.44 #53) >> Best rule #186 for best value: >> intensional similarity = 12 >> extensional distance = 6 >> proper extension: 036hv; >> query: (?x4321, 0bkj86) <- major_field_of_study(?x6271, ?x4321), major_field_of_study(?x4599, ?x4321), major_field_of_study(?x2948, ?x4321), major_field_of_study(?x741, ?x4321), ?x741 = 01w3v, major_field_of_study(?x4599, ?x5900), major_field_of_study(?x4599, ?x2981), ?x6271 = 015q1n, major_field_of_study(?x1527, ?x4321), ?x2981 = 02j62, school(?x799, ?x2948), ?x5900 = 0db86 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g26h major_field_of_study! 0bkj86 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 34.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #4080-016ppr PRED entity: 016ppr PRED relation: award PRED expected values: 01bgqh 03t5kl => 78 concepts (63 used for prediction) PRED predicted values (max 10 best out of 298): 01bgqh (0.79 #8824, 0.55 #11619, 0.38 #441), 01c427 (0.67 #7270, 0.38 #483, 0.38 #84), 01by1l (0.50 #8894, 0.44 #112, 0.43 #910), 02f73p (0.43 #986, 0.38 #1784, 0.22 #2183), 02f5qb (0.43 #954, 0.33 #1752, 0.23 #7342), 02f73b (0.42 #1881, 0.38 #1083, 0.21 #4674), 03r00m (0.38 #780, 0.25 #381, 0.08 #11977), 05zkcn5 (0.35 #1617, 0.29 #819, 0.14 #8803), 054ks3 (0.33 #940, 0.33 #1738, 0.22 #1339), 01cky2 (0.33 #594, 0.31 #195, 0.15 #12571) >> Best rule #8824 for best value: >> intensional similarity = 5 >> extensional distance = 185 >> proper extension: 028q6; 0ggl02; 05crg7; 02zmh5; 02cyfz; 0288fyj; 02qlg7s; 01x15dc; 017vkx; 05vzw3; ... >> query: (?x10740, 01bgqh) <- award(?x10740, ?x10169), award(?x7601, ?x10169), award(?x2963, ?x10169), ?x7601 = 01vzx45, ?x2963 = 0gcs9 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 32 EVAL 016ppr award 03t5kl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 78.000 63.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 016ppr award 01bgqh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 63.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4079-027hjff PRED entity: 027hjff PRED relation: award_winner PRED expected values: 066m4g 0cnl1c 02t_st => 30 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1977): 02tr7d (0.50 #7780, 0.44 #13830, 0.38 #18376), 0pz7h (0.50 #6159, 0.33 #9179, 0.33 #4649), 025mb_ (0.33 #14870, 0.33 #4289, 0.27 #16386), 0794g (0.33 #9552, 0.33 #2002, 0.25 #12579), 01ksr1 (0.33 #9551, 0.33 #2001, 0.23 #18638), 033jj1 (0.33 #10389, 0.33 #2839, 0.22 #14930), 01j7rd (0.33 #291, 0.29 #19955, 0.20 #22985), 04ns3gy (0.33 #1303, 0.29 #20967, 0.14 #11876), 043js (0.33 #374, 0.29 #10947, 0.23 #13599), 015pxr (0.33 #296, 0.29 #10869, 0.19 #7552) >> Best rule #7780 for best value: >> intensional similarity = 20 >> extensional distance = 2 >> proper extension: 0hr3c8y; >> query: (?x3624, 02tr7d) <- award_winner(?x3624, ?x3924), award_winner(?x3624, ?x968), ceremony(?x11179, ?x3624), ceremony(?x8250, ?x3624), ceremony(?x2853, ?x3624), ceremony(?x2771, ?x3624), ceremony(?x2257, ?x3624), ceremony(?x1670, ?x3624), ceremony(?x880, ?x3624), ?x2257 = 09td7p, honored_for(?x3624, ?x9701), ?x2853 = 09qv_s, ?x8250 = 0cqhb3, ?x880 = 0cqh46, ?x2771 = 03m73lj, profession(?x3924, ?x987), ?x11179 = 0cqhmg, ?x1670 = 0ck27z, award(?x9701, ?x68), gender(?x968, ?x231) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5184 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 03gyp30; *> query: (?x3624, 0cnl1c) <- award_winner(?x3624, ?x9272), award_winner(?x3624, ?x7821), award_winner(?x3624, ?x3924), ceremony(?x8250, ?x3624), ceremony(?x2853, ?x3624), ceremony(?x2771, ?x3624), ceremony(?x2257, ?x3624), ceremony(?x880, ?x3624), ceremony(?x704, ?x3624), ?x2257 = 09td7p, honored_for(?x3624, ?x9701), ?x2853 = 09qv_s, ?x8250 = 0cqhb3, ?x880 = 0cqh46, ?x2771 = 03m73lj, ?x3924 = 0h3mrc, ?x704 = 09sb52, ?x9272 = 05xpms, genre(?x9701, ?x258), film(?x7821, ?x522), film(?x1676, ?x9701) *> conf = 0.33 ranks of expected_values: 26, 142, 226 EVAL 027hjff award_winner 02t_st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 30.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 027hjff award_winner 0cnl1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 30.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 027hjff award_winner 066m4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 30.000 20.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #4078-0vkl2 PRED entity: 0vkl2 PRED relation: school_type PRED expected values: 07tf8 => 215 concepts (215 used for prediction) PRED predicted values (max 10 best out of 21): 05jxkf (0.64 #172, 0.61 #388, 0.59 #460), 05pcjw (0.32 #985, 0.31 #889, 0.30 #505), 01rs41 (0.27 #989, 0.26 #317, 0.25 #2538), 02p0qmm (0.25 #106, 0.16 #1177, 0.15 #682), 07tf8 (0.22 #1234, 0.19 #225, 0.19 #897), 01_9fk (0.18 #1010, 0.17 #842, 0.16 #1082), 0bwd5 (0.16 #1177, 0.07 #187, 0.07 #211), 01jlsn (0.13 #209, 0.06 #497, 0.05 #1121), 01_srz (0.10 #1083, 0.09 #1011, 0.09 #315), 01y64 (0.07 #204, 0.04 #684, 0.04 #372) >> Best rule #172 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 01nrnm; 02kzfw; 014xf6; >> query: (?x10240, 05jxkf) <- institution(?x1200, ?x10240), contains(?x1310, ?x10240), contains(?x362, ?x10240), ?x1310 = 02jx1, ?x362 = 04jpl >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #1234 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 126 *> proper extension: 01dbns; *> query: (?x10240, 07tf8) <- institution(?x4981, ?x10240), citytown(?x10240, ?x362), major_field_of_study(?x10240, ?x2981), ?x4981 = 03bwzr4 *> conf = 0.22 ranks of expected_values: 5 EVAL 0vkl2 school_type 07tf8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 215.000 215.000 0.643 http://example.org/education/educational_institution/school_type #4077-095l0 PRED entity: 095l0 PRED relation: teams PRED expected values: 01dtl => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 271): 014nzp (0.25 #298, 0.10 #1016, 0.08 #1375), 016gp5 (0.11 #476, 0.07 #1912, 0.04 #2989), 0ckf6 (0.11 #677, 0.07 #2113, 0.04 #3190), 01z1r (0.11 #510, 0.07 #1946, 0.04 #3023), 02_lt (0.11 #483, 0.07 #1919, 0.04 #2996), 01kc4s (0.11 #591, 0.07 #2027, 0.04 #3104), 0mmd6 (0.10 #1057, 0.04 #4288, 0.03 #4647), 04ltf (0.10 #897, 0.04 #4128, 0.03 #4487), 01634x (0.10 #914, 0.04 #4145, 0.03 #4504), 02b2np (0.10 #788, 0.04 #4019, 0.03 #4378) >> Best rule #298 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01_c4; >> query: (?x9929, 014nzp) <- location_of_ceremony(?x566, ?x9929), category(?x9929, ?x134), second_level_divisions(?x1310, ?x9929), ?x1310 = 02jx1 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 095l0 teams 01dtl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 167.000 0.250 http://example.org/sports/sports_team_location/teams #4076-0l15bq PRED entity: 0l15bq PRED relation: role PRED expected values: 01w4c9 => 81 concepts (49 used for prediction) PRED predicted values (max 10 best out of 78): 05842k (0.90 #3069, 0.89 #2757, 0.88 #3225), 0g2dz (0.87 #2497, 0.84 #2729, 0.82 #1659), 01w4dy (0.86 #3022, 0.86 #142, 0.84 #2170), 0mkg (0.86 #142, 0.84 #1563, 0.84 #2170), 0gkd1 (0.86 #142, 0.84 #1563, 0.84 #2170), 0l14j_ (0.86 #142, 0.84 #2170, 0.84 #363), 03gvt (0.86 #142, 0.84 #363, 0.84 #436), 01rhl (0.86 #142, 0.84 #363, 0.84 #436), 07c6l (0.86 #142, 0.84 #363, 0.84 #436), 0151b0 (0.82 #1771, 0.71 #1014, 0.67 #2455) >> Best rule #3069 for best value: >> intensional similarity = 13 >> extensional distance = 19 >> proper extension: 0dwsp; 0395lw; 026g73; 06rvn; >> query: (?x1574, 05842k) <- role(?x4078, ?x1574), role(?x2785, ?x1574), ?x4078 = 011k_j, role(?x3321, ?x1574), role(?x2698, ?x1574), performance_role(?x1432, ?x1574), instrumentalists(?x8168, ?x2698), profession(?x2698, ?x1183), award_nominee(?x3321, ?x1089), role(?x1165, ?x2785), artist(?x2149, ?x3321), award_winner(?x486, ?x2698), award(?x2698, ?x1079) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #865 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 4 *> proper extension: 0l14qv; *> query: (?x1574, 01w4c9) <- role(?x4078, ?x1574), role(?x3215, ?x1574), role(?x2785, ?x1574), role(?x2253, ?x1574), role(?x1433, ?x1574), role(?x569, ?x1574), ?x4078 = 011k_j, role(?x3321, ?x1574), performance_role(?x1432, ?x1574), ?x2785 = 0jtg0, performance_role(?x1817, ?x1574), ?x2253 = 01679d, award(?x3321, ?x724), ?x3215 = 0bxl5, ?x569 = 07c6l, role(?x1433, ?x1268), artist(?x2149, ?x3321) *> conf = 0.67 ranks of expected_values: 42 EVAL 0l15bq role 01w4c9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 81.000 49.000 0.905 http://example.org/music/performance_role/track_performances./music/track_contribution/role #4075-09gmmt6 PRED entity: 09gmmt6 PRED relation: film_release_region PRED expected values: 016wzw => 120 concepts (100 used for prediction) PRED predicted values (max 10 best out of 207): 09c7w0 (0.96 #12361, 0.93 #8084, 0.92 #9611), 03rjj (0.91 #2287, 0.90 #2135, 0.88 #1070), 0345h (0.89 #2463, 0.88 #2615, 0.86 #2311), 05b4w (0.87 #669, 0.82 #2496, 0.81 #1127), 03gj2 (0.85 #1088, 0.85 #630, 0.85 #2457), 02vzc (0.82 #2483, 0.81 #656, 0.81 #2939), 03spz (0.81 #700, 0.80 #2375, 0.78 #2223), 04gzd (0.78 #1074, 0.76 #616, 0.64 #2291), 03rt9 (0.76 #1078, 0.76 #2295, 0.74 #620), 03rk0 (0.70 #661, 0.66 #1119, 0.60 #2184) >> Best rule #12361 for best value: >> intensional similarity = 6 >> extensional distance = 1007 >> proper extension: 02d413; 0g22z; 018js4; 027qgy; 047q2k1; 0ckr7s; 08lr6s; 016fyc; 034qrh; 0b60sq; ... >> query: (?x6536, 09c7w0) <- film_release_distribution_medium(?x6536, ?x81), film_release_region(?x6536, ?x1603), film_release_region(?x633, ?x1603), contains(?x1603, ?x992), place_of_burial(?x10328, ?x1603), ?x633 = 0c40vxk >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #672 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 52 *> proper extension: 0401sg; 087wc7n; 03bx2lk; 07s3m4g; *> query: (?x6536, 016wzw) <- film_release_distribution_medium(?x6536, ?x81), film_release_region(?x6536, ?x1603), film_release_region(?x6536, ?x1122), film_release_region(?x6536, ?x304), ?x1603 = 06bnz, genre(?x6536, ?x53), ?x304 = 0d0vqn, ?x1122 = 09pmkv *> conf = 0.67 ranks of expected_values: 15 EVAL 09gmmt6 film_release_region 016wzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 120.000 100.000 0.961 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4074-0p9tm PRED entity: 0p9tm PRED relation: genre PRED expected values: 07s9rl0 => 95 concepts (92 used for prediction) PRED predicted values (max 10 best out of 87): 07s9rl0 (0.83 #1299, 0.70 #2479, 0.66 #1417), 02l7c8 (0.41 #133, 0.41 #369, 0.41 #251), 01jfsb (0.38 #11, 0.36 #1545, 0.32 #2017), 04xvlr (0.38 #2, 0.18 #1300, 0.18 #2480), 060__y (0.38 #16, 0.17 #606, 0.16 #1314), 01g6gs (0.29 #138, 0.20 #1082, 0.19 #728), 0lsxr (0.25 #8, 0.25 #1542, 0.23 #1188), 082gq (0.24 #148, 0.16 #1446, 0.15 #384), 04t36 (0.22 #359, 0.19 #241, 0.19 #477), 06n90 (0.21 #7453, 0.17 #1546, 0.15 #1664) >> Best rule #1299 for best value: >> intensional similarity = 4 >> extensional distance = 157 >> proper extension: 049xgc; 016ks5; 0sxns; 0286gm1; 02_06s; >> query: (?x7846, 07s9rl0) <- award_winner(?x7846, ?x3527), award(?x7846, ?x198), nominated_for(?x746, ?x7846), ?x746 = 04dn09n >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p9tm genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 92.000 0.830 http://example.org/film/film/genre #4073-0fnx1 PRED entity: 0fnx1 PRED relation: contains! PRED expected values: 0d060g => 78 concepts (37 used for prediction) PRED predicted values (max 10 best out of 287): 05rh2 (0.85 #32461, 0.82 #20729, 0.79 #19829), 0d060g (0.85 #20732, 0.81 #31564, 0.81 #27949), 0jcpw (0.76 #18028, 0.73 #20733, 0.71 #9911), 09c7w0 (0.76 #13521, 0.70 #25246, 0.69 #31567), 07c5l (0.39 #12114), 059t8 (0.33 #1415, 0.25 #7721, 0.17 #7205), 059s8 (0.29 #6064, 0.17 #7205, 0.15 #10817), 03rk0 (0.29 #4638, 0.17 #2837, 0.13 #19069), 03rjj (0.28 #10829, 0.20 #23449, 0.20 #14431), 01n7q (0.26 #19910, 0.14 #9992, 0.07 #31642) >> Best rule #32461 for best value: >> intensional similarity = 6 >> extensional distance = 52 >> proper extension: 0d33k; >> query: (?x13571, ?x13765) <- administrative_division(?x13571, ?x13765), adjoins(?x13765, ?x6842), contains(?x279, ?x13765), time_zones(?x279, ?x1638), locations(?x13643, ?x279), contains(?x6842, ?x2453) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #20732 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 37 *> proper extension: 0xrzh; 0l4vc; 0tt6k; 0txhf; *> query: (?x13571, ?x279) <- category(?x13571, ?x134), administrative_division(?x13571, ?x13765), adjoins(?x6842, ?x13765), adjoins(?x13765, ?x11542), contains(?x6842, ?x481), contains(?x279, ?x13765), adjoins(?x94, ?x279), time_zones(?x6842, ?x2674), featured_film_locations(?x1064, ?x279) *> conf = 0.85 ranks of expected_values: 2 EVAL 0fnx1 contains! 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 37.000 0.855 http://example.org/location/location/contains #4072-0fmyd PRED entity: 0fmyd PRED relation: time_zones PRED expected values: 03bdv => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 9): 03bdv (0.45 #304, 0.45 #357, 0.42 #436), 02hcv8 (0.26 #439, 0.26 #293, 0.25 #346), 02llzg (0.23 #58, 0.18 #85, 0.16 #72), 03plfd (0.12 #24, 0.09 #37, 0.03 #104), 02lcqs (0.11 #295, 0.11 #348, 0.10 #427), 02fqwt (0.10 #557, 0.09 #808, 0.09 #729), 0gsrz4 (0.08 #22, 0.03 #35), 02hczc (0.04 #226, 0.04 #239, 0.04 #252), 052vwh (0.03 #66, 0.03 #80, 0.03 #106) >> Best rule #304 for best value: >> intensional similarity = 7 >> extensional distance = 1259 >> proper extension: 0cchk3; 0qkyj; 0yx74; 0xgpv; >> query: (?x10885, ?x5327) <- contains(?x7360, ?x10885), jurisdiction_of_office(?x182, ?x7360), adjoins(?x7360, ?x2051), time_zones(?x7360, ?x5327), adjoins(?x7037, ?x7360), administrative_parent(?x7360, ?x551), currency(?x7360, ?x170) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fmyd time_zones 03bdv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.453 http://example.org/location/location/time_zones #4071-06thjt PRED entity: 06thjt PRED relation: colors PRED expected values: 0jc_p => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.46 #401, 0.40 #1085, 0.38 #724), 01g5v (0.40 #307, 0.33 #402, 0.33 #22), 01l849 (0.31 #1141, 0.27 #989, 0.25 #1844), 019sc (0.28 #728, 0.19 #1146, 0.17 #1887), 036k5h (0.20 #309, 0.17 #24, 0.16 #765), 038hg (0.17 #30, 0.15 #467, 0.09 #581), 04mkbj (0.17 #47, 0.14 #332, 0.14 #66), 02rnmb (0.15 #145, 0.14 #183, 0.10 #278), 0jc_p (0.10 #707, 0.09 #118, 0.09 #1144), 03wkwg (0.10 #337, 0.08 #774, 0.07 #964) >> Best rule #401 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 01cyd5; >> query: (?x10478, 083jv) <- student(?x10478, ?x7984), currency(?x10478, ?x170), ?x170 = 09nqf, influenced_by(?x364, ?x7984) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #707 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 48 *> proper extension: 017hnw; *> query: (?x10478, 0jc_p) <- student(?x10478, ?x4831), student(?x10478, ?x4325), company(?x4831, ?x1103), participant(?x4325, ?x3502), profession(?x4325, ?x319) *> conf = 0.10 ranks of expected_values: 9 EVAL 06thjt colors 0jc_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 166.000 166.000 0.458 http://example.org/education/educational_institution/colors #4070-034qrh PRED entity: 034qrh PRED relation: country PRED expected values: 09c7w0 => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 28): 09c7w0 (0.88 #724, 0.88 #363, 0.87 #2173), 07ssc (0.31 #258, 0.26 #860, 0.25 #739), 0f8l9c (0.15 #261, 0.10 #622, 0.10 #1104), 0d060g (0.08 #129, 0.08 #250, 0.06 #1937), 0chghy (0.08 #193, 0.06 #977, 0.05 #1339), 03_3d (0.08 #128, 0.04 #4537, 0.04 #4477), 06mkj (0.08 #281, 0.03 #822, 0.02 #1666), 01z4y (0.06 #3986, 0.06 #1807), 03rjj (0.04 #850, 0.04 #609, 0.03 #971), 03h64 (0.04 #648, 0.04 #1250, 0.04 #889) >> Best rule #724 for best value: >> intensional similarity = 3 >> extensional distance = 222 >> proper extension: 0209xj; >> query: (?x437, 09c7w0) <- film(?x436, ?x437), country(?x437, ?x1264), nominated_for(?x437, ?x4888) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034qrh country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.879 http://example.org/film/film/country #4069-05c4fys PRED entity: 05c4fys PRED relation: place_of_birth PRED expected values: 01b8w_ => 73 concepts (50 used for prediction) PRED predicted values (max 10 best out of 93): 04jpl (0.33 #28948, 0.31 #28242, 0.29 #24709), 0n90z (0.25 #685, 0.09 #1391, 0.06 #2097), 02_286 (0.11 #14842, 0.11 #15548, 0.10 #16254), 01llj3 (0.09 #1336, 0.06 #2748, 0.06 #2042), 02gw_w (0.09 #1343, 0.06 #2755, 0.06 #2049), 0m75g (0.09 #971, 0.06 #2383, 0.06 #1677), 01ngx6 (0.09 #1365, 0.06 #2777, 0.06 #2071), 0195j0 (0.09 #1208, 0.06 #2620, 0.06 #1914), 0fnm3 (0.06 #9880), 013wf1 (0.06 #2633, 0.06 #1927, 0.05 #3340) >> Best rule #28948 for best value: >> intensional similarity = 3 >> extensional distance = 2060 >> proper extension: 02d6n_; >> query: (?x7622, ?x362) <- nationality(?x7622, ?x1310), location(?x7622, ?x362), place_of_birth(?x1950, ?x362) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10214 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 111 *> proper extension: 03hh89; 01m7f5r; 044mvs; 01vzz1c; 0h1q6; 0dszr0; 042xh; *> query: (?x7622, 01b8w_) <- nationality(?x7622, ?x1310), location(?x7622, ?x362), ?x362 = 04jpl, profession(?x7622, ?x7623) *> conf = 0.02 ranks of expected_values: 34 EVAL 05c4fys place_of_birth 01b8w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 73.000 50.000 0.327 http://example.org/people/person/place_of_birth #4068-062zjtt PRED entity: 062zjtt PRED relation: film_distribution_medium PRED expected values: 0735l => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.38 #135, 0.25 #5, 0.24 #29), 029j_ (0.15 #19, 0.15 #13, 0.13 #25), 0dq6p (0.13 #33, 0.10 #64, 0.10 #9), 02nxhr (0.12 #14, 0.11 #26, 0.11 #32), 07z4p (0.03 #24, 0.01 #48) >> Best rule #135 for best value: >> intensional similarity = 6 >> extensional distance = 306 >> proper extension: 0522wp; >> query: (?x4273, 0735l) <- film(?x902, ?x4273), film(?x902, ?x5008), film(?x902, ?x4518), ?x5008 = 035w2k, nominated_for(?x591, ?x4518), music(?x4518, ?x7701) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 062zjtt film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.380 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #4067-01cw24 PRED entity: 01cw24 PRED relation: team! PRED expected values: 07y9k => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 8): 07y9k (0.44 #20, 0.39 #44, 0.39 #12), 0356lc (0.18 #57, 0.16 #81, 0.12 #289), 059yj (0.17 #93, 0.09 #229, 0.07 #173), 0355pl (0.13 #59, 0.12 #289, 0.09 #203), 021q23 (0.12 #8, 0.09 #48, 0.09 #32), 0h69c (0.12 #94, 0.12 #38, 0.07 #230), 03zv9 (0.12 #289, 0.07 #106, 0.06 #114), 01ddbl (0.02 #312, 0.02 #328, 0.02 #336) >> Best rule #20 for best value: >> intensional similarity = 10 >> extensional distance = 16 >> proper extension: 02_t6d; >> query: (?x9109, 07y9k) <- team(?x530, ?x9109), team(?x203, ?x9109), team(?x63, ?x9109), team(?x60, ?x9109), ?x63 = 02sdk9v, teams(?x2513, ?x9109), ?x203 = 0dgrmp, ?x60 = 02nzb8, ?x530 = 02_j1w, country(?x1009, ?x2513) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cw24 team! 07y9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.444 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #4066-03j2gxx PRED entity: 03j2gxx PRED relation: influenced_by! PRED expected values: 0399p => 147 concepts (69 used for prediction) PRED predicted values (max 10 best out of 458): 0p8jf (0.27 #2651, 0.14 #5191, 0.09 #7733), 0h0yt (0.25 #816, 0.20 #1324, 0.06 #3864), 01wd02c (0.25 #270, 0.11 #3826, 0.11 #20843), 0yxl (0.25 #356, 0.11 #20843, 0.09 #32032), 07w21 (0.25 #11, 0.11 #20843, 0.09 #32032), 0jt90f5 (0.25 #80, 0.10 #8210, 0.09 #7702), 03772 (0.25 #200, 0.08 #6296, 0.07 #5280), 0dz46 (0.25 #365, 0.06 #3921, 0.06 #3413), 0pkyh (0.25 #106, 0.06 #3662, 0.06 #3154), 01w8sf (0.20 #2634, 0.14 #2126, 0.11 #3142) >> Best rule #2651 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 06dl_; 03vrp; 0683n; 07lp1; >> query: (?x11104, 0p8jf) <- student(?x13328, ?x11104), influenced_by(?x3663, ?x11104), school_type(?x13328, ?x8834), ?x3663 = 02yl42 >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1850 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0gz_; 0m93; *> query: (?x11104, 0399p) <- profession(?x11104, ?x7290), gender(?x11104, ?x231), influenced_by(?x2161, ?x11104), ?x7290 = 04s2z *> conf = 0.09 ranks of expected_values: 79 EVAL 03j2gxx influenced_by! 0399p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 147.000 69.000 0.267 http://example.org/influence/influence_node/influenced_by #4065-05nn2c PRED entity: 05nn2c PRED relation: production_companies! PRED expected values: 09jcj6 => 54 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1661): 09jcj6 (0.45 #5711, 0.44 #13707, 0.43 #13706), 01q7h2 (0.45 #5711, 0.43 #11421, 0.43 #11420), 011wtv (0.33 #505, 0.14 #5072, 0.14 #6216), 048vhl (0.33 #957, 0.14 #5524, 0.14 #6668), 01cssf (0.33 #62, 0.14 #5773, 0.12 #1203), 01gc7 (0.33 #23, 0.12 #1164, 0.10 #4590), 020fcn (0.33 #127, 0.12 #2410, 0.10 #4694), 05sns6 (0.33 #472, 0.10 #5039, 0.09 #6183), 057lbk (0.33 #483, 0.10 #5050, 0.09 #6194), 026f__m (0.33 #854, 0.10 #5421, 0.09 #6565) >> Best rule #5711 for best value: >> intensional similarity = 8 >> extensional distance = 19 >> proper extension: 01w5m; 037bm2; 07wh1; >> query: (?x3323, ?x153) <- company(?x4660, ?x3323), produced_by(?x9614, ?x4660), produced_by(?x4953, ?x4660), produced_by(?x153, ?x4660), film(?x400, ?x4953), nominated_for(?x102, ?x4953), films(?x5179, ?x9614), film(?x525, ?x9614) >> conf = 0.45 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 05nn2c production_companies! 09jcj6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 35.000 0.451 http://example.org/film/film/production_companies #4064-01gsry PRED entity: 01gsry PRED relation: legislative_sessions! PRED expected values: 0b3wk => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 6): 0b3wk (0.89 #148, 0.88 #213, 0.88 #162), 0x2sv (0.07 #227, 0.07 #220), 0h6dy (0.05 #228, 0.05 #221), 0l_j_ (0.03 #229, 0.03 #222), 030p4s (0.02 #231, 0.02 #224), 0162kb (0.02 #223) >> Best rule #148 for best value: >> intensional similarity = 36 >> extensional distance = 17 >> proper extension: 070m6c; >> query: (?x9416, ?x2860) <- legislative_sessions(?x7973, ?x9416), legislative_sessions(?x4437, ?x9416), legislative_sessions(?x2712, ?x9416), district_represented(?x9416, ?x7518), district_represented(?x9416, ?x7405), district_represented(?x9416, ?x4776), district_represented(?x9416, ?x2713), district_represented(?x9416, ?x1755), district_represented(?x9416, ?x728), district_represented(?x9416, ?x177), ?x728 = 059f4, ?x177 = 05kkh, legislative_sessions(?x9416, ?x4812), ?x7518 = 026mj, ?x4776 = 06yxd, ?x2713 = 06btq, legislative_sessions(?x4665, ?x9416), legislative_sessions(?x2712, ?x3669), legislative_sessions(?x5401, ?x3669), district_represented(?x3669, ?x4758), district_represented(?x3669, ?x448), legislative_sessions(?x2860, ?x4437), ?x4758 = 0vbk, ?x1755 = 01x73, legislative_sessions(?x5742, ?x7973), state(?x7404, ?x7405), religion(?x7405, ?x109), country(?x7405, ?x94), ?x448 = 03v1s, taxonomy(?x7405, ?x939), jurisdiction_of_office(?x12303, ?x7405), jurisdiction_of_office(?x3959, ?x7405), ?x12303 = 0fkzq, category(?x7405, ?x134), ?x3959 = 0f6c3, contains(?x7405, ?x1476) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gsry legislative_sessions! 0b3wk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.889 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #4063-06qd3 PRED entity: 06qd3 PRED relation: administrative_parent PRED expected values: 02j71 => 177 concepts (107 used for prediction) PRED predicted values (max 10 best out of 57): 02j71 (0.85 #13928, 0.84 #10761, 0.82 #13654), 06qd3 (0.33 #32, 0.20 #305, 0.02 #13366), 09c7w0 (0.30 #11579, 0.24 #7303, 0.22 #8821), 048fz (0.26 #10199, 0.25 #137, 0.24 #13642), 0j0k (0.26 #10199, 0.25 #137, 0.24 #13642), 03rjj (0.15 #5246, 0.05 #2347, 0.05 #2208), 0345h (0.11 #1542, 0.09 #7880, 0.09 #2920), 03_3d (0.11 #1521, 0.04 #7859, 0.03 #10064), 049nq (0.10 #1063, 0.05 #1475, 0.05 #2026), 07ssc (0.09 #3877, 0.08 #3184, 0.05 #8142) >> Best rule #13928 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 07ytt; >> query: (?x1453, 02j71) <- countries_within(?x6956, ?x1453), countries_spoken_in(?x7926, ?x1453), contains(?x9954, ?x1453) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06qd3 administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 107.000 0.847 http://example.org/base/aareas/schema/administrative_area/administrative_parent #4062-02n5d PRED entity: 02n5d PRED relation: entity_involved PRED expected values: 05m0h => 57 concepts (45 used for prediction) PRED predicted values (max 10 best out of 170): 024pcx (0.50 #1928, 0.43 #1274, 0.33 #158), 01m41_ (0.42 #2690, 0.17 #910, 0.14 #6125), 025ndl (0.38 #1952, 0.33 #18, 0.25 #1624), 01h3dj (0.38 #2004, 0.28 #3784, 0.25 #1676), 0j06n (0.36 #953, 0.32 #4368, 0.30 #5689), 0cn_tpv (0.33 #124, 0.25 #600, 0.25 #440), 0j5b8 (0.33 #2634, 0.17 #854, 0.14 #6069), 0285m87 (0.25 #2656, 0.25 #2019, 0.20 #3139), 02jx1 (0.25 #473, 0.19 #1927, 0.17 #949), 02psqkz (0.25 #1971, 0.17 #3751, 0.15 #4244) >> Best rule #1928 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 01gjd0; >> query: (?x14038, ?x9328) <- locations(?x14038, ?x9328), combatants(?x9328, ?x13906), combatants(?x9328, ?x6371), official_language(?x6371, ?x254), entity_involved(?x9532, ?x13906), combatants(?x1777, ?x6371), jurisdiction_of_office(?x4689, ?x6371), combatants(?x8949, ?x9328), form_of_government(?x6371, ?x1926), split_to(?x6371, ?x512), combatants(?x9798, ?x9328) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3380 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 13 *> proper extension: 0f6rc; *> query: (?x14038, ?x5249) <- locations(?x14038, ?x9328), combatants(?x9328, ?x6371), nationality(?x5249, ?x9328), combatants(?x1777, ?x6371), jurisdiction_of_office(?x3119, ?x9328), combatants(?x9798, ?x9328), official_language(?x9328, ?x254), entity_involved(?x6982, ?x9328), entity_involved(?x14038, ?x2337) *> conf = 0.01 ranks of expected_values: 163 EVAL 02n5d entity_involved 05m0h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 57.000 45.000 0.500 http://example.org/base/culturalevent/event/entity_involved #4061-09p4w8 PRED entity: 09p4w8 PRED relation: film! PRED expected values: 01p6xx => 82 concepts (61 used for prediction) PRED predicted values (max 10 best out of 126): 06dkzt (0.41 #1100, 0.11 #10466, 0.10 #11018), 0gs5q (0.25 #4132, 0.23 #3854, 0.22 #8811), 04pqqb (0.25 #4132, 0.23 #3854, 0.22 #8811), 02mjf2 (0.12 #9361, 0.11 #6883, 0.11 #6057), 0794g (0.11 #10466, 0.10 #11018, 0.09 #9637), 0f5xn (0.11 #10466, 0.10 #11018, 0.09 #9637), 01rzqj (0.11 #10466, 0.10 #11018, 0.09 #9637), 086k8 (0.11 #10466, 0.10 #11018, 0.09 #9637), 0bwh6 (0.10 #34, 0.06 #309, 0.01 #7742), 030vmc (0.10 #220, 0.06 #495) >> Best rule #1100 for best value: >> intensional similarity = 3 >> extensional distance = 170 >> proper extension: 04dsnp; 091z_p; >> query: (?x4853, ?x8692) <- films(?x5069, ?x4853), nominated_for(?x2456, ?x4853), written_by(?x4853, ?x8692) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #762 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 81 *> proper extension: 0k2m6; *> query: (?x4853, 01p6xx) <- films(?x5069, ?x4853), nominated_for(?x2456, ?x4853), story_by(?x4853, ?x8744) *> conf = 0.02 ranks of expected_values: 22 EVAL 09p4w8 film! 01p6xx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 82.000 61.000 0.410 http://example.org/film/director/film #4060-0ll3 PRED entity: 0ll3 PRED relation: month! PRED expected values: 080h2 0cv3w 0ply0 02cft => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1380): 0cv3w (0.90 #67, 0.90 #27, 0.89 #57), 080h2 (0.90 #67, 0.90 #27, 0.89 #57), 0ply0 (0.90 #67, 0.90 #27, 0.89 #57), 02cft (0.90 #67, 0.90 #27, 0.89 #57), 03czqs (0.90 #67, 0.90 #27, 0.89 #57), 0l0mk (0.90 #27, 0.88 #58, 0.81 #42), 07gdw (0.54 #56, 0.50 #55), 02m77 (0.54 #56, 0.50 #55), 0r89d (0.54 #56, 0.31 #9, 0.09 #25), 02jx1 (0.54 #56, 0.31 #9) >> Best rule #67 for best value: >> intensional similarity = 122 >> extensional distance = 2 >> proper extension: 04w_7; >> query: (?x4925, ?x3026) <- month(?x12674, ?x4925), month(?x11197, ?x4925), month(?x10610, ?x4925), month(?x10143, ?x4925), month(?x9559, ?x4925), month(?x8977, ?x4925), month(?x8602, ?x4925), month(?x6703, ?x4925), month(?x6458, ?x4925), month(?x6054, ?x4925), month(?x5267, ?x4925), month(?x4627, ?x4925), month(?x4271, ?x4925), month(?x3125, ?x4925), month(?x3106, ?x4925), month(?x2645, ?x4925), month(?x2474, ?x4925), month(?x2316, ?x4925), month(?x2254, ?x4925), month(?x1649, ?x4925), month(?x1646, ?x4925), month(?x1523, ?x4925), month(?x659, ?x4925), month(?x108, ?x4925), seasonal_months(?x4925, ?x7298), seasonal_months(?x4925, ?x6303), seasonal_months(?x4925, ?x1650), seasonal_months(?x4925, ?x1459), ?x3125 = 0d6lp, ?x6054 = 0fn2g, ?x1650 = 06vkl, ?x11197 = 05l64, ?x1646 = 0156q, ?x2474 = 052p7, ?x6703 = 0f04v, ?x4271 = 06wjf, ?x2645 = 03h64, ?x10610 = 03902, ?x8602 = 0chgzm, ?x108 = 0rh6k, month(?x3026, ?x1459), month(?x1036, ?x1459), ?x1523 = 030qb3t, ?x3106 = 049d1, ?x5267 = 0d9jr, ?x6303 = 0lkm, ?x8977 = 02z0j, ?x659 = 02cl1, ?x2254 = 0dclg, ?x1649 = 01f62, ?x6458 = 08966, ?x1036 = 080h2, ?x7298 = 04wzr, ?x10143 = 0h3tv, ?x4627 = 05qtj, film_release_region(?x6536, ?x2316), film_release_region(?x6528, ?x2316), film_release_region(?x6394, ?x2316), film_release_region(?x6095, ?x2316), film_release_region(?x5825, ?x2316), film_release_region(?x5644, ?x2316), film_release_region(?x5400, ?x2316), film_release_region(?x5092, ?x2316), film_release_region(?x4707, ?x2316), film_release_region(?x4352, ?x2316), film_release_region(?x3843, ?x2316), film_release_region(?x3757, ?x2316), film_release_region(?x3491, ?x2316), film_release_region(?x2961, ?x2316), film_release_region(?x2788, ?x2316), film_release_region(?x2783, ?x2316), film_release_region(?x2628, ?x2316), film_release_region(?x2627, ?x2316), film_release_region(?x2501, ?x2316), film_release_region(?x1988, ?x2316), film_release_region(?x1932, ?x2316), film_release_region(?x1803, ?x2316), film_release_region(?x1701, ?x2316), film_release_region(?x1456, ?x2316), film_release_region(?x141, ?x2316), film_release_region(?x124, ?x2316), ?x2783 = 0879bpq, ?x5825 = 067ghz, ?x4352 = 09v71cj, genre(?x6528, ?x225), ?x5400 = 0bhwhj, film(?x665, ?x6528), ?x3843 = 080nwsb, olympics(?x2316, ?x778), ?x9559 = 07dfk, ?x1803 = 0g9wdmc, film_crew_role(?x6528, ?x137), film_release_region(?x6528, ?x1453), film_release_region(?x6528, ?x512), film_release_region(?x6528, ?x344), film_release_region(?x6528, ?x94), ?x4707 = 02xbyr, ?x1988 = 09k56b7, ?x6095 = 0bq6ntw, ?x2788 = 05q4y12, ?x2628 = 06wbm8q, ?x124 = 0g56t9t, ?x2627 = 0gz6b6g, ?x344 = 04gzd, currency(?x2316, ?x170), ?x2961 = 047p7fr, country(?x766, ?x2316), ?x5644 = 0dll_t2, ?x5092 = 0gg5qcw, ?x1932 = 0btyf5z, ?x6536 = 09gmmt6, ?x1456 = 0cz8mkh, ?x141 = 0gtsx8c, ?x6394 = 0cmdwwg, ?x12674 = 0g6xq, ?x3491 = 0gtvpkw, ?x1453 = 06qd3, ?x2501 = 040rmy, ?x94 = 09c7w0, ?x512 = 07ssc, ?x3757 = 02vr3gz, ?x1701 = 0bh8yn3 >> conf = 0.90 => this is the best rule for 5 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0ll3 month! 02cft CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.903 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0ll3 month! 0ply0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.903 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0ll3 month! 0cv3w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.903 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0ll3 month! 080h2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.903 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #4059-0992d9 PRED entity: 0992d9 PRED relation: country PRED expected values: 09c7w0 => 123 concepts (117 used for prediction) PRED predicted values (max 10 best out of 204): 09c7w0 (0.87 #4500, 0.85 #5545, 0.85 #4804), 07ssc (0.41 #1426, 0.39 #1303, 0.39 #3092), 01jfsb (0.31 #3137, 0.14 #3136, 0.09 #1593), 0f8l9c (0.24 #1059, 0.21 #1429, 0.21 #1306), 03rjj (0.20 #7, 0.18 #6710, 0.17 #68), 0j1z8 (0.20 #12, 0.17 #73, 0.04 #6279), 06mkj (0.18 #6710, 0.14 #5543, 0.13 #1101), 0k6nt (0.18 #6710, 0.13 #1101, 0.13 #1100), 0154j (0.14 #5543, 0.13 #1101, 0.13 #1100), 09blyk (0.14 #3136, 0.09 #1593, 0.07 #5420) >> Best rule #4500 for best value: >> intensional similarity = 7 >> extensional distance = 582 >> proper extension: 02y_lrp; 0sxg4; 02_fm2; 011yxg; 0dnvn3; 0ds11z; 0ds33; 01ln5z; 03h_yy; 02_1sj; ... >> query: (?x5730, 09c7w0) <- film_crew_role(?x5730, ?x137), film(?x7245, ?x5730), film(?x382, ?x5730), country(?x5730, ?x1264), award_winner(?x7245, ?x1596), nominated_for(?x7245, ?x5128), production_companies(?x5730, ?x2548) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0992d9 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 117.000 0.868 http://example.org/film/film/country #4058-03pp73 PRED entity: 03pp73 PRED relation: film PRED expected values: 02q0v8n => 92 concepts (40 used for prediction) PRED predicted values (max 10 best out of 769): 03mh94 (0.09 #3646, 0.04 #7228, 0.02 #12602), 017f3m (0.08 #10747, 0.08 #16121, 0.08 #17913), 0pv54 (0.07 #4541, 0.04 #8123, 0.02 #13497), 02qr3k8 (0.06 #8454, 0.04 #13828, 0.04 #4872), 0ptxj (0.05 #4486, 0.03 #8068, 0.02 #13442), 02_qt (0.05 #4216, 0.03 #7798, 0.02 #2425), 06fqlk (0.05 #4727, 0.03 #8309, 0.02 #13683), 016017 (0.05 #7087, 0.04 #1714, 0.02 #3505), 025ts_z (0.05 #6867, 0.04 #3285, 0.02 #1494), 09sr0 (0.04 #8685, 0.04 #5103, 0.03 #6894) >> Best rule #3646 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 0b_dy; >> query: (?x5130, 03mh94) <- award(?x5130, ?x3247), people(?x1446, ?x5130), ?x3247 = 0bdwqv >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03pp73 film 02q0v8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 40.000 0.088 http://example.org/film/actor/film./film/performance/film #4057-0846v PRED entity: 0846v PRED relation: religion PRED expected values: 0c8wxp => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 25): 0c8wxp (0.90 #327, 0.89 #52, 0.80 #152), 05w5d (0.81 #338, 0.79 #63, 0.73 #163), 092bf5 (0.48 #1578, 0.39 #2329, 0.37 #2455), 02t7t (0.48 #1578, 0.39 #2329, 0.37 #2455), 072w0 (0.48 #1578, 0.39 #2329, 0.37 #2455), 0flw86 (0.42 #753, 0.39 #1253, 0.39 #778), 03j6c (0.10 #1237, 0.09 #10, 0.08 #1262), 04t_mf (0.04 #793, 0.03 #593, 0.02 #1243), 0kpl (0.04 #429, 0.03 #656, 0.03 #4), 0n2g (0.04 #982, 0.03 #1232, 0.03 #5) >> Best rule #327 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05k7sb; >> query: (?x3086, 0c8wxp) <- district_represented(?x605, ?x3086), religion(?x3086, ?x109), adjoins(?x3086, ?x1024) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0846v religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 190.000 190.000 0.896 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #4056-01s0ps PRED entity: 01s0ps PRED relation: role! PRED expected values: 01wl38s 0kzy0 => 69 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1776): 050z2 (0.75 #8125, 0.56 #11227, 0.53 #10786), 01wxdn3 (0.62 #8335, 0.60 #10108, 0.57 #7894), 0137g1 (0.62 #8057, 0.57 #7176, 0.50 #6293), 01l4g5 (0.62 #8160, 0.50 #1982, 0.44 #8604), 05qhnq (0.62 #8239, 0.50 #6032, 0.43 #7798), 023slg (0.62 #8375, 0.50 #2197, 0.40 #5285), 082brv (0.60 #10864, 0.57 #7322, 0.50 #12194), 01wgjj5 (0.60 #4669, 0.57 #6878, 0.50 #5550), 01tp5bj (0.60 #4515, 0.50 #5396, 0.33 #6282), 01vsy7t (0.57 #7709, 0.50 #3295, 0.40 #10811) >> Best rule #8125 for best value: >> intensional similarity = 17 >> extensional distance = 6 >> proper extension: 026t6; 05r5c; 01vj9c; >> query: (?x2764, 050z2) <- role(?x1750, ?x2764), role(?x2764, ?x433), group(?x1750, ?x12211), group(?x1750, ?x11425), group(?x1750, ?x5493), group(?x1750, ?x4484), ?x11425 = 02vnpv, ?x12211 = 0jltp, performance_role(?x212, ?x1750), role(?x6162, ?x2764), instrumentalists(?x1750, ?x8873), ?x8873 = 0232lm, role(?x74, ?x1750), ?x4484 = 03xhj6, role(?x211, ?x1750), ?x6162 = 01w9wwg, ?x5493 = 0kr_t >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #7521 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 5 *> proper extension: 05148p4; 03qjg; 0dwt5; *> query: (?x2764, 01wl38s) <- role(?x7033, ?x2764), role(?x1750, ?x2764), role(?x2764, ?x1267), role(?x2764, ?x433), ?x1750 = 02hnl, role(?x3215, ?x2764), role(?x6162, ?x2764), role(?x3171, ?x2764), artists(?x5355, ?x3171), ?x1267 = 07brj, ?x3215 = 0bxl5, people(?x6260, ?x3171), profession(?x3171, ?x353), role(?x2764, ?x2048), ?x7033 = 0gkd1, role(?x211, ?x433), category(?x6162, ?x134) *> conf = 0.43 ranks of expected_values: 99, 284 EVAL 01s0ps role! 0kzy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 69.000 36.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01s0ps role! 01wl38s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 69.000 36.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role #4055-024jwt PRED entity: 024jwt PRED relation: gender PRED expected values: 05zppz => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #43, 0.87 #63, 0.86 #45), 02zsn (0.46 #270, 0.44 #4, 0.43 #62) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 07c37; >> query: (?x10694, 05zppz) <- location(?x10694, ?x739), influenced_by(?x7717, ?x10694), student(?x122, ?x10694) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024jwt gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.906 http://example.org/people/person/gender #4054-03qx_f PRED entity: 03qx_f PRED relation: category PRED expected values: 08mbj5d => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.79 #20, 0.78 #5, 0.75 #14) >> Best rule #20 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 02301; 01t0dy; >> query: (?x10504, 08mbj5d) <- child(?x3887, ?x10504), citytown(?x3887, ?x739), company(?x6151, ?x3887), state_province_region(?x3887, ?x335), ?x739 = 02_286 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qx_f category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.789 http://example.org/common/topic/webpage./common/webpage/category #4053-03f4xvm PRED entity: 03f4xvm PRED relation: artists! PRED expected values: 06by7 016_nr => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 212): 064t9 (0.57 #640, 0.50 #1578, 0.49 #3143), 017_qw (0.50 #65, 0.39 #6006, 0.37 #6318), 06by7 (0.42 #16268, 0.40 #10962, 0.39 #8776), 03_d0 (0.29 #3453, 0.24 #4078, 0.24 #6578), 06j6l (0.27 #8179, 0.26 #676, 0.26 #8803), 02lnbg (0.24 #687, 0.20 #1625, 0.19 #8190), 01lyv (0.23 #7852, 0.23 #8789, 0.18 #10350), 016clz (0.23 #16250, 0.22 #10944, 0.21 #1256), 05bt6j (0.22 #8798, 0.22 #8174, 0.21 #671), 025sc50 (0.22 #8805, 0.22 #8181, 0.19 #678) >> Best rule #640 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 016qtt; 02_fj; 02wb6yq; 0f7hc; 0c7xjb; 01vw8mh; 01wrcxr; 0bdxs5; 02ktrs; >> query: (?x4548, 064t9) <- award_winner(?x8501, ?x4548), artist(?x1954, ?x4548), people(?x2510, ?x4548) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #16268 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 829 *> proper extension: 0f0y8; 03c7ln; 0m19t; 032t2z; 0c7ct; 07qnf; 07_3qd; 01w923; 01ky2h; 012zng; ... *> query: (?x4548, 06by7) <- artists(?x2937, ?x4548), artist(?x1954, ?x4548) *> conf = 0.42 ranks of expected_values: 3, 99 EVAL 03f4xvm artists! 016_nr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 125.000 125.000 0.571 http://example.org/music/genre/artists EVAL 03f4xvm artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 125.000 0.571 http://example.org/music/genre/artists #4052-03cvfg PRED entity: 03cvfg PRED relation: place_of_birth PRED expected values: 0ttxp => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 99): 0r5y9 (0.25 #243), 02dtg (0.20 #1418, 0.02 #5644, 0.02 #10573), 0dclg (0.17 #2191, 0.14 #782, 0.09 #4303), 0cr3d (0.14 #3615, 0.09 #5024, 0.08 #2911), 05qtj (0.14 #871, 0.08 #2280, 0.04 #4392), 0281s1 (0.14 #992, 0.04 #4513, 0.02 #5922), 0d6lp (0.14 #818, 0.02 #5748, 0.02 #11382), 02_286 (0.12 #7061, 0.10 #10582, 0.10 #11993), 0vm5t (0.10 #2077), 0vfs8 (0.10 #1698) >> Best rule #243 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01xyt7; >> query: (?x1580, 0r5y9) <- student(?x1681, ?x1580), ?x1681 = 07szy, award_winner(?x10746, ?x1580), ?x10746 = 02r9qt >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03cvfg place_of_birth 0ttxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 74.000 0.250 http://example.org/people/person/place_of_birth #4051-0jkvj PRED entity: 0jkvj PRED relation: sports PRED expected values: 03fyrh => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 34): 06wrt (0.82 #175, 0.81 #341, 0.77 #133), 02y8z (0.80 #511, 0.77 #133, 0.76 #366), 01hp22 (0.77 #133, 0.76 #366, 0.76 #433), 0d1t3 (0.77 #133, 0.76 #366, 0.76 #433), 03_8r (0.77 #133, 0.76 #366, 0.76 #433), 064vjs (0.77 #133, 0.76 #366, 0.76 #433), 03fyrh (0.62 #349, 0.59 #416, 0.58 #283), 02_5h (0.39 #674, 0.36 #806, 0.12 #406), 019w9j (0.36 #217, 0.33 #117, 0.31 #350), 07bs0 (0.36 #507, 0.31 #340, 0.29 #407) >> Best rule #175 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 0lv1x; >> query: (?x7688, 06wrt) <- olympics(?x1229, ?x7688), olympics(?x1023, ?x7688), sports(?x7688, ?x4833), ?x1023 = 0ctw_b, locations(?x7688, ?x2474), country(?x4833, ?x142), ?x1229 = 059j2, athlete(?x4833, ?x1213) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #349 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 14 *> proper extension: 0l6mp; *> query: (?x7688, 03fyrh) <- olympics(?x1023, ?x7688), sports(?x7688, ?x3659), ?x1023 = 0ctw_b, olympics(?x3659, ?x1081), sports(?x775, ?x3659), sports(?x7688, ?x766), ?x775 = 0l998 *> conf = 0.62 ranks of expected_values: 7 EVAL 0jkvj sports 03fyrh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 49.000 49.000 0.818 http://example.org/user/jg/default_domain/olympic_games/sports #4050-01gtbb PRED entity: 01gtbb PRED relation: district_represented PRED expected values: 05fjf 050ks => 40 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1063): 05fjf (0.90 #1501, 0.88 #1366, 0.88 #1593), 04rrx (0.86 #875, 0.85 #829, 0.85 #965), 050ks (0.86 #875, 0.85 #829, 0.85 #965), 0vbk (0.86 #875, 0.85 #829, 0.85 #965), 02xry (0.86 #875, 0.85 #829, 0.85 #965), 07b_l (0.86 #875, 0.85 #829, 0.85 #965), 03s0w (0.86 #875, 0.85 #829, 0.85 #965), 0824r (0.86 #875, 0.85 #829, 0.85 #965), 01n7q (0.83 #828, 0.82 #362, 0.81 #684), 0g0syc (0.83 #828, 0.82 #362, 0.81 #684) >> Best rule #1501 for best value: >> intensional similarity = 26 >> extensional distance = 29 >> proper extension: 05rrw9; >> query: (?x2019, 05fjf) <- district_represented(?x2019, ?x4061), district_represented(?x2019, ?x3818), district_represented(?x2019, ?x760), ?x760 = 05fkf, contains(?x3818, ?x8016), jurisdiction_of_office(?x900, ?x3818), partially_contains(?x4061, ?x4540), religion(?x3818, ?x2591), religion(?x3818, ?x492), religion(?x3818, ?x109), major_field_of_study(?x8016, ?x1668), ?x492 = 0flw86, district_represented(?x5339, ?x4061), ?x1668 = 01mkq, state_province_region(?x2175, ?x4061), location(?x117, ?x4061), contains(?x4061, ?x2970), ?x109 = 01lp8, state_province_region(?x2313, ?x3818), location_of_ceremony(?x3426, ?x4061), ?x2591 = 0631_, colors(?x8016, ?x4557), currency(?x3818, ?x170), adjoins(?x2977, ?x4061), ?x5339 = 02glc4, country(?x3818, ?x94) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01gtbb district_represented 050ks CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 40.000 39.000 0.903 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtbb district_represented 05fjf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 39.000 0.903 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #4049-095sx6 PRED entity: 095sx6 PRED relation: program! PRED expected values: 03jl0_ => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 108): 0g5lhl7 (0.87 #344, 0.86 #288, 0.85 #231), 01f2w0 (0.40 #361, 0.36 #191, 0.36 #305), 025snf (0.33 #92, 0.19 #431, 0.05 #2424), 03lpbx (0.33 #89, 0.12 #428, 0.09 #201), 03jl0_ (0.33 #74, 0.06 #413, 0.03 #3783), 05gnf (0.31 #635, 0.29 #1763, 0.29 #126), 0gsg7 (0.29 #1864, 0.28 #2088, 0.28 #2200), 09d5h (0.29 #115, 0.20 #3, 0.17 #454), 02hmvw (0.25 #437, 0.17 #98, 0.05 #2424), 0cjdk (0.22 #514, 0.21 #965, 0.20 #5) >> Best rule #344 for best value: >> intensional similarity = 10 >> extensional distance = 13 >> proper extension: 05397h; >> query: (?x14278, 0g5lhl7) <- languages(?x14278, ?x254), program(?x13340, ?x14278), ?x254 = 02h40lc, genre(?x14278, ?x9669), award_winner(?x3486, ?x13340), genre(?x11726, ?x9669), genre(?x9668, ?x9669), industry(?x13340, ?x2271), honored_for(?x9450, ?x11726), actor(?x9668, ?x13195) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #74 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 017dtf; *> query: (?x14278, 03jl0_) <- genre(?x14278, ?x258), country_of_origin(?x14278, ?x550), ?x258 = 05p553, nationality(?x1408, ?x550), film_release_region(?x7493, ?x550), film_release_region(?x7126, ?x550), film_release_region(?x3981, ?x550), ?x7493 = 0btpm6, taxonomy(?x550, ?x939), form_of_government(?x550, ?x48), production_companies(?x7126, ?x7339), ?x3981 = 047tsx3 *> conf = 0.33 ranks of expected_values: 5 EVAL 095sx6 program! 03jl0_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 70.000 70.000 0.867 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #4048-0k0q73t PRED entity: 0k0q73t PRED relation: actor PRED expected values: 013v5j => 74 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1378): 01vxqyl (0.33 #674, 0.09 #2528, 0.07 #13917), 01wgfp6 (0.33 #482, 0.09 #2336, 0.06 #8830), 0d0l91 (0.33 #831, 0.09 #2685, 0.06 #9179), 0163t3 (0.25 #1611, 0.12 #9031, 0.10 #15529), 01vx5w7 (0.25 #1161, 0.06 #8581, 0.05 #12292), 01pfkw (0.25 #1282, 0.06 #8702, 0.05 #15200), 0gps0z (0.25 #1654, 0.06 #9074, 0.05 #15572), 0c7ct (0.25 #978, 0.06 #8398, 0.05 #14896), 016ywr (0.23 #3850, 0.19 #7561, 0.18 #10343), 0m8_v (0.23 #12058, 0.21 #11130, 0.19 #8348) >> Best rule #674 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 03bww6; >> query: (?x12265, 01vxqyl) <- actor(?x12265, ?x1213), profession(?x1213, ?x1032), participant(?x1213, ?x2237), award_winner(?x2325, ?x1213), award(?x770, ?x2325), award(?x286, ?x2325), ?x2237 = 01vs_v8 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k0q73t actor 013v5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 36.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #4047-0hr41p6 PRED entity: 0hr41p6 PRED relation: program! PRED expected values: 03mdt => 80 concepts (64 used for prediction) PRED predicted values (max 10 best out of 39): 05gnf (0.40 #71, 0.36 #642, 0.30 #471), 03mdt (0.33 #7, 0.16 #235, 0.14 #635), 0g5lhl7 (0.33 #6, 0.14 #805, 0.13 #748), 0gsg7 (0.28 #173, 0.22 #459, 0.20 #59), 0cjdk (0.20 #62, 0.17 #462, 0.16 #290), 09d5h (0.13 #1381, 0.13 #1149, 0.13 #1091), 01w92 (0.11 #179, 0.07 #522, 0.07 #579), 01fsyp (0.11 #678, 0.09 #507, 0.03 #906), 0ljc_ (0.11 #314, 0.09 #429, 0.07 #714), 0kctd (0.10 #370, 0.09 #428, 0.07 #713) >> Best rule #71 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01h72l; 01fs__; 0d7vtk; >> query: (?x14067, 05gnf) <- language(?x14067, ?x254), genre(?x14067, ?x809), genre(?x1708, ?x809), ?x1708 = 05cj_j >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0124k9; *> query: (?x14067, 03mdt) <- nominated_for(?x7510, ?x14067), nominated_for(?x364, ?x14067), ?x364 = 05ty4m, ?x7510 = 027gs1_ *> conf = 0.33 ranks of expected_values: 2 EVAL 0hr41p6 program! 03mdt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 64.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #4046-02s2ys PRED entity: 02s2ys PRED relation: team! PRED expected values: 07m69t => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 90): 07m69t (0.88 #215, 0.88 #214, 0.85 #1767), 0djvzd (0.33 #248, 0.33 #33, 0.12 #5301), 0135nb (0.33 #17, 0.12 #5301, 0.11 #232), 071h5c (0.33 #278, 0.10 #3752, 0.09 #1758), 054kmq (0.31 #353, 0.12 #5301, 0.11 #280), 0dv1hh (0.29 #193, 0.12 #5301, 0.11 #265), 05s_c38 (0.23 #308, 0.22 #235, 0.14 #163), 0f1pyf (0.22 #233, 0.12 #1713, 0.12 #5301), 07h1h5 (0.15 #301, 0.12 #5301, 0.11 #1004), 04v68c (0.15 #357, 0.12 #5301, 0.11 #284) >> Best rule #215 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 0223bl; 0284h6; 02b0yz; >> query: (?x9971, ?x8598) <- position(?x9971, ?x530), position(?x9971, ?x203), team(?x8598, ?x9971), ?x203 = 0dgrmp, ?x530 = 02_j1w, team(?x8712, ?x9971), team(?x8598, ?x348), ?x8712 = 0fp_xp >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02s2ys team! 07m69t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.875 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #4045-01w7nww PRED entity: 01w7nww PRED relation: award PRED expected values: 02f73p => 89 concepts (68 used for prediction) PRED predicted values (max 10 best out of 274): 031b3h (0.74 #10640, 0.71 #16552, 0.70 #19314), 02f716 (0.74 #10640, 0.71 #16552, 0.70 #19314), 01by1l (0.34 #1687, 0.32 #111, 0.29 #7204), 01bgqh (0.27 #1618, 0.23 #6741, 0.23 #7135), 02f6xy (0.24 #196, 0.14 #19313, 0.13 #25229), 02f79n (0.24 #333, 0.08 #1909, 0.08 #727), 03qbh5 (0.23 #1777, 0.19 #6900, 0.18 #5324), 0c4z8 (0.21 #1647, 0.20 #5982, 0.20 #5194), 02f76h (0.20 #174, 0.14 #19313, 0.13 #25229), 02f73p (0.20 #184, 0.10 #9641, 0.10 #1760) >> Best rule #10640 for best value: >> intensional similarity = 2 >> extensional distance = 745 >> proper extension: 0kc6x; 065y4w7; 01y67v; 099ks0; 02p10m; 01fkr_; 0cv_2; 06v99d; 0381pn; 01bfjy; >> query: (?x3176, ?x3365) <- award_winner(?x3365, ?x3176), category(?x3176, ?x134) >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #184 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 04r1t; 08w4pm; 012vm6; *> query: (?x3176, 02f73p) <- artists(?x505, ?x3176), artist(?x5021, ?x3176), ?x5021 = 04fcjt *> conf = 0.20 ranks of expected_values: 10 EVAL 01w7nww award 02f73p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 89.000 68.000 0.740 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4044-050l8 PRED entity: 050l8 PRED relation: district_represented! PRED expected values: 02glc4 => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 41): 03rl1g (0.65 #1, 0.62 #84, 0.60 #248), 043djx (0.65 #4, 0.62 #87, 0.58 #251), 01h7xx (0.57 #29, 0.54 #112, 0.53 #276), 02glc4 (0.55 #823, 0.54 #289, 0.52 #20), 032ft5 (0.55 #823, 0.54 #289, 0.17 #5), 0495ys (0.55 #823, 0.54 #289, 0.13 #2), 060ny2 (0.55 #823, 0.54 #289, 0.13 #23), 06r713 (0.55 #823, 0.54 #289, 0.13 #21), 04gp1d (0.55 #823, 0.54 #289, 0.13 #11), 05l2z4 (0.55 #823, 0.54 #289, 0.13 #3) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 04ych; >> query: (?x2049, 03rl1g) <- adjoins(?x2049, ?x1351), jurisdiction_of_office(?x900, ?x2049), district_represented(?x1137, ?x2049), ?x1137 = 02bqn1 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #823 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 0h5qxv; *> query: (?x2049, ?x355) <- adjoins(?x2049, ?x1351), jurisdiction_of_office(?x900, ?x2049), district_represented(?x1137, ?x2049), legislative_sessions(?x1137, ?x355) *> conf = 0.55 ranks of expected_values: 4 EVAL 050l8 district_represented! 02glc4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 173.000 173.000 0.652 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #4043-02jfc PRED entity: 02jfc PRED relation: major_field_of_study! PRED expected values: 03y5ky 02yxjs => 93 concepts (69 used for prediction) PRED predicted values (max 10 best out of 621): 08815 (0.67 #5634, 0.62 #10705, 0.62 #3378), 0bx8pn (0.67 #2296, 0.60 #1733, 0.56 #5678), 065y4w7 (0.67 #16339, 0.50 #8461, 0.50 #3954), 01w5m (0.62 #4053, 0.60 #10250, 0.57 #8560), 07t90 (0.62 #4098, 0.56 #6353, 0.56 #5224), 012mzw (0.62 #4226, 0.56 #5352, 0.50 #8733), 07wrz (0.60 #1748, 0.57 #2874, 0.56 #6820), 07tds (0.60 #1845, 0.56 #5790, 0.50 #4099), 02bqy (0.60 #1877, 0.56 #5822, 0.50 #4694), 01bm_ (0.60 #1943, 0.56 #5888, 0.50 #3632) >> Best rule #5634 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 01mkq; 0fdys; 064_8sq; 05qt0; >> query: (?x10391, 08815) <- major_field_of_study(?x8930, ?x10391), major_field_of_study(?x5486, ?x10391), major_field_of_study(?x10391, ?x1527), major_field_of_study(?x734, ?x10391), major_field_of_study(?x5486, ?x2014), ?x2014 = 04rjg, ?x8930 = 0373qt, school(?x662, ?x5486) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3116 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 5 *> proper extension: 05qfh; *> query: (?x10391, 02yxjs) <- major_field_of_study(?x6584, ?x10391), major_field_of_study(?x5486, ?x10391), major_field_of_study(?x4410, ?x10391), major_field_of_study(?x10391, ?x1695), major_field_of_study(?x865, ?x10391), ?x5486 = 0g8rj, colors(?x6584, ?x663), company(?x8404, ?x4410), service_location(?x4410, ?x94), ?x1695 = 06ms6, ?x865 = 02h4rq6 *> conf = 0.43 ranks of expected_values: 50, 337 EVAL 02jfc major_field_of_study! 02yxjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 93.000 69.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02jfc major_field_of_study! 03y5ky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 93.000 69.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #4042-02q3bb PRED entity: 02q3bb PRED relation: profession PRED expected values: 09jwl => 131 concepts (130 used for prediction) PRED predicted values (max 10 best out of 62): 09jwl (0.46 #19, 0.41 #3276, 0.39 #4312), 016z4k (0.37 #4, 0.30 #3261, 0.28 #4297), 01d_h8 (0.34 #1931, 0.34 #895, 0.34 #154), 03gjzk (0.33 #3420, 0.30 #163, 0.29 #311), 0dxtg (0.31 #7119, 0.31 #6083, 0.30 #3419), 0np9r (0.29 #1798, 0.23 #169, 0.21 #6978), 0dz3r (0.29 #2, 0.27 #3111, 0.26 #2963), 0n1h (0.23 #12, 0.16 #752, 0.16 #456), 02jknp (0.23 #9333, 0.22 #6077, 0.22 #11997), 0cbd2 (0.18 #1192, 0.17 #1340, 0.15 #1488) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0qdyf; 01bczm; >> query: (?x8091, 09jwl) <- diet(?x8091, ?x3130), award_nominee(?x2296, ?x8091), category(?x8091, ?x134) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q3bb profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 130.000 0.457 http://example.org/people/person/profession #4041-0g_zyp PRED entity: 0g_zyp PRED relation: film! PRED expected values: 020x5r => 81 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1049): 0164w8 (0.38 #83093, 0.38 #85171, 0.37 #81014), 081lh (0.33 #161, 0.06 #2237, 0.04 #10548), 01csvq (0.33 #109, 0.06 #2185, 0.04 #12572), 01y_px (0.33 #363, 0.06 #2439, 0.03 #4516), 016fjj (0.33 #633, 0.06 #2709, 0.03 #6864), 0h96g (0.33 #850, 0.06 #2926, 0.03 #13313), 021b_ (0.33 #1782, 0.06 #3858, 0.02 #14245), 031sg0 (0.33 #1689, 0.06 #3765), 0421st (0.33 #1344, 0.06 #3420), 0ddkf (0.33 #1202, 0.06 #3278) >> Best rule #83093 for best value: >> intensional similarity = 4 >> extensional distance = 847 >> proper extension: 0n2bh; 0gfzgl; 01f3p_; 02kk_c; 05fgr_; 05sy0cv; 0cskb; >> query: (?x9790, ?x8288) <- nominated_for(?x8288, ?x9790), nominated_for(?x4397, ?x9790), participant(?x719, ?x4397), film(?x4397, ?x240) >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g_zyp film! 020x5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 50.000 0.379 http://example.org/film/actor/film./film/performance/film #4040-02m30v PRED entity: 02m30v PRED relation: location_of_ceremony PRED expected values: 0d35y => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 92): 0cv3w (0.26 #2948, 0.20 #851, 0.20 #734), 06kx2 (0.22 #226, 0.04 #1394, 0.02 #3025), 02_286 (0.17 #2927, 0.16 #3045, 0.16 #3162), 0b90_r (0.16 #470, 0.07 #704, 0.07 #3036), 0k049 (0.14 #1056, 0.12 #1521, 0.10 #1404), 0l38x (0.11 #220, 0.02 #1039, 0.02 #1388), 01mb87 (0.11 #221, 0.02 #1040, 0.02 #1389), 0kc40 (0.11 #217, 0.02 #1036, 0.02 #1385), 0rj0z (0.11 #158, 0.02 #1326, 0.01 #2608), 03gh4 (0.11 #528, 0.05 #3094, 0.05 #3211) >> Best rule #2948 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 04_jsg; >> query: (?x14459, 0cv3w) <- nationality(?x14459, ?x94), location_of_ceremony(?x14459, ?x12931), county(?x12931, ?x4789) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #869 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 03m8lq; 0fpj9pm; *> query: (?x14459, 0d35y) <- spouse(?x14459, ?x5438), location_of_ceremony(?x14459, ?x12931), nationality(?x14459, ?x94), source(?x12931, ?x958) *> conf = 0.05 ranks of expected_values: 19 EVAL 02m30v location_of_ceremony 0d35y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 107.000 107.000 0.256 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #4039-0cqhk0 PRED entity: 0cqhk0 PRED relation: award! PRED expected values: 032xhg 0c4f4 0pyg6 011zd3 043js 07sgfsl 05p92jn 084m3 06dn58 06fc0b 03ywyk 03q3x5 => 49 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2560): 072bb1 (0.81 #13015, 0.81 #16268, 0.79 #26030), 03zqc1 (0.81 #13015, 0.81 #16268, 0.79 #26030), 011zd3 (0.81 #13015, 0.81 #16268, 0.79 #26030), 09yrh (0.81 #13015, 0.81 #16268, 0.79 #26030), 05xpms (0.81 #13015, 0.81 #16268, 0.79 #26030), 01wb8bs (0.81 #13015, 0.81 #16268, 0.79 #26030), 03h3vtz (0.81 #13015, 0.81 #16268, 0.79 #26030), 0h3mrc (0.81 #13015, 0.81 #16268, 0.79 #26030), 0863x_ (0.81 #13015, 0.81 #16268, 0.79 #26030), 038g2x (0.81 #13015, 0.81 #16268, 0.79 #26030) >> Best rule #13015 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 047sgz4; 02q1tc5; 02pz3j5; 027qq9b; 04jhhng; >> query: (?x678, ?x237) <- award_winner(?x678, ?x237), award(?x5386, ?x678), award(?x274, ?x678), producer_type(?x5386, ?x632) >> conf = 0.81 => this is the best rule for 14 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 178, 193, 224, 225, 226, 375, 376, 384, 475, 477, 506 EVAL 0cqhk0 award! 03q3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 03ywyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 06fc0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 06dn58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 084m3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 05p92jn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 07sgfsl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 043js CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 011zd3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 0pyg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 0c4f4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqhk0 award! 032xhg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 22.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4038-06rq2l PRED entity: 06rq2l PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (92 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.88 #5511, 0.88 #5411, 0.81 #4308), 0jrxx (0.39 #8320, 0.38 #5512, 0.33 #9224), 07ssc (0.19 #115, 0.17 #15, 0.12 #215), 02jx1 (0.11 #3640, 0.10 #6947, 0.10 #1737), 03spz (0.10 #669, 0.08 #267, 0.04 #1070), 03rt9 (0.08 #13, 0.03 #514, 0.03 #916), 03rk0 (0.07 #4554, 0.07 #4053, 0.06 #9066), 0chghy (0.06 #110, 0.04 #311, 0.03 #411), 0j5g9 (0.06 #162, 0.03 #463, 0.03 #965), 0345h (0.06 #1535, 0.03 #6914, 0.03 #2236) >> Best rule #5511 for best value: >> intensional similarity = 3 >> extensional distance = 1350 >> proper extension: 07m69t; >> query: (?x9204, ?x94) <- place_of_birth(?x9204, ?x8127), contains(?x94, ?x8127), ?x94 = 09c7w0 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06rq2l nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 92.000 0.883 http://example.org/people/person/nationality #4037-03z9585 PRED entity: 03z9585 PRED relation: film_release_region PRED expected values: 05v8c 015fr 0161c => 65 concepts (52 used for prediction) PRED predicted values (max 10 best out of 269): 05v8c (0.87 #779, 0.87 #267, 0.86 #1035), 03rk0 (0.87 #295, 0.80 #1063, 0.79 #935), 05b4w (0.83 #1070, 0.82 #942, 0.81 #814), 09c7w0 (0.82 #2051, 0.81 #2563, 0.80 #2179), 015fr (0.80 #1036, 0.80 #2060, 0.79 #908), 05qx1 (0.74 #794, 0.73 #282, 0.71 #1050), 01p1v (0.73 #292, 0.71 #1060, 0.71 #804), 01ls2 (0.73 #264, 0.69 #1032, 0.68 #392), 01mjq (0.70 #924, 0.69 #1052, 0.68 #412), 01pj7 (0.68 #417, 0.63 #1057, 0.61 #801) >> Best rule #779 for best value: >> intensional similarity = 11 >> extensional distance = 29 >> proper extension: 0gkz15s; 04hwbq; 0cz8mkh; 05qbckf; 09k56b7; 0407yfx; 08052t3; 04f52jw; 0407yj_; 0crc2cp; ... >> query: (?x8193, 05v8c) <- film_release_region(?x8193, ?x3951), film_release_region(?x8193, ?x2000), film_release_region(?x8193, ?x789), film_release_region(?x8193, ?x512), film_release_region(?x8193, ?x344), ?x512 = 07ssc, ?x2000 = 0d0kn, jurisdiction_of_office(?x182, ?x3951), ?x344 = 04gzd, country(?x668, ?x3951), ?x789 = 0f8l9c >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 18 EVAL 03z9585 film_release_region 0161c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 65.000 52.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03z9585 film_release_region 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 65.000 52.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03z9585 film_release_region 05v8c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 52.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #4036-0bsnm PRED entity: 0bsnm PRED relation: major_field_of_study PRED expected values: 06q83 => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 141): 02j62 (0.63 #5366, 0.53 #6732, 0.45 #279), 01mkq (0.53 #263, 0.44 #1379, 0.43 #1999), 03g3w (0.48 #5362, 0.40 #6728, 0.36 #275), 05qjt (0.40 #256, 0.32 #1868, 0.29 #1992), 01540 (0.40 #310, 0.21 #1302, 0.19 #1426), 04rjg (0.39 #1880, 0.38 #2004, 0.38 #1384), 062z7 (0.38 #276, 0.32 #5363, 0.32 #1392), 037mh8 (0.31 #317, 0.29 #2053, 0.28 #1929), 01lj9 (0.31 #288, 0.23 #1900, 0.23 #1280), 0g26h (0.31 #1283, 0.29 #291, 0.28 #1407) >> Best rule #5366 for best value: >> intensional similarity = 5 >> extensional distance = 248 >> proper extension: 0ymdn; 0ylvj; 0373qt; 0yl_3; >> query: (?x8191, 02j62) <- major_field_of_study(?x8191, ?x3490), major_field_of_study(?x7707, ?x3490), major_field_of_study(?x2313, ?x3490), ?x7707 = 01jt2w, ?x2313 = 07wrz >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #10057 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 412 *> proper extension: 05zjtn4; 04wlz2; 05krk; 01pl14; 02w2bc; 0288zy; 02cttt; 01hhvg; 01wdl3; 01bzw5; ... *> query: (?x8191, ?x254) <- contains(?x2474, ?x8191), institution(?x1526, ?x8191), major_field_of_study(?x8191, ?x1154), school_type(?x8191, ?x3092), major_field_of_study(?x1526, ?x254) *> conf = 0.06 ranks of expected_values: 63 EVAL 0bsnm major_field_of_study 06q83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 160.000 160.000 0.632 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #4035-06z6r PRED entity: 06z6r PRED relation: country PRED expected values: 05r4w 04gzd 05v8c 02k54 03gj2 035qy 01mjq 05v10 0162v 01n6c 0bjv6 0h8d 0jgx 077qn 0345_ 06s0l 07fb6 035yg 04v09 => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 349): 01mjq (0.86 #1288, 0.83 #1165, 0.80 #861), 035qy (0.75 #1102, 0.75 #980, 0.71 #1468), 02k54 (0.75 #976, 0.67 #1464, 0.67 #1035), 04gzd (0.70 #854, 0.67 #974, 0.64 #914), 05r4w (0.67 #972, 0.62 #1460, 0.60 #852), 03gj2 (0.67 #1036, 0.62 #1223, 0.60 #1344), 04xn_ (0.60 #705, 0.60 #586, 0.60 #367), 05b7q (0.60 #592, 0.58 #183, 0.57 #730), 077qn (0.60 #697, 0.58 #183, 0.57 #730), 05v8c (0.60 #555, 0.52 #971, 0.50 #735) >> Best rule #1288 for best value: >> intensional similarity = 45 >> extensional distance = 12 >> proper extension: 07bs0; >> query: (?x4045, 01mjq) <- country(?x4045, ?x4073), country(?x4045, ?x3432), country(?x4045, ?x3357), country(?x4045, ?x2316), country(?x4045, ?x1475), country(?x4045, ?x985), country(?x4045, ?x792), ?x985 = 0k6nt, film_release_region(?x7126, ?x1475), film_release_region(?x6446, ?x1475), film_release_region(?x5016, ?x1475), film_release_region(?x3981, ?x1475), film_release_region(?x1915, ?x1475), film_release_region(?x1642, ?x1475), film_release_region(?x1392, ?x1475), form_of_government(?x1475, ?x4763), ?x7126 = 0ds1glg, ?x1642 = 0bq8tmw, teams(?x3432, ?x2433), ?x3981 = 047tsx3, jurisdiction_of_office(?x182, ?x3432), ?x792 = 0hzlz, ?x1915 = 0fq7dv_, sports(?x358, ?x4045), organization(?x3357, ?x127), currency(?x3357, ?x170), ?x1392 = 017gm7, country(?x12404, ?x3432), contains(?x7273, ?x1475), country(?x343, ?x2316), film_release_region(?x11701, ?x2316), film_release_region(?x6886, ?x2316), film_release_region(?x6556, ?x2316), film_release_region(?x5162, ?x2316), film_release_region(?x1785, ?x2316), ?x6446 = 089j8p, ?x1785 = 0gj9tn5, religion(?x3357, ?x109), ?x5162 = 0j3d9tn, ?x11701 = 0gys2jp, ?x6556 = 05dss7, official_language(?x3432, ?x254), ?x6886 = 0gwjw0c, countries_spoken_in(?x13310, ?x4073), ?x5016 = 062zm5h >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 9, 10, 26, 36, 37, 41, 43, 87, 92, 94, 110, 112, 114 EVAL 06z6r country 04v09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 035yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 07fb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 06s0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 0345_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 077qn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 0h8d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 0bjv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 01n6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 0162v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 05v10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 01mjq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 02k54 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 05v8c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 04gzd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06z6r country 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #4034-016wzw PRED entity: 016wzw PRED relation: olympics PRED expected values: 018ctl => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 39): 0kbws (0.76 #92, 0.67 #248, 0.62 #14), 0kbvb (0.62 #7, 0.58 #85, 0.55 #280), 0kbvv (0.59 #24, 0.55 #102, 0.54 #297), 09n48 (0.46 #276, 0.46 #473, 0.45 #81), 09x3r (0.46 #430, 0.44 #825, 0.44 #824), 0jdk_ (0.45 #103, 0.44 #220, 0.44 #25), 018ctl (0.41 #478, 0.41 #320, 0.41 #8), 0jhn7 (0.41 #26, 0.40 #1063, 0.40 #510), 0l6mp (0.40 #1063, 0.40 #510, 0.39 #1023), 0l6m5 (0.40 #1063, 0.40 #510, 0.39 #1023) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 02k54; 06mzp; 0h7x; 01znc_; 01mjq; 07twz; >> query: (?x2843, 0kbws) <- organization(?x2843, ?x312), film_release_region(?x664, ?x2843), ?x664 = 0401sg >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #478 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 66 *> proper extension: 034tl; *> query: (?x2843, 018ctl) <- country(?x668, ?x2843), olympics(?x2843, ?x3729), ?x3729 = 0jdk_ *> conf = 0.41 ranks of expected_values: 7 EVAL 016wzw olympics 018ctl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 107.000 107.000 0.763 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #4033-016yxn PRED entity: 016yxn PRED relation: nominated_for! PRED expected values: 04dn09n => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 189): 02w9sd7 (0.67 #4703, 0.67 #3996, 0.67 #5409), 0gq9h (0.43 #2409, 0.42 #1939, 0.42 #3114), 0k611 (0.33 #1009, 0.33 #2419, 0.32 #1949), 0gq_v (0.32 #2369, 0.32 #3074, 0.31 #959), 040njc (0.31 #1887, 0.31 #2357, 0.30 #3062), 0p9sw (0.30 #960, 0.26 #2370, 0.25 #1900), 0gqy2 (0.29 #1059, 0.28 #1764, 0.28 #1999), 04dn09n (0.27 #2383, 0.27 #973, 0.26 #1678), 0gr0m (0.27 #996, 0.26 #1701, 0.26 #1936), 0l8z1 (0.25 #989, 0.24 #2399, 0.24 #1929) >> Best rule #4703 for best value: >> intensional similarity = 4 >> extensional distance = 502 >> proper extension: 07bz5; >> query: (?x11942, ?x591) <- honored_for(?x7105, ?x11942), nominated_for(?x1867, ?x11942), award(?x11942, ?x591), award(?x1867, ?x458) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2383 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 421 *> proper extension: 047vnkj; 02wwmhc; 042g97; *> query: (?x11942, 04dn09n) <- honored_for(?x7105, ?x11942), nominated_for(?x1867, ?x11942), award_nominee(?x1384, ?x1867), film(?x516, ?x11942) *> conf = 0.27 ranks of expected_values: 8 EVAL 016yxn nominated_for! 04dn09n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 96.000 96.000 0.673 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4032-0fpv_3_ PRED entity: 0fpv_3_ PRED relation: film_crew_role PRED expected values: 02rh1dz 094hwz => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 23): 09zzb8 (0.81 #34, 0.74 #100, 0.71 #1591), 0dxtw (0.36 #1500, 0.35 #1599, 0.35 #108), 02ynfr (0.31 #47, 0.24 #80, 0.24 #563), 01pvkk (0.30 #109, 0.29 #10, 0.28 #241), 02rh1dz (0.28 #74, 0.16 #272, 0.16 #107), 089g0h (0.24 #563, 0.15 #51, 0.14 #18), 0215hd (0.24 #563, 0.14 #17, 0.14 #1491), 01xy5l_ (0.24 #563, 0.14 #12, 0.14 #1491), 033smt (0.24 #563, 0.14 #25, 0.14 #1491), 04pyp5 (0.24 #563, 0.14 #1491, 0.12 #48) >> Best rule #34 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 011yxg; 020fcn; 029zqn; 026p4q7; 07w8fz; 04jplwp; >> query: (?x2340, 09zzb8) <- nominated_for(?x2393, ?x2340), nominated_for(?x484, ?x2340), award_winner(?x2340, ?x2086), ?x2393 = 02x258x, ?x484 = 0gq_v >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #74 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 064lsn; 05zvzf3; *> query: (?x2340, 02rh1dz) <- film_release_region(?x2340, ?x2152), film_release_region(?x2340, ?x404), ?x2152 = 06mkj, award(?x2340, ?x640), ?x404 = 047lj *> conf = 0.28 ranks of expected_values: 5, 17 EVAL 0fpv_3_ film_crew_role 094hwz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 79.000 79.000 0.808 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0fpv_3_ film_crew_role 02rh1dz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 79.000 0.808 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #4031-03mh_tp PRED entity: 03mh_tp PRED relation: titles! PRED expected values: 01z4y => 99 concepts (74 used for prediction) PRED predicted values (max 10 best out of 67): 01z4y (0.52 #1169, 0.52 #2104, 0.48 #1896), 07s9rl0 (0.33 #824, 0.33 #515, 0.30 #2587), 07ssc (0.27 #421, 0.20 #6358, 0.12 #319), 04xvlr (0.25 #107, 0.23 #1760, 0.23 #2383), 01jfsb (0.25 #123, 0.20 #6358, 0.20 #1361), 05p553 (0.23 #1445, 0.22 #206, 0.22 #1444), 01t_vv (0.23 #1445, 0.22 #206, 0.22 #1444), 02l7c8 (0.23 #1445, 0.22 #206, 0.22 #1444), 06cvj (0.23 #1445, 0.22 #206, 0.22 #1444), 04228s (0.22 #206, 0.22 #1444, 0.21 #926) >> Best rule #1169 for best value: >> intensional similarity = 7 >> extensional distance = 69 >> proper extension: 0gxfz; >> query: (?x3084, 01z4y) <- produced_by(?x3084, ?x3568), genre(?x3084, ?x239), film(?x11470, ?x3084), film(?x2317, ?x3084), type_of_union(?x11470, ?x566), ?x239 = 06cvj, nationality(?x2317, ?x94) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03mh_tp titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 74.000 0.521 http://example.org/media_common/netflix_genre/titles #4030-0tgcy PRED entity: 0tgcy PRED relation: place_of_birth! PRED expected values: 018gkb => 91 concepts (29 used for prediction) PRED predicted values (max 10 best out of 3030): 0cjsxp (0.20 #3362, 0.20 #751, 0.12 #5973), 0jfx1 (0.20 #3064, 0.20 #453, 0.12 #5675), 01j6mff (0.12 #7196, 0.08 #12418, 0.02 #18278), 0fmqp6 (0.09 #9262, 0.07 #20890, 0.07 #14484), 01nd6v (0.09 #10440, 0.07 #15662, 0.06 #18273), 04zn7g (0.09 #10399, 0.07 #15621, 0.06 #18232), 01fxfk (0.09 #10342, 0.07 #15564, 0.06 #18175), 08141d (0.09 #10335, 0.07 #15557, 0.06 #18168), 02bc74 (0.09 #10327, 0.07 #15549, 0.06 #18160), 044zvm (0.09 #10229, 0.07 #15451, 0.06 #18062) >> Best rule #3362 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0tct_; >> query: (?x10228, 0cjsxp) <- contains(?x4061, ?x10228), contains(?x94, ?x10228), ?x4061 = 0498y, time_zones(?x10228, ?x2674), ?x94 = 09c7w0 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #18278 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 06y57; 06pr6; 0nq_b; 012fzm; *> query: (?x10228, ?x51) <- place_of_birth(?x4863, ?x10228), contains(?x94, ?x10228), award_winner(?x499, ?x4863), cinematography(?x2057, ?x4863), nationality(?x51, ?x94) *> conf = 0.02 ranks of expected_values: 1396 EVAL 0tgcy place_of_birth! 018gkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 91.000 29.000 0.200 http://example.org/people/person/place_of_birth #4029-04jn6y7 PRED entity: 04jn6y7 PRED relation: film! PRED expected values: 0c1pj => 130 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1344): 0c1pj (0.38 #2176, 0.25 #93, 0.02 #37596), 06m6p7 (0.25 #3453, 0.25 #1370, 0.03 #49288), 0h5g_ (0.25 #74, 0.12 #2157, 0.08 #10491), 018p4y (0.25 #1921, 0.12 #4004, 0.05 #8170), 05dtsb (0.25 #1177, 0.12 #3260, 0.04 #11594), 02t_st (0.25 #1289, 0.12 #3372, 0.04 #28373), 01j5ts (0.25 #29, 0.12 #2112, 0.03 #31253), 09r9dp (0.25 #652, 0.12 #2735, 0.03 #27736), 05p606 (0.25 #1916, 0.12 #3999, 0.02 #37336), 04m064 (0.25 #1979, 0.12 #4062, 0.02 #41565) >> Best rule #2176 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 03h_yy; 0gyh2wm; 02yxbc; >> query: (?x12693, 0c1pj) <- film_crew_role(?x12693, ?x137), film(?x1286, ?x12693), country(?x12693, ?x94), production_companies(?x12693, ?x10629), ?x10629 = 0fvppk >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04jn6y7 film! 0c1pj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 48.000 0.375 http://example.org/film/actor/film./film/performance/film #4028-02lg3y PRED entity: 02lg3y PRED relation: actor! PRED expected values: 03ln8b => 106 concepts (88 used for prediction) PRED predicted values (max 10 best out of 87): 080dwhx (0.29 #6, 0.06 #1062, 0.03 #798), 06zsk51 (0.14 #182, 0.01 #5729), 05p9_ql (0.14 #134), 026b33f (0.11 #568, 0.06 #304), 0g60z (0.10 #796, 0.02 #5551, 0.02 #7135), 08jgk1 (0.07 #1078, 0.03 #1342, 0.02 #5569), 02_1q9 (0.06 #797, 0.06 #269, 0.06 #533), 0180mw (0.06 #911, 0.04 #1439, 0.02 #5666), 02k_4g (0.06 #806, 0.01 #1334), 07gbf (0.06 #983) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01wbg84; 01r42_g; 01l1sq; 01z7_f; 06dn58; >> query: (?x4401, 080dwhx) <- religion(?x4401, ?x1985), award_nominee(?x4401, ?x2965), ?x2965 = 01dy7j >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #822 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 01pcql; *> query: (?x4401, 03ln8b) <- place_of_birth(?x4401, ?x4253), award(?x4401, ?x2041), ?x2041 = 0bdx29 *> conf = 0.03 ranks of expected_values: 18 EVAL 02lg3y actor! 03ln8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 106.000 88.000 0.286 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #4027-015vq_ PRED entity: 015vq_ PRED relation: award PRED expected values: 09sb52 => 97 concepts (69 used for prediction) PRED predicted values (max 10 best out of 253): 09sb52 (0.88 #439, 0.78 #1241, 0.76 #840), 027b9j5 (0.70 #18460, 0.70 #23276, 0.70 #23678), 02z13jg (0.70 #18460, 0.70 #23276, 0.70 #23678), 09cm54 (0.70 #18460, 0.70 #23276, 0.70 #23678), 027986c (0.70 #18460, 0.70 #23276, 0.70 #23678), 0gq9h (0.34 #2080, 0.33 #2482, 0.09 #9302), 040njc (0.29 #2011, 0.26 #2413, 0.13 #16050), 0bfvd4 (0.25 #514, 0.19 #1717, 0.19 #915), 0gqy2 (0.25 #563, 0.19 #964, 0.17 #1365), 0gs9p (0.23 #2082, 0.17 #2484, 0.07 #9304) >> Best rule #439 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01vyv9; >> query: (?x4128, 09sb52) <- award_nominee(?x4128, ?x4154), award_nominee(?x4128, ?x4103), ?x4154 = 014g22, ?x4103 = 02jsgf >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015vq_ award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 69.000 0.875 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4026-01vsgrn PRED entity: 01vsgrn PRED relation: nationality PRED expected values: 09c7w0 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.79 #401, 0.77 #201, 0.75 #802), 04ych (0.33 #11211), 02jx1 (0.21 #133, 0.15 #4636, 0.14 #1334), 07ssc (0.11 #1116, 0.10 #1316, 0.09 #1516), 02dtg (0.07 #701, 0.03 #2402), 03rk0 (0.06 #7552, 0.06 #7852, 0.05 #7150), 0d060g (0.05 #1308, 0.05 #107, 0.05 #3109), 06q1r (0.05 #177, 0.03 #1378, 0.02 #2078), 0345h (0.05 #2032, 0.03 #631, 0.03 #1232), 03gyl (0.04 #366, 0.01 #767) >> Best rule #401 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0194xc; 0c_md_; 0pqzh; >> query: (?x5536, 09c7w0) <- award_winner(?x1323, ?x5536), celebrities_impersonated(?x5915, ?x5536), place_of_birth(?x5536, ?x12881) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vsgrn nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.791 http://example.org/people/person/nationality #4025-023slg PRED entity: 023slg PRED relation: role PRED expected values: 0342h 02hnl => 120 concepts (84 used for prediction) PRED predicted values (max 10 best out of 120): 0342h (0.64 #2666, 0.56 #102, 0.54 #1085), 042v_gx (0.62 #595, 0.44 #104, 0.32 #1087), 01vdm0 (0.54 #224, 0.44 #126, 0.41 #1109), 02fsn (0.44 #295, 0.37 #2762, 0.34 #2660), 07m2y (0.44 #295, 0.34 #983, 0.32 #2960), 02sgy (0.42 #889, 0.39 #1086, 0.38 #201), 05842k (0.39 #1450, 0.38 #270, 0.33 #172), 013y1f (0.33 #131, 0.31 #229, 0.27 #1114), 02qjv (0.31 #214, 0.13 #902, 0.12 #1099), 0l15bq (0.29 #623, 0.29 #34, 0.21 #525) >> Best rule #2666 for best value: >> intensional similarity = 5 >> extensional distance = 153 >> proper extension: 02fybl; 09g0h; >> query: (?x11916, 0342h) <- role(?x11916, ?x716), role(?x11916, ?x2888), instrumentalists(?x716, ?x4977), ?x4977 = 03f6fl0, role(?x716, ?x74) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 47 EVAL 023slg role 02hnl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 120.000 84.000 0.639 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 023slg role 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 84.000 0.639 http://example.org/music/artist/track_contributions./music/track_contribution/role #4024-0824r PRED entity: 0824r PRED relation: district_represented! PRED expected values: 070m6c 07p__7 02bn_p => 211 concepts (211 used for prediction) PRED predicted values (max 10 best out of 46): 07p__7 (0.88 #373, 0.83 #741, 0.80 #603), 070m6c (0.85 #740, 0.83 #464, 0.82 #372), 02bn_p (0.73 #374, 0.67 #466, 0.65 #604), 01gtdd (0.56 #1749, 0.55 #269, 0.46 #775), 01gt99 (0.56 #1749, 0.52 #272, 0.48 #778), 01gst_ (0.56 #1749, 0.48 #241, 0.46 #747), 01gtcc (0.56 #1749, 0.48 #337, 0.45 #245), 01gtbb (0.56 #1749, 0.48 #240, 0.44 #746), 01gtc0 (0.56 #1749, 0.45 #253, 0.44 #759), 01gsvp (0.56 #1749, 0.42 #764, 0.41 #258) >> Best rule #373 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 05kkh; 059rby; 03v1s; 05kj_; 059f4; 0hjy; 05fhy; 04ych; 01n7q; 06mz5; ... >> query: (?x4105, 07p__7) <- currency(?x4105, ?x170), contains(?x4105, ?x2680), district_represented(?x6933, ?x4105), ?x6933 = 024tkd >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0824r district_represented! 02bn_p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 211.000 211.000 0.879 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 0824r district_represented! 07p__7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 211.000 211.000 0.879 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 0824r district_represented! 070m6c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 211.000 211.000 0.879 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #4023-01lcxbb PRED entity: 01lcxbb PRED relation: people! PRED expected values: 0gk4g => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 43): 0gk4g (0.14 #274, 0.13 #2980, 0.12 #4036), 02y0js (0.11 #134, 0.07 #398, 0.06 #3764), 0qcr0 (0.11 #133, 0.06 #3631, 0.06 #2971), 0dq9p (0.09 #3119, 0.08 #4043, 0.08 #3779), 01n3bm (0.08 #109, 0.06 #175, 0.02 #835), 0kh3 (0.08 #84, 0.01 #612), 01dcqj (0.07 #408, 0.04 #276, 0.02 #2982), 01l2m3 (0.07 #280, 0.04 #412, 0.04 #544), 051_y (0.06 #180, 0.04 #444, 0.04 #246), 01_qc_ (0.06 #160, 0.04 #424, 0.03 #556) >> Best rule #274 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 023361; >> query: (?x3378, 0gk4g) <- award_winner(?x2561, ?x3378), artists(?x505, ?x3378), place_of_death(?x3378, ?x8026) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lcxbb people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.143 http://example.org/people/cause_of_death/people #4022-02hp70 PRED entity: 02hp70 PRED relation: colors PRED expected values: 01g5v => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.29 #223, 0.29 #83, 0.28 #743), 01l849 (0.29 #81, 0.27 #41, 0.26 #861), 019sc (0.25 #7, 0.18 #1047, 0.18 #867), 067z2v (0.25 #9, 0.06 #89, 0.05 #249), 03wkwg (0.18 #55, 0.14 #75, 0.09 #255), 02rnmb (0.18 #53, 0.05 #293, 0.05 #133), 06fvc (0.15 #162, 0.15 #1042, 0.15 #1002), 036k5h (0.11 #425, 0.11 #225, 0.10 #245), 04mkbj (0.10 #370, 0.10 #250, 0.10 #230), 038hg (0.10 #472, 0.09 #1052, 0.09 #712) >> Best rule #223 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 0k9wp; >> query: (?x11397, 01g5v) <- colors(?x11397, ?x663), major_field_of_study(?x11397, ?x1668), ?x1668 = 01mkq, contains(?x94, ?x11397) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hp70 colors 01g5v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.290 http://example.org/education/educational_institution/colors #4021-0xddr PRED entity: 0xddr PRED relation: category PRED expected values: 08mbj5d => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.79 #14, 0.79 #27, 0.79 #26) >> Best rule #14 for best value: >> intensional similarity = 6 >> extensional distance = 166 >> proper extension: 013jz2; 01m1_t; 0mb2b; 07l5z; 0_565; 0t_48; 0vm5t; >> query: (?x3819, 08mbj5d) <- county(?x3819, ?x10567), contains(?x1274, ?x3819), contains(?x94, ?x3819), ?x94 = 09c7w0, district_represented(?x605, ?x1274), location(?x1461, ?x1274) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xddr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.792 http://example.org/common/topic/webpage./common/webpage/category #4020-02f1c PRED entity: 02f1c PRED relation: award PRED expected values: 04njml => 105 concepts (94 used for prediction) PRED predicted values (max 10 best out of 300): 026mfs (0.82 #792, 0.79 #11857, 0.79 #8695), 026mff (0.82 #792, 0.79 #11857, 0.79 #8695), 026mml (0.82 #792, 0.79 #11857, 0.79 #8695), 03x3wf (0.82 #792, 0.79 #11857, 0.79 #8695), 09sb52 (0.48 #17823, 0.26 #18218, 0.26 #23752), 01bgqh (0.43 #3205, 0.36 #2415, 0.34 #2810), 01dpdh (0.30 #522, 0.06 #34393, 0.05 #6054), 03qbh5 (0.28 #2572, 0.25 #8499, 0.24 #5732), 02x17c2 (0.28 #2584, 0.16 #3374, 0.13 #31619), 054krc (0.28 #2457, 0.14 #3247, 0.10 #17867) >> Best rule #792 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 05cljf; 0137n0; 03gr7w; 015882; 01wmgrf; 016srn; 017xm3; 0dl567; 036px; 0x3b7; ... >> query: (?x8799, ?x1088) <- award(?x8799, ?x1361), profession(?x8799, ?x131), ?x1361 = 01c9f2, award_winner(?x1088, ?x8799) >> conf = 0.82 => this is the best rule for 4 predicted values *> Best rule #3261 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 0161c2; 024dgj; 01wbz9; 012wg; 01wf86y; 016s0m; 0pkgt; *> query: (?x8799, 04njml) <- award(?x8799, ?x1232), profession(?x8799, ?x131), ?x1232 = 0c4z8 *> conf = 0.17 ranks of expected_values: 23 EVAL 02f1c award 04njml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 105.000 94.000 0.825 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #4019-03gyh_z PRED entity: 03gyh_z PRED relation: award_nominee! PRED expected values: 058vfp4 => 157 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1124): 058vfp4 (0.82 #46659, 0.82 #44326, 0.82 #109633), 04_1nk (0.82 #46659, 0.82 #44326, 0.82 #109633), 0fmqp6 (0.62 #10908, 0.04 #17906, 0.02 #87882), 072twv (0.38 #9860, 0.33 #525, 0.25 #2859), 053vcrp (0.38 #11501, 0.04 #18499, 0.01 #34827), 07h1tr (0.25 #2931, 0.20 #5265, 0.12 #9932), 07hhnl (0.25 #3494, 0.20 #5828, 0.12 #10495), 051x52f (0.25 #11083, 0.04 #18081, 0.02 #64733), 076lxv (0.25 #9476, 0.04 #16474, 0.01 #32802), 03gyh_z (0.25 #10135, 0.04 #17133, 0.01 #63785) >> Best rule #46659 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 03zqc1; 0blt6; 0205dx; 0fn5bx; 067sqt; 09xvf7; >> query: (?x3548, ?x2801) <- award_nominee(?x3548, ?x2801), student(?x735, ?x3548), student(?x1771, ?x3548), institution(?x1771, ?x99) >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 03gyh_z award_nominee! 058vfp4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 66.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #4018-06lht1 PRED entity: 06lht1 PRED relation: profession PRED expected values: 02hrh1q => 83 concepts (82 used for prediction) PRED predicted values (max 10 best out of 58): 02hrh1q (0.89 #465, 0.88 #5119, 0.88 #7370), 01d_h8 (0.50 #6, 0.44 #906, 0.44 #1056), 03gjzk (0.33 #616, 0.32 #2267, 0.32 #1066), 0dxtg (0.31 #1214, 0.30 #2265, 0.30 #1064), 018gz8 (0.26 #618, 0.15 #768, 0.15 #918), 09jwl (0.25 #1220, 0.25 #1370, 0.24 #1520), 02jknp (0.25 #8, 0.21 #4211, 0.21 #7513), 02krf9 (0.25 #28, 0.14 #2279, 0.10 #1679), 0np9r (0.22 #1673, 0.20 #2423, 0.20 #2123), 0dz3r (0.20 #1202, 0.20 #1502, 0.20 #1352) >> Best rule #465 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 05bnp0; 02qgqt; 0785v8; 015grj; 03pmty; 0bg539; 031zkw; 045c66; 0fsm8c; 034np8; ... >> query: (?x4966, 02hrh1q) <- film(?x4966, ?x634), award(?x4966, ?x2252), ?x2252 = 02x8n1n >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06lht1 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 82.000 0.885 http://example.org/people/person/profession #4017-0342h PRED entity: 0342h PRED relation: group PRED expected values: 03t9sp 0167_s 018ndc 0b1zz 0178_w 081wh1 016vn3 014kyy 0jg77 => 66 concepts (65 used for prediction) PRED predicted values (max 10 best out of 497): 0cbm64 (0.62 #124, 0.50 #308, 0.33 #140), 01_wfj (0.62 #124, 0.40 #97, 0.38 #414), 0167_s (0.62 #124, 0.38 #296, 0.25 #253), 015srx (0.62 #124, 0.25 #301, 0.22 #210), 0k1bs (0.62 #124, 0.22 #210, 0.20 #188), 02mq_y (0.50 #278, 0.44 #340, 0.38 #256), 081wh1 (0.50 #262, 0.40 #115, 0.38 #305), 0178_w (0.44 #345, 0.42 #452, 0.33 #136), 016vn3 (0.40 #120, 0.38 #267, 0.38 #247), 01jkqfz (0.40 #117, 0.38 #264, 0.38 #244) >> Best rule #124 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 09lbv; >> query: (?x227, ?x646) <- split_to(?x227, ?x645), split_to(?x2659, ?x227), role(?x679, ?x645), group(?x645, ?x4909), group(?x645, ?x646), ?x4909 = 01cblr >> conf = 0.62 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 7, 8, 9, 12, 13, 14, 15, 18 EVAL 0342h group 0jg77 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 66.000 65.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0342h group 014kyy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 66.000 65.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0342h group 016vn3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 66.000 65.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0342h group 081wh1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 66.000 65.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0342h group 0178_w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 66.000 65.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0342h group 0b1zz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 66.000 65.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0342h group 018ndc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 66.000 65.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0342h group 0167_s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 65.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0342h group 03t9sp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 66.000 65.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group #4016-016t_3 PRED entity: 016t_3 PRED relation: institution PRED expected values: 09kvv 022lly 04sylm 017z88 01r3y2 02bb47 07vfj 0kw4j 01f1r4 01nnsv 02km0m 01p79b 0bsnm 021996 01wv24 0dzst 031n5b 027ybp 06thjt 02gnmp 01r47h 02kxx1 01pxcf 02tz9z 07wm6 07th_ => 24 concepts (23 used for prediction) PRED predicted values (max 10 best out of 482): 0ks67 (0.78 #7748, 0.75 #6400, 0.67 #7300), 09kvv (0.78 #7631, 0.73 #8079, 0.71 #5389), 07vjm (0.78 #7777, 0.71 #6702, 0.67 #7329), 01f1r4 (0.78 #7698, 0.71 #6702, 0.67 #7250), 01nnsv (0.75 #6849, 0.71 #6702, 0.67 #7745), 0lbfv (0.75 #6878, 0.67 #7774, 0.57 #5980), 07vfj (0.71 #5445, 0.71 #6702, 0.56 #7687), 01bk1y (0.71 #6021, 0.71 #6702, 0.55 #5808), 01bvw5 (0.71 #5847, 0.67 #7641, 0.62 #6745), 016ndm (0.71 #5461, 0.62 #6807, 0.58 #8600) >> Best rule #7748 for best value: >> intensional similarity = 23 >> extensional distance = 7 >> proper extension: 04zx3q1; >> query: (?x1200, 0ks67) <- major_field_of_study(?x1200, ?x6756), major_field_of_study(?x1200, ?x2981), major_field_of_study(?x1200, ?x1527), institution(?x1200, ?x12605), institution(?x1200, ?x5777), institution(?x1200, ?x4955), institution(?x1200, ?x4904), institution(?x1200, ?x4755), ?x4955 = 09f2j, major_field_of_study(?x1428, ?x6756), major_field_of_study(?x620, ?x1527), student(?x1200, ?x665), ?x1428 = 01j_06, category(?x12605, ?x134), colors(?x12605, ?x663), contains(?x362, ?x12605), ?x2981 = 02j62, school(?x2574, ?x4904), student(?x4904, ?x1683), ?x620 = 07s6fsf, school_type(?x5777, ?x3205), ?x2574 = 01y3v, company(?x920, ?x4755) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #7631 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 7 *> proper extension: 04zx3q1; *> query: (?x1200, 09kvv) <- major_field_of_study(?x1200, ?x6756), major_field_of_study(?x1200, ?x2981), major_field_of_study(?x1200, ?x1527), institution(?x1200, ?x12605), institution(?x1200, ?x5777), institution(?x1200, ?x4955), institution(?x1200, ?x4904), institution(?x1200, ?x4755), ?x4955 = 09f2j, major_field_of_study(?x1428, ?x6756), major_field_of_study(?x620, ?x1527), student(?x1200, ?x665), ?x1428 = 01j_06, category(?x12605, ?x134), colors(?x12605, ?x663), contains(?x362, ?x12605), ?x2981 = 02j62, school(?x2574, ?x4904), student(?x4904, ?x1683), ?x620 = 07s6fsf, school_type(?x5777, ?x3205), ?x2574 = 01y3v, company(?x920, ?x4755) *> conf = 0.78 ranks of expected_values: 2, 4, 5, 7, 31, 51, 59, 60, 66, 68, 88, 99, 102, 135, 141, 180, 209, 214, 228, 260, 266, 280, 294, 309, 341, 356 EVAL 016t_3 institution 07th_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 07wm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 02tz9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 01pxcf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 02kxx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 01r47h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 02gnmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 06thjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 027ybp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 031n5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 0dzst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 01wv24 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 021996 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 0bsnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 01p79b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 02km0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 01nnsv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 01f1r4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 0kw4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 07vfj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 02bb47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 01r3y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 017z88 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 04sylm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 022lly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 016t_3 institution 09kvv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 24.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #4015-0bj25 PRED entity: 0bj25 PRED relation: nominated_for! PRED expected values: 0gr4k 04dn09n => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 254): 0gs9p (0.67 #5528, 0.67 #2592, 0.67 #6679), 04dn09n (0.67 #5528, 0.67 #6679, 0.67 #2072), 02wwsh8 (0.67 #5528, 0.67 #6679, 0.67 #2072), 02y_j8g (0.67 #5528, 0.67 #6679, 0.67 #2072), 0gr4k (0.43 #1865, 0.40 #2557, 0.40 #2787), 0f4x7 (0.40 #1864, 0.40 #2556, 0.38 #2786), 02pqp12 (0.40 #2589, 0.39 #2819, 0.30 #1897), 02qyntr (0.38 #2935, 0.38 #2705, 0.30 #2013), 02ppm4q (0.37 #1950, 0.17 #1029, 0.16 #2181), 0l8z1 (0.33 #1661, 0.31 #3733, 0.27 #2583) >> Best rule #5528 for best value: >> intensional similarity = 3 >> extensional distance = 461 >> proper extension: 06mmr; >> query: (?x8769, ?x198) <- award(?x8769, ?x198), honored_for(?x11428, ?x8769), award_winner(?x8769, ?x574) >> conf = 0.67 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 5 EVAL 0bj25 nominated_for! 04dn09n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.673 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bj25 nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 76.000 0.673 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4014-0f4x7 PRED entity: 0f4x7 PRED relation: nominated_for PRED expected values: 016z5x 0_b3d 0bx0l 026p4q7 0ggbhy7 0g68zt 011yfd 0gcpc 09gb_4p 06t6dz 011ypx 011ykb 07jnt 0j90s 04165w 0h95927 0llcx 02cbg0 02p86pb 01lsl 07l50_1 0gt14 => 66 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1505): 09q5w2 (0.77 #19866, 0.67 #4383, 0.66 #34066), 0p_th (0.77 #19866, 0.66 #34066, 0.65 #36909), 0bmhn (0.77 #19866, 0.66 #34066, 0.65 #36909), 0c0zq (0.77 #19866, 0.66 #34066, 0.65 #36909), 064lsn (0.77 #19866, 0.66 #34066, 0.65 #36909), 042y1c (0.77 #19866, 0.66 #34066, 0.65 #36909), 06cm5 (0.77 #19866, 0.66 #34066, 0.65 #36909), 0bm2x (0.77 #19866, 0.66 #34066, 0.65 #36909), 0bl5c (0.77 #19866, 0.66 #34066, 0.65 #36909), 07l450 (0.77 #19866, 0.66 #34066, 0.65 #36909) >> Best rule #19866 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 06196; >> query: (?x591, ?x69) <- ceremony(?x591, ?x78), award_winner(?x591, ?x157), award(?x69, ?x591) >> conf = 0.77 => this is the best rule for 12 predicted values *> Best rule #8823 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 040njc; 0l8z1; 02pqp12; 04kxsb; 02qvyrt; 02ppm4q; 02qyntr; *> query: (?x591, 026p4q7) <- ceremony(?x591, ?x78), nominated_for(?x591, ?x2345), ?x2345 = 0c_j9x, award(?x123, ?x591) *> conf = 0.77 ranks of expected_values: 13, 48, 49, 55, 65, 69, 78, 79, 98, 104, 132, 182, 183, 187, 191, 210, 263, 278, 293, 395, 476, 534 EVAL 0f4x7 nominated_for 0gt14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 07l50_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 01lsl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 02p86pb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 02cbg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 0llcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 0h95927 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 04165w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 0j90s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 07jnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 011ykb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 011ypx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 06t6dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 09gb_4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 0gcpc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 011yfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 0g68zt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 0ggbhy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 026p4q7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 0bx0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 0_b3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0f4x7 nominated_for 016z5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 66.000 29.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4013-01hqhm PRED entity: 01hqhm PRED relation: nominated_for! PRED expected values: 099c8n 09sdmz => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 207): 05h5nb8 (0.67 #9517, 0.66 #13235, 0.66 #12770), 027cyf7 (0.67 #9517, 0.66 #13235, 0.66 #12770), 0gq9h (0.50 #1220, 0.49 #988, 0.47 #60), 019f4v (0.47 #1212, 0.45 #980, 0.43 #52), 0gs9p (0.44 #1222, 0.43 #990, 0.41 #4937), 0gr0m (0.40 #1218, 0.39 #986, 0.28 #5165), 04dn09n (0.39 #963, 0.38 #1195, 0.31 #35), 0k611 (0.38 #1231, 0.38 #999, 0.36 #5178), 0gq_v (0.38 #1180, 0.37 #948, 0.32 #6983), 040njc (0.35 #1167, 0.34 #935, 0.34 #5114) >> Best rule #9517 for best value: >> intensional similarity = 3 >> extensional distance = 510 >> proper extension: 02rq7nd; >> query: (?x2090, ?x4135) <- honored_for(?x7452, ?x2090), nominated_for(?x704, ?x2090), award(?x2090, ?x4135) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #2607 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 184 *> proper extension: 02hfk5; *> query: (?x2090, 099c8n) <- honored_for(?x7452, ?x2090), production_companies(?x2090, ?x2549), film_crew_role(?x2090, ?x137) *> conf = 0.35 ranks of expected_values: 12, 56 EVAL 01hqhm nominated_for! 09sdmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 97.000 97.000 0.674 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01hqhm nominated_for! 099c8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 97.000 97.000 0.674 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #4012-049dyj PRED entity: 049dyj PRED relation: profession PRED expected values: 018gz8 => 104 concepts (103 used for prediction) PRED predicted values (max 10 best out of 80): 03gjzk (0.86 #606, 0.75 #1198, 0.73 #1346), 0dxtg (0.86 #2974, 0.85 #2826, 0.82 #3418), 018gz8 (0.55 #904, 0.41 #608, 0.31 #312), 01d_h8 (0.50 #598, 0.38 #450, 0.37 #2078), 0np9r (0.38 #316, 0.36 #908, 0.32 #612), 02jknp (0.38 #451, 0.32 #599, 0.20 #8001), 09jwl (0.37 #4756, 0.34 #5200, 0.28 #1794), 02krf9 (0.31 #322, 0.22 #1950, 0.21 #914), 0nbcg (0.28 #4769, 0.26 #5213, 0.18 #1807), 016z4k (0.26 #4742, 0.24 #5186, 0.16 #1780) >> Best rule #606 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 04n7njg; 03yf4d; >> query: (?x1065, 03gjzk) <- nominated_for(?x1065, ?x5561), tv_program(?x1065, ?x6884), category(?x1065, ?x134) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #904 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 01wyzyl; 0glmv; 01p8r8; *> query: (?x1065, 018gz8) <- location(?x1065, ?x242), tv_program(?x1065, ?x6884), film(?x1065, ?x1066) *> conf = 0.55 ranks of expected_values: 3 EVAL 049dyj profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 103.000 0.864 http://example.org/people/person/profession #4011-0vh3 PRED entity: 0vh3 PRED relation: jurisdiction_of_office! PRED expected values: 02079p => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 22): 0fkvn (0.71 #50, 0.67 #73, 0.67 #27), 0f6c3 (0.60 #169, 0.53 #100, 0.51 #215), 0p5vf (0.57 #59, 0.56 #82, 0.50 #36), 0fkx3 (0.57 #67, 0.56 #90, 0.50 #44), 09n5b9 (0.52 #173, 0.51 #104, 0.47 #219), 060c4 (0.50 #1573, 0.45 #2037, 0.44 #1898), 060bp (0.44 #1571, 0.39 #1896, 0.38 #2035), 02079p (0.33 #877, 0.32 #1895, 0.30 #1919), 0fj45 (0.25 #20, 0.11 #3937, 0.07 #2082), 0pqc5 (0.21 #1669, 0.20 #2133, 0.20 #1924) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0chgr2; >> query: (?x12300, 0fkvn) <- contains(?x12300, ?x12051), country(?x12300, ?x390), adjoins(?x12300, ?x8506), ?x390 = 0chghy >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #877 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 124 *> proper extension: 05bkf; *> query: (?x12300, ?x900) <- adjoins(?x12300, ?x8506), country(?x8506, ?x390), adjoins(?x12854, ?x8506), jurisdiction_of_office(?x900, ?x12854) *> conf = 0.33 ranks of expected_values: 8 EVAL 0vh3 jurisdiction_of_office! 02079p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 172.000 172.000 0.714 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #4010-017jd9 PRED entity: 017jd9 PRED relation: titles! PRED expected values: 01hmnh => 69 concepts (42 used for prediction) PRED predicted values (max 10 best out of 52): 07s9rl0 (0.30 #1546, 0.30 #2065, 0.29 #1650), 04xvlr (0.25 #310, 0.23 #413, 0.23 #723), 01z4y (0.20 #36, 0.19 #2620, 0.18 #1376), 03k9fj (0.19 #1649, 0.19 #1545, 0.19 #1752), 02xlf (0.19 #1649, 0.19 #1545, 0.19 #1752), 060__y (0.19 #1649, 0.19 #1545, 0.19 #1752), 024qqx (0.17 #183, 0.16 #285, 0.15 #387), 01hmnh (0.14 #129, 0.13 #850, 0.13 #953), 01jfsb (0.12 #1152, 0.11 #636, 0.10 #1255), 07c52 (0.11 #2299, 0.11 #1782, 0.11 #1990) >> Best rule #1546 for best value: >> intensional similarity = 3 >> extensional distance = 469 >> proper extension: 02rlj20; 01fwzk; 0kt_4; 042g97; 0hr41p6; >> query: (?x4610, 07s9rl0) <- honored_for(?x1193, ?x4610), nominated_for(?x1194, ?x4610), genre(?x4610, ?x811) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #129 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 011yxg; 02qm_f; 048scx; 0k2sk; 026p4q7; 01qxc7; 035_2h; 07q1m; 027r9t; *> query: (?x4610, 01hmnh) <- award_winner(?x4610, ?x5653), crewmember(?x638, ?x5653), film_crew_role(?x4610, ?x137) *> conf = 0.14 ranks of expected_values: 8 EVAL 017jd9 titles! 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 69.000 42.000 0.299 http://example.org/media_common/netflix_genre/titles #4009-02py8_w PRED entity: 02py8_w PRED relation: team! PRED expected values: 0b_6jz => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 12): 0b_6zk (0.81 #219, 0.75 #174, 0.71 #165), 0b_71r (0.75 #181, 0.71 #172, 0.68 #136), 0b_6v_ (0.75 #179, 0.68 #136, 0.62 #224), 0br1x_ (0.71 #223, 0.68 #136, 0.67 #142), 0b_6s7 (0.71 #171, 0.68 #136, 0.62 #180), 0f9rw9 (0.68 #136, 0.60 #126, 0.50 #182), 0b_6jz (0.68 #136, 0.53 #137, 0.52 #220), 0b_6h7 (0.68 #136, 0.53 #137, 0.52 #222), 0br1xn (0.68 #136, 0.38 #176, 0.33 #149), 02z6gky (0.53 #137) >> Best rule #219 for best value: >> intensional similarity = 24 >> extensional distance = 19 >> proper extension: 03d555l; >> query: (?x6003, 0b_6zk) <- team(?x10673, ?x6003), team(?x6583, ?x6003), locations(?x10673, ?x9445), locations(?x10673, ?x6683), locations(?x10673, ?x659), team(?x10673, ?x9983), team(?x10673, ?x9909), ?x659 = 02cl1, ?x9983 = 02q4ntp, contains(?x94, ?x9445), ?x9909 = 026wlnm, location(?x427, ?x9445), state(?x9445, ?x1755), locations(?x6583, ?x108), category(?x9445, ?x134), featured_film_locations(?x103, ?x108), citytown(?x127, ?x108), place_of_birth(?x236, ?x108), adjoins(?x1426, ?x108), film_release_region(?x2878, ?x108), ?x6683 = 0djd3, month(?x108, ?x1459), teams(?x108, ?x662), location(?x2275, ?x108) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #136 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 4 *> proper extension: 03by7wc; *> query: (?x6003, ?x3797) <- team(?x13209, ?x6003), team(?x13045, ?x6003), team(?x9146, ?x6003), team(?x8824, ?x6003), team(?x6002, ?x6003), team(?x1348, ?x6003), ?x13045 = 0bqthy, position(?x10837, ?x1348), position(?x9937, ?x1348), locations(?x9146, ?x4978), locations(?x9146, ?x2087), team(?x9146, ?x9983), ?x4978 = 05jbn, team(?x6002, ?x4804), team(?x6002, ?x3798), ?x4804 = 03d555l, ?x10837 = 0jm7n, ?x8824 = 05g_nr, colors(?x9983, ?x3189), team(?x3797, ?x9983), ?x9937 = 0jmjr, sport(?x9983, ?x12913), position(?x9983, ?x4747), ?x3798 = 02ptzz0, time_zones(?x2087, ?x2088), category(?x2087, ?x134), ?x13209 = 0b_734 *> conf = 0.68 ranks of expected_values: 7 EVAL 02py8_w team! 0b_6jz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 57.000 57.000 0.810 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #4008-09jd9 PRED entity: 09jd9 PRED relation: gender PRED expected values: 02zsn => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #59, 0.91 #57, 0.90 #53), 02zsn (0.54 #252, 0.50 #195, 0.46 #274) >> Best rule #59 for best value: >> intensional similarity = 5 >> extensional distance = 136 >> proper extension: 079vf; >> query: (?x13624, 05zppz) <- student(?x892, ?x13624), story_by(?x596, ?x13624), student(?x892, ?x10372), institution(?x620, ?x892), nationality(?x10372, ?x94) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #252 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2548 *> proper extension: 01pwz; *> query: (?x13624, ?x231) <- place_of_birth(?x13624, ?x362), place_of_birth(?x6518, ?x362), place_of_birth(?x2487, ?x362), profession(?x2487, ?x1032), gender(?x6518, ?x231) *> conf = 0.54 ranks of expected_values: 2 EVAL 09jd9 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 136.000 0.906 http://example.org/people/person/gender #4007-021lby PRED entity: 021lby PRED relation: executive_produced_by! PRED expected values: 0d90m 01gglm => 120 concepts (46 used for prediction) PRED predicted values (max 10 best out of 258): 01bn3l (0.17 #2023, 0.17 #1492, 0.14 #430), 043h78 (0.17 #2071, 0.17 #1540, 0.01 #15387), 063zky (0.17 #1932, 0.17 #1401, 0.01 #15248), 0mbql (0.14 #379, 0.09 #5316, 0.06 #5317), 03t79f (0.14 #309, 0.04 #5093, 0.02 #2964), 047csmy (0.14 #302, 0.04 #5086, 0.02 #2957), 05zlld0 (0.14 #205, 0.04 #4989, 0.02 #2860), 0872p_c (0.14 #54, 0.04 #4838, 0.02 #2709), 09gdh6k (0.14 #411, 0.03 #2535, 0.02 #3066), 0bt4g (0.14 #423, 0.02 #3078, 0.02 #5207) >> Best rule #2023 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0dbpyd; 0l56b; 01c1px; 047cqr; >> query: (?x2464, 01bn3l) <- profession(?x2464, ?x8310), nationality(?x2464, ?x94), award_nominee(?x2464, ?x2182), ?x8310 = 0196pc >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #5230 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 0362q0; *> query: (?x2464, 01gglm) <- film(?x2464, ?x6620), featured_film_locations(?x6620, ?x726), film_release_region(?x6620, ?x87), executive_produced_by(?x1035, ?x2464) *> conf = 0.02 ranks of expected_values: 136, 253 EVAL 021lby executive_produced_by! 01gglm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 120.000 46.000 0.167 http://example.org/film/film/executive_produced_by EVAL 021lby executive_produced_by! 0d90m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 120.000 46.000 0.167 http://example.org/film/film/executive_produced_by #4006-0bkf72 PRED entity: 0bkf72 PRED relation: nationality PRED expected values: 09c7w0 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.84 #1, 0.83 #401, 0.81 #1202), 0chghy (0.38 #1101, 0.03 #3013, 0.02 #910), 02jx1 (0.10 #6639, 0.10 #6939, 0.10 #6439), 07ssc (0.09 #6621, 0.08 #3018, 0.08 #6821), 03rk0 (0.05 #9853, 0.05 #9153, 0.05 #9353), 0d060g (0.05 #3010, 0.05 #1007, 0.05 #907), 03spz (0.03 #267, 0.02 #167, 0.02 #2469), 03gj2 (0.03 #526, 0.01 #126, 0.01 #226), 03rjj (0.02 #105, 0.02 #5, 0.02 #1106), 0345h (0.02 #131, 0.02 #31, 0.02 #1132) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 06t8b; >> query: (?x8590, 09c7w0) <- produced_by(?x7243, ?x8590), award_winner(?x10618, ?x8590), producer_type(?x8590, ?x632) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bkf72 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.837 http://example.org/people/person/nationality #4005-01kxxq PRED entity: 01kxxq PRED relation: major_field_of_study PRED expected values: 02j62 0_jm => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 143): 02j62 (0.81 #357, 0.76 #354, 0.71 #508), 01mkq (0.81 #357, 0.76 #354, 0.70 #237), 03g3w (0.81 #357, 0.76 #354, 0.67 #717), 04gb7 (0.81 #357, 0.76 #354, 0.57 #521), 01r4k (0.81 #357, 0.76 #354, 0.57 #837), 062z7 (0.81 #357, 0.70 #237, 0.57 #596), 05qjt (0.81 #357, 0.59 #726, 0.57 #484), 01540 (0.81 #357, 0.57 #538, 0.50 #298), 0mg1w (0.81 #357, 0.50 #300, 0.47 #716), 06q83 (0.81 #357, 0.37 #715, 0.37 #714) >> Best rule #357 for best value: >> intensional similarity = 33 >> extensional distance = 2 >> proper extension: 02h4rq6; >> query: (?x9742, ?x2605) <- institution(?x9742, ?x11975), institution(?x9742, ?x9399), institution(?x9742, ?x9181), institution(?x9742, ?x9025), institution(?x9742, ?x8930), institution(?x9742, ?x7546), institution(?x9742, ?x6908), institution(?x9742, ?x5035), ?x9025 = 01vg0s, ?x8930 = 0373qt, ?x6908 = 01dthg, major_field_of_study(?x9742, ?x12158), major_field_of_study(?x9742, ?x3213), ?x3213 = 0g4gr, major_field_of_study(?x9181, ?x5179), major_field_of_study(?x9181, ?x2981), major_field_of_study(?x9181, ?x2605), currency(?x9181, ?x7888), ?x7888 = 0kz1h, state_province_region(?x9181, ?x8506), ?x5179 = 04gb7, contains(?x390, ?x5035), student(?x5035, ?x1738), ?x390 = 0chghy, institution(?x7636, ?x9181), ?x7636 = 01rr_d, student(?x9181, ?x6629), ?x12158 = 09s1f, ?x11975 = 050xpd, major_field_of_study(?x5035, ?x1668), ?x7546 = 01_qgp, ?x2981 = 02j62, ?x9399 = 02z6fs >> conf = 0.81 => this is the best rule for 10 predicted values ranks of expected_values: 1, 27 EVAL 01kxxq major_field_of_study 0_jm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 12.000 12.000 0.810 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 01kxxq major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.810 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #4004-0h0wc PRED entity: 0h0wc PRED relation: inductee! PRED expected values: 0qjfl => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 3): 0g2c8 (0.05 #56, 0.04 #155, 0.03 #74), 06szd3 (0.04 #29, 0.02 #300, 0.02 #438), 0qjfl (0.04 #12, 0.02 #40, 0.01 #67) >> Best rule #56 for best value: >> intensional similarity = 3 >> extensional distance = 238 >> proper extension: 05bpg3; 06s6hs; 019f9z; 0fq117k; 01wqmm8; >> query: (?x2551, 0g2c8) <- award_nominee(?x2551, ?x92), award_winner(?x1193, ?x2551), participant(?x2551, ?x6314) >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 01skmp; 02jr26; 01q8fxx; *> query: (?x2551, 0qjfl) <- award_winner(?x1716, ?x2551), ?x1716 = 02y_rq5, profession(?x2551, ?x1032) *> conf = 0.04 ranks of expected_values: 3 EVAL 0h0wc inductee! 0qjfl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 97.000 0.046 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #4003-0d99k_ PRED entity: 0d99k_ PRED relation: music PRED expected values: 07qy0b => 73 concepts (29 used for prediction) PRED predicted values (max 10 best out of 60): 0150t6 (0.10 #46, 0.05 #886, 0.04 #1306), 0csdzz (0.10 #187, 0.04 #607, 0.03 #1027), 02jxkw (0.10 #142, 0.03 #1823, 0.03 #772), 04ls53 (0.10 #79, 0.03 #1339, 0.03 #289), 07q1v4 (0.10 #15, 0.03 #225, 0.02 #855), 01m5m5b (0.10 #188, 0.02 #1028, 0.02 #1238), 07j8kh (0.10 #101, 0.02 #1151, 0.01 #1361), 02bh9 (0.05 #471, 0.05 #681, 0.05 #1522), 07v4dm (0.05 #613, 0.03 #823), 0146pg (0.05 #2538, 0.04 #1691, 0.04 #4860) >> Best rule #46 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0b1y_2; >> query: (?x11672, 0150t6) <- film_crew_role(?x11672, ?x3197), film_crew_role(?x11672, ?x281), language(?x11672, ?x254), ?x3197 = 02ynfr, ?x281 = 02_n3z, titles(?x1510, ?x11672) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #469 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 53 *> proper extension: 047gn4y; 0bth54; 03ckwzc; 09txzv; 05qbckf; 09g8vhw; 07yk1xz; 05p1qyh; 065z3_x; 05zy2cy; ... *> query: (?x11672, 07qy0b) <- film_crew_role(?x11672, ?x7591), film_crew_role(?x11672, ?x5136), country(?x11672, ?x94), ?x7591 = 0d2b38, ?x5136 = 089g0h, genre(?x11672, ?x225) *> conf = 0.02 ranks of expected_values: 38 EVAL 0d99k_ music 07qy0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 73.000 29.000 0.100 http://example.org/film/film/music #4002-05b2f_k PRED entity: 05b2f_k PRED relation: people! PRED expected values: 06gbnc => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 25): 06gbnc (0.58 #335, 0.04 #1105), 0g5y6 (0.33 #37, 0.06 #422, 0.05 #499), 07hwkr (0.17 #89, 0.11 #166, 0.09 #243), 02w7gg (0.16 #1080, 0.08 #310, 0.06 #2081), 02ctzb (0.11 #169, 0.11 #862, 0.09 #246), 033tf_ (0.11 #161, 0.09 #238, 0.06 #1162), 041rx (0.10 #3239, 0.10 #2622, 0.10 #3008), 0x67 (0.09 #1704, 0.09 #2474, 0.09 #2551), 02g7sp (0.08 #326, 0.02 #1096), 0d7wh (0.06 #1095, 0.02 #1634, 0.02 #1249) >> Best rule #335 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 07m69t; >> query: (?x8719, 06gbnc) <- nationality(?x8719, ?x4221), nationality(?x8719, ?x512), ?x512 = 07ssc, ?x4221 = 0j5g9 >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05b2f_k people! 06gbnc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.583 http://example.org/people/ethnicity/people #4001-01wkmgb PRED entity: 01wkmgb PRED relation: profession PRED expected values: 01d_h8 09jwl => 141 concepts (70 used for prediction) PRED predicted values (max 10 best out of 81): 09jwl (0.83 #5055, 0.82 #2685, 0.82 #2833), 016z4k (0.80 #152, 0.59 #3855, 0.56 #2522), 01d_h8 (0.77 #3561, 0.75 #2969, 0.68 #7718), 0n1h (0.70 #159, 0.38 #751, 0.36 #3862), 0nbcg (0.65 #1068, 0.63 #2698, 0.62 #2846), 0dxtg (0.64 #1494, 0.60 #7725, 0.59 #7577), 0dz3r (0.55 #2669, 0.53 #2817, 0.50 #1039), 039v1 (0.50 #4331, 0.45 #2703, 0.45 #1073), 03gjzk (0.42 #2977, 0.38 #3569, 0.37 #2087), 0cbd2 (0.38 #7, 0.31 #747, 0.24 #1340) >> Best rule #5055 for best value: >> intensional similarity = 5 >> extensional distance = 207 >> proper extension: 0f0y8; 03c7ln; 01vrx3g; 0fp_v1x; 01wl38s; 06cc_1; 032t2z; 0kzy0; 06y9c2; 01vvycq; ... >> query: (?x10329, 09jwl) <- profession(?x10329, ?x524), instrumentalists(?x227, ?x10329), artists(?x1572, ?x10329), role(?x10329, ?x432), artist(?x3240, ?x10329) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01wkmgb profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 70.000 0.828 http://example.org/people/person/profession EVAL 01wkmgb profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 70.000 0.828 http://example.org/people/person/profession #4000-03gfvsz PRED entity: 03gfvsz PRED relation: artist PRED expected values: 01vs_v8 01wmgrf 012z8_ 02jq1 01bczm 01k_mc 0g824 015cqh 0bdxs5 => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1590): 01jcxwp (0.71 #138, 0.50 #94, 0.33 #59), 07r1_ (0.50 #116, 0.50 #93, 0.33 #58), 017j6 (0.50 #121, 0.50 #83, 0.33 #48), 04rcr (0.50 #80, 0.43 #124, 0.33 #45), 0ycfj (0.50 #103, 0.43 #147, 0.33 #68), 01vsgrn (0.50 #90, 0.38 #40, 0.33 #55), 0c9l1 (0.50 #101, 0.33 #66, 0.32 #161), 0d193h (0.50 #88, 0.33 #53, 0.32 #161), 081wh1 (0.50 #92, 0.33 #57, 0.32 #161), 0b1hw (0.50 #102, 0.33 #67, 0.32 #161) >> Best rule #138 for best value: >> intensional similarity = 68 >> extensional distance = 5 >> proper extension: 0jrv_; 04f73rc; >> query: (?x6672, 01jcxwp) <- artist(?x6672, ?x10148), artist(?x6672, ?x7865), artist(?x6672, ?x5493), artist(?x6672, ?x2799), artist(?x6672, ?x1231), award(?x1231, ?x5123), award(?x1231, ?x4892), artist(?x1954, ?x1231), artists(?x3061, ?x1231), award_winner(?x5123, ?x487), award(?x5493, ?x6126), ceremony(?x5123, ?x6869), ceremony(?x5123, ?x2704), ceremony(?x5123, ?x1362), ceremony(?x5123, ?x486), ?x1362 = 019bk0, award(?x10565, ?x4892), artist(?x2149, ?x5493), artists(?x12070, ?x7865), artists(?x6210, ?x7865), artist(?x10992, ?x7865), category_of(?x5123, ?x2421), artists(?x3061, ?x6942), artists(?x3061, ?x6067), ?x6942 = 04b7xr, artists(?x2809, ?x2799), award_winner(?x6126, ?x646), award(?x12246, ?x6126), ?x6869 = 01xqqp, ?x2704 = 01mhwk, ?x6067 = 018y81, group(?x1166, ?x7865), artist(?x1954, ?x3894), artists(?x6210, ?x11186), artists(?x6210, ?x3657), artists(?x3928, ?x10148), award(?x10148, ?x3937), ?x12246 = 0bsj9, artists(?x3928, ?x11897), artists(?x3928, ?x8362), artists(?x3928, ?x6124), ?x3657 = 01w8n89, group(?x1166, ?x7966), group(?x1166, ?x7476), instrumentalists(?x1166, ?x10144), instrumentalists(?x1166, ?x6406), instrumentalists(?x1166, ?x6351), ?x486 = 02rjjll, role(?x74, ?x1166), group(?x5589, ?x7865), ?x7966 = 013rfk, parent_genre(?x12070, ?x5630), award(?x7865, ?x1389), ?x6351 = 01vsksr, category(?x10992, ?x134), ?x10565 = 0c9l1, ?x6124 = 0277c3, ?x11186 = 01304j, ?x6406 = 01386_, ?x3894 = 01vxlbm, role(?x1166, ?x885), parent_genre(?x3232, ?x3928), ?x10144 = 016wvy, role(?x680, ?x1166), ?x11897 = 01f2q5, origin(?x2799, ?x9026), ?x7476 = 048xh, ?x8362 = 01wg25j >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 71 *> extensional distance = 1 *> proper extension: 04y652m; *> query: (?x6672, ?x1345) <- artist(?x6672, ?x12810), artist(?x6672, ?x12357), artist(?x6672, ?x6380), artist(?x6672, ?x2824), artist(?x6672, ?x2614), artist(?x6672, ?x1231), artist(?x6672, ?x568), nationality(?x1231, ?x512), artists(?x3319, ?x1231), artist(?x4483, ?x1231), artist(?x1954, ?x1231), award(?x1231, ?x2238), award_nominee(?x568, ?x6382), award_winner(?x6380, ?x1751), artist(?x1954, ?x4983), artists(?x7329, ?x2824), artists(?x302, ?x2824), ?x4983 = 03f1d47, award(?x568, ?x567), participant(?x1231, ?x5665), award_nominee(?x6380, ?x3410), location(?x2824, ?x4151), student(?x1151, ?x6382), award_winner(?x2835, ?x6382), participant(?x891, ?x5665), profession(?x6382, ?x131), artist(?x12666, ?x12357), artists(?x1000, ?x1751), artists(?x302, ?x6876), artists(?x302, ?x3740), artists(?x302, ?x2945), parent_genre(?x837, ?x7329), ?x2945 = 01271h, role(?x1751, ?x314), nominated_for(?x6382, ?x9800), artist(?x4483, ?x2876), artist(?x4483, ?x1089), award_winner(?x2238, ?x1345), artists(?x7329, ?x7227), ?x3740 = 0fpj4lx, award_winner(?x3103, ?x1751), artists(?x3319, ?x1291), citytown(?x6455, ?x4151), ?x1089 = 01vrncs, group(?x1466, ?x12357), group(?x227, ?x12357), ?x1291 = 01kx_81, profession(?x2824, ?x220), ?x7227 = 01kcms4, gender(?x6382, ?x231), ceremony(?x2238, ?x9431), instrumentalists(?x316, ?x1231), participant(?x5665, ?x7527), gender(?x5665, ?x514), ?x2876 = 01vn35l, artists(?x5876, ?x2614), vacationer(?x362, ?x5665), ?x227 = 0342h, award_winner(?x342, ?x1751), artists(?x378, ?x6382), ?x1466 = 03bx0bm, artists(?x8187, ?x6380), award(?x12810, ?x9828), place_of_birth(?x5665, ?x13588), organization(?x4682, ?x12666), ?x9431 = 02cg41, origin(?x12810, ?x739), ?x6876 = 0ycp3, parent_genre(?x302, ?x2249), artists(?x5876, ?x8806), ?x8806 = 01d_h *> conf = 0.38 ranks of expected_values: 37, 48, 77, 78, 148, 354, 432, 486, 815 EVAL 03gfvsz artist 0bdxs5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.714 http://example.org/broadcast/content/artist EVAL 03gfvsz artist 015cqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.714 http://example.org/broadcast/content/artist EVAL 03gfvsz artist 0g824 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 5.000 5.000 0.714 http://example.org/broadcast/content/artist EVAL 03gfvsz artist 01k_mc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 5.000 5.000 0.714 http://example.org/broadcast/content/artist EVAL 03gfvsz artist 01bczm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 5.000 5.000 0.714 http://example.org/broadcast/content/artist EVAL 03gfvsz artist 02jq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.714 http://example.org/broadcast/content/artist EVAL 03gfvsz artist 012z8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 5.000 5.000 0.714 http://example.org/broadcast/content/artist EVAL 03gfvsz artist 01wmgrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.714 http://example.org/broadcast/content/artist EVAL 03gfvsz artist 01vs_v8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 5.000 5.000 0.714 http://example.org/broadcast/content/artist #3999-02lk1s PRED entity: 02lk1s PRED relation: nationality PRED expected values: 09c7w0 => 122 concepts (119 used for prediction) PRED predicted values (max 10 best out of 51): 09c7w0 (0.82 #201, 0.81 #1803, 0.80 #1903), 0d060g (0.34 #9327, 0.12 #507, 0.07 #1408), 02jx1 (0.16 #1634, 0.16 #1334, 0.13 #2937), 07ssc (0.16 #915, 0.13 #2619, 0.13 #2519), 03rk0 (0.06 #10077, 0.06 #10177, 0.06 #10378), 0j5g9 (0.06 #362, 0.02 #1062, 0.02 #862), 0345h (0.06 #3336, 0.06 #3536, 0.06 #3436), 0f8l9c (0.05 #3427, 0.04 #3327, 0.04 #3527), 01mjq (0.04 #640, 0.03 #4809, 0.01 #1341), 06m_5 (0.04 #683, 0.02 #983, 0.01 #1284) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 025cn2; 0bc71w; >> query: (?x881, 09c7w0) <- award_nominee(?x881, ?x829), student(?x741, ?x881), ?x741 = 01w3v >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lk1s nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 119.000 0.818 http://example.org/people/person/nationality #3998-01mr2g6 PRED entity: 01mr2g6 PRED relation: profession PRED expected values: 01___w => 140 concepts (93 used for prediction) PRED predicted values (max 10 best out of 99): 02hrh1q (0.94 #5249, 0.69 #7575, 0.68 #10629), 01d_h8 (0.75 #12077, 0.30 #3495, 0.28 #12222), 0nbcg (0.61 #4102, 0.59 #4538, 0.57 #465), 039v1 (0.45 #4107, 0.40 #4543, 0.38 #1196), 0kyk (0.44 #9626, 0.41 #1625, 0.34 #2644), 0dxtg (0.42 #12085, 0.40 #2774, 0.39 #1318), 02jknp (0.37 #12079, 0.23 #2040, 0.20 #12224), 01c72t (0.36 #7874, 0.33 #3949, 0.33 #7293), 0cbd2 (0.36 #2621, 0.33 #1311, 0.33 #5), 0n1h (0.33 #1026, 0.31 #881, 0.25 #1171) >> Best rule #5249 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 023s8; >> query: (?x8272, 02hrh1q) <- student(?x734, ?x8272), profession(?x8272, ?x5917), profession(?x1660, ?x5917), ?x1660 = 012x4t >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #1641 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 0q1lp; *> query: (?x8272, 01___w) <- student(?x734, ?x8272), religion(?x8272, ?x2694), category(?x8272, ?x134), student(?x741, ?x8272) *> conf = 0.03 ranks of expected_values: 64 EVAL 01mr2g6 profession 01___w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 140.000 93.000 0.944 http://example.org/people/person/profession #3997-07t90 PRED entity: 07t90 PRED relation: student PRED expected values: 083chw 02xyl => 116 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1458): 0n00 (0.29 #546, 0.14 #4717, 0.12 #10973), 07g2b (0.14 #75, 0.10 #8417, 0.08 #10502), 0kh6b (0.14 #614, 0.10 #8956, 0.08 #11041), 02m7r (0.14 #366, 0.10 #8708, 0.08 #10793), 0kn3g (0.14 #1662, 0.10 #10004, 0.08 #12089), 034bgm (0.14 #415, 0.10 #8757, 0.08 #10842), 03nk3t (0.14 #760, 0.10 #9102, 0.08 #11187), 030dr (0.14 #1871, 0.10 #10213, 0.08 #12298), 0xnc3 (0.14 #1437, 0.10 #9779, 0.08 #11864), 04hcw (0.14 #1259, 0.10 #9601, 0.08 #11686) >> Best rule #546 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02hcxm; >> query: (?x4599, 0n00) <- company(?x2998, ?x4599), company(?x3520, ?x4599), ?x2998 = 021q0l >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #22965 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 033hn8; 0fvppk; *> query: (?x4599, 083chw) <- company(?x3520, ?x4599), organization(?x346, ?x4599), type_of_union(?x3520, ?x566) *> conf = 0.02 ranks of expected_values: 342 EVAL 07t90 student 02xyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 81.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 07t90 student 083chw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 116.000 81.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #3996-04q5zw PRED entity: 04q5zw PRED relation: executive_produced_by! PRED expected values: 01k1k4 05567m => 122 concepts (30 used for prediction) PRED predicted values (max 10 best out of 390): 0bt4g (0.17 #417, 0.05 #1466, 0.03 #3039), 0mbql (0.17 #374, 0.05 #1423, 0.03 #2996), 01f7kl (0.17 #132, 0.05 #1181, 0.03 #2754), 0k_9j (0.17 #441, 0.03 #1490, 0.03 #3063), 04mcw4 (0.17 #252, 0.03 #1301, 0.03 #2874), 07b1gq (0.15 #2099, 0.11 #4723, 0.06 #1049), 0f2sx4 (0.09 #1573, 0.02 #1480), 05dptj (0.09 #1573), 0bpbhm (0.09 #1573), 074w86 (0.09 #1573) >> Best rule #417 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 059x0w; >> query: (?x3223, 0bt4g) <- executive_produced_by(?x1048, ?x3223), award_nominee(?x3223, ?x902), ?x902 = 05qd_ >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04q5zw executive_produced_by! 05567m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 30.000 0.167 http://example.org/film/film/executive_produced_by EVAL 04q5zw executive_produced_by! 01k1k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 30.000 0.167 http://example.org/film/film/executive_produced_by #3995-0cfdd PRED entity: 0cfdd PRED relation: role! PRED expected values: 0244r8 => 72 concepts (43 used for prediction) PRED predicted values (max 10 best out of 937): 01vs4ff (0.75 #6817, 0.67 #3553, 0.62 #7752), 05qhnq (0.71 #4490, 0.67 #3560, 0.67 #3097), 06x4l_ (0.71 #4306, 0.62 #5707, 0.62 #4772), 023l9y (0.67 #2537, 0.62 #7661, 0.62 #5793), 0161sp (0.67 #3380, 0.62 #7579, 0.50 #2455), 0770cd (0.67 #3329, 0.56 #7996, 0.50 #4725), 01gx5f (0.67 #3410, 0.50 #4806, 0.44 #8077), 0197tq (0.67 #3257, 0.44 #7924, 0.40 #1869), 01wxdn3 (0.62 #7859, 0.62 #6458, 0.62 #5991), 04bpm6 (0.62 #5655, 0.57 #4254, 0.57 #3787) >> Best rule #6817 for best value: >> intensional similarity = 22 >> extensional distance = 6 >> proper extension: 02fsn; >> query: (?x5926, 01vs4ff) <- role(?x1574, ?x5926), role(?x745, ?x5926), role(?x5926, ?x2785), role(?x5926, ?x1969), role(?x5926, ?x214), role(?x5926, ?x212), role(?x10239, ?x2785), role(?x2908, ?x2785), ?x1969 = 04rzd, group(?x5926, ?x1945), ?x214 = 02pprs, role(?x74, ?x2785), role(?x1467, ?x5926), ?x745 = 01vj9c, role(?x3667, ?x5926), ?x2908 = 0161sp, ?x10239 = 01p95y0, instrumentalists(?x2785, ?x1970), ?x212 = 026t6, role(?x211, ?x1574), role(?x433, ?x1574), performance_role(?x1432, ?x1574) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #17331 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 20 *> proper extension: 0319l; *> query: (?x5926, 0244r8) <- role(?x3161, ?x5926), role(?x5926, ?x2785), role(?x5926, ?x1969), role(?x9577, ?x2785), role(?x1969, ?x2377), role(?x1969, ?x1432), ?x2377 = 01bns_, group(?x1969, ?x1929), role(?x366, ?x1969), instrumentalists(?x1969, ?x1001), role(?x1436, ?x5926), role(?x1466, ?x2785), instrumentalists(?x2785, ?x3374), ?x9577 = 01nhkxp, ?x1432 = 0395lw, ?x3161 = 01v1d8, ?x1929 = 04r1t, ?x1466 = 03bx0bm *> conf = 0.05 ranks of expected_values: 721 EVAL 0cfdd role! 0244r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 72.000 43.000 0.750 http://example.org/music/artist/track_contributions./music/track_contribution/role #3994-07tds PRED entity: 07tds PRED relation: major_field_of_study PRED expected values: 06ms6 0h5k 04g7x => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 97): 02lp1 (0.56 #3652, 0.51 #3866, 0.51 #4187), 02h40lc (0.42 #539, 0.41 #753, 0.33 #967), 01r4k (0.42 #606, 0.35 #820, 0.29 #1034), 04sh3 (0.42 #597, 0.29 #811, 0.29 #2738), 01zc2w (0.42 #594, 0.29 #808, 0.24 #1022), 0g26h (0.39 #5172, 0.38 #3242, 0.36 #6135), 06ms6 (0.35 #763, 0.33 #549, 0.33 #335), 0193x (0.35 #776, 0.33 #562, 0.29 #990), 04g7x (0.35 #809, 0.33 #595, 0.29 #1023), 01tbp (0.34 #3904, 0.34 #4225, 0.32 #3690) >> Best rule #3652 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 01x5fb; >> query: (?x4672, 02lp1) <- student(?x4672, ?x264), major_field_of_study(?x4672, ?x742), list(?x4672, ?x2197) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #763 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0dx97; 019fz; *> query: (?x4672, 06ms6) <- organizations_founded(?x4672, ?x5487), organization(?x546, ?x5487), category(?x546, ?x134) *> conf = 0.35 ranks of expected_values: 7, 9, 13 EVAL 07tds major_field_of_study 04g7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 121.000 121.000 0.559 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07tds major_field_of_study 0h5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 121.000 121.000 0.559 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07tds major_field_of_study 06ms6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 121.000 121.000 0.559 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3993-0cx7f PRED entity: 0cx7f PRED relation: parent_genre PRED expected values: 06by7 => 61 concepts (37 used for prediction) PRED predicted values (max 10 best out of 281): 06by7 (0.92 #3444, 0.89 #3116, 0.75 #4101), 03_d0 (0.72 #2784, 0.33 #171, 0.20 #824), 05r6t (0.50 #4302, 0.33 #1194, 0.29 #1357), 0xhtw (0.43 #1317, 0.33 #337, 0.25 #499), 01243b (0.42 #2474, 0.33 #27, 0.27 #4276), 0155w (0.40 #886, 0.33 #395, 0.33 #233), 0133_p (0.33 #257, 0.29 #1726, 0.20 #2379), 02yv6b (0.33 #388, 0.25 #550, 0.20 #879), 0p9xd (0.33 #423, 0.25 #585, 0.20 #914), 0190_q (0.33 #186, 0.20 #839, 0.20 #673) >> Best rule #3444 for best value: >> intensional similarity = 9 >> extensional distance = 62 >> proper extension: 017ht; >> query: (?x9063, 06by7) <- parent_genre(?x9063, ?x1380), artists(?x1380, ?x11161), artists(?x1380, ?x9074), artists(?x1380, ?x8754), artists(?x1380, ?x6986), ?x6986 = 02vgh, ?x9074 = 01k47c, ?x11161 = 018gkb, profession(?x8754, ?x131) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cx7f parent_genre 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 37.000 0.922 http://example.org/music/genre/parent_genre #3992-029_3 PRED entity: 029_3 PRED relation: influenced_by! PRED expected values: 05rx__ => 154 concepts (100 used for prediction) PRED predicted values (max 10 best out of 432): 049fgvm (0.50 #772, 0.09 #4340, 0.08 #9957), 01xwv7 (0.40 #930, 0.13 #4498, 0.12 #10115), 016_mj (0.40 #564, 0.13 #4132, 0.10 #9749), 01wp_jm (0.40 #913, 0.07 #45400, 0.07 #41827), 0126rp (0.40 #580, 0.06 #18941, 0.06 #19451), 01x4r3 (0.30 #887, 0.14 #377, 0.08 #7010), 0q5hw (0.30 #611, 0.08 #6734, 0.07 #11325), 02g8h (0.30 #515, 0.03 #6638, 0.02 #17345), 04j_gs (0.29 #417, 0.12 #1945, 0.10 #3985), 05ty4m (0.20 #517, 0.16 #2045, 0.09 #4085) >> Best rule #772 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01k9lpl; >> query: (?x4065, 049fgvm) <- influenced_by(?x2127, ?x4065), profession(?x4065, ?x1032), ?x2127 = 01j7rd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7957 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 07c0j; *> query: (?x4065, 05rx__) <- participant(?x4065, ?x6190), influenced_by(?x692, ?x4065), award(?x6190, ?x678) *> conf = 0.08 ranks of expected_values: 85 EVAL 029_3 influenced_by! 05rx__ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 154.000 100.000 0.500 http://example.org/influence/influence_node/influenced_by #3991-06y3r PRED entity: 06y3r PRED relation: company PRED expected values: 0kk9v => 110 concepts (106 used for prediction) PRED predicted values (max 10 best out of 149): 0kk9v (0.55 #1707, 0.48 #1517, 0.47 #1897), 09c7w0 (0.22 #2657, 0.19 #3605, 0.17 #2467), 030_1_ (0.18 #1352, 0.17 #1542, 0.12 #1732), 02jd_7 (0.17 #333, 0.12 #1471, 0.11 #1661), 01f9wm (0.17 #351, 0.08 #731, 0.01 #4525), 0fvppk (0.17 #334, 0.06 #1662, 0.04 #1852), 01w5m (0.12 #427, 0.08 #3652, 0.08 #2704), 03d96s (0.12 #495, 0.08 #875, 0.07 #1064), 032j_n (0.12 #1475, 0.11 #1665, 0.08 #1855), 061dn_ (0.12 #1377, 0.11 #1567, 0.08 #1757) >> Best rule #1707 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 03qncl3; >> query: (?x9105, ?x3945) <- company(?x9105, ?x3920), organizations_founded(?x9105, ?x3945), production_companies(?x148, ?x3920) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06y3r company 0kk9v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 106.000 0.548 http://example.org/people/person/employment_history./business/employment_tenure/company #3990-02mhfy PRED entity: 02mhfy PRED relation: film PRED expected values: 01n30p 0291ck => 138 concepts (77 used for prediction) PRED predicted values (max 10 best out of 889): 0k4p0 (0.34 #125179, 0.02 #58204), 0yxm1 (0.25 #2538, 0.03 #20419, 0.03 #25784), 0dqytn (0.25 #1883, 0.03 #19764, 0.02 #57313), 01738w (0.25 #1128, 0.03 #70864, 0.03 #67288), 01l_pn (0.18 #6331, 0.12 #4542, 0.07 #18847), 0sxns (0.18 #6441, 0.12 #4652, 0.07 #18957), 0gkz15s (0.12 #3688, 0.09 #5477, 0.08 #12629), 02v63m (0.12 #3753, 0.09 #5542, 0.05 #7330), 0prrm (0.12 #4436, 0.09 #6225, 0.04 #13377), 013q0p (0.12 #4382, 0.09 #6171, 0.04 #13323) >> Best rule #125179 for best value: >> intensional similarity = 3 >> extensional distance = 1269 >> proper extension: 04cy8rb; 0dky9n; >> query: (?x2046, ?x5712) <- place_of_birth(?x2046, ?x13626), nominated_for(?x2046, ?x5712), nationality(?x2046, ?x94) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #60416 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 159 *> proper extension: 06b0d2; 03wpmd; 01dy7j; 06gh0t; 01pfkw; 06hgym; 02tf1y; 02_wxh; 047jhq; *> query: (?x2046, 01n30p) <- award(?x2046, ?x1254), actor(?x7657, ?x2046), languages(?x2046, ?x254) *> conf = 0.01 ranks of expected_values: 781 EVAL 02mhfy film 0291ck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 77.000 0.342 http://example.org/film/actor/film./film/performance/film EVAL 02mhfy film 01n30p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 138.000 77.000 0.342 http://example.org/film/actor/film./film/performance/film #3989-0603qp PRED entity: 0603qp PRED relation: award_nominee PRED expected values: 0fxky3 => 93 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1082): 01y0y6 (0.83 #23410, 0.83 #23409, 0.81 #95994), 06yrj6 (0.83 #23409, 0.81 #95994, 0.81 #49163), 04pz5c (0.83 #23409, 0.81 #95994, 0.81 #49163), 0c4f4 (0.83 #23409, 0.81 #95994, 0.81 #49163), 0q5hw (0.83 #23409, 0.81 #95994, 0.81 #49163), 0fxky3 (0.83 #23409, 0.81 #95994, 0.81 #49163), 0gls4q_ (0.83 #23409, 0.81 #95994, 0.81 #49163), 0603qp (0.50 #3683, 0.50 #1341, 0.40 #39796), 0pyww (0.40 #39796, 0.27 #110045, 0.17 #3481), 01w0yrc (0.40 #39796, 0.27 #110045, 0.15 #95995) >> Best rule #23410 for best value: >> intensional similarity = 3 >> extensional distance = 197 >> proper extension: 01vvydl; 01wbgdv; 01k5t_3; 015882; 01trhmt; 01qdjm; 0gbwp; 0191h5; 0bl60p; 01wj5hp; ... >> query: (?x5643, ?x3739) <- award_nominee(?x3739, ?x5643), people(?x1050, ?x5643), role(?x3739, ?x75) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #23409 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 197 *> proper extension: 01vvydl; 01wbgdv; 01k5t_3; 015882; 01trhmt; 01qdjm; 0gbwp; 0191h5; 0bl60p; 01wj5hp; ... *> query: (?x5643, ?x495) <- award_nominee(?x3739, ?x5643), award_nominee(?x495, ?x5643), people(?x1050, ?x5643), role(?x3739, ?x75) *> conf = 0.83 ranks of expected_values: 6 EVAL 0603qp award_nominee 0fxky3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 93.000 50.000 0.832 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3988-01wgjj5 PRED entity: 01wgjj5 PRED relation: location PRED expected values: 04jpl => 142 concepts (111 used for prediction) PRED predicted values (max 10 best out of 184): 0nbfm (0.55 #36201, 0.52 #42635, 0.48 #17695), 08809 (0.33 #2178), 0s3pw (0.33 #750), 0sbv7 (0.33 #734), 04jpl (0.21 #27368, 0.21 #25757, 0.21 #21734), 030qb3t (0.20 #18582, 0.20 #51567, 0.20 #49957), 02_286 (0.17 #49911, 0.17 #41063, 0.17 #54737), 0cr3d (0.09 #11406, 0.08 #14623, 0.08 #5776), 0fpzwf (0.08 #5109, 0.08 #4305, 0.05 #7522), 021npd (0.08 #5601, 0.08 #4797, 0.05 #8014) >> Best rule #36201 for best value: >> intensional similarity = 4 >> extensional distance = 282 >> proper extension: 01j5x6; 012vct; 02hy9p; 01npcy7; >> query: (?x5883, ?x10852) <- place_of_birth(?x5883, ?x10852), participant(?x4662, ?x5883), profession(?x5883, ?x1032), type_of_union(?x5883, ?x566) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #27368 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 209 *> proper extension: 021yzs; 015010; *> query: (?x5883, 04jpl) <- nationality(?x5883, ?x1310), ?x1310 = 02jx1, profession(?x5883, ?x1032), ?x1032 = 02hrh1q *> conf = 0.21 ranks of expected_values: 5 EVAL 01wgjj5 location 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 142.000 111.000 0.546 http://example.org/people/person/places_lived./people/place_lived/location #3987-0lw_s PRED entity: 0lw_s PRED relation: category PRED expected values: 08mbj5d => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.69 #4, 0.68 #6, 0.68 #49) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 150 >> proper extension: 0k3p; 0m__z; >> query: (?x14345, 08mbj5d) <- contains(?x1023, ?x14345), jurisdiction_of_office(?x1195, ?x14345), location(?x843, ?x1023), ?x1195 = 0pqc5 >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lw_s category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.691 http://example.org/common/topic/webpage./common/webpage/category #3986-0gs973 PRED entity: 0gs973 PRED relation: language PRED expected values: 02h40lc => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 33): 02h40lc (0.93 #653, 0.92 #594, 0.91 #2209), 064_8sq (0.60 #4732, 0.30 #318, 0.25 #259), 04306rv (0.48 #301, 0.27 #182, 0.27 #420), 02bjrlw (0.36 #178, 0.35 #297, 0.33 #1), 06nm1 (0.33 #11, 0.25 #248, 0.20 #70), 06b_j (0.33 #23, 0.25 #260, 0.20 #82), 0jzc (0.20 #79, 0.18 #197, 0.09 #494), 05zjd (0.20 #85, 0.08 #263, 0.05 #381), 032f6 (0.20 #115, 0.08 #293, 0.02 #1066), 02hxcvy (0.20 #93, 0.08 #271, 0.02 #1162) >> Best rule #653 for best value: >> intensional similarity = 5 >> extensional distance = 126 >> proper extension: 09sh8k; 011yxg; 09xbpt; 0czyxs; 01k1k4; 0gtv7pk; 01ln5z; 08720; 061681; 0p9lw; ... >> query: (?x5290, 02h40lc) <- featured_film_locations(?x5290, ?x205), film(?x874, ?x5290), production_companies(?x5290, ?x2549), category(?x5290, ?x134), genre(?x5290, ?x53) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gs973 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.930 http://example.org/film/film/language #3985-06q7n PRED entity: 06q7n PRED relation: genre! PRED expected values: 023ny6 0147w8 => 40 concepts (35 used for prediction) PRED predicted values (max 10 best out of 363): 0266s9 (0.62 #760, 0.33 #227, 0.25 #493), 05f4vxd (0.50 #612, 0.50 #345, 0.19 #2666), 03_b1g (0.50 #510, 0.38 #777, 0.33 #244), 09v38qj (0.50 #487, 0.33 #221, 0.25 #754), 04p5cr (0.50 #637, 0.33 #104, 0.23 #903), 099pks (0.50 #622, 0.29 #888, 0.28 #1154), 0123qq (0.50 #745, 0.25 #478, 0.23 #1543), 02r1ysd (0.50 #646, 0.25 #379, 0.23 #912), 05p9_ql (0.50 #653, 0.25 #386, 0.23 #919), 02py9yf (0.50 #740, 0.25 #473, 0.20 #1538) >> Best rule #760 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 07s9rl0; 05p553; 02l7c8; 01z4y; 01t_vv; >> query: (?x8805, 0266s9) <- genre(?x2829, ?x8805), genre(?x2078, ?x8805), tv_program(?x1056, ?x2829), category(?x2829, ?x134), nominated_for(?x3809, ?x2829), award(?x2829, ?x588), ?x134 = 08mbj5d, award_winner(?x2829, ?x10359), award_winner(?x3809, ?x415), ?x2078 = 03ln8b, honored_for(?x2751, ?x2829) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #723 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 6 *> proper extension: 07s9rl0; 05p553; 02l7c8; 01z4y; 01t_vv; *> query: (?x8805, 023ny6) <- genre(?x2829, ?x8805), genre(?x2078, ?x8805), tv_program(?x1056, ?x2829), category(?x2829, ?x134), nominated_for(?x3809, ?x2829), award(?x2829, ?x588), ?x134 = 08mbj5d, award_winner(?x2829, ?x10359), award_winner(?x3809, ?x415), ?x2078 = 03ln8b, honored_for(?x2751, ?x2829) *> conf = 0.25 ranks of expected_values: 68, 180 EVAL 06q7n genre! 0147w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 40.000 35.000 0.625 http://example.org/tv/tv_program/genre EVAL 06q7n genre! 023ny6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 40.000 35.000 0.625 http://example.org/tv/tv_program/genre #3984-05h95s PRED entity: 05h95s PRED relation: genre PRED expected values: 03k9fj 01htzx => 94 concepts (92 used for prediction) PRED predicted values (max 10 best out of 115): 07s9rl0 (0.87 #3055, 0.76 #2819, 0.68 #703), 01z4y (0.75 #719, 0.68 #1110, 0.64 #1972), 06n90 (0.45 #1262, 0.33 #325, 0.27 #1184), 0jxy (0.44 #342, 0.23 #1360, 0.22 #1201), 0c4xc (0.42 #1995, 0.41 #2231, 0.41 #1133), 03k9fj (0.39 #323, 0.33 #167, 0.30 #2436), 095bb (0.33 #1365, 0.33 #191, 0.32 #1206), 06nbt (0.33 #175, 0.26 #1112, 0.26 #1349), 02kdv5l (0.28 #315, 0.16 #1252, 0.08 #1174), 01htzx (0.26 #1265, 0.26 #2441, 0.26 #1346) >> Best rule #3055 for best value: >> intensional similarity = 5 >> extensional distance = 148 >> proper extension: 070ltt; 07qht4; >> query: (?x7566, 07s9rl0) <- genre(?x7566, ?x1510), titles(?x1510, ?x1734), genre(?x4669, ?x1510), ?x1734 = 029zqn, ?x4669 = 019kyn >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #323 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 16 *> proper extension: 088tp3; *> query: (?x7566, 03k9fj) <- genre(?x7566, ?x10159), genre(?x7566, ?x2540), genre(?x7566, ?x1510), ?x1510 = 01hmnh, genre(?x11599, ?x10159), ?x2540 = 0hcr, ?x11599 = 019g8j *> conf = 0.39 ranks of expected_values: 6, 10 EVAL 05h95s genre 01htzx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 94.000 92.000 0.873 http://example.org/tv/tv_program/genre EVAL 05h95s genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 94.000 92.000 0.873 http://example.org/tv/tv_program/genre #3983-070zc PRED entity: 070zc PRED relation: adjoins! PRED expected values: 07nf6 => 273 concepts (137 used for prediction) PRED predicted values (max 10 best out of 607): 06bnz (0.60 #4764, 0.25 #40589, 0.25 #3985), 06rf7 (0.50 #2795, 0.25 #2015, 0.16 #17620), 04p0c (0.46 #12656, 0.25 #40589, 0.25 #2515), 0163v (0.40 #4792, 0.25 #40589, 0.25 #4013), 03pn9 (0.40 #4804, 0.13 #99970, 0.07 #65717), 04g5k (0.40 #4937, 0.06 #37725, 0.06 #46319), 0d060g (0.33 #9371, 0.26 #16393, 0.19 #23421), 05rgl (0.29 #6337, 0.13 #14920, 0.11 #16482), 0f8l9c (0.28 #15640, 0.25 #3158, 0.23 #11740), 09krp (0.25 #40589, 0.25 #2719, 0.25 #1939) >> Best rule #4764 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 04gzd; 07t21; 0163v; >> query: (?x10524, 06bnz) <- contains(?x10524, ?x5560), adjoins(?x10524, ?x456), locations(?x12777, ?x10524), ?x456 = 05qhw >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #40589 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 05rh2; *> query: (?x10524, ?x1646) <- adjoins(?x10524, ?x7049), country(?x10524, ?x1264), adjoins(?x7049, ?x1646), administrative_division(?x5560, ?x10524) *> conf = 0.25 ranks of expected_values: 17 EVAL 070zc adjoins! 07nf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 273.000 137.000 0.600 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #3982-0kvgxk PRED entity: 0kvgxk PRED relation: film_crew_role PRED expected values: 02r96rf 0ch6mp2 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 26): 0ch6mp2 (0.79 #1065, 0.74 #1951, 0.73 #652), 02r96rf (0.69 #70, 0.68 #1061, 0.63 #1947), 01vx2h (0.38 #78, 0.34 #1069, 0.31 #1955), 01pvkk (0.33 #249, 0.31 #1070, 0.30 #657), 02ynfr (0.31 #83, 0.19 #1074, 0.17 #321), 02rh1dz (0.25 #9, 0.20 #43, 0.12 #1068), 02vs3x5 (0.20 #56, 0.07 #158, 0.06 #1081), 015h31 (0.15 #76, 0.09 #998, 0.08 #1953), 01xy5l_ (0.12 #1072, 0.11 #1958, 0.10 #149), 089g0h (0.12 #1077, 0.12 #1963, 0.11 #256) >> Best rule #1065 for best value: >> intensional similarity = 3 >> extensional distance = 664 >> proper extension: 0fq27fp; >> query: (?x2085, 0ch6mp2) <- film_crew_role(?x2085, ?x137), ?x137 = 09zzb8, currency(?x2085, ?x170) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0kvgxk film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.785 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0kvgxk film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.785 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3981-0q9kd PRED entity: 0q9kd PRED relation: award_winner! PRED expected values: 026kq4q => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 135): 09p30_ (0.12 #85, 0.09 #5923, 0.02 #3187), 0bzknt (0.12 #82, 0.09 #5923, 0.02 #8744), 0bzlrh (0.12 #104, 0.02 #8744, 0.01 #1373), 092t4b (0.09 #5923, 0.06 #52, 0.04 #3154), 092_25 (0.09 #5923, 0.06 #72, 0.03 #2469), 09p3h7 (0.09 #5923, 0.06 #71, 0.03 #2045), 0bx6zs (0.09 #5923, 0.06 #127, 0.02 #2665), 073h5b (0.09 #5923, 0.06 #134, 0.02 #8744), 03gyp30 (0.09 #5923, 0.05 #2514, 0.04 #3078), 09pnw5 (0.09 #5923, 0.04 #385, 0.04 #1372) >> Best rule #85 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 059fjj; >> query: (?x71, 09p30_) <- profession(?x71, ?x319), award_nominee(?x4360, ?x71), ?x4360 = 0f502 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #46 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 059fjj; *> query: (?x71, 026kq4q) <- profession(?x71, ?x319), award_nominee(?x4360, ?x71), ?x4360 = 0f502 *> conf = 0.06 ranks of expected_values: 23 EVAL 0q9kd award_winner! 026kq4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 86.000 86.000 0.118 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3980-03h2c PRED entity: 03h2c PRED relation: organization PRED expected values: 04k4l => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 18): 04k4l (0.56 #177, 0.44 #26, 0.41 #226), 01rz1 (0.45 #67, 0.43 #111, 0.40 #89), 0b6css (0.41 #98, 0.40 #120, 0.39 #76), 041288 (0.37 #790, 0.36 #878, 0.35 #658), 0_2v (0.32 #1524, 0.31 #313, 0.30 #512), 0gkjy (0.32 #1524, 0.27 #73, 0.27 #95), 0j7v_ (0.32 #1524, 0.27 #492, 0.26 #779), 018cqq (0.32 #1524, 0.25 #233, 0.22 #321), 085h1 (0.32 #1524, 0.23 #333, 0.04 #78), 02jxk (0.32 #1524, 0.18 #224, 0.18 #312) >> Best rule #177 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 059f4; 04rrx; 078lk; 05bcl; 0694j; 0cwx_; 0nj07; 04_1l0v; 04fh3; 0fhnf; ... >> query: (?x3720, ?x127) <- adjoins(?x4954, ?x3720), time_zones(?x3720, ?x1638), organization(?x4954, ?x127) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03h2c organization 04k4l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.561 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #3979-0dw3l PRED entity: 0dw3l PRED relation: profession PRED expected values: 0dz3r => 113 concepts (70 used for prediction) PRED predicted values (max 10 best out of 59): 039v1 (0.56 #468, 0.43 #2650, 0.41 #613), 016z4k (0.54 #4666, 0.47 #1311, 0.46 #2621), 0dz3r (0.50 #3934, 0.49 #1455, 0.48 #1600), 01c72t (0.36 #747, 0.34 #1620, 0.32 #1475), 01d_h8 (0.30 #7731, 0.26 #9628, 0.25 #7150), 0dxtg (0.27 #7738, 0.24 #9635, 0.24 #7157), 0n1h (0.24 #1318, 0.23 #591, 0.23 #2482), 03gjzk (0.21 #7739, 0.19 #7158, 0.18 #9056), 0fnpj (0.20 #1946, 0.20 #1073, 0.18 #637), 02jknp (0.19 #7733, 0.17 #9630, 0.15 #7152) >> Best rule #468 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 01vd7hn; >> query: (?x8048, 039v1) <- instrumentalists(?x2048, ?x8048), instrumentalists(?x1969, ?x8048), nationality(?x8048, ?x94), ?x2048 = 018j2, ?x1969 = 04rzd >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #3934 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 317 *> proper extension: 01p9hgt; 09mq4m; 02pzc4; 023l9y; 01l4g5; 01wxdn3; 06p03s; 023slg; *> query: (?x8048, 0dz3r) <- profession(?x8048, ?x2225), artists(?x302, ?x8048), role(?x8048, ?x212), profession(?x806, ?x2225), ?x806 = 03qd_ *> conf = 0.50 ranks of expected_values: 3 EVAL 0dw3l profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 70.000 0.556 http://example.org/people/person/profession #3978-01wl38s PRED entity: 01wl38s PRED relation: artists! PRED expected values: 016clz => 129 concepts (68 used for prediction) PRED predicted values (max 10 best out of 250): 06by7 (0.79 #15864, 0.68 #17417, 0.61 #7469), 017_qw (0.55 #6268, 0.52 #5958, 0.51 #5337), 064t9 (0.51 #18342, 0.48 #2807, 0.45 #10570), 016clz (0.50 #936, 0.50 #626, 0.45 #316), 05r6t (0.50 #704, 0.21 #1014, 0.18 #394), 03lty (0.50 #7476, 0.33 #650, 0.23 #15871), 02yv6b (0.45 #411, 0.29 #7547, 0.17 #6925), 05w3f (0.45 #349, 0.25 #7485, 0.20 #6863), 059kh (0.43 #980, 0.33 #670, 0.12 #6874), 0cx7f (0.36 #451, 0.25 #761, 0.15 #7587) >> Best rule #15864 for best value: >> intensional similarity = 3 >> extensional distance = 491 >> proper extension: 012vm6; 0qmny; 0qmpd; 0pqp3; >> query: (?x565, 06by7) <- artists(?x1000, ?x565), artists(?x1000, ?x442), ?x442 = 01t_xp_ >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #936 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 0dw4g; 016lmg; *> query: (?x565, 016clz) <- award_nominee(?x4239, ?x565), artists(?x2491, ?x565), ?x2491 = 011j5x *> conf = 0.50 ranks of expected_values: 4 EVAL 01wl38s artists! 016clz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 129.000 68.000 0.789 http://example.org/music/genre/artists #3977-02bg55 PRED entity: 02bg55 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 35): 09zzb8 (0.81 #149, 0.78 #891, 0.75 #186), 0ch6mp2 (0.80 #897, 0.73 #563, 0.73 #2213), 01vx2h (0.46 #456, 0.42 #827, 0.41 #902), 0dxtw (0.41 #567, 0.39 #455, 0.39 #789), 01pvkk (0.28 #2257, 0.28 #532, 0.28 #2408), 02rh1dz (0.24 #158, 0.21 #195, 0.19 #454), 02ynfr (0.21 #647, 0.20 #907, 0.19 #758), 0215hd (0.17 #798, 0.15 #576, 0.14 #910), 01xy5l_ (0.16 #905, 0.13 #3072, 0.12 #793), 0d2b38 (0.14 #917, 0.13 #3072, 0.12 #805) >> Best rule #149 for best value: >> intensional similarity = 6 >> extensional distance = 19 >> proper extension: 0gs973; >> query: (?x6520, 09zzb8) <- film(?x2549, ?x6520), titles(?x3613, ?x6520), ?x2549 = 024rgt, executive_produced_by(?x6520, ?x7830), genre(?x6520, ?x1013), genre(?x253, ?x3613) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #897 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 243 *> proper extension: 0kv9d3; *> query: (?x6520, 0ch6mp2) <- language(?x6520, ?x254), executive_produced_by(?x6520, ?x7830), country(?x6520, ?x94), genre(?x6520, ?x812), film_crew_role(?x6520, ?x468), ?x468 = 02r96rf *> conf = 0.80 ranks of expected_values: 2 EVAL 02bg55 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.810 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3976-0r2l7 PRED entity: 0r2l7 PRED relation: time_zones PRED expected values: 02lcqs => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 10): 02lcqs (0.78 #133, 0.78 #40, 0.78 #31), 02hcv8 (0.46 #501, 0.45 #318, 0.45 #344), 02fqwt (0.21 #485, 0.19 #694, 0.19 #264), 02hczc (0.21 #485, 0.16 #955, 0.15 #1125), 042g7t (0.21 #485, 0.16 #955, 0.15 #1125), 02lcrv (0.21 #485, 0.16 #955, 0.15 #1125), 02llzg (0.09 #150, 0.08 #397, 0.07 #332), 03bdv (0.06 #282, 0.06 #360, 0.06 #204), 03plfd (0.02 #898, 0.01 #638, 0.01 #169), 05jphn (0.01 #471) >> Best rule #133 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 013m43; >> query: (?x1661, ?x2950) <- contains(?x578, ?x1661), county(?x5783, ?x578), citytown(?x7350, ?x1661), time_zones(?x578, ?x2950) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r2l7 time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.780 http://example.org/location/location/time_zones #3975-018ctl PRED entity: 018ctl PRED relation: participating_countries PRED expected values: 03rk0 03spz => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 830): 06c1y (0.72 #618, 0.71 #121, 0.70 #371), 06t8v (0.72 #618, 0.71 #121, 0.70 #371), 0jhd (0.72 #618, 0.71 #121, 0.70 #371), 05sb1 (0.72 #618, 0.71 #121, 0.69 #372), 01ls2 (0.72 #618, 0.71 #121, 0.69 #372), 0bjv6 (0.72 #618, 0.71 #121, 0.69 #372), 056vv (0.72 #618, 0.71 #121, 0.69 #372), 04w58 (0.72 #618, 0.71 #121, 0.69 #372), 04j53 (0.72 #618, 0.71 #121, 0.69 #372), 01ppq (0.72 #618, 0.71 #121, 0.69 #372) >> Best rule #618 for best value: >> intensional similarity = 63 >> extensional distance = 2 >> proper extension: 09x3r; >> query: (?x784, ?x87) <- olympics(?x2188, ?x784), olympics(?x512, ?x784), olympics(?x344, ?x784), olympics(?x279, ?x784), olympics(?x87, ?x784), participating_countries(?x784, ?x2629), participating_countries(?x784, ?x1203), participating_countries(?x784, ?x172), jurisdiction_of_office(?x182, ?x2188), film_release_region(?x249, ?x2188), ?x172 = 0154j, country(?x4673, ?x1203), film_release_region(?x9002, ?x344), film_release_region(?x6882, ?x344), film_release_region(?x6235, ?x344), film_release_region(?x3784, ?x344), film_release_region(?x2746, ?x344), film_release_region(?x1150, ?x344), film_release_region(?x781, ?x344), film_release_region(?x409, ?x344), country(?x3309, ?x344), contains(?x7273, ?x1203), country(?x520, ?x2188), country(?x471, ?x2188), film_release_region(?x9652, ?x1203), film_release_region(?x3423, ?x1203), ?x9652 = 0ddbjy4, ?x471 = 02vx4, ?x6235 = 05b6rdt, ?x1150 = 0h3xztt, ?x512 = 07ssc, medal(?x2188, ?x422), organization(?x344, ?x127), ?x3784 = 0bmhvpr, film_release_region(?x9432, ?x279), film_release_region(?x8176, ?x279), film_release_region(?x8137, ?x279), film_release_region(?x5162, ?x279), film_release_region(?x4041, ?x279), ?x8176 = 0gvvm6l, featured_film_locations(?x1064, ?x279), ?x4041 = 0gy2y8r, nationality(?x8167, ?x279), ?x2746 = 04f52jw, ?x781 = 0gkz15s, ?x4673 = 07jbh, ?x8137 = 0gtx63s, ?x9432 = 0gvt53w, award_winner(?x1112, ?x8167), contains(?x279, ?x481), ?x409 = 0gtv7pk, jurisdiction_of_office(?x265, ?x1203), ?x3423 = 09g7vfw, ?x9002 = 0ndsl1x, award_nominee(?x848, ?x8167), ?x6882 = 043tvp3, countries_spoken_in(?x393, ?x279), ?x127 = 02vk52z, country(?x136, ?x279), official_language(?x2629, ?x2890), ?x520 = 01dys, jurisdiction_of_office(?x3119, ?x279), ?x5162 = 0j3d9tn >> conf = 0.72 => this is the best rule for 13 predicted values *> Best rule #371 for first EXPECTED value: *> intensional similarity = 60 *> extensional distance = 1 *> proper extension: 0kbws; *> query: (?x784, ?x1497) <- olympics(?x3277, ?x784), olympics(?x2346, ?x784), olympics(?x2188, ?x784), olympics(?x1917, ?x784), olympics(?x1603, ?x784), olympics(?x1264, ?x784), olympics(?x512, ?x784), olympics(?x410, ?x784), olympics(?x291, ?x784), ?x2188 = 0163v, sports(?x784, ?x11927), ?x1603 = 06bnz, country(?x11927, ?x1497), country(?x11927, ?x1471), medal(?x784, ?x422), participating_countries(?x784, ?x3730), participating_countries(?x784, ?x1558), participating_countries(?x784, ?x126), ?x410 = 01ls2, ?x2346 = 0d05w3, ?x512 = 07ssc, ?x1471 = 07t21, ?x3730 = 03shp, sports(?x1277, ?x11927), olympics(?x910, ?x1277), film_release_region(?x6931, ?x1558), film_release_region(?x6621, ?x1558), film_release_region(?x6527, ?x1558), film_release_region(?x4041, ?x1558), film_release_region(?x3757, ?x1558), film_release_region(?x2394, ?x1558), film_release_region(?x1498, ?x1558), film_release_region(?x781, ?x1558), olympics(?x1558, ?x391), ?x126 = 0160w, member_states(?x2106, ?x1558), country(?x6733, ?x1558), country(?x1557, ?x1558), film_release_region(?x80, ?x3277), olympics(?x3277, ?x1931), ?x1917 = 01p1v, country(?x6897, ?x1558), taxonomy(?x1558, ?x939), ?x6621 = 0h63gl9, ?x2394 = 0661ql3, ?x1557 = 07bs0, currency(?x3277, ?x170), ?x291 = 0h3y, ?x6931 = 09v3jyg, ?x6527 = 0gfh84d, ?x4041 = 0gy2y8r, ?x1498 = 04jkpgv, administrative_area_type(?x1558, ?x2792), ?x781 = 0gkz15s, ?x3757 = 02vr3gz, ?x1264 = 0345h, ?x6733 = 01sgl, countries_spoken_in(?x90, ?x3277), ?x6897 = 02nx2k, ?x90 = 02bjrlw *> conf = 0.70 ranks of expected_values: 15, 18 EVAL 018ctl participating_countries 03spz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 23.000 23.000 0.723 http://example.org/olympics/olympic_games/participating_countries EVAL 018ctl participating_countries 03rk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 23.000 23.000 0.723 http://example.org/olympics/olympic_games/participating_countries #3974-02fy0z PRED entity: 02fy0z PRED relation: student PRED expected values: 0sw62 => 58 concepts (46 used for prediction) PRED predicted values (max 10 best out of 819): 0ff3y (0.12 #2066, 0.03 #6244, 0.03 #8334), 0405l (0.08 #1850, 0.03 #6028, 0.03 #8118), 04t969 (0.08 #1278, 0.03 #5456, 0.02 #3367), 03ft8 (0.08 #256, 0.03 #6524, 0.02 #4434), 02cyfz (0.08 #332, 0.02 #4510, 0.01 #6600), 02vntj (0.08 #701, 0.02 #4879, 0.01 #6969), 03c6v3 (0.08 #1825, 0.02 #8093, 0.01 #6003), 01pqy_ (0.08 #896, 0.02 #7164, 0.01 #5074), 024y6w (0.08 #1450, 0.02 #9807, 0.01 #5628), 09v6tz (0.08 #1340, 0.01 #5518, 0.01 #7608) >> Best rule #2066 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 078bz; 03bmmc; 019c57; >> query: (?x3149, 0ff3y) <- institution(?x8398, ?x3149), institution(?x1368, ?x3149), ?x1368 = 014mlp, ?x8398 = 028dcg >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #1717 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 078bz; 03bmmc; 019c57; *> query: (?x3149, 0sw62) <- institution(?x8398, ?x3149), institution(?x1368, ?x3149), ?x1368 = 014mlp, ?x8398 = 028dcg *> conf = 0.04 ranks of expected_values: 248 EVAL 02fy0z student 0sw62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 58.000 46.000 0.115 http://example.org/education/educational_institution/students_graduates./education/education/student #3973-03m1n PRED entity: 03m1n PRED relation: school PRED expected values: 0trv => 67 concepts (53 used for prediction) PRED predicted values (max 10 best out of 667): 06pwq (0.91 #189, 0.89 #188, 0.73 #187), 07w0v (0.91 #189, 0.89 #188, 0.73 #187), 021w0_ (0.91 #189, 0.89 #188, 0.73 #187), 012vwb (0.91 #189, 0.89 #188, 0.73 #187), 01vs5c (0.91 #189, 0.89 #188, 0.73 #187), 01lnyf (0.91 #189, 0.89 #188, 0.73 #187), 01jq0j (0.91 #189, 0.89 #188, 0.73 #187), 02gr81 (0.91 #189, 0.89 #188, 0.73 #187), 0187nd (0.91 #189, 0.89 #188, 0.73 #187), 07t90 (0.91 #189, 0.89 #188, 0.73 #187) >> Best rule #189 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 0jmj7; >> query: (?x11361, ?x546) <- team(?x2010, ?x11361), school(?x11361, ?x7596), school(?x11361, ?x7338), school(?x11361, ?x2948), draft(?x11361, ?x4779), draft(?x11361, ?x3334), ?x7596 = 012mzw, draft(?x2174, ?x4779), school(?x4779, ?x388), ?x2948 = 0j_sncb, school(?x3334, ?x1011), ?x7338 = 01qgr3, team(?x8110, ?x2174), ?x1011 = 07w0v, school(?x2174, ?x546) >> conf = 0.91 => this is the best rule for 85 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 30 EVAL 03m1n school 0trv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 67.000 53.000 0.906 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #3972-023p29 PRED entity: 023p29 PRED relation: artist! PRED expected values: 03rhqg => 131 concepts (96 used for prediction) PRED predicted values (max 10 best out of 130): 0g768 (0.16 #1002, 0.15 #311, 0.14 #1278), 03rhqg (0.16 #2915, 0.15 #2086, 0.15 #3606), 01w40h (0.15 #303, 0.10 #2513, 0.09 #2098), 0n85g (0.15 #337, 0.09 #2961, 0.09 #3652), 043g7l (0.15 #306, 0.08 #4727, 0.08 #5279), 0fb0v (0.15 #283, 0.08 #3598, 0.08 #2493), 02p11jq (0.13 #2499, 0.11 #2084, 0.10 #4986), 0181dw (0.12 #4737, 0.12 #316, 0.11 #7090), 01trtc (0.12 #624, 0.09 #1038, 0.09 #5320), 01clyr (0.12 #585, 0.08 #4729, 0.07 #7774) >> Best rule #1002 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 01nn6c; 0fpj4lx; 0bkg4; 027dpx; 01386_; 021r7r; 0jsg0m; >> query: (?x10209, 0g768) <- student(?x7545, ?x10209), currency(?x10209, ?x170), artist(?x2299, ?x10209) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #2915 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 159 *> proper extension: 0fp_v1x; 01x66d; 0ftps; 01wz3cx; 01wsl7c; 016ntp; 01vxlbm; 01sb5r; 01l4g5; 094xh; ... *> query: (?x10209, 03rhqg) <- student(?x7545, ?x10209), artist(?x2299, ?x10209), award(?x10209, ?x1361) *> conf = 0.16 ranks of expected_values: 2 EVAL 023p29 artist! 03rhqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 96.000 0.156 http://example.org/music/record_label/artist #3971-036hf4 PRED entity: 036hf4 PRED relation: profession PRED expected values: 02hrh1q => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 69): 02hrh1q (0.90 #765, 0.90 #7970, 0.89 #2416), 01d_h8 (0.56 #156, 0.46 #3307, 0.44 #5257), 0dxtg (0.40 #164, 0.30 #3315, 0.30 #5265), 09jwl (0.38 #320, 0.35 #6303, 0.33 #5552), 0d1pc (0.37 #7655, 0.33 #52, 0.29 #802), 03gjzk (0.36 #166, 0.33 #3317, 0.32 #5267), 0dz3r (0.35 #6303, 0.33 #5552, 0.33 #10359), 039v1 (0.35 #6303, 0.33 #5552, 0.33 #10359), 05vyk (0.35 #6303, 0.33 #5552, 0.33 #10359), 018gz8 (0.32 #168, 0.29 #8856, 0.20 #5870) >> Best rule #765 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 018db8; 01l2fn; 011zd3; 01trhmt; 02js6_; 0dvmd; 02wb6yq; 01z0rcq; 07cjqy; 01wgcvn; ... >> query: (?x9084, 02hrh1q) <- participant(?x3503, ?x9084), friend(?x3503, ?x1896), vacationer(?x151, ?x9084) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 036hf4 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.902 http://example.org/people/person/profession #3970-0czp_ PRED entity: 0czp_ PRED relation: award! PRED expected values: 0gt14 => 49 concepts (18 used for prediction) PRED predicted values (max 10 best out of 991): 05sbv3 (0.67 #3029, 0.23 #4053, 0.12 #6101), 0hfzr (0.50 #2464, 0.30 #5536, 0.19 #3488), 0209hj (0.50 #2110, 0.26 #3134, 0.25 #63), 017jd9 (0.50 #2511, 0.26 #3535, 0.23 #5583), 04v8x9 (0.50 #2085, 0.26 #3109, 0.15 #5157), 0bmhn (0.50 #2986, 0.25 #939, 0.23 #4010), 07xtqq (0.50 #2079, 0.21 #5151, 0.16 #3103), 0ywrc (0.50 #2356, 0.19 #3380, 0.18 #5428), 0cq806 (0.50 #2909, 0.19 #3933, 0.15 #5981), 0jqj5 (0.50 #2571, 0.17 #5643, 0.16 #3595) >> Best rule #3029 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 019f4v; 0gq9h; 0gs9p; 0gqz2; >> query: (?x8153, 05sbv3) <- award(?x8333, ?x8153), award_winner(?x8153, ?x4477), ?x8333 = 0l6wj, nominated_for(?x8153, ?x3003), ceremony(?x8153, ?x3029) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1008 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0g9wd99; *> query: (?x8153, 0gt14) <- award(?x10883, ?x8153), award(?x4808, ?x8153), award_winner(?x8153, ?x4477), ?x4808 = 04107, music(?x6604, ?x10883) *> conf = 0.25 ranks of expected_values: 86 EVAL 0czp_ award! 0gt14 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 49.000 18.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #3969-03f5spx PRED entity: 03f5spx PRED relation: people! PRED expected values: 0x67 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 34): 0x67 (0.47 #241, 0.40 #318, 0.31 #626), 02w7gg (0.13 #156, 0.05 #2851, 0.05 #3929), 041rx (0.13 #2930, 0.12 #697, 0.12 #1544), 0xnvg (0.10 #706, 0.07 #1553, 0.07 #1245), 033tf_ (0.09 #700, 0.09 #1547, 0.08 #1239), 0432mrk (0.07 #226, 0.01 #1150), 07hwkr (0.04 #1552, 0.04 #1244, 0.04 #1167), 02ctzb (0.04 #1555, 0.03 #1247, 0.03 #1170), 01qhm_ (0.04 #699, 0.03 #1238, 0.03 #1161), 07bch9 (0.03 #2872, 0.03 #4643, 0.03 #4720) >> Best rule #241 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 01x1cn2; 01wz_ml; 015x1f; 015xp4; 01qgry; 01wrcxr; 0flpy; 01vsksr; 03f3_p3; 01vtg4q; ... >> query: (?x959, 0x67) <- artists(?x3319, ?x959), location(?x959, ?x2673), ?x3319 = 06j6l >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f5spx people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.471 http://example.org/people/ethnicity/people #3968-04jwp PRED entity: 04jwp PRED relation: nationality PRED expected values: 02jx1 => 142 concepts (141 used for prediction) PRED predicted values (max 10 best out of 56): 09c7w0 (0.74 #9118, 0.73 #3407, 0.73 #9418), 02jx1 (0.60 #1536, 0.56 #2538, 0.56 #2838), 07ssc (0.51 #1117, 0.38 #1918, 0.33 #2820), 0345h (0.27 #333, 0.25 #3006, 0.24 #733), 0f8l9c (0.25 #3006, 0.25 #224, 0.23 #3608), 06bnz (0.25 #3006, 0.23 #443, 0.23 #3608), 0cdbq (0.25 #3006, 0.23 #3608, 0.12 #5112), 03rt9 (0.23 #3608, 0.16 #3909, 0.15 #515), 03rjj (0.20 #107, 0.20 #2404, 0.16 #3909), 03b79 (0.20 #55, 0.12 #257, 0.09 #357) >> Best rule #9118 for best value: >> intensional similarity = 4 >> extensional distance = 1008 >> proper extension: 0lbj1; 05m63c; 09fqtq; 0lzb8; 0146pg; 08wq0g; 016khd; 01wbgdv; 01yb09; 031zkw; ... >> query: (?x5912, 09c7w0) <- type_of_union(?x5912, ?x566), profession(?x5912, ?x3746), student(?x11614, ?x5912), currency(?x11614, ?x1099) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1536 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 021yzs; 03hh89; 01m7f5r; 01qn8k; 03z0l6; 01vh3r; 0dszr0; *> query: (?x5912, 02jx1) <- profession(?x5912, ?x3746), location(?x5912, ?x362), ?x362 = 04jpl, student(?x2999, ?x5912) *> conf = 0.60 ranks of expected_values: 2 EVAL 04jwp nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 142.000 141.000 0.743 http://example.org/people/person/nationality #3967-015qq1 PRED entity: 015qq1 PRED relation: award_winner! PRED expected values: 030p35 => 122 concepts (103 used for prediction) PRED predicted values (max 10 best out of 218): 030p35 (0.47 #25026, 0.43 #69386, 0.43 #48914), 027pfb2 (0.36 #25025, 0.33 #29575, 0.29 #23887), 05jyb2 (0.36 #25025, 0.33 #29575, 0.29 #23887), 026y3cf (0.36 #25025, 0.33 #29575, 0.29 #23887), 08l0x2 (0.36 #25025, 0.33 #29575, 0.29 #23887), 09lxv9 (0.18 #89842, 0.10 #92115, 0.10 #89841), 07ghq (0.18 #89842, 0.10 #92115, 0.10 #70523), 03hj3b3 (0.11 #212, 0.08 #2489, 0.07 #3627), 07g1sm (0.11 #787, 0.02 #12167, 0.01 #13304), 025scjj (0.11 #1004, 0.02 #12384) >> Best rule #25026 for best value: >> intensional similarity = 4 >> extensional distance = 272 >> proper extension: 03h3vtz; >> query: (?x11380, ?x4639) <- actor(?x3725, ?x11380), nominated_for(?x11380, ?x4639), film(?x11380, ?x3524), program_creator(?x4639, ?x6913) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015qq1 award_winner! 030p35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 103.000 0.473 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #3966-06x58 PRED entity: 06x58 PRED relation: film PRED expected values: 03nqnnk 0640m69 => 87 concepts (62 used for prediction) PRED predicted values (max 10 best out of 911): 0p9lw (0.65 #24951, 0.49 #58818, 0.43 #58817), 0cs134 (0.65 #24951, 0.43 #58817, 0.41 #76645), 014lc_ (0.09 #2, 0.08 #1784, 0.02 #10694), 01qb5d (0.09 #138, 0.04 #1920, 0.01 #5484), 06gb1w (0.09 #733, 0.04 #4297, 0.02 #23901), 0d90m (0.09 #8, 0.01 #5354, 0.01 #8918), 06zn2v2 (0.09 #738, 0.01 #6084), 0cd2vh9 (0.09 #251, 0.01 #5597), 0422v0 (0.09 #1776), 076xkps (0.09 #1498) >> Best rule #24951 for best value: >> intensional similarity = 3 >> extensional distance = 422 >> proper extension: 03zqc1; 06jzh; 04shbh; 019_1h; 01hkhq; 02j9lm; 01438g; 02nwxc; 036hf4; 01bh6y; >> query: (?x1880, ?x994) <- participant(?x286, ?x1880), nominated_for(?x1880, ?x994), award(?x1880, ?x154) >> conf = 0.65 => this is the best rule for 2 predicted values *> Best rule #2803 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 22 *> proper extension: 03j90; *> query: (?x1880, 03nqnnk) <- notable_people_with_this_condition(?x1502, ?x1880), ?x1502 = 029sk *> conf = 0.04 ranks of expected_values: 39 EVAL 06x58 film 0640m69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 62.000 0.647 http://example.org/film/actor/film./film/performance/film EVAL 06x58 film 03nqnnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 87.000 62.000 0.647 http://example.org/film/actor/film./film/performance/film #3965-079dy PRED entity: 079dy PRED relation: gender PRED expected values: 05zppz => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #59, 0.84 #75, 0.84 #35), 02zsn (0.47 #97, 0.28 #127, 0.26 #183) >> Best rule #59 for best value: >> intensional similarity = 6 >> extensional distance = 34 >> proper extension: 019z7q; 0yfp; 03ft8; 01vsl3_; 032l1; 034bs; 014z8v; 0klw; 0282x; 01v9724; ... >> query: (?x12920, 05zppz) <- nationality(?x12920, ?x4092), religion(?x12920, ?x492), type_of_union(?x12920, ?x566), people(?x12672, ?x12920), profession(?x12920, ?x353), ?x353 = 0cbd2 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 079dy gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.861 http://example.org/people/person/gender #3964-04z257 PRED entity: 04z257 PRED relation: currency PRED expected values: 09nqf => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.86 #99, 0.83 #148, 0.81 #141), 01nv4h (0.26 #688, 0.20 #30, 0.12 #596), 088n7 (0.26 #688, 0.12 #596), 02l6h (0.12 #596, 0.02 #298, 0.02 #375), 02gsvk (0.12 #596, 0.02 #377, 0.01 #69), 0kz1h (0.12 #596, 0.01 #82, 0.01 #96) >> Best rule #99 for best value: >> intensional similarity = 7 >> extensional distance = 98 >> proper extension: 01gglm; >> query: (?x3612, 09nqf) <- executive_produced_by(?x3612, ?x4060), film(?x11684, ?x3612), film(?x4015, ?x3612), film_release_distribution_medium(?x3612, ?x81), films(?x9516, ?x3612), award_nominee(?x4015, ?x1194), award(?x11684, ?x1921) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04z257 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.860 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #3963-03kx49 PRED entity: 03kx49 PRED relation: film! PRED expected values: 021b_ => 44 concepts (35 used for prediction) PRED predicted values (max 10 best out of 960): 024bbl (0.33 #836, 0.04 #9104, 0.03 #17372), 016_mj (0.33 #294, 0.03 #8562, 0.03 #10629), 0pz91 (0.33 #211, 0.03 #8479, 0.02 #25018), 06t74h (0.33 #696, 0.03 #8964, 0.02 #17232), 063g7l (0.33 #1882, 0.01 #10150, 0.01 #26689), 04tnqn (0.33 #1640, 0.01 #9908, 0.01 #18176), 016pns (0.33 #501, 0.01 #8769, 0.01 #17037), 01rr9f (0.33 #80, 0.01 #8348, 0.01 #16616), 03dpqd (0.17 #4962, 0.04 #7029, 0.03 #9096), 06lht1 (0.17 #2955, 0.02 #29831, 0.01 #36033) >> Best rule #836 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 095z4q; >> query: (?x7723, 024bbl) <- film(?x4832, ?x7723), film(?x8081, ?x7723), ?x8081 = 02l3_5 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #22445 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 140 *> proper extension: 014zcr; 0bxtg; 0pz7h; 0tc7; 0c6qh; 0c9c0; 046lt; 0154qm; 0d1_f; 04sry; ... *> query: (?x7723, 021b_) <- list(?x7723, ?x3004), list(?x4404, ?x3004), film_festivals(?x4404, ?x3831) *> conf = 0.01 ranks of expected_values: 614 EVAL 03kx49 film! 021b_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 35.000 0.333 http://example.org/film/actor/film./film/performance/film #3962-0q_xk PRED entity: 0q_xk PRED relation: featured_film_locations! PRED expected values: 026mfbr 03tn80 => 80 concepts (62 used for prediction) PRED predicted values (max 10 best out of 498): 0g3zrd (0.19 #1625, 0.09 #3818, 0.06 #5280), 0bl1_ (0.19 #1802, 0.06 #1071, 0.03 #14229), 04dsnp (0.14 #1527, 0.12 #796, 0.10 #5182), 0m491 (0.14 #1587, 0.12 #856, 0.09 #3780), 047csmy (0.14 #1855, 0.12 #1124, 0.06 #5510), 0ds2n (0.14 #1692, 0.12 #961, 0.04 #5347), 0872p_c (0.14 #1539, 0.09 #3732, 0.06 #5194), 01_1hw (0.14 #2071, 0.06 #1340, 0.04 #5726), 033srr (0.14 #1739, 0.06 #1008, 0.02 #5394), 0473rc (0.13 #451, 0.12 #2644, 0.12 #1182) >> Best rule #1625 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 0fw4v; >> query: (?x8811, 0g3zrd) <- featured_film_locations(?x3619, ?x8811), source(?x8811, ?x958), costume_design_by(?x3619, ?x771) >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0q_xk featured_film_locations! 03tn80 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 62.000 0.190 http://example.org/film/film/featured_film_locations EVAL 0q_xk featured_film_locations! 026mfbr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 62.000 0.190 http://example.org/film/film/featured_film_locations #3961-09b_0m PRED entity: 09b_0m PRED relation: organization! PRED expected values: 0hm4q => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 13): 060c4 (0.73 #561, 0.73 #418, 0.72 #496), 07xl34 (0.34 #232, 0.28 #141, 0.27 #349), 05k17c (0.27 #306, 0.18 #345, 0.16 #280), 0dq_5 (0.24 #334, 0.23 #620, 0.23 #412), 0hm4q (0.14 #229, 0.14 #796, 0.14 #782), 08jcfy (0.14 #796, 0.14 #782, 0.08 #64), 05c0jwl (0.14 #796, 0.14 #782, 0.05 #590), 02wlwtm (0.08 #65), 01_fjr (0.08 #690, 0.03 #1437, 0.03 #1542), 01zq91 (0.08 #690, 0.03 #1437, 0.03 #1542) >> Best rule #561 for best value: >> intensional similarity = 7 >> extensional distance = 252 >> proper extension: 01qwb5; 04gd8j; >> query: (?x7575, 060c4) <- currency(?x7575, ?x5696), contains(?x1353, ?x7575), film_release_region(?x2695, ?x1353), film_release_region(?x511, ?x1353), ?x2695 = 047svrl, combatants(?x1353, ?x151), ?x511 = 0dscrwf >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #229 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 48 *> proper extension: 050fh; 01zzk4; *> query: (?x7575, 0hm4q) <- state_province_region(?x7575, ?x11096), contains(?x1353, ?x11096), country(?x359, ?x1353), adjoins(?x1353, ?x1497), administrative_parent(?x1353, ?x551), contains(?x6304, ?x1353) *> conf = 0.14 ranks of expected_values: 5 EVAL 09b_0m organization! 0hm4q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 184.000 184.000 0.732 http://example.org/organization/role/leaders./organization/leadership/organization #3960-02khs PRED entity: 02khs PRED relation: administrative_parent PRED expected values: 02j71 => 153 concepts (97 used for prediction) PRED predicted values (max 10 best out of 18): 02j71 (0.87 #7556, 0.85 #4117, 0.85 #6041), 09c7w0 (0.31 #4792, 0.26 #6854, 0.19 #9899), 0dg3n1 (0.14 #6991, 0.12 #10036, 0.10 #13083), 03rjj (0.07 #10732, 0.03 #12809, 0.01 #7684), 0d05w3 (0.07 #12015, 0.02 #11462, 0.02 #2923), 0345h (0.04 #12831, 0.03 #7706, 0.02 #12134), 0d060g (0.03 #12253, 0.03 #12811, 0.03 #7686), 07ssc (0.03 #2201, 0.02 #696, 0.02 #4529), 03rk0 (0.03 #12429, 0.02 #12150, 0.01 #7446), 03_3d (0.03 #12532) >> Best rule #7556 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 06sff; 0167v; >> query: (?x1756, 02j71) <- adjustment_currency(?x1756, ?x170), organization(?x1756, ?x312), ?x312 = 07t65, country(?x1121, ?x1756) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02khs administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 97.000 0.866 http://example.org/base/aareas/schema/administrative_area/administrative_parent #3959-0c_tl PRED entity: 0c_tl PRED relation: medal PRED expected values: 02lpp7 => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1): 02lpp7 (0.89 #39, 0.88 #36, 0.87 #51) >> Best rule #39 for best value: >> intensional similarity = 14 >> extensional distance = 23 >> proper extension: 018wrk; >> query: (?x2748, ?x422) <- olympics(?x789, ?x2748), olympics(?x608, ?x2748), sports(?x2748, ?x1121), ?x1121 = 0bynt, film_release_region(?x2695, ?x608), ?x2695 = 047svrl, country(?x3659, ?x608), combatants(?x7419, ?x608), ?x3659 = 0dwxr, contains(?x789, ?x790), country(?x251, ?x789), medal(?x789, ?x422), film_release_region(?x3998, ?x789), ?x3998 = 0184tc >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c_tl medal 02lpp7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.886 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #3958-0gtvpkw PRED entity: 0gtvpkw PRED relation: film! PRED expected values: 032dg7 => 77 concepts (71 used for prediction) PRED predicted values (max 10 best out of 249): 016tt2 (0.77 #283, 0.13 #1408, 0.13 #2462), 03xq0f (0.40 #2533, 0.17 #1128, 0.16 #988), 086k8 (0.29 #1196, 0.16 #4855, 0.15 #212), 05qd_ (0.28 #1202, 0.16 #148, 0.12 #4861), 016tw3 (0.22 #1415, 0.20 #1344, 0.19 #1765), 01795t (0.16 #156, 0.12 #436, 0.10 #647), 0jz9f (0.15 #211, 0.15 #1406, 0.11 #1335), 01gb54 (0.12 #166, 0.07 #1220, 0.06 #587), 04f525m (0.10 #500, 0.08 #853, 0.07 #782), 025jfl (0.10 #285, 0.07 #566, 0.07 #425) >> Best rule #283 for best value: >> intensional similarity = 9 >> extensional distance = 50 >> proper extension: 0bkq7; >> query: (?x3491, 016tt2) <- genre(?x3491, ?x1403), ?x1403 = 02l7c8, country(?x3491, ?x94), film(?x7526, ?x3491), film(?x1530, ?x3491), location(?x1530, ?x739), award(?x1530, ?x618), nominated_for(?x1530, ?x1531), service_language(?x7526, ?x254) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 32 *> proper extension: 04svwx; *> query: (?x3491, 032dg7) <- genre(?x3491, ?x1403), genre(?x3491, ?x258), ?x1403 = 02l7c8, country(?x3491, ?x205), ?x258 = 05p553, film_release_region(?x5271, ?x205), film_release_region(?x409, ?x205), ?x5271 = 047vnkj, ?x409 = 0gtv7pk *> conf = 0.06 ranks of expected_values: 16 EVAL 0gtvpkw film! 032dg7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 77.000 71.000 0.769 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #3957-01ps2h8 PRED entity: 01ps2h8 PRED relation: location PRED expected values: 02jx1 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 171): 0cc56 (0.47 #29537, 0.45 #17559, 0.43 #27140), 030qb3t (0.27 #4070, 0.24 #4868, 0.22 #8859), 09c7w0 (0.16 #3195, 0.01 #64665), 0rh6k (0.14 #4, 0.02 #11177, 0.02 #13571), 0cv5l (0.14 #762), 04jpl (0.12 #815, 0.12 #1613, 0.09 #4805), 06y57 (0.12 #1049, 0.12 #1847, 0.05 #2645), 02jx1 (0.12 #3260, 0.02 #4058, 0.02 #6452), 07ssc (0.10 #3218), 0ctw_b (0.09 #3241, 0.01 #4039, 0.01 #4837) >> Best rule #29537 for best value: >> intensional similarity = 3 >> extensional distance = 1228 >> proper extension: 0h1_w; >> query: (?x5283, ?x1131) <- film(?x5283, ?x5017), place_of_birth(?x5283, ?x1131), country(?x5017, ?x1003) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #3260 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 67 *> proper extension: 0466k4; *> query: (?x5283, 02jx1) <- location(?x5283, ?x2152), administrative_area_type(?x2152, ?x2792) *> conf = 0.12 ranks of expected_values: 8 EVAL 01ps2h8 location 02jx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 96.000 96.000 0.474 http://example.org/people/person/places_lived./people/place_lived/location #3956-0cj8x PRED entity: 0cj8x PRED relation: award PRED expected values: 0gq9h 02w9sd7 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 274): 0f4x7 (0.77 #33386, 0.70 #26142, 0.70 #24130), 02qkk9_ (0.77 #33386, 0.70 #26142, 0.70 #24130), 054ky1 (0.77 #33386, 0.70 #26142, 0.70 #24130), 027c95y (0.70 #26142, 0.70 #24130, 0.70 #22521), 09sb52 (0.43 #3256, 0.34 #1245, 0.34 #12102), 0gqy2 (0.43 #1369, 0.34 #3380, 0.19 #5391), 02w9sd7 (0.39 #3386, 0.15 #1375, 0.14 #1777), 0ck27z (0.32 #6525, 0.20 #12556, 0.20 #14969), 09qv_s (0.31 #3367, 0.15 #31774, 0.11 #1356), 0bfvd4 (0.30 #1320, 0.18 #30968, 0.18 #29761) >> Best rule #33386 for best value: >> intensional similarity = 3 >> extensional distance = 1631 >> proper extension: 01lcxbb; 03j90; >> query: (?x3002, ?x2060) <- nationality(?x3002, ?x94), award_winner(?x2060, ?x3002), ceremony(?x2060, ?x747) >> conf = 0.77 => this is the best rule for 3 predicted values *> Best rule #3386 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 97 *> proper extension: 01520h; *> query: (?x3002, 02w9sd7) <- award(?x3002, ?x2375), film(?x3002, ?x1944), ?x2375 = 04kxsb *> conf = 0.39 ranks of expected_values: 7, 36 EVAL 0cj8x award 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 128.000 128.000 0.767 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cj8x award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 128.000 128.000 0.767 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3955-0285r5d PRED entity: 0285r5d PRED relation: season! PRED expected values: 03lpp_ 0cqt41 07147 => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 352): 0cqt41 (0.80 #87, 0.80 #79, 0.78 #69), 07147 (0.72 #82, 0.67 #63, 0.67 #14), 03lpp_ (0.72 #82, 0.67 #14, 0.45 #94), 051wf (0.72 #82, 0.54 #75, 0.45 #30), 02hfgl (0.67 #14, 0.45 #94, 0.11 #7), 02h8p8 (0.67 #14, 0.45 #94, 0.11 #7), 04b5l3 (0.67 #14, 0.45 #94, 0.11 #7), 03qrh9 (0.67 #14, 0.45 #94, 0.11 #7), 02gtm4 (0.67 #14, 0.45 #94, 0.11 #7), 0132_h (0.67 #14, 0.45 #94, 0.11 #7) >> Best rule #87 for best value: >> intensional similarity = 90 >> extensional distance = 8 >> proper extension: 04110b0; >> query: (?x8517, ?x1632) <- season(?x8894, ?x8517), season(?x6823, ?x8517), season(?x4243, ?x8517), season(?x2174, ?x8517), season(?x2067, ?x8517), season(?x1823, ?x8517), season(?x260, ?x8517), ?x1823 = 01yhm, colors(?x6823, ?x12170), colors(?x6823, ?x8271), position(?x6823, ?x8520), position(?x6823, ?x4244), ?x8520 = 01z9v6, season(?x2174, ?x11501), school(?x2174, ?x6953), school(?x2174, ?x1884), school(?x2174, ?x735), school(?x2174, ?x581), teams(?x6088, ?x2174), school(?x4243, ?x5638), school(?x4243, ?x4599), school(?x4243, ?x1428), school(?x4243, ?x466), ?x1428 = 01j_06, team(?x261, ?x4243), ?x11501 = 027mvrc, ?x581 = 06pwq, school(?x6823, ?x3779), institution(?x865, ?x6953), teams(?x4144, ?x6823), draft(?x4243, ?x8499), organization(?x5510, ?x1884), ?x8499 = 02r6gw6, major_field_of_study(?x4599, ?x5900), major_field_of_study(?x4599, ?x5031), major_field_of_study(?x4599, ?x4100), major_field_of_study(?x4599, ?x2981), major_field_of_study(?x4599, ?x1154), ?x5900 = 0db86, company(?x2998, ?x1884), contains(?x94, ?x6953), school(?x3089, ?x4599), student(?x6953, ?x117), state_province_region(?x1884, ?x760), student(?x1884, ?x1815), ?x5031 = 0dc_v, draft(?x6823, ?x1161), category(?x4243, ?x134), ?x8271 = 02rnmb, ?x1154 = 02lp1, ?x4100 = 01lj9, organization(?x4599, ?x5487), major_field_of_study(?x5638, ?x2014), contains(?x4600, ?x4599), sport(?x6823, ?x5063), school(?x1632, ?x6953), school(?x1576, ?x6953), ?x735 = 065y4w7, ?x2014 = 04rjg, student(?x4599, ?x3273), currency(?x6953, ?x170), season(?x260, ?x8923), school(?x260, ?x1681), major_field_of_study(?x1884, ?x3995), school(?x2067, ?x7596), school_type(?x466, ?x1507), institution(?x620, ?x3779), ?x1576 = 05tfm, fraternities_and_sororities(?x1884, ?x3697), student(?x466, ?x3134), student(?x5638, ?x2239), institution(?x734, ?x1884), major_field_of_study(?x466, ?x947), contains(?x3778, ?x3779), colors(?x4599, ?x332), student(?x3779, ?x2409), state_province_region(?x466, ?x3908), list(?x3779, ?x2197), school(?x6462, ?x466), ?x6462 = 09l0x9, ?x2197 = 09g7thr, ?x2981 = 02j62, ?x4244 = 028c_8, ?x7596 = 012mzw, ?x1632 = 0cqt41, contains(?x7564, ?x5638), ?x8894 = 02d02, ?x620 = 07s6fsf, ?x8923 = 03c74_8, colors(?x1615, ?x12170) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0285r5d season! 07147 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.800 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 0285r5d season! 0cqt41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.800 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 0285r5d season! 03lpp_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 13.000 13.000 0.800 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #3954-02bxd PRED entity: 02bxd PRED relation: group PRED expected values: 046p9 => 59 concepts (38 used for prediction) PRED predicted values (max 10 best out of 202): 02vnpv (0.64 #3022, 0.63 #4942, 0.62 #5519), 05563d (0.60 #794, 0.55 #2706, 0.50 #2479), 07mvp (0.60 #644, 0.50 #3704, 0.42 #4861), 02dw1_ (0.60 #632, 0.47 #4849, 0.46 #3889), 02mq_y (0.60 #815, 0.45 #3297, 0.45 #3108), 07qnf (0.60 #766, 0.45 #2678, 0.43 #1529), 0134tg (0.60 #817, 0.43 #1580, 0.40 #2345), 01czx (0.60 #780, 0.43 #1543, 0.40 #2308), 02r3zy (0.60 #767, 0.43 #1530, 0.40 #2295), 01fchy (0.60 #895, 0.43 #1658, 0.38 #2044) >> Best rule #3022 for best value: >> intensional similarity = 25 >> extensional distance = 9 >> proper extension: 02fsn; >> query: (?x1662, 02vnpv) <- role(?x1662, ?x3161), role(?x1662, ?x2798), role(?x1662, ?x227), role(?x1662, ?x4311), role(?x1662, ?x3991), role(?x1662, ?x1432), role(?x1662, ?x1332), ?x227 = 0342h, role(?x1583, ?x1332), role(?x1831, ?x1332), role(?x1225, ?x1332), role(?x1148, ?x1332), group(?x1332, ?x3516), ?x2798 = 03qjg, ?x1432 = 0395lw, ?x1583 = 01kvqc, ?x3991 = 05842k, role(?x1332, ?x1267), ?x1148 = 02qjv, ?x1225 = 01qbl, role(?x1332, ?x885), instrumentalists(?x3161, ?x140), ?x4311 = 01xqw, ?x1831 = 03t22m, role(?x74, ?x3161) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #110 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 1 *> proper extension: 01vj9c; *> query: (?x1662, 046p9) <- role(?x1662, ?x3161), role(?x1662, ?x716), role(?x1662, ?x4311), role(?x1662, ?x2377), role(?x1662, ?x1432), role(?x1662, ?x1332), role(?x1662, ?x1166), role(?x1662, ?x228), role(?x1662, ?x212), ?x1332 = 03qlv7, ?x1166 = 05148p4, ?x212 = 026t6, ?x228 = 0l14qv, ?x1432 = 0395lw, ?x4311 = 01xqw, ?x716 = 018vs, role(?x3112, ?x2377), role(?x7449, ?x3161), role(?x5926, ?x3161), ?x7449 = 01vnt4, ?x5926 = 0cfdd, role(?x3160, ?x3161), ?x3112 = 0mbct, award_winner(?x247, ?x3160), location(?x3160, ?x2277), award_nominee(?x9116, ?x3160), gender(?x3160, ?x231) *> conf = 0.33 ranks of expected_values: 47 EVAL 02bxd group 046p9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 59.000 38.000 0.636 http://example.org/music/performance_role/regular_performances./music/group_membership/group #3953-0fphf3v PRED entity: 0fphf3v PRED relation: nominated_for! PRED expected values: 03c7tr1 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 188): 07cbcy (0.46 #538, 0.23 #1490, 0.21 #1966), 04ljl_l (0.44 #479, 0.15 #1431, 0.14 #3), 05p09zm (0.42 #569, 0.14 #93, 0.13 #1521), 05b4l5x (0.42 #482, 0.14 #1434, 0.14 #1910), 03c7tr1 (0.35 #522, 0.26 #2381, 0.19 #12145), 03hj5vf (0.29 #124, 0.04 #6313, 0.04 #362), 05p1dby (0.27 #557, 0.19 #12145, 0.19 #10716), 0gq9h (0.25 #5774, 0.23 #4108, 0.23 #7441), 0gs9p (0.22 #5776, 0.20 #7443, 0.19 #10540), 019f4v (0.21 #5765, 0.21 #7432, 0.20 #1480) >> Best rule #538 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 06zn1c; >> query: (?x7832, 07cbcy) <- nominated_for(?x688, ?x7832), titles(?x2480, ?x7832), ?x688 = 05b1610, genre(?x7832, ?x239) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #522 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 69 *> proper extension: 06zn1c; *> query: (?x7832, 03c7tr1) <- nominated_for(?x688, ?x7832), titles(?x2480, ?x7832), ?x688 = 05b1610, genre(?x7832, ?x239) *> conf = 0.35 ranks of expected_values: 5 EVAL 0fphf3v nominated_for! 03c7tr1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 77.000 0.465 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3952-0b7t3p PRED entity: 0b7t3p PRED relation: student! PRED expected values: 01t0dy 01p896 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 146): 0bwfn (0.17 #275, 0.09 #1329, 0.07 #7126), 04b_46 (0.17 #227, 0.06 #1281, 0.03 #5497), 01w5m (0.17 #105, 0.06 #3267, 0.05 #3794), 02g839 (0.17 #25, 0.02 #1079, 0.02 #1606), 02q253 (0.17 #505, 0.01 #2613), 01qd_r (0.17 #281, 0.01 #2389), 0fnmz (0.17 #101, 0.01 #2209), 017z88 (0.10 #609, 0.04 #8515, 0.04 #9569), 01jszm (0.10 #700), 065y4w7 (0.09 #1068, 0.06 #2122, 0.04 #8974) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0p_47; >> query: (?x6443, 0bwfn) <- award_nominee(?x8013, ?x6443), ?x8013 = 01mh8zn, gender(?x6443, ?x514) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1271 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 01y0y6; 04mx__; *> query: (?x6443, 01t0dy) <- award_nominee(?x364, ?x6443), people(?x4195, ?x6443), program(?x6443, ?x631) *> conf = 0.04 ranks of expected_values: 18, 50 EVAL 0b7t3p student! 01p896 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 94.000 94.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0b7t3p student! 01t0dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 94.000 94.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #3951-01wj5hp PRED entity: 01wj5hp PRED relation: type_of_union PRED expected values: 04ztj => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.74 #109, 0.74 #13, 0.74 #133), 01g63y (0.25 #357, 0.25 #2, 0.18 #82), 01bl8s (0.25 #357, 0.01 #27) >> Best rule #109 for best value: >> intensional similarity = 3 >> extensional distance = 589 >> proper extension: 0cl_m; >> query: (?x8829, 04ztj) <- nationality(?x8829, ?x94), ?x94 = 09c7w0, religion(?x8829, ?x8967) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wj5hp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.745 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3950-04h41v PRED entity: 04h41v PRED relation: titles! PRED expected values: 01z4y => 65 concepts (21 used for prediction) PRED predicted values (max 10 best out of 66): 07c52 (0.59 #335, 0.50 #746, 0.48 #644), 07s9rl0 (0.45 #408, 0.44 #512, 0.37 #820), 01hmnh (0.43 #948, 0.34 #641, 0.33 #743), 01z4y (0.40 #238, 0.32 #1059, 0.25 #136), 02l7c8 (0.25 #101, 0.25 #2044, 0.20 #2043), 0d63kt (0.25 #101, 0.25 #2044, 0.20 #2043), 0219x_ (0.25 #101, 0.20 #2043, 0.19 #1635), 05p553 (0.25 #101, 0.20 #2043, 0.19 #1635), 0vgkd (0.25 #101, 0.20 #2043, 0.19 #1635), 06cvj (0.25 #101, 0.20 #2043, 0.19 #1635) >> Best rule #335 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 0ddd0gc; 02xhpl; 02hct1; 02md2d; 030cx; 01fx1l; 05lfwd; 016zfm; 02qkq0; 0gvsh7l; ... >> query: (?x5966, 07c52) <- nominated_for(?x3499, ?x5966), award(?x5586, ?x3499), ?x5586 = 03rwng >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #238 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 43 *> proper extension: 0g5q34q; 0d8w2n; *> query: (?x5966, 01z4y) <- genre(?x5966, ?x2753), genre(?x5966, ?x258), film(?x5959, ?x5966), ?x2753 = 0219x_, ?x258 = 05p553 *> conf = 0.40 ranks of expected_values: 4 EVAL 04h41v titles! 01z4y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 65.000 21.000 0.589 http://example.org/media_common/netflix_genre/titles #3949-02qhm3 PRED entity: 02qhm3 PRED relation: people! PRED expected values: 01qhm_ => 142 concepts (116 used for prediction) PRED predicted values (max 10 best out of 45): 041rx (0.46 #596, 0.41 #744, 0.38 #818), 0x67 (0.25 #6229, 0.22 #6303, 0.21 #2969), 07hwkr (0.20 #3712, 0.08 #6231, 0.08 #3786), 02ctzb (0.17 #310, 0.15 #3715, 0.08 #162), 02w7gg (0.12 #2814, 0.11 #2666, 0.10 #6148), 0xnvg (0.11 #2898, 0.11 #3565, 0.11 #2676), 01qhm_ (0.11 #3707, 0.08 #1560, 0.08 #1412), 09vc4s (0.10 #3709, 0.10 #2006, 0.08 #1562), 07mqps (0.07 #240, 0.06 #3719, 0.04 #2016), 0bbz66j (0.07 #267, 0.03 #415, 0.02 #1377) >> Best rule #596 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 0jf1b; 0gl88b; 02vqpx8; >> query: (?x11612, 041rx) <- award_winner(?x7589, ?x11612), place_of_birth(?x11612, ?x12307), place_of_death(?x11612, ?x5895), people(?x1446, ?x11612) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #3707 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 401 *> proper extension: 012gbb; *> query: (?x11612, 01qhm_) <- nationality(?x11612, ?x94), people(?x5741, ?x11612), people(?x5741, ?x12525), taxonomy(?x12525, ?x939) *> conf = 0.11 ranks of expected_values: 7 EVAL 02qhm3 people! 01qhm_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 142.000 116.000 0.457 http://example.org/people/ethnicity/people #3948-02hxc3j PRED entity: 02hxc3j PRED relation: language! PRED expected values: 01_1hw => 30 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1855): 047vnkj (0.57 #4328, 0.50 #12969, 0.50 #2599), 0f4_2k (0.57 #4441, 0.45 #3457, 0.40 #9626), 06x43v (0.57 #4711, 0.45 #3457, 0.36 #6439), 041td_ (0.57 #4516, 0.36 #6244, 0.33 #9701), 03z9585 (0.50 #3083, 0.45 #6540, 0.45 #3457), 0dr_4 (0.50 #1964, 0.45 #3457, 0.43 #3693), 04fzfj (0.50 #1824, 0.45 #3457, 0.43 #3553), 061681 (0.50 #1828, 0.45 #3457, 0.36 #5285), 0bbw2z6 (0.50 #2511, 0.45 #3457, 0.36 #5968), 0ywrc (0.50 #2220, 0.45 #3457, 0.36 #5677) >> Best rule #4328 for best value: >> intensional similarity = 18 >> extensional distance = 5 >> proper extension: 02bjrlw; 03_9r; 064_8sq; 04h9h; >> query: (?x1049, 047vnkj) <- language(?x7741, ?x1049), language(?x7463, ?x1049), language(?x2714, ?x1049), language(?x1170, ?x1049), film_release_region(?x2714, ?x2316), film_release_region(?x2714, ?x1453), film_release_region(?x2714, ?x1353), film_release_region(?x2714, ?x142), ?x1453 = 06qd3, film_crew_role(?x1170, ?x137), ?x142 = 0jgd, ?x2316 = 06t2t, ?x7741 = 01xq8v, ?x1353 = 035qy, film(?x3495, ?x7463), genre(?x7463, ?x1510), ?x1510 = 01hmnh, film(?x548, ?x1170) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1408 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x1049, 01_1hw) <- language(?x9901, ?x1049), language(?x7741, ?x1049), language(?x7463, ?x1049), language(?x5070, ?x1049), language(?x2714, ?x1049), language(?x1170, ?x1049), language(?x1048, ?x1049), ?x2714 = 0kv238, official_language(?x9006, ?x1049), ?x1048 = 048scx, ?x1170 = 09gdm7q, languages_spoken(?x3584, ?x1049), ?x7463 = 02fj8n, ?x5070 = 0dt8xq, ?x7741 = 01xq8v, film(?x1104, ?x9901), film(?x100, ?x9901), genre(?x9901, ?x53) *> conf = 0.33 ranks of expected_values: 1344 EVAL 02hxc3j language! 01_1hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 16.000 0.571 http://example.org/film/film/language #3947-03hh89 PRED entity: 03hh89 PRED relation: film PRED expected values: 07yvsn => 99 concepts (83 used for prediction) PRED predicted values (max 10 best out of 456): 02rcwq0 (0.59 #3571, 0.58 #89278, 0.55 #12497), 0c3ybss (0.59 #3571, 0.58 #89278, 0.55 #12497), 0bvn25 (0.20 #7190, 0.06 #5405, 0.02 #10760), 02825cv (0.20 #8279, 0.06 #6494, 0.01 #88630), 04gv3db (0.16 #7891, 0.02 #11461, 0.02 #23960), 017jd9 (0.14 #2562, 0.14 #777, 0.12 #4348), 05q96q6 (0.14 #1937, 0.14 #152, 0.12 #3723), 0ndwt2w (0.14 #2781, 0.14 #996, 0.12 #4567), 05650n (0.14 #2793, 0.14 #1008, 0.12 #4579), 026qnh6 (0.14 #2605, 0.14 #820, 0.06 #4391) >> Best rule #3571 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02r5w9; 02dbn2; 013t9y; 02r6c_; >> query: (?x5446, ?x86) <- location(?x5446, ?x5036), nominated_for(?x5446, ?x86), ?x5036 = 06y57 >> conf = 0.59 => this is the best rule for 2 predicted values *> Best rule #9482 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 47 *> proper extension: 071pf2; *> query: (?x5446, 07yvsn) <- nationality(?x5446, ?x390), ?x390 = 0chghy *> conf = 0.02 ranks of expected_values: 326 EVAL 03hh89 film 07yvsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 99.000 83.000 0.593 http://example.org/film/actor/film./film/performance/film #3946-01_p6t PRED entity: 01_p6t PRED relation: award_nominee PRED expected values: 0372kf 0525b => 112 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1098): 0372kf (0.81 #123710, 0.81 #37346, 0.81 #81692), 0525b (0.81 #123710, 0.81 #37346, 0.81 #81692), 0g8st4 (0.81 #123710, 0.81 #37346, 0.81 #81692), 04t2l2 (0.76 #93365, 0.76 #67687, 0.76 #107371), 0dlglj (0.50 #336, 0.04 #5005, 0.03 #65689), 01yfm8 (0.50 #1665, 0.01 #92695, 0.01 #63018), 020_95 (0.25 #1277, 0.08 #3611, 0.04 #5946), 02p65p (0.25 #27, 0.06 #14030, 0.06 #18700), 051wwp (0.25 #1163, 0.04 #68850, 0.04 #66516), 02l4pj (0.25 #773, 0.04 #5442, 0.04 #66126) >> Best rule #123710 for best value: >> intensional similarity = 3 >> extensional distance = 1236 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 04bdxl; 02s2ft; 05vsxz; 06qgvf; 0grwj; ... >> query: (?x5758, ?x525) <- film(?x5758, ?x7887), award_nominee(?x525, ?x5758), genre(?x7887, ?x53) >> conf = 0.81 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2 EVAL 01_p6t award_nominee 0525b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 65.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 01_p6t award_nominee 0372kf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 65.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3945-01pp3p PRED entity: 01pp3p PRED relation: nationality PRED expected values: 09c7w0 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.90 #1302, 0.80 #4708, 0.78 #701), 0l2q3 (0.32 #5812), 01n7q (0.32 #5812), 02jx1 (0.15 #433, 0.14 #633, 0.14 #1033), 0h7x (0.13 #135, 0.04 #2138, 0.04 #2239), 03rjj (0.12 #205, 0.07 #505, 0.05 #3512), 07ssc (0.11 #1918, 0.10 #3022, 0.10 #5524), 03rk0 (0.09 #946, 0.08 #6259, 0.07 #5656), 0f8l9c (0.07 #522, 0.07 #122, 0.04 #422), 012m_ (0.07 #191, 0.02 #1291, 0.01 #2295) >> Best rule #1302 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 05m63c; 02qjj7; 01wjrn; 0chrwb; 04cr6qv; 01sfmyk; 06fc0b; 0194xc; 01r4bps; 04gc65; ... >> query: (?x4926, 09c7w0) <- profession(?x4926, ?x319), people(?x1446, ?x4926), ?x1446 = 033tf_ >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pp3p nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.898 http://example.org/people/person/nationality #3944-0gp_x9 PRED entity: 0gp_x9 PRED relation: profession PRED expected values: 0dxtg => 108 concepts (36 used for prediction) PRED predicted values (max 10 best out of 117): 0dxtg (0.64 #4453, 0.60 #1493, 0.51 #3713), 03gjzk (0.49 #4010, 0.42 #4602, 0.34 #4454), 02jknp (0.48 #3707, 0.47 #1635, 0.46 #2819), 09jwl (0.39 #2090, 0.20 #2386, 0.19 #4162), 0fj9f (0.33 #350, 0.25 #54, 0.09 #498), 02t8yb (0.25 #81, 0.14 #229, 0.11 #377), 0np9r (0.21 #3128, 0.14 #4312, 0.12 #2536), 0nbcg (0.18 #2103, 0.14 #179, 0.13 #3583), 02krf9 (0.18 #4614, 0.16 #4022, 0.15 #1654), 0cbd2 (0.17 #1486, 0.16 #4446, 0.14 #4594) >> Best rule #4453 for best value: >> intensional similarity = 6 >> extensional distance = 879 >> proper extension: 070w7s; >> query: (?x9554, 0dxtg) <- place_of_birth(?x9554, ?x4335), profession(?x9554, ?x319), profession(?x8683, ?x319), profession(?x4466, ?x319), ?x4466 = 01_x6d, ?x8683 = 05jjl >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gp_x9 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 36.000 0.638 http://example.org/people/person/profession #3943-0gltv PRED entity: 0gltv PRED relation: film! PRED expected values: 02s2ft => 112 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1156): 03rqww (0.49 #18711, 0.48 #43660, 0.47 #29106), 0146pg (0.42 #110206, 0.42 #68609, 0.41 #135162), 0284n42 (0.42 #110206, 0.42 #68609, 0.41 #135162), 0h7pj (0.29 #3621, 0.18 #26491, 0.08 #5699), 0kszw (0.22 #23287, 0.08 #4574, 0.05 #10812), 04fhn_ (0.20 #679, 0.14 #2758, 0.06 #6914), 08hp53 (0.20 #289, 0.13 #51978, 0.13 #91490), 01tnxc (0.20 #1424, 0.06 #7659, 0.02 #28451), 02js6_ (0.20 #445, 0.04 #126840, 0.02 #14999), 0147dk (0.20 #82, 0.04 #37504, 0.04 #27109) >> Best rule #18711 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 01gglm; >> query: (?x8736, ?x8248) <- award_winner(?x8736, ?x8248), film_format(?x8736, ?x909), films(?x11109, ?x8736), titles(?x53, ?x8736) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #6242 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 05sy0cv; *> query: (?x8736, 02s2ft) <- award_winner(?x8736, ?x8248), nominated_for(?x556, ?x8736), cinematography(?x634, ?x8248), student(?x735, ?x8248) *> conf = 0.06 ranks of expected_values: 180 EVAL 0gltv film! 02s2ft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 112.000 73.000 0.491 http://example.org/film/actor/film./film/performance/film #3942-01l79yc PRED entity: 01l79yc PRED relation: music! PRED expected values: 0dscrwf => 125 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1042): 05v38p (0.79 #11071, 0.73 #23147, 0.73 #22140), 064r97z (0.79 #11071, 0.73 #23147, 0.73 #22140), 09146g (0.09 #1190, 0.06 #3202, 0.04 #6220), 03h3x5 (0.09 #1267, 0.05 #5291, 0.04 #7303), 078mm1 (0.09 #1833, 0.05 #5857, 0.04 #7869), 02rrfzf (0.07 #11397, 0.05 #12403, 0.05 #5358), 07bzz7 (0.06 #8579, 0.06 #9586, 0.03 #26694), 08rr3p (0.06 #3292, 0.05 #1280, 0.04 #6310), 0btpm6 (0.06 #3762, 0.05 #1750, 0.04 #10806), 02fqrf (0.06 #3359, 0.05 #1347, 0.04 #10403) >> Best rule #11071 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 01271h; 02bh9; 02sj1x; 01pr6q7; 09swkk; 02w670; 01pbs9w; 0c_drn; 019x62; 0pj8m; >> query: (?x6251, ?x5682) <- music(?x167, ?x6251), award(?x6251, ?x1079), nominated_for(?x6251, ?x5682), ?x1079 = 0l8z1 >> conf = 0.79 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01l79yc music! 0dscrwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 31.000 0.787 http://example.org/film/film/music #3941-087z2 PRED entity: 087z2 PRED relation: symptom_of! PRED expected values: 0hgxh => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 73): 01j6t0 (0.91 #890, 0.89 #691, 0.88 #675), 098s1 (0.82 #182, 0.75 #214, 0.58 #254), 012qjw (0.82 #405, 0.80 #361, 0.74 #734), 08g5q7 (0.78 #388, 0.62 #329, 0.61 #839), 01cdt5 (0.73 #187, 0.71 #213, 0.69 #346), 0brgy (0.69 #346, 0.67 #200, 0.64 #428), 02tfl8 (0.64 #189, 0.64 #221, 0.60 #184), 0hgxh (0.64 #189, 0.64 #221, 0.53 #225), 04kllm9 (0.64 #189, 0.64 #221, 0.50 #379), 097ns (0.58 #512, 0.51 #642, 0.42 #473) >> Best rule #890 for best value: >> intensional similarity = 56 >> extensional distance = 33 >> proper extension: 04p3w; 02bft; 02psvcf; 097ns; 01g2q; 0c58k; 0g02vk; 0fltx; 0146bp; >> query: (?x14562, 01j6t0) <- symptom_of(?x10717, ?x14562), symptom_of(?x9509, ?x14562), symptom_of(?x6780, ?x14562), symptom_of(?x10717, ?x14376), symptom_of(?x10717, ?x14228), symptom_of(?x10717, ?x13744), symptom_of(?x10717, ?x13485), symptom_of(?x10717, ?x13131), symptom_of(?x10717, ?x11739), symptom_of(?x10717, ?x10613), symptom_of(?x10717, ?x10480), symptom_of(?x10717, ?x9898), symptom_of(?x10717, ?x8675), symptom_of(?x10717, ?x6656), symptom_of(?x10717, ?x6655), symptom_of(?x10717, ?x4291), symptom_of(?x10717, ?x3799), ?x9898 = 09jg8, ?x8675 = 01gkcc, symptom_of(?x6780, ?x11392), symptom_of(?x6780, ?x1158), ?x13131 = 0d19y2, symptom_of(?x9509, ?x14096), symptom_of(?x9509, ?x11064), symptom_of(?x9509, ?x4322), ?x13485 = 07s4l, people(?x1158, ?x1159), risk_factors(?x13744, ?x231), ?x11739 = 0167bx, ?x6656 = 03p41, notable_people_with_this_condition(?x14228, ?x5609), ?x11392 = 0lcdk, ?x4322 = 0gk4g, risk_factors(?x5784, ?x1158), risk_factors(?x1158, ?x8523), risk_factors(?x1158, ?x5802), ?x8523 = 0c58k, ?x10480 = 0h1n9, ?x5609 = 034rd, people(?x14228, ?x5912), ?x5802 = 0k95h, people(?x10613, ?x10516), people(?x10613, ?x3194), people(?x13744, ?x4204), ?x231 = 05zppz, risk_factors(?x14376, ?x514), ?x4291 = 07jwr, ?x11064 = 01n3bm, ?x4204 = 02dth1, ?x6655 = 09d11, ?x10516 = 0b22w, ?x514 = 02zsn, symptom_of(?x13373, ?x14096), ?x3194 = 0jrny, ?x13373 = 0f3kl, ?x3799 = 04psf >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #189 for first EXPECTED value: *> intensional similarity = 47 *> extensional distance = 3 *> proper extension: 09jg8; 0d19y2; *> query: (?x14562, ?x13605) <- symptom_of(?x10717, ?x14562), symptom_of(?x9509, ?x14562), symptom_of(?x6780, ?x14562), ?x10717 = 0cjf0, ?x6780 = 0j5fv, symptom_of(?x9509, ?x14096), symptom_of(?x9509, ?x13744), symptom_of(?x9509, ?x11739), symptom_of(?x9509, ?x11659), symptom_of(?x9509, ?x11126), symptom_of(?x9509, ?x11064), symptom_of(?x9509, ?x10480), symptom_of(?x9509, ?x9510), symptom_of(?x9509, ?x9119), symptom_of(?x9509, ?x6781), symptom_of(?x9509, ?x4322), symptom_of(?x9509, ?x3799), ?x11064 = 01n3bm, ?x9119 = 011zdm, ?x11739 = 0167bx, symptom_of(?x13373, ?x14096), symptom_of(?x3679, ?x14096), symptom_of(?x4905, ?x3799), risk_factors(?x3799, ?x13738), people(?x3799, ?x487), ?x6781 = 035482, risk_factors(?x11659, ?x13131), risk_factors(?x11126, ?x8524), ?x13373 = 0f3kl, ?x4322 = 0gk4g, ?x10480 = 0h1n9, people(?x13744, ?x2871), ?x4905 = 01j6t0, symptom_of(?x11126, ?x10199), ?x8524 = 01hbgs, risk_factors(?x13744, ?x231), ?x231 = 05zppz, symptom_of(?x13605, ?x9510), symptom_of(?x9510, ?x7006), ?x13131 = 0d19y2, risk_factors(?x8675, ?x11659), ?x3679 = 02tfl8, risk_factors(?x9510, ?x14024), risk_factors(?x13744, ?x8524), symptom_of(?x10717, ?x3799), risk_factors(?x11659, ?x13738), symptom_of(?x4905, ?x11659) *> conf = 0.64 ranks of expected_values: 8 EVAL 087z2 symptom_of! 0hgxh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 31.000 31.000 0.914 http://example.org/medicine/symptom/symptom_of #3940-0234pg PRED entity: 0234pg PRED relation: people! PRED expected values: 0x67 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 37): 0x67 (0.56 #549, 0.56 #472, 0.55 #164), 041rx (0.33 #4, 0.18 #1775, 0.17 #2006), 02ctzb (0.16 #785, 0.11 #1170, 0.10 #1093), 07hwkr (0.12 #89, 0.09 #166, 0.06 #397), 033tf_ (0.11 #2702, 0.09 #3164, 0.09 #3241), 0xnvg (0.09 #2708, 0.06 #3401, 0.06 #3556), 06v41q (0.09 #183, 0.06 #414, 0.06 #568), 0fqz6 (0.09 #196, 0.06 #427, 0.06 #581), 02w7gg (0.08 #3930, 0.07 #4315, 0.07 #4238), 0cn68 (0.06 #289, 0.06 #597, 0.06 #520) >> Best rule #549 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 08gwzt; 095nx; >> query: (?x10361, 0x67) <- athlete(?x1083, ?x10361), currency(?x10361, ?x170), team(?x10361, ?x934), nationality(?x10361, ?x94) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0234pg people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.556 http://example.org/people/ethnicity/people #3939-043q6n_ PRED entity: 043q6n_ PRED relation: place_of_birth PRED expected values: 030qb3t => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 84): 0rnmy (0.25 #95), 02_286 (0.20 #1427, 0.20 #723, 0.11 #3539), 0xl08 (0.20 #1649, 0.20 #945, 0.02 #3057), 02dtg (0.20 #714, 0.04 #41547, 0.03 #4938), 030qb3t (0.17 #3574, 0.06 #4982, 0.06 #9911), 0cr3d (0.04 #9951, 0.04 #17698, 0.04 #18403), 01_d4 (0.04 #41547, 0.04 #11332, 0.04 #6403), 0nbwf (0.04 #41547, 0.03 #3122, 0.02 #5939), 0chrx (0.04 #41547, 0.03 #3825), 0v9qg (0.04 #41547, 0.03 #3666) >> Best rule #95 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 0f721s; >> query: (?x1417, 0rnmy) <- program(?x1417, ?x8644), ?x8644 = 06dfz1 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3574 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 63 *> proper extension: 01_rh4; 04h07s; 0hgqq; 021r7r; 01mr2g6; 0f1jhc; 0ccqd7; 0sw62; 05xd_v; 049sb; ... *> query: (?x1417, 030qb3t) <- student(?x4955, ?x1417), ?x4955 = 09f2j *> conf = 0.17 ranks of expected_values: 5 EVAL 043q6n_ place_of_birth 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 78.000 78.000 0.250 http://example.org/people/person/place_of_birth #3938-0k6nt PRED entity: 0k6nt PRED relation: country! PRED expected values: 06f41 => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 45): 03_8r (0.83 #539, 0.80 #609, 0.80 #504), 06f41 (0.83 #535, 0.79 #430, 0.77 #500), 02y8z (0.72 #397, 0.67 #502, 0.66 #1097), 07gyv (0.70 #530, 0.70 #495, 0.69 #390), 01hp22 (0.67 #41, 0.63 #496, 0.59 #391), 03rbzn (0.67 #508, 0.62 #438, 0.60 #543), 09w1n (0.67 #505, 0.56 #1065, 0.56 #995), 07_53 (0.67 #62, 0.45 #447, 0.40 #517), 035d1m (0.63 #507, 0.57 #367, 0.56 #52), 02_5h (0.60 #499, 0.56 #44, 0.48 #429) >> Best rule #539 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 07z5n; >> query: (?x985, 03_8r) <- country(?x10585, ?x985), official_language(?x985, ?x7791), ?x10585 = 01gqfm >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #535 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 07z5n; *> query: (?x985, 06f41) <- country(?x10585, ?x985), official_language(?x985, ?x7791), ?x10585 = 01gqfm *> conf = 0.83 ranks of expected_values: 2 EVAL 0k6nt country! 06f41 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 181.000 181.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #3937-01mkq PRED entity: 01mkq PRED relation: major_field_of_study! PRED expected values: 01540 => 78 concepts (64 used for prediction) PRED predicted values (max 10 best out of 123): 01mkq (0.60 #1633, 0.50 #953, 0.50 #783), 03qsdpk (0.60 #1400, 0.27 #2942, 0.26 #1452), 0fdys (0.50 #2163, 0.50 #1140, 0.40 #1308), 01lj9 (0.50 #971, 0.43 #1907, 0.40 #1651), 03g3w (0.50 #1132, 0.41 #3270, 0.40 #1385), 05qt0 (0.50 #1153, 0.40 #1406, 0.38 #2176), 037mh8 (0.50 #737, 0.40 #259, 0.33 #567), 0_jm (0.43 #1921, 0.40 #1494, 0.40 #1323), 06mnr (0.40 #1415, 0.40 #259, 0.33 #174), 01400v (0.40 #1353, 0.38 #2208, 0.33 #416) >> Best rule #1633 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 06ms6; >> query: (?x1668, 01mkq) <- major_field_of_study(?x10576, ?x1668), major_field_of_study(?x10175, ?x1668), major_field_of_study(?x9200, ?x1668), major_field_of_study(?x4780, ?x1668), major_field_of_study(?x1668, ?x2014), ?x2014 = 04rjg, major_field_of_study(?x734, ?x1668), ?x734 = 04zx3q1, organization(?x346, ?x10175), ?x9200 = 0dzst, institution(?x2636, ?x4780), school_type(?x10576, ?x1044) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2221 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 6 *> proper extension: 0dc_v; *> query: (?x1668, ?x2601) <- major_field_of_study(?x2711, ?x1668), major_field_of_study(?x2313, ?x1668), major_field_of_study(?x1768, ?x1668), major_field_of_study(?x546, ?x1668), major_field_of_study(?x1668, ?x2014), major_field_of_study(?x388, ?x2014), major_field_of_study(?x2014, ?x732), ?x1768 = 09kvv, ?x2313 = 07wrz, school(?x4171, ?x546), fraternities_and_sororities(?x2711, ?x3697), major_field_of_study(?x734, ?x1668), major_field_of_study(?x546, ?x2601) *> conf = 0.15 ranks of expected_values: 67 EVAL 01mkq major_field_of_study! 01540 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 78.000 64.000 0.600 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #3936-0137n0 PRED entity: 0137n0 PRED relation: artist! PRED expected values: 015_1q 0g768 0181dw => 109 concepts (78 used for prediction) PRED predicted values (max 10 best out of 104): 03rhqg (0.29 #16, 0.17 #572, 0.17 #711), 02p11jq (0.29 #13, 0.09 #1682, 0.08 #1542), 015_1q (0.22 #993, 0.21 #576, 0.21 #1689), 0g768 (0.14 #37, 0.13 #1706, 0.12 #4775), 033hn8 (0.14 #14, 0.12 #2101, 0.11 #4752), 0181dw (0.14 #42, 0.11 #1711, 0.11 #1432), 01w40h (0.14 #28, 0.10 #862, 0.09 #167), 017l96 (0.14 #19, 0.10 #1827, 0.10 #575), 04fcjt (0.14 #29, 0.04 #1419, 0.04 #1837), 01t04r (0.14 #64, 0.04 #5917, 0.04 #2151) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 04r1t; >> query: (?x1270, 03rhqg) <- artists(?x13972, ?x1270), artist(?x4483, ?x1270), ?x13972 = 026g51 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #993 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 01wwvd2; 08n__5; *> query: (?x1270, 015_1q) <- award_nominee(?x2300, ?x1270), award_winner(?x2420, ?x1270), role(?x1270, ?x227) *> conf = 0.22 ranks of expected_values: 3, 4, 6 EVAL 0137n0 artist! 0181dw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 78.000 0.286 http://example.org/music/record_label/artist EVAL 0137n0 artist! 0g768 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 78.000 0.286 http://example.org/music/record_label/artist EVAL 0137n0 artist! 015_1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 78.000 0.286 http://example.org/music/record_label/artist #3935-0cm19f PRED entity: 0cm19f PRED relation: place_of_birth PRED expected values: 04vmp => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 53): 0hj6h (0.29 #1895, 0.17 #1190, 0.10 #3305), 06_kh (0.25 #5, 0.12 #2120, 0.09 #3529), 02_286 (0.20 #13407, 0.14 #7066, 0.14 #6362), 03b12 (0.18 #3931, 0.14 #4635, 0.10 #5341), 04vmp (0.17 #972, 0.14 #27751, 0.14 #28456), 0dlv0 (0.17 #1058, 0.10 #3173, 0.05 #6697), 0cvw9 (0.14 #1708, 0.10 #3118, 0.07 #4527), 0cr3d (0.14 #4322, 0.09 #7845, 0.09 #8549), 0fpzwf (0.12 #2321, 0.09 #3730, 0.05 #5845), 0nqv1 (0.12 #2514, 0.09 #3923, 0.05 #5333) >> Best rule #1895 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0b5x23; >> query: (?x13496, 0hj6h) <- sibling(?x13496, ?x11786), gender(?x13496, ?x231), nationality(?x13496, ?x2146), ?x2146 = 03rk0, place_of_death(?x11786, ?x7412) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #972 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 02756j; *> query: (?x13496, 04vmp) <- sibling(?x13496, ?x11786), gender(?x13496, ?x231), nationality(?x13496, ?x2146), ?x2146 = 03rk0, film(?x11786, ?x697) *> conf = 0.17 ranks of expected_values: 5 EVAL 0cm19f place_of_birth 04vmp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 83.000 83.000 0.286 http://example.org/people/person/place_of_birth #3934-039yzs PRED entity: 039yzs PRED relation: sport! PRED expected values: 02pqcfz 03d555l 02pyyld => 9 concepts (8 used for prediction) PRED predicted values (max 10 best out of 517): 0jm2v (0.88 #457, 0.84 #470, 0.84 #469), 02pyyld (0.88 #457, 0.84 #470, 0.84 #469), 02r2qt7 (0.88 #457, 0.84 #470, 0.84 #469), 0jm74 (0.88 #457, 0.84 #470, 0.84 #469), 0jmfb (0.88 #457, 0.84 #470, 0.84 #469), 0jm3b (0.88 #457, 0.84 #470, 0.84 #469), 0bwjj (0.88 #457, 0.84 #470, 0.84 #469), 0jmh7 (0.88 #457, 0.84 #470, 0.84 #469), 0fw9vx (0.88 #457, 0.84 #470, 0.84 #469), 0jm8l (0.88 #457, 0.84 #470, 0.84 #469) >> Best rule #457 for best value: >> intensional similarity = 102 >> extensional distance = 1 >> proper extension: 03tmr; >> query: (?x12913, ?x4369) <- sport(?x12370, ?x12913), sport(?x9983, ?x12913), sport(?x9576, ?x12913), sport(?x8728, ?x12913), sport(?x6003, ?x12913), sport(?x3798, ?x12913), sport(?x2303, ?x12913), teams(?x3983, ?x9576), colors(?x6003, ?x3315), colors(?x6003, ?x3189), colors(?x2303, ?x4557), colors(?x2303, ?x1101), team(?x4570, ?x12370), colors(?x12370, ?x9778), team(?x9070, ?x12370), teams(?x2740, ?x2303), ?x1101 = 06fvc, team(?x9266, ?x3798), dog_breed(?x2740, ?x6596), dog_breed(?x2740, ?x5194), dog_breed(?x2740, ?x3095), dog_breed(?x2740, ?x1706), ?x5194 = 01t032, ?x3315 = 0jc_p, contains(?x94, ?x2740), location(?x117, ?x2740), team(?x11924, ?x8728), team(?x1348, ?x6003), team(?x1348, ?x4369), ?x4557 = 019sc, featured_film_locations(?x2386, ?x2740), colors(?x9576, ?x9464), ?x6596 = 0km3f, colors(?x4296, ?x9464), contains(?x2740, ?x12485), ?x4296 = 07vyf, locations(?x9974, ?x2740), place_of_birth(?x4475, ?x2740), teams(?x3786, ?x9983), county_seat(?x10845, ?x2740), ?x9974 = 0b_6pv, teams(?x1248, ?x12370), team(?x11620, ?x9983), ?x1706 = 0km5c, vacationer(?x2740, ?x10770), colors(?x14319, ?x9778), colors(?x13695, ?x9778), colors(?x7900, ?x9778), colors(?x6732, ?x9778), colors(?x5780, ?x9778), colors(?x4410, ?x9778), category(?x3786, ?x134), contains(?x3786, ?x2775), ?x13695 = 0jksm, ?x134 = 08mbj5d, time_zones(?x2740, ?x2674), ?x14319 = 019tfm, place_of_birth(?x3785, ?x3786), location(?x1852, ?x3786), ?x5780 = 02zcz3, ?x3095 = 01_gx_, colors(?x13580, ?x3189), colors(?x10636, ?x3189), colors(?x10443, ?x3189), colors(?x10389, ?x3189), colors(?x9547, ?x3189), colors(?x6526, ?x3189), colors(?x2971, ?x3189), colors(?x1297, ?x3189), colors(?x978, ?x3189), colors(?x9066, ?x3189), colors(?x8879, ?x3189), colors(?x8694, ?x3189), colors(?x5306, ?x3189), colors(?x4220, ?x3189), ?x8879 = 0211jt, ?x4220 = 01v3ht, ?x8694 = 011xy1, ?x5306 = 0217m9, teams(?x5837, ?x3798), ?x6732 = 0gdm1, ?x7900 = 02nvg1, gender(?x11924, ?x231), type_of_union(?x9266, ?x566), administrative_division(?x1248, ?x3908), ?x9066 = 03l78j, ?x9547 = 04l5d0, ?x4410 = 017j69, ?x10389 = 08vk_r, team(?x4747, ?x9983), location(?x156, ?x5837), place_of_death(?x4473, ?x5837), ?x13580 = 01_1kk, ?x10443 = 03j6_5, ?x6526 = 03c0t9, ?x1297 = 03x746, ?x2971 = 04112r, team(?x1579, ?x3798), athlete(?x4833, ?x11924), ?x10636 = 04h54p, ?x978 = 03y_f8, county_seat(?x8854, ?x3786) >> conf = 0.88 => this is the best rule for 38 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 38, 503 EVAL 039yzs sport! 02pyyld CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 9.000 8.000 0.881 http://example.org/sports/sports_team/sport EVAL 039yzs sport! 03d555l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 9.000 8.000 0.881 http://example.org/sports/sports_team/sport EVAL 039yzs sport! 02pqcfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 9.000 8.000 0.881 http://example.org/sports/sports_team/sport #3933-02kz_ PRED entity: 02kz_ PRED relation: influenced_by! PRED expected values: 040_t 041_y 0mb0 => 189 concepts (75 used for prediction) PRED predicted values (max 10 best out of 444): 07lp1 (0.40 #2419, 0.20 #911, 0.17 #1416), 01hc9_ (0.40 #355, 0.17 #1362, 0.12 #32151), 0683n (0.33 #1339, 0.20 #834, 0.20 #332), 040db (0.33 #1081, 0.13 #4599, 0.10 #19664), 0lrh (0.33 #1109, 0.12 #32151, 0.10 #24611), 084w8 (0.33 #1008, 0.08 #18585, 0.07 #34666), 073v6 (0.33 #1121, 0.08 #18585, 0.07 #34666), 041xl (0.33 #1291, 0.08 #18585, 0.07 #34666), 040_t (0.33 #1257, 0.08 #18585, 0.07 #34666), 073bb (0.33 #1067, 0.05 #31646, 0.04 #11112) >> Best rule #2419 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 019z7q; 0m77m; 0lrh; 073v6; 01vdrw; >> query: (?x5336, 07lp1) <- award(?x5336, ?x921), influenced_by(?x5336, ?x3336), religion(?x5336, ?x1985), ?x3336 = 032l1 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1257 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 03f0324; 03_87; *> query: (?x5336, 040_t) <- influenced_by(?x5336, ?x118), influenced_by(?x8382, ?x5336), location(?x5336, ?x1658), ?x8382 = 0mb5x *> conf = 0.33 ranks of expected_values: 9, 49, 167 EVAL 02kz_ influenced_by! 0mb0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 189.000 75.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 02kz_ influenced_by! 041_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 189.000 75.000 0.400 http://example.org/influence/influence_node/influenced_by EVAL 02kz_ influenced_by! 040_t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 189.000 75.000 0.400 http://example.org/influence/influence_node/influenced_by #3932-024_vw PRED entity: 024_vw PRED relation: student! PRED expected values: 0bwfn => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 179): 03ksy (0.17 #6407, 0.15 #6932, 0.15 #5882), 02rff2 (0.17 #98, 0.14 #623, 0.11 #1148), 02ckl3 (0.17 #443, 0.10 #4644, 0.06 #4118), 02bq1j (0.17 #166, 0.09 #2266, 0.07 #2791), 07tgn (0.17 #17, 0.06 #3692, 0.05 #8946), 0f11p (0.17 #513, 0.06 #4188, 0.05 #4714), 0ymf1 (0.17 #523, 0.05 #4724, 0.02 #7349), 01mpwj (0.15 #5883, 0.14 #6408, 0.14 #4833), 08815 (0.14 #2627, 0.14 #527, 0.13 #3152), 0g8fs (0.14 #880, 0.11 #1405, 0.10 #1930) >> Best rule #6407 for best value: >> intensional similarity = 2 >> extensional distance = 27 >> proper extension: 03_js; >> query: (?x11605, 03ksy) <- legislative_sessions(?x11605, ?x5339), district_represented(?x5339, ?x335) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #10778 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 120 *> proper extension: 028r4y; 0ccqd7; 03c9pqt; 02k76g; *> query: (?x11605, 0bwfn) <- nationality(?x11605, ?x94), place_of_birth(?x11605, ?x739), ?x739 = 02_286, student(?x4016, ?x11605) *> conf = 0.11 ranks of expected_values: 19 EVAL 024_vw student! 0bwfn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 101.000 101.000 0.172 http://example.org/education/educational_institution/students_graduates./education/education/student #3931-0fb0v PRED entity: 0fb0v PRED relation: artist PRED expected values: 0137g1 01vsy95 01xzb6 0134pk 0mjn2 01nz1q6 01f2q5 => 78 concepts (36 used for prediction) PRED predicted values (max 10 best out of 954): 01wp8w7 (0.50 #6381, 0.33 #1652, 0.33 #864), 0178kd (0.50 #5940, 0.33 #1999, 0.18 #19344), 0bk1p (0.44 #14809, 0.33 #2197, 0.25 #19542), 01k23t (0.40 #8409, 0.19 #14716, 0.18 #19449), 0g824 (0.40 #8307, 0.17 #5943, 0.13 #26447), 0ffgh (0.40 #8357, 0.17 #5993, 0.09 #24920), 011z3g (0.40 #8329, 0.17 #5965, 0.07 #24892), 0qf3p (0.38 #14333, 0.33 #6450, 0.33 #1721), 01wg25j (0.33 #6886, 0.33 #6098, 0.33 #2945), 0gr69 (0.33 #6784, 0.33 #2843, 0.33 #1267) >> Best rule #6381 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 01clyr; 011k11; >> query: (?x1954, 01wp8w7) <- artist(?x1954, ?x10257), artist(?x1954, ?x8215), artist(?x1954, ?x7570), artist(?x1954, ?x5452), ?x10257 = 01v0sxx, instrumentalists(?x315, ?x7570), role(?x5452, ?x645), type_of_union(?x8215, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6518 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 01clyr; 011k11; *> query: (?x1954, 01vsy95) <- artist(?x1954, ?x10257), artist(?x1954, ?x8215), artist(?x1954, ?x7570), artist(?x1954, ?x5452), ?x10257 = 01v0sxx, instrumentalists(?x315, ?x7570), role(?x5452, ?x645), type_of_union(?x8215, ?x566) *> conf = 0.33 ranks of expected_values: 20, 42, 131, 298, 434, 518, 687 EVAL 0fb0v artist 01f2q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 78.000 36.000 0.500 http://example.org/music/record_label/artist EVAL 0fb0v artist 01nz1q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 78.000 36.000 0.500 http://example.org/music/record_label/artist EVAL 0fb0v artist 0mjn2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 78.000 36.000 0.500 http://example.org/music/record_label/artist EVAL 0fb0v artist 0134pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 78.000 36.000 0.500 http://example.org/music/record_label/artist EVAL 0fb0v artist 01xzb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 78.000 36.000 0.500 http://example.org/music/record_label/artist EVAL 0fb0v artist 01vsy95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 78.000 36.000 0.500 http://example.org/music/record_label/artist EVAL 0fb0v artist 0137g1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 78.000 36.000 0.500 http://example.org/music/record_label/artist #3930-02cgb8 PRED entity: 02cgb8 PRED relation: place_of_birth PRED expected values: 0ncy4 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 23): 04jpl (0.07 #712, 0.05 #8, 0.02 #15499), 02_286 (0.06 #4244, 0.06 #3540, 0.06 #23255), 030qb3t (0.05 #54, 0.04 #15545, 0.03 #26811), 0jgvy (0.05 #675), 0nbfm (0.05 #422), 01ngxm (0.05 #419), 01yj2 (0.05 #317), 0ygbf (0.05 #209), 04p3c (0.05 #163), 01jr6 (0.05 #143) >> Best rule #712 for best value: >> intensional similarity = 2 >> extensional distance = 332 >> proper extension: 07m69t; >> query: (?x5823, 04jpl) <- nationality(?x5823, ?x512), ?x512 = 07ssc >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02cgb8 place_of_birth 0ncy4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 61.000 0.072 http://example.org/people/person/place_of_birth #3929-01kt_j PRED entity: 01kt_j PRED relation: nominated_for! PRED expected values: 0gkr9q => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 191): 0gq9h (0.38 #10734, 0.35 #10497, 0.34 #10971), 0gs9p (0.34 #10736, 0.32 #10499, 0.31 #11448), 019f4v (0.33 #10725, 0.31 #10488, 0.30 #11437), 0gkr9q (0.32 #445, 0.24 #11383, 0.23 #919), 0bdw6t (0.32 #321, 0.20 #1269, 0.20 #1743), 0cjyzs (0.31 #81, 0.25 #1503, 0.24 #1740), 0k611 (0.28 #10744, 0.27 #10507, 0.26 #11218), 0gq_v (0.27 #10691, 0.26 #11403, 0.26 #11165), 040njc (0.27 #10678, 0.25 #10441, 0.25 #11390), 027gs1_ (0.26 #1608, 0.26 #1845, 0.25 #186) >> Best rule #10734 for best value: >> intensional similarity = 3 >> extensional distance = 487 >> proper extension: 04hwbq; 0gpx6; >> query: (?x10595, 0gq9h) <- award_winner(?x10595, ?x2296), honored_for(?x4760, ?x10595), nominated_for(?x686, ?x10595) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #445 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 09v38qj; 0gxr1c; *> query: (?x10595, 0gkr9q) <- actor(?x10595, ?x2296), genre(?x10595, ?x812), genre(?x10595, ?x604), ?x812 = 01jfsb, genre(?x54, ?x604) *> conf = 0.32 ranks of expected_values: 4 EVAL 01kt_j nominated_for! 0gkr9q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 87.000 0.376 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3928-011yrp PRED entity: 011yrp PRED relation: genre PRED expected values: 082gq => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 96): 02bjrlw (0.56 #2173, 0.55 #121, 0.50 #4345), 06cvj (0.52 #967, 0.30 #124, 0.25 #2418), 02kdv5l (0.41 #725, 0.33 #1571, 0.27 #4949), 01jfsb (0.35 #735, 0.32 #4959, 0.32 #4597), 03k9fj (0.32 #1580, 0.31 #734, 0.28 #132), 0lsxr (0.31 #8, 0.20 #2059, 0.20 #1334), 01hmnh (0.27 #740, 0.25 #1586, 0.23 #138), 082gq (0.25 #30, 0.24 #391, 0.20 #1478), 06n90 (0.25 #736, 0.21 #1582, 0.18 #616), 04xvlr (0.23 #2052, 0.21 #242, 0.21 #2295) >> Best rule #2173 for best value: >> intensional similarity = 4 >> extensional distance = 393 >> proper extension: 0140g4; 02_fm2; 0c0yh4; 0yyg4; 011yxg; 01hr1; 0ds11z; 050r1z; 02_1sj; 0n0bp; ... >> query: (?x303, ?x90) <- films(?x326, ?x303), country(?x303, ?x205), titles(?x90, ?x303), country(?x150, ?x205) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 03ckwzc; 019vhk; 03prz_; 02vjp3; 03xj05; *> query: (?x303, 082gq) <- films(?x326, ?x303), country(?x303, ?x205), titles(?x90, ?x303), ?x205 = 03rjj *> conf = 0.25 ranks of expected_values: 8 EVAL 011yrp genre 082gq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 83.000 83.000 0.557 http://example.org/film/film/genre #3927-01wrcxr PRED entity: 01wrcxr PRED relation: type_of_union PRED expected values: 04ztj => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.72 #117, 0.71 #137, 0.71 #29), 01g63y (0.31 #14, 0.21 #34, 0.20 #46), 0jgjn (0.19 #345), 01bl8s (0.19 #345) >> Best rule #117 for best value: >> intensional similarity = 3 >> extensional distance = 1090 >> proper extension: 049tjg; 06jzh; 0785v8; 04sx9_; 04shbh; 019_1h; 03f1zdw; 030znt; 02wrhj; 02k6rq; ... >> query: (?x6042, 04ztj) <- location(?x6042, ?x479), film(?x6042, ?x9800), film(?x2344, ?x9800) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wrcxr type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.718 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3926-08phg9 PRED entity: 08phg9 PRED relation: nominated_for! PRED expected values: 018wdw => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 201): 057xs89 (0.44 #341, 0.15 #2861, 0.14 #3090), 0gq9h (0.41 #8993, 0.40 #4870, 0.39 #8764), 0gs9p (0.37 #8995, 0.35 #8766, 0.35 #4872), 019f4v (0.35 #8984, 0.35 #4861, 0.34 #6694), 0k611 (0.35 #4881, 0.31 #5568, 0.30 #9004), 054krc (0.34 #3730, 0.30 #5564, 0.24 #2356), 0gr51 (0.33 #73, 0.23 #4885, 0.21 #9008), 02xj3rw (0.33 #198, 0.04 #885, 0.04 #1115), 099c8n (0.32 #4864, 0.28 #1198, 0.27 #739), 0l8z1 (0.32 #5546, 0.30 #3712, 0.26 #917) >> Best rule #341 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 02x8fs; >> query: (?x5128, 057xs89) <- award_winner(?x5128, ?x1596), genre(?x5128, ?x225), ?x225 = 02kdv5l, film(?x3558, ?x5128) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2233 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 122 *> proper extension: 0czyxs; 0gtv7pk; 064n1pz; 0184tc; 07kb7vh; 04nlb94; 07ykkx5; *> query: (?x5128, 018wdw) <- film_crew_role(?x5128, ?x281), nominated_for(?x277, ?x5128), film_distribution_medium(?x5128, ?x2099) *> conf = 0.17 ranks of expected_values: 50 EVAL 08phg9 nominated_for! 018wdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 108.000 108.000 0.438 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3925-03n08b PRED entity: 03n08b PRED relation: award PRED expected values: 05pcn59 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 238): 09sb52 (0.32 #4877, 0.32 #5683, 0.32 #6086), 0gq9h (0.26 #1286, 0.11 #4511, 0.08 #7332), 040njc (0.22 #1217, 0.09 #7263, 0.08 #4442), 05pcn59 (0.20 #2499, 0.20 #2096, 0.18 #18944), 01by1l (0.19 #6963, 0.11 #2126, 0.11 #2529), 0ck27z (0.18 #10571, 0.17 #4929, 0.14 #9765), 07bdd_ (0.18 #1274, 0.09 #4499, 0.06 #7320), 01bgqh (0.15 #6894, 0.10 #2057, 0.09 #2460), 05p1dby (0.14 #1315, 0.06 #4540, 0.05 #7361), 05p09zm (0.14 #2138, 0.13 #2541, 0.13 #17331) >> Best rule #4877 for best value: >> intensional similarity = 3 >> extensional distance = 618 >> proper extension: 05gnf; 039cq4; >> query: (?x1461, 09sb52) <- award_winner(?x1461, ?x2560), participant(?x1986, ?x2560), nominated_for(?x2560, ?x8775) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #2499 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 283 *> proper extension: 025ldg; 02x_h0; *> query: (?x1461, 05pcn59) <- award_winner(?x1460, ?x1461), participant(?x1461, ?x7830) *> conf = 0.20 ranks of expected_values: 4 EVAL 03n08b award 05pcn59 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 75.000 0.323 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3924-01jmv8 PRED entity: 01jmv8 PRED relation: type_of_union PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.83 #21, 0.77 #29, 0.76 #33), 01g63y (0.43 #133, 0.16 #26, 0.16 #22) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 02jt1k; 057hz; 01wk51; 02z1yj; 01bj6y; >> query: (?x8674, 04ztj) <- award(?x8674, ?x1132), film(?x8674, ?x1331), ?x1132 = 0bdwft >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jmv8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.829 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3923-01rwyq PRED entity: 01rwyq PRED relation: films! PRED expected values: 06d4h => 79 concepts (36 used for prediction) PRED predicted values (max 10 best out of 73): 02vnz (0.25 #124, 0.01 #594, 0.01 #1069), 05489 (0.12 #208, 0.06 #522, 0.05 #680), 0g1x2_ (0.12 #183, 0.03 #497, 0.03 #655), 07jdr (0.12 #191), 0fx2s (0.08 #543, 0.05 #701, 0.02 #3399), 07c52 (0.07 #648, 0.05 #490, 0.03 #1125), 0d1w9 (0.05 #506, 0.02 #349, 0.01 #3362), 081pw (0.05 #631, 0.03 #3012, 0.03 #1743), 0fzyg (0.05 #367, 0.03 #1159, 0.02 #1794), 0ddct (0.05 #401, 0.01 #1193, 0.01 #1351) >> Best rule #124 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05sns6; >> query: (?x3388, 02vnz) <- nominated_for(?x1033, ?x3388), film(?x1700, ?x3388), ?x1700 = 02jt1k, award(?x197, ?x1033) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #829 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 93 *> proper extension: 0415ggl; *> query: (?x3388, 06d4h) <- titles(?x1316, ?x3388), film(?x447, ?x3388), ?x1316 = 017fp, genre(?x3388, ?x6887) *> conf = 0.04 ranks of expected_values: 11 EVAL 01rwyq films! 06d4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 79.000 36.000 0.250 http://example.org/film/film_subject/films #3922-01kstn9 PRED entity: 01kstn9 PRED relation: artist! PRED expected values: 043ljr => 136 concepts (93 used for prediction) PRED predicted values (max 10 best out of 106): 03mp8k (0.41 #3008, 0.20 #906, 0.16 #486), 015_1q (0.29 #2961, 0.24 #3522, 0.24 #1279), 01f_3w (0.26 #454, 0.22 #874, 0.14 #1714), 043g7l (0.25 #2973, 0.20 #871, 0.16 #451), 01q940 (0.22 #2713, 0.13 #471, 0.12 #891), 0g768 (0.20 #37, 0.13 #1157, 0.13 #737), 0190vc (0.20 #85, 0.06 #1205, 0.05 #1765), 033hn8 (0.19 #2956, 0.15 #714, 0.11 #9404), 017l96 (0.17 #298, 0.17 #158, 0.12 #578), 01cszh (0.16 #431, 0.15 #2673, 0.15 #1691) >> Best rule #3008 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 019g40; 0136p1; 01cwhp; 018pj3; 01vx5w7; 018ndc; 01wj18h; 01wv9p; 025ldg; 02lvtb; ... >> query: (?x3539, 03mp8k) <- artist(?x7089, ?x3539), artist(?x7089, ?x1165), ?x1165 = 018y2s >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #4484 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 219 *> proper extension: 01pbxb; 01vrx3g; 0m2l9; 025xt8y; 018y2s; 01vs14j; 09qr6; 015_30; 02zmh5; 01vsnff; ... *> query: (?x3539, ?x3006) <- role(?x3539, ?x2048), award_nominee(?x4102, ?x3539), artist(?x3006, ?x4102) *> conf = 0.09 ranks of expected_values: 30 EVAL 01kstn9 artist! 043ljr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 136.000 93.000 0.413 http://example.org/music/record_label/artist #3921-01tpl1p PRED entity: 01tpl1p PRED relation: actor! PRED expected values: 019g8j => 100 concepts (68 used for prediction) PRED predicted values (max 10 best out of 229): 026bfsh (0.18 #1674, 0.18 #1411, 0.17 #2463), 024rwx (0.09 #368, 0.04 #10635, 0.02 #12213), 01xr2s (0.09 #556, 0.04 #1345, 0.03 #1608), 05f7w84 (0.09 #369, 0.03 #9321, 0.02 #10636), 026y3cf (0.07 #1826, 0.05 #2615, 0.05 #1563), 0kfv9 (0.05 #9242, 0.04 #10557, 0.03 #12135), 03kq98 (0.05 #2376, 0.05 #1324, 0.04 #1587), 02_1q9 (0.05 #1320, 0.05 #10535, 0.05 #531), 0464pz (0.05 #1338, 0.04 #2390, 0.03 #9238), 0828jw (0.05 #9319, 0.04 #10634, 0.03 #1682) >> Best rule #1674 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 02p65p; 0lzb8; 04y79_n; 044mm6; 09f0bj; 01zmpg; 0306ds; 044gyq; 05yh_t; 01d1st; ... >> query: (?x10607, 026bfsh) <- people(?x2510, ?x10607), ?x2510 = 0x67, nationality(?x10607, ?x94), actor(?x3144, ?x10607) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #491 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 02wrhj; *> query: (?x10607, 019g8j) <- location(?x10607, ?x9311), contains(?x279, ?x9311), actor(?x3144, ?x10607), ?x279 = 0d060g *> conf = 0.05 ranks of expected_values: 33 EVAL 01tpl1p actor! 019g8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 100.000 68.000 0.179 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #3920-050llt PRED entity: 050llt PRED relation: languages PRED expected values: 02h40lc => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 15): 02h40lc (0.90 #527, 0.89 #457, 0.88 #562), 0999q (0.44 #56, 0.08 #126, 0.06 #301), 09s02 (0.22 #68, 0.14 #33, 0.07 #138), 064_8sq (0.14 #13, 0.11 #48, 0.10 #538), 055qm (0.14 #22, 0.11 #57, 0.05 #127), 0121sr (0.14 #30, 0.03 #135, 0.01 #310), 01c7y (0.11 #63, 0.04 #98, 0.03 #133), 02bjrlw (0.05 #526, 0.05 #176, 0.04 #666), 06nm1 (0.04 #565, 0.04 #460, 0.03 #670), 04306rv (0.03 #458, 0.03 #528, 0.03 #563) >> Best rule #527 for best value: >> intensional similarity = 3 >> extensional distance = 358 >> proper extension: 01h4rj; >> query: (?x12189, 02h40lc) <- languages(?x12189, ?x1882), film(?x12189, ?x3742), type_of_union(?x12189, ?x566) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050llt languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.900 http://example.org/people/person/languages #3919-02lxj_ PRED entity: 02lxj_ PRED relation: place_of_death PRED expected values: 030qb3t => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 22): 0cv3w (0.20 #238, 0.02 #1210), 030qb3t (0.18 #605, 0.17 #1188, 0.14 #2935), 0k049 (0.18 #586, 0.09 #3111, 0.08 #1169), 02_286 (0.07 #2926, 0.07 #3121, 0.06 #596), 06_kh (0.07 #583, 0.07 #393, 0.06 #1171), 0f2wj (0.06 #595, 0.04 #2925, 0.04 #5841), 0r3w7 (0.06 #760, 0.04 #1343, 0.01 #3285), 04jpl (0.04 #1173, 0.03 #3115, 0.03 #5836), 0rd5k (0.04 #1022), 05qtj (0.02 #3755, 0.02 #5893, 0.02 #6282) >> Best rule #238 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 018qql; >> query: (?x1623, 0cv3w) <- participant(?x9777, ?x1623), people(?x12624, ?x1623), ?x12624 = 019dmc >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #605 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0lrh; 01fs_4; 044qx; 0432b; 0cf2h; 0bw87; 0c2tf; 02p5hf; 09x8ms; *> query: (?x1623, 030qb3t) <- participant(?x9777, ?x1623), people(?x12624, ?x1623), student(?x5288, ?x1623) *> conf = 0.18 ranks of expected_values: 2 EVAL 02lxj_ place_of_death 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 133.000 133.000 0.200 http://example.org/people/deceased_person/place_of_death #3918-017z49 PRED entity: 017z49 PRED relation: film_release_region PRED expected values: 06mkj => 119 concepts (107 used for prediction) PRED predicted values (max 10 best out of 153): 06mkj (0.89 #895, 0.89 #470, 0.88 #1320), 03rt9 (0.84 #1002, 0.74 #1852, 0.73 #2135), 05v8c (0.73 #1854, 0.71 #2137, 0.70 #1004), 04gzd (0.70 #998, 0.69 #1848, 0.69 #2131), 03rk0 (0.69 #469, 0.65 #1036, 0.60 #1886), 06qd3 (0.66 #452, 0.64 #877, 0.55 #735), 05qx1 (0.63 #455, 0.51 #1022, 0.50 #1872), 01p1v (0.62 #1032, 0.59 #1882, 0.57 #2165), 09pmkv (0.57 #444, 0.46 #1011, 0.44 #1861), 06f32 (0.54 #1045, 0.49 #1895, 0.49 #478) >> Best rule #895 for best value: >> intensional similarity = 5 >> extensional distance = 74 >> proper extension: 0c3ybss; 03g90h; 03bx2lk; 09v71cj; 02rmd_2; 04yg13l; 0glqh5_; 02825cv; 0bs8ndx; 076xkps; ... >> query: (?x3482, 06mkj) <- film(?x72, ?x3482), titles(?x812, ?x3482), film_release_region(?x3482, ?x1603), ?x1603 = 06bnz, currency(?x3482, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017z49 film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 107.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3917-02wr6r PRED entity: 02wr6r PRED relation: type_of_union PRED expected values: 04ztj => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.84 #41, 0.80 #45, 0.79 #37), 01g63y (0.15 #34, 0.15 #74, 0.14 #18) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 217 >> proper extension: 05hjmd; >> query: (?x9775, 04ztj) <- people(?x10199, ?x9775), nominated_for(?x9775, ?x7784), gender(?x9775, ?x231) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wr6r type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.840 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3916-02xjb PRED entity: 02xjb PRED relation: artists PRED expected values: 01jfr3y => 65 concepts (23 used for prediction) PRED predicted values (max 10 best out of 3680): 06mt91 (0.64 #1697, 0.57 #2782, 0.52 #6038), 03t9sp (0.60 #10978, 0.50 #2293, 0.50 #1208), 01x1cn2 (0.57 #2366, 0.57 #1281, 0.50 #3451), 01vtj38 (0.57 #2833, 0.57 #1748, 0.50 #664), 01vxlbm (0.57 #2512, 0.57 #1427, 0.50 #343), 0136p1 (0.57 #2313, 0.50 #3398, 0.50 #1228), 01r7pq (0.57 #2847, 0.50 #1762, 0.50 #678), 09889g (0.55 #4789, 0.50 #2620, 0.50 #1535), 01dwrc (0.50 #4863, 0.50 #2694, 0.50 #1609), 011z3g (0.50 #4943, 0.50 #2774, 0.50 #1689) >> Best rule #1697 for best value: >> intensional similarity = 8 >> extensional distance = 12 >> proper extension: 0m0jc; 06by7; 0y3_8; 06j6l; 025sc50; 08cyft; 026z9; 0ggx5q; 02vjzr; 035wcs; >> query: (?x4122, 06mt91) <- artists(?x4122, ?x4394), artists(?x4122, ?x4123), ?x4394 = 049qx, profession(?x4123, ?x1032), award_winner(?x1480, ?x4123), ?x1032 = 02hrh1q, award(?x4123, ?x884), currency(?x4123, ?x170) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #534 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 064t9; 02lnbg; *> query: (?x4122, 01jfr3y) <- artists(?x4122, ?x4394), artists(?x4122, ?x4123), ?x4394 = 049qx, ?x4123 = 01wv9p *> conf = 0.50 ranks of expected_values: 22 EVAL 02xjb artists 01jfr3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 65.000 23.000 0.643 http://example.org/music/genre/artists #3915-0130sy PRED entity: 0130sy PRED relation: nationality PRED expected values: 02jx1 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 30): 02jx1 (0.43 #131, 0.35 #627, 0.28 #1420), 07ssc (0.29 #113, 0.19 #1402, 0.16 #2892), 0l2v0 (0.28 #10226), 03_3d (0.14 #104, 0.06 #203, 0.03 #2386), 03rk0 (0.12 #6498, 0.08 #7092, 0.06 #9374), 03rjj (0.11 #202, 0.04 #301, 0.04 #698), 04jpl (0.07 #2183), 0d060g (0.06 #9235, 0.05 #3975, 0.05 #5070), 06bnz (0.06 #238, 0.04 #734, 0.02 #635), 03rt9 (0.06 #210, 0.04 #904, 0.03 #2790) >> Best rule #131 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01sxd1; 0ftqr; 01mxnvc; >> query: (?x6838, 02jx1) <- artists(?x11106, ?x6838), profession(?x6838, ?x220), artist(?x3265, ?x6838), ?x11106 = 0781g >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0130sy nationality 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.429 http://example.org/people/person/nationality #3914-0168dy PRED entity: 0168dy PRED relation: people! PRED expected values: 0lcdk => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 46): 0gk4g (0.26 #2378, 0.24 #2314, 0.24 #138), 0qcr0 (0.15 #1473, 0.13 #1985, 0.13 #2369), 04p3w (0.12 #331, 0.11 #523, 0.11 #715), 0m32h (0.10 #150, 0.08 #342, 0.05 #598), 0j8hd (0.10 #173, 0.08 #365, 0.05 #749), 019dmc (0.10 #496, 0.05 #176, 0.04 #240), 02y0js (0.08 #3970, 0.08 #322, 0.07 #3842), 01tf_6 (0.08 #350, 0.05 #606, 0.05 #542), 0jdk0 (0.08 #325, 0.05 #133, 0.04 #581), 02knxx (0.08 #2335, 0.07 #2399, 0.05 #607) >> Best rule #2378 for best value: >> intensional similarity = 3 >> extensional distance = 224 >> proper extension: 01vyp_; 0164w8; 01vsy9_; 01p7b6b; 0b_dh; 01hkck; 03csqj4; 02zfg3; >> query: (?x10770, 0gk4g) <- people(?x6260, ?x10770), nominated_for(?x10770, ?x2081), award(?x10770, ?x154) >> conf = 0.26 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0168dy people! 0lcdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 125.000 0.257 http://example.org/people/cause_of_death/people #3913-0f_y9 PRED entity: 0f_y9 PRED relation: instrumentalists! PRED expected values: 0342h => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 103): 0342h (0.75 #1124, 0.69 #3964, 0.67 #262), 05r5c (0.50 #1559, 0.48 #3624, 0.48 #1214), 018vs (0.42 #271, 0.33 #1133, 0.32 #1564), 03qjg (0.33 #309, 0.23 #653, 0.21 #481), 02hnl (0.25 #292, 0.20 #378, 0.20 #1585), 026t6 (0.20 #88, 0.14 #1122, 0.13 #1553), 042v_gx (0.20 #95, 0.05 #1129, 0.05 #353), 02sgy (0.20 #92, 0.05 #350, 0.04 #1212), 018j2 (0.17 #296, 0.11 #1158, 0.10 #1330), 06ncr (0.17 #302, 0.10 #474, 0.10 #388) >> Best rule #1124 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 01p45_v; 02fybl; >> query: (?x7345, 0342h) <- profession(?x7345, ?x2659), location(?x7345, ?x659), ?x2659 = 039v1, place_of_birth(?x1775, ?x659) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f_y9 instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.753 http://example.org/music/instrument/instrumentalists #3912-01d_h8 PRED entity: 01d_h8 PRED relation: film_crew_role! PRED expected values: 057lbk => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1277): 0bth54 (0.75 #15390, 0.56 #16667, 0.47 #17944), 0ct2tf5 (0.67 #16463, 0.47 #17740, 0.39 #19017), 09sh8k (0.67 #15339, 0.47 #16616, 0.39 #17893), 04n52p6 (0.67 #15533, 0.44 #16810, 0.37 #18087), 05pbl56 (0.67 #15519, 0.44 #16796, 0.37 #18073), 057lbk (0.67 #15883, 0.44 #17160, 0.37 #18437), 016dj8 (0.67 #16152, 0.41 #17429, 0.34 #18706), 014kq6 (0.67 #15597, 0.38 #16874, 0.32 #18151), 06_x996 (0.67 #15840, 0.38 #17117, 0.32 #18394), 07yk1xz (0.67 #15602, 0.38 #16879, 0.32 #18156) >> Best rule #15390 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 02_n3z; 02r96rf; 0ch6mp2; 0263ycg; 0215hd; 02zdwq; >> query: (?x319, 0bth54) <- film_crew_role(?x2006, ?x319), ?x2006 = 031778 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #15883 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 10 *> proper extension: 02_n3z; 02r96rf; 0ch6mp2; 0263ycg; 0215hd; 02zdwq; *> query: (?x319, 057lbk) <- film_crew_role(?x2006, ?x319), ?x2006 = 031778 *> conf = 0.67 ranks of expected_values: 6 EVAL 01d_h8 film_crew_role! 057lbk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 40.000 40.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3911-0hndn2q PRED entity: 0hndn2q PRED relation: award_winner PRED expected values: 04cl1 09zmys => 47 concepts (38 used for prediction) PRED predicted values (max 10 best out of 453): 02l840 (0.43 #7722, 0.33 #6197, 0.27 #9248), 0bwh6 (0.40 #3227, 0.18 #10854, 0.11 #7621), 0mz73 (0.40 #4176, 0.11 #7621), 01wmxfs (0.40 #3150), 0fvf9q (0.33 #6105, 0.29 #7630, 0.18 #9156), 023p29 (0.33 #7472, 0.29 #8997, 0.18 #10523), 02z4b_8 (0.33 #7139, 0.29 #8664, 0.18 #10190), 01vw20h (0.33 #6784, 0.29 #8309, 0.18 #9835), 02cx90 (0.33 #6753, 0.29 #8278, 0.18 #9804), 0fpjd_g (0.33 #6302, 0.29 #7827, 0.18 #9353) >> Best rule #7722 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0hhtgcw; >> query: (?x2515, 02l840) <- award_winner(?x2515, ?x1541), award_winner(?x2515, ?x1179), locations(?x2515, ?x191), nominated_for(?x1179, ?x3784), award_winner(?x1541, ?x3698), award(?x1179, ?x198) >> conf = 0.43 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hndn2q award_winner 09zmys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 38.000 0.429 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0hndn2q award_winner 04cl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 38.000 0.429 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3910-059x3p PRED entity: 059x3p PRED relation: production_companies! PRED expected values: 03ntbmw => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1549): 03ntbmw (0.49 #13779, 0.47 #33302, 0.32 #28707), 02q0k7v (0.40 #3152, 0.33 #4300, 0.12 #22671), 03clwtw (0.33 #4240, 0.20 #3092, 0.12 #8833), 04tc1g (0.33 #3539, 0.20 #2391, 0.06 #5835), 03rtz1 (0.33 #3563, 0.20 #2415, 0.06 #5859), 01cssf (0.25 #1212, 0.18 #4656, 0.18 #5804), 05sns6 (0.25 #1624, 0.18 #5068, 0.18 #7365), 01gc7 (0.25 #1173, 0.18 #4617, 0.17 #9210), 048vhl (0.25 #2115, 0.12 #22782, 0.12 #7856), 011wtv (0.25 #1657, 0.12 #8546, 0.12 #6249) >> Best rule #13779 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 02zc7f; >> query: (?x11557, ?x857) <- citytown(?x11557, ?x191), company(?x9316, ?x11557), award_winner(?x10806, ?x9316), produced_by(?x857, ?x9316) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059x3p production_companies! 03ntbmw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.493 http://example.org/film/film/production_companies #3909-0cpllql PRED entity: 0cpllql PRED relation: film_distribution_medium PRED expected values: 029j_ => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.63 #19, 0.17 #9, 0.14 #24), 029j_ (0.33 #16, 0.12 #21, 0.12 #26), 0dq6p (0.11 #17, 0.07 #22, 0.07 #27), 07c52 (0.03 #18), 07z4p (0.01 #20) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 0522wp; >> query: (?x626, 0735l) <- category(?x626, ?x134), film_distribution_medium(?x626, ?x627), film_release_distribution_medium(?x12648, ?x627), nominated_for(?x1312, ?x12648) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 0522wp; *> query: (?x626, 029j_) <- category(?x626, ?x134), film_distribution_medium(?x626, ?x627), film_release_distribution_medium(?x12648, ?x627), nominated_for(?x1312, ?x12648) *> conf = 0.33 ranks of expected_values: 2 EVAL 0cpllql film_distribution_medium 029j_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.627 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #3908-05p1dby PRED entity: 05p1dby PRED relation: award! PRED expected values: 04mcw4 => 48 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1598): 017jd9 (0.50 #4521, 0.33 #1477, 0.25 #2492), 04v8x9 (0.50 #4097, 0.33 #1053, 0.25 #2068), 0bdjd (0.33 #4795, 0.33 #1751, 0.25 #2766), 0y_9q (0.33 #4598, 0.33 #1554, 0.25 #2569), 0ccd3x (0.33 #4515, 0.33 #1471, 0.25 #2486), 0404j37 (0.33 #4724, 0.33 #1680, 0.25 #2695), 0pv3x (0.33 #4168, 0.33 #1124, 0.25 #2139), 042y1c (0.33 #4293, 0.33 #1249, 0.25 #2264), 01cmp9 (0.33 #4670, 0.33 #1626, 0.25 #2641), 0ywrc (0.33 #4367, 0.33 #1323, 0.25 #2338) >> Best rule #4521 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0p9sw; 0gr42; 02x1z2s; >> query: (?x2022, 017jd9) <- award(?x4397, ?x2022), award(?x382, ?x2022), ?x382 = 086k8, award(?x351, ?x2022), nominated_for(?x4397, ?x240) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1016 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 07bdd_; *> query: (?x2022, ?x148) <- award(?x9195, ?x2022), award(?x382, ?x2022), film(?x382, ?x83), ?x9195 = 030g9z, nominated_for(?x2022, ?x148) *> conf = 0.23 ranks of expected_values: 105 EVAL 05p1dby award! 04mcw4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 48.000 25.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #3907-02qjv PRED entity: 02qjv PRED relation: role! PRED expected values: 03gvt => 72 concepts (52 used for prediction) PRED predicted values (max 10 best out of 92): 01vdm0 (0.88 #2398, 0.84 #1091, 0.83 #3411), 0395lw (0.87 #2305, 0.84 #1091, 0.83 #2190), 05r5c (0.85 #906, 0.84 #1091, 0.83 #2190), 01s0ps (0.85 #906, 0.84 #1091, 0.83 #2190), 0680x0 (0.84 #1091, 0.83 #2190, 0.83 #1184), 0l14md (0.84 #1091, 0.83 #2190, 0.83 #1184), 04rzd (0.84 #1091, 0.83 #2190, 0.83 #1184), 07y_7 (0.84 #1091, 0.83 #2190, 0.83 #1184), 07brj (0.84 #1091, 0.83 #2190, 0.83 #1184), 01qbl (0.84 #1091, 0.83 #2190, 0.83 #1184) >> Best rule #2398 for best value: >> intensional similarity = 23 >> extensional distance = 14 >> proper extension: 07_l6; >> query: (?x1148, 01vdm0) <- role(?x3161, ?x1148), role(?x2798, ?x1148), role(?x1482, ?x1148), role(?x1473, ?x1148), role(?x745, ?x1148), ?x1473 = 0g2dz, role(?x1148, ?x315), ?x745 = 01vj9c, role(?x1482, ?x5417), role(?x1482, ?x1655), role(?x1148, ?x1432), ?x3161 = 01v1d8, ?x2798 = 03qjg, role(?x7794, ?x1148), role(?x5494, ?x1148), ?x5417 = 02w3w, award_winner(?x1323, ?x7794), award_winner(?x5493, ?x5494), award(?x5494, ?x1565), profession(?x5494, ?x131), gender(?x5494, ?x231), ?x1655 = 01hww_, role(?x1432, ?x960) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1972 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 9 *> proper extension: 0bxl5; *> query: (?x1148, 03gvt) <- role(?x5926, ?x1148), role(?x3161, ?x1148), role(?x2798, ?x1148), role(?x1482, ?x1148), role(?x1473, ?x1148), role(?x745, ?x1148), role(?x569, ?x1148), ?x1473 = 0g2dz, role(?x1148, ?x315), ?x745 = 01vj9c, role(?x1482, ?x615), role(?x1148, ?x75), ?x3161 = 01v1d8, role(?x317, ?x1148), instrumentalists(?x2798, ?x8308), instrumentalists(?x2798, ?x4790), role(?x74, ?x2798), ?x4790 = 01kph_c, ?x8308 = 04mx7s, ?x5926 = 0cfdd, ?x569 = 07c6l, group(?x2798, ?x11551), group(?x2798, ?x7227), ?x11551 = 0cfgd, ?x7227 = 01kcms4 *> conf = 0.73 ranks of expected_values: 22 EVAL 02qjv role! 03gvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 72.000 52.000 0.875 http://example.org/music/performance_role/track_performances./music/track_contribution/role #3906-089j8p PRED entity: 089j8p PRED relation: nominated_for! PRED expected values: 0fq9zcx => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 213): 0gq_v (0.57 #943, 0.32 #1636, 0.29 #2329), 0gq9h (0.51 #983, 0.34 #9068, 0.34 #9530), 019f4v (0.44 #975, 0.42 #9522, 0.27 #9060), 0gs9p (0.43 #985, 0.34 #9532, 0.30 #9070), 0gr0m (0.42 #981, 0.25 #3060, 0.22 #1674), 03c7tr1 (0.40 #44, 0.11 #9053, 0.06 #2354), 0k611 (0.39 #993, 0.27 #9540, 0.25 #3072), 0gqyl (0.39 #9085, 0.27 #1000, 0.21 #2386), 0l8z1 (0.36 #973, 0.20 #21259, 0.19 #19640), 040njc (0.31 #1392, 0.29 #930, 0.26 #9477) >> Best rule #943 for best value: >> intensional similarity = 4 >> extensional distance = 97 >> proper extension: 0gmgwnv; >> query: (?x6446, 0gq_v) <- film_release_region(?x6446, ?x87), nominated_for(?x2222, ?x6446), nominated_for(?x2805, ?x6446), ?x2222 = 0gs96 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1610 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 109 *> proper extension: 02qrv7; *> query: (?x6446, 0fq9zcx) <- film(?x3462, ?x6446), titles(?x512, ?x6446), ?x512 = 07ssc, language(?x6446, ?x254) *> conf = 0.05 ranks of expected_values: 95 EVAL 089j8p nominated_for! 0fq9zcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 105.000 105.000 0.566 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3905-05lb30 PRED entity: 05lb30 PRED relation: profession PRED expected values: 02hrh1q => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 49): 02hrh1q (0.89 #765, 0.88 #2265, 0.88 #4066), 03gjzk (0.33 #1366, 0.29 #3151, 0.24 #5267), 0dxtg (0.30 #1364, 0.27 #5265, 0.26 #9015), 01d_h8 (0.30 #5257, 0.29 #9007, 0.29 #3151), 0cbd2 (0.29 #3151, 0.15 #5408, 0.13 #457), 0np9r (0.29 #3151, 0.14 #9923, 0.14 #10073), 0d1pc (0.29 #3151, 0.11 #52, 0.09 #12302), 015cjr (0.29 #3151, 0.06 #51, 0.05 #201), 02jknp (0.20 #8409, 0.20 #9009, 0.19 #1208), 09jwl (0.19 #1520, 0.18 #5871, 0.18 #1670) >> Best rule #765 for best value: >> intensional similarity = 3 >> extensional distance = 685 >> proper extension: 023n39; 065mm1; 045gzq; >> query: (?x6632, 02hrh1q) <- place_of_birth(?x6632, ?x3269), film(?x6632, ?x6681), student(?x6637, ?x6632) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05lb30 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.886 http://example.org/people/person/profession #3904-04mjl PRED entity: 04mjl PRED relation: draft PRED expected values: 02x2khw => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 14): 02pq_x5 (0.77 #590, 0.57 #112, 0.57 #210), 02x2khw (0.77 #590, 0.51 #200, 0.51 #214), 05vsb7 (0.38 #226, 0.33 #240, 0.29 #576), 092j54 (0.38 #232, 0.31 #246, 0.31 #582), 09l0x9 (0.36 #234, 0.31 #248, 0.30 #795), 0g3zpp (0.36 #227, 0.30 #788, 0.30 #816), 03nt7j (0.33 #231, 0.29 #245, 0.26 #792), 025tn92 (0.33 #38, 0.26 #585, 0.26 #796), 0f4vx0 (0.33 #36, 0.26 #794, 0.26 #822), 09th87 (0.33 #39, 0.25 #68, 0.24 #839) >> Best rule #590 for best value: >> intensional similarity = 5 >> extensional distance = 70 >> proper extension: 04cxw5b; >> query: (?x7357, ?x1161) <- draft(?x7357, ?x8499), draft(?x1438, ?x8499), teams(?x1523, ?x7357), draft(?x1438, ?x1161), contains(?x94, ?x1523) >> conf = 0.77 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 04mjl draft 02x2khw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.772 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #3903-04ktcgn PRED entity: 04ktcgn PRED relation: crewmember! PRED expected values: 032sl_ 0419kt => 81 concepts (48 used for prediction) PRED predicted values (max 10 best out of 303): 02dr9j (0.37 #1506, 0.36 #1807, 0.35 #1205), 017jd9 (0.37 #1506, 0.36 #1807, 0.35 #1205), 0pc62 (0.37 #1506, 0.36 #1807, 0.35 #1205), 03y0pn (0.37 #1506, 0.36 #1807, 0.35 #1205), 0ndwt2w (0.37 #1506, 0.36 #1807, 0.35 #1205), 02c638 (0.37 #1506, 0.36 #1807, 0.35 #1205), 0dtfn (0.18 #948, 0.17 #1249, 0.14 #1550), 024mpp (0.17 #125, 0.12 #1029, 0.11 #1330), 024lt6 (0.17 #281, 0.12 #584, 0.06 #1185), 01cssf (0.17 #18, 0.12 #321, 0.06 #922) >> Best rule #1506 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 06cv1; >> query: (?x1983, ?x667) <- crewmember(?x2649, ?x1983), nominated_for(?x1983, ?x667), written_by(?x2649, ?x2650) >> conf = 0.37 => this is the best rule for 6 predicted values *> Best rule #1481 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 06cv1; *> query: (?x1983, 032sl_) <- crewmember(?x2649, ?x1983), nominated_for(?x1983, ?x667), written_by(?x2649, ?x2650) *> conf = 0.03 ranks of expected_values: 237 EVAL 04ktcgn crewmember! 0419kt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 48.000 0.369 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember EVAL 04ktcgn crewmember! 032sl_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 81.000 48.000 0.369 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #3902-049g_xj PRED entity: 049g_xj PRED relation: award PRED expected values: 05zrvfd => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 244): 09sb52 (0.54 #439, 0.50 #40, 0.36 #13606), 09sdmz (0.50 #201, 0.13 #33124, 0.13 #20352), 02x73k6 (0.50 #59, 0.13 #33124, 0.13 #20352), 0gqyl (0.46 #500, 0.17 #12071, 0.11 #14465), 02y_rq5 (0.40 #490, 0.09 #12061, 0.06 #14056), 02ppm4q (0.39 #550, 0.12 #12121, 0.09 #14116), 02z0dfh (0.33 #472, 0.10 #12043, 0.07 #14038), 0cqgl9 (0.33 #586, 0.09 #12157, 0.07 #2182), 0bdwft (0.31 #466, 0.12 #12037, 0.09 #14431), 0bfvw2 (0.26 #414, 0.11 #11985, 0.08 #14379) >> Best rule #439 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 01gv_f; 086sj; 02js9p; 0g_92; >> query: (?x1530, 09sb52) <- award_nominee(?x1530, ?x949), award(?x1530, ?x1245), ?x1245 = 0gqwc >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #18356 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1203 *> proper extension: 07s6tbm; 01gp_x; 027hnjh; 055sjw; *> query: (?x1530, ?x68) <- award_nominee(?x1530, ?x949), award_winner(?x2394, ?x1530), nominated_for(?x68, ?x2394) *> conf = 0.14 ranks of expected_values: 34 EVAL 049g_xj award 05zrvfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 104.000 104.000 0.537 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3901-044mrh PRED entity: 044mrh PRED relation: student! PRED expected values: 016wyn => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 66): 04b_46 (0.18 #1277, 0.02 #5477, 0.02 #7052), 01d34b (0.17 #256, 0.02 #3931, 0.02 #7081), 04s934 (0.17 #216), 0m4yg (0.09 #888, 0.02 #3513, 0.01 #12964), 0gjv_ (0.09 #731, 0.01 #3881), 014xf6 (0.09 #827), 017cy9 (0.09 #677), 02183k (0.09 #629), 02w2bc (0.09 #538), 01j_9c (0.09 #535) >> Best rule #1277 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 0203v; 032r1; >> query: (?x4965, 04b_46) <- award_winner(?x1670, ?x4965), student(?x7545, ?x4965), ?x7545 = 0bwfn >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 044mrh student! 016wyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 99.000 0.179 http://example.org/education/educational_institution/students_graduates./education/education/student #3900-02zfdp PRED entity: 02zfdp PRED relation: gender PRED expected values: 05zppz => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #43, 0.71 #101, 0.71 #128), 02zsn (0.51 #109, 0.32 #22, 0.32 #34) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 1168 >> proper extension: 016hvl; 063vn; 0c_mvb; 052h3; 0fpj4lx; 0372p; 0kvnn; 0dx97; 03bxh; 0x3r3; ... >> query: (?x9152, 05zppz) <- nationality(?x9152, ?x1310), student(?x2486, ?x9152), place_of_birth(?x9152, ?x12884) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zfdp gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.720 http://example.org/people/person/gender #3899-01w7nwm PRED entity: 01w7nwm PRED relation: artist! PRED expected values: 02bh8z 08pn_9 => 117 concepts (94 used for prediction) PRED predicted values (max 10 best out of 99): 01dtcb (0.50 #44, 0.11 #318, 0.08 #455), 01trtc (0.33 #69, 0.13 #754, 0.12 #1578), 015_1q (0.24 #3308, 0.22 #1937, 0.20 #701), 0g768 (0.20 #857, 0.16 #2503, 0.16 #1543), 01f_3w (0.17 #31, 0.08 #1678, 0.07 #1540), 01cszh (0.17 #9, 0.07 #2615, 0.07 #832), 01clyr (0.17 #30, 0.07 #6477, 0.07 #7438), 016ckq (0.17 #40, 0.07 #863, 0.05 #1549), 06wcbk7 (0.17 #3, 0.07 #1512, 0.06 #688), 01cl0d (0.17 #51, 0.07 #2657, 0.07 #1698) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01yzl2; 02k5sc; 01f2q5; >> query: (?x3175, 01dtcb) <- artists(?x12070, ?x3175), award(?x3175, ?x3365), ?x12070 = 01f9y_ >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2076 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 164 *> proper extension: 0lzkm; *> query: (?x3175, 02bh8z) <- artists(?x2937, ?x3175), award_winner(?x827, ?x3175), origin(?x3175, ?x1860) *> conf = 0.04 ranks of expected_values: 38, 84 EVAL 01w7nwm artist! 08pn_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 117.000 94.000 0.500 http://example.org/music/record_label/artist EVAL 01w7nwm artist! 02bh8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 117.000 94.000 0.500 http://example.org/music/record_label/artist #3898-015jr PRED entity: 015jr PRED relation: adjoins! PRED expected values: 03s5t => 271 concepts (135 used for prediction) PRED predicted values (max 10 best out of 691): 03s5t (0.84 #59456, 0.84 #59455, 0.82 #84506), 059ts (0.84 #59456, 0.84 #59455, 0.82 #84506), 087r4 (0.84 #59456, 0.84 #59455, 0.82 #84506), 09c7w0 (0.33 #2, 0.26 #43799, 0.26 #43798), 05rgl (0.33 #102, 0.23 #49275, 0.22 #92332), 059f4 (0.33 #35, 0.12 #5504, 0.08 #61837), 02dtg (0.33 #28, 0.10 #810, 0.08 #1592), 02gt5s (0.33 #622, 0.10 #1404, 0.08 #4530), 04_1l0v (0.33 #380, 0.08 #4288, 0.07 #43014), 04rrx (0.33 #105, 0.08 #4013, 0.06 #11825) >> Best rule #59456 for best value: >> intensional similarity = 2 >> extensional distance = 67 >> proper extension: 080h2; 01d26y; 01vqq1; 017j7y; >> query: (?x7468, ?x2049) <- adjoins(?x7468, ?x2049), featured_film_locations(?x1721, ?x7468) >> conf = 0.84 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 015jr adjoins! 03s5t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 271.000 135.000 0.839 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #3897-09_gdc PRED entity: 09_gdc PRED relation: film PRED expected values: 0b1y_2 047vnkj 025rxjq 0ptdz => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1886): 01pvxl (0.50 #219, 0.33 #47, 0.25 #306), 028kj0 (0.33 #84, 0.33 #75, 0.25 #334), 01l_pn (0.33 #50, 0.25 #340, 0.25 #309), 0dlngsd (0.33 #37, 0.25 #296, 0.25 #209), 062zm5h (0.33 #44, 0.25 #303, 0.25 #216), 047wh1 (0.33 #45, 0.25 #304, 0.25 #217), 0btpm6 (0.33 #64, 0.25 #323, 0.25 #236), 03177r (0.33 #24, 0.25 #283, 0.25 #196), 013q0p (0.33 #41, 0.25 #300, 0.25 #213), 0170th (0.33 #23, 0.25 #282, 0.25 #195) >> Best rule #219 for best value: >> intensional similarity = 89 >> extensional distance = 2 >> proper extension: 02t8yb; >> query: (?x3558, 01pvxl) <- film(?x3558, ?x6283), film(?x7046, ?x6283), special_performance_type(?x13366, ?x3558), special_performance_type(?x4478, ?x3558), special_performance_type(?x2726, ?x3558), special_performance_type(?x989, ?x3558), film_crew_role(?x6283, ?x468), film_release_region(?x6283, ?x2843), film_release_region(?x6283, ?x1499), film_release_region(?x6283, ?x774), film_release_region(?x6283, ?x205), film_release_region(?x6283, ?x151), genre(?x6283, ?x53), film_release_region(?x7680, ?x774), film_release_region(?x5418, ?x774), film_release_region(?x2094, ?x774), film_release_region(?x1035, ?x774), olympics(?x774, ?x7775), olympics(?x774, ?x778), film_release_region(?x6520, ?x1499), film_release_region(?x6215, ?x1499), film_release_region(?x6168, ?x1499), film_release_region(?x5400, ?x1499), film_release_region(?x4355, ?x1499), film_release_region(?x3377, ?x1499), film_release_region(?x3217, ?x1499), film_release_region(?x1118, ?x1499), film_release_region(?x511, ?x1499), film_release_region(?x299, ?x1499), first_level_division_of(?x5535, ?x774), ?x53 = 07s9rl0, ?x2094 = 05z7c, ?x511 = 0dscrwf, ?x3377 = 0gj8nq2, ?x6215 = 0jyb4, ?x778 = 0kbvb, ?x7775 = 01f1kd, nationality(?x1221, ?x774), contains(?x774, ?x1220), award_winner(?x7046, ?x2122), ?x4355 = 08tq4x, nominated_for(?x7046, ?x2191), olympics(?x774, ?x2131), jurisdiction_of_office(?x182, ?x1499), film_release_distribution_medium(?x6283, ?x81), ?x1035 = 08hmch, ?x468 = 02r96rf, country(?x668, ?x1499), award_nominee(?x450, ?x2726), actor(?x10661, ?x13366), ?x5400 = 0bhwhj, award(?x4478, ?x102), countries_within(?x455, ?x774), country(?x3507, ?x2843), ?x299 = 01gc7, organization(?x774, ?x3750), ?x6520 = 02bg55, film(?x2726, ?x240), nationality(?x11186, ?x151), nationality(?x10180, ?x151), ?x7680 = 0gh6j94, person(?x9646, ?x13366), nationality(?x101, ?x205), ?x1118 = 0_92w, contains(?x7273, ?x151), ?x3750 = 0_2v, location(?x4587, ?x205), award_nominee(?x2422, ?x989), ?x5418 = 026lgs, award_nominee(?x1345, ?x4478), member_states(?x7416, ?x1499), nationality(?x7703, ?x1499), award_winner(?x851, ?x2422), country(?x7191, ?x205), film(?x13366, ?x430), award_winner(?x1312, ?x989), country(?x89, ?x205), participant(?x545, ?x989), ?x11186 = 01304j, ?x10180 = 020hyj, country(?x359, ?x774), ?x2131 = 0lk8j, ?x3217 = 0gffmn8, ?x6168 = 0gj96ln, award(?x269, ?x1312), service_location(?x555, ?x205), award_nominee(?x989, ?x969), religion(?x13366, ?x8140), adjoins(?x2843, ?x8449) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #338 for first EXPECTED value: *> intensional similarity = 63 *> extensional distance = 2 *> proper extension: 014kbl; *> query: (?x3558, ?x3638) <- film(?x3558, ?x6283), film(?x3558, ?x3986), film(?x3558, ?x2006), film(?x395, ?x6283), special_performance_type(?x5834, ?x3558), special_performance_type(?x3557, ?x3558), special_performance_type(?x1324, ?x3558), film_crew_role(?x6283, ?x137), currency(?x6283, ?x170), film(?x609, ?x2006), nominated_for(?x4449, ?x2006), award_nominee(?x5834, ?x8445), award_nominee(?x5834, ?x2762), language(?x3986, ?x254), ?x137 = 09zzb8, ?x170 = 09nqf, film(?x609, ?x2954), film(?x609, ?x2868), film(?x609, ?x2441), ?x254 = 02h40lc, film_crew_role(?x2006, ?x1966), gender(?x5834, ?x231), award_nominee(?x2763, ?x5834), titles(?x600, ?x3986), film(?x9587, ?x3986), film(?x981, ?x2006), award_nominee(?x2762, ?x230), nominated_for(?x484, ?x3986), award_nominee(?x3557, ?x906), award_winner(?x1193, ?x2762), nominated_for(?x3637, ?x3986), nominated_for(?x3557, ?x2528), ?x2868 = 0dr3sl, award_winner(?x2763, ?x1867), type_of_union(?x5834, ?x566), award(?x3557, ?x678), film(?x1324, ?x2090), genre(?x2006, ?x811), film(?x2762, ?x972), place_of_birth(?x5834, ?x9233), film_crew_role(?x5608, ?x1966), film_crew_role(?x5372, ?x1966), film_crew_role(?x1192, ?x1966), nominated_for(?x2763, ?x351), nationality(?x4449, ?x512), award_nominee(?x2445, ?x3557), ?x2954 = 0crh5_f, ?x5372 = 03t79f, award_winner(?x782, ?x8445), profession(?x1109, ?x1966), featured_film_locations(?x3986, ?x739), award(?x2763, ?x154), location(?x2763, ?x1523), nationality(?x2763, ?x94), ?x2441 = 0cc5mcj, nominated_for(?x899, ?x2090), nominated_for(?x3637, ?x3638), profession(?x2763, ?x524), ?x5608 = 01l_pn, award_nominee(?x8445, ?x971), ?x1192 = 07sc6nw, nominated_for(?x507, ?x2006), award(?x8445, ?x401) *> conf = 0.09 ranks of expected_values: 303, 377, 383, 981 EVAL 09_gdc film 0ptdz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.500 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film EVAL 09_gdc film 025rxjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.500 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film EVAL 09_gdc film 047vnkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.500 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film EVAL 09_gdc film 0b1y_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.500 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #3896-01pgzn_ PRED entity: 01pgzn_ PRED relation: nationality PRED expected values: 09c7w0 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 39): 09c7w0 (0.85 #602, 0.82 #1907, 0.81 #4214), 0f8l9c (0.19 #22, 0.04 #5936, 0.03 #4735), 02jx1 (0.14 #6948, 0.13 #8249, 0.11 #333), 07ssc (0.12 #7430, 0.12 #5929, 0.11 #7931), 0d060g (0.11 #107, 0.07 #1913, 0.06 #608), 03rk0 (0.08 #9565, 0.08 #9665, 0.07 #8362), 0345h (0.07 #5244, 0.07 #5945, 0.06 #5644), 03_3d (0.06 #6, 0.06 #4819, 0.03 #9425), 0b90_r (0.06 #3), 0h7x (0.04 #5248, 0.03 #5949, 0.03 #335) >> Best rule #602 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 01m65sp; 044mfr; >> query: (?x2352, 09c7w0) <- participant(?x2352, ?x1416), actor(?x416, ?x2352), category(?x2352, ?x134) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pgzn_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.851 http://example.org/people/person/nationality #3895-01w5jwb PRED entity: 01w5jwb PRED relation: award PRED expected values: 01bgqh 0c4z8 => 122 concepts (99 used for prediction) PRED predicted values (max 10 best out of 266): 01cky2 (0.79 #12003, 0.77 #19614, 0.76 #18812), 025m8l (0.59 #1318, 0.47 #2118, 0.17 #118), 054ks3 (0.55 #1341, 0.45 #2141, 0.22 #2541), 031b3h (0.43 #600, 0.33 #200, 0.20 #1800), 023vrq (0.43 #723, 0.17 #3524, 0.13 #2723), 01bgqh (0.43 #2443, 0.41 #1243, 0.30 #6044), 0c4z8 (0.39 #2472, 0.38 #872, 0.36 #1272), 01c92g (0.39 #2498, 0.27 #1298, 0.20 #2098), 0gqz2 (0.37 #2081, 0.36 #1281, 0.15 #2882), 01d38g (0.34 #1628, 0.33 #28, 0.29 #428) >> Best rule #12003 for best value: >> intensional similarity = 4 >> extensional distance = 400 >> proper extension: 06lxn; >> query: (?x8722, ?x3835) <- category(?x8722, ?x134), artists(?x505, ?x8722), award_winner(?x3835, ?x8722), artist(?x2190, ?x8722) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #2443 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 52 *> proper extension: 03f0fnk; *> query: (?x8722, 01bgqh) <- award(?x8722, ?x12835), award(?x8722, ?x3926), category_of(?x12835, ?x2421), ?x3926 = 02f6xy, artists(?x505, ?x8722) *> conf = 0.43 ranks of expected_values: 6, 7 EVAL 01w5jwb award 0c4z8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 99.000 0.790 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01w5jwb award 01bgqh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 99.000 0.790 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3894-05pq9 PRED entity: 05pq9 PRED relation: music! PRED expected values: 0cq7tx => 99 concepts (67 used for prediction) PRED predicted values (max 10 best out of 20): 01jrbv (0.10 #1348, 0.06 #2364), 0c9k8 (0.10 #1311, 0.06 #2327), 015x74 (0.10 #1193, 0.06 #2209), 0ds11z (0.10 #1053, 0.06 #2069), 09d3b7 (0.06 #2877, 0.02 #7958), 06pyc2 (0.06 #2999), 0djlxb (0.06 #2354), 027rpym (0.03 #3543, 0.02 #4559, 0.01 #5575), 0cq7tx (0.03 #3483, 0.02 #4499, 0.01 #5515), 0bcndz (0.03 #3218, 0.02 #4234, 0.01 #5250) >> Best rule #1348 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 06y9c2; 0dpqk; 077rj; 01x2tm8; 071xj; >> query: (?x2483, 01jrbv) <- profession(?x2483, ?x6476), profession(?x2483, ?x353), ?x353 = 0cbd2, ?x6476 = 025352, type_of_union(?x2483, ?x566) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #3483 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 38 *> proper extension: 018p5f; *> query: (?x2483, 0cq7tx) <- award(?x2483, ?x3105), ?x3105 = 01l29r, award_winner(?x2874, ?x2483) *> conf = 0.03 ranks of expected_values: 9 EVAL 05pq9 music! 0cq7tx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 99.000 67.000 0.100 http://example.org/film/film/music #3893-03mnk PRED entity: 03mnk PRED relation: company! PRED expected values: 060c4 07t3gd => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 41): 060c4 (0.74 #871, 0.71 #953, 0.70 #1364), 05_wyz (0.57 #1874, 0.55 #1377, 0.54 #2534), 09d6p2 (0.55 #1378, 0.42 #1214, 0.41 #1875), 01yc02 (0.47 #587, 0.39 #1368, 0.39 #1865), 04192r (0.33 #118, 0.33 #36, 0.29 #159), 021q0l (0.33 #212, 0.21 #2725, 0.17 #958), 02y6fz (0.29 #602, 0.21 #2725, 0.18 #267), 01rk91 (0.25 #457, 0.21 #332, 0.21 #290), 06hpx2 (0.25 #71, 0.21 #2725, 0.17 #112), 021q1c (0.24 #1411, 0.22 #2197, 0.17 #1617) >> Best rule #871 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 08815; 04rwx; 0gsg7; 09kvv; 0l8sx; 01w5m; 03ksy; 02bh8z; 09b3v; 0bqxw; ... >> query: (?x3230, 060c4) <- company(?x554, ?x3230), company(?x9105, ?x3230), company(?x554, ?x8931), list(?x3230, ?x5997), ?x8931 = 01qygl >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 24 EVAL 03mnk company! 07t3gd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 177.000 177.000 0.739 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 03mnk company! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.739 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #3892-01wxyx1 PRED entity: 01wxyx1 PRED relation: participant! PRED expected values: 048lv => 118 concepts (86 used for prediction) PRED predicted values (max 10 best out of 410): 01wmxfs (0.83 #32968, 0.83 #37408, 0.82 #27895), 048lv (0.83 #32968, 0.83 #37408, 0.82 #27895), 019pm_ (0.20 #3167, 0.17 #5070, 0.12 #5704), 0h5g_ (0.20 #31), 01pgzn_ (0.12 #5704, 0.12 #6971, 0.11 #11407), 015f7 (0.12 #5704, 0.12 #6971, 0.11 #11407), 01pk8v (0.12 #5704, 0.12 #6971, 0.11 #11407), 02_hj4 (0.12 #5704, 0.12 #6971, 0.11 #11407), 0f4vbz (0.12 #5704, 0.11 #11407, 0.10 #24089), 01sl1q (0.12 #5704, 0.11 #11407, 0.10 #24089) >> Best rule #32968 for best value: >> intensional similarity = 3 >> extensional distance = 527 >> proper extension: 03qcq; 0411q; 05cljf; 06cv1; 06w2sn5; 08m4c8; 019g40; 0zjpz; 045zr; 02ld6x; ... >> query: (?x2108, ?x828) <- participant(?x2035, ?x2108), participant(?x2108, ?x828), nationality(?x2108, ?x429) >> conf = 0.83 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01wxyx1 participant! 048lv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 86.000 0.833 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #3891-08z39v PRED entity: 08z39v PRED relation: people! PRED expected values: 0d7wh => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 37): 02w7gg (0.23 #2082, 0.23 #2005, 0.20 #1312), 041rx (0.17 #620, 0.15 #2700, 0.15 #2546), 013xrm (0.10 #1561, 0.05 #636, 0.04 #944), 0dryh9k (0.10 #5794, 0.08 #6025, 0.08 #1557), 0x67 (0.09 #6712, 0.08 #6943, 0.08 #7097), 0d7wh (0.08 #2020, 0.08 #2097, 0.07 #1327), 033tf_ (0.07 #1394, 0.06 #1702, 0.06 #1471), 06gbnc (0.05 #2107, 0.04 #1337, 0.04 #1953), 07bch9 (0.05 #1487, 0.04 #5030, 0.04 #1718), 013b6_ (0.05 #669, 0.04 #977, 0.03 #438) >> Best rule #2082 for best value: >> intensional similarity = 4 >> extensional distance = 165 >> proper extension: 01m7f5r; >> query: (?x10078, 02w7gg) <- nationality(?x10078, ?x512), ?x512 = 07ssc, nominated_for(?x10078, ?x3882), profession(?x10078, ?x2265) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #2020 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 157 *> proper extension: 03q5dr; 01vh3r; 01c65z; *> query: (?x10078, 0d7wh) <- nationality(?x10078, ?x512), ?x512 = 07ssc, award(?x10078, ?x1243), nominated_for(?x10078, ?x3882) *> conf = 0.08 ranks of expected_values: 6 EVAL 08z39v people! 0d7wh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 135.000 135.000 0.234 http://example.org/people/ethnicity/people #3890-045zr PRED entity: 045zr PRED relation: type_of_union PRED expected values: 04ztj => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #45, 0.85 #165, 0.77 #25), 01g63y (0.28 #166, 0.27 #46, 0.20 #154), 0jgjn (0.01 #16) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 023tp8; 0159h6; 0c4f4; 04bs3j; 014x77; 0n6f8; 022_lg; 01gq0b; 01fh9; 01fwk3; ... >> query: (?x2662, 04ztj) <- award_winner(?x486, ?x2662), spouse(?x2662, ?x11533), award(?x2662, ?x724) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045zr type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.872 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3889-0164r9 PRED entity: 0164r9 PRED relation: film PRED expected values: 01jwxx => 100 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1119): 025twgf (0.40 #60797, 0.39 #57219, 0.38 #62586), 02qrv7 (0.12 #66163, 0.12 #67952, 0.04 #193), 0g5pv3 (0.12 #66163, 0.12 #67952, 0.02 #194), 02n72k (0.12 #66163, 0.12 #67952, 0.01 #2944), 0g5pvv (0.12 #66163, 0.12 #67952, 0.01 #2842), 01kf3_9 (0.12 #66163, 0.12 #67952, 0.01 #2077), 02sg5v (0.12 #66163, 0.12 #67952, 0.01 #1914), 0fztbq (0.12 #66163, 0.12 #67952, 0.01 #5285), 0fsw_7 (0.12 #66163, 0.12 #67952, 0.01 #4515), 0d1qmz (0.12 #66163, 0.12 #67952, 0.01 #4180) >> Best rule #60797 for best value: >> intensional similarity = 5 >> extensional distance = 652 >> proper extension: 01wz01; >> query: (?x7561, ?x8737) <- film(?x7561, ?x11362), film(?x7561, ?x6004), nominated_for(?x484, ?x6004), titles(?x162, ?x6004), prequel(?x11362, ?x8737) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #850 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 07hbxm; *> query: (?x7561, 01jwxx) <- film(?x7561, ?x6004), nationality(?x7561, ?x512), student(?x2486, ?x7561), ?x2486 = 015nl4 *> conf = 0.04 ranks of expected_values: 79 EVAL 0164r9 film 01jwxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 100.000 62.000 0.398 http://example.org/film/actor/film./film/performance/film #3888-02qr69m PRED entity: 02qr69m PRED relation: language PRED expected values: 02h40lc => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 55): 02h40lc (0.90 #766, 0.89 #471, 0.89 #3114), 064_8sq (0.27 #22, 0.20 #140, 0.15 #198), 04306rv (0.27 #5, 0.13 #123, 0.11 #240), 02bjrlw (0.18 #1, 0.08 #294, 0.07 #765), 06b_j (0.11 #258, 0.07 #492, 0.06 #374), 06nm1 (0.11 #187, 0.10 #362, 0.09 #775), 03_9r (0.10 #537, 0.07 #1241, 0.05 #1182), 0653m (0.09 #12, 0.05 #247, 0.04 #1126), 012w70 (0.09 #13, 0.02 #306, 0.02 #2303), 07zrf (0.06 #61, 0.03 #121, 0.01 #646) >> Best rule #766 for best value: >> intensional similarity = 4 >> extensional distance = 367 >> proper extension: 02d413; 015qsq; 0b2v79; 01sxly; 0pv2t; 018f8; 0hv1t; 0qm98; 07h9gp; 0jym0; ... >> query: (?x2488, 02h40lc) <- genre(?x2488, ?x53), nominated_for(?x372, ?x2488), production_companies(?x2488, ?x1104), featured_film_locations(?x2488, ?x1523) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qr69m language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.897 http://example.org/film/film/language #3887-071ynp PRED entity: 071ynp PRED relation: film PRED expected values: 05vxdh => 100 concepts (65 used for prediction) PRED predicted values (max 10 best out of 433): 031hcx (0.57 #6629, 0.33 #1271, 0.08 #8415), 03177r (0.50 #5821, 0.33 #463, 0.20 #2249), 03176f (0.50 #6063, 0.33 #705, 0.04 #7849), 03hxsv (0.33 #1114, 0.29 #6472, 0.20 #2900), 0407yfx (0.33 #344, 0.07 #5702, 0.04 #7488), 0gydcp7 (0.33 #330, 0.07 #5688, 0.04 #7474), 01npcx (0.33 #962, 0.07 #6320, 0.04 #8106), 0164qt (0.33 #125, 0.07 #5483, 0.04 #7269), 0418wg (0.33 #401, 0.07 #5759, 0.01 #36121), 02nx2k (0.33 #1212, 0.07 #6570) >> Best rule #6629 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0prjs; 05sq84; 03dpqd; >> query: (?x3225, 031hcx) <- film(?x3225, ?x2006), ?x2006 = 031778, nationality(?x3225, ?x6401) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 071ynp film 05vxdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 65.000 0.571 http://example.org/film/actor/film./film/performance/film #3886-05qhw PRED entity: 05qhw PRED relation: contains PRED expected values: 071g6 => 181 concepts (72 used for prediction) PRED predicted values (max 10 best out of 2896): 06fz_ (0.20 #3965, 0.09 #77392, 0.08 #39208), 0bwfn (0.13 #3981, 0.09 #12794, 0.07 #27475), 01b1nk (0.13 #5676, 0.05 #11550, 0.05 #70290), 024cg8 (0.13 #5525, 0.05 #11399, 0.05 #70139), 01z9j2 (0.13 #5813, 0.05 #11687, 0.05 #70427), 01vc3y (0.13 #5773, 0.05 #11647, 0.05 #70387), 011pcj (0.13 #5725, 0.05 #11599, 0.05 #70339), 017vdg (0.13 #5681, 0.05 #11555, 0.05 #70295), 027xq5 (0.13 #5559, 0.05 #11433, 0.05 #70173), 02fvv (0.13 #5488, 0.05 #11362, 0.05 #70102) >> Best rule #3965 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 06q1r; >> query: (?x456, 06fz_) <- film_release_region(?x66, ?x456), first_level_division_of(?x6265, ?x456), adjoins(?x456, ?x344) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05qhw contains 071g6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 181.000 72.000 0.200 http://example.org/location/location/contains #3885-09jwl PRED entity: 09jwl PRED relation: person PRED expected values: 016k62 => 57 concepts (40 used for prediction) PRED predicted values (max 10 best out of 90): 02b9g4 (0.40 #942, 0.29 #1388), 01jbx1 (0.40 #911, 0.29 #1357), 01yznp (0.40 #893, 0.29 #1339), 0mbw0 (0.29 #1400, 0.20 #954, 0.01 #2913), 03f3yfj (0.29 #1396, 0.20 #950, 0.01 #2909), 013v5j (0.29 #1349, 0.20 #903, 0.01 #2862), 01yg9y (0.29 #1378, 0.20 #932), 01kvqc (0.20 #898, 0.14 #1344, 0.02 #2494), 01s7z0 (0.20 #974, 0.14 #1420), 02bc74 (0.20 #973, 0.14 #1419) >> Best rule #942 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 043q4d; 026h21_; >> query: (?x1183, 02b9g4) <- person(?x1183, ?x1282), role(?x1282, ?x212), artists(?x378, ?x1282), award(?x1282, ?x4958) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09jwl person 016k62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 40.000 0.400 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #3884-045gzq PRED entity: 045gzq PRED relation: nationality PRED expected values: 09c7w0 => 103 concepts (78 used for prediction) PRED predicted values (max 10 best out of 89): 09c7w0 (0.88 #402, 0.88 #706, 0.86 #1209), 04_1l0v (0.33 #7762, 0.33 #4439, 0.30 #6760), 02jx1 (0.33 #536, 0.31 #636, 0.11 #838), 07ssc (0.24 #518, 0.23 #618, 0.18 #115), 0d060g (0.19 #201, 0.06 #208, 0.05 #3335), 03rk0 (0.07 #2972, 0.07 #3475, 0.07 #5491), 03_3d (0.06 #509, 0.05 #609, 0.03 #7864), 06q1r (0.05 #580, 0.04 #680, 0.02 #3607), 03rt9 (0.05 #516, 0.04 #616, 0.01 #3644), 03rjj (0.03 #508, 0.03 #7864, 0.03 #7863) >> Best rule #402 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 04gmp_z; >> query: (?x13928, 09c7w0) <- place_of_birth(?x13928, ?x108), jurisdiction_of_office(?x1195, ?x108), adjoins(?x1426, ?x108), state_province_region(?x3228, ?x108) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045gzq nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 78.000 0.885 http://example.org/people/person/nationality #3883-072x7s PRED entity: 072x7s PRED relation: featured_film_locations PRED expected values: 04v3q 0345h => 132 concepts (114 used for prediction) PRED predicted values (max 10 best out of 105): 030qb3t (0.18 #2394, 0.16 #742, 0.15 #1685), 052p7 (0.15 #290, 0.13 #525, 0.08 #55), 0345h (0.15 #266, 0.07 #501, 0.04 #737), 0rh6k (0.14 #1650, 0.12 #707, 0.12 #2359), 01_d4 (0.08 #12500, 0.05 #1693, 0.05 #3345), 0h7h6 (0.08 #12500, 0.04 #746, 0.03 #2398), 06c62 (0.08 #12500, 0.02 #1540, 0.02 #1775), 03h64 (0.08 #12500, 0.02 #765, 0.01 #1473), 094jv (0.08 #12500, 0.01 #2399, 0.01 #8292), 04lh6 (0.08 #12500) >> Best rule #2394 for best value: >> intensional similarity = 4 >> extensional distance = 147 >> proper extension: 02v63m; 04mcw4; 07xvf; 032sl_; >> query: (?x1685, 030qb3t) <- produced_by(?x1685, ?x846), genre(?x1685, ?x812), featured_film_locations(?x1685, ?x362), ?x812 = 01jfsb >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #266 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 015g28; *> query: (?x1685, 0345h) <- produced_by(?x1685, ?x2135), film_release_distribution_medium(?x1685, ?x81), ?x2135 = 06pj8, titles(?x812, ?x1685) *> conf = 0.15 ranks of expected_values: 3 EVAL 072x7s featured_film_locations 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 132.000 114.000 0.181 http://example.org/film/film/featured_film_locations EVAL 072x7s featured_film_locations 04v3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 114.000 0.181 http://example.org/film/film/featured_film_locations #3882-05hjnw PRED entity: 05hjnw PRED relation: award PRED expected values: 09cm54 02w_6xj => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 210): 04kxsb (0.27 #15511, 0.27 #14415, 0.27 #14416), 02qvyrt (0.27 #15511, 0.27 #14415, 0.27 #14416), 0gqyl (0.27 #15511, 0.27 #14415, 0.27 #14416), 0l8z1 (0.27 #15511, 0.27 #14415, 0.27 #14416), 0gqy2 (0.27 #15511, 0.27 #14415, 0.27 #14416), 09sdmz (0.27 #15511, 0.27 #14415, 0.27 #14416), 0gq9h (0.27 #15511, 0.27 #14415, 0.27 #14416), 0gr0m (0.27 #15511, 0.27 #14415, 0.27 #14416), 02qyntr (0.27 #15511, 0.27 #14415, 0.27 #14416), 0gs9p (0.27 #15511, 0.27 #14415, 0.27 #14416) >> Best rule #15511 for best value: >> intensional similarity = 4 >> extensional distance = 982 >> proper extension: 02_1q9; 027tbrc; 0524b41; 02_1kl; 03j63k; 06qwh; 0fpxp; 097h2; 04xbq3; 023ny6; ... >> query: (?x4939, ?x2341) <- award(?x4939, ?x13075), nominated_for(?x2341, ?x4939), award(?x276, ?x2341), award_winner(?x13075, ?x1554) >> conf = 0.27 => this is the best rule for 23 predicted values *> Best rule #8735 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 563 *> proper extension: 0564x; *> query: (?x4939, ?x384) <- film(?x902, ?x4939), award_winner(?x4939, ?x2248), award_winner(?x384, ?x2248), film_release_region(?x4939, ?x94) *> conf = 0.21 ranks of expected_values: 24, 48 EVAL 05hjnw award 02w_6xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 107.000 107.000 0.272 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 05hjnw award 09cm54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 107.000 107.000 0.272 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #3881-03902 PRED entity: 03902 PRED relation: place_of_death! PRED expected values: 03y3dk => 233 concepts (75 used for prediction) PRED predicted values (max 10 best out of 792): 03_87 (0.20 #298, 0.17 #1053, 0.05 #33256), 0bk5r (0.20 #228, 0.17 #983, 0.04 #42328), 07dnx (0.20 #435, 0.17 #1190, 0.04 #5725), 0420y (0.09 #8313, 0.07 #41570, 0.06 #46865), 07ym0 (0.08 #49890, 0.06 #1920, 0.05 #3434), 0dzkq (0.06 #1638, 0.05 #9070, 0.05 #7557), 0ct9_ (0.06 #1925, 0.05 #9070, 0.05 #7557), 0399p (0.06 #1907, 0.05 #9070, 0.05 #7557), 03jxw (0.06 #2040, 0.05 #33256, 0.05 #20406), 01rgr (0.06 #2004, 0.05 #33256, 0.05 #20406) >> Best rule #298 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 04qdj; >> query: (?x10610, 03_87) <- location_of_ceremony(?x566, ?x10610), country(?x10610, ?x774), ?x774 = 06mzp, ?x566 = 04ztj >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03902 place_of_death! 03y3dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 233.000 75.000 0.200 http://example.org/people/deceased_person/place_of_death #3880-081mh PRED entity: 081mh PRED relation: district_represented! PRED expected values: 02bqm0 024tkd => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 35): 02bqm0 (0.80 #117, 0.55 #257, 0.54 #351), 024tkd (0.70 #124, 0.68 #264, 0.66 #299), 043djx (0.55 #282, 0.55 #247, 0.50 #72), 03rl1g (0.55 #281, 0.55 #246, 0.50 #176), 01gt99 (0.50 #101, 0.46 #807, 0.45 #311), 01gtdd (0.50 #98, 0.46 #807, 0.43 #308), 01gst_ (0.50 #75, 0.46 #807, 0.43 #285), 01gtbb (0.50 #74, 0.46 #807, 0.42 #284), 01gsvp (0.50 #87, 0.46 #807, 0.40 #122), 01gssm (0.50 #78, 0.46 #807, 0.34 #288) >> Best rule #117 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0g0syc; >> query: (?x2977, 02bqm0) <- district_represented(?x1830, ?x2977), district_represented(?x1829, ?x2977), ?x1830 = 03z5xd, ?x1829 = 02bp37 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 081mh district_represented! 024tkd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.800 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 081mh district_represented! 02bqm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.800 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #3879-01y_px PRED entity: 01y_px PRED relation: people! PRED expected values: 041rx => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 35): 041rx (0.23 #4, 0.20 #81, 0.16 #158), 048z7l (0.15 #40, 0.13 #117, 0.05 #194), 033tf_ (0.13 #84, 0.12 #469, 0.12 #161), 0x67 (0.10 #934, 0.09 #1935, 0.09 #3167), 0xnvg (0.10 #244, 0.07 #1399, 0.07 #1322), 02w7gg (0.09 #2235, 0.08 #2620, 0.08 #2), 07hwkr (0.08 #12, 0.07 #89, 0.06 #166), 09vc4s (0.08 #9, 0.07 #86, 0.06 #240), 06v41q (0.08 #29, 0.07 #106, 0.03 #260), 07mqps (0.08 #19, 0.07 #96, 0.02 #558) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01mmslz; 027l0b; 01y665; 01bcq; 041c4; 018ygt; 06dn58; 0kjgl; 01z5tr; 033jj1; ... >> query: (?x2263, 041rx) <- nominated_for(?x2263, ?x2293), ?x2293 = 01q_y0, film(?x2263, ?x718) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y_px people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.231 http://example.org/people/ethnicity/people #3878-03r0rq PRED entity: 03r0rq PRED relation: titles! PRED expected values: 0ljc_ => 72 concepts (56 used for prediction) PRED predicted values (max 10 best out of 45): 0215n (0.50 #179, 0.43 #486, 0.40 #590), 0146mv (0.25 #191, 0.19 #705, 0.14 #810), 0ljc_ (0.25 #194, 0.12 #708, 0.10 #297), 03mdt (0.11 #4012, 0.10 #4431, 0.10 #2950), 01w5gp (0.11 #4700, 0.09 #3114, 0.08 #2382), 07s9rl0 (0.09 #4283, 0.08 #4078, 0.08 #4388), 01z4y (0.07 #5636, 0.06 #2801, 0.06 #5431), 03k9fj (0.07 #5636, 0.06 #2801, 0.06 #5431), 01z77k (0.07 #5388, 0.07 #4137, 0.06 #4342), 07qht4 (0.06 #4581, 0.03 #1019, 0.03 #1123) >> Best rule #179 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 09g_31; 07vqnc; >> query: (?x10278, 0215n) <- genre(?x10278, ?x2540), genre(?x10278, ?x811), category(?x10278, ?x134), ?x2540 = 0hcr, languages(?x10278, ?x254), ?x811 = 03k9fj, ?x134 = 08mbj5d >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #194 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 09g_31; 07vqnc; *> query: (?x10278, 0ljc_) <- genre(?x10278, ?x2540), genre(?x10278, ?x811), category(?x10278, ?x134), ?x2540 = 0hcr, languages(?x10278, ?x254), ?x811 = 03k9fj, ?x134 = 08mbj5d *> conf = 0.25 ranks of expected_values: 3 EVAL 03r0rq titles! 0ljc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 56.000 0.500 http://example.org/media_common/netflix_genre/titles #3877-031296 PRED entity: 031296 PRED relation: actor! PRED expected values: 024rwx => 119 concepts (95 used for prediction) PRED predicted values (max 10 best out of 151): 01bv8b (0.07 #302, 0.02 #564, 0.01 #3709), 01q_y0 (0.07 #296, 0.01 #1082, 0.01 #1344), 0cs134 (0.07 #473, 0.01 #7028, 0.01 #7815), 026bfsh (0.07 #1406, 0.05 #5864, 0.05 #1144), 02h2vv (0.05 #113, 0.04 #375, 0.02 #21254), 01fx1l (0.05 #99, 0.03 #623, 0.02 #1147), 0g60z (0.05 #4, 0.03 #3673, 0.02 #4986), 02rzdcp (0.05 #49, 0.02 #3718, 0.02 #5817), 017f3m (0.05 #85, 0.02 #6902, 0.02 #5067), 030p35 (0.05 #80, 0.02 #604, 0.01 #1390) >> Best rule #302 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 01hcj2; >> query: (?x3709, 01bv8b) <- award(?x3709, ?x11179), profession(?x3709, ?x1032), ?x11179 = 0cqhmg >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #2201 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 178 *> proper extension: 0418ft; 0q1lp; 04xhwn; *> query: (?x3709, 024rwx) <- languages(?x3709, ?x254), nationality(?x3709, ?x94), actor(?x3169, ?x3709) *> conf = 0.03 ranks of expected_values: 44 EVAL 031296 actor! 024rwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 119.000 95.000 0.071 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #3876-02y_3rt PRED entity: 02y_3rt PRED relation: nutrient! PRED expected values: 01645p => 15 concepts (12 used for prediction) PRED predicted values (max 10 best out of 24): 014j1m (0.92 #189, 0.92 #177, 0.92 #151), 0fbdb (0.91 #309, 0.91 #290, 0.91 #265), 061_f (0.89 #263, 0.89 #246, 0.89 #224), 04zpv (0.88 #331, 0.86 #202, 0.86 #182), 01645p (0.85 #268, 0.85 #252, 0.84 #199), 033cnk (0.84 #338, 0.84 #325, 0.84 #200), 09728 (0.83 #163, 0.83 #131, 0.82 #1), 01nkt (0.81 #270, 0.81 #249, 0.80 #351), 0cxn2 (0.81 #272, 0.81 #245, 0.80 #349), 0dj75 (0.80 #312, 0.80 #291, 0.79 #240) >> Best rule #189 for best value: >> intensional similarity = 142 >> extensional distance = 47 >> proper extension: 025s7x6; 0h1sg; 0h1tz; 02kd0rh; >> query: (?x14618, ?x6191) <- nutrient(?x5009, ?x14618), nutrient(?x4068, ?x14618), nutrient(?x2701, ?x14618), nutrient(?x1303, ?x14618), ?x5009 = 0fjfh, ?x1303 = 0fj52s, ?x2701 = 0hkxq, nutrient(?x4068, ?x13944), nutrient(?x4068, ?x13498), nutrient(?x4068, ?x12902), nutrient(?x4068, ?x12868), nutrient(?x4068, ?x12454), nutrient(?x4068, ?x12083), nutrient(?x4068, ?x11784), nutrient(?x4068, ?x11758), nutrient(?x4068, ?x11592), nutrient(?x4068, ?x11409), nutrient(?x4068, ?x11270), nutrient(?x4068, ?x10891), nutrient(?x4068, ?x10709), nutrient(?x4068, ?x10195), nutrient(?x4068, ?x10098), nutrient(?x4068, ?x9855), nutrient(?x4068, ?x9840), nutrient(?x4068, ?x9795), nutrient(?x4068, ?x9708), nutrient(?x4068, ?x9436), nutrient(?x4068, ?x9426), nutrient(?x4068, ?x9365), nutrient(?x4068, ?x8487), nutrient(?x4068, ?x8442), nutrient(?x4068, ?x8413), nutrient(?x4068, ?x8243), nutrient(?x4068, ?x7894), nutrient(?x4068, ?x7652), nutrient(?x4068, ?x7431), nutrient(?x4068, ?x7364), nutrient(?x4068, ?x7362), nutrient(?x4068, ?x7219), nutrient(?x4068, ?x7135), nutrient(?x4068, ?x6586), nutrient(?x4068, ?x6286), nutrient(?x4068, ?x6192), nutrient(?x4068, ?x6160), nutrient(?x4068, ?x6033), nutrient(?x4068, ?x6026), nutrient(?x4068, ?x5549), nutrient(?x4068, ?x5526), nutrient(?x4068, ?x5451), nutrient(?x4068, ?x5374), nutrient(?x4068, ?x5337), nutrient(?x4068, ?x5010), nutrient(?x4068, ?x4069), nutrient(?x4068, ?x3469), nutrient(?x4068, ?x3264), nutrient(?x4068, ?x3203), nutrient(?x4068, ?x2702), nutrient(?x4068, ?x2018), nutrient(?x4068, ?x1960), nutrient(?x4068, ?x1258), ?x9795 = 05v_8y, ?x3203 = 04kl74p, ?x13944 = 0f4kp, ?x12083 = 01n78x, ?x9426 = 0h1yy, ?x5337 = 06x4c, ?x10195 = 0hkwr, ?x1258 = 0h1wg, ?x11270 = 02kc008, ?x7219 = 0h1vg, ?x8442 = 02kcv4x, ?x3469 = 0h1zw, ?x12868 = 03d49, ?x8413 = 02kc4sf, ?x10709 = 0h1sz, ?x13498 = 07q0m, ?x6033 = 04zjxcz, ?x7894 = 0f4hc, ?x2018 = 01sh2, ?x10098 = 0h1_c, ?x6026 = 025sf8g, ?x12902 = 0fzjh, ?x9436 = 025sqz8, ?x11758 = 0q01m, ?x5526 = 09pbb, ?x1960 = 07hnp, ?x7135 = 025rsfk, ?x6586 = 05gh50, ?x6192 = 06jry, nutrient(?x9732, ?x9708), nutrient(?x9489, ?x9708), nutrient(?x8298, ?x9708), nutrient(?x7719, ?x9708), nutrient(?x7057, ?x9708), nutrient(?x6191, ?x9708), nutrient(?x3900, ?x9708), ?x11409 = 0h1yf, ?x9365 = 04k8n, ?x5451 = 05wvs, ?x11784 = 07zqy, ?x8298 = 037ls6, ?x5374 = 025s0zp, ?x4069 = 0hqw8p_, nutrient(?x6285, ?x8243), nutrient(?x6032, ?x8243), nutrient(?x5373, ?x8243), nutrient(?x1257, ?x8243), ?x7652 = 025s0s0, ?x9855 = 0d9t0, ?x7362 = 02kc5rj, ?x6285 = 01645p, ?x10891 = 0g5gq, ?x6160 = 041r51, ?x5549 = 025s7j4, ?x8487 = 014yzm, ?x7431 = 09gwd, ?x7364 = 09gvd, ?x11592 = 025sf0_, ?x6032 = 01nkt, nutrient(?x6159, ?x3264), ?x9732 = 05z55, nutrient(?x10612, ?x9840), nutrient(?x9005, ?x9840), nutrient(?x3468, ?x9840), nutrient(?x1959, ?x9840), ?x6159 = 033cnk, ?x6286 = 02y_3rf, ?x10612 = 0frq6, ?x5373 = 0971v, ?x3468 = 0cxn2, ?x9005 = 04zpv, ?x3900 = 061_f, ?x9489 = 07j87, ?x12454 = 025rw19, ?x7719 = 0dj75, ?x7057 = 0fbdb, ?x5010 = 0h1vz, ?x6191 = 014j1m, ?x1257 = 09728, ?x2702 = 0838f, ?x1959 = 0f25w9, nutrient(?x3264, ?x12454) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #268 for first EXPECTED value: *> intensional similarity = 145 *> extensional distance = 52 *> proper extension: 08lb68; 0h1tg; *> query: (?x14618, ?x6285) <- nutrient(?x5009, ?x14618), nutrient(?x4068, ?x14618), nutrient(?x2701, ?x14618), nutrient(?x1303, ?x14618), ?x5009 = 0fjfh, ?x1303 = 0fj52s, nutrient(?x2701, ?x13944), nutrient(?x2701, ?x13498), nutrient(?x2701, ?x13126), nutrient(?x2701, ?x12902), nutrient(?x2701, ?x12868), nutrient(?x2701, ?x12454), nutrient(?x2701, ?x12083), nutrient(?x2701, ?x11784), nutrient(?x2701, ?x11758), nutrient(?x2701, ?x11592), nutrient(?x2701, ?x11409), nutrient(?x2701, ?x11270), nutrient(?x2701, ?x10891), nutrient(?x2701, ?x10709), nutrient(?x2701, ?x10195), nutrient(?x2701, ?x10098), nutrient(?x2701, ?x9949), nutrient(?x2701, ?x9915), nutrient(?x2701, ?x9855), nutrient(?x2701, ?x9840), nutrient(?x2701, ?x9795), nutrient(?x2701, ?x9733), nutrient(?x2701, ?x9490), nutrient(?x2701, ?x9436), nutrient(?x2701, ?x9426), nutrient(?x2701, ?x9365), nutrient(?x2701, ?x8487), nutrient(?x2701, ?x8413), nutrient(?x2701, ?x7894), nutrient(?x2701, ?x7720), nutrient(?x2701, ?x7652), nutrient(?x2701, ?x7431), nutrient(?x2701, ?x7364), nutrient(?x2701, ?x7362), nutrient(?x2701, ?x7219), nutrient(?x2701, ?x7135), nutrient(?x2701, ?x6586), nutrient(?x2701, ?x6286), nutrient(?x2701, ?x6192), nutrient(?x2701, ?x6160), nutrient(?x2701, ?x6033), nutrient(?x2701, ?x6026), nutrient(?x2701, ?x5549), nutrient(?x2701, ?x5526), nutrient(?x2701, ?x5451), nutrient(?x2701, ?x5374), nutrient(?x2701, ?x5337), nutrient(?x2701, ?x5010), nutrient(?x2701, ?x4069), nutrient(?x2701, ?x3469), nutrient(?x2701, ?x2702), nutrient(?x2701, ?x2018), ?x7135 = 025rsfk, ?x6192 = 06jry, ?x9795 = 05v_8y, ?x11409 = 0h1yf, ?x7362 = 02kc5rj, ?x13498 = 07q0m, ?x10709 = 0h1sz, ?x9855 = 0d9t0, ?x5526 = 09pbb, ?x7894 = 0f4hc, ?x8413 = 02kc4sf, ?x6160 = 041r51, ?x11758 = 0q01m, ?x4069 = 0hqw8p_, ?x7652 = 025s0s0, ?x11270 = 02kc008, ?x6026 = 025sf8g, ?x7431 = 09gwd, ?x8487 = 014yzm, ?x10098 = 0h1_c, ?x9733 = 0h1tz, ?x5337 = 06x4c, ?x12083 = 01n78x, ?x10195 = 0hkwr, ?x9949 = 02kd0rh, ?x9490 = 0h1sg, ?x12868 = 03d49, nutrient(?x4068, ?x9708), nutrient(?x4068, ?x8243), nutrient(?x4068, ?x3203), nutrient(?x4068, ?x1960), nutrient(?x4068, ?x1258), ?x3203 = 04kl74p, ?x5451 = 05wvs, ?x3469 = 0h1zw, ?x1960 = 07hnp, ?x10891 = 0g5gq, ?x7364 = 09gvd, ?x13944 = 0f4kp, ?x9426 = 0h1yy, ?x6286 = 02y_3rf, ?x11784 = 07zqy, nutrient(?x10612, ?x9840), nutrient(?x9489, ?x9840), nutrient(?x9005, ?x9840), nutrient(?x7719, ?x9840), nutrient(?x7057, ?x9840), nutrient(?x6285, ?x9840), nutrient(?x6191, ?x9840), nutrient(?x6159, ?x9840), nutrient(?x6032, ?x9840), nutrient(?x3900, ?x9840), nutrient(?x3468, ?x9840), nutrient(?x1959, ?x9840), ?x9365 = 04k8n, ?x2018 = 01sh2, ?x5549 = 025s7j4, ?x3468 = 0cxn2, ?x6159 = 033cnk, ?x9436 = 025sqz8, ?x5010 = 0h1vz, ?x6032 = 01nkt, ?x5374 = 025s0zp, ?x9489 = 07j87, ?x1258 = 0h1wg, ?x6033 = 04zjxcz, ?x6285 = 01645p, ?x2702 = 0838f, ?x7219 = 0h1vg, ?x10612 = 0frq6, ?x12902 = 0fzjh, nutrient(?x5373, ?x13126), ?x7719 = 0dj75, nutrient(?x1257, ?x9915), ?x11592 = 025sf0_, ?x7720 = 025s7x6, ?x6586 = 05gh50, ?x9708 = 061xhr, ?x8243 = 014d7f, ?x12454 = 025rw19, ?x1257 = 09728, ?x7057 = 0fbdb, ?x5373 = 0971v, ?x6191 = 014j1m, ?x3900 = 061_f, ?x9005 = 04zpv, ?x1959 = 0f25w9 *> conf = 0.85 ranks of expected_values: 5 EVAL 02y_3rt nutrient! 01645p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 15.000 12.000 0.918 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #3875-042z_g PRED entity: 042z_g PRED relation: film PRED expected values: 05sxr_ => 71 concepts (48 used for prediction) PRED predicted values (max 10 best out of 259): 016z9n (0.79 #3949, 0.74 #5739, 0.64 #2159), 020y73 (0.18 #2156, 0.14 #3946, 0.11 #5736), 0260bz (0.14 #3915, 0.11 #5705, 0.10 #335), 03ntbmw (0.14 #5351, 0.11 #7141, 0.09 #3561), 01q7h2 (0.14 #5156, 0.11 #6946, 0.09 #3366), 016z5x (0.14 #3650, 0.11 #5440, 0.06 #85941), 02c7k4 (0.14 #4684, 0.11 #6474, 0.06 #85941), 04cv9m (0.11 #6072, 0.10 #702, 0.09 #2492), 04tqtl (0.11 #5880, 0.10 #510, 0.09 #2300), 08mg_b (0.11 #6493, 0.07 #4703, 0.06 #85941) >> Best rule #3949 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 03q1vd; 050t68; >> query: (?x5099, 016z9n) <- award_nominee(?x5099, ?x4482), award_nominee(?x5099, ?x1677), ?x1677 = 021vwt, ?x4482 = 03q95r >> conf = 0.79 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 042z_g film 05sxr_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 48.000 0.786 http://example.org/film/actor/film./film/performance/film #3874-02rv_dz PRED entity: 02rv_dz PRED relation: currency PRED expected values: 09nqf => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.83 #29, 0.83 #64, 0.82 #78), 01nv4h (0.07 #30, 0.06 #58, 0.04 #184), 0ptk_ (0.03 #38), 02l6h (0.03 #46, 0.02 #53, 0.01 #305), 02gsvk (0.03 #118, 0.02 #174, 0.01 #132) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 02v63m; 0m491; 03l6q0; 05n6sq; 09dv8h; 03cyslc; >> query: (?x1531, 09nqf) <- nominated_for(?x617, ?x1531), genre(?x1531, ?x6452), film(?x436, ?x1531), ?x6452 = 02b5_l >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rv_dz currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.833 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #3873-09wnnb PRED entity: 09wnnb PRED relation: film! PRED expected values: 01hkhq => 83 concepts (43 used for prediction) PRED predicted values (max 10 best out of 838): 046qq (0.72 #49911, 0.56 #2080, 0.46 #54072), 0169dl (0.33 #401, 0.06 #8720, 0.04 #4561), 0c6qh (0.33 #2495, 0.03 #54486, 0.03 #29528), 01gq0b (0.33 #303, 0.03 #27032), 02gvwz (0.33 #2268, 0.02 #35541, 0.01 #8506), 0q9kd (0.33 #4, 0.02 #54076, 0.02 #87346), 0205dx (0.33 #850, 0.02 #54922, 0.01 #48680), 050zr4 (0.33 #1457), 02tv80 (0.33 #1132), 053y4h (0.33 #918) >> Best rule #49911 for best value: >> intensional similarity = 4 >> extensional distance = 539 >> proper extension: 09xbpt; 047gn4y; 04kkz8; 08hmch; 03s5lz; 0bh8yn3; 0m491; 0gydcp7; 01hvjx; 075cph; ... >> query: (?x10130, ?x4277) <- production_companies(?x10130, ?x2156), nominated_for(?x4277, ?x10130), film_release_region(?x10130, ?x94), film(?x4277, ?x377) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #8732 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 011yxg; 07gp9; 01hr1; 01k1k4; 061681; 01qb5d; 017gm7; 07h9gp; 05qbckf; 07p62k; ... *> query: (?x10130, 01hkhq) <- nominated_for(?x102, ?x10130), film_release_distribution_medium(?x10130, ?x81), titles(?x811, ?x10130), prequel(?x10130, ?x3990) *> conf = 0.01 ranks of expected_values: 725 EVAL 09wnnb film! 01hkhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 83.000 43.000 0.724 http://example.org/film/actor/film./film/performance/film #3872-05mvd62 PRED entity: 05mvd62 PRED relation: produced_by! PRED expected values: 05zlld0 => 111 concepts (78 used for prediction) PRED predicted values (max 10 best out of 688): 011xg5 (0.07 #21629, 0.02 #3761, 0.02 #42316), 012mrr (0.07 #21629, 0.02 #42316, 0.01 #38555), 0ds1glg (0.07 #21629), 03bzyn4 (0.05 #1768, 0.05 #828, 0.04 #4589), 05h43ls (0.05 #1165, 0.05 #225, 0.04 #3986), 0g22z (0.04 #948, 0.03 #2828, 0.03 #8), 0pb33 (0.04 #10344, 0.04 #14106), 026p4q7 (0.03 #216, 0.03 #1156, 0.02 #3036), 07s846j (0.03 #354, 0.03 #1294, 0.02 #3174), 060__7 (0.03 #773, 0.03 #1713, 0.02 #3593) >> Best rule #21629 for best value: >> intensional similarity = 3 >> extensional distance = 451 >> proper extension: 01q415; 01ycck; 09pl3f; 02_0d2; 01hmk9; 060pl5; 0pksh; >> query: (?x7094, ?x7126) <- award_nominee(?x6698, ?x7094), award_winner(?x2928, ?x7094), written_by(?x7126, ?x6698) >> conf = 0.07 => this is the best rule for 3 predicted values *> Best rule #3761 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 85 *> proper extension: 0fvf9q; 0byfz; 0h5f5n; 0147dk; 02q_cc; 02ndbd; 04wvhz; 030pr; 0207wx; 01t6b4; ... *> query: (?x7094, ?x1173) <- award_nominee(?x7094, ?x2021), type_of_union(?x7094, ?x566), production_companies(?x1173, ?x2021) *> conf = 0.02 ranks of expected_values: 237 EVAL 05mvd62 produced_by! 05zlld0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 111.000 78.000 0.074 http://example.org/film/film/produced_by #3871-04bgy PRED entity: 04bgy PRED relation: people! PRED expected values: 01tf_6 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 38): 0dq9p (0.38 #413, 0.13 #1337, 0.12 #347), 08g5q7 (0.25 #42, 0.12 #240, 0.10 #306), 04p3w (0.19 #407, 0.07 #1199, 0.07 #1331), 0gk4g (0.14 #142, 0.13 #3970, 0.13 #604), 0qcr0 (0.14 #133, 0.12 #331, 0.08 #529), 01n3bm (0.14 #175, 0.08 #571, 0.06 #637), 02y0js (0.12 #398, 0.08 #1256, 0.06 #332), 01dcqj (0.07 #1266, 0.06 #408, 0.06 #1200), 019dmc (0.06 #446, 0.06 #380, 0.03 #1238), 0gg4h (0.06 #432, 0.06 #366, 0.03 #828) >> Best rule #413 for best value: >> intensional similarity = 6 >> extensional distance = 14 >> proper extension: 0cj2w; >> query: (?x6469, 0dq9p) <- profession(?x6469, ?x4773), profession(?x6469, ?x1614), ?x1614 = 01c72t, place_of_death(?x6469, ?x362), profession(?x3705, ?x4773), ?x3705 = 02114t >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #361 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 02rf51g; *> query: (?x6469, 01tf_6) <- profession(?x6469, ?x1614), profession(?x9297, ?x1614), profession(?x7559, ?x1614), ?x9297 = 0hr3g, artists(?x597, ?x7559), place_of_burial(?x6469, ?x14321) *> conf = 0.06 ranks of expected_values: 13 EVAL 04bgy people! 01tf_6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 97.000 97.000 0.375 http://example.org/people/cause_of_death/people #3870-051wf PRED entity: 051wf PRED relation: draft PRED expected values: 02rl201 02z6872 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 17): 02pq_rp (0.87 #284, 0.83 #429, 0.82 #197), 02z6872 (0.85 #251, 0.80 #779, 0.80 #163), 02r6gw6 (0.80 #779, 0.76 #709, 0.75 #435), 02rl201 (0.80 #779, 0.76 #709, 0.73 #193), 02pq_x5 (0.80 #779, 0.76 #709, 0.73 #188), 02qw1zx (0.57 #463, 0.40 #695, 0.39 #992), 09l0x9 (0.54 #470, 0.53 #702, 0.52 #576), 0g3zpp (0.53 #693, 0.52 #567, 0.50 #461), 092j54 (0.53 #699, 0.52 #573, 0.46 #467), 05vsb7 (0.50 #566, 0.49 #692, 0.46 #460) >> Best rule #284 for best value: >> intensional similarity = 15 >> extensional distance = 13 >> proper extension: 01d5z; 0713r; 02__x; 06wpc; >> query: (?x12956, 02pq_rp) <- team(?x12826, ?x12956), school(?x12956, ?x6919), school(?x12956, ?x5621), school(?x12956, ?x4955), category(?x5621, ?x134), season(?x12956, ?x3431), school(?x2574, ?x5621), ?x2574 = 01y3v, major_field_of_study(?x6919, ?x8925), school_type(?x6919, ?x3092), school(?x465, ?x5621), student(?x5621, ?x525), student(?x8925, ?x123), student(?x4955, ?x495), institution(?x620, ?x4955) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #251 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 11 *> proper extension: 049n7; *> query: (?x12956, 02z6872) <- school(?x12956, ?x5621), season(?x12956, ?x3431), draft(?x12956, ?x1161), institution(?x3437, ?x5621), institution(?x1771, ?x5621), state_province_region(?x5621, ?x6521), ?x3437 = 02_xgp2, school(?x5229, ?x5621), school(?x2198, ?x5621), school(?x465, ?x5621), ?x5229 = 07l2m, position(?x2198, ?x180), colors(?x2198, ?x663), ?x1771 = 019v9k, team(?x2247, ?x2198) *> conf = 0.85 ranks of expected_values: 2, 4 EVAL 051wf draft 02z6872 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.867 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 051wf draft 02rl201 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 103.000 0.867 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #3869-01w5m PRED entity: 01w5m PRED relation: child PRED expected values: 095kp => 97 concepts (94 used for prediction) PRED predicted values (max 10 best out of 119): 0kqj1 (0.08 #554, 0.08 #381, 0.04 #898), 0fqy4p (0.08 #556, 0.03 #1415, 0.01 #3467), 04gmlt (0.03 #1283, 0.03 #1454, 0.02 #2138), 056ws9 (0.03 #1257, 0.03 #1428, 0.02 #2112), 0yldt (0.03 #1348, 0.02 #1690, 0.02 #2032), 0ymb6 (0.03 #1277, 0.02 #1619, 0.02 #1961), 0ymbl (0.03 #1211, 0.02 #1553, 0.02 #1895), 03z19 (0.03 #1220, 0.02 #2075, 0.01 #3443), 032j_n (0.03 #2673, 0.02 #4042, 0.02 #4213), 016tw3 (0.03 #3266, 0.03 #3609, 0.02 #3951) >> Best rule #554 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 059j2; >> query: (?x3424, 0kqj1) <- company(?x346, ?x3424), organizations_founded(?x3424, ?x5487) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w5m child 095kp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 94.000 0.083 http://example.org/organization/organization/child./organization/organization_relationship/child #3868-047yc PRED entity: 047yc PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.88 #41, 0.88 #52, 0.87 #70) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 07bxhl; 04tr1; 036b_; 01rxw; 01c4pv; 06s_2; 04hvw; 06tgw; 05rznz; >> query: (?x1174, 0hzc9wc) <- form_of_government(?x1174, ?x6065), adjoins(?x1174, ?x1781), countries_within(?x6956, ?x1174) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047yc administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.881 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #3867-01vw87c PRED entity: 01vw87c PRED relation: artist! PRED expected values: 01w40h => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 97): 015_1q (0.19 #725, 0.18 #4250, 0.17 #1853), 03rhqg (0.16 #1426, 0.15 #16, 0.14 #4246), 0g768 (0.15 #742, 0.15 #37, 0.14 #1024), 0n85g (0.15 #63, 0.11 #204, 0.11 #768), 033hn8 (0.14 #1424, 0.11 #4244, 0.10 #1283), 0181dw (0.13 #42, 0.12 #747, 0.11 #1029), 01dtcb (0.13 #47, 0.11 #188, 0.10 #893), 01w40h (0.13 #28, 0.09 #1438, 0.08 #874), 01trtc (0.11 #355, 0.11 #637, 0.11 #778), 0fb0v (0.11 #7, 0.09 #289, 0.09 #712) >> Best rule #725 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 01l_vgt; >> query: (?x300, 015_1q) <- participant(?x6702, ?x300), artists(?x1000, ?x300), artist(?x4483, ?x300) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #28 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 01vv7sc; 01kx_81; 09qr6; 01j4ls; 04bpm6; 0zjpz; 0136pk; 045zr; 0gdh5; 0pkyh; ... *> query: (?x300, 01w40h) <- participant(?x6702, ?x300), artists(?x1000, ?x300), role(?x300, ?x227) *> conf = 0.13 ranks of expected_values: 8 EVAL 01vw87c artist! 01w40h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 63.000 63.000 0.186 http://example.org/music/record_label/artist #3866-02mscn PRED entity: 02mscn PRED relation: artists PRED expected values: 05d8vw 02cx90 => 47 concepts (13 used for prediction) PRED predicted values (max 10 best out of 3433): 050z2 (0.57 #2498, 0.44 #3570, 0.33 #1426), 0140t7 (0.57 #3004, 0.44 #4076, 0.33 #1932), 01mxnvc (0.57 #3104, 0.44 #4176, 0.33 #2032), 02ndj5 (0.57 #3037, 0.44 #4109, 0.33 #894), 01l_w0 (0.57 #2928, 0.44 #4000, 0.33 #785), 025xt8y (0.57 #2194, 0.44 #3266, 0.19 #2143), 06p03s (0.56 #4220, 0.43 #3148, 0.33 #2076), 01w8n89 (0.56 #3530, 0.43 #2458, 0.22 #8891), 0191h5 (0.56 #3860, 0.43 #2788, 0.19 #2143), 03j24kf (0.50 #1486, 0.44 #3630, 0.43 #2558) >> Best rule #2498 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0ggq0m; 0dl5d; 06by7; 01fh36; >> query: (?x13359, 050z2) <- artists(?x13359, ?x2409), artists(?x13359, ?x1407), ?x1407 = 0ftps, award(?x2409, ?x2139), artist(?x6672, ?x2409), ?x2139 = 01by1l >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1223 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 05bt6j; 016cjb; *> query: (?x13359, 05d8vw) <- artists(?x13359, ?x12743), artists(?x13359, ?x1407), parent_genre(?x4739, ?x13359), ?x12743 = 02bc74, artists(?x3916, ?x1407), ?x3916 = 08cyft *> conf = 0.33 ranks of expected_values: 178, 561 EVAL 02mscn artists 02cx90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 13.000 0.571 http://example.org/music/genre/artists EVAL 02mscn artists 05d8vw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 47.000 13.000 0.571 http://example.org/music/genre/artists #3865-027hnjh PRED entity: 027hnjh PRED relation: award PRED expected values: 0fbtbt => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 237): 0fbtbt (0.57 #234, 0.36 #640, 0.28 #2670), 0cjyzs (0.33 #2543, 0.32 #2949, 0.26 #2137), 0gkr9q (0.29 #335, 0.18 #741, 0.12 #21115), 0ck27z (0.26 #3341, 0.23 #3747, 0.20 #4559), 09sb52 (0.26 #4101, 0.25 #11410, 0.24 #7755), 0gr51 (0.26 #1319, 0.25 #913, 0.14 #1725), 0gr4k (0.25 #1251, 0.23 #845, 0.16 #1657), 04dn09n (0.21 #856, 0.20 #1262, 0.14 #1668), 05b1610 (0.17 #851, 0.15 #1257, 0.06 #1663), 0cqhk0 (0.17 #3285, 0.15 #3691, 0.13 #4503) >> Best rule #234 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 04h68j; >> query: (?x4671, 0fbtbt) <- award_nominee(?x4671, ?x3570), ?x3570 = 0b05xm, award_nominee(?x10216, ?x4671) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027hnjh award 0fbtbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3864-0fg04 PRED entity: 0fg04 PRED relation: titles! PRED expected values: 01jfsb => 121 concepts (48 used for prediction) PRED predicted values (max 10 best out of 65): 07s9rl0 (0.33 #1545, 0.32 #928, 0.31 #3402), 04xvlr (0.30 #211, 0.26 #4, 0.24 #931), 01hmnh (0.21 #1440, 0.20 #1132, 0.18 #4639), 02n4kr (0.21 #1440, 0.20 #1132, 0.18 #4639), 06www (0.21 #1440, 0.20 #1132, 0.18 #4639), 0vjs6 (0.21 #1440, 0.20 #1132, 0.18 #4639), 028v3 (0.21 #1440, 0.20 #1132, 0.18 #4639), 0fdjb (0.21 #1440, 0.20 #1132, 0.18 #4639), 04xvh5 (0.21 #1440, 0.20 #1132, 0.18 #4639), 01z4y (0.20 #755, 0.19 #1475, 0.19 #859) >> Best rule #1545 for best value: >> intensional similarity = 5 >> extensional distance = 232 >> proper extension: 06mr2s; 01b7h8; >> query: (?x708, 07s9rl0) <- nominated_for(?x3701, ?x708), nominated_for(?x707, ?x708), executive_produced_by(?x3423, ?x707), award_nominee(?x3701, ?x3756), honored_for(?x1084, ?x708) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1047 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 144 *> proper extension: 0c0yh4; 085bd1; 03n785; 02gd6x; 02r2j8; *> query: (?x708, 01jfsb) <- country(?x708, ?x1264), genre(?x708, ?x600), film(?x1469, ?x708), ?x1264 = 0345h *> conf = 0.18 ranks of expected_values: 11 EVAL 0fg04 titles! 01jfsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 121.000 48.000 0.329 http://example.org/media_common/netflix_genre/titles #3863-01q8hj PRED entity: 01q8hj PRED relation: student PRED expected values: 051wwp => 107 concepts (93 used for prediction) PRED predicted values (max 10 best out of 514): 02vntj (0.09 #704, 0.05 #2797, 0.05 #6983), 0gs1_ (0.06 #1135, 0.05 #3228, 0.04 #5321), 01l3mk3 (0.06 #1373, 0.05 #3466, 0.04 #5559), 04ls53 (0.06 #819, 0.05 #2912, 0.04 #5005), 037lyl (0.06 #662, 0.05 #2755, 0.04 #4848), 015wc0 (0.06 #1696, 0.05 #3789, 0.03 #7975), 01wjrn (0.06 #223, 0.05 #2316, 0.03 #6502), 0892sx (0.06 #425, 0.05 #2518, 0.03 #6704), 021bk (0.06 #353, 0.05 #2446, 0.03 #6632), 0306ds (0.06 #408, 0.05 #2501, 0.03 #6687) >> Best rule #704 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 02lwv5; >> query: (?x8589, 02vntj) <- contains(?x335, ?x8589), ?x335 = 059rby, category(?x8589, ?x134), student(?x8589, ?x6993) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #90013 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 522 *> proper extension: 03w1lf; 035qv8; *> query: (?x8589, ?x1485) <- contains(?x335, ?x8589), contains(?x335, ?x4794), citytown(?x6896, ?x335), student(?x4794, ?x1485) *> conf = 0.03 ranks of expected_values: 249 EVAL 01q8hj student 051wwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 107.000 93.000 0.091 http://example.org/education/educational_institution/students_graduates./education/education/student #3862-01h7bb PRED entity: 01h7bb PRED relation: film_crew_role PRED expected values: 0dxtw => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 21): 0dxtw (0.42 #237, 0.42 #719, 0.38 #591), 04pyp5 (0.25 #13, 0.09 #241, 0.07 #723), 02vs3x5 (0.25 #20, 0.06 #441, 0.05 #312), 089fss (0.24 #233, 0.08 #715, 0.08 #200), 02rh1dz (0.16 #236, 0.14 #40, 0.13 #590), 0215hd (0.15 #597, 0.14 #1307, 0.14 #307), 01xy5l_ (0.13 #239, 0.12 #303, 0.11 #721), 089g0h (0.13 #308, 0.12 #244, 0.12 #178), 0d2b38 (0.11 #604, 0.11 #250, 0.11 #990), 02_n3z (0.10 #583, 0.09 #969, 0.09 #261) >> Best rule #237 for best value: >> intensional similarity = 5 >> extensional distance = 174 >> proper extension: 0h95zbp; >> query: (?x522, 0dxtw) <- film_crew_role(?x522, ?x3197), film_crew_role(?x522, ?x2178), ?x3197 = 02ynfr, film_crew_role(?x770, ?x2178), ?x770 = 01r97z >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01h7bb film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.420 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3861-033p3_ PRED entity: 033p3_ PRED relation: student! PRED expected values: 02fgdx => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 108): 09f2j (0.08 #159, 0.05 #2794, 0.04 #3848), 02ldmw (0.08 #285, 0.03 #3447, 0.03 #3974), 07szy (0.08 #40, 0.02 #9000, 0.02 #11108), 08tyb_ (0.08 #498, 0.01 #3660, 0.01 #5768), 07tds (0.08 #149, 0.01 #9636), 04gd8j (0.08 #368), 065y4w7 (0.07 #541, 0.03 #46395, 0.03 #33744), 01qd_r (0.07 #808), 09wv__ (0.07 #695), 026gvfj (0.06 #1165, 0.05 #1692, 0.04 #6962) >> Best rule #159 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 0sz28; 01d0fp; 0btpx; >> query: (?x10325, 09f2j) <- participant(?x10325, ?x8159), participant(?x10325, ?x3017), award_winner(?x6653, ?x8159), place_of_birth(?x8159, ?x739), artist(?x6672, ?x3017) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 033p3_ student! 02fgdx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 123.000 0.077 http://example.org/education/educational_institution/students_graduates./education/education/student #3860-01pvxl PRED entity: 01pvxl PRED relation: language PRED expected values: 064_8sq => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 42): 03_9r (0.64 #351, 0.23 #694, 0.18 #3782), 064_8sq (0.27 #249, 0.26 #649, 0.23 #592), 06nm1 (0.27 #523, 0.20 #638, 0.18 #3782), 04306rv (0.26 #632, 0.26 #575, 0.24 #517), 0349s (0.25 #100, 0.18 #3782, 0.17 #157), 01wgr (0.25 #95, 0.17 #152, 0.05 #551), 0x82 (0.25 #110, 0.17 #167, 0.02 #566), 02bjrlw (0.18 #229, 0.18 #3782, 0.16 #629), 04h9h (0.18 #269, 0.05 #497, 0.02 #554), 05zjd (0.18 #3782, 0.09 #252, 0.09 #309) >> Best rule #351 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 015qy1; >> query: (?x5243, 03_9r) <- film(?x9609, ?x5243), genre(?x5243, ?x1510), profession(?x2264, ?x9609), titles(?x1510, ?x83) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #249 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 05_5rjx; *> query: (?x5243, 064_8sq) <- film(?x9257, ?x5243), film(?x8412, ?x5243), currency(?x5243, ?x170), award_winner(?x5841, ?x8412), ?x9257 = 01gkmx *> conf = 0.27 ranks of expected_values: 2 EVAL 01pvxl language 064_8sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.643 http://example.org/film/film/language #3859-01g5v PRED entity: 01g5v PRED relation: colors! PRED expected values: 01j_06 02hft3 01y9pk 0kw4j 0h6rm 027kp3 01r3w7 02lv2v 0sxgh 02hp70 02tz9z 07tlg 0d5fb => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 662): 01jq34 (0.50 #3955, 0.50 #3598, 0.50 #3241), 02vnp2 (0.50 #4163, 0.50 #3806, 0.50 #3449), 04cnp4 (0.50 #3416, 0.38 #4130, 0.38 #3916), 0k__z (0.50 #3414, 0.38 #4128, 0.38 #3771), 02nq10 (0.50 #3436, 0.38 #4150, 0.38 #3793), 01p896 (0.50 #3101, 0.33 #3457, 0.33 #2390), 027b43 (0.50 #3160, 0.33 #3516, 0.33 #2449), 02bvc5 (0.50 #3126, 0.33 #3482, 0.33 #2771), 0lyjf (0.50 #2957, 0.33 #2246, 0.33 #1891), 02qw_v (0.50 #3114, 0.33 #2403, 0.33 #2048) >> Best rule #3955 for best value: >> intensional similarity = 30 >> extensional distance = 6 >> proper extension: 067z2v; >> query: (?x3189, 01jq34) <- colors(?x11789, ?x3189), colors(?x9922, ?x3189), colors(?x12699, ?x3189), colors(?x10297, ?x3189), colors(?x8223, ?x3189), colors(?x5983, ?x3189), colors(?x5055, ?x3189), colors(?x3387, ?x3189), team(?x10594, ?x11789), colors(?x8223, ?x1101), ?x10594 = 0b_756, school(?x1160, ?x10297), institution(?x1519, ?x12699), state_province_region(?x10297, ?x1227), contains(?x94, ?x12699), major_field_of_study(?x5983, ?x2606), major_field_of_study(?x5983, ?x1154), major_field_of_study(?x3387, ?x2314), student(?x3387, ?x2136), ?x2606 = 062z7, student(?x8223, ?x1515), currency(?x5055, ?x170), service_language(?x5055, ?x254), contains(?x2254, ?x5055), student(?x10297, ?x2451), colors(?x3719, ?x1101), ?x1154 = 02lp1, team(?x60, ?x9922), ?x3719 = 044crp, contact_category(?x3387, ?x897) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2944 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 2 *> proper extension: 038hg; *> query: (?x3189, 0h6rm) <- colors(?x12807, ?x3189), colors(?x4546, ?x3189), colors(?x3552, ?x3189), colors(?x1632, ?x3189), colors(?x10297, ?x3189), colors(?x9108, ?x3189), colors(?x8223, ?x3189), colors(?x6460, ?x3189), colors(?x6455, ?x3189), colors(?x2399, ?x3189), team(?x3551, ?x3552), team(?x1935, ?x12807), currency(?x6455, ?x170), team(?x63, ?x12807), citytown(?x6455, ?x4151), student(?x8223, ?x1515), major_field_of_study(?x2399, ?x1527), school_type(?x8223, ?x3092), position(?x12807, ?x60), ?x9108 = 01v3k2, child(?x3360, ?x10297), citytown(?x8223, ?x362), school(?x1823, ?x2399), draft(?x4546, ?x685), contains(?x94, ?x6460), category(?x6455, ?x134), season(?x1632, ?x701), institution(?x620, ?x6455), sport(?x12807, ?x471), ?x60 = 02nzb8 *> conf = 0.50 ranks of expected_values: 20, 141, 168, 180, 184, 194, 204, 227, 267, 279, 294, 298 EVAL 01g5v colors! 0d5fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 07tlg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 02tz9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 02hp70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 0sxgh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 02lv2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 01r3w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 027kp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 0h6rm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 0kw4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 01y9pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 02hft3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 20.000 0.500 http://example.org/education/educational_institution/colors EVAL 01g5v colors! 01j_06 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 20.000 20.000 0.500 http://example.org/education/educational_institution/colors #3858-014mlp PRED entity: 014mlp PRED relation: institution PRED expected values: 01rtm4 052nd 02cttt 07w0v 01bzw5 033q4k 01j_cy 049dk 037s9x 07wrz 02301 0778p 01q0kg 07vyf 01k3s2 07t90 07tds 0345gh 0lyjf 07vhb 027mdh 03zj9 080z7 01jq4b 01y9qr 027ydt 01y20v 0cwx_ 05x_5 0885n 01qd_r 02gnh0 01hjy5 04rkkv 01q8hj 03gn1x 014zws 0dzst 0jhjl 034q81 031hxk 01clyb 016sd3 02mzg9 01hl_w 015fs3 02hp70 013719 07wtc 058z2d 01pxcf 0yl_3 03wv2g 01x5fb 01xcgf 0ym69 => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 303): 07tds (0.73 #4862, 0.60 #4580, 0.57 #4297), 0hsb3 (0.73 #4893, 0.60 #4611, 0.57 #4328), 01j_cy (0.71 #4251, 0.60 #5099, 0.60 #4534), 07w0v (0.71 #4239, 0.53 #5087, 0.50 #5369), 052nd (0.64 #4802, 0.60 #4520, 0.50 #3954), 07wrz (0.57 #4259, 0.55 #4824, 0.50 #4542), 02mzg9 (0.57 #4428, 0.53 #5276, 0.50 #5558), 0f1nl (0.57 #4261, 0.50 #4544, 0.50 #3414), 01qd_r (0.57 #4360, 0.50 #4643, 0.50 #3795), 03v6t (0.57 #4250, 0.50 #4533, 0.50 #3121) >> Best rule #4862 for best value: >> intensional similarity = 20 >> extensional distance = 9 >> proper extension: 027f2w; >> query: (?x1368, 07tds) <- student(?x1368, ?x10645), institution(?x1368, ?x9947), institution(?x1368, ?x8706), institution(?x1368, ?x5581), institution(?x1368, ?x5357), institution(?x1368, ?x3513), institution(?x1368, ?x3136), major_field_of_study(?x1368, ?x4268), major_field_of_study(?x1368, ?x866), ?x3513 = 0pspl, student(?x4268, ?x3329), citytown(?x8706, ?x4419), currency(?x5357, ?x170), student(?x3136, ?x5366), contains(?x94, ?x9947), profession(?x10645, ?x1032), major_field_of_study(?x1011, ?x866), fraternities_and_sororities(?x8706, ?x3697), spouse(?x3329, ?x4438), school(?x2820, ?x5581) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 5, 6, 7, 9, 13, 14, 15, 16, 18, 19, 22, 30, 38, 43, 44, 46, 48, 50, 51, 53, 55, 62, 65, 79, 80, 81, 84, 87, 92, 96, 104, 106, 107, 111, 120, 121, 152, 157, 158, 183, 185, 186, 187, 190, 226, 228, 229, 238, 247, 249 EVAL 014mlp institution 0ym69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01xcgf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01x5fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 03wv2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 0yl_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01pxcf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 058z2d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 07wtc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 013719 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 02hp70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 015fs3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01hl_w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 02mzg9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 016sd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01clyb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 031hxk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 034q81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 0jhjl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 0dzst CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 014zws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 03gn1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01q8hj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 04rkkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01hjy5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 02gnh0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01qd_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 0885n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 05x_5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 0cwx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01y20v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 027ydt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01y9qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01jq4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 080z7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 03zj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 027mdh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 07vhb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 0lyjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 0345gh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 07tds CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 07t90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01k3s2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 07vyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01q0kg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 0778p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 02301 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 07wrz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 037s9x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 049dk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01j_cy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 033q4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01bzw5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 07w0v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 02cttt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 052nd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 014mlp institution 01rtm4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 24.000 24.000 0.727 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3857-01hww_ PRED entity: 01hww_ PRED relation: role! PRED expected values: 0326tc => 63 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1063): 0161sp (0.62 #8580, 0.43 #5293, 0.33 #7644), 04bpm6 (0.60 #10399, 0.50 #4298, 0.46 #8522), 050z2 (0.53 #10516, 0.52 #14754, 0.50 #6293), 0137g1 (0.50 #7164, 0.50 #4345, 0.50 #3409), 03ryks (0.50 #7349, 0.50 #4999, 0.47 #11103), 05qhnq (0.50 #7828, 0.50 #4540, 0.47 #10641), 023l9y (0.50 #7259, 0.50 #3504, 0.47 #10541), 01wxdn3 (0.50 #7459, 0.50 #3704, 0.47 #10741), 01vs4ff (0.50 #3597, 0.46 #8757, 0.42 #7352), 0285c (0.50 #4780, 0.42 #7130, 0.38 #8535) >> Best rule #8580 for best value: >> intensional similarity = 20 >> extensional distance = 11 >> proper extension: 026t6; 0bxl5; 016622; >> query: (?x1655, 0161sp) <- role(?x1655, ?x2785), role(?x1655, ?x314), role(?x1655, ?x227), ?x227 = 0342h, role(?x4913, ?x1655), role(?x645, ?x1655), role(?x74, ?x4913), ?x314 = 02sgy, group(?x645, ?x9999), group(?x645, ?x6876), role(?x4917, ?x645), role(?x1147, ?x645), group(?x1655, ?x6475), ?x1147 = 07kc_, ?x2785 = 0jtg0, instrumentalists(?x4913, ?x1489), ?x9999 = 01_wfj, ?x6876 = 0ycp3, role(?x3716, ?x4913), ?x4917 = 06w7v >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #8801 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 11 *> proper extension: 026t6; 0bxl5; 016622; *> query: (?x1655, 0326tc) <- role(?x1655, ?x2785), role(?x1655, ?x314), role(?x1655, ?x227), ?x227 = 0342h, role(?x4913, ?x1655), role(?x645, ?x1655), role(?x74, ?x4913), ?x314 = 02sgy, group(?x645, ?x9999), group(?x645, ?x6876), role(?x4917, ?x645), role(?x1147, ?x645), group(?x1655, ?x6475), ?x1147 = 07kc_, ?x2785 = 0jtg0, instrumentalists(?x4913, ?x1489), ?x9999 = 01_wfj, ?x6876 = 0ycp3, role(?x3716, ?x4913), ?x4917 = 06w7v *> conf = 0.46 ranks of expected_values: 20 EVAL 01hww_ role! 0326tc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 63.000 45.000 0.615 http://example.org/music/artist/track_contributions./music/track_contribution/role #3856-039g82 PRED entity: 039g82 PRED relation: location PRED expected values: 030qb3t => 120 concepts (118 used for prediction) PRED predicted values (max 10 best out of 53): 030qb3t (0.25 #83, 0.18 #10525, 0.13 #26589), 02_286 (0.18 #42609, 0.16 #10479, 0.15 #1643), 04jpl (0.12 #17, 0.05 #81148, 0.05 #84362), 01n7q (0.12 #63, 0.02 #81194, 0.02 #84408), 0k049 (0.12 #8, 0.02 #5630, 0.02 #1614), 04rrd (0.12 #97, 0.01 #10539), 013f1h (0.12 #656), 0xkq4 (0.12 #59), 0cr3d (0.05 #42716, 0.05 #14602, 0.05 #10586), 0cc56 (0.04 #10499, 0.04 #1663, 0.03 #860) >> Best rule #83 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 01gy7r; >> query: (?x1784, 030qb3t) <- film(?x1784, ?x3619), ?x3619 = 0fphgb >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039g82 location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 118.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #3855-02zq43 PRED entity: 02zq43 PRED relation: nationality PRED expected values: 02jx1 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 16): 09c7w0 (0.75 #1001, 0.73 #1503, 0.73 #801), 02jx1 (0.63 #333, 0.56 #133, 0.56 #233), 07ssc (0.36 #315, 0.34 #115, 0.33 #215), 0f8l9c (0.31 #5318, 0.30 #5319, 0.21 #2004), 0chghy (0.31 #5318, 0.30 #5319, 0.21 #2004), 03rk0 (0.11 #646, 0.11 #1247, 0.10 #1347), 0d060g (0.05 #3615, 0.05 #1709, 0.05 #1007), 0345h (0.05 #31, 0.04 #131, 0.03 #231), 03rt9 (0.05 #13, 0.04 #113, 0.03 #213), 0j5g9 (0.05 #62, 0.01 #1964, 0.01 #762) >> Best rule #1001 for best value: >> intensional similarity = 3 >> extensional distance = 552 >> proper extension: 01bpc9; 02dh86; 01m65sp; 01vv6_6; 081jbk; 01kmd4; 09wlpl; 044_7j; 01vxqyl; 0163t3; ... >> query: (?x381, 09c7w0) <- gender(?x381, ?x231), location(?x381, ?x362), actor(?x8554, ?x381) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #333 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 141 *> proper extension: 03gt0c5; *> query: (?x381, 02jx1) <- profession(?x381, ?x1032), people(?x743, ?x381), ?x743 = 02w7gg *> conf = 0.63 ranks of expected_values: 2 EVAL 02zq43 nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 76.000 0.753 http://example.org/people/person/nationality #3854-0171cm PRED entity: 0171cm PRED relation: award PRED expected values: 09qv_s => 86 concepts (69 used for prediction) PRED predicted values (max 10 best out of 232): 0bfvd4 (0.71 #15808, 0.70 #15412, 0.69 #2371), 02x4w6g (0.71 #15808, 0.70 #15412, 0.69 #2371), 09cm54 (0.71 #15808, 0.70 #15412, 0.69 #2371), 09td7p (0.23 #117, 0.18 #15015, 0.16 #15411), 03qgjwc (0.23 #176, 0.13 #25691, 0.12 #20947), 0cqh6z (0.23 #63, 0.13 #25691, 0.12 #20947), 0ck27z (0.20 #3249, 0.20 #1668, 0.16 #15411), 0gqwc (0.18 #70, 0.18 #15015, 0.16 #15411), 02ppm4q (0.18 #152, 0.18 #15015, 0.16 #15411), 094qd5 (0.18 #41, 0.18 #15015, 0.16 #15411) >> Best rule #15808 for best value: >> intensional similarity = 3 >> extensional distance = 1531 >> proper extension: 01w806h; 03d9d6; 0cbm64; >> query: (?x2556, ?x1770) <- award(?x2556, ?x112), award_nominee(?x2556, ?x989), award_winner(?x1770, ?x2556) >> conf = 0.71 => this is the best rule for 3 predicted values *> Best rule #15015 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1519 *> proper extension: 0f721s; 0gsg7; 0cjdk; 01_8w2; 01p5yn; 018_q8; 0gsgr; 0283xx2; 01j53q; 0kc8y; ... *> query: (?x2556, ?x704) <- award_winner(?x2556, ?x988), award_winner(?x704, ?x988) *> conf = 0.18 ranks of expected_values: 19 EVAL 0171cm award 09qv_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 86.000 69.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3853-07l450 PRED entity: 07l450 PRED relation: genre PRED expected values: 01jfsb 082gq 01f9r0 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 107): 02kdv5l (0.44 #120, 0.37 #2390, 0.34 #2032), 017fp (0.38 #15, 0.20 #372, 0.19 #253), 01hmnh (0.38 #137, 0.16 #2407, 0.16 #8370), 01jfsb (0.37 #2521, 0.37 #2401, 0.37 #3235), 05p553 (0.35 #1796, 0.35 #9189, 0.34 #3583), 02l7c8 (0.33 #1089, 0.32 #2644, 0.32 #2286), 04pbhw (0.31 #174, 0.06 #2444, 0.06 #2086), 0btmb (0.31 #206, 0.03 #2476, 0.03 #2118), 06n90 (0.25 #132, 0.16 #2522, 0.16 #3950), 03k9fj (0.23 #2042, 0.23 #2400, 0.22 #7646) >> Best rule #120 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0d_wms; >> query: (?x9599, 02kdv5l) <- honored_for(?x5592, ?x9599), film(?x940, ?x9599), ?x5592 = 0275n3y, genre(?x9599, ?x53) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2521 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 425 *> proper extension: 0bh8yn3; *> query: (?x9599, 01jfsb) <- film_crew_role(?x9599, ?x1171), ?x1171 = 09vw2b7, production_companies(?x9599, ?x617), film(?x617, ?x136) *> conf = 0.37 ranks of expected_values: 4, 17, 25 EVAL 07l450 genre 01f9r0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 115.000 115.000 0.438 http://example.org/film/film/genre EVAL 07l450 genre 082gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 115.000 115.000 0.438 http://example.org/film/film/genre EVAL 07l450 genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 115.000 0.438 http://example.org/film/film/genre #3852-0k_9j PRED entity: 0k_9j PRED relation: film_distribution_medium PRED expected values: 0dq6p => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.71 #89, 0.63 #149, 0.62 #179), 02nxhr (0.25 #146, 0.25 #186, 0.24 #176), 0dq6p (0.25 #27, 0.21 #147, 0.20 #2), 07z4p (0.02 #150, 0.02 #35, 0.02 #90), 07c52 (0.01 #178) >> Best rule #89 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 0cnztc4; 064n1pz; 0crh5_f; 072r5v; 04nlb94; >> query: (?x8107, 0735l) <- film_distribution_medium(?x8107, ?x81), film_crew_role(?x8107, ?x468), titles(?x811, ?x8107) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 0dsvzh; 01vksx; 0dtfn; 031778; 082scv; 01qxc7; 02wgk1; 02w9k1c; 04k9y6; 03y0pn; ... *> query: (?x8107, 0dq6p) <- award(?x8107, ?x640), music(?x8107, ?x669), currency(?x8107, ?x170), film_distribution_medium(?x8107, ?x81) *> conf = 0.25 ranks of expected_values: 3 EVAL 0k_9j film_distribution_medium 0dq6p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 134.000 0.708 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #3851-01shy7 PRED entity: 01shy7 PRED relation: film_release_region PRED expected values: 02vzc => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 158): 09c7w0 (0.95 #1783, 0.94 #745, 0.93 #4310), 05r4w (0.88 #893, 0.87 #298, 0.86 #1041), 0k6nt (0.85 #319, 0.83 #914, 0.79 #468), 02vzc (0.84 #345, 0.78 #940, 0.77 #1088), 0345h (0.84 #923, 0.82 #328, 0.81 #1071), 03_3d (0.82 #303, 0.75 #452, 0.74 #1046), 0d060g (0.79 #899, 0.75 #1047, 0.74 #453), 0b90_r (0.73 #896, 0.69 #450, 0.69 #1044), 06qd3 (0.65 #332, 0.49 #927, 0.49 #481), 05v8c (0.64 #312, 0.62 #907, 0.56 #461) >> Best rule #1783 for best value: >> intensional similarity = 3 >> extensional distance = 292 >> proper extension: 0cpllql; >> query: (?x2644, 09c7w0) <- film(?x4295, ?x2644), film_release_region(?x2644, ?x142), diet(?x4295, ?x11141) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #345 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 87 *> proper extension: 07s3m4g; *> query: (?x2644, 02vzc) <- film_release_region(?x2644, ?x2513), film_release_region(?x2644, ?x429), ?x429 = 03rt9, ?x2513 = 05b4w, titles(?x2480, ?x2644) *> conf = 0.84 ranks of expected_values: 4 EVAL 01shy7 film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 64.000 64.000 0.946 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3850-0m63c PRED entity: 0m63c PRED relation: film_release_region PRED expected values: 05r4w 0jgd 0b90_r 0d060g 015fr 09pmkv 059j2 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 127): 0jgd (0.87 #1544, 0.77 #2664, 0.76 #2104), 059j2 (0.85 #1565, 0.81 #2685, 0.81 #585), 0b90_r (0.85 #1545, 0.73 #565, 0.64 #2105), 05r4w (0.84 #1542, 0.80 #2662, 0.79 #2102), 015fr (0.81 #1554, 0.71 #574, 0.70 #2674), 0154j (0.79 #1546, 0.67 #2106, 0.66 #2666), 0d060g (0.79 #567, 0.76 #1547, 0.63 #2667), 03rk0 (0.65 #606, 0.59 #1586, 0.34 #2706), 01p1v (0.63 #1582, 0.54 #602, 0.37 #2702), 04gzd (0.62 #570, 0.60 #1550, 0.42 #2110) >> Best rule #1544 for best value: >> intensional similarity = 5 >> extensional distance = 114 >> proper extension: 0g56t9t; 0gtsx8c; 011yrp; 0ds3t5x; 0gtv7pk; 0h1cdwq; 0dscrwf; 05p1tzf; 0gx9rvq; 0gkz15s; ... >> query: (?x7693, 0jgd) <- film(?x722, ?x7693), film_release_region(?x7693, ?x410), film_release_region(?x7693, ?x390), ?x390 = 0chghy, ?x410 = 01ls2 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 7, 13 EVAL 0m63c film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0m63c film_release_region 09pmkv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 72.000 72.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0m63c film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0m63c film_release_region 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0m63c film_release_region 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0m63c film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0m63c film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3849-06wrt PRED entity: 06wrt PRED relation: country PRED expected values: 05qhw 09pmkv 05b4w 056vv 06t8v 07twz => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 904): 05qhw (0.92 #5990, 0.92 #5683, 0.86 #4149), 06c1y (0.86 #4312, 0.83 #3701, 0.82 #3240), 05b4w (0.82 #3256, 0.80 #915, 0.78 #1070), 0d04z6 (0.80 #915, 0.78 #1070, 0.76 #1227), 05sb1 (0.80 #915, 0.78 #1070, 0.76 #1226), 05cgv (0.80 #915, 0.78 #1070, 0.76 #1226), 01crd5 (0.80 #915, 0.78 #1070, 0.76 #1226), 04xn_ (0.80 #915, 0.78 #1070, 0.55 #917), 0jdd (0.80 #915, 0.78 #1070, 0.55 #917), 0jhd (0.80 #915, 0.76 #1227, 0.75 #1071) >> Best rule #5990 for best value: >> intensional similarity = 34 >> extensional distance = 37 >> proper extension: 06zgc; >> query: (?x2315, 05qhw) <- sports(?x6464, ?x2315), country(?x2315, ?x5680), country(?x2315, ?x94), country(?x2315, ?x87), olympics(?x2315, ?x1931), olympics(?x5114, ?x6464), currency(?x5680, ?x170), film_release_region(?x11351, ?x87), film_release_region(?x10860, ?x87), film_release_region(?x9657, ?x87), film_release_region(?x8373, ?x87), film_release_region(?x7336, ?x87), film_release_region(?x6168, ?x87), film_release_region(?x3830, ?x87), film_release_region(?x3606, ?x87), film_release_region(?x1803, ?x87), film_release_region(?x1456, ?x87), ?x1803 = 0g9wdmc, ?x8373 = 0bs8hvm, ?x11351 = 02wtp6, ?x10860 = 049w1q, ?x3606 = 0gh65c5, ?x3830 = 0gjcrrw, ?x7336 = 0bdjd, ?x1456 = 0cz8mkh, film_release_region(?x54, ?x94), service_location(?x127, ?x94), country(?x89, ?x94), nationality(?x10473, ?x94), ?x9657 = 07jqjx, olympics(?x94, ?x358), ?x6168 = 0gj96ln, location(?x10473, ?x335), administrative_parent(?x108, ?x94) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 34, 59, 80, 90 EVAL 06wrt country 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 45.000 45.000 0.923 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06wrt country 06t8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 45.000 45.000 0.923 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06wrt country 056vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 45.000 45.000 0.923 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06wrt country 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 45.000 45.000 0.923 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06wrt country 09pmkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 45.000 45.000 0.923 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06wrt country 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.923 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #3848-025twgt PRED entity: 025twgt PRED relation: nominated_for! PRED expected values: 014kq6 => 148 concepts (58 used for prediction) PRED predicted values (max 10 best out of 183): 0g5pv3 (0.84 #6138, 0.82 #12291, 0.81 #4172), 025twgt (0.78 #729, 0.67 #1221, 0.67 #975), 025twgf (0.69 #3681, 0.69 #4909, 0.67 #5893), 014kq6 (0.67 #551, 0.62 #4417, 0.57 #305), 01kf5lf (0.58 #5646, 0.56 #4418, 0.52 #3189), 02jr6k (0.28 #6007, 0.26 #4532, 0.26 #4039), 01s9vc (0.28 #6126, 0.22 #4651, 0.22 #4158), 0k5g9 (0.26 #4492, 0.26 #3999, 0.25 #5967), 075cph (0.26 #4487, 0.26 #3994, 0.25 #5962), 01jr4j (0.26 #4612, 0.26 #4119, 0.22 #6087) >> Best rule #6138 for best value: >> intensional similarity = 7 >> extensional distance = 30 >> proper extension: 01771z; >> query: (?x11362, ?x1262) <- genre(?x11362, ?x812), nominated_for(?x836, ?x11362), ?x812 = 01jfsb, film_production_design_by(?x836, ?x5532), country(?x836, ?x94), nominated_for(?x11362, ?x1262), film(?x2538, ?x836) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #551 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 01kf4tt; *> query: (?x11362, 014kq6) <- genre(?x11362, ?x225), film_release_distribution_medium(?x11362, ?x81), currency(?x11362, ?x170), nominated_for(?x11362, ?x836), language(?x11362, ?x5671), prequel(?x11362, ?x8737), ?x836 = 02sg5v *> conf = 0.67 ranks of expected_values: 4 EVAL 025twgt nominated_for! 014kq6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 148.000 58.000 0.841 http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for #3847-03hkp PRED entity: 03hkp PRED relation: language! PRED expected values: 02r1c18 072x7s 02ctc6 027ct7c => 32 concepts (6 used for prediction) PRED predicted values (max 10 best out of 1776): 015qqg (0.87 #1716, 0.82 #1717, 0.60 #5148), 07024 (0.87 #1716, 0.82 #1717, 0.60 #5148), 0jqj5 (0.87 #1716, 0.82 #1717, 0.60 #5148), 0jsf6 (0.87 #1716, 0.82 #1717, 0.60 #5148), 011yg9 (0.87 #1716, 0.82 #1717, 0.60 #5148), 0462hhb (0.87 #1716, 0.82 #1717, 0.60 #5148), 016dj8 (0.87 #1716, 0.82 #1717, 0.60 #5148), 0_9l_ (0.87 #1716, 0.82 #1717, 0.60 #5148), 011yth (0.87 #1716, 0.82 #1717, 0.60 #5148), 0yx_w (0.87 #1716, 0.82 #1717, 0.60 #5148) >> Best rule #1716 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 02h40lc; >> query: (?x3966, ?x144) <- language(?x2370, ?x3966), nominated_for(?x7522, ?x2370), nominated_for(?x3066, ?x2370), nominated_for(?x749, ?x2370), languages_spoken(?x1050, ?x3966), ?x7522 = 0d608, award_winner(?x3066, ?x92), nominated_for(?x3066, ?x144), award(?x396, ?x749), award(?x696, ?x749), film(?x9796, ?x2370), people(?x1050, ?x65) >> conf = 0.87 => this is the best rule for 700 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 383, 519, 654, 1066 EVAL 03hkp language! 027ct7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 6.000 0.865 http://example.org/film/film/language EVAL 03hkp language! 02ctc6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 6.000 0.865 http://example.org/film/film/language EVAL 03hkp language! 072x7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 6.000 0.865 http://example.org/film/film/language EVAL 03hkp language! 02r1c18 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 32.000 6.000 0.865 http://example.org/film/film/language #3846-07bzp PRED entity: 07bzp PRED relation: group! PRED expected values: 021r7r => 99 concepts (51 used for prediction) PRED predicted values (max 10 best out of 96): 0k1bs (0.25 #916, 0.14 #1116, 0.01 #4533), 01vrnsk (0.17 #724, 0.06 #1525, 0.02 #2730), 01vsl3_ (0.17 #649, 0.06 #1450, 0.02 #2655), 01vs4ff (0.12 #927, 0.07 #1127, 0.01 #2933), 0ql36 (0.12 #1000, 0.07 #1200), 01vs4f3 (0.12 #963, 0.07 #1163), 0xsk8 (0.12 #955, 0.07 #1155), 017g21 (0.12 #937, 0.07 #1137), 02x8z_ (0.12 #885, 0.07 #1085), 01nn6c (0.12 #858, 0.07 #1058) >> Best rule #916 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01wv9xn; 0394y; 03k3b; 0qmny; 0jltp; 03qkcn9; >> query: (?x6241, 0k1bs) <- artist(?x3050, ?x6241), artists(?x7436, ?x6241), ?x7436 = 02l96k, group(?x212, ?x6241) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07bzp group! 021r7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 51.000 0.250 http://example.org/music/group_member/membership./music/group_membership/group #3845-02xnjd PRED entity: 02xnjd PRED relation: nationality PRED expected values: 09c7w0 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 85): 09c7w0 (0.86 #397, 0.86 #7944, 0.85 #8839), 07ssc (0.45 #6173, 0.39 #1190, 0.35 #5263), 0345h (0.43 #2582, 0.39 #1190, 0.35 #5263), 0d060g (0.43 #2582, 0.39 #1190, 0.35 #5263), 0j1z8 (0.39 #1190, 0.35 #5263, 0.32 #1488), 0f8l9c (0.35 #5263, 0.32 #1488, 0.10 #6180), 059rby (0.25 #8143, 0.25 #7844, 0.25 #8144), 02zp1t (0.25 #8143, 0.25 #7844, 0.25 #8144), 0cyn3 (0.20 #8142), 04_1l0v (0.20 #8142) >> Best rule #397 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 0byfz; 02ndbd; 03n93; 02_l96; 0bvg70; 09xvf7; >> query: (?x7976, 09c7w0) <- student(?x3394, ?x7976), nationality(?x7976, ?x4743), award_nominee(?x1914, ?x7976), production_companies(?x781, ?x1914), nominated_for(?x1914, ?x2742) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xnjd nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.860 http://example.org/people/person/nationality #3844-05fm6m PRED entity: 05fm6m PRED relation: language PRED expected values: 02h40lc => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 30): 02h40lc (0.96 #120, 0.93 #297, 0.91 #356), 064_8sq (0.20 #81, 0.15 #317, 0.15 #1626), 06b_j (0.20 #82, 0.07 #377, 0.06 #437), 06nm1 (0.14 #306, 0.11 #781, 0.11 #663), 04306rv (0.10 #300, 0.10 #64, 0.09 #894), 05zjd (0.10 #85, 0.02 #558, 0.02 #975), 0t_2 (0.10 #73, 0.02 #250, 0.01 #606), 03_9r (0.06 #187, 0.06 #899, 0.06 #246), 02bjrlw (0.06 #178, 0.06 #2261, 0.06 #2501), 04h9h (0.04 #338, 0.03 #457, 0.03 #754) >> Best rule #120 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 04tz52; 05fcbk7; 0gh65c5; 0bz3jx; 01svry; >> query: (?x7626, 02h40lc) <- film(?x2275, ?x7626), film(?x2275, ?x5839), ?x5839 = 05650n >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fm6m language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.958 http://example.org/film/film/language #3843-02lg3y PRED entity: 02lg3y PRED relation: award_nominee PRED expected values: 01l1sq => 106 concepts (56 used for prediction) PRED predicted values (max 10 best out of 603): 01z7_f (0.86 #6996, 0.85 #4664, 0.85 #2332), 059gkk (0.74 #5397, 0.72 #3065, 0.57 #733), 01l1sq (0.63 #5005, 0.61 #2673, 0.57 #341), 0bl60p (0.63 #6376, 0.61 #4044, 0.43 #1712), 02p_ycc (0.53 #5362, 0.50 #3030, 0.43 #698), 02lg3y (0.53 #5688, 0.50 #3356, 0.43 #1024), 0521rl1 (0.53 #4826, 0.50 #2494, 0.37 #6998), 0c1ps1 (0.47 #6804, 0.44 #4472, 0.43 #2140), 02lgj6 (0.47 #4969, 0.44 #2637, 0.37 #6998), 02tr7d (0.37 #6998, 0.29 #345, 0.18 #130564) >> Best rule #6996 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 01r42_g; >> query: (?x4401, ?x368) <- award_nominee(?x6920, ?x4401), award_nominee(?x369, ?x4401), award_nominee(?x368, ?x4401), ?x6920 = 02lgfh, award_winner(?x369, ?x446) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #5005 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 01r42_g; *> query: (?x4401, 01l1sq) <- award_nominee(?x6920, ?x4401), award_nominee(?x369, ?x4401), ?x6920 = 02lgfh, award_winner(?x369, ?x446) *> conf = 0.63 ranks of expected_values: 3 EVAL 02lg3y award_nominee 01l1sq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 56.000 0.861 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3842-019kyn PRED entity: 019kyn PRED relation: language PRED expected values: 02h40lc => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.94 #61, 0.91 #1124, 0.91 #769), 064_8sq (0.29 #81, 0.23 #22, 0.20 #553), 06nm1 (0.23 #11, 0.13 #542, 0.11 #424), 03_9r (0.14 #718, 0.10 #895, 0.10 #482), 04306rv (0.12 #123, 0.10 #1067, 0.09 #418), 02bjrlw (0.12 #119, 0.09 #414, 0.09 #60), 06b_j (0.09 #82, 0.08 #849, 0.08 #554), 0653m (0.06 #71, 0.05 #130, 0.03 #1786), 071fb (0.06 #77, 0.02 #372, 0.02 #549), 04h9h (0.04 #869, 0.04 #810, 0.04 #1105) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 0p_sc; 02q52q; 0fpv_3_; 0cqnss; 09p3_s; 0dnw1; 0p9tm; 02zk08; >> query: (?x4669, 02h40lc) <- production_companies(?x4669, ?x2156), award(?x4669, ?x1079), ?x1079 = 0l8z1 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019kyn language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.941 http://example.org/film/film/language #3841-027n4zv PRED entity: 027n4zv PRED relation: profession PRED expected values: 02hrh1q => 94 concepts (91 used for prediction) PRED predicted values (max 10 best out of 51): 02hrh1q (0.88 #1665, 0.88 #8721, 0.87 #465), 01d_h8 (0.31 #4959, 0.30 #1956, 0.29 #6460), 0dxtg (0.29 #6918, 0.28 #7355, 0.28 #4967), 0d1pc (0.29 #3902, 0.28 #7355, 0.27 #5554), 018gz8 (0.29 #318, 0.14 #1368, 0.13 #7673), 03gjzk (0.27 #5554, 0.26 #10508, 0.22 #7972), 0np9r (0.22 #1372, 0.20 #2873, 0.20 #2572), 02jknp (0.22 #4961, 0.20 #6462, 0.20 #7513), 09jwl (0.21 #3171, 0.20 #2270, 0.19 #2420), 0cbd2 (0.16 #907, 0.15 #6911, 0.15 #1507) >> Best rule #1665 for best value: >> intensional similarity = 3 >> extensional distance = 652 >> proper extension: 01wk7b7; 0309jm; 05r5w; 015njf; 02_p8v; 01wc7p; 03xn3s2; 03kxp7; 05w6cw; 037w7r; ... >> query: (?x8424, 02hrh1q) <- gender(?x8424, ?x231), actor(?x3310, ?x8424), award(?x8424, ?x1670) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027n4zv profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 91.000 0.876 http://example.org/people/person/profession #3840-0pksh PRED entity: 0pksh PRED relation: place_of_birth PRED expected values: 0hn4h => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 36): 03h64 (0.30 #33109, 0.28 #4931, 0.27 #45088), 02_286 (0.12 #32423, 0.11 #3541, 0.10 #4245), 0cr3d (0.07 #32498, 0.05 #4320, 0.04 #9250), 030qb3t (0.04 #5689, 0.04 #2872, 0.04 #9210), 01_d4 (0.04 #5701, 0.04 #10632, 0.04 #9927), 04jpl (0.03 #31002, 0.02 #6347, 0.02 #27480), 01531 (0.02 #3627, 0.02 #17011, 0.02 #44487), 0cc56 (0.02 #32437, 0.02 #19757, 0.01 #4259), 04f_d (0.02 #2891, 0.02 #6412, 0.01 #12048), 0dclg (0.02 #5009, 0.01 #7121, 0.01 #7826) >> Best rule #33109 for best value: >> intensional similarity = 3 >> extensional distance = 1117 >> proper extension: 076df9; >> query: (?x12529, ?x2645) <- location(?x12529, ?x2645), place_of_birth(?x3382, ?x2645), adjoins(?x2645, ?x12917) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pksh place_of_birth 0hn4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 123.000 0.298 http://example.org/people/person/place_of_birth #3839-03v1s PRED entity: 03v1s PRED relation: religion PRED expected values: 01y0s9 => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 22): 01y0s9 (0.58 #413, 0.58 #124, 0.57 #437), 0flw86 (0.39 #1376, 0.39 #1303, 0.38 #1014), 092bf5 (0.37 #2340, 0.36 #2461, 0.25 #1383), 072w0 (0.37 #2340, 0.36 #2461, 0.24 #351), 058x5 (0.36 #411, 0.35 #435, 0.35 #315), 03j6c (0.25 #35, 0.09 #1434, 0.09 #1532), 0kpl (0.25 #29, 0.03 #753, 0.03 #221), 07w8f (0.25 #42, 0.03 #234, 0.02 #307), 04t_mf (0.04 #1438, 0.04 #1536, 0.02 #1366), 078tg (0.03 #1322, 0.03 #1371, 0.03 #1443) >> Best rule #413 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 0hjy; >> query: (?x448, 01y0s9) <- jurisdiction_of_office(?x2358, ?x448), contains(?x448, ?x449), district_represented(?x176, ?x448) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03v1s religion 01y0s9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 188.000 0.585 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #3838-078sj4 PRED entity: 078sj4 PRED relation: film_crew_role PRED expected values: 01pvkk 02ynfr => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 24): 02ynfr (0.36 #13, 0.22 #46, 0.19 #482), 01pvkk (0.32 #9, 0.30 #443, 0.30 #42), 01xy5l_ (0.20 #11, 0.14 #211, 0.13 #377), 0215hd (0.19 #216, 0.17 #49, 0.14 #382), 089g0h (0.19 #50, 0.12 #217, 0.12 #383), 0d2b38 (0.17 #56, 0.14 #223, 0.12 #389), 02rh1dz (0.16 #442, 0.15 #477, 0.13 #208), 02_n3z (0.12 #201, 0.10 #267, 0.09 #367), 04pyp5 (0.12 #14, 0.08 #517, 0.08 #583), 089fss (0.11 #38, 0.10 #205, 0.08 #72) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 02rx2m5; 065z3_x; 0gbtbm; 046488; 0ptx_; 02nczh; >> query: (?x2814, 02ynfr) <- genre(?x2814, ?x10122), film_crew_role(?x2814, ?x137), ?x10122 = 01f9r0, language(?x2814, ?x254) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 078sj4 film_crew_role 02ynfr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.360 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 078sj4 film_crew_role 01pvkk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.360 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3837-018gqj PRED entity: 018gqj PRED relation: award PRED expected values: 0c4z8 02x17c2 02gdjb => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 293): 054krc (0.83 #1987, 0.83 #1674, 0.78 #16687), 0c4z8 (0.78 #16687, 0.78 #43701, 0.78 #17483), 01by1l (0.45 #2097, 0.44 #1301, 0.41 #2494), 0fhpv4 (0.38 #1779, 0.26 #2973, 0.16 #7342), 01bgqh (0.34 #2030, 0.34 #2427, 0.33 #1234), 09sb52 (0.34 #30231, 0.33 #26655, 0.26 #38973), 01c92g (0.26 #96, 0.21 #10824, 0.19 #890), 02f5qb (0.26 #152, 0.20 #549, 0.15 #1343), 05pcn59 (0.26 #4053, 0.10 #30270, 0.10 #12397), 05p09zm (0.26 #4095, 0.08 #16809, 0.08 #7672) >> Best rule #1987 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 012ljv; 02fgpf; 02cyfz; 06449; 01jpmpv; 01tc9r; 012wg; 04ls53; 02w670; 07j8kh; ... >> query: (?x6025, ?x1443) <- music(?x7846, ?x6025), award_winner(?x1443, ?x6025), ?x1443 = 054krc >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #16687 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 329 *> proper extension: 016qtt; 01vvydl; 028q6; 07s3vqk; 0197tq; 0411q; 0lbj1; 01vw87c; 01vrx3g; 01lmj3q; ... *> query: (?x6025, ?x1232) <- instrumentalists(?x316, ?x6025), award(?x6025, ?x1079), award_winner(?x1232, ?x6025) *> conf = 0.78 ranks of expected_values: 2, 17, 21 EVAL 018gqj award 02gdjb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 138.000 138.000 0.828 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 018gqj award 02x17c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 138.000 138.000 0.828 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 018gqj award 0c4z8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 138.000 0.828 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3836-035w2k PRED entity: 035w2k PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 25): 0ch6mp2 (0.79 #347, 0.78 #313, 0.74 #1544), 0dxtw (0.45 #145, 0.44 #827, 0.44 #895), 01pvkk (0.30 #760, 0.30 #828, 0.29 #896), 02ynfr (0.20 #82, 0.18 #900, 0.17 #832), 089fss (0.20 #73, 0.12 #618, 0.09 #687), 0d2b38 (0.19 #160, 0.15 #331, 0.13 #229), 015h31 (0.17 #8, 0.16 #213, 0.16 #144), 01xy5l_ (0.17 #353, 0.16 #148, 0.15 #319), 094hwz (0.17 #13, 0.10 #252, 0.09 #286), 0215hd (0.16 #324, 0.15 #392, 0.14 #358) >> Best rule #347 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 04nlb94; >> query: (?x5008, 0ch6mp2) <- region(?x5008, ?x512), country(?x5008, ?x94), film_crew_role(?x5008, ?x137), film_distribution_medium(?x5008, ?x2099) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035w2k film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.789 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3835-03ft8 PRED entity: 03ft8 PRED relation: written_by! PRED expected values: 015bpl => 227 concepts (196 used for prediction) PRED predicted values (max 10 best out of 403): 08phg9 (0.33 #11233, 0.30 #49555, 0.20 #56159), 014nq4 (0.33 #11233, 0.30 #49555, 0.20 #56159), 014lc_ (0.28 #8588, 0.21 #15855, 0.17 #50876), 06fqlk (0.15 #5069, 0.14 #8372, 0.14 #6390), 0cc5mcj (0.15 #4779, 0.14 #8082, 0.14 #6100), 047csmy (0.15 #4979, 0.14 #8282, 0.14 #6300), 05zlld0 (0.15 #4867, 0.14 #8170, 0.14 #6188), 012mrr (0.14 #847, 0.09 #3489, 0.08 #4150), 011xg5 (0.14 #1200, 0.09 #3842, 0.08 #4503), 07ghq (0.12 #11114, 0.08 #4505, 0.06 #10453) >> Best rule #11233 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0gyx4; >> query: (?x1683, ?x3221) <- profession(?x1683, ?x353), type_of_union(?x1683, ?x566), spouse(?x9957, ?x1683), story_by(?x3221, ?x1683) >> conf = 0.33 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 03ft8 written_by! 015bpl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 227.000 196.000 0.333 http://example.org/film/film/written_by #3834-02z4b_8 PRED entity: 02z4b_8 PRED relation: gender PRED expected values: 02zsn => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.89 #21, 0.89 #23, 0.81 #17), 05zppz (0.86 #24, 0.86 #42, 0.85 #40) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 0152cw; 01r9fv; 01wz3cx; 0161c2; 01wrcxr; 0dzlk; >> query: (?x7115, 02zsn) <- award(?x7115, ?x4796), ?x4796 = 01c99j, profession(?x7115, ?x220), nationality(?x7115, ?x1310) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02z4b_8 gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.891 http://example.org/people/person/gender #3833-02rdyk7 PRED entity: 02rdyk7 PRED relation: award! PRED expected values: 02kxbwx 0kft 06pjs 03fqv5 => 47 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2851): 06pj8 (0.77 #3335, 0.71 #23355, 0.67 #40041), 06t8b (0.77 #3335, 0.67 #46715, 0.66 #23354), 0bzyh (0.77 #3335, 0.67 #46715, 0.66 #23354), 0kvqv (0.77 #3335, 0.67 #46715, 0.66 #23354), 041jlr (0.77 #3335, 0.67 #46715, 0.66 #23354), 014hdb (0.77 #3335, 0.67 #46715, 0.66 #23354), 0hskw (0.60 #732, 0.09 #10741, 0.07 #17414), 014zcr (0.40 #51, 0.24 #10060, 0.19 #16733), 02kxbwx (0.40 #178, 0.21 #10187, 0.20 #6851), 02hfp_ (0.40 #2298, 0.21 #12307, 0.20 #8971) >> Best rule #3335 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0bm70b; >> query: (?x1587, ?x986) <- award_winner(?x1587, ?x986), award(?x4922, ?x1587), award(?x3572, ?x1587), ?x4922 = 01j2xj, film(?x3572, ?x392) >> conf = 0.77 => this is the best rule for 6 predicted values *> Best rule #178 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0bm70b; *> query: (?x1587, 02kxbwx) <- award_winner(?x1587, ?x986), award(?x4922, ?x1587), award(?x3572, ?x1587), ?x4922 = 01j2xj, film(?x3572, ?x392) *> conf = 0.40 ranks of expected_values: 9, 12, 45, 57 EVAL 02rdyk7 award! 03fqv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 47.000 19.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02rdyk7 award! 06pjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 47.000 19.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02rdyk7 award! 0kft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 47.000 19.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02rdyk7 award! 02kxbwx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 47.000 19.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3832-07bdd_ PRED entity: 07bdd_ PRED relation: nominated_for PRED expected values: 01r97z 04tc1g 03cvwkr 01771z 03nx8mj 03t79f 0dp7wt 06zn1c => 45 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1746): 06rmdr (0.67 #31957, 0.67 #25869, 0.66 #19776), 0n83s (0.67 #31957, 0.67 #25869, 0.66 #19776), 06wzvr (0.67 #31957, 0.67 #25869, 0.66 #19776), 02krdz (0.67 #31957, 0.67 #25869, 0.66 #19776), 05fm6m (0.67 #5661, 0.40 #4139, 0.33 #1100), 0bshwmp (0.60 #3173, 0.33 #4695, 0.19 #4561), 05c46y6 (0.50 #6458, 0.43 #7977, 0.36 #9496), 09k56b7 (0.50 #6349, 0.43 #7868, 0.36 #9387), 0j43swk (0.50 #6508, 0.43 #8027, 0.27 #9546), 0462hhb (0.50 #6788, 0.43 #8307, 0.27 #9826) >> Best rule #31957 for best value: >> intensional similarity = 3 >> extensional distance = 215 >> proper extension: 06196; 0fqnzts; >> query: (?x1105, ?x146) <- award(?x11876, ?x1105), award(?x146, ?x1105), profession(?x11876, ?x319) >> conf = 0.67 => this is the best rule for 4 predicted values *> Best rule #4673 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 05b1610; 05p09zm; *> query: (?x1105, 04tc1g) <- award(?x846, ?x1105), produced_by(?x153, ?x846), nominated_for(?x1105, ?x4839), ?x4839 = 0dqcs3 *> conf = 0.50 ranks of expected_values: 30, 34, 35, 38, 192, 194, 196, 198 EVAL 07bdd_ nominated_for 06zn1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 45.000 22.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07bdd_ nominated_for 0dp7wt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 45.000 22.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07bdd_ nominated_for 03t79f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 45.000 22.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07bdd_ nominated_for 03nx8mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 45.000 22.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07bdd_ nominated_for 01771z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 45.000 22.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07bdd_ nominated_for 03cvwkr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 45.000 22.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07bdd_ nominated_for 04tc1g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 45.000 22.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07bdd_ nominated_for 01r97z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 45.000 22.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3831-055td_ PRED entity: 055td_ PRED relation: film! PRED expected values: 016yvw => 78 concepts (57 used for prediction) PRED predicted values (max 10 best out of 878): 0yfp (0.72 #37446, 0.61 #93625, 0.49 #104026), 02xs5v (0.20 #1405, 0.07 #7645, 0.06 #9726), 030xr_ (0.20 #1591, 0.06 #9912, 0.03 #7831), 024n3z (0.20 #465, 0.03 #4625, 0.03 #17108), 0169dl (0.20 #402, 0.03 #17045, 0.03 #37850), 08s_lw (0.20 #1005, 0.02 #17648, 0.01 #34290), 01vvb4m (0.20 #523, 0.02 #40052, 0.02 #42133), 02s2ft (0.20 #7, 0.02 #54103, 0.01 #43698), 0408np (0.20 #460, 0.01 #15022), 016yzz (0.20 #687, 0.01 #25650, 0.01 #17330) >> Best rule #37446 for best value: >> intensional similarity = 4 >> extensional distance = 289 >> proper extension: 09fb5; >> query: (?x4399, ?x973) <- nominated_for(?x973, ?x4399), award_winner(?x7606, ?x973), award(?x7561, ?x7606), ?x7561 = 0164r9 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #7191 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 02bg8v; 09fn1w; 03cv_gy; 05znbh7; *> query: (?x4399, 016yvw) <- genre(?x4399, ?x3312), ?x3312 = 02p0szs, award_winner(?x4399, ?x4103), nominated_for(?x973, ?x4399) *> conf = 0.10 ranks of expected_values: 28 EVAL 055td_ film! 016yvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 78.000 57.000 0.718 http://example.org/film/actor/film./film/performance/film #3830-0b78hw PRED entity: 0b78hw PRED relation: company PRED expected values: 0194_r => 142 concepts (129 used for prediction) PRED predicted values (max 10 best out of 131): 01w5m (0.50 #240, 0.20 #432, 0.09 #2362), 0c5x_ (0.33 #123, 0.03 #2244, 0.03 #2821), 03p7gb (0.25 #265, 0.20 #457, 0.07 #1423), 07szy (0.25 #215, 0.20 #407, 0.07 #1373), 07wrz (0.25 #227, 0.17 #1191, 0.12 #2349), 03hdz8 (0.25 #300, 0.03 #2229, 0.03 #2422), 05zl0 (0.20 #475, 0.09 #3365, 0.09 #1828), 01w3v (0.17 #1171, 0.06 #3289, 0.05 #1560), 07tgn (0.17 #594, 0.05 #1562, 0.05 #1946), 030_1_ (0.17 #601, 0.05 #1953, 0.02 #2914) >> Best rule #240 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04411; 01dvtx; >> query: (?x4308, 01w5m) <- student(?x1368, ?x4308), profession(?x4308, ?x353), interests(?x4308, ?x6364), influenced_by(?x4308, ?x920) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #940 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 019fz; *> query: (?x4308, 0194_r) <- profession(?x4308, ?x353), influenced_by(?x4308, ?x1857), gender(?x4308, ?x231), ?x1857 = 026lj *> conf = 0.09 ranks of expected_values: 14 EVAL 0b78hw company 0194_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 142.000 129.000 0.500 http://example.org/people/person/employment_history./business/employment_tenure/company #3829-05f7w84 PRED entity: 05f7w84 PRED relation: languages PRED expected values: 02h40lc => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 55): 02h40lc (0.98 #574, 0.98 #564, 0.96 #434), 03_9r (0.18 #144, 0.11 #232, 0.11 #326), 064_8sq (0.17 #46, 0.11 #232, 0.07 #126), 04306rv (0.11 #232, 0.08 #43, 0.07 #63), 02bv9 (0.11 #232, 0.08 #48, 0.07 #68), 02bjrlw (0.11 #232, 0.08 #41, 0.07 #61), 0t_2 (0.11 #232, 0.07 #65, 0.06 #75), 05zjd (0.11 #232, 0.07 #67, 0.05 #613), 07qv_ (0.11 #232, 0.05 #613, 0.02 #725), 032f6 (0.05 #613, 0.02 #725) >> Best rule #574 for best value: >> intensional similarity = 9 >> extensional distance = 184 >> proper extension: 0d7m90; >> query: (?x5938, 02h40lc) <- country_of_origin(?x5938, ?x94), program(?x11954, ?x5938), languages(?x5938, ?x2502), countries_spoken_in(?x2502, ?x47), language(?x8218, ?x2502), language(?x4118, ?x2502), ?x8218 = 03m5y9p, languages(?x804, ?x2502), ?x4118 = 07jxpf >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05f7w84 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.984 http://example.org/tv/tv_program/languages #3828-0mb0 PRED entity: 0mb0 PRED relation: location PRED expected values: 04ly1 => 207 concepts (185 used for prediction) PRED predicted values (max 10 best out of 356): 0f2tj (0.71 #134305, 0.70 #135915, 0.70 #127870), 02_286 (0.64 #73217, 0.25 #23347, 0.25 #115841), 07b_l (0.38 #2597, 0.25 #186, 0.09 #52267), 030qb3t (0.26 #15350, 0.25 #2493, 0.25 #82), 0cr3d (0.25 #144, 0.12 #2555, 0.12 #1751), 04jpl (0.18 #72392, 0.06 #4841, 0.06 #143974), 0d6lp (0.16 #91684, 0.09 #52267, 0.06 #69156), 010016 (0.16 #91684, 0.09 #52267, 0.06 #69156), 04ly1 (0.15 #3417, 0.03 #48442, 0.02 #72577), 059rby (0.14 #48256, 0.12 #819, 0.08 #72391) >> Best rule #134305 for best value: >> intensional similarity = 3 >> extensional distance = 1527 >> proper extension: 04dyqk; >> query: (?x10598, ?x6769) <- place_of_birth(?x10598, ?x6769), location(?x10598, ?x1227), contains(?x94, ?x1227) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3417 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 01k7d9; 07d370; 0g28b1; 0d810y; *> query: (?x10598, 04ly1) <- place_of_birth(?x10598, ?x6769), nationality(?x10598, ?x94), award(?x10598, ?x8909), ?x6769 = 0f2tj *> conf = 0.15 ranks of expected_values: 9 EVAL 0mb0 location 04ly1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 207.000 185.000 0.705 http://example.org/people/person/places_lived./people/place_lived/location #3827-02b15h PRED entity: 02b15h PRED relation: team! PRED expected values: 0d9v9q => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 72): 04v68c (0.67 #651, 0.46 #1087, 0.40 #287), 0784v1 (0.46 #1021, 0.22 #1385, 0.22 #585), 0d3f83 (0.45 #849, 0.40 #777, 0.21 #1963), 0135nb (0.44 #523, 0.29 #580, 0.27 #1178), 05s_c38 (0.40 #746, 0.36 #818, 0.17 #1617), 09j028 (0.40 #1211, 0.22 #1429, 0.18 #1792), 080dyk (0.33 #874, 0.33 #75, 0.27 #1092), 07h1h5 (0.33 #883, 0.33 #84, 0.27 #1101), 02y0dd (0.33 #136, 0.27 #1153, 0.21 #1963), 0457w0 (0.33 #25, 0.20 #2399, 0.19 #506) >> Best rule #651 for best value: >> intensional similarity = 15 >> extensional distance = 7 >> proper extension: 02029f; 0hqzm6r; 0ckf6; >> query: (?x209, 04v68c) <- position(?x209, ?x530), position(?x209, ?x63), position(?x209, ?x60), ?x63 = 02sdk9v, ?x530 = 02_j1w, team(?x208, ?x209), team(?x208, ?x9358), team(?x208, ?x6340), team(?x208, ?x3702), ?x60 = 02nzb8, ?x6340 = 0j47s, nationality(?x208, ?x4071), ?x4071 = 05bcl, ?x9358 = 0272vm, team(?x6873, ?x3702) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2399 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 57 *> proper extension: 0j13b; *> query: (?x209, ?x1898) <- position(?x209, ?x530), position(?x209, ?x63), position(?x209, ?x60), ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x60 = 02nzb8, team(?x208, ?x209), team(?x208, ?x5993), team(?x208, ?x11955), team(?x9410, ?x11955), team(?x1898, ?x11955), colors(?x5993, ?x663), team(?x5420, ?x5993), ?x9410 = 0dv1hh *> conf = 0.20 ranks of expected_values: 26 EVAL 02b15h team! 0d9v9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 83.000 83.000 0.667 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #3826-0dwvl PRED entity: 0dwvl PRED relation: role! PRED expected values: 0770cd 01gx5f 01dhjz => 71 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1100): 050z2 (0.78 #4849, 0.60 #6251, 0.60 #2517), 023l9y (0.67 #5122, 0.67 #4874, 0.54 #10485), 02s6sh (0.67 #5096, 0.54 #11645, 0.54 #10707), 0161sp (0.67 #4789, 0.47 #13681, 0.44 #5257), 01pq5j7 (0.67 #931, 0.43 #2326, 0.27 #4191), 05qhnq (0.60 #2642, 0.58 #9649, 0.57 #3572), 03ryks (0.60 #6367, 0.58 #9640, 0.57 #3096), 0l12d (0.60 #1932, 0.56 #4730, 0.50 #538), 016ntp (0.60 #2006, 0.50 #9479, 0.50 #3868), 01vs4ff (0.56 #4968, 0.56 #4501, 0.46 #10579) >> Best rule #4849 for best value: >> intensional similarity = 26 >> extensional distance = 7 >> proper extension: 01vj9c; 05842k; >> query: (?x868, 050z2) <- role(?x4769, ?x868), role(?x3991, ?x868), role(?x3716, ?x868), role(?x316, ?x868), role(?x868, ?x2798), ?x3716 = 03gvt, role(?x1715, ?x868), ?x316 = 05r5c, role(?x1268, ?x3991), role(?x10237, ?x3991), role(?x4855, ?x3991), role(?x4595, ?x3991), role(?x3266, ?x3991), role(?x2799, ?x3991), ?x1268 = 0bm02, ?x4855 = 01l4g5, ?x4595 = 023l9y, ?x2799 = 01vsl3_, role(?x3991, ?x5480), ?x2798 = 03qjg, ?x4769 = 0dwt5, ?x5480 = 01w4c9, artist(?x2149, ?x3266), artists(?x505, ?x10237), religion(?x3266, ?x2694), ?x1715 = 04bpm6 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #7541 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 9 *> proper extension: 03gvt; *> query: (?x868, 0770cd) <- role(?x7033, ?x868), role(?x3716, ?x868), role(?x2460, ?x868), role(?x432, ?x868), role(?x314, ?x868), role(?x868, ?x2798), role(?x868, ?x2764), ?x2798 = 03qjg, performance_role(?x315, ?x3716), role(?x2876, ?x3716), ?x2460 = 01wy6, role(?x3716, ?x1166), ?x314 = 02sgy, ?x2876 = 01vn35l, instrumentalists(?x432, ?x133), ?x2764 = 01s0ps, role(?x569, ?x7033), role(?x894, ?x432), role(?x13413, ?x432), role(?x11635, ?x432), ?x13413 = 014cw2, ?x11635 = 01nrz4, role(?x1407, ?x7033), instrumentalists(?x7033, ?x2987) *> conf = 0.55 ranks of expected_values: 17, 18, 88 EVAL 0dwvl role! 01dhjz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 71.000 42.000 0.778 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0dwvl role! 01gx5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 71.000 42.000 0.778 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0dwvl role! 0770cd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 71.000 42.000 0.778 http://example.org/music/artist/track_contributions./music/track_contribution/role #3825-08nhfc1 PRED entity: 08nhfc1 PRED relation: film! PRED expected values: 025jfl => 112 concepts (102 used for prediction) PRED predicted values (max 10 best out of 49): 086k8 (0.18 #1364, 0.17 #530, 0.17 #759), 03xq0f (0.17 #156, 0.12 #1216, 0.12 #306), 01gb54 (0.17 #105, 0.08 #406, 0.07 #1391), 01795t (0.15 #395, 0.15 #244, 0.08 #18), 05qd_ (0.15 #386, 0.13 #160, 0.12 #1371), 016tw3 (0.15 #844, 0.14 #1373, 0.14 #920), 017s11 (0.13 #1289, 0.12 #1894, 0.12 #3645), 015c4g (0.12 #76, 0.05 #5161, 0.05 #5085), 0m_v0 (0.12 #76, 0.05 #5161, 0.05 #5085), 0flw6 (0.12 #76, 0.05 #5161, 0.05 #5085) >> Best rule #1364 for best value: >> intensional similarity = 4 >> extensional distance = 299 >> proper extension: 02vr3gz; >> query: (?x7635, 086k8) <- genre(?x7635, ?x53), film_crew_role(?x7635, ?x1284), award(?x7635, ?x591), produced_by(?x7635, ?x3442) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #915 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 261 *> proper extension: 0ds35l9; 0m313; 083shs; 02vp1f_; 01gc7; 0ds3t5x; 0ds11z; 04nl83; 011yph; 0pc62; ... *> query: (?x7635, 025jfl) <- honored_for(?x2220, ?x7635), film(?x2108, ?x7635), award(?x7635, ?x591), film_crew_role(?x7635, ?x1284) *> conf = 0.06 ranks of expected_values: 29 EVAL 08nhfc1 film! 025jfl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 112.000 102.000 0.176 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #3824-0bmhn PRED entity: 0bmhn PRED relation: nominated_for! PRED expected values: 01y8cr => 61 concepts (37 used for prediction) PRED predicted values (max 10 best out of 687): 0bvzp (0.39 #49047, 0.39 #16347, 0.36 #21019), 01y8cr (0.31 #63060, 0.27 #58387, 0.24 #42037), 034q3l (0.31 #63060, 0.27 #58387, 0.24 #42037), 017s11 (0.19 #7005, 0.14 #67730, 0.14 #2335), 0cb77r (0.17 #65395, 0.16 #70066, 0.13 #35029), 05218gr (0.17 #65395, 0.16 #70066, 0.13 #35029), 0c0tzp (0.17 #65395, 0.16 #70066, 0.13 #35029), 0f7h2g (0.16 #70066, 0.11 #79407, 0.10 #70067), 0k9j_ (0.13 #35029, 0.05 #4212, 0.04 #6547), 01d6jf (0.13 #35029) >> Best rule #49047 for best value: >> intensional similarity = 3 >> extensional distance = 544 >> proper extension: 03t97y; 03twd6; >> query: (?x10114, ?x6399) <- award_winner(?x10114, ?x199), music(?x10114, ?x6399), language(?x10114, ?x254) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #63060 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 738 *> proper extension: 02d413; 0ds35l9; 0d90m; 03qcfvw; 0m313; 02y_lrp; 0g22z; 083shs; 02vxq9m; 0b2v79; ... *> query: (?x10114, ?x4279) <- film(?x541, ?x10114), award(?x10114, ?x591), film(?x4279, ?x10114) *> conf = 0.31 ranks of expected_values: 2 EVAL 0bmhn nominated_for! 01y8cr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 61.000 37.000 0.387 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #3823-0ckm4x PRED entity: 0ckm4x PRED relation: profession PRED expected values: 0np9r => 75 concepts (74 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.90 #5378, 0.90 #2994, 0.89 #7912), 0np9r (0.80 #1362, 0.73 #1809, 0.68 #1213), 01d_h8 (0.49 #4774, 0.34 #2539, 0.34 #2390), 02jknp (0.42 #4776, 0.33 #306, 0.26 #902), 0cbd2 (0.41 #1050, 0.38 #901, 0.37 #1497), 09jwl (0.37 #5681, 0.36 #3446, 0.36 #3297), 03gjzk (0.36 #4783, 0.33 #313, 0.22 #5081), 02krf9 (0.33 #325, 0.15 #4795, 0.09 #5093), 0kyk (0.32 #1073, 0.32 #924, 0.30 #1520), 0nbcg (0.27 #5694, 0.26 #5247, 0.26 #3459) >> Best rule #5378 for best value: >> intensional similarity = 3 >> extensional distance = 1066 >> proper extension: 07nznf; 05vsxz; 05b__vr; 02lnhv; 016gr2; 04y79_n; 02lgj6; 02jm0n; 02bkdn; 09f0bj; ... >> query: (?x12353, 02hrh1q) <- student(?x6953, ?x12353), profession(?x12353, ?x987), film(?x12353, ?x6840) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1362 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 0678gl; *> query: (?x12353, 0np9r) <- actor(?x5430, ?x12353), film(?x14066, ?x5430), gender(?x14066, ?x231), genre(?x5430, ?x1626) *> conf = 0.80 ranks of expected_values: 2 EVAL 0ckm4x profession 0np9r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 74.000 0.904 http://example.org/people/person/profession #3822-0jgd PRED entity: 0jgd PRED relation: country! PRED expected values: 07rlg 03_8r => 218 concepts (218 used for prediction) PRED predicted values (max 10 best out of 25): 03_8r (0.87 #686, 0.85 #1161, 0.82 #1061), 0w0d (0.74 #1033, 0.72 #1158, 0.72 #783), 0194d (0.74 #971, 0.69 #246, 0.67 #1271), 07rlg (0.67 #326, 0.67 #126, 0.67 #101), 09qgm (0.67 #337, 0.57 #487, 0.55 #412), 0d1t3 (0.67 #339, 0.56 #139, 0.56 #114), 0d1tm (0.67 #127, 0.56 #102, 0.50 #327), 09_bl (0.67 #331, 0.52 #481, 0.52 #456), 01yfj (0.67 #99, 0.44 #149, 0.44 #124), 06zgc (0.61 #341, 0.50 #416, 0.48 #466) >> Best rule #686 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 07ylj; 015qh; 01p1v; 06t8v; 05r7t; >> query: (?x142, 03_8r) <- olympics(?x142, ?x775), film_release_region(?x9174, ?x142), teams(?x142, ?x7667), ?x9174 = 087pfc >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0jgd country! 03_8r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 218.000 218.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0jgd country! 07rlg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 218.000 218.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #3821-07qnf PRED entity: 07qnf PRED relation: artists! PRED expected values: 064t9 => 50 concepts (30 used for prediction) PRED predicted values (max 10 best out of 247): 064t9 (0.62 #14, 0.56 #939, 0.53 #631), 0xhtw (0.41 #2176, 0.40 #3412, 0.40 #3103), 016clz (0.41 #3091, 0.39 #4020, 0.38 #3711), 06j6l (0.39 #1588, 0.33 #971, 0.33 #1896), 0gywn (0.39 #981, 0.38 #56, 0.35 #673), 02yv6b (0.37 #2257, 0.29 #1331, 0.29 #2875), 0glt670 (0.36 #1581, 0.26 #4673, 0.23 #39), 02w4v (0.34 #2510, 0.30 #2201, 0.28 #2819), 0dl5d (0.33 #1253, 0.23 #3106, 0.23 #3415), 025sc50 (0.31 #48, 0.29 #665, 0.29 #1590) >> Best rule #14 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 01vrt_c; 01trhmt; 017yfz; 01vwbts; 0g824; 0jsg0m; 01k23t; 01whg97; 01r0t_j; 03f7m4h; >> query: (?x997, 064t9) <- artist(?x7089, ?x997), artist(?x2190, ?x997), ?x2190 = 01cszh, ?x7089 = 0181dw, artists(?x996, ?x997) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07qnf artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 30.000 0.615 http://example.org/music/genre/artists #3820-0175wg PRED entity: 0175wg PRED relation: film PRED expected values: 0gtv7pk 09zf_q => 73 concepts (44 used for prediction) PRED predicted values (max 10 best out of 732): 06_sc3 (0.82 #5362, 0.50 #1420, 0.05 #4994), 01cx_ (0.82 #5362), 048xyn (0.25 #1106, 0.05 #4680), 0401sg (0.25 #93, 0.05 #3667), 0gtv7pk (0.25 #59, 0.05 #3633), 01shy7 (0.25 #423, 0.05 #2210, 0.04 #53619), 0prrm (0.25 #860, 0.05 #2647, 0.03 #42895), 0296vv (0.25 #1395, 0.03 #4969, 0.02 #8546), 016z5x (0.25 #70, 0.03 #3644, 0.01 #17943), 0125xq (0.25 #741, 0.03 #4315, 0.01 #6103) >> Best rule #5362 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 014x77; 02hsgn; 05nzw6; 02p5hf; 04gc65; >> query: (?x5743, ?x8234) <- profession(?x5743, ?x4773), film(?x5743, ?x10590), split_to(?x10590, ?x8234) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #59 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0151ns; 03359d; *> query: (?x5743, 0gtv7pk) <- profession(?x5743, ?x4773), film(?x5743, ?x10590), ?x10590 = 080dfr7 *> conf = 0.25 ranks of expected_values: 5, 314 EVAL 0175wg film 09zf_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 73.000 44.000 0.818 http://example.org/film/actor/film./film/performance/film EVAL 0175wg film 0gtv7pk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 44.000 0.818 http://example.org/film/actor/film./film/performance/film #3819-02sjgpq PRED entity: 02sjgpq PRED relation: major_field_of_study PRED expected values: 01mkq => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 108): 01mkq (0.71 #473, 0.70 #1278, 0.65 #588), 062z7 (0.47 #599, 0.46 #1059, 0.43 #1174), 03g3w (0.46 #1058, 0.43 #483, 0.42 #6465), 0fdys (0.46 #494, 0.36 #1299, 0.35 #609), 05qfh (0.43 #491, 0.38 #1296, 0.38 #1181), 037mh8 (0.42 #865, 0.40 #1555, 0.38 #290), 04x_3 (0.41 #1057, 0.40 #482, 0.38 #1287), 0g26h (0.39 #37, 0.37 #497, 0.34 #1302), 06ms6 (0.37 #474, 0.37 #1049, 0.26 #244), 0db86 (0.37 #506, 0.28 #621, 0.26 #1311) >> Best rule #473 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0d06m5; 0d05fv; >> query: (?x7278, 01mkq) <- organization(?x7278, ?x5487), list(?x7278, ?x2197), category(?x7278, ?x134) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02sjgpq major_field_of_study 01mkq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3818-01s5nb PRED entity: 01s5nb PRED relation: religion! PRED expected values: 05kj_ 01x73 => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1023): 01n4w (0.80 #486, 0.78 #667, 0.78 #395), 0vmt (0.80 #464, 0.78 #373, 0.72 #645), 059_c (0.80 #467, 0.78 #376, 0.71 #285), 07h34 (0.78 #402, 0.71 #311, 0.70 #493), 0498y (0.78 #405, 0.71 #314, 0.70 #496), 05mph (0.78 #422, 0.71 #331, 0.70 #513), 05fjy (0.78 #418, 0.71 #327, 0.70 #509), 01x73 (0.78 #384, 0.70 #475, 0.67 #656), 06yxd (0.78 #411, 0.63 #449, 0.61 #357), 01n7q (0.72 #649, 0.70 #468, 0.67 #377) >> Best rule #486 for best value: >> intensional similarity = 14 >> extensional distance = 8 >> proper extension: 01y0s9; 03_gx; >> query: (?x10681, 01n4w) <- religion(?x3818, ?x10681), religion(?x3670, ?x10681), religion(?x2713, ?x10681), religion(?x1426, ?x10681), religion(?x1274, ?x10681), ?x3670 = 05tbn, ?x1274 = 04ykg, ?x1426 = 07z1m, contains(?x94, ?x2713), contains(?x2713, ?x2056), adjoins(?x1755, ?x2713), district_represented(?x176, ?x2713), state_province_region(?x1440, ?x3818), jurisdiction_of_office(?x900, ?x3818) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #384 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 7 *> proper extension: 0631_; 019cr; *> query: (?x10681, 01x73) <- religion(?x3670, ?x10681), religion(?x2831, ?x10681), religion(?x2768, ?x10681), religion(?x1426, ?x10681), religion(?x1274, ?x10681), ?x3670 = 05tbn, ?x1274 = 04ykg, time_zones(?x1426, ?x2674), location(?x1654, ?x1426), ?x2831 = 0gyh, ?x2768 = 03s5t, adjoins(?x108, ?x1426), partially_contains(?x1426, ?x10710), contains(?x1426, ?x347) *> conf = 0.78 ranks of expected_values: 8, 25 EVAL 01s5nb religion! 01x73 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 21.000 21.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01s5nb religion! 05kj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 21.000 21.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #3817-0kc8y PRED entity: 0kc8y PRED relation: award_winner! PRED expected values: 0g5lhl7 => 58 concepts (27 used for prediction) PRED predicted values (max 10 best out of 693): 017s11 (0.60 #3289, 0.13 #33803, 0.11 #11336), 03m9c8 (0.40 #2733, 0.20 #4342, 0.13 #33803), 05gnf (0.33 #1107, 0.23 #35415, 0.20 #2715), 05xbx (0.33 #871, 0.23 #35415, 0.20 #2479), 03jvmp (0.33 #350, 0.23 #35415, 0.20 #1958), 0g5lhl7 (0.32 #35416, 0.30 #35417, 0.27 #41857), 04cw0j (0.30 #35417, 0.30 #37027, 0.29 #37026), 0dbpwb (0.30 #35417, 0.30 #37027, 0.27 #41857), 0kc8y (0.30 #35417, 0.27 #41857, 0.24 #33805), 01w92 (0.30 #35417, 0.27 #41857, 0.24 #33805) >> Best rule #3289 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0fvf9q; 07f8wg; 030pr; 05zh9c; 02_l96; 031rq5; 03rwz3; >> query: (?x10844, 017s11) <- award_winner(?x11078, ?x10844), award_nominee(?x11078, ?x3170), category(?x11078, ?x134), ?x3170 = 04cw0j >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #35416 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 493 *> proper extension: 02c0mv; 023jq1; 08f3yq; *> query: (?x10844, ?x2776) <- award_winner(?x11078, ?x10844), award_winner(?x2776, ?x11078), program(?x2776, ?x10234), award_winner(?x2246, ?x2776) *> conf = 0.32 ranks of expected_values: 6 EVAL 0kc8y award_winner! 0g5lhl7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 58.000 27.000 0.600 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #3816-0gcrg PRED entity: 0gcrg PRED relation: genre PRED expected values: 05p553 => 96 concepts (61 used for prediction) PRED predicted values (max 10 best out of 100): 02kdv5l (0.51 #4204, 0.48 #5048, 0.48 #5168), 05p553 (0.50 #124, 0.32 #2165, 0.32 #6251), 02l7c8 (0.41 #2176, 0.40 #2056, 0.38 #2416), 04xvlr (0.41 #602, 0.20 #2282, 0.20 #1442), 01hmnh (0.39 #1218, 0.34 #4219, 0.33 #5183), 01jfsb (0.38 #492, 0.30 #1932, 0.30 #4092), 04xvh5 (0.33 #274, 0.11 #1835, 0.11 #635), 06n90 (0.32 #1213, 0.25 #5058, 0.25 #5178), 01g6gs (0.30 #1821, 0.28 #1701, 0.25 #140), 082gq (0.30 #631, 0.27 #391, 0.20 #871) >> Best rule #4204 for best value: >> intensional similarity = 4 >> extensional distance = 225 >> proper extension: 05p3738; 05fcbk7; 0ddcbd5; 03wh49y; 0h63q6t; 076tw54; >> query: (?x3909, 02kdv5l) <- genre(?x3909, ?x811), language(?x3909, ?x254), ?x811 = 03k9fj, film_crew_role(?x3909, ?x12763) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0b_5d; *> query: (?x3909, 05p553) <- genre(?x3909, ?x53), music(?x3909, ?x12188), film(?x788, ?x3909), ?x12188 = 07zhd7 *> conf = 0.50 ranks of expected_values: 2 EVAL 0gcrg genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 61.000 0.511 http://example.org/film/film/genre #3815-0q59y PRED entity: 0q59y PRED relation: award PRED expected values: 0bm7fy => 97 concepts (68 used for prediction) PRED predicted values (max 10 best out of 276): 0d085 (0.70 #23395, 0.70 #23394, 0.70 #19356), 02qkk9_ (0.70 #23395, 0.70 #23394, 0.70 #19356), 04dn09n (0.31 #43, 0.17 #5691, 0.16 #3270), 09sb52 (0.28 #14153, 0.25 #16170, 0.24 #23435), 04njml (0.26 #505, 0.06 #101, 0.04 #1715), 02h3d1 (0.26 #585, 0.04 #1795, 0.02 #3408), 01l78d (0.25 #289, 0.21 #693, 0.04 #7146), 0gr51 (0.25 #100, 0.18 #5748, 0.17 #3327), 03hkv_r (0.25 #16, 0.11 #5664, 0.11 #3243), 0gqz2 (0.21 #484, 0.08 #1290, 0.06 #2097) >> Best rule #23395 for best value: >> intensional similarity = 2 >> extensional distance = 1585 >> proper extension: 04rcr; 02r3zy; 03g5jw; 05crg7; 0dvqq; 03fbc; 0249kn; 018ndc; 017j6; 01w92; ... >> query: (?x3947, ?x9766) <- award_nominee(?x3947, ?x10920), award_winner(?x9766, ?x3947) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #1048 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 131 *> proper extension: 05hjmd; 03g62; 06zd1c; 01b0k1; *> query: (?x3947, 0bm7fy) <- type_of_union(?x3947, ?x566), nominated_for(?x3947, ?x12829), place_of_death(?x3947, ?x2866), people(?x1158, ?x3947) *> conf = 0.02 ranks of expected_values: 196 EVAL 0q59y award 0bm7fy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 97.000 68.000 0.703 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3814-031v3p PRED entity: 031v3p PRED relation: award_nominee! PRED expected values: 0443xn => 124 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1028): 0443xn (0.83 #2334, 0.82 #53663, 0.81 #20999), 0blt6 (0.83 #2334, 0.82 #53663, 0.81 #20999), 04h68j (0.44 #4453, 0.25 #100332), 031v3p (0.40 #2301, 0.25 #100332, 0.21 #95664), 0b05xm (0.38 #3141, 0.25 #100332, 0.22 #83997), 01gp_x (0.38 #2911, 0.25 #100332, 0.22 #83997), 07lwsz (0.31 #3142, 0.25 #100332, 0.22 #83997), 025y9fn (0.31 #4452, 0.25 #100332, 0.22 #83997), 027hnjh (0.31 #3446, 0.25 #100332, 0.22 #83997), 03y9ccy (0.31 #3177, 0.25 #100332, 0.22 #83997) >> Best rule #2334 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0443xn; >> query: (?x12417, ?x3583) <- award_nominee(?x940, ?x12417), award_nominee(?x12417, ?x3583), ?x940 = 03d_w3h >> conf = 0.83 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 031v3p award_nominee! 0443xn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 57.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3813-07h5d PRED entity: 07h5d PRED relation: gender PRED expected values: 05zppz => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #19, 0.87 #29, 0.86 #25), 02zsn (0.31 #56, 0.31 #70, 0.30 #68) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 02pp_q_; 027d5g5; >> query: (?x7352, 05zppz) <- written_by(?x1842, ?x7352), award_nominee(?x4987, ?x7352), film_crew_role(?x1842, ?x137) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07h5d gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.887 http://example.org/people/person/gender #3812-04yywz PRED entity: 04yywz PRED relation: award PRED expected values: 0gqy2 => 135 concepts (110 used for prediction) PRED predicted values (max 10 best out of 282): 0gqy2 (0.51 #5430, 0.25 #975, 0.25 #570), 09sb52 (0.35 #7330, 0.35 #6925, 0.33 #8545), 05p09zm (0.26 #5389, 0.14 #124, 0.12 #529), 0bdwqv (0.25 #578, 0.17 #5438, 0.14 #173), 0gkts9 (0.25 #979, 0.14 #169, 0.12 #574), 040njc (0.25 #817, 0.14 #2032, 0.10 #5677), 0gq9h (0.25 #887, 0.14 #5747, 0.13 #2102), 019f4v (0.25 #876, 0.10 #1686, 0.09 #5736), 0gs9p (0.25 #889, 0.10 #1699, 0.08 #16609), 02rdyk7 (0.25 #901, 0.10 #1711, 0.08 #1306) >> Best rule #5430 for best value: >> intensional similarity = 3 >> extensional distance = 342 >> proper extension: 0cbm64; >> query: (?x187, 0gqy2) <- award(?x187, ?x102), award(?x7522, ?x102), ?x7522 = 0d608 >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04yywz award 0gqy2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 110.000 0.506 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3811-01wqpnm PRED entity: 01wqpnm PRED relation: award PRED expected values: 01ck6h => 108 concepts (68 used for prediction) PRED predicted values (max 10 best out of 261): 02f5qb (0.63 #2151, 0.63 #2550, 0.52 #4944), 01by1l (0.49 #2108, 0.48 #2507, 0.40 #7694), 02v1m7 (0.40 #2508, 0.38 #2109, 0.27 #4902), 01bgqh (0.39 #2437, 0.38 #2038, 0.33 #7624), 02f71y (0.32 #2177, 0.31 #2576, 0.22 #4970), 01ckcd (0.30 #2726, 0.29 #2327, 0.22 #5120), 02f705 (0.28 #2148, 0.27 #2547, 0.20 #4941), 0c4z8 (0.28 #1668, 0.23 #1269, 0.23 #2865), 0gqz2 (0.25 #4071, 0.20 #15244, 0.19 #17638), 02f6yz (0.25 #2310, 0.24 #2709, 0.22 #315) >> Best rule #2151 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 089tm; 01pfr3; 02l840; 02r3zy; 01vrt_c; 01vrz41; 01r9fv; 01wv9xn; 0dtd6; 0dvqq; ... >> query: (?x10198, 02f5qb) <- artists(?x1000, ?x10198), award(?x10198, ?x3365), ?x3365 = 02f716, artist(?x1543, ?x10198) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #3714 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 018d6l; *> query: (?x10198, 01ck6h) <- artists(?x1000, ?x10198), profession(?x10198, ?x1032), ?x1000 = 0xhtw, artist(?x1543, ?x10198) *> conf = 0.19 ranks of expected_values: 22 EVAL 01wqpnm award 01ck6h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 108.000 68.000 0.631 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3810-02z6fs PRED entity: 02z6fs PRED relation: major_field_of_study PRED expected values: 04x_3 => 125 concepts (120 used for prediction) PRED predicted values (max 10 best out of 125): 02j62 (0.48 #1742, 0.47 #517, 0.44 #1619), 01mkq (0.44 #502, 0.41 #1727, 0.40 #1604), 062z7 (0.35 #1739, 0.34 #1616, 0.32 #514), 04rjg (0.35 #507, 0.33 #1609, 0.33 #3082), 05qjt (0.28 #1598, 0.28 #3071, 0.28 #2949), 0g26h (0.28 #653, 0.27 #530, 0.26 #776), 01540 (0.25 #671, 0.25 #794, 0.23 #1650), 01lj9 (0.23 #527, 0.23 #1752, 0.23 #1383), 05qfh (0.23 #523, 0.23 #1748, 0.23 #1379), 0_jm (0.22 #668, 0.21 #791, 0.20 #1770) >> Best rule #1742 for best value: >> intensional similarity = 6 >> extensional distance = 171 >> proper extension: 0kz2w; 01j_06; 07szy; 07vk2; 0dplh; 01jsn5; 027xx3; 05mv4; 027kp3; 08qnnv; ... >> query: (?x9399, 02j62) <- institution(?x1368, ?x9399), institution(?x865, ?x9399), ?x865 = 02h4rq6, ?x1368 = 014mlp, major_field_of_study(?x9399, ?x866), student(?x9399, ?x8097) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #759 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 137 *> proper extension: 08815; 01jssp; 05krk; 052nd; 01j_9c; 01fpvz; 02w2bc; 065y4w7; 07tgn; 07w0v; ... *> query: (?x9399, 04x_3) <- institution(?x1771, ?x9399), institution(?x865, ?x9399), ?x865 = 02h4rq6, state_province_region(?x9399, ?x9305), ?x1771 = 019v9k, contains(?x2146, ?x9399) *> conf = 0.19 ranks of expected_values: 17 EVAL 02z6fs major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 125.000 120.000 0.480 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3809-050kh5 PRED entity: 050kh5 PRED relation: country_of_origin PRED expected values: 03rk0 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.87 #473, 0.86 #368, 0.86 #428), 02jx1 (0.73 #461, 0.02 #413, 0.02 #763), 07ssc (0.71 #764, 0.33 #43, 0.25 #54), 03rk0 (0.62 #180, 0.62 #702, 0.56 #541), 0d060g (0.19 #555, 0.08 #302, 0.05 #254), 06f32 (0.19 #555), 03_3d (0.13 #826, 0.13 #837, 0.09 #1009), 01mjq (0.04 #617), 0f8l9c (0.04 #617), 05qhw (0.04 #617) >> Best rule #473 for best value: >> intensional similarity = 10 >> extensional distance = 75 >> proper extension: 0g60z; 080dwhx; 039fgy; 02k_4g; 019nnl; 08jgk1; 0464pz; 0kfv9; 03ln8b; 0gfzgl; ... >> query: (?x12165, 09c7w0) <- genre(?x12165, ?x12120), genre(?x12387, ?x12120), genre(?x4761, ?x12120), program(?x14356, ?x12165), program_creator(?x12165, ?x6937), program(?x12903, ?x12387), program(?x2554, ?x4761), actor(?x12165, ?x2382), nominated_for(?x11603, ?x4761), program_creator(?x12387, ?x4374) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #180 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 12 *> proper extension: 06qv_; *> query: (?x12165, ?x2146) <- genre(?x12165, ?x12120), genre(?x9580, ?x12120), actor(?x12165, ?x11799), actor(?x12165, ?x11462), actor(?x12165, ?x3129), type_of_union(?x11462, ?x566), place_of_birth(?x11462, ?x7412), religion(?x3129, ?x8967), nationality(?x11462, ?x2146), people(?x13008, ?x11799), diet(?x3129, ?x3130), languages(?x9580, ?x254) *> conf = 0.62 ranks of expected_values: 4 EVAL 050kh5 country_of_origin 03rk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 89.000 89.000 0.870 http://example.org/tv/tv_program/country_of_origin #3808-072r5v PRED entity: 072r5v PRED relation: film! PRED expected values: 016tt2 => 130 concepts (113 used for prediction) PRED predicted values (max 10 best out of 62): 0fvppk (0.72 #6198, 0.62 #671, 0.61 #4327), 03xq0f (0.68 #601, 0.59 #2621, 0.59 #3441), 054g1r (0.50 #109, 0.33 #35, 0.22 #183), 05qd_ (0.38 #455, 0.35 #306, 0.33 #157), 01gb54 (0.33 #29, 0.21 #625, 0.14 #1374), 04mkft (0.33 #259, 0.14 #855, 0.11 #1155), 025tlyv (0.27 #281, 0.20 #579, 0.18 #355), 016tw3 (0.22 #159, 0.20 #532, 0.19 #6134), 016tt2 (0.21 #675, 0.20 #227, 0.18 #1872), 086k8 (0.20 #2693, 0.20 #374, 0.20 #225) >> Best rule #6198 for best value: >> intensional similarity = 4 >> extensional distance = 746 >> proper extension: 02_1sj; 09p35z; 047qxs; 02pb2bp; 03mh_tp; 03l6q0; 05sw5b; 0gs973; 04xx9s; 03_wm6; ... >> query: (?x7917, ?x10629) <- country(?x7917, ?x94), production_companies(?x7917, ?x10629), film(?x7980, ?x7917), film(?x10629, ?x174) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #675 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 0m313; 01br2w; 02vp1f_; 09m6kg; 07gp9; 04nl83; 0209hj; 091z_p; 0g9wdmc; 015x74; ... *> query: (?x7917, 016tt2) <- film_release_region(?x7917, ?x94), film_crew_role(?x7917, ?x137), nominated_for(?x3458, ?x7917), ?x3458 = 0gqxm *> conf = 0.21 ranks of expected_values: 9 EVAL 072r5v film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 130.000 113.000 0.716 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #3807-017j69 PRED entity: 017j69 PRED relation: organization PRED expected values: 034h1h => 113 concepts (111 used for prediction) PRED predicted values (max 10 best out of 9): 034h1h (0.33 #155, 0.30 #443, 0.29 #875), 03mbdx_ (0.05 #48, 0.04 #96, 0.04 #120), 0b6css (0.02 #1116, 0.02 #372, 0.01 #516), 0_2v (0.02 #1109, 0.02 #365, 0.01 #509), 07t65 (0.02 #1106, 0.02 #362, 0.01 #506), 02vk52z (0.02 #1105, 0.02 #361, 0.01 #505), 059dn (0.02 #377, 0.01 #521), 018cqq (0.02 #373, 0.01 #517), 06nvzg (0.02 #405) >> Best rule #155 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 015g1w; >> query: (?x4410, 034h1h) <- major_field_of_study(?x4410, ?x1527), student(?x4410, ?x510), specialization_of(?x3197, ?x1527) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017j69 organization 034h1h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 111.000 0.333 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #3806-039cq4 PRED entity: 039cq4 PRED relation: producer_type PRED expected values: 0ckd1 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.87 #7, 0.82 #13, 0.79 #16) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0cpz4k; >> query: (?x6884, 0ckd1) <- tv_program(?x5019, ?x6884), tv_program(?x545, ?x6884), participant(?x989, ?x545), profession(?x5019, ?x319) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039cq4 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.867 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #3805-0fr63l PRED entity: 0fr63l PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.86 #81, 0.84 #116, 0.83 #61), 07c52 (0.17 #3, 0.04 #213, 0.04 #218), 02nxhr (0.06 #62, 0.05 #127, 0.05 #122), 07z4p (0.03 #220, 0.03 #225, 0.03 #215) >> Best rule #81 for best value: >> intensional similarity = 4 >> extensional distance = 270 >> proper extension: 05p3738; 07p12s; >> query: (?x721, 029j_) <- film_crew_role(?x721, ?x2154), language(?x721, ?x254), ?x2154 = 01vx2h, currency(?x721, ?x170) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fr63l film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.860 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #3804-052nd PRED entity: 052nd PRED relation: major_field_of_study PRED expected values: 05qjt => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 105): 05qjt (0.75 #346, 0.47 #1250, 0.46 #572), 062z7 (0.75 #363, 0.47 #1267, 0.43 #1041), 04x_3 (0.75 #362, 0.42 #1266, 0.41 #1379), 01mkq (0.75 #1257, 0.71 #1031, 0.71 #579), 02j62 (0.69 #479, 0.67 #253, 0.58 #1496), 04rjg (0.60 #584, 0.58 #358, 0.55 #1149), 01lj9 (0.58 #373, 0.41 #938, 0.41 #1277), 01tbp (0.50 #392, 0.42 #1296, 0.41 #1409), 01540 (0.50 #393, 0.37 #619, 0.33 #280), 02ky346 (0.50 #354, 0.37 #580, 0.31 #1258) >> Best rule #346 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 08815; 01w3v; 04rwx; 01w5m; 03ksy; 025v3k; 07t90; 07tds; 02zd460; 05zl0; >> query: (?x481, 05qjt) <- major_field_of_study(?x481, ?x3878), organization(?x481, ?x5487), ?x3878 = 03nfmq >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 052nd major_field_of_study 05qjt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3803-0pz91 PRED entity: 0pz91 PRED relation: award_winner! PRED expected values: 05zvj3m => 138 concepts (136 used for prediction) PRED predicted values (max 10 best out of 312): 099tbz (0.67 #2176, 0.09 #46226, 0.08 #17867), 09sb52 (0.56 #2159, 0.13 #26331, 0.13 #17850), 05p1dby (0.40 #102, 0.30 #4766, 0.15 #1798), 02x1z2s (0.40 #190, 0.07 #4854, 0.01 #22665), 05zvj3m (0.34 #7633, 0.34 #36894, 0.31 #47076), 0gq9h (0.27 #1770, 0.20 #8555, 0.20 #74), 01l29r (0.20 #159, 0.08 #4823, 0.08 #1855), 01lk0l (0.20 #270, 0.06 #15267, 0.05 #47075), 0cjyzs (0.20 #9006, 0.06 #10703, 0.04 #4765), 0f4x7 (0.19 #5118, 0.11 #4270, 0.09 #46226) >> Best rule #2176 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 02qgqt; 05cj4r; 09fqtq; 01sp81; 016gr2; 05k2s_; 015rkw; 06t61y; 065jlv; 015gw6; ... >> query: (?x1335, 099tbz) <- award_nominee(?x1335, ?x3461), film(?x1335, ?x821), ?x3461 = 02l4pj >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7633 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 133 *> proper extension: 04411; 04g865; 036jp8; 03f47xl; 017_pb; 06myp; *> query: (?x1335, ?x401) <- company(?x1335, ?x1836), award(?x1335, ?x401) *> conf = 0.34 ranks of expected_values: 5 EVAL 0pz91 award_winner! 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 136.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #3802-07sgdw PRED entity: 07sgdw PRED relation: nominated_for! PRED expected values: 05b4l5x => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 174): 05b1610 (0.64 #988, 0.60 #1466, 0.58 #1227), 07bdd_ (0.53 #1487, 0.50 #1248, 0.45 #1009), 04ljl_l (0.53 #1437, 0.50 #1198, 0.45 #959), 05q5t0b (0.36 #1078, 0.36 #839, 0.33 #1317), 0gq9h (0.35 #3410, 0.33 #7236, 0.32 #3171), 04dn09n (0.31 #2186, 0.27 #3382, 0.26 #3143), 05b4l5x (0.30 #3108, 0.29 #245, 0.27 #962), 05q8pss (0.30 #3108, 0.28 #3826, 0.28 #4544), 03c7tr1 (0.30 #3108, 0.28 #3826, 0.28 #4544), 018wdw (0.30 #3108, 0.20 #1614, 0.18 #897) >> Best rule #988 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0946bb; 074rg9; 06c0ns; >> query: (?x4749, 05b1610) <- honored_for(?x4749, ?x6746), nominated_for(?x350, ?x4749), ?x6746 = 059lwy, currency(?x4749, ?x170) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #3108 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 102 *> proper extension: 0g60z; 0180mw; 0ddf2bm; *> query: (?x4749, ?x102) <- honored_for(?x4749, ?x6746), nominated_for(?x350, ?x4749), nominated_for(?x6746, ?x2165), nominated_for(?x102, ?x6746) *> conf = 0.30 ranks of expected_values: 7 EVAL 07sgdw nominated_for! 05b4l5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 96.000 96.000 0.636 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3801-01btyw PRED entity: 01btyw PRED relation: time_zones PRED expected values: 02fqwt => 221 concepts (221 used for prediction) PRED predicted values (max 10 best out of 13): 02fqwt (0.43 #27, 0.32 #131, 0.32 #105), 02hcv8 (0.39 #770, 0.37 #705, 0.36 #1069), 02lcqs (0.33 #5, 0.30 #473, 0.24 #408), 02hczc (0.33 #15, 0.29 #41, 0.15 #2731), 03bdv (0.23 #214, 0.11 #500, 0.11 #71), 02llzg (0.17 #628, 0.15 #654, 0.14 #1239), 052vwh (0.05 #493, 0.05 #506, 0.05 #519), 03plfd (0.03 #1583, 0.03 #686, 0.02 #1050), 0gsrz4 (0.03 #1581), 02lcrv (0.02 #176, 0.02 #189, 0.02 #306) >> Best rule #27 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 014m1m; >> query: (?x8852, 02fqwt) <- country(?x8852, ?x151), ?x151 = 0b90_r, administrative_parent(?x8852, ?x151), contains(?x151, ?x8852) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01btyw time_zones 02fqwt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 221.000 221.000 0.429 http://example.org/location/location/time_zones #3800-0h1_c PRED entity: 0h1_c PRED relation: nutrient! PRED expected values: 0dj75 => 57 concepts (53 used for prediction) PRED predicted values (max 10 best out of 6): 0dj75 (0.90 #80, 0.89 #39, 0.89 #32), 06x4c (0.90 #80, 0.89 #39, 0.89 #32), 0dcfv (0.90 #80, 0.89 #39, 0.89 #32), 04k8n (0.03 #265, 0.02 #385), 05wvs (0.03 #265, 0.02 #385), 01sh2 (0.03 #265, 0.02 #385) >> Best rule #80 for best value: >> intensional similarity = 121 >> extensional distance = 10 >> proper extension: 025sf0_; 025rw19; >> query: (?x10098, ?x3264) <- nutrient(?x10612, ?x10098), nutrient(?x9732, ?x10098), nutrient(?x9489, ?x10098), nutrient(?x9005, ?x10098), nutrient(?x8298, ?x10098), nutrient(?x7057, ?x10098), nutrient(?x6285, ?x10098), nutrient(?x6191, ?x10098), nutrient(?x6159, ?x10098), nutrient(?x6032, ?x10098), nutrient(?x5373, ?x10098), nutrient(?x5009, ?x10098), nutrient(?x4068, ?x10098), nutrient(?x3900, ?x10098), nutrient(?x3468, ?x10098), nutrient(?x2701, ?x10098), nutrient(?x1959, ?x10098), nutrient(?x1303, ?x10098), nutrient(?x1257, ?x10098), ?x2701 = 0hkxq, ?x6159 = 033cnk, ?x8298 = 037ls6, ?x9732 = 05z55, ?x6032 = 01nkt, ?x10612 = 0frq6, ?x6285 = 01645p, ?x1303 = 0fj52s, ?x9005 = 04zpv, ?x5009 = 0fjfh, ?x9489 = 07j87, ?x7057 = 0fbdb, ?x3900 = 061_f, ?x5373 = 0971v, ?x1257 = 09728, nutrient(?x3468, ?x13545), nutrient(?x3468, ?x13126), nutrient(?x3468, ?x12083), nutrient(?x3468, ?x11758), nutrient(?x3468, ?x11409), nutrient(?x3468, ?x11270), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10453), nutrient(?x3468, ?x9949), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x9840), nutrient(?x3468, ?x9733), nutrient(?x3468, ?x9490), nutrient(?x3468, ?x9426), nutrient(?x3468, ?x9365), nutrient(?x3468, ?x8442), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x7894), nutrient(?x3468, ?x7720), nutrient(?x3468, ?x7652), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7364), nutrient(?x3468, ?x7362), nutrient(?x3468, ?x7219), nutrient(?x3468, ?x7135), nutrient(?x3468, ?x6586), nutrient(?x3468, ?x6286), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x6160), nutrient(?x3468, ?x6033), nutrient(?x3468, ?x6026), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5526), nutrient(?x3468, ?x5451), nutrient(?x3468, ?x5337), nutrient(?x3468, ?x5010), nutrient(?x3468, ?x4069), nutrient(?x3468, ?x3469), nutrient(?x3468, ?x3203), nutrient(?x3468, ?x2702), nutrient(?x3468, ?x2018), nutrient(?x3468, ?x1960), ?x4068 = 0fbw6, ?x5451 = 05wvs, ?x7894 = 0f4hc, ?x10453 = 075pwf, ?x6191 = 014j1m, ?x6286 = 02y_3rf, ?x9840 = 02p0tjr, ?x6586 = 05gh50, ?x8442 = 02kcv4x, ?x3203 = 04kl74p, ?x10891 = 0g5gq, ?x9733 = 0h1tz, ?x9490 = 0h1sg, ?x7652 = 025s0s0, ?x7720 = 025s7x6, ?x7431 = 09gwd, ?x13545 = 01w_3, ?x7362 = 02kc5rj, ?x9365 = 04k8n, ?x6033 = 04zjxcz, ?x12083 = 01n78x, ?x4069 = 0hqw8p_, ?x1959 = 0f25w9, ?x6192 = 06jry, ?x7135 = 025rsfk, ?x9949 = 02kd0rh, nutrient(?x3264, ?x5549), ?x9426 = 0h1yy, ?x1960 = 07hnp, ?x2018 = 01sh2, ?x5337 = 06x4c, ?x11270 = 02kc008, ?x2702 = 0838f, ?x6160 = 041r51, ?x5526 = 09pbb, ?x7219 = 0h1vg, ?x3469 = 0h1zw, ?x11758 = 0q01m, ?x11409 = 0h1yf, ?x6026 = 025sf8g, ?x7364 = 09gvd, ?x9915 = 025tkqy, ?x8413 = 02kc4sf, ?x13126 = 02kc_w5, ?x5010 = 0h1vz >> conf = 0.90 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0h1_c nutrient! 0dj75 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 53.000 0.896 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #3799-05p09zm PRED entity: 05p09zm PRED relation: nominated_for PRED expected values: 03cd0x 01mszz 03cmsqb => 51 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1497): 074rg9 (0.68 #20122, 0.67 #23218, 0.61 #10832), 02krdz (0.68 #20122, 0.67 #23218, 0.61 #10832), 03cmsqb (0.68 #20122, 0.67 #23218, 0.61 #10832), 0n83s (0.68 #20122, 0.67 #23218, 0.61 #10832), 02c638 (0.62 #6480, 0.50 #3386, 0.38 #8028), 05hjnw (0.62 #6928, 0.40 #3834, 0.33 #2289), 03hkch7 (0.62 #6630, 0.40 #3536, 0.31 #8178), 0gmgwnv (0.50 #4024, 0.46 #7118, 0.38 #8666), 0f4_l (0.50 #3396, 0.46 #6490, 0.38 #8038), 07g1sm (0.50 #4140, 0.38 #8782, 0.38 #7234) >> Best rule #20122 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 0fqnzts; >> query: (?x2325, ?x770) <- award(?x9781, ?x2325), film(?x9781, ?x755), award(?x770, ?x2325) >> conf = 0.68 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 165, 167 EVAL 05p09zm nominated_for 03cmsqb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 51.000 18.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05p09zm nominated_for 01mszz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 18.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05p09zm nominated_for 03cd0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 18.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3798-0f3m1 PRED entity: 0f3m1 PRED relation: film! PRED expected values: 0p8r1 => 116 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1279): 0146pg (0.46 #83159, 0.45 #89396, 0.44 #87316), 04wp63 (0.45 #89396, 0.44 #87316, 0.43 #70682), 02fcs2 (0.45 #89396, 0.44 #87316, 0.43 #70682), 016k6x (0.17 #2968, 0.03 #15443, 0.02 #29995), 0gn30 (0.16 #27974, 0.09 #42524, 0.09 #38365), 0151zx (0.14 #1547, 0.14 #14553, 0.08 #3625), 016_mj (0.14 #295, 0.08 #2373, 0.01 #43950), 02gf_l (0.14 #1266, 0.07 #19977, 0.06 #24136), 0151w_ (0.14 #164, 0.05 #6400, 0.03 #8479), 0169dl (0.14 #402, 0.03 #8717, 0.03 #12875) >> Best rule #83159 for best value: >> intensional similarity = 4 >> extensional distance = 251 >> proper extension: 04q01mn; >> query: (?x8486, ?x669) <- executive_produced_by(?x8486, ?x1387), award_winner(?x8486, ?x669), film(?x2916, ?x8486), nominated_for(?x484, ?x8486) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #15139 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 07gp9; 0bth54; 0b73_1d; 0pv3x; 0gmcwlb; 017gm7; 0f4_l; 0ggbhy7; 016kz1; 02rn00y; ... *> query: (?x8486, 0p8r1) <- executive_produced_by(?x8486, ?x1387), language(?x8486, ?x254), nominated_for(?x637, ?x8486), ?x637 = 02r22gf *> conf = 0.05 ranks of expected_values: 191 EVAL 0f3m1 film! 0p8r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 116.000 51.000 0.460 http://example.org/film/actor/film./film/performance/film #3797-0140t7 PRED entity: 0140t7 PRED relation: artists! PRED expected values: 07sbbz2 => 113 concepts (41 used for prediction) PRED predicted values (max 10 best out of 242): 0xhtw (0.57 #16, 0.34 #6716, 0.34 #5801), 02vjzr (0.46 #2259, 0.40 #2563, 0.38 #3173), 0gywn (0.34 #1575, 0.33 #2183, 0.33 #661), 016clz (0.31 #6400, 0.28 #7622, 0.28 #4266), 0mhfr (0.29 #22, 0.14 #935, 0.12 #326), 0155w (0.28 #7106, 0.22 #709, 0.20 #9241), 02yv6b (0.28 #701, 0.20 #7098, 0.19 #6183), 0glt670 (0.27 #9483, 0.21 #10399, 0.18 #342), 02lnbg (0.26 #4621, 0.20 #5230, 0.19 #11635), 0ggx5q (0.25 #1595, 0.24 #377, 0.20 #4640) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0dtd6; 0178_w; >> query: (?x9321, 0xhtw) <- artists(?x5300, ?x9321), award(?x9321, ?x4488), ?x5300 = 02k_kn, ?x4488 = 02gdjb >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #7013 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 159 *> proper extension: 053y0s; 01qkqwg; 0zjpz; 09prnq; 02jg92; 0565cz; 01m65sp; 01wz_ml; 01w8n89; 0fpj4lx; ... *> query: (?x9321, 07sbbz2) <- artists(?x1572, ?x9321), role(?x9321, ?x228), instrumentalists(?x212, ?x9321), ?x1572 = 06by7 *> conf = 0.17 ranks of expected_values: 21 EVAL 0140t7 artists! 07sbbz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 113.000 41.000 0.571 http://example.org/music/genre/artists #3796-01jrs46 PRED entity: 01jrs46 PRED relation: people! PRED expected values: 02y0js => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 31): 0gk4g (0.14 #1462, 0.13 #1594, 0.13 #1528), 0dq9p (0.10 #215, 0.09 #149, 0.07 #1469), 04p3w (0.09 #143, 0.06 #1529, 0.06 #869), 0qcr0 (0.07 #661, 0.07 #1453, 0.07 #859), 02k6hp (0.06 #37, 0.06 #169, 0.04 #697), 04psf (0.06 #7, 0.06 #139, 0.02 #667), 0d19y2 (0.06 #55, 0.04 #187, 0.03 #253), 02y0js (0.05 #1784, 0.05 #1520, 0.05 #1454), 01_qc_ (0.04 #160, 0.03 #226, 0.03 #1150), 01l2m3 (0.04 #148, 0.03 #16, 0.03 #808) >> Best rule #1462 for best value: >> intensional similarity = 3 >> extensional distance = 488 >> proper extension: 0fx02; 0gct_; 0k57l; 0835q; 01l3j; >> query: (?x10005, 0gk4g) <- nationality(?x10005, ?x94), type_of_union(?x10005, ?x566), place_of_death(?x10005, ?x739) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1784 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 638 *> proper extension: 032t2z; 01dvtx; *> query: (?x10005, 02y0js) <- nationality(?x10005, ?x94), profession(?x10005, ?x1614), place_of_death(?x10005, ?x739) *> conf = 0.05 ranks of expected_values: 8 EVAL 01jrs46 people! 02y0js CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 116.000 116.000 0.141 http://example.org/people/cause_of_death/people #3795-03f1d47 PRED entity: 03f1d47 PRED relation: award_winner! PRED expected values: 01bx35 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 117): 01bx35 (0.20 #148, 0.14 #994, 0.13 #289), 0gx1673 (0.20 #120, 0.11 #1953, 0.08 #1530), 0bz6sb (0.20 #205, 0.07 #346, 0.05 #1192), 01mhwk (0.17 #323, 0.10 #41, 0.08 #1169), 056878 (0.14 #1583, 0.13 #1724, 0.10 #10999), 01s695 (0.13 #285, 0.13 #1131, 0.09 #2400), 013b2h (0.13 #221, 0.11 #1208, 0.10 #362), 0jt3qpk (0.13 #1312, 0.07 #607, 0.06 #889), 02rjjll (0.12 #1556, 0.12 #2120, 0.12 #1697), 0gpjbt (0.11 #1016, 0.10 #10999, 0.09 #1721) >> Best rule #148 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 09g0h; >> query: (?x4983, 01bx35) <- film(?x4983, ?x2418), profession(?x4983, ?x6565), ?x6565 = 0fnpj, gender(?x4983, ?x514) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f1d47 award_winner! 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3794-02flpc PRED entity: 02flpc PRED relation: ceremony PRED expected values: 01s695 019bk0 01mhwk => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 128): 01s695 (0.87 #2, 0.87 #130, 0.82 #258), 019bk0 (0.85 #13, 0.84 #141, 0.83 #269), 01mhwk (0.84 #163, 0.82 #35, 0.80 #291), 0jzphpx (0.71 #161, 0.70 #289, 0.69 #33), 0gx1673 (0.51 #748, 0.51 #1004, 0.51 #492), 05c1t6z (0.24 #1164, 0.22 #780, 0.18 #2572), 02q690_ (0.22 #1210, 0.20 #826, 0.16 #2874), 03nnm4t (0.22 #835, 0.20 #1219, 0.15 #2883), 0gvstc3 (0.21 #1180, 0.19 #796, 0.17 #2588), 0gx_st (0.20 #799, 0.20 #1183, 0.15 #2591) >> Best rule #2 for best value: >> intensional similarity = 8 >> extensional distance = 53 >> proper extension: 02581q; 02wh75; 026mg3; 02g3gj; 01d38g; 02grdc; 01c9f2; 01c427; 01c4_6; 02nhxf; ... >> query: (?x3313, 01s695) <- ceremony(?x3313, ?x5656), ceremony(?x3313, ?x2186), ceremony(?x3313, ?x1480), ceremony(?x3313, ?x486), ?x2186 = 056878, ?x486 = 02rjjll, ?x1480 = 01c6qp, ?x5656 = 0466p0j >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 02flpc ceremony 01mhwk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.873 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02flpc ceremony 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.873 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02flpc ceremony 01s695 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.873 http://example.org/award/award_category/winners./award/award_honor/ceremony #3793-02fn5r PRED entity: 02fn5r PRED relation: award_nominee! PRED expected values: 01k_r5b => 97 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1048): 01p9hgt (0.81 #23266, 0.81 #97724, 0.80 #53520), 010hn (0.81 #23266, 0.81 #97724, 0.80 #53520), 02f1c (0.81 #23266, 0.81 #97724, 0.80 #53520), 0p_47 (0.77 #104703, 0.77 #81435, 0.77 #118664), 0pmw9 (0.77 #104703, 0.77 #81435, 0.77 #118664), 0ggjt (0.77 #104703, 0.77 #81435, 0.77 #118664), 01kv4mb (0.77 #104703, 0.77 #81435, 0.77 #118664), 01lmj3q (0.77 #104703, 0.77 #81435, 0.77 #118664), 03cfjg (0.71 #27920, 0.27 #123321, 0.14 #67478), 02cx90 (0.27 #123321, 0.25 #1014, 0.14 #67478) >> Best rule #23266 for best value: >> intensional similarity = 2 >> extensional distance = 136 >> proper extension: 017vkx; 037hgm; >> query: (?x2638, ?x1413) <- award_nominee(?x2638, ?x1413), role(?x2638, ?x227) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #123321 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1457 *> proper extension: 04glx0; *> query: (?x2638, ?x3202) <- award_nominee(?x1795, ?x2638), award_winner(?x2638, ?x3917), award_winner(?x3917, ?x3202) *> conf = 0.27 ranks of expected_values: 17 EVAL 02fn5r award_nominee! 01k_r5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 97.000 53.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3792-0ff2k PRED entity: 0ff2k PRED relation: influenced_by PRED expected values: 06kb_ => 144 concepts (65 used for prediction) PRED predicted values (max 10 best out of 372): 03hnd (0.44 #1409, 0.38 #536, 0.25 #4899), 03_87 (0.38 #639, 0.17 #3694, 0.16 #6312), 02zjd (0.38 #632, 0.15 #2815, 0.13 #3687), 0klw (0.33 #151, 0.06 #1896, 0.04 #4078), 032l1 (0.30 #3581, 0.25 #2709, 0.25 #526), 081k8 (0.30 #2776, 0.26 #3648, 0.25 #593), 02lt8 (0.25 #2740, 0.25 #557, 0.22 #3612), 02mpb (0.25 #704, 0.19 #1577, 0.12 #5067), 084w8 (0.25 #439, 0.15 #2622, 0.13 #3494), 0g5ff (0.25 #631, 0.15 #2814, 0.13 #3686) >> Best rule #1409 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01zwy; 0c4y8; >> query: (?x11598, 03hnd) <- influenced_by(?x11598, ?x5435), story_by(?x6078, ?x11598), place_of_death(?x11598, ?x1841), award_winner(?x4879, ?x11598) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #595 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 034bs; *> query: (?x11598, 06kb_) <- influenced_by(?x11598, ?x5435), influenced_by(?x2465, ?x11598), ?x5435 = 01v9724, award_winner(?x4879, ?x11598) *> conf = 0.12 ranks of expected_values: 52 EVAL 0ff2k influenced_by 06kb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 144.000 65.000 0.438 http://example.org/influence/influence_node/influenced_by #3791-09j_g PRED entity: 09j_g PRED relation: category PRED expected values: 08mbj5d => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #75, 0.86 #103, 0.85 #157) >> Best rule #75 for best value: >> intensional similarity = 6 >> extensional distance = 76 >> proper extension: 04wlz2; 02s62q; 035wtd; 0221g_; 033x5p; 017j69; 037njl; 06bw5; 03bmmc; 01tx9m; ... >> query: (?x5861, 08mbj5d) <- state_province_region(?x5861, ?x1227), currency(?x5861, ?x170), ?x170 = 09nqf, organization(?x4682, ?x5861), citytown(?x5861, ?x242), location(?x241, ?x242) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09j_g category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.859 http://example.org/common/topic/webpage./common/webpage/category #3790-0g5838s PRED entity: 0g5838s PRED relation: film_release_region PRED expected values: 0b90_r 0chghy 03rt9 0f8l9c 06c1y 01pj7 06t8v => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 103): 0f8l9c (0.93 #814, 0.91 #1081, 0.89 #282), 0b90_r (0.91 #269, 0.90 #668, 0.90 #402), 0chghy (0.88 #805, 0.86 #672, 0.86 #1072), 03rt9 (0.86 #410, 0.83 #277, 0.79 #676), 02k54 (0.66 #279, 0.57 #412, 0.48 #678), 06c1y (0.65 #432, 0.59 #698, 0.57 #299), 01ls2 (0.63 #408, 0.62 #674, 0.57 #275), 06qd3 (0.60 #694, 0.57 #428, 0.54 #295), 09pmkv (0.60 #287, 0.59 #420, 0.55 #686), 06t8v (0.60 #326, 0.59 #459, 0.51 #725) >> Best rule #814 for best value: >> intensional similarity = 6 >> extensional distance = 172 >> proper extension: 0ds35l9; 0gtsx8c; 0c3ybss; 011yrp; 0gx1bnj; 0h1cdwq; 0dscrwf; 0gx9rvq; 0401sg; 087wc7n; ... >> query: (?x3076, 0f8l9c) <- film_release_region(?x3076, ?x1658), film_release_region(?x3076, ?x1499), film_release_region(?x3076, ?x172), ?x172 = 0154j, ?x1499 = 01znc_, month(?x1658, ?x1459) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 10, 15 EVAL 0g5838s film_release_region 06t8v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 60.000 60.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5838s film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 60.000 60.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5838s film_release_region 06c1y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 60.000 60.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5838s film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5838s film_release_region 03rt9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5838s film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5838s film_release_region 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3789-06t2t2 PRED entity: 06t2t2 PRED relation: genre PRED expected values: 05p553 060__y => 87 concepts (73 used for prediction) PRED predicted values (max 10 best out of 91): 05p553 (0.69 #3791, 0.49 #475, 0.42 #949), 01jfsb (0.40 #248, 0.40 #130, 0.38 #366), 02kdv5l (0.40 #356, 0.37 #238, 0.37 #120), 06n90 (0.25 #367, 0.20 #249, 0.17 #131), 03k9fj (0.25 #1312, 0.24 #3088, 0.23 #247), 060__y (0.23 #606, 0.20 #962, 0.17 #3922), 04xvlr (0.20 #591, 0.19 #2486, 0.19 #1), 01hmnh (0.20 #253, 0.18 #3094, 0.17 #371), 04xvh5 (0.19 #624, 0.08 #2163, 0.08 #980), 0lsxr (0.18 #2612, 0.18 #3914, 0.18 #2967) >> Best rule #3791 for best value: >> intensional similarity = 3 >> extensional distance = 848 >> proper extension: 04svwx; >> query: (?x10470, 05p553) <- genre(?x10470, ?x239), genre(?x7626, ?x239), ?x7626 = 05fm6m >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 06t2t2 genre 060__y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 73.000 0.686 http://example.org/film/film/genre EVAL 06t2t2 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 73.000 0.686 http://example.org/film/film/genre #3788-06kbb6 PRED entity: 06kbb6 PRED relation: written_by! PRED expected values: 0ptxj 05qm9f 059lwy 02k1pr => 124 concepts (76 used for prediction) PRED predicted values (max 10 best out of 206): 0ptxj (0.23 #18514, 0.11 #7273, 0.10 #2645), 05qm9f (0.23 #18514, 0.11 #7273, 0.10 #2645), 0g_zyp (0.03 #600), 0291hr (0.03 #538), 0f2sx4 (0.03 #522), 0hv4t (0.03 #454), 011yth (0.03 #116), 072x7s (0.03 #102), 01719t (0.03 #90), 0c0nhgv (0.03 #68) >> Best rule #18514 for best value: >> intensional similarity = 4 >> extensional distance = 533 >> proper extension: 0b4rf3; >> query: (?x11772, ?x5212) <- profession(?x11772, ?x987), nominated_for(?x11772, ?x5212), nationality(?x11772, ?x94), ?x987 = 0dxtg >> conf = 0.23 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2 EVAL 06kbb6 written_by! 02k1pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 76.000 0.230 http://example.org/film/film/written_by EVAL 06kbb6 written_by! 059lwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 76.000 0.230 http://example.org/film/film/written_by EVAL 06kbb6 written_by! 05qm9f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 76.000 0.230 http://example.org/film/film/written_by EVAL 06kbb6 written_by! 0ptxj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 76.000 0.230 http://example.org/film/film/written_by #3787-05pbsry PRED entity: 05pbsry PRED relation: actor PRED expected values: 0bz5v2 => 112 concepts (75 used for prediction) PRED predicted values (max 10 best out of 779): 025mb_ (0.47 #23276, 0.38 #27000, 0.38 #22344), 06jntd (0.43 #16761, 0.41 #21413, 0.39 #28861), 05gnf (0.38 #27000, 0.38 #22344, 0.38 #32586), 01j7rd (0.20 #165, 0.04 #1096, 0.03 #13203), 08141d (0.20 #893, 0.02 #9279, 0.02 #8348), 0163r3 (0.20 #533, 0.02 #8919, 0.02 #7988), 01_x6d (0.20 #362, 0.02 #8748, 0.02 #7817), 01_x6v (0.20 #189, 0.02 #8575, 0.02 #7644), 01xdf5 (0.20 #13, 0.02 #12121, 0.01 #13051), 04zkj5 (0.20 #597, 0.01 #13635, 0.01 #15496) >> Best rule #23276 for best value: >> intensional similarity = 5 >> extensional distance = 106 >> proper extension: 03_8kz; >> query: (?x13443, ?x9140) <- nominated_for(?x9140, ?x13443), country_of_origin(?x13443, ?x94), nominated_for(?x3906, ?x13443), program(?x10068, ?x13443), location(?x9140, ?x1523) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #84 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 01j7mr; 05_z42; *> query: (?x13443, 0bz5v2) <- nominated_for(?x6678, ?x13443), genre(?x13443, ?x258), program(?x10068, ?x13443), actor(?x13443, ?x5490), ?x10068 = 01j7pt *> conf = 0.20 ranks of expected_values: 13 EVAL 05pbsry actor 0bz5v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 112.000 75.000 0.466 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #3786-013rxq PRED entity: 013rxq PRED relation: artists PRED expected values: 089tm => 60 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1129): 07s3vqk (0.60 #2177, 0.45 #4345, 0.44 #3260), 01wvxw1 (0.60 #2916, 0.36 #5084, 0.33 #6169), 01kx_81 (0.60 #2252, 0.36 #4420, 0.33 #5505), 012vd6 (0.60 #2649, 0.36 #4817, 0.33 #5902), 01wg25j (0.60 #2946, 0.36 #5114, 0.33 #6199), 01vwyqp (0.60 #2443, 0.36 #4611, 0.33 #5696), 044k8 (0.60 #2572, 0.36 #4740, 0.33 #5825), 0407f (0.60 #2448, 0.33 #282, 0.28 #6787), 020_4z (0.60 #3108, 0.33 #942, 0.28 #9618), 011z3g (0.60 #2772, 0.33 #606, 0.27 #4940) >> Best rule #2177 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 0gywn; 0155w; >> query: (?x14558, 07s3vqk) <- artists(?x14558, ?x11186), artists(?x14558, ?x9128), ?x9128 = 01d4cb, role(?x11186, ?x8014), role(?x11186, ?x3703), role(?x11186, ?x432), ?x8014 = 0214km, award_winner(?x342, ?x11186), ?x432 = 042v_gx, performance_role(?x3703, ?x212), gender(?x11186, ?x231), role(?x75, ?x3703) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #22 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 06by7; *> query: (?x14558, 089tm) <- artists(?x14558, ?x11186), artists(?x14558, ?x9128), artists(?x14558, ?x6635), ?x9128 = 01d4cb, ?x11186 = 01304j, ?x6635 = 015cxv *> conf = 0.33 ranks of expected_values: 303 EVAL 013rxq artists 089tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 60.000 17.000 0.600 http://example.org/music/genre/artists #3785-05qt0 PRED entity: 05qt0 PRED relation: major_field_of_study! PRED expected values: 04cnp4 01v3k2 0vkl2 014d4v => 90 concepts (60 used for prediction) PRED predicted values (max 10 best out of 670): 06pwq (0.73 #6969, 0.73 #6390, 0.67 #4070), 03ksy (0.72 #11133, 0.71 #11716, 0.71 #10553), 07szy (0.71 #10476, 0.67 #11056, 0.62 #11639), 01w3v (0.67 #4073, 0.65 #10450, 0.64 #6972), 09f2j (0.67 #4234, 0.65 #10611, 0.62 #11774), 07wrz (0.67 #4122, 0.50 #1803, 0.47 #10499), 05mv4 (0.67 #4203, 0.50 #1884, 0.43 #5363), 01w5m (0.60 #2437, 0.57 #5335, 0.57 #25050), 08815 (0.55 #6958, 0.55 #6379, 0.53 #10436), 01bm_ (0.55 #7227, 0.55 #6648, 0.50 #11285) >> Best rule #6969 for best value: >> intensional similarity = 8 >> extensional distance = 9 >> proper extension: 0mkz; >> query: (?x6364, 06pwq) <- major_field_of_study(?x6056, ?x6364), major_field_of_study(?x4390, ?x6364), student(?x6056, ?x445), major_field_of_study(?x6056, ?x3878), currency(?x6056, ?x170), company(?x1159, ?x6056), ?x4390 = 0h6rm, ?x3878 = 03nfmq >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1738 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 02ky346; *> query: (?x6364, ?x122) <- major_field_of_study(?x13827, ?x6364), major_field_of_study(?x6056, ?x6364), major_field_of_study(?x6364, ?x12035), major_field_of_study(?x6364, ?x2981), ?x6056 = 05zl0, ?x12035 = 01400v, major_field_of_study(?x122, ?x2981), student(?x13827, ?x1997), major_field_of_study(?x2981, ?x1527) *> conf = 0.29 ranks of expected_values: 244, 257, 277, 327 EVAL 05qt0 major_field_of_study! 014d4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 90.000 60.000 0.727 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qt0 major_field_of_study! 0vkl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 90.000 60.000 0.727 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qt0 major_field_of_study! 01v3k2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 90.000 60.000 0.727 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qt0 major_field_of_study! 04cnp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 90.000 60.000 0.727 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3784-01nz1q6 PRED entity: 01nz1q6 PRED relation: artists! PRED expected values: 06by7 => 165 concepts (77 used for prediction) PRED predicted values (max 10 best out of 270): 06by7 (0.74 #18630, 0.62 #14909, 0.53 #4052), 03_d0 (0.56 #2492, 0.40 #3732, 0.38 #6835), 016clz (0.50 #14893, 0.35 #4966, 0.33 #3726), 05w3f (0.50 #1279, 0.47 #4069, 0.29 #4999), 05bt6j (0.50 #1285, 0.45 #14932, 0.41 #23624), 02k_kn (0.50 #1307, 0.17 #18675, 0.17 #14024), 08jyyk (0.47 #5029, 0.47 #3789, 0.33 #4099), 0cx7f (0.41 #5100, 0.33 #3860, 0.33 #1690), 0dl5d (0.40 #3740, 0.35 #4980, 0.33 #2500), 0155w (0.40 #4138, 0.35 #7861, 0.33 #8481) >> Best rule #18630 for best value: >> intensional similarity = 5 >> extensional distance = 229 >> proper extension: 0fp_v1x; 01pfr3; 02mslq; 06cc_1; 03f5spx; 01jrz5j; 0lk90; 016kjs; 07c0j; 018y2s; ... >> query: (?x10924, 06by7) <- artists(?x11746, ?x10924), artists(?x671, ?x10924), artists(?x11746, ?x1412), ?x671 = 064t9, ?x1412 = 067mj >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nz1q6 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 77.000 0.740 http://example.org/music/genre/artists #3783-015xp4 PRED entity: 015xp4 PRED relation: award_winner! PRED expected values: 02cg41 => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 130): 02rjjll (0.27 #561, 0.15 #1534, 0.14 #3480), 01s695 (0.24 #559, 0.14 #3478, 0.14 #837), 02cg41 (0.24 #680, 0.14 #3599, 0.13 #4295), 0gpjbt (0.19 #584, 0.13 #1557, 0.11 #6564), 09n4nb (0.16 #602, 0.15 #324, 0.12 #880), 01c6qp (0.16 #574, 0.14 #852, 0.12 #6554), 013b2h (0.16 #4249, 0.16 #5083, 0.15 #5222), 05pd94v (0.14 #5007, 0.14 #4173, 0.14 #5146), 0jzphpx (0.14 #871, 0.10 #5042, 0.10 #5181), 01mh_q (0.14 #643, 0.13 #1616, 0.10 #5092) >> Best rule #561 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0197tq; 026ps1; 02r3zy; 01wbgdv; 0137n0; 012x4t; 01w60_p; 01trhmt; 09hnb; 016fmf; ... >> query: (?x5140, 02rjjll) <- award_winner(?x1362, ?x5140), award(?x5140, ?x6378), artist(?x4483, ?x5140), ?x1362 = 019bk0 >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #680 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 0197tq; 026ps1; 02r3zy; 01wbgdv; 0137n0; 012x4t; 01w60_p; 01trhmt; 09hnb; 016fmf; ... *> query: (?x5140, 02cg41) <- award_winner(?x1362, ?x5140), award(?x5140, ?x6378), artist(?x4483, ?x5140), ?x1362 = 019bk0 *> conf = 0.24 ranks of expected_values: 3 EVAL 015xp4 award_winner! 02cg41 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 153.000 153.000 0.270 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3782-01lmj3q PRED entity: 01lmj3q PRED relation: artists! PRED expected values: 0gg8l => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 204): 06by7 (0.50 #1895, 0.47 #2207, 0.47 #1271), 064t9 (0.47 #5949, 0.47 #1262, 0.47 #5637), 0gg8l (0.42 #445, 0.38 #133, 0.30 #1069), 03_d0 (0.30 #948, 0.28 #636, 0.20 #5320), 06j6l (0.29 #6296, 0.29 #5672, 0.29 #6608), 0xhtw (0.27 #1890, 0.24 #1266, 0.23 #2202), 05bt6j (0.25 #2229, 0.24 #2541, 0.22 #3478), 0glt670 (0.25 #5977, 0.25 #6289, 0.25 #6601), 0mhfr (0.25 #26, 0.19 #7810, 0.17 #650), 025sc50 (0.25 #5674, 0.25 #5986, 0.24 #3172) >> Best rule #1895 for best value: >> intensional similarity = 2 >> extensional distance = 88 >> proper extension: 018gm9; 01k_yf; 015srx; 01q99h; 02vgh; 01jcxwp; 017lb_; 033s6; 02cw1m; 03q_w5; ... >> query: (?x367, 06by7) <- artist(?x2299, ?x367), ?x2299 = 033hn8 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #445 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 0m_31; *> query: (?x367, 0gg8l) <- artist(?x2299, ?x367), award_nominee(?x367, ?x3146), ?x3146 = 0ggjt *> conf = 0.42 ranks of expected_values: 3 EVAL 01lmj3q artists! 0gg8l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 95.000 0.500 http://example.org/music/genre/artists #3781-01bk1y PRED entity: 01bk1y PRED relation: major_field_of_study PRED expected values: 01r4k => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 115): 02j62 (0.52 #5998, 0.50 #494, 0.37 #2838), 0g26h (0.51 #857, 0.50 #975, 0.41 #389), 062z7 (0.47 #491, 0.37 #842, 0.36 #1195), 01540 (0.47 #524, 0.29 #2516, 0.28 #1228), 02ky346 (0.42 #481, 0.26 #2473, 0.21 #1185), 03g3w (0.38 #22, 0.35 #724, 0.33 #2834), 0_jm (0.35 #991, 0.35 #873, 0.32 #405), 0db86 (0.32 #516, 0.18 #2508, 0.13 #1220), 0fdys (0.31 #35, 0.25 #737, 0.24 #503), 05qfh (0.29 #500, 0.27 #1204, 0.25 #851) >> Best rule #5998 for best value: >> intensional similarity = 4 >> extensional distance = 300 >> proper extension: 01pl14; 01j_9c; 049dk; 07vk2; 07xpm; 01vc5m; 01mpwj; 0pspl; 018m5q; 01b1pf; ... >> query: (?x7618, 02j62) <- institution(?x620, ?x7618), major_field_of_study(?x7618, ?x1154), major_field_of_study(?x3354, ?x1154), ?x3354 = 01q460 >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #547 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 078bz; 025v3k; 05zl0; 01j_5k; 01qqv5; *> query: (?x7618, 01r4k) <- institution(?x620, ?x7618), major_field_of_study(?x7618, ?x6859), major_field_of_study(?x7618, ?x1154), ?x1154 = 02lp1, ?x6859 = 01tbp *> conf = 0.16 ranks of expected_values: 31 EVAL 01bk1y major_field_of_study 01r4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 121.000 121.000 0.520 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3780-0170qf PRED entity: 0170qf PRED relation: film PRED expected values: 05p09dd => 114 concepts (75 used for prediction) PRED predicted values (max 10 best out of 702): 031786 (0.58 #104681, 0.52 #17741, 0.42 #120653), 02w9k1c (0.58 #104681, 0.52 #17741, 0.42 #120653), 02pjc1h (0.58 #104681, 0.52 #17741, 0.42 #120653), 0c3zjn7 (0.22 #944, 0.03 #44353, 0.01 #15136), 0466s8n (0.11 #1620, 0.03 #15812, 0.03 #44353), 09gq0x5 (0.11 #280, 0.03 #14472, 0.03 #44353), 03shpq (0.11 #1431, 0.03 #44353, 0.03 #15623), 08r4x3 (0.11 #152, 0.03 #44353, 0.02 #10796), 0hgnl3t (0.11 #753, 0.03 #44353, 0.02 #14945), 04gknr (0.11 #135, 0.03 #44353, 0.02 #14327) >> Best rule #104681 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 012c6x; 0f0p0; 028lc8; 036c_0; 01wxyx1; 0b_fw; 01wk7b7; 01pnn3; 01dw9z; 0241wg; ... >> query: (?x2280, ?x1448) <- film(?x2280, ?x186), profession(?x2280, ?x1032), nominated_for(?x2280, ?x1448) >> conf = 0.58 => this is the best rule for 3 predicted values *> Best rule #14951 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 237 *> proper extension: 0785v8; 03f1zdw; 01v42g; 01wb8bs; 03ym1; 0807ml; 01w23w; 031k24; 03k545; 05p606; *> query: (?x2280, 05p09dd) <- film(?x2280, ?x6448), nominated_for(?x6729, ?x6448), ?x6729 = 099ck7 *> conf = 0.01 ranks of expected_values: 605 EVAL 0170qf film 05p09dd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 114.000 75.000 0.585 http://example.org/film/actor/film./film/performance/film #3779-0d06vc PRED entity: 0d06vc PRED relation: combatants PRED expected values: 03_3d => 90 concepts (73 used for prediction) PRED predicted values (max 10 best out of 417): 09b6zr (0.50 #618, 0.41 #1612, 0.41 #1611), 0948xk (0.50 #618, 0.41 #1612, 0.41 #1611), 03f77 (0.50 #618, 0.41 #1612, 0.41 #1611), 02mjmr (0.50 #618, 0.41 #1612, 0.41 #1611), 08_hns (0.50 #618, 0.41 #1612, 0.41 #1611), 01cpp0 (0.50 #618, 0.41 #1612, 0.41 #1611), 079dy (0.50 #618, 0.41 #1612, 0.41 #1611), 0f8l9c (0.50 #509, 0.33 #3364, 0.33 #139), 01pj7 (0.50 #526, 0.33 #156, 0.30 #898), 03gj2 (0.50 #511, 0.33 #141, 0.30 #883) >> Best rule #618 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 07j9n; >> query: (?x2391, ?x2669) <- combatants(?x2391, ?x2152), combatants(?x2391, ?x456), ?x2152 = 06mkj, entity_involved(?x2391, ?x2669), film_release_region(?x8891, ?x456), combatants(?x456, ?x151), ?x8891 = 0gwlfnb, olympics(?x456, ?x391) >> conf = 0.50 => this is the best rule for 7 predicted values *> Best rule #6710 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 55 *> proper extension: 02n5d; *> query: (?x2391, ?x410) <- combatants(?x2391, ?x390), combatants(?x151, ?x390), entity_involved(?x2391, ?x2669), combatants(?x9203, ?x390), combatants(?x9203, ?x410), entity_involved(?x9203, ?x5803) *> conf = 0.10 ranks of expected_values: 131 EVAL 0d06vc combatants 03_3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 90.000 73.000 0.500 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #3778-046mxj PRED entity: 046mxj PRED relation: profession PRED expected values: 02hrh1q => 90 concepts (64 used for prediction) PRED predicted values (max 10 best out of 61): 02hrh1q (0.74 #5047, 0.70 #6823, 0.70 #6083), 01d_h8 (0.65 #3114, 0.64 #3558, 0.64 #3410), 02jknp (0.56 #3116, 0.55 #3560, 0.55 #3412), 0cbd2 (0.56 #1043, 0.50 #2523, 0.50 #2227), 018gz8 (0.39 #460, 0.22 #3864, 0.20 #4012), 02krf9 (0.35 #766, 0.32 #1802, 0.32 #2394), 0kyk (0.31 #1065, 0.28 #1213, 0.26 #2545), 0np9r (0.20 #20, 0.16 #168, 0.14 #3868), 0d8qb (0.20 #79, 0.05 #227, 0.04 #1263), 09jwl (0.20 #4458, 0.19 #4755, 0.18 #4903) >> Best rule #5047 for best value: >> intensional similarity = 3 >> extensional distance = 1144 >> proper extension: 03b78r; 024y6w; >> query: (?x5432, 02hrh1q) <- award_nominee(?x5432, ?x9326), place_of_birth(?x9326, ?x10763), location(?x5432, ?x1523) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 046mxj profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 64.000 0.743 http://example.org/people/person/profession #3777-0f2tj PRED entity: 0f2tj PRED relation: location! PRED expected values: 0c4y8 => 207 concepts (130 used for prediction) PRED predicted values (max 10 best out of 2237): 04n32 (0.49 #266342, 0.46 #263828, 0.45 #301516), 04twmk (0.49 #266342, 0.46 #263828, 0.45 #301516), 0c35b1 (0.49 #266342, 0.46 #263828, 0.45 #301516), 01k7d9 (0.49 #266342, 0.46 #263828, 0.45 #301516), 03kts (0.49 #266342, 0.46 #263828, 0.45 #301516), 0n6f8 (0.49 #266342, 0.46 #263828, 0.45 #301516), 05wm88 (0.49 #266342, 0.46 #263828, 0.45 #301516), 0d810y (0.46 #263828, 0.45 #301516, 0.44 #301515), 0g28b1 (0.46 #263828, 0.45 #301516, 0.44 #301515), 07d370 (0.46 #263828, 0.45 #301516, 0.44 #301515) >> Best rule #266342 for best value: >> intensional similarity = 3 >> extensional distance = 166 >> proper extension: 01423b; >> query: (?x6769, ?x1299) <- place_of_birth(?x1299, ?x6769), location_of_ceremony(?x566, ?x6769), film(?x1299, ?x861) >> conf = 0.49 => this is the best rule for 7 predicted values *> Best rule #6975 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 01mc11; *> query: (?x6769, 0c4y8) <- state(?x6769, ?x3908), vacationer(?x6769, ?x7025), contains(?x6769, ?x5596) *> conf = 0.06 ranks of expected_values: 681 EVAL 0f2tj location! 0c4y8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 207.000 130.000 0.490 http://example.org/people/person/places_lived./people/place_lived/location #3776-02f9wb PRED entity: 02f9wb PRED relation: award_winner PRED expected values: 04gnbv1 => 81 concepts (26 used for prediction) PRED predicted values (max 10 best out of 516): 02f9wb (0.50 #2623, 0.37 #6456, 0.33 #1010), 04gnbv1 (0.50 #2413, 0.37 #6456, 0.33 #800), 0c7t58 (0.37 #6456, 0.30 #38727, 0.25 #2234), 04x4s2 (0.37 #6456, 0.30 #38727, 0.25 #2233), 09_99w (0.29 #12912, 0.28 #14527, 0.06 #4584), 0bbxd3 (0.29 #12912, 0.28 #14527), 01vz80y (0.29 #12912, 0.28 #14527), 09r9dp (0.18 #41954, 0.02 #28060, 0.02 #24834), 03cbtlj (0.18 #41954, 0.01 #5788, 0.01 #4174), 0b1f49 (0.18 #41954, 0.01 #3875) >> Best rule #2623 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0cp9f9; >> query: (?x5958, 02f9wb) <- award_winner(?x5958, ?x4948), profession(?x5958, ?x353), ?x4948 = 02d6cy >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2413 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0cp9f9; *> query: (?x5958, 04gnbv1) <- award_winner(?x5958, ?x4948), profession(?x5958, ?x353), ?x4948 = 02d6cy *> conf = 0.50 ranks of expected_values: 2 EVAL 02f9wb award_winner 04gnbv1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 26.000 0.500 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #3775-05hjmd PRED entity: 05hjmd PRED relation: profession PRED expected values: 01d_h8 015cjr => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 86): 01d_h8 (0.84 #4506, 0.63 #2706, 0.58 #3006), 02hrh1q (0.66 #9766, 0.65 #9316, 0.65 #10666), 0dxtg (0.51 #4514, 0.37 #1664, 0.31 #7515), 03gjzk (0.50 #1666, 0.39 #4516, 0.38 #2116), 02jknp (0.47 #4508, 0.43 #1658, 0.32 #1808), 012t_z (0.35 #1813, 0.26 #2113, 0.25 #1063), 0dz3r (0.25 #1052, 0.19 #1802, 0.15 #2102), 09jwl (0.25 #1070, 0.17 #9921, 0.16 #8571), 0nbcg (0.25 #1083, 0.15 #2133, 0.13 #1833), 0fj9f (0.21 #2906, 0.19 #3806, 0.06 #5757) >> Best rule #4506 for best value: >> intensional similarity = 2 >> extensional distance = 385 >> proper extension: 024c1b; >> query: (?x11030, 01d_h8) <- produced_by(?x9993, ?x11030), genre(?x9993, ?x53) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 25 EVAL 05hjmd profession 015cjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 107.000 107.000 0.837 http://example.org/people/person/profession EVAL 05hjmd profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.837 http://example.org/people/person/profession #3774-04jplwp PRED entity: 04jplwp PRED relation: nominated_for! PRED expected values: 09td7p 02x2gy0 => 119 concepts (97 used for prediction) PRED predicted values (max 10 best out of 216): 019f4v (0.65 #974, 0.50 #50, 0.43 #4209), 0gs9p (0.59 #984, 0.50 #60, 0.45 #4219), 0gq9h (0.57 #982, 0.50 #751, 0.45 #4217), 0gr0m (0.54 #748, 0.53 #979, 0.47 #517), 0k611 (0.50 #69, 0.45 #993, 0.42 #762), 02pqp12 (0.50 #54, 0.41 #978, 0.40 #747), 0p9sw (0.50 #19, 0.40 #712, 0.39 #943), 02qyntr (0.50 #173, 0.40 #866, 0.38 #635), 02qvyrt (0.50 #91, 0.34 #784, 0.31 #1015), 02r22gf (0.50 #26, 0.34 #719, 0.27 #950) >> Best rule #974 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 0m313; 0b2v79; 011yxg; 0ds11z; 017gl1; 09q5w2; 0pv3x; 020fcn; 0dr_4; 0bcndz; ... >> query: (?x7880, 019f4v) <- nominated_for(?x2222, ?x7880), written_by(?x7880, ?x8042), honored_for(?x8964, ?x7880), ?x2222 = 0gs96 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #95 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 0dgst_d; *> query: (?x7880, 02x2gy0) <- nominated_for(?x704, ?x7880), nominated_for(?x4060, ?x7880), ?x4060 = 05hj_k, ceremony(?x704, ?x873) *> conf = 0.33 ranks of expected_values: 21, 27 EVAL 04jplwp nominated_for! 02x2gy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 119.000 97.000 0.647 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 04jplwp nominated_for! 09td7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 119.000 97.000 0.647 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3773-026c1 PRED entity: 026c1 PRED relation: profession PRED expected values: 02hrh1q => 134 concepts (133 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.91 #6682, 0.91 #3492, 0.91 #5667), 09jwl (0.37 #11036, 0.36 #10166, 0.36 #11326), 0nbcg (0.28 #13341, 0.26 #9599, 0.26 #11339), 012t_z (0.28 #13341, 0.10 #1461, 0.08 #3926), 02krf9 (0.26 #1909, 0.15 #3939, 0.15 #1039), 016z4k (0.24 #9574, 0.23 #11024, 0.22 #10154), 0dz3r (0.24 #9572, 0.22 #11022, 0.21 #7252), 0cbd2 (0.23 #586, 0.18 #6, 0.17 #1891), 018gz8 (0.20 #1319, 0.15 #4799, 0.15 #7264), 0np9r (0.15 #15825, 0.14 #15680, 0.14 #15970) >> Best rule #6682 for best value: >> intensional similarity = 3 >> extensional distance = 388 >> proper extension: 01_rh4; >> query: (?x2221, 02hrh1q) <- film(?x2221, ?x339), participant(?x2307, ?x2221), people(?x7562, ?x2307) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026c1 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 133.000 0.910 http://example.org/people/person/profession #3772-045hz5 PRED entity: 045hz5 PRED relation: languages PRED expected values: 03k50 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 24): 03k50 (0.75 #41, 0.70 #117, 0.44 #231), 07c9s (0.38 #50, 0.30 #126, 0.20 #202), 09s02 (0.25 #111, 0.25 #73, 0.10 #149), 02hxcvy (0.25 #291, 0.20 #139, 0.12 #253), 01c7y (0.25 #68, 0.20 #144, 0.12 #258), 055qm (0.25 #61, 0.20 #137, 0.12 #251), 0999q (0.25 #60, 0.13 #212, 0.12 #288), 064_8sq (0.12 #52, 0.12 #394, 0.10 #1192), 0121sr (0.12 #70, 0.10 #146, 0.06 #260), 032f6 (0.06 #303, 0.02 #531) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 03x31g; >> query: (?x13153, 03k50) <- award(?x13153, ?x10156), ?x10156 = 03r8v_, languages(?x13153, ?x254), ?x254 = 02h40lc >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045hz5 languages 03k50 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.750 http://example.org/people/person/languages #3771-0226cw PRED entity: 0226cw PRED relation: legislative_sessions PRED expected values: 07p__7 02bn_p 04gp1d => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 34): 07p__7 (0.75 #57, 0.75 #30, 0.73 #138), 02bn_p (0.75 #58, 0.75 #31, 0.67 #85), 04gp1d (0.38 #63, 0.38 #36, 0.33 #90), 01gtc0 (0.15 #298, 0.11 #256, 0.10 #283), 01h7xx (0.15 #298, 0.09 #209, 0.07 #236), 043djx (0.15 #298, 0.09 #191, 0.07 #218), 01gtcc (0.15 #298, 0.07 #251, 0.07 #278), 01gtbb (0.15 #298, 0.07 #249, 0.07 #276), 01grr2 (0.15 #298, 0.07 #260, 0.07 #287), 01gsvp (0.15 #298, 0.07 #259, 0.07 #286) >> Best rule #57 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 012v1t; >> query: (?x8607, 07p__7) <- legislative_sessions(?x8607, ?x3463), legislative_sessions(?x8607, ?x1137), ?x3463 = 02bqmq, jurisdiction_of_office(?x8607, ?x94), ?x1137 = 02bqn1, basic_title(?x8607, ?x2358) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0226cw legislative_sessions 04gp1d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.750 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 0226cw legislative_sessions 02bn_p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.750 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 0226cw legislative_sessions 07p__7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.750 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #3770-0hr6lkl PRED entity: 0hr6lkl PRED relation: ceremony! PRED expected values: 09tqxt => 43 concepts (40 used for prediction) PRED predicted values (max 10 best out of 333): 0k611 (0.67 #4337, 0.66 #4086, 0.60 #3584), 018wng (0.64 #4301, 0.63 #4050, 0.60 #3548), 0p9sw (0.64 #4286, 0.63 #4035, 0.57 #3533), 0gqy2 (0.64 #4387, 0.63 #4136, 0.57 #3634), 0gq_d (0.63 #4171, 0.62 #4422, 0.57 #3669), 0gr07 (0.62 #4435, 0.61 #4184, 0.58 #3180), 0gqyl (0.62 #4345, 0.61 #4094, 0.57 #3592), 0gqwc (0.61 #4074, 0.59 #4325, 0.53 #3572), 0gvx_ (0.59 #4401, 0.58 #4150, 0.54 #4651), 0f4x7 (0.59 #4291, 0.58 #4040, 0.54 #4541) >> Best rule #4337 for best value: >> intensional similarity = 16 >> extensional distance = 37 >> proper extension: 0c4hx0; >> query: (?x1442, 0k611) <- ceremony(?x451, ?x1442), honored_for(?x1442, ?x1916), film_release_region(?x1916, ?x279), film_production_design_by(?x1916, ?x3080), film_release_region(?x7393, ?x279), film_release_region(?x5255, ?x279), film_release_region(?x2318, ?x279), country(?x136, ?x279), combatants(?x326, ?x279), nationality(?x199, ?x279), ?x7393 = 02vz6dn, combatants(?x279, ?x792), ?x2318 = 06v9_x, country(?x1905, ?x279), ?x5255 = 01sby_, participating_countries(?x784, ?x279) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #818 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 0clfdj; *> query: (?x1442, 09tqxt) <- ceremony(?x1441, ?x1442), honored_for(?x1442, ?x1916), film_release_region(?x1916, ?x87), film_crew_role(?x1916, ?x137), ?x137 = 09zzb8, nominated_for(?x277, ?x1916), films(?x10705, ?x1916), ?x1441 = 099cng, award_winner(?x1916, ?x3080), film_release_region(?x9194, ?x87), film_release_region(?x7336, ?x87), film_release_region(?x5315, ?x87), film_release_region(?x3599, ?x87), ?x9194 = 0fpgp26, ?x7336 = 0bdjd, ?x3599 = 0kxf1, ?x5315 = 0glqh5_ *> conf = 0.50 ranks of expected_values: 22 EVAL 0hr6lkl ceremony! 09tqxt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 43.000 40.000 0.667 http://example.org/award/award_category/winners./award/award_honor/ceremony #3769-01vksx PRED entity: 01vksx PRED relation: country PRED expected values: 09c7w0 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 94): 09c7w0 (0.88 #252, 0.83 #436, 0.82 #1245), 07ssc (0.40 #17, 0.28 #573, 0.27 #390), 0chghy (0.38 #1733, 0.10 #4066, 0.08 #4065), 02jx1 (0.38 #1733, 0.04 #2592, 0.01 #711), 0345h (0.30 #28, 0.19 #1209, 0.17 #1699), 0f8l9c (0.20 #20, 0.11 #1324, 0.11 #1013), 03_3d (0.18 #69, 0.13 #818, 0.10 #4066), 0d060g (0.12 #320, 0.10 #4066, 0.10 #1252), 03rt9 (0.10 #4066, 0.10 #15, 0.08 #4065), 06mzp (0.10 #4066, 0.10 #19, 0.08 #4065) >> Best rule #252 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 018nnz; 03kx49; 02mc5v; 033pf1; >> query: (?x908, 09c7w0) <- film(?x629, ?x908), film_distribution_medium(?x908, ?x2007), film(?x609, ?x908), ?x2007 = 0dq6p >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vksx country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.881 http://example.org/film/film/country #3768-0gr4k PRED entity: 0gr4k PRED relation: award! PRED expected values: 0sxfd 0ywrc 0ktx_ => 55 concepts (32 used for prediction) PRED predicted values (max 10 best out of 921): 017jd9 (0.58 #7272, 0.50 #1415, 0.40 #12160), 0209hj (0.57 #5916, 0.50 #8849, 0.50 #1035), 01jc6q (0.57 #5868, 0.43 #4892, 0.33 #9779), 02jkkv (0.50 #2928, 0.50 #2808, 0.40 #4881), 0pv3x (0.50 #6935, 0.50 #1078, 0.36 #8892), 04v8x9 (0.50 #1011, 0.43 #5892, 0.43 #4916), 0bmhn (0.50 #1870, 0.43 #6751, 0.43 #5775), 0ywrc (0.50 #1267, 0.43 #9081, 0.42 #7124), 0ccd3x (0.50 #1410, 0.43 #5315, 0.33 #11178), 05sbv3 (0.50 #1911, 0.43 #6792, 0.33 #11679) >> Best rule #7272 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 02rdxsh; >> query: (?x601, 017jd9) <- nominated_for(?x601, ?x10829), nominated_for(?x601, ?x5849), nominated_for(?x601, ?x3219), ?x3219 = 011ydl, nominated_for(?x4057, ?x10829), film_release_region(?x5849, ?x87) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1267 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0gq9h; *> query: (?x601, 0ywrc) <- award(?x164, ?x601), nominated_for(?x601, ?x11355), ceremony(?x601, ?x2082), ?x2082 = 0gmdkyy, ?x11355 = 02q_ncg *> conf = 0.50 ranks of expected_values: 8, 60, 160 EVAL 0gr4k award! 0ktx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 32.000 0.583 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0gr4k award! 0ywrc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 55.000 32.000 0.583 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0gr4k award! 0sxfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 55.000 32.000 0.583 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #3767-09pjnd PRED entity: 09pjnd PRED relation: crewmember! PRED expected values: 01d2v1 => 115 concepts (85 used for prediction) PRED predicted values (max 10 best out of 317): 0f4yh (0.33 #1263, 0.31 #2210, 0.31 #1894), 06mmr (0.33 #1263, 0.31 #2210, 0.31 #1894), 0hx4y (0.18 #1048, 0.12 #1364, 0.10 #733), 024mpp (0.18 #1080, 0.11 #1711, 0.10 #2027), 07nxnw (0.18 #1186, 0.11 #1817, 0.10 #2133), 01hq1 (0.18 #1210, 0.10 #895, 0.08 #1526), 043tvp3 (0.18 #1187, 0.10 #872, 0.08 #1503), 011xg5 (0.18 #1219, 0.05 #1850, 0.05 #2166), 01f7jt (0.18 #1255, 0.05 #1886, 0.05 #2202), 0dtfn (0.16 #1628, 0.16 #1313, 0.14 #1944) >> Best rule #1263 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 04rcl7; >> query: (?x1643, ?x2366) <- award_winner(?x2366, ?x1643), award_winner(?x2209, ?x1643), ?x2209 = 0gr42 >> conf = 0.33 => this is the best rule for 2 predicted values *> Best rule #10430 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1047 *> proper extension: 0q59y; 0klw; 01pp3p; 046mxj; 058s44; 01w9wwg; 06qgjh; 06rgq; 034hck; 023zsh; ... *> query: (?x1643, ?x11174) <- award_nominee(?x1643, ?x930), nominated_for(?x1643, ?x11174), film_release_region(?x11174, ?x94) *> conf = 0.02 ranks of expected_values: 316 EVAL 09pjnd crewmember! 01d2v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 115.000 85.000 0.333 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #3766-019tzd PRED entity: 019tzd PRED relation: country PRED expected values: 03_3d 0chghy 05bmq => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 298): 0chghy (0.88 #1066, 0.88 #1062, 0.85 #4766), 06qd3 (0.88 #1066, 0.88 #1062, 0.83 #3554), 06f32 (0.88 #1066, 0.88 #1062, 0.79 #1416), 07f1x (0.88 #1066, 0.88 #1062, 0.78 #2425), 03spz (0.88 #1066, 0.88 #1062, 0.77 #2295), 0d04z6 (0.88 #1066, 0.88 #1062, 0.75 #1063), 047yc (0.88 #1066, 0.88 #1062, 0.75 #1063), 015qh (0.88 #1066, 0.79 #1416, 0.78 #2142), 06c1y (0.88 #1066, 0.78 #2144, 0.77 #2295), 01pj7 (0.88 #1066, 0.78 #2150, 0.75 #1063) >> Best rule #1066 for best value: >> intensional similarity = 43 >> extensional distance = 3 >> proper extension: 03hr1p; >> query: (?x5989, ?x1781) <- country(?x5989, ?x8958), country(?x5989, ?x1603), country(?x5989, ?x1471), country(?x5989, ?x1355), country(?x5989, ?x985), country(?x5989, ?x774), country(?x5989, ?x583), country(?x5989, ?x456), country(?x5989, ?x304), country(?x5989, ?x172), ?x1603 = 06bnz, ?x583 = 015fr, ?x304 = 0d0vqn, ?x172 = 0154j, ?x456 = 05qhw, sports(?x3971, ?x5989), sports(?x2966, ?x5989), ?x1355 = 0h7x, capital(?x8958, ?x13918), ?x1471 = 07t21, country(?x3015, ?x8958), teams(?x8958, ?x4281), administrative_area_type(?x8958, ?x2792), film_release_region(?x280, ?x8958), ?x2792 = 0hzc9wc, location(?x12565, ?x8958), olympics(?x5989, ?x1931), member_states(?x2106, ?x8958), ?x3971 = 0jhn7, organization(?x8958, ?x1062), olympics(?x2316, ?x2966), olympics(?x1781, ?x2966), olympics(?x4503, ?x2966), ?x2316 = 06t2t, combatants(?x1781, ?x4092), ?x4503 = 06z68, olympics(?x1122, ?x2966), ?x985 = 0k6nt, ?x774 = 06mzp, ?x3015 = 071t0, geographic_distribution(?x1571, ?x1122), film_release_region(?x86, ?x1122), organization(?x1781, ?x127) >> conf = 0.88 => this is the best rule for 18 predicted values ranks of expected_values: 1, 19, 78 EVAL 019tzd country 05bmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 51.000 51.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 019tzd country 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 019tzd country 03_3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 51.000 51.000 0.885 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #3765-0d1y7 PRED entity: 0d1y7 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 232 concepts (232 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.88 #109, 0.88 #53, 0.87 #45), 01g63y (0.14 #30, 0.14 #10, 0.12 #38), 0jgjn (0.07 #32, 0.06 #40, 0.05 #156) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 0k049; 0162v; 04qdj; 0rsjf; 01dg3s; >> query: (?x12569, 04ztj) <- location_of_ceremony(?x1864, ?x12569), category(?x1864, ?x134), award(?x1864, ?x5923), profession(?x1864, ?x319) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d1y7 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 232.000 232.000 0.885 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #3764-07b_l PRED entity: 07b_l PRED relation: state_province_region! PRED expected values: 0gztl 018p5f 0z07 012x8m => 204 concepts (151 used for prediction) PRED predicted values (max 10 best out of 771): 02ngbs (0.81 #18286, 0.81 #19019, 0.65 #35117), 01n_g9 (0.81 #18286, 0.81 #19019, 0.65 #35117), 02km0m (0.81 #18286, 0.81 #19019, 0.65 #35117), 0108xl (0.30 #49761, 0.23 #18285, 0.23 #19018), 013mtx (0.30 #49761, 0.23 #18285, 0.23 #19018), 013n60 (0.30 #49761, 0.23 #18285, 0.23 #19018), 010016 (0.30 #49761, 0.23 #18285, 0.23 #19018), 0f2sq (0.30 #49761, 0.23 #18285, 0.23 #19018), 0_z91 (0.30 #49761, 0.23 #18285, 0.23 #19018), 0106dv (0.30 #49761, 0.23 #18285, 0.23 #19018) >> Best rule #18286 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 03s0w; 05k7sb; 050l8; 01n4w; 05j49; 05fjy; 050ks; 059t8; 0j95; 0847q; >> query: (?x3634, ?x12795) <- contains(?x3634, ?x12795), district_represented(?x176, ?x3634), major_field_of_study(?x12795, ?x1668) >> conf = 0.81 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 07b_l state_province_region! 012x8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 151.000 0.810 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 07b_l state_province_region! 0z07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 151.000 0.810 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 07b_l state_province_region! 018p5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 151.000 0.810 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region EVAL 07b_l state_province_region! 0gztl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 204.000 151.000 0.810 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #3763-07c0j PRED entity: 07c0j PRED relation: award PRED expected values: 01c427 => 121 concepts (119 used for prediction) PRED predicted values (max 10 best out of 314): 01by1l (0.77 #33528, 0.76 #28737, 0.76 #29935), 054krc (0.50 #12057, 0.47 #9663, 0.35 #15250), 03qbh5 (0.41 #2197, 0.32 #13370, 0.28 #601), 0l8z1 (0.40 #12033, 0.39 #9639, 0.26 #15226), 02qvyrt (0.38 #12096, 0.37 #9702, 0.29 #15289), 0gqz2 (0.38 #9656, 0.34 #12050, 0.22 #15243), 0c4z8 (0.36 #13239, 0.22 #2066, 0.22 #22023), 054ks3 (0.34 #2136, 0.33 #540, 0.29 #9717), 02f77l (0.34 #3044, 0.18 #1049, 0.17 #11423), 02x17c2 (0.33 #615, 0.19 #2211, 0.15 #9792) >> Best rule #33528 for best value: >> intensional similarity = 2 >> extensional distance = 591 >> proper extension: 025vry; >> query: (?x1136, ?x2139) <- award_winner(?x2139, ?x1136), artists(?x671, ?x1136) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2877 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 02r3zy; 03g5jw; 0dvqq; 03fbc; 04qmr; 0163m1; 03d9d6; 07bzp; 09lwrt; 01dq9q; ... *> query: (?x1136, 01c427) <- artist(?x2149, ?x1136), award_nominee(?x1136, ?x538), group(?x227, ?x1136) *> conf = 0.29 ranks of expected_values: 16 EVAL 07c0j award 01c427 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 121.000 119.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3762-034b6k PRED entity: 034b6k PRED relation: executive_produced_by PRED expected values: 05prs8 => 70 concepts (46 used for prediction) PRED predicted values (max 10 best out of 47): 05hj_k (0.10 #97, 0.05 #350, 0.05 #853), 03y2kr (0.10 #186), 04flrx (0.10 #140), 02pq9yv (0.10 #84), 0343h (0.08 #295, 0.05 #798, 0.04 #1303), 02q_cc (0.07 #281, 0.03 #1289, 0.03 #1542), 0glyyw (0.05 #441, 0.04 #693, 0.03 #2964), 06q8hf (0.05 #2942, 0.05 #3957, 0.04 #3197), 079vf (0.04 #3542, 0.04 #758, 0.03 #1012), 030_3z (0.03 #360, 0.03 #863, 0.03 #1368) >> Best rule #97 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0gj8t_b; 05fgt1; 0d99k_; >> query: (?x10742, 05hj_k) <- film(?x3708, ?x10742), film(?x450, ?x10742), ?x3708 = 013knm, location(?x450, ?x242) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #2569 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 255 *> proper extension: 0fq27fp; *> query: (?x10742, 05prs8) <- crewmember(?x10742, ?x666), genre(?x10742, ?x225), currency(?x10742, ?x170) *> conf = 0.02 ranks of expected_values: 39 EVAL 034b6k executive_produced_by 05prs8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 70.000 46.000 0.100 http://example.org/film/film/executive_produced_by #3761-07rn0z PRED entity: 07rn0z PRED relation: person! PRED expected values: 0c5lg => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 4): 043q4d (0.04 #216, 0.03 #138, 0.03 #180), 0c5lg (0.04 #41, 0.03 #48, 0.02 #142), 02k13d (0.03 #45, 0.03 #52, 0.02 #59), 026h21_ (0.01 #192, 0.01 #115, 0.01 #221) >> Best rule #216 for best value: >> intensional similarity = 4 >> extensional distance = 627 >> proper extension: 0cg9y; 05qhnq; 02vwckw; 01wxdn3; >> query: (?x11568, 043q4d) <- category(?x11568, ?x134), gender(?x11568, ?x231), profession(?x11568, ?x1032), ?x1032 = 02hrh1q >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #41 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 01zp33; 09r_wb; 01s0l0; 0fg_hg; 03x31g; 08y7b9; *> query: (?x11568, 0c5lg) <- film(?x11568, ?x8657), people(?x5025, ?x11568), gender(?x11568, ?x231), ?x5025 = 0dryh9k *> conf = 0.04 ranks of expected_values: 2 EVAL 07rn0z person! 0c5lg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 117.000 0.040 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #3760-0gvsh7l PRED entity: 0gvsh7l PRED relation: nominated_for! PRED expected values: 0gkr9q => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 192): 0bdw1g (0.80 #4476, 0.79 #3531, 0.79 #4003), 0cqh6z (0.80 #4476, 0.79 #3531, 0.79 #4003), 0gq9h (0.39 #14657, 0.37 #15128, 0.37 #14893), 0gs9p (0.35 #14659, 0.33 #14895, 0.31 #15130), 019f4v (0.34 #14648, 0.33 #55, 0.33 #14884), 054krc (0.33 #71, 0.17 #14664, 0.17 #15135), 07cbcy (0.33 #65, 0.07 #14423, 0.07 #18197), 05b4l5x (0.33 #6, 0.07 #14364, 0.05 #19079), 02x201b (0.33 #180, 0.02 #13596, 0.01 #15009), 0gq_v (0.32 #13436, 0.28 #15084, 0.27 #14849) >> Best rule #4476 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 02rq7nd; >> query: (?x8316, ?x1111) <- producer_type(?x8316, ?x632), award(?x8316, ?x1111), award(?x376, ?x1111), ceremony(?x1111, ?x873) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #1853 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 02k_4g; 08jgk1; 0464pz; 027tbrc; 01b_lz; 015g28; 0hz55; 02r1ysd; 039cq4; 0266s9; ... *> query: (?x8316, 0gkr9q) <- honored_for(?x1764, ?x8316), actor(?x8316, ?x1890), award(?x8316, ?x686), program(?x3571, ?x8316) *> conf = 0.24 ranks of expected_values: 21 EVAL 0gvsh7l nominated_for! 0gkr9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 95.000 95.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3759-01mt1fy PRED entity: 01mt1fy PRED relation: award_winner! PRED expected values: 02g2wv => 113 concepts (107 used for prediction) PRED predicted values (max 10 best out of 226): 09qv3c (0.33 #51, 0.17 #915, 0.04 #1779), 094qd5 (0.20 #477, 0.06 #4797, 0.04 #6093), 02z1nbg (0.20 #627, 0.06 #6675, 0.05 #4947), 09cn0c (0.20 #751, 0.04 #7231, 0.04 #5071), 09qwmm (0.20 #466, 0.04 #6946, 0.04 #4786), 027b9k6 (0.20 #642, 0.02 #6258, 0.02 #4962), 02z0dfh (0.20 #508, 0.02 #6124, 0.02 #9148), 0gqyl (0.17 #970, 0.06 #1834, 0.05 #2266), 027571b (0.17 #1140, 0.03 #9780, 0.03 #13236), 02x4x18 (0.17 #998, 0.03 #9638, 0.02 #4886) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0q9kd; >> query: (?x4395, 09qv3c) <- film(?x4395, ?x11686), profession(?x4395, ?x1041), ?x11686 = 04180vy, ?x1041 = 03gjzk >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3270 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 02qjj7; 0162c8; 01hrqc; 03f3yfj; 01vhrz; *> query: (?x4395, 02g2wv) <- participant(?x538, ?x4395), profession(?x4395, ?x1041), student(?x6271, ?x4395), ?x1041 = 03gjzk *> conf = 0.01 ranks of expected_values: 183 EVAL 01mt1fy award_winner! 02g2wv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 113.000 107.000 0.333 http://example.org/award/award_category/winners./award/award_honor/award_winner #3758-0yx74 PRED entity: 0yx74 PRED relation: place PRED expected values: 0yx74 => 157 concepts (134 used for prediction) PRED predicted values (max 10 best out of 190): 0yx74 (0.29 #13396, 0.14 #53074, 0.11 #39678), 04tgp (0.29 #13396, 0.14 #53074, 0.11 #39678), 013kcv (0.14 #531, 0.05 #2076, 0.03 #3622), 013yq (0.14 #560, 0.05 #2105, 0.03 #3651), 0psxp (0.14 #661, 0.05 #2206, 0.03 #3752), 0c_m3 (0.14 #647, 0.04 #3222, 0.03 #3738), 0rh7t (0.14 #660, 0.02 #6326, 0.01 #6841), 0wq3z (0.13 #59775, 0.09 #1150, 0.09 #50496), 043yj (0.09 #1447, 0.07 #1962, 0.02 #4538), 0xgpv (0.09 #1514, 0.07 #2029) >> Best rule #13396 for best value: >> intensional similarity = 4 >> extensional distance = 114 >> proper extension: 0n5dt; >> query: (?x12883, ?x4622) <- contains(?x12883, ?x2821), source(?x12883, ?x958), student(?x2821, ?x672), contains(?x4622, ?x2821) >> conf = 0.29 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0yx74 place 0yx74 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 157.000 134.000 0.287 http://example.org/location/hud_county_place/place #3757-0f4yh PRED entity: 0f4yh PRED relation: film_distribution_medium PRED expected values: 0dq6p => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.69 #54, 0.65 #144, 0.65 #179), 02nxhr (0.50 #96, 0.50 #91, 0.50 #76), 0dq6p (0.44 #97, 0.43 #92, 0.38 #77), 07z4p (0.08 #40, 0.06 #5, 0.05 #80), 07c52 (0.03 #78, 0.02 #53, 0.02 #631) >> Best rule #54 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 0cnztc4; 0crh5_f; 02pcq92; >> query: (?x3535, 0735l) <- film_distribution_medium(?x3535, ?x81), language(?x3535, ?x254), titles(?x811, ?x3535), category(?x3535, ?x134) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 0353tm; *> query: (?x3535, 0dq6p) <- film_distribution_medium(?x3535, ?x81), film(?x1958, ?x3535), film(?x902, ?x3535), ?x81 = 029j_ *> conf = 0.44 ranks of expected_values: 3 EVAL 0f4yh film_distribution_medium 0dq6p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 144.000 144.000 0.686 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #3756-040_9s0 PRED entity: 040_9s0 PRED relation: award! PRED expected values: 0dz46 => 56 concepts (34 used for prediction) PRED predicted values (max 10 best out of 4041): 07w21 (0.85 #16792, 0.80 #16793, 0.79 #110858), 014ps4 (0.85 #16792, 0.80 #16793, 0.79 #110858), 0fpzt5 (0.85 #16792, 0.80 #16793, 0.79 #110858), 03772 (0.85 #16792, 0.80 #16793, 0.79 #110858), 048_p (0.67 #18412, 0.60 #11695, 0.42 #21773), 04mhl (0.67 #18049, 0.60 #11332, 0.33 #21410), 05kfs (0.67 #13596, 0.60 #6878, 0.08 #87501), 05ldnp (0.67 #14325, 0.60 #7607, 0.08 #98309), 03hy3g (0.67 #15284, 0.60 #8566, 0.06 #89189), 0h5f5n (0.67 #13494, 0.60 #6776, 0.05 #107559) >> Best rule #16792 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 0gr4k; >> query: (?x8909, ?x476) <- award_winner(?x8909, ?x5049), award_winner(?x8909, ?x476), award(?x11214, ?x8909), award(?x10598, ?x8909), award(?x3527, ?x8909), currency(?x11214, ?x170), location(?x5049, ?x1274), ?x3527 = 085pr, people(?x3584, ?x10598) >> conf = 0.85 => this is the best rule for 4 predicted values *> Best rule #20153 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 0262zm; 02664f; 0262x6; *> query: (?x8909, ?x5334) <- award_winner(?x8909, ?x477), award(?x11214, ?x8909), award(?x1376, ?x8909), gender(?x11214, ?x231), influenced_by(?x477, ?x5334), influenced_by(?x11214, ?x1029), ?x1376 = 01963w *> conf = 0.20 ranks of expected_values: 217 EVAL 040_9s0 award! 0dz46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 56.000 34.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3755-01czx PRED entity: 01czx PRED relation: artist! PRED expected values: 03rhqg 03qk20 => 104 concepts (64 used for prediction) PRED predicted values (max 10 best out of 119): 0g768 (0.66 #5975, 0.40 #7080, 0.23 #1002), 03rhqg (0.44 #153, 0.42 #2500, 0.40 #2085), 0mzkr (0.40 #25, 0.19 #301, 0.18 #439), 011k1h (0.36 #7054, 0.20 #10, 0.19 #976), 02p11jq (0.33 #151, 0.16 #2083, 0.15 #2498), 02y21l (0.33 #232, 0.12 #370, 0.12 #1612), 0181dw (0.33 #179, 0.11 #1835, 0.09 #7500), 01cf93 (0.32 #6410, 0.18 #5663, 0.12 #1299), 041bnw (0.25 #2552, 0.12 #481, 0.11 #2137), 01clyr (0.24 #722, 0.21 #1136, 0.20 #32) >> Best rule #5975 for best value: >> intensional similarity = 6 >> extensional distance = 150 >> proper extension: 0f0y8; 0411q; 01lmj3q; 01t_xp_; 0150jk; 0152cw; 03qmj9; 03t9sp; 04r1t; 0dtd6; ... >> query: (?x2073, 0g768) <- artist(?x5634, ?x2073), artists(?x1000, ?x2073), artist(?x5634, ?x4574), artist(?x5634, ?x2518), ?x4574 = 02dbp7, award_winner(?x2420, ?x2518) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 01w60_p; 01w524f; 03f0fnk; 01vsy3q; 013qvn; 01wg25j; 033s6; *> query: (?x2073, 03rhqg) <- artist(?x5634, ?x2073), artists(?x8011, ?x2073), ?x5634 = 01cl2y, influenced_by(?x5329, ?x2073), parent_genre(?x11973, ?x8011) *> conf = 0.44 ranks of expected_values: 2, 68 EVAL 01czx artist! 03qk20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 104.000 64.000 0.664 http://example.org/music/record_label/artist EVAL 01czx artist! 03rhqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 64.000 0.664 http://example.org/music/record_label/artist #3754-021q23 PRED entity: 021q23 PRED relation: team PRED expected values: 038zh6 => 26 concepts (26 used for prediction) PRED predicted values (max 10 best out of 350): 0329gm (0.33 #132, 0.25 #804, 0.12 #1142), 02w64f (0.33 #161, 0.25 #833, 0.12 #1171), 0272vm (0.33 #112, 0.25 #784, 0.12 #1122), 03ys48 (0.33 #111, 0.25 #783, 0.12 #1121), 037mp6 (0.33 #50, 0.25 #722, 0.12 #1060), 03y_f8 (0.33 #10, 0.25 #682, 0.12 #1020), 02ryyk (0.33 #166, 0.25 #838, 0.12 #1176), 03ym73 (0.33 #165, 0.25 #837, 0.12 #1175), 03z1c5 (0.33 #159, 0.25 #831, 0.12 #1169), 033g54 (0.33 #156, 0.25 #828, 0.12 #1166) >> Best rule #132 for best value: >> intensional similarity = 20 >> extensional distance = 1 >> proper extension: 07y9k; >> query: (?x13559, 0329gm) <- team(?x13559, ?x14520), team(?x13559, ?x14441), team(?x13559, ?x14238), colors(?x14520, ?x332), sport(?x14441, ?x12682), teams(?x2146, ?x14238), ?x332 = 01l849, category(?x13559, ?x134), nationality(?x111, ?x2146), contains(?x2146, ?x1391), organization(?x2146, ?x127), film_release_region(?x6528, ?x2146), film_release_region(?x4668, ?x2146), film_release_region(?x1178, ?x2146), film_release_region(?x204, ?x2146), countries_spoken_in(?x254, ?x2146), ?x1178 = 053rxgm, ?x6528 = 0dc_ms, ?x204 = 028_yv, ?x4668 = 0bh8x1y >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 021q23 team 038zh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 26.000 0.333 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #3753-027c924 PRED entity: 027c924 PRED relation: award! PRED expected values: 04vr_f 0hmm7 => 52 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1365): 0hfzr (0.50 #4369, 0.33 #2385, 0.33 #1393), 01hv3t (0.38 #4689, 0.33 #2705, 0.33 #1713), 0gmcwlb (0.38 #4090, 0.33 #1114, 0.33 #122), 0hmr4 (0.38 #4031, 0.33 #1055, 0.33 #63), 0h6r5 (0.38 #4363, 0.33 #1387, 0.33 #395), 017jd9 (0.38 #4416, 0.33 #448, 0.25 #3424), 017gl1 (0.38 #4057, 0.33 #1081, 0.25 #3065), 049xgc (0.33 #2535, 0.33 #551, 0.25 #4519), 0c0zq (0.33 #2861, 0.33 #877, 0.25 #4845), 04j4tx (0.33 #2387, 0.33 #403, 0.25 #4371) >> Best rule #4369 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 09d28z; 0262s1; >> query: (?x289, 0hfzr) <- award_winner(?x289, ?x4353), ?x4353 = 06mn7, award(?x2490, ?x289), film_crew_role(?x2490, ?x137) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #17883 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 194 *> proper extension: 02r0csl; 0bfvw2; 0gq_v; 0p9sw; 02r22gf; 02hsq3m; 0cqh46; 047byns; 0fq9zdn; 05zvq6g; ... *> query: (?x289, ?x8283) <- award_winner(?x289, ?x9313), award_winner(?x289, ?x4353), award_winner(?x289, ?x4056), award(?x197, ?x289), profession(?x4056, ?x319), award_nominee(?x4353, ?x4895), award_winner(?x8283, ?x9313) *> conf = 0.25 ranks of expected_values: 121, 206 EVAL 027c924 award! 0hmm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 52.000 21.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 027c924 award! 04vr_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 52.000 21.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #3752-01gbcf PRED entity: 01gbcf PRED relation: artists PRED expected values: 02ndj5 => 46 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1080): 02ndj5 (0.50 #904, 0.45 #9638, 0.40 #1995), 0191h5 (0.50 #6113, 0.45 #9387, 0.29 #19218), 01w8n89 (0.44 #5779, 0.40 #9053, 0.30 #16701), 06br6t (0.44 #6358, 0.35 #9632, 0.24 #6547), 05563d (0.44 #5771, 0.35 #9045, 0.19 #17785), 01gx5f (0.40 #9029, 0.40 #1386, 0.38 #5755), 03lgg (0.40 #1539, 0.25 #448, 0.20 #3723), 03t9sp (0.38 #16506, 0.36 #19779, 0.27 #24145), 0326tc (0.38 #6189, 0.35 #9463, 0.30 #5096), 0p76z (0.38 #6382, 0.35 #9656, 0.24 #6547) >> Best rule #904 for best value: >> intensional similarity = 14 >> extensional distance = 2 >> proper extension: 0g_bh; 029fbr; >> query: (?x301, 02ndj5) <- parent_genre(?x301, ?x10290), parent_genre(?x301, ?x302), ?x302 = 016clz, artists(?x10290, ?x9757), artists(?x10290, ?x7506), artists(?x10290, ?x7476), artists(?x10290, ?x5556), ?x7506 = 024dw0, ?x7476 = 048xh, nominated_for(?x5556, ?x2331), award(?x5556, ?x1079), ?x9757 = 06br6t, music(?x2251, ?x5556), profession(?x5556, ?x106) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gbcf artists 02ndj5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 25.000 0.500 http://example.org/music/genre/artists #3751-04bd8y PRED entity: 04bd8y PRED relation: location PRED expected values: 0bxbr => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 47): 0bxbr (0.70 #45784, 0.43 #30522, 0.43 #7228), 02_286 (0.29 #1643, 0.29 #37, 0.27 #840), 030qb3t (0.29 #83, 0.18 #886, 0.18 #2492), 0cr3d (0.14 #144, 0.09 #947, 0.07 #1750), 029cr (0.14 #128, 0.09 #931, 0.07 #1734), 0mp3l (0.14 #119, 0.09 #922, 0.07 #1725), 07z1m (0.14 #79, 0.09 #882, 0.07 #1685), 0r62v (0.14 #47, 0.09 #850, 0.07 #1653), 0r0m6 (0.14 #217, 0.07 #1823, 0.02 #5838), 013n2h (0.12 #2815) >> Best rule #45784 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 045bg; 028p0; 064p92m; 01ky2h; 014dq7; 02rgz97; 01lcxbb; 07h1h5; 01w8n89; 0p3r8; ... >> query: (?x820, ?x5962) <- place_of_birth(?x820, ?x5962), location(?x820, ?x1767) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bd8y location 0bxbr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #3750-03gr7w PRED entity: 03gr7w PRED relation: profession PRED expected values: 02hrh1q => 124 concepts (88 used for prediction) PRED predicted values (max 10 best out of 79): 02hrh1q (0.78 #12158, 0.73 #9821, 0.73 #8358), 0nbcg (0.61 #906, 0.57 #1490, 0.50 #4561), 0dz3r (0.57 #1462, 0.50 #1608, 0.46 #878), 01c72t (0.55 #606, 0.33 #2650, 0.31 #3235), 01d_h8 (0.42 #735, 0.32 #1903, 0.31 #1757), 0dxtg (0.33 #742, 0.28 #11865, 0.27 #10843), 03gjzk (0.29 #744, 0.22 #10114, 0.21 #12744), 02jknp (0.29 #737, 0.20 #1759, 0.19 #1905), 0kyk (0.27 #174, 0.21 #758, 0.17 #2364), 0cbd2 (0.25 #736, 0.18 #152, 0.18 #2196) >> Best rule #12158 for best value: >> intensional similarity = 3 >> extensional distance = 1786 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 04bdxl; 02s2ft; 05vsxz; 06qgvf; 0grwj; ... >> query: (?x1795, 02hrh1q) <- award_nominee(?x1795, ?x2638), profession(?x1795, ?x955), specialization_of(?x1776, ?x955) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03gr7w profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 88.000 0.776 http://example.org/people/person/profession #3749-02qkwl PRED entity: 02qkwl PRED relation: story_by PRED expected values: 03rx9 => 87 concepts (48 used for prediction) PRED predicted values (max 10 best out of 49): 025b3k (0.09 #164, 0.02 #383, 0.01 #1908), 060pl5 (0.09 #181), 01vl17 (0.09 #141), 041h0 (0.04 #224, 0.02 #661), 079vf (0.04 #1964, 0.03 #3047, 0.03 #1746), 0d6484 (0.03 #5874, 0.03 #7397, 0.03 #9133), 05nn4k (0.03 #5874, 0.03 #7397, 0.03 #9133), 0343h (0.03 #893, 0.03 #3063, 0.02 #3496), 081k8 (0.03 #1613, 0.02 #8353), 046_v (0.03 #2135, 0.02 #3218, 0.02 #3435) >> Best rule #164 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 0fpgp26; >> query: (?x8001, 025b3k) <- film(?x8092, ?x8001), film(?x3999, ?x8001), genre(?x8001, ?x53), film_release_region(?x8001, ?x94), ?x8092 = 02w29z, award_nominee(?x3999, ?x221) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #386 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 44 *> proper extension: 05cvgl; 0kvgtf; 0h6r5; 0qmjd; 0_9l_; 01f69m; *> query: (?x8001, 03rx9) <- film(?x521, ?x8001), genre(?x8001, ?x1509), produced_by(?x8001, ?x4660), ?x1509 = 060__y, friend(?x521, ?x6187) *> conf = 0.02 ranks of expected_values: 22 EVAL 02qkwl story_by 03rx9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 87.000 48.000 0.091 http://example.org/film/film/story_by #3748-018y81 PRED entity: 018y81 PRED relation: gender PRED expected values: 05zppz => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #33, 0.87 #67, 0.87 #97), 02zsn (0.46 #288, 0.46 #311, 0.45 #241) >> Best rule #33 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 03cs_z7; >> query: (?x6067, 05zppz) <- role(?x6067, ?x1750), ?x1750 = 02hnl, profession(?x6067, ?x1359), profession(?x5285, ?x1359), award(?x5285, ?x724) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018y81 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.872 http://example.org/people/person/gender #3747-01fmys PRED entity: 01fmys PRED relation: film! PRED expected values: 028d4v => 70 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1027): 0146pg (0.47 #95768, 0.41 #99933, 0.39 #91603), 0dzf_ (0.33 #812, 0.03 #19543, 0.02 #23706), 06cgy (0.33 #251, 0.03 #62454, 0.03 #35638), 0k525 (0.33 #1845, 0.02 #10170, 0.02 #14332), 012q4n (0.33 #1139, 0.02 #24033, 0.01 #26114), 025n3p (0.33 #492, 0.01 #19223), 02rrsz (0.33 #1612), 0335fp (0.33 #1385), 0py5b (0.19 #58290, 0.12 #74948, 0.09 #33303), 01r2c7 (0.16 #62455, 0.14 #64537, 0.12 #31220) >> Best rule #95768 for best value: >> intensional similarity = 3 >> extensional distance = 1280 >> proper extension: 03g9xj; >> query: (?x2050, ?x11851) <- nominated_for(?x11851, ?x2050), nationality(?x11851, ?x94), location(?x11851, ?x739) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #52046 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 501 *> proper extension: 01cgz; *> query: (?x2050, ?x397) <- films(?x5011, ?x2050), films(?x5011, ?x10711), films(?x5011, ?x2463), film_release_region(?x10711, ?x94), film(?x397, ?x2463) *> conf = 0.06 ranks of expected_values: 52 EVAL 01fmys film! 028d4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 70.000 48.000 0.471 http://example.org/film/actor/film./film/performance/film #3746-0qdyf PRED entity: 0qdyf PRED relation: profession PRED expected values: 02hrh1q => 142 concepts (119 used for prediction) PRED predicted values (max 10 best out of 83): 02hrh1q (0.81 #9064, 0.81 #10402, 0.79 #1940), 016z4k (0.59 #152, 0.54 #448, 0.50 #744), 0dz3r (0.52 #150, 0.44 #5928, 0.43 #8161), 0cbd2 (0.48 #7, 0.39 #4746, 0.39 #3709), 01c72t (0.44 #1060, 0.37 #912, 0.34 #4171), 0dxtg (0.41 #2679, 0.40 #1346, 0.39 #3716), 0n1h (0.37 #160, 0.31 #456, 0.28 #308), 039v1 (0.37 #4479, 0.37 #1220, 0.33 #1072), 01d_h8 (0.36 #1486, 0.32 #1931, 0.32 #12767), 0kyk (0.32 #30, 0.26 #6401, 0.26 #3732) >> Best rule #9064 for best value: >> intensional similarity = 3 >> extensional distance = 488 >> proper extension: 05m63c; 01wyzyl; 05wjnt; 012_53; 02tqkf; 0glmv; 03gkn5; 02_p5w; 01gy7r; 039crh; ... >> query: (?x3166, 02hrh1q) <- type_of_union(?x3166, ?x566), profession(?x3166, ?x1183), languages(?x3166, ?x254) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qdyf profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 119.000 0.808 http://example.org/people/person/profession #3745-03wbqc4 PRED entity: 03wbqc4 PRED relation: language PRED expected values: 02h40lc => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 37): 02h40lc (0.92 #535, 0.91 #594, 0.90 #475), 064_8sq (0.16 #614, 0.16 #318, 0.15 #910), 03_9r (0.14 #128, 0.14 #69, 0.14 #10), 06nm1 (0.14 #129, 0.14 #11, 0.13 #366), 06b_j (0.14 #141, 0.14 #23, 0.10 #260), 04306rv (0.11 #1605, 0.11 #1725, 0.11 #301), 02bjrlw (0.09 #712, 0.08 #1601, 0.08 #1721), 0jzc (0.05 #612, 0.05 #731, 0.05 #1383), 012w70 (0.04 #605, 0.04 #368, 0.03 #1376), 0653m (0.04 #604, 0.04 #1137, 0.04 #1494) >> Best rule #535 for best value: >> intensional similarity = 3 >> extensional distance = 198 >> proper extension: 0bhwhj; >> query: (?x4361, 02h40lc) <- genre(?x4361, ?x225), produced_by(?x4361, ?x5647), crewmember(?x4361, ?x1983) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03wbqc4 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.915 http://example.org/film/film/language #3744-05fgt1 PRED entity: 05fgt1 PRED relation: genre PRED expected values: 0vgkd => 93 concepts (37 used for prediction) PRED predicted values (max 10 best out of 97): 01z4y (0.58 #3197, 0.53 #3671, 0.53 #2012), 02kdv5l (0.42 #2962, 0.38 #475, 0.35 #1185), 01jfsb (0.36 #2496, 0.36 #484, 0.34 #958), 03k9fj (0.34 #1193, 0.29 #1311, 0.27 #483), 0lsxr (0.27 #7, 0.25 #480, 0.25 #244), 082gq (0.27 #29, 0.24 #3344, 0.15 #384), 01hmnh (0.26 #1199, 0.21 #1317, 0.19 #1081), 03bxz7 (0.22 #171, 0.12 #644, 0.09 #2657), 04xvlr (0.21 #3079, 0.20 #356, 0.20 #119), 060__y (0.20 #15, 0.19 #370, 0.18 #2619) >> Best rule #3197 for best value: >> intensional similarity = 4 >> extensional distance = 641 >> proper extension: 05jyb2; >> query: (?x2481, ?x2480) <- film_release_distribution_medium(?x2481, ?x81), titles(?x2480, ?x2481), genre(?x2481, ?x53), ?x53 = 07s9rl0 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 09xbpt; 03s6l2; 0fh694; 01hvjx; 078sj4; 01bb9r; 011ysn; 0bmhvpr; 06_x996; 01hqk; ... *> query: (?x2481, 0vgkd) <- film(?x1104, ?x2481), film(?x286, ?x2481), genre(?x2481, ?x53), film_crew_role(?x2481, ?x137), ?x286 = 014zcr *> conf = 0.13 ranks of expected_values: 17 EVAL 05fgt1 genre 0vgkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 93.000 37.000 0.578 http://example.org/film/film/genre #3743-06rq2l PRED entity: 06rq2l PRED relation: award_nominee PRED expected values: 017s11 => 96 concepts (35 used for prediction) PRED predicted values (max 10 best out of 918): 017s11 (0.09 #32793, 0.06 #37478, 0.04 #35242), 06rq1k (0.09 #32793), 054lpb6 (0.09 #32793), 04t2l2 (0.08 #2382, 0.05 #16435, 0.05 #14093), 02p65p (0.07 #51560, 0.05 #56244, 0.05 #72640), 086k8 (0.07 #30512, 0.06 #21142, 0.06 #35198), 06dv3 (0.06 #51576, 0.04 #72656, 0.03 #53918), 051wwp (0.06 #52703, 0.04 #55045, 0.03 #73783), 02qgqt (0.06 #56237, 0.05 #51553, 0.04 #53895), 06q8hf (0.06 #1660, 0.03 #6344, 0.03 #4002) >> Best rule #32793 for best value: >> intensional similarity = 3 >> extensional distance = 258 >> proper extension: 030pr; 043q6n_; >> query: (?x9204, ?x541) <- produced_by(?x10395, ?x9204), production_companies(?x10395, ?x541), titles(?x2480, ?x10395) >> conf = 0.09 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 06rq2l award_nominee 017s11 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 35.000 0.091 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3742-0121h7 PRED entity: 0121h7 PRED relation: adjoins! PRED expected values: 0hkq4 => 133 concepts (29 used for prediction) PRED predicted values (max 10 best out of 215): 0clzr (0.20 #2726, 0.20 #371, 0.18 #1941), 0clz7 (0.20 #164, 0.17 #949, 0.12 #1734), 01fj9_ (0.12 #2031, 0.10 #2816, 0.08 #3601), 01279v (0.12 #2330, 0.10 #3115, 0.08 #3900), 0hkq4 (0.10 #2455, 0.08 #3240, 0.07 #11003), 0jtf1 (0.10 #2727, 0.08 #3512, 0.07 #11003), 0m_w6 (0.08 #3889, 0.07 #18874, 0.07 #11003), 0m__z (0.07 #18874, 0.07 #11003, 0.06 #2269), 01qs54 (0.07 #18874, 0.04 #8644, 0.04 #4711), 0121h7 (0.07 #18874, 0.04 #8644, 0.04 #4711) >> Best rule #2726 for best value: >> intensional similarity = 2 >> extensional distance = 18 >> proper extension: 01xd9; 02cft; 01279v; >> query: (?x9635, 0clzr) <- country(?x9635, ?x429), ?x429 = 03rt9 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2455 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 18 *> proper extension: 01xd9; 02cft; 01279v; *> query: (?x9635, 0hkq4) <- country(?x9635, ?x429), ?x429 = 03rt9 *> conf = 0.10 ranks of expected_values: 5 EVAL 0121h7 adjoins! 0hkq4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 133.000 29.000 0.200 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #3741-0gl5_ PRED entity: 0gl5_ PRED relation: student PRED expected values: 030znt 073bb 044n3h => 77 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1314): 03rx9 (0.15 #14522, 0.11 #10373, 0.08 #20746), 0cbgl (0.11 #6217, 0.09 #2068, 0.03 #4143), 0ff3y (0.10 #8274, 0.08 #10349, 0.06 #14498), 06jkm (0.10 #3968, 0.05 #1893, 0.03 #14340), 0d3k14 (0.09 #1837, 0.08 #5986, 0.06 #14284), 0hnjt (0.09 #819, 0.05 #4968, 0.03 #13266), 0crqcc (0.09 #1213, 0.04 #9511, 0.03 #13660), 0683n (0.09 #1449, 0.03 #13896, 0.03 #5598), 01ry0f (0.08 #4972, 0.03 #7046, 0.03 #42314), 03_nq (0.06 #3627, 0.05 #1552, 0.04 #13999) >> Best rule #14522 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 0194_r; >> query: (?x6912, ?x9738) <- student(?x6912, ?x1564), location(?x1564, ?x2850), company(?x9738, ?x6912) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #12733 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 67 *> proper extension: 0194_r; *> query: (?x6912, 073bb) <- student(?x6912, ?x1564), location(?x1564, ?x2850), company(?x9738, ?x6912) *> conf = 0.01 ranks of expected_values: 1068 EVAL 0gl5_ student 044n3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 32.000 0.151 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0gl5_ student 073bb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 77.000 32.000 0.151 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0gl5_ student 030znt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 32.000 0.151 http://example.org/education/educational_institution/students_graduates./education/education/student #3740-048xh PRED entity: 048xh PRED relation: group! PRED expected values: 0l14md 02hnl => 105 concepts (71 used for prediction) PRED predicted values (max 10 best out of 115): 02hnl (0.92 #977, 0.83 #896, 0.80 #737), 0l14md (0.68 #796, 0.62 #875, 0.60 #2710), 05r5c (0.57 #321, 0.37 #797, 0.33 #717), 03qjg (0.43 #357, 0.33 #753, 0.33 #120), 01wy6 (0.43 #352, 0.33 #115, 0.13 #748), 07_l6 (0.43 #369, 0.20 #765, 0.16 #845), 028tv0 (0.43 #2716, 0.37 #3192, 0.37 #3432), 01vj9c (0.36 #963, 0.33 #882, 0.33 #90), 0mkg (0.33 #720, 0.29 #324, 0.26 #800), 04rzd (0.33 #740, 0.29 #344, 0.26 #820) >> Best rule #977 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 04rcr; 07c0j; 04r1t; 01czx; 07yg2; 0d193h; 05xq9; 0134tg; 07mvp; 01kcms4; ... >> query: (?x7476, 02hnl) <- group(?x4769, ?x7476), artist(?x382, ?x7476), influenced_by(?x4620, ?x7476), role(?x4769, ?x2888), ?x2888 = 02fsn >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 048xh group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 71.000 0.920 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 048xh group! 0l14md CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 71.000 0.920 http://example.org/music/performance_role/regular_performances./music/group_membership/group #3739-0pd6l PRED entity: 0pd6l PRED relation: country PRED expected values: 09c7w0 07ssc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 130): 09c7w0 (0.79 #1544, 0.77 #799, 0.77 #1237), 07ssc (0.47 #429, 0.31 #446, 0.29 #323), 0f8l9c (0.24 #1298, 0.12 #20, 0.12 #203), 0ctw_b (0.24 #1298, 0.07 #85, 0.06 #146), 0chghy (0.24 #1298, 0.05 #442, 0.05 #13), 0345h (0.12 #457, 0.12 #334, 0.10 #272), 03_3d (0.11 #621, 0.04 #552, 0.04 #4327), 04xvlr (0.07 #1852, 0.07 #1851, 0.06 #1666), 07s9rl0 (0.07 #1852, 0.07 #1851, 0.06 #1666), 03rjj (0.07 #68, 0.06 #129, 0.05 #251) >> Best rule #1544 for best value: >> intensional similarity = 4 >> extensional distance = 612 >> proper extension: 047svrl; >> query: (?x3992, 09c7w0) <- film(?x269, ?x3992), music(?x3992, ?x6131), titles(?x53, ?x3992), nominated_for(?x4561, ?x3992) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0pd6l country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.792 http://example.org/film/film/country EVAL 0pd6l country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.792 http://example.org/film/film/country #3738-01wy6 PRED entity: 01wy6 PRED relation: role PRED expected values: 042v_gx => 89 concepts (58 used for prediction) PRED predicted values (max 10 best out of 103): 03qjg (0.84 #5682, 0.83 #2390, 0.83 #1972), 013y1f (0.83 #2941, 0.83 #2390, 0.83 #1972), 0l14qv (0.83 #2390, 0.83 #1972, 0.83 #5312), 018vs (0.83 #2390, 0.83 #1972, 0.83 #5312), 03bx0bm (0.83 #2390, 0.83 #1972, 0.83 #5312), 07gql (0.83 #2390, 0.83 #1972, 0.83 #5312), 03t22m (0.83 #2390, 0.83 #1972, 0.83 #5312), 05r5c (0.83 #2390, 0.83 #1972, 0.83 #5312), 0dwt5 (0.83 #2390, 0.83 #1972, 0.83 #5312), 0gkd1 (0.83 #2390, 0.83 #1972, 0.83 #5312) >> Best rule #5682 for best value: >> intensional similarity = 18 >> extensional distance = 29 >> proper extension: 050rj; >> query: (?x2460, 03qjg) <- role(?x2460, ?x6039), role(?x2460, ?x1166), role(?x2460, ?x75), role(?x2048, ?x2460), role(?x228, ?x2048), instrumentalists(?x2048, ?x3867), instrumentalists(?x2048, ?x2799), ?x2799 = 01vsl3_, role(?x1524, ?x2460), role(?x8599, ?x2048), ?x8599 = 01nkxvx, role(?x1466, ?x2460), ?x3867 = 0bkg4, ?x1166 = 05148p4, instrumentalists(?x6039, ?x3030), role(?x2048, ?x780), role(?x2662, ?x6039), ?x75 = 07y_7 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1240 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 3 *> proper extension: 0l14j_; *> query: (?x2460, ?x2764) <- role(?x2460, ?x3239), role(?x2460, ?x1472), role(?x2460, ?x885), role(?x3156, ?x2460), role(?x2764, ?x2460), role(?x2048, ?x2460), role(?x1495, ?x2460), ?x2048 = 018j2, ?x1472 = 0319l, ?x1495 = 013y1f, role(?x2764, ?x228), role(?x1574, ?x2764), instrumentalists(?x2460, ?x680), ?x3239 = 03qmg1, role(?x1413, ?x2764), ?x1574 = 0l15bq, ?x1413 = 01p9hgt, ?x885 = 0dwtp, instrumentalists(?x3156, ?x10802) *> conf = 0.80 ranks of expected_values: 14 EVAL 01wy6 role 042v_gx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 89.000 58.000 0.839 http://example.org/music/performance_role/regular_performances./music/group_membership/role #3737-016dj8 PRED entity: 016dj8 PRED relation: film! PRED expected values: 017s11 => 77 concepts (47 used for prediction) PRED predicted values (max 10 best out of 51): 03rwz3 (0.54 #452, 0.53 #530, 0.15 #451), 030_1_ (0.50 #1359), 086k8 (0.33 #77, 0.29 #152, 0.18 #227), 030_1m (0.33 #89, 0.20 #14, 0.14 #164), 024rgt (0.33 #95, 0.14 #170, 0.07 #627), 03xq0f (0.20 #5, 0.19 #305, 0.18 #912), 016tt2 (0.20 #4, 0.17 #79, 0.16 #229), 05qd_ (0.20 #9, 0.14 #309, 0.14 #992), 06jntd (0.20 #31, 0.04 #1239, 0.04 #713), 017s11 (0.17 #78, 0.14 #1663, 0.13 #610) >> Best rule #452 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 02kk_c; 0qmk5; >> query: (?x6306, ?x7526) <- award_winner(?x6306, ?x7526), nominated_for(?x1533, ?x6306), production_companies(?x5157, ?x7526) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #78 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 035s95; *> query: (?x6306, 017s11) <- film(?x10050, ?x6306), ?x10050 = 01hmb_, film_crew_role(?x6306, ?x137), genre(?x6306, ?x225) *> conf = 0.17 ranks of expected_values: 10 EVAL 016dj8 film! 017s11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 77.000 47.000 0.540 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #3736-0gg8l PRED entity: 0gg8l PRED relation: parent_genre PRED expected values: 050g1v => 58 concepts (42 used for prediction) PRED predicted values (max 10 best out of 265): 06by7 (0.92 #2270, 0.82 #2109, 0.69 #2757), 03lty (0.39 #2921, 0.30 #2434, 0.19 #1304), 01m1y (0.33 #439, 0.25 #600, 0.17 #921), 02w4v (0.25 #673, 0.17 #833, 0.12 #1154), 05r9t (0.25 #707, 0.12 #1027, 0.06 #1188), 017371 (0.25 #748, 0.06 #2359, 0.06 #1229), 05r6t (0.24 #2795, 0.24 #2469, 0.24 #2147), 06j6l (0.16 #2774, 0.08 #5333, 0.07 #3421), 016clz (0.15 #6465, 0.12 #6790, 0.12 #2098), 01243b (0.14 #2121, 0.14 #2443, 0.13 #2769) >> Best rule #2270 for best value: >> intensional similarity = 7 >> extensional distance = 62 >> proper extension: 028cl7; 017ht; >> query: (?x8798, 06by7) <- parent_genre(?x8798, ?x2664), artists(?x2664, ?x4200), artists(?x2664, ?x2124), artists(?x2664, ?x1800), ?x4200 = 025ldg, ?x1800 = 015_30, ?x2124 = 01kv4mb >> conf = 0.92 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gg8l parent_genre 050g1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 58.000 42.000 0.922 http://example.org/music/genre/parent_genre #3735-02583l PRED entity: 02583l PRED relation: contains! PRED expected values: 0d060g => 212 concepts (132 used for prediction) PRED predicted values (max 10 best out of 285): 0d060g (0.93 #19693, 0.92 #7168, 0.86 #62614), 09c7w0 (0.75 #56355, 0.73 #67092, 0.71 #84085), 02jx1 (0.59 #77910, 0.53 #20662, 0.52 #21556), 07ssc (0.52 #108271, 0.40 #77855, 0.33 #96642), 059rby (0.46 #50110, 0.46 #51901, 0.36 #71581), 04jpl (0.28 #38485, 0.27 #39379, 0.27 #40274), 02_286 (0.28 #28670, 0.18 #38506, 0.17 #39400), 05qtj (0.22 #9225, 0.14 #12804, 0.14 #13699), 081yw (0.21 #28903, 0.09 #47683, 0.09 #49472), 0978r (0.19 #16308, 0.14 #20780, 0.14 #21674) >> Best rule #19693 for best value: >> intensional similarity = 5 >> extensional distance = 57 >> proper extension: 0j8p6; 01fd26; 0pml7; 04_lb; 0154fs; 01gbzb; 017j7y; >> query: (?x1306, 0d060g) <- category(?x1306, ?x134), contains(?x1905, ?x1306), contains(?x1905, ?x14016), ?x14016 = 018gmr, religion(?x1905, ?x109) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02583l contains! 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 132.000 0.932 http://example.org/location/location/contains #3734-0j1z8 PRED entity: 0j1z8 PRED relation: contains PRED expected values: 0j1z8 => 110 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2554): 0164b (0.25 #4495, 0.23 #10385, 0.20 #1550), 01p8s (0.25 #4403, 0.23 #10293, 0.20 #1458), 05c74 (0.25 #4017, 0.23 #9907, 0.20 #1072), 0345_ (0.25 #3579, 0.23 #9469, 0.20 #634), 03h2c (0.25 #3410, 0.23 #9300, 0.20 #465), 0b90_r (0.25 #2953, 0.23 #8843, 0.20 #8), 035v3 (0.25 #5127, 0.23 #11017, 0.20 #2182), 0j11 (0.25 #4496, 0.20 #1551, 0.15 #10386), 0d060g (0.25 #2966, 0.20 #21, 0.15 #8856), 05qx1 (0.25 #3110, 0.20 #165, 0.15 #9000) >> Best rule #4495 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 07c5l; >> query: (?x311, 0164b) <- contains(?x311, ?x3838), exported_to(?x252, ?x3838), ?x252 = 03_3d >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #8861 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 02qkt; 04wsz; *> query: (?x311, 0j1z8) <- contains(?x311, ?x3838), exported_to(?x94, ?x3838), location_of_ceremony(?x566, ?x3838) *> conf = 0.15 ranks of expected_values: 100 EVAL 0j1z8 contains 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 110.000 82.000 0.250 http://example.org/location/location/contains #3733-044ptm PRED entity: 044ptm PRED relation: type_of_union PRED expected values: 04ztj => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.90 #21, 0.90 #17, 0.75 #81), 01g63y (0.13 #170, 0.13 #198, 0.12 #246) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 03z_g7; >> query: (?x13874, 04ztj) <- profession(?x13874, ?x1032), award(?x13874, ?x1937), ?x1937 = 03r8tl, ?x1032 = 02hrh1q >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044ptm type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.897 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3732-0285c PRED entity: 0285c PRED relation: artists! PRED expected values: 06by7 => 154 concepts (119 used for prediction) PRED predicted values (max 10 best out of 283): 064t9 (0.62 #1833, 0.59 #9724, 0.59 #9421), 06by7 (0.55 #8520, 0.53 #5787, 0.53 #3056), 05bt6j (0.50 #1862, 0.34 #6414, 0.31 #7022), 0glt670 (0.47 #3682, 0.37 #9447, 0.36 #9750), 01_bkd (0.43 #15475, 0.33 #357, 0.11 #16438), 0xv2x (0.43 #15475, 0.33 #148, 0.04 #16533), 0b_6yv (0.43 #15475, 0.21 #35193, 0.21 #11227), 0cx7f (0.43 #15475, 0.16 #2256, 0.16 #5899), 08jyyk (0.43 #15475, 0.16 #2189, 0.15 #13112), 025sc50 (0.39 #3692, 0.33 #9760, 0.33 #9457) >> Best rule #1833 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 01vw87c; 0152cw; 0lk90; 01vrt_c; 0qf3p; 01vsl3_; 01w02sy; 0gy6z9; 01vsy7t; 03j24kf; ... >> query: (?x1955, 064t9) <- artist(?x1954, ?x1955), location_of_ceremony(?x1955, ?x1523), film(?x1955, ?x8495) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #8520 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 100 *> proper extension: 07h5d; *> query: (?x1955, 06by7) <- group(?x1955, ?x1060), nationality(?x1955, ?x94), location(?x1955, ?x5867) *> conf = 0.55 ranks of expected_values: 2 EVAL 0285c artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 154.000 119.000 0.615 http://example.org/music/genre/artists #3731-0nm3n PRED entity: 0nm3n PRED relation: adjoins! PRED expected values: 0nm9h 0n5_t => 125 concepts (46 used for prediction) PRED predicted values (max 10 best out of 539): 0k3k1 (0.50 #2766, 0.33 #1983, 0.25 #3549), 0k3l5 (0.33 #1890, 0.25 #3456, 0.25 #2673), 0n5yv (0.33 #349, 0.25 #3480, 0.24 #31378), 0n5y4 (0.27 #14113, 0.26 #16466, 0.26 #18820), 0n5yh (0.27 #14113, 0.26 #16466, 0.26 #18820), 0nm3n (0.27 #14113, 0.26 #16466, 0.26 #18820), 0n5_t (0.27 #14113, 0.26 #16466, 0.26 #18820), 0nm9h (0.27 #14113, 0.26 #16466, 0.26 #18820), 0k3hn (0.25 #8619, 0.25 #20392, 0.24 #24316), 0k3ll (0.25 #2795, 0.24 #31378, 0.23 #33737) >> Best rule #2766 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0n5yh; >> query: (?x7330, 0k3k1) <- adjoins(?x13066, ?x7330), ?x13066 = 0n5xb, time_zones(?x7330, ?x2674), ?x2674 = 02hcv8, contains(?x7058, ?x7330) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #14113 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 187 *> proper extension: 0n5j_; 0jcgs; 0mx4_; 0mwl2; 0mw93; 0m7fm; 0drsm; 0n5fl; 0mx6c; 0nvrd; ... *> query: (?x7330, ?x12290) <- adjoins(?x13066, ?x7330), adjoins(?x7954, ?x7330), county(?x7600, ?x13066), adjoins(?x12290, ?x7954), contains(?x7058, ?x7330), time_zones(?x7330, ?x2674) *> conf = 0.27 ranks of expected_values: 7, 8 EVAL 0nm3n adjoins! 0n5_t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 125.000 46.000 0.500 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins EVAL 0nm3n adjoins! 0nm9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 125.000 46.000 0.500 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #3730-024lt6 PRED entity: 024lt6 PRED relation: film_festivals PRED expected values: 0kfhjq0 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 17): 0kfhjq0 (0.08 #257, 0.08 #89, 0.08 #131), 04_m9gk (0.08 #13, 0.05 #55, 0.04 #139), 0cmd3zy (0.08 #19, 0.01 #229, 0.01 #61), 0gg7gsl (0.08 #85, 0.08 #127, 0.07 #43), 0j63cyr (0.07 #255, 0.06 #87, 0.06 #297), 0hrcs29 (0.04 #183, 0.04 #288, 0.03 #120), 05f5rsr (0.03 #32, 0.01 #95), 03wf1p2 (0.03 #98, 0.03 #140, 0.03 #182), 0bmj62v (0.03 #117, 0.03 #453, 0.03 #579), 03nn7l2 (0.03 #122, 0.02 #290, 0.02 #332) >> Best rule #257 for best value: >> intensional similarity = 5 >> extensional distance = 163 >> proper extension: 0gx1bnj; 0dtw1x; 053tj7; 04zyhx; 0cz8mkh; 0c8tkt; 06v9_x; 0661m4p; 09g7vfw; 0gtvpkw; ... >> query: (?x9941, 0kfhjq0) <- film_release_region(?x9941, ?x2267), film_release_region(?x9941, ?x512), ?x512 = 07ssc, genre(?x9941, ?x53), ?x2267 = 03rj0 >> conf = 0.08 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024lt6 film_festivals 0kfhjq0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.085 http://example.org/film/film/film_festivals #3729-03spz PRED entity: 03spz PRED relation: contains! PRED expected values: 02qkt => 235 concepts (166 used for prediction) PRED predicted values (max 10 best out of 259): 02qkt (0.69 #3925, 0.64 #58516, 0.64 #43299), 02j71 (0.65 #116383, 0.61 #106527, 0.55 #125341), 0dg3n1 (0.60 #127132, 0.55 #143256, 0.50 #102048), 0604m (0.60 #127132, 0.55 #143256, 0.50 #102048), 09c7w0 (0.55 #85038, 0.53 #143261, 0.51 #81454), 03rk0 (0.45 #40406, 0.29 #88757, 0.28 #52935), 04_1l0v (0.44 #34456, 0.36 #66675, 0.32 #20130), 02j9z (0.42 #2712, 0.39 #45666, 0.35 #35825), 07c5l (0.31 #23656, 0.26 #100651, 0.23 #119466), 07ssc (0.27 #85962, 0.15 #105664, 0.13 #114623) >> Best rule #3925 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 03_3d; 0k6nt; 06bnz; 05b4w; >> query: (?x4743, 02qkt) <- film_release_region(?x9961, ?x4743), film_release_region(?x1259, ?x4743), ?x1259 = 04hwbq, ?x9961 = 0bx_hnp >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03spz contains! 02qkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 235.000 166.000 0.692 http://example.org/location/location/contains #3728-01f_mw PRED entity: 01f_mw PRED relation: child! PRED expected values: 0g1rw => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 49): 086k8 (0.38 #337, 0.17 #420, 0.15 #1013), 049ql1 (0.33 #319, 0.25 #487, 0.25 #152), 0sxdg (0.25 #132, 0.17 #299, 0.12 #384), 016tw3 (0.25 #94, 0.17 #261, 0.08 #429), 0l8sx (0.25 #348, 0.12 #516, 0.11 #1024), 01f_mw (0.20 #214, 0.02 #1646, 0.02 #1729), 03d6fyn (0.17 #448, 0.17 #280, 0.06 #1293), 09b3v (0.15 #1039, 0.15 #1709, 0.12 #531), 016tt2 (0.12 #339, 0.08 #422, 0.06 #1267), 018_q8 (0.12 #544, 0.10 #629, 0.05 #714) >> Best rule #337 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 046b0s; 0c41qv; 05rrtf; >> query: (?x9001, 086k8) <- citytown(?x9001, ?x1523), ?x1523 = 030qb3t, production_companies(?x3433, ?x9001), industry(?x9001, ?x373), music(?x3433, ?x11281) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1771 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 09d5h; 05xbx; *> query: (?x9001, 0g1rw) <- film(?x9001, ?x3433), film(?x4748, ?x3433), genre(?x3433, ?x225), featured_film_locations(?x3433, ?x479) *> conf = 0.02 ranks of expected_values: 36 EVAL 01f_mw child! 0g1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 118.000 118.000 0.375 http://example.org/organization/organization/child./organization/organization_relationship/child #3727-014zfs PRED entity: 014zfs PRED relation: award_winner! PRED expected values: 0bdw6t => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 315): 024dzn (0.68 #1710, 0.68 #1601, 0.65 #2564), 0cjyzs (0.47 #1709, 0.47 #2563, 0.41 #427), 01by1l (0.27 #537, 0.26 #3526, 0.18 #3953), 057xs89 (0.25 #1012, 0.05 #1439, 0.05 #2293), 05zvj3m (0.25 #946, 0.05 #18008, 0.04 #4788), 03x3wf (0.18 #9879, 0.18 #491, 0.18 #3907), 054ks3 (0.18 #566, 0.16 #1421, 0.15 #2275), 01cw51 (0.18 #563, 0.13 #3552, 0.07 #5687), 02f71y (0.18 #604, 0.10 #3593, 0.09 #4020), 01cky2 (0.18 #615, 0.10 #3604, 0.07 #4031) >> Best rule #1710 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 09fb5; 01sbf2; 02fgpf; 016gkf; 01wd9lv; 0bvzp; 01x209s; 0kftt; 02fgp0; 01d4cb; ... >> query: (?x1145, ?x9372) <- award(?x1145, ?x9372), award_winner(?x3846, ?x1145), ?x9372 = 024dzn >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #3951 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 01d6jf; *> query: (?x1145, 0bdw6t) <- inductee(?x9953, ?x1145), type_of_union(?x1145, ?x566), award_winner(?x11007, ?x1145) *> conf = 0.07 ranks of expected_values: 104 EVAL 014zfs award_winner! 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 117.000 117.000 0.684 http://example.org/award/award_category/winners./award/award_honor/award_winner #3726-0fn7r PRED entity: 0fn7r PRED relation: category PRED expected values: 08mbj5d => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.73 #58, 0.72 #69, 0.72 #62) >> Best rule #58 for best value: >> intensional similarity = 5 >> extensional distance = 142 >> proper extension: 01n4nd; 0tk02; 0qplq; 017j7y; >> query: (?x11762, 08mbj5d) <- citytown(?x11761, ?x11762), school_type(?x11761, ?x12633), contains(?x8420, ?x11762), colors(?x11761, ?x332), country(?x1352, ?x8420) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fn7r category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.729 http://example.org/common/topic/webpage./common/webpage/category #3725-0bxl5 PRED entity: 0bxl5 PRED relation: role PRED expected values: 02fsn => 74 concepts (56 used for prediction) PRED predicted values (max 10 best out of 78): 0mkg (0.84 #1192, 0.84 #146, 0.82 #1191), 02fsn (0.84 #1192, 0.84 #146, 0.82 #1191), 03gvt (0.84 #1192, 0.84 #146, 0.82 #1191), 03q5t (0.84 #1192, 0.84 #146, 0.82 #1191), 07c6l (0.84 #1192, 0.84 #146, 0.82 #1191), 02pprs (0.84 #1192, 0.84 #146, 0.82 #1191), 01p970 (0.84 #146, 0.82 #1191, 0.82 #2172), 01679d (0.84 #146, 0.82 #1191, 0.82 #2172), 02dlh2 (0.81 #1190, 0.78 #1313, 0.76 #2171), 01qbl (0.81 #1190, 0.78 #1279, 0.76 #2171) >> Best rule #1192 for best value: >> intensional similarity = 16 >> extensional distance = 6 >> proper extension: 0dwsp; >> query: (?x3215, ?x569) <- role(?x3215, ?x2059), role(?x3215, ?x1147), ?x2059 = 0dwr4, role(?x7210, ?x3215), role(?x2908, ?x3215), role(?x2187, ?x3215), award_nominee(?x565, ?x7210), role(?x569, ?x3215), group(?x569, ?x1751), artists(?x1000, ?x2908), instrumentalists(?x1147, ?x2242), origin(?x7210, ?x8602), role(?x645, ?x569), ?x2187 = 01vsnff, instrumentalists(?x569, ?x642), role(?x3410, ?x569) >> conf = 0.84 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0bxl5 role 02fsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 56.000 0.840 http://example.org/music/performance_role/track_performances./music/track_contribution/role #3724-01mqz0 PRED entity: 01mqz0 PRED relation: people! PRED expected values: 013b6_ => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 45): 0x67 (0.18 #5184, 0.18 #5784, 0.17 #5484), 033tf_ (0.17 #2856, 0.17 #2181, 0.15 #3456), 02w7gg (0.15 #452, 0.12 #4577, 0.11 #3452), 07bch9 (0.15 #97, 0.10 #172, 0.07 #2872), 065b6q (0.15 #78, 0.05 #2178, 0.05 #1278), 09kr66 (0.14 #341, 0.03 #1316, 0.03 #1166), 022dp5 (0.11 #348, 0.05 #123, 0.03 #198), 01qhm_ (0.10 #80, 0.07 #230, 0.06 #3455), 0xnvg (0.10 #3462, 0.09 #2862, 0.09 #4587), 07hwkr (0.09 #1286, 0.08 #2186, 0.07 #2861) >> Best rule #5184 for best value: >> intensional similarity = 2 >> extensional distance = 1050 >> proper extension: 07h1q; >> query: (?x1607, 0x67) <- people(?x1050, ?x1607), place_of_birth(?x1607, ?x3014) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #351 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 42 *> proper extension: 01l4zqz; 03jldb; 05jjl; 03rx9; *> query: (?x1607, 013b6_) <- award(?x1607, ?x686), people(?x9428, ?x1607), ?x9428 = 048z7l *> conf = 0.09 ranks of expected_values: 11 EVAL 01mqz0 people! 013b6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 119.000 119.000 0.180 http://example.org/people/ethnicity/people #3723-0f7hw PRED entity: 0f7hw PRED relation: genre PRED expected values: 02l7c8 03p5xs => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 114): 02l7c8 (0.82 #135, 0.54 #736, 0.42 #1097), 01z4y (0.61 #2644, 0.55 #3606, 0.53 #601), 01jfsb (0.33 #11, 0.29 #612, 0.28 #5298), 02n4kr (0.33 #6, 0.16 #1088, 0.12 #246), 0219x_ (0.33 #26, 0.10 #146, 0.09 #5072), 02js9 (0.33 #72, 0.04 #7210, 0.01 #1154), 0glj9q (0.33 #27, 0.04 #7210), 02kdv5l (0.30 #242, 0.28 #603, 0.28 #362), 03k9fj (0.28 #250, 0.27 #370, 0.22 #611), 01hmnh (0.23 #1099, 0.18 #377, 0.16 #618) >> Best rule #135 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 02n9bh; >> query: (?x9424, 02l7c8) <- genre(?x9424, ?x239), ?x239 = 06cvj, film_release_distribution_medium(?x9424, ?x81), country(?x9424, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 44 EVAL 0f7hw genre 03p5xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 61.000 61.000 0.817 http://example.org/film/film/genre EVAL 0f7hw genre 02l7c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.817 http://example.org/film/film/genre #3722-06dv3 PRED entity: 06dv3 PRED relation: award PRED expected values: 09sb52 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 283): 09sb52 (0.89 #840, 0.57 #440, 0.50 #40), 027986c (0.73 #5205, 0.72 #2801, 0.72 #4804), 027b9j5 (0.73 #5205, 0.72 #2801, 0.72 #4804), 05pcn59 (0.33 #81, 0.31 #2481, 0.30 #3683), 0gqy2 (0.33 #163, 0.16 #963, 0.14 #25216), 057xs89 (0.33 #159, 0.14 #25216, 0.13 #559), 0gr51 (0.33 #100, 0.14 #25216, 0.13 #12508), 02x73k6 (0.33 #60, 0.14 #25216, 0.13 #34025), 02x4w6g (0.33 #114, 0.13 #514, 0.13 #34025), 0gq9h (0.32 #12485, 0.17 #77, 0.14 #25216) >> Best rule #840 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 0bgrsl; >> query: (?x262, 09sb52) <- award_nominee(?x262, ?x525), award_nominee(?x2033, ?x262), ?x525 = 017149 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06dv3 award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.892 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3721-0q74c PRED entity: 0q74c PRED relation: contains! PRED expected values: 0gyh => 83 concepts (30 used for prediction) PRED predicted values (max 10 best out of 110): 01n7q (0.23 #9030, 0.15 #6342, 0.15 #7238), 07b_l (0.18 #2011, 0.14 #2906, 0.12 #1116), 04_1l0v (0.14 #450, 0.12 #1345, 0.09 #7611), 0gyh (0.12 #174, 0.07 #1964, 0.06 #1069), 0d060g (0.09 #9862, 0.08 #10759, 0.07 #11655), 07ssc (0.08 #10778, 0.07 #14363, 0.07 #15259), 059rby (0.07 #21517, 0.07 #24204, 0.07 #25995), 0824r (0.07 #2936, 0.07 #2041, 0.06 #1146), 04tgp (0.07 #279, 0.06 #2069, 0.05 #1174), 02qkt (0.07 #11989) >> Best rule #9030 for best value: >> intensional similarity = 4 >> extensional distance = 457 >> proper extension: 0l30v; 02hyt; 0r3w7; 0l2nd; >> query: (?x10776, 01n7q) <- time_zones(?x10776, ?x1638), contains(?x94, ?x10776), contains(?x94, ?x2949), ?x2949 = 0kpys >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #174 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 95 *> proper extension: 04rrx; 0498y; 0824r; 04yf_; 0kwmc; 0135p7; *> query: (?x10776, 0gyh) <- time_zones(?x10776, ?x1638), contains(?x94, ?x10776), ?x94 = 09c7w0, ?x1638 = 02fqwt *> conf = 0.12 ranks of expected_values: 4 EVAL 0q74c contains! 0gyh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 30.000 0.229 http://example.org/location/location/contains #3720-01540 PRED entity: 01540 PRED relation: major_field_of_study! PRED expected values: 08815 04wlz2 04rwx 07tg4 017j69 07vjm 01bm_ 06fq2 02mzg9 => 53 concepts (34 used for prediction) PRED predicted values (max 10 best out of 590): 01w5m (0.80 #5914, 0.75 #7499, 0.75 #6971), 08815 (0.80 #5816, 0.75 #7401, 0.75 #6873), 01j_cy (0.75 #7436, 0.75 #6908, 0.50 #9557), 01j_9c (0.75 #4239, 0.67 #7938, 0.62 #9000), 07w0v (0.67 #7946, 0.62 #4247, 0.62 #9008), 07wrz (0.67 #4813, 0.60 #5869, 0.56 #5341), 017j69 (0.67 #5420, 0.54 #8592, 0.50 #4363), 0g8rj (0.67 #4927, 0.50 #4398, 0.50 #3869), 01bm_ (0.64 #6575, 0.58 #7632, 0.58 #7104), 07wjk (0.58 #7455, 0.55 #6398, 0.50 #6927) >> Best rule #5914 for best value: >> intensional similarity = 12 >> extensional distance = 8 >> proper extension: 03nfmq; >> query: (?x6870, 01w5m) <- major_field_of_study(?x9803, ?x6870), major_field_of_study(?x6315, ?x6870), major_field_of_study(?x6271, ?x6870), major_field_of_study(?x4672, ?x6870), major_field_of_study(?x3948, ?x6870), currency(?x9803, ?x170), ?x3948 = 025v3k, service_location(?x6315, ?x551), ?x4672 = 07tds, student(?x6315, ?x1400), school_type(?x9803, ?x1044), contains(?x94, ?x6271) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5816 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 8 *> proper extension: 03nfmq; *> query: (?x6870, 08815) <- major_field_of_study(?x9803, ?x6870), major_field_of_study(?x6315, ?x6870), major_field_of_study(?x6271, ?x6870), major_field_of_study(?x4672, ?x6870), major_field_of_study(?x3948, ?x6870), currency(?x9803, ?x170), ?x3948 = 025v3k, service_location(?x6315, ?x551), ?x4672 = 07tds, student(?x6315, ?x1400), school_type(?x9803, ?x1044), contains(?x94, ?x6271) *> conf = 0.80 ranks of expected_values: 2, 7, 9, 12, 17, 26, 55, 152, 209 EVAL 01540 major_field_of_study! 02mzg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 53.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01540 major_field_of_study! 06fq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 53.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01540 major_field_of_study! 01bm_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 53.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01540 major_field_of_study! 07vjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 53.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01540 major_field_of_study! 017j69 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 53.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01540 major_field_of_study! 07tg4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 53.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01540 major_field_of_study! 04rwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 53.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01540 major_field_of_study! 04wlz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01540 major_field_of_study! 08815 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 53.000 34.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3719-027nb PRED entity: 027nb PRED relation: member_states! PRED expected values: 085h1 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 13): 085h1 (0.75 #169, 0.74 #48, 0.74 #151), 059dn (0.33 #12, 0.30 #16, 0.27 #20), 018cqq (0.33 #10, 0.30 #14, 0.27 #18), 02jxk (0.25 #51, 0.24 #80, 0.20 #116), 07t65 (0.09 #84, 0.08 #221, 0.08 #176), 02vk52z (0.09 #84, 0.08 #221, 0.08 #176), 041288 (0.09 #84, 0.08 #221, 0.08 #176), 0j7v_ (0.09 #84, 0.08 #221, 0.08 #176), 0b6css (0.07 #374, 0.07 #373, 0.07 #383), 0gkjy (0.07 #374, 0.07 #373, 0.07 #383) >> Best rule #169 for best value: >> intensional similarity = 6 >> extensional distance = 90 >> proper extension: 05v10; 05c74; 07fb6; 02k1b; 0164b; >> query: (?x183, 085h1) <- organization(?x183, ?x127), administrative_area_type(?x183, ?x2792), currency(?x183, ?x170), official_language(?x183, ?x254), ?x127 = 02vk52z, country(?x1121, ?x183) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027nb member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.750 http://example.org/user/ktrueman/default_domain/international_organization/member_states #3718-0bx6zs PRED entity: 0bx6zs PRED relation: honored_for PRED expected values: 01q_y0 01rp13 => 24 concepts (17 used for prediction) PRED predicted values (max 10 best out of 739): 01q_y0 (0.67 #2369, 0.19 #5929, 0.16 #4880), 01j7mr (0.60 #2582, 0.60 #1990, 0.55 #3174), 07zhjj (0.60 #2273, 0.40 #2865, 0.36 #3457), 06mr2s (0.60 #2061, 0.40 #2653, 0.36 #3245), 01b7h8 (0.60 #2309, 0.40 #2901, 0.36 #3493), 04xbq3 (0.60 #2288, 0.40 #2880, 0.36 #3472), 0d68qy (0.60 #1926, 0.38 #3703, 0.36 #4299), 02gl58 (0.60 #2326, 0.33 #1140, 0.30 #2918), 027tbrc (0.60 #1923, 0.33 #737, 0.30 #2515), 07s8z_l (0.50 #2922, 0.45 #3514, 0.40 #2330) >> Best rule #2369 for best value: >> intensional similarity = 22 >> extensional distance = 3 >> proper extension: 05c1t6z; 02q690_; >> query: (?x9450, ?x2293) <- award_winner(?x9450, ?x9815), award_winner(?x9450, ?x7684), award_winner(?x9450, ?x5586), award_winner(?x9450, ?x3751), award_winner(?x9450, ?x1422), ceremony(?x7510, ?x9450), ceremony(?x5235, ?x9450), ceremony(?x2192, ?x9450), ceremony(?x2041, ?x9450), ?x2192 = 0bfvd4, ?x7510 = 027gs1_, instance_of_recurring_event(?x9450, ?x2758), award(?x3751, ?x384), ?x2041 = 0bdx29, ?x5235 = 09qrn4, ?x1422 = 0p_2r, honored_for(?x9450, ?x337), award_nominee(?x3751, ?x1039), actor(?x2293, ?x9815), award_winner(?x594, ?x7684), currency(?x9815, ?x170), profession(?x5586, ?x1032) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11 EVAL 0bx6zs honored_for 01rp13 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 24.000 17.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0bx6zs honored_for 01q_y0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 17.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3717-017vkx PRED entity: 017vkx PRED relation: award_winner! PRED expected values: 01mhwk => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 101): 07y9ts (0.33 #209, 0.17 #491, 0.08 #632), 07y_p6 (0.33 #239, 0.17 #521, 0.08 #662), 013b2h (0.33 #80, 0.16 #3182, 0.15 #2336), 01c6qp (0.33 #19, 0.13 #3121, 0.10 #3685), 01bx35 (0.33 #7, 0.11 #2968, 0.11 #3673), 01s695 (0.33 #3, 0.10 #4233, 0.08 #5361), 05pd94v (0.25 #707, 0.23 #989, 0.18 #1130), 0g5b0q5 (0.17 #443, 0.17 #302, 0.08 #584), 0g55tzk (0.17 #560, 0.17 #419, 0.08 #701), 0275n3y (0.17 #498, 0.17 #357, 0.08 #639) >> Best rule #209 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 01rcmg; >> query: (?x3856, 07y9ts) <- gender(?x3856, ?x231), ?x231 = 05zppz, award_winner(?x3856, ?x3290), actor(?x10873, ?x3856), place_of_birth(?x3856, ?x12491) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2297 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 108 *> proper extension: 0cj2w; *> query: (?x3856, 01mhwk) <- role(?x3856, ?x227), award_winner(?x3290, ?x3856), award_winner(?x1206, ?x3290), place_of_birth(?x3856, ?x12491) *> conf = 0.11 ranks of expected_values: 19 EVAL 017vkx award_winner! 01mhwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 130.000 130.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3716-01nvmd_ PRED entity: 01nvmd_ PRED relation: award PRED expected values: 0cqhb3 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 237): 0bfvd4 (0.40 #512, 0.38 #912, 0.08 #12912), 09sb52 (0.38 #9639, 0.37 #11239, 0.34 #18840), 0bdwqv (0.34 #570, 0.25 #970, 0.08 #17371), 0cqh46 (0.26 #450, 0.17 #850, 0.05 #11250), 0fbvqf (0.23 #446, 0.17 #846, 0.16 #27202), 0789_m (0.23 #419, 0.14 #819, 0.06 #12819), 0gqy2 (0.20 #962, 0.17 #562, 0.14 #17363), 04ljl_l (0.20 #403, 0.17 #803, 0.06 #11603), 05b4l5x (0.19 #6, 0.07 #36004, 0.06 #4006), 07cbcy (0.19 #77, 0.07 #36004, 0.05 #7277) >> Best rule #512 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 03pp73; >> query: (?x1129, 0bfvd4) <- people(?x1050, ?x1129), award(?x1129, ?x435), ?x435 = 0bp_b2 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #14001 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 889 *> proper extension: 01qkqwg; 01vw917; 013ybx; *> query: (?x1129, ?x1670) <- people(?x1050, ?x1129), award_nominee(?x7034, ?x1129), award(?x7034, ?x1670) *> conf = 0.13 ranks of expected_values: 25 EVAL 01nvmd_ award 0cqhb3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 123.000 123.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3715-071x0k PRED entity: 071x0k PRED relation: geographic_distribution PRED expected values: 05v8c 01z215 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 260): 0345h (0.33 #251, 0.29 #72, 0.25 #133), 06bnz (0.31 #315, 0.21 #435, 0.21 #374), 06t2t (0.29 #82, 0.25 #261, 0.25 #143), 03rk0 (0.29 #80, 0.25 #141, 0.18 #200), 06m_5 (0.29 #110, 0.25 #171, 0.18 #230), 07f1x (0.25 #168, 0.17 #286, 0.16 #299), 03spz (0.18 #216, 0.15 #336, 0.14 #456), 0hzlz (0.17 #247, 0.15 #308, 0.14 #68), 07b_l (0.17 #266, 0.14 #447, 0.14 #386), 01n7q (0.17 #250, 0.07 #431, 0.07 #370) >> Best rule #251 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 09vc4s; >> query: (?x1571, 0345h) <- geographic_distribution(?x1571, ?x1023), geographic_distribution(?x1571, ?x311), film_release_region(?x66, ?x1023), featured_film_locations(?x522, ?x1023), location(?x843, ?x1023), jurisdiction_of_office(?x182, ?x1023), adjoins(?x311, ?x1781) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #299 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 10 *> proper extension: 09vc4s; *> query: (?x1571, ?x1781) <- geographic_distribution(?x1571, ?x1023), geographic_distribution(?x1571, ?x311), film_release_region(?x66, ?x1023), featured_film_locations(?x522, ?x1023), location(?x843, ?x1023), jurisdiction_of_office(?x182, ?x1023), adjoins(?x311, ?x1781) *> conf = 0.16 ranks of expected_values: 13, 57 EVAL 071x0k geographic_distribution 01z215 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 33.000 33.000 0.333 http://example.org/people/ethnicity/geographic_distribution EVAL 071x0k geographic_distribution 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 33.000 33.000 0.333 http://example.org/people/ethnicity/geographic_distribution #3714-02r5w9 PRED entity: 02r5w9 PRED relation: profession PRED expected values: 0dgd_ => 112 concepts (27 used for prediction) PRED predicted values (max 10 best out of 49): 03gjzk (0.44 #1913, 0.37 #2207, 0.35 #888), 01c72t (0.25 #21, 0.11 #1775, 0.09 #1628), 02krf9 (0.24 #900, 0.23 #754, 0.23 #1046), 0cbd2 (0.22 #1613, 0.22 #2054, 0.18 #1907), 0kyk (0.17 #2075, 0.15 #1634, 0.11 #2514), 02vxn (0.16 #1901, 0.11 #1607, 0.07 #1754), 018gz8 (0.15 #2209, 0.14 #1915, 0.10 #2501), 09jwl (0.13 #162, 0.11 #2503, 0.10 #2357), 0np9r (0.13 #164, 0.10 #2213, 0.09 #3676), 0dgd_ (0.09 #612, 0.09 #320, 0.08 #466) >> Best rule #1913 for best value: >> intensional similarity = 3 >> extensional distance = 432 >> proper extension: 0bz5v2; 03bx_5q; >> query: (?x1197, 03gjzk) <- award_winner(?x2370, ?x1197), profession(?x1197, ?x987), ?x987 = 0dxtg >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #612 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 05h72z; 024tj; 05tjm3; *> query: (?x1197, 0dgd_) <- award(?x1197, ?x1313), gender(?x1197, ?x231), ?x1313 = 0gs9p *> conf = 0.09 ranks of expected_values: 10 EVAL 02r5w9 profession 0dgd_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 112.000 27.000 0.435 http://example.org/people/person/profession #3713-04d2yp PRED entity: 04d2yp PRED relation: nationality PRED expected values: 09c7w0 => 104 concepts (84 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.85 #201, 0.85 #2107, 0.83 #3622), 0kpys (0.32 #2811, 0.31 #3117, 0.31 #3116), 01n7q (0.32 #2811, 0.31 #3117, 0.31 #3116), 0rh6k (0.26 #3621, 0.25 #3923, 0.24 #6836), 02jx1 (0.14 #534, 0.13 #334, 0.13 #1136), 07ssc (0.13 #316, 0.10 #918, 0.09 #4138), 03rk0 (0.08 #1550, 0.04 #2857, 0.04 #2353), 03rt9 (0.07 #213, 0.03 #4424, 0.01 #4637), 0d060g (0.06 #4631, 0.06 #6039, 0.05 #1110), 0345h (0.04 #1836, 0.04 #332, 0.03 #3044) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 02qjj7; 02w4fkq; >> query: (?x11861, 09c7w0) <- location(?x11861, ?x4151), gender(?x11861, ?x231), ?x4151 = 0r0m6 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04d2yp nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 84.000 0.852 http://example.org/people/person/nationality #3712-05728w1 PRED entity: 05728w1 PRED relation: nationality PRED expected values: 09c7w0 => 103 concepts (101 used for prediction) PRED predicted values (max 10 best out of 85): 09c7w0 (0.73 #3005, 0.73 #901, 0.73 #1601), 0n2q0 (0.33 #9024), 05kkh (0.33 #9024), 0d060g (0.32 #7317, 0.05 #307, 0.05 #407), 03rk0 (0.15 #246, 0.10 #646, 0.05 #9070), 03rjj (0.14 #5, 0.04 #505, 0.03 #805), 02jx1 (0.10 #4641, 0.10 #633, 0.10 #5443), 07ssc (0.10 #715, 0.10 #315, 0.09 #1115), 03gj2 (0.10 #326, 0.09 #426, 0.01 #3407), 03rt9 (0.07 #113, 0.05 #313, 0.05 #413) >> Best rule #3005 for best value: >> intensional similarity = 3 >> extensional distance = 1232 >> proper extension: 07cjqy; 0cj2k3; 01w9k25; >> query: (?x2778, 09c7w0) <- award_nominee(?x10835, ?x2778), award_winner(?x10835, ?x11330), place_of_birth(?x2778, ?x4090) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05728w1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 101.000 0.733 http://example.org/people/person/nationality #3711-03j24kf PRED entity: 03j24kf PRED relation: artist! PRED expected values: 015_1q => 133 concepts (103 used for prediction) PRED predicted values (max 10 best out of 109): 0181dw (0.50 #41, 0.29 #317, 0.20 #455), 01w40h (0.39 #1131, 0.30 #441, 0.25 #27), 0n85g (0.30 #474, 0.25 #60, 0.17 #1164), 01cl2y (0.30 #443, 0.25 #29, 0.14 #305), 015_1q (0.28 #1122, 0.25 #18, 0.24 #3606), 03rhqg (0.25 #15, 0.24 #2361, 0.19 #2085), 01gfq4 (0.25 #21, 0.20 #435, 0.14 #297), 02y21l (0.25 #93, 0.20 #507, 0.14 #369), 03y5g8 (0.25 #106, 0.14 #382, 0.12 #1072), 01q940 (0.25 #50, 0.14 #326, 0.10 #464) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01vsy7t; 03f0fnk; >> query: (?x4701, 0181dw) <- influenced_by(?x4701, ?x2845), location_of_ceremony(?x4701, ?x362), performance_role(?x4701, ?x315) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1122 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 024qwq; 01q3_2; *> query: (?x4701, 015_1q) <- award(?x4701, ?x2322), ?x2322 = 01ck6h, award_winner(?x2799, ?x4701) *> conf = 0.28 ranks of expected_values: 5 EVAL 03j24kf artist! 015_1q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 133.000 103.000 0.500 http://example.org/music/record_label/artist #3710-06b_j PRED entity: 06b_j PRED relation: language! PRED expected values: 0164qt 062zm5h 02dwj 0bcp9b 014knw => 77 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1804): 0f4_2k (0.60 #20812, 0.50 #10876, 0.50 #7564), 034xyf (0.50 #21201, 0.50 #11265, 0.50 #7953), 08gsvw (0.50 #10037, 0.50 #6725, 0.50 #5069), 011yxg (0.50 #9973, 0.50 #6661, 0.50 #5005), 01ffx4 (0.50 #8756, 0.50 #7100, 0.50 #5444), 0gh6j94 (0.50 #21098, 0.50 #11162, 0.50 #6194), 041td_ (0.50 #20884, 0.50 #7636, 0.50 #5980), 08nvyr (0.50 #20579, 0.50 #10643, 0.50 #7331), 024l2y (0.50 #20105, 0.50 #10169, 0.50 #6857), 014kq6 (0.50 #10252, 0.50 #5284, 0.45 #24842) >> Best rule #20812 for best value: >> intensional similarity = 11 >> extensional distance = 8 >> proper extension: 03_9r; 04h9h; >> query: (?x5671, 0f4_2k) <- language(?x9786, ?x5671), language(?x6628, ?x5671), language(?x6533, ?x5671), language(?x1015, ?x5671), film(?x902, ?x9786), genre(?x6533, ?x225), person(?x1015, ?x1620), languages_spoken(?x3584, ?x5671), titles(?x1014, ?x1015), major_field_of_study(?x1681, ?x5671), film_crew_role(?x6628, ?x137) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6393 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 04306rv; *> query: (?x5671, 014knw) <- language(?x9786, ?x5671), language(?x6121, ?x5671), language(?x1956, ?x5671), ?x9786 = 06bc59, countries_spoken_in(?x5671, ?x279), nominated_for(?x198, ?x6121), film_release_region(?x6121, ?x1353), ?x1353 = 035qy, film(?x4731, ?x1956), film_crew_role(?x6121, ?x137) *> conf = 0.50 ranks of expected_values: 251, 336, 564, 986, 1550 EVAL 06b_j language! 014knw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 27.000 0.600 http://example.org/film/film/language EVAL 06b_j language! 0bcp9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 77.000 27.000 0.600 http://example.org/film/film/language EVAL 06b_j language! 02dwj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 77.000 27.000 0.600 http://example.org/film/film/language EVAL 06b_j language! 062zm5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 77.000 27.000 0.600 http://example.org/film/film/language EVAL 06b_j language! 0164qt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 77.000 27.000 0.600 http://example.org/film/film/language #3709-011w20 PRED entity: 011w20 PRED relation: people! PRED expected values: 041rx => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 39): 041rx (0.23 #235, 0.18 #312, 0.17 #543), 033tf_ (0.12 #84, 0.09 #392, 0.09 #1008), 0x67 (0.10 #2398, 0.09 #2244, 0.09 #2937), 07bch9 (0.09 #177, 0.04 #947, 0.04 #793), 03ts0c (0.09 #180, 0.02 #1387, 0.02 #642), 048z7l (0.07 #117, 0.05 #271, 0.05 #425), 013b6_ (0.07 #130, 0.05 #284, 0.03 #669), 0xnvg (0.07 #1322, 0.06 #1091, 0.06 #552), 07hwkr (0.06 #474, 0.06 #628, 0.06 #705), 07mqps (0.05 #96, 0.03 #250, 0.03 #404) >> Best rule #235 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 033hqf; 01kgg9; 0443c; >> query: (?x12152, 041rx) <- location(?x12152, ?x739), ?x739 = 02_286, place_of_death(?x12152, ?x1131), location(?x406, ?x1131) >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011w20 people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.230 http://example.org/people/ethnicity/people #3708-0hqly PRED entity: 0hqly PRED relation: currency PRED expected values: 09nqf => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.49 #34, 0.48 #31, 0.33 #22), 01nv4h (0.03 #14, 0.03 #20, 0.03 #26) >> Best rule #34 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 020ffd; >> query: (?x11019, 09nqf) <- award(?x11019, ?x102), profession(?x11019, ?x319), producer_type(?x11019, ?x632), film(?x11019, ?x6806) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hqly currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.494 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #3707-0fr61 PRED entity: 0fr61 PRED relation: time_zones PRED expected values: 02hcv8 => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.89 #55, 0.87 #106, 0.86 #160), 02lcqs (0.22 #178, 0.22 #205, 0.22 #151), 02fqwt (0.14 #1077, 0.13 #469, 0.13 #720), 02llzg (0.10 #123, 0.09 #258, 0.07 #459), 02hczc (0.10 #148, 0.10 #229, 0.09 #310), 03bdv (0.04 #1121, 0.03 #1135, 0.03 #1214), 03plfd (0.03 #689, 0.03 #847, 0.02 #264), 0gsrz4 (0.02 #845, 0.02 #937, 0.02 #979), 042g7t (0.01 #130, 0.01 #265, 0.01 #848), 052vwh (0.01 #467) >> Best rule #55 for best value: >> intensional similarity = 5 >> extensional distance = 174 >> proper extension: 01tlmw; 0pc7r; 0tz1x; 01m1_t; 0rd5k; 0mzvm; 01zmqw; 0t_gg; 0t_71; 0t_hx; ... >> query: (?x7420, 02hcv8) <- contains(?x1767, ?x7420), currency(?x7420, ?x170), ?x170 = 09nqf, district_represented(?x4787, ?x1767), ?x4787 = 01grpq >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fr61 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.886 http://example.org/location/location/time_zones #3706-01l_pn PRED entity: 01l_pn PRED relation: prequel PRED expected values: 01y9jr => 101 concepts (59 used for prediction) PRED predicted values (max 10 best out of 32): 016dj8 (0.03 #289, 0.03 #470, 0.02 #1015), 02825kb (0.03 #303, 0.01 #484, 0.01 #666), 02gpkt (0.03 #321, 0.01 #502), 02wgk1 (0.03 #264, 0.01 #445), 03r0g9 (0.03 #241, 0.01 #422), 014lc_ (0.03 #182, 0.01 #363), 013q0p (0.02 #998, 0.01 #635), 0164qt (0.01 #375, 0.01 #557), 01d2v1 (0.01 #538), 06r2h (0.01 #522) >> Best rule #289 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0k5g9; 0bl1_; 01mszz; >> query: (?x5608, 016dj8) <- film(?x1206, ?x5608), featured_film_locations(?x5608, ?x6226), nominated_for(?x154, ?x5608), film(?x4832, ?x5608) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01l_pn prequel 01y9jr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 59.000 0.032 http://example.org/film/film/prequel #3705-0kqj1 PRED entity: 0kqj1 PRED relation: student PRED expected values: 09b6zr 05nn4k => 189 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1539): 01my4f (0.40 #5377, 0.29 #11650, 0.25 #3286), 01kb2j (0.25 #2974, 0.20 #5065, 0.14 #11338), 01g257 (0.25 #2331, 0.20 #4422, 0.14 #10695), 0c1j_ (0.25 #3908, 0.20 #5999, 0.14 #12272), 054k_8 (0.25 #3047, 0.20 #5138, 0.14 #11411), 0sx5w (0.25 #3891, 0.20 #5982, 0.14 #12255), 051cc (0.25 #3566, 0.20 #5657, 0.14 #11930), 0hwbd (0.25 #3111, 0.20 #5202, 0.14 #11475), 05zjx (0.25 #3407, 0.20 #5498, 0.14 #11771), 01zz8t (0.25 #4103, 0.20 #6194, 0.14 #12467) >> Best rule #5377 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 04ycjk; >> query: (?x4278, 01my4f) <- citytown(?x4278, ?x3052), ?x3052 = 01cx_, school_type(?x4278, ?x3205), student(?x4278, ?x3291), institution(?x620, ?x4278) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #30084 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 15 *> proper extension: 01ky7c; 0gdm1; *> query: (?x4278, 05nn4k) <- citytown(?x4278, ?x3052), location(?x9289, ?x3052), location(?x8134, ?x3052), ?x8134 = 0kjrx, award_winner(?x2307, ?x9289), contains(?x3052, ?x1151) *> conf = 0.06 ranks of expected_values: 150, 168 EVAL 0kqj1 student 05nn4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 189.000 91.000 0.400 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0kqj1 student 09b6zr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 189.000 91.000 0.400 http://example.org/education/educational_institution/students_graduates./education/education/student #3704-03q3sy PRED entity: 03q3sy PRED relation: gender PRED expected values: 05zppz => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #9, 0.85 #33, 0.84 #21), 02zsn (0.33 #2, 0.31 #48, 0.30 #56) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0969fd; >> query: (?x5944, 05zppz) <- influenced_by(?x5944, ?x986), profession(?x986, ?x353), type_of_appearance(?x986, ?x3429) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q3sy gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.879 http://example.org/people/person/gender #3703-02jxmr PRED entity: 02jxmr PRED relation: category PRED expected values: 08mbj5d => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.79 #21, 0.78 #18, 0.76 #15) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 575 >> proper extension: 089tm; 01pfr3; 01v0sx2; 01wv9xn; 0frsw; 016fmf; 01vrwfv; 02lbrd; 0d193h; 0khth; ... >> query: (?x4428, 08mbj5d) <- award_winner(?x1854, ?x4428), award(?x4428, ?x1079), artists(?x4910, ?x4428) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jxmr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.794 http://example.org/common/topic/webpage./common/webpage/category #3702-017jq PRED entity: 017jq PRED relation: contains PRED expected values: 03jn4 => 109 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2054): 07ssc (0.64 #23560, 0.53 #29450, 0.33 #49), 034cm (0.63 #64797, 0.62 #61850, 0.12 #12426), 017jq (0.51 #64796, 0.51 #64795, 0.47 #26506), 02j9z (0.51 #64796, 0.51 #64795, 0.47 #26506), 02qkt (0.51 #64796, 0.51 #64795, 0.47 #26506), 0285m87 (0.42 #2944, 0.19 #38287, 0.17 #38286), 024pcx (0.42 #2944, 0.19 #38287, 0.17 #38286), 017v_ (0.42 #2944, 0.19 #38287, 0.17 #38286), 0432mrk (0.42 #2944, 0.19 #38287, 0.17 #38286), 040vgd (0.42 #2944, 0.19 #38287, 0.17 #38286) >> Best rule #23560 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 03fb3t; 014wxc; >> query: (?x11138, ?x512) <- contains(?x11138, ?x6371), jurisdiction_of_office(?x182, ?x6371), split_to(?x6371, ?x512), contains(?x512, ?x362) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #52538 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 0nccd; 088cp; *> query: (?x11138, 03jn4) <- contains(?x11138, ?x13274), contains(?x455, ?x13274), time_zones(?x13274, ?x5327), ?x455 = 02j9z *> conf = 0.05 ranks of expected_values: 1681 EVAL 017jq contains 03jn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 109.000 22.000 0.636 http://example.org/location/location/contains #3701-01718w PRED entity: 01718w PRED relation: genre PRED expected values: 03k9fj => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 90): 05p553 (0.56 #6257, 0.54 #244, 0.52 #364), 02l7c8 (0.35 #8072, 0.30 #2778, 0.29 #735), 02kdv5l (0.32 #122, 0.28 #2885, 0.27 #362), 03k9fj (0.26 #131, 0.26 #2894, 0.22 #6505), 060__y (0.20 #6269, 0.16 #1096, 0.15 #136), 04xvlr (0.20 #1443, 0.19 #2043, 0.18 #961), 082gq (0.19 #2072, 0.18 #1110, 0.14 #150), 01hmnh (0.18 #2900, 0.15 #497, 0.15 #6511), 02n4kr (0.14 #1088, 0.12 #3852, 0.12 #4092), 06n90 (0.14 #132, 0.13 #6506, 0.13 #5423) >> Best rule #6257 for best value: >> intensional similarity = 3 >> extensional distance = 1035 >> proper extension: 0vgkd; >> query: (?x8063, 05p553) <- genre(?x8063, ?x604), genre(?x4864, ?x604), ?x4864 = 0qf2t >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #131 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 016ztl; *> query: (?x8063, 03k9fj) <- film_release_distribution_medium(?x8063, ?x81), film(?x382, ?x8063), edited_by(?x8063, ?x323) *> conf = 0.26 ranks of expected_values: 4 EVAL 01718w genre 03k9fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 92.000 92.000 0.562 http://example.org/film/film/genre #3700-02cl1 PRED entity: 02cl1 PRED relation: time_zones PRED expected values: 02hczc => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 13): 02hcv8 (0.62 #237, 0.60 #315, 0.57 #926), 02hczc (0.60 #28, 0.33 #41, 0.16 #340), 02lcqs (0.52 #213, 0.36 #382, 0.35 #200), 02fqwt (0.42 #339, 0.38 #430, 0.30 #456), 02llzg (0.38 #147, 0.31 #160, 0.24 #485), 03bdv (0.20 #136, 0.16 #266, 0.11 #1033), 0d2t4g (0.09 #74, 0.04 #282, 0.03 #399), 042g7t (0.08 #89, 0.03 #466, 0.02 #999), 02lcrv (0.08 #111, 0.04 #254, 0.02 #631), 03plfd (0.07 #907, 0.07 #1037, 0.06 #1232) >> Best rule #237 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0mnzd; 0kpzy; 034lk7; 0kv5t; >> query: (?x659, 02hcv8) <- source(?x659, ?x958), second_level_divisions(?x94, ?x659), place_of_birth(?x1775, ?x659) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #28 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 031sn; *> query: (?x659, 02hczc) <- state(?x659, ?x2982), contains(?x94, ?x659), ?x2982 = 01n4w *> conf = 0.60 ranks of expected_values: 2 EVAL 02cl1 time_zones 02hczc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 170.000 170.000 0.625 http://example.org/location/location/time_zones #3699-02mzg9 PRED entity: 02mzg9 PRED relation: colors PRED expected values: 01jnf1 => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.59 #681, 0.51 #801, 0.39 #1201), 01g5v (0.29 #763, 0.28 #403, 0.28 #663), 019sc (0.24 #807, 0.18 #67, 0.18 #1408), 06fvc (0.18 #762, 0.15 #1403, 0.15 #662), 04mkbj (0.12 #110, 0.11 #810, 0.09 #990), 038hg (0.12 #692, 0.11 #172, 0.11 #112), 036k5h (0.11 #245, 0.11 #5, 0.10 #405), 09ggk (0.11 #116, 0.11 #216, 0.08 #176), 0jc_p (0.08 #984, 0.08 #1381, 0.07 #124), 03vtbc (0.08 #1381, 0.06 #1922, 0.05 #228) >> Best rule #681 for best value: >> intensional similarity = 4 >> extensional distance = 223 >> proper extension: 022xml; 0q19t; 02zc7f; 038czx; 057wlm; 06l32y; 016sd3; 02jztz; 02jx_v; 01x5fb; ... >> query: (?x10861, 083jv) <- colors(?x10861, ?x332), currency(?x10861, ?x170), colors(?x2497, ?x332), ?x2497 = 0f1nl >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #1922 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 640 *> proper extension: 04rkkv; 023p18; 033gn8; 041sbd; 04gxp2; 0ym4t; 0301dp; *> query: (?x10861, ?x332) <- institution(?x3437, ?x10861), institution(?x3437, ?x2760), institution(?x3437, ?x331), school(?x1115, ?x2760), colors(?x331, ?x332) *> conf = 0.06 ranks of expected_values: 17 EVAL 02mzg9 colors 01jnf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 151.000 151.000 0.587 http://example.org/education/educational_institution/colors #3698-0cc5mcj PRED entity: 0cc5mcj PRED relation: story_by PRED expected values: 04l3_z => 79 concepts (58 used for prediction) PRED predicted values (max 10 best out of 67): 09pl3f (0.11 #2165, 0.03 #4765, 0.03 #6505), 09pl3s (0.11 #2165, 0.03 #4765, 0.03 #6505), 0697kh (0.11 #2165), 01twdk (0.06 #5852, 0.06 #4112, 0.06 #6287), 06pj8 (0.05 #5199), 0343h (0.04 #234, 0.04 #451, 0.03 #2615), 0237jb (0.04 #345, 0.04 #562, 0.02 #778), 0h5f5n (0.04 #220, 0.04 #437, 0.02 #653), 04l3_z (0.04 #227, 0.04 #444, 0.01 #877), 02g3w (0.04 #404, 0.04 #621) >> Best rule #2165 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 02qrv7; 0g5pv3; 015bpl; >> query: (?x2441, ?x2442) <- genre(?x2441, ?x812), ?x812 = 01jfsb, film(?x609, ?x2441), written_by(?x2441, ?x2442) >> conf = 0.11 => this is the best rule for 3 predicted values *> Best rule #227 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0dl6fv; *> query: (?x2441, 04l3_z) <- production_companies(?x2441, ?x6560), production_companies(?x2441, ?x1478), program(?x6560, ?x2436), award_nominee(?x1478, ?x7274) *> conf = 0.04 ranks of expected_values: 9 EVAL 0cc5mcj story_by 04l3_z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 79.000 58.000 0.110 http://example.org/film/film/story_by #3697-0b79gfg PRED entity: 0b79gfg PRED relation: profession PRED expected values: 09zzb8 => 79 concepts (78 used for prediction) PRED predicted values (max 10 best out of 50): 02hrh1q (0.71 #2268, 0.68 #2118, 0.67 #2418), 01d_h8 (0.39 #1209, 0.36 #3009, 0.35 #3309), 03gjzk (0.35 #2119, 0.34 #2419, 0.33 #1519), 0dxtg (0.33 #3017, 0.31 #2117, 0.31 #6768), 02jknp (0.27 #761, 0.27 #3011, 0.26 #1211), 02tx6q (0.26 #204, 0.23 #505, 0.23 #355), 09jwl (0.18 #7524, 0.18 #7224, 0.18 #6174), 0np9r (0.16 #775, 0.09 #9781, 0.09 #2425), 089fss (0.15 #920, 0.09 #17, 0.09 #620), 02krf9 (0.15 #1531, 0.14 #2431, 0.14 #2131) >> Best rule #2268 for best value: >> intensional similarity = 3 >> extensional distance = 841 >> proper extension: 01r42_g; 01j5x6; 04cf09; 0162c8; 01wjrn; 08m4c8; 02j8nx; 0b05xm; 05dxl5; 04crrxr; ... >> query: (?x2887, 02hrh1q) <- nominated_for(?x2887, ?x5328), nominated_for(?x880, ?x5328), genre(?x5328, ?x53) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #9007 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2131 *> proper extension: 01w8sf; 04cbtrw; 07d370; 0l99s; *> query: (?x2887, ?x137) <- award(?x2887, ?x3458), nominated_for(?x2887, ?x2685), award(?x2871, ?x3458), profession(?x2871, ?x137) *> conf = 0.07 ranks of expected_values: 21 EVAL 0b79gfg profession 09zzb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 79.000 78.000 0.706 http://example.org/people/person/profession #3696-02vrgnr PRED entity: 02vrgnr PRED relation: executive_produced_by PRED expected values: 05mvd62 => 79 concepts (54 used for prediction) PRED predicted values (max 10 best out of 80): 06pj8 (0.11 #816, 0.06 #5117, 0.04 #4357), 0343h (0.07 #1308, 0.06 #1055, 0.06 #2065), 02q_cc (0.07 #789, 0.03 #28, 0.03 #5090), 04jspq (0.06 #151, 0.05 #1669, 0.05 #1922), 0glyyw (0.05 #950, 0.04 #696, 0.03 #6262), 06q8hf (0.05 #3708, 0.05 #3961, 0.05 #2948), 05hj_k (0.05 #2879, 0.04 #1364, 0.04 #3132), 030_3z (0.04 #869, 0.02 #5170), 0gg9_5q (0.03 #90, 0.03 #343, 0.02 #5911), 04q5zw (0.03 #81, 0.02 #1599, 0.02 #1852) >> Best rule #816 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 05c5z8j; 01qxc7; 02lk60; 02bj22; >> query: (?x4621, 06pj8) <- genre(?x4621, ?x258), film_crew_role(?x4621, ?x137), ?x258 = 05p553, film_distribution_medium(?x4621, ?x81) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02vrgnr executive_produced_by 05mvd62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 54.000 0.109 http://example.org/film/film/executive_produced_by #3695-0dh8v4 PRED entity: 0dh8v4 PRED relation: genre PRED expected values: 02kdv5l 0hcr => 84 concepts (71 used for prediction) PRED predicted values (max 10 best out of 134): 07s9rl0 (0.98 #5822, 0.98 #6308, 0.91 #5943), 04xvlr (0.83 #3634, 0.34 #3755, 0.31 #2301), 01jfsb (0.80 #5334, 0.80 #5456, 0.79 #3632), 0hcr (0.80 #2445, 0.80 #1840, 0.79 #2082), 02kdv5l (0.73 #4729, 0.60 #5701, 0.59 #1455), 03k9fj (0.65 #4617, 0.55 #1828, 0.48 #5711), 05p553 (0.56 #4367, 0.55 #3151, 0.51 #4123), 01hmnh (0.55 #1835, 0.44 #4624, 0.44 #988), 06n90 (0.50 #2072, 0.43 #499, 0.41 #1346), 02n4kr (0.49 #3519, 0.33 #4004, 0.19 #5221) >> Best rule #5822 for best value: >> intensional similarity = 9 >> extensional distance = 870 >> proper extension: 047n8xt; 0kb07; 01sby_; 0353xq; 01jw67; 03ffcz; 08cfr1; 02chhq; 08c6k9; 0k5px; ... >> query: (?x5430, 07s9rl0) <- film(?x9753, ?x5430), country(?x5430, ?x252), genre(?x5430, ?x1626), genre(?x5992, ?x1626), genre(?x4000, ?x1626), genre(?x1786, ?x1626), ?x4000 = 011yfd, ?x5992 = 0g5q34q, ?x1786 = 091z_p >> conf = 0.98 => this is the best rule for 1 predicted values *> Best rule #2445 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 28 *> proper extension: 015qy1; *> query: (?x5430, 0hcr) <- actor(?x5430, ?x5779), language(?x5430, ?x2164), genre(?x5430, ?x5937), genre(?x14357, ?x5937), genre(?x8752, ?x5937), ?x14357 = 03q4hl, ?x8752 = 076xkdz *> conf = 0.80 ranks of expected_values: 4, 5 EVAL 0dh8v4 genre 0hcr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 84.000 71.000 0.979 http://example.org/film/film/genre EVAL 0dh8v4 genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 84.000 71.000 0.979 http://example.org/film/film/genre #3694-0265wl PRED entity: 0265wl PRED relation: award! PRED expected values: 05x8n 0gd_s => 69 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1710): 0fpzt5 (0.70 #19375, 0.60 #9283, 0.57 #12647), 01963w (0.70 #17154, 0.57 #10426, 0.43 #23883), 0210f1 (0.69 #33646, 0.68 #33645, 0.65 #50472), 0gd_s (0.64 #26204, 0.62 #22840, 0.60 #19475), 05x8n (0.60 #18757, 0.60 #8665, 0.57 #25486), 04mhl (0.60 #18081, 0.60 #7989, 0.50 #24810), 01g6bk (0.60 #9918, 0.57 #26739, 0.54 #23375), 01zkxv (0.60 #6857, 0.54 #20314, 0.50 #23678), 0b0pf (0.60 #8285, 0.36 #25106, 0.33 #31836), 0jt86 (0.57 #26622, 0.54 #23258, 0.50 #19893) >> Best rule #19375 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 0262zm; >> query: (?x5050, 0fpzt5) <- disciplines_or_subjects(?x5050, ?x1013), award(?x7828, ?x5050), award(?x5034, ?x5050), award(?x1752, ?x5050), award(?x1752, ?x4418), award(?x1752, ?x575), ?x575 = 040vk98, ?x7828 = 014ps4, award_nominee(?x5034, ?x3571), ?x4418 = 02664f >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #26204 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 040vk98; *> query: (?x5050, 0gd_s) <- disciplines_or_subjects(?x5050, ?x5864), award(?x10275, ?x5050), award(?x1752, ?x5050), ?x1752 = 01dzz7, place_of_birth(?x10275, ?x4090), major_field_of_study(?x734, ?x5864) *> conf = 0.64 ranks of expected_values: 4, 5 EVAL 0265wl award! 0gd_s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 37.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0265wl award! 05x8n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 37.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3693-03w4sh PRED entity: 03w4sh PRED relation: film PRED expected values: 047qxs => 111 concepts (79 used for prediction) PRED predicted values (max 10 best out of 207): 03ln8b (0.59 #69664, 0.37 #121462, 0.36 #53588), 01q2nx (0.11 #6268, 0.06 #8054, 0.06 #4482), 015g28 (0.08 #12503, 0.07 #39298, 0.07 #32152), 063y9fp (0.07 #1526, 0.06 #5098, 0.06 #3312), 04y9mm8 (0.07 #1184, 0.06 #4756, 0.06 #2970), 09cr8 (0.07 #284, 0.06 #3856, 0.06 #2070), 04b_jc (0.07 #1673, 0.06 #5245, 0.06 #3459), 02qdrjx (0.07 #1558, 0.06 #5130, 0.06 #3344), 0640y35 (0.07 #1012, 0.06 #4584, 0.06 #2798), 09q23x (0.07 #850, 0.06 #4422, 0.06 #2636) >> Best rule #69664 for best value: >> intensional similarity = 3 >> extensional distance = 1273 >> proper extension: 03_vx9; 028lc8; 01wxyx1; 05hdf; 03tf_h; 09qh1; 01vtqml; 0c2ry; 01x72k; 084z0w; ... >> query: (?x6538, ?x2078) <- nationality(?x6538, ?x94), film(?x6538, ?x1562), nominated_for(?x6538, ?x2078) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03w4sh film 047qxs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 79.000 0.586 http://example.org/film/actor/film./film/performance/film #3692-027dtv3 PRED entity: 027dtv3 PRED relation: award_nominee PRED expected values: 03yj_0n => 85 concepts (28 used for prediction) PRED predicted values (max 10 best out of 694): 03yj_0n (0.86 #2334, 0.86 #809, 0.82 #2333), 0f830f (0.82 #2333, 0.82 #4667, 0.80 #44341), 040981l (0.82 #2333, 0.82 #4667, 0.80 #44341), 02sb1w (0.82 #2333, 0.82 #4667, 0.77 #53671), 027dtv3 (0.71 #4776, 0.71 #2442, 0.64 #108), 026v437 (0.43 #6128, 0.43 #3794, 0.36 #8460), 01dy7j (0.28 #32671, 0.24 #9333, 0.21 #5334), 02lfcm (0.28 #32671, 0.24 #9333, 0.18 #65337), 016gr2 (0.28 #32671, 0.24 #9333, 0.18 #65337), 03n_7k (0.28 #32671, 0.24 #9333, 0.18 #65337) >> Best rule #2334 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 026v437; 040981l; >> query: (?x561, ?x3594) <- award_winner(?x561, ?x3594), award(?x561, ?x1670), ?x3594 = 03yj_0n >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027dtv3 award_nominee 03yj_0n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 28.000 0.857 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3691-01_4mn PRED entity: 01_4mn PRED relation: industry PRED expected values: 01mw1 => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 45): 01mw1 (0.80 #5092, 0.77 #3632, 0.75 #1130), 02vxn (0.53 #1743, 0.47 #2830, 0.38 #1602), 015p1m (0.50 #309, 0.40 #262, 0.33 #827), 02h400t (0.40 #260, 0.33 #307, 0.29 #590), 02jjt (0.33 #855, 0.20 #243, 0.17 #337), 03qh03g (0.24 #1793, 0.22 #852, 0.17 #1228), 04rlf (0.20 #249, 0.17 #1237, 0.17 #296), 0g4gr (0.20 #195, 0.17 #383, 0.14 #619), 01b4x4 (0.20 #273, 0.17 #320, 0.14 #603), 019z7b (0.19 #6179, 0.18 #5847, 0.18 #6275) >> Best rule #5092 for best value: >> intensional similarity = 9 >> extensional distance = 73 >> proper extension: 01tlrp; 01tkfj; 043fz4; 070ny; >> query: (?x13872, 01mw1) <- industry(?x13872, ?x10022), industry(?x14248, ?x10022), industry(?x14118, ?x10022), industry(?x13714, ?x10022), industry(?x4683, ?x10022), ?x13714 = 06zl7g, ?x14248 = 03_kl4, ?x14118 = 026wmz6, ?x4683 = 04vgq5 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_4mn industry 01mw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 173.000 0.800 http://example.org/business/business_operation/industry #3690-0bthb PRED entity: 0bthb PRED relation: institution! PRED expected values: 03bwzr4 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 20): 03bwzr4 (0.54 #214, 0.42 #721, 0.34 #699), 02_xgp2 (0.49 #212, 0.40 #719, 0.39 #697), 016t_3 (0.46 #204, 0.40 #711, 0.36 #93), 07s6fsf (0.37 #202, 0.31 #224, 0.30 #23), 0bkj86 (0.36 #208, 0.35 #693, 0.33 #715), 04zx3q1 (0.25 #203, 0.22 #688, 0.20 #710), 013zdg (0.22 #207, 0.16 #940, 0.16 #692), 027f2w (0.20 #209, 0.17 #694, 0.16 #716), 022h5x (0.20 #220, 0.12 #727, 0.11 #705), 028dcg (0.17 #152, 0.16 #175, 0.15 #241) >> Best rule #214 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 02zkz7; >> query: (?x1772, 03bwzr4) <- fraternities_and_sororities(?x1772, ?x4348), currency(?x1772, ?x170), school_type(?x1772, ?x3205) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bthb institution! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.539 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3689-01nbq4 PRED entity: 01nbq4 PRED relation: gender PRED expected values: 05zppz => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #57, 0.81 #47, 0.80 #149), 02zsn (0.39 #70, 0.36 #108, 0.35 #114) >> Best rule #57 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 047g6; 011zwl; >> query: (?x10227, 05zppz) <- location(?x10227, ?x9986), company(?x10227, ?x2776), contains(?x1310, ?x9986), company(?x900, ?x2776) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nbq4 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.827 http://example.org/people/person/gender #3688-0vfs8 PRED entity: 0vfs8 PRED relation: place PRED expected values: 0vfs8 => 101 concepts (67 used for prediction) PRED predicted values (max 10 best out of 42): 08809 (0.21 #4639, 0.12 #20625), 02dtg (0.15 #18559, 0.12 #9, 0.07 #524), 0vm39 (0.12 #238, 0.07 #753, 0.05 #1269), 0v9qg (0.12 #91, 0.07 #606, 0.05 #1122), 0vrmb (0.12 #402, 0.07 #917, 0.05 #1433), 0v1xg (0.12 #229, 0.05 #1260, 0.04 #1776), 013d_f (0.07 #957, 0.05 #1473, 0.04 #1989), 0xckc (0.07 #703, 0.05 #1219, 0.04 #1735), 01fq7 (0.07 #519, 0.05 #1035, 0.04 #1551), 0vg8x (0.07 #775, 0.05 #1291) >> Best rule #4639 for best value: >> intensional similarity = 3 >> extensional distance = 185 >> proper extension: 09bkv; 0kdqw; 01llj3; >> query: (?x8115, ?x11359) <- place_of_birth(?x8149, ?x8115), artists(?x1000, ?x8149), location(?x8149, ?x11359) >> conf = 0.21 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0vfs8 place 0vfs8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 67.000 0.211 http://example.org/location/hud_county_place/place #3687-01jqr_5 PRED entity: 01jqr_5 PRED relation: artists! PRED expected values: 0557q => 144 concepts (63 used for prediction) PRED predicted values (max 10 best out of 191): 06by7 (0.86 #12905, 0.85 #13533, 0.54 #3477), 03_d0 (0.64 #7549, 0.19 #3466, 0.17 #8492), 064t9 (0.45 #13524, 0.44 #11006, 0.44 #11952), 01lyv (0.41 #3490, 0.21 #7573, 0.17 #12918), 0155w (0.31 #3564, 0.22 #12992, 0.19 #7647), 05bt6j (0.30 #13556, 0.28 #12928, 0.20 #3500), 016jny (0.30 #3562, 0.15 #7645, 0.13 #12990), 0glt670 (0.27 #7266, 0.23 #7895, 0.21 #11035), 016clz (0.27 #3459, 0.26 #12887, 0.26 #13515), 0xhtw (0.25 #12900, 0.23 #13528, 0.14 #15100) >> Best rule #12905 for best value: >> intensional similarity = 3 >> extensional distance = 452 >> proper extension: 089tm; 01t_xp_; 01pfr3; 0150jk; 02r3zy; 07c0j; 067mj; 01vsxdm; 03g5jw; 03t9sp; ... >> query: (?x2511, 06by7) <- artists(?x3108, ?x2511), artists(?x3108, ?x6635), ?x6635 = 015cxv >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #8651 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 268 *> proper extension: 09bx1k; *> query: (?x2511, 0557q) <- gender(?x2511, ?x231), student(?x5807, ?x2511), artists(?x3108, ?x2511) *> conf = 0.05 ranks of expected_values: 54 EVAL 01jqr_5 artists! 0557q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 144.000 63.000 0.857 http://example.org/music/genre/artists #3686-02t_vx PRED entity: 02t_vx PRED relation: award_nominee! PRED expected values: 034zc0 => 66 concepts (26 used for prediction) PRED predicted values (max 10 best out of 604): 017149 (0.81 #60367, 0.81 #60366, 0.81 #48758), 02ch1w (0.81 #60367, 0.81 #60366, 0.81 #48758), 03q1vd (0.81 #60367, 0.81 #60366, 0.81 #48758), 02t_vx (0.54 #4065, 0.52 #6387, 0.16 #41791), 034zc0 (0.43 #5995, 0.31 #3673, 0.16 #41791), 02qgqt (0.25 #19, 0.03 #16268, 0.03 #18589), 02l4pj (0.25 #779, 0.02 #14706, 0.02 #47215), 04bdxl (0.25 #6, 0.02 #16255, 0.02 #18576), 03mp9s (0.25 #1572, 0.01 #13178, 0.01 #43363), 01qqtr (0.25 #1937) >> Best rule #60367 for best value: >> intensional similarity = 3 >> extensional distance = 1648 >> proper extension: 0hvbj; >> query: (?x7923, ?x4463) <- award_nominee(?x7923, ?x4463), award_nominee(?x3660, ?x7923), film(?x4463, ?x204) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #5995 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 19 *> proper extension: 0652ty; *> query: (?x7923, 034zc0) <- film(?x7923, ?x2336), ?x2336 = 016z9n *> conf = 0.43 ranks of expected_values: 5 EVAL 02t_vx award_nominee! 034zc0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 66.000 26.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3685-01yz0x PRED entity: 01yz0x PRED relation: award! PRED expected values: 0mfc0 0fvt2 => 60 concepts (35 used for prediction) PRED predicted values (max 10 best out of 2371): 0210f1 (0.78 #22188, 0.78 #114155, 0.78 #23498), 0p8jf (0.78 #114155, 0.78 #23498, 0.77 #114154), 01dhmw (0.78 #114155, 0.78 #23498, 0.77 #114154), 02g75 (0.78 #114155, 0.78 #23498, 0.77 #114154), 033cw (0.78 #114155, 0.78 #23498, 0.77 #114154), 0821j (0.78 #114155, 0.78 #23498, 0.77 #114154), 0mfc0 (0.67 #22833, 0.50 #16119, 0.41 #29547), 0g5ff (0.56 #21908, 0.50 #18551, 0.50 #15194), 03hpr (0.50 #16303, 0.44 #23017, 0.35 #29731), 0b0pf (0.50 #14983, 0.44 #21697, 0.33 #8269) >> Best rule #22188 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 0262zm; 02664f; >> query: (?x3337, 0210f1) <- award_winner(?x3337, ?x13125), disciplines_or_subjects(?x3337, ?x6647), ?x6647 = 02xlf, award(?x9284, ?x3337), story_by(?x2475, ?x13125), ?x9284 = 0gd_s >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #22833 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 0262zm; 02664f; *> query: (?x3337, 0mfc0) <- award_winner(?x3337, ?x13125), disciplines_or_subjects(?x3337, ?x6647), ?x6647 = 02xlf, award(?x9284, ?x3337), story_by(?x2475, ?x13125), ?x9284 = 0gd_s *> conf = 0.67 ranks of expected_values: 7, 12 EVAL 01yz0x award! 0fvt2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 60.000 35.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01yz0x award! 0mfc0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 60.000 35.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3684-01453 PRED entity: 01453 PRED relation: sport PRED expected values: 02vx4 => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 56): 02vx4 (0.93 #601, 0.93 #593, 0.92 #665), 0z74 (0.47 #1379, 0.31 #1389, 0.27 #1489), 0jm_ (0.30 #421, 0.29 #231, 0.24 #367), 018jz (0.29 #369, 0.29 #233, 0.27 #396), 09xp_ (0.18 #397, 0.17 #61, 0.14 #370), 03tmr (0.16 #830, 0.16 #329, 0.15 #885), 018w8 (0.11 #332, 0.09 #1154, 0.09 #787), 039yzs (0.07 #836, 0.04 #1157, 0.04 #644), 06f3l (0.02 #646), 09f6b (0.01 #1012, 0.01 #1022, 0.01 #1223) >> Best rule #601 for best value: >> intensional similarity = 12 >> extensional distance = 39 >> proper extension: 03yl2t; 03_r_5; 02rqxc; 03yvgp; 03ytj1; 0329t7; 04k3jt; 04h5tx; 04n8xs; 03z1c5; >> query: (?x202, ?x471) <- position(?x202, ?x530), position(?x202, ?x203), position(?x202, ?x63), position(?x202, ?x60), team(?x5471, ?x202), ?x60 = 02nzb8, ?x63 = 02sdk9v, teams(?x9660, ?x202), ?x203 = 0dgrmp, team(?x5471, ?x10389), sport(?x10389, ?x471), ?x530 = 02_j1w >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01453 sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.927 http://example.org/sports/sports_team/sport #3683-01t21q PRED entity: 01t21q PRED relation: contains! PRED expected values: 07ssc => 117 concepts (89 used for prediction) PRED predicted values (max 10 best out of 247): 07ssc (0.82 #9865, 0.78 #8971, 0.76 #50111), 09c7w0 (0.78 #27725, 0.78 #21464, 0.73 #57238), 02w7gg (0.58 #58130), 01n7q (0.35 #5441, 0.35 #11700, 0.30 #17066), 036wy (0.25 #764, 0.20 #2552, 0.16 #5234), 0kpys (0.25 #5543, 0.19 #11802, 0.17 #12696), 0f485 (0.25 #822, 0.13 #2610, 0.08 #8868), 059rby (0.24 #8066, 0.11 #3596, 0.11 #2702), 02j9z (0.15 #14332, 0.03 #53685, 0.03 #7179), 04_1l0v (0.15 #21910, 0.13 #28171, 0.13 #41586) >> Best rule #9865 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 01m3b7; >> query: (?x4984, 07ssc) <- location(?x10973, ?x4984), contains(?x1310, ?x4984), ?x1310 = 02jx1, profession(?x10973, ?x1032) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01t21q contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 89.000 0.820 http://example.org/location/location/contains #3682-086qd PRED entity: 086qd PRED relation: influenced_by PRED expected values: 01wbgdv => 113 concepts (60 used for prediction) PRED predicted values (max 10 best out of 440): 0j3v (0.17 #1365, 0.07 #17889, 0.07 #14407), 032l1 (0.12 #1394, 0.10 #17918, 0.09 #17482), 05qmj (0.12 #1496, 0.09 #18020, 0.07 #20635), 042q3 (0.12 #1669, 0.07 #18193, 0.06 #14711), 01hmk9 (0.11 #11520, 0.05 #16306, 0.04 #12390), 08433 (0.11 #3933, 0.10 #3064, 0.08 #4368), 014z8v (0.11 #11422, 0.08 #1426, 0.07 #12292), 081k8 (0.11 #14503, 0.11 #17985, 0.08 #18856), 041mt (0.10 #3102, 0.06 #3971, 0.04 #4406), 0p_47 (0.10 #11408, 0.04 #21860, 0.03 #24470) >> Best rule #1365 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 03s9v; 04xfb; 07ym0; 04lg6; 04_by; >> query: (?x2138, 0j3v) <- diet(?x2138, ?x3130), profession(?x2138, ?x131), influenced_by(?x4593, ?x2138) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #16957 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 260 *> proper extension: 01d494; 03f5vvx; 04z0g; 05d1y; 01t_z; 059y0; 02g3w; 0d_w7; 019fz; 02wlk; *> query: (?x2138, ?x217) <- award_winner(?x724, ?x2138), influenced_by(?x2138, ?x2237), award_winner(?x724, ?x217) *> conf = 0.01 ranks of expected_values: 426 EVAL 086qd influenced_by 01wbgdv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 113.000 60.000 0.167 http://example.org/influence/influence_node/influenced_by #3681-03d555l PRED entity: 03d555l PRED relation: team! PRED expected values: 0b_734 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 17): 0b_6v_ (0.73 #193, 0.69 #210, 0.67 #108), 0b_72t (0.70 #174, 0.67 #106, 0.64 #345), 05g_nr (0.70 #177, 0.67 #245, 0.62 #262), 0b_71r (0.70 #183, 0.67 #115, 0.60 #251), 0bzrsh (0.70 #181, 0.56 #266, 0.56 #147), 0b_6mr (0.70 #185, 0.56 #151, 0.50 #356), 0b_6pv (0.67 #148, 0.64 #353, 0.60 #182), 0bzrxn (0.67 #141, 0.62 #260, 0.60 #243), 0b_756 (0.67 #116, 0.62 #218, 0.60 #184), 0b_6rk (0.64 #343, 0.62 #257, 0.60 #240) >> Best rule #193 for best value: >> intensional similarity = 7 >> extensional distance = 9 >> proper extension: 02q4ntp; 026dqjm; >> query: (?x4804, 0b_6v_) <- team(?x12162, ?x4804), team(?x6002, ?x4804), colors(?x4804, ?x332), team(?x12162, ?x3798), teams(?x6088, ?x4804), ?x6002 = 0cc8q3, ?x3798 = 02ptzz0 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #154 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 02pyyld; *> query: (?x4804, 0b_734) <- team(?x12162, ?x4804), colors(?x4804, ?x3189), team(?x12162, ?x4938), team(?x12162, ?x2303), ?x2303 = 02plv57, ?x4938 = 027yf83, ?x3189 = 01g5v *> conf = 0.56 ranks of expected_values: 15 EVAL 03d555l team! 0b_734 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 98.000 98.000 0.727 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #3680-0xsk8 PRED entity: 0xsk8 PRED relation: profession PRED expected values: 01c72t => 127 concepts (101 used for prediction) PRED predicted values (max 10 best out of 63): 016z4k (0.50 #148, 0.45 #293, 0.44 #729), 039v1 (0.40 #3665, 0.39 #2211, 0.38 #4247), 01d_h8 (0.35 #1748, 0.34 #4511, 0.32 #2473), 01c72t (0.33 #4818, 0.33 #3945, 0.32 #2054), 0dxtg (0.32 #1464, 0.32 #1755, 0.28 #2625), 0n1h (0.30 #446, 0.30 #591, 0.29 #1317), 0fnpj (0.27 #783, 0.19 #57, 0.18 #5289), 03gjzk (0.23 #11201, 0.22 #11781, 0.22 #11926), 018gz8 (0.21 #1758, 0.19 #2483, 0.18 #1467), 0cbd2 (0.20 #1458, 0.18 #1749, 0.18 #2619) >> Best rule #148 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 01q7cb_; 014488; >> query: (?x8032, 016z4k) <- film(?x8032, ?x6125), artist(?x2931, ?x8032), group(?x8032, ?x8637) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #4818 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 291 *> proper extension: 0c9d9; 0fp_v1x; 02rgz4; 0274ck; 07q1v4; 0lgsq; 01qvgl; 0ftps; 03qmj9; 01w923; ... *> query: (?x8032, 01c72t) <- role(?x8032, ?x212), profession(?x8032, ?x131), type_of_union(?x8032, ?x566) *> conf = 0.33 ranks of expected_values: 4 EVAL 0xsk8 profession 01c72t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 127.000 101.000 0.500 http://example.org/people/person/profession #3679-09qvc0 PRED entity: 09qvc0 PRED relation: nominated_for PRED expected values: 03ln8b => 52 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1579): 039cq4 (0.83 #3166, 0.79 #7920, 0.77 #15838), 016tvq (0.83 #3166, 0.79 #7920, 0.77 #15838), 01s81 (0.83 #3166, 0.79 #7920, 0.77 #15838), 0266s9 (0.83 #3166, 0.79 #7920, 0.77 #15838), 0d68qy (0.83 #3166, 0.77 #15838, 0.77 #17422), 07w8fz (0.43 #3624, 0.19 #17880, 0.17 #14712), 01g03q (0.40 #6115, 0.33 #2949, 0.21 #7700), 0kfv9 (0.40 #5009, 0.33 #1843, 0.19 #8179), 0g60z (0.40 #4792, 0.33 #1626, 0.18 #7962), 0180mw (0.40 #5762, 0.33 #2596, 0.18 #7347) >> Best rule #3166 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0cqhb3; >> query: (?x693, ?x2293) <- award(?x8061, ?x693), award(?x7489, ?x693), award(?x2293, ?x693), nominated_for(?x693, ?x631), ?x7489 = 084m3, ?x8061 = 0sw6g >> conf = 0.83 => this is the best rule for 5 predicted values *> Best rule #8216 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 054knh; 02py_sj; *> query: (?x693, 03ln8b) <- nominated_for(?x693, ?x7317), actor(?x7317, ?x2965), ceremony(?x693, ?x1265), nominated_for(?x3406, ?x7317) *> conf = 0.10 ranks of expected_values: 539 EVAL 09qvc0 nominated_for 03ln8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 18.000 0.833 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3678-01j_cy PRED entity: 01j_cy PRED relation: school! PRED expected values: 09th87 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 19): 0f4vx0 (0.61 #48, 0.25 #314, 0.23 #181), 02qw1zx (0.39 #43, 0.23 #309, 0.19 #233), 09l0x9 (0.29 #49, 0.17 #315, 0.14 #609), 02pq_x5 (0.21 #54, 0.15 #320, 0.14 #73), 04f4z1k (0.18 #17, 0.14 #609, 0.09 #321), 05vsb7 (0.18 #39, 0.18 #305, 0.14 #609), 03nt7j (0.18 #45, 0.15 #311, 0.14 #609), 06439y (0.18 #57, 0.14 #609, 0.13 #323), 025tn92 (0.16 #316, 0.14 #69, 0.14 #50), 0g3zpp (0.14 #40, 0.14 #609, 0.13 #306) >> Best rule #48 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 06mkj; 0d05w3; >> query: (?x1675, 0f4vx0) <- school(?x4171, ?x1675), contains(?x94, ?x1675), organization(?x1675, ?x5487) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #609 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 189 *> proper extension: 0fht9f; 0frm7n; *> query: (?x1675, ?x2569) <- school(?x10837, ?x1675), team(?x1348, ?x10837), draft(?x10837, ?x2569) *> conf = 0.14 ranks of expected_values: 14 EVAL 01j_cy school! 09th87 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 106.000 106.000 0.607 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #3677-05drq5 PRED entity: 05drq5 PRED relation: nationality PRED expected values: 09c7w0 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 25): 09c7w0 (0.84 #501, 0.73 #1802, 0.73 #2602), 02jx1 (0.13 #133, 0.12 #2934, 0.11 #633), 07ssc (0.10 #215, 0.09 #1315, 0.09 #3316), 03rk0 (0.07 #2247, 0.06 #1246, 0.06 #8353), 0f8l9c (0.06 #122, 0.02 #1222, 0.02 #1622), 0d060g (0.06 #207, 0.05 #307, 0.04 #407), 0chghy (0.05 #110, 0.03 #10, 0.02 #310), 0345h (0.05 #131, 0.03 #31, 0.02 #1631), 0ctw_b (0.03 #27, 0.03 #127), 03rjj (0.03 #5, 0.02 #405, 0.02 #1605) >> Best rule #501 for best value: >> intensional similarity = 4 >> extensional distance = 178 >> proper extension: 02qjj7; 044ntk; 02v406; 01h8f; 02sh8y; 021r7r; 07ftc0; 02yy_j; 031bf1; 0m76b; ... >> query: (?x1314, 09c7w0) <- profession(?x1314, ?x524), ?x524 = 02jknp, student(?x1011, ?x1314), school(?x260, ?x1011) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05drq5 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.839 http://example.org/people/person/nationality #3676-027j9wd PRED entity: 027j9wd PRED relation: film_crew_role PRED expected values: 0d2b38 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 28): 0ch6mp2 (0.77 #456, 0.75 #974, 0.73 #597), 09vw2b7 (0.75 #41, 0.68 #596, 0.65 #213), 02r96rf (0.72 #209, 0.70 #140, 0.69 #451), 09zzb8 (0.71 #967, 0.71 #659, 0.70 #1448), 0dxtw (0.49 #217, 0.45 #148, 0.42 #669), 0d2b38 (0.37 #231, 0.17 #93, 0.13 #614), 0215hd (0.29 #225, 0.15 #608, 0.15 #190), 01pvkk (0.28 #670, 0.28 #1459, 0.27 #1527), 02ynfr (0.25 #50, 0.24 #84, 0.18 #605), 05smlt (0.25 #54, 0.10 #88, 0.10 #226) >> Best rule #456 for best value: >> intensional similarity = 4 >> extensional distance = 156 >> proper extension: 07kb7vh; 0372j5; >> query: (?x6000, 0ch6mp2) <- film(?x318, ?x6000), film_distribution_medium(?x6000, ?x2099), film(?x574, ?x6000), film_crew_role(?x6000, ?x281) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #231 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 66 *> proper extension: 04lqvly; 0dkv90; 0581vn8; *> query: (?x6000, 0d2b38) <- film_crew_role(?x6000, ?x1966), ?x1966 = 015h31, nominated_for(?x804, ?x6000) *> conf = 0.37 ranks of expected_values: 6 EVAL 027j9wd film_crew_role 0d2b38 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 75.000 75.000 0.766 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3675-02dbp7 PRED entity: 02dbp7 PRED relation: role PRED expected values: 05148p4 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 77): 05r5c (0.60 #8, 0.46 #111, 0.41 #317), 0342h (0.36 #1860, 0.34 #1344, 0.33 #1551), 02sgy (0.23 #1861, 0.22 #1449, 0.22 #1345), 042v_gx (0.21 #1864, 0.20 #1348, 0.20 #1452), 05148p4 (0.15 #127, 0.13 #24, 0.13 #1879), 05842k (0.15 #1933, 0.13 #1521, 0.13 #78), 018vs (0.14 #1353, 0.13 #1869, 0.13 #14), 026t6 (0.13 #3, 0.13 #1858, 0.12 #1342), 03gvt (0.13 #77, 0.10 #180, 0.10 #2372), 04rzd (0.13 #44, 0.10 #2372, 0.08 #147) >> Best rule #8 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0dhqyw; >> query: (?x4574, 05r5c) <- artists(?x597, ?x4574), ?x597 = 0ggq0m, origin(?x4574, ?x8916) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #127 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 025xt8y; 0ftps; 01hw6wq; 050z2; 07r4c; 01p0vf; 03zrp; 02bc74; *> query: (?x4574, 05148p4) <- artists(?x597, ?x4574), ?x597 = 0ggq0m, award(?x4574, ?x724) *> conf = 0.15 ranks of expected_values: 5 EVAL 02dbp7 role 05148p4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 104.000 104.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role #3674-03lsq PRED entity: 03lsq PRED relation: school PRED expected values: 0jkhr 01jq0j => 69 concepts (62 used for prediction) PRED predicted values (max 10 best out of 191): 065y4w7 (0.50 #573, 0.50 #382, 0.45 #1700), 01jq0j (0.38 #679, 0.33 #1992, 0.33 #866), 06pwq (0.38 #380, 0.18 #10377, 0.18 #8668), 0j_sncb (0.38 #412, 0.15 #7180, 0.15 #8700), 05krk (0.27 #3385, 0.26 #3763, 0.21 #5268), 07w0v (0.27 #1703, 0.24 #5276, 0.23 #9623), 01vs5c (0.25 #4411, 0.25 #461, 0.24 #3281), 07szy (0.25 #393, 0.22 #958, 0.20 #1333), 01pl14 (0.25 #378, 0.18 #5269, 0.18 #3576), 01qgr3 (0.25 #493, 0.18 #1811, 0.15 #5384) >> Best rule #573 for best value: >> intensional similarity = 15 >> extensional distance = 6 >> proper extension: 025_64l; >> query: (?x4256, 065y4w7) <- team(?x3113, ?x4256), team(?x1792, ?x4256), team(?x180, ?x4256), ?x1792 = 05zm34, position(?x4256, ?x1717), ?x180 = 01r3hr, ?x1717 = 02g_6x, teams(?x4733, ?x4256), dog_breed(?x4733, ?x3095), place_of_birth(?x1093, ?x4733), position_s(?x6976, ?x3113), position_s(?x729, ?x3113), ?x729 = 05g3b, ?x6976 = 04vn5, ?x3095 = 01_gx_ >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #679 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 6 *> proper extension: 025_64l; *> query: (?x4256, 01jq0j) <- team(?x3113, ?x4256), team(?x1792, ?x4256), team(?x180, ?x4256), ?x1792 = 05zm34, position(?x4256, ?x1717), ?x180 = 01r3hr, ?x1717 = 02g_6x, teams(?x4733, ?x4256), dog_breed(?x4733, ?x3095), place_of_birth(?x1093, ?x4733), position_s(?x6976, ?x3113), position_s(?x729, ?x3113), ?x729 = 05g3b, ?x6976 = 04vn5, ?x3095 = 01_gx_ *> conf = 0.38 ranks of expected_values: 2, 56 EVAL 03lsq school 01jq0j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 62.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 03lsq school 0jkhr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 69.000 62.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #3673-05qx1 PRED entity: 05qx1 PRED relation: film_release_region! PRED expected values: 0gkz15s 08hmch 040rmy 0879bpq 03q0r1 02xbyr 0dc_ms 09v3jyg 0btpm6 0gwlfnb => 90 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1262): 08hmch (0.92 #7466, 0.91 #5014, 0.90 #3788), 0gg5qcw (0.91 #5502, 0.89 #3050, 0.81 #7954), 0ds3t5x (0.88 #6168, 0.84 #2490, 0.73 #9846), 0btpm6 (0.88 #7023, 0.79 #11927, 0.76 #10701), 02x3lt7 (0.86 #4964, 0.84 #2512, 0.84 #6190), 01fmys (0.85 #3894, 0.84 #2668, 0.84 #6346), 06wbm8q (0.84 #2727, 0.82 #5179, 0.81 #11309), 0gmcwlb (0.84 #2592, 0.82 #5044, 0.80 #6270), 0bc1yhb (0.84 #3076, 0.82 #5528, 0.80 #4302), 0dscrwf (0.84 #2502, 0.82 #4954, 0.80 #6180) >> Best rule #7466 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 07dfk; >> query: (?x1475, 08hmch) <- film_release_region(?x10860, ?x1475), film_release_region(?x2394, ?x1475), ?x10860 = 049w1q, film_release_distribution_medium(?x2394, ?x81) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 19, 24, 26, 49, 50, 61, 84, 88 EVAL 05qx1 film_release_region! 0gwlfnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 90.000 87.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qx1 film_release_region! 0btpm6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 87.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qx1 film_release_region! 09v3jyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 90.000 87.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qx1 film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 90.000 87.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qx1 film_release_region! 02xbyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 90.000 87.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qx1 film_release_region! 03q0r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 90.000 87.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qx1 film_release_region! 0879bpq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 90.000 87.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qx1 film_release_region! 040rmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 90.000 87.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qx1 film_release_region! 08hmch CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 87.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05qx1 film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 90.000 87.000 0.923 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3672-01_qp_ PRED entity: 01_qp_ PRED relation: artists PRED expected values: 02mq_y => 71 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1087): 05k79 (0.78 #8822, 0.75 #5569, 0.38 #6654), 03t9sp (0.62 #5545, 0.56 #8798, 0.38 #6630), 014pg1 (0.62 #7247, 0.50 #6162, 0.44 #9415), 016t0h (0.60 #3189, 0.50 #2105, 0.43 #5357), 03fbc (0.56 #8879, 0.50 #5626, 0.50 #1289), 02hzz (0.50 #7255, 0.50 #6170, 0.44 #9423), 01323p (0.50 #6121, 0.44 #9374, 0.40 #2868), 01k3qj (0.50 #1773, 0.44 #9363, 0.40 #2857), 016vn3 (0.50 #6367, 0.44 #9620, 0.38 #7452), 01w5n51 (0.50 #6115, 0.44 #9368, 0.33 #694) >> Best rule #8822 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 041738; >> query: (?x12149, 05k79) <- artists(?x12149, ?x11633), artists(?x12149, ?x5916), parent_genre(?x14058, ?x12149), ?x5916 = 02cpp, instrumentalists(?x716, ?x11633), role(?x716, ?x74), role(?x645, ?x716), role(?x569, ?x716) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #6988 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 03ckfl9; *> query: (?x12149, 02mq_y) <- artists(?x12149, ?x11633), artists(?x12149, ?x5916), parent_genre(?x14058, ?x12149), ?x11633 = 01ww_vs, group(?x227, ?x5916), artists(?x3370, ?x5916), ?x3370 = 059kh, award(?x5916, ?x3045), ?x3045 = 02sp_v *> conf = 0.25 ranks of expected_values: 117 EVAL 01_qp_ artists 02mq_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 71.000 30.000 0.778 http://example.org/music/genre/artists #3671-02pq_x5 PRED entity: 02pq_x5 PRED relation: draft! PRED expected values: 01d5z 05g76 => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 82): 04wmvz (0.82 #739, 0.80 #885, 0.77 #1108), 05xvj (0.82 #739, 0.80 #885, 0.77 #1108), 05g76 (0.82 #739, 0.80 #885, 0.77 #1108), 03m1n (0.82 #739, 0.80 #885, 0.77 #1108), 0713r (0.82 #739, 0.80 #885, 0.77 #1108), 04mjl (0.82 #739, 0.80 #885, 0.77 #1108), 01d5z (0.82 #739, 0.80 #885, 0.77 #1108), 051wf (0.82 #739, 0.80 #885, 0.77 #1108), 070xg (0.60 #588, 0.57 #980, 0.50 #685), 03lsq (0.60 #588, 0.57 #986, 0.50 #691) >> Best rule #739 for best value: >> intensional similarity = 53 >> extensional distance = 2 >> proper extension: 05vsb7; 09l0x9; >> query: (?x8786, ?x1010) <- school(?x8786, ?x6333), school(?x8786, ?x5486), school(?x8786, ?x4904), school(?x8786, ?x4211), school(?x8786, ?x735), draft(?x8901, ?x8786), draft(?x7399, ?x8786), draft(?x6074, ?x8786), draft(?x1438, ?x8786), school(?x6074, ?x2948), category(?x6074, ?x134), team(?x2010, ?x6074), draft(?x6074, ?x11905), state_province_region(?x4211, ?x3634), contains(?x94, ?x4211), school(?x8901, ?x8706), school(?x8901, ?x6732), school(?x8901, ?x4955), school(?x8901, ?x3777), school(?x8901, ?x2830), student(?x6333, ?x5350), fraternities_and_sororities(?x4211, ?x3697), ?x4955 = 09f2j, institution(?x1368, ?x4211), contains(?x2020, ?x6732), colors(?x1438, ?x663), type_of_union(?x5350, ?x566), ?x735 = 065y4w7, school_type(?x6732, ?x3205), colors(?x6074, ?x4557), currency(?x6732, ?x170), major_field_of_study(?x6333, ?x1154), draft(?x1010, ?x11905), student(?x6732, ?x3961), ?x5486 = 0g8rj, ?x1154 = 02lp1, team(?x261, ?x1438), company(?x3131, ?x6333), institution(?x1526, ?x8706), school(?x7399, ?x2497), teams(?x5771, ?x1438), ?x1368 = 014mlp, ?x1526 = 0bkj86, ?x4904 = 0lyjf, ?x566 = 04ztj, colors(?x8706, ?x332), fraternities_and_sororities(?x3777, ?x4348), sport(?x7399, ?x5063), student(?x8706, ?x1817), major_field_of_study(?x3777, ?x3213), ?x170 = 09nqf, ?x2497 = 0f1nl, organization(?x346, ?x2830) >> conf = 0.82 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 7 EVAL 02pq_x5 draft! 05g76 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 19.000 19.000 0.818 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02pq_x5 draft! 01d5z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 19.000 19.000 0.818 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #3670-0bx8pn PRED entity: 0bx8pn PRED relation: institution! PRED expected values: 019v9k => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 12): 019v9k (0.77 #69, 0.70 #173, 0.70 #201), 0bjrnt (0.43 #15, 0.31 #81, 0.30 #2), 028dcg (0.33 #23, 0.19 #76, 0.16 #89), 02mjs7 (0.33 #14, 0.19 #80, 0.14 #517), 01rr_d (0.29 #21, 0.25 #87, 0.25 #61), 02cq61 (0.19 #22, 0.14 #517, 0.12 #88), 071tyz (0.15 #4, 0.14 #517, 0.14 #17), 01ysy9 (0.14 #517, 0.10 #78, 0.06 #370), 02m4yg (0.14 #20, 0.12 #86, 0.07 #99), 01kxxq (0.03 #369, 0.02 #672, 0.02 #780) >> Best rule #69 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 06mkj; 0d05w3; >> query: (?x1884, 019v9k) <- school(?x4779, ?x1884), organization(?x1884, ?x5487), draft(?x260, ?x4779) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bx8pn institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.774 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3669-023zsh PRED entity: 023zsh PRED relation: film PRED expected values: 03m5y9p 0crd8q6 => 57 concepts (45 used for prediction) PRED predicted values (max 10 best out of 190): 091xrc (0.25 #1764, 0.03 #26791), 08r4x3 (0.12 #153, 0.05 #1939, 0.02 #3725), 03ntbmw (0.12 #1767, 0.05 #3553), 062zm5h (0.12 #854, 0.03 #46438, 0.03 #48225), 04tqtl (0.12 #508, 0.03 #46438, 0.03 #48225), 02q7yfq (0.12 #1200, 0.03 #46438, 0.03 #48225), 051ys82 (0.12 #1033, 0.03 #46438, 0.03 #48225), 02y_lrp (0.12 #13, 0.03 #46438, 0.03 #48225), 06_wqk4 (0.12 #126, 0.03 #26791, 0.02 #3698), 0bvn25 (0.12 #49, 0.03 #26791, 0.01 #8979) >> Best rule #1764 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 01sfmyk; >> query: (?x9780, 091xrc) <- film(?x9780, ?x136), ?x136 = 09sh8k >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 023zsh film 0crd8q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 45.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 023zsh film 03m5y9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 45.000 0.250 http://example.org/film/actor/film./film/performance/film #3668-01mmslz PRED entity: 01mmslz PRED relation: people! PRED expected values: 07hwkr => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 46): 07hwkr (0.38 #166, 0.29 #243, 0.20 #89), 07bch9 (0.24 #485, 0.18 #639, 0.08 #1024), 041rx (0.21 #235, 0.20 #4, 0.19 #389), 033tf_ (0.20 #7, 0.15 #1470, 0.15 #700), 065b6q (0.20 #3, 0.08 #157, 0.07 #234), 02ctzb (0.18 #477, 0.14 #631, 0.06 #1016), 0x67 (0.17 #2936, 0.15 #1088, 0.15 #1165), 0d7wh (0.12 #325, 0.04 #633, 0.03 #402), 01qhm_ (0.12 #853, 0.08 #1238, 0.08 #1315), 063k3h (0.11 #493, 0.08 #647, 0.07 #262) >> Best rule #166 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0jgd; >> query: (?x2416, 07hwkr) <- organization(?x2416, ?x4542), gender(?x2416, ?x514), ?x514 = 02zsn >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mmslz people! 07hwkr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.385 http://example.org/people/ethnicity/people #3667-06s6hs PRED entity: 06s6hs PRED relation: people! PRED expected values: 033tf_ => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 38): 033tf_ (0.30 #84, 0.22 #7, 0.15 #315), 041rx (0.15 #1933, 0.15 #543, 0.14 #698), 0x67 (0.11 #3945, 0.11 #10, 0.11 #781), 013xrm (0.11 #20, 0.10 #97, 0.06 #5245), 01qhm_ (0.11 #6, 0.10 #83, 0.05 #314), 02w7gg (0.10 #156, 0.07 #1159, 0.07 #1004), 0xnvg (0.10 #321, 0.07 #1248, 0.07 #244), 07hwkr (0.06 #243, 0.05 #474, 0.05 #320), 01g7zj (0.06 #1235, 0.06 #5245, 0.02 #360), 07bch9 (0.06 #254, 0.05 #485, 0.05 #1258) >> Best rule #84 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 034x61; 066m4g; 08m4c8; 09f0bj; 05np4c; 08yx9q; 025b5y; 095b70; >> query: (?x5809, 033tf_) <- award_nominee(?x5809, ?x5599), ?x5599 = 06czyr, participant(?x1918, ?x5809) >> conf = 0.30 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06s6hs people! 033tf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.300 http://example.org/people/ethnicity/people #3666-02s6sh PRED entity: 02s6sh PRED relation: performance_role PRED expected values: 02snj9 => 130 concepts (78 used for prediction) PRED predicted values (max 10 best out of 119): 05r5c (0.44 #76, 0.19 #668, 0.17 #740), 03gvt (0.18 #213, 0.14 #30, 0.11 #139), 0342h (0.17 #740, 0.16 #220, 0.15 #666), 02sgy (0.17 #740, 0.16 #220, 0.15 #1036), 05148p4 (0.17 #740, 0.16 #220, 0.15 #1036), 01s0ps (0.17 #740, 0.16 #220, 0.15 #1036), 01vj9c (0.17 #740, 0.16 #220, 0.15 #1036), 01vdm0 (0.17 #740, 0.16 #220, 0.13 #1035), 042v_gx (0.16 #220, 0.15 #1036, 0.13 #1035), 011k_j (0.16 #220, 0.15 #1036, 0.13 #1035) >> Best rule #76 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 012j5h; >> query: (?x10989, 05r5c) <- place_of_death(?x10989, ?x1523), performance_role(?x10989, ?x1466), role(?x1466, ?x74), group(?x1466, ?x442), role(?x115, ?x1466) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #319 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 0l12d; 012x4t; 01w923; 0285c; 014q2g; 01vn35l; 0qdyf; 01vsy95; 050z2; 0fhxv; ... *> query: (?x10989, 02snj9) <- role(?x10989, ?x745), artist(?x10504, ?x10989), profession(?x10989, ?x131), performance_role(?x10989, ?x1225), ?x745 = 01vj9c *> conf = 0.15 ranks of expected_values: 12 EVAL 02s6sh performance_role 02snj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 130.000 78.000 0.444 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #3665-05l2z4 PRED entity: 05l2z4 PRED relation: legislative_sessions! PRED expected values: 06hx2 => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 126): 024_vw (0.75 #621, 0.72 #523, 0.71 #519), 0d3qd0 (0.72 #523, 0.71 #504, 0.69 #24), 0bymv (0.72 #523, 0.71 #500, 0.69 #24), 012v1t (0.72 #523, 0.71 #509, 0.69 #24), 03txms (0.72 #523, 0.69 #24, 0.67 #144), 0d06m5 (0.72 #523, 0.69 #24, 0.67 #144), 016lh0 (0.72 #523, 0.69 #24, 0.67 #144), 01lct6 (0.72 #523, 0.69 #24, 0.67 #144), 02mjmr (0.72 #523, 0.69 #24, 0.66 #499), 06hx2 (0.59 #145, 0.48 #868, 0.48 #867) >> Best rule #621 for best value: >> intensional similarity = 51 >> extensional distance = 10 >> proper extension: 06f0dc; >> query: (?x356, 024_vw) <- legislative_sessions(?x4665, ?x356), legislative_sessions(?x356, ?x6139), legislative_sessions(?x356, ?x3540), legislative_sessions(?x356, ?x2976), legislative_sessions(?x356, ?x952), legislative_sessions(?x356, ?x606), legislative_sessions(?x356, ?x355), legislative_sessions(?x6933, ?x356), legislative_sessions(?x8607, ?x356), ?x6933 = 024tkd, district_represented(?x952, ?x7518), district_represented(?x952, ?x7405), district_represented(?x952, ?x6521), district_represented(?x952, ?x4758), district_represented(?x952, ?x4600), district_represented(?x952, ?x4061), district_represented(?x952, ?x3908), district_represented(?x952, ?x3818), district_represented(?x952, ?x3634), district_represented(?x952, ?x2831), district_represented(?x952, ?x2256), district_represented(?x952, ?x1782), district_represented(?x952, ?x1274), district_represented(?x952, ?x1138), district_represented(?x952, ?x335), ?x4061 = 0498y, ?x335 = 059rby, ?x7518 = 026mj, ?x1138 = 059_c, ?x2256 = 07srw, ?x7405 = 07_f2, ?x1274 = 04ykg, legislative_sessions(?x5266, ?x2976), ?x355 = 0495ys, ?x6139 = 060ny2, ?x3908 = 04ly1, ?x3818 = 03v0t, district_represented(?x3540, ?x12828), district_represented(?x3540, ?x1906), ?x2831 = 0gyh, ?x12828 = 0gj4fx, ?x1906 = 04rrx, ?x3634 = 07b_l, ?x4665 = 07t58, ?x1782 = 0488g, ?x4758 = 0vbk, ?x5266 = 016lh0, ?x8607 = 0226cw, ?x4600 = 081yw, ?x6521 = 05mph, ?x606 = 03ww_x >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #145 for first EXPECTED value: *> intensional similarity = 52 *> extensional distance = 1 *> proper extension: 02bqm0; *> query: (?x356, ?x5266) <- legislative_sessions(?x2860, ?x356), legislative_sessions(?x356, ?x6743), legislative_sessions(?x356, ?x4821), legislative_sessions(?x356, ?x4730), legislative_sessions(?x356, ?x3766), legislative_sessions(?x356, ?x3540), legislative_sessions(?x356, ?x3463), legislative_sessions(?x356, ?x2976), legislative_sessions(?x356, ?x1829), legislative_sessions(?x356, ?x1027), legislative_sessions(?x356, ?x952), legislative_sessions(?x6933, ?x356), legislative_sessions(?x1028, ?x356), legislative_sessions(?x9569, ?x356), legislative_sessions(?x9334, ?x356), legislative_sessions(?x652, ?x356), ?x6933 = 024tkd, ?x952 = 06f0dc, ?x1829 = 02bp37, ?x6743 = 04h1rz, ?x2976 = 03rtmz, ?x652 = 021sv1, district_represented(?x356, ?x335), district_represented(?x4821, ?x6521), district_represented(?x4821, ?x5575), district_represented(?x4821, ?x4105), district_represented(?x4821, ?x3670), district_represented(?x4821, ?x2256), district_represented(?x4821, ?x1227), district_represented(?x4821, ?x1025), district_represented(?x4821, ?x728), district_represented(?x4821, ?x448), ?x5575 = 05fjy, ?x3540 = 024tcq, ?x3766 = 02gkzs, ?x6521 = 05mph, ?x3463 = 02bqmq, ?x1027 = 02bn_p, legislative_sessions(?x2357, ?x4821), ?x4730 = 02cg7g, legislative_sessions(?x4821, ?x605), ?x9569 = 0194xc, ?x1227 = 01n7q, ?x448 = 03v1s, ?x1025 = 04ych, ?x335 = 059rby, legislative_sessions(?x5266, ?x1028), ?x728 = 059f4, ?x4105 = 0824r, ?x3670 = 05tbn, ?x9334 = 02hy5d, ?x2256 = 07srw *> conf = 0.59 ranks of expected_values: 10 EVAL 05l2z4 legislative_sessions! 06hx2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 38.000 38.000 0.750 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #3664-0y_yw PRED entity: 0y_yw PRED relation: film! PRED expected values: 01yzhn => 92 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1048): 05yzt_ (0.46 #4160, 0.44 #87322, 0.43 #81085), 0854hr (0.46 #4160, 0.44 #87322, 0.43 #81085), 0cdf37 (0.46 #4160, 0.44 #87322, 0.43 #81085), 015c4g (0.40 #779, 0.06 #2860, 0.04 #13253), 0j_c (0.22 #6648, 0.07 #8726, 0.06 #35750), 0252fh (0.20 #1350, 0.08 #14553, 0.03 #24217), 04__f (0.20 #1378, 0.08 #14553, 0.03 #13852), 014gf8 (0.20 #1006, 0.03 #7245, 0.03 #34269), 0c0k1 (0.20 #1505, 0.03 #13979, 0.03 #16058), 02vyw (0.18 #79006, 0.18 #72765, 0.18 #72766) >> Best rule #4160 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 03h_yy; >> query: (?x6097, ?x5389) <- nominated_for(?x5389, ?x6097), genre(?x6097, ?x600), genre(?x6097, ?x162), ?x162 = 04xvlr, ?x600 = 02n4kr >> conf = 0.46 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0y_yw film! 01yzhn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 44.000 0.465 http://example.org/film/actor/film./film/performance/film #3663-02gdjb PRED entity: 02gdjb PRED relation: ceremony PRED expected values: 09n4nb 0466p0j 013b2h => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 127): 013b2h (0.70 #579, 0.62 #637, 0.61 #255), 03nnm4t (0.62 #637, 0.61 #255, 0.36 #1401), 0clfdj (0.62 #637, 0.61 #255, 0.36 #1401), 09n4nb (0.61 #255, 0.60 #547, 0.60 #803), 0466p0j (0.61 #255, 0.60 #575, 0.59 #831), 073hkh (0.61 #255, 0.47 #765, 0.40 #383), 0bzjvm (0.61 #255, 0.40 #1911, 0.40 #480), 073h1t (0.61 #255, 0.40 #1911, 0.40 #403), 0bzmt8 (0.61 #255, 0.40 #1911, 0.40 #468), 0bz6sb (0.61 #255, 0.40 #1911, 0.40 #436) >> Best rule #579 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 01bgqh; 01c9f2; 026mfs; 01dk00; 02sp_v; 03qbh5; 03tk6z; >> query: (?x4488, 013b2h) <- award(?x1583, ?x4488), award(?x1270, ?x4488), award_winner(?x6869, ?x1583), ?x1270 = 0137n0, ?x6869 = 01xqqp >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 5 EVAL 02gdjb ceremony 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.700 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02gdjb ceremony 0466p0j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 41.000 0.700 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02gdjb ceremony 09n4nb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 41.000 0.700 http://example.org/award/award_category/winners./award/award_honor/ceremony #3662-01tjt2 PRED entity: 01tjt2 PRED relation: contains PRED expected values: 01yj2 => 87 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1687): 01yj2 (0.36 #53013, 0.36 #132528, 0.25 #1238), 01tjt2 (0.36 #53013, 0.36 #132528, 0.25 #1882), 0hzlz (0.36 #53013, 0.36 #132528, 0.25 #167878), 06f0y3 (0.25 #2608, 0.07 #11442, 0.01 #114856), 01tjvv (0.25 #1725, 0.07 #10559, 0.01 #114856), 01vg0s (0.25 #1308, 0.07 #10142, 0.01 #114856), 01_vrh (0.25 #75, 0.07 #8909, 0.01 #114856), 0c499 (0.25 #2452, 0.02 #97190, 0.01 #114856), 067z4 (0.25 #1599, 0.02 #97190, 0.01 #114856), 018lkp (0.25 #2545, 0.01 #114856) >> Best rule #53013 for best value: >> intensional similarity = 3 >> extensional distance = 205 >> proper extension: 06y9v; 0123_x; 09lk2; 02ly_; 0mgfs; 0125q1; 0ck1d; 0124jj; 0mczk; 0gqm3; ... >> query: (?x11536, ?x8751) <- contains(?x11536, ?x9861), country(?x11536, ?x792), contains(?x8751, ?x9861) >> conf = 0.36 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 01tjt2 contains 01yj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 59.000 0.359 http://example.org/location/location/contains #3661-043t8t PRED entity: 043t8t PRED relation: genre PRED expected values: 05p553 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 94): 05p553 (0.88 #122, 0.37 #240, 0.37 #476), 01z4y (0.51 #1891, 0.49 #6269, 0.48 #4497), 02kdv5l (0.48 #474, 0.46 #238, 0.44 #710), 02l7c8 (0.40 #841, 0.31 #1195, 0.29 #605), 01jfsb (0.38 #601, 0.35 #955, 0.34 #365), 017fp (0.38 #14, 0.14 #4141, 0.10 #1194), 06n90 (0.31 #248, 0.31 #484, 0.29 #720), 04xvlr (0.27 #827, 0.25 #1, 0.22 #945), 0lsxr (0.25 #598, 0.24 #362, 0.18 #952), 03bxz7 (0.25 #53, 0.14 #4141, 0.13 #407) >> Best rule #122 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 0ndsl1x; >> query: (?x4651, 05p553) <- nominated_for(?x3281, ?x4651), nominated_for(?x400, ?x4651), film(?x4468, ?x4651), ?x400 = 01q_ph, award_nominee(?x3281, ?x230) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043t8t genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.875 http://example.org/film/film/genre #3660-026rsl9 PRED entity: 026rsl9 PRED relation: award! PRED expected values: 01wv9p => 49 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2211): 0cc5tgk (0.77 #16888, 0.75 #13510, 0.75 #20268), 01wv9p (0.50 #7914, 0.50 #4536, 0.33 #1159), 01nkxvx (0.50 #9240, 0.33 #2485, 0.25 #5862), 01wj18h (0.33 #882, 0.30 #7637, 0.25 #20270), 09z1lg (0.33 #2752, 0.30 #9507, 0.25 #6129), 01qmy04 (0.33 #3212, 0.30 #9967, 0.25 #6589), 01dwrc (0.33 #1699, 0.30 #8454, 0.25 #5076), 05pdbs (0.33 #297, 0.25 #20270, 0.25 #3674), 0127s7 (0.33 #1741, 0.25 #5118, 0.20 #8496), 01w9wwg (0.33 #1803, 0.25 #5180, 0.20 #8558) >> Best rule #16888 for best value: >> intensional similarity = 6 >> extensional distance = 78 >> proper extension: 02qkk9_; >> query: (?x10028, ?x883) <- ceremony(?x10028, ?x12139), award_winner(?x10028, ?x10180), award_winner(?x10028, ?x883), award_winner(?x1238, ?x10180), artists(?x671, ?x10180), participant(?x2614, ?x10180) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #7914 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 01bgqh; 02681xs; 03qbnj; 02w7fs; 03m79j_; 02681_5; *> query: (?x10028, 01wv9p) <- ceremony(?x10028, ?x12139), award(?x2584, ?x10028), award(?x883, ?x10028), ?x883 = 01w61th, category(?x2584, ?x134), artists(?x597, ?x2584) *> conf = 0.50 ranks of expected_values: 2 EVAL 026rsl9 award! 01wv9p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 49.000 15.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3659-0j2jr PRED entity: 0j2jr PRED relation: teams! PRED expected values: 017_cl => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 78): 0pfd9 (0.20 #519, 0.10 #15151, 0.10 #15150), 016wrq (0.17 #772, 0.07 #1313, 0.06 #1584), 01fbb3 (0.10 #11352, 0.10 #15151, 0.10 #15150), 01m4pc (0.10 #11352, 0.10 #15151, 0.10 #15150), 01xbld (0.10 #11352, 0.08 #12975, 0.07 #15699), 0126hc (0.10 #11352, 0.08 #12975, 0.07 #15699), 03rt9 (0.10 #15151, 0.10 #15150, 0.08 #12975), 0fm2_ (0.10 #15151, 0.10 #15150, 0.08 #12975), 01w2dq (0.10 #15151, 0.10 #15150, 0.08 #12975), 04lh6 (0.10 #15151, 0.10 #15150, 0.08 #12975) >> Best rule #519 for best value: >> intensional similarity = 13 >> extensional distance = 3 >> proper extension: 01zhs3; 01rlz4; >> query: (?x13211, 0pfd9) <- position(?x13211, ?x203), position(?x13211, ?x63), position(?x13211, ?x60), ?x203 = 0dgrmp, team(?x11510, ?x13211), ?x60 = 02nzb8, ?x63 = 02sdk9v, team(?x9779, ?x13211), team(?x9779, ?x9922), ?x11510 = 0g9zjp, team(?x1935, ?x9922), colors(?x9922, ?x663), athlete(?x471, ?x9779) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0j2jr teams! 017_cl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 69.000 0.200 http://example.org/sports/sports_team_location/teams #3658-065ym0c PRED entity: 065ym0c PRED relation: film_release_region PRED expected values: 0j1z8 07ssc => 89 concepts (71 used for prediction) PRED predicted values (max 10 best out of 160): 0d0vqn (0.90 #1979, 0.90 #2143, 0.89 #1651), 06mkj (0.90 #2365, 0.86 #2037, 0.84 #1709), 07ssc (0.89 #1663, 0.89 #1991, 0.86 #1334), 035qy (0.89 #1683, 0.88 #2011, 0.86 #1354), 0b90_r (0.88 #1975, 0.88 #1647, 0.73 #2303), 05qhw (0.88 #1661, 0.86 #1989, 0.80 #1332), 0chghy (0.88 #1984, 0.86 #1656, 0.84 #2312), 047yc (0.88 #1020, 0.62 #2005, 0.61 #1677), 03gj2 (0.86 #2331, 0.86 #1346, 0.81 #1018), 02vzc (0.86 #1374, 0.85 #2031, 0.84 #1703) >> Best rule #1979 for best value: >> intensional similarity = 6 >> extensional distance = 70 >> proper extension: 0gj9qxr; 09v3jyg; >> query: (?x10080, 0d0vqn) <- film_release_region(?x10080, ?x1122), film_release_region(?x10080, ?x252), film_release_region(?x10080, ?x205), ?x252 = 03_3d, ?x205 = 03rjj, ?x1122 = 09pmkv >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1663 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 62 *> proper extension: 0h1cdwq; 08hmch; 03bx2lk; 0cz8mkh; 0661m4p; 07x4qr; 023gxx; 09g7vfw; 0cp0ph6; 0c3xw46; ... *> query: (?x10080, 07ssc) <- film_release_region(?x10080, ?x2645), film_release_region(?x10080, ?x1122), film_release_region(?x10080, ?x252), film_release_region(?x10080, ?x205), ?x252 = 03_3d, ?x205 = 03rjj, ?x1122 = 09pmkv, ?x2645 = 03h64 *> conf = 0.89 ranks of expected_values: 3, 51 EVAL 065ym0c film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 71.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 065ym0c film_release_region 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 89.000 71.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3657-02x17c2 PRED entity: 02x17c2 PRED relation: award! PRED expected values: 0lbj1 02cx72 01vvyc_ 018gqj => 49 concepts (28 used for prediction) PRED predicted values (max 10 best out of 2424): 01vsnff (0.83 #9971, 0.81 #6647, 0.80 #16620), 0g824 (0.83 #9971, 0.81 #6647, 0.80 #16620), 09swkk (0.83 #9971, 0.81 #6647, 0.80 #16620), 0pk41 (0.83 #9971, 0.81 #6647, 0.80 #16620), 02pt7h_ (0.83 #9971, 0.81 #6647, 0.80 #16620), 01vtj38 (0.83 #9971, 0.81 #6647, 0.80 #16620), 02zj61 (0.83 #9971, 0.81 #6647, 0.80 #16620), 09889g (0.65 #24687, 0.25 #14715, 0.24 #28011), 0dl567 (0.62 #14419, 0.50 #1123, 0.33 #4446), 01hgwkr (0.62 #15953, 0.24 #25925, 0.19 #26592) >> Best rule #9971 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 02g8mp; 01c9f2; >> query: (?x4481, ?x4940) <- award(?x523, ?x4481), ceremony(?x4481, ?x762), award_winner(?x4481, ?x4940), award_winner(?x4481, ?x2187), ?x2187 = 01vsnff >> conf = 0.83 => this is the best rule for 7 predicted values *> Best rule #13340 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01bgqh; 0c4z8; 03tk6z; 01c99j; *> query: (?x4481, 0lbj1) <- award(?x6418, ?x4481), ceremony(?x4481, ?x762), award_winner(?x4481, ?x2187), ?x6418 = 013423 *> conf = 0.50 ranks of expected_values: 18, 174, 199, 405 EVAL 02x17c2 award! 018gqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 49.000 28.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x17c2 award! 01vvyc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 28.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x17c2 award! 02cx72 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 28.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x17c2 award! 0lbj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 49.000 28.000 0.831 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3656-0285c PRED entity: 0285c PRED relation: role PRED expected values: 03bx0bm => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 123): 03bx0bm (0.56 #899, 0.55 #1395, 0.53 #1209), 05r5c (0.40 #259, 0.38 #189, 0.37 #1257), 026t6 (0.38 #189, 0.30 #378, 0.29 #315), 042v_gx (0.38 #189, 0.27 #3623, 0.25 #197), 02sgy (0.38 #189, 0.27 #3623, 0.24 #5066), 03qjg (0.33 #1104, 0.33 #40, 0.32 #1290), 05148p4 (0.33 #1080, 0.32 #1266, 0.28 #4956), 018vs (0.25 #138, 0.22 #1076, 0.22 #2448), 01vj9c (0.25 #139, 0.21 #2186, 0.20 #2499), 05842k (0.21 #2186, 0.20 #2499, 0.20 #3000) >> Best rule #899 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01kx_81; 01vv126; 0fhxv; 03h502k; 02r3cn; >> query: (?x1955, 03bx0bm) <- role(?x1955, ?x227), participant(?x4126, ?x1955), location(?x1955, ?x5867), artist(?x1954, ?x1955) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0285c role 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 188.000 0.562 http://example.org/music/group_member/membership./music/group_membership/role #3655-0hpyv PRED entity: 0hpyv PRED relation: origin! PRED expected values: 067mj => 203 concepts (115 used for prediction) PRED predicted values (max 10 best out of 472): 02t3ln (0.12 #3290, 0.11 #4835, 0.07 #1745), 01wdqrx (0.12 #2609, 0.06 #4669, 0.06 #3639), 01vrt_c (0.11 #3636, 0.09 #5697, 0.08 #6213), 04411 (0.09 #43295, 0.06 #5151, 0.06 #51541), 01s7ns (0.09 #6133, 0.08 #6649, 0.07 #2012), 06s7rd (0.09 #6025, 0.08 #6541, 0.06 #9635), 018ndc (0.09 #5783, 0.08 #6299, 0.06 #9393), 04n2vgk (0.09 #8655, 0.08 #7109, 0.06 #7624), 01d1st (0.08 #6995, 0.06 #7510, 0.06 #9574), 01q99h (0.08 #6450, 0.06 #9029, 0.06 #3873) >> Best rule #3290 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0cc56; 094jv; 0mp3l; 02xry; 01cx_; 0d6lp; 0t_gg; 0mn8t; 0fw4v; 0y62n; ... >> query: (?x8007, 02t3ln) <- category(?x8007, ?x134), currency(?x8007, ?x170), origin(?x838, ?x8007), ?x134 = 08mbj5d >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hpyv origin! 067mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 203.000 115.000 0.125 http://example.org/music/artist/origin #3654-02784z PRED entity: 02784z PRED relation: profession PRED expected values: 0cbd2 02hrh1q => 119 concepts (98 used for prediction) PRED predicted values (max 10 best out of 97): 02hrh1q (0.91 #5116, 0.91 #5716, 0.90 #5566), 01d_h8 (0.42 #606, 0.40 #1056, 0.32 #2856), 0cbd2 (0.36 #307, 0.27 #2857, 0.26 #1207), 0dxtg (0.35 #3914, 0.34 #1814, 0.34 #2864), 02jknp (0.29 #608, 0.26 #1058, 0.24 #3908), 0kyk (0.28 #331, 0.23 #1381, 0.22 #481), 018gz8 (0.25 #618, 0.20 #1068, 0.16 #5569), 03gjzk (0.20 #616, 0.19 #3916, 0.19 #6017), 0d1pc (0.20 #52, 0.10 #4402, 0.10 #5603), 09jwl (0.18 #7373, 0.17 #13378, 0.17 #13228) >> Best rule #5116 for best value: >> intensional similarity = 4 >> extensional distance = 412 >> proper extension: 01520h; >> query: (?x10454, 02hrh1q) <- film(?x10454, ?x1547), film_regional_debut_venue(?x1547, ?x3288), nominated_for(?x198, ?x1547), genre(?x1547, ?x53) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 02784z profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 98.000 0.911 http://example.org/people/person/profession EVAL 02784z profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 98.000 0.911 http://example.org/people/person/profession #3653-06r713 PRED entity: 06r713 PRED relation: legislative_sessions! PRED expected values: 06bss => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 144): 02hy5d (0.80 #734, 0.78 #703, 0.73 #673), 06bss (0.75 #729, 0.73 #638, 0.67 #698), 024_vw (0.73 #682, 0.73 #677, 0.71 #389), 0d3qd0 (0.66 #391, 0.65 #684, 0.64 #450), 016lh0 (0.66 #391, 0.65 #684, 0.64 #450), 0bymv (0.66 #391, 0.64 #450, 0.63 #594), 012v1t (0.66 #391, 0.64 #450, 0.63 #594), 03txms (0.66 #391, 0.64 #450, 0.59 #685), 01lct6 (0.66 #391, 0.64 #450, 0.59 #685), 0d06m5 (0.66 #391, 0.64 #450, 0.58 #655) >> Best rule #734 for best value: >> intensional similarity = 37 >> extensional distance = 18 >> proper extension: 05rrw9; >> query: (?x5977, 02hy5d) <- legislative_sessions(?x9569, ?x5977), legislative_sessions(?x8607, ?x5977), legislative_sessions(?x652, ?x5977), student(?x5750, ?x652), legislative_sessions(?x9569, ?x2976), legislative_sessions(?x9569, ?x653), major_field_of_study(?x5750, ?x4321), major_field_of_study(?x5750, ?x2606), place_of_birth(?x9569, ?x9863), jurisdiction_of_office(?x8607, ?x94), people(?x268, ?x9569), institution(?x7636, ?x5750), institution(?x1368, ?x5750), institution(?x865, ?x5750), profession(?x9569, ?x3342), ?x4321 = 0g26h, place_of_birth(?x652, ?x5193), currency(?x5750, ?x170), ?x1368 = 014mlp, location(?x8607, ?x7770), gender(?x652, ?x231), district_represented(?x2976, ?x2020), ?x2606 = 062z7, ?x94 = 09c7w0, district_represented(?x653, ?x1426), list(?x5750, ?x2197), profession(?x652, ?x14074), category(?x5750, ?x134), origin(?x2683, ?x5193), ?x865 = 02h4rq6, ?x3342 = 04gc2, ?x1426 = 07z1m, ?x2020 = 05k7sb, school(?x2067, ?x5750), student(?x3439, ?x9569), ?x7636 = 01rr_d, citytown(?x5750, ?x108) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #729 for first EXPECTED value: *> intensional similarity = 37 *> extensional distance = 18 *> proper extension: 05rrw9; *> query: (?x5977, 06bss) <- legislative_sessions(?x9569, ?x5977), legislative_sessions(?x8607, ?x5977), legislative_sessions(?x652, ?x5977), student(?x5750, ?x652), legislative_sessions(?x9569, ?x2976), legislative_sessions(?x9569, ?x653), major_field_of_study(?x5750, ?x4321), major_field_of_study(?x5750, ?x2606), place_of_birth(?x9569, ?x9863), jurisdiction_of_office(?x8607, ?x94), people(?x268, ?x9569), institution(?x7636, ?x5750), institution(?x1368, ?x5750), institution(?x865, ?x5750), profession(?x9569, ?x3342), ?x4321 = 0g26h, place_of_birth(?x652, ?x5193), currency(?x5750, ?x170), ?x1368 = 014mlp, location(?x8607, ?x7770), gender(?x652, ?x231), district_represented(?x2976, ?x2020), ?x2606 = 062z7, ?x94 = 09c7w0, district_represented(?x653, ?x1426), list(?x5750, ?x2197), profession(?x652, ?x14074), category(?x5750, ?x134), origin(?x2683, ?x5193), ?x865 = 02h4rq6, ?x3342 = 04gc2, ?x1426 = 07z1m, ?x2020 = 05k7sb, school(?x2067, ?x5750), student(?x3439, ?x9569), ?x7636 = 01rr_d, citytown(?x5750, ?x108) *> conf = 0.75 ranks of expected_values: 2 EVAL 06r713 legislative_sessions! 06bss CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 31.000 31.000 0.800 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #3652-01vw8k PRED entity: 01vw8k PRED relation: film! PRED expected values: 018009 => 75 concepts (54 used for prediction) PRED predicted values (max 10 best out of 965): 018009 (0.72 #68506, 0.67 #80965, 0.59 #97576), 0lpjn (0.25 #476, 0.05 #2554, 0.04 #4630), 04shbh (0.25 #164, 0.05 #2242, 0.03 #20921), 03kpvp (0.25 #630, 0.02 #52530, 0.01 #73290), 059_gf (0.25 #996, 0.02 #21753, 0.01 #30057), 0170pk (0.12 #279, 0.07 #4433, 0.06 #8583), 05qg6g (0.12 #735, 0.07 #80967, 0.05 #97577), 0d02km (0.12 #1062, 0.07 #80967, 0.05 #97577), 03l3jy (0.12 #767, 0.07 #80967, 0.05 #97577), 0dlglj (0.12 #256, 0.07 #80967, 0.05 #97577) >> Best rule #68506 for best value: >> intensional similarity = 4 >> extensional distance = 619 >> proper extension: 0372j5; >> query: (?x3979, ?x2499) <- country(?x3979, ?x94), nominated_for(?x2499, ?x3979), film(?x382, ?x3979), participant(?x2499, ?x91) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vw8k film! 018009 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 54.000 0.715 http://example.org/film/actor/film./film/performance/film #3651-0154qm PRED entity: 0154qm PRED relation: student! PRED expected values: 02j62 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 33): 02822 (0.18 #397, 0.15 #519, 0.04 #275), 03g3w (0.11 #82, 0.07 #509, 0.05 #387), 062z7 (0.11 #83, 0.04 #388, 0.03 #510), 041y2 (0.11 #111, 0.03 #538, 0.02 #416), 0g26h (0.11 #93, 0.01 #215, 0.01 #276), 03qsdpk (0.09 #402, 0.08 #524, 0.02 #158), 0w7c (0.08 #530, 0.06 #408, 0.01 #714), 0fdys (0.07 #395, 0.07 #517, 0.01 #273), 02h40lc (0.06 #369, 0.05 #491), 01zc2w (0.05 #108, 0.05 #413, 0.04 #535) >> Best rule #397 for best value: >> intensional similarity = 2 >> extensional distance = 120 >> proper extension: 05fg2; 0c_md_; 059y0; >> query: (?x3281, 02822) <- student(?x7070, ?x3281), award_winner(?x995, ?x3281) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #390 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 120 *> proper extension: 05fg2; 0c_md_; 059y0; *> query: (?x3281, 02j62) <- student(?x7070, ?x3281), award_winner(?x995, ?x3281) *> conf = 0.04 ranks of expected_values: 14 EVAL 0154qm student! 02j62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 95.000 95.000 0.180 http://example.org/education/field_of_study/students_majoring./education/education/student #3650-0x3r3 PRED entity: 0x3r3 PRED relation: influenced_by! PRED expected values: 01d494 => 146 concepts (66 used for prediction) PRED predicted values (max 10 best out of 339): 032r1 (0.33 #2542, 0.14 #992, 0.14 #4091), 0b78hw (0.33 #2234, 0.14 #684, 0.14 #3783), 047g6 (0.27 #2548, 0.18 #4097, 0.14 #998), 0x3r3 (0.27 #2307, 0.09 #3856, 0.07 #12119), 07h1q (0.23 #4025, 0.14 #926, 0.13 #2476), 0399p (0.23 #3945, 0.13 #2396, 0.12 #12208), 01d494 (0.21 #1601, 0.14 #3666, 0.13 #2117), 03_hd (0.20 #2247, 0.14 #1034, 0.14 #697), 09gnn (0.20 #2487, 0.14 #937, 0.09 #4036), 0h25 (0.20 #2488, 0.09 #4037, 0.07 #5072) >> Best rule #2542 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 04411; 026lj; 0j3v; 0dzkq; 043s3; 07c37; 04hcw; 048cl; 0nk72; 0cpvcd; ... >> query: (?x5796, 032r1) <- location(?x5796, ?x1705), student(?x6056, ?x5796), place_of_death(?x5796, ?x12697), interests(?x5796, ?x1858) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1601 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 0ct9_; *> query: (?x5796, 01d494) <- company(?x5796, ?x3485), major_field_of_study(?x3485, ?x5864), ?x5864 = 04g51 *> conf = 0.21 ranks of expected_values: 7 EVAL 0x3r3 influenced_by! 01d494 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 146.000 66.000 0.333 http://example.org/influence/influence_node/influenced_by #3649-01wwvc5 PRED entity: 01wwvc5 PRED relation: role PRED expected values: 05r5c => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 108): 05r5c (0.49 #608, 0.46 #208, 0.44 #408), 02sgy (0.31 #206, 0.25 #406, 0.25 #6), 0l14qv (0.26 #205, 0.17 #1511, 0.16 #605), 05148p4 (0.25 #1809, 0.24 #802, 0.24 #4533), 018j2 (0.25 #43, 0.05 #1953, 0.04 #4071), 05842k (0.19 #1682, 0.18 #1581, 0.17 #275), 01vj9c (0.16 #1621, 0.16 #4042, 0.14 #3237), 013y1f (0.16 #635, 0.14 #4063, 0.13 #3258), 026t6 (0.16 #1610, 0.16 #4031, 0.16 #1710), 01s0ps (0.14 #259, 0.08 #1565, 0.07 #4087) >> Best rule #608 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 02rgz4; 0lgsq; 01qvgl; 0244r8; 04zwjd; 06k02; 01l9v7n; 02z81h; 01p0vf; 01wf86y; ... >> query: (?x2731, 05r5c) <- role(?x2731, ?x227), artists(?x671, ?x2731), nominated_for(?x2731, ?x7354) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wwvc5 role 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.487 http://example.org/music/artist/track_contributions./music/track_contribution/role #3648-01csrl PRED entity: 01csrl PRED relation: gender PRED expected values: 02zsn => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #39, 0.85 #79, 0.84 #31), 02zsn (0.48 #24, 0.46 #48, 0.46 #197) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 308 >> proper extension: 03_hd; >> query: (?x2417, 05zppz) <- profession(?x2417, ?x1032), student(?x8056, ?x2417), place_of_death(?x2417, ?x12801) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #24 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 164 *> proper extension: 01trhmt; 012_53; 01n7qlf; 02t_99; 05myd2; 01ggc9; *> query: (?x2417, 02zsn) <- participant(?x2417, ?x2416), film(?x2417, ?x3294), actor(?x9951, ?x2417) *> conf = 0.48 ranks of expected_values: 2 EVAL 01csrl gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.848 http://example.org/people/person/gender #3647-08052t3 PRED entity: 08052t3 PRED relation: language PRED expected values: 04306rv => 106 concepts (103 used for prediction) PRED predicted values (max 10 best out of 54): 064_8sq (0.17 #309, 0.15 #483, 0.15 #2813), 02bjrlw (0.17 #1, 0.13 #115, 0.11 #173), 06b_j (0.13 #135, 0.10 #1127, 0.09 #1882), 04306rv (0.12 #1865, 0.11 #176, 0.11 #1110), 03_9r (0.11 #66, 0.09 #123, 0.07 #356), 0jzc (0.11 #75, 0.06 #1879, 0.05 #1124), 012w70 (0.11 #68, 0.06 #884, 0.05 #5641), 05zjd (0.11 #81, 0.05 #5641, 0.04 #138), 0653m (0.09 #124, 0.08 #883, 0.07 #357), 04h9h (0.07 #446, 0.05 #5641, 0.05 #622) >> Best rule #309 for best value: >> intensional similarity = 5 >> extensional distance = 52 >> proper extension: 06g77c; 0d8w2n; >> query: (?x2471, 064_8sq) <- titles(?x811, ?x2471), language(?x2471, ?x254), film_format(?x2471, ?x6392), ?x6392 = 0cj16, featured_film_locations(?x2471, ?x3052) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1865 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 351 *> proper extension: 02qjv1p; *> query: (?x2471, 04306rv) <- titles(?x811, ?x2471), genre(?x2471, ?x225), genre(?x5667, ?x225), genre(?x5441, ?x225), award(?x5667, ?x1007), ?x5441 = 04cbbz *> conf = 0.12 ranks of expected_values: 4 EVAL 08052t3 language 04306rv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 106.000 103.000 0.167 http://example.org/film/film/language #3646-0dxmyh PRED entity: 0dxmyh PRED relation: vacationer! PRED expected values: 031y2 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 88): 03gh4 (0.32 #1074, 0.24 #1820, 0.21 #454), 05qtj (0.24 #1811, 0.13 #941, 0.09 #5670), 04jpl (0.18 #133, 0.16 #1002, 0.14 #630), 0160w (0.16 #995, 0.12 #1119, 0.10 #499), 0f2v0 (0.15 #1802, 0.13 #932, 0.12 #1056), 0cv3w (0.13 #2667, 0.12 #1796, 0.09 #4908), 02_286 (0.13 #884, 0.09 #760, 0.09 #1754), 0b90_r (0.12 #996, 0.12 #1742, 0.09 #748), 015fr (0.10 #510, 0.08 #1006, 0.08 #261), 07fr_ (0.09 #219, 0.09 #2705, 0.05 #716) >> Best rule #1074 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 02mjmr; 024dgj; 07swvb; 016fnb; 03xnq9_; 0g824; 04d_mtq; 0c1j_; >> query: (?x10277, 03gh4) <- friend(?x5514, ?x10277), vacationer(?x6959, ?x10277), category(?x10277, ?x134), profession(?x10277, ?x987) >> conf = 0.32 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dxmyh vacationer! 031y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 117.000 0.320 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #3645-0gg4h PRED entity: 0gg4h PRED relation: people PRED expected values: 0hwqg 03cprft => 57 concepts (40 used for prediction) PRED predicted values (max 10 best out of 1454): 07pzc (0.57 #9923, 0.50 #11958, 0.50 #7210), 03d_zl4 (0.50 #7070, 0.43 #9783, 0.40 #3671), 01vz0g4 (0.50 #7144, 0.43 #9857, 0.40 #3745), 053yx (0.50 #5526, 0.40 #2809, 0.33 #12314), 0137hn (0.50 #8421, 0.33 #275, 0.21 #16567), 04__f (0.43 #9166, 0.17 #5771, 0.15 #15278), 01vsl3_ (0.40 #3487, 0.33 #6886, 0.33 #6204), 0136p1 (0.40 #2094, 0.25 #10921, 0.17 #7530), 06y7d (0.40 #2635, 0.25 #11462, 0.17 #8071), 02cvp8 (0.40 #2574, 0.25 #11401, 0.17 #8010) >> Best rule #9923 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 019dmc; >> query: (?x10069, 07pzc) <- people(?x10069, ?x4960), award(?x4960, ?x462), profession(?x4960, ?x131), category(?x4960, ?x134), artist(?x2299, ?x4960), artist(?x8738, ?x4960), location(?x4960, ?x1227), artists(?x671, ?x4960), participant(?x4960, ?x1126) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gg4h people 03cprft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 40.000 0.571 http://example.org/people/cause_of_death/people EVAL 0gg4h people 0hwqg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 40.000 0.571 http://example.org/people/cause_of_death/people #3644-029h45 PRED entity: 029h45 PRED relation: profession PRED expected values: 025352 => 92 concepts (49 used for prediction) PRED predicted values (max 10 best out of 59): 02hrh1q (0.95 #3543, 0.76 #5309, 0.69 #4426), 0cbd2 (0.60 #7, 0.45 #448, 0.40 #1624), 01d_h8 (0.53 #3388, 0.53 #3535, 0.52 #3976), 03gjzk (0.51 #6635, 0.48 #2073, 0.48 #3249), 02jknp (0.47 #890, 0.47 #3390, 0.47 #1184), 05sxg2 (0.30 #295, 0.20 #589, 0.20 #1), 01c72t (0.29 #5737, 0.29 #6326, 0.15 #318), 0kyk (0.29 #5737, 0.28 #471, 0.18 #1647), 018gz8 (0.26 #3546, 0.18 #3987, 0.18 #3251), 09jwl (0.23 #6933, 0.22 #5167, 0.19 #4726) >> Best rule #3543 for best value: >> intensional similarity = 5 >> extensional distance = 557 >> proper extension: 01xwqn; >> query: (?x5650, 02hrh1q) <- profession(?x5650, ?x2348), profession(?x5650, ?x987), ?x987 = 0dxtg, profession(?x140, ?x2348), ?x140 = 01vvydl >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 03kpvp; *> query: (?x5650, 025352) <- profession(?x5650, ?x987), ?x987 = 0dxtg, award_winner(?x3105, ?x5650), ?x3105 = 01l29r *> conf = 0.20 ranks of expected_values: 12 EVAL 029h45 profession 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 92.000 49.000 0.948 http://example.org/people/person/profession #3643-0cgbf PRED entity: 0cgbf PRED relation: films PRED expected values: 06krf3 => 160 concepts (129 used for prediction) PRED predicted values (max 10 best out of 9): 09sr0 (0.07 #4701, 0.04 #7891, 0.03 #8954), 03xj05 (0.07 #4744, 0.04 #7934), 02yvct (0.07 #4358, 0.04 #7548), 0b4lkx (0.07 #4664, 0.01 #12104, 0.01 #12635), 02vqsll (0.07 #4399, 0.01 #11839, 0.01 #12370), 080lkt7 (0.05 #6079, 0.02 #9268, 0.02 #9799), 049xgc (0.02 #10374), 07yk1xz (0.01 #11267, 0.01 #12861, 0.01 #13925), 03m5y9p (0.01 #11582, 0.01 #14240) >> Best rule #4701 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 07_m9_; 06c97; >> query: (?x6934, 09sr0) <- people(?x4322, ?x6934), person(?x7415, ?x6934), place_of_death(?x6934, ?x739) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cgbf films 06krf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 129.000 0.071 http://example.org/film/film_subject/films #3642-07w8fz PRED entity: 07w8fz PRED relation: film! PRED expected values: 016z2j => 70 concepts (27 used for prediction) PRED predicted values (max 10 best out of 781): 02vx4c2 (0.35 #51866), 020_95 (0.33 #961, 0.09 #11332, 0.06 #15480), 01pcq3 (0.33 #131, 0.03 #49791, 0.03 #53942), 018yj6 (0.33 #1524, 0.03 #11895, 0.02 #29044), 03yj_0n (0.20 #4759, 0.20 #2684, 0.12 #6833), 06cgy (0.20 #4396, 0.20 #2321, 0.12 #6470), 01900g (0.20 #4928, 0.20 #2853, 0.12 #7002), 01_xtx (0.20 #4807, 0.20 #2732, 0.12 #6881), 02x0dzw (0.20 #5655, 0.20 #3580, 0.12 #7729), 027bs_2 (0.20 #5422, 0.20 #3347, 0.12 #7496) >> Best rule #51866 for best value: >> intensional similarity = 4 >> extensional distance = 568 >> proper extension: 0275kr; >> query: (?x3133, ?x286) <- nominated_for(?x1922, ?x3133), nominated_for(?x286, ?x3133), award_winner(?x458, ?x1922), spouse(?x1922, ?x3183) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #49791 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 555 *> proper extension: 026mfbr; 02z3r8t; 035xwd; 04kkz8; 08hmch; 0gj8t_b; 03s5lz; 0c00zd0; 0c8tkt; 0m491; ... *> query: (?x3133, ?x1676) <- written_by(?x3133, ?x286), film(?x1922, ?x3133), film(?x969, ?x3133), student(?x2775, ?x1922), award_nominee(?x969, ?x1676) *> conf = 0.03 ranks of expected_values: 144 EVAL 07w8fz film! 016z2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 70.000 27.000 0.346 http://example.org/film/actor/film./film/performance/film #3641-01jsn5 PRED entity: 01jsn5 PRED relation: school! PRED expected values: 038981 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 18): 0f4vx0 (0.38 #101, 0.32 #65, 0.28 #83), 05vsb7 (0.28 #19, 0.23 #55, 0.20 #73), 02qw1zx (0.28 #239, 0.24 #95, 0.18 #59), 092j54 (0.23 #63, 0.22 #27, 0.21 #99), 02pq_rp (0.20 #8, 0.17 #26, 0.14 #98), 025tn92 (0.18 #247, 0.17 #103, 0.15 #505), 09l0x9 (0.17 #102, 0.17 #246, 0.14 #66), 02pq_x5 (0.17 #34, 0.15 #505, 0.14 #542), 03nt7j (0.17 #25, 0.14 #241, 0.14 #97), 038c0q (0.16 #240, 0.15 #505, 0.14 #542) >> Best rule #101 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 05krk; 01j_9c; 065y4w7; 07w0v; 01j_cy; 0bx8pn; 0f102; 078bz; 01wdj_; 0j_sncb; ... >> query: (?x2399, 0f4vx0) <- colors(?x2399, ?x663), major_field_of_study(?x2399, ?x10046), school(?x1823, ?x2399), ?x10046 = 041y2 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #505 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 170 *> proper extension: 01t8sr; 02jyr8; 04bfg; 029qzx; *> query: (?x2399, ?x2569) <- colors(?x2399, ?x663), school(?x4571, ?x2399), draft(?x4571, ?x2569), institution(?x620, ?x2399) *> conf = 0.15 ranks of expected_values: 13 EVAL 01jsn5 school! 038981 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 132.000 132.000 0.379 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #3640-0gs1_ PRED entity: 0gs1_ PRED relation: award PRED expected values: 09qv3c => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 290): 027c924 (0.72 #43807, 0.70 #34245, 0.70 #43009), 054ky1 (0.72 #43807, 0.70 #34245, 0.70 #43009), 02py7pj (0.70 #34245, 0.70 #43009, 0.69 #43806), 0gqy2 (0.50 #159, 0.14 #33846, 0.13 #1353), 0bdwqv (0.38 #166, 0.08 #2156, 0.08 #7332), 0789_m (0.38 #19, 0.08 #7185, 0.07 #8379), 02pqp12 (0.36 #3252, 0.32 #864, 0.23 #1262), 05pcn59 (0.35 #1668, 0.25 #872, 0.23 #16797), 09sb52 (0.33 #1631, 0.32 #23529, 0.30 #24328), 04dn09n (0.32 #838, 0.24 #3226, 0.17 #15170) >> Best rule #43807 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x6558, ?x1313) <- award_winner(?x1313, ?x6558), award(?x269, ?x1313) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #24338 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 939 *> proper extension: 01mvth; 064nh4k; 0bz5v2; 0806vbn; 07s6prs; 02p_ycc; 07z1_q; 062ftr; 01cbt3; 01vrnsk; ... *> query: (?x6558, 09qv3c) <- film(?x6558, ?x2423), award_winner(?x1454, ?x6558) *> conf = 0.03 ranks of expected_values: 186 EVAL 0gs1_ award 09qv3c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 120.000 120.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3639-05t0_2v PRED entity: 05t0_2v PRED relation: genre PRED expected values: 05p553 01hmnh => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 140): 05p553 (0.81 #120, 0.39 #1056, 0.38 #1174), 07s9rl0 (0.72 #1640, 0.66 #2577, 0.66 #3283), 01jfsb (0.57 #245, 0.55 #362, 0.54 #479), 06n90 (0.54 #363, 0.52 #246, 0.49 #480), 01hmnh (0.48 #367, 0.48 #250, 0.47 #484), 0btmb (0.43 #436, 0.42 #553, 0.37 #319), 04xvlr (0.22 #1641, 0.21 #3284, 0.20 #2110), 0lsxr (0.22 #242, 0.21 #476, 0.20 #359), 082gq (0.19 #2605, 0.19 #1902, 0.11 #1668), 060__y (0.17 #1654, 0.16 #3297, 0.16 #2591) >> Best rule #120 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 07kb7vh; >> query: (?x5945, 05p553) <- film(?x4465, ?x5945), award_winner(?x11272, ?x4465), cast_members(?x4465, ?x1942), award_nominee(?x4465, ?x2390) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 05t0_2v genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 54.000 54.000 0.812 http://example.org/film/film/genre EVAL 05t0_2v genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.812 http://example.org/film/film/genre #3638-09w1n PRED entity: 09w1n PRED relation: country PRED expected values: 01mjq 0163v 03rj0 0d05w3 016wzw => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 278): 07t21 (0.87 #4587, 0.85 #3766, 0.80 #5072), 0k6nt (0.86 #3905, 0.83 #1621, 0.79 #648), 015fr (0.86 #3905, 0.80 #5057, 0.79 #6026), 059j2 (0.86 #3905, 0.79 #648, 0.78 #2949), 0d05w3 (0.85 #3785, 0.83 #4931, 0.80 #4606), 01znc_ (0.83 #1621, 0.72 #3904, 0.71 #3906), 01mjq (0.80 #4427, 0.79 #648, 0.77 #4099), 07t_x (0.80 #4655, 0.78 #2695, 0.75 #649), 0163v (0.79 #648, 0.77 #3780, 0.75 #5086), 015qh (0.79 #648, 0.77 #3768, 0.75 #649) >> Best rule #4587 for best value: >> intensional similarity = 47 >> extensional distance = 13 >> proper extension: 019w9j; >> query: (?x3309, 07t21) <- country(?x3309, ?x8588), country(?x3309, ?x4059), country(?x3309, ?x2513), country(?x3309, ?x1355), sports(?x2630, ?x3309), film_release_region(?x11351, ?x4059), film_release_region(?x9839, ?x4059), film_release_region(?x9194, ?x4059), film_release_region(?x7832, ?x4059), film_release_region(?x4707, ?x4059), film_release_region(?x3784, ?x4059), film_release_region(?x3423, ?x4059), film_release_region(?x1518, ?x4059), ?x3423 = 09g7vfw, ?x4707 = 02xbyr, ?x11351 = 02wtp6, film_release_region(?x10346, ?x2513), film_release_region(?x9294, ?x2513), film_release_region(?x7629, ?x2513), film_release_region(?x7554, ?x2513), film_release_region(?x5315, ?x2513), film_release_region(?x4040, ?x2513), film_release_region(?x3812, ?x2513), film_release_region(?x1421, ?x2513), ?x9194 = 0fpgp26, ?x3784 = 0bmhvpr, teams(?x2513, ?x9109), ?x1421 = 07qg8v, nationality(?x4250, ?x4059), olympics(?x2513, ?x584), ?x5315 = 0glqh5_, ?x4040 = 02mt51, medal(?x2513, ?x422), olympics(?x453, ?x2630), ?x7554 = 01mgw, ?x7629 = 02825nf, ?x7832 = 0fphf3v, countries_spoken_in(?x8650, ?x4059), ?x10346 = 0dw4b0, ?x3812 = 0c3xw46, contains(?x4059, ?x11540), ?x9294 = 0m3gy, combatants(?x2513, ?x583), ?x9839 = 0gy7bj4, ?x8588 = 0jhd, ?x1518 = 04w7rn, service_location(?x896, ?x1355) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #3785 for first EXPECTED value: *> intensional similarity = 46 *> extensional distance = 11 *> proper extension: 01lb14; *> query: (?x3309, 0d05w3) <- country(?x3309, ?x8620), country(?x3309, ?x3912), country(?x3309, ?x3855), country(?x3309, ?x1790), country(?x3309, ?x1603), country(?x3309, ?x304), country(?x3309, ?x205), country(?x3309, ?x151), country(?x3309, ?x142), olympics(?x3309, ?x1277), olympics(?x3309, ?x418), ?x304 = 0d0vqn, participating_countries(?x418, ?x2146), participating_countries(?x418, ?x1203), participating_countries(?x418, ?x985), ?x2146 = 03rk0, country(?x4310, ?x3855), ?x151 = 0b90_r, ?x1603 = 06bnz, member_states(?x7695, ?x3855), partially_contains(?x455, ?x3855), film_release_region(?x428, ?x3855), ?x205 = 03rjj, ?x142 = 0jgd, sports(?x1277, ?x453), adjoins(?x3912, ?x12727), taxonomy(?x3912, ?x939), ?x1790 = 01pj7, ?x4310 = 064vjs, country(?x359, ?x1203), film_release_region(?x9174, ?x985), film_release_region(?x8770, ?x985), film_release_region(?x7651, ?x985), film_release_region(?x1080, ?x985), film_release_region(?x141, ?x985), ?x8770 = 025ts_z, ?x7651 = 0h95927, ?x1080 = 01c22t, organization(?x985, ?x127), participating_countries(?x1608, ?x3912), olympics(?x985, ?x391), ?x141 = 0gtsx8c, ?x9174 = 087pfc, olympics(?x1203, ?x2966), ?x1608 = 09x3r, jurisdiction_of_office(?x182, ?x8620) *> conf = 0.85 ranks of expected_values: 5, 7, 9, 15, 53 EVAL 09w1n country 016wzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 46.000 46.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09w1n country 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 46.000 46.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09w1n country 03rj0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 46.000 46.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09w1n country 0163v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 46.000 46.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09w1n country 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 46.000 46.000 0.867 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #3637-02wb6d PRED entity: 02wb6d PRED relation: music! PRED expected values: 03mr85 => 163 concepts (134 used for prediction) PRED predicted values (max 10 best out of 918): 09d38d (0.10 #4002, 0.08 #7026, 0.03 #21138), 0gvvm6l (0.07 #2820, 0.06 #7860, 0.05 #10884), 0gcrg (0.07 #380, 0.01 #12476, 0.01 #17516), 0b_5d (0.07 #296, 0.01 #12392, 0.01 #17432), 0k4f3 (0.07 #274, 0.01 #12370, 0.01 #17410), 0h3k3f (0.07 #3867, 0.05 #6891, 0.03 #21003), 0y_pg (0.07 #3808, 0.05 #6832, 0.02 #20944), 0gnjh (0.07 #3698, 0.03 #6722, 0.03 #5714), 0k5px (0.07 #3989, 0.03 #7013, 0.02 #21125), 07bzz7 (0.06 #4558, 0.05 #6574, 0.04 #22702) >> Best rule #4002 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 09bx1k; >> query: (?x6971, 09d38d) <- place_of_death(?x6971, ?x1523), nominated_for(?x6971, ?x4841), music(?x1308, ?x6971) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02wb6d music! 03mr85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 163.000 134.000 0.100 http://example.org/film/film/music #3636-0fsv2 PRED entity: 0fsv2 PRED relation: time_zones PRED expected values: 02hczc => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 12): 02hczc (0.84 #418, 0.83 #118, 0.83 #93), 02lcqs (0.52 #109, 0.46 #175, 0.43 #188), 02hcv8 (0.49 #772, 0.47 #1123, 0.47 #1149), 02fqwt (0.23 #614, 0.22 #262, 0.22 #288), 02llzg (0.10 #643, 0.08 #994, 0.08 #1228), 03bdv (0.08 #332, 0.07 #1321, 0.06 #1009), 02lcrv (0.04 #125, 0.04 #111, 0.04 #151), 03plfd (0.03 #597, 0.02 #493, 0.02 #1572), 052vwh (0.02 #742, 0.02 #234, 0.02 #351), 042g7t (0.02 #676, 0.02 #1014, 0.01 #845) >> Best rule #418 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 0l_q9; >> query: (?x13739, ?x2088) <- contains(?x938, ?x13739), county_seat(?x12253, ?x13739), country(?x13739, ?x94), time_zones(?x12253, ?x2088) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fsv2 time_zones 02hczc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 170.000 0.838 http://example.org/location/location/time_zones #3635-013fn PRED entity: 013fn PRED relation: category PRED expected values: 08mbj5d => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.84 #45, 0.84 #143, 0.84 #142) >> Best rule #45 for best value: >> intensional similarity = 6 >> extensional distance = 42 >> proper extension: 02r5dz; 01zpmq; 07xyn1; 0vlf; >> query: (?x12460, 08mbj5d) <- state_province_region(?x12460, ?x8506), adjoins(?x9494, ?x8506), capital(?x8506, ?x5036), company(?x8314, ?x12460), location(?x5282, ?x8506), contact_category(?x12460, ?x897) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013fn category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.841 http://example.org/common/topic/webpage./common/webpage/category #3634-03hmt9b PRED entity: 03hmt9b PRED relation: category PRED expected values: 08mbj5d => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.37 #2, 0.33 #5, 0.32 #4) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 0581vn8; >> query: (?x4007, 08mbj5d) <- film_crew_role(?x4007, ?x468), nominated_for(?x1198, ?x4007), ?x1198 = 02pqp12, ?x468 = 02r96rf >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hmt9b category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.367 http://example.org/common/topic/webpage./common/webpage/category #3633-04n65n PRED entity: 04n65n PRED relation: artists! PRED expected values: 06cp5 => 126 concepts (45 used for prediction) PRED predicted values (max 10 best out of 286): 064t9 (0.74 #4353, 0.45 #633, 0.45 #12427), 06by7 (0.61 #4362, 0.61 #5293, 0.58 #4052), 0xhtw (0.39 #9634, 0.36 #5288, 0.29 #7151), 025sc50 (0.38 #361, 0.38 #981, 0.23 #4391), 0m0jc (0.38 #318, 0.31 #938, 0.23 #4038), 01lyv (0.38 #2515, 0.25 #3755, 0.18 #12449), 05bt6j (0.34 #4074, 0.33 #2524, 0.31 #4384), 06j6l (0.33 #4389, 0.26 #12463, 0.25 #979), 0gywn (0.28 #989, 0.24 #4399, 0.23 #369), 02x8m (0.25 #949, 0.20 #1879, 0.19 #12414) >> Best rule #4353 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 01p45_v; 04bgy; >> query: (?x7201, 064t9) <- artists(?x2937, ?x7201), group(?x7201, ?x3390), artists(?x2937, ?x7162), ?x7162 = 0ffgh >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #12414 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 474 *> proper extension: 039cq4; *> query: (?x7201, ?x302) <- award_winner(?x7201, ?x3390), award(?x3390, ?x1565), artists(?x302, ?x3390) *> conf = 0.19 ranks of expected_values: 25 EVAL 04n65n artists! 06cp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 126.000 45.000 0.744 http://example.org/music/genre/artists #3632-0nvrd PRED entity: 0nvrd PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 107 concepts (60 used for prediction) PRED predicted values (max 10 best out of 7): 09c7w0 (0.91 #188, 0.90 #258, 0.88 #557), 03v0t (0.19 #468, 0.12 #397, 0.12 #720), 0nvrd (0.19 #468, 0.10 #370, 0.09 #383), 0l3kx (0.19 #468, 0.10 #370, 0.09 #383), 0nvg4 (0.19 #468, 0.10 #370, 0.09 #383), 02jx1 (0.04 #307, 0.03 #366, 0.03 #379), 03rt9 (0.03 #387, 0.01 #666, 0.01 #768) >> Best rule #188 for best value: >> intensional similarity = 4 >> extensional distance = 100 >> proper extension: 0njpq; >> query: (?x1963, 09c7w0) <- adjoins(?x10134, ?x1963), county_seat(?x1963, ?x8936), adjoins(?x11150, ?x10134), currency(?x1963, ?x170) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nvrd second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 60.000 0.912 http://example.org/location/country/second_level_divisions #3631-07zl1 PRED entity: 07zl1 PRED relation: profession PRED expected values: 0kyk => 125 concepts (54 used for prediction) PRED predicted values (max 10 best out of 77): 06q2q (0.78 #2962, 0.42 #4740, 0.36 #8000), 02hrh1q (0.73 #6975, 0.67 #4457, 0.66 #5494), 0kyk (0.71 #474, 0.50 #622, 0.46 #1658), 0dxtg (0.64 #6233, 0.58 #2826, 0.50 #6381), 01d_h8 (0.47 #6226, 0.36 #1338, 0.32 #6374), 09jwl (0.47 #4610, 0.42 #6091, 0.38 #4462), 02jknp (0.44 #6227, 0.24 #6375, 0.22 #2820), 0nbcg (0.34 #4623, 0.27 #6104, 0.27 #4475), 04s2z (0.33 #212, 0.04 #2285, 0.04 #2581), 016z4k (0.30 #4595, 0.27 #4447, 0.24 #5632) >> Best rule #2962 for best value: >> intensional similarity = 5 >> extensional distance = 238 >> proper extension: 03mz9r; 052h3; 0d5_f; 0bwx3; 02f9wb; 06c44; 08304; 023n39; 079ws; 0448r; ... >> query: (?x10438, ?x3802) <- student(?x6637, ?x10438), profession(?x10438, ?x11085), profession(?x10438, ?x353), specialization_of(?x11085, ?x3802), ?x353 = 0cbd2 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #474 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 03v36; *> query: (?x10438, 0kyk) <- award(?x10438, ?x11263), award(?x10438, ?x3337), ?x3337 = 01yz0x, ?x11263 = 01tgwv, gender(?x10438, ?x231) *> conf = 0.71 ranks of expected_values: 3 EVAL 07zl1 profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 54.000 0.784 http://example.org/people/person/profession #3630-06nnj PRED entity: 06nnj PRED relation: adjoins PRED expected values: 015fr => 148 concepts (97 used for prediction) PRED predicted values (max 10 best out of 387): 015fr (0.55 #801, 0.54 #1574, 0.50 #3120), 0345h (0.40 #66, 0.08 #6257, 0.08 #19389), 0165v (0.38 #2007, 0.36 #1234, 0.31 #3553), 016wzw (0.38 #1685, 0.36 #912, 0.31 #3231), 01ls2 (0.36 #794, 0.33 #2340, 0.31 #1567), 0jgd (0.31 #1549, 0.25 #3095, 0.18 #776), 07ylj (0.27 #834, 0.25 #18547, 0.24 #37859), 06nnj (0.25 #18547, 0.24 #37859, 0.23 #70323), 0j3b (0.25 #18547, 0.24 #37859, 0.23 #70323), 01p1v (0.23 #1651, 0.19 #3197, 0.18 #878) >> Best rule #801 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01ls2; 015fr; 02k1b; >> query: (?x9051, 015fr) <- medal(?x9051, ?x422), contains(?x12315, ?x9051), ?x12315 = 06n3y, country(?x4045, ?x9051) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06nnj adjoins 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 97.000 0.545 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #3629-01hcj2 PRED entity: 01hcj2 PRED relation: profession PRED expected values: 03gjzk => 137 concepts (114 used for prediction) PRED predicted values (max 10 best out of 74): 01d_h8 (0.50 #154, 0.47 #1190, 0.40 #3114), 02jknp (0.50 #156, 0.33 #8, 0.23 #9924), 03gjzk (0.37 #755, 0.33 #459, 0.33 #15), 09jwl (0.33 #1055, 0.32 #1203, 0.27 #463), 02krf9 (0.33 #26, 0.25 #174, 0.13 #16727), 018gz8 (0.30 #757, 0.19 #609, 0.14 #1645), 0dxtg (0.29 #8006, 0.28 #8302, 0.28 #10374), 0nbcg (0.27 #475, 0.21 #1363, 0.18 #1067), 016z4k (0.27 #448, 0.18 #1040, 0.17 #1188), 0d1pc (0.25 #346, 0.21 #2566, 0.17 #4194) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 07bsj; >> query: (?x9545, 01d_h8) <- participant(?x8571, ?x9545), ?x8571 = 0p17j, profession(?x9545, ?x1032) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #755 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 01_j71; 02_n5d; *> query: (?x9545, 03gjzk) <- award(?x9545, ?x11179), nationality(?x9545, ?x94), ?x11179 = 0cqhmg *> conf = 0.37 ranks of expected_values: 3 EVAL 01hcj2 profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 137.000 114.000 0.500 http://example.org/people/person/profession #3628-05sb1 PRED entity: 05sb1 PRED relation: contains PRED expected values: 05xb7q => 175 concepts (96 used for prediction) PRED predicted values (max 10 best out of 2866): 03rk0 (0.69 #108561, 0.62 #249410, 0.61 #272883), 04vmp (0.43 #129101, 0.05 #12754, 0.04 #15688), 05xb7q (0.29 #269948, 0.28 #20537, 0.27 #29341), 0hj6h (0.29 #269948), 059f4 (0.12 #26474, 0.12 #17670, 0.11 #41142), 0bwfn (0.10 #12779, 0.08 #18647, 0.06 #27451), 01lhdt (0.10 #12706, 0.04 #15640, 0.04 #71384), 0d34_ (0.10 #14282, 0.04 #17216, 0.04 #72960), 09f8q (0.10 #14021, 0.04 #16955, 0.04 #72699), 05bkf (0.10 #13948, 0.04 #16882, 0.04 #72626) >> Best rule #108561 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 0ck1d; >> query: (?x2236, ?x2146) <- contains(?x2236, ?x2365), adjoins(?x2365, ?x2146), administrative_parent(?x2236, ?x551) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #269948 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 120 *> proper extension: 06mx8; 027rqbx; 02v3m7; 08xpv_; 041_3z; *> query: (?x2236, ?x11914) <- contains(?x2236, ?x12577), contains(?x2236, ?x2365), adjoins(?x2365, ?x2146), contains(?x12577, ?x11914) *> conf = 0.29 ranks of expected_values: 3 EVAL 05sb1 contains 05xb7q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 175.000 96.000 0.691 http://example.org/location/location/contains #3627-0pnf3 PRED entity: 0pnf3 PRED relation: currency PRED expected values: 09nqf => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.33 #1, 0.31 #4, 0.22 #7), 01nv4h (0.04 #8, 0.03 #11, 0.02 #14) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 02dh86; 0p_jc; >> query: (?x10299, 09nqf) <- nominated_for(?x10299, ?x2500), inductee(?x9953, ?x10299), type_of_union(?x10299, ?x566) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pnf3 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #3626-04rzd PRED entity: 04rzd PRED relation: group PRED expected values: 0249kn 02mq_y => 83 concepts (59 used for prediction) PRED predicted values (max 10 best out of 699): 05563d (0.71 #3723, 0.60 #3053, 0.57 #6952), 014pg1 (0.71 #4118, 0.57 #2846, 0.50 #3447), 02vgh (0.67 #3429, 0.57 #4100, 0.57 #2846), 03qkcn9 (0.60 #2840, 0.57 #4187, 0.57 #4018), 0134tg (0.60 #3071, 0.57 #3909, 0.57 #2846), 01czx (0.60 #3040, 0.57 #3878, 0.57 #2846), 01k_yf (0.60 #2732, 0.57 #2846, 0.50 #4421), 01wv9xn (0.60 #3033, 0.57 #2846, 0.50 #1010), 07bzp (0.60 #3084, 0.57 #2846, 0.50 #1061), 02mq_y (0.60 #3069, 0.57 #2846, 0.50 #6285) >> Best rule #3723 for best value: >> intensional similarity = 15 >> extensional distance = 5 >> proper extension: 01wy6; >> query: (?x1969, 05563d) <- role(?x3296, ?x1969), role(?x2309, ?x1969), role(?x894, ?x1969), role(?x894, ?x4429), instrumentalists(?x894, ?x1231), ?x2309 = 06ncr, role(?x366, ?x1969), performance_role(?x212, ?x1969), role(?x1969, ?x3239), ?x3296 = 07_l6, instrumentalists(?x1969, ?x1001), group(?x1969, ?x6475), ?x4429 = 0g33q, ?x3239 = 03qmg1, group(?x1291, ?x6475) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #3069 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 3 *> proper extension: 03qjg; *> query: (?x1969, 02mq_y) <- role(?x3296, ?x1969), role(?x2309, ?x1969), role(?x2059, ?x1969), role(?x894, ?x1969), role(?x894, ?x614), instrumentalists(?x894, ?x6838), ?x2309 = 06ncr, role(?x366, ?x1969), performance_role(?x212, ?x1969), role(?x1969, ?x228), ?x3296 = 07_l6, instrumentalists(?x1969, ?x1001), role(?x2865, ?x1969), ?x2059 = 0dwr4, ?x6838 = 0130sy, group(?x1969, ?x1929) *> conf = 0.60 ranks of expected_values: 10, 113 EVAL 04rzd group 02mq_y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 83.000 59.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 04rzd group 0249kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 83.000 59.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #3625-02lyx4 PRED entity: 02lyx4 PRED relation: profession PRED expected values: 02hrh1q => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 74): 02hrh1q (0.91 #3615, 0.89 #9465, 0.89 #9916), 0dz3r (0.42 #302, 0.27 #2, 0.18 #3152), 016z4k (0.42 #304, 0.19 #3154, 0.18 #4504), 01d_h8 (0.39 #1806, 0.38 #1356, 0.37 #1956), 03gjzk (0.38 #1066, 0.36 #166, 0.25 #2266), 0d1pc (0.36 #52, 0.21 #3202, 0.20 #1552), 018gz8 (0.34 #1068, 0.29 #10352, 0.17 #618), 09jwl (0.29 #320, 0.29 #10352, 0.27 #3170), 0nbcg (0.29 #10352, 0.27 #3183, 0.25 #333), 0dxtg (0.26 #10966, 0.25 #9765, 0.25 #1064) >> Best rule #3615 for best value: >> intensional similarity = 3 >> extensional distance = 159 >> proper extension: 01vw87c; 01j4ls; 01pw2f1; 03rl84; 02mhfy; 0b_fw; 01mmslz; 0c01c; 01m65sp; 01jbx1; ... >> query: (?x10410, 02hrh1q) <- participant(?x4066, ?x10410), gender(?x10410, ?x514), actor(?x5529, ?x10410) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lyx4 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.907 http://example.org/people/person/profession #3624-0f4kp PRED entity: 0f4kp PRED relation: nutrient! PRED expected values: 0f25w9 033cnk => 55 concepts (51 used for prediction) PRED predicted values (max 10 best out of 7): 033cnk (0.91 #444, 0.91 #438, 0.90 #417), 0f25w9 (0.89 #16, 0.89 #315, 0.89 #51), 06x4c (0.89 #16, 0.89 #51, 0.88 #118), 0dcfv (0.89 #16, 0.89 #51, 0.88 #118), 04k8n (0.04 #473), 05wvs (0.04 #473), 01sh2 (0.04 #473) >> Best rule #444 for best value: >> intensional similarity = 101 >> extensional distance = 30 >> proper extension: 075pwf; >> query: (?x13944, ?x6159) <- nutrient(?x9005, ?x13944), nutrient(?x8298, ?x13944), nutrient(?x6032, ?x13944), nutrient(?x5373, ?x13944), nutrient(?x5009, ?x13944), nutrient(?x4068, ?x13944), nutrient(?x3468, ?x13944), nutrient(?x2701, ?x13944), nutrient(?x4068, ?x12902), nutrient(?x4068, ?x12868), nutrient(?x4068, ?x12083), nutrient(?x4068, ?x11784), nutrient(?x4068, ?x11758), nutrient(?x4068, ?x11409), nutrient(?x4068, ?x11270), nutrient(?x4068, ?x10709), nutrient(?x4068, ?x10195), nutrient(?x4068, ?x10098), nutrient(?x4068, ?x9915), nutrient(?x4068, ?x9840), nutrient(?x4068, ?x9795), nutrient(?x4068, ?x9436), nutrient(?x4068, ?x9426), nutrient(?x4068, ?x9365), nutrient(?x4068, ?x8442), nutrient(?x4068, ?x8413), nutrient(?x4068, ?x7894), nutrient(?x4068, ?x7652), nutrient(?x4068, ?x7431), nutrient(?x4068, ?x7362), nutrient(?x4068, ?x7219), nutrient(?x4068, ?x7135), nutrient(?x4068, ?x6586), nutrient(?x4068, ?x6286), nutrient(?x4068, ?x6192), nutrient(?x4068, ?x6160), nutrient(?x4068, ?x6033), nutrient(?x4068, ?x6026), nutrient(?x4068, ?x5549), nutrient(?x4068, ?x5526), nutrient(?x4068, ?x5337), nutrient(?x4068, ?x3469), nutrient(?x4068, ?x2702), nutrient(?x4068, ?x2018), nutrient(?x4068, ?x1960), nutrient(?x4068, ?x1258), ?x6286 = 02y_3rf, ?x5373 = 0971v, ?x6586 = 05gh50, ?x11758 = 0q01m, ?x9915 = 025tkqy, ?x9840 = 02p0tjr, ?x7362 = 02kc5rj, ?x7219 = 0h1vg, ?x6160 = 041r51, ?x12083 = 01n78x, ?x5337 = 06x4c, ?x9426 = 0h1yy, ?x6192 = 06jry, ?x5526 = 09pbb, ?x3469 = 0h1zw, ?x1258 = 0h1wg, ?x9436 = 025sqz8, ?x10709 = 0h1sz, nutrient(?x5009, ?x9949), nutrient(?x5009, ?x9619), nutrient(?x5009, ?x9490), nutrient(?x5009, ?x1304), ?x1960 = 07hnp, ?x7652 = 025s0s0, nutrient(?x6159, ?x12868), ?x11409 = 0h1yf, ?x7135 = 025rsfk, ?x9490 = 0h1sg, ?x8442 = 02kcv4x, ?x11270 = 02kc008, ?x6033 = 04zjxcz, ?x5549 = 025s7j4, ?x9365 = 04k8n, ?x1304 = 08lb68, ?x7431 = 09gwd, ?x3468 = 0cxn2, ?x9619 = 0h1tg, ?x6032 = 01nkt, ?x6026 = 025sf8g, nutrient(?x1959, ?x9795), ?x9005 = 04zpv, ?x11784 = 07zqy, ?x2018 = 01sh2, nutrient(?x2701, ?x13126), ?x8298 = 037ls6, ?x2702 = 0838f, ?x12902 = 0fzjh, ?x10098 = 0h1_c, ?x7894 = 0f4hc, ?x13126 = 02kc_w5, ?x8413 = 02kc4sf, ?x10195 = 0hkwr, ?x9949 = 02kd0rh, ?x1959 = 0f25w9, ?x6159 = 033cnk >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0f4kp nutrient! 033cnk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 51.000 0.906 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0f4kp nutrient! 0f25w9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 51.000 0.906 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #3623-0bkf72 PRED entity: 0bkf72 PRED relation: produced_by! PRED expected values: 01bb9r => 70 concepts (52 used for prediction) PRED predicted values (max 10 best out of 492): 01fszq (0.40 #4733, 0.16 #10415, 0.16 #13255), 01b9w3 (0.40 #4733, 0.16 #10415, 0.16 #13255), 0kvbl6 (0.05 #614, 0.03 #2507, 0.02 #4400), 0h03fhx (0.05 #425, 0.03 #2318, 0.02 #4211), 01s7w3 (0.05 #814, 0.02 #3653, 0.02 #8387), 02vqsll (0.05 #271, 0.02 #1217, 0.02 #2164), 0ds2l81 (0.05 #771, 0.02 #1717, 0.02 #2664), 0g54xkt (0.05 #289, 0.02 #2182, 0.01 #3128), 02qr69m (0.05 #216, 0.02 #2109, 0.01 #3055), 05fgt1 (0.05 #215, 0.02 #2108, 0.01 #4001) >> Best rule #4733 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 0qf43; 0p51w; 03tf_h; 01f8ld; 09p06; 01n9d9; 012rng; 01vsps; 0bs8d; 098n_m; ... >> query: (?x8590, ?x4384) <- award(?x8590, ?x1307), ?x1307 = 0gq9h, nominated_for(?x8590, ?x4384) >> conf = 0.40 => this is the best rule for 2 predicted values *> Best rule #1893 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 01w92; *> query: (?x8590, ?x339) <- award_winner(?x8590, ?x2179), award_nominee(?x8590, ?x382), production_companies(?x339, ?x382) *> conf = 0.01 ranks of expected_values: 436 EVAL 0bkf72 produced_by! 01bb9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 70.000 52.000 0.395 http://example.org/film/film/produced_by #3622-0dtfn PRED entity: 0dtfn PRED relation: nominated_for! PRED expected values: 0p9sw 0gr51 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 221): 054krc (0.78 #4136, 0.77 #1380, 0.77 #21582), 0gs96 (0.78 #4136, 0.77 #1380, 0.77 #21582), 02g3ft (0.78 #4136, 0.77 #1380, 0.74 #2758), 02g3gw (0.78 #4136, 0.77 #1380, 0.74 #2758), 02r0csl (0.67 #1155, 0.64 #1382, 0.40 #5743), 0p9sw (0.64 #1382, 0.58 #2547, 0.56 #1169), 02r22gf (0.64 #1382, 0.50 #714, 0.44 #1176), 02hsq3m (0.64 #1382, 0.44 #1177, 0.40 #5743), 018wng (0.64 #1382, 0.29 #11026, 0.27 #4137), 0gr0m (0.58 #2584, 0.44 #1206, 0.37 #3502) >> Best rule #4136 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0ds11z; 0b73_1d; 0fdv3; 0kv9d3; 01hqk; 06rhz7; 05fm6m; 011xg5; >> query: (?x1386, ?x484) <- award(?x1386, ?x484), crewmember(?x1386, ?x1585), award(?x1585, ?x500), category(?x1386, ?x134) >> conf = 0.78 => this is the best rule for 4 predicted values *> Best rule #1382 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 011yxg; *> query: (?x1386, ?x500) <- award(?x1386, ?x2222), crewmember(?x1386, ?x1585), award(?x1585, ?x500), ?x2222 = 0gs96 *> conf = 0.64 ranks of expected_values: 6, 53 EVAL 0dtfn nominated_for! 0gr51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 139.000 139.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0dtfn nominated_for! 0p9sw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 139.000 139.000 0.779 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3621-09vw2b7 PRED entity: 09vw2b7 PRED relation: film_crew_role! PRED expected values: 0170z3 0b76d_m 014_x2 0ds11z 03s6l2 04gknr 0bwfwpj 05q96q6 01f7gh 01719t 02rv_dz 05p3738 02rx2m5 03sxd2 047n8xt 035s95 065z3_x 0418wg 07cz2 0gfsq9 04q24zv 03kg2v 0ggbhy7 0x25q 0571m 07yvsn 033srr 0g83dv 0d61px 09v71cj 03tps5 06zn2v2 0fqt1ns 03b_fm5 026qnh6 07_fj54 02xs6_ 0bh8tgs 012s1d 04fv5b 03t79f 07_k0c0 02z2mr7 011ypx 05t0_2v 04pk1f 0404j37 0dc_ms 03_wm6 0421v9q 0drnwh 047rkcm 027pfg 08984j 027r9t 05nlx4 031786 0btpm6 03bzjpm 04165w 01633c 05pxnmb 01cycq 01n30p 03z9585 026wlxw 05zvzf3 023g6w 027x7z5 0ndsl1x 0gy0l_ 0h14ln 0gwf191 0dnkmq 028kj0 07vn_9 08g_jw 04nlb94 04q01mn => 33 concepts (22 used for prediction) PRED predicted values (max 10 best out of 544): 047p798 (0.67 #5410, 0.67 #4866, 0.62 #6498), 09g8vhw (0.67 #4462, 0.67 #3917, 0.50 #6094), 01gglm (0.67 #4800, 0.67 #4255, 0.50 #5888), 035bcl (0.67 #4678, 0.67 #4133, 0.50 #3589), 05nlx4 (0.67 #4207, 0.50 #5840, 0.50 #5296), 0cbv4g (0.67 #4645, 0.50 #5733, 0.50 #4100), 027x7z5 (0.67 #4279, 0.50 #5368, 0.50 #3735), 05t0_2v (0.67 #4683, 0.50 #5227, 0.50 #4138), 033srr (0.67 #4033, 0.50 #5122, 0.50 #3489), 0ds11z (0.67 #3832, 0.50 #4921, 0.50 #3288) >> Best rule #5410 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 01pvkk; 0d2b38; >> query: (?x1171, 047p798) <- film_crew_role(?x9421, ?x1171), film_crew_role(?x5746, ?x1171), film_crew_role(?x4592, ?x1171), ?x5746 = 02qsqmq, country(?x4592, ?x429), ?x9421 = 0ct2tf5 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4207 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 0dxtw; *> query: (?x1171, 05nlx4) <- film_crew_role(?x6005, ?x1171), film_crew_role(?x4939, ?x1171), film_crew_role(?x3612, ?x1171), genre(?x6005, ?x53), nominated_for(?x2248, ?x4939), ?x3612 = 04z257 *> conf = 0.67 ranks of expected_values: 5, 7, 8, 9, 10, 13, 15, 17, 18, 19, 24, 25, 27, 31, 32, 35, 42, 48, 49, 50, 52, 53, 56, 58, 60, 61, 62, 66, 67, 68, 76, 79, 82, 83, 87, 88, 89, 91, 93, 94, 95, 98, 99, 100, 102, 103, 105, 109, 113, 114, 116, 117, 124, 130, 132, 133, 139, 155, 164, 175, 178, 185, 217, 226, 229, 232, 286, 289, 290, 292, 293, 305, 316, 320, 341, 346, 374, 392, 431 EVAL 09vw2b7 film_crew_role! 04q01mn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 04nlb94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 08g_jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 07vn_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 028kj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0dnkmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0gwf191 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0h14ln CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0gy0l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0ndsl1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 027x7z5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 023g6w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 026wlxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 03z9585 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 01n30p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 01cycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 05pxnmb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 01633c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 04165w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 03bzjpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 031786 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 05nlx4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 027r9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 08984j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 047rkcm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0drnwh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0421v9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 03_wm6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0dc_ms CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 04pk1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 05t0_2v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 011ypx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 02z2mr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 07_k0c0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 03t79f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 04fv5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 012s1d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0bh8tgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 02xs6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 07_fj54 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 026qnh6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 03b_fm5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0fqt1ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 06zn2v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 03tps5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 09v71cj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0d61px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0g83dv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 033srr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 07yvsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0571m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0x25q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0ggbhy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 03kg2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 04q24zv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0gfsq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 07cz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0418wg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 065z3_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 035s95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 047n8xt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 03sxd2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 02rx2m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 05p3738 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 02rv_dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 01719t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 01f7gh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 05q96q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0bwfwpj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 04gknr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 03s6l2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0ds11z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 014_x2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0b76d_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 09vw2b7 film_crew_role! 0170z3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 33.000 22.000 0.667 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3620-06c0ns PRED entity: 06c0ns PRED relation: genre PRED expected values: 07s9rl0 05p553 => 74 concepts (54 used for prediction) PRED predicted values (max 10 best out of 91): 03k9fj (0.72 #3253, 0.39 #3013, 0.38 #3133), 07s9rl0 (0.71 #5285, 0.61 #601, 0.60 #1081), 01jfsb (0.50 #3014, 0.50 #12, 0.50 #3134), 05p553 (0.40 #3246, 0.34 #6009, 0.34 #4328), 02l7c8 (0.36 #496, 0.34 #376, 0.27 #4220), 06n90 (0.28 #3135, 0.27 #1921, 0.27 #2162), 082gq (0.27 #1921, 0.27 #2162, 0.13 #511), 01lrrt (0.27 #1921, 0.27 #2162, 0.12 #6247), 05c3mp2 (0.27 #1921, 0.27 #2162, 0.07 #290), 01drsx (0.27 #1921, 0.27 #2162, 0.05 #403) >> Best rule #3253 for best value: >> intensional similarity = 5 >> extensional distance = 509 >> proper extension: 0vgkd; >> query: (?x6963, 03k9fj) <- genre(?x6963, ?x5231), genre(?x9478, ?x5231), genre(?x6578, ?x5231), ?x9478 = 0f8j13, film_crew_role(?x6578, ?x468) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #5285 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1299 *> proper extension: 053tj7; 0gcrg; 016ztl; 02zk08; *> query: (?x6963, 07s9rl0) <- genre(?x6963, ?x225), language(?x6963, ?x254), genre(?x54, ?x225), ?x54 = 0170z3 *> conf = 0.71 ranks of expected_values: 2, 4 EVAL 06c0ns genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 74.000 54.000 0.720 http://example.org/film/film/genre EVAL 06c0ns genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 54.000 0.720 http://example.org/film/film/genre #3619-0b85mm PRED entity: 0b85mm PRED relation: music PRED expected values: 012201 => 90 concepts (52 used for prediction) PRED predicted values (max 10 best out of 95): 01l9v7n (0.15 #467), 01njxvw (0.12 #191, 0.12 #1241, 0.04 #821), 012ljv (0.12 #1, 0.08 #211, 0.04 #1051), 05y7hc (0.12 #126, 0.02 #2228, 0.02 #5593), 02z81h (0.12 #112, 0.02 #2214), 01x6v6 (0.09 #753, 0.04 #2646, 0.03 #1385), 0146pg (0.08 #220, 0.08 #850, 0.04 #9055), 01m3b1t (0.08 #346, 0.04 #2238), 01m5m5b (0.08 #398, 0.04 #818, 0.04 #3341), 023361 (0.08 #360, 0.03 #1832, 0.03 #6458) >> Best rule #467 for best value: >> intensional similarity = 2 >> extensional distance = 11 >> proper extension: 01cgz; >> query: (?x11809, 01l9v7n) <- films(?x1967, ?x11809), ?x1967 = 01cgz >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #781 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 21 *> proper extension: 028_yv; 040rmy; 01jwxx; 011ycb; 03nqnnk; 07jnt; 07g1sm; 0bdjd; 02vz6dn; 0pd64; ... *> query: (?x11809, 012201) <- film_release_region(?x11809, ?x1499), film_release_region(?x11809, ?x789), ?x1499 = 01znc_, ?x789 = 0f8l9c, films(?x1967, ?x11809), genre(?x11809, ?x53), film_regional_debut_venue(?x11809, ?x13344) *> conf = 0.04 ranks of expected_values: 28 EVAL 0b85mm music 012201 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 90.000 52.000 0.154 http://example.org/film/film/music #3618-0n6dc PRED entity: 0n6dc PRED relation: location! PRED expected values: 01vvyvk => 125 concepts (63 used for prediction) PRED predicted values (max 10 best out of 2102): 02hblj (0.50 #103093, 0.49 #95549, 0.49 #128237), 01qn8k (0.33 #1882, 0.20 #4396, 0.05 #29540), 0dszr0 (0.33 #2467, 0.20 #4981, 0.05 #10012), 0pyww (0.33 #980, 0.10 #3494, 0.09 #16067), 02sjf5 (0.33 #202, 0.10 #2716, 0.09 #15289), 0sx5w (0.33 #2138, 0.10 #4652, 0.08 #9683), 05myd2 (0.33 #1924, 0.10 #4438, 0.08 #9469), 0blt6 (0.33 #690, 0.10 #3204, 0.07 #15777), 015v3r (0.33 #600, 0.10 #3114, 0.05 #8145), 05d1y (0.33 #1678, 0.10 #4192, 0.05 #9223) >> Best rule #103093 for best value: >> intensional similarity = 4 >> extensional distance = 201 >> proper extension: 03s0w; 03hrz; >> query: (?x11843, ?x12084) <- location(?x8341, ?x11843), place_of_birth(?x12084, ?x11843), instrumentalists(?x75, ?x8341), contains(?x94, ?x11843) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8446 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 02_286; 030qb3t; 01_d4; 0dclg; 0f__1; 01531; 0ccvx; 0psxp; 013n0n; 0xn7b; ... *> query: (?x11843, 01vvyvk) <- location(?x4593, ?x11843), source(?x11843, ?x958), notable_people_with_this_condition(?x9118, ?x4593), profession(?x4593, ?x131) *> conf = 0.03 ranks of expected_values: 1130 EVAL 0n6dc location! 01vvyvk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 125.000 63.000 0.496 http://example.org/people/person/places_lived./people/place_lived/location #3617-02x258x PRED entity: 02x258x PRED relation: award! PRED expected values: 03cx282 0f3zsq 05br10 => 46 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2093): 04qvl7 (0.79 #33752, 0.69 #47257, 0.66 #47256), 02kxbx3 (0.50 #4364, 0.33 #24616, 0.27 #21240), 02kxbwx (0.50 #3554, 0.33 #23806, 0.27 #20430), 05ldnp (0.50 #4273, 0.29 #24525, 0.27 #21149), 0151w_ (0.50 #3609, 0.29 #23861, 0.21 #27235), 0184jw (0.50 #5641, 0.27 #29267, 0.27 #22517), 06m6z6 (0.50 #4486, 0.27 #21362, 0.21 #24738), 0qf43 (0.50 #3426, 0.21 #27052, 0.18 #20302), 0c00lh (0.50 #4950, 0.21 #28576, 0.18 #21826), 0136g9 (0.50 #3706, 0.21 #23958, 0.12 #10454) >> Best rule #33752 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 09v7wsg; >> query: (?x2393, ?x185) <- nominated_for(?x2393, ?x144), award_winner(?x2393, ?x185), award(?x1077, ?x2393), ceremony(?x2393, ?x762) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1468 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 0gr0m; *> query: (?x2393, 03cx282) <- award(?x8288, ?x2393), nominated_for(?x2393, ?x144), ?x8288 = 0164w8 *> conf = 0.33 ranks of expected_values: 45, 52, 955 EVAL 02x258x award! 05br10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 46.000 16.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x258x award! 0f3zsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 46.000 16.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x258x award! 03cx282 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 46.000 16.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3616-0147sh PRED entity: 0147sh PRED relation: nominated_for! PRED expected values: 0gq_v => 57 concepts (53 used for prediction) PRED predicted values (max 10 best out of 194): 0gq_v (0.53 #732, 0.50 #258, 0.39 #1680), 019f4v (0.48 #1004, 0.37 #767, 0.33 #55), 0gs9p (0.43 #1014, 0.37 #777, 0.35 #1725), 040njc (0.41 #956, 0.33 #7, 0.27 #719), 02qyntr (0.41 #1128, 0.17 #417, 0.16 #891), 0l8z1 (0.39 #1002, 0.24 #765, 0.21 #291), 0gr0m (0.34 #1010, 0.30 #1898, 0.27 #773), 04dn09n (0.34 #985, 0.27 #511, 0.19 #1696), 02pqp12 (0.34 #1009, 0.18 #1958, 0.17 #298), 0p9sw (0.33 #21, 0.33 #970, 0.33 #733) >> Best rule #732 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0bmc4cm; >> query: (?x878, 0gq_v) <- genre(?x878, ?x4757), ?x4757 = 06l3bl, nominated_for(?x1307, ?x878) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0147sh nominated_for! 0gq_v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 53.000 0.531 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3615-05g3v PRED entity: 05g3v PRED relation: school PRED expected values: 05krk => 84 concepts (64 used for prediction) PRED predicted values (max 10 best out of 314): 0lyjf (0.47 #6015, 0.45 #2358, 0.43 #1784), 07w0v (0.44 #2103, 0.42 #5369, 0.41 #5756), 0jkhr (0.43 #1822, 0.27 #2396, 0.25 #2011), 0g8rj (0.40 #847, 0.22 #2178, 0.18 #2561), 06pwq (0.37 #5752, 0.35 #6140, 0.33 #3822), 01jq0j (0.33 #2208, 0.33 #1446, 0.28 #8429), 0225bv (0.33 #1319, 0.33 #1130, 0.25 #558), 0j_sncb (0.33 #991, 0.29 #1558, 0.25 #419), 05krk (0.29 #1714, 0.27 #2479, 0.27 #3437), 012vwb (0.29 #1570, 0.25 #431, 0.23 #6184) >> Best rule #6015 for best value: >> intensional similarity = 17 >> extensional distance = 28 >> proper extension: 01y3c; 01xvb; 05l71; 0289q; 04vn5; 0ws7; 06x76; >> query: (?x2198, 0lyjf) <- position_s(?x2198, ?x2573), position_s(?x2198, ?x2147), team(?x2573, ?x13438), team(?x2573, ?x5822), team(?x2573, ?x3114), team(?x2573, ?x387), ?x3114 = 070xg, team(?x1792, ?x2198), ?x387 = 02896, draft(?x2198, ?x465), ?x2147 = 04nfpk, position(?x4519, ?x2573), position(?x684, ?x2573), ?x4519 = 084l5, ?x684 = 01ct6, ?x13438 = 02wvfxz, ?x5822 = 03wnh >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1714 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 5 *> proper extension: 05gg4; 01c_d; *> query: (?x2198, 05krk) <- position_s(?x2198, ?x3346), position_s(?x2198, ?x2573), position_s(?x2198, ?x2247), position_s(?x2198, ?x2147), position_s(?x2198, ?x1717), position_s(?x2198, ?x1240), position_s(?x2198, ?x935), ?x2573 = 05b3ts, ?x2247 = 01_9c1, ?x1717 = 02g_6x, ?x1240 = 023wyl, position(?x2198, ?x1114), draft(?x2198, ?x465), team(?x11323, ?x2198), ?x2147 = 04nfpk, school(?x2198, ?x735), ?x935 = 06b1q, ?x3346 = 02g_7z, sport(?x2198, ?x1083) *> conf = 0.29 ranks of expected_values: 9 EVAL 05g3v school 05krk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 84.000 64.000 0.467 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #3614-02qk3fk PRED entity: 02qk3fk PRED relation: film! PRED expected values: 03kts => 65 concepts (33 used for prediction) PRED predicted values (max 10 best out of 813): 01tsbmv (0.14 #1897, 0.04 #8134, 0.03 #10213), 0f4vbz (0.14 #363, 0.03 #35711, 0.03 #41950), 01pkhw (0.14 #698, 0.03 #43668, 0.03 #37429), 012ykt (0.14 #1091, 0.03 #5249, 0.02 #13566), 03hzl42 (0.14 #786, 0.03 #4944, 0.01 #22872), 08qxx9 (0.14 #1518, 0.03 #26469, 0.02 #28548), 0lpjn (0.14 #479, 0.02 #12954, 0.02 #15033), 03jj93 (0.14 #1895, 0.02 #22687, 0.02 #10211), 02ck7w (0.14 #938, 0.02 #21730, 0.01 #22872), 01l7qw (0.14 #1909, 0.01 #22872, 0.01 #20792) >> Best rule #1897 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 047qxs; 0292qb; >> query: (?x6422, 01tsbmv) <- film(?x382, ?x6422), film(?x2745, ?x6422), currency(?x6422, ?x170), ?x2745 = 038rzr, genre(?x6422, ?x53) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02qk3fk film! 03kts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 33.000 0.143 http://example.org/film/actor/film./film/performance/film #3613-0h7pj PRED entity: 0h7pj PRED relation: currency PRED expected values: 09nqf => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.47 #19, 0.47 #16, 0.45 #4), 01nv4h (0.07 #14, 0.07 #8, 0.04 #29) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 0bx_q; >> query: (?x8898, 09nqf) <- participant(?x1397, ?x8898), participant(?x2451, ?x8898), friend(?x917, ?x8898) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h7pj currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.474 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #3612-01k8rb PRED entity: 01k8rb PRED relation: student! PRED expected values: 0bjqh => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 86): 0fr9jp (0.14 #345, 0.03 #6142, 0.03 #3507), 017hnw (0.14 #509), 019dwp (0.14 #158), 031q3w (0.14 #124), 07tds (0.08 #676, 0.03 #1203, 0.03 #1730), 017j69 (0.08 #672, 0.03 #1726, 0.02 #13320), 0bwfn (0.05 #3437, 0.05 #27152, 0.05 #26625), 05nrkb (0.05 #1403, 0.04 #1930, 0.02 #4038), 015nl4 (0.04 #594, 0.04 #24836, 0.03 #30634), 053mhx (0.04 #822, 0.03 #1349, 0.03 #1876) >> Best rule #345 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0gsg7; >> query: (?x1397, 0fr9jp) <- nominated_for(?x1397, ?x1673), nominated_for(?x1397, ?x782), ?x782 = 02k_4g, category(?x1673, ?x134) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01k8rb student! 0bjqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 112.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #3611-0dhqyw PRED entity: 0dhqyw PRED relation: artists! PRED expected values: 0827d => 84 concepts (49 used for prediction) PRED predicted values (max 10 best out of 271): 064t9 (0.96 #8145, 0.95 #10966, 0.89 #1264), 06by7 (0.74 #12227, 0.70 #12853, 0.64 #10663), 03_d0 (0.69 #6892, 0.38 #1575, 0.34 #1888), 0dl5d (0.68 #5335, 0.17 #12851, 0.14 #5647), 025sc50 (0.63 #4427, 0.63 #3488, 0.59 #4740), 02lnbg (0.58 #3497, 0.57 #4436, 0.56 #1312), 06j6l (0.44 #10063, 0.42 #4738, 0.41 #1301), 05bt6j (0.38 #6299, 0.37 #1296, 0.34 #5985), 0glt670 (0.37 #3478, 0.37 #4730, 0.34 #4417), 016clz (0.37 #12836, 0.29 #11583, 0.26 #6259) >> Best rule #8145 for best value: >> intensional similarity = 8 >> extensional distance = 204 >> proper extension: 05d8vw; 01svw8n; 01ws9n6; 015srx; 08w4pm; 07hgm; >> query: (?x8604, 064t9) <- artists(?x5876, ?x8604), origin(?x8604, ?x9305), artists(?x5876, ?x7634), artists(?x5876, ?x2274), artists(?x5876, ?x2237), ?x2274 = 013v5j, ?x2237 = 01vs_v8, ?x7634 = 02bwjv >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 01nqfh_; 06k02; 01gg59; 05y7hc; 02sjp; *> query: (?x8604, 0827d) <- artists(?x4910, ?x8604), artists(?x597, ?x8604), category(?x8604, ?x134), ?x597 = 0ggq0m, ?x4910 = 017_qw, ?x134 = 08mbj5d, nationality(?x8604, ?x2146) *> conf = 0.29 ranks of expected_values: 14 EVAL 0dhqyw artists! 0827d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 84.000 49.000 0.961 http://example.org/music/genre/artists #3610-01tlrp PRED entity: 01tlrp PRED relation: citytown PRED expected values: 07dfk => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 87): 02_286 (0.53 #1868, 0.36 #1124, 0.33 #2609), 07bcn (0.30 #2223, 0.16 #2593, 0.16 #2592), 0rh6k (0.30 #2223, 0.16 #2592, 0.12 #4074), 07dfk (0.26 #5029, 0.25 #4658, 0.23 #5776), 0135g (0.20 #484, 0.09 #1483, 0.09 #1480), 080h2 (0.15 #1482, 0.14 #757, 0.09 #1483), 01xhb_ (0.15 #1482, 0.12 #4074, 0.09 #1483), 0hsqf (0.15 #1482, 0.09 #1483, 0.09 #1480), 0d6lp (0.15 #1482, 0.09 #1483, 0.09 #1480), 081yw (0.15 #1482, 0.09 #1483, 0.09 #1480) >> Best rule #1868 for best value: >> intensional similarity = 22 >> extensional distance = 15 >> proper extension: 01yx7f; 02l48d; >> query: (?x11939, 02_286) <- industry(?x11939, ?x12816), industry(?x9469, ?x12816), industry(?x6972, ?x12816), industry(?x2270, ?x12816), service_location(?x9469, ?x279), list(?x9469, ?x5997), company(?x4682, ?x9469), company(?x1491, ?x9469), ?x5997 = 04k4rt, citytown(?x2270, ?x108), ?x1491 = 0krdk, category(?x6972, ?x134), currency(?x2270, ?x170), service_language(?x9469, ?x254), ?x4682 = 0dq_5, gender(?x6972, ?x514), state_province_region(?x9469, ?x4600), ?x134 = 08mbj5d, contact_category(?x9469, ?x897), ?x279 = 0d060g, ?x254 = 02h40lc, company(?x265, ?x2270) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #5029 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 60 *> proper extension: 06q07; *> query: (?x11939, 07dfk) <- industry(?x11939, ?x12816), industry(?x11939, ?x245), industry(?x9469, ?x12816), industry(?x2270, ?x12816), ?x9469 = 04sv4, industry(?x12217, ?x245), industry(?x11303, ?x245), industry(?x6230, ?x245), industry(?x3253, ?x245), service_language(?x3253, ?x254), category(?x12217, ?x134), company(?x346, ?x3253), currency(?x3253, ?x170), ?x6230 = 073tm9, citytown(?x11303, ?x1658), service_location(?x3253, ?x279), service_language(?x11303, ?x2164), child(?x11303, ?x13919), company(?x265, ?x2270), place_founded(?x3253, ?x4578) *> conf = 0.26 ranks of expected_values: 4 EVAL 01tlrp citytown 07dfk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 40.000 40.000 0.529 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #3609-02rmd_2 PRED entity: 02rmd_2 PRED relation: film_release_region PRED expected values: 0b90_r 06mzp 035qy 015qh 01mjq 06t2t => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 141): 0f8l9c (0.93 #1333, 0.93 #310, 0.92 #1041), 06t2t (0.86 #346, 0.83 #639, 0.82 #1808), 05v8c (0.86 #305, 0.75 #1036, 0.74 #1328), 035qy (0.85 #760, 0.82 #321, 0.81 #1783), 03rk0 (0.82 #341, 0.58 #634, 0.56 #780), 0b90_r (0.79 #588, 0.79 #295, 0.76 #1757), 04gzd (0.79 #299, 0.58 #738, 0.56 #1761), 01p1v (0.75 #337, 0.58 #776, 0.56 #630), 016wzw (0.75 #351, 0.56 #1082, 0.54 #1374), 015qh (0.71 #327, 0.62 #620, 0.60 #1058) >> Best rule #1333 for best value: >> intensional similarity = 6 >> extensional distance = 72 >> proper extension: 0ds35l9; 07gp9; 0ddfwj1; 0h1cdwq; 08720; 017gl1; 08hmch; 01c22t; 0jjy0; 0c0nhgv; ... >> query: (?x4372, 0f8l9c) <- film_release_region(?x4372, ?x2645), film_release_region(?x4372, ?x2152), executive_produced_by(?x4372, ?x4060), ?x2645 = 03h64, ?x2152 = 06mkj, genre(?x4372, ?x239) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #346 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 26 *> proper extension: 043tvp3; *> query: (?x4372, 06t2t) <- film_release_region(?x4372, ?x2645), film_release_region(?x4372, ?x1536), film_release_region(?x4372, ?x142), executive_produced_by(?x4372, ?x4060), ?x2645 = 03h64, ?x1536 = 06c1y, ?x142 = 0jgd *> conf = 0.86 ranks of expected_values: 2, 4, 6, 10, 14, 16 EVAL 02rmd_2 film_release_region 06t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02rmd_2 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 99.000 99.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02rmd_2 film_release_region 015qh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 99.000 99.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02rmd_2 film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 99.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02rmd_2 film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 99.000 99.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02rmd_2 film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 99.000 0.932 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3608-01gvr1 PRED entity: 01gvr1 PRED relation: type_of_union PRED expected values: 04ztj => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.88 #33, 0.86 #69, 0.85 #25), 01g63y (0.39 #10, 0.33 #82, 0.32 #70) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 0167v4; >> query: (?x624, 04ztj) <- award(?x624, ?x375), spouse(?x624, ?x2835), artists(?x119, ?x2835) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gvr1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.877 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3607-01x66d PRED entity: 01x66d PRED relation: instrumentalists! PRED expected values: 05148p4 => 106 concepts (87 used for prediction) PRED predicted values (max 10 best out of 122): 0342h (0.78 #2947, 0.75 #3120, 0.70 #3382), 05148p4 (0.55 #449, 0.51 #882, 0.50 #19), 018vs (0.43 #1046, 0.41 #441, 0.40 #874), 01vdm0 (0.32 #689, 0.27 #2506, 0.26 #2157), 0l14md (0.27 #695, 0.25 #6, 0.22 #1041), 03qjg (0.25 #136, 0.20 #913, 0.19 #739), 03gvt (0.25 #64, 0.19 #580, 0.12 #666), 026t6 (0.25 #2, 0.14 #3380, 0.14 #691), 03f5mt (0.25 #82, 0.09 #945, 0.08 #857), 0mkg (0.25 #9, 0.09 #611, 0.08 #95) >> Best rule #2947 for best value: >> intensional similarity = 5 >> extensional distance = 464 >> proper extension: 01vt5c_; >> query: (?x1068, 0342h) <- instrumentalists(?x228, ?x1068), role(?x130, ?x228), role(?x74, ?x228), group(?x228, ?x5227), ?x5227 = 01j59b0 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #449 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 01vs14j; 02r4qs; 01lvcs1; 04m2zj; 01jgkj2; *> query: (?x1068, 05148p4) <- instrumentalists(?x228, ?x1068), ?x228 = 0l14qv, artists(?x1067, ?x1068), location(?x1068, ?x1523), category(?x1068, ?x134) *> conf = 0.55 ranks of expected_values: 2 EVAL 01x66d instrumentalists! 05148p4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 87.000 0.781 http://example.org/music/instrument/instrumentalists #3606-0275n3y PRED entity: 0275n3y PRED relation: award_winner PRED expected values: 0hvb2 0dvmd 08yx9q 042xrr 013pk3 02ktrs => 25 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2566): 01j7rd (0.59 #18319, 0.14 #19824, 0.13 #21329), 02xs0q (0.47 #18556, 0.10 #20061, 0.09 #21566), 081nh (0.44 #10847, 0.40 #12348, 0.33 #13852), 076lxv (0.40 #12109, 0.33 #13613, 0.33 #10608), 02sj1x (0.40 #12526, 0.33 #14030, 0.27 #17034), 072twv (0.40 #12353, 0.33 #13857, 0.27 #16861), 0h005 (0.40 #12708, 0.33 #14212, 0.27 #17216), 025jfl (0.33 #10590, 0.33 #9085, 0.33 #4577), 05fnl9 (0.33 #9239, 0.33 #226, 0.20 #16753), 07lt7b (0.33 #3090, 0.33 #86, 0.19 #19532) >> Best rule #18319 for best value: >> intensional similarity = 16 >> extensional distance = 15 >> proper extension: 05pd94v; >> query: (?x5592, 01j7rd) <- award_winner(?x5592, ?x4564), award_winner(?x5592, ?x2657), award_winner(?x5592, ?x1365), ceremony(?x899, ?x5592), student(?x13219, ?x2657), award_nominee(?x2912, ?x2657), award_nominee(?x949, ?x2657), gender(?x2657, ?x231), nominated_for(?x1365, ?x1118), award_nominee(?x274, ?x2912), award(?x1365, ?x198), award_winner(?x5296, ?x2657), award_winner(?x1105, ?x4564), award_winner(?x1630, ?x2912), award(?x949, ?x678), ?x5296 = 07y9ts >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #2154 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 027hjff; *> query: (?x5592, 08yx9q) <- award_winner(?x5592, ?x6324), award_winner(?x5592, ?x5996), award_winner(?x5592, ?x4564), award_winner(?x5592, ?x848), ceremony(?x2341, ?x5592), ?x5996 = 095b70, nominated_for(?x4564, ?x2709), award_nominee(?x1039, ?x4564), award(?x276, ?x2341), nominated_for(?x2341, ?x6288), nominated_for(?x2341, ?x1392), ?x1392 = 017gm7, film_release_region(?x2709, ?x87), ?x848 = 034x61, honored_for(?x5592, ?x10731), ?x6324 = 018ygt, ?x6288 = 01chpn, ?x10731 = 0cs134 *> conf = 0.33 ranks of expected_values: 31, 113, 127, 154, 158, 204 EVAL 0275n3y award_winner 02ktrs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 25.000 19.000 0.588 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0275n3y award_winner 013pk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 25.000 19.000 0.588 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0275n3y award_winner 042xrr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 25.000 19.000 0.588 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0275n3y award_winner 08yx9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 25.000 19.000 0.588 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0275n3y award_winner 0dvmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 25.000 19.000 0.588 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0275n3y award_winner 0hvb2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 25.000 19.000 0.588 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3605-0bq8tmw PRED entity: 0bq8tmw PRED relation: film_crew_role PRED expected values: 015h31 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 27): 09vw2b7 (0.67 #1346, 0.65 #908, 0.64 #1274), 0dxtw (0.62 #46, 0.45 #658, 0.44 #912), 01vx2h (0.46 #47, 0.38 #479, 0.38 #913), 01pvkk (0.29 #914, 0.29 #480, 0.28 #733), 02rh1dz (0.23 #45, 0.19 #225, 0.19 #477), 02ynfr (0.23 #52, 0.18 #1356, 0.18 #1284), 020xn5 (0.23 #43, 0.04 #331, 0.04 #475), 089g0h (0.15 #56, 0.14 #488, 0.12 #922), 01xy5l_ (0.15 #50, 0.11 #1282, 0.11 #626), 02_n3z (0.15 #37, 0.09 #1341, 0.09 #1521) >> Best rule #1346 for best value: >> intensional similarity = 4 >> extensional distance = 551 >> proper extension: 03xj05; >> query: (?x1642, 09vw2b7) <- nominated_for(?x3508, ?x1642), film_crew_role(?x1642, ?x468), ?x468 = 02r96rf, award(?x123, ?x3508) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #224 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 133 *> proper extension: 0gtsx8c; 0dtw1x; 08hmch; 07g_0c; 04zyhx; 03sxd2; 01b195; 04g9gd; 05fgt1; 065zlr; ... *> query: (?x1642, 015h31) <- film_release_region(?x1642, ?x87), film_release_distribution_medium(?x1642, ?x81), crewmember(?x1642, ?x1643), production_companies(?x1642, ?x541) *> conf = 0.14 ranks of expected_values: 13 EVAL 0bq8tmw film_crew_role 015h31 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 97.000 97.000 0.665 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3604-0170z3 PRED entity: 0170z3 PRED relation: film_distribution_medium PRED expected values: 0735l => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.80 #70, 0.18 #57, 0.17 #50), 029j_ (0.16 #66, 0.10 #166, 0.10 #172), 02nxhr (0.11 #67, 0.07 #255, 0.06 #167), 0dq6p (0.08 #68, 0.06 #256, 0.05 #168) >> Best rule #70 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 07kb7vh; >> query: (?x54, 0735l) <- language(?x54, ?x254), film(?x6157, ?x54), region(?x54, ?x512), award_nominee(?x6157, ?x397) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0170z3 film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.797 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #3603-01jfsb PRED entity: 01jfsb PRED relation: genre! PRED expected values: 014_x2 060v34 0fg04 01kf3_9 05qbckf 0gvrws1 01b195 0661ql3 04g9gd 07j8r 0j43swk 0299hs 016kv6 04ydr95 024lff 0g9yrw 0pd57 09gb_4p 0f42nz 0fsw_7 04cbbz 0284b56 02q87z6 034qbx 0bxxzb 0gvvf4j 02k1pr 06r2h 0crs0b8 0353tm 0m3gy 0jdr0 07l450 09wnnb 0gyv0b4 07p12s 027r7k 032xky 042g97 => 56 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1469): 09k56b7 (0.78 #15323, 0.77 #6964, 0.72 #11144), 02tktw (0.78 #15323, 0.77 #6964, 0.72 #11144), 02c638 (0.78 #15323, 0.77 #6964, 0.72 #11144), 02jr6k (0.78 #15323, 0.77 #6964, 0.72 #11144), 0b6tzs (0.78 #15323, 0.77 #6964, 0.72 #11144), 016fyc (0.78 #15323, 0.77 #6964, 0.72 #11144), 035zr0 (0.78 #15323, 0.77 #6964, 0.72 #11144), 02jkkv (0.78 #15323, 0.77 #6964, 0.72 #11144), 04jkpgv (0.78 #15323, 0.77 #6964, 0.72 #11144), 05zvzf3 (0.78 #15323, 0.77 #6964, 0.72 #11144) >> Best rule #15323 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 03npn; 02n4kr; 09blyk; >> query: (?x812, ?x394) <- genre(?x7275, ?x812), genre(?x1562, ?x812), titles(?x812, ?x394), film_release_region(?x7275, ?x87), ?x1562 = 026n4h6 >> conf = 0.78 => this is the best rule for 35 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 20, 36, 39, 98, 112, 118, 129, 165, 169, 180, 195, 199, 267, 269, 271, 272, 348, 355, 369, 546, 547, 566, 568, 569, 618, 628, 632, 652, 662, 682, 687, 691, 714, 785, 847, 996, 1047, 1067, 1256 EVAL 01jfsb genre! 042g97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 032xky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 027r7k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 07p12s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0gyv0b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 09wnnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 07l450 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0jdr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0m3gy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0353tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0crs0b8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 06r2h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 02k1pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0gvvf4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0bxxzb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 034qbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 02q87z6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0284b56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 04cbbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0fsw_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0f42nz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 09gb_4p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0pd57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0g9yrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 024lff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 04ydr95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 016kv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0299hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0j43swk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 07j8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 04g9gd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 01b195 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0gvrws1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 05qbckf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 01kf3_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 0fg04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 060v34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre EVAL 01jfsb genre! 014_x2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 45.000 0.778 http://example.org/film/film/genre #3602-0chghy PRED entity: 0chghy PRED relation: country_of_origin! PRED expected values: 02rq7nd => 244 concepts (170 used for prediction) PRED predicted values (max 10 best out of 395): 02gl58 (0.50 #2094, 0.25 #2634, 0.22 #3174), 06k176 (0.50 #2120, 0.22 #3200, 0.16 #4548), 04sskp (0.50 #2037, 0.22 #3117, 0.14 #2308), 05z43v (0.50 #2032, 0.22 #3112, 0.14 #2303), 0b005 (0.50 #2008, 0.22 #3088, 0.14 #2279), 01hn_t (0.50 #1954, 0.22 #3034, 0.14 #2225), 090s_0 (0.50 #1885, 0.22 #2965, 0.14 #2156), 027tbrc (0.25 #2457, 0.25 #1917, 0.16 #4345), 03cf9ly (0.25 #2122, 0.14 #2393, 0.12 #2662), 07g9f (0.25 #2089, 0.14 #2360, 0.12 #2629) >> Best rule #2094 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 09c7w0; 07ssc; >> query: (?x390, 02gl58) <- country(?x10260, ?x390), ?x10260 = 037cr1, film_release_region(?x66, ?x390) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0chghy country_of_origin! 02rq7nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 244.000 170.000 0.500 http://example.org/tv/tv_program/country_of_origin #3601-07f0tw PRED entity: 07f0tw PRED relation: location PRED expected values: 04vmp => 106 concepts (101 used for prediction) PRED predicted values (max 10 best out of 123): 04vmp (0.33 #2764, 0.28 #5176, 0.27 #3568), 030qb3t (0.33 #13743, 0.29 #10529, 0.28 #6511), 02_286 (0.23 #22535, 0.19 #20126, 0.19 #18519), 04jpl (0.15 #48230, 0.10 #19302, 0.09 #53049), 0cvw9 (0.13 #2808, 0.11 #398, 0.07 #3612), 07c98 (0.11 #1253, 0.11 #449, 0.08 #2056), 049lr (0.11 #1256, 0.08 #2059, 0.07 #3666), 09c6w (0.11 #1076, 0.08 #1879, 0.07 #2682), 086g2 (0.11 #653, 0.07 #3063, 0.03 #4671), 02p3my (0.08 #2370, 0.03 #5585, 0.03 #4781) >> Best rule #2764 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 06wvfq; 03fwln; 040nwr; >> query: (?x10462, 04vmp) <- award(?x10462, ?x10156), nationality(?x10462, ?x2146), ?x10156 = 03r8v_, ?x2146 = 03rk0, location(?x10462, ?x12210) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07f0tw location 04vmp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 101.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #3600-03kxdw PRED entity: 03kxdw PRED relation: category PRED expected values: 08mbj5d => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.52 #2, 0.40 #3, 0.40 #8) >> Best rule #2 for best value: >> intensional similarity = 2 >> extensional distance = 104 >> proper extension: 04r1t; >> query: (?x8780, 08mbj5d) <- influenced_by(?x6692, ?x8780), artist(?x12061, ?x6692) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03kxdw category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.519 http://example.org/common/topic/webpage./common/webpage/category #3599-01my4f PRED entity: 01my4f PRED relation: type_of_union PRED expected values: 04ztj => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.87 #81, 0.85 #97, 0.85 #69), 01g63y (0.25 #70, 0.25 #82, 0.24 #98) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 0c9d9; 06y9c2; 02v406; 01mt1fy; 01rc4p; 037s5h; 0f14q; 01hdht; 01nglk; >> query: (?x6913, 04ztj) <- student(?x6056, ?x6913), spouse(?x6913, ?x6278), gender(?x6913, ?x231) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01my4f type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.869 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3598-023p33 PRED entity: 023p33 PRED relation: film_release_region PRED expected values: 09c7w0 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 145): 09c7w0 (0.94 #4428, 0.93 #6730, 0.93 #9562), 0jgd (0.93 #1244, 0.87 #1598, 0.86 #1775), 0f8l9c (0.92 #1800, 0.91 #1623, 0.91 #1269), 0d0vqn (0.91 #1251, 0.91 #1782, 0.90 #1605), 05r4w (0.90 #1595, 0.88 #1772, 0.86 #2126), 06mkj (0.88 #1313, 0.85 #2375, 0.85 #1844), 059j2 (0.86 #2167, 0.86 #1636, 0.85 #1282), 0k6nt (0.86 #1627, 0.85 #1804, 0.79 #1273), 03gj2 (0.85 #1274, 0.84 #2159, 0.84 #2336), 0chghy (0.85 #2141, 0.84 #2318, 0.84 #5858) >> Best rule #4428 for best value: >> intensional similarity = 4 >> extensional distance = 206 >> proper extension: 09xbpt; 0dtw1x; 026mfbr; 053tj7; 028cg00; 0j_tw; 04g9gd; 08k40m; 0crh5_f; 0ckrgs; ... >> query: (?x2097, 09c7w0) <- category(?x2097, ?x134), production_companies(?x2097, ?x3920), film_release_region(?x2097, ?x429), ?x134 = 08mbj5d >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023p33 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.938 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3597-01713c PRED entity: 01713c PRED relation: gender PRED expected values: 05zppz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #9, 0.91 #5, 0.79 #3), 02zsn (0.56 #14, 0.48 #12, 0.48 #38) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 01l7qw; >> query: (?x1582, 05zppz) <- award(?x1582, ?x4091), ?x4091 = 09sdmz, film(?x1582, ?x186) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01713c gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.917 http://example.org/people/person/gender #3596-01vswx5 PRED entity: 01vswx5 PRED relation: profession PRED expected values: 0nbcg 039v1 => 109 concepts (82 used for prediction) PRED predicted values (max 10 best out of 78): 02hrh1q (0.78 #2681, 0.76 #8626, 0.76 #8774), 0dz3r (0.67 #1778, 0.55 #2816, 0.50 #2), 0nbcg (0.58 #4036, 0.57 #2846, 0.57 #3590), 01c72t (0.50 #24, 0.37 #1800, 0.31 #5661), 016z4k (0.48 #2374, 0.44 #3412, 0.43 #1928), 039v1 (0.40 #2851, 0.39 #4041, 0.39 #3595), 0n1h (0.35 #308, 0.34 #1344, 0.33 #1492), 01d_h8 (0.32 #2672, 0.31 #2524, 0.31 #2079), 0dxtg (0.32 #2680, 0.29 #3274, 0.28 #9809), 01c8w0 (0.26 #12165, 0.08 #9, 0.06 #5053) >> Best rule #2681 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 04bs3j; 0gthm; >> query: (?x5170, 02hrh1q) <- award(?x5170, ?x1323), nationality(?x5170, ?x512), person(?x10796, ?x5170) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4036 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 242 *> proper extension: 01p45_v; 04d_mtq; 015196; *> query: (?x5170, 0nbcg) <- artists(?x302, ?x5170), role(?x5170, ?x227), instrumentalists(?x716, ?x5170) *> conf = 0.58 ranks of expected_values: 3, 6 EVAL 01vswx5 profession 039v1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 109.000 82.000 0.779 http://example.org/people/person/profession EVAL 01vswx5 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 82.000 0.779 http://example.org/people/person/profession #3595-02s5v5 PRED entity: 02s5v5 PRED relation: award PRED expected values: 0fm3kw => 80 concepts (74 used for prediction) PRED predicted values (max 10 best out of 278): 04dn09n (0.57 #43, 0.55 #443, 0.50 #843), 03hkv_r (0.49 #16, 0.45 #416, 0.41 #816), 02n9nmz (0.40 #68, 0.38 #468, 0.34 #868), 0gs9p (0.36 #77, 0.34 #477, 0.31 #877), 09sb52 (0.35 #2840, 0.34 #9241, 0.32 #3640), 0gr51 (0.33 #98, 0.31 #898, 0.31 #498), 0gq9h (0.33 #75, 0.31 #475, 0.30 #875), 040njc (0.32 #8, 0.30 #408, 0.27 #808), 019f4v (0.31 #65, 0.29 #465, 0.26 #865), 03hl6lc (0.27 #176, 0.23 #576, 0.22 #976) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 0qf43; 0prjs; 01_vfy; 034bgm; 0gr36; 01q4qv; 085pr; 0171lb; 012wg; 098n_m; ... >> query: (?x3096, 04dn09n) <- award(?x3096, ?x601), ?x601 = 0gr4k, award_winner(?x8277, ?x3096) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02s5v5 award 0fm3kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 74.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3594-04x4vj PRED entity: 04x4vj PRED relation: film_crew_role PRED expected values: 09zzb8 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.75 #1580, 0.74 #1158, 0.71 #1510), 02r96rf (0.68 #1161, 0.67 #1583, 0.61 #1513), 0dxtw (0.40 #1589, 0.39 #115, 0.38 #746), 01vx2h (0.34 #1168, 0.31 #1590, 0.29 #2118), 01pvkk (0.30 #257, 0.29 #1134, 0.28 #1977), 0215hd (0.25 #228, 0.21 #158, 0.21 #263), 0d2b38 (0.22 #235, 0.19 #270, 0.18 #165), 089g0h (0.19 #229, 0.16 #264, 0.15 #159), 01xy5l_ (0.15 #154, 0.13 #1171, 0.12 #434), 02_n3z (0.14 #212, 0.12 #247, 0.09 #352) >> Best rule #1580 for best value: >> intensional similarity = 3 >> extensional distance = 658 >> proper extension: 03g90h; 0gx1bnj; 02_1sj; 026mfbr; 09p35z; 0963mq; 0gj8t_b; 03sxd2; 02vqhv0; 0j_tw; ... >> query: (?x4591, 09zzb8) <- film(?x526, ?x4591), film_crew_role(?x4591, ?x1078), produced_by(?x4591, ?x163) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04x4vj film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.748 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3593-05842k PRED entity: 05842k PRED relation: role! PRED expected values: 07_3qd 09swkk 01vswx5 0326tc 01nn3m => 77 concepts (47 used for prediction) PRED predicted values (max 10 best out of 932): 0326tc (0.71 #8995, 0.60 #11372, 0.57 #7402), 01vsyjy (0.71 #8970, 0.50 #11347, 0.50 #6982), 0133x7 (0.71 #7762, 0.50 #5000, 0.40 #6181), 01vsnff (0.60 #6003, 0.57 #7584, 0.50 #11155), 03j24kf (0.60 #6114, 0.57 #7695, 0.50 #5326), 01wp8w7 (0.60 #6370, 0.50 #3610, 0.43 #7557), 01vsy95 (0.60 #6054, 0.50 #4873, 0.36 #13584), 0144l1 (0.60 #6002, 0.33 #874, 0.33 #481), 0484q (0.60 #6196, 0.33 #1068, 0.33 #675), 01wwnh2 (0.60 #6283, 0.33 #1155, 0.33 #762) >> Best rule #8995 for best value: >> intensional similarity = 18 >> extensional distance = 5 >> proper extension: 0gkd1; >> query: (?x3991, 0326tc) <- role(?x3991, ?x6449), role(?x3991, ?x1166), role(?x3991, ?x433), role(?x3991, ?x227), role(?x6449, ?x75), role(?x12557, ?x3991), role(?x10091, ?x3991), role(?x8152, ?x3991), role(?x5494, ?x3991), ?x1166 = 05148p4, ?x433 = 025cbm, ?x227 = 0342h, artist(?x3888, ?x5494), artists(?x497, ?x5494), group(?x10091, ?x6699), category(?x8152, ?x134), ?x12557 = 04s5_s, role(?x745, ?x3991) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 17, 18, 58, 162 EVAL 05842k role! 01nn3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 77.000 47.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05842k role! 0326tc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 47.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05842k role! 01vswx5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 77.000 47.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05842k role! 09swkk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 77.000 47.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05842k role! 07_3qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 77.000 47.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #3592-0cfdd PRED entity: 0cfdd PRED relation: instrumentalists PRED expected values: 01vw_dv => 62 concepts (33 used for prediction) PRED predicted values (max 10 best out of 970): 01lvcs1 (0.73 #1853, 0.52 #3097, 0.52 #3093), 018x3 (0.73 #1853, 0.52 #3097, 0.52 #3093), 0140t7 (0.73 #1853, 0.52 #3097, 0.52 #3093), 0137g1 (0.73 #1853, 0.52 #3097, 0.52 #3093), 03h502k (0.73 #1853, 0.52 #3097, 0.52 #3093), 0fhxv (0.73 #1853, 0.52 #3097, 0.50 #3094), 0f0qfz (0.73 #1853, 0.49 #3717, 0.47 #3092), 050z2 (0.73 #1853, 0.49 #3717, 0.40 #5186), 0892sx (0.73 #1853, 0.49 #3717, 0.39 #11153), 01vsxdm (0.73 #1853, 0.49 #3717, 0.39 #11153) >> Best rule #1853 for best value: >> intensional similarity = 19 >> extensional distance = 1 >> proper extension: 06ncr; >> query: (?x5926, ?x1467) <- instrumentalists(?x5926, ?x140), role(?x5926, ?x227), role(?x5926, ?x75), ?x227 = 0342h, role(?x5926, ?x8172), role(?x5926, ?x1332), role(?x5926, ?x212), ?x1332 = 03qlv7, role(?x1473, ?x5926), role(?x1436, ?x5926), role(?x1467, ?x5926), award_nominee(?x527, ?x140), ?x212 = 026t6, ?x527 = 04lgymt, origin(?x140, ?x12107), performance_role(?x6052, ?x8172), role(?x8172, ?x615), ?x75 = 07y_7, artist(?x2299, ?x140) >> conf = 0.73 => this is the best rule for 10 predicted values *> Best rule #994 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 1 *> proper extension: 01v1d8; *> query: (?x5926, 01vw_dv) <- instrumentalists(?x5926, ?x7221), instrumentalists(?x5926, ?x140), role(?x5926, ?x2460), role(?x5926, ?x1647), role(?x5926, ?x227), ?x227 = 0342h, role(?x5926, ?x8172), role(?x5926, ?x1437), role(?x5926, ?x1332), ?x1332 = 03qlv7, ?x140 = 01vvydl, ?x1437 = 01vdm0, role(?x1467, ?x5926), role(?x5990, ?x8172), artists(?x302, ?x7221), award_winner(?x7221, ?x2392), place_of_birth(?x7221, ?x739), role(?x7210, ?x8172), group(?x5926, ?x1945), role(?x1647, ?x868), ?x5990 = 0192l, performance_role(?x2460, ?x1831) *> conf = 0.33 ranks of expected_values: 462 EVAL 0cfdd instrumentalists 01vw_dv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 33.000 0.733 http://example.org/music/instrument/instrumentalists #3591-0303jw PRED entity: 0303jw PRED relation: teams! PRED expected values: 0jhd => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 95): 0bjv6 (0.07 #96, 0.04 #7836, 0.04 #7835), 04v3q (0.07 #31, 0.04 #7836, 0.04 #7835), 0k6nt (0.07 #27, 0.04 #7836, 0.04 #7835), 06mzp (0.07 #23, 0.04 #7836, 0.04 #7835), 0d0vqn (0.07 #8, 0.04 #7836, 0.04 #7835), 05r4w (0.07 #1, 0.04 #7836, 0.04 #7835), 04w4s (0.07 #93, 0.04 #7836, 0.04 #7835), 06c1y (0.07 #51, 0.04 #7836, 0.04 #7835), 0163v (0.07 #68, 0.04 #7836, 0.04 #7835), 0m75g (0.05 #699, 0.03 #429, 0.02 #2049) >> Best rule #96 for best value: >> intensional similarity = 11 >> extensional distance = 12 >> proper extension: 01l3wr; 03yvln; >> query: (?x6983, 0bjv6) <- position(?x6983, ?x530), position(?x6983, ?x203), position(?x6983, ?x63), position(?x6983, ?x60), ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x203 = 0dgrmp, ?x60 = 02nzb8, team(?x8594, ?x6983), ?x8594 = 07y9k, team(?x63, ?x6983) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0303jw teams! 0jhd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 50.000 0.071 http://example.org/sports/sports_team_location/teams #3590-037jz PRED entity: 037jz PRED relation: influenced_by PRED expected values: 03f70xs => 105 concepts (44 used for prediction) PRED predicted values (max 10 best out of 367): 042q3 (0.50 #3421, 0.44 #3858, 0.10 #13859), 081k8 (0.47 #9741, 0.38 #3213, 0.33 #3650), 0gz_ (0.38 #3160, 0.33 #3597, 0.20 #2283), 0j3v (0.38 #3117, 0.33 #3554, 0.15 #3991), 03sbs (0.38 #3278, 0.33 #3715, 0.11 #13716), 02lt8 (0.36 #9705, 0.33 #119, 0.32 #5354), 041h0 (0.33 #446, 0.25 #1319, 0.25 #882), 06y8v (0.33 #215, 0.25 #1960, 0.12 #2836), 04093 (0.33 #291, 0.25 #2036, 0.12 #2912), 03hnd (0.32 #5333, 0.20 #2279, 0.15 #4030) >> Best rule #3421 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0j3v; 03pm9; 06c44; 08304; 048cl; 02wh0; >> query: (?x6810, 042q3) <- influenced_by(?x11554, ?x6810), influenced_by(?x9794, ?x6810), influenced_by(?x8383, ?x6810), ?x11554 = 03cdg, religion(?x8383, ?x8967), award(?x9794, ?x5354), gender(?x6810, ?x231) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5303 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 07w21; 09dt7; 014ps4; 06jcc; 06bng; 0yxl; 07d3x; 03j0d; 04x56; 03hpr; ... *> query: (?x6810, 03f70xs) <- influenced_by(?x3858, ?x6810), influenced_by(?x3279, ?x6810), profession(?x3279, ?x987), award(?x3279, ?x10270), influenced_by(?x1742, ?x3279), ?x3858 = 05jm7 *> conf = 0.16 ranks of expected_values: 32 EVAL 037jz influenced_by 03f70xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 105.000 44.000 0.500 http://example.org/influence/influence_node/influenced_by #3589-014488 PRED entity: 014488 PRED relation: award PRED expected values: 01by1l => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 275): 09lvl1 (0.70 #31518, 0.68 #22222, 0.67 #30709), 01bgqh (0.43 #849, 0.27 #5697, 0.26 #7313), 01by1l (0.40 #515, 0.35 #919, 0.31 #6575), 0ck27z (0.33 #1303, 0.15 #15848, 0.15 #29900), 05pcn59 (0.25 #80, 0.20 #1292, 0.18 #1696), 0f4x7 (0.25 #30, 0.16 #1646, 0.16 #2050), 07cbcy (0.25 #77, 0.16 #1693, 0.15 #29900), 05p09zm (0.25 #123, 0.15 #29900, 0.13 #31923), 04kxsb (0.25 #125, 0.15 #29900, 0.13 #31923), 03c7tr1 (0.25 #57, 0.15 #29900, 0.13 #31923) >> Best rule #31518 for best value: >> intensional similarity = 2 >> extensional distance = 2276 >> proper extension: 04f525m; 0hwd8; 027pdrh; 0b79gfg; 03q8ch; 056ws9; 014l4w; 015cxv; 081bls; 08mhyd; ... >> query: (?x3324, ?x7788) <- award(?x3324, ?x537), award_winner(?x7788, ?x3324) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 04dqdk; 03j24kf; 01w5gg6; *> query: (?x3324, 01by1l) <- location(?x3324, ?x739), artist(?x9120, ?x3324), ?x9120 = 025t8bv *> conf = 0.40 ranks of expected_values: 3 EVAL 014488 award 01by1l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 92.000 0.701 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3588-06wm0z PRED entity: 06wm0z PRED relation: vacationer! PRED expected values: 015fr => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 55): 05qtj (0.19 #933, 0.19 #810, 0.17 #1056), 0jbs5 (0.17 #98, 0.14 #221, 0.11 #467), 0160w (0.14 #125, 0.11 #371, 0.08 #617), 0b90_r (0.12 #864, 0.11 #741, 0.11 #987), 0cv3w (0.12 #918, 0.11 #1041, 0.11 #795), 0f2v0 (0.11 #924, 0.10 #801, 0.10 #1047), 04jpl (0.09 #870, 0.07 #993, 0.07 #624), 06c62 (0.07 #947, 0.06 #1070, 0.05 #824), 02_286 (0.06 #876, 0.06 #753, 0.06 #999), 0r0m6 (0.05 #930, 0.05 #1053, 0.04 #807) >> Best rule #933 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 04cr6qv; >> query: (?x5058, 05qtj) <- participant(?x5058, ?x6613), vacationer(?x6226, ?x5058), featured_film_locations(?x723, ?x6226) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #751 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 140 *> proper extension: 0456xp; 02lnhv; 0n6f8; 01wxyx1; 02mjmr; 0161c2; 05r5w; 07g2v; 01z0rcq; 024dgj; ... *> query: (?x5058, 015fr) <- participant(?x5058, ?x6613), vacationer(?x6226, ?x5058), award(?x5058, ?x704) *> conf = 0.04 ranks of expected_values: 17 EVAL 06wm0z vacationer! 015fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 124.000 124.000 0.192 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #3587-07fb8_ PRED entity: 07fb8_ PRED relation: film_format! PRED expected values: 017gl1 011yth 013q07 07jxpf 0g83dv 0fb7sd 091rc5 0prrm 0cbv4g 03cd0x 01l_pn 07gghl 02dr9j 035gnh 01cz7r 03cwwl 0_9l_ => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1634): 02_fm2 (0.40 #600, 0.33 #202, 0.25 #401), 05ch98 (0.33 #397, 0.33 #359, 0.25 #558), 0f4_2k (0.33 #311, 0.25 #510, 0.25 #395), 01chpn (0.33 #322, 0.25 #521, 0.20 #720), 0btpm6 (0.33 #348, 0.25 #547, 0.20 #746), 02mpyh (0.33 #368, 0.25 #567, 0.20 #766), 06_x996 (0.33 #281, 0.25 #480, 0.20 #679), 095zlp (0.33 #206, 0.25 #405, 0.20 #604), 011yxg (0.33 #203, 0.25 #402, 0.20 #601), 02mt51 (0.33 #280, 0.25 #479, 0.20 #678) >> Best rule #600 for best value: >> intensional similarity = 60 >> extensional distance = 3 >> proper extension: 0hcr; >> query: (?x909, 02_fm2) <- film_format(?x9421, ?x909), film_format(?x7199, ?x909), film_format(?x6298, ?x909), film_format(?x6018, ?x909), film_format(?x5189, ?x909), film_format(?x4839, ?x909), film_format(?x4304, ?x909), film_format(?x2734, ?x909), film_format(?x1224, ?x909), film_format(?x936, ?x909), film(?x541, ?x4304), nominated_for(?x1307, ?x1224), nominated_for(?x484, ?x1224), film_crew_role(?x9421, ?x1966), currency(?x5189, ?x170), film(?x8311, ?x4304), music(?x9421, ?x8806), film(?x166, ?x1224), film(?x1634, ?x7199), film(?x56, ?x9421), film(?x609, ?x7199), genre(?x1224, ?x225), award(?x71, ?x1307), film(?x1914, ?x4839), film(?x10834, ?x4839), award(?x253, ?x1307), genre(?x9421, ?x604), award_nominee(?x100, ?x1634), country(?x2734, ?x512), music(?x6298, ?x5536), film(?x4107, ?x6018), award_nominee(?x1634, ?x381), award(?x199, ?x484), nominated_for(?x2375, ?x2734), location(?x4107, ?x335), film_crew_role(?x10422, ?x1966), film_crew_role(?x5271, ?x1966), student(?x6925, ?x10834), country(?x936, ?x94), award(?x157, ?x2375), friend(?x1017, ?x1634), award_nominee(?x4107, ?x806), ?x5271 = 047vnkj, award(?x10834, ?x154), film(?x65, ?x936), ceremony(?x484, ?x4224), people(?x743, ?x1634), genre(?x2968, ?x225), genre(?x2441, ?x225), genre(?x1847, ?x225), genre(?x664, ?x225), ?x4224 = 05qb8vx, award(?x8311, ?x1323), ?x1847 = 02rb84n, genre(?x4304, ?x258), ?x664 = 0401sg, nominated_for(?x2585, ?x6298), ?x2968 = 025n07, ?x2441 = 0cc5mcj, ?x10422 = 05k4my >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #395 for first EXPECTED value: *> intensional similarity = 72 *> extensional distance = 1 *> proper extension: 0cj16; *> query: (?x909, ?x10446) <- film_format(?x10722, ?x909), film_format(?x10397, ?x909), film_format(?x9421, ?x909), film_format(?x7161, ?x909), film_format(?x6773, ?x909), film_format(?x5648, ?x909), film_format(?x5189, ?x909), film_format(?x4610, ?x909), film_format(?x4375, ?x909), film_format(?x4304, ?x909), film_format(?x4093, ?x909), film_format(?x3055, ?x909), film_format(?x2770, ?x909), film_format(?x1496, ?x909), film_format(?x1224, ?x909), film(?x541, ?x4304), nominated_for(?x637, ?x1224), nominated_for(?x451, ?x1224), nominated_for(?x788, ?x5189), film_release_distribution_medium(?x10397, ?x81), award(?x5189, ?x350), film(?x818, ?x10722), film(?x5690, ?x4304), ?x451 = 099jhq, genre(?x7161, ?x812), ?x1496 = 011yqc, film_crew_role(?x1224, ?x2154), film_crew_role(?x1224, ?x2095), country(?x2770, ?x390), film(?x2922, ?x2770), film_crew_role(?x9133, ?x2154), film_crew_role(?x7854, ?x2154), film_crew_role(?x6306, ?x2154), film_crew_role(?x2869, ?x2154), film_crew_role(?x2340, ?x2154), film_crew_role(?x2289, ?x2154), film_crew_role(?x522, ?x2154), film_crew_role(?x428, ?x2154), ?x2340 = 0fpv_3_, edited_by(?x7161, ?x6233), prequel(?x10446, ?x4375), film_release_region(?x4610, ?x2316), film_release_region(?x4610, ?x550), ?x2869 = 03177r, ?x428 = 0h1cdwq, ?x2289 = 02725hs, ?x637 = 02r22gf, honored_for(?x5902, ?x4610), ?x2095 = 0dxtw, award(?x2770, ?x298), produced_by(?x10722, ?x2332), nominated_for(?x2858, ?x9421), genre(?x2770, ?x1013), film(?x262, ?x1224), award_nominee(?x5690, ?x906), award_winner(?x4610, ?x628), ?x2316 = 06t2t, ?x7854 = 05ch98, ?x550 = 05v8c, ?x6306 = 016dj8, ?x1013 = 06n90, ?x522 = 01h7bb, award_winner(?x5648, ?x3281), film(?x56, ?x9421), currency(?x10397, ?x170), honored_for(?x6300, ?x4093), production_companies(?x3055, ?x382), ?x3281 = 0154qm, person(?x6773, ?x1620), ?x812 = 01jfsb, genre(?x9421, ?x604), ?x9133 = 0gy0l_ *> conf = 0.25 ranks of expected_values: 197, 213, 246, 296, 313, 380, 392, 395, 417, 535, 564, 610, 732, 857, 977, 1415, 1558 EVAL 07fb8_ film_format! 0_9l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 03cwwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 01cz7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 035gnh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 02dr9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 07gghl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 01l_pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 03cd0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 0cbv4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 0prrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 091rc5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 0fb7sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 0g83dv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 07jxpf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 013q07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 011yth CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 5.000 0.400 http://example.org/film/film/film_format EVAL 07fb8_ film_format! 017gl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 5.000 5.000 0.400 http://example.org/film/film/film_format #3586-03d2k PRED entity: 03d2k PRED relation: type_of_union PRED expected values: 04ztj => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.68 #377, 0.67 #45, 0.67 #29), 01g63y (0.23 #30, 0.19 #46, 0.16 #66) >> Best rule #377 for best value: >> intensional similarity = 3 >> extensional distance = 2871 >> proper extension: 05m63c; 033hqf; 07hbxm; 08b8vd; 0mdyn; 0k57l; 01xllf; 069z_5; 01r4bps; 0bqch; ... >> query: (?x9210, 04ztj) <- profession(?x9210, ?x2348), profession(?x6639, ?x2348), ?x6639 = 0137hn >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03d2k type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.677 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3585-07srw PRED entity: 07srw PRED relation: adjoins PRED expected values: 03s5t => 178 concepts (131 used for prediction) PRED predicted values (max 10 best out of 571): 03s5t (0.83 #97661, 0.83 #44609, 0.82 #43838), 0j3b (0.33 #58, 0.08 #3897, 0.08 #15436), 05rgl (0.33 #100, 0.08 #3939, 0.06 #24708), 0d060g (0.33 #10, 0.07 #11540, 0.07 #2314), 07srw (0.25 #19994, 0.25 #887, 0.23 #39223), 05kj_ (0.25 #19994, 0.25 #801, 0.23 #39223), 01n7q (0.25 #19994, 0.25 #830, 0.23 #39223), 05fhy (0.25 #19994, 0.23 #39223, 0.23 #39221), 081yw (0.25 #19994, 0.23 #39223, 0.23 #39221), 0ldff (0.25 #19994, 0.23 #39223, 0.23 #39221) >> Best rule #97661 for best value: >> intensional similarity = 3 >> extensional distance = 302 >> proper extension: 01z88t; 04tr1; 03548; 0nlc7; 025r_t; 01gpy4; 0qjd; 03188; 0ff0x; 0kzcv; ... >> query: (?x2256, ?x1138) <- adjoins(?x1138, ?x2256), adjoins(?x726, ?x1138), administrative_parent(?x2256, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07srw adjoins 03s5t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 131.000 0.835 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #3584-024y6w PRED entity: 024y6w PRED relation: gender PRED expected values: 02zsn => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #45, 0.73 #55, 0.73 #57), 02zsn (0.50 #4, 0.34 #18, 0.33 #2) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 546 >> proper extension: 01wbl_r; 01wj9y9; 024zq; 019389; >> query: (?x8371, 05zppz) <- type_of_union(?x8371, ?x566), artists(?x671, ?x8371), profession(?x8371, ?x1032) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 05r5w; *> query: (?x8371, 02zsn) <- award_winner(?x9628, ?x8371), ?x9628 = 02kgb7, profession(?x8371, ?x1032) *> conf = 0.50 ranks of expected_values: 2 EVAL 024y6w gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 103.000 0.730 http://example.org/people/person/gender #3583-01wg982 PRED entity: 01wg982 PRED relation: written_by! PRED expected values: 084qpk => 124 concepts (116 used for prediction) PRED predicted values (max 10 best out of 60): 03n3gl (0.25 #440, 0.02 #3740, 0.02 #7040), 01hvjx (0.25 #150, 0.02 #3450, 0.02 #6750), 0bz3jx (0.03 #5723, 0.02 #7043, 0.02 #7703), 050f0s (0.03 #6060, 0.02 #10680, 0.02 #13980), 084qpk (0.02 #6645, 0.02 #10605, 0.02 #3345), 01h7bb (0.02 #10584, 0.02 #5964, 0.02 #6624), 06fqlk (0.02 #3745, 0.02 #5725, 0.02 #6385), 025s1wg (0.02 #3934), 02q7yfq (0.02 #3761), 02pg45 (0.02 #3662) >> Best rule #440 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01_x6v; >> query: (?x2408, 03n3gl) <- location(?x2408, ?x3052), profession(?x2408, ?x1614), ?x1614 = 01c72t, ?x3052 = 01cx_ >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #6645 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 127 *> proper extension: 03wpmd; 05whq_9; 027l0b; 0gv5c; 0cm89v; 06n9lt; 098n_m; 0362q0; 043hg; 01pjr7; ... *> query: (?x2408, 084qpk) <- place_of_birth(?x2408, ?x11721), written_by(?x10651, ?x2408), profession(?x2408, ?x1032), ?x1032 = 02hrh1q *> conf = 0.02 ranks of expected_values: 5 EVAL 01wg982 written_by! 084qpk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 124.000 116.000 0.250 http://example.org/film/film/written_by #3582-0p5wz PRED entity: 0p5wz PRED relation: major_field_of_study PRED expected values: 02ky346 => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 102): 02lp1 (0.56 #12, 0.54 #381, 0.53 #504), 02j62 (0.50 #400, 0.49 #523, 0.48 #31), 04rjg (0.47 #20, 0.46 #389, 0.45 #512), 05qjt (0.44 #8, 0.41 #377, 0.40 #500), 01lj9 (0.41 #40, 0.41 #409, 0.40 #532), 03g3w (0.41 #27, 0.39 #396, 0.38 #519), 062z7 (0.38 #397, 0.38 #28, 0.37 #520), 0fdys (0.36 #408, 0.35 #39, 0.35 #531), 01540 (0.34 #430, 0.33 #553, 0.33 #61), 037mh8 (0.33 #68, 0.33 #437, 0.32 #560) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 08815; 05krk; 052nd; 06pwq; 065y4w7; 07tgn; 04rwx; 07szy; 09kvv; 0lfgr; ... >> query: (?x3362, 02lp1) <- list(?x3362, ?x2197), major_field_of_study(?x3362, ?x1668), school_type(?x3362, ?x3092) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 08815; 05krk; 052nd; 06pwq; 065y4w7; 07tgn; 04rwx; 07szy; 09kvv; 0lfgr; ... *> query: (?x3362, 02ky346) <- list(?x3362, ?x2197), major_field_of_study(?x3362, ?x1668), school_type(?x3362, ?x3092) *> conf = 0.21 ranks of expected_values: 18 EVAL 0p5wz major_field_of_study 02ky346 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 34.000 34.000 0.561 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3581-03x6m PRED entity: 03x6m PRED relation: team! PRED expected values: 03zv9 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 8): 03zv9 (0.60 #59, 0.50 #67, 0.38 #180), 0355pl (0.32 #810, 0.27 #197, 0.25 #432), 07y9k (0.32 #810, 0.25 #551, 0.19 #462), 0356lc (0.32 #810, 0.25 #551, 0.18 #769), 059yj (0.13 #475, 0.13 #644, 0.12 #798), 0h69c (0.11 #476, 0.10 #645, 0.08 #799), 01ddbl (0.06 #598, 0.04 #921, 0.03 #953), 021q23 (0.02 #922, 0.01 #1018, 0.01 #986) >> Best rule #59 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 03h0k1; 08vk_r; >> query: (?x8750, 03zv9) <- team(?x7484, ?x8750), team(?x7109, ?x8750), sport(?x8750, ?x471), team(?x60, ?x8750), ?x7109 = 08b0cj, team(?x7484, ?x7485), current_club(?x978, ?x8750), colors(?x7485, ?x663), ?x471 = 02vx4 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x6m team! 03zv9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.600 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #3580-02tz9z PRED entity: 02tz9z PRED relation: colors PRED expected values: 01g5v => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.50 #3, 0.33 #163, 0.32 #203), 01l849 (0.31 #61, 0.28 #221, 0.26 #1201), 06fvc (0.25 #2, 0.19 #322, 0.18 #162), 019sc (0.19 #1507, 0.18 #1207, 0.18 #1667), 036k5h (0.15 #65, 0.15 #85, 0.12 #405), 038hg (0.15 #72, 0.15 #92, 0.11 #732), 03wkwg (0.15 #75, 0.15 #95, 0.08 #195), 04d18d (0.15 #79, 0.10 #99, 0.07 #2121), 04mkbj (0.12 #330, 0.12 #370, 0.12 #170), 09ggk (0.10 #96, 0.08 #76, 0.07 #2121) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 037njl; 037q2p; >> query: (?x12127, 01g5v) <- school_type(?x12127, ?x3355), country(?x12127, ?x94), currency(?x12127, ?x170), ?x3355 = 06cs1 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tz9z colors 01g5v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.500 http://example.org/education/educational_institution/colors #3579-09zf_q PRED entity: 09zf_q PRED relation: genre PRED expected values: 07s9rl0 => 88 concepts (47 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.77 #1423, 0.70 #3321, 0.69 #3678), 01jfsb (0.60 #4873, 0.58 #1315, 0.56 #2499), 02l7c8 (0.38 #1437, 0.33 #3335, 0.32 #3692), 01hmnh (0.34 #2859, 0.33 #608, 0.33 #371), 05p553 (0.34 #5220, 0.32 #4510, 0.31 #2136), 04xvlr (0.30 #1424, 0.25 #2, 0.22 #1779), 0lsxr (0.25 #9, 0.24 #4870, 0.23 #2496), 02n4kr (0.25 #8, 0.22 #4869, 0.17 #3319), 082gq (0.25 #29, 0.20 #3467, 0.17 #3319), 04xvh5 (0.25 #33, 0.15 #1455, 0.12 #388) >> Best rule #1423 for best value: >> intensional similarity = 4 >> extensional distance = 181 >> proper extension: 03tn80; 02rlj20; 09tkzy; 03cffvv; >> query: (?x5054, 07s9rl0) <- genre(?x5054, ?x1509), film(?x72, ?x5054), ?x1509 = 060__y, nominated_for(?x3458, ?x5054) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09zf_q genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 47.000 0.765 http://example.org/film/film/genre #3578-0d1qmz PRED entity: 0d1qmz PRED relation: film! PRED expected values: 017jv5 => 78 concepts (70 used for prediction) PRED predicted values (max 10 best out of 52): 0g1rw (0.43 #233, 0.40 #158, 0.40 #83), 017jv5 (0.42 #315, 0.33 #15, 0.29 #240), 086k8 (0.24 #679, 0.21 #452, 0.21 #1357), 05qd_ (0.23 #459, 0.22 #686, 0.19 #1364), 017s11 (0.20 #153, 0.20 #78, 0.20 #1583), 016tt2 (0.20 #154, 0.20 #79, 0.20 #1583), 03rwz3 (0.20 #194, 0.20 #119, 0.20 #1583), 016tw3 (0.19 #537, 0.15 #386, 0.14 #2350), 03xq0f (0.17 #982, 0.17 #1058, 0.14 #1436), 01gb54 (0.11 #479, 0.08 #555, 0.07 #1612) >> Best rule #233 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02sg5v; 02qrv7; 0fztbq; >> query: (?x3643, 0g1rw) <- nominated_for(?x3643, ?x5399), ?x5399 = 0fsw_7, written_by(?x3643, ?x11598) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 01kf4tt; 0g5pvv; 025twgt; *> query: (?x3643, 017jv5) <- nominated_for(?x3643, ?x5399), ?x5399 = 0fsw_7, language(?x3643, ?x254) *> conf = 0.42 ranks of expected_values: 2 EVAL 0d1qmz film! 017jv5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 70.000 0.429 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #3577-02cg7g PRED entity: 02cg7g PRED relation: legislative_sessions PRED expected values: 02bn_p => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 38): 02bp37 (0.83 #310, 0.83 #309, 0.83 #348), 02bqn1 (0.83 #310, 0.83 #309, 0.83 #348), 070mff (0.83 #310, 0.83 #309, 0.83 #348), 02bn_p (0.83 #310, 0.83 #309, 0.83 #348), 02cg7g (0.83 #310, 0.83 #309, 0.82 #824), 01gsvb (0.46 #862, 0.46 #863, 0.45 #1375), 01gsvp (0.46 #862, 0.46 #863, 0.45 #864), 01gtcc (0.46 #862, 0.46 #863, 0.45 #864), 043djx (0.46 #862, 0.46 #863, 0.45 #864), 01gtcq (0.46 #862, 0.46 #863, 0.45 #864) >> Best rule #310 for best value: >> intensional similarity = 44 >> extensional distance = 2 >> proper extension: 02bqm0; >> query: (?x4730, ?x1137) <- legislative_sessions(?x9569, ?x4730), legislative_sessions(?x9334, ?x4730), legislative_sessions(?x8607, ?x4730), legislative_sessions(?x4730, ?x6933), legislative_sessions(?x4730, ?x4821), legislative_sessions(?x4730, ?x3765), legislative_sessions(?x4730, ?x3540), legislative_sessions(?x4730, ?x2976), legislative_sessions(?x4730, ?x605), legislative_sessions(?x4730, ?x356), legislative_sessions(?x4730, ?x355), district_represented(?x4730, ?x3670), district_represented(?x4730, ?x2049), district_represented(?x4730, ?x2020), district_represented(?x4730, ?x1906), district_represented(?x4730, ?x1767), district_represented(?x4730, ?x1755), district_represented(?x4730, ?x1025), district_represented(?x4730, ?x448), ?x448 = 03v1s, ?x9569 = 0194xc, ?x356 = 05l2z4, ?x2020 = 05k7sb, ?x1767 = 04rrd, ?x3540 = 024tcq, ?x2976 = 03rtmz, ?x1906 = 04rrx, legislative_sessions(?x1137, ?x3765), district_represented(?x4821, ?x12828), district_represented(?x4821, ?x2831), district_represented(?x4821, ?x1227), ?x12828 = 0gj4fx, ?x1227 = 01n7q, ?x605 = 077g7n, ?x6933 = 024tkd, ?x2049 = 050l8, ?x1025 = 04ych, ?x355 = 0495ys, legislative_sessions(?x4821, ?x1027), ?x3670 = 05tbn, ?x9334 = 02hy5d, ?x1755 = 01x73, ?x2831 = 0gyh, ?x8607 = 0226cw >> conf = 0.83 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 02cg7g legislative_sessions 02bn_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 45.000 45.000 0.833 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #3576-05qzv PRED entity: 05qzv PRED relation: people! PRED expected values: 02y0js => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 44): 06z5s (0.33 #25, 0.06 #223, 0.06 #817), 0m32h (0.20 #89, 0.07 #155, 0.06 #221), 01dcqj (0.20 #78, 0.06 #210, 0.03 #1201), 0gk4g (0.17 #802, 0.14 #3575, 0.14 #4499), 0dq9p (0.09 #1602, 0.09 #2130, 0.08 #2592), 0qcr0 (0.08 #661, 0.08 #3566, 0.08 #1190), 01l2m3 (0.08 #676, 0.07 #148, 0.06 #1205), 02y0js (0.08 #662, 0.06 #1191, 0.06 #530), 02k6hp (0.08 #763, 0.07 #433, 0.07 #1159), 01psyx (0.07 #177, 0.07 #507, 0.07 #441) >> Best rule #25 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0399p; >> query: (?x9982, 06z5s) <- influenced_by(?x9982, ?x5612), influenced_by(?x9982, ?x4265), ?x4265 = 06whf, ?x5612 = 058vp, nationality(?x9982, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #662 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 0kvnn; 02s6sh; 0223g8; *> query: (?x9982, 02y0js) <- category(?x9982, ?x134), place_of_death(?x9982, ?x13451), nationality(?x9982, ?x94), time_zones(?x13451, ?x2950) *> conf = 0.08 ranks of expected_values: 8 EVAL 05qzv people! 02y0js CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 141.000 141.000 0.333 http://example.org/people/cause_of_death/people #3575-01g5kv PRED entity: 01g5kv PRED relation: actor! PRED expected values: 0cskb => 54 concepts (43 used for prediction) PRED predicted values (max 10 best out of 48): 03wh49y (0.07 #96, 0.01 #361, 0.01 #626), 01xr2s (0.07 #30, 0.01 #295, 0.01 #560), 06cs95 (0.07 #7, 0.01 #272, 0.01 #537), 01hn_t (0.07 #72, 0.01 #337), 02_1q9 (0.07 #5, 0.01 #535), 0n2bh (0.07 #32), 026bfsh (0.06 #362, 0.04 #627, 0.02 #5398), 050kh5 (0.02 #505, 0.01 #770), 01kt_j (0.02 #739, 0.01 #474), 0kfpm (0.02 #543, 0.01 #278) >> Best rule #96 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 01vvycq; >> query: (?x13000, 03wh49y) <- profession(?x13000, ?x1032), profession(?x13000, ?x524), ?x524 = 02jknp, ?x1032 = 02hrh1q, diet(?x13000, ?x3130) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01g5kv actor! 0cskb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 43.000 0.071 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #3574-029q_y PRED entity: 029q_y PRED relation: film PRED expected values: 02p86pb => 90 concepts (69 used for prediction) PRED predicted values (max 10 best out of 615): 030p35 (0.63 #23272, 0.59 #57292, 0.41 #84145), 0418wg (0.58 #2191, 0.04 #34013, 0.04 #39386), 09xbpt (0.50 #1837, 0.04 #34013, 0.04 #39386), 03s6l2 (0.25 #1873, 0.04 #34013, 0.04 #39386), 0bz3jx (0.22 #1140, 0.01 #56641), 07bxqz (0.22 #1735), 02qr3k8 (0.17 #3080, 0.11 #1290, 0.02 #85435), 09cr8 (0.17 #2074, 0.05 #5654, 0.03 #107421), 02704ff (0.17 #2773, 0.04 #34013, 0.04 #39386), 06_wqk4 (0.17 #1917, 0.03 #16238, 0.03 #18028) >> Best rule #23272 for best value: >> intensional similarity = 3 >> extensional distance = 414 >> proper extension: 0d_84; 04bs3j; 0134w7; 0456xp; 0h1m9; 0n6f8; 01qvgl; 013cr; 0j582; 01mqz0; ... >> query: (?x7613, ?x4639) <- participant(?x516, ?x7613), profession(?x7613, ?x319), nominated_for(?x7613, ?x4639) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #34013 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 534 *> proper extension: 01xyt7; *> query: (?x7613, ?x1184) <- participant(?x516, ?x7613), participant(?x7613, ?x513), film(?x513, ?x1184) *> conf = 0.04 ranks of expected_values: 175 EVAL 029q_y film 02p86pb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 90.000 69.000 0.627 http://example.org/film/actor/film./film/performance/film #3573-02v_r7d PRED entity: 02v_r7d PRED relation: genre PRED expected values: 03q4nz 03mqtr => 101 concepts (100 used for prediction) PRED predicted values (max 10 best out of 87): 07s9rl0 (0.94 #8169, 0.94 #6245, 0.85 #3115), 02l7c8 (0.46 #1093, 0.45 #1933, 0.42 #2053), 05p553 (0.38 #4205, 0.37 #1441, 0.36 #5407), 01jfsb (0.37 #1689, 0.35 #1211, 0.35 #1570), 03k9fj (0.37 #1210, 0.37 #970, 0.36 #2170), 02kdv5l (0.35 #960, 0.32 #2160, 0.32 #1200), 060__y (0.34 #1334, 0.30 #2891, 0.30 #3011), 01hmnh (0.33 #18, 0.22 #1216, 0.21 #976), 015w9s (0.33 #33, 0.02 #3870, 0.02 #395), 082gq (0.19 #2068, 0.18 #1108, 0.18 #2905) >> Best rule #8169 for best value: >> intensional similarity = 7 >> extensional distance = 1065 >> proper extension: 0c0wvx; 02qjv1p; >> query: (?x6178, 07s9rl0) <- genre(?x6178, ?x6887), genre(?x9961, ?x6887), genre(?x4431, ?x6887), genre(?x1877, ?x6887), country(?x9961, ?x94), ?x4431 = 0pd4f, ?x1877 = 0cz_ym >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #512 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 65 *> proper extension: 03bdkd; *> query: (?x6178, 03mqtr) <- film(?x1958, ?x6178), currency(?x6178, ?x170), film(?x788, ?x6178), genre(?x6178, ?x6887), ?x6887 = 03bxz7 *> conf = 0.12 ranks of expected_values: 20, 29 EVAL 02v_r7d genre 03mqtr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 101.000 100.000 0.943 http://example.org/film/film/genre EVAL 02v_r7d genre 03q4nz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 101.000 100.000 0.943 http://example.org/film/film/genre #3572-0prhz PRED entity: 0prhz PRED relation: country PRED expected values: 07ssc => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 31): 03rjj (0.64 #1966, 0.59 #891, 0.11 #302), 07ssc (0.52 #253, 0.47 #194, 0.32 #1146), 0345h (0.17 #85, 0.14 #144, 0.14 #322), 03_3d (0.17 #66, 0.12 #185, 0.10 #244), 02jx1 (0.14 #145, 0.01 #1634), 0chghy (0.10 #1023, 0.04 #548, 0.04 #1082), 081k8 (0.07 #1311, 0.07 #1310, 0.07 #178), 04xvlr (0.07 #1311, 0.07 #1310, 0.07 #178), 07s9rl0 (0.07 #1311, 0.07 #1310, 0.07 #178), 03rt9 (0.05 #251, 0.02 #1621, 0.02 #1384) >> Best rule #1966 for best value: >> intensional similarity = 4 >> extensional distance = 512 >> proper extension: 0dkv90; >> query: (?x4678, ?x205) <- film(?x1742, ?x4678), nominated_for(?x112, ?x4678), nationality(?x1742, ?x205), titles(?x53, ?x4678) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #253 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 0gydcp7; 043tvp3; *> query: (?x4678, 07ssc) <- film(?x2531, ?x4678), genre(?x4678, ?x162), ?x2531 = 0kszw *> conf = 0.52 ranks of expected_values: 2 EVAL 0prhz country 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.644 http://example.org/film/film/country #3571-01dy7j PRED entity: 01dy7j PRED relation: student! PRED expected values: 0234_c => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 97): 0bwfn (0.11 #275, 0.06 #1856, 0.06 #15034), 08815 (0.06 #2111, 0.04 #2638, 0.04 #3165), 09f2j (0.06 #686, 0.05 #2268, 0.03 #8066), 053mhx (0.06 #822, 0.04 #2404, 0.04 #1349), 04b_46 (0.05 #227, 0.05 #1281, 0.04 #2863), 017z88 (0.05 #82, 0.04 #7989, 0.04 #6934), 01vg13 (0.05 #219, 0.03 #1800), 0ks67 (0.05 #189, 0.03 #2298, 0.02 #3352), 026gvfj (0.05 #111, 0.02 #1165, 0.02 #2220), 027kp3 (0.05 #153, 0.01 #680, 0.01 #4370) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 0bl60p; >> query: (?x2965, 0bwfn) <- nominated_for(?x2965, ?x1849), award_nominee(?x2965, ?x1059), ?x1059 = 021_rm >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01dy7j student! 0234_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 111.000 0.105 http://example.org/education/educational_institution/students_graduates./education/education/student #3570-0bmssv PRED entity: 0bmssv PRED relation: film! PRED expected values: 032w8h 0dzf_ => 63 concepts (33 used for prediction) PRED predicted values (max 10 best out of 657): 0863x_ (0.24 #4991, 0.09 #2914, 0.06 #7067), 0315q3 (0.24 #4973, 0.03 #68522, 0.03 #56064), 086nl7 (0.22 #783, 0.06 #4936, 0.06 #9088), 02mjf2 (0.22 #772, 0.06 #4925, 0.02 #9077), 04yqlk (0.18 #2851, 0.18 #4928, 0.12 #7004), 0sw6g (0.18 #5555, 0.11 #1402, 0.01 #15935), 01r2c7 (0.15 #4153), 01vsn38 (0.12 #6002, 0.11 #1849, 0.04 #10154), 032xhg (0.12 #4215, 0.11 #62, 0.04 #8367), 06cgy (0.12 #4401, 0.09 #2324, 0.06 #6477) >> Best rule #4991 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0ddfwj1; >> query: (?x4178, 0863x_) <- film(?x794, ?x4178), nominated_for(?x1691, ?x4178), ?x794 = 0mdqp, genre(?x4178, ?x225) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #807 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 034qmv; 02ryz24; 05c5z8j; 04gv3db; 07_fj54; 0642xf3; 087vnr5; *> query: (?x4178, 0dzf_) <- language(?x4178, ?x90), film(?x9161, ?x4178), genre(?x4178, ?x225), ?x9161 = 01nfys *> conf = 0.11 ranks of expected_values: 22, 121 EVAL 0bmssv film! 0dzf_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 63.000 33.000 0.235 http://example.org/film/actor/film./film/performance/film EVAL 0bmssv film! 032w8h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 63.000 33.000 0.235 http://example.org/film/actor/film./film/performance/film #3569-04zyhx PRED entity: 04zyhx PRED relation: film_release_region PRED expected values: 0154j 0d0vqn 0ctw_b 01mjq => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 174): 0d0vqn (0.92 #1680, 0.90 #5789, 0.90 #5636), 03_3d (0.90 #1830, 0.80 #1526, 0.78 #1374), 059j2 (0.87 #4290, 0.87 #3986, 0.86 #4442), 015fr (0.87 #2906, 0.85 #3972, 0.85 #2754), 0d060g (0.86 #1679, 0.86 #614, 0.80 #2744), 035qy (0.86 #641, 0.85 #2619, 0.84 #3989), 0154j (0.85 #2893, 0.83 #2741, 0.82 #2437), 06bnz (0.82 #1717, 0.80 #2478, 0.80 #2934), 06t2t (0.82 #1733, 0.71 #668, 0.71 #2646), 05v8c (0.78 #1840, 0.72 #2601, 0.71 #2905) >> Best rule #1680 for best value: >> intensional similarity = 7 >> extensional distance = 49 >> proper extension: 04969y; >> query: (?x1451, 0d0vqn) <- film_release_region(?x1451, ?x2267), film_release_region(?x1451, ?x1536), film_release_region(?x1451, ?x985), ?x2267 = 03rj0, genre(?x1451, ?x53), ?x985 = 0k6nt, ?x1536 = 06c1y >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 12, 14 EVAL 04zyhx film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 96.000 96.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04zyhx film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 96.000 96.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04zyhx film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04zyhx film_release_region 0154j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 96.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3568-05qjc PRED entity: 05qjc PRED relation: major_field_of_study! PRED expected values: 014mlp => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 21): 019v9k (0.83 #31, 0.82 #53, 0.81 #118), 02h4rq6 (0.82 #68, 0.82 #46, 0.75 #24), 014mlp (0.80 #71, 0.80 #49, 0.78 #136), 016t_3 (0.73 #156, 0.73 #134, 0.73 #69), 04zx3q1 (0.58 #23, 0.49 #110, 0.48 #45), 0bkj86 (0.55 #139, 0.53 #161, 0.53 #204), 03bwzr4 (0.54 #122, 0.51 #209, 0.51 #187), 01ysy9 (0.42 #42, 0.36 #306, 0.33 #21), 028dcg (0.36 #306, 0.33 #18, 0.27 #525), 0bjrnt (0.36 #306, 0.33 #28, 0.25 #159) >> Best rule #31 for best value: >> intensional similarity = 10 >> extensional distance = 10 >> proper extension: 05qjt; 01mkq; 03g3w; 062z7; 02j62; 04gb7; 01540; 0mg1w; 06q83; >> query: (?x5740, 019v9k) <- major_field_of_study(?x8357, ?x5740), student(?x8357, ?x8674), student(?x8357, ?x3281), ?x3281 = 0154qm, award_winner(?x472, ?x8674), nominated_for(?x8674, ?x1330), award(?x8674, ?x375), institution(?x5739, ?x8357), religion(?x8674, ?x1985), award_nominee(?x5330, ?x8674) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #71 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 43 *> proper extension: 064_8sq; *> query: (?x5740, 014mlp) <- major_field_of_study(?x8357, ?x5740), student(?x8357, ?x3281), award_nominee(?x2728, ?x3281), award_winner(?x995, ?x3281), ?x2728 = 01v9l67, major_field_of_study(?x8357, ?x6760), award_winner(?x972, ?x3281), nationality(?x3281, ?x390), student(?x6760, ?x665) *> conf = 0.80 ranks of expected_values: 3 EVAL 05qjc major_field_of_study! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 32.000 32.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #3567-0gndh PRED entity: 0gndh PRED relation: costume_design_by PRED expected values: 025_nbr => 61 concepts (35 used for prediction) PRED predicted values (max 10 best out of 15): 03y1mlp (0.05 #30, 0.03 #2, 0.03 #115), 02cqbx (0.04 #185, 0.03 #100, 0.03 #157), 0c4qzm (0.04 #199, 0.02 #198, 0.02 #426), 02h1rt (0.03 #14, 0.02 #70, 0.02 #42), 0bytfv (0.03 #95, 0.02 #408, 0.02 #180), 03mfqm (0.03 #74, 0.02 #217, 0.02 #415), 0gl88b (0.02 #174), 071jv5 (0.02 #198, 0.02 #426, 0.01 #863), 02mxbd (0.02 #216, 0.02 #385, 0.02 #414), 02pqgt8 (0.02 #96, 0.01 #466, 0.01 #409) >> Best rule #30 for best value: >> intensional similarity = 5 >> extensional distance = 126 >> proper extension: 0gtv7pk; 03qnvdl; 02nx2k; 09bw4_; 076xkdz; 080dfr7; >> query: (?x7677, 03y1mlp) <- nominated_for(?x484, ?x7677), genre(?x7677, ?x811), genre(?x7677, ?x225), ?x811 = 03k9fj, ?x225 = 02kdv5l >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gndh costume_design_by 025_nbr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 35.000 0.047 http://example.org/film/film/costume_design_by #3566-0bc1yhb PRED entity: 0bc1yhb PRED relation: film_release_region PRED expected values: 0b90_r 0k6nt 059j2 06t2t => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 152): 035qy (0.90 #738, 0.85 #2158, 0.81 #1022), 059j2 (0.90 #2155, 0.86 #735, 0.86 #2866), 06t2t (0.82 #760, 0.70 #1044, 0.69 #2180), 0k6nt (0.82 #303, 0.81 #1723, 0.80 #2149), 0b90_r (0.81 #713, 0.80 #997, 0.75 #2133), 03spz (0.76 #792, 0.70 #2212, 0.66 #1076), 01mjq (0.67 #745, 0.55 #2165, 0.53 #319), 016wzw (0.57 #764, 0.52 #1048, 0.45 #338), 06mzp (0.54 #726, 0.46 #2146, 0.45 #300), 01ls2 (0.53 #719, 0.43 #2139, 0.43 #1003) >> Best rule #738 for best value: >> intensional similarity = 6 >> extensional distance = 100 >> proper extension: 0fq27fp; 040rmy; 0g9zljd; >> query: (?x5270, 035qy) <- film_release_region(?x5270, ?x1497), film_release_region(?x5270, ?x583), film_release_region(?x5270, ?x172), ?x583 = 015fr, ?x172 = 0154j, ?x1497 = 015qh >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #2155 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 205 *> proper extension: 026njb5; 0bhwhj; 07l50vn; 0g5q34q; 0gh6j94; 05zvzf3; 08j7lh; 0g5qmbz; 0hz6mv2; *> query: (?x5270, 059j2) <- film_release_region(?x5270, ?x1497), film_release_region(?x5270, ?x583), film_release_region(?x5270, ?x172), ?x583 = 015fr, ?x172 = 0154j, country(?x668, ?x1497) *> conf = 0.90 ranks of expected_values: 2, 3, 4, 5 EVAL 0bc1yhb film_release_region 06t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bc1yhb film_release_region 059j2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bc1yhb film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0bc1yhb film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.902 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3565-03hkp PRED entity: 03hkp PRED relation: languages_spoken! PRED expected values: 013b6_ => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 71): 0bbz66j (0.50 #109, 0.33 #445, 0.25 #512), 0ffjqy (0.50 #322, 0.26 #135, 0.25 #255), 071x0k (0.50 #142, 0.25 #209, 0.25 #74), 02vsw1 (0.44 #379, 0.33 #446, 0.29 #647), 013b6_ (0.33 #314, 0.33 #45, 0.26 #135), 059_w (0.33 #428, 0.26 #135, 0.25 #227), 0x67 (0.33 #278, 0.26 #135, 0.25 #211), 033tf_ (0.33 #275, 0.26 #135, 0.25 #208), 04gfy7 (0.26 #1531, 0.26 #135, 0.25 #1598), 0g8_vp (0.26 #135, 0.25 #219, 0.25 #152) >> Best rule #109 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 0349s; >> query: (?x3966, 0bbz66j) <- language(?x7114, ?x3966), languages_spoken(?x3584, ?x3966), ?x7114 = 06rzwx, people(?x3584, ?x9545), people(?x3584, ?x9508), people(?x3584, ?x7872), influenced_by(?x587, ?x9508), profession(?x7872, ?x319), award_winner(?x7085, ?x9545), student(?x4672, ?x9508) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #314 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 0k0sv; *> query: (?x3966, 013b6_) <- language(?x7114, ?x3966), languages_spoken(?x1050, ?x3966), film(?x2587, ?x7114), produced_by(?x7114, ?x7036), award(?x7036, ?x1105), people(?x1050, ?x10663), people(?x1050, ?x4705), people(?x1050, ?x1594), ?x4705 = 0863x_, place_of_birth(?x10663, ?x13899), award(?x1594, ?x5455) *> conf = 0.33 ranks of expected_values: 5 EVAL 03hkp languages_spoken! 013b6_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 39.000 39.000 0.500 http://example.org/people/ethnicity/languages_spoken #3564-03k8th PRED entity: 03k8th PRED relation: genre PRED expected values: 07s9rl0 0lsxr 04t2t => 68 concepts (65 used for prediction) PRED predicted values (max 10 best out of 110): 07s9rl0 (0.86 #4257, 0.74 #5322, 0.66 #2130), 0lsxr (0.47 #362, 0.46 #244, 0.43 #1189), 05p553 (0.35 #7331, 0.34 #1775, 0.33 #4378), 02l7c8 (0.27 #4271, 0.27 #7224, 0.26 #5808), 09blyk (0.24 #1211, 0.24 #1447, 0.24 #1683), 04xvlr (0.22 #356, 0.19 #2131, 0.18 #2011), 03k9fj (0.21 #7220, 0.21 #4385, 0.20 #2614), 017fp (0.20 #14, 0.13 #132, 0.13 #5321), 03g3w (0.20 #23, 0.13 #141, 0.13 #5321), 082gq (0.19 #2038, 0.19 #2158, 0.18 #1918) >> Best rule #4257 for best value: >> intensional similarity = 4 >> extensional distance = 1171 >> proper extension: 060v34; 07f_7h; 03l6q0; 01sxdy; 048rn; 0h21v2; 02p76f9; 02cbg0; 027x7z5; 0353tm; ... >> query: (?x11296, 07s9rl0) <- genre(?x11296, ?x600), genre(?x1988, ?x600), genre(?x2078, ?x600), ?x1988 = 09k56b7 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 20 EVAL 03k8th genre 04t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 68.000 65.000 0.858 http://example.org/film/film/genre EVAL 03k8th genre 0lsxr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 65.000 0.858 http://example.org/film/film/genre EVAL 03k8th genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 65.000 0.858 http://example.org/film/film/genre #3563-035gnh PRED entity: 035gnh PRED relation: genre PRED expected values: 0lsxr => 68 concepts (66 used for prediction) PRED predicted values (max 10 best out of 71): 07s9rl0 (0.60 #2246, 0.60 #2482, 0.58 #5204), 02l7c8 (0.29 #2260, 0.28 #2496, 0.28 #2378), 03k9fj (0.25 #720, 0.24 #957, 0.23 #2138), 0lsxr (0.19 #363, 0.18 #245, 0.18 #8), 01hmnh (0.18 #726, 0.18 #963, 0.16 #1790), 04xvlr (0.17 #2247, 0.17 #2483, 0.15 #2365), 06n90 (0.16 #12, 0.15 #958, 0.15 #721), 060__y (0.14 #135, 0.14 #2261, 0.14 #2497), 02n4kr (0.12 #362, 0.12 #244, 0.11 #480), 017fp (0.09 #2259, 0.09 #2495, 0.08 #487) >> Best rule #2246 for best value: >> intensional similarity = 3 >> extensional distance = 1031 >> proper extension: 0cp08zg; 0d7vtk; 0k20s; >> query: (?x7428, 07s9rl0) <- country(?x7428, ?x94), titles(?x9360, ?x7428), nominated_for(?x382, ?x7428) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #363 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 616 *> proper extension: 026njb5; 011yfd; 02phtzk; 0g5q34q; 03q8xj; 0gh6j94; 02zk08; 0dmn0x; 0d8w2n; *> query: (?x7428, 0lsxr) <- featured_film_locations(?x7428, ?x3026), film_release_distribution_medium(?x7428, ?x81), genre(?x7428, ?x225) *> conf = 0.19 ranks of expected_values: 4 EVAL 035gnh genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 66.000 0.597 http://example.org/film/film/genre #3562-01sh2 PRED entity: 01sh2 PRED relation: nutrient! PRED expected values: 01nkt 01645p => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 73): 01nkt (0.93 #554, 0.93 #546, 0.92 #541), 01645p (0.93 #486, 0.93 #481, 0.92 #456), 0frq6 (0.89 #180, 0.89 #178, 0.89 #175), 0f25w9 (0.89 #180, 0.89 #178, 0.89 #175), 0971v (0.89 #180, 0.89 #178, 0.89 #175), 025rw19 (0.04 #45, 0.04 #114, 0.03 #35), 025tkqy (0.04 #45, 0.04 #114, 0.03 #35), 06jry (0.04 #45, 0.04 #114, 0.03 #35), 025sf8g (0.04 #45, 0.04 #114, 0.03 #35), 025s7j4 (0.04 #45, 0.04 #114, 0.03 #35) >> Best rule #554 for best value: >> intensional similarity = 116 >> extensional distance = 39 >> proper extension: 01w_3; 0f4k5; >> query: (?x2018, ?x6032) <- nutrient(?x9005, ?x2018), nutrient(?x8298, ?x2018), nutrient(?x7719, ?x2018), nutrient(?x6159, ?x2018), nutrient(?x3468, ?x2018), nutrient(?x2701, ?x2018), nutrient(?x1257, ?x2018), nutrient(?x7719, ?x13944), nutrient(?x7719, ?x13498), nutrient(?x7719, ?x12902), nutrient(?x7719, ?x12454), nutrient(?x7719, ?x11784), nutrient(?x7719, ?x11758), nutrient(?x7719, ?x11592), nutrient(?x7719, ?x11270), nutrient(?x7719, ?x10709), nutrient(?x7719, ?x9915), nutrient(?x7719, ?x9840), nutrient(?x7719, ?x9795), nutrient(?x7719, ?x9733), nutrient(?x7719, ?x9619), nutrient(?x7719, ?x9436), nutrient(?x7719, ?x9426), nutrient(?x7719, ?x9365), nutrient(?x7719, ?x8487), nutrient(?x7719, ?x8442), nutrient(?x7719, ?x8413), nutrient(?x7719, ?x8243), nutrient(?x7719, ?x7894), nutrient(?x7719, ?x7720), nutrient(?x7719, ?x7652), nutrient(?x7719, ?x7364), nutrient(?x7719, ?x7362), nutrient(?x7719, ?x7219), nutrient(?x7719, ?x7135), nutrient(?x7719, ?x6586), nutrient(?x7719, ?x6192), nutrient(?x7719, ?x6026), nutrient(?x7719, ?x5526), nutrient(?x7719, ?x5374), nutrient(?x7719, ?x5010), nutrient(?x7719, ?x4069), nutrient(?x7719, ?x3469), nutrient(?x7719, ?x3203), nutrient(?x7719, ?x1960), nutrient(?x7719, ?x1258), ?x8442 = 02kcv4x, ?x9005 = 04zpv, ?x9365 = 04k8n, ?x8487 = 014yzm, ?x11270 = 02kc008, ?x8413 = 02kc4sf, ?x9436 = 025sqz8, ?x7364 = 09gvd, nutrient(?x2701, ?x12083), nutrient(?x2701, ?x11409), nutrient(?x2701, ?x10891), nutrient(?x2701, ?x10195), nutrient(?x2701, ?x10098), nutrient(?x2701, ?x9949), nutrient(?x2701, ?x6160), nutrient(?x2701, ?x6033), nutrient(?x2701, ?x5549), nutrient(?x2701, ?x5451), ?x9619 = 0h1tg, ?x4069 = 0hqw8p_, ?x6586 = 05gh50, ?x11758 = 0q01m, ?x7362 = 02kc5rj, ?x9915 = 025tkqy, ?x3468 = 0cxn2, ?x10709 = 0h1sz, ?x7894 = 0f4hc, ?x6026 = 025sf8g, ?x9426 = 0h1yy, ?x7135 = 025rsfk, ?x5451 = 05wvs, ?x6033 = 04zjxcz, ?x5010 = 0h1vz, ?x11592 = 025sf0_, ?x5374 = 025s0zp, ?x6159 = 033cnk, ?x1258 = 0h1wg, nutrient(?x8298, ?x10453), ?x9733 = 0h1tz, ?x10891 = 0g5gq, nutrient(?x6285, ?x11784), nutrient(?x1959, ?x11784), ?x13498 = 07q0m, ?x6160 = 041r51, ?x1960 = 07hnp, ?x5549 = 025s7j4, ?x13944 = 0f4kp, ?x5526 = 09pbb, ?x6192 = 06jry, ?x10098 = 0h1_c, nutrient(?x1257, ?x14698), ?x12083 = 01n78x, ?x12454 = 025rw19, ?x3469 = 0h1zw, ?x11409 = 0h1yf, ?x9840 = 02p0tjr, ?x1959 = 0f25w9, ?x10195 = 0hkwr, ?x9949 = 02kd0rh, ?x7219 = 0h1vg, ?x7720 = 025s7x6, nutrient(?x6032, ?x8243), ?x10453 = 075pwf, ?x14698 = 02kb_jm, ?x6285 = 01645p, ?x6032 = 01nkt, ?x12902 = 0fzjh, ?x7652 = 025s0s0, ?x3203 = 04kl74p, ?x9795 = 05v_8y >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01sh2 nutrient! 01645p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.927 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 01sh2 nutrient! 01nkt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.927 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #3561-033cw PRED entity: 033cw PRED relation: nationality PRED expected values: 09c7w0 => 95 concepts (70 used for prediction) PRED predicted values (max 10 best out of 33): 09c7w0 (0.87 #2216, 0.87 #2316, 0.86 #4835), 0n5d1 (0.32 #1510, 0.28 #3123, 0.27 #6964), 05fjf (0.32 #1510, 0.28 #3123, 0.27 #6964), 02_286 (0.32 #1510, 0.28 #3123, 0.27 #6964), 07ssc (0.17 #1122, 0.15 #618, 0.14 #1525), 02jx1 (0.14 #1947, 0.14 #1140, 0.14 #1746), 06m_5 (0.13 #3826, 0.06 #83, 0.05 #284), 0d060g (0.09 #308, 0.07 #3330, 0.06 #408), 0cr3d (0.07 #101, 0.04 #1712, 0.04 #502), 0xqf3 (0.07 #101, 0.04 #502, 0.03 #1711) >> Best rule #2216 for best value: >> intensional similarity = 3 >> extensional distance = 206 >> proper extension: 03kpvp; 0fpj4lx; 03hbzj; 01r0t_j; 07lz9l; 0f3nn; 089z0z; 05h7tk; >> query: (?x10056, 09c7w0) <- place_of_birth(?x10056, ?x739), gender(?x10056, ?x231), ?x739 = 02_286 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033cw nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 70.000 0.870 http://example.org/people/person/nationality #3560-03n52j PRED entity: 03n52j PRED relation: film PRED expected values: 02ht1k 0b6m5fy => 94 concepts (36 used for prediction) PRED predicted values (max 10 best out of 692): 02md2d (0.44 #32153, 0.44 #39299, 0.43 #33940), 0symg (0.33 #3485, 0.15 #7057, 0.14 #8844), 0fpkhkz (0.33 #2018, 0.15 #5590, 0.14 #7377), 0m2kd (0.33 #62, 0.14 #3634, 0.08 #5420), 0dnqr (0.33 #485, 0.14 #4057, 0.08 #5843), 0n08r (0.17 #3487, 0.14 #5273, 0.08 #7059), 01kqq7 (0.17 #3413, 0.14 #5199, 0.08 #6985), 0bt4g (0.17 #3119, 0.14 #4905, 0.08 #6691), 01y9jr (0.17 #2946, 0.14 #4732, 0.08 #6518), 01l_pn (0.17 #2751, 0.14 #4537, 0.08 #6323) >> Best rule #32153 for best value: >> intensional similarity = 4 >> extensional distance = 251 >> proper extension: 0b6yp2; >> query: (?x5397, ?x4223) <- nominated_for(?x5397, ?x4223), nationality(?x5397, ?x94), category(?x5397, ?x134), ?x94 = 09c7w0 >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03n52j film 0b6m5fy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 36.000 0.443 http://example.org/film/actor/film./film/performance/film EVAL 03n52j film 02ht1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 36.000 0.443 http://example.org/film/actor/film./film/performance/film #3559-018p4y PRED entity: 018p4y PRED relation: film PRED expected values: 014kq6 => 100 concepts (82 used for prediction) PRED predicted values (max 10 best out of 669): 0164qt (0.57 #40971, 0.49 #10687, 0.48 #60571), 01hn_t (0.07 #30282, 0.06 #37408, 0.06 #23156), 08r4x3 (0.06 #154, 0.04 #10841, 0.02 #9059), 01shy7 (0.06 #424, 0.02 #11111, 0.02 #80594), 01l_pn (0.06 #960, 0.02 #2741, 0.01 #81130), 0pc62 (0.06 #94), 03bx2lk (0.04 #185, 0.02 #16216, 0.02 #9090), 02z3r8t (0.04 #108, 0.02 #9013, 0.02 #1889), 06_wqk4 (0.04 #127, 0.02 #9032, 0.01 #39316), 013q07 (0.04 #357, 0.02 #9262, 0.01 #16388) >> Best rule #40971 for best value: >> intensional similarity = 3 >> extensional distance = 902 >> proper extension: 04bdxl; 02bfmn; 0d_84; 0l8v5; 032xhg; 054_mz; 04wqr; 01rr9f; 02r_d4; 05zbm4; ... >> query: (?x11879, ?x835) <- profession(?x11879, ?x319), film(?x11879, ?x2755), award_winner(?x835, ?x11879) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #346 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 06sn8m; *> query: (?x11879, 014kq6) <- profession(?x11879, ?x319), award(?x11879, ?x2325), ?x2325 = 05p09zm *> conf = 0.02 ranks of expected_values: 146 EVAL 018p4y film 014kq6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 100.000 82.000 0.572 http://example.org/film/actor/film./film/performance/film #3558-0gyx4 PRED entity: 0gyx4 PRED relation: award_winner! PRED expected values: 054ky1 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 265): 0gq9h (0.37 #30195, 0.37 #31048, 0.37 #31047), 0gr51 (0.37 #30195, 0.37 #31048, 0.37 #31047), 0gr4k (0.37 #30195, 0.37 #31048, 0.37 #31047), 04dn09n (0.37 #30195, 0.37 #31048, 0.37 #31047), 03hl6lc (0.37 #30195, 0.37 #31048, 0.37 #31047), 0f4x7 (0.37 #30195, 0.37 #31048, 0.37 #31047), 04kxsb (0.37 #30195, 0.37 #31048, 0.37 #31047), 05pcn59 (0.37 #30195, 0.37 #31048, 0.37 #31047), 05p1dby (0.37 #30195, 0.37 #31048, 0.37 #31047), 07bdd_ (0.37 #30195, 0.37 #31048, 0.37 #31047) >> Best rule #30195 for best value: >> intensional similarity = 3 >> extensional distance = 1379 >> proper extension: 05vsxz; 0dbpyd; 0520r2x; 06j0md; 0197tq; 06gp3f; 0cnl80; 0hl3d; 01lmj3q; 086k8; ... >> query: (?x4397, ?x350) <- award(?x4397, ?x350), award_winner(?x3736, ?x4397), award_nominee(?x4397, ?x2028) >> conf = 0.37 => this is the best rule for 13 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 12 EVAL 0gyx4 award_winner! 054ky1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 112.000 112.000 0.369 http://example.org/award/award_category/winners./award/award_honor/award_winner #3557-05c26ss PRED entity: 05c26ss PRED relation: nominated_for! PRED expected values: 09tqxt 02x1z2s => 80 concepts (71 used for prediction) PRED predicted values (max 10 best out of 199): 09tqxt (0.60 #318, 0.15 #16157, 0.14 #2487), 0drtkx (0.40 #439, 0.15 #16157, 0.11 #17123), 0p9sw (0.36 #503, 0.16 #3154, 0.16 #3878), 07bdd_ (0.29 #536, 0.26 #3911, 0.25 #1741), 05b1610 (0.29 #515, 0.25 #3890, 0.24 #1720), 05f4m9q (0.29 #494, 0.22 #3869, 0.19 #1699), 05b4l5x (0.29 #488, 0.17 #1693, 0.15 #3863), 0gq9h (0.28 #3197, 0.27 #5609, 0.26 #1269), 05ztrmj (0.25 #137, 0.08 #1583, 0.07 #3029), 05zvj3m (0.25 #74, 0.07 #556, 0.07 #8513) >> Best rule #318 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 09146g; 06wbm8q; 01sbv9; >> query: (?x3839, 09tqxt) <- nominated_for(?x5970, ?x3839), film(?x396, ?x3839), ?x5970 = 056ws9, film_release_region(?x3839, ?x87) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 18 EVAL 05c26ss nominated_for! 02x1z2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 80.000 71.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05c26ss nominated_for! 09tqxt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 71.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3556-03d555l PRED entity: 03d555l PRED relation: sport PRED expected values: 039yzs => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 9): 039yzs (0.67 #97, 0.65 #160, 0.59 #124), 018w8 (0.63 #139, 0.62 #220, 0.60 #193), 02vx4 (0.50 #695, 0.50 #713, 0.49 #722), 03tmr (0.24 #226, 0.23 #199, 0.22 #73), 0jm_ (0.23 #183, 0.22 #273, 0.21 #237), 018jz (0.22 #212, 0.21 #230, 0.21 #302), 06f3l (0.11 #81, 0.05 #153, 0.02 #243), 09xp_ (0.03 #465, 0.03 #276, 0.03 #366), 0z74 (0.01 #305, 0.01 #323, 0.01 #350) >> Best rule #97 for best value: >> intensional similarity = 9 >> extensional distance = 10 >> proper extension: 02ptzz0; 02py8_w; 091tgz; 02qk2d5; 026wlnm; >> query: (?x4804, 039yzs) <- team(?x12162, ?x4804), team(?x5258, ?x4804), team(?x4803, ?x4804), team(?x4368, ?x4804), ?x12162 = 0b_6_l, team(?x4803, ?x4369), ?x4368 = 0b_6x2, locations(?x5258, ?x3983), ?x4369 = 02pqcfz >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03d555l sport 039yzs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.667 http://example.org/sports/sports_team/sport #3555-01w524f PRED entity: 01w524f PRED relation: category PRED expected values: 08mbj5d => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #25, 0.83 #80, 0.83 #79) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 0m19t; >> query: (?x4237, 08mbj5d) <- artists(?x302, ?x4237), artist(?x4868, ?x4237), ?x4868 = 01w40h >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w524f category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.879 http://example.org/common/topic/webpage./common/webpage/category #3554-01qqv5 PRED entity: 01qqv5 PRED relation: major_field_of_study PRED expected values: 01mkq => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 118): 01mkq (0.43 #1094, 0.40 #734, 0.40 #614), 02j62 (0.40 #267, 0.39 #1587, 0.38 #747), 03g3w (0.36 #743, 0.32 #623, 0.29 #1583), 062z7 (0.34 #384, 0.30 #264, 0.29 #1584), 0g26h (0.33 #280, 0.31 #1120, 0.24 #520), 01bt59 (0.30 #317, 0.13 #437, 0.11 #797), 01lj9 (0.28 #757, 0.26 #637, 0.25 #277), 02_7t (0.28 #302, 0.24 #422, 0.22 #662), 036hv (0.28 #250, 0.16 #730, 0.14 #6005), 01540 (0.25 #298, 0.22 #1138, 0.20 #1618) >> Best rule #1094 for best value: >> intensional similarity = 4 >> extensional distance = 154 >> proper extension: 014b4h; 05xb7q; 03q6zc; 03fcbb; >> query: (?x9166, 01mkq) <- contains(?x6895, ?x9166), institution(?x4981, ?x9166), ?x4981 = 03bwzr4, location_of_ceremony(?x566, ?x6895) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qqv5 major_field_of_study 01mkq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.429 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3553-07t_x PRED entity: 07t_x PRED relation: country! PRED expected values: 07bs0 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 43): 01lb14 (0.67 #140, 0.58 #183, 0.55 #699), 0w0d (0.56 #181, 0.56 #138, 0.55 #9), 07jbh (0.56 #152, 0.52 #195, 0.52 #109), 06wrt (0.54 #141, 0.52 #184, 0.48 #98), 0194d (0.54 #165, 0.52 #208, 0.48 #122), 07bs0 (0.48 #139, 0.44 #182, 0.38 #698), 09w1n (0.48 #146, 0.42 #189, 0.41 #490), 03rbzn (0.46 #149, 0.44 #192, 0.38 #20), 01gqfm (0.46 #168, 0.44 #211, 0.35 #39), 07jjt (0.46 #188, 0.42 #145, 0.38 #16) >> Best rule #140 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 02jx1; 06vbd; >> query: (?x6305, 01lb14) <- olympics(?x6305, ?x1931), adjoins(?x6305, ?x3352), country(?x13440, ?x6305) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #139 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 02jx1; 06vbd; *> query: (?x6305, 07bs0) <- olympics(?x6305, ?x1931), adjoins(?x6305, ?x3352), country(?x13440, ?x6305) *> conf = 0.48 ranks of expected_values: 6 EVAL 07t_x country! 07bs0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 56.000 56.000 0.673 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #3552-05zbm4 PRED entity: 05zbm4 PRED relation: film PRED expected values: 03qcfvw 0gm2_0 => 82 concepts (60 used for prediction) PRED predicted values (max 10 best out of 314): 047msdk (0.58 #33940, 0.42 #64309, 0.41 #42871), 0by1wkq (0.42 #64309, 0.38 #64308, 0.37 #105399), 0cmdwwg (0.42 #64309, 0.38 #64308, 0.37 #105399), 03p2xc (0.29 #1241, 0.03 #44658, 0.03 #66096), 01q7h2 (0.29 #1572, 0.03 #44658, 0.03 #66096), 0b76kw1 (0.14 #312, 0.05 #80388, 0.05 #83961), 08zrbl (0.14 #1374, 0.05 #80388, 0.03 #66096), 04jplwp (0.14 #1368, 0.05 #80388, 0.03 #66096), 0kvgnq (0.14 #996, 0.05 #80388, 0.03 #66096), 0yzvw (0.14 #341, 0.05 #80388, 0.03 #66096) >> Best rule #33940 for best value: >> intensional similarity = 3 >> extensional distance = 889 >> proper extension: 0h1_w; 015wfg; 0gm34; 0jvtp; >> query: (?x949, ?x1364) <- award(?x949, ?x678), film(?x949, ?x218), award_winner(?x1364, ?x949) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05zbm4 film 0gm2_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 60.000 0.577 http://example.org/film/actor/film./film/performance/film EVAL 05zbm4 film 03qcfvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 60.000 0.577 http://example.org/film/actor/film./film/performance/film #3551-0gvvm6l PRED entity: 0gvvm6l PRED relation: nominated_for! PRED expected values: 099cng => 97 concepts (90 used for prediction) PRED predicted values (max 10 best out of 201): 019f4v (0.62 #957, 0.56 #1865, 0.40 #3454), 03hl6lc (0.55 #574, 0.24 #1028, 0.17 #2390), 04dn09n (0.48 #941, 0.46 #1849, 0.38 #487), 040njc (0.45 #913, 0.42 #1821, 0.40 #459), 02qyntr (0.45 #624, 0.36 #1986, 0.36 #1078), 0gr4k (0.42 #3429, 0.40 #1840, 0.33 #932), 0k611 (0.42 #1880, 0.36 #972, 0.35 #3469), 099c8n (0.36 #1868, 0.36 #506, 0.31 #960), 04kxsb (0.36 #1901, 0.33 #993, 0.22 #10077), 0f4x7 (0.36 #3428, 0.36 #931, 0.32 #1839) >> Best rule #957 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0209xj; 091z_p; 0ywrc; 011ydl; 0dzz6g; 0jqj5; 064lsn; 02gd6x; 0404j37; 0yxf4; ... >> query: (?x8176, 019f4v) <- genre(?x8176, ?x53), production_companies(?x8176, ?x3331), nominated_for(?x1063, ?x8176), ?x1063 = 02rdxsh >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2330 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 01242_; 0yyn5; 02_06s; *> query: (?x8176, 099cng) <- genre(?x8176, ?x53), nominated_for(?x1716, ?x8176), ?x1716 = 02y_rq5, nominated_for(?x8767, ?x8176) *> conf = 0.27 ranks of expected_values: 27 EVAL 0gvvm6l nominated_for! 099cng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 97.000 90.000 0.619 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3550-0hz55 PRED entity: 0hz55 PRED relation: honored_for! PRED expected values: 0gx_st => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 81): 0gvstc3 (0.29 #139, 0.22 #944, 0.22 #829), 0lp_cd3 (0.24 #129, 0.16 #244, 0.16 #934), 03nnm4t (0.22 #173, 0.22 #978, 0.20 #863), 0gx_st (0.12 #832, 0.12 #947, 0.12 #717), 0jt3qpk (0.12 #146, 0.08 #261, 0.08 #376), 09pj68 (0.10 #200, 0.09 #85, 0.07 #315), 0gkxgfq (0.10 #202, 0.07 #1007, 0.07 #317), 0bxs_d (0.10 #785, 0.09 #900, 0.09 #1015), 04n2r9h (0.09 #33, 0.06 #723, 0.06 #838), 07z31v (0.08 #6441, 0.08 #137, 0.07 #712) >> Best rule #139 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 01b7h8; >> query: (?x4932, 0gvstc3) <- genre(?x4932, ?x225), honored_for(?x1112, ?x4932), tv_program(?x3571, ?x4932) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #832 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 0g60z; 080dwhx; 02py4c8; 0kfpm; 02k_4g; 0cwrr; 0358x_; 019nnl; 0ddd0gc; 08jgk1; ... *> query: (?x4932, 0gx_st) <- genre(?x4932, ?x225), honored_for(?x1112, ?x4932), actor(?x4932, ?x3366) *> conf = 0.12 ranks of expected_values: 4 EVAL 0hz55 honored_for! 0gx_st CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 69.000 0.286 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3549-075wx7_ PRED entity: 075wx7_ PRED relation: featured_film_locations PRED expected values: 0d060g => 73 concepts (50 used for prediction) PRED predicted values (max 10 best out of 79): 02_286 (0.31 #977, 0.30 #6493, 0.29 #5292), 02frhbc (0.25 #164, 0.03 #882, 0.02 #1839), 030qb3t (0.13 #5311, 0.12 #39, 0.12 #6512), 080h2 (0.12 #24, 0.04 #6497, 0.04 #4095), 05kj_ (0.12 #18, 0.03 #736, 0.03 #3609), 017j7y (0.12 #222, 0.03 #940), 01vqq1 (0.12 #178, 0.01 #896), 04jpl (0.11 #6482, 0.11 #5281, 0.10 #3600), 06y57 (0.10 #582, 0.09 #342, 0.07 #1060), 0rh6k (0.06 #3592, 0.06 #5273, 0.06 #6474) >> Best rule #977 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 0h0wd9; >> query: (?x1721, 02_286) <- nominated_for(?x963, ?x1721), film(?x561, ?x1721), nominated_for(?x1721, ?x857), featured_film_locations(?x1721, ?x7468) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #3599 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 233 *> proper extension: 0d8w2n; *> query: (?x1721, 0d060g) <- genre(?x1721, ?x53), featured_film_locations(?x1721, ?x7468), films(?x7173, ?x1721) *> conf = 0.02 ranks of expected_values: 36 EVAL 075wx7_ featured_film_locations 0d060g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 73.000 50.000 0.308 http://example.org/film/film/featured_film_locations #3548-03kq98 PRED entity: 03kq98 PRED relation: award PRED expected values: 0m7yy => 93 concepts (86 used for prediction) PRED predicted values (max 10 best out of 176): 0m7yy (0.50 #3176, 0.47 #3878, 0.45 #3644), 0cqh6z (0.27 #703, 0.27 #522, 0.12 #704), 0bdw6t (0.25 #14050, 0.19 #2343, 0.12 #1022), 02py7pj (0.25 #14050, 0.19 #2343, 0.12 #13581), 07cbcy (0.24 #2406, 0.03 #10366, 0.03 #12473), 0bdx29 (0.23 #551, 0.15 #787, 0.15 #1021), 0bfvd4 (0.22 #87, 0.08 #7026, 0.08 #321), 07z2lx (0.22 #165, 0.08 #399, 0.05 #869), 05f4m9q (0.22 #2354, 0.03 #13123, 0.03 #13826), 0bdw1g (0.20 #498, 0.19 #2343, 0.16 #968) >> Best rule #3176 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 02nf2c; >> query: (?x623, 0m7yy) <- genre(?x623, ?x53), country_of_origin(?x623, ?x94), award(?x623, ?x435), award_winner(?x623, ?x5559) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03kq98 award 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 86.000 0.504 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #3547-01mr2g6 PRED entity: 01mr2g6 PRED relation: student! PRED expected values: 02h4rq6 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 18): 02_xgp2 (0.24 #120, 0.14 #48, 0.12 #246), 016t_3 (0.21 #39, 0.10 #21, 0.08 #418), 019v9k (0.20 #26, 0.16 #333, 0.16 #711), 0bkj86 (0.16 #115, 0.10 #422, 0.10 #332), 02h4rq6 (0.14 #327, 0.10 #417, 0.10 #597), 028dcg (0.12 #503, 0.11 #431, 0.10 #809), 07s6fsf (0.10 #19, 0.07 #37, 0.07 #109), 013zdg (0.09 #114, 0.05 #253, 0.04 #150), 03mkk4 (0.08 #318, 0.08 #426, 0.07 #732), 01gkg3 (0.07 #50, 0.01 #807, 0.01 #501) >> Best rule #120 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 09bg4l; 06c97; >> query: (?x8272, 02_xgp2) <- profession(?x8272, ?x131), gender(?x8272, ?x231), company(?x8272, ?x4955), student(?x734, ?x8272) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #327 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 02rmfm; 05j12n; 0kst7v; 081t6; *> query: (?x8272, 02h4rq6) <- profession(?x8272, ?x131), gender(?x8272, ?x231), student(?x734, ?x8272), religion(?x8272, ?x2694) *> conf = 0.14 ranks of expected_values: 5 EVAL 01mr2g6 student! 02h4rq6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 137.000 137.000 0.244 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #3546-01gw8b PRED entity: 01gw8b PRED relation: people! PRED expected values: 041rx => 127 concepts (125 used for prediction) PRED predicted values (max 10 best out of 48): 02w7gg (0.22 #79, 0.18 #156, 0.17 #310), 041rx (0.20 #1390, 0.18 #1082, 0.18 #1159), 033tf_ (0.17 #7, 0.16 #392, 0.13 #623), 07hwkr (0.13 #166, 0.12 #320, 0.08 #243), 0xnvg (0.12 #398, 0.10 #475, 0.10 #860), 0x67 (0.12 #1704, 0.11 #1858, 0.11 #2012), 048z7l (0.10 #579, 0.09 #425, 0.07 #348), 07bch9 (0.08 #23, 0.07 #639, 0.06 #870), 09vc4s (0.08 #9, 0.07 #625, 0.05 #394), 022dp5 (0.08 #50, 0.04 #435, 0.03 #820) >> Best rule #79 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 03f2_rc; >> query: (?x10617, 02w7gg) <- location(?x10617, ?x739), award(?x10617, ?x1716), ?x1716 = 02y_rq5, award_nominee(?x3927, ?x10617) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1390 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 202 *> proper extension: 033hqf; 0ccqd7; *> query: (?x10617, 041rx) <- location(?x10617, ?x739), ?x739 = 02_286, nationality(?x10617, ?x94), student(?x263, ?x10617) *> conf = 0.20 ranks of expected_values: 2 EVAL 01gw8b people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 125.000 0.219 http://example.org/people/ethnicity/people #3545-093dqjy PRED entity: 093dqjy PRED relation: film_festivals PRED expected values: 0cmd3zy => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 18): 03wf1p2 (0.12 #153, 0.10 #173, 0.06 #33), 04_m9gk (0.11 #152, 0.10 #172, 0.03 #32), 0bmj62v (0.11 #151, 0.09 #171, 0.04 #11), 0gg7gsl (0.10 #141, 0.09 #41, 0.09 #61), 0kfhjq0 (0.10 #145, 0.08 #165, 0.04 #5), 04grdgy (0.10 #148, 0.08 #168, 0.03 #68), 0g57ws5 (0.08 #147, 0.07 #167, 0.01 #307), 03nn7l2 (0.07 #16, 0.06 #56, 0.05 #96), 0j63cyr (0.07 #163, 0.03 #23, 0.03 #43), 0hrcs29 (0.07 #154, 0.06 #174, 0.03 #34) >> Best rule #153 for best value: >> intensional similarity = 3 >> extensional distance = 207 >> proper extension: 0h3y; 042rnl; 02z13jg; 03kwtb; 01f7v_; 01c6l; 04ld94; 04r7p; 02y_j8g; 02404v; ... >> query: (?x3714, 03wf1p2) <- film_festivals(?x3714, ?x9080), film_festivals(?x5074, ?x9080), featured_film_locations(?x5074, ?x362) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #18 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 0j8f09z; *> query: (?x3714, 0cmd3zy) <- genre(?x3714, ?x812), nominated_for(?x2478, ?x3714), ?x2478 = 02x4x18 *> conf = 0.04 ranks of expected_values: 15 EVAL 093dqjy film_festivals 0cmd3zy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 64.000 64.000 0.120 http://example.org/film/film/film_festivals #3544-073h9x PRED entity: 073h9x PRED relation: award_winner PRED expected values: 0z4s => 30 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1499): 0cw67g (0.44 #9086, 0.33 #1405, 0.25 #7550), 03_gd (0.38 #15365, 0.37 #12288, 0.33 #96), 05kfs (0.38 #15365, 0.37 #12288, 0.25 #3164), 021yc7p (0.38 #15365, 0.37 #12288, 0.24 #7681), 01vwllw (0.38 #15365, 0.37 #12288, 0.24 #7681), 0z4s (0.38 #15365, 0.37 #12288, 0.24 #7681), 0klh7 (0.38 #15365, 0.37 #12288, 0.24 #7681), 06chf (0.38 #15365, 0.37 #12288, 0.24 #7681), 0gyx4 (0.38 #15365, 0.37 #12288, 0.11 #8356), 01fwk3 (0.38 #15365, 0.37 #12288, 0.05 #6145) >> Best rule #9086 for best value: >> intensional similarity = 14 >> extensional distance = 7 >> proper extension: 059x66; 0bzm81; 0bvfqq; 02yvhx; 02yxh9; >> query: (?x3254, 0cw67g) <- ceremony(?x1972, ?x3254), award_winner(?x3254, ?x4393), award_winner(?x3254, ?x2870), ?x1972 = 0gqyl, honored_for(?x3254, ?x3255), nominated_for(?x4393, ?x1173), gender(?x2870, ?x231), nominated_for(?x2028, ?x3255), award_winner(?x5349, ?x4393), film(?x1561, ?x3255), film_release_region(?x1173, ?x985), ?x985 = 0k6nt, crewmember(?x1386, ?x2870), ?x5349 = 02jp5r >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #15365 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 52 *> proper extension: 02yv_b; 0ftlkg; 0dthsy; 0ftlxj; 0bzkvd; 0fy59t; 0dznvw; *> query: (?x3254, ?x4397) <- ceremony(?x1972, ?x3254), award_winner(?x3254, ?x4393), award_winner(?x3254, ?x2870), ?x1972 = 0gqyl, honored_for(?x3254, ?x3255), nominated_for(?x4393, ?x1173), gender(?x2870, ?x231), nominated_for(?x2028, ?x3255), film(?x1561, ?x3255), film_release_region(?x1173, ?x985), ?x985 = 0k6nt, award_winner(?x3255, ?x4397), award_nominee(?x4393, ?x1983) *> conf = 0.38 ranks of expected_values: 6 EVAL 073h9x award_winner 0z4s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 30.000 17.000 0.444 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3543-0yyh PRED entity: 0yyh PRED relation: location_of_ceremony! PRED expected values: 04ztj => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.90 #273, 0.89 #264, 0.88 #200), 0jgjn (0.80 #146, 0.78 #250, 0.71 #21), 01g63y (0.71 #21, 0.11 #36, 0.11 #83), 01bl8s (0.04 #105, 0.04 #109, 0.02 #190) >> Best rule #273 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 0162v; 0f25y; >> query: (?x11812, 04ztj) <- location_of_ceremony(?x10591, ?x11812), location(?x4334, ?x11812), gender(?x4334, ?x231), nationality(?x4334, ?x2146) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0yyh location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 178.000 0.898 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #3542-03spz PRED entity: 03spz PRED relation: combatants! PRED expected values: 018vbf => 219 concepts (219 used for prediction) PRED predicted values (max 10 best out of 63): 081pw (0.51 #2601, 0.50 #2664, 0.50 #2283), 03gqgt3 (0.42 #373, 0.38 #1451, 0.37 #1005), 048n7 (0.40 #720, 0.40 #278, 0.39 #594), 01gjd0 (0.40 #258, 0.28 #574, 0.26 #637), 0cm2xh (0.32 #646, 0.31 #393, 0.29 #772), 0c3mz (0.30 #294, 0.23 #420, 0.22 #610), 01h6pn (0.30 #268, 0.23 #394, 0.22 #584), 018w0j (0.29 #1268, 0.29 #1269, 0.25 #101), 0jfgk (0.29 #1268, 0.29 #1269, 0.21 #1966), 0ql86 (0.29 #1268, 0.29 #1269, 0.21 #1966) >> Best rule #2601 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0285m87; >> query: (?x4743, 081pw) <- combatants(?x512, ?x4743), combatants(?x7419, ?x4743), jurisdiction_of_office(?x182, ?x4743) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #759 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 07ytt; *> query: (?x4743, 018vbf) <- entity_involved(?x7419, ?x4743), administrative_parent(?x4743, ?x551), countries_spoken_in(?x254, ?x4743) *> conf = 0.05 ranks of expected_values: 58 EVAL 03spz combatants! 018vbf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 219.000 219.000 0.511 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #3541-0bg4f9 PRED entity: 0bg4f9 PRED relation: sport PRED expected values: 02vx4 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 9): 02vx4 (0.89 #574, 0.88 #592, 0.88 #637), 0z74 (0.50 #737, 0.49 #766, 0.48 #672), 018jz (0.11 #428, 0.10 #747, 0.10 #727), 03tmr (0.11 #428, 0.10 #747, 0.10 #727), 0jm_ (0.11 #428, 0.10 #747, 0.10 #727), 018w8 (0.11 #428, 0.10 #747, 0.10 #727), 039yzs (0.11 #428, 0.10 #747, 0.10 #727), 06f3l (0.11 #428, 0.10 #747, 0.10 #727), 09xp_ (0.11 #428, 0.10 #747, 0.10 #727) >> Best rule #574 for best value: >> intensional similarity = 17 >> extensional distance = 157 >> proper extension: 0g701n; 0gxkm; 01kwhf; 017znw; 04wqsm; 0ljbg; 031zp2; 04mvk7; 03l7tr; 06zpgb2; ... >> query: (?x12526, 02vx4) <- position(?x12526, ?x203), position(?x12526, ?x60), team(?x63, ?x12526), colors(?x12526, ?x663), ?x203 = 0dgrmp, position(?x11225, ?x60), position(?x7899, ?x60), position(?x1360, ?x60), ?x11225 = 03ylxn, team(?x60, ?x11268), team(?x60, ?x10463), team(?x60, ?x2677), ?x1360 = 08pgl8, ?x11268 = 03b6j8, ?x7899 = 08k05y, ?x10463 = 032498, ?x2677 = 0g701n >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bg4f9 sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.887 http://example.org/sports/sports_team/sport #3540-0l76z PRED entity: 0l76z PRED relation: titles! PRED expected values: 07c52 => 113 concepts (68 used for prediction) PRED predicted values (max 10 best out of 73): 07s9rl0 (0.96 #6196, 0.46 #515, 0.33 #6095), 07c52 (0.91 #645, 0.84 #2402, 0.80 #4365), 09b3v (0.58 #975, 0.03 #1281, 0.02 #6556), 0hn10 (0.50 #530, 0.20 #5678, 0.08 #118), 01z4y (0.33 #36, 0.20 #1684, 0.18 #6543), 04xvlr (0.33 #6199, 0.20 #5678, 0.19 #6614), 015w9s (0.30 #561, 0.20 #5678, 0.14 #973), 0146mv (0.25 #190, 0.06 #292, 0.05 #807), 01hmnh (0.24 #953, 0.20 #5678, 0.10 #6534), 017fp (0.20 #5678, 0.16 #6219, 0.11 #538) >> Best rule #6196 for best value: >> intensional similarity = 4 >> extensional distance = 286 >> proper extension: 03h_yy; 04gknr; 01b195; 0bby9p5; 05n6sq; 04xx9s; 047gpsd; 01gglm; 03ntbmw; >> query: (?x4588, 07s9rl0) <- titles(?x3381, ?x4588), award_winner(?x4588, ?x2813), titles(?x3381, ?x5682), ?x5682 = 064r97z >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #645 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 54 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x4588, 07c52) <- genre(?x4588, ?x2480), genre(?x4588, ?x239), titles(?x3381, ?x4588), ?x2480 = 01z4y, genre(?x238, ?x239) *> conf = 0.91 ranks of expected_values: 2 EVAL 0l76z titles! 07c52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 113.000 68.000 0.958 http://example.org/media_common/netflix_genre/titles #3539-0fw2d3 PRED entity: 0fw2d3 PRED relation: nationality PRED expected values: 0f8l9c => 89 concepts (49 used for prediction) PRED predicted values (max 10 best out of 71): 09c7w0 (0.84 #4088, 0.80 #4188, 0.80 #4289), 0f8l9c (0.82 #3986, 0.48 #4892, 0.40 #3987), 07ssc (0.48 #2799, 0.39 #3001, 0.37 #412), 09lk2 (0.40 #3987, 0.34 #4893, 0.33 #199), 02jx1 (0.34 #3221, 0.34 #2917, 0.33 #132), 03rk0 (0.30 #3334, 0.26 #3433, 0.25 #3131), 0d060g (0.26 #2791, 0.22 #3093, 0.21 #3296), 0chghy (0.20 #507, 0.13 #905, 0.12 #1104), 015fr (0.15 #414, 0.13 #514, 0.12 #714), 05bcl (0.14 #357, 0.11 #456, 0.09 #756) >> Best rule #4088 for best value: >> intensional similarity = 6 >> extensional distance = 1468 >> proper extension: 044mz_; 07nznf; 02s2ft; 079vf; 03qcq; 05bnp0; 0dbpyd; 028q6; 0fvf9q; 04yywz; ... >> query: (?x7703, 09c7w0) <- gender(?x7703, ?x231), ?x231 = 05zppz, place_of_birth(?x7703, ?x3936), nationality(?x7703, ?x1499), film_release_region(?x1421, ?x1499), ?x1421 = 07qg8v >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #3986 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1420 *> proper extension: 049tjg; 09bx1k; 02q9kqf; 037mh8; 01h4rj; 07h1q; *> query: (?x7703, ?x789) <- gender(?x7703, ?x231), ?x231 = 05zppz, place_of_birth(?x7703, ?x3936), contains(?x789, ?x3936), country(?x251, ?x789), film_release_region(?x66, ?x789) *> conf = 0.82 ranks of expected_values: 2 EVAL 0fw2d3 nationality 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 49.000 0.839 http://example.org/people/person/nationality #3538-05p1dby PRED entity: 05p1dby PRED relation: award! PRED expected values: 01h1b 0c41qv => 40 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2106): 02_l96 (0.81 #9967, 0.69 #3322, 0.68 #49853), 0127m7 (0.81 #9967, 0.69 #3322, 0.68 #49853), 0m66w (0.81 #9967, 0.69 #3322, 0.68 #49853), 03v1w7 (0.81 #9967, 0.69 #3322, 0.68 #49853), 046b0s (0.81 #9967, 0.69 #3322, 0.68 #49853), 016tw3 (0.81 #9967, 0.69 #3322, 0.68 #49853), 0kx4m (0.81 #9967, 0.69 #3322, 0.68 #49853), 0f7hc (0.60 #1325, 0.38 #7970, 0.17 #4647), 0f502 (0.60 #1214, 0.38 #7859, 0.17 #4536), 0hqly (0.60 #2996, 0.17 #6318, 0.12 #9641) >> Best rule #9967 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 05b4l5x; 05b1610; 03c7tr1; 07bdd_; 057xs89; 0hnf5vm; >> query: (?x2022, ?x847) <- award(?x166, ?x2022), nominated_for(?x2022, ?x5608), award_winner(?x2022, ?x847), ?x5608 = 01l_pn >> conf = 0.81 => this is the best rule for 7 predicted values *> Best rule #53176 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 208 *> proper extension: 02pzxlw; 02p_04b; 0fqpg6b; *> query: (?x2022, ?x1104) <- award(?x4660, ?x2022), nominated_for(?x2022, ?x4551), award_nominee(?x4660, ?x1104), award(?x4551, ?x507) *> conf = 0.12 ranks of expected_values: 530, 532 EVAL 05p1dby award! 0c41qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 40.000 16.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05p1dby award! 01h1b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 40.000 16.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3537-03mdt PRED entity: 03mdt PRED relation: child PRED expected values: 030_1m => 157 concepts (144 used for prediction) PRED predicted values (max 10 best out of 184): 093h7p (0.25 #606, 0.25 #435, 0.20 #1286), 0kc9f (0.25 #664, 0.25 #493, 0.20 #1344), 02w_l9 (0.25 #653, 0.25 #482, 0.20 #1333), 02swsm (0.25 #639, 0.25 #468, 0.20 #1319), 04rcl7 (0.25 #610, 0.25 #439, 0.20 #1290), 032dg7 (0.25 #594, 0.25 #423, 0.20 #1274), 04gvyp (0.25 #592, 0.25 #421, 0.20 #1272), 020h2v (0.25 #585, 0.25 #414, 0.20 #1265), 054g1r (0.25 #558, 0.25 #387, 0.20 #1238), 0kk9v (0.25 #547, 0.25 #376, 0.20 #1227) >> Best rule #606 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0g5lhl7; >> query: (?x3381, 093h7p) <- titles(?x3381, ?x715), company(?x346, ?x3381), citytown(?x3381, ?x739) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2737 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 02slt7; 06q07; *> query: (?x3381, 030_1m) <- service_location(?x3381, ?x94), production_companies(?x4083, ?x3381) *> conf = 0.14 ranks of expected_values: 41 EVAL 03mdt child 030_1m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 157.000 144.000 0.250 http://example.org/organization/organization/child./organization/organization_relationship/child #3536-028kj0 PRED entity: 028kj0 PRED relation: film! PRED expected values: 028d4v => 61 concepts (45 used for prediction) PRED predicted values (max 10 best out of 932): 0fby2t (0.25 #752, 0.07 #2829, 0.03 #6983), 0cmt6q (0.25 #1143, 0.07 #3220, 0.02 #7374), 08vr94 (0.25 #674, 0.07 #2751, 0.02 #6905), 032w8h (0.25 #278, 0.07 #2355, 0.02 #6509), 0p_r5 (0.25 #2018, 0.01 #6172), 079vf (0.15 #2085, 0.05 #6239, 0.02 #10393), 086nl7 (0.12 #784, 0.07 #2861, 0.04 #7015), 05txrz (0.12 #764, 0.07 #2841, 0.04 #15303), 07cjqy (0.12 #600, 0.07 #2677, 0.03 #8908), 05wjnt (0.12 #409, 0.07 #2486, 0.02 #6640) >> Best rule #752 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02825cv; 011x_4; >> query: (?x10596, 0fby2t) <- film(?x7624, ?x10596), production_companies(?x10596, ?x902), ?x7624 = 01pjr7, film_release_distribution_medium(?x10596, ?x81) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #8698 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 113 *> proper extension: 02v8kmz; 03g90h; 047gn4y; 0bth54; 02z3r8t; 03ckwzc; 06_wqk4; 05sxzwc; 05pbl56; 09txzv; ... *> query: (?x10596, 028d4v) <- film(?x794, ?x10596), film_crew_role(?x10596, ?x5136), ?x5136 = 089g0h, genre(?x10596, ?x258) *> conf = 0.02 ranks of expected_values: 432 EVAL 028kj0 film! 028d4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 45.000 0.250 http://example.org/film/actor/film./film/performance/film #3535-01j_9c PRED entity: 01j_9c PRED relation: institution! PRED expected values: 03mkk4 => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 16): 03bwzr4 (0.68 #111, 0.59 #180, 0.57 #77), 04zx3q1 (0.55 #103, 0.43 #172, 0.43 #69), 027f2w (0.47 #107, 0.39 #176, 0.34 #296), 013zdg (0.38 #106, 0.31 #278, 0.27 #158), 0bjrnt (0.29 #71, 0.23 #225, 0.22 #139), 01rr_d (0.27 #148, 0.19 #80, 0.17 #114), 03mkk4 (0.26 #109, 0.24 #178, 0.24 #75), 02mjs7 (0.25 #138, 0.14 #430, 0.13 #224), 028dcg (0.23 #99, 0.19 #219, 0.16 #150), 02cq61 (0.10 #98, 0.10 #81, 0.09 #149) >> Best rule #111 for best value: >> intensional similarity = 2 >> extensional distance = 51 >> proper extension: 01prf3; >> query: (?x546, 03bwzr4) <- citytown(?x546, ?x310), organization(?x546, ?x5487) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #109 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 51 *> proper extension: 01prf3; *> query: (?x546, 03mkk4) <- citytown(?x546, ?x310), organization(?x546, ?x5487) *> conf = 0.26 ranks of expected_values: 7 EVAL 01j_9c institution! 03mkk4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 166.000 166.000 0.679 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3534-03xpsrx PRED entity: 03xpsrx PRED relation: award PRED expected values: 09sb52 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 220): 09sb52 (0.39 #40, 0.33 #9737, 0.32 #8525), 0ck27z (0.34 #4939, 0.27 #4535, 0.22 #6151), 05pcn59 (0.18 #80, 0.11 #888, 0.10 #10181), 0fbtbt (0.14 #6869, 0.13 #19394, 0.12 #28689), 0bdw6t (0.14 #6869, 0.13 #19394, 0.12 #28689), 0bdw1g (0.14 #6869, 0.13 #19394, 0.12 #28689), 05p09zm (0.14 #123, 0.11 #931, 0.10 #1739), 05zr6wv (0.14 #825, 0.12 #1633, 0.12 #421), 01by1l (0.14 #1323, 0.12 #1727, 0.10 #3747), 0gqwc (0.13 #73, 0.11 #2497, 0.10 #2901) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 187 >> proper extension: 036hf4; >> query: (?x2841, 09sb52) <- film(?x2841, ?x238), award_nominee(?x2841, ?x2842), spouse(?x2841, ?x9301) >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xpsrx award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.392 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3533-04ynx7 PRED entity: 04ynx7 PRED relation: titles! PRED expected values: 07s9rl0 0c3351 => 79 concepts (59 used for prediction) PRED predicted values (max 10 best out of 49): 07s9rl0 (0.74 #2125, 0.40 #403, 0.33 #1), 01z4y (0.40 #2259, 0.29 #2968, 0.21 #2867), 01jfsb (0.37 #504, 0.29 #1920, 0.29 #1837), 07c52 (0.29 #1139, 0.08 #2556, 0.08 #4995), 07ssc (0.24 #2233, 0.13 #2133, 0.13 #411), 03k9fj (0.22 #4559, 0.22 #503, 0.22 #4966), 060__y (0.22 #4559, 0.22 #503, 0.22 #4966), 02kdv5l (0.22 #4559, 0.22 #503, 0.22 #4966), 017fp (0.19 #2147, 0.11 #2247, 0.10 #223), 024qqx (0.15 #684, 0.14 #179, 0.14 #887) >> Best rule #2125 for best value: >> intensional similarity = 4 >> extensional distance = 464 >> proper extension: 03kq98; >> query: (?x9872, 07s9rl0) <- titles(?x162, ?x9872), nominated_for(?x6463, ?x9872), titles(?x162, ?x5648), ?x5648 = 049xgc >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12 EVAL 04ynx7 titles! 0c3351 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 79.000 59.000 0.742 http://example.org/media_common/netflix_genre/titles EVAL 04ynx7 titles! 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 59.000 0.742 http://example.org/media_common/netflix_genre/titles #3532-010xjr PRED entity: 010xjr PRED relation: award PRED expected values: 01c92g 0bdwqv => 88 concepts (66 used for prediction) PRED predicted values (max 10 best out of 269): 02z13jg (0.71 #17248, 0.70 #18453, 0.70 #18452), 04kxsb (0.50 #924, 0.36 #523, 0.27 #1325), 0c4z8 (0.50 #69, 0.17 #1673, 0.16 #3277), 054ks3 (0.50 #138, 0.14 #1742, 0.14 #2143), 01c92g (0.50 #94, 0.14 #2099, 0.13 #3302), 05q8pss (0.50 #210, 0.06 #611, 0.03 #9835), 025m8y (0.50 #96, 0.05 #9721, 0.05 #1700), 09qv_s (0.40 #950, 0.22 #549, 0.16 #1351), 02w9sd7 (0.38 #969, 0.24 #568, 0.18 #1370), 0gqy2 (0.35 #963, 0.26 #562, 0.19 #1364) >> Best rule #17248 for best value: >> intensional similarity = 3 >> extensional distance = 1568 >> proper extension: 026v1z; >> query: (?x9797, ?x594) <- award_nominee(?x793, ?x9797), award_winner(?x594, ?x9797), award(?x793, ?x112) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #94 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01vvycq; 015_30; *> query: (?x9797, 01c92g) <- award(?x9797, ?x2139), award(?x9797, ?x1312), ?x1312 = 07cbcy, ?x2139 = 01by1l *> conf = 0.50 ranks of expected_values: 5, 45 EVAL 010xjr award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 88.000 66.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 010xjr award 01c92g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 88.000 66.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3531-020ffd PRED entity: 020ffd PRED relation: award PRED expected values: 04fgkf_ => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 254): 09sb52 (0.32 #18221, 0.30 #11353, 0.27 #14181), 09qrn4 (0.29 #644, 0.25 #1452, 0.17 #240), 0gkvb7 (0.29 #431, 0.17 #27, 0.12 #835), 0cjyzs (0.27 #2127, 0.12 #6975, 0.09 #11015), 0ck27z (0.26 #8577, 0.23 #6961, 0.21 #7365), 05p1dby (0.25 #1320, 0.25 #916, 0.17 #108), 0gqy2 (0.17 #165, 0.14 #569, 0.12 #1377), 05p09zm (0.17 #125, 0.14 #529, 0.12 #1337), 027dtxw (0.17 #4, 0.14 #408, 0.12 #1216), 04ljl_l (0.17 #3, 0.14 #407, 0.12 #1215) >> Best rule #18221 for best value: >> intensional similarity = 2 >> extensional distance = 1236 >> proper extension: 02zyy4; 04rsd2; 01v3bn; 01wbsdz; 01vw917; 01qrbf; 01w5gg6; 03k48_; 02__ww; >> query: (?x6171, 09sb52) <- award_nominee(?x690, ?x6171), film(?x6171, ?x6588) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #695 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0187y5; 02v2jy; *> query: (?x6171, 04fgkf_) <- award(?x6171, ?x2750), ?x2750 = 02vm9nd, film(?x6171, ?x6588) *> conf = 0.14 ranks of expected_values: 18 EVAL 020ffd award 04fgkf_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 105.000 105.000 0.321 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3530-0grmhb PRED entity: 0grmhb PRED relation: profession PRED expected values: 02krf9 => 85 concepts (53 used for prediction) PRED predicted values (max 10 best out of 55): 0kyk (0.50 #26, 0.28 #7109, 0.28 #7546), 0np9r (0.33 #17, 0.28 #7109, 0.28 #7255), 025352 (0.33 #56, 0.28 #7109, 0.28 #7255), 02krf9 (0.28 #168, 0.28 #7109, 0.28 #7546), 0cbd2 (0.28 #7109, 0.28 #7546, 0.28 #7255), 0nbcg (0.28 #7109, 0.28 #7546, 0.28 #7255), 020xn5 (0.28 #7109, 0.28 #7546, 0.28 #7255), 02hv44_ (0.28 #7109, 0.28 #7255, 0.18 #7692), 01c72t (0.18 #7692, 0.17 #20, 0.16 #600), 0dz3r (0.18 #7692, 0.17 #2, 0.11 #6094) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 07d370; 0c408_; >> query: (?x8922, 0kyk) <- award_winner(?x4064, ?x8922), ?x4064 = 03bx_5q, profession(?x8922, ?x319) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #168 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 01vyv9; 022g44; 06w38l; *> query: (?x8922, 02krf9) <- people(?x6484, ?x8922), profession(?x8922, ?x1041), ?x1041 = 03gjzk *> conf = 0.28 ranks of expected_values: 4 EVAL 0grmhb profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 53.000 0.500 http://example.org/people/person/profession #3529-0jmwg PRED entity: 0jmwg PRED relation: parent_genre! PRED expected values: 01nd9f => 82 concepts (30 used for prediction) PRED predicted values (max 10 best out of 261): 06cp5 (0.60 #588, 0.33 #844, 0.31 #3612), 0ccxx6 (0.50 #2272, 0.31 #3612, 0.25 #3567), 05jt_ (0.43 #1904, 0.43 #1646, 0.43 #1388), 04f73rc (0.43 #2022, 0.43 #1764, 0.43 #1506), 0xv2x (0.40 #637, 0.33 #893, 0.25 #2185), 01h0kx (0.40 #639, 0.33 #895, 0.20 #4259), 05jg58 (0.40 #610, 0.31 #3612, 0.17 #866), 0g_bh (0.38 #2944, 0.38 #2167, 0.17 #1133), 03lty (0.33 #793, 0.31 #3612, 0.30 #3613), 06bpt_ (0.33 #91, 0.31 #3612, 0.29 #1894) >> Best rule #588 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 0xhtw; 0dls3; 05r6t; >> query: (?x7808, 06cp5) <- artists(?x7808, ?x7706), artists(?x7808, ?x3875), people(?x1050, ?x7706), role(?x7706, ?x74), ?x3875 = 0mgcr, parent_genre(?x3642, ?x7808), profession(?x7706, ?x131), nationality(?x7706, ?x94) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6710 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 40 *> proper extension: 028cl7; 088vmr; *> query: (?x7808, 01nd9f) <- parent_genre(?x7808, ?x5934), ?x5934 = 05r6t *> conf = 0.02 ranks of expected_values: 218 EVAL 0jmwg parent_genre! 01nd9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 82.000 30.000 0.600 http://example.org/music/genre/parent_genre #3528-0d8lm PRED entity: 0d8lm PRED relation: family! PRED expected values: 07y_7 => 83 concepts (63 used for prediction) PRED predicted values (max 10 best out of 120): 02dlh2 (0.33 #156, 0.33 #137, 0.25 #379), 0l1589 (0.33 #241, 0.33 #205, 0.14 #401), 02hnl (0.33 #110, 0.26 #402, 0.25 #352), 07brj (0.33 #97, 0.26 #402, 0.25 #339), 01v1d8 (0.33 #213, 0.26 #402, 0.18 #157), 011k_j (0.33 #139, 0.25 #381, 0.20 #1747), 01p970 (0.33 #138, 0.25 #380, 0.20 #1746), 026g73 (0.33 #140, 0.25 #382, 0.20 #1748), 0mbct (0.33 #129, 0.25 #371, 0.20 #1737), 023r2x (0.33 #69, 0.25 #473, 0.18 #157) >> Best rule #156 for best value: >> intensional similarity = 14 >> extensional distance = 1 >> proper extension: 0l14md; >> query: (?x10811, ?x3703) <- role(?x227, ?x10811), family(?x4311, ?x10811), instrumentalists(?x10811, ?x562), performance_role(?x10811, ?x2944), ?x2944 = 0l14j_, ?x227 = 0342h, ?x562 = 01nqfh_, performance_role(?x75, ?x4311), performance_role(?x4343, ?x10811), role(?x4311, ?x432), instrumentalists(?x4311, ?x2693), role(?x4311, ?x3703), artists(?x597, ?x2693), ?x3703 = 02dlh2 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #402 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 2 *> proper extension: 05r5c; *> query: (?x10811, ?x75) <- role(?x227, ?x10811), family(?x4311, ?x10811), instrumentalists(?x10811, ?x562), performance_role(?x10811, ?x2944), ?x2944 = 0l14j_, ?x227 = 0342h, ?x562 = 01nqfh_, performance_role(?x75, ?x4311), performance_role(?x4343, ?x10811), role(?x4311, ?x432), instrumentalists(?x4311, ?x2693), role(?x4311, ?x3703), artists(?x597, ?x2693), role(?x3703, ?x615) *> conf = 0.26 ranks of expected_values: 32 EVAL 0d8lm family! 07y_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 83.000 63.000 0.333 http://example.org/music/instrument/family #3527-0ds6bmk PRED entity: 0ds6bmk PRED relation: film_release_region PRED expected values: 05r4w 059j2 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 160): 05r4w (0.89 #2966, 0.87 #2030, 0.86 #3434), 059j2 (0.89 #812, 0.87 #2060, 0.86 #2996), 03rjj (0.87 #2969, 0.83 #3281, 0.82 #3437), 03h64 (0.85 #3032, 0.85 #2096, 0.82 #3500), 0b90_r (0.77 #2032, 0.77 #2968, 0.74 #3280), 01znc_ (0.75 #3006, 0.75 #2070, 0.73 #3318), 06t2t (0.75 #3027, 0.70 #2091, 0.67 #3339), 03rt9 (0.71 #2042, 0.67 #3446, 0.66 #2978), 03spz (0.71 #2124, 0.66 #3060, 0.65 #3528), 0ctw_b (0.65 #2053, 0.57 #3457, 0.54 #2989) >> Best rule #2966 for best value: >> intensional similarity = 7 >> extensional distance = 180 >> proper extension: 0b76d_m; 0gtsx8c; 0c3ybss; 03g90h; 0ddfwj1; 0gtv7pk; 0h1cdwq; 0dscrwf; 0c40vxk; 0gx9rvq; ... >> query: (?x6492, 05r4w) <- film_release_region(?x6492, ?x4698), film_release_region(?x6492, ?x1353), film_release_region(?x6492, ?x94), film(?x9643, ?x6492), ?x94 = 09c7w0, ?x1353 = 035qy, month(?x4698, ?x1459) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0ds6bmk film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.890 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds6bmk film_release_region 05r4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.890 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3526-047lj PRED entity: 047lj PRED relation: geographic_distribution! PRED expected values: 0g6ff => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 34): 0d29z (0.38 #301, 0.36 #381, 0.34 #341), 071x0k (0.38 #283, 0.34 #323, 0.33 #363), 0g6ff (0.25 #50, 0.25 #10, 0.14 #90), 04mvp8 (0.18 #394, 0.17 #354, 0.16 #514), 013b6_ (0.17 #67, 0.12 #27, 0.09 #147), 012f86 (0.17 #72, 0.12 #32, 0.09 #152), 01xhh5 (0.12 #20, 0.10 #540, 0.09 #500), 04gfy7 (0.12 #33, 0.08 #73, 0.07 #113), 0ffjqy (0.12 #31, 0.08 #71, 0.07 #111), 0cn68 (0.12 #29, 0.08 #69, 0.07 #109) >> Best rule #301 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 03rjj; 03_3d; 0d060g; 04gzd; 0chghy; 01ls2; ... >> query: (?x404, 0d29z) <- film_release_region(?x9194, ?x404), film_release_region(?x2783, ?x404), ?x2783 = 0879bpq, ?x9194 = 0fpgp26 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 10 *> proper extension: 049nq; *> query: (?x404, 0g6ff) <- form_of_government(?x404, ?x4763), partially_contains(?x455, ?x404) *> conf = 0.25 ranks of expected_values: 3 EVAL 047lj geographic_distribution! 0g6ff CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 105.000 0.382 http://example.org/people/ethnicity/geographic_distribution #3525-0525b PRED entity: 0525b PRED relation: nationality PRED expected values: 02jx1 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 29): 09c7w0 (0.74 #402, 0.73 #302, 0.73 #702), 02jx1 (0.61 #233, 0.40 #33, 0.33 #8824), 07ssc (0.34 #215, 0.33 #8824, 0.20 #15), 0dbdy (0.33 #8824), 06q1r (0.20 #77, 0.01 #4787, 0.01 #4988), 0j5g9 (0.17 #162, 0.01 #1966), 03rk0 (0.07 #2852, 0.06 #8668, 0.06 #5058), 0d060g (0.05 #1110, 0.05 #2211, 0.04 #508), 03_3d (0.03 #2210, 0.01 #2110, 0.01 #6322), 03rjj (0.03 #1609, 0.02 #2409, 0.02 #2009) >> Best rule #402 for best value: >> intensional similarity = 3 >> extensional distance = 266 >> proper extension: 01zmpg; >> query: (?x11858, 09c7w0) <- people(?x743, ?x11858), actor(?x8062, ?x11858), award_nominee(?x11858, ?x1871) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 03f70xs; 01m65sp; 082mw; 06g4_; 03hltjb; *> query: (?x11858, 02jx1) <- people(?x743, ?x11858), place_of_birth(?x11858, ?x12847), ?x743 = 02w7gg *> conf = 0.61 ranks of expected_values: 2 EVAL 0525b nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.735 http://example.org/people/person/nationality #3524-07phbc PRED entity: 07phbc PRED relation: film! PRED expected values: 016tt2 => 79 concepts (55 used for prediction) PRED predicted values (max 10 best out of 75): 017s11 (0.77 #761, 0.65 #1289, 0.46 #2499), 03rwz3 (0.77 #761, 0.65 #1289, 0.18 #229), 0g1rw (0.46 #2499, 0.46 #1517, 0.45 #1670), 086k8 (0.22 #383, 0.22 #610, 0.20 #231), 016tt2 (0.21 #4, 0.16 #841, 0.16 #766), 016tw3 (0.20 #316, 0.18 #544, 0.16 #1301), 047c9l (0.18 #229, 0.17 #457, 0.16 #760), 05qd_ (0.17 #693, 0.17 #921, 0.14 #9), 03xq0f (0.15 #234, 0.13 #613, 0.12 #462), 01795t (0.13 #1155, 0.10 #626, 0.10 #475) >> Best rule #761 for best value: >> intensional similarity = 4 >> extensional distance = 164 >> proper extension: 08cx5g; >> query: (?x10268, ?x7526) <- nominated_for(?x7526, ?x10268), industry(?x7526, ?x373), citytown(?x7526, ?x4801), award_nominee(?x519, ?x7526) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 06bc59; *> query: (?x10268, 016tt2) <- film_release_distribution_medium(?x10268, ?x81), country(?x10268, ?x1558), ?x1558 = 01mjq, language(?x10268, ?x254) *> conf = 0.21 ranks of expected_values: 5 EVAL 07phbc film! 016tt2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 55.000 0.772 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #3523-04pk1f PRED entity: 04pk1f PRED relation: genre PRED expected values: 0jdm8 => 106 concepts (105 used for prediction) PRED predicted values (max 10 best out of 99): 07s9rl0 (0.61 #961, 0.61 #4201, 0.61 #2761), 02kdv5l (0.56 #603, 0.52 #1803, 0.49 #723), 01jfsb (0.48 #1812, 0.47 #612, 0.43 #8177), 06n90 (0.37 #733, 0.36 #493, 0.26 #2053), 02l7c8 (0.35 #4096, 0.28 #6259, 0.28 #7821), 04xvh5 (0.33 #154, 0.17 #394, 0.14 #4594), 0lsxr (0.25 #2409, 0.25 #8174, 0.23 #2289), 0hcr (0.24 #3863, 0.17 #143, 0.16 #2063), 06cvj (0.23 #4084, 0.17 #124, 0.14 #10450), 04pbhw (0.20 #776, 0.13 #2336, 0.12 #896) >> Best rule #961 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 06mmr; >> query: (?x6078, 07s9rl0) <- award_winner(?x6078, ?x3410), music(?x124, ?x3410), location(?x3410, ?x2467) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #2362 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 124 *> proper extension: 02s4l6; *> query: (?x6078, 0jdm8) <- nominated_for(?x2444, ?x6078), award_nominee(?x398, ?x2444), participant(?x2444, ?x117), story_by(?x6078, ?x11598) *> conf = 0.03 ranks of expected_values: 73 EVAL 04pk1f genre 0jdm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 106.000 105.000 0.608 http://example.org/film/film/genre #3522-01cwcr PRED entity: 01cwcr PRED relation: award PRED expected values: 02xj3rw => 95 concepts (87 used for prediction) PRED predicted values (max 10 best out of 290): 09sb52 (0.58 #8439, 0.36 #1239, 0.34 #14840), 0gqy2 (0.54 #5362, 0.26 #8562, 0.12 #1762), 0bdwqv (0.35 #5370, 0.17 #8570, 0.15 #2170), 04kxsb (0.22 #523, 0.20 #123, 0.15 #5323), 02x4x18 (0.22 #530, 0.13 #26405, 0.05 #14931), 0gqwc (0.22 #472, 0.08 #2472, 0.08 #2872), 094qd5 (0.22 #443, 0.08 #2443, 0.07 #3243), 05b4l5x (0.22 #405, 0.08 #2405, 0.07 #3205), 09qwmm (0.22 #432, 0.07 #2032, 0.05 #8432), 0cqgl9 (0.22 #589, 0.05 #6989, 0.04 #5789) >> Best rule #8439 for best value: >> intensional similarity = 4 >> extensional distance = 630 >> proper extension: 04mz10g; >> query: (?x7277, 09sb52) <- nationality(?x7277, ?x512), award(?x7277, ?x783), award(?x851, ?x783), ?x851 = 016khd >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #26405 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2152 *> proper extension: 0kk9v; 0181hw; *> query: (?x7277, ?x678) <- award_nominee(?x7277, ?x8966), award_nominee(?x7277, ?x7276), award(?x7276, ?x678), award_winner(?x1716, ?x8966) *> conf = 0.13 ranks of expected_values: 47 EVAL 01cwcr award 02xj3rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 95.000 87.000 0.579 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3521-01vwyqp PRED entity: 01vwyqp PRED relation: award PRED expected values: 01cw7s => 127 concepts (106 used for prediction) PRED predicted values (max 10 best out of 288): 02f73b (0.53 #2663, 0.36 #1869, 0.29 #3060), 03qbh5 (0.47 #2981, 0.39 #1790, 0.38 #2584), 02f716 (0.47 #2556, 0.24 #2953, 0.22 #1762), 02f777 (0.44 #2686, 0.33 #1892, 0.24 #1495), 0c4z8 (0.44 #2850, 0.29 #3644, 0.22 #3247), 02f5qb (0.42 #2536, 0.36 #1742, 0.24 #2933), 01c92g (0.40 #2875, 0.23 #3669, 0.22 #3272), 02f72_ (0.38 #2607, 0.28 #1813, 0.19 #29381), 03qbnj (0.36 #2611, 0.31 #3008, 0.31 #1817), 02f73p (0.36 #2566, 0.29 #2963, 0.19 #1772) >> Best rule #2663 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 01r9fv; 03j0br4; 046p9; >> query: (?x3256, 02f73b) <- award(?x3256, ?x3488), artists(?x1127, ?x3256), ?x3488 = 02f71y >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #2642 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 43 *> proper extension: 01r9fv; 03j0br4; 046p9; *> query: (?x3256, 01cw7s) <- award(?x3256, ?x3488), artists(?x1127, ?x3256), ?x3488 = 02f71y *> conf = 0.11 ranks of expected_values: 64 EVAL 01vwyqp award 01cw7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 127.000 106.000 0.533 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3520-0gl02yg PRED entity: 0gl02yg PRED relation: film_release_region PRED expected values: 0154j 0chghy => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 187): 02vzc (0.89 #1029, 0.82 #2487, 0.81 #2001), 0154j (0.89 #978, 0.80 #2436, 0.79 #816), 06mkj (0.88 #873, 0.86 #3141, 0.86 #1683), 03gj2 (0.88 #1648, 0.85 #1972, 0.85 #2944), 015fr (0.88 #828, 0.82 #2448, 0.76 #2934), 0k6nt (0.87 #1971, 0.82 #2943, 0.82 #3105), 05r4w (0.86 #2918, 0.85 #3080, 0.83 #1622), 0chghy (0.86 #983, 0.85 #2441, 0.84 #2927), 03spz (0.83 #1074, 0.73 #1722, 0.73 #912), 0jgd (0.83 #2920, 0.82 #3082, 0.82 #814) >> Best rule #1029 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 087wc7n; 03bx2lk; 03mgx6z; >> query: (?x5826, 02vzc) <- film(?x1864, ?x5826), film_release_region(?x5826, ?x311), film_release_region(?x5826, ?x252), ?x252 = 03_3d, ?x311 = 0j1z8 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #978 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 087wc7n; 03bx2lk; 03mgx6z; *> query: (?x5826, 0154j) <- film(?x1864, ?x5826), film_release_region(?x5826, ?x311), film_release_region(?x5826, ?x252), ?x252 = 03_3d, ?x311 = 0j1z8 *> conf = 0.89 ranks of expected_values: 2, 8 EVAL 0gl02yg film_release_region 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 80.000 80.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gl02yg film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3519-01m1_t PRED entity: 01m1_t PRED relation: source PRED expected values: 0jbk9 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #7, 0.92 #15, 0.92 #14) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 196 >> proper extension: 0qlrh; >> query: (?x3163, 0jbk9) <- county(?x3163, ?x3164), time_zones(?x3163, ?x2674), category(?x3163, ?x134), ?x134 = 08mbj5d >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m1_t source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.919 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #3518-0315rp PRED entity: 0315rp PRED relation: film! PRED expected values: 04bd8y 01kb2j 012q4n => 109 concepts (61 used for prediction) PRED predicted values (max 10 best out of 957): 02q_cc (0.55 #74925, 0.44 #101976, 0.43 #72843), 0c6qh (0.45 #411, 0.06 #2082, 0.03 #52441), 03r1pr (0.44 #101976, 0.43 #72843, 0.43 #85334), 016dmx (0.44 #101976, 0.43 #72843, 0.43 #85334), 027rwmr (0.44 #101976, 0.43 #72843, 0.43 #85334), 06rnl9 (0.44 #101976, 0.43 #72843, 0.43 #85334), 016tw3 (0.44 #101976, 0.43 #72843, 0.43 #85334), 08qxx9 (0.24 #1518, 0.06 #2082, 0.02 #16653), 06pj8 (0.14 #45788, 0.14 #58274, 0.11 #22897), 026rm_y (0.13 #1511, 0.06 #2082, 0.02 #16653) >> Best rule #74925 for best value: >> intensional similarity = 4 >> extensional distance = 649 >> proper extension: 02sqkh; >> query: (?x8397, ?x846) <- nominated_for(?x2871, ?x8397), nominated_for(?x846, ?x8397), profession(?x2871, ?x137), spouse(?x4552, ?x846) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #3217 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 05dy7p; *> query: (?x8397, 012q4n) <- production_companies(?x8397, ?x1104), music(?x8397, ?x669), edited_by(?x8397, ?x4215), crewmember(?x8397, ?x1933) *> conf = 0.07 ranks of expected_values: 27, 185, 560 EVAL 0315rp film! 012q4n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 109.000 61.000 0.555 http://example.org/film/actor/film./film/performance/film EVAL 0315rp film! 01kb2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 109.000 61.000 0.555 http://example.org/film/actor/film./film/performance/film EVAL 0315rp film! 04bd8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 109.000 61.000 0.555 http://example.org/film/actor/film./film/performance/film #3517-01y9qr PRED entity: 01y9qr PRED relation: institution! PRED expected values: 014mlp => 216 concepts (198 used for prediction) PRED predicted values (max 10 best out of 23): 019v9k (0.85 #81, 0.69 #177, 0.65 #541), 014mlp (0.82 #2839, 0.70 #77, 0.70 #3158), 02h4rq6 (0.82 #75, 0.79 #171, 0.72 #1095), 02_xgp2 (0.71 #181, 0.64 #85, 0.55 #37), 03bwzr4 (0.67 #87, 0.65 #183, 0.52 #547), 0bkj86 (0.50 #176, 0.39 #80, 0.37 #1100), 07s6fsf (0.42 #73, 0.42 #169, 0.36 #1093), 013zdg (0.42 #79, 0.40 #175, 0.21 #953), 04zx3q1 (0.42 #170, 0.30 #74, 0.30 #2172), 01rr_d (0.41 #42, 0.19 #428, 0.18 #18) >> Best rule #81 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 01f1r4; 02zd460; 02bqy; >> query: (?x6038, 019v9k) <- currency(?x6038, ?x2244), category(?x6038, ?x134), major_field_of_study(?x6038, ?x4100), contains(?x279, ?x6038), ?x4100 = 01lj9 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #2839 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 435 *> proper extension: 01b1mj; 0dplh; 015nl4; 0373qg; 01q460; *> query: (?x6038, 014mlp) <- institution(?x1200, ?x6038), institution(?x1200, ?x11987), institution(?x1200, ?x11975), institution(?x1200, ?x6545), ?x11987 = 0159r9, ?x6545 = 01ky7c, ?x11975 = 050xpd *> conf = 0.82 ranks of expected_values: 2 EVAL 01y9qr institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 216.000 198.000 0.848 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3516-0g8rj PRED entity: 0g8rj PRED relation: school! PRED expected values: 0jmj7 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 87): 0jmj7 (0.66 #3506, 0.66 #2810, 0.60 #2288), 05m_8 (0.19 #2265, 0.16 #2439, 0.15 #873), 01slc (0.15 #2317, 0.12 #2491, 0.10 #2839), 01yhm (0.13 #1322, 0.12 #974, 0.11 #2279), 051vz (0.13 #2282, 0.12 #977, 0.11 #1325), 0713r (0.12 #2296, 0.11 #121, 0.11 #2470), 01d5z (0.12 #2271, 0.11 #2445, 0.08 #2793), 0jmm4 (0.11 #1373, 0.11 #1460, 0.08 #2504), 01yjl (0.11 #115, 0.11 #2290, 0.11 #2464), 06x68 (0.11 #2268, 0.11 #2442, 0.07 #3486) >> Best rule #3506 for best value: >> intensional similarity = 4 >> extensional distance = 186 >> proper extension: 0fht9f; 0frm7n; >> query: (?x5486, 0jmj7) <- school(?x1576, ?x5486), school(?x662, ?x5486), colors(?x662, ?x332), team(?x180, ?x1576) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g8rj school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.665 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #3515-02l96k PRED entity: 02l96k PRED relation: parent_genre PRED expected values: 05w3f => 60 concepts (41 used for prediction) PRED predicted values (max 10 best out of 194): 06by7 (0.66 #1822, 0.64 #2153, 0.60 #343), 07sbbz2 (0.33 #5, 0.26 #2136, 0.14 #169), 02l96k (0.33 #72, 0.14 #236, 0.11 #2137), 05r6t (0.27 #1861, 0.19 #2026, 0.18 #4331), 03_d0 (0.25 #2146, 0.20 #336, 0.14 #173), 011j5x (0.25 #513, 0.11 #841, 0.11 #677), 01243b (0.24 #1835, 0.18 #1971, 0.12 #3155), 03lty (0.21 #4295, 0.19 #1312, 0.19 #1167), 017371 (0.18 #1971, 0.17 #761, 0.17 #597), 06j6l (0.18 #1971, 0.17 #689, 0.09 #1017) >> Best rule #1822 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 0133k0; 028cl7; 01nd9f; 017ht; >> query: (?x7436, 06by7) <- parent_genre(?x7436, ?x9935), parent_genre(?x9935, ?x2996), artists(?x9935, ?x5618), ?x5618 = 03d9d6 >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #845 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 16 *> proper extension: 016clz; 0m0jc; 0fd3y; 0dl5d; 01243b; 08cyft; 08jyyk; 0mmp3; 09nwwf; 0cx7f; ... *> query: (?x7436, 05w3f) <- artists(?x7436, ?x7221), artists(?x7436, ?x6241), award(?x6241, ?x3045), award_nominee(?x8831, ?x6241), artist(?x3050, ?x6241), ?x7221 = 0191h5 *> conf = 0.17 ranks of expected_values: 16 EVAL 02l96k parent_genre 05w3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 60.000 41.000 0.656 http://example.org/music/genre/parent_genre #3514-0837ql PRED entity: 0837ql PRED relation: type_of_union PRED expected values: 04ztj => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.76 #121, 0.75 #149, 0.74 #133), 01g63y (0.18 #46, 0.17 #34, 0.15 #106) >> Best rule #121 for best value: >> intensional similarity = 2 >> extensional distance = 562 >> proper extension: 01xyt7; 0dj5q; >> query: (?x4836, 04ztj) <- award_winner(?x567, ?x4836), religion(?x4836, ?x492) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0837ql type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.764 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3513-0gq9h PRED entity: 0gq9h PRED relation: award! PRED expected values: 0byfz 02lf0c 030pr 0184dt 0cj8x 0b13g7 03n93 02vyh 06mn7 030_3z 0gv40 051wwp 01wd9lv 01vb6z 05mvd62 04sry 013tcv 026670 05strv 05hjmd 01lc5 0blpnz 09xvf7 => 43 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2752): 02kxbx3 (0.79 #55025, 0.79 #29133, 0.78 #51788), 029m83 (0.79 #55025, 0.79 #29133, 0.78 #51788), 0bwh6 (0.79 #55025, 0.79 #29133, 0.78 #51788), 0kr5_ (0.79 #55025, 0.79 #29133, 0.78 #51788), 016tt2 (0.79 #55025, 0.79 #29133, 0.78 #45314), 059x0w (0.79 #55025, 0.79 #29133, 0.78 #45314), 06m6z6 (0.60 #14003, 0.18 #45315, 0.17 #4291), 04sry (0.53 #14978, 0.18 #45315, 0.17 #5266), 01ts_3 (0.53 #14910, 0.17 #5198, 0.14 #48551), 05cgy8 (0.53 #14788, 0.17 #5076, 0.09 #18025) >> Best rule #55025 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 02r0d0; 02v1ws; >> query: (?x1307, ?x3434) <- award_winner(?x1307, ?x3434), category_of(?x1307, ?x3459), award(?x3434, ?x198) >> conf = 0.79 => this is the best rule for 6 predicted values *> Best rule #14978 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 05b1610; 02rdyk7; 02x4wr9; 02x4sn8; *> query: (?x1307, 04sry) <- award(?x144, ?x1307), award(?x777, ?x1307), nominated_for(?x1307, ?x161), ?x777 = 05kfs *> conf = 0.53 ranks of expected_values: 8, 13, 14, 26, 75, 76, 110, 133, 135, 259, 260, 290, 296, 352, 611, 615, 630, 680, 682, 688, 689, 701, 1393 EVAL 0gq9h award! 09xvf7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 0blpnz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 01lc5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 05hjmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 05strv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 026670 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 013tcv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 04sry CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 05mvd62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 01vb6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 01wd9lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 051wwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 0gv40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 030_3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 06mn7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 02vyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 03n93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 0b13g7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 0cj8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 0184dt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 030pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 02lf0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gq9h award! 0byfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 26.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3512-0c_md_ PRED entity: 0c_md_ PRED relation: location PRED expected values: 0v9qg => 167 concepts (140 used for prediction) PRED predicted values (max 10 best out of 311): 02_286 (0.36 #5660, 0.30 #4053, 0.29 #7269), 0rh6k (0.32 #12056, 0.31 #9646, 0.31 #8843), 0r3w7 (0.21 #52248, 0.20 #53857, 0.20 #50638), 0dclg (0.18 #4937, 0.05 #21814, 0.05 #12973), 030qb3t (0.17 #44284, 0.16 #45893, 0.13 #56351), 059rby (0.17 #819, 0.14 #6443, 0.07 #17693), 05k7sb (0.17 #912, 0.12 #3322, 0.06 #10554), 01cx_ (0.17 #966, 0.11 #20252, 0.08 #21057), 0d6lp (0.14 #7400, 0.10 #4184, 0.09 #5791), 0cr3d (0.12 #2555, 0.11 #73298, 0.10 #86162) >> Best rule #5660 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 012v1t; >> query: (?x9684, 02_286) <- place_of_birth(?x9684, ?x8451), jurisdiction_of_office(?x9684, ?x94), dog_breed(?x8451, ?x3095), ?x94 = 09c7w0 >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #17887 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 03cvfg; 03mz9r; 01vs_v8; 011zd3; 02fcs2; 06pwf6; 02l5rm; 0f6_x; 019vgs; 033w9g; ... *> query: (?x9684, 0v9qg) <- student(?x13215, ?x9684), profession(?x9684, ?x1032), contains(?x1906, ?x13215), ?x1906 = 04rrx *> conf = 0.04 ranks of expected_values: 131 EVAL 0c_md_ location 0v9qg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 167.000 140.000 0.364 http://example.org/people/person/places_lived./people/place_lived/location #3511-0gt14 PRED entity: 0gt14 PRED relation: nominated_for! PRED expected values: 0f4x7 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 222): 0gr51 (0.77 #4031, 0.66 #6404, 0.66 #4506), 0gs9p (0.64 #1722, 0.45 #300, 0.45 #774), 019f4v (0.54 #1712, 0.53 #290, 0.49 #764), 0k611 (0.53 #1731, 0.49 #309, 0.41 #783), 040njc (0.43 #1666, 0.42 #718, 0.35 #244), 04dn09n (0.41 #1693, 0.37 #34, 0.33 #271), 0f4x7 (0.39 #1684, 0.32 #736, 0.30 #1447), 0gr0m (0.37 #1718, 0.31 #296, 0.28 #770), 0p9sw (0.35 #257, 0.31 #731, 0.30 #1679), 02pqp12 (0.32 #1717, 0.24 #769, 0.22 #295) >> Best rule #4031 for best value: >> intensional similarity = 2 >> extensional distance = 691 >> proper extension: 06mmr; >> query: (?x12113, ?x1862) <- award(?x12113, ?x1862), ceremony(?x1862, ?x78) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1684 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 282 *> proper extension: 0gcrg; 0581vn8; 0cq8nx; 0cbl95; *> query: (?x12113, 0f4x7) <- nominated_for(?x541, ?x12113), nominated_for(?x1307, ?x12113), ?x1307 = 0gq9h *> conf = 0.39 ranks of expected_values: 7 EVAL 0gt14 nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 65.000 65.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3510-01t7jy PRED entity: 01t7jy PRED relation: industry PRED expected values: 01mw1 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 40): 01mw1 (0.82 #706, 0.80 #1414, 0.75 #800), 02vxn (0.50 #660, 0.44 #754, 0.40 #989), 03qh03g (0.34 #1083, 0.17 #1035, 0.13 #1885), 0hz28 (0.34 #1083, 0.17 #1035, 0.13 #1885), 0sydc (0.34 #1083, 0.17 #1035, 0.13 #1885), 01mf0 (0.18 #2168, 0.09 #970, 0.08 #1537), 019z7b (0.18 #2168, 0.06 #808, 0.04 #1422), 04rlf (0.12 #1191, 0.12 #672, 0.09 #766), 02jjt (0.10 #1987, 0.09 #1515, 0.09 #1374), 06mbny (0.09 #639, 0.04 #1394, 0.03 #1252) >> Best rule #706 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 01n073; 04vgq5; 046qpy; 049vhf; 02rfft; 03_05; >> query: (?x3147, 01mw1) <- industry(?x3147, ?x10022), ?x10022 = 020mfr, place_founded(?x3147, ?x3148), contains(?x94, ?x3148) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01t7jy industry 01mw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.821 http://example.org/business/business_operation/industry #3509-01nln PRED entity: 01nln PRED relation: organization PRED expected values: 0gkjy => 100 concepts (98 used for prediction) PRED predicted values (max 10 best out of 15): 0gkjy (0.73 #101, 0.65 #25, 0.60 #765), 0_2v (0.43 #79, 0.33 #155, 0.32 #385), 01rz1 (0.38 #77, 0.35 #58, 0.34 #307), 04k4l (0.36 #310, 0.36 #156, 0.34 #386), 018cqq (0.31 #85, 0.26 #315, 0.26 #161), 02jxk (0.22 #78, 0.20 #308, 0.19 #384), 034h1h (0.22 #1370, 0.21 #1274, 0.18 #1485), 02_l9 (0.07 #1489, 0.02 #1608), 059dn (0.07 #127, 0.06 #319, 0.06 #165), 085h1 (0.05 #29, 0.04 #181, 0.03 #124) >> Best rule #101 for best value: >> intensional similarity = 2 >> extensional distance = 57 >> proper extension: 0ftn8; 0lnfy; 0dbks; 0c1xm; 01pxqx; 0fnc_; >> query: (?x6974, 0gkjy) <- contains(?x2467, ?x6974), ?x2467 = 0dg3n1 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nln organization 0gkjy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 98.000 0.729 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #3508-03dpqd PRED entity: 03dpqd PRED relation: film PRED expected values: 05fcbk7 043n1r5 => 98 concepts (38 used for prediction) PRED predicted values (max 10 best out of 816): 09p0ct (0.40 #211, 0.33 #1993, 0.02 #3775), 031hcx (0.20 #1269, 0.17 #3051, 0.06 #15528), 033qdy (0.20 #1170, 0.17 #2952, 0.03 #4734), 03cfkrw (0.20 #746, 0.17 #2528, 0.03 #4310), 02ctc6 (0.20 #520, 0.17 #2302, 0.03 #7648), 03t97y (0.20 #160, 0.17 #1942, 0.02 #3724), 05c9zr (0.20 #684, 0.17 #2466, 0.02 #4248), 02vxq9m (0.20 #20, 0.17 #1802, 0.02 #3584), 02ywwy (0.20 #1441, 0.17 #3223, 0.02 #6787), 0pv3x (0.20 #178, 0.17 #1960, 0.02 #23173) >> Best rule #211 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 014x77; 0170qf; 05th8t; >> query: (?x4649, 09p0ct) <- film(?x4649, ?x10455), film(?x4649, ?x278), ?x278 = 0c0yh4, award(?x4649, ?x2880), music(?x10455, ?x6664) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #14091 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 172 *> proper extension: 03_0p; *> query: (?x4649, 043n1r5) <- award_winner(?x2880, ?x4649), gender(?x4649, ?x514), people(?x1050, ?x4649), ?x1050 = 041rx *> conf = 0.01 ranks of expected_values: 474, 593 EVAL 03dpqd film 043n1r5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 98.000 38.000 0.400 http://example.org/film/actor/film./film/performance/film EVAL 03dpqd film 05fcbk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 98.000 38.000 0.400 http://example.org/film/actor/film./film/performance/film #3507-04sh80 PRED entity: 04sh80 PRED relation: country PRED expected values: 09c7w0 => 85 concepts (73 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.83 #3886, 0.83 #3826, 0.82 #1111), 0chghy (0.64 #1108, 0.10 #196, 0.08 #320), 0d060g (0.37 #4195, 0.36 #3388, 0.15 #192), 07ssc (0.22 #3528, 0.22 #3466, 0.22 #1311), 0345h (0.15 #519, 0.14 #273, 0.12 #335), 0f8l9c (0.12 #142, 0.10 #511, 0.09 #265), 03rjj (0.12 #129, 0.03 #498, 0.03 #1733), 03_3d (0.10 #561, 0.04 #807, 0.04 #377), 0c3351 (0.07 #861, 0.07 #923, 0.06 #1356), 09blyk (0.07 #861, 0.07 #923, 0.06 #1356) >> Best rule #3886 for best value: >> intensional similarity = 6 >> extensional distance = 1162 >> proper extension: 0gtsx8c; 07kb7vh; 04xbq3; >> query: (?x11681, ?x94) <- film(?x10109, ?x11681), film(?x526, ?x11681), nationality(?x526, ?x94), profession(?x10109, ?x1032), award_winner(?x526, ?x496), ?x94 = 09c7w0 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sh80 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 73.000 0.827 http://example.org/film/film/country #3506-083p7 PRED entity: 083p7 PRED relation: taxonomy PRED expected values: 04n6k => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.77 #7, 0.71 #10, 0.67 #21) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 05m0h; >> query: (?x1157, 04n6k) <- company(?x1157, ?x94), contains(?x94, ?x12761), people(?x12781, ?x1157), major_field_of_study(?x12761, ?x1154) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 083p7 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.769 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #3505-0gldyz PRED entity: 0gldyz PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #136, 0.83 #158, 0.82 #21), 02nxhr (0.05 #17, 0.05 #12, 0.04 #48), 07c52 (0.04 #28, 0.04 #23, 0.03 #33), 07z4p (0.04 #25, 0.03 #30, 0.03 #232) >> Best rule #136 for best value: >> intensional similarity = 4 >> extensional distance = 361 >> proper extension: 072r5v; 0dmn0x; >> query: (?x10459, 029j_) <- film(?x902, ?x10459), film_crew_role(?x10459, ?x137), nominated_for(?x1691, ?x10459), featured_film_locations(?x10459, ?x2474) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gldyz film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.846 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #3504-087qxp PRED entity: 087qxp PRED relation: nominated_for PRED expected values: 0d68qy => 59 concepts (33 used for prediction) PRED predicted values (max 10 best out of 134): 01j7mr (0.79 #51958, 0.78 #40593, 0.78 #14608), 05_z42 (0.29 #899, 0.14 #2521, 0.11 #53584), 01b66d (0.12 #5336, 0.11 #6960, 0.09 #8585), 0d68qy (0.11 #53584, 0.04 #13359, 0.03 #5241), 0330r (0.10 #4660, 0.03 #14400, 0.03 #6282), 01h72l (0.10 #3623, 0.02 #5245, 0.02 #6869), 0gj50 (0.10 #5470, 0.09 #7094, 0.07 #8719), 01b66t (0.09 #5605, 0.08 #7229, 0.07 #8854), 0124k9 (0.08 #25978, 0.03 #3466, 0.02 #5088), 01y6dz (0.08 #5830, 0.07 #7454, 0.06 #9079) >> Best rule #51958 for best value: >> intensional similarity = 2 >> extensional distance = 1624 >> proper extension: 0dky9n; 04f525m; 024rbz; 027_tg; 06jntd; 034f0d; 056ws9; 081bls; 099ks0; 04rcl7; ... >> query: (?x7583, ?x3626) <- award_winner(?x3626, ?x7583), nominated_for(?x1040, ?x3626) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #53584 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1689 *> proper extension: 0jz9f; 0207wx; 09rp4r_; 030_1_; 032v0v; 0hpt3; 026c1; 03jvmp; 0g5lhl7; 02s5v5; ... *> query: (?x7583, ?x3626) <- award_nominee(?x1040, ?x7583), award_winner(?x1265, ?x1040), award_winner(?x3626, ?x1040) *> conf = 0.11 ranks of expected_values: 4 EVAL 087qxp nominated_for 0d68qy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 59.000 33.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #3503-0dnw1 PRED entity: 0dnw1 PRED relation: nominated_for! PRED expected values: 0gq_v => 94 concepts (90 used for prediction) PRED predicted values (max 10 best out of 190): 0l8z1 (0.68 #13390, 0.66 #3108, 0.66 #3348), 0gq9h (0.67 #1496, 0.65 #4367, 0.64 #1018), 0gq_v (0.65 #498, 0.64 #976, 0.63 #1454), 0gs9p (0.57 #1498, 0.56 #3890, 0.55 #4847), 0gqyl (0.50 #80, 0.36 #319, 0.35 #558), 019f4v (0.49 #3880, 0.47 #4837, 0.47 #4359), 0f4x7 (0.47 #1460, 0.47 #504, 0.42 #3852), 0k611 (0.47 #1029, 0.46 #1746, 0.42 #4856), 0gs96 (0.45 #1046, 0.38 #568, 0.36 #1763), 04dn09n (0.44 #6969, 0.37 #4340, 0.36 #3861) >> Best rule #13390 for best value: >> intensional similarity = 3 >> extensional distance = 975 >> proper extension: 03j63k; 097h2; 019g8j; 0147w8; 0300ml; 02rq7nd; >> query: (?x6094, ?x1079) <- nominated_for(?x601, ?x6094), award(?x6094, ?x1079), award(?x669, ?x1079) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #498 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 04v8x9; 0bcndz; 0k4kk; 02q52q; 070fnm; 083skw; 0kcn7; 0gxfz; 0k4f3; 0b_5d; ... *> query: (?x6094, 0gq_v) <- film_art_direction_by(?x6094, ?x4896), film(?x4349, ?x6094), film_sets_designed(?x2716, ?x6094), award_winner(?x6094, ?x7556) *> conf = 0.65 ranks of expected_values: 3 EVAL 0dnw1 nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 90.000 0.679 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3502-02778qt PRED entity: 02778qt PRED relation: nationality PRED expected values: 09c7w0 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.82 #301, 0.82 #801, 0.82 #701), 0d060g (0.35 #3804, 0.35 #3503, 0.04 #4811), 02jx1 (0.20 #133, 0.18 #233, 0.14 #33), 07ssc (0.20 #115, 0.18 #215, 0.14 #15), 07t21 (0.09 #237), 03rk0 (0.07 #2347, 0.05 #7150, 0.05 #7350), 0chghy (0.02 #2111, 0.02 #2511, 0.02 #2611), 0f8l9c (0.02 #2823, 0.02 #3826, 0.02 #4526), 03rjj (0.02 #2206, 0.02 #2406, 0.02 #2806), 0345h (0.02 #7135, 0.02 #7335, 0.02 #331) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 02z6l5f; 08xz51; >> query: (?x3082, 09c7w0) <- award_winner(?x1265, ?x3082), producer_type(?x3082, ?x632), profession(?x3082, ?x319) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02778qt nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.823 http://example.org/people/person/nationality #3501-01nm8w PRED entity: 01nm8w PRED relation: category PRED expected values: 08mbj5d => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #26, 0.91 #87, 0.90 #94) >> Best rule #26 for best value: >> intensional similarity = 7 >> extensional distance = 54 >> proper extension: 022xml; >> query: (?x9658, 08mbj5d) <- major_field_of_study(?x9658, ?x2014), school_type(?x9658, ?x3092), organization(?x4095, ?x9658), currency(?x9658, ?x5696), colors(?x9658, ?x3189), ?x2014 = 04rjg, colors(?x387, ?x3189) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nm8w category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.911 http://example.org/common/topic/webpage./common/webpage/category #3500-01dpts PRED entity: 01dpts PRED relation: group! PRED expected values: 028tv0 02hnl => 93 concepts (59 used for prediction) PRED predicted values (max 10 best out of 121): 02hnl (0.78 #1860, 0.77 #2035, 0.77 #1335), 0l14md (0.69 #966, 0.67 #1401, 0.65 #1314), 028tv0 (0.49 #972, 0.43 #1320, 0.38 #885), 02fsn (0.33 #48, 0.25 #309, 0.25 #222), 0l14qv (0.27 #1399, 0.27 #964, 0.24 #1837), 05r5c (0.24 #1315, 0.24 #2015, 0.22 #1840), 03qjg (0.24 #1441, 0.23 #1354, 0.23 #2054), 042v_gx (0.20 #357, 0.15 #968, 0.12 #881), 06ncr (0.18 #1432, 0.16 #997, 0.15 #2045), 04rzd (0.17 #553, 0.14 #1425, 0.13 #990) >> Best rule #1860 for best value: >> intensional similarity = 6 >> extensional distance = 150 >> proper extension: 02mq_y; >> query: (?x10265, 02hnl) <- category(?x10265, ?x134), group(?x227, ?x10265), artists(?x2249, ?x10265), instrumentalists(?x227, ?x8149), role(?x74, ?x227), ?x8149 = 01whg97 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01dpts group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 59.000 0.776 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01dpts group! 028tv0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 59.000 0.776 http://example.org/music/performance_role/regular_performances./music/group_membership/group #3499-0hzgf PRED entity: 0hzgf PRED relation: place_founded! PRED expected values: 05b5c => 131 concepts (61 used for prediction) PRED predicted values (max 10 best out of 2): 05b5c (0.25 #95, 0.20 #317, 0.20 #206), 099ks0 (0.01 #612) >> Best rule #95 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0c_zx; 0c75w; >> query: (?x13854, 05b5c) <- contains(?x1892, ?x13854), category(?x13854, ?x134), ?x134 = 08mbj5d, ?x1892 = 02vzc, country(?x13854, ?x1892) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hzgf place_founded! 05b5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 61.000 0.250 http://example.org/organization/organization/place_founded #3498-01jszm PRED entity: 01jszm PRED relation: contains! PRED expected values: 09c7w0 => 163 concepts (124 used for prediction) PRED predicted values (max 10 best out of 201): 0c1d0 (0.82 #35821, 0.77 #67171, 0.77 #85977), 09c7w0 (0.81 #58217, 0.81 #27763, 0.78 #75233), 01n7q (0.50 #67249, 0.14 #9928, 0.14 #3658), 04_1l0v (0.38 #63589), 0mw5x (0.38 #2352, 0.37 #105691, 0.33 #107485), 059rby (0.18 #27780, 0.13 #7181, 0.12 #23304), 02jx1 (0.15 #22474, 0.14 #28742, 0.13 #40388), 01cx_ (0.11 #6461, 0.05 #5566, 0.05 #4671), 04rrx (0.11 #67298, 0.05 #8185, 0.03 #10873), 07ssc (0.09 #22419, 0.09 #36751, 0.08 #34060) >> Best rule #35821 for best value: >> intensional similarity = 4 >> extensional distance = 229 >> proper extension: 06klyh; >> query: (?x5324, ?x8263) <- citytown(?x5324, ?x8263), school_type(?x5324, ?x3205), contains(?x8263, ?x2056), country(?x8263, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #58217 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 292 *> proper extension: 02_2kg; *> query: (?x5324, 09c7w0) <- state_province_region(?x5324, ?x2713), currency(?x5324, ?x170), district_represented(?x176, ?x2713), ?x170 = 09nqf *> conf = 0.81 ranks of expected_values: 2 EVAL 01jszm contains! 09c7w0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 163.000 124.000 0.819 http://example.org/location/location/contains #3497-0zm1 PRED entity: 0zm1 PRED relation: influenced_by! PRED expected values: 04cbtrw => 147 concepts (66 used for prediction) PRED predicted values (max 10 best out of 450): 01vsl3_ (0.50 #1107, 0.16 #4638, 0.06 #8172), 0d4jl (0.33 #114, 0.29 #1626, 0.25 #618), 01vdrw (0.33 #435, 0.25 #939, 0.22 #2957), 0683n (0.33 #2853, 0.14 #9918, 0.14 #1843), 0p8jf (0.33 #2631, 0.12 #2127, 0.12 #21202), 049gc (0.29 #1732, 0.12 #21202, 0.12 #26248), 041xl (0.29 #1794, 0.12 #21202, 0.12 #26248), 0dzkq (0.29 #1634, 0.12 #21202, 0.12 #26248), 0453t (0.29 #1588, 0.12 #21202, 0.12 #26248), 043tg (0.29 #1832, 0.12 #21202, 0.12 #26248) >> Best rule #1107 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0lrh; 02jq1; >> query: (?x4292, 01vsl3_) <- influenced_by(?x9610, ?x4292), influenced_by(?x1089, ?x4292), award(?x9610, ?x601), person(?x5725, ?x9610), ?x1089 = 01vrncs >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #21202 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 214 *> proper extension: 07c0j; 0j3v; 0dzkq; 099bk; 04107; 05xq9; 03sbs; 02ln1; 0bk1p; 07hgm; ... *> query: (?x4292, ?x8720) <- influenced_by(?x9610, ?x4292), influenced_by(?x1946, ?x4292), award(?x9610, ?x601), influenced_by(?x4292, ?x2610), influenced_by(?x8720, ?x1946) *> conf = 0.12 ranks of expected_values: 94 EVAL 0zm1 influenced_by! 04cbtrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 147.000 66.000 0.500 http://example.org/influence/influence_node/influenced_by #3496-0d61px PRED entity: 0d61px PRED relation: genre PRED expected values: 02kdv5l => 75 concepts (73 used for prediction) PRED predicted values (max 10 best out of 117): 04xvlr (0.64 #1326, 0.61 #5074, 0.54 #5073), 02l7c8 (0.38 #4848, 0.34 #376, 0.32 #4485), 05p553 (0.36 #2538, 0.33 #7969, 0.32 #4956), 02kdv5l (0.30 #6039, 0.27 #122, 0.27 #1933), 01jfsb (0.29 #734, 0.29 #1943, 0.29 #3756), 01hmnh (0.24 #860, 0.16 #6055, 0.15 #4367), 060__y (0.23 #1222, 0.22 #859, 0.19 #257), 0lsxr (0.21 #1456, 0.21 #1577, 0.21 #369), 082gq (0.19 #2323, 0.18 #2202, 0.17 #1840), 04xvh5 (0.19 #274, 0.18 #34, 0.17 #394) >> Best rule #1326 for best value: >> intensional similarity = 5 >> extensional distance = 348 >> proper extension: 027pfb2; >> query: (?x4175, ?x162) <- language(?x4175, ?x90), titles(?x162, ?x4175), titles(?x53, ?x4175), film(?x1867, ?x4175), ?x53 = 07s9rl0 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #6039 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1386 *> proper extension: 015qy1; *> query: (?x4175, 02kdv5l) <- language(?x4175, ?x90), genre(?x4175, ?x811), genre(?x6610, ?x811), ?x6610 = 07ghv5 *> conf = 0.30 ranks of expected_values: 4 EVAL 0d61px genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 75.000 73.000 0.642 http://example.org/film/film/genre #3495-0ctw_b PRED entity: 0ctw_b PRED relation: combatants! PRED expected values: 048n7 => 202 concepts (202 used for prediction) PRED predicted values (max 10 best out of 59): 048n7 (0.50 #305, 0.40 #476, 0.38 #647), 01h6pn (0.40 #181, 0.38 #295, 0.32 #751), 0d06vc (0.33 #231, 0.25 #288, 0.21 #1314), 01gjd0 (0.27 #229, 0.25 #457, 0.25 #286), 018w0j (0.27 #261, 0.25 #318, 0.24 #660), 0c3mz (0.27 #264, 0.25 #321, 0.20 #492), 01cpp0 (0.27 #280, 0.25 #337, 0.17 #1991), 0gjw_ (0.27 #258, 0.20 #486, 0.19 #657), 06k75 (0.20 #241, 0.20 #184, 0.20 #3265), 01y998 (0.20 #188, 0.19 #302, 0.15 #473) >> Best rule #305 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 059z0; >> query: (?x1023, 048n7) <- combatants(?x1023, ?x2629), combatants(?x1023, ?x456), nationality(?x226, ?x1023), ?x456 = 05qhw, film_release_region(?x66, ?x2629) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ctw_b combatants! 048n7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.500 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #3494-03hfmm PRED entity: 03hfmm PRED relation: category PRED expected values: 08mbj5d => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.32 #5, 0.31 #4, 0.29 #25) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 03h_yy; >> query: (?x8664, 08mbj5d) <- currency(?x8664, ?x170), titles(?x307, ?x8664), ?x307 = 04t36, film(?x194, ?x8664) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hfmm category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.320 http://example.org/common/topic/webpage./common/webpage/category #3493-0crh5_f PRED entity: 0crh5_f PRED relation: film_release_region PRED expected values: 05r4w => 116 concepts (95 used for prediction) PRED predicted values (max 10 best out of 261): 06mkj (0.92 #714, 0.88 #3656, 0.88 #4472), 0k6nt (0.92 #680, 0.85 #4438, 0.83 #6564), 05r4w (0.89 #3597, 0.89 #3434, 0.89 #4413), 015fr (0.89 #3450, 0.86 #4429, 0.85 #6555), 035qy (0.89 #3470, 0.84 #1507, 0.84 #1181), 0jgd (0.88 #983, 0.86 #3436, 0.85 #4251), 0chghy (0.86 #3607, 0.86 #4259, 0.86 #5076), 03gj2 (0.86 #3460, 0.84 #4275, 0.84 #1171), 05b4w (0.85 #3502, 0.84 #1213, 0.83 #4481), 06bnz (0.85 #3482, 0.82 #213, 0.81 #1519) >> Best rule #714 for best value: >> intensional similarity = 8 >> extensional distance = 22 >> proper extension: 0ds35l9; 0jqn5; 03qnvdl; 0gj9tn5; 05z7c; 0cc5mcj; 0kv238; 0dr3sl; 0hx4y; 024mpp; ... >> query: (?x2954, 06mkj) <- film_release_region(?x2954, ?x2645), film_release_region(?x2954, ?x456), ?x2645 = 03h64, film_crew_role(?x2954, ?x468), film(?x1104, ?x2954), ?x456 = 05qhw, genre(?x2954, ?x53), ?x1104 = 016tw3 >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #3597 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 79 *> proper extension: 0gkz15s; 04n52p6; 05q4y12; 03z9585; *> query: (?x2954, 05r4w) <- film_release_region(?x2954, ?x2645), film_release_region(?x2954, ?x1453), film_release_region(?x2954, ?x512), film_release_region(?x2954, ?x456), ?x2645 = 03h64, film_crew_role(?x2954, ?x468), genre(?x2954, ?x53), ?x1453 = 06qd3, ?x512 = 07ssc, ?x456 = 05qhw *> conf = 0.89 ranks of expected_values: 3 EVAL 0crh5_f film_release_region 05r4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 95.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3492-04pp9s PRED entity: 04pp9s PRED relation: student! PRED expected values: 03qsdpk => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 100): 0w7c (0.25 #41, 0.14 #102, 0.12 #1078), 03qsdpk (0.14 #1378, 0.14 #1624, 0.13 #1929), 0fdys (0.12 #29, 0.07 #1127, 0.07 #90), 05lls (0.12 #7, 0.07 #68, 0.05 #190), 0557q (0.12 #51, 0.07 #112, 0.05 #234), 05qjc (0.12 #37, 0.07 #98, 0.05 #220), 040p_q (0.12 #48, 0.07 #109, 0.03 #414), 01r2l (0.12 #39, 0.07 #100, 0.03 #405), 06nm1 (0.12 #18, 0.07 #79, 0.03 #384), 01tbp (0.12 #42, 0.07 #103, 0.03 #408) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 02r_d4; 02r34n; 02v406; >> query: (?x10092, 0w7c) <- student(?x1200, ?x10092), nationality(?x10092, ?x94), film(?x10092, ?x2920), ?x1200 = 016t_3 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1378 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 111 *> proper extension: 02lg9w; 0c6qh; 072bb1; 0c9c0; 03jjzf; 0jw67; 01pkhw; 03_l8m; 02ts3h; 012gbb; ... *> query: (?x10092, 03qsdpk) <- nationality(?x10092, ?x94), award(?x10092, ?x3184), student(?x4268, ?x10092), film(?x10092, ?x2920) *> conf = 0.14 ranks of expected_values: 2 EVAL 04pp9s student! 03qsdpk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 127.000 127.000 0.250 http://example.org/education/field_of_study/students_majoring./education/education/student #3491-01m23s PRED entity: 01m23s PRED relation: currency PRED expected values: 09nqf => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.62 #1, 0.42 #47, 0.42 #2) >> Best rule #1 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0rd5k; 0rd6b; 01m20m; >> query: (?x13745, 09nqf) <- place_of_birth(?x3698, ?x13745), contains(?x1755, ?x13745), contains(?x94, ?x13745), ?x94 = 09c7w0, ?x1755 = 01x73 >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m23s currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.625 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #3490-06t61y PRED entity: 06t61y PRED relation: gender PRED expected values: 02zsn => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #5, 0.72 #145, 0.71 #135), 02zsn (0.35 #4, 0.35 #2, 0.31 #18) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 016h9b; 0137hn; >> query: (?x1950, 05zppz) <- award_winner(?x1950, ?x3307), sibling(?x488, ?x1950), award_nominee(?x192, ?x3307) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 01p7yb; 0p_pd; 028d4v; 02f2dn; 02qx69; 01pcbg; 01515w; 0h7pj; *> query: (?x1950, 02zsn) <- award_nominee(?x4928, ?x1950), award(?x1950, ?x704), ?x4928 = 051wwp *> conf = 0.35 ranks of expected_values: 2 EVAL 06t61y gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 79.000 0.794 http://example.org/people/person/gender #3489-04b675 PRED entity: 04b675 PRED relation: parent_genre PRED expected values: 08z0wx => 70 concepts (59 used for prediction) PRED predicted values (max 10 best out of 257): 06by7 (0.64 #5443, 0.64 #6081, 0.61 #5761), 05r6t (0.49 #5000, 0.48 #5159, 0.40 #5318), 016clz (0.44 #1285, 0.22 #4633, 0.15 #1925), 0296y (0.33 #214, 0.33 #55, 0.25 #373), 0xhtw (0.33 #2255, 0.27 #1933, 0.25 #3213), 03_d0 (0.33 #9, 0.22 #1290, 0.19 #9423), 0dl5d (0.25 #975, 0.22 #1295, 0.20 #494), 0glt670 (0.25 #987, 0.17 #1626, 0.17 #1466), 0jrv_ (0.23 #2024, 0.22 #1863, 0.19 #2184), 01243b (0.22 #1308, 0.19 #4656, 0.17 #5295) >> Best rule #5443 for best value: >> intensional similarity = 6 >> extensional distance = 82 >> proper extension: 05hs4r; 015pdg; 0xhtw; 061fhg; 01756d; 0mhfr; 05w3f; 05bt6j; 0y3_8; 07v64s; ... >> query: (?x6805, 06by7) <- parent_genre(?x6805, ?x2249), artists(?x6805, ?x562), artists(?x2249, ?x10565), artists(?x2249, ?x9206), ?x9206 = 017mbb, ?x10565 = 0c9l1 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #1440 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 7 *> proper extension: 0g_bh; *> query: (?x6805, ?x302) <- parent_genre(?x6805, ?x5436), parent_genre(?x6805, ?x2249), artists(?x6805, ?x562), artists(?x5436, ?x8048), artists(?x5436, ?x7972), artists(?x2249, ?x8708), artists(?x2249, ?x7272), ?x7272 = 01vsyjy, parent_genre(?x302, ?x2249), ?x8708 = 01vn0t_, ?x8048 = 0dw3l, ?x7972 = 0326tc *> conf = 0.06 ranks of expected_values: 90 EVAL 04b675 parent_genre 08z0wx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 70.000 59.000 0.643 http://example.org/music/genre/parent_genre #3488-01qb559 PRED entity: 01qb559 PRED relation: language PRED expected values: 02h40lc => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 46): 02h40lc (0.94 #478, 0.93 #419, 0.92 #1492), 03x42 (0.25 #50, 0.03 #646, 0.03 #228), 064_8sq (0.19 #618, 0.18 #7177, 0.18 #200), 06nm1 (0.18 #7177, 0.17 #963, 0.15 #189), 06b_j (0.18 #7177, 0.10 #440, 0.10 #201), 02bjrlw (0.18 #7177, 0.09 #1730, 0.08 #1251), 01r2l (0.18 #7177, 0.09 #85, 0.03 #144), 04h9h (0.18 #7177, 0.06 #460, 0.05 #221), 0295r (0.18 #7177), 02jx1 (0.18 #7177) >> Best rule #478 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 0pvms; 045j3w; 07kdkfj; 0bs8ndx; 0m3gy; >> query: (?x7491, 02h40lc) <- currency(?x7491, ?x170), film_release_region(?x7491, ?x94), film(?x3056, ?x7491), participant(?x3056, ?x2258) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qb559 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.943 http://example.org/film/film/language #3487-09nqf PRED entity: 09nqf PRED relation: currency! PRED expected values: 07gp9 0dq626 01ln5z 01r97z 0p_sc 035xwd 02hxhz 06z8s_ 06krf3 0jqp3 05z_kps 04hwbq 053tj7 017gm7 04zyhx 02rv_dz 0c8tkt 02q52q 09146g 011yth 0fy34l 023p33 064n1pz 02qhqz4 016z9n 0ddjy 021y7yw 02q56mk 04t6fk 04f52jw 04yc76 08rr3p 05q4y12 04q24zv 04tz52 05fcbk7 03rz2b 0c9k8 01dvbd 014zwb 03hkch7 02ctc6 0571m 0jswp 0221zw 0f4yh 04z257 07b1gq 024mxd 024lff 023p7l 02_qt 02rq8k8 0blpg 02mt51 049mql 070g7 062zjtt 02q_4ph 057lbk 06gb1w 07k8rt4 02phtzk 04nnpw 0315w4 07_fj54 0fb7sd 05_5_22 0y_9q 03t79f 0bhwhj 026lgs 027ct7c 06bd5j 0kvgnq 025rvx0 05650n 011ypx 07cw4 01cmp9 0dnw1 0y_yw 06cm5 04pmnt 0gmgwnv 02tktw 047myg9 02754c9 0404j37 027gy0k 02bg55 02n72k 05dss7 0gnjh 02pw_n 063hp4 059lwy 065_cjc 047rkcm 04lhc4 027pfg 01jr4j 01gvts 0294mx 047vp1n 072zl1 07xvf 093l8p 01cz7r 011x_4 03kx49 0cf8qb 01xq8v 02wgbb 0b3n61 06znpjr 02qkwl 03cvvlg 060__7 0hhggmy 027x7z5 02qydsh 03lfd_ 0n_hp 05nyqk 0f7hw 0f8j13 03bzyn4 0cq8nx 01q7h2 08xvpn 07tlfx 0kb1g 037cr1 01s9vc 03tbg6 080dfr7 0jz71 02qlp4 04fjzv 04ltlj 0640m69 0d8w2n 03phtz 04hk0w 04q01mn => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 57): 03xj05 (0.33 #113, 0.25 #171, 0.20 #286), 07jqjx (0.33 #111, 0.25 #169, 0.20 #284), 0h14ln (0.33 #110, 0.25 #168, 0.20 #283), 0cvkv5 (0.33 #107, 0.25 #165, 0.20 #280), 0gvvm6l (0.33 #106, 0.25 #164, 0.20 #279), 02qyv3h (0.33 #95, 0.25 #153, 0.20 #268), 0cmc26r (0.33 #80, 0.25 #138, 0.20 #253), 0g5879y (0.33 #76, 0.25 #134, 0.20 #249), 040rmy (0.33 #72, 0.25 #130, 0.20 #245), 02rb607 (0.33 #71, 0.25 #129, 0.20 #244) >> Best rule #113 for best value: >> intensional similarity = 22 >> extensional distance = 1 >> proper extension: 02l6h; >> query: (?x170, 03xj05) <- currency(?x99, ?x170), currency(?x4672, ?x170), currency(?x5122, ?x170), currency(?x4778, ?x170), currency(?x3882, ?x170), currency(?x1135, ?x170), currency(?x756, ?x170), currency(?x94, ?x170), film_crew_role(?x1135, ?x1171), currency(?x2518, ?x170), currency(?x216, ?x170), currency(?x1908, ?x170), film_release_region(?x303, ?x756), film(?x71, ?x4778), award_winner(?x4778, ?x6275), ?x1171 = 09vw2b7, nominated_for(?x3080, ?x5122), genre(?x3882, ?x53), film(?x3705, ?x5122), nominated_for(?x2689, ?x1135), institution(?x734, ?x4672), instrumentalists(?x227, ?x2518) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09nqf currency! 04q01mn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04hk0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03phtz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0d8w2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0640m69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04ltlj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04fjzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02qlp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0jz71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 080dfr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03tbg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01s9vc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 037cr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0kb1g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 07tlfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 08xvpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01q7h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0cq8nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03bzyn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0f8j13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0f7hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 05nyqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0n_hp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03lfd_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02qydsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 027x7z5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0hhggmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 060__7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03cvvlg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02qkwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 06znpjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0b3n61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02wgbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01xq8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0cf8qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03kx49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 011x_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01cz7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 093l8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 07xvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 072zl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 047vp1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0294mx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01gvts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01jr4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04lhc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 047rkcm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 065_cjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 059lwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 063hp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02pw_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0gnjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 05dss7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02n72k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02bg55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 027gy0k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02754c9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 047myg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02tktw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0gmgwnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04pmnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 06cm5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0y_yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0dnw1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01cmp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 07cw4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 011ypx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 05650n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 025rvx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0kvgnq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 06bd5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 027ct7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 026lgs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0bhwhj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03t79f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0y_9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 05_5_22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0fb7sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 07_fj54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0315w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04nnpw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02phtzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 07k8rt4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 06gb1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 057lbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02q_4ph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 062zjtt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 070g7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 049mql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02mt51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0blpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02rq8k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02_qt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 023p7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 024lff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 024mxd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 07b1gq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04z257 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0f4yh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0221zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0jswp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0571m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02ctc6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03hkch7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 014zwb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01dvbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0c9k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03rz2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 05fcbk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04tz52 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04q24zv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 05q4y12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 08rr3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04yc76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04f52jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04t6fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02q56mk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 021y7yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0ddjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 016z9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02qhqz4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 064n1pz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 023p33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0fy34l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 011yth CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 09146g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02q52q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0c8tkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02rv_dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04zyhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 017gm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 053tj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 04hwbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 05z_kps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0jqp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 06krf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 06z8s_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 02hxhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 035xwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0p_sc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01r97z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 01ln5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0dq626 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 07gp9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.333 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #3486-0b_6pv PRED entity: 0b_6pv PRED relation: locations PRED expected values: 0lphb => 69 concepts (68 used for prediction) PRED predicted values (max 10 best out of 278): 0d9y6 (0.50 #2698, 0.50 #1308, 0.43 #1656), 030qb3t (0.43 #1596, 0.38 #1770, 0.36 #4547), 0f2rq (0.42 #3570, 0.40 #1138, 0.40 #964), 02cl1 (0.40 #1059, 0.40 #885, 0.33 #3491), 0dzt9 (0.40 #1180, 0.40 #1006, 0.33 #1528), 0kcw2 (0.40 #2772, 0.33 #3640, 0.33 #1382), 0ply0 (0.40 #934, 0.21 #7842, 0.20 #10161), 0lphb (0.38 #1846, 0.36 #3234, 0.33 #282), 06wxw (0.38 #2165, 0.33 #2338, 0.29 #1642), 029cr (0.36 #3179, 0.35 #4222, 0.31 #3875) >> Best rule #2698 for best value: >> intensional similarity = 15 >> extensional distance = 8 >> proper extension: 0b_6v_; >> query: (?x9974, 0d9y6) <- locations(?x9974, ?x6769), locations(?x9974, ?x5267), team(?x9974, ?x10846), team(?x9974, ?x9983), team(?x9974, ?x8728), team(?x9974, ?x4369), ?x8728 = 026xxv_, ?x9983 = 02q4ntp, place_of_birth(?x190, ?x6769), ?x4369 = 02pqcfz, citytown(?x5596, ?x6769), ?x10846 = 02pzy52, citytown(?x3543, ?x5267), instance_of_recurring_event(?x9974, ?x10863), location(?x1165, ?x5267) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1846 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 6 *> proper extension: 0bzrxn; *> query: (?x9974, 0lphb) <- locations(?x9974, ?x6769), team(?x9974, ?x10171), team(?x9974, ?x9983), team(?x9974, ?x8728), ?x8728 = 026xxv_, ?x9983 = 02q4ntp, place_of_birth(?x190, ?x6769), ?x10171 = 026w398, country(?x6769, ?x94), featured_film_locations(?x7107, ?x6769), citytown(?x5596, ?x6769), edited_by(?x7107, ?x11147), dog_breed(?x6769, ?x1706) *> conf = 0.38 ranks of expected_values: 8 EVAL 0b_6pv locations 0lphb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 69.000 68.000 0.500 http://example.org/time/event/locations #3485-03shp PRED entity: 03shp PRED relation: organization PRED expected values: 07t65 => 172 concepts (171 used for prediction) PRED predicted values (max 10 best out of 51): 07t65 (0.92 #1275, 0.92 #1229, 0.91 #1043), 04k4l (0.58 #2255, 0.53 #214, 0.44 #329), 0_2v (0.58 #2255, 0.46 #236, 0.45 #512), 01rz1 (0.58 #2255, 0.45 #441, 0.41 #813), 0j7v_ (0.58 #2255, 0.32 #3028, 0.26 #514), 018cqq (0.45 #451, 0.42 #82, 0.40 #336), 0b6css (0.45 #151, 0.44 #220, 0.43 #519), 041288 (0.35 #1710, 0.33 #2411, 0.32 #2110), 02jxk (0.32 #212, 0.32 #3028, 0.32 #73), 0gkjy (0.32 #3028, 0.30 #1213, 0.30 #1328) >> Best rule #1275 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 0d060g; 0h3y; 0d0vqn; 0j1z8; ... >> query: (?x3730, 07t65) <- country(?x668, ?x3730), olympics(?x3730, ?x584), adjoins(?x1499, ?x3730) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03shp organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 171.000 0.916 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #3484-026r8q PRED entity: 026r8q PRED relation: award_winner! PRED expected values: 099tbz => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 252): 05pcn59 (0.30 #32542, 0.30 #32541, 0.30 #34258), 027dtxw (0.30 #32542, 0.30 #32541, 0.30 #34258), 0gqy2 (0.30 #32542, 0.30 #32541, 0.30 #34258), 09sdmz (0.30 #32542, 0.30 #32541, 0.30 #34258), 099jhq (0.30 #32542, 0.30 #32541, 0.30 #34258), 01by1l (0.22 #2252, 0.22 #3108, 0.15 #6961), 099tbz (0.15 #22263, 0.15 #21834, 0.15 #20121), 02ppm4q (0.15 #22263, 0.15 #21834, 0.15 #20121), 05p09zm (0.15 #22263, 0.15 #21834, 0.15 #20121), 05zr6wv (0.15 #22263, 0.15 #21834, 0.15 #20121) >> Best rule #32542 for best value: >> intensional similarity = 2 >> extensional distance = 2890 >> proper extension: 041h0; 02lnhv; 073bb; 01wk7b7; 01w8sf; 0lrh; 02rgz97; 01x72k; 02dbn2; 02bxjp; ... >> query: (?x7346, ?x704) <- award(?x7346, ?x704), gender(?x7346, ?x231) >> conf = 0.30 => this is the best rule for 5 predicted values *> Best rule #22263 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1519 *> proper extension: 01_8w2; 018_q8; 0gsgr; *> query: (?x7346, ?x704) <- award_winner(?x7346, ?x192), award_winner(?x704, ?x192) *> conf = 0.15 ranks of expected_values: 7 EVAL 026r8q award_winner! 099tbz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 85.000 85.000 0.305 http://example.org/award/award_category/winners./award/award_honor/award_winner #3483-03qx63 PRED entity: 03qx63 PRED relation: sport PRED expected values: 02vx4 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 56): 02vx4 (0.89 #264, 0.89 #255, 0.88 #236), 0z74 (0.52 #633, 0.49 #908, 0.49 #815), 03tmr (0.30 #100, 0.16 #670, 0.12 #444), 0jm_ (0.20 #102, 0.14 #672, 0.11 #410), 018w8 (0.20 #103, 0.11 #673, 0.11 #411), 018jz (0.15 #412, 0.15 #448, 0.14 #674), 039yzs (0.11 #862, 0.11 #958, 0.04 #450), 09xp_ (0.11 #862, 0.11 #958, 0.01 #954), 01yfj (0.01 #834), 09f6b (0.01 #834) >> Best rule #264 for best value: >> intensional similarity = 12 >> extensional distance = 26 >> proper extension: 03z0dt; 0415zv; 046vvc; >> query: (?x1100, 02vx4) <- position(?x1100, ?x530), position(?x1100, ?x203), position(?x1100, ?x63), position(?x1100, ?x60), ?x203 = 0dgrmp, ?x530 = 02_j1w, ?x63 = 02sdk9v, teams(?x8745, ?x1100), ?x60 = 02nzb8, contains(?x1603, ?x8745), taxonomy(?x8745, ?x939), jurisdiction_of_office(?x900, ?x8745) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03qx63 sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.893 http://example.org/sports/sports_team/sport #3482-027dpx PRED entity: 027dpx PRED relation: category PRED expected values: 08mbj5d => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.81 #35, 0.81 #30, 0.79 #15) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 839 >> proper extension: 020jqv; >> query: (?x5437, 08mbj5d) <- artist(?x441, ?x5437), artist(?x441, ?x2408), artists(?x1000, ?x2408) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027dpx category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.815 http://example.org/common/topic/webpage./common/webpage/category #3481-01yndb PRED entity: 01yndb PRED relation: artist! PRED expected values: 03rhqg => 115 concepts (74 used for prediction) PRED predicted values (max 10 best out of 113): 011k1h (0.33 #10, 0.17 #290, 0.16 #430), 01clyr (0.33 #32, 0.13 #312, 0.12 #452), 01cf93 (0.29 #197, 0.09 #617, 0.06 #2298), 03vv61 (0.29 #239, 0.03 #800, 0.03 #1080), 017l96 (0.22 #299, 0.17 #19, 0.16 #439), 03rhqg (0.17 #296, 0.17 #997, 0.17 #16), 01dtcb (0.17 #326, 0.17 #46, 0.12 #606), 0n85g (0.17 #62, 0.09 #622, 0.09 #342), 03mp8k (0.17 #66, 0.09 #346, 0.08 #1327), 01t04r (0.17 #64, 0.04 #344, 0.04 #3990) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01mwsnc; 01kcms4; 020_4z; >> query: (?x9144, 011k1h) <- artists(?x7083, ?x9144), artists(?x283, ?x9144), ?x283 = 06cqb, ?x7083 = 02yv6b >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #296 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 0knjh; *> query: (?x9144, 03rhqg) <- artists(?x7083, ?x9144), artists(?x283, ?x9144), ?x283 = 06cqb, artists(?x7083, ?x7597), ?x7597 = 03c3yf *> conf = 0.17 ranks of expected_values: 6 EVAL 01yndb artist! 03rhqg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 115.000 74.000 0.333 http://example.org/music/record_label/artist #3480-0b60sq PRED entity: 0b60sq PRED relation: genre PRED expected values: 0hcr => 119 concepts (50 used for prediction) PRED predicted values (max 10 best out of 106): 0hcr (0.90 #1287, 0.88 #1747, 0.82 #1632), 07s9rl0 (0.80 #2533, 0.74 #3688, 0.65 #5647), 01jfsb (0.57 #3584, 0.51 #5428, 0.49 #4276), 06n90 (0.50 #1624, 0.50 #1279, 0.43 #1509), 05p553 (0.50 #234, 0.40 #5650, 0.40 #349), 04xvlr (0.49 #3689, 0.21 #1958, 0.21 #2764), 04t2t (0.33 #169, 0.33 #54, 0.29 #2010), 02n4kr (0.33 #123, 0.24 #3580, 0.17 #4156), 04xvh5 (0.33 #147, 0.23 #2794, 0.19 #3719), 03npn (0.33 #122, 0.16 #3579, 0.14 #927) >> Best rule #1287 for best value: >> intensional similarity = 6 >> extensional distance = 18 >> proper extension: 056k77g; 03d8jd1; >> query: (?x596, 0hcr) <- genre(?x596, ?x1403), genre(?x596, ?x225), actor(?x596, ?x1607), ?x225 = 02kdv5l, genre(?x2097, ?x1403), ?x2097 = 023p33 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b60sq genre 0hcr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 50.000 0.900 http://example.org/film/film/genre #3479-01yfm8 PRED entity: 01yfm8 PRED relation: student! PRED expected values: 06182p => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 46): 015nl4 (0.17 #1648, 0.12 #67, 0.10 #1121), 08815 (0.12 #2, 0.02 #21093, 0.02 #23729), 033gn8 (0.12 #378, 0.01 #7758), 01k3s2 (0.12 #140), 015zyd (0.12 #1), 04rkkv (0.08 #1888, 0.08 #834, 0.05 #1361), 02zd460 (0.08 #1751), 01ky7c (0.08 #751, 0.05 #1278), 01_qgp (0.08 #803, 0.04 #1857), 0234_c (0.08 #944) >> Best rule #1648 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 01rh0w; 0dlglj; 0241jw; 0c6g1l; 01v9l67; 015t56; 01846t; 0154qm; 05qg6g; 0884fm; ... >> query: (?x7401, 015nl4) <- film(?x7401, ?x924), award_nominee(?x7401, ?x2727), ?x2727 = 024n3z >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #2933 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 518 *> proper extension: 01sl1q; 044mz_; 0q9kd; 02s2ft; 06qgvf; 0grwj; 01vvydl; 01k7d9; 02p65p; 01xdf5; ... *> query: (?x7401, 06182p) <- film(?x7401, ?x924), award_nominee(?x7401, ?x91), actor(?x4223, ?x7401) *> conf = 0.01 ranks of expected_values: 33 EVAL 01yfm8 student! 06182p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 71.000 71.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #3478-01dw9z PRED entity: 01dw9z PRED relation: film PRED expected values: 034qbx => 122 concepts (93 used for prediction) PRED predicted values (max 10 best out of 729): 02h2vv (0.54 #30432, 0.43 #7161, 0.41 #118153), 02qr3k8 (0.06 #8450, 0.03 #80055, 0.02 #31721), 02x3lt7 (0.06 #3664, 0.05 #84, 0.03 #5454), 03nqnnk (0.06 #8185, 0.02 #36827, 0.02 #29665), 06_wqk4 (0.05 #127, 0.04 #3707, 0.04 #7288), 07bzz7 (0.05 #890, 0.04 #4470, 0.03 #13421), 01jnc_ (0.05 #24840, 0.05 #28420, 0.05 #10520), 01shy7 (0.05 #23694, 0.05 #9374, 0.04 #5793), 03459x (0.04 #4150, 0.03 #570, 0.02 #5940), 031hcx (0.04 #8435, 0.02 #29915, 0.02 #22755) >> Best rule #30432 for best value: >> intensional similarity = 3 >> extensional distance = 197 >> proper extension: 02dh86; 0bkmf; >> query: (?x2683, ?x6222) <- award_winner(?x724, ?x2683), spouse(?x2683, ?x919), nominated_for(?x2683, ?x6222) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #8323 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 107 *> proper extension: 012gbb; 01bh6y; 01dbgw; *> query: (?x2683, 034qbx) <- award_winner(?x724, ?x2683), award(?x2683, ?x1245), ?x1245 = 0gqwc *> conf = 0.02 ranks of expected_values: 150 EVAL 01dw9z film 034qbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 122.000 93.000 0.542 http://example.org/film/actor/film./film/performance/film #3477-05lfwd PRED entity: 05lfwd PRED relation: titles! PRED expected values: 07c52 => 109 concepts (50 used for prediction) PRED predicted values (max 10 best out of 59): 07c52 (0.75 #548, 0.70 #1277, 0.69 #3987), 07s9rl0 (0.24 #2289, 0.23 #3019, 0.08 #105), 01z4y (0.16 #2324, 0.15 #3054, 0.02 #3575), 0gsg7 (0.13 #1039, 0.12 #4482, 0.11 #1665), 03mdt (0.13 #980, 0.12 #1710, 0.12 #459), 04xvlr (0.12 #2292, 0.12 #3022, 0.04 #3543), 024qqx (0.09 #2369, 0.09 #3099), 04t36 (0.09 #3026, 0.08 #2296), 01z77k (0.08 #4335, 0.08 #2974, 0.08 #4649), 0djd22 (0.07 #4378, 0.06 #1769, 0.04 #3016) >> Best rule #548 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 06qxh; >> query: (?x5808, 07c52) <- program(?x10160, ?x5808), genre(?x5808, ?x53), award(?x10160, ?x688), ?x53 = 07s9rl0 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05lfwd titles! 07c52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 50.000 0.750 http://example.org/media_common/netflix_genre/titles #3476-06y_n PRED entity: 06y_n PRED relation: honored_for! PRED expected values: 05c1t6z => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 92): 05c1t6z (0.50 #131, 0.42 #371, 0.35 #611), 0gvstc3 (0.44 #27, 0.29 #1227, 0.29 #387), 0ds460j (0.27 #1801, 0.10 #8286, 0.09 #7805), 0gx1673 (0.27 #1801, 0.10 #8286, 0.09 #7805), 0gx_st (0.23 #390, 0.19 #870, 0.19 #630), 0lp_cd3 (0.22 #17, 0.18 #1457, 0.17 #1217), 02wzl1d (0.22 #127, 0.06 #367, 0.05 #487), 0hn821n (0.17 #233, 0.10 #473, 0.09 #1193), 04n2r9h (0.17 #156, 0.07 #1236, 0.07 #1476), 0418154 (0.17 #211, 0.07 #691, 0.05 #1171) >> Best rule #131 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 07zhjj; 07s8z_l; >> query: (?x9787, 05c1t6z) <- honored_for(?x5585, ?x9787), genre(?x9787, ?x258), award_winner(?x9787, ?x3972), ?x5585 = 03nnm4t >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06y_n honored_for! 05c1t6z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3475-03bwzr4 PRED entity: 03bwzr4 PRED relation: student PRED expected values: 01zwy => 56 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1237): 04z0g (0.50 #2660, 0.33 #1276, 0.33 #355), 0b78hw (0.50 #2632, 0.33 #1248, 0.33 #327), 06g4_ (0.50 #2745, 0.33 #1361, 0.33 #900), 024jwt (0.50 #2279, 0.33 #1127, 0.33 #436), 01_rh4 (0.50 #2596, 0.33 #1212, 0.25 #1672), 0d0vj4 (0.50 #2553, 0.33 #1169, 0.25 #1629), 01zh29 (0.50 #2697, 0.33 #1313, 0.25 #1773), 0453t (0.50 #2578, 0.33 #1194, 0.25 #1654), 07w21 (0.50 #2540, 0.33 #1156, 0.25 #1616), 03s9v (0.50 #2680, 0.33 #1296, 0.25 #1756) >> Best rule #2660 for best value: >> intensional similarity = 18 >> extensional distance = 2 >> proper extension: 0bkj86; >> query: (?x4981, 04z0g) <- major_field_of_study(?x4981, ?x3490), major_field_of_study(?x4981, ?x2014), institution(?x4981, ?x8694), institution(?x4981, ?x6455), institution(?x4981, ?x5638), institution(?x4981, ?x2948), institution(?x4981, ?x2760), institution(?x4981, ?x2682), institution(?x4981, ?x741), ?x741 = 01w3v, ?x3490 = 05qfh, ?x6455 = 026vcc, ?x2682 = 0f102, ?x2014 = 04rjg, ?x5638 = 02bqy, ?x2948 = 0j_sncb, list(?x8694, ?x2197), student(?x2760, ?x1934) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1552 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 02h4rq6; *> query: (?x4981, 01zwy) <- major_field_of_study(?x4981, ?x12206), major_field_of_study(?x4981, ?x3490), institution(?x4981, ?x11278), institution(?x4981, ?x8220), institution(?x4981, ?x7596), institution(?x4981, ?x4980), institution(?x4981, ?x4257), institution(?x4981, ?x3439), institution(?x4981, ?x3208), institution(?x4981, ?x741), ?x741 = 01w3v, ?x3490 = 05qfh, ?x12206 = 05fh2, ?x3439 = 03ksy, ?x11278 = 037q2p, ?x8220 = 0c5x_, ?x4980 = 01n6r0, ?x3208 = 01y17m, ?x4257 = 01q0kg, student(?x4981, ?x118), ?x7596 = 012mzw *> conf = 0.33 ranks of expected_values: 16 EVAL 03bwzr4 student 01zwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 56.000 34.000 0.500 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #3474-0154j PRED entity: 0154j PRED relation: organization PRED expected values: 04k4l => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 45): 04k4l (0.68 #401, 0.58 #2069, 0.50 #207), 059dn (0.68 #401, 0.58 #2069, 0.50 #207), 0j7v_ (0.68 #401, 0.50 #207, 0.31 #2363), 034h1h (0.57 #127, 0.48 #195, 0.30 #389), 041288 (0.55 #218, 0.38 #1924, 0.37 #1733), 0gkjy (0.52 #211, 0.31 #2363, 0.28 #1639), 085h1 (0.31 #2363, 0.06 #758, 0.06 #58), 02_l9 (0.20 #113, 0.17 #164, 0.15 #392), 01prf3 (0.05 #117, 0.04 #168, 0.03 #396), 03lb_v (0.05 #119, 0.04 #170, 0.03 #398) >> Best rule #401 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 072twv; 01v5h; 016z1c; 05hjmd; 01lc5; 026ck; 04rfq; 0gzh; >> query: (?x172, ?x127) <- organizations_founded(?x172, ?x1062), organizations_founded(?x304, ?x1062), organization(?x304, ?x127) >> conf = 0.68 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0154j organization 04k4l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.684 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #3473-06mkj PRED entity: 06mkj PRED relation: countries_within! PRED expected values: 02j9z => 235 concepts (171 used for prediction) PRED predicted values (max 10 best out of 40): 02j9z (0.50 #232, 0.50 #62, 0.46 #172), 0j0k (0.35 #384, 0.34 #430, 0.30 #535), 02qkt (0.31 #650, 0.29 #673, 0.28 #390), 0dg3n1 (0.23 #647, 0.22 #574, 0.22 #309), 04swx (0.18 #32, 0.17 #37, 0.15 #74), 0j3b (0.18 #32, 0.17 #37, 0.15 #74), 05g2v (0.18 #32, 0.17 #37, 0.15 #74), 059g4 (0.14 #17, 0.13 #409, 0.12 #30), 0cgs4 (0.01 #210), 020g9r (0.01 #210) >> Best rule #232 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 04j53; >> query: (?x2152, 02j9z) <- country(?x150, ?x2152), administrative_area_type(?x2152, ?x2792), country(?x689, ?x2152), adjoins(?x87, ?x2152) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mkj countries_within! 02j9z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 235.000 171.000 0.500 http://example.org/base/locations/continents/countries_within #3472-0c4y8 PRED entity: 0c4y8 PRED relation: location PRED expected values: 0f2tj => 170 concepts (160 used for prediction) PRED predicted values (max 10 best out of 347): 02_286 (0.30 #8829, 0.30 #8062, 0.25 #69894), 030qb3t (0.22 #53872, 0.20 #54675, 0.19 #33805), 01n7q (0.20 #866, 0.13 #36994, 0.07 #4878), 04lh6 (0.20 #1237, 0.05 #6052, 0.04 #8459), 04ych (0.20 #856, 0.04 #2462, 0.04 #17715), 05mph (0.20 #1120, 0.02 #37248, 0.02 #8342), 0f2w0 (0.20 #897, 0.02 #8119, 0.02 #8923), 04f_d (0.20 #911, 0.02 #8133, 0.02 #9739), 0z53k (0.20 #1431, 0.02 #8653, 0.02 #10259), 059rby (0.16 #36947, 0.07 #4831, 0.06 #10451) >> Best rule #8829 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 01w1ywm; 014g91; >> query: (?x9610, ?x739) <- nationality(?x9610, ?x94), place_of_death(?x9610, ?x739), ?x739 = 02_286, location(?x9610, ?x4356) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #19595 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 82 *> proper extension: 01pnn3; 01pcrw; 03_6y; 06_6j3; 0hgqq; 01pcvn; 012ykt; 0b7t3p; 01pwz; 04kjrv; ... *> query: (?x9610, 0f2tj) <- type_of_union(?x9610, ?x1873), religion(?x9610, ?x1985), ?x1873 = 01g63y, gender(?x9610, ?x231) *> conf = 0.02 ranks of expected_values: 149 EVAL 0c4y8 location 0f2tj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 170.000 160.000 0.304 http://example.org/people/person/places_lived./people/place_lived/location #3471-0bw6y PRED entity: 0bw6y PRED relation: profession PRED expected values: 01p5_g => 158 concepts (130 used for prediction) PRED predicted values (max 10 best out of 72): 01d_h8 (0.60 #2819, 0.56 #1190, 0.51 #3263), 02jknp (0.44 #2821, 0.43 #2369, 0.40 #1192), 0cbd2 (0.43 #2369, 0.25 #155, 0.22 #13477), 015cjr (0.43 #2369, 0.14 #345, 0.07 #3306), 0nbcg (0.42 #11873, 0.41 #13205, 0.16 #4472), 03gjzk (0.37 #3272, 0.36 #3716, 0.31 #7120), 016z4k (0.35 #11846, 0.35 #13178, 0.20 #448), 0dz3r (0.34 #13176, 0.32 #11844, 0.13 #3259), 0dxtg (0.34 #3715, 0.34 #3271, 0.33 #458), 01445t (0.25 #170, 0.05 #614, 0.02 #1354) >> Best rule #2819 for best value: >> intensional similarity = 5 >> extensional distance = 83 >> proper extension: 0fvf9q; 086k8; 017s11; 016tt2; 0g1rw; 02kxbwx; 04wvhz; 016tw3; 02pq9yv; 047q2wc; ... >> query: (?x6744, 01d_h8) <- award_winner(?x2060, ?x6744), award(?x9837, ?x2060), award(?x5869, ?x2060), award_winner(?x401, ?x5869), ?x9837 = 0qdwr >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1422 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 41 *> proper extension: 01dw9z; 03mp9s; *> query: (?x6744, 01p5_g) <- spouse(?x3525, ?x6744), award(?x6744, ?x1245), award(?x6744, ?x686), ?x1245 = 0gqwc, category_of(?x686, ?x2758) *> conf = 0.07 ranks of expected_values: 27 EVAL 0bw6y profession 01p5_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 158.000 130.000 0.600 http://example.org/people/person/profession #3470-0dr_4 PRED entity: 0dr_4 PRED relation: award PRED expected values: 02qt02v => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 212): 0gq9h (0.57 #225, 0.33 #59, 0.25 #3364), 040njc (0.57 #225, 0.33 #7, 0.25 #3364), 0gq_v (0.57 #225, 0.33 #18, 0.25 #3364), 09sb52 (0.57 #225, 0.33 #32, 0.25 #3364), 02hsq3m (0.57 #225, 0.33 #27, 0.25 #3364), 0gr42 (0.57 #225, 0.33 #82, 0.25 #3364), 02r0csl (0.57 #225, 0.25 #3364, 0.25 #3590), 02r22gf (0.57 #225, 0.25 #3364, 0.25 #3590), 0gr0m (0.57 #225, 0.25 #3364, 0.25 #3590), 04dn09n (0.57 #225, 0.25 #3364, 0.25 #3590) >> Best rule #225 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 017jd9; >> query: (?x1597, ?x143) <- nominated_for(?x4999, ?x1597), ?x4999 = 015t7v, nominated_for(?x143, ?x1597), honored_for(?x5349, ?x1597) >> conf = 0.57 => this is the best rule for 22 predicted values *> Best rule #7403 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 994 *> proper extension: 0275kr; *> query: (?x1597, ?x500) <- nominated_for(?x4999, ?x1597), nominated_for(?x4393, ?x1597), location(?x4999, ?x2997), award_winner(?x500, ?x4393), student(?x12746, ?x4999) *> conf = 0.12 ranks of expected_values: 50 EVAL 0dr_4 award 02qt02v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 76.000 76.000 0.571 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #3469-05rrtf PRED entity: 05rrtf PRED relation: production_companies! PRED expected values: 02pxmgz 05_5_22 03wjm2 => 158 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1257): 03wjm2 (0.49 #15811, 0.47 #22587, 0.46 #3388), 03z9585 (0.49 #15811, 0.47 #22587, 0.46 #3388), 02gpkt (0.49 #15811, 0.47 #22587, 0.46 #22588), 06y611 (0.49 #15811, 0.46 #22588, 0.36 #5647), 06x43v (0.46 #3388, 0.38 #13552, 0.08 #7591), 02_sr1 (0.46 #3388, 0.38 #13552, 0.08 #7217), 0gy0l_ (0.40 #968, 0.17 #5485, 0.14 #8873), 08fn5b (0.40 #457, 0.17 #4974, 0.14 #8362), 011xg5 (0.40 #904, 0.17 #5421, 0.14 #8809), 07y9w5 (0.40 #154, 0.17 #4671, 0.13 #9188) >> Best rule #15811 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 04gmlt; >> query: (?x7690, ?x511) <- organizations_founded(?x10430, ?x7690), executive_produced_by(?x103, ?x10430), award(?x10430, ?x1105), produced_by(?x511, ?x10430) >> conf = 0.49 => this is the best rule for 4 predicted values ranks of expected_values: 1, 24, 564 EVAL 05rrtf production_companies! 03wjm2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 112.000 0.490 http://example.org/film/film/production_companies EVAL 05rrtf production_companies! 05_5_22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 158.000 112.000 0.490 http://example.org/film/film/production_companies EVAL 05rrtf production_companies! 02pxmgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 158.000 112.000 0.490 http://example.org/film/film/production_companies #3468-04w58 PRED entity: 04w58 PRED relation: olympics PRED expected values: 0kbvv => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 39): 0kbvb (0.70 #6, 0.60 #44, 0.54 #120), 0kbws (0.61 #316, 0.55 #88, 0.54 #1229), 0kbvv (0.60 #23, 0.54 #327, 0.50 #61), 0swbd (0.60 #47, 0.50 #9, 0.39 #313), 0jdk_ (0.50 #62, 0.40 #24, 0.39 #328), 0swff (0.50 #59, 0.40 #21, 0.31 #135), 0l6mp (0.50 #54, 0.36 #92, 0.30 #16), 0sx7r (0.50 #41, 0.30 #3, 0.27 #79), 0ldqf (0.45 #110, 0.30 #72, 0.29 #338), 09x3r (0.40 #48, 0.40 #10, 0.39 #1256) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0154j; >> query: (?x3912, 0kbvb) <- adjoins(?x789, ?x3912), jurisdiction_of_office(?x182, ?x3912), ?x789 = 0f8l9c >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #23 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0154j; *> query: (?x3912, 0kbvv) <- adjoins(?x789, ?x3912), jurisdiction_of_office(?x182, ?x3912), ?x789 = 0f8l9c *> conf = 0.60 ranks of expected_values: 3 EVAL 04w58 olympics 0kbvv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 94.000 0.700 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #3467-02x8n1n PRED entity: 02x8n1n PRED relation: award! PRED expected values: 02p65p 04shbh 013cr 02qgyv 01f6zc 03fbb6 04t969 => 39 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2396): 01qscs (0.82 #3327, 0.69 #56573, 0.68 #63230), 01f7dd (0.82 #3327, 0.69 #56573, 0.68 #63230), 02mjf2 (0.82 #3327, 0.69 #56573, 0.68 #63230), 05vsxz (0.82 #3327, 0.69 #56573, 0.68 #69887), 0p_pd (0.82 #3327, 0.69 #56573, 0.68 #69887), 01wbg84 (0.82 #3327, 0.69 #56573, 0.68 #69887), 0dlglj (0.50 #396, 0.14 #36603, 0.14 #36602), 01nm3s (0.50 #1105, 0.14 #36603, 0.14 #36602), 015rkw (0.50 #439, 0.14 #36603, 0.14 #36602), 02s2ft (0.50 #8, 0.12 #3335, 0.12 #33273) >> Best rule #3327 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 09sb52; 0bfvd4; >> query: (?x2252, ?x100) <- award(?x9449, ?x2252), ?x9449 = 06bzwt, award(?x394, ?x2252), award_winner(?x2252, ?x100) >> conf = 0.82 => this is the best rule for 6 predicted values *> Best rule #27 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09sb52; 0bfvd4; *> query: (?x2252, 02p65p) <- award(?x9449, ?x2252), ?x9449 = 06bzwt, award(?x394, ?x2252), award_winner(?x2252, ?x100) *> conf = 0.25 ranks of expected_values: 61, 77, 117, 578, 1579, 1787 EVAL 02x8n1n award! 04t969 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 21.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x8n1n award! 03fbb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 39.000 21.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x8n1n award! 01f6zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 39.000 21.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x8n1n award! 02qgyv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 39.000 21.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x8n1n award! 013cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 21.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x8n1n award! 04shbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 21.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02x8n1n award! 02p65p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 39.000 21.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3466-01v3bn PRED entity: 01v3bn PRED relation: languages PRED expected values: 02h40lc => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 16): 02h40lc (0.50 #275, 0.40 #705, 0.37 #784), 064_8sq (0.06 #171, 0.05 #523, 0.05 #601), 06mp7 (0.04 #89, 0.02 #675, 0.02 #167), 02bjrlw (0.03 #665, 0.02 #1369, 0.02 #1564), 03115z (0.02 #145, 0.02 #184), 0t_2 (0.02 #126, 0.01 #517, 0.01 #595), 04306rv (0.02 #1098, 0.01 #198, 0.01 #472), 06nm1 (0.02 #319, 0.02 #1881, 0.02 #2389), 03_9r (0.02 #708, 0.01 #200, 0.01 #787), 03k50 (0.02 #1645, 0.02 #3951, 0.01 #1840) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 103 >> proper extension: 02lg9w; >> query: (?x3525, 02h40lc) <- gender(?x3525, ?x231), award_nominee(?x3525, ?x1357), student(?x8681, ?x3525) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01v3bn languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.495 http://example.org/people/person/languages #3465-0cmpn PRED entity: 0cmpn PRED relation: jurisdiction_of_office PRED expected values: 014tss => 82 concepts (67 used for prediction) PRED predicted values (max 10 best out of 7): 07ssc (0.33 #104, 0.33 #103, 0.03 #1004), 03rk0 (0.33 #104, 0.33 #103, 0.03 #1004), 0f8l9c (0.33 #64, 0.05 #1480), 09c7w0 (0.05 #1430, 0.04 #898, 0.04 #951), 01zst8 (0.02 #1110), 0h924 (0.02 #1110), 014tss (0.01 #1164) >> Best rule #104 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0dj5q; >> query: (?x9919, ?x512) <- gender(?x9919, ?x231), award_winner(?x9918, ?x9919), type_of_union(?x9919, ?x566), ?x9918 = 052m7n, nationality(?x9919, ?x512), titles(?x512, ?x144) >> conf = 0.33 => this is the best rule for 2 predicted values *> Best rule #1164 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 118 *> proper extension: 015qyf; 02dztn; 02cj_f; 027r0_f; *> query: (?x9919, ?x6371) <- place_of_burial(?x9919, ?x13771), gender(?x9919, ?x231), place_of_burial(?x6779, ?x13771), nationality(?x9919, ?x512), type_of_union(?x6779, ?x566), nationality(?x6779, ?x6371) *> conf = 0.01 ranks of expected_values: 7 EVAL 0cmpn jurisdiction_of_office 014tss CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 82.000 67.000 0.333 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #3464-020vx9 PRED entity: 020vx9 PRED relation: institution! PRED expected values: 027f2w 02_xgp2 => 200 concepts (143 used for prediction) PRED predicted values (max 10 best out of 26): 02h4rq6 (0.85 #153, 0.81 #205, 0.80 #259), 019v9k (0.85 #160, 0.78 #266, 0.77 #292), 014mlp (0.81 #288, 0.80 #262, 0.79 #1049), 0bkj86 (0.67 #159, 0.58 #211, 0.56 #185), 02_xgp2 (0.66 #270, 0.63 #244, 0.63 #296), 03bwzr4 (0.63 #166, 0.58 #218, 0.56 #272), 07s6fsf (0.59 #177, 0.56 #203, 0.44 #151), 016t_3 (0.59 #260, 0.56 #286, 0.56 #206), 04zx3q1 (0.41 #258, 0.40 #284, 0.39 #232), 01rr_d (0.36 #44, 0.28 #2790, 0.25 #436) >> Best rule #153 for best value: >> intensional similarity = 6 >> extensional distance = 25 >> proper extension: 01jt2w; >> query: (?x9688, 02h4rq6) <- major_field_of_study(?x9688, ?x3490), major_field_of_study(?x9688, ?x2981), organization(?x4095, ?x9688), category(?x9688, ?x134), ?x2981 = 02j62, ?x3490 = 05qfh >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #270 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 39 *> proper extension: 03ksy; 017cy9; 01bm_; 01_qgp; *> query: (?x9688, 02_xgp2) <- contains(?x455, ?x9688), major_field_of_study(?x9688, ?x2981), major_field_of_study(?x9688, ?x742), ?x2981 = 02j62, student(?x9688, ?x4292), ?x742 = 05qjt *> conf = 0.66 ranks of expected_values: 5, 12 EVAL 020vx9 institution! 02_xgp2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 200.000 143.000 0.852 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 020vx9 institution! 027f2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 200.000 143.000 0.852 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3463-0bk1p PRED entity: 0bk1p PRED relation: artist! PRED expected values: 02swsm => 114 concepts (83 used for prediction) PRED predicted values (max 10 best out of 131): 03rhqg (0.35 #285, 0.27 #825, 0.27 #3531), 01clyr (0.30 #30, 0.27 #165, 0.25 #570), 01w40h (0.29 #295, 0.15 #565, 0.14 #2051), 0g768 (0.24 #304, 0.21 #2060, 0.21 #2330), 015_1q (0.23 #3534, 0.23 #2584, 0.23 #4210), 0n85g (0.23 #867, 0.20 #57, 0.18 #327), 01cl2y (0.20 #567, 0.18 #162, 0.18 #297), 033hn8 (0.20 #1498, 0.17 #958, 0.17 #4205), 02y21l (0.20 #630, 0.12 #495, 0.12 #2251), 043g7l (0.20 #28, 0.11 #3409, 0.10 #1513) >> Best rule #285 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0m19t; 0167_s; 03xhj6; 02dw1_; 06gcn; 0p76z; >> query: (?x8999, 03rhqg) <- artist(?x4483, ?x8999), group(?x227, ?x8999), ?x4483 = 0mzkr, artists(?x671, ?x8999) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #89 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 02rgz4; *> query: (?x8999, 02swsm) <- artists(?x10306, ?x8999), artists(?x1380, ?x8999), ?x1380 = 0dl5d, music(?x6029, ?x8999), parent_genre(?x10306, ?x3061) *> conf = 0.10 ranks of expected_values: 28 EVAL 0bk1p artist! 02swsm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 114.000 83.000 0.353 http://example.org/music/record_label/artist #3462-01l63 PRED entity: 01l63 PRED relation: category PRED expected values: 08mbj5d => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.70 #61, 0.69 #36, 0.68 #72) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 216 >> proper extension: 013m43; 0l0mk; 0xpp5; 0qpn9; 0mgp; 0dqyw; 0tk02; 0xn7b; 0qplq; 018dk_; >> query: (?x12190, 08mbj5d) <- citytown(?x11459, ?x12190), location(?x11396, ?x12190), profession(?x11396, ?x1032), award(?x11396, ?x458) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l63 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.697 http://example.org/common/topic/webpage./common/webpage/category #3461-0bqsk5 PRED entity: 0bqsk5 PRED relation: award_winner PRED expected values: 043hg => 37 concepts (7 used for prediction) PRED predicted values (max 10 best out of 1660): 053yx (0.60 #616, 0.43 #5553, 0.03 #8020), 01vttb9 (0.43 #6595, 0.20 #1658, 0.10 #9062), 032nwy (0.43 #5014, 0.20 #77, 0.03 #14884), 0fpjd_g (0.43 #5249, 0.20 #312, 0.03 #10183), 01lwx (0.33 #4824, 0.07 #4935), 01vrncs (0.30 #7610, 0.11 #4934, 0.09 #12546), 01wd9lv (0.29 #6354, 0.20 #1417, 0.10 #11288), 0bdlj (0.29 #6547, 0.20 #1610, 0.10 #9014), 09hnb (0.29 #5511, 0.20 #574, 0.09 #15381), 012ky3 (0.29 #5842, 0.20 #905, 0.07 #8309) >> Best rule #616 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 02nbqh; 058vy5; 02tj96; >> query: (?x12729, 053yx) <- award_winner(?x12729, ?x10313), award_winner(?x12729, ?x7706), award(?x10313, ?x575), profession(?x10313, ?x353), artists(?x119, ?x7706), type_of_union(?x10313, ?x566), ?x119 = 01lxd4, influenced_by(?x2993, ?x10313) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #12340 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 67 *> proper extension: 02x1z2s; *> query: (?x12729, ?x118) <- award_winner(?x12729, ?x10313), award_winner(?x12729, ?x6072), award_winner(?x12729, ?x4568), award(?x10313, ?x11471), award(?x9854, ?x11471), award(?x118, ?x11471), ?x9854 = 0gthm, award_nominee(?x6071, ?x6072), award_winner(?x4568, ?x506) *> conf = 0.05 ranks of expected_values: 387 EVAL 0bqsk5 award_winner 043hg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 7.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #3460-0bzjf PRED entity: 0bzjf PRED relation: contains PRED expected values: 03qhnx => 173 concepts (74 used for prediction) PRED predicted values (max 10 best out of 2826): 02185j (0.71 #150054, 0.70 #129459, 0.68 #161822), 0bzjf (0.68 #88267, 0.66 #120632, 0.64 #176535), 03rjj (0.68 #88267, 0.66 #120632, 0.64 #176535), 057bxr (0.33 #737, 0.17 #33106, 0.14 #9565), 06c62 (0.33 #941, 0.14 #9769, 0.14 #6827), 098phg (0.33 #2268, 0.14 #11096, 0.14 #8154), 0947l (0.33 #1293, 0.14 #10121, 0.12 #33662), 0fhsz (0.33 #2088, 0.14 #10916, 0.12 #34457), 09pxc (0.33 #1976, 0.14 #10804, 0.12 #34345), 01jp4s (0.33 #2788, 0.14 #11616, 0.09 #14558) >> Best rule #150054 for best value: >> intensional similarity = 5 >> extensional distance = 78 >> proper extension: 0mhhc; >> query: (?x10495, ?x11488) <- administrative_parent(?x10495, ?x205), contains(?x10495, ?x14366), citytown(?x11488, ?x14366), category(?x11488, ?x134), major_field_of_study(?x11488, ?x3400) >> conf = 0.71 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bzjf contains 03qhnx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 74.000 0.707 http://example.org/location/location/contains #3459-0l39b PRED entity: 0l39b PRED relation: source PRED expected values: 0jbk9 => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #85, 0.91 #127, 0.86 #19) >> Best rule #85 for best value: >> intensional similarity = 4 >> extensional distance = 250 >> proper extension: 0mn0v; 0qlrh; >> query: (?x12088, 0jbk9) <- county(?x12088, ?x12087), time_zones(?x12088, ?x2088), time_zones(?x279, ?x2088), ?x279 = 0d060g >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l39b source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.917 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #3458-026spg PRED entity: 026spg PRED relation: award PRED expected values: 01cw7s => 109 concepts (99 used for prediction) PRED predicted values (max 10 best out of 273): 02f6ym (0.46 #3038, 0.29 #252, 0.14 #5824), 03qbnj (0.43 #227, 0.37 #3013, 0.25 #4207), 01cky2 (0.37 #1783, 0.14 #191, 0.13 #32638), 0c4z8 (0.36 #4049, 0.29 #69, 0.28 #4447), 031b3h (0.34 #1790, 0.07 #4178, 0.07 #8954), 09sb52 (0.31 #18745, 0.29 #18347, 0.25 #14367), 01cw7s (0.29 #259, 0.27 #1851, 0.14 #3045), 054ks3 (0.29 #138, 0.25 #2924, 0.23 #4914), 02f764 (0.29 #216, 0.24 #1808, 0.08 #6186), 026mfs (0.29 #125, 0.15 #4901, 0.14 #7289) >> Best rule #3038 for best value: >> intensional similarity = 5 >> extensional distance = 63 >> proper extension: 01qvgl; >> query: (?x4675, 02f6ym) <- award(?x4675, ?x4796), award(?x4675, ?x4018), ?x4796 = 01c99j, award(?x2614, ?x4018), ?x2614 = 04xrx >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #259 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 015mrk; *> query: (?x4675, 01cw7s) <- award(?x4675, ?x4796), award(?x4675, ?x4018), award(?x4675, ?x1389), ?x4796 = 01c99j, ?x4018 = 03qbh5, ?x1389 = 01c427 *> conf = 0.29 ranks of expected_values: 7 EVAL 026spg award 01cw7s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 109.000 99.000 0.462 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3457-04s2z PRED entity: 04s2z PRED relation: specialization_of! PRED expected values: 025tmkg => 71 concepts (59 used for prediction) PRED predicted values (max 10 best out of 128): 07s467s (0.33 #552, 0.33 #115, 0.25 #333), 05snw (0.33 #170, 0.25 #388, 0.17 #716), 04s2z (0.33 #149, 0.25 #367, 0.17 #695), 0h9c (0.33 #133, 0.25 #351, 0.17 #679), 01pxg (0.33 #199, 0.25 #417, 0.17 #745), 03m3mgq (0.33 #171, 0.25 #389, 0.17 #717), 06mq7 (0.33 #180, 0.25 #398, 0.17 #726), 036n1 (0.33 #196, 0.25 #414, 0.17 #742), 07lqg0 (0.33 #186, 0.25 #404, 0.17 #732), 03sbb (0.33 #165, 0.25 #383, 0.17 #711) >> Best rule #552 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 0fj9f; >> query: (?x7290, 07s467s) <- profession(?x7341, ?x7290), profession(?x3994, ?x7290), specialization_of(?x14736, ?x7290), student(?x12343, ?x3994), religion(?x7341, ?x492), place_of_birth(?x3994, ?x5577), ?x12343 = 01zzy3, interests(?x3994, ?x2014), influenced_by(?x1857, ?x3994) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04s2z specialization_of! 025tmkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 59.000 0.333 http://example.org/people/profession/specialization_of #3456-09j9h PRED entity: 09j9h PRED relation: profession! PRED expected values: 06vnh2 => 52 concepts (29 used for prediction) PRED predicted values (max 10 best out of 4162): 05vzw3 (0.72 #21196, 0.40 #38152, 0.36 #38153), 0412f5y (0.72 #21196, 0.40 #38152, 0.36 #38153), 02g40r (0.72 #21196, 0.40 #38152, 0.36 #38153), 02qlg7s (0.72 #21196, 0.40 #38152, 0.36 #38153), 05bm4sm (0.72 #21196, 0.36 #38153, 0.36 #59355), 092ys_y (0.72 #21196, 0.36 #38153, 0.36 #59355), 094tsh6 (0.72 #21196, 0.36 #38153, 0.36 #59355), 04ktcgn (0.72 #21196, 0.36 #38153, 0.36 #59355), 027y151 (0.72 #21196, 0.36 #38153, 0.36 #59355), 09dvgb8 (0.72 #21196, 0.36 #38153, 0.36 #59355) >> Best rule #21196 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 02hrh1q; 0np9r; 0d1pc; >> query: (?x8498, ?x211) <- profession(?x8566, ?x8498), specialization_of(?x5654, ?x8498), award_nominee(?x8566, ?x2559), award_nominee(?x8566, ?x2457), ?x2457 = 02cllz, profession(?x211, ?x5654), ?x2559 = 06mmb >> conf = 0.72 => this is the best rule for 18 predicted values No rule for expected values ranks of expected_values: EVAL 09j9h profession! 06vnh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 52.000 29.000 0.722 http://example.org/people/person/profession #3455-025x1t PRED entity: 025x1t PRED relation: actor PRED expected values: 04mlh8 => 100 concepts (79 used for prediction) PRED predicted values (max 10 best out of 800): 01tpl1p (0.50 #1719, 0.40 #2648, 0.33 #791), 06pj8 (0.45 #7437, 0.39 #7436, 0.39 #41848), 05vtbl (0.45 #7437, 0.39 #7436, 0.33 #4646), 06jrhz (0.39 #7436, 0.33 #4646, 0.21 #14887), 09b0xs (0.36 #10234, 0.35 #39991, 0.35 #49280), 031c2r (0.33 #865, 0.25 #1793, 0.20 #2722), 024my5 (0.33 #606, 0.25 #1534, 0.20 #2463), 029cpw (0.33 #550, 0.25 #1478, 0.20 #2407), 04hxyv (0.33 #906, 0.25 #1834, 0.20 #2763), 030x48 (0.25 #1176, 0.20 #2105, 0.12 #5827) >> Best rule #1719 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 028k2x; >> query: (?x11377, 01tpl1p) <- tv_program(?x5832, ?x11377), nominated_for(?x2135, ?x11377), ?x5832 = 06jrhz, actor(?x11377, ?x12054), actor(?x5286, ?x12054) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6149 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 04mx8h4; 019g8j; *> query: (?x11377, 04mlh8) <- genre(?x11377, ?x2540), producer_type(?x11377, ?x632), actor(?x11377, ?x1765), ?x2540 = 0hcr *> conf = 0.06 ranks of expected_values: 153 EVAL 025x1t actor 04mlh8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 100.000 79.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #3454-063hp4 PRED entity: 063hp4 PRED relation: genre PRED expected values: 02l7c8 => 70 concepts (69 used for prediction) PRED predicted values (max 10 best out of 86): 07s9rl0 (0.75 #365, 0.68 #243, 0.66 #1455), 02l7c8 (0.40 #137, 0.38 #16, 0.35 #258), 02kdv5l (0.34 #488, 0.33 #730, 0.28 #2912), 03k9fj (0.34 #496, 0.32 #738, 0.24 #3164), 01jfsb (0.33 #739, 0.31 #497, 0.31 #2921), 01hmnh (0.28 #503, 0.23 #745, 0.16 #3171), 04xvlr (0.25 #2, 0.22 #366, 0.21 #123), 06n90 (0.24 #498, 0.22 #740, 0.14 #2922), 01g6gs (0.23 #21, 0.16 #142, 0.09 #869), 060__y (0.20 #259, 0.18 #744, 0.17 #1228) >> Best rule #365 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 0209xj; 011ydl; 097zcz; 0bs4r; 0gl3hr; 0hv4t; 0llcx; 03pc89; 09tkzy; 015gm8; >> query: (?x6722, 07s9rl0) <- nominated_for(?x6311, ?x6722), nominated_for(?x198, ?x6722), ?x198 = 040njc >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #137 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 0fsw_7; *> query: (?x6722, 02l7c8) <- nominated_for(?x6311, ?x6722), titles(?x307, ?x6722), film_sets_designed(?x786, ?x6722) *> conf = 0.40 ranks of expected_values: 2 EVAL 063hp4 genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 69.000 0.750 http://example.org/film/film/genre #3453-06fqlk PRED entity: 06fqlk PRED relation: language PRED expected values: 02bjrlw => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 42): 064_8sq (0.38 #454, 0.35 #3150, 0.35 #508), 06nm1 (0.31 #553, 0.25 #3141, 0.14 #445), 03k50 (0.25 #7, 0.05 #3139, 0.04 #551), 0688f (0.25 #35, 0.01 #525, 0.01 #579), 02bjrlw (0.24 #545, 0.22 #491, 0.21 #437), 06b_j (0.17 #455, 0.12 #509, 0.09 #1276), 03hkp (0.15 #174, 0.06 #447, 0.04 #555), 03_9r (0.14 #3140, 0.13 #552, 0.08 #498), 0jzc (0.11 #452, 0.08 #179, 0.08 #506), 02hxcvy (0.08 #193, 0.02 #3162, 0.02 #574) >> Best rule #454 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 01jc6q; 04fv5b; >> query: (?x6489, 064_8sq) <- film(?x157, ?x6489), language(?x6489, ?x732), ?x732 = 04306rv, music(?x6489, ?x4727) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #545 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 93 *> proper extension: 024l2y; 05zlld0; 02_nsc; *> query: (?x6489, 02bjrlw) <- featured_film_locations(?x6489, ?x1646), language(?x6489, ?x2890), written_by(?x6489, ?x2442), film(?x157, ?x6489), titles(?x2890, ?x467) *> conf = 0.24 ranks of expected_values: 5 EVAL 06fqlk language 02bjrlw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 99.000 99.000 0.375 http://example.org/film/film/language #3452-0165v PRED entity: 0165v PRED relation: jurisdiction_of_office! PRED expected values: 0dq3c => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 20): 060bp (0.71 #89, 0.67 #595, 0.62 #793), 0f6c3 (0.49 #271, 0.49 #249, 0.30 #1151), 09n5b9 (0.49 #275, 0.49 #253, 0.26 #1155), 0fkvn (0.41 #267, 0.41 #245, 0.28 #1323), 0pqc5 (0.36 #2007, 0.36 #2051, 0.14 #1764), 0dq3c (0.36 #1893, 0.31 #2, 0.20 #24), 01gkgk (0.36 #1893, 0.10 #49, 0.08 #93), 0p5vf (0.25 #56, 0.18 #100, 0.18 #210), 04syw (0.19 #666, 0.18 #930, 0.18 #842), 0fkzq (0.16 #280, 0.16 #258, 0.08 #1160) >> Best rule #89 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 016zwt; >> query: (?x9816, 060bp) <- form_of_government(?x9816, ?x48), combatants(?x2843, ?x9816), adjoins(?x9816, ?x142) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1893 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 302 *> proper extension: 0f04v; *> query: (?x9816, ?x182) <- adjoins(?x583, ?x9816), adjoins(?x142, ?x9816), contains(?x7273, ?x142), jurisdiction_of_office(?x182, ?x583) *> conf = 0.36 ranks of expected_values: 6 EVAL 0165v jurisdiction_of_office! 0dq3c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 110.000 110.000 0.711 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #3451-07kc_ PRED entity: 07kc_ PRED relation: role PRED expected values: 02hnl => 79 concepts (54 used for prediction) PRED predicted values (max 10 best out of 110): 05148p4 (0.89 #3618, 0.89 #3529, 0.88 #5991), 02k84w (0.86 #2945, 0.86 #2872, 0.83 #2033), 03bx0bm (0.86 #2750, 0.83 #3427, 0.82 #4447), 05r5c (0.84 #2713, 0.83 #2033, 0.83 #445), 03qlv7 (0.83 #2033, 0.83 #445, 0.82 #2371), 06ncr (0.83 #2033, 0.83 #445, 0.82 #2371), 0gghm (0.83 #2033, 0.83 #445, 0.82 #2371), 02snj9 (0.83 #2033, 0.83 #445, 0.82 #2371), 03qjg (0.82 #4358, 0.80 #4926, 0.80 #4020), 02hnl (0.79 #5246, 0.75 #1622, 0.74 #5020) >> Best rule #3618 for best value: >> intensional similarity = 22 >> extensional distance = 16 >> proper extension: 02qjv; 0239kh; >> query: (?x1147, ?x1166) <- role(?x1166, ?x1147), role(?x716, ?x1147), role(?x645, ?x1147), role(?x227, ?x1147), performance_role(?x1147, ?x1495), role(?x1147, ?x2158), role(?x1147, ?x2764), role(?x2158, ?x2459), ?x716 = 018vs, group(?x645, ?x8058), group(?x645, ?x6876), group(?x645, ?x5279), family(?x2158, ?x7256), role(?x569, ?x645), ?x8058 = 014pg1, ?x2459 = 021bmf, ?x6876 = 0ycp3, ?x2764 = 01s0ps, performance_role(?x8323, ?x645), ?x5279 = 06nv27, ?x227 = 0342h, ?x1166 = 05148p4 >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #5246 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 31 *> proper extension: 03q5t; 07c6l; 01hww_; 0jtg0; 03_vpw; 05kms; *> query: (?x1147, 02hnl) <- role(?x2675, ?x1147), role(?x645, ?x1147), role(?x432, ?x1147), role(?x2460, ?x2675), role(?x3378, ?x2675), ?x645 = 028tv0, group(?x1147, ?x13142), role(?x1148, ?x1147), role(?x74, ?x432), role(?x7972, ?x432), role(?x4550, ?x432), role(?x4239, ?x432), ?x7972 = 0326tc, origin(?x13142, ?x4090), group(?x432, ?x1945), ?x4239 = 0x3b7, role(?x432, ?x736), ?x4550 = 0180w8, ?x1945 = 02_5x9, role(?x1750, ?x432), role(?x1291, ?x432), ?x2460 = 01wy6 *> conf = 0.79 ranks of expected_values: 10 EVAL 07kc_ role 02hnl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 79.000 54.000 0.889 http://example.org/music/performance_role/regular_performances./music/group_membership/role #3450-0p8jf PRED entity: 0p8jf PRED relation: location PRED expected values: 0cy8v => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 199): 0rh6k (0.77 #49036, 0.76 #66734, 0.76 #42606), 02_286 (0.39 #41839, 0.32 #44250, 0.32 #48269), 04jpl (0.19 #20117, 0.16 #21724, 0.16 #25742), 0cr3d (0.16 #48377, 0.12 #3362, 0.12 #949), 030qb3t (0.13 #44296, 0.13 #49925, 0.13 #91746), 0vzm (0.12 #3390, 0.12 #977, 0.09 #5801), 01n7q (0.12 #867, 0.12 #8103, 0.11 #7299), 02frhbc (0.12 #3685, 0.08 #4488, 0.04 #6096), 01531 (0.12 #962, 0.08 #48390, 0.04 #66088), 02m77 (0.12 #3547, 0.06 #20430, 0.05 #26055) >> Best rule #49036 for best value: >> intensional similarity = 4 >> extensional distance = 507 >> proper extension: 040j2_; 02x8kk; 0bm9xk; >> query: (?x2993, ?x108) <- place_of_birth(?x2993, ?x108), location(?x2993, ?x3976), gender(?x2993, ?x231), adjoins(?x1426, ?x108) >> conf = 0.77 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0p8jf location 0cy8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 148.000 0.770 http://example.org/people/person/places_lived./people/place_lived/location #3449-03bnv PRED entity: 03bnv PRED relation: award_nominee PRED expected values: 0m2l9 => 98 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1084): 0m2l9 (0.80 #102667, 0.80 #60666, 0.80 #34998), 01vrt_c (0.17 #4913, 0.05 #42244, 0.04 #46911), 03f2_rc (0.15 #20998, 0.10 #104, 0.07 #4770), 02zft0 (0.15 #20998, 0.09 #20067, 0.05 #29400), 03bnv (0.15 #20998, 0.07 #17078, 0.05 #26411), 01ycfv (0.15 #20998, 0.07 #6676, 0.05 #2010), 01wwvc5 (0.15 #20998, 0.06 #33267, 0.05 #49601), 0lbj1 (0.15 #20998, 0.05 #42, 0.03 #4708), 012x4t (0.15 #20998, 0.04 #2684, 0.03 #42348), 01vrz41 (0.15 #20998, 0.03 #4918, 0.03 #18916) >> Best rule #102667 for best value: >> intensional similarity = 3 >> extensional distance = 628 >> proper extension: 0fb0v; 018p5f; 04qzm; 09jm8; 016ppr; >> query: (?x3321, ?x483) <- award(?x3321, ?x724), category(?x3321, ?x134), award_nominee(?x483, ?x3321) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03bnv award_nominee 0m2l9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 51.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3448-0dqcm PRED entity: 0dqcm PRED relation: film PRED expected values: 0dnw1 => 142 concepts (75 used for prediction) PRED predicted values (max 10 best out of 952): 0dnw1 (0.53 #25019, 0.46 #53613, 0.46 #55401), 0jvt9 (0.20 #4114, 0.14 #5901, 0.02 #9475), 0168ls (0.20 #3816, 0.14 #5603, 0.01 #14538), 03rg2b (0.20 #4667, 0.04 #8241, 0.02 #10028), 0prh7 (0.20 #4410, 0.04 #7984, 0.02 #11558), 04954r (0.20 #4191, 0.01 #32784, 0.01 #89976), 0bykpk (0.20 #4636), 017kct (0.20 #4157), 03hj3b3 (0.20 #3881), 0m_mm (0.14 #5506, 0.02 #9080, 0.01 #14441) >> Best rule #25019 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 032wdd; >> query: (?x9095, ?x4504) <- languages(?x9095, ?x90), award_winner(?x4504, ?x9095), award_winner(?x9095, ?x1357) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dqcm film 0dnw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 75.000 0.526 http://example.org/film/actor/film./film/performance/film #3447-01bh6y PRED entity: 01bh6y PRED relation: award_nominee! PRED expected values: 030znt => 109 concepts (48 used for prediction) PRED predicted values (max 10 best out of 879): 030znt (0.81 #34878, 0.81 #83710, 0.81 #99990), 01bh6y (0.43 #6687, 0.27 #90687, 0.25 #4362), 05gnf (0.27 #90687), 06b0d2 (0.14 #4872, 0.05 #7197, 0.04 #14172), 01438g (0.14 #5342, 0.05 #7667, 0.03 #23943), 015pkc (0.14 #5012, 0.05 #7337, 0.02 #9662), 017149 (0.14 #4750, 0.03 #97764, 0.03 #93112), 035gjq (0.14 #4870, 0.02 #67653, 0.02 #14170), 0gnbw (0.14 #6290, 0.02 #10940, 0.02 #17915), 01dw4q (0.14 #4728, 0.02 #67511, 0.02 #93090) >> Best rule #34878 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 0j_c; 053yx; 03bnv; 0127gn; 03_0p; 01l1rw; 0cj2w; >> query: (?x9604, ?x1343) <- award_winner(?x9604, ?x5289), people(?x4659, ?x9604), award_nominee(?x9604, ?x1343) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bh6y award_nominee! 030znt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 48.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3446-0rjg8 PRED entity: 0rjg8 PRED relation: contains! PRED expected values: 09c7w0 => 83 concepts (27 used for prediction) PRED predicted values (max 10 best out of 145): 09c7w0 (0.77 #2682, 0.68 #15211, 0.67 #16105), 059rby (0.33 #10754, 0.16 #12545, 0.13 #21494), 04_1l0v (0.31 #8942, 0.29 #8047, 0.25 #12076), 01n7q (0.27 #4547, 0.24 #14391, 0.21 #13497), 05k7sb (0.23 #10867, 0.10 #4602, 0.09 #15341), 06pvr (0.20 #4634, 0.19 #2847, 0.18 #6424), 02qkt (0.19 #9288, 0.17 #10183, 0.16 #18236), 0d060g (0.13 #10747, 0.05 #18798, 0.05 #19695), 05tbn (0.12 #12748, 0.08 #21697, 0.08 #22590), 05fjf (0.12 #12898, 0.12 #6632, 0.12 #5737) >> Best rule #2682 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 0j_sncb; 02qvvv; 0lyjf; 0146hc; 09s5q8; 01jq0j; >> query: (?x6194, ?x94) <- contains(?x9290, ?x6194), contains(?x2623, ?x6194), ?x2623 = 02xry, contains(?x94, ?x9290), source(?x9290, ?x958) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rjg8 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 27.000 0.769 http://example.org/location/location/contains #3445-05l0j5 PRED entity: 05l0j5 PRED relation: people! PRED expected values: 02ctzb 0g96wd => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 36): 033tf_ (0.17 #469, 0.11 #7, 0.11 #623), 02w7gg (0.17 #310, 0.16 #541, 0.06 #2476), 041rx (0.14 #1084, 0.14 #2169, 0.14 #2091), 0xnvg (0.11 #13, 0.10 #90, 0.09 #475), 02ctzb (0.11 #15, 0.10 #92, 0.08 #246), 04dbw3 (0.11 #28, 0.10 #105, 0.08 #259), 022dp5 (0.11 #50, 0.10 #127, 0.08 #281), 0222qb (0.11 #44, 0.10 #121, 0.04 #506), 013xrm (0.11 #20, 0.10 #97, 0.03 #2185), 04gfy7 (0.10 #142, 0.08 #296) >> Best rule #469 for best value: >> intensional similarity = 3 >> extensional distance = 331 >> proper extension: 02w5q6; 0dszr0; 0w6w; >> query: (?x7752, 033tf_) <- religion(?x7752, ?x1985), nationality(?x7752, ?x512), ?x1985 = 0c8wxp >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #15 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 04t2l2; 083chw; 043js; 0bt4r4; 060j8b; 0cms7f; 05p92jn; *> query: (?x7752, 02ctzb) <- award_nominee(?x6532, ?x7752), award(?x7752, ?x678), ?x6532 = 0cmt6q, type_of_union(?x7752, ?x1873) *> conf = 0.11 ranks of expected_values: 5, 33 EVAL 05l0j5 people! 0g96wd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 79.000 79.000 0.174 http://example.org/people/ethnicity/people EVAL 05l0j5 people! 02ctzb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 79.000 0.174 http://example.org/people/ethnicity/people #3444-02vntj PRED entity: 02vntj PRED relation: participant! PRED expected values: 02tc5y => 123 concepts (65 used for prediction) PRED predicted values (max 10 best out of 328): 04bdzg (0.29 #12377, 0.26 #13682, 0.24 #8467), 0gyx4 (0.16 #1609, 0.08 #3562, 0.05 #17899), 0456xp (0.10 #1303, 0.05 #24761, 0.05 #28018), 04205z (0.10 #1303, 0.05 #24761, 0.05 #28018), 01pgzn_ (0.08 #149, 0.03 #6662, 0.03 #11874), 01dw4q (0.08 #18, 0.02 #10440, 0.02 #11743), 022q4j (0.08 #1887, 0.05 #3840, 0.02 #19481), 0dvmd (0.06 #6725, 0.05 #6073, 0.05 #863), 02t__3 (0.05 #1707, 0.05 #1055, 0.02 #6265), 01vvb4m (0.05 #858, 0.04 #4114, 0.03 #6720) >> Best rule #12377 for best value: >> intensional similarity = 3 >> extensional distance = 202 >> proper extension: 044mfr; 02w5q6; >> query: (?x4247, ?x6242) <- participant(?x4247, ?x5467), profession(?x4247, ?x955), participant(?x6242, ?x4247) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2550 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 66 *> proper extension: 032t2z; 03hbzj; 06c97; 02rsz0; *> query: (?x4247, 02tc5y) <- people(?x4195, ?x4247), ?x4195 = 02ctzb *> conf = 0.01 ranks of expected_values: 243 EVAL 02vntj participant! 02tc5y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 123.000 65.000 0.289 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #3443-02h3d1 PRED entity: 02h3d1 PRED relation: ceremony PRED expected values: 019bk0 0gpjbt => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 125): 0gpjbt (0.71 #399, 0.61 #649, 0.54 #900), 019bk0 (0.57 #387, 0.54 #637, 0.47 #888), 0ds460j (0.43 #616, 0.42 #876, 0.42 #1002), 0h_9252 (0.43 #549, 0.42 #876, 0.42 #1002), 05c1t6z (0.42 #876, 0.42 #1002, 0.25 #136), 02q690_ (0.42 #876, 0.42 #1002, 0.25 #181), 0bzm81 (0.42 #876, 0.42 #1002, 0.25 #142), 02yxh9 (0.42 #876, 0.42 #1002, 0.25 #212), 05q7cj (0.42 #876, 0.42 #1002, 0.25 #207), 0bz6l9 (0.42 #876, 0.42 #1002, 0.25 #167) >> Best rule #399 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 02g8mp; 02nhxf; 025m8y; 03tk6z; >> query: (?x3467, 0gpjbt) <- award(?x5298, ?x3467), award(?x1231, ?x3467), award_winner(?x3467, ?x3732), ?x5298 = 02_jkc, profession(?x1231, ?x131), award_nominee(?x215, ?x1231) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02h3d1 ceremony 0gpjbt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02h3d1 ceremony 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 44.000 0.714 http://example.org/award/award_category/winners./award/award_honor/ceremony #3442-02rnmb PRED entity: 02rnmb PRED relation: colors! PRED expected values: 02896 02qk2d5 => 20 concepts (16 used for prediction) PRED predicted values (max 10 best out of 960): 01ct6 (0.62 #2376, 0.60 #2723, 0.49 #1695), 05tfm (0.62 #2376, 0.60 #2739, 0.49 #1695), 03915c (0.62 #2376, 0.60 #2942, 0.43 #3623), 032498 (0.62 #2376, 0.57 #3615, 0.50 #2598), 0jnmj (0.62 #2376, 0.50 #2427, 0.50 #2082), 04l5d0 (0.62 #2376, 0.50 #2569, 0.50 #1888), 0j5m6 (0.62 #2376, 0.50 #1787, 0.50 #1446), 02hqt6 (0.62 #2376, 0.50 #2576, 0.50 #1554), 05gg4 (0.62 #2376, 0.50 #2454, 0.49 #1695), 01ypc (0.62 #2376, 0.50 #2382, 0.49 #1695) >> Best rule #2376 for best value: >> intensional similarity = 36 >> extensional distance = 2 >> proper extension: 06kqt3; >> query: (?x8271, ?x470) <- colors(?x11337, ?x8271), colors(?x9995, ?x8271), colors(?x8228, ?x8271), colors(?x7399, ?x8271), colors(?x5686, ?x8271), colors(?x5154, ?x8271), colors(?x2405, ?x8271), draft(?x9995, ?x8133), draft(?x5154, ?x12852), season(?x2405, ?x8517), teams(?x2879, ?x9995), school(?x9995, ?x4296), team(?x261, ?x2405), ?x8517 = 0285r5d, team(?x1348, ?x9995), colors(?x11559, ?x8271), colors(?x10899, ?x8271), colors(?x6637, ?x8271), colors(?x5154, ?x1101), school(?x12852, ?x2497), school_type(?x11559, ?x1044), school(?x5154, ?x2775), ?x10899 = 01fsv9, currency(?x11559, ?x170), contains(?x94, ?x11559), ?x2497 = 0f1nl, colors(?x481, ?x1101), colors(?x470, ?x1101), team(?x12607, ?x8228), current_club(?x676, ?x5686), major_field_of_study(?x6637, ?x742), student(?x6637, ?x395), position(?x5686, ?x60), team(?x5685, ?x11337), sport(?x7399, ?x5063), current_club(?x8511, ?x11337) >> conf = 0.62 => this is the best rule for 272 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 47, 313 EVAL 02rnmb colors! 02qk2d5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 20.000 16.000 0.624 http://example.org/sports/sports_team/colors EVAL 02rnmb colors! 02896 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 20.000 16.000 0.624 http://example.org/sports/sports_team/colors #3441-01jsn5 PRED entity: 01jsn5 PRED relation: institution! PRED expected values: 03bwzr4 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 19): 03bwzr4 (0.70 #133, 0.67 #338, 0.66 #214), 016t_3 (0.70 #124, 0.62 #205, 0.58 #103), 0bkj86 (0.61 #107, 0.58 #128, 0.58 #66), 04zx3q1 (0.45 #102, 0.42 #61, 0.40 #123), 013zdg (0.39 #106, 0.31 #65, 0.29 #332), 027f2w (0.37 #129, 0.35 #334, 0.31 #67), 022h5x (0.31 #77, 0.29 #118, 0.21 #284), 028dcg (0.26 #117, 0.23 #76, 0.16 #283), 03mkk4 (0.24 #336, 0.23 #69, 0.23 #110), 0bjrnt (0.23 #126, 0.18 #230, 0.18 #207) >> Best rule #133 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 015cz0; 050xpd; >> query: (?x2399, 03bwzr4) <- major_field_of_study(?x2399, ?x2601), school_type(?x2399, ?x1507), ?x2601 = 04x_3, student(?x2399, ?x3070) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jsn5 institution! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.698 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3440-03_fk9 PRED entity: 03_fk9 PRED relation: cinematography! PRED expected values: 02v8kmz => 113 concepts (69 used for prediction) PRED predicted values (max 10 best out of 342): 083skw (0.07 #1108, 0.05 #3845, 0.05 #3503), 0kbhf (0.05 #3960, 0.05 #3618, 0.04 #4986), 03cw411 (0.05 #3882, 0.05 #4224, 0.04 #4908), 084qpk (0.05 #3444, 0.04 #4812, 0.04 #4470), 0jymd (0.05 #3892, 0.04 #4918, 0.04 #4576), 0jvt9 (0.05 #3869, 0.04 #4895, 0.04 #4553), 0bbgvp (0.04 #1362, 0.03 #2731, 0.03 #4099), 03bdkd (0.04 #1350, 0.03 #2719, 0.03 #4087), 0h0wd9 (0.04 #1345, 0.03 #2714, 0.03 #4082), 06x77g (0.04 #1321, 0.03 #2690, 0.03 #4058) >> Best rule #1108 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 06cv1; 0f3zf_; 04g865; 02rgz97; 08t7nz; 02rybfn; 07z4p5; >> query: (?x10650, 083skw) <- cinematography(?x9755, ?x10650), profession(?x10650, ?x2265), award(?x10650, ?x1243), film_production_design_by(?x9755, ?x6096) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_fk9 cinematography! 02v8kmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 69.000 0.074 http://example.org/film/film/cinematography #3439-023fb PRED entity: 023fb PRED relation: team! PRED expected values: 0czmk1 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 74): 0czmk1 (0.83 #1335, 0.82 #2326, 0.82 #1617), 09j028 (0.33 #117, 0.25 #187, 0.17 #467), 02d9k (0.33 #5, 0.11 #1264, 0.09 #1196), 05_6_y (0.33 #1, 0.09 #4168, 0.09 #4167), 04v68c (0.33 #69, 0.08 #1475, 0.07 #2040), 071h5c (0.33 #63, 0.07 #1254, 0.06 #2034), 0g3b2z (0.33 #464, 0.06 #1308, 0.04 #1235), 080dyk (0.25 #144, 0.18 #2042, 0.09 #4168), 0135nb (0.25 #157, 0.11 #1264, 0.09 #4168), 0bn9sc (0.25 #142, 0.09 #4168, 0.09 #4167) >> Best rule #1335 for best value: >> intensional similarity = 8 >> extensional distance = 48 >> proper extension: 01vqc7; >> query: (?x6670, ?x9697) <- position(?x6670, ?x203), position(?x6670, ?x60), ?x203 = 0dgrmp, team(?x8324, ?x6670), ?x60 = 02nzb8, team(?x9697, ?x6670), team(?x8324, ?x1599), current_club(?x978, ?x6670) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023fb team! 0czmk1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.832 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #3438-09th87 PRED entity: 09th87 PRED relation: draft! PRED expected values: 0jm4v 0jm5b => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 248): 03lsq (0.60 #1210, 0.50 #1060, 0.47 #438), 043vc (0.60 #1211, 0.50 #1061, 0.45 #510), 06rpd (0.60 #1240, 0.50 #1090, 0.43 #794), 05l71 (0.60 #1206, 0.50 #1056, 0.43 #760), 02c_4 (0.60 #1232, 0.50 #1082, 0.43 #786), 05g3v (0.60 #1199, 0.50 #1049, 0.43 #753), 05g3b (0.60 #1185, 0.50 #1035, 0.43 #739), 06x76 (0.60 #1244, 0.50 #1094, 0.43 #798), 0jmh7 (0.54 #1177, 0.54 #1176, 0.53 #731), 0jm2v (0.54 #1177, 0.54 #1176, 0.53 #731) >> Best rule #1210 for best value: >> intensional similarity = 49 >> extensional distance = 8 >> proper extension: 05vsb7; >> query: (?x8542, 03lsq) <- school(?x8542, ?x6763), school(?x8542, ?x4599), draft(?x11168, ?x8542), draft(?x2398, ?x8542), team(?x1348, ?x2398), sport(?x2398, ?x4833), major_field_of_study(?x6763, ?x1154), student(?x6763, ?x426), currency(?x6763, ?x170), institution(?x4981, ?x6763), institution(?x1368, ?x6763), institution(?x620, ?x6763), ?x4981 = 03bwzr4, ?x620 = 07s6fsf, contains(?x94, ?x6763), state_province_region(?x4599, ?x4600), institution(?x1200, ?x4599), fraternities_and_sororities(?x6763, ?x3697), major_field_of_study(?x4599, ?x4268), ?x170 = 09nqf, colors(?x4599, ?x332), colors(?x2398, ?x663), draft(?x11168, ?x4979), organization(?x346, ?x4599), organization(?x4599, ?x5487), ?x346 = 060c4, ?x4268 = 02822, school(?x11168, ?x1884), school(?x1639, ?x4599), school(?x4979, ?x6973), school(?x4979, ?x4980), school(?x4979, ?x4296), ?x4296 = 07vyf, ?x4980 = 01n6r0, team(?x13926, ?x11168), institution(?x1368, ?x11185), institution(?x1368, ?x11128), institution(?x1368, ?x6787), institution(?x1368, ?x6732), institution(?x1368, ?x4582), major_field_of_study(?x1368, ?x3400), ?x11128 = 01pdgp, ?x11185 = 01n4w_, student(?x1368, ?x123), ?x4582 = 02897w, ?x3400 = 0pf2, ?x6787 = 016wyn, ?x6973 = 05x_5, ?x6732 = 0gdm1 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1177 for first EXPECTED value: *> intensional similarity = 49 *> extensional distance = 7 *> proper extension: 02pq_x5; *> query: (?x8542, ?x4369) <- school(?x8542, ?x6814), school(?x8542, ?x6763), school(?x8542, ?x4599), school(?x8542, ?x2399), school(?x8542, ?x2175), draft(?x9931, ?x8542), draft(?x8228, ?x8542), draft(?x2398, ?x8542), team(?x1348, ?x2398), sport(?x2398, ?x4833), major_field_of_study(?x6763, ?x1154), student(?x6763, ?x7359), currency(?x6763, ?x170), institution(?x4981, ?x6763), institution(?x620, ?x6763), ?x4981 = 03bwzr4, ?x620 = 07s6fsf, contains(?x94, ?x6763), state_province_region(?x4599, ?x4600), institution(?x1200, ?x4599), school(?x8228, ?x9745), ?x170 = 09nqf, colors(?x2398, ?x1101), award(?x7359, ?x2420), award_nominee(?x248, ?x7359), major_field_of_study(?x4599, ?x3489), major_field_of_study(?x4599, ?x3213), major_field_of_study(?x4599, ?x1695), school(?x1639, ?x4599), ?x1101 = 06fvc, ?x3489 = 0193x, teams(?x4733, ?x2398), category(?x2399, ?x134), ?x134 = 08mbj5d, team(?x1348, ?x4369), ?x1695 = 06ms6, major_field_of_study(?x6545, ?x3213), company(?x2998, ?x4599), school_type(?x2399, ?x1507), organization(?x346, ?x2175), student(?x4599, ?x3273), major_field_of_study(?x2399, ?x1527), citytown(?x6814, ?x8157), ?x6545 = 01ky7c, school(?x580, ?x6814), ?x9745 = 01jpqb, school(?x2398, ?x4980), major_field_of_study(?x1526, ?x3213), team(?x13926, ?x9931) *> conf = 0.54 ranks of expected_values: 11, 12 EVAL 09th87 draft! 0jm5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 20.000 20.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 09th87 draft! 0jm4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 20.000 20.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #3437-0fztbq PRED entity: 0fztbq PRED relation: genre PRED expected values: 02kdv5l => 113 concepts (79 used for prediction) PRED predicted values (max 10 best out of 100): 02kdv5l (0.73 #608, 0.71 #487, 0.71 #971), 07s9rl0 (0.71 #5205, 0.65 #1816, 0.65 #1695), 03k9fj (0.59 #2191, 0.58 #2070, 0.54 #860), 05p553 (0.54 #3757, 0.41 #8123, 0.40 #8244), 0lsxr (0.41 #978, 0.33 #7148, 0.33 #252), 02l7c8 (0.40 #4253, 0.40 #3285, 0.38 #3164), 06n90 (0.35 #1587, 0.33 #7148, 0.33 #7026), 01hmnh (0.33 #2197, 0.33 #19, 0.32 #2076), 018td (0.33 #7148, 0.33 #7026, 0.33 #6539), 082gq (0.33 #7148, 0.33 #7026, 0.33 #6539) >> Best rule #608 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 02sg5v; 0g5pv3; 01kf3_9; 0d1qmz; 025twgt; >> query: (?x11120, 02kdv5l) <- nominated_for(?x11120, ?x5399), language(?x11120, ?x732), ?x5399 = 0fsw_7, service_language(?x555, ?x732), countries_spoken_in(?x732, ?x172) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fztbq genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 79.000 0.727 http://example.org/film/film/genre #3436-018y2s PRED entity: 018y2s PRED relation: group PRED expected values: 013w8y => 150 concepts (77 used for prediction) PRED predicted values (max 10 best out of 93): 01qqwp9 (0.11 #347, 0.10 #130, 0.09 #563), 01v0sx2 (0.11 #5, 0.09 #439, 0.08 #655), 0123r4 (0.09 #370, 0.09 #478, 0.09 #586), 07mvp (0.07 #46, 0.07 #372, 0.07 #155), 02r1tx7 (0.07 #342, 0.06 #558, 0.04 #992), 0cbm64 (0.07 #186, 0.04 #619, 0.04 #77), 01wv9xn (0.07 #442, 0.06 #658, 0.03 #1416), 07c0j (0.05 #330, 0.04 #546, 0.03 #113), 01v0sxx (0.05 #411, 0.03 #1061, 0.02 #1385), 06nv27 (0.04 #467, 0.04 #1009, 0.02 #575) >> Best rule #347 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 01vrx3g; 01vsnff; 01wwvt2; 0qf3p; 01vn35l; 06449; 0407f; 02bh9; 0pmw9; 01vsy7t; ... >> query: (?x1165, 01qqwp9) <- role(?x1165, ?x227), film(?x1165, ?x1066), role(?x1165, ?x314) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018y2s group 013w8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 77.000 0.114 http://example.org/music/group_member/membership./music/group_membership/group #3435-026mj PRED entity: 026mj PRED relation: contains! PRED expected values: 04_1l0v => 171 concepts (114 used for prediction) PRED predicted values (max 10 best out of 197): 04_1l0v (0.88 #16581, 0.82 #14791, 0.81 #17477), 059rby (0.33 #915, 0.17 #1812, 0.15 #86091), 02qkt (0.31 #79245, 0.30 #89106, 0.29 #56830), 07ssc (0.31 #2721, 0.30 #89686, 0.20 #83413), 01n7q (0.30 #9040, 0.25 #18895, 0.24 #19790), 06pvr (0.30 #9128, 0.16 #10922, 0.12 #18983), 07c5l (0.21 #3979, 0.20 #7564, 0.14 #89154), 0dg3n1 (0.20 #79053, 0.20 #71881, 0.19 #88914), 02jx1 (0.19 #89741, 0.15 #2776, 0.14 #83468), 029jpy (0.18 #5593, 0.12 #10076, 0.10 #13661) >> Best rule #16581 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 07srw; >> query: (?x7518, 04_1l0v) <- adjoins(?x3670, ?x7518), district_represented(?x176, ?x7518), religion(?x7518, ?x492) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026mj contains! 04_1l0v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 114.000 0.875 http://example.org/location/location/contains #3434-0m491 PRED entity: 0m491 PRED relation: genre PRED expected values: 05p553 01j1n2 => 99 concepts (92 used for prediction) PRED predicted values (max 10 best out of 99): 01z4y (0.61 #9025, 0.59 #5652, 0.53 #7216), 05p553 (0.59 #2405, 0.45 #4213, 0.44 #844), 02kdv5l (0.53 #842, 0.48 #1442, 0.46 #602), 03k9fj (0.46 #611, 0.38 #491, 0.36 #971), 01jfsb (0.45 #1692, 0.42 #972, 0.38 #492), 01hmnh (0.43 #1338, 0.33 #18, 0.29 #258), 02l7c8 (0.37 #4946, 0.36 #4225, 0.34 #3381), 03bxz7 (0.35 #1375, 0.14 #295, 0.11 #5586), 02n4kr (0.33 #7, 0.17 #1927, 0.14 #1687), 02xlf (0.33 #53, 0.07 #1013, 0.05 #1133) >> Best rule #9025 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 024rwx; 0ctzf1; 09g_31; >> query: (?x1859, ?x2480) <- titles(?x2480, ?x1859), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #2405 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 213 *> proper extension: 02_kd; *> query: (?x1859, 05p553) <- production_companies(?x1859, ?x902), film(?x12602, ?x1859), film(?x902, ?x103), influenced_by(?x364, ?x12602) *> conf = 0.59 ranks of expected_values: 2, 55 EVAL 0m491 genre 01j1n2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 99.000 92.000 0.612 http://example.org/film/film/genre EVAL 0m491 genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 92.000 0.612 http://example.org/film/film/genre #3433-05xbx PRED entity: 05xbx PRED relation: award_winner! PRED expected values: 049rl0 => 154 concepts (126 used for prediction) PRED predicted values (max 10 best out of 802): 03fg0r (0.27 #26475, 0.10 #36111, 0.07 #63408), 0m66w (0.27 #26676, 0.10 #36312, 0.07 #63609), 070j61 (0.27 #26904, 0.10 #36540, 0.07 #63837), 046b0s (0.27 #26088, 0.07 #63021, 0.06 #71048), 05mvd62 (0.25 #15608, 0.25 #4372, 0.24 #152522), 01gb54 (0.25 #15231, 0.25 #3995, 0.24 #152522), 05gnf (0.25 #15549, 0.25 #149310, 0.19 #68537), 017s11 (0.25 #4887, 0.24 #152522, 0.17 #9702), 0gsg7 (0.25 #14713, 0.18 #22740, 0.18 #21134), 09d5h (0.25 #14761, 0.18 #22788, 0.18 #21182) >> Best rule #26475 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0b4rf3; >> query: (?x5007, 03fg0r) <- award_nominee(?x5007, ?x2776), program(?x2776, ?x10234), citytown(?x2776, ?x362) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05xbx award_winner! 049rl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 126.000 0.267 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #3432-01l9v7n PRED entity: 01l9v7n PRED relation: award PRED expected values: 054ks3 => 108 concepts (85 used for prediction) PRED predicted values (max 10 best out of 342): 01ckrr (0.54 #5455, 0.23 #3043, 0.23 #1435), 054ks3 (0.52 #5768, 0.43 #542, 0.42 #140), 02qvyrt (0.52 #2135, 0.45 #4145, 0.43 #4547), 025m8y (0.39 #2107, 0.38 #901, 0.34 #4117), 0c4z8 (0.35 #5699, 0.26 #71, 0.24 #6503), 025m8l (0.32 #5745, 0.24 #519, 0.23 #11777), 0fhpv4 (0.30 #2204, 0.27 #2606, 0.27 #4214), 01by1l (0.29 #512, 0.26 #110, 0.26 #6542), 01bgqh (0.28 #2857, 0.28 #1249, 0.24 #445), 09sb52 (0.27 #16123, 0.26 #15319, 0.24 #12907) >> Best rule #5455 for best value: >> intensional similarity = 5 >> extensional distance = 92 >> proper extension: 01t_xp_; 067mj; 0frsw; 047cx; 0l8g0; 048xh; 0b_xm; 07sbk; 03vhvp; 0jg77; >> query: (?x3134, 01ckrr) <- award(?x3134, ?x4488), award(?x5385, ?x4488), award(?x2440, ?x4488), location(?x2440, ?x1591), ?x5385 = 0134tg >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #5768 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 105 *> proper extension: 0fhxv; 02z4b_8; 0drc1; *> query: (?x3134, 054ks3) <- award(?x3134, ?x4488), award(?x3134, ?x1323), award(?x2440, ?x4488), ?x2440 = 01vvpjj, ?x1323 = 0gqz2 *> conf = 0.52 ranks of expected_values: 2 EVAL 01l9v7n award 054ks3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 85.000 0.543 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3431-03y317 PRED entity: 03y317 PRED relation: genre PRED expected values: 07s9rl0 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 79): 07s9rl0 (0.88 #2548, 0.75 #2972, 0.62 #257), 07qht4 (0.68 #682, 0.67 #425, 0.60 #510), 05p553 (0.58 #1110, 0.57 #1365, 0.55 #1280), 01z4y (0.40 #1379, 0.40 #1549, 0.39 #1124), 0hcr (0.40 #530, 0.19 #2991, 0.19 #4353), 01z77k (0.35 #627, 0.33 #540, 0.12 #3256), 09lmb (0.33 #372, 0.30 #457, 0.29 #629), 06n90 (0.33 #14, 0.29 #185, 0.25 #270), 02n4kr (0.33 #9, 0.29 #180, 0.25 #265), 01hmnh (0.33 #17, 0.29 #188, 0.25 #273) >> Best rule #2548 for best value: >> intensional similarity = 6 >> extensional distance = 126 >> proper extension: 02v5xg; 0dl6fv; >> query: (?x9032, 07s9rl0) <- genre(?x9032, ?x8805), languages(?x9032, ?x254), genre(?x12173, ?x8805), genre(?x8057, ?x8805), ?x8057 = 0jq2r, ?x12173 = 0pc_l >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03y317 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.883 http://example.org/tv/tv_program/genre #3430-02sj1x PRED entity: 02sj1x PRED relation: artists! PRED expected values: 017_qw => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 124): 017_qw (0.46 #5736, 0.45 #4476, 0.43 #5106), 06by7 (0.25 #1283, 0.21 #6008, 0.21 #1598), 0ggq0m (0.22 #643, 0.17 #1588, 0.11 #8520), 064t9 (0.21 #1274, 0.17 #1589, 0.16 #24588), 05lls (0.17 #646, 0.15 #331, 0.14 #1591), 0l8gh (0.17 #812, 0.15 #497, 0.14 #1757), 03ckfl9 (0.17 #1427, 0.15 #482, 0.14 #1742), 0827d (0.17 #1264, 0.14 #1579, 0.09 #634), 05w3f (0.15 #355, 0.12 #1300, 0.10 #1615), 08jyyk (0.15 #386, 0.12 #1331, 0.07 #1646) >> Best rule #5736 for best value: >> intensional similarity = 4 >> extensional distance = 135 >> proper extension: 02rgz4; 01nqfh_; 04k15; 0kvnn; 01lz4tf; 07m4c; 03_f0; 0232lm; 03f4k; 01mz9lt; ... >> query: (?x3519, 017_qw) <- music(?x8769, ?x3519), music(?x8617, ?x3519), nominated_for(?x591, ?x8617), written_by(?x8769, ?x8225) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02sj1x artists! 017_qw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.460 http://example.org/music/genre/artists #3429-026m3y PRED entity: 026m3y PRED relation: school_type PRED expected values: 05jxkf => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 22): 05jxkf (0.82 #460, 0.69 #628, 0.67 #748), 01rs41 (0.43 #341, 0.30 #869, 0.26 #1014), 05pcjw (0.27 #865, 0.26 #1058, 0.25 #1010), 07tf8 (0.23 #393, 0.20 #681, 0.16 #921), 02p0qmm (0.20 #322, 0.16 #442, 0.14 #538), 01_9fk (0.16 #746, 0.14 #1179, 0.13 #1709), 0257h9 (0.14 #44, 0.02 #1221, 0.02 #1245), 02dk5q (0.14 #31, 0.02 #1208, 0.02 #1040), 025tjcb (0.14 #47, 0.02 #1056, 0.01 #1224), 0bwd5 (0.08 #985, 0.07 #355, 0.03 #3761) >> Best rule #460 for best value: >> intensional similarity = 5 >> extensional distance = 36 >> proper extension: 0dplh; 02qwgk; 01tzfz; 014jyk; 013719; >> query: (?x10432, 05jxkf) <- currency(?x10432, ?x1099), organization(?x5510, ?x10432), ?x5510 = 07xl34, institution(?x1771, ?x10432), student(?x1771, ?x744) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026m3y school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.816 http://example.org/education/educational_institution/school_type #3428-01t38b PRED entity: 01t38b PRED relation: category PRED expected values: 08mbj5d => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #70, 0.91 #38, 0.90 #80) >> Best rule #70 for best value: >> intensional similarity = 5 >> extensional distance = 231 >> proper extension: 06klyh; >> query: (?x5846, 08mbj5d) <- citytown(?x5846, ?x7213), school_type(?x5846, ?x3092), country(?x7213, ?x1310), contains(?x512, ?x5846), contains(?x1310, ?x892) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01t38b category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.914 http://example.org/common/topic/webpage./common/webpage/category #3427-02y_3rf PRED entity: 02y_3rf PRED relation: nutrient! PRED expected values: 033cnk => 41 concepts (39 used for prediction) PRED predicted values (max 10 best out of 13): 033cnk (0.89 #750, 0.89 #772, 0.86 #21), 05z55 (0.88 #544, 0.88 #541, 0.86 #21), 037ls6 (0.88 #516, 0.86 #21, 0.86 #194), 0f25w9 (0.87 #608, 0.87 #595, 0.86 #21), 0frq6 (0.86 #21, 0.86 #194, 0.86 #590), 09728 (0.86 #21, 0.86 #194, 0.86 #43), 07j87 (0.86 #21, 0.86 #194, 0.86 #43), 0971v (0.86 #21, 0.86 #194, 0.86 #43), 0dcfv (0.86 #21, 0.86 #194, 0.86 #43), 06x4c (0.86 #21, 0.86 #194, 0.86 #43) >> Best rule #750 for best value: >> intensional similarity = 125 >> extensional distance = 45 >> proper extension: 075pwf; 01w_3; 0f4k5; >> query: (?x6286, 033cnk) <- nutrient(?x9005, ?x6286), nutrient(?x7057, ?x6286), nutrient(?x6032, ?x6286), nutrient(?x3900, ?x6286), nutrient(?x3468, ?x6286), nutrient(?x2701, ?x6286), nutrient(?x7057, ?x13944), nutrient(?x7057, ?x12902), nutrient(?x7057, ?x12868), nutrient(?x7057, ?x12454), nutrient(?x7057, ?x12083), nutrient(?x7057, ?x11758), nutrient(?x7057, ?x11592), nutrient(?x7057, ?x11409), nutrient(?x7057, ?x11270), nutrient(?x7057, ?x10891), nutrient(?x7057, ?x10709), nutrient(?x7057, ?x10098), nutrient(?x7057, ?x9949), nutrient(?x7057, ?x9915), nutrient(?x7057, ?x9855), nutrient(?x7057, ?x9840), nutrient(?x7057, ?x9795), nutrient(?x7057, ?x9733), nutrient(?x7057, ?x9619), nutrient(?x7057, ?x9490), nutrient(?x7057, ?x9436), nutrient(?x7057, ?x9426), nutrient(?x7057, ?x9365), nutrient(?x7057, ?x8413), nutrient(?x7057, ?x7720), nutrient(?x7057, ?x7652), nutrient(?x7057, ?x7431), nutrient(?x7057, ?x7364), nutrient(?x7057, ?x7362), nutrient(?x7057, ?x7135), nutrient(?x7057, ?x6192), nutrient(?x7057, ?x6160), nutrient(?x7057, ?x6033), nutrient(?x7057, ?x6026), nutrient(?x7057, ?x5549), nutrient(?x7057, ?x5526), nutrient(?x7057, ?x5451), nutrient(?x7057, ?x5374), nutrient(?x7057, ?x5337), nutrient(?x7057, ?x5010), nutrient(?x7057, ?x4069), nutrient(?x7057, ?x3469), nutrient(?x7057, ?x3264), nutrient(?x7057, ?x3203), nutrient(?x7057, ?x2702), nutrient(?x7057, ?x2018), nutrient(?x7057, ?x1960), nutrient(?x7057, ?x1304), ?x12454 = 025rw19, ?x5549 = 025s7j4, ?x9005 = 04zpv, ?x9365 = 04k8n, ?x10709 = 0h1sz, ?x3264 = 0dcfv, ?x7431 = 09gwd, ?x10098 = 0h1_c, ?x10891 = 0g5gq, ?x2702 = 0838f, ?x5526 = 09pbb, ?x6033 = 04zjxcz, ?x1304 = 08lb68, ?x5451 = 05wvs, ?x3468 = 0cxn2, ?x9733 = 0h1tz, ?x9855 = 0d9t0, nutrient(?x3900, ?x13498), nutrient(?x3900, ?x8487), nutrient(?x3900, ?x8442), ?x5010 = 0h1vz, ?x1960 = 07hnp, ?x9619 = 0h1tg, ?x5374 = 025s0zp, ?x8487 = 014yzm, ?x7652 = 025s0s0, nutrient(?x10612, ?x13944), nutrient(?x9732, ?x13944), nutrient(?x9489, ?x13944), nutrient(?x8298, ?x13944), nutrient(?x5373, ?x13944), nutrient(?x1257, ?x13944), ?x9426 = 0h1yy, ?x5373 = 0971v, ?x3203 = 04kl74p, ?x9436 = 025sqz8, ?x2018 = 01sh2, ?x9490 = 0h1sg, nutrient(?x2701, ?x13126), ?x12083 = 01n78x, ?x6026 = 025sf8g, ?x9795 = 05v_8y, ?x9915 = 025tkqy, ?x8442 = 02kcv4x, ?x8413 = 02kc4sf, ?x9732 = 05z55, ?x6160 = 041r51, ?x1257 = 09728, ?x5337 = 06x4c, ?x3469 = 0h1zw, ?x6032 = 01nkt, ?x11409 = 0h1yf, ?x6192 = 06jry, nutrient(?x1959, ?x11270), ?x11758 = 0q01m, ?x7364 = 09gvd, ?x8298 = 037ls6, ?x7135 = 025rsfk, ?x7362 = 02kc5rj, ?x12868 = 03d49, ?x13126 = 02kc_w5, ?x4069 = 0hqw8p_, ?x9840 = 02p0tjr, ?x7720 = 025s7x6, ?x13498 = 07q0m, ?x11592 = 025sf0_, ?x12902 = 0fzjh, ?x1959 = 0f25w9, ?x9949 = 02kd0rh, ?x10612 = 0frq6, ?x9489 = 07j87 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y_3rf nutrient! 033cnk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 39.000 0.894 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #3426-03_lsr PRED entity: 03_lsr PRED relation: teams! PRED expected values: 05qx1 => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 46): 0947l (0.02 #1802, 0.02 #3962, 0.02 #5042), 0d0vqn (0.02 #5138, 0.01 #7568, 0.01 #8108), 04jpl (0.02 #5949, 0.01 #8379, 0.01 #9), 02fvv (0.01 #252, 0.01 #9432, 0.01 #522), 04swd (0.01 #177, 0.01 #9357, 0.01 #447), 0m75g (0.01 #159, 0.01 #9339, 0.01 #429), 01rwbd (0.01 #269, 0.01 #539, 0.01 #809), 0pfd9 (0.01 #249, 0.01 #519, 0.01 #789), 01n43d (0.01 #211, 0.01 #481, 0.01 #751), 031y2 (0.01 #195, 0.01 #465, 0.01 #735) >> Best rule #1802 for best value: >> intensional similarity = 13 >> extensional distance = 104 >> proper extension: 0371rb; 041xyk; 03xzxb; 03ytj1; 011v3; 02b1k5; 07sqnh; 02gjt4; 02nt75; 01kkk4; ... >> query: (?x8406, 0947l) <- position(?x8406, ?x530), position(?x8406, ?x203), position(?x8406, ?x63), position(?x8406, ?x60), ?x63 = 02sdk9v, ?x60 = 02nzb8, ?x530 = 02_j1w, ?x203 = 0dgrmp, position(?x8406, ?x63), team(?x63, ?x8406), position(?x8406, ?x203), team(?x530, ?x8406), team(?x203, ?x8406) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_lsr teams! 05qx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 48.000 0.019 http://example.org/sports/sports_team_location/teams #3425-03ym1 PRED entity: 03ym1 PRED relation: award_winner! PRED expected values: 092c5f => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 137): 09qvms (0.07 #1973, 0.06 #1833, 0.06 #2953), 02cg41 (0.06 #265, 0.04 #6845, 0.04 #405), 05pd94v (0.06 #142, 0.04 #6722, 0.04 #282), 027hjff (0.06 #336, 0.05 #2016, 0.04 #2436), 01s695 (0.06 #143, 0.04 #6723, 0.04 #283), 01c6qp (0.06 #159, 0.04 #6739, 0.04 #299), 02rjjll (0.06 #145, 0.04 #6725, 0.04 #285), 03gyp30 (0.05 #2076, 0.05 #396, 0.05 #816), 0gx1673 (0.05 #259, 0.03 #399, 0.03 #539), 09g90vz (0.05 #3063, 0.05 #1943, 0.05 #2083) >> Best rule #1973 for best value: >> intensional similarity = 3 >> extensional distance = 660 >> proper extension: 026v1z; 0cbxl0; >> query: (?x5661, 09qvms) <- award_winner(?x5661, ?x5283), award_nominee(?x628, ?x5661), languages(?x5283, ?x90) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #714 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 347 *> proper extension: 02knnd; 03ds83; 02x0bdb; 039cq4; 02bwjv; 01d6jf; 0gdhhy; 051m56; *> query: (?x5661, 092c5f) <- award_winner(?x5661, ?x5283), location(?x5283, ?x739), participant(?x5898, ?x5283) *> conf = 0.05 ranks of expected_values: 13 EVAL 03ym1 award_winner! 092c5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 100.000 100.000 0.066 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3424-0416y94 PRED entity: 0416y94 PRED relation: language PRED expected values: 02h40lc => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 46): 02h40lc (0.99 #4222, 0.98 #5449, 0.96 #4396), 04306rv (0.22 #472, 0.22 #121, 0.22 #180), 02bjrlw (0.18 #117, 0.15 #293, 0.15 #584), 06nm1 (0.16 #127, 0.14 #186, 0.13 #69), 06b_j (0.12 #138, 0.10 #489, 0.10 #605), 0jzc (0.09 #195, 0.08 #136, 0.08 #312), 03_9r (0.07 #126, 0.07 #243, 0.07 #302), 05zjd (0.05 #492, 0.05 #258, 0.04 #608), 04h9h (0.05 #42, 0.05 #158, 0.05 #217), 05qqm (0.05 #40, 0.04 #156, 0.03 #215) >> Best rule #4222 for best value: >> intensional similarity = 4 >> extensional distance = 1290 >> proper extension: 0c0yh4; 090s_0; 01sxly; 0n0bp; 087wc7n; 0pv2t; 0kv2hv; 04969y; 06krf3; 0crfwmx; ... >> query: (?x1318, 02h40lc) <- language(?x1318, ?x5607), film_release_distribution_medium(?x1318, ?x81), language(?x9138, ?x5607), ?x9138 = 06x77g >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0416y94 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.985 http://example.org/film/film/language #3423-06nd8c PRED entity: 06nd8c PRED relation: profession PRED expected values: 02jknp => 105 concepts (20 used for prediction) PRED predicted values (max 10 best out of 59): 03gjzk (0.67 #15, 0.58 #1330, 0.27 #599), 0dxtg (0.61 #1329, 0.54 #14, 0.41 #598), 02jknp (0.53 #1323, 0.46 #8, 0.29 #592), 01d_h8 (0.50 #1321, 0.46 #590, 0.44 #6), 0cbd2 (0.23 #2491, 0.18 #2053, 0.16 #2638), 015h31 (0.18 #25, 0.06 #1925, 0.06 #2363), 0kyk (0.16 #2073, 0.15 #2511, 0.09 #2658), 0nbcg (0.14 #321, 0.12 #467, 0.11 #2660), 09jwl (0.13 #18, 0.10 #310, 0.10 #2649), 01c72t (0.12 #2653, 0.11 #314, 0.11 #2506) >> Best rule #15 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 01v3vp; >> query: (?x12804, 03gjzk) <- gender(?x12804, ?x231), profession(?x12804, ?x1943), profession(?x12804, ?x1383), ?x1383 = 0np9r, ?x1943 = 02krf9 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1323 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 135 *> proper extension: 06y9bd; *> query: (?x12804, 02jknp) <- profession(?x12804, ?x1943), student(?x7545, ?x12804), nationality(?x12804, ?x94), ?x1943 = 02krf9 *> conf = 0.53 ranks of expected_values: 3 EVAL 06nd8c profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 20.000 0.667 http://example.org/people/person/profession #3422-016k62 PRED entity: 016k62 PRED relation: people! PRED expected values: 018s6c => 108 concepts (105 used for prediction) PRED predicted values (max 10 best out of 42): 0x67 (0.39 #10, 0.24 #2012, 0.24 #1704), 041rx (0.15 #466, 0.13 #158, 0.13 #543), 033tf_ (0.12 #315, 0.08 #469, 0.07 #546), 0xnvg (0.09 #13, 0.06 #321, 0.06 #167), 07bch9 (0.09 #23, 0.06 #331, 0.06 #562), 013xrm (0.07 #405, 0.03 #790, 0.02 #2253), 07hwkr (0.07 #474, 0.06 #320, 0.06 #551), 0g6ff (0.06 #406, 0.02 #175, 0.02 #791), 048z7l (0.05 #502, 0.05 #579, 0.03 #348), 02w7gg (0.05 #2698, 0.05 #3160, 0.05 #3622) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0gbwp; 0c7xjb; >> query: (?x5151, 0x67) <- award_nominee(?x5125, ?x5151), company(?x5151, ?x2909), artists(?x505, ?x5151) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #605 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 85 *> proper extension: 03qncl3; *> query: (?x5151, 018s6c) <- award_nominee(?x5125, ?x5151), company(?x5151, ?x2909), award(?x5151, ?x2324) *> conf = 0.01 ranks of expected_values: 40 EVAL 016k62 people! 018s6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 108.000 105.000 0.391 http://example.org/people/ethnicity/people #3421-0194zl PRED entity: 0194zl PRED relation: honored_for! PRED expected values: 09p3h7 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 110): 0275n3y (0.07 #185, 0.06 #306, 0.05 #427), 04n2r9h (0.06 #36, 0.04 #520, 0.04 #278), 09p30_ (0.06 #71, 0.04 #555, 0.03 #434), 03tn9w (0.06 #79, 0.03 #926, 0.02 #1047), 059x66 (0.06 #497, 0.03 #1223, 0.02 #618), 05c1t6z (0.05 #3520, 0.04 #2794, 0.02 #5096), 09pj68 (0.05 #452, 0.04 #331, 0.03 #89), 09qftb (0.05 #460, 0.04 #339, 0.03 #581), 02q690_ (0.05 #3563, 0.05 #2837, 0.03 #3927), 09k5jh7 (0.05 #191, 0.04 #675, 0.04 #312) >> Best rule #185 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 0m313; 095zlp; 05jzt3; 0344gc; 0pv3x; 0gjk1d; 04qw17; 09k56b7; 02qr69m; 048htn; ... >> query: (?x4963, 0275n3y) <- nominated_for(?x1245, ?x4963), nominated_for(?x618, ?x4963), currency(?x4963, ?x170), ?x618 = 09qwmm, ?x1245 = 0gqwc >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #302 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 49 *> proper extension: 09m6kg; 0dr_4; 02q5g1z; 09tqkv2; 05vxdh; 02d49z; 02nczh; 0drnwh; 0h95927; 0234j5; ... *> query: (?x4963, 09p3h7) <- nominated_for(?x1245, ?x4963), nominated_for(?x618, ?x4963), currency(?x4963, ?x170), ?x618 = 09qwmm, award(?x241, ?x1245) *> conf = 0.04 ranks of expected_values: 29 EVAL 0194zl honored_for! 09p3h7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 90.000 90.000 0.075 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3420-02049g PRED entity: 02049g PRED relation: contains! PRED expected values: 0j5g9 => 87 concepts (21 used for prediction) PRED predicted values (max 10 best out of 337): 09c7w0 (0.72 #8077, 0.62 #8972, 0.60 #9868), 02jx1 (0.66 #981, 0.64 #2774, 0.64 #1876), 06q1r (0.64 #14348, 0.08 #351, 0.06 #1246), 0j5g9 (0.64 #14348, 0.05 #5379, 0.03 #5381), 05bcl (0.64 #14348, 0.05 #5379, 0.03 #5381), 02j9z (0.27 #6279, 0.12 #4510, 0.11 #5410), 01n7q (0.25 #6358, 0.11 #15323, 0.10 #18014), 059rby (0.19 #6301, 0.08 #15266, 0.07 #17957), 04jpl (0.15 #2710, 0.15 #1812, 0.12 #917), 02qkt (0.15 #4828, 0.14 #5728, 0.05 #5379) >> Best rule #8077 for best value: >> intensional similarity = 5 >> extensional distance = 1237 >> proper extension: 0rs6x; 015zyd; 0rh6k; 08815; 05kkh; 0k049; 05zjtn4; 01fq7; 06_kh; 01rtm4; ... >> query: (?x14454, 09c7w0) <- contains(?x512, ?x14454), country(?x124, ?x512), nationality(?x111, ?x512), place_of_birth(?x5184, ?x512), participating_countries(?x358, ?x512) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #14348 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1528 *> proper extension: 027rn; 09c7w0; 0jgd; 027nb; 01ls2; 03_r3; 015fr; 07ylj; 07ww5; 05v10; ... *> query: (?x14454, ?x6401) <- contains(?x512, ?x14454), contains(?x512, ?x10165), contains(?x512, ?x8653), location_of_ceremony(?x566, ?x10165), service_location(?x555, ?x512), first_level_division_of(?x8653, ?x6401) *> conf = 0.64 ranks of expected_values: 4 EVAL 02049g contains! 0j5g9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 21.000 0.720 http://example.org/location/location/contains #3419-0bzty PRED entity: 0bzty PRED relation: contains PRED expected values: 0ggyr 01tsq8 => 189 concepts (68 used for prediction) PRED predicted values (max 10 best out of 2772): 052fbt (0.87 #55846, 0.86 #55845, 0.86 #23514), 03wxvk (0.87 #55846, 0.86 #55845, 0.86 #23514), 01tsq8 (0.81 #64663, 0.69 #135220, 0.33 #1659), 0bzty (0.58 #64664, 0.56 #135222, 0.56 #135221), 0ggyr (0.58 #64664, 0.56 #135222, 0.56 #135221), 03rjj (0.58 #64664, 0.56 #135222, 0.56 #135221), 05p7tx (0.40 #4134, 0.38 #38209, 0.33 #1195), 026wmz6 (0.38 #38209), 0bwfn (0.33 #1046, 0.27 #21621, 0.20 #3985), 06c62 (0.33 #939, 0.20 #3878, 0.18 #21514) >> Best rule #55846 for best value: >> intensional similarity = 5 >> extensional distance = 36 >> proper extension: 0f8l9c; 0hzlz; >> query: (?x10706, ?x9792) <- adjoins(?x9230, ?x10706), contains(?x10706, ?x14229), administrative_parent(?x9792, ?x10706), category(?x14229, ?x134), country(?x9792, ?x205) >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #64663 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 02qkt; 07c5l; 04pnx; 049nq; *> query: (?x10706, ?x10691) <- contains(?x10706, ?x12142), contains(?x10706, ?x5695), company(?x3131, ?x5695), administrative_division(?x10691, ?x12142) *> conf = 0.81 ranks of expected_values: 3, 5 EVAL 0bzty contains 01tsq8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 189.000 68.000 0.869 http://example.org/location/location/contains EVAL 0bzty contains 0ggyr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 189.000 68.000 0.869 http://example.org/location/location/contains #3418-0sg4x PRED entity: 0sg4x PRED relation: place! PRED expected values: 0sg4x => 54 concepts (29 used for prediction) PRED predicted values (max 10 best out of 24): 0sf9_ (0.06 #87, 0.04 #602, 0.03 #1117), 0s2z0 (0.06 #375, 0.04 #890, 0.03 #1405), 0s9z_ (0.06 #332, 0.04 #847, 0.03 #1362), 0sd7v (0.06 #411, 0.04 #926, 0.03 #1441), 0s3y5 (0.06 #7, 0.04 #522, 0.03 #1037), 0sgxg (0.06 #462, 0.04 #977, 0.03 #1492), 0s9b_ (0.06 #436, 0.04 #951, 0.03 #1466), 0sjqm (0.06 #273, 0.04 #788, 0.03 #1303), 0sc6p (0.06 #489, 0.03 #1519, 0.03 #2035), 0sbv7 (0.06 #452, 0.03 #1482, 0.03 #1998) >> Best rule #87 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 0s5cg; 0sbbq; 0s987; 0s6g4; 0sbv7; 0sc6p; 0s4sj; >> query: (?x14549, 0sf9_) <- source(?x14549, ?x958), contains(?x3818, ?x14549), contains(?x94, ?x14549), ?x3818 = 03v0t, ?x94 = 09c7w0 >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0sg4x place! 0sg4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 29.000 0.056 http://example.org/location/hud_county_place/place #3417-0rvty PRED entity: 0rvty PRED relation: place_of_birth! PRED expected values: 01hrqc => 88 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1229): 0163kf (0.33 #2407, 0.02 #10246, 0.01 #12859), 0168dy (0.33 #2224, 0.02 #10063, 0.01 #12676), 030wkp (0.33 #2016, 0.02 #9855, 0.01 #12468), 06pjs (0.33 #1905, 0.02 #9744, 0.01 #12357), 051cc (0.33 #1771, 0.02 #9610, 0.01 #12223), 06t8b (0.33 #1641, 0.02 #9480, 0.01 #12093), 0gm8_p (0.33 #1625, 0.02 #9464, 0.01 #12077), 04zkj5 (0.33 #1588, 0.02 #9427, 0.01 #12040), 02b9g4 (0.33 #1457, 0.02 #9296, 0.01 #11909), 01vw_dv (0.33 #1372, 0.02 #9211, 0.01 #11824) >> Best rule #2407 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 013yq; >> query: (?x6966, 0163kf) <- contains(?x3038, ?x6966), contains(?x94, ?x6966), featured_film_locations(?x2362, ?x6966), ?x94 = 09c7w0, ?x3038 = 0d0x8 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0rvty place_of_birth! 01hrqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 62.000 0.333 http://example.org/people/person/place_of_birth #3416-01wmxfs PRED entity: 01wmxfs PRED relation: film PRED expected values: 0fb7sd => 114 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1012): 02r79_h (0.33 #228, 0.08 #3790, 0.02 #16257), 049mql (0.33 #681, 0.02 #14929, 0.02 #22053), 0gvrws1 (0.33 #319, 0.02 #14567, 0.01 #19910), 08nhfc1 (0.33 #1318, 0.01 #17347, 0.01 #65899), 04pmnt (0.33 #1069, 0.01 #17098), 048vhl (0.33 #1489, 0.01 #33547, 0.01 #65899), 02pjc1h (0.33 #218, 0.01 #65899), 0gxtknx (0.33 #246), 02mc5v (0.20 #3177, 0.08 #6739, 0.07 #8520), 06t6dz (0.20 #2599, 0.03 #56993, 0.03 #135366) >> Best rule #228 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01wxyx1; >> query: (?x828, 02r79_h) <- participant(?x828, ?x91), film(?x828, ?x10722), ?x10722 = 07p12s >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wmxfs film 0fb7sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 80.000 0.333 http://example.org/film/actor/film./film/performance/film #3415-0gcf2r PRED entity: 0gcf2r PRED relation: category_of! PRED expected values: 047byns 09qs08 09qvf4 07z2lx => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 311): 09qvf4 (0.90 #130, 0.32 #260, 0.29 #648), 09qs08 (0.90 #130, 0.32 #260, 0.29 #648), 07z2lx (0.90 #130, 0.32 #260, 0.23 #1691), 047byns (0.90 #130, 0.32 #260, 0.20 #1299), 03qbnj (0.32 #260, 0.29 #648, 0.25 #196), 024fz9 (0.32 #260, 0.29 #648, 0.25 #189), 01c92g (0.32 #260, 0.29 #648, 0.25 #157), 02grdc (0.32 #260, 0.29 #648, 0.25 #140), 0f4x7 (0.32 #260, 0.29 #648, 0.25 #9), 0gqwc (0.32 #260, 0.29 #648, 0.25 #19) >> Best rule #130 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 0c4ys; 0g_w; >> query: (?x2758, ?x4225) <- instance_of_recurring_event(?x5296, ?x2758), category_of(?x686, ?x2758), award_winner(?x5296, ?x2127), honored_for(?x5296, ?x2078), award_winner(?x2127, ?x236), profession(?x2127, ?x319), ceremony(?x4225, ?x5296), award_winner(?x686, ?x624), nominated_for(?x686, ?x337) >> conf = 0.90 => this is the best rule for 4 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 0gcf2r category_of! 07z2lx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 17.000 17.000 0.898 http://example.org/award/award_category/category_of EVAL 0gcf2r category_of! 09qvf4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 17.000 17.000 0.898 http://example.org/award/award_category/category_of EVAL 0gcf2r category_of! 09qs08 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 17.000 17.000 0.898 http://example.org/award/award_category/category_of EVAL 0gcf2r category_of! 047byns CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 17.000 17.000 0.898 http://example.org/award/award_category/category_of #3414-08k40m PRED entity: 08k40m PRED relation: film! PRED expected values: 01nfys => 119 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1207): 0525b (0.33 #1907, 0.09 #10208, 0.09 #6058), 01chc7 (0.33 #558, 0.09 #8859, 0.05 #15085), 0356dp (0.33 #1739, 0.05 #7965, 0.05 #10040), 03h2d4 (0.33 #745, 0.05 #9046, 0.04 #11121), 06j8wx (0.25 #3035, 0.10 #7185, 0.01 #36244), 0z4s (0.25 #2144, 0.06 #80959, 0.03 #35353), 03ym1 (0.25 #3087, 0.05 #7237, 0.03 #62278), 0q9kd (0.25 #2080, 0.03 #58127, 0.03 #45671), 02f_k_ (0.25 #3196, 0.03 #15647, 0.02 #17723), 016kb7 (0.25 #3442, 0.02 #17969, 0.02 #22122) >> Best rule #1907 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02z0f6l; >> query: (?x2939, 0525b) <- produced_by(?x2939, ?x3568), film(?x2938, ?x2939), genre(?x2939, ?x225), film(?x4832, ?x2939), ?x2938 = 01nwwl >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #34777 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 158 *> proper extension: 07g_0c; 05q4y12; 08cfr1; 0dpl44; 016z43; 0ckt6; *> query: (?x2939, 01nfys) <- production_companies(?x2939, ?x6554), genre(?x2939, ?x600), genre(?x2939, ?x258), ?x258 = 05p553, featured_film_locations(?x2939, ?x362), genre(?x7304, ?x600), film(?x488, ?x7304) *> conf = 0.03 ranks of expected_values: 738 EVAL 08k40m film! 01nfys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 119.000 56.000 0.333 http://example.org/film/actor/film./film/performance/film #3413-02bh_v PRED entity: 02bh_v PRED relation: current_club PRED expected values: 049n2l 02rh_0 => 82 concepts (68 used for prediction) PRED predicted values (max 10 best out of 741): 0xbm (0.56 #1333, 0.50 #749, 0.45 #1917), 080_y (0.40 #981, 0.25 #835, 0.20 #1565), 03x6m (0.33 #217, 0.25 #802, 0.25 #509), 0y54 (0.33 #153, 0.25 #738, 0.25 #445), 0cttx (0.33 #273, 0.25 #858, 0.25 #565), 0kqbh (0.33 #281, 0.25 #866, 0.25 #573), 0mmd6 (0.33 #288, 0.25 #873, 0.25 #580), 011v3 (0.33 #188, 0.25 #773, 0.25 #480), 02mplj (0.33 #168, 0.25 #753, 0.25 #460), 03fnmd (0.33 #185, 0.25 #770, 0.25 #477) >> Best rule #1333 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 01l3vx; 02s2lg; 03zrhb; 02s9vc; 03dj48; >> query: (?x9740, 0xbm) <- position(?x9740, ?x203), position(?x9740, ?x63), ?x203 = 0dgrmp, current_club(?x9740, ?x9089), current_club(?x11309, ?x9089), ?x11309 = 02pp1, ?x63 = 02sdk9v >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #967 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 02rqxc; *> query: (?x9740, 02rh_0) <- position(?x9740, ?x203), ?x203 = 0dgrmp, current_club(?x9740, ?x9089), current_club(?x9740, ?x7377), current_club(?x11309, ?x9089), team(?x1898, ?x11309), sport(?x9089, ?x471), ?x7377 = 06l22 *> conf = 0.20 ranks of expected_values: 44, 91 EVAL 02bh_v current_club 02rh_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 82.000 68.000 0.556 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club EVAL 02bh_v current_club 049n2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 82.000 68.000 0.556 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #3412-015pkc PRED entity: 015pkc PRED relation: profession PRED expected values: 02hrh1q => 127 concepts (126 used for prediction) PRED predicted values (max 10 best out of 79): 02hrh1q (0.92 #5534, 0.90 #8961, 0.89 #6279), 0dxtg (0.53 #4191, 0.51 #4937, 0.51 #4340), 0cbd2 (0.50 #6, 0.18 #1646, 0.17 #2093), 02jknp (0.49 #4185, 0.48 #4334, 0.47 #4931), 03gjzk (0.44 #3597, 0.40 #4342, 0.39 #4193), 0np9r (0.28 #3155, 0.25 #21, 0.24 #2557), 09jwl (0.28 #3303, 0.26 #1957, 0.25 #765), 0kyk (0.26 #13120, 0.25 #30, 0.12 #2117), 0d1pc (0.26 #13120, 0.22 #797, 0.22 #3484), 018gz8 (0.26 #13120, 0.16 #2553, 0.15 #3897) >> Best rule #5534 for best value: >> intensional similarity = 3 >> extensional distance = 423 >> proper extension: 04bs3j; 01mqz0; 01csrl; 044qx; 01fwf1; 06b_0; 04bdqk; >> query: (?x1733, 02hrh1q) <- film(?x1733, ?x167), participant(?x2763, ?x1733), nominated_for(?x1733, ?x13816) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015pkc profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 126.000 0.918 http://example.org/people/person/profession #3411-0hn821n PRED entity: 0hn821n PRED relation: award_winner PRED expected values: 01t6b4 01j7rd 0pyww => 35 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2349): 01j7rd (0.67 #7955, 0.50 #17158, 0.47 #6128), 04ns3gy (0.67 #8982, 0.36 #13581, 0.33 #4386), 01hkhq (0.60 #11075, 0.38 #15678, 0.33 #8010), 02tr7d (0.57 #9420, 0.49 #7659, 0.47 #6128), 02xs0q (0.50 #8199, 0.49 #7659, 0.35 #17402), 05bpg3 (0.50 #8492, 0.33 #3896, 0.33 #833), 05fnl9 (0.49 #7659, 0.47 #6128, 0.42 #6129), 03yj_0n (0.49 #7659, 0.47 #6128, 0.42 #6129), 03w1v2 (0.49 #7659, 0.47 #6128, 0.42 #6129), 02sb1w (0.49 #7659, 0.47 #6128, 0.42 #6129) >> Best rule #7955 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 02q690_; >> query: (?x10010, 01j7rd) <- honored_for(?x10010, ?x10447), honored_for(?x10010, ?x4083), award_winner(?x10010, ?x6693), award_winner(?x10010, ?x3571), award_winner(?x10010, ?x2789), ?x10447 = 07s8z_l, film(?x2789, ?x408), influenced_by(?x2143, ?x6693), ceremony(?x8660, ?x10010), ?x8660 = 02xcb6n, film(?x525, ?x4083), award_winner(?x4838, ?x2789), award_nominee(?x3571, ?x3366), film(?x6693, ?x4745) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 21, 149 EVAL 0hn821n award_winner 0pyww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 35.000 18.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0hn821n award_winner 01j7rd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 18.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0hn821n award_winner 01t6b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 35.000 18.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3410-062zjtt PRED entity: 062zjtt PRED relation: currency PRED expected values: 09nqf => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.86 #176, 0.85 #162, 0.83 #344), 01nv4h (0.12 #799, 0.03 #135, 0.03 #261), 088n7 (0.12 #799, 0.03 #189, 0.02 #70), 02gsvk (0.12 #799, 0.02 #118, 0.02 #160), 02l6h (0.12 #799, 0.02 #221, 0.01 #116), 0kz1h (0.12 #799), 0ptk_ (0.12 #799) >> Best rule #176 for best value: >> intensional similarity = 5 >> extensional distance = 137 >> proper extension: 03t97y; 01kff7; 09146g; 02nx2k; 065ym0c; 07p12s; 04hk0w; >> query: (?x4273, 09nqf) <- film_crew_role(?x4273, ?x2154), genre(?x4273, ?x225), film_release_distribution_medium(?x4273, ?x81), ?x225 = 02kdv5l, ?x2154 = 01vx2h >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 062zjtt currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.863 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #3409-01n7qlf PRED entity: 01n7qlf PRED relation: nationality PRED expected values: 09c7w0 => 85 concepts (84 used for prediction) PRED predicted values (max 10 best out of 69): 09c7w0 (0.80 #1701, 0.78 #4002, 0.78 #1101), 02jx1 (0.50 #33, 0.30 #133, 0.20 #3033), 07t21 (0.17 #37, 0.05 #537, 0.02 #1237), 07ssc (0.12 #3015, 0.08 #3916, 0.08 #5222), 03rjj (0.08 #205, 0.07 #1205, 0.03 #3105), 0d060g (0.08 #707, 0.07 #1007, 0.07 #307), 03rk0 (0.06 #6457, 0.06 #6557, 0.06 #6657), 05bcl (0.05 #560, 0.03 #5808, 0.02 #1260), 0345h (0.05 #1231, 0.04 #631, 0.04 #731), 0f8l9c (0.04 #4927, 0.03 #1522, 0.03 #5808) >> Best rule #1701 for best value: >> intensional similarity = 6 >> extensional distance = 86 >> proper extension: 02hhtj; >> query: (?x3611, 09c7w0) <- celebrity(?x3611, ?x3056), profession(?x3611, ?x220), profession(?x2782, ?x220), profession(?x672, ?x220), ?x2782 = 014q2g, ?x672 = 0168cl >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n7qlf nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 84.000 0.795 http://example.org/people/person/nationality #3408-06lgq8 PRED entity: 06lgq8 PRED relation: profession PRED expected values: 02hrh1q => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 47): 02hrh1q (0.88 #465, 0.87 #2565, 0.86 #7817), 03gjzk (0.35 #766, 0.24 #1216, 0.24 #1516), 01d_h8 (0.32 #1356, 0.31 #3456, 0.30 #4207), 0dxtg (0.31 #764, 0.29 #1514, 0.28 #3464), 02jknp (0.28 #4652, 0.25 #8703, 0.24 #3751), 0d1pc (0.28 #4652, 0.25 #8703, 0.24 #3751), 02hv44_ (0.28 #4652, 0.25 #8703, 0.24 #3751), 0np9r (0.28 #4652, 0.20 #622, 0.18 #322), 09jwl (0.16 #3921, 0.16 #1070, 0.16 #3020), 0cbd2 (0.15 #6009, 0.14 #7959, 0.14 #6459) >> Best rule #465 for best value: >> intensional similarity = 2 >> extensional distance = 721 >> proper extension: 01vw87c; 02nb2s; 0lzb8; 03ds3; 04hpck; 01j4ls; 05sq84; 01nczg; 01bpc9; 0b_fw; ... >> query: (?x2076, 02hrh1q) <- award(?x2076, ?x1670), actor(?x3822, ?x2076) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06lgq8 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.878 http://example.org/people/person/profession #3407-02yw1c PRED entity: 02yw1c PRED relation: parent_genre PRED expected values: 0296y 0jrv_ => 43 concepts (36 used for prediction) PRED predicted values (max 10 best out of 209): 0xhtw (0.67 #827, 0.45 #1152, 0.40 #502), 06by7 (0.64 #2792, 0.57 #2954, 0.51 #3612), 0296y (0.40 #383, 0.33 #58, 0.25 #220), 02yv6b (0.40 #553, 0.13 #878, 0.10 #1041), 05r6t (0.36 #3649, 0.25 #2005, 0.24 #1517), 0dl5d (0.32 #1154, 0.16 #1641, 0.15 #666), 03_d0 (0.28 #2290, 0.20 #334, 0.12 #4426), 05w3f (0.27 #1164, 0.20 #839, 0.20 #514), 01jwt (0.25 #207, 0.20 #370, 0.11 #1346), 01dqhq (0.25 #211, 0.20 #374, 0.08 #700) >> Best rule #827 for best value: >> intensional similarity = 8 >> extensional distance = 13 >> proper extension: 0dls3; 05r6t; 06cp5; 05jg58; 0xv2x; 0173b0; 04_sqm; 0b_6yv; >> query: (?x8230, 0xhtw) <- artists(?x8230, ?x9463), parent_genre(?x8230, ?x5436), artists(?x5436, ?x9841), artists(?x5436, ?x8012), artists(?x5436, ?x5437), ?x5437 = 027dpx, ?x9841 = 02ndj5, ?x8012 = 01wt4wc >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #383 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 05g7tj; *> query: (?x8230, 0296y) <- artists(?x8230, ?x9463), parent_genre(?x8230, ?x5436), ?x5436 = 0hdf8, artists(?x9248, ?x9463), artists(?x9248, ?x10091), artists(?x9248, ?x8308), ?x8308 = 04mx7s, ?x10091 = 048tgl *> conf = 0.40 ranks of expected_values: 3, 20 EVAL 02yw1c parent_genre 0jrv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 43.000 36.000 0.667 http://example.org/music/genre/parent_genre EVAL 02yw1c parent_genre 0296y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 43.000 36.000 0.667 http://example.org/music/genre/parent_genre #3406-0175rc PRED entity: 0175rc PRED relation: sport PRED expected values: 02vx4 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.93 #472, 0.88 #256, 0.88 #310), 0jm_ (0.25 #104, 0.25 #85, 0.23 #167), 03tmr (0.25 #92, 0.25 #1, 0.22 #129), 018jz (0.19 #286, 0.18 #376, 0.14 #574), 018w8 (0.16 #429, 0.15 #573, 0.15 #537), 09xp_ (0.07 #206, 0.05 #395, 0.02 #647), 039yzs (0.07 #648, 0.05 #558, 0.05 #576), 0z74 (0.03 #343, 0.03 #379, 0.03 #388) >> Best rule #472 for best value: >> intensional similarity = 10 >> extensional distance = 52 >> proper extension: 03yl2t; 02rytm; 03_r_5; 035qlx; 02rqxc; 03yvgp; 03ytj1; 03zrc_; 03rrdb; 06l7jj; ... >> query: (?x11507, 02vx4) <- position(?x11507, ?x530), position(?x11507, ?x203), position(?x11507, ?x63), position(?x11507, ?x60), ?x63 = 02sdk9v, ?x203 = 0dgrmp, ?x60 = 02nzb8, teams(?x2611, ?x11507), ?x530 = 02_j1w, contains(?x2611, ?x7154) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0175rc sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.926 http://example.org/sports/sports_team/sport #3405-04qhdf PRED entity: 04qhdf PRED relation: company! PRED expected values: 0krdk => 212 concepts (212 used for prediction) PRED predicted values (max 10 best out of 34): 060c4 (0.82 #4467, 0.73 #5503, 0.71 #4737), 0krdk (0.79 #2529, 0.79 #1538, 0.76 #997), 05_wyz (0.55 #2404, 0.53 #1908, 0.52 #1367), 0dq3c (0.50 #4104, 0.49 #2524, 0.47 #2074), 09d6p2 (0.44 #1909, 0.44 #2135, 0.43 #1368), 01kr6k (0.33 #1557, 0.30 #1376, 0.30 #2278), 0142rn (0.33 #25, 0.21 #7045, 0.16 #3831), 02211by (0.23 #1264, 0.18 #2076, 0.18 #2121), 04192r (0.23 #1300, 0.17 #1390, 0.17 #1751), 014l7h (0.21 #7045, 0.16 #3831, 0.16 #4536) >> Best rule #4467 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 02w2bc; 01qgr3; 02q253; >> query: (?x2276, 060c4) <- company(?x1907, ?x2276), state_province_region(?x2276, ?x3038), company(?x1907, ?x5710), team(?x208, ?x5710) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #2529 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 51 *> proper extension: 045c7b; *> query: (?x2276, 0krdk) <- company(?x1907, ?x2276), state_province_region(?x2276, ?x3038), currency(?x2276, ?x170), company(?x1907, ?x9968), ?x9968 = 0k9ts *> conf = 0.79 ranks of expected_values: 2 EVAL 04qhdf company! 0krdk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 212.000 212.000 0.816 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #3404-01cj6y PRED entity: 01cj6y PRED relation: people! PRED expected values: 041rx => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 33): 0x67 (0.60 #10, 0.16 #164, 0.11 #1011), 041rx (0.13 #312, 0.13 #235, 0.12 #2391), 033tf_ (0.12 #84, 0.11 #469, 0.10 #315), 02w7gg (0.11 #79, 0.07 #618, 0.07 #772), 03bkbh (0.07 #109, 0.03 #340, 0.03 #494), 0xnvg (0.06 #475, 0.06 #937, 0.06 #90), 07bch9 (0.05 #254, 0.04 #485, 0.04 #331), 07hwkr (0.05 #320, 0.04 #474, 0.04 #89), 02ctzb (0.04 #92, 0.03 #246, 0.02 #323), 01qhm_ (0.04 #468, 0.04 #314, 0.03 #237) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02xbw2; 02lhm2; 059_gf; >> query: (?x4337, 0x67) <- award_winner(?x4337, ?x3054), film(?x4337, ?x8501), ?x8501 = 03wj4r8 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #312 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 550 *> proper extension: 01l1b90; 01vw87c; 0152cw; 01j4ls; 031zkw; 0f2df; 02p21g; 01wz3cx; 02fb1n; 0qf3p; ... *> query: (?x4337, 041rx) <- award(?x4337, ?x591), participant(?x1634, ?x4337) *> conf = 0.13 ranks of expected_values: 2 EVAL 01cj6y people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 78.000 0.600 http://example.org/people/ethnicity/people #3403-071fb PRED entity: 071fb PRED relation: languages_spoken! PRED expected values: 078vc => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 71): 07hwkr (0.56 #228, 0.50 #300, 0.48 #876), 03w9bjf (0.50 #409, 0.35 #625, 0.33 #49), 04gfy7 (0.43 #420, 0.29 #636, 0.21 #996), 059_w (0.33 #243, 0.33 #27, 0.25 #315), 078vc (0.33 #43, 0.29 #403, 0.25 #187), 02vsw1 (0.33 #262, 0.25 #334, 0.25 #118), 071x0k (0.33 #8, 0.25 #152, 0.25 #80), 04czx7 (0.33 #69, 0.25 #213, 0.25 #141), 0bbz66j (0.33 #45, 0.25 #189, 0.25 #117), 0bhsnb (0.33 #71, 0.25 #215, 0.25 #143) >> Best rule #228 for best value: >> intensional similarity = 14 >> extensional distance = 7 >> proper extension: 02bjrlw; 06nm1; 07c9s; 064_8sq; 06b_j; >> query: (?x5003, 07hwkr) <- countries_spoken_in(?x5003, ?x910), language(?x6181, ?x5003), film(?x6239, ?x6181), genre(?x6181, ?x811), ?x811 = 03k9fj, nominated_for(?x5348, ?x6181), film_release_region(?x6181, ?x1892), film_release_region(?x6181, ?x789), film_release_region(?x6181, ?x151), ?x789 = 0f8l9c, ?x1892 = 02vzc, ?x151 = 0b90_r, country(?x6181, ?x512), nominated_for(?x198, ?x6181) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x5003, 078vc) <- countries_spoken_in(?x5003, ?x4752), countries_spoken_in(?x5003, ?x2804), language(?x6181, ?x5003), language(?x5002, ?x5003), language(?x4089, ?x5003), language(?x3559, ?x5003), ?x6181 = 0hv27, ?x5002 = 03tn80, ?x4089 = 02kfzz, ?x3559 = 02xtxw, olympics(?x4752, ?x2966), administrative_area_type(?x4752, ?x2792), form_of_government(?x4752, ?x48), languages(?x1515, ?x5003), adjoins(?x5457, ?x2804), ?x1515 = 07f3xb, taxonomy(?x4752, ?x939) *> conf = 0.33 ranks of expected_values: 5 EVAL 071fb languages_spoken! 078vc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 28.000 28.000 0.556 http://example.org/people/ethnicity/languages_spoken #3402-04b_jc PRED entity: 04b_jc PRED relation: nominated_for! PRED expected values: 02z0dfh => 62 concepts (60 used for prediction) PRED predicted values (max 10 best out of 221): 02z0dfh (0.66 #5128, 0.66 #5831, 0.66 #5363), 02ppm4q (0.40 #113, 0.29 #579, 0.24 #346), 027dtxw (0.40 #3, 0.21 #702, 0.19 #236), 09sdmz (0.40 #140, 0.21 #839, 0.14 #373), 099jhq (0.40 #17, 0.18 #716, 0.14 #250), 099c8n (0.29 #753, 0.26 #1220, 0.24 #1453), 0gqwc (0.29 #291, 0.25 #757, 0.23 #5830), 094qd5 (0.29 #267, 0.23 #5830, 0.21 #733), 0gq9h (0.28 #5188, 0.27 #4954, 0.27 #5423), 02x4x18 (0.26 #933, 0.23 #5830, 0.20 #11202) >> Best rule #5128 for best value: >> intensional similarity = 3 >> extensional distance = 890 >> proper extension: 01jc6q; 02vp1f_; 0n0bp; 02x3lt7; 04969y; 03cvwkr; 03m4mj; 01vfqh; 0283_zv; 0bm2g; ... >> query: (?x10732, ?x68) <- nominated_for(?x6977, ?x10732), award_nominee(?x6977, ?x91), award(?x10732, ?x68) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04b_jc nominated_for! 02z0dfh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 60.000 0.663 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3401-035hm PRED entity: 035hm PRED relation: countries_spoken_in! PRED expected values: 06nm1 => 214 concepts (214 used for prediction) PRED predicted values (max 10 best out of 57): 064_8sq (0.38 #1062, 0.33 #1007, 0.33 #512), 06nm1 (0.36 #722, 0.27 #2427, 0.24 #4957), 03k50 (0.33 #666, 0.33 #61, 0.18 #721), 02hxcvy (0.33 #85, 0.22 #690, 0.17 #965), 0121sr (0.33 #97, 0.22 #702, 0.17 #977), 09s02 (0.33 #100, 0.22 #705, 0.17 #980), 07c9s (0.33 #69, 0.17 #894, 0.15 #1114), 0688f (0.33 #90, 0.17 #915, 0.15 #1135), 0999q (0.33 #81, 0.14 #576, 0.11 #686), 09bnf (0.33 #110, 0.14 #605, 0.11 #715) >> Best rule #1062 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 052gtg; >> query: (?x9283, 064_8sq) <- adjoins(?x2152, ?x9283), contains(?x455, ?x9283), country(?x3897, ?x2152), ?x3897 = 02dpl9, film_release_region(?x66, ?x2152) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #722 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 027rn; 03_3d; 0162v; *> query: (?x9283, 06nm1) <- form_of_government(?x9283, ?x1926), countries_spoken_in(?x254, ?x9283), contains(?x455, ?x9283), location_of_ceremony(?x2799, ?x9283) *> conf = 0.36 ranks of expected_values: 2 EVAL 035hm countries_spoken_in! 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 214.000 214.000 0.385 http://example.org/language/human_language/countries_spoken_in #3400-02jxmr PRED entity: 02jxmr PRED relation: award_winner! PRED expected values: 0466p0j => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 133): 02rjjll (0.19 #146, 0.15 #1979, 0.12 #2825), 04n2r9h (0.17 #45, 0.03 #186, 0.02 #327), 03gwpw2 (0.17 #9, 0.02 #4803, 0.02 #5508), 0drtv8 (0.17 #66, 0.02 #2463, 0.02 #1758), 013b2h (0.15 #2054, 0.14 #2900, 0.10 #3464), 02cg41 (0.12 #2100, 0.10 #2946, 0.09 #267), 0466p0j (0.11 #2050, 0.10 #2896, 0.08 #3460), 01bx35 (0.11 #1981, 0.09 #2827, 0.07 #3391), 05pd94v (0.10 #1976, 0.10 #2822, 0.08 #3245), 01c6qp (0.10 #1993, 0.09 #442, 0.09 #160) >> Best rule #146 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0c9d9; >> query: (?x4428, 02rjjll) <- profession(?x4428, ?x131), role(?x4428, ?x228), spouse(?x4428, ?x7617) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #2050 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 197 *> proper extension: 0lzkm; 0f6lx; *> query: (?x4428, 0466p0j) <- profession(?x4428, ?x131), role(?x4428, ?x228), award_winner(?x3069, ?x4428) *> conf = 0.11 ranks of expected_values: 7 EVAL 02jxmr award_winner! 0466p0j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 111.000 111.000 0.188 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3399-0gdh5 PRED entity: 0gdh5 PRED relation: religion PRED expected values: 092bf5 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 22): 0c8wxp (0.23 #96, 0.19 #1761, 0.18 #231), 03_gx (0.16 #194, 0.08 #284, 0.08 #509), 01lp8 (0.15 #91, 0.04 #361, 0.03 #946), 0kpl (0.13 #505, 0.09 #325, 0.05 #190), 092bf5 (0.08 #106, 0.04 #376, 0.03 #151), 03j6c (0.05 #291, 0.04 #2091, 0.02 #2586), 019cr (0.04 #101, 0.03 #461, 0.03 #146), 0v53x (0.04 #119, 0.02 #479, 0.01 #524), 06nzl (0.04 #105, 0.02 #420, 0.02 #1095), 0flw86 (0.04 #902, 0.03 #1217, 0.03 #1127) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0147dk; 03f2_rc; 01vrz41; 012x4t; 0j1yf; 01vs_v8; 01pgzn_; 01w02sy; 02wb6yq; 0gy6z9; ... >> query: (?x2796, 0c8wxp) <- nominated_for(?x2796, ?x83), artists(?x302, ?x2796), friend(?x2796, ?x7056) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #106 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0147dk; 03f2_rc; 01vrz41; 012x4t; 0j1yf; 01vs_v8; 01pgzn_; 01w02sy; 02wb6yq; 0gy6z9; ... *> query: (?x2796, 092bf5) <- nominated_for(?x2796, ?x83), artists(?x302, ?x2796), friend(?x2796, ?x7056) *> conf = 0.08 ranks of expected_values: 5 EVAL 0gdh5 religion 092bf5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 116.000 116.000 0.231 http://example.org/people/person/religion #3398-0d6lp PRED entity: 0d6lp PRED relation: mode_of_transportation PRED expected values: 025t3bg => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 3): 025t3bg (0.78 #124, 0.77 #151, 0.77 #133), 0k4j (0.06 #86, 0.04 #188, 0.04 #68), 06d_3 (0.04 #189, 0.02 #153, 0.02 #171) >> Best rule #124 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0fhp9; 02h6_6p; 03hrz; 049d1; 0ply0; 06mxs; 0f2rq; 02sn34; 0f04v; 01lfy; ... >> query: (?x3125, 025t3bg) <- month(?x3125, ?x1459), place_of_birth(?x399, ?x3125), location(?x1802, ?x3125) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d6lp mode_of_transportation 025t3bg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.783 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #3397-084m3 PRED entity: 084m3 PRED relation: award PRED expected values: 0cqhk0 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 287): 0gs9p (0.43 #2456, 0.37 #3647, 0.36 #2059), 019f4v (0.43 #2444, 0.34 #62, 0.33 #3635), 040njc (0.38 #2389, 0.32 #1992, 0.32 #3580), 02pqp12 (0.36 #66, 0.28 #2448, 0.23 #2051), 0gq9h (0.34 #73, 0.30 #2455, 0.28 #2058), 09sb52 (0.33 #12741, 0.32 #19887, 0.32 #4404), 0gr51 (0.27 #94, 0.26 #2079, 0.26 #2476), 04dn09n (0.27 #40, 0.24 #2025, 0.22 #2422), 05pcn59 (0.25 #76, 0.23 #4443, 0.21 #6031), 0gr4k (0.23 #2015, 0.23 #2412, 0.19 #3603) >> Best rule #2456 for best value: >> intensional similarity = 3 >> extensional distance = 205 >> proper extension: 0jf1b; 022_lg; 01q4qv; 012vct; 06b_0; 02404v; >> query: (?x7489, 0gs9p) <- film(?x7489, ?x8971), award_winner(?x435, ?x7489), profession(?x7489, ?x319) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #431 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 0d02km; 0154d7; 01rzxl; *> query: (?x7489, 0cqhk0) <- student(?x481, ?x7489), actor(?x3169, ?x7489), currency(?x7489, ?x170) *> conf = 0.23 ranks of expected_values: 11 EVAL 084m3 award 0cqhk0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 111.000 111.000 0.430 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3396-06mzp PRED entity: 06mzp PRED relation: film_release_region! PRED expected values: 02x3lt7 07g_0c 017gm7 035yn8 02yvct 0gyfp9c 02dpl9 08tq4x 02rmd_2 0dzlbx 0hv8w 03yvf2 064lsn 043tvp3 05zvzf3 0ccck7 => 209 concepts (143 used for prediction) PRED predicted values (max 10 best out of 1175): 06ztvyx (0.81 #49327, 0.66 #64514, 0.66 #42319), 017gm7 (0.80 #30504, 0.79 #49193, 0.76 #38680), 0dzlbx (0.79 #49613, 0.76 #11066, 0.74 #39100), 043tvp3 (0.79 #49857, 0.76 #65044, 0.76 #39344), 062zm5h (0.79 #49618, 0.73 #42610, 0.71 #64805), 0872p_c (0.77 #49172, 0.71 #38659, 0.71 #10625), 0bh8yn3 (0.77 #49223, 0.66 #42215, 0.64 #38710), 07f_7h (0.76 #10773, 0.67 #49320, 0.60 #9605), 0661m4p (0.75 #49296, 0.73 #30607, 0.71 #38783), 0gkz15s (0.75 #49135, 0.71 #64322, 0.69 #38622) >> Best rule #49327 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 01ly5m; >> query: (?x774, 06ztvyx) <- film_release_region(?x2655, ?x774), film_release_region(?x1012, ?x774), ?x1012 = 0bwfwpj, film(?x1641, ?x2655) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #30504 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 025r_t; *> query: (?x774, 017gm7) <- adjoins(?x774, ?x205), service_location(?x896, ?x774), teams(?x774, ?x11564) *> conf = 0.80 ranks of expected_values: 2, 3, 4, 38, 53, 70, 73, 74, 86, 89, 109, 145, 159, 184, 231, 271 EVAL 06mzp film_release_region! 0ccck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 043tvp3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 064lsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 03yvf2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 0hv8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 0dzlbx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 02rmd_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 08tq4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 02dpl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 0gyfp9c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 02yvct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 035yn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 017gm7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 07g_0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mzp film_release_region! 02x3lt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 209.000 143.000 0.812 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3395-0jzc PRED entity: 0jzc PRED relation: major_field_of_study! PRED expected values: 07x4c => 80 concepts (61 used for prediction) PRED predicted values (max 10 best out of 616): 03ksy (0.75 #6056, 0.71 #5462, 0.64 #13784), 01w5m (0.71 #5461, 0.64 #13188, 0.62 #6055), 09f2j (0.57 #5522, 0.56 #22152, 0.50 #25715), 08815 (0.57 #5343, 0.50 #13070, 0.50 #5937), 0bwfn (0.57 #5645, 0.50 #6239, 0.44 #22275), 07tgn (0.57 #5359, 0.50 #5953, 0.41 #11881), 07wrz (0.57 #5408, 0.50 #6002, 0.40 #6597), 01k2wn (0.57 #5366, 0.50 #5960, 0.40 #6555), 01bm_ (0.57 #5621, 0.50 #6215, 0.40 #6810), 06pwq (0.54 #21984, 0.46 #13081, 0.46 #29705) >> Best rule #6056 for best value: >> intensional similarity = 9 >> extensional distance = 6 >> proper extension: 01400v; >> query: (?x5359, 03ksy) <- major_field_of_study(?x6364, ?x5359), ?x6364 = 05qt0, major_field_of_study(?x1771, ?x5359), major_field_of_study(?x1200, ?x5359), major_field_of_study(?x5359, ?x14397), ?x1771 = 019v9k, major_field_of_study(?x10240, ?x5359), institution(?x1200, ?x9691), ?x9691 = 0g8fs >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #13663 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 26 *> proper extension: 0l14j_; *> query: (?x5359, ?x2497) <- major_field_of_study(?x10240, ?x5359), category(?x10240, ?x134), contains(?x1310, ?x10240), contains(?x362, ?x10240), ?x362 = 04jpl, major_field_of_study(?x10240, ?x12035), ?x1310 = 02jx1, major_field_of_study(?x2497, ?x12035) *> conf = 0.33 ranks of expected_values: 170 EVAL 0jzc major_field_of_study! 07x4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 80.000 61.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3394-027fwmt PRED entity: 027fwmt PRED relation: story_by PRED expected values: 04mby => 68 concepts (42 used for prediction) PRED predicted values (max 10 best out of 54): 01p6xx (0.12 #3697, 0.10 #4136, 0.10 #3262), 0fx02 (0.06 #713, 0.02 #1799, 0.01 #2452), 01_vfy (0.04 #3914, 0.04 #434), 079vf (0.04 #436, 0.02 #655, 0.01 #1959), 0343h (0.04 #1975, 0.03 #2410, 0.02 #2191), 042xh (0.03 #649, 0.02 #2172, 0.01 #2607), 079ws (0.03 #565, 0.01 #2088, 0.01 #784), 056wb (0.03 #323, 0.01 #2498, 0.01 #1194), 081k8 (0.02 #1392, 0.02 #1609, 0.02 #2913), 04jspq (0.02 #550, 0.02 #2073) >> Best rule #3697 for best value: >> intensional similarity = 4 >> extensional distance = 440 >> proper extension: 0ds35l9; 0m313; 02y_lrp; 0g22z; 01br2w; 0140g4; 0b2v79; 09m6kg; 011yrp; 011yxg; ... >> query: (?x9800, ?x8928) <- genre(?x9800, ?x307), nominated_for(?x484, ?x9800), film(?x1104, ?x9800), written_by(?x9800, ?x8928) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027fwmt story_by 04mby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 42.000 0.116 http://example.org/film/film/story_by #3393-030b93 PRED entity: 030b93 PRED relation: nationality PRED expected values: 09c7w0 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 67): 09c7w0 (0.86 #1201, 0.86 #2201, 0.77 #2001), 02jx1 (0.11 #433, 0.10 #533, 0.10 #3833), 07ssc (0.10 #115, 0.08 #4315, 0.08 #1415), 0f8l9c (0.10 #122, 0.03 #322, 0.02 #822), 03rt9 (0.10 #113, 0.02 #8504, 0.02 #1313), 06q1r (0.10 #177, 0.02 #8504, 0.01 #2677), 03rk0 (0.09 #846, 0.07 #1546, 0.07 #1746), 0d060g (0.06 #507, 0.05 #607, 0.05 #1607), 0d0vqn (0.03 #409, 0.02 #209, 0.02 #8504), 0d05w3 (0.02 #850, 0.02 #1550, 0.02 #1750) >> Best rule #1201 for best value: >> intensional similarity = 3 >> extensional distance = 567 >> proper extension: 03c_8t; >> query: (?x7132, 09c7w0) <- student(?x6953, ?x7132), school(?x580, ?x6953), school(?x465, ?x6953) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030b93 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.863 http://example.org/people/person/nationality #3392-0n6f8 PRED entity: 0n6f8 PRED relation: location PRED expected values: 05jbn 0bp_7 => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 321): 0f2tj (0.70 #120493, 0.53 #73897, 0.52 #74701), 030qb3t (0.33 #886, 0.33 #15345, 0.29 #29000), 02_286 (0.22 #6461, 0.22 #5658, 0.21 #10476), 0f2wj (0.20 #1640, 0.09 #3246, 0.07 #5655), 01_d4 (0.18 #3314, 0.10 #1708, 0.08 #6526), 05fjf (0.18 #2740, 0.03 #4346, 0.03 #5149), 0cc56 (0.13 #8890, 0.11 #10496, 0.10 #1663), 01n7q (0.11 #8896, 0.11 #4881, 0.09 #3275), 04jpl (0.10 #1623, 0.09 #3229, 0.06 #57851), 05qtj (0.10 #1846, 0.03 #35583, 0.03 #20082) >> Best rule #120493 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 01pbxb; 05g8ky; 0fp_v1x; 0274ck; 025xt8y; 03f5spx; 04l3_z; 0k4gf; 045bg; 028p0; ... >> query: (?x1299, ?x6769) <- place_of_birth(?x1299, ?x6769), location(?x1299, ?x3908) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #5070 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 012x4t; *> query: (?x1299, 05jbn) <- award_winner(?x861, ?x1299), currency(?x1299, ?x170), friend(?x1299, ?x4536) *> conf = 0.08 ranks of expected_values: 29 EVAL 0n6f8 location 0bp_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 175.000 175.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0n6f8 location 05jbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 175.000 175.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #3391-09wnnb PRED entity: 09wnnb PRED relation: genre PRED expected values: 02n4kr 01jfsb => 81 concepts (61 used for prediction) PRED predicted values (max 10 best out of 91): 07s9rl0 (0.74 #6966, 0.65 #3842, 0.64 #361), 01jfsb (0.57 #492, 0.55 #1212, 0.49 #1812), 02l7c8 (0.39 #376, 0.31 #976, 0.28 #4577), 05p553 (0.36 #1324, 0.35 #3965, 0.35 #4205), 06n90 (0.36 #493, 0.32 #1213, 0.28 #1813), 01hmnh (0.28 #618, 0.24 #2418, 0.22 #1938), 0lsxr (0.27 #9, 0.24 #489, 0.23 #729), 02n4kr (0.26 #128, 0.16 #728, 0.14 #2168), 0hcr (0.23 #2424, 0.10 #1944, 0.10 #624), 04xvlr (0.20 #3843, 0.19 #4563, 0.18 #2042) >> Best rule #6966 for best value: >> intensional similarity = 5 >> extensional distance = 1365 >> proper extension: 06n90; >> query: (?x10130, 07s9rl0) <- genre(?x10130, ?x225), genre(?x6334, ?x225), genre(?x869, ?x225), ?x869 = 02z9hqn, ?x6334 = 0kvbl6 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #492 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 134 *> proper extension: 0d90m; 06w99h3; 04fzfj; 02pjc1h; 0m9p3; 02ryz24; 05q54f5; 02fqrf; 02rq8k8; 06sfk6; ... *> query: (?x10130, 01jfsb) <- genre(?x10130, ?x225), nominated_for(?x102, ?x10130), featured_film_locations(?x10130, ?x362), ?x225 = 02kdv5l *> conf = 0.57 ranks of expected_values: 2, 8 EVAL 09wnnb genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 61.000 0.736 http://example.org/film/film/genre EVAL 09wnnb genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 81.000 61.000 0.736 http://example.org/film/film/genre #3390-04vt98 PRED entity: 04vt98 PRED relation: profession PRED expected values: 02krf9 => 120 concepts (93 used for prediction) PRED predicted values (max 10 best out of 62): 01d_h8 (0.78 #1182, 0.77 #1476, 0.76 #2211), 02hrh1q (0.72 #895, 0.70 #5746, 0.69 #8100), 0cbd2 (0.39 #7063, 0.22 #5152, 0.20 #3829), 03gjzk (0.39 #9718, 0.37 #5159, 0.37 #3836), 02pjxr (0.25 #33, 0.03 #474, 0.03 #621), 02krf9 (0.24 #2231, 0.23 #1496, 0.22 #2525), 09jwl (0.22 #753, 0.20 #1341, 0.19 #9722), 0dz3r (0.20 #9706, 0.10 #1031, 0.10 #11617), 018gz8 (0.17 #5161, 0.17 #11043, 0.15 #3838), 0kyk (0.17 #7085, 0.15 #2381, 0.14 #3263) >> Best rule #1182 for best value: >> intensional similarity = 4 >> extensional distance = 185 >> proper extension: 042rnl; 058kqy; 052gzr; 05whq_9; 01vy_v8; 01twdk; 054bt3; 0gv2r; 02mz_6; 081l_; ... >> query: (?x9296, 01d_h8) <- award_winner(?x350, ?x9296), gender(?x9296, ?x231), film(?x9296, ?x8217), profession(?x9296, ?x524) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2231 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 227 *> proper extension: 04l3_z; 021lby; 0cm89v; 013zyw; 01z7s_; 01vz80y; 01wk51; 0jpdn; 05bht9; 0dh1n_; ... *> query: (?x9296, 02krf9) <- film(?x9296, ?x8217), nationality(?x9296, ?x512), gender(?x9296, ?x231) *> conf = 0.24 ranks of expected_values: 6 EVAL 04vt98 profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 120.000 93.000 0.781 http://example.org/people/person/profession #3389-02rg_4 PRED entity: 02rg_4 PRED relation: school_type PRED expected values: 04qbv => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 22): 05jxkf (0.52 #201, 0.50 #355, 0.50 #1323), 07tf8 (0.23 #271, 0.19 #359, 0.16 #161), 01_9fk (0.21 #529, 0.21 #551, 0.16 #265), 01_srz (0.16 #112, 0.13 #134, 0.12 #288), 01y64 (0.07 #32, 0.05 #318, 0.04 #230), 06cs1 (0.06 #136, 0.05 #290, 0.04 #224), 04qbv (0.06 #322, 0.03 #520, 0.03 #2421), 04399 (0.04 #122, 0.04 #144, 0.04 #342), 02p0qmm (0.04 #954, 0.04 #844, 0.04 #1064), 0bwd5 (0.04 #325, 0.03 #2421, 0.01 #941) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 59 >> proper extension: 06rjp; >> query: (?x4293, 05jxkf) <- major_field_of_study(?x4293, ?x5179), ?x5179 = 04gb7, contains(?x94, ?x4293) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #322 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 82 *> proper extension: 02_2kg; *> query: (?x4293, 04qbv) <- state_province_region(?x4293, ?x3670), school_type(?x4293, ?x3205), currency(?x4293, ?x170), ?x3205 = 01rs41 *> conf = 0.06 ranks of expected_values: 7 EVAL 02rg_4 school_type 04qbv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 127.000 127.000 0.525 http://example.org/education/educational_institution/school_type #3388-03spz PRED entity: 03spz PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 213 concepts (213 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.89 #18, 0.89 #85, 0.88 #43) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 047lj; >> query: (?x4743, 0hzc9wc) <- film_release_region(?x8646, ?x4743), film_release_region(?x1035, ?x4743), ?x1035 = 08hmch, ?x8646 = 05zvzf3 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03spz administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 213.000 213.000 0.893 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #3387-0xqf3 PRED entity: 0xqf3 PRED relation: location! PRED expected values: 0gd9k => 98 concepts (28 used for prediction) PRED predicted values (max 10 best out of 846): 0fpjyd (0.64 #7553, 0.54 #5035, 0.52 #25177), 019z7q (0.33 #143, 0.11 #2660, 0.03 #7696), 01l47f5 (0.33 #1314, 0.11 #3831, 0.03 #8867), 0gcs9 (0.33 #570, 0.11 #3087, 0.03 #8123), 0lrh (0.13 #5583, 0.11 #3065, 0.06 #35246), 0br1w (0.13 #5769, 0.11 #3251, 0.04 #10804), 0ddkf (0.11 #3902, 0.07 #6420, 0.06 #35246), 0m66w (0.11 #3723, 0.07 #6241, 0.06 #35246), 02wr6r (0.11 #4504, 0.07 #7022, 0.04 #14575), 01pj5q (0.11 #4065, 0.07 #6583, 0.03 #9101) >> Best rule #7553 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 0xrzh; >> query: (?x8944, ?x6907) <- contains(?x6895, ?x8944), ?x6895 = 05fjf, place_of_birth(?x6907, ?x8944), gender(?x6907, ?x231), student(?x1513, ?x6907) >> conf = 0.64 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0xqf3 location! 0gd9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 28.000 0.643 http://example.org/people/person/places_lived./people/place_lived/location #3386-09wv__ PRED entity: 09wv__ PRED relation: student PRED expected values: 0gl88b => 112 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1307): 0ff3y (0.15 #10407, 0.08 #29182, 0.06 #2063), 0306ds (0.12 #405, 0.09 #4577, 0.06 #23352), 015v3r (0.12 #496, 0.05 #4668, 0.04 #23443), 0c9xjl (0.12 #946, 0.05 #5118, 0.04 #23893), 013pp3 (0.12 #920, 0.04 #23867, 0.04 #30125), 049gc (0.12 #923, 0.04 #34301, 0.03 #44732), 037lyl (0.12 #2744, 0.09 #4830, 0.09 #6916), 02vntj (0.09 #4872, 0.09 #11130, 0.09 #6958), 015wc0 (0.09 #5862, 0.06 #1690, 0.06 #12120), 01l1rw (0.09 #5167, 0.06 #995, 0.06 #11425) >> Best rule #10407 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 0yjf0; 07tg4; 0gjv_; 0lbfv; 0gl6x; 026m3y; 0ymf1; >> query: (?x5238, 0ff3y) <- student(?x5238, ?x9519), award(?x9519, ?x3263), story_by(?x1644, ?x9519), profession(?x9519, ?x319), people(?x1158, ?x9519) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09wv__ student 0gl88b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 32.000 0.147 http://example.org/education/educational_institution/students_graduates./education/education/student #3385-02rv_dz PRED entity: 02rv_dz PRED relation: honored_for! PRED expected values: 03gt46z => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 94): 09gkdln (0.08 #2642, 0.05 #1544, 0.05 #1184), 09k5jh7 (0.08 #2642, 0.04 #70, 0.03 #310), 04n2r9h (0.08 #2642, 0.04 #1475, 0.04 #2075), 05zksls (0.08 #2642, 0.03 #1467, 0.03 #2067), 09bymc (0.08 #2642, 0.03 #343, 0.03 #1663), 09v0p2c (0.08 #2642, 0.02 #69, 0.02 #309), 0466p0j (0.08 #2642), 05c1t6z (0.05 #1450, 0.05 #2170, 0.05 #2050), 02q690_ (0.05 #1493, 0.05 #2213, 0.05 #1853), 0275n3y (0.04 #1503, 0.04 #2223, 0.04 #1863) >> Best rule #2642 for best value: >> intensional similarity = 5 >> extensional distance = 747 >> proper extension: 0n2bh; 01h1bf; 03y3bp7; 02kk_c; 05gnf; 05fgr_; 05sy0cv; 025x1t; 0gxsh4; 06ys2; >> query: (?x1531, ?x2220) <- nominated_for(?x6279, ?x1531), nominated_for(?x5351, ?x1531), produced_by(?x8158, ?x6279), award(?x5351, ?x198), award_winner(?x2220, ?x5351) >> conf = 0.08 => this is the best rule for 7 predicted values No rule for expected values ranks of expected_values: EVAL 02rv_dz honored_for! 03gt46z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 59.000 0.080 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3384-0c4ys PRED entity: 0c4ys PRED relation: category_of! PRED expected values: 02gx2k 025m8l 025mb9 0248jb 02v703 02fm4d 024_dt => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 237): 047sgz4 (0.33 #28, 0.25 #390, 0.17 #536), 09qvc0 (0.33 #10, 0.25 #372, 0.17 #518), 0gkvb7 (0.33 #6, 0.25 #368, 0.17 #514), 0cjcbg (0.33 #63, 0.25 #425, 0.17 #571), 09qrn4 (0.33 #40, 0.25 #402, 0.17 #548), 0fc9js (0.33 #35, 0.25 #397, 0.17 #543), 0bdwqv (0.33 #31, 0.25 #393, 0.17 #539), 0gkts9 (0.33 #30, 0.25 #392, 0.17 #538), 0bdwft (0.33 #16, 0.25 #378, 0.17 #524), 0bp_b2 (0.33 #3, 0.25 #365, 0.17 #511) >> Best rule #28 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 0gcf2r; >> query: (?x2421, 047sgz4) <- category_of(?x4837, ?x2421), category_of(?x2703, ?x2421), instance_of_recurring_event(?x9431, ?x2421), award(?x5760, ?x4837), award_winner(?x2703, ?x4184), award_winner(?x3391, ?x5760), category(?x2421, ?x134), artists(?x474, ?x5760), award_winner(?x9431, ?x248) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #436 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 01cd7p; *> query: (?x2421, ?x1443) <- category_of(?x1854, ?x2421), award_winner(?x1854, ?x7536), award_winner(?x1854, ?x5251), award_winner(?x1854, ?x1656), award_nominee(?x1974, ?x5251), category(?x2421, ?x134), award(?x1656, ?x1443), student(?x9479, ?x7536), nationality(?x1656, ?x6401), profession(?x1656, ?x131) *> conf = 0.14 ranks of expected_values: 59, 62, 64, 66, 76, 98, 103 EVAL 0c4ys category_of! 024_dt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 18.000 18.000 0.333 http://example.org/award/award_category/category_of EVAL 0c4ys category_of! 02fm4d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 18.000 18.000 0.333 http://example.org/award/award_category/category_of EVAL 0c4ys category_of! 02v703 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 18.000 18.000 0.333 http://example.org/award/award_category/category_of EVAL 0c4ys category_of! 0248jb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 18.000 18.000 0.333 http://example.org/award/award_category/category_of EVAL 0c4ys category_of! 025mb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 18.000 18.000 0.333 http://example.org/award/award_category/category_of EVAL 0c4ys category_of! 025m8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 18.000 18.000 0.333 http://example.org/award/award_category/category_of EVAL 0c4ys category_of! 02gx2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 18.000 18.000 0.333 http://example.org/award/award_category/category_of #3383-05t0_2v PRED entity: 05t0_2v PRED relation: production_companies PRED expected values: 054lpb6 => 72 concepts (55 used for prediction) PRED predicted values (max 10 best out of 79): 016tw3 (0.40 #1429, 0.39 #1261, 0.39 #584), 086k8 (0.33 #2, 0.15 #502, 0.09 #1937), 05rrtf (0.18 #224, 0.15 #308, 0.11 #391), 054lpb6 (0.18 #98, 0.14 #432, 0.12 #856), 017s11 (0.18 #86, 0.11 #420, 0.07 #761), 016tt2 (0.09 #87, 0.09 #1096, 0.08 #589), 024rgt (0.09 #108, 0.08 #442, 0.07 #783), 01795t (0.09 #188, 0.08 #272, 0.06 #1031), 046b0s (0.09 #190, 0.08 #274, 0.05 #357), 09b3v (0.09 #199, 0.08 #283, 0.05 #366) >> Best rule #1429 for best value: >> intensional similarity = 5 >> extensional distance = 339 >> proper extension: 04bp0l; >> query: (?x5945, ?x1104) <- nominated_for(?x1104, ?x5945), film(?x1104, ?x2746), film(?x1104, ?x394), film_release_region(?x2746, ?x87), film(?x395, ?x394) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #98 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 0bvn25; 0661m4p; 02ryz24; 02_sr1; 0fz3b1; 04gv3db; 027j9wd; 065_cjc; 02825nf; *> query: (?x5945, 054lpb6) <- genre(?x5945, ?x225), film_crew_role(?x5945, ?x137), film(?x4465, ?x5945), country(?x5945, ?x512), ?x4465 = 086nl7 *> conf = 0.18 ranks of expected_values: 4 EVAL 05t0_2v production_companies 054lpb6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 55.000 0.400 http://example.org/film/film/production_companies #3382-05d7rk PRED entity: 05d7rk PRED relation: student! PRED expected values: 02vnp2 => 118 concepts (57 used for prediction) PRED predicted values (max 10 best out of 121): 04rkkv (0.25 #305, 0.01 #6080, 0.01 #9756), 03w1lf (0.19 #874, 0.10 #1924, 0.09 #1399), 07tg4 (0.14 #2709, 0.08 #3759, 0.07 #4809), 0bwfn (0.13 #17601, 0.13 #18126, 0.11 #22329), 088gzp (0.12 #1036, 0.09 #1561, 0.08 #2086), 02hwww (0.12 #964, 0.03 #2539, 0.02 #3589), 02kxx1 (0.12 #981, 0.02 #1506, 0.02 #3606), 07tgn (0.12 #2642, 0.06 #3692, 0.06 #4742), 03ksy (0.09 #17432, 0.07 #25310, 0.06 #629), 065y4w7 (0.08 #17342, 0.07 #25220, 0.05 #28371) >> Best rule #305 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0gr36; >> query: (?x111, 04rkkv) <- film(?x111, ?x5519), ?x5519 = 09p3_s, nationality(?x111, ?x512), award_winner(?x4687, ?x111) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05d7rk student! 02vnp2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 57.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #3381-04fhn_ PRED entity: 04fhn_ PRED relation: location PRED expected values: 0sngf => 129 concepts (121 used for prediction) PRED predicted values (max 10 best out of 103): 02_286 (0.35 #5660, 0.30 #2446, 0.28 #12892), 0y1rf (0.33 #550, 0.07 #3762), 04jpl (0.20 #820, 0.14 #3229, 0.06 #81168), 0rh6k (0.20 #807, 0.14 #3216, 0.06 #6431), 013n2h (0.20 #1209, 0.07 #3618), 06y57 (0.20 #1058, 0.07 #3467), 05jbn (0.20 #1055, 0.07 #3464), 030qb3t (0.14 #33823, 0.13 #20166, 0.13 #37838), 0cc56 (0.10 #2466, 0.07 #12912, 0.06 #6484), 0gkgp (0.10 #2866, 0.06 #23297, 0.05 #56239) >> Best rule #5660 for best value: >> intensional similarity = 4 >> extensional distance = 61 >> proper extension: 09dt7; 011zf2; 0fpjd_g; 0lrh; 01f8ld; 015mrk; 085pr; 03ys2f; 0fd_1; 03rx9; ... >> query: (?x3952, 02_286) <- nationality(?x3952, ?x94), gender(?x3952, ?x231), student(?x3424, ?x3952), ?x3424 = 01w5m >> conf = 0.35 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04fhn_ location 0sngf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 121.000 0.349 http://example.org/people/person/places_lived./people/place_lived/location #3380-03j24kf PRED entity: 03j24kf PRED relation: artists! PRED expected values: 0xhtw => 121 concepts (70 used for prediction) PRED predicted values (max 10 best out of 261): 016clz (0.34 #3071, 0.27 #10734, 0.26 #4604), 06j6l (0.33 #11385, 0.29 #6789, 0.29 #1268), 025sc50 (0.32 #6791, 0.31 #11387, 0.23 #13225), 0glt670 (0.32 #6782, 0.23 #13216, 0.21 #16902), 0155w (0.32 #408, 0.31 #714, 0.28 #1020), 0xhtw (0.32 #2159, 0.31 #8904, 0.28 #319), 03_d0 (0.31 #621, 0.28 #927, 0.27 #1847), 02lnbg (0.30 #6800, 0.23 #11396, 0.13 #12008), 0gywn (0.29 #53, 0.27 #1585, 0.24 #11395), 02x8m (0.29 #15, 0.12 #3081, 0.12 #1547) >> Best rule #3071 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 03c7ln; 07_3qd; 01w923; 012zng; 0285c; 01tp5bj; 0137g1; 016ntp; 01gx5f; 01w8n89; ... >> query: (?x4701, 016clz) <- artists(?x378, ?x4701), role(?x4701, ?x716), ?x716 = 018vs >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #2159 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 014zfs; 01jrvr6; 09h_q; *> query: (?x4701, 0xhtw) <- artists(?x378, ?x4701), award(?x4701, ?x567), influenced_by(?x3929, ?x4701) *> conf = 0.32 ranks of expected_values: 6 EVAL 03j24kf artists! 0xhtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 121.000 70.000 0.342 http://example.org/music/genre/artists #3379-04f_d PRED entity: 04f_d PRED relation: teams PRED expected values: 049d_ => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 265): 01jvgt (0.10 #347, 0.09 #705, 0.03 #1421), 070xg (0.10 #54, 0.09 #412, 0.03 #1128), 01d6g (0.10 #191, 0.09 #549, 0.03 #1265), 027yf83 (0.10 #91, 0.09 #449, 0.03 #1165), 0jmfv (0.10 #24, 0.09 #382, 0.03 #1098), 0bszz (0.04 #2862, 0.03 #5727, 0.02 #2144), 0cqt41 (0.04 #3253, 0.04 #3611, 0.04 #5043), 0jmk7 (0.03 #1017, 0.03 #1375, 0.03 #1733), 0jnq8 (0.03 #943, 0.03 #1301, 0.03 #1659), 0jmjr (0.03 #936, 0.03 #1294, 0.03 #1652) >> Best rule #347 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 094jv; 099ty; 029cr; 0ftvz; 0vzm; 0d9jr; 0c_m3; 0lphb; >> query: (?x2017, 01jvgt) <- locations(?x7378, ?x2017), ?x7378 = 0bzrxn, place_of_birth(?x92, ?x2017) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04f_d teams 049d_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 177.000 177.000 0.100 http://example.org/sports/sports_team_location/teams #3378-060v34 PRED entity: 060v34 PRED relation: genre PRED expected values: 01jfsb => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 93): 01jfsb (0.64 #613, 0.63 #373, 0.43 #133), 07s9rl0 (0.62 #8902, 0.61 #7094, 0.61 #6613), 02kdv5l (0.50 #483, 0.44 #243, 0.43 #2165), 05p553 (0.48 #245, 0.45 #725, 0.43 #125), 03k9fj (0.36 #1693, 0.36 #2174, 0.32 #852), 0lsxr (0.33 #129, 0.31 #9, 0.24 #3495), 06n90 (0.30 #254, 0.23 #1695, 0.22 #2176), 02l7c8 (0.29 #6990, 0.29 #6268, 0.29 #8438), 02n4kr (0.29 #368, 0.21 #608, 0.15 #8), 09blyk (0.24 #633, 0.14 #393, 0.07 #7126) >> Best rule #613 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 06bc59; >> query: (?x570, 01jfsb) <- nominated_for(?x382, ?x570), film(?x1410, ?x570), genre(?x570, ?x571), ?x571 = 03npn >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 060v34 genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.643 http://example.org/film/film/genre #3377-0fk98 PRED entity: 0fk98 PRED relation: service_location! PRED expected values: 06_9lg => 152 concepts (101 used for prediction) PRED predicted values (max 10 best out of 124): 06_9lg (0.79 #1880, 0.78 #783, 0.73 #2154), 017vb_ (0.18 #1738, 0.15 #2423, 0.15 #2698), 01y81r (0.12 #1692, 0.12 #2377, 0.11 #2652), 064f29 (0.06 #7892, 0.06 #1705, 0.06 #5553), 0cv9b (0.05 #3579, 0.04 #6330, 0.03 #7569), 02lv2v (0.05 #3647, 0.03 #5572, 0.02 #7637), 01xdn1 (0.05 #3586, 0.03 #5511, 0.02 #7850), 0k9ts (0.05 #3660, 0.02 #7650, 0.02 #7924), 01n073 (0.05 #3591, 0.02 #7581, 0.02 #7855), 069b85 (0.04 #7687, 0.04 #7961, 0.03 #5622) >> Best rule #1880 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 027wvb; 01sv6k; >> query: (?x13551, 06_9lg) <- contains(?x2146, ?x13551), place_of_birth(?x13550, ?x13551), ?x2146 = 03rk0, profession(?x13550, ?x319), award(?x13550, ?x4443) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fk98 service_location! 06_9lg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 101.000 0.789 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #3376-0jzphpx PRED entity: 0jzphpx PRED relation: award_winner PRED expected values: 01vvydl 0k7pf 02x8z_ 0178rl 01jkqfz => 55 concepts (52 used for prediction) PRED predicted values (max 10 best out of 1525): 02qwg (0.67 #8039, 0.47 #11056, 0.40 #3516), 0x3b7 (0.56 #8169, 0.50 #2139, 0.40 #11186), 0gcs9 (0.53 #10986, 0.50 #6462, 0.38 #21550), 06fmdb (0.50 #2298, 0.44 #8328, 0.40 #11345), 032nwy (0.50 #1559, 0.40 #10606, 0.33 #7589), 01lmj3q (0.47 #10585, 0.40 #3045, 0.38 #6061), 0fpjd_g (0.47 #10762, 0.38 #6238, 0.33 #7745), 06rgq (0.44 #8735, 0.38 #7228, 0.33 #1198), 0161sp (0.44 #7955, 0.27 #10972, 0.25 #6448), 01vsy95 (0.40 #3511, 0.33 #11051, 0.33 #8034) >> Best rule #8039 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 01c6qp; 01xqqp; >> query: (?x2431, 02qwg) <- ceremony(?x10102, ?x2431), ceremony(?x6378, ?x2431), ceremony(?x2561, ?x2431), award_winner(?x2431, ?x2169), ?x6378 = 0249fn, ?x2561 = 02hgm4, award(?x2169, ?x4018), ?x10102 = 031b91 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #37724 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 42 *> proper extension: 0hhtgcw; *> query: (?x2431, ?x4343) <- award_winner(?x2431, ?x248), award_winner(?x4343, ?x248), role(?x248, ?x716), award_nominee(?x248, ?x3403), profession(?x248, ?x131), award(?x248, ?x247) *> conf = 0.30 ranks of expected_values: 57, 64, 170, 182, 447 EVAL 0jzphpx award_winner 01jkqfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 55.000 52.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0jzphpx award_winner 0178rl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 52.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0jzphpx award_winner 02x8z_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 52.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0jzphpx award_winner 0k7pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 55.000 52.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0jzphpx award_winner 01vvydl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 52.000 0.667 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3375-01mszz PRED entity: 01mszz PRED relation: nominated_for! PRED expected values: 05f4m9q 05p09zm => 99 concepts (95 used for prediction) PRED predicted values (max 10 best out of 205): 05f4m9q (0.82 #2377, 0.64 #2140, 0.54 #3321), 05q8pss (0.81 #2366, 0.76 #12053, 0.68 #15371), 05b4l5x (0.48 #2371, 0.38 #2134, 0.35 #2843), 05p09zm (0.45 #2221, 0.43 #2458, 0.30 #3402), 0gq9h (0.41 #4079, 0.40 #4316, 0.39 #5025), 019f4v (0.35 #4070, 0.34 #4307, 0.33 #5016), 04dn09n (0.33 #4052, 0.32 #4289, 0.32 #4998), 0gs9p (0.31 #4317, 0.30 #11878, 0.30 #4080), 05ztrmj (0.30 #842, 0.15 #1420, 0.12 #16084), 05zr6wv (0.30 #725, 0.07 #2144, 0.07 #22460) >> Best rule #2377 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 049mql; 0f2sx4; >> query: (?x6205, 05f4m9q) <- nominated_for(?x1105, ?x6205), nominated_for(?x102, ?x6205), nominated_for(?x5338, ?x6205), ?x102 = 04ljl_l, ?x1105 = 07bdd_ >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 01mszz nominated_for! 05p09zm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 95.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01mszz nominated_for! 05f4m9q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 95.000 0.818 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3374-01n6c PRED entity: 01n6c PRED relation: country! PRED expected values: 06z6r => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 57): 06z6r (0.74 #1173, 0.74 #1116, 0.73 #489), 03_8r (0.63 #935, 0.63 #764, 0.62 #1220), 01cgz (0.60 #641, 0.60 #698, 0.59 #926), 071t0 (0.57 #366, 0.54 #480, 0.54 #1164), 01lb14 (0.50 #472, 0.50 #358, 0.50 #244), 07gyv (0.42 #748, 0.42 #349, 0.40 #919), 06f41 (0.42 #357, 0.41 #243, 0.37 #642), 0w0d (0.42 #354, 0.37 #468, 0.36 #411), 07jbh (0.41 #264, 0.40 #378, 0.38 #435), 03hr1p (0.40 #367, 0.38 #253, 0.37 #652) >> Best rule #1173 for best value: >> intensional similarity = 3 >> extensional distance = 158 >> proper extension: 03f2w; >> query: (?x2468, 06z6r) <- country(?x1121, ?x2468), organization(?x2468, ?x127), countries_spoken_in(?x5003, ?x2468) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n6c country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.744 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #3373-0kszw PRED entity: 0kszw PRED relation: film PRED expected values: 09gq0x5 => 112 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1027): 011ywj (0.65 #4967, 0.56 #6739, 0.15 #26232), 09z2b7 (0.56 #37215, 0.55 #35442, 0.55 #42532), 043t8t (0.22 #6101, 0.09 #4329, 0.06 #25594), 09gq0x5 (0.13 #3826, 0.11 #5598, 0.06 #25091), 02qr3k8 (0.12 #1277, 0.11 #3049, 0.07 #13681), 03lvwp (0.12 #1036, 0.11 #2808, 0.04 #4580), 087pfc (0.12 #1516, 0.11 #3288, 0.02 #26325), 025rvx0 (0.12 #996, 0.11 #2768), 011yr9 (0.12 #688, 0.09 #4232, 0.07 #6004), 031786 (0.12 #1263, 0.09 #4807, 0.07 #6579) >> Best rule #4967 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 0184jc; 01q_ph; 09fqtq; 01sp81; 03f1zdw; 016gr2; 02tr7d; 06t61y; 065jlv; 02k6rq; ... >> query: (?x2531, 011ywj) <- award_nominee(?x2531, ?x1739), ?x1739 = 015rkw, award_winner(?x72, ?x2531) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #3826 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0184jc; 01q_ph; 09fqtq; 01sp81; 03f1zdw; 016gr2; 02tr7d; 06t61y; 065jlv; 02k6rq; ... *> query: (?x2531, 09gq0x5) <- award_nominee(?x2531, ?x1739), ?x1739 = 015rkw, award_winner(?x72, ?x2531) *> conf = 0.13 ranks of expected_values: 4 EVAL 0kszw film 09gq0x5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 112.000 86.000 0.652 http://example.org/film/actor/film./film/performance/film #3372-02k1pr PRED entity: 02k1pr PRED relation: written_by PRED expected values: 06kbb6 => 62 concepts (6 used for prediction) PRED predicted values (max 10 best out of 30): 019l68 (0.10 #1686, 0.08 #337, 0.07 #674), 039bp (0.10 #1686), 03s2y9 (0.08 #337, 0.07 #674, 0.07 #1349), 0146pg (0.08 #337, 0.07 #674, 0.07 #1349), 0gn30 (0.03 #168, 0.03 #842), 02vyw (0.03 #441, 0.02 #104, 0.02 #778), 0kb3n (0.02 #931, 0.02 #257, 0.02 #594), 0237jb (0.02 #234, 0.01 #908), 02mt4k (0.02 #156), 02kxbx3 (0.02 #102) >> Best rule #1686 for best value: >> intensional similarity = 3 >> extensional distance = 534 >> proper extension: 03t97y; 03twd6; 019kyn; 02x8fs; 047gpsd; 0564x; >> query: (?x8456, ?x1119) <- country(?x8456, ?x94), production_companies(?x8456, ?x574), award_winner(?x8456, ?x1119) >> conf = 0.10 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02k1pr written_by 06kbb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 6.000 0.101 http://example.org/film/film/written_by #3371-0qlnr PRED entity: 0qlnr PRED relation: student PRED expected values: 021vwt => 190 concepts (134 used for prediction) PRED predicted values (max 10 best out of 1525): 03h40_7 (0.14 #3907, 0.06 #6000, 0.06 #14372), 06pwf6 (0.12 #4649, 0.07 #2556, 0.06 #10928), 0p8jf (0.11 #6757, 0.05 #15129, 0.05 #23501), 0kc6 (0.11 #7995, 0.03 #16367, 0.03 #18460), 01_xtx (0.10 #630, 0.07 #2723, 0.06 #4816), 05sj55 (0.10 #1349, 0.07 #3442, 0.06 #5535), 03gkn5 (0.10 #556, 0.07 #2649, 0.06 #4742), 0fwy0h (0.10 #842, 0.07 #2935, 0.06 #5028), 0226cw (0.10 #1496, 0.07 #3589, 0.06 #5682), 04xbr4 (0.10 #2008, 0.07 #4101, 0.06 #6194) >> Best rule #3907 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0ylsr; >> query: (?x8943, 03h40_7) <- school_type(?x8943, ?x1044), time_zones(?x8943, ?x2674), institution(?x865, ?x8943), company(?x346, ?x8943) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qlnr student 021vwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 190.000 134.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #3370-02wwsh8 PRED entity: 02wwsh8 PRED relation: award_winner PRED expected values: 049l7 => 50 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1353): 0bwh6 (0.60 #5202, 0.19 #22483, 0.17 #10138), 02kxbx3 (0.50 #10643, 0.34 #22988, 0.13 #15580), 0h1p (0.50 #10300, 0.28 #22645, 0.25 #2896), 06t8b (0.50 #11586, 0.20 #6650, 0.13 #23931), 071xj (0.50 #4585, 0.06 #16926, 0.06 #24334), 0gdqy (0.50 #4617, 0.06 #24366, 0.04 #34566), 06pj8 (0.40 #5371, 0.33 #10307, 0.20 #7839), 0dbbz (0.40 #6966, 0.25 #4498, 0.11 #24247), 06b_0 (0.40 #6610, 0.17 #23891, 0.17 #11546), 0184jw (0.40 #6634, 0.17 #23915, 0.17 #11570) >> Best rule #5202 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 02x17c2; >> query: (?x8313, 0bwh6) <- award_winner(?x8313, ?x4732), award_winner(?x8313, ?x3022), award_winner(?x8313, ?x2671), film(?x3022, ?x1851), profession(?x4732, ?x319), type_of_union(?x3022, ?x566), ?x2671 = 04k25 >> conf = 0.60 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02wwsh8 award_winner 049l7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 50.000 29.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #3369-01wbl_r PRED entity: 01wbl_r PRED relation: nationality PRED expected values: 05r7t => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 92): 09c7w0 (0.78 #401, 0.75 #301, 0.74 #1304), 07ssc (0.15 #15, 0.09 #1016, 0.08 #2720), 0f8l9c (0.15 #22, 0.03 #822, 0.03 #722), 02jx1 (0.14 #633, 0.14 #1436, 0.14 #2738), 05r7t (0.13 #178, 0.01 #10417, 0.01 #878), 01ls2 (0.13 #111, 0.01 #10417, 0.01 #311), 03rjj (0.08 #5, 0.07 #105, 0.03 #705), 03rt9 (0.08 #13, 0.02 #6823, 0.01 #1916), 0162v (0.08 #45), 0d060g (0.07 #707, 0.07 #107, 0.06 #1610) >> Best rule #401 for best value: >> intensional similarity = 2 >> extensional distance = 93 >> proper extension: 0b80__; 01w_10; 03qncl3; >> query: (?x2031, 09c7w0) <- award_nominee(?x2031, ?x3930), company(?x2031, ?x12827) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #178 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 01w61th; 02b25y; 01wj18h; 0bqsy; 01wv9p; 043zg; 0127s7; 01nkxvx; 01w58n3; 020hyj; ... *> query: (?x2031, 05r7t) <- artists(?x12082, ?x2031), award(?x2031, ?x884), artist(?x5666, ?x2031), ?x12082 = 08vlns *> conf = 0.13 ranks of expected_values: 5 EVAL 01wbl_r nationality 05r7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 106.000 106.000 0.779 http://example.org/people/person/nationality #3368-01f1kd PRED entity: 01f1kd PRED relation: olympics! PRED expected values: 02vzc 05b4w => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 361): 02vzc (0.81 #2611, 0.80 #2485, 0.79 #2738), 03_3d (0.71 #1165, 0.71 #1036, 0.69 #2572), 05b4w (0.67 #2178, 0.67 #2109, 0.64 #2877), 0chghy (0.67 #2062, 0.59 #2190, 0.59 #2830), 03gj2 (0.67 #2075, 0.59 #2203, 0.57 #2463), 05qhw (0.63 #2067, 0.59 #2195, 0.57 #2455), 0ctw_b (0.60 #384, 0.58 #2952, 0.57 #2819), 01mk6 (0.60 #384, 0.58 #2952, 0.57 #2819), 06mkj (0.60 #384, 0.58 #2952, 0.57 #2819), 0345h (0.60 #896, 0.57 #1024, 0.57 #927) >> Best rule #2611 for best value: >> intensional similarity = 55 >> extensional distance = 34 >> proper extension: 0sxrz; >> query: (?x7775, 02vzc) <- olympics(?x3309, ?x7775), sports(?x7775, ?x453), olympics(?x3040, ?x7775), olympics(?x2984, ?x7775), olympics(?x304, ?x7775), film_release_region(?x10829, ?x2984), film_release_region(?x10404, ?x2984), film_release_region(?x10208, ?x2984), film_release_region(?x8373, ?x2984), film_release_region(?x6415, ?x2984), film_release_region(?x5825, ?x2984), film_release_region(?x5318, ?x2984), film_release_region(?x4448, ?x2984), film_release_region(?x3514, ?x2984), film_release_region(?x1452, ?x2984), country(?x1121, ?x2984), ?x3514 = 04vh83, ?x6415 = 02z44tp, ?x1452 = 0jqn5, ?x10829 = 0jz71, ?x5318 = 0353xq, administrative_area_type(?x3040, ?x2792), ?x10404 = 01s9vc, country(?x6345, ?x2984), countries_spoken_in(?x732, ?x2984), ?x8373 = 0bs8hvm, ?x5825 = 067ghz, olympics(?x1023, ?x7775), film_release_region(?x8646, ?x304), film_release_region(?x7494, ?x304), film_release_region(?x7493, ?x304), film_release_region(?x6247, ?x304), film_release_region(?x6178, ?x304), film_release_region(?x4441, ?x304), film_release_region(?x4040, ?x304), film_release_region(?x2878, ?x304), film_release_region(?x1552, ?x304), film_release_region(?x664, ?x304), ?x6178 = 02v_r7d, ?x7494 = 0dgrwqr, currency(?x3040, ?x170), ?x10208 = 09rfpk, ?x664 = 0401sg, ?x4441 = 0125xq, ?x4040 = 02mt51, ?x1552 = 0gj9qxr, ?x4448 = 01k60v, country(?x1009, ?x304), ?x7493 = 0btpm6, form_of_government(?x304, ?x1926), medal(?x7775, ?x422), country(?x3309, ?x142), ?x6247 = 09v9mks, ?x2878 = 0hx4y, ?x8646 = 05zvzf3 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01f1kd olympics! 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 24.000 24.000 0.806 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 01f1kd olympics! 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.806 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #3367-026mml PRED entity: 026mml PRED relation: ceremony PRED expected values: 0jzphpx 013b2h => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 121): 013b2h (0.80 #319, 0.80 #444, 0.60 #194), 0jzphpx (0.67 #281, 0.65 #406, 0.60 #156), 05c1t6z (0.18 #886, 0.12 #1511, 0.12 #1261), 02q690_ (0.17 #930, 0.11 #1430, 0.11 #1180), 0gvstc3 (0.16 #901, 0.10 #1526, 0.10 #1651), 0bzm81 (0.16 #766, 0.14 #891, 0.10 #1141), 0n8_m93 (0.16 #854, 0.14 #979, 0.10 #1229), 03nnm4t (0.15 #939, 0.11 #1439, 0.10 #1189), 02yxh9 (0.15 #837, 0.13 #962, 0.10 #1212), 0bc773 (0.15 #794, 0.13 #919, 0.10 #1169) >> Best rule #319 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 02581q; 02wh75; 026mg3; 02g3gj; 01d38g; 01bgqh; 03x3wf; 02g8mp; 01c9f2; 01ckbq; ... >> query: (?x8076, 013b2h) <- award(?x4635, ?x8076), ceremony(?x8076, ?x1362), award_nominee(?x4635, ?x1051), ?x1362 = 019bk0 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 026mml ceremony 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.803 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 026mml ceremony 0jzphpx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.803 http://example.org/award/award_category/winners./award/award_honor/ceremony #3366-01b66t PRED entity: 01b66t PRED relation: honored_for! PRED expected values: 0gkxgfq => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 86): 02q690_ (0.73 #901, 0.26 #3079, 0.26 #2232), 0jt3qpk (0.44 #155, 0.38 #397, 0.31 #518), 05c1t6z (0.37 #858, 0.26 #1100, 0.23 #3036), 0gx_st (0.37 #877, 0.15 #2208, 0.14 #3055), 0gkxgfq (0.33 #333, 0.25 #575, 0.24 #3631), 03nnm4t (0.29 #909, 0.21 #1151, 0.17 #3087), 0gvstc3 (0.24 #874, 0.23 #1842, 0.22 #1721), 09v0p2c (0.24 #3631, 0.17 #7870, 0.17 #69), 0hn821n (0.24 #3631, 0.17 #7870, 0.15 #7627), 03gt46z (0.24 #3631, 0.11 #294, 0.11 #173) >> Best rule #901 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 0gpjbt; >> query: (?x4721, 02q690_) <- honored_for(?x5469, ?x4721), award_winner(?x5469, ?x3895), ?x3895 = 06jnvs >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #333 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 02_1q9; *> query: (?x4721, 0gkxgfq) <- nominated_for(?x2720, ?x4721), nominated_for(?x588, ?x4721), ?x588 = 02p_7cr, ?x2720 = 02q1tc5 *> conf = 0.33 ranks of expected_values: 5 EVAL 01b66t honored_for! 0gkxgfq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 91.000 0.732 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3365-01vrx35 PRED entity: 01vrx35 PRED relation: profession PRED expected values: 02hrh1q 0nbcg => 113 concepts (102 used for prediction) PRED predicted values (max 10 best out of 72): 02hrh1q (0.84 #14755, 0.79 #7546, 0.75 #9607), 0nbcg (0.68 #1504, 0.59 #2979, 0.58 #4163), 016z4k (0.56 #3, 0.52 #1181, 0.49 #2951), 0dxtg (0.37 #2076, 0.34 #2666, 0.32 #4589), 01d_h8 (0.35 #2068, 0.33 #2215, 0.32 #2658), 01c72t (0.34 #760, 0.33 #317, 0.32 #2528), 0fnpj (0.30 #353, 0.26 #13712, 0.22 #796), 0cbd2 (0.29 #2069, 0.27 #2659, 0.26 #2216), 02jknp (0.26 #154, 0.24 #2070, 0.22 #2217), 01c8w0 (0.26 #13712, 0.10 #892, 0.09 #3103) >> Best rule #14755 for best value: >> intensional similarity = 3 >> extensional distance = 2978 >> proper extension: 06v8s0; 033hqf; 025p38; 067jsf; 01yh3y; 0177s6; 025tdwc; 012_53; 02dh86; 059xvg; ... >> query: (?x7668, 02hrh1q) <- profession(?x7668, ?x2659), profession(?x6467, ?x2659), ?x6467 = 01l47f5 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01vrx35 profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 102.000 0.838 http://example.org/people/person/profession EVAL 01vrx35 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 102.000 0.838 http://example.org/people/person/profession #3364-012z8_ PRED entity: 012z8_ PRED relation: artist! PRED expected values: 016ckq => 133 concepts (96 used for prediction) PRED predicted values (max 10 best out of 133): 03rhqg (0.38 #157, 0.25 #298, 0.24 #1144), 033hn8 (0.33 #296, 0.25 #155, 0.15 #1283), 01w40h (0.25 #310, 0.25 #169, 0.17 #1579), 02p11jq (0.25 #154, 0.17 #295, 0.12 #1564), 0229rs (0.25 #159, 0.17 #300, 0.06 #1005), 0181dw (0.23 #747, 0.15 #1311, 0.14 #3850), 017l96 (0.23 #1006, 0.13 #1570, 0.12 #160), 0n85g (0.17 #1332, 0.16 #2178, 0.12 #1755), 0g768 (0.17 #37, 0.15 #5396, 0.14 #3845), 01cszh (0.17 #293, 0.12 #152, 0.12 #1139) >> Best rule #157 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 044gyq; 026spg; 01vt9p3; 019f9z; >> query: (?x4576, 03rhqg) <- profession(?x4576, ?x131), artists(?x9789, ?x4576), ?x9789 = 02b71x, people(?x2510, ?x4576) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #3851 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 123 *> proper extension: 0qmny; *> query: (?x4576, 016ckq) <- artists(?x3928, ?x4576), ?x3928 = 0gywn *> conf = 0.14 ranks of expected_values: 20 EVAL 012z8_ artist! 016ckq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 133.000 96.000 0.375 http://example.org/music/record_label/artist #3363-01vsqvs PRED entity: 01vsqvs PRED relation: languages PRED expected values: 02h40lc => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 31): 02h40lc (0.94 #520, 0.92 #4628, 0.91 #4073), 03k50 (0.30 #1965, 0.14 #1114, 0.09 #781), 02bjrlw (0.27 #889, 0.27 #778, 0.24 #815), 07c9s (0.16 #1973, 0.07 #789, 0.05 #308), 04306rv (0.11 #1964, 0.11 #780, 0.10 #891), 0999q (0.07 #1982, 0.05 #317, 0.04 #946), 05zjd (0.07 #238, 0.03 #497, 0.02 #1015), 06b_j (0.07 #828, 0.06 #902, 0.04 #1383), 03_9r (0.06 #1966, 0.05 #301, 0.02 #745), 09s02 (0.05 #1995, 0.05 #330, 0.02 #811) >> Best rule #520 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 03zz8b; 01syr4; >> query: (?x9179, 02h40lc) <- profession(?x9179, ?x1032), origin(?x9179, ?x14383), film(?x9179, ?x5429), languages(?x9179, ?x2502) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vsqvs languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 170.000 0.939 http://example.org/people/person/languages #3362-02w9sd7 PRED entity: 02w9sd7 PRED relation: nominated_for PRED expected values: 03hj3b3 015whm 0284b56 0y_hb 0c0zq 04q827 => 42 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1458): 0p_th (0.76 #3044, 0.74 #1522, 0.72 #6092), 03xf_m (0.76 #3044, 0.74 #1522, 0.72 #6092), 0bl1_ (0.76 #3044, 0.74 #1522, 0.72 #6092), 016yxn (0.76 #3044, 0.74 #1522, 0.72 #6092), 016ks5 (0.76 #3044, 0.74 #1522, 0.72 #6092), 0d87hc (0.76 #3044, 0.74 #1522, 0.72 #6092), 01gkp1 (0.76 #3044, 0.74 #1522, 0.72 #6092), 049xgc (0.66 #2364, 0.18 #8460, 0.17 #9982), 026p4q7 (0.63 #1864, 0.21 #7960, 0.20 #342), 0m313 (0.61 #1534, 0.20 #7630, 0.19 #9152) >> Best rule #3044 for best value: >> intensional similarity = 5 >> extensional distance = 36 >> proper extension: 099c8n; >> query: (?x3209, ?x407) <- award(?x407, ?x3209), nominated_for(?x3209, ?x3012), nominated_for(?x3209, ?x1813), production_companies(?x3012, ?x3331), ?x1813 = 09gq0x5 >> conf = 0.76 => this is the best rule for 7 predicted values *> Best rule #2839 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 099c8n; *> query: (?x3209, 0c0zq) <- award(?x407, ?x3209), nominated_for(?x3209, ?x3012), nominated_for(?x3209, ?x1813), production_companies(?x3012, ?x3331), ?x1813 = 09gq0x5 *> conf = 0.42 ranks of expected_values: 32, 63, 124, 186, 237, 369 EVAL 02w9sd7 nominated_for 04q827 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 42.000 17.000 0.758 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02w9sd7 nominated_for 0c0zq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 42.000 17.000 0.758 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02w9sd7 nominated_for 0y_hb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 17.000 0.758 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02w9sd7 nominated_for 0284b56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 17.000 0.758 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02w9sd7 nominated_for 015whm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 17.000 0.758 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02w9sd7 nominated_for 03hj3b3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 42.000 17.000 0.758 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3361-01c6qp PRED entity: 01c6qp PRED relation: ceremony! PRED expected values: 01cky2 03tk6z 03q_g6 024_41 02fv3t 03ncb2 => 29 concepts (28 used for prediction) PRED predicted values (max 10 best out of 234): 02fv3t (0.85 #2685, 0.81 #3073, 0.78 #4156), 01cky2 (0.85 #2630, 0.81 #3073, 0.78 #4156), 024_41 (0.81 #3073, 0.78 #4156, 0.77 #2681), 03tk6z (0.81 #3073, 0.78 #4156, 0.77 #2638), 03ncb2 (0.81 #3073, 0.78 #4156, 0.76 #3974), 03qpp9 (0.81 #3073, 0.78 #4156, 0.76 #3974), 03t5kl (0.81 #3073, 0.78 #4156, 0.76 #3974), 03q_g6 (0.81 #3073, 0.78 #4156, 0.76 #3974), 026mmy (0.81 #3073, 0.78 #4156, 0.76 #3974), 03t5n3 (0.81 #3073, 0.78 #4156, 0.76 #3974) >> Best rule #2685 for best value: >> intensional similarity = 25 >> extensional distance = 11 >> proper extension: 0gpjbt; >> query: (?x1480, 02fv3t) <- ceremony(?x7691, ?x1480), ceremony(?x7594, ?x1480), ceremony(?x3903, ?x1480), ceremony(?x3467, ?x1480), ceremony(?x2576, ?x1480), ceremony(?x567, ?x1480), ceremony(?x341, ?x1480), ?x567 = 01d38g, award(?x5635, ?x3467), award(?x5298, ?x3467), award(?x3861, ?x3467), ?x7594 = 02v703, ?x3903 = 024vjd, ceremony(?x3467, ?x9431), award_winner(?x1480, ?x4712), ?x5635 = 0kxbc, ?x7691 = 026m9w, ?x2576 = 01dk00, ?x341 = 026mg3, ?x9431 = 02cg41, award_nominee(?x3861, ?x981), artist(?x2931, ?x4712), role(?x4712, ?x227), type_of_union(?x4712, ?x566), award_winner(?x215, ?x5298) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 8 EVAL 01c6qp ceremony! 03ncb2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 28.000 0.846 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c6qp ceremony! 02fv3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 28.000 0.846 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c6qp ceremony! 024_41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 28.000 0.846 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c6qp ceremony! 03q_g6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 29.000 28.000 0.846 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c6qp ceremony! 03tk6z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 28.000 0.846 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01c6qp ceremony! 01cky2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 28.000 0.846 http://example.org/award/award_category/winners./award/award_honor/ceremony #3360-02856r PRED entity: 02856r PRED relation: parent_genre! PRED expected values: 016ybr => 42 concepts (19 used for prediction) PRED predicted values (max 10 best out of 306): 0g_bh (0.44 #647, 0.16 #1187, 0.13 #1726), 0339z0 (0.33 #192, 0.14 #460, 0.11 #1001), 059kh (0.22 #581, 0.14 #1121, 0.11 #807), 06cp5 (0.22 #614, 0.12 #1154, 0.11 #807), 0y3_8 (0.22 #579, 0.12 #1119, 0.11 #807), 0dl5d (0.22 #554, 0.12 #1094, 0.11 #807), 01gbcf (0.22 #542, 0.11 #813, 0.11 #807), 0781g (0.22 #695, 0.11 #966, 0.11 #807), 01pfpt (0.22 #613, 0.11 #884, 0.11 #807), 0xv2x (0.22 #666, 0.11 #807, 0.10 #2283) >> Best rule #647 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 03_d0; >> query: (?x11759, 0g_bh) <- parent_genre(?x11759, ?x11960), artists(?x11759, ?x2170), artists(?x11960, ?x3171), role(?x3171, ?x228), ?x2170 = 0144l1 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #808 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 03_d0; *> query: (?x11759, ?x5355) <- parent_genre(?x11759, ?x11960), artists(?x11759, ?x2170), artists(?x11960, ?x3171), role(?x3171, ?x228), artists(?x5355, ?x3171), ?x2170 = 0144l1 *> conf = 0.06 ranks of expected_values: 113 EVAL 02856r parent_genre! 016ybr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 42.000 19.000 0.444 http://example.org/music/genre/parent_genre #3359-0b76kw1 PRED entity: 0b76kw1 PRED relation: language PRED expected values: 02h40lc => 65 concepts (63 used for prediction) PRED predicted values (max 10 best out of 29): 02h40lc (0.88 #713, 0.88 #3264, 0.88 #2732), 064_8sq (0.20 #22, 0.17 #672, 0.17 #852), 06nm1 (0.20 #11, 0.11 #70, 0.11 #483), 0jzc (0.20 #20, 0.11 #79, 0.10 #138), 06b_j (0.20 #23, 0.11 #82, 0.10 #141), 04306rv (0.11 #835, 0.10 #655, 0.10 #954), 02hxcvy (0.08 #270, 0.02 #506, 0.02 #565), 02bjrlw (0.08 #651, 0.07 #950, 0.07 #712), 03_9r (0.08 #305, 0.04 #2323, 0.04 #3331), 05zjd (0.08 #380, 0.02 #439, 0.02 #617) >> Best rule #713 for best value: >> intensional similarity = 3 >> extensional distance = 413 >> proper extension: 0bx_hnp; >> query: (?x1994, 02h40lc) <- honored_for(?x1553, ?x1994), genre(?x1994, ?x162), award_winner(?x1994, ?x1890) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b76kw1 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 63.000 0.884 http://example.org/film/film/language #3358-01d494 PRED entity: 01d494 PRED relation: influenced_by PRED expected values: 0dzkq 0x3r3 085gk => 171 concepts (59 used for prediction) PRED predicted values (max 10 best out of 292): 042q3 (0.52 #5900, 0.23 #5971, 0.15 #8033), 03sbs (0.50 #2350, 0.46 #4906, 0.43 #217), 048cl (0.43 #228, 0.38 #2361, 0.29 #4917), 0gz_ (0.43 #4792, 0.29 #7779, 0.25 #2236), 040db (0.43 #55, 0.19 #2188, 0.12 #11569), 0j3v (0.30 #5603, 0.19 #2193, 0.18 #11574), 0420y (0.29 #395, 0.25 #2528, 0.23 #5971), 02ln1 (0.29 #273, 0.25 #2406, 0.17 #4962), 0372p (0.29 #110, 0.23 #5971, 0.19 #2243), 081k8 (0.29 #153, 0.19 #18067, 0.18 #11667) >> Best rule #5900 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 0379s; 04k15; 03f0324; 05d1y; 0dw6b; 03jht; 039n1; 0h336; 04_by; 06myp; ... >> query: (?x1737, 042q3) <- influenced_by(?x1737, ?x9600), influenced_by(?x9600, ?x8177), ?x8177 = 03_f0 >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #5117 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 33 *> proper extension: 05qmj; *> query: (?x1737, ?x712) <- interests(?x1737, ?x6978), interests(?x8430, ?x6978), interests(?x1857, ?x6978), interests(?x712, ?x6978), ?x1857 = 026lj, ?x8430 = 0ct9_ *> conf = 0.12 ranks of expected_values: 52, 67, 71 EVAL 01d494 influenced_by 085gk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 171.000 59.000 0.523 http://example.org/influence/influence_node/influenced_by EVAL 01d494 influenced_by 0x3r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 171.000 59.000 0.523 http://example.org/influence/influence_node/influenced_by EVAL 01d494 influenced_by 0dzkq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 171.000 59.000 0.523 http://example.org/influence/influence_node/influenced_by #3357-01pctb PRED entity: 01pctb PRED relation: artist! PRED expected values: 02bh8z => 131 concepts (127 used for prediction) PRED predicted values (max 10 best out of 68): 0g768 (0.22 #322, 0.13 #464, 0.12 #748), 01trtc (0.22 #358, 0.13 #500, 0.12 #784), 043g7l (0.19 #600, 0.06 #742, 0.05 #2304), 015_1q (0.12 #872, 0.11 #304, 0.10 #5419), 01f_3w (0.12 #887, 0.11 #319, 0.07 #1739), 011k1h (0.12 #720, 0.11 #294, 0.07 #436), 03rhqg (0.11 #300, 0.07 #442, 0.06 #10674), 0fb0v (0.11 #291, 0.07 #433, 0.06 #717), 02p11jq (0.11 #297, 0.07 #439, 0.06 #723), 02bh8z (0.11 #306, 0.07 #448, 0.06 #732) >> Best rule #322 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02l840; 0gy6z9; 05r5w; 0478__m; 01vtj38; 0c1j_; 01pgk0; >> query: (?x4884, 0g768) <- vacationer(?x2983, ?x4884), producer_type(?x4884, ?x632), category(?x4884, ?x134) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #306 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 02l840; 0gy6z9; 05r5w; 0478__m; 01vtj38; 0c1j_; 01pgk0; *> query: (?x4884, 02bh8z) <- vacationer(?x2983, ?x4884), producer_type(?x4884, ?x632), category(?x4884, ?x134) *> conf = 0.11 ranks of expected_values: 10 EVAL 01pctb artist! 02bh8z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 131.000 127.000 0.222 http://example.org/music/record_label/artist #3356-025twgf PRED entity: 025twgf PRED relation: genre PRED expected values: 02kdv5l => 118 concepts (81 used for prediction) PRED predicted values (max 10 best out of 110): 07s9rl0 (0.66 #9694, 0.60 #2663, 0.59 #2784), 05p553 (0.66 #8003, 0.35 #2304, 0.34 #1699), 02kdv5l (0.65 #3511, 0.64 #245, 0.64 #124), 0bkbm (0.65 #3511, 0.19 #282, 0.14 #403), 01hmnh (0.50 #18, 0.44 #1833, 0.40 #865), 06n90 (0.43 #13, 0.39 #739, 0.35 #7768), 02l7c8 (0.33 #4134, 0.30 #3769, 0.30 #2921), 0lsxr (0.32 #7523, 0.29 #1462, 0.28 #1099), 03npn (0.26 #2905, 0.16 #7521, 0.15 #7763), 0c3351 (0.25 #6301, 0.25 #6300, 0.09 #7551) >> Best rule #9694 for best value: >> intensional similarity = 5 >> extensional distance = 970 >> proper extension: 0kvgnq; 0h3k3f; >> query: (?x8737, 07s9rl0) <- film_release_region(?x8737, ?x94), genre(?x8737, ?x811), ?x94 = 09c7w0, genre(?x1807, ?x811), ?x1807 = 018nnz >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #3511 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 138 *> proper extension: 06z8s_; 02vw1w2; 09146g; 02z5x7l; 027m67; *> query: (?x8737, ?x225) <- language(?x8737, ?x254), film_release_distribution_medium(?x8737, ?x81), ?x81 = 029j_, prequel(?x11362, ?x8737), genre(?x11362, ?x225) *> conf = 0.65 ranks of expected_values: 3 EVAL 025twgf genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 118.000 81.000 0.663 http://example.org/film/film/genre #3355-07m9cm PRED entity: 07m9cm PRED relation: profession PRED expected values: 02hrh1q => 115 concepts (73 used for prediction) PRED predicted values (max 10 best out of 62): 02hrh1q (0.91 #5599, 0.90 #6187, 0.89 #2364), 03gjzk (0.48 #1336, 0.37 #1483, 0.34 #1924), 09jwl (0.37 #4722, 0.37 #2222, 0.36 #5016), 0nbcg (0.26 #5029, 0.26 #4735, 0.25 #1206), 02krf9 (0.23 #466, 0.20 #1348, 0.16 #1495), 016z4k (0.23 #4709, 0.22 #4121, 0.22 #2209), 0cbd2 (0.22 #153, 0.21 #1476, 0.21 #6), 0dz3r (0.22 #4707, 0.21 #1178, 0.21 #5001), 018gz8 (0.18 #1338, 0.17 #1044, 0.15 #1485), 0kyk (0.17 #175, 0.15 #322, 0.13 #1498) >> Best rule #5599 for best value: >> intensional similarity = 3 >> extensional distance = 1117 >> proper extension: 025p38; 0kr5_; 012c6x; 0152cw; 0f0p0; 0h1m9; 02lnhv; 01j4ls; 0j582; 01xcqc; ... >> query: (?x4543, 02hrh1q) <- profession(?x4543, ?x319), award_winner(?x704, ?x4543), film(?x4543, ?x2189) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07m9cm profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 73.000 0.906 http://example.org/people/person/profession #3354-04p5cr PRED entity: 04p5cr PRED relation: honored_for! PRED expected values: 058m5m4 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 84): 02q690_ (0.31 #399, 0.29 #863, 0.29 #2371), 0bvhz9 (0.30 #572, 0.04 #3937, 0.02 #7069), 07y9ts (0.27 #2669, 0.18 #285, 0.11 #517), 058m5m4 (0.27 #2669, 0.04 #855, 0.04 #1783), 0bq_mx (0.27 #2669, 0.04 #1038, 0.03 #1386), 09v0p2c (0.27 #2669, 0.02 #761, 0.02 #993), 03nnm4t (0.27 #755, 0.23 #407, 0.22 #871), 0lp_cd3 (0.20 #944, 0.18 #828, 0.17 #1292), 0gx_st (0.20 #840, 0.18 #608, 0.15 #376), 09qftb (0.20 #208, 0.06 #324, 0.06 #1948) >> Best rule #399 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0431v3; 02py9yf; 05pbsry; >> query: (?x6439, 02q690_) <- actor(?x6439, ?x1538), genre(?x6439, ?x6674), nominated_for(?x435, ?x6439), ?x6674 = 01t_vv >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #2669 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 82 *> proper extension: 04kzqz; 06w7mlh; *> query: (?x6439, ?x5957) <- program(?x8522, ?x6439), producer_type(?x8522, ?x632), award_winner(?x5957, ?x8522) *> conf = 0.27 ranks of expected_values: 4 EVAL 04p5cr honored_for! 058m5m4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 87.000 0.308 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3353-01x2_q PRED entity: 01x2_q PRED relation: team PRED expected values: 0j86l => 74 concepts (13 used for prediction) PRED predicted values (max 10 best out of 381): 0hn2q (0.33 #295, 0.25 #651, 0.01 #2076), 0jnpc (0.33 #190, 0.25 #546, 0.01 #1971), 0jnmj (0.33 #58, 0.25 #414, 0.01 #1839), 015_z1 (0.20 #834, 0.07 #4397, 0.06 #3684), 03_44z (0.20 #1030, 0.05 #2099, 0.05 #2455), 029q3k (0.20 #929, 0.04 #4135, 0.04 #3779), 04mrfv (0.20 #976, 0.04 #2045, 0.04 #2401), 02gjt4 (0.20 #875, 0.03 #3725, 0.03 #1944), 01wx_y (0.20 #907, 0.03 #1976, 0.03 #2332), 04ngn (0.20 #874, 0.03 #1943, 0.03 #2299) >> Best rule #295 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 02y8bn; >> query: (?x13210, 0hn2q) <- nationality(?x13210, ?x1264), athlete(?x453, ?x13210), team(?x13210, ?x8037), team(?x2918, ?x8037), ?x2918 = 02qvl7, position(?x8037, ?x3299), ?x3299 = 02qvgy >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4632 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 88 *> proper extension: 0ct_yc; *> query: (?x13210, ?x2919) <- nationality(?x13210, ?x1264), athlete(?x453, ?x13210), team(?x13210, ?x8037), team(?x2918, ?x8037), team(?x2918, ?x2919), adjoins(?x172, ?x1264), country(?x150, ?x1264) *> conf = 0.01 ranks of expected_values: 329 EVAL 01x2_q team 0j86l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 74.000 13.000 0.333 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #3352-09vzz PRED entity: 09vzz PRED relation: major_field_of_study PRED expected values: 062z7 0fdys 04gb7 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 116): 02lp1 (0.65 #370, 0.39 #1454, 0.32 #2414), 01mkq (0.62 #374, 0.44 #1458, 0.41 #1818), 062z7 (0.41 #386, 0.37 #869, 0.36 #628), 04rjg (0.39 #1463, 0.39 #2783, 0.37 #2423), 04gb7 (0.38 #523, 0.32 #645, 0.30 #886), 01lj9 (0.35 #278, 0.29 #398, 0.29 #1482), 05qfh (0.35 #394, 0.34 #3999, 0.24 #1838), 037mh8 (0.35 #426, 0.25 #2830, 0.23 #1870), 01540 (0.35 #420, 0.23 #1864, 0.20 #1504), 0fdys (0.32 #397, 0.30 #517, 0.28 #880) >> Best rule #370 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 01jssp; 01pq4w; 017j69; 01g6l8; >> query: (?x12726, 02lp1) <- major_field_of_study(?x12726, ?x9111), institution(?x734, ?x12726), colors(?x12726, ?x332), ?x9111 = 04sh3 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #386 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 01jssp; 01pq4w; 017j69; 01g6l8; *> query: (?x12726, 062z7) <- major_field_of_study(?x12726, ?x9111), institution(?x734, ?x12726), colors(?x12726, ?x332), ?x9111 = 04sh3 *> conf = 0.41 ranks of expected_values: 3, 5, 10 EVAL 09vzz major_field_of_study 04gb7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 146.000 146.000 0.647 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 09vzz major_field_of_study 0fdys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 146.000 146.000 0.647 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 09vzz major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 146.000 146.000 0.647 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3351-0qf11 PRED entity: 0qf11 PRED relation: award PRED expected values: 01c92g 03qbh5 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 282): 01by1l (0.45 #2926, 0.39 #514, 0.33 #8554), 01bgqh (0.38 #2856, 0.33 #1248, 0.31 #846), 01d38g (0.33 #2841, 0.15 #1635, 0.12 #11685), 03qbh5 (0.33 #1411, 0.32 #3019, 0.23 #2215), 01c427 (0.30 #2898, 0.17 #10134, 0.15 #8526), 09sb52 (0.30 #7276, 0.21 #30998, 0.20 #28585), 0c4z8 (0.29 #1679, 0.26 #2885, 0.24 #3689), 054ks3 (0.28 #1347, 0.21 #5367, 0.21 #1749), 01cky2 (0.27 #3008, 0.15 #998, 0.11 #8636), 03qbnj (0.26 #3045, 0.16 #8673, 0.16 #10281) >> Best rule #2926 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 06cc_1; 01vvycq; 012x4t; 0cg9y; 01wwvc5; 01vx5w7; 01rm8b; 01s21dg; 0hvbj; 01dwrc; ... >> query: (?x4381, 01by1l) <- artists(?x3928, ?x4381), artists(?x671, ?x4381), ?x671 = 064t9, category(?x4381, ?x134), ?x3928 = 0gywn >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1411 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 01vsy9_; *> query: (?x4381, 03qbh5) <- nationality(?x4381, ?x1310), inductee(?x1091, ?x4381), artist(?x382, ?x4381), artists(?x671, ?x4381) *> conf = 0.33 ranks of expected_values: 4, 13 EVAL 0qf11 award 03qbh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 113.000 113.000 0.452 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0qf11 award 01c92g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 113.000 113.000 0.452 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3350-03ydlnj PRED entity: 03ydlnj PRED relation: genre PRED expected values: 04rlf => 138 concepts (91 used for prediction) PRED predicted values (max 10 best out of 107): 07ssc (0.62 #481, 0.61 #10222, 0.58 #10221), 01z4y (0.62 #481, 0.58 #10221, 0.57 #361), 02kdv5l (0.53 #2282, 0.47 #603, 0.45 #5047), 04xvh5 (0.50 #275, 0.25 #34, 0.23 #395), 01jfsb (0.41 #2292, 0.41 #3251, 0.39 #5057), 03k9fj (0.38 #1453, 0.34 #5056, 0.33 #252), 060__y (0.33 #257, 0.33 #136, 0.28 #736), 01hmnh (0.33 #258, 0.31 #378, 0.26 #5062), 04xvlr (0.33 #121, 0.31 #10102, 0.28 #962), 02n4kr (0.33 #248, 0.25 #7, 0.23 #368) >> Best rule #481 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 092vkg; 07sc6nw; 047tsx3; 05b6rdt; 0bw20; 02vz6dn; 02x0fs9; >> query: (?x8054, ?x512) <- titles(?x512, ?x8054), featured_film_locations(?x8054, ?x1310), film_crew_role(?x8054, ?x281), contains(?x1310, ?x892), nationality(?x3664, ?x1310), ?x3664 = 059xvg >> conf = 0.62 => this is the best rule for 2 predicted values *> Best rule #545 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 16 *> proper extension: 0c3z0; *> query: (?x8054, 04rlf) <- titles(?x512, ?x8054), featured_film_locations(?x8054, ?x1310), film_crew_role(?x8054, ?x281), contains(?x1310, ?x892), adjoins(?x1310, ?x4221), ?x281 = 02_n3z *> conf = 0.11 ranks of expected_values: 32 EVAL 03ydlnj genre 04rlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 138.000 91.000 0.625 http://example.org/film/film/genre #3349-03lpp_ PRED entity: 03lpp_ PRED relation: season PRED expected values: 0285r5d 027mvrc => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 11): 0285r5d (0.89 #311, 0.88 #289, 0.87 #278), 025ygws (0.83 #169, 0.74 #312, 0.74 #279), 027mvrc (0.83 #284, 0.81 #317, 0.80 #196), 026fmqm (0.81 #316, 0.79 #294, 0.78 #283), 025ygqm (0.81 #255, 0.79 #288, 0.79 #244), 05kcgsf (0.71 #78, 0.67 #122, 0.64 #155), 027pwzc (0.70 #315, 0.67 #293, 0.65 #282), 02h7s73 (0.56 #131, 0.55 #164, 0.50 #109), 04110b0 (0.56 #127, 0.50 #105, 0.50 #6), 03c6s24 (0.45 #165, 0.44 #132, 0.40 #198) >> Best rule #311 for best value: >> intensional similarity = 11 >> extensional distance = 25 >> proper extension: 049n7; >> query: (?x662, 0285r5d) <- team(?x4244, ?x662), colors(?x662, ?x663), season(?x662, ?x2406), colors(?x9879, ?x663), colors(?x4845, ?x663), colors(?x12612, ?x663), state_province_region(?x9879, ?x335), sport(?x12612, ?x471), currency(?x4845, ?x170), position(?x12612, ?x63), contains(?x550, ?x4845) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 03lpp_ season 027mvrc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 81.000 0.889 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 03lpp_ season 0285r5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.889 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #3348-026spg PRED entity: 026spg PRED relation: artists! PRED expected values: 03_d0 => 143 concepts (72 used for prediction) PRED predicted values (max 10 best out of 227): 06by7 (0.60 #13621, 0.50 #1258, 0.49 #9602), 03_d0 (0.54 #321, 0.22 #2484, 0.17 #4957), 0glt670 (0.46 #969, 0.39 #1896, 0.30 #3132), 025sc50 (0.38 #2214, 0.33 #6232, 0.32 #1596), 02lnbg (0.37 #2222, 0.26 #986, 0.24 #1913), 0ggx5q (0.36 #2241, 0.25 #6259, 0.23 #1932), 05bt6j (0.33 #45, 0.32 #2208, 0.29 #13644), 01lyv (0.27 #9615, 0.25 #4980, 0.22 #4671), 0xhtw (0.27 #1253, 0.22 #9597, 0.19 #11142), 016clz (0.23 #11130, 0.23 #17007, 0.22 #13604) >> Best rule #13621 for best value: >> intensional similarity = 3 >> extensional distance = 649 >> proper extension: 0123r4; >> query: (?x4675, 06by7) <- artists(?x3319, ?x4675), artists(?x3319, ?x8078), ?x8078 = 0134wr >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #321 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 22 *> proper extension: 05563d; 07yg2; *> query: (?x4675, 03_d0) <- artist(?x11171, ?x4675), artists(?x671, ?x4675), ?x11171 = 01xyqk *> conf = 0.54 ranks of expected_values: 2 EVAL 026spg artists! 03_d0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 72.000 0.598 http://example.org/music/genre/artists #3347-05p09zm PRED entity: 05p09zm PRED relation: award! PRED expected values: 07ymr5 01vwllw 02nwxc 0kjrx 032wdd 023zsh 04gr35 04zqmj 05wm88 => 49 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2881): 0dvmd (0.81 #39577, 0.81 #3296, 0.80 #36278), 0c6qh (0.81 #39577, 0.81 #3296, 0.80 #36278), 05dbf (0.81 #39577, 0.81 #3296, 0.80 #36278), 01ztgm (0.81 #39577, 0.81 #3296, 0.80 #36278), 0227vl (0.81 #3296, 0.80 #36278, 0.80 #9889), 014vk4 (0.81 #3296, 0.80 #36278, 0.80 #9889), 0pz91 (0.81 #3296, 0.80 #36278, 0.80 #9889), 0794g (0.81 #3296, 0.80 #36278, 0.80 #9889), 0m8_v (0.81 #3296, 0.80 #36278, 0.80 #9889), 02fcs2 (0.50 #13789, 0.50 #10493, 0.13 #52779) >> Best rule #39577 for best value: >> intensional similarity = 5 >> extensional distance = 94 >> proper extension: 02flpc; >> query: (?x2325, ?x2275) <- award_winner(?x2325, ?x2275), award_winner(?x2325, ?x521), award(?x286, ?x2325), artists(?x671, ?x521), vacationer(?x390, ?x2275) >> conf = 0.81 => this is the best rule for 4 predicted values *> Best rule #16284 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 05f4m9q; 05b1610; 03c7tr1; 07bdd_; *> query: (?x2325, 05wm88) <- award_winner(?x2325, ?x521), award(?x286, ?x2325), nominated_for(?x2325, ?x9379), ?x9379 = 09y6pb *> conf = 0.33 ranks of expected_values: 168, 221, 232, 435, 532, 685, 1707, 1880, 2379 EVAL 05p09zm award! 05wm88 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 49.000 22.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05p09zm award! 04zqmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 22.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05p09zm award! 04gr35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 22.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05p09zm award! 023zsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 22.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05p09zm award! 032wdd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 22.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05p09zm award! 0kjrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 22.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05p09zm award! 02nwxc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 22.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05p09zm award! 01vwllw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 22.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05p09zm award! 07ymr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 22.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3346-01wqlc PRED entity: 01wqlc PRED relation: parent_genre! PRED expected values: 0k345 => 60 concepts (35 used for prediction) PRED predicted values (max 10 best out of 299): 0283d (0.48 #1673, 0.17 #3181, 0.13 #1939), 01ym9b (0.30 #1627, 0.17 #3181, 0.13 #1893), 07lnk (0.22 #1613, 0.17 #3181, 0.10 #1854), 01wqlc (0.20 #327, 0.11 #7438, 0.11 #5311), 0l8gh (0.20 #412, 0.07 #3714, 0.03 #3448), 04b675 (0.20 #341, 0.06 #2726, 0.05 #2992), 064t9 (0.20 #273, 0.05 #1863, 0.05 #3182), 01wtlq (0.20 #277, 0.05 #3980, 0.05 #3182), 02yw0y (0.20 #388, 0.04 #1978, 0.03 #3448), 01fsz (0.20 #406, 0.03 #3448, 0.02 #1996) >> Best rule #1673 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 06__c; >> query: (?x5640, 0283d) <- parent_genre(?x497, ?x5640), parent_genre(?x2439, ?x497), artists(?x497, ?x8636), ?x8636 = 0k60 >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #3182 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 93 *> proper extension: 01gbcf; 02srgf; 01h0kx; 018ysx; 0bmfpc; 03gt7s; *> query: (?x5640, ?x1380) <- parent_genre(?x5640, ?x597), parent_genre(?x497, ?x5640), artists(?x497, ?x5494), parent_genre(?x2439, ?x497), artists(?x1380, ?x5494) *> conf = 0.05 ranks of expected_values: 163 EVAL 01wqlc parent_genre! 0k345 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 60.000 35.000 0.478 http://example.org/music/genre/parent_genre #3345-0c34mt PRED entity: 0c34mt PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 32): 0ch6mp2 (0.88 #323, 0.87 #287, 0.87 #500), 02r96rf (0.83 #425, 0.82 #319, 0.82 #283), 0dxtw (0.52 #433, 0.48 #46, 0.44 #291), 01pvkk (0.35 #434, 0.30 #2396, 0.30 #1857), 089fss (0.25 #286, 0.24 #322, 0.23 #499), 02rh1dz (0.22 #432, 0.21 #290, 0.18 #326), 0215hd (0.19 #440, 0.15 #1076, 0.14 #53), 015h31 (0.19 #431, 0.10 #467, 0.10 #396), 0d2b38 (0.17 #60, 0.16 #130, 0.15 #447), 089g0h (0.17 #54, 0.14 #124, 0.13 #441) >> Best rule #323 for best value: >> intensional similarity = 7 >> extensional distance = 137 >> proper extension: 025n07; >> query: (?x3531, 0ch6mp2) <- language(?x3531, ?x254), film_crew_role(?x3531, ?x3197), film_crew_role(?x3531, ?x137), genre(?x3531, ?x53), ?x3197 = 02ynfr, ?x137 = 09zzb8, film(?x1289, ?x3531) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c34mt film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.885 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3344-095zvfg PRED entity: 095zvfg PRED relation: crewmember! PRED expected values: 04vr_f 07cyl => 86 concepts (43 used for prediction) PRED predicted values (max 10 best out of 304): 0dtfn (0.17 #47, 0.16 #950, 0.15 #649), 024mpp (0.13 #1030, 0.10 #1331, 0.08 #127), 07nxnw (0.13 #1131, 0.10 #1432, 0.08 #228), 0bwfwpj (0.12 #34, 0.10 #937, 0.08 #636), 0jqn5 (0.12 #654, 0.12 #353, 0.10 #1256), 0hx4y (0.12 #697, 0.12 #396, 0.10 #998), 04gknr (0.12 #333, 0.08 #32, 0.08 #634), 031t2d (0.10 #1265, 0.08 #61, 0.06 #964), 01kff7 (0.10 #1250, 0.08 #46, 0.06 #949), 0ddjy (0.10 #983, 0.08 #80, 0.08 #682) >> Best rule #47 for best value: >> intensional similarity = 2 >> extensional distance = 22 >> proper extension: 05f260; >> query: (?x9151, 0dtfn) <- award(?x9151, ?x500), ?x500 = 0p9sw >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1317 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 03m49ly; *> query: (?x9151, 07cyl) <- crewmember(?x5361, ?x9151), crewmember(?x2402, ?x9151), production_companies(?x5361, ?x1914), film_crew_role(?x2402, ?x468) *> conf = 0.05 ranks of expected_values: 74 EVAL 095zvfg crewmember! 07cyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 86.000 43.000 0.167 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember EVAL 095zvfg crewmember! 04vr_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 43.000 0.167 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #3343-049mql PRED entity: 049mql PRED relation: film_crew_role PRED expected values: 09zzb8 01pvkk => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.79 #504, 0.77 #605, 0.73 #704), 01pvkk (0.38 #144, 0.32 #580, 0.32 #77), 02_n3z (0.25 #35, 0.11 #136, 0.10 #572), 02ynfr (0.22 #517, 0.22 #351, 0.21 #584), 02rh1dz (0.21 #579, 0.20 #478, 0.19 #312), 015h31 (0.20 #578, 0.18 #477, 0.18 #242), 0215hd (0.19 #49, 0.18 #586, 0.14 #16), 0d2b38 (0.18 #593, 0.17 #492, 0.16 #257), 089g0h (0.17 #587, 0.12 #520, 0.12 #354), 01xy5l_ (0.16 #582, 0.12 #515, 0.12 #349) >> Best rule #504 for best value: >> intensional similarity = 4 >> extensional distance = 254 >> proper extension: 01br2w; 04dsnp; 05dy7p; 040rmy; 02h22; 064lsn; 0gh6j94; 0gpx6; 03xj05; >> query: (?x4127, 09zzb8) <- films(?x12167, ?x4127), film_crew_role(?x4127, ?x1284), ?x1284 = 0ch6mp2, genre(?x4127, ?x53) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 049mql film_crew_role 01pvkk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.785 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 049mql film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.785 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3342-09kvv PRED entity: 09kvv PRED relation: colors PRED expected values: 07plts => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.37 #990, 0.37 #971, 0.35 #1199), 01g5v (0.28 #1201, 0.26 #973, 0.25 #992), 01l849 (0.27 #20, 0.25 #970, 0.25 #1198), 03wkwg (0.17 #147, 0.15 #109, 0.13 #204), 036k5h (0.17 #139, 0.10 #272, 0.09 #1203), 0jc_p (0.17 #5, 0.10 #100, 0.09 #24), 06fvc (0.15 #1200, 0.15 #972, 0.14 #991), 09ggk (0.14 #91, 0.06 #623, 0.06 #1003), 04mkbj (0.10 #104, 0.08 #997, 0.08 #218), 02rnmb (0.09 #31, 0.09 #145, 0.08 #69) >> Best rule #990 for best value: >> intensional similarity = 3 >> extensional distance = 341 >> proper extension: 03zw80; 021l5s; 01y9st; 0352gk; 01hnb; >> query: (?x1768, 083jv) <- state_province_region(?x1768, ?x1767), colors(?x1768, ?x4557), contains(?x94, ?x1768) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 10 *> proper extension: 06y3r; 023p29; 0n839; *> query: (?x1768, 07plts) <- list(?x1768, ?x2197), organizations_founded(?x1768, ?x5487) *> conf = 0.08 ranks of expected_values: 13 EVAL 09kvv colors 07plts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 99.000 99.000 0.367 http://example.org/education/educational_institution/colors #3341-01g7_r PRED entity: 01g7_r PRED relation: major_field_of_study PRED expected values: 03g3w => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 110): 04rjg (0.53 #2924, 0.50 #383, 0.40 #3892), 062z7 (0.45 #2690, 0.40 #391, 0.39 #2932), 01mkq (0.45 #2919, 0.42 #3040, 0.40 #3887), 03g3w (0.45 #2931, 0.40 #390, 0.32 #2689), 01lj9 (0.40 #402, 0.39 #2701, 0.37 #2943), 04x_3 (0.32 #2688, 0.22 #3898, 0.21 #3051), 02822 (0.32 #2702, 0.21 #2944, 0.20 #403), 0fdys (0.32 #2942, 0.30 #401, 0.29 #2700), 02h40lc (0.32 #2908, 0.23 #2666, 0.16 #3876), 02lp1 (0.31 #3883, 0.29 #1100, 0.29 #2915) >> Best rule #2924 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 07tgn; 07tg4; 01mpwj; 09hgk; 02f8zw; 017ztv; 023zl; >> query: (?x7092, 04rjg) <- contains(?x94, ?x7092), institution(?x1368, ?x7092), ?x1368 = 014mlp, company(?x1159, ?x7092) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #2931 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 07tgn; 07tg4; 01mpwj; 09hgk; 02f8zw; 017ztv; 023zl; *> query: (?x7092, 03g3w) <- contains(?x94, ?x7092), institution(?x1368, ?x7092), ?x1368 = 014mlp, company(?x1159, ?x7092) *> conf = 0.45 ranks of expected_values: 4 EVAL 01g7_r major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 170.000 170.000 0.526 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3340-026r8q PRED entity: 026r8q PRED relation: place_of_birth PRED expected values: 0yc84 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 75): 0mpbx (0.12 #442, 0.11 #1147, 0.01 #28875), 0s3pw (0.12 #643, 0.11 #1348, 0.01 #42963), 04f_d (0.12 #73, 0.11 #778), 0cr3d (0.12 #94, 0.03 #45875, 0.03 #26855), 01_d4 (0.11 #771, 0.03 #75419, 0.03 #53594), 013yq (0.11 #1488, 0.03 #4304, 0.01 #12049), 0psxp (0.11 #1620, 0.02 #4436, 0.01 #7957), 0853g (0.11 #1880, 0.01 #2584), 01ykl0 (0.11 #1591), 02_286 (0.10 #2836, 0.09 #4244, 0.08 #5652) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 03lq43; >> query: (?x7346, 0mpbx) <- award_winner(?x2580, ?x7346), ?x2580 = 0227tr, nationality(?x7346, ?x94) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026r8q place_of_birth 0yc84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 118.000 0.125 http://example.org/people/person/place_of_birth #3339-0l2wt PRED entity: 0l2wt PRED relation: currency PRED expected values: 09nqf => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.86 #9, 0.85 #41, 0.85 #40) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 0mmr1; 0mmty; 0kv7k; >> query: (?x9398, 09nqf) <- source(?x9398, ?x958), time_zones(?x9398, ?x2950), ?x2950 = 02lcqs, second_level_divisions(?x94, ?x9398) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l2wt currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.857 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #3338-04fzk PRED entity: 04fzk PRED relation: film PRED expected values: 0cp0t91 03wy8t => 99 concepts (75 used for prediction) PRED predicted values (max 10 best out of 751): 06ys2 (0.71 #10682, 0.70 #14243, 0.68 #17804), 099bhp (0.09 #1609, 0.05 #3390), 01shy7 (0.08 #7543, 0.06 #12884, 0.06 #9323), 0241y7 (0.06 #1064, 0.03 #2845, 0.02 #4625), 023p7l (0.06 #616, 0.03 #2397), 0_7w6 (0.06 #301, 0.03 #2082), 08r4x3 (0.05 #3715, 0.04 #9055, 0.04 #5495), 03bx2lk (0.05 #3746, 0.04 #9086, 0.03 #12647), 06_wqk4 (0.05 #5468, 0.05 #10809, 0.05 #14370), 0fphf3v (0.05 #6694, 0.05 #12035, 0.04 #15596) >> Best rule #10682 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 06jzh; 04shbh; >> query: (?x4106, ?x1490) <- participant(?x1896, ?x4106), participant(?x1733, ?x4106), nominated_for(?x4106, ?x1490) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #8698 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 137 *> proper extension: 023tp8; 01qscs; 04wqr; 014x77; 01kwld; 016khd; 03_vx9; 081lh; 0prjs; 01g257; ... *> query: (?x4106, 03wy8t) <- profession(?x4106, ?x1032), film(?x4106, ?x1490), celebrity(?x4106, ?x2626) *> conf = 0.01 ranks of expected_values: 502 EVAL 04fzk film 03wy8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 99.000 75.000 0.705 http://example.org/film/actor/film./film/performance/film EVAL 04fzk film 0cp0t91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 75.000 0.705 http://example.org/film/actor/film./film/performance/film #3337-0gd_s PRED entity: 0gd_s PRED relation: notable_people_with_this_condition! PRED expected values: 029sk => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 9): 0dcsx (0.09 #70, 0.07 #92, 0.03 #268), 068p_ (0.05 #196, 0.02 #306, 0.01 #570), 02vrr (0.04 #223, 0.03 #245, 0.01 #421), 03p41 (0.03 #270, 0.02 #292, 0.02 #314), 029sk (0.02 #287, 0.02 #375, 0.01 #463), 0h99n (0.02 #384, 0.01 #340, 0.01 #560), 01g2q (0.01 #779, 0.01 #846, 0.01 #449), 0g02vk (0.01 #518, 0.01 #628, 0.01 #650), 07jwr (0.01 #354) >> Best rule #70 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 07w21; 01dzz7; 0gd5z; 02yl42; 04r68; 018fq; 048_p; 07d3x; 0jt86; >> query: (?x9284, 0dcsx) <- award_winner(?x1288, ?x9284), award(?x9284, ?x12418), award(?x9284, ?x9285), ?x12418 = 045xh, ?x9285 = 0265vt >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #287 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 034bs; *> query: (?x9284, 029sk) <- award_winner(?x1288, ?x9284), award_winner(?x1288, ?x476), ?x476 = 07w21, influenced_by(?x9284, ?x1287) *> conf = 0.02 ranks of expected_values: 5 EVAL 0gd_s notable_people_with_this_condition! 029sk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 118.000 118.000 0.091 http://example.org/medicine/disease/notable_people_with_this_condition #3336-0fbvqf PRED entity: 0fbvqf PRED relation: nominated_for PRED expected values: 063ykwt => 53 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1416): 03d34x8 (0.77 #15717, 0.70 #23579, 0.65 #28300), 0fhzwl (0.56 #7592, 0.50 #6019, 0.33 #2877), 063ykwt (0.56 #6849, 0.50 #5276, 0.33 #2134), 07g9f (0.56 #7699, 0.50 #6126, 0.06 #13989), 07gbf (0.50 #6092, 0.33 #7665, 0.33 #2950), 01xr2s (0.44 #6573, 0.38 #5000, 0.04 #12863), 080dwhx (0.44 #6345, 0.33 #1630, 0.25 #4772), 04p5cr (0.38 #5712, 0.33 #7285, 0.33 #2570), 08bytj (0.38 #5880, 0.33 #7453, 0.33 #2738), 0pc_l (0.38 #6261, 0.33 #3119, 0.22 #7834) >> Best rule #15717 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 02py_sj; >> query: (?x783, ?x2009) <- nominated_for(?x783, ?x337), ceremony(?x783, ?x1265), award(?x2009, ?x783) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #6849 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 0ck27z; 0cqhb3; 0gkr9q; *> query: (?x783, 063ykwt) <- award(?x4248, ?x783), nominated_for(?x783, ?x4898), award_nominee(?x4248, ?x395), ?x4898 = 017f3m *> conf = 0.56 ranks of expected_values: 3 EVAL 0fbvqf nominated_for 063ykwt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 53.000 24.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3335-032xky PRED entity: 032xky PRED relation: genre PRED expected values: 03npn 01jfsb => 88 concepts (71 used for prediction) PRED predicted values (max 10 best out of 114): 01jfsb (0.65 #1920, 0.65 #2039, 0.58 #5621), 02kdv5l (0.60 #360, 0.32 #5492, 0.27 #6087), 05p553 (0.50 #600, 0.44 #7161, 0.43 #7280), 01z77k (0.41 #1668, 0.08 #1908, 0.08 #2504), 0lsxr (0.40 #1916, 0.40 #2035, 0.30 #5617), 03k9fj (0.40 #250, 0.37 #964, 0.33 #1202), 01hmnh (0.40 #257, 0.33 #971, 0.33 #495), 0gf28 (0.40 #422, 0.20 #660, 0.05 #6030), 02l7c8 (0.40 #6696, 0.36 #2162, 0.32 #7292), 060__y (0.32 #2163, 0.21 #6340, 0.20 #5507) >> Best rule #1920 for best value: >> intensional similarity = 6 >> extensional distance = 82 >> proper extension: 03g90h; 0yyg4; 0dq626; 03h_yy; 061681; 0dsvzh; 0jjy0; 04mzf8; 09p0ct; 03twd6; ... >> query: (?x11699, 01jfsb) <- film_release_distribution_medium(?x11699, ?x81), genre(?x11699, ?x600), genre(?x11699, ?x53), ?x53 = 07s9rl0, ?x600 = 02n4kr, ?x81 = 029j_ >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1, 17 EVAL 032xky genre 01jfsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 71.000 0.655 http://example.org/film/film/genre EVAL 032xky genre 03npn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 88.000 71.000 0.655 http://example.org/film/film/genre #3334-01y20v PRED entity: 01y20v PRED relation: major_field_of_study PRED expected values: 062z7 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 116): 01mkq (0.39 #3141, 0.36 #3891, 0.35 #2266), 02j62 (0.38 #3157, 0.36 #3907, 0.36 #3782), 02lp1 (0.36 #3137, 0.36 #3887, 0.29 #2262), 04rjg (0.35 #3146, 0.35 #2271, 0.33 #3771), 03g3w (0.33 #3153, 0.33 #3778, 0.29 #2278), 062z7 (0.31 #3154, 0.26 #3904, 0.25 #8529), 0g26h (0.30 #3919, 0.30 #3169, 0.22 #5419), 05qjt (0.28 #3133, 0.25 #3883, 0.20 #2258), 01tbp (0.24 #3187, 0.22 #3937, 0.16 #3812), 02_7t (0.23 #3192, 0.22 #3942, 0.17 #5442) >> Best rule #3141 for best value: >> intensional similarity = 3 >> extensional distance = 151 >> proper extension: 022r38; >> query: (?x6846, 01mkq) <- institution(?x1200, ?x6846), ?x1200 = 016t_3, student(?x6846, ?x5785) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #3154 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 151 *> proper extension: 022r38; *> query: (?x6846, 062z7) <- institution(?x1200, ?x6846), ?x1200 = 016t_3, student(?x6846, ?x5785) *> conf = 0.31 ranks of expected_values: 6 EVAL 01y20v major_field_of_study 062z7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 159.000 159.000 0.392 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3333-0ds2n PRED entity: 0ds2n PRED relation: music PRED expected values: 02g1jh => 93 concepts (38 used for prediction) PRED predicted values (max 10 best out of 75): 0147dk (0.07 #1901, 0.06 #7606, 0.06 #211), 08hp53 (0.07 #1901, 0.06 #7606, 0.06 #211), 02ts3h (0.07 #1901, 0.06 #7606, 0.06 #211), 016tt2 (0.07 #1901, 0.06 #7606, 0.06 #211), 02bh9 (0.06 #1741, 0.06 #51, 0.05 #3642), 0146pg (0.05 #3178, 0.05 #1278, 0.05 #642), 0150t6 (0.05 #891, 0.05 #46, 0.05 #1736), 0b6yp2 (0.05 #263, 0.02 #3855, 0.01 #5544), 02jxmr (0.04 #1764, 0.04 #4299, 0.03 #6620), 0csdzz (0.04 #1877, 0.04 #187, 0.02 #4412) >> Best rule #1901 for best value: >> intensional similarity = 4 >> extensional distance = 248 >> proper extension: 0gtvrv3; >> query: (?x3218, ?x521) <- film_crew_role(?x3218, ?x2154), ?x2154 = 01vx2h, nominated_for(?x521, ?x3218), film(?x434, ?x3218) >> conf = 0.07 => this is the best rule for 4 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 01gglm; *> query: (?x3218, 02g1jh) <- film_crew_role(?x3218, ?x2154), ?x2154 = 01vx2h, nominated_for(?x521, ?x3218), film_format(?x3218, ?x909) *> conf = 0.04 ranks of expected_values: 14 EVAL 0ds2n music 02g1jh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 93.000 38.000 0.070 http://example.org/film/film/music #3332-045nc5 PRED entity: 045nc5 PRED relation: country_of_origin PRED expected values: 09c7w0 03_3d => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 17): 03_3d (0.83 #149, 0.76 #138, 0.75 #127), 09c7w0 (0.81 #308, 0.81 #416, 0.80 #367), 0gqm3 (0.19 #124), 049yf (0.19 #124), 07ssc (0.14 #281, 0.11 #495, 0.09 #529), 0d060g (0.08 #219, 0.06 #161, 0.06 #172), 02jx1 (0.03 #544, 0.01 #353, 0.01 #497), 03h64 (0.03 #544), 0ctw_b (0.03 #544), 0f8l9c (0.03 #544) >> Best rule #149 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 088tp3; >> query: (?x13848, 03_3d) <- genre(?x13848, ?x5937), genre(?x13848, ?x2540), ?x2540 = 0hcr, ?x5937 = 0jxy >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 045nc5 country_of_origin 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.826 http://example.org/tv/tv_program/country_of_origin EVAL 045nc5 country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.826 http://example.org/tv/tv_program/country_of_origin #3331-0bs8d PRED entity: 0bs8d PRED relation: profession PRED expected values: 02jknp => 119 concepts (71 used for prediction) PRED predicted values (max 10 best out of 77): 02jknp (0.90 #883, 0.89 #299, 0.86 #1759), 0cbd2 (0.47 #2196, 0.44 #3073, 0.43 #4096), 03gjzk (0.45 #2349, 0.44 #2933, 0.43 #1911), 0kyk (0.32 #2218, 0.32 #3095, 0.28 #4118), 05z96 (0.27 #3652, 0.25 #4967, 0.16 #3108), 02hv44_ (0.27 #3652, 0.25 #4967, 0.13 #3123), 0xzm (0.27 #3652, 0.25 #4967, 0.02 #251), 09jwl (0.25 #163, 0.18 #8928, 0.15 #8782), 02krf9 (0.23 #1777, 0.23 #1339, 0.21 #2507), 018gz8 (0.23 #15, 0.20 #5712, 0.18 #6880) >> Best rule #883 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 05drq5; 036jb; 015njf; 01p1z_; 04v048; 06pjs; >> query: (?x5366, 02jknp) <- award(?x5366, ?x1107), ?x1107 = 019f4v, profession(?x5366, ?x319) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bs8d profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 71.000 0.896 http://example.org/people/person/profession #3330-05567m PRED entity: 05567m PRED relation: nominated_for! PRED expected values: 02r0csl => 101 concepts (94 used for prediction) PRED predicted values (max 10 best out of 191): 02r22gf (0.44 #1193, 0.39 #4455, 0.32 #3989), 0f_nbyh (0.43 #8, 0.12 #3736, 0.11 #3969), 099tbz (0.43 #45, 0.11 #3773, 0.09 #4239), 02r0csl (0.37 #4432, 0.27 #704, 0.25 #3500), 0p9sw (0.37 #3982, 0.35 #4448, 0.29 #21), 0gq_v (0.35 #4447, 0.30 #3515, 0.29 #3981), 05b4l5x (0.33 #239, 0.27 #705, 0.19 #2336), 07cbcy (0.33 #296, 0.27 #2626, 0.22 #1694), 05pcn59 (0.33 #532, 0.26 #9088, 0.25 #9089), 099c8n (0.33 #522, 0.21 #4483, 0.21 #3085) >> Best rule #1193 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 018nnz; 0ddt_; >> query: (?x9303, 02r22gf) <- film_distribution_medium(?x9303, ?x81), nominated_for(?x9303, ?x6751), ?x81 = 029j_, film(?x609, ?x9303) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #4432 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 03q5db; 0fh2v5; *> query: (?x9303, 02r0csl) <- nominated_for(?x640, ?x9303), film(?x609, ?x9303), titles(?x1510, ?x9303), ?x640 = 02hsq3m *> conf = 0.37 ranks of expected_values: 4 EVAL 05567m nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 101.000 94.000 0.438 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3329-01vrz41 PRED entity: 01vrz41 PRED relation: award PRED expected values: 01c427 099vwn 02f73b => 111 concepts (96 used for prediction) PRED predicted values (max 10 best out of 303): 02f5qb (0.55 #2445, 0.18 #17620, 0.16 #1679), 02f73p (0.53 #2473, 0.20 #174, 0.18 #17620), 02f72n (0.52 #2435, 0.20 #136, 0.18 #17620), 02f73b (0.48 #2566, 0.27 #1800, 0.24 #267), 09sb52 (0.41 #11531, 0.29 #7701, 0.28 #10765), 02v1m7 (0.35 #2406, 0.18 #17620, 0.15 #490), 099vwn (0.31 #583, 0.18 #1733, 0.18 #17620), 01ckcd (0.27 #2614, 0.13 #698, 0.11 #1465), 02f705 (0.26 #2442, 0.18 #1676, 0.18 #17620), 02f6ym (0.24 #1771, 0.20 #2537, 0.18 #17620) >> Best rule #2445 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 0lbj1; 089tm; 01pfr3; 02l840; 02r3zy; 01vsxdm; 01r9fv; 01v_pj6; 0dtd6; 01vs_v8; ... >> query: (?x1231, 02f5qb) <- award(?x1231, ?x4892), artist(?x1954, ?x1231), ?x4892 = 02f72_ >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2566 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 0lbj1; 089tm; 01pfr3; 02l840; 02r3zy; 01vsxdm; 01r9fv; 01v_pj6; 0dtd6; 01vs_v8; ... *> query: (?x1231, 02f73b) <- award(?x1231, ?x4892), artist(?x1954, ?x1231), ?x4892 = 02f72_ *> conf = 0.48 ranks of expected_values: 4, 7, 24 EVAL 01vrz41 award 02f73b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 96.000 0.545 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vrz41 award 099vwn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 111.000 96.000 0.545 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vrz41 award 01c427 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 111.000 96.000 0.545 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3328-0fh2v5 PRED entity: 0fh2v5 PRED relation: language PRED expected values: 02h40lc 04306rv 06nm1 0jzc => 92 concepts (79 used for prediction) PRED predicted values (max 10 best out of 56): 02h40lc (0.94 #2532, 0.94 #1089, 0.93 #1203), 04306rv (0.33 #118, 0.32 #290, 0.26 #577), 064_8sq (0.28 #249, 0.26 #422, 0.25 #77), 06b_j (0.25 #78, 0.22 #250, 0.16 #651), 0t_2 (0.17 #69, 0.07 #414, 0.07 #2413), 0jzc (0.15 #420, 0.11 #247, 0.08 #132), 06nm1 (0.11 #411, 0.11 #238, 0.10 #2772), 03_9r (0.09 #581, 0.07 #467, 0.07 #2413), 06mp7 (0.08 #128, 0.08 #14, 0.07 #2413), 02bv9 (0.08 #140, 0.08 #83, 0.07 #2413) >> Best rule #2532 for best value: >> intensional similarity = 7 >> extensional distance = 487 >> proper extension: 02vw1w2; 048rn; 01f39b; 014bpd; >> query: (?x9901, 02h40lc) <- genre(?x9901, ?x1509), genre(?x4610, ?x1509), genre(?x1224, ?x1509), ?x1224 = 020fcn, film(?x100, ?x9901), ?x4610 = 017jd9, language(?x9901, ?x90) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6, 7 EVAL 0fh2v5 language 0jzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 92.000 79.000 0.945 http://example.org/film/film/language EVAL 0fh2v5 language 06nm1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 92.000 79.000 0.945 http://example.org/film/film/language EVAL 0fh2v5 language 04306rv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 79.000 0.945 http://example.org/film/film/language EVAL 0fh2v5 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 79.000 0.945 http://example.org/film/film/language #3327-01pk3z PRED entity: 01pk3z PRED relation: profession PRED expected values: 018gz8 => 85 concepts (84 used for prediction) PRED predicted values (max 10 best out of 62): 0dxtg (0.38 #312, 0.33 #14, 0.28 #5826), 0np9r (0.38 #319, 0.28 #9243, 0.27 #8795), 01d_h8 (0.35 #751, 0.33 #6, 0.33 #5073), 0cbd2 (0.33 #156, 0.33 #7, 0.25 #305), 02jknp (0.33 #8, 0.30 #753, 0.22 #5075), 0kyk (0.33 #179, 0.12 #477, 0.12 #328), 0d1pc (0.28 #9243, 0.27 #8795, 0.25 #6409), 0n1h (0.28 #9243, 0.27 #8795, 0.25 #6409), 03gjzk (0.26 #1505, 0.25 #313, 0.23 #1654), 018gz8 (0.25 #315, 0.14 #3444, 0.14 #4488) >> Best rule #312 for best value: >> intensional similarity = 2 >> extensional distance = 6 >> proper extension: 0pgm3; >> query: (?x5541, 0dxtg) <- film(?x5541, ?x8072), ?x8072 = 02mc5v >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 6 *> proper extension: 0pgm3; *> query: (?x5541, 018gz8) <- film(?x5541, ?x8072), ?x8072 = 02mc5v *> conf = 0.25 ranks of expected_values: 10 EVAL 01pk3z profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 85.000 84.000 0.375 http://example.org/people/person/profession #3326-03gr7w PRED entity: 03gr7w PRED relation: location PRED expected values: 0r0m6 => 123 concepts (114 used for prediction) PRED predicted values (max 10 best out of 167): 030qb3t (0.23 #8916, 0.18 #1689, 0.18 #7310), 02_286 (0.17 #7264, 0.17 #13691, 0.16 #8067), 01_d4 (0.14 #102, 0.09 #1708, 0.07 #3314), 0f2tj (0.14 #328, 0.09 #1131, 0.02 #9161), 01531 (0.14 #158, 0.03 #11401, 0.03 #52362), 0_vw8 (0.14 #785), 05fkf (0.10 #4053, 0.02 #20117, 0.02 #24935), 07h34 (0.09 #1802, 0.08 #2605, 0.05 #3408), 0cr3d (0.09 #1751, 0.07 #5766, 0.06 #52349), 059rby (0.09 #1622, 0.06 #8849, 0.06 #8046) >> Best rule #8916 for best value: >> intensional similarity = 3 >> extensional distance = 113 >> proper extension: 0bg539; 03k7bd; 0tc7; 0q5hw; 03jjzf; 02f8lw; 01v3bn; 01pkhw; 0fby2t; 0ksrf8; ... >> query: (?x1795, 030qb3t) <- profession(?x1795, ?x220), award_nominee(?x226, ?x1795), student(?x8681, ?x1795) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #5839 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 079ws; 03gvpk; 011s9r; *> query: (?x1795, 0r0m6) <- profession(?x1795, ?x955), award_winner(?x1413, ?x1795), ?x955 = 0n1h *> conf = 0.04 ranks of expected_values: 33 EVAL 03gr7w location 0r0m6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 123.000 114.000 0.226 http://example.org/people/person/places_lived./people/place_lived/location #3325-04sqj PRED entity: 04sqj PRED relation: location! PRED expected values: 049fgvm => 271 concepts (197 used for prediction) PRED predicted values (max 10 best out of 2264): 01xndd (0.65 #115695, 0.25 #3298, 0.14 #8328), 09pl3f (0.65 #115695, 0.07 #57846, 0.06 #168511), 01ps2h8 (0.50 #3584, 0.29 #8614, 0.18 #16159), 023kzp (0.29 #8759, 0.27 #16304, 0.25 #3729), 02mjmr (0.29 #8045, 0.25 #3015, 0.18 #15590), 01p7yb (0.29 #7592, 0.25 #2562, 0.18 #15137), 0405l (0.29 #9747, 0.25 #4717, 0.18 #17292), 01gbn6 (0.29 #9472, 0.25 #4442, 0.15 #22047), 0151ns (0.27 #15174, 0.25 #2599, 0.23 #20204), 040db (0.27 #15479, 0.25 #2904, 0.15 #20509) >> Best rule #115695 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 019fh; 0pzmf; 071vr; 0gkgp; 0f2s6; 095l0; 0rk71; 0s987; >> query: (?x8181, ?x4035) <- location(?x2442, ?x8181), location_of_ceremony(?x566, ?x8181), program_creator(?x11895, ?x2442), program_creator(?x11895, ?x4035) >> conf = 0.65 => this is the best rule for 2 predicted values *> Best rule #72937 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0fs1v; *> query: (?x8181, ?x2167) <- capital(?x151, ?x8181), vacationer(?x151, ?x286), nationality(?x2167, ?x151) *> conf = 0.12 ranks of expected_values: 500 EVAL 04sqj location! 049fgvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 271.000 197.000 0.648 http://example.org/people/person/places_lived./people/place_lived/location #3324-0qdwr PRED entity: 0qdwr PRED relation: gender PRED expected values: 05zppz => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #29, 0.88 #45, 0.88 #13), 02zsn (0.28 #74, 0.28 #70, 0.27 #72) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 0gg9_5q; 0f7fy; 03y2kr; 012ycy; 07bty; 09gnn; 044kwr; 01hdht; 019fz; 026ck; ... >> query: (?x9837, 05zppz) <- organizations_founded(?x9837, ?x902), nationality(?x9837, ?x1003), type_of_union(?x9837, ?x566) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qdwr gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.893 http://example.org/people/person/gender #3323-01q8hj PRED entity: 01q8hj PRED relation: institution! PRED expected values: 014mlp => 130 concepts (75 used for prediction) PRED predicted values (max 10 best out of 23): 014mlp (0.76 #238, 0.75 #168, 0.71 #98), 02_xgp2 (0.62 #175, 0.58 #35, 0.48 #549), 03bwzr4 (0.61 #37, 0.52 #177, 0.49 #247), 016t_3 (0.55 #26, 0.50 #166, 0.43 #540), 0bkj86 (0.52 #171, 0.43 #101, 0.39 #31), 07s6fsf (0.40 #445, 0.35 #538, 0.33 #632), 04zx3q1 (0.37 #165, 0.35 #25, 0.28 #1322), 0bjrnt (0.31 #99, 0.28 #1322, 0.26 #29), 027f2w (0.31 #172, 0.28 #1322, 0.23 #32), 028dcg (0.29 #66, 0.23 #89, 0.19 #1466) >> Best rule #238 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 0kz2w; 09f2j; 022fj_; 02v992; >> query: (?x8589, 014mlp) <- currency(?x8589, ?x170), major_field_of_study(?x8589, ?x2606), ?x2606 = 062z7 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q8hj institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 75.000 0.759 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3322-01r32 PRED entity: 01r32 PRED relation: location! PRED expected values: 01k165 => 164 concepts (106 used for prediction) PRED predicted values (max 10 best out of 2177): 0l56b (0.49 #151047, 0.49 #118321, 0.47 #171187), 025_ql1 (0.49 #151047, 0.49 #118321, 0.47 #171187), 0pyww (0.25 #3498, 0.25 #981, 0.17 #8532), 01797x (0.25 #4610, 0.25 #2093, 0.17 #9644), 0151ns (0.25 #2601, 0.25 #84, 0.17 #7635), 05myd2 (0.25 #4444, 0.25 #1927, 0.17 #9478), 05mkhs (0.25 #3253, 0.25 #736, 0.11 #22658), 01w02sy (0.25 #3112, 0.25 #595, 0.11 #8146), 09fb5 (0.25 #2568, 0.25 #51, 0.11 #7602), 023kzp (0.25 #3733, 0.25 #1216, 0.11 #8767) >> Best rule #151047 for best value: >> intensional similarity = 3 >> extensional distance = 159 >> proper extension: 0g251; 09f8q; >> query: (?x1411, ?x2181) <- location(?x1410, ?x1411), state(?x1411, ?x10063), place_of_birth(?x2181, ?x1411) >> conf = 0.49 => this is the best rule for 2 predicted values *> Best rule #10661 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0179q0; *> query: (?x1411, 01k165) <- citytown(?x11632, ?x1411), contains(?x279, ?x1411), ?x279 = 0d060g *> conf = 0.06 ranks of expected_values: 853 EVAL 01r32 location! 01k165 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 164.000 106.000 0.488 http://example.org/people/person/places_lived./people/place_lived/location #3321-06y9c2 PRED entity: 06y9c2 PRED relation: profession PRED expected values: 012t_z => 153 concepts (93 used for prediction) PRED predicted values (max 10 best out of 82): 02hrh1q (0.91 #11263, 0.89 #13283, 0.88 #5345), 016z4k (0.56 #1301, 0.53 #2741, 0.53 #10389), 0dz3r (0.56 #434, 0.50 #8797, 0.47 #9087), 03gjzk (0.48 #2319, 0.38 #3328, 0.32 #4914), 01d_h8 (0.42 #1591, 0.41 #2311, 0.41 #4762), 01c72t (0.39 #2183, 0.38 #11128, 0.36 #9827), 0n1h (0.38 #2747, 0.36 #3180, 0.33 #442), 012wxt (0.36 #10818, 0.27 #10385, 0.11 #523), 0dxtg (0.34 #2317, 0.29 #5056, 0.28 #4336), 02jknp (0.34 #2312, 0.28 #1448, 0.28 #3321) >> Best rule #11263 for best value: >> intensional similarity = 5 >> extensional distance = 377 >> proper extension: 01k5t_3; 062dn7; 03q3sy; >> query: (?x677, 02hrh1q) <- profession(?x677, ?x1183), profession(?x677, ?x353), ?x1183 = 09jwl, profession(?x2845, ?x353), ?x2845 = 0lrh >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #1452 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 0grwj; *> query: (?x677, 012t_z) <- student(?x10341, ?x677), friend(?x677, ?x4741), colors(?x10341, ?x3189), participant(?x677, ?x2697) *> conf = 0.17 ranks of expected_values: 20 EVAL 06y9c2 profession 012t_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 153.000 93.000 0.905 http://example.org/people/person/profession #3320-014mlp PRED entity: 014mlp PRED relation: major_field_of_study PRED expected values: 06nm1 05qjc 02_7t 01zc2w 01x3g => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 125): 04x_3 (0.78 #818, 0.67 #327, 0.67 #622), 02_7t (0.67 #327, 0.67 #826, 0.60 #579), 02ky346 (0.67 #327, 0.67 #816, 0.60 #569), 02stgt (0.67 #327, 0.67 #835, 0.56 #884), 06mnr (0.67 #327, 0.67 #827, 0.56 #876), 0mkz (0.67 #327, 0.60 #572, 0.55 #141), 0db86 (0.67 #327, 0.56 #822, 0.55 #141), 01zc2w (0.67 #327, 0.55 #141, 0.51 #858), 01bt59 (0.67 #327, 0.55 #141, 0.51 #858), 01r4k (0.67 #327, 0.55 #141, 0.51 #858) >> Best rule #818 for best value: >> intensional similarity = 14 >> extensional distance = 7 >> proper extension: 01gkg3; >> query: (?x1368, 04x_3) <- institution(?x1368, ?x8095), institution(?x1368, ?x3204), student(?x1368, ?x6314), major_field_of_study(?x3204, ?x2014), award_nominee(?x539, ?x6314), major_field_of_study(?x1368, ?x2164), major_field_of_study(?x1368, ?x1695), film(?x6314, ?x2973), colors(?x8095, ?x332), language(?x11685, ?x2164), language(?x8955, ?x2164), student(?x1695, ?x3806), ?x11685 = 017n9, ?x8955 = 0g4pl7z >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #327 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 016t_3; *> query: (?x1368, ?x1668) <- institution(?x1368, ?x12293), institution(?x1368, ?x8220), institution(?x1368, ?x4603), institution(?x1368, ?x4410), institution(?x1368, ?x3821), institution(?x1368, ?x2948), ?x12293 = 01pj48, student(?x1368, ?x13084), student(?x1368, ?x9070), major_field_of_study(?x8220, ?x1668), major_field_of_study(?x8220, ?x1154), ?x4603 = 0hd7j, list(?x8220, ?x2197), state_province_region(?x3821, ?x108), ?x1154 = 02lp1, ?x2948 = 0j_sncb, major_field_of_study(?x1368, ?x254), gender(?x9070, ?x231), ?x4410 = 017j69, citytown(?x8220, ?x5783), ?x13084 = 01hbq0 *> conf = 0.67 ranks of expected_values: 2, 8, 27, 32, 39 EVAL 014mlp major_field_of_study 01x3g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 014mlp major_field_of_study 01zc2w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 014mlp major_field_of_study 02_7t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 014mlp major_field_of_study 05qjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 014mlp major_field_of_study 06nm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 25.000 25.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #3319-01vsxdm PRED entity: 01vsxdm PRED relation: role PRED expected values: 0l14qv 05r5c => 107 concepts (62 used for prediction) PRED predicted values (max 10 best out of 124): 0l14qv (0.73 #1545, 0.50 #312, 0.50 #210), 0342h (0.66 #2360, 0.55 #1440, 0.50 #209), 01vdm0 (0.64 #1571, 0.50 #338, 0.50 #236), 05r5c (0.62 #4636, 0.58 #1853, 0.54 #2159), 05842k (0.55 #1617, 0.50 #384, 0.50 #282), 01vj9c (0.55 #1555, 0.25 #4644, 0.25 #322), 042v_gx (0.54 #2364, 0.29 #828, 0.27 #1444), 018vs (0.36 #1553, 0.32 #2369, 0.25 #320), 05148p4 (0.36 #1562, 0.25 #329, 0.25 #227), 07brj (0.36 #1565, 0.25 #230, 0.09 #1461) >> Best rule #1545 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0892sx; 0137g1; 01lvcs1; 0f0qfz; 0fhxv; 0140t7; >> query: (?x1467, 0l14qv) <- role(?x1467, ?x5926), artists(?x302, ?x1467), ?x5926 = 0cfdd >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 01vsxdm role 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 107.000 62.000 0.727 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01vsxdm role 0l14qv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 62.000 0.727 http://example.org/music/artist/track_contributions./music/track_contribution/role #3318-0pqzh PRED entity: 0pqzh PRED relation: people! PRED expected values: 0gk4g => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 44): 01n3bm (0.33 #43, 0.07 #440, 0.02 #2156), 0dq9p (0.22 #876, 0.14 #2130, 0.12 #2460), 0gk4g (0.19 #2453, 0.18 #2123, 0.15 #1397), 01psyx (0.14 #442, 0.08 #177, 0.08 #243), 02k6hp (0.12 #1226, 0.08 #301, 0.08 #1688), 02y0js (0.11 #68, 0.07 #2115, 0.07 #3171), 01l2m3 (0.10 #1205, 0.08 #1667, 0.07 #413), 06z5s (0.09 #1346, 0.08 #157, 0.08 #223), 0dcsx (0.09 #874, 0.06 #1402, 0.05 #2128), 051_y (0.09 #1435, 0.07 #643, 0.03 #1105) >> Best rule #43 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 040db; >> query: (?x11404, 01n3bm) <- influenced_by(?x11404, ?x10000), influenced_by(?x11404, ?x3542), place_of_death(?x11404, ?x11086), ?x3542 = 03hnd, ?x10000 = 03j0d >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2453 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 94 *> proper extension: 0d9kl; 057ph; 0dng4; *> query: (?x11404, 0gk4g) <- celebrities_impersonated(?x3649, ?x11404), ?x3649 = 03m6t5 *> conf = 0.19 ranks of expected_values: 3 EVAL 0pqzh people! 0gk4g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 162.000 162.000 0.333 http://example.org/people/cause_of_death/people #3317-01wj92r PRED entity: 01wj92r PRED relation: profession PRED expected values: 02hrh1q => 132 concepts (130 used for prediction) PRED predicted values (max 10 best out of 93): 02hrh1q (0.87 #17349, 0.87 #17497, 0.86 #17793), 016z4k (0.57 #1040, 0.56 #2076, 0.56 #2372), 01d_h8 (0.50 #6, 0.46 #450, 0.45 #894), 0dz3r (0.45 #4596, 0.44 #150, 0.43 #742), 0dxtg (0.40 #2234, 0.29 #10092, 0.29 #7276), 039v1 (0.36 #776, 0.34 #2108, 0.31 #2404), 01c72t (0.31 #3282, 0.30 #5805, 0.26 #4468), 0n1h (0.29 #1048, 0.23 #4606, 0.22 #2084), 02jknp (0.28 #2228, 0.20 #15713, 0.20 #6233), 03gjzk (0.25 #2236, 0.24 #10094, 0.23 #7278) >> Best rule #17349 for best value: >> intensional similarity = 3 >> extensional distance = 2872 >> proper extension: 01p7b6b; >> query: (?x2806, 02hrh1q) <- profession(?x2806, ?x1183), profession(?x2359, ?x1183), ?x2359 = 0783m_ >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wj92r profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 130.000 0.869 http://example.org/people/person/profession #3316-05c1t6z PRED entity: 05c1t6z PRED relation: ceremony! PRED expected values: 0fbtbt 0gqmvn => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 309): 0gqy2 (0.80 #6759, 0.63 #6300, 0.50 #7449), 0gq_d (0.75 #6792, 0.58 #6333, 0.49 #7482), 0gqwc (0.73 #6701, 0.58 #6242, 0.49 #7391), 0k611 (0.73 #6714, 0.56 #6255, 0.49 #7404), 0gvx_ (0.72 #6772, 0.58 #6313, 0.48 #7462), 0gqyl (0.72 #6722, 0.55 #6263, 0.47 #7412), 018wng (0.71 #6679, 0.60 #6220, 0.47 #7138), 0f4x7 (0.71 #6671, 0.56 #6212, 0.47 #7130), 0p9sw (0.71 #6666, 0.52 #6207, 0.47 #7356), 0gq9h (0.69 #6702, 0.58 #6243, 0.47 #7161) >> Best rule #6759 for best value: >> intensional similarity = 16 >> extensional distance = 73 >> proper extension: 0h_9252; 0ds460j; >> query: (?x1265, 0gqy2) <- award_winner(?x1265, ?x9503), award_winner(?x1265, ?x4465), award_winner(?x1265, ?x1040), award_winner(?x1040, ?x7583), ceremony(?x2192, ?x1265), ceremony(?x757, ?x1265), award(?x1606, ?x2192), ?x1606 = 01xcqc, type_of_union(?x4465, ?x566), award_nominee(?x4328, ?x9503), award(?x6255, ?x757), award_nominee(?x6255, ?x496), film(?x6255, ?x1080), profession(?x4465, ?x353), place_of_birth(?x6255, ?x1860), award_nominee(?x4465, ?x3927) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2436 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 6 *> proper extension: 0hn821n; *> query: (?x1265, 0fbtbt) <- honored_for(?x1265, ?x8775), honored_for(?x1265, ?x3626), honored_for(?x1265, ?x3326), ceremony(?x6724, ?x1265), ceremony(?x870, ?x1265), ?x3626 = 01j7mr, award_winner(?x1265, ?x2127), award_winner(?x1265, ?x1422), award_winner(?x1265, ?x1285), ?x870 = 09qv3c, program_creator(?x8775, ?x1340), award_nominee(?x692, ?x1422), people(?x1050, ?x2127), award_winner(?x3326, ?x4563), nominated_for(?x6724, ?x337), place_of_birth(?x1422, ?x4743), nationality(?x1285, ?x94), award_winner(?x2127, ?x236), award_nominee(?x2156, ?x1285) *> conf = 0.62 ranks of expected_values: 19, 31 EVAL 05c1t6z ceremony! 0gqmvn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 36.000 36.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 05c1t6z ceremony! 0fbtbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 36.000 36.000 0.800 http://example.org/award/award_category/winners./award/award_honor/ceremony #3315-02p10m PRED entity: 02p10m PRED relation: company! PRED expected values: 060c4 => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 36): 0krdk (0.78 #2473, 0.74 #2077, 0.74 #1637), 060c4 (0.72 #3839, 0.70 #3927, 0.70 #3618), 0dq3c (0.69 #2468, 0.62 #1500, 0.56 #2248), 01yc02 (0.48 #1727, 0.47 #669, 0.46 #1507), 014l7h (0.40 #378, 0.36 #1568, 0.35 #950), 02k13d (0.40 #366, 0.25 #762, 0.24 #982), 01kr6k (0.31 #553, 0.29 #817, 0.27 #685), 0142rn (0.21 #2270, 0.17 #3571, 0.16 #2138), 02211by (0.17 #3571, 0.16 #2470, 0.15 #2250), 02y6fz (0.17 #3571, 0.15 #1740, 0.14 #2092) >> Best rule #2473 for best value: >> intensional similarity = 9 >> extensional distance = 49 >> proper extension: 01s73z; 04htfd; 0537b; 07gyp7; >> query: (?x11344, 0krdk) <- company(?x4792, ?x11344), company(?x4682, ?x11344), ?x4682 = 0dq_5, company(?x4792, ?x7008), company(?x4792, ?x5072), company(?x4792, ?x2607), ?x2607 = 01xdn1, ?x7008 = 03phgz, ?x5072 = 045c7b >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #3839 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 181 *> proper extension: 07wj1; 01j_x; 01skqzw; 07wh1; 07wg3; *> query: (?x11344, 060c4) <- company(?x4682, ?x11344), company(?x4682, ?x11727), company(?x4682, ?x11188), company(?x4682, ?x7390), company(?x4682, ?x3920), ?x11188 = 0z07, industry(?x7390, ?x3368), service_location(?x11727, ?x94), state_province_region(?x3920, ?x1227) *> conf = 0.72 ranks of expected_values: 2 EVAL 02p10m company! 060c4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 146.000 0.784 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #3314-01vd7hn PRED entity: 01vd7hn PRED relation: award_winner! PRED expected values: 013b2h => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 93): 013b2h (0.75 #221, 0.71 #80, 0.30 #503), 01c6qp (0.20 #301, 0.17 #4233, 0.17 #4232), 0gpjbt (0.20 #311, 0.17 #4233, 0.17 #4232), 0466p0j (0.17 #4233, 0.17 #4232, 0.17 #3808), 01bx35 (0.17 #4233, 0.17 #4232, 0.17 #3808), 01xqqp (0.17 #4233, 0.17 #4232, 0.17 #3808), 01mhwk (0.17 #4233, 0.17 #4232, 0.17 #3808), 05pd94v (0.17 #4233, 0.17 #4232, 0.17 #3808), 02cg41 (0.17 #4233, 0.17 #4232, 0.17 #3808), 09gkdln (0.17 #4233, 0.17 #4232, 0.17 #3808) >> Best rule #221 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0jdhp; 01k5t_3; 0m_v0; 01kstn9; 0x3b7; 01l03w2; >> query: (?x4101, 013b2h) <- award_nominee(?x4102, ?x4101), ?x4102 = 01m1dzc, award_winner(?x4101, ?x2929) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vd7hn award_winner! 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3313-01vn35l PRED entity: 01vn35l PRED relation: role PRED expected values: 01vdm0 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 120): 01vdm0 (0.71 #28, 0.57 #127, 0.38 #424), 018vs (0.35 #793, 0.35 #703, 0.31 #1685), 02sgy (0.35 #696, 0.29 #103, 0.29 #4), 07y_7 (0.31 #1685, 0.31 #4562, 0.31 #792), 0l14md (0.31 #1682, 0.31 #2382, 0.31 #1681), 02dlh2 (0.31 #1682, 0.31 #2382, 0.31 #1681), 03bx0bm (0.31 #1682, 0.31 #2382, 0.31 #1681), 02snj9 (0.31 #1682, 0.31 #2382, 0.31 #1681), 03qjg (0.31 #3768, 0.31 #3372, 0.29 #59), 042v_gx (0.31 #302, 0.29 #105, 0.29 #6) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0274ck; >> query: (?x2876, 01vdm0) <- performance_role(?x2876, ?x3214), ?x3214 = 02snj9, role(?x2876, ?x227), artists(?x378, ?x2876) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vn35l role 01vdm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #3312-03hdz8 PRED entity: 03hdz8 PRED relation: contains! PRED expected values: 0r02m => 146 concepts (84 used for prediction) PRED predicted values (max 10 best out of 222): 0r02m (0.79 #36676, 0.78 #46519, 0.76 #37571), 0345h (0.20 #23255, 0.18 #30413, 0.07 #1869), 02jx1 (0.16 #51973, 0.12 #45709, 0.12 #42129), 059rby (0.16 #12542, 0.14 #6283, 0.12 #32223), 05k7sb (0.14 #6395, 0.11 #1920, 0.10 #12654), 06pvr (0.12 #10898, 0.12 #19842, 0.02 #1953), 0kpys (0.12 #19857, 0.10 #10913, 0.01 #16278), 05tbn (0.11 #12745, 0.09 #222, 0.09 #6486), 030qb3t (0.09 #19777, 0.09 #10833, 0.08 #16198), 07ssc (0.09 #64445, 0.09 #65339, 0.08 #30445) >> Best rule #36676 for best value: >> intensional similarity = 4 >> extensional distance = 222 >> proper extension: 07lx1s; 02jyr8; 03zw80; 01v3ht; 01y9st; 0352gk; 02zcz3; 0ylsr; 057wlm; 05gm16l; ... >> query: (?x7178, ?x13255) <- citytown(?x7178, ?x13255), location(?x1092, ?x13255), colors(?x7178, ?x332), contains(?x94, ?x7178) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hdz8 contains! 0r02m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 84.000 0.786 http://example.org/location/location/contains #3311-0cv5l PRED entity: 0cv5l PRED relation: administrative_parent! PRED expected values: 0hc8h => 186 concepts (115 used for prediction) PRED predicted values (max 10 best out of 855): 01hvzr (0.33 #1171, 0.07 #6515, 0.07 #5922), 0crjn65 (0.33 #661, 0.07 #6005, 0.07 #5412), 0b_yz (0.33 #380, 0.05 #11074, 0.04 #12857), 0c9cw (0.33 #558, 0.05 #11252, 0.04 #13035), 01rvgx (0.33 #406, 0.05 #11100, 0.04 #12883), 0hc8h (0.30 #7720, 0.21 #4749, 0.18 #24361), 026mj (0.20 #1438, 0.04 #13322, 0.04 #19265), 07_f2 (0.20 #1431, 0.04 #13315, 0.04 #19258), 050ks (0.20 #1421, 0.04 #13305, 0.04 #19248), 05fjf (0.20 #1411, 0.04 #13295, 0.04 #19238) >> Best rule #1171 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0dbdy; >> query: (?x13888, 01hvzr) <- country(?x13888, ?x512), location_of_ceremony(?x566, ?x13888), administrative_parent(?x10338, ?x13888), second_level_divisions(?x1310, ?x13888) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7720 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0g14f; *> query: (?x13888, ?x12767) <- second_level_divisions(?x1310, ?x13888), ?x1310 = 02jx1, state(?x12767, ?x13888), contains(?x13888, ?x10338) *> conf = 0.30 ranks of expected_values: 6 EVAL 0cv5l administrative_parent! 0hc8h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 186.000 115.000 0.333 http://example.org/base/aareas/schema/administrative_area/administrative_parent #3310-0191n PRED entity: 0191n PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.83 #43, 0.83 #32, 0.82 #53), 02nxhr (0.04 #59, 0.03 #90, 0.03 #85), 07c52 (0.04 #34, 0.03 #39, 0.03 #18), 07z4p (0.03 #20, 0.03 #41, 0.02 #205) >> Best rule #43 for best value: >> intensional similarity = 3 >> extensional distance = 270 >> proper extension: 05p3738; 0992d9; 0bq6ntw; 01jr4j; 0ddbjy4; 09p5mwg; >> query: (?x5029, 029j_) <- genre(?x5029, ?x604), film(?x230, ?x5029), ?x604 = 0lsxr >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0191n film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.835 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #3309-047lj PRED entity: 047lj PRED relation: adjoins PRED expected values: 06bnz => 93 concepts (58 used for prediction) PRED predicted values (max 10 best out of 441): 06bnz (0.21 #27745, 0.21 #36983, 0.18 #20043), 047lj (0.21 #27745, 0.21 #36983, 0.18 #20043), 03rk0 (0.21 #27745, 0.21 #36983, 0.18 #20043), 01crd5 (0.21 #27745, 0.21 #36983, 0.18 #20043), 0jdd (0.21 #27745, 0.21 #36983, 0.18 #20043), 05b7q (0.21 #27745, 0.21 #36983, 0.18 #20043), 05sb1 (0.21 #27745, 0.21 #36983, 0.18 #20043), 04xn_ (0.21 #27745, 0.21 #36983, 0.18 #20043), 07bxhl (0.21 #27745, 0.21 #36983, 0.18 #20043), 016zwt (0.21 #27745, 0.21 #36983, 0.18 #20043) >> Best rule #27745 for best value: >> intensional similarity = 3 >> extensional distance = 106 >> proper extension: 080h2; 0135k2; 025r_t; 01zqy6t; >> query: (?x404, ?x3352) <- adjoins(?x404, ?x6305), teams(?x404, ?x11736), adjoins(?x3352, ?x6305) >> conf = 0.21 => this is the best rule for 14 predicted values ranks of expected_values: 1 EVAL 047lj adjoins 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 58.000 0.215 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #3308-01vrz41 PRED entity: 01vrz41 PRED relation: participant PRED expected values: 01vsgrn => 122 concepts (46 used for prediction) PRED predicted values (max 10 best out of 366): 043zg (0.83 #26913, 0.81 #17305, 0.80 #26272), 01wv9p (0.83 #26913, 0.81 #17305, 0.80 #26272), 0bmh4 (0.42 #7691, 0.41 #7689, 0.35 #5127), 09889g (0.42 #7691, 0.08 #344, 0.03 #6751), 01vsl3_ (0.41 #7689, 0.35 #5127, 0.35 #10256), 02wb6yq (0.10 #2147, 0.04 #7272, 0.03 #9839), 0c6qh (0.09 #1448, 0.08 #166, 0.06 #4652), 07r1h (0.09 #1694, 0.07 #7460, 0.06 #2335), 014zcr (0.09 #5145, 0.05 #7066, 0.04 #1300), 015f7 (0.08 #2157, 0.08 #234, 0.05 #7282) >> Best rule #26913 for best value: >> intensional similarity = 3 >> extensional distance = 581 >> proper extension: 01n5309; 01j5x6; 0n6f8; 01qvgl; 0993r; 01w02sy; 01jbx1; 04gycf; 06wm0z; 01pqy_; ... >> query: (?x1231, ?x4123) <- participant(?x1231, ?x2647), profession(?x1231, ?x131), participant(?x4123, ?x1231) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #1014 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 01w61th; 01vn35l; 0hvbj; 01dwrc; 01dq9q; 0bs1g5r; *> query: (?x1231, 01vsgrn) <- award_nominee(?x1231, ?x215), award(?x1231, ?x3488), ?x3488 = 02f71y *> conf = 0.03 ranks of expected_values: 134 EVAL 01vrz41 participant 01vsgrn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 122.000 46.000 0.827 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #3307-0jswp PRED entity: 0jswp PRED relation: honored_for! PRED expected values: 05hmp6 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 115): 0ftlkg (0.33 #20, 0.02 #508, 0.02 #264), 0fz0c2 (0.25 #213, 0.02 #579, 0.02 #335), 05c1t6z (0.13 #621, 0.03 #1841, 0.03 #2085), 02q690_ (0.12 #664, 0.04 #1884, 0.03 #2128), 0gvstc3 (0.11 #637, 0.02 #1857, 0.02 #2101), 03nnm4t (0.09 #673, 0.03 #1893, 0.02 #2137), 0gx_st (0.07 #640, 0.02 #1860, 0.01 #2104), 0lp_cd3 (0.06 #627, 0.02 #2091, 0.02 #1847), 0275n3y (0.04 #674, 0.03 #1894, 0.02 #2138), 0bxs_d (0.04 #710, 0.02 #1930, 0.01 #2174) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0gzy02; >> query: (?x3369, 0ftlkg) <- film(?x7676, ?x3369), award_winner(?x3369, ?x7130), ?x7130 = 06kxk2, participant(?x5239, ?x7676) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2197 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 682 *> proper extension: 03j63k; 097h2; 019g8j; 0147w8; 0300ml; *> query: (?x3369, ?x78) <- award(?x3369, ?x591), nominated_for(?x601, ?x3369), nominated_for(?x591, ?x54), ceremony(?x591, ?x78) *> conf = 0.02 ranks of expected_values: 80 EVAL 0jswp honored_for! 05hmp6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 82.000 82.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3306-015f7 PRED entity: 015f7 PRED relation: artist! PRED expected values: 03mp8k => 122 concepts (104 used for prediction) PRED predicted values (max 10 best out of 109): 015_1q (0.22 #5029, 0.21 #3083, 0.20 #2386), 0mzkr (0.20 #26, 0.14 #165, 0.11 #582), 03rhqg (0.20 #2382, 0.13 #10594, 0.13 #5025), 033hn8 (0.19 #709, 0.15 #2380, 0.14 #848), 017l96 (0.19 #714, 0.14 #853, 0.14 #158), 02swsm (0.18 #371, 0.09 #3156, 0.07 #232), 03mp8k (0.14 #760, 0.14 #204, 0.11 #899), 01trtc (0.14 #210, 0.12 #2437, 0.10 #766), 04fc6c (0.14 #214, 0.11 #631, 0.10 #75), 0n85g (0.13 #5070, 0.12 #2427, 0.11 #3124) >> Best rule #5029 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 013rds; >> query: (?x3397, 015_1q) <- award_winner(?x154, ?x3397), film(?x3397, ?x2084), artist(?x5666, ?x3397) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #760 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 01wphh2; *> query: (?x3397, 03mp8k) <- special_performance_type(?x3397, ?x4832), artists(?x671, ?x3397), artist(?x5666, ?x3397) *> conf = 0.14 ranks of expected_values: 7 EVAL 015f7 artist! 03mp8k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 122.000 104.000 0.224 http://example.org/music/record_label/artist #3305-025twgt PRED entity: 025twgt PRED relation: story_by PRED expected values: 0fx02 => 118 concepts (93 used for prediction) PRED predicted values (max 10 best out of 87): 0fx02 (0.57 #60, 0.36 #926, 0.35 #1576), 01y8d4 (0.19 #1437, 0.10 #3600, 0.10 #2301), 011s9r (0.19 #1498, 0.10 #3661, 0.10 #2362), 03kpvp (0.14 #61, 0.07 #927, 0.06 #1577), 042xh (0.12 #2811, 0.11 #3245, 0.10 #3895), 0343h (0.08 #4563, 0.07 #2182, 0.07 #1966), 05gpy (0.07 #978, 0.07 #1195), 09pl3f (0.07 #755, 0.06 #1405, 0.03 #3351), 0kb3n (0.07 #2307, 0.07 #2091, 0.06 #2523), 01lc5 (0.07 #1266) >> Best rule #60 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 02sg5v; >> query: (?x11362, 0fx02) <- genre(?x11362, ?x225), nominated_for(?x11362, ?x11120), nominated_for(?x11362, ?x5399), nominated_for(?x11362, ?x1851), ?x1851 = 01kf3_9, ?x5399 = 0fsw_7, language(?x11362, ?x5671), ?x11120 = 0fztbq >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025twgt story_by 0fx02 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 93.000 0.571 http://example.org/film/film/story_by #3304-02nf2c PRED entity: 02nf2c PRED relation: nominated_for! PRED expected values: 0bp_b2 => 73 concepts (62 used for prediction) PRED predicted values (max 10 best out of 229): 0m7yy (0.69 #1882, 0.67 #2588, 0.67 #2589), 0cqhk0 (0.68 #501, 0.62 #266, 0.62 #31), 05pcn59 (0.66 #3595, 0.07 #4770, 0.06 #12946), 09qvf4 (0.62 #381, 0.62 #146, 0.58 #616), 0cqhmg (0.62 #217, 0.58 #687, 0.50 #452), 09sb52 (0.59 #4738, 0.10 #11767, 0.08 #10855), 09qs08 (0.58 #578, 0.56 #343, 0.54 #108), 099c8n (0.40 #4760, 0.17 #10877, 0.16 #10407), 09qj50 (0.38 #37, 0.38 #272, 0.37 #507), 0gq9h (0.36 #4766, 0.35 #10883, 0.32 #12535) >> Best rule #1882 for best value: >> intensional similarity = 6 >> extensional distance = 69 >> proper extension: 015g28; 06w7mlh; >> query: (?x871, ?x3486) <- titles(?x2008, ?x871), genre(?x871, ?x258), program(?x1762, ?x871), award(?x871, ?x3486), award(?x871, ?x2016), award(?x201, ?x2016) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #12473 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 891 *> proper extension: 03rg2b; *> query: (?x871, ?x678) <- award_winner(?x871, ?x1762), award_winner(?x2078, ?x1762), award(?x2078, ?x678), honored_for(?x1112, ?x2078), nominated_for(?x1762, ?x782) *> conf = 0.22 ranks of expected_values: 27 EVAL 02nf2c nominated_for! 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 73.000 62.000 0.691 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3303-02hfk5 PRED entity: 02hfk5 PRED relation: nominated_for! PRED expected values: 0gr51 02qwdhq => 123 concepts (80 used for prediction) PRED predicted values (max 10 best out of 224): 02y_j8g (0.78 #237, 0.70 #1418, 0.68 #4965), 0468g4r (0.78 #237, 0.70 #1418, 0.68 #4965), 02qwdhq (0.75 #108, 0.11 #1762, 0.09 #6860), 03hl6lc (0.56 #1076, 0.45 #1312, 0.24 #1549), 0gr51 (0.53 #1023, 0.52 #1259, 0.23 #1968), 099c8n (0.50 #1237, 0.50 #1001, 0.34 #4548), 02pqp12 (0.50 #1239, 0.41 #1003, 0.26 #5971), 02qsfzv (0.50 #197, 0.09 #6860, 0.07 #18920), 02qyntr (0.45 #1361, 0.44 #1125, 0.27 #6093), 0gs9p (0.45 #1245, 0.40 #5265, 0.38 #5977) >> Best rule #237 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 014bpd; >> query: (?x4844, ?x77) <- titles(?x53, ?x4844), award(?x4844, ?x77), nominated_for(?x5824, ?x4844), currency(?x4844, ?x170), ?x5824 = 02qysm0 >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #108 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 014bpd; *> query: (?x4844, 02qwdhq) <- titles(?x53, ?x4844), award(?x4844, ?x77), nominated_for(?x5824, ?x4844), currency(?x4844, ?x170), ?x5824 = 02qysm0 *> conf = 0.75 ranks of expected_values: 3, 5 EVAL 02hfk5 nominated_for! 02qwdhq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 80.000 0.783 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02hfk5 nominated_for! 0gr51 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 123.000 80.000 0.783 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3302-03_6y PRED entity: 03_6y PRED relation: award_nominee PRED expected values: 015t56 014488 01mqc_ => 107 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1163): 014gf8 (0.86 #6993, 0.84 #9325, 0.81 #83916), 06151l (0.86 #6993, 0.84 #9325, 0.81 #83916), 015t56 (0.86 #6993, 0.84 #9325, 0.81 #83916), 014488 (0.86 #6993, 0.84 #9325, 0.81 #83916), 08swgx (0.86 #6993, 0.84 #9325, 0.81 #83916), 01d1st (0.86 #6993, 0.84 #9325, 0.81 #83916), 05cx7x (0.86 #6993, 0.84 #9325, 0.81 #83916), 03_6y (0.67 #5440, 0.55 #19428, 0.50 #7772), 01mqc_ (0.67 #6339, 0.50 #8671, 0.48 #20327), 0dlglj (0.33 #18986, 0.15 #132884, 0.13 #69931) >> Best rule #6993 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01p4vl; >> query: (?x3466, ?x221) <- vacationer(?x390, ?x3466), award_nominee(?x3324, ?x3466), award_nominee(?x221, ?x3466), ?x3324 = 014488 >> conf = 0.86 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 4, 9 EVAL 03_6y award_nominee 01mqc_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 107.000 59.000 0.859 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 03_6y award_nominee 014488 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 107.000 59.000 0.859 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 03_6y award_nominee 015t56 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 107.000 59.000 0.859 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3301-03p41 PRED entity: 03p41 PRED relation: notable_people_with_this_condition PRED expected values: 02fgp0 016dgz => 72 concepts (45 used for prediction) PRED predicted values (max 10 best out of 194): 0484q (0.33 #1739, 0.22 #3065, 0.20 #1072), 0n839 (0.33 #1773, 0.22 #3099, 0.15 #4100), 06x58 (0.33 #1686, 0.22 #3012, 0.15 #4013), 01pw2f1 (0.33 #1682, 0.22 #3008, 0.15 #4009), 0gt_k (0.33 #1777, 0.20 #1021, 0.20 #910), 01x6jd (0.33 #1777), 067sqt (0.33 #1777), 0405l (0.33 #1777), 0hwqg (0.33 #1777), 06jz0 (0.33 #1777) >> Best rule #1739 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 029sk; 0h99n; 0j8hd; >> query: (?x6656, 0484q) <- notable_people_with_this_condition(?x6656, ?x4142), artist(?x2190, ?x4142), profession(?x4142, ?x955), ?x955 = 0n1h, category(?x4142, ?x134), location(?x4142, ?x1860), gender(?x4142, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03p41 notable_people_with_this_condition 016dgz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 45.000 0.333 http://example.org/medicine/disease/notable_people_with_this_condition EVAL 03p41 notable_people_with_this_condition 02fgp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 45.000 0.333 http://example.org/medicine/disease/notable_people_with_this_condition #3300-0244r8 PRED entity: 0244r8 PRED relation: category PRED expected values: 08mbj5d => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #22, 0.79 #38, 0.79 #17) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 330 >> proper extension: 01vw87c; 0kzy0; 0152cw; 02whj; 0lgsq; 01qvgl; 01wp8w7; 01bpc9; 01vyp_; 01vvpjj; ... >> query: (?x1489, 08mbj5d) <- profession(?x1489, ?x1183), award_winner(?x1443, ?x1489), instrumentalists(?x614, ?x1489) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0244r8 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.804 http://example.org/common/topic/webpage./common/webpage/category #3299-06pr6 PRED entity: 06pr6 PRED relation: place_of_death! PRED expected values: 063tn => 295 concepts (129 used for prediction) PRED predicted values (max 10 best out of 808): 05hks (0.12 #1293, 0.09 #6560, 0.08 #12039), 08959 (0.11 #3741, 0.11 #2236, 0.10 #4493), 0835q (0.11 #3663, 0.11 #2158, 0.10 #4415), 03_nq (0.11 #3468, 0.11 #1963, 0.10 #4220), 0c_jc (0.11 #3272, 0.11 #1767, 0.10 #4024), 0dq2k (0.11 #3253, 0.11 #1748, 0.10 #4005), 083pr (0.11 #3071, 0.11 #1566, 0.10 #3823), 0jf1b (0.11 #3030, 0.11 #1525, 0.10 #3782), 0l99s (0.11 #2596, 0.11 #1844, 0.10 #4101), 034bs (0.11 #2415, 0.11 #1663, 0.10 #3920) >> Best rule #1293 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 01cr28; 020lpx; 020vx9; 02_jjm; >> query: (?x7184, 05hks) <- category(?x7184, ?x134), contains(?x1603, ?x7184), ?x134 = 08mbj5d, ?x1603 = 06bnz >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #88851 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 94 *> proper extension: 01r32; 06wxw; 07tcs; 074r0; *> query: (?x7184, ?x889) <- citytown(?x10223, ?x7184), location_of_ceremony(?x566, ?x7184), place_of_birth(?x2693, ?x7184), student(?x10223, ?x889) *> conf = 0.03 ranks of expected_values: 636 EVAL 06pr6 place_of_death! 063tn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 295.000 129.000 0.125 http://example.org/people/deceased_person/place_of_death #3298-0jdgr PRED entity: 0jdgr PRED relation: film! PRED expected values: 02tv80 050zr4 => 56 concepts (35 used for prediction) PRED predicted values (max 10 best out of 907): 0284n42 (0.46 #70757, 0.44 #39538, 0.43 #68675), 02wgln (0.22 #314, 0.12 #4474, 0.08 #6554), 01vvb4m (0.22 #521, 0.08 #6761, 0.06 #4681), 017r13 (0.17 #7352, 0.11 #1112, 0.06 #5272), 026dx (0.14 #12484, 0.13 #18728), 012q4n (0.13 #3218, 0.04 #24972, 0.02 #13622), 0dzf_ (0.13 #2890, 0.02 #42429, 0.02 #40349), 030h95 (0.13 #2369, 0.02 #16935, 0.01 #33584), 019f2f (0.13 #2517), 09fb5 (0.12 #6296, 0.12 #4216, 0.11 #56) >> Best rule #70757 for best value: >> intensional similarity = 3 >> extensional distance = 1363 >> proper extension: 01h72l; 03bzyn4; >> query: (?x2475, ?x666) <- nominated_for(?x666, ?x2475), genre(?x2475, ?x53), gender(?x666, ?x231) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #17779 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 173 *> proper extension: 0372j5; *> query: (?x2475, 02tv80) <- cinematography(?x2475, ?x5528), film(?x230, ?x2475), film(?x4703, ?x2475), film_release_distribution_medium(?x2475, ?x81) *> conf = 0.01 ranks of expected_values: 820 EVAL 0jdgr film! 050zr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 35.000 0.457 http://example.org/film/actor/film./film/performance/film EVAL 0jdgr film! 02tv80 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 56.000 35.000 0.457 http://example.org/film/actor/film./film/performance/film #3297-0gnkb PRED entity: 0gnkb PRED relation: film! PRED expected values: 0m6x4 => 78 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1073): 0c3dzk (0.44 #22918), 025vry (0.44 #22918), 02qx1m2 (0.42 #22919, 0.33 #12499, 0.33 #14583), 086k8 (0.42 #22919, 0.33 #12499, 0.33 #14583), 03n6r (0.14 #951, 0.07 #5117, 0.07 #7200), 012dtf (0.14 #1234, 0.05 #7483, 0.04 #5400), 0c2tf (0.14 #1339, 0.05 #7588, 0.01 #25005), 0g10g (0.14 #1831, 0.04 #5997, 0.02 #8080), 02drd3 (0.14 #1982, 0.02 #8231, 0.01 #25005), 0436zq (0.14 #1902, 0.02 #8151) >> Best rule #22918 for best value: >> intensional similarity = 4 >> extensional distance = 270 >> proper extension: 01gglm; >> query: (?x6890, ?x681) <- award_winner(?x6890, ?x681), titles(?x53, ?x6890), nominated_for(?x382, ?x6890), ?x53 = 07s9rl0 >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #5770 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 05css_; *> query: (?x6890, 0m6x4) <- genre(?x6890, ?x1805), film(?x382, ?x6890), ?x1805 = 01g6gs, music(?x6890, ?x681) *> conf = 0.04 ranks of expected_values: 161 EVAL 0gnkb film! 0m6x4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 78.000 13.000 0.441 http://example.org/film/actor/film./film/performance/film #3296-01ck6h PRED entity: 01ck6h PRED relation: award! PRED expected values: 03h_fqv 01wqpnm => 41 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2373): 0134pk (0.70 #22852, 0.50 #19505, 0.33 #9466), 0c9l1 (0.70 #22996, 0.33 #6264, 0.08 #29688), 0dvqq (0.60 #20699, 0.50 #17352, 0.33 #7313), 0frsw (0.50 #20737, 0.50 #17390, 0.33 #7351), 0dtd6 (0.50 #20597, 0.50 #17250, 0.33 #7211), 03h_fk5 (0.50 #14142, 0.33 #10796, 0.33 #4103), 0kr_t (0.50 #21676, 0.33 #8290, 0.33 #4944), 07r1_ (0.50 #22117, 0.33 #8731, 0.33 #5385), 0gr69 (0.50 #15454, 0.33 #12108, 0.33 #8761), 06mj4 (0.50 #22388, 0.33 #9002, 0.33 #5656) >> Best rule #22852 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 02f5qb; 02f716; 02f72_; 02f77l; 01d38t; 02x4wb; >> query: (?x2322, 0134pk) <- award(?x13142, ?x2322), award(?x4712, ?x2322), award(?x1092, ?x2322), artists(?x505, ?x1092), ?x13142 = 0jg77, role(?x1092, ?x227), award_winner(?x1480, ?x4712) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #12881 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 02f6xy; *> query: (?x2322, 01wqpnm) <- award(?x5285, ?x2322), award(?x4712, ?x2322), award(?x2784, ?x2322), award(?x300, ?x2322), ?x4712 = 03f0fnk, ?x5285 = 01xzb6, ?x300 = 01vw87c, ?x2784 = 0137g1 *> conf = 0.33 ranks of expected_values: 45, 833 EVAL 01ck6h award! 01wqpnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 41.000 15.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01ck6h award! 03h_fqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 15.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3295-047fjjr PRED entity: 047fjjr PRED relation: produced_by PRED expected values: 06q8hf => 90 concepts (57 used for prediction) PRED predicted values (max 10 best out of 180): 02xnjd (0.22 #661, 0.10 #7638, 0.09 #1049), 05prs8 (0.20 #54, 0.05 #7419, 0.04 #9365), 06dkzt (0.20 #300, 0.03 #3790, 0.02 #11946), 06cv1 (0.14 #1570, 0.02 #8552, 0.01 #7385), 04pqqb (0.11 #566, 0.06 #3668, 0.04 #7155), 0h1p (0.11 #454, 0.04 #2393, 0.03 #3556), 05zh9c (0.11 #563, 0.03 #2889, 0.03 #3665), 026c1 (0.11 #457, 0.03 #3559, 0.02 #4723), 08hp53 (0.11 #451, 0.03 #3553, 0.02 #4717), 043q6n_ (0.11 #439, 0.02 #4705, 0.01 #9362) >> Best rule #661 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 048yqf; >> query: (?x3850, 02xnjd) <- music(?x3850, ?x84), executive_produced_by(?x3850, ?x4060), film_crew_role(?x3850, ?x1284), language(?x3850, ?x2164), ?x1284 = 0ch6mp2, ?x2164 = 03_9r >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1801 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 025s1wg; *> query: (?x3850, 06q8hf) <- music(?x3850, ?x84), executive_produced_by(?x3850, ?x4060), ?x4060 = 05hj_k, currency(?x3850, ?x170), film(?x4859, ?x3850), film_release_distribution_medium(?x3850, ?x81) *> conf = 0.07 ranks of expected_values: 24 EVAL 047fjjr produced_by 06q8hf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 90.000 57.000 0.222 http://example.org/film/film/produced_by #3294-04mhl PRED entity: 04mhl PRED relation: profession PRED expected values: 0kyk => 92 concepts (71 used for prediction) PRED predicted values (max 10 best out of 75): 0kyk (0.69 #1073, 0.61 #477, 0.60 #1520), 02hrh1q (0.62 #8965, 0.62 #7621, 0.61 #9561), 0dxtg (0.47 #3441, 0.45 #3143, 0.44 #4186), 01d_h8 (0.34 #5224, 0.33 #5374, 0.33 #5524), 02jknp (0.30 #5225, 0.29 #5525, 0.29 #5375), 03gjzk (0.26 #3145, 0.22 #6131, 0.22 #3592), 09jwl (0.22 #8074, 0.17 #1956, 0.16 #9417), 018gz8 (0.21 #3296, 0.19 #3892, 0.19 #2998), 05z96 (0.21 #2278, 0.14 #2875, 0.13 #2725), 02hv44_ (0.17 #2293, 0.14 #3486, 0.13 #10593) >> Best rule #1073 for best value: >> intensional similarity = 6 >> extensional distance = 30 >> proper extension: 05jm7; 0klw; 049gc; 014ps4; 06bng; 07zl1; 05cv8; 042xh; >> query: (?x4417, 0kyk) <- award(?x4417, ?x3337), award(?x4417, ?x1288), award(?x576, ?x1288), ?x576 = 01zkxv, ?x3337 = 01yz0x, disciplines_or_subjects(?x1288, ?x1013) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mhl profession 0kyk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 71.000 0.688 http://example.org/people/person/profession #3293-019lty PRED entity: 019lty PRED relation: teams! PRED expected values: 06gmr => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 217): 04lh6 (0.25 #456, 0.25 #186, 0.17 #996), 0htqt (0.25 #489, 0.17 #759, 0.12 #2380), 0jp26 (0.17 #1180, 0.09 #2530, 0.08 #2800), 0b2h3 (0.17 #1221, 0.09 #2571, 0.08 #2841), 01f62 (0.17 #594, 0.04 #6535, 0.04 #6805), 013yq (0.17 #613, 0.04 #6824, 0.03 #7365), 04swd (0.09 #2608, 0.08 #5848, 0.08 #2878), 01ly5m (0.09 #2517, 0.08 #2787, 0.07 #3327), 0jgd (0.09 #2434, 0.08 #2704, 0.07 #3244), 0947l (0.08 #6393, 0.06 #4233, 0.06 #7744) >> Best rule #456 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 0kwv2; >> query: (?x4802, 04lh6) <- category(?x4802, ?x134), position(?x4802, ?x530), position(?x4802, ?x60), ?x60 = 02nzb8, ?x530 = 02_j1w, sport(?x4802, ?x471), ?x471 = 02vx4, ?x134 = 08mbj5d, colors(?x4802, ?x1101) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #9183 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 42 *> proper extension: 03dkx; *> query: (?x4802, ?x4521) <- category(?x4802, ?x134), ?x134 = 08mbj5d, sport(?x4802, ?x471), sport(?x7453, ?x471), sport(?x6153, ?x471), sport(?x3436, ?x471), team(?x60, ?x7453), teams(?x4521, ?x3436), colors(?x6153, ?x663) *> conf = 0.02 ranks of expected_values: 107 EVAL 019lty teams! 06gmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 94.000 94.000 0.250 http://example.org/sports/sports_team_location/teams #3292-01rwcgb PRED entity: 01rwcgb PRED relation: artists! PRED expected values: 0827d => 121 concepts (70 used for prediction) PRED predicted values (max 10 best out of 238): 064t9 (0.67 #956, 0.51 #2527, 0.50 #642), 016clz (0.59 #13534, 0.32 #4092, 0.29 #1261), 06by7 (0.50 #5368, 0.49 #3479, 0.49 #2221), 0ggq0m (0.40 #3785, 0.38 #6302, 0.18 #1897), 05bt6j (0.33 #988, 0.32 #1302, 0.27 #674), 0ggx5q (0.33 #1023, 0.30 #709, 0.30 #3853), 06j6l (0.33 #993, 0.29 #2564, 0.28 #2249), 02lnbg (0.30 #1004, 0.21 #1318, 0.20 #690), 0155w (0.26 #2308, 0.18 #8915, 0.17 #4197), 0xhtw (0.25 #8823, 0.21 #2216, 0.21 #3159) >> Best rule #956 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 01l_vgt; >> query: (?x10591, 064t9) <- origin(?x10591, ?x4335), type_of_union(?x10591, ?x566), ?x566 = 04ztj, location_of_ceremony(?x10591, ?x362), category(?x10591, ?x134) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 01kwlwp; 025tdwc; 05pq9; 06mt91; 05yzt_; 095p3z; *> query: (?x10591, 0827d) <- profession(?x10591, ?x8353), profession(?x10591, ?x2348), ?x2348 = 0nbcg, ?x8353 = 028kk_, nationality(?x10591, ?x2146), gender(?x10591, ?x231) *> conf = 0.20 ranks of expected_values: 16 EVAL 01rwcgb artists! 0827d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 121.000 70.000 0.667 http://example.org/music/genre/artists #3291-0f04v PRED entity: 0f04v PRED relation: citytown! PRED expected values: 01v3rb => 229 concepts (178 used for prediction) PRED predicted values (max 10 best out of 978): 01zpmq (0.40 #276, 0.07 #4302, 0.07 #13158), 0k8z (0.20 #151, 0.07 #4177, 0.05 #113586), 01rt2z (0.20 #562, 0.07 #13444, 0.05 #113586), 02pfymy (0.20 #525, 0.07 #13407, 0.05 #113586), 03s7h (0.20 #599, 0.05 #113586, 0.03 #13481), 06py2 (0.20 #728, 0.05 #113586, 0.03 #13610), 045c7b (0.20 #215, 0.05 #113586, 0.03 #13097), 03_c8p (0.17 #13455, 0.10 #22315, 0.08 #47285), 07l1c (0.15 #3546, 0.06 #7572, 0.06 #5962), 02975m (0.15 #3942, 0.06 #7968, 0.06 #6358) >> Best rule #276 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0r679; 0r6cx; 0r6c4; >> query: (?x6703, 01zpmq) <- citytown(?x6404, ?x6703), adjoins(?x6703, ?x11315), ?x11315 = 0r6ff >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #13676 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 024bqj; *> query: (?x6703, 01v3rb) <- citytown(?x7633, ?x6703), contact_category(?x7633, ?x6046), ?x6046 = 02zdwq *> conf = 0.03 ranks of expected_values: 431 EVAL 0f04v citytown! 01v3rb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 229.000 178.000 0.400 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #3290-01s73z PRED entity: 01s73z PRED relation: child PRED expected values: 02_l39 => 190 concepts (138 used for prediction) PRED predicted values (max 10 best out of 180): 03sb38 (0.63 #7849, 0.59 #12473, 0.56 #12817), 01dtcb (0.63 #7849, 0.59 #12473, 0.56 #12817), 0dwcl (0.63 #7849, 0.59 #12473, 0.56 #12817), 01jx9 (0.63 #7849, 0.59 #12473, 0.56 #12817), 05gnf (0.63 #7849, 0.59 #12473, 0.56 #12817), 025txrl (0.63 #7849, 0.59 #12473, 0.56 #12817), 09j_g (0.63 #7849, 0.59 #12473, 0.56 #12817), 02lw5z (0.63 #7849, 0.59 #12473, 0.56 #12817), 01jygk (0.63 #7849, 0.59 #12473, 0.56 #12817), 0kcdl (0.63 #7849, 0.59 #12473, 0.56 #12817) >> Best rule #7849 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 07733f; 05q78ky; >> query: (?x5108, ?x5260) <- child(?x5108, ?x1104), industry(?x1104, ?x373), child(?x10957, ?x1104), category(?x1104, ?x134), child(?x10957, ?x5260) >> conf = 0.63 => this is the best rule for 13 predicted values *> Best rule #12818 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 09glbnt; *> query: (?x5108, ?x10957) <- child(?x5108, ?x1104), citytown(?x5108, ?x2254), child(?x10957, ?x1104), citytown(?x10957, ?x739) *> conf = 0.09 ranks of expected_values: 78 EVAL 01s73z child 02_l39 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 190.000 138.000 0.633 http://example.org/organization/organization/child./organization/organization_relationship/child #3289-024_dt PRED entity: 024_dt PRED relation: category_of PRED expected values: 0c4ys => 43 concepts (34 used for prediction) PRED predicted values (max 10 best out of 3): 0c4ys (0.92 #194, 0.92 #173, 0.90 #85), 0gcf2r (0.16 #259, 0.14 #281, 0.12 #303), 0g_w (0.10 #260, 0.09 #282, 0.09 #304) >> Best rule #194 for best value: >> intensional similarity = 6 >> extensional distance = 95 >> proper extension: 056jm_; 03r00m; >> query: (?x12458, 0c4ys) <- ceremony(?x12458, ?x5766), award(?x352, ?x12458), award_winner(?x5766, ?x1674), ceremony(?x3647, ?x5766), ?x3647 = 01c9jp, artists(?x302, ?x1674) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024_dt category_of 0c4ys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 34.000 0.918 http://example.org/award/award_category/category_of #3288-0gyh PRED entity: 0gyh PRED relation: religion PRED expected values: 058x5 => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 22): 092bf5 (0.46 #2215, 0.33 #99, 0.33 #7), 02t7t (0.46 #2215, 0.32 #150, 0.30 #81), 058x5 (0.40 #625, 0.37 #579, 0.37 #232), 0flw86 (0.39 #1593, 0.39 #1362, 0.37 #1524), 03j6c (0.33 #10, 0.15 #263, 0.09 #1672), 0kpl (0.33 #4, 0.06 #257, 0.03 #1319), 07w8f (0.33 #17, 0.03 #270, 0.02 #895), 04t_mf (0.04 #1676, 0.03 #1007, 0.02 #1537), 01spm (0.03 #227, 0.03 #273, 0.02 #875), 078tg (0.03 #1542, 0.03 #1611, 0.03 #1681) >> Best rule #2215 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 04fh3; >> query: (?x2831, ?x109) <- adjoins(?x3778, ?x2831), jurisdiction_of_office(?x3959, ?x2831), religion(?x3778, ?x109) >> conf = 0.46 => this is the best rule for 2 predicted values *> Best rule #625 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 05kkh; 059rby; 03v1s; 05kj_; 059f4; 05fkf; 0vmt; 0hjy; 03s0w; 05fhy; ... *> query: (?x2831, 058x5) <- contains(?x2831, ?x1201), district_represented(?x653, ?x2831), adjoins(?x2623, ?x2831), ?x653 = 070m6c *> conf = 0.40 ranks of expected_values: 3 EVAL 0gyh religion 058x5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 176.000 176.000 0.456 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #3287-01f7dd PRED entity: 01f7dd PRED relation: award_nominee! PRED expected values: 01y_px => 93 concepts (47 used for prediction) PRED predicted values (max 10 best out of 939): 044mz_ (0.85 #4653, 0.81 #69809, 0.81 #76790), 01y_px (0.85 #4653, 0.81 #69809, 0.81 #76790), 03hzl42 (0.85 #4653, 0.81 #69809, 0.81 #76790), 015pkc (0.29 #58174, 0.27 #79119, 0.10 #74463), 01f7dd (0.29 #58174, 0.27 #79119, 0.10 #74463), 04fzk (0.29 #58174, 0.27 #79119, 0.10 #74463), 079ws (0.29 #58174, 0.27 #79119), 0284n42 (0.29 #58174, 0.27 #79119), 0794g (0.27 #79119, 0.10 #74463, 0.08 #93081), 021npv (0.27 #79119, 0.10 #74463, 0.08 #93081) >> Best rule #4653 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0flw6; 033w9g; 016kft; >> query: (?x6916, ?x57) <- award_nominee(?x6916, ?x57), film(?x6916, ?x11065), ?x11065 = 0n08r >> conf = 0.85 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01f7dd award_nominee! 01y_px CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 47.000 0.847 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3286-065zlr PRED entity: 065zlr PRED relation: titles! PRED expected values: 01z4y => 118 concepts (101 used for prediction) PRED predicted values (max 10 best out of 64): 01z4y (0.55 #139, 0.45 #2512, 0.29 #36), 024qqx (0.36 #286, 0.15 #1005, 0.14 #1107), 07s9rl0 (0.30 #3822, 0.27 #2064, 0.27 #4342), 09q17 (0.27 #179, 0.20 #9938, 0.18 #9937), 04xvlr (0.24 #3825, 0.22 #4962, 0.22 #1859), 06n90 (0.20 #9938, 0.18 #9937, 0.18 #6718), 02l7c8 (0.20 #9938, 0.18 #9937, 0.18 #6718), 05p553 (0.20 #9938, 0.18 #9937, 0.18 #6718), 06cvj (0.20 #9938, 0.18 #9937, 0.18 #6718), 01jfsb (0.17 #8501, 0.14 #20, 0.14 #3427) >> Best rule #139 for best value: >> intensional similarity = 2 >> extensional distance = 9 >> proper extension: 039cq4; >> query: (?x2494, 01z4y) <- nominated_for(?x4657, ?x2494), ?x4657 = 0f7hc >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065zlr titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 101.000 0.545 http://example.org/media_common/netflix_genre/titles #3285-0r5lz PRED entity: 0r5lz PRED relation: time_zones PRED expected values: 02lcqs => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 11): 02lcqs (0.86 #57, 0.58 #1405, 0.44 #70), 02hcv8 (0.51 #146, 0.50 #237, 0.48 #81), 02fqwt (0.33 #14, 0.28 #27, 0.23 #1549), 02hczc (0.23 #1549, 0.16 #1966, 0.15 #1980), 042g7t (0.23 #1549, 0.16 #1966, 0.15 #1980), 02lcrv (0.23 #1549, 0.16 #1966, 0.15 #1980), 02llzg (0.13 #303, 0.12 #368, 0.12 #199), 03bdv (0.08 #175, 0.07 #565, 0.06 #539), 03plfd (0.02 #1415, 0.02 #894, 0.02 #1441), 052vwh (0.02 #753, 0.02 #428, 0.02 #1078) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 06pwq; >> query: (?x5174, 02lcqs) <- category(?x5174, ?x134), state(?x5174, ?x1227), ?x1227 = 01n7q >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r5lz time_zones 02lcqs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.861 http://example.org/location/location/time_zones #3284-01_1hw PRED entity: 01_1hw PRED relation: language PRED expected values: 02hxc3j => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 40): 06nm1 (0.30 #123, 0.21 #295, 0.17 #527), 064_8sq (0.18 #306, 0.17 #595, 0.17 #768), 05qqm (0.17 #39, 0.10 #153, 0.06 #5339), 02bjrlw (0.15 #345, 0.12 #287, 0.12 #807), 06b_j (0.13 #250, 0.12 #78, 0.11 #654), 03hkp (0.12 #70, 0.06 #5339, 0.03 #185), 01r2l (0.12 #80, 0.03 #656, 0.03 #195), 03_9r (0.12 #641, 0.08 #1681, 0.07 #1219), 0jzc (0.11 #304, 0.08 #362, 0.06 #708), 0653m (0.08 #643, 0.07 #585, 0.07 #413) >> Best rule #123 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 03bx2lk; 05c9zr; 02ll45; 01_0f7; 04xg2f; >> query: (?x8631, 06nm1) <- film(?x5153, ?x8631), film(?x1867, ?x8631), film_release_distribution_medium(?x8631, ?x81), titles(?x8581, ?x8631), ?x1867 = 016ywr, location(?x5153, ?x739) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #753 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 118 *> proper extension: 0fvr1; 01dvbd; 0fjyzt; 04jn6y7; *> query: (?x8631, 02hxc3j) <- film(?x1549, ?x8631), genre(?x8631, ?x812), titles(?x8581, ?x8631), ?x812 = 01jfsb, executive_produced_by(?x8631, ?x8208) *> conf = 0.03 ranks of expected_values: 31 EVAL 01_1hw language 02hxc3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 102.000 102.000 0.300 http://example.org/film/film/language #3283-0njpq PRED entity: 0njpq PRED relation: time_zones PRED expected values: 02hcv8 => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.91 #225, 0.90 #133, 0.86 #396), 02fqwt (0.31 #1099, 0.27 #1193, 0.21 #134), 02lcqs (0.23 #387, 0.21 #853, 0.21 #415), 02hczc (0.12 #41, 0.11 #1706, 0.11 #691), 02lcrv (0.11 #1706, 0.11 #1759, 0.06 #46), 042g7t (0.11 #1706, 0.11 #1759, 0.06 #50), 02llzg (0.09 #879, 0.07 #1076, 0.07 #1009), 03bdv (0.05 #495, 0.03 #985, 0.03 #1633), 03plfd (0.03 #885, 0.03 #1068, 0.03 #1082), 0gsrz4 (0.02 #883, 0.02 #1173, 0.02 #1147) >> Best rule #225 for best value: >> intensional similarity = 5 >> extensional distance = 109 >> proper extension: 0mnzd; 0nm87; >> query: (?x13203, ?x2674) <- county_seat(?x13203, ?x3372), contains(?x1906, ?x13203), contains(?x94, ?x3372), time_zones(?x3372, ?x2674), category(?x3372, ?x134) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0njpq time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.910 http://example.org/location/location/time_zones #3282-05dxl_ PRED entity: 05dxl_ PRED relation: nationality PRED expected values: 09c7w0 => 84 concepts (80 used for prediction) PRED predicted values (max 10 best out of 74): 09c7w0 (0.89 #2717, 0.89 #3120, 0.88 #1408), 01n4w (0.35 #3019, 0.34 #4332, 0.34 #703), 01n7q (0.32 #2312, 0.31 #2010), 0f8l9c (0.12 #322, 0.06 #624, 0.04 #524), 03rk0 (0.12 #246, 0.09 #1052, 0.09 #548), 0d060g (0.11 #307, 0.07 #609, 0.05 #2521), 07ssc (0.11 #617, 0.09 #1121, 0.08 #2327), 02jx1 (0.11 #233, 0.10 #1139, 0.09 #2345), 06q1r (0.09 #377, 0.06 #679, 0.02 #7864), 0345h (0.04 #735, 0.03 #2443, 0.03 #1838) >> Best rule #2717 for best value: >> intensional similarity = 4 >> extensional distance = 763 >> proper extension: 0bl60p; >> query: (?x11664, 09c7w0) <- place_of_birth(?x11664, ?x659), featured_film_locations(?x1015, ?x659), dog_breed(?x659, ?x5194), teams(?x659, ?x660) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05dxl_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 80.000 0.889 http://example.org/people/person/nationality #3281-0b5x23 PRED entity: 0b5x23 PRED relation: profession PRED expected values: 02jknp 02hrh1q => 188 concepts (171 used for prediction) PRED predicted values (max 10 best out of 89): 02hrh1q (0.86 #4665, 0.85 #7815, 0.84 #12165), 02jknp (0.60 #1958, 0.50 #158, 0.45 #2558), 01d_h8 (0.42 #4956, 0.40 #7956, 0.40 #3006), 0dxtg (0.29 #5114, 0.29 #6014, 0.29 #17566), 0fj9f (0.25 #56, 0.22 #1556, 0.17 #2756), 0cbd2 (0.24 #7057, 0.20 #10057, 0.20 #9757), 018gz8 (0.23 #4368, 0.20 #318, 0.17 #2718), 03gjzk (0.22 #6016, 0.21 #5116, 0.19 #18768), 016z4k (0.22 #3754, 0.11 #13354, 0.11 #8104), 01c72t (0.20 #1825, 0.20 #625, 0.20 #325) >> Best rule #4665 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 087z12; 01x2tm8; 03z_g7; >> query: (?x11260, 02hrh1q) <- nationality(?x11260, ?x2146), languages(?x11260, ?x1882), ?x2146 = 03rk0, ?x1882 = 03k50, award(?x11260, ?x4687) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0b5x23 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 171.000 0.857 http://example.org/people/person/profession EVAL 0b5x23 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 171.000 0.857 http://example.org/people/person/profession #3280-020923 PRED entity: 020923 PRED relation: major_field_of_study PRED expected values: 04gb7 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 90): 01mkq (0.26 #646, 0.26 #898, 0.26 #1907), 02j62 (0.25 #4318, 0.25 #1041, 0.24 #4444), 02lp1 (0.25 #1021, 0.23 #1651, 0.21 #1525), 062z7 (0.23 #1038, 0.23 #1668, 0.20 #2046), 0g26h (0.22 #1054, 0.20 #1684, 0.18 #2062), 04rjg (0.20 #1030, 0.20 #1660, 0.19 #2038), 03g3w (0.19 #4314, 0.19 #1919, 0.19 #658), 05qjt (0.18 #638, 0.18 #1899, 0.17 #1395), 0_jm (0.17 #1070, 0.16 #1700, 0.15 #2078), 02_7t (0.15 #1077, 0.14 #1707, 0.13 #2085) >> Best rule #646 for best value: >> intensional similarity = 3 >> extensional distance = 180 >> proper extension: 024y8p; 02gr81; 071_8; 09f2j; 02zkz7; 0trv; 022fj_; 01p896; 02v992; 01c57n; >> query: (?x4227, 01mkq) <- school_type(?x4227, ?x3092), ?x3092 = 05jxkf, currency(?x4227, ?x170) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #803 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 184 *> proper extension: 01w_sh; 01dbns; 0c5x_; 07tjf; 01d650; 0mbwf; 012gyf; 02jx_v; 0jksm; *> query: (?x4227, 04gb7) <- school_type(?x4227, ?x3092), ?x3092 = 05jxkf, citytown(?x4227, ?x3976) *> conf = 0.12 ranks of expected_values: 16 EVAL 020923 major_field_of_study 04gb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 74.000 74.000 0.264 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3279-01t110 PRED entity: 01t110 PRED relation: award PRED expected values: 02f6ym => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 266): 01by1l (0.36 #4533, 0.32 #4935, 0.31 #10161), 0c4z8 (0.35 #876, 0.29 #72, 0.26 #1278), 01c92g (0.35 #900, 0.26 #1302, 0.19 #4920), 01bgqh (0.32 #4465, 0.27 #3259, 0.26 #10093), 054ks3 (0.29 #3357, 0.28 #4965, 0.24 #6573), 026mg3 (0.29 #12, 0.24 #816, 0.22 #1218), 01dk00 (0.29 #139, 0.18 #943, 0.13 #1345), 09sb52 (0.27 #8081, 0.26 #19740, 0.25 #23760), 05pcn59 (0.25 #484, 0.18 #12142, 0.17 #8122), 025m8l (0.24 #922, 0.22 #1324, 0.18 #4942) >> Best rule #4533 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 01q_ph; 0147dk; 01wmxfs; 0lk90; 01vrt_c; 086qd; 07ss8_; 01pgzn_; 01trhmt; 04xrx; ... >> query: (?x6461, 01by1l) <- artist(?x382, ?x6461), profession(?x6461, ?x220), award_nominee(?x1751, ?x6461), participant(?x6461, ?x3754) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #4679 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 101 *> proper extension: 01q_ph; 0147dk; 01wmxfs; 0lk90; 01vrt_c; 086qd; 07ss8_; 01pgzn_; 01trhmt; 04xrx; ... *> query: (?x6461, 02f6ym) <- artist(?x382, ?x6461), profession(?x6461, ?x220), award_nominee(?x1751, ?x6461), participant(?x6461, ?x3754) *> conf = 0.18 ranks of expected_values: 19 EVAL 01t110 award 02f6ym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 131.000 131.000 0.359 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3278-06w2sn5 PRED entity: 06w2sn5 PRED relation: special_performance_type PRED expected values: 01pb34 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 4): 01pb34 (0.20 #3, 0.16 #68, 0.15 #73), 02t8yb (0.05 #4, 0.04 #29, 0.03 #34), 09_gdc (0.04 #17, 0.03 #87, 0.03 #37), 01kyvx (0.01 #406, 0.01 #423, 0.01 #81) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 01wrcxr; >> query: (?x1462, 01pb34) <- gender(?x1462, ?x231), friend(?x6577, ?x1462), profession(?x1462, ?x131), ?x131 = 0dz3r >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06w2sn5 special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.200 http://example.org/film/actor/film./film/performance/special_performance_type #3277-047gpsd PRED entity: 047gpsd PRED relation: film! PRED expected values: 0bksh => 100 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1045): 03c9pqt (0.14 #14559, 0.13 #47846, 0.09 #4160), 055c8 (0.12 #2624, 0.03 #8863, 0.03 #19263), 046zh (0.09 #936, 0.04 #5096, 0.03 #15495), 019_1h (0.09 #168, 0.04 #6408, 0.02 #10567), 0b_fw (0.09 #350, 0.02 #10749, 0.02 #6590), 0gpprt (0.08 #3603, 0.04 #1523, 0.04 #7763), 01r93l (0.08 #2828, 0.04 #19467, 0.03 #27787), 059_gf (0.08 #3080, 0.04 #7240, 0.04 #5160), 014zcr (0.08 #2117, 0.04 #14596, 0.03 #39559), 02s2ft (0.08 #2087, 0.03 #18726, 0.03 #14566) >> Best rule #14559 for best value: >> intensional similarity = 5 >> extensional distance = 105 >> proper extension: 02r1c18; 03z20c; 0kv9d3; 016dj8; 0n_hp; >> query: (?x6719, ?x12790) <- genre(?x6719, ?x53), film(?x3477, ?x6719), country(?x6719, ?x94), crewmember(?x6719, ?x6166), executive_produced_by(?x6719, ?x12790) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #25814 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 199 *> proper extension: 0fq27fp; *> query: (?x6719, 0bksh) <- genre(?x6719, ?x53), crewmember(?x6719, ?x6166), film_crew_role(?x6719, ?x1171), ?x1171 = 09vw2b7 *> conf = 0.02 ranks of expected_values: 343 EVAL 047gpsd film! 0bksh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 100.000 29.000 0.145 http://example.org/film/actor/film./film/performance/film #3276-02d49z PRED entity: 02d49z PRED relation: nominated_for! PRED expected values: 02xj3rw => 63 concepts (48 used for prediction) PRED predicted values (max 10 best out of 178): 0gqwc (0.39 #530, 0.32 #294, 0.29 #60), 0gq9h (0.29 #62, 0.27 #532, 0.25 #2641), 04dn09n (0.29 #35, 0.27 #505, 0.22 #269), 0gr0m (0.29 #59, 0.22 #293, 0.17 #529), 0k611 (0.29 #73, 0.21 #2652, 0.20 #543), 0gr51 (0.29 #78, 0.20 #312, 0.16 #548), 03hl6lc (0.29 #127, 0.20 #361, 0.12 #11259), 019f4v (0.28 #523, 0.25 #287, 0.20 #2632), 094qd5 (0.27 #469, 0.27 #270, 0.22 #506), 0gr4k (0.27 #497, 0.17 #2606, 0.15 #6126) >> Best rule #530 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 0m313; 0c0yh4; 0ds3t5x; 095zlp; 02py4c8; 05jzt3; 0_b3d; 0jyx6; 09gdm7q; 032_wv; ... >> query: (?x4596, 0gqwc) <- nominated_for(?x4234, ?x4596), award_winner(?x9130, ?x4234), genre(?x4596, ?x714), ?x9130 = 09cn0c >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #11259 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1455 *> proper extension: 04mx8h4; *> query: (?x4596, ?x68) <- nominated_for(?x4234, ?x4596), nominated_for(?x4234, ?x4742), nominated_for(?x618, ?x4596), nominated_for(?x68, ?x4742) *> conf = 0.12 ranks of expected_values: 74 EVAL 02d49z nominated_for! 02xj3rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 63.000 48.000 0.385 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3275-01yfm8 PRED entity: 01yfm8 PRED relation: type_of_union PRED expected values: 04ztj => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.75 #1, 0.71 #93, 0.71 #109), 01g63y (0.33 #6, 0.14 #62, 0.13 #86) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0184jc; 04t7ts; 01kwsg; 016k6x; 0f5xn; 03cvv4; >> query: (?x7401, 04ztj) <- nominated_for(?x7401, ?x1064), film(?x7401, ?x924), ?x924 = 04gknr >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01yfm8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.750 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3274-0b9rdk PRED entity: 0b9rdk PRED relation: nominated_for! PRED expected values: 07cbcy 02qvyrt => 65 concepts (59 used for prediction) PRED predicted values (max 10 best out of 175): 0gq9h (0.55 #543, 0.38 #63, 0.21 #8944), 0gq_v (0.46 #20, 0.45 #500, 0.19 #2420), 0p9sw (0.38 #21, 0.21 #501, 0.19 #981), 0gs9p (0.36 #545, 0.18 #8946, 0.18 #9186), 0gr4k (0.36 #506, 0.15 #8907, 0.14 #9147), 0gqy2 (0.33 #604, 0.23 #124, 0.14 #9005), 0gr0m (0.31 #540, 0.15 #780, 0.14 #2460), 019f4v (0.26 #534, 0.19 #2454, 0.17 #8935), 0f4x7 (0.26 #505, 0.15 #25, 0.14 #11763), 0gqwc (0.24 #541, 0.12 #8942, 0.11 #10143) >> Best rule #543 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0jzw; 0kxf1; 024lff; 097zcz; 0gt1k; 0dnw1; 0bykpk; 0gl3hr; 0p9tm; 0ft18; ... >> query: (?x6029, 0gq9h) <- genre(?x6029, ?x225), production_companies(?x6029, ?x1104), film_release_region(?x6029, ?x94), film_sets_designed(?x12848, ?x6029) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #12724 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1557 *> proper extension: 016zfm; 0fpxp; 0fkwzs; 04mx8h4; *> query: (?x6029, ?x112) <- nominated_for(?x12848, ?x6029), profession(?x12848, ?x2450), nominated_for(?x12848, ?x9261), nominated_for(?x112, ?x9261) *> conf = 0.13 ranks of expected_values: 29, 52 EVAL 0b9rdk nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 65.000 59.000 0.548 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0b9rdk nominated_for! 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 65.000 59.000 0.548 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3273-0bfvd4 PRED entity: 0bfvd4 PRED relation: award_winner PRED expected values: 03xkps => 49 concepts (28 used for prediction) PRED predicted values (max 10 best out of 2431): 040z9 (0.57 #13916, 0.50 #8997, 0.43 #18830), 0k9j_ (0.50 #9834, 0.50 #9287, 0.41 #9830), 0bj9k (0.50 #7787, 0.43 #17620, 0.43 #12706), 0cgzj (0.50 #9442, 0.43 #14361, 0.14 #19275), 044qx (0.50 #8298, 0.43 #13217, 0.14 #18131), 0z4s (0.50 #7441, 0.41 #9830, 0.41 #9833), 09fb5 (0.50 #7433, 0.36 #17266, 0.29 #12352), 0bl2g (0.50 #7430, 0.33 #2514, 0.29 #17263), 039bp (0.50 #7582, 0.29 #17415, 0.29 #12501), 06cgy (0.50 #7678, 0.29 #12597, 0.21 #17511) >> Best rule #13916 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02py7pj; >> query: (?x2192, 040z9) <- ceremony(?x2192, ?x1265), award_winner(?x2192, ?x8151), ?x8151 = 0d6d2 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #9830 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 054ky1; *> query: (?x2192, ?x72) <- award(?x9000, ?x2192), award(?x3181, ?x2192), award(?x879, ?x2192), award(?x72, ?x2192), ?x9000 = 0k9j_, film(?x879, ?x1255), award_nominee(?x3181, ?x230) *> conf = 0.41 ranks of expected_values: 26 EVAL 0bfvd4 award_winner 03xkps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 49.000 28.000 0.571 http://example.org/award/award_category/winners./award/award_honor/award_winner #3272-03f2w PRED entity: 03f2w PRED relation: taxonomy PRED expected values: 04n6k => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.79 #16, 0.76 #8, 0.75 #7) >> Best rule #16 for best value: >> intensional similarity = 6 >> extensional distance = 70 >> proper extension: 0cdbq; >> query: (?x11872, 04n6k) <- participating_countries(?x4255, ?x11872), sports(?x4255, ?x4045), ?x4045 = 06z6r, olympics(?x142, ?x4255), film_release_region(?x80, ?x142), film_release_region(?x886, ?x142) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f2w taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.792 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #3271-0dl9_4 PRED entity: 0dl9_4 PRED relation: language PRED expected values: 02h40lc => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 43): 02h40lc (0.96 #2937, 0.96 #2102, 0.95 #1826), 064_8sq (0.59 #3490, 0.58 #3547, 0.25 #21), 06nm1 (0.59 #3490, 0.58 #3547, 0.15 #672), 02bjrlw (0.59 #3490, 0.58 #3547, 0.11 #663), 0295r (0.59 #3490, 0.58 #3547, 0.04 #137), 05f_3 (0.59 #3490, 0.58 #3547, 0.02 #300), 04306rv (0.13 #279, 0.12 #1828, 0.12 #723), 012w70 (0.08 #67, 0.08 #674, 0.08 #618), 04h9h (0.08 #94, 0.08 #259, 0.07 #534), 03_9r (0.08 #9, 0.07 #839, 0.06 #284) >> Best rule #2937 for best value: >> intensional similarity = 4 >> extensional distance = 475 >> proper extension: 018nnz; 03l6q0; 0prrm; 02x8fs; 048rn; 0h1fktn; 01cm8w; 048tv9; 02mc5v; 063y9fp; ... >> query: (?x5185, 02h40lc) <- executive_produced_by(?x5185, ?x4857), genre(?x5185, ?x53), film(?x2891, ?x5185), language(?x5185, ?x403) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dl9_4 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.962 http://example.org/film/film/language #3270-0232lm PRED entity: 0232lm PRED relation: instrumentalists! PRED expected values: 05148p4 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 124): 05148p4 (0.46 #872, 0.40 #2067, 0.37 #2837), 026t6 (0.30 #2905, 0.29 #2134, 0.28 #1621), 03qjg (0.23 #902, 0.20 #134, 0.19 #1413), 0l14md (0.16 #860, 0.15 #1116, 0.15 #2055), 018j2 (0.15 #889, 0.12 #1059, 0.11 #36), 0l14qv (0.13 #858, 0.11 #345, 0.11 #5), 06ncr (0.12 #127, 0.11 #42, 0.09 #468), 04rzd (0.11 #35, 0.11 #2083, 0.11 #1399), 07y_7 (0.10 #87, 0.09 #428, 0.08 #855), 03gvt (0.10 #148, 0.08 #2111, 0.08 #1427) >> Best rule #872 for best value: >> intensional similarity = 4 >> extensional distance = 115 >> proper extension: 0bg539; 09swkk; >> query: (?x8873, 05148p4) <- instrumentalists(?x716, ?x8873), type_of_union(?x8873, ?x566), ?x716 = 018vs, profession(?x8873, ?x131) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0232lm instrumentalists! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.462 http://example.org/music/instrument/instrumentalists #3269-02h2vv PRED entity: 02h2vv PRED relation: genre PRED expected values: 0c4xc => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 80): 0c4xc (0.73 #291, 0.73 #208, 0.70 #125), 07s9rl0 (0.54 #2659, 0.51 #831, 0.51 #2077), 01t_vv (0.38 #282, 0.32 #365, 0.32 #199), 0hcr (0.23 #2094, 0.22 #516, 0.19 #2593), 0pr6f (0.20 #50, 0.11 #548, 0.11 #2043), 03mqtr (0.20 #23, 0.02 #1102, 0.02 #1185), 06n90 (0.20 #2089, 0.18 #2671, 0.16 #2588), 03k9fj (0.17 #2087, 0.17 #2669, 0.15 #2586), 06nbt (0.17 #518, 0.15 #269, 0.14 #435), 01htzx (0.17 #2093, 0.16 #1678, 0.16 #1428) >> Best rule #291 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 0557yqh; 02rkkn1; 05pbsry; 0hr41p6; >> query: (?x6339, 0c4xc) <- nominated_for(?x1870, ?x6339), nominated_for(?x3906, ?x6339), nominated_for(?x678, ?x6339), ?x3906 = 03ccq3s, ceremony(?x678, ?x873) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02h2vv genre 0c4xc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.731 http://example.org/tv/tv_program/genre #3268-0mwl2 PRED entity: 0mwl2 PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 160 concepts (70 used for prediction) PRED predicted values (max 10 best out of 8): 09c7w0 (0.90 #187, 0.89 #150, 0.89 #212), 05tbn (0.36 #469, 0.23 #234, 0.22 #101), 0mwl2 (0.23 #234, 0.22 #101, 0.21 #331), 02jx1 (0.16 #122, 0.08 #291, 0.08 #316), 03rt9 (0.12 #4, 0.03 #423, 0.02 #746), 03rjj (0.02 #334, 0.01 #613, 0.01 #879), 0f8l9c (0.02 #618, 0.01 #241), 0d060g (0.02 #104, 0.01 #116, 0.01 #201) >> Best rule #187 for best value: >> intensional similarity = 4 >> extensional distance = 82 >> proper extension: 0nm87; >> query: (?x855, 09c7w0) <- county_seat(?x855, ?x854), time_zones(?x855, ?x2674), ?x2674 = 02hcv8, contains(?x3670, ?x855) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwl2 second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 70.000 0.905 http://example.org/location/country/second_level_divisions #3267-05xq9 PRED entity: 05xq9 PRED relation: category PRED expected values: 08mbj5d => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #59, 0.84 #89, 0.83 #11) >> Best rule #59 for best value: >> intensional similarity = 3 >> extensional distance = 225 >> proper extension: 03n0q5; 01r6jt2; 012wg; 02lfp4; 02w670; 025cn2; 02fybl; 01jrs46; 024yxd; >> query: (?x4942, 08mbj5d) <- origin(?x4942, ?x3052), location(?x1322, ?x3052), month(?x3052, ?x1459) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05xq9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.850 http://example.org/common/topic/webpage./common/webpage/category #3266-0gthm PRED entity: 0gthm PRED relation: influenced_by PRED expected values: 014635 => 108 concepts (25 used for prediction) PRED predicted values (max 10 best out of 301): 032l1 (0.46 #6610, 0.30 #6175, 0.16 #9655), 03f0324 (0.34 #6238, 0.16 #6673, 0.12 #9718), 01v9724 (0.33 #6699, 0.26 #6264, 0.22 #1480), 03_87 (0.30 #6289, 0.21 #6724, 0.17 #4117), 081k8 (0.25 #5373, 0.22 #6677, 0.20 #6242), 014z8v (0.24 #2294, 0.22 #3600, 0.16 #1859), 048cl (0.22 #234, 0.17 #1536, 0.09 #668), 01hmk9 (0.22 #3700, 0.21 #1959, 0.20 #2394), 02lt8 (0.20 #6206, 0.16 #6641, 0.13 #9686), 06whf (0.19 #4039, 0.15 #6211, 0.14 #5342) >> Best rule #6610 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 02m4t; >> query: (?x9854, 032l1) <- influenced_by(?x9854, ?x7334), influenced_by(?x10598, ?x7334), influenced_by(?x7334, ?x2994), ?x10598 = 0mb0 >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1412 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 052h3; 018zvb; 085gk; *> query: (?x9854, 014635) <- profession(?x9854, ?x9081), influenced_by(?x1725, ?x9854), ?x9081 = 0d8qb *> conf = 0.11 ranks of expected_values: 43 EVAL 0gthm influenced_by 014635 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 108.000 25.000 0.457 http://example.org/influence/influence_node/influenced_by #3265-054k_8 PRED entity: 054k_8 PRED relation: profession PRED expected values: 01d_h8 01tkqy => 115 concepts (83 used for prediction) PRED predicted values (max 10 best out of 67): 01d_h8 (0.67 #5929, 0.67 #6077, 0.65 #4892), 0dxtg (0.61 #6084, 0.60 #5936, 0.57 #4899), 09jwl (0.42 #6830, 0.37 #7422, 0.36 #6386), 018gz8 (0.40 #164, 0.20 #312, 0.17 #756), 03gjzk (0.28 #4900, 0.26 #6085, 0.25 #1198), 0nbcg (0.27 #6843, 0.27 #7435, 0.26 #6399), 0cbd2 (0.24 #5337, 0.20 #155, 0.20 #6226), 0dz3r (0.23 #6814, 0.22 #6370, 0.22 #7406), 016z4k (0.23 #6520, 0.23 #6372, 0.23 #7408), 02krf9 (0.22 #4912, 0.22 #5949, 0.20 #6097) >> Best rule #5929 for best value: >> intensional similarity = 3 >> extensional distance = 517 >> proper extension: 0c8hct; >> query: (?x5501, 01d_h8) <- type_of_union(?x5501, ?x566), profession(?x5501, ?x524), ?x524 = 02jknp >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11 EVAL 054k_8 profession 01tkqy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 115.000 83.000 0.672 http://example.org/people/person/profession EVAL 054k_8 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 83.000 0.672 http://example.org/people/person/profession #3264-06bw5 PRED entity: 06bw5 PRED relation: major_field_of_study PRED expected values: 037mh8 => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 116): 02j62 (0.46 #996, 0.45 #754, 0.45 #1238), 03g3w (0.41 #750, 0.36 #992, 0.34 #1840), 037mh8 (0.38 #550, 0.25 #1034, 0.23 #1155), 01mkq (0.38 #10315, 0.37 #1831, 0.33 #1588), 062z7 (0.37 #3052, 0.37 #1235, 0.34 #1841), 0g26h (0.34 #1250, 0.22 #1492, 0.22 #1856), 02h40lc (0.32 #730, 0.25 #972, 0.14 #1093), 01lj9 (0.31 #521, 0.29 #1005, 0.28 #1853), 01540 (0.31 #543, 0.23 #1875, 0.22 #422), 02lp1 (0.29 #1827, 0.27 #10311, 0.26 #1221) >> Best rule #996 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 0bqxw; >> query: (?x5777, 02j62) <- contains(?x94, ?x5777), major_field_of_study(?x5777, ?x2314), institution(?x1200, ?x5777), ?x2314 = 0h5k >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #550 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 037s9x; *> query: (?x5777, 037mh8) <- currency(?x5777, ?x170), major_field_of_study(?x5777, ?x742), ?x742 = 05qjt, student(?x5777, ?x9105) *> conf = 0.38 ranks of expected_values: 3 EVAL 06bw5 major_field_of_study 037mh8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 161.000 161.000 0.464 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3263-0468g4r PRED entity: 0468g4r PRED relation: award_winner PRED expected values: 076_74 => 70 concepts (31 used for prediction) PRED predicted values (max 10 best out of 2055): 01hkhq (0.50 #2995, 0.25 #17843, 0.25 #7944), 0fbx6 (0.50 #3413, 0.25 #18261, 0.25 #8362), 012g92 (0.50 #4848, 0.25 #19696, 0.25 #9797), 01f8ld (0.47 #20457, 0.32 #25408, 0.26 #32835), 01wd9lv (0.44 #33596, 0.13 #42078, 0.13 #54453), 04sry (0.40 #21419, 0.28 #36274, 0.27 #26370), 0js9s (0.40 #21254, 0.27 #26205, 0.22 #33632), 081lh (0.38 #34842, 0.33 #19987, 0.23 #24938), 02kxbx3 (0.34 #35426, 0.27 #20571, 0.18 #25522), 0h1p (0.33 #20226, 0.28 #35081, 0.23 #25177) >> Best rule #2995 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 05zvq6g; >> query: (?x12581, 01hkhq) <- award(?x8496, ?x12581), ?x8496 = 0cvkv5, award_winner(?x12581, ?x8572), award_nominee(?x8572, ?x4732), profession(?x8572, ?x353) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #19798 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 0fm3b5; 0fm3kw; *> query: (?x12581, ?x3862) <- award(?x8496, ?x12581), award(?x2085, ?x12581), ?x8496 = 0cvkv5, award_winner(?x2085, ?x3862), genre(?x2085, ?x53), film_crew_role(?x2085, ?x137) *> conf = 0.14 ranks of expected_values: 157 EVAL 0468g4r award_winner 076_74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 70.000 31.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #3262-0qf2t PRED entity: 0qf2t PRED relation: nominated_for! PRED expected values: 0159h6 => 67 concepts (22 used for prediction) PRED predicted values (max 10 best out of 558): 071ynp (0.32 #21030, 0.30 #23366, 0.30 #14018), 01bbwp (0.32 #21030, 0.30 #23366, 0.30 #14018), 02tr7d (0.32 #21030, 0.30 #23366, 0.30 #14018), 01j5x6 (0.32 #21030, 0.30 #23366, 0.30 #14018), 016tt2 (0.19 #107, 0.05 #9453, 0.04 #18801), 025jfl (0.16 #2337, 0.04 #11792, 0.02 #14128), 0338lq (0.16 #2337), 04wp63 (0.12 #2062, 0.04 #16080, 0.04 #23092), 0f276 (0.12 #1993, 0.03 #11339, 0.01 #20687), 0146pg (0.10 #14137, 0.09 #21149, 0.08 #23485) >> Best rule #21030 for best value: >> intensional similarity = 4 >> extensional distance = 150 >> proper extension: 011yxg; 0g5qs2k; 0dqytn; 0pv2t; 0344gc; 017gl1; 0jyx6; 0c0nhgv; 069q4f; 03m4mj; ... >> query: (?x4864, ?x891) <- film(?x891, ?x4864), nominated_for(?x166, ?x4864), genre(?x4864, ?x258), honored_for(?x4864, ?x5950) >> conf = 0.32 => this is the best rule for 4 predicted values *> Best rule #39720 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 239 *> proper extension: 026p_bs; 02sg5v; 0blpg; *> query: (?x4864, ?x1950) <- film(?x891, ?x4864), nominated_for(?x4864, ?x11001), genre(?x4864, ?x258), film(?x1950, ?x11001) *> conf = 0.06 ranks of expected_values: 40 EVAL 0qf2t nominated_for! 0159h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 67.000 22.000 0.321 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #3261-0r6ff PRED entity: 0r6ff PRED relation: category PRED expected values: 08mbj5d => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #69, 0.78 #126, 0.78 #58) >> Best rule #69 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0x335; >> query: (?x11315, 08mbj5d) <- place_of_birth(?x9586, ?x11315), country(?x11315, ?x94), state(?x11315, ?x1227), source(?x11315, ?x958) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r6ff category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.783 http://example.org/common/topic/webpage./common/webpage/category #3260-0160nk PRED entity: 0160nk PRED relation: institution! PRED expected values: 07s6fsf 03bwzr4 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 16): 03bwzr4 (0.80 #81, 0.67 #157, 0.62 #118), 02_xgp2 (0.79 #155, 0.72 #79, 0.72 #116), 016t_3 (0.66 #150, 0.60 #74, 0.51 #93), 0bkj86 (0.61 #153, 0.53 #77, 0.51 #114), 07s6fsf (0.57 #19, 0.51 #129, 0.50 #73), 01rr_d (0.42 #66, 0.28 #121, 0.17 #84), 028dcg (0.32 #105, 0.16 #1180, 0.15 #86), 013zdg (0.31 #152, 0.30 #76, 0.30 #58), 022h5x (0.29 #33, 0.23 #143, 0.23 #87), 0bjrnt (0.26 #112, 0.23 #75, 0.21 #151) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 01w5m; 09f2j; 0b1xl; 01nnsv; 0gl5_; 0g2jl; >> query: (?x10572, 03bwzr4) <- fraternities_and_sororities(?x10572, ?x4348), major_field_of_study(?x10572, ?x2606), ?x2606 = 062z7 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 0160nk institution! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0160nk institution! 07s6fsf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 105.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3259-018t8f PRED entity: 018t8f PRED relation: campuses! PRED expected values: 018t8f => 136 concepts (90 used for prediction) PRED predicted values (max 10 best out of 237): 0k9wp (0.06 #22942, 0.05 #43709, 0.04 #44802), 018t8f (0.06 #22942, 0.05 #43709, 0.04 #44802), 04ftdq (0.03 #309, 0.03 #855, 0.02 #1401), 017z88 (0.03 #73, 0.03 #619, 0.02 #1165), 03qdm (0.03 #405, 0.02 #1497, 0.01 #2044), 02lwv5 (0.03 #411, 0.02 #1503, 0.01 #2050), 027kp3 (0.03 #145, 0.02 #1237, 0.01 #1784), 01p7x7 (0.03 #423, 0.02 #1515, 0.01 #2608), 03_fmr (0.03 #415, 0.02 #1507, 0.01 #2600), 01n951 (0.03 #274, 0.02 #1366, 0.01 #2459) >> Best rule #22942 for best value: >> intensional similarity = 4 >> extensional distance = 303 >> proper extension: 02kj7g; >> query: (?x9237, ?x5983) <- school_type(?x9237, ?x3205), citytown(?x9237, ?x7770), location(?x230, ?x7770), citytown(?x5983, ?x7770) >> conf = 0.06 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 018t8f campuses! 018t8f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 90.000 0.060 http://example.org/education/educational_institution/campuses #3258-0ys4f PRED entity: 0ys4f PRED relation: category PRED expected values: 08mbj5d => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.79 #13, 0.79 #10, 0.75 #8) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 94 >> proper extension: 0f2tj; >> query: (?x7067, 08mbj5d) <- time_zones(?x7067, ?x1638), ?x1638 = 02fqwt, place(?x7067, ?x7067) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ys4f category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.792 http://example.org/common/topic/webpage./common/webpage/category #3257-02vwckw PRED entity: 02vwckw PRED relation: profession PRED expected values: 0n1h => 108 concepts (43 used for prediction) PRED predicted values (max 10 best out of 61): 09jwl (0.65 #1499, 0.60 #4325, 0.58 #1795), 0dz3r (0.57 #594, 0.56 #1038, 0.54 #1334), 016z4k (0.55 #1188, 0.48 #1632, 0.44 #2523), 01d_h8 (0.47 #4610, 0.47 #2229, 0.42 #3421), 0n1h (0.43 #604, 0.31 #1492, 0.28 #1344), 0dxtg (0.38 #2237, 0.31 #4618, 0.31 #4172), 01c72t (0.37 #2692, 0.36 #2843, 0.35 #2098), 03gjzk (0.36 #2238, 0.31 #3430, 0.30 #4619), 0d1pc (0.28 #1234, 0.25 #1678, 0.25 #1086), 02jknp (0.26 #2231, 0.21 #4612, 0.19 #4166) >> Best rule #1499 for best value: >> intensional similarity = 5 >> extensional distance = 66 >> proper extension: 01fxck; >> query: (?x8185, 09jwl) <- currency(?x8185, ?x170), profession(?x8185, ?x2348), profession(?x8185, ?x1032), ?x1032 = 02hrh1q, ?x2348 = 0nbcg >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #604 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 04lgymt; *> query: (?x8185, 0n1h) <- award(?x8185, ?x8705), award(?x8185, ?x4837), ?x8705 = 01c9dd, ceremony(?x4837, ?x139) *> conf = 0.43 ranks of expected_values: 5 EVAL 02vwckw profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 43.000 0.647 http://example.org/people/person/profession #3256-03j0d PRED entity: 03j0d PRED relation: religion PRED expected values: 0kq2 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 31): 03_gx (0.50 #2090, 0.31 #101, 0.21 #145), 0c8wxp (0.41 #3009, 0.40 #2260, 0.40 #931), 0n2g (0.16 #144, 0.15 #100, 0.14 #188), 0kq2 (0.13 #1234, 0.13 #1367, 0.12 #1813), 01lp8 (0.08 #89, 0.06 #266, 0.05 #486), 092bf5 (0.08 #103, 0.05 #588, 0.05 #147), 051kv (0.08 #93, 0.05 #137, 0.04 #358), 03j6c (0.08 #3199, 0.08 #3155, 0.07 #3332), 0631_ (0.07 #933, 0.02 #1644, 0.02 #2254), 0flw86 (0.05 #3049, 0.05 #3181, 0.05 #3314) >> Best rule #2090 for best value: >> intensional similarity = 3 >> extensional distance = 325 >> proper extension: 01w3v; 0mcf4; >> query: (?x10000, 03_gx) <- religion(?x10000, ?x2694), religion(?x7509, ?x2694), ?x7509 = 048cl >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1234 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 183 *> proper extension: 04wqr; 02lk1s; 01nczg; 0gt_k; 01wj9y9; 0f0kz; 0n00; 01gn36; 01jrvr6; 02jq1; ... *> query: (?x10000, ?x2694) <- influenced_by(?x9707, ?x10000), influenced_by(?x2161, ?x10000), religion(?x9707, ?x2694), location(?x10000, ?x2713), influenced_by(?x476, ?x2161) *> conf = 0.13 ranks of expected_values: 4 EVAL 03j0d religion 0kq2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 127.000 127.000 0.505 http://example.org/people/person/religion #3255-07_pf PRED entity: 07_pf PRED relation: vacationer PRED expected values: 0237fw => 126 concepts (92 used for prediction) PRED predicted values (max 10 best out of 208): 03lt8g (0.25 #373, 0.18 #899, 0.17 #1075), 0bbf1f (0.25 #410, 0.18 #936, 0.17 #1112), 01xyt7 (0.25 #476, 0.18 #1002, 0.17 #1178), 034x61 (0.25 #364, 0.18 #890, 0.17 #1066), 05r5w (0.25 #423, 0.17 #1125, 0.17 #773), 016fnb (0.25 #453, 0.17 #803, 0.16 #1685), 02mjf2 (0.25 #448, 0.17 #798, 0.09 #974), 0bksh (0.25 #457, 0.11 #1689, 0.09 #983), 01yf85 (0.25 #505, 0.11 #1737, 0.09 #1031), 09yrh (0.25 #450, 0.11 #1682, 0.09 #976) >> Best rule #373 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 03qhnx; >> query: (?x10496, 03lt8g) <- featured_film_locations(?x4786, ?x10496), ?x4786 = 0bbw2z6 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1627 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 02k54; 04wgh; *> query: (?x10496, 0237fw) <- contains(?x205, ?x10496), vacationer(?x10496, ?x1634), featured_film_locations(?x4786, ?x10496), taxonomy(?x10496, ?x939) *> conf = 0.11 ranks of expected_values: 52 EVAL 07_pf vacationer 0237fw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 126.000 92.000 0.250 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #3254-03f68r6 PRED entity: 03f68r6 PRED relation: profession PRED expected values: 09jwl => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 83): 02hrh1q (0.78 #14433, 0.76 #15185, 0.76 #14884), 01c72t (0.76 #776, 0.66 #3782, 0.63 #3032), 01c8w0 (0.65 #760, 0.43 #1511, 0.38 #1211), 09jwl (0.59 #13533, 0.57 #471, 0.56 #11580), 0nbcg (0.50 #333, 0.47 #1385, 0.44 #11593), 016z4k (0.43 #455, 0.42 #605, 0.40 #154), 0dz3r (0.42 #603, 0.40 #152, 0.37 #13515), 01d_h8 (0.40 #156, 0.33 #607, 0.33 #6), 02jknp (0.33 #8, 0.29 #14124, 0.26 #15027), 0n1h (0.33 #12, 0.18 #11572, 0.18 #1364) >> Best rule #14433 for best value: >> intensional similarity = 3 >> extensional distance = 904 >> proper extension: 01j5x6; >> query: (?x11119, 02hrh1q) <- nominated_for(?x11119, ?x2112), location(?x11119, ?x6555), honored_for(?x4445, ?x2112) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #13533 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 642 *> proper extension: 018d6l; 05qhnq; 021r7r; 01wxdn3; *> query: (?x11119, 09jwl) <- gender(?x11119, ?x231), ?x231 = 05zppz, artists(?x4910, ?x11119) *> conf = 0.59 ranks of expected_values: 4 EVAL 03f68r6 profession 09jwl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 146.000 146.000 0.779 http://example.org/people/person/profession #3253-06gd4 PRED entity: 06gd4 PRED relation: profession PRED expected values: 0nbcg 039v1 => 137 concepts (96 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.74 #4695, 0.73 #11164, 0.73 #4403), 0nbcg (0.64 #1785, 0.63 #1491, 0.61 #2077), 039v1 (0.52 #3985, 0.48 #1790, 0.43 #35), 016z4k (0.49 #7035, 0.46 #1464, 0.46 #6301), 0np9r (0.39 #13225, 0.10 #13955, 0.10 #13809), 01d_h8 (0.29 #443, 0.25 #11155, 0.25 #4248), 02jknp (0.26 #445, 0.19 #4250, 0.17 #5132), 01c8w0 (0.25 #3519, 0.09 #8508, 0.07 #8801), 0dxtg (0.23 #4256, 0.20 #6605, 0.20 #4402), 0n1h (0.22 #6016, 0.20 #2497, 0.20 #8069) >> Best rule #4695 for best value: >> intensional similarity = 4 >> extensional distance = 182 >> proper extension: 0436kgz; 05cx7x; 02h48; >> query: (?x3869, 02hrh1q) <- award(?x3869, ?x4912), profession(?x3869, ?x131), location_of_ceremony(?x3869, ?x11472), contains(?x512, ?x11472) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1785 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 011_vz; 017mbb; *> query: (?x3869, 0nbcg) <- category(?x3869, ?x134), artists(?x1000, ?x3869), role(?x3869, ?x227), ?x1000 = 0xhtw *> conf = 0.64 ranks of expected_values: 2, 3 EVAL 06gd4 profession 039v1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 96.000 0.745 http://example.org/people/person/profession EVAL 06gd4 profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 96.000 0.745 http://example.org/people/person/profession #3252-05cvgl PRED entity: 05cvgl PRED relation: film! PRED expected values: 0jrny => 75 concepts (28 used for prediction) PRED predicted values (max 10 best out of 659): 0hskw (0.65 #47895, 0.60 #22906, 0.56 #41646), 0z4s (0.48 #2082, 0.43 #22905, 0.42 #37481), 04ld94 (0.48 #2082, 0.43 #22905, 0.40 #56222), 02h761 (0.48 #2082, 0.43 #22905, 0.40 #56222), 039bp (0.17 #180, 0.07 #4343, 0.02 #14755), 059_gf (0.17 #1000, 0.07 #5163), 09l3p (0.17 #749, 0.05 #47896, 0.03 #11159), 01nxzv (0.17 #1909, 0.05 #6072), 046zh (0.17 #936, 0.04 #7182, 0.02 #11346), 02x7vq (0.17 #981, 0.04 #3063, 0.02 #5144) >> Best rule #47895 for best value: >> intensional similarity = 3 >> extensional distance = 653 >> proper extension: 0cskb; >> query: (?x2734, ?x2733) <- nominated_for(?x2733, ?x2734), people(?x1050, ?x2733), participant(?x2733, ?x4295) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #15121 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 245 *> proper extension: 02fn5r; *> query: (?x2734, 0jrny) <- nominated_for(?x1493, ?x2734), nominated_for(?x198, ?x1493) *> conf = 0.02 ranks of expected_values: 447 EVAL 05cvgl film! 0jrny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 75.000 28.000 0.648 http://example.org/film/actor/film./film/performance/film #3251-0lmgy PRED entity: 0lmgy PRED relation: location_of_ceremony! PRED expected values: 029pnn => 143 concepts (76 used for prediction) PRED predicted values (max 10 best out of 79): 01z7_f (0.33 #104, 0.25 #358, 0.17 #612), 02fn5 (0.12 #865, 0.11 #1123, 0.10 #1633), 0cgbf (0.06 #2464, 0.04 #2718, 0.02 #3227), 06lbp (0.06 #2454, 0.04 #2708, 0.02 #3472), 012cph (0.06 #2319, 0.04 #2573, 0.02 #3337), 02m30v (0.02 #4588, 0.02 #3315, 0.02 #5352), 0gdqy (0.02 #3030), 0677ng (0.02 #2977), 0g824 (0.02 #2961), 0lcx (0.02 #2900) >> Best rule #104 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02hrh0_; >> query: (?x9190, 01z7_f) <- contains(?x14447, ?x9190), ?x14447 = 014wxc, source(?x9190, ?x958), contains(?x9190, ?x9191), location_of_ceremony(?x566, ?x9190) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0lmgy location_of_ceremony! 029pnn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 143.000 76.000 0.333 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #3250-01wqpnm PRED entity: 01wqpnm PRED relation: artists! PRED expected values: 059kh 09jw2 => 121 concepts (46 used for prediction) PRED predicted values (max 10 best out of 268): 064t9 (0.66 #3097, 0.59 #939, 0.58 #1247), 016clz (0.62 #5, 0.43 #622, 0.29 #2163), 06j6l (0.54 #3129, 0.33 #355, 0.31 #4363), 0gywn (0.47 #3139, 0.25 #365, 0.22 #981), 025sc50 (0.45 #3131, 0.33 #357, 0.32 #973), 05w3f (0.39 #5586, 0.27 #2811, 0.25 #3736), 059kh (0.39 #664, 0.17 #356, 0.12 #2205), 0glt670 (0.34 #3122, 0.33 #348, 0.26 #4356), 02yv6b (0.30 #3797, 0.29 #5647, 0.29 #4106), 0ggx5q (0.29 #1002, 0.25 #3160, 0.25 #386) >> Best rule #3097 for best value: >> intensional similarity = 5 >> extensional distance = 129 >> proper extension: 03t9sp; 0163m1; 0hvbj; 01dwrc; 01dq9q; 016lmg; 07sbk; 016376; 016ppr; >> query: (?x10198, 064t9) <- award(?x10198, ?x3926), award_winner(?x3926, ?x4628), award_winner(?x3926, ?x2562), ?x2562 = 01trhmt, artists(?x671, ?x4628) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #664 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 06lxn; *> query: (?x10198, 059kh) <- artists(?x5934, ?x10198), artists(?x1572, ?x10198), award_winner(?x10169, ?x10198), ?x5934 = 05r6t, ?x1572 = 06by7 *> conf = 0.39 ranks of expected_values: 7, 41 EVAL 01wqpnm artists! 09jw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 121.000 46.000 0.656 http://example.org/music/genre/artists EVAL 01wqpnm artists! 059kh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 121.000 46.000 0.656 http://example.org/music/genre/artists #3249-027tbrc PRED entity: 027tbrc PRED relation: actor PRED expected values: 0h0jz 0k525 => 96 concepts (70 used for prediction) PRED predicted values (max 10 best out of 936): 027cxsm (0.40 #12058, 0.35 #12986, 0.34 #26897), 0b13g7 (0.40 #12058, 0.35 #12986, 0.34 #26897), 02q42j_ (0.40 #12058, 0.35 #12986, 0.34 #26897), 0kctd (0.40 #12058, 0.35 #12986, 0.34 #26897), 0h0jz (0.25 #19, 0.02 #6513, 0.02 #13933), 031y07 (0.25 #470, 0.01 #6964, 0.01 #38954), 017khj (0.25 #452, 0.01 #6946, 0.01 #38954), 0z4s (0.25 #35, 0.01 #38954), 022yb4 (0.20 #1578, 0.06 #3436, 0.05 #5291), 0382m4 (0.20 #1392, 0.04 #5105, 0.03 #3250) >> Best rule #12058 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 0hr41p6; >> query: (?x2447, ?x1630) <- honored_for(?x1265, ?x2447), genre(?x2447, ?x53), nominated_for(?x1630, ?x2447), genre(?x54, ?x53) >> conf = 0.40 => this is the best rule for 4 predicted values *> Best rule #19 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 02bg8v; 09rfpk; *> query: (?x2447, 0h0jz) <- program(?x6092, ?x2447), genre(?x2447, ?x3312), ?x3312 = 02p0szs, actor(?x2447, ?x2045) *> conf = 0.25 ranks of expected_values: 5 EVAL 027tbrc actor 0k525 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 70.000 0.396 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 027tbrc actor 0h0jz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 96.000 70.000 0.396 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #3248-02f9wb PRED entity: 02f9wb PRED relation: type_of_union PRED expected values: 04ztj => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.69 #94, 0.68 #219, 0.67 #267), 01g63y (0.20 #6, 0.16 #63, 0.15 #14) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 776 >> proper extension: 01nqfh_; 01mqz0; 07s8r0; 01vyp_; 0pyg6; 01q415; 04kj2v; 0hskw; 01nwwl; 0blt6; ... >> query: (?x5958, 04ztj) <- nationality(?x5958, ?x94), award_winner(?x3310, ?x5958), student(?x5280, ?x5958) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02f9wb type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.688 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3247-0fqjks PRED entity: 0fqjks PRED relation: profession PRED expected values: 02pjxr => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 61): 02hrh1q (0.66 #15616, 0.65 #9465, 0.65 #10665), 02pjxr (0.58 #485, 0.53 #185, 0.36 #1535), 089fss (0.57 #17, 0.47 #1517, 0.42 #1367), 01d_h8 (0.36 #5106, 0.34 #3006, 0.33 #8556), 0dxtg (0.28 #4364, 0.27 #10814, 0.27 #7814), 03gjzk (0.24 #7366, 0.24 #3766, 0.24 #7816), 02jknp (0.23 #5108, 0.23 #8558, 0.23 #2708), 09jwl (0.21 #1670, 0.20 #3470, 0.20 #3320), 0nbcg (0.17 #1683, 0.14 #16052, 0.13 #6333), 01c72t (0.16 #1675, 0.14 #16052, 0.11 #1825) >> Best rule #15616 for best value: >> intensional similarity = 3 >> extensional distance = 2489 >> proper extension: 031zkw; 087z12; 01m42d0; 05zdk2; 02x08c; 06wvfq; 03xk1_; 03fwln; 0ck91; 01tsbmv; ... >> query: (?x7528, 02hrh1q) <- award(?x7528, ?x484), nationality(?x7528, ?x94), nominated_for(?x484, ?x144) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #485 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 06cv1; 03gyh_z; 05b5_tj; *> query: (?x7528, 02pjxr) <- award_nominee(?x200, ?x7528), film_production_design_by(?x951, ?x7528), nominated_for(?x7528, ?x1745) *> conf = 0.58 ranks of expected_values: 2 EVAL 0fqjks profession 02pjxr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.657 http://example.org/people/person/profession #3246-01_lh1 PRED entity: 01_lh1 PRED relation: company! PRED expected values: 060c4 => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 45): 060c4 (0.82 #4604, 0.82 #4562, 0.80 #4646), 05_wyz (0.62 #57, 0.56 #184, 0.53 #1195), 09d6p2 (0.42 #2636, 0.39 #2337, 0.34 #2891), 02211by (0.27 #425, 0.24 #1308, 0.23 #340), 02y6fz (0.25 #105, 0.25 #63, 0.24 #1308), 09lq2c (0.25 #69, 0.24 #1308, 0.22 #2619), 01rk91 (0.24 #2320, 0.24 #1308, 0.22 #2619), 021q1c (0.24 #2320, 0.22 #2619, 0.18 #3971), 07t3gd (0.24 #2320, 0.22 #2619, 0.18 #3971), 04n1q6 (0.24 #2320, 0.22 #2619, 0.18 #3971) >> Best rule #4604 for best value: >> intensional similarity = 7 >> extensional distance = 158 >> proper extension: 08815; 017s11; 01j_9c; 025jfl; 0f8l9c; 0288zy; 07w0v; 0kz2w; 09kvv; 031n8c; ... >> query: (?x11558, 060c4) <- company(?x4682, ?x11558), company(?x265, ?x11558), jurisdiction_of_office(?x265, ?x2346), ?x2346 = 0d05w3, basic_title(?x966, ?x265), organization(?x4682, ?x2299), company(?x2461, ?x2299) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_lh1 company! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.825 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #3245-047fjjr PRED entity: 047fjjr PRED relation: language PRED expected values: 02h40lc => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 58): 02h40lc (0.90 #460, 0.89 #1848, 0.89 #1383), 064_8sq (0.15 #764, 0.14 #878, 0.14 #821), 0jzc (0.11 #188, 0.08 #533, 0.08 #246), 06nm1 (0.10 #1797, 0.09 #1159, 0.09 #754), 06b_j (0.08 #422, 0.07 #765, 0.07 #1053), 02bjrlw (0.08 #459, 0.08 #57, 0.07 #517), 032f6 (0.05 #224, 0.05 #282, 0.05 #3227), 02hxcvy (0.05 #202, 0.05 #3227, 0.04 #2366), 012v8 (0.05 #215, 0.04 #2366, 0.03 #273), 03k50 (0.05 #3227, 0.04 #810, 0.04 #2366) >> Best rule #460 for best value: >> intensional similarity = 7 >> extensional distance = 76 >> proper extension: 07k2mq; >> query: (?x3850, 02h40lc) <- film_release_region(?x3850, ?x404), film_release_region(?x3850, ?x311), film_release_region(?x2746, ?x311), film_release_distribution_medium(?x3850, ?x81), ?x2746 = 04f52jw, ?x404 = 047lj, country(?x3838, ?x311) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047fjjr language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.897 http://example.org/film/film/language #3244-0fn2g PRED entity: 0fn2g PRED relation: location! PRED expected values: 02tq2r => 153 concepts (85 used for prediction) PRED predicted values (max 10 best out of 2033): 01cqz5 (0.51 #211665, 0.50 #108346, 0.50 #85665), 06kbb6 (0.19 #7559, 0.18 #7560, 0.13 #178904), 09fb5 (0.17 #25245, 0.11 #73116, 0.11 #85716), 073749 (0.16 #5842, 0.11 #86468, 0.10 #99067), 0dvld (0.16 #6260, 0.06 #142325, 0.06 #74286), 0pyww (0.15 #16099, 0.13 #26176, 0.11 #74047), 023kzp (0.15 #16334, 0.12 #36491, 0.10 #94441), 0gl88b (0.15 #15488, 0.11 #5410, 0.10 #12969), 0jsg0m (0.15 #16614, 0.11 #6536, 0.10 #14095), 02mjmr (0.15 #15619, 0.11 #5541, 0.10 #13100) >> Best rule #211665 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 013kcv; 06gmr; 01r32; 0g284; 0k_q_; 0cr3d; 0fvzg; 01qh7; 01sn3; 081yw; ... >> query: (?x6054, ?x11755) <- location_of_ceremony(?x566, ?x6054), country(?x6054, ?x7747), place_of_birth(?x11755, ?x6054), religion(?x11755, ?x7422) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #86909 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 0r2gj; *> query: (?x6054, 02tq2r) <- place_of_death(?x11772, ?x6054), award(?x11772, ?x601), featured_film_locations(?x3257, ?x6054), nominated_for(?x11772, ?x5212) *> conf = 0.03 ranks of expected_values: 1457 EVAL 0fn2g location! 02tq2r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 153.000 85.000 0.506 http://example.org/people/person/places_lived./people/place_lived/location #3243-02qsfzv PRED entity: 02qsfzv PRED relation: nominated_for PRED expected values: 0dl9_4 01srq2 => 44 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1312): 017jd9 (0.70 #5477, 0.27 #7070, 0.25 #10249), 026p4q7 (0.60 #5126, 0.36 #9898, 0.28 #11487), 017gl1 (0.60 #4899, 0.36 #6492, 0.27 #8081), 0dr_4 (0.60 #4991, 0.25 #223, 0.25 #11352), 011yxg (0.60 #4805, 0.25 #37, 0.20 #9577), 035_2h (0.60 #5599, 0.25 #831, 0.10 #11960), 02c638 (0.55 #6669, 0.40 #8258, 0.35 #9848), 0m313 (0.50 #4780, 0.46 #6361, 0.29 #9552), 049xgc (0.50 #5644, 0.46 #6361, 0.29 #10416), 01mgw (0.50 #5921, 0.28 #12282, 0.27 #7514) >> Best rule #5477 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 027h4yd; >> query: (?x8224, 017jd9) <- award(?x5613, ?x8224), award_winner(?x144, ?x5613), costume_design_by(?x7974, ?x5613), costume_design_by(?x4458, ?x5613), featured_film_locations(?x4458, ?x362), genre(?x7974, ?x53) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #7470 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 0gr4k; 02rdxsh; 0gs9p; 02rdyk7; 02x17s4; 02w9sd7; 02qrwjt; *> query: (?x8224, 01srq2) <- nominated_for(?x8224, ?x8277), nominated_for(?x8224, ?x251), ?x8277 = 02r858_, film_release_region(?x251, ?x87), nominated_for(?x3458, ?x251), ?x3458 = 0gqxm *> conf = 0.27 ranks of expected_values: 128, 172 EVAL 02qsfzv nominated_for 01srq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 44.000 13.000 0.700 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qsfzv nominated_for 0dl9_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 13.000 0.700 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3242-03pc89 PRED entity: 03pc89 PRED relation: honored_for! PRED expected values: 026kqs9 => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 115): 0275n3y (0.09 #6293, 0.04 #3572, 0.04 #2362), 09p30_ (0.09 #6293, 0.03 #2370, 0.03 #2854), 09p3h7 (0.09 #6293, 0.02 #2358, 0.02 #2842), 0bzlrh (0.08 #88, 0.04 #209, 0.03 #451), 0bzmt8 (0.08 #83, 0.02 #446, 0.02 #204), 0dthsy (0.08 #55, 0.02 #418, 0.01 #660), 0ftlkg (0.08 #20, 0.02 #383, 0.01 #625), 0bzkvd (0.08 #98, 0.02 #219, 0.01 #461), 0c4hnm (0.08 #112, 0.02 #596, 0.01 #475), 09k5jh7 (0.06 #191, 0.03 #1643, 0.03 #2490) >> Best rule #6293 for best value: >> intensional similarity = 2 >> extensional distance = 1315 >> proper extension: 09fb5; 04bp0l; >> query: (?x8551, ?x6238) <- nominated_for(?x10626, ?x8551), award_winner(?x6238, ?x10626) >> conf = 0.09 => this is the best rule for 3 predicted values *> Best rule #439 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 07bz5; *> query: (?x8551, 026kqs9) <- nominated_for(?x3960, ?x8551), list(?x8551, ?x3004), award(?x8551, ?x1063) *> conf = 0.01 ranks of expected_values: 108 EVAL 03pc89 honored_for! 026kqs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 64.000 64.000 0.085 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3241-06btq PRED entity: 06btq PRED relation: contains! PRED expected values: 029jpy => 188 concepts (136 used for prediction) PRED predicted values (max 10 best out of 268): 029jpy (0.56 #81480, 0.20 #1109, 0.17 #215), 0mw5x (0.43 #65355, 0.12 #113733, 0.01 #27408), 06btq (0.43 #65355, 0.12 #113733, 0.01 #27016), 02qkt (0.34 #68388, 0.31 #99743, 0.30 #94368), 07ssc (0.32 #88677, 0.23 #42095, 0.22 #84197), 0dg3n1 (0.23 #78946, 0.19 #94176, 0.19 #97761), 02jx1 (0.20 #88732, 0.16 #84252, 0.14 #89628), 02j9z (0.17 #67174, 0.16 #68069, 0.15 #94049), 0j0k (0.17 #68419, 0.15 #94399, 0.15 #97984), 0d060g (0.15 #24174, 0.08 #42970, 0.08 #21488) >> Best rule #81480 for best value: >> intensional similarity = 3 >> extensional distance = 177 >> proper extension: 0rj0z; >> query: (?x2713, ?x94) <- location(?x4806, ?x2713), adjoins(?x2713, ?x1755), contains(?x94, ?x1755) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06btq contains! 029jpy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 136.000 0.561 http://example.org/location/location/contains #3240-0k4f3 PRED entity: 0k4f3 PRED relation: list PRED expected values: 05glt => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 1): 05glt (0.42 #9, 0.33 #2, 0.22 #23) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0kb07; >> query: (?x2779, 05glt) <- film(?x9320, ?x2779), music(?x2779, ?x12188), film_art_direction_by(?x2779, ?x2778), genre(?x2779, ?x239) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k4f3 list 05glt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.419 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #3239-0l2xl PRED entity: 0l2xl PRED relation: contains PRED expected values: 0mhdz => 183 concepts (78 used for prediction) PRED predicted values (max 10 best out of 2559): 0f04v (0.91 #120678, 0.88 #91242, 0.88 #85356), 0r6cx (0.81 #126563, 0.81 #111847, 0.75 #108903), 0f04c (0.81 #126563, 0.81 #111847, 0.75 #108903), 0mhdz (0.60 #5013, 0.42 #11775, 0.42 #11774), 0r5wt (0.60 #3530, 0.33 #587, 0.18 #18248), 0qymv (0.60 #4596, 0.33 #1653, 0.14 #19314), 0gdk0 (0.60 #3987, 0.33 #1044, 0.14 #18705), 0135g (0.60 #3619, 0.33 #676, 0.14 #18337), 01jr6 (0.60 #3443, 0.33 #500, 0.14 #18161), 02zd460 (0.60 #3628, 0.33 #685, 0.14 #18346) >> Best rule #120678 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 0n4m5; 0mtdx; 036k0s; 0123_x; 0ntpv; 03fb3t; 09lk2; 02ly_; 028n3; 0nrqh; ... >> query: (?x7964, ?x6703) <- contains(?x7964, ?x3794), second_level_divisions(?x94, ?x7964), contains(?x1227, ?x7964), administrative_division(?x6703, ?x7964) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #5013 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 06pvr; *> query: (?x7964, 0mhdz) <- contains(?x7964, ?x12691), contains(?x7964, ?x3794), ?x3794 = 0r679, adjoins(?x2935, ?x12691), time_zones(?x12691, ?x2950) *> conf = 0.60 ranks of expected_values: 4 EVAL 0l2xl contains 0mhdz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 183.000 78.000 0.911 http://example.org/location/location/contains #3238-023kzp PRED entity: 023kzp PRED relation: award_winner! PRED expected values: 07y_p6 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 109): 092c5f (0.10 #14, 0.04 #1001, 0.03 #3539), 0hndn2q (0.05 #40, 0.05 #322, 0.05 #181), 0drtv8 (0.05 #66, 0.04 #207, 0.02 #348), 092t4b (0.05 #52, 0.04 #898, 0.03 #3295), 09gkdln (0.05 #122, 0.03 #968, 0.03 #545), 0275n3y (0.05 #75, 0.03 #921, 0.03 #216), 09p30_ (0.05 #85, 0.03 #226, 0.03 #649), 09qftb (0.05 #113, 0.03 #1100, 0.02 #1241), 05zksls (0.05 #35, 0.02 #881, 0.02 #740), 058m5m4 (0.05 #55, 0.02 #6259, 0.02 #3298) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 06151l; 0c4f4; 0hvb2; 0h0wc; 08swgx; 014488; 03_6y; 0391jz; 04w391; 02k21g; ... >> query: (?x5925, 092c5f) <- award_nominee(?x2352, ?x5925), place_of_birth(?x5925, ?x3501), ?x2352 = 01pgzn_ >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #7148 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1294 *> proper extension: 0f721s; 01jq34; 01_8w2; 01p5yn; 03yxwq; 018_q8; 0gsgr; 0283xx2; 0kc8y; 04rqd; ... *> query: (?x5925, 07y_p6) <- award_winner(?x880, ?x5925), award_winner(?x5925, ?x157) *> conf = 0.01 ranks of expected_values: 109 EVAL 023kzp award_winner! 07y_p6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 99.000 99.000 0.100 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3237-01pqy_ PRED entity: 01pqy_ PRED relation: nationality PRED expected values: 09c7w0 => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.87 #6518, 0.86 #501, 0.85 #901), 03gh4 (0.33 #16655, 0.01 #8824), 014wxc (0.33 #16655), 03fb3t (0.33 #16655), 02jx1 (0.23 #633, 0.18 #433, 0.14 #1033), 07ssc (0.20 #15, 0.17 #215, 0.13 #3422), 0d060g (0.20 #7, 0.17 #207, 0.06 #3819), 0d0vqn (0.14 #309, 0.02 #1210, 0.01 #1510), 03rk0 (0.09 #8971, 0.08 #9172, 0.08 #9672), 03rjj (0.05 #1809, 0.04 #905, 0.04 #1709) >> Best rule #6518 for best value: >> intensional similarity = 3 >> extensional distance = 342 >> proper extension: 0dbpyd; 06n7h7; 04n7njg; 06brp0; 01gp_x; 04g865; 03gyh_z; 05_pkf; 0bxfmk; 02pt6k_; ... >> query: (?x5197, 09c7w0) <- nominated_for(?x5197, ?x3752), student(?x4955, ?x5197), school(?x4979, ?x4955) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pqy_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.869 http://example.org/people/person/nationality #3236-0k2cb PRED entity: 0k2cb PRED relation: honored_for! PRED expected values: 073hkh => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 112): 05c1t6z (0.10 #5491, 0.09 #7815, 0.09 #7816), 09gkdln (0.10 #5491, 0.09 #7815, 0.09 #7816), 09p2r9 (0.10 #5491, 0.09 #7815, 0.09 #7816), 02wzl1d (0.10 #5491, 0.09 #7815, 0.09 #7816), 03nnm4t (0.10 #5491, 0.09 #7815, 0.09 #7816), 03gwpw2 (0.09 #7815, 0.09 #7816, 0.09 #7691), 073hkh (0.09 #7815, 0.09 #7816, 0.09 #7691), 073h5b (0.09 #7815, 0.09 #7816, 0.09 #7691), 02q690_ (0.09 #7815, 0.09 #7816, 0.08 #6591), 05zksls (0.07 #150, 0.03 #1004, 0.02 #1492) >> Best rule #5491 for best value: >> intensional similarity = 4 >> extensional distance = 964 >> proper extension: 03cf9ly; >> query: (?x4751, ?x944) <- nominated_for(?x6314, ?x4751), award_winner(?x944, ?x6314), film(?x6314, ?x2973), award_winner(?x686, ?x6314) >> conf = 0.10 => this is the best rule for 5 predicted values *> Best rule #7815 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1301 *> proper extension: 04bp0l; *> query: (?x4751, ?x8964) <- nominated_for(?x6314, ?x4751), award_winner(?x8964, ?x6314), honored_for(?x8964, ?x1364) *> conf = 0.09 ranks of expected_values: 7 EVAL 0k2cb honored_for! 073hkh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 83.000 83.000 0.103 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3235-01nc3rh PRED entity: 01nc3rh PRED relation: music! PRED expected values: 06tpmy => 105 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1114): 0btpm6 (0.17 #740, 0.06 #3764, 0.05 #1748), 02fqrf (0.17 #339, 0.06 #3363, 0.05 #1347), 09146g (0.17 #182, 0.06 #3206, 0.05 #1190), 09q5w2 (0.17 #100, 0.05 #1108, 0.04 #2116), 034r25 (0.17 #439, 0.05 #1447, 0.04 #2455), 0gy0n (0.17 #986, 0.05 #1994, 0.04 #3002), 07bx6 (0.17 #738, 0.05 #1746, 0.04 #2754), 03y0pn (0.17 #719, 0.05 #1727, 0.04 #2735), 05nlx4 (0.17 #718, 0.05 #1726, 0.04 #2734), 011yn5 (0.17 #545, 0.05 #1553, 0.04 #2561) >> Best rule #740 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 07h1q; >> query: (?x10295, 0btpm6) <- gender(?x10295, ?x231), ?x231 = 05zppz, place_of_birth(?x10295, ?x8977), ?x8977 = 02z0j >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nc3rh music! 06tpmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 19.000 0.167 http://example.org/film/film/music #3234-02w3w PRED entity: 02w3w PRED relation: role! PRED expected values: 0319l => 66 concepts (42 used for prediction) PRED predicted values (max 10 best out of 94): 03bx0bm (0.85 #2979, 0.84 #809, 0.83 #1452), 0l14md (0.84 #809, 0.83 #1452, 0.82 #1549), 02hnl (0.84 #809, 0.83 #1452, 0.82 #1549), 02fsn (0.84 #809, 0.83 #1452, 0.82 #1549), 02k84w (0.84 #809, 0.83 #1452, 0.82 #1549), 013y1f (0.79 #3349, 0.76 #2791, 0.75 #1576), 0l14qv (0.78 #3234, 0.73 #3052, 0.73 #3517), 07xzm (0.78 #1755, 0.71 #916, 0.69 #444), 02w3w (0.77 #2465, 0.75 #1436, 0.71 #1340), 0dwt5 (0.75 #1711, 0.75 #1432, 0.73 #2646) >> Best rule #2979 for best value: >> intensional similarity = 22 >> extensional distance = 18 >> proper extension: 02pprs; 01rhl; 0192l; >> query: (?x5417, 03bx0bm) <- role(?x4583, ?x5417), role(?x2798, ?x5417), ?x4583 = 0bmnm, role(?x5417, ?x315), role(?x1291, ?x2798), role(?x74, ?x2798), group(?x315, ?x10502), group(?x315, ?x4957), group(?x315, ?x2395), performance_role(?x1148, ?x315), role(?x212, ?x2798), performance_role(?x5543, ?x315), ?x10502 = 016vn3, group(?x2798, ?x997), ?x2395 = 0dvqq, instrumentalists(?x315, ?x226), instrumentalists(?x2798, ?x1001), role(?x315, ?x1831), ?x4957 = 0g_g2, ?x1001 = 01gf5h, artists(?x302, ?x5543), ?x1291 = 01kx_81 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1294 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 5 *> proper extension: 02fsn; *> query: (?x5417, 0319l) <- role(?x4583, ?x5417), role(?x4311, ?x5417), role(?x3296, ?x5417), role(?x2923, ?x5417), role(?x2309, ?x5417), role(?x1482, ?x5417), instrumentalists(?x1482, ?x7581), role(?x2888, ?x4583), ?x3296 = 07_l6, role(?x5417, ?x1495), ?x4311 = 01xqw, role(?x1750, ?x4583), role(?x2297, ?x1482), role(?x2923, ?x212), instrumentalists(?x5417, ?x367), ?x1750 = 02hnl, ?x2297 = 051hrr, ?x7581 = 01wf86y, role(?x1433, ?x2923), ?x2888 = 02fsn, ?x1495 = 013y1f, role(?x1694, ?x5417), ?x2309 = 06ncr *> conf = 0.71 ranks of expected_values: 19 EVAL 02w3w role! 0319l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 66.000 42.000 0.850 http://example.org/music/performance_role/regular_performances./music/group_membership/role #3233-03_hd PRED entity: 03_hd PRED relation: influenced_by PRED expected values: 07c37 => 128 concepts (54 used for prediction) PRED predicted values (max 10 best out of 360): 03sbs (0.50 #4518, 0.50 #2365, 0.50 #648), 081k8 (0.49 #9186, 0.25 #1011, 0.25 #582), 015n8 (0.43 #4274, 0.40 #1693, 0.29 #7287), 0420y (0.40 #1687, 0.33 #2546, 0.25 #1258), 01lwx (0.40 #1691, 0.25 #1262, 0.25 #833), 04hcw (0.40 #1938, 0.20 #4520, 0.17 #16349), 03_hd (0.40 #1849, 0.18 #7013, 0.17 #16349), 03_87 (0.34 #12243, 0.21 #16548, 0.19 #14825), 07c37 (0.33 #2760, 0.29 #7065, 0.20 #1471), 0cpvcd (0.33 #2539, 0.29 #3832, 0.25 #1251) >> Best rule #4518 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0b78hw; 032r1; 01lwx; >> query: (?x4547, 03sbs) <- influenced_by(?x4547, ?x12441), influenced_by(?x4547, ?x11837), gender(?x4547, ?x231), profession(?x4547, ?x10210), ?x12441 = 0tfc, student(?x2637, ?x11837) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2760 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 05qmj; *> query: (?x4547, 07c37) <- influenced_by(?x8418, ?x4547), influenced_by(?x3941, ?x4547), peers(?x920, ?x3941), profession(?x4547, ?x10210), ?x8418 = 02ln1, interests(?x4547, ?x1858) *> conf = 0.33 ranks of expected_values: 9 EVAL 03_hd influenced_by 07c37 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 128.000 54.000 0.500 http://example.org/influence/influence_node/influenced_by #3232-01gqfm PRED entity: 01gqfm PRED relation: country PRED expected values: 06mzp 0hzlz 06bnz 07twz 04wlh => 35 concepts (34 used for prediction) PRED predicted values (max 10 best out of 346): 06bnz (0.94 #4446, 0.92 #4272, 0.92 #4976), 0f8l9c (0.90 #4956, 0.89 #4602, 0.86 #2828), 07t21 (0.88 #3562, 0.87 #3388, 0.87 #2676), 06c1y (0.87 #3219, 0.80 #1979, 0.79 #2507), 016wzw (0.86 #2828, 0.62 #3535, 0.61 #709), 06qd3 (0.82 #529, 0.80 #1974, 0.78 #1614), 0hzlz (0.82 #529, 0.80 #1963, 0.76 #2823), 0163v (0.80 #3404, 0.80 #1991, 0.79 #2519), 015qh (0.79 #2505, 0.78 #1617, 0.76 #2823), 02k54 (0.79 #2487, 0.77 #2133, 0.73 #2660) >> Best rule #4446 for best value: >> intensional similarity = 52 >> extensional distance = 34 >> proper extension: 09wz9; 03fyrh; >> query: (?x10585, 06bnz) <- olympics(?x10585, ?x1931), country(?x10585, ?x2513), country(?x10585, ?x1558), country(?x10585, ?x583), country(?x10585, ?x94), ?x1558 = 01mjq, ?x94 = 09c7w0, adjoins(?x583, ?x410), participating_countries(?x418, ?x583), country(?x12465, ?x583), film_release_region(?x9432, ?x583), film_release_region(?x6270, ?x583), film_release_region(?x6175, ?x583), film_release_region(?x4514, ?x583), film_release_region(?x4464, ?x583), film_release_region(?x3482, ?x583), film_release_region(?x3287, ?x583), film_release_region(?x2558, ?x583), film_release_region(?x2094, ?x583), film_release_region(?x1744, ?x583), film_release_region(?x1707, ?x583), film_release_region(?x86, ?x583), ?x1744 = 035yn8, country(?x1167, ?x583), ?x2094 = 05z7c, olympics(?x2513, ?x452), film_release_region(?x4828, ?x2513), film_release_region(?x3201, ?x2513), film_release_region(?x2627, ?x2513), ?x3201 = 01ffx4, ?x4514 = 06tpmy, ?x6175 = 0gg5kmg, country(?x4876, ?x2513), country(?x3345, ?x2513), combatants(?x2513, ?x789), ?x6270 = 0g9zljd, medal(?x583, ?x422), ?x4828 = 02fttd, geographic_distribution(?x9148, ?x583), ?x86 = 0ds35l9, ?x4876 = 0d1t3, ?x4464 = 05pdh86, ?x1707 = 04n52p6, ?x3345 = 09qgm, ?x789 = 0f8l9c, ?x3287 = 026njb5, jurisdiction_of_office(?x182, ?x583), ?x9432 = 0gvt53w, ?x3482 = 017z49, ?x2627 = 0gz6b6g, ?x9148 = 0d29z, ?x2558 = 0bby9p5 >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 30, 33, 73 EVAL 01gqfm country 04wlh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 35.000 34.000 0.944 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01gqfm country 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 35.000 34.000 0.944 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01gqfm country 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 34.000 0.944 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01gqfm country 0hzlz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 35.000 34.000 0.944 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01gqfm country 06mzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 35.000 34.000 0.944 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #3231-03cglm PRED entity: 03cglm PRED relation: film PRED expected values: 01kff7 => 86 concepts (81 used for prediction) PRED predicted values (max 10 best out of 554): 0cz_ym (0.47 #67788, 0.46 #17839, 0.41 #83837), 0qm8b (0.35 #5592, 0.01 #57332, 0.01 #59115), 03f7nt (0.25 #829), 0dnkmq (0.22 #7004), 08mg_b (0.18 #2904, 0.03 #6470, 0.01 #31451), 0pvms (0.18 #2195, 0.03 #5761, 0.01 #30742), 09wnnb (0.16 #6968), 05q54f5 (0.16 #5819), 078sj4 (0.12 #454, 0.08 #5803, 0.02 #14724), 0661m4p (0.12 #375, 0.08 #3941, 0.03 #94538) >> Best rule #67788 for best value: >> intensional similarity = 2 >> extensional distance = 1275 >> proper extension: 0b82vw; 04zwjd; 04k25; 04g865; 01l9v7n; 02wb6yq; 085pr; 02pqgt8; 0854hr; 09r9m7; ... >> query: (?x5888, ?x1877) <- location(?x5888, ?x177), nominated_for(?x5888, ?x1877) >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03cglm film 01kff7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 81.000 0.473 http://example.org/film/actor/film./film/performance/film #3230-0cc97st PRED entity: 0cc97st PRED relation: film_crew_role PRED expected values: 02r96rf => 60 concepts (60 used for prediction) PRED predicted values (max 10 best out of 28): 02r96rf (0.73 #339, 0.73 #227, 0.73 #302), 0ch6mp2 (0.71 #1203, 0.67 #344, 0.66 #121), 09zzb8 (0.70 #1195, 0.66 #113, 0.58 #336), 01vx2h (0.60 #50, 0.52 #162, 0.52 #199), 09vw2b7 (0.59 #1202, 0.56 #120, 0.53 #792), 015h31 (0.34 #197, 0.33 #160, 0.29 #48), 01pvkk (0.27 #312, 0.27 #1208, 0.26 #126), 033smt (0.20 #66, 0.18 #29, 0.14 #178), 02rh1dz (0.20 #124, 0.16 #235, 0.15 #310), 0d2b38 (0.19 #176, 0.18 #213, 0.17 #64) >> Best rule #339 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 0c3ybss; 0gj8t_b; 03bx2lk; 04zyhx; 03twd6; 0bby9p5; 023gxx; 0gtvpkw; 0cp0ph6; 0c3xw46; ... >> query: (?x5713, 02r96rf) <- film_release_region(?x5713, ?x344), genre(?x5713, ?x258), ?x344 = 04gzd >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cc97st film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 60.000 0.728 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3229-0b_cr PRED entity: 0b_cr PRED relation: place_of_birth! PRED expected values: 01hkck => 88 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1574): 01hkck (0.32 #54866, 0.29 #18292, 0.29 #18291), 01jz6d (0.10 #10450, 0.03 #2517, 0.02 #7742), 012xdf (0.10 #10450, 0.03 #1884, 0.02 #7109), 03l295 (0.10 #10450, 0.02 #6375, 0.02 #8987), 02lm0t (0.10 #10450), 01s9ftn (0.03 #2554, 0.03 #5166, 0.02 #7779), 0b7gr2 (0.03 #2358, 0.03 #4970, 0.02 #7583), 0534nr (0.03 #2227, 0.03 #4839, 0.02 #7452), 0pk41 (0.03 #1925, 0.03 #4537, 0.02 #7150), 01nzz8 (0.03 #1132, 0.03 #3744, 0.02 #6357) >> Best rule #54866 for best value: >> intensional similarity = 4 >> extensional distance = 374 >> proper extension: 0hsqf; >> query: (?x13692, ?x11311) <- contains(?x94, ?x13692), place_of_birth(?x4834, ?x13692), location(?x11311, ?x13692), service_location(?x127, ?x94) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_cr place_of_birth! 01hkck CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 33.000 0.319 http://example.org/people/person/place_of_birth #3228-0g2lq PRED entity: 0g2lq PRED relation: award_winner! PRED expected values: 07z31v => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 133): 09p3h7 (0.40 #69, 0.28 #10075, 0.08 #621), 05q7cj (0.28 #10075, 0.02 #18218, 0.02 #2852), 0jt3qpk (0.25 #317, 0.24 #179, 0.11 #1145), 0gkxgfq (0.22 #380, 0.21 #242, 0.09 #1208), 0gx_st (0.20 #35, 0.13 #587, 0.08 #2243), 0bxs_d (0.20 #112, 0.06 #3148, 0.06 #3010), 0hn821n (0.20 #128, 0.06 #2336, 0.05 #3026), 07z31v (0.20 #29, 0.05 #2237, 0.05 #581), 0418154 (0.20 #105, 0.05 #657, 0.04 #2313), 073h1t (0.20 #25, 0.03 #577, 0.03 #715) >> Best rule #69 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 04rtpt; >> query: (?x7837, 09p3h7) <- program(?x7837, ?x4881), ?x4881 = 02kk_c >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #29 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 3 *> proper extension: 04rtpt; *> query: (?x7837, 07z31v) <- program(?x7837, ?x4881), ?x4881 = 02kk_c *> conf = 0.20 ranks of expected_values: 8 EVAL 0g2lq award_winner! 07z31v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 140.000 140.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3227-03gyp30 PRED entity: 03gyp30 PRED relation: award_winner PRED expected values: 01dy7j 0cj36c 04qsdh => 27 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2274): 02kxbwx (0.57 #10680, 0.50 #7653, 0.50 #3119), 07lt7b (0.50 #7641, 0.50 #3107, 0.43 #10668), 025mb_ (0.50 #10338, 0.40 #7311, 0.27 #16383), 02kxbx3 (0.43 #11102, 0.33 #8075, 0.25 #3541), 01rzqj (0.40 #5024, 0.38 #15120, 0.32 #7558), 0382m4 (0.40 #5395, 0.27 #7555, 0.25 #3021), 0794g (0.40 #5018, 0.22 #14087, 0.20 #6527), 01kwld (0.40 #4610, 0.22 #13679, 0.11 #12171), 0cl0bk (0.38 #15120, 0.34 #3022, 0.33 #2483), 0cj36c (0.38 #15120, 0.34 #3022, 0.33 #2484) >> Best rule #10680 for best value: >> intensional similarity = 18 >> extensional distance = 5 >> proper extension: 03gt46z; >> query: (?x8347, 02kxbwx) <- award_winner(?x8347, ?x9526), award_winner(?x8347, ?x3284), ceremony(?x3722, ?x8347), honored_for(?x8347, ?x945), ?x945 = 0b6tzs, award(?x8674, ?x3722), award(?x8045, ?x3722), award(?x5460, ?x3722), award(?x5097, ?x3722), award(?x9526, ?x154), spouse(?x8045, ?x9404), location(?x3284, ?x2020), nominated_for(?x8674, ?x1330), award_nominee(?x5097, ?x157), people(?x3591, ?x3284), award_nominee(?x3267, ?x9526), nominated_for(?x3722, ?x531), location(?x5460, ?x12873) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #15120 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 7 *> proper extension: 058m5m4; 092_25; *> query: (?x8347, ?x3366) <- award_winner(?x8347, ?x6324), award_winner(?x8347, ?x840), ceremony(?x3722, ?x8347), ceremony(?x618, ?x8347), honored_for(?x8347, ?x4932), ?x3722 = 0cqgl9, award_winner(?x4932, ?x3366), people(?x1446, ?x6324), film(?x6324, ?x667), award_nominee(?x2422, ?x6324), nationality(?x840, ?x94), genre(?x4932, ?x225), award(?x4932, ?x2041), film(?x2422, ?x349), ?x618 = 09qwmm *> conf = 0.38 ranks of expected_values: 10, 37, 367 EVAL 03gyp30 award_winner 04qsdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 27.000 15.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03gyp30 award_winner 0cj36c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 27.000 15.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03gyp30 award_winner 01dy7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 27.000 15.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3226-0_z91 PRED entity: 0_z91 PRED relation: place! PRED expected values: 0_z91 => 110 concepts (55 used for prediction) PRED predicted values (max 10 best out of 136): 0f2rq (0.20 #139, 0.04 #2201, 0.03 #3232), 013m_x (0.20 #138, 0.04 #2200, 0.03 #3231), 013mzh (0.20 #457, 0.04 #2519, 0.03 #3550), 0f2w0 (0.17 #552, 0.07 #1067, 0.06 #1583), 010016 (0.17 #871, 0.07 #1386, 0.06 #1902), 0f2s6 (0.17 #776, 0.06 #1807, 0.04 #2323), 0nqph (0.17 #1002, 0.06 #2033, 0.03 #3580), 0__wm (0.06 #1858, 0.04 #2374, 0.03 #3405), 0msck (0.04 #11856, 0.04 #6701), 013n2h (0.04 #2287, 0.03 #3318, 0.03 #2802) >> Best rule #139 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 013m_x; 0f2rq; 013mzh; >> query: (?x10465, 0f2rq) <- contains(?x3634, ?x10465), ?x3634 = 07b_l, origin(?x3495, ?x10465), source(?x10465, ?x958), ?x958 = 0jbk9 >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0_z91 place! 0_z91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 55.000 0.200 http://example.org/location/hud_county_place/place #3225-02rrh1w PRED entity: 02rrh1w PRED relation: language PRED expected values: 06nm1 => 103 concepts (99 used for prediction) PRED predicted values (max 10 best out of 43): 064_8sq (0.24 #312, 0.23 #371, 0.14 #2722), 06nm1 (0.18 #184, 0.16 #537, 0.14 #894), 02bjrlw (0.14 #351, 0.12 #292, 0.09 #587), 0jzc (0.13 #251, 0.12 #310, 0.06 #5619), 03_9r (0.11 #125, 0.10 #536, 0.09 #183), 04306rv (0.10 #1418, 0.10 #1476, 0.10 #1359), 0653m (0.10 #538, 0.06 #5619, 0.06 #1483), 012w70 (0.10 #539, 0.06 #5619, 0.05 #896), 06b_j (0.08 #2139, 0.08 #1611, 0.08 #1377), 0459q4 (0.08 #563, 0.03 #920, 0.03 #979) >> Best rule #312 for best value: >> intensional similarity = 7 >> extensional distance = 15 >> proper extension: 07kh6f3; 0404j37; 05zvzf3; 0466s8n; >> query: (?x7792, 064_8sq) <- film_crew_role(?x7792, ?x4305), film_crew_role(?x7792, ?x2178), titles(?x11405, ?x7792), ?x4305 = 0215hd, genre(?x1415, ?x11405), ?x2178 = 01pvkk, ?x1415 = 09p0ct >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #184 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 067ghz; *> query: (?x7792, 06nm1) <- film_crew_role(?x7792, ?x137), film_release_distribution_medium(?x7792, ?x81), film(?x541, ?x7792), film(?x4771, ?x7792), ?x4771 = 0h96g *> conf = 0.18 ranks of expected_values: 2 EVAL 02rrh1w language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 99.000 0.235 http://example.org/film/film/language #3224-026036 PRED entity: 026036 PRED relation: citytown PRED expected values: 02_286 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 212): 02_286 (0.59 #5181, 0.45 #1860, 0.35 #2597), 019fh (0.25 #1187, 0.03 #28426, 0.03 #28798), 01cx_ (0.17 #1543, 0.05 #2281, 0.02 #15565), 01531 (0.17 #1539, 0.04 #2645, 0.03 #3014), 094jv (0.17 #1510, 0.01 #3355, 0.01 #3724), 05tbn (0.08 #4428, 0.07 #4059, 0.07 #3690), 059rby (0.07 #3320, 0.02 #7011, 0.02 #25099), 04jpl (0.07 #4435, 0.05 #2221, 0.04 #5911), 030qb3t (0.06 #4824, 0.06 #18111, 0.06 #8485), 0ccvx (0.06 #21040, 0.06 #11438, 0.06 #20301) >> Best rule #5181 for best value: >> intensional similarity = 2 >> extensional distance = 118 >> proper extension: 0jz9f; 087c7; 0c_j5d; 0gsg7; 0l8sx; 09d5h; 01xdn1; 0gvbw; 015_1q; 03mdt; ... >> query: (?x10386, 02_286) <- state_province_region(?x10386, ?x335), ?x335 = 059rby >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026036 citytown 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.592 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #3223-02q56mk PRED entity: 02q56mk PRED relation: music PRED expected values: 0fpjyd => 88 concepts (68 used for prediction) PRED predicted values (max 10 best out of 69): 0146pg (0.08 #2759, 0.07 #2546, 0.06 #4236), 0184jw (0.08 #212, 0.06 #5915, 0.05 #1902), 05qd_ (0.08 #212, 0.06 #5915, 0.05 #1902), 0kjgl (0.06 #10981, 0.06 #10980, 0.06 #4226), 01p7yb (0.06 #10981, 0.06 #10980, 0.06 #4226), 02jxmr (0.06 #920, 0.04 #74, 0.03 #497), 0150t6 (0.05 #2159, 0.05 #2369, 0.05 #1949), 02bh9 (0.05 #263, 0.05 #3222, 0.05 #474), 02cyfz (0.04 #246, 0.04 #457, 0.04 #34), 0bs1yy (0.04 #257, 0.04 #45, 0.04 #679) >> Best rule #2759 for best value: >> intensional similarity = 3 >> extensional distance = 245 >> proper extension: 02fn5r; >> query: (?x2613, 0146pg) <- nominated_for(?x7141, ?x2613), nominated_for(?x198, ?x7141), award(?x71, ?x198) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #971 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 141 *> proper extension: 025n07; *> query: (?x2613, 0fpjyd) <- production_companies(?x2613, ?x902), film_crew_role(?x2613, ?x468), genre(?x2613, ?x53), cinematography(?x2613, ?x7782) *> conf = 0.01 ranks of expected_values: 51 EVAL 02q56mk music 0fpjyd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 88.000 68.000 0.081 http://example.org/film/film/music #3222-01wkmgb PRED entity: 01wkmgb PRED relation: artists! PRED expected values: 0xhtw 0173b0 => 181 concepts (130 used for prediction) PRED predicted values (max 10 best out of 259): 064t9 (0.66 #9981, 0.66 #8115, 0.66 #27725), 016clz (0.62 #31144, 0.47 #34880, 0.45 #7793), 02lnbg (0.45 #6602, 0.35 #13451, 0.34 #8159), 0xhtw (0.43 #9363, 0.39 #10609, 0.35 #36137), 05bt6j (0.42 #34917, 0.39 #7208, 0.38 #9387), 0ggx5q (0.41 #6622, 0.34 #8179, 0.34 #9734), 025sc50 (0.38 #6593, 0.33 #13442, 0.32 #13753), 06924p (0.38 #1110, 0.33 #177, 0.22 #11704), 02k_kn (0.38 #998, 0.24 #6297, 0.20 #1620), 05r6t (0.36 #2261, 0.25 #3509, 0.20 #3197) >> Best rule #9981 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 015f7; 0c7xjb; 020hyj; >> query: (?x10329, 064t9) <- celebrity(?x3421, ?x10329), artists(?x1928, ?x10329), parent_genre(?x114, ?x1928), category(?x10329, ?x134) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #9363 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 04r1t; 07m4c; 08w4pm; *> query: (?x10329, 0xhtw) <- artists(?x2249, ?x10329), artists(?x1928, ?x10329), ?x1928 = 0mhfr, artists(?x2249, ?x5126), ?x5126 = 03h502k *> conf = 0.43 ranks of expected_values: 4, 23 EVAL 01wkmgb artists! 0173b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 181.000 130.000 0.659 http://example.org/music/genre/artists EVAL 01wkmgb artists! 0xhtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 181.000 130.000 0.659 http://example.org/music/genre/artists #3221-02bkdn PRED entity: 02bkdn PRED relation: location PRED expected values: 029cr => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 86): 01cx_ (0.47 #20915, 0.47 #6434, 0.42 #23328), 02_286 (0.20 #841, 0.14 #2449, 0.13 #5666), 030qb3t (0.15 #3299, 0.14 #2495, 0.13 #7323), 0cr3d (0.10 #145, 0.05 #949, 0.05 #1753), 06_kh (0.10 #11, 0.05 #1619, 0.02 #3227), 04ykg (0.10 #68, 0.05 #1676, 0.01 #11331), 0_jsl (0.10 #791, 0.05 #2399), 0djd3 (0.10 #324, 0.05 #1932), 0xl08 (0.10 #322, 0.05 #1930), 0f2wj (0.10 #838, 0.02 #2446, 0.02 #6468) >> Best rule #20915 for best value: >> intensional similarity = 2 >> extensional distance = 1265 >> proper extension: 01ty7ll; 01n7qlf; 0f2c8g; 023n39; 01x0sy; 0f14q; 085q5; 065mm1; 0cgfb; 063g7l; ... >> query: (?x1871, ?x3052) <- place_of_birth(?x1871, ?x3052), film(?x1871, ?x385) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #7239 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 605 *> proper extension: 079vf; 0c_mvb; 0c01c; 02wr2r; 023jq1; 0969fd; 08849; *> query: (?x1871, ?x739) <- award_winner(?x1871, ?x4586), student(?x2486, ?x1871), location(?x4586, ?x739) *> conf = 0.03 ranks of expected_values: 40 EVAL 02bkdn location 029cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 80.000 80.000 0.472 http://example.org/people/person/places_lived./people/place_lived/location #3220-027hjff PRED entity: 027hjff PRED relation: honored_for PRED expected values: 0bbm7r => 21 concepts (18 used for prediction) PRED predicted values (max 10 best out of 884): 04vr_f (0.60 #2427, 0.33 #62, 0.25 #1835), 0cbv4g (0.40 #2684, 0.33 #319, 0.04 #5649), 08nvyr (0.40 #2634, 0.33 #269, 0.04 #5599), 03y0pn (0.40 #2788, 0.33 #423, 0.04 #5753), 0bhwhj (0.40 #2692, 0.33 #327, 0.04 #5657), 091z_p (0.40 #2463, 0.33 #98, 0.04 #5428), 0gy0l_ (0.40 #2872, 0.05 #5243, 0.04 #5837), 01j7mr (0.35 #3765, 0.09 #7324, 0.09 #4358), 0kfv9 (0.33 #1289, 0.33 #696, 0.26 #3657), 0266s9 (0.33 #1166, 0.30 #2365, 0.23 #7704) >> Best rule #2427 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 0drtv8; 0bvhz9; >> query: (?x3624, 04vr_f) <- award_winner(?x3624, ?x4719), award_winner(?x3624, ?x4470), award_winner(?x3624, ?x4391), award_winner(?x3624, ?x2061), profession(?x4719, ?x353), ?x4470 = 02y_2y, place_of_birth(?x2061, ?x4733), student(?x6611, ?x2061), place_of_birth(?x4391, ?x3125), award(?x2061, ?x1111), nominated_for(?x2061, ?x5808), student(?x3439, ?x4719), award_winner(?x4719, ?x679), film(?x4391, ?x857), honored_for(?x3624, ?x1631), award_nominee(?x4391, ?x849) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2365 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 2 *> proper extension: 027n06w; *> query: (?x3624, ?x3413) <- award_winner(?x3624, ?x8423), award_winner(?x3624, ?x4719), award_winner(?x3624, ?x4470), award_winner(?x3624, ?x4391), award_winner(?x3624, ?x2602), ?x4719 = 08hsww, award_nominee(?x2602, ?x6532), award_nominee(?x2602, ?x4332), actor(?x3413, ?x4391), award_winner(?x6169, ?x4470), profession(?x8423, ?x1032), place_of_birth(?x4391, ?x3125), honored_for(?x3624, ?x1631), place_of_birth(?x4470, ?x12747), ?x6532 = 0cmt6q, student(?x735, ?x4470), film(?x8423, ?x4404), award(?x4470, ?x102), ?x4332 = 0cnl1c *> conf = 0.30 ranks of expected_values: 42 EVAL 027hjff honored_for 0bbm7r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 21.000 18.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3219-0dnw1 PRED entity: 0dnw1 PRED relation: genre PRED expected values: 01t_vv => 75 concepts (73 used for prediction) PRED predicted values (max 10 best out of 78): 01jfsb (0.37 #365, 0.33 #11, 0.32 #2371), 03k9fj (0.33 #600, 0.32 #128, 0.23 #4258), 02kdv5l (0.33 #592, 0.31 #356, 0.27 #2362), 04t36 (0.32 #122, 0.17 #476, 0.11 #830), 01g6gs (0.27 #491, 0.21 #137, 0.12 #727), 01hmnh (0.25 #606, 0.16 #4264, 0.16 #2730), 06n90 (0.22 #602, 0.21 #130, 0.14 #2726), 060__y (0.21 #133, 0.19 #605, 0.18 #841), 04xvlr (0.19 #827, 0.18 #1063, 0.17 #945), 0lsxr (0.18 #1305, 0.17 #2367, 0.17 #361) >> Best rule #365 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 0dh8v4; >> query: (?x6094, 01jfsb) <- language(?x6094, ?x6753), official_language(?x11816, ?x6753), ?x11816 = 04thp >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #879 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 346 *> proper extension: 0b60sq; 02n9bh; 02phtzk; 027ct7c; 0hv81; 09rfpk; *> query: (?x6094, 01t_vv) <- nominated_for(?x2716, ?x6094), genre(?x6094, ?x1403), language(?x6094, ?x254), ?x1403 = 02l7c8 *> conf = 0.11 ranks of expected_values: 17 EVAL 0dnw1 genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 75.000 73.000 0.371 http://example.org/film/film/genre #3218-0txhf PRED entity: 0txhf PRED relation: source PRED expected values: 0jbk9 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #19, 0.91 #20, 0.85 #4) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 0f2tj; 0_rwf; 0_wm_; 010bnr; 0104lr; >> query: (?x13402, 0jbk9) <- category(?x13402, ?x134), ?x134 = 08mbj5d, place(?x13402, ?x13402) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0txhf source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #3217-0vbk PRED entity: 0vbk PRED relation: jurisdiction_of_office! PRED expected values: 0157m => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 67): 0d1_f (0.08 #627, 0.06 #551, 0.06 #4214), 0fd_1 (0.06 #344, 0.04 #421, 0.04 #1411), 06c0j (0.04 #451, 0.04 #374, 0.04 #679), 02yy8 (0.04 #452, 0.04 #375, 0.04 #680), 07hyk (0.04 #437, 0.04 #360, 0.04 #665), 0rlz (0.04 #334, 0.04 #639, 0.03 #1095), 042d1 (0.04 #435, 0.04 #663, 0.03 #1425), 07cbs (0.04 #408, 0.04 #636, 0.03 #1398), 02mjmr (0.04 #396, 0.03 #1080, 0.03 #1386), 0gzh (0.04 #457, 0.03 #1141, 0.03 #1447) >> Best rule #627 for best value: >> intensional similarity = 2 >> extensional distance = 50 >> proper extension: 02j71; >> query: (?x4758, 0d1_f) <- administrative_parent(?x11403, ?x4758), taxonomy(?x4758, ?x939) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #390 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 0f8l9c; *> query: (?x4758, 0157m) <- adjoins(?x4758, ?x1025), partially_contains(?x4758, ?x4540), religion(?x4758, ?x109) *> conf = 0.02 ranks of expected_values: 54 EVAL 0vbk jurisdiction_of_office! 0157m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 119.000 119.000 0.077 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #3216-02x2jl_ PRED entity: 02x2jl_ PRED relation: titles! PRED expected values: 02qfv5d => 84 concepts (60 used for prediction) PRED predicted values (max 10 best out of 66): 07ssc (0.38 #10, 0.36 #110, 0.15 #313), 07s9rl0 (0.33 #5544, 0.33 #1712, 0.33 #1612), 01z4y (0.24 #34, 0.23 #941, 0.23 #134), 04xvlr (0.24 #4, 0.23 #1615, 0.23 #104), 09blyk (0.20 #551, 0.18 #245, 0.09 #1152), 0c3351 (0.18 #556, 0.15 #250, 0.07 #1157), 024qqx (0.12 #484, 0.12 #1186, 0.11 #787), 01hmnh (0.11 #734, 0.11 #5568, 0.11 #833), 07c52 (0.10 #3948, 0.08 #5471, 0.08 #4354), 02l7c8 (0.10 #23, 0.09 #123, 0.04 #1333) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 02725hs; 0bpx1k; 02vqsll; 03mh_tp; 0ctb4g; 011yr9; 05vxdh; 0glqh5_; 09r94m; 03_gz8; ... >> query: (?x11735, 07ssc) <- produced_by(?x11735, ?x5973), language(?x11735, ?x254), ?x5973 = 02q42j_, film(?x262, ?x11735) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #592 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 163 *> proper extension: 04svwx; *> query: (?x11735, 02qfv5d) <- genre(?x11735, ?x600), country(?x11735, ?x94), ?x600 = 02n4kr *> conf = 0.05 ranks of expected_values: 20 EVAL 02x2jl_ titles! 02qfv5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 84.000 60.000 0.381 http://example.org/media_common/netflix_genre/titles #3215-01b3bp PRED entity: 01b3bp PRED relation: profession PRED expected values: 01d_h8 03gjzk 02krf9 => 108 concepts (81 used for prediction) PRED predicted values (max 10 best out of 57): 01d_h8 (0.55 #10369, 0.31 #894, 0.30 #9481), 0dxtg (0.54 #10377, 0.32 #2678, 0.29 #458), 03gjzk (0.50 #163, 0.30 #10378, 0.28 #5478), 09jwl (0.37 #6829, 0.37 #6977, 0.37 #4904), 016z4k (0.33 #4, 0.28 #5478, 0.24 #5037), 02jknp (0.33 #10371, 0.28 #5478, 0.25 #156), 0cbd2 (0.32 #2671, 0.29 #451, 0.16 #10814), 018gz8 (0.28 #5478, 0.27 #1645, 0.27 #1201), 0nbcg (0.28 #5478, 0.27 #6989, 0.27 #6841), 02krf9 (0.28 #5478, 0.25 #174, 0.15 #10389) >> Best rule #10369 for best value: >> intensional similarity = 5 >> extensional distance = 1777 >> proper extension: 074qgb; >> query: (?x14101, 01d_h8) <- profession(?x14101, ?x1383), profession(?x6682, ?x1383), profession(?x237, ?x1383), ?x6682 = 04jspq, ?x237 = 04t2l2 >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 10 EVAL 01b3bp profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 108.000 81.000 0.548 http://example.org/people/person/profession EVAL 01b3bp profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 81.000 0.548 http://example.org/people/person/profession EVAL 01b3bp profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 81.000 0.548 http://example.org/people/person/profession #3214-0bytfv PRED entity: 0bytfv PRED relation: costume_design_by! PRED expected values: 06sfk6 => 98 concepts (44 used for prediction) PRED predicted values (max 10 best out of 182): 05dy7p (0.08 #40, 0.04 #944, 0.04 #1125), 0f42nz (0.08 #999, 0.07 #1360, 0.05 #819), 09d38d (0.07 #1260, 0.05 #899), 01c9d (0.07 #538, 0.05 #900, 0.04 #1080), 04fjzv (0.07 #536, 0.05 #898, 0.04 #1078), 0g_zyp (0.07 #525, 0.05 #887, 0.04 #1067), 01rnly (0.07 #524, 0.05 #886, 0.04 #1066), 04x4nv (0.07 #516, 0.05 #878, 0.04 #1058), 01qz5 (0.07 #505, 0.05 #867, 0.04 #1047), 0gg8z1f (0.07 #481, 0.05 #843, 0.04 #1023) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 012ljv; 01r93l; 02cx90; 02lp3c; 053xw6; 08mhyd; 029m83; 02hfp_; 02vr7; >> query: (?x3685, 05dy7p) <- gender(?x3685, ?x514), nominated_for(?x3685, ?x8084), award(?x3685, ?x2222), ?x8084 = 02cbhg >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bytfv costume_design_by! 06sfk6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 44.000 0.083 http://example.org/film/film/costume_design_by #3213-01xysf PRED entity: 01xysf PRED relation: contains! PRED expected values: 09c7w0 => 169 concepts (79 used for prediction) PRED predicted values (max 10 best out of 350): 09c7w0 (0.83 #5369, 0.82 #8949, 0.82 #40262), 0n3dv (0.69 #51892, 0.37 #44731, 0.36 #25050), 04_1l0v (0.37 #44731, 0.36 #25050, 0.36 #16999), 0yp21 (0.25 #707, 0.17 #1601, 0.03 #4284), 02jx1 (0.17 #23346, 0.12 #44818, 0.11 #53768), 0d060g (0.16 #60841, 0.08 #1802, 0.07 #6274), 04s7y (0.16 #60841), 01n7q (0.15 #5444, 0.15 #51074, 0.14 #57339), 059rby (0.15 #5386, 0.14 #61758, 0.12 #9861), 02_286 (0.12 #5409, 0.07 #8989, 0.06 #51039) >> Best rule #5369 for best value: >> intensional similarity = 5 >> extensional distance = 90 >> proper extension: 02bf58; >> query: (?x11516, 09c7w0) <- contains(?x9333, ?x11516), category(?x11516, ?x134), school_type(?x11516, ?x1507), county(?x9333, ?x13475), currency(?x11516, ?x170) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xysf contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 79.000 0.826 http://example.org/location/location/contains #3212-019z7q PRED entity: 019z7q PRED relation: influenced_by PRED expected values: 08433 01tz6vs 058vp => 120 concepts (53 used for prediction) PRED predicted values (max 10 best out of 412): 014z8v (0.19 #1842, 0.09 #551, 0.08 #1411), 0p_47 (0.17 #1828, 0.05 #3120, 0.05 #3550), 01hmk9 (0.16 #1940, 0.08 #3232, 0.07 #3662), 014zfs (0.16 #1746, 0.07 #455, 0.07 #3038), 03_87 (0.14 #7951, 0.11 #18097, 0.11 #13354), 0gs7x (0.14 #8183, 0.11 #18097, 0.11 #13354), 0mj0c (0.14 #8183, 0.03 #7859, 0.02 #6136), 02z4b_8 (0.14 #8183), 0czkbt (0.14 #8183), 0gct_ (0.14 #8183) >> Best rule #1842 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 05xq9; >> query: (?x916, 014z8v) <- influenced_by(?x916, ?x8768), influenced_by(?x916, ?x5336), student(?x7154, ?x8768), participant(?x1607, ?x5336) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #18097 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 464 *> proper extension: 01kcms4; 02m4t; 04sd0; 01d5g; *> query: (?x916, ?x3541) <- influenced_by(?x916, ?x5336), influenced_by(?x5336, ?x3541) *> conf = 0.11 ranks of expected_values: 26, 56, 68 EVAL 019z7q influenced_by 058vp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 120.000 53.000 0.191 http://example.org/influence/influence_node/influenced_by EVAL 019z7q influenced_by 01tz6vs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 120.000 53.000 0.191 http://example.org/influence/influence_node/influenced_by EVAL 019z7q influenced_by 08433 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 120.000 53.000 0.191 http://example.org/influence/influence_node/influenced_by #3211-01pllx PRED entity: 01pllx PRED relation: award_nominee PRED expected values: 0bksh => 113 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1139): 0bksh (0.81 #53892, 0.80 #60921, 0.07 #3480), 04bdxl (0.81 #53892, 0.80 #60921, 0.04 #28124), 017r13 (0.81 #53892, 0.80 #60921, 0.02 #20207), 01z7s_ (0.20 #16401, 0.19 #7030, 0.15 #11716), 01wkmgb (0.17 #49203, 0.15 #53890, 0.13 #53889), 0151w_ (0.17 #49203, 0.13 #53889, 0.12 #58577), 04zn7g (0.17 #49203, 0.13 #53889, 0.12 #58577), 01ggc9 (0.17 #49203, 0.13 #53889, 0.12 #58577), 0sz28 (0.17 #49203, 0.13 #53889, 0.12 #58577), 08yx9q (0.17 #49203, 0.13 #53889, 0.12 #58577) >> Best rule #53892 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 07c0j; 04qmr; >> query: (?x8927, ?x91) <- participant(?x10329, ?x8927), artist(?x3240, ?x10329), award_nominee(?x91, ?x8927) >> conf = 0.81 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 01pllx award_nominee 0bksh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 53.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3210-0276g40 PRED entity: 0276g40 PRED relation: gender PRED expected values: 05zppz => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #19, 0.77 #23, 0.76 #25), 02zsn (0.55 #152, 0.53 #149, 0.46 #197) >> Best rule #19 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 02vmzp; 03wpmd; 05j12n; 038b_x; 01x2tm8; 0kst7v; 0239zv; 0cct7p; 01q8wk7; 04qp06; ... >> query: (?x11476, 05zppz) <- profession(?x11476, ?x1032), religion(?x11476, ?x8967), ?x8967 = 03j6c, ?x1032 = 02hrh1q, nationality(?x11476, ?x2146) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0276g40 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.780 http://example.org/people/person/gender #3209-016wzw PRED entity: 016wzw PRED relation: organization PRED expected values: 02vk52z => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 50): 02vk52z (0.93 #997, 0.90 #953, 0.88 #775), 018cqq (0.56 #78, 0.54 #1702, 0.47 #100), 01rz1 (0.54 #248, 0.50 #69, 0.49 #226), 0b6css (0.47 #99, 0.43 #234, 0.40 #144), 0_2v (0.46 #250, 0.45 #228, 0.44 #316), 041288 (0.40 #1519, 0.38 #1387, 0.37 #1762), 02jxk (0.36 #70, 0.32 #227, 0.32 #2485), 0gkjy (0.32 #2485, 0.27 #1400, 0.26 #1422), 0j7v_ (0.32 #2485, 0.27 #1508, 0.26 #1376), 059dn (0.32 #2485, 0.17 #59, 0.17 #82) >> Best rule #997 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 07z5n; 03676; 01n8qg; >> query: (?x2843, 02vk52z) <- adjoins(?x410, ?x2843), form_of_government(?x2843, ?x48), currency(?x2843, ?x170) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016wzw organization 02vk52z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.935 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #3208-0f502 PRED entity: 0f502 PRED relation: type_of_union PRED expected values: 04ztj => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.91 #13, 0.89 #69, 0.88 #9), 01g63y (0.34 #74, 0.30 #2, 0.29 #102), 0jgjn (0.01 #8), 01bl8s (0.01 #27) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 84 >> proper extension: 024y6w; >> query: (?x4360, 04ztj) <- award_winner(?x4360, ?x382), location_of_ceremony(?x4360, ?x6495) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f502 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.907 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3207-04wvhz PRED entity: 04wvhz PRED relation: student! PRED expected values: 065y4w7 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 167): 065y4w7 (0.25 #14, 0.22 #541, 0.09 #1595), 021w0_ (0.11 #851, 0.03 #2432, 0.03 #2959), 02zcnq (0.11 #673, 0.02 #1727, 0.02 #2254), 01jssp (0.11 #532, 0.02 #6860, 0.02 #7387), 04hgpt (0.11 #678), 05zl0 (0.10 #1256, 0.02 #1783, 0.01 #3891), 06182p (0.10 #1352, 0.02 #8207, 0.02 #22966), 01bm_ (0.10 #1300, 0.02 #2881, 0.01 #3408), 01jt2w (0.10 #1337, 0.01 #4500), 07t90 (0.10 #1201) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 04rtpt; >> query: (?x1039, 065y4w7) <- program(?x1039, ?x4881), program(?x1039, ?x2436), ?x2436 = 02hct1, ?x4881 = 02kk_c >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wvhz student! 065y4w7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #3206-01jswq PRED entity: 01jswq PRED relation: major_field_of_study PRED expected values: 04x_3 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 113): 02j62 (0.53 #1091, 0.48 #2035, 0.41 #1681), 02_7t (0.50 #180, 0.45 #298, 0.37 #1124), 04rjg (0.49 #1080, 0.42 #4623, 0.40 #136), 062z7 (0.43 #1088, 0.39 #1206, 0.37 #1442), 03g3w (0.40 #2031, 0.39 #1087, 0.30 #2976), 01lj9 (0.33 #2045, 0.32 #1219, 0.32 #629), 05qjt (0.33 #2014, 0.30 #1424, 0.27 #2605), 04x_3 (0.33 #1086, 0.32 #1204, 0.28 #2148), 041y2 (0.33 #1137, 0.29 #1491, 0.28 #1373), 0_jm (0.32 #1591, 0.30 #2772, 0.29 #2299) >> Best rule #1091 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 02y9bj; >> query: (?x2711, 02j62) <- institution(?x3437, ?x2711), ?x3437 = 02_xgp2, school(?x1883, ?x2711), major_field_of_study(?x2711, ?x1154) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1086 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 47 *> proper extension: 02y9bj; *> query: (?x2711, 04x_3) <- institution(?x3437, ?x2711), ?x3437 = 02_xgp2, school(?x1883, ?x2711), major_field_of_study(?x2711, ?x1154) *> conf = 0.33 ranks of expected_values: 8 EVAL 01jswq major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 132.000 132.000 0.531 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3205-0l14md PRED entity: 0l14md PRED relation: role! PRED expected values: 01wl38s 027dpx => 84 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1205): 04mx7s (0.67 #2550, 0.64 #5156, 0.60 #1507), 08n__5 (0.67 #3013, 0.50 #4055, 0.50 #3271), 0473q (0.60 #2003, 0.60 #1481, 0.50 #3303), 01mxnvc (0.60 #2066, 0.60 #1544, 0.50 #2849), 037hgm (0.60 #1423, 0.50 #2867, 0.50 #2346), 01vsnff (0.60 #1872, 0.50 #2655, 0.50 #2393), 01v_pj6 (0.60 #1341, 0.50 #2646, 0.50 #298), 04kjrv (0.60 #1994, 0.50 #2515, 0.43 #3556), 01vs4ff (0.60 #1471, 0.50 #428, 0.40 #1993), 019389 (0.60 #2017, 0.40 #2280, 0.33 #2800) >> Best rule #2550 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 0l14qv; 05r5c; >> query: (?x315, 04mx7s) <- role(?x315, ?x2785), role(?x315, ?x212), performance_role(?x115, ?x315), instrumentalists(?x315, ?x9407), instrumentalists(?x315, ?x4595), artists(?x671, ?x9407), performance_role(?x315, ?x1225), role(?x1282, ?x315), ?x212 = 026t6, role(?x4595, ?x745), ?x2785 = 0jtg0, group(?x315, ?x475), artists(?x302, ?x475) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1573 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 0bxl5; *> query: (?x315, 01wl38s) <- role(?x315, ?x2048), performance_role(?x115, ?x315), instrumentalists(?x315, ?x11233), instrumentalists(?x315, ?x9407), artists(?x671, ?x9407), performance_role(?x315, ?x1225), group(?x315, ?x379), role(?x7210, ?x315), profession(?x11233, ?x131), ?x7210 = 05qhnq, ?x2048 = 018j2 *> conf = 0.40 ranks of expected_values: 81, 243 EVAL 0l14md role! 027dpx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 84.000 35.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role EVAL 0l14md role! 01wl38s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 84.000 35.000 0.667 http://example.org/music/group_member/membership./music/group_membership/role #3204-03hhd3 PRED entity: 03hhd3 PRED relation: award PRED expected values: 05ztrmj => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 236): 09sb52 (0.34 #7331, 0.32 #8951, 0.32 #9356), 0ck27z (0.31 #6573, 0.23 #4548, 0.23 #4953), 0cqhk0 (0.18 #6517, 0.16 #1252, 0.16 #4492), 04ljl_l (0.16 #408, 0.15 #3, 0.14 #813), 0bdwqv (0.16 #578, 0.15 #173, 0.14 #983), 02x8n1n (0.15 #11746, 0.13 #28358, 0.13 #27547), 0gqy2 (0.15 #165, 0.11 #1785, 0.11 #2190), 05pcn59 (0.15 #82, 0.11 #487, 0.10 #11017), 057xs89 (0.15 #161, 0.11 #566, 0.09 #971), 0789_m (0.15 #20, 0.11 #425, 0.09 #830) >> Best rule #7331 for best value: >> intensional similarity = 3 >> extensional distance = 700 >> proper extension: 01w02sy; 05slvm; 01wz01; 02vntj; 051wwp; 01520h; >> query: (?x8587, 09sb52) <- award_nominee(?x3013, ?x8587), student(?x3948, ?x8587), film(?x8587, ?x66) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #185 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 0jsw9l; *> query: (?x8587, 05ztrmj) <- student(?x8398, ?x8587), ?x8398 = 028dcg, student(?x5614, ?x8587) *> conf = 0.08 ranks of expected_values: 39 EVAL 03hhd3 award 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 87.000 87.000 0.336 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3203-0x67 PRED entity: 0x67 PRED relation: taxonomy PRED expected values: 04n6k => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.20 #6, 0.17 #15, 0.17 #5) >> Best rule #6 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 02zsn; 0k95h; 012jc; >> query: (?x2510, 04n6k) <- risk_factors(?x7260, ?x2510), risk_factors(?x6720, ?x2510), people(?x6720, ?x510), people(?x7260, ?x11469), award_winner(?x724, ?x11469), ?x510 = 0chsq >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0x67 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.200 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #3202-02gkxp PRED entity: 02gkxp PRED relation: fraternities_and_sororities PRED expected values: 035tlh => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 3): 0325pb (0.21 #4, 0.21 #79, 0.19 #19), 035tlh (0.18 #80, 0.18 #92, 0.17 #8), 04m8fy (0.05 #9, 0.04 #12, 0.04 #15) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 0gk7z; >> query: (?x10333, 0325pb) <- contains(?x94, ?x10333), major_field_of_study(?x10333, ?x5614), currency(?x10333, ?x170), ?x5614 = 03qsdpk >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #80 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 195 *> proper extension: 06xpp7; *> query: (?x10333, 035tlh) <- contains(?x94, ?x10333), student(?x10333, ?x4681), ?x94 = 09c7w0, award_nominee(?x4681, ?x71) *> conf = 0.18 ranks of expected_values: 2 EVAL 02gkxp fraternities_and_sororities 035tlh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 145.000 0.212 http://example.org/education/university/fraternities_and_sororities #3201-07tp2 PRED entity: 07tp2 PRED relation: adjoins PRED expected values: 07dzf => 104 concepts (91 used for prediction) PRED predicted values (max 10 best out of 392): 06tw8 (0.82 #26989, 0.82 #30076, 0.81 #33933), 07dzf (0.82 #26989, 0.82 #30076, 0.81 #33933), 019rg5 (0.82 #26989, 0.82 #30076, 0.81 #33933), 019pcs (0.25 #945, 0.17 #66343, 0.13 #1715), 07tp2 (0.23 #65572, 0.22 #60163, 0.22 #66344), 088vb (0.23 #65572, 0.22 #60163, 0.22 #66344), 0169t (0.23 #65572, 0.22 #60163, 0.22 #66344), 01nyl (0.23 #65572, 0.22 #60163, 0.22 #66344), 05rznz (0.23 #65572, 0.22 #60163, 0.22 #66344), 01rxw (0.23 #65572, 0.22 #60163, 0.22 #66344) >> Best rule #26989 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 0n3g; >> query: (?x9251, ?x910) <- contains(?x2467, ?x9251), form_of_government(?x9251, ?x48), adjoins(?x910, ?x9251) >> conf = 0.82 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07tp2 adjoins 07dzf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 91.000 0.822 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #3200-01xcqc PRED entity: 01xcqc PRED relation: student! PRED expected values: 0gdm1 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 121): 0lyjf (0.33 #157), 08815 (0.20 #529, 0.06 #2637, 0.05 #3164), 01q0kg (0.20 #661), 015nl4 (0.14 #5864, 0.13 #5337, 0.08 #8499), 06182p (0.06 #2406, 0.05 #3460, 0.05 #5041), 03ksy (0.06 #2214, 0.04 #16973, 0.04 #11173), 0bwfn (0.06 #15033, 0.05 #33480, 0.05 #37697), 06thjt (0.06 #6722, 0.05 #3560, 0.05 #7776), 0187nd (0.06 #3001, 0.04 #1420, 0.03 #1947), 01d34b (0.06 #2891, 0.02 #5526, 0.02 #16596) >> Best rule #157 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0gn30; >> query: (?x1606, 0lyjf) <- participant(?x1607, ?x1606), nominated_for(?x1606, ?x7370), ?x7370 = 0cf08, profession(?x1606, ?x524) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01xcqc student! 0gdm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 120.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #3199-07tlfx PRED entity: 07tlfx PRED relation: country PRED expected values: 09c7w0 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.86 #2905, 0.83 #1452, 0.83 #1027), 07ssc (0.28 #436, 0.24 #497, 0.22 #376), 0345h (0.21 #387, 0.20 #447, 0.19 #508), 0j1z8 (0.14 #71, 0.01 #311), 0f8l9c (0.12 #199, 0.11 #439, 0.10 #379), 0chghy (0.09 #312, 0.06 #553, 0.06 #794), 03_3d (0.08 #128, 0.08 #248, 0.07 #368), 03rk0 (0.08 #159, 0.02 #640, 0.01 #339), 0ctw_b (0.08 #263, 0.04 #383, 0.04 #323), 0d05w3 (0.08 #223, 0.03 #403, 0.03 #463) >> Best rule #2905 for best value: >> intensional similarity = 3 >> extensional distance = 791 >> proper extension: 05f67hw; >> query: (?x9978, 09c7w0) <- language(?x9978, ?x254), country(?x9978, ?x279), produced_by(?x9978, ?x2499) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tlfx country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.864 http://example.org/film/film/country #3198-0745k7 PRED entity: 0745k7 PRED relation: nominated_for PRED expected values: 02h2vv => 75 concepts (10 used for prediction) PRED predicted values (max 10 best out of 76): 043qqt5 (0.36 #14607, 0.34 #16231, 0.33 #11360), 063y9fp (0.15 #8114, 0.11 #3247), 085ccd (0.15 #8114, 0.11 #3247), 080dwhx (0.03 #9797, 0.02 #13044, 0.02 #14667), 0kfv9 (0.03 #10004, 0.02 #13251, 0.02 #14874), 03ln8b (0.03 #13287, 0.02 #14910, 0.01 #10040), 0330r (0.03 #7906, 0.02 #1415, 0.02 #6284), 0180mw (0.02 #14024, 0.02 #15647, 0.02 #10777), 01q_y0 (0.02 #338, 0.02 #1961, 0.01 #6829), 0g60z (0.02 #13025, 0.02 #9778, 0.02 #14648) >> Best rule #14607 for best value: >> intensional similarity = 4 >> extensional distance = 497 >> proper extension: 01qn8k; >> query: (?x13493, ?x11477) <- profession(?x13493, ?x1032), actor(?x11477, ?x13493), place_of_birth(?x13493, ?x739), ?x1032 = 02hrh1q >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0745k7 nominated_for 02h2vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 10.000 0.360 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #3197-0c8qq PRED entity: 0c8qq PRED relation: nominated_for! PRED expected values: 054krc => 88 concepts (82 used for prediction) PRED predicted values (max 10 best out of 200): 019f4v (0.67 #9946, 0.67 #2311, 0.66 #9945), 0gs9p (0.33 #2369, 0.32 #3062, 0.32 #2600), 0k611 (0.31 #1684, 0.27 #3071, 0.27 #2378), 040njc (0.29 #1162, 0.29 #1624, 0.29 #7), 099c8n (0.29 #1670, 0.20 #2826, 0.20 #3057), 02pqp12 (0.26 #1210, 0.24 #1672, 0.20 #2366), 02qyntr (0.24 #1790, 0.24 #1328, 0.21 #2484), 02r0csl (0.24 #698, 0.18 #1622, 0.15 #1160), 0p9sw (0.23 #1637, 0.22 #2793, 0.21 #3255), 02qvyrt (0.23 #9019, 0.22 #1707, 0.20 #783) >> Best rule #9946 for best value: >> intensional similarity = 2 >> extensional distance = 1000 >> proper extension: 06mmr; >> query: (?x3311, ?x746) <- award(?x3311, ?x746), nominated_for(?x746, ?x69) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #9019 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 962 *> proper extension: 0cwrr; 04glx0; 05fgr_; 05sy0cv; 06w7mlh; 07bz5; *> query: (?x3311, ?x1307) <- nominated_for(?x6488, ?x3311), award(?x3311, ?x746), award(?x6488, ?x1307) *> conf = 0.23 ranks of expected_values: 16 EVAL 0c8qq nominated_for! 054krc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 88.000 82.000 0.673 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3196-07c52 PRED entity: 07c52 PRED relation: student PRED expected values: 06pjs 012x2b => 60 concepts (48 used for prediction) PRED predicted values (max 10 best out of 237): 012x2b (0.22 #2346, 0.14 #1628, 0.12 #1867), 0bg539 (0.22 #2180, 0.14 #1462, 0.12 #1701), 04sry (0.22 #2308, 0.14 #1590, 0.12 #1829), 02hsgn (0.22 #2266, 0.04 #4898, 0.03 #6333), 03l3ln (0.14 #1583, 0.12 #1822, 0.11 #2301), 06ltr (0.14 #1559, 0.12 #1798, 0.11 #2277), 02qzjj (0.14 #1666, 0.12 #1905, 0.11 #2384), 06pjs (0.14 #1621, 0.12 #1860, 0.11 #2339), 0f4vbz (0.14 #1477, 0.12 #1716, 0.11 #2195), 049dyj (0.14 #1456, 0.12 #1695, 0.11 #1934) >> Best rule #2346 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02h40lc; 02vxn; 02j62; 02822; 04rlf; 01zc2w; 041y2; >> query: (?x2008, 012x2b) <- major_field_of_study(?x2008, ?x5614), ?x5614 = 03qsdpk, major_field_of_study(?x4904, ?x2008), currency(?x4904, ?x170) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 07c52 student 012x2b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 48.000 0.222 http://example.org/education/field_of_study/students_majoring./education/education/student EVAL 07c52 student 06pjs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 60.000 48.000 0.222 http://example.org/education/field_of_study/students_majoring./education/education/student #3195-02fwfb PRED entity: 02fwfb PRED relation: featured_film_locations PRED expected values: 06y57 => 72 concepts (49 used for prediction) PRED predicted values (max 10 best out of 65): 02_286 (0.18 #1942, 0.14 #2666, 0.14 #5565), 04jpl (0.10 #9, 0.10 #2896, 0.09 #1450), 0chghy (0.10 #10, 0.05 #490, 0.03 #971), 0ctw_b (0.10 #23, 0.04 #1464, 0.01 #1224), 06y57 (0.10 #103, 0.01 #1304, 0.01 #5407), 030qb3t (0.08 #760, 0.07 #279, 0.07 #2926), 05kj_ (0.08 #739, 0.07 #258, 0.01 #2664), 0qpqn (0.07 #400, 0.04 #881, 0.01 #1842), 0qr4n (0.07 #320, 0.04 #801), 01_d4 (0.05 #527, 0.04 #768, 0.03 #2209) >> Best rule #1942 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 0963mq; 0pdp8; 0k54q; 064q5v; 09v42sf; >> query: (?x7292, 02_286) <- film(?x72, ?x7292), genre(?x7292, ?x2753), ?x2753 = 0219x_, language(?x7292, ?x254) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #103 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 047n8xt; *> query: (?x7292, 06y57) <- nominated_for(?x7291, ?x7292), currency(?x7292, ?x170), ?x7291 = 0274v0r, genre(?x7292, ?x53) *> conf = 0.10 ranks of expected_values: 5 EVAL 02fwfb featured_film_locations 06y57 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 72.000 49.000 0.183 http://example.org/film/film/featured_film_locations #3194-0kszw PRED entity: 0kszw PRED relation: student! PRED expected values: 01722w => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 116): 07szy (0.20 #567, 0.14 #1094, 0.02 #6891), 02mw6c (0.20 #430, 0.02 #3065), 019vsw (0.20 #359), 015nl4 (0.14 #1648, 0.14 #1121, 0.14 #2175), 02l9wl (0.14 #1833, 0.06 #2360, 0.05 #4468), 01722w (0.14 #1359, 0.02 #6102, 0.01 #9791), 080z7 (0.14 #1242), 07xpm (0.14 #1118), 0m4yg (0.07 #3000, 0.06 #2473, 0.06 #4581), 07tg4 (0.06 #9045, 0.06 #9572, 0.06 #10099) >> Best rule #567 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 09byk; >> query: (?x2531, 07szy) <- film(?x2531, ?x8062), ?x8062 = 04sskp, location(?x2531, ?x6764) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1359 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 01ckhj; *> query: (?x2531, 01722w) <- film(?x2531, ?x8062), ?x8062 = 04sskp, award(?x2531, ?x618) *> conf = 0.14 ranks of expected_values: 6 EVAL 0kszw student! 01722w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 105.000 105.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #3193-01s21dg PRED entity: 01s21dg PRED relation: nationality PRED expected values: 09c7w0 => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 45): 09c7w0 (0.82 #8336, 0.81 #2118, 0.78 #811), 02jx1 (0.20 #5459, 0.19 #5959, 0.18 #2250), 07ssc (0.20 #15, 0.13 #5441, 0.11 #5941), 0f8l9c (0.20 #22, 0.05 #731, 0.04 #1135), 06q1r (0.20 #77, 0.03 #5503, 0.03 #3900), 013yq (0.09 #1012, 0.07 #201, 0.06 #302), 0d060g (0.07 #4431, 0.07 #4231, 0.06 #310), 0chghy (0.07 #211, 0.06 #515, 0.06 #413), 02k1b (0.07 #285, 0.06 #589, 0.06 #487), 0162v (0.07 #246, 0.06 #550, 0.06 #448) >> Best rule #8336 for best value: >> intensional similarity = 2 >> extensional distance = 349 >> proper extension: 049tjg; 012v1t; >> query: (?x4741, 09c7w0) <- location(?x4741, ?x739), ?x739 = 02_286 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01s21dg nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 180.000 180.000 0.818 http://example.org/people/person/nationality #3192-01sl1q PRED entity: 01sl1q PRED relation: languages PRED expected values: 02h40lc => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 12): 02h40lc (0.33 #2, 0.33 #665, 0.33 #353), 06b_j (0.33 #16), 06nm1 (0.09 #1327, 0.01 #1606, 0.01 #1137), 02ztjwg (0.09 #1327), 05zjd (0.09 #1327), 064_8sq (0.05 #873, 0.05 #678, 0.05 #912), 02bjrlw (0.03 #118, 0.03 #196, 0.02 #625), 04306rv (0.02 #120, 0.01 #393, 0.01 #861), 0t_2 (0.02 #165, 0.01 #711, 0.01 #282), 03k50 (0.02 #1994, 0.02 #3515, 0.02 #2540) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0151ns; >> query: (?x56, 02h40lc) <- film(?x56, ?x664), place_of_birth(?x56, ?x1719), ?x664 = 0401sg >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sl1q languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.333 http://example.org/people/person/languages #3191-0fpmrm3 PRED entity: 0fpmrm3 PRED relation: film_release_region PRED expected values: 0k6nt => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 112): 05qhw (0.94 #323, 0.78 #1875, 0.73 #2495), 09c7w0 (0.93 #3414, 0.92 #6829, 0.92 #9778), 03gj2 (0.88 #334, 0.78 #1886, 0.74 #2506), 0k6nt (0.85 #333, 0.79 #1885, 0.74 #2815), 02vzc (0.83 #360, 0.78 #1912, 0.76 #2842), 05b4w (0.81 #374, 0.70 #1926, 0.66 #2546), 0chghy (0.81 #1871, 0.77 #319, 0.77 #2491), 06t2t (0.73 #371, 0.63 #1923, 0.59 #2543), 03rj0 (0.63 #369, 0.57 #1921, 0.53 #2541), 0ctw_b (0.63 #335, 0.51 #1887, 0.47 #2507) >> Best rule #323 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0ds35l9; 011yrp; 0g5qs2k; 01vksx; 0c0nhgv; 05z_kps; 047msdk; 0gmcwlb; 0dtfn; 0fpkhkz; ... >> query: (?x2655, 05qhw) <- film_release_region(?x2655, ?x3277), award(?x2655, ?x1254), ?x3277 = 06t8v >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #333 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 0ds35l9; 011yrp; 0g5qs2k; 01vksx; 0c0nhgv; 05z_kps; 047msdk; 0gmcwlb; 0dtfn; 0fpkhkz; ... *> query: (?x2655, 0k6nt) <- film_release_region(?x2655, ?x3277), award(?x2655, ?x1254), ?x3277 = 06t8v *> conf = 0.85 ranks of expected_values: 4 EVAL 0fpmrm3 film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 86.000 0.942 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3190-0141kz PRED entity: 0141kz PRED relation: award PRED expected values: 027dtxw => 96 concepts (86 used for prediction) PRED predicted values (max 10 best out of 276): 09sb52 (0.81 #4485, 0.50 #848, 0.34 #2464), 0gqy2 (0.77 #16173, 0.76 #25075, 0.72 #28309), 0bdwqv (0.62 #2192, 0.47 #2596, 0.36 #576), 0f4x7 (0.57 #2859, 0.38 #2050, 0.36 #434), 0bfvd4 (0.46 #2135, 0.31 #2539, 0.18 #2944), 0cqh46 (0.41 #2071, 0.27 #2475, 0.13 #2880), 04kxsb (0.36 #2146, 0.28 #2955, 0.27 #2550), 027dtxw (0.36 #2024, 0.25 #812, 0.24 #2428), 02x73k6 (0.31 #2080, 0.21 #2484, 0.18 #464), 0bs0bh (0.27 #2527, 0.26 #2123, 0.10 #2932) >> Best rule #4485 for best value: >> intensional similarity = 5 >> extensional distance = 492 >> proper extension: 0c3jz; 01q9b9; 01gw8b; 015vql; >> query: (?x9808, 09sb52) <- award(?x9808, ?x458), award(?x4294, ?x458), award(?x1958, ?x458), ?x1958 = 02wgln, ?x4294 = 01r93l >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #2024 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 02qgqt; 0bl2g; 017149; 01yk13; 01tcf7; 048lv; 0170pk; 016ywr; 01ycbq; 0bj9k; ... *> query: (?x9808, 027dtxw) <- award(?x9808, ?x458), film(?x9808, ?x3549), award_winner(?x4700, ?x9808), ?x458 = 0789_m *> conf = 0.36 ranks of expected_values: 8 EVAL 0141kz award 027dtxw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 96.000 86.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3189-09r94m PRED entity: 09r94m PRED relation: award PRED expected values: 027b9ly => 113 concepts (104 used for prediction) PRED predicted values (max 10 best out of 234): 0gs9p (0.28 #3225, 0.28 #4147, 0.26 #2303), 0k611 (0.28 #3225, 0.28 #4147, 0.26 #2303), 02r22gf (0.28 #3225, 0.28 #4147, 0.26 #2303), 03hl6lc (0.28 #3225, 0.28 #4147, 0.26 #2303), 02qyp19 (0.28 #3225, 0.28 #4147, 0.26 #2303), 099c8n (0.28 #3225, 0.28 #4147, 0.26 #2303), 02w9sd7 (0.24 #123, 0.09 #2195, 0.07 #1734), 02rdxsh (0.24 #51, 0.06 #1662, 0.06 #2123), 0gq9h (0.20 #2134, 0.12 #62, 0.10 #11573), 0l8z1 (0.18 #52, 0.14 #2124, 0.10 #282) >> Best rule #3225 for best value: >> intensional similarity = 4 >> extensional distance = 198 >> proper extension: 01b64v; 01b66d; 01j7mr; 0gj50; 01b65l; 030cx; 01b66t; 05_z42; 03ctqqf; >> query: (?x5331, ?x68) <- award(?x5331, ?x1198), category(?x5331, ?x134), nominated_for(?x68, ?x5331), nominated_for(?x3568, ?x5331) >> conf = 0.28 => this is the best rule for 6 predicted values *> Best rule #2230 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 131 *> proper extension: 0147sh; 018f8; 0pd57; 0gndh; 02fqxm; *> query: (?x5331, 027b9ly) <- genre(?x5331, ?x53), production_companies(?x5331, ?x1104), nominated_for(?x1703, ?x5331), ?x1703 = 0k611 *> conf = 0.06 ranks of expected_values: 43 EVAL 09r94m award 027b9ly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 113.000 104.000 0.283 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #3188-09l0x9 PRED entity: 09l0x9 PRED relation: draft! PRED expected values: 01xvb => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 212): 01y3v (0.56 #135, 0.56 #134, 0.56 #210), 084l5 (0.56 #135, 0.56 #134, 0.56 #210), 01xvb (0.56 #135, 0.56 #134, 0.56 #210), 0wsr (0.56 #135, 0.56 #134, 0.48 #1042), 03915c (0.56 #135, 0.56 #134, 0.48 #1042), 0ftccy (0.56 #135, 0.56 #134, 0.48 #1042), 04ls81 (0.56 #135, 0.56 #134, 0.48 #1042), 0fht9f (0.56 #135, 0.56 #134, 0.48 #1042), 07k53y (0.56 #135, 0.56 #134, 0.48 #1042), 0fbftr (0.56 #135, 0.56 #134, 0.48 #1042) >> Best rule #135 for best value: >> intensional similarity = 43 >> extensional distance = 1 >> proper extension: 0f4vx0; >> query: (?x6462, ?x179) <- draft(?x4469, ?x6462), draft(?x4222, ?x6462), team(?x2573, ?x4469), team(?x935, ?x4469), school(?x6462, ?x7338), school(?x6462, ?x6814), school(?x6462, ?x5486), school(?x6462, ?x4904), school(?x6462, ?x546), ?x546 = 01j_9c, major_field_of_study(?x7338, ?x10264), major_field_of_study(?x7338, ?x6756), school(?x1160, ?x7338), colors(?x4222, ?x9778), institution(?x3437, ?x7338), institution(?x1771, ?x7338), ?x1771 = 019v9k, contains(?x94, ?x6814), school(?x8542, ?x6814), school(?x465, ?x6814), school(?x10279, ?x6814), school(?x3333, ?x6814), ?x10264 = 01bt59, currency(?x5486, ?x170), school_type(?x7338, ?x1507), ?x8542 = 09th87, school(?x662, ?x5486), major_field_of_study(?x6814, ?x1154), ?x9778 = 09ggk, position(?x2573, ?x1240), position(?x8329, ?x935), ?x10279 = 04wmvz, school(?x4469, ?x2948), ?x465 = 05vsb7, team(?x935, ?x179), student(?x5486, ?x118), teams(?x7930, ?x4469), ?x6756 = 0_jm, ?x3333 = 01yjl, institution(?x734, ?x5486), colors(?x4904, ?x3315), school_type(?x4904, ?x3205), ?x3437 = 02_xgp2 >> conf = 0.56 => this is the best rule for 59 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 09l0x9 draft! 01xvb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 20.000 20.000 0.561 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #3187-04n2vgk PRED entity: 04n2vgk PRED relation: artists! PRED expected values: 0glt670 => 103 concepts (45 used for prediction) PRED predicted values (max 10 best out of 248): 0glt670 (0.63 #346, 0.47 #1883, 0.46 #1268), 064t9 (0.56 #1856, 0.52 #1241, 0.49 #3392), 0xhtw (0.50 #630, 0.33 #16, 0.27 #4318), 025sc50 (0.41 #356, 0.38 #1893, 0.32 #1278), 02yv6b (0.39 #712, 0.38 #98, 0.17 #4400), 0dl5d (0.36 #633, 0.15 #4321, 0.12 #19), 06j6l (0.33 #2198, 0.32 #1891, 0.30 #1276), 08jyyk (0.29 #681, 0.21 #67, 0.10 #4369), 0cx7f (0.27 #751, 0.17 #137, 0.09 #4439), 05bt6j (0.26 #1578, 0.25 #42, 0.23 #7109) >> Best rule #346 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 02r3cn; >> query: (?x9262, 0glt670) <- artists(?x9630, ?x9262), profession(?x9262, ?x131), ?x9630 = 012yc >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04n2vgk artists! 0glt670 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 45.000 0.630 http://example.org/music/genre/artists #3186-0mpzm PRED entity: 0mpzm PRED relation: currency PRED expected values: 09nqf => 172 concepts (172 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.86 #42, 0.84 #61, 0.84 #72) >> Best rule #42 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 0l380; 0mwvq; 0kwgs; 0fc_9; 0n5y4; 0k3gw; 0mx7f; 0cv1h; 0cc07; 0nm87; ... >> query: (?x11228, 09nqf) <- contains(?x11228, ?x3269), time_zones(?x11228, ?x1638), administrative_division(?x3269, ?x3634), second_level_divisions(?x94, ?x11228) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mpzm currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 172.000 0.857 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #3185-0vlf PRED entity: 0vlf PRED relation: service_language PRED expected values: 02h40lc => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 117): 02h40lc (0.93 #884, 0.92 #1031, 0.92 #1220), 06nm1 (0.25 #216, 0.20 #909, 0.19 #783), 03_9r (0.17 #215, 0.10 #89, 0.07 #908), 064_8sq (0.16 #913, 0.14 #1396, 0.13 #1438), 04306rv (0.15 #87, 0.12 #213, 0.12 #591), 01r2l (0.15 #96, 0.08 #600, 0.06 #789), 05zjd (0.11 #55, 0.10 #97, 0.09 #181), 06b_j (0.10 #95, 0.05 #389, 0.04 #557), 02hwhyv (0.10 #100, 0.05 #394, 0.04 #562), 02bjrlw (0.05 #85, 0.04 #904, 0.04 #211) >> Best rule #884 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 01_qgp; >> query: (?x12452, 02h40lc) <- contact_category(?x12452, ?x897), service_location(?x12452, ?x94), state_province_region(?x12452, ?x1227), nationality(?x51, ?x94) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0vlf service_language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.926 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #3184-0345h PRED entity: 0345h PRED relation: film_release_region! PRED expected values: 02x3lt7 017gl1 0cnztc4 02r1c18 0ch26b_ 0btyf5z 0gvrws1 0407yfx 0j6b5 0fpv_3_ 08052t3 07x4qr 0dr3sl 040b5k 023gxx 06w839_ 0dgpwnk 03q0r1 0gyh2wm 0gy2y8r 080lkt7 03nm_fh 01rwpj 026lgs 02prwdh 0hv8w 0h95zbp 01d259 01f85k 0dc_ms 035zr0 0cbn7c 0gwlfnb 0g57wgv 024lt6 09tcg4 => 206 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1046): 03nm_fh (0.89 #31525, 0.87 #21495, 0.86 #26510), 07x4qr (0.87 #22282, 0.83 #26294, 0.77 #21279), 03yvf2 (0.87 #15576, 0.77 #22598, 0.74 #26610), 017gl1 (0.87 #31176, 0.87 #21146, 0.83 #26161), 0fpv_3_ (0.87 #21257, 0.85 #23263, 0.82 #33293), 0407yfx (0.86 #26256, 0.84 #22244, 0.83 #12212), 0ch26b_ (0.84 #22221, 0.80 #21218, 0.79 #23224), 0h95zbp (0.81 #22607, 0.75 #33640, 0.74 #26619), 03q0r1 (0.80 #21406, 0.78 #15387, 0.78 #12377), 0j6b5 (0.80 #26258, 0.78 #15224, 0.77 #22246) >> Best rule #31525 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0d0vqn; 04gzd; 06npd; 06mzp; 047yc; 0h7x; 06c1y; 01pj7; 05b4w; 03spz; >> query: (?x1264, 03nm_fh) <- film_release_region(?x8025, ?x1264), member_states(?x2106, ?x1264), ?x8025 = 03nsm5x >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 24, 28, 30, 31, 130, 131, 133, 134, 139, 140, 143, 152, 156, 160, 209, 229 EVAL 0345h film_release_region! 09tcg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 024lt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0g57wgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0gwlfnb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0cbn7c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 035zr0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0dc_ms CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 01f85k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 01d259 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0h95zbp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0hv8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 02prwdh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 026lgs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 01rwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 03nm_fh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 080lkt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0gy2y8r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0gyh2wm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 03q0r1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0dgpwnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 06w839_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 023gxx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 040b5k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0dr3sl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 07x4qr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 08052t3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0fpv_3_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0j6b5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0407yfx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0gvrws1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0btyf5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0ch26b_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 02r1c18 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 0cnztc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 017gl1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0345h film_release_region! 02x3lt7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 206.000 133.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3183-0m2rv PRED entity: 0m2rv PRED relation: place! PRED expected values: 0m2rv => 137 concepts (109 used for prediction) PRED predicted values (max 10 best out of 243): 0qpqn (0.14 #37658, 0.10 #37142, 0.08 #46949), 03spz (0.14 #37658, 0.10 #37142, 0.08 #46949), 0m2rv (0.14 #37658, 0.10 #37142, 0.08 #46949), 0njpq (0.11 #3608, 0.09 #10317, 0.08 #10833), 0r00l (0.11 #347, 0.05 #1378, 0.04 #1893), 0hptm (0.11 #157, 0.05 #1188, 0.04 #1703), 03b12 (0.11 #291, 0.05 #1322, 0.03 #2868), 0qxzd (0.11 #401, 0.05 #1432, 0.02 #4009), 02_286 (0.11 #14, 0.03 #2591, 0.02 #3622), 02dtg (0.10 #525, 0.04 #1555, 0.04 #2071) >> Best rule #37658 for best value: >> intensional similarity = 4 >> extensional distance = 325 >> proper extension: 0wp9b; 0dclg; 0ftvz; 0r1jr; 0m2lt; 01531; 0y2dl; 0l2hf; 0l380; 0ccvx; ... >> query: (?x3372, ?x4743) <- source(?x3372, ?x958), location(?x12255, ?x3372), ?x958 = 0jbk9, location(?x12255, ?x4743) >> conf = 0.14 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0m2rv place! 0m2rv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 137.000 109.000 0.143 http://example.org/location/hud_county_place/place #3182-0d0vj4 PRED entity: 0d0vj4 PRED relation: religion PRED expected values: 07y1z => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 40): 02rsw (0.33 #24, 0.21 #429, 0.16 #294), 07y1z (0.33 #43, 0.14 #223, 0.13 #268), 0c8wxp (0.28 #501, 0.27 #96, 0.21 #1491), 019cr (0.25 #56, 0.21 #191, 0.14 #371), 0v53x (0.25 #74, 0.14 #389, 0.14 #344), 05sfs (0.20 #228, 0.17 #3, 0.14 #183), 0631_ (0.19 #323, 0.17 #413, 0.16 #278), 03j6c (0.18 #156, 0.08 #1866, 0.06 #3486), 0kpl (0.17 #460, 0.17 #10, 0.16 #1090), 051kv (0.17 #5, 0.14 #365, 0.14 #320) >> Best rule #24 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 034ls; >> query: (?x966, 02rsw) <- gender(?x966, ?x231), student(?x122, ?x966), profession(?x966, ?x967), person(?x4312, ?x966), ?x122 = 08815 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 034ls; *> query: (?x966, 07y1z) <- gender(?x966, ?x231), student(?x122, ?x966), profession(?x966, ?x967), person(?x4312, ?x966), ?x122 = 08815 *> conf = 0.33 ranks of expected_values: 2 EVAL 0d0vj4 religion 07y1z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 161.000 161.000 0.333 http://example.org/people/person/religion #3181-0j582 PRED entity: 0j582 PRED relation: profession PRED expected values: 01d_h8 => 129 concepts (73 used for prediction) PRED predicted values (max 10 best out of 122): 01d_h8 (0.79 #7111, 0.78 #6815, 0.77 #7259), 0cbd2 (0.71 #8596, 0.52 #6224, 0.48 #4743), 0dxtg (0.64 #902, 0.60 #1494, 0.53 #2974), 03gjzk (0.59 #5788, 0.46 #4899, 0.46 #5936), 02jknp (0.43 #4596, 0.41 #5929, 0.40 #6521), 018gz8 (0.32 #3569, 0.26 #3273, 0.24 #1941), 09jwl (0.29 #759, 0.25 #3275, 0.22 #4311), 02krf9 (0.29 #767, 0.19 #5800, 0.17 #5948), 0np9r (0.29 #761, 0.17 #465, 0.16 #3277), 0nbcg (0.29 #771, 0.17 #1067, 0.15 #1511) >> Best rule #7111 for best value: >> intensional similarity = 4 >> extensional distance = 240 >> proper extension: 05cv94; 03kpvp; 0gg9_5q; 03y2kr; 0glyyw; 05zrx3v; 03c9pqt; 01g04k; 0g_rs_; >> query: (?x1548, 01d_h8) <- executive_produced_by(?x1547, ?x1548), profession(?x1548, ?x2225), profession(?x6914, ?x2225), ?x6914 = 02b29 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j582 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 73.000 0.785 http://example.org/people/person/profession #3180-0dc_ms PRED entity: 0dc_ms PRED relation: film_crew_role PRED expected values: 09vw2b7 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 24): 09vw2b7 (0.76 #200, 0.75 #38, 0.73 #521), 02rh1dz (0.43 #73, 0.31 #203, 0.29 #331), 02ynfr (0.29 #77, 0.25 #207, 0.25 #45), 0d2b38 (0.21 #86, 0.16 #537, 0.13 #344), 089fss (0.21 #69, 0.12 #37, 0.11 #199), 089g0h (0.15 #339, 0.12 #565, 0.12 #1153), 0215hd (0.14 #531, 0.14 #338, 0.14 #1152), 015h31 (0.14 #523, 0.14 #202, 0.13 #330), 01xy5l_ (0.14 #526, 0.13 #559, 0.13 #1147), 04pyp5 (0.12 #46, 0.10 #2279, 0.08 #1150) >> Best rule #200 for best value: >> intensional similarity = 8 >> extensional distance = 70 >> proper extension: 02y_lrp; >> query: (?x6528, 09vw2b7) <- film(?x541, ?x6528), film_crew_role(?x6528, ?x2178), film_crew_role(?x6528, ?x2095), film_crew_role(?x6528, ?x468), ?x2095 = 0dxtw, genre(?x6528, ?x225), ?x2178 = 01pvkk, ?x468 = 02r96rf >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dc_ms film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.764 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3179-0828jw PRED entity: 0828jw PRED relation: genre PRED expected values: 07s9rl0 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 75): 07s9rl0 (0.80 #317, 0.79 #396, 0.79 #238), 05p553 (0.68 #1352, 0.47 #1035, 0.45 #1432), 01z4y (0.44 #1360, 0.35 #725, 0.34 #646), 0hcr (0.39 #567, 0.26 #1361, 0.22 #172), 0lsxr (0.36 #246, 0.33 #325, 0.32 #404), 0c4xc (0.30 #1385, 0.24 #671, 0.23 #750), 0c3351 (0.29 #259, 0.27 #338, 0.26 #417), 03npn (0.25 #87, 0.21 #245, 0.21 #403), 02kdv5l (0.25 #82, 0.21 #398, 0.19 #556), 0vgkd (0.25 #89, 0.15 #1357, 0.14 #247) >> Best rule #317 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 06dfz1; >> query: (?x5810, 07s9rl0) <- genre(?x5810, ?x812), ?x812 = 01jfsb, program(?x2650, ?x5810) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0828jw genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 62.000 0.800 http://example.org/tv/tv_program/genre #3178-02184q PRED entity: 02184q PRED relation: student! PRED expected values: 01y9st => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 144): 07wjk (0.33 #2698, 0.26 #1644, 0.17 #590), 02g839 (0.17 #552, 0.05 #1606, 0.02 #5822), 031ns1 (0.17 #518), 07vhb (0.17 #169), 01d34b (0.11 #3418, 0.02 #10797, 0.02 #9215), 07tg4 (0.09 #4302, 0.07 #6937, 0.03 #4829), 03ksy (0.08 #2741, 0.06 #4849, 0.05 #1687), 01w5m (0.08 #3794, 0.07 #6429, 0.06 #1159), 0bwfn (0.08 #3964, 0.06 #11870, 0.06 #1329), 05nrkb (0.07 #3511, 0.02 #21960, 0.01 #29865) >> Best rule #2698 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 0m2l9; 0gd5z; 01k165; 01vqrm; 012rng; 02kz_; 03xyp_; 0d_w7; 04rfq; >> query: (?x9819, 07wjk) <- type_of_union(?x9819, ?x566), location(?x9819, ?x1658), ?x566 = 04ztj, ?x1658 = 0h7h6, nationality(?x9819, ?x279) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02184q student! 01y9st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 129.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #3177-01wdj_ PRED entity: 01wdj_ PRED relation: school! PRED expected values: 0g3zpp => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 19): 0f4vx0 (0.47 #200, 0.31 #181, 0.31 #162), 02qw1zx (0.35 #156, 0.32 #270, 0.31 #80), 09l0x9 (0.29 #163, 0.27 #287, 0.26 #286), 03nt7j (0.29 #158, 0.27 #287, 0.24 #177), 092j54 (0.27 #179, 0.27 #287, 0.25 #160), 0g3zpp (0.27 #287, 0.21 #153, 0.20 #267), 02pq_x5 (0.26 #206, 0.19 #168, 0.16 #417), 025tn92 (0.24 #126, 0.21 #164, 0.20 #183), 02z6872 (0.19 #199, 0.15 #161, 0.14 #123), 09th87 (0.18 #185, 0.14 #280, 0.13 #166) >> Best rule #200 for best value: >> intensional similarity = 5 >> extensional distance = 56 >> proper extension: 06mkj; 0d05w3; >> query: (?x2830, 0f4vx0) <- contains(?x2831, ?x2830), school(?x465, ?x2830), school(?x465, ?x9131), draft(?x387, ?x465), ?x9131 = 02pptm >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #287 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 64 *> proper extension: 0fht9f; *> query: (?x2830, ?x465) <- school(?x1639, ?x2830), team(?x11323, ?x1639), draft(?x1639, ?x6462), draft(?x1639, ?x465), position(?x1639, ?x180), ?x6462 = 09l0x9 *> conf = 0.27 ranks of expected_values: 6 EVAL 01wdj_ school! 0g3zpp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 173.000 173.000 0.466 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #3176-01hq1 PRED entity: 01hq1 PRED relation: nominated_for! PRED expected values: 0gqxm 0641kkh => 83 concepts (62 used for prediction) PRED predicted values (max 10 best out of 196): 099c8n (0.50 #288, 0.50 #54, 0.29 #756), 09sb52 (0.43 #265, 0.33 #31, 0.20 #1201), 0gq_v (0.42 #7742, 0.25 #2592, 0.19 #1890), 040njc (0.36 #239, 0.33 #5, 0.22 #2579), 04dn09n (0.36 #266, 0.33 #32, 0.22 #1202), 09qv_s (0.36 #345, 0.17 #1281, 0.17 #111), 0gq9h (0.34 #7784, 0.33 #2634, 0.33 #60), 0gr0m (0.34 #7781, 0.21 #291, 0.21 #759), 0gs9p (0.33 #62, 0.29 #2636, 0.29 #296), 019f4v (0.33 #51, 0.29 #753, 0.29 #285) >> Best rule #288 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 02rx2m5; 016z9n; 07w8fz; 016kv6; 0h03fhx; 09ps01; 0gmgwnv; >> query: (?x7881, 099c8n) <- country(?x7881, ?x94), nominated_for(?x71, ?x7881), film(?x71, ?x11149), ?x11149 = 016017 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #12406 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1262 *> proper extension: 0267wwv; *> query: (?x7881, ?x198) <- country(?x7881, ?x94), nominated_for(?x71, ?x7881), profession(?x71, ?x319), award(?x71, ?x198) *> conf = 0.20 ranks of expected_values: 39, 93 EVAL 01hq1 nominated_for! 0641kkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 83.000 62.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01hq1 nominated_for! 0gqxm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 83.000 62.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3175-02zmh5 PRED entity: 02zmh5 PRED relation: award_nominee PRED expected values: 0412f5y => 127 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1060): 01wd9vs (0.16 #1625, 0.12 #3962, 0.05 #15648), 02zft0 (0.16 #1406, 0.12 #3743, 0.04 #15429), 02l840 (0.14 #21194, 0.11 #16520, 0.07 #9509), 01vw20h (0.14 #22092, 0.11 #17418, 0.06 #1058), 04lgymt (0.10 #21137, 0.08 #11789, 0.07 #18800), 02cx90 (0.10 #26723, 0.09 #31397, 0.07 #33734), 0288fyj (0.09 #21534, 0.06 #16860, 0.06 #5175), 01vttb9 (0.09 #1694, 0.07 #4031, 0.02 #4675), 01vs_v8 (0.08 #93489, 0.03 #21511, 0.02 #5152), 01pq5j7 (0.08 #93489, 0.02 #19933, 0.02 #4675) >> Best rule #1625 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 02lfp4; >> query: (?x2083, 01wd9vs) <- award(?x2083, ?x4317), ?x4317 = 05q8pss, award_nominee(?x2083, ?x3397) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #19514 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 105 *> proper extension: 0288fyj; 01bmlb; 03cd1q; *> query: (?x2083, 0412f5y) <- award(?x2083, ?x724), nationality(?x2083, ?x304), ?x724 = 01bgqh *> conf = 0.04 ranks of expected_values: 104 EVAL 02zmh5 award_nominee 0412f5y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 127.000 46.000 0.156 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3174-09rfh9 PRED entity: 09rfh9 PRED relation: genre PRED expected values: 03npn => 118 concepts (79 used for prediction) PRED predicted values (max 10 best out of 120): 07s9rl0 (0.88 #6200, 0.84 #4413, 0.80 #2144), 02n4kr (0.81 #2033, 0.64 #3466, 0.64 #961), 060__y (0.76 #3832, 0.33 #136, 0.25 #374), 03npn (0.73 #8472, 0.71 #246, 0.71 #3100), 02kdv5l (0.61 #1551, 0.54 #3222, 0.54 #3103), 03k9fj (0.50 #3231, 0.46 #3112, 0.39 #1560), 02l7c8 (0.43 #6817, 0.27 #4428, 0.26 #3831), 05p553 (0.38 #4655, 0.36 #6925, 0.35 #7641), 0c3351 (0.33 #2777, 0.33 #2061, 0.33 #1942), 04xvlr (0.33 #121, 0.27 #6683, 0.21 #3817) >> Best rule #6200 for best value: >> intensional similarity = 7 >> extensional distance = 342 >> proper extension: 0b76d_m; 0ds3t5x; 0h3xztt; 04zyhx; 0bcndz; 02q52q; 0fpmrm3; 0c9k8; 0crh5_f; 0c8qq; ... >> query: (?x10309, 07s9rl0) <- genre(?x10309, ?x3613), production_companies(?x10309, ?x1914), film_release_region(?x10309, ?x94), titles(?x571, ?x10309), genre(?x4136, ?x3613), titles(?x3613, ?x1308), ?x4136 = 02jr6k >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #8472 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 519 *> proper extension: 01gglm; *> query: (?x10309, ?x571) <- film_release_distribution_medium(?x10309, ?x81), ?x81 = 029j_, titles(?x571, ?x10309), production_companies(?x10309, ?x1914), genre(?x5198, ?x571), titles(?x811, ?x5198) *> conf = 0.73 ranks of expected_values: 4 EVAL 09rfh9 genre 03npn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 118.000 79.000 0.884 http://example.org/film/film/genre #3173-02rjv2w PRED entity: 02rjv2w PRED relation: award_winner PRED expected values: 03j24kf => 99 concepts (46 used for prediction) PRED predicted values (max 10 best out of 750): 01kwsg (0.26 #6572, 0.20 #67369, 0.12 #42719), 015g_7 (0.26 #6572, 0.20 #67369, 0.12 #42719), 0f0p0 (0.26 #6572, 0.20 #67369, 0.12 #42719), 03j24kf (0.17 #49290, 0.15 #13141, 0.12 #67368), 0pj8m (0.17 #49290, 0.15 #13141, 0.12 #67368), 018dyl (0.17 #49290, 0.15 #13141, 0.12 #67368), 01kv4mb (0.17 #49290, 0.15 #13141, 0.12 #67368), 01vrncs (0.17 #49290, 0.15 #13141, 0.12 #67368), 02qwg (0.17 #49290, 0.15 #13141, 0.12 #67368), 0gt_k (0.17 #49290, 0.15 #13141, 0.12 #67368) >> Best rule #6572 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 03l6q0; >> query: (?x2729, ?x1021) <- currency(?x2729, ?x170), award_winner(?x2729, ?x3321), film(?x1021, ?x2729), diet(?x3321, ?x3130) >> conf = 0.26 => this is the best rule for 3 predicted values *> Best rule #49290 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 366 *> proper extension: 0cwrr; 04glx0; 05fgr_; 06mmr; *> query: (?x2729, ?x1089) <- award_winner(?x2729, ?x6947), award(?x2729, ?x746), honored_for(?x4700, ?x2729), award_winner(?x1089, ?x6947) *> conf = 0.17 ranks of expected_values: 4 EVAL 02rjv2w award_winner 03j24kf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 46.000 0.264 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #3172-03g5_y PRED entity: 03g5_y PRED relation: religion PRED expected values: 0c8wxp => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 21): 0c8wxp (0.35 #186, 0.28 #96, 0.26 #411), 03_gx (0.19 #645, 0.18 #736, 0.12 #284), 0kpl (0.18 #10, 0.17 #641, 0.16 #1049), 0kq2 (0.06 #18, 0.04 #1057, 0.04 #1147), 0flw86 (0.06 #2, 0.03 #815, 0.02 #996), 092bf5 (0.04 #106, 0.04 #512, 0.04 #421), 019cr (0.04 #101, 0.03 #507, 0.02 #191), 01lp8 (0.04 #91, 0.02 #136, 0.02 #1402), 051kv (0.04 #95, 0.02 #140, 0.01 #230), 05sfs (0.04 #93, 0.01 #138, 0.01 #228) >> Best rule #186 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 0cbm64; >> query: (?x7872, 0c8wxp) <- award_nominee(?x7872, ?x8716), award(?x7872, ?x2325), ?x2325 = 05p09zm >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03g5_y religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.354 http://example.org/people/person/religion #3171-020ffd PRED entity: 020ffd PRED relation: gender PRED expected values: 02zsn => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.82 #31, 0.77 #3, 0.77 #25), 02zsn (0.67 #2, 0.53 #101, 0.48 #22) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 199 >> proper extension: 0bjkpt; 01zlh5; >> query: (?x6171, 05zppz) <- profession(?x6171, ?x1032), award_nominee(?x690, ?x6171), producer_type(?x6171, ?x632) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 06jvj7; 047q2wc; 02pt6k_; 06jw0s; 05sj55; 05yjhm; *> query: (?x6171, 02zsn) <- profession(?x6171, ?x1032), award_nominee(?x690, ?x6171), ?x690 = 06n7h7 *> conf = 0.67 ranks of expected_values: 2 EVAL 020ffd gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 92.000 0.821 http://example.org/people/person/gender #3170-0c4hx0 PRED entity: 0c4hx0 PRED relation: award_winner PRED expected values: 0p9qb 01fxfk => 41 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1323): 0cw67g (0.40 #15236, 0.29 #7554, 0.25 #22931), 04ktcgn (0.33 #277, 0.29 #6422, 0.25 #1813), 021yc7p (0.33 #217, 0.29 #6362, 0.25 #1753), 02h1rt (0.33 #743, 0.25 #2279, 0.20 #3815), 01tc9r (0.33 #593, 0.25 #2129, 0.20 #3665), 0pmhf (0.33 #376, 0.25 #1912, 0.20 #3448), 01nwwl (0.33 #438, 0.25 #1974, 0.20 #3510), 0fgg4 (0.33 #780, 0.25 #2316, 0.20 #3852), 05mcjs (0.33 #1008, 0.25 #2544, 0.20 #4080), 06dkzt (0.33 #1240, 0.25 #2776, 0.20 #4312) >> Best rule #15236 for best value: >> intensional similarity = 19 >> extensional distance = 8 >> proper extension: 073hmq; >> query: (?x9667, 0cw67g) <- ceremony(?x6860, ?x9667), ceremony(?x3066, ?x9667), ceremony(?x1972, ?x9667), ceremony(?x1243, ?x9667), ?x6860 = 018wdw, ?x3066 = 0gqy2, ?x1243 = 0gr0m, award_winner(?x9667, ?x11729), award_winner(?x9667, ?x8423), award(?x11729, ?x1854), artists(?x10332, ?x11729), ?x1972 = 0gqyl, nationality(?x11729, ?x94), honored_for(?x9667, ?x2640), gender(?x11729, ?x231), instance_of_recurring_event(?x9667, ?x3459), nationality(?x8423, ?x1310), ?x1854 = 025m8y, profession(?x8423, ?x1032) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #33835 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 49 *> proper extension: 02wzl1d; 026kq4q; 09p3h7; 026kqs9; 09pnw5; 09pj68; 0418154; 09qftb; *> query: (?x9667, ?x2426) <- ceremony(?x6860, ?x9667), ceremony(?x3617, ?x9667), ceremony(?x3066, ?x9667), nominated_for(?x6860, ?x6007), award_winner(?x9667, ?x2641), ceremony(?x6860, ?x1084), award(?x6187, ?x3066), nominated_for(?x3066, ?x4734), nominated_for(?x3066, ?x4093), ?x4734 = 0sxmx, award(?x195, ?x3066), award_winner(?x7452, ?x6187), place_of_death(?x2641, ?x362), award_winner(?x3617, ?x2426), ?x4093 = 0h6r5, ?x6007 = 0dgq_kn, award_nominee(?x2641, ?x669), honored_for(?x1084, ?x708) *> conf = 0.02 ranks of expected_values: 922 EVAL 0c4hx0 award_winner 01fxfk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 41.000 24.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0c4hx0 award_winner 0p9qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 41.000 24.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3169-0_j_z PRED entity: 0_j_z PRED relation: source PRED expected values: 0jbk9 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #60, 0.90 #39, 0.89 #14) >> Best rule #60 for best value: >> intensional similarity = 3 >> extensional distance = 229 >> proper extension: 0mn0v; 0qlrh; >> query: (?x13019, 0jbk9) <- time_zones(?x13019, ?x2674), county(?x13019, ?x10067), second_level_divisions(?x94, ?x10067) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_j_z source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.926 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #3168-01k53x PRED entity: 01k53x PRED relation: film PRED expected values: 01k1k4 => 158 concepts (148 used for prediction) PRED predicted values (max 10 best out of 1129): 0cs134 (0.67 #91194, 0.59 #193124, 0.50 #123383), 09cr8 (0.09 #282, 0.04 #3858, 0.03 #50349), 025s1wg (0.09 #1704, 0.03 #12433, 0.03 #44619), 02pg45 (0.09 #929, 0.03 #11658, 0.02 #47420), 0cmf0m0 (0.09 #1427, 0.03 #12156, 0.02 #44342), 029k4p (0.09 #834, 0.03 #43749, 0.02 #49113), 01svry (0.09 #1190, 0.02 #31589, 0.02 #19072), 06zn2v2 (0.09 #736, 0.02 #4312, 0.02 #7888), 0gmd3k7 (0.09 #1107, 0.02 #4683, 0.02 #47598), 01kff7 (0.09 #206, 0.02 #3782, 0.02 #9146) >> Best rule #91194 for best value: >> intensional similarity = 3 >> extensional distance = 376 >> proper extension: 0j582; 09qh1; 044qx; 046qq; 0432b; 03m6pk; 0gd9k; 063_t; 0btxr; 012x2b; ... >> query: (?x9585, ?x10731) <- film(?x9585, ?x97), participant(?x9585, ?x65), nominated_for(?x9585, ?x10731) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #16151 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 91 *> proper extension: 01n5309; 03h_9lg; 034x61; 03knl; 0134w7; 018grr; 011zd3; 01trhmt; 0pmhf; 02mjmr; ... *> query: (?x9585, 01k1k4) <- place_of_birth(?x9585, ?x3976), vacationer(?x151, ?x9585), location(?x846, ?x3976) *> conf = 0.02 ranks of expected_values: 370 EVAL 01k53x film 01k1k4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 158.000 148.000 0.671 http://example.org/film/actor/film./film/performance/film #3167-01y_px PRED entity: 01y_px PRED relation: film PRED expected values: 02qr3k8 => 109 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1075): 01q_y0 (0.64 #30306, 0.61 #7130, 0.54 #17829), 08s6mr (0.40 #6661, 0.02 #8444, 0.01 #19143), 01gkp1 (0.30 #6158, 0.14 #2593, 0.04 #62393), 02qr3k8 (0.20 #4849, 0.04 #11981, 0.03 #8414), 07bxqz (0.20 #5293), 0jyx6 (0.20 #3733), 058kh7 (0.14 #3355, 0.10 #6920, 0.03 #12270), 0gtvpkw (0.14 #2343, 0.10 #4126, 0.02 #7691), 01jrbb (0.14 #2251, 0.10 #5816, 0.02 #18298), 02nt3d (0.14 #2860, 0.05 #8208, 0.02 #9991) >> Best rule #30306 for best value: >> intensional similarity = 3 >> extensional distance = 406 >> proper extension: 02wb6yq; 01507p; >> query: (?x2263, ?x2293) <- nominated_for(?x2263, ?x2293), nationality(?x2263, ?x94), participant(?x4775, ?x2263) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #4849 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0421st; *> query: (?x2263, 02qr3k8) <- film(?x2263, ?x718), award(?x2263, ?x693), ?x718 = 0hmr4 *> conf = 0.20 ranks of expected_values: 4 EVAL 01y_px film 02qr3k8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 64.000 0.640 http://example.org/film/actor/film./film/performance/film #3166-0372kf PRED entity: 0372kf PRED relation: film PRED expected values: 0sxfd 04kzqz 01f69m => 98 concepts (54 used for prediction) PRED predicted values (max 10 best out of 433): 029zqn (0.71 #1782, 0.59 #57004, 0.58 #67693), 05jf85 (0.71 #1782, 0.59 #57004, 0.58 #67693), 0h1x5f (0.29 #1577, 0.03 #92638, 0.02 #28499), 02b6n9 (0.14 #1567, 0.03 #39188, 0.03 #35625), 0gmblvq (0.14 #671, 0.03 #39188, 0.03 #35625), 05p1qyh (0.14 #374, 0.03 #39188, 0.03 #35625), 03ntbmw (0.14 #1762, 0.03 #39188, 0.03 #35625), 091rc5 (0.14 #851), 02825cv (0.07 #1137, 0.03 #39188, 0.03 #20730), 049xgc (0.07 #967, 0.03 #39188, 0.03 #35625) >> Best rule #1782 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 017149; 015grj; 0fsm8c; 02bkdn; 01gq0b; 05dbf; 02qgyv; 01kb2j; 0g8st4; 02t_st; ... >> query: (?x5156, ?x306) <- award_nominee(?x5156, ?x5758), nominated_for(?x5156, ?x306), ?x5758 = 01_p6t >> conf = 0.71 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0372kf film 01f69m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 54.000 0.714 http://example.org/film/actor/film./film/performance/film EVAL 0372kf film 04kzqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 54.000 0.714 http://example.org/film/actor/film./film/performance/film EVAL 0372kf film 0sxfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 54.000 0.714 http://example.org/film/actor/film./film/performance/film #3165-0g57wgv PRED entity: 0g57wgv PRED relation: film_crew_role PRED expected values: 09zzb8 => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 33): 09zzb8 (0.80 #435, 0.69 #472, 0.53 #325), 09vw2b7 (0.69 #441, 0.55 #478, 0.47 #331), 01vx2h (0.41 #445, 0.37 #263, 0.36 #335), 01xy5l_ (0.40 #50, 0.10 #194, 0.10 #230), 01pvkk (0.30 #84, 0.30 #446, 0.27 #483), 02ynfr (0.25 #16, 0.17 #450, 0.14 #88), 015h31 (0.25 #9, 0.11 #261, 0.10 #443), 0d2b38 (0.20 #62, 0.18 #134, 0.12 #170), 0215hd (0.20 #55, 0.16 #127, 0.16 #380), 089g0h (0.20 #56, 0.15 #128, 0.12 #200) >> Best rule #435 for best value: >> intensional similarity = 3 >> extensional distance = 427 >> proper extension: 03_wm6; >> query: (?x9859, 09zzb8) <- film_crew_role(?x9859, ?x2095), ?x2095 = 0dxtw, genre(?x9859, ?x53) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g57wgv film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.802 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3164-08g_jw PRED entity: 08g_jw PRED relation: featured_film_locations PRED expected values: 0cvw9 => 79 concepts (62 used for prediction) PRED predicted values (max 10 best out of 79): 04jpl (0.22 #9, 0.13 #1918, 0.12 #3116), 030qb3t (0.13 #1947, 0.13 #4820, 0.12 #6737), 04vmp (0.11 #136, 0.03 #13637), 01zxx9 (0.11 #235), 0rh6k (0.07 #3108, 0.06 #1910, 0.06 #4783), 080h2 (0.05 #3130, 0.05 #1453, 0.04 #5765), 01_d4 (0.05 #1955, 0.04 #3153, 0.04 #2195), 02nd_ (0.05 #830, 0.02 #2264, 0.02 #2024), 05qtj (0.05 #333, 0.04 #571, 0.02 #810), 03gh4 (0.04 #1306, 0.03 #1068, 0.02 #2263) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0h3xztt; >> query: (?x10842, 04jpl) <- language(?x10842, ?x1882), production_companies(?x10842, ?x617), ?x1882 = 03k50, titles(?x53, ?x10842) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08g_jw featured_film_locations 0cvw9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 62.000 0.222 http://example.org/film/film/featured_film_locations #3163-05lb65 PRED entity: 05lb65 PRED relation: film PRED expected values: 01cycq => 97 concepts (57 used for prediction) PRED predicted values (max 10 best out of 175): 03ln8b (0.48 #35853, 0.46 #17926, 0.41 #21512), 02bqvs (0.05 #1499, 0.05 #102178), 093l8p (0.05 #1320, 0.05 #102178), 02v5_g (0.05 #793, 0.02 #6170, 0.01 #9755), 03nqnnk (0.05 #1025, 0.01 #27915), 0879bpq (0.05 #450, 0.01 #16583), 01q2nx (0.05 #914, 0.01 #18840, 0.01 #17047), 02v8kmz (0.05 #28, 0.01 #3613, 0.01 #8990), 09sr0 (0.05 #1522, 0.01 #3314, 0.01 #14069), 01v1ln (0.05 #1231, 0.01 #3023, 0.01 #8400) >> Best rule #35853 for best value: >> intensional similarity = 3 >> extensional distance = 969 >> proper extension: 049tjg; 01qvgl; 01gvyp; 03jj93; 033071; 0pgm3; 01hbq0; 014kg4; >> query: (?x6851, ?x2078) <- type_of_union(?x6851, ?x566), location(?x6851, ?x9605), nominated_for(?x6851, ?x2078) >> conf = 0.48 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05lb65 film 01cycq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 57.000 0.484 http://example.org/film/actor/film./film/performance/film #3162-013b2h PRED entity: 013b2h PRED relation: ceremony! PRED expected values: 02g3gj 0c4z8 02g8mp 01c427 01ckbq 026mff 031b3h 02gdjb 03t5kl 025mbn 026mml 03ncb2 02gm9n => 30 concepts (28 used for prediction) PRED predicted values (max 10 best out of 242): 026mml (0.87 #2768, 0.82 #2395, 0.80 #2582), 02g8mp (0.80 #2472, 0.78 #1725, 0.78 #4104), 01c427 (0.80 #2479, 0.78 #4104, 0.77 #4480), 02gdjb (0.80 #2543, 0.78 #4104, 0.77 #4480), 031b3h (0.78 #1788, 0.78 #4104, 0.77 #4480), 02gm9n (0.78 #1857, 0.78 #4104, 0.77 #4480), 025mbn (0.78 #4104, 0.77 #4480, 0.77 #4292), 02g3gj (0.78 #4104, 0.77 #4480, 0.77 #4292), 03t5kl (0.78 #4104, 0.77 #4480, 0.77 #4292), 026mff (0.78 #4104, 0.77 #4480, 0.77 #4292) >> Best rule #2768 for best value: >> intensional similarity = 18 >> extensional distance = 13 >> proper extension: 01xqqp; >> query: (?x5766, 026mml) <- ceremony(?x10881, ?x5766), ceremony(?x4958, ?x5766), ceremony(?x2238, ?x5766), award_winner(?x5766, ?x6715), award_winner(?x5766, ?x5172), award_winner(?x5766, ?x1206), award(?x12565, ?x4958), award(?x883, ?x4958), ?x883 = 01w61th, award_nominee(?x5172, ?x1399), ?x2238 = 025m8l, instance_of_recurring_event(?x5766, ?x2421), award(?x6715, ?x2877), award_nominee(?x140, ?x1206), award_winner(?x10881, ?x793), artists(?x3319, ?x1206), ?x12565 = 063t3j, ?x3319 = 06j6l >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 19 EVAL 013b2h ceremony! 02gm9n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 03ncb2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 026mml CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 025mbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 03t5kl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 02gdjb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 031b3h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 026mff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 01ckbq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 01c427 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 02g8mp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 0c4z8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 013b2h ceremony! 02g3gj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 28.000 0.867 http://example.org/award/award_category/winners./award/award_honor/ceremony #3161-04cf09 PRED entity: 04cf09 PRED relation: profession PRED expected values: 01d_h8 => 86 concepts (85 used for prediction) PRED predicted values (max 10 best out of 58): 01d_h8 (0.37 #1645, 0.36 #1794, 0.35 #1496), 03gjzk (0.33 #2832, 0.33 #3443, 0.27 #1654), 0d1pc (0.33 #2832, 0.15 #349, 0.13 #647), 015cjr (0.33 #2832, 0.05 #2136, 0.05 #2732), 08z956 (0.33 #2832, 0.01 #377, 0.01 #2761), 0dxtg (0.30 #3442, 0.29 #4038, 0.26 #8807), 09jwl (0.24 #317, 0.20 #2701, 0.20 #1807), 02jknp (0.22 #157, 0.19 #6565, 0.19 #1647), 0np9r (0.21 #2853, 0.20 #3598, 0.18 #2256), 0nbcg (0.18 #330, 0.13 #2714, 0.12 #2118) >> Best rule #1645 for best value: >> intensional similarity = 2 >> extensional distance = 421 >> proper extension: 063_t; >> query: (?x1205, 01d_h8) <- participant(?x5889, ?x1205), nominated_for(?x1205, ?x3787) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cf09 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 85.000 0.366 http://example.org/people/person/profession #3160-06z9yh PRED entity: 06z9yh PRED relation: written_by! PRED expected values: 032xky => 56 concepts (38 used for prediction) PRED predicted values (max 10 best out of 86): 0g9z_32 (0.04 #486, 0.01 #1808, 0.01 #1147), 0gg5qcw (0.02 #345, 0.01 #1667, 0.01 #1006), 07w8fz (0.02 #200, 0.01 #1522, 0.01 #861), 02wgbb (0.02 #512, 0.01 #1834), 0k4p0 (0.02 #381, 0.01 #1042), 01h7bb (0.02 #24, 0.01 #685), 06fpsx (0.02 #508), 0cn_b8 (0.02 #244), 0291hr (0.01 #1860, 0.01 #538, 0.01 #1199), 03wy8t (0.01 #1919) >> Best rule #486 for best value: >> intensional similarity = 6 >> extensional distance = 83 >> proper extension: 0bkf72; >> query: (?x13392, 0g9z_32) <- profession(?x13392, ?x1943), profession(?x13392, ?x1041), profession(?x13392, ?x319), ?x1943 = 02krf9, ?x319 = 01d_h8, ?x1041 = 03gjzk >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06z9yh written_by! 032xky CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 38.000 0.035 http://example.org/film/film/written_by #3159-02g40r PRED entity: 02g40r PRED relation: role PRED expected values: 0l14qv => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 112): 05r5c (0.60 #529, 0.51 #1153, 0.38 #4493), 0342h (0.50 #5, 0.45 #1149, 0.41 #213), 01vdm0 (0.44 #1178, 0.27 #4518, 0.26 #5146), 02sgy (0.41 #215, 0.36 #1151, 0.25 #7), 026t6 (0.37 #1147, 0.25 #3, 0.17 #2084), 042v_gx (0.31 #1154, 0.25 #10, 0.22 #3343), 01vj9c (0.29 #1161, 0.25 #17, 0.18 #2098), 0l14md (0.29 #1152, 0.25 #8, 0.06 #2089), 0l14qv (0.27 #1150, 0.15 #3131, 0.15 #2087), 018vs (0.26 #1159, 0.25 #15, 0.20 #2096) >> Best rule #529 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 01wl38s; 04f9r2; >> query: (?x10574, 05r5c) <- music(?x755, ?x10574), award_winner(?x565, ?x10574), role(?x10574, ?x3161) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1150 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 76 *> proper extension: 018y81; *> query: (?x10574, 0l14qv) <- role(?x10574, ?x3991), ?x3991 = 05842k, profession(?x10574, ?x131) *> conf = 0.27 ranks of expected_values: 9 EVAL 02g40r role 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 135.000 135.000 0.596 http://example.org/music/artist/track_contributions./music/track_contribution/role #3158-02py8_w PRED entity: 02py8_w PRED relation: teams! PRED expected values: 0s3pw => 74 concepts (69 used for prediction) PRED predicted values (max 10 best out of 124): 0fr0t (0.33 #657, 0.25 #2717, 0.25 #2557), 0fvzg (0.33 #87, 0.25 #1717, 0.12 #6628), 02_286 (0.25 #6291, 0.25 #1652, 0.18 #8721), 071cn (0.25 #2829, 0.25 #2010, 0.17 #5011), 0d9y6 (0.25 #3126, 0.20 #4216, 0.17 #5310), 0t6hk (0.25 #3491, 0.20 #4034, 0.11 #7035), 0snty (0.25 #3779, 0.17 #4867, 0.14 #5689), 0f__1 (0.25 #2527, 0.08 #8803, 0.08 #2719), 030qb3t (0.20 #4134, 0.18 #7681, 0.13 #9320), 01_d4 (0.17 #4419, 0.12 #6058, 0.10 #7149) >> Best rule #657 for best value: >> intensional similarity = 31 >> extensional distance = 1 >> proper extension: 02qk2d5; >> query: (?x6003, 0fr0t) <- team(?x13045, ?x6003), team(?x12162, ?x6003), team(?x11210, ?x6003), team(?x9974, ?x6003), team(?x9908, ?x6003), team(?x9146, ?x6003), team(?x7042, ?x6003), team(?x6583, ?x6003), team(?x5897, ?x6003), team(?x4368, ?x6003), team(?x2302, ?x6003), team(?x1348, ?x6003), position(?x6003, ?x4570), ?x12162 = 0b_6_l, ?x2302 = 0b_77q, ?x5897 = 0b_6rk, ?x9974 = 0b_6pv, ?x11210 = 0b_6q5, ?x13045 = 0bqthy, ?x7042 = 0b_72t, ?x6583 = 0b_75k, ?x9146 = 0b_6qj, team(?x9908, ?x9833), team(?x9908, ?x5551), ?x5551 = 02pjzvh, ?x9833 = 03y9p40, ?x4368 = 0b_6x2, position(?x12141, ?x1348), position(?x7136, ?x1348), ?x7136 = 0jm74, ?x12141 = 0jmk7 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02py8_w teams! 0s3pw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 69.000 0.333 http://example.org/sports/sports_team_location/teams #3157-018yj6 PRED entity: 018yj6 PRED relation: award PRED expected values: 07cbcy => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 310): 09sb52 (0.50 #1256, 0.46 #2471, 0.38 #3686), 0gqy2 (0.43 #165, 0.33 #570, 0.17 #1785), 09sdmz (0.43 #207, 0.22 #612, 0.19 #1422), 099jhq (0.43 #19, 0.11 #2449, 0.11 #1639), 027dtxw (0.29 #4, 0.12 #1219, 0.11 #2434), 02x73k6 (0.29 #61, 0.12 #1276, 0.11 #1681), 04kxsb (0.25 #1341, 0.17 #1746, 0.15 #36049), 01bgqh (0.23 #4093, 0.19 #5308, 0.17 #3688), 05pcn59 (0.22 #487, 0.17 #8992, 0.17 #2512), 02x4w6g (0.22 #1735, 0.15 #36049, 0.15 #24301) >> Best rule #1256 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02qgqt; 0sz28; 07hbxm; 02cllz; 06mmb; 016xh5; 01qrbf; 04954; 022411; >> query: (?x8813, 09sb52) <- type_of_union(?x8813, ?x566), award_nominee(?x5454, ?x8813), ?x5454 = 020_95 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #484 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 02ts3h; *> query: (?x8813, 07cbcy) <- type_of_union(?x8813, ?x566), film(?x8813, ?x9060), ?x9060 = 02p86pb *> conf = 0.22 ranks of expected_values: 13 EVAL 018yj6 award 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 121.000 121.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3156-07vjm PRED entity: 07vjm PRED relation: school_type PRED expected values: 05jxkf => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.61 #119, 0.55 #50, 0.50 #809), 05pcjw (0.44 #139, 0.36 #208, 0.35 #185), 01rs41 (0.26 #1802, 0.26 #1040, 0.25 #1454), 01_9fk (0.25 #577, 0.25 #232, 0.25 #209), 02p0qmm (0.08 #124, 0.07 #55, 0.06 #32), 01_srz (0.08 #2466, 0.06 #187, 0.06 #256), 04399 (0.08 #2466, 0.06 #36, 0.03 #128), 04qbv (0.08 #2466, 0.03 #61, 0.03 #130), 047951 (0.08 #2466, 0.03 #307, 0.02 #905), 06cs1 (0.08 #2466, 0.03 #144, 0.02 #811) >> Best rule #119 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 030nwm; >> query: (?x6637, 05jxkf) <- contains(?x1227, ?x6637), school_type(?x6637, ?x4994), time_zones(?x6637, ?x2950) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07vjm school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.611 http://example.org/education/educational_institution/school_type #3155-06jvj7 PRED entity: 06jvj7 PRED relation: profession PRED expected values: 09jwl => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 60): 02hrh1q (0.74 #1952, 0.73 #760, 0.72 #1058), 015cjr (0.50 #51, 0.31 #5068, 0.28 #4024), 09jwl (0.41 #1212, 0.40 #1361, 0.40 #467), 03gjzk (0.36 #314, 0.31 #5068, 0.30 #165), 01d_h8 (0.33 #1049, 0.33 #1943, 0.32 #751), 0kyk (0.31 #5068, 0.28 #4024, 0.28 #6261), 04gc2 (0.31 #5068, 0.28 #6859, 0.28 #6411), 05t4q (0.31 #5068, 0.28 #6859, 0.28 #6411), 01d30f (0.31 #5068, 0.28 #6411, 0.25 #9393), 0nbcg (0.29 #1374, 0.29 #1225, 0.28 #6261) >> Best rule #1952 for best value: >> intensional similarity = 2 >> extensional distance = 452 >> proper extension: 012t1; 02lymt; 01c6l; 01vt5c_; 01wj5hp; 0hqly; >> query: (?x3074, 02hrh1q) <- award_nominee(?x690, ?x3074), religion(?x3074, ?x8249) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1212 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 317 *> proper extension: 0gsg7; 0cjdk; 0khth; *> query: (?x3074, 09jwl) <- award_winner(?x691, ?x3074), category(?x3074, ?x134), award_winner(?x2751, ?x3074) *> conf = 0.41 ranks of expected_values: 3 EVAL 06jvj7 profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 78.000 0.736 http://example.org/people/person/profession #3154-028tv0 PRED entity: 028tv0 PRED relation: group PRED expected values: 0150jk 04r1t 0394y 081wh1 01dpts 016vn3 01518s => 72 concepts (61 used for prediction) PRED predicted values (max 10 best out of 888): 02vnpv (0.83 #3385, 0.82 #3020, 0.80 #2898), 02cw1m (0.71 #2519, 0.70 #2883, 0.64 #3005), 07mvp (0.71 #2483, 0.67 #3580, 0.67 #1990), 047cx (0.71 #2220, 0.67 #1729, 0.60 #1482), 0dtd6 (0.71 #2203, 0.67 #1712, 0.57 #1572), 07rnh (0.71 #2389, 0.57 #1572, 0.50 #2876), 0187x8 (0.67 #2002, 0.67 #1757, 0.57 #1572), 0b_xm (0.67 #2123, 0.67 #1755, 0.57 #1572), 0dvqq (0.67 #2081, 0.67 #1713, 0.57 #1572), 04r1t (0.67 #2077, 0.67 #1709, 0.57 #1572) >> Best rule #3385 for best value: >> intensional similarity = 14 >> extensional distance = 10 >> proper extension: 0dwtp; >> query: (?x645, 02vnpv) <- role(?x6039, ?x645), role(?x2785, ?x645), role(?x1495, ?x645), role(?x1147, ?x645), ?x1495 = 013y1f, group(?x645, ?x646), role(?x1955, ?x645), ?x1147 = 07kc_, performance_role(?x645, ?x716), role(?x6039, ?x2459), group(?x8640, ?x646), role(?x212, ?x2785), ?x2459 = 021bmf, artists(?x302, ?x1955) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #2077 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 4 *> proper extension: 01vj9c; *> query: (?x645, 04r1t) <- role(?x7772, ?x645), role(?x3214, ?x645), role(?x2309, ?x645), role(?x1495, ?x645), role(?x1147, ?x645), ?x1495 = 013y1f, group(?x645, ?x1060), role(?x1955, ?x645), role(?x1345, ?x645), role(?x716, ?x1147), ?x7772 = 0j862, ?x2309 = 06ncr, ?x3214 = 02snj9, ?x1060 = 02r3zy, profession(?x1955, ?x1032), people(?x3584, ?x1955), award(?x1345, ?x1323) *> conf = 0.67 ranks of expected_values: 10, 28, 29, 32, 70, 85, 97 EVAL 028tv0 group 01518s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 72.000 61.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 028tv0 group 016vn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 72.000 61.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 028tv0 group 01dpts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 72.000 61.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 028tv0 group 081wh1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 72.000 61.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 028tv0 group 0394y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 72.000 61.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 028tv0 group 04r1t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 72.000 61.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 028tv0 group 0150jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 72.000 61.000 0.833 http://example.org/music/performance_role/regular_performances./music/group_membership/group #3153-02fgm7 PRED entity: 02fgm7 PRED relation: film PRED expected values: 05qbbfb => 63 concepts (55 used for prediction) PRED predicted values (max 10 best out of 390): 017jd9 (0.59 #17920, 0.46 #10751, 0.44 #37637), 03cf9ly (0.59 #17920, 0.46 #10751, 0.44 #37637), 017gl1 (0.39 #143, 0.29 #3725, 0.29 #1934), 0ndwt2w (0.18 #1001, 0.15 #4583, 0.13 #2792), 043t8t (0.16 #2580, 0.15 #4371, 0.04 #789), 0djlxb (0.11 #535, 0.07 #4117, 0.06 #34053), 08s6mr (0.11 #3111, 0.10 #4902, 0.07 #1320), 01gkp1 (0.11 #2607, 0.10 #4398, 0.07 #816), 026p4q7 (0.10 #3980, 0.08 #2189, 0.04 #398), 049xgc (0.08 #2764, 0.07 #4555, 0.07 #973) >> Best rule #17920 for best value: >> intensional similarity = 3 >> extensional distance = 1074 >> proper extension: 04yywz; 049tjg; 02g8h; 0d_84; 0h1_w; 02nb2s; 0151ns; 03_vx9; 0456xp; 04shbh; ... >> query: (?x7505, ?x4610) <- nominated_for(?x7505, ?x4610), type_of_union(?x7505, ?x566), film(?x7505, ?x1392) >> conf = 0.59 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02fgm7 film 05qbbfb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 55.000 0.588 http://example.org/film/actor/film./film/performance/film #3152-02lfns PRED entity: 02lfns PRED relation: people! PRED expected values: 0xnvg => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 27): 041rx (0.13 #1236, 0.13 #312, 0.12 #1852), 0x67 (0.11 #10, 0.11 #1473, 0.11 #241), 033tf_ (0.09 #161, 0.09 #7, 0.08 #854), 0xnvg (0.07 #13, 0.06 #475, 0.05 #167), 02w7gg (0.07 #541, 0.07 #156, 0.06 #849), 07hwkr (0.04 #1244, 0.04 #628, 0.04 #4324), 09vc4s (0.04 #9, 0.03 #163, 0.03 #86), 07bch9 (0.04 #1332, 0.04 #177, 0.03 #639), 01qhm_ (0.04 #160, 0.03 #1007, 0.03 #83), 03bkbh (0.03 #32, 0.02 #186, 0.02 #956) >> Best rule #1236 for best value: >> intensional similarity = 3 >> extensional distance = 979 >> proper extension: 017r2; 02jt1k; 0m32_; 01jbx1; 01v3vp; 04l19_; 06sn8m; 06gn7r; 09nz_c; 07t3x8; ... >> query: (?x1169, 041rx) <- location(?x1169, ?x739), student(?x4824, ?x1169), award(?x1169, ?x783) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 319 *> proper extension: 03xmy1; 01nrq5; 01skmp; 02dlfh; 02j490; 047jhq; 01hbq0; *> query: (?x1169, 0xnvg) <- location(?x1169, ?x739), award_winner(?x1670, ?x1169), actor(?x1849, ?x1169) *> conf = 0.07 ranks of expected_values: 4 EVAL 02lfns people! 0xnvg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 93.000 93.000 0.131 http://example.org/people/ethnicity/people #3151-06n8j PRED entity: 06n8j PRED relation: time_zones PRED expected values: 02llzg => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 11): 02llzg (0.66 #27, 0.51 #67, 0.51 #57), 02hcv8 (0.34 #96, 0.33 #122, 0.32 #148), 02fqwt (0.16 #146, 0.15 #172, 0.15 #107), 03plfd (0.14 #23, 0.12 #77, 0.09 #90), 02lcqs (0.13 #98, 0.12 #163, 0.12 #150), 03bdv (0.08 #19, 0.08 #46, 0.07 #464), 0gsrz4 (0.08 #21, 0.03 #75, 0.03 #35), 02hczc (0.05 #225, 0.05 #108, 0.05 #264), 052vwh (0.03 #25, 0.03 #39, 0.02 #196), 0d2t4g (0.03 #36, 0.02 #49, 0.02 #76) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 0fngy; >> query: (?x13196, ?x2864) <- capital(?x6435, ?x13196), time_zones(?x6435, ?x2864), administrative_parent(?x6435, ?x551) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06n8j time_zones 02llzg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.658 http://example.org/location/location/time_zones #3150-01mgw PRED entity: 01mgw PRED relation: nominated_for! PRED expected values: 02r0csl 02qyntr => 98 concepts (90 used for prediction) PRED predicted values (max 10 best out of 197): 02qyntr (0.73 #357, 0.55 #562, 0.55 #1590), 02rdxsh (0.72 #657, 0.21 #3737, 0.18 #5788), 09v4bym (0.69 #2260, 0.67 #12322, 0.67 #12323), 0262s1 (0.69 #2260, 0.67 #12322, 0.67 #12323), 04dn09n (0.68 #232, 0.63 #437, 0.52 #642), 0p9sw (0.53 #1454, 0.49 #426, 0.46 #2070), 04kxsb (0.51 #281, 0.49 #486, 0.36 #2746), 0f4x7 (0.45 #430, 0.42 #5766, 0.41 #3715), 02r0csl (0.43 #209, 0.33 #414, 0.27 #1442), 0gqyl (0.38 #266, 0.35 #471, 0.32 #676) >> Best rule #357 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 09p0ct; 0661ql3; 0ctb4g; 07s846j; 09gb_4p; 0h03fhx; 0k2cb; 011yg9; 0jyb4; 04165w; ... >> query: (?x7554, 02qyntr) <- nominated_for(?x1198, ?x7554), nominated_for(?x1079, ?x7554), film_release_region(?x7554, ?x94), ?x1079 = 0l8z1, ?x1198 = 02pqp12 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 01mgw nominated_for! 02qyntr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 90.000 0.730 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01mgw nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 98.000 90.000 0.730 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #3149-031x_3 PRED entity: 031x_3 PRED relation: artists! PRED expected values: 0ggq0m => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 241): 03_d0 (0.73 #7217, 0.35 #2204, 0.33 #325), 017_qw (0.71 #6018, 0.37 #1318, 0.33 #65), 064t9 (0.59 #1580, 0.52 #7846, 0.47 #5654), 0ggq0m (0.57 #1266, 0.33 #13, 0.20 #7218), 06by7 (0.53 #1588, 0.43 #11611, 0.42 #15057), 05bt6j (0.41 #1611, 0.23 #7877, 0.22 #15080), 06q6jz (0.40 #1443, 0.33 #190, 0.11 #6143), 059kh (0.34 #1617, 0.09 #4437, 0.08 #4123), 0glt670 (0.34 #6934, 0.33 #6308, 0.32 #3801), 021dvj (0.33 #1306, 0.33 #53, 0.10 #6006) >> Best rule #7217 for best value: >> intensional similarity = 2 >> extensional distance = 188 >> proper extension: 01nqfh_; 0h08p; >> query: (?x8583, 03_d0) <- artists(?x888, ?x8583), major_field_of_study(?x2767, ?x888) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1266 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 0hgqq; 043d4; 03d6q; 0kn3g; 0hqgp; 0c73g; *> query: (?x8583, 0ggq0m) <- artists(?x888, ?x8583), ?x888 = 05lls, nationality(?x8583, ?x94) *> conf = 0.57 ranks of expected_values: 4 EVAL 031x_3 artists! 0ggq0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 132.000 0.726 http://example.org/music/genre/artists #3148-0287477 PRED entity: 0287477 PRED relation: film! PRED expected values: 055c8 => 101 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1046): 0h32q (0.33 #772, 0.25 #2853, 0.02 #29912), 01l2fn (0.33 #262, 0.08 #4425, 0.06 #12752), 02cff1 (0.33 #1487, 0.02 #26463, 0.02 #11896), 03bxsw (0.33 #571, 0.01 #42199, 0.01 #25547), 027z0pl (0.25 #4163, 0.13 #54116, 0.12 #83259), 03h304l (0.25 #4163, 0.13 #54116, 0.12 #83259), 086sj (0.25 #2792, 0.03 #4874, 0.03 #112406), 0168dy (0.25 #3886, 0.03 #5968, 0.03 #8050), 0prfz (0.25 #2137, 0.03 #6301, 0.02 #33359), 05vsxz (0.25 #2090, 0.02 #52043, 0.01 #49961) >> Best rule #772 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0ctb4g; >> query: (?x6119, 0h32q) <- nominated_for(?x500, ?x6119), executive_produced_by(?x6119, ?x4946), nominated_for(?x3295, ?x6119), ?x3295 = 06mnps >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4704 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 02q52q; 03kx49; 01dc0c; *> query: (?x6119, 055c8) <- film_distribution_medium(?x6119, ?x627), category(?x6119, ?x134), film(?x1019, ?x6119), music(?x6119, ?x3410) *> conf = 0.08 ranks of expected_values: 21 EVAL 0287477 film! 055c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 101.000 62.000 0.333 http://example.org/film/actor/film./film/performance/film #3147-02jx1 PRED entity: 02jx1 PRED relation: location! PRED expected values: 014x77 04cf09 01ps2h8 => 234 concepts (181 used for prediction) PRED predicted values (max 10 best out of 2360): 02q42j_ (0.50 #202467, 0.48 #72478, 0.47 #5000), 066yfh (0.50 #202467, 0.48 #72478, 0.47 #5000), 02g40r (0.50 #202467, 0.48 #72478, 0.47 #5000), 04_1nk (0.50 #202467, 0.48 #72478, 0.47 #5000), 02pq9yv (0.50 #202467, 0.48 #72478, 0.47 #5000), 099d4 (0.40 #37337, 0.40 #27339, 0.20 #49832), 032r1 (0.40 #34790, 0.25 #17295, 0.25 #14796), 09fb5 (0.40 #30043, 0.17 #40040, 0.11 #70029), 01797x (0.33 #4579, 0.25 #9578, 0.25 #7079), 01qn8k (0.33 #4371, 0.25 #9370, 0.25 #6871) >> Best rule #202467 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 013t2y; >> query: (?x1310, ?x3528) <- location(?x981, ?x1310), place_of_birth(?x3528, ?x1310), country(?x1310, ?x512) >> conf = 0.50 => this is the best rule for 5 predicted values *> Best rule #9999 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01z3d2; *> query: (?x1310, ?x100) <- contains(?x1310, ?x9741), contains(?x1310, ?x5987), ?x9741 = 019vsw, student(?x5987, ?x100) *> conf = 0.16 ranks of expected_values: 651, 797, 1080 EVAL 02jx1 location! 01ps2h8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 234.000 181.000 0.498 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02jx1 location! 04cf09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 234.000 181.000 0.498 http://example.org/people/person/places_lived./people/place_lived/location EVAL 02jx1 location! 014x77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 234.000 181.000 0.498 http://example.org/people/person/places_lived./people/place_lived/location #3146-0gnkb PRED entity: 0gnkb PRED relation: genre PRED expected values: 02l7c8 => 99 concepts (67 used for prediction) PRED predicted values (max 10 best out of 95): 07s9rl0 (0.93 #6513, 0.84 #238, 0.83 #592), 02l7c8 (0.93 #2383, 0.37 #1200, 0.37 #725), 02kdv5l (0.65 #120, 0.61 #4383, 0.29 #3673), 05p553 (0.40 #2371, 0.40 #7705, 0.36 #5215), 01jfsb (0.35 #4393, 0.32 #7357, 0.30 #3683), 01hmnh (0.28 #135, 0.19 #3688, 0.18 #5110), 082gq (0.24 #502, 0.22 #384, 0.19 #4410), 04xvh5 (0.24 #270, 0.23 #151, 0.22 #1217), 06cvj (0.23 #2370, 0.19 #3, 0.10 #1307), 03bxz7 (0.23 #999, 0.19 #762, 0.18 #526) >> Best rule #6513 for best value: >> intensional similarity = 4 >> extensional distance = 1009 >> proper extension: 0ckr7s; 0gx1bnj; 0dq626; 0gx9rvq; 0gj8t_b; 07ng9k; 018nnz; 0gtxj2q; 01q2nx; 05pyrb; ... >> query: (?x6890, 07s9rl0) <- genre(?x6890, ?x162), film(?x457, ?x6890), genre(?x6446, ?x162), ?x6446 = 089j8p >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #2383 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 492 *> proper extension: 0fq27fp; *> query: (?x6890, 02l7c8) <- genre(?x6890, ?x1805), genre(?x5818, ?x1805), genre(?x5499, ?x1805), ?x5818 = 0ktpx, ?x5499 = 0gt1k *> conf = 0.93 ranks of expected_values: 2 EVAL 0gnkb genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 67.000 0.931 http://example.org/film/film/genre #3145-09y2k2 PRED entity: 09y2k2 PRED relation: split_to PRED expected values: 0222qb => 32 concepts (13 used for prediction) PRED predicted values (max 10 best out of 3): 07qv_ (0.25 #77, 0.20 #171, 0.12 #372), 0349s (0.06 #981, 0.06 #883, 0.05 #1187), 07ssc (0.06 #1009, 0.05 #1110, 0.04 #1212) >> Best rule #77 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 02h40lc; 038rz; >> query: (?x12386, 07qv_) <- language(?x7239, ?x12386), ?x7239 = 0bl3nn >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09y2k2 split_to 0222qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 32.000 13.000 0.250 http://example.org/dataworld/gardening_hint/split_to #3144-0329qp PRED entity: 0329qp PRED relation: sport PRED expected values: 02vx4 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 59): 02vx4 (0.96 #400, 0.96 #373, 0.95 #344), 0z74 (0.47 #768, 0.47 #897, 0.47 #860), 018jz (0.25 #61, 0.14 #555, 0.12 #573), 09xp_ (0.21 #92, 0.17 #62, 0.15 #242), 0jm_ (0.17 #688, 0.15 #761, 0.15 #553), 03tmr (0.17 #569, 0.15 #551, 0.12 #632), 018w8 (0.13 #554, 0.10 #635, 0.10 #572), 039yzs (0.05 #638, 0.05 #575, 0.04 #566), 0194d (0.02 #163, 0.02 #86, 0.02 #428), 07_53 (0.02 #163, 0.02 #86, 0.02 #428) >> Best rule #400 for best value: >> intensional similarity = 9 >> extensional distance = 47 >> proper extension: 03_9hm; 01n_2f; 03zm00; 03zb6t; >> query: (?x11532, 02vx4) <- position(?x11532, ?x530), teams(?x1497, ?x11532), ?x530 = 02_j1w, olympics(?x1497, ?x584), film_release_region(?x2656, ?x1497), film_release_region(?x1202, ?x1497), award(?x2656, ?x372), country(?x668, ?x1497), film(?x815, ?x1202) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0329qp sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.959 http://example.org/sports/sports_team/sport #3143-03lfd_ PRED entity: 03lfd_ PRED relation: film_crew_role PRED expected values: 0263ycg => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.80 #46, 0.75 #640, 0.75 #677), 02r96rf (0.73 #41, 0.69 #635, 0.69 #672), 09zzb8 (0.72 #1525, 0.71 #1376, 0.71 #2267), 09vw2b7 (0.62 #639, 0.61 #676, 0.60 #1681), 0dxtw (0.35 #644, 0.35 #1686, 0.35 #2279), 01vx2h (0.35 #682, 0.34 #645, 0.31 #51), 01pvkk (0.28 #1539, 0.28 #1390, 0.28 #1873), 02ynfr (0.25 #557, 0.22 #1114, 0.17 #650), 089g0h (0.25 #557, 0.22 #1114, 0.14 #244), 01xy5l_ (0.25 #557, 0.22 #1114, 0.11 #239) >> Best rule #46 for best value: >> intensional similarity = 5 >> extensional distance = 62 >> proper extension: 0qm8b; >> query: (?x8867, 0ch6mp2) <- nominated_for(?x298, ?x8867), ?x298 = 05ztjjw, film_crew_role(?x8867, ?x4305), nominated_for(?x617, ?x8867), award(?x8867, ?x3646) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #557 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 229 *> proper extension: 0h3y; 042rnl; 02z13jg; 01_vfy; 01q4qv; 01ycck; 01f7v_; 01c6l; 04ld94; 04r7p; ... *> query: (?x8867, ?x137) <- film_festivals(?x8867, ?x6557), film_festivals(?x5070, ?x6557), film_crew_role(?x5070, ?x137), film_release_region(?x5070, ?x87) *> conf = 0.25 ranks of expected_values: 13 EVAL 03lfd_ film_crew_role 0263ycg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 89.000 89.000 0.797 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3142-03r8gp PRED entity: 03r8gp PRED relation: major_field_of_study! PRED expected values: 028dcg => 89 concepts (70 used for prediction) PRED predicted values (max 10 best out of 20): 02_xgp2 (0.82 #503, 0.71 #604, 0.70 #251), 04zx3q1 (0.82 #494, 0.60 #242, 0.57 #595), 02h4rq6 (0.78 #203, 0.76 #495, 0.74 #596), 019v9k (0.77 #601, 0.75 #883, 0.73 #803), 028dcg (0.67 #117, 0.49 #282, 0.43 #177), 0bkj86 (0.62 #944, 0.61 #882, 0.60 #268), 03bwzr4 (0.57 #605, 0.53 #949, 0.53 #504), 07s6fsf (0.49 #282, 0.37 #471, 0.36 #325), 022h5x (0.49 #282, 0.37 #471, 0.36 #325), 027f2w (0.37 #471, 0.36 #325, 0.36 #389) >> Best rule #503 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 01540; >> query: (?x10705, 02_xgp2) <- major_field_of_study(?x1200, ?x10705), major_field_of_study(?x7545, ?x10705), major_field_of_study(?x6637, ?x10705), adjoins(?x1659, ?x6637), ?x7545 = 0bwfn, ?x1200 = 016t_3 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #117 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 02822; 0w7c; *> query: (?x10705, 028dcg) <- major_field_of_study(?x1368, ?x10705), student(?x10705, ?x123), ?x1368 = 014mlp, major_field_of_study(?x6611, ?x10705), major_field_of_study(?x2606, ?x10705), ?x6611 = 04b_46 *> conf = 0.67 ranks of expected_values: 5 EVAL 03r8gp major_field_of_study! 028dcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 89.000 70.000 0.824 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #3141-02nb2s PRED entity: 02nb2s PRED relation: location PRED expected values: 0f2r6 => 90 concepts (49 used for prediction) PRED predicted values (max 10 best out of 99): 02_286 (0.22 #37, 0.17 #20131, 0.17 #21740), 030qb3t (0.17 #7318, 0.17 #1688, 0.16 #9729), 0cr3d (0.12 #1750, 0.09 #947, 0.07 #27472), 04jpl (0.11 #17, 0.09 #820, 0.09 #3231), 0gyvgw (0.11 #796), 0ycht (0.11 #691), 0chrx (0.11 #404), 059rby (0.10 #1622, 0.09 #819, 0.05 #3230), 0r0m6 (0.10 #1823, 0.03 #7453, 0.03 #8257), 05qtj (0.09 #1043, 0.05 #1846, 0.02 #36413) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 033071; >> query: (?x532, 02_286) <- film(?x532, ?x11610), actor(?x531, ?x532), ?x11610 = 03cffvv >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1639 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 0g5ff; *> query: (?x532, 0f2r6) <- nationality(?x532, ?x94), award(?x532, ?x2183), location(?x532, ?x1227), ?x1227 = 01n7q *> conf = 0.02 ranks of expected_values: 66 EVAL 02nb2s location 0f2r6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 90.000 49.000 0.222 http://example.org/people/person/places_lived./people/place_lived/location #3140-0_816 PRED entity: 0_816 PRED relation: genre PRED expected values: 02l7c8 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 93): 02kdv5l (0.54 #358, 0.52 #477, 0.33 #2262), 03bxz7 (0.52 #887, 0.35 #1006, 0.18 #3096), 01jfsb (0.50 #368, 0.48 #487, 0.32 #606), 02l7c8 (0.50 #252, 0.38 #609, 0.35 #2394), 060__y (0.38 #253, 0.36 #134, 0.30 #15), 05p553 (0.35 #598, 0.35 #2502, 0.34 #7503), 04xvh5 (0.33 #271, 0.15 #2413, 0.13 #866), 03k9fj (0.31 #1438, 0.30 #10, 0.27 #1200), 03g3w (0.24 #975, 0.20 #856, 0.11 #2879), 0219x_ (0.20 #739, 0.18 #3096, 0.13 #2881) >> Best rule #358 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0by1wkq; 01cycq; >> query: (?x3255, 02kdv5l) <- nominated_for(?x484, ?x3255), film(?x1561, ?x3255), ?x1561 = 030_1m, nominated_for(?x771, ?x3255) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #252 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0fgpvf; 03bxp5; *> query: (?x3255, 02l7c8) <- nominated_for(?x2222, ?x3255), nominated_for(?x3255, ?x1230), ?x2222 = 0gs96, film(?x1057, ?x3255) *> conf = 0.50 ranks of expected_values: 4 EVAL 0_816 genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 86.000 0.538 http://example.org/film/film/genre #3139-024_dt PRED entity: 024_dt PRED relation: ceremony PRED expected values: 05pd94v => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 124): 05pd94v (0.87 #499, 0.83 #375, 0.75 #2484), 0bzm81 (0.20 #17, 0.15 #638, 0.14 #762), 0h_cssd (0.20 #23, 0.14 #271, 0.08 #768), 0n8_m93 (0.20 #103, 0.14 #724, 0.13 #848), 02yxh9 (0.20 #86, 0.14 #707, 0.13 #831), 0bc773 (0.20 #44, 0.14 #665, 0.13 #789), 02yw5r (0.20 #9, 0.14 #630, 0.13 #754), 02yvhx (0.20 #66, 0.13 #687, 0.12 #811), 02hn5v (0.20 #33, 0.13 #654, 0.12 #778), 0bvfqq (0.20 #26, 0.13 #647, 0.12 #771) >> Best rule #499 for best value: >> intensional similarity = 6 >> extensional distance = 66 >> proper extension: 03nl5k; 0257pw; >> query: (?x12458, 05pd94v) <- award(?x13167, ?x12458), ceremony(?x12458, ?x2054), ceremony(?x12458, ?x342), artists(?x888, ?x13167), ?x342 = 01s695, ?x2054 = 0gpjbt >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024_dt ceremony 05pd94v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.868 http://example.org/award/award_category/winners./award/award_honor/ceremony #3138-071xj PRED entity: 071xj PRED relation: student! PRED expected values: 014mlp => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 15): 014mlp (0.38 #386, 0.37 #406, 0.37 #266), 019v9k (0.15 #150, 0.14 #130, 0.11 #510), 0bkj86 (0.12 #149, 0.11 #129, 0.11 #169), 02_xgp2 (0.11 #134, 0.07 #154, 0.06 #294), 03mkk4 (0.11 #133, 0.07 #313, 0.06 #393), 04zx3q1 (0.10 #142, 0.09 #162, 0.06 #122), 02h4rq6 (0.09 #123, 0.07 #163, 0.07 #283), 028dcg (0.09 #258, 0.08 #278, 0.08 #318), 016t_3 (0.07 #164, 0.07 #384, 0.07 #504), 07s6fsf (0.04 #161, 0.02 #281, 0.02 #361) >> Best rule #386 for best value: >> intensional similarity = 4 >> extensional distance = 124 >> proper extension: 063vn; 038rzr; 0fby2t; 04h07s; 0dx97; 04z542; 0ksrf8; 05drr9; 0d02km; 03l3ln; ... >> query: (?x10117, 014mlp) <- student(?x7574, ?x10117), student(?x2981, ?x10117), profession(?x10117, ?x319), place_of_birth(?x10117, ?x8297) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 071xj student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.381 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #3137-028knk PRED entity: 028knk PRED relation: award_winner! PRED expected values: 09p2r9 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 116): 09pnw5 (0.09 #101, 0.06 #239, 0.05 #377), 09g90vz (0.07 #397, 0.05 #811, 0.05 #259), 09p30_ (0.06 #221, 0.06 #83, 0.04 #359), 02rjjll (0.06 #143, 0.06 #281, 0.04 #4973), 09q_6t (0.06 #146, 0.05 #284, 0.03 #560), 09qftb (0.06 #111, 0.06 #387, 0.04 #801), 013b2h (0.06 #354, 0.05 #5046, 0.05 #4908), 03gyp30 (0.06 #391, 0.05 #253, 0.04 #805), 0hndn2q (0.06 #315, 0.05 #177, 0.04 #1557), 09qvms (0.06 #289, 0.05 #4843, 0.05 #5119) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0151ns; 03xmy1; 01vhb0; 05r5w; 01gw4f; 02g0rb; 01cpqk; 017m2y; >> query: (?x2028, 09pnw5) <- spouse(?x2028, ?x4397), award(?x2028, ?x1007), ?x1007 = 03c7tr1 >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #2575 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 303 *> proper extension: 07qy0b; 01mkn_d; 01my_c; *> query: (?x2028, 09p2r9) <- award_nominee(?x2028, ?x4397), place_of_birth(?x2028, ?x5093), film(?x4397, ?x240) *> conf = 0.02 ranks of expected_values: 75 EVAL 028knk award_winner! 09p2r9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 100.000 100.000 0.091 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3136-03061d PRED entity: 03061d PRED relation: award_nominee PRED expected values: 0306bt => 120 concepts (79 used for prediction) PRED predicted values (max 10 best out of 948): 0306bt (0.81 #147582, 0.81 #121816, 0.80 #154610), 03061d (0.37 #58572, 0.26 #185064, 0.26 #173350), 043js (0.37 #58572, 0.26 #185064, 0.26 #173350), 07k2p6 (0.37 #58572, 0.26 #185064, 0.26 #173350), 084m3 (0.26 #185064, 0.26 #173350, 0.11 #1681), 04twmk (0.26 #185064, 0.26 #173350, 0.05 #2022), 030znt (0.26 #185064, 0.26 #173350, 0.02 #68228), 021vwt (0.26 #185064, 0.26 #173350, 0.02 #68302), 018z_c (0.26 #185064, 0.26 #173350, 0.01 #68983), 024jwt (0.26 #185064, 0.26 #173350) >> Best rule #147582 for best value: >> intensional similarity = 3 >> extensional distance = 1212 >> proper extension: 0m2wm; 02zq43; 04wqr; 07lmxq; 0f830f; 03m8lq; 08w7vj; 01j5x6; 01v3s2_; 0bz5v2; ... >> query: (?x11884, ?x1397) <- film(?x11884, ?x4626), award_nominee(?x1397, ?x11884), award_nominee(?x11884, ?x2615) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03061d award_nominee 0306bt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 79.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3135-01z28b PRED entity: 01z28b PRED relation: contains! PRED expected values: 02jx1 => 136 concepts (27 used for prediction) PRED predicted values (max 10 best out of 206): 02jx1 (0.85 #16099, 0.85 #15288, 0.79 #9837), 09c7w0 (0.42 #11628, 0.34 #19682, 0.30 #9840), 04_1l0v (0.40 #12075, 0.27 #10287, 0.26 #21025), 0345h (0.38 #16180, 0.16 #17969, 0.13 #20656), 04jpl (0.23 #8070, 0.20 #7176, 0.15 #22387), 01w0v (0.23 #15409, 0.04 #6467, 0.03 #9149), 0dg3n1 (0.22 #10885, 0.14 #21625, 0.08 #5520), 0dbdy (0.17 #9057, 0.08 #5481, 0.05 #23374), 059rby (0.17 #18804, 0.08 #3597, 0.07 #9857), 0j5g9 (0.15 #5626, 0.06 #16359, 0.04 #6520) >> Best rule #16099 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 01h8sf; >> query: (?x12597, ?x1310) <- contains(?x11971, ?x12597), second_level_divisions(?x1310, ?x11971), state(?x9168, ?x11971) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01z28b contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 27.000 0.854 http://example.org/location/location/contains #3134-02r8hh_ PRED entity: 02r8hh_ PRED relation: genre PRED expected values: 03bxz7 => 100 concepts (78 used for prediction) PRED predicted values (max 10 best out of 155): 03k9fj (0.79 #1101, 0.72 #615, 0.62 #2312), 017fp (0.77 #6048, 0.75 #4956, 0.72 #9309), 04xvlr (0.72 #9309, 0.71 #3746, 0.71 #6895), 0f8l9c (0.63 #6047, 0.63 #2179, 0.63 #4955), 064_8sq (0.63 #6047, 0.63 #2179, 0.63 #4955), 05p553 (0.63 #1580, 0.57 #2304, 0.55 #607), 03bxz7 (0.62 #778, 0.55 #2113, 0.55 #900), 02kdv5l (0.60 #6170, 0.41 #7741, 0.34 #1698), 01hmnh (0.54 #502, 0.50 #19, 0.48 #1108), 03mqtr (0.45 #150, 0.11 #997, 0.11 #4864) >> Best rule #1101 for best value: >> intensional similarity = 6 >> extensional distance = 60 >> proper extension: 02vw1w2; >> query: (?x1724, 03k9fj) <- film_release_distribution_medium(?x1724, ?x81), genre(?x1724, ?x2540), genre(?x1724, ?x53), ?x2540 = 0hcr, genre(?x1866, ?x53), ?x1866 = 02rx2m5 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #778 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 48 *> proper extension: 06nr2h; 01qbg5; 04vq33; *> query: (?x1724, 03bxz7) <- titles(?x1316, ?x1724), ?x1316 = 017fp, film(?x1208, ?x1724), film(?x5959, ?x1724), award(?x1724, ?x8313) *> conf = 0.62 ranks of expected_values: 7 EVAL 02r8hh_ genre 03bxz7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 100.000 78.000 0.790 http://example.org/film/film/genre #3133-07wtc PRED entity: 07wtc PRED relation: major_field_of_study PRED expected values: 05qjt => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 101): 01mkq (0.44 #636, 0.43 #760, 0.33 #1876), 01lj9 (0.44 #661, 0.43 #785, 0.30 #1281), 062z7 (0.41 #648, 0.39 #772, 0.38 #1268), 02j62 (0.38 #1271, 0.37 #775, 0.33 #651), 03g3w (0.37 #771, 0.36 #647, 0.36 #1267), 05qjt (0.37 #752, 0.33 #628, 0.30 #1248), 02lp1 (0.33 #756, 0.31 #632, 0.28 #1872), 05qfh (0.33 #781, 0.31 #657, 0.25 #1277), 0fdys (0.31 #660, 0.30 #784, 0.29 #40), 04sh3 (0.31 #697, 0.30 #821, 0.20 #1317) >> Best rule #636 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 045c7b; 03y7ml; 07gyp7; >> query: (?x11740, 01mkq) <- organization(?x5510, ?x11740), company(?x5370, ?x11740), list(?x11740, ?x2197) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #752 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 44 *> proper extension: 0l8sx; *> query: (?x11740, 05qjt) <- company(?x5370, ?x11740), list(?x11740, ?x2197), profession(?x5370, ?x319) *> conf = 0.37 ranks of expected_values: 6 EVAL 07wtc major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 68.000 68.000 0.436 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3132-0qjfl PRED entity: 0qjfl PRED relation: inductee PRED expected values: 03h_9lg 0h0wc => 86 concepts (47 used for prediction) PRED predicted values (max 10 best out of 205): 01zlh5 (0.50 #228, 0.40 #367, 0.33 #506), 016t00 (0.40 #397, 0.33 #122, 0.25 #946), 015cbq (0.33 #524, 0.25 #934, 0.25 #798), 029b9k (0.33 #513, 0.25 #923, 0.25 #787), 0grwj (0.33 #415, 0.25 #689, 0.25 #553), 06m61 (0.33 #58, 0.25 #194, 0.20 #333), 0p7h7 (0.33 #52, 0.25 #188, 0.20 #327), 06lxn (0.33 #135, 0.25 #271, 0.20 #410), 0p8h0 (0.33 #134, 0.25 #270, 0.20 #409), 012x03 (0.33 #131, 0.25 #267, 0.20 #406) >> Best rule #228 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 06szd3; >> query: (?x11145, 01zlh5) <- inductee(?x11145, ?x10539), inductee(?x11145, ?x4126), award_nominee(?x10539, ?x163), film(?x4126, ?x124), celebrity(?x1205, ?x4126) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qjfl inductee 0h0wc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 47.000 0.500 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 0qjfl inductee 03h_9lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 47.000 0.500 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #3131-0gs9p PRED entity: 0gs9p PRED relation: ceremony PRED expected values: 0fy6bh 0bzn6_ 02pgky2 0c4hgj 073hgx => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 86): 0c4hgj (0.60 #569, 0.50 #314, 0.50 #229), 0fy6bh (0.60 #543, 0.50 #203, 0.50 #33), 0fzrhn (0.60 #592, 0.50 #252, 0.50 #82), 073hgx (0.50 #317, 0.50 #232, 0.50 #62), 0bzn6_ (0.50 #292, 0.50 #207, 0.50 #37), 02pgky2 (0.50 #312, 0.50 #57, 0.43 #737), 0h_cssd (0.50 #1379, 0.33 #1209, 0.33 #1124), 0ftlxj (0.40 #555, 0.29 #895, 0.27 #1065), 0ftlkg (0.40 #528, 0.29 #868, 0.27 #1038), 0drtv8 (0.40 #382, 0.29 #807, 0.25 #127) >> Best rule #569 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0gqwc; >> query: (?x1313, 0c4hgj) <- nominated_for(?x1313, ?x9611), nominated_for(?x1313, ?x3826), nominated_for(?x1313, ?x1402), award(?x269, ?x1313), ?x3826 = 0yx7h, ?x1402 = 0sxfd, ?x9611 = 0cq8nx >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5, 6 EVAL 0gs9p ceremony 073hgx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.600 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gs9p ceremony 0c4hgj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.600 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gs9p ceremony 02pgky2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.600 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gs9p ceremony 0bzn6_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.600 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gs9p ceremony 0fy6bh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.600 http://example.org/award/award_category/winners./award/award_honor/ceremony #3130-0jmj PRED entity: 0jmj PRED relation: award_nominee! PRED expected values: 04yqlk => 105 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1133): 03x3qv (0.81 #57907, 0.81 #55590, 0.81 #81077), 04yqlk (0.81 #57907, 0.81 #55590, 0.81 #81077), 0pz7h (0.36 #2497, 0.02 #11761), 02778qt (0.32 #3006, 0.15 #81078, 0.01 #37749), 02773nt (0.32 #2474, 0.15 #81078, 0.01 #37217), 02773m2 (0.32 #2475, 0.15 #81078, 0.01 #51115), 0266r6h (0.32 #3428, 0.15 #81078), 0gkydb (0.32 #2949, 0.15 #81078), 0jmj (0.29 #101928, 0.15 #81078, 0.15 #74125), 01ggc9 (0.29 #101928, 0.15 #81078, 0.03 #9043) >> Best rule #57907 for best value: >> intensional similarity = 3 >> extensional distance = 811 >> proper extension: 02knnd; >> query: (?x4346, ?x336) <- location(?x4346, ?x739), award_nominee(?x4346, ?x336), place_of_birth(?x4346, ?x3014) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0jmj award_nominee! 04yqlk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 46.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #3129-04dqdk PRED entity: 04dqdk PRED relation: artists! PRED expected values: 059kh => 112 concepts (103 used for prediction) PRED predicted values (max 10 best out of 228): 0xhtw (0.47 #17, 0.28 #6558, 0.19 #6246), 0cx7f (0.37 #139, 0.09 #1074, 0.08 #6368), 05w3f (0.33 #37, 0.12 #6578, 0.11 #6266), 06j6l (0.30 #2538, 0.29 #358, 0.29 #1916), 016clz (0.28 #6546, 0.28 #14639, 0.26 #940), 0gywn (0.27 #368, 0.25 #1926, 0.23 #1304), 025sc50 (0.26 #2540, 0.25 #2229, 0.25 #7212), 0glt670 (0.26 #8136, 0.25 #7825, 0.25 #7203), 0155w (0.25 #418, 0.22 #1042, 0.19 #730), 01lyv (0.25 #1902, 0.24 #4394, 0.24 #656) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 01t_xp_; 01wv9xn; 0167_s; 01vrwfv; 0fcsd; 02jqjm; 013w2r; 02vgh; 01kcms4; 06gcn; ... >> query: (?x1381, 0xhtw) <- origin(?x1381, ?x4627), artists(?x1380, ?x1381), ?x1380 = 0dl5d >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #6589 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 346 *> proper extension: 05563d; *> query: (?x1381, 059kh) <- artists(?x1572, ?x1381), artist(?x2299, ?x1381), ?x1572 = 06by7 *> conf = 0.11 ranks of expected_values: 34 EVAL 04dqdk artists! 059kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 112.000 103.000 0.469 http://example.org/music/genre/artists #3128-02bgmr PRED entity: 02bgmr PRED relation: artists! PRED expected values: 05bt6j 08jyyk => 120 concepts (40 used for prediction) PRED predicted values (max 10 best out of 240): 05r6t (0.45 #1304, 0.43 #1610, 0.31 #998), 05bt6j (0.41 #959, 0.29 #8321, 0.29 #1265), 0glt670 (0.36 #3717, 0.34 #2797, 0.32 #1876), 01lyv (0.35 #7087, 0.20 #5551, 0.20 #9845), 06j6l (0.35 #2804, 0.31 #2498, 0.31 #3724), 0m0jc (0.33 #314, 0.19 #8288, 0.14 #926), 08jyyk (0.33 #371, 0.16 #1595, 0.14 #7120), 0fd3y (0.33 #316, 0.07 #2155, 0.07 #8290), 025sc50 (0.33 #2805, 0.32 #3725, 0.26 #3418), 0gywn (0.30 #2813, 0.26 #3426, 0.24 #3733) >> Best rule #1304 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 05xq9; 02mq_y; 013rfk; 02hzz; 070b4; 079kr; 07n68; >> query: (?x5768, 05r6t) <- artists(?x2491, ?x5768), ?x2491 = 011j5x, origin(?x5768, ?x2911) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #959 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 03t9sp; 05k79; 0frsw; 02cpp; 0178kd; 0143q0; 01323p; 014pg1; 017lb_; 011_vz; ... *> query: (?x5768, 05bt6j) <- artists(?x2491, ?x5768), award(?x5768, ?x884), ?x2491 = 011j5x *> conf = 0.41 ranks of expected_values: 2, 7 EVAL 02bgmr artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 120.000 40.000 0.452 http://example.org/music/genre/artists EVAL 02bgmr artists! 05bt6j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 40.000 0.452 http://example.org/music/genre/artists #3127-01j5ws PRED entity: 01j5ws PRED relation: film PRED expected values: 087vnr5 => 119 concepts (81 used for prediction) PRED predicted values (max 10 best out of 939): 0f4_l (0.25 #347, 0.02 #39578, 0.02 #41361), 01gkp1 (0.20 #2595, 0.06 #7945, 0.06 #11512), 01d2v1 (0.20 #3491, 0.06 #12408, 0.05 #14191), 0pc62 (0.20 #1876, 0.03 #10793, 0.02 #12576), 03np63f (0.20 #3156, 0.03 #12073, 0.02 #13856), 02_qt (0.12 #631, 0.10 #2414, 0.04 #4197), 0f2sx4 (0.12 #1381, 0.10 #3164, 0.03 #12081), 01f69m (0.12 #1730, 0.10 #3513), 02qhqz4 (0.12 #341, 0.07 #3907, 0.03 #9257), 015ynm (0.12 #1430, 0.06 #6779, 0.06 #10346) >> Best rule #347 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 04fhxp; 032zg9; >> query: (?x3025, 0f4_l) <- film(?x3025, ?x9361), location(?x3025, ?x4499), ?x9361 = 02jkkv >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1451 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 04fhxp; 032zg9; *> query: (?x3025, 087vnr5) <- film(?x3025, ?x9361), location(?x3025, ?x4499), ?x9361 = 02jkkv *> conf = 0.12 ranks of expected_values: 47 EVAL 01j5ws film 087vnr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 119.000 81.000 0.250 http://example.org/film/actor/film./film/performance/film #3126-085wqm PRED entity: 085wqm PRED relation: film! PRED expected values: 016tw3 => 75 concepts (63 used for prediction) PRED predicted values (max 10 best out of 45): 086k8 (0.33 #224, 0.27 #298, 0.25 #150), 03xq0f (0.33 #227, 0.27 #301, 0.16 #375), 017s11 (0.33 #3, 0.20 #77, 0.16 #447), 0g1rw (0.25 #156, 0.09 #452, 0.06 #2839), 0338lq (0.25 #155, 0.02 #673, 0.01 #821), 04mkft (0.22 #257, 0.18 #331, 0.03 #775), 05qd_ (0.21 #379, 0.16 #749, 0.15 #675), 016tt2 (0.20 #78, 0.16 #374, 0.13 #1040), 017jv5 (0.20 #89, 0.07 #459, 0.05 #2100), 016tw3 (0.15 #381, 0.14 #973, 0.14 #603) >> Best rule #224 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 031778; 06nr2h; 04xg2f; 03tbg6; >> query: (?x10397, 086k8) <- film(?x4649, ?x10397), nominated_for(?x102, ?x10397), ?x4649 = 03dpqd, film(?x5636, ?x10397) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #381 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 107 *> proper extension: 03n0cd; 03whyr; *> query: (?x10397, 016tw3) <- film(?x2387, ?x10397), film_crew_role(?x10397, ?x2091), ?x2091 = 02rh1dz, genre(?x10397, ?x571) *> conf = 0.15 ranks of expected_values: 10 EVAL 085wqm film! 016tw3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 75.000 63.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #3125-040vk98 PRED entity: 040vk98 PRED relation: award! PRED expected values: 06bng 01v_0b => 61 concepts (36 used for prediction) PRED predicted values (max 10 best out of 3064): 07zl1 (0.81 #53487, 0.81 #53486, 0.80 #56833), 01zkxv (0.81 #53487, 0.81 #53486, 0.80 #56833), 05jm7 (0.60 #14434, 0.60 #11091, 0.50 #27807), 02y49 (0.60 #12577, 0.50 #29293, 0.50 #22606), 03hpr (0.40 #16232, 0.40 #12889, 0.38 #29605), 09889g (0.39 #61616, 0.13 #51586, 0.13 #85015), 0187y5 (0.33 #36918, 0.26 #46949, 0.21 #53638), 04x56 (0.33 #2853, 0.25 #9539, 0.20 #16224), 042xh (0.33 #6666, 0.20 #13351, 0.17 #23380), 0jmj (0.32 #48029, 0.30 #37998, 0.21 #54718) >> Best rule #53487 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 040njc; 02wh75; 0gr4k; 01bgqh; 0l8z1; 019f4v; 0c4z8; 02662b; 0gq9h; 0gs9p; ... >> query: (?x575, ?x5506) <- award_winner(?x575, ?x5506), award(?x4895, ?x575), influenced_by(?x4895, ?x3542), influenced_by(?x1683, ?x4895), notable_people_with_this_condition(?x13845, ?x4895) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #9115 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 0265vt; *> query: (?x575, 06bng) <- award_winner(?x575, ?x5506), award(?x11262, ?x575), award(?x4895, ?x575), ?x5506 = 048_p, student(?x2999, ?x11262), people(?x12624, ?x4895) *> conf = 0.25 ranks of expected_values: 25, 588 EVAL 040vk98 award! 01v_0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 36.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 040vk98 award! 06bng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 61.000 36.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3124-03knl PRED entity: 03knl PRED relation: participant! PRED expected values: 0gyx4 => 117 concepts (47 used for prediction) PRED predicted values (max 10 best out of 427): 0gyx4 (0.80 #22239, 0.80 #17152, 0.80 #13338), 0127m7 (0.10 #9522, 0.09 #26055, 0.09 #10160), 06pjs (0.10 #9522, 0.09 #26055, 0.09 #10160), 06g2d1 (0.10 #9522, 0.09 #10160, 0.08 #11431), 02lymt (0.10 #9522, 0.09 #10160, 0.08 #11431), 0f7hc (0.09 #26055, 0.09 #10160, 0.08 #11431), 01rr9f (0.08 #3840, 0.05 #3205, 0.05 #7650), 0227vl (0.08 #3069, 0.05 #4339, 0.03 #3704), 011zd3 (0.08 #2690, 0.03 #3325, 0.02 #3960), 03lt8g (0.07 #3240, 0.05 #3875, 0.04 #2605) >> Best rule #22239 for best value: >> intensional similarity = 4 >> extensional distance = 405 >> proper extension: 017f4y; >> query: (?x971, ?x970) <- participant(?x971, ?x2275), participant(?x971, ?x970), people(?x3715, ?x2275), type_of_union(?x2275, ?x566) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03knl participant! 0gyx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 47.000 0.805 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #3123-06bnz PRED entity: 06bnz PRED relation: adjoins! PRED expected values: 047lj => 191 concepts (129 used for prediction) PRED predicted values (max 10 best out of 553): 0163v (0.86 #36436, 0.86 #41864, 0.86 #775), 0jhd (0.86 #36436, 0.86 #41864, 0.86 #775), 06bnz (0.43 #85, 0.21 #99271, 0.15 #35746), 03pn9 (0.43 #122, 0.21 #99271, 0.07 #25706), 0345h (0.21 #99271, 0.20 #4717, 0.18 #10143), 03rk0 (0.21 #99271, 0.19 #9412, 0.14 #44303), 01mjq (0.21 #99271, 0.14 #82, 0.10 #8610), 06npd (0.21 #99271, 0.14 #35, 0.10 #8563), 070zc (0.21 #99271, 0.14 #502, 0.05 #22985), 0hyyq (0.21 #99271, 0.14 #507, 0.04 #27641) >> Best rule #36436 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 059t8; 059s8; 05rh2; >> query: (?x1603, ?x344) <- adjoins(?x1603, ?x344), administrative_parent(?x1603, ?x551), geographic_distribution(?x5590, ?x1603) >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #99271 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 193 *> proper extension: 02hyt; *> query: (?x1603, ?x2517) <- adjoins(?x1603, ?x344), organization(?x344, ?x127), adjoins(?x2517, ?x344) *> conf = 0.21 ranks of expected_values: 25 EVAL 06bnz adjoins! 047lj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 191.000 129.000 0.864 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #3122-02cbg0 PRED entity: 02cbg0 PRED relation: language PRED expected values: 02h40lc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 34): 02h40lc (0.97 #177, 0.96 #1117, 0.95 #4761), 064_8sq (0.21 #1469, 0.14 #22, 0.14 #666), 04306rv (0.21 #1469, 0.13 #766, 0.12 #532), 06nm1 (0.21 #1469, 0.12 #596, 0.12 #186), 02bjrlw (0.21 #1469, 0.10 #762, 0.08 #1174), 06b_j (0.21 #1469, 0.08 #140, 0.08 #82), 0jzc (0.21 #1469, 0.06 #664, 0.05 #723), 04h9h (0.21 #1469, 0.05 #160, 0.04 #687), 03_9r (0.21 #1469, 0.05 #4240, 0.05 #3477), 03hkp (0.21 #1469, 0.02 #776, 0.02 #542) >> Best rule #177 for best value: >> intensional similarity = 4 >> extensional distance = 197 >> proper extension: 031t2d; 0cc5mcj; 014zwb; 04fv5b; 034qbx; 057__d; 085wqm; >> query: (?x8436, 02h40lc) <- film_crew_role(?x8436, ?x137), language(?x8436, ?x13310), music(?x8436, ?x2214), crewmember(?x8436, ?x666) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cbg0 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.970 http://example.org/film/film/language #3121-01x73 PRED entity: 01x73 PRED relation: religion PRED expected values: 051kv 01s5nb => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 21): 051kv (0.91 #219, 0.83 #146, 0.83 #122), 01s5nb (0.46 #134, 0.46 #231, 0.42 #401), 092bf5 (0.33 #54, 0.30 #102, 0.29 #756), 0kpl (0.32 #654, 0.25 #27, 0.07 #2338), 0g5llry (0.32 #654), 072w0 (0.28 #232, 0.25 #135, 0.23 #402), 02t7t (0.26 #229, 0.25 #374, 0.25 #350), 03j6c (0.25 #57, 0.25 #33, 0.09 #1146), 07w8f (0.25 #42, 0.07 #2338, 0.02 #333), 0n2g (0.08 #52, 0.07 #2338, 0.05 #76) >> Best rule #219 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 0l3h; >> query: (?x1755, 051kv) <- contains(?x94, ?x1755), religion(?x1755, ?x2769), ?x2769 = 019cr >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01x73 religion 01s5nb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.913 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 01x73 religion 051kv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.913 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #3120-0mx2h PRED entity: 0mx2h PRED relation: adjoins PRED expected values: 0mx0f => 134 concepts (43 used for prediction) PRED predicted values (max 10 best out of 295): 0mx0f (0.82 #32499, 0.81 #20886, 0.81 #32498), 0mxhc (0.33 #774, 0.29 #680, 0.25 #26305), 0mx2h (0.33 #774, 0.25 #26305, 0.25 #19340), 0mx48 (0.33 #774, 0.25 #26305, 0.25 #19340), 0mx5p (0.29 #654, 0.15 #1430, 0.03 #2202), 0mx6c (0.29 #108, 0.15 #884, 0.03 #1656), 0mx3k (0.25 #17791, 0.25 #3866, 0.24 #30951), 0mxbq (0.21 #453, 0.11 #1229, 0.02 #2001), 0mx4_ (0.21 #33, 0.11 #809, 0.02 #1581), 0l339 (0.14 #439, 0.07 #1215, 0.03 #1987) >> Best rule #32499 for best value: >> intensional similarity = 4 >> extensional distance = 312 >> proper extension: 0f04v; >> query: (?x13508, ?x9568) <- adjoins(?x9568, ?x13508), contains(?x726, ?x9568), source(?x9568, ?x958), time_zones(?x13508, ?x2950) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mx2h adjoins 0mx0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 43.000 0.820 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #3119-06pjs PRED entity: 06pjs PRED relation: award PRED expected values: 02rdyk7 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 315): 02w_6xj (0.70 #40605, 0.68 #45431, 0.67 #53875), 0gs9p (0.60 #2892, 0.41 #14149, 0.41 #3294), 040njc (0.51 #2822, 0.37 #9254, 0.36 #9657), 02pqp12 (0.46 #2883, 0.32 #873, 0.32 #471), 0gq9h (0.42 #2890, 0.39 #11333, 0.38 #4900), 09sb52 (0.31 #6071, 0.27 #44265, 0.26 #20142), 02rdyk7 (0.30 #2904, 0.22 #9649, 0.20 #3306), 0gr4k (0.27 #2847, 0.25 #21742, 0.23 #9682), 05pcn59 (0.26 #884, 0.24 #482, 0.21 #6110), 04kxsb (0.26 #526, 0.18 #928, 0.13 #6154) >> Best rule #40605 for best value: >> intensional similarity = 2 >> extensional distance = 1294 >> proper extension: 0f721s; 01jq34; 01w92; 01_8w2; 01p5yn; 04glx0; 03yxwq; 018_q8; 0gsgr; 0283xx2; ... >> query: (?x9153, ?x5398) <- award_winner(?x9153, ?x2551), award_winner(?x5398, ?x9153) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2904 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 02kxbwx; 032v0v; 0b_c7; 04y8r; 0b_7k; 07rd7; 026dx; 015njf; 0mm1q; 012vct; ... *> query: (?x9153, 02rdyk7) <- film(?x9153, ?x3388), titles(?x162, ?x3388), ?x162 = 04xvlr *> conf = 0.30 ranks of expected_values: 7 EVAL 06pjs award 02rdyk7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 147.000 147.000 0.699 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3118-072r5v PRED entity: 072r5v PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.81 #417, 0.75 #520, 0.73 #142), 01pvkk (0.34 #146, 0.33 #421, 0.30 #524), 02rh1dz (0.25 #420, 0.21 #343, 0.20 #523), 02ynfr (0.23 #425, 0.22 #528, 0.21 #343), 0d2b38 (0.21 #343, 0.16 #435, 0.14 #538), 01xy5l_ (0.21 #343, 0.14 #423, 0.13 #148), 089g0h (0.21 #343, 0.13 #429, 0.12 #154), 015h31 (0.21 #343, 0.13 #419, 0.11 #522), 04pyp5 (0.21 #343, 0.10 #1654, 0.09 #151), 089fss (0.21 #343, 0.10 #1654, 0.08 #416) >> Best rule #417 for best value: >> intensional similarity = 4 >> extensional distance = 227 >> proper extension: 01q2nx; >> query: (?x7917, 0ch6mp2) <- genre(?x7917, ?x225), film_crew_role(?x7917, ?x1171), ?x225 = 02kdv5l, ?x1171 = 09vw2b7 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 072r5v film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.808 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3117-06ryl PRED entity: 06ryl PRED relation: administrative_parent PRED expected values: 02j71 => 134 concepts (86 used for prediction) PRED predicted values (max 10 best out of 53): 02j71 (0.85 #4970, 0.84 #5381, 0.84 #7304), 0261m (0.29 #138, 0.16 #2749, 0.15 #2471), 0d05w3 (0.29 #47, 0.11 #7199, 0.11 #7477), 09c7w0 (0.23 #2613, 0.20 #2335, 0.14 #10205), 07c5l (0.16 #2749, 0.15 #2471, 0.12 #137), 03rjj (0.07 #9373, 0.04 #10490), 049nq (0.06 #509, 0.05 #786, 0.04 #2847), 03rk0 (0.05 #11088, 0.05 #11370), 07ssc (0.04 #3739, 0.04 #2900, 0.03 #1797), 0chghy (0.03 #2472, 0.03 #2750, 0.03 #2473) >> Best rule #4970 for best value: >> intensional similarity = 5 >> extensional distance = 102 >> proper extension: 0160w; 0f8l9c; 0hzlz; 06s_2; 04hvw; >> query: (?x4402, 02j71) <- administrative_area_type(?x4402, ?x2792), adjustment_currency(?x4402, ?x170), organization(?x4402, ?x127), form_of_government(?x4402, ?x1926), jurisdiction_of_office(?x182, ?x4402) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06ryl administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 86.000 0.846 http://example.org/base/aareas/schema/administrative_area/administrative_parent #3116-094xh PRED entity: 094xh PRED relation: artists! PRED expected values: 0xhtw 01lyv => 245 concepts (108 used for prediction) PRED predicted values (max 10 best out of 296): 016clz (0.43 #3089, 0.41 #4324, 0.32 #4015), 025sc50 (0.40 #10844, 0.36 #12077, 0.35 #25028), 06j6l (0.40 #12383, 0.39 #25026, 0.37 #14236), 02w4v (0.40 #4053, 0.38 #3436, 0.29 #967), 02yv6b (0.36 #14286, 0.34 #12433, 0.29 #3490), 0ggx5q (0.34 #10871, 0.31 #9021, 0.30 #12104), 02lnbg (0.34 #10853, 0.31 #9003, 0.30 #12086), 016jny (0.33 #3496, 0.32 #4113, 0.29 #1027), 0xhtw (0.33 #3410, 0.31 #12353, 0.31 #10504), 02vjzr (0.33 #5069, 0.18 #12776, 0.16 #10927) >> Best rule #3089 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 01q7cb_; 09qr6; 0j1yf; 0285c; 01vsnff; 0137g1; 07g2v; 03h502k; 02r3cn; 0484q; ... >> query: (?x5312, 016clz) <- type_of_union(?x5312, ?x566), instrumentalists(?x227, ?x5312), artists(?x671, ?x5312), celebrity(?x5312, ?x4140) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3410 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 018d6l; *> query: (?x5312, 0xhtw) <- student(?x8427, ?x5312), instrumentalists(?x2798, ?x5312), ?x2798 = 03qjg, artist(?x2149, ?x5312) *> conf = 0.33 ranks of expected_values: 9, 13 EVAL 094xh artists! 01lyv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 245.000 108.000 0.435 http://example.org/music/genre/artists EVAL 094xh artists! 0xhtw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 245.000 108.000 0.435 http://example.org/music/genre/artists #3115-048s0r PRED entity: 048s0r PRED relation: award PRED expected values: 099tbz => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 228): 01by1l (0.24 #2947, 0.10 #10237, 0.09 #11858), 0gqyl (0.19 #12558, 0.17 #10936, 0.17 #2130), 02ppm4q (0.19 #12558, 0.17 #10936, 0.16 #12152), 09td7p (0.19 #12558, 0.17 #10936, 0.16 #12152), 099t8j (0.19 #12558, 0.17 #10936, 0.16 #12152), 0cqhk0 (0.19 #12558, 0.17 #10936, 0.16 #12152), 0cqgl9 (0.19 #12558, 0.17 #10936, 0.16 #12152), 057xs89 (0.19 #12558, 0.17 #10936, 0.16 #12152), 099tbz (0.19 #12558, 0.17 #10936, 0.16 #12152), 0bsjcw (0.19 #12558, 0.17 #10936, 0.16 #12152) >> Best rule #2947 for best value: >> intensional similarity = 3 >> extensional distance = 794 >> proper extension: 089tm; 0kzy0; 0152cw; 01v0sx2; 01r9fv; 01wv9xn; 03t9sp; 0dtd6; 0frsw; 03j0br4; ... >> query: (?x7157, 01by1l) <- award(?x7157, ?x704), award(?x1896, ?x704), ?x1896 = 0j1yf >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #12558 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1268 *> proper extension: 01_8w2; 0gsgr; 0kc8y; *> query: (?x7157, ?x995) <- award_winner(?x3078, ?x7157), award_winner(?x704, ?x7157), award_winner(?x995, ?x3078) *> conf = 0.19 ranks of expected_values: 9 EVAL 048s0r award 099tbz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 84.000 84.000 0.237 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3114-08ns5s PRED entity: 08ns5s PRED relation: position! PRED expected values: 06rny => 29 concepts (22 used for prediction) PRED predicted values (max 10 best out of 409): 01y49 (0.86 #251, 0.84 #328, 0.84 #451), 01c_d (0.86 #251, 0.84 #328, 0.84 #327), 0ftf0f (0.84 #328, 0.84 #451, 0.83 #207), 0fgg8c (0.83 #207, 0.83 #253, 0.83 #250), 026lg0s (0.83 #253, 0.83 #250, 0.82 #113), 07l2m (0.80 #71, 0.79 #384, 0.78 #354), 0g0z58 (0.80 #71, 0.76 #407, 0.76 #163), 0bs09lb (0.80 #71, 0.76 #407, 0.75 #353), 06rny (0.79 #393, 0.73 #439, 0.73 #419), 026ldz7 (0.75 #288, 0.75 #264, 0.73 #455) >> Best rule #251 for best value: >> intensional similarity = 22 >> extensional distance = 5 >> proper extension: 01_9c1; >> query: (?x7079, ?x4256) <- position(?x9172, ?x7079), position(?x7643, ?x7079), position(?x4519, ?x7079), position(?x4469, ?x7079), position(?x4256, ?x7079), position(?x7079, ?x3346), ?x4469 = 043vc, team(?x11323, ?x4256), ?x9172 = 06rpd, position(?x4546, ?x7079), school(?x4256, ?x2711), position_s(?x5229, ?x7079), team(?x2247, ?x7643), draft(?x4256, ?x4171), ?x4171 = 092j54, institution(?x865, ?x2711), ?x2247 = 01_9c1, ?x4519 = 084l5, category(?x7643, ?x134), ?x5229 = 07l2m, ?x4546 = 05gg4, student(?x2711, ?x5200) >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #393 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 12 *> proper extension: 03h42s4; *> query: (?x7079, 06rny) <- team(?x7079, ?x7312), team(?x7079, ?x6466), team(?x7079, ?x6379), team(?x7079, ?x1639), category(?x6466, ?x134), position(?x7312, ?x2247), position(?x7312, ?x1717), ?x1717 = 02g_6x, draft(?x7312, ?x6462), ?x6379 = 0bjkk9, position(?x1639, ?x1517), position_s(?x1639, ?x3113), ?x6462 = 09l0x9, sport(?x1639, ?x1083), school(?x7312, ?x466), team(?x11282, ?x7312), colors(?x1639, ?x663), ?x2247 = 01_9c1 *> conf = 0.79 ranks of expected_values: 9 EVAL 08ns5s position! 06rny CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 29.000 22.000 0.856 http://example.org/sports/sports_team/roster./american_football/football_roster_position/position #3113-01b7h8 PRED entity: 01b7h8 PRED relation: nominated_for! PRED expected values: 0136g9 => 41 concepts (38 used for prediction) PRED predicted values (max 10 best out of 889): 043zg (0.52 #7003, 0.43 #14007, 0.32 #16344), 04xrx (0.52 #7003, 0.43 #14007, 0.32 #16344), 09k2t1 (0.52 #7003, 0.43 #14007, 0.28 #18679), 047sxrj (0.52 #7003, 0.32 #16344, 0.28 #18679), 02ktrs (0.52 #7003, 0.32 #16344, 0.28 #18679), 05cljf (0.52 #7003, 0.28 #18679, 0.24 #16343), 01gbbz (0.52 #7003, 0.28 #18679, 0.24 #16343), 01wmjkb (0.52 #7003, 0.28 #18679, 0.24 #16343), 025ldg (0.52 #7003, 0.28 #18679, 0.24 #16343), 0cjdk (0.32 #18680, 0.29 #14009, 0.24 #21016) >> Best rule #7003 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01h1bf; 03czz87; >> query: (?x9788, ?x226) <- program(?x226, ?x9788), nominated_for(?x6937, ?x9788), honored_for(?x1265, ?x9788) >> conf = 0.52 => this is the best rule for 9 predicted values *> Best rule #25686 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 89 *> proper extension: 03j63k; *> query: (?x9788, ?x1367) <- program_creator(?x9788, ?x6937), award_nominee(?x6937, ?x1367) *> conf = 0.28 ranks of expected_values: 11 EVAL 01b7h8 nominated_for! 0136g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 41.000 38.000 0.524 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #3112-0hn10 PRED entity: 0hn10 PRED relation: genre! PRED expected values: 02q56mk 06kl78 011yn5 02zk08 0p7pw 02b6n9 09v42sf => 49 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1965): 02nczh (0.80 #5412, 0.79 #9024, 0.78 #9026), 046f3p (0.80 #5412, 0.79 #9024, 0.78 #9026), 02d413 (0.80 #5412, 0.79 #9024, 0.78 #9026), 02s4l6 (0.80 #5412, 0.79 #9024, 0.78 #9026), 01633c (0.80 #5412, 0.79 #9024, 0.78 #9026), 02py4c8 (0.80 #5412, 0.79 #9024, 0.78 #9026), 01q_y0 (0.80 #5412, 0.79 #9024, 0.78 #9026), 04h41v (0.80 #5412, 0.79 #9024, 0.78 #9026), 04y5j64 (0.80 #5412, 0.79 #9024, 0.78 #9026), 0sxkh (0.80 #5412, 0.79 #9024, 0.78 #9026) >> Best rule #5412 for best value: >> intensional similarity = 13 >> extensional distance = 1 >> proper extension: 07s9rl0; >> query: (?x714, ?x69) <- genre(?x7501, ?x714), genre(?x5109, ?x714), genre(?x3251, ?x714), genre(?x2961, ?x714), genre(?x2488, ?x714), genre(?x1903, ?x714), ?x3251 = 0571m, titles(?x714, ?x69), ?x2961 = 047p7fr, ?x5109 = 0b44shh, ?x7501 = 0gd92, ?x2488 = 02qr69m, ?x1903 = 026gyn_ >> conf = 0.80 => this is the best rule for 12 predicted values *> Best rule #26095 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 6 *> proper extension: 0glj9q; *> query: (?x714, 06kl78) <- genre(?x9452, ?x714), genre(?x7967, ?x714), genre(?x3251, ?x714), genre(?x2961, ?x714), ?x3251 = 0571m, titles(?x714, ?x69), films(?x4450, ?x2961), award(?x9452, ?x198), nominated_for(?x102, ?x7967), film_release_region(?x2961, ?x87), music(?x9452, ?x6783) *> conf = 0.62 ranks of expected_values: 47, 82, 429, 701, 732, 767, 1304 EVAL 0hn10 genre! 09v42sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 49.000 19.000 0.797 http://example.org/film/film/genre EVAL 0hn10 genre! 02b6n9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 19.000 0.797 http://example.org/film/film/genre EVAL 0hn10 genre! 0p7pw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 19.000 0.797 http://example.org/film/film/genre EVAL 0hn10 genre! 02zk08 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 19.000 0.797 http://example.org/film/film/genre EVAL 0hn10 genre! 011yn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 19.000 0.797 http://example.org/film/film/genre EVAL 0hn10 genre! 06kl78 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 49.000 19.000 0.797 http://example.org/film/film/genre EVAL 0hn10 genre! 02q56mk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 19.000 0.797 http://example.org/film/film/genre #3111-05mc99 PRED entity: 05mc99 PRED relation: type_of_union PRED expected values: 04ztj => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.72 #25, 0.72 #33, 0.71 #165), 01g63y (0.15 #6, 0.14 #34, 0.14 #54) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 986 >> proper extension: 079vf; 05d7rk; 014x77; 0kr5_; 03gm48; 04hpck; 02lnhv; 05sq84; 0j582; 03qmj9; ... >> query: (?x7595, 04ztj) <- film(?x7595, ?x324), gender(?x7595, ?x231), student(?x3394, ?x7595) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05mc99 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.723 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3110-054fvj PRED entity: 054fvj PRED relation: type_of_union PRED expected values: 04ztj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.80 #161, 0.80 #73, 0.80 #141), 01g63y (0.17 #106, 0.14 #198, 0.14 #230) >> Best rule #161 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 03yf3z; >> query: (?x9952, 04ztj) <- student(?x1519, ?x9952), location(?x9952, ?x2504), nationality(?x9952, ?x94), ?x94 = 09c7w0 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 054fvj type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.805 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3109-01dq0z PRED entity: 01dq0z PRED relation: institution! PRED expected values: 02m4yg => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 19): 03bwzr4 (0.54 #112, 0.52 #273, 0.49 #294), 02_xgp2 (0.54 #110, 0.51 #292, 0.51 #271), 07s6fsf (0.46 #101, 0.44 #41, 0.40 #262), 0bkj86 (0.46 #106, 0.41 #247, 0.37 #267), 02mjs7 (0.33 #43, 0.22 #23, 0.17 #1744), 04zx3q1 (0.32 #102, 0.30 #143, 0.28 #1440), 013zdg (0.32 #105, 0.23 #266, 0.23 #146), 02m4yg (0.28 #1440, 0.14 #1765, 0.09 #155), 01gkg3 (0.28 #1440, 0.14 #1765, 0.02 #254), 027f2w (0.25 #228, 0.23 #148, 0.22 #268) >> Best rule #112 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 017j69; >> query: (?x13670, 03bwzr4) <- category(?x13670, ?x134), citytown(?x13670, ?x1196), major_field_of_study(?x13670, ?x1695), ?x1695 = 06ms6 >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1440 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 550 *> proper extension: 03bwzr4; *> query: (?x13670, ?x865) <- major_field_of_study(?x13670, ?x2601), major_field_of_study(?x3437, ?x2601), major_field_of_study(?x865, ?x2601), ?x3437 = 02_xgp2 *> conf = 0.28 ranks of expected_values: 8 EVAL 01dq0z institution! 02m4yg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 134.000 134.000 0.541 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3108-0f7hc PRED entity: 0f7hc PRED relation: film PRED expected values: 0b1y_2 => 130 concepts (114 used for prediction) PRED predicted values (max 10 best out of 841): 026f__m (0.73 #128188, 0.73 #126406, 0.66 #128187), 05pxnmb (0.73 #128188, 0.73 #126406, 0.66 #128187), 0b1y_2 (0.73 #128188, 0.66 #128187, 0.65 #126405), 039cq4 (0.66 #128187, 0.65 #126405, 0.60 #124624), 0642xf3 (0.18 #2650, 0.01 #31135), 01shy7 (0.17 #421, 0.10 #7543, 0.07 #30687), 031t2d (0.17 #253, 0.09 #2034, 0.04 #5595), 011ykb (0.17 #1137, 0.09 #2918, 0.01 #75909), 011yn5 (0.17 #922, 0.04 #4483, 0.04 #6264), 032zq6 (0.17 #687, 0.04 #4248, 0.04 #6029) >> Best rule #128188 for best value: >> intensional similarity = 3 >> extensional distance = 1031 >> proper extension: 05wjnt; 05hdf; 01pnn3; 02zrv7; 01lqnff; 0418ft; 012x2b; 065d1h; 01mylz; >> query: (?x4657, ?x886) <- nominated_for(?x4657, ?x886), film(?x4657, ?x4650), film(?x902, ?x886) >> conf = 0.73 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0f7hc film 0b1y_2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 114.000 0.728 http://example.org/film/actor/film./film/performance/film #3107-014d4v PRED entity: 014d4v PRED relation: major_field_of_study PRED expected values: 05qt0 => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 105): 01mkq (0.71 #261, 0.63 #876, 0.63 #507), 02lp1 (0.60 #257, 0.55 #503, 0.53 #872), 02j62 (0.52 #277, 0.49 #400, 0.49 #892), 04rjg (0.49 #266, 0.46 #512, 0.45 #881), 05qjt (0.43 #499, 0.43 #376, 0.41 #253), 01lj9 (0.43 #286, 0.40 #532, 0.40 #901), 03g3w (0.43 #273, 0.40 #519, 0.38 #888), 062z7 (0.40 #274, 0.37 #520, 0.37 #889), 064_8sq (0.38 #47, 0.06 #908, 0.06 #416), 01540 (0.37 #308, 0.33 #923, 0.33 #554) >> Best rule #261 for best value: >> intensional similarity = 3 >> extensional distance = 61 >> proper extension: 01jssp; 0277jc; 01pq4w; 017j69; 017cy9; 02bqy; 0hsb3; 01qd_r; >> query: (?x11583, 01mkq) <- institution(?x3437, ?x11583), list(?x11583, ?x2197), ?x3437 = 02_xgp2 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 052nd; 0dplh; 01y8zd; 011xy1; 0373qt; 0gl6x; *> query: (?x11583, 05qt0) <- institution(?x1368, ?x11583), list(?x11583, ?x2197), currency(?x11583, ?x1099) *> conf = 0.12 ranks of expected_values: 44 EVAL 014d4v major_field_of_study 05qt0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 48.000 48.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #3106-0249kn PRED entity: 0249kn PRED relation: group! PRED expected values: 05148p4 04rzd => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 106): 05148p4 (0.70 #954, 0.69 #784, 0.61 #529), 03bx0bm (0.65 #620, 0.64 #790, 0.61 #535), 018vs (0.62 #948, 0.61 #778, 0.52 #523), 0l14md (0.59 #772, 0.58 #942, 0.51 #602), 028tv0 (0.50 #352, 0.43 #607, 0.38 #777), 05r5c (0.36 #348, 0.32 #603, 0.30 #518), 0l14qv (0.24 #940, 0.22 #770, 0.17 #515), 013y1f (0.22 #538, 0.21 #368, 0.14 #963), 06ncr (0.17 #548, 0.15 #973, 0.14 #378), 04rzd (0.17 #542, 0.14 #372, 0.12 #967) >> Best rule #954 for best value: >> intensional similarity = 3 >> extensional distance = 183 >> proper extension: 0123r4; 0qmpd; 06br6t; >> query: (?x2906, 05148p4) <- group(?x2798, ?x2906), role(?x74, ?x2798), instrumentalists(?x2798, ?x211) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 0249kn group! 04rzd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 95.000 95.000 0.697 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0249kn group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.697 http://example.org/music/performance_role/regular_performances./music/group_membership/group #3105-01ts_3 PRED entity: 01ts_3 PRED relation: award_winner! PRED expected values: 027b9ly 0789r6 => 93 concepts (74 used for prediction) PRED predicted values (max 10 best out of 334): 09d28z (0.39 #1592, 0.36 #2024, 0.25 #2887), 02w_6xj (0.38 #1960, 0.34 #1528, 0.18 #2823), 0gs9p (0.34 #3017, 0.34 #2664, 0.33 #3096), 02rdyk7 (0.32 #2153, 0.31 #2585, 0.31 #3016), 02pqp12 (0.32 #2153, 0.31 #2585, 0.31 #3016), 0gr51 (0.32 #2153, 0.31 #2585, 0.31 #3016), 04dn09n (0.32 #2153, 0.31 #2585, 0.31 #3016), 019f4v (0.32 #2153, 0.31 #2585, 0.31 #3016), 040njc (0.32 #2153, 0.31 #2585, 0.31 #3016), 02qyp19 (0.32 #2153, 0.31 #2585, 0.31 #3016) >> Best rule #1592 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 09qc1; 020x5r; 0gdqy; >> query: (?x7068, 09d28z) <- profession(?x7068, ?x319), award(?x7068, ?x1587), ?x1587 = 02rdyk7, ?x319 = 01d_h8 >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1964 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 07mb57; *> query: (?x7068, 027b9ly) <- profession(?x7068, ?x319), award(?x7068, ?x1587), ?x1587 = 02rdyk7 *> conf = 0.23 ranks of expected_values: 14, 22 EVAL 01ts_3 award_winner! 0789r6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 93.000 74.000 0.386 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01ts_3 award_winner! 027b9ly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 93.000 74.000 0.386 http://example.org/award/award_category/winners./award/award_honor/award_winner #3104-0c9l1 PRED entity: 0c9l1 PRED relation: artists! PRED expected values: 05jt_ => 99 concepts (56 used for prediction) PRED predicted values (max 10 best out of 260): 016clz (0.73 #314, 0.59 #930, 0.50 #5), 064t9 (0.59 #13589, 0.57 #1867, 0.54 #5261), 05jt_ (0.50 #125, 0.18 #434, 0.08 #1359), 06j6l (0.37 #11770, 0.29 #5295, 0.29 #665), 05jg58 (0.36 #430, 0.17 #121, 0.14 #1046), 0xv2x (0.36 #462, 0.17 #153, 0.07 #5865), 02yv6b (0.35 #4419, 0.27 #2877, 0.22 #4110), 05bt6j (0.34 #4054, 0.34 #1896, 0.33 #5290), 0ggx5q (0.33 #695, 0.21 #3164, 0.20 #3472), 0jrv_ (0.33 #179, 0.14 #1413, 0.07 #5865) >> Best rule #314 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 048tgl; >> query: (?x10565, 016clz) <- artists(?x3753, ?x10565), artists(?x1572, ?x10565), artists(?x1000, ?x10565), ?x1572 = 06by7, ?x1000 = 0xhtw, ?x3753 = 01_bkd >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #125 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 01j59b0; 01shhf; 016lj_; *> query: (?x10565, 05jt_) <- artists(?x9248, ?x10565), group(?x227, ?x10565), origin(?x10565, ?x12875), ?x9248 = 02t8gf *> conf = 0.50 ranks of expected_values: 3 EVAL 0c9l1 artists! 05jt_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 56.000 0.727 http://example.org/music/genre/artists #3103-013t85 PRED entity: 013t85 PRED relation: contains! PRED expected values: 07ssc 02jx1 => 66 concepts (11 used for prediction) PRED predicted values (max 10 best out of 309): 02jx1 (0.86 #2781, 0.81 #3592, 0.81 #1793), 07ssc (0.81 #3592, 0.81 #1791, 0.81 #927), 094vy (0.81 #3592, 0.81 #1793, 0.73 #6293), 059rby (0.61 #1815, 0.24 #5413, 0.14 #6315), 09c7w0 (0.37 #9000, 0.37 #6298, 0.37 #7198), 0345h (0.20 #4575, 0.07 #1877, 0.04 #6377), 01n7q (0.18 #6373, 0.18 #7273, 0.18 #8174), 04jpl (0.17 #3616, 0.14 #2716, 0.10 #917), 02_286 (0.16 #1838, 0.09 #4536, 0.05 #5436), 036wy (0.15 #1660, 0.04 #2560, 0.03 #3459) >> Best rule #2781 for best value: >> intensional similarity = 7 >> extensional distance = 254 >> proper extension: 0yl27; 01l63; 0kd69; 0dwfw; 0cmb3; 01l5rz; >> query: (?x14320, 02jx1) <- contains(?x12381, ?x14320), administrative_parent(?x8755, ?x12381), contains(?x1310, ?x12381), contains(?x512, ?x12381), contains(?x9985, ?x8755), ?x512 = 07ssc, nationality(?x57, ?x1310) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 013t85 contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 11.000 0.859 http://example.org/location/location/contains EVAL 013t85 contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 11.000 0.859 http://example.org/location/location/contains #3102-01_mdl PRED entity: 01_mdl PRED relation: honored_for! PRED expected values: 0275n3y => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 118): 0275n3y (0.40 #64, 0.20 #186, 0.07 #2016), 05zksls (0.22 #272, 0.12 #394, 0.10 #1004), 05qb8vx (0.18 #414, 0.14 #658, 0.11 #292), 09gkdln (0.14 #960, 0.13 #1082, 0.12 #472), 09k5jh7 (0.13 #1047, 0.12 #437, 0.11 #315), 04n2r9h (0.13 #768, 0.09 #1378, 0.06 #1500), 09g90vz (0.11 #352, 0.10 #718, 0.07 #1084), 02yvhx (0.11 #309, 0.04 #797, 0.04 #2993), 0clfdj (0.11 #246, 0.04 #734, 0.03 #856), 09p3h7 (0.11 #304, 0.04 #9887, 0.03 #914) >> Best rule #64 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 024mxd; >> query: (?x1072, 0275n3y) <- nominated_for(?x1072, ?x12214), film(?x773, ?x1072), ?x12214 = 042g97, country(?x1072, ?x94) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_mdl honored_for! 0275n3y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #3101-09x3r PRED entity: 09x3r PRED relation: participating_countries PRED expected values: 06f32 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 170): 06mkj (0.50 #168, 0.18 #5744, 0.17 #6306), 059j2 (0.50 #150, 0.16 #5726, 0.15 #6288), 0d04z6 (0.50 #205, 0.14 #3257, 0.13 #3538), 0d05w3 (0.25 #172, 0.25 #35, 0.17 #309), 06bnz (0.25 #159, 0.25 #22, 0.17 #296), 047lj (0.25 #143, 0.25 #6, 0.17 #280), 06qd3 (0.25 #16, 0.17 #290, 0.13 #5729), 04hqz (0.25 #230, 0.16 #5806, 0.15 #6368), 03h64 (0.25 #175, 0.13 #4862, 0.11 #5751), 07f1x (0.25 #233, 0.13 #4862, 0.09 #5852) >> Best rule #168 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 0kbws; >> query: (?x1608, 06mkj) <- olympics(?x1121, ?x1608), sports(?x1608, ?x471), ?x1121 = 0bynt, participating_countries(?x1608, ?x1353), participating_countries(?x1608, ?x1264), participating_countries(?x1608, ?x142), locations(?x1608, ?x1646), sports(?x358, ?x471), films(?x471, ?x6451), ?x1353 = 035qy, ?x1264 = 0345h, ?x142 = 0jgd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #174 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 0kbws; *> query: (?x1608, 06f32) <- olympics(?x1121, ?x1608), sports(?x1608, ?x471), ?x1121 = 0bynt, participating_countries(?x1608, ?x1353), participating_countries(?x1608, ?x1264), participating_countries(?x1608, ?x142), locations(?x1608, ?x1646), sports(?x358, ?x471), films(?x471, ?x6451), ?x1353 = 035qy, ?x1264 = 0345h, ?x142 = 0jgd *> conf = 0.25 ranks of expected_values: 12 EVAL 09x3r participating_countries 06f32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 61.000 61.000 0.500 http://example.org/olympics/olympic_games/participating_countries #3100-01p7yb PRED entity: 01p7yb PRED relation: award_winner! PRED expected values: 027b9k6 => 111 concepts (109 used for prediction) PRED predicted values (max 10 best out of 308): 094qd5 (0.37 #22957, 0.35 #16155, 0.34 #23808), 09sb52 (0.37 #22957, 0.35 #16155, 0.34 #23808), 09td7p (0.37 #22957, 0.35 #16155, 0.34 #23808), 02ppm4q (0.37 #22957, 0.35 #16155, 0.34 #23808), 0bfvw2 (0.37 #22957, 0.35 #16155, 0.34 #23808), 02x4x18 (0.37 #22957, 0.35 #16155, 0.34 #23808), 02z0dfh (0.37 #22957, 0.35 #16155, 0.34 #23808), 0gqyl (0.23 #954, 0.19 #103, 0.15 #1381), 027571b (0.21 #1547, 0.12 #1120, 0.06 #269), 027b9k6 (0.19 #1482, 0.07 #1055, 0.06 #204) >> Best rule #22957 for best value: >> intensional similarity = 2 >> extensional distance = 1454 >> proper extension: 02mslq; 0kzy0; 03qmj9; 01czx; 016fmf; 0137g1; 0134s5; 024dgj; 02lbrd; 0d193h; ... >> query: (?x396, ?x375) <- award_winner(?x7144, ?x396), award(?x396, ?x375) >> conf = 0.37 => this is the best rule for 7 predicted values *> Best rule #1482 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 03zqc1; 0gjvqm; 0f4vbz; 02d4ct; 043kzcr; 019f2f; 0lpjn; 01xcfy; 02jsgf; 0fbx6; ... *> query: (?x396, 027b9k6) <- award_nominee(?x157, ?x396), award(?x396, ?x618), ?x618 = 09qwmm *> conf = 0.19 ranks of expected_values: 10 EVAL 01p7yb award_winner! 027b9k6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 111.000 109.000 0.367 http://example.org/award/award_category/winners./award/award_honor/award_winner #3099-03r1pr PRED entity: 03r1pr PRED relation: type_of_union PRED expected values: 04ztj => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.79 #37, 0.78 #53, 0.78 #61), 01g63y (0.20 #441, 0.12 #118, 0.12 #218), 0jgjn (0.20 #441, 0.02 #32), 01bl8s (0.20 #441) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 282 >> proper extension: 0k4gf; 028rk; 0zm1; 080r3; 04jwp; 0h0p_; 06c44; 036jp8; 06lbp; 0k1bs; ... >> query: (?x2871, 04ztj) <- gender(?x2871, ?x231), student(?x5486, ?x2871), people(?x13744, ?x2871) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03r1pr type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.789 http://example.org/people/person/spouse_s./people/marriage/type_of_union #3098-02sjgpq PRED entity: 02sjgpq PRED relation: institution! PRED expected values: 02h4rq6 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 18): 02h4rq6 (0.75 #154, 0.74 #97, 0.72 #212), 0bkj86 (0.68 #25, 0.66 #101, 0.57 #254), 027f2w (0.50 #26, 0.47 #102, 0.39 #179), 07s6fsf (0.49 #96, 0.46 #20, 0.43 #173), 013zdg (0.43 #24, 0.38 #100, 0.32 #43), 0bjrnt (0.29 #23, 0.29 #4, 0.28 #1337), 03mkk4 (0.29 #28, 0.26 #104, 0.24 #181), 01rr_d (0.28 #1337, 0.26 #51, 0.25 #32), 022h5x (0.28 #1337, 0.25 #35, 0.19 #111), 071tyz (0.28 #1337, 0.12 #46, 0.10 #802) >> Best rule #154 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 03gn1x; >> query: (?x7278, 02h4rq6) <- major_field_of_study(?x7278, ?x4100), contains(?x94, ?x7278), ?x4100 = 01lj9 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02sjgpq institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #3097-04rrd PRED entity: 04rrd PRED relation: location! PRED expected values: 02wr2r 05cv8 => 218 concepts (94 used for prediction) PRED predicted values (max 10 best out of 2067): 05gp3x (0.33 #1230, 0.25 #6234, 0.12 #11238), 0164nb (0.33 #733, 0.25 #5737, 0.07 #13243), 01pk3z (0.33 #1131, 0.25 #6135, 0.07 #13641), 02lt8 (0.33 #792, 0.25 #5796, 0.07 #43326), 012v1t (0.33 #1209, 0.25 #6213, 0.06 #18723), 07r4c (0.33 #1250, 0.25 #6254, 0.06 #178907), 0l12d (0.33 #287, 0.25 #5291, 0.05 #42821), 01vvzb1 (0.33 #1080, 0.25 #6084, 0.05 #43614), 02whj (0.33 #180, 0.25 #5184, 0.05 #45216), 03l26m (0.33 #2275, 0.25 #7279, 0.04 #139893) >> Best rule #1230 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 094jv; >> query: (?x1767, 05gp3x) <- contains(?x1767, ?x9212), ?x9212 = 0sxgh, featured_film_locations(?x2754, ?x1767) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #10920 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 6 *> proper extension: 0f8x_r; *> query: (?x1767, 02wr2r) <- adjoins(?x1767, ?x1426), ?x1426 = 07z1m *> conf = 0.12 ranks of expected_values: 75 EVAL 04rrd location! 05cv8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 218.000 94.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location EVAL 04rrd location! 02wr2r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 218.000 94.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #3096-05b3ts PRED entity: 05b3ts PRED relation: position_s! PRED expected values: 0fbq2n => 33 concepts (24 used for prediction) PRED predicted values (max 10 best out of 449): 0fsb_6 (0.83 #627, 0.79 #496, 0.79 #973), 05tg3 (0.83 #627, 0.79 #496, 0.78 #747), 01xvb (0.83 #627, 0.79 #496, 0.78 #747), 05l71 (0.83 #627, 0.79 #496, 0.78 #747), 0fbtm7 (0.83 #627, 0.72 #624, 0.71 #65), 0bs09lb (0.83 #627, 0.72 #624, 0.71 #65), 057xlyq (0.83 #627, 0.72 #624, 0.71 #65), 02wvfxl (0.83 #627, 0.72 #624, 0.70 #628), 01jv_6 (0.83 #627, 0.71 #65, 0.70 #628), 06x76 (0.79 #496, 0.78 #747, 0.77 #309) >> Best rule #627 for best value: >> intensional similarity = 31 >> extensional distance = 3 >> proper extension: 02g_7z; >> query: (?x2573, ?x934) <- position_s(?x8902, ?x2573), position_s(?x7892, ?x2573), position_s(?x4723, ?x2573), position_s(?x4469, ?x2573), position_s(?x4222, ?x2573), position_s(?x2574, ?x2573), position_s(?x2198, ?x2573), position_s(?x1576, ?x2573), team(?x2573, ?x6292), ?x4222 = 051q5, ?x2198 = 05g3v, position(?x1717, ?x2573), position(?x1517, ?x2573), ?x4469 = 043vc, ?x6292 = 02pd1q9, ?x8902 = 01c_d, ?x2574 = 01y3v, team(?x1517, ?x6294), team(?x1517, ?x4494), team(?x1517, ?x934), position(?x4723, ?x2312), position(?x4723, ?x935), ?x2312 = 02qpbqj, ?x7892 = 07kbp5, ?x4494 = 057xkj_, position(?x4193, ?x1517), ?x935 = 06b1q, ?x6294 = 02663p2, team(?x1177, ?x934), position(?x3346, ?x1717), ?x1576 = 05tfm >> conf = 0.83 => this is the best rule for 9 predicted values *> Best rule #814 for first EXPECTED value: *> intensional similarity = 26 *> extensional distance = 6 *> proper extension: 08ns5s; *> query: (?x2573, 0fbq2n) <- position_s(?x7312, ?x2573), position_s(?x4546, ?x2573), position_s(?x4222, ?x2573), position_s(?x1639, ?x2573), position_s(?x729, ?x2573), position_s(?x684, ?x2573), position_s(?x387, ?x2573), team(?x2573, ?x7539), ?x729 = 05g3b, ?x684 = 01ct6, ?x7539 = 02px_23, position(?x4222, ?x1792), draft(?x4222, ?x685), team(?x3113, ?x7312), ?x1792 = 05zm34, school(?x4222, ?x546), draft(?x4546, ?x1883), colors(?x7312, ?x332), sport(?x1639, ?x1083), team(?x11323, ?x1639), position(?x2573, ?x935), school(?x387, ?x388), colors(?x4546, ?x663), ?x685 = 0g3zpp, ?x388 = 05krk, ?x11323 = 059yj *> conf = 0.50 ranks of expected_values: 43 EVAL 05b3ts position_s! 0fbq2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 33.000 24.000 0.831 http://example.org/sports/sports_team/roster./american_football/football_historical_roster_position/position_s #3095-0ddbjy4 PRED entity: 0ddbjy4 PRED relation: film! PRED expected values: 06mnbn => 96 concepts (61 used for prediction) PRED predicted values (max 10 best out of 1096): 0151ns (0.14 #94, 0.10 #2178, 0.04 #8430), 0h96g (0.14 #853, 0.10 #2937, 0.03 #17525), 012c6x (0.13 #2200, 0.07 #116, 0.04 #16788), 0jrny (0.13 #2630, 0.07 #546, 0.03 #17218), 0175wg (0.11 #1022, 0.03 #3106, 0.03 #19778), 039bp (0.10 #2265, 0.07 #181, 0.03 #16853), 02mxw0 (0.10 #2547, 0.07 #463, 0.03 #19219), 04mlmx (0.10 #3525, 0.04 #1441, 0.03 #18113), 03kpvp (0.08 #17305, 0.04 #6885, 0.04 #633), 0p8r1 (0.08 #4754, 0.05 #23510, 0.04 #13090) >> Best rule #94 for best value: >> intensional similarity = 7 >> extensional distance = 26 >> proper extension: 01kf3_9; 042fgh; >> query: (?x9652, 0151ns) <- genre(?x9652, ?x1013), country(?x9652, ?x512), film(?x2727, ?x9652), ?x512 = 07ssc, ?x1013 = 06n90, production_companies(?x9652, ?x6413), film(?x1914, ?x9652) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #40290 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 258 *> proper extension: 0dj0m5; 085bd1; 016kv6; 017kct; 02_kd; 02rq8k8; 06nr2h; 03prz_; 016ywb; 03p2xc; ... *> query: (?x9652, 06mnbn) <- genre(?x9652, ?x1013), country(?x9652, ?x512), film(?x2727, ?x9652), ?x512 = 07ssc, genre(?x4084, ?x1013), genre(?x419, ?x1013), ?x419 = 020qr4, ?x4084 = 01rf57 *> conf = 0.01 ranks of expected_values: 863 EVAL 0ddbjy4 film! 06mnbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 96.000 61.000 0.143 http://example.org/film/actor/film./film/performance/film #3094-0gl5_ PRED entity: 0gl5_ PRED relation: organization! PRED expected values: 060c4 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 9): 060c4 (0.79 #158, 0.76 #301, 0.76 #236), 0dq_5 (0.40 #126, 0.37 #217, 0.32 #282), 05k17c (0.27 #72, 0.23 #20, 0.20 #7), 07xl34 (0.24 #414, 0.22 #388, 0.21 #740), 0hm4q (0.13 #34, 0.07 #255, 0.06 #47), 05c0jwl (0.04 #421, 0.04 #226, 0.04 #838), 08jcfy (0.02 #389, 0.02 #624, 0.02 #546), 04n1q6 (0.02 #227, 0.02 #253, 0.01 #592), 0dq3c (0.02 #27, 0.01 #79) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 016sd3; 03wv2g; >> query: (?x6912, 060c4) <- colors(?x6912, ?x663), currency(?x6912, ?x170), school(?x2820, ?x6912) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gl5_ organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.792 http://example.org/organization/role/leaders./organization/leadership/organization #3093-01qxc7 PRED entity: 01qxc7 PRED relation: award PRED expected values: 0gqxm => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 171): 0gqxm (0.25 #938, 0.25 #15234, 0.24 #10311), 02r0csl (0.25 #938, 0.25 #15234, 0.24 #10311), 02hsq3m (0.25 #938, 0.25 #15234, 0.24 #10311), 0gq_v (0.23 #722, 0.07 #6343, 0.07 #5875), 0gs96 (0.20 #793, 0.08 #6414, 0.07 #6559), 0p9sw (0.18 #723, 0.08 #6344, 0.07 #2598), 019f4v (0.18 #757, 0.08 #6378, 0.07 #2632), 027b9ly (0.17 #160, 0.09 #863, 0.03 #3441), 0gr51 (0.17 #79, 0.07 #6559, 0.07 #6403), 02x73k6 (0.17 #49, 0.05 #2627, 0.05 #13826) >> Best rule #938 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 02p76f9; >> query: (?x4489, ?x640) <- film(?x2383, ?x4489), nominated_for(?x640, ?x4489), nominated_for(?x143, ?x4489), film(?x4314, ?x4489), ?x143 = 02r0csl >> conf = 0.25 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 01qxc7 award 0gqxm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.255 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #3092-03tc8d PRED entity: 03tc8d PRED relation: sport PRED expected values: 02vx4 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 9): 02vx4 (0.88 #264, 0.88 #137, 0.87 #707), 0z74 (0.27 #944, 0.26 #934), 018jz (0.13 #145, 0.12 #733, 0.11 #624), 0jm_ (0.13 #145, 0.12 #733, 0.11 #624), 03tmr (0.13 #145, 0.12 #733, 0.11 #624), 018w8 (0.13 #145, 0.12 #733, 0.11 #624), 039yzs (0.13 #145, 0.12 #733, 0.11 #624), 09xp_ (0.13 #145, 0.12 #733, 0.11 #624), 06f3l (0.13 #145, 0.12 #733, 0.11 #624) >> Best rule #264 for best value: >> intensional similarity = 17 >> extensional distance = 73 >> proper extension: 075q_; 03c0vy; 03_qj1; 01s0t3; 019lvv; 019m9h; 01rlzn; 0175tv; 019ltg; 0272vm; ... >> query: (?x11560, 02vx4) <- colors(?x11560, ?x3189), position(?x11560, ?x530), position(?x11560, ?x203), position(?x11560, ?x63), position(?x11560, ?x60), ?x63 = 02sdk9v, ?x60 = 02nzb8, ?x203 = 0dgrmp, ?x530 = 02_j1w, colors(?x7122, ?x3189), colors(?x5756, ?x3189), colors(?x978, ?x3189), colors(?x9988, ?x3189), ?x9988 = 0pz6q, ?x7122 = 01zhs3, ?x978 = 03y_f8, school(?x5756, ?x466) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03tc8d sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.880 http://example.org/sports/sports_team/sport #3091-0rnmy PRED entity: 0rnmy PRED relation: place_of_birth! PRED expected values: 03ywyk => 143 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1957): 013w7j (0.42 #135907, 0.27 #75797, 0.27 #135906), 02m501 (0.42 #135907, 0.27 #75797, 0.27 #135906), 01w58n3 (0.42 #135907, 0.27 #75797, 0.27 #135906), 03ywyk (0.27 #75797, 0.27 #135906, 0.27 #122842), 01vh18t (0.09 #15681, 0.06 #120228, 0.06 #120229), 0q59y (0.09 #15681, 0.06 #120228, 0.06 #120229), 016h9b (0.09 #15681, 0.06 #120228, 0.06 #120229), 01nd6v (0.06 #2608, 0.04 #5221, 0.04 #7834), 04zn7g (0.06 #2567, 0.04 #5180, 0.04 #7793), 01fxfk (0.06 #2510, 0.04 #5123, 0.04 #7736) >> Best rule #135907 for best value: >> intensional similarity = 5 >> extensional distance = 159 >> proper extension: 02lf_x; >> query: (?x2866, ?x9418) <- contains(?x94, ?x2866), location(?x9418, ?x2866), location(?x6151, ?x2866), company(?x6151, ?x3887), place_of_birth(?x9418, ?x10708) >> conf = 0.42 => this is the best rule for 3 predicted values *> Best rule #75797 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 89 *> proper extension: 0144wg; *> query: (?x2866, ?x9232) <- contains(?x94, ?x2866), location(?x9232, ?x2866), location(?x6151, ?x2866), company(?x6151, ?x3887), location_of_ceremony(?x566, ?x2866) *> conf = 0.27 ranks of expected_values: 4 EVAL 0rnmy place_of_birth! 03ywyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 143.000 56.000 0.417 http://example.org/people/person/place_of_birth #3090-01pxcf PRED entity: 01pxcf PRED relation: educational_institution PRED expected values: 01pxcf => 67 concepts (57 used for prediction) PRED predicted values (max 10 best out of 44): 01bcwk (0.07 #146, 0.02 #1224), 01_qgp (0.07 #255), 0jhjl (0.02 #877, 0.01 #1956, 0.01 #3034), 01dyk8 (0.02 #868, 0.01 #1947, 0.01 #3025), 01hjy5 (0.02 #828, 0.01 #1907, 0.01 #2985), 01qd_r (0.02 #799, 0.01 #1878, 0.01 #2956), 0bwfn (0.02 #793, 0.01 #1872, 0.01 #2950), 01lhdt (0.02 #778, 0.01 #1857, 0.01 #2935), 017v3q (0.02 #766, 0.01 #1845, 0.01 #2923), 0hsb3 (0.02 #733, 0.01 #1812, 0.01 #2890) >> Best rule #146 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 02ckm7; 0g34_; 0mgp; 01sgmd; 01b8jj; 0g4g7; 0gxbl; 01kq5; 01qrcx; 01l53f; ... >> query: (?x12051, 01bcwk) <- contains(?x12300, ?x12051), adjoins(?x8506, ?x12300), ?x8506 = 05fly >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pxcf educational_institution 01pxcf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 57.000 0.067 http://example.org/education/educational_institution_campus/educational_institution #3089-016376 PRED entity: 016376 PRED relation: artist! PRED expected values: 041p3y => 99 concepts (69 used for prediction) PRED predicted values (max 10 best out of 124): 015_1q (0.48 #4011, 0.40 #417, 0.33 #550), 02p11jq (0.33 #13, 0.25 #146, 0.17 #545), 086k8 (0.25 #267, 0.20 #400, 0.17 #533), 01t04r (0.25 #324, 0.20 #457, 0.17 #590), 01dtcb (0.25 #3236, 0.17 #573, 0.14 #972), 01clyr (0.23 #3226, 0.14 #696, 0.14 #1228), 03mp8k (0.20 #459, 0.20 #991, 0.19 #1124), 043g7l (0.20 #428, 0.17 #561, 0.16 #960), 04fcjt (0.20 #426, 0.17 #559, 0.08 #958), 0n85g (0.18 #987, 0.17 #1120, 0.16 #1519) >> Best rule #4011 for best value: >> intensional similarity = 6 >> extensional distance = 259 >> proper extension: 01wmxfs; >> query: (?x10712, 015_1q) <- artist(?x2931, ?x10712), award(?x10712, ?x567), artist(?x2931, ?x6124), artist(?x2931, ?x379), ?x379 = 089tm, category(?x6124, ?x134) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #7992 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 681 *> proper extension: 01sfmyk; *> query: (?x10712, ?x3240) <- artist(?x8392, ?x10712), artist(?x8392, ?x8393), artist(?x8392, ?x7259), category(?x10712, ?x134), artist(?x3240, ?x8393), nationality(?x7259, ?x94) *> conf = 0.04 ranks of expected_values: 54 EVAL 016376 artist! 041p3y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 99.000 69.000 0.479 http://example.org/music/record_label/artist #3088-02bgmr PRED entity: 02bgmr PRED relation: instrumentalists! PRED expected values: 01v1d8 => 94 concepts (90 used for prediction) PRED predicted values (max 10 best out of 127): 05r5c (0.56 #793, 0.50 #968, 0.48 #1056), 05148p4 (0.45 #805, 0.44 #542, 0.43 #2307), 02sgy (0.34 #2463, 0.30 #2285, 0.29 #1137), 02hnl (0.33 #34, 0.24 #1082, 0.23 #819), 03qjg (0.23 #573, 0.19 #836, 0.18 #1099), 04rzd (0.22 #37, 0.12 #1085, 0.10 #647), 06w7v (0.21 #333, 0.11 #72, 0.08 #594), 0l14md (0.20 #355, 0.18 #792, 0.17 #268), 026t6 (0.15 #788, 0.14 #1051, 0.13 #2200), 0l14qv (0.13 #965, 0.13 #790, 0.11 #2202) >> Best rule #793 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 07_3qd; >> query: (?x5768, 05r5c) <- role(?x5768, ?x227), artists(?x671, ?x5768), instrumentalists(?x716, ?x5768), ?x671 = 064t9 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1581 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 241 *> proper extension: 094xh; 014g91; *> query: (?x5768, ?x75) <- role(?x5768, ?x314), artists(?x302, ?x5768), location(?x5768, ?x2911), role(?x314, ?x75) *> conf = 0.04 ranks of expected_values: 48 EVAL 02bgmr instrumentalists! 01v1d8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 94.000 90.000 0.557 http://example.org/music/instrument/instrumentalists #3087-05397h PRED entity: 05397h PRED relation: country_of_origin PRED expected values: 07ssc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 60): 09c7w0 (0.81 #236, 0.80 #258, 0.79 #533), 07ssc (0.80 #109, 0.75 #64, 0.71 #53), 03rjj (0.67 #521, 0.67 #508, 0.57 #337), 02jx1 (0.49 #314, 0.47 #664, 0.46 #651), 04jpl (0.49 #314, 0.47 #664, 0.46 #651), 0d060g (0.14 #421, 0.14 #819, 0.13 #773), 03rt9 (0.14 #421, 0.14 #819, 0.13 #773), 03_3d (0.14 #819, 0.13 #773, 0.13 #561), 0d0vqn (0.14 #819, 0.13 #773, 0.12 #760), 0chghy (0.14 #819, 0.12 #760) >> Best rule #236 for best value: >> intensional similarity = 15 >> extensional distance = 57 >> proper extension: 0cwrr; 0d68qy; 03y3bp7; >> query: (?x13282, 09c7w0) <- genre(?x13282, ?x9446), genre(?x13282, ?x8805), genre(?x7317, ?x9446), genre(?x6482, ?x9446), ?x7317 = 05p9_ql, genre(?x10731, ?x8805), genre(?x8686, ?x8805), genre(?x2829, ?x8805), genre(?x69, ?x9446), ?x10731 = 0cs134, program(?x2062, ?x2829), category(?x13282, ?x134), nominated_for(?x192, ?x6482), languages(?x8686, ?x254), tv_program(?x1056, ?x2829) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #109 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 8 *> proper extension: 02pvqmz; *> query: (?x13282, 07ssc) <- program(?x10344, ?x13282), program(?x2776, ?x13282), languages(?x13282, ?x254), ?x254 = 02h40lc, ?x10344 = 01f2w0, genre(?x13282, ?x53), program(?x2776, ?x11818), program(?x2776, ?x8686), program(?x2776, ?x7424), ?x8686 = 02qfh, program(?x3405, ?x11818), titles(?x162, ?x7424), genre(?x11818, ?x811) *> conf = 0.80 ranks of expected_values: 2 EVAL 05397h country_of_origin 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.814 http://example.org/tv/tv_program/country_of_origin #3086-02dj3 PRED entity: 02dj3 PRED relation: citytown PRED expected values: 0cdw6 => 119 concepts (74 used for prediction) PRED predicted values (max 10 best out of 138): 036k0s (0.25 #2212, 0.25 #2211, 0.22 #6641), 0d060g (0.25 #2212, 0.25 #2211, 0.22 #6641), 059t8 (0.25 #2212, 0.25 #2211, 0.22 #6641), 0h7h6 (0.22 #767, 0.07 #2615, 0.06 #2244), 05l5n (0.14 #4466, 0.09 #4098, 0.04 #9635), 02_286 (0.11 #15895, 0.11 #22165, 0.08 #18480), 05ksh (0.11 #757, 0.04 #2234, 0.04 #2605), 0978r (0.11 #4136, 0.11 #4504, 0.11 #1917), 04jpl (0.09 #1849, 0.08 #4068, 0.08 #4436), 052p7 (0.08 #47, 0.06 #415, 0.06 #783) >> Best rule #2212 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 01v3k2; >> query: (?x5085, ?x9370) <- contains(?x9370, ?x5085), major_field_of_study(?x5085, ?x947), colors(?x5085, ?x332), currency(?x5085, ?x2244) >> conf = 0.25 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 02dj3 citytown 0cdw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 74.000 0.252 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #3085-01_qgp PRED entity: 01_qgp PRED relation: list PRED expected values: 09g7thr => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 4): 01ptsx (0.59 #96, 0.55 #138, 0.55 #110), 04k4rt (0.50 #18, 0.47 #25, 0.42 #109), 09g7thr (0.44 #141, 0.44 #148, 0.41 #64), 01pd60 (0.34 #97, 0.34 #111, 0.34 #139) >> Best rule #96 for best value: >> intensional similarity = 5 >> extensional distance = 59 >> proper extension: 0vlf; >> query: (?x7546, 01ptsx) <- state_province_region(?x7546, ?x9494), contact_category(?x7546, ?x897), service_location(?x7546, ?x390), ?x897 = 03w5xm, nationality(?x72, ?x390) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #141 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 75 *> proper extension: 01jssp; 01pq4w; 0kw4j; 017cy9; 02bqy; 01bk1y; 01qd_r; 02f4s3; *> query: (?x7546, 09g7thr) <- major_field_of_study(?x7546, ?x1668), currency(?x7546, ?x7888), ?x1668 = 01mkq, student(?x7546, ?x3281), organization(?x4682, ?x7546) *> conf = 0.44 ranks of expected_values: 3 EVAL 01_qgp list 09g7thr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 154.000 154.000 0.590 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #3084-0k049 PRED entity: 0k049 PRED relation: citytown! PRED expected values: 09xwz => 133 concepts (72 used for prediction) PRED predicted values (max 10 best out of 652): 078bz (0.25 #107, 0.07 #26575, 0.02 #21042), 0225bv (0.25 #669, 0.03 #11944, 0.03 #14359), 01dtcb (0.18 #1189, 0.17 #2799, 0.16 #5216), 01nds (0.18 #1379, 0.17 #2184, 0.16 #5406), 06182p (0.12 #1199, 0.11 #3615, 0.11 #2809), 0146mv (0.12 #1388, 0.11 #3804, 0.11 #2998), 0338lq (0.12 #830, 0.11 #2440, 0.11 #1635), 024rdh (0.12 #1071, 0.11 #2681, 0.11 #5098), 04f525m (0.12 #837, 0.11 #2447, 0.11 #4864), 02x2097 (0.12 #1345, 0.09 #6983, 0.06 #2955) >> Best rule #107 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 071cn; >> query: (?x191, 078bz) <- locations(?x1553, ?x191), location(?x6187, ?x191), ?x6187 = 07r1h >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k049 citytown! 09xwz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 72.000 0.250 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #3083-088vmr PRED entity: 088vmr PRED relation: artists PRED expected values: 07r4c => 31 concepts (16 used for prediction) PRED predicted values (max 10 best out of 964): 0pkyh (0.43 #1334, 0.33 #242, 0.29 #2427), 0134tg (0.43 #1581, 0.29 #2674, 0.18 #3766), 0m2l9 (0.43 #1118, 0.29 #2211, 0.18 #3303), 0lccn (0.43 #1269, 0.24 #3454, 0.21 #2362), 01kd57 (0.43 #1599, 0.24 #3784, 0.21 #2692), 0fpj4lx (0.43 #1419, 0.23 #4701, 0.21 #2512), 01kcms4 (0.43 #1747, 0.22 #6123, 0.21 #2840), 01wvxw1 (0.43 #1843, 0.21 #2936, 0.18 #4028), 0b_j2 (0.43 #1691, 0.21 #2784, 0.18 #3876), 01vwyqp (0.43 #1369, 0.21 #2462, 0.18 #3554) >> Best rule #1334 for best value: >> intensional similarity = 13 >> extensional distance = 5 >> proper extension: 06by7; 016jny; 0155w; 01gjw; >> query: (?x14058, 0pkyh) <- parent_genre(?x14058, ?x5934), parent_genre(?x14058, ?x3108), ?x3108 = 02w4v, artists(?x5934, ?x10209), artists(?x5934, ?x4936), artists(?x5934, ?x2946), artists(?x5934, ?x2395), role(?x2946, ?x212), award_winner(?x2054, ?x10209), award_nominee(?x2807, ?x10209), student(?x7545, ?x10209), award_winner(?x724, ?x2395), profession(?x4936, ?x220) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #8221 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 63 *> proper extension: 05hs4r; 01gbcf; 016clz; 0m0jc; 015pdg; 064t9; 016jhr; 0xhtw; 0dl5d; 061fhg; ... *> query: (?x14058, 07r4c) <- parent_genre(?x14058, ?x3108), artists(?x3108, ?x6635), artists(?x3108, ?x5405), artists(?x3108, ?x4062), artists(?x3108, ?x3632), artists(?x3108, ?x2807), ?x4062 = 0bqsy, award_nominee(?x1206, ?x5405), award_winner(?x3290, ?x5405), ?x6635 = 015cxv, ?x3632 = 01309x, ?x2807 = 03h_fk5 *> conf = 0.06 ranks of expected_values: 707 EVAL 088vmr artists 07r4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 16.000 0.429 http://example.org/music/genre/artists #3082-09p2r9 PRED entity: 09p2r9 PRED relation: award_winner PRED expected values: 01dw4q 01kx_81 028knk 04y8r 02zfdp => 48 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2365): 05183k (0.58 #21461, 0.50 #7663, 0.46 #27595), 05ldnp (0.44 #30660, 0.38 #35265, 0.35 #26061), 053ksp (0.44 #30660, 0.35 #26061, 0.33 #15332), 0h0wc (0.40 #12630, 0.40 #9558, 0.40 #8025), 04sry (0.40 #13345, 0.40 #10273, 0.37 #29127), 0bytkq (0.40 #17320, 0.40 #8119, 0.33 #7665), 01ycbq (0.40 #7943, 0.33 #278, 0.30 #17144), 081lh (0.40 #7794, 0.33 #129, 0.25 #4727), 01vsgrn (0.40 #11585, 0.06 #34587, 0.03 #36122), 08h79x (0.33 #7665, 0.32 #6129, 0.30 #24529) >> Best rule #21461 for best value: >> intensional similarity = 14 >> extensional distance = 16 >> proper extension: 027hjff; >> query: (?x6631, ?x1532) <- ceremony(?x746, ?x6631), honored_for(?x6631, ?x5648), award_winner(?x6631, ?x8070), type_of_union(?x8070, ?x566), nominated_for(?x9151, ?x5648), nominated_for(?x166, ?x5648), award_winner(?x384, ?x8070), nominated_for(?x2880, ?x5648), written_by(?x1298, ?x8070), written_by(?x5648, ?x1532), award_nominee(?x9151, ?x6232), award(?x5043, ?x2880), ?x166 = 0jz9f, ?x5043 = 015q43 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #7665 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 0g5b0q5; *> query: (?x6631, ?x166) <- ceremony(?x1107, ?x6631), honored_for(?x6631, ?x5648), award_winner(?x6631, ?x8070), award_winner(?x6631, ?x1292), type_of_union(?x8070, ?x566), nominated_for(?x9151, ?x5648), nominated_for(?x166, ?x5648), award_winner(?x384, ?x8070), nominated_for(?x68, ?x5648), written_by(?x1298, ?x8070), written_by(?x5648, ?x1532), ?x1107 = 019f4v, music(?x148, ?x1292), role(?x1292, ?x227), crewmember(?x303, ?x9151), award(?x1292, ?x4317) *> conf = 0.33 ranks of expected_values: 12, 64, 94, 97, 1478 EVAL 09p2r9 award_winner 02zfdp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 48.000 24.000 0.577 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09p2r9 award_winner 04y8r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 48.000 24.000 0.577 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09p2r9 award_winner 028knk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 48.000 24.000 0.577 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09p2r9 award_winner 01kx_81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 24.000 0.577 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09p2r9 award_winner 01dw4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 48.000 24.000 0.577 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #3081-01qz5 PRED entity: 01qz5 PRED relation: genre PRED expected values: 07s9rl0 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.78 #970, 0.76 #364, 0.71 #2670), 07ssc (0.57 #1942, 0.53 #8018, 0.52 #8141), 0hcr (0.47 #1602, 0.08 #146, 0.08 #267), 02l7c8 (0.43 #501, 0.33 #1229, 0.33 #744), 01hmnh (0.42 #140, 0.38 #261, 0.25 #1596), 03k9fj (0.36 #1589, 0.35 #617, 0.33 #12), 05p553 (0.35 #6563, 0.33 #4, 0.33 #9844), 06n90 (0.33 #135, 0.33 #14, 0.31 #256), 03bxz7 (0.33 #419, 0.12 #2725, 0.11 #904), 01jfsb (0.33 #1103, 0.31 #3896, 0.31 #2561) >> Best rule #970 for best value: >> intensional similarity = 3 >> extensional distance = 137 >> proper extension: 01c9d; >> query: (?x8188, 07s9rl0) <- film(?x1371, ?x8188), nominated_for(?x1198, ?x8188), ?x1198 = 02pqp12 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qz5 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.777 http://example.org/film/film/genre #3080-071t0 PRED entity: 071t0 PRED relation: sports! PRED expected values: 018ljb => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 20): 0blg2 (0.82 #195, 0.82 #194, 0.82 #750), 018ljb (0.82 #194, 0.82 #750, 0.82 #520), 0lv1x (0.71 #374, 0.71 #528, 0.67 #418), 09n48 (0.53 #431, 0.49 #387, 0.48 #323), 018ctl (0.53 #431, 0.49 #387, 0.48 #323), 0blfl (0.53 #431, 0.49 #387, 0.48 #323), 0sx8l (0.53 #431, 0.49 #387, 0.48 #323), 018wrk (0.53 #431, 0.49 #387, 0.48 #323), 0swbd (0.53 #431, 0.49 #387, 0.48 #323), 0sx7r (0.53 #431, 0.49 #387, 0.48 #323) >> Best rule #195 for best value: >> intensional similarity = 45 >> extensional distance = 3 >> proper extension: 06wrt; >> query: (?x3015, ?x7441) <- country(?x3015, ?x8620), country(?x3015, ?x4954), country(?x3015, ?x2267), country(?x3015, ?x1203), country(?x3015, ?x774), film_release_region(?x9839, ?x2267), film_release_region(?x7897, ?x2267), film_release_region(?x7493, ?x2267), film_release_region(?x6886, ?x2267), film_release_region(?x5576, ?x2267), film_release_region(?x5347, ?x2267), film_release_region(?x3498, ?x2267), film_release_region(?x2656, ?x2267), film_release_region(?x2471, ?x2267), film_release_region(?x2394, ?x2267), film_release_region(?x2155, ?x2267), film_release_region(?x1785, ?x2267), film_release_region(?x1080, ?x2267), film_release_region(?x1035, ?x2267), ?x1203 = 07ylj, sports(?x7441, ?x3015), sports(?x391, ?x3015), ?x1785 = 0gj9tn5, participating_countries(?x418, ?x2267), ?x774 = 06mzp, country(?x1121, ?x2267), ?x1121 = 0bynt, ?x9839 = 0gy7bj4, ?x3498 = 02fqrf, ?x5347 = 02ylg6, ?x7897 = 03np63f, ?x2471 = 08052t3, ?x1080 = 01c22t, ?x2656 = 03qnc6q, ?x391 = 0l6vl, ?x4954 = 0345_, currency(?x8620, ?x170), ?x1035 = 08hmch, ?x7493 = 0btpm6, sports(?x7441, ?x359), ?x2155 = 0407yfx, ?x359 = 02bkg, ?x2394 = 0661ql3, ?x5576 = 0gbfn9, ?x6886 = 0gwjw0c >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #194 for first EXPECTED value: *> intensional similarity = 46 *> extensional distance = 3 *> proper extension: 06wrt; *> query: (?x3015, ?x584) <- country(?x3015, ?x8620), country(?x3015, ?x4954), country(?x3015, ?x2267), country(?x3015, ?x1203), country(?x3015, ?x774), film_release_region(?x9839, ?x2267), film_release_region(?x7897, ?x2267), film_release_region(?x7493, ?x2267), film_release_region(?x6886, ?x2267), film_release_region(?x5576, ?x2267), film_release_region(?x5347, ?x2267), film_release_region(?x3498, ?x2267), film_release_region(?x2656, ?x2267), film_release_region(?x2471, ?x2267), film_release_region(?x2394, ?x2267), film_release_region(?x2155, ?x2267), film_release_region(?x1785, ?x2267), film_release_region(?x1080, ?x2267), film_release_region(?x1035, ?x2267), ?x1203 = 07ylj, sports(?x7441, ?x3015), sports(?x584, ?x3015), sports(?x391, ?x3015), ?x1785 = 0gj9tn5, participating_countries(?x418, ?x2267), ?x774 = 06mzp, country(?x1121, ?x2267), ?x1121 = 0bynt, ?x9839 = 0gy7bj4, ?x3498 = 02fqrf, ?x5347 = 02ylg6, ?x7897 = 03np63f, ?x2471 = 08052t3, ?x1080 = 01c22t, ?x2656 = 03qnc6q, ?x391 = 0l6vl, ?x4954 = 0345_, currency(?x8620, ?x170), ?x1035 = 08hmch, ?x7493 = 0btpm6, sports(?x7441, ?x359), ?x2155 = 0407yfx, ?x359 = 02bkg, ?x2394 = 0661ql3, ?x5576 = 0gbfn9, ?x6886 = 0gwjw0c *> conf = 0.82 ranks of expected_values: 2 EVAL 071t0 sports! 018ljb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 40.000 40.000 0.820 http://example.org/olympics/olympic_games/sports #3079-06r_by PRED entity: 06r_by PRED relation: student! PRED expected values: 02s62q => 98 concepts (78 used for prediction) PRED predicted values (max 10 best out of 73): 065y4w7 (0.33 #540, 0.31 #2118, 0.11 #4748), 0187nd (0.33 #365, 0.02 #5099, 0.01 #7729), 0bwfn (0.17 #801, 0.14 #5535, 0.13 #11847), 07w0v (0.17 #546, 0.08 #2124, 0.06 #2650), 0dy04 (0.17 #597, 0.02 #3753, 0.02 #4279), 09f2j (0.11 #1737, 0.09 #4893, 0.08 #7523), 02zd460 (0.11 #1222, 0.08 #2274, 0.06 #2800), 026gvfj (0.11 #1689, 0.01 #6423, 0.01 #6949), 01_r9k (0.11 #1957), 03ksy (0.10 #7470, 0.09 #4840, 0.09 #11678) >> Best rule #540 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0280mv7; >> query: (?x6062, 065y4w7) <- student(?x8008, ?x6062), award_nominee(?x815, ?x6062), cinematography(?x153, ?x6062) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06r_by student! 02s62q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 78.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #3078-036px PRED entity: 036px PRED relation: instrumentalists! PRED expected values: 03qjg => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 105): 018vs (0.35 #442, 0.33 #270, 0.25 #12), 05148p4 (0.31 #3120, 0.23 #2173, 0.23 #1138), 03qjg (0.25 #394, 0.25 #50, 0.22 #480), 02hnl (0.25 #34, 0.22 #292, 0.20 #120), 018j2 (0.25 #38, 0.20 #124, 0.13 #468), 026t6 (0.25 #3, 0.20 #89, 0.13 #433), 02sgy (0.25 #6, 0.20 #92, 0.11 #264), 04rzd (0.25 #37, 0.20 #123, 0.11 #295), 07c6l (0.20 #95, 0.17 #353, 0.11 #267), 07xzm (0.20 #107, 0.13 #451, 0.08 #365) >> Best rule #442 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 06y9c2; 0bkg4; 01mr2g6; 0c73z; >> query: (?x4191, 018vs) <- student(?x5615, ?x4191), profession(?x4191, ?x220), instrumentalists(?x227, ?x4191) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #394 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0qf3p; 016h4r; 021yw7; *> query: (?x4191, 03qjg) <- student(?x5615, ?x4191), award(?x4191, ?x2420), award(?x3632, ?x2420), ?x3632 = 01309x *> conf = 0.25 ranks of expected_values: 3 EVAL 036px instrumentalists! 03qjg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 119.000 0.348 http://example.org/music/instrument/instrumentalists #3077-095sx6 PRED entity: 095sx6 PRED relation: genre PRED expected values: 03fpg => 35 concepts (34 used for prediction) PRED predicted values (max 10 best out of 138): 0hcr (0.71 #436, 0.69 #352, 0.53 #520), 0pr6f (0.71 #467, 0.69 #383, 0.33 #216), 07s9rl0 (0.66 #1843, 0.66 #1928, 0.65 #2096), 01z4y (0.65 #854, 0.63 #602, 0.63 #770), 0dm00 (0.60 #155, 0.40 #72, 0.33 #238), 0jxy (0.47 #533, 0.15 #1457, 0.15 #1372), 0c4xc (0.46 #879, 0.43 #1046, 0.42 #795), 01hmnh (0.43 #433, 0.38 #349, 0.28 #1271), 06nbt (0.40 #104, 0.32 #417, 0.20 #21), 025s89p (0.38 #385, 0.36 #469, 0.13 #636) >> Best rule #436 for best value: >> intensional similarity = 15 >> extensional distance = 12 >> proper extension: 020qr4; >> query: (?x14278, 0hcr) <- genre(?x14278, ?x9669), genre(?x14278, ?x258), genre(?x13288, ?x9669), genre(?x9668, ?x9669), genre(?x9557, ?x9669), genre(?x6694, ?x9669), ?x258 = 05p553, ?x13288 = 05631, category(?x9557, ?x134), languages(?x9557, ?x254), program(?x2554, ?x9668), ?x254 = 02h40lc, ?x6694 = 0b005, titles(?x2008, ?x9668), program(?x6382, ?x9668) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1424 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 146 *> proper extension: 05sy0cv; 0gxsh4; 017dbx; *> query: (?x14278, ?x53) <- genre(?x14278, ?x258), genre(?x11806, ?x258), genre(?x7566, ?x258), genre(?x5060, ?x258), genre(?x3630, ?x258), genre(?x802, ?x258), ?x802 = 0cwrr, award(?x11806, ?x693), genre(?x11806, ?x53), actor(?x11806, ?x5888), ?x7566 = 05h95s, nominated_for(?x757, ?x3630), award_winner(?x5060, ?x822) *> conf = 0.10 ranks of expected_values: 38 EVAL 095sx6 genre 03fpg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 35.000 34.000 0.714 http://example.org/tv/tv_program/genre #3076-02r4qs PRED entity: 02r4qs PRED relation: award_winner! PRED expected values: 01by1l => 120 concepts (113 used for prediction) PRED predicted values (max 10 best out of 244): 0c4z8 (0.42 #4285, 0.40 #1715, 0.37 #34254), 0gqz2 (0.42 #4285, 0.40 #1715, 0.37 #34254), 02nhxf (0.42 #4285, 0.40 #1715, 0.37 #34254), 02x17c2 (0.42 #4285, 0.40 #1715, 0.37 #34254), 03tcnt (0.42 #4285, 0.40 #1715, 0.37 #34254), 0g9wd99 (0.25 #371), 0c_dx (0.25 #271), 0ddd9 (0.25 #55), 054ks3 (0.23 #1426, 0.21 #997, 0.20 #568), 01bgqh (0.23 #20551, 0.15 #33824, 0.15 #34684) >> Best rule #4285 for best value: >> intensional similarity = 3 >> extensional distance = 209 >> proper extension: 01vvydl; 07s3vqk; 0197tq; 0411q; 0lbj1; 01vrx3g; 01lmj3q; 0m2l9; 032nwy; 026ps1; ... >> query: (?x1504, ?x247) <- award_winner(?x2704, ?x1504), award(?x1504, ?x247), role(?x1504, ?x316) >> conf = 0.42 => this is the best rule for 5 predicted values *> Best rule #1398 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 84 *> proper extension: 03f2_rc; 01kwlwp; 02fgpf; 02cyfz; 010hn; 018pj3; 04xrx; 0259r0; 01vx5w7; 01k98nm; ... *> query: (?x1504, 01by1l) <- award_winner(?x2704, ?x1504), award(?x1504, ?x1232), ?x1232 = 0c4z8 *> conf = 0.22 ranks of expected_values: 24 EVAL 02r4qs award_winner! 01by1l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 120.000 113.000 0.420 http://example.org/award/award_category/winners./award/award_honor/award_winner #3075-04mky3 PRED entity: 04mky3 PRED relation: student! PRED expected values: 06thjt => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 70): 01w5m (0.17 #1159, 0.03 #4321, 0.03 #24348), 015wy_ (0.17 #1509, 0.02 #4671, 0.01 #8887), 01g0p5 (0.17 #1261), 02g839 (0.12 #1606, 0.06 #4768, 0.05 #3187), 04bfg (0.06 #1807, 0.05 #2334, 0.03 #3388), 0fr9jp (0.06 #1926, 0.05 #2453, 0.03 #3507), 01dq5z (0.06 #1677, 0.05 #2204, 0.03 #3258), 0gjv_ (0.06 #1787, 0.03 #3368, 0.02 #4422), 0gl6x (0.06 #1960, 0.03 #3541), 0bwfn (0.05 #2383, 0.04 #24518, 0.04 #22937) >> Best rule #1159 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 032t2z; >> query: (?x11947, 01w5m) <- instrumentalists(?x4769, ?x11947), ?x4769 = 0dwt5, artist(?x8518, ?x11947), artists(?x302, ?x11947) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04mky3 student! 06thjt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 76.000 76.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #3074-0134wr PRED entity: 0134wr PRED relation: award PRED expected values: 02qvyrt => 108 concepts (95 used for prediction) PRED predicted values (max 10 best out of 278): 09sb52 (0.38 #21698, 0.34 #23302, 0.23 #10067), 02f77l (0.37 #3059, 0.17 #1054, 0.16 #3460), 03tcnt (0.35 #966, 0.34 #2971, 0.25 #565), 0gqz2 (0.33 #79, 0.26 #15720, 0.20 #13235), 05q8pss (0.33 #210, 0.20 #13235, 0.17 #18048), 04njml (0.33 #99, 0.20 #13235, 0.17 #18048), 02qvyrt (0.33 #124, 0.20 #13235, 0.17 #18048), 01ckcd (0.33 #3541, 0.32 #3140, 0.30 #1135), 02f6yz (0.32 #3123, 0.26 #1118, 0.15 #7936), 054ks3 (0.30 #1342, 0.29 #15780, 0.28 #4149) >> Best rule #21698 for best value: >> intensional similarity = 3 >> extensional distance = 809 >> proper extension: 03h2d4; 0g2mbn; 025hzx; >> query: (?x8078, 09sb52) <- award_nominee(?x4866, ?x8078), award_winner(?x4866, ?x11469), spouse(?x4867, ?x4866) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #124 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 07hgkd; *> query: (?x8078, 02qvyrt) <- award_winner(?x5637, ?x8078), gender(?x8078, ?x514), ?x5637 = 016890, artists(?x671, ?x8078) *> conf = 0.33 ranks of expected_values: 7 EVAL 0134wr award 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 108.000 95.000 0.377 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3073-02d003 PRED entity: 02d003 PRED relation: production_companies PRED expected values: 06rq1k 025hwq => 83 concepts (71 used for prediction) PRED predicted values (max 10 best out of 58): 017s11 (0.33 #2578, 0.22 #252, 0.19 #335), 016tt2 (0.31 #336, 0.29 #419, 0.28 #502), 0g1rw (0.29 #91, 0.22 #257, 0.12 #340), 024rgt (0.22 #274, 0.20 #25, 0.14 #108), 054lpb6 (0.17 #679, 0.10 #930, 0.10 #1679), 086k8 (0.15 #750, 0.12 #1250, 0.11 #1417), 0c41qv (0.14 #139, 0.12 #388, 0.12 #471), 016tw3 (0.14 #95, 0.12 #178, 0.11 #760), 04rtpt (0.14 #132, 0.11 #298, 0.06 #381), 08wjc1 (0.14 #111, 0.11 #277, 0.06 #360) >> Best rule #2578 for best value: >> intensional similarity = 5 >> extensional distance = 669 >> proper extension: 0gtvrv3; 047svrl; 07kb7vh; 07k2mq; 0372j5; >> query: (?x7072, ?x541) <- currency(?x7072, ?x170), film(?x287, ?x7072), film_release_distribution_medium(?x7072, ?x81), film(?x541, ?x7072), film_crew_role(?x7072, ?x137) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #350 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 14 *> proper extension: 01fmys; 03h3x5; 074w86; 026hxwx; 0291hr; 0291ck; 0df2zx; *> query: (?x7072, 06rq1k) <- currency(?x7072, ?x170), genre(?x7072, ?x7323), genre(?x7072, ?x258), ?x7323 = 09q17, ?x258 = 05p553, film(?x287, ?x7072) *> conf = 0.06 ranks of expected_values: 18, 41 EVAL 02d003 production_companies 025hwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 83.000 71.000 0.325 http://example.org/film/film/production_companies EVAL 02d003 production_companies 06rq1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 83.000 71.000 0.325 http://example.org/film/film/production_companies #3072-027g6p7 PRED entity: 027g6p7 PRED relation: nutrient! PRED expected values: 0fjfh 033cnk => 17 concepts (13 used for prediction) PRED predicted values (max 10 best out of 25): 0fjfh (0.93 #204, 0.93 #180, 0.93 #144), 0fj52s (0.91 #192, 0.91 #175, 0.91 #161), 0fbdb (0.90 #344, 0.90 #331, 0.90 #309), 04zpv (0.88 #318, 0.88 #297, 0.87 #206), 0fbw6 (0.88 #363, 0.87 #195, 0.87 #179), 033cnk (0.86 #279, 0.86 #256, 0.85 #203), 01nkt (0.85 #414, 0.85 #404, 0.85 #162), 0cxn2 (0.82 #229, 0.80 #178, 0.80 #156), 0f25w9 (0.82 #229, 0.80 #171, 0.80 #140), 0dj75 (0.82 #229, 0.80 #196, 0.79 #332) >> Best rule #204 for best value: >> intensional similarity = 146 >> extensional distance = 44 >> proper extension: 01sh2; 06x4c; 06jry; 025sqz8; >> query: (?x12481, ?x5009) <- nutrient(?x6285, ?x12481), nutrient(?x6191, ?x12481), nutrient(?x3900, ?x12481), nutrient(?x2701, ?x12481), nutrient(?x6285, ?x13944), nutrient(?x6285, ?x13498), nutrient(?x6285, ?x12902), nutrient(?x6285, ?x12868), nutrient(?x6285, ?x12454), nutrient(?x6285, ?x11784), nutrient(?x6285, ?x11758), nutrient(?x6285, ?x11592), nutrient(?x6285, ?x11409), nutrient(?x6285, ?x11270), nutrient(?x6285, ?x10891), nutrient(?x6285, ?x10709), nutrient(?x6285, ?x10195), nutrient(?x6285, ?x10098), nutrient(?x6285, ?x9949), nutrient(?x6285, ?x9915), nutrient(?x6285, ?x9855), nutrient(?x6285, ?x9840), nutrient(?x6285, ?x9795), nutrient(?x6285, ?x9733), nutrient(?x6285, ?x9619), nutrient(?x6285, ?x9490), nutrient(?x6285, ?x9426), nutrient(?x6285, ?x9365), nutrient(?x6285, ?x8487), nutrient(?x6285, ?x8442), nutrient(?x6285, ?x8413), nutrient(?x6285, ?x8243), nutrient(?x6285, ?x7894), nutrient(?x6285, ?x7720), nutrient(?x6285, ?x7652), nutrient(?x6285, ?x7364), nutrient(?x6285, ?x7362), nutrient(?x6285, ?x7219), nutrient(?x6285, ?x6586), nutrient(?x6285, ?x6286), nutrient(?x6285, ?x6160), nutrient(?x6285, ?x6033), nutrient(?x6285, ?x6026), nutrient(?x6285, ?x5549), nutrient(?x6285, ?x5526), nutrient(?x6285, ?x5451), nutrient(?x6285, ?x5374), nutrient(?x6285, ?x5010), nutrient(?x6285, ?x4069), nutrient(?x6285, ?x3901), nutrient(?x6285, ?x3469), nutrient(?x6285, ?x3203), nutrient(?x6285, ?x2702), nutrient(?x6285, ?x1960), nutrient(?x6285, ?x1304), nutrient(?x6285, ?x1258), ?x3469 = 0h1zw, ?x9365 = 04k8n, ?x8243 = 014d7f, ?x1304 = 08lb68, ?x6191 = 014j1m, ?x2701 = 0hkxq, ?x11409 = 0h1yf, ?x4069 = 0hqw8p_, ?x8487 = 014yzm, ?x11592 = 025sf0_, ?x13498 = 07q0m, ?x5451 = 05wvs, ?x7219 = 0h1vg, ?x9619 = 0h1tg, ?x10195 = 0hkwr, ?x6286 = 02y_3rf, ?x9426 = 0h1yy, ?x13944 = 0f4kp, ?x9490 = 0h1sg, ?x1960 = 07hnp, ?x9915 = 025tkqy, ?x1258 = 0h1wg, ?x9795 = 05v_8y, ?x5374 = 025s0zp, ?x7652 = 025s0s0, ?x5549 = 025s7j4, ?x11758 = 0q01m, ?x9949 = 02kd0rh, ?x10709 = 0h1sz, ?x12902 = 0fzjh, ?x7362 = 02kc5rj, ?x7720 = 025s7x6, ?x9855 = 0d9t0, ?x6160 = 041r51, ?x9733 = 0h1tz, ?x8413 = 02kc4sf, ?x5010 = 0h1vz, ?x11270 = 02kc008, ?x8442 = 02kcv4x, ?x9840 = 02p0tjr, ?x12454 = 025rw19, ?x6026 = 025sf8g, ?x6033 = 04zjxcz, nutrient(?x10612, ?x2702), nutrient(?x9732, ?x2702), nutrient(?x9489, ?x2702), nutrient(?x9005, ?x2702), nutrient(?x8298, ?x2702), nutrient(?x7719, ?x2702), nutrient(?x7057, ?x2702), nutrient(?x6159, ?x2702), nutrient(?x6032, ?x2702), nutrient(?x5337, ?x2702), nutrient(?x5009, ?x2702), nutrient(?x4068, ?x2702), nutrient(?x1959, ?x2702), nutrient(?x1303, ?x2702), nutrient(?x1257, ?x2702), ?x7364 = 09gvd, ?x6032 = 01nkt, ?x9005 = 04zpv, ?x3900 = 061_f, ?x7057 = 0fbdb, ?x10612 = 0frq6, ?x5009 = 0fjfh, ?x5526 = 09pbb, ?x3203 = 04kl74p, ?x6159 = 033cnk, ?x4068 = 0fbw6, ?x1257 = 09728, ?x9732 = 05z55, ?x7719 = 0dj75, ?x11784 = 07zqy, ?x3901 = 0466p20, ?x10891 = 0g5gq, ?x10098 = 0h1_c, ?x1303 = 0fj52s, ?x8298 = 037ls6, ?x1959 = 0f25w9, ?x9489 = 07j87, ?x5337 = 06x4c, ?x7894 = 0f4hc, ?x6586 = 05gh50, nutrient(?x6191, ?x2702), nutrient(?x1303, ?x12868), nutrient(?x7057, ?x12868), nutrient(?x7719, ?x12868), nutrient(?x6191, ?x12868), nutrient(?x4068, ?x12868), nutrient(?x2701, ?x12868) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 027g6p7 nutrient! 033cnk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 17.000 13.000 0.935 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 027g6p7 nutrient! 0fjfh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 17.000 13.000 0.935 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #3071-01k_yf PRED entity: 01k_yf PRED relation: artists! PRED expected values: 016clz => 59 concepts (38 used for prediction) PRED predicted values (max 10 best out of 242): 016clz (0.85 #3387, 0.79 #2775, 0.79 #3694), 06by7 (0.79 #9243, 0.71 #4631, 0.70 #1868), 064t9 (0.59 #6163, 0.55 #8009, 0.54 #6775), 0dl5d (0.58 #2174, 0.27 #5243, 0.26 #4629), 0xhtw (0.56 #5240, 0.51 #4626, 0.50 #5548), 0cx7f (0.50 #2290, 0.33 #445, 0.33 #137), 05w3f (0.42 #2191, 0.33 #38, 0.26 #4646), 05bt6j (0.38 #4959, 0.38 #1273, 0.36 #6192), 0y3_8 (0.33 #661, 0.33 #355, 0.25 #1277), 016ybr (0.33 #740, 0.33 #434, 0.25 #1356) >> Best rule #3387 for best value: >> intensional similarity = 6 >> extensional distance = 38 >> proper extension: 01tp5bj; 01m65sp; 03xnq9_; 03wjb7; >> query: (?x5407, 016clz) <- artists(?x9935, ?x5407), artists(?x2996, ?x5407), ?x2996 = 01243b, artists(?x9935, ?x10502), ?x10502 = 016vn3, parent_genre(?x2809, ?x9935) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k_yf artists! 016clz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 38.000 0.850 http://example.org/music/genre/artists #3070-01nds PRED entity: 01nds PRED relation: citytown PRED expected values: 0hsqf => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 220): 02_286 (0.43 #37851, 0.43 #27764, 0.39 #41460), 0r6cx (0.43 #37851, 0.39 #41460, 0.18 #9729), 06_kh (0.43 #37851, 0.39 #41460, 0.18 #9729), 0k049 (0.43 #37851, 0.39 #41460, 0.18 #9729), 0nbfm (0.43 #37851, 0.39 #41460, 0.18 #9729), 05jbn (0.43 #37851, 0.39 #41460, 0.12 #4066), 013yq (0.43 #37851, 0.20 #7246, 0.18 #9729), 06y57 (0.43 #37851, 0.20 #106, 0.18 #9729), 024bqj (0.43 #37851, 0.18 #9729, 0.14 #555), 0hsqf (0.43 #37851, 0.18 #9729, 0.14 #562) >> Best rule #37851 for best value: >> intensional similarity = 6 >> extensional distance = 345 >> proper extension: 02hrb2; >> query: (?x11304, ?x8951) <- organization(?x4682, ?x11304), citytown(?x11304, ?x9559), citytown(?x11304, ?x6598), citytown(?x14343, ?x9559), time_zones(?x6598, ?x2950), citytown(?x14343, ?x8951) >> conf = 0.43 => this is the best rule for 15 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 10 EVAL 01nds citytown 0hsqf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 155.000 155.000 0.430 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #3069-01zlh5 PRED entity: 01zlh5 PRED relation: profession PRED expected values: 012t_z => 106 concepts (75 used for prediction) PRED predicted values (max 10 best out of 87): 09jwl (0.62 #886, 0.61 #1031, 0.49 #1611), 02krf9 (0.50 #169, 0.33 #459, 0.33 #24), 0nbcg (0.43 #1044, 0.42 #899, 0.36 #1624), 016z4k (0.41 #1019, 0.40 #874, 0.31 #1599), 02jknp (0.38 #297, 0.38 #152, 0.27 #5373), 0dz3r (0.31 #872, 0.29 #1017, 0.27 #4062), 039v1 (0.30 #1049, 0.29 #904, 0.22 #1484), 018gz8 (0.27 #3349, 0.25 #594, 0.23 #1319), 0np9r (0.27 #453, 0.25 #163, 0.22 #598), 01c72t (0.26 #1181, 0.21 #891, 0.20 #1036) >> Best rule #886 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05crg7; 0163m1; 0dw4g; 07bzp; 0134wr; 014kyy; >> query: (?x8205, 09jwl) <- category(?x8205, ?x134), award_nominee(?x8205, ?x12194), inductee(?x1091, ?x8205) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #157 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 01pcmd; 024swd; 044f7; 05hrq4; 03mstc; 0488g9; *> query: (?x8205, 012t_z) <- place_of_death(?x8205, ?x242), program(?x8205, ?x8837), inductee(?x1091, ?x8205) *> conf = 0.12 ranks of expected_values: 18 EVAL 01zlh5 profession 012t_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 106.000 75.000 0.625 http://example.org/people/person/profession #3068-0638kv PRED entity: 0638kv PRED relation: produced_by! PRED expected values: 04954r => 156 concepts (108 used for prediction) PRED predicted values (max 10 best out of 78): 083skw (0.25 #948, 0.25 #228, 0.22 #11376), 0jdr0 (0.25 #825, 0.20 #1773, 0.02 #8410), 0kbhf (0.25 #557, 0.20 #1505, 0.02 #8142), 0ktpx (0.25 #554, 0.20 #1502, 0.02 #8139), 04mzf8 (0.25 #117, 0.20 #1065, 0.02 #7702), 025s1wg (0.06 #3751, 0.05 #5647), 02q7yfq (0.06 #3495, 0.05 #5391), 01svry (0.06 #3490, 0.05 #5386), 084qpk (0.06 #2915, 0.05 #4811), 03mh94 (0.05 #4778) >> Best rule #948 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0m6x4; >> query: (?x4830, ?x2612) <- type_of_union(?x4830, ?x566), nominated_for(?x4830, ?x2612), award(?x4830, ?x484), ?x2612 = 083skw >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0638kv produced_by! 04954r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 156.000 108.000 0.250 http://example.org/film/film/produced_by #3067-03lrht PRED entity: 03lrht PRED relation: produced_by PRED expected values: 05ty4m => 83 concepts (47 used for prediction) PRED predicted values (max 10 best out of 118): 0mdqp (0.37 #8932, 0.37 #9320, 0.04 #804), 0fvf9q (0.12 #6, 0.09 #394, 0.09 #783), 01zfmm (0.12 #96, 0.01 #2037, 0.01 #2428), 06cgy (0.12 #56, 0.01 #5495), 0lx2l (0.12 #3109, 0.12 #4275, 0.12 #3498), 01vlj1g (0.10 #12818, 0.10 #3499, 0.09 #5439), 092kgw (0.09 #584, 0.05 #1749, 0.01 #2916), 03v1w7 (0.09 #612, 0.03 #2165, 0.02 #4110), 07rd7 (0.09 #537, 0.03 #2090, 0.01 #4424), 0d_skg (0.09 #618, 0.01 #2171) >> Best rule #8932 for best value: >> intensional similarity = 4 >> extensional distance = 486 >> proper extension: 0k2m6; 02wk7b; 0267wwv; >> query: (?x1692, ?x794) <- film_crew_role(?x1692, ?x1284), film_crew_role(?x641, ?x1284), film(?x794, ?x1692), ?x641 = 08720 >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #1953 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 133 *> proper extension: 025x1t; 0gxsh4; *> query: (?x1692, 05ty4m) <- nominated_for(?x2534, ?x1692), participant(?x1817, ?x2534), influenced_by(?x2534, ?x1145), film(?x2534, ?x339) *> conf = 0.02 ranks of expected_values: 55 EVAL 03lrht produced_by 05ty4m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 83.000 47.000 0.369 http://example.org/film/film/produced_by #3066-0bmc4cm PRED entity: 0bmc4cm PRED relation: genre PRED expected values: 02kdv5l => 146 concepts (86 used for prediction) PRED predicted values (max 10 best out of 121): 04t2t (0.85 #4930, 0.77 #6223, 0.74 #1877), 05p553 (0.82 #3879, 0.48 #9288, 0.38 #1174), 04xvh5 (0.78 #6374, 0.25 #149, 0.24 #1790), 03g3w (0.77 #2017, 0.33 #2370, 0.29 #374), 03_9r (0.70 #4929, 0.68 #1876, 0.64 #6222), 03_3d (0.70 #4929, 0.68 #1876, 0.64 #6222), 02kdv5l (0.69 #3406, 0.66 #3169, 0.65 #7874), 0jxy (0.45 #4736, 0.20 #627, 0.14 #393), 01hmnh (0.45 #6240, 0.41 #6121, 0.40 #251), 0hcr (0.43 #4716, 0.21 #2251, 0.20 #607) >> Best rule #4930 for best value: >> intensional similarity = 6 >> extensional distance = 68 >> proper extension: 03t97y; 01kff7; 05p3738; >> query: (?x3135, ?x7160) <- film_release_distribution_medium(?x3135, ?x81), genre(?x3135, ?x811), ?x811 = 03k9fj, titles(?x7160, ?x3135), genre(?x6219, ?x7160), ?x6219 = 05znbh7 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #3406 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 47 *> proper extension: 0d6_s; *> query: (?x3135, 02kdv5l) <- film(?x609, ?x3135), genre(?x3135, ?x812), genre(?x3135, ?x811), genre(?x3135, ?x53), ?x811 = 03k9fj, titles(?x252, ?x3135), ?x812 = 01jfsb, titles(?x53, ?x54) *> conf = 0.69 ranks of expected_values: 7 EVAL 0bmc4cm genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 146.000 86.000 0.851 http://example.org/film/film/genre #3065-064lsn PRED entity: 064lsn PRED relation: film_release_region PRED expected values: 0b90_r 03rjj 07ssc 015fr 06npd 06mzp 03gj2 03rj0 06t8v 03spz => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 171): 07ssc (0.90 #13, 0.87 #164, 0.84 #1227), 03rjj (0.88 #1218, 0.86 #764, 0.85 #1977), 03gj2 (0.87 #175, 0.87 #1238, 0.86 #784), 015fr (0.82 #1229, 0.81 #15, 0.76 #2744), 0b90_r (0.81 #3, 0.75 #763, 0.74 #154), 05v8c (0.81 #14, 0.71 #165, 0.63 #774), 03rk0 (0.81 #52, 0.65 #203, 0.49 #1266), 03spz (0.79 #1306, 0.77 #243, 0.76 #92), 03rt9 (0.72 #1225, 0.68 #1984, 0.68 #162), 05qx1 (0.71 #38, 0.58 #189, 0.47 #798) >> Best rule #13 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 0ds35l9; 05p1tzf; 017gl1; 04hwbq; 0gmcwlb; 017gm7; 0ch26b_; 09k56b7; 02yvct; 0fpv_3_; ... >> query: (?x6121, 07ssc) <- award(?x6121, ?x198), film_release_region(?x6121, ?x404), film_release_region(?x6121, ?x344), ?x404 = 047lj, ?x344 = 04gzd >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 8, 14, 17, 22, 26 EVAL 064lsn film_release_region 03spz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 98.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 064lsn film_release_region 06t8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 98.000 98.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 064lsn film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 98.000 98.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 064lsn film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 064lsn film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 98.000 98.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 064lsn film_release_region 06npd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 98.000 98.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 064lsn film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 064lsn film_release_region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 064lsn film_release_region 03rjj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 064lsn film_release_region 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #3064-0j8cb PRED entity: 0j8cb PRED relation: teams! PRED expected values: 0fvyg => 31 concepts (26 used for prediction) PRED predicted values (max 10 best out of 59): 0dclg (0.06 #72, 0.06 #342, 0.05 #612), 02cl1 (0.06 #20, 0.06 #290, 0.05 #560), 068p2 (0.06 #123, 0.06 #393, 0.05 #663), 02dtg (0.06 #16, 0.06 #286, 0.05 #556), 0n1rj (0.06 #142, 0.06 #412, 0.05 #682), 03pzf (0.06 #209, 0.06 #479, 0.05 #749), 0hptm (0.06 #144, 0.06 #414, 0.05 #684), 080h2 (0.06 #30, 0.06 #300, 0.05 #570), 0qplq (0.06 #500, 0.05 #770, 0.04 #1580), 030qb3t (0.05 #860, 0.05 #1130, 0.04 #1400) >> Best rule #72 for best value: >> intensional similarity = 10 >> extensional distance = 14 >> proper extension: 0c41y70; 0hn6d; 0jnrk; 02fp3; 04l5d0; 02hqt6; 0jnnx; 0jbqf; 030ykh; 0hn2q; ... >> query: (?x14183, 0dclg) <- position(?x14183, ?x5234), position(?x14183, ?x3724), position(?x14183, ?x2918), ?x3724 = 02qvzf, ?x2918 = 02qvl7, ?x5234 = 02qvdc, sport(?x14183, ?x453), ?x453 = 03tmr, team(?x3724, ?x14183), team(?x2918, ?x14183) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0j8cb teams! 0fvyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 26.000 0.062 http://example.org/sports/sports_team_location/teams #3063-031rq5 PRED entity: 031rq5 PRED relation: production_companies! PRED expected values: 01zfzb => 99 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1679): 02q0k7v (0.29 #1983, 0.25 #845, 0.20 #7675), 072kp (0.27 #17074), 026hh0m (0.25 #1044, 0.14 #2182, 0.13 #7874), 027gy0k (0.25 #732, 0.14 #1870, 0.13 #7562), 0x25q (0.25 #338, 0.14 #1476, 0.13 #7168), 01dyvs (0.25 #192, 0.14 #1330, 0.13 #7022), 026wlxw (0.25 #904, 0.14 #2042, 0.13 #7734), 01718w (0.25 #892, 0.14 #2030, 0.13 #7722), 04pk1f (0.25 #673, 0.14 #1811, 0.13 #7503), 09g7vfw (0.25 #374, 0.14 #1512, 0.13 #7204) >> Best rule #1983 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02bh8z; 07733f; >> query: (?x5908, 02q0k7v) <- child(?x7526, ?x5908), child(?x1908, ?x5908), ?x1908 = 0l8sx, film(?x7526, ?x723) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #6287 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 11 *> proper extension: 0l2tk; 01ygv2; 0c0sl; *> query: (?x5908, 01zfzb) <- award_winner(?x1105, ?x5908), country(?x5908, ?x94) *> conf = 0.08 ranks of expected_values: 384 EVAL 031rq5 production_companies! 01zfzb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 99.000 26.000 0.286 http://example.org/film/film/production_companies #3062-082_p PRED entity: 082_p PRED relation: influenced_by PRED expected values: 017r2 => 153 concepts (72 used for prediction) PRED predicted values (max 10 best out of 398): 081k8 (0.45 #156, 0.33 #2338, 0.27 #4080), 04xjp (0.36 #56, 0.20 #1365, 0.12 #493), 03_87 (0.27 #202, 0.20 #1511, 0.19 #2384), 032l1 (0.27 #89, 0.20 #1398, 0.19 #526), 043s3 (0.27 #116, 0.20 #1425, 0.19 #553), 01v9724 (0.27 #177, 0.19 #2359, 0.19 #614), 028p0 (0.27 #30, 0.15 #22690, 0.15 #1339), 03hnd (0.27 #99, 0.15 #1408, 0.12 #536), 03_dj (0.27 #413, 0.15 #1722, 0.12 #850), 05qmj (0.27 #192, 0.15 #1501, 0.10 #18082) >> Best rule #156 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 03_hd; 0hky; >> query: (?x8938, 081k8) <- influenced_by(?x2608, ?x8938), profession(?x8938, ?x987), place_of_death(?x8938, ?x863), ?x2608 = 01hb6v >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #10505 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 75 *> proper extension: 03f70xs; 04k15; 02lt8; 02lhm2; 06c44; 06y8v; 01w9ph_; 02633g; 01whg97; 02z3zp; ... *> query: (?x8938, 017r2) <- influenced_by(?x1900, ?x8938), people(?x743, ?x8938), influenced_by(?x8938, ?x587), gender(?x8938, ?x231), place_of_birth(?x8938, ?x12600) *> conf = 0.03 ranks of expected_values: 217 EVAL 082_p influenced_by 017r2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 153.000 72.000 0.455 http://example.org/influence/influence_node/influenced_by #3061-01sbv9 PRED entity: 01sbv9 PRED relation: region PRED expected values: 07ssc => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 5): 07ssc (0.87 #98, 0.81 #52, 0.57 #168), 09c7w0 (0.04 #232, 0.03 #139, 0.03 #279), 05v8c (0.02 #255), 0d060g (0.02 #142), 059j2 (0.01 #194, 0.01 #240) >> Best rule #98 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 0cnztc4; 064n1pz; 0crh5_f; 0d8w2n; 04nlb94; >> query: (?x10192, 07ssc) <- film_distribution_medium(?x10192, ?x2099), film_release_distribution_medium(?x10192, ?x81), ?x2099 = 0735l, genre(?x10192, ?x811) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sbv9 region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.874 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #3060-0h336 PRED entity: 0h336 PRED relation: people! PRED expected values: 013xrm => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 41): 013xrm (0.43 #713, 0.40 #1560, 0.33 #20), 041rx (0.36 #697, 0.31 #1082, 0.27 #928), 07bch9 (0.30 #408, 0.20 #793, 0.14 #639), 063k3h (0.30 #416, 0.20 #801, 0.04 #1956), 03ts0c (0.25 #334, 0.20 #257, 0.04 #3800), 02ctzb (0.20 #400, 0.13 #785, 0.08 #2556), 0x67 (0.18 #2166, 0.16 #3398, 0.15 #1396), 013b6_ (0.18 #592, 0.14 #669, 0.08 #2055), 0g6ff (0.10 #4930, 0.03 #1176, 0.02 #3717), 048z7l (0.10 #502, 0.07 #656, 0.05 #887) >> Best rule #713 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 03j43; 02ln1; 047g6; >> query: (?x10605, 013xrm) <- influenced_by(?x3336, ?x10605), interests(?x10605, ?x10606), influenced_by(?x10605, ?x7250), place_of_death(?x10605, ?x4861), ?x7250 = 03sbs >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h336 people! 013xrm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.429 http://example.org/people/ethnicity/people #3059-03_r3 PRED entity: 03_r3 PRED relation: olympics PRED expected values: 0l98s 0lgxj => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 31): 0lgxj (0.79 #236, 0.70 #391, 0.53 #1428), 0lv1x (0.75 #227, 0.63 #382, 0.45 #134), 0nbjq (0.71 #230, 0.63 #385, 0.45 #137), 0lbd9 (0.67 #240, 0.60 #395, 0.40 #147), 0lbbj (0.63 #384, 0.62 #229, 0.43 #198), 09x3r (0.62 #224, 0.57 #379, 0.53 #100), 0lk8j (0.62 #228, 0.57 #383, 0.40 #135), 0ldqf (0.62 #244, 0.57 #399, 0.37 #120), 0l98s (0.54 #221, 0.50 #376, 0.40 #128), 018ctl (0.53 #1428, 0.53 #1427, 0.48 #1491) >> Best rule #236 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 09c7w0; 02k54; 059j2; 0h7x; 03shp; 01mk6; 06m_5; >> query: (?x421, 0lgxj) <- olympics(?x421, ?x2134), ?x2134 = 0blg2, nationality(?x2538, ?x421) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 03_r3 olympics 0lgxj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.792 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 03_r3 olympics 0l98s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 140.000 140.000 0.792 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #3058-0sb1r PRED entity: 0sb1r PRED relation: contains! PRED expected values: 09c7w0 => 109 concepts (64 used for prediction) PRED predicted values (max 10 best out of 265): 09c7w0 (0.97 #6271, 0.87 #34007, 0.77 #17013), 07ssc (0.45 #28638, 0.42 #57278, 0.10 #54623), 02xry (0.23 #7327, 0.19 #10907, 0.08 #1952), 01n7q (0.20 #21559, 0.20 #15298, 0.18 #17088), 05fjf (0.16 #2162, 0.13 #3059, 0.13 #4850), 0kpys (0.13 #3763, 0.11 #7345, 0.09 #10925), 04_1l0v (0.13 #9406, 0.12 #10300, 0.12 #12986), 059rby (0.12 #25972, 0.08 #55507, 0.08 #2706), 02jx1 (0.12 #32302, 0.08 #18885, 0.08 #9937), 0sbbq (0.10 #5374, 0.08 #48316, 0.08 #7164) >> Best rule #6271 for best value: >> intensional similarity = 6 >> extensional distance = 112 >> proper extension: 03qdm; >> query: (?x3982, 09c7w0) <- category(?x3982, ?x134), contains(?x8552, ?x3982), contains(?x3818, ?x3982), county_seat(?x8552, ?x8553), contains(?x3818, ?x2838), ?x2838 = 065r8g >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sb1r contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 64.000 0.974 http://example.org/location/location/contains #3057-01vvycq PRED entity: 01vvycq PRED relation: instrumentalists! PRED expected values: 026t6 03qjg => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 116): 03qjg (0.47 #275, 0.33 #198, 0.30 #1741), 018j2 (0.44 #185, 0.14 #2501, 0.13 #262), 03bx0bm (0.41 #386, 0.39 #3320, 0.38 #1543), 028tv0 (0.41 #386, 0.39 #3320, 0.38 #1543), 06w7v (0.33 #217, 0.20 #63, 0.18 #449), 04rzd (0.33 #184, 0.17 #107, 0.13 #261), 026t6 (0.26 #311, 0.22 #157, 0.19 #1700), 07xzm (0.17 #92, 0.11 #169, 0.07 #246), 02fsn (0.17 #122, 0.08 #2205, 0.06 #1742), 06ncr (0.14 #423, 0.12 #1502, 0.10 #3277) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0fpj4lx; 012ycy; >> query: (?x702, 03qjg) <- instrumentalists(?x716, ?x702), ?x716 = 018vs, diet(?x702, ?x11141) >> conf = 0.47 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 01vvycq instrumentalists! 03qjg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.467 http://example.org/music/instrument/instrumentalists EVAL 01vvycq instrumentalists! 026t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 120.000 120.000 0.467 http://example.org/music/instrument/instrumentalists #3056-04t2t PRED entity: 04t2t PRED relation: titles PRED expected values: 048yqf => 41 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1866): 0296rz (0.60 #4495, 0.40 #6045, 0.33 #7593), 07z6xs (0.60 #3846, 0.40 #5396, 0.33 #6944), 03h_yy (0.60 #3158, 0.40 #4708, 0.33 #6256), 0f4m2z (0.60 #3463, 0.40 #5013, 0.33 #6561), 047fjjr (0.60 #3628, 0.40 #5178, 0.33 #6726), 01q7h2 (0.60 #4427, 0.40 #5977, 0.33 #7525), 0191n (0.60 #3827, 0.40 #5377, 0.33 #6925), 06gjk9 (0.60 #3548, 0.40 #5098, 0.33 #6646), 02c638 (0.60 #3381, 0.40 #4931, 0.33 #6479), 035zr0 (0.60 #4200, 0.40 #5750, 0.33 #7298) >> Best rule #4495 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 02n4kr; >> query: (?x7160, 0296rz) <- genre(?x6219, ?x7160), genre(?x5945, ?x7160), genre(?x2889, ?x7160), genre(?x763, ?x7160), ?x763 = 061681, titles(?x7160, ?x1185), language(?x6219, ?x254), film(?x1596, ?x5945), film_release_region(?x2889, ?x94), honored_for(?x8762, ?x2889), film_release_distribution_medium(?x6219, ?x81) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3092 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 07s9rl0; *> query: (?x7160, ?x148) <- genre(?x6219, ?x7160), genre(?x2889, ?x7160), genre(?x1628, ?x7160), genre(?x763, ?x7160), genre(?x148, ?x7160), ?x763 = 061681, ?x6219 = 05znbh7, ?x2889 = 040b5k, titles(?x7160, ?x1185), genre(?x1628, ?x811), production_companies(?x1628, ?x1914), ?x811 = 03k9fj *> conf = 0.32 ranks of expected_values: 464 EVAL 04t2t titles 048yqf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 41.000 16.000 0.600 http://example.org/media_common/netflix_genre/titles #3055-0d4htf PRED entity: 0d4htf PRED relation: film! PRED expected values: 0521rl1 0kftt => 98 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1102): 02fgpf (0.49 #103988, 0.48 #89428, 0.45 #60307), 0b25vg (0.49 #103988, 0.45 #60307, 0.45 #29110), 02_p5w (0.35 #2724, 0.12 #11040, 0.09 #17278), 02gf_l (0.29 #3346, 0.12 #11662, 0.09 #17900), 01v3vp (0.18 #2788, 0.08 #11104, 0.08 #709), 019803 (0.18 #4015, 0.02 #22727, 0.02 #12331), 0p8r1 (0.16 #10980, 0.12 #17218, 0.04 #35933), 0jfx1 (0.15 #406, 0.08 #14959, 0.08 #10801), 01l2fn (0.15 #262, 0.07 #14815, 0.05 #21053), 01tsbmv (0.15 #1896, 0.06 #12291, 0.05 #16449) >> Best rule #103988 for best value: >> intensional similarity = 4 >> extensional distance = 951 >> proper extension: 0n2bh; 025x1t; >> query: (?x5513, ?x1894) <- titles(?x1510, ?x5513), nominated_for(?x1894, ?x5513), award(?x1894, ?x1232), people(?x1050, ?x1894) >> conf = 0.49 => this is the best rule for 2 predicted values *> Best rule #16019 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 0gtsx8c; *> query: (?x5513, 0kftt) <- film(?x609, ?x5513), crewmember(?x5513, ?x3879), film(?x848, ?x5513), film_distribution_medium(?x5513, ?x81) *> conf = 0.02 ranks of expected_values: 773 EVAL 0d4htf film! 0kftt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 98.000 60.000 0.488 http://example.org/film/actor/film./film/performance/film EVAL 0d4htf film! 0521rl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 60.000 0.488 http://example.org/film/actor/film./film/performance/film #3054-02630g PRED entity: 02630g PRED relation: award_winner! PRED expected values: 0m7yy => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 11): 0m7yy (0.14 #612, 0.13 #23940, 0.11 #20484), 05p1dby (0.10 #1836, 0.09 #10476, 0.08 #12636), 0gq9h (0.10 #1806, 0.04 #17358, 0.04 #10446), 02x1z2s (0.07 #15318, 0.07 #20070, 0.07 #10566), 07bdd_ (0.07 #10434, 0.06 #12594, 0.05 #19938), 01l29r (0.06 #1462, 0.05 #1894, 0.04 #13990), 0b6jkkg (0.05 #1961, 0.02 #9305, 0.01 #17513), 01lk0l (0.05 #2006, 0.02 #10646, 0.02 #11078), 018wng (0.02 #10410, 0.02 #12570, 0.01 #17322), 0p9sw (0.02 #14280, 0.02 #16872, 0.01 #17736) >> Best rule #612 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 01skqzw; >> query: (?x6638, 0m7yy) <- company(?x1491, ?x6638), state_province_region(?x6638, ?x1426), company(?x1491, ?x5072), ?x1426 = 07z1m, contact_category(?x5072, ?x897) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02630g award_winner! 0m7yy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.143 http://example.org/award/award_category/winners./award/award_honor/award_winner #3053-03xmy1 PRED entity: 03xmy1 PRED relation: nationality PRED expected values: 0k6nt => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 32): 09c7w0 (0.85 #6816, 0.79 #5813, 0.77 #4308), 02jx1 (0.15 #434, 0.14 #133, 0.13 #2536), 07ssc (0.14 #115, 0.12 #2518, 0.10 #2719), 0d060g (0.14 #107, 0.07 #5617, 0.07 #1108), 03rk0 (0.12 #947, 0.11 #3952, 0.07 #3151), 0345h (0.06 #231, 0.04 #1833, 0.03 #932), 03rjj (0.05 #806, 0.04 #706, 0.04 #1006), 03rt9 (0.04 #614, 0.03 #914, 0.03 #3017), 0f8l9c (0.04 #723, 0.04 #823, 0.03 #1323), 01xbgx (0.04 #381, 0.02 #882, 0.01 #1282) >> Best rule #6816 for best value: >> intensional similarity = 3 >> extensional distance = 1314 >> proper extension: 069d71; >> query: (?x1888, 09c7w0) <- location(?x1888, ?x11000), gender(?x1888, ?x514), source(?x11000, ?x958) >> conf = 0.85 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03xmy1 nationality 0k6nt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 122.000 0.847 http://example.org/people/person/nationality #3052-05kwx2 PRED entity: 05kwx2 PRED relation: award PRED expected values: 04kxsb => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 230): 04kxsb (0.20 #125, 0.13 #19396, 0.13 #22226), 09sdmz (0.20 #206, 0.13 #19396, 0.13 #22226), 04ljl_l (0.20 #3, 0.05 #3639, 0.05 #10509), 0ck27z (0.15 #4131, 0.15 #2919, 0.14 #4939), 0gqy2 (0.13 #19396, 0.13 #22226, 0.13 #6061), 0f4x7 (0.13 #19396, 0.13 #22226, 0.13 #6061), 02x73k6 (0.13 #19396, 0.13 #22226, 0.13 #6061), 02w9sd7 (0.13 #19396, 0.13 #22226, 0.13 #6061), 09qv_s (0.13 #19396, 0.13 #22226, 0.13 #6061), 099jhq (0.13 #19396, 0.13 #22226, 0.13 #6061) >> Best rule #125 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0gm8_p; 01l7qw; >> query: (?x6227, 04kxsb) <- film(?x6227, ?x5318), film(?x6227, ?x522), country(?x5318, ?x512), ?x522 = 01h7bb >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05kwx2 award 04kxsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.200 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3051-02scbv PRED entity: 02scbv PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 6): 029j_ (0.86 #167, 0.86 #127, 0.86 #187), 02nxhr (0.18 #528, 0.10 #93, 0.10 #12), 07z4p (0.18 #528, 0.03 #111, 0.03 #285), 0735l (0.18 #528), 0dq6p (0.18 #528), 07c52 (0.03 #525, 0.03 #309, 0.02 #194) >> Best rule #167 for best value: >> intensional similarity = 4 >> extensional distance = 145 >> proper extension: 03h4fq7; >> query: (?x6918, 029j_) <- production_companies(?x6918, ?x1561), genre(?x6918, ?x225), language(?x6918, ?x254), nominated_for(?x2165, ?x6918) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02scbv film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #3050-03f0324 PRED entity: 03f0324 PRED relation: profession PRED expected values: 04gc2 => 173 concepts (144 used for prediction) PRED predicted values (max 10 best out of 108): 02hrh1q (0.82 #20765, 0.76 #17508, 0.75 #5798), 0dxtg (0.82 #19876, 0.81 #17211, 0.70 #20616), 02jknp (0.50 #451, 0.47 #20906, 0.44 #1780), 01d_h8 (0.50 #450, 0.45 #20905, 0.45 #17204), 02hv44_ (0.44 #1687, 0.44 #1780, 0.43 #1779), 09jwl (0.44 #1780, 0.43 #1779, 0.42 #3578), 05z96 (0.44 #1780, 0.43 #1779, 0.40 #1822), 03gjzk (0.44 #1780, 0.43 #1779, 0.33 #17213), 0d8qb (0.44 #1780, 0.43 #1779, 0.32 #14680), 0q04f (0.44 #1780, 0.43 #1779, 0.31 #1630) >> Best rule #20765 for best value: >> intensional similarity = 4 >> extensional distance = 1405 >> proper extension: 016qtt; 0gcdzz; 0cg9y; 0993r; 02_4fn; 03x22w; 0lh0c; 01lqf49; 017lqp; 0fs9jn; ... >> query: (?x4915, 02hrh1q) <- people(?x1050, ?x4915), profession(?x4915, ?x353), profession(?x11440, ?x353), ?x11440 = 01lct6 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #7714 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 124 *> proper extension: 073bb; 05qw5; 0gd5z; 0bv7t; 01_6dw; 01q9b9; 042v2; 041jlr; 01hc9_; 06pjs; ... *> query: (?x4915, ?x353) <- location(?x4915, ?x1458), influenced_by(?x4915, ?x10654), profession(?x10654, ?x3746), profession(?x10654, ?x353), ?x3746 = 05z96 *> conf = 0.34 ranks of expected_values: 17 EVAL 03f0324 profession 04gc2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 173.000 144.000 0.823 http://example.org/people/person/profession #3049-01qzt1 PRED entity: 01qzt1 PRED relation: titles PRED expected values: 0353xq => 83 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1946): 0bz6sq (0.43 #10647, 0.33 #16884, 0.33 #1293), 016z7s (0.42 #22112, 0.38 #25232, 0.23 #23671), 0209hj (0.42 #21916, 0.38 #25036, 0.23 #23475), 0dtzkt (0.40 #9242, 0.17 #24946, 0.17 #23274), 041td_ (0.38 #25877, 0.33 #930, 0.29 #10284), 0296rz (0.33 #23241, 0.31 #29479, 0.31 #26361), 03h_yy (0.33 #64, 0.31 #28129, 0.29 #29690), 03hfmm (0.33 #1261, 0.31 #24647, 0.25 #29326), 011yr9 (0.33 #22415, 0.31 #25535, 0.23 #23974), 01719t (0.33 #22023, 0.31 #25143, 0.23 #23582) >> Best rule #10647 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0ggq0m; 064t9; 01z4y; 01chg; >> query: (?x5138, 0bz6sq) <- titles(?x5138, ?x3566), artists(?x5138, ?x248), profession(?x248, ?x131), award_nominee(?x248, ?x3403), award(?x248, ?x247), film_release_region(?x3566, ?x94) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #787 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 04t36; *> query: (?x5138, 0353xq) <- titles(?x5138, ?x3566), titles(?x5138, ?x1619), artists(?x5138, ?x248), ?x3566 = 04jpk2, crewmember(?x1619, ?x9151), genre(?x1619, ?x307) *> conf = 0.33 ranks of expected_values: 38 EVAL 01qzt1 titles 0353xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 83.000 33.000 0.429 http://example.org/media_common/netflix_genre/titles #3048-074j87 PRED entity: 074j87 PRED relation: genre PRED expected values: 066wd => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 83): 066wd (0.73 #1788, 0.68 #2728, 0.62 #1447), 06ntj (0.73 #1788, 0.68 #2728, 0.62 #1447), 05p553 (0.71 #771, 0.56 #1452, 0.56 #856), 07s9rl0 (0.60 #426, 0.51 #3495, 0.50 #2814), 06nbt (0.57 #788, 0.44 #873, 0.33 #1043), 01z4y (0.47 #1636, 0.44 #1466, 0.43 #785), 0c4xc (0.43 #810, 0.33 #1661, 0.33 #895), 0gf28 (0.43 #809, 0.33 #894, 0.17 #1064), 0lsxr (0.40 #435, 0.33 #10, 0.15 #3504), 01jfsb (0.40 #438, 0.33 #13, 0.12 #2484) >> Best rule #1788 for best value: >> intensional similarity = 9 >> extensional distance = 29 >> proper extension: 050kh5; >> query: (?x13964, ?x11043) <- actor(?x13964, ?x11630), profession(?x11630, ?x319), ?x319 = 01d_h8, actor(?x13068, ?x11630), genre(?x13068, ?x11043), type_of_union(?x11630, ?x566), program(?x13067, ?x13068), program(?x12664, ?x13964), category(?x11630, ?x134) >> conf = 0.73 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 074j87 genre 066wd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.726 http://example.org/tv/tv_program/genre #3047-06whf PRED entity: 06whf PRED relation: athlete! PRED expected values: 09xp_ => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 4): 0jm_ (0.03 #353, 0.03 #393, 0.02 #423), 09xp_ (0.03 #129, 0.02 #159, 0.02 #319), 02vx4 (0.02 #1552, 0.02 #1512, 0.02 #1492), 018w8 (0.01 #416, 0.01 #246, 0.01 #1046) >> Best rule #353 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 0l6qt; 0prfz; 01x66d; 039bp; 03yf3z; 02tqkf; 02kxbx3; 01gy7r; 01z_g6; 06jw0s; ... >> query: (?x4265, 0jm_) <- gender(?x4265, ?x231), student(?x1771, ?x4265), location(?x4265, ?x4627), place_of_birth(?x771, ?x4627) >> conf = 0.03 => this is the best rule for 1 predicted values *> Best rule #129 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 01_x6v; *> query: (?x4265, 09xp_) <- gender(?x4265, ?x231), influenced_by(?x4265, ?x1236), location(?x4265, ?x1591), student(?x90, ?x4265) *> conf = 0.03 ranks of expected_values: 2 EVAL 06whf athlete! 09xp_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 167.000 167.000 0.028 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #3046-02rv_dz PRED entity: 02rv_dz PRED relation: film_crew_role PRED expected values: 09vw2b7 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 28): 09vw2b7 (0.65 #1345, 0.63 #1980, 0.60 #746), 01vx2h (0.51 #116, 0.49 #81, 0.46 #11), 0dxtw (0.36 #185, 0.36 #1984, 0.35 #2588), 01xy5l_ (0.27 #13, 0.23 #83, 0.22 #118), 02rh1dz (0.20 #114, 0.19 #184, 0.19 #9), 015h31 (0.20 #113, 0.19 #78, 0.15 #8), 089g0h (0.18 #124, 0.16 #89, 0.15 #19), 0215hd (0.17 #158, 0.15 #53, 0.15 #193), 02ynfr (0.16 #1354, 0.16 #1989, 0.15 #2664), 0d2b38 (0.13 #130, 0.12 #588, 0.12 #200) >> Best rule #1345 for best value: >> intensional similarity = 4 >> extensional distance = 435 >> proper extension: 04gknr; 03t97y; 047gpsd; >> query: (?x1531, 09vw2b7) <- award_winner(?x1531, ?x617), film_crew_role(?x1531, ?x1284), film(?x436, ?x1531), ?x1284 = 0ch6mp2 >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rv_dz film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.645 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #3045-01w02sy PRED entity: 01w02sy PRED relation: gender PRED expected values: 02zsn => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #63, 0.83 #71, 0.80 #103), 02zsn (0.59 #12, 0.52 #60, 0.52 #16) >> Best rule #63 for best value: >> intensional similarity = 3 >> extensional distance = 206 >> proper extension: 07_3qd; 094xh; 04mx7s; 011_vz; 017mbb; >> query: (?x3118, 05zppz) <- artists(?x302, ?x3118), role(?x3118, ?x227), category(?x3118, ?x134) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #12 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0159h6; 01rr9f; 0151w_; 0456xp; 02d9k; 0170qf; 01wk7b7; 0jfx1; 0lx2l; 047hpm; ... *> query: (?x3118, 02zsn) <- profession(?x3118, ?x131), friend(?x7571, ?x3118), spouse(?x3118, ?x7375) *> conf = 0.59 ranks of expected_values: 2 EVAL 01w02sy gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 143.000 0.846 http://example.org/people/person/gender #3044-0wp9b PRED entity: 0wp9b PRED relation: category PRED expected values: 08mbj5d => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #1, 0.78 #39, 0.77 #17) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0hd7j; 01dzg0; 01fsv9; >> query: (?x1505, 08mbj5d) <- contains(?x4622, ?x1505), contains(?x94, ?x1505), ?x4622 = 04tgp, ?x94 = 09c7w0 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0wp9b category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.818 http://example.org/common/topic/webpage./common/webpage/category #3043-0cj16 PRED entity: 0cj16 PRED relation: film_format! PRED expected values: 01jc6q 0dckvs 0344gc 0hmm7 0jym0 04q24zv 01rwpj 0mb8c 0hv8w 0cy__l 02qyv3h 03rg2b 0294mx 047vp1n 03kx49 05zvzf3 07l50_1 => 5 concepts (4 used for prediction) PRED predicted values (max 10 best out of 1751): 05zlld0 (0.40 #729, 0.40 #513, 0.33 #301), 049xgc (0.38 #210, 0.33 #353, 0.23 #426), 0h6r5 (0.38 #210, 0.33 #311, 0.23 #426), 04vr_f (0.38 #210, 0.33 #241, 0.23 #426), 05dy7p (0.38 #210, 0.33 #274, 0.21 #428), 09txzv (0.38 #210, 0.33 #39, 0.20 #683), 0cc5mcj (0.38 #210, 0.33 #60, 0.20 #704), 06cm5 (0.38 #210, 0.23 #426, 0.21 #428), 0ch26b_ (0.38 #210, 0.23 #426, 0.21 #428), 07cw4 (0.38 #210, 0.23 #426, 0.17 #427) >> Best rule #729 for best value: >> intensional similarity = 90 >> extensional distance = 3 >> proper extension: 0hcr; >> query: (?x6392, 05zlld0) <- film_format(?x12641, ?x6392), film_format(?x10225, ?x6392), film_format(?x4086, ?x6392), film_format(?x3392, ?x6392), film_format(?x2471, ?x6392), film_format(?x1490, ?x6392), film_format(?x695, ?x6392), country(?x1490, ?x1264), nominated_for(?x143, ?x695), nominated_for(?x372, ?x1490), currency(?x3392, ?x170), film(?x4782, ?x2471), film(?x609, ?x12641), film_release_region(?x11065, ?x1264), film_release_region(?x7693, ?x1264), film_release_region(?x6283, ?x1264), film_release_region(?x6216, ?x1264), film_release_region(?x6168, ?x1264), film_release_region(?x5588, ?x1264), film_release_region(?x4998, ?x1264), film_release_region(?x4610, ?x1264), film_release_region(?x4336, ?x1264), film_release_region(?x2896, ?x1264), film_release_region(?x2394, ?x1264), film_release_region(?x1386, ?x1264), film_release_region(?x1173, ?x1264), film_release_region(?x903, ?x1264), film_release_region(?x141, ?x1264), ?x5588 = 0gtt5fb, ?x4998 = 0dzlbx, contains(?x1264, ?x196), country(?x10722, ?x1264), country(?x7760, ?x1264), country(?x6721, ?x1264), country(?x6499, ?x1264), country(?x5290, ?x1264), country(?x5001, ?x1264), country(?x4194, ?x1264), adjoins(?x172, ?x1264), ?x7760 = 017kz7, ?x4194 = 04cv9m, organization(?x1264, ?x7416), ?x11065 = 0n08r, music(?x10225, ?x565), film_crew_role(?x695, ?x137), ?x6721 = 017180, ?x2394 = 0661ql3, country(?x6150, ?x1264), country(?x4673, ?x1264), country(?x3309, ?x1264), country(?x3015, ?x1264), country(?x150, ?x1264), award(?x4782, ?x1007), ?x6283 = 0gmd3k7, ?x7693 = 0m63c, film_crew_role(?x2471, ?x468), ?x3309 = 09w1n, ?x4336 = 0bpm4yw, ?x4673 = 07jbh, ?x6168 = 0gj96ln, film_release_region(?x1133, ?x1264), olympics(?x1264, ?x4255), ?x4610 = 017jd9, ?x6150 = 07_53, genre(?x695, ?x53), nominated_for(?x6729, ?x10225), ?x6499 = 04xx9s, participant(?x1896, ?x4782), nationality(?x380, ?x1264), ?x6216 = 06fcqw, ?x1173 = 0872p_c, combatants(?x792, ?x1264), film_release_distribution_medium(?x4086, ?x81), ?x7416 = 018cqq, production_companies(?x4086, ?x902), ?x5001 = 09q23x, ?x5290 = 0gs973, ?x4255 = 0lgxj, ?x141 = 0gtsx8c, ?x1386 = 0dtfn, ?x903 = 04969y, ?x150 = 07rlg, ?x3015 = 071t0, ?x10722 = 07p12s, ?x2896 = 0645k5, film(?x72, ?x10225), religion(?x1264, ?x492), olympics(?x1264, ?x1081), ?x792 = 0hzlz, production_companies(?x3392, ?x1104) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 82 *> extensional distance = 1 *> proper extension: 07fb8_; *> query: (?x6392, 0344gc) <- film_format(?x13183, ?x6392), film_format(?x7538, ?x6392), film_format(?x7493, ?x6392), film_format(?x5992, ?x6392), film_format(?x4276, ?x6392), film_format(?x3287, ?x6392), film_format(?x2788, ?x6392), film_format(?x2471, ?x6392), film_format(?x1619, ?x6392), film_format(?x1490, ?x6392), film_format(?x1315, ?x6392), film_format(?x392, ?x6392), film_format(?x218, ?x6392), film_format(?x204, ?x6392), country(?x1490, ?x304), featured_film_locations(?x2788, ?x4074), nominated_for(?x2671, ?x1490), film_release_region(?x1490, ?x3277), film_release_region(?x1490, ?x1892), film_release_region(?x1490, ?x279), ?x3277 = 06t8v, genre(?x13183, ?x812), genre(?x13183, ?x600), film_crew_role(?x4276, ?x468), genre(?x3287, ?x2753), film(?x4809, ?x4276), genre(?x4276, ?x1509), titles(?x5138, ?x1619), film_release_region(?x2471, ?x2146), film_release_region(?x2471, ?x1475), award(?x3287, ?x941), titles(?x811, ?x2471), film(?x7310, ?x1619), film(?x6262, ?x2788), film_release_distribution_medium(?x4276, ?x81), film_release_distribution_medium(?x1490, ?x2099), nominated_for(?x1008, ?x1490), nominated_for(?x372, ?x1490), ?x1892 = 02vzc, ?x1475 = 05qx1, currency(?x2471, ?x170), award(?x810, ?x372), ?x468 = 02r96rf, award(?x748, ?x1008), produced_by(?x1315, ?x1039), ?x2146 = 03rk0, crewmember(?x392, ?x7675), nominated_for(?x1008, ?x8670), film(?x1104, ?x1315), titles(?x2480, ?x2788), film(?x1382, ?x218), ?x812 = 01jfsb, person(?x1619, ?x1291), ?x8670 = 04h4c9, ?x279 = 0d060g, film(?x4210, ?x204), participant(?x436, ?x6262), film(?x496, ?x392), genre(?x1619, ?x1014), award_winner(?x6262, ?x275), executive_produced_by(?x13183, ?x4857), award(?x1365, ?x372), genre(?x5992, ?x258), category(?x1382, ?x134), film(?x8394, ?x5992), production_companies(?x7538, ?x7303), ?x600 = 02n4kr, award_nominee(?x237, ?x6262), film(?x1253, ?x2471), film_festivals(?x4276, ?x11147), film_crew_role(?x2471, ?x1776), award_winner(?x13183, ?x100), ?x4210 = 02t1cp, student(?x6925, ?x6262), film_release_region(?x7538, ?x1523), films(?x2286, ?x5992), genre(?x6932, ?x1509), language(?x392, ?x393), ?x6932 = 027pfg, nominated_for(?x112, ?x7493), written_by(?x7493, ?x2533), ?x1523 = 030qb3t *> conf = 0.33 ranks of expected_values: 177, 336, 341, 370, 436, 641, 688, 794, 934, 1298, 1304, 1306, 1324, 1330, 1424, 1469, 1501 EVAL 0cj16 film_format! 07l50_1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 03kx49 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 047vp1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 0294mx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 03rg2b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 02qyv3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 0cy__l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 0hv8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 0mb8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 01rwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 04q24zv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 0jym0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 0hmm7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 0344gc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 0dckvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 0cj16 film_format! 01jc6q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 5.000 4.000 0.400 http://example.org/film/film/film_format #3042-0htcn PRED entity: 0htcn PRED relation: profession PRED expected values: 0dxtg => 87 concepts (82 used for prediction) PRED predicted values (max 10 best out of 51): 0dxtg (0.71 #2529, 0.70 #2677, 0.69 #3269), 02hrh1q (0.66 #8450, 0.65 #7118, 0.65 #8746), 03gjzk (0.43 #2531, 0.40 #2679, 0.40 #2827), 02krf9 (0.27 #2543, 0.27 #2839, 0.25 #2691), 09jwl (0.17 #6679, 0.17 #5199, 0.17 #5347), 0cbd2 (0.17 #2523, 0.16 #2671, 0.16 #3263), 012t_z (0.15 #159, 0.14 #603, 0.12 #1492), 0nbcg (0.14 #10512, 0.12 #5212, 0.12 #5360), 0dz3r (0.14 #10512, 0.11 #5183, 0.11 #5331), 016z4k (0.14 #10512, 0.09 #8293, 0.09 #7257) >> Best rule #2529 for best value: >> intensional similarity = 4 >> extensional distance = 149 >> proper extension: 07nznf; 0q9kd; 02rchht; 0byfz; 014zcr; 05ty4m; 0bxtg; 06cv1; 03f2_rc; 0c1pj; ... >> query: (?x10226, 0dxtg) <- award(?x10226, ?x198), award_nominee(?x788, ?x10226), film(?x10226, ?x1804), gender(?x10226, ?x231) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0htcn profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 82.000 0.709 http://example.org/people/person/profession #3041-0330r PRED entity: 0330r PRED relation: genre PRED expected values: 01z4y => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 73): 07s9rl0 (0.65 #1314, 0.55 #739, 0.55 #83), 01z4y (0.34 #182, 0.33 #756, 0.32 #346), 01z77k (0.26 #111, 0.13 #603, 0.12 #767), 0hcr (0.22 #1660, 0.19 #1744, 0.19 #1826), 01t_vv (0.21 #198, 0.17 #772, 0.17 #444), 01htzx (0.19 #755, 0.18 #263, 0.16 #591), 06n90 (0.19 #1654, 0.16 #1326, 0.16 #1903), 06q7n (0.18 #536, 0.15 #947, 0.15 #372), 03k9fj (0.17 #749, 0.17 #1652, 0.16 #257), 01hmnh (0.15 #1657, 0.14 #1329, 0.13 #98) >> Best rule #1314 for best value: >> intensional similarity = 3 >> extensional distance = 199 >> proper extension: 07qht4; >> query: (?x9541, 07s9rl0) <- genre(?x9541, ?x258), genre(?x5429, ?x258), ?x5429 = 02psgq >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #182 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 07s8z_l; *> query: (?x9541, 01z4y) <- award_winner(?x9541, ?x636), genre(?x9541, ?x258), honored_for(?x2126, ?x9541) *> conf = 0.34 ranks of expected_values: 2 EVAL 0330r genre 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.652 http://example.org/tv/tv_program/genre #3040-01pgzn_ PRED entity: 01pgzn_ PRED relation: currency PRED expected values: 09nqf => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.53 #22, 0.52 #25, 0.50 #10), 01nv4h (0.05 #74, 0.02 #113, 0.02 #116) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 01l2fn; 01trhmt; 07cjqy; >> query: (?x2352, 09nqf) <- friend(?x2352, ?x2697), vacationer(?x126, ?x2352), award_nominee(?x2352, ?x221) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pgzn_ currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.526 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #3039-02cbvn PRED entity: 02cbvn PRED relation: organization! PRED expected values: 07xl34 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 10): 060c4 (0.71 #106, 0.69 #145, 0.68 #184), 07xl34 (0.50 #11, 0.35 #141, 0.33 #219), 0dq_5 (0.17 #724, 0.16 #737, 0.16 #542), 05k17c (0.10 #436, 0.10 #449, 0.09 #410), 0hm4q (0.07 #216, 0.06 #138, 0.06 #203), 05c0jwl (0.04 #460, 0.04 #395, 0.04 #603), 0fj45 (0.03 #976, 0.02 #1238, 0.02 #1198), 060bp (0.03 #976, 0.02 #1238, 0.02 #1198), 08jcfy (0.02 #207, 0.02 #610, 0.02 #480), 04n1q6 (0.01 #97, 0.01 #318, 0.01 #474) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 144 >> proper extension: 02zc7f; 02bd_f; >> query: (?x4289, 060c4) <- school_type(?x4289, ?x3092), contains(?x11181, ?x4289), student(?x4289, ?x7531), administrative_division(?x6373, ?x11181) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #11 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 03q6zc; *> query: (?x4289, 07xl34) <- institution(?x4981, ?x4289), contains(?x10632, ?x4289), ?x10632 = 01hpnh, ?x4981 = 03bwzr4 *> conf = 0.50 ranks of expected_values: 2 EVAL 02cbvn organization! 07xl34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 119.000 0.705 http://example.org/organization/role/leaders./organization/leadership/organization #3038-06g4_ PRED entity: 06g4_ PRED relation: nationality PRED expected values: 07ssc => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 43): 09c7w0 (0.86 #8436, 0.81 #3372, 0.80 #9035), 02jx1 (0.33 #131, 0.29 #428, 0.28 #1319), 07ssc (0.33 #114, 0.22 #312, 0.22 #3882), 0345h (0.17 #129, 0.17 #30, 0.14 #822), 0h7x (0.17 #34, 0.11 #1123, 0.09 #2313), 06mzp (0.17 #21, 0.07 #813, 0.06 #1705), 084n_ (0.17 #94, 0.05 #589, 0.04 #886), 0k6nt (0.17 #24, 0.02 #1907, 0.02 #2105), 0cdbq (0.11 #854, 0.05 #557, 0.03 #1151), 0d060g (0.10 #502, 0.05 #8442, 0.05 #5263) >> Best rule #8436 for best value: >> intensional similarity = 3 >> extensional distance = 1404 >> proper extension: 0h1_w; 0784v1; 09lhln; 0457w0; 027rfxc; 0ct_yc; 09hd6f; 04gtq43; >> query: (?x11018, 09c7w0) <- nationality(?x11018, ?x910), place_of_birth(?x11018, ?x13481), time_zones(?x13481, ?x6582) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #114 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 040dv; *> query: (?x11018, 07ssc) <- nationality(?x11018, ?x910), influenced_by(?x5345, ?x11018), ?x5345 = 0282x *> conf = 0.33 ranks of expected_values: 3 EVAL 06g4_ nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 107.000 107.000 0.858 http://example.org/people/person/nationality #3037-04s934 PRED entity: 04s934 PRED relation: colors PRED expected values: 06fvc => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.47 #1143, 0.44 #1694, 0.32 #98), 01l849 (0.40 #96, 0.38 #115, 0.38 #1426), 06fvc (0.33 #895, 0.25 #1693, 0.17 #1142), 0jc_p (0.19 #118, 0.16 #99, 0.14 #175), 019sc (0.19 #2097, 0.18 #2116, 0.18 #1242), 09ggk (0.14 #34, 0.14 #699, 0.12 #53), 09q2t (0.14 #14, 0.04 #489, 0.03 #679), 038hg (0.13 #506, 0.12 #696, 0.11 #1437), 04mkbj (0.09 #1511, 0.09 #2043, 0.09 #1017), 036k5h (0.09 #1183, 0.09 #974, 0.09 #2095) >> Best rule #1143 for best value: >> intensional similarity = 4 >> extensional distance = 237 >> proper extension: 01bvw5; 07vht; 029d_; 0k9wp; 02zkz7; 02s8qk; 01314k; 01jpyb; 0ylsr; 01g6l8; ... >> query: (?x6396, 01g5v) <- category(?x6396, ?x134), colors(?x6396, ?x663), colors(?x8689, ?x663), ?x8689 = 03v9yw >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #895 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 200 *> proper extension: 02bjhv; 01y17m; 018m5q; 01v3ht; 01xrlm; 01y9qr; 05d9y_; 06b19; 0211jt; 03l78j; ... *> query: (?x6396, 06fvc) <- category(?x6396, ?x134), colors(?x6396, ?x663), colors(?x6856, ?x663), colors(?x260, ?x663), ?x6856 = 0jkhr *> conf = 0.33 ranks of expected_values: 3 EVAL 04s934 colors 06fvc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 142.000 142.000 0.473 http://example.org/education/educational_institution/colors #3036-0hdx8 PRED entity: 0hdx8 PRED relation: organization PRED expected values: 0b6css 041288 => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 48): 041288 (0.45 #34, 0.40 #114, 0.36 #316), 0b6css (0.45 #8, 0.36 #189, 0.35 #168), 01rz1 (0.34 #1, 0.27 #161, 0.27 #182), 04k4l (0.31 #144, 0.28 #185, 0.27 #164), 0_2v (0.31 #163, 0.30 #184, 0.29 #445), 085h1 (0.21 #181, 0.21 #202, 0.04 #10), 018cqq (0.21 #169, 0.20 #190, 0.18 #149), 034h1h (0.18 #729, 0.02 #1053), 02jxk (0.18 #2, 0.15 #162, 0.15 #183), 02_l9 (0.07 #734, 0.05 #152, 0.02 #857) >> Best rule #34 for best value: >> intensional similarity = 2 >> extensional distance = 65 >> proper extension: 0h44w; >> query: (?x9563, 041288) <- countries_spoken_in(?x254, ?x9563), ?x254 = 02h40lc >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0hdx8 organization 041288 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.448 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 0hdx8 organization 0b6css CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.448 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #3035-01934k PRED entity: 01934k PRED relation: award PRED expected values: 09qj50 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 284): 0bdw1g (0.79 #35574, 0.71 #38002, 0.71 #41242), 094qd5 (0.60 #2065, 0.49 #3681, 0.48 #3277), 09sb52 (0.54 #2061, 0.35 #3273, 0.34 #3677), 0gqyl (0.46 #2125, 0.40 #3337, 0.40 #3741), 09qwmm (0.45 #2054, 0.36 #3266, 0.34 #3670), 02y_rq5 (0.40 #2115, 0.37 #3327, 0.37 #3731), 02ppm4q (0.39 #2177, 0.30 #3793, 0.29 #3389), 099cng (0.35 #2106, 0.25 #3318, 0.24 #3722), 02x4x18 (0.33 #2153, 0.27 #3365, 0.25 #1749), 0cqgl9 (0.33 #2213, 0.27 #3829, 0.24 #3425) >> Best rule #35574 for best value: >> intensional similarity = 3 >> extensional distance = 1380 >> proper extension: 0kk9v; 0627sn; 0f1jhc; >> query: (?x8543, ?x1245) <- award_winner(?x1245, ?x8543), ceremony(?x1245, ?x78), nominated_for(?x1245, ?x144) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #46904 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2309 *> proper extension: 031rx9; 0fqy4p; 0c41qv; *> query: (?x8543, ?x757) <- award_nominee(?x6808, ?x8543), award(?x6808, ?x757) *> conf = 0.13 ranks of expected_values: 34 EVAL 01934k award 09qj50 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 132.000 132.000 0.787 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3034-03tbg6 PRED entity: 03tbg6 PRED relation: executive_produced_by PRED expected values: 0glyyw => 96 concepts (81 used for prediction) PRED predicted values (max 10 best out of 104): 0glyyw (0.27 #1707, 0.05 #1959, 0.05 #6515), 04jspq (0.25 #151, 0.20 #657, 0.20 #404), 06pj8 (0.20 #308, 0.07 #1069, 0.07 #3340), 02q_cc (0.20 #281, 0.05 #3313, 0.03 #6607), 0488g9 (0.20 #483, 0.02 #1748), 03mstc (0.20 #458, 0.02 #1723), 0ds2sb (0.20 #476), 02vyw (0.14 #848, 0.02 #1354, 0.01 #5655), 02q42j_ (0.14 #897, 0.02 #1655, 0.01 #10256), 0b13g7 (0.14 #846, 0.02 #1604, 0.01 #10205) >> Best rule #1707 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 02n9bh; 05dl1s; >> query: (?x10455, 0glyyw) <- country(?x10455, ?x390), genre(?x10455, ?x225), ?x390 = 0chghy, language(?x10455, ?x254) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03tbg6 executive_produced_by 0glyyw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 81.000 0.269 http://example.org/film/film/executive_produced_by #3033-0jhd PRED entity: 0jhd PRED relation: medal PRED expected values: 02lq67 02lq5w => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 02lq5w (0.83 #6, 0.79 #10, 0.77 #46), 02lq67 (0.82 #9, 0.78 #5, 0.76 #45) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 02j71; >> query: (?x8588, 02lq5w) <- currency(?x8588, ?x170), administrative_parent(?x11419, ?x8588), location(?x10293, ?x11419) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0jhd medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.826 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal EVAL 0jhd medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.826 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #3032-01chc7 PRED entity: 01chc7 PRED relation: film PRED expected values: 0bc1yhb 076xkps => 92 concepts (60 used for prediction) PRED predicted values (max 10 best out of 699): 09m6kg (0.41 #97976, 0.38 #97975, 0.33 #55217), 01_1hw (0.22 #1466, 0.11 #3247, 0.03 #7125), 09cr8 (0.22 #2065, 0.03 #18095, 0.02 #27000), 0cbv4g (0.22 #911, 0.03 #7125, 0.03 #74809), 02jkkv (0.22 #3326, 0.01 #5107, 0.01 #14013), 0bc1yhb (0.22 #2686, 0.01 #13373, 0.01 #18716), 062zjtt (0.22 #2489), 0g9z_32 (0.11 #3051, 0.11 #1270, 0.04 #4832), 0cc846d (0.11 #2224, 0.11 #443, 0.03 #7125), 016fyc (0.11 #56, 0.06 #39185, 0.05 #96193) >> Best rule #97976 for best value: >> intensional similarity = 2 >> extensional distance = 1771 >> proper extension: 03czrpj; 09mfvx; 05d6q1; 0fvppk; 0kcdl; 0kc9f; >> query: (?x3274, ?x1868) <- nominated_for(?x3274, ?x1868), genre(?x1868, ?x53) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #2686 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 7 *> proper extension: 0c9xjl; *> query: (?x3274, 0bc1yhb) <- film(?x3274, ?x1956), ?x1956 = 05qbckf *> conf = 0.22 ranks of expected_values: 6 EVAL 01chc7 film 076xkps CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 60.000 0.413 http://example.org/film/actor/film./film/performance/film EVAL 01chc7 film 0bc1yhb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 92.000 60.000 0.413 http://example.org/film/actor/film./film/performance/film #3031-0d05w3 PRED entity: 0d05w3 PRED relation: combatants PRED expected values: 0g970 => 249 concepts (207 used for prediction) PRED predicted values (max 10 best out of 311): 05vz3zq (0.85 #1909, 0.84 #2697, 0.84 #870), 05b7q (0.85 #1909, 0.84 #2697, 0.84 #870), 0g970 (0.85 #1909, 0.84 #2697, 0.84 #870), 0chghy (0.68 #2617, 0.61 #1829, 0.52 #4880), 0154j (0.65 #1825, 0.50 #2613, 0.50 #1564), 05qhw (0.61 #1830, 0.58 #791, 0.50 #2618), 01mk6 (0.61 #1884, 0.58 #845, 0.46 #2672), 0d060g (0.61 #1827, 0.55 #1566, 0.54 #2615), 035qy (0.61 #1845, 0.50 #2633, 0.50 #806), 09c7w0 (0.61 #2611, 0.58 #784, 0.55 #609) >> Best rule #1909 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0hw29; >> query: (?x2346, ?x7287) <- combatants(?x7287, ?x2346), combatants(?x5114, ?x2346), combatants(?x1140, ?x2346), ?x5114 = 05vz3zq >> conf = 0.85 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0d05w3 combatants 0g970 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 249.000 207.000 0.847 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #3030-02m501 PRED entity: 02m501 PRED relation: nominated_for PRED expected values: 05qbckf => 124 concepts (56 used for prediction) PRED predicted values (max 10 best out of 544): 04tqtl (0.37 #8108, 0.36 #25942, 0.33 #37297), 04t6fk (0.37 #8108, 0.36 #25942, 0.33 #37297), 076tw54 (0.37 #8108, 0.36 #25942, 0.33 #37297), 0n08r (0.37 #8108, 0.36 #25942, 0.33 #37297), 04g9gd (0.37 #8108, 0.36 #25942, 0.33 #37297), 053rxgm (0.37 #8108, 0.36 #25942, 0.33 #37297), 04vr_f (0.09 #3402, 0.07 #6645, 0.03 #34211), 03kq98 (0.08 #1705, 0.05 #84, 0.04 #4948), 06z8s_ (0.06 #3364, 0.05 #6607, 0.02 #34173), 03hkch7 (0.06 #3715, 0.05 #6958, 0.01 #71831) >> Best rule #8108 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 06dv3; 0bl2g; 0785v8; 048lv; 0170pk; 01fh9; 0170qf; 0170s4; 0c6qh; 0m31m; ... >> query: (?x9886, ?x1178) <- award(?x9886, ?x2853), ?x2853 = 09qv_s, film(?x9886, ?x1178) >> conf = 0.37 => this is the best rule for 6 predicted values *> Best rule #18120 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 97 *> proper extension: 02w5q6; 01npcy7; *> query: (?x9886, 05qbckf) <- participant(?x9886, ?x1089), place_of_birth(?x9886, ?x11360), participant(?x9886, ?x950) *> conf = 0.02 ranks of expected_values: 265 EVAL 02m501 nominated_for 05qbckf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 124.000 56.000 0.371 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #3029-0184jw PRED entity: 0184jw PRED relation: award_winner! PRED expected values: 027c924 => 94 concepts (92 used for prediction) PRED predicted values (max 10 best out of 201): 019f4v (0.50 #65, 0.35 #1699, 0.34 #1273), 0gq9h (0.50 #76, 0.35 #1699, 0.34 #1273), 0gs9p (0.45 #1352, 0.40 #2127, 0.40 #1778), 02pqp12 (0.35 #1699, 0.34 #1273, 0.33 #2125), 040njc (0.35 #1699, 0.34 #1273, 0.33 #2125), 04dn09n (0.35 #1699, 0.34 #1273, 0.33 #2125), 02n9nmz (0.35 #1699, 0.34 #1273, 0.33 #2125), 027c924 (0.31 #1285, 0.28 #1711, 0.21 #2562), 0gr51 (0.25 #98, 0.15 #946, 0.11 #1798), 09sb52 (0.25 #39, 0.11 #16592, 0.11 #15744) >> Best rule #65 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 03_gd; 0151w_; >> query: (?x7815, 019f4v) <- produced_by(?x1496, ?x7815), award_winner(?x5349, ?x7815), ?x5349 = 02jp5r >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1285 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 042l3v; 0kr5_; 0j_c; 03tf_h; 09p06; 06m6z6; 01ycck; 01n9d9; 012rng; 07rd7; ... *> query: (?x7815, 027c924) <- produced_by(?x1496, ?x7815), award(?x7815, ?x1107), ?x1107 = 019f4v *> conf = 0.31 ranks of expected_values: 8 EVAL 0184jw award_winner! 027c924 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 94.000 92.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #3028-01hx2t PRED entity: 01hx2t PRED relation: school! PRED expected values: 0jmj7 => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 173): 0jmj7 (0.79 #1108, 0.79 #388, 0.74 #748), 0713r (0.29 #35, 0.22 #755, 0.21 #485), 01yjl (0.29 #30, 0.21 #480, 0.17 #750), 05m_8 (0.22 #723, 0.22 #1714, 0.21 #363), 07l8x (0.22 #784, 0.21 #424, 0.15 #1144), 0512p (0.22 #734, 0.15 #1094, 0.15 #1905), 04wmvz (0.22 #797, 0.15 #1157, 0.14 #77), 07l4z (0.21 #518, 0.17 #788, 0.15 #1959), 0289q (0.21 #492, 0.17 #582, 0.14 #1982), 07147 (0.21 #515, 0.15 #1505, 0.15 #1956) >> Best rule #1108 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 01w3v; 03v6t; 078bz; 04hgpt; 02ln0f; 05zl0; 01j_5k; 0bwfn; 04ftdq; 01nhgd; >> query: (?x8479, 0jmj7) <- colors(?x8479, ?x3315), major_field_of_study(?x8479, ?x6859), school(?x7060, ?x8479), ?x6859 = 01tbp, team(?x2010, ?x7060) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hx2t school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.788 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #3027-01vrnsk PRED entity: 01vrnsk PRED relation: artists! PRED expected values: 06by7 => 133 concepts (79 used for prediction) PRED predicted values (max 10 best out of 236): 06by7 (0.52 #5957, 0.51 #2206, 0.50 #11894), 016clz (0.45 #1252, 0.25 #2813, 0.24 #3126), 017_qw (0.39 #5060, 0.37 #5372, 0.37 #4748), 06j6l (0.36 #11921, 0.29 #7234, 0.27 #984), 0xhtw (0.35 #5952, 0.30 #6890, 0.29 #1264), 05bt6j (0.35 #11916, 0.30 #979, 0.29 #1291), 0m0jc (0.33 #8, 0.23 #13123, 0.16 #1881), 025sc50 (0.32 #11923, 0.24 #7236, 0.23 #986), 0gywn (0.30 #994, 0.28 #2243, 0.26 #11931), 0glt670 (0.29 #7226, 0.23 #976, 0.23 #2225) >> Best rule #5957 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 09g0h; >> query: (?x6947, 06by7) <- group(?x6947, ?x1136), role(?x6947, ?x212), instrumentalists(?x315, ?x6947) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrnsk artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 79.000 0.518 http://example.org/music/genre/artists #3026-06k02 PRED entity: 06k02 PRED relation: role PRED expected values: 01vdm0 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 103): 042v_gx (0.56 #202, 0.21 #2740, 0.20 #1760), 0342h (0.44 #199, 0.36 #2737, 0.34 #1367), 02sgy (0.44 #200, 0.23 #2738, 0.22 #1758), 05842k (0.44 #266, 0.15 #2804, 0.13 #1824), 018vs (0.33 #206, 0.14 #401, 0.14 #1374), 01vdm0 (0.27 #2762, 0.26 #1950, 0.26 #487), 0l15bq (0.22 #229, 0.14 #424, 0.14 #132), 01vj9c (0.22 #208, 0.13 #2746, 0.13 #1766), 0dwt5 (0.22 #273, 0.10 #2537, 0.06 #371), 01qzyz (0.22 #209, 0.10 #2537, 0.04 #1850) >> Best rule #202 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0l12d; 0lzkm; 01gg59; 0677ng; >> query: (?x2306, 042v_gx) <- artists(?x10290, ?x2306), award_winner(?x1656, ?x2306), instrumentalists(?x316, ?x2306), ?x10290 = 03ckfl9 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2762 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 333 *> proper extension: 0jfx1; 02ryx0; 0kp2_; 0cj2w; 05mxw33; *> query: (?x2306, 01vdm0) <- profession(?x2306, ?x131), award(?x2306, ?x1079), role(?x2306, ?x228) *> conf = 0.27 ranks of expected_values: 6 EVAL 06k02 role 01vdm0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 92.000 92.000 0.556 http://example.org/music/artist/track_contributions./music/track_contribution/role #3025-0436f4 PRED entity: 0436f4 PRED relation: nationality PRED expected values: 09c7w0 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.87 #101, 0.86 #301, 0.77 #401), 0cymp (0.27 #8115), 02_286 (0.27 #8115), 059rby (0.27 #8115), 07ssc (0.24 #215, 0.10 #1916, 0.09 #515), 03rjj (0.20 #105, 0.18 #305, 0.12 #205), 02jx1 (0.12 #1934, 0.11 #533, 0.11 #3636), 03rk0 (0.06 #8161, 0.05 #8464, 0.05 #8664), 06q1r (0.06 #277, 0.02 #577, 0.01 #2378), 0d060g (0.05 #2610, 0.04 #2409, 0.04 #407) >> Best rule #101 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 02lg3y; 0bl60p; >> query: (?x446, 09c7w0) <- award_nominee(?x2965, ?x446), award_nominee(?x1651, ?x446), ?x2965 = 01dy7j, ?x1651 = 02lg9w >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0436f4 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.867 http://example.org/people/person/nationality #3024-09qh1 PRED entity: 09qh1 PRED relation: religion PRED expected values: 0n2g => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 30): 0c8wxp (0.24 #186, 0.23 #366, 0.22 #546), 0kpl (0.18 #1450, 0.16 #1765, 0.15 #1945), 03_gx (0.13 #1454, 0.12 #509, 0.12 #284), 0631_ (0.08 #233, 0.06 #188, 0.03 #818), 06nzl (0.07 #330, 0.07 #420, 0.07 #465), 01lp8 (0.07 #361, 0.06 #136, 0.06 #946), 0g5llry (0.06 #163, 0.03 #253, 0.02 #568), 019cr (0.06 #191, 0.04 #371, 0.03 #596), 0v53x (0.06 #209, 0.02 #299, 0.02 #884), 0kq2 (0.05 #1458, 0.05 #1773, 0.04 #2043) >> Best rule #186 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 0157m; >> query: (?x3627, 0c8wxp) <- participant(?x3627, ?x3628), celebrities_impersonated(?x3649, ?x3627), type_of_union(?x3627, ?x566) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #1768 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 413 *> proper extension: 03sbs; 07h1q; *> query: (?x3627, 0n2g) <- gender(?x3627, ?x231), ?x231 = 05zppz, influenced_by(?x3627, ?x11626) *> conf = 0.03 ranks of expected_values: 17 EVAL 09qh1 religion 0n2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 116.000 116.000 0.242 http://example.org/people/person/religion #3023-01svw8n PRED entity: 01svw8n PRED relation: nationality PRED expected values: 09c7w0 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 39): 09c7w0 (0.91 #501, 0.78 #6003, 0.77 #3101), 06n3y (0.49 #6205), 059g4 (0.49 #6205), 02jx1 (0.32 #433, 0.26 #1033, 0.23 #2233), 07ssc (0.27 #215, 0.21 #715, 0.17 #2015), 0d060g (0.11 #907, 0.09 #207, 0.08 #1807), 03rt9 (0.09 #213, 0.06 #713, 0.05 #813), 0f8l9c (0.09 #222, 0.03 #6126, 0.02 #4524), 0ctw_b (0.09 #227, 0.01 #1427, 0.01 #1727), 03rk0 (0.08 #5148, 0.08 #6551, 0.08 #6851) >> Best rule #501 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 02zrv7; >> query: (?x3930, 09c7w0) <- participant(?x3930, ?x6817), participant(?x2697, ?x3930), person(?x9858, ?x3930) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01svw8n nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.909 http://example.org/people/person/nationality #3022-01f8hf PRED entity: 01f8hf PRED relation: genre PRED expected values: 02kdv5l => 110 concepts (107 used for prediction) PRED predicted values (max 10 best out of 96): 07s9rl0 (0.71 #3663, 0.70 #1890, 0.69 #10286), 02kdv5l (0.61 #2246, 0.48 #4139, 0.45 #239), 01hmnh (0.42 #1197, 0.30 #1433, 0.28 #2023), 05p553 (0.41 #10646, 0.36 #6268, 0.35 #8162), 02l7c8 (0.41 #132, 0.38 #368, 0.35 #840), 0lsxr (0.33 #4145, 0.20 #1780, 0.19 #1072), 04xvlr (0.25 #1891, 0.20 #2837, 0.19 #2718), 02n4kr (0.23 #4144, 0.13 #4380, 0.13 #1779), 060__y (0.20 #1904, 0.20 #2850, 0.20 #2495), 04xvh5 (0.20 #1922, 0.15 #505, 0.12 #2868) >> Best rule #3663 for best value: >> intensional similarity = 4 >> extensional distance = 407 >> proper extension: 01h72l; >> query: (?x4680, 07s9rl0) <- nominated_for(?x2214, ?x4680), award_winner(?x4680, ?x800), genre(?x4680, ?x571), honored_for(?x3579, ?x4680) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2246 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 212 *> proper extension: 06n90; *> query: (?x4680, 02kdv5l) <- genre(?x4680, ?x12344), genre(?x4680, ?x1013), genre(?x8370, ?x12344), ?x8370 = 07ghq, ?x1013 = 06n90 *> conf = 0.61 ranks of expected_values: 2 EVAL 01f8hf genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 107.000 0.711 http://example.org/film/film/genre #3021-01w60_p PRED entity: 01w60_p PRED relation: location PRED expected values: 0c_m3 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 223): 02_286 (0.21 #2446, 0.19 #4052, 0.17 #6461), 030qb3t (0.15 #58714, 0.15 #60320, 0.15 #41850), 01531 (0.11 #4173, 0.08 #3370, 0.05 #2567), 0cr3d (0.10 #12995, 0.10 #6569, 0.07 #12192), 02dtg (0.08 #4039, 0.07 #2433, 0.06 #3236), 0h7h6 (0.08 #4105, 0.04 #3302, 0.03 #8924), 0fhp9 (0.07 #2452, 0.05 #12090, 0.04 #16909), 05qtj (0.06 #9877, 0.06 #15500, 0.05 #17910), 04lh6 (0.06 #3647, 0.06 #4450, 0.03 #9269), 09c7w0 (0.06 #3, 0.01 #9640, 0.01 #15263) >> Best rule #2446 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0459z; >> query: (?x2169, 02_286) <- influenced_by(?x3403, ?x2169), instrumentalists(?x227, ?x2169), location(?x2169, ?x3778) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #4285 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 02ndbd; 01pcmd; 086sj; 01zlh5; 03mstc; 0488g9; *> query: (?x2169, 0c_m3) <- award_nominee(?x215, ?x2169), location(?x2169, ?x3778), inductee(?x1091, ?x2169) *> conf = 0.06 ranks of expected_values: 12 EVAL 01w60_p location 0c_m3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 116.000 116.000 0.209 http://example.org/people/person/places_lived./people/place_lived/location #3020-016zp5 PRED entity: 016zp5 PRED relation: award_winner! PRED expected values: 0bs0bh => 86 concepts (80 used for prediction) PRED predicted values (max 10 best out of 226): 0gqy2 (0.37 #19276, 0.37 #19275, 0.36 #11138), 02w9sd7 (0.37 #19276, 0.37 #19275, 0.36 #11138), 02x73k6 (0.37 #19276, 0.37 #19275, 0.36 #11138), 0bdwqv (0.37 #19276, 0.37 #19275, 0.36 #11138), 0bfvd4 (0.37 #19276, 0.37 #19275, 0.36 #11138), 027b9ly (0.25 #240, 0.11 #28277, 0.10 #1097), 09d28z (0.25 #300, 0.11 #28277, 0.10 #1157), 0gr4k (0.25 #32, 0.10 #889, 0.10 #461), 025m8y (0.25 #97, 0.10 #954, 0.10 #526), 02x4wr9 (0.25 #133, 0.10 #990, 0.10 #562) >> Best rule #19276 for best value: >> intensional similarity = 2 >> extensional distance = 1462 >> proper extension: 0411q; 0hl3d; 01lmj3q; 026ps1; 06cc_1; 07g2b; 04rcr; 03f5spx; 0b68vs; 05pdbs; ... >> query: (?x5495, ?x3209) <- award(?x5495, ?x3209), award_winner(?x628, ?x5495) >> conf = 0.37 => this is the best rule for 5 predicted values *> Best rule #2670 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 164 *> proper extension: 03bpn6; 02gyl0; 02sh8y; 0m0hw; 01p1z_; 01v90t; 03xx3m; 01m42d0; 023nlj; 05xpv; ... *> query: (?x5495, 0bs0bh) <- award(?x5495, ?x3066), film(?x5495, ?x972), ?x3066 = 0gqy2 *> conf = 0.04 ranks of expected_values: 105 EVAL 016zp5 award_winner! 0bs0bh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 86.000 80.000 0.368 http://example.org/award/award_category/winners./award/award_honor/award_winner #3019-019vgs PRED entity: 019vgs PRED relation: nationality PRED expected values: 09c7w0 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 31): 09c7w0 (0.80 #3409, 0.79 #1, 0.79 #6317), 07ssc (0.16 #817, 0.13 #2118, 0.13 #2320), 02jx1 (0.15 #835, 0.11 #2539, 0.11 #2740), 0d060g (0.14 #7, 0.05 #409, 0.05 #208), 03rk0 (0.08 #4559, 0.08 #4959, 0.06 #9165), 0345h (0.06 #3238, 0.06 #2234, 0.05 #1533), 0h7x (0.03 #35, 0.03 #2238, 0.03 #1537), 04hqz (0.03 #79), 0f8l9c (0.03 #824, 0.03 #3330, 0.03 #3129), 03rt9 (0.03 #815, 0.03 #1615, 0.02 #2116) >> Best rule #3409 for best value: >> intensional similarity = 3 >> extensional distance = 608 >> proper extension: 06rnl9; >> query: (?x3853, 09c7w0) <- award_winner(?x1474, ?x3853), student(?x621, ?x3853), colors(?x621, ?x332) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019vgs nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.802 http://example.org/people/person/nationality #3018-01vwbts PRED entity: 01vwbts PRED relation: award PRED expected values: 0gqz2 02f6ym => 111 concepts (102 used for prediction) PRED predicted values (max 10 best out of 283): 02f73b (0.59 #1074, 0.55 #3059, 0.54 #3853), 02f72_ (0.57 #3002, 0.55 #3796, 0.50 #1017), 01bgqh (0.56 #837, 0.39 #3616, 0.38 #2822), 0gqz2 (0.50 #81, 0.22 #8021, 0.19 #4448), 01by1l (0.49 #2892, 0.48 #3686, 0.41 #907), 02f72n (0.43 #3720, 0.43 #2926, 0.38 #941), 02v1m7 (0.40 #3687, 0.38 #2893, 0.38 #908), 0c4z8 (0.38 #866, 0.26 #4042, 0.25 #6424), 03qbh5 (0.38 #997, 0.24 #4173, 0.24 #6952), 02f71y (0.32 #2961, 0.31 #3755, 0.31 #976) >> Best rule #1074 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0152cw; 0frsw; >> query: (?x4693, 02f73b) <- instrumentalists(?x227, ?x4693), award(?x4693, ?x2877), ?x2877 = 02f5qb >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #81 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 05q9g1; *> query: (?x4693, 0gqz2) <- award_nominee(?x3890, ?x4693), ?x3890 = 01gg59, award(?x4693, ?x2877) *> conf = 0.50 ranks of expected_values: 4, 11 EVAL 01vwbts award 02f6ym CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 111.000 102.000 0.594 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vwbts award 0gqz2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 111.000 102.000 0.594 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3017-027kmrb PRED entity: 027kmrb PRED relation: executive_produced_by! PRED expected values: 0170z3 => 108 concepts (79 used for prediction) PRED predicted values (max 10 best out of 344): 0h1v19 (0.14 #149, 0.05 #4384, 0.04 #2265), 0bdjd (0.11 #10590, 0.10 #12180, 0.10 #11650), 0kvbl6 (0.11 #10590, 0.10 #12180, 0.10 #11650), 06rhz7 (0.11 #10590, 0.10 #12180, 0.10 #11650), 049xgc (0.07 #2439, 0.04 #5088, 0.04 #9325), 01pj_5 (0.07 #2366, 0.04 #5015, 0.03 #5544), 03s6l2 (0.07 #2139, 0.04 #4788, 0.03 #5317), 0gwjw0c (0.07 #2503, 0.04 #5152, 0.03 #5681), 0fsd9t (0.07 #2584, 0.04 #5233, 0.03 #5762), 03ntbmw (0.07 #2639, 0.04 #5288, 0.03 #5817) >> Best rule #149 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02qx1m2; >> query: (?x5647, 0h1v19) <- award_winner(?x382, ?x5647), ?x382 = 086k8, award(?x5647, ?x198) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027kmrb executive_produced_by! 0170z3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 79.000 0.143 http://example.org/film/film/executive_produced_by #3016-0jqkh PRED entity: 0jqkh PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #182, 0.81 #172, 0.81 #76), 07c52 (0.12 #13, 0.05 #45, 0.04 #139), 07z4p (0.04 #47, 0.03 #141, 0.02 #201), 02nxhr (0.03 #108, 0.03 #264, 0.03 #223) >> Best rule #182 for best value: >> intensional similarity = 3 >> extensional distance = 1198 >> proper extension: 0gkz15s; >> query: (?x7666, 029j_) <- currency(?x7666, ?x170), film(?x8065, ?x7666), award_nominee(?x521, ?x8065) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jqkh film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.816 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #3015-07dzf PRED entity: 07dzf PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.92 #16, 0.90 #5, 0.88 #11) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 03_3d; 06qd3; 06t2t; 06f32; 07fsv; 06s9y; >> query: (?x5360, 0hzc9wc) <- currency(?x5360, ?x170), exported_to(?x311, ?x5360), organization(?x5360, ?x127) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07dzf administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.925 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #3014-01p1z_ PRED entity: 01p1z_ PRED relation: student! PRED expected values: 06182p => 122 concepts (89 used for prediction) PRED predicted values (max 10 best out of 296): 0bwfn (0.27 #275, 0.25 #801, 0.17 #1327), 07tgn (0.18 #17, 0.17 #543, 0.06 #3699), 03ksy (0.17 #1158, 0.12 #3788, 0.09 #106), 01w5m (0.11 #3787, 0.09 #11151, 0.08 #11677), 0373qt (0.09 #325, 0.08 #851, 0.02 #1903), 0yjf0 (0.09 #48, 0.08 #574, 0.01 #7938), 0gk7z (0.09 #362, 0.08 #888), 031vy_ (0.09 #277, 0.08 #803), 065y4w7 (0.08 #20528, 0.06 #34732, 0.05 #35784), 08815 (0.08 #1054, 0.07 #1580, 0.06 #2106) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01q4qv; 0jgwf; 014hdb; >> query: (?x6993, 0bwfn) <- award_winner(?x9171, ?x6993), ?x9171 = 05h5nb8, award(?x6993, ?x198) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1876 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 03bxh; *> query: (?x6993, 06182p) <- student(?x8589, ?x6993), nationality(?x6993, ?x94), place_of_burial(?x6993, ?x3153), state_province_region(?x8589, ?x335) *> conf = 0.04 ranks of expected_values: 34 EVAL 01p1z_ student! 06182p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 122.000 89.000 0.273 http://example.org/education/educational_institution/students_graduates./education/education/student #3013-096gm PRED entity: 096gm PRED relation: capital! PRED expected values: 0cgs4 => 224 concepts (68 used for prediction) PRED predicted values (max 10 best out of 188): 02k54 (0.20 #416, 0.20 #283, 0.12 #549), 02psqkz (0.20 #863, 0.14 #998, 0.11 #1932), 01znc_ (0.20 #434, 0.12 #567, 0.07 #1102), 0163v (0.20 #312, 0.12 #578, 0.07 #1113), 04gzd (0.20 #276, 0.12 #542, 0.05 #7360), 015qh (0.12 #566, 0.10 #833, 0.07 #1101), 03bxbql (0.12 #588, 0.10 #855, 0.07 #1123), 04gqr (0.12 #600, 0.02 #7294, 0.02 #7565), 09c7w0 (0.11 #667, 0.07 #935, 0.07 #1334), 03rjj (0.11 #671, 0.07 #939, 0.06 #1473) >> Best rule #416 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 09949m; >> query: (?x4962, 02k54) <- contains(?x1536, ?x4962), place_of_birth(?x4379, ?x4962), category(?x4962, ?x134), time_zones(?x4962, ?x10735), ?x10735 = 03plfd >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 096gm capital! 0cgs4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 224.000 68.000 0.200 http://example.org/location/country/capital #3012-0dr3sl PRED entity: 0dr3sl PRED relation: prequel! PRED expected values: 02qydsh => 79 concepts (27 used for prediction) PRED predicted values (max 10 best out of 65): 0fpgp26 (0.07 #329, 0.04 #1042, 0.02 #2290), 03y0pn (0.04 #121, 0.04 #299, 0.03 #477), 0bpm4yw (0.04 #74, 0.04 #252, 0.03 #430), 0gffmn8 (0.04 #59, 0.04 #237, 0.01 #1307), 013q07 (0.04 #225, 0.04 #1116), 05nlx4 (0.04 #298, 0.03 #476, 0.03 #654), 031778 (0.04 #222, 0.03 #400, 0.03 #578), 03177r (0.04 #232, 0.03 #410, 0.03 #588), 09v8clw (0.04 #356, 0.03 #534, 0.01 #1782), 063fh9 (0.04 #291, 0.03 #469, 0.01 #1717) >> Best rule #329 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 06_sc3; 02bj22; >> query: (?x2868, 0fpgp26) <- genre(?x2868, ?x258), film_crew_role(?x2868, ?x468), region(?x2868, ?x512), prequel(?x4656, ?x2868) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #324 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 06_sc3; 02bj22; *> query: (?x2868, 02qydsh) <- genre(?x2868, ?x258), film_crew_role(?x2868, ?x468), region(?x2868, ?x512), prequel(?x4656, ?x2868) *> conf = 0.04 ranks of expected_values: 14 EVAL 0dr3sl prequel! 02qydsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 79.000 27.000 0.074 http://example.org/film/film/prequel #3011-02p8v8 PRED entity: 02p8v8 PRED relation: student! PRED expected values: 02y9bj => 96 concepts (64 used for prediction) PRED predicted values (max 10 best out of 180): 08815 (0.31 #6314, 0.12 #2632, 0.08 #11574), 0bwfn (0.28 #7638, 0.26 #8690, 0.17 #11846), 022xml (0.25 #51), 03ksy (0.17 #2736, 0.12 #3788, 0.11 #11678), 01mpwj (0.15 #2737, 0.09 #3789, 0.08 #3263), 017z88 (0.12 #7446, 0.12 #8498, 0.10 #6920), 01w3v (0.12 #6327, 0.02 #14217, 0.02 #16848), 065y4w7 (0.10 #11586, 0.08 #14216, 0.07 #16847), 05zl0 (0.09 #6513, 0.03 #3357, 0.03 #3883), 07tds (0.09 #674, 0.08 #6460, 0.02 #11720) >> Best rule #6314 for best value: >> intensional similarity = 3 >> extensional distance = 180 >> proper extension: 0bkg4; 019r_1; 02x8mt; 016wvy; 02nygk; >> query: (?x9686, 08815) <- student(?x3779, ?x9686), institution(?x6117, ?x3779), ?x6117 = 02m4yg >> conf = 0.31 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02p8v8 student! 02y9bj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 64.000 0.313 http://example.org/education/educational_institution/students_graduates./education/education/student #3010-02f4s3 PRED entity: 02f4s3 PRED relation: student PRED expected values: 02t_99 => 133 concepts (60 used for prediction) PRED predicted values (max 10 best out of 752): 017r13 (0.10 #1093, 0.09 #3184, 0.08 #5275), 02jsgf (0.10 #679, 0.09 #2770, 0.08 #4861), 02ndbd (0.10 #113, 0.09 #2204, 0.01 #35660), 02nwxc (0.10 #994, 0.04 #3085, 0.01 #36541), 01gv_f (0.10 #622, 0.04 #2713, 0.01 #36169), 06y9c2 (0.10 #87, 0.04 #2178, 0.01 #35634), 0chsq (0.10 #63, 0.04 #2154, 0.01 #35610), 073v6 (0.09 #2617, 0.05 #526, 0.02 #17254), 06pwf6 (0.06 #8826, 0.06 #13008, 0.02 #17190), 024y6w (0.06 #13999, 0.03 #9817, 0.02 #24454) >> Best rule #1093 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 03v52f; >> query: (?x9676, 017r13) <- category(?x9676, ?x134), state_province_region(?x9676, ?x3818), ?x3818 = 03v0t, ?x134 = 08mbj5d >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02f4s3 student 02t_99 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 60.000 0.095 http://example.org/education/educational_institution/students_graduates./education/education/student #3009-03fcbb PRED entity: 03fcbb PRED relation: currency PRED expected values: 09nqf => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.72 #73, 0.66 #79, 0.64 #13), 01nv4h (0.11 #56, 0.11 #134, 0.11 #146), 0ptk_ (0.05 #63, 0.04 #81, 0.03 #135), 02l6h (0.03 #64, 0.02 #118, 0.01 #136) >> Best rule #73 for best value: >> intensional similarity = 4 >> extensional distance = 342 >> proper extension: 01qwb5; 04gd8j; >> query: (?x14069, 09nqf) <- contains(?x7466, ?x14069), place_of_death(?x4863, ?x7466), adjoins(?x7466, ?x1879), organization(?x346, ?x14069) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03fcbb currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.715 http://example.org/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency #3008-02r22gf PRED entity: 02r22gf PRED relation: award! PRED expected values: 09dvgb8 => 53 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2630): 0c94fn (0.82 #30366, 0.80 #40493, 0.79 #40494), 094tsh6 (0.82 #30366, 0.80 #40493, 0.79 #40494), 095zvfg (0.82 #30366, 0.80 #40493, 0.79 #40494), 0b6mgp_ (0.82 #30366, 0.80 #40493, 0.79 #40494), 09dvgb8 (0.53 #37120, 0.10 #8968, 0.08 #15716), 02kxbwx (0.50 #3552, 0.50 #179, 0.44 #23797), 03_gd (0.50 #3542, 0.50 #169, 0.33 #23787), 03hy3g (0.50 #5226, 0.50 #1853, 0.29 #28846), 02kxbx3 (0.50 #986, 0.39 #24604, 0.38 #4359), 01ts_3 (0.50 #2053, 0.38 #5426, 0.28 #25671) >> Best rule #30366 for best value: >> intensional similarity = 5 >> extensional distance = 19 >> proper extension: 02x17c2; >> query: (?x637, ?x1585) <- award_winner(?x637, ?x9151), award_winner(?x637, ?x1585), ceremony(?x637, ?x2032), crewmember(?x5960, ?x9151), featured_film_locations(?x5960, ?x1523) >> conf = 0.82 => this is the best rule for 4 predicted values *> Best rule #37120 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 30 *> proper extension: 047xyn; *> query: (?x637, ?x8415) <- award_winner(?x637, ?x3574), award_winner(?x637, ?x1933), award_nominee(?x8415, ?x3574), crewmember(?x308, ?x3574), nominated_for(?x1933, ?x324) *> conf = 0.53 ranks of expected_values: 5 EVAL 02r22gf award! 09dvgb8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 25.000 0.816 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #3007-02cyfz PRED entity: 02cyfz PRED relation: music! PRED expected values: 01q2nx 072r5v => 137 concepts (86 used for prediction) PRED predicted values (max 10 best out of 883): 04ynx7 (0.06 #61329, 0.06 #41541, 0.06 #84084), 02ht1k (0.06 #1348, 0.04 #3326, 0.03 #4315), 01s7w3 (0.05 #3817, 0.04 #7773, 0.03 #10740), 07bzz7 (0.05 #3480, 0.04 #7436, 0.02 #9414), 09d3b7 (0.04 #3788, 0.03 #7744, 0.02 #9722), 0pdp8 (0.04 #1206, 0.03 #6151, 0.03 #7140), 08rr3p (0.04 #1253, 0.03 #3231, 0.02 #4220), 0jzw (0.04 #1055, 0.03 #3033, 0.02 #4022), 0888c3 (0.04 #1777, 0.03 #3755, 0.02 #4744), 03h3x5 (0.04 #1241, 0.02 #4208, 0.02 #6186) >> Best rule #61329 for best value: >> intensional similarity = 3 >> extensional distance = 1199 >> proper extension: 024rbz; 01nzs7; 0gp9mp; 05wjnt; 05hdf; 07xr3w; 01lqnff; 03cp7b3; 0f3zsq; >> query: (?x2214, ?x299) <- award_winner(?x10742, ?x2214), nominated_for(?x2214, ?x299), film(?x556, ?x299) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02cyfz music! 072r5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 86.000 0.062 http://example.org/film/film/music EVAL 02cyfz music! 01q2nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 86.000 0.062 http://example.org/film/film/music #3006-048cl PRED entity: 048cl PRED relation: nationality PRED expected values: 01k6y1 => 175 concepts (133 used for prediction) PRED predicted values (max 10 best out of 118): 09c7w0 (0.87 #12914, 0.82 #12815, 0.78 #12519), 06jtd (0.40 #12420, 0.22 #8270), 0156q (0.39 #12120, 0.28 #12020, 0.27 #12121), 02jx1 (0.38 #4952, 0.36 #1308, 0.36 #3177), 07nf6 (0.28 #12020, 0.27 #12121, 0.27 #6101), 018_7x (0.28 #12020, 0.27 #12121, 0.27 #6101), 03rt9 (0.27 #1290, 0.25 #8763, 0.24 #7973), 0150n (0.27 #6101, 0.25 #10833, 0.25 #9058), 0h7x (0.25 #131, 0.24 #1604, 0.22 #1702), 0f8l9c (0.25 #8763, 0.24 #7973, 0.24 #9157) >> Best rule #12914 for best value: >> intensional similarity = 4 >> extensional distance = 1070 >> proper extension: 0456xp; 01g4zr; 01fdc0; 03h2p5; 016kft; 02c7lt; 0gpmp; >> query: (?x7509, 09c7w0) <- student(?x7508, ?x7509), place_of_birth(?x7509, ?x12802), nationality(?x7509, ?x512), region(?x54, ?x512) >> conf = 0.87 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 048cl nationality 01k6y1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 175.000 133.000 0.870 http://example.org/people/person/nationality #3005-01hmnh PRED entity: 01hmnh PRED relation: titles PRED expected values: 0gj8nq2 02dpl9 017jd9 => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1523): 016z7s (0.60 #11677, 0.25 #7392, 0.22 #25961), 0209hj (0.60 #11503, 0.25 #7218, 0.22 #25787), 08y2fn (0.60 #12420, 0.25 #8135, 0.22 #26704), 04x4gw (0.60 #12801, 0.25 #8516, 0.20 #11371), 02ll45 (0.60 #12094, 0.25 #7809, 0.20 #10664), 09p3_s (0.60 #12160, 0.25 #7875, 0.17 #26444), 0qmd5 (0.60 #11811, 0.25 #7526, 0.17 #26095), 011yr9 (0.60 #11956, 0.25 #7671, 0.17 #26240), 02z0f6l (0.60 #12361, 0.25 #8076, 0.17 #26645), 08sfxj (0.60 #12117, 0.25 #7832, 0.17 #26401) >> Best rule #11677 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 07ssc; >> query: (?x1510, 016z7s) <- titles(?x1510, ?x5759), titles(?x1510, ?x4273), titles(?x1510, ?x2207), ?x5759 = 03prz_, nominated_for(?x2022, ?x2207), film_crew_role(?x4273, ?x137) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #39043 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 58 *> proper extension: 0jgd; 04306rv; 0f8l9c; 04jjy; 0345h; 01cgz; 06mkj; 0d05w3; 06nm1; 0653m; ... *> query: (?x1510, 02dpl9) <- titles(?x1510, ?x5759), titles(?x1510, ?x3053), titles(?x1510, ?x1804), nominated_for(?x143, ?x5759), film_release_region(?x3053, ?x142), honored_for(?x8964, ?x1804) *> conf = 0.03 ranks of expected_values: 1466 EVAL 01hmnh titles 017jd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.600 http://example.org/media_common/netflix_genre/titles EVAL 01hmnh titles 02dpl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 53.000 0.600 http://example.org/media_common/netflix_genre/titles EVAL 01hmnh titles 0gj8nq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 53.000 0.600 http://example.org/media_common/netflix_genre/titles #3004-0k3ll PRED entity: 0k3ll PRED relation: contains! PRED expected values: 05k7sb => 129 concepts (58 used for prediction) PRED predicted values (max 10 best out of 127): 05k7sb (0.84 #8090, 0.61 #45865, 0.61 #6292), 059f4 (0.79 #9888, 0.61 #45865, 0.61 #6292), 04_1l0v (0.65 #4945, 0.49 #11242, 0.43 #14839), 09c7w0 (0.63 #4497, 0.59 #43169, 0.58 #49464), 029jpy (0.61 #6292, 0.60 #16186, 0.58 #21587), 059g4 (0.61 #6292, 0.60 #16186, 0.58 #21587), 06btq (0.61 #6292, 0.60 #16186, 0.58 #21587), 0k3ll (0.34 #17986, 0.34 #28784, 0.27 #48562), 059rby (0.34 #9009, 0.19 #44089, 0.18 #46786), 07z1m (0.29 #3688, 0.28 #2791, 0.07 #9081) >> Best rule #8090 for best value: >> intensional similarity = 5 >> extensional distance = 56 >> proper extension: 0g_wn2; >> query: (?x9504, ?x2020) <- second_level_divisions(?x94, ?x9504), county_seat(?x9504, ?x3046), ?x94 = 09c7w0, time_zones(?x9504, ?x2674), state(?x3046, ?x2020) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k3ll contains! 05k7sb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 58.000 0.845 http://example.org/location/location/contains #3003-0_3cs PRED entity: 0_3cs PRED relation: source PRED expected values: 0jbk9 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #47, 0.91 #64, 0.90 #45) >> Best rule #47 for best value: >> intensional similarity = 4 >> extensional distance = 196 >> proper extension: 0qlrh; >> query: (?x854, 0jbk9) <- category(?x854, ?x134), time_zones(?x854, ?x2674), ?x134 = 08mbj5d, county(?x854, ?x855) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_3cs source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.919 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #3002-04v3q PRED entity: 04v3q PRED relation: administrative_parent PRED expected values: 02j71 => 191 concepts (111 used for prediction) PRED predicted values (max 10 best out of 42): 02j71 (0.87 #8379, 0.86 #9758, 0.86 #14161), 09c7w0 (0.43 #8091, 0.41 #4929, 0.40 #5753), 03rjj (0.40 #141, 0.33 #4, 0.10 #8231), 02j9z (0.28 #9745, 0.25 #137, 0.21 #5889), 04swx (0.28 #9745, 0.25 #137, 0.21 #5889), 02qkt (0.28 #9745, 0.25 #137, 0.21 #5889), 07ssc (0.12 #557, 0.10 #3016, 0.07 #2471), 0d05w3 (0.07 #457, 0.05 #1002, 0.05 #1138), 049nq (0.04 #1460, 0.04 #1323, 0.04 #2007), 0f8l9c (0.04 #13615, 0.03 #3987) >> Best rule #8379 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0hg5; 04w58; 07dvs; 06sff; 0167v; 04xn_; 05b7q; 04hhv; 04ty8; >> query: (?x1061, 02j71) <- country(?x4045, ?x1061), contains(?x6304, ?x1061), ?x6304 = 02qkt, organization(?x1061, ?x312) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04v3q administrative_parent 02j71 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 111.000 0.866 http://example.org/base/aareas/schema/administrative_area/administrative_parent #3001-04j53 PRED entity: 04j53 PRED relation: organization PRED expected values: 07t65 => 139 concepts (135 used for prediction) PRED predicted values (max 10 best out of 51): 07t65 (0.94 #555, 0.93 #95, 0.93 #417), 02vk52z (0.88 #600, 0.88 #554, 0.87 #1480), 0b6css (0.63 #995, 0.56 #1851, 0.55 #104), 0_2v (0.63 #995, 0.56 #1851, 0.52 #97), 02jxk (0.63 #995, 0.56 #1851, 0.45 #142), 04k4l (0.63 #995, 0.56 #1851, 0.41 #121), 018cqq (0.48 #105, 0.48 #12, 0.43 #82), 0j7v_ (0.40 #306, 0.34 #237, 0.32 #2570), 0gkjy (0.35 #561, 0.30 #331, 0.29 #677), 041288 (0.35 #1613, 0.33 #317, 0.33 #1891) >> Best rule #555 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 05rznz; >> query: (?x3040, 07t65) <- adjoins(?x774, ?x3040), countries_within(?x455, ?x3040), form_of_government(?x3040, ?x1926) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04j53 organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 135.000 0.940 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #3000-01nn3m PRED entity: 01nn3m PRED relation: role PRED expected values: 05842k => 102 concepts (89 used for prediction) PRED predicted values (max 10 best out of 121): 05842k (0.71 #72, 0.50 #849, 0.31 #169), 026t6 (0.60 #780, 0.46 #100, 0.32 #3022), 0342h (0.42 #2537, 0.41 #199, 0.39 #3517), 02sgy (0.35 #201, 0.25 #2931, 0.24 #590), 0l14qv (0.35 #977, 0.29 #6, 0.18 #395), 02hnl (0.32 #3022, 0.29 #292, 0.24 #4689), 06ch55 (0.32 #3022, 0.29 #292, 0.23 #4688), 013y1f (0.25 #1006, 0.16 #3547, 0.15 #3741), 042v_gx (0.24 #202, 0.23 #1758, 0.23 #2540), 018vs (0.21 #984, 0.18 #3525, 0.17 #3719) >> Best rule #72 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 026dx; >> query: (?x12623, 05842k) <- profession(?x12623, ?x1032), nationality(?x12623, ?x1310), role(?x12623, ?x315), ?x1032 = 02hrh1q, ?x315 = 0l14md >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nn3m role 05842k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 89.000 0.714 http://example.org/music/artist/track_contributions./music/track_contribution/role #2999-0sxdg PRED entity: 0sxdg PRED relation: category PRED expected values: 08mbj5d => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #93, 0.82 #126, 0.81 #102) >> Best rule #93 for best value: >> intensional similarity = 3 >> extensional distance = 160 >> proper extension: 0f721s; 01c333; 017v71; 02km0m; 015y3j; 0l0wv; 03205_; 02tz9z; >> query: (?x9077, 08mbj5d) <- currency(?x9077, ?x170), ?x170 = 09nqf, currency(?x9077, ?x170) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sxdg category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 152.000 0.821 http://example.org/common/topic/webpage./common/webpage/category #2998-07dfk PRED entity: 07dfk PRED relation: location! PRED expected values: 0kft => 253 concepts (163 used for prediction) PRED predicted values (max 10 best out of 2220): 04r7p (0.77 #406275, 0.53 #115362, 0.52 #42641), 05bp8g (0.53 #115362, 0.52 #42641, 0.50 #47656), 01kym3 (0.53 #115362, 0.52 #42641, 0.50 #47656), 02t1dv (0.53 #115362, 0.52 #42641, 0.50 #47656), 03cz4j (0.53 #115362, 0.52 #42641, 0.50 #47656), 099d4 (0.40 #7371, 0.25 #4864, 0.25 #2357), 0pyww (0.38 #13519, 0.25 #18537, 0.22 #16029), 04z0g (0.33 #11206, 0.25 #1177, 0.22 #16225), 0dn3n (0.33 #10616, 0.25 #13125, 0.22 #15635), 0gs1_ (0.33 #11351, 0.25 #13860, 0.11 #16370) >> Best rule #406275 for best value: >> intensional similarity = 4 >> extensional distance = 175 >> proper extension: 0x335; >> query: (?x9559, ?x6958) <- location(?x2306, ?x9559), place_of_birth(?x6958, ?x9559), contains(?x9559, ?x8951), location(?x6958, ?x14309) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #125393 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 0fn7r; *> query: (?x9559, ?x9149) <- place_of_death(?x14003, ?x9559), citytown(?x4079, ?x9559), award_nominee(?x9149, ?x14003), award(?x14003, ?x198) *> conf = 0.03 ranks of expected_values: 1806 EVAL 07dfk location! 0kft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 253.000 163.000 0.770 http://example.org/people/person/places_lived./people/place_lived/location #2997-01jz6d PRED entity: 01jz6d PRED relation: location PRED expected values: 0f2v0 => 94 concepts (88 used for prediction) PRED predicted values (max 10 best out of 134): 02_286 (0.31 #40157, 0.17 #25718, 0.17 #43366), 030qb3t (0.25 #40203, 0.22 #25764, 0.20 #1687), 0cr3d (0.20 #947, 0.18 #7364, 0.17 #10574), 0hptm (0.20 #1906, 0.12 #4312, 0.12 #9928), 0ply0 (0.20 #1781, 0.12 #4187, 0.09 #7396), 0f2w0 (0.20 #1698, 0.12 #4104, 0.09 #7313), 0b_cr (0.20 #2354, 0.09 #7969, 0.07 #9574), 05kkh (0.20 #1611, 0.06 #10429, 0.04 #13648), 0rv97 (0.20 #2025, 0.04 #14062, 0.02 #18878), 0qy5v (0.20 #1075) >> Best rule #40157 for best value: >> intensional similarity = 3 >> extensional distance = 1084 >> proper extension: 01hkck; 033071; 042fk; >> query: (?x12922, 02_286) <- location(?x12922, ?x7919), profession(?x12922, ?x1581), county(?x7919, ?x7786) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #13020 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 040j2_; 06s27s; *> query: (?x12922, 0f2v0) <- athlete(?x4833, ?x12922), team(?x12922, ?x10837), school(?x10837, ?x1675), country(?x4833, ?x94) *> conf = 0.05 ranks of expected_values: 60 EVAL 01jz6d location 0f2v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 94.000 88.000 0.313 http://example.org/people/person/places_lived./people/place_lived/location #2996-0f2w0 PRED entity: 0f2w0 PRED relation: location! PRED expected values: 081jbk => 129 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2015): 01sl1q (0.46 #130425, 0.44 #97820, 0.44 #178080), 09fb5 (0.09 #5067, 0.07 #10083, 0.06 #25133), 0443c (0.09 #7508, 0.07 #12524, 0.02 #52656), 03h_fk5 (0.08 #27591, 0.08 #35116, 0.07 #25082), 01q_ph (0.08 #2558, 0.05 #7574, 0.05 #50), 05m63c (0.08 #2539, 0.05 #7555, 0.03 #15080), 016pns (0.08 #3070, 0.03 #15611, 0.03 #18119), 0grwj (0.08 #2514, 0.03 #15055, 0.03 #20071), 081nh (0.08 #2950, 0.03 #17999, 0.03 #20507), 02zjd (0.08 #3758, 0.02 #31349, 0.02 #36366) >> Best rule #130425 for best value: >> intensional similarity = 3 >> extensional distance = 250 >> proper extension: 0177z; 03kjh; >> query: (?x1719, ?x56) <- contains(?x94, ?x1719), place_of_birth(?x56, ?x1719), citytown(?x7390, ?x1719) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #3597 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 01zlx; *> query: (?x1719, 081jbk) <- time_zones(?x1719, ?x1638), teams(?x1719, ?x10409), ?x1638 = 02fqwt *> conf = 0.04 ranks of expected_values: 327 EVAL 0f2w0 location! 081jbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 129.000 95.000 0.455 http://example.org/people/person/places_lived./people/place_lived/location #2995-01xcgf PRED entity: 01xcgf PRED relation: institution! PRED expected values: 014mlp => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 25): 014mlp (0.78 #581, 0.74 #556, 0.72 #506), 02_xgp2 (0.75 #414, 0.75 #289, 0.68 #364), 019v9k (0.71 #335, 0.67 #585, 0.65 #410), 02h4rq6 (0.70 #578, 0.69 #278, 0.67 #428), 03bwzr4 (0.67 #441, 0.62 #291, 0.60 #516), 016t_3 (0.60 #404, 0.59 #579, 0.59 #329), 04zx3q1 (0.57 #427, 0.52 #502, 0.48 #552), 0bkj86 (0.53 #334, 0.52 #434, 0.52 #509), 027f2w (0.52 #436, 0.52 #511, 0.48 #561), 07s6fsf (0.41 #326, 0.35 #401, 0.33 #551) >> Best rule #581 for best value: >> intensional similarity = 5 >> extensional distance = 25 >> proper extension: 01k2wn; 01q0kg; >> query: (?x14049, 014mlp) <- school_type(?x14049, ?x5931), citytown(?x14049, ?x553), company(?x4831, ?x14049), country(?x553, ?x94), ?x94 = 09c7w0 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xcgf institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2994-07_m9_ PRED entity: 07_m9_ PRED relation: nationality PRED expected values: 0h7x => 191 concepts (158 used for prediction) PRED predicted values (max 10 best out of 190): 09c7w0 (0.82 #10144, 0.78 #12550, 0.78 #10540), 0f8l9c (0.33 #1413, 0.28 #3482, 0.11 #9669), 0d060g (0.33 #205, 0.20 #899, 0.13 #3787), 03rt9 (0.33 #13, 0.06 #4393, 0.06 #4591), 0bq0p9 (0.33 #19, 0.06 #4597, 0.02 #2785), 059z0 (0.33 #9747, 0.09 #2771, 0.07 #4081), 084n_ (0.33 #9747, 0.09 #2779, 0.07 #4075), 01k6y1 (0.33 #9747, 0.01 #9710), 03rjj (0.28 #3482, 0.17 #1396, 0.11 #1996), 0h7x (0.28 #3482, 0.10 #2520, 0.07 #595) >> Best rule #10144 for best value: >> intensional similarity = 5 >> extensional distance = 100 >> proper extension: 014g91; >> query: (?x4736, 09c7w0) <- place_of_death(?x4736, ?x1646), citytown(?x196, ?x1646), adjoins(?x1646, ?x6325), month(?x1646, ?x2255), ?x2255 = 040fv >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #3482 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 04pwg; 0jrg; *> query: (?x4736, ?x205) <- films(?x4736, ?x10619), type_of_union(?x4736, ?x566), film_crew_role(?x10619, ?x137), genre(?x10619, ?x53), country(?x10619, ?x205) *> conf = 0.28 ranks of expected_values: 10 EVAL 07_m9_ nationality 0h7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 191.000 158.000 0.824 http://example.org/people/person/nationality #2993-054krc PRED entity: 054krc PRED relation: nominated_for PRED expected values: 016z5x 0bth54 0c8qq 0cc5qkt 0pd57 0fgrm 0j80w 02k1pr => 49 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1566): 017jd9 (0.80 #11644, 0.79 #10188, 0.78 #23291), 0gmcwlb (0.80 #11644, 0.79 #10188, 0.78 #23291), 049xgc (0.80 #11644, 0.79 #10188, 0.78 #23291), 02ll45 (0.80 #11644, 0.79 #10188, 0.78 #23291), 0dtfn (0.80 #11644, 0.79 #10188, 0.78 #23291), 011yxg (0.80 #11644, 0.79 #10188, 0.78 #23291), 0ccck7 (0.80 #11644, 0.79 #10188, 0.78 #23291), 0jnwx (0.80 #11644, 0.79 #10188, 0.78 #23291), 0_7w6 (0.80 #11644, 0.79 #10188, 0.78 #23291), 0kv9d3 (0.80 #11644, 0.79 #10188, 0.78 #23291) >> Best rule #11644 for best value: >> intensional similarity = 6 >> extensional distance = 20 >> proper extension: 0f4x7; 09qwmm; 02ppm4q; >> query: (?x1443, ?x308) <- nominated_for(?x1443, ?x2490), nominated_for(?x1443, ?x253), nominated_for(?x846, ?x2490), award(?x84, ?x1443), award(?x308, ?x1443), ?x253 = 09m6kg >> conf = 0.80 => this is the best rule for 10 predicted values *> Best rule #1936 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 02qvyrt; *> query: (?x1443, 0cc5qkt) <- nominated_for(?x1443, ?x2490), ?x2490 = 026p4q7, award(?x308, ?x1443), award(?x6783, ?x1443), ?x6783 = 01x6v6 *> conf = 0.60 ranks of expected_values: 13, 15, 203, 229, 230, 235, 398, 438 EVAL 054krc nominated_for 02k1pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 23.000 0.799 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 054krc nominated_for 0j80w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 23.000 0.799 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 054krc nominated_for 0fgrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 23.000 0.799 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 054krc nominated_for 0pd57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 23.000 0.799 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 054krc nominated_for 0cc5qkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 49.000 23.000 0.799 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 054krc nominated_for 0c8qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 23.000 0.799 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 054krc nominated_for 0bth54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 49.000 23.000 0.799 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 054krc nominated_for 016z5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 49.000 23.000 0.799 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2992-01b66t PRED entity: 01b66t PRED relation: languages PRED expected values: 02h40lc => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 9): 02h40lc (0.89 #310, 0.89 #288, 0.88 #200), 0t_2 (0.06 #61, 0.04 #127, 0.04 #138), 06nm1 (0.04 #137, 0.04 #159, 0.04 #170), 03_9r (0.04 #378, 0.04 #466, 0.04 #444), 064_8sq (0.02 #95, 0.02 #84, 0.01 #282), 02bv9 (0.02 #97, 0.02 #86, 0.01 #141), 04306rv (0.02 #91, 0.02 #80, 0.01 #135), 02bjrlw (0.02 #89, 0.02 #78, 0.01 #133), 01jb8r (0.01 #176, 0.01 #187) >> Best rule #310 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 0n2bh; 0h95b81; >> query: (?x4721, 02h40lc) <- nominated_for(?x415, ?x4721), country_of_origin(?x4721, ?x94), award_winner(?x912, ?x415) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01b66t languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.889 http://example.org/tv/tv_program/languages #2991-0tbql PRED entity: 0tbql PRED relation: citytown! PRED expected values: 01xl5 => 168 concepts (94 used for prediction) PRED predicted values (max 10 best out of 648): 01nds (0.06 #14317, 0.05 #17549, 0.04 #7851), 07733f (0.05 #11266, 0.05 #12074, 0.04 #6416), 064f29 (0.05 #11628, 0.04 #15670, 0.04 #9204), 01pf21 (0.05 #480, 0.04 #6137, 0.04 #8563), 027lf1 (0.05 #573, 0.04 #6230, 0.03 #11888), 060ppp (0.05 #551, 0.03 #1360, 0.03 #2168), 02l424 (0.05 #476, 0.03 #1285, 0.03 #2093), 01qygl (0.05 #427, 0.03 #1236, 0.03 #2044), 02ln0f (0.05 #250, 0.03 #1059, 0.03 #1867), 07vyf (0.05 #183, 0.03 #992, 0.03 #1800) >> Best rule #14317 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 068cn; 0l178; >> query: (?x3521, 01nds) <- contains(?x94, ?x3521), locations(?x12451, ?x3521), film_release_region(?x280, ?x94) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #60624 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 202 *> proper extension: 06pwq; 0kf14; *> query: (?x3521, ?x1783) <- category(?x3521, ?x134), ?x134 = 08mbj5d, state(?x3521, ?x1782), state_province_region(?x1783, ?x1782) *> conf = 0.02 ranks of expected_values: 111 EVAL 0tbql citytown! 01xl5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 168.000 94.000 0.056 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #2990-0jzc PRED entity: 0jzc PRED relation: languages_spoken! PRED expected values: 07hwkr 033qxt => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 69): 07hwkr (0.61 #1711, 0.58 #894, 0.57 #1098), 03w9bjf (0.45 #591, 0.33 #115, 0.25 #319), 059_w (0.33 #161, 0.33 #93, 0.25 #297), 0c41n (0.33 #204, 0.33 #136, 0.25 #340), 0fk3s (0.33 #197, 0.33 #129, 0.25 #333), 03x1x (0.33 #186, 0.33 #118, 0.25 #322), 0g8_vp (0.33 #153, 0.33 #85, 0.25 #289), 02vsw1 (0.33 #180, 0.30 #520, 0.29 #792), 0bbz66j (0.33 #111, 0.30 #519, 0.25 #315), 078vc (0.33 #109, 0.27 #585, 0.25 #313) >> Best rule #1711 for best value: >> intensional similarity = 11 >> extensional distance = 26 >> proper extension: 01bkv; >> query: (?x5359, 07hwkr) <- language(?x6365, ?x5359), language(?x3965, ?x5359), language(?x1685, ?x5359), official_language(?x291, ?x5359), countries_spoken_in(?x5359, ?x1781), currency(?x1781, ?x170), films(?x5954, ?x6365), languages_spoken(?x1176, ?x5359), film_crew_role(?x1685, ?x137), featured_film_locations(?x1685, ?x362), country(?x3965, ?x94) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1, 32 EVAL 0jzc languages_spoken! 033qxt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 78.000 78.000 0.607 http://example.org/people/ethnicity/languages_spoken EVAL 0jzc languages_spoken! 07hwkr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.607 http://example.org/people/ethnicity/languages_spoken #2989-013nws PRED entity: 013nws PRED relation: source PRED expected values: 0jbk9 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #19, 0.87 #6, 0.84 #36) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x9453, 0jbk9) <- category(?x9453, ?x134), ?x134 = 08mbj5d, place(?x9453, ?x9453) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013nws source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #2988-05ksh PRED entity: 05ksh PRED relation: teams PRED expected values: 05pcr => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 217): 0j6tr (0.20 #1048, 0.05 #2488, 0.04 #2848), 01lpwh (0.20 #960, 0.05 #2400, 0.04 #2760), 07l4z (0.20 #909, 0.05 #2349, 0.04 #2709), 0cgwt8 (0.20 #838, 0.05 #2278, 0.04 #2638), 0j46b (0.07 #1337, 0.05 #2417, 0.04 #2777), 0j47s (0.07 #1201, 0.05 #2281, 0.04 #2641), 01h0b0 (0.07 #1180, 0.05 #2260, 0.04 #2620), 0bszz (0.07 #1436, 0.02 #9716, 0.02 #11156), 0jnlm (0.07 #1432, 0.02 #9712, 0.02 #11152), 0jmk7 (0.07 #1383, 0.02 #9663, 0.02 #11103) >> Best rule #1048 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01dbxr; >> query: (?x1196, 0j6tr) <- second_level_divisions(?x279, ?x1196), contains(?x1905, ?x1196), ?x279 = 0d060g >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05ksh teams 05pcr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 151.000 151.000 0.200 http://example.org/sports/sports_team_location/teams #2987-0yx7h PRED entity: 0yx7h PRED relation: nominated_for! PRED expected values: 019f4v => 59 concepts (47 used for prediction) PRED predicted values (max 10 best out of 201): 019f4v (0.51 #1901, 0.25 #3749, 0.22 #4904), 0f4x7 (0.40 #1873, 0.19 #3721, 0.18 #6933), 040njc (0.40 #1855, 0.19 #3703, 0.18 #4858), 04dn09n (0.37 #1882, 0.20 #3730, 0.18 #496), 0gr0m (0.37 #1907, 0.18 #3755, 0.18 #6933), 0gq_v (0.34 #1867, 0.21 #6489, 0.21 #3715), 0gqy2 (0.33 #1964, 0.20 #3812, 0.18 #6586), 0p9sw (0.33 #1868, 0.18 #6933, 0.17 #6490), 02pqp12 (0.32 #1906, 0.18 #6933, 0.16 #751), 0l8z1 (0.27 #1899, 0.18 #6933, 0.14 #6521) >> Best rule #1901 for best value: >> intensional similarity = 4 >> extensional distance = 290 >> proper extension: 06mmr; >> query: (?x3826, 019f4v) <- award(?x3826, ?x6909), nominated_for(?x6909, ?x288), award(?x800, ?x6909), ?x288 = 0yyg4 >> conf = 0.51 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0yx7h nominated_for! 019f4v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 47.000 0.514 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2986-06krf3 PRED entity: 06krf3 PRED relation: award_winner PRED expected values: 029ghl => 75 concepts (21 used for prediction) PRED predicted values (max 10 best out of 198): 0hwbd (0.44 #9879, 0.43 #23055, 0.43 #29644), 05qd_ (0.44 #9879, 0.43 #23055, 0.43 #29644), 05dtsb (0.18 #16467, 0.18 #32937, 0.18 #29642), 01vttb9 (0.17 #21407, 0.17 #14821, 0.14 #13173), 086k8 (0.14 #1691, 0.02 #26349, 0.02 #9880), 017s11 (0.09 #1727, 0.02 #6667, 0.02 #26349), 016tt2 (0.09 #1733, 0.02 #26349, 0.02 #9880), 032dg7 (0.09 #2997, 0.02 #26349, 0.02 #9880), 03ktjq (0.09 #2601, 0.02 #26349, 0.02 #9880), 014zcr (0.08 #37, 0.06 #3329, 0.01 #13210) >> Best rule #9879 for best value: >> intensional similarity = 4 >> extensional distance = 578 >> proper extension: 05zvzf3; >> query: (?x1006, ?x902) <- nominated_for(?x902, ?x1006), award(?x1006, ?x1007), award_winner(?x1007, ?x971), film_release_region(?x1006, ?x94) >> conf = 0.44 => this is the best rule for 2 predicted values *> Best rule #3045 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 06wzvr; 01kff7; 026n4h6; 06rmdr; 082scv; 02krdz; 02rq8k8; 0g9yrw; 0n83s; 047csmy; ... *> query: (?x1006, 029ghl) <- film(?x6701, ?x1006), award(?x1006, ?x1105), language(?x1006, ?x254), ?x1105 = 07bdd_ *> conf = 0.05 ranks of expected_values: 26 EVAL 06krf3 award_winner 029ghl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 75.000 21.000 0.444 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #2985-034qmv PRED entity: 034qmv PRED relation: language PRED expected values: 03k50 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 41): 02bjrlw (0.27 #55, 0.25 #109, 0.14 #439), 06nm1 (0.19 #9, 0.16 #2240, 0.14 #117), 04h9h (0.16 #2240, 0.06 #146, 0.05 #2571), 06b_j (0.14 #127, 0.12 #73, 0.11 #457), 0jzc (0.09 #71, 0.09 #125, 0.08 #345), 03_9r (0.09 #62, 0.09 #116, 0.08 #1100), 05qqm (0.08 #145, 0.07 #91, 0.02 #365), 0653m (0.07 #10, 0.06 #556, 0.06 #64), 05zjd (0.05 #2571, 0.05 #130, 0.05 #460), 03hkp (0.05 #2571, 0.04 #66, 0.04 #120) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 03ckwzc; 025n07; 09rsjpv; 047fjjr; 0bmch_x; 01jwxx; 07bwr; 02qyv3h; 06fqlk; 03_wm6; ... >> query: (?x148, 02bjrlw) <- film_crew_role(?x148, ?x468), language(?x148, ?x732), ?x732 = 04306rv, currency(?x148, ?x170) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #2571 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1550 *> proper extension: 01qn7n; 07hpv3; 09kn9; 0n2bh; 0gfzgl; 01cjhz; 03y3bp7; 01f3p_; 05sy2k_; 08cx5g; ... *> query: (?x148, ?x254) <- titles(?x7160, ?x148), titles(?x7160, ?x7554), titles(?x7160, ?x5230), language(?x5230, ?x254), genre(?x7554, ?x53) *> conf = 0.05 ranks of expected_values: 15 EVAL 034qmv language 03k50 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 65.000 65.000 0.269 http://example.org/film/film/language #2984-02jq1 PRED entity: 02jq1 PRED relation: inductee! PRED expected values: 0g2c8 => 202 concepts (202 used for prediction) PRED predicted values (max 10 best out of 5): 0g2c8 (0.57 #91, 0.50 #100, 0.50 #55), 0qjfl (0.33 #3, 0.14 #30, 0.14 #21), 06szd3 (0.25 #11, 0.15 #74, 0.12 #119), 04dm2n (0.04 #188, 0.04 #179, 0.04 #170), 04045y (0.02 #609, 0.01 #573, 0.01 #582) >> Best rule #91 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 033s6; >> query: (?x5442, 0g2c8) <- artists(?x3061, ?x5442), influenced_by(?x483, ?x5442), award(?x5442, ?x724), ?x3061 = 05bt6j >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jq1 inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 202.000 202.000 0.571 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #2983-0bm7fy PRED entity: 0bm7fy PRED relation: award! PRED expected values: 0q59y => 35 concepts (13 used for prediction) PRED predicted values (max 10 best out of 3771): 02645b (0.62 #790, 0.40 #4168, 0.31 #7545), 02kxbwx (0.62 #180, 0.30 #3558, 0.25 #13690), 02f93t (0.62 #2704, 0.30 #6082, 0.23 #9459), 01_f_5 (0.62 #1839, 0.30 #5217, 0.23 #8594), 02l5rm (0.62 #819, 0.30 #4197, 0.23 #7574), 0c12h (0.62 #1825, 0.25 #15335, 0.20 #5203), 03hy3g (0.62 #1858, 0.20 #5236, 0.19 #15368), 02vyw (0.62 #1010, 0.20 #4388, 0.15 #7765), 0151w_ (0.50 #234, 0.31 #6989, 0.30 #3612), 05kfs (0.50 #164, 0.30 #3542, 0.25 #13674) >> Best rule #790 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 040njc; 0gs9p; 0gr51; >> query: (?x5367, 02645b) <- award(?x5366, ?x5367), award(?x2733, ?x5367), award_nominee(?x2733, ?x488), producer_type(?x2733, ?x632), ?x5366 = 0bs8d >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #4481 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 019f4v; 02pqp12; 0gq9h; 01l29r; 0fbtbt; 01l78d; 07kjk7c; 0bm70b; *> query: (?x5367, 0q59y) <- award(?x11364, ?x5367), award(?x2733, ?x5367), ?x2733 = 0hskw, film(?x11364, ?x697), student(?x2486, ?x11364) *> conf = 0.20 ranks of expected_values: 200 EVAL 0bm7fy award! 0q59y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 35.000 13.000 0.625 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2982-0y_9q PRED entity: 0y_9q PRED relation: currency PRED expected values: 09nqf => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.77 #50, 0.76 #43, 0.75 #169), 01nv4h (0.06 #72, 0.03 #44, 0.02 #51), 0kz1h (0.01 #19), 02l6h (0.01 #242, 0.01 #249, 0.01 #102), 02gsvk (0.01 #160) >> Best rule #50 for best value: >> intensional similarity = 3 >> extensional distance = 280 >> proper extension: 08hmch; 03sxd2; 0k4d7; 08k40m; 023gxx; 0gj8nq2; 0f4_2k; 0b7l4x; 03rg2b; 01gwk3; ... >> query: (?x5304, 09nqf) <- genre(?x5304, ?x53), films(?x7734, ?x5304), produced_by(?x5304, ?x2451) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0y_9q currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.770 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #2981-03dpqd PRED entity: 03dpqd PRED relation: type_of_union PRED expected values: 01g63y => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.73 #153, 0.73 #177, 0.73 #161), 01g63y (0.17 #22, 0.17 #2, 0.17 #50), 0jgjn (0.01 #36, 0.01 #48) >> Best rule #153 for best value: >> intensional similarity = 4 >> extensional distance = 914 >> proper extension: 01h4rj; >> query: (?x4649, 04ztj) <- film(?x4649, ?x638), award(?x4649, ?x2880), student(?x2999, ?x4649), currency(?x638, ?x170) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #22 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 015c1b; *> query: (?x4649, 01g63y) <- gender(?x4649, ?x514), ?x514 = 02zsn, people(?x1050, ?x4649), ?x1050 = 041rx *> conf = 0.17 ranks of expected_values: 2 EVAL 03dpqd type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.728 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2980-0kp2_ PRED entity: 0kp2_ PRED relation: profession PRED expected values: 0cbd2 => 182 concepts (139 used for prediction) PRED predicted values (max 10 best out of 126): 0nbcg (0.89 #11286, 0.56 #5806, 0.52 #6990), 0cbd2 (0.83 #11410, 0.77 #3412, 0.76 #11114), 02hrh1q (0.75 #8159, 0.73 #16899, 0.71 #17197), 09jwl (0.74 #11274, 0.73 #12310, 0.72 #3128), 01c72t (0.56 #2836, 0.39 #3133, 0.35 #10095), 01d_h8 (0.56 #5189, 0.54 #14667, 0.52 #16298), 02krf9 (0.50 #471, 0.27 #1211, 0.21 #2395), 0dz3r (0.49 #6961, 0.48 #5777, 0.47 #11257), 02jknp (0.48 #5191, 0.46 #14669, 0.44 #16300), 016z4k (0.45 #4891, 0.44 #10371, 0.43 #1040) >> Best rule #11286 for best value: >> intensional similarity = 4 >> extensional distance = 237 >> proper extension: 032t2z; 023l9y; 01ydzx; 0191h5; >> query: (?x6795, 0nbcg) <- profession(?x6795, ?x987), role(?x6795, ?x614), profession(?x7142, ?x987), ?x7142 = 0bc71w >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #11410 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 258 *> proper extension: 07kb5; 028p0; 01dzz7; 014dq7; 0c9c0; 0379s; 0693l; 058vp; 05rx__; 07ym0; ... *> query: (?x6795, 0cbd2) <- profession(?x6795, ?x2225), influenced_by(?x6795, ?x4072), profession(?x3336, ?x2225), ?x3336 = 032l1 *> conf = 0.83 ranks of expected_values: 2 EVAL 0kp2_ profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 182.000 139.000 0.891 http://example.org/people/person/profession #2979-0gd0c7x PRED entity: 0gd0c7x PRED relation: nominated_for! PRED expected values: 0gr42 => 95 concepts (85 used for prediction) PRED predicted values (max 10 best out of 207): 0gq_v (0.68 #6980, 0.37 #2420, 0.18 #8420), 0gq9h (0.39 #7023, 0.39 #2463, 0.25 #1743), 02qyntr (0.36 #422, 0.21 #7142, 0.18 #902), 019f4v (0.33 #7014, 0.30 #2454, 0.24 #1254), 0gs9p (0.33 #2465, 0.32 #7025, 0.21 #8465), 0gs96 (0.32 #7051, 0.18 #2491, 0.12 #811), 02g3v6 (0.32 #982, 0.29 #742, 0.28 #1222), 099c8n (0.32 #1737, 0.17 #7017, 0.16 #1977), 0gr0m (0.30 #7020, 0.18 #1740, 0.18 #300), 0k611 (0.30 #2474, 0.29 #7034, 0.25 #1754) >> Best rule #6980 for best value: >> intensional similarity = 7 >> extensional distance = 334 >> proper extension: 04z_x4v; >> query: (?x1999, 0gq_v) <- nominated_for(?x640, ?x1999), nominated_for(?x640, ?x7741), nominated_for(?x640, ?x3218), nominated_for(?x640, ?x751), ?x751 = 01hp5, ?x7741 = 01xq8v, film(?x434, ?x3218) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #810 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 15 *> proper extension: 01vksx; 017gl1; 08hmch; 053rxgm; 04n52p6; 0661ql3; 040b5k; 0645k5; 07s846j; 047vnkj; ... *> query: (?x1999, 0gr42) <- film_release_region(?x1999, ?x1355), film_release_region(?x1999, ?x1174), film_release_region(?x1999, ?x608), film_release_region(?x1999, ?x390), ?x1174 = 047yc, ?x390 = 0chghy, ?x1355 = 0h7x, produced_by(?x1999, ?x2803), ?x608 = 02k54 *> conf = 0.18 ranks of expected_values: 33 EVAL 0gd0c7x nominated_for! 0gr42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 95.000 85.000 0.685 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2978-06lht1 PRED entity: 06lht1 PRED relation: film PRED expected values: 01jzyf 040_lv => 85 concepts (40 used for prediction) PRED predicted values (max 10 best out of 480): 01g03q (0.36 #67467, 0.34 #47939, 0.33 #49715), 07tlfx (0.29 #3371, 0.17 #1596), 0294zg (0.17 #1210, 0.14 #2985, 0.08 #4760), 0qm8b (0.17 #241, 0.14 #2016, 0.01 #26869), 03h0byn (0.17 #1688, 0.14 #3463), 047tsx3 (0.17 #649, 0.14 #2424), 05q54f5 (0.17 #466, 0.14 #2241), 02cbhg (0.17 #1393, 0.05 #6718, 0.04 #8493), 0284b56 (0.17 #978, 0.05 #6303, 0.04 #8078), 065z3_x (0.17 #381, 0.05 #5706, 0.04 #7481) >> Best rule #67467 for best value: >> intensional similarity = 3 >> extensional distance = 1578 >> proper extension: 09d5h; >> query: (?x4966, ?x9350) <- award_nominee(?x4966, ?x5645), nominated_for(?x4966, ?x9350), award_winner(?x5645, ?x222) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #2380 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0c6qh; 01z7_f; 0bl60p; 02j490; *> query: (?x4966, 01jzyf) <- film(?x4966, ?x6175), type_of_union(?x4966, ?x566), ?x6175 = 0gg5kmg *> conf = 0.14 ranks of expected_values: 68, 88 EVAL 06lht1 film 040_lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 85.000 40.000 0.356 http://example.org/film/actor/film./film/performance/film EVAL 06lht1 film 01jzyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 85.000 40.000 0.356 http://example.org/film/actor/film./film/performance/film #2977-03q0r1 PRED entity: 03q0r1 PRED relation: award PRED expected values: 025m8l => 75 concepts (62 used for prediction) PRED predicted values (max 10 best out of 271): 02x1z2s (0.28 #4903, 0.28 #4902, 0.26 #4435), 0gqzz (0.28 #4903, 0.28 #4902, 0.26 #4435), 026mmy (0.28 #4903, 0.28 #4902, 0.26 #4435), 02qyxs5 (0.28 #4903, 0.28 #4902, 0.26 #4435), 0gr51 (0.20 #78, 0.09 #4279, 0.07 #5214), 099tbz (0.20 #46, 0.04 #980, 0.03 #4481), 02rdxsh (0.20 #51, 0.04 #3267, 0.03 #4486), 063y_ky (0.20 #99, 0.04 #3267, 0.02 #9343), 05b1610 (0.17 #265, 0.14 #499, 0.05 #3065), 05b4l5x (0.17 #239, 0.14 #473, 0.04 #1406) >> Best rule #4903 for best value: >> intensional similarity = 4 >> extensional distance = 417 >> proper extension: 02nf2c; 04p5cr; 0m123; 02gl58; >> query: (?x3854, ?x1723) <- award_winner(?x3854, ?x10310), nominated_for(?x1723, ?x3854), category(?x10310, ?x134), award(?x224, ?x1723) >> conf = 0.28 => this is the best rule for 4 predicted values *> Best rule #11215 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1077 *> proper extension: 0cq8nx; 06zn1c; *> query: (?x3854, ?x1323) <- film(?x2156, ?x3854), nominated_for(?x4850, ?x3854), award_winner(?x1323, ?x4850), nominated_for(?x1053, ?x3854) *> conf = 0.12 ranks of expected_values: 19 EVAL 03q0r1 award 025m8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 75.000 62.000 0.277 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #2976-0cw10 PRED entity: 0cw10 PRED relation: organizations_founded PRED expected values: 02mw6c => 120 concepts (61 used for prediction) PRED predicted values (max 10 best out of 20): 017jv5 (0.33 #120, 0.25 #426, 0.14 #2263), 082x5 (0.20 #815, 0.20 #713, 0.17 #1121), 082mc (0.20 #812, 0.20 #710, 0.17 #1118), 06qmk (0.20 #805, 0.20 #703, 0.17 #1111), 04gdr (0.20 #886, 0.17 #988, 0.12 #1396), 06dr9 (0.18 #1820, 0.14 #2433, 0.14 #2535), 015dvh (0.17 #1115, 0.17 #1013, 0.14 #1319), 03z19 (0.17 #1041, 0.09 #2062, 0.05 #2470), 07wbk (0.17 #1039, 0.09 #2060, 0.05 #2468), 05f4p (0.11 #1706, 0.09 #2115, 0.05 #2523) >> Best rule #120 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 04rfq; >> query: (?x11911, 017jv5) <- people(?x10900, ?x11911), ?x10900 = 08g5q7, gender(?x11911, ?x514), ?x514 = 02zsn, religion(?x11911, ?x13975) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0cw10 organizations_founded 02mw6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 61.000 0.333 http://example.org/organization/organization_founder/organizations_founded #2975-081lh PRED entity: 081lh PRED relation: location_of_ceremony PRED expected values: 07_pf => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 33): 0k049 (0.03 #962, 0.03 #3114, 0.02 #2276), 0cv3w (0.03 #8158, 0.03 #3145, 0.03 #1710), 04jpl (0.03 #607, 0.02 #2641, 0.02 #1087), 01_d4 (0.03 #143, 0.02 #263, 0.01 #383), 0r0m6 (0.02 #1008, 0.02 #1844, 0.01 #2322), 030qb3t (0.02 #2291, 0.02 #1813, 0.01 #617), 02_286 (0.02 #7180, 0.02 #3600, 0.01 #2047), 0ycht (0.02 #353, 0.01 #473, 0.01 #593), 05qtj (0.02 #294, 0.01 #534, 0.01 #893), 03_3d (0.02 #246, 0.01 #486, 0.01 #1205) >> Best rule #962 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 01w_10; >> query: (?x986, 0k049) <- location(?x986, ?x3014), celebrity(?x719, ?x986), award_winner(?x68, ?x986) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 081lh location_of_ceremony 07_pf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 170.000 170.000 0.035 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #2974-01qzt1 PRED entity: 01qzt1 PRED relation: artists PRED expected values: 0gr69 => 75 concepts (42 used for prediction) PRED predicted values (max 10 best out of 989): 0b_j2 (0.62 #7054, 0.50 #2748, 0.33 #5977), 033s6 (0.62 #7318, 0.42 #28020, 0.33 #1936), 011z3g (0.62 #7062, 0.33 #5985, 0.33 #1680), 0178_w (0.62 #7072, 0.33 #1690, 0.25 #3842), 02jq1 (0.62 #6950, 0.33 #1568, 0.25 #2644), 01y_rz (0.55 #22629, 0.50 #4179, 0.33 #951), 01vsyg9 (0.55 #22629, 0.50 #3742, 0.23 #9127), 01vsyjy (0.55 #22629, 0.50 #3889, 0.23 #8196), 01wwvt2 (0.55 #22629, 0.33 #182, 0.17 #5563), 01ttg5 (0.55 #22629, 0.33 #346, 0.17 #5727) >> Best rule #7054 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 02k_kn; >> query: (?x5138, 0b_j2) <- artists(?x5138, ?x10671), artists(?x5138, ?x5312), artists(?x5138, ?x300), ?x5312 = 094xh, influenced_by(?x5329, ?x10671), film(?x300, ?x7373), award(?x300, ?x1323) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #645 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 05r6t; *> query: (?x5138, 0gr69) <- parent_genre(?x12618, ?x5138), titles(?x5138, ?x3566), artists(?x5138, ?x8060), film(?x406, ?x3566), ?x8060 = 06mj4, parent_genre(?x5138, ?x1572) *> conf = 0.33 ranks of expected_values: 471 EVAL 01qzt1 artists 0gr69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 75.000 42.000 0.625 http://example.org/music/genre/artists #2973-02k5sc PRED entity: 02k5sc PRED relation: artists! PRED expected values: 06cp5 01gjw => 64 concepts (29 used for prediction) PRED predicted values (max 10 best out of 256): 05w3f (0.50 #336, 0.25 #3348, 0.14 #2143), 0xhtw (0.42 #317, 0.35 #3329, 0.25 #3931), 03_d0 (0.41 #3324, 0.41 #2119, 0.24 #6941), 011j5x (0.40 #30, 0.31 #2439, 0.25 #3945), 0dl5d (0.39 #3331, 0.33 #319, 0.20 #18), 06j6l (0.39 #1549, 0.34 #4862, 0.32 #2151), 0gywn (0.39 #1558, 0.33 #4269, 0.29 #4871), 059kh (0.34 #2453, 0.32 #1851, 0.29 #3959), 0glt670 (0.33 #1543, 0.32 #1844, 0.25 #6363), 025sc50 (0.33 #1551, 0.28 #4262, 0.28 #4864) >> Best rule #336 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0qmpd; >> query: (?x7865, 05w3f) <- artists(?x6210, ?x7865), ?x6210 = 01fh36, group(?x227, ?x7865), group(?x5589, ?x7865) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1891 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 01wg982; 03sww; 04n65n; 024qwq; *> query: (?x7865, 06cp5) <- artists(?x5934, ?x7865), artist(?x3265, ?x7865), ?x5934 = 05r6t, award_nominee(?x5906, ?x7865) *> conf = 0.09 ranks of expected_values: 73, 200 EVAL 02k5sc artists! 01gjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 64.000 29.000 0.500 http://example.org/music/genre/artists EVAL 02k5sc artists! 06cp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 64.000 29.000 0.500 http://example.org/music/genre/artists #2972-02mt51 PRED entity: 02mt51 PRED relation: film_release_region PRED expected values: 0jgd 0b90_r 01mjq 03ryn => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 118): 06t2t (0.83 #620, 0.75 #477, 0.75 #51), 0jgd (0.82 #572, 0.81 #3, 0.81 #2418), 0b90_r (0.81 #4, 0.77 #573, 0.75 #430), 01mjq (0.81 #35, 0.62 #604, 0.58 #461), 0d060g (0.78 #575, 0.74 #2421, 0.69 #432), 016wzw (0.69 #55, 0.53 #624, 0.50 #481), 04gzd (0.62 #8, 0.57 #577, 0.48 #434), 09pmkv (0.62 #22, 0.51 #591, 0.48 #448), 0ctw_b (0.62 #20, 0.50 #2435, 0.48 #589), 01ls2 (0.56 #11, 0.53 #580, 0.42 #437) >> Best rule #620 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 0g56t9t; 0gtv7pk; 0g5qs2k; 09146g; 0by1wkq; 0btyf5z; 0gd0c7x; 0407yfx; 06wbm8q; 04f52jw; ... >> query: (?x4040, 06t2t) <- film_release_region(?x4040, ?x7747), nominated_for(?x68, ?x4040), ?x7747 = 07f1x >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #572 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 0g56t9t; 0gtv7pk; 0g5qs2k; 09146g; 0by1wkq; 0btyf5z; 0gd0c7x; 0407yfx; 06wbm8q; 04f52jw; ... *> query: (?x4040, 0jgd) <- film_release_region(?x4040, ?x7747), nominated_for(?x68, ?x4040), ?x7747 = 07f1x *> conf = 0.82 ranks of expected_values: 2, 3, 4, 21 EVAL 02mt51 film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 98.000 98.000 0.831 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02mt51 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.831 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02mt51 film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.831 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02mt51 film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 98.000 0.831 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2971-01f7jt PRED entity: 01f7jt PRED relation: film_distribution_medium PRED expected values: 029j_ => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 3): 0735l (0.66 #19, 0.66 #15, 0.65 #33), 029j_ (0.38 #60, 0.36 #21, 0.35 #31), 07z4p (0.03 #29, 0.02 #34, 0.02 #38) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0crh5_f; >> query: (?x10943, 0735l) <- production_companies(?x10943, ?x1686), film_crew_role(?x10943, ?x468), film_distribution_medium(?x10943, ?x627), genre(?x10943, ?x258) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #60 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 203 *> proper extension: 0522wp; *> query: (?x10943, 029j_) <- film_distribution_medium(?x10943, ?x627), film_release_distribution_medium(?x5089, ?x627), film_release_region(?x5089, ?x47) *> conf = 0.38 ranks of expected_values: 2 EVAL 01f7jt film_distribution_medium 029j_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.660 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #2970-016fjj PRED entity: 016fjj PRED relation: participant! PRED expected values: 01vwllw => 111 concepts (69 used for prediction) PRED predicted values (max 10 best out of 145): 01vwllw (0.84 #11071, 0.84 #14979, 0.82 #9768), 0151w_ (0.11 #715, 0.03 #3970, 0.02 #5923), 019pm_ (0.11 #840, 0.02 #6048, 0.01 #8653), 026c1 (0.11 #787, 0.02 #4042, 0.02 #5995), 01pcvn (0.11 #1035, 0.02 #4290, 0.01 #4941), 02g0mx (0.11 #862, 0.02 #6070, 0.02 #7373), 01pcrw (0.11 #861, 0.02 #6069, 0.02 #8023), 0127s7 (0.11 #1051, 0.02 #6910, 0.01 #10168), 04fzk (0.11 #938, 0.01 #4844, 0.01 #8100), 01gq0b (0.11 #762, 0.01 #5970, 0.01 #6621) >> Best rule #11071 for best value: >> intensional similarity = 3 >> extensional distance = 381 >> proper extension: 03n93; 01hrqc; 0knjh; >> query: (?x3701, ?x3210) <- participant(?x4285, ?x3701), participant(?x3701, ?x3210), award_nominee(?x3701, ?x3756) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016fjj participant! 01vwllw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 69.000 0.843 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #2969-024vjd PRED entity: 024vjd PRED relation: ceremony PRED expected values: 019bk0 => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 125): 019bk0 (0.83 #262, 0.82 #512, 0.78 #12), 0gx1673 (0.56 #355, 0.51 #605, 0.47 #105), 08pc1x (0.27 #3254, 0.12 #3003, 0.03 #1500), 05c1t6z (0.18 #1136, 0.18 #1262, 0.17 #1011), 0n8_m93 (0.18 #1377, 0.16 #853, 0.14 #1228), 0bzm81 (0.18 #1377, 0.16 #767, 0.14 #1142), 02yxh9 (0.18 #1377, 0.15 #836, 0.13 #1211), 0bc773 (0.18 #1377, 0.15 #794, 0.13 #1169), 02yw5r (0.18 #1377, 0.15 #758, 0.13 #1133), 02yvhx (0.18 #1377, 0.15 #816, 0.13 #1191) >> Best rule #262 for best value: >> intensional similarity = 7 >> extensional distance = 62 >> proper extension: 026mg3; 01c9f2; 02nhxf; 025m8y; 0257yf; 03tcnt; 031b3h; 03qbh5; 024fz9; 03qbnj; ... >> query: (?x3903, 019bk0) <- award(?x5150, ?x3903), ceremony(?x3903, ?x6869), ceremony(?x3903, ?x6487), ceremony(?x3903, ?x139), award_winner(?x6869, ?x1128), ?x6487 = 01mh_q, ?x139 = 05pd94v >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024vjd ceremony 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.828 http://example.org/award/award_category/winners./award/award_honor/ceremony #2968-0j_t1 PRED entity: 0j_t1 PRED relation: music PRED expected values: 01wmcbg => 63 concepts (18 used for prediction) PRED predicted values (max 10 best out of 90): 03h610 (0.10 #495, 0.04 #2384, 0.03 #2594), 01tc9r (0.08 #694, 0.04 #2372, 0.03 #1744), 0146pg (0.07 #2318, 0.05 #640, 0.04 #2739), 02bh9 (0.06 #2358, 0.04 #1939, 0.04 #2568), 0bzyh (0.05 #1052, 0.05 #3358, 0.03 #2518), 011_3s (0.05 #1052, 0.05 #3358), 04d2yp (0.05 #1052), 0150t6 (0.04 #2353, 0.03 #886, 0.03 #1097), 02wb6d (0.04 #1178, 0.04 #967, 0.03 #1388), 03975z (0.04 #374) >> Best rule #495 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 05cj_j; 01hvjx; 08k40m; 09v71cj; 0b7l4x; 040_lv; 0473rc; 034qbx; 02d003; 047vp1n; ... >> query: (?x2719, 03h610) <- film(?x2263, ?x2719), genre(?x2719, ?x809), ?x809 = 0vgkd >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0j_t1 music 01wmcbg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 18.000 0.103 http://example.org/film/film/music #2967-02r3cn PRED entity: 02r3cn PRED relation: gender PRED expected values: 05zppz => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #129, 0.86 #171, 0.85 #91), 02zsn (0.57 #94, 0.57 #64, 0.55 #72) >> Best rule #129 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 08wq0g; 07s6prs; 0pmw9; >> query: (?x6035, 05zppz) <- role(?x6035, ?x1466), profession(?x6035, ?x1183), location(?x6035, ?x1767), ?x1183 = 09jwl >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r3cn gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 178.000 178.000 0.870 http://example.org/people/person/gender #2966-016dsy PRED entity: 016dsy PRED relation: type_of_union PRED expected values: 01g63y => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 3): 01g63y (0.27 #118, 0.22 #73, 0.22 #10), 01bl8s (0.03 #11), 0jgjn (0.02 #15, 0.02 #18, 0.02 #60) >> Best rule #118 for best value: >> intensional similarity = 4 >> extensional distance = 422 >> proper extension: 03n93; 04mlmx; 01nfys; 01syr4; 06c0j; >> query: (?x4082, 01g63y) <- profession(?x4082, ?x1032), gender(?x4082, ?x514), type_of_union(?x4082, ?x566), participant(?x3403, ?x4082) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016dsy type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.267 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2965-026gvfj PRED entity: 026gvfj PRED relation: student PRED expected values: 026_w57 018ygt => 33 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1586): 05bnp0 (0.05 #11, 0.04 #2090, 0.03 #4169), 083chw (0.05 #2105, 0.04 #26, 0.03 #4184), 0ff3y (0.05 #4135, 0.04 #6214, 0.03 #2056), 01l1hr (0.05 #2646, 0.03 #4725, 0.03 #567), 0405l (0.04 #1840, 0.04 #3919, 0.03 #5998), 07ymr5 (0.04 #288, 0.04 #2367, 0.03 #4446), 05kfs (0.04 #97, 0.04 #2176, 0.03 #4255), 0prfz (0.04 #42, 0.04 #2121, 0.03 #6279), 0h0wc (0.04 #388, 0.04 #2467, 0.02 #8705), 0306ds (0.04 #403, 0.04 #2482, 0.02 #4561) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 01314k; 02km0m; 035gt8; >> query: (?x3564, 05bnp0) <- major_field_of_study(?x3564, ?x6760), major_field_of_study(?x2909, ?x6760), ?x2909 = 017z88 >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #1092 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 73 *> proper extension: 01314k; 02km0m; 035gt8; *> query: (?x3564, 018ygt) <- major_field_of_study(?x3564, ?x6760), major_field_of_study(?x2909, ?x6760), ?x2909 = 017z88 *> conf = 0.03 ranks of expected_values: 65, 830 EVAL 026gvfj student 018ygt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 33.000 5.000 0.053 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 026gvfj student 026_w57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 5.000 0.053 http://example.org/education/educational_institution/students_graduates./education/education/student #2964-03m8y5 PRED entity: 03m8y5 PRED relation: titles! PRED expected values: 01z4y => 59 concepts (38 used for prediction) PRED predicted values (max 10 best out of 100): 01z4y (0.71 #762, 0.50 #866, 0.41 #2014), 07s9rl0 (0.50 #414, 0.50 #310, 0.34 #2085), 04xvlr (0.40 #417, 0.38 #313, 0.33 #107), 01z77k (0.33 #61, 0.10 #474, 0.02 #3975), 01g6gs (0.33 #2188, 0.25 #2400, 0.21 #412), 017fp (0.25 #333, 0.20 #437, 0.09 #854), 05p553 (0.21 #412, 0.20 #2187, 0.20 #1245), 01t_vv (0.21 #412, 0.20 #2187, 0.20 #1245), 0gf28 (0.21 #412, 0.20 #2187, 0.20 #1245), 0219x_ (0.21 #412, 0.20 #2187, 0.20 #1245) >> Best rule #762 for best value: >> intensional similarity = 7 >> extensional distance = 29 >> proper extension: 0ckt6; >> query: (?x2529, 01z4y) <- genre(?x2529, ?x8467), film(?x10188, ?x2529), film(?x8674, ?x2529), nationality(?x8674, ?x390), featured_film_locations(?x2529, ?x739), ?x8467 = 0gf28, award_winner(?x1670, ?x10188) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03m8y5 titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 38.000 0.710 http://example.org/media_common/netflix_genre/titles #2963-0dn44 PRED entity: 0dn44 PRED relation: profession PRED expected values: 0np9r => 80 concepts (32 used for prediction) PRED predicted values (max 10 best out of 64): 09jwl (0.81 #715, 0.43 #1122, 0.42 #2678), 01d_h8 (0.78 #847, 0.77 #1128, 0.66 #987), 0kyk (0.57 #25, 0.43 #1122, 0.24 #445), 0nbcg (0.54 #728, 0.27 #2271, 0.27 #2691), 0dz3r (0.47 #703, 0.24 #1825, 0.24 #1965), 016z4k (0.43 #1122, 0.36 #705, 0.26 #2248), 02krf9 (0.43 #1122, 0.26 #1705, 0.24 #1144), 01c72t (0.43 #1122, 0.24 #720, 0.17 #1281), 025352 (0.43 #1122, 0.08 #753, 0.05 #613), 015h31 (0.43 #1122, 0.04 #1004, 0.04 #1145) >> Best rule #715 for best value: >> intensional similarity = 3 >> extensional distance = 166 >> proper extension: 04mx7s; >> query: (?x11797, 09jwl) <- gender(?x11797, ?x231), nationality(?x11797, ?x512), group(?x11797, ?x12459) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #436 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 03xmy1; 02t_99; 01g4bk; 05_zc7; 047jhq; *> query: (?x11797, 0np9r) <- profession(?x11797, ?x4725), type_of_union(?x11797, ?x566), ?x4725 = 015cjr *> conf = 0.20 ranks of expected_values: 13 EVAL 0dn44 profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 80.000 32.000 0.810 http://example.org/people/person/profession #2962-020_95 PRED entity: 020_95 PRED relation: award_winner! PRED expected values: 058m5m4 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 133): 092_25 (0.17 #10745, 0.05 #341, 0.03 #2109), 09q_6t (0.17 #10745, 0.03 #2048, 0.02 #2592), 04n2r9h (0.17 #10745, 0.02 #6162, 0.02 #5618), 0bx6zs (0.17 #10745, 0.02 #1210, 0.02 #394), 09qvms (0.09 #285, 0.06 #1645, 0.06 #2053), 03gyp30 (0.08 #384, 0.05 #2152, 0.04 #2560), 09g90vz (0.08 #391, 0.05 #1207, 0.05 #1751), 027hjff (0.07 #326, 0.05 #2502, 0.04 #2638), 0hr3c8y (0.07 #282, 0.07 #10, 0.05 #2050), 092t4b (0.07 #49, 0.06 #321, 0.05 #2089) >> Best rule #10745 for best value: >> intensional similarity = 2 >> extensional distance = 1699 >> proper extension: 04n7njg; >> query: (?x5454, ?x2126) <- nominated_for(?x5454, ?x9541), honored_for(?x2126, ?x9541) >> conf = 0.17 => this is the best rule for 4 predicted values *> Best rule #324 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 346 *> proper extension: 02pbp9; *> query: (?x5454, 058m5m4) <- award_winner(?x1553, ?x5454), actor(?x5328, ?x5454) *> conf = 0.06 ranks of expected_values: 14 EVAL 020_95 award_winner! 058m5m4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 123.000 123.000 0.173 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2961-05ywg PRED entity: 05ywg PRED relation: location_of_ceremony! PRED expected values: 04ztj => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.85 #45, 0.84 #73, 0.84 #89), 0jgjn (0.25 #12, 0.12 #20, 0.11 #28), 01g63y (0.12 #14, 0.12 #10, 0.10 #30), 01bl8s (0.04 #47, 0.03 #63, 0.03 #71) >> Best rule #45 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 0h3tv; >> query: (?x1458, 04ztj) <- month(?x1458, ?x7298), month(?x1458, ?x4827), ?x4827 = 03_ly, ?x7298 = 04wzr, vacationer(?x1458, ?x4394) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05ywg location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 181.000 0.846 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #2960-042g97 PRED entity: 042g97 PRED relation: genre PRED expected values: 05p553 01jfsb => 86 concepts (83 used for prediction) PRED predicted values (max 10 best out of 107): 05p553 (0.78 #5514, 0.70 #5865, 0.69 #1766), 07s9rl0 (0.75 #1175, 0.73 #3521, 0.73 #1529), 01jfsb (0.70 #5405, 0.63 #5639, 0.55 #5990), 02l7c8 (0.45 #6578, 0.37 #2480, 0.33 #3535), 02n4kr (0.44 #3879, 0.24 #5635, 0.21 #594), 0hcr (0.39 #3308, 0.24 #3190, 0.20 #373), 0lsxr (0.33 #2239, 0.27 #6690, 0.25 #5636), 04xvlr (0.24 #1176, 0.23 #1294, 0.23 #3522), 01t_vv (0.22 #2518, 0.18 #1815, 0.15 #1227), 060__y (0.22 #2950, 0.20 #1190, 0.20 #1308) >> Best rule #5514 for best value: >> intensional similarity = 7 >> extensional distance = 750 >> proper extension: 06wzvr; 01k1k4; 02_1sj; 026mfbr; 02hxhz; 07g_0c; 03s5lz; 0gxtknx; 0bq8tmw; 031t2d; ... >> query: (?x12214, 05p553) <- genre(?x12214, ?x1510), genre(?x7199, ?x1510), genre(?x5139, ?x1510), genre(?x4707, ?x1510), ?x5139 = 07bzz7, ?x4707 = 02xbyr, ?x7199 = 05nlx4 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 042g97 genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 83.000 0.775 http://example.org/film/film/genre EVAL 042g97 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 83.000 0.775 http://example.org/film/film/genre #2959-02p2zq PRED entity: 02p2zq PRED relation: artists! PRED expected values: 06by7 => 126 concepts (123 used for prediction) PRED predicted values (max 10 best out of 259): 064t9 (0.62 #4719, 0.59 #4093, 0.58 #2837), 06by7 (0.55 #963, 0.54 #1904, 0.54 #649), 0xhtw (0.37 #2526, 0.34 #3154, 0.31 #5351), 06j6l (0.33 #4129, 0.32 #2873, 0.32 #4755), 016clz (0.32 #1258, 0.30 #1572, 0.26 #2513), 025sc50 (0.30 #4131, 0.28 #4757, 0.28 #2875), 0glt670 (0.29 #4122, 0.27 #6942, 0.27 #4748), 05w3f (0.27 #980, 0.26 #666, 0.24 #2548), 0gywn (0.26 #6959, 0.24 #2883, 0.24 #5078), 01lyv (0.25 #2231, 0.24 #6622, 0.23 #36) >> Best rule #4719 for best value: >> intensional similarity = 3 >> extensional distance = 175 >> proper extension: 01pfr3; 01v0sx2; 01wv9xn; 0frsw; 01vrwfv; 0d193h; 016z1t; 0d9xq; 02lvtb; 02jqjm; ... >> query: (?x7549, 064t9) <- award(?x7549, ?x2139), ?x2139 = 01by1l, artists(?x3061, ?x7549) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #963 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 53 *> proper extension: 04cr6qv; 044mfr; 04f7c55; 0jsg0m; 04d_mtq; 0ql36; *> query: (?x7549, 06by7) <- group(?x7549, ?x12427), people(?x1050, ?x7549), category(?x7549, ?x134) *> conf = 0.55 ranks of expected_values: 2 EVAL 02p2zq artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 123.000 0.616 http://example.org/music/genre/artists #2958-0glmv PRED entity: 0glmv PRED relation: award_winner! PRED expected values: 05c1t6z => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 106): 0gvstc3 (0.29 #3105, 0.28 #5080, 0.19 #2258), 0275n3y (0.29 #3105, 0.28 #5080, 0.19 #2258), 05c1t6z (0.29 #3105, 0.28 #5080, 0.19 #2258), 0gx_st (0.29 #3105, 0.28 #5080, 0.19 #2258), 09gkdln (0.14 #122, 0.05 #2238, 0.05 #2803), 092_25 (0.14 #72, 0.04 #777, 0.03 #636), 07z31v (0.14 #31, 0.04 #1441, 0.03 #877), 0fqpc7d (0.14 #36, 0.02 #741, 0.02 #4974), 0n8_m93 (0.14 #118, 0.01 #823, 0.01 #2376), 09qvms (0.09 #1423, 0.06 #2129, 0.05 #2412) >> Best rule #3105 for best value: >> intensional similarity = 4 >> extensional distance = 626 >> proper extension: 02q9kqf; >> query: (?x3242, ?x1265) <- place_of_birth(?x3242, ?x5037), award_winner(?x3180, ?x3242), gender(?x3242, ?x231), honored_for(?x1265, ?x3180) >> conf = 0.29 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0glmv award_winner! 05c1t6z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 75.000 75.000 0.292 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2957-0fx02 PRED entity: 0fx02 PRED relation: story_by! PRED expected values: 02vxq9m 0164qt 01kf3_9 025twgt => 134 concepts (80 used for prediction) PRED predicted values (max 10 best out of 244): 0fztbq (0.26 #4597, 0.25 #5584, 0.13 #5255), 02qrv7 (0.26 #4597, 0.25 #5584, 0.13 #5255), 025twgt (0.26 #4597, 0.25 #5584, 0.10 #6245), 01kf3_9 (0.26 #4597, 0.25 #5584, 0.10 #6245), 0164qt (0.26 #4597, 0.25 #5584, 0.10 #6245), 01s9vc (0.26 #4597, 0.25 #5584), 014kq6 (0.26 #4597, 0.25 #5584), 026p_bs (0.26 #4597, 0.25 #5584), 02q_4ph (0.11 #4410, 0.10 #5397, 0.02 #17547), 01_mdl (0.10 #1022, 0.08 #4306, 0.07 #5293) >> Best rule #4597 for best value: >> intensional similarity = 6 >> extensional distance = 36 >> proper extension: 07nznf; 03qcq; 03_gd; 05_k56; 0343h; 08hp53; 052gzr; 05pq9; 02645b; 0p8jf; ... >> query: (?x3686, ?x835) <- story_by(?x5598, ?x3686), story_by(?x2506, ?x3686), profession(?x3686, ?x353), language(?x5598, ?x254), country(?x2506, ?x512), nominated_for(?x835, ?x2506) >> conf = 0.26 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 4, 5 EVAL 0fx02 story_by! 025twgt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 80.000 0.265 http://example.org/film/film/story_by EVAL 0fx02 story_by! 01kf3_9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 80.000 0.265 http://example.org/film/film/story_by EVAL 0fx02 story_by! 0164qt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 80.000 0.265 http://example.org/film/film/story_by EVAL 0fx02 story_by! 02vxq9m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 80.000 0.265 http://example.org/film/film/story_by #2956-016tw3 PRED entity: 016tw3 PRED relation: production_companies! PRED expected values: 0d87hc 085wqm 0ccck7 => 142 concepts (129 used for prediction) PRED predicted values (max 10 best out of 1140): 0gc_c_ (0.45 #31311, 0.44 #15657, 0.41 #18788), 05t0_2v (0.45 #31311, 0.44 #15657, 0.41 #18788), 016mhd (0.45 #31311, 0.44 #15657, 0.37 #27135), 0bpx1k (0.45 #31311, 0.44 #15657, 0.37 #27135), 01cz7r (0.45 #31311, 0.44 #15657, 0.37 #27135), 01bn3l (0.45 #31311, 0.44 #15657, 0.37 #27135), 0f2sx4 (0.45 #31311, 0.44 #15657, 0.37 #27135), 0c9t0y (0.45 #31311, 0.44 #15657, 0.37 #27135), 02vqsll (0.45 #31311, 0.44 #15657, 0.37 #27135), 0g4pl7z (0.45 #31311, 0.44 #15657, 0.37 #27135) >> Best rule #31311 for best value: >> intensional similarity = 3 >> extensional distance = 41 >> proper extension: 0g5lhl7; 0kk9v; 056ws9; 04rcl7; 02x2097; >> query: (?x1104, ?x2881) <- production_companies(?x3748, ?x1104), nominated_for(?x1104, ?x2881), film(?x609, ?x3748) >> conf = 0.45 => this is the best rule for 11 predicted values *> Best rule #17744 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0jz9f; 086k8; 017s11; 016tt2; 025jfl; 0338lq; 0g1rw; 05qd_; 030_1m; 017jv5; ... *> query: (?x1104, ?x86) <- film(?x1104, ?x86), production_companies(?x253, ?x1104), award_nominee(?x846, ?x1104) *> conf = 0.37 ranks of expected_values: 22, 44 EVAL 016tw3 production_companies! 0ccck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 142.000 129.000 0.449 http://example.org/film/film/production_companies EVAL 016tw3 production_companies! 085wqm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 129.000 0.449 http://example.org/film/film/production_companies EVAL 016tw3 production_companies! 0d87hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 142.000 129.000 0.449 http://example.org/film/film/production_companies #2955-01bzs9 PRED entity: 01bzs9 PRED relation: student PRED expected values: 02j8nx => 126 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1264): 01tdnyh (0.12 #2973, 0.05 #9235, 0.04 #7147), 0l6qt (0.12 #2103, 0.05 #8365, 0.04 #6277), 0xnc3 (0.12 #3526, 0.05 #9788, 0.02 #13962), 01n1gc (0.12 #2696, 0.05 #11045, 0.02 #17306), 0ff3y (0.12 #4151, 0.05 #18761, 0.03 #22935), 034bgm (0.12 #2499, 0.03 #12935, 0.03 #8761), 02m7r (0.12 #2451, 0.03 #12887, 0.03 #8713), 03nk3t (0.12 #2845, 0.03 #9107, 0.02 #13281), 0kn3g (0.12 #3751, 0.03 #10013, 0.02 #14187), 030dr (0.12 #3959, 0.03 #10221, 0.02 #14395) >> Best rule #2973 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0277jc; 07vk2; 0yldt; 0ym20; >> query: (?x11963, 01tdnyh) <- institution(?x2759, ?x11963), major_field_of_study(?x11963, ?x947), student(?x11963, ?x361), ?x2759 = 071tyz >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01bzs9 student 02j8nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 126.000 70.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #2954-01jq4b PRED entity: 01jq4b PRED relation: institution! PRED expected values: 014mlp => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 20): 014mlp (0.68 #614, 0.67 #784, 0.66 #572), 03bwzr4 (0.62 #138, 0.50 #411, 0.49 #537), 02_xgp2 (0.62 #136, 0.45 #346, 0.43 #283), 016t_3 (0.50 #128, 0.47 #317, 0.43 #23), 04zx3q1 (0.50 #127, 0.29 #1482, 0.28 #1506), 0bkj86 (0.47 #132, 0.39 #405, 0.36 #616), 027f2w (0.38 #133, 0.23 #343, 0.22 #280), 0bjrnt (0.29 #1482, 0.28 #1506, 0.25 #131), 01rr_d (0.23 #141, 0.13 #625, 0.12 #309), 028dcg (0.20 #17, 0.17 #143, 0.14 #38) >> Best rule #614 for best value: >> intensional similarity = 3 >> extensional distance = 253 >> proper extension: 09r4xx; 023p18; >> query: (?x5907, 014mlp) <- student(?x5907, ?x3762), institution(?x620, ?x5907), award_winner(?x3762, ?x3763) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jq4b institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.678 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2953-0l56b PRED entity: 0l56b PRED relation: profession PRED expected values: 01d_h8 0n1h 0dxtg => 141 concepts (115 used for prediction) PRED predicted values (max 10 best out of 92): 02hrh1q (0.75 #3122, 0.74 #5046, 0.74 #6971), 01d_h8 (0.64 #1190, 0.54 #4446, 0.53 #1338), 09jwl (0.56 #167, 0.50 #6531, 0.46 #5495), 0dz3r (0.50 #150, 0.43 #594, 0.43 #446), 0nbcg (0.50 #180, 0.43 #624, 0.38 #476), 03gjzk (0.39 #1051, 0.38 #1347, 0.37 #1495), 016z4k (0.38 #2076, 0.35 #5924, 0.34 #6516), 0dxtg (0.38 #2233, 0.35 #3269, 0.35 #901), 02jknp (0.35 #895, 0.30 #1191, 0.30 #1635), 0kyk (0.33 #2250, 0.20 #3434, 0.17 #1658) >> Best rule #3122 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 05th8t; 059xvg; 012j5h; 01d1yr; 03q43g; 02wd48; 069z_5; 01gc7h; 021r6w; 07gknc; ... >> query: (?x2181, 02hrh1q) <- nationality(?x2181, ?x279), profession(?x2181, ?x353), ?x279 = 0d060g, type_of_union(?x2181, ?x566) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1190 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 042f1; *> query: (?x2181, 01d_h8) <- organizations_founded(?x2181, ?x14451), profession(?x2181, ?x353), state_province_region(?x14451, ?x1227) *> conf = 0.64 ranks of expected_values: 2, 8, 15 EVAL 0l56b profession 0dxtg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 141.000 115.000 0.747 http://example.org/people/person/profession EVAL 0l56b profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 141.000 115.000 0.747 http://example.org/people/person/profession EVAL 0l56b profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 115.000 0.747 http://example.org/people/person/profession #2952-07024 PRED entity: 07024 PRED relation: award PRED expected values: 02w_6xj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 185): 0gs9p (0.27 #2255, 0.27 #6085, 0.27 #6084), 0gq9h (0.27 #2255, 0.27 #6085, 0.27 #6084), 0gq_v (0.27 #2255, 0.27 #6085, 0.27 #6084), 054krc (0.27 #2255, 0.27 #6085, 0.27 #6084), 0l8z1 (0.27 #2255, 0.27 #6085, 0.27 #6084), 040njc (0.27 #2255, 0.27 #6085, 0.27 #6084), 04dn09n (0.27 #2255, 0.27 #6085, 0.27 #6084), 0f4x7 (0.27 #2255, 0.27 #6085, 0.27 #6084), 0gr51 (0.27 #2255, 0.27 #6085, 0.27 #6084), 02qyntr (0.27 #2255, 0.27 #6085, 0.27 #6084) >> Best rule #2255 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 027ct7c; >> query: (?x2928, ?x143) <- honored_for(?x747, ?x2928), nominated_for(?x500, ?x2928), nominated_for(?x143, ?x2928), ?x500 = 0p9sw >> conf = 0.27 => this is the best rule for 22 predicted values *> Best rule #2181 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 111 *> proper extension: 027ct7c; *> query: (?x2928, 02w_6xj) <- honored_for(?x747, ?x2928), nominated_for(?x500, ?x2928), ?x500 = 0p9sw *> conf = 0.08 ranks of expected_values: 34 EVAL 07024 award 02w_6xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 98.000 98.000 0.274 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #2951-018db8 PRED entity: 018db8 PRED relation: award PRED expected values: 09qv_s => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 302): 026mmy (0.74 #4358, 0.69 #14657, 0.68 #33276), 05pcn59 (0.41 #3643, 0.37 #4039, 0.32 #6020), 09qv_s (0.31 #6882, 0.13 #39616, 0.12 #38823), 01by1l (0.30 #2485, 0.18 #32086, 0.14 #4467), 05p09zm (0.29 #5667, 0.28 #3686, 0.28 #6063), 0bdwqv (0.27 #6900, 0.08 #25518, 0.08 #2146), 05zr6wv (0.24 #16, 0.21 #4770, 0.20 #3581), 05b4l5x (0.23 #3570, 0.21 #3966, 0.20 #6343), 03c7tr1 (0.22 #4809, 0.19 #55, 0.18 #6393), 0789_m (0.21 #6752, 0.06 #3979, 0.05 #25370) >> Best rule #4358 for best value: >> intensional similarity = 2 >> extensional distance = 66 >> proper extension: 01ccr8; >> query: (?x793, ?x451) <- participant(?x843, ?x793), award_winner(?x451, ?x793) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #6882 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 97 *> proper extension: 05sq84; 015dnt; 0164r9; 0151xv; 016z68; 01tsbmv; 01l7qw; 044ptm; *> query: (?x793, 09qv_s) <- award(?x793, ?x2375), ?x2375 = 04kxsb *> conf = 0.31 ranks of expected_values: 3 EVAL 018db8 award 09qv_s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 113.000 0.740 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2950-01lqf49 PRED entity: 01lqf49 PRED relation: artists! PRED expected values: 02lnbg 03mb9 035wcs => 85 concepts (40 used for prediction) PRED predicted values (max 10 best out of 231): 064t9 (0.85 #3105, 0.84 #6506, 0.81 #2178), 06by7 (0.71 #8369, 0.59 #9605, 0.54 #10533), 0glt670 (0.64 #2206, 0.51 #3133, 0.41 #5565), 0ggx5q (0.50 #3169, 0.43 #2242, 0.27 #3479), 02lnbg (0.49 #3150, 0.45 #2223, 0.28 #3460), 0gywn (0.45 #5313, 0.45 #2222, 0.41 #5565), 02x8m (0.41 #5565, 0.26 #5275, 0.23 #2184), 016_rm (0.41 #5565, 0.06 #546, 0.05 #855), 04pcmw (0.41 #5565, 0.03 #12058), 05bt6j (0.33 #9009, 0.30 #6537, 0.28 #3136) >> Best rule #3105 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 01k23t; >> query: (?x8848, 064t9) <- origin(?x8848, ?x6960), artists(?x3562, ?x8848), ?x3562 = 025sc50, profession(?x8848, ?x220) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #3150 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 01k23t; *> query: (?x8848, 02lnbg) <- origin(?x8848, ?x6960), artists(?x3562, ?x8848), ?x3562 = 025sc50, profession(?x8848, ?x220) *> conf = 0.49 ranks of expected_values: 5, 72, 100 EVAL 01lqf49 artists! 035wcs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 85.000 40.000 0.851 http://example.org/music/genre/artists EVAL 01lqf49 artists! 03mb9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 85.000 40.000 0.851 http://example.org/music/genre/artists EVAL 01lqf49 artists! 02lnbg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 85.000 40.000 0.851 http://example.org/music/genre/artists #2949-019fh PRED entity: 019fh PRED relation: location! PRED expected values: 07zhd7 => 119 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1780): 069nzr (0.51 #25054, 0.49 #75167, 0.48 #110244), 029cpw (0.51 #25054, 0.49 #75167, 0.48 #110244), 07hgkd (0.49 #75167, 0.48 #110244, 0.48 #155343), 06pj8 (0.13 #2890, 0.05 #384, 0.05 #5395), 083p7 (0.12 #40085, 0.10 #62636, 0.10 #80178), 07csf4 (0.11 #107738, 0.03 #290, 0.03 #2796), 03swmf (0.11 #107738, 0.03 #1845, 0.03 #4351), 05hj_k (0.11 #107738, 0.01 #25846), 0gd_s (0.11 #107738), 0b1f49 (0.11 #107738) >> Best rule #25054 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 0tbql; 01jr6; 0ncj8; 0h778; 0z20d; 0r2kh; 0f25y; 0c4kv; 0ytph; >> query: (?x3689, ?x7001) <- location(?x3688, ?x3689), state(?x3689, ?x335), place_of_birth(?x7001, ?x3689), actor(?x3144, ?x7001) >> conf = 0.51 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 019fh location! 07zhd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 111.000 0.514 http://example.org/people/person/places_lived./people/place_lived/location #2948-0880p PRED entity: 0880p PRED relation: language! PRED expected values: 03q0r1 => 37 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1881): 0cf8qb (0.50 #1286, 0.44 #3018, 0.36 #11681), 047vnkj (0.50 #6070, 0.40 #4337, 0.36 #11266), 0dl9_4 (0.50 #858, 0.22 #2590, 0.20 #4324), 02qrv7 (0.44 #1911, 0.40 #5378, 0.36 #10574), 061681 (0.44 #1830, 0.30 #5297, 0.29 #10493), 0f4_2k (0.40 #6183, 0.37 #14845, 0.36 #11379), 0g5qmbz (0.40 #4965, 0.36 #11894, 0.33 #13627), 0dr_4 (0.40 #5433, 0.36 #10629, 0.33 #1966), 0hfzr (0.40 #5875, 0.33 #2408, 0.29 #11071), 0pd6l (0.40 #5829, 0.33 #2362, 0.29 #11025) >> Best rule #1286 for best value: >> intensional similarity = 16 >> extensional distance = 2 >> proper extension: 0cjk9; 06b_j; >> query: (?x12272, 0cf8qb) <- countries_spoken_in(?x12272, ?x4743), countries_spoken_in(?x12272, ?x3041), countries_spoken_in(?x12272, ?x1603), languages_spoken(?x1050, ?x12272), ?x1603 = 06bnz, ?x3041 = 04w4s, language(?x6345, ?x12272), nominated_for(?x77, ?x6345), film_release_region(?x3748, ?x4743), film_release_region(?x1785, ?x4743), film_release_region(?x249, ?x4743), adjoins(?x608, ?x4743), ?x3748 = 05zlld0, genre(?x6345, ?x53), ?x1785 = 0gj9tn5, ?x249 = 0c3ybss >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5805 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 8 *> proper extension: 03_9r; 04h9h; *> query: (?x12272, 03q0r1) <- language(?x715, ?x12272), award(?x715, ?x1132), titles(?x714, ?x715), film(?x488, ?x715), award_winner(?x1132, ?x4234), award(?x5884, ?x1132), award(?x2683, ?x1132), ceremony(?x1132, ?x1265), nominated_for(?x1132, ?x1434), languages_spoken(?x1050, ?x12272), ?x4234 = 01kp66, ?x5884 = 0hwqz, ?x2683 = 01dw9z *> conf = 0.20 ranks of expected_values: 379 EVAL 0880p language! 03q0r1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 37.000 23.000 0.500 http://example.org/film/film/language #2947-026wlxw PRED entity: 026wlxw PRED relation: film! PRED expected values: 01fkv0 => 84 concepts (61 used for prediction) PRED predicted values (max 10 best out of 915): 015wnl (0.20 #646, 0.10 #2720, 0.04 #19320), 015c4g (0.20 #776, 0.04 #6999, 0.03 #19450), 0bj9k (0.20 #325, 0.03 #14847, 0.03 #16923), 02q4mt (0.15 #4149, 0.11 #22824, 0.11 #8298), 04__f (0.10 #1377, 0.08 #7600, 0.03 #20051), 01vwllw (0.10 #544, 0.06 #4693, 0.02 #37890), 01_xtx (0.10 #660, 0.06 #4809, 0.02 #8959), 039bp (0.10 #177, 0.06 #6400, 0.05 #2251), 06mr6 (0.10 #1037, 0.05 #3111, 0.04 #7260), 05dbf (0.10 #362, 0.05 #2436, 0.03 #4511) >> Best rule #646 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0m9p3; >> query: (?x8214, 015wnl) <- currency(?x8214, ?x170), film(?x7780, ?x8214), ?x7780 = 0252fh, film_release_distribution_medium(?x8214, ?x81) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2238 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 04fzfj; 05jzt3; 04w7rn; 0cd2vh9; 05qbckf; 026p4q7; 0d1qmz; 02wgk1; 04mcw4; 08ct6; ... *> query: (?x8214, 01fkv0) <- currency(?x8214, ?x170), story_by(?x8214, ?x11873), language(?x8214, ?x5671), ?x5671 = 06b_j *> conf = 0.05 ranks of expected_values: 133 EVAL 026wlxw film! 01fkv0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 84.000 61.000 0.200 http://example.org/film/actor/film./film/performance/film #2946-09lq2c PRED entity: 09lq2c PRED relation: company PRED expected values: 01npw8 => 35 concepts (24 used for prediction) PRED predicted values (max 10 best out of 773): 060ppp (0.73 #2615, 0.60 #1599, 0.57 #3291), 0z90c (0.67 #1864, 0.60 #1524, 0.55 #2365), 019rl6 (0.64 #2529, 0.60 #1513, 0.57 #2190), 0300cp (0.64 #2416, 0.60 #1400, 0.57 #2077), 02r5dz (0.64 #2438, 0.60 #1422, 0.57 #2099), 087c7 (0.64 #2372, 0.60 #1356, 0.55 #2365), 07xyn1 (0.64 #2554, 0.60 #1538, 0.55 #2365), 0vlf (0.60 #1641, 0.55 #2365, 0.55 #2657), 01npw8 (0.60 #1643, 0.55 #2365, 0.55 #2659), 01c6k4 (0.60 #1359, 0.55 #2365, 0.50 #3730) >> Best rule #2615 for best value: >> intensional similarity = 12 >> extensional distance = 9 >> proper extension: 01rk91; >> query: (?x9158, 060ppp) <- company(?x9158, ?x11636), company(?x9158, ?x10637), company(?x9158, ?x7442), company(?x12865, ?x11636), ?x12865 = 04192r, citytown(?x7442, ?x1860), contact_category(?x10637, ?x6046), service_language(?x10637, ?x254), ?x6046 = 02zdwq, citytown(?x10637, ?x8993), category(?x10637, ?x134), country(?x7442, ?x94) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1643 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 3 *> proper extension: 060c4; *> query: (?x9158, 01npw8) <- company(?x9158, ?x11636), company(?x9158, ?x10637), company(?x9158, ?x9873), company(?x9158, ?x7442), ?x7442 = 03v52f, company(?x4792, ?x10637), service_location(?x9873, ?x94), ?x4792 = 05_wyz, ?x94 = 09c7w0, industry(?x9873, ?x12352), service_location(?x10637, ?x335), ?x11636 = 03s7h, industry(?x10637, ?x12014) *> conf = 0.60 ranks of expected_values: 9 EVAL 09lq2c company 01npw8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 35.000 24.000 0.727 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #2945-0js9s PRED entity: 0js9s PRED relation: profession PRED expected values: 02hrh1q => 97 concepts (93 used for prediction) PRED predicted values (max 10 best out of 58): 02hrh1q (0.90 #11629, 0.88 #3983, 0.88 #5012), 03gjzk (0.45 #1336, 0.44 #2072, 0.42 #7660), 02tx6q (0.28 #344, 0.02 #2844, 0.01 #3286), 0cbd2 (0.27 #1329, 0.18 #2506, 0.17 #153), 02krf9 (0.24 #2672, 0.23 #1789, 0.23 #1642), 09jwl (0.19 #5164, 0.18 #6046, 0.17 #5752), 018gz8 (0.17 #1338, 0.13 #7956, 0.12 #9573), 026sdt1 (0.17 #361, 0.08 #12794, 0.02 #8449), 0dgd_ (0.14 #176, 0.10 #617, 0.10 #764), 0np9r (0.14 #9577, 0.13 #7960, 0.12 #2225) >> Best rule #11629 for best value: >> intensional similarity = 3 >> extensional distance = 2770 >> proper extension: 0d0vj4; 06y3r; 081t6; >> query: (?x6589, 02hrh1q) <- profession(?x6589, ?x524), profession(?x11562, ?x524), ?x11562 = 0d0l91 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0js9s profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 93.000 0.901 http://example.org/people/person/profession #2944-09p2r9 PRED entity: 09p2r9 PRED relation: ceremony! PRED expected values: 027s4dn => 44 concepts (41 used for prediction) PRED predicted values (max 10 best out of 368): 0gqz2 (0.78 #6954, 0.77 #6190, 0.65 #4400), 0gq_d (0.74 #6286, 0.74 #7050, 0.65 #4496), 0gqy2 (0.74 #7012, 0.73 #6248, 0.65 #4458), 0gqwc (0.72 #6951, 0.71 #6187, 0.63 #4397), 0k611 (0.72 #6963, 0.71 #6199, 0.61 #4409), 0gvx_ (0.72 #7027, 0.71 #6263, 0.59 #4473), 0gs9p (0.71 #6953, 0.70 #6189, 0.61 #4399), 0f4x7 (0.71 #6917, 0.70 #6153, 0.55 #4363), 018wng (0.69 #6927, 0.68 #6163, 0.61 #4373), 0gq9h (0.69 #6952, 0.68 #6188, 0.61 #4398) >> Best rule #6954 for best value: >> intensional similarity = 15 >> extensional distance = 66 >> proper extension: 0fz20l; 0fz2y7; >> query: (?x6631, 0gqz2) <- award_winner(?x6631, ?x241), ceremony(?x2585, ?x6631), award(?x6877, ?x2585), award(?x6382, ?x2585), award(?x5536, ?x2585), award(?x2641, ?x2585), award(?x2237, ?x2585), award(?x1089, ?x2585), ?x6877 = 0ddkf, ?x1089 = 01vrncs, nominated_for(?x2585, ?x83), ?x6382 = 01wd9lv, ?x2641 = 03n0q5, participant(?x2790, ?x2237), participant(?x5536, ?x140) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2488 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 6 *> proper extension: 0drtv8; *> query: (?x6631, 027s4dn) <- award_winner(?x6631, ?x4295), award_winner(?x6631, ?x3101), ceremony(?x7965, ?x6631), participant(?x4295, ?x1942), film(?x4295, ?x755), participant(?x4295, ?x1582), ?x7965 = 054knh, award_winner(?x618, ?x4295), award_nominee(?x5485, ?x3101), film(?x3101, ?x638), award(?x4295, ?x154), ?x5485 = 01pk8v, people(?x1050, ?x4295) *> conf = 0.62 ranks of expected_values: 18 EVAL 09p2r9 ceremony! 027s4dn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 44.000 41.000 0.779 http://example.org/award/award_category/winners./award/award_honor/ceremony #2943-03bx2lk PRED entity: 03bx2lk PRED relation: music PRED expected values: 08c9b0 => 54 concepts (38 used for prediction) PRED predicted values (max 10 best out of 73): 02bh9 (0.50 #50, 0.20 #258, 0.09 #675), 0146pg (0.15 #2721, 0.15 #2930, 0.15 #2301), 02fgpf (0.14 #1071, 0.14 #1279, 0.12 #1487), 0150t6 (0.14 #670, 0.09 #878, 0.05 #1919), 02cyfz (0.11 #450, 0.04 #2116, 0.04 #2534), 08c9b0 (0.11 #497, 0.01 #2372, 0.01 #2581), 012ljv (0.11 #417, 0.01 #1875), 02z81h (0.11 #526), 016szr (0.11 #1120, 0.10 #1328, 0.09 #1536), 06fxnf (0.08 #1941, 0.06 #900, 0.05 #692) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0kv238; >> query: (?x1219, 02bh9) <- film_release_region(?x1219, ?x5482), ?x5482 = 04g5k, edited_by(?x1219, ?x323), music(?x1219, ?x2363) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #497 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0sxg4; *> query: (?x1219, 08c9b0) <- titles(?x3920, ?x1219), film(?x7830, ?x1219), film(?x374, ?x1219), award_nominee(?x221, ?x7830), ?x374 = 05cj4r *> conf = 0.11 ranks of expected_values: 6 EVAL 03bx2lk music 08c9b0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 54.000 38.000 0.500 http://example.org/film/film/music #2942-025twgt PRED entity: 025twgt PRED relation: language PRED expected values: 02h40lc => 107 concepts (106 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.99 #5716, 0.99 #5658, 0.98 #5131), 064_8sq (0.77 #3573, 0.69 #479, 0.60 #1109), 04306rv (0.40 #62, 0.38 #233, 0.37 #3847), 06nm1 (0.37 #2464, 0.36 #1204, 0.31 #870), 02bjrlw (0.37 #2464, 0.36 #1204, 0.17 #1031), 02ztjwg (0.37 #2464, 0.36 #1204, 0.10 #88), 0jzc (0.37 #2464, 0.23 #535, 0.20 #20), 03_9r (0.37 #2464, 0.11 #755, 0.10 #467), 0653m (0.20 #12, 0.11 #757, 0.08 #515), 012w70 (0.20 #13, 0.10 #70, 0.09 #127) >> Best rule #5716 for best value: >> intensional similarity = 8 >> extensional distance = 1615 >> proper extension: 08g_jw; >> query: (?x11362, 02h40lc) <- language(?x11362, ?x5671), genre(?x11362, ?x225), language(?x5271, ?x5671), language(?x3943, ?x5671), ?x3943 = 015whm, ?x5271 = 047vnkj, genre(?x4127, ?x225), film(?x382, ?x4127) >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025twgt language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 106.000 0.988 http://example.org/film/film/language #2941-04mkft PRED entity: 04mkft PRED relation: film PRED expected values: 07sc6nw 03f7xg 02fqrf 03hxsv 063y9fp => 110 concepts (42 used for prediction) PRED predicted values (max 10 best out of 1833): 0b6l1st (0.50 #5814, 0.33 #8956, 0.33 #2674), 04pk1f (0.50 #5637, 0.33 #2497, 0.14 #7852), 09w6br (0.50 #6182, 0.33 #3042, 0.14 #7852), 031786 (0.50 #5827, 0.33 #2687, 0.14 #7852), 03tbg6 (0.50 #6165, 0.33 #3025, 0.14 #7852), 031hcx (0.50 #5826, 0.33 #2686, 0.14 #7852), 03h3x5 (0.50 #5078, 0.33 #1938, 0.14 #7852), 0bh8yn3 (0.50 #4934, 0.33 #1794, 0.14 #7852), 0372j5 (0.50 #5761, 0.33 #2621, 0.12 #26175), 0404j37 (0.50 #5709, 0.33 #2569, 0.11 #8851) >> Best rule #5814 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 03xq0f; >> query: (?x5854, 0b6l1st) <- film(?x5854, ?x6620), film(?x5854, ?x5839), film(?x5854, ?x280), ?x6620 = 0mbql, film_release_region(?x280, ?x550), ?x550 = 05v8c, ?x5839 = 05650n >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5692 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 03xq0f; *> query: (?x5854, 03hxsv) <- film(?x5854, ?x6620), film(?x5854, ?x5839), film(?x5854, ?x280), ?x6620 = 0mbql, film_release_region(?x280, ?x550), ?x550 = 05v8c, ?x5839 = 05650n *> conf = 0.50 ranks of expected_values: 15, 17, 133, 221, 656 EVAL 04mkft film 063y9fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 110.000 42.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 04mkft film 03hxsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 110.000 42.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 04mkft film 02fqrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 110.000 42.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 04mkft film 03f7xg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 110.000 42.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 04mkft film 07sc6nw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 110.000 42.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #2940-09r9dp PRED entity: 09r9dp PRED relation: award PRED expected values: 09qvc0 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 228): 09sb52 (0.34 #3249, 0.32 #7259, 0.32 #7660), 0cqhk0 (0.20 #37, 0.17 #10427, 0.15 #14840), 0fbvqf (0.20 #48, 0.15 #14840, 0.15 #14438), 05pcn59 (0.20 #1285, 0.20 #1686, 0.15 #14840), 05p09zm (0.17 #10427, 0.15 #14840, 0.15 #14438), 0gr51 (0.17 #10427, 0.15 #14840, 0.15 #14438), 0gqy2 (0.17 #10427, 0.15 #14840, 0.15 #14438), 0gq9h (0.17 #10427, 0.15 #14840, 0.15 #14438), 0bdwqv (0.17 #10427, 0.15 #14840, 0.15 #14438), 07cbcy (0.17 #10427, 0.15 #14840, 0.15 #14438) >> Best rule #3249 for best value: >> intensional similarity = 3 >> extensional distance = 712 >> proper extension: 08hp53; 0jrqq; 03ktjq; 06chvn; 01l1ls; >> query: (?x3789, 09sb52) <- award_winner(?x3789, ?x92), award(?x3789, ?x1670), film(?x3789, ?x1192) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #20095 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1714 *> proper extension: 03xmy1; 03j0br4; 03bpn6; 039xcr; 05_zc7; *> query: (?x3789, 09qvc0) <- film(?x3789, ?x1192), award(?x3789, ?x1670) *> conf = 0.04 ranks of expected_values: 113 EVAL 09r9dp award 09qvc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 84.000 84.000 0.339 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2939-094tsh6 PRED entity: 094tsh6 PRED relation: nominated_for PRED expected values: 0drnwh => 83 concepts (33 used for prediction) PRED predicted values (max 10 best out of 655): 03176f (0.50 #3219, 0.38 #2249, 0.20 #12881), 01qb5d (0.50 #3219, 0.01 #13007), 0jqn5 (0.29 #9660, 0.26 #8048, 0.25 #8047), 027gy0k (0.29 #9660, 0.26 #8048, 0.25 #8047), 0dfw0 (0.29 #9660, 0.26 #8048, 0.25 #8047), 03nsm5x (0.29 #9660, 0.26 #8048, 0.25 #8047), 0bt3j9 (0.29 #9660, 0.26 #8048, 0.25 #8047), 0642xf3 (0.29 #9660, 0.26 #8048, 0.25 #8047), 01f7gh (0.29 #9660, 0.26 #8048, 0.25 #8047), 060__7 (0.29 #9660, 0.25 #8047, 0.23 #9659) >> Best rule #3219 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0146pg; 03y1mlp; 071ywj; 013_vh; 0bbxx9b; 0d5wn3; >> query: (?x9391, ?x936) <- nominated_for(?x9391, ?x4392), nominated_for(?x9391, ?x2006), ?x2006 = 031778, prequel(?x4392, ?x936) >> conf = 0.50 => this is the best rule for 2 predicted values *> Best rule #2668 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 0146pg; 03y1mlp; 071ywj; 013_vh; 0bbxx9b; 0d5wn3; *> query: (?x9391, 0drnwh) <- nominated_for(?x9391, ?x4392), nominated_for(?x9391, ?x2006), ?x2006 = 031778, prequel(?x4392, ?x936) *> conf = 0.12 ranks of expected_values: 76 EVAL 094tsh6 nominated_for 0drnwh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 83.000 33.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #2938-03p2xc PRED entity: 03p2xc PRED relation: titles! PRED expected values: 01z4y => 65 concepts (32 used for prediction) PRED predicted values (max 10 best out of 58): 01z4y (0.75 #135, 0.64 #235, 0.35 #336), 05mrx8 (0.34 #602, 0.21 #1902, 0.20 #2607), 0gf28 (0.34 #602, 0.21 #1902, 0.20 #2607), 0cshrf (0.34 #602, 0.21 #1902, 0.20 #2607), 06nbt (0.34 #602, 0.21 #1902, 0.20 #2607), 05p553 (0.34 #602, 0.21 #1902, 0.20 #2607), 07ssc (0.27 #411, 0.18 #1401, 0.16 #1310), 04xvlr (0.23 #1805, 0.23 #505, 0.22 #1104), 02qfv5d (0.23 #587, 0.02 #2592, 0.02 #1887), 04t36 (0.12 #107, 0.09 #207, 0.06 #711) >> Best rule #135 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 02v8kmz; >> query: (?x7128, 01z4y) <- genre(?x7128, ?x13467), genre(?x7128, ?x8467), film_release_distribution_medium(?x7128, ?x81), ?x13467 = 05mrx8, ?x8467 = 0gf28 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03p2xc titles! 01z4y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 32.000 0.750 http://example.org/media_common/netflix_genre/titles #2937-03ncb2 PRED entity: 03ncb2 PRED relation: award_winner PRED expected values: 02qtywd => 34 concepts (9 used for prediction) PRED predicted values (max 10 best out of 880): 09hnb (0.43 #17309, 0.40 #19784, 0.33 #19783), 01l7cxq (0.40 #19784, 0.33 #19783, 0.32 #22259), 0gcs9 (0.14 #645, 0.13 #3117, 0.12 #5590), 01wd9lv (0.13 #6362, 0.12 #11308, 0.12 #8835), 09889g (0.11 #11015, 0.11 #3596, 0.11 #1124), 0dw4g (0.11 #1255, 0.10 #3727, 0.10 #8673), 0m_v0 (0.11 #742, 0.10 #3214, 0.09 #5687), 02qwg (0.11 #735, 0.10 #3207, 0.09 #5680), 0g824 (0.11 #6363, 0.10 #8836, 0.08 #11309), 012x4t (0.10 #17310, 0.10 #19785, 0.07 #7755) >> Best rule #17309 for best value: >> intensional similarity = 5 >> extensional distance = 132 >> proper extension: 04jhhng; >> query: (?x8409, ?x2698) <- award(?x2698, ?x8409), award_winner(?x8409, ?x547), category_of(?x8409, ?x2421), award_winner(?x217, ?x2698), award_winner(?x1079, ?x2698) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #17310 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 132 *> proper extension: 04jhhng; *> query: (?x8409, ?x217) <- award(?x2698, ?x8409), award_winner(?x8409, ?x547), category_of(?x8409, ?x2421), award_winner(?x217, ?x2698), award_winner(?x1079, ?x2698) *> conf = 0.10 ranks of expected_values: 14 EVAL 03ncb2 award_winner 02qtywd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 34.000 9.000 0.428 http://example.org/award/award_category/winners./award/award_honor/award_winner #2936-0345h PRED entity: 0345h PRED relation: exported_to! PRED expected values: 07dzf => 220 concepts (146 used for prediction) PRED predicted values (max 10 best out of 219): 0h3y (0.27 #550, 0.21 #661, 0.15 #1158), 0ctw_b (0.22 #395, 0.21 #670, 0.19 #615), 0jdd (0.22 #414, 0.21 #1019, 0.19 #634), 0j4b (0.22 #368, 0.15 #1196, 0.13 #588), 0d060g (0.22 #385, 0.13 #549, 0.12 #605), 0n3g (0.22 #420, 0.13 #584, 0.12 #640), 047t_ (0.21 #693, 0.19 #638, 0.19 #1190), 05r4w (0.20 #710, 0.12 #1097, 0.11 #381), 03rjj (0.20 #438, 0.11 #383, 0.11 #327), 0d05w3 (0.19 #2286, 0.19 #2834, 0.18 #2945) >> Best rule #550 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 049nq; >> query: (?x1264, 0h3y) <- nationality(?x380, ?x1264), country(?x1679, ?x1264), first_level_division_of(?x1646, ?x1264) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1024 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 22 *> proper extension: 09nm_; *> query: (?x1264, 07dzf) <- region(?x1315, ?x1264), film_release_region(?x1315, ?x94) *> conf = 0.17 ranks of expected_values: 11 EVAL 0345h exported_to! 07dzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 220.000 146.000 0.267 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #2935-0263tn1 PRED entity: 0263tn1 PRED relation: profession PRED expected values: 02hrh1q => 84 concepts (82 used for prediction) PRED predicted values (max 10 best out of 47): 02hrh1q (0.92 #1803, 0.90 #1505, 0.88 #1356), 01d_h8 (0.36 #3731, 0.34 #3284, 0.33 #1943), 0dxtg (0.33 #3739, 0.29 #5080, 0.29 #6272), 016z4k (0.27 #8495, 0.25 #7749, 0.10 #302), 0np9r (0.27 #8495, 0.20 #2257, 0.20 #2406), 02jknp (0.26 #3733, 0.24 #3286, 0.22 #5074), 018gz8 (0.25 #7749, 0.14 #1061, 0.13 #1508), 03gjzk (0.25 #2996, 0.25 #3741, 0.24 #2847), 09jwl (0.17 #6278, 0.16 #318, 0.16 #5533), 0cbd2 (0.16 #156, 0.12 #11186, 0.11 #11782) >> Best rule #1803 for best value: >> intensional similarity = 3 >> extensional distance = 687 >> proper extension: 0m32_; 01jbx1; 01v3vp; >> query: (?x8325, 02hrh1q) <- actor(?x1434, ?x8325), award(?x8325, ?x1670), profession(?x8325, ?x4773) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0263tn1 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 82.000 0.922 http://example.org/people/person/profession #2934-016kft PRED entity: 016kft PRED relation: student! PRED expected values: 08815 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 54): 015nl4 (0.09 #593, 0.05 #2697, 0.05 #8483), 017j69 (0.09 #145, 0.03 #2775, 0.02 #1723), 05nrkb (0.09 #875, 0.02 #2979, 0.02 #8765), 033gn8 (0.09 #904, 0.02 #3008, 0.02 #8794), 02q253 (0.09 #1030), 02237m (0.09 #923), 02607j (0.09 #629), 017hnw (0.09 #508), 02j416 (0.09 #431), 04s934 (0.09 #216) >> Best rule #593 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 041rhq; 02m501; 044bn; >> query: (?x9359, 015nl4) <- film(?x9359, ?x11065), gender(?x9359, ?x231), ?x11065 = 0n08r >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #11574 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1168 *> proper extension: 03wjb7; 03z0l6; 01h2_6; 02k76g; *> query: (?x9359, 08815) <- nationality(?x9359, ?x94), student(?x11112, ?x9359), place_of_birth(?x9359, ?x3007) *> conf = 0.03 ranks of expected_values: 21 EVAL 016kft student! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 100.000 100.000 0.091 http://example.org/education/educational_institution/students_graduates./education/education/student #2933-02f6xy PRED entity: 02f6xy PRED relation: award_winner PRED expected values: 02vr7 => 45 concepts (27 used for prediction) PRED predicted values (max 10 best out of 2082): 0478__m (0.44 #10860, 0.33 #1024, 0.16 #13318), 09889g (0.41 #49178, 0.36 #66397, 0.34 #63937), 01wwvc5 (0.41 #49178, 0.36 #66397, 0.34 #63937), 0140t7 (0.41 #49178, 0.36 #66397, 0.34 #63937), 03bnv (0.41 #49178, 0.36 #66397, 0.34 #63937), 01s21dg (0.41 #49178, 0.36 #66397, 0.34 #63937), 01wcp_g (0.41 #49178, 0.36 #66397, 0.34 #63937), 03h_fk5 (0.41 #49178, 0.36 #66397, 0.34 #63937), 016kjs (0.41 #49178, 0.36 #66397, 0.34 #63937), 0fhxv (0.41 #49178, 0.36 #66397, 0.34 #63937) >> Best rule #10860 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 02f716; 02f77y; >> query: (?x3926, 0478__m) <- award_winner(?x3926, ?x1896), award_winner(?x3926, ?x1462), award(?x300, ?x3926), profession(?x1462, ?x955), ?x955 = 0n1h, ?x1896 = 0j1yf >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #14080 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 47 *> proper extension: 02f6yz; *> query: (?x3926, 02vr7) <- award_winner(?x3926, ?x521), award(?x1125, ?x3926), award(?x883, ?x3926), award(?x1125, ?x8705), artists(?x284, ?x883), ?x8705 = 01c9dd *> conf = 0.02 ranks of expected_values: 906 EVAL 02f6xy award_winner 02vr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 45.000 27.000 0.444 http://example.org/award/award_category/winners./award/award_honor/award_winner #2932-0162v PRED entity: 0162v PRED relation: nationality! PRED expected values: 0d9v9q => 93 concepts (76 used for prediction) PRED predicted values (max 10 best out of 4206): 0d1_f (0.60 #40684, 0.45 #77293, 0.07 #25394), 059xvg (0.15 #37668, 0.11 #33601, 0.09 #62073), 07y_r (0.14 #19863, 0.14 #15794, 0.13 #23932), 03y3dk (0.14 #18946, 0.14 #14877, 0.13 #23015), 01qq_lp (0.14 #17425, 0.14 #13356, 0.13 #21494), 0cmpn (0.13 #27547, 0.13 #23479, 0.12 #31615), 02ply6j (0.13 #26621, 0.13 #22553, 0.12 #30689), 04cbtrw (0.13 #25246, 0.13 #21178, 0.12 #29314), 05d7rk (0.13 #24427, 0.13 #20359, 0.12 #28495), 070px (0.13 #24251, 0.12 #32387, 0.11 #36455) >> Best rule #40684 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 06frc; >> query: (?x1957, ?x3444) <- form_of_government(?x1957, ?x1926), jurisdiction_of_office(?x3444, ?x1957), contains(?x8882, ?x1957) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #38853 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 06frc; *> query: (?x1957, 0d9v9q) <- form_of_government(?x1957, ?x1926), jurisdiction_of_office(?x3444, ?x1957), contains(?x8882, ?x1957) *> conf = 0.07 ranks of expected_values: 153 EVAL 0162v nationality! 0d9v9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 93.000 76.000 0.600 http://example.org/people/person/nationality #2931-0g0vx PRED entity: 0g0vx PRED relation: profession! PRED expected values: 034rd 042d1 => 71 concepts (34 used for prediction) PRED predicted values (max 10 best out of 4142): 03j24kf (0.50 #82052, 0.50 #14226, 0.43 #99008), 0168cl (0.50 #8637, 0.40 #21351, 0.25 #12875), 012_53 (0.50 #9223, 0.40 #21937, 0.19 #89764), 0cqt90 (0.50 #9671, 0.40 #22385, 0.17 #81735), 03s2y9 (0.50 #12250, 0.40 #24964, 0.17 #84314), 0grwj (0.50 #8489, 0.40 #21203, 0.10 #80553), 04bgy (0.50 #14823, 0.33 #2109, 0.20 #82649), 01wdqrx (0.50 #13051, 0.33 #337, 0.20 #80877), 0285c (0.50 #13264, 0.33 #550, 0.20 #81090), 023322 (0.50 #16099, 0.33 #3385, 0.17 #83925) >> Best rule #82052 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 0dz3r; 016z4k; 01d_h8; 02jknp; 0gbbt; 0n1h; 0dxtg; 02hrh1q; 03gjzk; 09jwl; ... >> query: (?x12647, 03j24kf) <- profession(?x8114, ?x12647), instrumentalists(?x212, ?x8114), artists(?x302, ?x8114), ?x212 = 026t6, role(?x8114, ?x228) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #27270 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 099md; 0c5lg; *> query: (?x12647, 034rd) <- profession(?x8114, ?x12647), profession(?x5742, ?x12647), profession(?x5254, ?x12647), gender(?x8114, ?x231), ?x231 = 05zppz, ?x5742 = 0rlz, place_of_death(?x5254, ?x2298), place_of_birth(?x8114, ?x11299) *> conf = 0.40 ranks of expected_values: 42, 2246 EVAL 0g0vx profession! 042d1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 34.000 0.500 http://example.org/people/person/profession EVAL 0g0vx profession! 034rd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 71.000 34.000 0.500 http://example.org/people/person/profession #2930-016ndm PRED entity: 016ndm PRED relation: major_field_of_study PRED expected values: 0fdys 037mh8 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 117): 03g3w (0.55 #27, 0.49 #150, 0.38 #1013), 05qjt (0.45 #8, 0.29 #624, 0.28 #131), 01mkq (0.45 #631, 0.42 #1001, 0.40 #1124), 02j62 (0.44 #154, 0.43 #1263, 0.42 #1017), 04rjg (0.37 #1006, 0.31 #636, 0.30 #4578), 062z7 (0.33 #151, 0.32 #644, 0.31 #1014), 0fdys (0.33 #163, 0.24 #287, 0.24 #1026), 01tbp (0.32 #676, 0.31 #1169, 0.27 #60), 04sh3 (0.30 #1185, 0.23 #199, 0.20 #692), 0g26h (0.28 #660, 0.25 #1276, 0.25 #1030) >> Best rule #27 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0373qg; 025v3k; 015cz0; 0jpkw; 01csqg; >> query: (?x4199, 03g3w) <- contains(?x279, ?x4199), major_field_of_study(?x4199, ?x1154), ?x1154 = 02lp1, currency(?x4199, ?x2244) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #163 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 01b1pf; *> query: (?x4199, 0fdys) <- institution(?x7636, ?x4199), colors(?x4199, ?x663), ?x7636 = 01rr_d *> conf = 0.33 ranks of expected_values: 7, 14 EVAL 016ndm major_field_of_study 037mh8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 137.000 137.000 0.545 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 016ndm major_field_of_study 0fdys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 137.000 137.000 0.545 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #2929-0nlh7 PRED entity: 0nlh7 PRED relation: split_to PRED expected values: 0nlh7 => 166 concepts (68 used for prediction) PRED predicted values (max 10 best out of 7): 06rny (0.04 #624, 0.03 #1195, 0.03 #1291), 01k6zy (0.03 #1231, 0.03 #1327, 0.01 #1230), 07ssc (0.02 #1527, 0.02 #2005, 0.02 #2101), 09c7w0 (0.01 #2873, 0.01 #3353), 06wjf (0.01 #3014), 0jnmj (0.01 #1230, 0.01 #1423, 0.01 #1805), 03fb3t (0.01 #3396) >> Best rule #624 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 0cy07; >> query: (?x10718, 06rny) <- place_of_birth(?x8596, ?x10718), notable_people_with_this_condition(?x9933, ?x8596), award_winner(?x434, ?x8596) >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0nlh7 split_to 0nlh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 166.000 68.000 0.042 http://example.org/dataworld/gardening_hint/split_to #2928-02b25y PRED entity: 02b25y PRED relation: profession PRED expected values: 0dz3r => 108 concepts (87 used for prediction) PRED predicted values (max 10 best out of 91): 02hrh1q (0.83 #3096, 0.82 #4413, 0.79 #2068), 0dxtg (0.69 #10852, 0.30 #10268, 0.28 #12460), 0fj9f (0.57 #1226, 0.18 #1959, 0.14 #932), 016z4k (0.51 #736, 0.48 #443, 0.48 #297), 0dz3r (0.50 #149, 0.45 #3523, 0.41 #4254), 03gjzk (0.49 #10270, 0.26 #10854, 0.24 #895), 01c72t (0.47 #5886, 0.43 #171, 0.38 #3691), 01d_h8 (0.36 #10844, 0.33 #2646, 0.32 #8652), 02jknp (0.30 #10846, 0.19 #11862, 0.18 #8800), 05vyk (0.29 #240, 0.15 #1560, 0.14 #386) >> Best rule #3096 for best value: >> intensional similarity = 3 >> extensional distance = 216 >> proper extension: 01r42_g; 04smkr; 03_wpf; 023jq1; >> query: (?x2584, 02hrh1q) <- profession(?x2584, ?x1183), award_winner(?x2584, ?x2461), languages(?x2584, ?x90) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #149 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0lbj1; 01vrz41; 01kx_81; 01w02sy; 016szr; 01l4g5; 03f1d47; 0277c3; 01r7pq; 01k3qj; ... *> query: (?x2584, 0dz3r) <- artist(?x7793, ?x2584), ?x7793 = 01dtcb, student(?x6784, ?x2584), profession(?x2584, ?x1183) *> conf = 0.50 ranks of expected_values: 5 EVAL 02b25y profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 87.000 0.835 http://example.org/people/person/profession #2927-0282x PRED entity: 0282x PRED relation: influenced_by PRED expected values: 01v9724 => 172 concepts (61 used for prediction) PRED predicted values (max 10 best out of 409): 01hmk9 (0.40 #1076, 0.33 #217, 0.25 #6228), 01wp_jm (0.40 #1196, 0.33 #337, 0.25 #4200), 040db (0.40 #1343, 0.33 #2201, 0.17 #15959), 03f47xl (0.40 #1490, 0.33 #2348, 0.10 #19544), 0p_47 (0.40 #965, 0.25 #3969, 0.13 #6977), 01s7qqw (0.33 #163, 0.20 #5745, 0.20 #1022), 0427y (0.33 #320, 0.20 #1179, 0.12 #4183), 01gn36 (0.33 #135, 0.20 #994, 0.12 #3998), 02h48 (0.33 #407, 0.20 #1266, 0.12 #4270), 03f0324 (0.27 #19493, 0.26 #22938, 0.26 #16055) >> Best rule #1076 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 02_j7t; 0126rp; >> query: (?x5345, 01hmk9) <- influenced_by(?x5345, ?x12459), influenced_by(?x5345, ?x8981), profession(?x5345, ?x353), ?x12459 = 04sd0, story_by(?x7320, ?x8981) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #16080 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 04107; *> query: (?x5345, 01v9724) <- influenced_by(?x5345, ?x11018), influenced_by(?x11018, ?x12146), type_of_union(?x5345, ?x566), student(?x734, ?x11018) *> conf = 0.13 ranks of expected_values: 100 EVAL 0282x influenced_by 01v9724 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 172.000 61.000 0.400 http://example.org/influence/influence_node/influenced_by #2926-0cp9f9 PRED entity: 0cp9f9 PRED relation: award_winner! PRED expected values: 02xcb6n => 113 concepts (111 used for prediction) PRED predicted values (max 10 best out of 232): 02xcb6n (0.38 #23711, 0.37 #30184, 0.37 #28888), 0cjyzs (0.29 #3124, 0.19 #3555, 0.17 #3986), 09cm54 (0.17 #959, 0.08 #528, 0.03 #1390), 040njc (0.17 #439, 0.04 #2163, 0.03 #3025), 09sb52 (0.13 #6075, 0.13 #7368, 0.12 #14267), 02x4w6g (0.12 #977, 0.03 #1408, 0.01 #7873), 02grdc (0.11 #1325, 0.04 #3049, 0.03 #2187), 0ck27z (0.11 #7420, 0.10 #8713, 0.10 #8282), 0cqhk0 (0.10 #5209, 0.09 #5640, 0.08 #8226), 0fc9js (0.09 #1507, 0.05 #3231, 0.04 #18538) >> Best rule #23711 for best value: >> intensional similarity = 3 >> extensional distance = 1248 >> proper extension: 07mvp; >> query: (?x8229, ?x4921) <- award_winner(?x8229, ?x8933), award(?x8229, ?x4921), award_winner(?x1265, ?x8933) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cp9f9 award_winner! 02xcb6n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 111.000 0.376 http://example.org/award/award_category/winners./award/award_honor/award_winner #2925-0249kn PRED entity: 0249kn PRED relation: artist! PRED expected values: 03rhqg => 89 concepts (62 used for prediction) PRED predicted values (max 10 best out of 97): 033hn8 (0.33 #148, 0.19 #418, 0.16 #688), 015_1q (0.26 #423, 0.24 #1098, 0.24 #1638), 03rhqg (0.26 #420, 0.22 #150, 0.21 #285), 0g768 (0.22 #170, 0.15 #845, 0.14 #980), 01trtc (0.22 #205, 0.10 #1285, 0.09 #2096), 01dtcb (0.22 #180, 0.10 #720, 0.10 #450), 0n85g (0.22 #195, 0.09 #3991, 0.08 #735), 0229rs (0.17 #422, 0.09 #827, 0.09 #962), 01clyr (0.14 #436, 0.12 #31, 0.11 #166), 03mp8k (0.13 #334, 0.10 #1279, 0.09 #1684) >> Best rule #148 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0kvnn; >> query: (?x2906, 033hn8) <- artist(?x8392, ?x2906), origin(?x2906, ?x8468), ?x8392 = 04gmlt >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #420 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 07qnf; 02r3zy; 01wv9xn; 01czx; 02r1tx7; 01vrwfv; 05563d; 0d193h; 0394y; 0khth; ... *> query: (?x2906, 03rhqg) <- artist(?x2241, ?x2906), group(?x2798, ?x2906), ?x2798 = 03qjg *> conf = 0.26 ranks of expected_values: 3 EVAL 0249kn artist! 03rhqg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 62.000 0.333 http://example.org/music/record_label/artist #2924-035_2h PRED entity: 035_2h PRED relation: genre PRED expected values: 0lsxr => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 78): 07s9rl0 (0.79 #481, 0.71 #1921, 0.70 #1561), 05p553 (0.40 #124, 0.36 #10329, 0.35 #4086), 03k9fj (0.38 #1212, 0.38 #1452, 0.27 #732), 04xvlr (0.33 #482, 0.19 #2042, 0.18 #1922), 06n90 (0.28 #1453, 0.28 #1213, 0.15 #7576), 0lsxr (0.24 #1209, 0.20 #1449, 0.20 #129), 082gq (0.23 #750, 0.21 #510, 0.13 #1590), 03bxz7 (0.21 #535, 0.13 #775, 0.12 #1975), 04t36 (0.21 #486, 0.09 #2046, 0.08 #2286), 060__y (0.20 #136, 0.19 #736, 0.18 #496) >> Best rule #481 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 01jc6q; 03hjv97; 0cwy47; 0p7qm; 0qmfk; >> query: (?x5294, 07s9rl0) <- award(?x5294, ?x484), produced_by(?x5294, ?x4397), ?x484 = 0gq_v >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1209 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 365 *> proper extension: 015qy1; *> query: (?x5294, 0lsxr) <- film_release_distribution_medium(?x5294, ?x81), genre(?x5294, ?x225), ?x225 = 02kdv5l *> conf = 0.24 ranks of expected_values: 6 EVAL 035_2h genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 92.000 92.000 0.788 http://example.org/film/film/genre #2923-01w_10 PRED entity: 01w_10 PRED relation: gender PRED expected values: 02zsn => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #67, 0.82 #61, 0.80 #83), 02zsn (0.54 #8, 0.45 #54, 0.44 #26) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 0j3v; 0cl_m; 03h40_7; 047g6; 011zwl; >> query: (?x8122, 05zppz) <- nationality(?x8122, ?x94), company(?x8122, ?x1762), student(?x5486, ?x8122) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 11 *> proper extension: 047q2wc; *> query: (?x8122, 02zsn) <- award_winner(?x8122, ?x3150), ?x3150 = 049_zz *> conf = 0.54 ranks of expected_values: 2 EVAL 01w_10 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.841 http://example.org/people/person/gender #2922-0kz4w PRED entity: 0kz4w PRED relation: current_club! PRED expected values: 035qgm => 88 concepts (48 used for prediction) PRED predicted values (max 10 best out of 52): 01_lhg (0.50 #39, 0.44 #318, 0.30 #163), 02s2lg (0.50 #37, 0.27 #191, 0.20 #253), 03yl2t (0.25 #96, 0.25 #66, 0.20 #127), 035qgm (0.25 #81, 0.25 #19, 0.16 #216), 0329r5 (0.25 #45, 0.20 #169, 0.16 #216), 03z8bw (0.25 #83, 0.16 #216, 0.14 #238), 03y_f8 (0.25 #65, 0.16 #216, 0.12 #313), 033nzk (0.25 #2, 0.16 #216, 0.12 #312), 03ys48 (0.25 #80, 0.16 #216, 0.12 #110), 02rqxc (0.25 #40, 0.16 #216, 0.11 #1132) >> Best rule #39 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 01453; 045xx; >> query: (?x11253, 01_lhg) <- team(?x60, ?x11253), team(?x5471, ?x11253), team(?x9106, ?x11253), colors(?x11253, ?x3189), position(?x11253, ?x203), ?x9106 = 09j028, colors(?x331, ?x3189) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #81 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 03x6m; *> query: (?x11253, 035qgm) <- team(?x60, ?x11253), team(?x7109, ?x11253), ?x7109 = 08b0cj, sport(?x11253, ?x471), colors(?x11253, ?x3189), position(?x11253, ?x203), ?x60 = 02nzb8, ?x203 = 0dgrmp *> conf = 0.25 ranks of expected_values: 4 EVAL 0kz4w current_club! 035qgm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 48.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #2921-02qgqt PRED entity: 02qgqt PRED relation: award_nominee PRED expected values: 01g23m => 91 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1089): 023kzp (0.81 #60077, 0.81 #43900, 0.81 #53143), 02mt4k (0.81 #60077, 0.81 #43900, 0.81 #53143), 01kb2j (0.81 #60077, 0.81 #43900, 0.81 #53143), 02qgyv (0.81 #60077, 0.81 #43900, 0.81 #53143), 02wgln (0.81 #60077, 0.81 #43900, 0.81 #53143), 04t7ts (0.81 #60077, 0.81 #43900, 0.81 #53143), 030hcs (0.81 #60077, 0.81 #43900, 0.81 #53143), 034np8 (0.81 #60077, 0.81 #43900, 0.81 #53143), 02x7vq (0.77 #57766, 0.76 #83186, 0.76 #85498), 02qgqt (0.25 #13866, 0.15 #25418, 0.08 #50832) >> Best rule #60077 for best value: >> intensional similarity = 3 >> extensional distance = 1212 >> proper extension: 0m2wm; 02zq43; 04wqr; 07lmxq; 0f830f; 03m8lq; 08w7vj; 01j5x6; 01v3s2_; 0bz5v2; ... >> query: (?x157, ?x92) <- film(?x157, ?x974), award_nominee(?x92, ?x157), award_nominee(?x157, ?x91) >> conf = 0.81 => this is the best rule for 8 predicted values *> Best rule #10140 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 712 *> proper extension: 018swb; 02t_v1; *> query: (?x157, 01g23m) <- award(?x157, ?x112), film(?x157, ?x974), award_winner(?x157, ?x91) *> conf = 0.01 ranks of expected_values: 853 EVAL 02qgqt award_nominee 01g23m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 91.000 37.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2920-0pqc5 PRED entity: 0pqc5 PRED relation: jurisdiction_of_office PRED expected values: 02cl1 0fvvz 0dclg 0ply0 05jbn 0ftlx 01smm 0f04v 0f2tj 0gp5l6 0c5v2 0kcw2 0nqph => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 462): 01n7q (0.67 #1854, 0.50 #1554, 0.46 #2455), 081yw (0.62 #1663, 0.56 #1963, 0.40 #3466), 059rby (0.62 #1519, 0.44 #1819, 0.33 #3322), 07z1m (0.55 #1503, 0.50 #1565, 0.50 #1202), 05tbn (0.55 #1503, 0.50 #1634, 0.50 #1202), 03v1s (0.55 #1503, 0.50 #1202, 0.46 #2103), 04rrd (0.55 #1503, 0.50 #1202, 0.46 #2103), 0d060g (0.55 #1503, 0.50 #1202, 0.46 #2103), 0mpbx (0.55 #1503, 0.50 #1202, 0.46 #2103), 0kpzy (0.55 #1503, 0.50 #1202, 0.46 #2103) >> Best rule #1854 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 01gkgk; 0789n; 09n5b9; 01t7n9; >> query: (?x1195, 01n7q) <- jurisdiction_of_office(?x1195, ?x9767), jurisdiction_of_office(?x1195, ?x9331), jurisdiction_of_office(?x1195, ?x7328), jurisdiction_of_office(?x1195, ?x3125), adjoins(?x12358, ?x9331), featured_film_locations(?x7584, ?x9331), origin(?x1751, ?x3125), location(?x7345, ?x9767), place_founded(?x5956, ?x3125), featured_film_locations(?x590, ?x3125), place_of_birth(?x5202, ?x7328) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3908 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 15 *> proper extension: 09d6p2; *> query: (?x1195, ?x12358) <- jurisdiction_of_office(?x1195, ?x9331), jurisdiction_of_office(?x1195, ?x3125), jurisdiction_of_office(?x1195, ?x3026), adjoins(?x12358, ?x9331), basic_title(?x744, ?x1195), place_of_birth(?x399, ?x3125), location(?x1773, ?x3026) *> conf = 0.38 ranks of expected_values: 58, 252, 269, 280 EVAL 0pqc5 jurisdiction_of_office 0nqph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 0kcw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 0c5v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 0gp5l6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 0f2tj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 0f04v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 01smm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 0ftlx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 05jbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 0ply0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 0dclg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 0fvvz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 0pqc5 jurisdiction_of_office 02cl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 17.000 17.000 0.667 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #2919-051z6rz PRED entity: 051z6rz PRED relation: crewmember! PRED expected values: 035s95 03tbg6 => 88 concepts (77 used for prediction) PRED predicted values (max 10 best out of 307): 0bwfwpj (0.33 #35, 0.13 #1255, 0.12 #645), 0hx4y (0.33 #96, 0.13 #1316, 0.10 #1926), 07gp9 (0.33 #6, 0.13 #1226, 0.10 #1836), 024mpp (0.33 #128, 0.13 #1348, 0.09 #2568), 011wtv (0.33 #149, 0.13 #1369, 0.06 #2284), 04gknr (0.33 #32, 0.07 #1252, 0.07 #2472), 016017 (0.33 #302, 0.07 #1522, 0.06 #2437), 037cr1 (0.33 #291, 0.07 #1511, 0.06 #2426), 02ylg6 (0.33 #175, 0.07 #1395, 0.06 #2310), 0407yfx (0.33 #77, 0.07 #1297, 0.06 #2212) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0c94fn; >> query: (?x6166, 0bwfwpj) <- crewmember(?x97, ?x6166), profession(?x6166, ?x524), place_of_birth(?x6166, ?x3976), ?x97 = 0d90m >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 051z6rz crewmember! 03tbg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 77.000 0.333 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember EVAL 051z6rz crewmember! 035s95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 77.000 0.333 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #2918-05g76 PRED entity: 05g76 PRED relation: team! PRED expected values: 01z9v6 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 68): 01z9v6 (0.90 #787, 0.87 #833, 0.87 #649), 02_j1w (0.69 #3249, 0.64 #3063, 0.60 #3685), 02sdk9v (0.69 #3060, 0.66 #1951, 0.63 #3246), 02dwpf (0.66 #563, 0.66 #421, 0.65 #3291), 01yvvn (0.66 #563, 0.66 #421, 0.65 #3291), 02rsl1 (0.66 #563, 0.66 #421, 0.59 #324), 017drs (0.66 #563, 0.66 #421, 0.59 #324), 02sddg (0.66 #563, 0.66 #421, 0.59 #324), 049k4w (0.66 #563, 0.66 #421, 0.59 #324), 02sg4b (0.66 #563, 0.66 #421, 0.52 #3680) >> Best rule #787 for best value: >> intensional similarity = 8 >> extensional distance = 18 >> proper extension: 02gtm4; 04b5l3; 02hfgl; >> query: (?x2067, 01z9v6) <- team(?x5727, ?x2067), team(?x4244, ?x2067), team(?x261, ?x2067), ?x4244 = 028c_8, colors(?x2067, ?x3189), position(?x580, ?x261), ?x5727 = 02wszf, sport(?x2067, ?x5063) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05g76 team! 01z9v6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.900 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #2917-09c7w0 PRED entity: 09c7w0 PRED relation: film_release_region! PRED expected values: 0c0yh4 087wc7n 02hxhz 0cwy47 02d44q 0g9wdmc 01kf3_9 023p33 0yzvw 06ybb1 0bmc4cm 0gh8zks 0c8qq 05zlld0 03hmt9b 05c5z8j 07j94 0315w4 0b44shh 02pg45 0992d9 03prz_ 0n1s0 051ys82 01f85k 089j8p 063hp4 07bx6 04gcyg 03nsm5x 0gvvm6l 0hhggmy 01_1hw 02bqvs 029jt9 02bj22 08j7lh 03bzyn4 0hz6mv2 015qy1 07tlfx 03xj05 027r7k 025twgt 042g97 04nlb94 => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 773): 05zlld0 (0.89 #4521, 0.82 #1900, 0.77 #5287), 047vnkj (0.83 #4538, 0.82 #1917, 0.77 #3120), 0gvvm6l (0.82 #1945, 0.78 #4566, 0.65 #3148), 03z9585 (0.82 #1946, 0.75 #4567, 0.67 #2822), 02d44q (0.82 #1862, 0.72 #4483, 0.66 #5249), 089j8p (0.82 #1934, 0.67 #4555, 0.62 #3137), 0gd0c7x (0.81 #4496, 0.77 #3078, 0.73 #1875), 0by1wkq (0.81 #4494, 0.73 #3076, 0.73 #5260), 087wc7n (0.78 #4480, 0.73 #3062, 0.73 #5246), 05pdh86 (0.78 #4531, 0.73 #3113, 0.71 #2786) >> Best rule #4521 for best value: >> intensional similarity = 2 >> extensional distance = 34 >> proper extension: 07ww5; >> query: (?x94, 05zlld0) <- country(?x54, ?x94), service_location(?x127, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 5, 6, 9, 13, 14, 15, 36, 39, 40, 43, 50, 58, 59, 66, 77, 78, 79, 140, 156, 157, 227, 265, 279, 419, 550, 555, 557, 566, 567 EVAL 09c7w0 film_release_region! 04nlb94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 042g97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 025twgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 027r7k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 03xj05 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 07tlfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 015qy1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0hz6mv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 03bzyn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 08j7lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 02bj22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 029jt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 02bqvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 01_1hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0hhggmy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0gvvm6l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 03nsm5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 04gcyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 07bx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 063hp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 089j8p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 01f85k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 051ys82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0n1s0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 03prz_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0992d9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 02pg45 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0b44shh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0315w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 07j94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 05c5z8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 03hmt9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 05zlld0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0c8qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0gh8zks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0bmc4cm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 06ybb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0yzvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 023p33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 01kf3_9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0g9wdmc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 02d44q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0cwy47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 02hxhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 087wc7n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09c7w0 film_release_region! 0c0yh4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 186.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2916-0d3f83 PRED entity: 0d3f83 PRED relation: profession PRED expected values: 0gl2ny2 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 93): 0gl2ny2 (0.71 #1269, 0.69 #2170, 0.68 #1719), 02hrh1q (0.58 #5266, 0.58 #4516, 0.58 #4666), 0dxtg (0.25 #4815, 0.25 #6466, 0.24 #1364), 01d_h8 (0.25 #4807, 0.24 #6458, 0.23 #4657), 0cbd2 (0.22 #1357, 0.17 #1957, 0.15 #2408), 02jknp (0.18 #3759, 0.17 #4809, 0.17 #6460), 03gjzk (0.17 #5267, 0.17 #4667, 0.17 #4517), 01445t (0.17 #3475, 0.15 #3325, 0.15 #3175), 09jwl (0.16 #4821, 0.15 #6472, 0.14 #5571), 01c72t (0.16 #1375, 0.12 #2426, 0.12 #1975) >> Best rule #1269 for best value: >> intensional similarity = 6 >> extensional distance = 33 >> proper extension: 06yj20; >> query: (?x9231, 0gl2ny2) <- nationality(?x9231, ?x789), athlete(?x471, ?x9231), ?x471 = 02vx4, film_release_region(?x9839, ?x789), combatants(?x94, ?x789), ?x9839 = 0gy7bj4 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d3f83 profession 0gl2ny2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.714 http://example.org/people/person/profession #2915-0jqkh PRED entity: 0jqkh PRED relation: featured_film_locations PRED expected values: 02_286 => 41 concepts (30 used for prediction) PRED predicted values (max 10 best out of 44): 02_286 (0.26 #500, 0.21 #980, 0.15 #260), 030qb3t (0.08 #279, 0.06 #1722, 0.06 #999), 04jpl (0.06 #489, 0.05 #3619, 0.05 #969), 03rjj (0.06 #486), 05qtj (0.05 #336, 0.02 #576, 0.02 #1056), 06c62 (0.05 #610), 02nd_ (0.04 #836, 0.03 #356, 0.02 #1076), 0rh6k (0.03 #481, 0.03 #1443, 0.02 #6026), 0345h (0.03 #513, 0.02 #273, 0.01 #1475), 0dclg (0.03 #293, 0.03 #1013) >> Best rule #500 for best value: >> intensional similarity = 4 >> extensional distance = 84 >> proper extension: 0gh6j94; >> query: (?x7666, 02_286) <- language(?x7666, ?x254), language(?x7666, ?x90), ?x254 = 02h40lc, ?x90 = 02bjrlw >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jqkh featured_film_locations 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 30.000 0.256 http://example.org/film/film/featured_film_locations #2914-09tqkv2 PRED entity: 09tqkv2 PRED relation: genre PRED expected values: 0d63kt => 93 concepts (82 used for prediction) PRED predicted values (max 10 best out of 109): 01z4y (0.61 #8109, 0.50 #9183, 0.48 #9780), 02l7c8 (0.51 #6453, 0.37 #491, 0.35 #1562), 0219x_ (0.38 #383, 0.25 #145, 0.12 #978), 01jfsb (0.33 #11, 0.33 #5376, 0.30 #6806), 01hmnh (0.33 #17, 0.18 #5382, 0.17 #6217), 03npn (0.33 #5, 0.12 #124, 0.07 #5370), 02kdv5l (0.33 #5367, 0.30 #6202, 0.29 #7514), 03bxz7 (0.27 #292, 0.16 #649, 0.13 #530), 04xvlr (0.25 #4770, 0.21 #1905, 0.20 #3694), 060__y (0.25 #135, 0.18 #2873, 0.18 #254) >> Best rule #8109 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 024rwx; 0ctzf1; 09g_31; >> query: (?x2052, ?x2480) <- titles(?x2480, ?x2052), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #9781 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1550 *> proper extension: 07hpv3; 09kn9; 05sy2k_; 02648p; 01p4wv; 099pks; 05r1_t; 06r4f; 06qxh; 03r0rq; ... *> query: (?x2052, ?x53) <- titles(?x2480, ?x2052), titles(?x2480, ?x6272), genre(?x6272, ?x53) *> conf = 0.06 ranks of expected_values: 46 EVAL 09tqkv2 genre 0d63kt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 93.000 82.000 0.612 http://example.org/film/film/genre #2913-016zwt PRED entity: 016zwt PRED relation: contains PRED expected values: 08hbxv => 156 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2981): 0205m3 (0.17 #2784, 0.07 #5728, 0.06 #11618), 03x3l (0.17 #1334, 0.07 #4278, 0.06 #10168), 01z53w (0.17 #1992, 0.07 #4936, 0.06 #10826), 0k33p (0.17 #1486, 0.07 #4430, 0.06 #10320), 04lh6 (0.17 #1309, 0.07 #4253, 0.06 #10143), 020d8d (0.17 #1239, 0.07 #4183, 0.06 #10073), 01b_d4 (0.17 #722, 0.07 #3666, 0.06 #9556), 01hvzr (0.17 #2824, 0.07 #5768, 0.06 #11658), 04682_ (0.17 #2806, 0.07 #5750, 0.06 #11640), 0202wk (0.17 #2805, 0.07 #5749, 0.06 #11639) >> Best rule #2784 for best value: >> intensional similarity = 2 >> extensional distance = 4 >> proper extension: 0bvz6; 0q307; >> query: (?x8620, 0205m3) <- combatants(?x10351, ?x8620), ?x10351 = 03z8w6 >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016zwt contains 08hbxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 156.000 122.000 0.167 http://example.org/location/location/contains #2912-040db PRED entity: 040db PRED relation: influenced_by PRED expected values: 0j3v 0372p 015n8 085gk => 150 concepts (51 used for prediction) PRED predicted values (max 10 best out of 340): 032l1 (0.38 #1734, 0.38 #907, 0.29 #6270), 06whf (0.38 #1767, 0.12 #940, 0.09 #19370), 03sbs (0.32 #4330, 0.25 #1028, 0.24 #7628), 02kz_ (0.31 #1810, 0.15 #2222, 0.09 #19370), 058vp (0.25 #997, 0.15 #1824, 0.11 #6360), 0gz_ (0.24 #4221, 0.21 #7519, 0.15 #18639), 01tz6vs (0.23 #1816, 0.14 #578, 0.12 #17306), 014635 (0.23 #1753, 0.14 #515, 0.12 #17306), 07g2b (0.23 #2073, 0.12 #17306, 0.11 #6197), 0l99s (0.23 #1859, 0.09 #1446, 0.09 #19370) >> Best rule #1734 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0399p; >> query: (?x2161, 032l1) <- influenced_by(?x2161, ?x118), ?x118 = 084w8, place_of_birth(?x2161, ?x2911) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1624 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 02lk1s; *> query: (?x2161, 015n8) <- influenced_by(?x476, ?x2161), company(?x2161, ?x7661), student(?x1368, ?x476), award(?x2161, ?x4418) *> conf = 0.18 ranks of expected_values: 18, 21, 45, 134 EVAL 040db influenced_by 085gk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 150.000 51.000 0.385 http://example.org/influence/influence_node/influenced_by EVAL 040db influenced_by 015n8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 150.000 51.000 0.385 http://example.org/influence/influence_node/influenced_by EVAL 040db influenced_by 0372p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 150.000 51.000 0.385 http://example.org/influence/influence_node/influenced_by EVAL 040db influenced_by 0j3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 150.000 51.000 0.385 http://example.org/influence/influence_node/influenced_by #2911-07h9gp PRED entity: 07h9gp PRED relation: genre PRED expected values: 0gf28 => 80 concepts (79 used for prediction) PRED predicted values (max 10 best out of 91): 07s9rl0 (0.75 #238, 0.65 #2375, 0.63 #1664), 01z4y (0.56 #950, 0.55 #831, 0.55 #356), 02l7c8 (0.45 #253, 0.39 #1322, 0.37 #372), 01jfsb (0.34 #1199, 0.33 #724, 0.31 #1794), 04xvlr (0.33 #239, 0.20 #952, 0.19 #2376), 060__y (0.32 #254, 0.20 #491, 0.19 #610), 03k9fj (0.32 #1080, 0.26 #604, 0.26 #485), 02kdv5l (0.30 #1072, 0.29 #1785, 0.28 #121), 0lsxr (0.25 #8, 0.21 #839, 0.20 #1195), 06cvj (0.25 #360, 0.21 #1904, 0.20 #1310) >> Best rule #238 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 0fjyzt; 05dptj; >> query: (?x1728, 07s9rl0) <- titles(?x2480, ?x1728), film(?x166, ?x1728), ?x166 = 0jz9f, film(?x1678, ?x1728) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #419 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 91 *> proper extension: 0ddfwj1; *> query: (?x1728, 0gf28) <- titles(?x2480, ?x1728), ?x2480 = 01z4y, executive_produced_by(?x1728, ?x4060) *> conf = 0.19 ranks of expected_values: 12 EVAL 07h9gp genre 0gf28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 80.000 79.000 0.750 http://example.org/film/film/genre #2910-01d2v1 PRED entity: 01d2v1 PRED relation: nominated_for! PRED expected values: 02hsq3m => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 170): 0gq9h (0.24 #2465, 0.23 #8466, 0.22 #7746), 019f4v (0.23 #2456, 0.19 #4136, 0.19 #4856), 0gs9p (0.22 #2467, 0.20 #8468, 0.19 #4147), 0gq_v (0.21 #2421, 0.19 #3621, 0.19 #3381), 04kxsb (0.20 #10084, 0.19 #11525, 0.19 #9843), 02w9sd7 (0.20 #10084, 0.19 #11525, 0.19 #9843), 02x73k6 (0.20 #10084, 0.19 #11525, 0.19 #9843), 02x4w6g (0.20 #10084, 0.19 #11525, 0.19 #9843), 0279c15 (0.20 #10084, 0.19 #11525, 0.19 #9843), 02hsq3m (0.20 #10084, 0.19 #11525, 0.19 #9843) >> Best rule #2465 for best value: >> intensional similarity = 4 >> extensional distance = 383 >> proper extension: 0372j5; >> query: (?x11174, 0gq9h) <- nominated_for(?x397, ?x11174), written_by(?x11174, ?x7522), film_release_distribution_medium(?x11174, ?x81), nominated_for(?x2209, ?x11174) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #10084 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1421 *> proper extension: 0c3xpwy; *> query: (?x11174, ?x591) <- nominated_for(?x397, ?x11174), nationality(?x397, ?x94), award_winner(?x696, ?x397), award(?x397, ?x591) *> conf = 0.20 ranks of expected_values: 10 EVAL 01d2v1 nominated_for! 02hsq3m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 56.000 56.000 0.242 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2909-05ch98 PRED entity: 05ch98 PRED relation: country PRED expected values: 09c7w0 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 175): 09c7w0 (0.88 #5055, 0.86 #4995, 0.84 #602), 07ssc (0.61 #4829, 0.28 #1038, 0.28 #1699), 0f8l9c (0.53 #440, 0.24 #4832, 0.20 #1943), 06mkj (0.25 #160, 0.06 #400, 0.04 #1662), 0b90_r (0.25 #125, 0.06 #365, 0.03 #4569), 03rjj (0.18 #427, 0.08 #1930, 0.05 #4819), 06mzp (0.17 #259, 0.12 #319, 0.12 #439), 04j53 (0.17 #289, 0.12 #349, 0.05 #649), 0154j (0.12 #426, 0.03 #4569, 0.03 #6812), 03_3d (0.12 #4820, 0.06 #849, 0.06 #1089) >> Best rule #5055 for best value: >> intensional similarity = 5 >> extensional distance = 723 >> proper extension: 0d7vtk; >> query: (?x7854, 09c7w0) <- language(?x7854, ?x254), country(?x7854, ?x1264), produced_by(?x7854, ?x7903), ?x254 = 02h40lc, nominated_for(?x7903, ?x876) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05ch98 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.879 http://example.org/film/film/country #2908-06hwzy PRED entity: 06hwzy PRED relation: category PRED expected values: 08mbj5d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.49 #47, 0.49 #45, 0.49 #42) >> Best rule #47 for best value: >> intensional similarity = 9 >> extensional distance = 67 >> proper extension: 02gd6x; 0170k0; >> query: (?x2583, 08mbj5d) <- honored_for(?x10010, ?x2583), country_of_origin(?x2583, ?x94), genre(?x2583, ?x5728), ceremony(?x3247, ?x10010), award(?x4969, ?x3247), award(?x2451, ?x3247), ?x4969 = 016k6x, ?x2451 = 0127m7, award(?x715, ?x3247) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06hwzy category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.493 http://example.org/common/topic/webpage./common/webpage/category #2907-05fkf PRED entity: 05fkf PRED relation: taxonomy PRED expected values: 04n6k => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.91 #11, 0.90 #10, 0.90 #7) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 06mz5; 026mj; >> query: (?x760, 04n6k) <- jurisdiction_of_office(?x900, ?x760), ?x900 = 0fkvn, district_represented(?x605, ?x760) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05fkf taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 170.000 0.909 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #2906-09r9dp PRED entity: 09r9dp PRED relation: film PRED expected values: 080lkt7 => 110 concepts (81 used for prediction) PRED predicted values (max 10 best out of 660): 0ds35l9 (0.47 #58907, 0.42 #116038, 0.41 #142830), 02rzdcp (0.36 #71404, 0.34 #119610, 0.34 #76761), 0b3n61 (0.07 #4926, 0.04 #3141, 0.03 #6711), 01gkp1 (0.05 #4384, 0.03 #6169, 0.02 #7954), 01633c (0.04 #1324, 0.04 #3109, 0.03 #4894), 032016 (0.04 #503, 0.04 #2288, 0.02 #9428), 09rvwmy (0.04 #1690, 0.04 #3475, 0.02 #7045), 02825cv (0.04 #1139, 0.04 #48196, 0.02 #2924), 06fpsx (0.04 #1335, 0.02 #3120, 0.02 #17400), 0gldyz (0.04 #1653, 0.02 #3438, 0.02 #5223) >> Best rule #58907 for best value: >> intensional similarity = 3 >> extensional distance = 746 >> proper extension: 0bxfmk; 0gv07g; 01m7f5r; 01507p; 03g62; >> query: (?x3789, ?x86) <- location(?x3789, ?x1523), student(?x1011, ?x3789), nominated_for(?x3789, ?x86) >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09r9dp film 080lkt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 110.000 81.000 0.467 http://example.org/film/actor/film./film/performance/film #2905-0gj4fx PRED entity: 0gj4fx PRED relation: district_represented! PRED expected values: 06f0dc => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 50): 06f0dc (0.87 #558, 0.85 #257, 0.84 #458), 043djx (0.57 #256, 0.56 #306, 0.56 #557), 02bqmq (0.57 #266, 0.55 #756, 0.55 #452), 03rl1g (0.56 #302, 0.56 #553, 0.55 #252), 02bqn1 (0.55 #756, 0.55 #452, 0.53 #603), 02cg7g (0.55 #756, 0.55 #452, 0.53 #603), 02gkzs (0.55 #756, 0.55 #452, 0.53 #603), 03rtmz (0.55 #756, 0.55 #452, 0.53 #603), 02glc4 (0.55 #756, 0.55 #452, 0.53 #603), 03tcbx (0.55 #756, 0.55 #452, 0.53 #603) >> Best rule #558 for best value: >> intensional similarity = 5 >> extensional distance = 50 >> proper extension: 05kkh; 05fhy; 07z1m; 05k7sb; 06btq; 07b_l; 081yw; >> query: (?x12828, 06f0dc) <- district_represented(?x6728, ?x12828), adjoins(?x13269, ?x12828), district_represented(?x6728, ?x3908), ?x3908 = 04ly1, legislative_sessions(?x355, ?x6728) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gj4fx district_represented! 06f0dc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.865 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #2904-05lfwd PRED entity: 05lfwd PRED relation: honored_for! PRED expected values: 0gvstc3 0gx_st 0bq_mx => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 113): 0bvhz9 (0.30 #337, 0.05 #6675, 0.02 #6205), 05c1t6z (0.28 #470, 0.26 #815, 0.26 #1851), 0gvstc3 (0.26 #1176, 0.24 #1292, 0.23 #486), 03nnm4t (0.25 #519, 0.23 #864, 0.23 #1209), 0lp_cd3 (0.18 #1282, 0.17 #1166, 0.17 #1051), 0hhtgcw (0.18 #5408, 0.17 #6791, 0.16 #7368), 027n06w (0.17 #6791, 0.16 #7368, 0.14 #7022), 0bq_mx (0.17 #6791, 0.16 #7368, 0.14 #7022), 0gx_st (0.15 #489, 0.15 #374, 0.14 #604), 07y9ts (0.12 #513, 0.11 #282, 0.11 #398) >> Best rule #337 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 0d_wms; 06fqlk; >> query: (?x5808, 0bvhz9) <- honored_for(?x5592, ?x5808), honored_for(?x3624, ?x5808), award_winner(?x3624, ?x237), ?x5592 = 0275n3y >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #1176 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 80 *> proper extension: 06hwzy; *> query: (?x5808, 0gvstc3) <- honored_for(?x1112, ?x5808), producer_type(?x5808, ?x632), award_winner(?x1112, ?x56) *> conf = 0.26 ranks of expected_values: 3, 8, 9 EVAL 05lfwd honored_for! 0bq_mx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 99.000 99.000 0.296 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 05lfwd honored_for! 0gx_st CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 99.000 99.000 0.296 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 05lfwd honored_for! 0gvstc3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 99.000 0.296 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2903-049gc PRED entity: 049gc PRED relation: student! PRED expected values: 01lj9 => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 50): 02822 (0.18 #2229, 0.10 #1678, 0.09 #2595), 0fdys (0.13 #577, 0.12 #394, 0.11 #760), 03g3w (0.12 #203, 0.08 #264, 0.06 #996), 03qsdpk (0.11 #2234, 0.08 #2600, 0.07 #3578), 037mh8 (0.09 #594, 0.04 #1143, 0.03 #1204), 05qjt (0.09 #737, 0.04 #554, 0.03 #1653), 04rlf (0.09 #778, 0.04 #900, 0.03 #2245), 0w7c (0.08 #2240, 0.05 #1200, 0.04 #1139), 09s1f (0.08 #302, 0.03 #790, 0.03 #851), 034ns (0.07 #358, 0.02 #1029, 0.01 #1274) >> Best rule #2229 for best value: >> intensional similarity = 3 >> extensional distance = 151 >> proper extension: 02lg9w; >> query: (?x5346, 02822) <- award(?x5346, ?x3337), nationality(?x5346, ?x94), student(?x2314, ?x5346) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #761 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 017yfz; 0d5_f; 059y0; *> query: (?x5346, 01lj9) <- student(?x741, ?x5346), influenced_by(?x5346, ?x3969), location(?x5346, ?x448), student(?x2314, ?x5346) *> conf = 0.03 ranks of expected_values: 32 EVAL 049gc student! 01lj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 171.000 171.000 0.183 http://example.org/education/field_of_study/students_majoring./education/education/student #2902-0ply0 PRED entity: 0ply0 PRED relation: location! PRED expected values: 04gycf => 219 concepts (124 used for prediction) PRED predicted values (max 10 best out of 2072): 02lymt (0.62 #113256, 0.60 #178696, 0.54 #302027), 048tgl (0.50 #30204, 0.47 #133389, 0.47 #229036), 023kzp (0.31 #1216, 0.29 #3733, 0.25 #6250), 0hvbj (0.30 #266792, 0.27 #118290, 0.26 #226519), 01s21dg (0.23 #965, 0.21 #3482, 0.18 #8517), 01q_ph (0.23 #50, 0.21 #2567, 0.18 #7602), 0pyww (0.23 #982, 0.21 #3499, 0.18 #8534), 0jsg0m (0.23 #1495, 0.21 #4012, 0.18 #9047), 02mjmr (0.23 #502, 0.21 #3019, 0.18 #8054), 0151ns (0.19 #5118, 0.15 #84, 0.14 #2601) >> Best rule #113256 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 0pfd9; >> query: (?x3373, ?x4777) <- contains(?x2623, ?x3373), teams(?x3373, ?x5756), place_of_birth(?x4777, ?x3373), participant(?x4777, ?x971) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #73666 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 0ggh3; *> query: (?x3373, 04gycf) <- location_of_ceremony(?x566, ?x3373), state(?x3373, ?x2623), origin(?x4842, ?x3373), location(?x6380, ?x3373) *> conf = 0.02 ranks of expected_values: 1686 EVAL 0ply0 location! 04gycf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 219.000 124.000 0.620 http://example.org/people/person/places_lived./people/place_lived/location #2901-05mvd62 PRED entity: 05mvd62 PRED relation: executive_produced_by! PRED expected values: 02vrgnr => 105 concepts (68 used for prediction) PRED predicted values (max 10 best out of 230): 027r9t (0.10 #6915, 0.09 #3723, 0.04 #10111), 047csmy (0.10 #6915, 0.09 #3723, 0.04 #10111), 07024 (0.09 #3723, 0.04 #10111, 0.04 #7447), 025ts_z (0.04 #3661, 0.03 #6853, 0.02 #8451), 0bt4g (0.03 #423, 0.03 #954, 0.03 #1486), 0mbql (0.03 #379, 0.03 #910, 0.03 #1442), 01f7kl (0.03 #134, 0.03 #665, 0.03 #1197), 04mcw4 (0.03 #256, 0.03 #787, 0.03 #1319), 0k_9j (0.03 #447, 0.03 #978, 0.03 #1510), 02scbv (0.03 #389, 0.03 #1452, 0.02 #3580) >> Best rule #6915 for best value: >> intensional similarity = 3 >> extensional distance = 195 >> proper extension: 02qggqc; >> query: (?x7094, ?x5277) <- nominated_for(?x7094, ?x5277), award(?x7094, ?x198), executive_produced_by(?x1450, ?x7094) >> conf = 0.10 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 05mvd62 executive_produced_by! 02vrgnr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 68.000 0.097 http://example.org/film/film/executive_produced_by #2900-02238b PRED entity: 02238b PRED relation: profession PRED expected values: 018gz8 => 125 concepts (106 used for prediction) PRED predicted values (max 10 best out of 77): 0cbd2 (0.48 #3921, 0.46 #5951, 0.44 #5661), 018gz8 (0.43 #1609, 0.40 #594, 0.39 #1174), 02jknp (0.36 #732, 0.30 #587, 0.29 #2327), 0kyk (0.34 #3941, 0.31 #2056, 0.31 #5971), 02krf9 (0.33 #603, 0.28 #748, 0.28 #313), 015cjr (0.30 #6236, 0.13 #771, 0.13 #1206), 0fj9f (0.30 #6236, 0.08 #6867, 0.07 #9767), 08z956 (0.30 #6236, 0.04 #800, 0.02 #2105), 02dsz (0.30 #6236, 0.03 #1793, 0.03 #923), 014ktf (0.30 #6236, 0.02 #822, 0.01 #1257) >> Best rule #3921 for best value: >> intensional similarity = 2 >> extensional distance = 291 >> proper extension: 099bk; 07c37; >> query: (?x7022, 0cbd2) <- influenced_by(?x7022, ?x4065), student(?x8706, ?x7022) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #1609 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 106 *> proper extension: 01kcms4; 01d5g; *> query: (?x7022, 018gz8) <- influenced_by(?x7022, ?x4065), participant(?x4065, ?x1145) *> conf = 0.43 ranks of expected_values: 2 EVAL 02238b profession 018gz8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 106.000 0.485 http://example.org/people/person/profession #2899-03s9kp PRED entity: 03s9kp PRED relation: genre PRED expected values: 07s9rl0 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 137): 07s9rl0 (0.96 #1780, 0.71 #119, 0.67 #593), 01jfsb (0.62 #365, 0.43 #129, 0.32 #2854), 05p553 (0.60 #4, 0.52 #240, 0.38 #1311), 03k9fj (0.50 #10, 0.22 #246, 0.21 #2972), 0hcr (0.50 #22, 0.17 #258, 0.07 #8782), 02kdv5l (0.43 #120, 0.27 #5575, 0.27 #2845), 01zhp (0.40 #75, 0.04 #311, 0.02 #2327), 02l7c8 (0.31 #1204, 0.30 #2031, 0.30 #1440), 06n90 (0.29 #130, 0.14 #2264, 0.13 #4044), 060__y (0.21 #134, 0.16 #370, 0.16 #1795) >> Best rule #1780 for best value: >> intensional similarity = 4 >> extensional distance = 718 >> proper extension: 0fq27fp; >> query: (?x11996, 07s9rl0) <- genre(?x11996, ?x162), film_crew_role(?x11996, ?x137), titles(?x162, ?x7424), ?x7424 = 08y2fn >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03s9kp genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.957 http://example.org/film/film/genre #2898-01k2wn PRED entity: 01k2wn PRED relation: student PRED expected values: 03x16f => 197 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1705): 0nk72 (0.20 #39542, 0.14 #37459, 0.14 #39541), 083q7 (0.19 #37460, 0.14 #37459, 0.14 #39541), 03_nq (0.17 #3639, 0.12 #5720, 0.08 #39017), 0d3k14 (0.17 #3926, 0.08 #37222, 0.08 #39304), 06jkm (0.17 #3982, 0.07 #51851, 0.06 #12306), 06hx2 (0.17 #3150, 0.06 #11474, 0.06 #9394), 0194xc (0.17 #3717, 0.06 #12041, 0.06 #9961), 02d6cy (0.17 #2941, 0.06 #11265, 0.06 #9185), 02yy8 (0.17 #4093, 0.06 #12417, 0.06 #10337), 01lct6 (0.17 #3975, 0.06 #12299, 0.06 #10219) >> Best rule #39542 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 09c7w0; >> query: (?x1103, ?x8404) <- contains(?x1755, ?x1103), company(?x8404, ?x1103), location(?x8404, ?x3622), influenced_by(?x8404, ?x3712) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #88929 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 69 *> proper extension: 0k89p; 02mbs4; *> query: (?x1103, 03x16f) <- citytown(?x1103, ?x12912), currency(?x12912, ?x170), contains(?x94, ?x12912), ?x170 = 09nqf *> conf = 0.01 ranks of expected_values: 1255 EVAL 01k2wn student 03x16f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 197.000 80.000 0.204 http://example.org/education/educational_institution/students_graduates./education/education/student #2897-014gf8 PRED entity: 014gf8 PRED relation: award_nominee PRED expected values: 08swgx => 98 concepts (41 used for prediction) PRED predicted values (max 10 best out of 866): 04w391 (0.81 #62793, 0.81 #44186, 0.81 #55815), 01p4vl (0.81 #62793, 0.81 #44186, 0.81 #55815), 01d1st (0.81 #62793, 0.81 #44186, 0.81 #55815), 0z4s (0.81 #62793, 0.81 #44186, 0.81 #55815), 016vg8 (0.81 #62793, 0.81 #44186, 0.81 #55815), 07h565 (0.81 #62793, 0.81 #44186, 0.81 #55815), 0btpx (0.81 #62793, 0.81 #44186, 0.81 #55815), 08swgx (0.81 #62793, 0.81 #44186, 0.81 #55815), 05cx7x (0.81 #62793, 0.81 #44186, 0.81 #55815), 02qgqt (0.22 #20, 0.16 #53489, 0.07 #51163) >> Best rule #62793 for best value: >> intensional similarity = 3 >> extensional distance = 1212 >> proper extension: 04n32; >> query: (?x5626, ?x450) <- film(?x5626, ?x136), award_nominee(?x5626, ?x221), award_nominee(?x450, ?x5626) >> conf = 0.81 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 8 EVAL 014gf8 award_nominee 08swgx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 98.000 41.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2896-07t_x PRED entity: 07t_x PRED relation: olympics PRED expected values: 0jdk_ => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 39): 06sks6 (0.81 #101, 0.71 #140, 0.64 #922), 0jdk_ (0.69 #103, 0.58 #924, 0.58 #142), 0l6mp (0.58 #95, 0.42 #134, 0.42 #916), 0kbvv (0.58 #1682, 0.39 #102, 0.34 #24), 0lgxj (0.56 #104, 0.48 #26, 0.47 #143), 0lbd9 (0.53 #108, 0.39 #147, 0.35 #929), 0jkvj (0.53 #113, 0.39 #191, 0.34 #35), 09n48 (0.52 #509, 0.52 #979, 0.52 #978), 0l6m5 (0.50 #87, 0.47 #126, 0.46 #908), 0lbbj (0.50 #96, 0.45 #18, 0.41 #174) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 0h7x; 01p1v; 0163v; 0d05w3; 0jhd; >> query: (?x6305, 06sks6) <- capital(?x6305, ?x13440), country(?x2867, ?x6305), ?x2867 = 02y8z >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #103 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 0h7x; 01p1v; 0163v; 0d05w3; 0jhd; *> query: (?x6305, 0jdk_) <- capital(?x6305, ?x13440), country(?x2867, ?x6305), ?x2867 = 02y8z *> conf = 0.69 ranks of expected_values: 2 EVAL 07t_x olympics 0jdk_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.806 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #2895-07h07 PRED entity: 07h07 PRED relation: written_by! PRED expected values: 0jwmp => 83 concepts (34 used for prediction) PRED predicted values (max 10 best out of 199): 0gy7bj4 (0.26 #3301, 0.21 #5282, 0.08 #19153), 064lsn (0.26 #3301, 0.21 #5282, 0.08 #19153), 025rvx0 (0.26 #3301, 0.21 #5282, 0.08 #19153), 055td_ (0.06 #953, 0.06 #293, 0.02 #1613), 03tn80 (0.06 #996, 0.02 #1656, 0.01 #2316), 0296rz (0.06 #612, 0.02 #1932, 0.01 #3252), 0qmjd (0.06 #465, 0.02 #1785, 0.01 #3105), 0n1s0 (0.06 #403, 0.02 #1723, 0.01 #3043), 02xs6_ (0.06 #334, 0.02 #1654, 0.01 #2974), 0b2v79 (0.06 #8, 0.02 #1328, 0.01 #2648) >> Best rule #3301 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 0p_jc; >> query: (?x4008, ?x144) <- nominated_for(?x4008, ?x144), influenced_by(?x12392, ?x4008), profession(?x4008, ?x987) >> conf = 0.26 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 07h07 written_by! 0jwmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 34.000 0.262 http://example.org/film/film/written_by #2894-05zkcn5 PRED entity: 05zkcn5 PRED relation: award! PRED expected values: 01cwhp 0137g1 015bwt => 48 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2645): 0fhxv (0.75 #18137, 0.71 #21497, 0.50 #28216), 016pns (0.60 #14240, 0.50 #7522, 0.47 #24320), 01vvycq (0.59 #20303, 0.57 #10224, 0.56 #16943), 01xzb6 (0.56 #18333, 0.53 #21693, 0.45 #28412), 0kr_t (0.55 #28487, 0.41 #21768, 0.38 #18408), 01vsgrn (0.53 #25141, 0.50 #28500, 0.50 #18421), 02l840 (0.53 #23695, 0.38 #16975, 0.35 #20335), 015cxv (0.50 #8633, 0.33 #5273, 0.33 #1914), 02r3zy (0.50 #27128, 0.33 #3611, 0.29 #23769), 01vn35l (0.47 #20945, 0.44 #17585, 0.35 #24305) >> Best rule #18137 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 01c92g; 01ck6h; 02f72n; 02x17c2; 02f72_; 02f73b; >> query: (?x462, 0fhxv) <- award(?x7601, ?x462), award(?x5550, ?x462), award(?x4476, ?x462), type_of_union(?x7601, ?x566), ?x5550 = 01bczm, award_winner(?x2704, ?x7601), friend(?x2280, ?x4476) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #17546 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 14 *> proper extension: 01c92g; 01ck6h; 02f72n; 02x17c2; 02f72_; 02f73b; *> query: (?x462, 0137g1) <- award(?x7601, ?x462), award(?x5550, ?x462), award(?x4476, ?x462), type_of_union(?x7601, ?x566), ?x5550 = 01bczm, award_winner(?x2704, ?x7601), friend(?x2280, ?x4476) *> conf = 0.44 ranks of expected_values: 17, 142, 466 EVAL 05zkcn5 award! 015bwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 17.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zkcn5 award! 0137g1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 48.000 17.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05zkcn5 award! 01cwhp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 48.000 17.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2893-09889g PRED entity: 09889g PRED relation: participant PRED expected values: 01f5q5 => 127 concepts (91 used for prediction) PRED predicted values (max 10 best out of 272): 01f5q5 (0.80 #47139, 0.80 #41405, 0.80 #51598), 0bx_q (0.34 #29305, 0.01 #29046), 01kx_81 (0.22 #1909, 0.17 #3183, 0.14 #3821), 01vvycq (0.22 #1909, 0.17 #3183, 0.14 #3821), 015f7 (0.14 #1504, 0.12 #10426, 0.09 #4054), 09889g (0.14 #1613, 0.11 #2887, 0.10 #3525), 02bc74 (0.12 #628, 0.08 #1264, 0.06 #7000), 06mt91 (0.12 #445, 0.08 #1081, 0.05 #10639), 01vrt_c (0.12 #77, 0.08 #713), 0237fw (0.12 #159, 0.03 #28827, 0.02 #34559) >> Best rule #47139 for best value: >> intensional similarity = 2 >> extensional distance = 492 >> proper extension: 0d_84; 0134w7; 0456xp; 04shbh; 0h1m9; 0n6f8; 013cr; 031zkw; 01pw2f1; 02p21g; ... >> query: (?x4960, ?x1126) <- participant(?x1126, ?x4960), location(?x4960, ?x1227) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09889g participant 01f5q5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 91.000 0.801 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #2892-032nl2 PRED entity: 032nl2 PRED relation: profession PRED expected values: 0dz3r => 103 concepts (89 used for prediction) PRED predicted values (max 10 best out of 61): 0dxtg (0.83 #1015, 0.67 #1587, 0.58 #1301), 03gjzk (0.74 #1016, 0.55 #1588, 0.44 #1731), 0nbcg (0.68 #5180, 0.57 #4032, 0.56 #6471), 01d_h8 (0.57 #10467, 0.42 #1436, 0.41 #1579), 0dz3r (0.55 #574, 0.47 #6160, 0.44 #2005), 02jknp (0.33 #10469, 0.25 #11327, 0.20 #865), 0cbd2 (0.30 #435, 0.26 #1437, 0.22 #1008), 01c72t (0.29 #5603, 0.29 #7182, 0.28 #6607), 0kyk (0.29 #1313, 0.20 #883, 0.18 #740), 0n1h (0.29 #1871, 0.29 #2729, 0.28 #3158) >> Best rule #1015 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 086nl7; 01z5tr; 02dlfh; >> query: (?x8053, 0dxtg) <- profession(?x8053, ?x1383), profession(?x8053, ?x1146), ?x1146 = 018gz8, participant(?x8053, ?x5662), ?x1383 = 0np9r >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #574 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 01zmpg; 01w724; 04b7xr; *> query: (?x8053, 0dz3r) <- profession(?x8053, ?x220), artist(?x7448, ?x8053), ?x220 = 016z4k, ?x7448 = 016ckq, instrumentalists(?x212, ?x8053) *> conf = 0.55 ranks of expected_values: 5 EVAL 032nl2 profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 103.000 89.000 0.826 http://example.org/people/person/profession #2891-01wsl7c PRED entity: 01wsl7c PRED relation: artists! PRED expected values: 016jny => 148 concepts (58 used for prediction) PRED predicted values (max 10 best out of 259): 06j6l (0.50 #46, 0.42 #969, 0.31 #2204), 05bt6j (0.38 #965, 0.31 #10216, 0.30 #15463), 02yv6b (0.38 #98, 0.31 #1329, 0.23 #3798), 0mhfr (0.34 #2181, 0.27 #946, 0.25 #23), 0xhtw (0.34 #3716, 0.29 #5565, 0.27 #15437), 03_d0 (0.28 #4635, 0.28 #4326, 0.27 #4943), 07sbbz2 (0.27 #622, 0.27 #2165, 0.25 #7), 02k_kn (0.27 #987, 0.23 #679, 0.21 #2529), 0dl5d (0.26 #1558, 0.17 #5876, 0.17 #1250), 0gywn (0.25 #56, 0.19 #979, 0.17 #8379) >> Best rule #46 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 016qtt; >> query: (?x1997, 06j6l) <- instrumentalists(?x227, ?x1997), award(?x1997, ?x4796), artists(?x9197, ?x1997), artist(?x9224, ?x1997), ?x9197 = 017510 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #719 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 01vsnff; *> query: (?x1997, 016jny) <- role(?x1997, ?x432), artists(?x1572, ?x1997), origin(?x1997, ?x14595), ?x1572 = 06by7, ?x432 = 042v_gx *> conf = 0.23 ranks of expected_values: 13 EVAL 01wsl7c artists! 016jny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 148.000 58.000 0.500 http://example.org/music/genre/artists #2890-01w92 PRED entity: 01w92 PRED relation: program_creator! PRED expected values: 0k0q73t => 119 concepts (93 used for prediction) PRED predicted values (max 10 best out of 26): 050kh5 (0.17 #353, 0.14 #594, 0.03 #3007), 01b7h8 (0.17 #333, 0.14 #574, 0.03 #2987), 06mr2s (0.17 #281, 0.14 #522, 0.03 #2935), 015g28 (0.14 #392, 0.08 #5064, 0.08 #753), 0dk0dj (0.08 #677, 0.04 #2245, 0.02 #3451), 0ctzf1 (0.07 #1027, 0.06 #1268, 0.06 #1509), 039c26 (0.05 #5665), 047m_w (0.02 #481), 02q_x_l (0.02 #481), 016zfm (0.02 #481) >> Best rule #353 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01my_c; >> query: (?x3487, 050kh5) <- citytown(?x3487, ?x362), ?x362 = 04jpl, award_nominee(?x3487, ?x2246) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w92 program_creator! 0k0q73t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 93.000 0.167 http://example.org/tv/tv_program/program_creator #2889-0dqcs3 PRED entity: 0dqcs3 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.82 #52, 0.82 #117, 0.81 #142), 02nxhr (0.06 #17, 0.06 #22, 0.03 #53), 07z4p (0.06 #20, 0.04 #25, 0.03 #40), 07c52 (0.03 #159, 0.03 #38, 0.03 #180) >> Best rule #52 for best value: >> intensional similarity = 3 >> extensional distance = 295 >> proper extension: 07l50vn; 0cvkv5; 0dmn0x; >> query: (?x4839, 029j_) <- genre(?x4839, ?x571), film_format(?x4839, ?x909), nominated_for(?x688, ?x4839) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dqcs3 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 66.000 0.825 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #2888-06r2_ PRED entity: 06r2_ PRED relation: crewmember PRED expected values: 03h26tm => 74 concepts (48 used for prediction) PRED predicted values (max 10 best out of 33): 092ys_y (0.14 #20, 0.08 #67, 0.05 #114), 051z6rz (0.14 #29, 0.08 #76, 0.05 #123), 0js9s (0.14 #33, 0.08 #80, 0.05 #127), 02xc1w4 (0.11 #121, 0.08 #74, 0.03 #215), 04ktcgn (0.08 #59, 0.06 #294, 0.05 #106), 0bbxx9b (0.08 #68, 0.05 #115, 0.03 #303), 027rwmr (0.08 #53, 0.05 #100, 0.03 #382), 0b79gfg (0.08 #65, 0.05 #112, 0.02 #728), 094tsh6 (0.08 #86, 0.03 #368, 0.02 #463), 04wp63 (0.05 #136, 0.04 #324, 0.04 #183) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 04zl8; >> query: (?x3524, 092ys_y) <- nominated_for(?x10747, ?x3524), film(?x3395, ?x3524), film_release_distribution_medium(?x3524, ?x81), ?x10747 = 0262s1 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #242 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 86 *> proper extension: 01h1bf; 05gnf; 06ys2; *> query: (?x3524, 03h26tm) <- nominated_for(?x7489, ?x3524), profession(?x7489, ?x319), actor(?x3169, ?x7489), special_performance_type(?x7489, ?x4832) *> conf = 0.01 ranks of expected_values: 33 EVAL 06r2_ crewmember 03h26tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 74.000 48.000 0.143 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #2887-023p29 PRED entity: 023p29 PRED relation: nationality PRED expected values: 09c7w0 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 55): 09c7w0 (0.80 #1803, 0.79 #1602, 0.77 #2607), 059rby (0.33 #5915, 0.33 #11828, 0.33 #11526), 02jx1 (0.27 #533, 0.13 #3141, 0.12 #1334), 0d060g (0.12 #507, 0.09 #1007, 0.08 #107), 07ssc (0.11 #615, 0.09 #5527, 0.09 #3823), 03rk0 (0.08 #5558, 0.08 #5258, 0.08 #4557), 0chghy (0.08 #110, 0.07 #210, 0.05 #410), 05vz3zq (0.07 #970, 0.07 #1070, 0.04 #1271), 0345h (0.05 #931, 0.05 #1031, 0.05 #431), 0cdbq (0.05 #963, 0.05 #1063, 0.04 #1264) >> Best rule #1803 for best value: >> intensional similarity = 3 >> extensional distance = 200 >> proper extension: 02y0dd; >> query: (?x10209, 09c7w0) <- place_of_birth(?x10209, ?x3415), currency(?x10209, ?x170), contains(?x3415, ?x3148) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023p29 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.802 http://example.org/people/person/nationality #2886-0vqcq PRED entity: 0vqcq PRED relation: time_zones PRED expected values: 02hcv8 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.81 #42, 0.73 #16, 0.69 #3), 02fqwt (0.51 #366, 0.51 #380, 0.48 #408), 02lcqs (0.28 #339, 0.22 #148, 0.20 #174), 02hczc (0.28 #339, 0.17 #889, 0.16 #835), 02lcrv (0.28 #339, 0.17 #889, 0.16 #835), 042g7t (0.16 #835, 0.15 #849, 0.15 #821), 02llzg (0.05 #958, 0.05 #440, 0.05 #645), 03bdv (0.03 #428, 0.03 #511, 0.03 #525), 03plfd (0.01 #418) >> Best rule #42 for best value: >> intensional similarity = 6 >> extensional distance = 25 >> proper extension: 0njvn; 0wh3; 0nj07; 0nj7b; 0vg8x; 0nj0m; 0njlp; 0nj3m; 0njdm; 0vrmb; ... >> query: (?x13168, 02hcv8) <- source(?x13168, ?x958), contains(?x1906, ?x13168), contains(?x94, ?x13168), ?x1906 = 04rrx, contains(?x94, ?x7660), major_field_of_study(?x7660, ?x373) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0vqcq time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.815 http://example.org/location/location/time_zones #2885-0g10g PRED entity: 0g10g PRED relation: award_winner! PRED expected values: 01by1l 02qkk9_ => 118 concepts (90 used for prediction) PRED predicted values (max 10 best out of 271): 054ks3 (0.59 #1863, 0.39 #2293, 0.27 #1001), 0gkvb7 (0.41 #1722, 0.41 #1721, 0.38 #35282), 0gqwc (0.41 #1722, 0.41 #1721, 0.38 #35282), 0gqyl (0.41 #1722, 0.41 #1721, 0.38 #35282), 0gqz2 (0.40 #1803, 0.27 #2233, 0.27 #941), 01c427 (0.33 #85, 0.29 #515, 0.23 #1375), 01c9jp (0.29 #616, 0.17 #186, 0.12 #1476), 01by1l (0.23 #1402, 0.18 #972, 0.17 #112), 01bgqh (0.23 #1333, 0.18 #903, 0.17 #43), 0c4z8 (0.23 #1794, 0.18 #2224, 0.17 #72) >> Best rule #1863 for best value: >> intensional similarity = 5 >> extensional distance = 73 >> proper extension: 0152cw; 0x3n; >> query: (?x10973, 054ks3) <- award_winner(?x4796, ?x10973), award(?x7115, ?x4796), award(?x5310, ?x4796), ?x5310 = 012vd6, ?x7115 = 02z4b_8 >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #1402 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 086qd; 01cwhp; 01dw9z; 01vwyqp; 025ldg; 0d9xq; 01n44c; 01pq5j7; 0133x7; 02z4b_8; ... *> query: (?x10973, 01by1l) <- award_winner(?x4796, ?x10973), ?x4796 = 01c99j, award(?x10973, ?x537) *> conf = 0.23 ranks of expected_values: 8, 16 EVAL 0g10g award_winner! 02qkk9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 118.000 90.000 0.587 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0g10g award_winner! 01by1l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 118.000 90.000 0.587 http://example.org/award/award_category/winners./award/award_honor/award_winner #2884-0d05w3 PRED entity: 0d05w3 PRED relation: jurisdiction_of_office! PRED expected values: 0fkx3 => 239 concepts (239 used for prediction) PRED predicted values (max 10 best out of 19): 060bp (0.76 #1121, 0.74 #861, 0.71 #1001), 0f6c3 (0.52 #2306, 0.49 #1806, 0.48 #2126), 09n5b9 (0.49 #1809, 0.44 #2309, 0.42 #2129), 0fkvn (0.49 #2302, 0.41 #1802, 0.39 #3222), 0pqc5 (0.39 #2643, 0.38 #2223, 0.36 #4344), 04syw (0.36 #4121, 0.21 #545, 0.18 #3025), 0fj45 (0.36 #4121, 0.12 #577, 0.12 #557), 0p5vf (0.33 #570, 0.33 #550, 0.32 #490), 0789n (0.27 #227, 0.20 #467, 0.20 #107), 02079p (0.25 #468, 0.20 #108, 0.19 #668) >> Best rule #1121 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 09c7w0; >> query: (?x2346, 060bp) <- film_release_region(?x186, ?x2346), locations(?x3654, ?x2346), olympics(?x2346, ?x418) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #418 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 03bxbql; 01rdm0; *> query: (?x2346, 0fkx3) <- combatants(?x5114, ?x2346), capital(?x2346, ?x206), ?x5114 = 05vz3zq *> conf = 0.06 ranks of expected_values: 18 EVAL 0d05w3 jurisdiction_of_office! 0fkx3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 239.000 239.000 0.756 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #2883-0gv40 PRED entity: 0gv40 PRED relation: award PRED expected values: 0gq9h => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 280): 019f4v (0.77 #33634, 0.73 #39315, 0.72 #40128), 040njc (0.77 #33634, 0.73 #39315, 0.72 #40128), 07bdd_ (0.58 #1281, 0.53 #2091, 0.19 #6547), 05p1dby (0.42 #1322, 0.36 #2132, 0.15 #38908), 0gq9h (0.38 #888, 0.36 #1293, 0.35 #6964), 02pqp12 (0.35 #476, 0.21 #5337, 0.16 #4931), 09sb52 (0.31 #4496, 0.30 #9764, 0.28 #16246), 05pcn59 (0.26 #486, 0.20 #9804, 0.19 #4536), 04dn09n (0.26 #449, 0.18 #5310, 0.17 #4904), 0gr51 (0.23 #505, 0.21 #5366, 0.16 #4960) >> Best rule #33634 for best value: >> intensional similarity = 3 >> extensional distance = 1631 >> proper extension: 02mslq; 011zf2; 0288fyj; 01vd7hn; 01yzl2; 0g5ff; 031x_3; 01wyq0w; >> query: (?x4652, ?x1313) <- nationality(?x4652, ?x94), award_winner(?x1313, ?x4652), ceremony(?x1313, ?x78) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #888 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 03bw6; *> query: (?x4652, 0gq9h) <- nationality(?x4652, ?x94), film(?x4652, ?x4653), place_of_death(?x4652, ?x1523) *> conf = 0.38 ranks of expected_values: 5 EVAL 0gv40 award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 120.000 120.000 0.766 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2882-027pfg PRED entity: 027pfg PRED relation: currency PRED expected values: 09nqf => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.87 #50, 0.84 #57, 0.83 #29), 088n7 (0.06 #14, 0.05 #21, 0.03 #42), 01nv4h (0.03 #58, 0.03 #72, 0.03 #65), 02l6h (0.02 #137, 0.02 #46, 0.02 #158) >> Best rule #50 for best value: >> intensional similarity = 5 >> extensional distance = 73 >> proper extension: 011yxg; 031778; 0dnqr; 01qxc7; 01s3vk; 049xgc; 031786; 02z9rr; 01hq1; 0y_pg; ... >> query: (?x6932, 09nqf) <- nominated_for(?x1180, ?x6932), nominated_for(?x640, ?x6932), music(?x6932, ?x3410), ?x640 = 02hsq3m, award(?x638, ?x1180) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027pfg currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.867 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #2881-04tz52 PRED entity: 04tz52 PRED relation: currency PRED expected values: 09nqf => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.84 #50, 0.81 #239, 0.80 #253), 088n7 (0.08 #14, 0.08 #63, 0.06 #35), 02gsvk (0.04 #195), 01nv4h (0.04 #247, 0.02 #268, 0.02 #303), 02l6h (0.01 #249) >> Best rule #50 for best value: >> intensional similarity = 7 >> extensional distance = 29 >> proper extension: 07k2mq; >> query: (?x2816, 09nqf) <- film(?x902, ?x2816), titles(?x307, ?x2816), film_release_region(?x2816, ?x142), genre(?x2084, ?x307), genre(?x1009, ?x307), ?x1009 = 01m13b, award(?x2084, ?x1007) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04tz52 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.839 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #2880-02bgmr PRED entity: 02bgmr PRED relation: profession PRED expected values: 016z4k 0nbcg => 122 concepts (70 used for prediction) PRED predicted values (max 10 best out of 62): 02hrh1q (0.76 #10122, 0.73 #9830, 0.67 #8364), 0nbcg (0.57 #4270, 0.51 #2514, 0.51 #1636), 016z4k (0.54 #1609, 0.54 #2487, 0.48 #2340), 0n1h (0.31 #741, 0.25 #887, 0.23 #2495), 01d_h8 (0.28 #10113, 0.27 #9526, 0.27 #9821), 0fnpj (0.25 #58, 0.20 #1810, 0.20 #1664), 012t_z (0.25 #12, 0.11 #158, 0.06 #888), 0dxtg (0.24 #7922, 0.24 #9534, 0.23 #6748), 03gjzk (0.20 #10123, 0.19 #7924, 0.19 #9831), 025352 (0.19 #933, 0.19 #787, 0.17 #1079) >> Best rule #10122 for best value: >> intensional similarity = 3 >> extensional distance = 1248 >> proper extension: 01sl1q; 0q9kd; 0184jc; 04bdxl; 06qgvf; 0grwj; 03qcq; 07fq1y; 02qgqt; 0fvf9q; ... >> query: (?x5768, 02hrh1q) <- award_nominee(?x1566, ?x5768), profession(?x5768, ?x131), location(?x5768, ?x2911) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #4270 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 349 *> proper extension: 07_3qd; 04mx7s; *> query: (?x5768, 0nbcg) <- artists(?x9063, ?x5768), instrumentalists(?x227, ?x5768), artists(?x9063, ?x10502), ?x10502 = 016vn3 *> conf = 0.57 ranks of expected_values: 2, 3 EVAL 02bgmr profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 70.000 0.758 http://example.org/people/person/profession EVAL 02bgmr profession 016z4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 70.000 0.758 http://example.org/people/person/profession #2879-0g_wn2 PRED entity: 0g_wn2 PRED relation: featured_film_locations! PRED expected values: 015bpl => 150 concepts (61 used for prediction) PRED predicted values (max 10 best out of 709): 0btbyn (0.25 #283, 0.05 #3227, 0.04 #8380), 0ddjy (0.25 #167, 0.03 #13417, 0.03 #3111), 04cbbz (0.25 #407, 0.03 #3351, 0.02 #8504), 070g7 (0.25 #304, 0.03 #3248, 0.02 #8401), 07jxpf (0.25 #292, 0.03 #3236, 0.02 #8389), 0872p_c (0.17 #2286, 0.11 #3022, 0.07 #8175), 0ds2n (0.17 #2438, 0.08 #3174, 0.06 #8327), 033srr (0.17 #2487, 0.08 #3223, 0.06 #8376), 051zy_b (0.17 #2460, 0.06 #8349, 0.04 #13502), 02q0v8n (0.17 #2840, 0.05 #3576, 0.04 #7257) >> Best rule #283 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0qr8z; 0qf5p; >> query: (?x6497, 0btbyn) <- featured_film_locations(?x1866, ?x6497), contains(?x94, ?x6497), source(?x6497, ?x958), ?x1866 = 02rx2m5 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g_wn2 featured_film_locations! 015bpl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 150.000 61.000 0.250 http://example.org/film/film/featured_film_locations #2878-06ztvyx PRED entity: 06ztvyx PRED relation: nominated_for! PRED expected values: 02x1z2s => 78 concepts (67 used for prediction) PRED predicted values (max 10 best out of 190): 02hsq3m (0.35 #747, 0.33 #30, 0.29 #508), 0gq9h (0.33 #6038, 0.27 #6516, 0.26 #5082), 02x1z2s (0.33 #2534, 0.18 #10519, 0.17 #15063), 0l8z1 (0.31 #6027, 0.24 #6266, 0.18 #10519), 0gq_v (0.30 #5995, 0.21 #6234, 0.20 #9582), 0gqwc (0.29 #300, 0.15 #10998, 0.14 #9623), 054krc (0.29 #6045, 0.24 #309, 0.24 #6284), 0k611 (0.29 #6049, 0.22 #6288, 0.20 #6527), 019f4v (0.28 #6029, 0.20 #9616, 0.20 #6268), 02ppm4q (0.27 #356, 0.09 #6331, 0.09 #4897) >> Best rule #747 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 03bzyn4; >> query: (?x2709, 02hsq3m) <- film_format(?x2709, ?x10390), film(?x147, ?x2709), film_distribution_medium(?x2709, ?x2099) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #2534 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 07ng9k; 0dr1c2; 02v5xg; 05vc35; *> query: (?x2709, 02x1z2s) <- genre(?x2709, ?x2540), film(?x147, ?x2709), ?x2540 = 0hcr *> conf = 0.33 ranks of expected_values: 3 EVAL 06ztvyx nominated_for! 02x1z2s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 67.000 0.346 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2877-01z8f0 PRED entity: 01z8f0 PRED relation: place_of_birth! PRED expected values: 04pf4r => 61 concepts (30 used for prediction) PRED predicted values (max 10 best out of 753): 035gjq (0.25 #177, 0.01 #23699), 0ksrf8 (0.20 #3757, 0.14 #8985, 0.14 #6370), 03d9wk (0.20 #5215, 0.14 #10443, 0.14 #7828), 0kbg6 (0.20 #5199, 0.14 #10427, 0.14 #7812), 09jd9 (0.20 #5193, 0.14 #10421, 0.14 #7806), 026sb55 (0.20 #5176, 0.14 #10404, 0.14 #7789), 06lhbl (0.20 #5149, 0.14 #10377, 0.14 #7762), 03f4w4 (0.20 #5099, 0.14 #10327, 0.14 #7712), 01b0k1 (0.20 #5098, 0.14 #10326, 0.14 #7711), 0935jw (0.20 #5097, 0.14 #10325, 0.14 #7710) >> Best rule #177 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01z53w; 014kj2; >> query: (?x9390, 035gjq) <- contains(?x2199, ?x9390), contains(?x512, ?x9390), ?x512 = 07ssc, category(?x9390, ?x134), ?x2199 = 0121c1 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01z8f0 place_of_birth! 04pf4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 30.000 0.250 http://example.org/people/person/place_of_birth #2876-0372j5 PRED entity: 0372j5 PRED relation: film_release_region PRED expected values: 05qhw 059j2 06mkj 05b4w => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 181): 059j2 (0.93 #1160, 0.87 #3747, 0.84 #5846), 06mkj (0.86 #3935, 0.85 #1186, 0.84 #5872), 03spz (0.85 #1229, 0.75 #101, 0.60 #3816), 06bnz (0.83 #46, 0.78 #1174, 0.67 #5860), 05b4w (0.83 #67, 0.75 #1195, 0.69 #3782), 06t2t (0.83 #64, 0.75 #1192, 0.59 #5878), 0d060g (0.83 #6, 0.67 #5820, 0.65 #3721), 04gzd (0.83 #9, 0.57 #1137, 0.43 #5823), 03h64 (0.82 #1198, 0.78 #3785, 0.76 #5884), 05qhw (0.82 #1143, 0.76 #3730, 0.71 #5829) >> Best rule #1160 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 0401sg; 053tj7; >> query: (?x6751, 059j2) <- film_distribution_medium(?x6751, ?x81), film_release_region(?x6751, ?x1353), film_release_region(?x6751, ?x985), ?x1353 = 035qy, ?x985 = 0k6nt >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 10 EVAL 0372j5 film_release_region 05b4w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 121.000 121.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0372j5 film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0372j5 film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0372j5 film_release_region 05qhw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 121.000 121.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2875-01x42h PRED entity: 01x42h PRED relation: second_level_divisions! PRED expected values: 0d060g => 66 concepts (32 used for prediction) PRED predicted values (max 10 best out of 9): 0d060g (0.26 #172, 0.24 #184, 0.23 #196), 09c7w0 (0.25 #161, 0.25 #211, 0.25 #249), 02jx1 (0.06 #56, 0.04 #130, 0.03 #393), 05kr_ (0.06 #59, 0.05 #396, 0.03 #133), 0f8l9c (0.02 #54), 07ssc (0.02 #53), 03rjj (0.02 #49), 03rt9 (0.01 #278), 0h7h6 (0.01 #413) >> Best rule #172 for best value: >> intensional similarity = 4 >> extensional distance = 968 >> proper extension: 01q0l; >> query: (?x14514, ?x279) <- contains(?x1905, ?x14514), country(?x1905, ?x279), time_zones(?x1905, ?x2674), taxonomy(?x1905, ?x939) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x42h second_level_divisions! 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 32.000 0.264 http://example.org/location/country/second_level_divisions #2874-034h1h PRED entity: 034h1h PRED relation: organization! PRED expected values: 0j_sncb 017j69 0bwfn => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 326): 02vzc (0.78 #1367, 0.57 #2934, 0.56 #1104), 0k6nt (0.78 #1339, 0.57 #2906, 0.56 #1076), 0d0vqn (0.78 #1320, 0.57 #2887, 0.56 #1057), 03rjj (0.78 #1316, 0.57 #2883, 0.56 #1053), 05b4w (0.78 #1385, 0.56 #1122, 0.50 #2952), 06mkj (0.78 #1375, 0.56 #1112, 0.50 #2942), 07ssc (0.67 #1329, 0.67 #1066, 0.64 #2896), 0345h (0.67 #1348, 0.67 #1085, 0.50 #2915), 059j2 (0.67 #1346, 0.57 #2913, 0.56 #1083), 03rt9 (0.67 #1327, 0.57 #2894, 0.53 #3156) >> Best rule #1367 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01rz1; 04k4l; >> query: (?x5487, 02vzc) <- organization(?x6973, ?x5487), organization(?x5288, ?x5487), school(?x465, ?x6973), contains(?x94, ?x5288) >> conf = 0.78 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 034h1h organization! 0bwfn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.778 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 034h1h organization! 017j69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.778 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 034h1h organization! 0j_sncb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.778 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #2873-0688f PRED entity: 0688f PRED relation: languages! PRED expected values: 08d6bd 04cmrt 047jhq => 46 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1243): 040nwr (0.67 #5217, 0.67 #3245, 0.62 #6526), 0894_x (0.60 #2583, 0.50 #5207, 0.50 #3235), 09r_wb (0.60 #8312, 0.50 #3065, 0.40 #8967), 046rfv (0.60 #8296, 0.50 #3049, 0.40 #8951), 01x2tm8 (0.50 #8368, 0.50 #6402, 0.50 #3121), 03x31g (0.50 #8453, 0.50 #3206, 0.40 #2554), 06kl0k (0.50 #3156, 0.40 #8403, 0.40 #2504), 050llt (0.50 #3232, 0.40 #8479, 0.40 #2580), 05vzql (0.50 #3181, 0.40 #2529, 0.38 #6462), 04cmrt (0.50 #3222, 0.40 #2570, 0.33 #7811) >> Best rule #5217 for best value: >> intensional similarity = 16 >> extensional distance = 4 >> proper extension: 0121sr; >> query: (?x10323, 040nwr) <- languages_spoken(?x7838, ?x10323), languages(?x10750, ?x10323), language(?x10774, ?x10323), language(?x2882, ?x10323), award_winner(?x10774, ?x2065), titles(?x2146, ?x10774), ?x2146 = 03rk0, genre(?x10774, ?x307), genre(?x10774, ?x53), ?x307 = 04t36, ?x53 = 07s9rl0, award(?x10774, ?x4443), countries_spoken_in(?x10323, ?x279), gender(?x10750, ?x231), nominated_for(?x1937, ?x10774), country(?x2882, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3222 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 07c9s; *> query: (?x10323, 04cmrt) <- languages_spoken(?x11665, ?x10323), languages(?x7517, ?x10323), language(?x10774, ?x10323), language(?x657, ?x10323), award_winner(?x10774, ?x2065), titles(?x1882, ?x10774), place_of_birth(?x7517, ?x9466), film(?x1445, ?x10774), award_winner(?x657, ?x7969), produced_by(?x657, ?x2618), award(?x657, ?x5018), award_nominee(?x1445, ?x1554), ?x11665 = 03w9bjf *> conf = 0.50 ranks of expected_values: 10, 569, 573 EVAL 0688f languages! 047jhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 27.000 0.667 http://example.org/people/person/languages EVAL 0688f languages! 04cmrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 46.000 27.000 0.667 http://example.org/people/person/languages EVAL 0688f languages! 08d6bd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 27.000 0.667 http://example.org/people/person/languages #2872-01x73 PRED entity: 01x73 PRED relation: district_represented! PRED expected values: 01h7xx => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 15): 03z5xd (0.56 #80, 0.56 #151, 0.29 #50), 03ww_x (0.56 #78, 0.56 #151, 0.29 #48), 01h7xx (0.56 #151, 0.50 #222, 0.50 #86), 032ft5 (0.56 #151, 0.38 #79, 0.29 #49), 0495ys (0.56 #151, 0.31 #76, 0.29 #46), 060ny2 (0.56 #151, 0.29 #54, 0.25 #84), 06r713 (0.56 #151, 0.29 #53, 0.25 #83), 04gp1d (0.56 #151, 0.29 #52, 0.25 #82), 05l2z4 (0.56 #151, 0.29 #47, 0.25 #77), 04h1rz (0.56 #151, 0.19 #85, 0.14 #55) >> Best rule #80 for best value: >> intensional similarity = 2 >> extensional distance = 14 >> proper extension: 0g0syc; >> query: (?x1755, 03z5xd) <- district_represented(?x5339, ?x1755), ?x5339 = 02glc4 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #151 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 0ldff; *> query: (?x1755, ?x176) <- adjoins(?x1755, ?x2713), category(?x1755, ?x134), district_represented(?x176, ?x2713) *> conf = 0.56 ranks of expected_values: 3 EVAL 01x73 district_represented! 01h7xx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 147.000 147.000 0.562 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #2871-08815 PRED entity: 08815 PRED relation: major_field_of_study PRED expected values: 0w7c 01540 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 90): 02lp1 (0.62 #892, 0.54 #1088, 0.52 #1480), 01540 (0.44 #242, 0.42 #340, 0.41 #830), 02ky346 (0.42 #307, 0.36 #503, 0.34 #699), 0g26h (0.40 #3166, 0.38 #912, 0.35 #4245), 06ms6 (0.38 #896, 0.37 #1092, 0.33 #308), 01tbp (0.36 #927, 0.32 #1515, 0.32 #1123), 0h5k (0.33 #313, 0.33 #19, 0.31 #901), 0db86 (0.33 #919, 0.33 #331, 0.25 #135), 0dc_v (0.33 #31, 0.31 #717, 0.29 #1109), 0193x (0.33 #321, 0.25 #125, 0.24 #811) >> Best rule #892 for best value: >> intensional similarity = 2 >> extensional distance = 37 >> proper extension: 0d06m5; 0d05fv; >> query: (?x122, 02lp1) <- list(?x122, ?x2197), organization(?x122, ?x5487) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #242 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 07wj1; 0d6qjf; 07y0n; *> query: (?x122, 01540) <- company(?x346, ?x122), company(?x3520, ?x122), ?x3520 = 03gkn5 *> conf = 0.44 ranks of expected_values: 2, 25 EVAL 08815 major_field_of_study 01540 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 08815 major_field_of_study 0w7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 107.000 107.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #2870-0872p_c PRED entity: 0872p_c PRED relation: film! PRED expected values: 01l1b90 0f13b => 87 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1111): 02qzjj (0.43 #76575, 0.42 #31046, 0.40 #37257), 034x61 (0.43 #76575, 0.42 #31046, 0.40 #37257), 0b6mgp_ (0.43 #76575, 0.42 #31046, 0.40 #37257), 05qd_ (0.43 #76575, 0.42 #31046, 0.40 #37257), 06pj8 (0.14 #18626, 0.09 #33116, 0.09 #33117), 0h5g_ (0.12 #73, 0.03 #10422, 0.03 #55952), 0c0k1 (0.08 #3571, 0.08 #5642, 0.07 #93130), 01vy_v8 (0.08 #2799, 0.08 #4870, 0.03 #13148), 0hvb2 (0.07 #93130, 0.06 #2367, 0.06 #4438), 015t56 (0.07 #93130, 0.06 #469, 0.03 #111760) >> Best rule #76575 for best value: >> intensional similarity = 4 >> extensional distance = 529 >> proper extension: 09fc83; 06zsk51; >> query: (?x1173, ?x848) <- featured_film_locations(?x1173, ?x108), nominated_for(?x848, ?x1173), language(?x1173, ?x254), film(?x396, ?x1173) >> conf = 0.43 => this is the best rule for 4 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 0kvgxk; *> query: (?x1173, 01l1b90) <- production_companies(?x1173, ?x2021), contact_category(?x2021, ?x897), ?x897 = 03w5xm *> conf = 0.06 ranks of expected_values: 43, 93 EVAL 0872p_c film! 0f13b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 87.000 54.000 0.435 http://example.org/film/actor/film./film/performance/film EVAL 0872p_c film! 01l1b90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 87.000 54.000 0.435 http://example.org/film/actor/film./film/performance/film #2869-0n_hp PRED entity: 0n_hp PRED relation: titles! PRED expected values: 017fp => 113 concepts (51 used for prediction) PRED predicted values (max 10 best out of 55): 03bxz7 (0.83 #3446, 0.37 #3445, 0.35 #3548), 060__y (0.37 #3445, 0.35 #3548, 0.35 #3547), 0lsxr (0.37 #3445, 0.35 #3548, 0.35 #3547), 01jfsb (0.35 #826, 0.30 #1534, 0.30 #1330), 01z4y (0.25 #742, 0.24 #1045, 0.23 #4596), 07ssc (0.23 #4772, 0.19 #2239, 0.18 #2745), 024qqx (0.20 #1090, 0.19 #483, 0.18 #787), 01hmnh (0.20 #4283, 0.17 #3674, 0.13 #3978), 017fp (0.19 #2253, 0.18 #2759, 0.18 #3468), 0c3351 (0.14 #151, 0.09 #556, 0.09 #1872) >> Best rule #3446 for best value: >> intensional similarity = 5 >> extensional distance = 333 >> proper extension: 0d8w2n; >> query: (?x9129, ?x6887) <- titles(?x53, ?x9129), genre(?x9129, ?x6887), ?x53 = 07s9rl0, genre(?x10048, ?x6887), ?x10048 = 09tcg4 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #2253 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 224 *> proper extension: 02bg8v; 016z9n; 03prz_; 08y2fn; 01fwzk; 06zsk51; 09qycb; 04x4gw; *> query: (?x9129, 017fp) <- titles(?x162, ?x9129), genre(?x9129, ?x604), film(?x539, ?x9129), ?x162 = 04xvlr, nominated_for(?x1007, ?x9129) *> conf = 0.19 ranks of expected_values: 9 EVAL 0n_hp titles! 017fp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 113.000 51.000 0.829 http://example.org/media_common/netflix_genre/titles #2868-0fd3y PRED entity: 0fd3y PRED relation: parent_genre! PRED expected values: 0g_bh 01hydr => 63 concepts (14 used for prediction) PRED predicted values (max 10 best out of 278): 0g_bh (0.50 #1137, 0.45 #1654, 0.44 #1395), 01gbcf (0.38 #1036, 0.27 #1553, 0.22 #1294), 0mmp3 (0.33 #81, 0.28 #1290, 0.16 #2845), 0y3_8 (0.33 #39, 0.27 #1588, 0.25 #2626), 029fbr (0.33 #1439, 0.27 #1698, 0.25 #1181), 03xnwz (0.33 #1317, 0.27 #1576, 0.25 #1059), 0193f (0.33 #96, 0.25 #612, 0.12 #1128), 03mb9 (0.33 #82, 0.17 #2669, 0.16 #2845), 07gxw (0.33 #46, 0.16 #2845, 0.11 #2633), 0163zw (0.33 #177, 0.11 #1467, 0.09 #1726) >> Best rule #1137 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 025tm81; >> query: (?x497, 0g_bh) <- parent_genre(?x7280, ?x497), artists(?x497, ?x9757), artists(?x497, ?x4701), artists(?x497, ?x498), ?x9757 = 06br6t, parent_genre(?x1127, ?x7280), ?x498 = 0m19t, instrumentalists(?x227, ?x4701) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 96 EVAL 0fd3y parent_genre! 01hydr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 63.000 14.000 0.500 http://example.org/music/genre/parent_genre EVAL 0fd3y parent_genre! 0g_bh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 14.000 0.500 http://example.org/music/genre/parent_genre #2867-0c53vt PRED entity: 0c53vt PRED relation: award_winner PRED expected values: 03bpn6 => 30 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1705): 02lz1s (0.60 #4859, 0.21 #12277, 0.20 #30710), 076lxv (0.50 #1627, 0.33 #92, 0.20 #3160), 076psv (0.40 #5284, 0.20 #3750, 0.17 #8354), 0cb77r (0.37 #15349, 0.30 #15350, 0.25 #1548), 0c0tzp (0.37 #15349, 0.30 #15350, 0.25 #3042), 0c6g29 (0.37 #15349, 0.30 #15350, 0.21 #12277), 0dg3jz (0.37 #15349, 0.30 #15350, 0.21 #12277), 072twv (0.33 #346, 0.25 #1881, 0.20 #3414), 0h005 (0.33 #713, 0.25 #2248, 0.15 #14527), 01pp3p (0.33 #771, 0.25 #2306, 0.08 #11510) >> Best rule #4859 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 0fzrtf; 0fk0xk; 0c6vcj; >> query: (?x8015, 02lz1s) <- ceremony(?x1313, ?x8015), award_winner(?x8015, ?x12188), award_winner(?x8015, ?x7876), award_winner(?x8015, ?x3771), instance_of_recurring_event(?x8015, ?x3459), ?x3771 = 01vvdm, gender(?x12188, ?x231), type_of_union(?x12188, ?x566), music(?x2779, ?x12188), ?x231 = 05zppz, honored_for(?x8015, ?x8735), ?x1313 = 0gs9p, award_nominee(?x786, ?x7876), profession(?x7876, ?x1078), nationality(?x7876, ?x94) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #30709 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 54 *> proper extension: 0h_9252; 0ds460j; *> query: (?x8015, ?x276) <- ceremony(?x1313, ?x8015), award_winner(?x8015, ?x4180), award_winner(?x8015, ?x3770), award_winner(?x8015, ?x199), award_winner(?x200, ?x199), award(?x3770, ?x1869), profession(?x199, ?x1078), award_winner(?x1313, ?x276), ?x1869 = 04njml, award_winner(?x8735, ?x4180), award(?x269, ?x1313) *> conf = 0.05 ranks of expected_values: 703 EVAL 0c53vt award_winner 03bpn6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 30.000 21.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2866-033qdy PRED entity: 033qdy PRED relation: film_distribution_medium PRED expected values: 02nxhr => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.69 #155, 0.63 #216, 0.62 #143), 02nxhr (0.39 #69, 0.35 #73, 0.26 #214), 07z4p (0.03 #532, 0.03 #76, 0.02 #144), 07c52 (0.03 #532) >> Best rule #155 for best value: >> intensional similarity = 6 >> extensional distance = 131 >> proper extension: 0ds35l9; 03qcfvw; 0c3ybss; 03g90h; 01gc7; 0dq626; 0czyxs; 0gtv7pk; 0ds11z; 01r97z; ... >> query: (?x6624, 0735l) <- language(?x6624, ?x254), country(?x6624, ?x94), film(?x1104, ?x6624), genre(?x6624, ?x53), film_distribution_medium(?x6624, ?x81), film_crew_role(?x6624, ?x137) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #69 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 36 *> proper extension: 02pcq92; *> query: (?x6624, 02nxhr) <- language(?x6624, ?x254), country(?x6624, ?x94), film(?x1104, ?x6624), genre(?x6624, ?x53), film_distribution_medium(?x6624, ?x81), prequel(?x6624, ?x9786) *> conf = 0.39 ranks of expected_values: 2 EVAL 033qdy film_distribution_medium 02nxhr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 136.000 0.692 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #2865-042xh PRED entity: 042xh PRED relation: gender PRED expected values: 02zsn => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.93 #82, 0.91 #55, 0.90 #59), 02zsn (0.46 #303, 0.46 #300, 0.46 #297) >> Best rule #82 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 03qcq; 08433; 03ft8; 01gp_x; 03hnd; 0br1w; 0c5tl; 01v9724; 056wb; 02zjd; ... >> query: (?x13644, 05zppz) <- influenced_by(?x13644, ?x2343), story_by(?x7304, ?x13644), profession(?x13644, ?x319), film_crew_role(?x7304, ?x137), genre(?x7304, ?x600) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #303 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4243 *> proper extension: 04s9n; *> query: (?x13644, ?x231) <- profession(?x13644, ?x353), profession(?x7332, ?x353), profession(?x3975, ?x353), gender(?x3975, ?x231), type_of_union(?x7332, ?x566) *> conf = 0.46 ranks of expected_values: 2 EVAL 042xh gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 149.000 149.000 0.929 http://example.org/people/person/gender #2864-04hqz PRED entity: 04hqz PRED relation: country! PRED expected values: 0bynt 02y8z => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 50): 0bynt (0.90 #660, 0.88 #160, 0.86 #410), 03hr1p (0.81 #172, 0.72 #372, 0.67 #672), 06f41 (0.78 #165, 0.74 #365, 0.66 #665), 01lb14 (0.78 #166, 0.70 #366, 0.66 #666), 064vjs (0.78 #178, 0.57 #678, 0.56 #378), 06wrt (0.75 #167, 0.70 #367, 0.59 #667), 01cgz (0.73 #1064, 0.70 #1414, 0.69 #764), 0194d (0.72 #193, 0.63 #393, 0.59 #693), 0w0d (0.63 #362, 0.57 #662, 0.56 #162), 07bs0 (0.62 #163, 0.56 #363, 0.53 #663) >> Best rule #660 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 06npd; 06mzp; 03gj2; 030qb3t; 04g5k; 01crd5; >> query: (?x7413, 0bynt) <- film_release_region(?x6886, ?x7413), film_release_region(?x1956, ?x7413), ?x1956 = 05qbckf, language(?x6886, ?x254) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14 EVAL 04hqz country! 02y8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 125.000 125.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 04hqz country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.897 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #2863-01wgjj5 PRED entity: 01wgjj5 PRED relation: role PRED expected values: 0l14md 05148p4 => 127 concepts (89 used for prediction) PRED predicted values (max 10 best out of 120): 02sgy (0.39 #873, 0.33 #1354, 0.30 #3762), 013y1f (0.25 #31, 0.21 #995, 0.18 #1380), 0gkd1 (0.25 #89, 0.10 #569, 0.08 #1438), 0l14j_ (0.25 #61, 0.06 #6854, 0.05 #4822), 026t6 (0.24 #1351, 0.21 #290, 0.21 #3759), 0l14qv (0.22 #872, 0.21 #968, 0.20 #1353), 05148p4 (0.19 #498, 0.15 #3775, 0.14 #1367), 03qjg (0.13 #538, 0.12 #250, 0.11 #637), 01s0ps (0.13 #1020, 0.09 #924, 0.08 #3524), 0dwt5 (0.12 #271, 0.06 #6854, 0.06 #559) >> Best rule #873 for best value: >> intensional similarity = 4 >> extensional distance = 85 >> proper extension: 025xt8y; 03f5spx; 01gf5h; 01vv7sc; 01vrncs; 02whj; 01k5t_3; 07_3qd; 03qmj9; 0770cd; ... >> query: (?x5883, 02sgy) <- artists(?x302, ?x5883), place_of_birth(?x5883, ?x10852), role(?x5883, ?x227), ?x227 = 0342h >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #498 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 02qx69; 0p_47; *> query: (?x5883, 05148p4) <- role(?x5883, ?x5676), role(?x1663, ?x5676), participant(?x4662, ?x5883), role(?x228, ?x1663) *> conf = 0.19 ranks of expected_values: 7, 18 EVAL 01wgjj5 role 05148p4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 127.000 89.000 0.391 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01wgjj5 role 0l14md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 127.000 89.000 0.391 http://example.org/music/artist/track_contributions./music/track_contribution/role #2862-03c3yf PRED entity: 03c3yf PRED relation: artists! PRED expected values: 015pdg 07ym47 => 85 concepts (68 used for prediction) PRED predicted values (max 10 best out of 238): 01lyv (0.84 #7294, 0.61 #11843, 0.60 #2448), 03_d0 (0.72 #11520, 0.47 #15151, 0.41 #1219), 064t9 (0.59 #16665, 0.49 #19386, 0.47 #12731), 0xhtw (0.58 #619, 0.43 #8793, 0.42 #6370), 0dl5d (0.58 #621, 0.33 #17, 0.31 #1226), 02l96k (0.50 #403, 0.22 #8172, 0.18 #14837), 05w3f (0.42 #638, 0.25 #336, 0.22 #8172), 05hs4r (0.41 #907, 0.22 #8172, 0.18 #14837), 016jny (0.41 #11913, 0.36 #1611, 0.33 #100), 05bt6j (0.38 #1551, 0.34 #1249, 0.34 #15787) >> Best rule #7294 for best value: >> intensional similarity = 5 >> extensional distance = 140 >> proper extension: 01lmj3q; 0152cw; 01vrz41; 01kx_81; 01p9hgt; 01kv4mb; 02jg92; 010hn; 03yf3z; 014q2g; ... >> query: (?x7597, 01lyv) <- artists(?x1928, ?x7597), artists(?x1928, ?x12659), artists(?x1928, ?x3957), ?x12659 = 01dpsv, ?x3957 = 09r8l >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #914 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 01j4ls; 016t00; *> query: (?x7597, 015pdg) <- artists(?x10318, ?x7597), artists(?x1928, ?x7597), artists(?x1928, ?x133), ?x10318 = 03jsvl, profession(?x133, ?x220) *> conf = 0.23 ranks of expected_values: 37, 137 EVAL 03c3yf artists! 07ym47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 85.000 68.000 0.838 http://example.org/music/genre/artists EVAL 03c3yf artists! 015pdg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 85.000 68.000 0.838 http://example.org/music/genre/artists #2861-0124ld PRED entity: 0124ld PRED relation: medal PRED expected values: 02lq5w => 19 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1): 02lq5w (0.87 #21, 0.85 #19, 0.85 #17) >> Best rule #21 for best value: >> intensional similarity = 43 >> extensional distance = 40 >> proper extension: 0l6vl; 0kbws; 016r9z; 0ldqf; >> query: (?x7429, ?x422) <- sports(?x7429, ?x2631), country(?x2631, ?x3357), country(?x2631, ?x2843), country(?x2631, ?x2756), country(?x2631, ?x2152), country(?x2631, ?x1892), country(?x2631, ?x1603), country(?x2631, ?x1536), country(?x2631, ?x1471), country(?x2631, ?x789), country(?x2631, ?x583), country(?x2631, ?x390), country(?x2631, ?x279), country(?x2631, ?x142), ?x390 = 0chghy, sports(?x8584, ?x2631), sports(?x2630, ?x2631), ?x3357 = 04w8f, ?x142 = 0jgd, ?x789 = 0f8l9c, ?x2756 = 0hg5, ?x583 = 015fr, olympics(?x6305, ?x2630), olympics(?x1229, ?x2630), ?x6305 = 07t_x, ?x2152 = 06mkj, olympics(?x2631, ?x784), participating_countries(?x784, ?x3730), participating_countries(?x784, ?x1917), participating_countries(?x784, ?x512), medal(?x8584, ?x422), ?x1603 = 06bnz, ?x512 = 07ssc, ?x1471 = 07t21, ?x279 = 0d060g, ?x1536 = 06c1y, ?x1917 = 01p1v, ?x1892 = 02vzc, sports(?x7775, ?x2631), ?x3730 = 03shp, ?x2843 = 016wzw, ?x1229 = 059j2, sports(?x784, ?x3345) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0124ld medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 19.000 19.000 0.869 http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal #2860-0hgqq PRED entity: 0hgqq PRED relation: artists! PRED expected values: 01wqlc => 135 concepts (93 used for prediction) PRED predicted values (max 10 best out of 230): 064t9 (0.62 #3145, 0.62 #2519, 0.58 #640), 06by7 (0.55 #4405, 0.50 #648, 0.49 #5973), 0ggq0m (0.50 #13, 0.18 #326, 0.18 #5650), 025sc50 (0.42 #678, 0.38 #3183, 0.36 #365), 03_d0 (0.39 #3456, 0.31 #951, 0.22 #4082), 0155w (0.36 #4492, 0.29 #6060, 0.26 #1361), 05bt6j (0.33 #671, 0.25 #5367, 0.25 #45), 0xhtw (0.29 #4400, 0.29 #5968, 0.21 #1269), 06j6l (0.29 #4433, 0.29 #1929, 0.28 #4120), 02lnbg (0.28 #3191, 0.25 #686, 0.24 #1939) >> Best rule #3145 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 01vvydl; 0gbwp; 0c7xjb; 01f9zw; >> query: (?x4831, 064t9) <- artists(?x888, ?x4831), company(?x4831, ?x1103), nationality(?x4831, ?x94), profession(?x4831, ?x1032) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #389 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 016k62; 0g7k2g; 01s7ns; *> query: (?x4831, 01wqlc) <- artists(?x888, ?x4831), gender(?x4831, ?x231), company(?x4831, ?x1103), student(?x4955, ?x4831) *> conf = 0.09 ranks of expected_values: 58 EVAL 0hgqq artists! 01wqlc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 135.000 93.000 0.621 http://example.org/music/genre/artists #2859-0gfzfj PRED entity: 0gfzfj PRED relation: film! PRED expected values: 0ccqd7 => 59 concepts (28 used for prediction) PRED predicted values (max 10 best out of 800): 027j79k (0.33 #12438, 0.19 #47680, 0.19 #41461), 09l3p (0.20 #744, 0.12 #4891, 0.03 #4147), 021yzs (0.20 #845, 0.07 #20732, 0.01 #25723), 016h4r (0.20 #592, 0.07 #20732, 0.01 #25470), 081lh (0.20 #162, 0.07 #10527, 0.04 #16748), 02114t (0.20 #630, 0.04 #25508, 0.04 #29654), 05txrz (0.20 #761, 0.04 #11126, 0.04 #13199), 046zh (0.20 #930, 0.04 #15442, 0.03 #29954), 0151w_ (0.20 #164, 0.04 #16750, 0.03 #25042), 07cjqy (0.20 #598, 0.03 #4147, 0.02 #8891) >> Best rule #12438 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 04dsnp; >> query: (?x10942, ?x1537) <- genre(?x10942, ?x258), film(?x574, ?x10942), written_by(?x10942, ?x1537), ?x258 = 05p553 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gfzfj film! 0ccqd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 28.000 0.326 http://example.org/film/actor/film./film/performance/film #2858-0pyg6 PRED entity: 0pyg6 PRED relation: type_of_union PRED expected values: 04ztj => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.80 #5, 0.77 #9, 0.77 #1), 01g63y (0.44 #461, 0.24 #70, 0.24 #78), 0jgjn (0.01 #48, 0.01 #52) >> Best rule #5 for best value: >> intensional similarity = 2 >> extensional distance = 18 >> proper extension: 02bkdn; >> query: (?x2194, 04ztj) <- award(?x2194, ?x5841), ?x5841 = 02lp0w >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0pyg6 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.800 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2857-02x4sn8 PRED entity: 02x4sn8 PRED relation: nominated_for PRED expected values: 0b76t12 0dzz6g 03vyw8 0bnzd 0h95927 0170yd => 59 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1386): 0gmgwnv (0.73 #8711, 0.60 #2489, 0.44 #5598), 02c638 (0.69 #8075, 0.60 #1853, 0.44 #4962), 026p4q7 (0.62 #8123, 0.44 #5010, 0.42 #6566), 05hjnw (0.62 #8523, 0.40 #2301, 0.25 #6966), 0h95927 (0.60 #10889, 0.54 #8907, 0.44 #5794), 0f4_l (0.60 #10889, 0.42 #8085, 0.25 #6528), 011yl_ (0.60 #2069, 0.50 #6734, 0.38 #8291), 09gq0x5 (0.60 #1801, 0.50 #8023, 0.33 #6466), 0c0nhgv (0.60 #1709, 0.38 #7931, 0.33 #4818), 011yth (0.60 #1817, 0.35 #8039, 0.33 #6482) >> Best rule #8711 for best value: >> intensional similarity = 7 >> extensional distance = 24 >> proper extension: 02rdxsh; >> query: (?x2902, 0gmgwnv) <- nominated_for(?x2902, ?x7858), nominated_for(?x2902, ?x7129), nominated_for(?x2902, ?x4690), ?x7858 = 04b2qn, film(?x396, ?x4690), award(?x7129, ?x899), film_release_region(?x4690, ?x87) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #10889 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 33 *> proper extension: 02wwsh8; 02vl9ln; *> query: (?x2902, ?x394) <- award_winner(?x2902, ?x3572), award(?x394, ?x2902), award_winner(?x372, ?x3572), award_winner(?x277, ?x3572), ?x372 = 02wkmx, award(?x163, ?x277) *> conf = 0.60 ranks of expected_values: 5, 156, 218, 325, 376, 489 EVAL 02x4sn8 nominated_for 0170yd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 59.000 23.000 0.731 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x4sn8 nominated_for 0h95927 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 59.000 23.000 0.731 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x4sn8 nominated_for 0bnzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 59.000 23.000 0.731 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x4sn8 nominated_for 03vyw8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 23.000 0.731 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x4sn8 nominated_for 0dzz6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 59.000 23.000 0.731 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x4sn8 nominated_for 0b76t12 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 59.000 23.000 0.731 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2856-05zlld0 PRED entity: 05zlld0 PRED relation: film_release_region PRED expected values: 09c7w0 0b90_r 0hzlz 07ylj 03rj0 03ryn => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 112): 09c7w0 (0.94 #3630, 0.93 #6638, 0.93 #377), 0b90_r (0.87 #1630, 0.86 #1254, 0.85 #1880), 03rj0 (0.86 #38, 0.76 #163, 0.74 #663), 01mjq (0.76 #151, 0.64 #26, 0.61 #1652), 03ryn (0.64 #58, 0.53 #183, 0.45 #1058), 07t21 (0.57 #24, 0.45 #149, 0.40 #649), 0jgx (0.57 #59, 0.24 #1309, 0.21 #684), 01pj7 (0.53 #156, 0.45 #1031, 0.44 #1657), 07ylj (0.50 #17, 0.45 #642, 0.45 #142), 077qn (0.50 #61, 0.44 #1061, 0.37 #1687) >> Best rule #3630 for best value: >> intensional similarity = 3 >> extensional distance = 244 >> proper extension: 02v8kmz; 016z5x; 07p62k; 013q07; 0jdgr; 097zcz; 02x8fs; 07tj4c; >> query: (?x3748, 09c7w0) <- film_release_region(?x3748, ?x2513), country(?x359, ?x2513), story_by(?x3748, ?x6001) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5, 9, 15 EVAL 05zlld0 film_release_region 03ryn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zlld0 film_release_region 03rj0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zlld0 film_release_region 07ylj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 109.000 109.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zlld0 film_release_region 0hzlz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 109.000 109.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zlld0 film_release_region 0b90_r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05zlld0 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2855-01mmslz PRED entity: 01mmslz PRED relation: actor! PRED expected values: 019g8j => 115 concepts (95 used for prediction) PRED predicted values (max 10 best out of 172): 01q_y0 (0.45 #10308, 0.34 #13746, 0.31 #12160), 05f7w84 (0.19 #1427, 0.15 #1691, 0.08 #2483), 09g_31 (0.11 #1750, 0.09 #1486, 0.06 #2542), 0jwl2 (0.11 #1394, 0.10 #1658, 0.05 #2450), 015w8_ (0.09 #1367, 0.08 #1631, 0.05 #2423), 01h72l (0.09 #1359, 0.08 #1623, 0.05 #2415), 024rwx (0.09 #1426, 0.06 #1690, 0.05 #2482), 0kfpm (0.08 #277, 0.05 #541, 0.04 #1334), 030k94 (0.08 #311, 0.05 #575, 0.03 #839), 02h2vv (0.08 #377, 0.05 #641, 0.03 #905) >> Best rule #10308 for best value: >> intensional similarity = 3 >> extensional distance = 496 >> proper extension: 0cj2t3; 0347xl; 03hh89; 03q3sy; 0g69lg; 02vqpx8; 02js_6; >> query: (?x2416, ?x2293) <- location(?x2416, ?x8093), nominated_for(?x2416, ?x2293), genre(?x2293, ?x258) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1550 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 066l3y; 09fp45; 084x96; 07glc4; *> query: (?x2416, 019g8j) <- language(?x2416, ?x254), actor(?x5529, ?x2416), place_of_birth(?x2416, ?x8093) *> conf = 0.04 ranks of expected_values: 19 EVAL 01mmslz actor! 019g8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 115.000 95.000 0.455 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #2854-073w14 PRED entity: 073w14 PRED relation: award_nominee PRED expected values: 01v42g 0509bl => 107 concepts (39 used for prediction) PRED predicted values (max 10 best out of 651): 0509bl (0.81 #18715, 0.81 #60828, 0.81 #56147), 073w14 (0.27 #3352, 0.16 #91242, 0.16 #65507), 01v42g (0.27 #2605, 0.16 #91242, 0.16 #65507), 05qd_ (0.18 #2522, 0.02 #49311, 0.01 #14219), 06pj8 (0.18 #2793, 0.01 #12150, 0.01 #47243), 02q_cc (0.18 #2509, 0.01 #49298), 0b455l (0.18 #4435), 02kv5k (0.18 #4063), 01wd9lv (0.18 #3813), 03qmx_f (0.18 #2928) >> Best rule #18715 for best value: >> intensional similarity = 3 >> extensional distance = 351 >> proper extension: 01wj5hp; 07pzc; >> query: (?x4345, ?x1290) <- profession(?x4345, ?x1032), languages(?x4345, ?x254), award_nominee(?x1290, ?x4345) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 073w14 award_nominee 0509bl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 39.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 073w14 award_nominee 01v42g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 39.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2853-0cmf0m0 PRED entity: 0cmf0m0 PRED relation: film_release_region PRED expected values: 059j2 06c1y 03rk0 03spz => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 111): 059j2 (0.88 #291, 0.86 #1101, 0.83 #1776), 03spz (0.88 #347, 0.85 #1157, 0.70 #212), 03rk0 (0.85 #311, 0.68 #176, 0.61 #1121), 03rj0 (0.80 #314, 0.75 #179, 0.74 #1124), 016wzw (0.76 #319, 0.66 #1129, 0.60 #184), 0ctw_b (0.72 #1097, 0.65 #152, 0.54 #287), 06mzp (0.62 #1093, 0.54 #1228, 0.50 #148), 06t8v (0.61 #330, 0.53 #1140, 0.47 #195), 06c1y (0.59 #300, 0.48 #1110, 0.47 #165), 06qd3 (0.58 #1242, 0.58 #1107, 0.56 #1782) >> Best rule #291 for best value: >> intensional similarity = 5 >> extensional distance = 39 >> proper extension: 0c40vxk; 03qnvdl; 0gd0c7x; 0661m4p; 08052t3; 0gj8nq2; 09g7vfw; 0gh65c5; 0gtxj2q; 0glqh5_; ... >> query: (?x8292, 059j2) <- film_release_region(?x8292, ?x2236), film_release_region(?x8292, ?x304), film_crew_role(?x8292, ?x468), ?x304 = 0d0vqn, ?x2236 = 05sb1 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 9 EVAL 0cmf0m0 film_release_region 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cmf0m0 film_release_region 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cmf0m0 film_release_region 06c1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 61.000 61.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0cmf0m0 film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.878 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2852-01c92g PRED entity: 01c92g PRED relation: award! PRED expected values: 01vvyfh 0qf11 01kph_c 01jgkj2 010xjr => 47 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2585): 01htxr (0.79 #53213, 0.78 #29930, 0.78 #73175), 01vsykc (0.79 #53213, 0.78 #29930, 0.78 #73175), 01q3_2 (0.79 #53213, 0.78 #29930, 0.78 #73175), 016z1t (0.78 #29930, 0.78 #73175, 0.78 #63193), 04n32 (0.78 #29930, 0.78 #73175, 0.78 #63193), 0ffgh (0.67 #12027, 0.50 #2052, 0.40 #5377), 04xrx (0.60 #4015, 0.56 #10665, 0.50 #690), 0dl567 (0.60 #4452, 0.50 #1127, 0.33 #11102), 01hgwkr (0.60 #5984, 0.50 #2659, 0.22 #12634), 0dw4g (0.60 #4934, 0.50 #1609, 0.22 #11584) >> Best rule #53213 for best value: >> intensional similarity = 4 >> extensional distance = 168 >> proper extension: 05qck; 058vy5; 0bqsk5; 02q3s; >> query: (?x1801, ?x1231) <- award_winner(?x1801, ?x1231), award_nominee(?x215, ?x1231), artist(?x1954, ?x1231), award(?x1231, ?x462) >> conf = 0.79 => this is the best rule for 3 predicted values *> Best rule #12563 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 01d38g; 01c427; 01cw51; 01cky2; 031b3h; *> query: (?x1801, 01jgkj2) <- award(?x5904, ?x1801), ceremony(?x1801, ?x139), ?x5904 = 01k_mc *> conf = 0.33 ranks of expected_values: 110, 140, 236, 298, 352 EVAL 01c92g award! 010xjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 26.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c92g award! 01jgkj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 47.000 26.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c92g award! 01kph_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 47.000 26.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c92g award! 0qf11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 26.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01c92g award! 01vvyfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 47.000 26.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2851-09y6pb PRED entity: 09y6pb PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.87 #31, 0.87 #96, 0.86 #116), 02nxhr (0.24 #426, 0.09 #12, 0.08 #7), 07c52 (0.24 #426, 0.04 #43, 0.03 #108), 07z4p (0.24 #426, 0.03 #155, 0.02 #337) >> Best rule #31 for best value: >> intensional similarity = 4 >> extensional distance = 67 >> proper extension: 047gn4y; 02qm_f; 0k2sk; 07h9gp; 0_7w6; 050gkf; 01_1pv; 065z3_x; 0kcn7; 05zy2cy; ... >> query: (?x9379, 029j_) <- film(?x5636, ?x9379), ?x5636 = 054g1r, genre(?x9379, ?x53), nominated_for(?x2200, ?x9379) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09y6pb film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #2850-073hd1 PRED entity: 073hd1 PRED relation: award_winner PRED expected values: 01cbt3 => 46 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1471): 06rnl9 (0.50 #3504, 0.25 #17350, 0.24 #18888), 03r1pr (0.50 #3505, 0.19 #17351, 0.18 #11194), 0c94fn (0.50 #3351, 0.15 #1538, 0.12 #17197), 0b6mgp_ (0.50 #3757, 0.15 #1538, 0.12 #17603), 076psv (0.43 #6839, 0.38 #8378, 0.33 #5301), 02sj1x (0.43 #6683, 0.29 #14376, 0.25 #8222), 04sry (0.33 #2625, 0.33 #1082, 0.21 #30765), 085pr (0.33 #5147, 0.29 #6685, 0.25 #8224), 04gmp_z (0.33 #5028, 0.25 #8105, 0.15 #1538), 0gcs9 (0.33 #442, 0.25 #3524, 0.15 #1538) >> Best rule #3504 for best value: >> intensional similarity = 21 >> extensional distance = 2 >> proper extension: 073h1t; 073h9x; >> query: (?x7105, 06rnl9) <- award_winner(?x7105, ?x8814), award_winner(?x7105, ?x7733), award_winner(?x7105, ?x3183), ceremony(?x3066, ?x7105), ceremony(?x2222, ?x7105), ceremony(?x1323, ?x7105), ceremony(?x1307, ?x7105), ceremony(?x1243, ?x7105), ceremony(?x720, ?x7105), ?x1323 = 0gqz2, honored_for(?x7105, ?x4093), ?x1307 = 0gq9h, ?x720 = 018wng, ?x2222 = 0gs96, ?x1243 = 0gr0m, crewmember(?x924, ?x8814), ?x3066 = 0gqy2, nationality(?x8814, ?x94), ?x924 = 04gknr, actor(?x7904, ?x3183), award(?x7733, ?x102) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #13127 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 11 *> proper extension: 0hhtgcw; *> query: (?x7105, 01cbt3) <- award_winner(?x7105, ?x5896), award_winner(?x1232, ?x5896), honored_for(?x7105, ?x4093), location(?x5896, ?x739), award_winner(?x1232, ?x6383), award(?x4474, ?x1232), ?x4474 = 01vvyvk, film(?x446, ?x4093), ceremony(?x1232, ?x139), ?x6383 = 0g824, honored_for(?x6300, ?x4093), titles(?x53, ?x4093) *> conf = 0.08 ranks of expected_values: 595 EVAL 073hd1 award_winner 01cbt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 46.000 30.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2849-03xyp_ PRED entity: 03xyp_ PRED relation: nationality PRED expected values: 077qn => 152 concepts (117 used for prediction) PRED predicted values (max 10 best out of 113): 09c7w0 (0.91 #5443, 0.89 #7774, 0.81 #5848), 087vz (0.80 #10304, 0.56 #4123, 0.42 #2813), 02j71 (0.73 #3520, 0.42 #5745, 0.40 #6561), 01rhrd (0.58 #1907, 0.48 #2712, 0.47 #3719), 077qn (0.42 #2813, 0.40 #4124, 0.39 #10305), 02j9z (0.42 #2813, 0.40 #4124, 0.39 #10305), 02jx1 (0.37 #4054, 0.24 #4156, 0.22 #4357), 07ssc (0.27 #4036, 0.17 #4138, 0.17 #1220), 0160w (0.24 #1807, 0.20 #2914, 0.20 #2212), 0f8l9c (0.21 #2633, 0.19 #1828, 0.09 #3841) >> Best rule #5443 for best value: >> intensional similarity = 5 >> extensional distance = 587 >> proper extension: 01sl1q; 0197tq; 05zbm4; 0134w7; 021_rm; 039bp; 0h1mt; 045bg; 01963w; 03xgm3; ... >> query: (?x8696, 09c7w0) <- place_of_birth(?x8696, ?x11540), gender(?x8696, ?x231), nationality(?x8696, ?x279), location(?x8696, ?x126), jurisdiction_of_office(?x1195, ?x11540) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #2813 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 81 *> proper extension: 0520r2x; 067pl7; *> query: (?x8696, ?x455) <- place_of_birth(?x8696, ?x11540), gender(?x8696, ?x231), nationality(?x8696, ?x279), ?x279 = 0d060g, contains(?x455, ?x11540) *> conf = 0.42 ranks of expected_values: 5 EVAL 03xyp_ nationality 077qn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 152.000 117.000 0.912 http://example.org/people/person/nationality #2848-01q_y0 PRED entity: 01q_y0 PRED relation: languages PRED expected values: 02h40lc => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 11): 02h40lc (0.91 #299, 0.90 #233, 0.90 #222), 03_9r (0.05 #368, 0.04 #379, 0.04 #478), 06nm1 (0.04 #82, 0.04 #104, 0.04 #115), 0t_2 (0.04 #83, 0.04 #28, 0.03 #325), 064_8sq (0.03 #40, 0.02 #62, 0.02 #161), 02bv9 (0.03 #42, 0.02 #64, 0.01 #86), 04306rv (0.03 #36, 0.02 #58, 0.01 #80), 02bjrlw (0.03 #34, 0.02 #56, 0.01 #78), 01jb8r (0.01 #110, 0.01 #121), 07qv_ (0.01 #131) >> Best rule #299 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 01cjhz; 05r1_t; 0jq2r; 06f0k; >> query: (?x2293, 02h40lc) <- program(?x6678, ?x2293), genre(?x2293, ?x258), titles(?x714, ?x2293) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q_y0 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.910 http://example.org/tv/tv_program/languages #2847-01k56k PRED entity: 01k56k PRED relation: student! PRED expected values: 01w5m => 123 concepts (95 used for prediction) PRED predicted values (max 10 best out of 168): 01w5m (0.25 #1157, 0.21 #2209, 0.15 #1683), 07tgn (0.20 #17, 0.17 #3173, 0.14 #3699), 0ylvj (0.20 #201, 0.13 #2831, 0.11 #3357), 09f2j (0.20 #159, 0.09 #28575, 0.08 #1737), 025v3k (0.20 #120, 0.08 #1698, 0.07 #2224), 0g8fs (0.20 #357, 0.08 #1935, 0.02 #7195), 0bwfn (0.16 #28691, 0.09 #37114, 0.09 #18165), 02zd460 (0.15 #1748, 0.13 #2800, 0.11 #3326), 07wrz (0.14 #588, 0.11 #5848, 0.08 #1640), 02301 (0.14 #600, 0.08 #1126, 0.08 #1652) >> Best rule #1157 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 01dzz7; 018fq; 01g6bk; >> query: (?x12614, 01w5m) <- award(?x12614, ?x3337), award(?x12614, ?x1288), award(?x12614, ?x575), ?x1288 = 02662b, ?x3337 = 01yz0x, ?x575 = 040vk98, influenced_by(?x12614, ?x1287) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k56k student! 01w5m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 95.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #2846-071nw5 PRED entity: 071nw5 PRED relation: film_crew_role PRED expected values: 02r96rf => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 30): 02r96rf (0.80 #68, 0.74 #465, 0.72 #498), 0d2b38 (0.57 #89, 0.14 #486, 0.13 #420), 01xy5l_ (0.49 #79, 0.16 #476, 0.13 #1142), 02ynfr (0.47 #15, 0.29 #81, 0.20 #346), 01vx2h (0.46 #76, 0.37 #473, 0.35 #506), 0dxtw (0.40 #604, 0.40 #75, 0.40 #838), 01pvkk (0.33 #11, 0.32 #972, 0.31 #1972), 02rh1dz (0.33 #8, 0.14 #504, 0.14 #471), 015h31 (0.17 #73, 0.13 #7, 0.11 #503), 0263ycg (0.17 #83, 0.09 #4257, 0.07 #50) >> Best rule #68 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 09lcsj; >> query: (?x6200, 02r96rf) <- films(?x3359, ?x6200), genre(?x6200, ?x53), film_crew_role(?x6200, ?x5136), ?x5136 = 089g0h >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 071nw5 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2845-06py2 PRED entity: 06py2 PRED relation: company! PRED expected values: 0dq3c => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 35): 0dq3c (0.90 #1925, 0.76 #2165, 0.69 #1362), 0krdk (0.73 #2688, 0.71 #1808, 0.71 #205), 05_wyz (0.69 #1362, 0.53 #654, 0.48 #2377), 09d6p2 (0.69 #1362, 0.40 #55, 0.38 #695), 01kr6k (0.29 #702, 0.29 #222, 0.28 #2065), 09lq2c (0.25 #266, 0.25 #26, 0.20 #106), 02y6fz (0.25 #20, 0.20 #100, 0.20 #2764), 01rk91 (0.25 #1, 0.17 #4294, 0.17 #121), 06hpx2 (0.17 #4294, 0.14 #3605, 0.13 #3686), 02h53vq (0.17 #4294, 0.14 #3605, 0.13 #3809) >> Best rule #1925 for best value: >> intensional similarity = 6 >> extensional distance = 50 >> proper extension: 09c7w0; 0hm0k; >> query: (?x13314, 0dq3c) <- company(?x6403, ?x13314), company(?x2998, ?x13314), company(?x6403, ?x3578), company(?x2998, ?x8611), ?x8611 = 04gvyp, ?x3578 = 08z129 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06py2 company! 0dq3c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 158.000 0.904 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #2844-01y64_ PRED entity: 01y64_ PRED relation: award PRED expected values: 02ppm4q => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 207): 0ck27z (0.33 #3709, 0.32 #4111, 0.29 #5317), 0cqhk0 (0.20 #4057, 0.19 #3655, 0.17 #439), 05pcn59 (0.14 #80, 0.11 #8924, 0.11 #10130), 0gqy2 (0.14 #162, 0.09 #3378, 0.09 #15036), 0f4x7 (0.13 #31, 0.13 #22916, 0.08 #1237), 0gqwc (0.13 #22916, 0.12 #22513, 0.12 #8114), 02ppm4q (0.13 #22916, 0.12 #22513, 0.12 #154), 094qd5 (0.13 #22916, 0.12 #22513, 0.09 #44), 09qwmm (0.13 #22916, 0.12 #22513, 0.06 #34), 05zr6wv (0.13 #22916, 0.12 #17, 0.10 #419) >> Best rule #3709 for best value: >> intensional similarity = 3 >> extensional distance = 525 >> proper extension: 0bz5v2; 08_83x; >> query: (?x4440, 0ck27z) <- actor(?x4084, ?x4440), nominated_for(?x4440, ?x5752), award_nominee(?x4440, ?x1958) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #22916 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2276 *> proper extension: 03n93; 0c41qv; 0181hw; *> query: (?x4440, ?x401) <- award_nominee(?x4440, ?x6444), award_nominee(?x4440, ?x4439), award_nominee(?x4439, ?x1975), award(?x6444, ?x401) *> conf = 0.13 ranks of expected_values: 7 EVAL 01y64_ award 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 76.000 76.000 0.332 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2843-02xyl PRED entity: 02xyl PRED relation: student! PRED expected values: 07t90 => 165 concepts (164 used for prediction) PRED predicted values (max 10 best out of 242): 01b1mj (0.25 #22), 01w5m (0.20 #632, 0.15 #1159, 0.13 #2213), 06thjt (0.20 #925, 0.08 #1452, 0.06 #1979), 032r4n (0.20 #1014, 0.04 #1541, 0.03 #2068), 07tgn (0.16 #1598, 0.12 #2652, 0.10 #5287), 0bwfn (0.12 #1329, 0.08 #32953, 0.07 #24521), 03ksy (0.11 #8011, 0.09 #10120, 0.09 #4322), 09f2j (0.10 #686, 0.08 #1213, 0.05 #3321), 065y4w7 (0.10 #541, 0.07 #8974, 0.07 #6338), 07tg4 (0.10 #613, 0.06 #13263, 0.05 #12208) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03cvv4; >> query: (?x13125, 01b1mj) <- profession(?x13125, ?x353), place_of_birth(?x13125, ?x9973), ?x9973 = 010t4v, award_winner(?x3337, ?x13125) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02xyl student! 07t90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 164.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #2842-0dzst PRED entity: 0dzst PRED relation: institution! PRED expected values: 016t_3 014mlp 03bwzr4 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 16): 03bwzr4 (0.70 #94, 0.69 #43, 0.68 #77), 014mlp (0.69 #378, 0.69 #191, 0.68 #1255), 016t_3 (0.66 #87, 0.66 #36, 0.64 #70), 03mkk4 (0.33 #23, 0.28 #1310, 0.27 #58), 028dcg (0.33 #30, 0.28 #1310, 0.16 #150), 0bjrnt (0.31 #39, 0.28 #1310, 0.24 #56), 022h5x (0.28 #1310, 0.20 #66, 0.19 #83), 01rr_d (0.28 #1310, 0.20 #63, 0.19 #97), 02mjs7 (0.28 #1310, 0.14 #37, 0.14 #122), 02cq61 (0.28 #1310, 0.14 #47, 0.10 #64) >> Best rule #94 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 019q50; >> query: (?x9200, 03bwzr4) <- state_province_region(?x9200, ?x760), institution(?x620, ?x9200), list(?x9200, ?x2197) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0dzst institution! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.698 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0dzst institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.698 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0dzst institution! 016t_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.698 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2841-05v38p PRED entity: 05v38p PRED relation: executive_produced_by PRED expected values: 06q8hf => 68 concepts (55 used for prediction) PRED predicted values (max 10 best out of 49): 06q8hf (0.25 #166, 0.18 #418, 0.05 #922), 06pj8 (0.06 #1313, 0.06 #1815, 0.05 #1564), 0343h (0.05 #1300, 0.03 #1551, 0.02 #1049), 02z6l5f (0.05 #2128, 0.05 #873, 0.05 #369), 029m83 (0.05 #428, 0.03 #680), 02z2xdf (0.05 #409, 0.03 #2168, 0.02 #913), 02hfp_ (0.05 #429, 0.01 #681), 05bnx3j (0.05 #496), 015vq_ (0.05 #351), 079vf (0.04 #1762, 0.02 #2515, 0.02 #6799) >> Best rule #166 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0bz3jx; >> query: (?x6445, 06q8hf) <- film(?x4254, ?x6445), titles(?x162, ?x6445), ?x4254 = 0fbx6 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v38p executive_produced_by 06q8hf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 55.000 0.250 http://example.org/film/film/executive_produced_by #2840-0yx1m PRED entity: 0yx1m PRED relation: language PRED expected values: 02h40lc => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 34): 02h40lc (0.92 #298, 0.89 #1730, 0.89 #894), 064_8sq (0.17 #141, 0.16 #853, 0.15 #555), 04306rv (0.12 #656, 0.12 #419, 0.11 #597), 06nm1 (0.10 #783, 0.09 #1082, 0.09 #603), 02bjrlw (0.09 #652, 0.09 #60, 0.08 #1), 06b_j (0.08 #319, 0.07 #437, 0.06 #674), 04h9h (0.06 #102, 0.03 #457, 0.03 #1294), 03_9r (0.06 #483, 0.05 #1081, 0.05 #782), 0jzc (0.04 #434, 0.04 #1209, 0.03 #671), 0653m (0.04 #308, 0.03 #485, 0.03 #2640) >> Best rule #298 for best value: >> intensional similarity = 4 >> extensional distance = 153 >> proper extension: 0d1qmz; 02x8fs; 08sk8l; 02r2j8; 0ckt6; >> query: (?x8330, 02h40lc) <- written_by(?x8330, ?x4385), film(?x851, ?x8330), genre(?x8330, ?x53), cinematography(?x8330, ?x5014) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0yx1m language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.916 http://example.org/film/film/language #2839-02k1pr PRED entity: 02k1pr PRED relation: genre PRED expected values: 01jfsb => 58 concepts (53 used for prediction) PRED predicted values (max 10 best out of 77): 05p553 (0.35 #3457, 0.35 #3576, 0.34 #3218), 01hmnh (0.33 #17, 0.17 #2399, 0.16 #1922), 082gq (0.33 #30, 0.12 #625, 0.12 #863), 0hcr (0.33 #23, 0.07 #2405, 0.07 #2762), 06l3bl (0.33 #38, 0.06 #514, 0.06 #633), 02l7c8 (0.32 #610, 0.29 #1681, 0.28 #1086), 01jfsb (0.30 #1916, 0.30 #368, 0.29 #3465), 0lsxr (0.30 #127, 0.22 #246, 0.18 #365), 04xvlr (0.18 #596, 0.17 #1, 0.16 #1072), 04t36 (0.17 #5, 0.12 #6080, 0.11 #481) >> Best rule #3457 for best value: >> intensional similarity = 4 >> extensional distance = 1106 >> proper extension: 047n8xt; 0k4d7; 01sby_; 01jr4j; >> query: (?x8456, 05p553) <- genre(?x8456, ?x53), film(?x2378, ?x8456), production_companies(?x8456, ?x574), gender(?x2378, ?x231) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1916 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 781 *> proper extension: 0gtvrv3; *> query: (?x8456, 01jfsb) <- country(?x8456, ?x94), music(?x8456, ?x669), artists(?x505, ?x669) *> conf = 0.30 ranks of expected_values: 7 EVAL 02k1pr genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 58.000 53.000 0.351 http://example.org/film/film/genre #2838-0mbql PRED entity: 0mbql PRED relation: film_distribution_medium PRED expected values: 0735l => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 3): 0735l (0.96 #149, 0.80 #83, 0.69 #167), 07z4p (0.04 #117, 0.03 #105, 0.03 #69), 07c52 (0.04 #49, 0.01 #115) >> Best rule #149 for best value: >> intensional similarity = 10 >> extensional distance = 119 >> proper extension: 0522wp; >> query: (?x6620, 0735l) <- film_distribution_medium(?x6620, ?x81), film(?x609, ?x6620), film(?x382, ?x6620), ?x609 = 03xq0f, film(?x382, ?x5735), film(?x382, ?x3790), film(?x382, ?x1701), ?x1701 = 0bh8yn3, ?x5735 = 0h21v2, film_crew_role(?x3790, ?x137) >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mbql film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.959 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #2837-01lz4tf PRED entity: 01lz4tf PRED relation: artists! PRED expected values: 03lty => 173 concepts (108 used for prediction) PRED predicted values (max 10 best out of 284): 06by7 (0.80 #13070, 0.71 #31400, 0.56 #14312), 064t9 (0.65 #15856, 0.61 #19580, 0.60 #17718), 03lty (0.59 #5304, 0.48 #26121, 0.46 #19257), 0jrv_ (0.50 #489, 0.10 #8874, 0.07 #5455), 05bt6j (0.49 #14645, 0.47 #21163, 0.35 #31422), 0ggx5q (0.47 #3803, 0.33 #1318, 0.27 #1940), 05r6t (0.46 #19257, 0.40 #702, 0.28 #16235), 0cx7f (0.46 #19257, 0.24 #9144, 0.19 #26231), 0dls3 (0.46 #19257, 0.15 #2535, 0.14 #5949), 01738f (0.46 #19257, 0.09 #16269, 0.06 #2910) >> Best rule #13070 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 01wkmgb; >> query: (?x7233, 06by7) <- participant(?x3754, ?x7233), artists(?x1000, ?x7233), artists(?x1000, ?x12211), ?x12211 = 0jltp >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5304 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 01vsxdm; 05crg7; *> query: (?x7233, 03lty) <- role(?x7233, ?x314), artists(?x1000, ?x7233), ?x1000 = 0xhtw, ?x314 = 02sgy *> conf = 0.59 ranks of expected_values: 3 EVAL 01lz4tf artists! 03lty CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 173.000 108.000 0.797 http://example.org/music/genre/artists #2836-0nbzp PRED entity: 0nbzp PRED relation: country PRED expected values: 09c7w0 => 127 concepts (71 used for prediction) PRED predicted values (max 10 best out of 25): 09c7w0 (0.83 #958, 0.83 #871, 0.82 #1131), 059g4 (0.41 #2694, 0.41 #4099), 04_1l0v (0.41 #2694, 0.41 #4099), 029jpy (0.41 #2694, 0.41 #4099), 0czr9_ (0.35 #5689, 0.28 #3132, 0.27 #3044), 0n5xb (0.28 #3132, 0.27 #3044, 0.27 #2606), 059f4 (0.14 #4190, 0.14 #4281, 0.12 #4635), 0d060g (0.05 #3407, 0.04 #1483, 0.03 #1396), 059j2 (0.05 #2637, 0.01 #3429, 0.01 #1853), 0345h (0.03 #2639, 0.03 #1680, 0.03 #1855) >> Best rule #958 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 0l_q9; 0mnm2; 01m94f; 0bx9y; 0nvg4; 0tz41; 0jpy_; >> query: (?x14091, 09c7w0) <- currency(?x14091, ?x170), state(?x14091, ?x728), ?x170 = 09nqf, time_zones(?x14091, ?x2674), contains(?x13066, ?x14091) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nbzp country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 71.000 0.829 http://example.org/base/biblioness/bibs_location/country #2835-0cwy47 PRED entity: 0cwy47 PRED relation: film! PRED expected values: 01kt17 01kkx2 => 98 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1206): 076psv (0.56 #66590, 0.45 #66589, 0.44 #70752), 057bc6m (0.45 #66589, 0.44 #70752, 0.43 #70751), 0dqzkv (0.45 #66589, 0.44 #70752, 0.43 #70751), 0579tg2 (0.45 #66589, 0.43 #83238, 0.42 #83237), 0gl88b (0.44 #70752, 0.43 #70751, 0.43 #52023), 0fqjks (0.44 #70752, 0.43 #70751, 0.43 #52023), 08ff1k (0.44 #70752, 0.43 #70751, 0.43 #52023), 027vps (0.44 #70752, 0.43 #70751, 0.43 #91561), 02bn75 (0.44 #70752, 0.43 #70751, 0.43 #91561), 044qx (0.25 #732, 0.20 #4894, 0.07 #21544) >> Best rule #66590 for best value: >> intensional similarity = 3 >> extensional distance = 532 >> proper extension: 04gknr; 019kyn; 02x8fs; 02gs6r; 07ghq; 0564x; >> query: (?x951, ?x11011) <- award_winner(?x951, ?x11011), film_release_region(?x951, ?x142), location(?x11011, ?x9026) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #33154 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 03m8y5; 05_5rjx; 05r3qc; 0bl3nn; 03n0cd; 06y611; *> query: (?x951, 01kkx2) <- film_crew_role(?x951, ?x137), film_production_design_by(?x951, ?x7528), genre(?x951, ?x53) *> conf = 0.01 ranks of expected_values: 1115 EVAL 0cwy47 film! 01kkx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 98.000 71.000 0.562 http://example.org/film/actor/film./film/performance/film EVAL 0cwy47 film! 01kt17 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 71.000 0.562 http://example.org/film/actor/film./film/performance/film #2834-01rp13 PRED entity: 01rp13 PRED relation: honored_for! PRED expected values: 0bx6zs => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 87): 05c1t6z (0.32 #963, 0.30 #1082, 0.26 #1558), 02q690_ (0.29 #1005, 0.29 #1124, 0.28 #1243), 0gvstc3 (0.26 #979, 0.24 #1098, 0.23 #1455), 03nnm4t (0.22 #1014, 0.21 #1133, 0.21 #1252), 0lp_cd3 (0.18 #136, 0.16 #1445, 0.15 #1564), 0275n3y (0.17 #5594, 0.10 #1015, 0.10 #1134), 0bzknt (0.17 #5594, 0.09 #6787, 0.09 #6786), 09gkdln (0.17 #5594, 0.07 #1174, 0.06 #817), 092c5f (0.16 #10, 0.09 #248, 0.08 #367), 07y9ts (0.11 #56, 0.10 #1008, 0.10 #1127) >> Best rule #963 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 07s8z_l; >> query: (?x6341, 05c1t6z) <- titles(?x2008, ?x6341), ?x2008 = 07c52, genre(?x6341, ?x258), honored_for(?x2292, ?x6341) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #108 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 072kp; 0124k9; 01q_y0; 0d68qy; 01bv8b; 02r5qtm; 01s81; 0l76z; 05f4vxd; 0vjr; ... *> query: (?x6341, 0bx6zs) <- titles(?x2008, ?x6341), nominated_for(?x4225, ?x6341), nominated_for(?x3210, ?x6341), ?x4225 = 09qvf4 *> conf = 0.11 ranks of expected_values: 12 EVAL 01rp13 honored_for! 0bx6zs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 79.000 79.000 0.324 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2833-04cygb3 PRED entity: 04cygb3 PRED relation: production_companies! PRED expected values: 0cc5mcj => 34 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1790): 0drnwh (0.33 #3057, 0.33 #1907, 0.33 #757), 0glqh5_ (0.33 #2901, 0.33 #601, 0.25 #9797), 047vnkj (0.33 #1744, 0.33 #594, 0.25 #9790), 07p62k (0.33 #2541, 0.33 #241, 0.25 #9437), 09q23x (0.33 #2863, 0.33 #563, 0.25 #9759), 01jft4 (0.33 #3106, 0.33 #806, 0.17 #10002), 0298n7 (0.33 #3163, 0.33 #863, 0.17 #10059), 03cp4cn (0.33 #3008, 0.33 #708, 0.17 #9904), 0gwlfnb (0.33 #970, 0.29 #6718, 0.29 #5568), 02y_lrp (0.33 #12, 0.29 #5760, 0.29 #4610) >> Best rule #3057 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 025jfl; 016tw3; 01gb54; >> query: (?x6413, 0drnwh) <- production_companies(?x9652, ?x6413), production_companies(?x5113, ?x6413), film(?x2092, ?x5113), film(?x237, ?x5113), film_crew_role(?x9652, ?x137), country(?x5113, ?x94), influenced_by(?x2092, ?x3917), ?x237 = 04t2l2, country(?x9652, ?x512), student(?x9823, ?x2092), nominated_for(?x6798, ?x5113), category(?x6413, ?x134), genre(?x9652, ?x225) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #9459 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 10 *> proper extension: 086k8; 0g1rw; 024rbz; 054lpb6; 046b0s; *> query: (?x6413, 0cc5mcj) <- production_companies(?x9652, ?x6413), production_companies(?x5113, ?x6413), film(?x2092, ?x5113), film(?x237, ?x5113), film_crew_role(?x9652, ?x137), country(?x5113, ?x94), influenced_by(?x2092, ?x3917), ?x237 = 04t2l2, country(?x9652, ?x2146), student(?x9823, ?x2092), film_crew_role(?x5113, ?x1776), nationality(?x111, ?x2146) *> conf = 0.17 ranks of expected_values: 162 EVAL 04cygb3 production_companies! 0cc5mcj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 34.000 14.000 0.333 http://example.org/film/film/production_companies #2832-03kwtb PRED entity: 03kwtb PRED relation: nationality PRED expected values: 02jx1 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 32): 09c7w0 (0.77 #7338, 0.75 #4163, 0.74 #8031), 02jx1 (0.39 #2807, 0.36 #6544, 0.31 #6444), 0d05w3 (0.12 #49, 0.01 #2725, 0.01 #2626), 03rt9 (0.07 #211, 0.03 #112, 0.02 #1598), 03rk0 (0.07 #3018, 0.06 #6787, 0.06 #6985), 03rjj (0.07 #104, 0.05 #203, 0.03 #599), 0d060g (0.06 #304, 0.06 #799, 0.06 #1889), 0chghy (0.06 #307, 0.04 #10, 0.02 #1100), 06q1r (0.04 #2851, 0.03 #670, 0.03 #769), 01n7rc (0.04 #2081, 0.02 #991) >> Best rule #7338 for best value: >> intensional similarity = 3 >> extensional distance = 1940 >> proper extension: 03qcq; 07lmxq; 04cf09; 02knnd; 05cv94; 07hbxm; 04rsd2; 02j8nx; 01v3bn; 03h2d4; ... >> query: (?x1292, 09c7w0) <- profession(?x1292, ?x131), nationality(?x1292, ?x512), award_nominee(?x1291, ?x1292) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2807 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 332 *> proper extension: 0d1_f; 0xnc3; 0cw10; *> query: (?x1292, 02jx1) <- nationality(?x1292, ?x512), ?x512 = 07ssc *> conf = 0.39 ranks of expected_values: 2 EVAL 03kwtb nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 105.000 0.769 http://example.org/people/person/nationality #2831-0cq86w PRED entity: 0cq86w PRED relation: film_release_region PRED expected values: 06mkj => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 175): 0345h (0.91 #1180, 0.88 #1017, 0.86 #691), 06bnz (0.91 #1193, 0.88 #1030, 0.86 #704), 03h64 (0.88 #1054, 0.86 #1217, 0.86 #728), 03rk0 (0.88 #1041, 0.86 #715, 0.77 #1204), 03rjj (0.86 #1149, 0.81 #986, 0.79 #660), 0b90_r (0.86 #1147, 0.81 #984, 0.79 #658), 015fr (0.86 #1163, 0.79 #674, 0.75 #1000), 04gzd (0.86 #665, 0.81 #991, 0.77 #1154), 05r4w (0.83 #6374, 0.83 #5394, 0.82 #4415), 06mkj (0.82 #6925, 0.82 #6435, 0.82 #5455) >> Best rule #1180 for best value: >> intensional similarity = 6 >> extensional distance = 20 >> proper extension: 0h1cdwq; 0cz8mkh; 0crc2cp; 09g7vfw; 0ds1glg; >> query: (?x5873, 0345h) <- production_companies(?x5873, ?x541), film_release_region(?x5873, ?x3855), film_release_region(?x5873, ?x362), ?x3855 = 0jgx, language(?x5873, ?x254), origin(?x1407, ?x362) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #6925 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 250 *> proper extension: 087wc7n; 03bx2lk; 02qk3fk; 07s3m4g; 07ghq; 0m3gy; 01xlqd; *> query: (?x5873, 06mkj) <- film_release_region(?x5873, ?x5114), film_release_region(?x5873, ?x1892), film_release_region(?x5873, ?x252), genre(?x5873, ?x53), ?x252 = 03_3d, ?x1892 = 02vzc, olympics(?x5114, ?x391) *> conf = 0.82 ranks of expected_values: 10 EVAL 0cq86w film_release_region 06mkj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 129.000 129.000 0.909 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2830-025tmkg PRED entity: 025tmkg PRED relation: specialization_of PRED expected values: 04s2z => 2 concepts (2 used for prediction) No prediction ranks of expected_values: EVAL 025tmkg specialization_of 04s2z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 2.000 2.000 0.000 http://example.org/people/profession/specialization_of #2829-0419kt PRED entity: 0419kt PRED relation: nominated_for! PRED expected values: 03c7tr1 => 102 concepts (90 used for prediction) PRED predicted values (max 10 best out of 207): 05p1dby (0.67 #13637, 0.67 #13873, 0.66 #13401), 0p9sw (0.53 #19, 0.30 #3309, 0.20 #5661), 07cbcy (0.41 #766, 0.27 #61, 0.20 #18817), 0gq9h (0.37 #5702, 0.36 #5937, 0.35 #3350), 05b4l5x (0.35 #710, 0.20 #18817, 0.12 #15289), 0gs9p (0.34 #5704, 0.34 #5939, 0.31 #3352), 019f4v (0.34 #5693, 0.33 #5928, 0.31 #3341), 0gqyl (0.33 #7365, 0.20 #78, 0.18 #5720), 0k611 (0.33 #3361, 0.28 #5713, 0.28 #5948), 0gqwc (0.31 #7345, 0.20 #18817, 0.20 #2643) >> Best rule #13637 for best value: >> intensional similarity = 4 >> extensional distance = 971 >> proper extension: 07bz5; >> query: (?x11372, ?x2022) <- award(?x11372, ?x2022), award_winner(?x2022, ?x4660), award(?x166, ?x2022), nominated_for(?x4660, ?x7849) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #14344 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 991 *> proper extension: 0275kr; *> query: (?x11372, ?x1105) <- award_winner(?x11372, ?x6203), nominated_for(?x574, ?x11372), award_winner(?x1105, ?x574), gender(?x6203, ?x231) *> conf = 0.22 ranks of expected_values: 21 EVAL 0419kt nominated_for! 03c7tr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 102.000 90.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2828-0gcrg PRED entity: 0gcrg PRED relation: costume_design_by PRED expected values: 04vzv4 => 120 concepts (56 used for prediction) PRED predicted values (max 10 best out of 28): 02cqbx (0.21 #100, 0.17 #355, 0.16 #128), 0gl88b (0.20 #33, 0.07 #146, 0.06 #232), 026lyl4 (0.20 #51, 0.07 #164, 0.01 #504), 03y1mlp (0.20 #58, 0.06 #455, 0.03 #570), 04vzv4 (0.14 #97, 0.10 #183, 0.08 #125), 0dck27 (0.10 #178, 0.08 #120, 0.07 #291), 03mfqm (0.08 #586, 0.07 #794, 0.06 #615), 0c6g29 (0.07 #290, 0.07 #177, 0.06 #234), 09x8ms (0.07 #112, 0.07 #198, 0.06 #255), 0fx0j2 (0.07 #105, 0.03 #248, 0.03 #276) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0jqb8; >> query: (?x3909, 02cqbx) <- film_release_distribution_medium(?x3909, ?x81), cinematography(?x3909, ?x10720), list(?x3909, ?x3004), film_art_direction_by(?x3909, ?x2449) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 0jqb8; *> query: (?x3909, 04vzv4) <- film_release_distribution_medium(?x3909, ?x81), cinematography(?x3909, ?x10720), list(?x3909, ?x3004), film_art_direction_by(?x3909, ?x2449) *> conf = 0.14 ranks of expected_values: 5 EVAL 0gcrg costume_design_by 04vzv4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 120.000 56.000 0.214 http://example.org/film/film/costume_design_by #2827-0121rx PRED entity: 0121rx PRED relation: influenced_by! PRED expected values: 01xwqn => 145 concepts (57 used for prediction) PRED predicted values (max 10 best out of 171): 0ph2w (0.20 #1706, 0.08 #4803, 0.06 #7903), 0bqs56 (0.10 #1800, 0.08 #4897, 0.04 #13165), 052hl (0.10 #1820, 0.03 #2853, 0.03 #4917), 081lh (0.10 #1577, 0.03 #3126, 0.03 #3642), 0p_47 (0.10 #1692, 0.03 #7889, 0.03 #8406), 014zfs (0.10 #1582, 0.03 #7779, 0.03 #9330), 01hmk9 (0.10 #1835, 0.03 #4932, 0.02 #10617), 012gq6 (0.10 #1677, 0.03 #4774, 0.02 #7874), 03b78r (0.10 #1846, 0.02 #14245, 0.02 #7009), 015pxr (0.05 #4721, 0.05 #5237, 0.02 #5754) >> Best rule #1706 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0g51l1; 044f7; 01w_10; 0l5yl; 04ns3gy; 01l1ls; >> query: (?x13073, 0ph2w) <- award_winner(?x3486, ?x13073), award_winner(?x2062, ?x13073), location(?x13073, ?x2850), ?x3486 = 0m7yy >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #13359 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 103 *> proper extension: 0bz5v2; *> query: (?x13073, 01xwqn) <- award_winner(?x2062, ?x13073), profession(?x13073, ?x1146), ?x1146 = 018gz8 *> conf = 0.03 ranks of expected_values: 28 EVAL 0121rx influenced_by! 01xwqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 145.000 57.000 0.200 http://example.org/influence/influence_node/influenced_by #2826-017gl1 PRED entity: 017gl1 PRED relation: film_format PRED expected values: 07fb8_ => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.62 #1, 0.50 #6, 0.24 #11), 0cj16 (0.21 #28, 0.17 #38, 0.16 #60), 017fx5 (0.09 #19, 0.07 #24, 0.06 #39) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0x25q; >> query: (?x972, 07fb8_) <- nominated_for(?x2922, ?x972), language(?x972, ?x254), nominated_for(?x143, ?x972), ?x2922 = 016ypb >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017gl1 film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.625 http://example.org/film/film/film_format #2825-01d8wq PRED entity: 01d8wq PRED relation: location! PRED expected values: 02rsz0 => 173 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1842): 040dv (0.20 #4342, 0.11 #32052, 0.10 #6862), 0cgfb (0.15 #32749, 0.01 #100771), 0l6px (0.15 #32749, 0.01 #100771), 0465_ (0.14 #23969, 0.08 #49160, 0.06 #69312), 0g7k2g (0.13 #67241, 0.06 #117633, 0.03 #170543), 03j2gxx (0.10 #7250, 0.10 #4730, 0.08 #12288), 040_t (0.10 #6323, 0.10 #3803, 0.08 #11361), 02g3w (0.10 #7296, 0.10 #4776, 0.08 #12334), 06g4_ (0.10 #7238, 0.10 #4718, 0.08 #12276), 02d42t (0.10 #6030, 0.08 #11068, 0.08 #8549) >> Best rule #4342 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 071zb; 0174qm; 0b_yz; 0q34g; 0jgvy; >> query: (?x8564, 040dv) <- state(?x8564, ?x5376), contains(?x512, ?x8564), ?x512 = 07ssc, contains(?x5376, ?x5375), location(?x2493, ?x5376) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #118408 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 62 *> proper extension: 0fnc_; *> query: (?x8564, ?x111) <- time_zones(?x8564, ?x5327), country(?x8564, ?x512), contains(?x512, ?x362), nationality(?x111, ?x512), currency(?x512, ?x170) *> conf = 0.02 ranks of expected_values: 1388 EVAL 01d8wq location! 02rsz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 173.000 88.000 0.200 http://example.org/people/person/places_lived./people/place_lived/location #2824-02rxbmt PRED entity: 02rxbmt PRED relation: profession PRED expected values: 0dz3r => 114 concepts (28 used for prediction) PRED predicted values (max 10 best out of 56): 09jwl (0.43 #2776, 0.21 #3066, 0.19 #3356), 0nbcg (0.33 #2789, 0.13 #3079, 0.12 #3514), 0dz3r (0.31 #2763, 0.12 #3053, 0.12 #3343), 016z4k (0.31 #2765, 0.14 #3055, 0.11 #3490), 0gl2ny2 (0.25 #209), 0cbd2 (0.25 #1458, 0.22 #1603, 0.19 #1312), 02krf9 (0.24 #313, 0.22 #604, 0.22 #1184), 01c72t (0.20 #2781, 0.13 #455, 0.10 #3361), 018gz8 (0.20 #1465, 0.11 #739, 0.10 #884), 0kyk (0.16 #1623, 0.14 #1478, 0.13 #3222) >> Best rule #2776 for best value: >> intensional similarity = 3 >> extensional distance = 705 >> proper extension: 01t_xp_; 0150jk; 067mj; 05k79; 0dtd6; 0dm5l; 014hr0; 01rm8b; 0fcsd; 047cx; ... >> query: (?x5342, 09jwl) <- award(?x5342, ?x7594), ceremony(?x7594, ?x1362), ?x1362 = 019bk0 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2763 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 705 *> proper extension: 01t_xp_; 0150jk; 067mj; 05k79; 0dtd6; 0dm5l; 014hr0; 01rm8b; 0fcsd; 047cx; ... *> query: (?x5342, 0dz3r) <- award(?x5342, ?x7594), ceremony(?x7594, ?x1362), ?x1362 = 019bk0 *> conf = 0.31 ranks of expected_values: 3 EVAL 02rxbmt profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 28.000 0.434 http://example.org/people/person/profession #2823-01pny5 PRED entity: 01pny5 PRED relation: people! PRED expected values: 0gk4g => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 26): 01_qc_ (0.07 #94, 0.03 #358, 0.02 #424), 0m32h (0.06 #155, 0.04 #221, 0.04 #287), 0qcr0 (0.04 #595, 0.03 #925, 0.03 #1387), 01tf_6 (0.04 #625, 0.03 #955, 0.03 #1087), 0gk4g (0.04 #7468, 0.04 #6082, 0.03 #6016), 0dq9p (0.02 #413, 0.02 #677, 0.02 #6485), 032s66 (0.02 #1171, 0.02 #643, 0.02 #1501), 051_y (0.02 #642, 0.02 #1632, 0.02 #2490), 01l2m3 (0.02 #610, 0.02 #940, 0.01 #1072), 097ns (0.02 #1413, 0.02 #819, 0.01 #1149) >> Best rule #94 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 01vrwfv; 02jqjm; 015cqh; >> query: (?x12791, 01_qc_) <- artists(?x6210, ?x12791), artists(?x1380, ?x12791), artists(?x1000, ?x12791), ?x1380 = 0dl5d, ?x1000 = 0xhtw, ?x6210 = 01fh36 >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #7468 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 3218 *> proper extension: 04lgymt; 03h26tm; 01x15dc; 0g4gr; 0bbxx9b; 0b6mgp_; 01k6y1; 075wq; 03mz5b; 08lpkq; ... *> query: (?x12791, 0gk4g) <- gender(?x12791, ?x231), ?x231 = 05zppz *> conf = 0.04 ranks of expected_values: 5 EVAL 01pny5 people! 0gk4g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 130.000 130.000 0.071 http://example.org/people/cause_of_death/people #2822-02d413 PRED entity: 02d413 PRED relation: language PRED expected values: 02h40lc => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.89 #2979, 0.89 #1967, 0.89 #1609), 06nm1 (0.21 #773, 0.10 #247, 0.09 #1258), 064_8sq (0.17 #81, 0.16 #437, 0.15 #556), 03_9r (0.17 #10, 0.06 #187, 0.06 #69), 04306rv (0.12 #480, 0.11 #717, 0.11 #599), 02bjrlw (0.09 #297, 0.08 #119, 0.08 #713), 06b_j (0.07 #498, 0.07 #319, 0.07 #200), 0jzc (0.06 #79, 0.05 #316, 0.04 #197), 01wgr (0.06 #99, 0.03 #158, 0.01 #276), 04h9h (0.06 #102, 0.03 #220, 0.03 #518) >> Best rule #2979 for best value: >> intensional similarity = 3 >> extensional distance = 1471 >> proper extension: 02sg5v; 0fxmbn; 0hhggmy; 025twgf; 0gwlfnb; 08c6k9; 07p12s; 025twgt; >> query: (?x69, 02h40lc) <- film(?x7977, ?x69), award_winner(?x820, ?x7977), gender(?x7977, ?x231) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02d413 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.894 http://example.org/film/film/language #2821-07_pf PRED entity: 07_pf PRED relation: location_of_ceremony! PRED expected values: 081lh => 179 concepts (138 used for prediction) PRED predicted values (max 10 best out of 200): 0gdqy (0.25 #481, 0.17 #1245, 0.14 #1760), 0c9c0 (0.25 #323, 0.17 #1087, 0.14 #1602), 06wvj (0.25 #316, 0.17 #1080, 0.14 #1595), 06x58 (0.25 #298, 0.17 #1062, 0.14 #1577), 03lt8g (0.25 #280, 0.17 #1044, 0.14 #1559), 0f4vbz (0.20 #767, 0.20 #562, 0.08 #6124), 0dvld (0.20 #659, 0.09 #6016, 0.03 #9849), 01vzxld (0.20 #732, 0.05 #6089, 0.04 #7875), 03m2fg (0.20 #694, 0.05 #6051, 0.03 #9884), 02yy8 (0.20 #759, 0.05 #6116, 0.03 #9949) >> Best rule #481 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 03qhnx; >> query: (?x10496, 0gdqy) <- featured_film_locations(?x4786, ?x10496), ?x4786 = 0bbw2z6 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07_pf location_of_ceremony! 081lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 179.000 138.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #2820-0453t PRED entity: 0453t PRED relation: people! PRED expected values: 07bch9 => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 47): 041rx (0.40 #158, 0.33 #81, 0.26 #620), 01qhm_ (0.33 #83, 0.20 #160, 0.11 #237), 013xrm (0.31 #405, 0.22 #251, 0.20 #174), 0x67 (0.15 #1319, 0.14 #1396, 0.12 #7788), 013b6_ (0.14 #746, 0.11 #284, 0.11 #1208), 07bch9 (0.12 #1101, 0.09 #4028, 0.09 #3797), 048z7l (0.12 #1118, 0.07 #502, 0.06 #579), 07hwkr (0.11 #243, 0.09 #320, 0.08 #859), 01p7s6 (0.11 #290, 0.05 #675, 0.05 #752), 03ttfc (0.09 #2465, 0.02 #2087, 0.01 #2164) >> Best rule #158 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 047g6; >> query: (?x2239, 041rx) <- place_of_birth(?x2239, ?x12931), influenced_by(?x2239, ?x2240), ?x2240 = 0j3v, company(?x2239, ?x263) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1101 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 01nbq4; *> query: (?x2239, 07bch9) <- type_of_union(?x2239, ?x566), student(?x3995, ?x2239), company(?x2239, ?x263), profession(?x2239, ?x353) *> conf = 0.12 ranks of expected_values: 6 EVAL 0453t people! 07bch9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 170.000 170.000 0.400 http://example.org/people/ethnicity/people #2819-0178kd PRED entity: 0178kd PRED relation: award PRED expected values: 01ckcd => 87 concepts (63 used for prediction) PRED predicted values (max 10 best out of 202): 02f6yz (0.85 #3217, 0.83 #3620, 0.80 #19309), 02f73b (0.85 #3217, 0.83 #3620, 0.80 #19309), 01c427 (0.58 #2095, 0.18 #4107, 0.16 #2899), 01by1l (0.53 #14994, 0.35 #15396, 0.33 #113), 01ckcd (0.50 #2344, 0.43 #3551, 0.33 #3954), 01c9jp (0.42 #2198, 0.22 #4210, 0.21 #3808), 01c99j (0.33 #225, 0.25 #1431, 0.25 #1029), 02f6ym (0.33 #256, 0.25 #1462, 0.25 #1060), 01ckbq (0.33 #89, 0.25 #1295, 0.25 #893), 02g3gj (0.33 #25, 0.25 #1231, 0.25 #829) >> Best rule #3217 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 051m56; >> query: (?x6368, ?x7535) <- artist(?x2299, ?x6368), ?x2299 = 033hn8, artists(?x302, ?x6368), award_winner(?x7535, ?x6368) >> conf = 0.85 => this is the best rule for 2 predicted values *> Best rule #2344 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 03t9sp; 0fcsd; 09lwrt; 07hgm; 0mjn2; 016vn3; 016t0h; 012x1l; 027kwc; *> query: (?x6368, 01ckcd) <- artist(?x3887, ?x6368), group(?x227, ?x6368), ?x3887 = 02bh8z, artists(?x302, ?x6368) *> conf = 0.50 ranks of expected_values: 5 EVAL 0178kd award 01ckcd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 63.000 0.847 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2818-025m8l PRED entity: 025m8l PRED relation: award_winner PRED expected values: 0178rl => 49 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1487): 01l3mk3 (0.52 #17190, 0.38 #4910, 0.38 #4909), 01vvycq (0.52 #17190, 0.38 #4910, 0.38 #4909), 0146pg (0.52 #17190, 0.38 #4910, 0.38 #4909), 02jxmr (0.52 #17190, 0.38 #4910, 0.38 #4909), 02bh9 (0.52 #17190, 0.38 #4910, 0.38 #4909), 01c7p_ (0.52 #17190, 0.38 #4910, 0.38 #4909), 02g1jh (0.52 #17190, 0.38 #4910, 0.38 #4909), 01vsgrn (0.52 #17190, 0.38 #4910, 0.38 #4909), 028k57 (0.52 #17190, 0.38 #4910, 0.38 #4909), 01mh8zn (0.52 #17190, 0.38 #4910, 0.38 #4909) >> Best rule #17190 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 03nl5k; >> query: (?x2238, ?x7027) <- award(?x7027, ?x2238), ceremony(?x2238, ?x2704), ?x2704 = 01mhwk, music(?x723, ?x7027) >> conf = 0.52 => this is the best rule for 10 predicted values *> Best rule #3629 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 0gqz2; *> query: (?x2238, 0178rl) <- award(?x12724, ?x2238), award(?x3673, ?x2238), ?x3673 = 021yw7, ?x12724 = 02zj61 *> conf = 0.33 ranks of expected_values: 77 EVAL 025m8l award_winner 0178rl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 49.000 23.000 0.521 http://example.org/award/award_category/winners./award/award_honor/award_winner #2817-01p9hgt PRED entity: 01p9hgt PRED relation: artists! PRED expected values: 017371 => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 173): 0gg8l (0.44 #132, 0.25 #443, 0.06 #2931), 064t9 (0.43 #2812, 0.40 #3123, 0.35 #3434), 0155w (0.37 #729, 0.16 #3839, 0.15 #4151), 02yv6b (0.32 #721, 0.11 #3831, 0.11 #99), 016cjb (0.22 #75, 0.17 #386, 0.11 #697), 016clz (0.22 #3737, 0.20 #4049, 0.20 #4361), 05bt6j (0.21 #2842, 0.18 #3153, 0.17 #5647), 025sc50 (0.21 #2848, 0.18 #3159, 0.14 #4719), 03_d0 (0.21 #633, 0.19 #3743, 0.18 #4055), 05w3f (0.21 #659, 0.10 #3769, 0.09 #4081) >> Best rule #132 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 0p_47; 0pmw9; >> query: (?x1413, 0gg8l) <- profession(?x1413, ?x220), nominated_for(?x1413, ?x2124) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #798 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 60 *> proper extension: 089tm; 0134s5; 05563d; 015srx; 03c3yf; 0134pk; 016376; 012x03; 027kwc; 011xhx; *> query: (?x1413, 017371) <- artists(?x378, ?x1413), ?x378 = 07sbbz2 *> conf = 0.08 ranks of expected_values: 34 EVAL 01p9hgt artists! 017371 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 39.000 39.000 0.444 http://example.org/music/genre/artists #2816-0fhxv PRED entity: 0fhxv PRED relation: artists! PRED expected values: 0xhtw => 111 concepts (66 used for prediction) PRED predicted values (max 10 best out of 257): 0xhtw (0.46 #2764, 0.46 #1847, 0.21 #9496), 0gywn (0.32 #971, 0.22 #5559, 0.22 #7701), 05w3f (0.32 #1867, 0.30 #2784, 0.13 #2478), 03lty (0.31 #1857, 0.28 #2774, 0.15 #4916), 01fh36 (0.31 #1914, 0.26 #2831, 0.11 #3137), 06j6l (0.29 #962, 0.27 #7692, 0.27 #3713), 01lyv (0.29 #948, 0.24 #3699, 0.24 #7678), 016clz (0.27 #9484, 0.25 #2752, 0.24 #1835), 025sc50 (0.25 #963, 0.24 #5551, 0.23 #7693), 03_d0 (0.23 #3371, 0.23 #3984, 0.22 #3065) >> Best rule #2764 for best value: >> intensional similarity = 2 >> extensional distance = 97 >> proper extension: 05563d; 0qmny; >> query: (?x4646, 0xhtw) <- artists(?x1380, ?x4646), ?x1380 = 0dl5d >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fhxv artists! 0xhtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 66.000 0.465 http://example.org/music/genre/artists #2815-07tgn PRED entity: 07tgn PRED relation: company! PRED expected values: 021q0l => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 44): 0krdk (0.81 #145, 0.55 #329, 0.55 #376), 0dq_5 (0.69 #155, 0.59 #386, 0.45 #1682), 07xl34 (0.57 #2316, 0.55 #2409, 0.45 #369), 060c4 (0.56 #141, 0.50 #557, 0.47 #1343), 021q0l (0.50 #56, 0.25 #194, 0.25 #102), 01yc02 (0.44 #147, 0.27 #378, 0.24 #1349), 05_wyz (0.41 #387, 0.38 #156, 0.30 #1358), 09d6p2 (0.38 #157, 0.32 #388, 0.30 #341), 0dq3c (0.28 #1667, 0.26 #556, 0.26 #1342), 01kr6k (0.25 #165, 0.20 #349, 0.14 #1692) >> Best rule #145 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 01frpd; >> query: (?x892, 0krdk) <- child(?x892, ?x893), state_province_region(?x892, ?x2235), list(?x892, ?x2197) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0yjf0; *> query: (?x892, 021q0l) <- student(?x892, ?x4330), major_field_of_study(?x892, ?x742), ?x4330 = 0kvqv *> conf = 0.50 ranks of expected_values: 5 EVAL 07tgn company! 021q0l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 118.000 118.000 0.812 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #2814-01hgwkr PRED entity: 01hgwkr PRED relation: instrumentalists! PRED expected values: 0342h => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 77): 0342h (0.65 #5, 0.50 #1342, 0.50 #95), 05r5c (0.55 #9, 0.53 #99, 0.43 #1346), 05148p4 (0.50 #22, 0.35 #112, 0.32 #1359), 018vs (0.40 #14, 0.31 #1351, 0.28 #639), 03qjg (0.35 #53, 0.18 #678, 0.15 #1390), 02hnl (0.30 #36, 0.19 #1373, 0.18 #661), 04rzd (0.27 #1696, 0.26 #1964, 0.26 #180), 042v_gx (0.27 #1696, 0.26 #1964, 0.26 #180), 0l14md (0.20 #8, 0.14 #633, 0.13 #1345), 0l14qv (0.20 #6, 0.11 #1343, 0.10 #3397) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0kxbc; >> query: (?x9442, 0342h) <- award_winner(?x2420, ?x9442), role(?x9442, ?x432), diet(?x9442, ?x11141) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01hgwkr instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.650 http://example.org/music/instrument/instrumentalists #2813-040fv PRED entity: 040fv PRED relation: month! PRED expected values: 04jpl 03hrz 0d6lp 0ply0 0d9jr 02sn34 0k3p => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1200): 0d6lp (0.90 #37, 0.90 #24, 0.89 #83), 02sn34 (0.90 #37, 0.90 #24, 0.89 #83), 04jpl (0.90 #37, 0.90 #24, 0.89 #83), 0k3p (0.90 #37, 0.90 #24, 0.89 #83), 03hrz (0.90 #37, 0.90 #24, 0.89 #83), 0d9jr (0.90 #37, 0.90 #24, 0.89 #83), 0ply0 (0.90 #37, 0.90 #24, 0.89 #83), 03czqs (0.90 #37, 0.90 #24, 0.89 #83), 0l0mk (0.90 #37, 0.90 #24, 0.89 #83), 0sjqm (0.46 #93, 0.02 #119) >> Best rule #37 for best value: >> intensional similarity = 100 >> extensional distance = 1 >> proper extension: 03_ly; >> query: (?x2255, ?x362) <- month(?x12674, ?x2255), month(?x11237, ?x2255), month(?x11197, ?x2255), month(?x10610, ?x2255), month(?x9559, ?x2255), month(?x8977, ?x2255), month(?x6960, ?x2255), month(?x6959, ?x2255), month(?x6703, ?x2255), month(?x6458, ?x2255), month(?x6357, ?x2255), month(?x6054, ?x2255), month(?x5168, ?x2255), month(?x5036, ?x2255), month(?x4826, ?x2255), month(?x4627, ?x2255), month(?x4271, ?x2255), month(?x3501, ?x2255), month(?x3106, ?x2255), month(?x2645, ?x2255), month(?x2474, ?x2255), month(?x2316, ?x2255), month(?x2254, ?x2255), month(?x1860, ?x2255), month(?x1658, ?x2255), month(?x1646, ?x2255), month(?x1036, ?x2255), month(?x863, ?x2255), month(?x739, ?x2255), month(?x206, ?x2255), month(?x108, ?x2255), ?x1658 = 0h7h6, ?x11197 = 05l64, ?x6703 = 0f04v, ?x1646 = 0156q, ?x12674 = 0g6xq, ?x4627 = 05qtj, ?x206 = 01914, ?x4826 = 0177z, seasonal_months(?x7298, ?x2255), seasonal_months(?x6303, ?x2255), seasonal_months(?x3270, ?x2255), seasonal_months(?x1650, ?x2255), ?x863 = 0fhp9, ?x2316 = 06t2t, ?x8977 = 02z0j, ?x7298 = 04wzr, ?x4271 = 06wjf, ?x3501 = 0f2v0, ?x9559 = 07dfk, ?x2645 = 03h64, ?x6054 = 0fn2g, ?x3270 = 05cw8, ?x10610 = 03902, ?x6959 = 06c62, ?x3106 = 049d1, ?x1650 = 06vkl, ?x1036 = 080h2, ?x2254 = 0dclg, ?x5168 = 06mxs, month(?x6494, ?x6303), month(?x3125, ?x6303), month(?x362, ?x6303), seasonal_months(?x2255, ?x2140), ?x2474 = 052p7, ?x3125 = 0d6lp, ?x11237 = 03khn, ?x739 = 02_286, ?x6458 = 08966, ?x5036 = 06y57, contains(?x6960, ?x1659), mode_of_transportation(?x6960, ?x4272), ?x6494 = 02sn34, place_of_death(?x5806, ?x6960), country(?x6960, ?x94), source(?x6960, ?x958), ?x1860 = 01_d4, place_of_birth(?x10814, ?x6960), place_of_birth(?x4782, ?x6960), place_of_birth(?x3633, ?x6960), location(?x540, ?x6960), category(?x6357, ?x134), dog_breed(?x6960, ?x1706), film(?x4782, ?x1811), celebrity(?x1896, ?x4782), origin(?x1321, ?x6357), participant(?x4782, ?x2221), award(?x4782, ?x1007), ?x108 = 0rh6k, participant(?x3633, ?x3183), award_nominee(?x4782, ?x91), featured_film_locations(?x8302, ?x6960), vacationer(?x126, ?x4782), participant(?x250, ?x4782), award(?x3633, ?x435), award_winner(?x4517, ?x3633), gender(?x3633, ?x231), award_winner(?x873, ?x10814), award_nominee(?x540, ?x539), film(?x10814, ?x3921) >> conf = 0.90 => this is the best rule for 9 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7 EVAL 040fv month! 0k3p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fv month! 02sn34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fv month! 0d9jr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fv month! 0ply0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fv month! 0d6lp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fv month! 03hrz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 040fv month! 04jpl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #2812-01kv4mb PRED entity: 01kv4mb PRED relation: role PRED expected values: 013y1f => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 92): 0342h (0.39 #2330, 0.38 #2224, 0.36 #3172), 02sgy (0.31 #427, 0.29 #7, 0.23 #2121), 0l14md (0.31 #1161, 0.05 #3704, 0.05 #3809), 042v_gx (0.29 #9, 0.24 #324, 0.23 #2123), 04rzd (0.29 #46, 0.17 #949, 0.09 #572), 01vdm0 (0.27 #4151, 0.26 #2147, 0.25 #3519), 05148p4 (0.26 #2537, 0.26 #1267, 0.26 #3274), 03gvt (0.26 #3274, 0.26 #3486, 0.24 #4118), 013y1f (0.19 #458, 0.17 #949, 0.17 #1093), 018vs (0.19 #856, 0.18 #2339, 0.18 #3181) >> Best rule #2330 for best value: >> intensional similarity = 3 >> extensional distance = 156 >> proper extension: 01vzz1c; >> query: (?x2124, 0342h) <- role(?x2124, ?x1166), profession(?x2124, ?x2348), ?x2348 = 0nbcg >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #458 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 01hw6wq; 02bh9; 02qfhb; 0jn5l; 01p0vf; *> query: (?x2124, 013y1f) <- role(?x2124, ?x1166), award_nominee(?x538, ?x2124), music(?x8677, ?x2124) *> conf = 0.19 ranks of expected_values: 9 EVAL 01kv4mb role 013y1f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 126.000 126.000 0.392 http://example.org/music/artist/track_contributions./music/track_contribution/role #2811-0824r PRED entity: 0824r PRED relation: contains PRED expected values: 0ml25 03l6bs => 162 concepts (74 used for prediction) PRED predicted values (max 10 best out of 2810): 03l6bs (0.52 #76157, 0.48 #137682, 0.46 #76156), 09c7w0 (0.49 #196277, 0.16 #111316, 0.04 #26361), 0824r (0.49 #196277, 0.05 #12235, 0.04 #9306), 0fr0t (0.33 #502, 0.25 #6358, 0.07 #12216), 0qplq (0.33 #2155, 0.25 #8011, 0.05 #13869), 0qpsn (0.33 #2133, 0.25 #7989, 0.05 #13847), 0qpjt (0.33 #1300, 0.25 #7156, 0.05 #13014), 0m257 (0.33 #2442, 0.25 #8298, 0.05 #14156), 0m241 (0.33 #2391, 0.25 #8247, 0.05 #14105), 0m2dk (0.33 #1814, 0.25 #7670, 0.05 #13528) >> Best rule #76157 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 01vsb_; >> query: (?x4105, ?x5581) <- administrative_parent(?x4105, ?x94), state_province_region(?x5581, ?x4105), category(?x5581, ?x134) >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0824r contains 03l6bs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 74.000 0.523 http://example.org/location/location/contains EVAL 0824r contains 0ml25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 162.000 74.000 0.523 http://example.org/location/location/contains #2810-03f0r5w PRED entity: 03f0r5w PRED relation: profession PRED expected values: 02krf9 => 97 concepts (71 used for prediction) PRED predicted values (max 10 best out of 52): 09jwl (0.39 #2030, 0.37 #5056, 0.36 #5200), 02krf9 (0.38 #166, 0.33 #22, 0.31 #3912), 0np9r (0.38 #4914, 0.25 #16, 0.23 #448), 0nbcg (0.29 #2043, 0.26 #2331, 0.26 #5213), 0cbd2 (0.28 #150, 0.20 #438, 0.18 #582), 016z4k (0.27 #2020, 0.25 #1588, 0.24 #4038), 0dz3r (0.26 #2018, 0.25 #1586, 0.24 #2306), 0kyk (0.18 #169, 0.13 #889, 0.11 #457), 01c72t (0.17 #883, 0.15 #2035, 0.15 #4053), 015cjr (0.17 #45, 0.09 #477, 0.08 #189) >> Best rule #2030 for best value: >> intensional similarity = 3 >> extensional distance = 583 >> proper extension: 01pbxb; 028q6; 0197tq; 05cljf; 0hl3d; 01lmj3q; 0m2l9; 032nwy; 026ps1; 01wl38s; ... >> query: (?x4744, 09jwl) <- award_nominee(?x4744, ?x10512), profession(?x4744, ?x319), category(?x4744, ?x134) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 0k57l; 06zmg7m; 03mv0b; 04rg6; 0894_x; 05b1062; *> query: (?x4744, 02krf9) <- profession(?x4744, ?x1146), profession(?x4744, ?x524), ?x524 = 02jknp, ?x1146 = 018gz8 *> conf = 0.38 ranks of expected_values: 2 EVAL 03f0r5w profession 02krf9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 71.000 0.393 http://example.org/people/person/profession #2809-091n7z PRED entity: 091n7z PRED relation: nationality PRED expected values: 09c7w0 => 135 concepts (74 used for prediction) PRED predicted values (max 10 best out of 97): 09c7w0 (0.90 #3810, 0.89 #4010, 0.88 #6145), 04tgp (0.35 #5430, 0.35 #4922, 0.34 #5735), 07ssc (0.20 #616, 0.15 #1016, 0.14 #1116), 0d060g (0.20 #6142, 0.10 #2615, 0.08 #2414), 02j71 (0.20 #6142), 0chghy (0.14 #811, 0.06 #6748, 0.05 #2215), 02jx1 (0.12 #1836, 0.11 #2037, 0.11 #7083), 03_3d (0.11 #3215, 0.10 #3115, 0.10 #2915), 03rk0 (0.09 #6392, 0.09 #4968, 0.09 #6593), 03rt9 (0.08 #1014, 0.07 #1114, 0.06 #1215) >> Best rule #3810 for best value: >> intensional similarity = 5 >> extensional distance = 250 >> proper extension: 01438g; 01wb8bs; 094xh; 012v1t; 01xyt7; 03bw6; >> query: (?x12811, 09c7w0) <- religion(?x12811, ?x109), place_of_birth(?x12811, ?x13556), location(?x8749, ?x13556), gender(?x12811, ?x514), source(?x13556, ?x958) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 091n7z nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 74.000 0.897 http://example.org/people/person/nationality #2808-049tjg PRED entity: 049tjg PRED relation: languages PRED expected values: 02h40lc => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 12): 02h40lc (0.34 #1602, 0.32 #1524, 0.30 #978), 04306rv (0.08 #81, 0.05 #703, 0.04 #3200), 064_8sq (0.08 #93, 0.05 #703, 0.03 #1771), 02bv9 (0.04 #98, 0.04 #3200), 0t_2 (0.04 #3200), 06mp7 (0.03 #206), 02bjrlw (0.02 #235, 0.02 #1094, 0.02 #352), 03k50 (0.02 #746, 0.02 #1175, 0.02 #3164), 06nm1 (0.02 #357, 0.02 #279, 0.02 #787), 07c9s (0.01 #1184, 0.01 #3173, 0.01 #2510) >> Best rule #1602 for best value: >> intensional similarity = 4 >> extensional distance = 663 >> proper extension: 01sl1q; 0q9kd; 04bdxl; 06qgvf; 0grwj; 07fq1y; 02qgqt; 04yywz; 06688p; 07s3vqk; ... >> query: (?x305, 02h40lc) <- location(?x305, ?x739), film(?x305, ?x306), gender(?x305, ?x231), people(?x1423, ?x305) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 049tjg languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.341 http://example.org/people/person/languages #2807-06ncr PRED entity: 06ncr PRED relation: role! PRED expected values: 05qhnq 04vrxh => 85 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1076): 050z2 (0.82 #13626, 0.71 #9450, 0.67 #7131), 04bpm6 (0.73 #13510, 0.67 #7015, 0.60 #6090), 01wxdn3 (0.71 #9211, 0.60 #6429, 0.57 #15240), 05qhnq (0.67 #7255, 0.60 #5401, 0.60 #4938), 0326tc (0.67 #7291, 0.60 #4510, 0.57 #10072), 06x4l_ (0.67 #7068, 0.60 #4751, 0.57 #9387), 0770cd (0.67 #7020, 0.57 #9339, 0.50 #11196), 0m_v0 (0.67 #7107, 0.57 #9426, 0.50 #11283), 01gx5f (0.67 #7101, 0.57 #9420, 0.50 #2466), 0l12d (0.60 #6086, 0.60 #5157, 0.50 #7011) >> Best rule #13626 for best value: >> intensional similarity = 17 >> extensional distance = 9 >> proper extension: 03gvt; >> query: (?x2309, 050z2) <- role(?x2309, ?x2460), role(?x2309, ?x2310), role(?x2309, ?x1969), role(?x2309, ?x1750), role(?x2309, ?x615), instrumentalists(?x2309, ?x10625), instrumentalists(?x2309, ?x3492), instrumentalists(?x1750, ?x7753), role(?x1969, ?x228), role(?x1955, ?x1750), ?x7753 = 03mszl, award(?x10625, ?x247), place_of_birth(?x3492, ?x2850), ?x2310 = 0gghm, role(?x367, ?x1969), type_of_union(?x10625, ?x566), ?x2460 = 01wy6 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #7255 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 4 *> proper extension: 013y1f; *> query: (?x2309, 05qhnq) <- role(?x2309, ?x1969), role(?x2309, ?x1750), role(?x2309, ?x1437), role(?x2309, ?x1166), role(?x2309, ?x615), instrumentalists(?x2309, ?x3492), ?x1750 = 02hnl, group(?x2309, ?x5838), award_nominee(?x3492, ?x3493), performance_role(?x2944, ?x2309), ?x1166 = 05148p4, artists(?x505, ?x3492), ?x1437 = 01vdm0, ?x5838 = 02dw1_, role(?x3492, ?x228), ?x1969 = 04rzd *> conf = 0.67 ranks of expected_values: 4, 97 EVAL 06ncr role! 04vrxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 85.000 45.000 0.818 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 06ncr role! 05qhnq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 45.000 0.818 http://example.org/music/artist/track_contributions./music/track_contribution/role #2806-05gnf PRED entity: 05gnf PRED relation: award_winner! PRED expected values: 030_1_ 07ymr5 => 177 concepts (129 used for prediction) PRED predicted values (max 10 best out of 1132): 05kh_ (0.93 #43232, 0.87 #68865, 0.87 #40030), 0g5lhl7 (0.93 #43232, 0.87 #68865, 0.87 #40030), 0g51l1 (0.93 #43232, 0.87 #68865, 0.87 #40030), 030_1_ (0.93 #43232, 0.87 #68865, 0.87 #40030), 05gnf (0.50 #60358, 0.50 #7506, 0.47 #16008), 01bh6y (0.47 #16008, 0.40 #15828, 0.25 #169769), 09bx1k (0.47 #16008, 0.40 #15309, 0.25 #169769), 07ymr5 (0.47 #16008, 0.40 #89690, 0.25 #169769), 04glx0 (0.47 #16008, 0.33 #1089, 0.25 #13896), 01l1ls (0.47 #16008, 0.33 #1420, 0.25 #14227) >> Best rule #43232 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 01jq34; >> query: (?x6678, ?x1686) <- category(?x6678, ?x134), citytown(?x6678, ?x739), award_winner(?x6678, ?x1686) >> conf = 0.93 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 8 EVAL 05gnf award_winner! 07ymr5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 177.000 129.000 0.931 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner EVAL 05gnf award_winner! 030_1_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 177.000 129.000 0.931 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #2805-026dg51 PRED entity: 026dg51 PRED relation: award_winner! PRED expected values: 01b66t => 70 concepts (37 used for prediction) PRED predicted values (max 10 best out of 72): 0gj50 (0.60 #4532, 0.59 #2265, 0.57 #3399), 01b66t (0.60 #4532, 0.54 #5668, 0.47 #6802), 01y6dz (0.59 #2265, 0.57 #3399, 0.57 #3398), 01b64v (0.24 #11337, 0.20 #1439, 0.15 #2572), 0phrl (0.24 #11337, 0.13 #1520, 0.10 #2653), 01b65l (0.24 #11337, 0.10 #448, 0.03 #3847), 0170k0 (0.24 #11337), 08jgk1 (0.10 #10373, 0.04 #12640, 0.04 #4702), 02_1kl (0.08 #24942, 0.07 #28341), 02_1q9 (0.08 #24942, 0.07 #28341) >> Best rule #4532 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 0glmv; >> query: (?x912, ?x4011) <- award_winner(?x589, ?x912), gender(?x912, ?x514), tv_program(?x912, ?x4011) >> conf = 0.60 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 026dg51 award_winner! 01b66t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 37.000 0.602 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #2804-05x8n PRED entity: 05x8n PRED relation: type_of_union PRED expected values: 04ztj => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.81 #89, 0.80 #45, 0.80 #93), 01g63y (0.42 #161, 0.11 #231, 0.11 #235), 01bl8s (0.02 #55, 0.02 #59, 0.01 #75) >> Best rule #89 for best value: >> intensional similarity = 5 >> extensional distance = 265 >> proper extension: 084w8; 028q6; 01k7d9; 07s3vqk; 0h1_w; 032nwy; 0chsq; 012cj0; 076lxv; 019z7q; ... >> query: (?x6688, 04ztj) <- award(?x6688, ?x575), gender(?x6688, ?x231), nationality(?x6688, ?x94), ?x94 = 09c7w0, people(?x268, ?x6688) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05x8n type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.809 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2803-01wrwf PRED entity: 01wrwf PRED relation: school_type PRED expected values: 05pcjw => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 19): 05pcjw (0.56 #70, 0.49 #116, 0.40 #93), 05jxkf (0.56 #142, 0.51 #857, 0.49 #995), 07tf8 (0.19 #146, 0.14 #54, 0.13 #861), 01_9fk (0.12 #439, 0.11 #485, 0.11 #462), 01y64 (0.08 #333, 0.06 #126, 0.05 #80), 02dk5q (0.08 #121, 0.04 #236, 0.03 #305), 01_srz (0.07 #440, 0.06 #279, 0.06 #118), 04qbv (0.07 #268, 0.06 #291, 0.05 #337), 02p0qmm (0.06 #9, 0.05 #55, 0.04 #538), 0257h9 (0.06 #134, 0.03 #318, 0.02 #249) >> Best rule #70 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 02kth6; 027kp3; 02kbtf; 0hpv3; 0l0wv; 03205_; >> query: (?x13963, 05pcjw) <- school_type(?x13963, ?x3205), contains(?x335, ?x13963), category(?x13963, ?x134), ?x335 = 059rby >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wrwf school_type 05pcjw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.564 http://example.org/education/educational_institution/school_type #2802-050kh5 PRED entity: 050kh5 PRED relation: actor PRED expected values: 0147dk 02756j 040nwr => 62 concepts (51 used for prediction) PRED predicted values (max 10 best out of 916): 025p38 (0.56 #12960, 0.43 #9253, 0.43 #9254), 03fwln (0.56 #12960, 0.43 #9253, 0.43 #9254), 03xmy1 (0.50 #3846, 0.40 #5696, 0.11 #10324), 0163t3 (0.33 #1612, 0.25 #9013, 0.22 #10866), 01vx5w7 (0.33 #1161, 0.25 #8562, 0.20 #6712), 01pfkw (0.33 #1281, 0.20 #6832, 0.20 #4982), 0gps0z (0.33 #1654, 0.20 #7205, 0.20 #5355), 0c7ct (0.33 #976, 0.20 #6527, 0.20 #4677), 02r_d4 (0.33 #2827, 0.20 #4678, 0.15 #12085), 0b4rf3 (0.33 #832, 0.20 #7309, 0.08 #11939) >> Best rule #12960 for best value: >> intensional similarity = 9 >> extensional distance = 11 >> proper extension: 0cpz4k; >> query: (?x12165, ?x656) <- genre(?x12165, ?x5728), actor(?x12165, ?x11799), actor(?x12165, ?x3129), program(?x14356, ?x12165), type_of_union(?x3129, ?x566), languages(?x3129, ?x254), program(?x656, ?x12165), profession(?x11799, ?x319), people(?x13008, ?x11799) >> conf = 0.56 => this is the best rule for 2 predicted values *> Best rule #4585 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 03bww6; 0k0q73t; *> query: (?x12165, 040nwr) <- genre(?x12165, ?x5728), actor(?x12165, ?x11170), actor(?x12165, ?x3129), program(?x14356, ?x12165), award_winner(?x2617, ?x3129), film(?x3129, ?x2381), ?x5728 = 09lmb, languages(?x11170, ?x254), award(?x11170, ?x10156) *> conf = 0.25 ranks of expected_values: 22, 297 EVAL 050kh5 actor 040nwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 62.000 51.000 0.556 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 050kh5 actor 02756j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 62.000 51.000 0.556 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 050kh5 actor 0147dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 51.000 0.556 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #2801-05qbckf PRED entity: 05qbckf PRED relation: music PRED expected values: 01mkn_d => 85 concepts (53 used for prediction) PRED predicted values (max 10 best out of 88): 06fxnf (0.18 #279, 0.09 #911, 0.06 #1543), 02jxmr (0.14 #74, 0.09 #916, 0.07 #494), 0146pg (0.14 #10, 0.09 #2330, 0.08 #1694), 0bwh6 (0.14 #22, 0.01 #2132), 016szr (0.13 #501, 0.09 #291, 0.05 #1344), 0150t6 (0.13 #466, 0.06 #2578, 0.06 #3426), 01tc9r (0.12 #695, 0.07 #485, 0.06 #2175), 02bh9 (0.10 #1735, 0.07 #1314, 0.07 #3006), 0bs1yy (0.10 #1729, 0.03 #4483, 0.02 #6820), 04pf4r (0.09 #278, 0.09 #910, 0.06 #1542) >> Best rule #279 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 05p1tzf; 04n52p6; 047vnkj; >> query: (?x1956, 06fxnf) <- film_release_region(?x1956, ?x4302), film_release_region(?x1956, ?x1122), film_release_region(?x1956, ?x774), ?x774 = 06mzp, medal(?x1122, ?x422), ?x4302 = 06vbd >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #1805 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 0d_wms; 03d8jd1; *> query: (?x1956, 01mkn_d) <- genre(?x1956, ?x6888), film(?x192, ?x1956), ?x6888 = 04pbhw *> conf = 0.02 ranks of expected_values: 60 EVAL 05qbckf music 01mkn_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 85.000 53.000 0.182 http://example.org/film/film/music #2800-0j43swk PRED entity: 0j43swk PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.81 #76, 0.81 #86, 0.81 #241), 02nxhr (0.10 #12, 0.08 #32, 0.07 #47), 07c52 (0.07 #53, 0.06 #33, 0.06 #48), 07z4p (0.06 #50, 0.06 #20, 0.05 #55), 0735l (0.01 #14) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 418 >> proper extension: 09p35z; 018nnz; 03mh_tp; 0gs973; 0h1fktn; 02tktw; 04y9mm8; 047gpsd; 08984j; 0bw20; ... >> query: (?x3035, 029j_) <- film(?x194, ?x3035), country(?x3035, ?x94), executive_produced_by(?x3035, ?x8345), ?x94 = 09c7w0 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j43swk film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.814 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #2799-02kxbx3 PRED entity: 02kxbx3 PRED relation: award PRED expected values: 03hj5vf => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 282): 04dn09n (0.77 #28423, 0.71 #40108, 0.71 #28422), 0gq9h (0.77 #28423, 0.71 #40108, 0.71 #28422), 02wkmx (0.71 #40108, 0.71 #28422, 0.71 #23358), 027b9ly (0.71 #40108, 0.71 #28422, 0.71 #23358), 02wypbh (0.71 #40108, 0.71 #28422, 0.71 #23358), 027c924 (0.71 #40108, 0.71 #28422, 0.71 #23358), 09d28z (0.71 #40108, 0.71 #28422, 0.71 #23358), 09sb52 (0.59 #3932, 0.36 #817, 0.33 #36), 0k611 (0.38 #2034, 0.23 #12073, 0.22 #8959), 05pcn59 (0.33 #72, 0.27 #853, 0.11 #2800) >> Best rule #28423 for best value: >> intensional similarity = 3 >> extensional distance = 1371 >> proper extension: 028q6; 07s3vqk; 05cljf; 02rchht; 0hl3d; 01vrx3g; 0prfz; 025h4z; 0m2l9; 026ps1; ... >> query: (?x3572, ?x1307) <- award_nominee(?x3572, ?x163), award_winner(?x1307, ?x3572), ceremony(?x1307, ?x1084) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #6387 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 166 *> proper extension: 0gv40; 0htcn; *> query: (?x3572, 03hj5vf) <- award(?x3572, ?x68), award_nominee(?x163, ?x3572), film(?x3572, ?x392) *> conf = 0.04 ranks of expected_values: 137 EVAL 02kxbx3 award 03hj5vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 129.000 129.000 0.771 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2798-0cc56 PRED entity: 0cc56 PRED relation: place_of_birth! PRED expected values: 01gq0b 04wx2v => 145 concepts (120 used for prediction) PRED predicted values (max 10 best out of 2186): 01gzm2 (0.46 #5173, 0.41 #201690, 0.35 #7759), 05kfs (0.46 #5173, 0.41 #201690, 0.35 #7759), 032w8h (0.46 #5173, 0.41 #201690, 0.35 #7759), 0py5b (0.46 #5173, 0.35 #7759, 0.32 #5172), 0237fw (0.41 #201690, 0.35 #7759, 0.32 #5172), 01vrlqd (0.41 #201690, 0.35 #7759, 0.32 #5172), 02sjf5 (0.41 #201690, 0.35 #7759, 0.32 #5172), 01bmlb (0.41 #201690, 0.35 #7759, 0.32 #5172), 02114t (0.41 #201690, 0.35 #7759, 0.32 #5172), 01rh0w (0.41 #201690, 0.35 #7759, 0.32 #5172) >> Best rule #5173 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02_286; >> query: (?x1131, ?x8408) <- location(?x10645, ?x1131), location(?x8408, ?x1131), ?x10645 = 0sx5w, written_by(?x689, ?x8408) >> conf = 0.46 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 0cc56 place_of_birth! 04wx2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 145.000 120.000 0.462 http://example.org/people/person/place_of_birth EVAL 0cc56 place_of_birth! 01gq0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 145.000 120.000 0.462 http://example.org/people/person/place_of_birth #2797-04cw0n4 PRED entity: 04cw0n4 PRED relation: place_of_death PRED expected values: 0f2wj => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 19): 030qb3t (0.17 #607, 0.15 #803, 0.12 #1194), 0k_p5 (0.08 #869, 0.06 #1260, 0.06 #673), 06_kh (0.08 #786, 0.06 #1177, 0.05 #1374), 0f2wj (0.07 #207, 0.06 #597, 0.05 #793), 01_d4 (0.07 #224, 0.05 #419, 0.02 #1790), 06c62 (0.03 #1862), 0b2ds (0.03 #694, 0.03 #890, 0.02 #1086), 0r3w7 (0.03 #762, 0.03 #958, 0.02 #1349), 015zxh (0.03 #610, 0.03 #806, 0.02 #1197), 02_286 (0.02 #5104, 0.02 #1969, 0.02 #2361) >> Best rule #607 for best value: >> intensional similarity = 6 >> extensional distance = 33 >> proper extension: 06cv1; 0f3zf_; 0gp9mp; 079hvk; 04g865; 0693l; 0dqzkv; 07xr3w; 06g60w; 0280mv7; ... >> query: (?x13048, 030qb3t) <- cinematography(?x9993, ?x13048), nationality(?x13048, ?x205), nationality(?x13048, ?x94), ?x94 = 09c7w0, gender(?x13048, ?x231), film_release_region(?x66, ?x205) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #207 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 01gw4f; *> query: (?x13048, 0f2wj) <- type_of_union(?x13048, ?x566), nationality(?x13048, ?x205), nationality(?x13048, ?x94), ?x94 = 09c7w0, ?x566 = 04ztj, ?x205 = 03rjj *> conf = 0.07 ranks of expected_values: 4 EVAL 04cw0n4 place_of_death 0f2wj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 42.000 42.000 0.171 http://example.org/people/deceased_person/place_of_death #2796-01bzr4 PRED entity: 01bzr4 PRED relation: place_of_birth PRED expected values: 0dclg => 117 concepts (74 used for prediction) PRED predicted values (max 10 best out of 91): 019fh (0.50 #1411, 0.50 #838, 0.50 #705), 02_286 (0.16 #3546, 0.15 #9900, 0.13 #14132), 0ycht (0.10 #581, 0.07 #1287, 0.02 #6223), 030qb3t (0.07 #26830, 0.07 #28242, 0.07 #30357), 0cr3d (0.07 #35384, 0.05 #1506, 0.05 #16326), 01_d4 (0.06 #21245, 0.06 #2888, 0.05 #12063), 03b12 (0.05 #1819, 0.04 #5344, 0.03 #3229), 0fhp9 (0.05 #1436, 0.03 #8491, 0.03 #2846), 0qkcb (0.05 #1704, 0.03 #3114, 0.02 #3819), 013n0n (0.05 #1925, 0.03 #3335, 0.02 #4040) >> Best rule #1411 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 01fxck; >> query: (?x5605, ?x3689) <- location(?x5605, ?x3689), nationality(?x5605, ?x94), ?x3689 = 019fh, profession(?x5605, ?x319), film_release_region(?x54, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2900 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 022p06; 04rg6; *> query: (?x5605, 0dclg) <- profession(?x5605, ?x319), ?x319 = 01d_h8, place_of_burial(?x5605, ?x11261) *> conf = 0.03 ranks of expected_values: 23 EVAL 01bzr4 place_of_birth 0dclg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 117.000 74.000 0.500 http://example.org/people/person/place_of_birth #2795-016tb7 PRED entity: 016tb7 PRED relation: profession PRED expected values: 03gjzk => 117 concepts (116 used for prediction) PRED predicted values (max 10 best out of 58): 01d_h8 (0.39 #1623, 0.38 #888, 0.37 #1476), 03gjzk (0.30 #1631, 0.29 #6616, 0.28 #2072), 02jknp (0.29 #6616, 0.26 #13674, 0.23 #8977), 02krf9 (0.29 #6616, 0.26 #13674, 0.13 #172), 0q04f (0.29 #6616, 0.26 #13674, 0.02 #980), 09jwl (0.27 #752, 0.26 #13674, 0.25 #1046), 0cbd2 (0.26 #13674, 0.16 #1771, 0.16 #9564), 0d1pc (0.20 #1078, 0.20 #784, 0.20 #637), 0dz3r (0.19 #884, 0.16 #737, 0.15 #1031), 0nbcg (0.18 #765, 0.16 #1059, 0.15 #912) >> Best rule #1623 for best value: >> intensional similarity = 3 >> extensional distance = 275 >> proper extension: 01nczg; 0lrh; 0d06m5; 0164nb; 012rng; 01s3kv; 012dr7; 03d_zl4; 0f13b; 014g_s; >> query: (?x3694, 01d_h8) <- award(?x3694, ?x678), participant(?x3694, ?x513), student(?x6501, ?x3694) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1631 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 275 *> proper extension: 01nczg; 0lrh; 0d06m5; 0164nb; 012rng; 01s3kv; 012dr7; 03d_zl4; 0f13b; 014g_s; *> query: (?x3694, 03gjzk) <- award(?x3694, ?x678), participant(?x3694, ?x513), student(?x6501, ?x3694) *> conf = 0.30 ranks of expected_values: 2 EVAL 016tb7 profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 116.000 0.386 http://example.org/people/person/profession #2794-02q636 PRED entity: 02q636 PRED relation: student PRED expected values: 0j582 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 963): 02vntj (0.06 #704, 0.06 #2797, 0.05 #4890), 0306ds (0.05 #4594, 0.04 #8780, 0.04 #408), 037lyl (0.04 #662, 0.04 #2755, 0.03 #6941), 015wc0 (0.04 #1696, 0.04 #3789, 0.03 #5882), 01l1rw (0.04 #999, 0.04 #3092, 0.03 #5185), 03rs8y (0.04 #46, 0.04 #2139, 0.03 #4232), 0892sx (0.04 #425, 0.04 #2518, 0.03 #4611), 016fjj (0.04 #594, 0.02 #21524, 0.02 #2687), 01nglk (0.04 #1961, 0.02 #4054, 0.02 #29170), 01cj6y (0.04 #2824, 0.03 #7010, 0.03 #11196) >> Best rule #704 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 0gsg7; 09d5h; 01xdn1; 02bh8z; 01r3kd; 0jvs0; 0sxdg; 02_l39; 03qbm; 06nvzg; >> query: (?x2980, 02vntj) <- state_province_region(?x2980, ?x335), currency(?x2980, ?x170), ?x335 = 059rby >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #234 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 0gsg7; 09d5h; 01xdn1; 02bh8z; 01r3kd; 0jvs0; 0sxdg; 02_l39; 03qbm; 06nvzg; *> query: (?x2980, 0j582) <- state_province_region(?x2980, ?x335), currency(?x2980, ?x170), ?x335 = 059rby *> conf = 0.02 ranks of expected_values: 195 EVAL 02q636 student 0j582 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 98.000 98.000 0.061 http://example.org/education/educational_institution/students_graduates./education/education/student #2793-0q9kd PRED entity: 0q9kd PRED relation: profession PRED expected values: 02jknp 02hrh1q => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.90 #1742, 0.89 #14567, 0.89 #8661), 02jknp (0.88 #871, 0.88 #727, 0.67 #2894), 0cbd2 (0.27 #3326, 0.20 #150, 0.16 #582), 09jwl (0.21 #6792, 0.21 #6504, 0.20 #6936), 0kyk (0.20 #3345, 0.12 #9105, 0.12 #6657), 0d1pc (0.15 #5670, 0.13 #6966, 0.12 #5094), 0nbcg (0.14 #6515, 0.13 #6947, 0.13 #6227), 015h31 (0.14 #455, 0.14 #311, 0.12 #599), 012t_z (0.14 #1165, 0.10 #3188, 0.08 #4052), 016z4k (0.14 #6492, 0.13 #5628, 0.13 #6924) >> Best rule #1742 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 0241wg; 0k8y7; 06wvfq; 03fwln; 040nwr; >> query: (?x71, 02hrh1q) <- award(?x71, ?x198), film(?x71, ?x407), special_performance_type(?x71, ?x4832) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0q9kd profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.897 http://example.org/people/person/profession EVAL 0q9kd profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.897 http://example.org/people/person/profession #2792-05zvj3m PRED entity: 05zvj3m PRED relation: award_winner PRED expected values: 0pz91 => 48 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1555): 02h1rt (0.43 #10930, 0.38 #15863, 0.04 #23267), 0237fw (0.33 #505, 0.20 #2972, 0.14 #5439), 09fb5 (0.33 #61, 0.18 #17332, 0.14 #7462), 02fn5 (0.33 #944, 0.14 #10814, 0.14 #5878), 0f276 (0.33 #2059, 0.14 #11929, 0.14 #6993), 0pmhf (0.33 #545, 0.14 #5479, 0.12 #12881), 09yrh (0.33 #1010, 0.14 #5944, 0.12 #13346), 026r8q (0.33 #1621, 0.14 #6555, 0.12 #13957), 011zd3 (0.33 #471, 0.14 #5405, 0.12 #12807), 01qqtr (0.33 #1914, 0.14 #6848, 0.12 #14250) >> Best rule #10930 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 05ztjjw; >> query: (?x1691, 02h1rt) <- nominated_for(?x1691, ?x10274), nominated_for(?x1691, ?x7199), ?x7199 = 05nlx4, award_winner(?x1691, ?x237), titles(?x2480, ?x10274) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #49365 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 203 *> proper extension: 0262zm; 024vjd; 02664f; 06fp11; 0262yt; 0grw_; 02f6yz; 027x4ws; 0j6j8; 031b91; ... *> query: (?x1691, ?x3917) <- award(?x3917, ?x1691), award_nominee(?x6324, ?x3917), influenced_by(?x236, ?x3917) *> conf = 0.33 ranks of expected_values: 12 EVAL 05zvj3m award_winner 0pz91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 48.000 23.000 0.429 http://example.org/award/award_category/winners./award/award_honor/award_winner #2791-03nm_fh PRED entity: 03nm_fh PRED relation: film_distribution_medium PRED expected values: 0735l => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.82 #35, 0.31 #5, 0.20 #60), 029j_ (0.21 #13, 0.14 #19, 0.14 #25), 02nxhr (0.18 #14, 0.11 #20, 0.11 #26), 0dq6p (0.08 #58, 0.08 #70, 0.08 #3) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0522wp; >> query: (?x4684, 0735l) <- film(?x609, ?x4684), category(?x4684, ?x134), ?x609 = 03xq0f >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03nm_fh film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.824 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #2790-02xb2bt PRED entity: 02xb2bt PRED relation: profession PRED expected values: 02hrh1q => 76 concepts (64 used for prediction) PRED predicted values (max 10 best out of 54): 02hrh1q (0.89 #915, 0.88 #765, 0.88 #1065), 01d_h8 (0.29 #3907, 0.28 #4960, 0.28 #6463), 0d1pc (0.28 #5255, 0.28 #4954, 0.28 #4503), 0np9r (0.28 #5255, 0.25 #5706, 0.25 #9611), 016z4k (0.28 #4954, 0.28 #4503, 0.28 #4202), 0cbd2 (0.27 #7, 0.25 #5706, 0.24 #457), 0dxtg (0.27 #3915, 0.26 #4667, 0.26 #5269), 018gz8 (0.25 #5706, 0.25 #9611, 0.25 #8559), 03gjzk (0.25 #5706, 0.23 #4669, 0.23 #6173), 02jknp (0.25 #5706, 0.21 #6465, 0.20 #3909) >> Best rule #915 for best value: >> intensional similarity = 3 >> extensional distance = 515 >> proper extension: 0277990; 06gh0t; 018z_c; 08n__5; 030b93; 01q9b9; 07q0g5; 06hgym; 02tf1y; 03rgvr; ... >> query: (?x2371, 02hrh1q) <- award_nominee(?x931, ?x2371), actor(?x1434, ?x2371), nominated_for(?x375, ?x1434) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02xb2bt profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 64.000 0.888 http://example.org/people/person/profession #2789-01p7yb PRED entity: 01p7yb PRED relation: award PRED expected values: 0gqyl => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 253): 02z1nbg (0.72 #30284, 0.70 #19269, 0.70 #18875), 0gqyl (0.63 #885, 0.20 #99, 0.14 #18088), 0bdwft (0.33 #851, 0.09 #5960, 0.08 #3209), 02y_rq5 (0.22 #875, 0.06 #5984, 0.06 #3626), 0cqgl9 (0.21 #968, 0.06 #3326, 0.06 #4112), 09sdmz (0.20 #196, 0.14 #18088, 0.12 #28710), 099tbz (0.20 #54, 0.12 #28710, 0.07 #23990), 02x8n1n (0.20 #114, 0.12 #28710, 0.07 #23990), 0cqhk0 (0.20 #35, 0.10 #428, 0.10 #3179), 054ks3 (0.20 #133, 0.10 #3277, 0.06 #7207) >> Best rule #30284 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 01wz_ml; 06lxn; >> query: (?x396, ?x1441) <- award_winner(?x1441, ?x396), award(?x516, ?x1441) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #885 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 01wk51; *> query: (?x396, 0gqyl) <- nationality(?x396, ?x94), award(?x396, ?x2880), ?x2880 = 02ppm4q *> conf = 0.63 ranks of expected_values: 2 EVAL 01p7yb award 0gqyl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2788-0292qb PRED entity: 0292qb PRED relation: film_crew_role PRED expected values: 02r96rf 0ch6mp2 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 19): 0ch6mp2 (0.79 #406, 0.78 #8, 0.76 #60), 02r96rf (0.70 #267, 0.69 #188, 0.69 #214), 09vw2b7 (0.70 #405, 0.66 #192, 0.65 #59), 04pyp5 (0.28 #13, 0.12 #1116, 0.09 #65), 02rh1dz (0.22 #62, 0.19 #221, 0.19 #274), 089g0h (0.14 #412, 0.12 #66, 0.12 #278), 02_n3z (0.12 #1116, 0.10 #399, 0.09 #239), 094hwz (0.12 #1116, 0.09 #118, 0.06 #64), 089fss (0.12 #1116, 0.08 #404, 0.07 #616), 020xn5 (0.12 #1116, 0.04 #61, 0.03 #88) >> Best rule #406 for best value: >> intensional similarity = 4 >> extensional distance = 428 >> proper extension: 047svrl; >> query: (?x7263, 0ch6mp2) <- produced_by(?x7263, ?x7848), currency(?x7263, ?x170), film_crew_role(?x7263, ?x137), film_release_distribution_medium(?x7263, ?x81) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0292qb film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.791 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0292qb film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.791 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2787-09v9mks PRED entity: 09v9mks PRED relation: film_release_region PRED expected values: 03rt9 06qd3 06mkj => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 154): 06mkj (0.91 #1839, 0.89 #2585, 0.88 #1243), 03h64 (0.88 #956, 0.88 #1105, 0.86 #1850), 02vzc (0.85 #1238, 0.84 #492, 0.83 #940), 05v8c (0.84 #463, 0.75 #1060, 0.72 #1358), 0k6nt (0.83 #1216, 0.80 #1812, 0.80 #1514), 0b90_r (0.82 #1048, 0.81 #899, 0.81 #1346), 05b4w (0.82 #654, 0.81 #1102, 0.80 #803), 03_3d (0.82 #602, 0.80 #901, 0.79 #1795), 03rj0 (0.82 #650, 0.76 #949, 0.73 #1247), 0d060g (0.80 #752, 0.79 #454, 0.78 #2989) >> Best rule #1839 for best value: >> intensional similarity = 6 >> extensional distance = 131 >> proper extension: 02vxq9m; 01c22t; 0gj8t_b; 04hwbq; 0dtfn; 011yqc; 02r8hh_; 0gj9tn5; 0_7w6; 09k56b7; ... >> query: (?x6247, 06mkj) <- film_release_region(?x6247, ?x4743), film_release_region(?x6247, ?x2316), film_crew_role(?x6247, ?x137), ?x4743 = 03spz, language(?x6247, ?x254), ?x2316 = 06t2t >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14, 16 EVAL 09v9mks film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.910 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v9mks film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 83.000 83.000 0.910 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09v9mks film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 83.000 83.000 0.910 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2786-09qv_s PRED entity: 09qv_s PRED relation: nominated_for PRED expected values: 011yth 0170th 0dl9_4 0404j37 07jnt 02cbg0 => 49 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1499): 0gmcwlb (0.78 #4758, 0.77 #12235, 0.75 #3230), 0y_9q (0.78 #5385, 0.75 #3857, 0.62 #13032), 0hv4t (0.78 #5582, 0.75 #4054, 0.55 #13229), 0jqj5 (0.78 #5357, 0.75 #3829, 0.50 #6885), 07cyl (0.78 #5071, 0.75 #3543, 0.48 #12718), 0cf08 (0.78 #5664, 0.75 #4136, 0.45 #13311), 0qmd5 (0.78 #5023, 0.75 #3495, 0.42 #9611), 0qmhk (0.78 #5411, 0.75 #3883, 0.42 #9999), 0pd64 (0.78 #5709, 0.75 #4181, 0.42 #7237), 0cq86w (0.78 #5462, 0.75 #3934, 0.42 #6990) >> Best rule #4758 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 019f4v; >> query: (?x2853, 0gmcwlb) <- nominated_for(?x2853, ?x4939), nominated_for(?x2853, ?x1199), nominated_for(?x2853, ?x696), award(?x123, ?x2853), ?x1199 = 0pv3x, ?x696 = 0209xj, award(?x4939, ?x112) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4028 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 040njc; 0f4x7; 02pqp12; 0gq9h; 04kxsb; 02qyntr; *> query: (?x2853, 0404j37) <- nominated_for(?x2853, ?x4939), nominated_for(?x2853, ?x1199), nominated_for(?x2853, ?x696), award(?x123, ?x2853), ?x1199 = 0pv3x, ?x696 = 0209xj, ?x4939 = 05hjnw *> conf = 0.75 ranks of expected_values: 16, 80, 267, 341, 486, 609 EVAL 09qv_s nominated_for 02cbg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 19.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qv_s nominated_for 07jnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 19.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qv_s nominated_for 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 49.000 19.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qv_s nominated_for 0dl9_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 19.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qv_s nominated_for 0170th CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 19.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qv_s nominated_for 011yth CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 49.000 19.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2785-01wj18h PRED entity: 01wj18h PRED relation: languages PRED expected values: 02h40lc => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 21): 02h40lc (0.94 #261, 0.93 #224, 0.92 #853), 064_8sq (0.10 #1087, 0.10 #1346, 0.09 #1420), 03k50 (0.08 #1780, 0.07 #1336, 0.06 #929), 02bjrlw (0.07 #75, 0.06 #38, 0.06 #149), 07c9s (0.04 #1788, 0.04 #1344, 0.03 #1492), 06b_j (0.03 #200, 0.02 #274, 0.01 #1347), 04306rv (0.03 #1409, 0.03 #1335, 0.03 #1076), 03_9r (0.03 #227, 0.02 #264, 0.02 #190), 0x82 (0.02 #72, 0.02 #109, 0.01 #183), 02bv9 (0.02 #55, 0.02 #92, 0.01 #166) >> Best rule #261 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 041mt; >> query: (?x3200, 02h40lc) <- artists(?x284, ?x3200), languages(?x3200, ?x2502), official_language(?x47, ?x2502) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wj18h languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.938 http://example.org/people/person/languages #2784-09rfh9 PRED entity: 09rfh9 PRED relation: language PRED expected values: 02h40lc => 151 concepts (112 used for prediction) PRED predicted values (max 10 best out of 43): 02h40lc (0.97 #1549, 0.95 #1132, 0.93 #1667), 06b_j (0.25 #201, 0.22 #260, 0.18 #437), 06nm1 (0.25 #11, 0.20 #1497, 0.19 #1558), 064_8sq (0.18 #377, 0.18 #735, 0.17 #915), 04306rv (0.18 #360, 0.17 #124, 0.17 #64), 02bjrlw (0.18 #356, 0.15 #835, 0.15 #1249), 03_9r (0.18 #365, 0.15 #844, 0.12 #723), 01r2l (0.12 #203, 0.11 #262, 0.09 #439), 012w70 (0.12 #726, 0.10 #1261, 0.09 #1915), 04h9h (0.12 #756, 0.09 #398, 0.08 #1232) >> Best rule #1549 for best value: >> intensional similarity = 6 >> extensional distance = 57 >> proper extension: 0d90m; 053rxgm; 031778; 0c_j9x; 01kf4tt; 03177r; 07cyl; 065dc4; 02d478; 01rxyb; ... >> query: (?x10309, 02h40lc) <- nominated_for(?x3019, ?x10309), prequel(?x11538, ?x10309), genre(?x10309, ?x3613), genre(?x11313, ?x3613), ?x11313 = 0by17xn, titles(?x3613, ?x1308) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09rfh9 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 112.000 0.966 http://example.org/film/film/language #2783-017f4y PRED entity: 017f4y PRED relation: nationality PRED expected values: 09c7w0 => 187 concepts (185 used for prediction) PRED predicted values (max 10 best out of 88): 09c7w0 (0.91 #10483, 0.90 #13120, 0.89 #7856), 07b_l (0.34 #14643, 0.34 #14232, 0.33 #17179), 02jx1 (0.30 #835, 0.23 #3350, 0.20 #4962), 02xry (0.27 #11797, 0.26 #14848, 0.25 #15864), 059g4 (0.27 #11797, 0.26 #14848), 04_1l0v (0.27 #11797, 0.26 #14848), 04pnx (0.27 #11797, 0.26 #14848), 07c5l (0.27 #11797, 0.26 #14848), 0rh7t (0.25 #15864, 0.15 #601, 0.09 #2210), 01p8s (0.24 #602, 0.18 #2211, 0.15 #601) >> Best rule #10483 for best value: >> intensional similarity = 4 >> extensional distance = 708 >> proper extension: 09hd6f; >> query: (?x10738, 09c7w0) <- place_of_birth(?x10738, ?x5719), time_zones(?x5719, ?x1638), source(?x5719, ?x958), origin(?x2697, ?x5719) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017f4y nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 185.000 0.906 http://example.org/people/person/nationality #2782-03j_hq PRED entity: 03j_hq PRED relation: artists! PRED expected values: 03339m 04f73rc => 70 concepts (36 used for prediction) PRED predicted values (max 10 best out of 254): 06by7 (0.91 #7122, 0.69 #8976, 0.68 #4648), 016clz (0.85 #5870, 0.81 #6178, 0.70 #6797), 0xhtw (0.67 #1248, 0.67 #940, 0.64 #3103), 07bbw (0.57 #743, 0.43 #436, 0.23 #2464), 02x8m (0.49 #4955, 0.24 #7428, 0.17 #2487), 0glt670 (0.46 #6521, 0.39 #7448, 0.22 #9303), 064t9 (0.45 #7114, 0.44 #9895, 0.43 #10206), 09nwwf (0.43 #1366, 0.33 #4762, 0.26 #5379), 059kh (0.38 #2205, 0.36 #1896, 0.34 #2516), 01cbwl (0.36 #1888, 0.33 #963, 0.31 #2508) >> Best rule #7122 for best value: >> intensional similarity = 7 >> extensional distance = 324 >> proper extension: 01pbxb; 07s3vqk; 0lbj1; 01vw87c; 01vrx3g; 089tm; 01t_xp_; 01pfr3; 0m2l9; 032nwy; ... >> query: (?x11627, 06by7) <- artists(?x10930, ?x11627), award(?x11627, ?x3045), artists(?x10930, ?x4877), artists(?x10930, ?x3657), ?x3657 = 01w8n89, ?x4877 = 03sww, parent_genre(?x5580, ?x10930) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #879 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 04rcr; 01jcxwp; 01fchy; 0560w; 0bsj9; *> query: (?x11627, 04f73rc) <- artists(?x10930, ?x11627), artists(?x2249, ?x11627), award(?x11627, ?x3045), ?x10930 = 0jrv_, group(?x227, ?x11627), ?x2249 = 03lty, ?x227 = 0342h *> conf = 0.29 ranks of expected_values: 21, 55 EVAL 03j_hq artists! 04f73rc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 70.000 36.000 0.911 http://example.org/music/genre/artists EVAL 03j_hq artists! 03339m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 70.000 36.000 0.911 http://example.org/music/genre/artists #2781-01f69m PRED entity: 01f69m PRED relation: film! PRED expected values: 0372kf => 73 concepts (40 used for prediction) PRED predicted values (max 10 best out of 670): 0272kv (0.52 #47831, 0.44 #51990, 0.42 #62387), 014g22 (0.52 #47831, 0.44 #51990, 0.42 #62387), 016szr (0.44 #51990, 0.42 #62387, 0.42 #66549), 05b2gsm (0.44 #51990, 0.42 #66548, 0.41 #58229), 0p8r1 (0.42 #2665, 0.02 #17219, 0.02 #52574), 0bxtg (0.25 #2156, 0.03 #8396, 0.03 #16710), 01nm3s (0.25 #2769, 0.02 #6930, 0.02 #4851), 01q_ph (0.25 #2137, 0.02 #45807, 0.02 #18774), 0bj9k (0.25 #326, 0.02 #4489, 0.02 #10726), 0dqcm (0.25 #1558, 0.01 #14037) >> Best rule #47831 for best value: >> intensional similarity = 4 >> extensional distance = 693 >> proper extension: 0cskb; >> query: (?x11483, ?x9363) <- nominated_for(?x9363, ?x11483), award_winner(?x8480, ?x9363), people(?x10035, ?x9363), location(?x9363, ?x7190) >> conf = 0.52 => this is the best rule for 2 predicted values *> Best rule #11320 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 276 *> proper extension: 0g5q34q; 0gh6j94; *> query: (?x11483, 0372kf) <- film_release_distribution_medium(?x11483, ?x81), films(?x5069, ?x11483), film_release_region(?x11483, ?x94) *> conf = 0.01 ranks of expected_values: 538 EVAL 01f69m film! 0372kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 40.000 0.522 http://example.org/film/actor/film./film/performance/film #2780-0g824 PRED entity: 0g824 PRED relation: award PRED expected values: 03qbh5 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 327): 02x17c2 (0.80 #10587, 0.78 #17646, 0.78 #23528), 02f705 (0.80 #10587, 0.78 #17646, 0.78 #23528), 01d38g (0.80 #10587, 0.78 #17646, 0.78 #23528), 02f764 (0.80 #10587, 0.78 #17646, 0.78 #23528), 09sb52 (0.38 #5530, 0.36 #3962, 0.32 #12981), 01c4_6 (0.36 #1264, 0.25 #479, 0.09 #871), 03qbh5 (0.34 #8825, 0.32 #10393, 0.29 #1768), 05p09zm (0.33 #4042, 0.29 #4434, 0.27 #6786), 054ks3 (0.33 #139, 0.29 #1708, 0.26 #8765), 05pcn59 (0.33 #4001, 0.28 #6745, 0.27 #4393) >> Best rule #10587 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 0ggl02; 05crg7; 0288fyj; 01x15dc; 015cxv; 03cd1q; >> query: (?x6383, ?x567) <- award(?x6383, ?x724), ?x724 = 01bgqh, award_winner(?x567, ?x6383) >> conf = 0.80 => this is the best rule for 4 predicted values *> Best rule #8825 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 120 *> proper extension: 028q6; 02zmh5; 02cyfz; 02qlg7s; 017vkx; 05vzw3; 01kd57; 0415mzy; 016732; 04n32; ... *> query: (?x6383, 03qbh5) <- award(?x6383, ?x724), ?x724 = 01bgqh, profession(?x6383, ?x131) *> conf = 0.34 ranks of expected_values: 7 EVAL 0g824 award 03qbh5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 145.000 145.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2779-02tr7d PRED entity: 02tr7d PRED relation: award_winner PRED expected values: 03v3xp => 84 concepts (37 used for prediction) PRED predicted values (max 10 best out of 550): 0bx0lc (0.82 #47636, 0.82 #30167, 0.82 #30166), 08w7vj (0.82 #47636, 0.82 #30167, 0.82 #30166), 02l4pj (0.82 #47636, 0.82 #30167, 0.82 #30166), 01ksr1 (0.82 #47636, 0.82 #30167, 0.82 #30166), 01713c (0.82 #47636, 0.82 #30167, 0.82 #30166), 03v3xp (0.82 #47636, 0.82 #30167, 0.82 #30166), 01sp81 (0.82 #47636, 0.82 #30167, 0.82 #30166), 0f4dx2 (0.82 #30167, 0.82 #30166, 0.82 #34931), 040981l (0.80 #1237, 0.73 #4410, 0.65 #4762), 01kwsg (0.65 #4762, 0.48 #50812, 0.48 #50811) >> Best rule #47636 for best value: >> intensional similarity = 3 >> extensional distance = 1048 >> proper extension: 079ws; >> query: (?x1669, ?x926) <- award_winner(?x926, ?x1669), people(?x743, ?x926), award_winner(?x1669, ?x2372) >> conf = 0.82 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 02tr7d award_winner 03v3xp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 84.000 37.000 0.824 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #2778-083wr9 PRED entity: 083wr9 PRED relation: profession PRED expected values: 02hrh1q => 72 concepts (52 used for prediction) PRED predicted values (max 10 best out of 44): 02hrh1q (0.92 #4339, 0.91 #4637, 0.91 #4786), 0dxtg (0.78 #3295, 0.72 #3891, 0.54 #5831), 01d_h8 (0.40 #3287, 0.39 #3883, 0.37 #5823), 02jknp (0.37 #5825, 0.35 #3885, 0.35 #3289), 03gjzk (0.32 #3893, 0.31 #3297, 0.27 #2103), 018gz8 (0.26 #2105, 0.25 #1956, 0.24 #1807), 02krf9 (0.20 #3904, 0.15 #5844, 0.13 #3308), 0cbd2 (0.18 #3288, 0.17 #3884, 0.14 #5824), 01c72t (0.17 #5841, 0.07 #6586, 0.07 #7629), 09jwl (0.17 #5539, 0.16 #6582, 0.15 #3748) >> Best rule #4339 for best value: >> intensional similarity = 4 >> extensional distance = 1462 >> proper extension: 01x1cn2; 02fx3c; 016ksk; 0fhxv; 048wrb; 01dw_f; 0p17j; 030g9z; 0306bt; 01vzxld; ... >> query: (?x13587, 02hrh1q) <- profession(?x13587, ?x1383), film(?x13587, ?x5277), featured_film_locations(?x5277, ?x739), film(?x609, ?x5277) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 083wr9 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 52.000 0.917 http://example.org/people/person/profession #2777-02bqy PRED entity: 02bqy PRED relation: school_type PRED expected values: 05pcjw => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 18): 05jxkf (0.49 #676, 0.49 #580, 0.49 #460), 01rs41 (0.30 #509, 0.28 #485, 0.28 #773), 05pcjw (0.27 #769, 0.25 #505, 0.24 #481), 01_9fk (0.26 #290, 0.25 #362, 0.21 #602), 07tf8 (0.21 #465, 0.17 #585, 0.17 #297), 01_srz (0.07 #939, 0.06 #795, 0.06 #771), 02p0qmm (0.05 #706, 0.04 #850, 0.03 #1186), 04399 (0.04 #614, 0.04 #662, 0.02 #470), 01y64 (0.03 #828, 0.03 #756, 0.03 #516), 01jlsn (0.03 #977, 0.03 #1121, 0.02 #833) >> Best rule #676 for best value: >> intensional similarity = 3 >> extensional distance = 196 >> proper extension: 01dbns; 0jksm; >> query: (?x5638, 05jxkf) <- institution(?x3437, ?x5638), ?x3437 = 02_xgp2, major_field_of_study(?x5638, ?x254) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #769 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 259 *> proper extension: 02zc7f; 02h7qr; 0sxgh; 043q2z; *> query: (?x5638, 05pcjw) <- student(?x5638, ?x2239), contains(?x94, ?x5638), ?x94 = 09c7w0 *> conf = 0.27 ranks of expected_values: 3 EVAL 02bqy school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 103.000 103.000 0.495 http://example.org/education/educational_institution/school_type #2776-0fpgp26 PRED entity: 0fpgp26 PRED relation: film_release_region PRED expected values: 0154j 0k6nt 06bnz 03spz 0345_ => 52 concepts (44 used for prediction) PRED predicted values (max 10 best out of 105): 0154j (0.86 #454, 0.85 #682, 0.76 #795), 0k6nt (0.83 #1147, 0.83 #921, 0.83 #1374), 03spz (0.82 #516, 0.78 #744, 0.72 #857), 06bnz (0.80 #479, 0.78 #707, 0.70 #820), 0h7x (0.55 #927, 0.55 #1153, 0.54 #814), 02k54 (0.35 #802, 0.35 #915, 0.34 #1141), 03ryn (0.32 #508, 0.30 #736, 0.29 #849), 0hzlz (0.28 #692, 0.27 #464, 0.27 #918), 0161c (0.24 #504, 0.20 #732, 0.16 #680), 0d0kn (0.24 #486, 0.19 #714, 0.16 #680) >> Best rule #454 for best value: >> intensional similarity = 8 >> extensional distance = 64 >> proper extension: 040rmy; 0g9zljd; >> query: (?x9194, 0154j) <- film_release_region(?x9194, ?x2629), film_release_region(?x9194, ?x2513), film_release_region(?x9194, ?x1558), film_release_region(?x9194, ?x47), ?x2513 = 05b4w, ?x2629 = 06f32, ?x1558 = 01mjq, administrative_parent(?x47, ?x551) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 20 EVAL 0fpgp26 film_release_region 0345_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 52.000 44.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fpgp26 film_release_region 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 44.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fpgp26 film_release_region 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 44.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fpgp26 film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 44.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0fpgp26 film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 44.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2775-05lwjc PRED entity: 05lwjc PRED relation: artists PRED expected values: 011z3g 03f3yfj => 40 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1217): 011z3g (0.75 #10257, 0.67 #4890, 0.60 #7036), 01wzlxj (0.67 #2472, 0.60 #6767, 0.60 #1400), 0g824 (0.67 #2715, 0.60 #7010, 0.60 #1643), 0gbwp (0.67 #10006, 0.60 #6785, 0.60 #1418), 015mrk (0.67 #2394, 0.60 #1322, 0.56 #4543), 01f2q5 (0.67 #5304, 0.60 #6377, 0.55 #8522), 01w272y (0.67 #2431, 0.60 #1359, 0.50 #9947), 0770cd (0.67 #2272, 0.60 #1200, 0.50 #6567), 03f1d47 (0.67 #2591, 0.60 #1519, 0.50 #448), 019g40 (0.64 #8724, 0.60 #6577, 0.60 #1210) >> Best rule #10257 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 0m0jc; 06by7; >> query: (?x11787, 011z3g) <- artists(?x11787, ?x6289), artists(?x11787, ?x3176), people(?x2510, ?x3176), award_nominee(?x3176, ?x1338), award(?x3176, ?x704), ?x6289 = 0x3n >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 22 EVAL 05lwjc artists 03f3yfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 40.000 21.000 0.750 http://example.org/music/genre/artists EVAL 05lwjc artists 011z3g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 21.000 0.750 http://example.org/music/genre/artists #2774-01gst9 PRED entity: 01gst9 PRED relation: district_represented PRED expected values: 05tbn 04ly1 0498y => 32 concepts (31 used for prediction) PRED predicted values (max 10 best out of 296): 05tbn (0.92 #146, 0.91 #595, 0.85 #1220), 0498y (0.92 #146, 0.91 #595, 0.85 #1170), 0g0syc (0.92 #146, 0.80 #497, 0.74 #245), 0gyh (0.91 #595, 0.82 #958, 0.82 #761), 04ych (0.91 #595, 0.82 #749, 0.82 #700), 050ks (0.91 #595, 0.80 #497, 0.80 #1139), 04ly1 (0.91 #595, 0.80 #497, 0.80 #1139), 0vbk (0.80 #497, 0.74 #245, 0.74 #693), 04rrx (0.80 #497, 0.74 #245, 0.74 #693), 03s0w (0.80 #497, 0.74 #245, 0.74 #693) >> Best rule #146 for best value: >> intensional similarity = 42 >> extensional distance = 2 >> proper extension: 01gt99; >> query: (?x6712, ?x3670) <- legislative_sessions(?x6712, ?x9416), legislative_sessions(?x6712, ?x7973), legislative_sessions(?x6712, ?x6021), legislative_sessions(?x6712, ?x5005), district_represented(?x6712, ?x7518), district_represented(?x6712, ?x6895), district_represented(?x6712, ?x4622), district_represented(?x6712, ?x3818), district_represented(?x6712, ?x3778), district_represented(?x6712, ?x2020), district_represented(?x6712, ?x1767), district_represented(?x6712, ?x1755), district_represented(?x6712, ?x1426), district_represented(?x6712, ?x760), district_represented(?x6712, ?x728), ?x4622 = 04tgp, ?x760 = 05fkf, ?x1426 = 07z1m, legislative_sessions(?x2860, ?x6712), ?x1767 = 04rrd, legislative_sessions(?x7714, ?x6712), ?x2860 = 0b3wk, ?x5005 = 01gstn, ?x1755 = 01x73, ?x7973 = 01gsvb, legislative_sessions(?x4787, ?x9416), ?x7518 = 026mj, legislative_sessions(?x7891, ?x7714), legislative_sessions(?x4787, ?x7715), legislative_sessions(?x4812, ?x7714), ?x6021 = 01gsvp, ?x6895 = 05fjf, ?x3818 = 03v0t, ?x7715 = 01grp0, ?x728 = 059f4, district_represented(?x4787, ?x3670), profession(?x7891, ?x5805), religion(?x7891, ?x2591), jurisdiction_of_office(?x7891, ?x94), basic_title(?x7891, ?x900), ?x2020 = 05k7sb, ?x3778 = 07h34 >> conf = 0.92 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2, 7 EVAL 01gst9 district_represented 0498y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.917 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gst9 district_represented 04ly1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 32.000 31.000 0.917 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gst9 district_represented 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.917 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #2773-01wqpnm PRED entity: 01wqpnm PRED relation: artist! PRED expected values: 011k1h => 124 concepts (59 used for prediction) PRED predicted values (max 10 best out of 116): 015_1q (0.20 #4080, 0.20 #7441, 0.19 #3380), 03rhqg (0.19 #295, 0.17 #5757, 0.15 #6457), 0fb0v (0.18 #426, 0.18 #146, 0.14 #706), 01w40h (0.16 #28, 0.13 #588, 0.11 #448), 02y21l (0.16 #95, 0.12 #375, 0.08 #795), 0g768 (0.15 #877, 0.15 #1297, 0.14 #5779), 033hn8 (0.14 #1133, 0.13 #5755, 0.12 #13), 011k1h (0.13 #849, 0.12 #9, 0.12 #1269), 0mzkr (0.12 #25, 0.08 #865, 0.08 #2545), 02bh8z (0.12 #21, 0.08 #1281, 0.07 #301) >> Best rule #4080 for best value: >> intensional similarity = 4 >> extensional distance = 329 >> proper extension: 01q_ph; 03f1r6t; 0m0hw; 04l19_; 013pk3; 0h7pj; 016jll; 020jqv; >> query: (?x10198, 015_1q) <- type_of_union(?x10198, ?x1873), profession(?x10198, ?x1032), artist(?x1543, ?x10198), award(?x10198, ?x2634) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #849 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 82 *> proper extension: 094xh; *> query: (?x10198, 011k1h) <- type_of_union(?x10198, ?x1873), artists(?x3061, ?x10198), nationality(?x10198, ?x1310), ?x3061 = 05bt6j *> conf = 0.13 ranks of expected_values: 8 EVAL 01wqpnm artist! 011k1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 124.000 59.000 0.199 http://example.org/music/record_label/artist #2772-0x3r3 PRED entity: 0x3r3 PRED relation: interests PRED expected values: 05qt0 => 158 concepts (141 used for prediction) PRED predicted values (max 10 best out of 12): 0gt_hv (0.50 #16, 0.11 #128, 0.10 #536), 02jhc (0.38 #56, 0.29 #201, 0.21 #427), 05qt0 (0.31 #55, 0.19 #200, 0.17 #39), 02jcc (0.30 #161, 0.25 #33, 0.25 #1), 04s0m (0.25 #170, 0.17 #42, 0.15 #58), 097df (0.25 #14, 0.05 #174, 0.04 #433), 0x0w (0.14 #206, 0.10 #536, 0.10 #173), 06ms6 (0.10 #536, 0.08 #35, 0.05 #196), 09xq9d (0.10 #166, 0.08 #38, 0.08 #54), 04g7x (0.08 #59, 0.06 #123, 0.05 #204) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 047g6; >> query: (?x5796, 0gt_hv) <- company(?x5796, ?x741), gender(?x5796, ?x231), influenced_by(?x5796, ?x4547), ?x4547 = 03_hd >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 0lcx; 043tg; 09gnn; *> query: (?x5796, 05qt0) <- influenced_by(?x5796, ?x13698), influenced_by(?x5796, ?x7509), profession(?x5796, ?x8340), people(?x12333, ?x13698), ?x7509 = 048cl *> conf = 0.31 ranks of expected_values: 3 EVAL 0x3r3 interests 05qt0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 158.000 141.000 0.500 http://example.org/user/alexander/philosophy/philosopher/interests #2771-04gp1d PRED entity: 04gp1d PRED relation: legislative_sessions! PRED expected values: 02bn_p => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 40): 02bqm0 (0.89 #256, 0.88 #343, 0.88 #171), 024tkd (0.89 #256, 0.88 #343, 0.88 #171), 04h1rz (0.89 #256, 0.88 #343, 0.88 #171), 032ft5 (0.89 #256, 0.88 #343, 0.88 #171), 02bn_p (0.88 #859, 0.87 #300, 0.86 #342), 077g7n (0.88 #859, 0.87 #300, 0.86 #342), 04gp1d (0.88 #859, 0.87 #300, 0.86 #342), 01gst_ (0.36 #1499, 0.35 #1159, 0.34 #1406), 01gsvb (0.35 #1159, 0.34 #1406, 0.33 #1280), 01gsvp (0.35 #1159, 0.34 #1406, 0.33 #1280) >> Best rule #256 for best value: >> intensional similarity = 46 >> extensional distance = 2 >> proper extension: 04h1rz; >> query: (?x3765, ?x1028) <- legislative_sessions(?x11605, ?x3765), legislative_sessions(?x9334, ?x3765), legislative_sessions(?x6728, ?x3765), legislative_sessions(?x5339, ?x3765), legislative_sessions(?x4730, ?x3765), legislative_sessions(?x3766, ?x3765), legislative_sessions(?x3540, ?x3765), legislative_sessions(?x2976, ?x3765), legislative_sessions(?x1830, ?x3765), legislative_sessions(?x1829, ?x3765), legislative_sessions(?x952, ?x3765), legislative_sessions(?x653, ?x3765), legislative_sessions(?x606, ?x3765), legislative_sessions(?x356, ?x3765), legislative_sessions(?x355, ?x3765), ?x1829 = 02bp37, legislative_sessions(?x3765, ?x6933), legislative_sessions(?x3765, ?x6743), legislative_sessions(?x3765, ?x1028), ?x3540 = 024tcq, ?x952 = 06f0dc, ?x6728 = 070mff, ?x606 = 03ww_x, district_represented(?x3765, ?x2977), district_represented(?x3765, ?x2020), ?x9334 = 02hy5d, ?x355 = 0495ys, ?x1830 = 03z5xd, ?x356 = 05l2z4, ?x4730 = 02cg7g, legislative_sessions(?x4665, ?x6743), ?x2976 = 03rtmz, ?x6933 = 024tkd, ?x2977 = 081mh, nationality(?x11605, ?x94), ?x653 = 070m6c, ?x2020 = 05k7sb, ?x3766 = 02gkzs, ?x5339 = 02glc4, ?x94 = 09c7w0, profession(?x11605, ?x5805), location(?x11605, ?x1131), ?x4665 = 07t58, place_of_birth(?x722, ?x1131), place_of_death(?x1047, ?x1131), citytown(?x1762, ?x1131) >> conf = 0.89 => this is the best rule for 4 predicted values *> Best rule #859 for first EXPECTED value: *> intensional similarity = 52 *> extensional distance = 6 *> proper extension: 03z5xd; *> query: (?x3765, ?x1027) <- legislative_sessions(?x6742, ?x3765), legislative_sessions(?x5977, ?x3765), legislative_sessions(?x4730, ?x3765), legislative_sessions(?x3540, ?x3765), legislative_sessions(?x2976, ?x3765), legislative_sessions(?x1829, ?x3765), legislative_sessions(?x952, ?x3765), legislative_sessions(?x355, ?x3765), ?x1829 = 02bp37, legislative_sessions(?x3765, ?x6933), legislative_sessions(?x3765, ?x1028), ?x3540 = 024tcq, district_represented(?x952, ?x7518), district_represented(?x952, ?x6895), district_represented(?x952, ?x5575), district_represented(?x952, ?x4105), district_represented(?x952, ?x3908), district_represented(?x952, ?x3778), district_represented(?x952, ?x3670), district_represented(?x952, ?x3038), district_represented(?x952, ?x2831), district_represented(?x952, ?x2768), district_represented(?x952, ?x2020), district_represented(?x952, ?x448), district_represented(?x952, ?x335), district_represented(?x952, ?x177), legislative_sessions(?x2357, ?x952), ?x2976 = 03rtmz, ?x6742 = 06bss, legislative_sessions(?x2860, ?x952), ?x3038 = 0d0x8, ?x2768 = 03s5t, ?x355 = 0495ys, ?x2831 = 0gyh, ?x3908 = 04ly1, ?x335 = 059rby, ?x2020 = 05k7sb, ?x3670 = 05tbn, ?x177 = 05kkh, ?x1028 = 032ft5, legislative_sessions(?x1027, ?x952), legislative_sessions(?x605, ?x952), ?x4730 = 02cg7g, ?x7518 = 026mj, ?x605 = 077g7n, ?x448 = 03v1s, ?x6895 = 05fjf, district_represented(?x6933, ?x728), ?x5575 = 05fjy, ?x3778 = 07h34, ?x4105 = 0824r, ?x5977 = 06r713 *> conf = 0.88 ranks of expected_values: 5 EVAL 04gp1d legislative_sessions! 02bn_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 40.000 40.000 0.889 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #2770-01s7w3 PRED entity: 01s7w3 PRED relation: production_companies PRED expected values: 030_1_ => 149 concepts (129 used for prediction) PRED predicted values (max 10 best out of 92): 01gb54 (0.50 #279, 0.18 #603, 0.15 #522), 05qd_ (0.33 #171, 0.25 #252, 0.14 #1064), 030_1_ (0.32 #582, 0.18 #1475, 0.16 #988), 016tt2 (0.25 #327, 0.20 #408, 0.12 #489), 0hpt3 (0.25 #262, 0.03 #668, 0.03 #6498), 054lpb6 (0.21 #662, 0.14 #1718, 0.12 #3510), 017s11 (0.20 #83, 0.12 #245, 0.09 #7973), 02jd_7 (0.20 #149, 0.06 #1123, 0.06 #1366), 0fvppk (0.17 #231, 0.01 #1529), 0kx4m (0.13 #900, 0.07 #1468, 0.07 #2039) >> Best rule #279 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0435vm; 027r9t; >> query: (?x9154, 01gb54) <- produced_by(?x9154, ?x7094), film(?x2557, ?x9154), award_nominee(?x525, ?x2557), ?x7094 = 05mvd62 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #582 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 01j95; *> query: (?x9154, 030_1_) <- award_winner(?x9154, ?x6045), influenced_by(?x6045, ?x3969), program(?x6045, ?x6482) *> conf = 0.32 ranks of expected_values: 3 EVAL 01s7w3 production_companies 030_1_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 149.000 129.000 0.500 http://example.org/film/film/production_companies #2769-044k8 PRED entity: 044k8 PRED relation: languages PRED expected values: 02h40lc => 176 concepts (176 used for prediction) PRED predicted values (max 10 best out of 11): 02h40lc (0.38 #2, 0.37 #1368, 0.35 #159), 064_8sq (0.07 #1381, 0.05 #796, 0.05 #1615), 06nm1 (0.06 #45, 0.05 #436, 0.03 #358), 04306rv (0.05 #81, 0.02 #550, 0.02 #589), 03k50 (0.03 #1526, 0.02 #3515, 0.02 #4412), 0t_2 (0.02 #517, 0.02 #634, 0.02 #712), 06b_j (0.02 #602, 0.02 #758, 0.02 #797), 02bjrlw (0.02 #1757, 0.02 #1874, 0.02 #3239), 03_9r (0.01 #2034, 0.01 #1761), 07c9s (0.01 #3017, 0.01 #3524, 0.01 #3446) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0f7fy; >> query: (?x4608, 02h40lc) <- location(?x4608, ?x3269), people(?x3584, ?x4608), ?x3269 = 0vzm >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044k8 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 176.000 176.000 0.375 http://example.org/people/person/languages #2768-04w1j9 PRED entity: 04w1j9 PRED relation: executive_produced_by! PRED expected values: 02h22 => 107 concepts (25 used for prediction) PRED predicted values (max 10 best out of 339): 02pw_n (0.33 #377, 0.01 #1439, 0.01 #8874), 0f4_l (0.33 #117, 0.01 #1179, 0.01 #8614), 016yxn (0.11 #6904, 0.05 #7967, 0.02 #13276), 0dp7wt (0.11 #6904, 0.05 #7967), 08sfxj (0.10 #6903, 0.10 #5841, 0.02 #13276), 025ts_z (0.04 #3655, 0.03 #5779, 0.03 #6841), 049xgc (0.04 #856, 0.03 #3510, 0.03 #5634), 0bt4g (0.04 #1484, 0.02 #954, 0.02 #3608), 0mbql (0.04 #1440, 0.02 #910, 0.02 #3564), 01f7kl (0.04 #1197, 0.02 #667, 0.02 #3321) >> Best rule #377 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0q9kd; >> query: (?x4931, 02pw_n) <- produced_by(?x10300, ?x4931), ?x10300 = 0296rz, nominated_for(?x4931, ?x7822) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #868 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 81 *> proper extension: 027zz; *> query: (?x4931, 02h22) <- location(?x4931, ?x739), executive_produced_by(?x3640, ?x4931), award_winner(?x4931, ?x3447) *> conf = 0.01 ranks of expected_values: 214 EVAL 04w1j9 executive_produced_by! 02h22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 107.000 25.000 0.333 http://example.org/film/film/executive_produced_by #2767-01vsn38 PRED entity: 01vsn38 PRED relation: people! PRED expected values: 041rx 07bch9 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 36): 0x67 (0.27 #1165, 0.24 #1704, 0.23 #1242), 041rx (0.27 #4, 0.17 #312, 0.15 #1313), 0xnvg (0.13 #13, 0.07 #90, 0.07 #321), 033tf_ (0.11 #315, 0.07 #4088, 0.07 #3780), 02g7sp (0.09 #95, 0.03 #480, 0.02 #326), 02w7gg (0.08 #2312, 0.07 #3775, 0.07 #4622), 048z7l (0.07 #40, 0.05 #348, 0.04 #656), 01qhm_ (0.07 #6, 0.03 #545, 0.03 #1007), 09vc4s (0.07 #9, 0.02 #1703, 0.02 #2319), 013xrm (0.07 #20, 0.02 #174, 0.02 #636) >> Best rule #1165 for best value: >> intensional similarity = 3 >> extensional distance = 301 >> proper extension: 09bx1k; >> query: (?x11233, 0x67) <- award_nominee(?x11233, ?x2275), artists(?x1000, ?x11233), film(?x2275, ?x308) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 04t2l2; 0mdqp; 032w8h; 01pcbg; 05txrz; 04fcx7; 030vnj; 01jz6x; *> query: (?x11233, 041rx) <- award_nominee(?x11233, ?x2275), film(?x11233, ?x2102), ?x2102 = 034qzw *> conf = 0.27 ranks of expected_values: 2, 15 EVAL 01vsn38 people! 07bch9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 111.000 111.000 0.267 http://example.org/people/ethnicity/people EVAL 01vsn38 people! 041rx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 111.000 111.000 0.267 http://example.org/people/ethnicity/people #2766-017b2p PRED entity: 017b2p PRED relation: artists! PRED expected values: 06by7 => 96 concepts (50 used for prediction) PRED predicted values (max 10 best out of 244): 025sc50 (0.82 #979, 0.60 #50, 0.57 #360), 06by7 (0.71 #9925, 0.68 #11164, 0.62 #11783), 02lnbg (0.63 #987, 0.43 #368, 0.40 #58), 0gywn (0.60 #57, 0.57 #367, 0.53 #986), 017_qw (0.46 #1610, 0.12 #9654, 0.12 #14296), 0glt670 (0.45 #970, 0.40 #41, 0.34 #1280), 01lyv (0.45 #4365, 0.29 #6531, 0.23 #9937), 05bt6j (0.33 #5303, 0.32 #664, 0.32 #6232), 016clz (0.32 #13003, 0.23 #5884, 0.23 #11766), 03_d0 (0.31 #8674, 0.23 #2487, 0.20 #12) >> Best rule #979 for best value: >> intensional similarity = 5 >> extensional distance = 36 >> proper extension: 0hvbj; >> query: (?x8947, 025sc50) <- artists(?x5876, ?x8947), artists(?x3319, ?x8947), artist(?x4079, ?x8947), ?x3319 = 06j6l, ?x5876 = 0ggx5q >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #9925 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 489 *> proper extension: 089tm; 01t_xp_; 01pfr3; 0150jk; 02r3zy; 07c0j; 067mj; 01vsxdm; 03g5jw; 03t9sp; ... *> query: (?x8947, 06by7) <- artists(?x3319, ?x8947), artist(?x4079, ?x8947), artists(?x3319, ?x5878), artists(?x3319, ?x1413), ?x1413 = 01p9hgt, nationality(?x5878, ?x94) *> conf = 0.71 ranks of expected_values: 2 EVAL 017b2p artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 50.000 0.816 http://example.org/music/genre/artists #2765-01mszz PRED entity: 01mszz PRED relation: honored_for! PRED expected values: 02scbv => 110 concepts (57 used for prediction) PRED predicted values (max 10 best out of 124): 01mszz (0.60 #255, 0.60 #104, 0.38 #557), 02scbv (0.60 #271, 0.60 #120, 0.28 #573), 0kv2hv (0.17 #468, 0.06 #1223, 0.06 #1072), 0cf08 (0.17 #2269, 0.05 #7418, 0.04 #4688), 07gghl (0.17 #567, 0.05 #1322, 0.05 #1171), 053rxgm (0.17 #2269, 0.04 #4688, 0.03 #4840), 03clwtw (0.17 #2269, 0.03 #4840, 0.01 #1965), 08984j (0.17 #2269, 0.03 #4840, 0.01 #1965), 0gffmn8 (0.17 #2269, 0.03 #4840), 04tc1g (0.14 #469, 0.05 #1224, 0.05 #1073) >> Best rule #255 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 059lwy; >> query: (?x6205, 01mszz) <- featured_film_locations(?x6205, ?x682), honored_for(?x6205, ?x5667), honored_for(?x6205, ?x1311), ?x5667 = 074rg9, ?x1311 = 069q4f >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #271 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 059lwy; *> query: (?x6205, 02scbv) <- featured_film_locations(?x6205, ?x682), honored_for(?x6205, ?x5667), honored_for(?x6205, ?x1311), ?x5667 = 074rg9, ?x1311 = 069q4f *> conf = 0.60 ranks of expected_values: 2 EVAL 01mszz honored_for! 02scbv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 57.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #2764-050xxm PRED entity: 050xxm PRED relation: film! PRED expected values: 01jgpsh 013zs9 => 68 concepts (30 used for prediction) PRED predicted values (max 10 best out of 730): 07rd7 (0.55 #35310, 0.42 #51928, 0.38 #45696), 0p_pd (0.22 #54, 0.03 #20825, 0.03 #29134), 014zcr (0.11 #37, 0.04 #2113, 0.02 #24962), 01trf3 (0.11 #725, 0.04 #2801, 0.01 #4877), 01q_ph (0.11 #57, 0.04 #20828, 0.03 #29137), 02qgqt (0.11 #18, 0.03 #4170, 0.03 #47774), 05kwx2 (0.11 #1090, 0.03 #47774, 0.03 #49851), 015rkw (0.11 #282, 0.03 #47774, 0.03 #49851), 01tspc6 (0.11 #162, 0.03 #12624, 0.01 #4314), 0h0wc (0.11 #422, 0.02 #8727, 0.02 #23269) >> Best rule #35310 for best value: >> intensional similarity = 4 >> extensional distance = 594 >> proper extension: 01b7h8; >> query: (?x1797, ?x3410) <- nominated_for(?x3410, ?x1797), gender(?x3410, ?x231), spouse(?x3410, ?x3361), type_of_union(?x3410, ?x566) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1503 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0g56t9t; 01pv91; 0gtsxr4; 01hw5kk; 08s6mr; 04z_3pm; 01d2v1; *> query: (?x1797, 013zs9) <- genre(?x1797, ?x12344), genre(?x1797, ?x258), film(?x926, ?x1797), ?x258 = 05p553, ?x12344 = 06qln *> conf = 0.11 ranks of expected_values: 26 EVAL 050xxm film! 013zs9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 68.000 30.000 0.554 http://example.org/film/actor/film./film/performance/film EVAL 050xxm film! 01jgpsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 30.000 0.554 http://example.org/film/actor/film./film/performance/film #2763-027hm_ PRED entity: 027hm_ PRED relation: profession PRED expected values: 016z4k 025352 => 133 concepts (24 used for prediction) PRED predicted values (max 10 best out of 50): 016z4k (0.60 #151, 0.51 #1920, 0.50 #1624), 0nbcg (0.54 #912, 0.52 #1798, 0.51 #2832), 0dz3r (0.46 #2064, 0.46 #1918, 0.43 #2067), 039v1 (0.42 #1212, 0.40 #182, 0.35 #1803), 04f2zj (0.33 #2065, 0.31 #1768, 0.30 #3543), 0n1h (0.29 #2077, 0.29 #453, 0.24 #1632), 01d_h8 (0.29 #2366, 0.19 #2071, 0.14 #1626), 0fnpj (0.21 #941, 0.20 #353, 0.20 #206), 03gjzk (0.21 #2375, 0.16 #750, 0.10 #2080), 0dxtg (0.19 #2374, 0.11 #749, 0.07 #2079) >> Best rule #151 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01693z; >> query: (?x8246, 016z4k) <- artists(?x482, ?x8246), ?x482 = 015pdg, profession(?x8246, ?x1032), place_of_birth(?x8246, ?x12405) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14 EVAL 027hm_ profession 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 133.000 24.000 0.600 http://example.org/people/person/profession EVAL 027hm_ profession 016z4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 24.000 0.600 http://example.org/people/person/profession #2762-0gd_s PRED entity: 0gd_s PRED relation: award PRED expected values: 0265wl 01tgwv => 151 concepts (128 used for prediction) PRED predicted values (max 10 best out of 327): 0265wl (0.70 #1817, 0.39 #3401, 0.35 #4589), 039yzf (0.39 #3512, 0.25 #2324, 0.22 #5492), 0ddd9 (0.38 #1243, 0.10 #26935, 0.09 #16689), 01tgwv (0.33 #2337, 0.23 #5505, 0.20 #5901), 058bzgm (0.33 #2346, 0.18 #5514, 0.16 #5910), 04hddx (0.33 #360, 0.12 #1548, 0.10 #26935), 0208wk (0.30 #1923, 0.17 #5487, 0.17 #2319), 0fbvqf (0.20 #839, 0.13 #2819, 0.05 #50703), 01bgqh (0.20 #834, 0.11 #15884, 0.10 #7170), 0gqy2 (0.20 #955, 0.10 #29477, 0.09 #31061) >> Best rule #1817 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0g5ff; >> query: (?x9284, 0265wl) <- award(?x9284, ?x1375), award(?x9284, ?x575), influenced_by(?x1752, ?x9284), ?x575 = 040vk98, ?x1375 = 0262zm >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0gd_s award 01tgwv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 151.000 128.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gd_s award 0265wl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 128.000 0.700 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2761-05ch98 PRED entity: 05ch98 PRED relation: film! PRED expected values: 04shbh => 63 concepts (38 used for prediction) PRED predicted values (max 10 best out of 973): 015p3p (0.60 #9414, 0.09 #7334, 0.09 #23975), 02lymt (0.40 #852, 0.29 #2932, 0.18 #7093), 014zcr (0.33 #4161, 0.12 #66576, 0.12 #66575), 06t8b (0.33 #4161, 0.12 #66575, 0.11 #62413), 02bj6k (0.29 #5545, 0.02 #30507, 0.01 #13866), 07y8l9 (0.20 #972, 0.14 #5133, 0.14 #3052), 02qx69 (0.20 #554, 0.14 #2634, 0.09 #6795), 05mlqj (0.20 #1604, 0.14 #3684, 0.09 #7845), 04l19_ (0.20 #1171, 0.14 #3251, 0.09 #7412), 0320jz (0.20 #305, 0.14 #2385, 0.09 #6546) >> Best rule #9414 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 06w99h3; 0dgpwnk; 062zm5h; 02d003; 01kqq7; >> query: (?x7854, 015p3p) <- film(?x3495, ?x7854), film(?x3495, ?x10931), award(?x3495, ?x1232), role(?x3495, ?x227), ?x10931 = 06pyc2 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4327 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0h7t36; *> query: (?x7854, 04shbh) <- film(?x8966, ?x7854), film(?x3495, ?x7854), award(?x3495, ?x1232), gender(?x3495, ?x231), ?x8966 = 01qqtr *> conf = 0.14 ranks of expected_values: 59 EVAL 05ch98 film! 04shbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 63.000 38.000 0.600 http://example.org/film/actor/film./film/performance/film #2760-0f2c8g PRED entity: 0f2c8g PRED relation: nationality PRED expected values: 03rk0 => 94 concepts (64 used for prediction) PRED predicted values (max 10 best out of 34): 03rk0 (0.87 #146, 0.84 #448, 0.34 #1607), 09c7w0 (0.79 #705, 0.77 #905, 0.75 #1005), 0byh8j (0.34 #1607, 0.34 #5424, 0.33 #1405), 0f8l9c (0.17 #323, 0.03 #5626, 0.03 #6433), 02jx1 (0.10 #1037, 0.10 #937, 0.10 #2746), 07ssc (0.08 #4937, 0.08 #4335, 0.08 #4135), 0345h (0.07 #31, 0.07 #332, 0.05 #533), 0d060g (0.07 #7, 0.07 #308, 0.04 #5330), 0h7x (0.07 #35, 0.07 #336, 0.04 #537), 0ctw_b (0.07 #27, 0.03 #228) >> Best rule #146 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 0cfywh; >> query: (?x6249, 03rk0) <- type_of_union(?x6249, ?x566), people(?x5025, ?x6249), ?x5025 = 0dryh9k, ?x566 = 04ztj, place_of_birth(?x6249, ?x6250) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2c8g nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 64.000 0.867 http://example.org/people/person/nationality #2759-063t3j PRED entity: 063t3j PRED relation: artist! PRED expected values: 02bh8z => 116 concepts (55 used for prediction) PRED predicted values (max 10 best out of 116): 015_1q (0.27 #1979, 0.26 #999, 0.25 #2260), 043g7l (0.16 #451, 0.15 #731, 0.12 #1991), 017l96 (0.16 #578, 0.15 #1978, 0.13 #2259), 0181dw (0.15 #1581, 0.12 #461, 0.12 #3262), 0n85g (0.14 #902, 0.10 #5104, 0.10 #622), 0mzkr (0.14 #445, 0.13 #725, 0.12 #585), 011k1h (0.14 #1970, 0.12 #5052, 0.12 #430), 033hn8 (0.13 #154, 0.13 #5056, 0.13 #6737), 03mp8k (0.13 #766, 0.12 #486, 0.10 #1606), 01cl2y (0.12 #590, 0.11 #730, 0.11 #2831) >> Best rule #1979 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 016qtt; 0m2l9; 02r3zy; 03g5jw; 01sbf2; 012x4t; 086qd; 0frsw; 01trhmt; 03fbc; ... >> query: (?x12565, 015_1q) <- artist(?x2931, ?x12565), award(?x12565, ?x724), ?x724 = 01bgqh >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #861 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 0f0y8; 03xl77; 0565cz; 01lcxbb; 01l87db; 0130sy; 02l_7y; 01vsyjy; 01mr2g6; 01vtg4q; ... *> query: (?x12565, 02bh8z) <- artist(?x6474, ?x12565), nationality(?x12565, ?x512), artists(?x1572, ?x12565), ?x6474 = 0g768 *> conf = 0.09 ranks of expected_values: 22 EVAL 063t3j artist! 02bh8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 116.000 55.000 0.269 http://example.org/music/record_label/artist #2758-0210f1 PRED entity: 0210f1 PRED relation: profession PRED expected values: 0cbd2 => 121 concepts (72 used for prediction) PRED predicted values (max 10 best out of 99): 02hrh1q (0.89 #6277, 0.86 #6724, 0.84 #7917), 0cbd2 (0.86 #454, 0.86 #1348, 0.83 #901), 09jwl (0.84 #5089, 0.43 #7325, 0.40 #5984), 0dxtg (0.83 #4038, 0.60 #6872, 0.59 #3441), 0nbcg (0.59 #5996, 0.41 #5101, 0.27 #8828), 01d_h8 (0.49 #3881, 0.45 #4030, 0.44 #5821), 03gjzk (0.45 #2549, 0.40 #6874, 0.36 #4040), 02jknp (0.41 #3883, 0.36 #4032, 0.34 #5823), 016z4k (0.37 #5073, 0.28 #3133, 0.24 #7309), 0dz3r (0.35 #5071, 0.25 #5966, 0.22 #8798) >> Best rule #6277 for best value: >> intensional similarity = 5 >> extensional distance = 743 >> proper extension: 07sgfsl; 0gs6vr; 0418ft; 01tpl1p; 067hq2; 07_bv_; >> query: (?x7055, 02hrh1q) <- profession(?x7055, ?x2225), gender(?x7055, ?x514), ?x514 = 02zsn, profession(?x7855, ?x2225), ?x7855 = 01g1lp >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #454 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 20 *> proper extension: 01963w; 01dhmw; 02yl42; 03772; 041xl; 014ps4; 033cw; *> query: (?x7055, 0cbd2) <- profession(?x7055, ?x2225), award(?x7055, ?x6687), award(?x7055, ?x4418), award(?x7055, ?x575), ?x4418 = 02664f, ?x575 = 040vk98, disciplines_or_subjects(?x6687, ?x1013) *> conf = 0.86 ranks of expected_values: 2 EVAL 0210f1 profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 72.000 0.893 http://example.org/people/person/profession #2757-0jnrk PRED entity: 0jnrk PRED relation: teams! PRED expected values: 0f04v => 101 concepts (56 used for prediction) PRED predicted values (max 10 best out of 110): 0nlh7 (0.33 #210, 0.25 #1290, 0.20 #1830), 0dclg (0.33 #612, 0.20 #1692, 0.05 #6027), 0b2lw (0.33 #432, 0.04 #6928, 0.03 #2703), 04ykg (0.33 #311, 0.04 #6807, 0.03 #2703), 030qb3t (0.25 #1130, 0.20 #1400, 0.10 #4109), 01cx_ (0.20 #1444, 0.04 #7130, 0.03 #2703), 0k9p4 (0.20 #1810, 0.04 #8309, 0.04 #8039), 0ftxw (0.17 #2515, 0.11 #3601, 0.11 #3328), 02_286 (0.17 #2452, 0.11 #3538, 0.11 #3265), 0f2v0 (0.17 #2533, 0.11 #3619, 0.11 #3346) >> Best rule #210 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 0jnmj; >> query: (?x8037, 0nlh7) <- sport(?x8037, ?x453), company(?x6010, ?x8037), position(?x8037, ?x5234), position(?x8037, ?x3724), position(?x8037, ?x3299), position(?x8037, ?x2918), ?x5234 = 02qvdc, ?x3299 = 02qvgy, ?x453 = 03tmr, position(?x9515, ?x3724), position(?x8541, ?x3724), position(?x8270, ?x3724), ?x8541 = 0jnpc, ?x8270 = 0j8js, ?x9515 = 0j2zj, ?x2918 = 02qvl7 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jnrk teams! 0f04v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 101.000 56.000 0.333 http://example.org/sports/sports_team_location/teams #2756-04bdxl PRED entity: 04bdxl PRED relation: award PRED expected values: 09sb52 02ppm4q => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 245): 09sb52 (0.50 #441, 0.42 #8844, 0.38 #41), 0gqwc (0.45 #1274, 0.25 #474, 0.17 #4476), 0cqgl9 (0.42 #1388, 0.17 #588, 0.14 #3602), 02y_rq5 (0.31 #1294, 0.18 #21605, 0.17 #494), 02ppm4q (0.30 #1352, 0.17 #552, 0.14 #3602), 0bfvw2 (0.30 #1215, 0.14 #3602, 0.08 #415), 094qd5 (0.29 #1245, 0.17 #445, 0.14 #3602), 0bb57s (0.27 #1440, 0.15 #34407, 0.15 #36008), 03qgjwc (0.25 #579, 0.18 #21605, 0.15 #34407), 0ck27z (0.25 #491, 0.16 #14494, 0.16 #14894) >> Best rule #441 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 02l4pj; 0dzf_; 02d42t; 046m59; 03mp9s; 02__7n; 0sw6g; 01qqtr; 016kft; 01nxzv; >> query: (?x91, 09sb52) <- award_nominee(?x5504, ?x91), nominated_for(?x91, ?x1064), ?x5504 = 02x7vq >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 04bdxl award 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 108.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04bdxl award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2755-0bkbm PRED entity: 0bkbm PRED relation: genre! PRED expected values: 02q0k7v => 55 concepts (28 used for prediction) PRED predicted values (max 10 best out of 2033): 01v1ln (0.81 #9153, 0.70 #29310, 0.67 #51309), 02vxq9m (0.81 #9153, 0.50 #9174, 0.50 #7342), 0fjyzt (0.71 #13770, 0.56 #17435, 0.33 #2781), 02qm_f (0.71 #11145, 0.50 #7480, 0.50 #3819), 02qydsh (0.71 #12508, 0.50 #8843, 0.50 #5182), 04f52jw (0.71 #11433, 0.50 #7768, 0.50 #4107), 06ztvyx (0.71 #11424, 0.50 #7759, 0.50 #4098), 09146g (0.71 #11288, 0.50 #7623, 0.50 #3962), 03twd6 (0.70 #29310, 0.67 #51309, 0.67 #9382), 01kf3_9 (0.70 #29310, 0.67 #51309, 0.56 #1831) >> Best rule #9153 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 05p553; >> query: (?x5104, ?x6994) <- genre(?x8443, ?x5104), genre(?x5598, ?x5104), genre(?x3643, ?x5104), genre(?x763, ?x5104), ?x8443 = 02ywwy, featured_film_locations(?x763, ?x108), country(?x5598, ?x512), nominated_for(?x637, ?x5598), film(?x2422, ?x763), prequel(?x6994, ?x5598), ?x637 = 02r22gf, film_release_distribution_medium(?x3643, ?x81) >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #6841 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 2 *> proper extension: 02kdv5l; *> query: (?x5104, 02q0k7v) <- genre(?x11120, ?x5104), genre(?x8443, ?x5104), genre(?x7726, ?x5104), genre(?x7444, ?x5104), genre(?x5598, ?x5104), genre(?x3904, ?x5104), genre(?x763, ?x5104), genre(?x650, ?x5104), ?x8443 = 02ywwy, ?x763 = 061681, ?x7726 = 0cf8qb, film(?x8366, ?x650), ?x5598 = 01npcx, film_release_distribution_medium(?x11120, ?x81), ?x3904 = 02rq8k8, ?x7444 = 0hwpz, film_release_region(?x650, ?x94) *> conf = 0.50 ranks of expected_values: 226 EVAL 0bkbm genre! 02q0k7v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 28.000 0.814 http://example.org/film/film/genre #2754-01lc5 PRED entity: 01lc5 PRED relation: people! PRED expected values: 06j2v => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 56): 041rx (0.44 #81, 0.21 #3316, 0.18 #1698), 02w7gg (0.26 #2851, 0.24 #4550, 0.24 #4242), 01qhm_ (0.20 #6, 0.10 #622, 0.09 #160), 0x67 (0.19 #1550, 0.17 #1011, 0.17 #2012), 06v41q (0.18 #183, 0.07 #260, 0.04 #414), 033tf_ (0.16 #931, 0.13 #1470, 0.13 #3706), 07bch9 (0.14 #793, 0.14 #254, 0.12 #639), 02ctzb (0.14 #246, 0.12 #631, 0.12 #400), 07mqps (0.14 #250, 0.08 #404, 0.08 #327), 013xrm (0.12 #1868, 0.07 #3023, 0.06 #3332) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0f0kz; 03s9b; 0mb5x; 01cspq; 016ggh; >> query: (?x11265, 041rx) <- influenced_by(?x1855, ?x11265), award(?x11265, ?x2523), ?x2523 = 03nqnk3 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #840 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 0kvnn; 09ld6g; *> query: (?x11265, 06j2v) <- languages(?x11265, ?x254), ?x254 = 02h40lc, people(?x1158, ?x11265) *> conf = 0.02 ranks of expected_values: 41 EVAL 01lc5 people! 06j2v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 146.000 146.000 0.444 http://example.org/people/ethnicity/people #2753-021npv PRED entity: 021npv PRED relation: profession PRED expected values: 03gjzk => 135 concepts (77 used for prediction) PRED predicted values (max 10 best out of 61): 0dxtg (0.34 #4305, 0.34 #4453, 0.33 #3417), 02jknp (0.30 #7, 0.28 #2671, 0.27 #5928), 03gjzk (0.30 #14, 0.27 #458, 0.27 #3270), 0np9r (0.23 #1796, 0.21 #316, 0.21 #6089), 09jwl (0.21 #1202, 0.21 #1054, 0.21 #462), 0cbd2 (0.17 #10372, 0.15 #10964, 0.14 #7705), 0nbcg (0.16 #1215, 0.15 #1067, 0.13 #8767), 018gz8 (0.15 #1792, 0.14 #7122, 0.14 #3568), 0dz3r (0.12 #8738, 0.12 #4739, 0.11 #5627), 0kyk (0.12 #1213, 0.11 #10395, 0.10 #10987) >> Best rule #4305 for best value: >> intensional similarity = 4 >> extensional distance = 726 >> proper extension: 02dth1; >> query: (?x12123, 0dxtg) <- award_winner(?x4734, ?x12123), student(?x9847, ?x12123), nominated_for(?x12123, ?x3820), institution(?x620, ?x9847) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 01vttb9; *> query: (?x12123, 03gjzk) <- award_winner(?x4734, ?x12123), student(?x9847, ?x12123), location_of_ceremony(?x12123, ?x13006), gender(?x12123, ?x231) *> conf = 0.30 ranks of expected_values: 3 EVAL 021npv profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 135.000 77.000 0.338 http://example.org/people/person/profession #2752-0n04r PRED entity: 0n04r PRED relation: film_release_region PRED expected values: 03rjj 02_286 030qb3t 06mkj => 101 concepts (99 used for prediction) PRED predicted values (max 10 best out of 155): 09c7w0 (0.94 #11111, 0.93 #7073, 0.93 #1350), 0f8l9c (0.92 #3055, 0.92 #3223, 0.91 #2887), 03rjj (0.88 #512, 0.84 #2868, 0.83 #3036), 059j2 (0.86 #3067, 0.84 #2899, 0.84 #4076), 06mkj (0.86 #571, 0.84 #2927, 0.83 #3095), 03h64 (0.84 #583, 0.77 #2939, 0.76 #3107), 0chghy (0.83 #4052, 0.82 #519, 0.82 #3211), 0345h (0.81 #545, 0.77 #4078, 0.76 #3237), 035qy (0.79 #547, 0.76 #2903, 0.76 #3239), 0jgd (0.79 #3033, 0.78 #2865, 0.77 #4042) >> Best rule #11111 for best value: >> intensional similarity = 4 >> extensional distance = 1126 >> proper extension: 0gtsx8c; 0ckr7s; 0dq626; 0czyxs; 0gtv7pk; 0h1cdwq; 0c40vxk; 0gx9rvq; 0401sg; 0crfwmx; ... >> query: (?x4024, 09c7w0) <- film(?x541, ?x4024), film_release_region(?x4024, ?x479), place_of_birth(?x478, ?x479), origin(?x1660, ?x479) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #512 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 55 *> proper extension: 087wc7n; *> query: (?x4024, 03rjj) <- film_release_region(?x4024, ?x1499), film_release_region(?x4024, ?x87), ?x1499 = 01znc_, ?x87 = 05r4w, film_format(?x4024, ?x6392) *> conf = 0.88 ranks of expected_values: 3, 5, 15, 24 EVAL 0n04r film_release_region 06mkj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 101.000 99.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0n04r film_release_region 030qb3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 101.000 99.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0n04r film_release_region 02_286 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 101.000 99.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0n04r film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 99.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2751-09pmkv PRED entity: 09pmkv PRED relation: medal PRED expected values: 02lq5w => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 2): 02lq5w (0.83 #49, 0.83 #5, 0.81 #9), 02lpp7 (0.81 #10, 0.78 #4, 0.76 #44) >> Best rule #49 for best value: >> intensional similarity = 2 >> extensional distance = 46 >> proper extension: 03_3d; 02k54; 06qd3; 0d0kn; 07twz; >> query: (?x1122, 02lq5w) <- film_release_region(?x4707, ?x1122), ?x4707 = 02xbyr >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09pmkv medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.833 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #2750-059j1m PRED entity: 059j1m PRED relation: student! PRED expected values: 0187nd => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 45): 09f2j (0.20 #686, 0.17 #1213, 0.03 #7010), 017z88 (0.14 #82, 0.05 #1663, 0.03 #2190), 015zyd (0.14 #1, 0.01 #4744, 0.01 #3163), 0234_c (0.14 #417), 01vc5m (0.14 #94), 065y4w7 (0.07 #541, 0.06 #1068, 0.05 #1595), 015q1n (0.07 #739, 0.06 #1266, 0.05 #1793), 01hb1t (0.07 #618, 0.06 #1145, 0.05 #1672), 03k7dn (0.07 #960, 0.06 #1487), 026036 (0.07 #920, 0.06 #1447) >> Best rule #686 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 04t2l2; 05ty4m; 0mdqp; 01v3s2_; 032w8h; 01pcbg; 05txrz; 0315q3; 04fcx7; 030vnj; ... >> query: (?x8440, 09f2j) <- film(?x8440, ?x2102), ?x2102 = 034qzw, award_nominee(?x8440, ?x237) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 059j1m student! 0187nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 67.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #2749-02gs6r PRED entity: 02gs6r PRED relation: film! PRED expected values: 01kyvx => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 4): 01kyvx (0.85 #81, 0.82 #101, 0.80 #96), 01pb34 (0.09 #73, 0.07 #154, 0.06 #164), 02t8yb (0.06 #109, 0.06 #151, 0.03 #129), 09_gdc (0.06 #151, 0.04 #153, 0.02 #178) >> Best rule #81 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 02vw1w2; >> query: (?x5286, 01kyvx) <- actor(?x5286, ?x10919), profession(?x10919, ?x1032), gender(?x10919, ?x514), film_release_distribution_medium(?x5286, ?x81), ?x1032 = 02hrh1q >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02gs6r film! 01kyvx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.846 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #2748-0kt_4 PRED entity: 0kt_4 PRED relation: prequel! PRED expected values: 04vh83 => 73 concepts (33 used for prediction) PRED predicted values (max 10 best out of 3): 0ckrnn (0.04 #349), 0jsf6 (0.04 #287), 04vh83 (0.01 #2346, 0.01 #3430) >> Best rule #349 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0sxg4; >> query: (?x8984, 0ckrnn) <- nominated_for(?x591, ?x8984), ?x591 = 0f4x7, nominated_for(?x269, ?x8984), story_by(?x8984, ?x5004) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #2346 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 477 *> proper extension: 048rn; *> query: (?x8984, ?x3514) <- film(?x269, ?x8984), award_nominee(?x269, ?x1850), written_by(?x3514, ?x269) *> conf = 0.01 ranks of expected_values: 3 EVAL 0kt_4 prequel! 04vh83 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 73.000 33.000 0.042 http://example.org/film/film/prequel #2747-0sw62 PRED entity: 0sw62 PRED relation: type_of_union PRED expected values: 04ztj => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.95 #37, 0.95 #274, 0.95 #250), 0jgjn (0.19 #319), 01bl8s (0.19 #319) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 225 >> proper extension: 0q9kd; 079vf; 04t2l2; 05bp8g; 0h0jz; 02g8h; 0bl2g; 01rrwf6; 0159h6; 0h5g_; ... >> query: (?x10109, 04ztj) <- gender(?x10109, ?x514), profession(?x10109, ?x1383), ?x1383 = 0np9r, type_of_union(?x10109, ?x1873) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sw62 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.952 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2746-07z542 PRED entity: 07z542 PRED relation: instrumentalists! PRED expected values: 03t22m => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 83): 0342h (0.66 #861, 0.66 #1375, 0.62 #1032), 05r5c (0.48 #1637, 0.47 #2151, 0.45 #179), 05148p4 (0.45 #192, 0.41 #536, 0.39 #1392), 018vs (0.42 #1384, 0.30 #955, 0.30 #184), 0l14j_ (0.42 #601, 0.42 #1458, 0.40 #515), 03qjg (0.33 #49, 0.25 #220, 0.18 #1420), 04rzd (0.33 #37, 0.15 #552, 0.13 #380), 018j2 (0.23 #124, 0.12 #342, 0.10 #1409), 02hnl (0.23 #205, 0.22 #34, 0.22 #1405), 0l14md (0.22 #7, 0.14 #178, 0.13 #1378) >> Best rule #861 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 0157m; >> query: (?x1524, 0342h) <- award_winner(?x158, ?x1524), instrumentalists(?x214, ?x1524), category(?x1524, ?x134) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #35 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 01cv3n; 024dgj; 04kjrv; 06p03s; *> query: (?x1524, 03t22m) <- instrumentalists(?x2460, ?x1524), role(?x1524, ?x2944), ?x2460 = 01wy6 *> conf = 0.11 ranks of expected_values: 29 EVAL 07z542 instrumentalists! 03t22m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 100.000 100.000 0.661 http://example.org/music/instrument/instrumentalists #2745-0m66w PRED entity: 0m66w PRED relation: award_winner! PRED expected values: 092c5f => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 124): 0bxs_d (0.25 #112, 0.12 #664, 0.09 #1354), 05c1t6z (0.24 #705, 0.21 #981, 0.12 #1257), 09v0p2c (0.18 #771, 0.16 #1047, 0.04 #6015), 05zksls (0.12 #449, 0.12 #725, 0.11 #1001), 09g90vz (0.12 #535, 0.12 #811, 0.11 #1087), 05q7cj (0.12 #507, 0.05 #921, 0.03 #2577), 03nnm4t (0.12 #1314, 0.12 #762, 0.11 #1038), 09q_6t (0.12 #698, 0.11 #974, 0.09 #1250), 0hndn2q (0.12 #730, 0.11 #1006, 0.07 #2524), 0418154 (0.12 #795, 0.11 #1071, 0.06 #1347) >> Best rule #112 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01pcmd; 070j61; >> query: (?x5889, 0bxs_d) <- award_nominee(?x4589, ?x5889), location(?x5889, ?x739), ?x4589 = 03fg0r >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1118 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 28 *> proper extension: 08f3b1; *> query: (?x5889, 092c5f) <- student(?x5522, ?x5889), award(?x5889, ?x1007), ?x1007 = 03c7tr1 *> conf = 0.10 ranks of expected_values: 16 EVAL 0m66w award_winner! 092c5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 143.000 143.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2744-01grqd PRED entity: 01grqd PRED relation: district_represented PRED expected values: 05fkf 07h34 => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 1055): 05fkf (0.91 #634, 0.90 #528, 0.89 #367), 07h34 (0.91 #634, 0.90 #528, 0.89 #367), 05kkh (0.91 #634, 0.90 #528, 0.89 #367), 04ly1 (0.90 #528, 0.86 #793, 0.86 #738), 03v1s (0.86 #793, 0.86 #738, 0.80 #526), 04tgp (0.86 #793, 0.86 #738, 0.80 #526), 04ych (0.86 #793, 0.86 #738, 0.80 #526), 03v0t (0.86 #793, 0.86 #738, 0.80 #526), 0gyh (0.86 #793, 0.86 #738, 0.80 #526), 050ks (0.86 #793, 0.86 #738, 0.80 #526) >> Best rule #634 for best value: >> intensional similarity = 41 >> extensional distance = 2 >> proper extension: 01gssz; >> query: (?x5256, ?x177) <- legislative_sessions(?x5256, ?x11142), legislative_sessions(?x5256, ?x7914), legislative_sessions(?x5256, ?x7714), legislative_sessions(?x5256, ?x4787), legislative_sessions(?x5256, ?x4437), legislative_sessions(?x5256, ?x3973), legislative_sessions(?x2860, ?x11142), district_represented(?x5256, ?x7518), district_represented(?x5256, ?x7405), district_represented(?x5256, ?x6895), district_represented(?x5256, ?x4776), district_represented(?x5256, ?x4061), district_represented(?x5256, ?x3670), district_represented(?x5256, ?x2713), district_represented(?x5256, ?x2020), district_represented(?x5256, ?x335), ?x4776 = 06yxd, ?x7518 = 026mj, ?x3973 = 01gssm, legislative_sessions(?x4437, ?x5005), legislative_sessions(?x9046, ?x4437), ?x3670 = 05tbn, ?x335 = 059rby, ?x4061 = 0498y, ?x2713 = 06btq, ?x6895 = 05fjf, ?x7405 = 07_f2, ?x5005 = 01gstn, legislative_sessions(?x4787, ?x7715), ?x7714 = 01grr2, legislative_sessions(?x4665, ?x4787), ?x2020 = 05k7sb, ?x4665 = 07t58, ?x2860 = 0b3wk, ?x9046 = 03_nq, ?x7914 = 01grrf, ?x7715 = 01grp0, legislative_sessions(?x5742, ?x4787), district_represented(?x4437, ?x760), district_represented(?x4437, ?x177), ?x760 = 05fkf >> conf = 0.91 => this is the best rule for 3 predicted values ranks of expected_values: 1, 2 EVAL 01grqd district_represented 07h34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.912 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01grqd district_represented 05fkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 34.000 34.000 0.912 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #2743-0djb3vw PRED entity: 0djb3vw PRED relation: genre PRED expected values: 02l7c8 => 100 concepts (95 used for prediction) PRED predicted values (max 10 best out of 93): 03k9fj (0.61 #254, 0.47 #496, 0.46 #617), 02kdv5l (0.54 #5581, 0.43 #244, 0.40 #486), 05p553 (0.45 #851, 0.36 #4611, 0.35 #6312), 01jfsb (0.38 #497, 0.36 #376, 0.35 #1225), 02l7c8 (0.33 #743, 0.31 #1714, 0.30 #1107), 04xvlr (0.33 #122, 0.30 #1, 0.17 #2790), 03bxz7 (0.30 #56, 0.20 #177, 0.11 #782), 06n90 (0.25 #5593, 0.25 #256, 0.23 #377), 01hmnh (0.25 #1352, 0.23 #624, 0.23 #382), 03g3w (0.20 #26, 0.13 #147, 0.08 #2815) >> Best rule #254 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 01fmys; >> query: (?x542, 03k9fj) <- film_release_region(?x542, ?x608), film_release_region(?x542, ?x205), ?x608 = 02k54, film(?x7855, ?x542), ?x205 = 03rjj >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #743 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 85 *> proper extension: 09tqkv2; 05q7874; *> query: (?x542, 02l7c8) <- film_crew_role(?x542, ?x468), currency(?x542, ?x170), film_festivals(?x542, ?x2686), nominated_for(?x3722, ?x542) *> conf = 0.33 ranks of expected_values: 5 EVAL 0djb3vw genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 100.000 95.000 0.607 http://example.org/film/film/genre #2742-01jr6 PRED entity: 01jr6 PRED relation: contains PRED expected values: 02zd460 => 130 concepts (121 used for prediction) PRED predicted values (max 10 best out of 2263): 02zd460 (0.76 #241475, 0.69 #141352, 0.68 #47124), 01jj4x (0.48 #241474, 0.45 #238528, 0.45 #226748), 05cwl_ (0.40 #3682, 0.05 #18409, 0.04 #24299), 01bzw5 (0.40 #3078, 0.05 #17805, 0.04 #23695), 03b8c4 (0.40 #5187, 0.05 #19914, 0.04 #25804), 06b7s9 (0.40 #5057, 0.05 #19784, 0.04 #25674), 06kknt (0.40 #4989, 0.05 #19716, 0.04 #25606), 02gnmp (0.40 #4754, 0.05 #19481, 0.04 #25371), 05q2c (0.40 #4152, 0.05 #18879, 0.04 #24769), 0l1pj (0.40 #4041, 0.05 #18768, 0.04 #24658) >> Best rule #241475 for best value: >> intensional similarity = 2 >> extensional distance = 266 >> proper extension: 0fngy; >> query: (?x3976, ?x5288) <- citytown(?x5288, ?x3976), contains(?x94, ?x5288) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jr6 contains 02zd460 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 121.000 0.760 http://example.org/location/location/contains #2741-0m0hw PRED entity: 0m0hw PRED relation: film PRED expected values: 016y_f => 139 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1006): 0ds5_72 (0.19 #3244, 0.05 #47945, 0.04 #44369), 0jvt9 (0.18 #4115, 0.17 #7691, 0.13 #11267), 04954r (0.14 #616, 0.06 #2404, 0.06 #54257), 0bm2g (0.14 #338, 0.06 #2126, 0.02 #12854), 031t2d (0.12 #2043, 0.09 #5619, 0.07 #255), 02qzh2 (0.12 #2481, 0.05 #20361, 0.05 #6057), 032sl_ (0.12 #3347, 0.05 #6923, 0.02 #15863), 03rg2b (0.09 #4670, 0.08 #8246, 0.07 #1094), 07bzz7 (0.09 #4466, 0.08 #8042, 0.05 #11618), 04gv3db (0.09 #6117, 0.03 #25786, 0.03 #38302) >> Best rule #3244 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0ql36; >> query: (?x6668, 0ds5_72) <- gender(?x6668, ?x231), artist(?x3240, ?x6668), special_performance_type(?x6668, ?x4832), ?x4832 = 01pb34 >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #61541 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 187 *> proper extension: 03m9c8; *> query: (?x6668, 016y_f) <- award_winner(?x2060, ?x6668), award_winner(?x2060, ?x3002), ?x3002 = 0cj8x *> conf = 0.01 ranks of expected_values: 942 EVAL 0m0hw film 016y_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 139.000 82.000 0.188 http://example.org/film/actor/film./film/performance/film #2740-03cd0x PRED entity: 03cd0x PRED relation: production_companies PRED expected values: 046b0s => 86 concepts (62 used for prediction) PRED predicted values (max 10 best out of 77): 046b0s (0.35 #270, 0.34 #435, 0.33 #518), 030_1m (0.33 #15, 0.17 #97, 0.03 #2158), 017s11 (0.17 #84, 0.09 #2145, 0.09 #2890), 0g1rw (0.17 #89, 0.06 #1323, 0.05 #2150), 05qd_ (0.13 #586, 0.13 #668, 0.13 #2152), 016tw3 (0.12 #175, 0.10 #2899, 0.09 #2154), 020h2v (0.12 #223, 0.04 #1457, 0.03 #1293), 025hwq (0.12 #222, 0.02 #717, 0.02 #964), 0jz9f (0.12 #165, 0.02 #413, 0.02 #496), 054lpb6 (0.10 #1166, 0.10 #1578, 0.08 #837) >> Best rule #270 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 02qyv3h; >> query: (?x5388, 046b0s) <- country(?x5388, ?x390), film(?x2200, ?x5388), ?x390 = 0chghy, people(?x1423, ?x2200) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cd0x production_companies 046b0s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 62.000 0.354 http://example.org/film/film/production_companies #2739-053yx PRED entity: 053yx PRED relation: profession PRED expected values: 09jwl 0nbcg 029bkp => 191 concepts (184 used for prediction) PRED predicted values (max 10 best out of 95): 09jwl (0.82 #1194, 0.77 #5167, 0.75 #459), 0nbcg (0.64 #1206, 0.62 #471, 0.60 #3266), 01d_h8 (0.59 #1035, 0.50 #594, 0.50 #153), 016z4k (0.56 #445, 0.50 #1180, 0.48 #1915), 0dxtg (0.55 #1777, 0.50 #160, 0.48 #8988), 02jknp (0.53 #743, 0.50 #155, 0.36 #1037), 0cbd2 (0.50 #5451, 0.48 #8688, 0.48 #8835), 0dz3r (0.44 #10741, 0.43 #13249, 0.43 #12954), 039v1 (0.44 #476, 0.41 #1211, 0.39 #5184), 029bkp (0.42 #2353, 0.18 #341, 0.12 #3283) >> Best rule #1194 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 06y9c2; 01vtqml; 02r3cn; 01nz1q6; >> query: (?x2835, 09jwl) <- spouse(?x2835, ?x624), role(?x2835, ?x2206), instrumentalists(?x1495, ?x2835) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 10 EVAL 053yx profession 029bkp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 191.000 184.000 0.818 http://example.org/people/person/profession EVAL 053yx profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 184.000 0.818 http://example.org/people/person/profession EVAL 053yx profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 184.000 0.818 http://example.org/people/person/profession #2738-0lk0l PRED entity: 0lk0l PRED relation: institution! PRED expected values: 02mjs7 => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 18): 02h4rq6 (0.83 #212, 0.71 #135, 0.67 #907), 014mlp (0.70 #214, 0.68 #909, 0.65 #717), 0bkj86 (0.49 #140, 0.41 #217, 0.37 #720), 07s6fsf (0.41 #1005, 0.38 #211, 0.37 #134), 027f2w (0.34 #141, 0.28 #218, 0.19 #1469), 01ysy9 (0.33 #18, 0.20 #56, 0.19 #1469), 013zdg (0.25 #178, 0.25 #139, 0.23 #159), 01rr_d (0.21 #224, 0.19 #147, 0.19 #1469), 0bjrnt (0.21 #138, 0.19 #1469, 0.14 #215), 03mkk4 (0.19 #1469, 0.16 #143, 0.15 #723) >> Best rule #212 for best value: >> intensional similarity = 5 >> extensional distance = 110 >> proper extension: 01w5m; 02gr81; 017j69; 09f2j; 08qnnv; 0gl5_; 01r3w7; 01jt2w; 0trv; >> query: (?x12823, 02h4rq6) <- institution(?x1771, ?x12823), institution(?x1200, ?x12823), ?x1771 = 019v9k, major_field_of_study(?x12823, ?x2014), ?x1200 = 016t_3 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1469 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 452 *> proper extension: 09r4xx; *> query: (?x12823, ?x620) <- institution(?x1771, ?x12823), institution(?x1771, ?x7350), student(?x12823, ?x916), institution(?x620, ?x7350) *> conf = 0.19 ranks of expected_values: 11 EVAL 0lk0l institution! 02mjs7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 158.000 158.000 0.830 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2737-0gry51 PRED entity: 0gry51 PRED relation: place_of_death PRED expected values: 0l1pj => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 39): 0k049 (0.17 #197, 0.14 #585, 0.11 #973), 030qb3t (0.14 #992, 0.13 #2158, 0.13 #2546), 02_286 (0.13 #401, 0.12 #789, 0.12 #1372), 0f2wj (0.12 #12, 0.11 #400, 0.10 #788), 06_kh (0.09 #5, 0.06 #393, 0.06 #781), 04jpl (0.05 #977, 0.04 #395, 0.04 #589), 0k_p5 (0.04 #476, 0.04 #864, 0.03 #88), 027l4q (0.04 #914, 0.03 #138, 0.03 #1303), 05qtj (0.03 #1034, 0.03 #64, 0.03 #1423), 0978r (0.03 #1018, 0.03 #242, 0.02 #630) >> Best rule #197 for best value: >> intensional similarity = 6 >> extensional distance = 38 >> proper extension: 0h1_w; 0436zq; >> query: (?x13488, 0k049) <- people(?x5801, ?x13488), notable_people_with_this_condition(?x5801, ?x510), award_winner(?x102, ?x510), type_of_union(?x510, ?x566), film(?x510, ?x499), award_nominee(?x510, ?x509) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gry51 place_of_death 0l1pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 48.000 0.175 http://example.org/people/deceased_person/place_of_death #2736-01323p PRED entity: 01323p PRED relation: origin PRED expected values: 0k33p => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 106): 02_286 (0.17 #252, 0.14 #960, 0.07 #1196), 06y57 (0.17 #329, 0.04 #1745, 0.03 #2453), 062qg (0.17 #383, 0.02 #2507, 0.01 #2743), 04jpl (0.14 #714, 0.12 #1422, 0.11 #2602), 030qb3t (0.14 #742, 0.10 #3338, 0.10 #3574), 0k9p4 (0.09 #629, 0.07 #1337, 0.02 #1573), 01cx_ (0.09 #536, 0.04 #1244, 0.02 #1480), 0fm2_ (0.09 #498, 0.04 #1206, 0.02 #1442), 05l64 (0.09 #656, 0.04 #1364, 0.02 #1600), 0fpzwf (0.09 #576, 0.04 #1284, 0.02 #1520) >> Best rule #252 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 023l9y; >> query: (?x7682, 02_286) <- artist(?x2149, ?x7682), artists(?x3370, ?x7682), category(?x7682, ?x134), ?x2149 = 011k1h, ?x3370 = 059kh >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1579 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 01vs8ng; *> query: (?x7682, 0k33p) <- artists(?x3243, ?x7682), artists(?x671, ?x7682), ?x3243 = 0y3_8, ?x671 = 064t9 *> conf = 0.02 ranks of expected_values: 38 EVAL 01323p origin 0k33p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 77.000 77.000 0.167 http://example.org/music/artist/origin #2735-0l35f PRED entity: 0l35f PRED relation: place_of_death! PRED expected values: 0gt3p => 148 concepts (73 used for prediction) PRED predicted values (max 10 best out of 500): 02drd3 (0.25 #2197, 0.06 #3709, 0.02 #10513), 051y1hd (0.25 #2119, 0.06 #3631, 0.02 #10435), 03f68r6 (0.25 #2110, 0.06 #3622, 0.02 #10426), 0pj8m (0.25 #1890, 0.06 #3402, 0.02 #10206), 07fzq3 (0.25 #1824, 0.06 #3336, 0.02 #10140), 034zc0 (0.25 #1778, 0.06 #3290, 0.02 #10094), 05683cn (0.06 #3540, 0.01 #13369, 0.01 #14884), 0h326 (0.03 #5291, 0.02 #9071, 0.02 #8315), 05f0r8 (0.03 #5285, 0.02 #9065, 0.02 #8309), 01l3j (0.03 #5280, 0.02 #9060, 0.02 #8304) >> Best rule #2197 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 071vr; >> query: (?x7369, 02drd3) <- contains(?x1227, ?x7369), mode_of_transportation(?x7369, ?x6665), ?x1227 = 01n7q >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0l35f place_of_death! 0gt3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 73.000 0.250 http://example.org/people/deceased_person/place_of_death #2734-0b9rdk PRED entity: 0b9rdk PRED relation: film! PRED expected values: 06ltr => 62 concepts (32 used for prediction) PRED predicted values (max 10 best out of 828): 03qhyn8 (0.43 #52059, 0.43 #43730, 0.41 #33318), 044qx (0.12 #734, 0.11 #2816, 0.02 #17391), 0chsq (0.12 #79, 0.02 #2161, 0.01 #10490), 0cf2h (0.12 #1101, 0.02 #3183), 04gc65 (0.12 #1976), 0272kv (0.11 #29152), 079vf (0.09 #6254, 0.05 #8337, 0.03 #4172), 0h96g (0.07 #7100, 0.07 #5018, 0.04 #9183), 0j_c (0.07 #2493, 0.06 #411, 0.03 #17068), 0f13b (0.07 #5645, 0.04 #7727, 0.03 #9810) >> Best rule #52059 for best value: >> intensional similarity = 3 >> extensional distance = 983 >> proper extension: 028k2x; 01hvv0; 03g9xj; 0h95b81; 06r1k; 025x1t; 03czz87; >> query: (?x6029, ?x12848) <- titles(?x1510, ?x6029), nominated_for(?x12848, ?x6029), genre(?x419, ?x1510) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #15524 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 272 *> proper extension: 014lc_; 034qmv; 06w99h3; 02vp1f_; 01gc7; 0gzy02; 047gn4y; 0m2kd; 03mh94; 04v8x9; ... *> query: (?x6029, 06ltr) <- genre(?x6029, ?x811), currency(?x6029, ?x170), ?x811 = 03k9fj *> conf = 0.03 ranks of expected_values: 125 EVAL 0b9rdk film! 06ltr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 62.000 32.000 0.428 http://example.org/film/actor/film./film/performance/film #2733-01dw9z PRED entity: 01dw9z PRED relation: profession PRED expected values: 018gz8 => 120 concepts (90 used for prediction) PRED predicted values (max 10 best out of 65): 03gjzk (0.83 #2478, 0.82 #1171, 0.77 #591), 09jwl (0.80 #1610, 0.62 #5973, 0.61 #5390), 0dxtg (0.65 #2477, 0.63 #1170, 0.56 #590), 0nbcg (0.56 #1623, 0.52 #1768, 0.47 #5986), 0dz3r (0.47 #1597, 0.42 #1307, 0.42 #5085), 02jknp (0.40 #6982, 0.38 #585, 0.35 #2182), 039v1 (0.39 #1628, 0.24 #5408, 0.23 #5991), 01c72t (0.35 #310, 0.28 #6560, 0.28 #4230), 018gz8 (0.33 #1028, 0.32 #593, 0.26 #158), 02krf9 (0.31 #1183, 0.27 #2490, 0.25 #603) >> Best rule #2478 for best value: >> intensional similarity = 2 >> extensional distance = 241 >> proper extension: 07f7jp; >> query: (?x2683, 03gjzk) <- profession(?x2683, ?x220), producer_type(?x2683, ?x632) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1028 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 132 *> proper extension: 023n39; 027hq5f; *> query: (?x2683, 018gz8) <- profession(?x2683, ?x353), film(?x2683, ?x2128), ?x353 = 0cbd2 *> conf = 0.33 ranks of expected_values: 9 EVAL 01dw9z profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 120.000 90.000 0.831 http://example.org/people/person/profession #2732-0byfz PRED entity: 0byfz PRED relation: award PRED expected values: 0gq9h => 121 concepts (113 used for prediction) PRED predicted values (max 10 best out of 321): 03nqnk3 (0.78 #36487, 0.76 #36881, 0.71 #23921), 09cm54 (0.71 #23921, 0.70 #30988, 0.70 #33345), 027c95y (0.71 #23921, 0.70 #30988, 0.70 #33345), 03c7tr1 (0.40 #7896, 0.11 #12209, 0.10 #838), 0gr4k (0.39 #1596, 0.32 #11006, 0.29 #2773), 05p09zm (0.39 #2468, 0.34 #3644, 0.33 #4036), 09sb52 (0.37 #2389, 0.33 #9054, 0.32 #26314), 019f4v (0.33 #11433, 0.26 #1630, 0.26 #8296), 05zr6wv (0.31 #2367, 0.25 #3543, 0.24 #3935), 0gr51 (0.31 #11072, 0.30 #8328, 0.29 #1662) >> Best rule #36487 for best value: >> intensional similarity = 3 >> extensional distance = 1897 >> proper extension: 01ky2h; 01lcxbb; 01wz_ml; 0lzkm; 01vsy3q; 08xz51; 0f6lx; 03j90; 06lxn; >> query: (?x269, ?x198) <- award_winner(?x198, ?x269), ceremony(?x198, ?x2032), award(?x71, ?x198) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1641 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 02pp_q_; *> query: (?x269, 0gq9h) <- award_winner(?x102, ?x269), written_by(?x3514, ?x269), people(?x268, ?x269) *> conf = 0.26 ranks of expected_values: 14 EVAL 0byfz award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 121.000 113.000 0.778 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2731-02h4rq6 PRED entity: 02h4rq6 PRED relation: major_field_of_study PRED expected values: 04_tv 0pf2 04g7x 041y2 02mgp => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 92): 0pf2 (0.64 #1018, 0.60 #656, 0.59 #823), 04g7x (0.59 #823, 0.57 #853, 0.50 #913), 04_tv (0.59 #823, 0.53 #1071, 0.50 #772), 04g51 (0.59 #823, 0.50 #784, 0.50 #603), 05qt0 (0.59 #823, 0.50 #605, 0.47 #466), 01lhy (0.59 #823, 0.50 #589, 0.47 #466), 05r79 (0.59 #823, 0.50 #592, 0.47 #466), 01jzxy (0.59 #823, 0.50 #594, 0.47 #466), 036nz (0.59 #823, 0.47 #466, 0.47 #701), 041y2 (0.59 #823, 0.47 #466, 0.47 #701) >> Best rule #1018 for best value: >> intensional similarity = 20 >> extensional distance = 9 >> proper extension: 03mkk4; >> query: (?x865, 0pf2) <- institution(?x865, ?x9200), institution(?x865, ?x7202), institution(?x865, ?x6132), institution(?x865, ?x4916), institution(?x865, ?x4846), institution(?x865, ?x1768), institution(?x865, ?x481), major_field_of_study(?x865, ?x254), colors(?x6132, ?x332), student(?x481, ?x2319), contains(?x279, ?x481), ?x1768 = 09kvv, currency(?x4846, ?x170), student(?x865, ?x1117), contains(?x94, ?x4916), list(?x481, ?x2197), category(?x9200, ?x134), student(?x6132, ?x1291), school(?x465, ?x4916), school_type(?x7202, ?x1044) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 10, 37 EVAL 02h4rq6 major_field_of_study 02mgp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 25.000 25.000 0.636 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 02h4rq6 major_field_of_study 041y2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 25.000 25.000 0.636 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 02h4rq6 major_field_of_study 04g7x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.636 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 02h4rq6 major_field_of_study 0pf2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.636 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 02h4rq6 major_field_of_study 04_tv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.636 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #2730-025m8l PRED entity: 025m8l PRED relation: award! PRED expected values: 0770cd 02qwg 0178rl 01c7qd => 60 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2933): 02cx72 (0.82 #6632, 0.81 #29844, 0.81 #29845), 012201 (0.82 #6632, 0.81 #29844, 0.81 #29845), 02fgpf (0.82 #6632, 0.81 #29844, 0.81 #29845), 02fgp0 (0.82 #6632, 0.81 #29844, 0.81 #29845), 0pgjm (0.82 #6632, 0.81 #29844, 0.81 #29845), 0ffgh (0.82 #6632, 0.81 #29844, 0.81 #29845), 02sjp (0.62 #19154, 0.25 #5890, 0.25 #2574), 01x0yrt (0.62 #22392, 0.25 #5812, 0.20 #12444), 01hgwkr (0.62 #22545, 0.25 #25861, 0.20 #12597), 01271h (0.62 #17380, 0.05 #43914, 0.05 #33962) >> Best rule #6632 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 01c427; 054ks3; >> query: (?x2238, ?x1345) <- award_winner(?x2238, ?x4537), award_winner(?x2238, ?x1345), ceremony(?x2238, ?x486), award(?x4080, ?x2238), ?x4537 = 01817f, ?x4080 = 0dl567 >> conf = 0.82 => this is the best rule for 6 predicted values *> Best rule #12683 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0c4z8; *> query: (?x2238, 01c7qd) <- award_winner(?x2238, ?x1345), ceremony(?x2238, ?x486), award(?x8799, ?x2238), award(?x3235, ?x2238), ?x3235 = 02v3yy, ?x8799 = 02f1c *> conf = 0.60 ranks of expected_values: 22, 27, 39, 364 EVAL 025m8l award! 01c7qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 60.000 24.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 025m8l award! 0178rl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 60.000 24.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 025m8l award! 02qwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 60.000 24.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 025m8l award! 0770cd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 60.000 24.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2729-027c924 PRED entity: 027c924 PRED relation: award_winner PRED expected values: 01_vfy 04sry 0184jw => 37 concepts (8 used for prediction) PRED predicted values (max 10 best out of 935): 09l3p (0.50 #940, 0.29 #10724, 0.06 #13171), 046zh (0.50 #1172, 0.24 #10956, 0.06 #13403), 0l6px (0.50 #483, 0.18 #10267, 0.11 #7822), 05dbf (0.50 #460, 0.18 #10244, 0.05 #12691), 019f2f (0.50 #540, 0.18 #10324, 0.03 #12771), 0159h6 (0.50 #78, 0.14 #14679, 0.14 #19576), 0dvld (0.50 #1316, 0.12 #11100, 0.10 #19577), 01kp66 (0.50 #916, 0.12 #10700, 0.07 #13147), 015nhn (0.50 #1777, 0.12 #11561, 0.05 #14008), 0h1mt (0.50 #212, 0.12 #9996, 0.05 #12443) >> Best rule #940 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 094qd5; 0gqwc; >> query: (?x289, 09l3p) <- award(?x11429, ?x289), award(?x6174, ?x289), award(?x288, ?x289), nominated_for(?x8626, ?x6174), ?x8626 = 03lvyj, award_winner(?x289, ?x767), nominated_for(?x500, ?x288), ?x11429 = 0_9l_ >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #8938 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 02qyp19; 02pqp12; 0gr51; 0gqyl; 0gqy2; 03hl6lc; 09d28z; *> query: (?x289, 04sry) <- award(?x6174, ?x289), award(?x2203, ?x289), ?x6174 = 0sxns, award_winner(?x289, ?x767), film_crew_role(?x2203, ?x137), featured_film_locations(?x2203, ?x2204), film_format(?x2203, ?x6392) *> conf = 0.22 ranks of expected_values: 82, 97, 692 EVAL 027c924 award_winner 0184jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 37.000 8.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027c924 award_winner 04sry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 37.000 8.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 027c924 award_winner 01_vfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 8.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #2728-020jqv PRED entity: 020jqv PRED relation: nationality PRED expected values: 09c7w0 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 25): 09c7w0 (0.73 #3002, 0.71 #101, 0.71 #1001), 02jx1 (0.16 #633, 0.15 #2034, 0.15 #2134), 03_3d (0.15 #6, 0.13 #706, 0.06 #1006), 07ssc (0.14 #415, 0.11 #515, 0.10 #315), 03rk0 (0.12 #746, 0.08 #46, 0.06 #9254), 0d060g (0.08 #7, 0.07 #407, 0.05 #307), 03rjj (0.08 #5, 0.02 #205, 0.02 #905), 0345h (0.06 #231, 0.04 #931, 0.02 #2032), 06q1r (0.03 #277, 0.03 #377, 0.02 #1378), 0f8l9c (0.03 #922, 0.02 #2423, 0.02 #1923) >> Best rule #3002 for best value: >> intensional similarity = 3 >> extensional distance = 577 >> proper extension: 02wrhj; >> query: (?x10527, 09c7w0) <- nominated_for(?x10527, ?x7723), actor(?x7647, ?x10527), film(?x10527, ?x857) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 020jqv nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.725 http://example.org/people/person/nationality #2727-01p1v PRED entity: 01p1v PRED relation: medal PRED expected values: 02lq5w => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 1): 02lq5w (0.79 #12, 0.78 #40, 0.77 #13) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0h7x; 07twz; >> query: (?x1917, 02lq5w) <- film_release_region(?x1392, ?x1917), film_release_region(?x1386, ?x1917), ?x1392 = 017gm7, ?x1386 = 0dtfn >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p1v medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.791 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #2726-05lb65 PRED entity: 05lb65 PRED relation: award_winner! PRED expected values: 030znt => 99 concepts (37 used for prediction) PRED predicted values (max 10 best out of 525): 05lb87 (0.82 #52828, 0.81 #44823, 0.81 #38419), 03zqc1 (0.82 #52828, 0.81 #44823, 0.81 #38419), 030znt (0.82 #52828, 0.81 #44823, 0.81 #38419), 05dxl5 (0.82 #52828, 0.81 #38419, 0.81 #52827), 0443y3 (0.82 #52828, 0.81 #38419, 0.81 #52827), 05lb65 (0.73 #2714, 0.50 #5916, 0.43 #1112), 0gd_b_ (0.55 #22412, 0.55 #3202, 0.55 #14408), 04vmqg (0.55 #22412, 0.55 #3202, 0.55 #14408), 03w4sh (0.55 #22412, 0.55 #3202, 0.55 #14408), 07z1_q (0.55 #22412, 0.55 #3202, 0.52 #38418) >> Best rule #52828 for best value: >> intensional similarity = 4 >> extensional distance = 1195 >> proper extension: 0f721s; 0c_mvb; 01p5yn; 014hdb; 05s34b; >> query: (?x6851, ?x2578) <- award_winner(?x6851, ?x2578), award_winner(?x6851, ?x2129), nominated_for(?x2578, ?x2078), award_winner(?x1112, ?x2129) >> conf = 0.82 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 05lb65 award_winner! 030znt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 37.000 0.815 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #2725-071fb PRED entity: 071fb PRED relation: language! PRED expected values: 07l450 => 44 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1871): 03qcfvw (0.79 #17266, 0.75 #17267, 0.64 #17265), 03twd6 (0.79 #17266, 0.75 #17267, 0.63 #6906), 03t97y (0.79 #17266, 0.75 #17267, 0.50 #3605), 0dr_4 (0.75 #5180, 0.63 #6906, 0.57 #8869), 02yvct (0.75 #5180, 0.63 #6906, 0.57 #8968), 0ft18 (0.75 #5180, 0.63 #6906, 0.57 #9976), 0cbl95 (0.75 #5180, 0.63 #6906, 0.57 #10348), 042y1c (0.75 #5180, 0.63 #6906, 0.57 #8993), 0pv3x (0.75 #5180, 0.63 #6906, 0.50 #7077), 0f4yh (0.75 #5180, 0.63 #6906, 0.50 #7463) >> Best rule #17266 for best value: >> intensional similarity = 14 >> extensional distance = 10 >> proper extension: 055qm; >> query: (?x5003, ?x1470) <- languages(?x1515, ?x5003), profession(?x1515, ?x4773), profession(?x1515, ?x1032), ?x1032 = 02hrh1q, film(?x1515, ?x1470), film(?x1515, ?x103), place_of_birth(?x1515, ?x13032), ?x4773 = 0d1pc, actor(?x5810, ?x1515), nominated_for(?x102, ?x103), language(?x1470, ?x90), nominated_for(?x902, ?x103), genre(?x103, ?x225), country(?x103, ?x94) >> conf = 0.79 => this is the best rule for 3 predicted values *> Best rule #5180 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 0jzc; *> query: (?x5003, ?x144) <- official_language(?x910, ?x5003), countries_spoken_in(?x5003, ?x1577), language(?x6181, ?x5003), language(?x5044, ?x5003), honored_for(?x9400, ?x6181), nominated_for(?x5348, ?x6181), nominated_for(?x1245, ?x6181), film_release_distribution_medium(?x6181, ?x81), ?x5044 = 0413cff, film_release_region(?x6181, ?x1355), film_release_region(?x6181, ?x1229), film_crew_role(?x6181, ?x137), ?x1355 = 0h7x, ?x1229 = 059j2, award(?x241, ?x1245), nominated_for(?x1245, ?x144) *> conf = 0.75 ranks of expected_values: 17 EVAL 071fb language! 07l450 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 44.000 14.000 0.787 http://example.org/film/film/language #2724-02184q PRED entity: 02184q PRED relation: film PRED expected values: 018js4 => 107 concepts (76 used for prediction) PRED predicted values (max 10 best out of 724): 05sw5b (0.29 #815, 0.01 #36579, 0.01 #41944), 0gjk1d (0.29 #182, 0.01 #7334), 06gb1w (0.14 #734, 0.10 #2522, 0.03 #22193), 0ds5_72 (0.14 #1455, 0.02 #13973, 0.02 #10395), 01xdxy (0.14 #1565, 0.02 #8717), 02ntb8 (0.14 #839, 0.02 #11568, 0.02 #9779), 03l6q0 (0.14 #543, 0.02 #11272, 0.01 #13061), 0jzw (0.14 #119, 0.02 #10848, 0.01 #12637), 020bv3 (0.14 #318, 0.02 #19989, 0.02 #21777), 07x4qr (0.14 #404, 0.02 #16499, 0.02 #14711) >> Best rule #815 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 03ds83; 01mylz; >> query: (?x9819, 05sw5b) <- film(?x9819, ?x7834), profession(?x9819, ?x319), ?x7834 = 01cycq >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02184q film 018js4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 76.000 0.286 http://example.org/film/actor/film./film/performance/film #2723-02fn5r PRED entity: 02fn5r PRED relation: artist! PRED expected values: 06x2ww => 110 concepts (87 used for prediction) PRED predicted values (max 10 best out of 96): 0bfp0l (0.29 #105, 0.22 #245, 0.05 #945), 03rhqg (0.25 #855, 0.22 #295, 0.15 #1975), 015_1q (0.23 #859, 0.23 #4502, 0.20 #2960), 0fb0v (0.17 #287, 0.11 #427, 0.07 #1827), 017l96 (0.14 #858, 0.14 #18, 0.11 #158), 06x2ww (0.14 #48, 0.13 #328, 0.11 #188), 043ljr (0.14 #16, 0.11 #156, 0.09 #856), 01clyr (0.14 #33, 0.11 #173, 0.09 #313), 01cl0d (0.14 #54, 0.11 #194, 0.06 #6499), 0181hw (0.14 #50, 0.11 #190, 0.04 #470) >> Best rule #105 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01p9hgt; 01kv4mb; 0ggjt; 0bhvtc; 03cfjg; >> query: (?x2638, 0bfp0l) <- role(?x2638, ?x227), nominated_for(?x2638, ?x1413), artists(?x2664, ?x2638) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 01p9hgt; 01kv4mb; 0ggjt; 0bhvtc; 03cfjg; *> query: (?x2638, 06x2ww) <- role(?x2638, ?x227), nominated_for(?x2638, ?x1413), artists(?x2664, ?x2638) *> conf = 0.14 ranks of expected_values: 6 EVAL 02fn5r artist! 06x2ww CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 110.000 87.000 0.286 http://example.org/music/record_label/artist #2722-0lhql PRED entity: 0lhql PRED relation: place_of_birth! PRED expected values: 01nx_8 => 151 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1987): 041mt (0.31 #169871, 0.31 #13066, 0.30 #107152), 018fwv (0.12 #5194, 0.08 #7807, 0.06 #13033), 018417 (0.12 #5153, 0.08 #7766, 0.06 #12992), 070yzk (0.12 #4389, 0.08 #7002, 0.06 #12228), 0bl60p (0.12 #4213, 0.08 #6826, 0.06 #12052), 0jsg0m (0.12 #4154, 0.08 #6767, 0.06 #11993), 0gv07g (0.12 #4105, 0.08 #6718, 0.06 #11944), 05gp3x (0.12 #3863, 0.08 #6476, 0.06 #11702), 059_gf (0.12 #3767, 0.08 #6380, 0.06 #11606), 0f5xn (0.12 #3733, 0.08 #6346, 0.06 #11572) >> Best rule #169871 for best value: >> intensional similarity = 3 >> extensional distance = 183 >> proper extension: 0f94t; 0284jb; 0281s1; >> query: (?x4144, ?x2208) <- contains(?x4143, ?x4144), location(?x2208, ?x4144), source(?x4143, ?x958) >> conf = 0.31 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0lhql place_of_birth! 01nx_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 151.000 112.000 0.313 http://example.org/people/person/place_of_birth #2721-025ygws PRED entity: 025ygws PRED relation: season! PRED expected values: 01ypc 05m_8 07l8f 02d02 => 14 concepts (14 used for prediction) PRED predicted values (max 10 best out of 332): 02d02 (0.88 #25, 0.86 #127, 0.83 #116), 01ypc (0.88 #25, 0.82 #100, 0.79 #132), 05m_8 (0.88 #25, 0.75 #34, 0.66 #38), 04wmvz (0.88 #25, 0.66 #38, 0.66 #73), 07l8f (0.88 #25, 0.66 #38, 0.66 #73), 03lpp_ (0.88 #25, 0.66 #38, 0.66 #73), 0jmj7 (0.75 #34, 0.58 #9, 0.55 #35), 03qrh9 (0.66 #38, 0.66 #73, 0.58 #36), 02hfgl (0.66 #38, 0.66 #73, 0.58 #36), 02gtm4 (0.66 #38, 0.66 #73, 0.58 #36) >> Best rule #25 for best value: >> intensional similarity = 117 >> extensional distance = 1 >> proper extension: 03c6sl9; >> query: (?x8529, ?x580) <- season(?x12956, ?x8529), season(?x11361, ?x8529), season(?x10939, ?x8529), season(?x8995, ?x8529), season(?x8901, ?x8529), season(?x8111, ?x8529), season(?x7725, ?x8529), season(?x7399, ?x8529), season(?x7357, ?x8529), season(?x7060, ?x8529), season(?x4487, ?x8529), season(?x4208, ?x8529), season(?x3333, ?x8529), season(?x2174, ?x8529), season(?x1823, ?x8529), season(?x1438, ?x8529), season(?x1160, ?x8529), season(?x1010, ?x8529), school(?x12956, ?x10666), school(?x12956, ?x10297), school(?x12956, ?x5621), school(?x12956, ?x4955), ?x4487 = 01ync, ?x2174 = 051vz, ?x11361 = 03m1n, ?x3333 = 01yjl, ?x10666 = 01dzg0, ?x8111 = 07147, team(?x11844, ?x12956), ?x8901 = 07l4z, season(?x12956, ?x3431), ?x7060 = 01slc, ?x1010 = 01d5z, ?x7399 = 06wpc, ?x10939 = 0x0d, ?x4208 = 061xq, ?x1823 = 01yhm, draft(?x12956, ?x11905), draft(?x12956, ?x10600), draft(?x12956, ?x1161), teams(?x5771, ?x1438), draft(?x1438, ?x8786), draft(?x1438, ?x8499), draft(?x1438, ?x4779), ?x10600 = 04f4z1k, ?x1160 = 049n7, position(?x1438, ?x4244), position(?x1438, ?x2010), school(?x1438, ?x8706), school(?x1438, ?x3948), school(?x1438, ?x2497), school(?x1438, ?x1011), school(?x1438, ?x581), school(?x1438, ?x466), sport(?x1438, ?x5063), ?x7357 = 04mjl, season(?x1438, ?x11501), season(?x1438, ?x9267), season(?x1438, ?x8517), ?x5063 = 018jz, ?x4244 = 028c_8, team(?x261, ?x1438), ?x8499 = 02r6gw6, ?x4955 = 09f2j, ?x11905 = 047dpm0, category(?x1438, ?x134), country(?x5771, ?x94), ?x581 = 06pwq, location(?x702, ?x5771), ?x4779 = 02z6872, place_of_birth(?x4572, ?x5771), ?x7725 = 07l8x, ?x2010 = 02lyr4, colors(?x1438, ?x8271), ?x8517 = 0285r5d, school(?x465, ?x5621), institution(?x865, ?x5621), ?x8786 = 02pq_x5, currency(?x5621, ?x170), ?x94 = 09c7w0, ?x11501 = 027mvrc, major_field_of_study(?x2497, ?x11820), student(?x5621, ?x525), ?x8271 = 02rnmb, locations(?x3797, ?x5771), citytown(?x5621, ?x13702), ?x11820 = 0w7s, organization(?x1011, ?x5487), major_field_of_study(?x5621, ?x254), student(?x10297, ?x2451), major_field_of_study(?x1011, ?x2601), major_field_of_study(?x1011, ?x947), ?x947 = 036hv, ?x8706 = 0trv, fraternities_and_sororities(?x10297, ?x3697), citytown(?x1011, ?x3269), ?x8995 = 01d6g, student(?x1011, ?x400), colors(?x2497, ?x332), school(?x2569, ?x1011), institution(?x1526, ?x1011), institution(?x734, ?x1011), school_type(?x1011, ?x1507), state_province_region(?x5621, ?x6521), ?x2601 = 04x_3, season(?x580, ?x9267), ?x1526 = 0bkj86, organization(?x346, ?x2497), ?x1161 = 02x2khw, ?x734 = 04zx3q1, time_zones(?x466, ?x1638), student(?x466, ?x3134), citytown(?x466, ?x1248), ?x3431 = 025ygqm, ?x3948 = 025v3k, major_field_of_study(?x466, ?x1695), state_province_region(?x1011, ?x3634) >> conf = 0.88 => this is the best rule for 6 predicted values ranks of expected_values: 1, 2, 3, 5 EVAL 025ygws season! 02d02 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.879 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 025ygws season! 07l8f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 14.000 14.000 0.879 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 025ygws season! 05m_8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.879 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season EVAL 025ygws season! 01ypc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 14.000 14.000 0.879 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #2720-0m24v PRED entity: 0m24v PRED relation: source PRED expected values: 0jbk9 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #56, 0.91 #22, 0.91 #21) >> Best rule #56 for best value: >> intensional similarity = 4 >> extensional distance = 237 >> proper extension: 0nh57; 043z0; 09dfcj; 0l2mg; >> query: (?x12253, 0jbk9) <- adjoins(?x12253, ?x7659), time_zones(?x12253, ?x2088), adjoins(?x5449, ?x12253), second_level_divisions(?x94, ?x12253) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m24v source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.921 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #2719-03cx282 PRED entity: 03cx282 PRED relation: cinematography! PRED expected values: 047wh1 027pfg => 109 concepts (18 used for prediction) PRED predicted values (max 10 best out of 339): 03wy8t (0.10 #1321, 0.09 #1660, 0.08 #1999), 03cw411 (0.08 #458, 0.06 #2492, 0.06 #797), 07tlfx (0.08 #650, 0.06 #989, 0.03 #2684), 03cvvlg (0.08 #617, 0.06 #956, 0.03 #2651), 07jnt (0.08 #569, 0.06 #908, 0.03 #2603), 01j5ql (0.08 #568, 0.06 #907, 0.03 #2602), 011yhm (0.08 #559, 0.06 #898, 0.03 #2593), 02q87z6 (0.08 #538, 0.06 #877, 0.03 #2572), 07bwr (0.08 #506, 0.06 #845, 0.03 #2540), 06sfk6 (0.08 #488, 0.06 #827, 0.03 #2522) >> Best rule #1321 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0c3dzk; >> query: (?x4997, 03wy8t) <- award_winner(?x6616, ?x4997), award_winner(?x1243, ?x4997), ?x1243 = 0gr0m, nationality(?x4997, ?x789) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03cx282 cinematography! 027pfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 18.000 0.095 http://example.org/film/film/cinematography EVAL 03cx282 cinematography! 047wh1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 18.000 0.095 http://example.org/film/film/cinematography #2718-0crc2cp PRED entity: 0crc2cp PRED relation: film_release_region PRED expected values: 03_3d 0d060g 0k6nt 059j2 02vzc 05sb1 => 114 concepts (105 used for prediction) PRED predicted values (max 10 best out of 199): 0d060g (0.90 #145, 0.88 #285, 0.85 #1408), 03_3d (0.90 #1407, 0.86 #1967, 0.86 #144), 02vzc (0.89 #1444, 0.88 #1724, 0.85 #2004), 059j2 (0.87 #1289, 0.87 #3251, 0.87 #2409), 0k6nt (0.82 #3246, 0.81 #4087, 0.79 #1984), 03rt9 (0.82 #1415, 0.81 #1695, 0.81 #1975), 01ls2 (0.81 #150, 0.75 #290, 0.63 #1413), 01p1v (0.76 #182, 0.75 #322, 0.63 #1865), 016wzw (0.75 #334, 0.70 #1877, 0.70 #1597), 03rj0 (0.74 #1451, 0.73 #1311, 0.71 #1731) >> Best rule #145 for best value: >> intensional similarity = 9 >> extensional distance = 19 >> proper extension: 0407yj_; >> query: (?x3191, 0d060g) <- film_release_region(?x3191, ?x1592), film_release_region(?x3191, ?x583), film_release_region(?x3191, ?x550), film_release_region(?x3191, ?x205), ?x205 = 03rjj, ?x1592 = 05v10, ?x550 = 05v8c, ?x583 = 015fr, film(?x629, ?x3191) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 19 EVAL 0crc2cp film_release_region 05sb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 114.000 105.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0crc2cp film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 105.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0crc2cp film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 105.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0crc2cp film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 105.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0crc2cp film_release_region 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 105.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0crc2cp film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 105.000 0.905 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2717-0dlm_ PRED entity: 0dlm_ PRED relation: category PRED expected values: 08mbj5d => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.68 #60, 0.67 #53, 0.67 #26) >> Best rule #60 for best value: >> intensional similarity = 14 >> extensional distance = 17 >> proper extension: 0ftjx; >> query: (?x14491, 08mbj5d) <- capital(?x1781, ?x14491), member_states(?x7695, ?x1781), adjoins(?x3951, ?x1781), administrative_area_type(?x1781, ?x2792), combatants(?x1781, ?x608), currency(?x3951, ?x170), contains(?x3951, ?x13896), organization(?x3951, ?x127), countries_spoken_in(?x254, ?x3951), administrative_parent(?x3951, ?x551), country(?x2978, ?x1781), medal(?x1781, ?x422), contains(?x6304, ?x1781), ?x2792 = 0hzc9wc >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dlm_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.684 http://example.org/common/topic/webpage./common/webpage/category #2716-02bqn1 PRED entity: 02bqn1 PRED relation: legislative_sessions! PRED expected values: 06bss 03txms => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 692): 06bss (0.78 #242, 0.74 #413, 0.72 #167), 03txms (0.72 #167, 0.71 #232, 0.71 #200), 016lh0 (0.72 #167, 0.71 #232, 0.71 #231), 0d06m5 (0.72 #167, 0.71 #232, 0.71 #231), 02mjmr (0.72 #167, 0.71 #232, 0.71 #231), 01lct6 (0.71 #232, 0.71 #231, 0.69 #81), 06hx2 (0.69 #81, 0.53 #403, 0.53 #402), 0dq2k (0.22 #476, 0.17 #678, 0.17 #539), 042fk (0.17 #678, 0.16 #728, 0.15 #491), 0rlz (0.17 #678, 0.16 #728, 0.11 #457) >> Best rule #242 for best value: >> intensional similarity = 30 >> extensional distance = 7 >> proper extension: 03rtmz; >> query: (?x1137, 06bss) <- district_represented(?x1137, ?x6226), district_represented(?x1137, ?x4061), district_represented(?x1137, ?x3670), district_represented(?x1137, ?x938), district_represented(?x1137, ?x448), legislative_sessions(?x1829, ?x1137), ?x3670 = 05tbn, jurisdiction_of_office(?x900, ?x938), religion(?x938, ?x8613), legislative_sessions(?x652, ?x1137), ?x8613 = 04pk9, location(?x1817, ?x938), adjoins(?x938, ?x2256), ?x448 = 03v1s, award(?x1817, ?x537), country(?x938, ?x94), contains(?x4061, ?x4765), legislative_sessions(?x1137, ?x356), contains(?x938, ?x3983), district_represented(?x7715, ?x4061), ?x356 = 05l2z4, ?x6226 = 03gh4, ?x7715 = 01grp0, adjoins(?x177, ?x4061), artists(?x378, ?x1817), award_winner(?x1817, ?x4239), ?x1829 = 02bp37, state(?x9331, ?x938), place_of_birth(?x6388, ?x938), time_zones(?x4765, ?x2674) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02bqn1 legislative_sessions! 03txms CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.778 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 02bqn1 legislative_sessions! 06bss CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.778 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #2715-03nqnnk PRED entity: 03nqnnk PRED relation: film_release_distribution_medium PRED expected values: 07c52 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 3): 07z4p (0.17 #16, 0.11 #137, 0.09 #149), 02nxhr (0.17 #29, 0.10 #142, 0.09 #134), 07c52 (0.12 #135, 0.10 #155, 0.09 #126) >> Best rule #16 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 02bqvs; >> query: (?x5929, 07z4p) <- film(?x1343, ?x5929), film_release_region(?x5929, ?x94), award_nominee(?x1094, ?x1343), ?x1094 = 035gjq, actor(?x2078, ?x1343) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #135 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 100 *> proper extension: 0jqzt; *> query: (?x5929, 07c52) <- film(?x3183, ?x5929), film_release_region(?x5929, ?x252), ?x252 = 03_3d, award(?x3183, ?x537), featured_film_locations(?x5929, ?x8136) *> conf = 0.12 ranks of expected_values: 3 EVAL 03nqnnk film_release_distribution_medium 07c52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 85.000 0.167 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #2714-0fvf9q PRED entity: 0fvf9q PRED relation: produced_by! PRED expected values: 02q87z6 0g9z_32 => 115 concepts (85 used for prediction) PRED predicted values (max 10 best out of 458): 0b6tzs (0.67 #919, 0.50 #920, 0.50 #80), 05fgt1 (0.50 #208, 0.22 #2047, 0.18 #3885), 011yhm (0.50 #609, 0.22 #2448, 0.18 #4286), 02704ff (0.50 #526, 0.22 #2365, 0.18 #4203), 01jzyf (0.50 #319, 0.22 #2158, 0.18 #3996), 07bwr (0.50 #454, 0.22 #2293, 0.18 #4131), 01vfqh (0.50 #118, 0.22 #1957, 0.18 #3795), 0dnvn3 (0.25 #31, 0.11 #1870, 0.09 #3708), 02r1c18 (0.25 #134, 0.11 #1973, 0.09 #3811), 01l_pn (0.20 #3276, 0.03 #8788, 0.03 #7869) >> Best rule #919 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02kxbwx; 02kxbx3; >> query: (?x163, ?x4047) <- award_winner(?x4047, ?x163), award_winner(?x945, ?x163), ?x945 = 0b6tzs, produced_by(?x414, ?x163) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #8820 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 01w92; 063472; 026v1z; *> query: (?x163, 02q87z6) <- award_winner(?x541, ?x163), award_nominee(?x902, ?x163), production_companies(?x80, ?x541) *> conf = 0.01 ranks of expected_values: 288, 333 EVAL 0fvf9q produced_by! 0g9z_32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 115.000 85.000 0.667 http://example.org/film/film/produced_by EVAL 0fvf9q produced_by! 02q87z6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 115.000 85.000 0.667 http://example.org/film/film/produced_by #2713-03rjj PRED entity: 03rjj PRED relation: country! PRED expected values: 079yb => 254 concepts (180 used for prediction) PRED predicted values (max 10 best out of 680): 06c62 (0.51 #20533, 0.44 #84563, 0.44 #16909), 0c7hq (0.49 #10267, 0.44 #16909, 0.33 #18722), 07kg3 (0.49 #10267, 0.33 #18722, 0.32 #86378), 06w92 (0.49 #10267, 0.33 #18722, 0.30 #32009), 078lk (0.49 #10267, 0.33 #18722, 0.30 #32009), 0bzty (0.49 #10267, 0.33 #18722, 0.30 #32009), 068cn (0.49 #10267, 0.33 #18722, 0.30 #32009), 0nr2v (0.49 #10267, 0.33 #18722, 0.30 #32009), 0jfvs (0.49 #10267, 0.33 #18722, 0.30 #32009), 01ngn3 (0.49 #10267, 0.33 #18722, 0.30 #32009) >> Best rule #20533 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 025ndl; >> query: (?x205, ?x6959) <- adjoins(?x205, ?x774), capital(?x205, ?x6959), combatants(?x94, ?x205) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #86378 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 108 *> proper extension: 02bb26; *> query: (?x205, ?x13691) <- contains(?x205, ?x13691), location_of_ceremony(?x566, ?x13691), jurisdiction_of_office(?x182, ?x205) *> conf = 0.32 ranks of expected_values: 30 EVAL 03rjj country! 079yb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 254.000 180.000 0.514 http://example.org/base/biblioness/bibs_location/country #2712-0gkd1 PRED entity: 0gkd1 PRED relation: instrumentalists PRED expected values: 01p95y0 => 88 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1447): 016h9b (0.67 #611, 0.64 #7964, 0.60 #2448), 03c7ln (0.67 #611, 0.64 #7964, 0.60 #2448), 01bpnd (0.67 #611, 0.64 #7964, 0.60 #2448), 0ftps (0.67 #611, 0.64 #7964, 0.60 #2448), 0161sp (0.64 #10570, 0.60 #7963, 0.59 #7961), 01vvycq (0.64 #10442, 0.58 #11055, 0.50 #11668), 018y81 (0.64 #10759, 0.50 #11372, 0.50 #8315), 0473q (0.64 #10812, 0.50 #3468, 0.50 #2857), 03h_fqv (0.62 #7962, 0.60 #7963, 0.59 #7961), 01271h (0.62 #7962, 0.60 #7963, 0.59 #7961) >> Best rule #611 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 05r5c; >> query: (?x7033, ?x211) <- performance_role(?x7033, ?x432), performance_role(?x7033, ?x228), role(?x211, ?x7033), role(?x1715, ?x7033), role(?x7033, ?x2944), role(?x7033, ?x1495), role(?x7033, ?x1166), ?x2944 = 0l14j_, ?x1166 = 05148p4, ?x1495 = 013y1f, role(?x7033, ?x4917), role(?x7033, ?x433), ?x433 = 025cbm, ?x228 = 0l14qv, role(?x2638, ?x432), ?x4917 = 06w7v, ?x2638 = 02fn5r >> conf = 0.67 => this is the best rule for 4 predicted values *> Best rule #11020 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 9 *> proper extension: 0l14md; 018vs; 04rzd; 03qjg; *> query: (?x7033, ?x120) <- performance_role(?x7033, ?x227), role(?x2865, ?x7033), role(?x1715, ?x7033), role(?x7033, ?x2944), ?x2865 = 016h9b, role(?x2944, ?x74), group(?x7033, ?x4715), role(?x120, ?x2944), role(?x2944, ?x4471), role(?x2944, ?x3328), ?x4471 = 026g73, role(?x433, ?x7033), ?x3328 = 016622, role(?x1225, ?x2944) *> conf = 0.26 ranks of expected_values: 440 EVAL 0gkd1 instrumentalists 01p95y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 88.000 47.000 0.673 http://example.org/music/instrument/instrumentalists #2711-0gl6x PRED entity: 0gl6x PRED relation: major_field_of_study PRED expected values: 01r4k => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 111): 02j62 (0.43 #4144, 0.37 #4507, 0.37 #5112), 04rjg (0.40 #1591, 0.38 #4133, 0.36 #3043), 03g3w (0.37 #4140, 0.36 #1719, 0.36 #872), 062z7 (0.32 #4141, 0.28 #4262, 0.27 #1962), 0fdys (0.25 #1611, 0.23 #3063, 0.21 #4153), 0g26h (0.24 #4156, 0.24 #2824, 0.23 #2098), 037mh8 (0.23 #4181, 0.22 #1760, 0.21 #1639), 04x_3 (0.22 #1597, 0.21 #3049, 0.21 #4139), 02_7t (0.22 #1636, 0.20 #4178, 0.19 #3088), 05qfh (0.22 #4150, 0.20 #1608, 0.19 #3060) >> Best rule #4144 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 0yldt; >> query: (?x10071, 02j62) <- institution(?x3437, ?x10071), ?x3437 = 02_xgp2, student(?x10071, ?x3542) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3108 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 031ns1; *> query: (?x10071, 01r4k) <- student(?x10071, ?x3542), company(?x2998, ?x10071), profession(?x3542, ?x7397) *> conf = 0.12 ranks of expected_values: 34 EVAL 0gl6x major_field_of_study 01r4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 92.000 92.000 0.427 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #2710-01vv6xv PRED entity: 01vv6xv PRED relation: profession PRED expected values: 01d_h8 => 198 concepts (140 used for prediction) PRED predicted values (max 10 best out of 86): 02hrh1q (0.89 #14203, 0.89 #15816, 0.89 #11708), 0nbcg (0.66 #2512, 0.64 #6313, 0.64 #2074), 01d_h8 (0.44 #11114, 0.43 #15661, 0.42 #2632), 0n1h (0.41 #2784, 0.37 #1762, 0.34 #2930), 01c72t (0.35 #4988, 0.34 #10548, 0.34 #7035), 03gjzk (0.34 #2642, 0.33 #3518, 0.32 #15671), 0fnpj (0.33 #58, 0.30 #1810, 0.29 #5024), 04f2zj (0.33 #94, 0.18 #1408, 0.15 #678), 0dxtg (0.30 #15669, 0.30 #11122, 0.30 #15231), 02jknp (0.25 #1028, 0.21 #736, 0.20 #13460) >> Best rule #14203 for best value: >> intensional similarity = 4 >> extensional distance = 241 >> proper extension: 023tp8; 01mqz0; 06b_0; 0cbkc; 01cwkq; >> query: (?x11443, 02hrh1q) <- profession(?x11443, ?x131), place_of_birth(?x11443, ?x11444), participant(?x11443, ?x2279), people(?x3584, ?x11443) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #11114 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 187 *> proper extension: 03lh3v; *> query: (?x11443, 01d_h8) <- currency(?x11443, ?x170), place_of_birth(?x11443, ?x11444), ?x170 = 09nqf, people(?x3584, ?x11443) *> conf = 0.44 ranks of expected_values: 3 EVAL 01vv6xv profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 198.000 140.000 0.893 http://example.org/people/person/profession #2709-0hzlz PRED entity: 0hzlz PRED relation: countries_spoken_in! PRED expected values: 07c9s 0349s => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 44): 0x82 (0.67 #5660, 0.53 #2669, 0.04 #4317), 0688f (0.25 #120, 0.09 #166, 0.07 #994), 06nm1 (0.21 #465, 0.20 #4146, 0.19 #2351), 064_8sq (0.21 #5028, 0.18 #5212, 0.18 #151), 02bjrlw (0.14 #461, 0.13 #277, 0.12 #323), 02hwhyv (0.13 #295, 0.11 #479, 0.10 #1445), 07c9s (0.12 #103, 0.09 #149, 0.08 #425), 03x42 (0.12 #127, 0.09 #173, 0.07 #219), 0h407 (0.12 #129, 0.09 #175, 0.07 #221), 083tk (0.12 #116, 0.09 #162, 0.07 #208) >> Best rule #5660 for best value: >> intensional similarity = 2 >> extensional distance = 135 >> proper extension: 02wzv; >> query: (?x792, ?x254) <- currency(?x792, ?x170), official_language(?x792, ?x254) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #103 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0853g; *> query: (?x792, 07c9s) <- exported_to(?x792, ?x5360), contains(?x792, ?x841), place_of_birth(?x5298, ?x792) *> conf = 0.12 ranks of expected_values: 7, 24 EVAL 0hzlz countries_spoken_in! 0349s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 200.000 200.000 0.669 http://example.org/language/human_language/countries_spoken_in EVAL 0hzlz countries_spoken_in! 07c9s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 200.000 200.000 0.669 http://example.org/language/human_language/countries_spoken_in #2708-09qvms PRED entity: 09qvms PRED relation: award_winner PRED expected values: 030znt 038g2x 033w9g => 21 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1870): 018ygt (0.54 #11442, 0.45 #9942, 0.44 #8439), 033jkj (0.50 #3657, 0.36 #1500, 0.30 #10507), 015c2f (0.50 #3399, 0.29 #6401, 0.25 #4900), 01p85y (0.50 #4210, 0.29 #7212, 0.25 #5711), 0pz7h (0.38 #10621, 0.33 #112, 0.27 #9121), 02tr7d (0.36 #9225, 0.33 #7722, 0.31 #10725), 025b5y (0.36 #1500, 0.33 #2344, 0.33 #6003), 05np4c (0.36 #1500, 0.33 #1985, 0.33 #6003), 09f0bj (0.36 #1500, 0.33 #1773, 0.33 #6003), 034x61 (0.36 #1500, 0.33 #1603, 0.33 #6003) >> Best rule #11442 for best value: >> intensional similarity = 16 >> extensional distance = 11 >> proper extension: 05c1t6z; 03nnm4t; >> query: (?x1112, 018ygt) <- ceremony(?x2853, ?x1112), ceremony(?x678, ?x1112), nominated_for(?x2853, ?x7087), nominated_for(?x2853, ?x6288), nominated_for(?x2853, ?x303), film_crew_role(?x6288, ?x137), award(?x7276, ?x678), film(?x91, ?x6288), award_winner(?x1112, ?x56), film_release_distribution_medium(?x7087, ?x81), produced_by(?x7087, ?x5371), ?x7276 = 05gnf9, award(?x123, ?x2853), genre(?x7087, ?x53), award_winner(?x303, ?x4854), award(?x631, ?x678) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #1500 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 09g90vz; *> query: (?x1112, ?x848) <- ceremony(?x2853, ?x1112), ?x2853 = 09qv_s, award_winner(?x1112, ?x4670), award_winner(?x1112, ?x1299), honored_for(?x1112, ?x5808), participant(?x3293, ?x1299), celebrity(?x1299, ?x2626), currency(?x1299, ?x170), student(?x2999, ?x4670), film(?x1299, ?x1298), nominated_for(?x4670, ?x5871), friend(?x1299, ?x4536), award_winner(?x8039, ?x1299), actor(?x5808, ?x848) *> conf = 0.36 ranks of expected_values: 15, 16, 111 EVAL 09qvms award_winner 033w9g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 21.000 13.000 0.538 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09qvms award_winner 038g2x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 21.000 13.000 0.538 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09qvms award_winner 030znt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 21.000 13.000 0.538 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2707-0kbvb PRED entity: 0kbvb PRED relation: olympics! PRED expected values: 0d060g 06npd 07t21 05r7t => 27 concepts (27 used for prediction) PRED predicted values (max 10 best out of 348): 0chghy (0.87 #72, 0.84 #1947, 0.82 #1870), 0154j (0.79 #1119, 0.78 #218, 0.76 #439), 0d060g (0.78 #1347, 0.78 #1648, 0.78 #1422), 06t2t (0.78 #218, 0.76 #439, 0.75 #742), 06t8v (0.78 #218, 0.75 #742, 0.74 #291), 05v10 (0.78 #218, 0.75 #742, 0.74 #291), 07twz (0.78 #218, 0.75 #742, 0.74 #291), 04v09 (0.78 #218, 0.75 #742, 0.74 #291), 087vz (0.71 #698, 0.62 #144, 0.54 #1116), 0h3y (0.67 #599, 0.50 #369, 0.47 #518) >> Best rule #72 for best value: >> intensional similarity = 75 >> extensional distance = 1 >> proper extension: 06sks6; >> query: (?x778, ?x390) <- olympics(?x8588, ?x778), olympics(?x6974, ?x778), olympics(?x5147, ?x778), olympics(?x2513, ?x778), olympics(?x2346, ?x778), olympics(?x2000, ?x778), olympics(?x1264, ?x778), olympics(?x1229, ?x778), olympics(?x1003, ?x778), olympics(?x774, ?x778), olympics(?x410, ?x778), olympics(?x404, ?x778), olympics(?x304, ?x778), olympics(?x205, ?x778), ?x774 = 06mzp, olympics(?x4876, ?x778), olympics(?x3641, ?x778), olympics(?x3598, ?x778), olympics(?x2978, ?x778), olympics(?x2044, ?x778), olympics(?x1967, ?x778), olympics(?x150, ?x778), ?x2000 = 0d0kn, ?x205 = 03rjj, ?x1003 = 03gj2, ?x2513 = 05b4w, medal(?x778, ?x422), ?x8588 = 0jhd, ?x410 = 01ls2, sports(?x778, ?x4045), ?x404 = 047lj, ?x3641 = 03fyrh, ?x1264 = 0345h, ?x3598 = 03rbzn, ?x5147 = 0d04z6, ?x4876 = 0d1t3, participating_countries(?x778, ?x390), ?x2044 = 06f41, ?x2978 = 03_8r, olympics(?x172, ?x778), ?x150 = 07rlg, administrative_parent(?x6974, ?x551), ?x1967 = 01cgz, film_release_region(?x7265, ?x304), film_release_region(?x7114, ?x304), film_release_region(?x5877, ?x304), film_release_region(?x4518, ?x304), film_release_region(?x3938, ?x304), film_release_region(?x3784, ?x304), film_release_region(?x3524, ?x304), film_release_region(?x1602, ?x304), film_release_region(?x1386, ?x304), film_release_region(?x1163, ?x304), olympics(?x304, ?x1277), ?x3524 = 06r2_, ?x7265 = 04tng0, taxonomy(?x304, ?x939), organization(?x304, ?x127), contains(?x304, ?x5168), country(?x1009, ?x304), ?x3938 = 024mpp, nationality(?x2083, ?x304), ?x1277 = 0swbd, ?x4045 = 06z6r, ?x1386 = 0dtfn, ?x3784 = 0bmhvpr, jurisdiction_of_office(?x182, ?x304), administrative_area_type(?x304, ?x2792), ?x4518 = 0hgnl3t, ?x1229 = 059j2, ?x2346 = 0d05w3, ?x5877 = 02qyv3h, ?x1163 = 0c0nhgv, film(?x494, ?x1602), genre(?x7114, ?x53) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1347 for first EXPECTED value: *> intensional similarity = 71 *> extensional distance = 21 *> proper extension: 015pkt; *> query: (?x778, 0d060g) <- olympics(?x5147, ?x778), olympics(?x4569, ?x778), olympics(?x4521, ?x778), olympics(?x2629, ?x778), olympics(?x1499, ?x778), olympics(?x789, ?x778), olympics(?x774, ?x778), olympics(?x429, ?x778), olympics(?x94, ?x778), olympics(?x87, ?x778), ?x774 = 06mzp, ?x94 = 09c7w0, nationality(?x10965, ?x4521), country(?x7687, ?x4521), country(?x4045, ?x4521), countries_spoken_in(?x5359, ?x4521), sports(?x778, ?x171), ?x7687 = 03krj, ?x789 = 0f8l9c, country(?x11662, ?x4569), film_release_region(?x4336, ?x4569), film_release_region(?x11313, ?x1499), film_release_region(?x11074, ?x1499), film_release_region(?x9839, ?x1499), film_release_region(?x6235, ?x1499), film_release_region(?x4352, ?x1499), film_release_region(?x3784, ?x1499), film_release_region(?x3745, ?x1499), film_release_region(?x2889, ?x1499), film_release_region(?x2342, ?x1499), film_release_region(?x2163, ?x1499), film_release_region(?x1642, ?x1499), film_release_region(?x1552, ?x1499), film_release_region(?x1535, ?x1499), film_release_region(?x1364, ?x1499), film_release_region(?x141, ?x1499), ?x1364 = 047msdk, ?x11313 = 0by17xn, ?x4352 = 09v71cj, ?x2889 = 040b5k, ?x2163 = 0j6b5, ?x9839 = 0gy7bj4, ?x4045 = 06z6r, medal(?x5147, ?x422), ?x1642 = 0bq8tmw, ?x141 = 0gtsx8c, adjoins(?x3855, ?x1499), film_release_region(?x10475, ?x87), film_release_region(?x7628, ?x87), film_release_region(?x6446, ?x87), film_release_region(?x5849, ?x87), teams(?x1499, ?x4306), ?x3745 = 03cw411, jurisdiction_of_office(?x1913, ?x5147), ?x5849 = 02h22, ?x1552 = 0gj9qxr, ?x7628 = 0bcp9b, ?x6235 = 05b6rdt, organization(?x4521, ?x312), ?x1535 = 02r1c18, contains(?x5147, ?x10708), ?x3784 = 0bmhvpr, nationality(?x294, ?x429), film_release_region(?x66, ?x2629), ?x6446 = 089j8p, ?x11074 = 0jqzt, administrative_parent(?x1789, ?x429), combatants(?x2629, ?x172), second_level_divisions(?x429, ?x1788), ?x2342 = 0ct5zc, ?x10475 = 047p798 *> conf = 0.78 ranks of expected_values: 3, 31, 38, 41 EVAL 0kbvb olympics! 05r7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 27.000 27.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0kbvb olympics! 07t21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 27.000 27.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0kbvb olympics! 06npd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 27.000 27.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0kbvb olympics! 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 27.000 27.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #2706-02704ff PRED entity: 02704ff PRED relation: executive_produced_by PRED expected values: 0b13g7 => 49 concepts (38 used for prediction) PRED predicted values (max 10 best out of 49): 05hj_k (0.08 #2112, 0.05 #98, 0.04 #4379), 06q8hf (0.07 #2180, 0.05 #166, 0.04 #920), 02z6l5f (0.06 #118, 0.03 #2383, 0.02 #3892), 02z2xdf (0.05 #157, 0.03 #2422), 0glyyw (0.04 #690, 0.03 #439, 0.03 #1699), 09d5d5 (0.03 #192), 02kxbwx (0.02 #4788, 0.02 #1007, 0.02 #8073), 02kxbx3 (0.02 #4788, 0.02 #8073, 0.02 #8328), 03c9pqt (0.02 #497, 0.02 #3266, 0.02 #748), 0gg9_5q (0.02 #341, 0.02 #592, 0.02 #4878) >> Best rule #2112 for best value: >> intensional similarity = 5 >> extensional distance = 237 >> proper extension: 02d44q; >> query: (?x5694, 05hj_k) <- nominated_for(?x112, ?x5694), nominated_for(?x112, ?x3790), nominated_for(?x112, ?x1224), ?x3790 = 07kh6f3, ?x1224 = 020fcn >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #2100 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 237 *> proper extension: 02d44q; *> query: (?x5694, 0b13g7) <- nominated_for(?x112, ?x5694), nominated_for(?x112, ?x3790), nominated_for(?x112, ?x1224), ?x3790 = 07kh6f3, ?x1224 = 020fcn *> conf = 0.01 ranks of expected_values: 31 EVAL 02704ff executive_produced_by 0b13g7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 49.000 38.000 0.075 http://example.org/film/film/executive_produced_by #2705-05v10 PRED entity: 05v10 PRED relation: country! PRED expected values: 06z6r => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 52): 06z6r (0.86 #185, 0.83 #289, 0.81 #341), 071t0 (0.83 #176, 0.81 #124, 0.81 #280), 03hr1p (0.74 #177, 0.72 #281, 0.71 #333), 06wrt (0.74 #170, 0.72 #274, 0.71 #326), 01lb14 (0.74 #169, 0.72 #273, 0.71 #325), 0w0d (0.71 #167, 0.66 #271, 0.65 #323), 01cgz (0.69 #12, 0.67 #116, 0.66 #272), 0194d (0.66 #305, 0.65 #357, 0.64 #201), 064vjs (0.62 #186, 0.62 #134, 0.62 #290), 02y8z (0.62 #277, 0.60 #329, 0.60 #173) >> Best rule #185 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 04gzd; 0chghy; ... >> query: (?x1592, 06z6r) <- film_release_region(?x1012, ?x1592), ?x1012 = 0bwfwpj, countries_spoken_in(?x403, ?x1592) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v10 country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.857 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #2704-0fsw_7 PRED entity: 0fsw_7 PRED relation: films! PRED expected values: 0fzyg => 109 concepts (62 used for prediction) PRED predicted values (max 10 best out of 65): 0fzyg (0.17 #367, 0.11 #211, 0.11 #54), 05489 (0.09 #1464, 0.08 #1307, 0.05 #1779), 07s2s (0.08 #1197, 0.06 #2456, 0.03 #5125), 01vq3 (0.08 #666, 0.07 #980, 0.06 #1610), 018h2 (0.08 #805, 0.02 #4422, 0.02 #1120), 081pw (0.06 #628, 0.05 #4716, 0.05 #786), 07c52 (0.05 #803, 0.05 #1275, 0.02 #7413), 0fx2s (0.05 #856, 0.03 #1171, 0.03 #4786), 07_nf (0.05 #850, 0.03 #1322, 0.02 #5412), 01cgz (0.05 #802, 0.02 #1746, 0.02 #5364) >> Best rule #367 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0d1qmz; 025twgt; >> query: (?x5399, 0fzyg) <- currency(?x5399, ?x170), nominated_for(?x6533, ?x5399), language(?x5399, ?x254), ?x6533 = 02n72k >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fsw_7 films! 0fzyg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 62.000 0.167 http://example.org/film/film_subject/films #2703-05mrf_p PRED entity: 05mrf_p PRED relation: country PRED expected values: 0f8l9c => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 144): 09c7w0 (0.83 #5373, 0.82 #420, 0.82 #1797), 0f8l9c (0.45 #3110, 0.43 #2990, 0.20 #19), 02jx1 (0.45 #3110, 0.43 #2990, 0.04 #505), 05qhw (0.45 #3110, 0.43 #2990, 0.03 #434), 06q1r (0.45 #3110, 0.43 #2990), 02qfv5d (0.27 #538, 0.18 #60, 0.08 #121), 03mqtr (0.27 #538, 0.18 #60, 0.08 #121), 01jfsb (0.27 #538, 0.18 #60, 0.08 #121), 0chghy (0.08 #73, 0.07 #134, 0.06 #193), 06mkj (0.07 #160, 0.06 #219, 0.05 #457) >> Best rule #5373 for best value: >> intensional similarity = 3 >> extensional distance = 1651 >> proper extension: 02vl9ln; >> query: (?x5074, 09c7w0) <- country(?x5074, ?x512), combatants(?x512, ?x94), nationality(?x111, ?x512) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #3110 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 819 *> proper extension: 07s8z_l; 01j95; *> query: (?x5074, ?x6401) <- award_winner(?x5074, ?x3580), titles(?x512, ?x5074), nationality(?x3580, ?x6401) *> conf = 0.45 ranks of expected_values: 2 EVAL 05mrf_p country 0f8l9c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 96.000 0.829 http://example.org/film/film/country #2702-01vg0s PRED entity: 01vg0s PRED relation: major_field_of_study PRED expected values: 04sh3 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 117): 04rjg (0.41 #738, 0.38 #258, 0.33 #498), 03g3w (0.40 #265, 0.34 #385, 0.33 #745), 0fdys (0.32 #516, 0.31 #276, 0.24 #396), 037mh8 (0.31 #305, 0.29 #545, 0.25 #785), 04gb7 (0.31 #882, 0.14 #762, 0.13 #4565), 0g26h (0.29 #400, 0.26 #760, 0.25 #280), 05qfh (0.29 #273, 0.28 #753, 0.27 #513), 05qjt (0.28 #727, 0.23 #247, 0.22 #487), 04x_3 (0.27 #264, 0.27 #504, 0.22 #384), 01lj9 (0.27 #277, 0.25 #517, 0.25 #757) >> Best rule #738 for best value: >> intensional similarity = 2 >> extensional distance = 136 >> proper extension: 03bwzr4; >> query: (?x9025, 04rjg) <- major_field_of_study(?x9025, ?x1668), ?x1668 = 01mkq >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #313 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 08815; 049dk; 0l2tk; 01mpwj; 01f1r4; 03x83_; 02zd460; 02bqy; 0677j; 023zl; ... *> query: (?x9025, 04sh3) <- major_field_of_study(?x9025, ?x254), contains(?x792, ?x9025), ?x254 = 02h40lc *> conf = 0.21 ranks of expected_values: 15 EVAL 01vg0s major_field_of_study 04sh3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 82.000 82.000 0.413 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #2701-03czqs PRED entity: 03czqs PRED relation: religion PRED expected values: 03kbr 04t_mf => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 31): 0c8wxp (0.80 #765, 0.70 #1227, 0.67 #1491), 05sfs (0.77 #762, 0.67 #1224, 0.54 #1389), 01lp8 (0.73 #760, 0.71 #1222, 0.65 #1486), 051kv (0.70 #764, 0.62 #1226, 0.49 #1490), 019cr (0.70 #770, 0.58 #1232, 0.47 #1496), 0631_ (0.67 #767, 0.58 #1229, 0.46 #1493), 05w5d (0.63 #780, 0.53 #1242, 0.43 #1506), 04pk9 (0.63 #778, 0.53 #1240, 0.43 #1504), 0flw86 (0.55 #299, 0.50 #35, 0.45 #1388), 092bf5 (0.55 #312, 0.36 #1236, 0.29 #1401) >> Best rule #765 for best value: >> intensional similarity = 5 >> extensional distance = 28 >> proper extension: 05kr_; >> query: (?x11103, 0c8wxp) <- religion(?x11103, ?x9040), country(?x11103, ?x2146), religion(?x7517, ?x9040), location_of_ceremony(?x566, ?x11103), languages(?x7517, ?x254) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1707 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 103 *> proper extension: 07t21; 015qh; 0d0kn; 0jdd; 07t_x; 07fb6; 0jhd; 04wlh; 019fv4; *> query: (?x11103, 04t_mf) <- religion(?x11103, ?x9040), contains(?x2146, ?x11103), religion(?x9039, ?x9040), gender(?x9039, ?x231), profession(?x9039, ?x1032) *> conf = 0.04 ranks of expected_values: 23 EVAL 03czqs religion 04t_mf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 167.000 167.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 03czqs religion 03kbr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 167.000 167.000 0.800 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #2700-02_jkc PRED entity: 02_jkc PRED relation: role PRED expected values: 07y_7 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 121): 05r5c (0.37 #433, 0.27 #221, 0.27 #327), 026t6 (0.36 #215, 0.14 #3, 0.13 #321), 01vdm0 (0.30 #352, 0.22 #564, 0.18 #246), 05842k (0.27 #399, 0.14 #81, 0.13 #505), 0l14qv (0.27 #324, 0.12 #536, 0.12 #642), 0342h (0.25 #535, 0.20 #323, 0.19 #641), 013y1f (0.20 #357, 0.14 #145, 0.13 #569), 02sgy (0.18 #431, 0.18 #219, 0.16 #537), 042v_gx (0.18 #222, 0.18 #540, 0.14 #116), 018vs (0.18 #227, 0.17 #333, 0.11 #651) >> Best rule #433 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 04ls53; 01l79yc; 02sjp; 0csdzz; >> query: (?x5298, 05r5c) <- award(?x5298, ?x1854), award_winner(?x1362, ?x5298), ?x1854 = 025m8y >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #1168 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 360 *> proper extension: 0cnl80; 03qd_; 03f5spx; 01gf5h; 0jdhp; 01l2fn; 0770cd; 07ymr5; 016h9b; 01w7nwm; ... *> query: (?x5298, ?x74) <- award_winner(?x5298, ?x8049), instrumentalists(?x614, ?x8049), role(?x614, ?x74) *> conf = 0.02 ranks of expected_values: 72 EVAL 02_jkc role 07y_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 84.000 84.000 0.368 http://example.org/music/artist/track_contributions./music/track_contribution/role #2699-02ptczs PRED entity: 02ptczs PRED relation: honored_for! PRED expected values: 0bzkvd => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 113): 04n2r9h (0.06 #158, 0.04 #524, 0.04 #1012), 0bzlrh (0.06 #211, 0.03 #577, 0.03 #455), 05c1t6z (0.04 #1841, 0.04 #2573, 0.03 #1963), 0bzk2h (0.04 #161, 0.03 #405, 0.02 #527), 0dznvw (0.04 #240, 0.02 #606, 0.02 #1460), 0bvfqq (0.03 #1002, 0.02 #270, 0.01 #2588), 0c53zb (0.03 #538, 0.02 #1148, 0.02 #172), 03gwpw2 (0.03 #249, 0.03 #1713, 0.03 #859), 0g55tzk (0.03 #364, 0.02 #242, 0.02 #1950), 02yw5r (0.03 #252, 0.02 #130, 0.02 #862) >> Best rule #158 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 03rg2b; >> query: (?x9772, 04n2r9h) <- production_companies(?x9772, ?x902), nominated_for(?x6525, ?x9772), ?x902 = 05qd_, religion(?x6525, ?x1985) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #221 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 03rg2b; *> query: (?x9772, 0bzkvd) <- production_companies(?x9772, ?x902), nominated_for(?x6525, ?x9772), ?x902 = 05qd_, religion(?x6525, ?x1985) *> conf = 0.02 ranks of expected_values: 53 EVAL 02ptczs honored_for! 0bzkvd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 104.000 104.000 0.059 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2698-08984j PRED entity: 08984j PRED relation: production_companies PRED expected values: 025hwq => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 92): 016tt2 (0.45 #3782, 0.40 #8064, 0.36 #7817), 030_1m (0.33 #254, 0.09 #734, 0.05 #9845), 016tw3 (0.30 #6049, 0.17 #7583, 0.16 #1293), 054lpb6 (0.29 #333, 0.16 #2909, 0.15 #2504), 086k8 (0.22 #7657, 0.18 #8066, 0.18 #8391), 05qd_ (0.22 #7663, 0.18 #1291, 0.18 #8072), 04rtpt (0.20 #207, 0.08 #2619, 0.06 #1651), 0hpt3 (0.20 #179, 0.05 #9845, 0.05 #9603), 06q07 (0.20 #218, 0.05 #9845, 0.05 #9603), 017jv5 (0.17 #1057, 0.17 #257, 0.14 #337) >> Best rule #3782 for best value: >> intensional similarity = 5 >> extensional distance = 197 >> proper extension: 091z_p; 0gy0l_; >> query: (?x7080, ?x574) <- production_companies(?x7080, ?x541), written_by(?x7080, ?x5338), film_release_distribution_medium(?x7080, ?x81), film_crew_role(?x7080, ?x137), film(?x574, ?x7080) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #776 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 07tw_b; 0prh7; *> query: (?x7080, 025hwq) <- production_companies(?x7080, ?x541), written_by(?x7080, ?x5338), film(?x5338, ?x188), place_of_birth(?x5338, ?x739), spouse(?x1888, ?x5338) *> conf = 0.06 ranks of expected_values: 29 EVAL 08984j production_companies 025hwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 164.000 164.000 0.454 http://example.org/film/film/production_companies #2697-0jrny PRED entity: 0jrny PRED relation: film PRED expected values: 05cvgl => 142 concepts (87 used for prediction) PRED predicted values (max 10 best out of 1109): 024mxd (0.40 #2392, 0.33 #4180, 0.13 #78681), 0291ck (0.20 #3353, 0.11 #5141, 0.08 #6929), 01jrbv (0.20 #2340, 0.11 #4128, 0.07 #33975), 090s_0 (0.20 #1825, 0.11 #3613, 0.05 #7189), 017kct (0.20 #2370, 0.11 #4158, 0.04 #41710), 032sl_ (0.20 #3347, 0.11 #5135, 0.04 #12287), 08mg_b (0.20 #2910, 0.11 #4698, 0.04 #11850), 01jwxx (0.20 #2636, 0.11 #4424, 0.03 #41976), 0b4lkx (0.20 #3178, 0.11 #4966, 0.03 #15695), 0m9p3 (0.20 #2175, 0.11 #3963, 0.02 #34362) >> Best rule #2392 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 039bp; 02mxw0; 01fwf1; >> query: (?x3194, 024mxd) <- student(?x741, ?x3194), nationality(?x3194, ?x94), film(?x3194, ?x1072), ?x1072 = 01_mdl >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #11165 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 02wb6yq; 0261x8t; *> query: (?x3194, 05cvgl) <- friend(?x4563, ?x3194), profession(?x3194, ?x1032), award_winner(?x1480, ?x3194), religion(?x3194, ?x7300) *> conf = 0.04 ranks of expected_values: 217 EVAL 0jrny film 05cvgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 142.000 87.000 0.400 http://example.org/film/actor/film./film/performance/film #2696-09d3b7 PRED entity: 09d3b7 PRED relation: honored_for! PRED expected values: 0bzjgq => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 91): 03gwpw2 (0.05 #249, 0.03 #371, 0.03 #2933), 04n2r9h (0.05 #646, 0.05 #402, 0.04 #524), 02wzl1d (0.04 #373, 0.03 #7, 0.03 #129), 0hr6lkl (0.04 #378, 0.03 #1842, 0.03 #256), 0bzmt8 (0.04 #450, 0.01 #1914, 0.01 #572), 0bzlrh (0.03 #89, 0.01 #821, 0.01 #577), 05zksls (0.03 #150, 0.03 #394, 0.02 #2956), 09gkdln (0.03 #228, 0.02 #838, 0.02 #3034), 09qftb (0.03 #220, 0.02 #464, 0.01 #2050), 09pnw5 (0.03 #210, 0.01 #820, 0.01 #2040) >> Best rule #249 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 0170z3; 01br2w; 0ddfwj1; 0djb3vw; 011yph; 0344gc; 01m13b; 04dsnp; 0jyx6; 02c6d; ... >> query: (?x8677, 03gwpw2) <- nominated_for(?x500, ?x8677), genre(?x8677, ?x2753), film_crew_role(?x8677, ?x1284), ?x2753 = 0219x_ >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09d3b7 honored_for! 0bzjgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.051 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2695-0ds2l81 PRED entity: 0ds2l81 PRED relation: genre PRED expected values: 07s9rl0 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 94): 07s9rl0 (0.71 #5138, 0.66 #2441, 0.59 #2075), 05p553 (0.57 #5, 0.43 #127, 0.42 #2445), 02kdv5l (0.50 #125, 0.39 #735, 0.31 #369), 01jfsb (0.41 #746, 0.37 #990, 0.36 #1722), 03k9fj (0.36 #135, 0.31 #623, 0.25 #867), 06n90 (0.36 #137, 0.21 #747, 0.18 #1235), 060__y (0.29 #18, 0.18 #2458, 0.15 #750), 06cvj (0.24 #2444, 0.11 #370, 0.10 #6847), 04xvlr (0.22 #490, 0.19 #978, 0.17 #1710), 01hmnh (0.21 #141, 0.21 #19, 0.19 #629) >> Best rule #5138 for best value: >> intensional similarity = 4 >> extensional distance = 1290 >> proper extension: 05jyb2; >> query: (?x8377, 07s9rl0) <- genre(?x8377, ?x1403), country(?x8377, ?x94), genre(?x7563, ?x1403), ?x7563 = 03bzjpm >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ds2l81 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.707 http://example.org/film/film/genre #2694-09lxtg PRED entity: 09lxtg PRED relation: olympics PRED expected values: 0kbws => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 37): 0l998 (0.82 #6, 0.69 #117, 0.58 #43), 0lk8j (0.82 #16, 0.62 #127, 0.58 #53), 0lv1x (0.82 #15, 0.62 #126, 0.50 #52), 018ctl (0.82 #7, 0.62 #118, 0.50 #44), 0kbvv (0.82 #24, 0.62 #135, 0.50 #61), 0nbjq (0.82 #18, 0.56 #129, 0.50 #55), 06sks6 (0.77 #208, 0.75 #134, 0.73 #23), 0l6mp (0.75 #54, 0.73 #17, 0.69 #128), 0l98s (0.75 #42, 0.69 #116, 0.64 #5), 0l6vl (0.75 #39, 0.69 #113, 0.64 #2) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 09c7w0; 03rjj; 0d060g; 05qhw; 07ssc; 06mzp; 0f8l9c; 059j2; 0h7x; >> query: (?x4569, 0l998) <- first_level_division_of(?x11662, ?x4569), olympics(?x4569, ?x2134), ?x2134 = 0blg2 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #421 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 07ww5; *> query: (?x4569, 0kbws) <- country(?x2315, ?x4569), jurisdiction_of_office(?x182, ?x4569), ?x2315 = 06wrt *> conf = 0.68 ranks of expected_values: 17 EVAL 09lxtg olympics 0kbws CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 98.000 98.000 0.818 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #2693-0149xx PRED entity: 0149xx PRED relation: nationality PRED expected values: 0jhd => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 58): 0jhd (0.92 #7283, 0.90 #3148, 0.84 #4822), 0cdbq (0.40 #160, 0.03 #11703, 0.02 #356), 06bnz (0.25 #8362, 0.25 #5610, 0.20 #138), 04swd (0.25 #8362, 0.25 #5610), 07ssc (0.18 #1683, 0.10 #3260, 0.10 #2273), 02jx1 (0.17 #1701, 0.16 #2587, 0.16 #2489), 0f8l9c (0.15 #217, 0.08 #1690, 0.05 #3069), 0d060g (0.06 #1185, 0.06 #1283, 0.05 #790), 03rk0 (0.06 #11256, 0.06 #1714, 0.05 #11355), 03rjj (0.05 #396, 0.05 #494, 0.04 #592) >> Best rule #7283 for best value: >> intensional similarity = 3 >> extensional distance = 1379 >> proper extension: 0784v1; 0bhtzw; >> query: (?x5125, ?x8588) <- nationality(?x5125, ?x94), place_of_birth(?x5125, ?x11419), country(?x11419, ?x8588) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0149xx nationality 0jhd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.922 http://example.org/people/person/nationality #2692-032w8h PRED entity: 032w8h PRED relation: award_nominee! PRED expected values: 01fx5l => 88 concepts (45 used for prediction) PRED predicted values (max 10 best out of 764): 029_l (0.84 #6972, 0.81 #20916, 0.81 #27891), 032w8h (0.50 #359, 0.18 #97609, 0.16 #104586), 05ty4m (0.25 #52, 0.18 #97609, 0.16 #104586), 04zkj5 (0.25 #1701, 0.16 #104586, 0.14 #4025), 072bb1 (0.25 #562, 0.16 #104586, 0.14 #2886), 0cmt6q (0.25 #1483, 0.16 #104586, 0.14 #3807), 0bt4r4 (0.25 #649, 0.16 #104586, 0.14 #2973), 08wq0g (0.25 #126, 0.16 #104586, 0.14 #2450), 0bt7ws (0.25 #868, 0.16 #104586, 0.14 #3192), 027cxsm (0.25 #336, 0.16 #104586, 0.14 #2660) >> Best rule #6972 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 04yj5z; 03q1vd; 05yh_t; 03_wtr; >> query: (?x1736, ?x237) <- award_nominee(?x1736, ?x237), film(?x1736, ?x638), ?x638 = 01cssf >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #104586 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1703 *> proper extension: 0kk9v; *> query: (?x1736, ?x274) <- award_nominee(?x1736, ?x237), nominated_for(?x1736, ?x167), award_nominee(?x237, ?x274) *> conf = 0.16 ranks of expected_values: 65 EVAL 032w8h award_nominee! 01fx5l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 88.000 45.000 0.837 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2691-015882 PRED entity: 015882 PRED relation: artists! PRED expected values: 09qxq7 => 142 concepts (118 used for prediction) PRED predicted values (max 10 best out of 220): 06924p (0.60 #1069, 0.12 #1669, 0.10 #2270), 06j6l (0.57 #1243, 0.44 #2144, 0.34 #5750), 0155w (0.50 #1302, 0.21 #2804, 0.20 #1002), 0gywn (0.46 #2153, 0.44 #1552, 0.43 #1252), 02k_kn (0.44 #2161, 0.17 #5767, 0.14 #1260), 02qdgx (0.40 #935, 0.21 #2136, 0.13 #2737), 025sc50 (0.38 #2145, 0.38 #1544, 0.25 #6052), 0ggx5q (0.33 #2174, 0.25 #373, 0.22 #3979), 02lnbg (0.31 #2154, 0.25 #1553, 0.25 #353), 0glt670 (0.29 #6045, 0.28 #8746, 0.24 #6646) >> Best rule #1069 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01n8gr; >> query: (?x1817, 06924p) <- place_of_birth(?x1817, ?x3983), award_nominee(?x1817, ?x8799), ?x8799 = 02f1c >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2325 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 0knjh; *> query: (?x1817, 09qxq7) <- location(?x1817, ?x938), artists(?x9007, ?x1817), ?x9007 = 02vjzr *> conf = 0.05 ranks of expected_values: 94 EVAL 015882 artists! 09qxq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 142.000 118.000 0.600 http://example.org/music/genre/artists #2690-02fgpf PRED entity: 02fgpf PRED relation: award_winner! PRED expected values: 024dzn => 110 concepts (104 used for prediction) PRED predicted values (max 10 best out of 300): 02qvyrt (0.42 #2971, 0.40 #19941, 0.39 #24187), 0fhpv4 (0.42 #2971, 0.40 #19941, 0.39 #24187), 026mmy (0.42 #2971, 0.40 #19941, 0.39 #24187), 0c4z8 (0.42 #2971, 0.40 #19941, 0.39 #24187), 04njml (0.42 #2971, 0.40 #19941, 0.39 #24187), 02h3d1 (0.42 #2971, 0.40 #19941, 0.39 #24187), 024dzn (0.42 #2971, 0.40 #19941, 0.39 #24187), 01by1l (0.19 #7322, 0.17 #8594, 0.15 #1381), 099vwn (0.17 #22063, 0.14 #25460, 0.08 #26310), 04mqgr (0.17 #22063, 0.14 #25460, 0.08 #26310) >> Best rule #2971 for best value: >> intensional similarity = 3 >> extensional distance = 138 >> proper extension: 07q1v4; 01vsxdm; 02jqjm; 07mvp; 0bdlj; 0bk1p; >> query: (?x1894, ?x1232) <- award(?x1894, ?x1232), award_winner(?x1079, ?x1894), music(?x188, ?x1894) >> conf = 0.42 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 7 EVAL 02fgpf award_winner! 024dzn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 110.000 104.000 0.421 http://example.org/award/award_category/winners./award/award_honor/award_winner #2689-01ww_vs PRED entity: 01ww_vs PRED relation: artist! PRED expected values: 03qk20 => 141 concepts (134 used for prediction) PRED predicted values (max 10 best out of 128): 01dtcb (0.43 #326, 0.38 #604, 0.29 #465), 03rhqg (0.33 #155, 0.25 #711, 0.19 #4048), 0g768 (0.33 #733, 0.17 #177, 0.15 #2263), 01clyr (0.33 #729, 0.13 #4066, 0.12 #590), 04t53l (0.33 #147, 0.02 #14049, 0.02 #13631), 0mzkr (0.29 #443, 0.29 #304, 0.25 #582), 01w40h (0.29 #446, 0.29 #307, 0.25 #585), 011k1h (0.28 #1400, 0.25 #1122, 0.18 #4042), 015_1q (0.25 #2384, 0.24 #2523, 0.24 #3357), 02p3cr5 (0.21 #862, 0.10 #4060, 0.08 #723) >> Best rule #326 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0lbj1; >> query: (?x11633, 01dtcb) <- religion(?x11633, ?x2694), artists(?x3370, ?x11633), ?x3370 = 059kh, role(?x11633, ?x227) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #205 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 07rnh; *> query: (?x11633, 03qk20) <- artists(?x2491, ?x11633), artist(?x14593, ?x11633), ?x14593 = 03x9yr *> conf = 0.17 ranks of expected_values: 20 EVAL 01ww_vs artist! 03qk20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 141.000 134.000 0.429 http://example.org/music/record_label/artist #2688-0167q3 PRED entity: 0167q3 PRED relation: place_of_birth! PRED expected values: 015zql => 188 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2379): 01vw_dv (0.42 #125224, 0.41 #7827, 0.33 #187835), 01tj34 (0.25 #812, 0.03 #37334, 0.03 #42553), 03975z (0.25 #1953, 0.03 #38475, 0.03 #43694), 013cr (0.25 #235, 0.03 #36757, 0.03 #41976), 01vh08 (0.25 #1874, 0.03 #38396, 0.03 #43615), 01tnbn (0.25 #1243, 0.03 #37765, 0.03 #42984), 01xsc9 (0.25 #2390, 0.03 #38912, 0.03 #44131), 0h10vt (0.25 #1984, 0.03 #38506, 0.03 #43725), 013bd1 (0.25 #1972, 0.03 #38494, 0.03 #43713), 044lyq (0.25 #1503, 0.03 #38025, 0.03 #43244) >> Best rule #125224 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 0160w; >> query: (?x6930, ?x6659) <- featured_film_locations(?x4551, ?x6930), location(?x6659, ?x6930), location_of_ceremony(?x566, ?x6930), place_of_birth(?x6659, ?x2277) >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0167q3 place_of_birth! 015zql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 188.000 122.000 0.415 http://example.org/people/person/place_of_birth #2687-07f7jp PRED entity: 07f7jp PRED relation: type_of_union PRED expected values: 04ztj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.82 #5, 0.81 #33, 0.75 #81), 01g63y (0.24 #18, 0.14 #110, 0.13 #134), 01bl8s (0.03 #43, 0.02 #47, 0.01 #55), 0jgjn (0.01 #56) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0gd5z; 0dzkq; 02vq8xn; 0kvsb; 07hyk; 0d3k14; 0d_w7; 0cbgl; >> query: (?x12140, 04ztj) <- company(?x12140, ?x382), student(?x3439, ?x12140), gender(?x12140, ?x231), ?x3439 = 03ksy >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07f7jp type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.818 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2686-02lnhv PRED entity: 02lnhv PRED relation: film PRED expected values: 0fdv3 => 144 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1083): 03bzjpm (0.12 #3099, 0.09 #4886, 0.07 #10247), 01l_pn (0.12 #4539, 0.09 #9900, 0.08 #2752), 04cv9m (0.10 #700, 0.04 #2487, 0.03 #36440), 020y73 (0.10 #365, 0.04 #2152, 0.02 #11087), 03ntbmw (0.10 #1768, 0.04 #3555, 0.02 #14277), 01gglm (0.10 #1402, 0.03 #4976, 0.03 #31781), 016z5x (0.10 #70, 0.02 #62617, 0.02 #12579), 0c9k8 (0.10 #483, 0.02 #18353, 0.01 #30862), 01q7h2 (0.10 #1573), 0260bz (0.10 #334) >> Best rule #3099 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 02qjj7; 02hhtj; >> query: (?x1207, 03bzjpm) <- vacationer(?x2316, ?x1207), profession(?x1207, ?x319), ?x319 = 01d_h8, participant(?x1725, ?x1207) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #37808 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 79 *> proper extension: 03h_0_z; 01skmp; 0g476; *> query: (?x1207, 0fdv3) <- participant(?x1725, ?x1207), place_of_birth(?x1207, ?x1885), award_winner(?x11115, ?x1207), nominated_for(?x11115, ?x5826) *> conf = 0.05 ranks of expected_values: 163 EVAL 02lnhv film 0fdv3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 144.000 105.000 0.125 http://example.org/film/actor/film./film/performance/film #2685-042xh PRED entity: 042xh PRED relation: story_by! PRED expected values: 03hxsv 031786 => 169 concepts (117 used for prediction) PRED predicted values (max 10 best out of 214): 04w7rn (0.25 #389, 0.17 #729, 0.05 #3450), 032zq6 (0.25 #138, 0.05 #2859, 0.04 #5239), 0gfsq9 (0.25 #90, 0.05 #2811, 0.04 #5191), 0b60sq (0.25 #360, 0.05 #2741, 0.02 #9542), 0bv8h2 (0.11 #2840, 0.09 #1479, 0.08 #4880), 03wh49y (0.10 #1214, 0.09 #1554, 0.06 #1894), 01cycq (0.10 #1274, 0.09 #1614, 0.05 #2975), 023cjg (0.10 #1345, 0.09 #1685, 0.05 #3046), 0ndwt2w (0.10 #1224, 0.09 #1564, 0.05 #2925), 017jd9 (0.10 #1179, 0.09 #1519, 0.05 #2880) >> Best rule #389 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 09jd9; >> query: (?x13644, 04w7rn) <- award_winner(?x3337, ?x13644), award(?x13644, ?x11084), award(?x13644, ?x9629), story_by(?x2006, ?x13644), ?x9629 = 0j6j8, ?x11084 = 02tzwd >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 042xh story_by! 031786 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 169.000 117.000 0.250 http://example.org/film/film/story_by EVAL 042xh story_by! 03hxsv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 169.000 117.000 0.250 http://example.org/film/film/story_by #2684-0j6j8 PRED entity: 0j6j8 PRED relation: award_winner PRED expected values: 05jm7 => 54 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1153): 07w21 (0.50 #9985, 0.50 #7506, 0.44 #12462), 0210f1 (0.42 #8995, 0.40 #11474, 0.32 #13951), 0fpzt5 (0.42 #9337, 0.33 #1905, 0.30 #11816), 05jm7 (0.38 #37162, 0.35 #19820, 0.34 #9907), 042xh (0.38 #37162, 0.34 #9907, 0.34 #19819), 09jd9 (0.38 #37162, 0.34 #9907, 0.34 #19819), 03ftmg (0.34 #9907, 0.34 #19819, 0.33 #24774), 0kp2_ (0.34 #9907, 0.34 #19819, 0.33 #24774), 03772 (0.33 #8578, 0.33 #1146, 0.25 #11057), 04r68 (0.33 #8582, 0.33 #1150, 0.25 #11061) >> Best rule #9985 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 02664f; 0c_dx; 01ppdy; 04hddx; >> query: (?x9629, 07w21) <- award(?x3858, ?x9629), disciplines_or_subjects(?x9629, ?x4403), influenced_by(?x3858, ?x11104), ?x11104 = 03j2gxx, profession(?x3858, ?x319) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #37162 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 71 *> proper extension: 018wng; 02z13jg; 0fq9zdn; 07h0cl; 03qgjwc; 0dgshf6; 0b6jkkg; 09v0wy2; 04zx08r; 0fq9zdv; ... *> query: (?x9629, ?x3858) <- award(?x3858, ?x9629), disciplines_or_subjects(?x9629, ?x4403), award_winner(?x9629, ?x6796), award_winner(?x10222, ?x3858) *> conf = 0.38 ranks of expected_values: 4 EVAL 0j6j8 award_winner 05jm7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 54.000 16.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #2683-01kb2j PRED entity: 01kb2j PRED relation: award_nominee PRED expected values: 0bqdvt => 94 concepts (45 used for prediction) PRED predicted values (max 10 best out of 827): 05dbf (0.81 #9289, 0.81 #71988, 0.81 #55733), 02bkdn (0.81 #9289, 0.81 #71988, 0.81 #55733), 0525b (0.81 #9289, 0.81 #71988, 0.81 #55733), 02wcx8c (0.41 #316, 0.15 #81282, 0.04 #2638), 02d4ct (0.35 #499, 0.15 #81282, 0.10 #71989), 03pmty (0.35 #195, 0.15 #81282, 0.04 #2517), 0bsb4j (0.35 #556, 0.15 #81282, 0.04 #2878), 0gpprt (0.35 #1899, 0.15 #81282), 014zcr (0.29 #46, 0.15 #81282, 0.10 #13934), 031k24 (0.29 #1779, 0.15 #81282, 0.08 #4101) >> Best rule #9289 for best value: >> intensional similarity = 3 >> extensional distance = 320 >> proper extension: 0277c3; 05l0j5; 01wmcbg; >> query: (?x5097, ?x192) <- award_nominee(?x192, ?x5097), film(?x5097, ?x414), religion(?x5097, ?x2694) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #74313 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1251 *> proper extension: 03yxwq; 0kc8y; 07k2d; 04rqd; *> query: (?x5097, ?x902) <- award_winner(?x1132, ?x5097), award_winner(?x6187, ?x5097), award_nominee(?x902, ?x6187) *> conf = 0.18 ranks of expected_values: 26 EVAL 01kb2j award_nominee 0bqdvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 94.000 45.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2682-048xyn PRED entity: 048xyn PRED relation: genre PRED expected values: 082gq 03bxz7 => 82 concepts (63 used for prediction) PRED predicted values (max 10 best out of 92): 07s9rl0 (0.95 #4632, 0.85 #475, 0.82 #357), 04xvlr (0.72 #5106, 0.71 #5936, 0.67 #356), 0f8l9c (0.67 #356, 0.64 #1188, 0.63 #1187), 02kdv5l (0.45 #833, 0.45 #952, 0.31 #121), 01jfsb (0.39 #963, 0.39 #844, 0.31 #2509), 02l7c8 (0.37 #727, 0.34 #7260, 0.33 #1206), 05p553 (0.37 #2027, 0.36 #3446, 0.36 #2264), 03bxz7 (0.36 #53, 0.35 #290, 0.32 #527), 03k9fj (0.34 #962, 0.34 #843, 0.32 #13), 082gq (0.32 #29, 0.19 #1548, 0.19 #1457) >> Best rule #4632 for best value: >> intensional similarity = 4 >> extensional distance = 985 >> proper extension: 0ckr7s; 02z9hqn; 07ng9k; 018nnz; 05pyrb; 059lwy; 02v5xg; 0jqb8; 058kh7; 023cjg; ... >> query: (?x6273, 07s9rl0) <- genre(?x6273, ?x1626), film(?x558, ?x6273), genre(?x8137, ?x1626), ?x8137 = 0gtx63s >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #53 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 26 *> proper extension: 0bmc4cm; *> query: (?x6273, 03bxz7) <- genre(?x6273, ?x3312), film_release_distribution_medium(?x6273, ?x81), nominated_for(?x1007, ?x6273), ?x3312 = 02p0szs *> conf = 0.36 ranks of expected_values: 8, 10 EVAL 048xyn genre 03bxz7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 82.000 63.000 0.953 http://example.org/film/film/genre EVAL 048xyn genre 082gq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 82.000 63.000 0.953 http://example.org/film/film/genre #2681-07tlfx PRED entity: 07tlfx PRED relation: currency PRED expected values: 09nqf => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.85 #29, 0.82 #43, 0.81 #15), 01nv4h (0.04 #23, 0.03 #198, 0.02 #30), 02l6h (0.01 #319, 0.01 #81, 0.01 #368), 02gsvk (0.01 #90) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 46 >> proper extension: 0gyy53; 09rsjpv; 03z106; >> query: (?x9978, 09nqf) <- production_companies(?x9978, ?x382), genre(?x9978, ?x2605), film_crew_role(?x9978, ?x137), ?x2605 = 03g3w >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tlfx currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.854 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #2680-09dfcj PRED entity: 09dfcj PRED relation: contains! PRED expected values: 05tbn => 78 concepts (45 used for prediction) PRED predicted values (max 10 best out of 96): 05tbn (0.69 #14396, 0.68 #18901, 0.63 #1799), 09c7w0 (0.69 #14396, 0.68 #18901, 0.61 #40499), 04rrd (0.57 #13494, 0.55 #16197, 0.50 #117), 01n7q (0.26 #1877, 0.14 #2777, 0.14 #27075), 02qkt (0.18 #28244, 0.03 #17446, 0.02 #38144), 08xpv_ (0.17 #1738, 0.03 #11638, 0.03 #13434), 059rby (0.17 #3619, 0.16 #6315, 0.15 #8117), 04_1l0v (0.14 #28348, 0.13 #38248, 0.12 #36451), 05fjf (0.12 #1272, 0.11 #4870, 0.11 #7570), 0vmt (0.12 #1854, 0.04 #34255, 0.04 #27052) >> Best rule #14396 for best value: >> intensional similarity = 5 >> extensional distance = 236 >> proper extension: 0nvrd; 01cx_; 0n2vl; >> query: (?x11384, ?x94) <- adjoins(?x11384, ?x4357), currency(?x11384, ?x170), contains(?x94, ?x4357), time_zones(?x4357, ?x2674), source(?x4357, ?x958) >> conf = 0.69 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 09dfcj contains! 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 45.000 0.688 http://example.org/location/location/contains #2679-09qj50 PRED entity: 09qj50 PRED relation: ceremony PRED expected values: 0hn821n => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 136): 0hn821n (0.57 #395, 0.50 #125, 0.28 #800), 0bxs_d (0.50 #109, 0.43 #379, 0.25 #784), 0bx6zs (0.50 #121, 0.43 #391, 0.25 #796), 07z31v (0.50 #30, 0.43 #300, 0.21 #5539), 0gpjbt (0.48 #1378, 0.32 #3404, 0.24 #1513), 09n4nb (0.47 #1395, 0.32 #3421, 0.25 #1530), 0466p0j (0.46 #1421, 0.31 #3447, 0.24 #1556), 05pd94v (0.46 #1352, 0.31 #3378, 0.24 #1487), 056878 (0.46 #1381, 0.31 #3407, 0.25 #1516), 02rjjll (0.46 #1355, 0.31 #3381, 0.24 #1490) >> Best rule #395 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 09qs08; >> query: (?x757, 0hn821n) <- award(?x444, ?x757), award(?x4517, ?x757), ?x4517 = 01s81, ceremony(?x757, ?x1265) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09qj50 ceremony 0hn821n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.571 http://example.org/award/award_category/winners./award/award_honor/ceremony #2678-06nvzg PRED entity: 06nvzg PRED relation: citytown PRED expected values: 02_286 => 88 concepts (86 used for prediction) PRED predicted values (max 10 best out of 204): 02_286 (0.62 #4442, 0.61 #5549, 0.59 #7758), 0rh6k (0.17 #1108, 0.08 #3690, 0.06 #6272), 0dclg (0.17 #1149, 0.05 #8894, 0.04 #3731), 0cc56 (0.13 #16986, 0.07 #17728, 0.06 #23290), 019fh (0.13 #16986, 0.07 #17728, 0.06 #23290), 0ccvx (0.13 #16986, 0.06 #23290, 0.06 #21065), 01mb87 (0.13 #16986, 0.06 #23290, 0.06 #21065), 0ycht (0.13 #16986, 0.06 #23290, 0.06 #21065), 0177z (0.12 #3795), 0cr3d (0.11 #2636, 0.09 #4850, 0.08 #3374) >> Best rule #4442 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 0c_j5d; 01xdn1; 0gvbw; 02bh8z; 0jvs0; 01ym8l; 0sxdg; 0d2fd7; 05th69; 01_4lx; ... >> query: (?x12371, 02_286) <- state_province_region(?x12371, ?x335), ?x335 = 059rby, currency(?x12371, ?x170), ?x170 = 09nqf, currency(?x12371, ?x170) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06nvzg citytown 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 86.000 0.618 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #2677-04s430 PRED entity: 04s430 PRED relation: profession PRED expected values: 09jwl 0np9r => 73 concepts (53 used for prediction) PRED predicted values (max 10 best out of 44): 01d_h8 (0.67 #6, 0.31 #1190, 0.30 #1338), 0dxtg (0.54 #1346, 0.54 #1494, 0.54 #1198), 03gjzk (0.46 #1051, 0.40 #1199, 0.40 #1347), 0np9r (0.42 #908, 0.35 #1056, 0.29 #168), 0cbd2 (0.38 #451, 0.33 #895, 0.20 #1191), 02jknp (0.20 #748, 0.19 #2969, 0.18 #1340), 09jwl (0.18 #4607, 0.17 #1646, 0.17 #18), 0nbcg (0.17 #31, 0.14 #327, 0.14 #179), 0d1pc (0.17 #50, 0.13 #1678, 0.07 #3159), 0dz3r (0.17 #2, 0.09 #4591, 0.08 #4443) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0fby2t; >> query: (?x5915, 01d_h8) <- film(?x5915, ?x1642), gender(?x5915, ?x231), ?x1642 = 0bq8tmw, profession(?x5915, ?x1032) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #908 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 01v3s2_; 0pz7h; 07ymr5; 08vr94; 086nl7; 02k21g; 04h07s; 05drr9; 030wkp; 06cddt; *> query: (?x5915, 0np9r) <- cast_members(?x5915, ?x905), gender(?x5915, ?x231), profession(?x5915, ?x1032), film(?x5915, ?x1642) *> conf = 0.42 ranks of expected_values: 4, 7 EVAL 04s430 profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 73.000 53.000 0.667 http://example.org/people/person/profession EVAL 04s430 profession 09jwl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 73.000 53.000 0.667 http://example.org/people/person/profession #2676-01k3tq PRED entity: 01k3tq PRED relation: dog_breed! PRED expected values: 04f_d => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 930): 059rby (0.43 #24, 0.14 #7, 0.01 #8), 0q8s4 (0.43 #24, 0.01 #8), 0cr3d (0.43 #24, 0.01 #8), 01x73 (0.43 #24, 0.01 #8), 0cc56 (0.43 #24), 02frhbc (0.33 #30, 0.33 #22, 0.33 #14), 0fvyg (0.33 #31, 0.33 #23, 0.33 #15), 0_vn7 (0.33 #28, 0.33 #20, 0.33 #12), 02hrh0_ (0.33 #29, 0.33 #21, 0.33 #4), 04f_d (0.33 #19, 0.33 #11, 0.33 #2) >> Best rule #24 for best value: >> intensional similarity = 121 >> extensional distance = 1 >> proper extension: 0km5c; >> query: (?x11363, ?x335) <- dog_breed(?x9713, ?x11363), dog_breed(?x8451, ?x11363), dog_breed(?x6769, ?x11363), dog_breed(?x6555, ?x11363), dog_breed(?x6088, ?x11363), dog_breed(?x5719, ?x11363), dog_breed(?x5381, ?x11363), dog_breed(?x4419, ?x11363), dog_breed(?x4356, ?x11363), dog_breed(?x4090, ?x11363), dog_breed(?x3373, ?x11363), dog_breed(?x3125, ?x11363), dog_breed(?x2879, ?x11363), dog_breed(?x2740, ?x11363), dog_breed(?x2277, ?x11363), dog_breed(?x1719, ?x11363), dog_breed(?x1705, ?x11363), dog_breed(?x1523, ?x11363), dog_breed(?x108, ?x11363), ?x3373 = 0ply0, ?x6555 = 01snm, ?x1705 = 094jv, location(?x8450, ?x8451), location(?x4992, ?x8451), location(?x4387, ?x8451), location(?x3054, ?x8451), ?x9713 = 0f2s6, ?x5719 = 0f2rq, dog_breed(?x8451, ?x5194), dog_breed(?x8451, ?x3095), ?x1523 = 030qb3t, country(?x8451, ?x94), ?x3125 = 0d6lp, ?x5381 = 0c_m3, ?x5194 = 01t032, ?x3095 = 01_gx_, influenced_by(?x6771, ?x8450), ?x2740 = 0f__1, award(?x8450, ?x112), languages(?x8450, ?x254), ?x1719 = 0f2w0, ?x4356 = 06wxw, ?x4090 = 01sn3, ?x2879 = 0ftxw, ?x108 = 0rh6k, contains(?x1024, ?x8451), place_of_birth(?x5769, ?x8451), profession(?x8450, ?x1032), location(?x10770, ?x2277), location(?x7903, ?x2277), location(?x7040, ?x2277), location(?x2925, ?x2277), location(?x2437, ?x2277), location(?x1126, ?x2277), jurisdiction_of_office(?x1195, ?x2277), origin(?x6264, ?x2277), origin(?x1206, ?x2277), mode_of_transportation(?x2277, ?x8731), locations(?x11210, ?x2277), citytown(?x5866, ?x2277), nationality(?x8450, ?x1264), capital(?x3038, ?x2277), ?x254 = 02h40lc, place_of_birth(?x7821, ?x2277), religion(?x10770, ?x2672), ?x4419 = 0d35y, participant(?x5536, ?x10770), film(?x8450, ?x3438), team(?x11210, ?x2303), ?x8731 = 01bjv, citytown(?x12219, ?x8451), award(?x10770, ?x154), award(?x7903, ?x68), ?x4992 = 0lkr7, celebrity(?x1126, ?x4397), teams(?x2277, ?x13438), profession(?x10770, ?x1183), ?x6088 = 0dyl9, profession(?x7903, ?x987), participant(?x1206, ?x1207), award_winner(?x749, ?x1126), ?x1032 = 02hrh1q, participant(?x7903, ?x286), artists(?x2996, ?x4387), award(?x1206, ?x1479), category(?x1206, ?x134), artists(?x302, ?x1206), film(?x7821, ?x522), currency(?x12219, ?x170), artist(?x1693, ?x4387), participant(?x10770, ?x7830), participant(?x5582, ?x3054), award_nominee(?x140, ?x1206), contains(?x2277, ?x2497), month(?x2277, ?x6303), award_nominee(?x1367, ?x7040), award_nominee(?x4337, ?x3054), influenced_by(?x2437, ?x9024), artist(?x2190, ?x1206), actor(?x5529, ?x2925), origin(?x4387, ?x9605), colors(?x5866, ?x663), profession(?x2925, ?x220), award(?x2925, ?x528), ?x2303 = 02plv57, people(?x2510, ?x3054), team(?x180, ?x13438), ?x6769 = 0f2tj, film(?x6264, ?x3700), location(?x2437, ?x335), ?x6303 = 0lkm, profession(?x6264, ?x131), award_winner(?x6264, ?x192), award_nominee(?x3836, ?x1126), film(?x7040, ?x365), award_winner(?x2437, ?x364), participant(?x444, ?x7040), people(?x1050, ?x2437), award_winner(?x289, ?x7903), award_nominee(?x6264, ?x1290), artists(?x8123, ?x6264) >> conf = 0.43 => this is the best rule for 5 predicted values *> Best rule #19 for first EXPECTED value: *> intensional similarity = 120 *> extensional distance = 1 *> proper extension: 0km5c; *> query: (?x11363, 04f_d) <- dog_breed(?x9713, ?x11363), dog_breed(?x8451, ?x11363), dog_breed(?x6769, ?x11363), dog_breed(?x6555, ?x11363), dog_breed(?x6088, ?x11363), dog_breed(?x5719, ?x11363), dog_breed(?x5381, ?x11363), dog_breed(?x4419, ?x11363), dog_breed(?x4356, ?x11363), dog_breed(?x4090, ?x11363), dog_breed(?x3373, ?x11363), dog_breed(?x3125, ?x11363), dog_breed(?x2879, ?x11363), dog_breed(?x2740, ?x11363), dog_breed(?x2277, ?x11363), dog_breed(?x1719, ?x11363), dog_breed(?x1705, ?x11363), dog_breed(?x1523, ?x11363), dog_breed(?x108, ?x11363), ?x3373 = 0ply0, ?x6555 = 01snm, ?x1705 = 094jv, location(?x8450, ?x8451), location(?x4992, ?x8451), location(?x4387, ?x8451), location(?x3054, ?x8451), ?x9713 = 0f2s6, ?x5719 = 0f2rq, dog_breed(?x8451, ?x5194), dog_breed(?x8451, ?x3095), ?x1523 = 030qb3t, country(?x8451, ?x94), ?x3125 = 0d6lp, ?x5381 = 0c_m3, ?x5194 = 01t032, ?x3095 = 01_gx_, influenced_by(?x6771, ?x8450), ?x2740 = 0f__1, award(?x8450, ?x112), languages(?x8450, ?x254), ?x1719 = 0f2w0, ?x4356 = 06wxw, ?x4090 = 01sn3, ?x2879 = 0ftxw, ?x108 = 0rh6k, contains(?x1024, ?x8451), place_of_birth(?x5769, ?x8451), profession(?x8450, ?x1032), location(?x10770, ?x2277), location(?x7903, ?x2277), location(?x7040, ?x2277), location(?x2925, ?x2277), location(?x2437, ?x2277), location(?x1126, ?x2277), jurisdiction_of_office(?x1195, ?x2277), origin(?x6264, ?x2277), origin(?x1206, ?x2277), mode_of_transportation(?x2277, ?x8731), locations(?x11210, ?x2277), citytown(?x5866, ?x2277), nationality(?x8450, ?x1264), capital(?x3038, ?x2277), ?x254 = 02h40lc, place_of_birth(?x7821, ?x2277), religion(?x10770, ?x2672), ?x4419 = 0d35y, participant(?x5536, ?x10770), film(?x8450, ?x3438), team(?x11210, ?x2303), ?x8731 = 01bjv, citytown(?x12219, ?x8451), award(?x10770, ?x154), award(?x7903, ?x68), ?x4992 = 0lkr7, celebrity(?x1126, ?x4397), teams(?x2277, ?x13438), profession(?x10770, ?x1183), ?x6088 = 0dyl9, profession(?x7903, ?x987), participant(?x1206, ?x1207), award_winner(?x749, ?x1126), ?x1032 = 02hrh1q, participant(?x7903, ?x286), artists(?x2996, ?x4387), award(?x1206, ?x1479), category(?x1206, ?x134), artists(?x302, ?x1206), film(?x7821, ?x522), currency(?x12219, ?x170), artist(?x1693, ?x4387), participant(?x10770, ?x7830), participant(?x5582, ?x3054), award_nominee(?x140, ?x1206), contains(?x2277, ?x2497), month(?x2277, ?x6303), award_nominee(?x1367, ?x7040), award_nominee(?x4337, ?x3054), influenced_by(?x2437, ?x9024), artist(?x2190, ?x1206), actor(?x5529, ?x2925), origin(?x4387, ?x9605), colors(?x5866, ?x663), profession(?x2925, ?x220), award(?x2925, ?x528), ?x2303 = 02plv57, people(?x2510, ?x3054), team(?x180, ?x13438), ?x6769 = 0f2tj, film(?x6264, ?x3700), ?x6303 = 0lkm, profession(?x6264, ?x131), award_winner(?x6264, ?x192), award_nominee(?x3836, ?x1126), film(?x7040, ?x365), award_winner(?x2437, ?x364), participant(?x444, ?x7040), people(?x1050, ?x2437), award_winner(?x289, ?x7903), award_nominee(?x6264, ?x1290), artists(?x8123, ?x6264) *> conf = 0.33 ranks of expected_values: 10 EVAL 01k3tq dog_breed! 04f_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 5.000 5.000 0.429 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #2675-012vwb PRED entity: 012vwb PRED relation: school! PRED expected values: 0jmk7 => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 140): 0jmj7 (0.68 #1526, 0.68 #1858, 0.68 #1609), 07l8x (0.44 #60, 0.38 #309, 0.21 #392), 05xvj (0.44 #78, 0.17 #659, 0.15 #327), 0512p (0.33 #14, 0.31 #263, 0.21 #346), 0713r (0.33 #30, 0.31 #279, 0.17 #611), 0cqt41 (0.33 #17, 0.29 #349, 0.15 #666), 04wmvz (0.33 #70, 0.23 #319, 0.15 #666), 03m1n (0.33 #75, 0.23 #324, 0.15 #666), 070xg (0.33 #25, 0.21 #357, 0.11 #1166), 02d02 (0.33 #62, 0.17 #665, 0.17 #643) >> Best rule #1526 for best value: >> intensional similarity = 4 >> extensional distance = 137 >> proper extension: 03ksy; 0b1xl; 037fqp; 01nnsv; 0gl5_; 04p_hy; 0bwfn; 01p79b; 02mp0g; 0c5x_; ... >> query: (?x3777, 0jmj7) <- institution(?x865, ?x3777), school(?x260, ?x3777), colors(?x3777, ?x3315), currency(?x3777, ?x170) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #329 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 035ktt; 0gdm1; *> query: (?x3777, 0jmk7) <- currency(?x3777, ?x170), school(?x8901, ?x3777), ?x8901 = 07l4z, colors(?x3777, ?x3315) *> conf = 0.15 ranks of expected_values: 30 EVAL 012vwb school! 0jmk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 147.000 147.000 0.683 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #2674-09gkx35 PRED entity: 09gkx35 PRED relation: film_release_region PRED expected values: 0d0vqn => 108 concepts (74 used for prediction) PRED predicted values (max 10 best out of 152): 0d0vqn (0.90 #3732, 0.90 #4042, 0.90 #3266), 05qhw (0.87 #3274, 0.84 #1099, 0.78 #4050), 03h64 (0.85 #3327, 0.83 #3017, 0.83 #3793), 0k6nt (0.80 #3440, 0.80 #644, 0.78 #3751), 0b90_r (0.78 #1089, 0.77 #3264, 0.72 #623), 06t2t (0.77 #3322, 0.68 #1147, 0.67 #3788), 01znc_ (0.74 #3768, 0.74 #3302, 0.72 #4078), 03rj0 (0.67 #3320, 0.58 #4096, 0.56 #3786), 05v8c (0.66 #3276, 0.57 #4052, 0.57 #2966), 06mzp (0.63 #1106, 0.56 #640, 0.52 #3281) >> Best rule #3732 for best value: >> intensional similarity = 6 >> extensional distance = 205 >> proper extension: 087wc7n; 03z9585; >> query: (?x3603, 0d0vqn) <- film_release_region(?x3603, ?x1264), film_release_region(?x3603, ?x205), film_release_region(?x3603, ?x87), ?x1264 = 0345h, ?x205 = 03rjj, ?x87 = 05r4w >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09gkx35 film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 74.000 0.903 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2673-059yj PRED entity: 059yj PRED relation: team PRED expected values: 051q5 0ws7 06rpd => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 204): 0fbftr (0.38 #147, 0.04 #440), 04czgbh (0.38 #147, 0.04 #440), 02wvfxz (0.38 #147, 0.04 #440), 02wvf2s (0.38 #147, 0.04 #440), 0fw9n7 (0.38 #147, 0.04 #440), 0g2hw4 (0.38 #147, 0.04 #440), 0f24cc (0.38 #147, 0.04 #440), 026l1lq (0.38 #147, 0.04 #440), 0fwwkj (0.38 #147, 0.04 #440), 03915c (0.38 #147, 0.04 #440) >> Best rule #147 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 0h69c; >> query: (?x11323, ?x179) <- team(?x11323, ?x6976), team(?x11323, ?x4256), team(?x11323, ?x1115), school(?x4256, ?x2711), sport(?x6976, ?x1083), team(?x1240, ?x4256), team(?x1240, ?x179), draft(?x4256, ?x465), colors(?x1115, ?x663) >> conf = 0.38 => this is the best rule for 58 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 17, 25, 44 EVAL 059yj team 06rpd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 55.000 55.000 0.381 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team EVAL 059yj team 0ws7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 55.000 55.000 0.381 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team EVAL 059yj team 051q5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 55.000 55.000 0.381 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #2672-01slc PRED entity: 01slc PRED relation: colors PRED expected values: 083jv => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 18): 083jv (0.97 #1243, 0.97 #1185, 0.86 #589), 01g5v (0.57 #573, 0.35 #1300, 0.33 #166), 01l849 (0.55 #771, 0.33 #1, 0.31 #470), 06fvc (0.50 #75, 0.40 #111, 0.38 #1186), 02rnmb (0.42 #484, 0.37 #503, 0.36 #264), 0jc_p (0.33 #41, 0.24 #789, 0.24 #569), 038hg (0.26 #733, 0.24 #569, 0.21 #1220), 06kqt3 (0.26 #733, 0.21 #568, 0.20 #141), 036k5h (0.24 #789, 0.24 #569, 0.21 #883), 07plts (0.21 #883, 0.21 #568, 0.20 #472) >> Best rule #1243 for best value: >> intensional similarity = 18 >> extensional distance = 227 >> proper extension: 04088s0; 026xxv_; >> query: (?x7060, 083jv) <- colors(?x7060, ?x5325), colors(?x8825, ?x5325), colors(?x5324, ?x5325), colors(?x14123, ?x5325), colors(?x10085, ?x5325), colors(?x9995, ?x5325), colors(?x387, ?x5325), ?x5324 = 01jszm, ?x10085 = 02fbb5, contains(?x94, ?x8825), ?x9995 = 0jm9w, currency(?x8825, ?x170), institution(?x1519, ?x8825), organization(?x346, ?x8825), team(?x2918, ?x14123), major_field_of_study(?x8825, ?x1858), ?x2918 = 02qvl7, ?x387 = 02896 >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01slc colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.969 http://example.org/sports/sports_team/colors #2671-015srx PRED entity: 015srx PRED relation: artists! PRED expected values: 0dn16 02x8m => 78 concepts (57 used for prediction) PRED predicted values (max 10 best out of 252): 0xhtw (0.59 #3723, 0.54 #2486, 0.49 #1869), 05bt6j (0.54 #8698, 0.46 #969, 0.45 #3133), 02x8m (0.50 #636, 0.50 #326, 0.47 #1562), 025sc50 (0.50 #667, 0.45 #3139, 0.37 #1593), 016cjb (0.50 #692, 0.25 #1309, 0.17 #1618), 02b71x (0.50 #769, 0.12 #1386, 0.07 #2779), 016clz (0.44 #9584, 0.43 #2786, 0.43 #13903), 02lnbg (0.38 #1292, 0.27 #3147, 0.20 #5311), 0ggx5q (0.38 #1311, 0.25 #694, 0.21 #6564), 03lty (0.36 #3734, 0.35 #2497, 0.32 #4045) >> Best rule #3723 for best value: >> intensional similarity = 7 >> extensional distance = 54 >> proper extension: 0167_s; >> query: (?x5793, 0xhtw) <- artists(?x671, ?x5793), group(?x645, ?x5793), group(?x315, ?x5793), ?x645 = 028tv0, artists(?x671, ?x5550), performance_role(?x227, ?x315), ?x5550 = 01bczm >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #636 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 019f9z; *> query: (?x5793, 02x8m) <- artists(?x5792, ?x5793), artists(?x1572, ?x5793), artists(?x378, ?x5793), ?x1572 = 06by7, origin(?x5793, ?x10584), ?x5792 = 026z9, artist(?x2299, ?x5793), ?x378 = 07sbbz2 *> conf = 0.50 ranks of expected_values: 3, 60 EVAL 015srx artists! 02x8m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 78.000 57.000 0.589 http://example.org/music/genre/artists EVAL 015srx artists! 0dn16 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 78.000 57.000 0.589 http://example.org/music/genre/artists #2670-05znxx PRED entity: 05znxx PRED relation: nominated_for! PRED expected values: 0gq9h 02qyntr => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 210): 02qyntr (0.53 #396, 0.24 #5166, 0.24 #2439), 0gq9h (0.48 #5050, 0.44 #5277, 0.42 #2323), 02qvyrt (0.45 #314, 0.28 #4542, 0.24 #4543), 02pqp12 (0.44 #277, 0.22 #2320, 0.22 #5047), 0gs9p (0.41 #5052, 0.38 #5279, 0.37 #2325), 0gq_v (0.40 #243, 0.38 #5013, 0.36 #5240), 040njc (0.39 #233, 0.32 #2276, 0.30 #5230), 099c8n (0.34 #275, 0.23 #2318, 0.22 #5045), 0gr4k (0.31 #5246, 0.31 #5019, 0.23 #2292), 0f4x7 (0.29 #5245, 0.28 #2291, 0.28 #4542) >> Best rule #396 for best value: >> intensional similarity = 4 >> extensional distance = 112 >> proper extension: 02zk08; >> query: (?x5098, 02qyntr) <- genre(?x5098, ?x53), nominated_for(?x637, ?x5098), ?x637 = 02r22gf, nominated_for(?x4428, ?x5098) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05znxx nominated_for! 02qyntr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.526 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05znxx nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.526 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2669-04n_g PRED entity: 04n_g PRED relation: profession PRED expected values: 02hrh1q => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 86): 02hrh1q (0.89 #12915, 0.89 #2265, 0.89 #9315), 01d_h8 (0.34 #8556, 0.33 #6, 0.33 #9306), 02jknp (0.32 #5408, 0.31 #4058, 0.24 #7508), 0dxtg (0.30 #5414, 0.28 #24316, 0.28 #4064), 09jwl (0.29 #20, 0.20 #15770, 0.20 #16070), 03gjzk (0.27 #2266, 0.22 #3916, 0.22 #5566), 0cbd2 (0.22 #1207, 0.22 #1057, 0.21 #10507), 0fnpj (0.19 #62, 0.09 #662, 0.08 #812), 0nbcg (0.18 #783, 0.18 #2133, 0.15 #2583), 01c72t (0.18 #2125, 0.16 #3175, 0.16 #2725) >> Best rule #12915 for best value: >> intensional similarity = 3 >> extensional distance = 482 >> proper extension: 04cr6qv; 02hhtj; >> query: (?x3891, 02hrh1q) <- participant(?x3891, ?x7632), nationality(?x3891, ?x94), film(?x3891, ?x3137) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04n_g profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.893 http://example.org/people/person/profession #2668-0jhz_ PRED entity: 0jhz_ PRED relation: location_of_ceremony! PRED expected values: 04ztj => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.39 #45, 0.36 #49, 0.31 #58), 01g63y (0.05 #22, 0.04 #46, 0.03 #42), 0jgjn (0.01 #48) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 94 >> proper extension: 02j9z; 0dg3n1; 02qkt; 07c5l; 06srk; 04q_g; 086g2; >> query: (?x8707, 04ztj) <- contains(?x8707, ?x7930), location_of_ceremony(?x566, ?x7930), location(?x3817, ?x7930), origin(?x1929, ?x7930) >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jhz_ location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.385 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #2667-01ty4 PRED entity: 01ty4 PRED relation: influenced_by PRED expected values: 039n1 => 136 concepts (67 used for prediction) PRED predicted values (max 10 best out of 311): 05qmj (0.54 #10066, 0.33 #194, 0.23 #2817), 0mj0c (0.54 #10066, 0.25 #9186, 0.25 #9625), 01lwx (0.54 #10066, 0.22 #410, 0.13 #2596), 01ty4 (0.54 #10066, 0.21 #8311, 0.17 #10944), 01v9724 (0.54 #10066, 0.17 #2802, 0.11 #179), 0420y (0.54 #10066, 0.14 #3028, 0.11 #24071), 02lt8 (0.54 #10066, 0.14 #2744, 0.10 #11065), 039n1 (0.54 #10066, 0.11 #2950, 0.11 #327), 043s3 (0.54 #10066, 0.11 #24071, 0.11 #117), 0379s (0.54 #10066, 0.11 #79, 0.09 #2702) >> Best rule #10066 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 03d9d6; >> query: (?x12103, ?x4033) <- peers(?x3941, ?x12103), peers(?x920, ?x3941), influenced_by(?x920, ?x4033) >> conf = 0.54 => this is the best rule for 13 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 8 EVAL 01ty4 influenced_by 039n1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 136.000 67.000 0.536 http://example.org/influence/influence_node/influenced_by #2666-0vmt PRED entity: 0vmt PRED relation: adjoins PRED expected values: 01n7q => 207 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1375): 05rgl (0.25 #101, 0.19 #6248, 0.15 #2406), 0j3b (0.25 #59, 0.15 #6206, 0.15 #2364), 0d060g (0.25 #10, 0.13 #778, 0.12 #4618), 0m25p (0.25 #340, 0.06 #44149, 0.06 #18448), 0m2by (0.25 #391, 0.06 #18448, 0.04 #44200), 0m2dk (0.25 #568, 0.06 #18448, 0.04 #44377), 0m2cb (0.25 #390, 0.06 #18448, 0.03 #6917), 03s5t (0.23 #89153, 0.21 #80698, 0.20 #902), 05fhy (0.23 #89153, 0.21 #80698, 0.19 #1590), 0846v (0.23 #89153, 0.21 #80698, 0.13 #13987) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0m27n; >> query: (?x938, 05rgl) <- adjoins(?x938, ?x1138), contains(?x938, ?x9010), ?x9010 = 0qpjt >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #89153 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 199 *> proper extension: 0p0cw; 0n2m7; 0l30v; 0n6mc; 0mvn6; 0mvxt; *> query: (?x938, ?x2768) <- adjoins(?x938, ?x2256), adjoins(?x2768, ?x2256), administrative_division(?x4419, ?x938) *> conf = 0.23 ranks of expected_values: 14 EVAL 0vmt adjoins 01n7q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 207.000 122.000 0.250 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #2665-07s9rl0 PRED entity: 07s9rl0 PRED relation: titles PRED expected values: 02vp1f_ 050r1z 0sxfd 02q5g1z 09gq0x5 03hj3b3 0260bz 0mcl0 0bpbhm 03np63f 01qz5 0170yd 04ynx7 03s9kp => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1390): 0168ls (0.75 #1163, 0.73 #1162, 0.70 #2327), 01_0f7 (0.75 #1163, 0.73 #1162, 0.70 #2327), 0kcn7 (0.75 #1163, 0.73 #1162, 0.70 #2327), 02q52q (0.75 #1163, 0.73 #1162, 0.70 #2327), 017kz7 (0.75 #1163, 0.73 #1162, 0.70 #2327), 02py4c8 (0.75 #1163, 0.73 #1162, 0.70 #2327), 02mpyh (0.75 #1163, 0.73 #1162, 0.70 #2327), 0hv4t (0.75 #1163, 0.73 #1162, 0.70 #2327), 0cwy47 (0.75 #1163, 0.73 #1162, 0.70 #2327), 0b2v79 (0.75 #1163, 0.73 #1162, 0.70 #2327) >> Best rule #1163 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 03mqtr; >> query: (?x53, ?x7917) <- genre(?x8137, ?x53), genre(?x7917, ?x53), ?x8137 = 0gtx63s, genre(?x273, ?x53), nominated_for(?x666, ?x7917), titles(?x53, ?x253) >> conf = 0.75 => this is the best rule for 541 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 113, 181, 188, 189, 316, 447, 476, 820, 880, 882, 883, 888, 930 EVAL 07s9rl0 titles 03s9kp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 04ynx7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 0170yd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 01qz5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 03np63f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 0bpbhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 0mcl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 0260bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 03hj3b3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 09gq0x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 02q5g1z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 0sxfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 050r1z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles EVAL 07s9rl0 titles 02vp1f_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 64.000 64.000 0.754 http://example.org/media_common/netflix_genre/titles #2664-01nln PRED entity: 01nln PRED relation: adjoins! PRED expected values: 01nyl => 98 concepts (93 used for prediction) PRED predicted values (max 10 best out of 389): 01nyl (0.83 #12520, 0.82 #72046, 0.82 #72045), 01rxw (0.83 #12520, 0.82 #72046, 0.82 #72045), 01nln (0.22 #71261, 0.22 #57931, 0.22 #72047), 05cc1 (0.22 #71261, 0.21 #72832, 0.21 #72831), 0164v (0.22 #71261, 0.21 #72832, 0.21 #72831), 01p1b (0.22 #57931, 0.22 #72047, 0.22 #66554), 03548 (0.22 #57931, 0.22 #72047, 0.22 #66554), 088xp (0.22 #57931, 0.22 #72047, 0.22 #66554), 06tw8 (0.22 #72047, 0.22 #66554, 0.21 #58716), 05rznz (0.22 #72047, 0.22 #66554, 0.21 #58716) >> Best rule #12520 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 080h2; 0156q; 0dclg; 0f04v; 0135k2; 025r_t; 01zlx; 01zqy6t; >> query: (?x6974, ?x6863) <- adjoins(?x6974, ?x6863), adjoins(?x1241, ?x6974), teams(?x6974, ?x1598) >> conf = 0.83 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 01nln adjoins! 01nyl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 93.000 0.827 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #2663-0bdt8 PRED entity: 0bdt8 PRED relation: gender PRED expected values: 02zsn => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.92 #10, 0.89 #6, 0.52 #76), 05zppz (0.86 #127, 0.85 #155, 0.85 #105) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 01938t; >> query: (?x6440, 02zsn) <- award_winner(?x1245, ?x6440), ?x1245 = 0gqwc, award_winner(?x6958, ?x6440) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bdt8 gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.917 http://example.org/people/person/gender #2662-0hz55 PRED entity: 0hz55 PRED relation: producer_type PRED expected values: 0ckd1 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.80 #8, 0.79 #6, 0.79 #18) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 03czz87; >> query: (?x4932, 0ckd1) <- actor(?x4932, ?x3366), honored_for(?x1112, ?x4932), nominated_for(?x1039, ?x4932), tv_program(?x4299, ?x4932) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hz55 producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.800 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #2661-0d6lp PRED entity: 0d6lp PRED relation: county_seat PRED expected values: 0d6lp => 212 concepts (210 used for prediction) PRED predicted values (max 10 best out of 54): 013807 (0.22 #26148, 0.04 #11981, 0.03 #35800), 02jmst (0.22 #26148, 0.04 #11981, 0.03 #35800), 07vfz (0.22 #26148, 0.04 #11981, 0.03 #35800), 02cl1 (0.10 #548, 0.05 #1273, 0.04 #2726), 0r1jr (0.10 #563, 0.03 #4192, 0.02 #8551), 0g_wn2 (0.06 #800, 0.01 #11513, 0.01 #14054), 0mp36 (0.06 #872), 0c_m3 (0.06 #786), 0r8bh (0.06 #1059, 0.04 #1603, 0.03 #4508), 0cv3w (0.06 #930, 0.04 #1474, 0.03 #4379) >> Best rule #26148 for best value: >> intensional similarity = 2 >> extensional distance = 247 >> proper extension: 0mtdx; 01w0v; 0123_x; 03fb3t; 09lk2; 028n3; 0dyjz; 0l1k8; 052fbt; 0dmy0; ... >> query: (?x3125, ?x8281) <- second_level_divisions(?x94, ?x3125), contains(?x3125, ?x8281) >> conf = 0.22 => this is the best rule for 3 predicted values *> Best rule #29423 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 367 *> proper extension: 0fngy; *> query: (?x3125, ?x2552) <- citytown(?x1168, ?x3125), citytown(?x1168, ?x2552) *> conf = 0.01 ranks of expected_values: 51 EVAL 0d6lp county_seat 0d6lp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 212.000 210.000 0.224 http://example.org/location/us_county/county_seat #2660-0j86l PRED entity: 0j86l PRED relation: team! PRED expected values: 01x2_q => 72 concepts (61 used for prediction) PRED predicted values (max 10 best out of 91): 03n69x (0.56 #6321, 0.17 #938, 0.12 #1168), 040j2_ (0.56 #6321, 0.17 #966, 0.12 #2105), 02y8bn (0.25 #322, 0.22 #437, 0.20 #665), 054c1 (0.12 #1356, 0.06 #2150, 0.05 #2265), 049sb (0.11 #1474, 0.08 #1015, 0.06 #2154), 012xdf (0.09 #2576, 0.07 #2691, 0.06 #2917), 04g9sq (0.08 #1028, 0.08 #3661, 0.06 #1373), 0cg39k (0.08 #986, 0.06 #1331, 0.05 #2469), 03vrv9 (0.08 #1002, 0.05 #3402, 0.03 #3981), 054fvj (0.08 #992, 0.02 #2475, 0.02 #4666) >> Best rule #6321 for best value: >> intensional similarity = 8 >> extensional distance = 147 >> proper extension: 02279c; >> query: (?x14022, ?x5412) <- teams(?x2277, ?x14022), teams(?x2277, ?x2405), contains(?x94, ?x2277), team(?x5412, ?x2405), team(?x261, ?x2405), team(?x2918, ?x14022), nationality(?x51, ?x94), film_release_region(?x54, ?x94) >> conf = 0.56 => this is the best rule for 2 predicted values *> Best rule #3664 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 71 *> proper extension: 0jmnl; *> query: (?x14022, ?x11825) <- sport(?x14022, ?x453), sport(?x14258, ?x453), sport(?x11826, ?x453), sport(?x5233, ?x453), sport(?x4426, ?x453), sports(?x452, ?x453), team(?x11825, ?x11826), country(?x453, ?x1558), ?x1558 = 01mjq, sports(?x418, ?x453), team(?x2918, ?x4426), teams(?x2474, ?x14258), colors(?x5233, ?x332) *> conf = 0.07 ranks of expected_values: 12 EVAL 0j86l team! 01x2_q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 72.000 61.000 0.556 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #2659-0gqng PRED entity: 0gqng PRED relation: nominated_for PRED expected values: 02rb607 => 58 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1502): 07l50vn (0.86 #9505, 0.77 #26933, 0.77 #17427), 0b2qtl (0.77 #26933, 0.77 #17427, 0.77 #19013), 09gq0x5 (0.62 #3422, 0.62 #5006, 0.57 #6590), 017gl1 (0.62 #3300, 0.57 #1715, 0.54 #4884), 09q5w2 (0.62 #3319, 0.57 #1734, 0.54 #4903), 0gmgwnv (0.62 #10468, 0.57 #2546, 0.54 #5715), 026p4q7 (0.62 #3525, 0.57 #1940, 0.50 #356), 011yqc (0.62 #3376, 0.57 #1791, 0.50 #207), 0ywrc (0.62 #3634, 0.57 #2049, 0.50 #465), 02ll45 (0.62 #3949, 0.57 #2364, 0.50 #780) >> Best rule #9505 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 02x4wr9; 03qgjwc; 05ztrmj; 09v0wy2; 09v92_x; 09v4bym; 0262s1; 09v478h; >> query: (?x77, ?x9893) <- award(?x9893, ?x77), award(?x7554, ?x77), ?x7554 = 01mgw, film_format(?x9893, ?x6392), ?x6392 = 0cj16, award(?x1872, ?x77) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #3515 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 019f4v; 02pqp12; 02qvyrt; 054knh; *> query: (?x77, 02rb607) <- award(?x9893, ?x77), award(?x7554, ?x77), ?x7554 = 01mgw, ceremony(?x77, ?x78), genre(?x9893, ?x53) *> conf = 0.25 ranks of expected_values: 357 EVAL 0gqng nominated_for 02rb607 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 17.000 0.864 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2658-04n2r9h PRED entity: 04n2r9h PRED relation: award_winner PRED expected values: 05fnl9 01jgpsh 0gpprt => 33 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1475): 02tr7d (0.56 #3282, 0.33 #226, 0.33 #24456), 0h0wc (0.38 #4944, 0.36 #6472, 0.33 #8000), 026rm_y (0.38 #5820, 0.36 #7348, 0.33 #8876), 018ygt (0.33 #4010, 0.33 #954, 0.31 #5540), 01wbg84 (0.33 #3088, 0.33 #32, 0.20 #4583), 0dvld (0.33 #908, 0.33 #24456, 0.29 #4585), 02sb1w (0.33 #957, 0.25 #4586, 0.25 #1527), 0hsn_ (0.33 #1232, 0.25 #2759, 0.12 #11932), 02lfns (0.33 #151, 0.23 #19873, 0.20 #4583), 07s8hms (0.33 #572, 0.22 #3628, 0.20 #4583) >> Best rule #3282 for best value: >> intensional similarity = 15 >> extensional distance = 7 >> proper extension: 0clfdj; >> query: (?x2988, 02tr7d) <- award_winner(?x2988, ?x3842), honored_for(?x2988, ?x7493), honored_for(?x2988, ?x6213), honored_for(?x2988, ?x641), film(?x722, ?x641), category(?x641, ?x134), award_winner(?x3842, ?x6359), nominated_for(?x3842, ?x493), nominated_for(?x591, ?x6213), ?x6359 = 026v437, award_winner(?x6213, ?x1554), film_release_region(?x7493, ?x1264), film_release_region(?x951, ?x1264), nationality(?x380, ?x1264), contains(?x1264, ?x196) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #4585 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 7 *> proper extension: 0clfdj; *> query: (?x2988, ?x1554) <- award_winner(?x2988, ?x3842), honored_for(?x2988, ?x7493), honored_for(?x2988, ?x6213), honored_for(?x2988, ?x641), film(?x722, ?x641), category(?x641, ?x134), award_winner(?x3842, ?x6359), nominated_for(?x3842, ?x493), nominated_for(?x591, ?x6213), ?x6359 = 026v437, award_winner(?x6213, ?x1554), film_release_region(?x7493, ?x1264), film_release_region(?x951, ?x1264), nationality(?x380, ?x1264), contains(?x1264, ?x196) *> conf = 0.29 ranks of expected_values: 78, 1420 EVAL 04n2r9h award_winner 0gpprt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 33.000 21.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 04n2r9h award_winner 01jgpsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 33.000 21.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 04n2r9h award_winner 05fnl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 33.000 21.000 0.556 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2657-0f1sm PRED entity: 0f1sm PRED relation: place! PRED expected values: 0f1sm => 163 concepts (110 used for prediction) PRED predicted values (max 10 best out of 211): 0f1sm (0.18 #40761, 0.18 #35599, 0.16 #31461), 01m1_t (0.18 #40761, 0.18 #35599, 0.16 #31461), 01x73 (0.15 #44889, 0.09 #53149, 0.07 #46954), 09c7w0 (0.09 #53149, 0.07 #46954, 0.05 #49018), 030qb3t (0.04 #8763, 0.04 #13917, 0.04 #18560), 013yq (0.04 #8763, 0.04 #13917, 0.04 #18560), 0d9jr (0.04 #8763, 0.04 #13917, 0.04 #18560), 0f2r6 (0.04 #8763, 0.04 #13917, 0.04 #18560), 0f__1 (0.04 #8763, 0.04 #13917, 0.04 #18560), 0d9y6 (0.04 #8763, 0.04 #13917, 0.04 #18560) >> Best rule #40761 for best value: >> intensional similarity = 2 >> extensional distance = 288 >> proper extension: 013hvr; 03qzj4; >> query: (?x9445, ?x3163) <- contains(?x3164, ?x9445), county(?x3163, ?x3164) >> conf = 0.18 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0f1sm place! 0f1sm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 110.000 0.183 http://example.org/location/hud_county_place/place #2656-092vkg PRED entity: 092vkg PRED relation: genre PRED expected values: 04xvlr 0lsxr => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 90): 07s9rl0 (0.77 #611, 0.72 #245, 0.67 #1), 05p553 (0.56 #127, 0.35 #983, 0.34 #5646), 03k9fj (0.39 #379, 0.21 #991, 0.21 #2712), 01jfsb (0.36 #1114, 0.35 #746, 0.32 #992), 0219x_ (0.36 #272, 0.22 #150, 0.11 #28), 02l7c8 (0.33 #17, 0.33 #505, 0.32 #627), 060__y (0.33 #140, 0.19 #628, 0.13 #1241), 02kdv5l (0.29 #1103, 0.28 #981, 0.28 #2702), 0vgkd (0.22 #134, 0.22 #12, 0.06 #256), 0lsxr (0.22 #132, 0.21 #254, 0.20 #742) >> Best rule #611 for best value: >> intensional similarity = 3 >> extensional distance = 223 >> proper extension: 06mmr; >> query: (?x1064, 07s9rl0) <- award(?x1064, ?x591), nominated_for(?x591, ?x1308), ?x1308 = 04mzf8 >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #132 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0642xf3; 03hp2y1; *> query: (?x1064, 0lsxr) <- film_crew_role(?x1064, ?x281), film(?x91, ?x1064), ?x91 = 04bdxl *> conf = 0.22 ranks of expected_values: 10, 11 EVAL 092vkg genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 70.000 70.000 0.773 http://example.org/film/film/genre EVAL 092vkg genre 04xvlr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 70.000 70.000 0.773 http://example.org/film/film/genre #2655-04zkj5 PRED entity: 04zkj5 PRED relation: student! PRED expected values: 05mv4 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 96): 0bwfn (0.10 #802, 0.07 #275, 0.06 #1856), 017z88 (0.10 #609, 0.04 #6933, 0.03 #7460), 065y4w7 (0.09 #1595, 0.05 #3703, 0.04 #1068), 03ksy (0.07 #106, 0.05 #633, 0.04 #8538), 01rtm4 (0.07 #4, 0.05 #531, 0.01 #3693), 02xwzh (0.07 #388, 0.05 #915, 0.01 #5658), 08htt0 (0.07 #493, 0.05 #1020), 02lwv5 (0.07 #415, 0.05 #942), 02kbtf (0.07 #344, 0.05 #871), 01tx9m (0.07 #209, 0.05 #736) >> Best rule #802 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 06151l; 06mfvc; 0cnl09; >> query: (?x7663, 0bwfn) <- award_nominee(?x237, ?x7663), award_winner(?x275, ?x7663), ?x275 = 083chw >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04zkj5 student! 05mv4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 92.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student #2654-0ymff PRED entity: 0ymff PRED relation: educational_institution! PRED expected values: 0ymff => 189 concepts (89 used for prediction) PRED predicted values (max 10 best out of 274): 07tgn (0.21 #39400, 0.12 #15, 0.04 #5947), 0ymff (0.21 #39400, 0.05 #22665, 0.03 #7552), 01722w (0.21 #39400), 0138t4 (0.12 #395, 0.04 #6327, 0.03 #6867), 0lbfv (0.12 #208, 0.04 #6140, 0.03 #8300), 07w4j (0.12 #54, 0.04 #5986, 0.03 #8146), 0677j (0.12 #313, 0.03 #8405, 0.02 #10561), 030nwm (0.12 #526), 01v2xl (0.11 #975, 0.03 #7552, 0.03 #7551), 01lnyf (0.11 #670, 0.03 #7552, 0.03 #7142) >> Best rule #39400 for best value: >> intensional similarity = 5 >> extensional distance = 275 >> proper extension: 0j_sncb; 02fy0z; 01k3s2; 05zl0; 01j_5k; 02j04_; 019pwv; 01p79b; 02mp0g; 02m0sc; ... >> query: (?x10393, ?x892) <- colors(?x10393, ?x3621), student(?x10393, ?x11239), category(?x10393, ?x134), contains(?x1310, ?x10393), student(?x892, ?x11239) >> conf = 0.21 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0ymff educational_institution! 0ymff CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 189.000 89.000 0.215 http://example.org/education/educational_institution_campus/educational_institution #2653-01gv_f PRED entity: 01gv_f PRED relation: award PRED expected values: 0gqyl => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 272): 03qbh5 (0.34 #3791, 0.33 #1796, 0.29 #998), 01by1l (0.34 #3699, 0.29 #1704, 0.28 #5694), 09sb52 (0.33 #22383, 0.32 #28368, 0.32 #29166), 01bgqh (0.32 #3632, 0.30 #5627, 0.30 #5228), 0ck27z (0.31 #12856, 0.26 #17245, 0.25 #18043), 054ks3 (0.29 #1734, 0.24 #3729, 0.24 #936), 0bfvd4 (0.29 #111, 0.10 #510, 0.10 #6495), 05p09zm (0.29 #120, 0.09 #12489, 0.08 #9297), 063y_ky (0.29 #128, 0.03 #29255, 0.03 #26063), 0c4z8 (0.22 #3660, 0.18 #5655, 0.18 #5256) >> Best rule #3791 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 06cc_1; 09hnb; >> query: (?x3836, 03qbh5) <- performance_role(?x3836, ?x1466), award_winner(?x1126, ?x3836), category(?x3836, ?x134) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #500 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 02zmh5; 05dppk; 0889x; *> query: (?x3836, 0gqyl) <- nationality(?x3836, ?x304), award(?x3836, ?x154), ?x304 = 0d0vqn *> conf = 0.20 ranks of expected_values: 14 EVAL 01gv_f award 0gqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 132.000 132.000 0.341 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2652-0bm2nq PRED entity: 0bm2nq PRED relation: film_crew_role PRED expected values: 02r96rf => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 24): 02r96rf (0.84 #202, 0.69 #235, 0.67 #367), 02ynfr (0.27 #13, 0.21 #212, 0.21 #245), 02rh1dz (0.23 #208, 0.21 #241, 0.12 #2009), 015h31 (0.20 #207, 0.18 #8, 0.13 #240), 0215hd (0.19 #248, 0.17 #116, 0.17 #215), 0d2b38 (0.17 #222, 0.15 #255, 0.12 #123), 01xy5l_ (0.17 #111, 0.13 #210, 0.13 #144), 089g0h (0.13 #216, 0.12 #2009, 0.12 #249), 089fss (0.12 #106, 0.12 #2009, 0.12 #238), 02_n3z (0.12 #2009, 0.12 #233, 0.10 #365) >> Best rule #202 for best value: >> intensional similarity = 5 >> extensional distance = 125 >> proper extension: 01gglm; >> query: (?x10201, 02r96rf) <- nominated_for(?x488, ?x10201), film_crew_role(?x10201, ?x2154), film_crew_role(?x10201, ?x2095), ?x2154 = 01vx2h, ?x2095 = 0dxtw >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bm2nq film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.843 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2651-02mt51 PRED entity: 02mt51 PRED relation: currency PRED expected values: 09nqf => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 7): 09nqf (0.83 #36, 0.82 #120, 0.81 #64), 01nv4h (0.11 #519, 0.09 #9, 0.03 #135), 02l6h (0.11 #519, 0.04 #25, 0.03 #95), 088n7 (0.11 #519, 0.02 #21), 02gsvk (0.11 #519, 0.01 #188), 0kz1h (0.11 #519), 0ptk_ (0.11 #519) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 08984j; >> query: (?x4040, 09nqf) <- film(?x2534, ?x4040), nominated_for(?x4040, ?x7580), music(?x4040, ?x1715), film_crew_role(?x4040, ?x137) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02mt51 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.826 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #2650-0bt4r4 PRED entity: 0bt4r4 PRED relation: nationality PRED expected values: 09c7w0 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.83 #401, 0.81 #501, 0.80 #301), 0m2fr (0.33 #11123), 01x73 (0.33 #11123), 059rby (0.25 #7915), 07ssc (0.11 #1517, 0.10 #715, 0.09 #1417), 02jx1 (0.10 #6244, 0.10 #7246, 0.10 #5844), 03rk0 (0.10 #1949, 0.08 #1046, 0.08 #2450), 07t21 (0.09 #37), 0d060g (0.06 #907, 0.05 #307, 0.05 #807), 03rt9 (0.05 #313, 0.02 #1515, 0.02 #1114) >> Best rule #401 for best value: >> intensional similarity = 3 >> extensional distance = 144 >> proper extension: 04l3_z; 02xp18; 05r5w; 02v2jy; 01svq8; >> query: (?x2912, 09c7w0) <- award(?x2912, ?x678), type_of_union(?x2912, ?x566), producer_type(?x2912, ?x632) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bt4r4 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.829 http://example.org/people/person/nationality #2649-06mn7 PRED entity: 06mn7 PRED relation: religion PRED expected values: 03_gx => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 20): 03_gx (0.71 #499, 0.20 #2221, 0.19 #58), 0c8wxp (0.34 #2617, 0.34 #2213, 0.34 #2661), 0kpl (0.18 #186, 0.18 #495, 0.16 #628), 03j6c (0.07 #2631, 0.07 #2227, 0.07 #2675), 092bf5 (0.06 #236, 0.06 #148, 0.05 #501), 01lp8 (0.05 #265, 0.05 #1, 0.05 #2208), 0n2g (0.05 #1030, 0.05 #1602, 0.04 #189), 0flw86 (0.05 #2209, 0.05 #2613, 0.05 #2657), 0631_ (0.03 #848, 0.02 #2215, 0.02 #1861), 051kv (0.03 #49, 0.03 #93, 0.03 #137) >> Best rule #499 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 015c1b; >> query: (?x4353, 03_gx) <- people(?x1050, ?x4353), religion(?x4353, ?x8140), ?x1050 = 041rx >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mn7 religion 03_gx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.707 http://example.org/people/person/religion #2648-087pfc PRED entity: 087pfc PRED relation: produced_by PRED expected values: 043q6n_ => 92 concepts (52 used for prediction) PRED predicted values (max 10 best out of 156): 02bfxb (0.29 #499, 0.04 #2428, 0.02 #3583), 0js9s (0.29 #613, 0.03 #2542, 0.01 #3697), 0fvf9q (0.17 #1164, 0.04 #6184, 0.04 #10050), 0184dt (0.15 #1625, 0.04 #2396, 0.03 #3551), 0cv9fc (0.14 #746, 0.08 #1904), 0h1p (0.14 #451, 0.03 #2380, 0.02 #1995), 0py5b (0.14 #761, 0.01 #3845, 0.01 #2690), 01pk8v (0.10 #18551, 0.09 #4627, 0.08 #18164), 054_mz (0.09 #788, 0.08 #1174, 0.02 #6194), 09d5d5 (0.09 #1067, 0.08 #1839, 0.04 #3765) >> Best rule #499 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0fpv_3_; >> query: (?x9174, 02bfxb) <- produced_by(?x9174, ?x1335), film(?x3651, ?x9174), film_release_region(?x9174, ?x6559), ?x6559 = 05r7t >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #5451 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 269 *> proper extension: 02y_lrp; 011yxg; 01hr1; 0ds33; 03h_yy; 02_1sj; 0209xj; 035xwd; 03rtz1; 0sxfd; ... *> query: (?x9174, 043q6n_) <- produced_by(?x9174, ?x1335), film(?x5485, ?x9174), participant(?x4053, ?x5485), country(?x9174, ?x94) *> conf = 0.02 ranks of expected_values: 69 EVAL 087pfc produced_by 043q6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 92.000 52.000 0.286 http://example.org/film/film/produced_by #2647-07qg8v PRED entity: 07qg8v PRED relation: language PRED expected values: 02h40lc => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 45): 02h40lc (0.94 #3097, 0.93 #3275, 0.90 #534), 04306rv (0.56 #1307, 0.22 #300, 0.12 #900), 02bjrlw (0.56 #1307, 0.13 #296, 0.10 #236), 06nm1 (0.12 #306, 0.12 #543, 0.12 #424), 02hxcvy (0.11 #93, 0.02 #989, 0.02 #1221), 06b_j (0.10 #317, 0.08 #199, 0.07 #257), 03k50 (0.10 #69, 0.05 #3801, 0.02 #965), 0jzc (0.06 #315, 0.06 #255, 0.05 #3801), 03_9r (0.06 #305, 0.05 #363, 0.05 #3801), 0653m (0.06 #365, 0.05 #3801, 0.04 #544) >> Best rule #3097 for best value: >> intensional similarity = 3 >> extensional distance = 1314 >> proper extension: 01fs__; >> query: (?x1421, 02h40lc) <- nominated_for(?x1008, ?x1421), language(?x1421, ?x5607), languages_spoken(?x1176, ?x5607) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07qg8v language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.935 http://example.org/film/film/language #2646-01dc60 PRED entity: 01dc60 PRED relation: film_format! PRED expected values: 027rpym => 5 concepts (5 used for prediction) PRED predicted values (max 10 best out of 1804): 01mgw (0.50 #664, 0.40 #1040, 0.33 #1418), 0bpx1k (0.50 #490, 0.40 #866, 0.33 #1244), 032_wv (0.50 #418, 0.40 #794, 0.33 #1172), 0fgpvf (0.50 #394, 0.40 #770, 0.33 #1148), 05g8pg (0.50 #509, 0.40 #885, 0.33 #1263), 011yqc (0.50 #429, 0.40 #805, 0.33 #1183), 05zlld0 (0.40 #896, 0.33 #1274, 0.33 #144), 0639bg (0.33 #148, 0.25 #749, 0.25 #524), 0n83s (0.33 #211, 0.25 #587, 0.22 #373), 09qljs (0.33 #349, 0.25 #725, 0.20 #1101) >> Best rule #664 for best value: >> intensional similarity = 70 >> extensional distance = 2 >> proper extension: 0cj16; >> query: (?x14581, 01mgw) <- film_format(?x11356, ?x14581), film_format(?x3909, ?x14581), music(?x11356, ?x7168), nominated_for(?x1323, ?x11356), award_winner(?x1323, ?x10412), award_winner(?x1323, ?x7556), award_winner(?x1323, ?x5251), award_winner(?x1323, ?x2641), award_winner(?x1323, ?x1934), award_winner(?x1323, ?x1894), ceremony(?x1323, ?x7940), ceremony(?x1323, ?x7515), ceremony(?x1323, ?x7105), ceremony(?x1323, ?x7038), ceremony(?x1323, ?x5053), ceremony(?x1323, ?x3618), ceremony(?x1323, ?x2210), ceremony(?x1323, ?x1998), ceremony(?x1323, ?x1601), ceremony(?x1323, ?x602), ?x7556 = 01vttb9, ?x1894 = 02fgpf, film_release_distribution_medium(?x11356, ?x81), nominated_for(?x2068, ?x11356), award(?x10700, ?x1323), award(?x6783, ?x1323), award(?x6382, ?x1323), award(?x5720, ?x1323), award(?x5206, ?x1323), award(?x669, ?x1323), nominated_for(?x1323, ?x3438), ?x2641 = 03n0q5, film_release_region(?x11356, ?x94), ?x1934 = 0b82vw, location(?x7168, ?x3689), ?x7105 = 073hd1, honored_for(?x8015, ?x3909), type_of_union(?x7168, ?x566), nominated_for(?x10920, ?x3909), nominated_for(?x1307, ?x3909), ?x5251 = 01cbt3, film(?x788, ?x3909), award(?x2368, ?x1323), titles(?x307, ?x11356), ?x6783 = 01x6v6, ?x5720 = 01l1rw, ?x669 = 0146pg, ?x2210 = 0bvfqq, ?x1307 = 0gq9h, ?x7940 = 0bzjvm, production_companies(?x186, ?x788), ?x3438 = 0glnm, award_winner(?x788, ?x1850), film(?x788, ?x3845), ?x94 = 09c7w0, type_of_union(?x10920, ?x1873), ?x1601 = 073hmq, ?x5053 = 0dthsy, ?x3845 = 0639bg, award_nominee(?x10920, ?x3947), ?x602 = 0bzk8w, ?x6382 = 01wd9lv, ?x7515 = 0bzlrh, ?x7038 = 073hgx, ?x5206 = 02w670, film_crew_role(?x3909, ?x12763), ?x1998 = 073h1t, ?x10412 = 016jll, ?x10700 = 01m5m5b, ?x3618 = 0bzn6_ >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #747 for first EXPECTED value: *> intensional similarity = 70 *> extensional distance = 2 *> proper extension: 0cj16; *> query: (?x14581, ?x186) <- film_format(?x11356, ?x14581), film_format(?x3909, ?x14581), music(?x11356, ?x7168), nominated_for(?x1323, ?x11356), award_winner(?x1323, ?x10412), award_winner(?x1323, ?x7556), award_winner(?x1323, ?x5251), award_winner(?x1323, ?x2641), award_winner(?x1323, ?x1934), award_winner(?x1323, ?x1894), ceremony(?x1323, ?x7940), ceremony(?x1323, ?x7515), ceremony(?x1323, ?x7105), ceremony(?x1323, ?x7038), ceremony(?x1323, ?x5053), ceremony(?x1323, ?x3618), ceremony(?x1323, ?x2210), ceremony(?x1323, ?x1998), ceremony(?x1323, ?x1601), ceremony(?x1323, ?x602), ?x7556 = 01vttb9, ?x1894 = 02fgpf, film_release_distribution_medium(?x11356, ?x81), nominated_for(?x2068, ?x11356), award(?x10700, ?x1323), award(?x6783, ?x1323), award(?x6382, ?x1323), award(?x5720, ?x1323), award(?x5206, ?x1323), award(?x669, ?x1323), nominated_for(?x1323, ?x3438), ?x2641 = 03n0q5, film_release_region(?x11356, ?x94), ?x1934 = 0b82vw, location(?x7168, ?x3689), ?x7105 = 073hd1, honored_for(?x8015, ?x3909), type_of_union(?x7168, ?x566), nominated_for(?x10920, ?x3909), nominated_for(?x1307, ?x3909), ?x5251 = 01cbt3, film(?x788, ?x3909), award(?x2368, ?x1323), titles(?x307, ?x11356), ?x6783 = 01x6v6, ?x5720 = 01l1rw, ?x669 = 0146pg, ?x2210 = 0bvfqq, ?x1307 = 0gq9h, ?x7940 = 0bzjvm, production_companies(?x186, ?x788), ?x3438 = 0glnm, award_winner(?x788, ?x1850), film(?x788, ?x3845), ?x94 = 09c7w0, type_of_union(?x10920, ?x1873), ?x1601 = 073hmq, ?x5053 = 0dthsy, ?x3845 = 0639bg, award_nominee(?x10920, ?x3947), ?x602 = 0bzk8w, ?x6382 = 01wd9lv, ?x7515 = 0bzlrh, ?x7038 = 073hgx, ?x5206 = 02w670, film_crew_role(?x3909, ?x12763), ?x1998 = 073h1t, ?x10412 = 016jll, ?x10700 = 01m5m5b, ?x3618 = 0bzn6_ *> conf = 0.11 ranks of expected_values: 498 EVAL 01dc60 film_format! 027rpym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 5.000 0.500 http://example.org/film/film/film_format #2645-027rwmr PRED entity: 027rwmr PRED relation: crewmember! PRED expected values: 02vqhv0 => 100 concepts (51 used for prediction) PRED predicted values (max 10 best out of 209): 0hx4y (0.29 #99, 0.24 #612, 0.17 #405), 0315rp (0.24 #612, 0.07 #306, 0.03 #1186), 0295sy (0.24 #612, 0.07 #306, 0.02 #2749), 01hq1 (0.17 #565, 0.14 #259, 0.06 #1176), 043tvp3 (0.17 #543, 0.14 #237, 0.06 #1154), 0dtfn (0.14 #48, 0.09 #965, 0.09 #660), 0jqn5 (0.14 #53, 0.08 #359, 0.06 #970), 07gp9 (0.14 #7, 0.08 #313, 0.06 #924), 085wqm (0.14 #294, 0.08 #600, 0.03 #1211), 02p76f9 (0.14 #266, 0.08 #572, 0.03 #1183) >> Best rule #99 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0146pg; 06pj8; 06rnl9; 03r1pr; 01r93l; >> query: (?x929, 0hx4y) <- nominated_for(?x929, ?x8349), award_nominee(?x929, ?x930), ?x8349 = 011xg5 >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 027rwmr crewmember! 02vqhv0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 51.000 0.286 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #2644-04t6fk PRED entity: 04t6fk PRED relation: language PRED expected values: 04306rv => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 38): 05zjd (0.33 #24, 0.02 #1053, 0.02 #768), 064_8sq (0.20 #77, 0.14 #419, 0.13 #191), 02bjrlw (0.20 #58, 0.09 #1144, 0.09 #514), 06nm1 (0.17 #123, 0.16 #237, 0.11 #753), 04306rv (0.12 #346, 0.12 #232, 0.12 #1147), 06b_j (0.11 #192, 0.09 #765, 0.08 #135), 0653m (0.06 #754, 0.06 #238, 0.06 #982), 012w70 (0.06 #239, 0.06 #296, 0.04 #812), 0jzc (0.06 #246, 0.04 #762, 0.04 #990), 01wgr (0.04 #209, 0.02 #782, 0.02 #1010) >> Best rule #24 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 080lkt7; >> query: (?x2699, 05zjd) <- film(?x1031, ?x2699), ?x1031 = 04hpck, titles(?x2480, ?x2699), cinematography(?x2699, ?x7758), language(?x2699, ?x254) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #346 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 80 *> proper extension: 0cq8nx; *> query: (?x2699, 04306rv) <- genre(?x2699, ?x225), cinematography(?x2699, ?x7758), films(?x326, ?x2699), music(?x2699, ?x669) *> conf = 0.12 ranks of expected_values: 5 EVAL 04t6fk language 04306rv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 62.000 62.000 0.333 http://example.org/film/film/language #2643-0c6qh PRED entity: 0c6qh PRED relation: award_nominee PRED expected values: 02f2dn 02lj6p => 132 concepts (81 used for prediction) PRED predicted values (max 10 best out of 1114): 014zcr (0.81 #169442, 0.81 #132305, 0.81 #160156), 01q6bg (0.81 #169442, 0.81 #132305, 0.81 #160156), 0kvqv (0.81 #169442, 0.81 #132305, 0.81 #160156), 0154qm (0.81 #169442, 0.81 #132305, 0.81 #160156), 02f2dn (0.81 #169442, 0.81 #132305, 0.81 #160156), 01v42g (0.81 #169442, 0.81 #132305, 0.81 #160156), 06r_by (0.81 #169442, 0.81 #132305, 0.81 #160156), 0z4s (0.76 #146234, 0.76 #125342, 0.75 #143912), 04g3p5 (0.76 #146234, 0.75 #143912, 0.75 #185691), 07h565 (0.26 #5966, 0.05 #38462, 0.04 #17570) >> Best rule #169442 for best value: >> intensional similarity = 3 >> extensional distance = 1212 >> proper extension: 06qgvf; 02bfmn; 03x3qv; 02zq43; 054_mz; 07lmxq; 044rvb; 02r_d4; 05ml_s; 02ndbd; ... >> query: (?x2499, ?x192) <- film(?x2499, ?x349), award_nominee(?x192, ?x2499), award_nominee(?x2499, ?x1995) >> conf = 0.81 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 30 EVAL 0c6qh award_nominee 02lj6p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 132.000 81.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 0c6qh award_nominee 02f2dn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 132.000 81.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2642-01cwcr PRED entity: 01cwcr PRED relation: category PRED expected values: 08mbj5d => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.41 #2, 0.40 #3, 0.38 #7) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 03f77; 06y8v; 0tfc; 011zwl; >> query: (?x7277, 08mbj5d) <- people(?x3715, ?x7277), ?x3715 = 03lmx1, nationality(?x7277, ?x512) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cwcr category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.407 http://example.org/common/topic/webpage./common/webpage/category #2641-0g57wgv PRED entity: 0g57wgv PRED relation: film! PRED expected values: 04f525m => 63 concepts (53 used for prediction) PRED predicted values (max 10 best out of 53): 016tw3 (0.17 #87, 0.14 #463, 0.14 #1140), 086k8 (0.17 #904, 0.16 #1283, 0.16 #1131), 0134w7 (0.16 #1281, 0.06 #1053, 0.06 #1280), 03xq0f (0.13 #382, 0.12 #231, 0.12 #156), 017s11 (0.13 #1132, 0.12 #380, 0.12 #905), 05qd_ (0.12 #1290, 0.11 #2354, 0.11 #2279), 016tt2 (0.11 #906, 0.11 #80, 0.11 #1133), 0jz9f (0.09 #453, 0.07 #603, 0.07 #753), 03xsby (0.09 #167, 0.07 #16, 0.07 #318), 024rdh (0.08 #37, 0.08 #188, 0.07 #339) >> Best rule #87 for best value: >> intensional similarity = 4 >> extensional distance = 150 >> proper extension: 03mh_tp; 058kh7; >> query: (?x9859, 016tw3) <- genre(?x9859, ?x1403), produced_by(?x9859, ?x6279), student(?x1440, ?x6279), ?x1403 = 02l7c8 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 145 *> proper extension: 0sxg4; 0yyg4; 0b73_1d; 04mzf8; 0sxfd; 0p_th; 09tqkv2; 0gyy53; 0mcl0; 03hmt9b; ... *> query: (?x9859, 04f525m) <- genre(?x9859, ?x53), nominated_for(?x981, ?x9859), film(?x1736, ?x9859), film_festivals(?x9859, ?x4903) *> conf = 0.03 ranks of expected_values: 26 EVAL 0g57wgv film! 04f525m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 63.000 53.000 0.171 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #2640-0pkyh PRED entity: 0pkyh PRED relation: group PRED expected values: 04k05 => 130 concepts (49 used for prediction) PRED predicted values (max 10 best out of 75): 01v0sx2 (0.40 #5, 0.09 #114, 0.08 #2838), 0123r4 (0.09 #153, 0.06 #699, 0.04 #807), 01qqwp9 (0.06 #130, 0.04 #892, 0.03 #566), 081wh1 (0.05 #1360, 0.01 #1252), 02r1tx7 (0.03 #1324, 0.02 #235, 0.02 #1760), 0cfgd (0.03 #1404, 0.02 #532, 0.01 #1077), 01v0sxx (0.03 #1393, 0.02 #2373), 014_lq (0.03 #1343, 0.01 #1235), 01wv9xn (0.03 #1316, 0.01 #4804), 0bk1p (0.03 #1382) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0m2l9; 0gcs9; >> query: (?x2930, 01v0sx2) <- award(?x2930, ?x724), influenced_by(?x2930, ?x477), artists(?x1928, ?x2930), ?x1928 = 0mhfr >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1398 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 012zng; 09prnq; 0gkg6; 01nn6c; 01vv6_6; 01w8n89; 0fpj4lx; 0bkg4; 01vsy3q; 01s7qqw; ... *> query: (?x2930, 04k05) <- artists(?x1000, ?x2930), instrumentalists(?x227, ?x2930), ?x1000 = 0xhtw *> conf = 0.02 ranks of expected_values: 25 EVAL 0pkyh group 04k05 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 130.000 49.000 0.400 http://example.org/music/group_member/membership./music/group_membership/group #2639-01qklj PRED entity: 01qklj PRED relation: gender PRED expected values: 05zppz => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #111, 0.78 #125, 0.78 #163), 02zsn (0.47 #10, 0.46 #219, 0.39 #42) >> Best rule #111 for best value: >> intensional similarity = 4 >> extensional distance = 1224 >> proper extension: 06j0md; 03ckxdg; 026dcvf; 02773m2; 0265v21; 01pr_j6; 04wtx1; 05drq5; 0284gcb; 09gffmz; ... >> query: (?x9567, 05zppz) <- profession(?x9567, ?x1383), profession(?x9650, ?x1383), specialization_of(?x1383, ?x1032), ?x9650 = 0q1lp >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qklj gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.788 http://example.org/people/person/gender #2638-08qvhv PRED entity: 08qvhv PRED relation: film PRED expected values: 0h3k3f => 102 concepts (71 used for prediction) PRED predicted values (max 10 best out of 193): 03_8kz (0.47 #35860, 0.39 #64546, 0.39 #109359), 014gjp (0.47 #35860, 0.39 #64546, 0.39 #109359), 01j67j (0.06 #59166, 0.06 #71718, 0.05 #127287), 0q9jk (0.06 #59166, 0.06 #71718, 0.02 #44826), 0yx1m (0.06 #59166, 0.06 #71718), 011yn5 (0.06 #59166, 0.06 #71718), 0sxfd (0.06 #59166, 0.06 #71718), 0kfpm (0.06 #59166, 0.06 #71718), 04vr_f (0.04 #16305, 0.01 #32446, 0.01 #34238), 07nxvj (0.03 #16831, 0.01 #32972) >> Best rule #35860 for best value: >> intensional similarity = 3 >> extensional distance = 659 >> proper extension: 0n6f8; 034np8; 03xmy1; 043js; 01w02sy; 01nrq5; 019vgs; 062ftr; 035rnz; 02xwq9; ... >> query: (?x4303, ?x7551) <- type_of_union(?x4303, ?x566), award_winner(?x7551, ?x4303), location(?x4303, ?x4090) >> conf = 0.47 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 08qvhv film 0h3k3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 71.000 0.465 http://example.org/film/actor/film./film/performance/film #2637-09k9d0 PRED entity: 09k9d0 PRED relation: colors PRED expected values: 04mkbj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.26 #563, 0.25 #923, 0.25 #363), 01l849 (0.24 #41, 0.24 #961, 0.24 #921), 019sc (0.17 #967, 0.17 #927, 0.16 #827), 06fvc (0.17 #22, 0.16 #422, 0.16 #562), 038hg (0.15 #52, 0.14 #572, 0.10 #972), 036k5h (0.12 #245, 0.11 #5, 0.11 #485), 04mkbj (0.12 #490, 0.11 #130, 0.11 #10), 088fh (0.10 #46, 0.08 #66, 0.06 #6), 0jc_p (0.08 #824, 0.07 #844, 0.07 #244), 09ggk (0.07 #56, 0.06 #396, 0.06 #376) >> Best rule #563 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 01p896; >> query: (?x11824, 01g5v) <- currency(?x11824, ?x170), colors(?x11824, ?x663), ?x663 = 083jv >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #490 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 119 *> proper extension: 03ksy; 027kp3; 01nnsv; 022r38; *> query: (?x11824, 04mkbj) <- school_type(?x11824, ?x1044), institution(?x1305, ?x11824), ?x1044 = 05pcjw *> conf = 0.12 ranks of expected_values: 7 EVAL 09k9d0 colors 04mkbj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 98.000 98.000 0.263 http://example.org/education/educational_institution/colors #2636-01cmp9 PRED entity: 01cmp9 PRED relation: currency PRED expected values: 09nqf => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.81 #29, 0.80 #71, 0.80 #78), 01nv4h (0.06 #2, 0.03 #86, 0.02 #128), 0ptk_ (0.06 #3), 02l6h (0.01 #53, 0.01 #67, 0.01 #326), 02gsvk (0.01 #251, 0.01 #258) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 112 >> proper extension: 01hp5; 08gsvw; 02qr69m; 02fqrf; 03r0g9; 03176f; 09gb_4p; 01npcx; 0ndwt2w; 0581vn8; ... >> query: (?x6048, 09nqf) <- nominated_for(?x1585, ?x6048), nominated_for(?x637, ?x6048), ?x637 = 02r22gf >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cmp9 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.807 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #2635-03bnv PRED entity: 03bnv PRED relation: award_winner! PRED expected values: 02cg41 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 133): 02rjjll (0.12 #1125, 0.11 #1265, 0.11 #2525), 01c6qp (0.11 #159, 0.10 #11061, 0.09 #2539), 0gpjbt (0.11 #869, 0.10 #11061, 0.09 #2549), 05pd94v (0.10 #1122, 0.10 #2522, 0.10 #1262), 09n4nb (0.10 #1168, 0.10 #1308, 0.08 #3828), 013b2h (0.10 #2599, 0.10 #11061, 0.10 #499), 01s695 (0.10 #11061, 0.10 #843, 0.10 #2523), 01bx35 (0.10 #11061, 0.09 #2527, 0.08 #3787), 01mhwk (0.10 #11061, 0.08 #1161, 0.08 #3821), 01xqqp (0.10 #11061, 0.08 #1215, 0.07 #1355) >> Best rule #1125 for best value: >> intensional similarity = 3 >> extensional distance = 141 >> proper extension: 01sbf2; >> query: (?x3321, 02rjjll) <- artists(?x671, ?x3321), ?x671 = 064t9, award_winner(?x3321, ?x1089) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #265 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 08wq0g; 0pz91; 07s6prs; 021bk; 0jfx1; 02qfhb; 02wk4d; 02ryx0; 0h7pj; 020jqv; *> query: (?x3321, 02cg41) <- award_winner(?x2729, ?x3321), profession(?x3321, ?x131), instrumentalists(?x228, ?x3321) *> conf = 0.10 ranks of expected_values: 15 EVAL 03bnv award_winner! 02cg41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 110.000 110.000 0.119 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2634-03jldb PRED entity: 03jldb PRED relation: student! PRED expected values: 013807 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 69): 078bz (0.12 #77, 0.03 #1131, 0.02 #2185), 033gn8 (0.12 #378, 0.01 #6702, 0.01 #10918), 01jsk6 (0.12 #413), 02ln0f (0.12 #186), 086xm (0.12 #92), 02w2bc (0.12 #13), 02s62q (0.11 #579, 0.03 #1106), 07wrz (0.11 #589, 0.01 #4805), 01n4w_ (0.11 #949), 02ccqg (0.11 #622) >> Best rule #77 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 031k24; >> query: (?x1537, 078bz) <- award_nominee(?x12551, ?x1537), ?x12551 = 0736qr, people(?x1050, ?x1537) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03jldb student! 013807 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 69.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #2633-01k60v PRED entity: 01k60v PRED relation: film_crew_role PRED expected values: 01pvkk => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 31): 02r96rf (0.66 #40, 0.66 #934, 0.59 #1009), 09vw2b7 (0.66 #938, 0.58 #118, 0.57 #1125), 0dxtw (0.40 #942, 0.35 #48, 0.34 #122), 01vx2h (0.30 #943, 0.28 #1018, 0.26 #1130), 01pvkk (0.29 #50, 0.29 #944, 0.27 #497), 02ynfr (0.19 #91, 0.18 #948, 0.16 #390), 02rh1dz (0.16 #158, 0.15 #47, 0.11 #941), 02vs3x5 (0.16 #99, 0.14 #136, 0.14 #25), 0215hd (0.16 #243, 0.15 #280, 0.13 #318), 089g0h (0.14 #21, 0.10 #952, 0.10 #244) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 0gcrg; >> query: (?x4448, 02r96rf) <- film_crew_role(?x4448, ?x137), nominated_for(?x4447, ?x4448), edited_by(?x3510, ?x4447), nominated_for(?x1107, ?x4448) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 0gcrg; *> query: (?x4448, 01pvkk) <- film_crew_role(?x4448, ?x137), nominated_for(?x4447, ?x4448), edited_by(?x3510, ?x4447), nominated_for(?x1107, ?x4448) *> conf = 0.29 ranks of expected_values: 5 EVAL 01k60v film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 71.000 71.000 0.662 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2632-01x73 PRED entity: 01x73 PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 21): 0fkvn (0.79 #361, 0.78 #130, 0.76 #277), 0pqc5 (0.69 #1581, 0.69 #1623, 0.63 #1875), 060c4 (0.52 #1747, 0.49 #1978, 0.48 #171), 060bp (0.46 #1745, 0.43 #1976, 0.40 #169), 0fkzq (0.29 #78, 0.25 #456, 0.24 #351), 0789n (0.25 #9, 0.20 #30, 0.16 #198), 02079p (0.25 #10, 0.18 #73, 0.08 #136), 01t7n9 (0.25 #17, 0.16 #206, 0.13 #374), 0dq3c (0.25 #2, 0.09 #1704, 0.09 #1788), 01gkgk (0.25 #6, 0.08 #657, 0.08 #699) >> Best rule #361 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 0hjy; 06nrt; >> query: (?x1755, 0fkvn) <- location(?x1897, ?x1755), district_represented(?x176, ?x1755), contains(?x1755, ?x503) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x73 jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.788 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #2631-0978r PRED entity: 0978r PRED relation: citytown! PRED expected values: 07tlg => 174 concepts (128 used for prediction) PRED predicted values (max 10 best out of 673): 07tk7 (0.75 #23787, 0.71 #8721, 0.70 #77740), 0d07s (0.75 #23787, 0.71 #8721, 0.70 #77740), 07tlg (0.75 #23787, 0.71 #8721, 0.70 #77740), 059wk (0.12 #2825, 0.08 #10753, 0.05 #4410), 07733f (0.12 #3120, 0.05 #4705, 0.04 #5499), 018c_r (0.12 #3025, 0.05 #4610, 0.04 #5404), 02975m (0.12 #3085, 0.05 #4670, 0.03 #18151), 01l50r (0.12 #3046, 0.05 #4631, 0.03 #18112), 032j_n (0.12 #2913, 0.05 #4498, 0.03 #17979), 07l1c (0.12 #2695, 0.05 #4280, 0.03 #17761) >> Best rule #23787 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 031sn; >> query: (?x3301, ?x1369) <- time_zones(?x3301, ?x5327), contains(?x3301, ?x1369), citytown(?x1098, ?x3301), state(?x3301, ?x3302) >> conf = 0.75 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0978r citytown! 07tlg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 174.000 128.000 0.754 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #2630-02773m2 PRED entity: 02773m2 PRED relation: award_winner! PRED expected values: 03gt46z => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 104): 0418154 (0.42 #104, 0.17 #2605, 0.17 #3291), 0lp_cd3 (0.38 #159, 0.03 #296, 0.02 #570), 03gt46z (0.25 #62, 0.23 #199, 0.17 #2605), 0gvstc3 (0.23 #170, 0.10 #6310, 0.10 #5760), 07z31v (0.23 #167, 0.10 #5898, 0.07 #304), 0gx_st (0.23 #173, 0.10 #310, 0.08 #36), 07y9ts (0.23 #203, 0.07 #340, 0.05 #614), 09g90vz (0.17 #2605, 0.17 #3291, 0.17 #3841), 03gyp30 (0.17 #2605, 0.17 #3291, 0.17 #3841), 0bx6zs (0.17 #2605, 0.17 #3291, 0.17 #3841) >> Best rule #104 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 02773nt; 0pz7h; 0p_2r; 0284gcb; 02778qt; 06jnvs; 026w_gk; 02778yp; 018ygt; 02778tk; >> query: (?x830, 0418154) <- award_nominee(?x830, ?x832), ?x832 = 02778pf, award_nominee(?x364, ?x830) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #62 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 02773nt; 0pz7h; 0p_2r; 0284gcb; 02778qt; 06jnvs; 026w_gk; 02778yp; 018ygt; 02778tk; *> query: (?x830, 03gt46z) <- award_nominee(?x830, ?x832), ?x832 = 02778pf, award_nominee(?x364, ?x830) *> conf = 0.25 ranks of expected_values: 3 EVAL 02773m2 award_winner! 03gt46z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 68.000 0.417 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2629-026mfbr PRED entity: 026mfbr PRED relation: featured_film_locations PRED expected values: 0q_xk => 133 concepts (100 used for prediction) PRED predicted values (max 10 best out of 124): 04jpl (0.26 #9, 0.16 #973, 0.14 #1215), 02_286 (0.25 #262, 0.21 #20, 0.19 #2669), 030qb3t (0.21 #39, 0.15 #281, 0.14 #762), 0rh6k (0.06 #2890, 0.05 #3130, 0.04 #18783), 01_d4 (0.06 #770, 0.05 #1253, 0.04 #2936), 080h2 (0.05 #988, 0.05 #24, 0.05 #8930), 035p3 (0.05 #1197, 0.05 #233, 0.05 #1439), 03pzf (0.05 #176, 0.05 #1382, 0.03 #2585), 0q_xk (0.05 #153, 0.03 #2562, 0.03 #1117), 0chgzm (0.05 #149, 0.03 #3038, 0.03 #872) >> Best rule #9 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 07gp9; 02rn00y; 011yn5; >> query: (?x750, 04jpl) <- film_release_distribution_medium(?x750, ?x81), film_crew_role(?x750, ?x281), production_companies(?x750, ?x1478), ?x281 = 02_n3z, executive_produced_by(?x750, ?x2101), film_release_region(?x750, ?x94) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 07gp9; 02rn00y; 011yn5; *> query: (?x750, 0q_xk) <- film_release_distribution_medium(?x750, ?x81), film_crew_role(?x750, ?x281), production_companies(?x750, ?x1478), ?x281 = 02_n3z, executive_produced_by(?x750, ?x2101), film_release_region(?x750, ?x94) *> conf = 0.05 ranks of expected_values: 9 EVAL 026mfbr featured_film_locations 0q_xk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 133.000 100.000 0.263 http://example.org/film/film/featured_film_locations #2628-07jxpf PRED entity: 07jxpf PRED relation: film_format PRED expected values: 07fb8_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 4): 07fb8_ (0.45 #11, 0.19 #36, 0.16 #47), 0cj16 (0.12 #167, 0.12 #74, 0.12 #269), 017fx5 (0.09 #14, 0.05 #24, 0.04 #29), 01dc60 (0.03 #20) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 09p4w8; 01chpn; 0c0zq; >> query: (?x4118, 07fb8_) <- film(?x92, ?x4118), nominated_for(?x6233, ?x4118), ?x92 = 02s2ft, film_crew_role(?x4118, ?x137) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07jxpf film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.455 http://example.org/film/film/film_format #2627-0s3y5 PRED entity: 0s3y5 PRED relation: adjoins PRED expected values: 0s3pw => 127 concepts (60 used for prediction) PRED predicted values (max 10 best out of 309): 0s3pw (0.89 #9292, 0.86 #4646, 0.83 #16259), 04kcn (0.33 #588, 0.04 #6785, 0.04 #6010), 0psxp (0.33 #259, 0.04 #6456, 0.04 #5681), 0s5cg (0.33 #236, 0.04 #6433, 0.04 #5658), 0r6ff (0.14 #5998, 0.06 #7546, 0.05 #4449), 0r679 (0.11 #5606, 0.09 #4057, 0.06 #7154), 05tbn (0.11 #6374, 0.07 #14888, 0.06 #20310), 05fjf (0.11 #6497, 0.06 #15786, 0.04 #20433), 0f2rq (0.09 #7228, 0.06 #12647, 0.03 #15744), 05kr_ (0.09 #1653, 0.07 #6301, 0.07 #14815) >> Best rule #9292 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 05kr_; >> query: (?x405, ?x13681) <- adjoins(?x13681, ?x405), country(?x405, ?x94), source(?x13681, ?x958), contains(?x3818, ?x13681) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0s3y5 adjoins 0s3pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 60.000 0.887 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #2626-01znc_ PRED entity: 01znc_ PRED relation: country! PRED expected values: 071t0 03rbzn 0486tv => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 42): 071t0 (0.86 #1318, 0.86 #730, 0.85 #1150), 06f41 (0.82 #640, 0.79 #556, 0.76 #724), 064vjs (0.71 #737, 0.68 #653, 0.65 #1325), 02y8z (0.69 #727, 0.63 #559, 0.62 #643), 01sgl (0.67 #75, 0.61 #705, 0.53 #579), 07bs0 (0.67 #51, 0.60 #723, 0.59 #681), 01gqfm (0.67 #80, 0.57 #752, 0.56 #710), 07rlg (0.67 #43, 0.55 #715, 0.53 #547), 0d1t3 (0.67 #67, 0.50 #25, 0.46 #697), 019tzd (0.62 #744, 0.59 #702, 0.58 #450) >> Best rule #1318 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 04w58; 06sw9; >> query: (?x1499, 071t0) <- organization(?x1499, ?x127), country(?x2315, ?x1499), ?x2315 = 06wrt >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 12, 14 EVAL 01znc_ country! 0486tv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 201.000 201.000 0.860 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01znc_ country! 03rbzn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 201.000 201.000 0.860 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01znc_ country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 201.000 201.000 0.860 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #2625-09q23x PRED entity: 09q23x PRED relation: film! PRED expected values: 02mxw0 => 88 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1007): 01d8yn (0.73 #49917, 0.49 #76966, 0.48 #64480), 046zh (0.73 #49917, 0.49 #76966, 0.48 #64480), 02jxmr (0.49 #76966, 0.48 #64480, 0.47 #49916), 0hskw (0.14 #20798), 0415svh (0.13 #8317, 0.11 #37437, 0.10 #35357), 05nzw6 (0.09 #1191, 0.08 #3270, 0.06 #9508), 0z4s (0.09 #67, 0.08 #2146, 0.04 #10464), 01yfm8 (0.08 #3371, 0.06 #1292, 0.05 #70724), 023kzp (0.08 #3134, 0.06 #1055, 0.05 #70724), 04gc65 (0.08 #4052, 0.06 #1973, 0.04 #6131) >> Best rule #49917 for best value: >> intensional similarity = 4 >> extensional distance = 836 >> proper extension: 04ddm4; 043n0v_; 05_5_22; 0hv27; 02z0f6l; 05pt0l; 0jdr0; >> query: (?x5001, ?x496) <- genre(?x5001, ?x53), nominated_for(?x496, ?x5001), film_crew_role(?x5001, ?x137), film(?x496, ?x69) >> conf = 0.73 => this is the best rule for 2 predicted values *> Best rule #2540 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 04lqvlr; *> query: (?x5001, 02mxw0) <- genre(?x5001, ?x3506), language(?x5001, ?x254), film_crew_role(?x5001, ?x137), ?x3506 = 03mqtr *> conf = 0.05 ranks of expected_values: 32 EVAL 09q23x film! 02mxw0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 88.000 43.000 0.725 http://example.org/film/actor/film./film/performance/film #2624-02cg41 PRED entity: 02cg41 PRED relation: award_winner PRED expected values: 03bnv 015xp4 01k_mc 01htxr 04vrxh => 32 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1931): 05pdbs (0.57 #10616, 0.50 #15103, 0.50 #4642), 02qwg (0.57 #9451, 0.36 #18418, 0.33 #19913), 01dwrc (0.50 #6840, 0.50 #5347, 0.43 #12817), 09hnb (0.50 #15321, 0.50 #7845, 0.33 #19799), 01s21dg (0.50 #5205, 0.43 #11179, 0.38 #15666), 044gyq (0.50 #7972, 0.43 #12457, 0.38 #15448), 02l840 (0.43 #12052, 0.38 #15043, 0.33 #7567), 058s57 (0.43 #12189, 0.38 #15180, 0.33 #16671), 02qlg7s (0.43 #12291, 0.38 #15282, 0.33 #19760), 0g824 (0.43 #11393, 0.38 #15880, 0.33 #8404) >> Best rule #10616 for best value: >> intensional similarity = 20 >> extensional distance = 5 >> proper extension: 0466p0j; >> query: (?x9431, 05pdbs) <- award_winner(?x9431, ?x4640), award_winner(?x9431, ?x4394), award_winner(?x9431, ?x3632), ceremony(?x6378, ?x9431), ceremony(?x5123, ?x9431), ceremony(?x4488, ?x9431), ceremony(?x2561, ?x9431), award(?x7211, ?x4488), award(?x6635, ?x4488), award(?x3321, ?x4488), award_nominee(?x6264, ?x4640), ?x3321 = 03bnv, ?x6264 = 01vw37m, ?x2561 = 02hgm4, ?x7211 = 0135xb, ?x6635 = 015cxv, ?x6378 = 0249fn, award_winner(?x3854, ?x3632), artists(?x474, ?x4394), ?x5123 = 025m98 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #9873 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 5 *> proper extension: 0gx1673; *> query: (?x9431, 01htxr) <- award_winner(?x9431, ?x4640), award_winner(?x9431, ?x3374), award_winner(?x9431, ?x1399), ceremony(?x6652, ?x9431), ceremony(?x4488, ?x9431), ceremony(?x3045, ?x9431), ceremony(?x1827, ?x9431), award(?x3321, ?x4488), award(?x1751, ?x4488), award_nominee(?x140, ?x4640), ?x3321 = 03bnv, ?x3045 = 02sp_v, profession(?x4640, ?x131), ?x1751 = 05crg7, award_winner(?x1827, ?x1826), ceremony(?x6652, ?x2054), award_winner(?x1720, ?x1399), artists(?x597, ?x1399), ?x3374 = 01vsy95, ?x2054 = 0gpjbt, ?x1720 = 01qkqwg *> conf = 0.43 ranks of expected_values: 14, 66, 238, 263, 589 EVAL 02cg41 award_winner 04vrxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 32.000 20.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02cg41 award_winner 01htxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 32.000 20.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02cg41 award_winner 01k_mc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 32.000 20.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02cg41 award_winner 015xp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 32.000 20.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 02cg41 award_winner 03bnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 20.000 0.571 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2623-018h2 PRED entity: 018h2 PRED relation: films PRED expected values: 04jwly 02jxrw => 51 concepts (8 used for prediction) PRED predicted values (max 10 best out of 61): 0n6ds (0.16 #3092, 0.13 #3608, 0.10 #2577), 0194zl (0.16 #3092, 0.13 #3608, 0.10 #2577), 095zlp (0.16 #3092, 0.13 #3608, 0.10 #2577), 04y5j64 (0.16 #3092, 0.13 #3608, 0.10 #2577), 01l2b3 (0.14 #3418, 0.09 #2386, 0.08 #2902), 04h41v (0.13 #3608, 0.10 #2577, 0.07 #2576), 04w7rn (0.09 #2129, 0.08 #2645, 0.07 #3161), 0b85mm (0.09 #2563, 0.08 #3079, 0.07 #3595), 0gy0n (0.09 #2560, 0.08 #3076, 0.07 #3592), 04q827 (0.09 #2545, 0.08 #3061, 0.07 #3577) >> Best rule #3092 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 018w8; >> query: (?x2286, ?x2287) <- titles(?x2286, ?x2287), films(?x2286, ?x6111), nominated_for(?x4320, ?x2287), film(?x2035, ?x6111), nominated_for(?x198, ?x6111), produced_by(?x2287, ?x7324) >> conf = 0.16 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 018h2 films 02jxrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 8.000 0.159 http://example.org/film/film_subject/films EVAL 018h2 films 04jwly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 8.000 0.159 http://example.org/film/film_subject/films #2622-0133sq PRED entity: 0133sq PRED relation: place_of_birth PRED expected values: 0crjn65 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 145): 04f_d (0.20 #777, 0.02 #1481, 0.02 #5705), 02_286 (0.11 #1427, 0.10 #4947, 0.09 #6355), 030qb3t (0.08 #1462, 0.05 #5686, 0.04 #30333), 04jpl (0.07 #15498, 0.07 #12680, 0.06 #3528), 0cr3d (0.06 #6430, 0.06 #2206, 0.05 #12062), 01_d4 (0.06 #8514, 0.05 #5698, 0.04 #1474), 0b_yz (0.03 #3953, 0.01 #3249, 0.01 #5361), 013yq (0.03 #5007, 0.02 #1487, 0.02 #9231), 04vmp (0.03 #2380, 0.02 #10828, 0.02 #4492), 02m77 (0.03 #2364, 0.02 #12924, 0.01 #15742) >> Best rule #777 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 08xz51; >> query: (?x10854, 04f_d) <- award_winner(?x4573, ?x10854), award_winner(?x3008, ?x10854), ?x4573 = 0gq_d >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0133sq place_of_birth 0crjn65 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 115.000 0.200 http://example.org/people/person/place_of_birth #2621-03n6r PRED entity: 03n6r PRED relation: people! PRED expected values: 01k9gb => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 43): 0gk4g (0.25 #76, 0.22 #1330, 0.21 #934), 0c58k (0.17 #162, 0.11 #228, 0.04 #1218), 04p3w (0.16 #803, 0.13 #869, 0.12 #671), 0dq9p (0.13 #1535, 0.12 #1997, 0.11 #2195), 02y0js (0.11 #200, 0.10 #530, 0.08 #860), 02k6hp (0.11 #235, 0.09 #367, 0.09 #301), 012hw (0.11 #250, 0.09 #382, 0.09 #316), 0qcr0 (0.10 #463, 0.09 #1387, 0.09 #1651), 019dmc (0.09 #842, 0.08 #908, 0.08 #710), 06z5s (0.09 #355, 0.09 #289, 0.08 #685) >> Best rule #76 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0jvtp; >> query: (?x5348, 0gk4g) <- film(?x5348, ?x11356), location(?x5348, ?x739), ?x11356 = 09d38d >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03n6r people! 01k9gb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 139.000 139.000 0.250 http://example.org/people/cause_of_death/people #2620-03bnb PRED entity: 03bnb PRED relation: state_province_region PRED expected values: 01x73 => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 219): 01n7q (0.62 #2362, 0.56 #1621, 0.50 #387), 059rby (0.42 #2714, 0.41 #2842, 0.37 #3335), 01x73 (0.33 #271, 0.29 #14624, 0.25 #17590), 05k7sb (0.30 #10318, 0.05 #16998, 0.04 #17122), 09c7w0 (0.29 #14624, 0.25 #17590, 0.24 #17963), 0m2fr (0.29 #14624, 0.25 #17590, 0.24 #17963), 02jx1 (0.20 #759, 0.20 #636, 0.05 #6948), 03v0t (0.20 #669, 0.17 #1162, 0.14 #1408), 081yw (0.20 #1911, 0.09 #4262, 0.09 #7612), 07b_l (0.14 #2517, 0.11 #3135, 0.10 #3756) >> Best rule #2362 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 01swmr; >> query: (?x10808, 01n7q) <- child(?x10808, ?x6678), organizations_founded(?x10251, ?x10808), profession(?x10251, ?x319), nationality(?x10251, ?x94) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #271 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 02h30z; *> query: (?x10808, 01x73) <- citytown(?x10808, ?x3450), category(?x10808, ?x134), ?x134 = 08mbj5d, ?x3450 = 0rd5k *> conf = 0.33 ranks of expected_values: 3 EVAL 03bnb state_province_region 01x73 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 148.000 148.000 0.615 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #2619-07nnp_ PRED entity: 07nnp_ PRED relation: genre PRED expected values: 09blyk => 76 concepts (62 used for prediction) PRED predicted values (max 10 best out of 98): 07s9rl0 (0.77 #357, 0.71 #2604, 0.70 #2131), 09blyk (0.69 #3670, 0.67 #238, 0.61 #5567), 05p553 (0.50 #479, 0.43 #5334, 0.41 #954), 082gq (0.48 #2159, 0.11 #1450, 0.11 #3579), 02l7c8 (0.46 #372, 0.42 #2619, 0.29 #4990), 03k9fj (0.44 #250, 0.37 #604, 0.33 #1551), 0lsxr (0.39 #3679, 0.38 #1076, 0.31 #2966), 02kdv5l (0.38 #595, 0.36 #3673, 0.34 #1306), 03npn (0.33 #127, 0.25 #8, 0.22 #246), 06n90 (0.33 #251, 0.25 #13, 0.20 #487) >> Best rule #357 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 026njb5; >> query: (?x12393, 07s9rl0) <- genre(?x12393, ?x9637), nominated_for(?x1007, ?x12393), ?x9637 = 02js9, award(?x504, ?x1007) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #3670 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 601 *> proper extension: 03kq98; 01q_y0; 039c26; *> query: (?x12393, ?x812) <- award_winner(?x12393, ?x8796), titles(?x812, ?x12393), nominated_for(?x102, ?x12393), genre(?x66, ?x812) *> conf = 0.69 ranks of expected_values: 2 EVAL 07nnp_ genre 09blyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 76.000 62.000 0.769 http://example.org/film/film/genre #2618-05z7c PRED entity: 05z7c PRED relation: film_release_region PRED expected values: 0d0vqn 01mjq 06f32 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 141): 0d0vqn (0.90 #3712, 0.89 #1244, 0.88 #5408), 0chghy (0.80 #3717, 0.79 #5413, 0.78 #1249), 0jgd (0.77 #3708, 0.75 #2784, 0.73 #5404), 0154j (0.77 #3710, 0.72 #1242, 0.61 #5406), 0b90_r (0.72 #3709, 0.71 #1241, 0.60 #5405), 03spz (0.69 #3799, 0.68 #1331, 0.54 #5495), 06t2t (0.68 #3766, 0.65 #1298, 0.51 #5462), 03rt9 (0.68 #1253, 0.65 #3721, 0.53 #5417), 03rj0 (0.63 #1296, 0.60 #3764, 0.50 #5460), 05v8c (0.62 #1255, 0.57 #3723, 0.51 #5419) >> Best rule #3712 for best value: >> intensional similarity = 4 >> extensional distance = 156 >> proper extension: 0ddfwj1; 0gj96ln; 0b85mm; >> query: (?x2094, 0d0vqn) <- nominated_for(?x1972, ?x2094), film_release_region(?x2094, ?x1603), ?x1603 = 06bnz, award(?x91, ?x1972) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11, 16 EVAL 05z7c film_release_region 06f32 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 119.000 119.000 0.899 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05z7c film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 119.000 119.000 0.899 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05z7c film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.899 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2617-06c0ns PRED entity: 06c0ns PRED relation: film_sets_designed! PRED expected values: 05b4rcb => 85 concepts (77 used for prediction) PRED predicted values (max 10 best out of 20): 0c0tzp (0.07 #139, 0.03 #211, 0.02 #431), 053j4w4 (0.06 #201, 0.02 #396, 0.02 #250), 07h1tr (0.05 #172, 0.03 #293, 0.03 #586), 0584j4n (0.05 #175, 0.01 #224, 0.01 #272), 076psv (0.03 #198, 0.03 #588, 0.02 #343), 058z1hb (0.03 #216, 0.02 #361, 0.02 #386), 04_1nk (0.03 #200, 0.02 #345, 0.02 #664), 057bc6m (0.02 #348, 0.02 #373, 0.02 #398), 05b49tt (0.02 #374, 0.02 #643, 0.02 #399), 02x2t07 (0.02 #362, 0.02 #387, 0.01 #509) >> Best rule #139 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0kv2hv; 075wx7_; 0bx0l; 0fpmrm3; 06_x996; 04nnpw; 0jyb4; 0k7tq; 027r9t; 01qbg5; ... >> query: (?x6963, 0c0tzp) <- honored_for(?x6963, ?x5731), genre(?x6963, ?x225), nominated_for(?x154, ?x5731), film_release_region(?x6963, ?x1264) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #340 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 121 *> proper extension: 07xtqq; 0ds11z; 0209hj; 0m_mm; 0_92w; 03rtz1; 0pv3x; 05j82v; 0340hj; 0168ls; ... *> query: (?x6963, 05b4rcb) <- film(?x382, ?x6963), country(?x6963, ?x94), film_production_design_by(?x6963, ?x9062), nominated_for(?x154, ?x6963) *> conf = 0.02 ranks of expected_values: 12 EVAL 06c0ns film_sets_designed! 05b4rcb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 85.000 77.000 0.071 http://example.org/film/film_set_designer/film_sets_designed #2616-01jgkj2 PRED entity: 01jgkj2 PRED relation: role PRED expected values: 01vdm0 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 83): 026t6 (0.47 #419, 0.16 #2725, 0.12 #629), 0342h (0.39 #631, 0.39 #421, 0.37 #2727), 05r5c (0.39 #2731, 0.36 #425, 0.36 #740), 01vdm0 (0.31 #450, 0.27 #2756, 0.26 #660), 02sgy (0.31 #423, 0.24 #633, 0.23 #2729), 05842k (0.28 #495, 0.17 #2801, 0.15 #705), 018vs (0.26 #1255, 0.25 #1570, 0.24 #3560), 0l14qv (0.26 #1255, 0.25 #1570, 0.24 #3560), 02hnl (0.26 #1255, 0.25 #1570, 0.24 #3560), 042v_gx (0.24 #636, 0.20 #2732, 0.20 #741) >> Best rule #419 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 09g0h; >> query: (?x9176, 026t6) <- instrumentalists(?x1750, ?x9176), ?x1750 = 02hnl, role(?x9176, ?x1574) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #450 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 09g0h; *> query: (?x9176, 01vdm0) <- instrumentalists(?x1750, ?x9176), ?x1750 = 02hnl, role(?x9176, ?x1574) *> conf = 0.31 ranks of expected_values: 4 EVAL 01jgkj2 role 01vdm0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 114.000 114.000 0.473 http://example.org/music/artist/track_contributions./music/track_contribution/role #2615-07c0j PRED entity: 07c0j PRED relation: inductee! PRED expected values: 0g2c8 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 4): 0g2c8 (0.44 #10, 0.42 #37, 0.30 #100), 06szd3 (0.06 #210, 0.06 #237, 0.04 #164), 0qjfl (0.03 #165, 0.03 #93, 0.01 #310), 04dm2n (0.01 #216) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01vsxdm; 0134s5; 02jqjm; 07mvp; 015cxv; 0p8h0; >> query: (?x1136, 0g2c8) <- music(?x5139, ?x1136), group(?x227, ?x1136), category(?x1136, ?x134) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07c0j inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.444 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #2614-0g9lm2 PRED entity: 0g9lm2 PRED relation: nominated_for! PRED expected values: 0gq9h => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 198): 0gr4k (0.60 #22, 0.27 #6712, 0.25 #3345), 02x17s4 (0.60 #76, 0.25 #3345, 0.20 #9408), 02n9nmz (0.40 #45, 0.20 #9408, 0.19 #14430), 0gq9h (0.38 #6738, 0.38 #3811, 0.34 #4229), 0k611 (0.33 #266, 0.29 #6747, 0.28 #3820), 054krc (0.33 #262, 0.25 #3345, 0.24 #10664), 099ck7 (0.33 #362, 0.25 #3345, 0.21 #2452), 05ztjjw (0.33 #214, 0.25 #3345, 0.12 #10036), 09qv_s (0.33 #304, 0.22 #2394, 0.17 #5436), 0f4x7 (0.29 #2320, 0.23 #6711, 0.23 #3784) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 09z2b7; 0cw3yd; 0462hhb; >> query: (?x4359, 0gr4k) <- nominated_for(?x166, ?x4359), nominated_for(?x3902, ?x4359), ?x3902 = 02z1nbg >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #6738 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 747 *> proper extension: 0m5s5; 023cjg; *> query: (?x4359, 0gq9h) <- nominated_for(?x749, ?x4359), nominated_for(?x749, ?x414), ?x414 = 095zlp *> conf = 0.38 ranks of expected_values: 4 EVAL 0g9lm2 nominated_for! 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 83.000 0.600 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2613-018x3 PRED entity: 018x3 PRED relation: role PRED expected values: 0342h 0319l 03gvt => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 107): 05r5c (0.60 #8, 0.57 #1138, 0.54 #573), 0342h (0.55 #5376, 0.51 #570, 0.50 #5), 042v_gx (0.40 #9, 0.36 #103, 0.36 #857), 02sgy (0.40 #6, 0.33 #760, 0.32 #194), 0mkg (0.40 #12, 0.14 #577, 0.09 #2929), 0l14md (0.36 #660, 0.20 #572, 0.20 #7), 026t6 (0.30 #3, 0.29 #568, 0.18 #2920), 03gvt (0.30 #72, 0.17 #637, 0.12 #826), 0395lw (0.30 #26, 0.11 #591, 0.07 #2943), 018j2 (0.30 #41, 0.06 #3012, 0.06 #1830) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 082brv; >> query: (?x5494, 05r5c) <- role(?x5494, ?x4769), role(?x5494, ?x1437), ?x1437 = 01vdm0, ?x4769 = 0dwt5 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #5376 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 311 *> proper extension: 0p5mw; 018y81; 02fybl; 02mx98; 04m2zj; 03f1zhf; 048tgl; 023slg; *> query: (?x5494, 0342h) <- role(?x5494, ?x1437), role(?x11533, ?x1437), ?x11533 = 02qtywd *> conf = 0.55 ranks of expected_values: 2, 8, 63 EVAL 018x3 role 03gvt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 132.000 132.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 018x3 role 0319l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 132.000 132.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 018x3 role 0342h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 132.000 0.600 http://example.org/music/artist/track_contributions./music/track_contribution/role #2612-03h40_7 PRED entity: 03h40_7 PRED relation: produced_by! PRED expected values: 02gpkt => 91 concepts (85 used for prediction) PRED predicted values (max 10 best out of 346): 0gm2_0 (0.33 #840, 0.22 #1779, 0.06 #28177), 0bbw2z6 (0.17 #442, 0.11 #1381, 0.10 #49787), 0ch26b_ (0.17 #163, 0.11 #1102, 0.06 #28177), 049xgc (0.17 #532, 0.11 #1471, 0.06 #28177), 09g7vfw (0.17 #304, 0.11 #1243, 0.06 #28177), 0c38gj (0.17 #427, 0.11 #1366, 0.06 #28177), 02z0f6l (0.17 #653, 0.11 #1592, 0.06 #28177), 02wwmhc (0.17 #888, 0.11 #1827, 0.06 #28177), 07kh6f3 (0.17 #336, 0.11 #1275, 0.06 #28177), 0435vm (0.17 #344, 0.11 #1283, 0.02 #6919) >> Best rule #840 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 024jwt; >> query: (?x10715, 0gm2_0) <- award_nominee(?x10715, ?x4946), place_of_birth(?x10715, ?x479), ?x4946 = 03h304l >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #28177 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 568 *> proper extension: 044mz_; 04bdxl; 02s2ft; 06qgvf; 03qcq; 05bnp0; 02p65p; 0337vz; 06151l; 01j5ts; ... *> query: (?x10715, ?x511) <- award_nominee(?x10715, ?x4946), place_of_birth(?x10715, ?x479), produced_by(?x511, ?x4946) *> conf = 0.06 ranks of expected_values: 48 EVAL 03h40_7 produced_by! 02gpkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 91.000 85.000 0.333 http://example.org/film/film/produced_by #2611-0bx_hnp PRED entity: 0bx_hnp PRED relation: person PRED expected values: 03bnv 02qwg => 127 concepts (109 used for prediction) PRED predicted values (max 10 best out of 199): 09b6zr (0.38 #2608, 0.33 #3693, 0.28 #4597), 04sry (0.33 #3255, 0.25 #2712, 0.25 #1807), 019z7q (0.33 #377, 0.25 #557, 0.20 #1279), 06pjs (0.33 #507, 0.20 #1409, 0.08 #3942), 0407f (0.33 #422, 0.20 #1324, 0.08 #3857), 01w60_p (0.33 #402, 0.20 #1304, 0.08 #3837), 0144l1 (0.33 #222, 0.17 #1668, 0.11 #3116), 020hh3 (0.33 #1768, 0.15 #3938, 0.10 #3397), 02h8hr (0.33 #89), 095zvfg (0.25 #1807, 0.25 #361, 0.10 #2531) >> Best rule #2608 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0cz_ym; 0d_wms; 043mk4y; >> query: (?x9961, 09b6zr) <- person(?x9961, ?x1291), film(?x7310, ?x9961), honored_for(?x1764, ?x9961), language(?x9961, ?x254), award_winner(?x1764, ?x1040) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #6513 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 0h1fktn; *> query: (?x9961, ?x1089) <- person(?x9961, ?x4701), person(?x9961, ?x4288), award(?x4701, ?x567), award_winner(?x4288, ?x1089), genre(?x9961, ?x1014) *> conf = 0.04 ranks of expected_values: 165, 166 EVAL 0bx_hnp person 02qwg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 127.000 109.000 0.375 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person EVAL 0bx_hnp person 03bnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 127.000 109.000 0.375 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #2610-07sbbz2 PRED entity: 07sbbz2 PRED relation: artists PRED expected values: 03h_fk5 01vw20_ 01vwyqp 017vkx 01vsyg9 0k1bs 01w9ph_ 0140t7 01vvybv => 54 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1032): 02cw1m (0.71 #6984, 0.50 #2884, 0.33 #1859), 01309x (0.60 #5420, 0.50 #3370, 0.42 #8500), 02z4b_8 (0.60 #5725, 0.50 #4700, 0.38 #7777), 01wz3cx (0.60 #5260, 0.42 #8340, 0.33 #1160), 02pt7h_ (0.60 #5699, 0.38 #7751, 0.33 #1599), 03h_fk5 (0.60 #5339, 0.33 #8419, 0.33 #1239), 018ndc (0.60 #5366, 0.33 #8446, 0.33 #1266), 01vtj38 (0.60 #5749, 0.33 #8829, 0.33 #1649), 0x3b7 (0.60 #5477, 0.33 #8557, 0.33 #1377), 03mszl (0.60 #5785, 0.33 #8865, 0.33 #1685) >> Best rule #6984 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 03lty; 05bt6j; 02k_kn; >> query: (?x378, 02cw1m) <- artists(?x378, ?x12810), artists(?x378, ?x4918), artists(?x378, ?x1413), ?x12810 = 027kwc, award_winner(?x341, ?x1413), instrumentalists(?x227, ?x4918), award_nominee(?x1413, ?x1795) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5339 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 02w4v; *> query: (?x378, 03h_fk5) <- artists(?x378, ?x12810), artists(?x378, ?x4918), artists(?x378, ?x2963), artists(?x378, ?x2806), ?x2963 = 0gcs9, role(?x4918, ?x227), role(?x4918, ?x745), group(?x228, ?x12810), ?x2806 = 01wj92r *> conf = 0.60 ranks of expected_values: 6, 36, 88, 109, 139, 143, 198, 410, 707 EVAL 07sbbz2 artists 01vvybv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 54.000 23.000 0.714 http://example.org/music/genre/artists EVAL 07sbbz2 artists 0140t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 54.000 23.000 0.714 http://example.org/music/genre/artists EVAL 07sbbz2 artists 01w9ph_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 54.000 23.000 0.714 http://example.org/music/genre/artists EVAL 07sbbz2 artists 0k1bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 54.000 23.000 0.714 http://example.org/music/genre/artists EVAL 07sbbz2 artists 01vsyg9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 23.000 0.714 http://example.org/music/genre/artists EVAL 07sbbz2 artists 017vkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 54.000 23.000 0.714 http://example.org/music/genre/artists EVAL 07sbbz2 artists 01vwyqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 54.000 23.000 0.714 http://example.org/music/genre/artists EVAL 07sbbz2 artists 01vw20_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 54.000 23.000 0.714 http://example.org/music/genre/artists EVAL 07sbbz2 artists 03h_fk5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 54.000 23.000 0.714 http://example.org/music/genre/artists #2609-0c408_ PRED entity: 0c408_ PRED relation: profession PRED expected values: 0dxtg => 56 concepts (33 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.83 #590, 0.81 #878, 0.69 #1454), 0dxtg (0.66 #733, 0.44 #13, 0.41 #301), 0nbcg (0.54 #1324, 0.49 #460, 0.34 #2188), 09jwl (0.49 #2178, 0.46 #1314, 0.43 #450), 01d_h8 (0.48 #725, 0.40 #581, 0.33 #293), 0cbd2 (0.38 #2166, 0.28 #294, 0.22 #6), 02jknp (0.33 #7, 0.24 #727, 0.21 #2311), 02krf9 (0.26 #744, 0.16 #1032, 0.16 #888), 018gz8 (0.26 #4755, 0.25 #880, 0.23 #304), 02hv44_ (0.26 #4755, 0.19 #486, 0.14 #1350) >> Best rule #590 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 0grwj; 06dv3; 01q_ph; 01tvz5j; 0159h6; 01rr9f; 0147dk; 03f2_rc; 0187y5; 01hxs4; ... >> query: (?x11007, 02hrh1q) <- profession(?x11007, ?x131), award_nominee(?x11007, ?x1145), friend(?x11007, ?x5345) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #733 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 149 *> proper extension: 01dw9z; 02wr2r; 09pl3f; 02c0mv; 08xz51; 01l1ls; 023jq1; 08f3yq; *> query: (?x11007, 0dxtg) <- profession(?x11007, ?x131), award_winner(?x11007, ?x1145), producer_type(?x11007, ?x632) *> conf = 0.66 ranks of expected_values: 2 EVAL 0c408_ profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 56.000 33.000 0.830 http://example.org/people/person/profession #2608-0sb1r PRED entity: 0sb1r PRED relation: featured_film_locations! PRED expected values: 0m491 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 567): 0g3zrd (0.20 #901, 0.17 #2375, 0.17 #1638), 04dsnp (0.08 #7436, 0.04 #8174, 0.04 #9651), 0473rc (0.06 #7824, 0.04 #8562, 0.03 #9300), 061681 (0.06 #7417, 0.04 #8155, 0.03 #8893), 0872p_c (0.06 #5237, 0.05 #7448, 0.03 #8186), 0m491 (0.06 #5285, 0.03 #8234, 0.03 #8972), 0btpm6 (0.06 #5708, 0.03 #9395, 0.02 #8657), 0jqd3 (0.06 #5634, 0.02 #8583, 0.02 #7108), 0gw7p (0.06 #5600, 0.02 #8549, 0.02 #7074), 01cmp9 (0.06 #5604, 0.02 #8553, 0.02 #7078) >> Best rule #901 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0s69k; 0psxp; >> query: (?x3982, 0g3zrd) <- contains(?x8552, ?x3982), contains(?x3818, ?x3982), ?x3818 = 03v0t, location(?x4631, ?x3982), adjoins(?x6410, ?x8552), currency(?x8552, ?x170) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #5285 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 0s3y5; 01_d4; 0sf9_; 0s5cg; 0sbbq; 0sjqm; 0s987; 0s2z0; 0sbv7; 0s3pw; *> query: (?x3982, 0m491) <- contains(?x8552, ?x3982), contains(?x3818, ?x3982), ?x3818 = 03v0t, location(?x4631, ?x3982), adjoins(?x6410, ?x8552) *> conf = 0.06 ranks of expected_values: 6 EVAL 0sb1r featured_film_locations! 0m491 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 88.000 0.200 http://example.org/film/film/featured_film_locations #2607-01sv6k PRED entity: 01sv6k PRED relation: country PRED expected values: 03rk0 => 94 concepts (45 used for prediction) PRED predicted values (max 10 best out of 16): 03rk0 (0.54 #262, 0.54 #221, 0.52 #614), 09c7w0 (0.41 #881, 0.36 #1140, 0.36 #1922), 049lr (0.33 #261, 0.30 #349, 0.29 #613), 0j0k (0.12 #3884, 0.12 #3974, 0.12 #3791), 07ssc (0.07 #2921, 0.06 #3629, 0.05 #2114), 02jx1 (0.04 #2938, 0.04 #3646, 0.02 #2131), 0345h (0.03 #3645, 0.02 #1342, 0.02 #1862), 0d060g (0.03 #1060, 0.03 #1406, 0.03 #887), 03z8w6 (0.02 #2993), 0k4y6 (0.02 #2993) >> Best rule #262 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 020skc; 0c8tk; 0cvw9; 01j922; 019fbp; 0fk98; 050tt8; >> query: (?x13121, ?x2146) <- place_of_birth(?x12675, ?x13121), contains(?x2146, ?x13121), ?x2146 = 03rk0, profession(?x12675, ?x1032), languages(?x12675, ?x254) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sv6k country 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 45.000 0.538 http://example.org/base/biblioness/bibs_location/country #2606-035dk PRED entity: 035dk PRED relation: member_states! PRED expected values: 085h1 => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 13): 085h1 (0.79 #15, 0.75 #149, 0.74 #198), 018cqq (0.35 #33, 0.26 #19, 0.26 #14), 02jxk (0.24 #147, 0.24 #32, 0.23 #155), 059dn (0.24 #35, 0.20 #150, 0.20 #190), 041288 (0.09 #200, 0.08 #142, 0.07 #529), 0b6css (0.09 #200, 0.08 #142, 0.07 #529), 0gkjy (0.09 #200, 0.08 #142, 0.07 #529), 07t65 (0.09 #200, 0.08 #142, 0.07 #529), 02vk52z (0.09 #200, 0.08 #142, 0.07 #529), 0j7v_ (0.09 #200, 0.08 #142, 0.07 #529) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 0cwx_; >> query: (?x2051, 085h1) <- organization(?x2051, ?x127), featured_film_locations(?x5044, ?x2051), adjoins(?x7360, ?x2051) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035dk member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.789 http://example.org/user/ktrueman/default_domain/international_organization/member_states #2605-01vhrz PRED entity: 01vhrz PRED relation: student! PRED expected values: 04gd8j => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 162): 065y4w7 (0.15 #539, 0.14 #2646, 0.14 #3172), 0bwfn (0.13 #15002, 0.12 #16052, 0.08 #25503), 09f2j (0.11 #23288, 0.08 #158, 0.07 #2790), 03ksy (0.08 #7998, 0.08 #105, 0.07 #5894), 021w0_ (0.08 #322, 0.05 #1899, 0.04 #2426), 023znp (0.08 #118, 0.02 #9064, 0.02 #9589), 01jq34 (0.08 #56, 0.01 #23186), 01jt2w (0.08 #281), 0bjqh (0.08 #570, 0.05 #1096, 0.05 #1622), 02cttt (0.08 #544, 0.05 #1070, 0.05 #1596) >> Best rule #539 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 05cv94; 081nh; 0gg9_5q; 05hj_k; 0b478; 04pqqb; 022p06; 0m593; 06q8hf; 03fw4y; >> query: (?x9373, 065y4w7) <- produced_by(?x8284, ?x9373), organizations_founded(?x9373, ?x4564), film(?x4564, ?x253) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #5104 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 06dv3; 014zcr; 0bxtg; 0147dk; 06cv1; 0mdqp; 0151w_; 0sz28; 0n6f8; 0pz91; ... *> query: (?x9373, 04gd8j) <- produced_by(?x8284, ?x9373), participant(?x9373, ?x2662), participant(?x7331, ?x9373) *> conf = 0.03 ranks of expected_values: 69 EVAL 01vhrz student! 04gd8j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 110.000 110.000 0.154 http://example.org/education/educational_institution/students_graduates./education/education/student #2604-0dl5d PRED entity: 0dl5d PRED relation: artists PRED expected values: 0c9d9 01vn35l 03xl77 0qdyf 047cx 01vsy3q 018d6l 0473q 017g21 02pt27 => 75 concepts (41 used for prediction) PRED predicted values (max 10 best out of 901): 03t9sp (0.71 #8020, 0.60 #5054, 0.50 #4066), 03xhj6 (0.71 #8266, 0.60 #5300, 0.50 #4312), 03f5spx (0.71 #7959, 0.60 #4993, 0.50 #4005), 011z3g (0.60 #5503, 0.57 #8469, 0.50 #4515), 0473q (0.60 #5547, 0.57 #8513, 0.50 #4559), 01vvycq (0.60 #4984, 0.57 #7950, 0.50 #3996), 01vtj38 (0.60 #5554, 0.57 #8520, 0.50 #4566), 03j24kf (0.60 #5324, 0.57 #8290, 0.50 #4336), 016t0h (0.60 #5870, 0.57 #8836, 0.50 #4882), 0137hn (0.60 #5497, 0.57 #8463, 0.50 #4509) >> Best rule #8020 for best value: >> intensional similarity = 9 >> extensional distance = 5 >> proper extension: 0m0jc; 0y3_8; >> query: (?x1380, 03t9sp) <- artists(?x1380, ?x9999), artists(?x1380, ?x7227), artists(?x1380, ?x6986), ?x6986 = 02vgh, category(?x9999, ?x134), influenced_by(?x7227, ?x1136), parent_genre(?x301, ?x1380), group(?x228, ?x9999), parent_genre(?x1380, ?x1000) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5547 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 064t9; *> query: (?x1380, 0473q) <- artists(?x1380, ?x9999), artists(?x1380, ?x7227), artists(?x1380, ?x6986), artists(?x1380, ?x442), ?x6986 = 02vgh, category(?x9999, ?x134), ?x7227 = 01kcms4, artist(?x441, ?x442), group(?x314, ?x442) *> conf = 0.60 ranks of expected_values: 5, 143, 263, 265, 270, 294, 356, 446, 691, 718 EVAL 0dl5d artists 02pt27 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 75.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0dl5d artists 017g21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 75.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0dl5d artists 0473q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 75.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0dl5d artists 018d6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 75.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0dl5d artists 01vsy3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 75.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0dl5d artists 047cx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 75.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0dl5d artists 0qdyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 75.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0dl5d artists 03xl77 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 75.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0dl5d artists 01vn35l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 75.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0dl5d artists 0c9d9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 75.000 41.000 0.714 http://example.org/music/genre/artists #2603-02dq8f PRED entity: 02dq8f PRED relation: category PRED expected values: 08mbj5d => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #48, 0.90 #6, 0.89 #41) >> Best rule #48 for best value: >> intensional similarity = 3 >> extensional distance = 301 >> proper extension: 0yl_j; >> query: (?x4889, 08mbj5d) <- citytown(?x4889, ?x4890), currency(?x4889, ?x170), currency(?x54, ?x170) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02dq8f category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.901 http://example.org/common/topic/webpage./common/webpage/category #2602-0f0kz PRED entity: 0f0kz PRED relation: film PRED expected values: 017jd9 => 84 concepts (39 used for prediction) PRED predicted values (max 10 best out of 596): 017jd9 (0.69 #4318, 0.62 #2544, 0.04 #51451), 017kct (0.20 #572, 0.16 #5894, 0.03 #65646), 01jwxx (0.20 #838, 0.04 #6160, 0.02 #32771), 04vh83 (0.20 #563, 0.04 #5885, 0.01 #7659), 027r7k (0.20 #1707, 0.04 #7029, 0.01 #8803), 0bx0l (0.20 #340, 0.04 #5662), 0dtfn (0.20 #206, 0.04 #5528), 02qr3k8 (0.20 #1276, 0.03 #65646, 0.03 #60323), 0pd6l (0.20 #648, 0.03 #65646, 0.03 #60323), 0prh7 (0.20 #6148, 0.02 #7922, 0.02 #9696) >> Best rule #4318 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 01kwld; 02gvwz; 01l2fn; 0154qm; >> query: (?x3028, 017jd9) <- award_nominee(?x629, ?x3028), location(?x3028, ?x362), ?x629 = 09wj5 >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f0kz film 017jd9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 39.000 0.688 http://example.org/film/actor/film./film/performance/film #2601-015w8_ PRED entity: 015w8_ PRED relation: genre PRED expected values: 095bb 0pr6f => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 80): 01z4y (0.71 #100, 0.43 #1660, 0.41 #346), 0pr6f (0.60 #50, 0.57 #214, 0.36 #543), 0c4xc (0.57 #124, 0.31 #1684, 0.28 #781), 095bb (0.57 #201, 0.23 #612, 0.22 #530), 07s9rl0 (0.52 #1807, 0.52 #1068, 0.51 #1972), 06n90 (0.40 #13, 0.29 #177, 0.26 #588), 01htzx (0.40 #17, 0.19 #510, 0.18 #1577), 01hmnh (0.29 #180, 0.26 #591, 0.25 #509), 03k9fj (0.29 #175, 0.21 #586, 0.21 #421), 06q7n (0.20 #290, 0.19 #701, 0.16 #2179) >> Best rule #100 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0584r4; >> query: (?x3144, 01z4y) <- actor(?x3144, ?x7001), nominated_for(?x3263, ?x3144), award_winner(?x3144, ?x2135), organizations_founded(?x2135, ?x1686) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0vhm; 05f7w84; 09g_31; *> query: (?x3144, 0pr6f) <- actor(?x3144, ?x12244), actor(?x3144, ?x7811), languages(?x3144, ?x254), ?x12244 = 031c2r, location(?x7811, ?x739) *> conf = 0.60 ranks of expected_values: 2, 4 EVAL 015w8_ genre 0pr6f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.714 http://example.org/tv/tv_program/genre EVAL 015w8_ genre 095bb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.714 http://example.org/tv/tv_program/genre #2600-060c4 PRED entity: 060c4 PRED relation: company PRED expected values: 016tw3 05g76 03mnk 01p5yn 07tds 0jvs0 04htfd 018_q8 04fv0k 02bm1v 04rcl7 03bnb 02p10m 01_lh1 0841v 01bfjy => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 670): 04fv0k (0.62 #3955, 0.56 #5247, 0.55 #6103), 0797c7 (0.62 #4003, 0.45 #6151, 0.44 #5295), 01_lh1 (0.62 #3988, 0.45 #6136, 0.33 #5280), 0dq23 (0.50 #4017, 0.50 #2097, 0.45 #6165), 01zpmq (0.50 #3906, 0.50 #1986, 0.45 #6054), 0168nq (0.50 #3889, 0.50 #1969, 0.45 #6037), 03mnk (0.50 #3870, 0.50 #2372, 0.36 #6018), 0841v (0.50 #4021, 0.45 #6169, 0.44 #5313), 018_q8 (0.50 #2008, 0.44 #5220, 0.38 #3928), 0dmtp (0.50 #2416, 0.44 #5206, 0.38 #3914) >> Best rule #3955 for best value: >> intensional similarity = 11 >> extensional distance = 6 >> proper extension: 01kr6k; >> query: (?x346, 04fv0k) <- company(?x346, ?x8287), company(?x346, ?x7690), company(?x346, ?x2975), company(?x346, ?x2497), company(?x346, ?x502), list(?x2497, ?x2197), ?x502 = 087c7, production_companies(?x6679, ?x7690), state_province_region(?x8287, ?x2049), service_language(?x2975, ?x254), country(?x6679, ?x252) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 7, 8, 9, 14, 16, 17, 53, 343, 346, 347 EVAL 060c4 company 01bfjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 0841v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 01_lh1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 02p10m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 03bnb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 04rcl7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 02bm1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 04fv0k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 018_q8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 04htfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 0jvs0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 07tds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 01p5yn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 03mnk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 05g76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 060c4 company 016tw3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 55.000 0.625 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #2599-02qw2xb PRED entity: 02qw2xb PRED relation: profession PRED expected values: 02hrh1q => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 50): 02hrh1q (0.89 #615, 0.88 #4066, 0.87 #5866), 01d_h8 (0.40 #756, 0.33 #1501, 0.32 #1056), 0dxtg (0.33 #1501, 0.28 #6015, 0.27 #3165), 02jknp (0.33 #1501, 0.26 #10052, 0.21 #5109), 089fss (0.33 #1501, 0.26 #10052, 0.09 #17), 09jwl (0.27 #20, 0.26 #10052, 0.25 #320), 03gjzk (0.27 #166, 0.25 #766, 0.25 #916), 018gz8 (0.26 #10052, 0.19 #168, 0.12 #8419), 0nbcg (0.26 #10052, 0.17 #333, 0.14 #783), 0kyk (0.26 #10052, 0.13 #481, 0.10 #331) >> Best rule #615 for best value: >> intensional similarity = 3 >> extensional distance = 226 >> proper extension: 02wrhj; >> query: (?x7797, 02hrh1q) <- award_winner(?x6297, ?x7797), actor(?x5060, ?x7797), location(?x7797, ?x739) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qw2xb profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.886 http://example.org/people/person/profession #2598-0dpl44 PRED entity: 0dpl44 PRED relation: film_crew_role PRED expected values: 01pvkk => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 24): 0dxtw (0.40 #1138, 0.40 #1278, 0.39 #1348), 01vx2h (0.32 #610, 0.32 #961, 0.31 #1527), 01pvkk (0.29 #1350, 0.29 #1280, 0.28 #1140), 015h31 (0.25 #7, 0.09 #430, 0.09 #537), 02ynfr (0.18 #403, 0.18 #615, 0.18 #1144), 0215hd (0.14 #406, 0.14 #618, 0.13 #1535), 089g0h (0.11 #407, 0.11 #1536, 0.10 #1148), 02rh1dz (0.11 #1137, 0.11 #608, 0.11 #1277), 01xy5l_ (0.11 #401, 0.11 #1142, 0.10 #1282), 0d2b38 (0.11 #95, 0.11 #1542, 0.10 #200) >> Best rule #1138 for best value: >> intensional similarity = 4 >> extensional distance = 802 >> proper extension: 0c40vxk; 09g8vhw; 02725hs; 047p7fr; 0gh65c5; 03q5db; 0gtt5fb; 05r3qc; 02v_r7d; 06rzwx; ... >> query: (?x7103, 0dxtw) <- film(?x820, ?x7103), film_crew_role(?x7103, ?x137), ?x137 = 09zzb8, award_winner(?x337, ?x820) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1350 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 878 *> proper extension: 0fq27fp; 0bs8hvm; 0g5qmbz; 0j8f09z; 03xj05; *> query: (?x7103, 01pvkk) <- film_crew_role(?x7103, ?x137), ?x137 = 09zzb8, genre(?x7103, ?x53) *> conf = 0.29 ranks of expected_values: 3 EVAL 0dpl44 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 89.000 0.403 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2597-03v1s PRED entity: 03v1s PRED relation: jurisdiction_of_office! PRED expected values: 0fkvn 0fkzq => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 17): 0fkvn (0.82 #365, 0.82 #1068, 0.81 #23), 0pqc5 (0.65 #1278, 0.57 #1449, 0.55 #1259), 060c4 (0.55 #1694, 0.52 #1713, 0.52 #2131), 060bp (0.49 #1692, 0.47 #1711, 0.45 #2129), 0fkzq (0.38 #89, 0.38 #32, 0.32 #70), 01t7n9 (0.33 #15, 0.15 #167, 0.13 #452), 02079p (0.33 #8, 0.11 #1186, 0.08 #597), 0dq3c (0.33 #2, 0.09 #1693, 0.09 #1712), 0p5vf (0.13 #218, 0.11 #123, 0.11 #845), 01q24l (0.13 #1454, 0.09 #1264, 0.08 #1815) >> Best rule #365 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 0ny1p; >> query: (?x448, 0fkvn) <- location(?x5346, ?x448), capital(?x448, ?x2879), jurisdiction_of_office(?x2358, ?x448) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 03v1s jurisdiction_of_office! 0fkzq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 183.000 183.000 0.820 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 03v1s jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.820 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #2596-0631_ PRED entity: 0631_ PRED relation: religion! PRED expected values: 07ssc 07z1m => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 578): 07z1m (0.70 #756, 0.69 #959, 0.67 #554), 05fky (0.60 #841, 0.60 #774, 0.60 #169), 07_f2 (0.60 #182, 0.52 #1288, 0.51 #1494), 05kr_ (0.52 #1288, 0.51 #1494, 0.46 #1032), 0694j (0.52 #1288, 0.51 #1494, 0.39 #2252), 0d060g (0.52 #1288, 0.51 #1494, 0.39 #2252), 02_286 (0.52 #1288, 0.51 #1494, 0.39 #2252), 02gt5s (0.52 #1288, 0.51 #1494, 0.39 #2252), 081yw (0.52 #1288, 0.51 #1494, 0.39 #2252), 04ych (0.52 #1288, 0.51 #1494, 0.39 #2252) >> Best rule #756 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 01lp8; 0c8wxp; 021_0p; >> query: (?x2591, 07z1m) <- religion(?x4622, ?x2591), religion(?x2982, ?x2591), religion(?x335, ?x2591), religion(?x457, ?x2591), location(?x101, ?x335), district_represented(?x355, ?x335), ?x4622 = 04tgp, state_province_region(?x166, ?x335), administrative_parent(?x334, ?x335), ?x2982 = 01n4w >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 32 EVAL 0631_ religion! 07z1m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.700 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 0631_ religion! 07ssc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 39.000 39.000 0.700 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #2595-02h40lc PRED entity: 02h40lc PRED relation: service_language! PRED expected values: 03mnk 03mdt 02fgdx 07tds 01yfp7 04f0xq 07xyn1 0mgkg 04fv0k 03_c8p 01m_zd 0vlf => 74 concepts (59 used for prediction) PRED predicted values (max 10 best out of 5): 04fv0k (0.38 #68, 0.33 #73, 0.33 #3), 02slt7 (0.33 #1, 0.12 #66, 0.11 #71), 01m_zd (0.25 #30, 0.25 #25, 0.20 #47), 0mgkg (0.25 #22, 0.20 #44, 0.12 #67), 03_c8p (0.11 #74, 0.10 #98, 0.10 #93) >> Best rule #68 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 05zjd; 02bv9; >> query: (?x254, 04fv0k) <- languages(?x118, ?x254), language(?x3603, ?x254), service_language(?x127, ?x254), film_release_region(?x3603, ?x87), languages(?x50, ?x254), countries_spoken_in(?x254, ?x126) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 5 EVAL 02h40lc service_language! 0vlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 01m_zd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 03_c8p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 04fv0k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 0mgkg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 07xyn1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 04f0xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 01yfp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 07tds CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 02fgdx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 03mdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language EVAL 02h40lc service_language! 03mnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 59.000 0.375 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language #2594-0g56t9t PRED entity: 0g56t9t PRED relation: language PRED expected values: 02h40lc => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.89 #832, 0.89 #1909, 0.89 #1849), 064_8sq (0.13 #496, 0.11 #793, 0.11 #912), 06nm1 (0.11 #249, 0.10 #603, 0.10 #367), 06b_j (0.11 #379, 0.05 #1032, 0.05 #1091), 04306rv (0.09 #479, 0.08 #597, 0.08 #895), 03_9r (0.08 #189, 0.07 #129, 0.06 #543), 07c9s (0.07 #138), 02bjrlw (0.06 #416, 0.06 #239, 0.06 #831), 04h9h (0.06 #281, 0.04 #222, 0.02 #755), 03k50 (0.04 #188, 0.03 #365, 0.02 #660) >> Best rule #832 for best value: >> intensional similarity = 4 >> extensional distance = 546 >> proper extension: 01gglm; >> query: (?x124, 02h40lc) <- written_by(?x124, ?x4314), film(?x5427, ?x124), award_nominee(?x230, ?x5427), location(?x5427, ?x362) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g56t9t language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 45.000 0.891 http://example.org/film/film/language #2593-080h2 PRED entity: 080h2 PRED relation: contains! PRED expected values: 0d060g => 234 concepts (209 used for prediction) PRED predicted values (max 10 best out of 432): 0d060g (0.79 #87779, 0.79 #24185, 0.79 #23302), 09c7w0 (0.71 #82407, 0.71 #37623, 0.69 #8960), 081yw (0.66 #137045, 0.09 #3861, 0.08 #6548), 01n7q (0.57 #134437, 0.54 #105770, 0.35 #13513), 0kpys (0.44 #1972, 0.30 #2869, 0.27 #3764), 06pvr (0.35 #13601, 0.31 #8227, 0.30 #19872), 07c5l (0.33 #395, 0.19 #43388, 0.15 #101609), 059g4 (0.33 #462, 0.10 #55100, 0.07 #71223), 02qkt (0.32 #111414, 0.21 #58568, 0.19 #149038), 07ssc (0.31 #103934, 0.28 #120057, 0.27 #78855) >> Best rule #87779 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 04llb; 05zhg; 0l3q2; >> query: (?x1036, ?x279) <- country(?x1036, ?x279), teams(?x1036, ?x934), team(?x1177, ?x934) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 080h2 contains! 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 234.000 209.000 0.792 http://example.org/location/location/contains #2592-09z2b7 PRED entity: 09z2b7 PRED relation: nominated_for! PRED expected values: 03hkv_r 09td7p => 77 concepts (67 used for prediction) PRED predicted values (max 10 best out of 224): 0gq9h (0.69 #56, 0.65 #960, 0.63 #1864), 0gs9p (0.67 #58, 0.58 #962, 0.55 #2092), 019f4v (0.58 #51, 0.51 #955, 0.48 #1633), 02pqp12 (0.49 #2540, 0.38 #54, 0.30 #958), 03hkv_r (0.47 #14, 0.42 #918, 0.40 #1822), 04dn09n (0.45 #33, 0.38 #937, 0.36 #259), 040njc (0.44 #6, 0.43 #910, 0.41 #2492), 0k611 (0.41 #970, 0.40 #2100, 0.40 #66), 0gqy2 (0.40 #112, 0.38 #1016, 0.37 #1920), 02qyntr (0.40 #2655, 0.29 #395, 0.27 #169) >> Best rule #56 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 011yfd; >> query: (?x1508, 0gq9h) <- titles(?x162, ?x1508), nominated_for(?x601, ?x1508), ?x601 = 0gr4k, featured_film_locations(?x1508, ?x362) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 011yfd; *> query: (?x1508, 03hkv_r) <- titles(?x162, ?x1508), nominated_for(?x601, ?x1508), ?x601 = 0gr4k, featured_film_locations(?x1508, ?x362) *> conf = 0.47 ranks of expected_values: 5, 28 EVAL 09z2b7 nominated_for! 09td7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 77.000 67.000 0.691 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09z2b7 nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 67.000 0.691 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2591-01cvtf PRED entity: 01cvtf PRED relation: genre PRED expected values: 0lsxr => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 76): 05p553 (0.53 #336, 0.51 #502, 0.49 #1332), 01z4y (0.38 #267, 0.35 #682, 0.35 #516), 0c4xc (0.36 #209, 0.28 #541, 0.26 #873), 06n90 (0.23 #1922, 0.20 #3002, 0.17 #345), 0hcr (0.23 #3008, 0.19 #3507, 0.19 #3424), 01htzx (0.22 #349, 0.20 #930, 0.19 #1926), 01t_vv (0.22 #366, 0.19 #1611, 0.18 #1943), 03k9fj (0.17 #3000, 0.17 #1920, 0.17 #924), 0vgkd (0.16 #259, 0.14 #508, 0.13 #1670), 06nbt (0.16 #436, 0.16 #353, 0.14 #602) >> Best rule #336 for best value: >> intensional similarity = 4 >> extensional distance = 56 >> proper extension: 05f7w84; >> query: (?x11250, 05p553) <- program(?x9571, ?x11250), program(?x6678, ?x11250), tv_program(?x9571, ?x3303), award_winner(?x9571, ?x6765) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1918 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 130 *> proper extension: 07qht4; *> query: (?x11250, 0lsxr) <- genre(?x11250, ?x53), ?x53 = 07s9rl0 *> conf = 0.14 ranks of expected_values: 13 EVAL 01cvtf genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 88.000 88.000 0.534 http://example.org/tv/tv_program/genre #2590-038bh3 PRED entity: 038bh3 PRED relation: films! PRED expected values: 04gb7 => 97 concepts (70 used for prediction) PRED predicted values (max 10 best out of 63): 081pw (0.09 #2831, 0.06 #4252, 0.04 #1099), 07jq_ (0.09 #550, 0.05 #1020, 0.04 #3858), 01vq3 (0.09 #509, 0.05 #666, 0.03 #3659), 0bq3x (0.07 #968, 0.05 #655, 0.04 #1282), 05489 (0.06 #1304, 0.04 #834, 0.03 #2248), 0ddct (0.05 #1653, 0.05 #1968, 0.04 #2756), 07_nf (0.05 #1005, 0.04 #2895, 0.04 #1319), 07c52 (0.05 #645, 0.02 #5702, 0.02 #6494), 02m4t (0.05 #702), 06c97 (0.05 #673) >> Best rule #2831 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 05y0cr; 0k20s; >> query: (?x4626, 081pw) <- genre(?x4626, ?x53), language(?x4626, ?x5607), ?x5607 = 064_8sq, ?x53 = 07s9rl0 >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #5089 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 209 *> proper extension: 0cq8nx; *> query: (?x4626, 04gb7) <- genre(?x4626, ?x53), language(?x4626, ?x90), music(?x4626, ?x6907), cinematography(?x4626, ?x7384) *> conf = 0.03 ranks of expected_values: 23 EVAL 038bh3 films! 04gb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 97.000 70.000 0.093 http://example.org/film/film_subject/films #2589-05c9zr PRED entity: 05c9zr PRED relation: film_distribution_medium PRED expected values: 0735l => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.83 #167, 0.73 #143, 0.19 #149), 029j_ (0.14 #163, 0.11 #157, 0.11 #187), 02nxhr (0.14 #164, 0.10 #146, 0.10 #188), 0dq6p (0.08 #165, 0.08 #189, 0.07 #159), 07c52 (0.03 #142) >> Best rule #167 for best value: >> intensional similarity = 5 >> extensional distance = 94 >> proper extension: 0bmc4cm; >> query: (?x4132, 0735l) <- film(?x574, ?x4132), film_release_region(?x4132, ?x94), region(?x4132, ?x512), film(?x574, ?x5070), ?x5070 = 0dt8xq >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05c9zr film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.833 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #2588-0jn38 PRED entity: 0jn38 PRED relation: group! PRED expected values: 02hnl => 132 concepts (105 used for prediction) PRED predicted values (max 10 best out of 125): 02hnl (0.82 #5202, 0.82 #5027, 0.80 #2873), 028tv0 (0.62 #1823, 0.62 #960, 0.56 #2081), 0l14qv (0.50 #177, 0.38 #3539, 0.34 #4314), 06ncr (0.50 #1074, 0.32 #1677, 0.30 #1935), 05r5c (0.48 #1818, 0.44 #1560, 0.38 #3197), 03qjg (0.44 #1600, 0.38 #1858, 0.37 #3323), 01vj9c (0.33 #3806, 0.33 #5361, 0.32 #5187), 05842k (0.27 #5786, 0.27 #5174, 0.09 #6394), 013y1f (0.25 #112, 0.22 #1579, 0.19 #1837), 085jw (0.25 #225, 0.14 #2932, 0.11 #6922) >> Best rule #5202 for best value: >> intensional similarity = 7 >> extensional distance = 75 >> proper extension: 014kyy; >> query: (?x8614, 02hnl) <- group(?x227, ?x8614), group(?x5437, ?x8614), category(?x8614, ?x134), instrumentalists(?x227, ?x1674), ?x1674 = 01v_pj6, role(?x1247, ?x227), role(?x74, ?x227) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jn38 group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 105.000 0.818 http://example.org/music/performance_role/regular_performances./music/group_membership/group #2587-0159h6 PRED entity: 0159h6 PRED relation: award PRED expected values: 0fq9zdn 05zvq6g 0bdwft 02ppm4q 0fq9zcx => 126 concepts (109 used for prediction) PRED predicted values (max 10 best out of 292): 09cn0c (0.71 #3892, 0.70 #18682, 0.70 #27245), 02z1nbg (0.71 #3892, 0.70 #18682, 0.70 #27245), 027571b (0.71 #3892, 0.70 #18682, 0.70 #27245), 05pcn59 (0.29 #3576, 0.29 #74, 0.27 #852), 05zr6wv (0.29 #16, 0.17 #3518, 0.15 #5853), 0gr51 (0.26 #5151, 0.25 #4373, 0.23 #7096), 05p09zm (0.24 #3228, 0.21 #3617, 0.15 #6341), 057xs89 (0.20 #1317, 0.20 #539, 0.16 #3652), 04kxsb (0.20 #506, 0.16 #29191, 0.15 #1284), 0f4x7 (0.20 #419, 0.16 #29191, 0.15 #3532) >> Best rule #3892 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 01w_10; >> query: (?x488, ?x384) <- award_nominee(?x100, ?x488), award_winner(?x384, ?x488), celebrity(?x7638, ?x488) >> conf = 0.71 => this is the best rule for 3 predicted values *> Best rule #452 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 06dv3; 0hvb2; 03n_7k; 02cllz; 04v7kt; *> query: (?x488, 0bdwft) <- award_nominee(?x100, ?x488), award_winner(?x6861, ?x488), ?x100 = 05vsxz *> conf = 0.20 ranks of expected_values: 13, 30, 33, 34, 63 EVAL 0159h6 award 0fq9zcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 126.000 109.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0159h6 award 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 126.000 109.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0159h6 award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 126.000 109.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0159h6 award 05zvq6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 126.000 109.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0159h6 award 0fq9zdn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 126.000 109.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2586-0f2r6 PRED entity: 0f2r6 PRED relation: teams PRED expected values: 041xyk => 228 concepts (228 used for prediction) PRED predicted values (max 10 best out of 290): 02qk2d5 (0.09 #573, 0.03 #2729, 0.02 #3088), 01jvgt (0.09 #707, 0.02 #3222, 0.02 #3581), 070xg (0.09 #414, 0.02 #2929, 0.02 #3288), 01y49 (0.09 #396, 0.02 #2911, 0.02 #3270), 0jm9w (0.09 #585, 0.02 #3100, 0.02 #3818), 03wnh (0.09 #465, 0.02 #2980, 0.02 #3698), 01k8vh (0.09 #627, 0.02 #3142, 0.02 #3860), 01y3c (0.09 #379, 0.02 #2894, 0.02 #3612), 0jnlm (0.09 #710, 0.02 #3225, 0.02 #4302), 0jm74 (0.09 #506, 0.02 #3021, 0.02 #4098) >> Best rule #573 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01_d4; 013yq; 0f__1; 0ftxw; 071cn; 0fr0t; 0d9jr; 01snm; 0fsb8; >> query: (?x674, 02qk2d5) <- contains(?x673, ?x674), locations(?x9974, ?x674), location(?x436, ?x674), ?x9974 = 0b_6pv >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f2r6 teams 041xyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 228.000 228.000 0.091 http://example.org/sports/sports_team_location/teams #2585-06jjbp PRED entity: 06jjbp PRED relation: parent_genre PRED expected values: 08cyft => 62 concepts (40 used for prediction) PRED predicted values (max 10 best out of 199): 06by7 (0.84 #1983, 0.76 #2476, 0.67 #2639), 0gywn (0.71 #1188, 0.33 #41, 0.29 #1679), 05r6t (0.66 #1364, 0.26 #2021, 0.25 #1037), 0glt670 (0.51 #1830, 0.43 #2159, 0.18 #1175), 064t9 (0.50 #994, 0.18 #2142, 0.15 #1321), 02x8m (0.35 #1161, 0.33 #997, 0.29 #1816), 06j6l (0.31 #1836, 0.29 #1181, 0.25 #2165), 08cyft (0.26 #1515, 0.25 #1023, 0.24 #1678), 02w4v (0.25 #195, 0.20 #359, 0.14 #687), 011j5x (0.25 #1005, 0.17 #1332, 0.14 #1989) >> Best rule #1983 for best value: >> intensional similarity = 8 >> extensional distance = 48 >> proper extension: 01gbcf; >> query: (?x12594, 06by7) <- parent_genre(?x6833, ?x12594), parent_genre(?x12594, ?x5792), artists(?x5792, ?x7570), artists(?x5792, ?x6129), artists(?x5792, ?x1128), ?x7570 = 01dw_f, ?x6129 = 016jfw, ?x1128 = 01wbgdv >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1515 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 40 *> proper extension: 0dn16; 0193f; 02cqny; 0781g; 01hydr; *> query: (?x12594, 08cyft) <- parent_genre(?x12594, ?x5792), artists(?x12594, ?x3382), artists(?x5792, ?x4960), artists(?x5792, ?x3856), place_of_birth(?x3382, ?x2645), ?x3856 = 017vkx, award(?x4960, ?x462) *> conf = 0.26 ranks of expected_values: 8 EVAL 06jjbp parent_genre 08cyft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 62.000 40.000 0.840 http://example.org/music/genre/parent_genre #2584-07jqjx PRED entity: 07jqjx PRED relation: film_release_region PRED expected values: 01znc_ => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 129): 03rjj (0.92 #438, 0.90 #294, 0.80 #148), 01znc_ (0.86 #324, 0.80 #468, 0.72 #178), 05b4w (0.80 #489, 0.76 #199, 0.76 #345), 05v8c (0.73 #448, 0.71 #304, 0.68 #158), 05qx1 (0.64 #176, 0.55 #322, 0.55 #466), 015qh (0.60 #177, 0.58 #467, 0.49 #323), 04gzd (0.58 #442, 0.57 #298, 0.44 #152), 0ctw_b (0.57 #310, 0.52 #164, 0.52 #454), 01ls2 (0.55 #301, 0.47 #445, 0.44 #155), 016wzw (0.53 #348, 0.52 #492, 0.48 #202) >> Best rule #438 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 0gtvrv3; >> query: (?x9657, 03rjj) <- country(?x9657, ?x205), film(?x4286, ?x9657), film_release_region(?x9657, ?x756), ?x756 = 06npd >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #324 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 47 *> proper extension: 0g5qs2k; 0gkz15s; *> query: (?x9657, 01znc_) <- film_release_region(?x9657, ?x756), film_release_region(?x9657, ?x512), film_release_region(?x9657, ?x142), ?x142 = 0jgd, ?x756 = 06npd, ?x512 = 07ssc *> conf = 0.86 ranks of expected_values: 2 EVAL 07jqjx film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.922 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2583-01vw26l PRED entity: 01vw26l PRED relation: award PRED expected values: 05zvj3m => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 287): 01by1l (0.64 #517, 0.58 #1732, 0.54 #2137), 01bgqh (0.49 #1662, 0.46 #2067, 0.32 #2877), 03qbh5 (0.47 #1826, 0.44 #2231, 0.24 #3041), 0c4z8 (0.44 #1691, 0.42 #2096, 0.36 #476), 05p09zm (0.43 #124, 0.14 #2554, 0.14 #5389), 01c92g (0.40 #1717, 0.35 #2122, 0.23 #2932), 09sb52 (0.38 #14215, 0.36 #18670, 0.31 #19480), 03t5kl (0.36 #633, 0.23 #4278, 0.13 #33214), 03qbnj (0.31 #1854, 0.29 #2259, 0.22 #3069), 02f73p (0.29 #2213, 0.29 #1808, 0.24 #3023) >> Best rule #517 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 04lgymt; 0288fyj; >> query: (?x3494, 01by1l) <- award_nominee(?x6835, ?x3494), award_winner(?x286, ?x3494), ?x6835 = 06mt91 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #93 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 046qq; 033tln; 01xllf; *> query: (?x3494, 05zvj3m) <- profession(?x3494, ?x131), film(?x3494, ?x3507), ?x3507 = 03459x *> conf = 0.14 ranks of expected_values: 56 EVAL 01vw26l award 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 99.000 99.000 0.643 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2582-02fqwt PRED entity: 02fqwt PRED relation: time_zones! PRED expected values: 03s0w 0488g 0345_ 0rkkv 0tj4y 0fpzwf 0nht0 010bxh 0nh1v 04_1l0v 0chrx 01btyw 0105y2 0nmj 0ntxg 0109vk 0fvwz 013n0n 01fscv 0102t4 0xgpv 01gfhk 0nppc 0sg4x => 13 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1923): 0psxp (0.81 #1052, 0.79 #1054, 0.78 #3161), 0s5cg (0.81 #1052, 0.79 #1054, 0.78 #3161), 0fpzwf (0.81 #1052, 0.79 #1054, 0.76 #2105), 0sf9_ (0.81 #1052, 0.79 #1054, 0.76 #2105), 0s3y5 (0.81 #1052, 0.79 #1054, 0.76 #2105), 0s9b_ (0.81 #1052, 0.79 #1054, 0.76 #2105), 0xddr (0.81 #1052, 0.79 #1054, 0.58 #3158), 0s4sj (0.81 #1052, 0.79 #1054, 0.58 #3158), 0sd7v (0.81 #1052, 0.79 #1054, 0.58 #3158), 0s6g4 (0.81 #1052, 0.79 #1054, 0.58 #3158) >> Best rule #1052 for best value: >> intensional similarity = 27 >> extensional distance = 1 >> proper extension: 02hczc; >> query: (?x1638, ?x13979) <- time_zones(?x13523, ?x1638), time_zones(?x12859, ?x1638), time_zones(?x12384, ?x1638), time_zones(?x11644, ?x1638), time_zones(?x4852, ?x1638), time_zones(?x4198, ?x1638), time_zones(?x1025, ?x1638), time_zones(?x1024, ?x1638), time_zones(?x279, ?x1638), ?x4198 = 05fky, jurisdiction_of_office(?x900, ?x1025), ?x1024 = 05fhy, location(?x2662, ?x13523), district_represented(?x176, ?x1025), source(?x4852, ?x958), ?x279 = 0d060g, place_of_birth(?x2801, ?x1025), county(?x13979, ?x12859), citytown(?x12126, ?x12384), ?x900 = 0fkvn, contains(?x1025, ?x7439), state_province_region(?x7177, ?x1025), location(?x587, ?x1025), contains(?x9311, ?x13523), profession(?x2662, ?x131), state(?x11644, ?x961), role(?x2662, ?x227) >> conf = 0.81 => this is the best rule for 15 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 16, 18, 44, 45, 58, 67, 68, 70, 91, 92, 93, 94, 199, 201, 230, 231, 248, 249, 250, 251, 258, 624 EVAL 02fqwt time_zones! 0sg4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0nppc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 01gfhk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0xgpv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0102t4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 01fscv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 013n0n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0fvwz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0109vk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0ntxg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0nmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0105y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 01btyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0chrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 04_1l0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0nh1v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 010bxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0nht0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0fpzwf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0tj4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0rkkv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0345_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 0488g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 13.000 13.000 0.812 http://example.org/location/location/time_zones EVAL 02fqwt time_zones! 03s0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 13.000 13.000 0.812 http://example.org/location/location/time_zones #2581-03_3d PRED entity: 03_3d PRED relation: nationality! PRED expected values: 01wy61y 01g4bk 04f62k 0b9f7t 042gr4 01kym3 => 144 concepts (105 used for prediction) PRED predicted values (max 10 best out of 4150): 0m6x4 (0.53 #281649, 0.43 #100584, 0.42 #168990), 0b9f7t (0.53 #281649, 0.43 #100584, 0.42 #168990), 01kym3 (0.53 #281649, 0.23 #177039, 0.03 #422467), 01wy61y (0.53 #281649), 04f62k (0.43 #100584, 0.42 #168990, 0.41 #237391), 01g4bk (0.43 #100584, 0.42 #168990, 0.41 #237391), 01515w (0.41 #237391, 0.03 #422467), 02h761 (0.38 #132777, 0.17 #9197, 0.15 #33336), 01xcfy (0.38 #132777, 0.17 #8864, 0.15 #28980), 0bytkq (0.38 #132777, 0.17 #8921, 0.10 #16967) >> Best rule #281649 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 01w0v; >> query: (?x252, ?x4162) <- administrative_parent(?x536, ?x252), contains(?x252, ?x4163), place_of_birth(?x4162, ?x4163) >> conf = 0.53 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 3, 4, 5, 6, 4005 EVAL 03_3d nationality! 01kym3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 105.000 0.533 http://example.org/people/person/nationality EVAL 03_3d nationality! 042gr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 144.000 105.000 0.533 http://example.org/people/person/nationality EVAL 03_3d nationality! 0b9f7t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 105.000 0.533 http://example.org/people/person/nationality EVAL 03_3d nationality! 04f62k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 105.000 0.533 http://example.org/people/person/nationality EVAL 03_3d nationality! 01g4bk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 105.000 0.533 http://example.org/people/person/nationality EVAL 03_3d nationality! 01wy61y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 105.000 0.533 http://example.org/people/person/nationality #2580-0bzm__ PRED entity: 0bzm__ PRED relation: award_winner PRED expected values: 09rp4r_ 03xp8d5 => 37 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1583): 01l3mk3 (0.50 #6148, 0.25 #12304, 0.24 #18458), 03f2_rc (0.33 #4609, 0.33 #3072, 0.33 #65), 03n0pv (0.33 #4609, 0.33 #3072, 0.25 #6096), 03n0q5 (0.33 #4609, 0.33 #3072, 0.25 #4990), 020jqv (0.33 #4609, 0.33 #3072, 0.24 #18458), 01lw3kh (0.33 #4609, 0.33 #3072, 0.24 #18458), 016jll (0.33 #4609, 0.33 #3072, 0.12 #19862), 025cn2 (0.33 #4609, 0.33 #3072, 0.12 #15380), 02lfp4 (0.33 #4609, 0.33 #3072, 0.12 #15380), 0178rl (0.33 #4609, 0.33 #3072, 0.10 #6964) >> Best rule #6148 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 0c4hx0; >> query: (?x6344, ?x7955) <- award_winner(?x6344, ?x7088), award_winner(?x6344, ?x6011), nationality(?x6011, ?x94), award_nominee(?x6011, ?x669), ceremony(?x77, ?x6344), ?x669 = 0146pg, profession(?x6011, ?x987), award_winner(?x6011, ?x7955), award_winner(?x1443, ?x7088), award(?x7088, ?x2379), award_winner(?x7088, ?x1974), award(?x308, ?x1443), nominated_for(?x1443, ?x155) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1535 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 1 *> proper extension: 0bzkvd; *> query: (?x6344, ?x5298) <- award_winner(?x6344, ?x7167), award_winner(?x6344, ?x6011), award_winner(?x6344, ?x5297), award_winner(?x6344, ?x4436), award_winner(?x6344, ?x2530), ?x6011 = 02zft0, film(?x4436, ?x351), award(?x4436, ?x102), ceremony(?x1307, ?x6344), ceremony(?x601, ?x6344), ?x7167 = 01wd9vs, award_nominee(?x1979, ?x4436), ?x1307 = 0gq9h, honored_for(?x6344, ?x6030), nationality(?x2530, ?x304), award(?x164, ?x601), award_winner(?x5297, ?x5298), award(?x167, ?x601) *> conf = 0.23 ranks of expected_values: 150, 715 EVAL 0bzm__ award_winner 03xp8d5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 37.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bzm__ award_winner 09rp4r_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 37.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2579-05r7t PRED entity: 05r7t PRED relation: country! PRED expected values: 071t0 => 207 concepts (207 used for prediction) PRED predicted values (max 10 best out of 52): 071t0 (0.93 #1529, 0.89 #2309, 0.85 #2153), 06f41 (0.85 #1521, 0.78 #2301, 0.76 #1729), 03hr1p (0.85 #1530, 0.76 #2154, 0.73 #2310), 06wrt (0.85 #1523, 0.76 #2147, 0.72 #1731), 01lb14 (0.81 #1522, 0.74 #2146, 0.73 #2302), 07jbh (0.81 #1540, 0.70 #344, 0.67 #3516), 0w0d (0.78 #1519, 0.69 #1727, 0.68 #2143), 07gyv (0.76 #2295, 0.75 #371, 0.74 #1515), 0194d (0.74 #1553, 0.74 #2177, 0.69 #1761), 064vjs (0.74 #1538, 0.68 #2318, 0.64 #2838) >> Best rule #1529 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 05r4w; 0jgd; 0154j; 0k6nt; 06qd3; 015qh; 06bnz; 05b4w; 06f32; 03spz; >> query: (?x6559, 071t0) <- form_of_government(?x6559, ?x48), film_release_region(?x2501, ?x6559), ?x2501 = 040rmy >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05r7t country! 071t0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 207.000 207.000 0.926 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #2578-01tqfs PRED entity: 01tqfs PRED relation: sport PRED expected values: 02vx4 => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.89 #92, 0.88 #119, 0.87 #146), 0z74 (0.50 #310, 0.49 #291, 0.46 #200), 0jm_ (0.32 #129, 0.25 #156, 0.20 #239), 018jz (0.25 #131, 0.23 #158, 0.14 #296), 03tmr (0.18 #19, 0.16 #347, 0.15 #292), 018w8 (0.18 #22, 0.15 #213, 0.11 #295), 09xp_ (0.08 #159, 0.06 #24, 0.04 #408), 039yzs (0.04 #484, 0.04 #317, 0.04 #353) >> Best rule #92 for best value: >> intensional similarity = 10 >> extensional distance = 34 >> proper extension: 03y_f8; 044l47; 02rytm; 02rqxc; 03yvgp; 03ytj1; 0329t7; 03zmc7; 03ys48; 02rjz5; ... >> query: (?x7198, 02vx4) <- position(?x7198, ?x530), position(?x7198, ?x63), position(?x7198, ?x60), teams(?x7213, ?x7198), position(?x7198, ?x203), ?x63 = 02sdk9v, ?x203 = 0dgrmp, team(?x8194, ?x7198), ?x530 = 02_j1w, ?x60 = 02nzb8 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tqfs sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.889 http://example.org/sports/sports_team/sport #2577-01wqflx PRED entity: 01wqflx PRED relation: group PRED expected values: 0d193h => 91 concepts (34 used for prediction) PRED predicted values (max 10 best out of 51): 01v0sx2 (0.06 #977, 0.05 #221, 0.05 #545), 01qqwp9 (0.06 #21, 0.05 #561, 0.04 #885), 0123r4 (0.06 #44, 0.04 #368, 0.04 #1124), 07mvp (0.06 #46, 0.03 #1451, 0.03 #478), 02r1tx7 (0.03 #556, 0.03 #16, 0.03 #1421), 06nv27 (0.03 #33, 0.02 #1113, 0.01 #2196), 07c0j (0.03 #4, 0.02 #544, 0.02 #868), 016l09 (0.03 #82, 0.01 #406), 0b1zz (0.03 #42, 0.01 #1447), 014_lq (0.03 #35, 0.01 #1440) >> Best rule #977 for best value: >> intensional similarity = 3 >> extensional distance = 135 >> proper extension: 04l19_; 020jqv; >> query: (?x8556, 01v0sx2) <- artist(?x2299, ?x8556), film(?x8556, ?x6480), type_of_union(?x8556, ?x566) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #242 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 0m19t; 067mj; 0249kn; 01309x; 07yg2; 0394y; 01cblr; 0134tg; 01q99h; 01kcms4; ... *> query: (?x8556, 0d193h) <- artists(?x7329, ?x8556), artist(?x2299, ?x8556), ?x7329 = 016jny *> conf = 0.01 ranks of expected_values: 34 EVAL 01wqflx group 0d193h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 91.000 34.000 0.058 http://example.org/music/group_member/membership./music/group_membership/group #2576-05b_gq PRED entity: 05b_gq PRED relation: language PRED expected values: 02bjrlw => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 42): 064_8sq (0.29 #78, 0.20 #135, 0.18 #307), 03_9r (0.29 #66, 0.10 #123, 0.08 #2767), 04h9h (0.29 #99, 0.07 #328, 0.06 #444), 0jzc (0.29 #76, 0.04 #2777, 0.03 #2603), 04306rv (0.20 #4, 0.14 #61, 0.13 #347), 02bjrlw (0.20 #1, 0.14 #58, 0.11 #344), 06b_j (0.14 #79, 0.13 #308, 0.11 #481), 03hkp (0.14 #71, 0.02 #1964, 0.02 #242), 01bkv (0.14 #114), 02hxc3j (0.14 #63) >> Best rule #78 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 072x7s; 011ysn; 06rzwx; >> query: (?x6244, 064_8sq) <- language(?x6244, ?x11590), genre(?x6244, ?x239), film(?x5565, ?x6244), ?x11590 = 0349s >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 01kf3_9; 01kf4tt; *> query: (?x6244, 02bjrlw) <- nominated_for(?x2160, ?x6244), music(?x6244, ?x10906), nominated_for(?x688, ?x6244), ?x2160 = 014kq6 *> conf = 0.20 ranks of expected_values: 6 EVAL 05b_gq language 02bjrlw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 106.000 106.000 0.286 http://example.org/film/film/language #2575-04hwbq PRED entity: 04hwbq PRED relation: film_release_region PRED expected values: 0154j 015fr 059j2 035qy => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 131): 059j2 (0.87 #917, 0.85 #1429, 0.84 #1045), 035qy (0.85 #920, 0.80 #1688, 0.78 #1432), 015fr (0.83 #907, 0.77 #1675, 0.75 #1035), 0154j (0.78 #900, 0.76 #1156, 0.76 #1412), 01mjq (0.65 #926, 0.55 #1182, 0.54 #1694), 01p1v (0.49 #1702, 0.46 #934, 0.39 #1446), 06t8v (0.46 #1208, 0.45 #952, 0.43 #1464), 07f1x (0.45 #989, 0.40 #1117, 0.38 #1757), 01pj7 (0.41 #931, 0.36 #1187, 0.34 #1699), 06npd (0.41 #910, 0.36 #1166, 0.33 #1038) >> Best rule #917 for best value: >> intensional similarity = 5 >> extensional distance = 90 >> proper extension: 0gtsx8c; >> query: (?x1259, 059j2) <- film_release_region(?x1259, ?x774), film_release_region(?x1259, ?x550), film(?x2718, ?x1259), ?x550 = 05v8c, ?x774 = 06mzp >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 04hwbq film_release_region 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04hwbq film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04hwbq film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04hwbq film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2574-06wzvr PRED entity: 06wzvr PRED relation: featured_film_locations PRED expected values: 02_286 => 103 concepts (55 used for prediction) PRED predicted values (max 10 best out of 60): 02_286 (0.33 #20, 0.20 #2423, 0.18 #1462), 030qb3t (0.14 #2924, 0.14 #1000, 0.12 #519), 04jpl (0.12 #1691, 0.08 #489, 0.07 #2412), 0rh6k (0.08 #1443, 0.05 #1923, 0.04 #2164), 0h7h6 (0.05 #1244, 0.05 #283, 0.04 #763), 01_d4 (0.05 #1248, 0.04 #767, 0.03 #2690), 03gh4 (0.05 #1557, 0.04 #595, 0.03 #2278), 03pzf (0.05 #416, 0.04 #896, 0.03 #1377), 07b_l (0.05 #317, 0.04 #797, 0.02 #7779), 0b90_r (0.05 #244, 0.04 #724, 0.01 #8433) >> Best rule #20 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0h0wd9; >> query: (?x270, 02_286) <- film_crew_role(?x270, ?x12763), genre(?x270, ?x258), ?x258 = 05p553, ?x12763 = 018rn4, nominated_for(?x154, ?x270) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06wzvr featured_film_locations 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 55.000 0.333 http://example.org/film/film/featured_film_locations #2573-0m313 PRED entity: 0m313 PRED relation: genre PRED expected values: 06cvj => 86 concepts (79 used for prediction) PRED predicted values (max 10 best out of 93): 07ssc (0.52 #5165, 0.50 #8215, 0.50 #8214), 01jfsb (0.43 #11, 0.36 #716, 0.33 #1536), 02kdv5l (0.42 #706, 0.35 #1526, 0.34 #7393), 03k9fj (0.29 #598, 0.27 #7402, 0.26 #715), 082gq (0.25 #263, 0.18 #1789, 0.18 #1319), 01hmnh (0.25 #133, 0.19 #7408, 0.18 #721), 06cvj (0.24 #1999, 0.14 #2, 0.13 #472), 0219x_ (0.24 #495, 0.15 #376, 0.14 #25), 03bxz7 (0.24 #286, 0.16 #403, 0.13 #1108), 0lsxr (0.22 #946, 0.20 #829, 0.20 #1297) >> Best rule #5165 for best value: >> intensional similarity = 3 >> extensional distance = 990 >> proper extension: 0413cff; >> query: (?x144, ?x162) <- currency(?x144, ?x170), ?x170 = 09nqf, titles(?x162, ?x144) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #1999 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 457 *> proper extension: 04svwx; *> query: (?x144, 06cvj) <- genre(?x144, ?x1403), ?x1403 = 02l7c8 *> conf = 0.24 ranks of expected_values: 7 EVAL 0m313 genre 06cvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 86.000 79.000 0.524 http://example.org/film/film/genre #2572-05gnf9 PRED entity: 05gnf9 PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.86 #4710, 0.79 #201, 0.78 #602), 0cv0r (0.33 #10231), 02jx1 (0.10 #333, 0.09 #8555, 0.09 #7050), 07ssc (0.08 #8135, 0.08 #7935, 0.08 #3923), 03rk0 (0.06 #10075, 0.05 #10477, 0.05 #10577), 0d060g (0.05 #1210, 0.05 #1810, 0.05 #1610), 06q1r (0.04 #277, 0.02 #3985, 0.02 #5287), 0345h (0.03 #331, 0.02 #1234, 0.02 #732), 03rjj (0.02 #405, 0.02 #706, 0.02 #10536), 0chghy (0.02 #2013, 0.02 #1113, 0.02 #1313) >> Best rule #4710 for best value: >> intensional similarity = 2 >> extensional distance = 999 >> proper extension: 01vsy9_; 033071; >> query: (?x7276, 09c7w0) <- student(?x6271, ?x7276), school(?x1578, ?x6271) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05gnf9 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.860 http://example.org/people/person/nationality #2571-059dn PRED entity: 059dn PRED relation: member_states PRED expected values: 0k6nt 03gj2 035qy 06t8v => 116 concepts (67 used for prediction) PRED predicted values (max 10 best out of 202): 0345h (0.50 #360, 0.50 #247, 0.43 #337), 03gj2 (0.50 #354, 0.50 #241, 0.43 #337), 0k6nt (0.50 #353, 0.50 #240, 0.43 #337), 0h7x (0.50 #362, 0.50 #249, 0.18 #2291), 03rt9 (0.50 #346, 0.50 #233, 0.18 #2275), 0d0vqn (0.50 #342, 0.50 #229, 0.18 #2271), 06qd3 (0.50 #250, 0.43 #337, 0.25 #363), 0ctw_b (0.50 #242, 0.43 #337, 0.25 #355), 0chghy (0.50 #231, 0.43 #337, 0.25 #344), 06mzp (0.50 #237, 0.25 #350, 0.17 #1027) >> Best rule #360 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 02jxk; 085h1; >> query: (?x8868, 0345h) <- member_states(?x8868, ?x1497), member_states(?x8868, ?x1229), member_states(?x8868, ?x512), member_states(?x8868, ?x87), ?x1497 = 015qh, ?x1229 = 059j2, citytown(?x8868, ?x4826), ?x512 = 07ssc, ?x87 = 05r4w >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #354 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 02jxk; 085h1; *> query: (?x8868, 03gj2) <- member_states(?x8868, ?x1497), member_states(?x8868, ?x1229), member_states(?x8868, ?x512), member_states(?x8868, ?x87), ?x1497 = 015qh, ?x1229 = 059j2, citytown(?x8868, ?x4826), ?x512 = 07ssc, ?x87 = 05r4w *> conf = 0.50 ranks of expected_values: 2, 3, 13, 134 EVAL 059dn member_states 06t8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 116.000 67.000 0.500 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 059dn member_states 035qy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 116.000 67.000 0.500 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 059dn member_states 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 67.000 0.500 http://example.org/user/ktrueman/default_domain/international_organization/member_states EVAL 059dn member_states 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 67.000 0.500 http://example.org/user/ktrueman/default_domain/international_organization/member_states #2570-086k8 PRED entity: 086k8 PRED relation: production_companies! PRED expected values: 01gwk3 => 105 concepts (101 used for prediction) PRED predicted values (max 10 best out of 1105): 02ywwy (0.74 #5138, 0.68 #5137, 0.60 #1028), 05650n (0.74 #5138, 0.68 #5137, 0.60 #1028), 035gnh (0.74 #5138, 0.68 #5137, 0.60 #1028), 060v34 (0.74 #5138, 0.68 #5137, 0.60 #1028), 09p4w8 (0.74 #5138, 0.68 #5137, 0.60 #1028), 0gzlb9 (0.74 #5138, 0.68 #5137, 0.60 #1028), 05nyqk (0.74 #5138, 0.68 #5137, 0.60 #1028), 0kb1g (0.74 #5138, 0.68 #5137, 0.60 #1028), 08mg_b (0.74 #5138, 0.68 #5137, 0.60 #1028), 0kvbl6 (0.74 #5138, 0.68 #5137, 0.39 #21572) >> Best rule #5138 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0mzkr; >> query: (?x382, ?x7801) <- artist(?x382, ?x547), film(?x382, ?x7801), produced_by(?x7801, ?x6086) >> conf = 0.74 => this is the best rule for 89 predicted values No rule for expected values ranks of expected_values: EVAL 086k8 production_companies! 01gwk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 101.000 0.739 http://example.org/film/film/production_companies #2569-0rp46 PRED entity: 0rp46 PRED relation: place! PRED expected values: 0rp46 => 85 concepts (36 used for prediction) PRED predicted values (max 10 best out of 78): 0s5cg (0.13 #16000), 05qtj (0.10 #13931, 0.09 #11868, 0.04 #516), 0rp46 (0.10 #13931, 0.09 #11868, 0.04 #516), 01_d4 (0.10 #13931, 0.09 #11868, 0.04 #516), 0h7h6 (0.10 #13931, 0.09 #11868, 0.04 #516), 0jrq9 (0.06 #4126, 0.05 #9287, 0.04 #8770), 0f2v0 (0.04 #74, 0.02 #590), 0_xdd (0.04 #114), 030qb3t (0.04 #30), 02_286 (0.04 #14) >> Best rule #16000 for best value: >> intensional similarity = 4 >> extensional distance = 564 >> proper extension: 01vskn; >> query: (?x3259, ?x5037) <- location(?x5336, ?x3259), profession(?x5336, ?x987), place_of_birth(?x5336, ?x5037), nationality(?x5336, ?x94) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #13931 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 439 *> proper extension: 0s3y5; 0plyy; 01tlmw; 018jk2; 0ydpd; 0hjy; 05fhy; 0fvvz; 013jz2; 0s69k; ... *> query: (?x3259, ?x1658) <- location(?x5336, ?x3259), contains(?x94, ?x3259), category(?x3259, ?x134), location(?x5336, ?x1658) *> conf = 0.10 ranks of expected_values: 3 EVAL 0rp46 place! 0rp46 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 85.000 36.000 0.132 http://example.org/location/hud_county_place/place #2568-09_9n PRED entity: 09_9n PRED relation: country PRED expected values: 0h7x 06qd3 => 38 concepts (36 used for prediction) PRED predicted values (max 10 best out of 636): 0h7x (0.89 #3645, 0.88 #3242, 0.88 #3037), 0d05w3 (0.87 #5079, 0.78 #6283, 0.78 #3670), 06mkj (0.84 #6220, 0.83 #5213, 0.78 #4608), 0d0vqn (0.82 #3216, 0.79 #2597, 0.79 #2403), 07ssc (0.78 #2390, 0.78 #3809, 0.76 #6438), 0chghy (0.78 #3809, 0.78 #6235, 0.77 #6036), 06c1y (0.76 #3247, 0.75 #2026, 0.72 #3650), 03rj0 (0.75 #1182, 0.70 #3409, 0.69 #3814), 0k6nt (0.75 #3406, 0.70 #3409, 0.69 #3814), 0163v (0.73 #2652, 0.71 #5073, 0.70 #4670) >> Best rule #3645 for best value: >> intensional similarity = 50 >> extensional distance = 16 >> proper extension: 035d1m; >> query: (?x8190, 0h7x) <- olympics(?x8190, ?x3110), sports(?x12388, ?x8190), sports(?x4424, ?x8190), sports(?x452, ?x8190), country(?x8190, ?x756), country(?x8190, ?x205), sports(?x4424, ?x453), olympics(?x172, ?x4424), participating_countries(?x4424, ?x1353), participating_countries(?x4424, ?x512), participating_countries(?x4424, ?x142), ?x756 = 06npd, olympics(?x6564, ?x4424), medal(?x12388, ?x422), olympics(?x1023, ?x452), sports(?x3110, ?x520), olympics(?x87, ?x3110), ?x205 = 03rjj, ?x172 = 0154j, film_release_region(?x9565, ?x512), film_release_region(?x7170, ?x512), film_release_region(?x7009, ?x512), film_release_region(?x6446, ?x512), film_release_region(?x6168, ?x512), film_release_region(?x3471, ?x512), film_release_region(?x2512, ?x512), film_release_region(?x1498, ?x512), country(?x136, ?x512), nationality(?x111, ?x512), country_of_origin(?x2447, ?x512), nationality(?x1940, ?x142), olympics(?x512, ?x391), contains(?x512, ?x362), entity_involved(?x4908, ?x142), country(?x3302, ?x512), ?x7009 = 0bs8s1p, ?x6168 = 0gj96ln, country(?x150, ?x512), ?x6446 = 089j8p, film_release_region(?x499, ?x512), ?x3471 = 07cyl, ?x2512 = 07x4qr, ?x1498 = 04jkpgv, ?x1353 = 035qy, ?x7170 = 02pxst, religion(?x512, ?x492), organization(?x512, ?x127), film_release_region(?x251, ?x142), ?x9565 = 0hz6mv2, combatants(?x151, ?x512) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14 EVAL 09_9n country 06qd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 38.000 36.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09_9n country 0h7x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 36.000 0.889 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #2567-01vw87c PRED entity: 01vw87c PRED relation: artists! PRED expected values: 06by7 => 121 concepts (85 used for prediction) PRED predicted values (max 10 best out of 263): 06by7 (0.88 #21002, 0.61 #10820, 0.56 #1873), 01lyv (0.67 #1884, 0.20 #1576, 0.19 #11756), 064t9 (0.60 #1557, 0.54 #3100, 0.50 #3717), 0glt670 (0.39 #3126, 0.36 #3743, 0.32 #7756), 016clz (0.37 #8646, 0.32 #2164, 0.32 #6796), 025sc50 (0.32 #7765, 0.31 #11156, 0.29 #3135), 02lnbg (0.32 #1601, 0.23 #1292, 0.22 #3144), 06j6l (0.32 #3133, 0.30 #3750, 0.28 #11154), 02yv6b (0.29 #10896, 0.22 #1949, 0.22 #1024), 0ggx5q (0.28 #1620, 0.27 #1311, 0.21 #7793) >> Best rule #21002 for best value: >> intensional similarity = 3 >> extensional distance = 442 >> proper extension: 04r1t; 0167_s; 02r1tx7; 05563d; 03xhj6; 0394y; 01j59b0; 06nv27; 02mq_y; 02vgh; ... >> query: (?x300, 06by7) <- artists(?x11040, ?x300), artists(?x11040, ?x9830), ?x9830 = 01m7pwq >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vw87c artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 85.000 0.876 http://example.org/music/genre/artists #2566-085pr PRED entity: 085pr PRED relation: type_of_union PRED expected values: 04ztj => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.80 #13, 0.78 #37, 0.78 #33), 01g63y (0.15 #186, 0.14 #6, 0.12 #234), 01bl8s (0.02 #39, 0.01 #55) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 01nrq5; >> query: (?x3527, 04ztj) <- nominated_for(?x3527, ?x7846), influenced_by(?x10944, ?x3527), location(?x3527, ?x11639) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 085pr type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.800 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2565-018wng PRED entity: 018wng PRED relation: award_winner PRED expected values: 09p0q => 62 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1895): 0207wx (0.39 #37133, 0.33 #59412, 0.33 #51985), 0g1rw (0.39 #39610, 0.36 #4954, 0.33 #4956), 016tt2 (0.39 #39610, 0.36 #4954, 0.33 #4953), 05qd_ (0.39 #39610, 0.36 #4954, 0.33 #2643), 04wp63 (0.39 #39610, 0.36 #4954, 0.33 #59412), 0l15n (0.39 #39610, 0.36 #4954, 0.33 #59412), 0p51w (0.39 #39610, 0.36 #4954, 0.33 #51985), 047q2wc (0.33 #3346, 0.30 #8300, 0.16 #69320), 017s11 (0.33 #2570, 0.30 #7524, 0.11 #12384), 0127m7 (0.33 #2987, 0.20 #7941, 0.16 #69320) >> Best rule #37133 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 0grw_; >> query: (?x720, ?x1172) <- award(?x1172, ?x720), category_of(?x720, ?x3459), location(?x1172, ?x6253), ?x6253 = 0hptm >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018wng award_winner 09p0q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 31.000 0.395 http://example.org/award/award_category/winners./award/award_honor/award_winner #2564-03mqtr PRED entity: 03mqtr PRED relation: titles PRED expected values: 03hkch7 04nm0n0 08sfxj 02prwdh 02v_r7d 04tng0 => 44 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1776): 01qbg5 (0.50 #2523, 0.38 #9980, 0.33 #12966), 02wyzmv (0.50 #2448, 0.33 #958, 0.29 #6920), 0f4k49 (0.50 #2152, 0.33 #662, 0.25 #9609), 045r_9 (0.50 #2773, 0.33 #1283, 0.25 #13216), 0b6m5fy (0.50 #2390, 0.33 #900, 0.25 #3880), 064r97z (0.50 #2275, 0.33 #785, 0.25 #3765), 02q_x_l (0.50 #2734, 0.33 #1244, 0.25 #4224), 07z6xs (0.43 #6677, 0.42 #12648, 0.33 #715), 03h_yy (0.43 #6024, 0.33 #11995, 0.33 #62), 0f4m2z (0.43 #6318, 0.33 #12289, 0.33 #356) >> Best rule #2523 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 015w9s; >> query: (?x3506, 01qbg5) <- titles(?x3506, ?x7768), titles(?x3506, ?x5555), titles(?x3506, ?x3222), nominated_for(?x2853, ?x3222), ?x7768 = 043mk4y, genre(?x288, ?x3506), genre(?x3222, ?x714), nominated_for(?x1549, ?x5555) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9366 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 082gq; *> query: (?x3506, 03hkch7) <- genre(?x2943, ?x3506), genre(?x1685, ?x3506), ?x2943 = 0c9k8, featured_film_locations(?x1685, ?x362), nominated_for(?x669, ?x1685) *> conf = 0.38 ranks of expected_values: 22, 28, 75, 106, 160, 574 EVAL 03mqtr titles 04tng0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 24.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 03mqtr titles 02v_r7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 24.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 03mqtr titles 02prwdh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 44.000 24.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 03mqtr titles 08sfxj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 44.000 24.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 03mqtr titles 04nm0n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 44.000 24.000 0.500 http://example.org/media_common/netflix_genre/titles EVAL 03mqtr titles 03hkch7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 44.000 24.000 0.500 http://example.org/media_common/netflix_genre/titles #2563-065_cjc PRED entity: 065_cjc PRED relation: country PRED expected values: 0f8l9c => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 95): 09c7w0 (0.92 #598, 0.87 #1493, 0.87 #2151), 0d060g (0.37 #4362, 0.17 #68, 0.08 #483), 09q17 (0.31 #595, 0.30 #716, 0.29 #415), 01z4y (0.31 #595, 0.30 #716, 0.29 #415), 0345h (0.24 #1638, 0.22 #145, 0.20 #204), 0f8l9c (0.21 #1630, 0.20 #675, 0.19 #554), 0chghy (0.11 #131, 0.06 #848, 0.05 #487), 01mjq (0.11 #153, 0.03 #271, 0.03 #509), 03_3d (0.10 #1619, 0.10 #784, 0.04 #482), 03rjj (0.07 #1618, 0.06 #243, 0.06 #663) >> Best rule #598 for best value: >> intensional similarity = 5 >> extensional distance = 145 >> proper extension: 02n9bh; >> query: (?x6752, 09c7w0) <- film(?x3462, ?x6752), titles(?x2480, ?x6752), ?x2480 = 01z4y, film_crew_role(?x6752, ?x137), country(?x6752, ?x2152) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1630 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 657 *> proper extension: 05hd32; 02vl9ln; *> query: (?x6752, 0f8l9c) <- country(?x6752, ?x512), film_release_region(?x66, ?x512), jurisdiction_of_office(?x5402, ?x512), titles(?x512, ?x144) *> conf = 0.21 ranks of expected_values: 6 EVAL 065_cjc country 0f8l9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 77.000 0.918 http://example.org/film/film/country #2562-0n5yh PRED entity: 0n5yh PRED relation: source PRED expected values: 0jbk9 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #4, 0.93 #5, 0.93 #38) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 0mpfn; 0f6_j; 0nm3n; 0nm42; 0n5_g; 0f4y3; 0nm8n; 0n5_t; 0k3j0; >> query: (?x5088, 0jbk9) <- adjoins(?x9065, ?x5088), second_level_divisions(?x94, ?x5088), county(?x8171, ?x9065), currency(?x8171, ?x170) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n5yh source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.941 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #2561-048rn PRED entity: 048rn PRED relation: music PRED expected values: 01pr6q7 => 74 concepts (70 used for prediction) PRED predicted values (max 10 best out of 109): 02sj1x (0.33 #477, 0.11 #1739, 0.10 #1949), 02wb6d (0.33 #758, 0.10 #2020, 0.08 #3071), 01tc9r (0.20 #275, 0.06 #5322, 0.05 #3850), 02jxmr (0.20 #284, 0.05 #3859, 0.05 #3228), 01pr6q7 (0.18 #1324, 0.14 #903, 0.11 #1534), 07z4fy (0.17 #629, 0.06 #1891, 0.06 #1680), 01vttb9 (0.17 #771, 0.06 #1823, 0.05 #2033), 02bn75 (0.17 #775, 0.06 #1616, 0.05 #2037), 015wc0 (0.14 #1017, 0.12 #1438, 0.11 #1859), 0150t6 (0.12 #1098, 0.03 #3200, 0.03 #12887) >> Best rule #477 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0jymd; 0bkq7; 0bj25; 0ktx_; >> query: (?x5198, 02sj1x) <- featured_film_locations(?x5198, ?x739), film(?x12439, ?x5198), film_sets_designed(?x200, ?x5198), ?x200 = 0cb77r >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1324 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 04v8x9; 0_92w; 02q52q; 0cqnss; 0cy__l; 0cbn7c; 0kb1g; *> query: (?x5198, 01pr6q7) <- film(?x382, ?x5198), ?x382 = 086k8, list(?x5198, ?x3004), titles(?x811, ?x5198) *> conf = 0.18 ranks of expected_values: 5 EVAL 048rn music 01pr6q7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 74.000 70.000 0.333 http://example.org/film/film/music #2560-02lfp4 PRED entity: 02lfp4 PRED relation: profession PRED expected values: 025352 => 103 concepts (75 used for prediction) PRED predicted values (max 10 best out of 63): 0nbcg (0.75 #4882, 0.53 #1942, 0.47 #3118), 01d_h8 (0.75 #888, 0.70 #1329, 0.66 #741), 0kyk (0.70 #4586, 0.34 #1793, 0.12 #4880), 02hrh1q (0.70 #6042, 0.69 #5159, 0.69 #8689), 09jwl (0.63 #166, 0.59 #3106, 0.56 #2665), 02jknp (0.60 #890, 0.58 #1331, 0.52 #743), 0cbd2 (0.47 #1771, 0.44 #4564, 0.28 #889), 016z4k (0.44 #2650, 0.44 #3091, 0.44 #1474), 0dz3r (0.42 #2942, 0.42 #2795, 0.38 #1913), 01c8w0 (0.38 #597, 0.35 #450, 0.30 #7940) >> Best rule #4882 for best value: >> intensional similarity = 3 >> extensional distance = 564 >> proper extension: 0f0y8; 053y0s; 032t2z; 01q7cb_; 01p45_v; 0zjpz; 09prnq; 025tdwc; 02jg92; 0gkg6; ... >> query: (?x4951, 0nbcg) <- profession(?x4951, ?x6421), profession(?x4505, ?x6421), ?x4505 = 012wg >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #7940 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1313 *> proper extension: 01r42_g; 04wqr; 0f830f; 02pp_q_; 08wq0g; 06n7h7; 03ldxq; 03m8lq; 08w7vj; 0bz5v2; ... *> query: (?x4951, ?x131) <- award_nominee(?x4951, ?x669), profession(?x4951, ?x987), award_winner(?x4951, ?x5949), profession(?x5949, ?x131) *> conf = 0.30 ranks of expected_values: 15 EVAL 02lfp4 profession 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 103.000 75.000 0.751 http://example.org/people/person/profession #2559-015f47 PRED entity: 015f47 PRED relation: category PRED expected values: 08mbj5d => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #55, 0.10 #54, 0.09 #41) >> Best rule #55 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x2438, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015f47 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #2558-02661h PRED entity: 02661h PRED relation: profession PRED expected values: 02hrh1q 03gjzk => 122 concepts (118 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.93 #1495, 0.90 #2679, 0.89 #13042), 03gjzk (0.83 #1200, 0.83 #1052, 0.82 #904), 0dxtg (0.66 #1198, 0.66 #1050, 0.64 #1346), 02jknp (0.55 #7260, 0.46 #3412, 0.45 #5632), 018gz8 (0.53 #313, 0.35 #461, 0.33 #757), 02krf9 (0.37 #766, 0.32 #915, 0.28 #1359), 09jwl (0.22 #1796, 0.18 #8456, 0.17 #6828), 0cbd2 (0.20 #1191, 0.19 #746, 0.19 #1339), 01c72t (0.17 #11274, 0.10 #1800, 0.10 #4908), 0kyk (0.15 #1954, 0.13 #4470, 0.13 #3582) >> Best rule #1495 for best value: >> intensional similarity = 3 >> extensional distance = 329 >> proper extension: 07sgfsl; >> query: (?x8022, 02hrh1q) <- profession(?x8022, ?x319), award_winner(?x1193, ?x8022), actor(?x11806, ?x8022) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02661h profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 118.000 0.934 http://example.org/people/person/profession EVAL 02661h profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 118.000 0.934 http://example.org/people/person/profession #2557-03j_hq PRED entity: 03j_hq PRED relation: origin PRED expected values: 0dzt9 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 103): 0tz14 (0.33 #189, 0.25 #425, 0.17 #897), 04jpl (0.20 #478, 0.17 #3310, 0.17 #3546), 0cr3d (0.20 #528, 0.05 #2888, 0.02 #8081), 09c7w0 (0.20 #473, 0.04 #3305, 0.04 #3541), 02jx1 (0.20 #504, 0.04 #3808, 0.03 #4516), 07ssc (0.20 #483, 0.03 #3315, 0.03 #3551), 02_286 (0.17 #1904, 0.16 #1432, 0.12 #1196), 030qb3t (0.16 #1450, 0.14 #1922, 0.12 #1214), 01_d4 (0.08 #984, 0.04 #4052, 0.03 #5704), 0d9jr (0.06 #1278, 0.06 #2222, 0.05 #1514) >> Best rule #189 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 01wg982; >> query: (?x11627, 0tz14) <- artist(?x14692, ?x11627), artists(?x14090, ?x11627), artists(?x2407, ?x11627), artist(?x14692, ?x10265), ?x2407 = 020ngt, ?x10265 = 01dpts, ?x14090 = 02lw8j >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03j_hq origin 0dzt9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 80.000 0.333 http://example.org/music/artist/origin #2556-06btq PRED entity: 06btq PRED relation: category PRED expected values: 08mbj5d => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.75 #3, 0.72 #137, 0.71 #6) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 03v1s; 07h34; 04ly1; 04tgp; 0vbk; >> query: (?x2713, 08mbj5d) <- district_represented(?x10291, ?x2713), adjoins(?x2713, ?x1755), ?x10291 = 01gtdd >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06btq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 187.000 0.750 http://example.org/common/topic/webpage./common/webpage/category #2555-05kj_ PRED entity: 05kj_ PRED relation: school! PRED expected values: 01ct6 => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 94): 0jmj7 (0.60 #5778, 0.58 #6436, 0.57 #7096), 01ypc (0.29 #377, 0.18 #189, 0.08 #5750), 05m_8 (0.29 #379, 0.17 #7070, 0.16 #5752), 01yjl (0.29 #407, 0.12 #5780, 0.11 #6438), 07147 (0.29 #444, 0.10 #7135, 0.10 #6475), 0512p (0.29 #391, 0.09 #203, 0.09 #7082), 01y3v (0.29 #404, 0.09 #216, 0.08 #5777), 01d6g (0.27 #261, 0.24 #449, 0.09 #5822), 04wmvz (0.27 #268, 0.24 #456, 0.09 #7147), 02d02 (0.27 #258, 0.18 #446, 0.10 #6477) >> Best rule #5778 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 05krk; 01j_9c; 02w2bc; 07w0v; 01b1mj; 01wdl3; 01j_06; 01t8sr; 01j_cy; 049dk; ... >> query: (?x726, 0jmj7) <- school(?x6462, ?x726), contains(?x94, ?x726), ?x94 = 09c7w0 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7162 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 112 *> proper extension: 06pwq; 02gr81; 017j69; 09f2j; 027mdh; 0ks67; 02zkz7; 08qnnv; 0trv; *> query: (?x726, ?x387) <- school(?x6462, ?x726), draft(?x387, ?x6462) *> conf = 0.11 ranks of expected_values: 55 EVAL 05kj_ school! 01ct6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 181.000 181.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #2554-053y4h PRED entity: 053y4h PRED relation: award PRED expected values: 09sb52 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 217): 0ck27z (0.42 #900, 0.18 #4125, 0.14 #3319), 09sb52 (0.33 #445, 0.32 #3267, 0.31 #4073), 09td7p (0.33 #526, 0.25 #122, 0.13 #20560), 099t8j (0.33 #546, 0.25 #142, 0.13 #20560), 02x4x18 (0.25 #134, 0.17 #538, 0.14 #11288), 0bdwft (0.25 #69, 0.17 #473, 0.14 #11288), 02z0dfh (0.25 #76, 0.17 #480, 0.14 #11288), 0gqyl (0.25 #106, 0.17 #510, 0.13 #20560), 0bfvd4 (0.25 #116, 0.13 #20560, 0.13 #20559), 0gqy2 (0.25 #165, 0.13 #20560, 0.13 #20559) >> Best rule #900 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 02bfmn; 07lmxq; 06jzh; 0785v8; 05ml_s; 0h1nt; 045c66; 07s8r0; 0f6_dy; 0306ds; ... >> query: (?x5133, 0ck27z) <- award_nominee(?x9152, ?x5133), award_nominee(?x6279, ?x5133), award(?x6279, ?x112), ?x9152 = 02zfdp >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #445 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 04bdxl; 0bksh; 01pllx; 01gkmx; *> query: (?x5133, 09sb52) <- award_nominee(?x6279, ?x5133), ?x6279 = 017r13, film(?x5133, ?x2262) *> conf = 0.33 ranks of expected_values: 2 EVAL 053y4h award 09sb52 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.417 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2553-043t8t PRED entity: 043t8t PRED relation: nominated_for! PRED expected values: 02x1dht => 78 concepts (68 used for prediction) PRED predicted values (max 10 best out of 188): 0p9sw (0.30 #21, 0.17 #5545, 0.15 #3385), 02x17s4 (0.30 #96, 0.14 #817, 0.10 #2019), 0gr42 (0.30 #90, 0.11 #1051, 0.11 #1291), 018wdw (0.30 #181, 0.08 #3785, 0.07 #4265), 07bdd_ (0.29 #774, 0.22 #16094, 0.21 #1922), 07cbcy (0.29 #785, 0.22 #16094, 0.21 #1922), 0gq9h (0.28 #1504, 0.26 #5827, 0.26 #1744), 0gq_v (0.28 #1461, 0.19 #5544, 0.18 #9387), 05b1610 (0.27 #754, 0.20 #33, 0.19 #1714), 05f4m9q (0.25 #733, 0.20 #12, 0.16 #1693) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01kt_j; >> query: (?x4651, 0p9sw) <- nominated_for(?x7980, ?x4651), award_nominee(?x2156, ?x7980), ?x2156 = 01795t >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #16094 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1584 *> proper extension: 01tspc6; 06g60w; 04z_x4v; *> query: (?x4651, ?x3911) <- nominated_for(?x7980, ?x4651), award(?x7980, ?x3911), nominated_for(?x3911, ?x124) *> conf = 0.22 ranks of expected_values: 41 EVAL 043t8t nominated_for! 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 78.000 68.000 0.300 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2552-0gkz3nz PRED entity: 0gkz3nz PRED relation: music PRED expected values: 0h6sv => 83 concepts (56 used for prediction) PRED predicted values (max 10 best out of 159): 0146pg (0.10 #1062, 0.05 #4861, 0.04 #5708), 03h610 (0.09 #287, 0.08 #77, 0.06 #497), 0csdzz (0.06 #819, 0.06 #10369, 0.06 #9310), 0fvf9q (0.06 #10369, 0.06 #9310, 0.06 #9095), 051wwp (0.06 #10369, 0.06 #9310, 0.06 #9095), 01p7yb (0.06 #10369, 0.06 #9310, 0.06 #9095), 0p_pd (0.06 #10369, 0.06 #9310, 0.06 #9095), 01515w (0.06 #10369, 0.06 #9310, 0.06 #9095), 02bh9 (0.05 #2156, 0.04 #2367, 0.04 #683), 0150t6 (0.05 #678, 0.04 #2783, 0.04 #5533) >> Best rule #1062 for best value: >> intensional similarity = 4 >> extensional distance = 138 >> proper extension: 058kh7; >> query: (?x4690, 0146pg) <- produced_by(?x4690, ?x163), produced_by(?x2613, ?x163), languages(?x163, ?x254), nominated_for(?x902, ?x2613) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gkz3nz music 0h6sv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 56.000 0.100 http://example.org/film/film/music #2551-0dgr5xp PRED entity: 0dgr5xp PRED relation: nominated_for PRED expected values: 043n0v_ => 52 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1351): 01mgw (0.67 #2756, 0.60 #1158, 0.29 #4354), 065ym0c (0.60 #1437, 0.50 #3035, 0.29 #4633), 043n0v_ (0.60 #788, 0.50 #2386, 0.24 #3984), 0df92l (0.60 #901, 0.50 #2499, 0.24 #4097), 0233bn (0.60 #1150, 0.50 #2748, 0.14 #4346), 01f85k (0.40 #1010, 0.33 #2608, 0.29 #4206), 0mb8c (0.40 #827, 0.33 #2425, 0.24 #4023), 0432_5 (0.40 #711, 0.33 #2309, 0.24 #3907), 0dkv90 (0.40 #1195, 0.33 #2793, 0.24 #4391), 027m67 (0.40 #1122, 0.33 #2720, 0.24 #4318) >> Best rule #2756 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 05ztrmj; >> query: (?x8117, 01mgw) <- award(?x10271, ?x8117), award(?x147, ?x8117), ?x147 = 012d40, profession(?x10271, ?x319), type_of_union(?x10271, ?x566) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #788 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 09v0wy2; 09v92_x; 09v51c2; *> query: (?x8117, 043n0v_) <- disciplines_or_subjects(?x8117, ?x373), ?x373 = 02vxn, award_winner(?x8117, ?x754), nominated_for(?x8117, ?x9175), ?x9175 = 02qd04y *> conf = 0.60 ranks of expected_values: 3 EVAL 0dgr5xp nominated_for 043n0v_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 52.000 24.000 0.667 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2550-06mx8 PRED entity: 06mx8 PRED relation: titles PRED expected values: 011yxy => 115 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1790): 06kl78 (0.33 #2254, 0.33 #695, 0.25 #5372), 035zr0 (0.33 #2675, 0.33 #1116, 0.25 #5793), 05mrf_p (0.33 #2307, 0.33 #748, 0.25 #5425), 0c_j9x (0.33 #1878, 0.33 #319, 0.25 #4996), 09p0ct (0.33 #1741, 0.33 #182, 0.25 #4859), 02jr6k (0.33 #2143, 0.33 #584, 0.25 #5261), 026njb5 (0.33 #2014, 0.33 #455, 0.25 #5132), 02yy9r (0.33 #3114, 0.33 #1555, 0.25 #6232), 034hwx (0.33 #2883, 0.33 #1324, 0.25 #6001), 01rwpj (0.33 #2305, 0.33 #746, 0.25 #5423) >> Best rule #2254 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 01jfsb; >> query: (?x6820, 06kl78) <- titles(?x6820, ?x12641), titles(?x6820, ?x2098), titles(?x6820, ?x1498), ?x2098 = 064n1pz, ?x1498 = 04jkpgv, language(?x12641, ?x2502), region(?x12641, ?x512) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5759 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 06mp7; *> query: (?x6820, 011yxy) <- titles(?x6820, ?x2098), titles(?x6820, ?x1498), ?x2098 = 064n1pz, film(?x3280, ?x1498), language(?x1498, ?x254) *> conf = 0.25 ranks of expected_values: 300 EVAL 06mx8 titles 011yxy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 115.000 54.000 0.333 http://example.org/media_common/netflix_genre/titles #2549-04g73n PRED entity: 04g73n PRED relation: genre PRED expected values: 0hcr => 78 concepts (45 used for prediction) PRED predicted values (max 10 best out of 79): 0hcr (0.79 #23, 0.34 #142, 0.20 #261), 07s9rl0 (0.65 #1196, 0.64 #478, 0.61 #3826), 04t36 (0.64 #6, 0.19 #125, 0.11 #1201), 01jfsb (0.60 #2639, 0.36 #2281, 0.30 #4196), 02kdv5l (0.59 #2272, 0.48 #959, 0.44 #2630), 09b3v (0.52 #3945, 0.52 #5023, 0.48 #4783), 01hmnh (0.36 #17, 0.34 #973, 0.32 #2286), 02l7c8 (0.29 #15, 0.28 #3840, 0.28 #4918), 04xvh5 (0.29 #34, 0.12 #153, 0.10 #511), 04rlf (0.21 #65, 0.09 #184, 0.03 #303) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 023p7l; 0m63c; >> query: (?x8112, 0hcr) <- nominated_for(?x2156, ?x8112), production_companies(?x8112, ?x10685), film(?x806, ?x8112), ?x10685 = 04rcl7 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04g73n genre 0hcr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 45.000 0.786 http://example.org/film/film/genre #2548-01pq4w PRED entity: 01pq4w PRED relation: contact_category PRED expected values: 02zdwq => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 02zdwq (0.34 #26, 0.32 #62, 0.31 #52), 014dgf (0.24 #23, 0.24 #61, 0.23 #33) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 04f0xq; >> query: (?x3779, 02zdwq) <- list(?x3779, ?x2197), category(?x3779, ?x134), contact_category(?x3779, ?x897) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pq4w contact_category 02zdwq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.340 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #2547-06b0d2 PRED entity: 06b0d2 PRED relation: student! PRED expected values: 065y4w7 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 54): 0bwfn (0.11 #275, 0.08 #1329, 0.06 #2910), 07vjm (0.11 #228, 0.04 #755, 0.02 #1282), 07w0v (0.11 #20, 0.01 #3182), 0cwx_ (0.11 #241), 01ymvk (0.11 #121), 01j_cy (0.11 #39), 04b_46 (0.04 #754, 0.04 #1281, 0.02 #2335), 017z88 (0.04 #609, 0.03 #6933, 0.03 #15900), 01w5m (0.04 #632, 0.02 #14340, 0.02 #37013), 06182p (0.04 #825, 0.02 #1352, 0.01 #7149) >> Best rule #275 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 0mfc0; >> query: (?x1116, 0bwfn) <- place_of_birth(?x1116, ?x3269), ?x3269 = 0vzm >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #14249 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1259 *> proper extension: 05bp8g; 0d_84; 07w21; 01rrwf6; 01ty7ll; 0151ns; 01nqfh_; 021sv1; 041ly3; 012c6x; ... *> query: (?x1116, 065y4w7) <- place_of_birth(?x1116, ?x3269), jurisdiction_of_office(?x1195, ?x3269) *> conf = 0.04 ranks of expected_values: 24 EVAL 06b0d2 student! 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 82.000 82.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #2546-015g28 PRED entity: 015g28 PRED relation: honored_for! PRED expected values: 0bxs_d => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 105): 02q690_ (0.24 #2595, 0.21 #2232, 0.20 #2474), 05c1t6z (0.24 #2552, 0.20 #2189, 0.20 #2310), 0hn821n (0.23 #3389, 0.12 #235, 0.08 #719), 0bxs_d (0.23 #3389, 0.09 #2640, 0.08 #2519), 09gkdln (0.23 #3389, 0.08 #589, 0.08 #831), 0418154 (0.23 #3389, 0.08 #697, 0.08 #818), 073h1t (0.23 #3389, 0.08 #626, 0.06 #10892), 059x66 (0.23 #3389, 0.08 #618, 0.06 #10892), 02wzl1d (0.23 #3389, 0.06 #10892, 0.04 #1459), 0hndn2q (0.23 #3389, 0.06 #10892, 0.04 #2331) >> Best rule #2595 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 01xr2s; 02xhpl; 01h1bf; 0phrl; 0d66j2; 02r5qtm; 02ppg1r; 02rcwq0; 01vnbh; 0431v3; ... >> query: (?x4037, 02q690_) <- nominated_for(?x496, ?x4037), languages(?x4037, ?x90), honored_for(?x5392, ?x4037) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #3389 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 111 *> proper extension: 01h72l; 03cf9ly; *> query: (?x4037, ?x8238) <- program(?x2135, ?x4037), nominated_for(?x10215, ?x4037), nominated_for(?x3575, ?x4037), award_nominee(?x1129, ?x3575), award_winner(?x8238, ?x10215) *> conf = 0.23 ranks of expected_values: 4 EVAL 015g28 honored_for! 0bxs_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 115.000 115.000 0.240 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2545-018gkb PRED entity: 018gkb PRED relation: group PRED expected values: 047cx => 107 concepts (51 used for prediction) PRED predicted values (max 10 best out of 79): 01qqwp9 (0.25 #21, 0.08 #345, 0.07 #669), 02r1tx7 (0.25 #16, 0.08 #340, 0.07 #448), 07c0j (0.25 #4, 0.08 #328, 0.07 #436), 018ndc (0.22 #236, 0.13 #452, 0.08 #344), 0123r4 (0.11 #260, 0.08 #368, 0.07 #476), 06mj4 (0.11 #281, 0.08 #389, 0.07 #497), 02ndj5 (0.11 #299, 0.08 #407, 0.07 #515), 02_5x9 (0.11 #227, 0.08 #335, 0.07 #443), 0ycp3 (0.11 #266, 0.08 #374, 0.07 #482), 02vgh (0.11 #159, 0.02 #1024) >> Best rule #21 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 01gg59; 01vrnsk; >> query: (?x11161, 01qqwp9) <- instrumentalists(?x2944, ?x11161), instrumentalists(?x2888, ?x11161), award(?x11161, ?x2634), profession(?x11161, ?x2659), ?x2888 = 02fsn, ?x2944 = 0l14j_ >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #354 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 01wwvt2; 01lvcs1; 03j24kf; 06h2w; 018phr; 01fh0q; 02qtywd; 023slg; *> query: (?x11161, 047cx) <- instrumentalists(?x2888, ?x11161), award(?x11161, ?x2634), profession(?x11161, ?x2659), ?x2888 = 02fsn, role(?x11161, ?x314) *> conf = 0.08 ranks of expected_values: 11 EVAL 018gkb group 047cx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 107.000 51.000 0.250 http://example.org/music/group_member/membership./music/group_membership/group #2544-06151l PRED entity: 06151l PRED relation: film PRED expected values: 084qpk => 84 concepts (76 used for prediction) PRED predicted values (max 10 best out of 423): 017jd9 (0.44 #4360, 0.03 #98470, 0.02 #33003), 01g03q (0.34 #44757, 0.33 #75196, 0.32 #80567), 017gl1 (0.33 #3723, 0.03 #98470, 0.03 #82358), 017gm7 (0.31 #3790, 0.03 #98470, 0.03 #82358), 0ndwt2w (0.18 #4581, 0.03 #82358, 0.03 #34014), 0286vp (0.12 #3017, 0.10 #1227, 0.03 #98470), 03cd0x (0.10 #937, 0.06 #2727, 0.03 #98470), 02chhq (0.10 #1388, 0.06 #3178, 0.03 #98470), 06gb1w (0.10 #734, 0.06 #2524, 0.03 #82358), 0gy30w (0.10 #1444, 0.06 #3234, 0.03 #82358) >> Best rule #4360 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 0f0kz; 014488; 03_6y; 01z7s_; 023kzp; 073x6y; 01d1st; 05cx7x; >> query: (?x221, 017jd9) <- award_nominee(?x2762, ?x221), award_nominee(?x221, ?x1987), ?x2762 = 015t56 >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #82358 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1570 *> proper extension: 01933d; *> query: (?x221, ?x1045) <- profession(?x221, ?x319), award_nominee(?x221, ?x5925), film(?x5925, ?x1045) *> conf = 0.03 ranks of expected_values: 153 EVAL 06151l film 084qpk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 84.000 76.000 0.436 http://example.org/film/actor/film./film/performance/film #2543-059xnf PRED entity: 059xnf PRED relation: film PRED expected values: 02wgbb => 84 concepts (79 used for prediction) PRED predicted values (max 10 best out of 196): 0524b41 (0.58 #60774, 0.38 #50048, 0.38 #91162), 04f6df0 (0.18 #3179, 0.10 #1392), 07f_t4 (0.10 #1329, 0.09 #3116, 0.04 #42898), 05z43v (0.10 #1351, 0.09 #3138, 0.03 #35743), 07w8fz (0.10 #512, 0.09 #2299, 0.01 #30893), 04cv9m (0.10 #700, 0.09 #2487, 0.01 #11422), 03s9kp (0.10 #1760, 0.09 #3547), 04b_jc (0.10 #1674, 0.09 #3461), 02x0fs9 (0.10 #1650, 0.09 #3437), 03cwwl (0.10 #1609, 0.09 #3396) >> Best rule #60774 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 02nb2s; 0lzb8; 025p38; 03_vx9; 04hpck; 01sxq9; 05sq84; 0j582; 028lc8; 03xmy1; ... >> query: (?x7047, ?x616) <- nominated_for(?x7047, ?x616), film(?x7047, ?x148) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 059xnf film 02wgbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 79.000 0.585 http://example.org/film/actor/film./film/performance/film #2542-086nl7 PRED entity: 086nl7 PRED relation: nationality PRED expected values: 09c7w0 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 66): 09c7w0 (0.80 #801, 0.79 #901, 0.78 #701), 0d060g (0.32 #9716, 0.30 #10418, 0.17 #107), 07ssc (0.32 #9716, 0.30 #10418, 0.13 #1415), 0345h (0.32 #9716, 0.30 #10418, 0.06 #2131), 03h64 (0.32 #9716, 0.30 #10418), 06mkj (0.32 #9716, 0.30 #10418), 0j1z8 (0.30 #10418), 0b90_r (0.14 #403), 02jx1 (0.11 #4636, 0.11 #1433, 0.11 #3735), 03rk0 (0.08 #5249, 0.08 #4949, 0.07 #3247) >> Best rule #801 for best value: >> intensional similarity = 2 >> extensional distance = 249 >> proper extension: 04n7njg; 0bbxd3; 03yf4d; 07lz9l; 02k76g; >> query: (?x4465, 09c7w0) <- program(?x4465, ?x9787), profession(?x4465, ?x353) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 086nl7 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.805 http://example.org/people/person/nationality #2541-01l3j PRED entity: 01l3j PRED relation: gender PRED expected values: 05zppz => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 5): 05zppz (0.91 #15, 0.89 #60, 0.88 #7), 02zsn (0.46 #217, 0.39 #67, 0.32 #81), 0fltx (0.12 #136), 01hbgs (0.12 #136), 0c58k (0.12 #136) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0py5b; >> query: (?x13735, 05zppz) <- type_of_union(?x13735, ?x566), people(?x4322, ?x13735), ?x4322 = 0gk4g, place_of_burial(?x13735, ?x11327) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l3j gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.909 http://example.org/people/person/gender #2540-0gkxgfq PRED entity: 0gkxgfq PRED relation: ceremony! PRED expected values: 02vm9nd => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 262): 0gqy2 (0.50 #5363, 0.50 #5614, 0.45 #5864), 0gq_d (0.50 #5398, 0.49 #5649, 0.45 #4398), 0k611 (0.49 #5315, 0.48 #5566, 0.43 #4065), 0gqwc (0.49 #5302, 0.48 #5553, 0.43 #5803), 0gvx_ (0.48 #5376, 0.47 #5627, 0.43 #4376), 0gqyl (0.48 #5323, 0.47 #5574, 0.42 #5824), 018wng (0.47 #5276, 0.47 #5527, 0.43 #4026), 0f4x7 (0.47 #5266, 0.47 #5517, 0.42 #4266), 0p9sw (0.47 #5262, 0.47 #5513, 0.42 #5763), 0gq9h (0.46 #5303, 0.46 #5554, 0.42 #4053) >> Best rule #5363 for best value: >> intensional similarity = 13 >> extensional distance = 117 >> proper extension: 0bzk8w; 0fz20l; 03gt46z; 0dthsy; 0fk0xk; 0c6vcj; 0c4hx0; 0c4hnm; 0bq_mx; >> query: (?x7721, 0gqy2) <- award_winner(?x7721, ?x3809), award_winner(?x7721, ?x2894), profession(?x2894, ?x987), ceremony(?x2720, ?x7721), nominated_for(?x3809, ?x2829), award(?x2894, ?x537), location(?x2894, ?x191), award(?x9160, ?x2720), award_nominee(?x3809, ?x2476), honored_for(?x7721, ?x802), nominated_for(?x2720, ?x416), type_of_union(?x2894, ?x1873), producer_type(?x9160, ?x632) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #359 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 1 *> proper extension: 0jt3qpk; *> query: (?x7721, 02vm9nd) <- award_winner(?x7721, ?x9038), award_winner(?x7721, ?x5574), award_winner(?x7721, ?x4762), award_winner(?x7721, ?x4374), award_winner(?x7721, ?x3975), award_winner(?x7721, ?x3974), award_winner(?x7721, ?x3074), award_winner(?x7721, ?x2894), ?x2894 = 01gbbz, ceremony(?x3263, ?x7721), honored_for(?x7721, ?x802), ?x3975 = 02v0ff, ?x9038 = 05yjhm, ?x3263 = 0cc8l6d, award_winner(?x6171, ?x4762), ?x6171 = 020ffd, award_nominee(?x5647, ?x3974), ?x3074 = 06jvj7, participant(?x4374, ?x3884), type_of_union(?x5574, ?x566), award_winner(?x5304, ?x3974), location(?x4374, ?x362), produced_by(?x4361, ?x5647) *> conf = 0.33 ranks of expected_values: 104 EVAL 0gkxgfq ceremony! 02vm9nd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 25.000 25.000 0.504 http://example.org/award/award_category/winners./award/award_honor/ceremony #2539-0k3kv PRED entity: 0k3kv PRED relation: adjoins PRED expected values: 0k3g3 => 157 concepts (48 used for prediction) PRED predicted values (max 10 best out of 384): 0k3gw (0.82 #35590, 0.82 #35588, 0.82 #23974), 0k3g3 (0.82 #1544, 0.81 #35587, 0.81 #23971), 0k3ll (0.50 #1210, 0.40 #1985, 0.33 #437), 0k3k1 (0.40 #1958, 0.32 #1546, 0.25 #30162), 0mw5x (0.33 #3096, 0.32 #1546, 0.25 #30162), 0k3kv (0.32 #1546, 0.25 #30162, 0.25 #23973), 0k3hn (0.32 #1546, 0.25 #30162, 0.25 #23973), 059f4 (0.29 #2357, 0.03 #6989, 0.03 #13958), 09c7w0 (0.29 #2325, 0.02 #20884), 0n5yh (0.20 #1784, 0.09 #3333, 0.07 #4106) >> Best rule #35590 for best value: >> intensional similarity = 4 >> extensional distance = 256 >> proper extension: 0y62n; 0m2mk; >> query: (?x5874, ?x10893) <- adjoins(?x10893, ?x5874), adjoins(?x10067, ?x10893), second_level_divisions(?x94, ?x5874), source(?x5874, ?x958) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1544 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0k3kg; *> query: (?x5874, ?x7309) <- adjoins(?x10893, ?x5874), adjoins(?x7309, ?x5874), adjoins(?x4990, ?x5874), ?x10893 = 0k3gw, contains(?x5874, ?x5875), contains(?x2020, ?x5874), adjoins(?x9065, ?x4990) *> conf = 0.82 ranks of expected_values: 2 EVAL 0k3kv adjoins 0k3g3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 157.000 48.000 0.822 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #2538-047rkcm PRED entity: 047rkcm PRED relation: executive_produced_by PRED expected values: 0gg9_5q => 61 concepts (42 used for prediction) PRED predicted values (max 10 best out of 69): 05txrz (0.17 #104, 0.02 #857, 0.01 #355), 0h5f5n (0.17 #10, 0.01 #512), 06q8hf (0.06 #1927, 0.05 #2179, 0.05 #417), 05hj_k (0.06 #1859, 0.05 #2111, 0.05 #349), 02z6l5f (0.04 #871, 0.04 #369, 0.03 #2131), 03c9pqt (0.04 #497, 0.02 #2259, 0.02 #999), 079vf (0.04 #504, 0.02 #755, 0.02 #1763), 06pj8 (0.02 #808, 0.02 #557, 0.02 #5590), 02z2xdf (0.02 #910, 0.01 #659, 0.01 #3175), 027z0pl (0.02 #721, 0.02 #1980, 0.01 #1224) >> Best rule #104 for best value: >> intensional similarity = 2 >> extensional distance = 4 >> proper extension: 05lfwd; >> query: (?x6762, 05txrz) <- nominated_for(?x5593, ?x6762), ?x5593 = 025b5y >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1600 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 221 *> proper extension: 0hmr4; 03lrqw; 04tz52; 08k40m; 0prrm; 02x8fs; 02mc5v; 03wjm2; *> query: (?x6762, 0gg9_5q) <- genre(?x6762, ?x258), titles(?x2480, ?x6762), ?x258 = 05p553, ?x2480 = 01z4y *> conf = 0.02 ranks of expected_values: 16 EVAL 047rkcm executive_produced_by 0gg9_5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 61.000 42.000 0.167 http://example.org/film/film/executive_produced_by #2537-014bpd PRED entity: 014bpd PRED relation: country PRED expected values: 0d060g => 108 concepts (100 used for prediction) PRED predicted values (max 10 best out of 65): 09c7w0 (0.83 #615, 0.80 #1229, 0.80 #859), 0d060g (0.61 #1104, 0.58 #2891, 0.57 #2892), 06npd (0.61 #1104, 0.58 #2891, 0.57 #2892), 0345h (0.29 #89, 0.12 #1438, 0.10 #824), 07ssc (0.23 #323, 0.22 #2536, 0.22 #3095), 03_3d (0.21 #682, 0.18 #499, 0.17 #376), 0f8l9c (0.14 #81, 0.10 #2974, 0.09 #3715), 06mzp (0.14 #80, 0.03 #5236, 0.01 #325), 03rt9 (0.14 #76, 0.03 #5236, 0.01 #1364), 01hmnh (0.07 #2954, 0.07 #4188, 0.06 #6157) >> Best rule #615 for best value: >> intensional similarity = 5 >> extensional distance = 107 >> proper extension: 03qcfvw; 06w99h3; 02vp1f_; 01gc7; 0gzy02; 0dj0m5; 02qm_f; 020fcn; 0dtfn; 04w7rn; ... >> query: (?x7927, 09c7w0) <- currency(?x7927, ?x170), award(?x7927, ?x2599), ?x170 = 09nqf, genre(?x7927, ?x811), ?x811 = 03k9fj >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #1104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 286 *> proper extension: 028k2x; 03d17dg; *> query: (?x7927, ?x279) <- award_winner(?x7927, ?x595), nationality(?x595, ?x279), gender(?x595, ?x231), executive_produced_by(?x6375, ?x595) *> conf = 0.61 ranks of expected_values: 2 EVAL 014bpd country 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 100.000 0.826 http://example.org/film/film/country #2536-01vw20_ PRED entity: 01vw20_ PRED relation: artists! PRED expected values: 07sbbz2 => 151 concepts (114 used for prediction) PRED predicted values (max 10 best out of 256): 064t9 (0.80 #1201, 0.65 #3879, 0.60 #1498), 02lnbg (0.60 #1243, 0.41 #3921, 0.27 #647), 0ggx5q (0.60 #1263, 0.39 #3941, 0.26 #8403), 05bt6j (0.48 #24131, 0.40 #1227, 0.38 #929), 025sc50 (0.47 #1234, 0.39 #3912, 0.30 #2126), 05r6t (0.38 #77, 0.25 #374, 0.21 #24171), 06j6l (0.37 #3910, 0.33 #1232, 0.28 #8372), 02k_kn (0.27 #1250, 0.18 #654, 0.17 #3034), 0gywn (0.27 #3920, 0.25 #9572, 0.24 #14929), 02x8m (0.25 #16, 0.11 #9536, 0.11 #16379) >> Best rule #1201 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0lk90; 07ss8_; 04xrx; 015f7; 043zg; 06mt91; 01vtj38; >> query: (?x2987, 064t9) <- participant(?x8490, ?x2987), award_nominee(?x1573, ?x2987), artists(?x114, ?x2987), participant(?x1503, ?x2987) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #13692 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 160 *> proper extension: 03c7ln; 0274ck; 01w923; 012zng; 09prnq; 01tp5bj; 0gkg6; 01nn6c; 01vv6_6; 01w8n89; ... *> query: (?x2987, 07sbbz2) <- profession(?x2987, ?x220), instrumentalists(?x716, ?x2987), ?x716 = 018vs *> conf = 0.10 ranks of expected_values: 57 EVAL 01vw20_ artists! 07sbbz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 151.000 114.000 0.800 http://example.org/music/genre/artists #2535-09px1w PRED entity: 09px1w PRED relation: award_winner! PRED expected values: 02q690_ => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 99): 02q690_ (0.40 #347, 0.33 #65, 0.20 #206), 05c1t6z (0.33 #15, 0.20 #297, 0.20 #156), 03nnm4t (0.33 #74, 0.20 #215, 0.19 #2680), 0bx6zs (0.33 #127, 0.20 #268, 0.19 #2680), 0418154 (0.33 #108, 0.20 #249, 0.03 #1236), 0gvstc3 (0.20 #316, 0.20 #175, 0.19 #2680), 02rjjll (0.20 #287, 0.18 #6206, 0.11 #1274), 02cg41 (0.20 #408, 0.18 #6206, 0.09 #549), 0gpjbt (0.20 #311, 0.18 #6206, 0.09 #452), 0lp_cd3 (0.19 #2680, 0.03 #1574, 0.02 #2420) >> Best rule #347 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 039cq4; >> query: (?x8139, 02q690_) <- award_winner(?x8139, ?x6678), award_winner(?x8139, ?x1942), ?x1942 = 07ymr5, ?x6678 = 05gnf >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09px1w award_winner! 02q690_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2534-02nczh PRED entity: 02nczh PRED relation: country PRED expected values: 09c7w0 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 116): 09c7w0 (0.81 #618, 0.80 #63, 0.80 #434), 07ssc (0.33 #1740, 0.31 #1493, 0.24 #1247), 07s9rl0 (0.16 #1786, 0.13 #1539, 0.09 #1785), 0345h (0.14 #335, 0.14 #1013, 0.12 #582), 0f8l9c (0.10 #1743, 0.10 #1496, 0.10 #2051), 04jjy (0.09 #1785, 0.08 #1538, 0.07 #2522), 0hn10 (0.09 #1785, 0.08 #1538, 0.07 #2522), 06mkj (0.09 #41, 0.03 #287, 0.03 #163), 0chghy (0.07 #320, 0.06 #690, 0.06 #998), 03_3d (0.06 #376, 0.04 #191, 0.04 #1300) >> Best rule #618 for best value: >> intensional similarity = 4 >> extensional distance = 293 >> proper extension: 0dq626; 09g7vfw; 0gtxj2q; 03rg2b; 0hhggmy; 0gwlfnb; 08c6k9; 01jnc_; >> query: (?x6427, 09c7w0) <- film_crew_role(?x6427, ?x2095), genre(?x6427, ?x53), production_companies(?x6427, ?x617), ?x2095 = 0dxtw >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02nczh country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.810 http://example.org/film/film/country #2533-015vq_ PRED entity: 015vq_ PRED relation: film PRED expected values: 02vqhv0 => 123 concepts (87 used for prediction) PRED predicted values (max 10 best out of 741): 083shs (0.50 #1796, 0.04 #99516, 0.02 #72860), 0260bz (0.33 #334, 0.02 #72860, 0.01 #5665), 03h3x5 (0.20 #3973, 0.01 #43068), 0fgrm (0.20 #4338, 0.01 #6115), 011yxg (0.20 #1819), 01cssf (0.17 #88, 0.10 #1865, 0.02 #72860), 08r4x3 (0.17 #153, 0.04 #99516, 0.02 #72860), 07cyl (0.17 #558, 0.04 #99516, 0.02 #72860), 035bcl (0.17 #1005, 0.04 #99516, 0.02 #72860), 02xs6_ (0.17 #846, 0.04 #99516, 0.02 #72860) >> Best rule #1796 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 015pkc; 032w8h; 01nwwl; 01w7nww; 0k269; 05slvm; 022g44; 01fx5l; >> query: (?x4128, 083shs) <- place_of_birth(?x4128, ?x12337), award_nominee(?x4128, ?x7269), ?x7269 = 0gnbw >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015vq_ film 02vqhv0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 87.000 0.500 http://example.org/film/actor/film./film/performance/film #2532-070m12 PRED entity: 070m12 PRED relation: profession PRED expected values: 0dxtg => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 45): 02hrh1q (0.76 #1065, 0.71 #915, 0.66 #465), 0dxtg (0.75 #14, 0.67 #314, 0.61 #164), 03gjzk (0.61 #316, 0.36 #616, 0.34 #466), 01d_h8 (0.30 #3156, 0.30 #4806, 0.29 #1656), 02krf9 (0.21 #328, 0.14 #628, 0.14 #928), 02jknp (0.21 #4808, 0.20 #6458, 0.19 #5858), 09jwl (0.19 #1370, 0.18 #1520, 0.18 #1220), 0cbd2 (0.17 #307, 0.11 #10507, 0.11 #10657), 0dz3r (0.13 #2252, 0.13 #1202, 0.12 #1352), 0nbcg (0.13 #2283, 0.13 #1233, 0.12 #1383) >> Best rule #1065 for best value: >> intensional similarity = 2 >> extensional distance = 874 >> proper extension: 0jgd; 058j2; 02sch9; 02jyhv; 02bh_v; 042gr4; 015c1b; 01nd9f; 0513yzt; >> query: (?x4862, 02hrh1q) <- gender(?x4862, ?x514), ?x514 = 02zsn >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 02_2v2; *> query: (?x4862, 0dxtg) <- award_winner(?x415, ?x4862), award_nominee(?x4862, ?x4147), ?x4147 = 026n3rs *> conf = 0.75 ranks of expected_values: 2 EVAL 070m12 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.759 http://example.org/people/person/profession #2531-0b_6v_ PRED entity: 0b_6v_ PRED relation: locations PRED expected values: 029cr => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 205): 0fsb8 (0.50 #1866, 0.50 #1344, 0.42 #1170), 029cr (0.43 #1266, 0.40 #226, 0.36 #1788), 0156q (0.41 #2465, 0.35 #3160, 0.35 #2986), 0f2rq (0.40 #446, 0.36 #1486, 0.32 #2353), 0f2r6 (0.36 #1754, 0.36 #1232, 0.33 #19), 071cn (0.36 #1807, 0.36 #1285, 0.33 #1111), 0lphb (0.33 #110, 0.29 #1845, 0.29 #1323), 010h9y (0.33 #1193, 0.29 #1541, 0.21 #6636), 030qb3t (0.33 #34, 0.25 #1073, 0.21 #1421), 04gxf (0.33 #117, 0.25 #1156, 0.21 #1504) >> Best rule #1866 for best value: >> intensional similarity = 9 >> extensional distance = 12 >> proper extension: 0b_6zk; 0b_6x2; 0b_75k; 0b_72t; 0bzrxn; 0b_6s7; 0bzrsh; 0b_6pv; 0b_6mr; 0b_6_l; ... >> query: (?x8527, 0fsb8) <- locations(?x8527, ?x6088), locations(?x8527, ?x3046), team(?x8527, ?x8528), ?x8528 = 091tgz, place_of_birth(?x2794, ?x6088), time_zones(?x6088, ?x1638), jurisdiction_of_office(?x1195, ?x6088), teams(?x6088, ?x2174), state(?x3046, ?x2020) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1266 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 12 *> proper extension: 0br1xn; *> query: (?x8527, 029cr) <- locations(?x8527, ?x6088), team(?x8527, ?x8528), ?x8528 = 091tgz, place_of_birth(?x4662, ?x6088), film(?x4662, ?x408), contains(?x94, ?x6088), religion(?x4662, ?x1985), award_nominee(?x157, ?x4662) *> conf = 0.43 ranks of expected_values: 2 EVAL 0b_6v_ locations 029cr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 51.000 51.000 0.500 http://example.org/time/event/locations #2530-0ch26b_ PRED entity: 0ch26b_ PRED relation: film_crew_role PRED expected values: 02r96rf => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 24): 02r96rf (0.76 #190, 0.72 #221, 0.72 #283), 02vs3x5 (0.33 #19, 0.08 #829, 0.08 #361), 01pvkk (0.31 #444, 0.29 #258, 0.29 #2437), 0d2b38 (0.16 #456, 0.14 #239, 0.13 #363), 0215hd (0.16 #449, 0.13 #356, 0.13 #824), 094hwz (0.14 #106, 0.07 #261, 0.06 #354), 089g0h (0.13 #357, 0.10 #450, 0.10 #1943), 01xy5l_ (0.13 #446, 0.12 #229, 0.12 #353), 015h31 (0.12 #849, 0.11 #39, 0.11 #226), 089fss (0.09 #224, 0.09 #193, 0.08 #286) >> Best rule #190 for best value: >> intensional similarity = 6 >> extensional distance = 53 >> proper extension: 0h1cdwq; 087wc7n; 0cz8mkh; 0gj9qxr; 08052t3; 0crc2cp; 0dll_t2; 0bq6ntw; 043tvp3; 03z9585; >> query: (?x1916, 02r96rf) <- film_release_region(?x1916, ?x3749), film_release_region(?x1916, ?x1499), film_release_region(?x1916, ?x87), ?x1499 = 01znc_, ?x3749 = 03ryn, ?x87 = 05r4w >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ch26b_ film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.764 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2529-01kx1j PRED entity: 01kx1j PRED relation: place_of_death PRED expected values: 0ps1q => 79 concepts (67 used for prediction) PRED predicted values (max 10 best out of 40): 0156q (0.25 #607, 0.20 #1192, 0.20 #996), 0hkpn (0.20 #1292, 0.05 #3246, 0.05 #3442), 04jpl (0.17 #1566, 0.09 #7074, 0.07 #4891), 06mkj (0.17 #1592, 0.05 #2961, 0.04 #3744), 030qb3t (0.16 #7285, 0.15 #7481, 0.15 #7677), 04swd (0.14 #2071, 0.06 #2656, 0.04 #4221), 0rh6k (0.11 #2733, 0.10 #3321, 0.09 #3517), 02h6_6p (0.10 #2965, 0.09 #3748, 0.08 #3943), 02_286 (0.09 #7276, 0.08 #7472, 0.08 #7668), 0cpyv (0.08 #3974, 0.06 #4561, 0.05 #2996) >> Best rule #607 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 07_m9_; 02lmk; >> query: (?x9178, 0156q) <- people(?x6821, ?x9178), nationality(?x9178, ?x3142), entity_involved(?x10764, ?x9178), ?x6821 = 06z5s, combatants(?x612, ?x3142), official_language(?x3142, ?x732), combatants(?x279, ?x3142) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01kx1j place_of_death 0ps1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 67.000 0.250 http://example.org/people/deceased_person/place_of_death #2528-018n1k PRED entity: 018n1k PRED relation: category PRED expected values: 08mbj5d => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.77 #8, 0.71 #55, 0.70 #49) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 05ksh; 01y8zd; 0843m; 016ndm; 01y9st; 018dcy; 06b19; 018dk_; 0gf14; 01glqw; ... >> query: (?x12164, 08mbj5d) <- contains(?x1905, ?x12164), contains(?x279, ?x12164), ?x279 = 0d060g, ?x1905 = 05kr_ >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018n1k category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.774 http://example.org/common/topic/webpage./common/webpage/category #2527-01vwllw PRED entity: 01vwllw PRED relation: participant PRED expected values: 016fjj => 147 concepts (108 used for prediction) PRED predicted values (max 10 best out of 300): 01438g (0.82 #29091, 0.81 #35554, 0.81 #21329), 016fjj (0.82 #29091, 0.81 #35554, 0.81 #21329), 01gbn6 (0.30 #22622, 0.25 #20682, 0.23 #23916), 05k2s_ (0.30 #22622, 0.25 #20682, 0.23 #23916), 02g0mx (0.25 #216, 0.20 #862, 0.05 #4094), 0c6qh (0.25 #167, 0.20 #813, 0.03 #3398), 046zh (0.11 #26504, 0.07 #26503, 0.07 #18742), 04fzk (0.11 #26504, 0.07 #26503, 0.07 #18742), 0170s4 (0.05 #3388, 0.05 #4035, 0.05 #4681), 0h1mt (0.05 #3310, 0.05 #3957, 0.05 #4603) >> Best rule #29091 for best value: >> intensional similarity = 3 >> extensional distance = 288 >> proper extension: 01sl1q; 01dw4q; 03w1v2; 01rr9f; 03lt8g; 0j1yf; 07ymr5; 0gz5hs; 06mfvc; 0pyg6; ... >> query: (?x3210, ?x1208) <- award_winner(?x1209, ?x3210), film(?x3210, ?x670), participant(?x1208, ?x3210) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01vwllw participant 016fjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 108.000 0.816 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #2526-06kbb6 PRED entity: 06kbb6 PRED relation: profession PRED expected values: 01d_h8 => 131 concepts (42 used for prediction) PRED predicted values (max 10 best out of 56): 02hrh1q (0.90 #2234, 0.62 #902, 0.62 #4454), 01d_h8 (0.70 #6, 0.56 #1634, 0.56 #2966), 02jknp (0.57 #8, 0.50 #2968, 0.47 #4448), 02krf9 (0.29 #2542, 0.28 #1654, 0.28 #3578), 018gz8 (0.28 #1644, 0.24 #2532, 0.20 #5048), 0cbd2 (0.28 #7, 0.24 #3115, 0.22 #2079), 02hv44_ (0.17 #57, 0.09 #3165, 0.09 #3017), 0kyk (0.16 #3137, 0.14 #2101, 0.14 #2989), 0np9r (0.16 #1648, 0.16 #2536, 0.14 #4312), 01c72t (0.15 #763, 0.13 #1947, 0.13 #319) >> Best rule #2234 for best value: >> intensional similarity = 4 >> extensional distance = 309 >> proper extension: 0p3sf; 06hgj; 04hqbbz; 022q4j; 0kc6; 04g_wd; 05dxl_; 0cfz_z; 01hkg9; 026c0p; ... >> query: (?x11772, 02hrh1q) <- profession(?x11772, ?x1041), place_of_death(?x11772, ?x6054), profession(?x513, ?x1041), ?x513 = 01rr9f >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 81 *> proper extension: 0qf43; 0693l; 098n_m; *> query: (?x11772, 01d_h8) <- profession(?x11772, ?x987), nominated_for(?x11772, ?x5212), award(?x11772, ?x746), ?x746 = 04dn09n *> conf = 0.70 ranks of expected_values: 2 EVAL 06kbb6 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 42.000 0.904 http://example.org/people/person/profession #2525-06r2h PRED entity: 06r2h PRED relation: genre PRED expected values: 01jfsb 06n90 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 80): 07s9rl0 (0.69 #1445, 0.66 #1082, 0.66 #1805), 06n90 (0.56 #133, 0.29 #1577, 0.24 #1215), 01jfsb (0.51 #1576, 0.44 #132, 0.39 #1214), 05p553 (0.34 #4822, 0.34 #3496, 0.34 #2171), 0gf28 (0.33 #185, 0.04 #786, 0.04 #4883), 02l7c8 (0.27 #2664, 0.27 #2062, 0.27 #4230), 01hmnh (0.26 #739, 0.25 #499, 0.25 #1220), 02m4t (0.22 #188, 0.02 #428, 0.01 #308), 0lsxr (0.21 #1573, 0.20 #1090, 0.18 #1813), 04xvlr (0.21 #1446, 0.20 #1083, 0.19 #1806) >> Best rule #1445 for best value: >> intensional similarity = 3 >> extensional distance = 411 >> proper extension: 012jfb; 06zn1c; >> query: (?x9017, 07s9rl0) <- nominated_for(?x2774, ?x9017), nominated_for(?x1429, ?x9017), films(?x11523, ?x9017) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #133 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 7 *> proper extension: 06qw_; *> query: (?x9017, 06n90) <- nominated_for(?x10747, ?x9017), ?x10747 = 0262s1 *> conf = 0.56 ranks of expected_values: 2, 3 EVAL 06r2h genre 06n90 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.695 http://example.org/film/film/genre EVAL 06r2h genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.695 http://example.org/film/film/genre #2524-02hct1 PRED entity: 02hct1 PRED relation: actor PRED expected values: 01l9p => 69 concepts (52 used for prediction) PRED predicted values (max 10 best out of 917): 04wvhz (0.45 #15745, 0.38 #27792, 0.38 #7409), 06brp0 (0.38 #27792, 0.37 #26865, 0.36 #1852), 0cjdk (0.38 #7409, 0.37 #26865, 0.37 #15744), 03q3sy (0.37 #26865, 0.36 #1852, 0.36 #1851), 0284gcb (0.37 #26865, 0.36 #1852, 0.36 #1851), 01qr1_ (0.11 #13892, 0.09 #279, 0.08 #1204), 04m_zp (0.11 #13892, 0.05 #20379, 0.05 #21307), 0ds2sb (0.11 #13892, 0.05 #20379, 0.05 #21307), 0bxtg (0.11 #13892, 0.05 #20379, 0.05 #21307), 07ym6ss (0.11 #13892, 0.05 #20379, 0.05 #21307) >> Best rule #15745 for best value: >> intensional similarity = 4 >> extensional distance = 100 >> proper extension: 06w7mlh; >> query: (?x2436, ?x10127) <- award(?x2436, ?x2016), award_winner(?x2436, ?x10127), place_of_birth(?x10127, ?x2850), genre(?x2436, ?x258) >> conf = 0.45 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02hct1 actor 01l9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 52.000 0.452 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #2523-03v9w PRED entity: 03v9w PRED relation: partially_contains PRED expected values: 06mkj => 166 concepts (38 used for prediction) PRED predicted values (max 10 best out of 66): 0f8l9c (0.45 #162, 0.42 #281, 0.31 #445), 0lcd (0.39 #735, 0.25 #1146, 0.21 #1106), 026zt (0.29 #703, 0.29 #1154, 0.23 #464), 04yf_ (0.28 #1428, 0.04 #1102, 0.04 #1266), 0lm0n (0.28 #1281, 0.18 #1034, 0.12 #1443), 06mkj (0.27 #166, 0.25 #285, 0.17 #728), 0k3nk (0.22 #89, 0.17 #329, 0.15 #493), 06c6l (0.22 #106, 0.17 #346, 0.15 #510), 05vz3zq (0.20 #128, 0.18 #170, 0.17 #289), 0cdbq (0.20 #126, 0.18 #168, 0.17 #287) >> Best rule #162 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 05g2v; >> query: (?x12094, 0f8l9c) <- contains(?x12094, ?x4698), partially_contains(?x12094, ?x87), film_release_region(?x80, ?x87), olympics(?x87, ?x778) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 05g2v; *> query: (?x12094, 06mkj) <- contains(?x12094, ?x4698), partially_contains(?x12094, ?x87), film_release_region(?x80, ?x87), olympics(?x87, ?x778) *> conf = 0.27 ranks of expected_values: 6 EVAL 03v9w partially_contains 06mkj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 166.000 38.000 0.455 http://example.org/location/location/partially_contains #2522-0ctw_b PRED entity: 0ctw_b PRED relation: participating_countries! PRED expected values: 09n48 => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 37): 09n48 (0.63 #423, 0.62 #213, 0.60 #809), 06sks6 (0.54 #231, 0.50 #196, 0.40 #371), 016r9z (0.47 #508, 0.46 #228, 0.44 #263), 0c_tl (0.42 #195, 0.38 #230, 0.35 #370), 0blfl (0.40 #514, 0.38 #1600, 0.38 #549), 0kbvb (0.25 #3400, 0.24 #4522, 0.24 #596), 0jhn7 (0.25 #3400, 0.24 #4522, 0.24 #596), 0sxrz (0.25 #3400, 0.24 #4522, 0.24 #596), 01f1kd (0.25 #3400, 0.24 #4522, 0.17 #208), 0kbvv (0.25 #3400, 0.24 #4522, 0.10 #1822) >> Best rule #423 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 02j71; >> query: (?x1023, 09n48) <- currency(?x1023, ?x170), service_location(?x1492, ?x1023), administrative_parent(?x6291, ?x1023) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ctw_b participating_countries! 09n48 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.630 http://example.org/olympics/olympic_games/participating_countries #2521-0gjcrrw PRED entity: 0gjcrrw PRED relation: film_crew_role PRED expected values: 02r96rf => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.81 #809, 0.79 #386, 0.79 #1056), 02r96rf (0.77 #249, 0.76 #214, 0.75 #812), 01vx2h (0.50 #221, 0.49 #256, 0.44 #81), 02_n3z (0.44 #72, 0.13 #2407, 0.09 #1057), 0dxtw (0.44 #818, 0.41 #995, 0.41 #1065), 0215hd (0.33 #88, 0.25 #298, 0.25 #18), 089g0h (0.33 #89, 0.25 #19, 0.20 #299), 0263ycg (0.25 #17, 0.13 #2407, 0.11 #87), 02ynfr (0.23 #155, 0.22 #85, 0.22 #225), 01xy5l_ (0.22 #83, 0.16 #293, 0.13 #2407) >> Best rule #809 for best value: >> intensional similarity = 5 >> extensional distance = 434 >> proper extension: 03t97y; 0bscw; 0qm8b; 0g54xkt; 01s3vk; 02nx2k; 0dpl44; 0888c3; 0cqr0q; 07phbc; ... >> query: (?x3830, 09zzb8) <- film_crew_role(?x3830, ?x1284), film_crew_role(?x3830, ?x1171), ?x1171 = 09vw2b7, production_companies(?x3830, ?x3462), ?x1284 = 0ch6mp2 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #249 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 45 *> proper extension: 0gh6j94; *> query: (?x3830, 02r96rf) <- film_release_region(?x3830, ?x2346), film_release_region(?x3830, ?x1061), ?x2346 = 0d05w3, film_crew_role(?x3830, ?x1078), countries_spoken_in(?x254, ?x1061) *> conf = 0.77 ranks of expected_values: 2 EVAL 0gjcrrw film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 75.000 0.807 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2520-02wwmhc PRED entity: 02wwmhc PRED relation: film_crew_role PRED expected values: 02ynfr => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 30): 0ch6mp2 (0.81 #1273, 0.77 #1068, 0.72 #1445), 0215hd (0.38 #17, 0.23 #85, 0.15 #1284), 01pvkk (0.28 #1864, 0.27 #1933, 0.27 #1449), 01xy5l_ (0.25 #12, 0.15 #80, 0.12 #1279), 02vs3x5 (0.25 #22, 0.08 #90, 0.06 #192), 02ynfr (0.18 #1076, 0.18 #1281, 0.18 #490), 0d2b38 (0.16 #774, 0.15 #844, 0.14 #809), 02rh1dz (0.16 #315, 0.15 #485, 0.14 #829), 015h31 (0.15 #484, 0.14 #758, 0.14 #348), 089g0h (0.12 #18, 0.12 #1285, 0.11 #1080) >> Best rule #1273 for best value: >> intensional similarity = 3 >> extensional distance = 729 >> proper extension: 0fq27fp; >> query: (?x10778, 0ch6mp2) <- genre(?x10778, ?x53), film_crew_role(?x10778, ?x1171), ?x1171 = 09vw2b7 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1076 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 471 *> proper extension: 07k2mq; *> query: (?x10778, 02ynfr) <- film_release_distribution_medium(?x10778, ?x81), nominated_for(?x2444, ?x10778), film_crew_role(?x10778, ?x468), ?x468 = 02r96rf *> conf = 0.18 ranks of expected_values: 6 EVAL 02wwmhc film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 85.000 85.000 0.807 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2519-01_d4 PRED entity: 01_d4 PRED relation: place! PRED expected values: 01_d4 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 263): 0s6jm (0.33 #235, 0.13 #72686, 0.02 #19822), 0_vw8 (0.25 #1019, 0.13 #72686), 01_d4 (0.15 #18556, 0.13 #72686, 0.04 #81977), 0d9jr (0.15 #18556, 0.07 #2190, 0.04 #5286), 026mj (0.15 #18556), 030qb3t (0.14 #1060, 0.13 #72686, 0.12 #1575), 02_286 (0.14 #1044, 0.13 #72686, 0.12 #1559), 02cl1 (0.14 #1042, 0.03 #8774, 0.03 #11352), 0rh6k (0.13 #72686, 0.07 #2577, 0.07 #2062), 01sn3 (0.13 #72686, 0.07 #2668, 0.05 #4216) >> Best rule #235 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0s6jm; >> query: (?x1860, 0s6jm) <- country(?x1860, ?x94), location(?x2259, ?x1860), ?x2259 = 01wyzyl >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #18556 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 61 *> proper extension: 01xhb_; 01qq80; *> query: (?x1860, ?x5267) <- citytown(?x3795, ?x1860), place_founded(?x3795, ?x5267) *> conf = 0.15 ranks of expected_values: 3 EVAL 01_d4 place! 01_d4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 165.000 165.000 0.333 http://example.org/location/hud_county_place/place #2518-02tq2r PRED entity: 02tq2r PRED relation: location PRED expected values: 0fn2g => 98 concepts (91 used for prediction) PRED predicted values (max 10 best out of 195): 029kpy (0.72 #12053, 0.60 #15271, 0.60 #4821), 030qb3t (0.33 #32226, 0.21 #4100, 0.19 #46687), 04jpl (0.25 #17, 0.10 #3231, 0.09 #46621), 0h7h6 (0.25 #90, 0.04 #32233, 0.03 #8125), 0f2v0 (0.25 #183, 0.02 #3397, 0.02 #5004), 07ylj (0.25 #61, 0.02 #3275, 0.02 #4882), 015fr (0.25 #31, 0.02 #3245, 0.02 #4852), 02_286 (0.24 #46641, 0.15 #49052, 0.15 #52264), 01hpnh (0.17 #2129, 0.03 #2933, 0.02 #3736), 0k049 (0.12 #812, 0.07 #7239, 0.06 #9649) >> Best rule #12053 for best value: >> intensional similarity = 5 >> extensional distance = 72 >> proper extension: 0b9f7t; >> query: (?x6098, ?x7771) <- profession(?x6098, ?x1032), film(?x6098, ?x5247), special_performance_type(?x6098, ?x4832), place_of_birth(?x6098, ?x7771), location(?x6098, ?x7412) >> conf = 0.72 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02tq2r location 0fn2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 91.000 0.716 http://example.org/people/person/places_lived./people/place_lived/location #2517-02z0j PRED entity: 02z0j PRED relation: month PRED expected values: 05lf_ => 198 concepts (198 used for prediction) PRED predicted values (max 10 best out of 1): 05lf_ (0.90 #16, 0.89 #28, 0.89 #15) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 06y57; >> query: (?x8977, 05lf_) <- month(?x8977, ?x4925), ?x4925 = 0ll3, origin(?x3069, ?x8977) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02z0j month 05lf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 198.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #2516-0gps0z PRED entity: 0gps0z PRED relation: person! PRED expected values: 043q4d => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 5): 043q4d (0.88 #103, 0.56 #24, 0.55 #31), 026h21_ (0.11 #29, 0.09 #36, 0.05 #285), 0c5lg (0.11 #28, 0.08 #107, 0.06 #284), 02k13d (0.11 #281, 0.02 #327, 0.01 #451), 09jwl (0.01 #279, 0.01 #403, 0.01 #295) >> Best rule #103 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 06mmb; 018z_c; 0hfml; 010p3; 035wq7; >> query: (?x9639, 043q4d) <- profession(?x9639, ?x131), nationality(?x9639, ?x94), program(?x9639, ?x2583), ?x2583 = 06hwzy >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gps0z person! 043q4d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.885 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #2515-0154fs PRED entity: 0154fs PRED relation: contains! PRED expected values: 05kr_ => 146 concepts (78 used for prediction) PRED predicted values (max 10 best out of 496): 09c7w0 (0.72 #12542, 0.71 #9856, 0.71 #51047), 05kr_ (0.70 #1916, 0.69 #2811, 0.65 #4602), 02jx1 (0.43 #45759, 0.42 #46654, 0.13 #24261), 01n7q (0.39 #34994, 0.38 #36787, 0.31 #47541), 0345h (0.33 #26047, 0.27 #27837, 0.24 #29627), 04_1l0v (0.32 #10303, 0.26 #11198, 0.25 #20150), 07ssc (0.28 #45704, 0.27 #46599, 0.23 #24206), 03rjj (0.24 #27766, 0.22 #29556, 0.15 #34927), 02qkt (0.18 #37951, 0.17 #41538, 0.17 #40640), 015jr (0.17 #7577, 0.17 #6681, 0.16 #8474) >> Best rule #12542 for best value: >> intensional similarity = 4 >> extensional distance = 106 >> proper extension: 0pmp2; 018jcq; >> query: (?x13138, 09c7w0) <- category(?x13138, ?x134), location(?x5944, ?x13138), jurisdiction_of_office(?x1195, ?x13138), film(?x5944, ?x603) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1916 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 0h7h6; *> query: (?x13138, 05kr_) <- time_zones(?x13138, ?x2674), contains(?x279, ?x13138), ?x279 = 0d060g, location(?x5944, ?x13138), ?x2674 = 02hcv8 *> conf = 0.70 ranks of expected_values: 2 EVAL 0154fs contains! 05kr_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 78.000 0.722 http://example.org/location/location/contains #2514-01k_mc PRED entity: 01k_mc PRED relation: award_winner! PRED expected values: 01mh_q 02cg41 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 117): 01s695 (0.14 #144, 0.12 #3, 0.10 #567), 02rjjll (0.13 #710, 0.10 #992, 0.10 #9450), 02cg41 (0.12 #126, 0.10 #9450, 0.10 #9451), 013b2h (0.12 #785, 0.11 #2336, 0.11 #1067), 05pd94v (0.11 #707, 0.10 #989, 0.08 #2399), 09n4nb (0.10 #1035, 0.10 #9450, 0.10 #9451), 0jzphpx (0.10 #9450, 0.10 #9451, 0.10 #180), 01mhwk (0.10 #9450, 0.10 #9451, 0.06 #605), 0466p0j (0.10 #1063, 0.09 #781, 0.08 #2332), 01mh_q (0.10 #230, 0.07 #2486, 0.06 #4655) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0197tq; 0lbj1; 03f2_rc; 06cc_1; 01wbgdv; 09qr6; 015882; 05d8vw; 0pyg6; 01cwhp; ... >> query: (?x5904, 01s695) <- award_nominee(?x5904, ?x2614), artists(?x9007, ?x5904), ?x9007 = 02vjzr >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #126 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 02qfhb; *> query: (?x5904, 02cg41) <- award_nominee(?x5904, ?x2614), music(?x7647, ?x5904), film(?x5904, ?x9800) *> conf = 0.12 ranks of expected_values: 3, 10 EVAL 01k_mc award_winner! 02cg41 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 91.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01k_mc award_winner! 01mh_q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 91.000 91.000 0.143 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2513-03d1y3 PRED entity: 03d1y3 PRED relation: award PRED expected values: 040njc 019f4v => 143 concepts (111 used for prediction) PRED predicted values (max 10 best out of 314): 04dn09n (0.50 #4463, 0.18 #1247, 0.17 #1649), 03hkv_r (0.41 #4437, 0.13 #1221, 0.11 #1623), 05b4l5x (0.34 #4026, 0.13 #6036, 0.12 #4830), 02n9nmz (0.34 #4489, 0.09 #9649, 0.05 #2479), 07bdd_ (0.33 #1269, 0.30 #1671, 0.28 #867), 03c7tr1 (0.32 #4076, 0.13 #6086, 0.10 #12117), 040njc (0.31 #812, 0.27 #4430, 0.22 #1616), 0gr51 (0.31 #4519, 0.20 #1705, 0.18 #1303), 0gq9h (0.30 #4497, 0.26 #2085, 0.17 #22990), 05p09zm (0.29 #4141, 0.14 #121, 0.11 #1729) >> Best rule #4463 for best value: >> intensional similarity = 5 >> extensional distance = 108 >> proper extension: 01q415; 01q4qv; 0c921; 014g9y; >> query: (?x7380, 04dn09n) <- award(?x7380, ?x601), award(?x7380, ?x350), nominated_for(?x350, ?x103), profession(?x7380, ?x319), ?x601 = 0gr4k >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #812 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 0854hr; *> query: (?x7380, 040njc) <- award(?x7380, ?x350), gender(?x7380, ?x231), location(?x7380, ?x2254), ?x350 = 05f4m9q *> conf = 0.31 ranks of expected_values: 7, 13 EVAL 03d1y3 award 019f4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 143.000 111.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03d1y3 award 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 143.000 111.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2512-099ck7 PRED entity: 099ck7 PRED relation: nominated_for PRED expected values: 0c0nhgv 04b2qn 08zrbl => 40 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1466): 092vkg (0.79 #4629, 0.78 #4628, 0.77 #1543), 08zrbl (0.79 #4629, 0.78 #4628, 0.77 #1543), 0m313 (0.68 #3098, 0.21 #4641, 0.19 #6184), 0f4_l (0.55 #3388, 0.29 #302, 0.22 #4931), 0pv3x (0.55 #3240, 0.29 #154, 0.16 #6326), 016mhd (0.55 #4264, 0.29 #1178, 0.16 #5807), 0c0zq (0.50 #4422, 0.29 #1336, 0.16 #5965), 047d21r (0.50 #3617, 0.22 #2074, 0.14 #531), 02yvct (0.50 #3390, 0.17 #23159, 0.16 #6476), 095zlp (0.50 #3138, 0.15 #6224, 0.14 #4681) >> Best rule #4629 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 099c8n; >> query: (?x6729, ?x3124) <- award(?x3124, ?x6729), nominated_for(?x6729, ?x964), titles(?x53, ?x3124), ?x964 = 0fh694 >> conf = 0.79 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 12, 23 EVAL 099ck7 nominated_for 08zrbl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 40.000 16.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099ck7 nominated_for 04b2qn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 40.000 16.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 099ck7 nominated_for 0c0nhgv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 40.000 16.000 0.786 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2511-0f7hw PRED entity: 0f7hw PRED relation: currency PRED expected values: 09nqf => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.78 #36, 0.78 #85, 0.77 #78), 088n7 (0.04 #21, 0.03 #28), 01nv4h (0.04 #51, 0.02 #177, 0.02 #247), 02l6h (0.01 #130, 0.01 #95, 0.01 #123) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 167 >> proper extension: 0hgnl3t; >> query: (?x9424, 09nqf) <- film(?x902, ?x9424), film(?x427, ?x9424), ?x902 = 05qd_, award_nominee(?x427, ?x3747) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f7hw currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.781 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #2510-04344j PRED entity: 04344j PRED relation: major_field_of_study PRED expected values: 01mkq => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 117): 01mkq (0.45 #766, 0.40 #641, 0.40 #891), 02lp1 (0.42 #762, 0.42 #137, 0.41 #387), 02j62 (0.42 #156, 0.40 #4533, 0.36 #3406), 0g26h (0.39 #794, 0.38 #419, 0.38 #669), 04rjg (0.37 #146, 0.35 #771, 0.30 #521), 03g3w (0.30 #4530, 0.28 #778, 0.27 #3403), 01lj9 (0.30 #166, 0.27 #791, 0.25 #916), 05qjt (0.30 #133, 0.26 #758, 0.24 #508), 01tbp (0.30 #187, 0.25 #937, 0.24 #687), 05qfh (0.28 #787, 0.26 #162, 0.26 #412) >> Best rule #766 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 0b1xl; 01nnsv; 0ks67; 08qnnv; 0gl5_; 0g2jl; >> query: (?x2970, 01mkq) <- fraternities_and_sororities(?x2970, ?x3697), major_field_of_study(?x2970, ?x2606), school_type(?x2970, ?x3205) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04344j major_field_of_study 01mkq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.455 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #2509-04ns3gy PRED entity: 04ns3gy PRED relation: nationality PRED expected values: 09c7w0 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 37): 09c7w0 (0.85 #1908, 0.82 #1105, 0.82 #2211), 02_286 (0.33 #10228), 059rby (0.33 #10228), 02jx1 (0.18 #434, 0.10 #4449, 0.10 #4349), 07ssc (0.11 #1721, 0.09 #917, 0.08 #3029), 0d060g (0.07 #408, 0.05 #909, 0.05 #508), 06q1r (0.07 #278, 0.04 #478, 0.02 #5093), 03rjj (0.07 #206, 0.03 #2013, 0.03 #3320), 03spz (0.06 #1372, 0.04 #1672, 0.02 #869), 03rk0 (0.06 #10674, 0.06 #10774, 0.05 #10874) >> Best rule #1908 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 04l3_z; 02p21g; 02xp18; 06t8b; 023qfd; 01xwv7; 0hqly; 02v2jy; 01svq8; >> query: (?x9503, 09c7w0) <- type_of_union(?x9503, ?x566), producer_type(?x9503, ?x632), place_of_birth(?x9503, ?x1131) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ns3gy nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.845 http://example.org/people/person/nationality #2508-0h6l4 PRED entity: 0h6l4 PRED relation: location! PRED expected values: 09b0xs => 81 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1363): 01ggc9 (0.38 #7091, 0.03 #9609, 0.03 #14644), 03ywyk (0.33 #1868, 0.01 #27038), 0837ql (0.33 #986), 039xcr (0.25 #4556), 022yb4 (0.23 #6742, 0.04 #12585, 0.04 #7550), 023kzp (0.23 #6249, 0.03 #31419, 0.03 #13802), 05ry0p (0.23 #7193, 0.03 #12228, 0.03 #17263), 023mdt (0.23 #6897, 0.03 #11932, 0.03 #16967), 0gl88b (0.23 #5403, 0.03 #7921, 0.03 #12956), 01515w (0.23 #6284, 0.03 #8802, 0.02 #31454) >> Best rule #7091 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 02_286; 0r62v; 07z1m; 030qb3t; 04rrd; 0mp3l; 029cr; 0cr3d; 01cx_; 06yxd; ... >> query: (?x12314, 01ggc9) <- contains(?x94, ?x12314), location(?x6791, ?x12314), award_nominee(?x6791, ?x4586), ?x4586 = 04bcb1 >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0h6l4 location! 09b0xs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 59.000 0.385 http://example.org/people/person/places_lived./people/place_lived/location #2507-03f0vvr PRED entity: 03f0vvr PRED relation: type_of_union PRED expected values: 04ztj => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.75 #13, 0.71 #33, 0.70 #45), 01g63y (0.30 #442, 0.30 #421, 0.16 #10), 0jgjn (0.30 #442, 0.30 #421, 0.03 #8), 01bl8s (0.03 #19) >> Best rule #13 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 01vw20_; 01jfnvd; >> query: (?x4798, 04ztj) <- instrumentalists(?x227, ?x4798), artist(?x6672, ?x4798), profession(?x4798, ?x1032), artist(?x3240, ?x4798) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f0vvr type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.750 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2506-0hr41p6 PRED entity: 0hr41p6 PRED relation: genre PRED expected values: 01z4y => 104 concepts (74 used for prediction) PRED predicted values (max 10 best out of 121): 05p553 (0.92 #1592, 0.89 #1009, 0.89 #1426), 07s9rl0 (0.91 #2174, 0.68 #1088, 0.65 #1255), 01z4y (0.76 #855, 0.76 #1605, 0.76 #1356), 0c4xc (0.71 #1463, 0.61 #1629, 0.58 #1046), 06nbt (0.36 #1191, 0.29 #441, 0.21 #692), 0hcr (0.32 #1189, 0.22 #1772, 0.22 #3293), 01hmnh (0.29 #687, 0.25 #519, 0.21 #1103), 01htzx (0.26 #1104, 0.19 #2274, 0.19 #2190), 0djd22 (0.26 #1276, 0.18 #1192, 0.16 #1109), 04gm78f (0.26 #1300, 0.18 #1216, 0.16 #1133) >> Best rule #1592 for best value: >> intensional similarity = 5 >> extensional distance = 36 >> proper extension: 01lv85; >> query: (?x14067, 05p553) <- genre(?x14067, ?x809), nominated_for(?x757, ?x14067), ceremony(?x757, ?x1265), award_winner(?x757, ?x516), ?x516 = 03zqc1 >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #855 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 0557yqh; 01b9w3; 01s81; 05p9_ql; 02czd5; 01fszq; *> query: (?x14067, 01z4y) <- genre(?x14067, ?x809), program(?x364, ?x14067), nominated_for(?x757, ?x14067), ?x757 = 09qj50, award_nominee(?x364, ?x237) *> conf = 0.76 ranks of expected_values: 3 EVAL 0hr41p6 genre 01z4y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 104.000 74.000 0.921 http://example.org/tv/tv_program/genre #2505-0h3k3f PRED entity: 0h3k3f PRED relation: film! PRED expected values: 08qvhv => 120 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1073): 0171lb (0.46 #125063, 0.43 #58353, 0.43 #112557), 07hhnl (0.46 #125063, 0.43 #58353, 0.43 #31264), 07h1tr (0.46 #125063, 0.43 #58353, 0.43 #31264), 0c4qzm (0.43 #58353, 0.43 #31264, 0.42 #35432), 0gn30 (0.09 #36382, 0.09 #23877, 0.09 #40550), 02cj_f (0.09 #3707, 0.04 #20382, 0.03 #18297), 0h0wc (0.08 #21268, 0.05 #31689, 0.05 #44191), 044qx (0.08 #27095, 0.08 #39600, 0.07 #6986), 0j_c (0.08 #27095, 0.08 #39600, 0.06 #2494), 0f0p0 (0.08 #27095, 0.08 #39600, 0.03 #169) >> Best rule #125063 for best value: >> intensional similarity = 4 >> extensional distance = 654 >> proper extension: 0bx_hnp; >> query: (?x8735, ?x8803) <- award_winner(?x8735, ?x4180), film_release_region(?x8735, ?x94), nominated_for(?x8803, ?x8735), profession(?x8803, ?x220) >> conf = 0.46 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 0h3k3f film! 08qvhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 75.000 0.458 http://example.org/film/actor/film./film/performance/film #2504-099vwn PRED entity: 099vwn PRED relation: award! PRED expected values: 05cljf 0lgsq 01vrz41 01vsnff 01vvyvk 02lk95 012vd6 => 53 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2895): 0hvbj (0.79 #10031, 0.78 #3343, 0.78 #43478), 01vw20h (0.54 #1273, 0.24 #10032, 0.23 #7961), 02l840 (0.46 #179, 0.24 #10032, 0.20 #33442), 05mt_q (0.46 #334, 0.24 #10032, 0.20 #33442), 01wgxtl (0.46 #732, 0.18 #7420, 0.12 #14110), 016pns (0.46 #800, 0.15 #7488, 0.11 #14178), 01vs_v8 (0.38 #578, 0.32 #7266, 0.28 #13956), 01vvyc_ (0.38 #1684, 0.24 #10032, 0.23 #10033), 018n6m (0.38 #1330, 0.22 #8018, 0.15 #14708), 015mrk (0.38 #836, 0.17 #7524, 0.16 #23409) >> Best rule #10031 for best value: >> intensional similarity = 5 >> extensional distance = 63 >> proper extension: 02581q; 02wh75; 05f4m9q; 05zkcn5; 0gkvb7; 01d38g; 01bgqh; 03x3wf; 0c4z8; 01ckbq; ... >> query: (?x4416, ?x248) <- award_winner(?x4416, ?x5536), award_winner(?x4416, ?x248), award_winner(?x140, ?x5536), artist(?x8738, ?x5536), award_nominee(?x527, ?x5536) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #6980 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 02581q; 02wh75; 05f4m9q; 05zkcn5; 0gkvb7; 01d38g; 01bgqh; 03x3wf; 0c4z8; 01ckbq; ... *> query: (?x4416, 01vrz41) <- award_winner(?x4416, ?x5536), award_winner(?x140, ?x5536), artist(?x8738, ?x5536), award_nominee(?x527, ?x5536) *> conf = 0.26 ranks of expected_values: 35, 46, 202, 462, 572, 884, 1186 EVAL 099vwn award! 012vd6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 14.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099vwn award! 02lk95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 14.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099vwn award! 01vvyvk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 53.000 14.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099vwn award! 01vsnff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 53.000 14.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099vwn award! 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 53.000 14.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099vwn award! 0lgsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 14.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 099vwn award! 05cljf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 53.000 14.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2503-0nlh7 PRED entity: 0nlh7 PRED relation: split_to! PRED expected values: 0nlh7 => 169 concepts (79 used for prediction) PRED predicted values (max 10 best out of 10): 04gvyp (0.02 #1047, 0.01 #1343, 0.01 #1442), 05c9zr (0.02 #1020, 0.01 #1316, 0.01 #1415), 01j_x (0.02 #1067, 0.01 #1462, 0.01 #1658), 014tss (0.02 #1037, 0.01 #1432, 0.01 #1628), 02jx1 (0.02 #993, 0.01 #1388, 0.01 #1584), 02w7gg (0.01 #1380, 0.01 #1576, 0.01 #1675), 09c7w0 (0.01 #1470, 0.01 #1766), 013xrm (0.01 #1821), 0jdx (0.01 #2134), 02k54 (0.01 #2070) >> Best rule #1047 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 01ls2; >> query: (?x10718, 04gvyp) <- contains(?x279, ?x10718), origin(?x9442, ?x10718), jurisdiction_of_office(?x1195, ?x10718), teams(?x10718, ?x3298) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0nlh7 split_to! 0nlh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 169.000 79.000 0.020 http://example.org/dataworld/gardening_hint/split_to #2502-03dpqd PRED entity: 03dpqd PRED relation: nationality PRED expected values: 0chghy => 96 concepts (92 used for prediction) PRED predicted values (max 10 best out of 33): 09c7w0 (0.79 #1668, 0.78 #2357, 0.78 #2947), 0jt5zcn (0.33 #7079, 0.32 #5210, 0.32 #5899), 0d060g (0.30 #5998, 0.17 #105, 0.07 #1380), 0345h (0.30 #5998, 0.05 #422, 0.05 #324), 0h7x (0.30 #5998, 0.05 #327, 0.03 #1210), 0chghy (0.30 #5998, 0.02 #795, 0.02 #1481), 03gj2 (0.30 #5998, 0.01 #1202), 01ls2 (0.08 #207), 06q1r (0.07 #761, 0.05 #958, 0.04 #1154), 03rk0 (0.06 #4563, 0.06 #4170, 0.06 #7417) >> Best rule #1668 for best value: >> intensional similarity = 4 >> extensional distance = 620 >> proper extension: 02dh86; 0d4jl; 034bs; 05x8n; 06bng; 07zl1; >> query: (?x4649, 09c7w0) <- student(?x6837, ?x4649), award_winner(?x2880, ?x4649), award(?x253, ?x2880), colors(?x6837, ?x3189) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #5998 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1828 *> proper extension: 01v6480; 070px; *> query: (?x4649, ?x279) <- film(?x4649, ?x278), country(?x278, ?x279), gender(?x4649, ?x514) *> conf = 0.30 ranks of expected_values: 6 EVAL 03dpqd nationality 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 92.000 0.789 http://example.org/people/person/nationality #2501-016yvw PRED entity: 016yvw PRED relation: award_winner! PRED expected values: 02wzl1d 0n8_m93 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 129): 0gx1673 (0.31 #528, 0.07 #665, 0.02 #8063), 04n2r9h (0.25 #319, 0.17 #182, 0.08 #456), 05pd94v (0.23 #413, 0.12 #550, 0.04 #10689), 02wzl1d (0.17 #8084, 0.17 #148, 0.12 #285), 03gwpw2 (0.17 #8084, 0.17 #146, 0.12 #283), 02cg41 (0.17 #8084, 0.08 #533, 0.07 #670), 0ftlkg (0.17 #8084, 0.08 #437, 0.01 #4547), 09q_6t (0.17 #8084, 0.03 #4666, 0.03 #4940), 0bc773 (0.17 #8084, 0.01 #4986, 0.01 #739), 09qftb (0.17 #248, 0.12 #385, 0.08 #522) >> Best rule #528 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01wbl_r; >> query: (?x5363, 0gx1673) <- award_nominee(?x3930, ?x5363), ?x3930 = 01svw8n, award(?x5363, ?x591) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #8084 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1134 *> proper extension: 01p5yn; 05s34b; 06lxn; *> query: (?x5363, ?x762) <- award_winner(?x591, ?x5363), award_winner(?x748, ?x5363), award_winner(?x762, ?x748) *> conf = 0.17 ranks of expected_values: 4, 88 EVAL 016yvw award_winner! 0n8_m93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 117.000 117.000 0.308 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 016yvw award_winner! 02wzl1d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 117.000 0.308 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2500-03wh95l PRED entity: 03wh95l PRED relation: profession PRED expected values: 0dxtg => 73 concepts (44 used for prediction) PRED predicted values (max 10 best out of 43): 02krf9 (0.81 #23, 0.33 #1886, 0.32 #603), 0dxtg (0.76 #156, 0.71 #11, 0.67 #736), 0kyk (0.27 #316, 0.24 #461, 0.13 #171), 018gz8 (0.25 #4207, 0.19 #1608, 0.19 #1463), 09jwl (0.16 #4076, 0.16 #5817, 0.16 #6253), 02hv44_ (0.16 #489, 0.16 #199, 0.15 #344), 0np9r (0.13 #1612, 0.13 #1467, 0.13 #1322), 0nbcg (0.12 #4089, 0.11 #5830, 0.11 #6266), 0dz3r (0.11 #4063, 0.10 #5804, 0.10 #6240), 016z4k (0.10 #4065, 0.09 #5806, 0.09 #6242) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 03m_k0; >> query: (?x11581, 02krf9) <- award(?x11581, ?x9640), profession(?x11581, ?x319), ?x9640 = 0gkr9q >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #156 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 120 *> proper extension: 04107; *> query: (?x11581, 0dxtg) <- award(?x11581, ?x2016), story_by(?x1295, ?x11581), nominated_for(?x2016, ?x758) *> conf = 0.76 ranks of expected_values: 2 EVAL 03wh95l profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 44.000 0.810 http://example.org/people/person/profession #2499-016dj8 PRED entity: 016dj8 PRED relation: film_crew_role PRED expected values: 02ynfr => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 17): 01pvkk (0.32 #269, 0.27 #1426, 0.27 #1347), 02ynfr (0.30 #10, 0.27 #36, 0.21 #273), 089g0h (0.20 #64, 0.16 #275, 0.12 #747), 015h31 (0.18 #268, 0.13 #57, 0.09 #31), 02vs3x5 (0.18 #42, 0.10 #16, 0.07 #68), 01xy5l_ (0.15 #271, 0.13 #60, 0.12 #743), 04pyp5 (0.13 #63, 0.10 #11, 0.09 #37), 033smt (0.13 #71, 0.10 #282, 0.04 #256), 02zdwq (0.13 #67, 0.01 #331, 0.01 #278), 05smlt (0.07 #65, 0.04 #276, 0.03 #748) >> Best rule #269 for best value: >> intensional similarity = 3 >> extensional distance = 363 >> proper extension: 0h95zbp; 03_wm6; 09rfpk; >> query: (?x6306, 01pvkk) <- film_crew_role(?x6306, ?x2154), ?x2154 = 01vx2h, genre(?x6306, ?x225) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 02qkwl; *> query: (?x6306, 02ynfr) <- nominated_for(?x298, ?x6306), film(?x521, ?x6306), ?x521 = 0147dk, film_crew_role(?x6306, ?x137) *> conf = 0.30 ranks of expected_values: 2 EVAL 016dj8 film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.318 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2498-03_gz8 PRED entity: 03_gz8 PRED relation: genre PRED expected values: 02l7c8 => 77 concepts (76 used for prediction) PRED predicted values (max 10 best out of 130): 082gq (0.40 #28, 0.31 #145, 0.14 #4483), 06l3bl (0.40 #36, 0.31 #153, 0.06 #1329), 01jfsb (0.40 #246, 0.38 #363, 0.29 #3647), 05p553 (0.36 #2585, 0.35 #3404, 0.35 #3873), 02kdv5l (0.33 #235, 0.31 #352, 0.26 #3871), 02l7c8 (0.31 #1308, 0.31 #956, 0.29 #4470), 03k9fj (0.31 #128, 0.26 #245, 0.24 #362), 04xvh5 (0.25 #149, 0.20 #32, 0.10 #1325), 0lsxr (0.24 #242, 0.23 #359, 0.18 #477), 03mqtr (0.20 #27, 0.11 #144, 0.06 #496) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 048scx; 0260bz; 01_0f7; >> query: (?x6362, 082gq) <- language(?x6362, ?x2502), genre(?x6362, ?x3312), ?x2502 = 06nm1, ?x3312 = 02p0szs >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1308 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 415 *> proper extension: 019kyn; *> query: (?x6362, 02l7c8) <- honored_for(?x6594, ?x6362), country(?x6362, ?x512), film(?x1738, ?x6362) *> conf = 0.31 ranks of expected_values: 6 EVAL 03_gz8 genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 76.000 0.400 http://example.org/film/film/genre #2497-08gg47 PRED entity: 08gg47 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.82 #172, 0.80 #706, 0.79 #573), 01vx2h (0.45 #176, 0.39 #677, 0.38 #243), 01pvkk (0.32 #78, 0.31 #177, 0.29 #210), 02rh1dz (0.29 #175, 0.20 #208, 0.15 #242), 02ynfr (0.19 #715, 0.19 #181, 0.18 #582), 0215hd (0.18 #183, 0.17 #617, 0.16 #418), 089g0h (0.17 #184, 0.15 #618, 0.13 #217), 015h31 (0.15 #241, 0.14 #309, 0.13 #174), 0d2b38 (0.14 #256, 0.14 #189, 0.13 #324), 01xy5l_ (0.13 #613, 0.13 #179, 0.12 #580) >> Best rule #172 for best value: >> intensional similarity = 4 >> extensional distance = 141 >> proper extension: 0fq27fp; >> query: (?x3304, 0ch6mp2) <- film_release_region(?x3304, ?x94), film_crew_role(?x3304, ?x1171), ?x1171 = 09vw2b7, crewmember(?x3304, ?x8415) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08gg47 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.818 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2496-0jym0 PRED entity: 0jym0 PRED relation: film_format PRED expected values: 0cj16 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 4): 0cj16 (0.20 #3, 0.14 #18, 0.12 #166), 07fb8_ (0.17 #66, 0.13 #55, 0.12 #107), 017fx5 (0.02 #117, 0.02 #110, 0.02 #139), 01dc60 (0.01 #25, 0.01 #59, 0.01 #41) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03kx49; >> query: (?x2057, 0cj16) <- film(?x8081, ?x2057), list(?x2057, ?x3004), genre(?x2057, ?x53), ?x8081 = 02l3_5 >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jym0 film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.200 http://example.org/film/film/film_format #2495-0d2by PRED entity: 0d2by PRED relation: languages_spoken PRED expected values: 03115z => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 51): 0t_2 (0.75 #927, 0.75 #570, 0.60 #417), 064_8sq (0.60 #373, 0.25 #1546, 0.25 #985), 06nm1 (0.33 #773, 0.33 #59, 0.27 #875), 07qv_ (0.33 #181, 0.33 #130, 0.20 #436), 03_9r (0.33 #109, 0.20 #415, 0.12 #568), 07zrf (0.33 #104, 0.20 #410, 0.12 #563), 01jb8r (0.33 #196, 0.12 #655, 0.10 #1318), 0c_v2 (0.33 #12, 0.07 #1338), 03115z (0.33 #31, 0.04 #1357), 03x42 (0.25 #297, 0.25 #246, 0.18 #1215) >> Best rule #927 for best value: >> intensional similarity = 9 >> extensional distance = 10 >> proper extension: 0dryh9k; >> query: (?x7562, 0t_2) <- languages_spoken(?x7562, ?x254), people(?x7562, ?x2307), award_winner(?x678, ?x2307), student(?x1681, ?x2307), award_winner(?x2307, ?x3815), award_nominee(?x3815, ?x1129), ?x678 = 0cqhk0, service_language(?x127, ?x254), languages(?x118, ?x254) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #31 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 1 *> proper extension: 04l_pt; *> query: (?x7562, 03115z) <- geographic_distribution(?x7562, ?x6226), geographic_distribution(?x7562, ?x3634), languages_spoken(?x7562, ?x5974), languages_spoken(?x7562, ?x2890), vacationer(?x6226, ?x513), ?x5974 = 01r2l, language(?x6450, ?x2890), language(?x1889, ?x2890), prequel(?x1889, ?x1074), currency(?x6226, ?x170), religion(?x3634, ?x109), film(?x826, ?x6450) *> conf = 0.33 ranks of expected_values: 9 EVAL 0d2by languages_spoken 03115z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 47.000 47.000 0.750 http://example.org/people/ethnicity/languages_spoken #2494-01j851 PRED entity: 01j851 PRED relation: award PRED expected values: 03c7tr1 => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 279): 0gqwc (0.55 #1681, 0.44 #73, 0.40 #877), 03c7tr1 (0.45 #1666, 0.44 #58, 0.27 #5284), 0gqyl (0.40 #2114, 0.36 #1712, 0.33 #104), 0gqy2 (0.40 #2576, 0.14 #17450, 0.13 #8204), 094qd5 (0.36 #1652, 0.22 #44, 0.20 #4466), 0cqgl9 (0.33 #190, 0.18 #1798, 0.10 #2200), 0bsjcw (0.33 #201, 0.11 #603, 0.09 #1809), 0bb57s (0.33 #242, 0.09 #1850, 0.09 #1448), 02z0dfh (0.30 #2084, 0.18 #1682, 0.13 #9320), 09sb52 (0.30 #6472, 0.27 #1648, 0.26 #17728) >> Best rule #1681 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0fb1q; 046zh; 02nwxc; 0hwbd; 0kjrx; 0btpx; 01jw4r; 031sg0; 01gw8b; >> query: (?x9573, 0gqwc) <- participant(?x10592, ?x9573), participant(?x8667, ?x9573), award(?x9573, ?x3184), ?x3184 = 0gkts9 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1666 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0fb1q; 046zh; 02nwxc; 0hwbd; 0kjrx; 0btpx; 01jw4r; 031sg0; 01gw8b; *> query: (?x9573, 03c7tr1) <- participant(?x10592, ?x9573), participant(?x8667, ?x9573), award(?x9573, ?x3184), ?x3184 = 0gkts9 *> conf = 0.45 ranks of expected_values: 2 EVAL 01j851 award 03c7tr1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 167.000 167.000 0.545 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2493-07ssc PRED entity: 07ssc PRED relation: religion PRED expected values: 0631_ 06yyp => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 32): 01lp8 (0.70 #3125, 0.70 #1715, 0.68 #3035), 019cr (0.66 #3132, 0.63 #3042, 0.57 #1752), 0631_ (0.66 #3129, 0.63 #3039, 0.57 #1719), 05sfs (0.65 #1746, 0.64 #3126, 0.62 #3036), 04pk9 (0.61 #3139, 0.60 #3049, 0.55 #1729), 05w5d (0.60 #3142, 0.58 #3052, 0.55 #1732), 01y0s9 (0.46 #3130, 0.45 #1720, 0.45 #3040), 021_0p (0.46 #3138, 0.45 #3048, 0.40 #3318), 092bf5 (0.35 #1124, 0.33 #824, 0.28 #3045), 01s5nb (0.33 #3144, 0.33 #1764, 0.32 #3054) >> Best rule #3125 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 05kkh; 05kj_; 059f4; 05fhy; 01n4w; 0846v; 05fky; 04tgp; 06yxd; 026mj; >> query: (?x512, 01lp8) <- contains(?x512, ?x3362), religion(?x512, ?x492), major_field_of_study(?x3362, ?x1668) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #3129 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 05kkh; 05kj_; 059f4; 05fhy; 01n4w; 0846v; 05fky; 04tgp; 06yxd; 026mj; *> query: (?x512, 0631_) <- contains(?x512, ?x3362), religion(?x512, ?x492), major_field_of_study(?x3362, ?x1668) *> conf = 0.66 ranks of expected_values: 3, 19 EVAL 07ssc religion 06yyp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 200.000 200.000 0.701 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 07ssc religion 0631_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 200.000 200.000 0.701 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #2492-0tln7 PRED entity: 0tln7 PRED relation: place PRED expected values: 0tln7 => 93 concepts (63 used for prediction) PRED predicted values (max 10 best out of 96): 04ly1 (0.26 #4124), 09c7w0 (0.26 #4124), 0fvvz (0.25 #539, 0.25 #24, 0.20 #1054), 013d7t (0.25 #638, 0.20 #1153, 0.07 #1668), 0tk02 (0.25 #328, 0.07 #1873, 0.07 #2388), 0f2tj (0.20 #1201, 0.04 #14956, 0.04 #17538), 013gz (0.07 #2047, 0.07 #2562, 0.06 #3077), 0tn9j (0.07 #1936, 0.07 #2451, 0.06 #2966), 0tln7 (0.04 #14956, 0.04 #17538, 0.01 #28381), 019tfm (0.01 #12889) >> Best rule #4124 for best value: >> intensional similarity = 4 >> extensional distance = 100 >> proper extension: 0f04v; >> query: (?x5015, ?x3908) <- country(?x5015, ?x94), citytown(?x14319, ?x5015), ?x94 = 09c7w0, contains(?x3908, ?x14319) >> conf = 0.26 => this is the best rule for 2 predicted values *> Best rule #14956 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 217 *> proper extension: 0l_q9; *> query: (?x5015, ?x1248) <- country(?x5015, ?x94), contains(?x3908, ?x5015), state(?x1248, ?x3908), district_represented(?x176, ?x3908) *> conf = 0.04 ranks of expected_values: 9 EVAL 0tln7 place 0tln7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 93.000 63.000 0.261 http://example.org/location/hud_county_place/place #2491-02z6l5f PRED entity: 02z6l5f PRED relation: award_nominee PRED expected values: 06chf => 125 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1178): 06chf (0.81 #23446, 0.81 #35170, 0.81 #103170), 053xw6 (0.30 #98480, 0.08 #51582, 0.07 #53927), 016nff (0.30 #98480, 0.08 #51582, 0.07 #53927), 04wvhz (0.25 #2557, 0.25 #213, 0.04 #58829), 04glx0 (0.25 #1516, 0.20 #6204), 031rx9 (0.25 #958, 0.20 #5646), 01w92 (0.25 #786, 0.20 #5474), 02z6l5f (0.25 #1151, 0.13 #82065, 0.06 #79720), 0pz91 (0.25 #2621, 0.06 #9654, 0.06 #7309), 016dmx (0.25 #4197, 0.06 #11230, 0.06 #8885) >> Best rule #23446 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 081_zm; 01pfkw; 05nn4k; 0f5mdz; >> query: (?x4857, ?x2803) <- company(?x4857, ?x2776), nominated_for(?x4857, ?x7982), award_nominee(?x2803, ?x4857) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02z6l5f award_nominee 06chf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 46.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2490-0g293 PRED entity: 0g293 PRED relation: artists PRED expected values: 020_4z => 45 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1010): 04bgy (0.60 #1654, 0.57 #2727, 0.33 #3801), 016fmf (0.60 #1283, 0.57 #2356, 0.33 #3430), 01gf5h (0.60 #1134, 0.57 #2207, 0.33 #62), 01q99h (0.60 #1630, 0.57 #2703, 0.33 #558), 04b7xr (0.60 #1691, 0.57 #2764, 0.33 #619), 02cw1m (0.60 #1948, 0.57 #3021, 0.33 #876), 02z4b_8 (0.60 #1706, 0.50 #3853, 0.46 #6001), 011z3g (0.60 #1672, 0.46 #7039, 0.43 #2745), 01vvycq (0.60 #1119, 0.43 #2192, 0.42 #3266), 03t9sp (0.60 #1195, 0.43 #2268, 0.42 #3342) >> Best rule #1654 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 06by7; 05bt6j; >> query: (?x9225, 04bgy) <- parent_genre(?x9225, ?x284), artists(?x9225, ?x11182), artists(?x9225, ?x4123), artists(?x9225, ?x2584), ?x11182 = 03x82v, profession(?x4123, ?x131), friend(?x6187, ?x4123), nationality(?x4123, ?x94), artist(?x5891, ?x2584) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3079 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 03lty; 02pl5bx; *> query: (?x9225, 020_4z) <- parent_genre(?x9225, ?x284), artists(?x9225, ?x11182), artists(?x9225, ?x4123), artists(?x9225, ?x2584), ?x11182 = 03x82v, profession(?x4123, ?x131), instrumentalists(?x227, ?x2584) *> conf = 0.29 ranks of expected_values: 401 EVAL 0g293 artists 020_4z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 23.000 0.600 http://example.org/music/genre/artists #2489-086k8 PRED entity: 086k8 PRED relation: organization! PRED expected values: 0dq_5 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 10): 0dq_5 (0.79 #165, 0.73 #178, 0.64 #152), 060c4 (0.39 #964, 0.33 #925, 0.28 #977), 07xl34 (0.12 #310, 0.12 #934, 0.11 #986), 0krdk (0.08 #133, 0.04 #328, 0.04 #315), 01yc02 (0.08 #134, 0.04 #329, 0.04 #316), 0hm4q (0.07 #814, 0.05 #905, 0.05 #853), 05k17c (0.06 #969, 0.05 #930, 0.05 #956), 0dq3c (0.04 #261, 0.02 #547, 0.01 #586), 05c0jwl (0.03 #928, 0.03 #629, 0.02 #954), 08jcfy (0.02 #935, 0.02 #558, 0.01 #571) >> Best rule #165 for best value: >> intensional similarity = 2 >> extensional distance = 12 >> proper extension: 018_q8; >> query: (?x382, 0dq_5) <- award_winner(?x382, ?x1300), child(?x382, ?x2548) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 086k8 organization! 0dq_5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.786 http://example.org/organization/role/leaders./organization/leadership/organization #2488-072kp PRED entity: 072kp PRED relation: honored_for! PRED expected values: 09bymc => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 80): 05c1t6z (0.35 #133, 0.23 #499, 0.20 #11), 03nnm4t (0.30 #185, 0.20 #63, 0.20 #673), 0gvstc3 (0.25 #149, 0.20 #27, 0.20 #881), 02q690_ (0.24 #664, 0.23 #542, 0.22 #420), 0lp_cd3 (0.20 #17, 0.15 #139, 0.12 #1847), 0bx6zs (0.20 #111, 0.11 #965, 0.11 #5492), 0gx_st (0.20 #640, 0.16 #396, 0.16 #1006), 0bxs_d (0.17 #344, 0.10 #1076, 0.09 #466), 07y_p6 (0.12 #327, 0.09 #449, 0.09 #937), 0275n3y (0.12 #674, 0.11 #918, 0.09 #1040) >> Best rule #133 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 06hwzy; >> query: (?x631, 05c1t6z) <- program(?x1564, ?x631), program(?x6678, ?x631), country_of_origin(?x631, ?x94) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #227 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 06hwzy; *> query: (?x631, 09bymc) <- program(?x1564, ?x631), program(?x6678, ?x631), country_of_origin(?x631, ?x94) *> conf = 0.05 ranks of expected_values: 39 EVAL 072kp honored_for! 09bymc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 93.000 93.000 0.350 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2487-016vn3 PRED entity: 016vn3 PRED relation: artists! PRED expected values: 06by7 05r6t => 81 concepts (52 used for prediction) PRED predicted values (max 10 best out of 262): 06by7 (0.72 #1236, 0.72 #1541, 0.71 #2456), 02lnbg (0.64 #966, 0.23 #10731, 0.22 #12860), 0ggx5q (0.57 #986, 0.25 #74, 0.24 #10751), 02ny8t (0.57 #1041, 0.10 #8062, 0.06 #10806), 06j6l (0.50 #44, 0.40 #14987, 0.36 #956), 03_d0 (0.50 #11, 0.20 #15258, 0.18 #11905), 05r6t (0.40 #382, 0.21 #13111, 0.18 #6097), 025sc50 (0.36 #957, 0.32 #10722, 0.31 #3092), 0gywn (0.35 #3100, 0.33 #3404, 0.33 #4012), 0dl5d (0.31 #4280, 0.26 #5198, 0.25 #6114) >> Best rule #1236 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 01pfr3; 02r3zy; 01vsxdm; 0dtd6; 0dvqq; 0frsw; 03fbc; 016fmf; 014_lq; 03d9d6; ... >> query: (?x10502, 06by7) <- group(?x1166, ?x10502), ?x1166 = 05148p4, award(?x10502, ?x4892), ?x4892 = 02f72_, artist(?x2193, ?x10502) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 016vn3 artists! 05r6t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 81.000 52.000 0.720 http://example.org/music/genre/artists EVAL 016vn3 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 52.000 0.720 http://example.org/music/genre/artists #2486-053x8hr PRED entity: 053x8hr PRED relation: country_of_origin PRED expected values: 07ssc => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 17): 09c7w0 (0.94 #211, 0.93 #344, 0.93 #278), 02jx1 (0.77 #233, 0.74 #401, 0.13 #781), 06q1r (0.77 #233, 0.74 #401, 0.05 #1055), 07ssc (0.44 #20, 0.28 #64, 0.27 #31), 03_3d (0.17 #473, 0.15 #595, 0.13 #781), 0d060g (0.13 #781, 0.10 #137, 0.08 #92), 03rjj (0.13 #781, 0.06 #46, 0.04 #90), 03rt9 (0.13 #781, 0.04 #96, 0.04 #107), 0d0vqn (0.13 #781, 0.02 #171, 0.01 #249), 04jpl (0.13 #781) >> Best rule #211 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 04kzqz; >> query: (?x10234, 09c7w0) <- nominated_for(?x7027, ?x10234), award_nominee(?x1001, ?x7027), titles(?x2008, ?x10234), producer_type(?x10234, ?x632) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 064r97z; 05z43v; *> query: (?x10234, 07ssc) <- nominated_for(?x7850, ?x10234), nominated_for(?x2192, ?x10234), genre(?x10234, ?x53), ?x2192 = 0bfvd4, ?x7850 = 07kjk7c *> conf = 0.44 ranks of expected_values: 4 EVAL 053x8hr country_of_origin 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 109.000 109.000 0.936 http://example.org/tv/tv_program/country_of_origin #2485-0971v PRED entity: 0971v PRED relation: nutrient PRED expected values: 0838f 05gh50 02kd0rh 0g5gq => 22 concepts (22 used for prediction) PRED predicted values (max 10 best out of 41): 06x4c (0.86 #23, 0.73 #458, 0.67 #494), 01sh2 (0.86 #23, 0.73 #458, 0.67 #499), 05gh50 (0.86 #23, 0.73 #458, 0.62 #472), 0g5gq (0.86 #23, 0.73 #458, 0.62 #464), 04kl74p (0.86 #23, 0.73 #458, 0.62 #471), 0hqw8p_ (0.86 #23, 0.73 #458, 0.62 #465), 0838f (0.86 #23, 0.73 #458, 0.62 #438), 02p0tjr (0.86 #23, 0.73 #458, 0.50 #459), 02y_3rf (0.86 #23, 0.73 #458, 0.50 #460), 0hkwr (0.86 #23, 0.73 #458, 0.50 #468) >> Best rule #23 for best value: >> intensional similarity = 132 >> extensional distance = 1 >> proper extension: 014j1m; >> query: (?x5373, ?x2018) <- nutrient(?x5373, ?x13944), nutrient(?x5373, ?x13498), nutrient(?x5373, ?x13126), nutrient(?x5373, ?x12902), nutrient(?x5373, ?x12454), nutrient(?x5373, ?x12083), nutrient(?x5373, ?x11758), nutrient(?x5373, ?x11592), nutrient(?x5373, ?x11409), nutrient(?x5373, ?x11270), nutrient(?x5373, ?x10709), nutrient(?x5373, ?x10098), nutrient(?x5373, ?x9915), nutrient(?x5373, ?x9795), nutrient(?x5373, ?x9733), nutrient(?x5373, ?x9619), nutrient(?x5373, ?x9436), nutrient(?x5373, ?x9426), nutrient(?x5373, ?x9365), nutrient(?x5373, ?x8442), nutrient(?x5373, ?x8413), nutrient(?x5373, ?x8243), nutrient(?x5373, ?x7720), nutrient(?x5373, ?x7652), nutrient(?x5373, ?x7364), nutrient(?x5373, ?x7362), nutrient(?x5373, ?x7219), nutrient(?x5373, ?x7135), nutrient(?x5373, ?x6192), nutrient(?x5373, ?x6160), nutrient(?x5373, ?x6033), nutrient(?x5373, ?x6026), nutrient(?x5373, ?x5549), nutrient(?x5373, ?x5526), nutrient(?x5373, ?x5451), nutrient(?x5373, ?x5374), nutrient(?x5373, ?x5010), nutrient(?x5373, ?x3469), nutrient(?x5373, ?x1960), nutrient(?x5373, ?x1258), ?x6033 = 04zjxcz, ?x1258 = 0h1wg, ?x12083 = 01n78x, ?x13944 = 0f4kp, ?x11592 = 025sf0_, ?x6160 = 041r51, ?x13126 = 02kc_w5, ?x10098 = 0h1_c, ?x9365 = 04k8n, ?x10709 = 0h1sz, ?x9426 = 0h1yy, ?x9795 = 05v_8y, ?x11270 = 02kc008, ?x11758 = 0q01m, ?x5374 = 025s0zp, ?x7135 = 025rsfk, nutrient(?x10612, ?x9915), nutrient(?x9732, ?x9915), nutrient(?x9489, ?x9915), nutrient(?x9005, ?x9915), nutrient(?x8298, ?x9915), nutrient(?x7719, ?x9915), nutrient(?x7057, ?x9915), nutrient(?x6285, ?x9915), nutrient(?x6159, ?x9915), nutrient(?x6032, ?x9915), nutrient(?x5337, ?x9915), nutrient(?x4068, ?x9915), nutrient(?x3900, ?x9915), nutrient(?x3468, ?x9915), nutrient(?x3264, ?x9915), nutrient(?x2701, ?x9915), nutrient(?x1303, ?x9915), nutrient(?x1257, ?x9915), ?x8298 = 037ls6, ?x13498 = 07q0m, ?x9733 = 0h1tz, ?x4068 = 0fbw6, ?x7219 = 0h1vg, ?x3900 = 061_f, ?x6159 = 033cnk, nutrient(?x5009, ?x6026), nutrient(?x1959, ?x6026), ?x1960 = 07hnp, ?x3469 = 0h1zw, ?x6032 = 01nkt, ?x5451 = 05wvs, ?x12454 = 025rw19, ?x9732 = 05z55, ?x9005 = 04zpv, ?x7362 = 02kc5rj, ?x8243 = 014d7f, ?x9489 = 07j87, ?x8413 = 02kc4sf, ?x7719 = 0dj75, ?x9619 = 0h1tg, ?x6192 = 06jry, ?x1959 = 0f25w9, ?x11409 = 0h1yf, ?x7720 = 025s7x6, ?x7057 = 0fbdb, ?x5337 = 06x4c, ?x9436 = 025sqz8, ?x2701 = 0hkxq, ?x5549 = 025s7j4, ?x7364 = 09gvd, ?x5526 = 09pbb, ?x12902 = 0fzjh, ?x1257 = 09728, ?x1303 = 0fj52s, nutrient(?x3468, ?x11784), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x9840), nutrient(?x3468, ?x6586), nutrient(?x3468, ?x6286), nutrient(?x3468, ?x4069), nutrient(?x3468, ?x3203), nutrient(?x3468, ?x2018), ?x6286 = 02y_3rf, ?x8442 = 02kcv4x, ?x3264 = 0dcfv, ?x5009 = 0fjfh, ?x9840 = 02p0tjr, ?x7652 = 025s0s0, ?x10612 = 0frq6, ?x10891 = 0g5gq, ?x3203 = 04kl74p, ?x5010 = 0h1vz, ?x6586 = 05gh50, ?x4069 = 0hqw8p_, ?x6285 = 01645p, ?x11784 = 07zqy >> conf = 0.86 => this is the best rule for 20 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 4, 7, 12 EVAL 0971v nutrient 0g5gq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 22.000 0.864 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0971v nutrient 02kd0rh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 22.000 22.000 0.864 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0971v nutrient 05gh50 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 22.000 0.864 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 0971v nutrient 0838f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 22.000 22.000 0.864 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #2484-0yyn5 PRED entity: 0yyn5 PRED relation: film! PRED expected values: 048lv 018ygt => 104 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1167): 0hskw (0.64 #99781, 0.39 #99780, 0.36 #68600), 01817f (0.39 #99780, 0.36 #68600, 0.36 #29102), 0kszw (0.25 #420, 0.12 #4576, 0.04 #35761), 02qgqt (0.25 #2096, 0.07 #14568, 0.06 #12488), 01r93l (0.25 #2825, 0.05 #15297, 0.04 #13217), 02wr6r (0.25 #1665, 0.04 #9977, 0.02 #51556), 04fhn_ (0.25 #2759, 0.04 #13151, 0.04 #11071), 0f6_x (0.25 #4782, 0.02 #62989, 0.02 #15176), 05xd_v (0.25 #1814, 0.02 #22602, 0.02 #51705), 015q43 (0.25 #901, 0.02 #40400, 0.01 #17531) >> Best rule #99781 for best value: >> intensional similarity = 3 >> extensional distance = 794 >> proper extension: 01b7h8; 0clpml; >> query: (?x5584, ?x2733) <- nominated_for(?x2733, ?x5584), award_winner(?x715, ?x2733), participant(?x4295, ?x2733) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #90503 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 712 *> proper extension: 0dq626; 0gydcp7; 02qhqz4; 0661m4p; 07x4qr; 03mh_tp; 0gffmn8; 0gjc4d3; 09g7vfw; 0gtvpkw; ... *> query: (?x5584, 018ygt) <- film(?x6470, ?x5584), film(?x574, ?x5584), participant(?x6658, ?x6470), spouse(?x6470, ?x2282) *> conf = 0.02 ranks of expected_values: 563, 618 EVAL 0yyn5 film! 018ygt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 104.000 54.000 0.640 http://example.org/film/actor/film./film/performance/film EVAL 0yyn5 film! 048lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 104.000 54.000 0.640 http://example.org/film/actor/film./film/performance/film #2483-03h_yfh PRED entity: 03h_yfh PRED relation: artist! PRED expected values: 011k11 => 94 concepts (88 used for prediction) PRED predicted values (max 10 best out of 93): 015_1q (0.41 #1852, 0.38 #1570, 0.20 #160), 03rhqg (0.25 #2131, 0.14 #1567, 0.14 #580), 01xyqk (0.20 #222, 0.20 #81, 0.19 #1632), 011k11 (0.19 #1586, 0.18 #1868, 0.03 #8213), 011k1h (0.17 #2125, 0.17 #1138, 0.17 #433), 01cf93 (0.17 #2173, 0.15 #2314, 0.14 #622), 01clyr (0.17 #456, 0.13 #2853, 0.08 #1161), 0181dw (0.17 #2157, 0.12 #2298, 0.10 #5823), 01cl0d (0.17 #337, 0.11 #760, 0.10 #901), 02p3cr5 (0.17 #450, 0.11 #732, 0.08 #1155) >> Best rule #1852 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 01fl3; >> query: (?x7803, 015_1q) <- artists(?x12513, ?x7803), artists(?x9427, ?x7803), ?x9427 = 0m40d, parent_genre(?x12513, ?x505) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1586 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 01vtj38; *> query: (?x7803, 011k11) <- profession(?x7803, ?x220), artists(?x9427, ?x7803), ?x9427 = 0m40d *> conf = 0.19 ranks of expected_values: 4 EVAL 03h_yfh artist! 011k11 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 88.000 0.409 http://example.org/music/record_label/artist #2482-07wrz PRED entity: 07wrz PRED relation: category PRED expected values: 08mbj5d => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #15, 0.91 #66, 0.90 #13) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 019_6d; >> query: (?x2313, 08mbj5d) <- institution(?x734, ?x2313), company(?x2669, ?x2313), colors(?x2313, ?x663) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07wrz category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.923 http://example.org/common/topic/webpage./common/webpage/category #2481-0239zv PRED entity: 0239zv PRED relation: location PRED expected values: 04vmp => 151 concepts (144 used for prediction) PRED predicted values (max 10 best out of 266): 02_286 (0.33 #37, 0.29 #4058, 0.25 #12905), 030qb3t (0.29 #4104, 0.25 #887, 0.17 #67678), 04jpl (0.25 #821, 0.22 #16087, 0.20 #4021), 059rby (0.25 #820, 0.14 #4037, 0.07 #24957), 01dzq6 (0.25 #1390, 0.14 #4607, 0.02 #28747), 04vmp (0.22 #30930, 0.14 #37364, 0.12 #38169), 0fhp9 (0.20 #2455, 0.12 #4868, 0.09 #16130), 0cc56 (0.20 #1665, 0.11 #12120, 0.10 #16950), 04ly1 (0.20 #1811, 0.04 #10657, 0.03 #14680), 052p7 (0.20 #3343, 0.03 #16214, 0.01 #23458) >> Best rule #37 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 02kxbx3; >> query: (?x10074, 02_286) <- student(?x1771, ?x10074), sibling(?x12024, ?x10074), profession(?x10074, ?x987), ?x987 = 0dxtg, gender(?x10074, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #30930 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 104 *> proper extension: 0338lq; *> query: (?x10074, 04vmp) <- award(?x10074, ?x1937), nominated_for(?x1937, ?x11114), ?x11114 = 02tcgh *> conf = 0.22 ranks of expected_values: 6 EVAL 0239zv location 04vmp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 151.000 144.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #2480-05p92jn PRED entity: 05p92jn PRED relation: award_nominee! PRED expected values: 0cms7f => 88 concepts (30 used for prediction) PRED predicted values (max 10 best out of 861): 04t2l2 (0.81 #53288, 0.81 #57924, 0.81 #55606), 03hh89 (0.81 #53288, 0.81 #57924, 0.81 #55606), 0bt7ws (0.81 #53288, 0.81 #57924, 0.81 #55606), 0cj36c (0.81 #53288, 0.81 #57924, 0.81 #55606), 02_j8x (0.81 #53288, 0.81 #57924, 0.81 #55606), 0cms7f (0.81 #53288, 0.81 #57924, 0.81 #55606), 0cnl80 (0.81 #53288, 0.81 #57924, 0.81 #55606), 05p92jn (0.40 #1499, 0.25 #3816, 0.17 #48653), 02p65p (0.25 #2343, 0.04 #23196, 0.04 #9293), 01kwsg (0.20 #1109, 0.15 #16219, 0.15 #69509) >> Best rule #53288 for best value: >> intensional similarity = 3 >> extensional distance = 1205 >> proper extension: 043q6n_; 02bwc7; 09btt1; 03f4xvm; 05bpg3; 092kgw; 050_qx; >> query: (?x6622, ?x237) <- award_nominee(?x10617, ?x6622), participant(?x2035, ?x10617), award_nominee(?x6622, ?x237) >> conf = 0.81 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 05p92jn award_nominee! 0cms7f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 30.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2479-05prs8 PRED entity: 05prs8 PRED relation: profession PRED expected values: 0cbd2 0dxtg => 100 concepts (82 used for prediction) PRED predicted values (max 10 best out of 60): 0dxtg (0.83 #457, 0.77 #5193, 0.56 #2381), 02hrh1q (0.76 #3418, 0.74 #5046, 0.69 #4010), 02jknp (0.68 #451, 0.49 #2671, 0.49 #2523), 02krf9 (0.26 #2394, 0.26 #2986, 0.20 #26), 02hv44_ (0.25 #11399, 0.13 #501, 0.07 #353), 012t_z (0.25 #11399, 0.10 #160, 0.08 #1492), 0cbd2 (0.24 #450, 0.20 #154, 0.19 #5186), 018gz8 (0.22 #2976, 0.18 #2384, 0.16 #5196), 0dz3r (0.20 #2, 0.13 #3258, 0.12 #5478), 0dgd_ (0.20 #30, 0.05 #474, 0.05 #4766) >> Best rule #457 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 04107; >> query: (?x1533, 0dxtg) <- award(?x1533, ?x1862), ?x1862 = 0gr51, nationality(?x1533, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 05prs8 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 82.000 0.827 http://example.org/people/person/profession EVAL 05prs8 profession 0cbd2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 100.000 82.000 0.827 http://example.org/people/person/profession #2478-016ywr PRED entity: 016ywr PRED relation: award_winner! PRED expected values: 0cqh46 => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 230): 04kxsb (0.39 #26117, 0.37 #26974, 0.37 #28687), 0bdwqv (0.39 #26117, 0.37 #26974, 0.37 #28687), 0bfvd4 (0.39 #26117, 0.37 #26974, 0.37 #28687), 0cqh46 (0.39 #26117, 0.37 #26974, 0.37 #28687), 0789_m (0.39 #26117, 0.37 #26974, 0.37 #28687), 05pcn59 (0.39 #26117, 0.37 #26974, 0.37 #28687), 057xs89 (0.39 #26117, 0.37 #26974, 0.37 #28687), 027c95y (0.26 #1867, 0.05 #13001, 0.05 #5720), 0cjyzs (0.24 #532, 0.04 #17660, 0.04 #20228), 0fbtbt (0.20 #655, 0.03 #15215, 0.03 #16927) >> Best rule #26117 for best value: >> intensional similarity = 3 >> extensional distance = 1275 >> proper extension: 016fmf; 0134s5; 02lbrd; 0d193h; 0g_g2; 0134tg; 0b1zz; 07h76; 0l8g0; 015cxv; ... >> query: (?x1867, ?x458) <- award_winner(?x2292, ?x1867), award_winner(?x591, ?x1867), award(?x1867, ?x458) >> conf = 0.39 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 016ywr award_winner! 0cqh46 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 103.000 0.388 http://example.org/award/award_category/winners./award/award_honor/award_winner #2477-018fwv PRED entity: 018fwv PRED relation: profession PRED expected values: 02hrh1q => 68 concepts (66 used for prediction) PRED predicted values (max 10 best out of 125): 02hrh1q (0.88 #5119, 0.87 #3917, 0.87 #3016), 0np9r (0.42 #22, 0.24 #472, 0.22 #1222), 01d_h8 (0.32 #156, 0.31 #306, 0.28 #6460), 03gjzk (0.31 #616, 0.31 #316, 0.26 #166), 0dxtg (0.31 #314, 0.30 #614, 0.27 #5568), 0cbd2 (0.31 #307, 0.21 #157, 0.16 #757), 018gz8 (0.25 #18, 0.16 #468, 0.16 #168), 02jknp (0.20 #6462, 0.18 #4211, 0.18 #5262), 0kyk (0.19 #331, 0.14 #481, 0.11 #2132), 09jwl (0.17 #4673, 0.16 #5574, 0.16 #5274) >> Best rule #5119 for best value: >> intensional similarity = 3 >> extensional distance = 1804 >> proper extension: 03j0br4; 05gnf9; 039xcr; >> query: (?x13635, 02hrh1q) <- film(?x13635, ?x383), nominated_for(?x507, ?x383), film(?x382, ?x383) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018fwv profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 66.000 0.882 http://example.org/people/person/profession #2476-04954r PRED entity: 04954r PRED relation: language PRED expected values: 06nm1 => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 25): 04306rv (0.20 #4, 0.10 #290, 0.09 #175), 06nm1 (0.12 #10, 0.10 #296, 0.10 #641), 02bjrlw (0.12 #1, 0.08 #287, 0.07 #229), 06b_j (0.09 #21, 0.07 #135, 0.07 #307), 03_9r (0.07 #9, 0.06 #180, 0.05 #3642), 0653m (0.06 #68, 0.04 #182, 0.03 #11), 0jzc (0.04 #19, 0.04 #190, 0.03 #76), 04h9h (0.04 #41, 0.03 #615, 0.03 #327), 012w70 (0.04 #69, 0.04 #183, 0.02 #643), 05zjd (0.04 #24, 0.01 #195, 0.01 #770) >> Best rule #4 for best value: >> intensional similarity = 5 >> extensional distance = 136 >> proper extension: 0d7vtk; >> query: (?x3755, 04306rv) <- language(?x3755, ?x5607), language(?x3755, ?x254), ?x254 = 02h40lc, ?x5607 = 064_8sq, nominated_for(?x484, ?x3755) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 136 *> proper extension: 0d7vtk; *> query: (?x3755, 06nm1) <- language(?x3755, ?x5607), language(?x3755, ?x254), ?x254 = 02h40lc, ?x5607 = 064_8sq, nominated_for(?x484, ?x3755) *> conf = 0.12 ranks of expected_values: 2 EVAL 04954r language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.196 http://example.org/film/film/language #2475-0738b8 PRED entity: 0738b8 PRED relation: type_of_union PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.75 #17, 0.74 #33, 0.73 #169), 01g63y (0.24 #26, 0.23 #30, 0.21 #62) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 01wyzyl; 05wjnt; 01_rh4; 02v406; 0dfjb8; 015lhm; 03xnq9_; 04f7c55; 02wmbg; 03d9v8; ... >> query: (?x2437, 04ztj) <- film(?x2437, ?x428), languages(?x2437, ?x254), religion(?x2437, ?x2694) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0738b8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.753 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2474-0h3y PRED entity: 0h3y PRED relation: adjoins PRED expected values: 07fj_ 05cc1 => 160 concepts (62 used for prediction) PRED predicted values (max 10 best out of 479): 05cc1 (0.84 #15406, 0.83 #12323, 0.82 #19257), 04vjh (0.84 #15406, 0.83 #12323, 0.82 #19257), 07fj_ (0.84 #15406, 0.83 #12323, 0.82 #19257), 0h3y (0.29 #780, 0.18 #3090, 0.16 #9255), 01699 (0.29 #1045, 0.14 #9520, 0.12 #3355), 0164v (0.29 #1171, 0.12 #1941, 0.08 #9646), 03676 (0.29 #1113, 0.12 #3423, 0.08 #9588), 06tw8 (0.24 #3324, 0.14 #9489, 0.12 #27975), 01znc_ (0.20 #2391, 0.13 #19256, 0.13 #10096), 01z215 (0.20 #2405, 0.06 #17811, 0.06 #18581) >> Best rule #15406 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 05r4w; 01znc_; >> query: (?x291, ?x1273) <- adjoins(?x1273, ?x291), exported_to(?x291, ?x94), currency(?x291, ?x170) >> conf = 0.84 => this is the best rule for 3 predicted values ranks of expected_values: 1, 3 EVAL 0h3y adjoins 05cc1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 62.000 0.841 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins EVAL 0h3y adjoins 07fj_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 160.000 62.000 0.841 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #2473-054ky1 PRED entity: 054ky1 PRED relation: award! PRED expected values: 06pj8 043gj 01t9qj_ => 46 concepts (28 used for prediction) PRED predicted values (max 10 best out of 2648): 0gs1_ (0.81 #6696, 0.80 #26784, 0.80 #26785), 0bwh6 (0.81 #6696, 0.80 #26784, 0.80 #26785), 01v5h (0.81 #6696, 0.80 #26784, 0.80 #26785), 0z4s (0.81 #6696, 0.80 #26784, 0.80 #26785), 039bp (0.81 #6696, 0.80 #26784, 0.80 #26785), 0dzf_ (0.81 #6696, 0.80 #26784, 0.80 #26785), 0cj8x (0.81 #6696, 0.80 #26784, 0.80 #26785), 0j_c (0.81 #6696, 0.80 #26784, 0.80 #26785), 03_bcg (0.81 #6696, 0.80 #26784, 0.80 #26785), 0kjgl (0.81 #6696, 0.80 #26784, 0.80 #26785) >> Best rule #6696 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 040njc; 019f4v; 0gq9h; 0gs9p; >> query: (?x2060, ?x450) <- ceremony(?x2060, ?x747), award(?x4075, ?x2060), ?x4075 = 01n9d9, award_winner(?x2060, ?x450) >> conf = 0.81 => this is the best rule for 14 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 11, 55 EVAL 054ky1 award! 01t9qj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 28.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ky1 award! 043gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 46.000 28.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 054ky1 award! 06pj8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 46.000 28.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2472-03wv2g PRED entity: 03wv2g PRED relation: institution! PRED expected values: 014mlp => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 24): 02h4rq6 (0.81 #129, 0.81 #254, 0.79 #204), 014mlp (0.70 #132, 0.69 #182, 0.69 #207), 019v9k (0.70 #261, 0.68 #136, 0.68 #211), 03bwzr4 (0.60 #142, 0.58 #267, 0.56 #192), 02_xgp2 (0.52 #265, 0.48 #215, 0.45 #140), 016t_3 (0.51 #130, 0.44 #180, 0.44 #255), 0bkj86 (0.43 #135, 0.41 #260, 0.38 #210), 07s6fsf (0.42 #252, 0.41 #202, 0.39 #378), 04zx3q1 (0.36 #128, 0.30 #277, 0.30 #253), 013zdg (0.30 #277, 0.28 #134, 0.26 #403) >> Best rule #129 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 08qnnv; >> query: (?x13736, 02h4rq6) <- fraternities_and_sororities(?x13736, ?x3697), school(?x2820, ?x13736), ?x3697 = 0325pb, school_type(?x13736, ?x3205) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #132 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 08qnnv; *> query: (?x13736, 014mlp) <- fraternities_and_sororities(?x13736, ?x3697), school(?x2820, ?x13736), ?x3697 = 0325pb, school_type(?x13736, ?x3205) *> conf = 0.70 ranks of expected_values: 2 EVAL 03wv2g institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.809 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2471-0l998 PRED entity: 0l998 PRED relation: olympics! PRED expected values: 015qh 06c1y 087vz 07fj_ => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 196): 06c1y (0.75 #532, 0.70 #2797, 0.67 #327), 05b4w (0.69 #1780, 0.67 #345, 0.65 #3435), 06mkj (0.67 #339, 0.62 #544, 0.61 #1225), 05b7q (0.67 #383, 0.62 #588, 0.60 #180), 05cgv (0.67 #219, 0.60 #117, 0.50 #525), 015qh (0.62 #531, 0.53 #2277, 0.52 #2796), 05r4w (0.61 #1225, 0.60 #103, 0.50 #511), 01mjq (0.61 #1225, 0.60 #125, 0.50 #533), 03spz (0.61 #1225, 0.50 #369, 0.42 #203), 03h64 (0.61 #1225, 0.50 #4335, 0.40 #143) >> Best rule #532 for best value: >> intensional similarity = 14 >> extensional distance = 6 >> proper extension: 0l98s; 06sks6; >> query: (?x775, 06c1y) <- sports(?x775, ?x3015), sports(?x775, ?x2315), medal(?x775, ?x422), olympics(?x3635, ?x775), olympics(?x304, ?x775), ?x3015 = 071t0, locations(?x775, ?x8181), ?x2315 = 06wrt, ?x3635 = 019pcs, organization(?x304, ?x127), film_release_region(?x4441, ?x304), film_release_region(?x984, ?x304), ?x4441 = 0125xq, ?x984 = 0m_mm >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 21, 25 EVAL 0l998 olympics! 07fj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 59.000 59.000 0.750 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l998 olympics! 087vz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 59.000 59.000 0.750 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l998 olympics! 06c1y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.750 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0l998 olympics! 015qh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 59.000 59.000 0.750 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #2470-02_286 PRED entity: 02_286 PRED relation: film_regional_debut_venue! PRED expected values: 0bpm4yw 043sct5 => 178 concepts (178 used for prediction) PRED predicted values (max 10 best out of 174): 0gffmn8 (0.30 #929, 0.16 #2323, 0.13 #1103), 09v42sf (0.20 #1039, 0.13 #2433, 0.06 #1388), 0cnztc4 (0.20 #889, 0.13 #2283, 0.06 #1238), 043sct5 (0.10 #950, 0.10 #2344, 0.06 #1299), 0fq7dv_ (0.10 #910, 0.10 #2304), 0bpm4yw (0.10 #947, 0.07 #1121, 0.06 #1296), 0dr_4 (0.10 #901, 0.07 #1075, 0.06 #1250), 04z_3pm (0.10 #1014, 0.06 #2408), 05ft32 (0.10 #999, 0.06 #2393), 0ds6bmk (0.10 #993, 0.06 #2387) >> Best rule #929 for best value: >> intensional similarity = 2 >> extensional distance = 8 >> proper extension: 07751; 02fzs; >> query: (?x739, 0gffmn8) <- vacationer(?x739, ?x444), film_regional_debut_venue(?x2047, ?x739) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #950 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 8 *> proper extension: 07751; 02fzs; *> query: (?x739, 043sct5) <- vacationer(?x739, ?x444), film_regional_debut_venue(?x2047, ?x739) *> conf = 0.10 ranks of expected_values: 4, 6 EVAL 02_286 film_regional_debut_venue! 043sct5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 178.000 178.000 0.300 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue EVAL 02_286 film_regional_debut_venue! 0bpm4yw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 178.000 178.000 0.300 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #2469-0170qf PRED entity: 0170qf PRED relation: award_nominee! PRED expected values: 0fbx6 => 83 concepts (41 used for prediction) PRED predicted values (max 10 best out of 754): 03_wj_ (0.81 #90677, 0.81 #76727, 0.81 #76728), 0fbx6 (0.81 #90677, 0.81 #76727, 0.81 #76728), 0278x6s (0.76 #76729, 0.76 #51150, 0.19 #93005), 0m31m (0.23 #13951, 0.22 #6976, 0.19 #2325), 015rkw (0.19 #93005, 0.18 #95332, 0.18 #65104), 0170pk (0.19 #93005, 0.18 #95332, 0.18 #65104), 01kwld (0.19 #93005, 0.18 #95332, 0.18 #65104), 016gr2 (0.19 #93005, 0.18 #95332, 0.18 #65104), 0170qf (0.19 #93005, 0.18 #95332, 0.18 #65104), 07f3xb (0.19 #93005, 0.18 #95332, 0.18 #65104) >> Best rule #90677 for best value: >> intensional similarity = 3 >> extensional distance = 1514 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 04bdxl; 02s2ft; 05vsxz; 06qgvf; 0grwj; ... >> query: (?x2280, ?x57) <- award_nominee(?x2280, ?x57), nominated_for(?x2280, ?x278), gender(?x2280, ?x231) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0170qf award_nominee! 0fbx6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 41.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2468-014_xj PRED entity: 014_xj PRED relation: artist! PRED expected values: 01dtcb => 104 concepts (82 used for prediction) PRED predicted values (max 10 best out of 124): 0g768 (0.32 #5929, 0.21 #7300, 0.20 #34), 03rhqg (0.30 #14, 0.25 #7280, 0.24 #973), 01cszh (0.30 #10, 0.12 #1654, 0.09 #3027), 01cf93 (0.27 #192, 0.15 #877, 0.12 #1288), 02p3cr5 (0.26 #3315, 0.20 #2356, 0.20 #2767), 043g7l (0.25 #302, 0.20 #6334, 0.19 #576), 03mp8k (0.25 #611, 0.19 #748, 0.16 #3628), 0k_kr (0.22 #1411, 0.22 #1548, 0.19 #2373), 0n85g (0.22 #2392, 0.20 #471, 0.20 #1567), 03vtrv (0.20 #98, 0.18 #235, 0.06 #646) >> Best rule #5929 for best value: >> intensional similarity = 4 >> extensional distance = 258 >> proper extension: 0fp_v1x; 0m2l9; 01gf5h; 0ftps; 0136p1; 06k02; 045zr; 033wx9; 01w724; 0bqsy; ... >> query: (?x12449, 0g768) <- category(?x12449, ?x134), artist(?x3265, ?x12449), artist(?x3265, ?x6162), ?x6162 = 01w9wwg >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1140 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 04rcr; 0150jk; 0dtd6; 01czx; 0dvqq; 01vrwfv; 017j6; 0134s5; 01rm8b; 0mgcr; ... *> query: (?x12449, 01dtcb) <- artist(?x11912, ?x12449), group(?x227, ?x12449), ?x227 = 0342h, artist(?x2149, ?x12449) *> conf = 0.14 ranks of expected_values: 17 EVAL 014_xj artist! 01dtcb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 104.000 82.000 0.323 http://example.org/music/record_label/artist #2467-0d2b38 PRED entity: 0d2b38 PRED relation: profession! PRED expected values: 04n7njg 0488g9 => 76 concepts (8 used for prediction) PRED predicted values (max 10 best out of 3188): 0kc6 (0.50 #33028, 0.50 #16055, 0.11 #33946), 07d370 (0.50 #30814, 0.50 #13841, 0.11 #33946), 02465 (0.50 #33362, 0.50 #16389), 0ff3y (0.50 #16860, 0.33 #33833, 0.11 #33946), 05wm88 (0.50 #16552, 0.33 #33525, 0.11 #33946), 01jrp0 (0.50 #16494, 0.33 #33467, 0.11 #33946), 0d3k14 (0.50 #16360, 0.33 #33333, 0.11 #33946), 01nz1q6 (0.50 #16324, 0.33 #33297, 0.11 #33946), 029k55 (0.50 #16316, 0.33 #33289, 0.11 #33946), 0ds2sb (0.50 #16251, 0.33 #33224, 0.11 #33946) >> Best rule #33028 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 01nxfc; >> query: (?x7591, 0kc6) <- profession(?x12154, ?x7591), profession(?x5287, ?x7591), ?x5287 = 0534v, specialization_of(?x7591, ?x955), nationality(?x12154, ?x94), country(?x108, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #16468 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0dxtg; 0kyk; *> query: (?x7591, 0488g9) <- profession(?x12154, ?x7591), profession(?x5287, ?x7591), ?x5287 = 0534v, specialization_of(?x7591, ?x955), nationality(?x12154, ?x94), ?x94 = 09c7w0, tv_program(?x12154, ?x8017) *> conf = 0.25 ranks of expected_values: 124, 754 EVAL 0d2b38 profession! 0488g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 76.000 8.000 0.500 http://example.org/people/person/profession EVAL 0d2b38 profession! 04n7njg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 76.000 8.000 0.500 http://example.org/people/person/profession #2466-0fkvn PRED entity: 0fkvn PRED relation: basic_title! PRED expected values: 038w8 => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 491): 03_js (0.50 #459, 0.33 #641, 0.33 #158), 034ls (0.50 #452, 0.33 #634, 0.33 #151), 0c_md_ (0.50 #464, 0.33 #646, 0.33 #163), 0f7fy (0.50 #451, 0.33 #633, 0.33 #150), 06c97 (0.50 #444, 0.33 #626, 0.33 #143), 042fk (0.38 #1092, 0.33 #660, 0.33 #177), 0424m (0.38 #1062, 0.33 #630, 0.33 #147), 0dq2k (0.33 #625, 0.33 #82, 0.33 #21), 03_nq (0.33 #642, 0.33 #159, 0.33 #38), 081t6 (0.33 #296, 0.33 #176, 0.25 #477) >> Best rule #459 for best value: >> intensional similarity = 19 >> extensional distance = 2 >> proper extension: 0dq3c; >> query: (?x900, 03_js) <- jurisdiction_of_office(?x900, ?x9559), jurisdiction_of_office(?x900, ?x7405), jurisdiction_of_office(?x900, ?x4776), jurisdiction_of_office(?x900, ?x2020), jurisdiction_of_office(?x900, ?x938), basic_title(?x4196, ?x900), film_release_region(?x903, ?x9559), contains(?x2020, ?x6295), contains(?x938, ?x8726), contains(?x938, ?x4296), entity_involved(?x2391, ?x4196), ?x6295 = 0tygl, citytown(?x4079, ?x9559), ?x8726 = 0m2cb, contains(?x94, ?x938), ?x4296 = 07vyf, religion(?x7405, ?x109), location(?x397, ?x4776), person(?x1015, ?x4196) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #172 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 1 *> proper extension: 060c4; *> query: (?x900, 038w8) <- jurisdiction_of_office(?x900, ?x12522), jurisdiction_of_office(?x900, ?x9559), jurisdiction_of_office(?x900, ?x2020), jurisdiction_of_office(?x900, ?x938), basic_title(?x4196, ?x900), film_release_region(?x7538, ?x9559), contains(?x2020, ?x6295), contains(?x938, ?x8726), entity_involved(?x2391, ?x4196), ?x6295 = 0tygl, citytown(?x4079, ?x9559), ?x8726 = 0m2cb, administrative_division(?x12876, ?x12522), ?x7538 = 035zr0, contains(?x252, ?x9559), religion(?x938, ?x109) *> conf = 0.33 ranks of expected_values: 21 EVAL 0fkvn basic_title! 038w8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 46.000 46.000 0.500 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #2465-02cbhg PRED entity: 02cbhg PRED relation: film_crew_role PRED expected values: 09zzb8 02ynfr => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.77 #364, 0.75 #1, 0.73 #1521), 01vx2h (0.43 #156, 0.36 #229, 0.34 #807), 0dxtw (0.41 #191, 0.37 #806, 0.34 #1965), 01pvkk (0.30 #1532, 0.28 #1605, 0.28 #375), 02rh1dz (0.25 #9, 0.21 #154, 0.20 #190), 01xy5l_ (0.24 #159, 0.17 #123, 0.12 #377), 0215hd (0.19 #92, 0.17 #128, 0.15 #382), 02ynfr (0.17 #812, 0.17 #523, 0.16 #379), 089g0h (0.17 #165, 0.13 #383, 0.13 #201), 015h31 (0.17 #153, 0.11 #226, 0.09 #334) >> Best rule #364 for best value: >> intensional similarity = 4 >> extensional distance = 203 >> proper extension: 0dkv90; >> query: (?x8084, 09zzb8) <- film_format(?x8084, ?x909), nominated_for(?x143, ?x8084), film_release_region(?x8084, ?x94), film_crew_role(?x8084, ?x468) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8 EVAL 02cbhg film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 94.000 94.000 0.766 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02cbhg film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.766 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2464-01kb2j PRED entity: 01kb2j PRED relation: award PRED expected values: 09td7p 099t8j 02pzz3p => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 256): 027b9k6 (0.70 #14548, 0.70 #17693, 0.70 #12187), 027cyf7 (0.70 #14548, 0.70 #17693, 0.70 #12187), 02z1nbg (0.70 #14548, 0.70 #17693, 0.70 #12187), 02pzz3p (0.70 #14548, 0.70 #17693, 0.70 #12187), 027571b (0.70 #14548, 0.70 #17693, 0.70 #12187), 05ztrmj (0.40 #174, 0.14 #22805, 0.13 #28311), 02y_rq5 (0.33 #481, 0.29 #874, 0.14 #22805), 099t8j (0.33 #525, 0.29 #918, 0.13 #28311), 0gqy2 (0.29 #941, 0.26 #1334, 0.17 #548), 027dtxw (0.29 #790, 0.21 #1183, 0.20 #4) >> Best rule #14548 for best value: >> intensional similarity = 3 >> extensional distance = 1452 >> proper extension: 012t1; 01sbf2; 01dzz7; 01q415; 01ycck; 0gv40; 013423; 01vt5c_; 027vps; >> query: (?x5097, ?x1132) <- award_nominee(?x192, ?x5097), profession(?x5097, ?x1032), award_winner(?x1132, ?x5097) >> conf = 0.70 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 8, 20 EVAL 01kb2j award 02pzz3p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 87.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01kb2j award 099t8j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 87.000 87.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01kb2j award 09td7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 87.000 87.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2463-024y8p PRED entity: 024y8p PRED relation: category PRED expected values: 08mbj5d => 200 concepts (200 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.93 #58, 0.91 #77, 0.91 #116) >> Best rule #58 for best value: >> intensional similarity = 5 >> extensional distance = 67 >> proper extension: 05cwl_; >> query: (?x1635, 08mbj5d) <- organization(?x5510, ?x1635), currency(?x1635, ?x170), ?x170 = 09nqf, citytown(?x1635, ?x8993), locations(?x4368, ?x8993) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024y8p category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 200.000 200.000 0.928 http://example.org/common/topic/webpage./common/webpage/category #2462-010bnr PRED entity: 010bnr PRED relation: contains! PRED expected values: 07b_l => 34 concepts (18 used for prediction) PRED predicted values (max 10 best out of 195): 09c7w0 (0.79 #1795, 0.77 #899, 0.76 #3), 01n7q (0.29 #78, 0.29 #1870, 0.27 #2766), 0f8l9c (0.24 #6273, 0.12 #14436, 0.07 #15343), 0d060g (0.24 #6273, 0.07 #15343, 0.06 #14427), 04_1l0v (0.19 #9435, 0.17 #12162, 0.17 #11252), 01ly5m (0.17 #9884, 0.07 #12612, 0.02 #13517), 02qkt (0.15 #15713, 0.03 #13877, 0.02 #12058), 0345h (0.12 #14436, 0.11 #14433, 0.07 #15343), 0jgd (0.12 #14436, 0.11 #14433, 0.07 #15343), 06bnz (0.12 #14436, 0.07 #15343, 0.07 #14437) >> Best rule #1795 for best value: >> intensional similarity = 6 >> extensional distance = 82 >> proper extension: 04f_d; 0m2rv; 01snm; 0qpjt; 0fwc0; 0pc56; 0r4h3; 0r2bv; >> query: (?x12527, 09c7w0) <- jurisdiction_of_office(?x1195, ?x12527), source(?x12527, ?x958), category(?x12527, ?x134), ?x958 = 0jbk9, ?x1195 = 0pqc5, ?x134 = 08mbj5d >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #2910 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 98 *> proper extension: 02_286; 0wh3; 030qb3t; 0mn0v; 0f__1; 0y2dl; 0r4xt; 01sn3; 0r04p; 02hrh0_; ... *> query: (?x12527, 07b_l) <- jurisdiction_of_office(?x1195, ?x12527), source(?x12527, ?x958), ?x958 = 0jbk9, ?x1195 = 0pqc5, place(?x12527, ?x12527) *> conf = 0.07 ranks of expected_values: 24 EVAL 010bnr contains! 07b_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 34.000 18.000 0.786 http://example.org/location/location/contains #2461-03lpp_ PRED entity: 03lpp_ PRED relation: draft PRED expected values: 02r6gw6 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 18): 02r6gw6 (0.85 #322, 0.83 #139, 0.81 #358), 047dpm0 (0.79 #489, 0.78 #1047, 0.78 #880), 02rl201 (0.78 #1047, 0.78 #880, 0.78 #491), 04f4z1k (0.78 #1047, 0.78 #880, 0.78 #491), 02z6872 (0.78 #1047, 0.78 #880, 0.78 #491), 02x2khw (0.78 #1047, 0.78 #880, 0.78 #491), 0g3zpp (0.57 #456, 0.55 #110, 0.54 #584), 092j54 (0.55 #116, 0.54 #590, 0.54 #462), 09l0x9 (0.55 #119, 0.54 #593, 0.54 #465), 05vsb7 (0.54 #455, 0.50 #583, 0.47 #547) >> Best rule #322 for best value: >> intensional similarity = 14 >> extensional distance = 18 >> proper extension: 01yhm; >> query: (?x662, 02r6gw6) <- position(?x662, ?x8520), position(?x662, ?x5727), ?x8520 = 01z9v6, season(?x662, ?x2406), school(?x662, ?x5486), sport(?x662, ?x5063), colors(?x662, ?x332), team(?x5727, ?x8995), team(?x5727, ?x4208), position(?x7399, ?x5727), ?x8995 = 01d6g, ?x4208 = 061xq, ?x5063 = 018jz, ?x7399 = 06wpc >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03lpp_ draft 02r6gw6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.850 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #2460-0ltv PRED entity: 0ltv PRED relation: genre! PRED expected values: 08952r 02sfnv 08c6k9 => 36 concepts (7 used for prediction) PRED predicted values (max 10 best out of 1855): 0g4pl7z (0.79 #1876, 0.78 #1875, 0.59 #11255), 02_nsc (0.78 #1875, 0.62 #7501, 0.61 #13134), 027gy0k (0.78 #1875, 0.59 #11255, 0.55 #13132), 0czyxs (0.60 #1930, 0.58 #3804, 0.13 #5678), 01hqk (0.60 #2627, 0.58 #4501, 0.07 #6375), 03cd0x (0.60 #2843, 0.50 #4717, 0.07 #6591), 07b1gq (0.60 #2502, 0.50 #4376, 0.07 #6250), 03s6l2 (0.60 #1964, 0.42 #3838, 0.33 #87), 03kg2v (0.60 #2373, 0.42 #4247, 0.33 #496), 0dlngsd (0.60 #2686, 0.42 #4560, 0.27 #6434) >> Best rule #1876 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 02l7c8; >> query: (?x10308, ?x8955) <- titles(?x10308, ?x9642), titles(?x10308, ?x8955), ?x9642 = 02_nsc, genre(?x9421, ?x10308), award_winner(?x8955, ?x5973), film_release_region(?x8955, ?x87), film(?x1104, ?x8955), nominated_for(?x5886, ?x8955) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #3448 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 3 *> proper extension: 02kdv5l; 0lsxr; *> query: (?x10308, 08c6k9) <- genre(?x9421, ?x10308), ?x9421 = 0ct2tf5 *> conf = 0.60 ranks of expected_values: 51, 1091, 1096 EVAL 0ltv genre! 08c6k9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 36.000 7.000 0.786 http://example.org/film/film/genre EVAL 0ltv genre! 02sfnv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 7.000 0.786 http://example.org/film/film/genre EVAL 0ltv genre! 08952r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 7.000 0.786 http://example.org/film/film/genre #2459-040vk98 PRED entity: 040vk98 PRED relation: disciplines_or_subjects PRED expected values: 04g51 => 52 concepts (52 used for prediction) PRED predicted values (max 10 best out of 32): 04g51 (0.96 #529, 0.88 #384, 0.86 #348), 01hmnh (0.64 #298, 0.64 #262, 0.57 #335), 02vxn (0.38 #690, 0.38 #726, 0.35 #762), 014dfn (0.33 #97, 0.33 #25, 0.20 #133), 0707q (0.29 #174, 0.21 #318, 0.20 #800), 0l67h (0.29 #177, 0.20 #800, 0.20 #799), 08_lx0 (0.20 #800, 0.20 #799, 0.20 #798), 0j7v_ (0.20 #800, 0.20 #799, 0.20 #798), 02n4kr (0.20 #800, 0.20 #799, 0.20 #798), 01jfsb (0.20 #800, 0.20 #799, 0.20 #798) >> Best rule #529 for best value: >> intensional similarity = 7 >> extensional distance = 44 >> proper extension: 02xzd9; 05j9_f; 05j085; >> query: (?x575, 04g51) <- disciplines_or_subjects(?x575, ?x6060), disciplines_or_subjects(?x10505, ?x6060), disciplines_or_subjects(?x8909, ?x6060), disciplines_or_subjects(?x3337, ?x6060), ?x3337 = 01yz0x, ?x8909 = 040_9s0, ?x10505 = 0208wk >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 040vk98 disciplines_or_subjects 04g51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 52.000 0.957 http://example.org/award/award_category/disciplines_or_subjects #2458-05pyrb PRED entity: 05pyrb PRED relation: genre PRED expected values: 05p553 03k9fj => 92 concepts (82 used for prediction) PRED predicted values (max 10 best out of 156): 02kdv5l (0.85 #6682, 0.71 #6444, 0.70 #2744), 03k9fj (0.84 #5856, 0.79 #5260, 0.74 #7170), 05p553 (0.78 #7880, 0.56 #7999, 0.54 #3105), 06n90 (0.69 #6216, 0.56 #2515, 0.55 #2756), 03q4nz (0.57 #1448, 0.50 #853, 0.40 #1924), 01jfsb (0.54 #8128, 0.46 #6693, 0.43 #6455), 02l7c8 (0.50 #732, 0.48 #5146, 0.36 #2041), 04xvlr (0.47 #7040, 0.15 #9193, 0.15 #9433), 02n4kr (0.44 #6927, 0.27 #7644, 0.20 #8123), 0lsxr (0.42 #7645, 0.21 #8124, 0.21 #3946) >> Best rule #6682 for best value: >> intensional similarity = 11 >> extensional distance = 395 >> proper extension: 0gzy02; 04v8x9; 0ds33; 04fzfj; 03t97y; 0g5pv3; 01kff7; 0340hj; 0fdv3; 01kf3_9; ... >> query: (?x5732, 02kdv5l) <- film_release_distribution_medium(?x5732, ?x81), genre(?x5732, ?x5937), language(?x5732, ?x2164), genre(?x8444, ?x5937), ?x8444 = 045qmr, genre(?x9201, ?x5937), genre(?x5633, ?x5937), genre(?x2508, ?x5937), ?x5633 = 0cks1m, ?x9201 = 056k77g, production_companies(?x2508, ?x1561) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #5856 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 149 *> proper extension: 085bd1; 09fc83; 01f39b; 0h63q6t; *> query: (?x5732, 03k9fj) <- genre(?x5732, ?x2540), genre(?x5732, ?x1510), ?x1510 = 01hmnh, genre(?x10826, ?x2540), genre(?x7806, ?x2540), genre(?x6216, ?x2540), genre(?x2628, ?x2540), genre(?x11377, ?x2540), ?x10826 = 0564x, ?x2628 = 06wbm8q, ?x7806 = 0b3n61, nominated_for(?x2135, ?x11377), ?x6216 = 06fcqw *> conf = 0.84 ranks of expected_values: 2, 3 EVAL 05pyrb genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 82.000 0.849 http://example.org/film/film/genre EVAL 05pyrb genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 92.000 82.000 0.849 http://example.org/film/film/genre #2457-09p3_s PRED entity: 09p3_s PRED relation: nominated_for! PRED expected values: 040njc 02qvyrt => 77 concepts (72 used for prediction) PRED predicted values (max 10 best out of 206): 094qd5 (0.77 #7791, 0.68 #4895, 0.68 #5119), 09d28z (0.68 #4895, 0.68 #5119, 0.67 #5118), 027c924 (0.68 #4895, 0.68 #5119, 0.67 #5118), 02z1nbg (0.68 #4895, 0.68 #5119, 0.67 #5118), 027c95y (0.68 #4895, 0.68 #5119, 0.67 #5118), 040njc (0.51 #450, 0.49 #894, 0.43 #1560), 0gqy2 (0.42 #995, 0.40 #551, 0.38 #4333), 02pqp12 (0.40 #495, 0.38 #939, 0.34 #1605), 0f4x7 (0.39 #910, 0.39 #466, 0.37 #4248), 02qyntr (0.39 #609, 0.38 #1053, 0.37 #1719) >> Best rule #7791 for best value: >> intensional similarity = 3 >> extensional distance = 689 >> proper extension: 06w7mlh; >> query: (?x5519, ?x1079) <- award(?x5519, ?x1079), nominated_for(?x1079, ?x167), ceremony(?x1079, ?x78) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #450 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 134 *> proper extension: 0gcrg; 0581vn8; 0cq8nx; *> query: (?x5519, 040njc) <- nominated_for(?x1703, ?x5519), nominated_for(?x1307, ?x5519), ?x1703 = 0k611, ?x1307 = 0gq9h, film(?x541, ?x5519) *> conf = 0.51 ranks of expected_values: 6, 14 EVAL 09p3_s nominated_for! 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 77.000 72.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09p3_s nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 72.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2456-013dy7 PRED entity: 013dy7 PRED relation: place! PRED expected values: 013dy7 => 71 concepts (29 used for prediction) PRED predicted values (max 10 best out of 11): 013d_f (0.06 #442, 0.05 #957), 0vm39 (0.06 #238, 0.05 #753), 0xckc (0.06 #188, 0.05 #703), 0v9qg (0.06 #91, 0.05 #606), 02dtg (0.06 #9, 0.05 #524), 01fq7 (0.06 #4, 0.05 #519), 0vm5t (0.06 #493), 04pry (0.06 #385), 0vm4s (0.06 #187), 0v1xg (0.05 #744) >> Best rule #442 for best value: >> intensional similarity = 5 >> extensional distance = 14 >> proper extension: 02gt5s; 0vm5t; >> query: (?x11900, 013d_f) <- contains(?x1906, ?x11900), contains(?x94, ?x11900), ?x94 = 09c7w0, ?x1906 = 04rrx, time_zones(?x11900, ?x2674) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 013dy7 place! 013dy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 71.000 29.000 0.062 http://example.org/location/hud_county_place/place #2455-01vs14j PRED entity: 01vs14j PRED relation: artists! PRED expected values: 016clz => 117 concepts (55 used for prediction) PRED predicted values (max 10 best out of 249): 064t9 (0.58 #12881, 0.50 #1896, 0.49 #6291), 016clz (0.37 #11933, 0.37 #2828, 0.32 #3456), 05bt6j (0.35 #984, 0.32 #6321, 0.31 #4752), 0xhtw (0.30 #11946, 0.29 #4726, 0.28 #2214), 06j6l (0.29 #6013, 0.29 #1931, 0.27 #675), 03lty (0.28 #2225, 0.25 #1597, 0.25 #1283), 03_d0 (0.27 #3149, 0.25 #2208, 0.19 #5347), 0glt670 (0.26 #5062, 0.25 #6005, 0.22 #11655), 0155w (0.25 #1363, 0.25 #1049, 0.20 #1677), 02yv6b (0.25 #1669, 0.18 #4809, 0.17 #8262) >> Best rule #12881 for best value: >> intensional similarity = 3 >> extensional distance = 608 >> proper extension: 0123r4; >> query: (?x1321, 064t9) <- artists(?x1572, ?x1321), artists(?x1572, ?x2237), ?x2237 = 01vs_v8 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #11933 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 566 *> proper extension: 07rnh; *> query: (?x1321, 016clz) <- artists(?x1572, ?x1321), artists(?x1572, ?x6876), ?x6876 = 0ycp3 *> conf = 0.37 ranks of expected_values: 2 EVAL 01vs14j artists! 016clz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 55.000 0.577 http://example.org/music/genre/artists #2454-0172rj PRED entity: 0172rj PRED relation: artists PRED expected values: 01fchy 015196 01518s => 86 concepts (41 used for prediction) PRED predicted values (max 10 best out of 981): 02y7sr (0.71 #6187, 0.45 #8345, 0.43 #5108), 014_lq (0.57 #4799, 0.43 #5878, 0.42 #41067), 048tgl (0.57 #6307, 0.43 #5228, 0.38 #10624), 0fpj4lx (0.57 #5721, 0.43 #4642, 0.38 #10038), 01vng3b (0.57 #4878, 0.43 #5957, 0.38 #10274), 01y_rz (0.57 #6348, 0.43 #5269, 0.36 #8506), 0kxbc (0.57 #5911, 0.43 #4832, 0.36 #8069), 015196 (0.57 #6361, 0.38 #11759, 0.36 #8519), 012zng (0.57 #5530, 0.36 #7688, 0.33 #134), 03lgg (0.57 #5841, 0.36 #7999, 0.33 #445) >> Best rule #6187 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 0xhtw; 03lty; 05r6t; 0jmwg; >> query: (?x8011, 02y7sr) <- parent_genre(?x11973, ?x8011), artists(?x8011, ?x12121), artists(?x8011, ?x1955), ?x1955 = 0285c, origin(?x12121, ?x11903), artists(?x9063, ?x12121), location(?x12121, ?x1523), ?x9063 = 0cx7f >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6361 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 0xhtw; 03lty; 05r6t; 0jmwg; *> query: (?x8011, 015196) <- parent_genre(?x11973, ?x8011), artists(?x8011, ?x12121), artists(?x8011, ?x1955), ?x1955 = 0285c, origin(?x12121, ?x11903), artists(?x9063, ?x12121), location(?x12121, ?x1523), ?x9063 = 0cx7f *> conf = 0.57 ranks of expected_values: 8, 54, 239 EVAL 0172rj artists 01518s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 86.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0172rj artists 015196 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 86.000 41.000 0.714 http://example.org/music/genre/artists EVAL 0172rj artists 01fchy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 86.000 41.000 0.714 http://example.org/music/genre/artists #2453-02zs4 PRED entity: 02zs4 PRED relation: list PRED expected values: 01pd60 => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 4): 01pd60 (0.81 #732, 0.81 #726, 0.77 #595), 09g7thr (0.56 #423, 0.53 #510, 0.53 #500), 05glt (0.38 #722, 0.38 #728, 0.09 #591), 026cl_m (0.09 #723, 0.09 #729, 0.07 #592) >> Best rule #732 for best value: >> intensional similarity = 3 >> extensional distance = 300 >> proper extension: 07bz5; >> query: (?x266, ?x8915) <- list(?x266, ?x5997), list(?x4793, ?x5997), list(?x4793, ?x8915) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zs4 list 01pd60 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.814 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #2452-02vntj PRED entity: 02vntj PRED relation: award_nominee PRED expected values: 0237fw => 140 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1260): 03zz8b (0.81 #100312, 0.81 #149302, 0.80 #163302), 0c9c0 (0.81 #100312, 0.81 #149302, 0.80 #163302), 0237fw (0.81 #100312, 0.81 #149302, 0.80 #163302), 03mp9s (0.81 #100312, 0.81 #149302, 0.80 #163302), 01k5zk (0.81 #100312, 0.81 #149302, 0.80 #163302), 0h5g_ (0.35 #111977, 0.02 #9414, 0.02 #63065), 01h910 (0.35 #111977, 0.02 #43423), 03j9ml (0.35 #111977), 06b4wb (0.35 #111977), 0sw62 (0.35 #111977) >> Best rule #100312 for best value: >> intensional similarity = 2 >> extensional distance = 452 >> proper extension: 01q32bd; 0277c3; 05l0j5; >> query: (?x4247, ?x123) <- award_nominee(?x123, ?x4247), religion(?x4247, ?x1985) >> conf = 0.81 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 02vntj award_nominee 0237fw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 71.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2451-08qvhv PRED entity: 08qvhv PRED relation: profession PRED expected values: 0dxtg 02krf9 => 132 concepts (111 used for prediction) PRED predicted values (max 10 best out of 49): 0dxtg (0.89 #1346, 0.89 #902, 0.89 #754), 01d_h8 (0.54 #2078, 0.51 #2670, 0.50 #3558), 02krf9 (0.37 #174, 0.35 #2542, 0.34 #766), 0cbd2 (0.30 #303, 0.26 #599, 0.22 #451), 02jknp (0.29 #2672, 0.28 #2080, 0.27 #3560), 09jwl (0.22 #5050, 0.21 #5198, 0.20 #4754), 0np9r (0.20 #20, 0.15 #2092, 0.13 #612), 018gz8 (0.16 #3568, 0.15 #608, 0.13 #3420), 016z4k (0.14 #5036, 0.12 #5184, 0.12 #6812), 0nbcg (0.13 #4471, 0.13 #5063, 0.12 #15573) >> Best rule #1346 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 02ndbd; 06pj8; 0br1w; 06jrhz; 04mx__; 023jq1; >> query: (?x4303, 0dxtg) <- program_creator(?x9551, ?x4303), profession(?x4303, ?x1032), award_winner(?x9551, ?x2062) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 08qvhv profession 02krf9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 111.000 0.895 http://example.org/people/person/profession EVAL 08qvhv profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 111.000 0.895 http://example.org/people/person/profession #2450-0jgx PRED entity: 0jgx PRED relation: olympics PRED expected values: 0jdk_ 0jhn7 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 42): 0jhn7 (0.76 #238, 0.74 #154, 0.71 #744), 0kbvb (0.74 #217, 0.71 #723, 0.70 #386), 06sks6 (0.71 #741, 0.70 #530, 0.68 #404), 0jdk_ (0.68 #237, 0.63 #153, 0.62 #406), 0l6m5 (0.67 #136, 0.65 #220, 0.57 #389), 0lgxj (0.67 #155, 0.62 #239, 0.59 #408), 018ctl (0.60 #759, 0.59 #3417, 0.58 #253), 09n48 (0.60 #759, 0.58 #253, 0.48 #1854), 0kbvv (0.59 #3417, 0.41 #152, 0.32 #405), 0l998 (0.56 #216, 0.52 #132, 0.46 #385) >> Best rule #238 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 0cdbq; >> query: (?x3855, 0jhn7) <- nationality(?x6406, ?x3855), participating_countries(?x418, ?x3855), instrumentalists(?x227, ?x6406) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0jgx olympics 0jhn7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.765 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 0jgx olympics 0jdk_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 125.000 0.765 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #2449-01wz_ml PRED entity: 01wz_ml PRED relation: profession PRED expected values: 09jwl 0nbcg => 158 concepts (88 used for prediction) PRED predicted values (max 10 best out of 86): 09jwl (0.75 #8849, 0.74 #1489, 0.73 #7669), 0nbcg (0.70 #766, 0.57 #4592, 0.56 #4003), 016z4k (0.56 #1475, 0.52 #3976, 0.50 #4123), 0dz3r (0.47 #4710, 0.47 #2944, 0.45 #5152), 0dxtg (0.43 #6782, 0.43 #6341, 0.42 #8255), 02jknp (0.41 #6335, 0.40 #6776, 0.39 #7364), 0cbd2 (0.40 #7953, 0.38 #9132, 0.37 #9427), 03gjzk (0.39 #8256, 0.39 #6783, 0.38 #7371), 01c72t (0.34 #4437, 0.33 #1641, 0.33 #4879), 0kyk (0.33 #29, 0.27 #7976, 0.26 #5326) >> Best rule #8849 for best value: >> intensional similarity = 4 >> extensional distance = 419 >> proper extension: 0h7pj; 020jqv; >> query: (?x3401, 09jwl) <- instrumentalists(?x227, ?x3401), gender(?x3401, ?x231), artist(?x9243, ?x3401), profession(?x3401, ?x319) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01wz_ml profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 88.000 0.753 http://example.org/people/person/profession EVAL 01wz_ml profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 158.000 88.000 0.753 http://example.org/people/person/profession #2448-0288zy PRED entity: 0288zy PRED relation: major_field_of_study PRED expected values: 05qfh => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 112): 02j62 (0.40 #5613, 0.38 #6234, 0.36 #4249), 01mkq (0.39 #5597, 0.38 #7086, 0.38 #3241), 04rjg (0.38 #3246, 0.37 #5602, 0.36 #1013), 02lp1 (0.36 #6214, 0.34 #5593, 0.33 #3237), 03g3w (0.34 #3253, 0.33 #648, 0.33 #7966), 062z7 (0.31 #3005, 0.29 #5610, 0.28 #7099), 05qjt (0.31 #3233, 0.29 #628, 0.29 #5589), 0g26h (0.30 #5624, 0.26 #6245, 0.25 #663), 01lj9 (0.29 #1033, 0.28 #1281, 0.27 #1405), 06ms6 (0.29 #1010, 0.25 #1258, 0.24 #1382) >> Best rule #5613 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 0ylvj; >> query: (?x817, 02j62) <- citytown(?x817, ?x2552), institution(?x1200, ?x817), student(?x817, ?x158), ?x1200 = 016t_3 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3262 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 0yjf0; 0j_sncb; 02ln0f; 05zl0; 0hsb3; 02y9bj; 0k__z; 011xy1; 013nky; 02gkxp; ... *> query: (?x817, 05qfh) <- currency(?x817, ?x170), major_field_of_study(?x817, ?x3440), company(?x346, ?x817), student(?x817, ?x158) *> conf = 0.22 ranks of expected_values: 16 EVAL 0288zy major_field_of_study 05qfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 170.000 170.000 0.396 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #2447-06y9c2 PRED entity: 06y9c2 PRED relation: nationality PRED expected values: 09c7w0 => 117 concepts (113 used for prediction) PRED predicted values (max 10 best out of 47): 09c7w0 (0.87 #3268, 0.85 #6539, 0.83 #1981), 02jx1 (0.36 #1022, 0.33 #32, 0.25 #230), 0d060g (0.36 #4958, 0.12 #1393, 0.10 #1591), 07ssc (0.17 #411, 0.14 #609, 0.14 #510), 03rjj (0.13 #4956, 0.04 #6340, 0.03 #1787), 0chghy (0.10 #4961, 0.04 #6340, 0.03 #1792), 03rt9 (0.09 #4964, 0.02 #4469, 0.02 #2389), 03spz (0.09 #1056, 0.01 #2640), 03rk0 (0.06 #9065, 0.06 #8867, 0.06 #9363), 0h7x (0.06 #2410, 0.02 #2806) >> Best rule #3268 for best value: >> intensional similarity = 5 >> extensional distance = 126 >> proper extension: 02qw2xb; >> query: (?x677, 09c7w0) <- friend(?x677, ?x4741), nationality(?x677, ?x1264), contains(?x1264, ?x196), film_release_region(?x1490, ?x1264), ?x1490 = 0fpkhkz >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06y9c2 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 113.000 0.867 http://example.org/people/person/nationality #2446-059j2 PRED entity: 059j2 PRED relation: organization PRED expected values: 0_2v => 227 concepts (227 used for prediction) PRED predicted values (max 10 best out of 12): 034h1h (0.75 #166, 0.73 #736, 0.64 #440), 0_2v (0.70 #534, 0.68 #289, 0.67 #730), 0j7v_ (0.67 #730, 0.50 #434, 0.42 #130), 041288 (0.34 #2788, 0.32 #2563, 0.31 #2822), 0gkjy (0.28 #701, 0.27 #1590, 0.27 #1018), 085h1 (0.19 #2221, 0.14 #2355, 0.08 #135), 02_l9 (0.18 #346, 0.17 #410, 0.15 #722), 03mbdx_ (0.10 #746, 0.08 #450, 0.01 #3121), 01prf3 (0.05 #349, 0.04 #413, 0.03 #725), 03lb_v (0.05 #351, 0.04 #415, 0.03 #727) >> Best rule #166 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 05qd_; >> query: (?x1229, 034h1h) <- organizations_founded(?x1229, ?x1062), company(?x10118, ?x1229) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #534 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 05r7t; *> query: (?x1229, 0_2v) <- country(?x3407, ?x1229), film_release_region(?x66, ?x1229), service_location(?x610, ?x1229) *> conf = 0.70 ranks of expected_values: 2 EVAL 059j2 organization 0_2v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 227.000 227.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #2445-05qtj PRED entity: 05qtj PRED relation: location! PRED expected values: 05d1y => 218 concepts (153 used for prediction) PRED predicted values (max 10 best out of 2448): 03d0ns (0.58 #163592, 0.51 #359438, 0.50 #260284), 0b80__ (0.58 #163592, 0.50 #260284, 0.49 #111534), 0fbx6 (0.51 #359438, 0.49 #111534, 0.49 #111531), 02hh8j (0.51 #359438, 0.49 #111534, 0.49 #111531), 06b_0 (0.51 #359438, 0.49 #111534, 0.49 #111531), 0csdzz (0.51 #359438, 0.49 #111534, 0.49 #111531), 043tg (0.49 #111534, 0.49 #111531, 0.47 #337131), 012ky3 (0.49 #111534, 0.49 #111531, 0.47 #337131), 02w0dc0 (0.49 #111534, 0.49 #111531, 0.47 #337131), 012g92 (0.49 #111531, 0.47 #337131, 0.46 #299943) >> Best rule #163592 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 0bxbb; 010r6f; >> query: (?x4627, ?x4774) <- place_of_birth(?x4774, ?x4627), citytown(?x4619, ?x4627), spouse(?x4774, ?x8543) >> conf = 0.58 => this is the best rule for 2 predicted values *> Best rule #1657 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0r15k; *> query: (?x4627, 05d1y) <- location(?x8080, ?x4627), place_of_death(?x3428, ?x4627), ?x8080 = 09h_q *> conf = 0.20 ranks of expected_values: 118 EVAL 05qtj location! 05d1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 218.000 153.000 0.577 http://example.org/people/person/places_lived./people/place_lived/location #2444-0ywrc PRED entity: 0ywrc PRED relation: nominated_for! PRED expected values: 0gr4k => 117 concepts (107 used for prediction) PRED predicted values (max 10 best out of 208): 0gs96 (0.77 #10181, 0.77 #10404, 0.72 #222), 02qt02v (0.72 #222, 0.69 #1328, 0.68 #16382), 0gr4k (0.46 #22, 0.27 #12417, 0.23 #9980), 0gqy2 (0.41 #107, 0.30 #550, 0.25 #329), 0f4x7 (0.38 #21, 0.33 #464, 0.28 #243), 099c8n (0.31 #1153, 0.30 #269, 0.26 #47), 03hkv_r (0.26 #12, 0.20 #1118, 0.16 #1782), 02rdyk7 (0.26 #56, 0.19 #20594, 0.19 #19709), 0gqyl (0.24 #508, 0.23 #65, 0.22 #12460), 02w9sd7 (0.23 #110, 0.19 #20594, 0.19 #19709) >> Best rule #10181 for best value: >> intensional similarity = 4 >> extensional distance = 661 >> proper extension: 0g60z; 02_1q9; 080dwhx; 02_1rq; 03kq98; 072kp; 039fgy; 02py4c8; 0kfpm; 02k_4g; ... >> query: (?x3157, ?x1703) <- award(?x3157, ?x1703), nominated_for(?x294, ?x3157), nominated_for(?x1703, ?x144), ceremony(?x1703, ?x78) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #22 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 05sbv3; *> query: (?x3157, 0gr4k) <- award(?x3157, ?x1107), nominated_for(?x294, ?x3157), film_release_region(?x3157, ?x87), ?x1107 = 019f4v *> conf = 0.46 ranks of expected_values: 3 EVAL 0ywrc nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 107.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2443-03c7ln PRED entity: 03c7ln PRED relation: artists! PRED expected values: 06by7 => 117 concepts (56 used for prediction) PRED predicted values (max 10 best out of 248): 06by7 (0.67 #17134, 0.65 #15892, 0.60 #645), 03_d0 (0.64 #3741, 0.31 #1875, 0.28 #2805), 01lyv (0.47 #658, 0.24 #3450, 0.22 #3764), 0mhfr (0.47 #648, 0.13 #3440, 0.13 #2508), 016clz (0.43 #5594, 0.34 #15875, 0.33 #4045), 01243b (0.43 #5594, 0.10 #354, 0.07 #3459), 02w4v (0.40 #669, 0.33 #3775, 0.20 #3461), 03jsvl (0.40 #789, 0.05 #3581, 0.04 #3895), 0xhtw (0.35 #4989, 0.34 #4368, 0.32 #4679), 05bt6j (0.34 #15915, 0.27 #2528, 0.27 #978) >> Best rule #17134 for best value: >> intensional similarity = 4 >> extensional distance = 517 >> proper extension: 05563d; 01v0sxx; >> query: (?x211, 06by7) <- artists(?x12215, ?x211), artists(?x12215, ?x366), ?x366 = 01vrx3g, artist(?x5634, ?x211) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03c7ln artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 56.000 0.669 http://example.org/music/genre/artists #2442-02k13d PRED entity: 02k13d PRED relation: person PRED expected values: 05sj55 => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 89): 02b9g4 (0.50 #339, 0.40 #489, 0.40 #416), 01jbx1 (0.50 #315, 0.40 #465, 0.40 #392), 01yznp (0.50 #298, 0.40 #448, 0.40 #375), 0mbw0 (0.40 #500, 0.33 #649, 0.33 #576), 03f3yfj (0.40 #496, 0.33 #645, 0.33 #572), 013v5j (0.40 #458, 0.33 #607, 0.33 #534), 025ldg (0.40 #403, 0.25 #326, 0.20 #476), 029_3 (0.33 #29, 0.25 #325, 0.20 #475), 02bc74 (0.33 #71, 0.25 #367, 0.20 #517), 0427y (0.33 #58, 0.25 #354, 0.20 #504) >> Best rule #339 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 043q4d; >> query: (?x3775, 02b9g4) <- person(?x3775, ?x6170), person(?x3775, ?x5413), award_winner(?x3975, ?x6170), award_winner(?x691, ?x6170), award_winner(?x2751, ?x5413), nationality(?x5413, ?x789), nationality(?x691, ?x94), profession(?x5413, ?x1032), place_of_birth(?x5413, ?x4627), award_nominee(?x5413, ?x5222), nominated_for(?x691, ?x3075), location(?x3975, ?x4253) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #74 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 026h21_; *> query: (?x3775, ?x3974) <- person(?x3775, ?x6171), person(?x3775, ?x5413), person(?x3775, ?x4817), ?x5413 = 01yg9y, award_winner(?x9038, ?x6171), award_winner(?x4377, ?x6171), nationality(?x4817, ?x94), producer_type(?x6171, ?x632), award_winner(?x6171, ?x691), place_of_birth(?x4377, ?x4356), nominated_for(?x4377, ?x3075), profession(?x9038, ?x4725), award_nominee(?x4377, ?x3974) *> conf = 0.06 ranks of expected_values: 76 EVAL 02k13d person 05sj55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 36.000 36.000 0.500 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #2441-0hkwr PRED entity: 0hkwr PRED relation: nutrient! PRED expected values: 0dj75 => 42 concepts (38 used for prediction) PRED predicted values (max 10 best out of 18): 01nkt (0.93 #445, 0.93 #433, 0.91 #295), 05z55 (0.92 #414, 0.91 #299, 0.91 #290), 04zpv (0.91 #755, 0.91 #750, 0.89 #665), 033cnk (0.87 #21, 0.87 #152, 0.87 #147), 0f25w9 (0.87 #21, 0.87 #152, 0.87 #147), 0971v (0.87 #21, 0.87 #152, 0.87 #147), 0frq6 (0.87 #21, 0.87 #152, 0.87 #147), 0dj75 (0.87 #21, 0.87 #152, 0.87 #147), 06x4c (0.87 #21, 0.87 #152, 0.87 #147), 0dcfv (0.87 #21, 0.87 #152, 0.87 #147) >> Best rule #445 for best value: >> intensional similarity = 131 >> extensional distance = 25 >> proper extension: 014yzm; >> query: (?x10195, ?x6032) <- nutrient(?x9489, ?x10195), nutrient(?x8298, ?x10195), nutrient(?x7057, ?x10195), nutrient(?x6285, ?x10195), nutrient(?x6191, ?x10195), nutrient(?x5009, ?x10195), nutrient(?x4068, ?x10195), nutrient(?x3900, ?x10195), nutrient(?x3468, ?x10195), nutrient(?x1303, ?x10195), nutrient(?x1257, ?x10195), ?x1303 = 0fj52s, ?x6191 = 014j1m, nutrient(?x3468, ?x14210), nutrient(?x3468, ?x13126), nutrient(?x3468, ?x11784), nutrient(?x3468, ?x11758), nutrient(?x3468, ?x11409), nutrient(?x3468, ?x11270), nutrient(?x3468, ?x10891), nutrient(?x3468, ?x10709), nutrient(?x3468, ?x10098), nutrient(?x3468, ?x9949), nutrient(?x3468, ?x9915), nutrient(?x3468, ?x9840), nutrient(?x3468, ?x9733), nutrient(?x3468, ?x9619), nutrient(?x3468, ?x9490), nutrient(?x3468, ?x9436), nutrient(?x3468, ?x8442), nutrient(?x3468, ?x8413), nutrient(?x3468, ?x7894), nutrient(?x3468, ?x7720), nutrient(?x3468, ?x7652), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x7364), nutrient(?x3468, ?x7219), nutrient(?x3468, ?x6586), nutrient(?x3468, ?x6286), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x6160), nutrient(?x3468, ?x6033), nutrient(?x3468, ?x6026), nutrient(?x3468, ?x5549), nutrient(?x3468, ?x5451), nutrient(?x3468, ?x5337), nutrient(?x3468, ?x5010), nutrient(?x3468, ?x4069), nutrient(?x3468, ?x3469), nutrient(?x3468, ?x3203), nutrient(?x3468, ?x2018), nutrient(?x3468, ?x1960), nutrient(?x3468, ?x1304), nutrient(?x3468, ?x1258), ?x11784 = 07zqy, ?x5009 = 0fjfh, ?x1960 = 07hnp, ?x10891 = 0g5gq, nutrient(?x7057, ?x12868), nutrient(?x7057, ?x12454), nutrient(?x7057, ?x11592), nutrient(?x7057, ?x9855), nutrient(?x7057, ?x9795), nutrient(?x7057, ?x9708), nutrient(?x7057, ?x8243), nutrient(?x7057, ?x5374), nutrient(?x7057, ?x3264), ?x4069 = 0hqw8p_, ?x6026 = 025sf8g, ?x6160 = 041r51, ?x5549 = 025s7j4, ?x1258 = 0h1wg, ?x11758 = 0q01m, ?x9619 = 0h1tg, ?x9855 = 0d9t0, ?x12868 = 03d49, ?x7431 = 09gwd, ?x7894 = 0f4hc, ?x11592 = 025sf0_, ?x5451 = 05wvs, ?x6285 = 01645p, ?x8442 = 02kcv4x, ?x6033 = 04zjxcz, ?x9949 = 02kd0rh, ?x1304 = 08lb68, nutrient(?x10612, ?x14210), nutrient(?x9005, ?x14210), nutrient(?x6159, ?x14210), nutrient(?x6032, ?x14210), nutrient(?x5373, ?x14210), ?x8243 = 014d7f, ?x1257 = 09728, ?x9708 = 061xhr, ?x6586 = 05gh50, ?x11270 = 02kc008, ?x9915 = 025tkqy, ?x10709 = 0h1sz, ?x6192 = 06jry, ?x2018 = 01sh2, ?x9005 = 04zpv, ?x5337 = 06x4c, ?x9490 = 0h1sg, ?x3203 = 04kl74p, ?x10098 = 0h1_c, ?x3264 = 0dcfv, ?x11409 = 0h1yf, ?x7364 = 09gvd, ?x9436 = 025sqz8, ?x9795 = 05v_8y, ?x9840 = 02p0tjr, ?x9733 = 0h1tz, ?x3900 = 061_f, nutrient(?x1959, ?x13126), ?x8298 = 037ls6, ?x6032 = 01nkt, ?x6159 = 033cnk, ?x8413 = 02kc4sf, ?x5373 = 0971v, ?x10612 = 0frq6, nutrient(?x9489, ?x13498), ?x6286 = 02y_3rf, ?x4068 = 0fbw6, ?x7652 = 025s0s0, ?x1959 = 0f25w9, ?x3469 = 0h1zw, ?x7219 = 0h1vg, ?x5374 = 025s0zp, ?x12454 = 025rw19, ?x13498 = 07q0m, ?x7720 = 025s7x6, ?x5010 = 0h1vz >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #21 for first EXPECTED value: *> intensional similarity = 134 *> extensional distance = 17 *> proper extension: 0838f; *> query: (?x10195, ?x6032) <- nutrient(?x9489, ?x10195), nutrient(?x8298, ?x10195), nutrient(?x7057, ?x10195), nutrient(?x6285, ?x10195), nutrient(?x6191, ?x10195), nutrient(?x5009, ?x10195), nutrient(?x4068, ?x10195), nutrient(?x3900, ?x10195), nutrient(?x3468, ?x10195), nutrient(?x2701, ?x10195), nutrient(?x1303, ?x10195), nutrient(?x1257, ?x10195), ?x1303 = 0fj52s, ?x6191 = 014j1m, ?x3468 = 0cxn2, ?x5009 = 0fjfh, ?x2701 = 0hkxq, ?x8298 = 037ls6, ?x6285 = 01645p, ?x9489 = 07j87, ?x1257 = 09728, nutrient(?x3900, ?x13944), nutrient(?x3900, ?x13498), nutrient(?x3900, ?x12902), nutrient(?x3900, ?x12481), nutrient(?x3900, ?x12454), nutrient(?x3900, ?x11784), nutrient(?x3900, ?x11758), nutrient(?x3900, ?x11592), nutrient(?x3900, ?x11409), nutrient(?x3900, ?x11270), nutrient(?x3900, ?x10891), nutrient(?x3900, ?x10709), nutrient(?x3900, ?x10098), nutrient(?x3900, ?x9915), nutrient(?x3900, ?x9855), nutrient(?x3900, ?x9840), nutrient(?x3900, ?x9795), nutrient(?x3900, ?x9733), nutrient(?x3900, ?x9490), nutrient(?x3900, ?x9436), nutrient(?x3900, ?x9426), nutrient(?x3900, ?x9365), nutrient(?x3900, ?x8487), nutrient(?x3900, ?x8442), nutrient(?x3900, ?x8413), nutrient(?x3900, ?x8243), nutrient(?x3900, ?x7894), nutrient(?x3900, ?x7720), nutrient(?x3900, ?x7652), nutrient(?x3900, ?x7364), nutrient(?x3900, ?x7362), nutrient(?x3900, ?x7219), nutrient(?x3900, ?x6586), nutrient(?x3900, ?x6286), nutrient(?x3900, ?x6033), nutrient(?x3900, ?x6026), nutrient(?x3900, ?x5549), nutrient(?x3900, ?x5526), nutrient(?x3900, ?x5451), nutrient(?x3900, ?x5374), nutrient(?x3900, ?x5337), nutrient(?x3900, ?x5010), nutrient(?x3900, ?x4069), nutrient(?x3900, ?x3901), nutrient(?x3900, ?x3469), nutrient(?x3900, ?x1960), nutrient(?x3900, ?x1304), nutrient(?x3900, ?x1258), ?x5549 = 025s7j4, ?x8487 = 014yzm, ?x6286 = 02y_3rf, ?x8243 = 014d7f, ?x12481 = 027g6p7, ?x1960 = 07hnp, ?x5374 = 025s0zp, ?x7364 = 09gvd, ?x13498 = 07q0m, ?x10891 = 0g5gq, ?x3469 = 0h1zw, ?x6033 = 04zjxcz, ?x10098 = 0h1_c, ?x6026 = 025sf8g, ?x1304 = 08lb68, ?x9795 = 05v_8y, ?x11409 = 0h1yf, ?x12454 = 025rw19, ?x1258 = 0h1wg, ?x3901 = 0466p20, ?x5526 = 09pbb, ?x9915 = 025tkqy, ?x7894 = 0f4hc, ?x8413 = 02kc4sf, ?x5451 = 05wvs, ?x9426 = 0h1yy, ?x7219 = 0h1vg, ?x12902 = 0fzjh, ?x4068 = 0fbw6, ?x13944 = 0f4kp, ?x9490 = 0h1sg, ?x6586 = 05gh50, ?x4069 = 0hqw8p_, ?x8442 = 02kcv4x, ?x10709 = 0h1sz, ?x7362 = 02kc5rj, ?x7720 = 025s7x6, nutrient(?x9005, ?x5337), nutrient(?x7719, ?x5337), nutrient(?x6159, ?x5337), nutrient(?x6032, ?x5337), nutrient(?x3264, ?x5337), nutrient(?x10612, ?x9436), nutrient(?x9732, ?x9436), nutrient(?x5373, ?x9436), nutrient(?x1959, ?x9436), ?x11592 = 025sf0_, ?x9365 = 04k8n, ?x3264 = 0dcfv, ?x9733 = 0h1tz, ?x11758 = 0q01m, ?x11270 = 02kc008, ?x5373 = 0971v, ?x1959 = 0f25w9, ?x7652 = 025s0s0, ?x5010 = 0h1vz, ?x7719 = 0dj75, ?x10612 = 0frq6, ?x9005 = 04zpv, ?x9732 = 05z55, ?x7057 = 0fbdb, ?x9840 = 02p0tjr, ?x9855 = 0d9t0, ?x11784 = 07zqy, ?x6159 = 033cnk *> conf = 0.87 ranks of expected_values: 8 EVAL 0hkwr nutrient! 0dj75 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 42.000 38.000 0.926 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #2440-07zhjj PRED entity: 07zhjj PRED relation: nominated_for! PRED expected values: 0cjyzs 027gs1_ => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 215): 027gs1_ (0.44 #2351, 0.43 #910, 0.42 #2111), 0cjyzs (0.43 #803, 0.42 #2244, 0.41 #2004), 0gq9h (0.38 #11108, 0.34 #11348, 0.34 #11829), 09qs08 (0.37 #831, 0.35 #2272, 0.31 #2032), 0gs9p (0.35 #305, 0.34 #11110, 0.31 #11831), 0k611 (0.35 #314, 0.29 #8478, 0.28 #11119), 03ccq3s (0.33 #865, 0.31 #2306, 0.30 #2066), 0cqhk0 (0.33 #752, 0.27 #2193, 0.26 #1953), 019f4v (0.33 #11099, 0.31 #10138, 0.31 #9658), 09qrn4 (0.31 #2328, 0.30 #887, 0.30 #2088) >> Best rule #2351 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 01f3p_; 016zfm; 0fpxp; 01hvv0; 0sw0q; 07wqr6; 023ny6; 0123qq; 015pnb; >> query: (?x8775, 027gs1_) <- nominated_for(?x1341, ?x8775), actor(?x8775, ?x2579), genre(?x8775, ?x258), ?x258 = 05p553 >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07zhjj nominated_for! 027gs1_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.436 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07zhjj nominated_for! 0cjyzs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.436 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2439-0565cz PRED entity: 0565cz PRED relation: award PRED expected values: 02ddq4 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 227): 01by1l (0.32 #2955, 0.31 #2549, 0.31 #3361), 01bgqh (0.27 #449, 0.26 #855, 0.26 #7351), 09sb52 (0.25 #16281, 0.24 #16687, 0.20 #8973), 03qbh5 (0.23 #3049, 0.21 #2643, 0.21 #3455), 0c4z8 (0.23 #2102, 0.22 #2914, 0.20 #2508), 054ks3 (0.20 #2985, 0.19 #2173, 0.18 #2579), 026mfs (0.19 #130, 0.17 #2566, 0.15 #2972), 01c92g (0.18 #2940, 0.17 #2128, 0.15 #3346), 0gqz2 (0.18 #487, 0.15 #2111, 0.13 #2517), 02f5qb (0.18 #563, 0.11 #7465, 0.10 #2999) >> Best rule #2955 for best value: >> intensional similarity = 3 >> extensional distance = 239 >> proper extension: 0h7pj; 020jqv; >> query: (?x2964, 01by1l) <- instrumentalists(?x227, ?x2964), award_nominee(?x2964, ?x2321), artist(?x2299, ?x2964) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #19489 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2258 *> proper extension: 03xsby; 0181hw; *> query: (?x2964, ?x10316) <- award_nominee(?x2964, ?x2321), award(?x2321, ?x10316), ceremony(?x10316, ?x139) *> conf = 0.14 ranks of expected_values: 18 EVAL 0565cz award 02ddq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 68.000 68.000 0.315 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2438-0jwvf PRED entity: 0jwvf PRED relation: nominated_for! PRED expected values: 040njc => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 201): 0gqwc (0.75 #1017, 0.28 #1497, 0.22 #5571), 02z1nbg (0.69 #7189, 0.68 #1436, 0.68 #6229), 0gq9h (0.60 #1499, 0.56 #1019, 0.43 #4134), 019f4v (0.56 #1010, 0.36 #4125, 0.34 #6523), 04dn09n (0.56 #991, 0.33 #4106, 0.31 #2669), 0gq_v (0.52 #3133, 0.48 #1456, 0.47 #5530), 0gr0m (0.44 #1496, 0.38 #1016, 0.28 #2694), 0gr51 (0.44 #1034, 0.18 #12534, 0.18 #3430), 094qd5 (0.44 #992, 0.18 #6265, 0.16 #6505), 09qwmm (0.44 #983, 0.13 #1703, 0.12 #4098) >> Best rule #1017 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0sxfd; >> query: (?x5856, 0gqwc) <- award(?x5856, ?x3902), produced_by(?x5856, ?x2465), ?x3902 = 02z1nbg, nominated_for(?x601, ?x5856) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #963 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0sxfd; *> query: (?x5856, 040njc) <- award(?x5856, ?x3902), produced_by(?x5856, ?x2465), ?x3902 = 02z1nbg, nominated_for(?x601, ?x5856) *> conf = 0.31 ranks of expected_values: 17 EVAL 0jwvf nominated_for! 040njc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 148.000 148.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2437-07vfqj PRED entity: 07vfqj PRED relation: gender PRED expected values: 05zppz => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #79, 0.92 #77, 0.91 #66), 02zsn (0.47 #145, 0.46 #257, 0.46 #254) >> Best rule #79 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 03l26m; >> query: (?x6812, 05zppz) <- team(?x6812, ?x4094), nationality(?x6812, ?x1453), team(?x60, ?x4094), profession(?x6812, ?x7623) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07vfqj gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.917 http://example.org/people/person/gender #2436-06w38l PRED entity: 06w38l PRED relation: gender PRED expected values: 05zppz => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 6): 05zppz (0.93 #15, 0.91 #13, 0.90 #23), 02zsn (0.46 #216, 0.33 #26, 0.28 #147), 01hbgs (0.13 #79), 0c58k (0.13 #79), 0jpmt (0.13 #79), 0x67 (0.13 #79) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 063vn; 06c97; 03txms; >> query: (?x12891, 05zppz) <- people(?x7260, ?x12891), profession(?x12891, ?x987), student(?x1771, ?x12891) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06w38l gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.926 http://example.org/people/person/gender #2435-030pr PRED entity: 030pr PRED relation: profession PRED expected values: 01d_h8 02jknp 0dxtg => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 94): 02jknp (0.89 #3608, 0.89 #4508, 0.89 #5108), 01d_h8 (0.85 #906, 0.84 #5856, 0.84 #6756), 02hrh1q (0.84 #1065, 0.80 #10216, 0.80 #10666), 0dxtg (0.71 #5714, 0.71 #2564, 0.70 #5414), 03gjzk (0.44 #3616, 0.44 #5866, 0.44 #6166), 0fj9f (0.31 #356, 0.18 #1256, 0.14 #1406), 09jwl (0.30 #3170, 0.29 #1670, 0.29 #2720), 0cbd2 (0.29 #1207, 0.28 #307, 0.22 #9307), 02krf9 (0.28 #2878, 0.26 #3628, 0.26 #4528), 018gz8 (0.21 #1368, 0.20 #1218, 0.17 #3468) >> Best rule #3608 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 02ndbd; 021lby; 03f0r5w; 04fcx7; 01z7s_; 01vz80y; 04rvy8; 034hck; 04h68j; 042kbj; ... >> query: (?x1134, 02jknp) <- award_nominee(?x1134, ?x541), film(?x1134, ?x1133), type_of_union(?x1134, ?x566) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 030pr profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 153.000 153.000 0.895 http://example.org/people/person/profession EVAL 030pr profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.895 http://example.org/people/person/profession EVAL 030pr profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.895 http://example.org/people/person/profession #2434-0g1rw PRED entity: 0g1rw PRED relation: child PRED expected values: 01f_mw => 141 concepts (107 used for prediction) PRED predicted values (max 10 best out of 190): 032j_n (0.40 #270, 0.11 #608, 0.10 #1793), 0c41qv (0.29 #407, 0.20 #745, 0.18 #914), 031rq5 (0.20 #1743, 0.14 #389, 0.13 #3270), 016tw3 (0.15 #1703, 0.10 #3230, 0.06 #5772), 05s_k6 (0.14 #462, 0.11 #631, 0.10 #1816), 013x0b (0.14 #343, 0.11 #512, 0.08 #1359), 07733f (0.14 #486, 0.10 #1840, 0.10 #824), 02bh8z (0.14 #371, 0.10 #1725, 0.10 #709), 024rgt (0.14 #362, 0.10 #1716, 0.10 #700), 046b0s (0.14 #361, 0.10 #1715, 0.10 #699) >> Best rule #270 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0jz9f; 061dn_; 04g2mkf; >> query: (?x788, 032j_n) <- film(?x788, ?x4158), ?x4158 = 0g83dv, category(?x788, ?x134) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0g1rw child 01f_mw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 141.000 107.000 0.400 http://example.org/organization/organization/child./organization/organization_relationship/child #2433-03_6y PRED entity: 03_6y PRED relation: award_nominee! PRED expected values: 01mqc_ => 89 concepts (46 used for prediction) PRED predicted values (max 10 best out of 915): 01mqc_ (0.83 #6291, 0.48 #8605, 0.45 #3976), 03_6y (0.56 #5404, 0.45 #3089, 0.36 #7718), 02p65p (0.36 #6970, 0.17 #106509, 0.15 #104194), 02vy5j (0.33 #7422, 0.17 #106509, 0.02 #30571), 0b_dy (0.33 #7640, 0.15 #104194, 0.13 #53255), 01tnxc (0.33 #8735, 0.15 #104194, 0.13 #53255), 0kjrx (0.33 #1787, 0.01 #11045, 0.01 #13361), 054_mz (0.33 #80), 01qscs (0.30 #7006, 0.17 #106509, 0.15 #104194), 04sx9_ (0.30 #7127, 0.17 #106509, 0.10 #11573) >> Best rule #6291 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 06151l; 0z4s; 0hvb2; 014488; 0278x6s; 073x6y; 05cx7x; >> query: (?x3466, 01mqc_) <- award_nominee(?x3466, ?x5628), award_nominee(?x3466, ?x3999), ?x5628 = 07h565, ?x3999 = 04w391 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_6y award_nominee! 01mqc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 46.000 0.833 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2432-0hzlz PRED entity: 0hzlz PRED relation: films PRED expected values: 0581vn8 => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 16): 0jqp3 (0.07 #579, 0.06 #1110, 0.06 #1642), 02725hs (0.07 #644, 0.04 #2770, 0.03 #4894), 02fwfb (0.06 #1436, 0.06 #1968, 0.05 #2499), 01znj1 (0.06 #1348, 0.06 #1880, 0.05 #2411), 0bs5k8r (0.06 #1275, 0.05 #2338, 0.04 #2870), 0bz3jx (0.03 #7238, 0.03 #9363, 0.02 #13082), 05dmmc (0.03 #7134, 0.03 #9259, 0.02 #12978), 05q7874 (0.03 #7215, 0.03 #9340, 0.02 #13059), 09q5w2 (0.02 #12799, 0.02 #21301, 0.02 #24488), 04j4tx (0.02 #16148, 0.02 #18803, 0.02 #18272) >> Best rule #579 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 049nq; >> query: (?x792, 0jqp3) <- contains(?x792, ?x841), nationality(?x477, ?x792), taxonomy(?x841, ?x939) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hzlz films 0581vn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 203.000 203.000 0.071 http://example.org/film/film_subject/films #2431-01vrz41 PRED entity: 01vrz41 PRED relation: type_of_appearance PRED expected values: 01jdpf => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 1): 01jdpf (0.17 #2, 0.10 #7, 0.07 #23) >> Best rule #2 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0gs6vr; >> query: (?x1231, 01jdpf) <- artist(?x1954, ?x1231), participant(?x1231, ?x2647), person(?x4359, ?x1231) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrz41 type_of_appearance 01jdpf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.167 http://example.org/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance #2430-0glt670 PRED entity: 0glt670 PRED relation: artists PRED expected values: 0147dk 016kjs 01wbl_r 03fbc 01vw26l 01v40wd 01vxlbm 018n6m 0837ql 01vw8mh 011z3g 0677ng 01vxqyl 04n2vgk => 59 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1108): 011z3g (0.75 #11955, 0.60 #10048, 0.50 #7187), 0bs1g5r (0.62 #12087, 0.50 #7319, 0.40 #11133), 01wk7ql (0.62 #12221, 0.50 #7453, 0.40 #10314), 019f9z (0.62 #11951, 0.50 #7183, 0.25 #6231), 016376 (0.62 #12274, 0.50 #7506, 0.25 #6554), 012z8_ (0.62 #11778, 0.50 #6058, 0.25 #7010), 01k23t (0.62 #12044, 0.50 #7276, 0.25 #6324), 012vd6 (0.62 #11857, 0.40 #9950, 0.28 #11436), 024qwq (0.62 #12192, 0.25 #7424, 0.25 #6472), 0fpj4lx (0.60 #9808, 0.53 #14573, 0.42 #13620) >> Best rule #11955 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 0m0jc; 064t9; 02x8m; 06j6l; 0gywn; >> query: (?x2937, 011z3g) <- parent_genre(?x1952, ?x2937), artists(?x2937, ?x6289), artists(?x2937, ?x5340), artists(?x2937, ?x2987), instrumentalists(?x227, ?x2987), artist(?x2149, ?x5340), ?x6289 = 0x3n, profession(?x2987, ?x220) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 13, 21, 47, 48, 50, 51, 105, 109, 282, 292, 294, 295, 304 EVAL 0glt670 artists 04n2vgk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 01vxqyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 0677ng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 011z3g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 01vw8mh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 0837ql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 018n6m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 01vxlbm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 01v40wd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 01vw26l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 03fbc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 01wbl_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 016kjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 59.000 28.000 0.750 http://example.org/music/genre/artists EVAL 0glt670 artists 0147dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 59.000 28.000 0.750 http://example.org/music/genre/artists #2429-01g7zj PRED entity: 01g7zj PRED relation: people PRED expected values: 049fgvm 04n2vgk => 26 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1828): 01r4bps (0.33 #1519, 0.29 #3236, 0.09 #10106), 02byfd (0.33 #1253, 0.14 #2970, 0.12 #6405), 01tfck (0.33 #276, 0.14 #1993, 0.12 #5428), 043zg (0.33 #761, 0.14 #2478, 0.11 #3434), 08swgx (0.33 #381, 0.14 #2098, 0.10 #3817), 03f0qd7 (0.33 #1577, 0.14 #3294, 0.10 #5013), 01w58n3 (0.33 #1326, 0.14 #3043, 0.10 #4762), 04yj5z (0.33 #99, 0.14 #1816, 0.04 #5251), 0311wg (0.29 #2005, 0.21 #5440, 0.15 #8875), 0227tr (0.29 #2049, 0.17 #5484, 0.12 #8919) >> Best rule #1519 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 025rpb0; >> query: (?x11321, 01r4bps) <- people(?x11321, ?x10287), people(?x11321, ?x1817), people(?x11321, ?x1117), ?x1117 = 03lt8g, nationality(?x10287, ?x94), award_winner(?x1817, ?x4239), award(?x1817, ?x537), artists(?x378, ?x1817) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4738 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 071x0k; *> query: (?x11321, 04n2vgk) <- languages_spoken(?x11321, ?x3592), languages_spoken(?x11321, ?x2502), ?x2502 = 06nm1, language(?x1163, ?x3592), languages(?x3848, ?x3592) *> conf = 0.10 ranks of expected_values: 302 EVAL 01g7zj people 04n2vgk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 26.000 20.000 0.333 http://example.org/people/ethnicity/people EVAL 01g7zj people 049fgvm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 26.000 20.000 0.333 http://example.org/people/ethnicity/people #2428-015w8_ PRED entity: 015w8_ PRED relation: program! PRED expected values: 02fp82 => 103 concepts (83 used for prediction) PRED predicted values (max 10 best out of 53): 0ljc_ (0.33 #27, 0.25 #82, 0.07 #693), 0187wh (0.33 #24, 0.10 #857, 0.08 #912), 05gnf (0.31 #343, 0.24 #1295, 0.24 #1011), 0gsg7 (0.25 #57, 0.23 #1453, 0.21 #2747), 0146mv (0.25 #80, 0.09 #691, 0.03 #2547), 0215n (0.13 #1735, 0.09 #1905, 0.09 #2185), 03mdt (0.13 #503, 0.12 #1344, 0.12 #1570), 03lpbx (0.09 #697, 0.03 #2553, 0.03 #3051), 02hmvw (0.07 #707, 0.03 #3061, 0.02 #2841), 0g5lhl7 (0.07 #1683, 0.07 #1455, 0.06 #3024) >> Best rule #27 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 028k2x; >> query: (?x3144, 0ljc_) <- actor(?x3144, ?x10607), program(?x2135, ?x3144), languages(?x3144, ?x254), ?x10607 = 01tpl1p >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #711 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 02648p; 0jwl2; 024rwx; 0ctzf1; 031kyy; 01lk02; 0199wf; 04svwx; 017dbx; 02rhwjr; ... *> query: (?x3144, 02fp82) <- actor(?x3144, ?x7811), program(?x2062, ?x3144), genre(?x3144, ?x258), language(?x7811, ?x254) *> conf = 0.02 ranks of expected_values: 47 EVAL 015w8_ program! 02fp82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 103.000 83.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #2427-015q1n PRED entity: 015q1n PRED relation: student PRED expected values: 01wc7p 05z775 => 129 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1251): 09v6tz (0.12 #7606, 0.07 #9696, 0.05 #15964), 03rqww (0.08 #7694, 0.04 #9784, 0.04 #11873), 02lp3c (0.08 #7348, 0.04 #9438, 0.04 #11527), 03ft8 (0.08 #6524, 0.04 #8614, 0.03 #14882), 0ff3y (0.08 #8334, 0.04 #10424, 0.03 #16692), 01pqy_ (0.08 #7164, 0.04 #9254, 0.03 #15522), 02cyfz (0.08 #6600, 0.04 #8690, 0.03 #14958), 01wwvt2 (0.08 #6631, 0.04 #8721, 0.03 #14989), 024y6w (0.08 #7717, 0.04 #9807, 0.03 #16075), 05xd_v (0.08 #8090, 0.04 #10180, 0.03 #16448) >> Best rule #7606 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 01prf3; 03_c8p; >> query: (?x6271, 09v6tz) <- organization(?x6271, ?x5487), citytown(?x6271, ?x11511), place_of_death(?x1029, ?x11511) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015q1n student 05z775 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 107.000 0.120 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 015q1n student 01wc7p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 107.000 0.120 http://example.org/education/educational_institution/students_graduates./education/education/student #2426-0g4vmj8 PRED entity: 0g4vmj8 PRED relation: genre PRED expected values: 03j0dp => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 89): 03k9fj (0.43 #486, 0.42 #129, 0.42 #248), 05p553 (0.35 #241, 0.35 #1195, 0.35 #2626), 02l7c8 (0.33 #1684, 0.30 #3232, 0.29 #3113), 02n4kr (0.26 #1080, 0.24 #1916, 0.23 #2392), 01hmnh (0.25 #492, 0.23 #611, 0.22 #254), 04xvlr (0.25 #715, 0.24 #358, 0.21 #835), 060__y (0.23 #15, 0.20 #372, 0.16 #1685), 0hcr (0.20 #498, 0.19 #617, 0.16 #141), 06n90 (0.20 #1920, 0.20 #2396, 0.19 #249), 04xvh5 (0.17 #390, 0.10 #1703, 0.10 #747) >> Best rule #486 for best value: >> intensional similarity = 5 >> extensional distance = 86 >> proper extension: 014lc_; 0h3xztt; 0407yj_; 045j3w; 0j43swk; 0m63c; 0fpgp26; >> query: (?x7275, 03k9fj) <- nominated_for(?x451, ?x7275), film_release_region(?x7275, ?x1174), film_release_region(?x7275, ?x1003), ?x1174 = 047yc, ?x1003 = 03gj2 >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1116 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 208 *> proper extension: 03t97y; *> query: (?x7275, 03j0dp) <- genre(?x7275, ?x812), ?x812 = 01jfsb, award_winner(?x7275, ?x1401) *> conf = 0.02 ranks of expected_values: 62 EVAL 0g4vmj8 genre 03j0dp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 67.000 67.000 0.432 http://example.org/film/film/genre #2425-019pm_ PRED entity: 019pm_ PRED relation: award PRED expected values: 0641kkh => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 256): 0gq9h (0.37 #7257, 0.36 #8454, 0.32 #9252), 0bfvd4 (0.33 #910, 0.12 #24739, 0.10 #1708), 040njc (0.30 #8386, 0.26 #7189, 0.25 #9184), 02z0dfh (0.27 #1270, 0.07 #14437, 0.06 #13240), 02xj3rw (0.25 #322, 0.01 #8701, 0.01 #10696), 0gs9p (0.22 #8456, 0.18 #4067, 0.17 #10451), 019f4v (0.21 #8443, 0.17 #4054, 0.17 #10438), 07bdd_ (0.20 #7245, 0.18 #9240, 0.17 #8442), 0gqyl (0.20 #1299, 0.20 #501, 0.14 #8880), 05zr6wv (0.20 #1612, 0.19 #4006, 0.19 #2410) >> Best rule #7257 for best value: >> intensional similarity = 2 >> extensional distance = 204 >> proper extension: 0gdhhy; >> query: (?x2763, 0gq9h) <- award_winner(?x2763, ?x1384), produced_by(?x408, ?x2763) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #24739 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1669 *> proper extension: 02r3zy; 01dq9q; 02k5sc; 076df9; *> query: (?x2763, ?x704) <- award_nominee(?x2763, ?x5834), award_nominee(?x2763, ?x5283), film(?x5283, ?x306), award(?x5834, ?x704) *> conf = 0.12 ranks of expected_values: 83 EVAL 019pm_ award 0641kkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 87.000 87.000 0.369 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2424-034bs PRED entity: 034bs PRED relation: influenced_by! PRED expected values: 056wb 06jcc => 191 concepts (92 used for prediction) PRED predicted values (max 10 best out of 448): 040db (0.50 #3130, 0.33 #4145, 0.21 #8720), 034bs (0.50 #5235, 0.20 #5743, 0.20 #1680), 0mb5x (0.44 #4399, 0.14 #7956, 0.12 #3384), 032r1 (0.40 #2040, 0.40 #1997, 0.13 #2038), 0683n (0.40 #5417, 0.25 #3387, 0.22 #4402), 0mb0 (0.40 #5506, 0.22 #4491, 0.07 #9066), 01vs4f3 (0.38 #3399, 0.17 #2892, 0.11 #4414), 03_87 (0.33 #4325, 0.30 #5340, 0.12 #5592), 01v_0b (0.33 #4546, 0.25 #3531, 0.20 #5561), 084w8 (0.33 #4071, 0.25 #3056, 0.20 #5086) >> Best rule #3130 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 041h0; 06kb_; 03_87; 03jxw; 0ky1; 03j2gxx; >> query: (?x4055, 040db) <- influenced_by(?x7828, ?x4055), influenced_by(?x4055, ?x2994), location(?x4055, ?x2152), ?x7828 = 014ps4 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5391 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 040dv; *> query: (?x4055, 06jcc) <- influenced_by(?x3279, ?x4055), influenced_by(?x2485, ?x4055), profession(?x3279, ?x987), ?x2485 = 0gd5z *> conf = 0.20 ranks of expected_values: 59, 115 EVAL 034bs influenced_by! 06jcc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 191.000 92.000 0.500 http://example.org/influence/influence_node/influenced_by EVAL 034bs influenced_by! 056wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 191.000 92.000 0.500 http://example.org/influence/influence_node/influenced_by #2423-0gr42 PRED entity: 0gr42 PRED relation: award! PRED expected values: 0k4kk 03y0pn => 53 concepts (20 used for prediction) PRED predicted values (max 10 best out of 860): 0hfzr (0.60 #1394, 0.57 #2389, 0.46 #4381), 0h03fhx (0.60 #1439, 0.29 #4426, 0.29 #2434), 0bs4r (0.60 #1591, 0.29 #2586, 0.21 #4578), 0cq806 (0.60 #1830, 0.21 #2825, 0.21 #4817), 0bl1_ (0.60 #1448, 0.17 #4435, 0.12 #5433), 0ywrc (0.57 #2285, 0.40 #1290, 0.33 #4277), 0pv3x (0.50 #2093, 0.40 #1098, 0.33 #4085), 07s846j (0.50 #2375, 0.38 #4367, 0.28 #5365), 03hmt9b (0.46 #4360, 0.43 #2368, 0.40 #1373), 0bx0l (0.43 #2198, 0.25 #4190, 0.24 #3196) >> Best rule #1394 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 040njc; 0gr4k; 0gq9h; >> query: (?x2209, 0hfzr) <- nominated_for(?x2209, ?x324), award(?x9357, ?x2209), award_winner(?x2209, ?x2870), ?x9357 = 04rvy8, ceremony(?x2209, ?x78) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #705 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 05ztjjw; *> query: (?x2209, 03y0pn) <- nominated_for(?x2209, ?x4998), nominated_for(?x2209, ?x4610), nominated_for(?x2209, ?x2223), ?x4610 = 017jd9, ?x4998 = 0dzlbx, film_release_region(?x2223, ?x94), award(?x1392, ?x2209) *> conf = 0.25 ranks of expected_values: 72, 426 EVAL 0gr42 award! 03y0pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 53.000 20.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0gr42 award! 0k4kk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 53.000 20.000 0.600 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #2422-09mq4m PRED entity: 09mq4m PRED relation: award PRED expected values: 01bgqh => 95 concepts (81 used for prediction) PRED predicted values (max 10 best out of 268): 01by1l (0.77 #4401, 0.77 #19609, 0.76 #28824), 025m8l (0.41 #1717, 0.33 #2117, 0.23 #1317), 02f5qb (0.33 #553, 0.25 #5354, 0.20 #10403), 02f72n (0.33 #543, 0.23 #5344, 0.12 #943), 03qbh5 (0.33 #603, 0.23 #3403, 0.23 #3803), 02f72_ (0.33 #625, 0.22 #5426, 0.20 #10403), 02f73b (0.33 #683, 0.21 #5484, 0.20 #10403), 054krc (0.32 #2086, 0.14 #1686, 0.10 #4086), 02f6xy (0.27 #598, 0.22 #5399, 0.20 #1398), 02f73p (0.27 #585, 0.21 #5386, 0.19 #24018) >> Best rule #4401 for best value: >> intensional similarity = 4 >> extensional distance = 210 >> proper extension: 01qkqwg; 0lzkm; >> query: (?x1826, ?x1232) <- award_winner(?x1826, ?x2862), instrumentalists(?x212, ?x1826), profession(?x1826, ?x131), award_winner(?x1232, ?x1826) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #5244 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 254 *> proper extension: 089tm; 01t_xp_; 01pfr3; 0150jk; 01vsxdm; 01wv9xn; 01fl3; 0dtd6; 03fbc; 0fcsd; ... *> query: (?x1826, 01bgqh) <- award(?x1826, ?x4481), award(?x7188, ?x4481), ?x7188 = 0gr69 *> conf = 0.26 ranks of expected_values: 14 EVAL 09mq4m award 01bgqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 95.000 81.000 0.768 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2421-058dm9 PRED entity: 058dm9 PRED relation: team! PRED expected values: 02_j1w => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 35): 02_j1w (0.83 #904, 0.82 #951, 0.82 #1002), 03f0fp (0.54 #1503, 0.50 #1453), 02qvgy (0.50 #1453, 0.01 #1425), 02g_6x (0.06 #1464), 06b1q (0.06 #1458), 02g_7z (0.06 #1476), 01r3hr (0.06 #1454), 04nfpk (0.06 #1468), 02g_6j (0.06 #1462), 01_9c1 (0.05 #1469) >> Best rule #904 for best value: >> intensional similarity = 35 >> extensional distance = 553 >> proper extension: 05jx2d; 05kjc6; 03mqj_; 02gys2; 08pgl8; 0d_q40; 02jgm0; 02q3n9c; 0266sb_; 024tsn; ... >> query: (?x8954, 02_j1w) <- position(?x8954, ?x203), position(?x8954, ?x63), ?x63 = 02sdk9v, position(?x14337, ?x203), position(?x12269, ?x203), position(?x11507, ?x203), position(?x11225, ?x203), position(?x10468, ?x203), position(?x9543, ?x203), position(?x9182, ?x203), position(?x8387, ?x203), position(?x6964, ?x203), position(?x6537, ?x203), position(?x4973, ?x203), position(?x3383, ?x203), ?x11507 = 0175rc, ?x9182 = 029q3k, team(?x203, ?x12537), team(?x203, ?x5828), team(?x203, ?x2883), team(?x203, ?x852), ?x2883 = 09pgj2, ?x12269 = 0gfnqh, ?x3383 = 03m10r, ?x4973 = 082wbh, ?x10468 = 03j755, ?x14337 = 07sqhm, ?x6537 = 01s0t3, ?x5828 = 01rl_3, ?x852 = 05kjc6, ?x12537 = 03_44z, ?x9543 = 07s8qm7, ?x11225 = 03ylxn, ?x8387 = 019lvv, ?x6964 = 047fwlg >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 058dm9 team! 02_j1w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.825 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #2420-0gd9k PRED entity: 0gd9k PRED relation: location PRED expected values: 0xqf3 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 197): 02_286 (0.22 #3254, 0.21 #7276, 0.19 #6472), 030qb3t (0.19 #1691, 0.17 #50753, 0.17 #52361), 0cr3d (0.19 #1753, 0.17 #3362, 0.16 #6580), 0cc56 (0.13 #3274, 0.11 #8100, 0.05 #8904), 027l4q (0.08 #1302, 0.05 #8541, 0.05 #10149), 0r7fy (0.08 #881, 0.05 #2489, 0.04 #4098), 0r0m6 (0.08 #1022, 0.05 #2630, 0.04 #4239), 0qpqn (0.08 #1257, 0.05 #2865, 0.04 #4474), 0rk71 (0.08 #1307, 0.05 #2915, 0.04 #4524), 01snm (0.08 #1124, 0.05 #2732, 0.04 #4341) >> Best rule #3254 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 01n4f8; 0126rp; 014z8v; 02z3zp; 01xwv7; 0sx5w; >> query: (?x7984, 02_286) <- producer_type(?x7984, ?x632), influenced_by(?x364, ?x7984), profession(?x7984, ?x319) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gd9k location 0xqf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 130.000 0.217 http://example.org/people/person/places_lived./people/place_lived/location #2419-039n1 PRED entity: 039n1 PRED relation: place_of_birth PRED expected values: 0727_ => 150 concepts (129 used for prediction) PRED predicted values (max 10 best out of 157): 02_286 (0.33 #2836, 0.16 #24691, 0.11 #43722), 0n2z (0.25 #1844, 0.01 #27928), 0hpyv (0.17 #3104, 0.09 #4516, 0.05 #5926), 0156q (0.15 #22555, 0.10 #52866, 0.10 #35953), 05qtj (0.11 #22017, 0.07 #15672, 0.07 #7919), 0rh6k (0.10 #3525, 0.07 #18327, 0.06 #11279), 09f8q (0.10 #4089, 0.03 #8318, 0.03 #10433), 06cn5 (0.10 #3818, 0.03 #10162, 0.01 #24967), 01sn04 (0.10 #3563, 0.03 #9907, 0.01 #28239), 02z0j (0.09 #4560, 0.08 #5265, 0.06 #47930) >> Best rule #2836 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 02lk1s; >> query: (?x9600, 02_286) <- influenced_by(?x11499, ?x9600), company(?x9600, ?x4096), location(?x11499, ?x3052), ?x3052 = 01cx_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #20709 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 026rm_y; 01kx1j; 06vnh2; 01llxp; 0bhtzw; 01x2_q; *> query: (?x9600, 0727_) <- nationality(?x9600, ?x1264), gender(?x9600, ?x231), ?x1264 = 0345h, ?x231 = 05zppz *> conf = 0.02 ranks of expected_values: 103 EVAL 039n1 place_of_birth 0727_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 150.000 129.000 0.333 http://example.org/people/person/place_of_birth #2418-026mj PRED entity: 026mj PRED relation: religion PRED expected values: 01lp8 => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 24): 01lp8 (0.85 #235, 0.78 #313, 0.77 #365), 05sfs (0.80 #236, 0.73 #314, 0.73 #106), 01y0s9 (0.68 #239, 0.62 #317, 0.61 #343), 01s5nb (0.43 #1746, 0.42 #380, 0.41 #250), 058x5 (0.43 #1746, 0.41 #237, 0.40 #367), 092bf5 (0.43 #1746, 0.37 #2267, 0.30 #191), 02t7t (0.27 #248, 0.24 #326, 0.24 #612), 03j6c (0.24 #1303, 0.15 #194, 0.10 #64), 0kpl (0.24 #1303, 0.05 #188, 0.05 #58), 0b06q (0.24 #1303, 0.05 #193, 0.03 #845) >> Best rule #235 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 059rby; 03v1s; 05kj_; 059f4; 05fkf; 0vmt; 03s0w; 059_c; 01n7q; 04ykg; ... >> query: (?x7518, 01lp8) <- contains(?x94, ?x7518), district_represented(?x6728, ?x7518), ?x6728 = 070mff, religion(?x7518, ?x492) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026mj religion 01lp8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.854 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #2417-05dtwm PRED entity: 05dtwm PRED relation: film PRED expected values: 0b2km_ => 88 concepts (69 used for prediction) PRED predicted values (max 10 best out of 484): 0hz55 (0.65 #14299, 0.60 #50053, 0.58 #57203), 04jwly (0.25 #458, 0.10 #2245, 0.03 #103678), 06ztvyx (0.25 #431, 0.10 #2218, 0.03 #78652), 020bv3 (0.25 #318, 0.10 #2105, 0.03 #78652), 0bxsk (0.25 #1208, 0.10 #2995, 0.03 #78652), 07jxpf (0.25 #682, 0.10 #2469, 0.03 #78652), 060v34 (0.25 #75, 0.10 #1862, 0.03 #78652), 040_lv (0.10 #2834, 0.03 #78652, 0.02 #13558), 0bs5f0b (0.10 #3410, 0.03 #78652), 047vp1n (0.10 #3064, 0.03 #78652) >> Best rule #14299 for best value: >> intensional similarity = 3 >> extensional distance = 378 >> proper extension: 02jm0n; 01wxyx1; 02lq10; 046qq; 021yzs; 03m6pk; 0jlv5; 01gbb4; 04jb97; 02wr6r; ... >> query: (?x5542, ?x4932) <- nominated_for(?x5542, ?x4932), film(?x5542, ?x66), film(?x3558, ?x66) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05dtwm film 0b2km_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 69.000 0.645 http://example.org/film/actor/film./film/performance/film #2416-0fdjb PRED entity: 0fdjb PRED relation: genre! PRED expected values: 0cskb 0123qq => 41 concepts (17 used for prediction) PRED predicted values (max 10 best out of 308): 0123qq (0.60 #2006, 0.54 #2301, 0.47 #2596), 0cskb (0.60 #1980, 0.54 #2275, 0.47 #2570), 0fhzwl (0.57 #1658, 0.43 #1361, 0.33 #477), 03ln8b (0.54 #2098, 0.47 #2393, 0.40 #1803), 01rf57 (0.50 #1837, 0.38 #2132, 0.33 #2427), 020qr4 (0.50 #1776, 0.38 #2071, 0.33 #2366), 0h3mh3q (0.43 #1672, 0.43 #1375, 0.40 #1967), 0d_rw (0.43 #1754, 0.40 #2049, 0.38 #2344), 0c3xpwy (0.43 #1577, 0.33 #691, 0.33 #396), 0828jw (0.43 #1284, 0.33 #400, 0.33 #106) >> Best rule #2006 for best value: >> intensional similarity = 13 >> extensional distance = 8 >> proper extension: 01htzx; 06q7n; >> query: (?x6277, 0123qq) <- genre(?x3822, ?x6277), genre(?x3413, ?x6277), ?x3413 = 01f3p_, nominated_for(?x3193, ?x3822), nominated_for(?x1404, ?x3822), titles(?x2008, ?x3822), film(?x3193, ?x5736), place_of_birth(?x3193, ?x13996), actor(?x3822, ?x3687), ?x2008 = 07c52, award_nominee(?x1404, ?x2077), profession(?x3193, ?x319), category(?x3193, ?x134) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0fdjb genre! 0123qq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 17.000 0.600 http://example.org/tv/tv_program/genre EVAL 0fdjb genre! 0cskb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 17.000 0.600 http://example.org/tv/tv_program/genre #2415-04180vy PRED entity: 04180vy PRED relation: film_distribution_medium PRED expected values: 02nxhr => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 5): 0735l (0.68 #64, 0.68 #69, 0.66 #49), 02nxhr (0.32 #26, 0.28 #46, 0.27 #41), 0dq6p (0.14 #47, 0.14 #67, 0.14 #62), 07c52 (0.03 #43), 07z4p (0.02 #30, 0.01 #65, 0.01 #70) >> Best rule #64 for best value: >> intensional similarity = 5 >> extensional distance = 139 >> proper extension: 0ds35l9; 0d90m; 03qcfvw; 0gtsx8c; 03g90h; 01gc7; 0dq626; 0czyxs; 01k1k4; 0ds11z; ... >> query: (?x11686, 0735l) <- film(?x4395, ?x11686), film_crew_role(?x11686, ?x137), language(?x11686, ?x90), film_distribution_medium(?x11686, ?x81), student(?x6271, ?x4395) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 48 *> proper extension: 072r5v; *> query: (?x11686, 02nxhr) <- film_crew_role(?x11686, ?x468), country(?x11686, ?x94), film_distribution_medium(?x11686, ?x81), featured_film_locations(?x11686, ?x739), ?x468 = 02r96rf *> conf = 0.32 ranks of expected_values: 2 EVAL 04180vy film_distribution_medium 02nxhr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 77.000 77.000 0.681 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #2414-0283sdr PRED entity: 0283sdr PRED relation: contains! PRED expected values: 05nrg => 128 concepts (46 used for prediction) PRED predicted values (max 10 best out of 320): 09c7w0 (0.90 #25947, 0.84 #26841, 0.81 #27735), 05nrg (0.62 #5038, 0.14 #5367, 0.12 #3248), 07ssc (0.56 #3609, 0.52 #5400, 0.32 #3577), 02yc5b (0.33 #841, 0.07 #2629, 0.06 #3523), 0d060g (0.32 #3577, 0.30 #24167, 0.16 #6275), 0chghy (0.32 #3577, 0.20 #1811, 0.12 #24177), 02jx1 (0.28 #3663, 0.26 #5454, 0.20 #9033), 0345h (0.26 #24235, 0.08 #975, 0.03 #23260), 03rjj (0.23 #24164, 0.08 #904, 0.03 #22375), 0hzlz (0.20 #1836, 0.08 #3625, 0.07 #5416) >> Best rule #25947 for best value: >> intensional similarity = 6 >> extensional distance = 320 >> proper extension: 0pmcz; 0pz6q; 02p72j; >> query: (?x10285, 09c7w0) <- colors(?x10285, ?x332), contains(?x1023, ?x10285), film_release_region(?x5162, ?x1023), film_release_region(?x1163, ?x1023), ?x1163 = 0c0nhgv, ?x5162 = 0j3d9tn >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #5038 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 0chghy; 0ctw_b; 02wt0; 07z5n; 05qkp; 07fsv; 07fb6; 06s9y; 01n8qg; 03188; ... *> query: (?x10285, 05nrg) <- contains(?x1023, ?x10285), contains(?x1023, ?x12293), contains(?x10150, ?x1023), taxonomy(?x1023, ?x939), ?x12293 = 01pj48 *> conf = 0.62 ranks of expected_values: 2 EVAL 0283sdr contains! 05nrg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 46.000 0.901 http://example.org/location/location/contains #2413-02sn34 PRED entity: 02sn34 PRED relation: contains! PRED expected values: 0cdbq 0212ny => 164 concepts (128 used for prediction) PRED predicted values (max 10 best out of 245): 07t21 (0.95 #83308, 0.95 #78825, 0.95 #76138), 09c7w0 (0.82 #59114, 0.77 #42094, 0.74 #43886), 02j71 (0.56 #41193), 04_1l0v (0.43 #27315, 0.31 #38057, 0.30 #39849), 02qkt (0.33 #51946, 0.31 #69311, 0.30 #74690), 02j9z (0.30 #105703, 0.18 #93166, 0.18 #3610), 09b69 (0.30 #105703, 0.18 #93166, 0.10 #90476), 07ssc (0.25 #928, 0.23 #9882, 0.21 #6301), 0345h (0.25 #4559, 0.23 #5455, 0.21 #9932), 02jx1 (0.25 #983, 0.21 #6356, 0.18 #9937) >> Best rule #83308 for best value: >> intensional similarity = 3 >> extensional distance = 233 >> proper extension: 0lpp8; 01bh3l; 0drsm; 0m_xy; 065zr; 075mb; 078lk; 04p0c; 0dlw0; 0j5g9; ... >> query: (?x6494, ?x1471) <- administrative_parent(?x6494, ?x1471), contains(?x1603, ?x6494), film_release_region(?x124, ?x1471) >> conf = 0.95 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02sn34 contains! 0212ny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 164.000 128.000 0.953 http://example.org/location/location/contains EVAL 02sn34 contains! 0cdbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 164.000 128.000 0.953 http://example.org/location/location/contains #2412-05ldxl PRED entity: 05ldxl PRED relation: nominated_for! PRED expected values: 02pqp12 => 100 concepts (78 used for prediction) PRED predicted values (max 10 best out of 218): 0262s1 (0.67 #14569, 0.66 #15728, 0.66 #16194), 09d28z (0.67 #14569, 0.66 #15728, 0.66 #16194), 0f4x7 (0.67 #720, 0.57 #25, 0.46 #1183), 04dn09n (0.65 #1192, 0.52 #2347, 0.47 #4892), 04kxsb (0.52 #785, 0.39 #1248, 0.37 #1016), 0p9sw (0.47 #251, 0.30 #1178, 0.30 #8347), 02pqp12 (0.46 #1214, 0.40 #2369, 0.39 #982), 0gr51 (0.46 #8861, 0.34 #998, 0.31 #2385), 0gqy2 (0.43 #117, 0.40 #4975, 0.38 #8444), 0gqyl (0.43 #74, 0.39 #2387, 0.39 #1232) >> Best rule #14569 for best value: >> intensional similarity = 5 >> extensional distance = 764 >> proper extension: 06w7mlh; >> query: (?x8258, ?x10747) <- titles(?x512, ?x8258), award(?x8258, ?x10747), award(?x8258, ?x8364), award(?x2182, ?x10747), award_winner(?x8364, ?x698) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #1214 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 67 *> proper extension: 0p_th; 09cr8; 026p4q7; 02rjv2w; 019vhk; 03hmt9b; 0jsqk; 0jqj5; 0yyn5; 0k4p0; ... *> query: (?x8258, 02pqp12) <- nominated_for(?x1313, ?x8258), nominated_for(?x1107, ?x8258), featured_film_locations(?x8258, ?x362), ?x1313 = 0gs9p, ?x1107 = 019f4v *> conf = 0.46 ranks of expected_values: 7 EVAL 05ldxl nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 100.000 78.000 0.668 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2411-02tktw PRED entity: 02tktw PRED relation: film! PRED expected values: 0gx_p 0c0k1 => 109 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1211): 0c9xjl (0.30 #20835, 0.17 #12499, 0.17 #4166), 0p8r1 (0.29 #6836, 0.11 #19338, 0.10 #15170), 06jzh (0.29 #6337, 0.04 #27171, 0.03 #23005), 02qgqt (0.25 #8351, 0.14 #4184, 0.10 #12517), 016k6x (0.25 #9225, 0.14 #5058, 0.06 #23809), 07b2lv (0.25 #367, 0.04 #17036, 0.04 #21202), 015p3p (0.25 #1096, 0.04 #17765, 0.04 #21931), 0h7pj (0.25 #1545, 0.04 #57799, 0.03 #122368), 016vg8 (0.25 #833, 0.03 #13332, 0.02 #36252), 048hf (0.25 #1369, 0.02 #18038, 0.02 #22204) >> Best rule #20835 for best value: >> intensional similarity = 5 >> extensional distance = 53 >> proper extension: 02hxhz; 0dtfn; 03sxd2; 0hmm7; 0ddjy; 01jrbb; >> query: (?x6293, ?x5470) <- genre(?x6293, ?x53), crewmember(?x6293, ?x3782), film_crew_role(?x6293, ?x137), written_by(?x6293, ?x5470), film(?x5470, ?x1364) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #5676 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 09lcsj; *> query: (?x6293, 0c0k1) <- genre(?x6293, ?x3613), crewmember(?x6293, ?x3782), film_crew_role(?x6293, ?x137), ?x3613 = 09blyk, production_companies(?x6293, ?x574), music(?x6293, ?x7701) *> conf = 0.14 ranks of expected_values: 30, 437 EVAL 02tktw film! 0c0k1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 109.000 60.000 0.298 http://example.org/film/actor/film./film/performance/film EVAL 02tktw film! 0gx_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 109.000 60.000 0.298 http://example.org/film/actor/film./film/performance/film #2410-0d6lp PRED entity: 0d6lp PRED relation: source PRED expected values: 0jbk9 => 235 concepts (235 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #157, 0.92 #151, 0.91 #179) >> Best rule #157 for best value: >> intensional similarity = 3 >> extensional distance = 191 >> proper extension: 0ntwb; >> query: (?x3125, 0jbk9) <- adjoins(?x3125, ?x6815), county(?x5232, ?x6815), currency(?x6815, ?x170) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d6lp source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 235.000 235.000 0.922 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #2409-024bbl PRED entity: 024bbl PRED relation: film PRED expected values: 03m5y9p => 89 concepts (69 used for prediction) PRED predicted values (max 10 best out of 762): 0gmcwlb (0.59 #53355, 0.44 #51576, 0.38 #48018), 0180mw (0.59 #53355, 0.44 #51576, 0.38 #48018), 0gg5kmg (0.33 #1072), 02704ff (0.17 #977, 0.12 #2755, 0.02 #8089), 01718w (0.17 #1390, 0.12 #3168), 04jwly (0.17 #455, 0.11 #4011, 0.05 #37348), 0c3xw46 (0.17 #622, 0.11 #4178, 0.03 #51575), 07tlfx (0.17 #1598, 0.11 #5154, 0.03 #90703), 02bqvs (0.17 #1487, 0.11 #5043, 0.03 #90703), 0_9wr (0.17 #1226, 0.10 #6560, 0.03 #51575) >> Best rule #53355 for best value: >> intensional similarity = 3 >> extensional distance = 1315 >> proper extension: 015npr; 03tf_h; 01fs_4; 01y8cr; 08c9b0; 02wk4d; 01wrcxr; 03s9b; 02nfhx; 017yxq; ... >> query: (?x4681, ?x1370) <- film(?x4681, ?x1496), award(?x4681, ?x435), nominated_for(?x4681, ?x1370) >> conf = 0.59 => this is the best rule for 2 predicted values *> Best rule #3189 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01skmp; 07nx9j; 04gc65; *> query: (?x4681, 03m5y9p) <- film(?x4681, ?x8557), film(?x4681, ?x2386), film_crew_role(?x8557, ?x137), ?x2386 = 065z3_x *> conf = 0.12 ranks of expected_values: 61 EVAL 024bbl film 03m5y9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 89.000 69.000 0.588 http://example.org/film/actor/film./film/performance/film #2408-01jpqb PRED entity: 01jpqb PRED relation: school! PRED expected values: 0f4vx0 => 205 concepts (205 used for prediction) PRED predicted values (max 10 best out of 19): 0f4vx0 (0.33 #390, 0.32 #352, 0.31 #428), 092j54 (0.33 #27, 0.20 #502, 0.18 #122), 06439y (0.33 #38, 0.15 #513, 0.14 #646), 02qw1zx (0.28 #347, 0.28 #385, 0.27 #423), 03nt7j (0.25 #63, 0.17 #557, 0.16 #652), 09th87 (0.25 #71, 0.14 #508, 0.13 #641), 02pq_rp (0.25 #64, 0.14 #121, 0.13 #140), 025tn92 (0.21 #430, 0.21 #354, 0.20 #335), 038981 (0.20 #53, 0.09 #357, 0.09 #395), 09l0x9 (0.19 #505, 0.18 #353, 0.17 #391) >> Best rule #390 for best value: >> intensional similarity = 5 >> extensional distance = 67 >> proper extension: 01jssp; 05krk; 01pl14; 01j_9c; 06pwq; 02w2bc; 01wdl3; 01j_06; 01t8sr; 07szy; ... >> query: (?x9745, 0f4vx0) <- currency(?x9745, ?x170), school(?x2569, ?x9745), school(?x700, ?x9745), student(?x9745, ?x7022), gender(?x7022, ?x231) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jpqb school! 0f4vx0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 205.000 205.000 0.333 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #2407-0qf2t PRED entity: 0qf2t PRED relation: award_winner PRED expected values: 0159h6 => 75 concepts (33 used for prediction) PRED predicted values (max 10 best out of 405): 0jz9f (0.46 #13153, 0.44 #19728, 0.39 #26303), 01wk3c (0.46 #13153, 0.44 #19728, 0.39 #26303), 06pk8 (0.33 #157, 0.09 #1801, 0.03 #8377), 02tr7d (0.20 #8219, 0.18 #8218, 0.18 #19729), 071ynp (0.20 #8219, 0.18 #8218, 0.18 #19729), 01j5x6 (0.20 #8219, 0.18 #8218, 0.18 #19729), 02x7vq (0.18 #2551, 0.02 #7481, 0.01 #30501), 01bbwp (0.18 #8218, 0.18 #19729, 0.15 #26304), 02mt4k (0.17 #821, 0.09 #2465, 0.02 #7395), 0h0wc (0.17 #409, 0.05 #3696, 0.04 #13562) >> Best rule #13153 for best value: >> intensional similarity = 4 >> extensional distance = 133 >> proper extension: 0ds3t5x; 0g5qs2k; 0kv2hv; 0bcndz; 0fy34l; 03rz2b; 0p4v_; 07b1gq; 04nnpw; 0cqnss; ... >> query: (?x4864, ?x276) <- genre(?x4864, ?x258), honored_for(?x4864, ?x5950), film(?x166, ?x4864), nominated_for(?x276, ?x4864) >> conf = 0.46 => this is the best rule for 2 predicted values *> Best rule #3352 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 0c0nhgv; 0cbv4g; 01flv_; *> query: (?x4864, 0159h6) <- genre(?x4864, ?x258), honored_for(?x4864, ?x5950), nominated_for(?x1180, ?x4864), ?x1180 = 02n9nmz *> conf = 0.05 ranks of expected_values: 93 EVAL 0qf2t award_winner 0159h6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 75.000 33.000 0.458 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #2406-05k7sb PRED entity: 05k7sb PRED relation: time_zones PRED expected values: 02hcv8 => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.87 #56, 0.74 #944, 0.70 #458), 02fqwt (0.38 #172, 0.35 #211, 0.35 #250), 02hczc (0.25 #16, 0.23 #212, 0.21 #290), 042g7t (0.25 #25, 0.09 #155, 0.06 #508), 02lcrv (0.25 #21, 0.03 #138, 0.03 #151), 02lcqs (0.23 #136, 0.20 #1707, 0.18 #1210), 02llzg (0.23 #148, 0.19 #828, 0.18 #162), 03bdv (0.07 #1812, 0.07 #1042, 0.07 #333), 03plfd (0.07 #940, 0.06 #1280, 0.06 #1228), 0gsrz4 (0.05 #728, 0.05 #793, 0.05 #1369) >> Best rule #56 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 059rby; 059f4; 05fkf; 07z1m; 01x73; 04rrd; 06btq; 0d0x8; 05tbn; 0498y; ... >> query: (?x2020, 02hcv8) <- district_represented(?x7715, ?x2020), religion(?x2020, ?x109), ?x7715 = 01grp0 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05k7sb time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 179.000 0.867 http://example.org/location/location/time_zones #2405-01tntf PRED entity: 01tntf PRED relation: registering_agency PRED expected values: 03z19 => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.83 #30, 0.81 #27, 0.79 #33) >> Best rule #30 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 01k2wn; 01c333; 0pspl; 01jzyx; 017v71; 02km0m; 03cz83; 015y3j; 0l0wv; 03205_; ... >> query: (?x10178, 03z19) <- contains(?x94, ?x10178), major_field_of_study(?x10178, ?x1527), currency(?x10178, ?x170) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tntf registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.833 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #2404-05qhnq PRED entity: 05qhnq PRED relation: role PRED expected values: 06ncr 03qjg => 102 concepts (44 used for prediction) PRED predicted values (max 10 best out of 109): 02sgy (0.42 #1510, 0.33 #1422, 0.30 #2486), 042v_gx (0.37 #1511, 0.30 #2486, 0.26 #2665), 05148p4 (0.30 #2486, 0.26 #2665, 0.26 #2485), 02hnl (0.30 #2486, 0.26 #2665, 0.26 #2485), 0l14qv (0.25 #4, 0.25 #624, 0.22 #536), 0l15bq (0.25 #28, 0.10 #560, 0.09 #3467), 02dlh2 (0.25 #65, 0.09 #3467, 0.09 #1241), 0l1589 (0.25 #50, 0.09 #3467, 0.09 #1241), 0cfdd (0.25 #78, 0.08 #610, 0.08 #254), 0dwsp (0.25 #9, 0.07 #453, 0.07 #2215) >> Best rule #1510 for best value: >> intensional similarity = 4 >> extensional distance = 170 >> proper extension: 07_3qd; 04mx7s; 06br6t; >> query: (?x7210, 02sgy) <- role(?x7210, ?x8172), role(?x7210, ?x227), ?x227 = 0342h, role(?x228, ?x8172) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #761 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 03f2_rc; 015882; 0pyg6; 02b25y; 04pf4r; 0jbyg; 031x_3; 0163kf; *> query: (?x7210, 03qjg) <- artists(?x302, ?x7210), performance_role(?x7210, ?x212), award_nominee(?x565, ?x7210) *> conf = 0.10 ranks of expected_values: 13, 50 EVAL 05qhnq role 03qjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 102.000 44.000 0.419 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 05qhnq role 06ncr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 102.000 44.000 0.419 http://example.org/music/artist/track_contributions./music/track_contribution/role #2403-04z257 PRED entity: 04z257 PRED relation: language PRED expected values: 04306rv => 94 concepts (88 used for prediction) PRED predicted values (max 10 best out of 47): 06nm1 (0.23 #122, 0.17 #65, 0.12 #515), 06b_j (0.22 #20, 0.09 #414, 0.08 #245), 04306rv (0.15 #116, 0.14 #172, 0.13 #228), 04h9h (0.14 #209, 0.08 #96, 0.08 #153), 06mp7 (0.11 #14, 0.03 #2948, 0.03 #464), 03hkp (0.11 #13, 0.03 #2948, 0.03 #182), 01jb8r (0.11 #51, 0.03 #2948), 07qv_ (0.11 #30, 0.03 #2948), 0jzc (0.09 #243, 0.09 #299, 0.06 #581), 03_9r (0.08 #233, 0.07 #1764, 0.07 #289) >> Best rule #122 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 084qpk; 0dgst_d; 0gjcrrw; 0gwjw0c; 0gvvf4j; >> query: (?x3612, 06nm1) <- film(?x2443, ?x3612), production_companies(?x3612, ?x3462), ?x3462 = 061dn_, country(?x3612, ?x94), genre(?x3612, ?x258), award(?x2443, ?x112) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #116 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 084qpk; 0dgst_d; 0gjcrrw; 0gwjw0c; 0gvvf4j; *> query: (?x3612, 04306rv) <- film(?x2443, ?x3612), production_companies(?x3612, ?x3462), ?x3462 = 061dn_, country(?x3612, ?x94), genre(?x3612, ?x258), award(?x2443, ?x112) *> conf = 0.15 ranks of expected_values: 3 EVAL 04z257 language 04306rv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 88.000 0.231 http://example.org/film/film/language #2402-0c0tzp PRED entity: 0c0tzp PRED relation: nominated_for PRED expected values: 0cq7kw => 97 concepts (48 used for prediction) PRED predicted values (max 10 best out of 234): 0h0wd9 (0.81 #68027, 0.79 #37245, 0.78 #42106), 0cq7kw (0.51 #14570, 0.48 #16192, 0.47 #6475), 0ft18 (0.51 #14570, 0.48 #16192, 0.47 #6475), 0k7tq (0.51 #14570, 0.48 #16192, 0.47 #6475), 0cy__l (0.51 #14570, 0.48 #16192, 0.47 #6475), 0cq8qq (0.40 #822, 0.15 #48588, 0.11 #50209), 05dmmc (0.28 #12023, 0.28 #10405, 0.17 #3926), 0bj25 (0.20 #1333, 0.14 #6187, 0.12 #7808), 0kbf1 (0.20 #830, 0.07 #5684, 0.06 #7305), 0jymd (0.20 #598, 0.07 #5452, 0.06 #7073) >> Best rule #68027 for best value: >> intensional similarity = 3 >> extensional distance = 1422 >> proper extension: 01nqfh_; 01mqz0; 0241wg; 01vsy9_; 01m7f5r; 0bxy67; 01hkck; 02js_6; >> query: (?x12378, ?x4280) <- profession(?x12378, ?x1078), award_winner(?x4280, ?x12378), nominated_for(?x12378, ?x4970) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #14570 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 0584j4n; *> query: (?x12378, ?x4280) <- film_sets_designed(?x12378, ?x4280), award_nominee(?x12378, ?x7528), award_winner(?x1745, ?x7528) *> conf = 0.51 ranks of expected_values: 2 EVAL 0c0tzp nominated_for 0cq7kw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 48.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #2401-02pjc1h PRED entity: 02pjc1h PRED relation: genre PRED expected values: 01t_vv => 119 concepts (103 used for prediction) PRED predicted values (max 10 best out of 117): 07ssc (0.51 #4650, 0.48 #2907, 0.46 #5929), 02l7c8 (0.36 #128, 0.35 #1058, 0.34 #4545), 03k9fj (0.25 #705, 0.24 #3961, 0.24 #1404), 04xvlr (0.23 #4534, 0.20 #1047, 0.20 #5813), 06cvj (0.23 #5349, 0.22 #5233, 0.21 #2560), 060__y (0.21 #1059, 0.20 #1525, 0.18 #4663), 01t_vv (0.18 #5281, 0.17 #2608, 0.16 #5397), 06n90 (0.18 #1405, 0.14 #11524, 0.14 #6054), 04xvh5 (0.18 #1077, 0.13 #4681, 0.12 #4564), 01hmnh (0.16 #3967, 0.16 #6756, 0.16 #2572) >> Best rule #4650 for best value: >> intensional similarity = 4 >> extensional distance = 350 >> proper extension: 01fs__; 0d7vtk; >> query: (?x1448, ?x512) <- honored_for(?x7767, ?x1448), titles(?x512, ?x1448), nominated_for(?x112, ?x1448), language(?x1448, ?x254) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #5281 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 401 *> proper extension: 027qgy; 047q2k1; 034qrh; 04tc1g; 0c5dd; 03fts; 07h9gp; 06rmdr; 0407yfx; 01pv91; ... *> query: (?x1448, 01t_vv) <- nominated_for(?x1414, ?x1448), genre(?x1448, ?x258), ?x258 = 05p553, nominated_for(?x112, ?x1448) *> conf = 0.18 ranks of expected_values: 7 EVAL 02pjc1h genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 119.000 103.000 0.515 http://example.org/film/film/genre #2400-05ldxl PRED entity: 05ldxl PRED relation: genre PRED expected values: 0lsxr => 91 concepts (84 used for prediction) PRED predicted values (max 10 best out of 101): 02kdv5l (0.61 #1699, 0.61 #245, 0.59 #608), 07ssc (0.57 #2789, 0.57 #3276, 0.52 #7280), 01jfsb (0.50 #619, 0.48 #256, 0.47 #1710), 03k9fj (0.46 #618, 0.44 #255, 0.43 #1709), 05p553 (0.40 #4374, 0.37 #973, 0.35 #1823), 02l7c8 (0.35 #864, 0.31 #2561, 0.30 #1349), 01hmnh (0.28 #261, 0.27 #1715, 0.27 #624), 0lsxr (0.27 #5833, 0.20 #1828, 0.20 #1463), 04xvlr (0.25 #2667, 0.23 #3154, 0.21 #1455), 060__y (0.22 #1471, 0.21 #865, 0.20 #1350) >> Best rule #1699 for best value: >> intensional similarity = 3 >> extensional distance = 195 >> proper extension: 02vw1w2; >> query: (?x8258, 02kdv5l) <- language(?x8258, ?x254), genre(?x8258, ?x1013), ?x1013 = 06n90 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #5833 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 824 *> proper extension: 01h72l; 02bj22; 02qdrjx; *> query: (?x8258, 0lsxr) <- nominated_for(?x4353, ?x8258), genre(?x8258, ?x1013), disciplines_or_subjects(?x575, ?x1013) *> conf = 0.27 ranks of expected_values: 8 EVAL 05ldxl genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 91.000 84.000 0.609 http://example.org/film/film/genre #2399-09snz PRED entity: 09snz PRED relation: place PRED expected values: 09snz => 178 concepts (138 used for prediction) PRED predicted values (max 10 best out of 224): 0fw1y (0.38 #7211, 0.20 #494, 0.14 #20104), 010v8k (0.20 #206, 0.14 #20104, 0.14 #15461), 0d9jr (0.20 #130, 0.14 #20104, 0.07 #645), 04gxf (0.15 #50038, 0.14 #15461, 0.12 #47460), 09snz (0.14 #20104, 0.14 #15461, 0.12 #47460), 030qb3t (0.14 #15461, 0.12 #47460, 0.11 #49522), 0dq16 (0.07 #630, 0.03 #2175, 0.03 #3205), 019fh (0.07 #593, 0.02 #4713, 0.01 #7289), 02_286 (0.07 #529, 0.01 #7225, 0.01 #10317), 0lpk3 (0.07 #749) >> Best rule #7211 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 0j8p6; >> query: (?x9141, ?x14023) <- state(?x9141, ?x4600), administrative_division(?x14023, ?x4600), district_represented(?x605, ?x4600), administrative_division(?x9141, ?x10733) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #20104 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 135 *> proper extension: 0l_q9; 0g_wn2; 0qf5p; *> query: (?x9141, ?x5267) <- state(?x9141, ?x4600), source(?x9141, ?x958), state(?x5267, ?x4600), category(?x4600, ?x134) *> conf = 0.14 ranks of expected_values: 5 EVAL 09snz place 09snz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 178.000 138.000 0.375 http://example.org/location/hud_county_place/place #2398-05g76 PRED entity: 05g76 PRED relation: season PRED expected values: 025ygqm => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 6): 025ygqm (0.78 #163, 0.77 #271, 0.77 #193), 04110b0 (0.43 #45, 0.38 #231, 0.38 #99), 02h7s73 (0.43 #46, 0.38 #232, 0.32 #274), 03c6s24 (0.31 #233, 0.29 #161, 0.29 #47), 03c74_8 (0.27 #230, 0.24 #158, 0.23 #98), 04n36qk (0.12 #162, 0.08 #228, 0.08 #222) >> Best rule #163 for best value: >> intensional similarity = 7 >> extensional distance = 16 >> proper extension: 06x68; 0x2p; 0713r; 04mjl; 03m1n; >> query: (?x2067, 025ygqm) <- season(?x2067, ?x9498), draft(?x2067, ?x11905), school(?x2067, ?x1276), team(?x4244, ?x2067), ?x4244 = 028c_8, ?x11905 = 047dpm0, ?x9498 = 027pwzc >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05g76 season 025ygqm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.778 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #2397-017959 PRED entity: 017959 PRED relation: artists! PRED expected values: 0mhfr => 69 concepts (25 used for prediction) PRED predicted values (max 10 best out of 228): 07sbbz2 (0.73 #3105, 0.52 #3414, 0.38 #1867), 064t9 (0.57 #4041, 0.49 #6213, 0.49 #6832), 0mhfr (0.57 #1259, 0.56 #1570, 0.40 #948), 05hs4r (0.50 #1860, 0.40 #926, 0.29 #1237), 03lty (0.47 #2813, 0.38 #2504, 0.27 #3742), 01lyv (0.44 #1892, 0.35 #3439, 0.33 #1580), 06j6l (0.43 #4075, 0.35 #3145, 0.29 #1284), 05bt6j (0.41 #2520, 0.36 #2210, 0.32 #6242), 0155w (0.40 #1032, 0.33 #415, 0.33 #107), 015pdg (0.40 #935, 0.33 #318, 0.31 #1869) >> Best rule #3105 for best value: >> intensional similarity = 7 >> extensional distance = 72 >> proper extension: 053y0s; 01m65sp; >> query: (?x9638, 07sbbz2) <- artists(?x10318, ?x9638), artists(?x1572, ?x9638), ?x1572 = 06by7, artists(?x10318, ?x3442), artists(?x10318, ?x1654), ?x1654 = 01bpc9, ?x3442 = 0m_v0 >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1259 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 01cblr; 03c3yf; *> query: (?x9638, 0mhfr) <- group(?x1166, ?x9638), group(?x228, ?x9638), artists(?x10318, ?x9638), ?x10318 = 03jsvl, ?x1166 = 05148p4, role(?x228, ?x75), award(?x9638, ?x724) *> conf = 0.57 ranks of expected_values: 3 EVAL 017959 artists! 0mhfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 69.000 25.000 0.730 http://example.org/music/genre/artists #2396-0gr0m PRED entity: 0gr0m PRED relation: award_winner PRED expected values: 07xr3w => 54 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1730): 04ls53 (0.50 #5976, 0.25 #10877, 0.15 #23131), 012ljv (0.50 #4907, 0.25 #9808, 0.12 #22062), 012201 (0.50 #6714, 0.25 #11615, 0.12 #23869), 02kxbx3 (0.47 #17926, 0.36 #13025, 0.33 #3223), 0c921 (0.45 #16671, 0.33 #4419, 0.33 #1968), 06x77g (0.45 #34310, 0.30 #7352, 0.30 #22055), 02vyw (0.36 #15494, 0.33 #3242, 0.33 #791), 0151w_ (0.36 #14894, 0.33 #2642, 0.33 #191), 0js9s (0.36 #16149, 0.33 #1446, 0.29 #8798), 03_gd (0.36 #14841, 0.33 #138, 0.29 #7490) >> Best rule #5976 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0l8z1; 054krc; 02qvyrt; >> query: (?x1243, 04ls53) <- nominated_for(?x1243, ?x7941), nominated_for(?x1243, ?x4007), ?x4007 = 03hmt9b, award(?x185, ?x1243), ?x7941 = 03b1l8 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #61282 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 211 *> proper extension: 0h53c_5; *> query: (?x1243, ?x276) <- nominated_for(?x1243, ?x4007), award_winner(?x1243, ?x2466), award_winner(?x4007, ?x276) *> conf = 0.08 ranks of expected_values: 532 EVAL 0gr0m award_winner 07xr3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 54.000 29.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #2395-0flry PRED entity: 0flry PRED relation: films PRED expected values: 016ywb => 87 concepts (62 used for prediction) PRED predicted values (max 10 best out of 761): 08hmch (0.40 #1635, 0.22 #6400, 0.13 #11699), 0286vp (0.33 #3535, 0.33 #887, 0.29 #5122), 0kvgtf (0.33 #3364, 0.33 #716, 0.29 #4951), 02fwfb (0.33 #3021, 0.20 #7786, 0.02 #20670), 0294mx (0.33 #901, 0.17 #3549, 0.17 #3020), 04j14qc (0.33 #955, 0.17 #3603, 0.14 #5190), 0gd92 (0.33 #915, 0.17 #3563, 0.14 #5150), 01qbg5 (0.33 #903, 0.17 #3551, 0.14 #5138), 01z452 (0.33 #985, 0.17 #3633, 0.14 #5220), 0b7l4x (0.33 #829, 0.17 #3477, 0.14 #5064) >> Best rule #1635 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0kbq; >> query: (?x11183, 08hmch) <- entity_involved(?x11183, ?x5274), films(?x11183, ?x6273), combatants(?x9203, ?x5274), form_of_government(?x5274, ?x1926), jurisdiction_of_office(?x182, ?x5274), combatants(?x11183, ?x985), taxonomy(?x5274, ?x939) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0flry films 016ywb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 62.000 0.400 http://example.org/film/film_subject/films #2394-03mp9s PRED entity: 03mp9s PRED relation: film PRED expected values: 0djlxb => 93 concepts (66 used for prediction) PRED predicted values (max 10 best out of 530): 04b_jc (0.59 #42920, 0.54 #35764, 0.53 #42919), 011ywj (0.53 #8588, 0.03 #66172, 0.02 #31834), 031778 (0.25 #315, 0.05 #7468, 0.03 #9256), 016ywb (0.25 #1237, 0.05 #8390, 0.01 #13754), 01qz5 (0.25 #1415, 0.05 #8568), 02qhlwd (0.25 #701, 0.05 #7854), 03ydlnj (0.25 #1396, 0.03 #94789), 03m8y5 (0.25 #407, 0.01 #14712, 0.01 #16501), 01mszz (0.25 #1086, 0.01 #17180), 04ltlj (0.25 #1718) >> Best rule #42920 for best value: >> intensional similarity = 3 >> extensional distance = 974 >> proper extension: 04yywz; 049tjg; 02g8h; 0d_84; 042l3v; 02nb2s; 0151ns; 025p38; 0htlr; 019z7q; ... >> query: (?x6977, ?x1263) <- location(?x6977, ?x1523), nominated_for(?x6977, ?x1263), film(?x981, ?x1263) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #2324 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 01w23w; *> query: (?x6977, 0djlxb) <- award_nominee(?x6977, ?x2626), ?x2626 = 02js6_, award(?x6977, ?x618) *> conf = 0.17 ranks of expected_values: 23 EVAL 03mp9s film 0djlxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 93.000 66.000 0.591 http://example.org/film/actor/film./film/performance/film #2393-07pd_j PRED entity: 07pd_j PRED relation: genre PRED expected values: 05p553 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 89): 07s9rl0 (0.72 #367, 0.67 #1954, 0.67 #1343), 01z4y (0.61 #5630, 0.50 #7096, 0.49 #7463), 02kdv5l (0.58 #3, 0.55 #491, 0.55 #613), 05p553 (0.58 #4406, 0.42 #1347, 0.42 #1469), 01jfsb (0.47 #14, 0.44 #258, 0.43 #624), 03k9fj (0.45 #623, 0.43 #13, 0.43 #257), 01hmnh (0.31 #629, 0.30 #507, 0.25 #4420), 06cvj (0.25 #1346, 0.24 #1468, 0.18 #1957), 082gq (0.24 #1985, 0.10 #2351, 0.09 #3697), 0lsxr (0.23 #132, 0.22 #254, 0.22 #10) >> Best rule #367 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 0sxg4; 0yyg4; 0b73_1d; 04mzf8; 0sxfd; 0p_th; 09tqkv2; 0mcl0; 03hmt9b; 05c5z8j; ... >> query: (?x6684, 07s9rl0) <- award_winner(?x6684, ?x1773), nominated_for(?x1460, ?x6684), film_festivals(?x6684, ?x6557) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #4406 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1006 *> proper extension: 0vgkd; *> query: (?x6684, 05p553) <- genre(?x6684, ?x1403), genre(?x6099, ?x1403), ?x6099 = 0473rc *> conf = 0.58 ranks of expected_values: 4 EVAL 07pd_j genre 05p553 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 73.000 73.000 0.724 http://example.org/film/film/genre #2392-023jq1 PRED entity: 023jq1 PRED relation: nationality PRED expected values: 0j5g9 => 128 concepts (124 used for prediction) PRED predicted values (max 10 best out of 37): 09c7w0 (0.86 #1090, 0.85 #5651, 0.84 #1486), 02jx1 (0.42 #131, 0.40 #3102, 0.38 #428), 0j5g9 (0.36 #10317, 0.03 #3131, 0.03 #1645), 03rk0 (0.17 #2817, 0.17 #2718, 0.11 #5099), 03rt9 (0.11 #112, 0.05 #409, 0.03 #1597), 06q1r (0.09 #274, 0.09 #1264, 0.08 #472), 0d060g (0.07 #304, 0.07 #4068, 0.06 #997), 0f8l9c (0.05 #1011, 0.03 #6465, 0.03 #6663), 0345h (0.03 #3001, 0.03 #1218, 0.03 #6474), 0h7x (0.03 #3005, 0.02 #1222, 0.02 #4890) >> Best rule #1090 for best value: >> intensional similarity = 5 >> extensional distance = 84 >> proper extension: 0f7hc; >> query: (?x9793, 09c7w0) <- nationality(?x9793, ?x512), profession(?x9793, ?x1041), profession(?x9793, ?x353), ?x1041 = 03gjzk, ?x353 = 0cbd2 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #10317 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2325 *> proper extension: 0784v1; 0cfywh; *> query: (?x9793, ?x4221) <- nationality(?x9793, ?x512), place_of_birth(?x9793, ?x9818), contains(?x4221, ?x9818) *> conf = 0.36 ranks of expected_values: 3 EVAL 023jq1 nationality 0j5g9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 128.000 124.000 0.860 http://example.org/people/person/nationality #2391-0bh72t PRED entity: 0bh72t PRED relation: film! PRED expected values: 0dt645q => 93 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1073): 0dt645q (0.40 #1767, 0.33 #14272, 0.33 #5935), 05bp8g (0.38 #6285, 0.15 #18792, 0.15 #14621), 03fghg (0.33 #12735, 0.33 #4398, 0.24 #25243), 013km (0.33 #18760, 0.14 #85483, 0.13 #93824), 03q64h (0.25 #8293, 0.20 #2040, 0.17 #6208), 04j5fx (0.22 #14351, 0.17 #6014, 0.12 #10183), 079vf (0.18 #29191, 0.14 #33361, 0.12 #39614), 01rmnp (0.17 #5759, 0.12 #24519, 0.12 #9928), 0b9f7t (0.17 #6204, 0.12 #10373, 0.11 #14541), 0f5xn (0.17 #3056, 0.12 #32240, 0.11 #11393) >> Best rule #1767 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 076xkdz; >> query: (?x6649, 0dt645q) <- film(?x296, ?x6649), genre(?x6649, ?x5937), genre(?x6649, ?x1013), genre(?x6649, ?x225), ?x225 = 02kdv5l, actor(?x6649, ?x6414), country(?x6649, ?x252), ?x5937 = 0jxy, ?x296 = 01kyvx, ?x252 = 03_3d, ?x1013 = 06n90, ?x6414 = 05v954 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bh72t film! 0dt645q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 47.000 0.400 http://example.org/film/actor/film./film/performance/film #2390-02yw5r PRED entity: 02yw5r PRED relation: honored_for PRED expected values: 07cz2 098s2w => 40 concepts (23 used for prediction) PRED predicted values (max 10 best out of 926): 04q827 (0.50 #1751, 0.40 #1186, 0.40 #1152), 03xf_m (0.50 #1574, 0.40 #975, 0.33 #383), 049xgc (0.50 #1528, 0.40 #929, 0.33 #337), 04b2qn (0.50 #1653, 0.40 #1054, 0.33 #462), 04nl83 (0.50 #1215, 0.40 #616, 0.33 #24), 02qm_f (0.33 #1245, 0.33 #54, 0.20 #646), 02mt51 (0.33 #238, 0.20 #830, 0.17 #1429), 02wgk1 (0.33 #265, 0.20 #857, 0.17 #1456), 04165w (0.33 #443, 0.20 #1035, 0.17 #1634), 04k9y6 (0.33 #358, 0.20 #950, 0.17 #1549) >> Best rule #1751 for best value: >> intensional similarity = 16 >> extensional distance = 4 >> proper extension: 09bymc; >> query: (?x1084, 04q827) <- award_winner(?x1084, ?x7831), ceremony(?x3458, ?x1084), ceremony(?x1245, ?x1084), ?x7831 = 0mz73, nominated_for(?x3458, ?x7336), nominated_for(?x3458, ?x6981), nominated_for(?x3458, ?x144), award(?x2871, ?x3458), film_regional_debut_venue(?x7336, ?x13344), film_crew_role(?x6981, ?x137), nominated_for(?x1245, ?x3035), award(?x988, ?x1245), spouse(?x988, ?x6612), ?x3035 = 0j43swk, ?x144 = 0m313, film(?x496, ?x7336) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #990 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 3 *> proper extension: 09p2r9; 09pnw5; *> query: (?x1084, 098s2w) <- award_winner(?x1084, ?x7831), ceremony(?x3458, ?x1084), ceremony(?x1245, ?x1084), ceremony(?x601, ?x1084), ?x7831 = 0mz73, nominated_for(?x3458, ?x7336), ?x7336 = 0bdjd, nominated_for(?x1245, ?x10806), nominated_for(?x1245, ?x8890), nominated_for(?x1245, ?x3035), nominated_for(?x601, ?x5228), award(?x241, ?x1245), ?x10806 = 04q827, ?x3035 = 0j43swk, award_winner(?x601, ?x647), award_winner(?x8890, ?x7310), film(?x4353, ?x5228) *> conf = 0.20 ranks of expected_values: 13, 536 EVAL 02yw5r honored_for 098s2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 40.000 23.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 02yw5r honored_for 07cz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 40.000 23.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2389-03t22m PRED entity: 03t22m PRED relation: instrumentalists PRED expected values: 07z542 => 87 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1042): 02mslq (0.73 #2463, 0.60 #5551, 0.60 #5550), 01kvqc (0.73 #2463, 0.60 #5551, 0.60 #5550), 01w923 (0.73 #2463, 0.60 #5550, 0.46 #13577), 018y81 (0.67 #7753, 0.56 #10220, 0.44 #11452), 01sb5r (0.67 #7642, 0.56 #10109, 0.40 #7025), 05cljf (0.67 #7413, 0.56 #9880, 0.36 #12968), 09hnb (0.60 #12483, 0.39 #3081, 0.36 #13101), 018gkb (0.55 #13531, 0.50 #7976, 0.44 #10443), 01vw20_ (0.55 #13122, 0.39 #3081, 0.37 #20547), 0fhxv (0.55 #13227, 0.39 #3081, 0.33 #10139) >> Best rule #2463 for best value: >> intensional similarity = 22 >> extensional distance = 1 >> proper extension: 06ncr; >> query: (?x1831, ?x1583) <- performance_role(?x1831, ?x1332), role(?x9413, ?x1831), role(?x3991, ?x1831), role(?x2944, ?x1831), role(?x1267, ?x1831), instrumentalists(?x1831, ?x5815), instrumentalists(?x1831, ?x120), group(?x1831, ?x4010), role(?x9413, ?x1472), role(?x9413, ?x228), ?x1267 = 07brj, role(?x1583, ?x1831), role(?x547, ?x1831), ?x120 = 0f0y8, instrumentalists(?x9413, ?x2945), ?x228 = 0l14qv, ?x3991 = 05842k, ?x1472 = 0319l, ?x5815 = 01l7cxq, ?x547 = 02mslq, ?x2944 = 0l14j_, role(?x9413, ?x745) >> conf = 0.73 => this is the best rule for 3 predicted values *> Best rule #6245 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 3 *> proper extension: 02pprs; *> query: (?x1831, 07z542) <- performance_role(?x1831, ?x1332), role(?x10843, ?x1831), role(?x9413, ?x1831), role(?x1495, ?x1831), ?x9413 = 07m2y, instrumentalists(?x1831, ?x120), role(?x3161, ?x1831), ?x1495 = 013y1f, role(?x1831, ?x1432), gender(?x120, ?x231), role(?x547, ?x1831), nationality(?x120, ?x94), role(?x74, ?x1432), ?x74 = 03q5t, group(?x3161, ?x3682), role(?x645, ?x3161), ?x10843 = 0l15f_, instrumentalists(?x3161, ?x140) *> conf = 0.40 ranks of expected_values: 56 EVAL 03t22m instrumentalists 07z542 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 87.000 55.000 0.733 http://example.org/music/instrument/instrumentalists #2388-0_9l_ PRED entity: 0_9l_ PRED relation: film_format PRED expected values: 07fb8_ => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 4): 07fb8_ (0.17 #39, 0.14 #214, 0.14 #77), 017fx5 (0.14 #4, 0.03 #58, 0.03 #25), 0cj16 (0.12 #273, 0.11 #349, 0.11 #57), 01dc60 (0.04 #11, 0.01 #32, 0.01 #21) >> Best rule #39 for best value: >> intensional similarity = 4 >> extensional distance = 155 >> proper extension: 0cnztc4; >> query: (?x11429, 07fb8_) <- language(?x11429, ?x254), genre(?x11429, ?x1509), ?x1509 = 060__y, film_release_distribution_medium(?x11429, ?x81) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_9l_ film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.166 http://example.org/film/film/film_format #2387-01ptt7 PRED entity: 01ptt7 PRED relation: institution! PRED expected values: 02_xgp2 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 18): 02_xgp2 (0.65 #29, 0.60 #258, 0.59 #162), 0bkj86 (0.45 #25, 0.41 #254, 0.39 #407), 07s6fsf (0.44 #210, 0.44 #249, 0.43 #134), 04zx3q1 (0.40 #21, 0.34 #250, 0.32 #154), 027f2w (0.40 #26, 0.29 #255, 0.26 #159), 013zdg (0.30 #24, 0.26 #253, 0.25 #138), 03mkk4 (0.25 #28, 0.17 #1207, 0.17 #257), 01rr_d (0.20 #33, 0.17 #262, 0.17 #1207), 028dcg (0.17 #1207, 0.15 #206, 0.15 #35), 0bjrnt (0.17 #1207, 0.15 #23, 0.14 #252) >> Best rule #29 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 01pl14; 06pwq; 065y4w7; 01wdl3; 049dk; 078bz; 0j_sncb; 0pspl; 02t4yc; 07vyf; ... >> query: (?x2175, 02_xgp2) <- institution(?x865, ?x2175), school(?x8133, ?x2175), school(?x580, ?x2175), ?x8133 = 025tn92 >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ptt7 institution! 02_xgp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.650 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2386-0c0sl PRED entity: 0c0sl PRED relation: company! PRED expected values: 02k13d => 191 concepts (191 used for prediction) PRED predicted values (max 10 best out of 35): 0dq_5 (0.70 #2192, 0.70 #2162, 0.67 #474), 0krdk (0.68 #1864, 0.67 #1961, 0.64 #2151), 060c4 (0.67 #429, 0.58 #1860, 0.54 #1908), 05_wyz (0.54 #1018, 0.43 #1113, 0.41 #2163), 0dq3c (0.46 #1001, 0.45 #1859, 0.41 #2146), 09d6p2 (0.40 #257, 0.38 #1019, 0.36 #1114), 014l7h (0.40 #266, 0.33 #694, 0.33 #407), 01yc02 (0.39 #1866, 0.33 #2106, 0.33 #1963), 02k13d (0.33 #392, 0.24 #1300, 0.22 #679), 01kr6k (0.31 #1027, 0.30 #836, 0.29 #1122) >> Best rule #2192 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 09b3v; 09j_g; 01tmng; >> query: (?x13177, ?x4682) <- currency(?x13177, ?x170), organization(?x4682, ?x13177), ?x170 = 09nqf, ?x4682 = 0dq_5, citytown(?x13177, ?x108) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #392 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 03mdt; *> query: (?x13177, 02k13d) <- award_winner(?x3486, ?x13177), contact_category(?x13177, ?x897), ?x3486 = 0m7yy, organization(?x4682, ?x13177), citytown(?x13177, ?x108) *> conf = 0.33 ranks of expected_values: 9 EVAL 0c0sl company! 02k13d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 191.000 191.000 0.705 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #2385-09pbb PRED entity: 09pbb PRED relation: nutrient! PRED expected values: 037ls6 => 59 concepts (55 used for prediction) PRED predicted values (max 10 best out of 11): 037ls6 (0.90 #337, 0.90 #333, 0.89 #301), 06x4c (0.89 #10, 0.89 #109, 0.89 #115), 0dcfv (0.89 #10, 0.89 #109, 0.89 #115), 01sh2 (0.04 #187, 0.04 #204, 0.04 #459), 04k8n (0.04 #459, 0.03 #438, 0.02 #395), 05wvs (0.04 #459, 0.03 #438, 0.02 #395), 025rw19 (0.02 #407), 025tkqy (0.02 #407), 014d7f (0.02 #407), 06jry (0.02 #407) >> Best rule #337 for best value: >> intensional similarity = 119 >> extensional distance = 18 >> proper extension: 01n78x; >> query: (?x5526, ?x8298) <- nutrient(?x9489, ?x5526), nutrient(?x9005, ?x5526), nutrient(?x7719, ?x5526), nutrient(?x7057, ?x5526), nutrient(?x6159, ?x5526), nutrient(?x5373, ?x5526), nutrient(?x5009, ?x5526), nutrient(?x4068, ?x5526), nutrient(?x3900, ?x5526), nutrient(?x2701, ?x5526), nutrient(?x1959, ?x5526), nutrient(?x1257, ?x5526), ?x7057 = 0fbdb, nutrient(?x3900, ?x13944), nutrient(?x3900, ?x13498), nutrient(?x3900, ?x12902), nutrient(?x3900, ?x12454), nutrient(?x3900, ?x11784), nutrient(?x3900, ?x11758), nutrient(?x3900, ?x11592), nutrient(?x3900, ?x11409), nutrient(?x3900, ?x11270), nutrient(?x3900, ?x10891), nutrient(?x3900, ?x10709), nutrient(?x3900, ?x10195), nutrient(?x3900, ?x10098), nutrient(?x3900, ?x9855), nutrient(?x3900, ?x9840), nutrient(?x3900, ?x9795), nutrient(?x3900, ?x9733), nutrient(?x3900, ?x9708), nutrient(?x3900, ?x9490), nutrient(?x3900, ?x9436), nutrient(?x3900, ?x9426), nutrient(?x3900, ?x8487), nutrient(?x3900, ?x8442), nutrient(?x3900, ?x8413), nutrient(?x3900, ?x7894), nutrient(?x3900, ?x7720), nutrient(?x3900, ?x7652), nutrient(?x3900, ?x7431), nutrient(?x3900, ?x7364), nutrient(?x3900, ?x7362), nutrient(?x3900, ?x7219), nutrient(?x3900, ?x6586), nutrient(?x3900, ?x6286), nutrient(?x3900, ?x6192), nutrient(?x3900, ?x6160), nutrient(?x3900, ?x6033), nutrient(?x3900, ?x5549), nutrient(?x3900, ?x5374), nutrient(?x3900, ?x5337), nutrient(?x3900, ?x5010), nutrient(?x3900, ?x4069), nutrient(?x3900, ?x3901), nutrient(?x3900, ?x3469), nutrient(?x3900, ?x2018), ?x13944 = 0f4kp, ?x6160 = 041r51, ?x6159 = 033cnk, ?x8442 = 02kcv4x, ?x9436 = 025sqz8, ?x11784 = 07zqy, ?x3901 = 0466p20, ?x3469 = 0h1zw, ?x6033 = 04zjxcz, ?x11409 = 0h1yf, ?x1959 = 0f25w9, ?x2018 = 01sh2, ?x1257 = 09728, ?x13498 = 07q0m, ?x5337 = 06x4c, ?x9005 = 04zpv, ?x7431 = 09gwd, ?x6586 = 05gh50, ?x8413 = 02kc4sf, ?x7364 = 09gvd, ?x12902 = 0fzjh, ?x7720 = 025s7x6, ?x7894 = 0f4hc, ?x12454 = 025rw19, ?x6286 = 02y_3rf, ?x11758 = 0q01m, ?x5009 = 0fjfh, nutrient(?x7719, ?x12868), nutrient(?x7719, ?x7135), nutrient(?x7719, ?x3264), nutrient(?x7719, ?x2702), nutrient(?x8298, ?x9708), ?x8298 = 037ls6, ?x7219 = 0h1vg, ?x9795 = 05v_8y, ?x7362 = 02kc5rj, ?x9840 = 02p0tjr, ?x12868 = 03d49, ?x9855 = 0d9t0, ?x4069 = 0hqw8p_, ?x10891 = 0g5gq, ?x5374 = 025s0zp, ?x10098 = 0h1_c, ?x9733 = 0h1tz, ?x7652 = 025s0s0, ?x4068 = 0fbw6, ?x9426 = 0h1yy, ?x9490 = 0h1sg, ?x7135 = 025rsfk, ?x10195 = 0hkwr, ?x5373 = 0971v, ?x5549 = 025s7j4, ?x5010 = 0h1vz, ?x3264 = 0dcfv, ?x8487 = 014yzm, ?x2701 = 0hkxq, ?x10709 = 0h1sz, ?x9489 = 07j87, ?x6192 = 06jry, ?x11270 = 02kc008, ?x11592 = 025sf0_, ?x2702 = 0838f >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09pbb nutrient! 037ls6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 55.000 0.900 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #2384-0pc62 PRED entity: 0pc62 PRED relation: nominated_for! PRED expected values: 01795t 09b3v => 84 concepts (53 used for prediction) PRED predicted values (max 10 best out of 705): 01t6b4 (0.64 #44329, 0.46 #16329, 0.45 #116645), 0cjsxp (0.36 #44328, 0.35 #79320, 0.34 #86320), 014v6f (0.36 #44328, 0.35 #79320, 0.34 #86320), 046qq (0.36 #44328, 0.35 #79320, 0.34 #86320), 018ygt (0.36 #44328, 0.35 #79320, 0.34 #86320), 025j1t (0.36 #44328, 0.35 #79320, 0.34 #86320), 08x5c_ (0.36 #44328, 0.35 #79320, 0.34 #86320), 0gm8_p (0.36 #44328, 0.35 #79320, 0.34 #86320), 0d608 (0.36 #44328, 0.35 #79320, 0.34 #86320), 01pk8v (0.36 #44328, 0.35 #79320, 0.34 #86320) >> Best rule #44329 for best value: >> intensional similarity = 3 >> extensional distance = 240 >> proper extension: 0dgst_d; 02r1c18; 04n52p6; 02q5g1z; 02qr69m; 0k4f3; 084302; 0gvs1kt; 06gb1w; 026qnh6; ... >> query: (?x667, ?x1285) <- honored_for(?x5703, ?x667), film(?x989, ?x667), produced_by(?x667, ?x1285) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #65756 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 341 *> proper extension: 04bp0l; *> query: (?x667, 01795t) <- nominated_for(?x7980, ?x667), film(?x7980, ?x7834), genre(?x7834, ?x225) *> conf = 0.03 ranks of expected_values: 77 EVAL 0pc62 nominated_for! 09b3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 53.000 0.637 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 0pc62 nominated_for! 01795t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 84.000 53.000 0.637 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #2383-0r3tb PRED entity: 0r3tb PRED relation: contains! PRED expected values: 09c7w0 => 150 concepts (93 used for prediction) PRED predicted values (max 10 best out of 286): 09c7w0 (0.91 #81429, 0.81 #28620, 0.75 #77842), 0k_s5 (0.75 #77842, 0.19 #4265, 0.16 #8736), 0d060g (0.60 #59047, 0.25 #77857, 0.11 #75168), 0kpys (0.44 #3756, 0.37 #8227, 0.37 #7333), 07ssc (0.42 #6291, 0.27 #39381, 0.24 #42065), 02jx1 (0.26 #6345, 0.21 #42119, 0.20 #39435), 030qb3t (0.23 #15301, 0.21 #8147, 0.19 #3676), 05fjf (0.23 #12890, 0.10 #37932, 0.10 #27201), 06pvr (0.23 #46671, 0.20 #43987, 0.19 #28782), 05kr_ (0.22 #59159, 0.13 #75280, 0.10 #77969) >> Best rule #81429 for best value: >> intensional similarity = 5 >> extensional distance = 433 >> proper extension: 0n5j_; 030qb3t; 0dclg; 0f__1; 0qr4n; 07bcn; 0h3lt; 0xrz2; 0kcrd; 0kwgs; ... >> query: (?x8448, 09c7w0) <- source(?x8448, ?x958), ?x958 = 0jbk9, contains(?x1227, ?x8448), contains(?x1227, ?x9417), ?x9417 = 0k9p4 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r3tb contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 93.000 0.906 http://example.org/location/location/contains #2382-0cc63l PRED entity: 0cc63l PRED relation: gender PRED expected values: 05zppz => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #76, 0.84 #80, 0.84 #101), 02zsn (0.46 #234, 0.46 #35, 0.46 #185) >> Best rule #76 for best value: >> intensional similarity = 4 >> extensional distance = 462 >> proper extension: 012d40; 042l3v; 0m2l9; 02nb2s; 02pp_q_; 02kxbwx; 01q7cb_; 04l3_z; 06pk8; 07vc_9; ... >> query: (?x5709, 05zppz) <- profession(?x5709, ?x524), type_of_union(?x5709, ?x566), nationality(?x5709, ?x2146), ?x524 = 02jknp >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cc63l gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.845 http://example.org/people/person/gender #2381-0lgxj PRED entity: 0lgxj PRED relation: participating_countries PRED expected values: 0160w 05v8c 06m_5 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 256): 0hzlz (0.82 #659, 0.76 #1311, 0.75 #1312), 0b90_r (0.82 #659, 0.76 #1311, 0.75 #1312), 05qhw (0.82 #659, 0.76 #1311, 0.75 #1312), 0154j (0.82 #659, 0.76 #1311, 0.75 #1312), 06c1y (0.82 #659, 0.76 #1311, 0.75 #1312), 02k54 (0.82 #659, 0.76 #1311, 0.75 #1312), 059j2 (0.71 #129, 0.68 #398, 0.68 #130), 082fr (0.71 #129, 0.68 #398, 0.68 #130), 0165v (0.71 #129, 0.50 #1297, 0.33 #647), 01p8s (0.71 #129, 0.50 #1296, 0.33 #646) >> Best rule #659 for best value: >> intensional similarity = 37 >> extensional distance = 1 >> proper extension: 09x3r; >> query: (?x4255, ?x151) <- sports(?x4255, ?x171), participating_countries(?x4255, ?x5274), participating_countries(?x4255, ?x2629), participating_countries(?x4255, ?x789), sports(?x4255, ?x3127), olympics(?x792, ?x4255), olympics(?x151, ?x4255), ?x792 = 0hzlz, ?x5274 = 04g61, film_release_region(?x10475, ?x2629), film_release_region(?x8471, ?x2629), film_release_region(?x7204, ?x2629), film_release_region(?x7016, ?x2629), film_release_region(?x6216, ?x2629), film_release_region(?x6095, ?x2629), film_release_region(?x5162, ?x2629), film_release_region(?x4998, ?x2629), film_release_region(?x3745, ?x2629), film_release_region(?x3226, ?x2629), ?x8471 = 0cp0t91, ?x7016 = 07g1sm, nationality(?x690, ?x2629), ?x6216 = 06fcqw, ?x7204 = 0280061, medal(?x4255, ?x422), ?x10475 = 047p798, ?x3745 = 03cw411, ?x4998 = 0dzlbx, jurisdiction_of_office(?x346, ?x2629), olympics(?x2629, ?x775), contains(?x6304, ?x2629), contains(?x2629, ?x10324), ?x6095 = 0bq6ntw, currency(?x2629, ?x170), ?x3226 = 0gyfp9c, ?x789 = 0f8l9c, ?x5162 = 0j3d9tn >> conf = 0.82 => this is the best rule for 6 predicted values *> Best rule #129 for first EXPECTED value: *> intensional similarity = 36 *> extensional distance = 1 *> proper extension: 0kbws; *> query: (?x4255, ?x172) <- sports(?x4255, ?x3015), sports(?x4255, ?x2885), participating_countries(?x4255, ?x11872), participating_countries(?x4255, ?x8593), participating_countries(?x4255, ?x6307), participating_countries(?x4255, ?x4743), participating_countries(?x4255, ?x1453), participating_countries(?x4255, ?x1003), participating_countries(?x4255, ?x789), ?x789 = 0f8l9c, olympics(?x151, ?x4255), olympics(?x11872, ?x584), sports(?x5395, ?x2885), sports(?x3729, ?x2885), sports(?x358, ?x2885), ?x4743 = 03spz, olympics(?x2885, ?x1608), ?x3015 = 071t0, ?x5395 = 018qb4, ?x3729 = 0jdk_, official_language(?x11872, ?x732), medal(?x11872, ?x422), combatants(?x4373, ?x8593), film_release_region(?x2340, ?x8593), film_release_region(?x504, ?x8593), ?x504 = 0g5qs2k, ?x2340 = 0fpv_3_, country(?x9310, ?x1453), film_release_region(?x7494, ?x1453), ?x358 = 018wrk, ?x7494 = 0dgrwqr, country(?x2885, ?x172), nationality(?x1373, ?x1003), jurisdiction_of_office(?x265, ?x1453), ?x6307 = 04xn_, film_release_region(?x80, ?x1003) *> conf = 0.71 ranks of expected_values: 13, 32, 74 EVAL 0lgxj participating_countries 06m_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 57.000 57.000 0.821 http://example.org/olympics/olympic_games/participating_countries EVAL 0lgxj participating_countries 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 57.000 57.000 0.821 http://example.org/olympics/olympic_games/participating_countries EVAL 0lgxj participating_countries 0160w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 57.000 57.000 0.821 http://example.org/olympics/olympic_games/participating_countries #2380-092vkg PRED entity: 092vkg PRED relation: music PRED expected values: 01m5m5b => 56 concepts (46 used for prediction) PRED predicted values (max 10 best out of 70): 0146pg (0.12 #431, 0.08 #1485, 0.03 #4228), 04bdxl (0.06 #5273, 0.06 #5905, 0.06 #4429), 01yfm8 (0.06 #5273, 0.06 #5905, 0.06 #4429), 0dlglj (0.06 #5273, 0.06 #5905, 0.06 #4429), 02qgqt (0.06 #5273, 0.06 #5905, 0.06 #4429), 02s2ft (0.06 #5273, 0.06 #5905, 0.06 #4429), 051wwp (0.06 #5273, 0.06 #5905, 0.06 #4429), 02jxkw (0.06 #352, 0.02 #3727, 0.02 #2249), 0jn5l (0.06 #306, 0.01 #728, 0.01 #1993), 0417z2 (0.06 #382, 0.01 #804, 0.01 #2279) >> Best rule #431 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 02fn5r; >> query: (?x1064, 0146pg) <- nominated_for(?x2323, ?x1064), category(?x1064, ?x134), ?x134 = 08mbj5d >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #609 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 48 *> proper extension: 02fn5r; *> query: (?x1064, 01m5m5b) <- nominated_for(?x2323, ?x1064), category(?x1064, ?x134), ?x134 = 08mbj5d *> conf = 0.02 ranks of expected_values: 33 EVAL 092vkg music 01m5m5b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 56.000 46.000 0.120 http://example.org/film/film/music #2379-0mcf4 PRED entity: 0mcf4 PRED relation: artist PRED expected values: 09hnb => 63 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1032): 01vsy7t (0.58 #3623, 0.33 #315, 0.25 #2796), 02vr7 (0.50 #1430, 0.42 #3911, 0.33 #603), 09hnb (0.50 #986, 0.33 #3467, 0.33 #159), 03d2k (0.50 #1497, 0.33 #670, 0.25 #3978), 0dbb3 (0.50 #1560, 0.33 #733, 0.17 #4869), 0163kf (0.50 #1619, 0.33 #792, 0.17 #4100), 0bdlj (0.50 #1345, 0.33 #518, 0.17 #3826), 01w524f (0.50 #2768, 0.25 #3595, 0.22 #4423), 0285c (0.50 #2581, 0.11 #4236, 0.08 #3408), 03xhj6 (0.33 #3609, 0.33 #301, 0.28 #4437) >> Best rule #3623 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 03rhqg; 01gfq4; 01w40h; 0181dw; 0k_kr; 01q940; 0n85g; 041bnw; >> query: (?x8919, 01vsy7t) <- category(?x8919, ?x134), artist(?x8919, ?x2747), award_nominee(?x2698, ?x2747), role(?x2747, ?x1332), ?x1332 = 03qlv7, award_winner(?x2561, ?x2747) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #986 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 01xyqk; *> query: (?x8919, 09hnb) <- category(?x8919, ?x134), artist(?x8919, ?x3378), artist(?x8919, ?x2747), artist(?x8919, ?x217), ?x2747 = 01qdjm, artists(?x302, ?x217), gender(?x3378, ?x231), nationality(?x217, ?x94) *> conf = 0.50 ranks of expected_values: 3 EVAL 0mcf4 artist 09hnb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 63.000 24.000 0.583 http://example.org/music/record_label/artist #2378-0pk1p PRED entity: 0pk1p PRED relation: genre PRED expected values: 0gf28 016vh2 => 101 concepts (71 used for prediction) PRED predicted values (max 10 best out of 94): 07s9rl0 (0.74 #1202, 0.69 #1683, 0.67 #1443), 01jfsb (0.65 #251, 0.60 #11, 0.60 #611), 01z4y (0.61 #1322, 0.53 #5286, 0.52 #8532), 02l7c8 (0.52 #4100, 0.39 #1216, 0.38 #1457), 03k9fj (0.39 #2772, 0.38 #2532, 0.36 #370), 06n90 (0.28 #2534, 0.28 #2774, 0.26 #372), 04xvlr (0.28 #3486, 0.21 #1203, 0.20 #963), 02n4kr (0.23 #607, 0.12 #5172, 0.12 #7096), 060__y (0.21 #1217, 0.19 #1097, 0.19 #1698), 04btyz (0.20 #68, 0.07 #188, 0.06 #308) >> Best rule #1202 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 02v8kmz; 0g3zrd; 02q87z6; 0cbn7c; >> query: (?x8578, 07s9rl0) <- costume_design_by(?x8578, ?x1760), film(?x8898, ?x8578), nominated_for(?x926, ?x8578), titles(?x2480, ?x8578) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #105 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 04sntd; *> query: (?x8578, 016vh2) <- costume_design_by(?x8578, ?x1760), film(?x8898, ?x8578), language(?x8578, ?x254), ?x8898 = 0h7pj *> conf = 0.20 ranks of expected_values: 11, 19 EVAL 0pk1p genre 016vh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 101.000 71.000 0.738 http://example.org/film/film/genre EVAL 0pk1p genre 0gf28 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 101.000 71.000 0.738 http://example.org/film/film/genre #2377-0xr0t PRED entity: 0xr0t PRED relation: contains! PRED expected values: 05fjf => 104 concepts (47 used for prediction) PRED predicted values (max 10 best out of 129): 05fjf (0.19 #33099, 0.10 #373, 0.09 #9314), 059g4 (0.19 #33099, 0.03 #1355, 0.02 #28192), 07c5l (0.19 #33099, 0.01 #20071, 0.01 #8441), 04pnx (0.19 #33099), 01n7q (0.18 #4547, 0.16 #40338, 0.15 #1865), 07ssc (0.16 #22392, 0.16 #9866, 0.15 #21497), 059rby (0.14 #24171, 0.13 #8960, 0.11 #26856), 02jx1 (0.12 #32290, 0.12 #22447, 0.11 #39450), 05k7sb (0.11 #1026, 0.10 #24284, 0.08 #26969), 05kkh (0.08 #8949, 0.07 #26845, 0.06 #8) >> Best rule #33099 for best value: >> intensional similarity = 4 >> extensional distance = 621 >> proper extension: 020d8d; 02cb1j; 022tq4; 09ctj; >> query: (?x12892, ?x6895) <- contains(?x8173, ?x12892), place_of_birth(?x7048, ?x12892), adjoins(?x6252, ?x8173), contains(?x6895, ?x6252) >> conf = 0.19 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 0xr0t contains! 05fjf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 47.000 0.186 http://example.org/location/location/contains #2376-041td_ PRED entity: 041td_ PRED relation: genre PRED expected values: 05p553 03bxz7 => 94 concepts (43 used for prediction) PRED predicted values (max 10 best out of 91): 07ssc (0.65 #3937, 0.62 #925, 0.61 #694), 01z4y (0.62 #925, 0.61 #694, 0.61 #693), 05p553 (0.60 #3, 0.43 #1508, 0.41 #234), 03bxz7 (0.54 #628, 0.49 #860, 0.21 #397), 01t_vv (0.40 #50, 0.20 #281, 0.13 #1555), 01jfsb (0.34 #1285, 0.31 #3831, 0.30 #4872), 02kdv5l (0.27 #4862, 0.24 #2200, 0.23 #4746), 06cvj (0.26 #1507, 0.09 #3244, 0.09 #118), 0lsxr (0.23 #123, 0.23 #3827, 0.21 #701), 060__y (0.23 #939, 0.23 #1055, 0.22 #3487) >> Best rule #3937 for best value: >> intensional similarity = 5 >> extensional distance = 654 >> proper extension: 0cnztc4; 0crh5_f; 0413cff; 02pcq92; 0d8w2n; >> query: (?x6272, ?x512) <- titles(?x512, ?x6272), titles(?x512, ?x4772), titles(?x512, ?x3772), film_release_region(?x3772, ?x94), ?x4772 = 06kl78 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 03s6l2; *> query: (?x6272, 05p553) <- titles(?x2480, ?x6272), titles(?x1316, ?x6272), film(?x1846, ?x6272), ?x1316 = 017fp, ?x2480 = 01z4y *> conf = 0.60 ranks of expected_values: 3, 4 EVAL 041td_ genre 03bxz7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 43.000 0.649 http://example.org/film/film/genre EVAL 041td_ genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 43.000 0.649 http://example.org/film/film/genre #2375-08984j PRED entity: 08984j PRED relation: film_crew_role PRED expected values: 09vw2b7 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 29): 09vw2b7 (0.78 #1028, 0.75 #756, 0.72 #3331), 0dxtw (0.47 #521, 0.47 #998, 0.46 #248), 02rh1dz (0.42 #247, 0.34 #520, 0.26 #384), 04pyp5 (0.30 #83, 0.27 #117, 0.25 #15), 02ynfr (0.29 #525, 0.27 #1002, 0.26 #1070), 05smlt (0.25 #19, 0.12 #394, 0.11 #530), 01xy5l_ (0.23 #250, 0.18 #387, 0.16 #728), 0d2b38 (0.22 #296, 0.18 #1046, 0.17 #1183), 015h31 (0.21 #519, 0.19 #280, 0.17 #315), 0215hd (0.20 #85, 0.16 #528, 0.16 #1347) >> Best rule #1028 for best value: >> intensional similarity = 5 >> extensional distance = 81 >> proper extension: 0872p_c; 05pbl56; 03cp4cn; >> query: (?x7080, 09vw2b7) <- film(?x541, ?x7080), language(?x7080, ?x254), film_crew_role(?x7080, ?x468), nominated_for(?x188, ?x7080), ?x468 = 02r96rf >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08984j film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.783 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2374-05l5n PRED entity: 05l5n PRED relation: contains! PRED expected values: 02jx1 => 197 concepts (153 used for prediction) PRED predicted values (max 10 best out of 338): 02jx1 (0.76 #16100, 0.68 #101992, 0.66 #30417), 09c7w0 (0.68 #70676, 0.67 #124368, 0.64 #67993), 05l5n (0.54 #75148, 0.53 #72462, 0.52 #76937), 07tgn (0.54 #75148, 0.53 #72462, 0.52 #76937), 04_1l0v (0.48 #27285, 0.46 #8498, 0.44 #12972), 019rg5 (0.41 #126155, 0.32 #88569), 01n7q (0.23 #34968, 0.22 #64488, 0.20 #40336), 04jpl (0.21 #13439, 0.17 #2706, 0.09 #102014), 02qkt (0.19 #99652, 0.18 #115761, 0.16 #23603), 059rby (0.17 #32226, 0.07 #33120, 0.07 #35805) >> Best rule #16100 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0fnx1; >> query: (?x1841, ?x1310) <- time_zones(?x1841, ?x5327), administrative_division(?x1841, ?x2235), country(?x2235, ?x1310) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05l5n contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 197.000 153.000 0.756 http://example.org/location/location/contains #2373-03x9yr PRED entity: 03x9yr PRED relation: artist PRED expected values: 06lxn => 58 concepts (22 used for prediction) PRED predicted values (max 10 best out of 943): 03xhj6 (0.43 #4498, 0.40 #1985, 0.33 #3661), 01hgwkr (0.40 #2368, 0.33 #4044, 0.33 #3204), 01wg25j (0.40 #2299, 0.33 #3975, 0.33 #3135), 01wg6y (0.40 #2340, 0.33 #4016, 0.33 #3176), 016szr (0.40 #2023, 0.33 #3699, 0.33 #1186), 01817f (0.40 #1987, 0.33 #3663, 0.33 #1150), 01wx756 (0.40 #2469, 0.33 #4145, 0.33 #1632), 0x3b7 (0.40 #1972, 0.33 #3648, 0.33 #1135), 011lvx (0.40 #2213, 0.33 #3889, 0.33 #1376), 05xq9 (0.40 #2032, 0.33 #3708, 0.33 #356) >> Best rule #4498 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 023rwm; >> query: (?x14593, 03xhj6) <- artist(?x14593, ?x8199), artist(?x14593, ?x7966), award(?x8199, ?x8331), award(?x8199, ?x3365), award(?x8199, ?x1565), ?x3365 = 02f716, ?x7966 = 013rfk, ceremony(?x8331, ?x139), artists(?x9012, ?x8199), award(?x2395, ?x1565), ?x2395 = 0dvqq, parent_genre(?x9012, ?x2937) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3346 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 4 *> proper extension: 0g768; *> query: (?x14593, 06lxn) <- artist(?x14593, ?x8199), artist(?x14593, ?x7966), award(?x8199, ?x8331), award(?x8199, ?x3365), ?x3365 = 02f716, ?x7966 = 013rfk, award(?x1004, ?x8331), ceremony(?x8331, ?x139), ?x1004 = 01vv7sc, artists(?x474, ?x8199), artists(?x474, ?x5916), ?x5916 = 02cpp *> conf = 0.17 ranks of expected_values: 205 EVAL 03x9yr artist 06lxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 58.000 22.000 0.429 http://example.org/music/record_label/artist #2372-01j4ls PRED entity: 01j4ls PRED relation: instrumentalists! PRED expected values: 0342h => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 82): 0342h (0.82 #718, 0.76 #540, 0.69 #272), 05r5c (0.62 #276, 0.47 #722, 0.42 #900), 05148p4 (0.52 #557, 0.46 #289, 0.45 #735), 03qjg (0.50 #766, 0.45 #588, 0.22 #231), 018vs (0.46 #281, 0.38 #905, 0.38 #727), 03bx0bm (0.33 #357), 02hnl (0.28 #749, 0.27 #571, 0.23 #303), 042v_gx (0.26 #3386, 0.15 #277, 0.07 #456), 06w7v (0.23 #341, 0.07 #1054, 0.07 #520), 04rzd (0.22 #217, 0.09 #1464, 0.09 #663) >> Best rule #718 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 01gf5h; 0136pk; 0qf11; 0fq117k; 01vrx35; 0167v4; >> query: (?x1398, 0342h) <- award(?x1398, ?x2322), nationality(?x1398, ?x94), artists(?x378, ?x1398), ?x2322 = 01ck6h >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j4ls instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.825 http://example.org/music/instrument/instrumentalists #2371-023vcd PRED entity: 023vcd PRED relation: genre PRED expected values: 02l7c8 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 81): 07s9rl0 (0.67 #123, 0.62 #2445, 0.59 #1589), 01z4y (0.61 #5254, 0.51 #6109, 0.48 #4887), 02kdv5l (0.50 #3, 0.30 #1347, 0.29 #1469), 01jfsb (0.40 #13, 0.32 #1479, 0.32 #1357), 02l7c8 (0.29 #2461, 0.28 #2338, 0.27 #5148), 03k9fj (0.26 #2089, 0.26 #502, 0.26 #1966), 0gf28 (0.20 #66, 0.05 #1410, 0.05 #1532), 01hmnh (0.19 #2096, 0.17 #1973, 0.16 #2218), 0lsxr (0.18 #1231, 0.18 #1597, 0.17 #2819), 04xvlr (0.16 #5133, 0.16 #2323, 0.16 #4766) >> Best rule #123 for best value: >> intensional similarity = 5 >> extensional distance = 97 >> proper extension: 053tj7; 0g5q34q; 0g9zljd; 0gh6j94; 0g5qmbz; 0j8f09z; >> query: (?x10246, 07s9rl0) <- film_release_region(?x10246, ?x142), film_release_region(?x10246, ?x94), ?x142 = 0jgd, ?x94 = 09c7w0, film_regional_debut_venue(?x10246, ?x6557) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2461 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 811 *> proper extension: 011yfd; 05_61y; 05y0cr; 0cq8nx; 06zn1c; 05dl1s; *> query: (?x10246, 02l7c8) <- country(?x10246, ?x94), genre(?x10246, ?x258), award(?x10246, ?x3508) *> conf = 0.29 ranks of expected_values: 5 EVAL 023vcd genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 68.000 68.000 0.667 http://example.org/film/film/genre #2370-023kzp PRED entity: 023kzp PRED relation: film PRED expected values: 0c9t0y => 108 concepts (85 used for prediction) PRED predicted values (max 10 best out of 892): 01hqhm (0.64 #57063, 0.64 #41013, 0.58 #112344), 0298n7 (0.64 #57063, 0.64 #41013, 0.58 #112344), 011yhm (0.64 #57063, 0.64 #41013, 0.58 #112344), 01bb9r (0.25 #486, 0.04 #35663), 0c0nhgv (0.25 #172, 0.02 #14436, 0.01 #28700), 0df2zx (0.25 #1709), 0jzw (0.13 #7251, 0.10 #3685, 0.10 #1902), 093l8p (0.11 #48146, 0.04 #53496), 02v5_g (0.10 #4355, 0.10 #2572, 0.04 #7921), 026n4h6 (0.10 #3808, 0.09 #7374, 0.04 #35663) >> Best rule #57063 for best value: >> intensional similarity = 3 >> extensional distance = 349 >> proper extension: 0134w7; 02_hj4; 029_3; 033jkj; 0gv40; 02lymt; 01c6l; >> query: (?x5925, ?x2090) <- award_nominee(?x92, ?x5925), nominated_for(?x5925, ?x2090), participant(?x516, ?x5925) >> conf = 0.64 => this is the best rule for 3 predicted values *> Best rule #60630 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 380 *> proper extension: 029b9k; 07pzc; *> query: (?x5925, ?x1045) <- award_nominee(?x8445, ?x5925), participant(?x5925, ?x516), film(?x8445, ?x1045) *> conf = 0.03 ranks of expected_values: 290 EVAL 023kzp film 0c9t0y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 108.000 85.000 0.643 http://example.org/film/actor/film./film/performance/film #2369-014g22 PRED entity: 014g22 PRED relation: gender PRED expected values: 02zsn => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.86 #4, 0.81 #2, 0.45 #6), 05zppz (0.72 #181, 0.71 #175, 0.71 #189) >> Best rule #4 for best value: >> intensional similarity = 2 >> extensional distance = 64 >> proper extension: 02c7lt; >> query: (?x4154, 02zsn) <- award(?x4154, ?x1254), ?x1254 = 02z0dfh >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014g22 gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.864 http://example.org/people/person/gender #2368-06__m6 PRED entity: 06__m6 PRED relation: music PRED expected values: 07v4dm => 99 concepts (67 used for prediction) PRED predicted values (max 10 best out of 88): 0c00lh (0.17 #211, 0.15 #7593, 0.14 #422), 021bk (0.09 #247, 0.03 #36), 01r4hry (0.07 #143, 0.06 #354, 0.02 #1200), 01mh8zn (0.07 #147, 0.03 #782, 0.03 #358), 02jxkw (0.07 #142, 0.02 #8158, 0.02 #7103), 0gv07g (0.07 #132, 0.01 #1612), 0146pg (0.06 #2543, 0.06 #2754, 0.05 #1911), 0150t6 (0.06 #892, 0.06 #681, 0.04 #1314), 0pgjm (0.06 #232, 0.03 #21), 028k57 (0.06 #286) >> Best rule #211 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 0cbv4g; 07gghl; >> query: (?x5991, ?x5351) <- genre(?x5991, ?x8467), award_winner(?x5991, ?x5351), ?x8467 = 0gf28 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #616 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 0pvms; *> query: (?x5991, 07v4dm) <- genre(?x5991, ?x6674), written_by(?x5991, ?x5351), film(?x643, ?x5991), ?x6674 = 01t_vv *> conf = 0.05 ranks of expected_values: 12 EVAL 06__m6 music 07v4dm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 99.000 67.000 0.167 http://example.org/film/film/music #2367-026m0 PRED entity: 026m0 PRED relation: people! PRED expected values: 0gk4g => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 41): 0gk4g (0.14 #670, 0.14 #1661, 0.14 #604), 0dq9p (0.10 #1536, 0.09 #1139, 0.09 #941), 0qcr0 (0.10 #1652, 0.09 #595, 0.08 #1520), 012hw (0.09 #52, 0.06 #448, 0.06 #250), 02y0js (0.09 #2, 0.06 #266, 0.06 #926), 02k6hp (0.08 #301, 0.08 #961, 0.07 #1358), 04p3w (0.07 #275, 0.07 #1530, 0.06 #2520), 02knxx (0.05 #626, 0.05 #890, 0.04 #1683), 0m32h (0.04 #881, 0.04 #287, 0.04 #1145), 01mtqf (0.04 #268, 0.03 #664, 0.02 #70) >> Best rule #670 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 02vkvcz; >> query: (?x10819, 0gk4g) <- location(?x10819, ?x2982), place_of_death(?x10819, ?x1523), award(?x10819, ?x601), nominated_for(?x10819, ?x10435) >> conf = 0.14 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026m0 people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.144 http://example.org/people/cause_of_death/people #2366-01vvdm PRED entity: 01vvdm PRED relation: place_of_death PRED expected values: 030qb3t => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 20): 0k049 (0.40 #3, 0.14 #197, 0.05 #586), 030qb3t (0.29 #216, 0.20 #22, 0.13 #799), 02_286 (0.07 #401, 0.05 #1166, 0.05 #1374), 06_kh (0.07 #393, 0.02 #782, 0.02 #976), 0f2wj (0.05 #595, 0.04 #400, 0.04 #789), 0r3w7 (0.04 #565), 015zxh (0.04 #413), 04jpl (0.02 #395, 0.02 #784, 0.02 #590), 0k_p5 (0.02 #476, 0.02 #865, 0.01 #1254), 05jbn (0.02 #459, 0.01 #1626, 0.01 #1042) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 019l68; >> query: (?x3771, 0k049) <- award_winner(?x7226, ?x3771), people(?x1050, ?x3771), ?x7226 = 0c6vcj >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #216 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 04gmp_z; *> query: (?x3771, 030qb3t) <- award_nominee(?x3771, ?x1852), award_winner(?x7226, ?x3771), ?x7226 = 0c6vcj *> conf = 0.29 ranks of expected_values: 2 EVAL 01vvdm place_of_death 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 97.000 97.000 0.400 http://example.org/people/deceased_person/place_of_death #2365-07g2v PRED entity: 07g2v PRED relation: instrumentalists! PRED expected values: 05148p4 => 186 concepts (136 used for prediction) PRED predicted values (max 10 best out of 121): 05148p4 (0.60 #959, 0.47 #873, 0.45 #3265), 026t6 (0.58 #1541, 0.41 #2650, 0.37 #3759), 0l14md (0.39 #2733, 0.39 #7087, 0.39 #7086), 03qjg (0.29 #3722, 0.24 #3295, 0.24 #2353), 0l14qv (0.20 #1287, 0.20 #859, 0.16 #1201), 06w7v (0.20 #924, 0.17 #2460, 0.14 #3316), 018j2 (0.18 #3709, 0.16 #1232, 0.15 #5242), 04rzd (0.17 #2425, 0.17 #3708, 0.16 #1231), 0mkg (0.16 #1206, 0.14 #351, 0.10 #1377), 06ncr (0.15 #1665, 0.14 #383, 0.10 #7041) >> Best rule #959 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 02yygk; >> query: (?x3422, 05148p4) <- role(?x3422, ?x212), participant(?x3422, ?x777), instrumentalists(?x227, ?x3422), languages(?x3422, ?x254) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07g2v instrumentalists! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 186.000 136.000 0.600 http://example.org/music/instrument/instrumentalists #2364-02y7sr PRED entity: 02y7sr PRED relation: group PRED expected values: 0ycp3 => 138 concepts (45 used for prediction) PRED predicted values (max 10 best out of 96): 0123r4 (0.20 #151, 0.20 #44, 0.12 #473), 0frsw (0.20 #122, 0.20 #15, 0.12 #444), 0b1zz (0.17 #364, 0.17 #256, 0.06 #901), 014_lq (0.17 #357, 0.05 #787, 0.03 #894), 016l09 (0.17 #295, 0.05 #833, 0.02 #1156), 02r3zy (0.17 #325, 0.03 #862, 0.02 #1186), 0b1hw (0.17 #305, 0.01 #1596, 0.01 #1703), 02_5x9 (0.12 #440, 0.10 #547, 0.09 #763), 01wv9xn (0.12 #437, 0.10 #544, 0.05 #1083), 0jg77 (0.12 #535, 0.10 #642, 0.05 #858) >> Best rule #151 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 01qvgl; >> query: (?x8560, 0123r4) <- instrumentalists(?x7938, ?x8560), ?x7938 = 048j4l, role(?x8560, ?x716), student(?x9865, ?x8560), type_of_union(?x8560, ?x566) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #802 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 02ldv0; *> query: (?x8560, 0ycp3) <- gender(?x8560, ?x231), nationality(?x8560, ?x94), role(?x8560, ?x716), ?x716 = 018vs, ?x94 = 09c7w0 *> conf = 0.05 ranks of expected_values: 27 EVAL 02y7sr group 0ycp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 138.000 45.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group #2363-017xm3 PRED entity: 017xm3 PRED relation: location PRED expected values: 081yw => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 282): 030qb3t (0.26 #5713, 0.24 #23412, 0.23 #22608), 02_286 (0.17 #9691, 0.17 #21758, 0.16 #5667), 07h34 (0.14 #196, 0.11 #1001, 0.06 #1805), 0vbk (0.14 #246, 0.11 #1051, 0.06 #1855), 0b1t1 (0.14 #473, 0.04 #5299, 0.03 #11735), 0k_q_ (0.14 #128, 0.02 #4954, 0.02 #5758), 0cr3d (0.11 #1754, 0.10 #10603, 0.10 #13016), 04jpl (0.11 #1626, 0.09 #5647, 0.07 #23346), 0f2v0 (0.11 #1792, 0.05 #10641, 0.04 #14662), 04lh6 (0.11 #2045, 0.04 #3654, 0.04 #6066) >> Best rule #5713 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 01nbq4; >> query: (?x3426, 030qb3t) <- languages(?x3426, ?x254), nationality(?x3426, ?x94), location_of_ceremony(?x3426, ?x4061) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #5868 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 01nbq4; *> query: (?x3426, 081yw) <- languages(?x3426, ?x254), nationality(?x3426, ?x94), location_of_ceremony(?x3426, ?x4061) *> conf = 0.02 ranks of expected_values: 158 EVAL 017xm3 location 081yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 147.000 147.000 0.263 http://example.org/people/person/places_lived./people/place_lived/location #2362-0156q PRED entity: 0156q PRED relation: citytown! PRED expected values: 01trxd => 289 concepts (247 used for prediction) PRED predicted values (max 10 best out of 743): 01trxd (0.55 #18569, 0.49 #51671, 0.36 #44405), 0gsg7 (0.22 #3289, 0.11 #17014, 0.10 #4903), 01dtcb (0.22 #3613, 0.10 #5227, 0.07 #38329), 02vk52z (0.20 #809, 0.17 #2424, 0.11 #4038), 0dn_w (0.20 #1578, 0.17 #3193, 0.11 #4807), 01z_jj (0.20 #1555, 0.17 #3170, 0.11 #4784), 07k5l (0.20 #1553, 0.17 #3168, 0.11 #4782), 0c0sl (0.20 #1529, 0.17 #3144, 0.11 #4758), 01kcmr (0.20 #1522, 0.17 #3137, 0.11 #4751), 07w42 (0.20 #1500, 0.17 #3115, 0.11 #4729) >> Best rule #18569 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0l35f; >> query: (?x1646, ?x196) <- contains(?x1646, ?x196), mode_of_transportation(?x1646, ?x4272), adjoins(?x1646, ?x6325) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0156q citytown! 01trxd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 289.000 247.000 0.547 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #2361-0h03fhx PRED entity: 0h03fhx PRED relation: genre PRED expected values: 07s9rl0 02p0szs => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 91): 07s9rl0 (0.81 #977, 0.80 #733, 0.79 #1710), 05p553 (0.41 #127, 0.34 #1470, 0.34 #4156), 02kdv5l (0.35 #125, 0.35 #2200, 0.33 #3787), 0lsxr (0.35 #10, 0.19 #498, 0.19 #2207), 03k9fj (0.34 #135, 0.31 #13, 0.29 #257), 060__y (0.31 #18, 0.19 #3558, 0.17 #4415), 02l7c8 (0.31 #3557, 0.30 #383, 0.30 #4414), 082gq (0.22 #1253, 0.18 #2352, 0.18 #1741), 04xvlr (0.20 #3542, 0.18 #4399, 0.17 #1223), 03bxz7 (0.19 #57, 0.15 #1155, 0.14 #789) >> Best rule #977 for best value: >> intensional similarity = 4 >> extensional distance = 117 >> proper extension: 0kb57; 01fwzk; >> query: (?x4607, 07s9rl0) <- nominated_for(?x1703, ?x4607), ?x1703 = 0k611, produced_by(?x4607, ?x286), nominated_for(?x968, ?x4607) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1, 36 EVAL 0h03fhx genre 02p0szs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 81.000 81.000 0.807 http://example.org/film/film/genre EVAL 0h03fhx genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.807 http://example.org/film/film/genre #2360-0gz_ PRED entity: 0gz_ PRED relation: interests PRED expected values: 02jhc => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 14): 05r79 (0.62 #60, 0.29 #187, 0.29 #155), 02jcc (0.60 #113, 0.56 #141, 0.50 #184), 02jhc (0.50 #191, 0.50 #36, 0.43 #50), 0gt_hv (0.38 #70, 0.29 #155, 0.20 #240), 09xq9d (0.29 #155, 0.24 #133, 0.20 #119), 05qfh (0.29 #155, 0.20 #240, 0.16 #255), 097df (0.29 #155, 0.20 #240, 0.16 #255), 06ms6 (0.29 #155, 0.16 #255, 0.14 #45), 04rjg (0.29 #155, 0.16 #255, 0.13 #117), 06mq7 (0.29 #155, 0.16 #255, 0.07 #111) >> Best rule #60 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 07kb5; 026lj; >> query: (?x3712, 05r79) <- influenced_by(?x12167, ?x3712), influenced_by(?x9600, ?x3712), influenced_by(?x4547, ?x3712), ?x4547 = 03_hd, influenced_by(?x920, ?x9600), interests(?x3712, ?x6364), gender(?x12167, ?x231) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #191 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 0x3r3; *> query: (?x3712, 02jhc) <- gender(?x3712, ?x231), influenced_by(?x3712, ?x6015), interests(?x3712, ?x8405), interests(?x2240, ?x8405), people(?x5855, ?x2240) *> conf = 0.50 ranks of expected_values: 3 EVAL 0gz_ interests 02jhc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 95.000 0.625 http://example.org/user/alexander/philosophy/philosopher/interests #2359-018m5q PRED entity: 018m5q PRED relation: educational_institution! PRED expected values: 018m5q => 168 concepts (79 used for prediction) PRED predicted values (max 10 best out of 251): 07tl0 (0.17 #564, 0.14 #1103, 0.12 #1642), 07tk7 (0.17 #977, 0.14 #1516, 0.12 #2055), 01nn7r (0.17 #1033, 0.10 #3189, 0.07 #15112), 01f2xy (0.14 #1325, 0.12 #1864, 0.11 #2403), 0d07s (0.14 #1322, 0.12 #1861, 0.11 #2400), 013nky (0.12 #1993, 0.11 #2532, 0.10 #3071), 0c_zj (0.11 #2285, 0.07 #15112, 0.03 #37801), 01sjz_ (0.10 #2919, 0.09 #27527, 0.07 #15112), 0k2h6 (0.09 #27527, 0.07 #15112, 0.05 #35095), 018m5q (0.09 #27527, 0.07 #15112, 0.05 #35637) >> Best rule #564 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 01nn7r; >> query: (?x3671, 07tl0) <- state_province_region(?x3671, ?x3302), ?x3302 = 01w0v, contains(?x3301, ?x3671), school_type(?x3671, ?x5931), ?x3301 = 0978r >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #27527 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 283 *> proper extension: 0301dp; *> query: (?x3671, ?x7355) <- state_province_region(?x3671, ?x3302), state_province_region(?x10348, ?x3302), state_province_region(?x7355, ?x3302), organization(?x2361, ?x10348), currency(?x7355, ?x1099), contains(?x512, ?x7355) *> conf = 0.09 ranks of expected_values: 10 EVAL 018m5q educational_institution! 018m5q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 168.000 79.000 0.167 http://example.org/education/educational_institution_campus/educational_institution #2358-0c9k8 PRED entity: 0c9k8 PRED relation: currency PRED expected values: 09nqf => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.78 #274, 0.78 #267, 0.78 #78), 01nv4h (0.03 #135, 0.03 #107, 0.03 #9), 02gsvk (0.02 #20, 0.01 #195, 0.01 #160), 02l6h (0.02 #11, 0.01 #46, 0.01 #53) >> Best rule #274 for best value: >> intensional similarity = 3 >> extensional distance = 691 >> proper extension: 01gglm; >> query: (?x2943, 09nqf) <- titles(?x53, ?x2943), nominated_for(?x406, ?x2943), production_companies(?x2943, ?x902) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c9k8 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.784 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #2357-024tkd PRED entity: 024tkd PRED relation: legislative_sessions! PRED expected values: 0b3wk => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 6): 0b3wk (0.93 #180, 0.90 #187, 0.90 #173), 0x2sv (0.07 #221, 0.05 #208, 0.03 #189), 0h6dy (0.05 #222, 0.03 #190, 0.03 #209), 0l_j_ (0.03 #223, 0.03 #191, 0.03 #210), 0162kb (0.03 #192), 030p4s (0.02 #225) >> Best rule #180 for best value: >> intensional similarity = 34 >> extensional distance = 26 >> proper extension: 01gtdd; 01gt99; >> query: (?x6933, ?x2860) <- legislative_sessions(?x4821, ?x6933), legislative_sessions(?x845, ?x6933), district_represented(?x6933, ?x3086), district_represented(?x6933, ?x1782), district_represented(?x6933, ?x448), district_represented(?x6933, ?x335), legislative_sessions(?x9334, ?x6933), legislative_sessions(?x8607, ?x6933), legislative_sessions(?x3445, ?x6933), ?x448 = 03v1s, jurisdiction_of_office(?x3959, ?x1782), religion(?x1782, ?x2769), religion(?x1782, ?x1985), ?x1985 = 0c8wxp, ?x2769 = 019cr, legislative_sessions(?x6933, ?x356), legislative_sessions(?x2860, ?x4821), contains(?x1782, ?x6271), contains(?x3086, ?x3087), taxonomy(?x3086, ?x939), student(?x3228, ?x8607), district_represented(?x845, ?x2020), ?x335 = 059rby, ?x2020 = 05k7sb, contains(?x94, ?x3086), basic_title(?x8607, ?x2358), school(?x9760, ?x6271), ?x2358 = 01gkgk, ?x939 = 04n6k, adjoins(?x2982, ?x1782), religion(?x3445, ?x962), major_field_of_study(?x6271, ?x947), profession(?x9334, ?x1032), ?x9760 = 0bwjj >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024tkd legislative_sessions! 0b3wk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.926 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #2356-09swkk PRED entity: 09swkk PRED relation: music! PRED expected values: 02d478 => 124 concepts (71 used for prediction) PRED predicted values (max 10 best out of 867): 08c4yn (0.08 #3013, 0.02 #4025, 0.01 #6049), 0466s8n (0.08 #2954, 0.02 #3966, 0.01 #5990), 07tlfx (0.08 #2936, 0.02 #3948, 0.01 #5972), 0h7t36 (0.08 #2981, 0.01 #6017, 0.01 #8041), 040_lv (0.08 #2635, 0.01 #5671, 0.01 #7695), 02ylg6 (0.08 #2572, 0.01 #5608, 0.01 #7632), 02mt51 (0.08 #2421, 0.01 #5457, 0.01 #7481), 0gtsxr4 (0.08 #2338, 0.01 #5374, 0.01 #7398), 01hqhm (0.08 #2226, 0.01 #5262, 0.01 #7286), 026mfbr (0.08 #2083, 0.01 #5119, 0.01 #7143) >> Best rule #3013 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0djywgn; >> query: (?x4940, 08c4yn) <- role(?x4940, ?x212), ?x212 = 026t6, nominated_for(?x4940, ?x4541) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09swkk music! 02d478 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 71.000 0.083 http://example.org/film/film/music #2355-02ylg6 PRED entity: 02ylg6 PRED relation: nominated_for! PRED expected values: 02wkmx 05pcn59 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 220): 0gq9h (0.33 #5106, 0.32 #5587, 0.31 #3666), 0gs9p (0.32 #3668, 0.29 #5108, 0.26 #5589), 019f4v (0.28 #5097, 0.28 #3657, 0.26 #5578), 0k611 (0.24 #3677, 0.24 #5117, 0.23 #1755), 0l8z1 (0.24 #4375, 0.20 #293, 0.19 #1733), 054krc (0.23 #4393, 0.17 #311, 0.16 #3673), 0gq_v (0.23 #5062, 0.22 #4102, 0.21 #7704), 040njc (0.23 #3609, 0.22 #5049, 0.20 #5530), 04dn09n (0.22 #5078, 0.22 #3638, 0.21 #1716), 0gr0m (0.22 #4143, 0.20 #15607, 0.20 #16568) >> Best rule #5106 for best value: >> intensional similarity = 4 >> extensional distance = 593 >> proper extension: 02ppg1r; 03cv_gy; 05z43v; 04xbq3; >> query: (?x5347, 0gq9h) <- nominated_for(?x10597, ?x5347), nominated_for(?x9754, ?x5347), film(?x881, ?x5347), film(?x9754, ?x2090) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5523 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 604 *> proper extension: 0g60z; 080dwhx; 06cs95; 039fgy; 0kfpm; 02k_4g; 0ddd0gc; 0124k9; 08jgk1; 0kfv9; ... *> query: (?x5347, ?x1587) <- nominated_for(?x10597, ?x5347), nominated_for(?x9754, ?x5347), award_winner(?x1587, ?x9754), written_by(?x2090, ?x9754) *> conf = 0.20 ranks of expected_values: 15, 50 EVAL 02ylg6 nominated_for! 05pcn59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 86.000 86.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02ylg6 nominated_for! 02wkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 86.000 86.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2354-01g6l8 PRED entity: 01g6l8 PRED relation: school_type PRED expected values: 05jxkf => 225 concepts (225 used for prediction) PRED predicted values (max 10 best out of 20): 05jxkf (0.61 #436, 0.56 #844, 0.56 #820), 05pcjw (0.36 #145, 0.35 #793, 0.33 #217), 01rs41 (0.32 #581, 0.30 #317, 0.29 #2190), 07tf8 (0.21 #153, 0.21 #249, 0.19 #177), 01_9fk (0.14 #1946, 0.13 #1274, 0.13 #2091), 02p0qmm (0.08 #1042, 0.08 #658, 0.07 #778), 01y64 (0.08 #1993, 0.08 #3534, 0.05 #636), 01_srz (0.07 #2188, 0.07 #1587, 0.07 #1707), 047951 (0.05 #200, 0.05 #224, 0.04 #416), 06cs1 (0.05 #582, 0.04 #726, 0.03 #870) >> Best rule #436 for best value: >> intensional similarity = 5 >> extensional distance = 47 >> proper extension: 02qwgk; >> query: (?x7818, 05jxkf) <- institution(?x1200, ?x7818), currency(?x7818, ?x5696), currency(?x639, ?x5696), contains(?x1892, ?x7818), colors(?x7818, ?x3189) >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01g6l8 school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 225.000 225.000 0.612 http://example.org/education/educational_institution/school_type #2353-05qw5 PRED entity: 05qw5 PRED relation: instrumentalists! PRED expected values: 0342h => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 89): 0342h (0.63 #1590, 0.53 #1767, 0.52 #2735), 05r5c (0.43 #1594, 0.39 #1771, 0.38 #2739), 05148p4 (0.34 #1784, 0.33 #1607, 0.30 #286), 018vs (0.31 #1776, 0.26 #278, 0.25 #2392), 02hnl (0.17 #1798, 0.17 #1621, 0.15 #2766), 03qjg (0.17 #1637, 0.17 #844, 0.16 #316), 026t6 (0.13 #1765, 0.10 #1588, 0.09 #2557), 0l14md (0.12 #1770, 0.12 #1593, 0.10 #2386), 0l14qv (0.11 #270, 0.10 #1768, 0.10 #1591), 06ncr (0.09 #1808, 0.08 #1631, 0.07 #2600) >> Best rule #1590 for best value: >> intensional similarity = 4 >> extensional distance = 284 >> proper extension: 03_0p; >> query: (?x2120, 0342h) <- type_of_union(?x2120, ?x566), ?x566 = 04ztj, award(?x2120, ?x1479), instrumentalists(?x2460, ?x2120) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05qw5 instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.633 http://example.org/music/instrument/instrumentalists #2352-022840 PRED entity: 022840 PRED relation: locations PRED expected values: 059g4 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 308): 059g4 (0.67 #1602, 0.60 #1234, 0.50 #867), 06n3y (0.40 #1456, 0.21 #10570, 0.20 #2574), 0j3b (0.33 #577, 0.17 #2986, 0.17 #1495), 04jpl (0.30 #2415, 0.06 #7432, 0.05 #8366), 0f8l9c (0.26 #4470, 0.18 #2795, 0.13 #6512), 0261m (0.25 #3292, 0.25 #1992, 0.25 #883), 02j9z (0.25 #3156, 0.25 #2973, 0.21 #10570), 0dg3n1 (0.25 #3015, 0.20 #2643, 0.20 #1156), 01lxw6 (0.25 #907, 0.20 #1274, 0.20 #1091), 02613 (0.25 #901, 0.20 #1268, 0.20 #1085) >> Best rule #1602 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 086m1; >> query: (?x7734, 059g4) <- combatants(?x7734, ?x13662), entity_involved(?x7734, ?x6371), people(?x13662, ?x3930), combatants(?x1777, ?x6371), geographic_distribution(?x13662, ?x8483), award_nominee(?x748, ?x3930) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022840 locations 059g4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.667 http://example.org/time/event/locations #2351-0jm_ PRED entity: 0jm_ PRED relation: athlete PRED expected values: 039g82 043gj 01kwsg 0443c => 70 concepts (52 used for prediction) PRED predicted values (max 10 best out of 113): 02hg53 (0.40 #999, 0.40 #886, 0.33 #1113), 054c1 (0.40 #997, 0.40 #884, 0.33 #1111), 03m5111 (0.33 #113, 0.25 #681, 0.25 #455), 04v68c (0.33 #110, 0.25 #678, 0.25 #452), 02zbjhq (0.33 #105, 0.25 #673, 0.25 #447), 0bhtzw (0.33 #99, 0.25 #667, 0.25 #441), 0d3mlc (0.33 #98, 0.25 #666, 0.25 #440), 06yj20 (0.33 #97, 0.25 #665, 0.25 #439), 054kmq (0.33 #96, 0.25 #664, 0.25 #438), 02qny_ (0.33 #90, 0.25 #658, 0.25 #432) >> Best rule #999 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 037hz; >> query: (?x1083, 02hg53) <- athlete(?x1083, ?x11878), athlete(?x1083, ?x9180), athlete(?x1083, ?x2663), athlete(?x1083, ?x1377), athlete(?x1083, ?x445), place_of_birth(?x11878, ?x1110), participant(?x445, ?x444), gender(?x445, ?x231), profession(?x445, ?x319), location(?x9180, ?x2949), people(?x10199, ?x2663), student(?x735, ?x1377) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1016 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 037hz; *> query: (?x1083, 0443c) <- athlete(?x1083, ?x11878), athlete(?x1083, ?x9180), athlete(?x1083, ?x2663), athlete(?x1083, ?x1377), athlete(?x1083, ?x445), place_of_birth(?x11878, ?x1110), participant(?x445, ?x444), gender(?x445, ?x231), profession(?x445, ?x319), location(?x9180, ?x2949), people(?x10199, ?x2663), student(?x735, ?x1377) *> conf = 0.20 ranks of expected_values: 92 EVAL 0jm_ athlete 0443c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 70.000 52.000 0.400 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete EVAL 0jm_ athlete 01kwsg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 52.000 0.400 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete EVAL 0jm_ athlete 043gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 52.000 0.400 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete EVAL 0jm_ athlete 039g82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 52.000 0.400 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #2350-0jnr_ PRED entity: 0jnr_ PRED relation: team! PRED expected values: 02qvzf => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 49): 02qvzf (0.83 #928, 0.81 #1076, 0.81 #1375), 02sdk9v (0.78 #4008, 0.69 #3859, 0.64 #3703), 02_j1w (0.75 #4012, 0.64 #3863, 0.61 #3707), 02qvkj (0.74 #4056, 0.69 #3803, 0.67 #3751), 02nzb8 (0.74 #4007, 0.64 #3858, 0.60 #3702), 0dgrmp (0.63 #3861, 0.63 #4010, 0.60 #3705), 02g_7z (0.55 #1926, 0.52 #1174, 0.42 #1674), 05b3ts (0.50 #1671, 0.50 #471, 0.45 #1923), 06b1q (0.50 #1910, 0.47 #660, 0.37 #1658), 05zm34 (0.50 #1917, 0.45 #1165, 0.42 #1665) >> Best rule #928 for best value: >> intensional similarity = 39 >> extensional distance = 22 >> proper extension: 0j8js; >> query: (?x10950, 02qvzf) <- team(?x5234, ?x10950), team(?x2918, ?x10950), ?x5234 = 02qvdc, position(?x14123, ?x2918), position(?x13326, ?x2918), position(?x13166, ?x2918), position(?x11368, ?x2918), position(?x10941, ?x2918), position(?x10755, ?x2918), position(?x10713, ?x2918), position(?x10644, ?x2918), position(?x10034, ?x2918), position(?x9835, ?x2918), position(?x9515, ?x2918), position(?x8892, ?x2918), position(?x8541, ?x2918), position(?x8037, ?x2918), ?x10034 = 0jnq8, sport(?x10950, ?x453), team(?x2918, ?x14124), team(?x2918, ?x13608), team(?x2918, ?x12757), ?x10941 = 030ykh, ?x10755 = 0jbqf, ?x8541 = 0jnpc, ?x8037 = 0jnrk, ?x8892 = 02fp3, ?x10644 = 0jnnx, ?x14123 = 04l59s, ?x10713 = 0gx159f, ?x9835 = 02hqt6, ?x13326 = 0hm2b, ?x11368 = 032yps, colors(?x10950, ?x8271), ?x13608 = 0jnl5, ?x13166 = 0j6tr, ?x9515 = 0j2zj, ?x14124 = 04l590, ?x12757 = 0hmtk >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jnr_ team! 02qvzf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.833 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #2349-0178rl PRED entity: 0178rl PRED relation: award_winner! PRED expected values: 025m8l => 105 concepts (83 used for prediction) PRED predicted values (max 10 best out of 304): 02qvyrt (0.42 #2979, 0.42 #2978, 0.41 #2126), 02x17c2 (0.42 #2979, 0.42 #2978, 0.41 #2126), 05q8pss (0.42 #2979, 0.42 #2978, 0.41 #2126), 0g_w (0.42 #2978, 0.41 #2126, 0.40 #18288), 025m8y (0.37 #1798, 0.33 #2650, 0.25 #948), 0l8z1 (0.35 #1764, 0.31 #2616, 0.14 #489), 025m8l (0.33 #117, 0.14 #542, 0.12 #967), 05zkcn5 (0.33 #22, 0.14 #447, 0.10 #19564), 054krc (0.25 #936, 0.25 #1786, 0.22 #2638), 01by1l (0.20 #5216, 0.14 #535, 0.14 #5641) >> Best rule #2979 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 03ryks; 017l4; >> query: (?x5223, ?x2585) <- award(?x5223, ?x2585), award(?x5223, ?x2379), ?x2379 = 02qvyrt, award_winner(?x2585, ?x248) >> conf = 0.42 => this is the best rule for 3 predicted values *> Best rule #117 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 01bczm; *> query: (?x5223, 025m8l) <- award_nominee(?x5223, ?x7955), ?x7955 = 01l3mk3, category(?x5223, ?x134) *> conf = 0.33 ranks of expected_values: 7 EVAL 0178rl award_winner! 025m8l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 105.000 83.000 0.417 http://example.org/award/award_category/winners./award/award_honor/award_winner #2348-0ctw_b PRED entity: 0ctw_b PRED relation: contains PRED expected values: 0gslw => 214 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2885): 020p1 (0.83 #131839, 0.83 #82032, 0.81 #90823), 0ctw_b (0.60 #84963, 0.48 #301764, 0.33 #97), 0gslw (0.60 #84963, 0.48 #301764), 02y9wq (0.60 #84963, 0.48 #301764), 05nrg (0.60 #84963, 0.48 #301764), 01b8jj (0.33 #1937, 0.14 #7796, 0.11 #22446), 0mgp (0.33 #1291, 0.14 #7150, 0.11 #21800), 06y57 (0.33 #644, 0.14 #6503, 0.06 #12363), 062qg (0.33 #1252, 0.14 #7111, 0.06 #12971), 03ryn (0.33 #467, 0.05 #193826, 0.03 #311019) >> Best rule #131839 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 09c7w0; 05v8c; 059j2; 0h7x; 05sb1; 0162b; >> query: (?x1023, ?x6291) <- country(?x150, ?x1023), film_release_region(?x66, ?x1023), country(?x6291, ?x1023) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #84963 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 0n5gq; *> query: (?x1023, ?x14790) <- contains(?x1023, ?x13715), jurisdiction_of_office(?x3444, ?x1023), contains(?x14790, ?x13715) *> conf = 0.60 ranks of expected_values: 3 EVAL 0ctw_b contains 0gslw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 214.000 108.000 0.830 http://example.org/location/location/contains #2347-04zqmj PRED entity: 04zqmj PRED relation: award PRED expected values: 05p09zm => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 221): 09sb52 (0.35 #4495, 0.34 #5711, 0.33 #445), 05p09zm (0.18 #13773, 0.11 #529, 0.10 #1339), 05b4l5x (0.18 #13773, 0.09 #6, 0.09 #1626), 03c7tr1 (0.18 #13773, 0.08 #1678, 0.08 #58), 0ck27z (0.15 #2117, 0.14 #11839, 0.14 #5763), 05zrvfd (0.15 #14584, 0.14 #8507, 0.13 #19850), 05pcn59 (0.15 #81, 0.14 #1701, 0.13 #486), 05zvj3m (0.14 #8507, 0.13 #19850, 0.04 #93), 01by1l (0.13 #3352, 0.09 #11049, 0.08 #7403), 0gq9h (0.12 #482, 0.12 #1292, 0.11 #887) >> Best rule #4495 for best value: >> intensional similarity = 3 >> extensional distance = 1027 >> proper extension: 01vvycq; 01wmgrf; >> query: (?x11381, 09sb52) <- award_nominee(?x3536, ?x11381), participant(?x3536, ?x4106), award_winner(?x1336, ?x4106) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #13773 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1519 *> proper extension: 018_q8; 0gsgr; *> query: (?x11381, ?x154) <- award_winner(?x11381, ?x8793), award_winner(?x154, ?x8793) *> conf = 0.18 ranks of expected_values: 2 EVAL 04zqmj award 05p09zm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 81.000 0.351 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2346-0kr5_ PRED entity: 0kr5_ PRED relation: award_winner! PRED expected values: 0bzmt8 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 135): 0ftlkg (0.22 #167, 0.02 #1013), 02wzl1d (0.15 #293, 0.12 #11, 0.09 #998), 03gwpw2 (0.15 #291, 0.07 #996, 0.05 #1137), 09pnw5 (0.15 #385, 0.05 #526, 0.05 #667), 02yxh9 (0.15 #383, 0.05 #1088, 0.04 #1229), 03gt46z (0.15 #345, 0.05 #1050, 0.04 #1191), 0gvstc3 (0.15 #316, 0.03 #1726, 0.02 #1867), 09q_6t (0.12 #8, 0.08 #290, 0.07 #995), 0hndn2q (0.12 #40, 0.07 #1027, 0.07 #745), 09p3h7 (0.12 #71, 0.07 #776, 0.06 #917) >> Best rule #167 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 012cj0; 04bdlg; >> query: (?x698, 0ftlkg) <- award(?x698, ?x198), nominated_for(?x698, ?x2924), nationality(?x698, ?x1310), ?x2924 = 0p7qm >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #16076 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2935 *> proper extension: 0411q; 01lmj3q; 089tm; 01pfr3; 06cc_1; 01zkxv; 04rcr; 0kzy0; 01w61th; 01kwlwp; ... *> query: (?x698, ?x78) <- award(?x698, ?x1313), ceremony(?x1313, ?x78) *> conf = 0.02 ranks of expected_values: 118 EVAL 0kr5_ award_winner! 0bzmt8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 124.000 124.000 0.222 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2345-02hp70 PRED entity: 02hp70 PRED relation: major_field_of_study PRED expected values: 088tb => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 109): 037mh8 (0.62 #185, 0.40 #305, 0.37 #425), 02j62 (0.59 #388, 0.47 #628, 0.46 #869), 02lp1 (0.51 #372, 0.50 #252, 0.45 #732), 01lj9 (0.50 #278, 0.46 #398, 0.38 #158), 04x_3 (0.50 #144, 0.40 #264, 0.32 #384), 05qfh (0.50 #154, 0.34 #394, 0.30 #274), 02_7t (0.40 #302, 0.36 #542, 0.25 #182), 01tbp (0.40 #297, 0.32 #417, 0.25 #777), 062z7 (0.38 #145, 0.37 #385, 0.34 #745), 05qjt (0.38 #128, 0.34 #368, 0.33 #608) >> Best rule #185 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 07tds; >> query: (?x11397, 037mh8) <- major_field_of_study(?x11397, ?x2014), major_field_of_study(?x11397, ?x1527), school_type(?x11397, ?x1507), ?x2014 = 04rjg, ?x1527 = 04_tv >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #841 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 94 *> proper extension: 01w5m; 027mdh; 0gl5_; 0c5x_; 01d650; 0mbwf; 013719; 0jksm; *> query: (?x11397, ?x732) <- major_field_of_study(?x11397, ?x2014), major_field_of_study(?x11397, ?x1668), state_province_region(?x11397, ?x728), ?x1668 = 01mkq, major_field_of_study(?x732, ?x2014) *> conf = 0.20 ranks of expected_values: 42 EVAL 02hp70 major_field_of_study 088tb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 107.000 107.000 0.625 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #2344-015bwt PRED entity: 015bwt PRED relation: award PRED expected values: 05zkcn5 01d38g 01bgqh => 89 concepts (68 used for prediction) PRED predicted values (max 10 best out of 267): 01cky2 (0.50 #2600, 0.28 #5006, 0.18 #17647), 02f72_ (0.49 #3434, 0.26 #3835, 0.24 #5439), 01d38g (0.47 #2434, 0.19 #4840, 0.16 #11630), 02f716 (0.46 #3385, 0.33 #979, 0.29 #578), 02f5qb (0.46 #3364, 0.29 #557, 0.22 #958), 02f6ym (0.43 #656, 0.33 #1057, 0.19 #2661), 01by1l (0.43 #4924, 0.39 #2518, 0.38 #6127), 02f73b (0.41 #3492, 0.29 #685, 0.22 #1086), 01ckcd (0.40 #333, 0.36 #3942, 0.35 #5546), 02f72n (0.40 #146, 0.35 #3354, 0.21 #5359) >> Best rule #2600 for best value: >> intensional similarity = 5 >> extensional distance = 34 >> proper extension: 0770cd; >> query: (?x11455, 01cky2) <- award_nominee(?x11455, ?x2614), artists(?x3928, ?x11455), artists(?x2937, ?x11455), ?x2937 = 0glt670, ?x3928 = 0gywn >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2434 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 0770cd; *> query: (?x11455, 01d38g) <- award_nominee(?x11455, ?x2614), artists(?x3928, ?x11455), artists(?x2937, ?x11455), ?x2937 = 0glt670, ?x3928 = 0gywn *> conf = 0.47 ranks of expected_values: 3, 12, 43 EVAL 015bwt award 01bgqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 89.000 68.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 015bwt award 01d38g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 89.000 68.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 015bwt award 05zkcn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 89.000 68.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2343-0jkhr PRED entity: 0jkhr PRED relation: contains! PRED expected values: 09c7w0 => 138 concepts (67 used for prediction) PRED predicted values (max 10 best out of 289): 09c7w0 (0.83 #2685, 0.82 #11625, 0.81 #14307), 0n3ll (0.28 #51872, 0.04 #1564), 059rby (0.23 #17901, 0.13 #42943, 0.12 #37574), 02_286 (0.23 #17924, 0.08 #20606, 0.08 #8089), 02jx1 (0.22 #12602, 0.21 #9026, 0.19 #20649), 01n7q (0.20 #17958, 0.15 #77, 0.14 #51949), 07ssc (0.17 #12548, 0.16 #8972, 0.13 #20595), 030qb3t (0.13 #17981, 0.04 #21557, 0.03 #20663), 0d060g (0.11 #12529, 0.10 #8953, 0.10 #13), 04jpl (0.10 #22, 0.08 #12538, 0.06 #20585) >> Best rule #2685 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 06mkj; 0d05w3; >> query: (?x6856, 09c7w0) <- school(?x8542, ?x6856), draft(?x6128, ?x8542), contains(?x760, ?x6856), ?x6128 = 0jm64 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jkhr contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 67.000 0.833 http://example.org/location/location/contains #2342-02vmzp PRED entity: 02vmzp PRED relation: special_performance_type PRED expected values: 01pb34 => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.29 #13, 0.21 #89, 0.21 #73), 09_gdc (0.11 #78, 0.04 #149, 0.04 #144), 014kbl (0.01 #208) >> Best rule #13 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 03z_g7; >> query: (?x2145, 01pb34) <- award_winner(?x4687, ?x2145), profession(?x2145, ?x524), ?x524 = 02jknp, ?x4687 = 03rbj2, nationality(?x2145, ?x2146) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vmzp special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.286 http://example.org/film/actor/film./film/performance/special_performance_type #2341-025v1sx PRED entity: 025v1sx PRED relation: team! PRED expected values: 02g_7z => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 49): 02g_7z (0.89 #356, 0.87 #460, 0.85 #1124), 05b3ts (0.88 #579, 0.87 #528, 0.83 #833), 01_9c1 (0.88 #1087, 0.85 #983, 0.84 #881), 0b13yt (0.84 #886, 0.82 #683, 0.81 #582), 01r3hr (0.83 #2334, 0.80 #970, 0.80 #412), 04nfpk (0.80 #371, 0.79 #1289, 0.78 #2069), 05zm34 (0.80 #368, 0.75 #1427, 0.72 #1286), 047g8h (0.79 #1283, 0.77 #1762, 0.77 #1598), 02qpbqj (0.79 #1035, 0.78 #219, 0.77 #1770), 06b1q (0.78 #560, 0.78 #262, 0.76 #773) >> Best rule #356 for best value: >> intensional similarity = 19 >> extensional distance = 8 >> proper extension: 0fjzsy; >> query: (?x6570, ?x3346) <- team(?x1717, ?x6570), team(?x1517, ?x6570), team(?x1240, ?x6570), colors(?x6570, ?x1101), ?x1240 = 023wyl, position_s(?x6570, ?x3346), colors(?x10217, ?x1101), colors(?x13154, ?x1101), colors(?x12202, ?x1101), colors(?x12042, ?x1101), colors(?x260, ?x1101), ?x1517 = 02g_6j, category(?x12042, ?x134), ?x10217 = 03818y, position(?x387, ?x3346), position(?x12202, ?x60), team(?x8594, ?x13154), ?x1717 = 02g_6x, season(?x260, ?x701) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025v1sx team! 02g_7z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.890 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #2340-0g2lq PRED entity: 0g2lq PRED relation: film PRED expected values: 01hw5kk => 120 concepts (110 used for prediction) PRED predicted values (max 10 best out of 357): 016zfm (0.60 #26341, 0.09 #68354, 0.09 #68355), 02kk_c (0.60 #26341, 0.09 #68354, 0.09 #68355), 04ynx7 (0.53 #20578, 0.53 #21402, 0.28 #9054), 0cc5mcj (0.53 #20578, 0.53 #21402, 0.28 #9054), 0djb3vw (0.53 #20578, 0.53 #21402, 0.28 #9054), 0g54xkt (0.53 #20578, 0.53 #21402, 0.28 #9054), 02qr69m (0.53 #20578, 0.53 #21402, 0.28 #9054), 0f4_2k (0.53 #20578, 0.53 #21402, 0.28 #9054), 053tj7 (0.53 #20578, 0.53 #21402, 0.28 #9054), 02hct1 (0.28 #31283, 0.10 #46938, 0.09 #55178) >> Best rule #26341 for best value: >> intensional similarity = 3 >> extensional distance = 197 >> proper extension: 0362q0; >> query: (?x7837, ?x4881) <- film(?x7837, ?x253), award(?x7837, ?x198), award_winner(?x4881, ?x7837) >> conf = 0.60 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0g2lq film 01hw5kk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 110.000 0.599 http://example.org/film/director/film #2339-02gpkt PRED entity: 02gpkt PRED relation: produced_by PRED expected values: 03h40_7 => 86 concepts (74 used for prediction) PRED predicted values (max 10 best out of 165): 012d40 (0.29 #6962, 0.13 #22841, 0.12 #6575), 01900g (0.12 #6575, 0.12 #7735, 0.11 #10057), 02bfxb (0.08 #886, 0.03 #500), 01t6b4 (0.06 #429, 0.04 #1202, 0.02 #8553), 03h40_7 (0.06 #733, 0.03 #2278, 0.03 #3050), 0js9s (0.06 #999, 0.05 #227, 0.03 #613), 04pqqb (0.05 #178, 0.03 #1723, 0.03 #2109), 020l9r (0.05 #294, 0.03 #680, 0.02 #2997), 06pk8 (0.05 #34, 0.03 #420, 0.02 #2737), 03_gd (0.05 #29, 0.03 #415, 0.01 #1960) >> Best rule #6962 for best value: >> intensional similarity = 3 >> extensional distance = 575 >> proper extension: 06mr2s; >> query: (?x7541, ?x147) <- nominated_for(?x147, ?x7541), profession(?x147, ?x319), executive_produced_by(?x148, ?x147) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #733 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 014_x2; 01k1k4; 0ds33; 01vksx; 02qm_f; 026390q; 017gm7; 0pb33; 0dr_4; 0gj9tn5; ... *> query: (?x7541, 03h40_7) <- music(?x7541, ?x1940), nominated_for(?x401, ?x7541), language(?x7541, ?x254), ?x401 = 05zr6wv *> conf = 0.06 ranks of expected_values: 5 EVAL 02gpkt produced_by 03h40_7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 74.000 0.289 http://example.org/film/film/produced_by #2338-05qhw PRED entity: 05qhw PRED relation: country! PRED expected values: 063fh9 => 167 concepts (141 used for prediction) PRED predicted values (max 10 best out of 1839): 01m13b (0.41 #10367, 0.38 #6959, 0.30 #12072), 0gl3hr (0.27 #129445, 0.05 #2745, 0.04 #4448), 0bl5c (0.27 #129445, 0.05 #2633, 0.04 #4336), 0kb57 (0.27 #129445, 0.05 #2167, 0.04 #3870), 049mql (0.25 #4048, 0.19 #7454, 0.19 #10862), 0dscrwf (0.25 #3474, 0.19 #6880, 0.19 #10288), 023g6w (0.23 #8211, 0.22 #11619, 0.21 #4805), 0fjyzt (0.21 #4296, 0.19 #7702, 0.19 #11110), 06_sc3 (0.21 #4749, 0.19 #8155, 0.19 #11563), 0bmch_x (0.21 #4194, 0.19 #7600, 0.16 #5897) >> Best rule #10367 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 03rt9; 06mzp; 01znc_; 03rj0; 03h64; 06t8v; >> query: (?x456, 01m13b) <- film_release_region(?x1451, ?x456), olympics(?x456, ?x391), ?x1451 = 04zyhx >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #11339 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 03rt9; 06mzp; 01znc_; 03rj0; 03h64; 06t8v; *> query: (?x456, 063fh9) <- film_release_region(?x1451, ?x456), olympics(?x456, ?x391), ?x1451 = 04zyhx *> conf = 0.11 ranks of expected_values: 397 EVAL 05qhw country! 063fh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 167.000 141.000 0.407 http://example.org/film/film/country #2337-01g42 PRED entity: 01g42 PRED relation: location PRED expected values: 02_286 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 248): 0cc56 (0.49 #21725, 0.48 #23334, 0.47 #73211), 0rh6k (0.25 #809, 0.17 #1613, 0.11 #2417), 02_286 (0.19 #27392, 0.18 #3255, 0.17 #1646), 0k049 (0.17 #1617, 0.05 #5641, 0.04 #2421), 030qb3t (0.13 #16980, 0.12 #20199, 0.11 #32267), 0cr3d (0.10 #4973, 0.10 #27500, 0.08 #78040), 0ccvx (0.08 #78040, 0.07 #2635, 0.05 #4245), 0r0m6 (0.08 #78040, 0.07 #2631, 0.05 #5851), 01n7q (0.08 #78040, 0.04 #2476, 0.04 #5696), 0psxp (0.08 #78040, 0.03 #3507, 0.02 #78845) >> Best rule #21725 for best value: >> intensional similarity = 3 >> extensional distance = 557 >> proper extension: 012v1t; 07h1q; 01cqz5; 015n8; >> query: (?x8634, ?x1131) <- place_of_birth(?x8634, ?x1131), religion(?x8634, ?x962), gender(?x8634, ?x231) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #27392 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 894 *> proper extension: 0bhtzw; *> query: (?x8634, 02_286) <- place_of_birth(?x8634, ?x1131), adjoins(?x1131, ?x10856) *> conf = 0.19 ranks of expected_values: 3 EVAL 01g42 location 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 117.000 0.490 http://example.org/people/person/places_lived./people/place_lived/location #2336-09jwl PRED entity: 09jwl PRED relation: profession! PRED expected values: 0197tq 01tvz5j 02mslq 01n5309 01yk13 0pz7h 081lh 01vrz41 01kx_81 01j4ls 01kvqc 011zd3 01_x6v 04xrx 02fn5r 01qdjm 0137g1 0259r0 03h_fk5 053yx 0565cz 01vw20_ 06jvj7 0p3sf 07qy0b 01vsy95 01gx5f 01wz_ml 016h4r 012gq6 050z2 028qdb 02j3d4 01l03w2 03sww 04s430 01mvjl0 0k1bs 01t110 05cgy8 04b7xr 01vrnsk 0d0mbj 0bz60q 01p0vf 01vs4ff 03m6_z 04n65n 01yfm8 024dw0 01s1zk 013pk3 017l4 06fc0b 03j1p2n 018phr 0j6cj 02q3bb 02mx98 0h953 031x_3 01vn0t_ 04bbv7 04lg6 024qwq 01wkmgb 01rwcgb 01tpl1p 03f68r6 0cj2w 04s5_s => 55 concepts (33 used for prediction) PRED predicted values (max 10 best out of 3668): 017b2p (0.71 #53183, 0.60 #35066, 0.50 #24194), 02lk1s (0.67 #40042, 0.57 #54536, 0.51 #36236), 08hsww (0.67 #41101, 0.57 #55595, 0.51 #36236), 0c9c0 (0.67 #40525, 0.57 #55019, 0.51 #36236), 0dn44 (0.67 #43077, 0.57 #57571, 0.51 #36236), 0mbw0 (0.67 #42134, 0.57 #56628, 0.40 #34887), 052hl (0.67 #41684, 0.57 #56178, 0.33 #63426), 015pxr (0.67 #40353, 0.57 #54847, 0.33 #62095), 03nb5v (0.67 #41619, 0.57 #56113, 0.33 #5384), 086nl7 (0.67 #41027, 0.57 #55521, 0.33 #4792) >> Best rule #53183 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 025352; >> query: (?x1183, 017b2p) <- profession(?x9731, ?x1183), profession(?x2728, ?x1183), profession(?x1997, ?x1183), profession(?x1092, ?x1183), ?x9731 = 01q3_2, artist(?x9224, ?x1997), award_nominee(?x230, ?x2728), role(?x1092, ?x227) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #40048 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0cbd2; 03gjzk; 018gz8; *> query: (?x1183, 0pz7h) <- profession(?x8272, ?x1183), profession(?x425, ?x1183), religion(?x8272, ?x2694), ?x425 = 01yznp, artists(?x302, ?x8272) *> conf = 0.67 ranks of expected_values: 20, 25, 39, 43, 44, 45, 77, 78, 90, 97, 104, 107, 115, 154, 158, 166, 168, 181, 182, 185, 275, 279, 281, 310, 578, 598, 609, 611, 689, 693, 696, 737, 786, 790, 792, 822, 837, 846, 854, 860, 969, 970, 976, 979, 981, 1066, 1069, 1076, 1082, 1222, 1311, 1445, 1471, 1485, 1487, 1496, 1519, 1695, 1699, 1833, 1912, 1974, 2011, 2529, 2562, 2570, 2598, 3187, 3610 EVAL 09jwl profession! 04s5_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0cj2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 03f68r6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01tpl1p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01rwcgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01wkmgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 024qwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 04lg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 04bbv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01vn0t_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 031x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0h953 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 02mx98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 02q3bb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0j6cj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 018phr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 03j1p2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 06fc0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 017l4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 013pk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01s1zk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 024dw0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01yfm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 04n65n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 03m6_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01vs4ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01p0vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0bz60q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0d0mbj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01vrnsk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 04b7xr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 05cgy8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01t110 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0k1bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01mvjl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 04s430 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 03sww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01l03w2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 02j3d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 028qdb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 050z2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 012gq6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 016h4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01wz_ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01gx5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01vsy95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 07qy0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0p3sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 06jvj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01vw20_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0565cz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 053yx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 03h_fk5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0259r0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0137g1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01qdjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 02fn5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 04xrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01_x6v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 011zd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01kvqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01j4ls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01kx_81 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01vrz41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 081lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0pz7h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01yk13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01n5309 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 02mslq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 01tvz5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 55.000 33.000 0.714 http://example.org/people/person/profession EVAL 09jwl profession! 0197tq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 55.000 33.000 0.714 http://example.org/people/person/profession #2335-07j9n PRED entity: 07j9n PRED relation: combatants PRED expected values: 05kyr => 59 concepts (45 used for prediction) PRED predicted values (max 10 best out of 443): 07ytt (0.60 #937, 0.50 #819, 0.50 #702), 014tss (0.60 #647, 0.44 #1350, 0.44 #236), 09c7w0 (0.55 #3199, 0.50 #939, 0.49 #3923), 0chghy (0.55 #1885, 0.44 #236, 0.43 #3091), 059j2 (0.55 #1662, 0.44 #236, 0.33 #258), 0285m87 (0.44 #236, 0.43 #1249, 0.33 #77), 0cdbq (0.44 #236, 0.40 #753, 0.40 #637), 0ctw_b (0.44 #236, 0.36 #1660, 0.33 #957), 0d060g (0.44 #236, 0.36 #1648, 0.33 #128), 035qy (0.44 #236, 0.33 #260, 0.33 #144) >> Best rule #937 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 0845v; 05lbzg; 0ql76; 0ql86; >> query: (?x10119, ?x9376) <- combatants(?x10119, ?x9328), combatants(?x10119, ?x1003), entity_involved(?x10119, ?x9376), ?x9328 = 024pcx, nationality(?x1373, ?x1003), combatants(?x1497, ?x1003), adjoins(?x1003, ?x756), jurisdiction_of_office(?x182, ?x1003) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #236 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 081pw; *> query: (?x10119, ?x1679) <- combatants(?x10119, ?x10801), combatants(?x10119, ?x8949), combatants(?x10119, ?x5127), combatants(?x10119, ?x1790), combatants(?x10119, ?x1003), taxonomy(?x10119, ?x939), ?x1003 = 03gj2, ?x1790 = 01pj7, participating_countries(?x2553, ?x10801), combatants(?x8949, ?x1679), contains(?x1536, ?x5127) *> conf = 0.44 ranks of expected_values: 45 EVAL 07j9n combatants 05kyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 59.000 45.000 0.600 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #2334-08s_lw PRED entity: 08s_lw PRED relation: place_of_birth PRED expected values: 030qb3t => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 34): 02_286 (0.07 #46493, 0.07 #11284, 0.07 #20439), 030qb3t (0.04 #46528, 0.03 #7094, 0.03 #26106), 0cr3d (0.03 #2910, 0.03 #4318, 0.03 #46568), 01_d4 (0.03 #46540, 0.03 #32457, 0.03 #42315), 0vzm (0.02 #117, 0.01 #12674, 0.01 #37321), 0cv3w (0.02 #106, 0.01 #12674, 0.01 #37321), 0c_m3 (0.02 #197, 0.01 #37321, 0.01 #35208), 0rt80 (0.02 #668, 0.01 #37321, 0.01 #35208), 06y57 (0.02 #180, 0.01 #37321, 0.01 #35208), 094jv (0.02 #61, 0.01 #4285) >> Best rule #46493 for best value: >> intensional similarity = 2 >> extensional distance = 2749 >> proper extension: 07qnf; 07m69t; 09f5rr; 02x8kk; 09fqdt; 069d71; 01nvdc; 03cxqp5; 09f5pp; >> query: (?x5617, 02_286) <- nationality(?x5617, ?x94), ?x94 = 09c7w0 >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #46528 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2749 *> proper extension: 07qnf; 07m69t; 09f5rr; 02x8kk; 09fqdt; 069d71; 01nvdc; 03cxqp5; 09f5pp; *> query: (?x5617, 030qb3t) <- nationality(?x5617, ?x94), ?x94 = 09c7w0 *> conf = 0.04 ranks of expected_values: 2 EVAL 08s_lw place_of_birth 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 78.000 0.070 http://example.org/people/person/place_of_birth #2333-0d61px PRED entity: 0d61px PRED relation: film! PRED expected values: 017r13 => 68 concepts (36 used for prediction) PRED predicted values (max 10 best out of 609): 018swb (0.30 #2422, 0.25 #4502, 0.14 #342), 02qgyv (0.29 #384, 0.20 #2464, 0.17 #4544), 04sry (0.29 #1277, 0.20 #3357, 0.17 #5437), 0dvld (0.29 #1061, 0.17 #5221, 0.10 #3141), 0hvb2 (0.17 #4459, 0.14 #299, 0.10 #2379), 0154qm (0.14 #561, 0.10 #2641, 0.08 #4721), 01r93l (0.14 #748, 0.10 #2828, 0.08 #4908), 0bksh (0.14 #855, 0.10 #2935, 0.08 #5015), 07vc_9 (0.14 #203, 0.10 #2283, 0.08 #4363), 01s7zw (0.14 #426, 0.10 #2506, 0.08 #4586) >> Best rule #2422 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01cssf; 0dr_4; 0661ql3; 019vhk; 0g54xkt; 0c38gj; 049xgc; 01flv_; >> query: (?x4175, 018swb) <- nominated_for(?x3101, ?x4175), ?x3101 = 0dvmd, titles(?x53, ?x4175), film(?x1867, ?x4175) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #17759 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 403 *> proper extension: 02q0k7v; *> query: (?x4175, 017r13) <- nominated_for(?x3101, ?x4175), award_nominee(?x3101, ?x2353), films(?x11024, ?x4175), award_nominee(?x2353, ?x157) *> conf = 0.02 ranks of expected_values: 222 EVAL 0d61px film! 017r13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 68.000 36.000 0.300 http://example.org/film/actor/film./film/performance/film #2332-04syw PRED entity: 04syw PRED relation: jurisdiction_of_office PRED expected values: 0160w 0chghy 07ssc 025ndl => 45 concepts (40 used for prediction) PRED predicted values (max 10 best out of 626): 07ssc (0.73 #10507, 0.40 #1337, 0.33 #436), 0f8l9c (0.70 #3532, 0.60 #1347, 0.54 #7026), 09c7w0 (0.64 #4374, 0.50 #5248, 0.50 #3061), 03rj0 (0.50 #3599, 0.40 #1414, 0.38 #7093), 0hzlz (0.50 #3533, 0.40 #1348, 0.38 #7027), 03rk0 (0.50 #974, 0.33 #539, 0.30 #3156), 0160w (0.50 #881, 0.33 #446, 0.20 #3063), 03gj2 (0.40 #3541, 0.40 #2665, 0.40 #1356), 035qy (0.40 #3560, 0.40 #1375, 0.38 #7054), 015fr (0.40 #3524, 0.40 #3085, 0.36 #4398) >> Best rule #10507 for best value: >> intensional similarity = 12 >> extensional distance = 18 >> proper extension: 058yv_; >> query: (?x3119, ?x512) <- jurisdiction_of_office(?x3119, ?x6401), jurisdiction_of_office(?x3119, ?x3040), jurisdiction_of_office(?x3119, ?x279), jurisdiction_of_office(?x3119, ?x172), contains(?x6401, ?x13617), administrative_parent(?x6885, ?x6401), second_level_divisions(?x512, ?x13617), contains(?x172, ?x12196), contains(?x455, ?x3040), contains(?x279, ?x13138), location(?x5944, ?x13138), featured_film_locations(?x1448, ?x12196) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 98, 282 EVAL 04syw jurisdiction_of_office 025ndl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 45.000 40.000 0.727 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 04syw jurisdiction_of_office 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 40.000 0.727 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 04syw jurisdiction_of_office 0chghy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 45.000 40.000 0.727 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 04syw jurisdiction_of_office 0160w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 45.000 40.000 0.727 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #2331-01k23t PRED entity: 01k23t PRED relation: category PRED expected values: 08mbj5d => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.94 #3, 0.93 #2, 0.87 #20) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 0197tq; 01w61th; 07c0j; 01vrt_c; 01vs_v8; 01cwhp; 045zr; 0892sx; 016fmf; 01vsykc; ... >> query: (?x7794, 08mbj5d) <- award(?x7794, ?x4958), origin(?x7794, ?x8653), ?x4958 = 03qbnj, artist(?x2190, ?x7794) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01k23t category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.941 http://example.org/common/topic/webpage./common/webpage/category #2330-01gbbz PRED entity: 01gbbz PRED relation: award PRED expected values: 03ccq3s => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 296): 0fbtbt (0.44 #1024, 0.30 #9780, 0.08 #17740), 05zr6wv (0.38 #2803, 0.25 #3599, 0.20 #6385), 09sb52 (0.36 #438, 0.33 #2826, 0.31 #7602), 05pcn59 (0.31 #2866, 0.23 #6448, 0.23 #7642), 0cjyzs (0.29 #9656, 0.25 #900, 0.12 #31444), 019f4v (0.25 #2055, 0.19 #861, 0.11 #3647), 05ztrmj (0.25 #2968, 0.15 #6948, 0.14 #4162), 07cbcy (0.25 #2863, 0.12 #7639, 0.12 #4455), 05p09zm (0.23 #2908, 0.22 #1316, 0.20 #122), 03c7tr1 (0.22 #1251, 0.17 #2047, 0.15 #6823) >> Best rule #1024 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0721cy; 01dw9z; 0hskw; 04gnbv1; 01pctb; 026w_gk; 0382m4; 0m66w; 01my4f; 04sry; ... >> query: (?x2894, 0fbtbt) <- nominated_for(?x2894, ?x1542), spouse(?x2894, ?x2308), producer_type(?x2894, ?x632) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #992 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0721cy; 01dw9z; 0hskw; 04gnbv1; 01pctb; 026w_gk; 0382m4; 0m66w; 01my4f; 04sry; ... *> query: (?x2894, 03ccq3s) <- nominated_for(?x2894, ?x1542), spouse(?x2894, ?x2308), producer_type(?x2894, ?x632) *> conf = 0.12 ranks of expected_values: 38 EVAL 01gbbz award 03ccq3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 103.000 103.000 0.438 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2329-04w58 PRED entity: 04w58 PRED relation: adjoins! PRED expected values: 04swx => 95 concepts (93 used for prediction) PRED predicted values (max 10 best out of 460): 04swx (0.82 #67352, 0.82 #52454, 0.82 #31316), 0d05w3 (0.13 #22820, 0.09 #30656, 0.09 #39264), 07z5n (0.12 #2469, 0.09 #4033, 0.09 #4815), 06mkj (0.12 #115, 0.08 #1680, 0.08 #897), 0345h (0.12 #65, 0.08 #1630, 0.06 #11802), 0chghy (0.12 #21, 0.08 #7063, 0.05 #25855), 07h34 (0.11 #14276, 0.06 #15058, 0.06 #19757), 077qn (0.10 #15853, 0.10 #16636, 0.08 #11155), 06bnz (0.10 #22784, 0.10 #11824, 0.07 #15739), 04p0c (0.10 #11915, 0.07 #17396, 0.02 #51848) >> Best rule #67352 for best value: >> intensional similarity = 2 >> extensional distance = 733 >> proper extension: 0m2gk; 0mlyw; 059qw; 02j7k; 0hyyq; 02m4d; 03khn; 02613; 0f8x_r; 0d8h4; ... >> query: (?x3912, ?x789) <- adjoins(?x3912, ?x789), adjoins(?x12778, ?x3912) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04w58 adjoins! 04swx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 93.000 0.823 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #2328-03k3b PRED entity: 03k3b PRED relation: group! PRED expected values: 06ncr => 57 concepts (41 used for prediction) PRED predicted values (max 10 best out of 119): 0l14md (0.80 #340, 0.80 #172, 0.75 #87), 03bx0bm (0.70 #2176, 0.58 #1936, 0.58 #2024), 06ncr (0.70 #2176, 0.50 #457, 0.50 #371), 03qjg (0.62 #127, 0.60 #212, 0.35 #380), 028tv0 (0.41 #1503, 0.38 #93, 0.37 #1588), 07gql (0.40 #201, 0.38 #116, 0.30 #369), 07c6l (0.40 #175, 0.38 #90, 0.30 #343), 03qlv7 (0.38 #100, 0.30 #185, 0.26 #1577), 05r5c (0.32 #2088, 0.32 #1498, 0.30 #173), 013y1f (0.32 #2088, 0.26 #1577, 0.25 #107) >> Best rule #340 for best value: >> intensional similarity = 11 >> extensional distance = 18 >> proper extension: 05crg7; 0dtd6; 047cx; 01q99h; 048xh; 014pg1; 01l_w0; 0qmpd; 0pqp3; 027kwc; >> query: (?x7896, 0l14md) <- group(?x2944, ?x7896), group(?x745, ?x7896), ?x2944 = 0l14j_, role(?x2956, ?x745), role(?x894, ?x745), role(?x654, ?x745), artists(?x1000, ?x7896), ?x2956 = 0myk8, role(?x211, ?x745), ?x894 = 03m5k, role(?x745, ?x645) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #2176 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 186 *> proper extension: 0hvbj; 015bwt; *> query: (?x7896, ?x315) <- group(?x2944, ?x7896), performance_role(?x315, ?x2944), role(?x74, ?x2944), role(?x2944, ?x885), role(?x212, ?x315), role(?x460, ?x315), group(?x315, ?x13039), performance_role(?x615, ?x315), ?x13039 = 0fsyx *> conf = 0.70 ranks of expected_values: 3 EVAL 03k3b group! 06ncr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 57.000 41.000 0.800 http://example.org/music/performance_role/regular_performances./music/group_membership/group #2327-03x400 PRED entity: 03x400 PRED relation: location PRED expected values: 0cr3d => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 170): 06wxw (0.50 #1032, 0.12 #61944, 0.01 #30800), 030qb3t (0.26 #4911, 0.26 #6520, 0.26 #12153), 06_kh (0.25 #11, 0.04 #2424, 0.03 #6448), 0r00l (0.25 #606, 0.02 #6239, 0.02 #14285), 0ftyc (0.25 #259, 0.01 #5892), 02_286 (0.23 #2450, 0.22 #5670, 0.22 #15325), 04tgp (0.17 #1044, 0.01 #4263, 0.01 #5068), 01n7q (0.13 #1671, 0.08 #4891, 0.07 #6500), 0cc56 (0.08 #5690, 0.05 #22587, 0.05 #12932), 04jpl (0.07 #1625, 0.06 #12892, 0.06 #18524) >> Best rule #1032 for best value: >> intensional similarity = 2 >> extensional distance = 4 >> proper extension: 02g75; 0c4y8; >> query: (?x6618, 06wxw) <- student(?x10576, ?x6618), ?x10576 = 0g2jl >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #17042 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 238 *> proper extension: 06w2sn5; 045zr; 01wgxtl; 03f3yfj; 01vhrz; 01s7ns; *> query: (?x6618, 0cr3d) <- participant(?x6618, ?x9207), award_nominee(?x6618, ?x488), participant(?x6618, ?x2626) *> conf = 0.06 ranks of expected_values: 11 EVAL 03x400 location 0cr3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 114.000 114.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #2326-01515w PRED entity: 01515w PRED relation: nationality PRED expected values: 09c7w0 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.90 #301, 0.87 #1202, 0.85 #401), 0d060g (0.30 #5522, 0.26 #3208, 0.09 #507), 0345h (0.30 #5522, 0.26 #3208, 0.04 #1332), 0d05w3 (0.30 #5522, 0.26 #3208, 0.03 #951), 0b90_r (0.30 #5522, 0.26 #3208, 0.01 #1405), 06mkj (0.30 #5522, 0.26 #3208), 01mjq (0.30 #5522, 0.26 #3208), 02jx1 (0.10 #5656, 0.10 #6358, 0.10 #7358), 07ssc (0.09 #5638, 0.09 #4731, 0.09 #6340), 03rk0 (0.08 #146, 0.06 #9778, 0.05 #10283) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 04ns3gy; >> query: (?x6157, 09c7w0) <- award_nominee(?x396, ?x6157), student(?x122, ?x6157), ?x122 = 08815 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01515w nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.903 http://example.org/people/person/nationality #2325-09p06 PRED entity: 09p06 PRED relation: profession PRED expected values: 02jknp => 71 concepts (41 used for prediction) PRED predicted values (max 10 best out of 65): 02jknp (0.86 #595, 0.85 #1036, 0.68 #889), 0dxtg (0.70 #1042, 0.68 #601, 0.53 #895), 03gjzk (0.43 #1190, 0.39 #749, 0.39 #1925), 0cbd2 (0.28 #153, 0.21 #1476, 0.20 #1623), 02krf9 (0.25 #1055, 0.21 #614, 0.18 #761), 02hv44_ (0.24 #204, 0.08 #1527, 0.08 #1674), 0dgd_ (0.20 #30, 0.08 #618, 0.06 #1059), 089fss (0.20 #15, 0.02 #2514, 0.02 #3396), 02pjxr (0.20 #33, 0.02 #3414, 0.02 #4002), 0kyk (0.17 #176, 0.15 #1499, 0.14 #1646) >> Best rule #595 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 022_lg; >> query: (?x3637, 02jknp) <- award(?x3637, ?x198), ?x198 = 040njc, film(?x3637, ?x3638) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09p06 profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 41.000 0.856 http://example.org/people/person/profession #2324-025vl4m PRED entity: 025vl4m PRED relation: nationality PRED expected values: 09c7w0 => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 19): 09c7w0 (0.84 #601, 0.83 #401, 0.83 #1201), 02jx1 (0.11 #3233, 0.10 #7235, 0.10 #8335), 07ssc (0.08 #7017, 0.08 #6217, 0.08 #3315), 03rk0 (0.05 #9248, 0.05 #9048, 0.05 #9148), 0d060g (0.05 #707, 0.04 #2107, 0.04 #4007), 0ctw_b (0.02 #227, 0.02 #127, 0.02 #327), 0f8l9c (0.02 #122, 0.02 #1722, 0.02 #7224), 06q1r (0.02 #177, 0.01 #2377, 0.01 #277), 02k54 (0.02 #118, 0.01 #218, 0.01 #318), 0b90_r (0.02 #103, 0.01 #203, 0.01 #303) >> Best rule #601 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 013cr; 02dlfh; >> query: (?x7426, 09c7w0) <- tv_program(?x7426, ?x1280), award_winner(?x589, ?x7426), nominated_for(?x588, ?x589) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025vl4m nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.841 http://example.org/people/person/nationality #2323-016nff PRED entity: 016nff PRED relation: film PRED expected values: 031778 03hxsv => 117 concepts (49 used for prediction) PRED predicted values (max 10 best out of 835): 01w8g3 (0.60 #50012, 0.49 #71449, 0.48 #46439), 031778 (0.53 #2102, 0.50 #316, 0.38 #19648), 03hxsv (0.44 #1115, 0.41 #2901, 0.38 #19648), 031786 (0.38 #19648, 0.38 #1272, 0.35 #3058), 0dl6fv (0.19 #1484, 0.18 #3270, 0.04 #8628), 011yg9 (0.19 #1026, 0.18 #2812, 0.03 #4598), 04jpg2p (0.19 #1459, 0.18 #3245, 0.03 #5031), 0gydcp7 (0.19 #331, 0.18 #2117, 0.02 #11047), 020bv3 (0.12 #319, 0.12 #2105, 0.03 #5677), 0_9l_ (0.12 #1732, 0.12 #3518, 0.03 #5304) >> Best rule #50012 for best value: >> intensional similarity = 4 >> extensional distance = 911 >> proper extension: 06151l; 023tp8; 01kwld; 064nh4k; 034x61; 01713c; 016ywr; 06t61y; 02xb2bt; 0170s4; ... >> query: (?x6997, ?x4027) <- film(?x6997, ?x2155), award_winner(?x749, ?x6997), award(?x6997, ?x941), nominated_for(?x6997, ?x4027) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2102 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 025t9b; *> query: (?x6997, 031778) <- film(?x6997, ?x7304), gender(?x6997, ?x514), ?x7304 = 031hcx *> conf = 0.53 ranks of expected_values: 2, 3 EVAL 016nff film 03hxsv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 49.000 0.598 http://example.org/film/actor/film./film/performance/film EVAL 016nff film 031778 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 49.000 0.598 http://example.org/film/actor/film./film/performance/film #2322-01nyl PRED entity: 01nyl PRED relation: form_of_government PRED expected values: 06cx9 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 6): 06cx9 (0.78 #1, 0.70 #7, 0.53 #19), 01d9r3 (0.70 #11, 0.67 #5, 0.34 #29), 01fpfn (0.39 #57, 0.39 #69, 0.38 #75), 018wl5 (0.34 #50, 0.31 #80, 0.29 #110), 01q20 (0.28 #52, 0.27 #82, 0.24 #256), 026wp (0.11 #6, 0.10 #12, 0.07 #42) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05rznz; >> query: (?x7871, 06cx9) <- adjoins(?x7871, ?x2804), ?x2804 = 088xp, administrative_parent(?x7871, ?x551) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nyl form_of_government 06cx9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.778 http://example.org/location/country/form_of_government #2321-029q_y PRED entity: 029q_y PRED relation: actor! PRED expected values: 09v38qj => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 122): 02z44tp (0.15 #117, 0.03 #1174, 0.02 #1438), 039cq4 (0.14 #657, 0.13 #921, 0.10 #393), 030p35 (0.08 #1137, 0.08 #13482, 0.08 #15865), 026bfsh (0.08 #1681, 0.04 #8020, 0.04 #7756), 080dwhx (0.08 #6, 0.01 #7666, 0.01 #7930), 06zsk51 (0.08 #182, 0.01 #8106, 0.01 #7842), 072kp (0.08 #10, 0.01 #9775, 0.01 #13747), 01ft14 (0.08 #203), 05lfwd (0.06 #1688, 0.02 #3536, 0.02 #4064), 09fc83 (0.05 #1146, 0.04 #1410, 0.02 #7749) >> Best rule #117 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0cf_h9; 01gw4f; 01cpqk; 01wk51; 01pg1d; >> query: (?x7613, 02z44tp) <- type_of_union(?x7613, ?x566), film(?x7613, ?x9755), ?x9755 = 03wy8t >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 029q_y actor! 09v38qj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 83.000 0.154 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #2320-01243b PRED entity: 01243b PRED relation: artists PRED expected values: 03g5jw 0137g1 0838y 04bbv7 016l09 02ndj5 => 61 concepts (26 used for prediction) PRED predicted values (max 10 best out of 945): 067mj (0.67 #5320, 0.62 #8462, 0.60 #4275), 05563d (0.67 #5523, 0.50 #8665, 0.50 #3433), 0326tc (0.67 #5922, 0.50 #9064, 0.50 #3832), 02ndj5 (0.62 #9231, 0.60 #12363, 0.50 #15501), 011z3g (0.62 #9991, 0.55 #14173, 0.36 #13128), 01wvxw1 (0.62 #10130, 0.55 #14312, 0.33 #2810), 020_4z (0.62 #10315, 0.50 #6129, 0.45 #14497), 01vvycq (0.62 #9453, 0.45 #13635, 0.33 #2133), 016s0m (0.62 #10207, 0.45 #14389, 0.33 #2887), 01vng3b (0.60 #4718, 0.50 #8905, 0.50 #5763) >> Best rule #5320 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 0dl5d; 05w3f; >> query: (?x2996, 067mj) <- artists(?x2996, ?x12121), artists(?x2996, ?x7221), artists(?x2996, ?x6234), artists(?x2996, ?x4741), ?x6234 = 0l8g0, ?x7221 = 0191h5, artists(?x12800, ?x4741), award(?x12121, ?x884), ?x12800 = 09qxq7 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #9231 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 05r6t; *> query: (?x2996, 02ndj5) <- artists(?x2996, ?x6234), artists(?x2996, ?x4741), ?x6234 = 0l8g0, artists(?x7083, ?x4741), role(?x4741, ?x227), ?x7083 = 02yv6b, parent_genre(?x301, ?x2996) *> conf = 0.62 ranks of expected_values: 4, 60, 96, 120, 190, 229 EVAL 01243b artists 02ndj5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 61.000 26.000 0.667 http://example.org/music/genre/artists EVAL 01243b artists 016l09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 61.000 26.000 0.667 http://example.org/music/genre/artists EVAL 01243b artists 04bbv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 61.000 26.000 0.667 http://example.org/music/genre/artists EVAL 01243b artists 0838y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 26.000 0.667 http://example.org/music/genre/artists EVAL 01243b artists 0137g1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 61.000 26.000 0.667 http://example.org/music/genre/artists EVAL 01243b artists 03g5jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 26.000 0.667 http://example.org/music/genre/artists #2319-03krj PRED entity: 03krj PRED relation: country PRED expected values: 05qhw 087vz => 40 concepts (35 used for prediction) PRED predicted values (max 10 best out of 308): 03rjj (0.96 #6572, 0.95 #5797, 0.92 #5216), 09c7w0 (0.95 #6174, 0.94 #5598, 0.92 #5211), 05qhw (0.86 #4271, 0.85 #4653, 0.84 #5226), 0chghy (0.85 #572, 0.81 #3470, 0.79 #6184), 035qy (0.85 #572, 0.81 #3470, 0.78 #1149), 059j2 (0.85 #572, 0.81 #3470, 0.78 #1149), 06mzp (0.85 #572, 0.81 #3470, 0.78 #1149), 05r4w (0.85 #572, 0.81 #3470, 0.78 #1149), 0h7x (0.85 #572, 0.81 #3470, 0.78 #1149), 01znc_ (0.85 #572, 0.81 #3470, 0.78 #1149) >> Best rule #6572 for best value: >> intensional similarity = 38 >> extensional distance = 44 >> proper extension: 07_53; >> query: (?x7687, 03rjj) <- country(?x7687, ?x1536), country(?x7687, ?x279), sports(?x391, ?x7687), sports(?x2233, ?x7687), film_release_region(?x8377, ?x1536), film_release_region(?x6270, ?x1536), film_release_region(?x4430, ?x1536), film_release_region(?x2933, ?x1536), film_release_region(?x2746, ?x1536), film_release_region(?x2340, ?x1536), film_release_region(?x1999, ?x1536), film_release_region(?x1642, ?x1536), ?x2340 = 0fpv_3_, contains(?x6304, ?x1536), nationality(?x8002, ?x1536), combatants(?x1536, ?x756), adjustment_currency(?x1536, ?x170), ?x2933 = 0407yj_, ?x2746 = 04f52jw, ?x6304 = 02qkt, ?x8377 = 0ds2l81, ?x4430 = 043sct5, ?x6270 = 0g9zljd, country(?x4045, ?x1536), country(?x1967, ?x1536), student(?x2730, ?x8002), ?x1999 = 0gd0c7x, nominated_for(?x1691, ?x1642), ?x4045 = 06z6r, country(?x1036, ?x279), contains(?x279, ?x481), nationality(?x1422, ?x279), ?x1967 = 01cgz, country(?x136, ?x279), produced_by(?x1642, ?x1417), ?x1422 = 0p_2r, category(?x1642, ?x134), olympics(?x279, ?x358) >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #4271 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 12 *> proper extension: 07gyv; *> query: (?x7687, 05qhw) <- country(?x7687, ?x4059), country(?x7687, ?x1536), country(?x7687, ?x985), country(?x7687, ?x279), sports(?x391, ?x7687), sports(?x2233, ?x7687), film_release_region(?x8377, ?x1536), film_release_region(?x6270, ?x1536), film_release_region(?x4430, ?x1536), film_release_region(?x2933, ?x1536), film_release_region(?x2746, ?x1536), film_release_region(?x2340, ?x1536), film_release_region(?x1999, ?x1536), film_release_region(?x1642, ?x1536), ?x2340 = 0fpv_3_, contains(?x6304, ?x1536), nationality(?x8002, ?x1536), combatants(?x1536, ?x756), adjustment_currency(?x1536, ?x170), ?x2933 = 0407yj_, ?x2746 = 04f52jw, ?x6304 = 02qkt, ?x8377 = 0ds2l81, ?x4430 = 043sct5, ?x6270 = 0g9zljd, country(?x4045, ?x1536), student(?x2730, ?x8002), ?x1999 = 0gd0c7x, nominated_for(?x1691, ?x1642), ?x4045 = 06z6r, film_release_region(?x1642, ?x8593), film_release_region(?x1642, ?x344), ?x279 = 0d060g, ?x4059 = 077qn, first_level_division_of(?x4962, ?x1536), production_companies(?x1642, ?x541), ?x8593 = 01crd5, featured_film_locations(?x8084, ?x1536), ?x344 = 04gzd, film_release_region(?x5980, ?x985), ?x5980 = 0hv81 *> conf = 0.86 ranks of expected_values: 3, 77 EVAL 03krj country 087vz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 40.000 35.000 0.957 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03krj country 05qhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 40.000 35.000 0.957 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #2318-0x335 PRED entity: 0x335 PRED relation: category PRED expected values: 08mbj5d => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #112, 0.78 #119, 0.77 #27) >> Best rule #112 for best value: >> intensional similarity = 3 >> extensional distance = 470 >> proper extension: 0rs6x; 0ybkj; 0rqyx; 0136jw; 0zrlp; 0109vk; 010bnr; 0tn9j; 0vqcq; 0102t4; ... >> query: (?x10816, 08mbj5d) <- source(?x10816, ?x958), ?x958 = 0jbk9, place(?x10816, ?x10816) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0x335 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.778 http://example.org/common/topic/webpage./common/webpage/category #2317-017v3q PRED entity: 017v3q PRED relation: student PRED expected values: 0308kx => 118 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1451): 0blt6 (0.33 #2661, 0.07 #6835, 0.05 #19359), 01my4f (0.33 #3281, 0.05 #11630, 0.03 #28330), 0d3k14 (0.17 #3938, 0.11 #12287, 0.06 #28987), 0n00 (0.17 #2634, 0.11 #10983, 0.05 #25595), 05p92jn (0.17 #3229, 0.11 #11578, 0.04 #17840), 0hnjt (0.17 #2910, 0.11 #11259, 0.04 #17521), 01n1gc (0.17 #2699, 0.08 #19397, 0.07 #6873), 01d494 (0.17 #2352, 0.08 #16963, 0.07 #6526), 07f7jp (0.17 #4064, 0.08 #18675, 0.07 #8238), 03xx9l (0.17 #3408, 0.08 #18019, 0.07 #7582) >> Best rule #2661 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 08815; >> query: (?x6919, 0blt6) <- state_province_region(?x6919, ?x1426), major_field_of_study(?x6919, ?x9829), ?x9829 = 06bvp >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 017v3q student 0308kx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 102.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #2316-071vr PRED entity: 071vr PRED relation: mode_of_transportation PRED expected values: 025t3bg => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 3): 025t3bg (0.82 #40, 0.81 #70, 0.79 #52), 0k4j (0.06 #2, 0.04 #95, 0.03 #23), 06d_3 (0.04 #96, 0.03 #21, 0.03 #24) >> Best rule #40 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 02cl1; 05ywg; 013yq; 03hrz; 0f2v0; 06wjf; 0177z; 02cft; 02sn34; 0f04v; ... >> query: (?x6960, 025t3bg) <- place_of_birth(?x1182, ?x6960), month(?x6960, ?x3270), ?x3270 = 05cw8 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 071vr mode_of_transportation 025t3bg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.818 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #2315-0pc62 PRED entity: 0pc62 PRED relation: language PRED expected values: 03_9r => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 41): 04306rv (0.15 #118, 0.12 #4, 0.12 #1038), 02bjrlw (0.15 #115, 0.09 #1206, 0.09 #518), 06nm1 (0.11 #757, 0.11 #814, 0.11 #699), 0653m (0.10 #182, 0.10 #68, 0.09 #470), 03_9r (0.10 #66, 0.07 #2416, 0.07 #584), 02hwyss (0.10 #97, 0.04 #154, 0.02 #442), 02002f (0.10 #86), 0jzc (0.07 #133, 0.06 #1167, 0.06 #478), 06b_j (0.07 #1055, 0.07 #250, 0.07 #423), 05zjd (0.04 #483, 0.04 #195, 0.04 #310) >> Best rule #118 for best value: >> intensional similarity = 4 >> extensional distance = 25 >> proper extension: 04m1bm; >> query: (?x667, 04306rv) <- nominated_for(?x350, ?x667), film_release_region(?x667, ?x94), film_crew_role(?x667, ?x137), honored_for(?x5703, ?x667) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #66 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 02f6g5; 048qrd; 04t6fk; 0f2sx4; 01d2v1; *> query: (?x667, 03_9r) <- nominated_for(?x350, ?x667), production_companies(?x667, ?x7980), film(?x7522, ?x667), ?x7522 = 0d608 *> conf = 0.10 ranks of expected_values: 5 EVAL 0pc62 language 03_9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 89.000 89.000 0.148 http://example.org/film/film/language #2314-0b13g7 PRED entity: 0b13g7 PRED relation: profession PRED expected values: 05sxg2 => 86 concepts (82 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.83 #1939, 0.77 #458, 0.76 #606), 02jknp (0.56 #6372, 0.54 #2080, 0.52 #1192), 0dxtg (0.56 #309, 0.56 #1198, 0.56 #3566), 02krf9 (0.28 #2543, 0.28 #3727, 0.27 #322), 0cbd2 (0.26 #746, 0.20 #6, 0.20 #1339), 018gz8 (0.22 #3273, 0.20 #4309, 0.19 #2681), 09jwl (0.20 #4755, 0.19 #5347, 0.18 #5051), 0kyk (0.19 #769, 0.18 #1362, 0.09 #2694), 0np9r (0.14 #3277, 0.14 #4313, 0.13 #4165), 0dz3r (0.14 #4739, 0.13 #6071, 0.12 #5331) >> Best rule #1939 for best value: >> intensional similarity = 3 >> extensional distance = 243 >> proper extension: 04shbh; 02756j; 02vkvcz; >> query: (?x3568, 02hrh1q) <- nominated_for(?x3568, ?x2447), gender(?x3568, ?x231), spouse(?x12652, ?x3568) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #889 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 144 *> proper extension: 01w92; 0c41qv; 0181hw; *> query: (?x3568, 05sxg2) <- award_nominee(?x3568, ?x6554), production_companies(?x3012, ?x6554), film_crew_role(?x3012, ?x137) *> conf = 0.05 ranks of expected_values: 25 EVAL 0b13g7 profession 05sxg2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 86.000 82.000 0.829 http://example.org/people/person/profession #2313-01vx5w7 PRED entity: 01vx5w7 PRED relation: nationality PRED expected values: 09c7w0 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 38): 09c7w0 (0.83 #201, 0.80 #1102, 0.74 #4207), 0d060g (0.30 #8918, 0.10 #2208, 0.06 #707), 03rjj (0.30 #8918, 0.06 #10925, 0.04 #6512), 02jx1 (0.24 #133, 0.22 #1234, 0.13 #533), 03rk0 (0.18 #2247, 0.06 #846, 0.05 #10770), 07ssc (0.14 #115, 0.12 #1216, 0.09 #2817), 03_3d (0.08 #306, 0.06 #1607, 0.04 #106), 0f8l9c (0.07 #2223, 0.06 #10925, 0.05 #22), 0chghy (0.06 #10925, 0.05 #10, 0.05 #2211), 035qy (0.06 #10925, 0.05 #34, 0.04 #6512) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 025p38; 01v3s2_; 0bz5v2; 07ymr5; 06mmb; 012_53; 049_zz; 039crh; 01pctb; 0g2mbn; ... >> query: (?x2925, 09c7w0) <- gender(?x2925, ?x514), film(?x2925, ?x9213), program(?x2925, ?x7433) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vx5w7 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.828 http://example.org/people/person/nationality #2312-0ply0 PRED entity: 0ply0 PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 22): 0pqc5 (0.84 #373, 0.81 #488, 0.78 #281), 060c4 (0.29 #1429, 0.29 #2671, 0.26 #2395), 060bp (0.26 #2393, 0.26 #1427, 0.26 #2669), 0f6c3 (0.22 #1894, 0.22 #2952, 0.21 #2331), 09n5b9 (0.20 #2956, 0.18 #3163, 0.18 #1898), 0fkvn (0.20 #970, 0.20 #3155, 0.19 #1890), 01q24l (0.18 #83, 0.16 #290, 0.14 #129), 0p5vf (0.17 #151, 0.12 #979, 0.10 #1324), 01zq91 (0.13 #153, 0.06 #1142, 0.06 #2407), 04syw (0.09 #1962, 0.08 #1318, 0.08 #1387) >> Best rule #373 for best value: >> intensional similarity = 4 >> extensional distance = 36 >> proper extension: 03l2n; 05jbn; 01smm; >> query: (?x3373, 0pqc5) <- place_of_birth(?x4777, ?x3373), dog_breed(?x3373, ?x1706), origin(?x4842, ?x3373), contains(?x2623, ?x3373) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ply0 jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 220.000 220.000 0.842 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #2311-087vnr5 PRED entity: 087vnr5 PRED relation: person PRED expected values: 01g0jn => 86 concepts (62 used for prediction) PRED predicted values (max 10 best out of 113): 0157m (0.14 #973), 09b6zr (0.11 #1022), 06c97 (0.09 #1046), 01n4f8 (0.09 #974), 011hdn (0.08 #448), 0127s7 (0.06 #1050, 0.01 #1425), 046lt (0.06 #1002, 0.01 #1377), 079vf (0.06 #944, 0.01 #1319), 042kg (0.06 #1115), 0d3k14 (0.06 #1112) >> Best rule #973 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0dtw1x; >> query: (?x8492, 0157m) <- film_release_region(?x8492, ?x94), genre(?x8492, ?x225), film_crew_role(?x8492, ?x137), person(?x8492, ?x5625) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 087vnr5 person 01g0jn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 62.000 0.143 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #2310-07s8z_l PRED entity: 07s8z_l PRED relation: country_of_origin PRED expected values: 09c7w0 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 10): 09c7w0 (0.94 #348, 0.93 #393, 0.92 #302), 0d060g (0.25 #15, 0.11 #37, 0.09 #1031), 07ssc (0.19 #108, 0.18 #164, 0.15 #141), 03_3d (0.09 #998, 0.08 #965, 0.08 #1069), 03rjj (0.09 #1031, 0.07 #1507, 0.07 #1346), 03rt9 (0.09 #1031, 0.07 #1507, 0.07 #1346), 02jx1 (0.09 #1031, 0.07 #1346, 0.01 #905), 09lmb (0.03 #687, 0.03 #755, 0.03 #801), 07c52 (0.03 #687, 0.03 #755, 0.03 #801), 05v8c (0.01 #481, 0.01 #492, 0.01 #1005) >> Best rule #348 for best value: >> intensional similarity = 5 >> extensional distance = 62 >> proper extension: 0m123; >> query: (?x10447, 09c7w0) <- languages(?x10447, ?x254), award_winner(?x10447, ?x1285), producer_type(?x10447, ?x632), nominated_for(?x1285, ?x10595), titles(?x2008, ?x10447) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07s8z_l country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.938 http://example.org/tv/tv_program/country_of_origin #2309-0ds2l81 PRED entity: 0ds2l81 PRED relation: film_release_region PRED expected values: 0b90_r 03_3d 06bnz => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 219): 09c7w0 (0.92 #7386, 0.81 #2, 0.80 #4156), 03h64 (0.91 #675, 0.90 #368, 0.89 #215), 0b90_r (0.91 #618, 0.90 #311, 0.89 #158), 035qy (0.91 #644, 0.88 #1564, 0.88 #1103), 0jgd (0.90 #310, 0.89 #157, 0.85 #617), 03rt9 (0.89 #167, 0.85 #320, 0.81 #627), 06t2t (0.89 #59, 0.87 #365, 0.87 #672), 0chghy (0.89 #1083, 0.88 #1236, 0.88 #930), 07ssc (0.87 #782, 0.87 #629, 0.86 #1856), 06bnz (0.85 #656, 0.84 #196, 0.83 #43) >> Best rule #7386 for best value: >> intensional similarity = 8 >> extensional distance = 1328 >> proper extension: 0170z3; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; 018js4; ... >> query: (?x8377, 09c7w0) <- film_release_region(?x8377, ?x1174), film_release_region(?x11313, ?x1174), film_release_region(?x7275, ?x1174), film_release_region(?x1456, ?x1174), ?x1456 = 0cz8mkh, ?x7275 = 0g4vmj8, form_of_government(?x1174, ?x6065), ?x11313 = 0by17xn >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #618 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 51 *> proper extension: 0cc7hmk; 0661ql3; 0gffmn8; 0bmhvpr; *> query: (?x8377, 0b90_r) <- film_release_region(?x8377, ?x1499), film_release_region(?x8377, ?x1174), film_release_region(?x8377, ?x985), ?x1174 = 047yc, ?x1499 = 01znc_, produced_by(?x8377, ?x3568), olympics(?x985, ?x418), olympics(?x985, ?x391) *> conf = 0.91 ranks of expected_values: 3, 10, 11 EVAL 0ds2l81 film_release_region 06bnz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 68.000 68.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds2l81 film_release_region 03_3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 68.000 68.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds2l81 film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 68.000 0.917 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2308-0bl3nn PRED entity: 0bl3nn PRED relation: language PRED expected values: 02bjrlw => 101 concepts (98 used for prediction) PRED predicted values (max 10 best out of 36): 064_8sq (0.29 #133, 0.17 #77, 0.16 #1093), 02bjrlw (0.21 #113, 0.17 #57, 0.08 #1988), 06b_j (0.20 #248, 0.18 #304, 0.17 #192), 06nm1 (0.16 #236, 0.14 #292, 0.12 #180), 04306rv (0.14 #116, 0.12 #174, 0.12 #230), 04h9h (0.14 #42, 0.07 #154, 0.06 #5260), 03_9r (0.09 #1769, 0.09 #912, 0.08 #1309), 0jzc (0.08 #189, 0.08 #75, 0.08 #245), 01r2l (0.08 #194, 0.08 #250, 0.06 #5260), 0653m (0.08 #67, 0.06 #5260, 0.06 #5033) >> Best rule #133 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 03qcfvw; 02q6gfp; 02nx2k; >> query: (?x7239, 064_8sq) <- titles(?x1510, ?x7239), film_release_distribution_medium(?x7239, ?x81), country(?x7239, ?x1558), ?x1558 = 01mjq >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #113 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 03qcfvw; 02q6gfp; 02nx2k; *> query: (?x7239, 02bjrlw) <- titles(?x1510, ?x7239), film_release_distribution_medium(?x7239, ?x81), country(?x7239, ?x1558), ?x1558 = 01mjq *> conf = 0.21 ranks of expected_values: 2 EVAL 0bl3nn language 02bjrlw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 98.000 0.286 http://example.org/film/film/language #2307-02sp_v PRED entity: 02sp_v PRED relation: ceremony PRED expected values: 019bk0 0466p0j => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 125): 0466p0j (0.86 #941, 0.85 #1066, 0.84 #1191), 019bk0 (0.83 #1012, 0.82 #1262, 0.82 #1137), 02glmx (0.47 #2251, 0.28 #2627, 0.27 #4504), 073h1t (0.47 #2251, 0.28 #2627, 0.27 #4504), 0418154 (0.47 #2251, 0.28 #2627, 0.27 #4504), 0bvhz9 (0.28 #2627, 0.27 #4504, 0.27 #4755), 02q690_ (0.28 #2627, 0.27 #4504, 0.27 #4755), 0bzkvd (0.28 #2627, 0.27 #4504, 0.27 #4755), 0h_cssd (0.28 #2627, 0.27 #4504, 0.27 #4755), 0lp_cd3 (0.28 #2627, 0.27 #4504, 0.27 #4755) >> Best rule #941 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 01c9f2; 02nbqh; 02581c; 0257w4; 024vjd; 024fz9; 024_fw; 0249fn; 0248jb; 02v703; ... >> query: (?x3045, 0466p0j) <- ceremony(?x3045, ?x1480), ?x1480 = 01c6qp, award(?x4712, ?x3045), instrumentalists(?x1166, ?x4712), group(?x1166, ?x475) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02sp_v ceremony 0466p0j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.863 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02sp_v ceremony 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.863 http://example.org/award/award_category/winners./award/award_honor/ceremony #2306-0d6b7 PRED entity: 0d6b7 PRED relation: language PRED expected values: 04306rv => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.96 #353, 0.95 #2643, 0.95 #706), 064_8sq (0.17 #492, 0.16 #432, 0.16 #551), 06nm1 (0.12 #362, 0.12 #303, 0.11 #832), 04306rv (0.10 #1471, 0.09 #1412, 0.09 #1180), 06b_j (0.09 #198, 0.09 #315, 0.09 #139), 0653m (0.09 #482, 0.08 #541, 0.04 #1245), 02bjrlw (0.08 #176, 0.08 #117, 0.08 #471), 03_9r (0.08 #598, 0.06 #1944, 0.06 #1885), 03k50 (0.08 #479, 0.03 #1242, 0.03 #538), 012w70 (0.06 #483, 0.06 #542, 0.03 #1947) >> Best rule #353 for best value: >> intensional similarity = 4 >> extensional distance = 137 >> proper extension: 03kx49; 03hp2y1; >> query: (?x1546, 02h40lc) <- language(?x1546, ?x12328), nominated_for(?x8708, ?x1546), country(?x1546, ?x1264), artist(?x2149, ?x8708) >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #1471 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 488 *> proper extension: 05jf85; 011yxg; 0gzy02; 01hr1; 0dnvn3; 0ds11z; 0ds33; 01sxly; 050r1z; 0dj0m5; ... *> query: (?x1546, 04306rv) <- language(?x1546, ?x12328), nominated_for(?x7088, ?x1546), written_by(?x1546, ?x8043), profession(?x8043, ?x319) *> conf = 0.10 ranks of expected_values: 4 EVAL 0d6b7 language 04306rv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 81.000 0.957 http://example.org/film/film/language #2305-03cfjg PRED entity: 03cfjg PRED relation: role PRED expected values: 042v_gx => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 80): 05r5c (0.39 #2750, 0.36 #1907, 0.33 #9), 0342h (0.37 #2746, 0.33 #1903, 0.30 #110), 01vdm0 (0.27 #2775, 0.24 #1932, 0.17 #2881), 02w3w (0.26 #1159, 0.26 #1477, 0.24 #2424), 02sgy (0.23 #1905, 0.23 #2748, 0.20 #112), 042v_gx (0.22 #1908, 0.20 #2751, 0.20 #115), 05842k (0.17 #2821, 0.15 #1978, 0.10 #2927), 05148p4 (0.17 #25, 0.13 #1923, 0.12 #2766), 018j2 (0.17 #47, 0.10 #152, 0.10 #2530), 01s0ps (0.17 #63, 0.10 #168, 0.10 #2530) >> Best rule #2750 for best value: >> intensional similarity = 2 >> extensional distance = 438 >> proper extension: 03c7ln; 032t2z; 07_3qd; 01vsxdm; 0dm5l; 016ntp; 0fpj4lx; 023l9y; 01l4g5; 027dpx; ... >> query: (?x3419, 05r5c) <- artists(?x482, ?x3419), role(?x3419, ?x1969) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1908 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 268 *> proper extension: 02nfjp; *> query: (?x3419, 042v_gx) <- award_winner(?x341, ?x3419), role(?x3419, ?x1969) *> conf = 0.22 ranks of expected_values: 6 EVAL 03cfjg role 042v_gx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 95.000 95.000 0.386 http://example.org/music/artist/track_contributions./music/track_contribution/role #2304-086nl7 PRED entity: 086nl7 PRED relation: award PRED expected values: 09qv3c => 119 concepts (116 used for prediction) PRED predicted values (max 10 best out of 281): 0cjyzs (0.37 #1727, 0.35 #2942, 0.33 #3752), 09sb52 (0.36 #17457, 0.33 #15027, 0.32 #18267), 0fbtbt (0.33 #3069, 0.32 #1854, 0.32 #3879), 0drtkx (0.25 #705, 0.03 #1920, 0.03 #3135), 05pcn59 (0.24 #4537, 0.18 #6562, 0.15 #10612), 0ck27z (0.23 #12648, 0.16 #17509, 0.15 #15484), 0c422z4 (0.22 #144, 0.15 #954, 0.13 #38073), 05zvj3m (0.22 #94, 0.15 #904, 0.12 #13366), 09qj50 (0.22 #46, 0.15 #856, 0.05 #30782), 05zr6wv (0.20 #4472, 0.15 #6497, 0.13 #6902) >> Best rule #1727 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 0f721s; >> query: (?x4465, 0cjyzs) <- award_winner(?x7000, ?x4465), award_winner(?x1265, ?x7000), program(?x4465, ?x9787) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #2481 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 152 *> proper extension: 02cm2m; *> query: (?x4465, 09qv3c) <- award_nominee(?x4465, ?x2390), profession(?x4465, ?x1383), ?x1383 = 0np9r *> conf = 0.06 ranks of expected_values: 90 EVAL 086nl7 award 09qv3c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 119.000 116.000 0.372 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2303-0bpm4yw PRED entity: 0bpm4yw PRED relation: film! PRED expected values: 0gnbw => 83 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1231): 0184dt (0.19 #66413, 0.19 #68489, 0.15 #74717), 0gnbw (0.15 #1265, 0.07 #13715, 0.05 #9565), 02gf_l (0.13 #3338, 0.07 #13713, 0.05 #9563), 06ltr (0.12 #11316, 0.06 #29994, 0.05 #7166), 09y20 (0.12 #10620, 0.05 #20997, 0.05 #6470), 0l6px (0.12 #10760, 0.05 #29438, 0.05 #6610), 0134w7 (0.12 #10532, 0.05 #6382, 0.04 #20909), 065jlv (0.12 #10685, 0.05 #6535, 0.04 #21062), 079vf (0.10 #6233, 0.05 #22836, 0.05 #8308), 02lkcc (0.10 #6464, 0.04 #2314, 0.04 #23067) >> Best rule #66413 for best value: >> intensional similarity = 3 >> extensional distance = 543 >> proper extension: 048rn; 04sh80; >> query: (?x4336, ?x2533) <- written_by(?x4336, ?x2533), film(?x382, ?x4336), film(?x489, ?x4336) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1265 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 0g56t9t; 0g5qs2k; 0fpkhkz; 0bq8tmw; 05qbckf; 0gd0c7x; 01fmys; 06wbm8q; 06ztvyx; 04f52jw; ... *> query: (?x4336, 0gnbw) <- nominated_for(?x640, ?x4336), film_release_region(?x4336, ?x1536), film_release_region(?x4336, ?x1471), ?x1471 = 07t21, ?x1536 = 06c1y *> conf = 0.15 ranks of expected_values: 2 EVAL 0bpm4yw film! 0gnbw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 55.000 0.193 http://example.org/film/actor/film./film/performance/film #2302-030xr_ PRED entity: 030xr_ PRED relation: actor! PRED expected values: 0hz55 => 108 concepts (80 used for prediction) PRED predicted values (max 10 best out of 45): 02pqs8l (0.12 #60, 0.11 #325, 0.01 #1387), 03ln8b (0.12 #31, 0.11 #296), 02zv4b (0.12 #25, 0.11 #290), 0464pz (0.12 #23, 0.11 #288), 01cmp9 (0.10 #2925, 0.10 #7445, 0.10 #8245), 01ffx4 (0.08 #20740, 0.08 #4255, 0.08 #4522), 03q4hl (0.02 #795, 0.02 #1060), 05f7w84 (0.02 #637, 0.02 #902), 024rwx (0.02 #636, 0.01 #7818), 09g_31 (0.02 #961) >> Best rule #60 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0408np; 01vvb4m; 01l1hr; 02jsgf; 05683p; 08s_lw; >> query: (?x9289, 02pqs8l) <- award_nominee(?x9289, ?x2307), film(?x9289, ?x7945), ?x7945 = 02_fz3 >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 030xr_ actor! 0hz55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 80.000 0.125 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #2301-0xbm PRED entity: 0xbm PRED relation: current_club! PRED expected values: 03yl2t => 163 concepts (154 used for prediction) PRED predicted values (max 10 best out of 43): 03y_f8 (0.40 #75, 0.33 #173, 0.29 #197), 02pp1 (0.40 #94, 0.17 #192, 0.17 #144), 02bh_v (0.33 #139, 0.22 #237, 0.17 #187), 03_qj1 (0.33 #32, 0.20 #80, 0.17 #104), 03zrhb (0.20 #86, 0.14 #208, 0.13 #402), 03d8m4 (0.17 #103, 0.14 #201, 0.11 #227), 01l3wr (0.17 #190, 0.14 #214, 0.11 #240), 03_qrp (0.17 #108, 0.14 #206, 0.06 #616), 02s9vc (0.17 #189, 0.13 #407, 0.12 #745), 03_44z (0.17 #170, 0.13 #412, 0.11 #508) >> Best rule #75 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 050fh; 023fb; >> query: (?x3158, 03y_f8) <- position(?x3158, ?x60), ?x60 = 02nzb8, current_club(?x2427, ?x3158), organization(?x4682, ?x3158), team(?x6812, ?x3158) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #150 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 01453; 0y54; 01cwm1; 01rly6; *> query: (?x3158, 03yl2t) <- position(?x3158, ?x60), ?x60 = 02nzb8, current_club(?x3587, ?x3158), team(?x6812, ?x3158), ?x3587 = 02s2lg *> conf = 0.17 ranks of expected_values: 12 EVAL 0xbm current_club! 03yl2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 163.000 154.000 0.400 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #2300-0404j37 PRED entity: 0404j37 PRED relation: language PRED expected values: 02h40lc => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.95 #3225, 0.95 #1951, 0.94 #3568), 04306rv (0.31 #5, 0.26 #62, 0.23 #119), 064_8sq (0.23 #21, 0.22 #192, 0.21 #249), 02bjrlw (0.16 #58, 0.15 #1, 0.12 #286), 06nm1 (0.15 #125, 0.12 #1442, 0.11 #1038), 06b_j (0.09 #706, 0.09 #763, 0.09 #820), 03_9r (0.08 #751, 0.06 #409, 0.06 #2471), 03hkp (0.08 #15, 0.06 #2471, 0.05 #72), 04h9h (0.08 #41, 0.06 #2471, 0.05 #98), 05qqm (0.08 #40, 0.06 #2471, 0.05 #97) >> Best rule #3225 for best value: >> intensional similarity = 4 >> extensional distance = 716 >> proper extension: 03t97y; >> query: (?x6448, 02h40lc) <- language(?x6448, ?x5359), genre(?x6448, ?x53), award_winner(?x6448, ?x9449), film(?x9449, ?x1009) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0404j37 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.951 http://example.org/film/film/language #2299-05r7t PRED entity: 05r7t PRED relation: olympics PRED expected values: 0kbvb => 205 concepts (205 used for prediction) PRED predicted values (max 10 best out of 37): 0kbvb (0.78 #1045, 0.68 #971, 0.67 #155), 0kbws (0.72 #1051, 0.69 #1187, 0.69 #1348), 0jdk_ (0.72 #616, 0.68 #505, 0.65 #431), 0lgxj (0.61 #989, 0.60 #1063, 0.59 #778), 09n48 (0.59 #778, 0.53 #2599, 0.53 #2598), 09x3r (0.54 #491, 0.50 #417, 0.45 #1048), 0l6mp (0.53 #980, 0.50 #1054, 0.50 #682), 0l998 (0.50 #487, 0.47 #970, 0.45 #1044), 0lv1x (0.46 #495, 0.46 #421, 0.45 #978), 0lbbj (0.46 #498, 0.44 #1501, 0.43 #535) >> Best rule #1045 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 06mzp; 0h7x; 01mjq; 088q4; 01crd5; >> query: (?x6559, 0kbvb) <- country(?x766, ?x6559), contains(?x6559, ?x13728), olympics(?x6559, ?x867), administrative_parent(?x8428, ?x6559) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05r7t olympics 0kbvb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 205.000 205.000 0.775 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #2298-0259r0 PRED entity: 0259r0 PRED relation: profession PRED expected values: 09jwl => 149 concepts (99 used for prediction) PRED predicted values (max 10 best out of 60): 09jwl (0.72 #5762, 0.71 #3700, 0.70 #8417), 0nbcg (0.56 #471, 0.53 #766, 0.53 #2386), 0dz3r (0.45 #1032, 0.43 #1621, 0.43 #885), 05z96 (0.34 #2840, 0.08 #1365, 0.06 #630), 01d_h8 (0.31 #9733, 0.29 #8995, 0.29 #13559), 01c72t (0.31 #758, 0.31 #4588, 0.29 #8422), 039v1 (0.31 #3718, 0.30 #5780, 0.29 #2687), 05vyk (0.28 #12378, 0.08 #829, 0.08 #2597), 0cbd2 (0.27 #2805, 0.14 #1330, 0.12 #12678), 02jknp (0.25 #7, 0.19 #12532, 0.18 #9735) >> Best rule #5762 for best value: >> intensional similarity = 3 >> extensional distance = 446 >> proper extension: 03qd_; 0kp2_; >> query: (?x2786, 09jwl) <- instrumentalists(?x227, ?x2786), category(?x2786, ?x134), profession(?x2786, ?x220) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0259r0 profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 99.000 0.723 http://example.org/people/person/profession #2297-0ds35l9 PRED entity: 0ds35l9 PRED relation: nominated_for! PRED expected values: 01gw8b => 91 concepts (24 used for prediction) PRED predicted values (max 10 best out of 775): 063g7l (0.31 #49020, 0.30 #32674, 0.29 #21004), 0jfx1 (0.23 #2837, 0.18 #504, 0.06 #9840), 02pq9yv (0.18 #738, 0.15 #3071, 0.02 #40419), 09yhzs (0.18 #643, 0.15 #2976, 0.02 #9979), 0347db (0.18 #1534, 0.15 #3867), 015f7 (0.18 #713, 0.15 #3046), 0151w_ (0.13 #23338, 0.09 #25672, 0.02 #21204), 018swb (0.13 #23338, 0.09 #25672, 0.01 #21433), 0ksrf8 (0.13 #23338, 0.09 #25672, 0.01 #22239), 0bbvr84 (0.13 #23338, 0.09 #25672) >> Best rule #49020 for best value: >> intensional similarity = 5 >> extensional distance = 268 >> proper extension: 0c0yh4; 0209xj; 02py4c8; 01s3vk; 02z9rr; 02rlj20; 01gvsn; 03cffvv; 016z43; >> query: (?x86, ?x3927) <- award(?x86, ?x1691), film(?x4490, ?x86), film(?x3927, ?x86), nominated_for(?x68, ?x86), friend(?x3553, ?x4490) >> conf = 0.31 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0ds35l9 nominated_for! 01gw8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 24.000 0.313 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #2296-0hsmh PRED entity: 0hsmh PRED relation: place_of_death PRED expected values: 030qb3t => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 29): 030qb3t (0.17 #1769, 0.17 #2742, 0.16 #1575), 02_286 (0.12 #595, 0.09 #1566, 0.09 #2927), 0k049 (0.11 #1750, 0.08 #585, 0.08 #973), 03v1s (0.09 #3109, 0.07 #5247, 0.06 #4274), 0f2wj (0.07 #982, 0.05 #1176, 0.04 #594), 0qlrh (0.06 #379), 06_kh (0.05 #4084, 0.05 #2919, 0.04 #5056), 027l4q (0.04 #720, 0.02 #526, 0.02 #1108), 04jpl (0.04 #2727, 0.04 #5449, 0.03 #6617), 0k_p5 (0.03 #1058, 0.03 #3002, 0.02 #4167) >> Best rule #1769 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 0h1_w; 015wfg; 012gbb; 0jvtp; >> query: (?x10554, 030qb3t) <- award(?x10554, ?x198), people(?x4322, ?x10554), award_winner(?x4404, ?x10554) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0hsmh place_of_death 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.172 http://example.org/people/deceased_person/place_of_death #2295-0ddfwj1 PRED entity: 0ddfwj1 PRED relation: category PRED expected values: 08mbj5d => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.32 #20, 0.31 #4, 0.29 #26) >> Best rule #20 for best value: >> intensional similarity = 6 >> extensional distance = 205 >> proper extension: 014zwb; 02bqvs; 03bzyn4; >> query: (?x370, 08mbj5d) <- film_crew_role(?x370, ?x1284), film_crew_role(?x370, ?x137), ?x1284 = 0ch6mp2, genre(?x370, ?x258), ?x137 = 09zzb8, ?x258 = 05p553 >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ddfwj1 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.319 http://example.org/common/topic/webpage./common/webpage/category #2294-0hky PRED entity: 0hky PRED relation: nationality PRED expected values: 07ssc 02jx1 => 143 concepts (127 used for prediction) PRED predicted values (max 10 best out of 54): 09c7w0 (0.89 #3002, 0.88 #3504, 0.85 #7714), 03rk0 (0.79 #2347, 0.79 #2247, 0.75 #2447), 02jx1 (0.56 #433, 0.42 #633, 0.36 #733), 07ssc (0.43 #715, 0.42 #615, 0.33 #415), 0d060g (0.24 #7109, 0.20 #207, 0.07 #2708), 0345h (0.24 #7109, 0.13 #831, 0.10 #2532), 0bq0p9 (0.20 #119, 0.01 #2220, 0.01 #2320), 06bnz (0.11 #541, 0.04 #1841, 0.03 #2942), 0h7x (0.10 #2136, 0.10 #2036, 0.08 #1535), 03rjj (0.08 #1505, 0.06 #905, 0.05 #1205) >> Best rule #3002 for best value: >> intensional similarity = 4 >> extensional distance = 115 >> proper extension: 07xr3w; 0584j4n; 06hzsx; 02s6sh; 044bn; 02_33l; 044kwr; 011k4g; 06f_qn; 01vq3nl; ... >> query: (?x6037, 09c7w0) <- gender(?x6037, ?x231), place_of_death(?x6037, ?x1523), ?x1523 = 030qb3t, ?x231 = 05zppz >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #433 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 01m7f5r; *> query: (?x6037, 02jx1) <- student(?x13049, ?x6037), profession(?x6037, ?x353), ?x13049 = 0dzbl *> conf = 0.56 ranks of expected_values: 3, 4 EVAL 0hky nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 143.000 127.000 0.889 http://example.org/people/person/nationality EVAL 0hky nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 143.000 127.000 0.889 http://example.org/people/person/nationality #2293-0137g1 PRED entity: 0137g1 PRED relation: instrumentalists! PRED expected values: 02hnl => 137 concepts (75 used for prediction) PRED predicted values (max 10 best out of 122): 05r5c (0.50 #1227, 0.47 #5393, 0.46 #4740), 02hnl (0.43 #192, 0.40 #111, 0.29 #355), 03qjg (0.40 #127, 0.30 #1383, 0.30 #3751), 013y1f (0.30 #1383, 0.30 #3751, 0.30 #3506), 026t6 (0.30 #1383, 0.30 #3751, 0.30 #3506), 0l14qv (0.30 #1383, 0.30 #3751, 0.30 #3506), 0jtg0 (0.30 #1383, 0.30 #3751, 0.30 #3506), 042v_gx (0.30 #1383, 0.30 #3751, 0.30 #3506), 02sgy (0.30 #1383, 0.30 #3751, 0.30 #3506), 06w87 (0.30 #1383, 0.30 #3751, 0.30 #3506) >> Best rule #1227 for best value: >> intensional similarity = 4 >> extensional distance = 116 >> proper extension: 03ds3; 081lh; 021bk; 02b25y; 02w4fkq; 015mrk; 01wgcvn; 0dl567; 01kh2m1; 01817f; ... >> query: (?x2784, 05r5c) <- instrumentalists(?x227, ?x2784), award_winner(?x2322, ?x2784), gender(?x2784, ?x231), people(?x913, ?x2784) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #192 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 01sb5r; *> query: (?x2784, 02hnl) <- instrumentalists(?x4917, ?x2784), ?x4917 = 06w7v, origin(?x2784, ?x1523), artists(?x302, ?x2784) *> conf = 0.43 ranks of expected_values: 2 EVAL 0137g1 instrumentalists! 02hnl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 75.000 0.500 http://example.org/music/instrument/instrumentalists #2292-015_30 PRED entity: 015_30 PRED relation: celebrities_impersonated! PRED expected values: 018grr => 93 concepts (50 used for prediction) PRED predicted values (max 10 best out of 7): 0pz04 (0.32 #21, 0.20 #42, 0.20 #49), 01n5309 (0.15 #43, 0.14 #36, 0.12 #29), 03d_zl4 (0.15 #33, 0.14 #40, 0.13 #47), 04s430 (0.11 #39, 0.11 #46, 0.10 #32), 0f7hc (0.05 #17, 0.05 #31, 0.05 #38), 018grr (0.02 #51, 0.02 #30, 0.02 #37), 0d608 (0.02 #55, 0.02 #69, 0.02 #83) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 02whj; 016z1t; 02jq1; 013qvn; >> query: (?x1800, 0pz04) <- nationality(?x1800, ?x94), celebrities_impersonated(?x3649, ?x1800), artist(?x2241, ?x1800) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #51 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 79 *> proper extension: 014dq7; 0ly5n; 06c97; 0f7fy; 0bkmf; 044bn; 02_01w; 01l3j; 0h326; *> query: (?x1800, 018grr) <- nationality(?x1800, ?x94), celebrities_impersonated(?x3649, ?x1800), ?x3649 = 03m6t5 *> conf = 0.02 ranks of expected_values: 6 EVAL 015_30 celebrities_impersonated! 018grr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 93.000 50.000 0.316 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #2291-02rp117 PRED entity: 02rp117 PRED relation: artists PRED expected values: 0kvnn 04mky3 => 59 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1066): 06br6t (0.60 #3065, 0.44 #6315, 0.43 #5231), 01m65sp (0.60 #2440, 0.43 #4606, 0.43 #3523), 047cx (0.47 #8014, 0.21 #10839, 0.12 #12351), 0l8g0 (0.44 #5984, 0.43 #4900, 0.40 #2734), 01w5n51 (0.43 #3945, 0.40 #8280, 0.40 #2862), 03d9d6 (0.43 #3766, 0.33 #5933, 0.31 #9186), 0191h5 (0.42 #10406, 0.40 #8238, 0.40 #2820), 04mky3 (0.42 #10783, 0.40 #3197, 0.38 #9700), 07hgm (0.40 #3047, 0.37 #10633, 0.29 #5213), 070b4 (0.40 #2992, 0.37 #10578, 0.29 #5158) >> Best rule #3065 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 01243b; 08jyyk; >> query: (?x11862, 06br6t) <- parent_genre(?x11862, ?x7329), artists(?x11862, ?x5407), artists(?x11862, ?x2012), ?x5407 = 01k_yf, actor(?x3180, ?x2012), place_of_birth(?x2012, ?x1523), location(?x2012, ?x279), profession(?x2012, ?x220) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #10783 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 17 *> proper extension: 02mscn; *> query: (?x11862, 04mky3) <- artists(?x11862, ?x11425), artists(?x11862, ?x2012), artist(?x8518, ?x11425), ?x8518 = 037h1k, artists(?x1572, ?x11425), place_of_birth(?x2012, ?x1523), parent_genre(?x114, ?x1572), film(?x2012, ?x1246) *> conf = 0.42 ranks of expected_values: 8, 168 EVAL 02rp117 artists 04mky3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 59.000 24.000 0.600 http://example.org/music/genre/artists EVAL 02rp117 artists 0kvnn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 59.000 24.000 0.600 http://example.org/music/genre/artists #2290-0bwfn PRED entity: 0bwfn PRED relation: institution! PRED expected values: 03mkk4 => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 12): 03mkk4 (0.35 #259, 0.29 #125, 0.26 #1022), 0bjrnt (0.35 #160, 0.29 #123, 0.26 #1022), 01rr_d (0.26 #166, 0.26 #1022, 0.25 #398), 022h5x (0.26 #192, 0.26 #1022, 0.22 #338), 071tyz (0.26 #1022, 0.25 #15, 0.12 #76), 02mjs7 (0.26 #1022, 0.19 #122, 0.17 #440), 02m4yg (0.26 #1022, 0.10 #397, 0.09 #165), 01ysy9 (0.26 #1022, 0.09 #169, 0.08 #327), 0g26h (0.26 #1022, 0.02 #334, 0.02 #395), 01kxxq (0.04 #193, 0.03 #1092, 0.02 #1263) >> Best rule #259 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0473m9; 02zccd; 027mdh; 0l0wv; 035gt8; >> query: (?x7545, 03mkk4) <- major_field_of_study(?x7545, ?x373), institution(?x8398, ?x7545), ?x8398 = 028dcg >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bwfn institution! 03mkk4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 155.000 155.000 0.350 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2289-01fh0q PRED entity: 01fh0q PRED relation: artists! PRED expected values: 06by7 01fh36 => 134 concepts (80 used for prediction) PRED predicted values (max 10 best out of 218): 0gt_0v (0.67 #83, 0.06 #394, 0.04 #705), 06by7 (0.49 #1265, 0.48 #9058, 0.47 #12488), 016clz (0.37 #938, 0.24 #10911, 0.24 #7791), 0glt670 (0.37 #2219, 0.32 #1596, 0.28 #5646), 01fh36 (0.33 #87, 0.12 #7873, 0.10 #11618), 05bt6j (0.33 #9081, 0.27 #12511, 0.25 #1288), 025sc50 (0.32 #3786, 0.30 #9087, 0.29 #2228), 0gywn (0.32 #1613, 0.30 #3171, 0.27 #12525), 0ggx5q (0.25 #3815, 0.22 #9116, 0.20 #12546), 0ggq0m (0.25 #12, 0.11 #2502, 0.10 #4371) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 05pdbs; 0fpjd_g; 01vsy95; >> query: (?x8972, 0gt_0v) <- award_nominee(?x8972, ?x1563), award(?x8972, ?x2561), ?x2561 = 02hgm4 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1265 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 01czx; 01vv126; 0134s5; 01vtqml; 0d193h; 018gm9; 0134tg; 01k_yf; 02r3cn; 01q99h; ... *> query: (?x8972, 06by7) <- category(?x8972, ?x134), artist(?x2299, ?x8972), ?x2299 = 033hn8 *> conf = 0.49 ranks of expected_values: 2, 5 EVAL 01fh0q artists! 01fh36 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 134.000 80.000 0.667 http://example.org/music/genre/artists EVAL 01fh0q artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 134.000 80.000 0.667 http://example.org/music/genre/artists #2288-0999q PRED entity: 0999q PRED relation: languages! PRED expected values: 05gc0h => 39 concepts (8 used for prediction) PRED predicted values (max 10 best out of 3507): 040nwr (0.67 #4469, 0.67 #3186, 0.60 #2546), 05vzql (0.60 #2484, 0.50 #4407, 0.50 #3765), 03wpmd (0.60 #2043, 0.50 #1401, 0.33 #3966), 04cmrt (0.50 #4447, 0.50 #3805, 0.50 #1882), 03vrnh (0.50 #4249, 0.50 #1684, 0.43 #4889), 02qvhbb (0.50 #4472, 0.50 #1907, 0.43 #5112), 05g3ss (0.50 #3817, 0.50 #1894, 0.40 #2536), 0241wg (0.50 #3381, 0.50 #1458, 0.40 #2100), 03fwln (0.50 #1852, 0.40 #2494, 0.33 #4417), 02wyc0 (0.50 #1822, 0.40 #2464, 0.33 #4387) >> Best rule #4469 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 09s02; >> query: (?x8098, 040nwr) <- languages_spoken(?x12951, ?x8098), countries_spoken_in(?x8098, ?x3016), languages(?x11170, ?x8098), languages(?x8097, ?x8098), ?x11170 = 03x31g, countries_spoken_in(?x5607, ?x3016), capital(?x3016, ?x13852), currency(?x3016, ?x170), organization(?x3016, ?x312), organization(?x3016, ?x127), contains(?x6304, ?x3016), olympics(?x3016, ?x2966), ?x127 = 02vk52z, ?x8097 = 046rfv, ?x312 = 07t65, language(?x9642, ?x5607), ?x9642 = 02_nsc >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #5127 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 5 *> proper extension: 09bnf; *> query: (?x8098, ?x51) <- languages(?x8097, ?x8098), languages(?x3873, ?x8098), award(?x3873, ?x1313), nationality(?x3873, ?x94), award_winner(?x1313, ?x276), award(?x197, ?x1313), nominated_for(?x1313, ?x144), ?x8097 = 046rfv, nationality(?x51, ?x94), film_release_region(?x54, ?x94), organization(?x94, ?x127), contains(?x94, ?x95), country(?x89, ?x94), country(?x150, ?x94), jurisdiction_of_office(?x265, ?x94) *> conf = 0.08 ranks of expected_values: 1933 EVAL 0999q languages! 05gc0h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 39.000 8.000 0.667 http://example.org/people/person/languages #2287-0fhp9 PRED entity: 0fhp9 PRED relation: month PRED expected values: 06vkl 05lf_ 0lkm => 271 concepts (271 used for prediction) PRED predicted values (max 10 best out of 3): 05lf_ (0.91 #176, 0.88 #116, 0.87 #197), 0lkm (0.88 #219, 0.86 #177, 0.85 #156), 06vkl (0.87 #196, 0.84 #175, 0.83 #217) >> Best rule #176 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 0177z; 0f04v; >> query: (?x863, 05lf_) <- place_of_birth(?x2800, ?x863), month(?x863, ?x4827), month(?x863, ?x1459), ?x1459 = 04w_7, ?x4827 = 03_ly >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0fhp9 month 0lkm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 271.000 271.000 0.907 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0fhp9 month 05lf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 271.000 271.000 0.907 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0fhp9 month 06vkl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 271.000 271.000 0.907 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #2286-03ccq3s PRED entity: 03ccq3s PRED relation: award! PRED expected values: 0pz7h 01gbbz 0jmj 09v6gc9 08n__5 03wh8kl 070j61 03q45x => 43 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2828): 0crx5w (0.81 #23444, 0.79 #40192, 0.79 #40191), 0pz7h (0.81 #23444, 0.79 #40192, 0.79 #40191), 04cl1 (0.43 #8043, 0.33 #11390, 0.33 #1349), 05hrq4 (0.33 #12625, 0.33 #5931, 0.33 #2584), 018ygt (0.33 #11875, 0.33 #1834, 0.29 #8528), 08qvhv (0.33 #4554, 0.33 #1207, 0.29 #7901), 06j0md (0.33 #3380, 0.33 #33, 0.29 #6727), 06v_gh (0.33 #3777, 0.33 #430, 0.29 #7124), 098n5 (0.33 #4320, 0.33 #973, 0.14 #7667), 04t2l2 (0.33 #42, 0.29 #6736, 0.28 #30145) >> Best rule #23444 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 09v7wsg; >> query: (?x3906, ?x906) <- award(?x1631, ?x3906), nominated_for(?x3906, ?x12535), tv_program(?x1294, ?x12535), award_winner(?x3906, ?x906), genre(?x12535, ?x258) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 14, 22, 23, 30, 31, 37, 447 EVAL 03ccq3s award! 03q45x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 43.000 19.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03ccq3s award! 070j61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 43.000 19.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03ccq3s award! 03wh8kl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 43.000 19.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03ccq3s award! 08n__5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 43.000 19.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03ccq3s award! 09v6gc9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 43.000 19.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03ccq3s award! 0jmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 43.000 19.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03ccq3s award! 01gbbz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 43.000 19.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03ccq3s award! 0pz7h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 43.000 19.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2285-0cd25 PRED entity: 0cd25 PRED relation: taxonomy PRED expected values: 04n6k => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.71 #12, 0.65 #24, 0.65 #11) >> Best rule #12 for best value: >> intensional similarity = 10 >> extensional distance = 22 >> proper extension: 02mgp; >> query: (?x1468, 04n6k) <- major_field_of_study(?x892, ?x1468), major_field_of_study(?x1468, ?x9111), student(?x892, ?x12441), student(?x892, ?x10536), student(?x892, ?x8938), student(?x892, ?x5131), ?x12441 = 0tfc, profession(?x5131, ?x353), place_of_death(?x8938, ?x863), story_by(?x1518, ?x10536) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cd25 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.708 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #2284-01trhmt PRED entity: 01trhmt PRED relation: award_winner! PRED expected values: 056878 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 100): 0jzphpx (0.17 #38, 0.06 #2540, 0.05 #455), 02rjjll (0.13 #700, 0.12 #422, 0.10 #978), 02cg41 (0.11 #263, 0.10 #541, 0.09 #819), 09n4nb (0.10 #742, 0.09 #186, 0.09 #1020), 01c6qp (0.10 #713, 0.08 #1686, 0.08 #991), 01s695 (0.10 #698, 0.08 #976, 0.07 #1115), 0gpjbt (0.09 #167, 0.08 #1696, 0.07 #445), 01mhwk (0.09 #318, 0.05 #596, 0.05 #457), 05pd94v (0.09 #1670, 0.09 #697, 0.09 #419), 027hjff (0.09 #2141, 0.08 #2002, 0.06 #3114) >> Best rule #38 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0m2wm; 01pcvn; 05g7q; >> query: (?x2562, 0jzphpx) <- nationality(?x2562, ?x94), participant(?x3083, ?x2562), ?x3083 = 01pcrw >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #726 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 100 *> proper extension: 02bwjv; 02yygk; *> query: (?x2562, 056878) <- artists(?x671, ?x2562), participant(?x1898, ?x2562), award_nominee(?x2562, ?x6162) *> conf = 0.07 ranks of expected_values: 17 EVAL 01trhmt award_winner! 056878 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 123.000 123.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2283-05v38p PRED entity: 05v38p PRED relation: film! PRED expected values: 01kj0p => 72 concepts (44 used for prediction) PRED predicted values (max 10 best out of 690): 01kj0p (0.72 #70698, 0.71 #60301, 0.71 #64462), 01y64_ (0.72 #70698, 0.71 #60301, 0.68 #8315), 03v1xb (0.56 #2079, 0.46 #60300, 0.45 #64461), 01l79yc (0.56 #2079, 0.46 #60300, 0.45 #64461), 03kpvp (0.27 #630, 0.04 #17261), 0l6px (0.18 #387, 0.05 #60302, 0.05 #19096), 0171cm (0.18 #423, 0.05 #60302, 0.04 #62382), 0dgskx (0.18 #1153, 0.05 #60302, 0.04 #62382), 02cllz (0.18 #407, 0.05 #60302, 0.04 #76935), 04shbh (0.18 #166, 0.03 #2245, 0.02 #25112) >> Best rule #70698 for best value: >> intensional similarity = 4 >> extensional distance = 1037 >> proper extension: 084qpk; 05jyb2; 02v5_g; 0k5fg; 02t_h3; >> query: (?x6445, ?x2805) <- country(?x6445, ?x94), nominated_for(?x2805, ?x6445), nominated_for(?x143, ?x6445), film(?x2805, ?x144) >> conf = 0.72 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 05v38p film! 01kj0p CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 44.000 0.718 http://example.org/film/actor/film./film/performance/film #2282-01n5309 PRED entity: 01n5309 PRED relation: student! PRED expected values: 014mlp => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 10): 014mlp (0.33 #6, 0.12 #206, 0.09 #86), 019v9k (0.05 #270, 0.04 #70, 0.03 #90), 028dcg (0.05 #218, 0.02 #938, 0.02 #538), 03mkk4 (0.03 #93, 0.03 #133, 0.02 #193), 02mjs7 (0.03 #145), 02h4rq6 (0.02 #203, 0.02 #243, 0.02 #383), 0bkj86 (0.02 #829, 0.02 #1549, 0.02 #1589), 04zx3q1 (0.02 #822, 0.01 #1502, 0.01 #1542), 02_xgp2 (0.02 #1474, 0.02 #1554, 0.02 #1594), 016t_3 (0.02 #484, 0.01 #324) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0pz04; >> query: (?x692, 014mlp) <- celebrities_impersonated(?x692, ?x4360), ?x4360 = 0f502, award_nominee(?x692, ?x1422) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n5309 student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.333 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #2281-01w9ph_ PRED entity: 01w9ph_ PRED relation: student! PRED expected values: 09f2j 01jq0j => 216 concepts (216 used for prediction) PRED predicted values (max 10 best out of 251): 03ksy (0.25 #106, 0.17 #633, 0.15 #17497), 07tgn (0.25 #17, 0.17 #544, 0.10 #3706), 0ym17 (0.25 #407, 0.02 #16744, 0.02 #16217), 01w5m (0.24 #7483, 0.14 #11699, 0.10 #18023), 0dzbl (0.17 #1028, 0.07 #17365, 0.06 #15257), 0yldt (0.17 #1041, 0.06 #6838, 0.05 #9473), 07wrz (0.17 #589, 0.04 #20615, 0.03 #12183), 01w3v (0.17 #542, 0.03 #22676, 0.02 #16352), 02mw6c (0.17 #957, 0.03 #15186, 0.02 #16240), 01k7xz (0.17 #593, 0.03 #14822, 0.02 #16403) >> Best rule #106 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 07h1q; >> query: (?x8004, 03ksy) <- peers(?x8004, ?x4608), influenced_by(?x8004, ?x9595), influenced_by(?x8004, ?x2845), ?x9595 = 01rgr, award(?x2845, ?x1901) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #21239 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 01gp_x; *> query: (?x8004, 09f2j) <- influenced_by(?x8004, ?x916), profession(?x8004, ?x524), type_of_union(?x8004, ?x11744), ?x524 = 02jknp *> conf = 0.05 ranks of expected_values: 47, 183 EVAL 01w9ph_ student! 01jq0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 216.000 216.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01w9ph_ student! 09f2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 216.000 216.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #2280-0863x_ PRED entity: 0863x_ PRED relation: gender PRED expected values: 05zppz => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.72 #15, 0.71 #13, 0.71 #158), 02zsn (0.52 #137, 0.32 #30, 0.30 #48) >> Best rule #15 for best value: >> intensional similarity = 2 >> extensional distance = 522 >> proper extension: 02y0dd; >> query: (?x4705, 05zppz) <- currency(?x4705, ?x170), currency(?x99, ?x170) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0863x_ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.718 http://example.org/people/person/gender #2279-09ftwr PRED entity: 09ftwr PRED relation: nationality PRED expected values: 09c7w0 => 97 concepts (95 used for prediction) PRED predicted values (max 10 best out of 30): 09c7w0 (0.82 #1303, 0.82 #1103, 0.81 #801), 0d060g (0.40 #8458, 0.28 #7450, 0.26 #4125), 02jx1 (0.40 #8458, 0.28 #7450, 0.09 #8892), 07ssc (0.40 #8458, 0.28 #7450, 0.08 #6759), 03rt9 (0.40 #8458, 0.28 #7450, 0.08 #3317), 03rjj (0.40 #8458, 0.28 #7450, 0.08 #3317), 0f8l9c (0.40 #8458, 0.28 #7450, 0.08 #3317), 0345h (0.40 #8458, 0.28 #7450, 0.08 #3317), 03_3d (0.40 #8458, 0.28 #7450, 0.08 #3317), 0chghy (0.12 #410, 0.08 #3317, 0.03 #510) >> Best rule #1303 for best value: >> intensional similarity = 4 >> extensional distance = 359 >> proper extension: 02s58t; >> query: (?x2687, 09c7w0) <- location(?x2687, ?x739), place_of_birth(?x65, ?x739), location(?x2135, ?x739), ?x2135 = 06pj8 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09ftwr nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 95.000 0.820 http://example.org/people/person/nationality #2278-07mvp PRED entity: 07mvp PRED relation: artist! PRED expected values: 01clyr 0n85g 01w56k => 119 concepts (92 used for prediction) PRED predicted values (max 10 best out of 127): 03rhqg (0.38 #568, 0.38 #706, 0.31 #2224), 02p11jq (0.33 #13, 0.25 #289, 0.18 #4846), 056252 (0.33 #43, 0.25 #319, 0.12 #457), 03qk20 (0.33 #64, 0.25 #340, 0.02 #8076), 0mzkr (0.33 #163, 0.17 #2371, 0.11 #4304), 01cf93 (0.33 #194, 0.12 #470, 0.10 #4335), 086k8 (0.25 #277, 0.19 #691, 0.15 #553), 01t04r (0.25 #339, 0.16 #891, 0.11 #5587), 0229rs (0.21 #846, 0.15 #570, 0.12 #3055), 0k_kr (0.21 #1422, 0.14 #1008, 0.11 #1836) >> Best rule #568 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 06lxn; >> query: (?x6475, 03rhqg) <- group(?x716, ?x6475), category(?x6475, ?x134), ?x716 = 018vs, award_winner(?x10944, ?x6475) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1027 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 0l56b; *> query: (?x6475, 0n85g) <- award_winner(?x2180, ?x6475), ?x2180 = 02v1m7, category(?x6475, ?x134), ?x134 = 08mbj5d *> conf = 0.19 ranks of expected_values: 11, 13, 77 EVAL 07mvp artist! 01w56k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 119.000 92.000 0.385 http://example.org/music/record_label/artist EVAL 07mvp artist! 0n85g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 119.000 92.000 0.385 http://example.org/music/record_label/artist EVAL 07mvp artist! 01clyr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 119.000 92.000 0.385 http://example.org/music/record_label/artist #2277-062ftr PRED entity: 062ftr PRED relation: nationality PRED expected values: 09c7w0 => 90 concepts (87 used for prediction) PRED predicted values (max 10 best out of 45): 09c7w0 (0.83 #2307, 0.80 #402, 0.79 #201), 04rrx (0.45 #6919, 0.28 #6920, 0.26 #2407), 02jx1 (0.34 #6617, 0.11 #935, 0.10 #735), 07ssc (0.34 #6617, 0.09 #717, 0.09 #1018), 07t21 (0.34 #6617, 0.04 #237), 03rk0 (0.07 #1549, 0.05 #6160, 0.05 #7669), 0chghy (0.06 #401, 0.05 #702, 0.04 #1003), 03rjj (0.06 #401, 0.05 #702, 0.04 #1003), 0f8l9c (0.06 #401, 0.05 #702, 0.04 #1003), 0b90_r (0.06 #401, 0.05 #702, 0.04 #1003) >> Best rule #2307 for best value: >> intensional similarity = 3 >> extensional distance = 1109 >> proper extension: 05m63c; 033hqf; 0f1vrl; 0130sy; 02n9k; 01qn8k; 01qklj; 0ccqd7; 0cymln; 09fqd3; ... >> query: (?x3940, 09c7w0) <- location(?x3940, ?x7321), county(?x7321, ?x8968), contains(?x94, ?x7321) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 062ftr nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 87.000 0.829 http://example.org/people/person/nationality #2276-07t2k PRED entity: 07t2k PRED relation: location PRED expected values: 05kkh => 174 concepts (132 used for prediction) PRED predicted values (max 10 best out of 250): 07z1m (0.33 #78, 0.20 #3291, 0.17 #4897), 05jbn (0.33 #1055, 0.12 #8285, 0.09 #13106), 04jpl (0.33 #820, 0.11 #83669, 0.10 #95719), 0d9jr (0.33 #1071, 0.02 #68645, 0.01 #54968), 080h2 (0.33 #856, 0.01 #83705, 0.01 #98165), 030qb3t (0.26 #83734, 0.23 #95784, 0.22 #84537), 07b_l (0.25 #1792, 0.23 #74005, 0.17 #4202), 0rh6k (0.23 #74005, 0.20 #23314, 0.17 #24924), 013n2h (0.23 #74005, 0.20 #2816, 0.17 #4422), 0rd6b (0.23 #74005, 0.20 #2938, 0.17 #4544) >> Best rule #78 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 06f5j; >> query: (?x7558, 07z1m) <- entity_involved(?x11988, ?x7558), ?x11988 = 0kbq, location(?x7558, ?x739), religion(?x7558, ?x1624), profession(?x7558, ?x5805) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #28146 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 01d494; *> query: (?x7558, 05kkh) <- company(?x7558, ?x94), gender(?x7558, ?x231), type_of_union(?x7558, ?x566), organization(?x94, ?x127) *> conf = 0.03 ranks of expected_values: 141 EVAL 07t2k location 05kkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 174.000 132.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #2275-025cbm PRED entity: 025cbm PRED relation: role! PRED expected values: 09hnb 02s6sh => 78 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1137): 05qhnq (0.80 #9169, 0.75 #4970, 0.71 #3573), 050z2 (0.80 #7645, 0.70 #9045, 0.70 #8580), 01vs4ff (0.80 #9162, 0.62 #4963, 0.60 #8230), 023l9y (0.75 #5337, 0.67 #3010, 0.62 #4406), 0770cd (0.71 #11269, 0.70 #8003, 0.62 #4736), 04bpm6 (0.70 #8930, 0.70 #7530, 0.67 #11732), 0lzkm (0.70 #7629, 0.67 #2969, 0.62 #4365), 01wxdn3 (0.67 #3209, 0.62 #5536, 0.62 #4605), 0137g1 (0.67 #6641, 0.62 #5243, 0.62 #3847), 02s6sh (0.67 #3235, 0.62 #4631, 0.60 #7895) >> Best rule #9169 for best value: >> intensional similarity = 24 >> extensional distance = 8 >> proper extension: 026t6; >> query: (?x433, 05qhnq) <- role(?x4425, ?x433), role(?x2764, ?x433), role(?x1473, ?x433), role(?x8344, ?x433), role(?x3399, ?x433), role(?x2908, ?x433), ?x4425 = 0979zs, ?x1473 = 0g2dz, award(?x8344, ?x1801), role(?x2764, ?x1147), location(?x8344, ?x7321), ?x1801 = 01c92g, role(?x7112, ?x2764), role(?x5587, ?x2764), role(?x4140, ?x2764), role(?x3869, ?x2764), artists(?x302, ?x3399), ?x7112 = 0133x7, ?x1147 = 07kc_, role(?x2764, ?x314), ?x4140 = 01sb5r, nationality(?x3399, ?x94), ?x5587 = 01mxt_, ?x2908 = 0161sp >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3235 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 4 *> proper extension: 0342h; 01vj9c; 01vdm0; *> query: (?x433, 02s6sh) <- role(?x4425, ?x433), role(?x3409, ?x433), role(?x3215, ?x433), role(?x2764, ?x433), role(?x1495, ?x433), role(?x1473, ?x433), role(?x8344, ?x433), role(?x211, ?x433), ?x4425 = 0979zs, ?x1473 = 0g2dz, award(?x8344, ?x1232), ?x2764 = 01s0ps, ?x1495 = 013y1f, artists(?x10290, ?x211), gender(?x8344, ?x231), ?x3409 = 0680x0, profession(?x211, ?x131), profession(?x8344, ?x1032), nationality(?x211, ?x94), group(?x8344, ?x10263), ?x3215 = 0bxl5, ?x10290 = 03ckfl9, instrumentalists(?x227, ?x8344) *> conf = 0.67 ranks of expected_values: 10, 85 EVAL 025cbm role! 02s6sh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 78.000 51.000 0.800 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 025cbm role! 09hnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 78.000 51.000 0.800 http://example.org/music/artist/track_contributions./music/track_contribution/role #2274-0pkgt PRED entity: 0pkgt PRED relation: award_winner! PRED expected values: 02h3d1 => 75 concepts (48 used for prediction) PRED predicted values (max 10 best out of 319): 04njml (0.58 #431, 0.41 #3442, 0.40 #3874), 02h3d1 (0.58 #431, 0.41 #3442, 0.40 #3874), 05q8pss (0.58 #431, 0.41 #3442, 0.40 #3874), 0gqz2 (0.31 #941, 0.19 #1371, 0.18 #3522), 02gdjb (0.25 #218, 0.07 #3229, 0.06 #2369), 0gr51 (0.25 #100, 0.04 #1391, 0.03 #20230), 0gr4k (0.25 #33, 0.04 #1324, 0.03 #11653), 0gs9p (0.25 #79, 0.03 #11699, 0.03 #14282), 019f4v (0.25 #67, 0.03 #20230, 0.03 #11687), 0gq9h (0.25 #77, 0.03 #11697, 0.03 #2228) >> Best rule #431 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02mslq; >> query: (?x11238, ?x1232) <- nationality(?x11238, ?x94), award(?x11238, ?x1232), music(?x10362, ?x11238), notable_people_with_this_condition(?x6656, ?x11238) >> conf = 0.58 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0pkgt award_winner! 02h3d1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 75.000 48.000 0.583 http://example.org/award/award_category/winners./award/award_honor/award_winner #2273-05x2t7 PRED entity: 05x2t7 PRED relation: award_winner! PRED expected values: 0c6vcj => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 135): 0dznvw (0.30 #133, 0.15 #828, 0.11 #967), 0bzm81 (0.20 #161, 0.05 #717, 0.05 #578), 0fv89q (0.19 #400, 0.11 #539, 0.10 #678), 0fzrhn (0.12 #414, 0.10 #136, 0.09 #1248), 0c53zb (0.12 #339, 0.10 #61, 0.09 #3119), 05hmp6 (0.12 #364, 0.10 #1059, 0.08 #3144), 0fy6bh (0.12 #11819, 0.10 #603, 0.10 #186), 0fk0xk (0.12 #11819, 0.10 #216, 0.10 #77), 0ftlkg (0.12 #11819, 0.10 #165, 0.10 #26), 050yyb (0.10 #733, 0.10 #594, 0.07 #872) >> Best rule #133 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0dck27; 04vzv4; 0cbxl0; >> query: (?x2069, 0dznvw) <- award_nominee(?x2068, ?x2069), costume_design_by(?x4865, ?x2069), film(?x1666, ?x4865) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #11122 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1371 *> proper extension: 01dq9q; 03vhvp; *> query: (?x2069, ?x78) <- award_winner(?x2222, ?x2069), award_nominee(?x2069, ?x2068), ceremony(?x2222, ?x78) *> conf = 0.05 ranks of expected_values: 60 EVAL 05x2t7 award_winner! 0c6vcj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 133.000 133.000 0.300 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2272-0sxmx PRED entity: 0sxmx PRED relation: film! PRED expected values: 0g1rw => 89 concepts (48 used for prediction) PRED predicted values (max 10 best out of 49): 05qd_ (0.29 #9, 0.24 #231, 0.20 #83), 016tw3 (0.24 #233, 0.16 #529, 0.16 #1277), 086k8 (0.22 #745, 0.21 #819, 0.21 #968), 03xq0f (0.20 #79, 0.12 #896, 0.10 #598), 024rbz (0.20 #86, 0.08 #234, 0.07 #382), 017s11 (0.16 #894, 0.14 #373, 0.14 #3), 016tt2 (0.16 #300, 0.16 #152, 0.16 #522), 0jz9f (0.14 #1, 0.13 #445, 0.12 #297), 017jv5 (0.14 #15, 0.10 #89, 0.08 #311), 03xsby (0.14 #16, 0.05 #907, 0.03 #1653) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 08cfr1; >> query: (?x4734, 05qd_) <- genre(?x4734, ?x12340), ?x12340 = 01fc50, films(?x7455, ?x4734), award(?x4734, ?x500) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1496 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 218 *> proper extension: 0bs8hvm; *> query: (?x4734, 0g1rw) <- genre(?x4734, ?x225), film(?x777, ?x4734), cinematography(?x4734, ?x6062) *> conf = 0.08 ranks of expected_values: 21 EVAL 0sxmx film! 0g1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 89.000 48.000 0.286 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #2271-027b43 PRED entity: 027b43 PRED relation: contains! PRED expected values: 09c7w0 => 171 concepts (59 used for prediction) PRED predicted values (max 10 best out of 112): 09c7w0 (0.84 #11629, 0.82 #19680, 0.80 #22363), 04_1l0v (0.35 #27727, 0.28 #46514, 0.24 #49199), 0ynfz (0.33 #511, 0.25 #1405, 0.17 #2299), 01n7q (0.17 #8125, 0.13 #10809, 0.13 #11704), 02jx1 (0.16 #10818, 0.16 #12607, 0.11 #17976), 0d060g (0.16 #43828, 0.07 #28634, 0.07 #34896), 04s7y (0.16 #43828), 07b_l (0.16 #29737, 0.07 #8269, 0.06 #10953), 02xry (0.16 #29678, 0.04 #9106, 0.04 #8210), 03v0t (0.15 #29748, 0.10 #37567, 0.06 #8280) >> Best rule #11629 for best value: >> intensional similarity = 5 >> extensional distance = 244 >> proper extension: 01pj48; >> query: (?x12132, 09c7w0) <- contains(?x4198, ?x12132), currency(?x12132, ?x170), jurisdiction_of_office(?x900, ?x4198), religion(?x4198, ?x109), ?x900 = 0fkvn >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027b43 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 59.000 0.841 http://example.org/location/location/contains #2270-065d1h PRED entity: 065d1h PRED relation: type_of_union PRED expected values: 04ztj => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.76 #37, 0.75 #45, 0.75 #29), 01g63y (0.12 #18, 0.12 #155, 0.12 #163), 0jgjn (0.01 #16) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 602 >> proper extension: 03ckxdg; 01vvycq; 027rwmr; 05drq5; 02_4fn; 047q2wc; 01pp3p; 030b93; 0522wp; 0454s1; ... >> query: (?x10573, 04ztj) <- profession(?x10573, ?x524), ?x524 = 02jknp, gender(?x10573, ?x231) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065d1h type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.758 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2269-0c6vcj PRED entity: 0c6vcj PRED relation: ceremony! PRED expected values: 0gr0m => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 350): 0l8z1 (0.83 #2456, 0.80 #2939, 0.80 #2698), 018wdw (0.82 #891, 0.77 #5562, 0.77 #5561), 0gr0m (0.82 #3190, 0.82 #5126, 0.81 #4643), 0gqxm (0.77 #5562, 0.77 #5561, 0.77 #4351), 0czp_ (0.77 #5562, 0.77 #5561, 0.77 #4351), 0gqzz (0.77 #5562, 0.77 #5561, 0.77 #4351), 02x201b (0.77 #5562, 0.77 #5561, 0.77 #4351), 054krc (0.23 #4890, 0.22 #5859, 0.21 #6100), 054ks3 (0.22 #4926, 0.21 #5895, 0.21 #6136), 04dn09n (0.22 #4862, 0.21 #5831, 0.19 #5588) >> Best rule #2456 for best value: >> intensional similarity = 18 >> extensional distance = 27 >> proper extension: 0bzkgg; 0bzk2h; 0bzlrh; 0bzjvm; 0bzjgq; >> query: (?x7226, 0l8z1) <- ceremony(?x2209, ?x7226), ceremony(?x1323, ?x7226), ceremony(?x601, ?x7226), ceremony(?x591, ?x7226), ceremony(?x77, ?x7226), ?x1323 = 0gqz2, honored_for(?x7226, ?x499), award_winner(?x7226, ?x3771), award(?x382, ?x2209), award(?x13176, ?x2209), nominated_for(?x2209, ?x7834), award_winner(?x2209, ?x2870), ?x13176 = 06mmr, ?x77 = 0gqng, ?x601 = 0gr4k, story_by(?x7834, ?x8433), award(?x3771, ?x1079), ?x591 = 0f4x7 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #3190 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 37 *> proper extension: 0bzk8w; 073h1t; 0bzm__; 073hgx; 073hd1; *> query: (?x7226, 0gr0m) <- ceremony(?x3617, ?x7226), ceremony(?x2209, ?x7226), ceremony(?x1703, ?x7226), ceremony(?x1323, ?x7226), ?x1323 = 0gqz2, honored_for(?x7226, ?x499), award_winner(?x7226, ?x3771), award(?x7758, ?x2209), award(?x382, ?x2209), award(?x324, ?x2209), nominated_for(?x2209, ?x504), award_winner(?x2209, ?x2870), ?x3617 = 0gvx_, ?x7758 = 08t7nz, ?x1703 = 0k611, award_winner(?x3770, ?x3771), nominated_for(?x382, ?x522), instance_of_recurring_event(?x7226, ?x3459) *> conf = 0.82 ranks of expected_values: 3 EVAL 0c6vcj ceremony! 0gr0m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 36.000 36.000 0.828 http://example.org/award/award_category/winners./award/award_honor/ceremony #2268-09qrn4 PRED entity: 09qrn4 PRED relation: nominated_for PRED expected values: 0d68qy 01rp13 02r1ysd => 47 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1425): 0d68qy (0.65 #22061, 0.64 #22062, 0.25 #1935), 02z44tp (0.60 #4148, 0.08 #7297, 0.07 #8873), 01g03q (0.40 #4509, 0.24 #7658, 0.21 #9234), 0kfv9 (0.40 #3406, 0.24 #6555, 0.21 #8131), 0g60z (0.40 #3190, 0.22 #26795, 0.22 #6339), 0180mw (0.40 #4158, 0.22 #26795, 0.20 #25218), 02rcwq0 (0.40 #3927, 0.22 #26795, 0.20 #25218), 02k_4g (0.40 #3254, 0.22 #26795, 0.20 #25218), 02md2d (0.40 #3783, 0.22 #26795, 0.20 #25218), 02rzdcp (0.40 #3634, 0.22 #6783, 0.21 #8359) >> Best rule #22061 for best value: >> intensional similarity = 5 >> extensional distance = 186 >> proper extension: 0m7yy; 02wwsh8; 03ybrwc; 02vl9ln; 0468g4r; >> query: (?x5235, ?x2293) <- award_winner(?x5235, ?x5523), award(?x7511, ?x5235), award(?x2293, ?x5235), award_nominee(?x1979, ?x5523), titles(?x2008, ?x7511) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1, 30, 848 EVAL 09qrn4 nominated_for 02r1ysd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 18.000 0.646 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qrn4 nominated_for 01rp13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 47.000 18.000 0.646 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 09qrn4 nominated_for 0d68qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 18.000 0.646 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2267-0cxgc PRED entity: 0cxgc PRED relation: contains PRED expected values: 01dzq6 => 181 concepts (88 used for prediction) PRED predicted values (max 10 best out of 2588): 01x5fb (0.77 #202972, 0.68 #223568, 0.57 #188260), 01zzk4 (0.77 #202972, 0.55 #132368, 0.52 #200028), 02jx1 (0.56 #223569, 0.55 #5883, 0.54 #205915), 0cxgc (0.56 #223569, 0.55 #5883, 0.54 #205915), 04jpl (0.56 #223569, 0.55 #5883, 0.54 #205915), 07ssc (0.56 #223569, 0.55 #5883, 0.54 #205915), 036wy (0.56 #223569, 0.55 #5883, 0.54 #205915), 0m4yg (0.40 #4418, 0.09 #16184, 0.09 #13243), 0nbfm (0.40 #4646, 0.09 #13471, 0.08 #22294), 09bkv (0.40 #4462, 0.09 #13287, 0.08 #22110) >> Best rule #202972 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 065zr; 075mb; 07371; 075_t2; 01hpnh; 07nf6; 055vr; 0vh3; 036wy; 0jhwd; >> query: (?x11432, ?x13887) <- state_province_region(?x13887, ?x11432), country(?x11432, ?x512), contains(?x11432, ?x6432), contains(?x1310, ?x13887) >> conf = 0.77 => this is the best rule for 2 predicted values *> Best rule #4846 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 049kw; *> query: (?x11432, 01dzq6) <- contains(?x11432, ?x6432), ?x6432 = 01314k, contains(?x1310, ?x11432) *> conf = 0.40 ranks of expected_values: 16 EVAL 0cxgc contains 01dzq6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 181.000 88.000 0.766 http://example.org/location/location/contains #2266-02jxbw PRED entity: 02jxbw PRED relation: nominated_for! PRED expected values: 03hkv_r 0k611 => 98 concepts (93 used for prediction) PRED predicted values (max 10 best out of 209): 0gqy2 (0.68 #4919, 0.67 #5155, 0.67 #5154), 0gq9h (0.65 #58, 0.49 #6621, 0.43 #2166), 0gs9p (0.44 #60, 0.42 #6623, 0.38 #2168), 0k611 (0.41 #68, 0.37 #6631, 0.33 #2176), 040njc (0.32 #7, 0.31 #2115, 0.31 #3521), 0f4x7 (0.31 #6587, 0.30 #259, 0.29 #24), 0gr4k (0.31 #6588, 0.30 #260, 0.29 #1430), 0gs96 (0.29 #85, 0.26 #6648, 0.24 #2193), 0l8z1 (0.29 #49, 0.25 #2157, 0.25 #6329), 0gqyl (0.28 #6638, 0.26 #310, 0.24 #3589) >> Best rule #4919 for best value: >> intensional similarity = 4 >> extensional distance = 495 >> proper extension: 0kfpm; 0358x_; 0ddd0gc; 02hct1; 01b64v; 0phrl; 01j7mr; 0gj50; 01b65l; 030cx; ... >> query: (?x6184, ?x500) <- honored_for(?x7038, ?x6184), nominated_for(?x2214, ?x6184), award(?x6184, ?x500), nominated_for(?x484, ?x6184) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #68 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 0bth54; 0209hj; 0cwy47; 017gl1; 0m_mm; 017gm7; 0168ls; 0k4kk; 026gyn_; 083skw; ... *> query: (?x6184, 0k611) <- genre(?x6184, ?x4757), award(?x6184, ?x500), language(?x6184, ?x254), ?x4757 = 06l3bl *> conf = 0.41 ranks of expected_values: 4, 56 EVAL 02jxbw nominated_for! 0k611 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 93.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02jxbw nominated_for! 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 98.000 93.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2265-030_1_ PRED entity: 030_1_ PRED relation: award_winner PRED expected values: 05gnf => 117 concepts (87 used for prediction) PRED predicted values (max 10 best out of 785): 05gnf (0.84 #58158, 0.84 #67854, 0.83 #32298), 016tw3 (0.52 #134090, 0.52 #132474, 0.45 #137320), 030_1_ (0.33 #259, 0.27 #129242, 0.12 #14792), 0f721s (0.30 #6670, 0.12 #14744, 0.11 #16359), 0gsg7 (0.27 #129242, 0.20 #6731, 0.19 #14805), 0g5lhl7 (0.27 #129242, 0.20 #6911, 0.17 #3683), 09d5h (0.27 #129242, 0.20 #6778, 0.15 #31001), 031rq5 (0.27 #129242, 0.11 #17153, 0.10 #7464), 09px1w (0.27 #129242, 0.10 #7751, 0.09 #31974), 091yn0 (0.27 #129242, 0.10 #7531, 0.09 #31754) >> Best rule #58158 for best value: >> intensional similarity = 3 >> extensional distance = 69 >> proper extension: 02drd3; >> query: (?x1686, ?x6678) <- award(?x1686, ?x3911), award_winner(?x6678, ?x1686), film(?x6678, ?x6649) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030_1_ award_winner 05gnf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 87.000 0.841 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #2264-022q32 PRED entity: 022q32 PRED relation: vacationer! PRED expected values: 0cv3w 03gh4 => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 107): 05qtj (0.33 #197, 0.20 #574, 0.18 #1075), 04jpl (0.33 #134, 0.12 #511, 0.11 #1263), 0chghy (0.33 #10, 0.05 #1264, 0.04 #1515), 05ywg (0.33 #35, 0.01 #2544, 0.01 #3045), 0cv3w (0.20 #935, 0.20 #559, 0.17 #810), 02_286 (0.17 #140, 0.11 #642, 0.10 #893), 0f2v0 (0.17 #188, 0.11 #1820, 0.10 #3577), 0kygv (0.17 #218, 0.04 #1598, 0.04 #2727), 0h3tv (0.17 #229, 0.04 #1736, 0.03 #1987), 0r1yc (0.17 #154, 0.02 #1786, 0.01 #2413) >> Best rule #197 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0127s7; >> query: (?x10777, 05qtj) <- participant(?x1733, ?x10777), participant(?x56, ?x10777), ?x1733 = 015pkc, profession(?x10777, ?x2225) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #935 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 28 *> proper extension: 01sb5r; 01xzb6; 094xh; 01wx756; *> query: (?x10777, 0cv3w) <- gender(?x10777, ?x514), celebrity(?x6035, ?x10777), artists(?x302, ?x6035), type_of_union(?x6035, ?x566) *> conf = 0.20 ranks of expected_values: 5, 11 EVAL 022q32 vacationer! 03gh4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 171.000 171.000 0.333 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 022q32 vacationer! 0cv3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 171.000 171.000 0.333 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #2263-0c5tl PRED entity: 0c5tl PRED relation: profession PRED expected values: 0kyk => 104 concepts (94 used for prediction) PRED predicted values (max 10 best out of 88): 02hrh1q (0.89 #12314, 0.87 #12462, 0.86 #13054), 0dxtg (0.69 #605, 0.67 #161, 0.62 #6241), 01d_h8 (0.64 #11566, 0.43 #11714, 0.39 #4306), 0kyk (0.54 #1066, 0.49 #10850, 0.48 #3435), 02jknp (0.43 #11567, 0.43 #747, 0.40 #11715), 03gjzk (0.38 #11723, 0.33 #459, 0.32 #6970), 02hv44_ (0.34 #1241, 0.32 #1537, 0.30 #1389), 09jwl (0.25 #4468, 0.23 #7729, 0.21 #9210), 01c72t (0.25 #6969, 0.15 #7734, 0.13 #9215), 018gz8 (0.22 #6987, 0.22 #17, 0.22 #7875) >> Best rule #12314 for best value: >> intensional similarity = 5 >> extensional distance = 2800 >> proper extension: 06v8s0; 01sl1q; 044mz_; 0184jc; 04bdxl; 02s2ft; 05vsxz; 06qgvf; 05d7rk; 016qtt; ... >> query: (?x5091, 02hrh1q) <- profession(?x5091, ?x353), profession(?x13195, ?x353), profession(?x164, ?x353), ?x13195 = 0dszr0, ?x164 = 0l6qt >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1066 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 07g2b; 0gd5z; 032l1; 02yl42; 014635; 034bs; 02lt8; 03f0324; 03772; 0hky; ... *> query: (?x5091, 0kyk) <- profession(?x5091, ?x353), ?x353 = 0cbd2, influenced_by(?x5091, ?x5435), ?x5435 = 01v9724 *> conf = 0.54 ranks of expected_values: 4 EVAL 0c5tl profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 104.000 94.000 0.892 http://example.org/people/person/profession #2262-0645k5 PRED entity: 0645k5 PRED relation: country PRED expected values: 0345h => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 99): 03spz (0.48 #2039, 0.45 #3350, 0.44 #3470), 05cgv (0.45 #3771, 0.40 #3410), 0345h (0.22 #329, 0.22 #4902, 0.15 #509), 0f8l9c (0.22 #4902, 0.21 #321, 0.13 #4840), 0d060g (0.22 #4902, 0.13 #4840, 0.10 #726), 03rjj (0.22 #4902, 0.13 #4840, 0.10 #726), 059j2 (0.22 #4902, 0.13 #4840, 0.10 #726), 05qhw (0.22 #4902, 0.13 #4840, 0.10 #726), 035qy (0.22 #4902, 0.13 #4840, 0.10 #726), 06mkj (0.22 #4902, 0.10 #726, 0.09 #3650) >> Best rule #2039 for best value: >> intensional similarity = 4 >> extensional distance = 745 >> proper extension: 02xhpl; 016zfm; 043qqt5; >> query: (?x2896, ?x94) <- award_winner(?x2896, ?x4295), nominated_for(?x507, ?x2896), nationality(?x4295, ?x94), location(?x4295, ?x108) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #329 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 152 *> proper extension: 04jn6y7; *> query: (?x2896, 0345h) <- film_crew_role(?x2896, ?x468), country(?x2896, ?x512), ?x512 = 07ssc, ?x468 = 02r96rf *> conf = 0.22 ranks of expected_values: 3 EVAL 0645k5 country 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 100.000 100.000 0.481 http://example.org/film/film/country #2261-0645k5 PRED entity: 0645k5 PRED relation: films! PRED expected values: 04jjy => 84 concepts (16 used for prediction) PRED predicted values (max 10 best out of 59): 018h2 (0.14 #495, 0.14 #652, 0.01 #1125), 0kbq (0.10 #419, 0.08 #105, 0.04 #1208), 04jjy (0.09 #480, 0.05 #637, 0.02 #795), 07_nf (0.08 #67, 0.05 #381, 0.04 #1170), 07yjb (0.08 #65, 0.05 #379, 0.03 #853), 01vq3 (0.08 #41, 0.05 #355, 0.03 #514), 048n7 (0.08 #76, 0.05 #390, 0.01 #1179), 0fx2s (0.07 #230, 0.03 #2124, 0.03 #546), 0cm2xh (0.07 #204, 0.01 #1150), 05kh_ (0.07 #206) >> Best rule #495 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 0sxg4; 021y7yw; 04jwly; 02xtxw; 0194zl; 04xg2f; 09y6pb; 0c0zq; 0h1x5f; >> query: (?x2896, 018h2) <- genre(?x2896, ?x714), award_winner(?x2896, ?x4295), film_crew_role(?x2896, ?x137), ?x714 = 0hn10 >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #480 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 0sxg4; 021y7yw; 04jwly; 02xtxw; 0194zl; 04xg2f; 09y6pb; 0c0zq; 0h1x5f; *> query: (?x2896, 04jjy) <- genre(?x2896, ?x714), award_winner(?x2896, ?x4295), film_crew_role(?x2896, ?x137), ?x714 = 0hn10 *> conf = 0.09 ranks of expected_values: 3 EVAL 0645k5 films! 04jjy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 16.000 0.143 http://example.org/film/film_subject/films #2260-02w4fkq PRED entity: 02w4fkq PRED relation: artists! PRED expected values: 01gjw => 128 concepts (78 used for prediction) PRED predicted values (max 10 best out of 240): 06j6l (0.68 #4304, 0.36 #10389, 0.35 #1870), 06by7 (0.65 #16148, 0.61 #629, 0.58 #2151), 025sc50 (0.47 #4306, 0.31 #10391, 0.30 #4611), 0155w (0.44 #102, 0.23 #4362, 0.22 #710), 02lnbg (0.35 #662, 0.24 #3097, 0.23 #4314), 02k_kn (0.33 #365, 0.29 #974, 0.22 #61), 0glt670 (0.32 #4298, 0.27 #6733, 0.24 #1864), 0xhtw (0.31 #2146, 0.27 #929, 0.21 #8231), 02x8m (0.30 #4278, 0.16 #10363, 0.13 #626), 03_d0 (0.27 #4271, 0.25 #2749, 0.23 #16138) >> Best rule #4304 for best value: >> intensional similarity = 2 >> extensional distance = 123 >> proper extension: 0qmny; >> query: (?x2824, 06j6l) <- artists(?x3928, ?x2824), ?x3928 = 0gywn >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #4422 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 123 *> proper extension: 0qmny; *> query: (?x2824, 01gjw) <- artists(?x3928, ?x2824), ?x3928 = 0gywn *> conf = 0.03 ranks of expected_values: 137 EVAL 02w4fkq artists! 01gjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 128.000 78.000 0.680 http://example.org/music/genre/artists #2259-08hbxv PRED entity: 08hbxv PRED relation: contains! PRED expected values: 016zwt => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 4): 09c7w0 (0.06 #3), 07ssc (0.02 #32), 02jx1 (0.01 #87), 01n7q (0.01 #78) >> Best rule #3 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14823, 09c7w0) <- >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08hbxv contains! 016zwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 1.000 1.000 0.060 http://example.org/location/location/contains #2258-0m7yy PRED entity: 0m7yy PRED relation: award! PRED expected values: 03kq98 01h1bf 0vjr 0dsx3f 015ppk 04xbq3 0330r => 59 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1082): 015ppk (0.67 #3570, 0.50 #1644, 0.19 #15124), 030p35 (0.50 #1407, 0.33 #3333, 0.16 #14887), 02py4c8 (0.38 #8725, 0.21 #5776, 0.19 #14504), 01fx1l (0.33 #3430, 0.25 #1504, 0.11 #30807), 0ch3qr1 (0.33 #4399, 0.22 #5361, 0.17 #7288), 026n4h6 (0.33 #3993, 0.22 #4955, 0.17 #6882), 05b_gq (0.33 #4467, 0.22 #5429, 0.17 #7356), 080dwhx (0.33 #2924, 0.11 #30807, 0.11 #25030), 02yvct (0.33 #6946, 0.06 #16574, 0.06 #23310), 02lxrv (0.33 #7313, 0.03 #28492, 0.03 #23677) >> Best rule #3570 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0ck27z; 0gkr9q; >> query: (?x3486, 015ppk) <- award_winner(?x3486, ?x902), award(?x5810, ?x3486), ?x5810 = 0828jw, award_nominee(?x902, ?x163) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 35, 42, 73, 177, 544, 797 EVAL 0m7yy award! 0330r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 59.000 36.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0m7yy award! 04xbq3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 59.000 36.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0m7yy award! 015ppk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 36.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0m7yy award! 0dsx3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 59.000 36.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0m7yy award! 0vjr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 59.000 36.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0m7yy award! 01h1bf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 59.000 36.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 0m7yy award! 03kq98 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 59.000 36.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #2257-042f1 PRED entity: 042f1 PRED relation: nationality PRED expected values: 09c7w0 => 152 concepts (100 used for prediction) PRED predicted values (max 10 best out of 105): 09c7w0 (0.86 #9575, 0.86 #8464, 0.84 #6838), 07h34 (0.56 #5422, 0.56 #4317, 0.55 #4618), 0mtl5 (0.30 #9171), 04rrd (0.23 #3714, 0.03 #5528, 0.01 #10083), 0dn8b (0.23 #3714), 05fkf (0.23 #3111, 0.22 #4719, 0.08 #1703), 0jfqp (0.23 #3111, 0.22 #4719), 024pcx (0.17 #292, 0.05 #1694, 0.02 #4007), 07ssc (0.16 #6146, 0.15 #3830, 0.13 #7154), 02jx1 (0.14 #6164, 0.12 #5154, 0.11 #6264) >> Best rule #9575 for best value: >> intensional similarity = 4 >> extensional distance = 610 >> proper extension: 043q6n_; >> query: (?x9765, 09c7w0) <- student(?x1884, ?x9765), citytown(?x1884, ?x8322), currency(?x1884, ?x170), school(?x580, ?x1884) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042f1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 100.000 0.858 http://example.org/people/person/nationality #2256-078mm1 PRED entity: 078mm1 PRED relation: film_release_region PRED expected values: 03rjj 0d0vqn 0k6nt 01znc_ 02vzc => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 282): 0d0vqn (0.90 #697, 0.89 #2241, 0.89 #2412), 03h64 (0.89 #1798, 0.86 #768, 0.81 #2483), 0345h (0.88 #728, 0.80 #2443, 0.78 #1758), 059j2 (0.87 #726, 0.86 #2441, 0.85 #1756), 05qhw (0.86 #1736, 0.83 #706, 0.81 #2421), 05r4w (0.86 #1719, 0.82 #689, 0.80 #2404), 0jgd (0.86 #1721, 0.82 #691, 0.75 #2406), 0chghy (0.86 #1732, 0.81 #2417, 0.81 #702), 02vzc (0.86 #751, 0.79 #2466, 0.79 #2295), 03gj2 (0.85 #1748, 0.83 #718, 0.79 #2433) >> Best rule #697 for best value: >> intensional similarity = 6 >> extensional distance = 75 >> proper extension: 02vr3gz; 01mgw; >> query: (?x8477, 0d0vqn) <- film_release_region(?x8477, ?x2152), film_release_region(?x8477, ?x550), produced_by(?x8477, ?x8480), genre(?x8477, ?x53), ?x2152 = 06mkj, ?x550 = 05v8c >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9, 11, 14, 19 EVAL 078mm1 film_release_region 02vzc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 82.000 82.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 078mm1 film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 82.000 82.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 078mm1 film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 82.000 82.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 078mm1 film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 078mm1 film_release_region 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 82.000 82.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2255-07l50_1 PRED entity: 07l50_1 PRED relation: film_format PRED expected values: 0cj16 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 3): 0cj16 (0.32 #18, 0.24 #28, 0.22 #68), 07fb8_ (0.20 #1, 0.15 #36, 0.14 #41), 017fx5 (0.10 #4, 0.08 #44, 0.07 #54) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 092vkg; 02prw4h; 047msdk; 0gmcwlb; 02rqwhl; 0fpmrm3; 07w8fz; 0dzz6g; 0dx8gj; 0dr_9t7; ... >> query: (?x11619, 0cj16) <- nominated_for(?x2183, ?x11619), ?x2183 = 02x4w6g, film_release_distribution_medium(?x11619, ?x81), film_crew_role(?x11619, ?x137) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07l50_1 film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.321 http://example.org/film/film/film_format #2254-0l_q9 PRED entity: 0l_q9 PRED relation: category PRED expected values: 08mbj5d => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #11, 0.83 #18, 0.80 #45) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0ftxw; 0f25y; 0fvyz; 0ftxc; >> query: (?x5244, 08mbj5d) <- capital(?x953, ?x5244), source(?x5244, ?x958), ?x958 = 0jbk9, time_zones(?x5244, ?x6498) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l_q9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.862 http://example.org/common/topic/webpage./common/webpage/category #2253-03pmzt PRED entity: 03pmzt PRED relation: profession PRED expected values: 02hrh1q 0np9r => 125 concepts (110 used for prediction) PRED predicted values (max 10 best out of 53): 02hrh1q (0.90 #2250, 0.90 #9403, 0.89 #13279), 0np9r (0.77 #766, 0.70 #617, 0.69 #915), 0dxtg (0.53 #1057, 0.31 #1802, 0.30 #6272), 03gjzk (0.40 #1059, 0.28 #463, 0.23 #1804), 01d_h8 (0.33 #155, 0.31 #1198, 0.31 #1347), 0d1pc (0.33 #200, 0.12 #349, 0.09 #5713), 02jknp (0.21 #6266, 0.21 #6565, 0.21 #1200), 012t_z (0.20 #13, 0.17 #162, 0.03 #2099), 0cbd2 (0.20 #7, 0.16 #1050, 0.15 #7309), 015cjr (0.20 #50, 0.09 #1093, 0.04 #2881) >> Best rule #2250 for best value: >> intensional similarity = 4 >> extensional distance = 583 >> proper extension: 01wgfp6; 047jhq; >> query: (?x2910, 02hrh1q) <- type_of_union(?x2910, ?x566), ?x566 = 04ztj, actor(?x4339, ?x2910), profession(?x2910, ?x1146) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03pmzt profession 0np9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 110.000 0.904 http://example.org/people/person/profession EVAL 03pmzt profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 110.000 0.904 http://example.org/people/person/profession #2252-020d8d PRED entity: 020d8d PRED relation: location! PRED expected values: 0g2mbn => 152 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1706): 01wg3q (0.60 #15118, 0.51 #47878, 0.50 #52919), 0465_ (0.50 #1297, 0.18 #8855, 0.08 #13895), 016yzz (0.25 #775, 0.10 #28494, 0.09 #8333), 016z2j (0.25 #430, 0.10 #28149, 0.09 #7988), 01vs_v8 (0.25 #403, 0.10 #28122, 0.09 #7961), 03rl84 (0.25 #362, 0.10 #28081, 0.09 #7920), 01w02sy (0.25 #596, 0.10 #50995, 0.09 #8154), 0dx97 (0.25 #1066, 0.09 #8624, 0.08 #13664), 01lwx (0.25 #2362, 0.09 #9920, 0.08 #14960), 0134w7 (0.25 #163, 0.09 #7721, 0.08 #12761) >> Best rule #15118 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 02jx1; 0hyxv; 0m75g; 0fg6k; 0b_yz; 01hvzr; >> query: (?x8755, ?x8754) <- place_of_birth(?x8754, ?x8755), country(?x8755, ?x512), origin(?x7966, ?x8755), ?x512 = 07ssc, contains(?x1310, ?x8755) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #28768 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 0hzlz; *> query: (?x8755, 0g2mbn) <- place_of_birth(?x8754, ?x8755), administrative_parent(?x8755, ?x12381), award(?x8754, ?x4912), profession(?x8754, ?x1614), ?x1614 = 01c72t *> conf = 0.05 ranks of expected_values: 593 EVAL 020d8d location! 0g2mbn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 152.000 30.000 0.600 http://example.org/people/person/places_lived./people/place_lived/location #2251-01_d4 PRED entity: 01_d4 PRED relation: place PRED expected values: 01_d4 => 224 concepts (202 used for prediction) PRED predicted values (max 10 best out of 270): 0ftxc (0.16 #41787), 02_286 (0.14 #529, 0.14 #14, 0.13 #98592), 030qb3t (0.14 #545, 0.13 #98592, 0.08 #1060), 02cl1 (0.14 #527, 0.08 #1042, 0.04 #6199), 01_d4 (0.13 #98592, 0.08 #50045, 0.05 #95493), 01cx_ (0.13 #98592, 0.07 #1609, 0.07 #2641), 0rh6k (0.13 #98592, 0.06 #3095, 0.04 #5673), 0r0m6 (0.13 #98592, 0.04 #4734, 0.04 #4219), 01sn3 (0.13 #98592, 0.04 #5764, 0.04 #6280), 0dc95 (0.13 #98592, 0.04 #5203, 0.03 #10878) >> Best rule #41787 for best value: >> intensional similarity = 3 >> extensional distance = 105 >> proper extension: 079yb; 09b93; >> query: (?x1860, ?x11811) <- contains(?x3818, ?x1860), place_of_death(?x8619, ?x1860), administrative_division(?x11811, ?x3818) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #98592 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 502 *> proper extension: 0tz54; *> query: (?x1860, ?x1523) <- contains(?x3818, ?x1860), place_of_birth(?x8704, ?x1860), location(?x8704, ?x1523) *> conf = 0.13 ranks of expected_values: 5 EVAL 01_d4 place 01_d4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 224.000 202.000 0.161 http://example.org/location/hud_county_place/place #2250-0n1xp PRED entity: 0n1xp PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 130 concepts (65 used for prediction) PRED predicted values (max 10 best out of 5): 09c7w0 (0.89 #370, 0.89 #358, 0.88 #344), 05kkh (0.10 #883, 0.10 #490, 0.08 #385), 03rt9 (0.09 #38, 0.06 #63, 0.03 #152), 02jx1 (0.03 #68, 0.01 #785), 03rjj (0.03 #61) >> Best rule #370 for best value: >> intensional similarity = 3 >> extensional distance = 242 >> proper extension: 0n5j_; 0fm9_; 0f4y_; 0jcgs; 0mx4_; 0mwl2; 0mw89; 0mw93; 0m7fm; 0drsm; ... >> query: (?x6954, ?x94) <- adjoins(?x10235, ?x6954), currency(?x6954, ?x170), second_level_divisions(?x94, ?x10235) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n1xp second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 65.000 0.885 http://example.org/location/country/second_level_divisions #2249-0mmd6 PRED entity: 0mmd6 PRED relation: sport PRED expected values: 02vx4 => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 8): 02vx4 (0.90 #752, 0.90 #697, 0.89 #625), 0z74 (0.27 #1316, 0.02 #914, 0.01 #987), 018jz (0.20 #464, 0.12 #846, 0.12 #984), 0jm_ (0.16 #790, 0.15 #799, 0.14 #982), 03tmr (0.15 #842, 0.13 #460, 0.11 #788), 018w8 (0.12 #845, 0.11 #1074, 0.09 #791), 09xp_ (0.07 #465, 0.02 #793, 0.02 #985), 039yzs (0.04 #986, 0.04 #1241, 0.04 #1104) >> Best rule #752 for best value: >> intensional similarity = 9 >> extensional distance = 82 >> proper extension: 046vvc; >> query: (?x13542, 02vx4) <- position(?x13542, ?x203), position(?x13542, ?x63), teams(?x9026, ?x13542), team(?x530, ?x13542), ?x63 = 02sdk9v, ?x530 = 02_j1w, ?x203 = 0dgrmp, contains(?x1310, ?x9026), locations(?x14038, ?x1310) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mmd6 sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.905 http://example.org/sports/sports_team/sport #2248-081lh PRED entity: 081lh PRED relation: influenced_by! PRED expected values: 05rx__ => 140 concepts (76 used for prediction) PRED predicted values (max 10 best out of 398): 0c00lh (0.25 #3741, 0.24 #4244, 0.13 #5752), 0343h (0.20 #541, 0.04 #5569, 0.04 #3558), 040rjq (0.14 #1477, 0.11 #6003, 0.10 #6505), 02yy_j (0.14 #1380, 0.10 #1883, 0.08 #4398), 081lh (0.14 #1030, 0.10 #1533, 0.07 #35205), 085pr (0.14 #1128, 0.10 #1631, 0.07 #27657), 01d5g (0.14 #1483, 0.05 #2991, 0.05 #3494), 01cpqk (0.10 #3015), 01hb6v (0.10 #15672, 0.07 #20198, 0.07 #26737), 05rx__ (0.10 #10857, 0.05 #10355, 0.04 #13369) >> Best rule #3741 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 05ty4m; 03dbds; 023w9s; 0py5b; >> query: (?x986, 0c00lh) <- film(?x986, ?x1330), film(?x489, ?x1330), influenced_by(?x329, ?x986) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #10857 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 015cbq; *> query: (?x986, 05rx__) <- film(?x986, ?x718), profession(?x986, ?x353), influenced_by(?x329, ?x986) *> conf = 0.10 ranks of expected_values: 10 EVAL 081lh influenced_by! 05rx__ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 140.000 76.000 0.250 http://example.org/influence/influence_node/influenced_by #2247-017jd9 PRED entity: 017jd9 PRED relation: genre PRED expected values: 07s9rl0 01hmnh 06l3bl => 89 concepts (88 used for prediction) PRED predicted values (max 10 best out of 110): 07s9rl0 (0.73 #8910, 0.71 #721, 0.68 #1563), 01jfsb (0.43 #7479, 0.42 #7719, 0.37 #1214), 05p553 (0.41 #3858, 0.37 #2049, 0.36 #125), 02kdv5l (0.36 #603, 0.36 #1084, 0.34 #7469), 02l7c8 (0.31 #1579, 0.29 #5316, 0.28 #3025), 0lsxr (0.29 #10, 0.25 #7476, 0.25 #7716), 01hmnh (0.25 #1099, 0.23 #618, 0.22 #7484), 06n90 (0.24 #1095, 0.22 #614, 0.20 #494), 04xvlr (0.19 #1564, 0.17 #1443, 0.16 #722), 03bxz7 (0.18 #295, 0.12 #775, 0.12 #1496) >> Best rule #8910 for best value: >> intensional similarity = 3 >> extensional distance = 1383 >> proper extension: 04svwx; >> query: (?x4610, 07s9rl0) <- genre(?x4610, ?x6647), genre(?x5538, ?x6647), ?x5538 = 039zft >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7, 13 EVAL 017jd9 genre 06l3bl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 89.000 88.000 0.726 http://example.org/film/film/genre EVAL 017jd9 genre 01hmnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 89.000 88.000 0.726 http://example.org/film/film/genre EVAL 017jd9 genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 88.000 0.726 http://example.org/film/film/genre #2246-01w9ph_ PRED entity: 01w9ph_ PRED relation: influenced_by PRED expected values: 0453t => 146 concepts (68 used for prediction) PRED predicted values (max 10 best out of 367): 040_9 (0.40 #527, 0.25 #1390, 0.12 #13473), 08433 (0.38 #1315, 0.25 #20, 0.20 #452), 06whf (0.33 #985, 0.25 #1417, 0.25 #122), 03_87 (0.33 #1062, 0.25 #199, 0.22 #13577), 02kz_ (0.33 #1031, 0.25 #168, 0.20 #600), 073v6 (0.33 #947, 0.25 #84, 0.12 #1379), 084w8 (0.33 #866, 0.20 #435, 0.13 #13810), 01w60_p (0.30 #2215, 0.09 #6962, 0.08 #5666), 042q3 (0.25 #361, 0.17 #1224, 0.16 #15037), 01v9724 (0.25 #175, 0.17 #1038, 0.13 #13810) >> Best rule #527 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03qcq; 08433; >> query: (?x8004, 040_9) <- influenced_by(?x8004, ?x2845), type_of_union(?x8004, ?x11744), ?x2845 = 0lrh, people(?x5855, ?x8004) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w9ph_ influenced_by 0453t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 146.000 68.000 0.400 http://example.org/influence/influence_node/influenced_by #2245-0d9jr PRED entity: 0d9jr PRED relation: film_regional_debut_venue! PRED expected values: 0crh5_f => 266 concepts (260 used for prediction) PRED predicted values (max 10 best out of 22): 0crh5_f (0.36 #428, 0.17 #1731, 0.14 #1545), 01sby_ (0.18 #472, 0.13 #659, 0.12 #1031), 0b44shh (0.18 #469, 0.13 #656, 0.12 #1028), 0blpg (0.18 #446, 0.13 #633, 0.12 #1005), 0gffmn8 (0.18 #432, 0.13 #619, 0.10 #1549), 0btpm6 (0.18 #516, 0.13 #703, 0.10 #1633), 01s9vc (0.18 #550, 0.13 #737, 0.10 #1667), 0hv81 (0.09 #485, 0.09 #1788, 0.07 #3462), 0267wwv (0.09 #547, 0.07 #734, 0.06 #1106), 0cbn7c (0.09 #524, 0.07 #711, 0.06 #1083) >> Best rule #428 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0rh6k; 04jpl; 02cl1; 02_286; 030qb3t; 01_d4; 013yq; 0vzm; 05qtj; >> query: (?x5267, 0crh5_f) <- teams(?x5267, ?x3114), locations(?x4368, ?x5267), featured_film_locations(?x641, ?x5267), place_founded(?x3795, ?x5267) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d9jr film_regional_debut_venue! 0crh5_f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 266.000 260.000 0.364 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #2244-0gq_v PRED entity: 0gq_v PRED relation: ceremony PRED expected values: 02yv_b 0ftlkg 073h1t 0c53zb 02ywhz 02glmx 09306z => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 90): 0dth6b (0.81 #558, 0.40 #108, 0.29 #468), 02ywhz (0.81 #593, 0.40 #143, 0.29 #503), 09306z (0.81 #610, 0.40 #160, 0.29 #520), 02yv_b (0.76 #559, 0.40 #109, 0.29 #469), 073h1t (0.76 #561, 0.24 #471, 0.22 #381), 0c4hnm (0.71 #622, 0.40 #172, 0.29 #532), 0c4hx0 (0.71 #621, 0.40 #171, 0.29 #531), 02glmx (0.71 #595, 0.40 #145, 0.24 #505), 0bzkvd (0.71 #612, 0.40 #162, 0.24 #522), 0fz20l (0.62 #578, 0.40 #128, 0.21 #3781) >> Best rule #558 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 018wng; 0gq_d; 0gr07; >> query: (?x484, 0dth6b) <- award(?x9062, ?x484), place_of_birth(?x9062, ?x1131), ceremony(?x484, ?x7100), ?x7100 = 0bzmt8 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 018wng; 0gq_d; 0gr07; *> query: (?x484, 02ywhz) <- award(?x9062, ?x484), place_of_birth(?x9062, ?x1131), ceremony(?x484, ?x7100), ?x7100 = 0bzmt8 *> conf = 0.81 ranks of expected_values: 2, 3, 4, 5, 8, 21, 27 EVAL 0gq_v ceremony 09306z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 46.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq_v ceremony 02glmx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 46.000 46.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq_v ceremony 02ywhz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 46.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq_v ceremony 0c53zb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 46.000 46.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq_v ceremony 073h1t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 46.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq_v ceremony 0ftlkg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 46.000 46.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gq_v ceremony 02yv_b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 46.000 0.810 http://example.org/award/award_category/winners./award/award_honor/ceremony #2243-0k_mf PRED entity: 0k_mf PRED relation: place_of_death! PRED expected values: 05yzt_ => 79 concepts (27 used for prediction) PRED predicted values (max 10 best out of 509): 0b82vw (0.14 #64, 0.07 #820, 0.05 #1576), 09xvf7 (0.14 #710, 0.07 #1466, 0.05 #2222), 06lk0_ (0.14 #700, 0.07 #1456, 0.05 #2212), 01200d (0.14 #668, 0.07 #1424, 0.05 #2180), 02_01w (0.14 #660, 0.07 #1416, 0.05 #2172), 0blpnz (0.14 #659, 0.07 #1415, 0.05 #2171), 04d2yp (0.14 #655, 0.07 #1411, 0.05 #2167), 0jnb0 (0.14 #645, 0.07 #1401, 0.05 #2157), 012c6j (0.14 #644, 0.07 #1400, 0.05 #2156), 03zrp (0.14 #590, 0.07 #1346, 0.05 #2102) >> Best rule #64 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0k049; 0r0m6; 0r00l; >> query: (?x11600, 0b82vw) <- contains(?x2949, ?x11600), contains(?x1227, ?x11600), ?x1227 = 01n7q, citytown(?x8363, ?x11600), ?x2949 = 0kpys >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k_mf place_of_death! 05yzt_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 27.000 0.143 http://example.org/people/deceased_person/place_of_death #2242-0807ml PRED entity: 0807ml PRED relation: profession PRED expected values: 02hrh1q => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 44): 02hrh1q (0.89 #1515, 0.88 #5265, 0.88 #3165), 03gjzk (0.34 #616, 0.34 #766, 0.33 #466), 01d_h8 (0.32 #306, 0.30 #2406, 0.30 #3006), 0dxtg (0.31 #764, 0.30 #614, 0.29 #464), 02jknp (0.26 #7653, 0.25 #6002, 0.24 #158), 02krf9 (0.26 #7653, 0.25 #6002, 0.20 #28), 0np9r (0.26 #7653, 0.25 #6002, 0.14 #6774), 018gz8 (0.26 #7653, 0.25 #6002, 0.13 #768), 0cbd2 (0.26 #7653, 0.25 #6002, 0.12 #1957), 0gl2ny2 (0.20 #69, 0.03 #1719, 0.03 #2019) >> Best rule #1515 for best value: >> intensional similarity = 3 >> extensional distance = 968 >> proper extension: 033071; >> query: (?x6361, 02hrh1q) <- nominated_for(?x6361, ?x2009), location(?x6361, ?x1131), film(?x6361, ?x394) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0807ml profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.890 http://example.org/people/person/profession #2241-03sww PRED entity: 03sww PRED relation: category PRED expected values: 08mbj5d => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #21, 0.82 #7, 0.78 #34) >> Best rule #21 for best value: >> intensional similarity = 2 >> extensional distance = 643 >> proper extension: 04l19_; 020jqv; >> query: (?x4877, 08mbj5d) <- award(?x4877, ?x2456), artist(?x2149, ?x4877) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03sww category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.822 http://example.org/common/topic/webpage./common/webpage/category #2240-04sry PRED entity: 04sry PRED relation: religion PRED expected values: 0kq2 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 23): 03_gx (0.18 #277, 0.17 #2656, 0.11 #13), 0kpl (0.17 #97, 0.16 #2652, 0.16 #141), 019cr (0.11 #10, 0.03 #890, 0.03 #1022), 0v53x (0.11 #28, 0.02 #908, 0.02 #512), 0631_ (0.11 #7, 0.02 #2650, 0.02 #491), 092bf5 (0.08 #147, 0.04 #2658, 0.03 #103), 03j6c (0.07 #2663, 0.04 #1912, 0.03 #108), 0kq2 (0.07 #105, 0.07 #61, 0.04 #501), 0flw86 (0.05 #2645, 0.02 #1894, 0.02 #1850), 01lp8 (0.05 #2644, 0.03 #881, 0.03 #397) >> Best rule #277 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 01twdk; >> query: (?x7310, 03_gx) <- award_winner(?x493, ?x7310), people(?x3591, ?x7310), film(?x7310, ?x1619) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #105 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 01p1z_; 020x5r; 0mb5x; *> query: (?x7310, 0kq2) <- award(?x7310, ?x746), ?x746 = 04dn09n, film(?x7310, ?x1916) *> conf = 0.07 ranks of expected_values: 8 EVAL 04sry religion 0kq2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 127.000 127.000 0.183 http://example.org/people/person/religion #2239-03ym1 PRED entity: 03ym1 PRED relation: profession PRED expected values: 02hrh1q => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 70): 02hrh1q (0.91 #1215, 0.90 #1065, 0.89 #465), 0dxtg (0.89 #614, 0.34 #764, 0.31 #914), 01d_h8 (0.64 #606, 0.46 #906, 0.42 #1506), 02jknp (0.43 #608, 0.36 #308, 0.33 #8), 03gjzk (0.36 #616, 0.33 #916, 0.30 #1516), 0np9r (0.35 #3301, 0.26 #472, 0.20 #4973), 09jwl (0.35 #3301, 0.25 #2270, 0.23 #1520), 05z96 (0.35 #3301, 0.05 #794, 0.05 #494), 0lgw7 (0.35 #3301, 0.05 #499), 0cbd2 (0.32 #757, 0.29 #607, 0.16 #3608) >> Best rule #1215 for best value: >> intensional similarity = 3 >> extensional distance = 286 >> proper extension: 04bs3j; 0151ns; 0htlr; 04shbh; 0n6f8; 0prjs; 0f2df; 040wdl; 0285c; 03rl84; ... >> query: (?x5661, 02hrh1q) <- film(?x5661, ?x97), place_of_birth(?x5661, ?x7925), languages(?x5661, ?x254) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03ym1 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.906 http://example.org/people/person/profession #2238-01fdc0 PRED entity: 01fdc0 PRED relation: type_of_union PRED expected values: 04ztj => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #49, 0.87 #53, 0.85 #45), 01g63y (0.48 #221, 0.30 #10, 0.19 #42), 01bl8s (0.02 #31, 0.02 #35) >> Best rule #49 for best value: >> intensional similarity = 2 >> extensional distance = 228 >> proper extension: 0lh0c; >> query: (?x3533, 04ztj) <- gender(?x3533, ?x514), location_of_ceremony(?x3533, ?x335) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01fdc0 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.870 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2237-03yf4d PRED entity: 03yf4d PRED relation: profession PRED expected values: 0dxtg => 110 concepts (58 used for prediction) PRED predicted values (max 10 best out of 63): 01d_h8 (0.93 #1746, 0.88 #1166, 0.88 #5373), 02hrh1q (0.91 #3930, 0.90 #4075, 0.88 #4220), 0dxtg (0.85 #1319, 0.84 #1464, 0.84 #1029), 02jknp (0.73 #153, 0.67 #298, 0.42 #5375), 018gz8 (0.63 #742, 0.47 #1612, 0.40 #1177), 02krf9 (0.45 #1186, 0.33 #1621, 0.32 #1041), 09jwl (0.42 #6691, 0.38 #7563, 0.37 #6111), 0cbd2 (0.41 #877, 0.27 #587, 0.25 #297), 015cjr (0.30 #772, 0.14 #1207, 0.14 #1642), 0196pc (0.29 #360, 0.27 #215, 0.20 #70) >> Best rule #1746 for best value: >> intensional similarity = 5 >> extensional distance = 82 >> proper extension: 01t6b4; 02j8nx; 07lwsz; 07fvf1; 03xp8d5; 0m66w; 0dbc1s; 0bkf72; >> query: (?x12154, 01d_h8) <- type_of_union(?x12154, ?x566), profession(?x12154, ?x2848), program(?x12154, ?x8017), film_crew_role(?x1644, ?x2848), country(?x1644, ?x94) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1319 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 60 *> proper extension: 05qsxy; *> query: (?x12154, 0dxtg) <- type_of_union(?x12154, ?x566), nationality(?x12154, ?x94), tv_program(?x12154, ?x8017), student(?x99, ?x12154) *> conf = 0.85 ranks of expected_values: 3 EVAL 03yf4d profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 110.000 58.000 0.929 http://example.org/people/person/profession #2236-084w8 PRED entity: 084w8 PRED relation: influenced_by! PRED expected values: 0bv7t => 170 concepts (73 used for prediction) PRED predicted values (max 10 best out of 396): 05jm7 (0.45 #3639, 0.40 #3138, 0.19 #4641), 084w8 (0.40 #1001, 0.33 #1, 0.25 #4005), 073v6 (0.40 #1113, 0.31 #4117, 0.15 #4618), 0d4jl (0.40 #1112, 0.19 #4116, 0.14 #2114), 034bs (0.33 #150, 0.25 #4154, 0.11 #2652), 0zm1 (0.33 #161, 0.25 #4165, 0.11 #2663), 07dnx (0.33 #351, 0.20 #2002, 0.20 #1851), 041_y (0.33 #273, 0.19 #4277, 0.11 #2775), 058vp (0.33 #227, 0.19 #4231, 0.11 #2729), 04hcw (0.33 #280, 0.15 #5790, 0.12 #4284) >> Best rule #3639 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0g5ff; >> query: (?x118, 05jm7) <- influenced_by(?x5335, ?x118), influenced_by(?x5034, ?x118), ?x5034 = 03772, profession(?x5335, ?x353) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #5008 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 0ldd; *> query: (?x118, ?x476) <- gender(?x118, ?x231), influenced_by(?x118, ?x5004), influenced_by(?x118, ?x4072), ?x4072 = 02lt8, influenced_by(?x476, ?x5004) *> conf = 0.07 ranks of expected_values: 181 EVAL 084w8 influenced_by! 0bv7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 170.000 73.000 0.455 http://example.org/influence/influence_node/influenced_by #2235-01fyzy PRED entity: 01fyzy PRED relation: company PRED expected values: 06rq1k => 109 concepts (98 used for prediction) PRED predicted values (max 10 best out of 16): 07wh1 (0.09 #375, 0.03 #569, 0.02 #763), 03zj9 (0.05 #282, 0.02 #670, 0.01 #1058), 03mp8k (0.05 #332), 01q940 (0.05 #320), 0gsg7 (0.03 #414, 0.02 #802, 0.01 #996), 06rq1k (0.02 #611, 0.02 #805, 0.01 #999), 0ky6d (0.02 #768, 0.01 #1156), 017z88 (0.02 #625, 0.01 #1013), 09c7w0 (0.02 #8138, 0.02 #6006, 0.01 #6199), 01skqzw (0.01 #6572) >> Best rule #375 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 01pfkw; >> query: (?x5975, 07wh1) <- nationality(?x5975, ?x94), tv_program(?x5975, ?x6884), languages(?x5975, ?x254) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #611 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 0cj2nl; 04511f; 054187; 070j61; 0brkwj; 0b2_xp; 0cj2k3; 027j79k; 06y9bd; 03p01x; *> query: (?x5975, 06rq1k) <- nationality(?x5975, ?x94), tv_program(?x5975, ?x6884), written_by(?x4054, ?x5975) *> conf = 0.02 ranks of expected_values: 6 EVAL 01fyzy company 06rq1k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 109.000 98.000 0.091 http://example.org/people/person/employment_history./business/employment_tenure/company #2234-01s21dg PRED entity: 01s21dg PRED relation: profession PRED expected values: 0nbcg => 149 concepts (95 used for prediction) PRED predicted values (max 10 best out of 66): 0dxtg (0.65 #437, 0.55 #579, 0.54 #3846), 03gjzk (0.65 #438, 0.40 #580, 0.39 #7119), 0nbcg (0.60 #2583, 0.57 #879, 0.56 #311), 01d_h8 (0.50 #430, 0.41 #6116, 0.41 #6542), 01c72t (0.39 #1297, 0.36 #3711, 0.35 #5561), 0np9r (0.31 #4263, 0.30 #442, 0.29 #584), 0kyk (0.31 #4263, 0.29 #593, 0.25 #25), 02jknp (0.31 #4263, 0.24 #3272, 0.22 #290), 0d1pc (0.28 #2316, 0.24 #4307, 0.21 #754), 025352 (0.22 #337, 0.17 #53, 0.12 #1615) >> Best rule #437 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 02zrv7; 02pzck; >> query: (?x4741, 0dxtg) <- profession(?x4741, ?x1146), participant(?x4741, ?x4536), ?x1146 = 018gz8 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #2583 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 95 *> proper extension: 01gx5f; 06br6t; *> query: (?x4741, 0nbcg) <- role(?x4741, ?x227), artists(?x302, ?x4741), ?x302 = 016clz *> conf = 0.60 ranks of expected_values: 3 EVAL 01s21dg profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 149.000 95.000 0.650 http://example.org/people/person/profession #2233-047n8xt PRED entity: 047n8xt PRED relation: films! PRED expected values: 0465_ => 90 concepts (51 used for prediction) PRED predicted values (max 10 best out of 71): 0fx2s (0.09 #387, 0.06 #3224, 0.05 #2907), 01w1sx (0.08 #564, 0.07 #1195, 0.07 #1510), 01d5g (0.06 #1999, 0.05 #1372, 0.03 #3418), 03hzt (0.06 #292, 0.04 #449, 0.03 #1711), 07_nf (0.06 #224, 0.04 #698, 0.04 #540), 0l8bg (0.06 #274, 0.02 #748, 0.02 #1063), 0cw10 (0.06 #261, 0.02 #735, 0.02 #1050), 03f5vvx (0.06 #189, 0.02 #978, 0.02 #1608), 07c1v (0.06 #302, 0.02 #1091, 0.02 #1721), 0mkz (0.06 #185, 0.02 #974, 0.02 #1604) >> Best rule #387 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 053tj7; >> query: (?x2121, 0fx2s) <- genre(?x2121, ?x1316), ?x1316 = 017fp, category(?x2121, ?x134) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 047n8xt films! 0465_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 90.000 51.000 0.087 http://example.org/film/film_subject/films #2232-0f0p0 PRED entity: 0f0p0 PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 41): 09c7w0 (0.84 #2602, 0.83 #801, 0.78 #901), 07ssc (0.40 #9837, 0.18 #615, 0.14 #515), 0kpys (0.32 #4915, 0.32 #4813), 01n7q (0.32 #4915, 0.32 #4813), 02jx1 (0.14 #633, 0.14 #533, 0.14 #1733), 0f8l9c (0.07 #3707, 0.05 #6123, 0.04 #1422), 059j2 (0.07 #3707, 0.05 #6123, 0.03 #6727), 082fr (0.07 #3707, 0.05 #6123, 0.03 #6727), 03gj2 (0.07 #3707, 0.05 #6123, 0.03 #6727), 0154j (0.07 #3707, 0.05 #6123, 0.03 #6727) >> Best rule #2602 for best value: >> intensional similarity = 3 >> extensional distance = 333 >> proper extension: 0cl_m; 015n8; >> query: (?x1021, 09c7w0) <- place_of_death(?x1021, ?x12299), gender(?x1021, ?x231), state(?x12299, ?x1227) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f0p0 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.836 http://example.org/people/person/nationality #2231-02j69w PRED entity: 02j69w PRED relation: film! PRED expected values: 0151w_ => 70 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1027): 03dbds (0.24 #16648, 0.22 #29134, 0.21 #33298), 0bxtg (0.20 #77, 0.07 #29132, 0.07 #4240), 025j1t (0.20 #1074, 0.07 #29132, 0.07 #41623), 03fbb6 (0.20 #976, 0.07 #29132, 0.07 #41623), 04wg38 (0.20 #1340, 0.07 #29132, 0.07 #41623), 0169dl (0.20 #401, 0.07 #23290, 0.07 #10805), 078mgh (0.20 #1421, 0.07 #41623, 0.06 #56193), 025n3p (0.20 #490, 0.07 #41623, 0.06 #56193), 0zcbl (0.20 #1219, 0.05 #3301, 0.02 #17867), 04t7ts (0.20 #210, 0.04 #10614, 0.02 #29344) >> Best rule #16648 for best value: >> intensional similarity = 5 >> extensional distance = 121 >> proper extension: 0pvms; 0cn_b8; 042fgh; >> query: (?x4694, ?x7621) <- film(?x2927, ?x4694), country(?x4694, ?x94), celebrity(?x5240, ?x2927), written_by(?x4694, ?x7621), featured_film_locations(?x4694, ?x3269) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #10567 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 74 *> proper extension: 01gvpz; 01c9d; *> query: (?x4694, 0151w_) <- film(?x2927, ?x4694), country(?x4694, ?x94), celebrity(?x5240, ?x2927), written_by(?x4694, ?x7621), films(?x1083, ?x4694) *> conf = 0.04 ranks of expected_values: 142 EVAL 02j69w film! 0151w_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 70.000 36.000 0.243 http://example.org/film/actor/film./film/performance/film #2230-03ln8b PRED entity: 03ln8b PRED relation: honored_for! PRED expected values: 09bymc => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 83): 09qftb (0.34 #2205, 0.28 #2438, 0.26 #3832), 07y_p6 (0.34 #2205, 0.28 #2438, 0.26 #3832), 09gkdln (0.34 #2205, 0.28 #2438, 0.26 #3832), 05c1t6z (0.33 #126, 0.25 #2215, 0.25 #2448), 0gvstc3 (0.33 #142, 0.24 #722, 0.23 #1766), 09g90vz (0.33 #218, 0.11 #7780, 0.08 #9408), 0drtv8 (0.33 #518, 0.10 #402, 0.06 #1678), 0418154 (0.33 #203, 0.08 #783, 0.06 #1015), 05zksls (0.33 #143, 0.07 #607, 0.06 #1999), 02wzl1d (0.33 #123, 0.07 #587, 0.05 #1631) >> Best rule #2205 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 01f3p_; 06qwh; 0fkwzs; 04mx8h4; 03g9xj; >> query: (?x2078, ?x1112) <- actor(?x2078, ?x2129), nominated_for(?x515, ?x2078), award_winner(?x1112, ?x2129), producer_type(?x2078, ?x632) >> conf = 0.34 => this is the best rule for 3 predicted values *> Best rule #7780 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 844 *> proper extension: 0407yfx; *> query: (?x2078, ?x1112) <- nominated_for(?x3955, ?x2078), award(?x2078, ?x678), award_winner(?x1112, ?x3955) *> conf = 0.11 ranks of expected_values: 22 EVAL 03ln8b honored_for! 09bymc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 93.000 93.000 0.339 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2229-0gg8l PRED entity: 0gg8l PRED relation: artists PRED expected values: 01lmj3q 01kstn9 02cx90 05sq0m => 56 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1006): 067mj (0.60 #6471, 0.50 #5409, 0.50 #4345), 01kv4mb (0.50 #5465, 0.50 #5308, 0.50 #4401), 0gcs9 (0.50 #5550, 0.50 #4486, 0.50 #2364), 01vrncs (0.50 #5377, 0.50 #4313, 0.50 #2191), 03c3yf (0.50 #7043, 0.50 #5981, 0.50 #4917), 01309x (0.50 #5618, 0.50 #4554, 0.40 #6680), 0qf11 (0.50 #5680, 0.50 #4616, 0.40 #6742), 01kph_c (0.50 #5721, 0.50 #4657, 0.40 #6783), 01vrx35 (0.50 #5989, 0.50 #4925, 0.40 #7051), 0394y (0.50 #6766, 0.50 #4640, 0.39 #19120) >> Best rule #6471 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 016jhr; >> query: (?x8798, 067mj) <- artists(?x8798, ?x6456), artists(?x8798, ?x3202), ?x6456 = 0k1bs, award_winner(?x341, ?x3202), category(?x3202, ?x134), profession(?x3202, ?x131), artist(?x2241, ?x3202), place_of_birth(?x3202, ?x9302) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2766 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 01lyv; 02qdgx; 02w4v; *> query: (?x8798, 05sq0m) <- artists(?x8798, ?x7937), artists(?x8798, ?x6456), profession(?x6456, ?x955), artists(?x7083, ?x6456), people(?x7322, ?x6456), location(?x6456, ?x2633), ?x7937 = 018phr, ?x7083 = 02yv6b *> conf = 0.33 ranks of expected_values: 133, 141, 143, 192 EVAL 0gg8l artists 05sq0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 56.000 29.000 0.600 http://example.org/music/genre/artists EVAL 0gg8l artists 02cx90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 56.000 29.000 0.600 http://example.org/music/genre/artists EVAL 0gg8l artists 01kstn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 56.000 29.000 0.600 http://example.org/music/genre/artists EVAL 0gg8l artists 01lmj3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 56.000 29.000 0.600 http://example.org/music/genre/artists #2228-02sdwt PRED entity: 02sdwt PRED relation: state_province_region PRED expected values: 059rby => 74 concepts (47 used for prediction) PRED predicted values (max 10 best out of 52): 059rby (0.60 #127, 0.56 #991, 0.50 #4), 0cr3d (0.27 #3596, 0.27 #370, 0.27 #1611), 02_286 (0.27 #3596, 0.27 #370, 0.27 #1611), 01n7q (0.18 #636, 0.17 #512, 0.17 #760), 05k7sb (0.14 #897, 0.05 #401, 0.05 #649), 05kkh (0.06 #744, 0.04 #1365, 0.03 #1613), 05tbn (0.05 #917, 0.05 #3523, 0.05 #297), 0jt5zcn (0.05 #280, 0.04 #652, 0.04 #1274), 05kr_ (0.05 #275, 0.04 #399, 0.02 #1392), 0rh6k (0.05 #495, 0.02 #1736, 0.02 #2356) >> Best rule #127 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 021q2j; >> query: (?x10621, 059rby) <- contains(?x2850, ?x10621), contains(?x739, ?x10621), ?x2850 = 0cr3d, ?x739 = 02_286, campuses(?x10621, ?x10621) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02sdwt state_province_region 059rby CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 47.000 0.600 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #2227-01k23t PRED entity: 01k23t PRED relation: artists! PRED expected values: 05bt6j => 123 concepts (100 used for prediction) PRED predicted values (max 10 best out of 250): 0xhtw (0.33 #3090, 0.28 #5552, 0.28 #4013), 05bt6j (0.32 #2193, 0.31 #1578, 0.31 #1271), 0155w (0.31 #410, 0.27 #1332, 0.20 #3485), 016clz (0.30 #5541, 0.29 #4002, 0.28 #2156), 03_d0 (0.28 #318, 0.20 #3701, 0.20 #8317), 0ggx5q (0.27 #1303, 0.16 #5919, 0.16 #1610), 01lyv (0.25 #340, 0.23 #6492, 0.22 #7725), 02k_kn (0.25 #1290, 0.19 #368, 0.13 #2212), 0glt670 (0.24 #11112, 0.24 #5884, 0.24 #7731), 02lnbg (0.23 #1283, 0.18 #1590, 0.15 #5899) >> Best rule #3090 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 053y0s; >> query: (?x7794, 0xhtw) <- role(?x7794, ?x1148), gender(?x7794, ?x514), group(?x7794, ?x7407) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2193 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 51 *> proper extension: 0153nq; *> query: (?x7794, 05bt6j) <- artist(?x7793, ?x7794), ?x7793 = 01dtcb *> conf = 0.32 ranks of expected_values: 2 EVAL 01k23t artists! 05bt6j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 123.000 100.000 0.331 http://example.org/music/genre/artists #2226-029g_vk PRED entity: 029g_vk PRED relation: industry! PRED expected values: 01s73z => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 409): 04sv4 (0.78 #4353, 0.33 #111, 0.30 #6236), 06q07 (0.50 #1276, 0.12 #7400, 0.11 #4337), 045c7b (0.33 #294, 0.33 #58, 0.29 #2887), 0py9b (0.33 #93, 0.29 #2922, 0.25 #800), 018p5f (0.33 #323, 0.25 #1031, 0.25 #559), 061v5m (0.33 #311, 0.25 #1019, 0.25 #547), 0k8z (0.33 #4283, 0.25 #1222, 0.14 #3342), 09glbnt (0.30 #4782, 0.25 #4077, 0.25 #3842), 02b07b (0.25 #4214, 0.25 #3979, 0.25 #914), 0181hw (0.25 #4103, 0.25 #3868, 0.25 #803) >> Best rule #4353 for best value: >> intensional similarity = 12 >> extensional distance = 7 >> proper extension: 01mw1; 019z7b; 0hz28; 06xw2; 01mfj; >> query: (?x6575, 04sv4) <- industry(?x7177, ?x6575), industry(?x6676, ?x6575), list(?x6676, ?x5997), state_province_region(?x6676, ?x3818), company(?x4682, ?x6676), company(?x346, ?x6676), company(?x233, ?x6676), currency(?x6676, ?x170), ?x233 = 01rk91, ?x4682 = 0dq_5, service_location(?x7177, ?x94), ?x346 = 060c4 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #7541 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 24 *> proper extension: 01zhp; *> query: (?x6575, ?x94) <- industry(?x6676, ?x6575), industry(?x6574, ?x6575), industry(?x3379, ?x6575), category(?x3379, ?x134), company(?x1491, ?x3379), company(?x346, ?x3379), organization(?x4682, ?x6676), currency(?x6676, ?x170), state_province_region(?x3379, ?x1767), currency(?x6574, ?x1099), ?x1491 = 0krdk, company(?x346, ?x94) *> conf = 0.02 ranks of expected_values: 348 EVAL 029g_vk industry! 01s73z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 83.000 83.000 0.778 http://example.org/business/business_operation/industry #2225-0b_7k PRED entity: 0b_7k PRED relation: award PRED expected values: 019f4v => 94 concepts (62 used for prediction) PRED predicted values (max 10 best out of 307): 09sb52 (0.60 #39, 0.26 #16888, 0.24 #9668), 019f4v (0.46 #868, 0.43 #4478, 0.37 #467), 01by1l (0.38 #1313, 0.30 #8533, 0.28 #9336), 01bgqh (0.31 #1245, 0.21 #8465, 0.20 #9268), 0gq9h (0.30 #4488, 0.28 #4087, 0.28 #3686), 02f6xy (0.30 #1606, 0.22 #9227, 0.22 #402), 02f716 (0.30 #1606, 0.19 #9629, 0.19 #5617), 03tcnt (0.30 #1606, 0.19 #9629, 0.19 #5617), 02f72n (0.30 #1606, 0.19 #9629, 0.19 #5617), 01ckcd (0.30 #1606, 0.19 #9629, 0.19 #5617) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 0hm0k; >> query: (?x2793, 09sb52) <- award_winner(?x2793, ?x7188), award_winner(?x3045, ?x7188), split_to(?x7188, ?x7238) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #868 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 022_lg; 01f8ld; 015njf; 0mm1q; 03s9b; 017yxq; 025jbj; 026670; 0b_dh; *> query: (?x2793, 019f4v) <- profession(?x2793, ?x524), film(?x2793, ?x2057), spouse(?x2793, ?x12462) *> conf = 0.46 ranks of expected_values: 2 EVAL 0b_7k award 019f4v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 94.000 62.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2224-07fj_ PRED entity: 07fj_ PRED relation: form_of_government PRED expected values: 01fpfn => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 5): 01fpfn (0.50 #127, 0.47 #152, 0.47 #147), 01d9r3 (0.48 #184, 0.36 #254, 0.36 #404), 018wl5 (0.39 #26, 0.37 #56, 0.36 #46), 01q20 (0.35 #83, 0.33 #28, 0.32 #88), 026wp (0.11 #155, 0.10 #55, 0.09 #70) >> Best rule #127 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 05bcl; 020d5; >> query: (?x4521, 01fpfn) <- form_of_government(?x4521, ?x48), taxonomy(?x4521, ?x939), nationality(?x10965, ?x4521) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07fj_ form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.500 http://example.org/location/country/form_of_government #2223-01g4yw PRED entity: 01g4yw PRED relation: organization! PRED expected values: 07xl34 => 197 concepts (197 used for prediction) PRED predicted values (max 10 best out of 10): 060c4 (0.70 #1250, 0.70 #1276, 0.68 #522), 07xl34 (0.62 #141, 0.45 #128, 0.43 #102), 0dq_5 (0.33 #555, 0.33 #9, 0.32 #828), 05c0jwl (0.33 #83, 0.32 #148, 0.29 #96), 08jcfy (0.17 #51, 0.14 #64, 0.09 #77), 05k17c (0.17 #20, 0.12 #176, 0.11 #228), 0hm4q (0.10 #515, 0.09 #73, 0.09 #359), 04n1q6 (0.07 #240, 0.06 #149, 0.05 #201), 02wlwtm (0.03 #156, 0.02 #247, 0.01 #468), 09d6p2 (0.02 #309) >> Best rule #1250 for best value: >> intensional similarity = 5 >> extensional distance = 316 >> proper extension: 04bfg; 02qwgk; 02rk23; >> query: (?x13052, 060c4) <- colors(?x13052, ?x663), contains(?x1310, ?x13052), state_province_region(?x13052, ?x12774), institution(?x1368, ?x13052), major_field_of_study(?x1368, ?x254) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #141 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 27 *> proper extension: 0ymcz; *> query: (?x13052, 07xl34) <- currency(?x13052, ?x1099), school_type(?x13052, ?x3092), ?x1099 = 01nv4h, institution(?x1368, ?x13052), ?x1368 = 014mlp *> conf = 0.62 ranks of expected_values: 2 EVAL 01g4yw organization! 07xl34 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 197.000 197.000 0.698 http://example.org/organization/role/leaders./organization/leadership/organization #2222-02csf PRED entity: 02csf PRED relation: major_field_of_study! PRED expected values: 027xq5 => 50 concepts (42 used for prediction) PRED predicted values (max 10 best out of 796): 015nl4 (0.68 #20254, 0.55 #19660, 0.48 #21445), 09f2j (0.67 #7326, 0.57 #8516, 0.57 #9112), 01w5m (0.61 #11440, 0.60 #7265, 0.56 #10846), 027xq5 (0.58 #11914, 0.54 #8335, 0.52 #11320), 0h6rm (0.58 #11914, 0.54 #8335, 0.52 #11320), 017j69 (0.57 #1950, 0.56 #3737, 0.50 #6711), 03ksy (0.54 #11441, 0.53 #7266, 0.52 #19781), 02zd460 (0.54 #11517, 0.50 #13305, 0.48 #12112), 08815 (0.53 #7147, 0.53 #13707, 0.50 #13110), 07szy (0.53 #7189, 0.48 #1190, 0.43 #8379) >> Best rule #20254 for best value: >> intensional similarity = 11 >> extensional distance = 69 >> proper extension: 02jhc; >> query: (?x14034, ?x2486) <- major_field_of_study(?x10940, ?x14034), category(?x10940, ?x134), contains(?x362, ?x10940), student(?x10940, ?x5591), student(?x2486, ?x5591), award(?x5591, ?x68), location(?x5591, ?x1758), profession(?x5591, ?x1943), written_by(?x6272, ?x5591), ?x134 = 08mbj5d, ?x1943 = 02krf9 >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #11914 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 26 *> proper extension: 0dc_v; *> query: (?x14034, ?x4390) <- student(?x14034, ?x5332), major_field_of_study(?x14034, ?x373), award(?x5332, ?x112), award_winner(?x112, ?x395), award(?x144, ?x112), profession(?x5332, ?x1032), nominated_for(?x112, ?x167), student(?x4390, ?x5332), major_field_of_study(?x735, ?x373), disciplines_or_subjects(?x277, ?x373) *> conf = 0.58 ranks of expected_values: 4 EVAL 02csf major_field_of_study! 027xq5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 50.000 42.000 0.675 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #2221-02nt3d PRED entity: 02nt3d PRED relation: film_crew_role PRED expected values: 089fss => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 25): 02r96rf (0.68 #183, 0.68 #39, 0.64 #979), 0dxtw (0.40 #46, 0.38 #190, 0.36 #986), 01vx2h (0.34 #1414, 0.33 #47, 0.31 #191), 01pvkk (0.34 #1414, 0.30 #192, 0.29 #1536), 02ynfr (0.34 #1414, 0.26 #361, 0.25 #16), 0215hd (0.34 #1414, 0.26 #361, 0.25 #19), 089g0h (0.34 #1414, 0.26 #361, 0.19 #20), 01xy5l_ (0.34 #1414, 0.26 #361, 0.10 #990), 015h31 (0.34 #1414, 0.26 #361, 0.08 #1094), 02vs3x5 (0.34 #1414, 0.26 #361, 0.07 #60) >> Best rule #183 for best value: >> intensional similarity = 4 >> extensional distance = 128 >> proper extension: 01f7gh; 0ddt_; 02qzh2; 011xg5; >> query: (?x6198, 02r96rf) <- nominated_for(?x1312, ?x6198), nominated_for(?x6198, ?x2102), film_crew_role(?x6198, ?x137), nominated_for(?x794, ?x6198) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 02pxmgz; 03459x; 0ch3qr1; 0bxxzb; 02mmwk; 02v570; 02z9rr; 0gzlb9; 02bqvs; 0gwgn1k; ... *> query: (?x6198, 089fss) <- nominated_for(?x1312, ?x6198), film_release_distribution_medium(?x6198, ?x81), ?x1312 = 07cbcy, film_crew_role(?x6198, ?x137) *> conf = 0.12 ranks of expected_values: 17 EVAL 02nt3d film_crew_role 089fss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 84.000 84.000 0.685 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2220-01ppq PRED entity: 01ppq PRED relation: country! PRED expected values: 06f41 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 49): 03_8r (0.73 #265, 0.71 #510, 0.71 #608), 01cgz (0.66 #306, 0.62 #257, 0.62 #600), 01lb14 (0.59 #14, 0.55 #259, 0.53 #602), 03hr1p (0.56 #266, 0.54 #21, 0.51 #903), 06f41 (0.53 #258, 0.51 #601, 0.50 #895), 07jbh (0.53 #274, 0.50 #911, 0.49 #29), 064vjs (0.52 #272, 0.44 #321, 0.44 #517), 07gyv (0.51 #301, 0.51 #497, 0.49 #595), 03fyrh (0.47 #270, 0.44 #368, 0.42 #613), 0486tv (0.47 #280, 0.40 #525, 0.39 #35) >> Best rule #265 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 06t2t; >> query: (?x8958, 03_8r) <- contains(?x6304, ?x8958), ?x6304 = 02qkt, olympics(?x8958, ?x2966) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #258 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 62 *> proper extension: 06t2t; *> query: (?x8958, 06f41) <- contains(?x6304, ?x8958), ?x6304 = 02qkt, olympics(?x8958, ?x2966) *> conf = 0.53 ranks of expected_values: 5 EVAL 01ppq country! 06f41 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 63.000 63.000 0.734 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #2219-03c9pqt PRED entity: 03c9pqt PRED relation: gender PRED expected values: 05zppz => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #23, 0.88 #13, 0.88 #19), 02zsn (0.46 #179, 0.23 #134, 0.23 #106) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 146 >> proper extension: 02p65p; 02lfcm; 054_mz; 0147dk; 03f2_rc; 05k2s_; 0pz91; 0343h; 06cgy; 07s93v; ... >> query: (?x12790, 05zppz) <- place_of_birth(?x12790, ?x739), profession(?x12790, ?x319), executive_produced_by(?x1518, ?x12790), location(?x163, ?x739) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03c9pqt gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.878 http://example.org/people/person/gender #2218-01hb1t PRED entity: 01hb1t PRED relation: educational_institution! PRED expected values: 01hb1t => 143 concepts (82 used for prediction) PRED predicted values (max 10 best out of 305): 0cwx_ (0.20 #225, 0.02 #2921, 0.01 #5078), 0bjqh (0.20 #581, 0.02 #2738, 0.01 #7052), 01jssp (0.20 #5, 0.01 #4858, 0.01 #5937), 02482c (0.20 #850), 017z88 (0.18 #26962, 0.04 #1151, 0.03 #1690), 065y4w7 (0.18 #26962, 0.03 #1630, 0.02 #2170), 01hb1t (0.18 #26962, 0.02 #29121, 0.02 #36140), 078bz (0.04 #1148, 0.03 #1687, 0.02 #2227), 0bwfn (0.04 #1332, 0.03 #1871, 0.02 #2411), 01vg13 (0.04 #1283, 0.02 #2901, 0.02 #3440) >> Best rule #225 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01jssp; 0c5x_; >> query: (?x3123, 0cwx_) <- major_field_of_study(?x3123, ?x11691), student(?x3123, ?x7851), story_by(?x4502, ?x7851), ?x4502 = 02wgk1 >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #26962 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 321 *> proper extension: 01b1mj; 01t8sr; 01y9pk; 02t4yc; 02fs_d; 02xpy5; 04bfg; 01w_sh; 02yr3z; 01xk7r; ... *> query: (?x3123, ?x735) <- colors(?x3123, ?x332), student(?x3123, ?x65), institution(?x1200, ?x3123), student(?x735, ?x65) *> conf = 0.18 ranks of expected_values: 7 EVAL 01hb1t educational_institution! 01hb1t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 143.000 82.000 0.200 http://example.org/education/educational_institution_campus/educational_institution #2217-03rk0 PRED entity: 03rk0 PRED relation: country_of_origin! PRED expected values: 050kh5 => 180 concepts (129 used for prediction) PRED predicted values (max 10 best out of 286): 02gl58 (0.25 #1835, 0.20 #2105, 0.16 #2375), 06k176 (0.17 #1861, 0.16 #2401, 0.14 #2672), 027tbrc (0.17 #1657, 0.14 #2468, 0.14 #846), 04sskp (0.17 #1778, 0.14 #967, 0.13 #2048), 05z43v (0.17 #1773, 0.14 #962, 0.13 #2043), 0b005 (0.17 #1748, 0.14 #937, 0.13 #2018), 01hn_t (0.17 #1694, 0.14 #883, 0.13 #1964), 090s_0 (0.17 #1625, 0.14 #814, 0.13 #1895), 03cf9ly (0.17 #1863, 0.13 #2133, 0.11 #2403), 07g9f (0.17 #1830, 0.13 #2100, 0.11 #2370) >> Best rule #1835 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 05b7q; >> query: (?x2146, 02gl58) <- administrative_parent(?x3411, ?x2146), religion(?x2146, ?x109), combatants(?x2146, ?x10457) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03rk0 country_of_origin! 050kh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 180.000 129.000 0.250 http://example.org/tv/tv_program/country_of_origin #2216-01y9jr PRED entity: 01y9jr PRED relation: production_companies PRED expected values: 017s11 => 72 concepts (34 used for prediction) PRED predicted values (max 10 best out of 57): 017s11 (0.31 #1667, 0.08 #169, 0.07 #919), 086k8 (0.22 #85, 0.10 #586, 0.10 #503), 054lpb6 (0.22 #98, 0.08 #1431, 0.07 #1514), 04rtpt (0.22 #132, 0.03 #1465, 0.02 #2726), 01gb54 (0.11 #121, 0.08 #788, 0.07 #1454), 016tt2 (0.11 #87, 0.08 #754, 0.06 #1586), 02j_j0 (0.11 #131, 0.06 #297, 0.04 #1464), 0c41qv (0.11 #139, 0.05 #222, 0.04 #723), 031rq5 (0.11 #127, 0.02 #960, 0.02 #877), 08wjc1 (0.11 #111, 0.02 #1444, 0.02 #778) >> Best rule #1667 for best value: >> intensional similarity = 4 >> extensional distance = 655 >> proper extension: 0gpx6; >> query: (?x6578, ?x541) <- genre(?x6578, ?x225), award(?x6578, ?x401), award_winner(?x6578, ?x2221), film(?x541, ?x6578) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y9jr production_companies 017s11 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 34.000 0.307 http://example.org/film/film/production_companies #2215-0sz28 PRED entity: 0sz28 PRED relation: nationality PRED expected values: 09c7w0 => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 68): 09c7w0 (0.78 #3606, 0.74 #5207, 0.74 #3806), 01n7q (0.25 #10216), 0f8l9c (0.17 #22, 0.03 #10417, 0.02 #4528), 02jx1 (0.16 #633, 0.14 #733, 0.14 #4639), 07ssc (0.16 #315, 0.12 #915, 0.12 #4621), 03rk0 (0.11 #646, 0.11 #746, 0.10 #5953), 03rt9 (0.06 #313, 0.05 #413, 0.03 #10417), 0chghy (0.06 #310, 0.03 #410, 0.03 #10417), 0d060g (0.06 #1308, 0.05 #1708, 0.05 #5513), 03rjj (0.03 #905, 0.03 #305, 0.03 #10417) >> Best rule #3606 for best value: >> intensional similarity = 2 >> extensional distance = 304 >> proper extension: 09x8ms; >> query: (?x1208, 09c7w0) <- student(?x9865, ?x1208), participant(?x1554, ?x1208) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sz28 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.778 http://example.org/people/person/nationality #2214-0bz6l9 PRED entity: 0bz6l9 PRED relation: award_winner PRED expected values: 01q4qv 0cwtm => 30 concepts (14 used for prediction) PRED predicted values (max 10 best out of 640): 0cw67g (0.33 #1409, 0.19 #2948, 0.14 #4487), 04wp63 (0.33 #1394, 0.10 #2933, 0.09 #4472), 02pqgt8 (0.33 #640, 0.10 #2179, 0.09 #3718), 03_fk9 (0.33 #1431, 0.10 #2970, 0.05 #6049), 02v3yy (0.33 #489, 0.08 #1539, 0.06 #4618), 01k98nm (0.33 #488, 0.08 #1539, 0.06 #4618), 03q8ch (0.33 #645, 0.06 #11418, 0.06 #14497), 0bw87 (0.33 #1005, 0.05 #16395, 0.05 #17937), 05b2f_k (0.33 #1242, 0.05 #2781, 0.05 #4320), 09pjnd (0.33 #225, 0.05 #1764, 0.05 #3303) >> Best rule #1409 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 0bzm81; >> query: (?x3332, 0cw67g) <- ceremony(?x3066, ?x3332), ceremony(?x1703, ?x3332), ceremony(?x484, ?x3332), ?x1703 = 0k611, honored_for(?x3332, ?x10362), ?x3066 = 0gqy2, film(?x8473, ?x10362), award_winner(?x3332, ?x7528), award_winner(?x3332, ?x6025), ?x484 = 0gq_v, film_release_region(?x10362, ?x94), ?x6025 = 018gqj, award_nominee(?x200, ?x7528), genre(?x10362, ?x239), nominated_for(?x749, ?x10362) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2015 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 19 *> proper extension: 0fz2y7; *> query: (?x3332, 01q4qv) <- ceremony(?x3066, ?x3332), ceremony(?x1703, ?x3332), ceremony(?x484, ?x3332), ?x1703 = 0k611, honored_for(?x3332, ?x10362), ?x3066 = 0gqy2, film(?x8473, ?x10362), award_winner(?x3332, ?x6025), ?x484 = 0gq_v, film_release_region(?x10362, ?x94), nominated_for(?x749, ?x10362), origin(?x6025, ?x2017) *> conf = 0.05 ranks of expected_values: 251 EVAL 0bz6l9 award_winner 0cwtm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 14.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bz6l9 award_winner 01q4qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 30.000 14.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2213-05np2 PRED entity: 05np2 PRED relation: place_of_birth PRED expected values: 02cft => 169 concepts (135 used for prediction) PRED predicted values (max 10 best out of 173): 02cft (0.40 #5863, 0.31 #12206, 0.14 #933), 01xd9 (0.28 #93737, 0.28 #42284, 0.28 #8455), 05qtj (0.16 #7211, 0.15 #17615, 0.12 #17077), 02_286 (0.12 #9884, 0.12 #38074, 0.11 #42305), 0s5cg (0.12 #1589, 0.05 #7225, 0.03 #13567), 0f94t (0.12 #1436, 0.03 #14119, 0.02 #23989), 0b2lw (0.12 #1675, 0.03 #15063, 0.03 #15768), 0n920 (0.12 #2558, 0.02 #24407), 0pc7r (0.12 #2220), 0cr3d (0.11 #2910, 0.10 #15595, 0.10 #4318) >> Best rule #5863 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 02y0dd; >> query: (?x6975, 02cft) <- location(?x6975, ?x1591), ?x1591 = 01xd9, nationality(?x6975, ?x429), type_of_union(?x6975, ?x566) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05np2 place_of_birth 02cft CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 135.000 0.400 http://example.org/people/person/place_of_birth #2212-02x9cv PRED entity: 02x9cv PRED relation: category PRED expected values: 08mbj5d => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.92 #10, 0.91 #35, 0.91 #3) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 155 >> proper extension: 01cyd5; 01w3vc; >> query: (?x8825, 08mbj5d) <- state_province_region(?x8825, ?x2049), school_type(?x8825, ?x4994), school_type(?x11688, ?x4994), ?x11688 = 0558_1 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02x9cv category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.917 http://example.org/common/topic/webpage./common/webpage/category #2211-01bpnd PRED entity: 01bpnd PRED relation: notable_people_with_this_condition! PRED expected values: 029sk => 216 concepts (216 used for prediction) PRED predicted values (max 10 best out of 14): 0h99n (0.17 #10, 0.07 #428, 0.07 #604), 01g2q (0.17 #9, 0.05 #405, 0.05 #119), 029sk (0.09 #67, 0.08 #89, 0.07 #441), 0g02vk (0.05 #122, 0.02 #628, 0.02 #650), 0j8hd (0.05 #433, 0.03 #851, 0.02 #1181), 0dcsx (0.03 #268, 0.02 #444, 0.01 #928), 0d19y2 (0.03 #326, 0.03 #414, 0.02 #546), 03p41 (0.03 #314, 0.03 #402, 0.02 #534), 04nz3 (0.03 #349, 0.02 #459, 0.02 #481), 0m32h (0.03 #315, 0.02 #601, 0.01 #909) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 01w02sy; >> query: (?x5872, 0h99n) <- role(?x5872, ?x432), location_of_ceremony(?x5872, ?x3026), artist(?x2299, ?x5872), ?x3026 = 0cv3w >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 03qcq; 07rd7; 0hwbd; 04__f; *> query: (?x5872, 029sk) <- nationality(?x5872, ?x1310), participant(?x2444, ?x5872), gender(?x5872, ?x231), ?x2444 = 0jfx1 *> conf = 0.09 ranks of expected_values: 3 EVAL 01bpnd notable_people_with_this_condition! 029sk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 216.000 216.000 0.167 http://example.org/medicine/disease/notable_people_with_this_condition #2210-01j_9c PRED entity: 01j_9c PRED relation: school! PRED expected values: 03nt7j => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 17): 02qw1zx (0.39 #39, 0.28 #90, 0.22 #226), 05vsb7 (0.23 #86, 0.18 #35, 0.18 #239), 02pq_x5 (0.21 #48, 0.16 #65, 0.15 #286), 06439y (0.18 #51, 0.15 #102, 0.13 #289), 03nt7j (0.18 #41, 0.14 #228, 0.14 #245), 025tn92 (0.18 #248, 0.17 #282, 0.15 #231), 0g3zpp (0.17 #87, 0.14 #36, 0.13 #274), 038c0q (0.17 #91, 0.14 #40, 0.12 #227), 09th87 (0.17 #97, 0.13 #250, 0.13 #284), 02pq_rp (0.15 #93, 0.14 #42, 0.11 #246) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 06mkj; 0d05w3; >> query: (?x546, 02qw1zx) <- contains(?x94, ?x546), school(?x4171, ?x546), organization(?x546, ?x5487) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #41 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 06mkj; 0d05w3; *> query: (?x546, 03nt7j) <- contains(?x94, ?x546), school(?x4171, ?x546), organization(?x546, ?x5487) *> conf = 0.18 ranks of expected_values: 5 EVAL 01j_9c school! 03nt7j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 103.000 103.000 0.393 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #2209-06s_2 PRED entity: 06s_2 PRED relation: jurisdiction_of_office! PRED expected values: 060c4 => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 21): 060c4 (0.85 #1170, 0.75 #664, 0.73 #2139), 060bp (0.70 #640, 0.68 #155, 0.67 #618), 0f6c3 (0.68 #844, 0.66 #910, 0.58 #690), 09n5b9 (0.63 #848, 0.61 #914, 0.54 #694), 0pqc5 (0.39 #467, 0.38 #2008, 0.37 #1766), 01zq91 (0.37 #1366, 0.36 #3061, 0.15 #124), 0p5vf (0.36 #166, 0.33 #56, 0.32 #188), 04syw (0.25 #249, 0.24 #447, 0.23 #160), 01t7n9 (0.25 #18, 0.14 #150, 0.13 #701), 0dq3c (0.24 #333, 0.17 #24, 0.16 #1544) >> Best rule #1170 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 02wm6l; >> query: (?x10450, 060c4) <- form_of_government(?x10450, ?x48), ?x48 = 06cx9 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06s_2 jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 173.000 0.849 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #2208-02py7pj PRED entity: 02py7pj PRED relation: award_winner PRED expected values: 0j582 02_fj 0bw6y 0kftt 0k9j_ => 47 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2325): 0k9j_ (0.60 #14120, 0.33 #1908, 0.25 #9235), 043gj (0.60 #13258, 0.33 #1046, 0.25 #8373), 09fb5 (0.50 #7390, 0.40 #12275, 0.33 #4948), 0cj8x (0.50 #7975, 0.40 #12860, 0.33 #648), 0kjgl (0.50 #9040, 0.40 #13925, 0.33 #1713), 0bj9k (0.50 #7743, 0.40 #12628, 0.33 #416), 039bp (0.50 #7538, 0.40 #12423, 0.33 #211), 0z4s (0.50 #7398, 0.40 #12283, 0.33 #71), 06cgy (0.50 #7634, 0.40 #12519, 0.33 #307), 01vsy9_ (0.50 #9198, 0.40 #14083, 0.33 #1871) >> Best rule #14120 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 05qck; >> query: (?x8459, 0k9j_) <- award_winner(?x8459, ?x10929), award_winner(?x8459, ?x4279), award_winner(?x8459, ?x4240), nationality(?x4279, ?x94), ?x4240 = 044qx, award(?x10929, ?x375), film(?x10929, ?x1973) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 94, 154, 155, 191 EVAL 02py7pj award_winner 0k9j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 22.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02py7pj award_winner 0kftt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 47.000 22.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02py7pj award_winner 0bw6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 47.000 22.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02py7pj award_winner 02_fj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 47.000 22.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02py7pj award_winner 0j582 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 47.000 22.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #2207-07q3s PRED entity: 07q3s PRED relation: contains! PRED expected values: 06mkj => 86 concepts (82 used for prediction) PRED predicted values (max 10 best out of 207): 09c7w0 (0.62 #5382, 0.61 #22420, 0.58 #41243), 06mkj (0.51 #61865, 0.49 #16139, 0.49 #69935), 03v9w (0.33 #706, 0.25 #3396, 0.25 #2498), 059rby (0.25 #5399, 0.24 #8983, 0.15 #12571), 0dg3n1 (0.25 #2845, 0.22 #58274, 0.20 #3741), 04_1l0v (0.25 #6726, 0.18 #7622, 0.16 #24660), 02xry (0.25 #1955, 0.05 #11815, 0.04 #44093), 07ssc (0.20 #3618, 0.19 #13480, 0.17 #14377), 02jx1 (0.20 #3673, 0.16 #49311, 0.12 #6362), 021y1s (0.20 #4259, 0.05 #13224, 0.03 #15018) >> Best rule #5382 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 02_286; 07z1m; 094jv; 0dclg; 0dq16; >> query: (?x7852, 09c7w0) <- location_of_ceremony(?x566, ?x7852), location(?x2162, ?x7852), influenced_by(?x8389, ?x2162), influenced_by(?x118, ?x2162), profession(?x2162, ?x2225), ?x566 = 04ztj, ?x118 = 084w8, ?x8389 = 0683n >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #61865 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 347 *> proper extension: 0b2h3; 0ftvg; 01vskn; 0dgfx; *> query: (?x7852, ?x2152) <- category(?x7852, ?x134), location(?x2162, ?x7852), profession(?x2162, ?x6421), specialization_of(?x6421, ?x353), nationality(?x2162, ?x2152) *> conf = 0.51 ranks of expected_values: 2 EVAL 07q3s contains! 06mkj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 82.000 0.625 http://example.org/location/location/contains #2206-0j5g9 PRED entity: 0j5g9 PRED relation: taxonomy PRED expected values: 04n6k => 205 concepts (205 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.85 #32, 0.81 #53, 0.81 #30) >> Best rule #32 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 0jdx; >> query: (?x4221, 04n6k) <- nationality(?x6663, ?x4221), nationality(?x3708, ?x4221), participant(?x5665, ?x6663), gender(?x3708, ?x514), contains(?x4221, ?x4220) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j5g9 taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 205.000 205.000 0.848 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #2205-03sbs PRED entity: 03sbs PRED relation: influenced_by PRED expected values: 0372p => 108 concepts (56 used for prediction) PRED predicted values (max 10 best out of 317): 03sbs (0.60 #6284, 0.56 #2819, 0.54 #5421), 081k8 (0.60 #1018, 0.44 #2753, 0.38 #4921), 042q3 (0.56 #2961, 0.46 #5129, 0.46 #4694), 07c37 (0.50 #618, 0.43 #1917, 0.38 #2350), 030dr (0.50 #817, 0.29 #2116, 0.25 #2549), 0372p (0.43 #1843, 0.27 #5741, 0.25 #544), 0j3v (0.40 #924, 0.33 #5690, 0.33 #2659), 039n1 (0.38 #5524, 0.30 #10272, 0.22 #9839), 02wh0 (0.35 #14212, 0.33 #2979, 0.31 #5147), 0tfc (0.33 #4312, 0.27 #3443, 0.25 #3877) >> Best rule #6284 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0nk72; >> query: (?x7250, 03sbs) <- influenced_by(?x7250, ?x4033), people(?x5540, ?x7250), interests(?x7250, ?x713), profession(?x4033, ?x6630) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1843 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0372p; 039n1; 02wh0; *> query: (?x7250, 0372p) <- influenced_by(?x8233, ?x7250), influenced_by(?x2240, ?x7250), ?x8233 = 0399p, influenced_by(?x2239, ?x2240), ?x2239 = 0453t *> conf = 0.43 ranks of expected_values: 6 EVAL 03sbs influenced_by 0372p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 108.000 56.000 0.600 http://example.org/influence/influence_node/influenced_by #2204-0kbvb PRED entity: 0kbvb PRED relation: locations PRED expected values: 0n2z => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 152): 06y57 (0.33 #95, 0.25 #1642, 0.25 #1056), 04jpl (0.33 #203, 0.25 #780, 0.14 #2523), 01f62 (0.33 #420, 0.17 #1969, 0.12 #2934), 013yq (0.25 #1600, 0.25 #1404, 0.17 #1790), 030qb3t (0.25 #995, 0.17 #1966, 0.11 #4107), 01914 (0.25 #776, 0.14 #2327, 0.12 #2904), 04sqj (0.25 #1479, 0.10 #3419, 0.06 #3810), 05qtj (0.25 #1246, 0.06 #3772, 0.06 #3968), 06mxs (0.25 #1255, 0.06 #4172, 0.05 #4565), 052p7 (0.17 #1793, 0.12 #2953, 0.06 #3738) >> Best rule #95 for best value: >> intensional similarity = 47 >> extensional distance = 1 >> proper extension: 0jdk_; >> query: (?x778, 06y57) <- olympics(?x3041, ?x778), olympics(?x2000, ?x778), olympics(?x1264, ?x778), olympics(?x1023, ?x778), olympics(?x404, ?x778), olympics(?x304, ?x778), olympics(?x126, ?x778), olympics(?x6941, ?x778), olympics(?x2044, ?x778), olympics(?x779, ?x778), ?x1023 = 0ctw_b, ?x2044 = 06f41, ?x779 = 096f8, ?x6941 = 02y74, ?x2000 = 0d0kn, ?x126 = 0160w, sports(?x778, ?x171), film_release_region(?x1283, ?x404), film_release_region(?x86, ?x404), ?x1283 = 0cnztc4, ?x3041 = 04w4s, ?x86 = 0ds35l9, ?x304 = 0d0vqn, country(?x1646, ?x1264), country(?x6489, ?x1264), service_location(?x555, ?x1264), film_release_region(?x9839, ?x1264), film_release_region(?x3252, ?x1264), film_release_region(?x1518, ?x1264), film_release_region(?x1496, ?x1264), participating_countries(?x2748, ?x1264), nationality(?x380, ?x1264), featured_film_locations(?x2423, ?x1264), ?x2748 = 0c_tl, teams(?x404, ?x11736), partially_contains(?x455, ?x404), contains(?x1264, ?x196), adjoins(?x1264, ?x9714), ?x9839 = 0gy7bj4, exported_to(?x5622, ?x1264), organization(?x404, ?x127), currency(?x1264, ?x170), film(?x157, ?x6489), ?x3252 = 0gh8zks, language(?x6489, ?x254), ?x1496 = 011yqc, ?x1518 = 04w7rn >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4627 for first EXPECTED value: *> intensional similarity = 51 *> extensional distance = 17 *> proper extension: 0c_tl; *> query: (?x778, 0n2z) <- olympics(?x8588, ?x778), olympics(?x2188, ?x778), olympics(?x1355, ?x778), olympics(?x1023, ?x778), olympics(?x410, ?x778), olympics(?x304, ?x778), olympics(?x6733, ?x778), olympics(?x779, ?x778), film_release_region(?x10475, ?x1023), film_release_region(?x9432, ?x1023), film_release_region(?x5644, ?x1023), film_release_region(?x3745, ?x1023), film_release_region(?x3226, ?x1023), film_release_region(?x2695, ?x1023), film_release_region(?x1470, ?x1023), film_release_region(?x607, ?x1023), ?x1470 = 03twd6, combatants(?x1023, ?x172), organization(?x8588, ?x127), origin(?x3200, ?x410), country(?x1847, ?x1023), nationality(?x226, ?x1023), film_release_region(?x2598, ?x410), film_release_region(?x2512, ?x410), ?x9432 = 0gvt53w, jurisdiction_of_office(?x182, ?x1023), country(?x5963, ?x1023), olympics(?x410, ?x784), combatants(?x12031, ?x1023), adjoins(?x1475, ?x410), ?x2598 = 07f_7h, sports(?x391, ?x779), ?x6733 = 01sgl, ?x607 = 02x3lt7, sports(?x778, ?x171), ?x1355 = 0h7x, medal(?x304, ?x422), contains(?x304, ?x5168), ?x2512 = 07x4qr, ?x2695 = 047svrl, entity_involved(?x12031, ?x2663), featured_film_locations(?x10475, ?x3634), ?x3745 = 03cw411, ?x5644 = 0dll_t2, film_release_region(?x5688, ?x304), film_release_region(?x204, ?x304), adjustment_currency(?x2188, ?x170), ?x204 = 028_yv, taxonomy(?x2188, ?x939), ?x3226 = 0gyfp9c, ?x5688 = 0dr89x *> conf = 0.05 ranks of expected_values: 43 EVAL 0kbvb locations 0n2z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 31.000 31.000 0.333 http://example.org/time/event/locations #2203-02h2z_ PRED entity: 02h2z_ PRED relation: combatants PRED expected values: 06v9sf => 69 concepts (59 used for prediction) PRED predicted values (max 10 best out of 357): 02psqkz (0.62 #1036, 0.60 #6924, 0.57 #1922), 059j2 (0.50 #1257, 0.48 #1746, 0.47 #246), 05qhw (0.50 #1247, 0.48 #1746, 0.47 #246), 01h3dj (0.50 #1055, 0.43 #1941, 0.33 #1433), 02lmk (0.49 #1620, 0.43 #1234, 0.41 #2880), 028rk (0.49 #1620, 0.43 #1234, 0.41 #2880), 0d060g (0.48 #1746, 0.47 #246, 0.39 #1359), 0f8l9c (0.48 #1746, 0.47 #246, 0.39 #1359), 06c1y (0.48 #1746, 0.39 #1359, 0.35 #1364), 05vz3zq (0.47 #246, 0.43 #798, 0.42 #1678) >> Best rule #1036 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 0dl4z; >> query: (?x12789, 02psqkz) <- combatants(?x12789, ?x8687), locations(?x12789, ?x1273), combatants(?x1536, ?x8687), combatants(?x9856, ?x8687), combatants(?x5530, ?x8687), ?x1536 = 06c1y, ?x5530 = 01h6pn, form_of_government(?x1273, ?x1926), films(?x9856, ?x1786), olympics(?x1273, ?x778) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #246 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 081pw; *> query: (?x12789, ?x279) <- combatants(?x12789, ?x11095), combatants(?x12789, ?x8687), combatants(?x12789, ?x1023), combatants(?x12789, ?x613), ?x8687 = 059z0, locations(?x12789, ?x291), combatants(?x613, ?x1229), combatants(?x613, ?x279), ?x1229 = 059j2, countries_spoken_in(?x11590, ?x11095), combatants(?x11122, ?x613), ?x1023 = 0ctw_b, ?x11122 = 0c3mz *> conf = 0.47 ranks of expected_values: 24 EVAL 02h2z_ combatants 06v9sf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 69.000 59.000 0.625 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #2202-01pq5j7 PRED entity: 01pq5j7 PRED relation: origin PRED expected values: 04jpl => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 72): 030qb3t (0.12 #743, 0.06 #4760, 0.05 #16577), 02_286 (0.12 #725, 0.05 #13488, 0.04 #961), 04jpl (0.08 #243, 0.07 #715, 0.05 #479), 04lh6 (0.05 #150, 0.04 #387, 0.03 #3457), 03b12 (0.05 #171, 0.04 #408, 0.03 #644), 0vzm (0.05 #67, 0.04 #304, 0.02 #776), 013mzh (0.05 #222, 0.04 #459, 0.02 #1167), 013yq (0.05 #754, 0.02 #2408, 0.02 #9025), 0ccvx (0.05 #790), 0c_m3 (0.04 #2227, 0.02 #4591) >> Best rule #743 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 01pfr3; 0152cw; 05mt_q; 06w2sn5; 058s57; 0dvqq; 0137g1; 0161sp; 0161c2; 025ldg; ... >> query: (?x5225, 030qb3t) <- award_winner(?x1232, ?x5225), award(?x5225, ?x2855), ?x2855 = 02f705 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #243 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 0lrh; 0czkbt; 0j6cj; *> query: (?x5225, 04jpl) <- award(?x5225, ?x724), artists(?x505, ?x5225), peers(?x5225, ?x4713) *> conf = 0.08 ranks of expected_values: 3 EVAL 01pq5j7 origin 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 122.000 122.000 0.122 http://example.org/music/artist/origin #2201-0b44shh PRED entity: 0b44shh PRED relation: film_regional_debut_venue PRED expected values: 018cvf => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 26): 0bmj62v (0.23 #682, 0.23 #583, 0.19 #649), 018cvf (0.22 #241, 0.14 #925, 0.14 #631), 015hr (0.19 #239, 0.11 #271, 0.11 #306), 0prpt (0.17 #253, 0.07 #1005, 0.07 #806), 0gg7gsl (0.10 #232, 0.05 #103, 0.05 #655), 07zmj (0.10 #256, 0.04 #809, 0.03 #613), 0kfhjq0 (0.07 #240, 0.07 #630, 0.05 #924), 07751 (0.07 #234, 0.06 #266, 0.06 #201), 0j63cyr (0.06 #562, 0.06 #628, 0.06 #661), 03nn7l2 (0.06 #60, 0.02 #254, 0.02 #321) >> Best rule #682 for best value: >> intensional similarity = 4 >> extensional distance = 152 >> proper extension: 05q7874; >> query: (?x5109, ?x10083) <- film_festivals(?x5109, ?x10083), nominated_for(?x2532, ?x5109), genre(?x5109, ?x53), award_winner(?x2532, ?x777) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 01kqq7; *> query: (?x5109, 018cvf) <- film_regional_debut_venue(?x5109, ?x739), film_release_distribution_medium(?x5109, ?x81), award(?x5109, ?x112), genre(?x5109, ?x53) *> conf = 0.22 ranks of expected_values: 2 EVAL 0b44shh film_regional_debut_venue 018cvf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.231 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue #2200-01swck PRED entity: 01swck PRED relation: religion PRED expected values: 0c8wxp => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 22): 0c8wxp (0.18 #231, 0.16 #321, 0.15 #6), 0kpl (0.13 #55, 0.07 #10, 0.07 #370), 03_gx (0.11 #104, 0.08 #284, 0.07 #239), 03j6c (0.09 #291, 0.08 #246, 0.04 #21), 01lp8 (0.07 #91, 0.06 #226, 0.04 #271), 02rsw (0.05 #159, 0.04 #204, 0.03 #69), 0kq2 (0.04 #18, 0.02 #243, 0.02 #378), 051kv (0.04 #5, 0.02 #140, 0.01 #185), 0flw86 (0.04 #227, 0.02 #272, 0.02 #407), 06nzl (0.04 #105, 0.02 #240, 0.02 #285) >> Best rule #231 for best value: >> intensional similarity = 3 >> extensional distance = 83 >> proper extension: 049gc; 05xd8x; 045hz5; >> query: (?x4520, 0c8wxp) <- award(?x4520, ?x372), location(?x4520, ?x2850), special_performance_type(?x4520, ?x4832) >> conf = 0.18 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01swck religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.176 http://example.org/people/person/religion #2199-07y1z PRED entity: 07y1z PRED relation: religion! PRED expected values: 0d0vj4 => 36 concepts (28 used for prediction) PRED predicted values (max 10 best out of 4410): 0bymv (0.50 #1070, 0.33 #8727, 0.29 #15146), 06bss (0.50 #1070, 0.33 #9128, 0.29 #15547), 01lct6 (0.50 #1070, 0.25 #3083, 0.17 #12716), 0d3qd0 (0.50 #1070, 0.20 #4657, 0.20 #3587), 024_vw (0.50 #1070, 0.20 #5235, 0.20 #4165), 012v1t (0.50 #1070, 0.20 #4779, 0.20 #3709), 016lh0 (0.50 #1070, 0.17 #8999, 0.14 #15418), 02hy5d (0.50 #1070, 0.17 #9342, 0.14 #15761), 03txms (0.50 #1070, 0.17 #9221, 0.14 #15640), 021sv1 (0.50 #1070, 0.17 #11810, 0.12 #20366) >> Best rule #1070 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 02rsw; >> query: (?x14541, ?x652) <- religion(?x4196, ?x14541), religion(?x3445, ?x14541), ?x4196 = 09b6zr, legislative_sessions(?x3445, ?x653), student(?x122, ?x3445), legislative_sessions(?x2860, ?x653), legislative_sessions(?x652, ?x653), legislative_sessions(?x653, ?x355), politician(?x1912, ?x3445), district_represented(?x653, ?x2713), ?x2713 = 06btq >> conf = 0.50 => this is the best rule for 13 predicted values *> Best rule #24608 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 11 *> proper extension: 0flw86; *> query: (?x14541, ?x1620) <- religion(?x4196, ?x14541), place_of_birth(?x4196, ?x9336), person(?x6773, ?x4196), student(?x122, ?x4196), gender(?x4196, ?x231), film_crew_role(?x6773, ?x137), profession(?x4196, ?x967), person(?x6773, ?x1620), risk_factors(?x1158, ?x231) *> conf = 0.17 ranks of expected_values: 635 EVAL 07y1z religion! 0d0vj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 28.000 0.500 http://example.org/people/person/religion #2198-029q_y PRED entity: 029q_y PRED relation: place_of_birth PRED expected values: 071vr => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 114): 0r0ls (0.20 #564, 0.17 #1268, 0.01 #5493), 0t0n5 (0.20 #217, 0.17 #921, 0.01 #5146), 0cr3d (0.17 #1502, 0.05 #2206, 0.04 #5727), 0v1xg (0.17 #1023), 02_286 (0.10 #5652, 0.07 #14100, 0.07 #10580), 013h9 (0.08 #1839, 0.01 #4655), 018djs (0.08 #2073), 0xhj2 (0.08 #1897), 0r0f7 (0.08 #1719), 02cl1 (0.08 #1424) >> Best rule #564 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0127m7; 019pm_; >> query: (?x7613, 0r0ls) <- participant(?x516, ?x7613), participant(?x7613, ?x8898), ?x8898 = 0h7pj, award_nominee(?x7613, ?x192) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #4482 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 67 *> proper extension: 0k9j_; *> query: (?x7613, 071vr) <- participant(?x7613, ?x513), award_nominee(?x190, ?x7613), place_of_death(?x190, ?x191) *> conf = 0.01 ranks of expected_values: 80 EVAL 029q_y place_of_birth 071vr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 99.000 99.000 0.200 http://example.org/people/person/place_of_birth #2197-01yhm PRED entity: 01yhm PRED relation: teams! PRED expected values: 01sn3 => 82 concepts (70 used for prediction) PRED predicted values (max 10 best out of 122): 01_d4 (0.33 #330, 0.24 #6008, 0.23 #4115), 0fpzwf (0.20 #1488, 0.20 #1218, 0.20 #948), 0nqph (0.20 #1609, 0.20 #1339, 0.17 #1879), 06wxw (0.20 #931, 0.17 #2011, 0.14 #2551), 094jv (0.20 #596, 0.14 #2486, 0.09 #3297), 068p2 (0.20 #663, 0.14 #2553, 0.09 #3364), 02_286 (0.17 #3807, 0.14 #2452, 0.09 #8682), 0rh6k (0.17 #1892, 0.10 #2973, 0.08 #4328), 01cx_ (0.17 #1714, 0.09 #3606, 0.08 #3879), 02dtg (0.14 #2176, 0.09 #3528, 0.08 #3801) >> Best rule #330 for best value: >> intensional similarity = 20 >> extensional distance = 1 >> proper extension: 01yjl; >> query: (?x1823, 01_d4) <- draft(?x1823, ?x11905), draft(?x1823, ?x1161), colors(?x1823, ?x1101), colors(?x1823, ?x663), season(?x1823, ?x11834), season(?x1823, ?x9498), season(?x1823, ?x8529), season(?x1823, ?x3431), ?x3431 = 025ygqm, ?x1101 = 06fvc, ?x9498 = 027pwzc, ?x1161 = 02x2khw, sport(?x1823, ?x5063), ?x8529 = 025ygws, ?x663 = 083jv, position(?x1823, ?x2010), school(?x1823, ?x546), category(?x1823, ?x134), ?x11834 = 02h7s73, ?x11905 = 047dpm0 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #12841 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 77 *> proper extension: 0jmdb; 0jmcb; 0jm7n; *> query: (?x1823, 01sn3) <- draft(?x1823, ?x11905), draft(?x1823, ?x1161), school(?x1823, ?x946), team(?x2010, ?x1823), colors(?x946, ?x332), draft(?x1632, ?x11905), school(?x1161, ?x8202), school(?x1161, ?x4209), currency(?x4209, ?x170), institution(?x865, ?x946), colors(?x1632, ?x663), school(?x2820, ?x8202), sport(?x1823, ?x5063), list(?x8202, ?x2197) *> conf = 0.03 ranks of expected_values: 43 EVAL 01yhm teams! 01sn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 82.000 70.000 0.333 http://example.org/sports/sports_team_location/teams #2196-035tjy PRED entity: 035tjy PRED relation: teams! PRED expected values: 0d060g => 109 concepts (107 used for prediction) PRED predicted values (max 10 best out of 160): 02fvv (0.33 #252, 0.14 #792, 0.02 #3770), 0947l (0.20 #993, 0.08 #1263, 0.08 #1537), 0k33p (0.14 #739, 0.02 #4799, 0.02 #5340), 01_d4 (0.08 #1141, 0.06 #2496, 0.05 #2766), 01r32 (0.08 #1126, 0.04 #3293, 0.03 #2481), 01s3v (0.08 #1282, 0.04 #2096, 0.03 #2907), 0n1rj (0.08 #1223, 0.03 #2578, 0.03 #2848), 013yq (0.08 #1154, 0.03 #2509, 0.03 #2779), 0jp26 (0.08 #1454, 0.07 #1724, 0.04 #1994), 081m_ (0.08 #1539, 0.07 #1809, 0.04 #2079) >> Best rule #252 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 01wx_y; >> query: (?x12414, 02fvv) <- colors(?x12414, ?x1101), position(?x12414, ?x63), team(?x7907, ?x12414), position(?x12414, ?x203), ?x203 = 0dgrmp, sport(?x12414, ?x471), ?x7907 = 0841zn, ?x471 = 02vx4, ?x63 = 02sdk9v >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7843 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 81 *> proper extension: 0cnk2q; 02279c; 01bdxz; 04b4yg; 049bmk; 07r78j; 01kckd; 027pwl; 03_9hm; 02b0xq; ... *> query: (?x12414, ?x279) <- team(?x7907, ?x12414), team(?x530, ?x12414), team(?x63, ?x12414), team(?x60, ?x12414), ?x530 = 02_j1w, ?x60 = 02nzb8, position(?x12414, ?x203), ?x203 = 0dgrmp, ?x63 = 02sdk9v, nationality(?x7907, ?x279) *> conf = 0.05 ranks of expected_values: 16 EVAL 035tjy teams! 0d060g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 109.000 107.000 0.333 http://example.org/sports/sports_team_location/teams #2195-0dbc1s PRED entity: 0dbc1s PRED relation: award_nominee! PRED expected values: 06yrj6 => 99 concepts (32 used for prediction) PRED predicted values (max 10 best out of 638): 06yrj6 (0.81 #37273, 0.80 #37274, 0.79 #46595), 04pz5c (0.81 #37273, 0.80 #37274, 0.79 #46595), 0dbc1s (0.67 #8580, 0.60 #6249, 0.45 #39606), 09gffmz (0.48 #39605, 0.45 #39606, 0.26 #39604), 06v_gh (0.48 #39605, 0.45 #39606, 0.26 #39604), 0b7t3p (0.48 #39605, 0.45 #39606, 0.22 #41936), 02wk_43 (0.33 #4419, 0.26 #39604, 0.20 #6749), 0988cp (0.33 #3674, 0.26 #39604, 0.20 #6004), 01w0yrc (0.26 #39604, 0.22 #41936, 0.20 #6766), 0q9vf (0.26 #39604, 0.22 #41936, 0.20 #6170) >> Best rule #37273 for best value: >> intensional similarity = 4 >> extensional distance = 199 >> proper extension: 01r216; 01z7_f; >> query: (?x7002, ?x2817) <- award_nominee(?x7002, ?x9845), award_nominee(?x7002, ?x2817), profession(?x9845, ?x319), producer_type(?x7002, ?x632) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0dbc1s award_nominee! 06yrj6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 32.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2194-06fcqw PRED entity: 06fcqw PRED relation: film_crew_role PRED expected values: 02r96rf => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 31): 09zzb8 (0.77 #1726, 0.70 #1840, 0.70 #1499), 01vx2h (0.73 #311, 0.40 #162, 0.36 #87), 0ch6mp2 (0.72 #1734, 0.71 #1848, 0.71 #1507), 02r96rf (0.72 #302, 0.70 #153, 0.64 #378), 09vw2b7 (0.64 #1733, 0.63 #306, 0.60 #1506), 0dxtw (0.49 #310, 0.37 #1510, 0.36 #1737), 01pvkk (0.30 #1739, 0.27 #1853, 0.27 #686), 0215hd (0.27 #319, 0.17 #21, 0.12 #1746), 033smt (0.26 #327, 0.07 #141, 0.06 #103), 089g0h (0.25 #320, 0.17 #22, 0.10 #1747) >> Best rule #1726 for best value: >> intensional similarity = 4 >> extensional distance = 1100 >> proper extension: 02y_lrp; 0sxg4; 083shs; 06wzvr; 0dnvn3; 0ds33; 03h_yy; 02_1sj; 04fzfj; 035xwd; ... >> query: (?x6216, 09zzb8) <- genre(?x6216, ?x258), film(?x4360, ?x6216), film_crew_role(?x6216, ?x1966), profession(?x1109, ?x1966) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #302 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 02qm_f; 031778; 07yk1xz; 0k4d7; 014zwb; 03t79f; 0415ggl; 0bl3nn; 0292qb; 0dp7wt; ... *> query: (?x6216, 02r96rf) <- genre(?x6216, ?x258), film(?x4360, ?x6216), film_crew_role(?x6216, ?x1966), ?x1966 = 015h31 *> conf = 0.72 ranks of expected_values: 4 EVAL 06fcqw film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 71.000 71.000 0.766 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2193-0mb5x PRED entity: 0mb5x PRED relation: influenced_by PRED expected values: 06whf => 120 concepts (68 used for prediction) PRED predicted values (max 10 best out of 371): 040_9 (0.33 #527, 0.22 #956, 0.13 #15032), 03f70xs (0.33 #69, 0.22 #927, 0.13 #15032), 028p0 (0.33 #459, 0.22 #888, 0.13 #15032), 034bs (0.33 #546, 0.15 #14173, 0.14 #4293), 037jz (0.33 #635, 0.13 #15032, 0.12 #15463), 0448r (0.33 #687, 0.13 #15032, 0.12 #15463), 0zm1 (0.33 #127, 0.13 #15032, 0.12 #15463), 085gk (0.33 #409, 0.13 #15032, 0.12 #15463), 0465_ (0.33 #1054, 0.13 #15032, 0.12 #15463), 06myp (0.33 #800, 0.11 #1229, 0.09 #5523) >> Best rule #527 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0d4jl; >> query: (?x8382, 040_9) <- influenced_by(?x8382, ?x5336), influenced_by(?x8382, ?x587), ?x587 = 07g2b, ?x5336 = 02kz_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #11291 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 160 *> proper extension: 01xdf5; 02g8h; 05ty4m; 0m2l9; 01zkxv; 07c0j; 014zfs; 022_lg; 01wp8w7; 0c3kw; ... *> query: (?x8382, 06whf) <- influenced_by(?x8382, ?x587), place_of_death(?x587, ?x362), award_winner(?x921, ?x587) *> conf = 0.10 ranks of expected_values: 65 EVAL 0mb5x influenced_by 06whf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 120.000 68.000 0.333 http://example.org/influence/influence_node/influenced_by #2192-036c_0 PRED entity: 036c_0 PRED relation: film PRED expected values: 04t6fk => 88 concepts (33 used for prediction) PRED predicted values (max 10 best out of 271): 0g60z (0.59 #26873, 0.59 #32247, 0.59 #41204), 051zy_b (0.09 #579, 0.04 #2370, 0.04 #4161), 09rvwmy (0.09 #1695, 0.04 #3486, 0.04 #5277), 05pxnmb (0.09 #1346, 0.04 #3137, 0.04 #4928), 02v8kmz (0.09 #28, 0.04 #1819, 0.04 #3610), 01q2nx (0.04 #913, 0.04 #2704, 0.04 #4495), 046f3p (0.04 #1330, 0.04 #3121, 0.04 #4912), 03x7hd (0.04 #561, 0.04 #2352, 0.04 #4143), 0cd2vh9 (0.04 #252, 0.04 #2043, 0.04 #3834), 09sr0 (0.04 #1521, 0.02 #3312, 0.02 #5103) >> Best rule #26873 for best value: >> intensional similarity = 4 >> extensional distance = 980 >> proper extension: 049tjg; 0h1_w; 04shbh; 019_1h; 03f1zdw; 01v42g; 030znt; 0f6_dy; 01hkhq; 03q1vd; ... >> query: (?x2103, ?x337) <- film(?x2103, ?x2104), type_of_union(?x2103, ?x566), nominated_for(?x2103, ?x337), gender(?x2103, ?x231) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 036c_0 film 04t6fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 33.000 0.594 http://example.org/film/actor/film./film/performance/film #2191-0jjy0 PRED entity: 0jjy0 PRED relation: film_crew_role PRED expected values: 02r96rf => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 31): 09zzb8 (0.79 #631, 0.78 #112, 0.77 #1648), 02r96rf (0.73 #634, 0.71 #486, 0.69 #672), 01vx2h (0.43 #382, 0.42 #1241, 0.40 #642), 0dxtw (0.41 #1958, 0.37 #641, 0.37 #122), 01pvkk (0.30 #124, 0.29 #2340, 0.28 #2416), 02ynfr (0.24 #387, 0.23 #313, 0.21 #647), 02rh1dz (0.23 #232, 0.22 #84, 0.22 #380), 0215hd (0.20 #57, 0.18 #20, 0.15 #94), 0d2b38 (0.19 #101, 0.18 #27, 0.16 #583), 089g0h (0.18 #21, 0.15 #95, 0.13 #651) >> Best rule #631 for best value: >> intensional similarity = 6 >> extensional distance = 73 >> proper extension: 04fzfj; 02hxhz; 0b73_1d; 048scx; 0k2sk; 0cz_ym; 01j8wk; 0260bz; 019vhk; 0kv9d3; ... >> query: (?x1108, 09zzb8) <- country(?x1108, ?x94), genre(?x1108, ?x53), language(?x1108, ?x254), titles(?x4205, ?x1108), executive_produced_by(?x1108, ?x846), crewmember(?x1108, ?x5653) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #634 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 73 *> proper extension: 04fzfj; 02hxhz; 0b73_1d; 048scx; 0k2sk; 0cz_ym; 01j8wk; 0260bz; 019vhk; 0kv9d3; ... *> query: (?x1108, 02r96rf) <- country(?x1108, ?x94), genre(?x1108, ?x53), language(?x1108, ?x254), titles(?x4205, ?x1108), executive_produced_by(?x1108, ?x846), crewmember(?x1108, ?x5653) *> conf = 0.73 ranks of expected_values: 2 EVAL 0jjy0 film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 105.000 0.787 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2190-02t901 PRED entity: 02t901 PRED relation: profession PRED expected values: 02jknp => 143 concepts (57 used for prediction) PRED predicted values (max 10 best out of 67): 03gjzk (0.62 #603, 0.61 #4131, 0.61 #1926), 018gz8 (0.61 #1340, 0.50 #311, 0.44 #605), 0dxtg (0.55 #1190, 0.54 #4130, 0.54 #1925), 02jknp (0.54 #1184, 0.52 #1919, 0.52 #4124), 01d_h8 (0.51 #1182, 0.48 #1917, 0.47 #1476), 0nbcg (0.50 #177, 0.30 #3558, 0.24 #2235), 09jwl (0.39 #3547, 0.36 #5901, 0.32 #2224), 01d30f (0.33 #69, 0.04 #510, 0.02 #4332), 0dz3r (0.33 #3530, 0.25 #149, 0.20 #5884), 016z4k (0.25 #3532, 0.25 #151, 0.24 #2209) >> Best rule #603 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 05fnl9; 0p8r1; 015pvh; 02zrv7; 032nl2; 05vtbl; >> query: (?x12765, 03gjzk) <- film(?x12765, ?x1769), profession(?x12765, ?x1943), profession(?x12765, ?x1383), ?x1383 = 0np9r, ?x1943 = 02krf9 >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #1184 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 0c8hct; *> query: (?x12765, 02jknp) <- place_of_birth(?x12765, ?x2495), location(?x12765, ?x7152), profession(?x12765, ?x1943), ?x1943 = 02krf9 *> conf = 0.54 ranks of expected_values: 4 EVAL 02t901 profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 143.000 57.000 0.625 http://example.org/people/person/profession #2189-06gst PRED entity: 06gst PRED relation: artist PRED expected values: 03f0fnk => 85 concepts (55 used for prediction) PRED predicted values (max 10 best out of 987): 0d9xq (0.60 #2859, 0.31 #11181, 0.25 #18679), 0277c3 (0.40 #2928, 0.31 #11250, 0.25 #18748), 0dbb3 (0.40 #3235, 0.31 #11557, 0.25 #19055), 02mslq (0.40 #2523, 0.15 #10845, 0.15 #24169), 05563d (0.40 #2741, 0.15 #11063, 0.12 #18561), 02cpp (0.38 #11244, 0.31 #18742, 0.20 #2922), 016szr (0.35 #24486, 0.24 #32808, 0.23 #33640), 0qf3p (0.31 #18467, 0.31 #10969, 0.21 #31783), 01vn0t_ (0.31 #18955, 0.23 #11457, 0.20 #33935), 0kzy0 (0.31 #10851, 0.25 #18349, 0.20 #15018) >> Best rule #2859 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 01cl2y; 011k11; 01xyqk; >> query: (?x13059, 0d9xq) <- artist(?x13059, ?x13136), artist(?x13059, ?x8803), artist(?x13059, ?x5181), ?x8803 = 01vsy9_, people(?x2510, ?x5181), group(?x75, ?x13136), artists(?x671, ?x13136), award_winner(?x4796, ?x5181) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #11149 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 011k1h; 03rhqg; 017l96; 015_1q; 02p3cr5; 016ckq; 01sqd7; 02y21l; *> query: (?x13059, 03f0fnk) <- artist(?x13059, ?x8803), award_winner(?x3003, ?x8803), award_winner(?x591, ?x8803), inductee(?x14591, ?x8803), award(?x3003, ?x601), nominated_for(?x902, ?x3003) *> conf = 0.23 ranks of expected_values: 37 EVAL 06gst artist 03f0fnk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 85.000 55.000 0.600 http://example.org/music/record_label/artist #2188-0l15n PRED entity: 0l15n PRED relation: award PRED expected values: 05f4m9q => 117 concepts (104 used for prediction) PRED predicted values (max 10 best out of 298): 02qt02v (0.71 #21269, 0.71 #15248, 0.71 #15650), 0gq9h (0.53 #5289, 0.43 #475, 0.34 #876), 0gr51 (0.34 #898, 0.28 #497, 0.27 #3706), 0gr4k (0.34 #433, 0.26 #834, 0.23 #2037), 02rdyk7 (0.32 #488, 0.27 #87, 0.22 #889), 04dn09n (0.32 #844, 0.25 #2047, 0.25 #443), 09sb52 (0.27 #8063, 0.27 #8464, 0.26 #9266), 03hl6lc (0.21 #977, 0.17 #2180, 0.17 #3785), 0gq_v (0.20 #5236, 0.05 #26888, 0.05 #26889), 05f4m9q (0.19 #2419, 0.17 #2820, 0.17 #1616) >> Best rule #21269 for best value: >> intensional similarity = 3 >> extensional distance = 1275 >> proper extension: 04rcr; 05crg7; 016fmf; 0134s5; 02lbrd; 028qdb; 0khth; 0134tg; 01yzl2; 0b1zz; ... >> query: (?x11297, ?x3233) <- award(?x11297, ?x198), award_winner(?x8478, ?x11297), award_winner(?x3233, ?x11297) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2419 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 160 *> proper extension: 07nznf; 02rchht; 042l3v; 06cv1; 0kr5_; 0mdqp; 02ndbd; 04l3_z; 0prjs; 0343h; ... *> query: (?x11297, 05f4m9q) <- profession(?x11297, ?x524), place_of_birth(?x11297, ?x5037), film(?x11297, ?x188), award(?x11297, ?x198) *> conf = 0.19 ranks of expected_values: 10 EVAL 0l15n award 05f4m9q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 117.000 104.000 0.710 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2187-04wmvz PRED entity: 04wmvz PRED relation: sport PRED expected values: 018jz => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 9): 018jz (0.78 #416, 0.75 #324, 0.74 #360), 02vx4 (0.56 #771, 0.54 #477, 0.53 #403), 0jm_ (0.42 #451, 0.37 #671, 0.36 #340), 018w8 (0.34 #434, 0.34 #663, 0.32 #726), 03tmr (0.21 #182, 0.20 #293, 0.20 #218), 0z74 (0.15 #950, 0.10 #979), 039yzs (0.12 #729, 0.10 #979, 0.06 #601), 09xp_ (0.10 #979, 0.07 #214, 0.04 #636), 06f3l (0.02 #621, 0.01 #695) >> Best rule #416 for best value: >> intensional similarity = 13 >> extensional distance = 30 >> proper extension: 0x2p; >> query: (?x10279, 018jz) <- school(?x10279, ?x1011), season(?x10279, ?x2406), draft(?x10279, ?x1161), major_field_of_study(?x1011, ?x10391), contains(?x94, ?x1011), school(?x6089, ?x1011), school(?x3333, ?x1011), institution(?x620, ?x1011), sport(?x6089, ?x4833), student(?x1011, ?x400), draft(?x3333, ?x8499), major_field_of_study(?x10391, ?x1527), team(?x11844, ?x3333) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04wmvz sport 018jz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.781 http://example.org/sports/sports_team/sport #2186-0bzkvd PRED entity: 0bzkvd PRED relation: honored_for PRED expected values: 0cq86w 02ptczs => 32 concepts (19 used for prediction) PRED predicted values (max 10 best out of 579): 043n1r5 (0.33 #541, 0.19 #4767, 0.13 #10134), 0sxgv (0.33 #360, 0.04 #2146, 0.04 #2740), 02gd6x (0.33 #390, 0.04 #2176, 0.04 #2770), 0bl06 (0.33 #936, 0.03 #4513, 0.02 #10479), 0j80w (0.33 #889, 0.03 #4466, 0.02 #10432), 014kkm (0.33 #902, 0.03 #4479, 0.02 #10445), 018f8 (0.19 #4767, 0.13 #10134, 0.12 #1785), 05ldxl (0.19 #4767, 0.13 #10134, 0.12 #1785), 0cq86w (0.19 #4767, 0.12 #1785, 0.10 #1786), 0pd6l (0.19 #4767, 0.10 #1786, 0.10 #1189) >> Best rule #541 for best value: >> intensional similarity = 20 >> extensional distance = 1 >> proper extension: 0bzm__; >> query: (?x8150, 043n1r5) <- instance_of_recurring_event(?x8150, ?x3459), ceremony(?x5409, ?x8150), ceremony(?x1703, ?x8150), ceremony(?x1313, ?x8150), ceremony(?x1307, ?x8150), ceremony(?x1245, ?x8150), ?x1245 = 0gqwc, ?x5409 = 0gr07, award_winner(?x8150, ?x6771), award_winner(?x8150, ?x6011), ?x1307 = 0gq9h, ?x1313 = 0gs9p, honored_for(?x8150, ?x1903), ?x6011 = 02zft0, ?x1703 = 0k611, ?x3459 = 0g_w, film(?x6771, ?x1210), award_winner(?x693, ?x6771), award_winner(?x2794, ?x6771), film_release_region(?x1210, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4767 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 30 *> proper extension: 0fy6bh; 0bc773; *> query: (?x8150, ?x2168) <- award_winner(?x8150, ?x10758), award_winner(?x8150, ?x6514), award_winner(?x8150, ?x538), ceremony(?x3066, ?x8150), ceremony(?x2222, ?x8150), ceremony(?x1243, ?x8150), ceremony(?x500, ?x8150), ?x1243 = 0gr0m, ?x500 = 0p9sw, award(?x6514, ?x484), award_winner(?x2168, ?x6514), place_of_birth(?x6514, ?x362), ?x2222 = 0gs96, gender(?x6514, ?x231), ?x3066 = 0gqy2, award_nominee(?x772, ?x538), music(?x2755, ?x538), nationality(?x10758, ?x1310) *> conf = 0.19 ranks of expected_values: 9, 151 EVAL 0bzkvd honored_for 02ptczs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 32.000 19.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0bzkvd honored_for 0cq86w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 32.000 19.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2185-01w60_p PRED entity: 01w60_p PRED relation: instrumentalists! PRED expected values: 05r5c => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 69): 05r5c (0.46 #273, 0.35 #184, 0.33 #1593), 05148p4 (0.37 #286, 0.27 #1606, 0.27 #197), 03qjg (0.24 #228, 0.23 #317, 0.18 #493), 018vs (0.23 #3012, 0.22 #3633, 0.19 #189), 03gvt (0.17 #331, 0.15 #507, 0.12 #683), 02hnl (0.17 #300, 0.14 #3034, 0.13 #476), 04rzd (0.17 #38, 0.10 #1623, 0.09 #126), 06ch55 (0.17 #83, 0.05 #1668, 0.03 #1932), 06w7v (0.14 #249, 0.08 #338, 0.07 #514), 0l14md (0.12 #95, 0.12 #272, 0.11 #183) >> Best rule #273 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 01pbxb; 05qw5; 0136pk; 0qf11; 015x1f; 01vtg4q; 01wg25j; >> query: (?x2169, 05r5c) <- inductee(?x1091, ?x2169), profession(?x2169, ?x131), artist(?x1954, ?x2169) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01w60_p instrumentalists! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.462 http://example.org/music/instrument/instrumentalists #2184-07vc_9 PRED entity: 07vc_9 PRED relation: profession PRED expected values: 02hrh1q => 102 concepts (100 used for prediction) PRED predicted values (max 10 best out of 65): 02hrh1q (0.90 #610, 0.88 #1057, 0.88 #6571), 01d_h8 (0.67 #2837, 0.67 #3135, 0.37 #304), 0dxtg (0.61 #3142, 0.60 #2844, 0.30 #311), 03gjzk (0.25 #2846, 0.25 #3144, 0.23 #6274), 02krf9 (0.22 #2858, 0.21 #3156, 0.20 #12520), 0np9r (0.20 #12520, 0.20 #3597, 0.18 #3001), 09jwl (0.20 #12520, 0.17 #19, 0.16 #9558), 0nbcg (0.20 #12520, 0.15 #330, 0.11 #9571), 01c72t (0.20 #12520, 0.15 #322, 0.09 #918), 018gz8 (0.20 #12520, 0.13 #8513, 0.13 #6127) >> Best rule #610 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 02dh86; 0163t3; >> query: (?x1286, 02hrh1q) <- spouse(?x1286, ?x8346), location(?x1286, ?x6959), actor(?x5328, ?x1286) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07vc_9 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 100.000 0.896 http://example.org/people/person/profession #2183-03fbb6 PRED entity: 03fbb6 PRED relation: award PRED expected values: 02x8n1n => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 238): 05zvj3m (0.40 #496, 0.05 #3728, 0.04 #4940), 05pcn59 (0.33 #80, 0.20 #484, 0.13 #29500), 05b4l5x (0.33 #6, 0.10 #814, 0.05 #4854), 0bdw1g (0.33 #38, 0.05 #846, 0.03 #4886), 0cqh6z (0.33 #66, 0.05 #874, 0.02 #4914), 0f4x7 (0.20 #435, 0.15 #28286, 0.14 #16971), 09qv_s (0.20 #555, 0.15 #28286, 0.14 #16971), 0279c15 (0.20 #540, 0.15 #28286, 0.13 #29500), 09cm54 (0.20 #499, 0.15 #28286, 0.08 #5657), 04kxsb (0.20 #529, 0.14 #16971, 0.13 #29500) >> Best rule #496 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0p_pd; 02114t; 01pk3z; >> query: (?x5500, 05zvj3m) <- award_nominee(?x5500, ?x496), film(?x5500, ?x696), ?x696 = 0209xj >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #29500 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2301 *> proper extension: 0l56b; 0gcs9; 01wgfp6; 0gfmc_; 0knjh; *> query: (?x5500, ?x401) <- award_nominee(?x5500, ?x496), award(?x496, ?x401) *> conf = 0.13 ranks of expected_values: 59 EVAL 03fbb6 award 02x8n1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 87.000 87.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2182-01p5yn PRED entity: 01p5yn PRED relation: industry PRED expected values: 02jjt => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 39): 01mw1 (0.34 #2546, 0.30 #2687, 0.29 #2734), 03qh03g (0.32 #1419, 0.24 #1890, 0.22 #2078), 02jjt (0.29 #1469, 0.20 #2081, 0.19 #2128), 020mfr (0.28 #2561, 0.25 #2702, 0.24 #2749), 04rlf (0.22 #1240, 0.15 #1758, 0.15 #1852), 029g_vk (0.19 #566, 0.15 #1755, 0.15 #1849), 0hz28 (0.19 #566, 0.12 #2310, 0.10 #2103), 0sydc (0.19 #566, 0.12 #2310, 0.08 #173), 0h6dj (0.19 #566, 0.12 #2310, 0.01 #1448), 07c52 (0.08 #191, 0.07 #238, 0.07 #285) >> Best rule #2546 for best value: >> intensional similarity = 4 >> extensional distance = 177 >> proper extension: 0dwl2; 01xdn1; 01t7jy; 03mnk; 01n073; 08t9df; 03sc8; 03d6fyn; 04vgq5; 01jx9; ... >> query: (?x3944, 01mw1) <- industry(?x3944, ?x373), industry(?x7526, ?x373), contact_category(?x7526, ?x897), child(?x7526, ?x1561) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #1469 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 70 *> proper extension: 0c_j5d; 03xsby; 04qhdf; 08wjc1; 02r5dz; 09b3v; 081g_l; 073tm9; 06q07; 099ks0; ... *> query: (?x3944, 02jjt) <- industry(?x3944, ?x373), industry(?x10884, ?x373), industry(?x7526, ?x373), ?x7526 = 03rwz3, film(?x10884, ?x814) *> conf = 0.29 ranks of expected_values: 3 EVAL 01p5yn industry 02jjt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 97.000 0.335 http://example.org/business/business_operation/industry #2181-0gqzz PRED entity: 0gqzz PRED relation: award! PRED expected values: 02rxbmt => 61 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2719): 0133sq (0.81 #27035, 0.80 #27034, 0.80 #23655), 013t9y (0.81 #27035, 0.80 #27034, 0.80 #23655), 02kxbx3 (0.58 #14502, 0.27 #11122, 0.25 #17881), 0184dt (0.58 #14189, 0.12 #7428, 0.10 #37850), 06pj8 (0.50 #7313, 0.27 #10694, 0.25 #17453), 01_f_5 (0.50 #15354, 0.25 #8593, 0.18 #11974), 0g2lq (0.50 #9030, 0.10 #25929, 0.09 #12411), 0693l (0.42 #14371, 0.38 #7610, 0.12 #51550), 02kxbwx (0.42 #13697, 0.27 #10317, 0.25 #17076), 022wxh (0.42 #14739, 0.09 #11359, 0.08 #64216) >> Best rule #27035 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0gr07; >> query: (?x1053, ?x1052) <- ceremony(?x1053, ?x5902), award_winner(?x1053, ?x1052), award_winner(?x2893, ?x1052), ?x5902 = 02glmx >> conf = 0.81 => this is the best rule for 2 predicted values *> Best rule #55641 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 135 *> proper extension: 05ztjjw; 02rdxsh; 099c8n; 03m73lj; 02qt02v; 02qysm0; 054knh; 02qwzkm; *> query: (?x1053, 02rxbmt) <- nominated_for(?x1053, ?x5713), nominated_for(?x1053, ?x224), film_release_region(?x5713, ?x2267), award(?x5713, ?x1723), genre(?x224, ?x225), ?x2267 = 03rj0 *> conf = 0.02 ranks of expected_values: 1998 EVAL 0gqzz award! 02rxbmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 20.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2180-027hnjh PRED entity: 027hnjh PRED relation: profession PRED expected values: 0dxtg => 96 concepts (93 used for prediction) PRED predicted values (max 10 best out of 61): 02hrh1q (0.71 #3862, 0.68 #11858, 0.65 #8305), 0dxtg (0.71 #901, 0.68 #1493, 0.67 #753), 02krf9 (0.57 #26, 0.30 #1506, 0.29 #1950), 01d_h8 (0.50 #1634, 0.50 #1042, 0.49 #1338), 02jknp (0.32 #1191, 0.31 #599, 0.31 #5922), 0kyk (0.25 #1213, 0.25 #621, 0.10 #10689), 09jwl (0.20 #3570, 0.20 #2238, 0.19 #4162), 018gz8 (0.19 #3272, 0.19 #3124, 0.16 #1496), 0nbcg (0.15 #2251, 0.13 #3583, 0.13 #3731), 02hv44_ (0.15 #1241, 0.14 #649, 0.04 #2277) >> Best rule #3862 for best value: >> intensional similarity = 3 >> extensional distance = 976 >> proper extension: 05cv94; 01yhvv; 06w2sn5; 015_30; 01zmpg; 047sxrj; 04rsd2; 033wx9; 014q2g; 01vn35l; ... >> query: (?x4671, 02hrh1q) <- nationality(?x4671, ?x94), award_nominee(?x4671, ?x3727), currency(?x3727, ?x170) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #901 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 174 *> proper extension: 04rtpt; *> query: (?x4671, 0dxtg) <- program(?x4671, ?x10089), tv_program(?x2643, ?x10089) *> conf = 0.71 ranks of expected_values: 2 EVAL 027hnjh profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 93.000 0.711 http://example.org/people/person/profession #2179-011z3g PRED entity: 011z3g PRED relation: artists! PRED expected values: 0glt670 05lwjc => 122 concepts (54 used for prediction) PRED predicted values (max 10 best out of 265): 01lyv (0.42 #6822, 0.32 #915, 0.27 #1210), 017_qw (0.37 #5665, 0.20 #55, 0.11 #5369), 0glt670 (0.34 #4761, 0.32 #15100, 0.31 #6534), 0ggx5q (0.30 #2134, 0.24 #6566, 0.24 #659), 07sbbz2 (0.30 #1482, 0.29 #1777, 0.23 #2367), 0ggq0m (0.25 #5324, 0.23 #5620, 0.18 #3551), 0xhtw (0.24 #604, 0.23 #5921, 0.23 #8284), 08cyft (0.24 #641, 0.11 #346, 0.09 #2116), 02w4v (0.23 #7128, 0.23 #4173, 0.19 #6832), 09nwwf (0.22 #3961, 0.18 #713, 0.14 #7507) >> Best rule #6822 for best value: >> intensional similarity = 4 >> extensional distance = 158 >> proper extension: 01wqflx; >> query: (?x6715, 01lyv) <- award_winner(?x1362, ?x6715), artists(?x9630, ?x6715), artists(?x9630, ?x2784), ?x2784 = 0137g1 >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #4761 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 88 *> proper extension: 06cc_1; 01kx_81; 0136p1; 07ss8_; 0cg9y; 01w724; 01vsykc; 0407f; 01vvyfh; 0163m1; ... *> query: (?x6715, 0glt670) <- artist(?x2190, ?x6715), award(?x6715, ?x724), artists(?x3928, ?x6715), award_winner(?x2634, ?x6715), ?x3928 = 0gywn *> conf = 0.34 ranks of expected_values: 3, 34 EVAL 011z3g artists! 05lwjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 122.000 54.000 0.425 http://example.org/music/genre/artists EVAL 011z3g artists! 0glt670 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 122.000 54.000 0.425 http://example.org/music/genre/artists #2178-067xw PRED entity: 067xw PRED relation: tv_program PRED expected values: 01j95 => 108 concepts (94 used for prediction) PRED predicted values (max 10 best out of 23): 03g9xj (0.08 #498, 0.06 #585, 0.06 #1108), 07vqnc (0.08 #514, 0.04 #775, 0.04 #688), 039cq4 (0.07 #2226, 0.06 #1268, 0.05 #1442), 099pks (0.06 #562, 0.04 #736, 0.04 #649), 01j95 (0.04 #782, 0.04 #695, 0.04 #869), 07c72 (0.04 #717, 0.04 #630, 0.04 #804), 01h72l (0.04 #713, 0.04 #626, 0.04 #800), 06qw_ (0.04 #955), 06r1k (0.04 #945), 06qwh (0.04 #921) >> Best rule #498 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 03h2p5; >> query: (?x7180, 03g9xj) <- profession(?x7180, ?x8310), profession(?x7180, ?x2225), ?x8310 = 0196pc, profession(?x9117, ?x2225), ?x9117 = 0167v4 >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #782 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 22 *> proper extension: 021lby; 01c1px; *> query: (?x7180, 01j95) <- profession(?x7180, ?x8310), profession(?x7180, ?x353), type_of_union(?x7180, ?x566), ?x8310 = 0196pc, profession(?x5033, ?x353), ?x5033 = 05y5fw *> conf = 0.04 ranks of expected_values: 5 EVAL 067xw tv_program 01j95 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 94.000 0.083 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #2177-05sy0cv PRED entity: 05sy0cv PRED relation: producer_type PRED expected values: 0ckd1 => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.72 #4, 0.72 #11, 0.71 #8) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 02pvqmz; >> query: (?x8837, 0ckd1) <- program(?x8205, ?x8837), program(?x8050, ?x8837), award_winner(?x9667, ?x8050), award_nominee(?x8205, ?x12194) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05sy0cv producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.724 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #2176-0kft PRED entity: 0kft PRED relation: award_winner! PRED expected values: 02wkmx => 128 concepts (70 used for prediction) PRED predicted values (max 10 best out of 320): 0gs9p (0.46 #505, 0.41 #8566, 0.38 #1285), 040njc (0.41 #8566, 0.38 #857, 0.38 #435), 02n9nmz (0.41 #8566, 0.38 #2143, 0.38 #1782), 0776drd (0.41 #8566, 0.38 #1285, 0.35 #24829), 04zx08r (0.41 #8566, 0.38 #1285, 0.35 #24829), 02w_6xj (0.32 #5378, 0.31 #4523, 0.31 #1094), 02rdyk7 (0.32 #3947, 0.31 #4375, 0.30 #6085), 0gr4k (0.31 #888, 0.29 #2174, 0.29 #1745), 09d28z (0.31 #1157, 0.28 #4586, 0.27 #5441), 019f4v (0.29 #7346, 0.25 #5206, 0.23 #4351) >> Best rule #505 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 03bw6; >> query: (?x9149, 0gs9p) <- award(?x9149, ?x198), people(?x10798, ?x9149), award_winner(?x289, ?x9149), people(?x1158, ?x9149), ?x198 = 040njc >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #3871 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 02kxbwx; 0c3ns; 01q4qv; 05ldnp; 02kxbx3; 07rd7; 026dx; 0js9s; 06t8b; 02r6c_; ... *> query: (?x9149, 02wkmx) <- award(?x9149, ?x1198), profession(?x9149, ?x319), ?x1198 = 02pqp12, written_by(?x7978, ?x9149), type_of_union(?x9149, ?x566) *> conf = 0.26 ranks of expected_values: 11 EVAL 0kft award_winner! 02wkmx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 128.000 70.000 0.462 http://example.org/award/award_category/winners./award/award_honor/award_winner #2175-0cwtm PRED entity: 0cwtm PRED relation: award_winner! PRED expected values: 0bz6l9 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 130): 0hndn2q (0.17 #181, 0.17 #40, 0.12 #322), 0c4hnm (0.17 #270, 0.12 #411, 0.04 #14806), 0c6vcj (0.17 #243, 0.12 #384, 0.04 #14806), 01c6qp (0.17 #160, 0.12 #301, 0.04 #1006), 01s695 (0.17 #144, 0.12 #285, 0.04 #990), 02rjjll (0.17 #146, 0.12 #287, 0.04 #4799), 0gmdkyy (0.17 #30, 0.12 #453, 0.04 #14806), 04n2r9h (0.17 #45, 0.05 #609, 0.04 #468), 03tn9w (0.17 #94, 0.04 #14806, 0.04 #517), 0bc773 (0.17 #54, 0.04 #14806, 0.04 #477) >> Best rule #181 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 024y6w; >> query: (?x9782, 0hndn2q) <- profession(?x9782, ?x10649), award_winner(?x1972, ?x9782), student(?x3821, ?x9782), ?x10649 = 01p5_g >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #14806 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1897 *> proper extension: 06lxn; *> query: (?x9782, ?x78) <- award_winner(?x1972, ?x9782), award(?x91, ?x1972), ceremony(?x1972, ?x78) *> conf = 0.04 ranks of expected_values: 73 EVAL 0cwtm award_winner! 0bz6l9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 140.000 140.000 0.167 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2174-02756j PRED entity: 02756j PRED relation: people! PRED expected values: 02sch9 => 108 concepts (98 used for prediction) PRED predicted values (max 10 best out of 51): 02sch9 (0.32 #186, 0.20 #262, 0.17 #34), 041rx (0.24 #2360, 0.23 #2056, 0.20 #3196), 0bpjh3 (0.22 #100, 0.12 #404, 0.07 #328), 0x67 (0.17 #1910, 0.16 #5178, 0.16 #2518), 033tf_ (0.15 #2135, 0.14 #463, 0.14 #1223), 02w7gg (0.13 #2054, 0.10 #2510, 0.10 #1218), 01rv7x (0.10 #1102, 0.07 #342, 0.07 #950), 07hwkr (0.10 #1228, 0.08 #1912, 0.07 #2368), 0xnvg (0.09 #2141, 0.08 #2065, 0.08 #1229), 04mvp8 (0.08 #1130, 0.05 #218, 0.05 #978) >> Best rule #186 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 05yvfd; >> query: (?x6312, 02sch9) <- location(?x6312, ?x7412), ?x7412 = 04vmp, people(?x5025, ?x6312) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02756j people! 02sch9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 98.000 0.316 http://example.org/people/ethnicity/people #2173-02hmw9 PRED entity: 02hmw9 PRED relation: institution! PRED expected values: 02_xgp2 => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 21): 019v9k (0.79 #1784, 0.71 #332, 0.67 #587), 02h4rq6 (0.78 #327, 0.72 #746, 0.70 #257), 02_xgp2 (0.60 #266, 0.57 #755, 0.55 #336), 03bwzr4 (0.56 #268, 0.51 #757, 0.47 #872), 0bkj86 (0.49 #261, 0.47 #331, 0.47 #586), 04zx3q1 (0.40 #1725, 0.40 #256, 0.37 #745), 0bjrnt (0.40 #1725, 0.36 #604, 0.32 #1698), 07s6fsf (0.39 #744, 0.33 #859, 0.33 #1), 01rr_d (0.33 #17, 0.21 #596, 0.20 #667), 02mjs7 (0.33 #4, 0.20 #2913, 0.18 #654) >> Best rule #1784 for best value: >> intensional similarity = 6 >> extensional distance = 333 >> proper extension: 03fcbb; >> query: (?x6837, 019v9k) <- institution(?x1200, ?x6837), contains(?x1310, ?x6837), institution(?x1200, ?x7447), institution(?x1200, ?x6271), ?x7447 = 01r3w7, ?x6271 = 015q1n >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #266 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 41 *> proper extension: 01dbns; *> query: (?x6837, 02_xgp2) <- major_field_of_study(?x6837, ?x5671), citytown(?x6837, ?x3301), major_field_of_study(?x2606, ?x5671), languages(?x558, ?x5671), language(?x508, ?x5671) *> conf = 0.60 ranks of expected_values: 3 EVAL 02hmw9 institution! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 188.000 188.000 0.794 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2172-0dwsp PRED entity: 0dwsp PRED relation: role! PRED expected values: 026t6 => 77 concepts (56 used for prediction) PRED predicted values (max 10 best out of 92): 0dwtp (0.87 #2823, 0.85 #2456, 0.85 #2091), 05148p4 (0.87 #2823, 0.82 #1352, 0.78 #2565), 02sgy (0.85 #2456, 0.85 #2091, 0.85 #1356), 02hnl (0.85 #2456, 0.85 #2091, 0.85 #1356), 07y_7 (0.85 #2456, 0.85 #2091, 0.85 #1356), 0dwr4 (0.85 #2456, 0.85 #2091, 0.85 #1356), 03ndd (0.85 #2456, 0.85 #2091, 0.85 #1356), 01rhl (0.85 #2456, 0.85 #2091, 0.85 #1356), 06w7v (0.85 #2733, 0.78 #449, 0.76 #993), 02qjv (0.80 #2751, 0.78 #449, 0.76 #993) >> Best rule #2823 for best value: >> intensional similarity = 23 >> extensional distance = 8 >> proper extension: 0jtg0; 06w7v; >> query: (?x615, ?x885) <- role(?x1969, ?x615), role(?x1436, ?x615), role(?x1212, ?x615), role(?x716, ?x615), role(?x228, ?x615), ?x1969 = 04rzd, role(?x2309, ?x615), role(?x885, ?x615), role(?x7972, ?x885), ?x7972 = 0326tc, role(?x1563, ?x615), role(?x615, ?x75), award(?x1563, ?x2561), role(?x2253, ?x1436), role(?x1647, ?x885), role(?x212, ?x885), profession(?x1563, ?x6565), ?x2309 = 06ncr, ?x716 = 018vs, ?x228 = 0l14qv, ?x6565 = 0fnpj, ?x1647 = 05ljv7, role(?x2377, ?x1212) >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #3008 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 12 *> proper extension: 018j2; 02fsn; 01xqw; *> query: (?x615, 026t6) <- role(?x1969, ?x615), role(?x1436, ?x615), role(?x716, ?x615), role(?x228, ?x615), ?x1969 = 04rzd, role(?x2309, ?x615), role(?x885, ?x615), role(?x7972, ?x885), ?x7972 = 0326tc, role(?x1563, ?x615), role(?x615, ?x75), award(?x1563, ?x2561), role(?x2253, ?x1436), role(?x569, ?x885), role(?x212, ?x885), profession(?x1563, ?x1614), ?x2309 = 06ncr, ?x716 = 018vs, ?x228 = 0l14qv, award_nominee(?x1563, ?x1238), award_winner(?x139, ?x1563) *> conf = 0.79 ranks of expected_values: 12 EVAL 0dwsp role! 026t6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 77.000 56.000 0.868 http://example.org/music/performance_role/track_performances./music/track_contribution/role #2171-0bzrxn PRED entity: 0bzrxn PRED relation: locations PRED expected values: 0f2r6 0dc95 => 60 concepts (45 used for prediction) PRED predicted values (max 10 best out of 411): 013yq (0.67 #2138, 0.55 #1442, 0.50 #2487), 0f2r6 (0.50 #367, 0.45 #1411, 0.43 #1759), 0d9y6 (0.50 #93, 0.44 #963, 0.43 #1833), 0ftxw (0.50 #234, 0.36 #1452, 0.33 #930), 0fsb8 (0.45 #1522, 0.36 #1870, 0.35 #2567), 0f2rq (0.44 #966, 0.33 #2184, 0.33 #270), 02cl1 (0.33 #888, 0.33 #192, 0.27 #1584), 0fpzwf (0.33 #967, 0.33 #271, 0.20 #2534), 030qb3t (0.33 #382, 0.32 #2821, 0.28 #3346), 071cn (0.33 #245, 0.30 #2508, 0.30 #2334) >> Best rule #2138 for best value: >> intensional similarity = 9 >> extensional distance = 13 >> proper extension: 0b_72t; 0bzrsh; >> query: (?x7378, 013yq) <- locations(?x7378, ?x6952), team(?x7378, ?x10846), team(?x7378, ?x9833), team(?x9974, ?x10846), team(?x4803, ?x10846), ?x9833 = 03y9p40, ?x4803 = 0b_6jz, ?x9974 = 0b_6pv, contains(?x94, ?x6952) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #367 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 0b_6mr; *> query: (?x7378, 0f2r6) <- locations(?x7378, ?x6952), team(?x7378, ?x10846), team(?x7378, ?x9833), team(?x9974, ?x10846), team(?x4803, ?x10846), ?x9833 = 03y9p40, ?x4803 = 0b_6jz, ?x9974 = 0b_6pv, ?x6952 = 0lphb *> conf = 0.50 ranks of expected_values: 2, 41 EVAL 0bzrxn locations 0dc95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 60.000 45.000 0.667 http://example.org/time/event/locations EVAL 0bzrxn locations 0f2r6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 60.000 45.000 0.667 http://example.org/time/event/locations #2170-0h08p PRED entity: 0h08p PRED relation: artists PRED expected values: 01w60_p => 77 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1454): 011z3g (0.67 #2752, 0.29 #9211, 0.28 #8133), 02z4b_8 (0.58 #2785, 0.44 #3860, 0.28 #8166), 0qf11 (0.58 #2531, 0.33 #380, 0.25 #3606), 025ldg (0.58 #2521, 0.31 #3596, 0.27 #8980), 01vvyfh (0.58 #2493, 0.21 #7874, 0.20 #8952), 01vvycq (0.50 #2198, 0.31 #3273, 0.26 #7579), 01wp8w7 (0.50 #2257, 0.31 #3332, 0.25 #1181), 019f9z (0.50 #2747, 0.29 #9206, 0.25 #3822), 0134wr (0.50 #2885, 0.25 #3960, 0.20 #9344), 0178_w (0.50 #2762, 0.25 #3837, 0.19 #10297) >> Best rule #2752 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 01fh36; >> query: (?x12220, 011z3g) <- artists(?x12220, ?x9321), artists(?x12220, ?x6331), participant(?x6331, ?x703), film(?x6331, ?x10276), parent_genre(?x12220, ?x505), country(?x10276, ?x94), ?x9321 = 0140t7 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #2309 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 10 *> proper extension: 01fh36; *> query: (?x12220, 01w60_p) <- artists(?x12220, ?x9321), artists(?x12220, ?x6331), participant(?x6331, ?x703), film(?x6331, ?x10276), parent_genre(?x12220, ?x505), country(?x10276, ?x94), ?x9321 = 0140t7 *> conf = 0.25 ranks of expected_values: 281 EVAL 0h08p artists 01w60_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 77.000 36.000 0.667 http://example.org/music/genre/artists #2169-01cpp0 PRED entity: 01cpp0 PRED relation: combatants PRED expected values: 0k6nt => 66 concepts (45 used for prediction) PRED predicted values (max 10 best out of 421): 0xff (0.59 #2374, 0.38 #4999, 0.38 #1748), 05vz3zq (0.50 #56, 0.18 #3430, 0.17 #4182), 06qd3 (0.50 #23, 0.15 #4375, 0.15 #5251), 0d05w3 (0.50 #39, 0.11 #247, 0.10 #5252), 05b7q (0.50 #66, 0.11 #247, 0.10 #5252), 079dy (0.38 #4999, 0.38 #1748, 0.37 #2373), 09b6zr (0.38 #4999, 0.38 #1748, 0.37 #2373), 0f8l9c (0.34 #2503, 0.26 #2640, 0.25 #14), 0ctw_b (0.34 #2503, 0.25 #18, 0.20 #3392), 059j2 (0.34 #2503, 0.25 #20, 0.15 #4375) >> Best rule #2374 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 09x7p1; >> query: (?x13381, ?x12262) <- entity_involved(?x13381, ?x12262), locations(?x13381, ?x4092), combatants(?x11531, ?x12262), contains(?x6304, ?x4092), contains(?x4092, ?x13482) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #2503 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 34 *> proper extension: 0727h; *> query: (?x13381, ?x87) <- entity_involved(?x13381, ?x4196), locations(?x13381, ?x4092), locations(?x13022, ?x4092), adjoins(?x4092, ?x4302), combatants(?x13022, ?x87), film_release_region(?x559, ?x4302) *> conf = 0.34 ranks of expected_values: 18 EVAL 01cpp0 combatants 0k6nt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 66.000 45.000 0.587 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #2168-051ys82 PRED entity: 051ys82 PRED relation: film_release_region PRED expected values: 09c7w0 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 141): 09c7w0 (0.93 #10889, 0.81 #2320, 0.79 #2142), 0d0vqn (0.51 #6604, 0.50 #5711, 0.50 #6247), 0d060g (0.51 #6604, 0.50 #5711, 0.50 #6247), 03rjj (0.48 #1255, 0.46 #1433, 0.42 #542), 0f8l9c (0.44 #1456, 0.44 #1278, 0.43 #6456), 0345h (0.42 #1471, 0.42 #6471, 0.42 #580), 06mkj (0.42 #1500, 0.42 #1322, 0.42 #609), 059j2 (0.42 #1469, 0.40 #6469, 0.40 #1291), 02vzc (0.40 #1494, 0.40 #1316, 0.38 #603), 05r4w (0.40 #6427, 0.38 #1427, 0.38 #1249) >> Best rule #10889 for best value: >> intensional similarity = 3 >> extensional distance = 615 >> proper extension: 076xkdz; >> query: (?x6005, 09c7w0) <- award(?x6005, ?x154), genre(?x6005, ?x53), film_release_region(?x6005, ?x512) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051ys82 film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2167-0b_6qj PRED entity: 0b_6qj PRED relation: team PRED expected values: 02pqcfz 026xxv_ 026wlnm => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 27): 03by7wc (0.86 #227, 0.79 #186, 0.79 #156), 026w398 (0.80 #201, 0.79 #190, 0.79 #160), 026xxv_ (0.80 #199, 0.79 #188, 0.73 #128), 026wlnm (0.77 #230, 0.73 #129, 0.71 #159), 091tgz (0.73 #228, 0.71 #157, 0.70 #198), 02pqcfz (0.63 #182, 0.60 #193, 0.55 #223), 02pjzvh (0.62 #105, 0.59 #226, 0.58 #185), 04088s0 (0.50 #225, 0.50 #84, 0.38 #144), 02ptzz0 (0.50 #41, 0.42 #181, 0.40 #192), 03d555l (0.50 #73, 0.35 #194, 0.33 #23) >> Best rule #227 for best value: >> intensional similarity = 12 >> extensional distance = 20 >> proper extension: 0b_71r; 0b_770; >> query: (?x9146, 03by7wc) <- team(?x9146, ?x9975), team(?x9146, ?x9833), team(?x9146, ?x9147), teams(?x11246, ?x9147), team(?x5897, ?x9975), team(?x9956, ?x9147), team(?x8992, ?x9147), ?x5897 = 0b_6rk, ?x9833 = 03y9p40, ?x8992 = 0b_6s7, ?x9956 = 0bzrsh, position(?x9975, ?x1579) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #199 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 18 *> proper extension: 0br1xn; *> query: (?x9146, 026xxv_) <- team(?x9146, ?x9147), teams(?x11246, ?x9147), locations(?x9146, ?x4978), locations(?x9146, ?x3983), administrative_division(?x4978, ?x3778), location(?x105, ?x4978), adjoins(?x1025, ?x3778), place_of_birth(?x1817, ?x3983), time_zones(?x3778, ?x1638) *> conf = 0.80 ranks of expected_values: 3, 4, 6 EVAL 0b_6qj team 026wlnm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.864 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_6qj team 026xxv_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.864 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0b_6qj team 02pqcfz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 77.000 0.864 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #2166-03lfd_ PRED entity: 03lfd_ PRED relation: film_release_region PRED expected values: 01mjq => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 156): 0d0vqn (0.94 #331, 0.91 #1789, 0.91 #1465), 0f8l9c (0.91 #347, 0.91 #1805, 0.91 #1481), 059j2 (0.85 #2949, 0.85 #1814, 0.84 #2463), 0jgd (0.85 #327, 0.82 #1461, 0.81 #2920), 06t2t (0.85 #1847, 0.84 #1523, 0.72 #389), 0b90_r (0.85 #1462, 0.82 #1786, 0.70 #328), 01znc_ (0.84 #1826, 0.84 #1502, 0.83 #368), 06bnz (0.84 #1831, 0.81 #1507, 0.74 #373), 015fr (0.84 #1799, 0.81 #1475, 0.73 #2448), 03h64 (0.83 #1853, 0.81 #1529, 0.81 #395) >> Best rule #331 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 0j43swk; 072hx4; >> query: (?x8867, 0d0vqn) <- film_release_region(?x8867, ?x279), film_release_region(?x8867, ?x87), honored_for(?x8762, ?x8867), ?x279 = 0d060g, ?x87 = 05r4w >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #1505 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 95 *> proper extension: 014lc_; 0crfwmx; 045j3w; 0gffmn8; 0gwjw0c; 0fpgp26; *> query: (?x8867, 01mjq) <- film_release_region(?x8867, ?x1174), film_release_region(?x8867, ?x390), film_release_region(?x8867, ?x94), ?x390 = 0chghy, ?x1174 = 047yc, ?x94 = 09c7w0 *> conf = 0.62 ranks of expected_values: 19 EVAL 03lfd_ film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 100.000 100.000 0.936 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2165-04ck0_ PRED entity: 04ck0_ PRED relation: sport PRED expected values: 02vx4 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 39): 02vx4 (0.95 #536, 0.93 #473, 0.93 #446), 09xp_ (0.29 #187, 0.20 #96, 0.12 #387), 018jz (0.20 #231, 0.19 #657, 0.16 #612), 0jm_ (0.19 #655, 0.18 #610, 0.17 #637), 03tmr (0.17 #691, 0.17 #635, 0.14 #581), 018w8 (0.15 #638, 0.14 #584, 0.13 #694), 039yzs (0.11 #881, 0.07 #724, 0.04 #614), 0z74 (0.11 #881, 0.03 #680), 09f6b (0.02 #145), 01gqfm (0.02 #145) >> Best rule #536 for best value: >> intensional similarity = 12 >> extensional distance = 35 >> proper extension: 04h5tx; >> query: (?x12839, 02vx4) <- team(?x530, ?x12839), team(?x203, ?x12839), team(?x60, ?x12839), ?x203 = 0dgrmp, teams(?x2911, ?x12839), ?x530 = 02_j1w, ?x60 = 02nzb8, contains(?x12315, ?x2911), teams(?x2911, ?x7667), jurisdiction_of_office(?x1195, ?x2911), sport(?x7667, ?x471), administrative_parent(?x2911, ?x142) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ck0_ sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.946 http://example.org/sports/sports_team/sport #2164-016zp5 PRED entity: 016zp5 PRED relation: type_of_union PRED expected values: 04ztj 01g63y => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.87 #41, 0.87 #17, 0.86 #25), 01g63y (0.30 #38, 0.27 #46, 0.26 #42) >> Best rule #41 for best value: >> intensional similarity = 2 >> extensional distance = 253 >> proper extension: 067jsf; 06c0j; >> query: (?x5495, 04ztj) <- award_winner(?x112, ?x5495), spouse(?x5144, ?x5495) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 016zp5 type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.875 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 016zp5 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.875 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2163-01bx35 PRED entity: 01bx35 PRED relation: ceremony! PRED expected values: 01c9f2 01c427 02581c 026mfs 01dpdh 01cw51 0257yf 01c9jp 03t5b6 025mbn 03nc9d => 47 concepts (39 used for prediction) PRED predicted values (max 10 best out of 229): 01c9jp (0.83 #1769, 0.83 #1584, 0.80 #1400), 02grdc (0.80 #1309, 0.78 #2584, 0.78 #3875), 01c427 (0.80 #1346, 0.78 #2584, 0.78 #3875), 03t5b6 (0.80 #1406, 0.78 #2584, 0.78 #3875), 026mfs (0.78 #2584, 0.78 #3875, 0.78 #7196), 025mbn (0.78 #2584, 0.78 #3875, 0.78 #7196), 01cw51 (0.78 #2584, 0.78 #3875, 0.78 #7196), 02581c (0.78 #2584, 0.78 #3875, 0.78 #7196), 01c9f2 (0.78 #2584, 0.78 #3875, 0.78 #7196), 0257yf (0.78 #2584, 0.78 #3875, 0.78 #7196) >> Best rule #1769 for best value: >> intensional similarity = 20 >> extensional distance = 10 >> proper extension: 019bk0; 056878; >> query: (?x725, 01c9jp) <- ceremony(?x12458, ?x725), ceremony(?x11068, ?x725), ceremony(?x10137, ?x725), ceremony(?x1237, ?x725), ceremony(?x567, ?x725), ?x12458 = 024_dt, ?x1237 = 02g8mp, award_winner(?x725, ?x8393), award_winner(?x725, ?x4568), award_winner(?x725, ?x248), ?x11068 = 02x4wb, ?x567 = 01d38g, role(?x4568, ?x227), ?x10137 = 0257wh, origin(?x8393, ?x3269), category(?x248, ?x134), award_nominee(?x8393, ?x1270), artists(?x505, ?x248), award_nominee(?x248, ?x4029), artists(?x284, ?x4568) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 EVAL 01bx35 ceremony! 03nc9d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01bx35 ceremony! 025mbn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01bx35 ceremony! 03t5b6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01bx35 ceremony! 01c9jp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01bx35 ceremony! 0257yf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01bx35 ceremony! 01cw51 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01bx35 ceremony! 01dpdh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01bx35 ceremony! 026mfs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01bx35 ceremony! 02581c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01bx35 ceremony! 01c427 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 01bx35 ceremony! 01c9f2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 47.000 39.000 0.833 http://example.org/award/award_category/winners./award/award_honor/ceremony #2162-012ycy PRED entity: 012ycy PRED relation: place_of_birth PRED expected values: 0rh6k => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 165): 0ncj8 (0.25 #849, 0.03 #9301, 0.02 #14230), 030qb3t (0.14 #1462, 0.12 #2167, 0.07 #13434), 0cr3d (0.14 #1502, 0.12 #2207, 0.04 #16998), 0f2tj (0.14 #1656, 0.12 #2361, 0.02 #14333), 0r03f (0.14 #1909, 0.04 #5431, 0.04 #6135), 0f94t (0.12 #2141, 0.08 #2845, 0.04 #4254), 04n3l (0.12 #2236, 0.01 #19140), 02_286 (0.11 #11287, 0.09 #4245, 0.08 #2836), 04lh6 (0.09 #4559, 0.08 #5263, 0.06 #8785), 010rvx (0.08 #3470, 0.04 #4879, 0.04 #5583) >> Best rule #849 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 01w03jv; >> query: (?x9603, 0ncj8) <- type_of_union(?x9603, ?x566), artists(?x5934, ?x9603), ?x5934 = 05r6t, profession(?x9603, ?x131), organizations_founded(?x9603, ?x9121) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #42258 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 264 *> proper extension: 01t6b4; 0241jw; 0170s4; 01zfmm; 01gw4f; 049fgvm; 048hf; 0154d7; 01nbq4; 01rzxl; ... *> query: (?x9603, 0rh6k) <- type_of_union(?x9603, ?x566), nationality(?x9603, ?x94), ?x566 = 04ztj, profession(?x9603, ?x131), currency(?x9603, ?x170) *> conf = 0.03 ranks of expected_values: 57 EVAL 012ycy place_of_birth 0rh6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 140.000 140.000 0.250 http://example.org/people/person/place_of_birth #2161-03kxdw PRED entity: 03kxdw PRED relation: location PRED expected values: 071cn => 82 concepts (76 used for prediction) PRED predicted values (max 10 best out of 190): 030qb3t (0.22 #16174, 0.19 #18588, 0.19 #20197), 02_286 (0.20 #37, 0.15 #8083, 0.15 #10496), 059rby (0.20 #16, 0.04 #16107, 0.04 #3233), 01n7q (0.20 #63, 0.04 #867, 0.03 #7303), 0r0m6 (0.20 #218, 0.04 #1022, 0.03 #8264), 06yxd (0.20 #247, 0.02 #5878, 0.02 #7487), 01sn3 (0.20 #215, 0.01 #16306, 0.01 #6651), 0cr3d (0.07 #4166, 0.06 #5776, 0.06 #7385), 04jpl (0.06 #16108, 0.06 #18522, 0.06 #8063), 05k7sb (0.05 #4934, 0.04 #5740, 0.04 #4130) >> Best rule #16174 for best value: >> intensional similarity = 3 >> extensional distance = 416 >> proper extension: 022769; >> query: (?x8780, 030qb3t) <- languages(?x8780, ?x254), ?x254 = 02h40lc, film(?x8780, ?x2463) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #1001 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 51 *> proper extension: 03j90; *> query: (?x8780, 071cn) <- languages(?x8780, ?x254), influenced_by(?x6692, ?x8780), nationality(?x8780, ?x94), language(?x54, ?x254) *> conf = 0.02 ranks of expected_values: 90 EVAL 03kxdw location 071cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 82.000 76.000 0.218 http://example.org/people/person/places_lived./people/place_lived/location #2160-0cqh46 PRED entity: 0cqh46 PRED relation: award! PRED expected values: 017149 0170vn 0b_dy 01ft2l 01nm3s 02t_st 0sw6g 02j490 04m064 => 50 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2541): 0bj9k (0.83 #6661, 0.76 #46639, 0.76 #46640), 09y20 (0.83 #6661, 0.76 #46639, 0.76 #46640), 0432cd (0.83 #6661, 0.76 #46639, 0.76 #46640), 09r9dp (0.71 #4366, 0.17 #1035, 0.14 #11025), 05fnl9 (0.57 #3751, 0.21 #10410, 0.18 #7081), 07r1h (0.50 #1776, 0.21 #11766, 0.18 #8437), 0z4s (0.50 #87, 0.21 #10077, 0.18 #6748), 0dvmd (0.50 #840, 0.21 #10830, 0.18 #7501), 01vvb4m (0.50 #828, 0.18 #7489, 0.14 #10818), 0f502 (0.50 #1219, 0.14 #4550, 0.10 #24533) >> Best rule #6661 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0ck27z; 0bdw6t; 057xs89; 0fbtbt; 0cqhb3; >> query: (?x880, ?x1549) <- award(?x3366, ?x880), ?x3366 = 01rzqj, award_winner(?x880, ?x1549) >> conf = 0.83 => this is the best rule for 3 predicted values *> Best rule #5628 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0ck27z; 0bdw6t; 057xs89; 0fbtbt; 0cqhb3; *> query: (?x880, 0sw6g) <- award(?x3366, ?x880), ?x3366 = 01rzqj, award_winner(?x880, ?x1549) *> conf = 0.29 ranks of expected_values: 80, 187, 204, 216, 579, 877, 884, 1101, 1559 EVAL 0cqh46 award! 04m064 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 21.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqh46 award! 02j490 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 21.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqh46 award! 0sw6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 50.000 21.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqh46 award! 02t_st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 21.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqh46 award! 01nm3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 21.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqh46 award! 01ft2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 50.000 21.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqh46 award! 0b_dy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 50.000 21.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqh46 award! 0170vn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 50.000 21.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cqh46 award! 017149 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 50.000 21.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2159-03f5vvx PRED entity: 03f5vvx PRED relation: type_of_union PRED expected values: 04ztj => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.86 #109, 0.85 #141, 0.85 #129), 01g63y (0.34 #474, 0.27 #389, 0.19 #575), 0jgjn (0.27 #389, 0.02 #188, 0.02 #304), 01bl8s (0.19 #575, 0.09 #55, 0.05 #91) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0dj5q; >> query: (?x3864, 04ztj) <- people(?x4195, ?x3864), jurisdiction_of_office(?x3864, ?x512), region(?x54, ?x512), country(?x124, ?x512) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f5vvx type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.865 http://example.org/people/person/spouse_s./people/marriage/type_of_union #2158-0bc1yhb PRED entity: 0bc1yhb PRED relation: genre PRED expected values: 02kdv5l 03k9fj => 81 concepts (79 used for prediction) PRED predicted values (max 10 best out of 102): 03k9fj (0.96 #1586, 0.70 #255, 0.62 #1102), 01hmnh (0.89 #1108, 0.61 #3048, 0.44 #1592), 01jfsb (0.75 #1829, 0.56 #1951, 0.53 #1345), 02kdv5l (0.66 #1818, 0.66 #2183, 0.59 #1940), 05p553 (0.60 #2306, 0.46 #1457, 0.42 #2671), 07s9rl0 (0.55 #5946, 0.55 #2667, 0.55 #5824), 0hcr (0.43 #1477, 0.21 #1598, 0.18 #1114), 02n4kr (0.40 #251, 0.22 #1219, 0.16 #3768), 0lsxr (0.31 #1220, 0.28 #1341, 0.26 #978), 02l7c8 (0.26 #5718, 0.26 #5962, 0.26 #6449) >> Best rule #1586 for best value: >> intensional similarity = 6 >> extensional distance = 69 >> proper extension: 0dnqr; >> query: (?x5270, 03k9fj) <- prequel(?x5270, ?x1956), genre(?x5270, ?x6647), genre(?x7692, ?x6647), genre(?x6332, ?x6647), ?x6332 = 03hxsv, ?x7692 = 0bt4g >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0bc1yhb genre 03k9fj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 79.000 0.958 http://example.org/film/film/genre EVAL 0bc1yhb genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 81.000 79.000 0.958 http://example.org/film/film/genre #2157-05fnl9 PRED entity: 05fnl9 PRED relation: profession PRED expected values: 02jknp => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 44): 0dxtg (0.45 #158, 0.43 #738, 0.27 #7409), 01d_h8 (0.36 #151, 0.31 #6, 0.30 #7402), 0kyk (0.31 #751, 0.08 #10613, 0.08 #11918), 09jwl (0.23 #17, 0.23 #1741, 0.19 #3498), 02jknp (0.23 #1741, 0.21 #5808, 0.21 #7403), 018gz8 (0.23 #1741, 0.17 #160, 0.13 #3641), 01c72t (0.23 #1741, 0.09 #6402, 0.09 #3502), 0dz3r (0.13 #3483, 0.12 #6383, 0.12 #6963), 0nbcg (0.13 #3509, 0.12 #10905, 0.12 #6409), 016z4k (0.12 #3485, 0.11 #6385, 0.11 #10881) >> Best rule #158 for best value: >> intensional similarity = 2 >> extensional distance = 40 >> proper extension: 05gnf; 053xw6; 0hsn_; 02g9z1; 02pbp9; >> query: (?x1676, 0dxtg) <- award_winner(?x1265, ?x1676), ?x1265 = 05c1t6z >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1741 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 599 *> proper extension: 02tf1y; 076df9; 0f87jy; *> query: (?x1676, ?x319) <- award_nominee(?x1676, ?x989), actor(?x2009, ?x1676), profession(?x989, ?x319) *> conf = 0.23 ranks of expected_values: 5 EVAL 05fnl9 profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 88.000 88.000 0.452 http://example.org/people/person/profession #2156-06mtq PRED entity: 06mtq PRED relation: adjoins PRED expected values: 05ff6 => 157 concepts (71 used for prediction) PRED predicted values (max 10 best out of 505): 0g39h (0.84 #37833, 0.83 #47093, 0.83 #36288), 06mtq (0.57 #2997, 0.50 #2227, 0.43 #3768), 07_f2 (0.40 #1097, 0.15 #5721, 0.10 #8035), 05ff6 (0.23 #49407, 0.22 #37834, 0.22 #36289), 0vh3 (0.22 #37834, 0.22 #36289, 0.22 #33969), 0d060g (0.20 #781, 0.17 #4634, 0.15 #5405), 0j3b (0.20 #831, 0.15 #5455, 0.10 #7769), 059rby (0.20 #788, 0.10 #16989, 0.10 #13125), 0694j (0.20 #1067, 0.10 #8005, 0.08 #4920), 059f4 (0.20 #805, 0.10 #7743, 0.08 #5429) >> Best rule #37833 for best value: >> intensional similarity = 4 >> extensional distance = 188 >> proper extension: 01914; >> query: (?x12854, ?x9725) <- adjoins(?x9725, ?x12854), administrative_parent(?x12854, ?x390), contains(?x12854, ?x8963), adjoins(?x12908, ?x9725) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #49407 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 239 *> proper extension: 0l2mg; *> query: (?x12854, ?x12908) <- adjoins(?x9725, ?x12854), administrative_division(?x11731, ?x9725), adjoins(?x12908, ?x9725), place_of_birth(?x4153, ?x11731) *> conf = 0.23 ranks of expected_values: 4 EVAL 06mtq adjoins 05ff6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 157.000 71.000 0.837 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #2155-0bl3nn PRED entity: 0bl3nn PRED relation: country PRED expected values: 07ssc => 123 concepts (107 used for prediction) PRED predicted values (max 10 best out of 172): 07ssc (0.61 #1572, 0.46 #1864, 0.43 #306), 02jx1 (0.61 #1572, 0.46 #1864, 0.37 #2390), 0chghy (0.47 #6019, 0.37 #2390, 0.33 #4498), 03rk0 (0.37 #2390, 0.33 #4498, 0.02 #1901), 0f8l9c (0.20 #19, 0.17 #77, 0.11 #4749), 0d060g (0.17 #66, 0.11 #646, 0.10 #704), 0d05w3 (0.17 #99, 0.05 #505, 0.03 #3078), 03_3d (0.10 #2280, 0.09 #2577, 0.07 #3624), 0ctw_b (0.09 #429, 0.07 #603, 0.06 #1011), 01hmnh (0.07 #4966, 0.06 #5901, 0.06 #6196) >> Best rule #1572 for best value: >> intensional similarity = 5 >> extensional distance = 123 >> proper extension: 014lc_; 0g5pv3; 0jdgr; 07b1gq; 09fc83; 063y9fp; 0gyv0b4; >> query: (?x7239, ?x512) <- story_by(?x7239, ?x8753), executive_produced_by(?x7239, ?x5869), language(?x7239, ?x254), genre(?x7239, ?x225), nationality(?x8753, ?x512) >> conf = 0.61 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0bl3nn country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 107.000 0.606 http://example.org/film/film/country #2154-0c4hnm PRED entity: 0c4hnm PRED relation: ceremony! PRED expected values: 0gr51 0gvx_ => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 334): 0gr51 (0.90 #2013, 0.90 #1768, 0.89 #2985), 0gvx_ (0.89 #3528, 0.89 #3771, 0.88 #2799), 0gq_v (0.86 #2449, 0.84 #1718, 0.81 #1963), 0gqz2 (0.84 #2242, 0.84 #1754, 0.83 #3700), 0gqxm (0.75 #5353, 0.63 #601, 0.61 #1578), 0gqzz (0.75 #5353, 0.32 #522, 0.27 #767), 02x201b (0.75 #5353, 0.25 #415, 0.11 #3095), 0czp_ (0.75 #5353, 0.18 #1410, 0.17 #3844), 054krc (0.36 #1027, 0.31 #727, 0.31 #1949), 04dn09n (0.36 #1000, 0.31 #727, 0.31 #1949) >> Best rule #2013 for best value: >> intensional similarity = 14 >> extensional distance = 29 >> proper extension: 0c4hx0; >> query: (?x9899, 0gr51) <- ceremony(?x1313, ?x9899), ceremony(?x601, ?x9899), ceremony(?x591, ?x9899), award_winner(?x9899, ?x9170), award_winner(?x9899, ?x5043), award(?x9170, ?x1232), gender(?x9170, ?x231), music(?x3826, ?x9170), ?x1313 = 0gs9p, ?x601 = 0gr4k, profession(?x9170, ?x563), ?x591 = 0f4x7, award_winner(?x2812, ?x5043), titles(?x812, ?x3826) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0c4hnm ceremony! 0gvx_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.903 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c4hnm ceremony! 0gr51 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 35.000 0.903 http://example.org/award/award_category/winners./award/award_honor/ceremony #2153-01hx2t PRED entity: 01hx2t PRED relation: student PRED expected values: 03k1vm => 198 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1374): 096lf_ (0.12 #1717, 0.11 #3810, 0.08 #5903), 04ld94 (0.12 #1020, 0.11 #3113, 0.08 #5206), 07ftc0 (0.12 #1423, 0.11 #3516, 0.08 #5609), 01qbjg (0.12 #1371, 0.11 #3464, 0.08 #5557), 01v3vp (0.12 #682, 0.11 #2775, 0.08 #4868), 03ldxq (0.12 #91, 0.11 #2184, 0.08 #4277), 01d4cb (0.12 #1575, 0.11 #3668, 0.08 #5761), 06y3r (0.12 #1574, 0.11 #3667, 0.08 #5760), 015qq1 (0.12 #1893, 0.11 #3986, 0.06 #12358), 0cbgl (0.06 #12552, 0.06 #14645, 0.05 #18831) >> Best rule #1717 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0lwkh; >> query: (?x8479, 096lf_) <- state_province_region(?x8479, ?x726), organization(?x346, ?x8479), organization(?x346, ?x99), institution(?x1368, ?x99), ?x726 = 05kj_ >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01hx2t student 03k1vm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 198.000 95.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #2152-0356dp PRED entity: 0356dp PRED relation: film PRED expected values: 031778 => 90 concepts (70 used for prediction) PRED predicted values (max 10 best out of 889): 0524b41 (0.47 #80391, 0.35 #101825, 0.12 #8932), 0cfhfz (0.22 #11208, 0.02 #50507, 0.01 #43359), 017jd9 (0.20 #11495, 0.14 #777, 0.04 #110756), 017gm7 (0.19 #10928, 0.14 #210, 0.02 #7355), 017gl1 (0.17 #10861, 0.14 #143, 0.04 #110756), 05q96q6 (0.14 #153, 0.05 #10871, 0.05 #26793), 011yph (0.14 #85, 0.04 #110756, 0.03 #119688), 034qmv (0.14 #15, 0.03 #3587, 0.02 #5374), 033srr (0.14 #654, 0.02 #7799, 0.02 #11372), 03cwwl (0.14 #1608, 0.02 #12326, 0.01 #23042) >> Best rule #80391 for best value: >> intensional similarity = 2 >> extensional distance = 1275 >> proper extension: 02rgz4; 02wb6yq; 07z4fy; >> query: (?x10282, ?x7119) <- location(?x10282, ?x362), nominated_for(?x10282, ?x7119) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #7460 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 59 *> proper extension: 01ztgm; 01wv9p; 0382m4; 079kdz; 07q0g5; 0163t3; *> query: (?x10282, 031778) <- place_of_birth(?x10282, ?x6764), actor(?x7119, ?x10282), spouse(?x10282, ?x374) *> conf = 0.02 ranks of expected_values: 563 EVAL 0356dp film 031778 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 90.000 70.000 0.473 http://example.org/film/actor/film./film/performance/film #2151-05fcbk7 PRED entity: 05fcbk7 PRED relation: film! PRED expected values: 01hkhq 03dpqd 0301bq => 70 concepts (36 used for prediction) PRED predicted values (max 10 best out of 993): 0kszw (0.22 #58142, 0.15 #58141, 0.04 #56482), 0525b (0.22 #58142, 0.15 #58141, 0.02 #57973), 09byk (0.22 #58142, 0.15 #58141, 0.01 #56176), 016ggh (0.22 #58142, 0.04 #57929, 0.03 #24917), 0f6_x (0.22 #58142, 0.03 #56689, 0.02 #8931), 06mnbn (0.22 #58142, 0.01 #56754), 07f3xb (0.22 #2318, 0.10 #4153, 0.03 #26994), 01sl1q (0.22 #2077, 0.10 #4153, 0.03 #26994), 0p8r1 (0.21 #8890, 0.08 #17195, 0.06 #584), 03ym1 (0.17 #3086, 0.03 #57074, 0.03 #26994) >> Best rule #58142 for best value: >> intensional similarity = 6 >> extensional distance = 446 >> proper extension: 04xbq3; >> query: (?x2847, ?x3651) <- film(?x12733, ?x2847), film(?x12733, ?x8062), actor(?x8062, ?x731), film(?x3651, ?x8062), genre(?x8062, ?x53), country_of_origin(?x8062, ?x94) >> conf = 0.22 => this is the best rule for 6 predicted values *> Best rule #4981 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 18 *> proper extension: 06zn1c; *> query: (?x2847, 03dpqd) <- genre(?x2847, ?x2540), genre(?x2847, ?x811), country(?x2847, ?x390), ?x811 = 03k9fj, ?x390 = 0chghy, genre(?x419, ?x2540) *> conf = 0.15 ranks of expected_values: 13, 214, 400 EVAL 05fcbk7 film! 0301bq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 70.000 36.000 0.223 http://example.org/film/actor/film./film/performance/film EVAL 05fcbk7 film! 03dpqd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 70.000 36.000 0.223 http://example.org/film/actor/film./film/performance/film EVAL 05fcbk7 film! 01hkhq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 70.000 36.000 0.223 http://example.org/film/actor/film./film/performance/film #2150-01_vrh PRED entity: 01_vrh PRED relation: capital! PRED expected values: 0c4b8 => 26 concepts (13 used for prediction) PRED predicted values (max 10 best out of 3): 0hzlz (0.20 #273, 0.20 #158, 0.19 #272), 0c4b8 (0.20 #221), 01bh3l (0.19 #272, 0.03 #1101, 0.02 #962) >> Best rule #273 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 01bh3l; 0g284; 01yj2; 01vg0s; 031hxk; 01tjsl; 067z4; 01tjt2; 0c499; 018lkp; ... >> query: (?x842, ?x792) <- contains(?x841, ?x842), contains(?x792, ?x842), ?x792 = 0hzlz, taxonomy(?x841, ?x939) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #221 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 01bh3l; 0g284; 01yj2; 01vg0s; 031hxk; 01tjsl; 067z4; 01tjt2; 0c499; 018lkp; ... *> query: (?x842, 0c4b8) <- contains(?x841, ?x842), contains(?x792, ?x842), ?x792 = 0hzlz, taxonomy(?x841, ?x939) *> conf = 0.20 ranks of expected_values: 2 EVAL 01_vrh capital! 0c4b8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 26.000 13.000 0.200 http://example.org/location/country/capital #2149-0g3b2z PRED entity: 0g3b2z PRED relation: athlete! PRED expected values: 02vx4 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 5): 02vx4 (0.91 #114, 0.90 #204, 0.89 #244), 0jm_ (0.26 #165, 0.26 #105, 0.18 #185), 018w8 (0.21 #168, 0.18 #108, 0.13 #188), 018jz (0.10 #179, 0.07 #189, 0.06 #251), 03tmr (0.02 #234, 0.02 #245, 0.02 #163) >> Best rule #114 for best value: >> intensional similarity = 6 >> extensional distance = 42 >> proper extension: 08jbxf; >> query: (?x8860, 02vx4) <- team(?x8860, ?x12050), team(?x8860, ?x12225), current_club(?x6180, ?x12050), team(?x60, ?x12050), profession(?x8860, ?x7623), ?x7623 = 0gl2ny2 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g3b2z athlete! 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.909 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #2148-012d40 PRED entity: 012d40 PRED relation: religion PRED expected values: 092bf5 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 18): 0c8wxp (0.25 #6, 0.23 #231, 0.22 #96), 03_gx (0.25 #59, 0.09 #194, 0.08 #329), 01lp8 (0.11 #91, 0.05 #181, 0.04 #451), 0kpl (0.08 #55, 0.07 #550, 0.07 #415), 092bf5 (0.08 #61, 0.07 #151, 0.04 #241), 06nzl (0.06 #105, 0.03 #240, 0.03 #195), 0g5llry (0.06 #118, 0.01 #208), 03j6c (0.05 #1281, 0.04 #1101, 0.03 #1011), 0n2g (0.03 #238, 0.02 #283, 0.02 #688), 0flw86 (0.02 #767, 0.02 #1262, 0.02 #1217) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 03f1zhf; >> query: (?x147, 0c8wxp) <- artists(?x13968, ?x147), location(?x147, ?x191), ?x191 = 0k049 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #61 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 012ykt; *> query: (?x147, 092bf5) <- nominated_for(?x147, ?x4038), film(?x147, ?x2709), ?x2709 = 06ztvyx *> conf = 0.08 ranks of expected_values: 5 EVAL 012d40 religion 092bf5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 109.000 109.000 0.250 http://example.org/people/person/religion #2147-06mvq PRED entity: 06mvq PRED relation: geographic_distribution PRED expected values: 02vzc => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 245): 09c7w0 (0.80 #2092, 0.76 #2166, 0.75 #1418), 07ssc (0.62 #759, 0.50 #384, 0.40 #235), 09pmkv (0.50 #766, 0.40 #242, 0.33 #465), 06t2t (0.40 #259, 0.38 #783, 0.33 #482), 07f1x (0.40 #287, 0.33 #510, 0.25 #811), 0hzlz (0.40 #239, 0.33 #462, 0.25 #763), 0d060g (0.38 #755, 0.27 #1721, 0.20 #231), 03rk0 (0.33 #404, 0.25 #779, 0.20 #255), 06m_5 (0.33 #439, 0.25 #814, 0.20 #290), 012wgb (0.33 #45, 0.25 #195, 0.20 #344) >> Best rule #2092 for best value: >> intensional similarity = 15 >> extensional distance = 18 >> proper extension: 01xhh5; 0ffjqy; >> query: (?x7790, 09c7w0) <- geographic_distribution(?x7790, ?x985), people(?x7790, ?x2281), country(?x12943, ?x985), film_release_region(?x3035, ?x985), film_release_region(?x2958, ?x985), film_release_region(?x2717, ?x985), film_release_region(?x1625, ?x985), film_release_region(?x972, ?x985), ?x3035 = 0j43swk, ?x1625 = 01f8gz, ?x12943 = 01yfj, film_art_direction_by(?x2958, ?x11330), ?x972 = 017gl1, form_of_government(?x985, ?x1926), ?x2717 = 0k5g9 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1791 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 13 *> proper extension: 04l_pt; *> query: (?x7790, ?x142) <- geographic_distribution(?x7790, ?x1264), religion(?x1264, ?x492), film_release_region(?x6932, ?x1264), film_release_region(?x6543, ?x1264), film_release_region(?x5016, ?x1264), film_release_region(?x2878, ?x1264), country(?x150, ?x1264), country(?x1646, ?x1264), combatants(?x94, ?x1264), ?x5016 = 062zm5h, film_release_region(?x6543, ?x142), film_release_region(?x6543, ?x87), ?x6932 = 027pfg, film(?x539, ?x2878), ?x87 = 05r4w *> conf = 0.04 ranks of expected_values: 89 EVAL 06mvq geographic_distribution 02vzc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 55.000 55.000 0.800 http://example.org/people/ethnicity/geographic_distribution #2146-0vrmb PRED entity: 0vrmb PRED relation: country PRED expected values: 09c7w0 => 182 concepts (147 used for prediction) PRED predicted values (max 10 best out of 98): 09c7w0 (0.80 #9005, 0.79 #7603, 0.79 #7426), 04_1l0v (0.39 #7425, 0.39 #7602, 0.39 #6547), 04rrx (0.33 #3918, 0.31 #348, 0.30 #3917), 0nj07 (0.33 #3918, 0.31 #348, 0.30 #3917), 02gt5s (0.33 #3918, 0.31 #348, 0.30 #3917), 0d060g (0.25 #95, 0.10 #1313, 0.10 #1226), 02j71 (0.16 #870, 0.15 #1914), 0345h (0.10 #4567, 0.07 #5790, 0.06 #2557), 07ssc (0.08 #4110, 0.07 #5513, 0.06 #8060), 03rk0 (0.06 #2920, 0.05 #3703, 0.05 #3877) >> Best rule #9005 for best value: >> intensional similarity = 5 >> extensional distance = 240 >> proper extension: 013hxv; 062qg; 0jpy_; 012q8y; >> query: (?x12794, 09c7w0) <- state(?x12794, ?x1906), adjoins(?x177, ?x1906), contains(?x1906, ?x169), district_represented(?x176, ?x1906), location(?x5574, ?x1906) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0vrmb country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 147.000 0.798 http://example.org/base/biblioness/bibs_location/country #2145-03772 PRED entity: 03772 PRED relation: award_nominee PRED expected values: 07b3r9 => 88 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1024): 07b3r9 (0.81 #72603, 0.81 #91353, 0.79 #30443), 05m9f9 (0.26 #98379, 0.21 #44497, 0.02 #55088), 07fpm3 (0.26 #98379, 0.21 #44497, 0.02 #96875), 03mdt (0.26 #98379, 0.21 #44497, 0.02 #42919), 03772 (0.26 #98379, 0.21 #44497, 0.02 #43356), 03rgvr (0.26 #98379, 0.21 #44497, 0.01 #18557), 0356dp (0.26 #98379, 0.21 #44497, 0.01 #18517), 02624g (0.26 #98379, 0.21 #44497, 0.01 #17997), 059xnf (0.26 #98379, 0.21 #44497, 0.01 #17996), 06mnbn (0.26 #98379, 0.21 #44497, 0.01 #17313) >> Best rule #72603 for best value: >> intensional similarity = 3 >> extensional distance = 325 >> proper extension: 06n7h7; 03ldxq; 01v3s2_; 0bz5v2; 06jvj7; 049_zz; 073749; 0fqy4p; 01pctb; 0g2mbn; ... >> query: (?x5034, ?x4383) <- award_nominee(?x4383, ?x5034), nominated_for(?x5034, ?x7119), category(?x5034, ?x134) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03772 award_nominee 07b3r9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 44.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2144-03n08b PRED entity: 03n08b PRED relation: nominated_for PRED expected values: 033f8n => 96 concepts (61 used for prediction) PRED predicted values (max 10 best out of 594): 01k0vq (0.78 #95767, 0.78 #61681, 0.78 #71421), 0c0zq (0.50 #6272, 0.10 #4649, 0.10 #3027), 033f8n (0.35 #12980, 0.35 #11356, 0.33 #755), 07pd_j (0.35 #12980, 0.35 #11356, 0.33 #17853), 03n0cd (0.35 #12980, 0.35 #11356, 0.33 #17853), 04cppj (0.35 #12980, 0.35 #11356, 0.33 #17853), 09rx7tx (0.35 #12980, 0.35 #11356, 0.33 #17853), 0fpgp26 (0.35 #12980, 0.35 #11356, 0.33 #17853), 01g3gq (0.35 #12980, 0.35 #11356, 0.33 #17853), 027j9wd (0.35 #12980, 0.35 #11356, 0.33 #17853) >> Best rule #95767 for best value: >> intensional similarity = 2 >> extensional distance = 1507 >> proper extension: 0627sn; >> query: (?x1461, ?x7579) <- award_winner(?x7579, ?x1461), profession(?x1461, ?x1032) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #12980 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 079vf; 012d40; 02qgqt; 02p65p; 083chw; 014zcr; 05ty4m; 01q_ph; 02lfcm; 0z4s; ... *> query: (?x1461, ?x3084) <- executive_produced_by(?x141, ?x1461), award_winner(?x1460, ?x1461), film(?x1461, ?x3084) *> conf = 0.35 ranks of expected_values: 3 EVAL 03n08b nominated_for 033f8n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 96.000 61.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #2143-0k4gf PRED entity: 0k4gf PRED relation: instrumentalists! PRED expected values: 013y1f 07_l6 => 239 concepts (239 used for prediction) PRED predicted values (max 10 best out of 73): 0342h (0.62 #11718, 0.58 #5814, 0.58 #12158), 05148p4 (0.38 #5038, 0.34 #4509, 0.32 #4862), 07_l6 (0.33 #239, 0.33 #63, 0.29 #503), 018vs (0.29 #12166, 0.24 #11726, 0.21 #4678), 07gql (0.25 #1627, 0.21 #1187, 0.19 #2155), 013y1f (0.25 #560, 0.11 #4344, 0.07 #3640), 07y_7 (0.22 #706, 0.21 #1234, 0.21 #1146), 03qjg (0.20 #140, 0.19 #5157, 0.15 #5861), 026t6 (0.20 #91, 0.16 #4491, 0.15 #5020), 0g2dz (0.20 #118, 0.09 #910, 0.08 #1086) >> Best rule #11718 for best value: >> intensional similarity = 3 >> extensional distance = 353 >> proper extension: 07s6prs; 02wk4d; >> query: (?x1211, 0342h) <- profession(?x1211, ?x563), location(?x1211, ?x1646), instrumentalists(?x316, ?x1211) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #239 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 02r38; *> query: (?x1211, 07_l6) <- artists(?x10853, ?x1211), people(?x1158, ?x1211), ?x10853 = 0l8gh *> conf = 0.33 ranks of expected_values: 3, 6 EVAL 0k4gf instrumentalists! 07_l6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 239.000 239.000 0.623 http://example.org/music/instrument/instrumentalists EVAL 0k4gf instrumentalists! 013y1f CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 239.000 239.000 0.623 http://example.org/music/instrument/instrumentalists #2142-04bdzg PRED entity: 04bdzg PRED relation: profession PRED expected values: 02hrh1q => 87 concepts (84 used for prediction) PRED predicted values (max 10 best out of 55): 02hrh1q (0.89 #5974, 0.89 #908, 0.88 #461), 0dxtg (0.48 #2397, 0.28 #9102, 0.28 #7314), 02jknp (0.45 #2391, 0.38 #156, 0.25 #9388), 03gjzk (0.31 #2399, 0.22 #5677, 0.22 #4634), 09jwl (0.25 #9388, 0.22 #913, 0.19 #1211), 0d1pc (0.25 #9388, 0.21 #647, 0.20 #945), 02hv44_ (0.25 #9388, 0.03 #9147, 0.03 #11533), 0q04f (0.25 #9388, 0.01 #2633, 0.01 #4719), 0cbd2 (0.17 #1496, 0.13 #3582, 0.12 #4774), 0np9r (0.15 #5981, 0.14 #8067, 0.14 #7024) >> Best rule #5974 for best value: >> intensional similarity = 3 >> extensional distance = 1817 >> proper extension: 01n7qlf; 0f2c8g; 01d5vk; 01x0sy; 085q5; 0fs9jn; 065mm1; 0652ty; 03k1vm; 02hblj; ... >> query: (?x6242, 02hrh1q) <- gender(?x6242, ?x231), profession(?x6242, ?x319), film(?x6242, ?x253) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bdzg profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 84.000 0.889 http://example.org/people/person/profession #2141-0ds5_72 PRED entity: 0ds5_72 PRED relation: film_release_region PRED expected values: 0d0vqn 07ssc => 92 concepts (74 used for prediction) PRED predicted values (max 10 best out of 179): 0d0vqn (0.93 #2062, 0.90 #4118, 0.33 #9), 0f8l9c (0.91 #2081, 0.88 #4137, 0.33 #28), 0chghy (0.89 #2066, 0.82 #4122, 0.33 #13), 05r4w (0.87 #2055, 0.85 #4111, 0.33 #2), 059j2 (0.87 #4149, 0.83 #2093, 0.33 #40), 06mkj (0.86 #2121, 0.86 #4177, 0.33 #68), 03gj2 (0.86 #2086, 0.79 #4142, 0.33 #33), 0jgd (0.86 #2057, 0.78 #4113, 0.27 #3771), 03rjj (0.85 #4115, 0.84 #2059, 0.33 #6), 07ssc (0.84 #2073, 0.81 #4129, 0.33 #20) >> Best rule #2062 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 0g5qmbz; >> query: (?x8495, 0d0vqn) <- executive_produced_by(?x8495, ?x6985), film_release_region(?x8495, ?x1499), film_release_region(?x8495, ?x94), ?x94 = 09c7w0, ?x1499 = 01znc_ >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10 EVAL 0ds5_72 film_release_region 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 92.000 74.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ds5_72 film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 74.000 0.929 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2140-0133_p PRED entity: 0133_p PRED relation: parent_genre! PRED expected values: 01h0kx 018ysx 08cg36 => 70 concepts (42 used for prediction) PRED predicted values (max 10 best out of 305): 0xv2x (0.60 #1427, 0.50 #644, 0.40 #1166), 0g_bh (0.50 #626, 0.40 #1409, 0.40 #1148), 0bt7w (0.50 #608, 0.40 #1391, 0.40 #1130), 01gbcf (0.50 #524, 0.40 #1307, 0.40 #1046), 0xjl2 (0.50 #558, 0.40 #1341, 0.40 #1080), 01243b (0.50 #556, 0.40 #1339, 0.40 #1078), 0pm85 (0.50 #389, 0.40 #910, 0.33 #129), 059kh (0.50 #302, 0.40 #823, 0.33 #42), 0y3_8 (0.50 #300, 0.40 #821, 0.33 #40), 02k_kn (0.50 #313, 0.40 #834, 0.33 #53) >> Best rule #1427 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0xhtw; >> query: (?x9935, 0xv2x) <- parent_genre(?x10306, ?x9935), artists(?x9935, ?x6225), parent_genre(?x9935, ?x2996), ?x6225 = 01vng3b, parent_genre(?x14058, ?x10306), ?x14058 = 088vmr >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1429 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0xhtw; *> query: (?x9935, 01h0kx) <- parent_genre(?x10306, ?x9935), artists(?x9935, ?x6225), parent_genre(?x9935, ?x2996), ?x6225 = 01vng3b, parent_genre(?x14058, ?x10306), ?x14058 = 088vmr *> conf = 0.40 ranks of expected_values: 16, 22, 30 EVAL 0133_p parent_genre! 08cg36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 70.000 42.000 0.600 http://example.org/music/genre/parent_genre EVAL 0133_p parent_genre! 018ysx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 70.000 42.000 0.600 http://example.org/music/genre/parent_genre EVAL 0133_p parent_genre! 01h0kx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 70.000 42.000 0.600 http://example.org/music/genre/parent_genre #2139-086vfb PRED entity: 086vfb PRED relation: award! PRED expected values: 0c3p7 => 40 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2314): 01dw9z (0.50 #4097, 0.33 #721, 0.09 #14226), 01bcq (0.33 #1432, 0.25 #4808, 0.17 #8185), 02kxwk (0.33 #1240, 0.25 #4616, 0.12 #14745), 0c3p7 (0.33 #1848, 0.25 #5224, 0.11 #8601), 01l1ls (0.33 #2738, 0.25 #6114, 0.11 #9491), 06r3p2 (0.33 #3285, 0.25 #6661, 0.11 #10038), 0159h6 (0.33 #99, 0.25 #3475, 0.11 #13604), 020_95 (0.33 #1604, 0.25 #4980, 0.10 #15109), 0dvld (0.33 #1750, 0.25 #5126, 0.09 #18632), 02__7n (0.33 #2118, 0.25 #5494, 0.08 #15623) >> Best rule #4097 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03tk6z; >> query: (?x1058, 01dw9z) <- award(?x8412, ?x1058), award(?x5105, ?x1058), ?x8412 = 01ccr8, ceremony(?x1058, ?x4141), participant(?x3751, ?x5105) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1848 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 09qvf4; *> query: (?x1058, 0c3p7) <- award(?x8412, ?x1058), award(?x5105, ?x1058), award(?x1057, ?x1058), ?x8412 = 01ccr8, ceremony(?x1058, ?x4141), ?x5105 = 047c9l, ?x1057 = 01sxq9 *> conf = 0.33 ranks of expected_values: 4 EVAL 086vfb award! 0c3p7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 40.000 18.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2138-06fxnf PRED entity: 06fxnf PRED relation: music! PRED expected values: 087wc7n 035s95 056xkh => 116 concepts (93 used for prediction) PRED predicted values (max 10 best out of 326): 0dr3sl (0.41 #2971, 0.08 #33666, 0.07 #42580), 01s7w3 (0.03 #10752, 0.03 #12732, 0.03 #3821), 07bzz7 (0.03 #516, 0.03 #3487, 0.02 #7448), 02rrfzf (0.03 #3288, 0.03 #7249, 0.03 #10219), 09d3b7 (0.03 #3793, 0.02 #7754, 0.02 #2802), 02ht1k (0.03 #3331, 0.02 #6301, 0.02 #12242), 0pdp8 (0.03 #3191, 0.02 #220, 0.02 #2200), 09d38d (0.03 #3931, 0.02 #7892, 0.01 #10862), 04tqtl (0.03 #3272, 0.02 #7233, 0.01 #10203), 0h3k3f (0.03 #3796, 0.02 #7757, 0.01 #10727) >> Best rule #2971 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 04gycf; 03d9d6; 019389; >> query: (?x4020, ?x1904) <- award_nominee(?x1292, ?x4020), instrumentalists(?x75, ?x4020), nominated_for(?x4020, ?x1904) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #7135 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 177 *> proper extension: 0fp_v1x; 02rgz4; 02mslq; 01nqfh_; 03ds3; 07q1v4; 0k4gf; 01vsxdm; 01ky2h; 0p5mw; ... *> query: (?x4020, 035s95) <- music(?x9194, ?x4020), music(?x2746, ?x4020), film(?x2473, ?x2746), film_release_region(?x9194, ?x47) *> conf = 0.01 ranks of expected_values: 199 EVAL 06fxnf music! 056xkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 93.000 0.414 http://example.org/film/film/music EVAL 06fxnf music! 035s95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 116.000 93.000 0.414 http://example.org/film/film/music EVAL 06fxnf music! 087wc7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 93.000 0.414 http://example.org/film/film/music #2137-01w0yrc PRED entity: 01w0yrc PRED relation: film PRED expected values: 0241y7 => 96 concepts (63 used for prediction) PRED predicted values (max 10 best out of 448): 072kp (0.57 #46555, 0.48 #62670, 0.45 #44764), 0dtw1x (0.20 #1791, 0.18 #5373, 0.12 #3582), 0h1fktn (0.12 #969, 0.07 #4551, 0.02 #17086), 0jwl2 (0.07 #35812, 0.07 #10744, 0.07 #17908), 013q07 (0.06 #356, 0.04 #3938, 0.02 #2147), 0prrm (0.05 #2651, 0.04 #860, 0.04 #4442), 03q0r1 (0.05 #2427, 0.04 #636, 0.03 #4218), 02qr3k8 (0.04 #1289, 0.04 #3080, 0.03 #4871), 0295sy (0.04 #959, 0.04 #2750, 0.03 #4541), 016dj8 (0.04 #1114, 0.02 #2905, 0.02 #6487) >> Best rule #46555 for best value: >> intensional similarity = 2 >> extensional distance = 939 >> proper extension: 0241wg; 044qx; 06b_0; 0bxy67; >> query: (?x10153, ?x631) <- award_winner(?x631, ?x10153), film(?x10153, ?x1120) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w0yrc film 0241y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 63.000 0.574 http://example.org/film/actor/film./film/performance/film #2136-02v570 PRED entity: 02v570 PRED relation: film! PRED expected values: 01pb34 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 3): 01pb34 (0.09 #13, 0.09 #73, 0.08 #33), 09_gdc (0.09 #12, 0.04 #42, 0.03 #47), 01kyvx (0.01 #377, 0.01 #372, 0.01 #316) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 0dtw1x; 02847m9; 0h1fktn; 0hz6mv2; 056xkh; 0dtzkt; >> query: (?x7462, 01pb34) <- person(?x7462, ?x989), film(?x166, ?x7462), executive_produced_by(?x7462, ?x4060) >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v570 film! 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.091 http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film #2135-01pk3z PRED entity: 01pk3z PRED relation: award PRED expected values: 05ztrmj => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 213): 0gqyl (0.33 #103, 0.20 #506, 0.15 #22170), 02x4w6g (0.33 #112, 0.20 #515, 0.13 #20960), 0gqy2 (0.33 #163, 0.15 #22170, 0.13 #16122), 027dtxw (0.33 #4, 0.15 #22170, 0.13 #16122), 099t8j (0.33 #139, 0.15 #22170, 0.13 #16122), 02x4x18 (0.33 #131, 0.15 #22170, 0.13 #16122), 099cng (0.33 #85, 0.15 #22170, 0.13 #16122), 0gqwc (0.33 #74, 0.13 #477, 0.13 #20960), 05ztrmj (0.27 #586, 0.17 #183, 0.15 #22170), 094qd5 (0.27 #447, 0.17 #44, 0.13 #20960) >> Best rule #103 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0237fw; 02vntj; 01w23w; 03mp9s; >> query: (?x5541, 0gqyl) <- award_nominee(?x5541, ?x2626), award(?x5541, ?x704), ?x2626 = 02js6_ >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #586 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 05bnp0; 0kszw; 0c9c0; 01k5zk; 01g23m; 02pjvc; 058frd; 03zz8b; 0633p0; 0h10vt; *> query: (?x5541, 05ztrmj) <- award_nominee(?x5541, ?x4247), award(?x5541, ?x704), ?x4247 = 02vntj *> conf = 0.27 ranks of expected_values: 9 EVAL 01pk3z award 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 80.000 80.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2134-02ywwy PRED entity: 02ywwy PRED relation: nominated_for! PRED expected values: 0pgm3 => 88 concepts (43 used for prediction) PRED predicted values (max 10 best out of 744): 04pqqb (0.47 #35059, 0.45 #60780, 0.45 #60778), 06mr6 (0.31 #25704, 0.29 #88837, 0.29 #58442), 05cj4r (0.29 #88837, 0.29 #58442, 0.29 #81824), 0126rp (0.29 #58442, 0.29 #51434, 0.29 #56105), 01nwwl (0.29 #58442, 0.29 #51434, 0.29 #56105), 04y9dk (0.29 #58442, 0.29 #51434, 0.29 #56105), 04kj2v (0.19 #5192, 0.14 #517), 01b9ck (0.16 #70130, 0.10 #70129, 0.09 #11686), 0127m7 (0.16 #70130, 0.10 #70129, 0.09 #11686), 01qg7c (0.16 #70130, 0.10 #70129, 0.09 #11686) >> Best rule #35059 for best value: >> intensional similarity = 3 >> extensional distance = 544 >> proper extension: 0yyg4; 05cj_j; 05z7c; 07nt8p; 0h1v19; 01rwyq; 0glnm; 04vh83; 01sxdy; 02jr6k; ... >> query: (?x8443, ?x4854) <- produced_by(?x8443, ?x4854), award_winner(?x4854, ?x6426), nominated_for(?x382, ?x8443) >> conf = 0.47 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02ywwy nominated_for! 0pgm3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 43.000 0.467 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #2133-0c8tkt PRED entity: 0c8tkt PRED relation: film_crew_role PRED expected values: 01pvkk => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 28): 0ch6mp2 (0.80 #719, 0.79 #1015, 0.78 #978), 02r96rf (0.73 #151, 0.73 #339, 0.72 #788), 09vw2b7 (0.68 #977, 0.67 #1500, 0.67 #718), 01vx2h (0.44 #982, 0.41 #160, 0.39 #198), 01pvkk (0.32 #161, 0.31 #274, 0.30 #872), 02ynfr (0.25 #91, 0.25 #54, 0.22 #728), 0215hd (0.25 #57, 0.19 #168, 0.16 #879), 02_n3z (0.25 #38, 0.16 #149, 0.11 #823), 089fss (0.25 #43, 0.11 #192, 0.09 #976), 02rh1dz (0.21 #347, 0.17 #498, 0.15 #981) >> Best rule #719 for best value: >> intensional similarity = 5 >> extensional distance = 170 >> proper extension: 0ds11z; 0170_p; 09p35z; 02rqwhl; 01pgp6; 0kvgxk; 035s95; 0d_2fb; 08952r; 05c5z8j; ... >> query: (?x1743, 0ch6mp2) <- film_crew_role(?x1743, ?x137), film(?x215, ?x1743), titles(?x307, ?x1743), production_companies(?x1743, ?x1104), category(?x1743, ?x134) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 0kv9d3; 043t8t; 02z0f6l; 02825kb; *> query: (?x1743, 01pvkk) <- film_crew_role(?x1743, ?x137), film(?x11861, ?x1743), titles(?x307, ?x1743), award_nominee(?x11861, ?x8871), film(?x4832, ?x1743) *> conf = 0.32 ranks of expected_values: 5 EVAL 0c8tkt film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 93.000 0.797 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2132-0ksrf8 PRED entity: 0ksrf8 PRED relation: student! PRED expected values: 07tg4 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 130): 07w0v (0.33 #20, 0.14 #546, 0.02 #1072), 0187nd (0.33 #365, 0.14 #891, 0.02 #1417), 0bwfn (0.14 #800, 0.08 #9217, 0.08 #16584), 01q7q2 (0.14 #818), 07tg4 (0.08 #2716, 0.03 #3242, 0.02 #35873), 04b_46 (0.07 #1805, 0.07 #2331, 0.06 #1279), 08815 (0.07 #1580, 0.06 #3158, 0.05 #1054), 09f2j (0.07 #2263, 0.04 #16469, 0.04 #9102), 017j69 (0.06 #1197, 0.05 #1723, 0.03 #2249), 015nl4 (0.05 #2697, 0.04 #9010, 0.03 #16377) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09r9dp; >> query: (?x5563, 07w0v) <- award_winner(?x2123, ?x5563), student(?x8925, ?x5563), ?x2123 = 0f6_dy >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2716 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 127 *> proper extension: 01vzz1c; *> query: (?x5563, 07tg4) <- location(?x5563, ?x362), ?x362 = 04jpl *> conf = 0.08 ranks of expected_values: 5 EVAL 0ksrf8 student! 07tg4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 91.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #2131-07jjt PRED entity: 07jjt PRED relation: olympics PRED expected values: 0kbvb 0kbws => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 27): 0kbws (0.83 #866, 0.83 #670, 0.83 #538), 0kbvb (0.69 #660, 0.69 #638, 0.67 #861), 0l6m5 (0.60 #182, 0.56 #640, 0.55 #115), 0lgxj (0.55 #115, 0.52 #203, 0.50 #131), 0lbbj (0.55 #115, 0.52 #203, 0.50 #113), 0l98s (0.55 #115, 0.52 #203, 0.50 #113), 0jkvj (0.55 #115, 0.52 #203, 0.50 #113), 0l6mp (0.50 #113, 0.50 #98, 0.48 #201), 0c_tl (0.50 #113, 0.48 #201, 0.44 #27), 018wrk (0.50 #113, 0.48 #201, 0.42 #230) >> Best rule #866 for best value: >> intensional similarity = 51 >> extensional distance = 28 >> proper extension: 03_8r; 035d1m; 07jbh; 01gqfm; >> query: (?x2885, 0kbws) <- sports(?x867, ?x2885), country(?x2885, ?x1023), country(?x2885, ?x205), country(?x5963, ?x1023), exported_to(?x1023, ?x6923), film_release_region(?x9652, ?x1023), film_release_region(?x7651, ?x1023), film_release_region(?x6621, ?x1023), film_release_region(?x4441, ?x1023), film_release_region(?x3854, ?x1023), film_release_region(?x3839, ?x1023), film_release_region(?x2746, ?x1023), film_release_region(?x1927, ?x1023), film_release_region(?x1915, ?x1023), film_release_region(?x1035, ?x1023), film_release_region(?x634, ?x1023), film_release_region(?x249, ?x1023), ?x4441 = 0125xq, featured_film_locations(?x5767, ?x1023), featured_film_locations(?x3845, ?x1023), location(?x13173, ?x1023), ?x1927 = 0by1wkq, language(?x5767, ?x254), ?x9652 = 0ddbjy4, ?x634 = 0gx9rvq, ?x6621 = 0h63gl9, ?x6923 = 07fsv, ?x205 = 03rjj, ?x1035 = 08hmch, ?x3839 = 05c26ss, olympics(?x2885, ?x1608), administrative_parent(?x6291, ?x1023), ?x2746 = 04f52jw, ?x249 = 0c3ybss, nominated_for(?x143, ?x3845), olympics(?x7747, ?x867), olympics(?x2843, ?x867), nominated_for(?x1983, ?x5767), nominated_for(?x277, ?x7651), ?x2843 = 016wzw, combatants(?x5776, ?x1023), film(?x382, ?x5767), ?x1915 = 0fq7dv_, film(?x13173, ?x4502), ?x5776 = 0g8bw, nationality(?x226, ?x1023), ?x3854 = 03q0r1, nominated_for(?x2156, ?x3845), film(?x629, ?x5767), ?x7747 = 07f1x, film_crew_role(?x3845, ?x137) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07jjt olympics 0kbws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 07jjt olympics 0kbvb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.833 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics #2130-03nnm4t PRED entity: 03nnm4t PRED relation: honored_for PRED expected values: 01b_lz 043mk4y 03g9xj => 36 concepts (19 used for prediction) PRED predicted values (max 10 best out of 574): 01b_lz (0.50 #2497, 0.43 #3071, 0.36 #4225), 05lfwd (0.43 #3214, 0.33 #2063, 0.25 #1487), 0kfv9 (0.36 #4711, 0.36 #4136, 0.33 #8634), 0g60z (0.36 #4622, 0.36 #4047, 0.31 #5197), 02k_4g (0.36 #4648, 0.36 #4073, 0.31 #5223), 01vnbh (0.36 #4917, 0.33 #3765, 0.33 #2614), 030k94 (0.33 #2486, 0.33 #759, 0.29 #3060), 06nr2h (0.33 #1980, 0.33 #830, 0.29 #3131), 0180mw (0.33 #2689, 0.33 #962, 0.27 #4417), 0266s9 (0.33 #2862, 0.33 #1135, 0.27 #4590) >> Best rule #2497 for best value: >> intensional similarity = 15 >> extensional distance = 4 >> proper extension: 05c1t6z; 02q690_; 0bxs_d; >> query: (?x5585, 01b_lz) <- award_winner(?x5585, ?x10542), honored_for(?x5585, ?x2078), ceremony(?x7510, ?x5585), ceremony(?x686, ?x5585), gender(?x10542, ?x231), nominated_for(?x3906, ?x2078), genre(?x2078, ?x1403), award_winner(?x2078, ?x444), ?x686 = 0bdw1g, place_of_birth(?x10542, ?x6960), actor(?x2078, ?x2579), nominated_for(?x10542, ?x3471), nominated_for(?x3051, ?x2078), ?x1403 = 02l7c8, ?x7510 = 027gs1_ >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 38, 179 EVAL 03nnm4t honored_for 03g9xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 36.000 19.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 03nnm4t honored_for 043mk4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 36.000 19.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 03nnm4t honored_for 01b_lz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 19.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2129-01qf54 PRED entity: 01qf54 PRED relation: industry PRED expected values: 01mw1 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 43): 01mw1 (0.82 #801, 0.81 #848, 0.80 #1602), 02vxn (0.50 #96, 0.33 #2, 0.25 #1839), 01mf0 (0.24 #3298, 0.09 #1348, 0.09 #1395), 02jjt (0.15 #620, 0.14 #714, 0.13 #1939), 03qh03g (0.12 #1889, 0.12 #2218, 0.12 #2265), 0191_7 (0.10 #651, 0.09 #745, 0.06 #933), 0sydc (0.10 #361, 0.08 #502, 0.05 #1020), 0h6dj (0.10 #362, 0.08 #503, 0.02 #1115), 019z7b (0.09 #385, 0.08 #527, 0.06 #574), 04rlf (0.08 #2274, 0.08 #1898, 0.07 #2227) >> Best rule #801 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 02rfft; >> query: (?x9626, 01mw1) <- place_founded(?x9626, ?x1227), industry(?x9626, ?x10022), ?x10022 = 020mfr, contains(?x94, ?x1227) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qf54 industry 01mw1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.821 http://example.org/business/business_operation/industry #2128-04w7rn PRED entity: 04w7rn PRED relation: film! PRED expected values: 02kxwk 02zfdp => 98 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1138): 034bgm (0.45 #37414, 0.44 #83152, 0.40 #20783), 024rgt (0.45 #37414, 0.44 #83152, 0.40 #20783), 03c9pqt (0.13 #54045, 0.12 #41573, 0.12 #12470), 016z2j (0.11 #2466, 0.04 #14936, 0.03 #29485), 06ltr (0.09 #5103, 0.06 #13416, 0.04 #44597), 016ypb (0.09 #4655, 0.06 #2576, 0.05 #10889), 09y20 (0.09 #4405, 0.04 #16874, 0.04 #12718), 0l6px (0.09 #4544, 0.04 #12857, 0.03 #25327), 0134w7 (0.09 #4318, 0.04 #12631, 0.03 #16787), 065jlv (0.09 #4470, 0.04 #12783, 0.03 #16939) >> Best rule #37414 for best value: >> intensional similarity = 4 >> extensional distance = 220 >> proper extension: 0b76d_m; 0g56t9t; 02vxq9m; 028_yv; 0c3ybss; 02vp1f_; 01gc7; 011yrp; 0ds3t5x; 0dckvs; ... >> query: (?x1518, ?x2275) <- genre(?x1518, ?x53), film_release_region(?x1518, ?x1229), ?x1229 = 059j2, nominated_for(?x2275, ?x1518) >> conf = 0.45 => this is the best rule for 2 predicted values *> Best rule #18191 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 133 *> proper extension: 03t97y; 0d_2fb; 08sk8l; 02nx2k; 0h14ln; 016017; 0ptdz; 04nlb94; *> query: (?x1518, 02zfdp) <- genre(?x1518, ?x1510), film_crew_role(?x1518, ?x137), ?x1510 = 01hmnh, film_release_distribution_medium(?x1518, ?x81) *> conf = 0.03 ranks of expected_values: 176, 684 EVAL 04w7rn film! 02zfdp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 98.000 41.000 0.451 http://example.org/film/actor/film./film/performance/film EVAL 04w7rn film! 02kxwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 98.000 41.000 0.451 http://example.org/film/actor/film./film/performance/film #2127-03k7bd PRED entity: 03k7bd PRED relation: award_nominee! PRED expected values: 016khd => 112 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1345): 016yvw (0.81 #127856, 0.81 #130181, 0.81 #2325), 03k7bd (0.50 #389, 0.30 #2714, 0.15 #86016), 016khd (0.50 #170, 0.15 #86016, 0.02 #148780), 0sz28 (0.26 #120882, 0.25 #242, 0.16 #81366), 01_6dw (0.26 #120882, 0.16 #81366, 0.15 #86016), 0146pg (0.26 #120882, 0.16 #81366), 0dlglj (0.25 #332, 0.06 #7307, 0.03 #65423), 03mp9s (0.25 #1577, 0.06 #8552, 0.01 #66668), 026_w57 (0.25 #828, 0.05 #5477, 0.01 #12453), 02qgqt (0.25 #18, 0.04 #65109, 0.04 #67433) >> Best rule #127856 for best value: >> intensional similarity = 3 >> extensional distance = 1319 >> proper extension: 0blpnz; >> query: (?x1865, ?x91) <- place_of_birth(?x1865, ?x4090), award_nominee(?x1865, ?x91), award_nominee(?x2657, ?x1865) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #170 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 04bdxl; *> query: (?x1865, 016khd) <- place_of_birth(?x1865, ?x4090), award_nominee(?x1865, ?x8099), ?x8099 = 01nms7 *> conf = 0.50 ranks of expected_values: 3 EVAL 03k7bd award_nominee! 016khd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 64.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2126-0n228 PRED entity: 0n228 PRED relation: adjoins! PRED expected values: 0myn8 => 126 concepts (50 used for prediction) PRED predicted values (max 10 best out of 328): 0myn8 (0.82 #38420, 0.82 #28226, 0.81 #35280), 0mwjk (0.82 #38420, 0.82 #28226, 0.81 #35280), 0n2q0 (0.40 #1864, 0.25 #1081, 0.23 #32927), 0n1tx (0.40 #2708, 0.23 #32927, 0.20 #1925), 0n228 (0.26 #7838, 0.25 #35281, 0.25 #273), 0n2vl (0.26 #7838, 0.25 #35281, 0.25 #672), 0mwxl (0.26 #7838, 0.25 #35281, 0.24 #36851), 0mw89 (0.25 #49, 0.02 #34545, 0.02 #28276), 0m7fm (0.25 #70, 0.02 #6338, 0.02 #7123), 0m7d0 (0.25 #169, 0.01 #34665, 0.01 #36235) >> Best rule #38420 for best value: >> intensional similarity = 4 >> extensional distance = 311 >> proper extension: 05r4w; 0b90_r; 0154j; 0h3y; 04gzd; 03rt9; 03s0w; 0k6nt; 0j3b; 0h7x; ... >> query: (?x6051, ?x9101) <- adjoins(?x6954, ?x6051), adjoins(?x6051, ?x9101), contains(?x6051, ?x1629), time_zones(?x9101, ?x2674) >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0n228 adjoins! 0myn8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 50.000 0.819 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #2125-010tkc PRED entity: 010tkc PRED relation: place_of_birth! PRED expected values: 01q3_2 => 77 concepts (24 used for prediction) PRED predicted values (max 10 best out of 155): 0f276 (0.14 #2035, 0.01 #4648), 02y7sr (0.14 #1778, 0.01 #4391), 01pm0_ (0.14 #1304, 0.01 #3917), 03rwng (0.14 #1152, 0.01 #3765), 01vsy3q (0.14 #1002, 0.01 #3615), 04smkr (0.14 #416, 0.01 #3029), 05m883 (0.14 #192, 0.01 #2805), 01gf5h (0.14 #157, 0.01 #2770), 083chw (0.14 #35, 0.01 #2648), 02jm9c (0.01 #5224) >> Best rule #2035 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 01b1mj; >> query: (?x10213, 0f276) <- contains(?x4600, ?x10213), contains(?x94, ?x10213), ?x94 = 09c7w0, ?x4600 = 081yw >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 010tkc place_of_birth! 01q3_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 24.000 0.143 http://example.org/people/person/place_of_birth #2124-0k049 PRED entity: 0k049 PRED relation: place_of_death! PRED expected values: 086qd 0d9xq 0272kv 030s5g 018qpb => 109 concepts (97 used for prediction) PRED predicted values (max 10 best out of 733): 0pyg6 (0.06 #22118, 0.05 #31394, 0.04 #38527), 03f1zhf (0.06 #22118, 0.04 #38527, 0.04 #38526), 012d40 (0.06 #22118, 0.04 #38527, 0.04 #38526), 01tnbn (0.05 #31394, 0.04 #38527, 0.04 #38526), 01n1gc (0.05 #31394, 0.04 #38527, 0.04 #38526), 0252fh (0.05 #31394, 0.04 #38527, 0.04 #38526), 07r1h (0.05 #31394, 0.04 #38527, 0.04 #38526), 0q9kd (0.05 #31394, 0.04 #38527, 0.04 #38526), 02d9k (0.05 #31394, 0.04 #38527, 0.04 #38526), 029pnn (0.05 #31394, 0.04 #38527, 0.04 #38526) >> Best rule #22118 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 09c7w0; 0xkq4; 02xry; 0978r; 03pbf; 019fh; 0lhql; 03l2n; 0cpyv; 0r5lz; ... >> query: (?x191, ?x147) <- place_of_death(?x190, ?x191), location(?x147, ?x191), artists(?x13968, ?x147) >> conf = 0.06 => this is the best rule for 3 predicted values *> Best rule #38527 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 140 *> proper extension: 06mkj; 0l0mk; 0dq16; 096gm; 09c6w; 0pzmf; 0g251; 02z0j; 02d6c; 0t_07; ... *> query: (?x191, ?x7780) <- place_of_death(?x190, ?x191), location(?x7780, ?x191), nationality(?x7780, ?x94) *> conf = 0.04 ranks of expected_values: 27 EVAL 0k049 place_of_death! 018qpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 97.000 0.057 http://example.org/people/deceased_person/place_of_death EVAL 0k049 place_of_death! 030s5g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 109.000 97.000 0.057 http://example.org/people/deceased_person/place_of_death EVAL 0k049 place_of_death! 0272kv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 97.000 0.057 http://example.org/people/deceased_person/place_of_death EVAL 0k049 place_of_death! 0d9xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 97.000 0.057 http://example.org/people/deceased_person/place_of_death EVAL 0k049 place_of_death! 086qd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 97.000 0.057 http://example.org/people/deceased_person/place_of_death #2123-09c7w0 PRED entity: 09c7w0 PRED relation: countries_spoken_in! PRED expected values: 0t_2 => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 103): 02h40lc (0.56 #882, 0.52 #1598, 0.45 #717), 04306rv (0.27 #830, 0.20 #3798, 0.18 #2097), 064_8sq (0.20 #3798, 0.20 #842, 0.19 #6675), 0jzc (0.20 #3798, 0.20 #3372, 0.18 #1997), 02bjrlw (0.20 #3798, 0.20 #826, 0.16 #1377), 0880p (0.20 #3798, 0.20 #865, 0.13 #1306), 03k50 (0.20 #3798, 0.18 #722, 0.11 #887), 0cjk9 (0.20 #3798, 0.14 #554, 0.09 #1490), 02hwhyv (0.20 #3798, 0.13 #1291, 0.13 #850), 07zrf (0.20 #3798, 0.13 #828, 0.06 #1434) >> Best rule #882 for best value: >> intensional similarity = 2 >> extensional distance = 16 >> proper extension: 034tl; >> query: (?x94, 02h40lc) <- country(?x150, ?x94), place_of_birth(?x129, ?x94) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #3798 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 63 *> proper extension: 01vfwd; 02bd41; 05rh2; *> query: (?x94, ?x2164) <- geographic_distribution(?x12136, ?x94), languages_spoken(?x12136, ?x2164) *> conf = 0.20 ranks of expected_values: 37 EVAL 09c7w0 countries_spoken_in! 0t_2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 164.000 164.000 0.556 http://example.org/language/human_language/countries_spoken_in #2122-0dfjb8 PRED entity: 0dfjb8 PRED relation: nationality PRED expected values: 03rk0 => 102 concepts (98 used for prediction) PRED predicted values (max 10 best out of 102): 03rk0 (0.86 #347, 0.85 #146, 0.84 #1148), 09c7w0 (0.75 #2708, 0.74 #4221, 0.73 #1304), 0j1z8 (0.37 #8659, 0.33 #8557), 02jx1 (0.11 #2140, 0.10 #3746, 0.10 #6478), 07ssc (0.09 #5248, 0.09 #4944, 0.09 #5148), 03shp (0.07 #557, 0.03 #758, 0.02 #9763), 0d060g (0.06 #5341, 0.05 #1813, 0.05 #4024), 0f8l9c (0.05 #724, 0.04 #523, 0.02 #2529), 05sb1 (0.04 #549, 0.03 #650, 0.03 #750), 02k54 (0.04 #519, 0.03 #720, 0.02 #9763) >> Best rule #347 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 02qy3py; 046rfv; 0gp8sg; 04cmrt; >> query: (?x5120, 03rk0) <- people(?x5025, ?x5120), languages(?x5120, ?x5121), profession(?x5120, ?x319), type_of_union(?x5120, ?x566), ?x5121 = 07c9s >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dfjb8 nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 98.000 0.857 http://example.org/people/person/nationality #2121-05jbn PRED entity: 05jbn PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 216 concepts (216 used for prediction) PRED predicted values (max 10 best out of 23): 0pqc5 (0.57 #534, 0.56 #396, 0.56 #143), 0f6c3 (0.40 #1135, 0.40 #905, 0.40 #422), 09n5b9 (0.38 #863, 0.37 #1139, 0.37 #426), 0fkvn (0.37 #855, 0.35 #418, 0.34 #901), 060c4 (0.33 #3, 0.24 #2419, 0.23 #808), 0789n (0.33 #10, 0.14 #424, 0.11 #56), 01t7n9 (0.33 #19, 0.12 #433, 0.11 #65), 01gkgk (0.33 #6, 0.11 #52, 0.07 #420), 0dq3c (0.33 #2, 0.11 #48, 0.04 #1865), 02079p (0.33 #11, 0.11 #57, 0.04 #149) >> Best rule #534 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 080h2; 01r32; 019fh; >> query: (?x4978, 0pqc5) <- citytown(?x1506, ?x4978), teams(?x4978, ?x5229), state(?x4978, ?x3778) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05jbn jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 216.000 216.000 0.571 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #2120-0mb2b PRED entity: 0mb2b PRED relation: source PRED expected values: 0jbk9 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #54, 0.92 #53, 0.91 #71) >> Best rule #54 for best value: >> intensional similarity = 4 >> extensional distance = 243 >> proper extension: 0mn0v; >> query: (?x8304, ?x958) <- county(?x8304, ?x12075), second_level_divisions(?x94, ?x12075), ?x94 = 09c7w0, source(?x12075, ?x958) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mb2b source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.922 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #2119-09sdmz PRED entity: 09sdmz PRED relation: ceremony PRED expected values: 09g90vz => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 132): 09g90vz (0.69 #377, 0.22 #4850, 0.13 #508), 0n8_m93 (0.42 #240, 0.25 #764, 0.25 #109), 02yvhx (0.42 #201, 0.25 #725, 0.25 #70), 0bvfqq (0.42 #161, 0.25 #685, 0.25 #30), 0bzm81 (0.42 #150, 0.25 #674, 0.25 #19), 02yxh9 (0.42 #224, 0.25 #4587, 0.25 #93), 0bc773 (0.42 #181, 0.25 #4587, 0.25 #50), 04110lv (0.42 #233, 0.25 #4587, 0.25 #102), 02jp5r (0.42 #194, 0.25 #4587, 0.25 #63), 0bzm__ (0.42 #211, 0.25 #4587, 0.25 #80) >> Best rule #377 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 02py7pj; >> query: (?x4091, 09g90vz) <- award_winner(?x4091, ?x525), ceremony(?x4091, ?x8347), ceremony(?x4091, ?x3624), ?x8347 = 03gyp30, ?x3624 = 027hjff >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09sdmz ceremony 09g90vz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.692 http://example.org/award/award_category/winners./award/award_honor/ceremony #2118-01lct6 PRED entity: 01lct6 PRED relation: gender PRED expected values: 05zppz => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #23, 0.80 #27, 0.79 #7), 02zsn (0.31 #30, 0.30 #106, 0.29 #34) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 147 >> proper extension: 0453t; 032l1; 040_9; 014635; 02lt8; 017yfz; 0d5_f; 0b78hw; 03f0324; 01h8f; ... >> query: (?x11440, 05zppz) <- profession(?x11440, ?x2225), profession(?x11440, ?x353), ?x2225 = 0kyk, ?x353 = 0cbd2 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lct6 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.805 http://example.org/people/person/gender #2117-01vvpjj PRED entity: 01vvpjj PRED relation: profession PRED expected values: 0nbcg => 147 concepts (87 used for prediction) PRED predicted values (max 10 best out of 68): 0nbcg (0.58 #2517, 0.57 #2369, 0.52 #5590), 016z4k (0.58 #1319, 0.51 #1465, 0.50 #4395), 0cbd2 (0.44 #152, 0.14 #6, 0.11 #11130), 01d_h8 (0.44 #3666, 0.41 #5859, 0.41 #5128), 039v1 (0.42 #2374, 0.40 #2522, 0.30 #5595), 0kyk (0.33 #173, 0.29 #27, 0.14 #3688), 0dxtg (0.32 #3674, 0.29 #5136, 0.29 #5867), 0n1h (0.29 #3232, 0.27 #1327, 0.26 #4842), 02hv44_ (0.28 #201, 0.14 #55, 0.05 #347), 03gjzk (0.27 #5868, 0.26 #7331, 0.26 #7477) >> Best rule #2517 for best value: >> intensional similarity = 4 >> extensional distance = 147 >> proper extension: 01w9ph_; 01vzz1c; >> query: (?x2440, 0nbcg) <- location(?x2440, ?x1591), artists(?x2439, ?x2440), role(?x2440, ?x1166), parent_genre(?x12831, ?x2439) >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vvpjj profession 0nbcg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 87.000 0.584 http://example.org/people/person/profession #2116-0421v9q PRED entity: 0421v9q PRED relation: film_crew_role PRED expected values: 09vw2b7 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 22): 09vw2b7 (0.67 #754, 0.66 #686, 0.66 #1097), 0dxtw (0.38 #1101, 0.37 #861, 0.37 #758), 01pvkk (0.29 #759, 0.28 #862, 0.28 #691), 02ynfr (0.20 #763, 0.19 #934, 0.19 #695), 02rh1dz (0.19 #43, 0.13 #9, 0.12 #417), 02_n3z (0.14 #103, 0.12 #307, 0.11 #409), 089g0h (0.13 #120, 0.12 #324, 0.11 #698), 0d2b38 (0.13 #92, 0.13 #330, 0.12 #58), 01xy5l_ (0.12 #115, 0.12 #693, 0.12 #421), 015h31 (0.11 #76, 0.10 #178, 0.10 #42) >> Best rule #754 for best value: >> intensional similarity = 4 >> extensional distance = 389 >> proper extension: 011yxg; 0ds33; 02_1sj; 02z3r8t; 03ckwzc; 0dsvzh; 03t97y; 04qw17; 050gkf; 047qxs; ... >> query: (?x6543, 09vw2b7) <- featured_film_locations(?x6543, ?x12472), film_crew_role(?x6543, ?x137), country(?x6543, ?x94), currency(?x6543, ?x170) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0421v9q film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.668 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2115-03hvk2 PRED entity: 03hvk2 PRED relation: major_field_of_study PRED expected values: 05qjt => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 118): 02lp1 (0.55 #12, 0.44 #880, 0.43 #1624), 01mkq (0.51 #884, 0.46 #1876, 0.46 #1628), 0g26h (0.44 #911, 0.40 #167, 0.40 #1903), 062z7 (0.39 #896, 0.34 #3128, 0.34 #1888), 037mh8 (0.36 #441, 0.24 #3169, 0.23 #1433), 03g3w (0.35 #3127, 0.29 #4367, 0.28 #9460), 01lj9 (0.30 #908, 0.28 #1652, 0.28 #1900), 05qjt (0.30 #3108, 0.26 #876, 0.25 #1372), 04x_3 (0.30 #1638, 0.28 #1886, 0.27 #1514), 05qfh (0.29 #1896, 0.27 #904, 0.25 #1648) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0jpn8; >> query: (?x13148, 02lp1) <- currency(?x13148, ?x170), fraternities_and_sororities(?x13148, ?x4348), registering_agency(?x13148, ?x1982), state_province_region(?x13148, ?x177) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #3108 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 112 *> proper extension: 01b1pf; *> query: (?x13148, 05qjt) <- school_type(?x13148, ?x1044), major_field_of_study(?x13148, ?x2981), ?x2981 = 02j62, category(?x13148, ?x134) *> conf = 0.30 ranks of expected_values: 8 EVAL 03hvk2 major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 137.000 137.000 0.545 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #2114-0fthdk PRED entity: 0fthdk PRED relation: nationality PRED expected values: 09c7w0 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 41): 09c7w0 (0.82 #1602, 0.80 #2802, 0.77 #1502), 0gx1l (0.27 #9516), 04_1l0v (0.27 #9516), 0kpys (0.27 #9516), 02jx1 (0.19 #233, 0.12 #1134, 0.10 #3835), 07ssc (0.12 #1116, 0.12 #715, 0.11 #315), 03rk0 (0.08 #3848, 0.08 #4952, 0.06 #9562), 0d060g (0.06 #207, 0.06 #3409, 0.06 #2008), 0j5g9 (0.06 #362, 0.05 #462, 0.05 #662), 0345h (0.06 #331, 0.05 #531, 0.04 #731) >> Best rule #1602 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 044mfr; >> query: (?x9314, 09c7w0) <- currency(?x9314, ?x170), ?x170 = 09nqf, participant(?x9314, ?x7901) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fthdk nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.817 http://example.org/people/person/nationality #2113-069d68 PRED entity: 069d68 PRED relation: athlete! PRED expected values: 07bs0 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 6): 018w8 (0.39 #16, 0.38 #26, 0.01 #256), 018jz (0.28 #17, 0.19 #27), 0jm_ (0.19 #23, 0.17 #13, 0.01 #253), 07bs0 (0.11 #14, 0.10 #24), 037hz (0.06 #20, 0.05 #30), 02vx4 (0.02 #582, 0.02 #623, 0.02 #562) >> Best rule #16 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 01sg7_; 01g0jn; >> query: (?x8395, 018w8) <- gender(?x8395, ?x231), profession(?x8395, ?x1581), ?x231 = 05zppz, ?x1581 = 01445t, location(?x8395, ?x3501) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 01sg7_; 01g0jn; *> query: (?x8395, 07bs0) <- gender(?x8395, ?x231), profession(?x8395, ?x1581), ?x231 = 05zppz, ?x1581 = 01445t, location(?x8395, ?x3501) *> conf = 0.11 ranks of expected_values: 4 EVAL 069d68 athlete! 07bs0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 77.000 77.000 0.389 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #2112-0dclg PRED entity: 0dclg PRED relation: place_of_birth! PRED expected values: 01bzr4 0dbb3 => 200 concepts (172 used for prediction) PRED predicted values (max 10 best out of 2242): 0pz7h (0.39 #54221, 0.33 #441533, 0.33 #444115), 0b1s_q (0.39 #54221, 0.33 #441533, 0.33 #444115), 02qtywd (0.33 #2298, 0.04 #28116, 0.02 #84925), 0h5jg5 (0.33 #1498, 0.04 #27316, 0.02 #84125), 054187 (0.33 #1482, 0.04 #27300, 0.02 #84109), 08q3s0 (0.33 #1086, 0.04 #26904, 0.02 #83713), 09v6gc9 (0.33 #1026, 0.04 #26844, 0.02 #83653), 0h584v (0.33 #783, 0.04 #26601, 0.02 #83410), 0884hk (0.33 #780, 0.04 #26598, 0.02 #83407), 05b4rcb (0.33 #403, 0.04 #26221, 0.02 #83030) >> Best rule #54221 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0s987; 010rvx; >> query: (?x2254, ?x12754) <- location(?x12754, ?x2254), place_of_birth(?x487, ?x2254), program_creator(?x4535, ?x12754) >> conf = 0.39 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0dclg place_of_birth! 0dbb3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 200.000 172.000 0.388 http://example.org/people/person/place_of_birth EVAL 0dclg place_of_birth! 01bzr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 200.000 172.000 0.388 http://example.org/people/person/place_of_birth #2111-0k269 PRED entity: 0k269 PRED relation: profession PRED expected values: 03gjzk => 104 concepts (93 used for prediction) PRED predicted values (max 10 best out of 49): 01d_h8 (0.49 #3707, 0.48 #3559, 0.40 #746), 02jknp (0.42 #3709, 0.42 #3561, 0.24 #4745), 0np9r (0.40 #20, 0.14 #316, 0.14 #3277), 03gjzk (0.37 #3567, 0.37 #3715, 0.28 #754), 0cbd2 (0.22 #3560, 0.22 #3708, 0.14 #6817), 09jwl (0.22 #610, 0.21 #2091, 0.21 #2239), 0kyk (0.20 #29, 0.14 #325, 0.12 #3730), 018gz8 (0.17 #3717, 0.17 #3569, 0.15 #2977), 02krf9 (0.15 #3579, 0.15 #3727, 0.09 #4023), 0nbcg (0.15 #623, 0.14 #2104, 0.13 #2252) >> Best rule #3707 for best value: >> intensional similarity = 3 >> extensional distance = 880 >> proper extension: 05dxl_; >> query: (?x3580, 01d_h8) <- profession(?x3580, ?x987), ?x987 = 0dxtg, gender(?x3580, ?x231) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #3567 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 860 *> proper extension: 05g8ky; 01p45_v; 03ft8; 01c58j; 0177s6; 014dq7; 025tdwc; 08n9ng; 01v9724; 066l3y; ... *> query: (?x3580, 03gjzk) <- profession(?x3580, ?x987), nationality(?x3580, ?x6401), ?x987 = 0dxtg *> conf = 0.37 ranks of expected_values: 4 EVAL 0k269 profession 03gjzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 104.000 93.000 0.489 http://example.org/people/person/profession #2110-02h40lc PRED entity: 02h40lc PRED relation: countries_spoken_in PRED expected values: 0d05w3 0l3h => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 486): 0d060g (0.63 #2213, 0.61 #1515, 0.60 #1056), 014tss (0.63 #2213, 0.61 #1515, 0.58 #4193), 04vs9 (0.63 #2213, 0.61 #1515, 0.58 #4193), 0164b (0.63 #2213, 0.61 #1515, 0.58 #4193), 0ctw_b (0.63 #2213, 0.61 #1515, 0.58 #4193), 05sb1 (0.63 #2213, 0.61 #1515, 0.58 #4193), 049nq (0.63 #2213, 0.61 #1515, 0.58 #4193), 0n3g (0.63 #2213, 0.61 #1515, 0.58 #4193), 04tr1 (0.63 #2213, 0.61 #1515, 0.58 #4193), 07tp2 (0.63 #2213, 0.61 #1515, 0.58 #4193) >> Best rule #2213 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 05zjd; 02bv9; >> query: (?x254, ?x183) <- language(?x5819, ?x254), languages(?x118, ?x254), languages(?x50, ?x254), official_language(?x183, ?x254), genre(?x5819, ?x53) >> conf = 0.63 => this is the best rule for 28 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 24, 108 EVAL 02h40lc countries_spoken_in 0l3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 76.000 76.000 0.633 http://example.org/language/human_language/countries_spoken_in EVAL 02h40lc countries_spoken_in 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 76.000 76.000 0.633 http://example.org/language/human_language/countries_spoken_in #2109-0g9zjp PRED entity: 0g9zjp PRED relation: nationality PRED expected values: 02jx1 => 78 concepts (58 used for prediction) PRED predicted values (max 10 best out of 67): 09c7w0 (0.88 #3984, 0.84 #4482, 0.84 #4581), 02jx1 (0.60 #428, 0.57 #627, 0.50 #230), 07ssc (0.54 #3001, 0.44 #3100, 0.30 #3200), 0ctw_b (0.29 #621, 0.25 #224, 0.20 #422), 0j5g9 (0.25 #160, 0.20 #556, 0.18 #2888), 06sw9 (0.25 #270, 0.18 #2888, 0.14 #667), 03rjj (0.24 #2893, 0.07 #2688, 0.06 #3191), 0chghy (0.20 #505, 0.18 #2888, 0.18 #2898), 035yg (0.20 #485, 0.18 #2888, 0.14 #684), 019rg5 (0.20 #320, 0.18 #2888, 0.07 #2688) >> Best rule #3984 for best value: >> intensional similarity = 7 >> extensional distance = 2981 >> proper extension: 01pbxb; 01vvydl; 06151l; 0lbj1; 05m63c; 023tp8; 033hqf; 04bs3j; 01nqfh_; 01kwld; ... >> query: (?x11510, 09c7w0) <- profession(?x11510, ?x7623), nationality(?x11510, ?x429), film_release_region(?x4604, ?x429), film_release_region(?x4041, ?x429), second_level_divisions(?x429, ?x1788), ?x4041 = 0gy2y8r, ?x4604 = 0432_5 >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #428 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 3 *> proper extension: 0d1swh; 0czmk1; *> query: (?x11510, 02jx1) <- team(?x11510, ?x11139), team(?x11510, ?x6064), position(?x11139, ?x203), athlete(?x471, ?x11510), ?x203 = 0dgrmp, ?x6064 = 01x4wq, position(?x11139, ?x60), current_club(?x59, ?x11139) *> conf = 0.60 ranks of expected_values: 2 EVAL 0g9zjp nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 58.000 0.878 http://example.org/people/person/nationality #2108-019f4v PRED entity: 019f4v PRED relation: nominated_for PRED expected values: 011yxg 072x7s 03hj3b3 04cj79 0bmhvpr 0yx7h 0n04r 03cfkrw 0_b9f 015qqg 011yn5 0jsf6 0404j37 02dr9j 08xvpn => 61 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1732): 0209hj (0.77 #20564, 0.71 #11044, 0.67 #5558), 09sr0 (0.77 #20564, 0.67 #6628, 0.50 #3888), 0c0zq (0.77 #20564, 0.62 #9406, 0.53 #12149), 05hjnw (0.77 #20564, 0.58 #12983, 0.53 #11612), 0c8qq (0.77 #20564, 0.56 #10003, 0.47 #16452), 0h03fhx (0.77 #20564, 0.50 #8816, 0.47 #16452), 02rcdc2 (0.77 #20564, 0.50 #3100, 0.47 #16452), 0bl06 (0.77 #20564, 0.47 #16452, 0.47 #16451), 0170th (0.77 #20564, 0.47 #16452, 0.47 #16451), 0k419 (0.67 #10852, 0.50 #3997, 0.40 #5367) >> Best rule #20564 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 0fqnzts; >> query: (?x1107, ?x299) <- ceremony(?x1107, ?x7452), award_winner(?x7452, ?x538), award(?x299, ?x1107), award(?x276, ?x1107) >> conf = 0.77 => this is the best rule for 9 predicted values *> Best rule #10998 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 15 *> proper extension: 02qyp19; 02hsq3m; 0l8z1; 02pqp12; 0gr0m; 0k611; 0gs96; 02qvyrt; 02qyntr; *> query: (?x1107, 011yxg) <- nominated_for(?x1107, ?x11597), nominated_for(?x1107, ?x5028), award(?x276, ?x1107), ?x5028 = 02ll45, award(?x11597, ?x1243) *> conf = 0.65 ranks of expected_values: 14, 15, 16, 23, 30, 31, 32, 52, 89, 104, 119, 151, 168, 202, 269 EVAL 019f4v nominated_for 08xvpn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 02dr9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 0jsf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 011yn5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 015qqg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 0_b9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 03cfkrw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 0n04r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 0yx7h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 0bmhvpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 04cj79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 03hj3b3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 072x7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 019f4v nominated_for 011yxg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 61.000 20.000 0.771 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2107-048scx PRED entity: 048scx PRED relation: genre PRED expected values: 02qfv5d => 86 concepts (67 used for prediction) PRED predicted values (max 10 best out of 118): 02kdv5l (0.58 #572, 0.50 #457, 0.48 #1951), 01hmnh (0.55 #2081, 0.28 #473, 0.27 #588), 04xvh5 (0.48 #30, 0.43 #144, 0.18 #945), 03k9fj (0.46 #582, 0.37 #467, 0.31 #2075), 02l7c8 (0.35 #930, 0.30 #5394, 0.30 #4591), 05p553 (0.33 #6297, 0.33 #6755, 0.33 #5610), 0lsxr (0.33 #1958, 0.19 #7452, 0.18 #2187), 06n90 (0.32 #583, 0.28 #468, 0.28 #697), 017fp (0.29 #1274, 0.18 #128, 0.17 #929), 03bxz7 (0.29 #164, 0.26 #1310, 0.21 #965) >> Best rule #572 for best value: >> intensional similarity = 4 >> extensional distance = 107 >> proper extension: 09sh8k; 0czyxs; 053rxgm; 02pxmgz; 024l2y; 02q56mk; 014nq4; 0gj8nq2; 03459x; 0gh65c5; ... >> query: (?x1048, 02kdv5l) <- film_crew_role(?x1048, ?x2091), film(?x2451, ?x1048), ?x2091 = 02rh1dz, genre(?x1048, ?x53) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #5951 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1103 *> proper extension: 06cs95; *> query: (?x1048, ?x53) <- titles(?x4757, ?x1048), genre(?x2797, ?x4757), titles(?x53, ?x2797), film(?x548, ?x2797) *> conf = 0.05 ranks of expected_values: 37 EVAL 048scx genre 02qfv5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 86.000 67.000 0.578 http://example.org/film/film/genre #2106-02b14q PRED entity: 02b14q PRED relation: team! PRED expected values: 0dgrmp => 42 concepts (42 used for prediction) PRED predicted values (max 10 best out of 32): 0dgrmp (0.86 #250, 0.86 #202, 0.85 #800), 03f0fp (0.54 #2055, 0.54 #2005, 0.50 #1955), 02md_2 (0.50 #1955, 0.01 #26), 02g_6x (0.07 #1966, 0.06 #2016), 06b1q (0.07 #1960, 0.06 #2010), 02g_7z (0.07 #1978, 0.06 #2028), 01r3hr (0.07 #1956, 0.06 #2006), 04nfpk (0.06 #1970, 0.06 #2020), 02g_6j (0.06 #1964, 0.06 #2014), 01_9c1 (0.06 #1971, 0.05 #2021) >> Best rule #250 for best value: >> intensional similarity = 12 >> extensional distance = 110 >> proper extension: 03qx63; 03fhm5; 02mplj; 03j722; 08r98b; 0177gl; 0515_6; 041jk9; 01tqfs; 0175tv; ... >> query: (?x8698, ?x203) <- position(?x8698, ?x530), position(?x8698, ?x203), position(?x8698, ?x63), position(?x8698, ?x60), ?x203 = 0dgrmp, ?x530 = 02_j1w, ?x60 = 02nzb8, ?x63 = 02sdk9v, team(?x530, ?x8698), position(?x8698, ?x60), team(?x63, ?x8698), position(?x8698, ?x63) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02b14q team! 0dgrmp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 42.000 42.000 0.857 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #2105-0f94t PRED entity: 0f94t PRED relation: place_of_birth! PRED expected values: 017_pb => 86 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1829): 03f7jfh (0.40 #46878, 0.39 #52088, 0.38 #36460), 03f19q4 (0.38 #36460, 0.37 #114592, 0.34 #93756), 032zg9 (0.32 #85943, 0.32 #88548, 0.32 #57296), 04mg6l (0.12 #1151, 0.05 #3754, 0.04 #8962), 01mqc_ (0.12 #1547, 0.05 #4150, 0.04 #9358), 04cw0j (0.12 #602, 0.05 #3205, 0.04 #8413), 01pgzn_ (0.12 #426, 0.05 #3029, 0.04 #8237), 0fvf9q (0.12 #15, 0.05 #2618, 0.04 #7826), 05f0r8 (0.12 #2590, 0.05 #5193, 0.04 #10401), 05h7tk (0.12 #2562, 0.05 #5165, 0.04 #10373) >> Best rule #46878 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 059j2; 0498y; >> query: (?x1005, ?x8874) <- origin(?x8874, ?x1005), role(?x8874, ?x212), location(?x4667, ?x1005) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f94t place_of_birth! 017_pb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 57.000 0.397 http://example.org/people/person/place_of_birth #2104-01cky2 PRED entity: 01cky2 PRED relation: award! PRED expected values: 01trhmt 01wd9lv => 45 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2681): 09889g (0.78 #66753, 0.78 #66752, 0.62 #18113), 015mrk (0.78 #66753, 0.78 #66752, 0.50 #17519), 01vvycq (0.78 #66753, 0.78 #66752, 0.50 #6821), 067nsm (0.78 #66753, 0.78 #66752, 0.50 #8553), 030155 (0.78 #66753, 0.78 #66752, 0.33 #900), 01w5jwb (0.78 #66753, 0.78 #66752, 0.25 #9151), 01lvcs1 (0.78 #66753, 0.78 #66752, 0.16 #33376), 024qwq (0.78 #66753, 0.78 #66752, 0.16 #33376), 01vs_v8 (0.62 #23939, 0.40 #13925, 0.40 #10587), 01xzb6 (0.60 #11533, 0.50 #8196, 0.38 #18208) >> Best rule #66753 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 02v1ws; >> query: (?x3835, ?x6383) <- award_winner(?x3835, ?x6383), award_winner(?x5799, ?x6383), award(?x140, ?x5799), category_of(?x3835, ?x2421) >> conf = 0.78 => this is the best rule for 8 predicted values *> Best rule #10687 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 02f716; 02f6xy; *> query: (?x3835, 01trhmt) <- award(?x6162, ?x3835), award(?x4476, ?x3835), award(?x3834, ?x3835), ?x3834 = 01wzlxj, ?x4476 = 01vw20h, award_nominee(?x399, ?x6162), role(?x6162, ?x212) *> conf = 0.40 ranks of expected_values: 64, 109 EVAL 01cky2 award! 01wd9lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 45.000 20.000 0.780 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01cky2 award! 01trhmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 45.000 20.000 0.780 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2103-01cycq PRED entity: 01cycq PRED relation: film_crew_role PRED expected values: 09vw2b7 0ch6mp2 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 25): 0ch6mp2 (0.73 #1079, 0.72 #1281, 0.71 #1213), 09vw2b7 (0.68 #205, 0.62 #1078, 0.60 #1280), 02ynfr (0.40 #14, 0.21 #80, 0.20 #213), 01pvkk (0.32 #209, 0.29 #1284, 0.27 #1653), 094hwz (0.20 #13, 0.09 #212, 0.08 #817), 05smlt (0.20 #19, 0.07 #218, 0.05 #118), 015h31 (0.16 #813, 0.14 #242, 0.13 #42), 0d2b38 (0.13 #827, 0.13 #256, 0.13 #56), 01xy5l_ (0.12 #211, 0.09 #1286, 0.09 #1354), 0215hd (0.12 #1359, 0.12 #1122, 0.12 #1291) >> Best rule #1079 for best value: >> intensional similarity = 3 >> extensional distance = 816 >> proper extension: 0gtsx8c; 047svrl; 0gh8zks; 07kb7vh; 0hgnl3t; 07k2mq; >> query: (?x7834, 0ch6mp2) <- film_crew_role(?x7834, ?x137), production_companies(?x7834, ?x1561), film(?x3034, ?x7834) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01cycq film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.730 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 01cycq film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.730 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2102-0146pg PRED entity: 0146pg PRED relation: award PRED expected values: 02qvyrt 02x201b => 136 concepts (117 used for prediction) PRED predicted values (max 10 best out of 319): 047sgz4 (0.78 #1583, 0.78 #13450, 0.77 #13054), 025m98 (0.78 #1583, 0.78 #13450, 0.77 #13054), 02qvyrt (0.58 #518, 0.46 #1705, 0.46 #4080), 02x17c2 (0.36 #1005, 0.20 #1400, 0.19 #1796), 0c4z8 (0.33 #863, 0.25 #2445, 0.24 #1258), 03qbh5 (0.33 #2573, 0.22 #991, 0.21 #11275), 09sb52 (0.31 #10326, 0.29 #9534, 0.28 #19815), 01bgqh (0.28 #10723, 0.27 #11515, 0.26 #2417), 01c427 (0.28 #12346, 0.14 #2457, 0.12 #10763), 0gqy2 (0.23 #159, 0.15 #30846, 0.14 #38362) >> Best rule #1583 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 016yzz; 02nfjp; 02qmncd; >> query: (?x669, ?x1079) <- award_winner(?x1079, ?x669), nominated_for(?x669, ?x670), role(?x669, ?x316) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #518 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 025vry; 01vttb9; 02rf51g; *> query: (?x669, 02qvyrt) <- award_winner(?x1079, ?x669), music(?x670, ?x669), ?x1079 = 0l8z1 *> conf = 0.58 ranks of expected_values: 3, 11 EVAL 0146pg award 02x201b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 136.000 117.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0146pg award 02qvyrt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 136.000 117.000 0.782 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2101-03x33n PRED entity: 03x33n PRED relation: institution! PRED expected values: 02h4rq6 => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.85 #917, 0.84 #676, 0.83 #364), 019v9k (0.75 #250, 0.75 #226, 0.73 #370), 03bwzr4 (0.67 #111, 0.63 #376, 0.59 #232), 0bkj86 (0.59 #225, 0.57 #249, 0.56 #56), 016t_3 (0.54 #197, 0.53 #100, 0.53 #509), 02_xgp2 (0.54 #374, 0.49 #446, 0.48 #422), 07s6fsf (0.50 #194, 0.48 #362, 0.47 #97), 013zdg (0.44 #55, 0.37 #200, 0.35 #176), 04zx3q1 (0.33 #50, 0.31 #171, 0.30 #219), 028dcg (0.33 #68, 0.21 #189, 0.20 #116) >> Best rule #917 for best value: >> intensional similarity = 3 >> extensional distance = 142 >> proper extension: 01nnsv; 08qnnv; >> query: (?x4161, 02h4rq6) <- school(?x2174, ?x4161), student(?x4161, ?x1583), major_field_of_study(?x4161, ?x2606) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x33n institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.847 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2100-04j5fx PRED entity: 04j5fx PRED relation: gender PRED expected values: 05zppz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #91, 0.79 #107, 0.73 #73), 02zsn (0.46 #209, 0.46 #222, 0.38 #24) >> Best rule #91 for best value: >> intensional similarity = 6 >> extensional distance = 669 >> proper extension: 017r2; 0p8jf; 037d35; 06gn7r; 04v048; 04093; 04ns3gy; 05cqhl; 0f1jhc; 0bt23; ... >> query: (?x11146, 05zppz) <- location(?x11146, ?x13415), profession(?x11146, ?x1383), profession(?x9819, ?x1383), profession(?x7638, ?x1383), ?x9819 = 02184q, ?x7638 = 013pk3 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04j5fx gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.796 http://example.org/people/person/gender #2099-0pz7h PRED entity: 0pz7h PRED relation: award_winner! PRED expected values: 03gt46z 02q690_ => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 110): 02q690_ (0.26 #594, 0.13 #1126, 0.12 #1392), 0bx6zs (0.12 #8647, 0.11 #651, 0.04 #784), 027hjff (0.12 #8647, 0.07 #4709, 0.05 #4177), 03gt46z (0.12 #8647, 0.05 #592, 0.03 #1124), 09pj68 (0.12 #8647, 0.04 #765, 0.03 #898), 0gvstc3 (0.11 #165, 0.11 #564, 0.10 #298), 09qvms (0.09 #4667, 0.07 #5998, 0.07 #1475), 027n06w (0.09 #1134, 0.07 #1400, 0.06 #1666), 0gx_st (0.09 #1099, 0.07 #1365, 0.06 #1631), 02rjjll (0.08 #404, 0.08 #3064, 0.07 #1867) >> Best rule #594 for best value: >> intensional similarity = 3 >> extensional distance = 17 >> proper extension: 02lk1s; 02pb53; 0f7hc; >> query: (?x906, 02q690_) <- award_nominee(?x906, ?x237), nominated_for(?x906, ?x6884), ?x6884 = 039cq4 >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 0pz7h award_winner! 02q690_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.263 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0pz7h award_winner! 03gt46z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 112.000 0.263 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2098-07yvsn PRED entity: 07yvsn PRED relation: film! PRED expected values: 02yplc 03hh89 01nfys => 74 concepts (19 used for prediction) PRED predicted values (max 10 best out of 799): 01_f_5 (0.30 #37355, 0.27 #37354, 0.15 #31128), 02pqgt8 (0.30 #37355, 0.27 #37354), 0p8r1 (0.09 #10959, 0.07 #15109, 0.03 #2659), 0h0wc (0.07 #2498, 0.05 #17023, 0.04 #8723), 02xs5v (0.06 #1403, 0.04 #3478, 0.04 #5553), 016ggh (0.06 #1863, 0.04 #3938, 0.03 #12238), 0jfx1 (0.06 #6630, 0.06 #17005, 0.05 #23231), 0169dl (0.05 #4550, 0.04 #19076, 0.04 #21151), 02_p5w (0.05 #11019, 0.05 #15169, 0.03 #644), 0bxtg (0.05 #77, 0.04 #10452, 0.04 #14602) >> Best rule #37355 for best value: >> intensional similarity = 3 >> extensional distance = 555 >> proper extension: 0cwrr; 01h1bf; 06mr2s; 02kk_c; 0c3xpwy; 04glx0; 01b7h8; 07bz5; >> query: (?x3441, ?x6275) <- honored_for(?x9921, ?x3441), nominated_for(?x6275, ?x3441), profession(?x6275, ?x319) >> conf = 0.30 => this is the best rule for 2 predicted values *> Best rule #961 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 64 *> proper extension: 05z43v; *> query: (?x3441, 03hh89) <- genre(?x3441, ?x2605), film(?x1286, ?x3441), nominated_for(?x143, ?x3441), ?x2605 = 03g3w *> conf = 0.02 ranks of expected_values: 410, 690 EVAL 07yvsn film! 01nfys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 19.000 0.296 http://example.org/film/actor/film./film/performance/film EVAL 07yvsn film! 03hh89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 74.000 19.000 0.296 http://example.org/film/actor/film./film/performance/film EVAL 07yvsn film! 02yplc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 74.000 19.000 0.296 http://example.org/film/actor/film./film/performance/film #2097-0hsb3 PRED entity: 0hsb3 PRED relation: institution! PRED expected values: 02mjs7 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 14): 014mlp (0.73 #1524, 0.70 #274, 0.67 #229), 07s6fsf (0.62 #227, 0.56 #272, 0.50 #603), 0bkj86 (0.52 #336, 0.51 #381, 0.48 #260), 027f2w (0.36 #637, 0.34 #337, 0.33 #382), 022h5x (0.30 #283, 0.29 #238, 0.28 #1683), 071tyz (0.28 #1683, 0.17 #1760, 0.15 #383), 01ysy9 (0.28 #1683, 0.17 #1760, 0.08 #391), 028dcg (0.26 #282, 0.25 #237, 0.20 #613), 03mkk4 (0.24 #339, 0.23 #609, 0.20 #263), 02mjs7 (0.20 #604, 0.20 #258, 0.17 #589) >> Best rule #1524 for best value: >> intensional similarity = 3 >> extensional distance = 490 >> proper extension: 014b4h; 01t8sr; 01y9pk; 02t4yc; 01v3ht; 0ymgk; 04jr87; 04bfg; 01b7lc; 0ylsr; ... >> query: (?x6132, 014mlp) <- institution(?x1519, ?x6132), institution(?x1519, ?x4582), ?x4582 = 02897w >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #604 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 58 *> proper extension: 01b1mj; 04ycjk; 08qs09; 063576; *> query: (?x6132, 02mjs7) <- institution(?x1519, ?x6132), ?x1519 = 013zdg, student(?x6132, ?x1291) *> conf = 0.20 ranks of expected_values: 10 EVAL 0hsb3 institution! 02mjs7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 136.000 136.000 0.732 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2096-018mmw PRED entity: 018mmw PRED relation: place_of_burial! PRED expected values: 0gm34 015qq1 02nygk => 75 concepts (35 used for prediction) PRED predicted values (max 10 best out of 145): 022p06 (0.29 #172, 0.29 #39, 0.25 #304), 0bkmf (0.29 #83, 0.25 #348, 0.15 #873), 01t94_1 (0.29 #77, 0.25 #342, 0.15 #867), 03bw6 (0.29 #60, 0.25 #325, 0.15 #850), 0cf2h (0.29 #50, 0.25 #315, 0.15 #840), 0hnp7 (0.29 #47, 0.25 #312, 0.15 #837), 081nh (0.29 #148, 0.22 #412, 0.20 #674), 0c2ry (0.28 #133, 0.26 #397, 0.14 #163), 0l5yl (0.28 #133, 0.26 #397, 0.14 #69), 015cbq (0.28 #133, 0.26 #397) >> Best rule #172 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0k_q_; 018mm4; 018mmj; 0bvqq; 018mrd; >> query: (?x8044, 022p06) <- place_of_burial(?x10770, ?x8044), place_of_burial(?x1545, ?x8044), people(?x1446, ?x1545), location_of_ceremony(?x10770, ?x1523), award_nominee(?x10770, ?x5536) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018mmw place_of_burial! 02nygk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 35.000 0.286 http://example.org/people/deceased_person/place_of_burial EVAL 018mmw place_of_burial! 015qq1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 35.000 0.286 http://example.org/people/deceased_person/place_of_burial EVAL 018mmw place_of_burial! 0gm34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 75.000 35.000 0.286 http://example.org/people/deceased_person/place_of_burial #2095-011yg9 PRED entity: 011yg9 PRED relation: story_by PRED expected values: 040dv => 97 concepts (20 used for prediction) PRED predicted values (max 10 best out of 53): 042xh (0.19 #648, 0.03 #2383, 0.03 #2167), 029m83 (0.10 #1084, 0.09 #1518), 02mt4k (0.08 #301), 0343h (0.06 #885, 0.06 #1536, 0.06 #1319), 03j2gxx (0.06 #615, 0.02 #1049, 0.02 #1483), 0ldd (0.05 #857, 0.01 #1725), 0c2dl (0.05 #720, 0.01 #1588), 0fx02 (0.04 #927, 0.04 #3748, 0.03 #1361), 0kb3n (0.04 #1010, 0.03 #1444, 0.03 #2095), 04jspq (0.04 #983, 0.03 #1417, 0.01 #1634) >> Best rule #648 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 03wjm2; >> query: (?x5950, 042xh) <- language(?x5950, ?x254), currency(?x5950, ?x170), film(?x1549, ?x5950), ?x1549 = 09y20 >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1025 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 0g5pv3; 03h4fq7; 02n72k; 08984j; *> query: (?x5950, 040dv) <- nominated_for(?x4864, ?x5950), executive_produced_by(?x5950, ?x8041), genre(?x5950, ?x53), music(?x5950, ?x4911) *> conf = 0.02 ranks of expected_values: 22 EVAL 011yg9 story_by 040dv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 97.000 20.000 0.188 http://example.org/film/film/story_by #2094-03rwng PRED entity: 03rwng PRED relation: gender PRED expected values: 02zsn => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.85 #4, 0.38 #6, 0.37 #22), 05zppz (0.73 #163, 0.72 #225, 0.72 #143) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 01j851; >> query: (?x5586, 02zsn) <- award(?x5586, ?x3499), profession(?x5586, ?x1032), ?x3499 = 03qgjwc >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rwng gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.848 http://example.org/people/person/gender #2093-05w3f PRED entity: 05w3f PRED relation: parent_genre! PRED expected values: 016jhr 02l96k => 73 concepts (39 used for prediction) PRED predicted values (max 10 best out of 273): 0g_bh (0.44 #3682, 0.33 #5986, 0.33 #4447), 016jhr (0.43 #2818, 0.33 #1796, 0.29 #2297), 0xv2x (0.43 #2163, 0.33 #1397, 0.25 #4464), 09jw2 (0.38 #3451, 0.29 #2937, 0.29 #2171), 03lty (0.33 #3599, 0.33 #1297, 0.29 #2297), 01pfpt (0.33 #1347, 0.33 #326, 0.29 #2297), 01gbcf (0.33 #1280, 0.33 #259, 0.29 #2046), 0dls3 (0.33 #1317, 0.29 #2083, 0.27 #1531), 05r6t (0.33 #1341, 0.29 #2107, 0.27 #1531), 01_bkd (0.33 #298, 0.29 #2340, 0.25 #3063) >> Best rule #3682 for best value: >> intensional similarity = 10 >> extensional distance = 7 >> proper extension: 02k_kn; >> query: (?x2809, 0g_bh) <- artists(?x2809, ?x7084), artists(?x2809, ?x6225), artists(?x2809, ?x3869), artists(?x2809, ?x1751), ?x7084 = 01vs4ff, award_winner(?x724, ?x1751), award_winner(?x3065, ?x1751), type_of_union(?x6225, ?x566), role(?x3869, ?x227), parent_genre(?x2809, ?x505) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2818 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 06by7; 016jny; *> query: (?x2809, 016jhr) <- parent_genre(?x13553, ?x2809), artists(?x13553, ?x1412), artists(?x2809, ?x6456), ?x1412 = 067mj, parent_genre(?x9955, ?x13553), ?x6456 = 0k1bs *> conf = 0.43 ranks of expected_values: 2, 166 EVAL 05w3f parent_genre! 02l96k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 73.000 39.000 0.444 http://example.org/music/genre/parent_genre EVAL 05w3f parent_genre! 016jhr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 73.000 39.000 0.444 http://example.org/music/genre/parent_genre #2092-05_5_22 PRED entity: 05_5_22 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.88 #116, 0.86 #141, 0.85 #171), 07c52 (0.33 #3, 0.13 #28, 0.07 #83), 02nxhr (0.13 #27, 0.09 #17, 0.08 #72), 07z4p (0.03 #315, 0.03 #115, 0.02 #406) >> Best rule #116 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 03g90h; 047gn4y; 026mfbr; 04grkmd; 04ydr95; 05m_jsg; 0660b9b; 0b7l4x; 05pdd86; 0b6l1st; ... >> query: (?x5201, 029j_) <- produced_by(?x5201, ?x364), film_crew_role(?x5201, ?x4305), film(?x585, ?x5201), ?x4305 = 0215hd >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05_5_22 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #2091-034np8 PRED entity: 034np8 PRED relation: film PRED expected values: 0640y35 => 106 concepts (88 used for prediction) PRED predicted values (max 10 best out of 529): 027r9t (0.59 #92670, 0.58 #71285, 0.38 #112275), 04hwbq (0.25 #189, 0.05 #1971, 0.03 #34048), 01xdxy (0.25 #1559, 0.05 #3341, 0.03 #35418), 01c22t (0.25 #162, 0.05 #1944, 0.02 #34021), 03d8jd1 (0.16 #3499, 0.03 #7064, 0.01 #35576), 026q3s3 (0.16 #1983, 0.02 #34060), 02z5x7l (0.14 #2984, 0.03 #6549, 0.01 #35061), 0dh8v4 (0.14 #2717, 0.02 #6282, 0.01 #34794), 02vw1w2 (0.14 #1993, 0.01 #34070), 0cpllql (0.12 #85, 0.07 #1867, 0.02 #5432) >> Best rule #92670 for best value: >> intensional similarity = 3 >> extensional distance = 1393 >> proper extension: 049tjg; 04shbh; 02wrhj; 015wfg; 05typm; 01bcq; 02756j; 0gm34; 0jvtp; 015qq1; ... >> query: (?x1814, ?x7141) <- film(?x1814, ?x2288), nominated_for(?x1814, ?x7141), language(?x2288, ?x254) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 034np8 film 0640y35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 88.000 0.585 http://example.org/film/actor/film./film/performance/film #2090-06cx9 PRED entity: 06cx9 PRED relation: form_of_government! PRED expected values: 09c7w0 019rg5 07ylj 07t21 05v10 02khs 01699 06dfg 0fv4v 01nyl 06m_5 0gtzp => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 375): 07bxhl (0.50 #571, 0.50 #129, 0.33 #314), 0d05w3 (0.50 #129, 0.44 #509, 0.34 #125), 0jdd (0.50 #129, 0.44 #509, 0.34 #125), 07f1x (0.50 #606, 0.44 #509, 0.33 #349), 0fv4v (0.50 #507, 0.44 #509, 0.33 #506), 01n8qg (0.50 #617, 0.44 #509, 0.33 #360), 0h3y (0.50 #507, 0.44 #509, 0.33 #403), 04vjh (0.50 #507, 0.44 #509, 0.33 #121), 05cc1 (0.50 #507, 0.44 #509, 0.33 #121), 01699 (0.50 #507, 0.44 #509, 0.33 #121) >> Best rule #571 for best value: >> intensional similarity = 189 >> extensional distance = 2 >> proper extension: 01q20; >> query: (?x48, 07bxhl) <- form_of_government(?x8588, ?x48), form_of_government(?x7709, ?x48), form_of_government(?x6559, ?x48), form_of_government(?x6305, ?x48), form_of_government(?x5680, ?x48), form_of_government(?x4714, ?x48), form_of_government(?x4521, ?x48), form_of_government(?x2188, ?x48), form_of_government(?x1603, ?x48), form_of_government(?x1577, ?x48), form_of_government(?x550, ?x48), medal(?x6305, ?x422), olympics(?x8588, ?x2966), olympics(?x8588, ?x1931), participating_countries(?x418, ?x6305), country(?x13440, ?x6305), adjoins(?x404, ?x6305), country(?x5396, ?x2188), country(?x4673, ?x2188), country(?x3659, ?x2188), country(?x2867, ?x2188), country(?x2266, ?x2188), country(?x1121, ?x2188), country(?x359, ?x2188), film_release_region(?x11313, ?x2188), film_release_region(?x1701, ?x2188), official_language(?x5680, ?x5607), contains(?x2467, ?x4521), adjoins(?x4521, ?x291), country(?x3309, ?x8588), administrative_area_type(?x5680, ?x2792), ?x359 = 02bkg, currency(?x6559, ?x170), taxonomy(?x2188, ?x939), film_release_region(?x9174, ?x6559), film_release_region(?x3854, ?x6559), ?x3309 = 09w1n, countries_within(?x455, ?x2188), organization(?x7709, ?x4230), organization(?x7709, ?x127), country(?x8428, ?x6559), exported_to(?x87, ?x4521), ?x127 = 02vk52z, adjoins(?x2188, ?x456), ?x5396 = 0486tv, geographic_distribution(?x5590, ?x2188), nationality(?x12121, ?x6559), teams(?x6559, ?x9821), ?x1701 = 0bh8yn3, ?x4230 = 04k4l, film_release_region(?x9657, ?x1603), film_release_region(?x9565, ?x1603), film_release_region(?x8891, ?x1603), film_release_region(?x7832, ?x1603), film_release_region(?x7700, ?x1603), film_release_region(?x7693, ?x1603), film_release_region(?x7275, ?x1603), film_release_region(?x6931, ?x1603), film_release_region(?x6621, ?x1603), film_release_region(?x6543, ?x1603), film_release_region(?x6528, ?x1603), film_release_region(?x6321, ?x1603), film_release_region(?x6247, ?x1603), film_release_region(?x6216, ?x1603), film_release_region(?x6175, ?x1603), film_release_region(?x5713, ?x1603), film_release_region(?x5704, ?x1603), film_release_region(?x5496, ?x1603), film_release_region(?x5315, ?x1603), film_release_region(?x5089, ?x1603), film_release_region(?x4811, ?x1603), film_release_region(?x4352, ?x1603), film_release_region(?x3035, ?x1603), film_release_region(?x2896, ?x1603), film_release_region(?x2163, ?x1603), film_release_region(?x1932, ?x1603), film_release_region(?x1518, ?x1603), film_release_region(?x1108, ?x1603), film_release_region(?x634, ?x1603), film_release_region(?x504, ?x1603), film_release_region(?x467, ?x1603), film_release_region(?x409, ?x1603), artists(?x302, ?x12121), contains(?x1603, ?x992), ?x409 = 0gtv7pk, countries_spoken_in(?x2502, ?x7709), combatants(?x3728, ?x1603), combatants(?x1023, ?x1603), combatants(?x172, ?x1603), combatants(?x151, ?x1603), ?x9174 = 087pfc, ?x939 = 04n6k, nationality(?x889, ?x1603), jurisdiction_of_office(?x346, ?x8588), locations(?x12789, ?x4521), location_of_ceremony(?x566, ?x5680), ?x1108 = 0jjy0, ?x504 = 0g5qs2k, religion(?x8588, ?x492), country(?x11419, ?x8588), ?x9657 = 07jqjx, contains(?x7273, ?x7709), profession(?x12121, ?x1183), profession(?x12121, ?x131), adjoins(?x1603, ?x2346), service_location(?x8931, ?x6559), ?x8891 = 0gwlfnb, organization(?x1577, ?x9102), ?x2966 = 06sks6, ?x4352 = 09v71cj, service_location(?x555, ?x1603), ?x5713 = 0cc97st, ?x2266 = 01lb14, ?x7700 = 0cp08zg, ?x9102 = 041288, locations(?x7241, ?x8588), ?x2896 = 0645k5, ?x7275 = 0g4vmj8, ?x170 = 09nqf, ?x6175 = 0gg5kmg, ?x6321 = 0gg8z1f, ?x6247 = 09v9mks, jurisdiction_of_office(?x3959, ?x1603), contains(?x6304, ?x550), ?x5315 = 0glqh5_, ?x6216 = 06fcqw, film_release_region(?x6283, ?x550), film_release_region(?x6178, ?x550), film_release_region(?x5849, ?x550), film_release_region(?x3981, ?x550), film_release_region(?x3745, ?x550), olympics(?x1603, ?x784), ?x3854 = 03q0r1, ?x172 = 0154j, place_founded(?x12640, ?x550), ?x5849 = 02h22, location(?x12121, ?x1523), ?x1183 = 09jwl, ?x1932 = 0btyf5z, ?x2163 = 0j6b5, film_release_region(?x7832, ?x429), ?x3745 = 03cw411, ?x1931 = 0kbws, jurisdiction_of_office(?x265, ?x550), ?x131 = 0dz3r, ?x5496 = 07l50vn, ?x1518 = 04w7rn, olympics(?x550, ?x1608), ?x2867 = 02y8z, ?x3035 = 0j43swk, ?x6543 = 0421v9q, ?x467 = 0dckvs, ?x1121 = 0bynt, company(?x346, ?x122), ?x3728 = 087vz, ?x429 = 03rt9, ?x11313 = 0by17xn, countries_spoken_in(?x13310, ?x6305), ?x3981 = 047tsx3, country(?x5969, ?x550), ?x5704 = 0h95zbp, ?x5089 = 0bh8tgs, basic_title(?x1157, ?x346), country(?x3554, ?x404), ?x6621 = 0h63gl9, ?x6931 = 09v3jyg, award_winner(?x7693, ?x2156), ?x1023 = 0ctw_b, locations(?x9939, ?x456), organization(?x404, ?x3750), ?x6283 = 0gmd3k7, ?x9565 = 0hz6mv2, ?x634 = 0gx9rvq, partially_contains(?x11687, ?x404), ?x4673 = 07jbh, written_by(?x4811, ?x3555), ?x6528 = 0dc_ms, ?x151 = 0b90_r, country(?x150, ?x1603), sports(?x584, ?x3659), film_release_region(?x903, ?x456), genre(?x6178, ?x162), language(?x7149, ?x2502), ?x7149 = 01jr4j, languages(?x804, ?x2502), film(?x5889, ?x7832), participating_countries(?x1741, ?x456), ?x5889 = 0m66w, administrative_parent(?x4714, ?x551) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #507 for first EXPECTED value: *> intensional similarity = 203 *> extensional distance = 1 *> proper extension: 01d9r3; *> query: (?x48, ?x291) <- form_of_government(?x13717, ?x48), form_of_government(?x11593, ?x48), form_of_government(?x10450, ?x48), form_of_government(?x9563, ?x48), form_of_government(?x9251, ?x48), form_of_government(?x9035, ?x48), form_of_government(?x8866, ?x48), form_of_government(?x8197, ?x48), form_of_government(?x7807, ?x48), form_of_government(?x7665, ?x48), form_of_government(?x7430, ?x48), form_of_government(?x7037, ?x48), form_of_government(?x6559, ?x48), form_of_government(?x5680, ?x48), form_of_government(?x4737, ?x48), form_of_government(?x4714, ?x48), form_of_government(?x4092, ?x48), form_of_government(?x2291, ?x48), form_of_government(?x2188, ?x48), form_of_government(?x1453, ?x48), ?x13717 = 05rznz, combatants(?x13069, ?x7430), combatants(?x456, ?x7430), combatants(?x279, ?x7430), combatants(?x151, ?x7430), film_release_region(?x2050, ?x7430), olympics(?x7430, ?x12388), olympics(?x7430, ?x2134), olympics(?x7430, ?x1617), contains(?x7430, ?x1458), taxonomy(?x6559, ?x939), jurisdiction_of_office(?x182, ?x2291), organization(?x7430, ?x312), ?x1453 = 06qd3, ?x7665 = 03676, country(?x6733, ?x7430), country(?x2885, ?x7430), country(?x2044, ?x7430), country(?x1557, ?x7430), country(?x1175, ?x7430), ?x11593 = 03188, country(?x8428, ?x6559), ?x151 = 0b90_r, medal(?x7430, ?x1242), origin(?x9631, ?x6559), origin(?x7547, ?x6559), film_release_region(?x1392, ?x6559), country(?x4310, ?x8197), country(?x3309, ?x8197), country(?x1121, ?x8197), country(?x471, ?x8197), ?x3309 = 09w1n, combatants(?x326, ?x7430), olympics(?x2291, ?x2966), ?x2050 = 01fmys, countries_within(?x6956, ?x4092), ?x9563 = 0hdx8, ?x279 = 0d060g, ?x1175 = 02_5h, combatants(?x13381, ?x4092), administrative_area_type(?x4092, ?x2792), nationality(?x4915, ?x7430), adjoins(?x4092, ?x1174), country(?x4673, ?x2291), country(?x3641, ?x4092), country(?x1967, ?x4092), participating_countries(?x2496, ?x7430), ?x9251 = 07tp2, ?x12388 = 015pkt, ?x2885 = 07jjt, teams(?x4714, ?x10006), adjoins(?x9035, ?x7360), adjoins(?x9035, ?x291), countries_within(?x8483, ?x4714), ?x1121 = 0bynt, currency(?x4092, ?x170), film_release_region(?x8377, ?x456), film_release_region(?x7336, ?x456), film_release_region(?x7275, ?x456), film_release_region(?x6932, ?x456), film_release_region(?x6886, ?x456), film_release_region(?x6782, ?x456), film_release_region(?x6587, ?x456), film_release_region(?x6516, ?x456), film_release_region(?x6321, ?x456), film_release_region(?x6270, ?x456), film_release_region(?x6216, ?x456), film_release_region(?x6175, ?x456), film_release_region(?x5578, ?x456), film_release_region(?x5418, ?x456), film_release_region(?x5109, ?x456), film_release_region(?x5089, ?x456), film_release_region(?x4422, ?x456), film_release_region(?x3998, ?x456), film_release_region(?x3843, ?x456), film_release_region(?x3606, ?x456), film_release_region(?x3482, ?x456), film_release_region(?x3035, ?x456), film_release_region(?x2961, ?x456), film_release_region(?x2868, ?x456), film_release_region(?x2656, ?x456), film_release_region(?x1916, ?x456), film_release_region(?x1915, ?x456), film_release_region(?x1724, ?x456), film_release_region(?x1525, ?x456), film_release_region(?x1002, ?x456), film_release_region(?x791, ?x456), film_release_region(?x633, ?x456), film_release_region(?x343, ?x456), film_release_region(?x66, ?x456), ?x3482 = 017z49, countries_spoken_in(?x5359, ?x4092), ?x3998 = 0184tc, ?x5089 = 0bh8tgs, ?x7360 = 0fv4v, ?x1525 = 03qnvdl, ?x6886 = 0gwjw0c, ?x1916 = 0ch26b_, ?x343 = 0gx1bnj, ?x6321 = 0gg8z1f, adjoins(?x2517, ?x13069), ?x1967 = 01cgz, ?x1724 = 02r8hh_, teams(?x8197, ?x7453), country(?x1498, ?x456), ?x3035 = 0j43swk, ?x1617 = 01f1jy, ?x5680 = 06sw9, country(?x5989, ?x456), country(?x3127, ?x456), organization(?x4714, ?x127), ?x4737 = 07twz, award_winner(?x884, ?x9631), participating_countries(?x784, ?x8197), group(?x227, ?x9631), ?x6175 = 0gg5kmg, olympics(?x456, ?x1277), ?x6516 = 04cppj, ?x6587 = 07s3m4g, sports(?x2496, ?x359), ?x1915 = 0fq7dv_, ?x4310 = 064vjs, ?x6216 = 06fcqw, ?x1002 = 0_b3d, location(?x793, ?x8428), entity_involved(?x8865, ?x8866), olympics(?x1310, ?x2496), administrative_parent(?x1458, ?x1558), ?x3641 = 03fyrh, olympics(?x4092, ?x6464), ?x5989 = 019tzd, month(?x1458, ?x3270), month(?x1458, ?x1459), contains(?x456, ?x6265), ?x5418 = 026lgs, administrative_parent(?x9035, ?x551), ?x13381 = 01cpp0, place_of_birth(?x10180, ?x8428), influenced_by(?x4915, ?x2240), contains(?x6304, ?x4092), ?x7807 = 02kcz, ?x1310 = 02jx1, ?x2868 = 0dr3sl, jurisdiction_of_office(?x3959, ?x6559), countries_within(?x455, ?x456), ?x6932 = 027pfg, influenced_by(?x12392, ?x4915), ?x1242 = 02lq5w, adjoins(?x2290, ?x2291), ?x6782 = 07jnt, ?x3606 = 0gh65c5, ?x2961 = 047p7fr, ?x8377 = 0ds2l81, ?x2134 = 0blg2, ?x7275 = 0g4vmj8, locations(?x2496, ?x10099), ?x12392 = 040rjq, ?x471 = 02vx4, ?x4422 = 06zn2v2, olympics(?x9035, ?x778), ?x66 = 014lc_, ?x10450 = 06s_2, ?x3270 = 05cw8, ?x6733 = 01sgl, ?x3843 = 080nwsb, ?x7037 = 04hzj, ?x1277 = 0swbd, ?x2656 = 03qnc6q, ?x791 = 087wc7n, vacationer(?x4714, ?x3503), ?x3127 = 03hr1p, ?x2188 = 0163v, ?x7336 = 0bdjd, ?x1557 = 07bs0, ?x6270 = 0g9zljd, ?x5109 = 0b44shh, artists(?x284, ?x9631), ?x5578 = 0ddj0x, ?x633 = 0c40vxk, award_nominee(?x7547, ?x1125), ?x2044 = 06f41, ?x1459 = 04w_7, ?x2966 = 06sks6 *> conf = 0.50 ranks of expected_values: 5, 10, 26, 32, 50, 52, 57, 60, 68, 82, 91 EVAL 06cx9 form_of_government! 0gtzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 06m_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 01nyl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 0fv4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 06dfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 01699 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 02khs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 05v10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 07t21 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 019rg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 6.000 6.000 0.500 http://example.org/location/country/form_of_government EVAL 06cx9 form_of_government! 09c7w0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 6.000 6.000 0.500 http://example.org/location/country/form_of_government #2089-05kyr PRED entity: 05kyr PRED relation: combatants! PRED expected values: 07j9n => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 84): 07j9n (0.75 #323, 0.67 #204, 0.45 #560), 081pw (0.73 #1549, 0.53 #1608, 0.50 #240), 03jqfx (0.70 #60, 0.59 #655, 0.58 #2082), 025rzfc (0.50 #262, 0.50 #142, 0.15 #1571), 05nqz (0.50 #128, 0.17 #248, 0.09 #3507), 0dr7s (0.45 #520, 0.40 #104, 0.22 #1947), 0gfq9 (0.40 #66, 0.40 #6, 0.22 #1909), 0cwt70 (0.40 #97, 0.40 #37, 0.18 #513), 01_3rn (0.40 #87, 0.36 #503, 0.20 #27), 0k4y6 (0.40 #81, 0.27 #556, 0.26 #1924) >> Best rule #323 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 07l75; 0dv0z; >> query: (?x4815, 07j9n) <- combatants(?x4815, ?x10120), combatants(?x4815, ?x3141), ?x10120 = 01fvhp, entity_involved(?x10764, ?x3141), combatants(?x3141, ?x456) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05kyr combatants! 07j9n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.750 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #2088-0flbm PRED entity: 0flbm PRED relation: time_zones PRED expected values: 02hczc => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 9): 02hczc (0.75 #54, 0.71 #15, 0.64 #522), 02hcv8 (0.62 #199, 0.56 #277, 0.56 #251), 02lcqs (0.31 #96, 0.24 #240, 0.24 #214), 02fqwt (0.17 #771, 0.16 #745, 0.16 #758), 02llzg (0.06 #540, 0.06 #408, 0.05 #526), 03bdv (0.03 #1063, 0.03 #1024, 0.03 #1076), 03plfd (0.02 #505, 0.02 #414, 0.02 #546), 0gsrz4 (0.02 #503, 0.02 #557, 0.02 #570), 042g7t (0.01 #415, 0.01 #506, 0.01 #599) >> Best rule #54 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0d1qn; 01vsl; 0p07_; 060wq; >> query: (?x14360, 02hczc) <- contains(?x2982, ?x14360), source(?x14360, ?x958), ?x2982 = 01n4w, ?x958 = 0jbk9 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0flbm time_zones 02hczc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.750 http://example.org/location/location/time_zones #2087-0152x_ PRED entity: 0152x_ PRED relation: place_founded PRED expected values: 05fjf => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 43): 02_286 (0.20 #199, 0.20 #143, 0.17 #1061), 0r679 (0.14 #27, 0.07 #226, 0.06 #491), 030qb3t (0.11 #411, 0.09 #876, 0.07 #1074), 0d6lp (0.11 #89, 0.07 #221, 0.06 #354), 01n7q (0.07 #211, 0.06 #278, 0.06 #476), 09d4_ (0.07 #238, 0.06 #503, 0.05 #703), 01smm (0.06 #307, 0.04 #1167, 0.03 #1431), 06wjf (0.06 #297, 0.03 #1421, 0.02 #1817), 06pwq (0.06 #471, 0.05 #671, 0.05 #870), 0qcrj (0.06 #528, 0.05 #728, 0.05 #927) >> Best rule #199 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 016ckq; 0kcdl; >> query: (?x4818, ?x739) <- citytown(?x4818, ?x739), ?x739 = 02_286, company(?x5574, ?x4818), award_winner(?x5574, ?x3974), nominated_for(?x3974, ?x3075) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #5379 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 205 *> proper extension: 03pmfw; 07gqbk; *> query: (?x4818, ?x335) <- organization(?x4682, ?x4818), citytown(?x4818, ?x739), citytown(?x5077, ?x739), citytown(?x738, ?x739), state_province_region(?x738, ?x335), industry(?x5077, ?x245) *> conf = 0.01 ranks of expected_values: 40 EVAL 0152x_ place_founded 05fjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 137.000 137.000 0.200 http://example.org/organization/organization/place_founded #2086-044gyq PRED entity: 044gyq PRED relation: profession PRED expected values: 02hrh1q => 106 concepts (67 used for prediction) PRED predicted values (max 10 best out of 69): 02hrh1q (0.92 #4726, 0.88 #3841, 0.86 #3694), 0cbd2 (0.65 #5013, 0.44 #2949, 0.42 #3540), 09jwl (0.65 #900, 0.58 #1341, 0.55 #312), 0dxtg (0.63 #9730, 0.54 #453, 0.42 #5019), 01d_h8 (0.48 #446, 0.34 #9723, 0.33 #593), 0dz3r (0.45 #149, 0.41 #2, 0.39 #3241), 0nbcg (0.44 #1354, 0.41 #913, 0.41 #31), 0kyk (0.44 #5036, 0.30 #2972, 0.28 #3563), 03gjzk (0.34 #455, 0.25 #9732, 0.23 #602), 018gz8 (0.31 #457, 0.24 #604, 0.17 #2959) >> Best rule #4726 for best value: >> intensional similarity = 3 >> extensional distance = 687 >> proper extension: 02nb2s; 04hpck; 03xmy1; 02mhfy; 02fb1n; 01vhb0; 0jt90f5; 015rhv; 03wpmd; 01mmslz; ... >> query: (?x3493, 02hrh1q) <- award(?x3493, ?x567), profession(?x3493, ?x220), actor(?x5529, ?x3493) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044gyq profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 67.000 0.922 http://example.org/people/person/profession #2085-02rh_0 PRED entity: 02rh_0 PRED relation: current_club! PRED expected values: 02bh_v => 118 concepts (59 used for prediction) PRED predicted values (max 10 best out of 38): 02ltg3 (0.60 #113, 0.50 #33, 0.19 #696), 033nzk (0.43 #771, 0.21 #877, 0.17 #106), 02bh_v (0.40 #123, 0.25 #43, 0.20 #786), 03y_f8 (0.33 #532, 0.33 #3, 0.30 #268), 032jlh (0.33 #24, 0.25 #103, 0.25 #77), 02w64f (0.33 #213, 0.25 #52, 0.20 #132), 03yl2t (0.25 #57, 0.25 #30, 0.20 #110), 03ylxn (0.25 #101, 0.22 #1087, 0.20 #287), 03_qrp (0.25 #38, 0.20 #118, 0.17 #106), 03z8bw (0.25 #44, 0.20 #124, 0.17 #106) >> Best rule #113 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 01634x; >> query: (?x10196, 02ltg3) <- current_club(?x12089, ?x10196), colors(?x10196, ?x663), team(?x10129, ?x10196), team(?x203, ?x10196), team(?x63, ?x10196), ?x203 = 0dgrmp, ?x12089 = 01352_, position(?x13306, ?x63), ?x13306 = 06ylv0 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 01634x; *> query: (?x10196, 02bh_v) <- current_club(?x12089, ?x10196), colors(?x10196, ?x663), team(?x10129, ?x10196), team(?x203, ?x10196), team(?x63, ?x10196), ?x203 = 0dgrmp, ?x12089 = 01352_, position(?x13306, ?x63), ?x13306 = 06ylv0 *> conf = 0.40 ranks of expected_values: 3 EVAL 02rh_0 current_club! 02bh_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 118.000 59.000 0.600 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #2084-0cfywh PRED entity: 0cfywh PRED relation: gender PRED expected values: 05zppz => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.75 #21, 0.74 #25, 0.74 #63), 02zsn (0.46 #194, 0.46 #193, 0.46 #131) >> Best rule #21 for best value: >> intensional similarity = 6 >> extensional distance = 22 >> proper extension: 06pwf6; 03fw4y; 071xj; 01rwcgb; 07jmgz; 0cb1ky; 0bkg87; 0tj9; 01q8wk7; 0b66qd; >> query: (?x14088, 05zppz) <- type_of_union(?x14088, ?x566), nationality(?x14088, ?x2146), ?x566 = 04ztj, ?x2146 = 03rk0, place_of_birth(?x14088, ?x2236), contains(?x2236, ?x2364) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cfywh gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.750 http://example.org/people/person/gender #2083-01vvycq PRED entity: 01vvycq PRED relation: currency PRED expected values: 09nqf => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.44 #118, 0.42 #58, 0.41 #19), 01nv4h (0.11 #29, 0.08 #56, 0.06 #77), 02l6h (0.01 #105) >> Best rule #118 for best value: >> intensional similarity = 3 >> extensional distance = 124 >> proper extension: 02qjj7; 03n93; 02zrv7; 06fc0b; 01f492; 01nfys; 01p47r; 0fqjhm; 0dxmyh; 01gc7h; ... >> query: (?x702, 09nqf) <- gender(?x702, ?x231), ?x231 = 05zppz, participant(?x702, ?x2237) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vvycq currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.444 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #2082-0qx1w PRED entity: 0qx1w PRED relation: location_of_ceremony! PRED expected values: 04ztj => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.25 #45, 0.25 #49, 0.25 #13), 01g63y (0.14 #141) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 579 >> proper extension: 0xkq4; 04ykg; 03s5t; 0d0x8; 0vzm; 013m43; 07b_l; 0qkcb; 01s3v; 07mgr; ... >> query: (?x13205, 04ztj) <- contains(?x94, ?x13205), time_zones(?x13205, ?x1638), category(?x13205, ?x134) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qx1w location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.253 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #2081-01p7b6b PRED entity: 01p7b6b PRED relation: gender PRED expected values: 05zppz => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #1, 0.89 #13, 0.87 #43), 02zsn (0.46 #163, 0.31 #10, 0.29 #106) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 01r6jt2; 04k15; 0bvzp; 082db; 08c7cz; 0drc1; 03f4k; >> query: (?x10146, 05zppz) <- place_of_death(?x10146, ?x1523), music(?x1255, ?x10146), profession(?x10146, ?x563) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p7b6b gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.918 http://example.org/people/person/gender #2080-0778_3 PRED entity: 0778_3 PRED relation: contains! PRED expected values: 0d060g => 124 concepts (75 used for prediction) PRED predicted values (max 10 best out of 233): 0d060g (0.80 #5379, 0.80 #4484, 0.67 #3590), 09c7w0 (0.75 #8051, 0.69 #25059, 0.67 #19690), 02jx1 (0.59 #44821, 0.50 #1875, 0.42 #9031), 07ssc (0.41 #44766, 0.33 #1820, 0.30 #66262), 05kr_ (0.40 #5492, 0.33 #4597, 0.33 #3703), 02_286 (0.39 #8091, 0.30 #13466, 0.29 #14361), 059rby (0.34 #8068, 0.26 #16127, 0.23 #13443), 04jpl (0.33 #1810, 0.32 #17026, 0.24 #23287), 0843m (0.33 #235, 0.01 #12527), 01n7q (0.29 #56452, 0.28 #58242, 0.21 #65413) >> Best rule #5379 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 01y9st; >> query: (?x12918, 0d060g) <- contains(?x2474, ?x12918), currency(?x12918, ?x2244), ?x2244 = 0ptk_, category(?x12918, ?x134), ?x134 = 08mbj5d >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0778_3 contains! 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 75.000 0.800 http://example.org/location/location/contains #2079-080knyg PRED entity: 080knyg PRED relation: nationality PRED expected values: 09c7w0 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 18): 09c7w0 (0.85 #802, 0.78 #201, 0.77 #401), 0d060g (0.21 #6208, 0.20 #107, 0.17 #207), 01p8s (0.21 #6208, 0.07 #94, 0.07 #194), 05sb1 (0.21 #6208), 03rk0 (0.11 #746, 0.07 #947, 0.06 #5051), 02jx1 (0.11 #1835, 0.10 #2236, 0.10 #1535), 07ssc (0.09 #1517, 0.08 #3620, 0.08 #1817), 01j28z (0.07 #801, 0.01 #5306), 064t9 (0.07 #801, 0.01 #5306), 0chghy (0.03 #1312, 0.02 #1512, 0.02 #1412) >> Best rule #802 for best value: >> intensional similarity = 2 >> extensional distance = 282 >> proper extension: 0cymln; 0443c; >> query: (?x2360, 09c7w0) <- people(?x2510, ?x2360), ?x2510 = 0x67 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 080knyg nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.852 http://example.org/people/person/nationality #2078-02wgln PRED entity: 02wgln PRED relation: award PRED expected values: 08_vwq => 106 concepts (77 used for prediction) PRED predicted values (max 10 best out of 255): 0gqy2 (0.36 #8163, 0.23 #1361, 0.20 #561), 05pcn59 (0.25 #78, 0.21 #3279, 0.19 #1278), 054krc (0.25 #84, 0.17 #8086, 0.13 #26411), 094qd5 (0.25 #41, 0.13 #3242, 0.13 #26411), 0gqwc (0.25 #71, 0.13 #26411, 0.13 #30814), 09qwmm (0.25 #31, 0.13 #26411, 0.13 #30814), 099cng (0.25 #83, 0.13 #26411, 0.13 #30814), 0l8z1 (0.25 #60, 0.13 #26411, 0.12 #26812), 09qvf4 (0.25 #206, 0.13 #30814, 0.13 #29613), 0cjyzs (0.25 #103, 0.13 #30814, 0.13 #29613) >> Best rule #8163 for best value: >> intensional similarity = 3 >> extensional distance = 480 >> proper extension: 0fp_v1x; 07q1v4; 0hwd8; 09ftwr; 050z2; 04107; 03flwk; 043hg; 01t94_1; 064177; ... >> query: (?x1958, 0gqy2) <- award(?x1958, ?x451), nominated_for(?x451, ?x2814), ?x2814 = 078sj4 >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #5468 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 327 *> proper extension: 05_pkf; 022_q8; 017c87; 02sjp; 029ghl; 0405l; *> query: (?x1958, 08_vwq) <- award(?x1958, ?x112), nominated_for(?x1958, ?x2090), languages(?x1958, ?x90) *> conf = 0.03 ranks of expected_values: 179 EVAL 02wgln award 08_vwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 106.000 77.000 0.361 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #2077-0127xk PRED entity: 0127xk PRED relation: religion PRED expected values: 0c8wxp => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 25): 0c8wxp (0.20 #456, 0.16 #141, 0.15 #231), 03_gx (0.16 #824, 0.14 #689, 0.12 #374), 0kpl (0.16 #685, 0.15 #820, 0.14 #1180), 0n2g (0.06 #823, 0.05 #688, 0.04 #1138), 0kq2 (0.05 #828, 0.05 #1008, 0.05 #693), 01lp8 (0.03 #811, 0.03 #361, 0.03 #1), 092bf5 (0.03 #376, 0.03 #16, 0.03 #826), 0631_ (0.03 #8, 0.03 #98, 0.02 #188), 051kv (0.03 #275, 0.03 #140, 0.02 #590), 019cr (0.03 #56, 0.03 #146, 0.02 #416) >> Best rule #456 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 01l3j; >> query: (?x11334, 0c8wxp) <- nationality(?x11334, ?x94), ?x94 = 09c7w0, place_of_burial(?x11334, ?x8044) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0127xk religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.204 http://example.org/people/person/religion #2076-0qr8z PRED entity: 0qr8z PRED relation: location_of_ceremony! PRED expected values: 04ztj => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.88 #51, 0.87 #109, 0.86 #35), 01g63y (0.74 #72, 0.74 #71, 0.37 #10), 0jgjn (0.09 #8, 0.07 #50, 0.04 #70) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0hzlz; >> query: (?x8654, 04ztj) <- location_of_ceremony(?x4831, ?x8654), profession(?x4831, ?x1032), student(?x4955, ?x4831), ?x1032 = 02hrh1q >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qr8z location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.884 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #2075-02cllz PRED entity: 02cllz PRED relation: religion PRED expected values: 0c8wxp => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 15): 0c8wxp (0.20 #6, 0.18 #456, 0.15 #727), 0kpl (0.12 #55, 0.09 #280, 0.06 #100), 092bf5 (0.12 #61, 0.02 #241, 0.02 #737), 03_gx (0.08 #464, 0.08 #2179, 0.07 #2133), 0n2g (0.06 #103, 0.04 #283, 0.01 #463), 025t7ly (0.03 #102), 03j6c (0.03 #1734, 0.02 #2140, 0.02 #2638), 0kq2 (0.02 #198, 0.02 #288, 0.02 #559), 058x5 (0.02 #139, 0.01 #184), 0g5llry (0.02 #163) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 07vc_9; 0170qf; 026g801; >> query: (?x2457, 0c8wxp) <- film(?x2457, ?x1415), award_nominee(?x100, ?x2457), ?x1415 = 09p0ct >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02cllz religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.200 http://example.org/people/person/religion #2074-05wjnt PRED entity: 05wjnt PRED relation: profession PRED expected values: 018gz8 => 150 concepts (98 used for prediction) PRED predicted values (max 10 best out of 73): 018gz8 (0.56 #898, 0.40 #309, 0.25 #1927), 01d_h8 (0.40 #1771, 0.40 #1918, 0.39 #12653), 09jwl (0.40 #311, 0.30 #752, 0.20 #458), 02jknp (0.38 #1772, 0.35 #1331, 0.32 #1919), 03gjzk (0.38 #1778, 0.28 #896, 0.28 #1925), 0kyk (0.36 #2969, 0.24 #1940, 0.20 #2234), 0np9r (0.28 #902, 0.20 #754, 0.20 #313), 05z96 (0.25 #41, 0.20 #482, 0.13 #2982), 01c8w0 (0.25 #155, 0.20 #596, 0.04 #2214), 02krf9 (0.21 #1055, 0.20 #319, 0.18 #1790) >> Best rule #898 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 03qmj9; 0fby2t; 0cmt6q; 06q5t7; >> query: (?x2473, 018gz8) <- film(?x2473, ?x7141), film(?x2473, ?x365), ?x365 = 0bvn25, profession(?x2473, ?x353), film(?x541, ?x7141) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05wjnt profession 018gz8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 98.000 0.556 http://example.org/people/person/profession #2073-0kw4j PRED entity: 0kw4j PRED relation: student PRED expected values: 03lgg => 174 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1634): 0157m (0.22 #2340, 0.01 #180098, 0.01 #117356), 05sj55 (0.14 #1349, 0.11 #3440, 0.05 #5531), 01vh18t (0.14 #1620, 0.11 #3711, 0.05 #5802), 0bv7t (0.14 #909, 0.11 #3000, 0.05 #5091), 0blbxk (0.14 #186, 0.11 #2277, 0.05 #4368), 042xrr (0.14 #790, 0.11 #2881, 0.05 #4972), 031x_3 (0.14 #1492, 0.11 #3583, 0.05 #5674), 013w7j (0.14 #1072, 0.11 #3163, 0.05 #5254), 0277c3 (0.14 #1069, 0.11 #3160, 0.05 #5251), 095b70 (0.14 #1047, 0.11 #3138, 0.05 #5229) >> Best rule #2340 for best value: >> intensional similarity = 2 >> extensional distance = 7 >> proper extension: 07xhy; >> query: (?x3821, 0157m) <- state_province_region(?x3821, ?x108), ?x108 = 0rh6k >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #87827 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 130 *> proper extension: 03rj0; 04hzj; *> query: (?x3821, ?x1222) <- company(?x346, ?x3821), contains(?x94, ?x3821), location(?x1222, ?x94) *> conf = 0.02 ranks of expected_values: 750 EVAL 0kw4j student 03lgg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 174.000 91.000 0.222 http://example.org/education/educational_institution/students_graduates./education/education/student #2072-03lmzl PRED entity: 03lmzl PRED relation: actor! PRED expected values: 06cs95 => 92 concepts (63 used for prediction) PRED predicted values (max 10 best out of 127): 090s_0 (0.29 #267, 0.01 #7901, 0.01 #7638), 0199wf (0.17 #205), 0147w8 (0.14 #495, 0.01 #6812), 024hbv (0.14 #500), 02sqkh (0.14 #343), 0gbtbm (0.14 #339), 0jwl2 (0.07 #1389, 0.05 #2178, 0.02 #7707), 0vjr (0.07 #2726, 0.03 #2989, 0.03 #1937), 0cs134 (0.06 #1791, 0.05 #2843, 0.05 #3106), 03ln8b (0.06 #1611, 0.05 #2926, 0.03 #2663) >> Best rule #267 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 05xd_v; 04gc65; >> query: (?x8871, 090s_0) <- film(?x8871, ?x2719), actor(?x3303, ?x8871), ?x2719 = 0j_t1 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2376 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 07qy0b; 03ys2f; 03ysmg; *> query: (?x8871, 06cs95) <- award_nominee(?x8871, ?x12809), sibling(?x10445, ?x8871), profession(?x8871, ?x1032) *> conf = 0.02 ranks of expected_values: 80 EVAL 03lmzl actor! 06cs95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 92.000 63.000 0.286 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #2071-06w38l PRED entity: 06w38l PRED relation: place_of_death PRED expected values: 015zxh => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 37): 030qb3t (0.17 #3911, 0.15 #3133, 0.15 #216), 02_286 (0.10 #3124, 0.10 #207, 0.09 #3902), 0k049 (0.09 #197, 0.07 #2918, 0.06 #3892), 01_d4 (0.08 #3306, 0.06 #3110, 0.04 #5642), 0f2wj (0.05 #1373, 0.04 #3901, 0.04 #2927), 06_kh (0.04 #4089, 0.04 #3894, 0.04 #2920), 04jpl (0.04 #201, 0.03 #4285, 0.02 #3118), 0r3w7 (0.02 #371, 0.01 #1538, 0.01 #4066), 0rnmy (0.02 #236, 0.01 #1403), 04vmp (0.02 #4192, 0.01 #4386, 0.01 #3219) >> Best rule #3911 for best value: >> intensional similarity = 3 >> extensional distance = 428 >> proper extension: 03q95r; 012gbb; 01bh6y; 03f68r6; 04bdlg; 01l3j; 0443c; >> query: (?x12891, 030qb3t) <- nationality(?x12891, ?x94), ?x94 = 09c7w0, people(?x7260, ?x12891) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #413 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 88 *> proper extension: 0h5f5n; 0jf1b; 05kfs; 081lh; 0151w_; 012t1; 05_k56; 05drq5; 0136g9; 0343h; ... *> query: (?x12891, 015zxh) <- profession(?x12891, ?x987), award(?x12891, ?x1862), ?x1862 = 0gr51, ?x987 = 0dxtg *> conf = 0.01 ranks of expected_values: 29 EVAL 06w38l place_of_death 015zxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 104.000 104.000 0.170 http://example.org/people/deceased_person/place_of_death #2070-0jmhr PRED entity: 0jmhr PRED relation: team! PRED expected values: 0355dz => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 45): 0355dz (0.86 #586, 0.81 #489, 0.72 #392), 0ctt4z (0.71 #1369, 0.71 #924, 0.66 #1368), 02sdk9v (0.70 #1617, 0.66 #1713, 0.62 #1569), 02_j1w (0.65 #1621, 0.64 #1717, 0.57 #1573), 02nzb8 (0.65 #1616, 0.63 #1712, 0.58 #1568), 0dgrmp (0.64 #1619, 0.56 #1571, 0.53 #1667), 0619m3 (0.51 #1467, 0.50 #147, 0.49 #1318), 05b3ts (0.46 #1338, 0.25 #1487, 0.18 #1040), 01r3hr (0.44 #1321, 0.34 #1470, 0.21 #1023), 02g_7z (0.44 #1343, 0.34 #1492, 0.19 #1443) >> Best rule #586 for best value: >> intensional similarity = 26 >> extensional distance = 20 >> proper extension: 04cxw5b; >> query: (?x11420, ?x5755) <- team(?x6848, ?x11420), team(?x4747, ?x11420), team(?x4570, ?x11420), ?x4570 = 03558l, draft(?x11420, ?x4979), draft(?x11420, ?x2569), position(?x11420, ?x5755), ?x6848 = 02_ssl, draft(?x9937, ?x2569), draft(?x9931, ?x2569), draft(?x6089, ?x2569), position(?x13785, ?x4747), position(?x7136, ?x4747), position(?x5419, ?x4747), position(?x660, ?x4747), ?x660 = 0jmdb, ?x13785 = 0fw9vx, ?x9937 = 0jmjr, ?x6089 = 0jmbv, school(?x2569, ?x621), ?x9931 = 0jm3b, school(?x4979, ?x2171), ?x5419 = 0jmmn, ?x7136 = 0jm74, award_winner(?x3486, ?x2171), institution(?x734, ?x2171) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jmhr team! 0355dz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.865 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #2069-06gb1w PRED entity: 06gb1w PRED relation: film! PRED expected values: 016tt2 => 96 concepts (71 used for prediction) PRED predicted values (max 10 best out of 86): 0c_j5d (0.51 #1031, 0.46 #2062, 0.45 #1179), 016tt2 (0.38 #4, 0.27 #77, 0.25 #150), 086k8 (0.34 #516, 0.33 #148, 0.31 #590), 017s11 (0.25 #3, 0.18 #76, 0.18 #444), 05qd_ (0.21 #1777, 0.18 #449, 0.16 #1408), 016tw3 (0.19 #893, 0.16 #3850, 0.15 #1998), 03rwz3 (0.17 #189, 0.12 #43, 0.10 #557), 0g1rw (0.14 #964, 0.11 #1555, 0.10 #1629), 06jntd (0.12 #30, 0.09 #103, 0.08 #1872), 025jfl (0.12 #5, 0.09 #78, 0.05 #298) >> Best rule #1031 for best value: >> intensional similarity = 5 >> extensional distance = 85 >> proper extension: 02d413; 0g22z; 02vxq9m; 09m6kg; 011yxg; 0gzy02; 01sxly; 050r1z; 08720; 0170_p; ... >> query: (?x4392, ?x738) <- genre(?x4392, ?x225), production_companies(?x4392, ?x738), cinematography(?x4392, ?x7782), honored_for(?x5592, ?x4392), film(?x96, ?x4392) >> conf = 0.51 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 02wgk1; *> query: (?x4392, 016tt2) <- genre(?x4392, ?x11401), prequel(?x4392, ?x936), ?x11401 = 0btmb, film(?x96, ?x4392), film_crew_role(?x4392, ?x137) *> conf = 0.38 ranks of expected_values: 2 EVAL 06gb1w film! 016tt2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 96.000 71.000 0.509 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #2068-02_286 PRED entity: 02_286 PRED relation: time_zones PRED expected values: 02hcv8 => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 13): 02hcv8 (0.75 #42, 0.50 #3, 0.49 #1342), 02lcqs (0.42 #486, 0.32 #96, 0.30 #291), 02llzg (0.24 #264, 0.23 #238, 0.21 #459), 02fqwt (0.23 #53, 0.23 #378, 0.21 #1002), 03bdv (0.20 #71, 0.10 #604, 0.09 #669), 02hczc (0.18 #184, 0.14 #28, 0.13 #197), 03plfd (0.08 #608, 0.08 #738, 0.07 #335), 0d2t4g (0.08 #61, 0.03 #139, 0.02 #360), 042g7t (0.07 #76, 0.06 #505, 0.06 #310), 052vwh (0.07 #77, 0.04 #246, 0.04 #233) >> Best rule #42 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 0mvsg; >> query: (?x739, 02hcv8) <- source(?x739, ?x958), partially_contains(?x739, ?x10856) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_286 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.750 http://example.org/location/location/time_zones #2067-0209hj PRED entity: 0209hj PRED relation: language PRED expected values: 02h40lc => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 42): 02h40lc (0.96 #352, 0.96 #2110, 0.95 #3049), 064_8sq (0.40 #21, 0.35 #79, 0.27 #312), 06nm1 (0.20 #10, 0.12 #185, 0.12 #360), 04306rv (0.15 #471, 0.14 #530, 0.12 #413), 03mqtr (0.12 #117, 0.03 #350, 0.03 #1755), 017fp (0.12 #117, 0.03 #350, 0.03 #1755), 07ssc (0.12 #117, 0.03 #350, 0.03 #1755), 04xvlr (0.12 #117, 0.03 #350, 0.03 #1755), 07s9rl0 (0.12 #117, 0.03 #350, 0.03 #1755), 06b_j (0.10 #197, 0.08 #488, 0.08 #430) >> Best rule #352 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 0dgst_d; 02704ff; >> query: (?x697, 02h40lc) <- nominated_for(?x112, ?x697), language(?x697, ?x1882), ?x112 = 027dtxw >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0209hj language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.964 http://example.org/film/film/language #2066-025hl8 PRED entity: 025hl8 PRED relation: symptom_of! PRED expected values: 0gxb2 => 66 concepts (66 used for prediction) PRED predicted values (max 10 best out of 73): 0cjf0 (0.66 #1183, 0.50 #1072, 0.50 #333), 0gxb2 (0.63 #768, 0.43 #403, 0.43 #355), 0brgy (0.43 #401, 0.43 #353, 0.42 #766), 012qjw (0.43 #380, 0.43 #354, 0.42 #767), 0j5fv (0.40 #192, 0.37 #782, 0.35 #318), 04kllm9 (0.40 #290, 0.35 #318, 0.33 #41), 0hgxh (0.37 #782, 0.35 #318, 0.33 #33), 097ns (0.37 #782, 0.21 #344, 0.20 #1338), 01pf6 (0.35 #318, 0.31 #1293, 0.21 #344), 0hg45 (0.35 #318, 0.31 #1293, 0.21 #344) >> Best rule #1183 for best value: >> intensional similarity = 12 >> extensional distance = 30 >> proper extension: 087z2; >> query: (?x3680, 0cjf0) <- symptom_of(?x13487, ?x3680), symptom_of(?x13487, ?x14024), symptom_of(?x13487, ?x11126), symptom_of(?x13487, ?x6260), symptom_of(?x13487, ?x4959), symptom_of(?x13487, ?x4291), ?x4291 = 07jwr, risk_factors(?x11126, ?x11678), ?x14024 = 0h1wz, ?x11678 = 0fltx, people(?x4959, ?x598), ?x6260 = 0dq9p >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #768 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 17 *> proper extension: 011zdm; 0hg45; 072hv; *> query: (?x3680, 0gxb2) <- risk_factors(?x3680, ?x514), symptom_of(?x3679, ?x3680), symptom_of(?x3679, ?x14096), symptom_of(?x3679, ?x12536), symptom_of(?x3679, ?x7260), ?x14096 = 0h3bn, people(?x7260, ?x1737), risk_factors(?x7260, ?x231), risk_factors(?x11659, ?x12536) *> conf = 0.63 ranks of expected_values: 2 EVAL 025hl8 symptom_of! 0gxb2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 66.000 66.000 0.656 http://example.org/medicine/symptom/symptom_of #2065-0bksh PRED entity: 0bksh PRED relation: film PRED expected values: 047gpsd 02qydsh 0f8j13 => 134 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1109): 0cp0ph6 (0.59 #143867, 0.42 #156301, 0.38 #156300), 0dr3sl (0.59 #143867, 0.42 #156301, 0.38 #156300), 0d61px (0.17 #692, 0.09 #2468, 0.03 #158078), 013q07 (0.17 #355, 0.07 #7460, 0.07 #19892), 02p76f9 (0.17 #1416, 0.07 #4969, 0.04 #15625), 02pw_n (0.17 #1162, 0.07 #4715, 0.03 #13595), 02x2jl_ (0.17 #1741, 0.04 #10622, 0.03 #39039), 011ykb (0.17 #1133, 0.04 #101237, 0.03 #158078), 051zy_b (0.17 #575, 0.03 #18336, 0.03 #27216), 02qcr (0.17 #1507, 0.03 #6836, 0.03 #15716) >> Best rule #143867 for best value: >> intensional similarity = 3 >> extensional distance = 1315 >> proper extension: 0h1_w; 01mvth; 0yfp; 01vrncs; 02sjf5; 028lc8; 02lf70; 0b_7k; 02rmfm; 027xbpw; ... >> query: (?x4782, ?x2868) <- film(?x4782, ?x1811), award(?x4782, ?x1007), nominated_for(?x4782, ?x2868) >> conf = 0.59 => this is the best rule for 2 predicted values *> Best rule #158078 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1566 *> proper extension: 03mz9r; 07sgfsl; 0565cz; 0fwy0h; 0280mv7; 02x0bdb; 013ybx; *> query: (?x4782, ?x339) <- award_nominee(?x2534, ?x4782), profession(?x4782, ?x1032), film(?x2534, ?x339) *> conf = 0.03 ranks of expected_values: 422, 506 EVAL 0bksh film 0f8j13 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 134.000 99.000 0.588 http://example.org/film/actor/film./film/performance/film EVAL 0bksh film 02qydsh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 134.000 99.000 0.588 http://example.org/film/actor/film./film/performance/film EVAL 0bksh film 047gpsd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 99.000 0.588 http://example.org/film/actor/film./film/performance/film #2064-05f7w84 PRED entity: 05f7w84 PRED relation: program! PRED expected values: 0146mv => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 62): 0cjdk (0.46 #1767, 0.30 #1818, 0.26 #1106), 02hmvw (0.44 #756, 0.33 #536, 0.30 #1818), 03mdt (0.35 #1769, 0.14 #1990, 0.13 #1714), 0146mv (0.33 #577, 0.33 #192, 0.33 #137), 03lpbx (0.33 #527, 0.30 #1818, 0.24 #1022), 01y67v (0.30 #1818, 0.17 #499, 0.11 #719), 01j7pt (0.25 #241, 0.07 #901, 0.05 #1122), 0gsg7 (0.23 #2874, 0.22 #3550, 0.19 #2709), 0187wh (0.23 #2874, 0.20 #356, 0.14 #1182), 09d5h (0.23 #2874, 0.13 #1545, 0.12 #4440) >> Best rule #1767 for best value: >> intensional similarity = 8 >> extensional distance = 50 >> proper extension: 0bx_hnp; >> query: (?x5938, 0cjdk) <- program(?x12812, ?x5938), program(?x12812, ?x11454), program(?x12812, ?x419), category(?x12812, ?x134), languages(?x419, ?x5607), genre(?x419, ?x53), ?x5607 = 064_8sq, nominated_for(?x3263, ?x11454) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #577 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 4 *> proper extension: 0fkwzs; 043qqt5; *> query: (?x5938, 0146mv) <- genre(?x5938, ?x2540), genre(?x5938, ?x1510), genre(?x5938, ?x809), program(?x11954, ?x5938), ?x2540 = 0hcr, ?x1510 = 01hmnh, actor(?x5938, ?x12054), genre(?x670, ?x809), category(?x5938, ?x134), student(?x5288, ?x12054) *> conf = 0.33 ranks of expected_values: 4 EVAL 05f7w84 program! 0146mv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 84.000 84.000 0.462 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #2063-0gzh PRED entity: 0gzh PRED relation: politician! PRED expected values: 07wdw => 146 concepts (146 used for prediction) PRED predicted values (max 10 best out of 27): 0d075m (0.64 #562, 0.56 #258, 0.50 #165), 02245 (0.20 #601, 0.18 #344, 0.17 #367), 03z19 (0.20 #70, 0.14 #560, 0.08 #233), 0_00 (0.20 #57, 0.08 #407, 0.08 #383), 07wf9 (0.17 #1148, 0.17 #424, 0.15 #1056), 01fml (0.17 #144, 0.14 #237, 0.08 #446), 07wpm (0.17 #155, 0.09 #879, 0.08 #457), 01c9x (0.14 #236, 0.11 #657, 0.10 #726), 049tb (0.14 #216, 0.11 #707, 0.10 #753), 02189 (0.14 #223, 0.10 #317, 0.09 #340) >> Best rule #562 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 083p7; 083pr; 02mjmr; 012gx2; 0226cw; 0gs5q; 02hy5d; 038w8; 042fk; >> query: (?x13698, 0d075m) <- profession(?x13698, ?x3342), location(?x13698, ?x11811), ?x3342 = 04gc2, county_seat(?x13426, ?x11811) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #1149 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 012v1t; *> query: (?x13698, 07wdw) <- jurisdiction_of_office(?x13698, ?x94), ?x94 = 09c7w0, basic_title(?x13698, ?x346), type_of_union(?x13698, ?x566) *> conf = 0.08 ranks of expected_values: 15 EVAL 0gzh politician! 07wdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 146.000 146.000 0.643 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #2062-0431v3 PRED entity: 0431v3 PRED relation: genre PRED expected values: 0lsxr 06nbt => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 77): 0c4xc (0.45 #434, 0.44 #513, 0.33 #117), 06nbt (0.39 #96, 0.19 #413, 0.19 #492), 0hcr (0.36 #1201, 0.33 #94, 0.23 #490), 0djd22 (0.27 #176, 0.17 #97, 0.14 #18), 04gm78f (0.27 #200, 0.17 #121, 0.14 #42), 01htzx (0.25 #1200, 0.20 #2073, 0.16 #1596), 06n90 (0.23 #2069, 0.22 #1196, 0.18 #2782), 02n4kr (0.21 #7, 0.13 #323, 0.12 #165), 01hmnh (0.21 #2072, 0.17 #92, 0.15 #2785), 0lsxr (0.17 #87, 0.15 #324, 0.13 #1828) >> Best rule #434 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 0cwrr; 01h72l; 03y3bp7; 01f3p_; 01hvv0; 07wqr6; 0cskb; 0123qq; 025x1t; 03_b1g; >> query: (?x5561, 0c4xc) <- actor(?x5561, ?x1065), genre(?x5561, ?x258), nominated_for(?x1379, ?x5561), ?x258 = 05p553 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #96 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 07hpv3; 05f7w84; *> query: (?x5561, 06nbt) <- actor(?x5561, ?x1065), genre(?x5561, ?x809), languages(?x5561, ?x254), ?x809 = 0vgkd *> conf = 0.39 ranks of expected_values: 2, 10 EVAL 0431v3 genre 06nbt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.449 http://example.org/tv/tv_program/genre EVAL 0431v3 genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 84.000 84.000 0.449 http://example.org/tv/tv_program/genre #2061-0bqs56 PRED entity: 0bqs56 PRED relation: tv_program PRED expected values: 039cq4 => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 19): 09fc83 (0.11 #123, 0.07 #295, 0.02 #983), 039cq4 (0.10 #477, 0.10 #907, 0.07 #1079), 01j95 (0.07 #343, 0.02 #1031, 0.02 #1203), 0ph24 (0.05 #423, 0.02 #939, 0.02 #1111), 02xhwm (0.05 #417), 0gfzgl (0.03 #444), 06y_n (0.03 #665, 0.01 #1439, 0.01 #1525), 0180mw (0.03 #647), 05_z42 (0.03 #814, 0.01 #1502, 0.01 #1588), 01j7mr (0.03 #799, 0.01 #1487, 0.01 #1573) >> Best rule #123 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 07w21; 0jt90f5; 0kp2_; 03ftmg; 0h0yt; 06g4_; >> query: (?x6008, 09fc83) <- influenced_by(?x6008, ?x6771), student(?x1368, ?x6008), friend(?x6771, ?x9849) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #477 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 27 *> proper extension: 0b7t3p; *> query: (?x6008, 039cq4) <- influenced_by(?x6008, ?x986), celebrity(?x719, ?x986) *> conf = 0.10 ranks of expected_values: 2 EVAL 0bqs56 tv_program 039cq4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 143.000 0.111 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #2060-03d_zl4 PRED entity: 03d_zl4 PRED relation: people! PRED expected values: 0g8_vp => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 38): 041rx (0.25 #620, 0.19 #1467, 0.17 #1852), 033tf_ (0.14 #161, 0.14 #84, 0.12 #1008), 0x67 (0.14 #164, 0.11 #241, 0.09 #2936), 0g8_vp (0.13 #869, 0.12 #715, 0.04 #638), 0xnvg (0.12 #629, 0.10 #475, 0.08 #398), 01qhm_ (0.12 #391, 0.05 #1007, 0.03 #2470), 07hwkr (0.11 #320, 0.08 #1244, 0.08 #1706), 03bkbh (0.11 #263, 0.08 #417, 0.07 #494), 02w7gg (0.08 #387, 0.07 #2389, 0.07 #464), 07bch9 (0.08 #408, 0.07 #485, 0.06 #562) >> Best rule #620 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 01hxs4; 0ph2w; 01s21dg; 02tf1y; 01xwqn; >> query: (?x6707, 041rx) <- profession(?x6707, ?x1146), gender(?x6707, ?x231), ?x1146 = 018gz8, participant(?x6707, ?x6328) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #869 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 02y8bn; *> query: (?x6707, 0g8_vp) <- location(?x6707, ?x1755), nationality(?x6707, ?x279), ?x279 = 0d060g *> conf = 0.13 ranks of expected_values: 4 EVAL 03d_zl4 people! 0g8_vp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 105.000 0.250 http://example.org/people/ethnicity/people #2059-0ng8v PRED entity: 0ng8v PRED relation: location_of_ceremony! PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.21 #33, 0.21 #17, 0.21 #53), 01g63y (0.06 #165) >> Best rule #33 for best value: >> intensional similarity = 7 >> extensional distance = 419 >> proper extension: 0bmm4; >> query: (?x14760, 04ztj) <- contains(?x429, ?x14760), film_release_region(?x3217, ?x429), film_release_region(?x1518, ?x429), film_release_region(?x1150, ?x429), ?x1518 = 04w7rn, ?x1150 = 0h3xztt, ?x3217 = 0gffmn8 >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ng8v location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.209 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #2058-0d0vqn PRED entity: 0d0vqn PRED relation: country! PRED expected values: 03tmr 0d1t3 0194d => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 24): 0194d (0.74 #594, 0.68 #709, 0.66 #525), 07jjt (0.70 #143, 0.68 #189, 0.68 #465), 0d1t3 (0.70 #150, 0.63 #196, 0.60 #12), 035d1m (0.70 #144, 0.57 #75, 0.54 #466), 03rbzn (0.68 #191, 0.64 #306, 0.63 #697), 09_bl (0.63 #186, 0.60 #140, 0.55 #209), 0dwxr (0.60 #146, 0.59 #307, 0.58 #192), 06z68 (0.60 #148, 0.53 #194, 0.50 #309), 0152n0 (0.60 #154, 0.49 #1197, 0.47 #200), 09f6b (0.60 #159, 0.49 #1197, 0.47 #1359) >> Best rule #594 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 02j71; >> query: (?x304, 0194d) <- currency(?x304, ?x170), ?x170 = 09nqf, service_location(?x555, ?x304) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 18 EVAL 0d0vqn country! 0194d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 187.000 187.000 0.743 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d0vqn country! 0d1t3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 187.000 187.000 0.743 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 0d0vqn country! 03tmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 187.000 187.000 0.743 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #2057-03tps5 PRED entity: 03tps5 PRED relation: language PRED expected values: 06nm1 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 33): 064_8sq (0.33 #77, 0.18 #134, 0.17 #420), 06nm1 (0.22 #181, 0.21 #296, 0.18 #124), 0c_v2 (0.17 #15, 0.01 #358), 04306rv (0.15 #233, 0.14 #290, 0.13 #175), 03_9r (0.11 #66, 0.09 #180, 0.06 #409), 02bjrlw (0.11 #58, 0.08 #344, 0.08 #230), 012w70 (0.09 #298, 0.09 #183, 0.08 #355), 0653m (0.08 #354, 0.07 #297, 0.06 #125), 06b_j (0.08 #250, 0.07 #307, 0.06 #707), 01wgr (0.06 #152, 0.02 #495, 0.02 #552) >> Best rule #77 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01shy7; 03t79f; >> query: (?x4409, 064_8sq) <- film(?x2789, ?x4409), film(?x400, ?x4409), ?x400 = 01q_ph >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #181 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 011yxg; 07gp9; 01hr1; 09xbpt; 01k1k4; 03t97y; 04hwbq; 0dtfn; 017gm7; 075wx7_; ... *> query: (?x4409, 06nm1) <- award_winner(?x4409, ?x398), prequel(?x4409, ?x4920), titles(?x2480, ?x4409), genre(?x4409, ?x258) *> conf = 0.22 ranks of expected_values: 2 EVAL 03tps5 language 06nm1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.333 http://example.org/film/film/language #2056-05nzw6 PRED entity: 05nzw6 PRED relation: film PRED expected values: 0j_tw 05q54f5 => 108 concepts (61 used for prediction) PRED predicted values (max 10 best out of 570): 061681 (0.33 #109, 0.29 #7219, 0.20 #5442), 0418wg (0.33 #399, 0.20 #5732, 0.14 #7509), 04vr_f (0.33 #171, 0.20 #5504, 0.14 #7281), 02qzh2 (0.33 #691, 0.20 #6024, 0.14 #7801), 0prrm (0.33 #856, 0.20 #6189, 0.14 #7966), 09xbpt (0.33 #47, 0.20 #5380, 0.14 #7157), 06z8s_ (0.33 #130, 0.20 #5463, 0.14 #7240), 02mpyh (0.33 #1453, 0.20 #6786, 0.14 #8563), 03k8th (0.33 #1708, 0.20 #7041, 0.14 #8818), 02x3y41 (0.33 #1354, 0.20 #6687, 0.14 #8464) >> Best rule #109 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0169dl; >> query: (?x6777, 061681) <- film(?x6777, ?x8633), film(?x6777, ?x5731), film(?x6777, ?x4848), film_release_region(?x5731, ?x94), ?x8633 = 057__d, nominated_for(?x68, ?x4848), honored_for(?x5731, ?x188) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #23573 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 196 *> proper extension: 04cw0j; 01s21dg; 01vw_dv; *> query: (?x6777, 05q54f5) <- profession(?x6777, ?x1032), student(?x1011, ?x6777), languages(?x6777, ?x254), place_of_birth(?x6777, ?x1719) *> conf = 0.02 ranks of expected_values: 335 EVAL 05nzw6 film 05q54f5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 108.000 61.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 05nzw6 film 0j_tw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 61.000 0.333 http://example.org/film/actor/film./film/performance/film #2055-010xjr PRED entity: 010xjr PRED relation: nationality PRED expected values: 03rt9 => 89 concepts (85 used for prediction) PRED predicted values (max 10 best out of 19): 09c7w0 (0.76 #501, 0.75 #802, 0.74 #3015), 02jx1 (0.56 #333, 0.25 #233, 0.14 #133), 07ssc (0.38 #215, 0.33 #4622, 0.29 #115), 012wgb (0.33 #7330, 0.27 #7128), 0p54z (0.33 #7330, 0.01 #1305), 03rt9 (0.33 #4622, 0.27 #7128, 0.02 #1015), 0345h (0.33 #4622, 0.02 #1939, 0.02 #1738), 03rjj (0.25 #5, 0.14 #105, 0.12 #205), 0b90_r (0.14 #103, 0.12 #203), 06q1r (0.12 #377, 0.01 #4497, 0.01 #3694) >> Best rule #501 for best value: >> intensional similarity = 3 >> extensional distance = 393 >> proper extension: 05n19y; 03f3_p3; 027y_; 03p01x; 01tpl1p; 054kmq; 047s_cr; 01k31p; 012pd4; >> query: (?x9797, 09c7w0) <- profession(?x9797, ?x106), location(?x9797, ?x9895), administrative_division(?x10314, ?x9895) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #4622 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1863 *> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 04bdxl; 02s2ft; 079vf; 05vsxz; 06qgvf; ... *> query: (?x9797, ?x94) <- film(?x9797, ?x2006), currency(?x2006, ?x170), country(?x2006, ?x94) *> conf = 0.33 ranks of expected_values: 6 EVAL 010xjr nationality 03rt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 89.000 85.000 0.759 http://example.org/people/person/nationality #2054-06fq2 PRED entity: 06fq2 PRED relation: company! PRED expected values: 04n1q6 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 37): 0dq_5 (0.60 #385, 0.38 #17, 0.37 #1490), 0krdk (0.56 #374, 0.35 #696, 0.31 #1479), 0dq3c (0.37 #370, 0.23 #692, 0.22 #1475), 05_wyz (0.32 #386, 0.25 #18, 0.20 #708), 01yc02 (0.29 #376, 0.25 #8, 0.20 #698), 09d6p2 (0.25 #19, 0.24 #387, 0.15 #709), 021q1c (0.22 #148, 0.21 #102, 0.19 #194), 01kr6k (0.19 #395, 0.11 #717, 0.09 #809), 05k17c (0.18 #472, 0.15 #426, 0.14 #748), 07t3gd (0.14 #482, 0.13 #436, 0.10 #114) >> Best rule #385 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 02zs4; 087c7; 0cv9b; 0gsg7; 0l8sx; 0hpt3; 09d5h; 01xdn1; 0gvbw; 03mnk; ... >> query: (?x8202, 0dq_5) <- state_province_region(?x8202, ?x3634), company(?x346, ?x8202), list(?x8202, ?x2197) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #425 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 90 *> proper extension: 01cyd5; *> query: (?x8202, 04n1q6) <- state_province_region(?x8202, ?x3634), company(?x346, ?x8202), student(?x8202, ?x2248) *> conf = 0.08 ranks of expected_values: 18 EVAL 06fq2 company! 04n1q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 95.000 95.000 0.604 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #2053-011ykb PRED entity: 011ykb PRED relation: genre PRED expected values: 05p553 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 81): 05p553 (0.42 #830, 0.38 #357, 0.34 #2130), 01jfsb (0.30 #3560, 0.29 #4978, 0.29 #5807), 02kdv5l (0.28 #3550, 0.27 #5797, 0.26 #4968), 03bxz7 (0.23 #53, 0.21 #171, 0.16 #289), 0lsxr (0.21 #126, 0.19 #716, 0.18 #1190), 03k9fj (0.21 #5806, 0.21 #3559, 0.21 #8404), 04xvlr (0.20 #709, 0.20 #119, 0.19 #1183), 082gq (0.19 #1211, 0.18 #737, 0.13 #265), 060__y (0.17 #724, 0.17 #843, 0.16 #1198), 017fp (0.17 #15, 0.15 #133, 0.13 #251) >> Best rule #830 for best value: >> intensional similarity = 2 >> extensional distance = 457 >> proper extension: 04svwx; >> query: (?x6472, 05p553) <- genre(?x6472, ?x1403), ?x1403 = 02l7c8 >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 011ykb genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.425 http://example.org/film/film/genre #2052-014z8v PRED entity: 014z8v PRED relation: influenced_by PRED expected values: 01k9lpl => 164 concepts (112 used for prediction) PRED predicted values (max 10 best out of 424): 014z8v (0.45 #1850, 0.16 #9596, 0.16 #7872), 01k9lpl (0.30 #2037, 0.12 #4618, 0.11 #35773), 0l5yl (0.25 #268, 0.22 #1135, 0.17 #701), 013tjc (0.25 #373, 0.20 #2101, 0.09 #8123), 01t9qj_ (0.25 #255, 0.11 #1122, 0.02 #5424), 012gq6 (0.25 #97, 0.10 #1825, 0.07 #33621), 01wj9y9 (0.25 #62, 0.10 #1790, 0.07 #33621), 0127xk (0.25 #385, 0.07 #33621, 0.07 #33620), 01lc5 (0.25 #383, 0.06 #1681, 0.04 #7703), 015cbq (0.25 #326, 0.05 #5495, 0.03 #8076) >> Best rule #1850 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 014zfs; 056wb; 02_wxh; 0sx5w; 01svq8; >> query: (?x4112, 014z8v) <- category(?x4112, ?x134), influenced_by(?x4112, ?x4554), producer_type(?x4112, ?x632) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2037 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 18 *> proper extension: 014zfs; 056wb; 02_wxh; 0sx5w; 01svq8; *> query: (?x4112, 01k9lpl) <- category(?x4112, ?x134), influenced_by(?x4112, ?x4554), producer_type(?x4112, ?x632) *> conf = 0.30 ranks of expected_values: 2 EVAL 014z8v influenced_by 01k9lpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 164.000 112.000 0.450 http://example.org/influence/influence_node/influenced_by #2051-01l3j PRED entity: 01l3j PRED relation: special_performance_type PRED expected values: 01pb34 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 4): 01pb34 (0.15 #23, 0.13 #88, 0.13 #53), 01kyvx (0.05 #232, 0.03 #262, 0.03 #272), 02t8yb (0.02 #144, 0.02 #94, 0.01 #220), 09_gdc (0.02 #263, 0.01 #273, 0.01 #258) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 03mv0b; >> query: (?x13735, 01pb34) <- people(?x4322, ?x13735), ?x4322 = 0gk4g, place_of_death(?x13735, ?x1523), celebrities_impersonated(?x3649, ?x13735) >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l3j special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.154 http://example.org/film/actor/film./film/performance/special_performance_type #2050-01sn3 PRED entity: 01sn3 PRED relation: dog_breed PRED expected values: 01t032 0km3f => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 2): 0km3f (0.90 #26, 0.64 #4, 0.52 #10), 01t032 (0.82 #25, 0.64 #3, 0.50 #51) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0f2w0; 0fr0t; 07bcn; 0fsb8; >> query: (?x4090, 0km3f) <- jurisdiction_of_office(?x1195, ?x4090), location(?x971, ?x4090), dog_breed(?x4090, ?x1706) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01sn3 dog_breed 0km3f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.900 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed EVAL 01sn3 dog_breed 01t032 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.900 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #2049-05f0r8 PRED entity: 05f0r8 PRED relation: profession PRED expected values: 02jknp => 114 concepts (55 used for prediction) PRED predicted values (max 10 best out of 72): 01d_h8 (0.57 #4150, 0.55 #2522, 0.53 #6372), 02jknp (0.49 #4152, 0.45 #6374, 0.45 #1488), 03gjzk (0.40 #2530, 0.39 #4158, 0.38 #6380), 018gz8 (0.33 #2532, 0.27 #6382, 0.25 #4160), 0cbd2 (0.27 #1487, 0.21 #2523, 0.20 #2079), 09jwl (0.25 #3718, 0.14 #610, 0.12 #1054), 0dz3r (0.22 #3702, 0.09 #742, 0.08 #890), 0nbcg (0.19 #3731, 0.14 #919, 0.12 #1067), 02krf9 (0.18 #4170, 0.17 #6392, 0.15 #2542), 016z4k (0.15 #3704, 0.06 #892, 0.05 #2076) >> Best rule #4150 for best value: >> intensional similarity = 5 >> extensional distance = 320 >> proper extension: 04bs3j; 0jf1b; 015pxr; 0738b8; 027l0b; 01nwwl; 0347xl; 0blt6; 03xp8d5; 0gyx4; ... >> query: (?x13945, 01d_h8) <- place_of_birth(?x13945, ?x739), profession(?x13945, ?x1032), profession(?x13945, ?x987), ?x1032 = 02hrh1q, ?x987 = 0dxtg >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #4152 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 320 *> proper extension: 04bs3j; 0jf1b; 015pxr; 0738b8; 027l0b; 01nwwl; 0347xl; 0blt6; 03xp8d5; 0gyx4; ... *> query: (?x13945, 02jknp) <- place_of_birth(?x13945, ?x739), profession(?x13945, ?x1032), profession(?x13945, ?x987), ?x1032 = 02hrh1q, ?x987 = 0dxtg *> conf = 0.49 ranks of expected_values: 2 EVAL 05f0r8 profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 55.000 0.575 http://example.org/people/person/profession #2048-01rwf_ PRED entity: 01rwf_ PRED relation: country PRED expected values: 07ssc => 99 concepts (66 used for prediction) PRED predicted values (max 10 best out of 12): 07ssc (0.42 #488, 0.42 #439, 0.40 #243), 02jx1 (0.21 #3511, 0.21 #487, 0.21 #3574), 022_6 (0.18 #486, 0.15 #241, 0.14 #550), 09c7w0 (0.14 #853, 0.14 #675, 0.13 #912), 0345h (0.10 #2233, 0.10 #3760, 0.10 #4007), 0chghy (0.09 #132, 0.01 #1522, 0.01 #2065), 0jgd (0.05 #125), 03_3d (0.04 #1336, 0.02 #2662, 0.01 #2904), 03rk0 (0.03 #1361, 0.02 #3356, 0.02 #3482), 03rt9 (0.02 #1874, 0.02 #2729, 0.01 #1643) >> Best rule #488 for best value: >> intensional similarity = 5 >> extensional distance = 70 >> proper extension: 0jt5zcn; 06y9v; 094vy; 01q1j; 0366c; 04523f; 01zst8; 01_5bb; 0dzz_; 0cv5l; ... >> query: (?x11369, ?x512) <- contains(?x1310, ?x11369), contains(?x512, ?x11369), location(?x628, ?x11369), ?x512 = 07ssc, state_province_region(?x963, ?x1310) >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rwf_ country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 66.000 0.417 http://example.org/location/administrative_division/country #2047-035yg PRED entity: 035yg PRED relation: country! PRED expected values: 06z6r => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 55): 06z6r (0.84 #911, 0.83 #1407, 0.81 #691), 071t0 (0.73 #682, 0.70 #627, 0.69 #902), 03_8r (0.69 #1011, 0.68 #791, 0.67 #1397), 01lb14 (0.59 #674, 0.56 #1004, 0.54 #619), 03hr1p (0.56 #683, 0.53 #1013, 0.53 #628), 07jbh (0.56 #694, 0.53 #1024, 0.53 #639), 06f41 (0.55 #1003, 0.54 #673, 0.53 #893), 06wrt (0.53 #675, 0.51 #1005, 0.50 #620), 0w0d (0.51 #671, 0.51 #616, 0.50 #891), 064vjs (0.51 #692, 0.49 #1022, 0.47 #637) >> Best rule #911 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 0h3y; 01ls2; 015fr; 07ylj; 01p1v; 088q4; 034m8; >> query: (?x8884, 06z6r) <- countries_spoken_in(?x254, ?x8884), organization(?x8884, ?x312), teams(?x8884, ?x7396) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035yg country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.838 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #2046-0jmwg PRED entity: 0jmwg PRED relation: artists PRED expected values: 04_jsg 012ycy 09jvl => 70 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1033): 06mj4 (0.67 #4972, 0.60 #3911, 0.50 #10619), 0fpj4lx (0.60 #3506, 0.56 #7755, 0.50 #4567), 048tgl (0.60 #4081, 0.50 #5142, 0.33 #10454), 01y_rz (0.60 #4121, 0.50 #5182, 0.33 #10494), 0kxbc (0.60 #3693, 0.50 #4754, 0.33 #7942), 012x1l (0.60 #4204, 0.50 #5265, 0.33 #2081), 01gx5f (0.58 #8784, 0.44 #7723, 0.40 #3474), 02ndj5 (0.58 #3184, 0.50 #10619, 0.50 #10441), 02r3zy (0.58 #3184, 0.50 #10619, 0.50 #2186), 0ycp3 (0.58 #3184, 0.50 #10619, 0.40 #3790) >> Best rule #4972 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 05w3f; >> query: (?x7808, 06mj4) <- parent_genre(?x3642, ?x7808), artists(?x7808, ?x8560), artists(?x7808, ?x8272), artists(?x7808, ?x6471), artists(?x7808, ?x1838), ?x1838 = 012zng, gender(?x8560, ?x231), award_winner(?x6126, ?x6471), student(?x734, ?x8272), type_of_union(?x8560, ?x566) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3184 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 016clz; 03p7rp; *> query: (?x7808, ?x677) <- parent_genre(?x10933, ?x7808), artists(?x7808, ?x6471), artists(?x7808, ?x1955), ?x6471 = 0143q0, parent_genre(?x10933, ?x2996), artists(?x10933, ?x677), ?x1955 = 0285c, ?x2996 = 01243b *> conf = 0.58 ranks of expected_values: 14, 15, 112 EVAL 0jmwg artists 09jvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 70.000 28.000 0.667 http://example.org/music/genre/artists EVAL 0jmwg artists 012ycy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 70.000 28.000 0.667 http://example.org/music/genre/artists EVAL 0jmwg artists 04_jsg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 70.000 28.000 0.667 http://example.org/music/genre/artists #2045-03yvln PRED entity: 03yvln PRED relation: sport PRED expected values: 02vx4 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 59): 02vx4 (0.96 #362, 0.96 #326, 0.95 #278), 0z74 (0.47 #867, 0.27 #987, 0.24 #967), 0jm_ (0.17 #624, 0.17 #614, 0.15 #768), 03tmr (0.17 #649, 0.14 #450, 0.11 #522), 018jz (0.15 #598, 0.13 #653, 0.13 #562), 018w8 (0.13 #597, 0.11 #652, 0.11 #561), 09xp_ (0.12 #39, 0.09 #293, 0.07 #407), 039yzs (0.06 #655, 0.04 #600, 0.03 #564), 0194d (0.04 #33, 0.03 #22, 0.03 #110), 07jbh (0.04 #33, 0.03 #22, 0.03 #110) >> Best rule #362 for best value: >> intensional similarity = 10 >> extensional distance = 53 >> proper extension: 04nrcg; >> query: (?x11898, 02vx4) <- position(?x11898, ?x63), position(?x11898, ?x60), ?x60 = 02nzb8, teams(?x2188, ?x11898), country(?x766, ?x2188), olympics(?x2188, ?x418), contains(?x455, ?x2188), ?x63 = 02sdk9v, country(?x766, ?x512), ?x512 = 07ssc >> conf = 0.96 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03yvln sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.964 http://example.org/sports/sports_team/sport #2044-04110lv PRED entity: 04110lv PRED relation: honored_for! PRED expected values: 05c1t6z => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1): 02q690_ (0.02 #2519, 0.01 #3258, 0.01 #3508) >> Best rule #2519 for best value: >> intensional similarity = 10 >> extensional distance = 64 >> proper extension: 0gpjbt; >> query: (?x7936, 02q690_) <- ceremony(?x1313, ?x7936), honored_for(?x7936, ?x2490), nominated_for(?x1313, ?x5429), award(?x3662, ?x1313), award(?x2803, ?x1313), award(?x197, ?x1313), ?x5429 = 02psgq, award_nominee(?x2803, ?x1039), award_winner(?x8408, ?x3662), award_winner(?x1313, ?x276) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04110lv honored_for! 05c1t6z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 39.000 39.000 0.015 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #2043-01t6xz PRED entity: 01t6xz PRED relation: profession PRED expected values: 01d_h8 => 79 concepts (52 used for prediction) PRED predicted values (max 10 best out of 54): 0dxtg (0.56 #2531, 0.55 #1495, 0.55 #2087), 01d_h8 (0.53 #1339, 0.49 #1487, 0.49 #1191), 02krf9 (0.43 #26, 0.28 #6070, 0.26 #1507), 02jknp (0.29 #1341, 0.28 #6070, 0.27 #1489), 018gz8 (0.22 #1349, 0.20 #2089, 0.19 #2533), 09jwl (0.18 #2979, 0.18 #758, 0.18 #5347), 0cbd2 (0.17 #895, 0.15 #7113, 0.15 #4892), 0np9r (0.16 #612, 0.16 #1649, 0.15 #7275), 0d1pc (0.16 #494, 0.12 #790, 0.11 #1975), 0kyk (0.14 #917, 0.11 #4914, 0.10 #6691) >> Best rule #2531 for best value: >> intensional similarity = 3 >> extensional distance = 573 >> proper extension: 01pr_j6; >> query: (?x6481, 0dxtg) <- profession(?x6481, ?x1041), ?x1041 = 03gjzk, nationality(?x6481, ?x94) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1339 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 450 *> proper extension: 03qkgyl; 0168ql; *> query: (?x6481, 01d_h8) <- profession(?x6481, ?x1041), ?x1041 = 03gjzk, type_of_union(?x6481, ?x566) *> conf = 0.53 ranks of expected_values: 2 EVAL 01t6xz profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 52.000 0.560 http://example.org/people/person/profession #2042-01gbb4 PRED entity: 01gbb4 PRED relation: student! PRED expected values: 014mlp => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 9): 014mlp (0.08 #166, 0.07 #466, 0.06 #746), 019v9k (0.05 #10, 0.03 #170, 0.03 #90), 02_xgp2 (0.03 #94, 0.02 #174, 0.02 #114), 02h4rq6 (0.02 #103, 0.02 #303, 0.02 #223), 028dcg (0.02 #78, 0.02 #378, 0.02 #38), 0bkj86 (0.02 #229, 0.02 #169, 0.01 #89), 03mkk4 (0.02 #33, 0.01 #53, 0.01 #73), 016t_3 (0.01 #164), 04zx3q1 (0.01 #162) >> Best rule #166 for best value: >> intensional similarity = 4 >> extensional distance = 453 >> proper extension: 099bk; 0cl_m; >> query: (?x7137, 014mlp) <- nationality(?x7137, ?x94), gender(?x7137, ?x231), student(?x5981, ?x7137), religion(?x7137, ?x2694) >> conf = 0.08 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gbb4 student! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.084 http://example.org/education/educational_degree/people_with_this_degree./education/education/student #2041-01swxv PRED entity: 01swxv PRED relation: institution! PRED expected values: 03bwzr4 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 18): 03bwzr4 (0.82 #91, 0.80 #50, 0.58 #557), 019v9k (0.66 #184, 0.64 #86, 0.64 #223), 016t_3 (0.61 #83, 0.60 #42, 0.56 #549), 04zx3q1 (0.48 #41, 0.46 #82, 0.42 #1168), 022h5x (0.42 #1168, 0.41 #1107, 0.33 #1672), 01gkg3 (0.42 #1168, 0.41 #1107, 0.33 #1672), 027f2w (0.40 #46, 0.39 #87, 0.26 #1028), 01ysy9 (0.33 #1672, 0.29 #1612, 0.29 #1792), 02m4yg (0.33 #1672, 0.29 #1612, 0.29 #1792), 013zdg (0.32 #85, 0.32 #44, 0.27 #551) >> Best rule #91 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 0bx8pn; 0j_sncb; 027xx3; 02183k; 01w5m; 03ksy; 01q0kg; 07vyf; 07t90; 01h8rk; ... >> query: (?x2959, 03bwzr4) <- major_field_of_study(?x2959, ?x4321), major_field_of_study(?x2959, ?x2606), ?x4321 = 0g26h, ?x2606 = 062z7, student(?x2959, ?x5804) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01swxv institution! 03bwzr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.821 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #2040-06mkj PRED entity: 06mkj PRED relation: film_release_region! PRED expected values: 0dckvs 0gx9rvq 0401sg 0gkz15s 0cwy47 0h3xztt 05z_kps 07g_0c 01f8gz 0c8tkt 035yn8 0fq7dv_ 01jrbb 01ffx4 017z49 0gh65c5 03cw411 0bmhvpr 0c3xw46 0n04r 043sct5 0bhwhj 02prwdh 0cc97st 0cq86w 09v9mks 02qk3fk 0421v9q 0372j5 043tvp3 0ds1glg 02pxst 0280061 0233bn 03nsm5x 08j7lh => 253 concepts (123 used for prediction) PRED predicted values (max 10 best out of 1231): 043tvp3 (0.89 #29756, 0.85 #53040, 0.85 #40428), 0dll_t2 (0.89 #29619, 0.81 #17977, 0.74 #40291), 040rmy (0.83 #19609, 0.82 #39983, 0.78 #43864), 0421v9q (0.83 #20024, 0.82 #30697, 0.81 #17114), 01jrbb (0.82 #40016, 0.78 #19642, 0.75 #43897), 0gkz15s (0.82 #24320, 0.81 #29171, 0.81 #43724), 0fq7dv_ (0.81 #29259, 0.81 #17617, 0.76 #39931), 03nsm5x (0.81 #29850, 0.79 #30821, 0.78 #20148), 0cc97st (0.81 #29626, 0.75 #30597, 0.75 #17984), 0bmhvpr (0.81 #29426, 0.74 #40098, 0.73 #63380) >> Best rule #29756 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 0b90_r; 0chghy; 03rt9; 06mzp; 0ctw_b; 0345h; 01znc_; 06c1y; 06bnz; 01p1v; ... >> query: (?x2152, 043tvp3) <- film_release_region(?x1868, ?x2152), country(?x689, ?x2152), ?x1868 = 0cc7hmk >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 21, 23, 24, 25, 28, 29, 32, 35, 36, 37, 39, 41, 42, 43, 48, 54, 56, 79, 81, 93 EVAL 06mkj film_release_region! 08j7lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 03nsm5x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0233bn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0280061 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 02pxst CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0ds1glg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 043tvp3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0372j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0421v9q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 02qk3fk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 09v9mks CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0cq86w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0cc97st CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 02prwdh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0bhwhj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 043sct5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0n04r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0c3xw46 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0bmhvpr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 03cw411 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0gh65c5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 017z49 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 01ffx4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 01jrbb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0fq7dv_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 035yn8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0c8tkt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 01f8gz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 07g_0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 05z_kps CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0h3xztt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0cwy47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0401sg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0gx9rvq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06mkj film_release_region! 0dckvs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 253.000 123.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2039-03mh94 PRED entity: 03mh94 PRED relation: film_crew_role PRED expected values: 01vx2h => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 34): 0ch6mp2 (0.73 #86, 0.69 #48, 0.64 #124), 09zzb8 (0.67 #79, 0.62 #41, 0.56 #194), 09vw2b7 (0.60 #85, 0.54 #47, 0.48 #123), 0dxtw (0.40 #90, 0.38 #52, 0.32 #128), 01vx2h (0.35 #167, 0.31 #129, 0.27 #91), 02ynfr (0.23 #58, 0.20 #96, 0.19 #134), 01pvkk (0.21 #664, 0.21 #702, 0.21 #740), 015h31 (0.17 #1454, 0.17 #10, 0.13 #164), 094hwz (0.17 #1454, 0.17 #17, 0.08 #171), 02rh1dz (0.17 #1454, 0.15 #165, 0.12 #127) >> Best rule #86 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0gj8t_b; 0gtvrv3; 07024; 0dzz6g; 0dln8jk; 04yg13l; 0gg5qcw; 047wh1; 047myg9; 0cp0790; ... >> query: (?x463, 0ch6mp2) <- film(?x6980, ?x463), ?x6980 = 0zcbl, film_release_region(?x463, ?x94) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #167 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 259 *> proper extension: 06zn1c; *> query: (?x463, 01vx2h) <- genre(?x463, ?x811), ?x811 = 03k9fj, nominated_for(?x4563, ?x463) *> conf = 0.35 ranks of expected_values: 5 EVAL 03mh94 film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 44.000 44.000 0.733 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2038-01kvqc PRED entity: 01kvqc PRED relation: profession PRED expected values: 09jwl => 125 concepts (119 used for prediction) PRED predicted values (max 10 best out of 81): 09jwl (0.71 #166, 0.70 #5615, 0.69 #6205), 01d_h8 (0.54 #2354, 0.45 #1183, 0.36 #1624), 025352 (0.54 #2354, 0.14 #205, 0.12 #352), 028kk_ (0.54 #2354, 0.14 #221, 0.11 #515), 0nbcg (0.48 #5627, 0.48 #4595, 0.47 #6217), 0dz3r (0.44 #5598, 0.43 #4418, 0.42 #5894), 016z4k (0.43 #2800, 0.41 #4420, 0.38 #4273), 03gjzk (0.40 #1927, 0.30 #1192, 0.26 #1045), 012t_z (0.40 #13, 0.14 #160, 0.11 #1925), 0d1pc (0.40 #49, 0.13 #2845, 0.13 #3729) >> Best rule #166 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 016h9b; 01m3x5p; 09889g; 016jfw; 015076; >> query: (?x1583, 09jwl) <- award(?x1583, ?x1869), role(?x1583, ?x214), sibling(?x4184, ?x1583) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01kvqc profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 119.000 0.714 http://example.org/people/person/profession #2037-0dnw1 PRED entity: 0dnw1 PRED relation: nominated_for! PRED expected values: 0dqcm => 94 concepts (55 used for prediction) PRED predicted values (max 10 best out of 517): 0dqcm (0.79 #93414, 0.79 #121443, 0.79 #91079), 01vttb9 (0.79 #93414, 0.79 #121443, 0.79 #91079), 014dq7 (0.45 #14013, 0.29 #11677, 0.24 #21019), 01dvms (0.37 #119106, 0.35 #32698, 0.35 #39703), 034q3l (0.37 #119106, 0.35 #32698, 0.35 #39703), 0m0nq (0.35 #32698, 0.35 #39703, 0.30 #119105), 05683cn (0.33 #4342, 0.12 #128449, 0.09 #58385), 071jv5 (0.25 #2265, 0.12 #128449, 0.09 #58385), 0171lb (0.25 #897, 0.05 #7902, 0.04 #5567), 01vsy9_ (0.25 #1855, 0.01 #8860) >> Best rule #93414 for best value: >> intensional similarity = 4 >> extensional distance = 741 >> proper extension: 02bg8v; 04vh83; 016kv6; 043n0v_; 0gl02yg; 02754c9; 08y2fn; 06zsk51; >> query: (?x6094, ?x7556) <- nominated_for(?x2716, ?x6094), award_winner(?x6094, ?x7556), genre(?x6094, ?x53), award(?x6094, ?x1079) >> conf = 0.79 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0dnw1 nominated_for! 0dqcm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 55.000 0.791 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #2036-07j8kh PRED entity: 07j8kh PRED relation: nationality PRED expected values: 09c7w0 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 36): 09c7w0 (0.83 #1201, 0.75 #5209, 0.73 #3306), 0d060g (0.40 #10927, 0.21 #2202, 0.08 #107), 03rjj (0.40 #10927, 0.17 #405, 0.12 #105), 02_286 (0.25 #6111), 059rby (0.25 #6111), 07ssc (0.21 #2202, 0.15 #615, 0.11 #1015), 03_3d (0.21 #2202, 0.08 #206, 0.06 #506), 03rt9 (0.21 #2202, 0.03 #1313, 0.03 #513), 0b90_r (0.21 #2202), 02jx1 (0.17 #233, 0.15 #633, 0.14 #1633) >> Best rule #1201 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 02x8kk; 02x8mt; >> query: (?x5556, 09c7w0) <- place_of_birth(?x5556, ?x2850), ?x2850 = 0cr3d, gender(?x5556, ?x231) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07j8kh nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.832 http://example.org/people/person/nationality #2035-016yxn PRED entity: 016yxn PRED relation: language PRED expected values: 02h40lc => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 33): 02h40lc (0.89 #122, 0.89 #3313, 0.89 #1440), 03_9r (0.48 #189, 0.32 #70, 0.06 #2291), 04306rv (0.20 #5, 0.11 #65, 0.10 #365), 0jzc (0.20 #20, 0.07 #80, 0.04 #199), 064_8sq (0.17 #502, 0.15 #620, 0.14 #82), 06nm1 (0.12 #131, 0.12 #550, 0.10 #431), 06b_j (0.08 #562, 0.06 #621, 0.06 #443), 02bjrlw (0.07 #599, 0.06 #540, 0.06 #660), 04h9h (0.04 #343, 0.04 #163, 0.03 #284), 0653m (0.04 #72, 0.03 #2293, 0.03 #3445) >> Best rule #122 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 063y9fp; >> query: (?x11942, 02h40lc) <- genre(?x11942, ?x53), film(?x516, ?x11942), award_nominee(?x1116, ?x516), ?x1116 = 06b0d2 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016yxn language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.893 http://example.org/film/film/language #2034-025m8l PRED entity: 025m8l PRED relation: category_of PRED expected values: 0c4ys => 45 concepts (22 used for prediction) PRED predicted values (max 10 best out of 3): 0c4ys (0.80 #106, 0.67 #127, 0.60 #85), 0gcf2r (0.16 #257, 0.14 #366, 0.14 #388), 0g_w (0.12 #192, 0.10 #258, 0.10 #367) >> Best rule #106 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 01d38g; 02nhxf; 02v1m7; 01cky2; 031b3h; 02tj96; >> query: (?x2238, 0c4ys) <- award(?x4537, ?x2238), award(?x2731, ?x2238), ?x2731 = 01wwvc5, award_winner(?x2238, ?x1345), student(?x263, ?x4537), ceremony(?x2238, ?x486) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025m8l category_of 0c4ys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 45.000 22.000 0.800 http://example.org/award/award_category/category_of #2033-0gh6j94 PRED entity: 0gh6j94 PRED relation: currency PRED expected values: 02l6h => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.92 #78, 0.89 #99, 0.86 #281), 02l6h (0.14 #4, 0.10 #25, 0.10 #95), 01nv4h (0.14 #121, 0.12 #163, 0.11 #16), 02gsvk (0.03 #552, 0.01 #615, 0.01 #503), 0kz1h (0.02 #201, 0.02 #229, 0.02 #222) >> Best rule #78 for best value: >> intensional similarity = 8 >> extensional distance = 22 >> proper extension: 0p3_y; 0bz3jx; >> query: (?x7680, 09nqf) <- language(?x7680, ?x90), featured_film_locations(?x7680, ?x6959), films(?x14173, ?x7680), country(?x7680, ?x205), genre(?x7680, ?x53), films(?x6959, ?x1077), month(?x6959, ?x1459), place_of_death(?x4732, ?x6959) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 5 *> proper extension: 0c0nhgv; 09gdm7q; 0g9wdmc; 0cmc26r; 0btpm6; *> query: (?x7680, 02l6h) <- film_release_region(?x7680, ?x4743), film_release_region(?x7680, ?x2843), film_release_region(?x7680, ?x2152), film_release_region(?x7680, ?x1264), ?x1264 = 0345h, film_regional_debut_venue(?x7680, ?x2686), genre(?x7680, ?x53), ?x53 = 07s9rl0, film_crew_role(?x7680, ?x137), ?x2152 = 06mkj, ?x4743 = 03spz, films(?x14173, ?x7680), ?x2843 = 016wzw *> conf = 0.14 ranks of expected_values: 2 EVAL 0gh6j94 currency 02l6h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 116.000 0.917 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #2032-010z5n PRED entity: 010z5n PRED relation: category PRED expected values: 08mbj5d => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #16, 0.78 #27, 0.76 #8) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 347 >> proper extension: 01m1_t; 0tct_; 013h1c; 0c5v2; 0t_48; 0txhf; 031sn; 0ghtf; >> query: (?x12583, 08mbj5d) <- contains(?x94, ?x12583), ?x94 = 09c7w0, place(?x12583, ?x12583) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 010z5n category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.782 http://example.org/common/topic/webpage./common/webpage/category #2031-01lf293 PRED entity: 01lf293 PRED relation: artists! PRED expected values: 06by7 => 82 concepts (40 used for prediction) PRED predicted values (max 10 best out of 268): 06by7 (0.79 #3146, 0.73 #10987, 0.72 #3773), 064t9 (0.67 #326, 0.57 #9418, 0.57 #3137), 0xhtw (0.67 #1267, 0.54 #1891, 0.46 #3768), 0dl5d (0.56 #1270, 0.29 #2831, 0.26 #4714), 02yv6b (0.50 #1349, 0.40 #100, 0.27 #2910), 06j6l (0.50 #361, 0.35 #1610, 0.35 #4114), 016clz (0.48 #3755, 0.45 #6265, 0.40 #5326), 01lyv (0.44 #5984, 0.31 #3159, 0.21 #4101), 03lty (0.40 #30, 0.33 #8490, 0.33 #1279), 07sbbz2 (0.40 #8, 0.19 #2193, 0.17 #2505) >> Best rule #3146 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 0gt_k; 02qlg7s; 028qdb; 01ldw4; 03mszl; 01m7pwq; >> query: (?x8429, 06by7) <- artists(?x2809, ?x8429), award_winner(?x2139, ?x8429), ?x2139 = 01by1l, artists(?x2809, ?x4642), ?x4642 = 0394y >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lf293 artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 40.000 0.786 http://example.org/music/genre/artists #2030-017l4 PRED entity: 017l4 PRED relation: artists! PRED expected values: 064t9 => 160 concepts (82 used for prediction) PRED predicted values (max 10 best out of 257): 064t9 (0.87 #22462, 0.70 #2509, 0.69 #2196), 06by7 (0.65 #19665, 0.62 #2205, 0.60 #2518), 0gywn (0.65 #6608, 0.45 #2556, 0.33 #683), 017_qw (0.56 #2874, 0.55 #3185, 0.54 #3496), 06j6l (0.53 #6598, 0.45 #2546, 0.33 #673), 016clz (0.50 #1251, 0.29 #4057, 0.29 #4370), 02x8m (0.45 #6567, 0.43 #953, 0.42 #2515), 0ggx5q (0.38 #2263, 0.35 #2576, 0.33 #392), 05bt6j (0.38 #2228, 0.31 #1915, 0.30 #2541), 0xhtw (0.38 #1264, 0.21 #10616, 0.17 #22151) >> Best rule #22462 for best value: >> intensional similarity = 5 >> extensional distance = 404 >> proper extension: 01pfr3; 0m19t; 07c0j; 01v0sx2; 03t9sp; 01fl3; 05k79; 03fbc; 016fmf; 0249kn; ... >> query: (?x7799, 064t9) <- artists(?x12594, ?x7799), artists(?x5792, ?x7799), parent_genre(?x6833, ?x12594), artists(?x5792, ?x649), ?x649 = 0c7ct >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017l4 artists! 064t9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 82.000 0.867 http://example.org/music/genre/artists #2029-0gyy53 PRED entity: 0gyy53 PRED relation: executive_produced_by PRED expected values: 02z6l5f => 112 concepts (79 used for prediction) PRED predicted values (max 10 best out of 149): 02z6l5f (0.42 #1374, 0.08 #1122, 0.06 #4141), 02z2xdf (0.42 #1414, 0.07 #2673, 0.06 #3930), 01qg7c (0.25 #212, 0.01 #2979, 0.01 #3482), 03c9pqt (0.20 #748, 0.20 #497, 0.08 #1251), 02q42j_ (0.20 #638, 0.04 #2149, 0.04 #9941), 0b13g7 (0.20 #587, 0.04 #2098, 0.03 #9638), 01my_c (0.20 #658, 0.02 #2421), 06q8hf (0.19 #2933, 0.19 #2179, 0.17 #3436), 0h5f5n (0.19 #7794, 0.16 #2013, 0.15 #2265), 05hj_k (0.17 #2110, 0.17 #2864, 0.15 #9650) >> Best rule #1374 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 09p35z; 0dgst_d; 02q5g1z; 0gydcp7; 05c5z8j; 0bh8x1y; 09jcj6; 0dgq_kn; 089j8p; 0bs8s1p; ... >> query: (?x2932, 02z6l5f) <- executive_produced_by(?x2932, ?x163), production_companies(?x2932, ?x9518), film(?x3705, ?x2932), ?x9518 = 0283xx2 >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gyy53 executive_produced_by 02z6l5f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 79.000 0.421 http://example.org/film/film/executive_produced_by #2028-054ks3 PRED entity: 054ks3 PRED relation: award_winner PRED expected values: 012x4t => 49 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1767): 016szr (0.60 #5954, 0.43 #10821, 0.41 #12173), 01vttb9 (0.41 #12173, 0.40 #6499, 0.38 #7305), 01x6v6 (0.41 #12173, 0.40 #6346, 0.38 #7305), 01cbt3 (0.41 #12173, 0.40 #6040, 0.38 #7305), 02cyfz (0.41 #12173, 0.40 #5317, 0.38 #7305), 0dl567 (0.41 #12173, 0.38 #7305, 0.36 #12172), 01vsgrn (0.41 #12173, 0.38 #7305, 0.36 #12172), 05dbf (0.41 #12173, 0.38 #7305, 0.36 #12172), 02cx90 (0.41 #12173, 0.38 #7305, 0.36 #12172), 0146pg (0.41 #12173, 0.38 #7305, 0.36 #12172) >> Best rule #5954 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0gqz2; 025m8l; 025m98; >> query: (?x2585, 016szr) <- award(?x1231, ?x2585), nominated_for(?x2585, ?x83), ?x1231 = 01vrz41, ceremony(?x2585, ?x944) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #330 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 03qbh5; *> query: (?x2585, 012x4t) <- award(?x3997, ?x2585), award(?x2584, ?x2585), award(?x2275, ?x2585), participant(?x262, ?x2275), ?x3997 = 0gbwp, ?x2584 = 02b25y *> conf = 0.33 ranks of expected_values: 96 EVAL 054ks3 award_winner 012x4t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 49.000 24.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #2027-0f2wj PRED entity: 0f2wj PRED relation: place_of_death! PRED expected values: 076lxv 01pbs9w 05f2jk 0l9k1 04cw0n4 => 109 concepts (92 used for prediction) PRED predicted values (max 10 best out of 755): 019fz (0.25 #646, 0.02 #7880, 0.02 #9327), 01jqr_5 (0.25 #90, 0.02 #7324, 0.02 #8771), 083p7 (0.25 #32, 0.01 #22465, 0.01 #23188), 0579tg2 (0.08 #1376, 0.05 #16639, 0.04 #2822), 058vfp4 (0.08 #1216, 0.05 #16639, 0.04 #2662), 057bc6m (0.08 #1123, 0.05 #16639, 0.04 #2569), 01l3mk3 (0.08 #1085, 0.05 #16639, 0.04 #2531), 01vttb9 (0.08 #1069, 0.05 #16639, 0.04 #2515), 0fmqp6 (0.08 #1028, 0.05 #16639, 0.04 #2474), 043gj (0.08 #916, 0.05 #16639, 0.04 #2362) >> Best rule #646 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0dclg; 019fh; >> query: (?x682, 019fz) <- location(?x11008, ?x682), location_of_ceremony(?x566, ?x682), ?x11008 = 01507p >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #16639 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 64 *> proper extension: 0r540; *> query: (?x682, ?x788) <- place_of_death(?x9467, ?x682), place_of_death(?x4785, ?x682), award_nominee(?x4785, ?x788), nationality(?x9467, ?x94) *> conf = 0.05 ranks of expected_values: 250 EVAL 0f2wj place_of_death! 04cw0n4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 92.000 0.250 http://example.org/people/deceased_person/place_of_death EVAL 0f2wj place_of_death! 0l9k1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 92.000 0.250 http://example.org/people/deceased_person/place_of_death EVAL 0f2wj place_of_death! 05f2jk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 92.000 0.250 http://example.org/people/deceased_person/place_of_death EVAL 0f2wj place_of_death! 01pbs9w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 92.000 0.250 http://example.org/people/deceased_person/place_of_death EVAL 0f2wj place_of_death! 076lxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 109.000 92.000 0.250 http://example.org/people/deceased_person/place_of_death #2026-0bs1yy PRED entity: 0bs1yy PRED relation: student! PRED expected values: 065y4w7 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 100): 017z88 (0.33 #82, 0.25 #609, 0.08 #5353), 065y4w7 (0.31 #1068, 0.16 #1595, 0.15 #2650), 07vjm (0.25 #755), 0bwfn (0.11 #1856, 0.08 #2383, 0.08 #12397), 09f2j (0.08 #1213, 0.07 #3849, 0.05 #1740), 07w0v (0.08 #1074, 0.05 #1601, 0.04 #2128), 09kvv (0.08 #1095, 0.05 #1622, 0.04 #2149), 01w3v (0.08 #1069, 0.05 #1596, 0.04 #2123), 021w0_ (0.08 #1378, 0.04 #2432, 0.04 #2960), 06kknt (0.08 #3630, 0.03 #5211, 0.02 #4157) >> Best rule #82 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 048lv; >> query: (?x3042, 017z88) <- profession(?x3042, ?x524), award_winner(?x394, ?x3042), ?x394 = 016fyc, ?x524 = 02jknp >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1068 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 03q8ch; 08h79x; 027rfxc; 0bn3jg; *> query: (?x3042, 065y4w7) <- gender(?x3042, ?x231), place_of_birth(?x3042, ?x6960), edited_by(?x394, ?x3042) *> conf = 0.31 ranks of expected_values: 2 EVAL 0bs1yy student! 065y4w7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 93.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #2025-03s7h PRED entity: 03s7h PRED relation: company! PRED expected values: 0krdk 0142rn => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 27): 0krdk (0.80 #1018, 0.77 #982, 0.77 #513), 09d6p2 (0.51 #774, 0.49 #810, 0.47 #412), 0142rn (0.25 #91, 0.18 #528, 0.18 #491), 01rk91 (0.15 #328, 0.14 #3364, 0.12 #219), 014l7h (0.14 #3364, 0.09 #3925, 0.08 #1759), 02h53vq (0.14 #3364, 0.07 #4807, 0.06 #676), 06hpx2 (0.14 #3364, 0.07 #4807, 0.05 #931), 033smt (0.14 #3364, 0.07 #4807, 0.05 #1581), 02k13d (0.14 #3364, 0.05 #1640, 0.05 #842), 02zdwq (0.14 #3364, 0.05 #1032, 0.03 #1358) >> Best rule #1018 for best value: >> intensional similarity = 6 >> extensional distance = 42 >> proper extension: 02zs4; 0gsg7; 0hpt3; 09d5h; 0gvbw; 09b3v; 03sc8; 01s73z; 01yfp7; 061v5m; ... >> query: (?x11636, 0krdk) <- company(?x4792, ?x11636), company(?x346, ?x11636), ?x346 = 060c4, company(?x4792, ?x5072), ?x5072 = 045c7b, currency(?x11636, ?x170) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 03s7h company! 0142rn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 148.000 148.000 0.795 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 03s7h company! 0krdk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.795 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #2024-0cv72h PRED entity: 0cv72h PRED relation: team PRED expected values: 0wsr => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 354): 0jmk7 (0.22 #307, 0.14 #662, 0.11 #2082), 05g3b (0.22 #13, 0.06 #1788, 0.04 #3563), 0cqt41 (0.17 #1806, 0.14 #386, 0.12 #1096), 0jm2v (0.14 #384, 0.11 #1804, 0.11 #29), 0bwjj (0.14 #584, 0.09 #2004, 0.07 #2714), 085v7 (0.12 #2582, 0.09 #1162, 0.09 #2227), 0jmbv (0.11 #109, 0.09 #464, 0.06 #1884), 01lpx8 (0.11 #204, 0.09 #1979, 0.07 #3754), 06rpd (0.11 #214, 0.09 #1989, 0.06 #3764), 01c_d (0.11 #203, 0.09 #1978, 0.05 #558) >> Best rule #307 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 019y64; 03l295; 01xyt7; 01gct2; 095nx; >> query: (?x7064, 0jmk7) <- team(?x7064, ?x3658), type_of_union(?x7064, ?x566), award_winner(?x10746, ?x7064) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #486 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 20 *> proper extension: 040j2_; 02lm0t; *> query: (?x7064, 0wsr) <- location(?x7064, ?x5090), team(?x7064, ?x3658), people(?x7063, ?x7064) *> conf = 0.05 ranks of expected_values: 89 EVAL 0cv72h team 0wsr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 95.000 95.000 0.222 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #2023-01npw8 PRED entity: 01npw8 PRED relation: company! PRED expected values: 09lq2c => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 35): 0dq3c (0.58 #658, 0.50 #453, 0.50 #412), 01yc02 (0.50 #89, 0.47 #294, 0.40 #212), 014l7h (0.37 #1172, 0.27 #1049, 0.19 #2917), 0142rn (0.24 #739, 0.20 #4358, 0.19 #2917), 02211by (0.23 #659, 0.20 #988, 0.19 #1111), 021q1c (0.19 #2917, 0.18 #337, 0.17 #378), 04192r (0.19 #2917, 0.16 #692, 0.16 #2340), 02y6fz (0.19 #2917, 0.16 #2340, 0.15 #3412), 05k17c (0.19 #2917, 0.16 #2340, 0.15 #3412), 09lq2c (0.19 #2917, 0.16 #2340, 0.15 #3412) >> Best rule #658 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 03d6fyn; >> query: (?x12471, 0dq3c) <- industry(?x12471, ?x12380), citytown(?x12471, ?x3026), list(?x12471, ?x7472), currency(?x12471, ?x170) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #2917 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 105 *> proper extension: 0jbk9; 049n7; 05g76; 01r3kd; 01gb54; 01s0l0; 07y0n; 0797c7; 01jygk; *> query: (?x12471, ?x8314) <- company(?x5161, ?x12471), company(?x5161, ?x14343), company(?x5161, ?x11344), company(?x5161, ?x6016), company(?x8314, ?x14343), ?x6016 = 01zpmq, ?x11344 = 02p10m *> conf = 0.19 ranks of expected_values: 10 EVAL 01npw8 company! 09lq2c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 169.000 169.000 0.581 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #2022-0djywgn PRED entity: 0djywgn PRED relation: award_nominee! PRED expected values: 06mmb => 70 concepts (37 used for prediction) PRED predicted values (max 10 best out of 912): 06mmb (0.81 #78807, 0.81 #83446, 0.81 #78806), 05tk7y (0.81 #78807, 0.81 #78806, 0.81 #85764), 0djywgn (0.62 #1860, 0.55 #4177, 0.24 #76487), 04t2l2 (0.29 #6988, 0.03 #57980, 0.02 #62617), 09wj5 (0.24 #76487, 0.16 #78808, 0.16 #81126), 0jfx1 (0.24 #76487, 0.16 #78808, 0.16 #81126), 02wgln (0.24 #76487, 0.16 #78808, 0.16 #81126), 0dvld (0.24 #76487, 0.16 #78808, 0.16 #81126), 03x400 (0.24 #76487, 0.16 #78808, 0.16 #81126), 0hskw (0.24 #76487, 0.16 #78808, 0.16 #81126) >> Best rule #78807 for best value: >> intensional similarity = 2 >> extensional distance = 1678 >> proper extension: 02v49c; 02q6cv4; >> query: (?x8566, ?x1222) <- award_nominee(?x8566, ?x1222), film(?x1222, ?x695) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0djywgn award_nominee! 06mmb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 37.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #2021-02xlf PRED entity: 02xlf PRED relation: major_field_of_study! PRED expected values: 07wlf => 94 concepts (75 used for prediction) PRED predicted values (max 10 best out of 599): 08815 (0.73 #14211, 0.69 #14804, 0.46 #23709), 03ksy (0.69 #14922, 0.67 #14329, 0.63 #21925), 06pwq (0.69 #14815, 0.67 #14222, 0.61 #23720), 07szy (0.67 #14253, 0.62 #14846, 0.50 #22564), 01w3v (0.63 #21925, 0.61 #17175, 0.60 #14225), 02zd460 (0.63 #21925, 0.61 #17175, 0.59 #20735), 07tgn (0.63 #21925, 0.61 #17175, 0.59 #20735), 01mpwj (0.63 #21925, 0.61 #17175, 0.59 #20735), 07vhb (0.63 #21925, 0.61 #17175, 0.59 #20735), 0gjv_ (0.63 #21925, 0.61 #17175, 0.59 #20735) >> Best rule #14211 for best value: >> intensional similarity = 8 >> extensional distance = 13 >> proper extension: 02h40lc; 02lp1; 06ms6; 04x_3; 0fdys; 037mh8; 01zc2w; >> query: (?x6647, 08815) <- major_field_of_study(?x6925, ?x6647), major_field_of_study(?x3424, ?x6647), major_field_of_study(?x3386, ?x6647), ?x3424 = 01w5m, ?x6925 = 01bm_, institution(?x3386, ?x99), major_field_of_study(?x3386, ?x10705), ?x10705 = 03r8gp >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #14291 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 13 *> proper extension: 02h40lc; 02lp1; 06ms6; 04x_3; 0fdys; 037mh8; 01zc2w; *> query: (?x6647, 07wlf) <- major_field_of_study(?x6925, ?x6647), major_field_of_study(?x3424, ?x6647), major_field_of_study(?x3386, ?x6647), ?x3424 = 01w5m, ?x6925 = 01bm_, institution(?x3386, ?x99), major_field_of_study(?x3386, ?x10705), ?x10705 = 03r8gp *> conf = 0.47 ranks of expected_values: 33 EVAL 02xlf major_field_of_study! 07wlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 94.000 75.000 0.733 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #2020-035w2k PRED entity: 035w2k PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.87 #62, 0.84 #42, 0.83 #112), 02nxhr (0.06 #68, 0.05 #33, 0.04 #58), 07c52 (0.05 #8, 0.04 #13, 0.04 #18), 0735l (0.05 #31), 07z4p (0.03 #101, 0.03 #261, 0.02 #276) >> Best rule #62 for best value: >> intensional similarity = 5 >> extensional distance = 242 >> proper extension: 04lqvlr; 0h95zbp; 03_wm6; >> query: (?x5008, 029j_) <- film_crew_role(?x5008, ?x2154), film_crew_role(?x5008, ?x137), ?x137 = 09zzb8, ?x2154 = 01vx2h, language(?x5008, ?x254) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035w2k film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #2019-06_x996 PRED entity: 06_x996 PRED relation: nominated_for! PRED expected values: 02n9nmz => 84 concepts (73 used for prediction) PRED predicted values (max 10 best out of 214): 027c95y (0.68 #5479, 0.67 #5039, 0.67 #6357), 09cm54 (0.68 #5479, 0.67 #5039, 0.67 #6357), 0gqwc (0.67 #3996, 0.20 #1367, 0.20 #2463), 0k611 (0.57 #2473, 0.47 #2692, 0.35 #4882), 02pqp12 (0.55 #2680, 0.47 #2461, 0.37 #3556), 09sb52 (0.43 #3535, 0.27 #2440, 0.18 #1125), 0gr0m (0.40 #2462, 0.30 #2681, 0.29 #928), 0gq_v (0.36 #4179, 0.32 #2427, 0.31 #5935), 0gr51 (0.36 #2478, 0.32 #2039, 0.29 #1382), 0gqy2 (0.36 #2515, 0.31 #1419, 0.30 #4267) >> Best rule #5479 for best value: >> intensional similarity = 4 >> extensional distance = 415 >> proper extension: 04q00lw; 05m_jsg; 0k7tq; >> query: (?x4086, ?x384) <- award(?x4086, ?x384), currency(?x4086, ?x170), film_crew_role(?x4086, ?x137), film(?x286, ?x4086) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #2679 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 130 *> proper extension: 0gh8zks; *> query: (?x4086, 02n9nmz) <- nominated_for(?x2880, ?x4086), nominated_for(?x2375, ?x4086), award_winner(?x2880, ?x2185), award(?x156, ?x2880), ?x2375 = 04kxsb *> conf = 0.30 ranks of expected_values: 16 EVAL 06_x996 nominated_for! 02n9nmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 84.000 73.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #2018-056wb PRED entity: 056wb PRED relation: religion PRED expected values: 0kpl => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 22): 0kpl (0.20 #910, 0.16 #505, 0.15 #2845), 0c8wxp (0.17 #141, 0.16 #276, 0.14 #366), 03_gx (0.17 #149, 0.14 #509, 0.13 #959), 0kq2 (0.13 #423, 0.09 #513, 0.08 #648), 0n2g (0.10 #418, 0.08 #643, 0.04 #1453), 01lp8 (0.08 #721, 0.03 #361, 0.03 #316), 092bf5 (0.05 #421, 0.04 #916, 0.03 #646), 019cr (0.05 #461, 0.04 #146, 0.01 #776), 03j6c (0.04 #1056, 0.03 #2766, 0.03 #2811), 04pk9 (0.04 #155, 0.03 #290, 0.02 #2360) >> Best rule #910 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 01d494; >> query: (?x6045, 0kpl) <- people(?x268, ?x6045), influenced_by(?x6045, ?x3969), place_of_birth(?x6045, ?x1860) >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 056wb religion 0kpl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.200 http://example.org/people/person/religion #2017-04z257 PRED entity: 04z257 PRED relation: film! PRED expected values: 03xq0f => 110 concepts (100 used for prediction) PRED predicted values (max 10 best out of 167): 03xq0f (0.85 #1072, 0.85 #1001, 0.82 #1143), 04f525m (0.53 #4212, 0.50 #2424, 0.50 #2638), 0g1rw (0.53 #4212, 0.50 #2424, 0.50 #2638), 061dn_ (0.53 #4212, 0.50 #2424, 0.50 #2638), 05qd_ (0.34 #791, 0.32 #578, 0.30 #649), 086k8 (0.27 #4784, 0.25 #998, 0.22 #2639), 017s11 (0.25 #2, 0.20 #4785, 0.20 #73), 020h2v (0.22 #398, 0.09 #896, 0.09 #541), 016tw3 (0.22 #4149, 0.19 #2648, 0.19 #5222), 016tt2 (0.20 #4786, 0.18 #431, 0.17 #4357) >> Best rule #1072 for best value: >> intensional similarity = 5 >> extensional distance = 87 >> proper extension: 0170z3; 04jkpgv; 0f4_l; 07x4qr; 0gz6b6g; 06w839_; 09gkx35; 05c9zr; 062zjtt; 01l2b3; ... >> query: (?x3612, 03xq0f) <- film_release_region(?x3612, ?x94), film_crew_role(?x3612, ?x137), genre(?x3612, ?x258), region(?x3612, ?x512), ?x512 = 07ssc >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04z257 film! 03xq0f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 100.000 0.854 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #2016-02tkzn PRED entity: 02tkzn PRED relation: nationality PRED expected values: 09c7w0 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.89 #9115, 0.86 #201, 0.80 #601), 01_d4 (0.25 #5810), 07ssc (0.14 #115, 0.11 #2219, 0.10 #1818), 0d060g (0.13 #808, 0.11 #1008, 0.08 #7), 05kkh (0.11 #6311), 02jx1 (0.11 #6143, 0.10 #9247, 0.10 #4840), 0345h (0.08 #31, 0.07 #1032, 0.05 #832), 03rk0 (0.08 #3151, 0.08 #5655, 0.08 #5856), 03_3d (0.06 #2110, 0.03 #5215, 0.02 #4513), 0h7x (0.06 #836, 0.06 #1036, 0.02 #1838) >> Best rule #9115 for best value: >> intensional similarity = 2 >> extensional distance = 1420 >> proper extension: 07m69t; >> query: (?x5559, 09c7w0) <- place_of_birth(?x5559, ?x2017), source(?x2017, ?x958) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02tkzn nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.891 http://example.org/people/person/nationality #2015-02q8ms8 PRED entity: 02q8ms8 PRED relation: film_release_region PRED expected values: 0d060g => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 225): 0f8l9c (0.45 #1279, 0.39 #565, 0.39 #1815), 07ssc (0.44 #1271, 0.40 #557, 0.40 #1449), 0d0vqn (0.44 #1259, 0.40 #1437, 0.39 #1795), 0345h (0.43 #1294, 0.38 #1830, 0.38 #1472), 03rjj (0.42 #1255, 0.38 #1433, 0.37 #1791), 06mkj (0.42 #1323, 0.38 #1501, 0.37 #1859), 059j2 (0.41 #1292, 0.39 #578, 0.38 #1470), 05r4w (0.41 #1250, 0.38 #1428, 0.37 #1786), 0d060g (0.39 #1258, 0.35 #1436, 0.34 #1794), 0chghy (0.39 #1264, 0.33 #1800, 0.33 #1442) >> Best rule #1279 for best value: >> intensional similarity = 4 >> extensional distance = 173 >> proper extension: 0gh8zks; >> query: (?x6229, 0f8l9c) <- country(?x6229, ?x94), film_festivals(?x6229, ?x9189), language(?x6229, ?x254), film_release_region(?x54, ?x94) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1258 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 173 *> proper extension: 0gh8zks; *> query: (?x6229, 0d060g) <- country(?x6229, ?x94), film_festivals(?x6229, ?x9189), language(?x6229, ?x254), film_release_region(?x54, ?x94) *> conf = 0.39 ranks of expected_values: 9 EVAL 02q8ms8 film_release_region 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 72.000 72.000 0.451 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #2014-0dxtw PRED entity: 0dxtw PRED relation: film_crew_role! PRED expected values: 0d90m 083shs 0gx1bnj 01h7bb 03s6l2 011yph 08gsvw 0qm8b 029zqn 091z_p 02725hs 026p4q7 05zy2cy 0gfsq9 047p7fr 03mh_tp 0crc2cp 0ds2n 04vh83 02dpl9 01vw8k 0kv9d3 02qhlwd 033f8n 01d259 02h22 0cq86w 051ys82 04pk1f 05r3qc 0jqd3 0404j37 01_0f7 0642ykh 043tvp3 04lhc4 0cp0790 01v1ln 0cf8qb 02_fz3 0gvvm6l 0cmf0m0 0ds5_72 04h4c9 063_j5 05nyqk 0gwgn1k 07tlfx 026hh0m 0d87hc 0dnkmq 04jn6y7 => 29 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1332): 02w9k1c (0.75 #11532, 0.71 #10682, 0.62 #13230), 048scx (0.75 #11117, 0.71 #10267, 0.60 #7718), 08zrbl (0.75 #11704, 0.71 #10854, 0.60 #8305), 07g_0c (0.75 #11137, 0.71 #10287, 0.60 #7738), 047qxs (0.75 #11206, 0.71 #10356, 0.60 #7807), 07yk1xz (0.75 #12918, 0.67 #8669, 0.64 #13768), 05zpghd (0.75 #13208, 0.67 #9811, 0.62 #12359), 027m5wv (0.75 #13256, 0.67 #9859, 0.60 #6463), 0d99k_ (0.75 #13569, 0.62 #12720, 0.60 #8472), 0b1y_2 (0.75 #12989, 0.62 #12140, 0.60 #7892) >> Best rule #11532 for best value: >> intensional similarity = 13 >> extensional distance = 6 >> proper extension: 02vs3x5; >> query: (?x2095, 02w9k1c) <- film_crew_role(?x6556, ?x2095), film_crew_role(?x6306, ?x2095), film_crew_role(?x936, ?x2095), film_crew_role(?x835, ?x2095), film_crew_role(?x485, ?x2095), ?x835 = 0164qt, nominated_for(?x507, ?x485), ?x507 = 02g3v6, award_winner(?x485, ?x1532), nominated_for(?x521, ?x6306), language(?x936, ?x90), genre(?x485, ?x53), film_release_region(?x6556, ?x87) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #10376 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 5 *> proper extension: 04pyp5; *> query: (?x2095, 02725hs) <- film_crew_role(?x9599, ?x2095), film_crew_role(?x6427, ?x2095), film_crew_role(?x5927, ?x2095), film_crew_role(?x3157, ?x2095), film_crew_role(?x835, ?x2095), film_crew_role(?x485, ?x2095), ?x835 = 0164qt, nominated_for(?x507, ?x485), ?x507 = 02g3v6, award_winner(?x485, ?x1532), ?x9599 = 07l450, genre(?x5927, ?x53), film(?x6618, ?x6427), nominated_for(?x294, ?x3157) *> conf = 0.71 ranks of expected_values: 16, 28, 30, 37, 41, 56, 66, 79, 80, 85, 88, 97, 99, 107, 110, 143, 157, 159, 189, 194, 197, 226, 234, 256, 257, 277, 286, 307, 337, 382, 383, 386, 420, 424, 435, 436, 446, 462, 471, 506, 555, 561, 566, 611, 674, 692, 693, 694, 724, 832, 847, 890 EVAL 0dxtw film_crew_role! 04jn6y7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0dnkmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0d87hc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 026hh0m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 07tlfx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0gwgn1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 05nyqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 063_j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 04h4c9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0ds5_72 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0cmf0m0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0gvvm6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 02_fz3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0cf8qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 01v1ln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0cp0790 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 04lhc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 043tvp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0642ykh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 01_0f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0404j37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0jqd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 05r3qc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 04pk1f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 051ys82 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0cq86w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 02h22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 01d259 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 033f8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 02qhlwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0kv9d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 01vw8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 02dpl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 04vh83 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0ds2n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0crc2cp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 03mh_tp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 047p7fr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0gfsq9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 05zy2cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 026p4q7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 02725hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 091z_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 029zqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0qm8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 08gsvw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 011yph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 03s6l2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 01h7bb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0gx1bnj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 083shs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0dxtw film_crew_role! 0d90m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 29.000 25.000 0.750 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #2013-0k4d7 PRED entity: 0k4d7 PRED relation: genre PRED expected values: 02l7c8 => 98 concepts (95 used for prediction) PRED predicted values (max 10 best out of 101): 07s9rl0 (0.69 #2890, 0.68 #2528, 0.68 #2649), 03k9fj (0.66 #854, 0.60 #3863, 0.58 #492), 05p553 (0.53 #847, 0.45 #1567, 0.44 #485), 02kdv5l (0.45 #3854, 0.44 #724, 0.39 #603), 02l7c8 (0.39 #1099, 0.30 #1219, 0.30 #979), 06n90 (0.35 #3865, 0.23 #614, 0.22 #735), 01jfsb (0.33 #4708, 0.32 #4466, 0.31 #3624), 01zhp (0.31 #919, 0.25 #1639, 0.14 #557), 04xvh5 (0.29 #154, 0.14 #514, 0.11 #876), 04xvlr (0.25 #2650, 0.21 #3011, 0.21 #2286) >> Best rule #2890 for best value: >> intensional similarity = 4 >> extensional distance = 379 >> proper extension: 01b9w3; >> query: (?x2425, 07s9rl0) <- nominated_for(?x2426, ?x2425), award(?x2426, ?x1307), ?x1307 = 0gq9h, nationality(?x2426, ?x94) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1099 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 92 *> proper extension: 021pqy; 09dv8h; 03kx49; 052_mn; *> query: (?x2425, 02l7c8) <- nominated_for(?x1934, ?x2425), country(?x2425, ?x94), genre(?x2425, ?x307), ?x307 = 04t36 *> conf = 0.39 ranks of expected_values: 5 EVAL 0k4d7 genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 98.000 95.000 0.690 http://example.org/film/film/genre #2012-0198b6 PRED entity: 0198b6 PRED relation: cinematography PRED expected values: 02404v => 95 concepts (78 used for prediction) PRED predicted values (max 10 best out of 51): 02404v (0.10 #38, 0.08 #164, 0.06 #227), 0f3zf_ (0.09 #192, 0.07 #962, 0.06 #1279), 01f7v_ (0.08 #702, 0.08 #895, 0.08 #959), 06r_by (0.06 #212, 0.05 #276, 0.05 #341), 069_0y (0.05 #37, 0.04 #675, 0.04 #868), 0854hr (0.05 #19, 0.03 #208, 0.03 #272), 06qn87 (0.05 #1245, 0.04 #671, 0.04 #864), 01d1yr (0.05 #2293, 0.04 #703, 0.04 #2292), 06t8b (0.04 #679, 0.04 #872, 0.04 #936), 06p0s1 (0.04 #696, 0.04 #889, 0.03 #1207) >> Best rule #38 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 07gp9; 01vksx; >> query: (?x3886, 02404v) <- prequel(?x6376, ?x3886), award(?x3886, ?x10597), film_release_region(?x3886, ?x252), ?x252 = 03_3d >> conf = 0.10 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0198b6 cinematography 02404v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 78.000 0.100 http://example.org/film/film/cinematography #2011-01jpqb PRED entity: 01jpqb PRED relation: school! PRED expected values: 0jmfv => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 89): 0jmhr (0.20 #261, 0.11 #2049, 0.05 #1418), 0jm5b (0.20 #262, 0.11 #2049, 0.04 #1597), 07147 (0.19 #421, 0.12 #1400, 0.10 #1489), 05m_8 (0.18 #1962, 0.18 #1517, 0.17 #1784), 07l8x (0.15 #420, 0.13 #1399, 0.11 #1844), 07l4z (0.15 #423, 0.12 #1402, 0.12 #1847), 0512p (0.15 #371, 0.11 #1350, 0.10 #1795), 04wmvz (0.15 #432, 0.11 #1055, 0.10 #1500), 02d02 (0.15 #422, 0.11 #1579, 0.10 #1846), 01ypc (0.15 #358, 0.09 #1337, 0.09 #1782) >> Best rule #261 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0797c7; >> query: (?x9745, 0jmhr) <- category(?x9745, ?x134), state_province_region(?x9745, ?x1138), ?x134 = 08mbj5d, ?x1138 = 059_c >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2049 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 102 *> proper extension: 0frm7n; *> query: (?x9745, ?x799) <- school(?x6089, ?x9745), school(?x2569, ?x9745), draft(?x799, ?x2569), teams(?x6088, ?x6089) *> conf = 0.11 ranks of expected_values: 39 EVAL 01jpqb school! 0jmfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 174.000 174.000 0.200 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #2010-06crk PRED entity: 06crk PRED relation: award_winner! PRED expected values: 0dt39 => 176 concepts (173 used for prediction) PRED predicted values (max 10 best out of 305): 02pqp12 (0.25 #503, 0.13 #2231, 0.03 #5255), 020qjg (0.21 #2114, 0.12 #3410, 0.06 #6002), 0m57f (0.20 #427, 0.12 #1291, 0.07 #1723), 058vy5 (0.20 #355, 0.07 #1651, 0.04 #9427), 06zrp44 (0.14 #2142, 0.12 #3438, 0.07 #5598), 05fmy (0.14 #2152, 0.06 #3016, 0.04 #8200), 03x3wf (0.12 #497, 0.12 #3521, 0.07 #2225), 03nqnk3 (0.12 #2727, 0.10 #8775, 0.06 #3591), 0gs9p (0.12 #512, 0.09 #3968, 0.08 #8720), 01l29r (0.12 #598, 0.07 #1462, 0.07 #2326) >> Best rule #503 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0hskw; 063tn; >> query: (?x6342, 02pqp12) <- student(?x6056, ?x6342), people(?x5590, ?x6342), profession(?x6342, ?x3802), ?x5590 = 0g6ff >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3394 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 15 *> proper extension: 0prfz; *> query: (?x6342, 0dt39) <- student(?x6056, ?x6342), location(?x6342, ?x94), student(?x3437, ?x6342), nationality(?x51, ?x94) *> conf = 0.12 ranks of expected_values: 26 EVAL 06crk award_winner! 0dt39 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 176.000 173.000 0.250 http://example.org/award/award_category/winners./award/award_honor/award_winner #2009-07l5z PRED entity: 07l5z PRED relation: time_zones PRED expected values: 02hcv8 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.75 #144, 0.46 #147, 0.45 #199), 02fqwt (0.29 #40, 0.25 #53, 0.20 #210), 02lcqs (0.27 #201, 0.25 #5, 0.24 #31), 02hczc (0.16 #1016, 0.14 #41, 0.12 #54), 02lcrv (0.16 #1016, 0.01 #98, 0.01 #111), 02llzg (0.09 #369, 0.09 #226, 0.08 #291), 03bdv (0.06 #163, 0.05 #306, 0.05 #410), 03plfd (0.05 #232, 0.05 #375, 0.03 #518), 0gsrz4 (0.02 #516), 042g7t (0.02 #63, 0.02 #519, 0.02 #102) >> Best rule #144 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 0pbhz; 0jq27; >> query: (?x11058, ?x2674) <- administrative_division(?x11058, ?x8178), contains(?x177, ?x8178), time_zones(?x8178, ?x2674) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07l5z time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.749 http://example.org/location/location/time_zones #2008-069d68 PRED entity: 069d68 PRED relation: sibling! PRED expected values: 069d71 => 98 concepts (33 used for prediction) PRED predicted values (max 10 best out of 2): 013rds (0.11 #110, 0.09 #225, 0.04 #571), 016cff (0.07 #303) >> Best rule #110 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01s7ns; 03h8_g; 01mskc3; >> query: (?x8395, 013rds) <- location(?x8395, ?x3501), profession(?x8395, ?x1581), gender(?x8395, ?x231), ?x231 = 05zppz, ?x3501 = 0f2v0 >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 069d68 sibling! 069d71 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 33.000 0.111 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #2007-018w8 PRED entity: 018w8 PRED relation: athlete PRED expected values: 03l26m => 53 concepts (37 used for prediction) PRED predicted values (max 10 best out of 121): 019tzd (0.33 #35, 0.10 #1480, 0.03 #2323), 03n69x (0.25 #1354, 0.20 #1594, 0.20 #872), 02m501 (0.25 #445, 0.17 #1168, 0.10 #1529), 03m5111 (0.20 #963, 0.20 #841, 0.17 #1204), 04v68c (0.20 #962, 0.20 #840, 0.17 #1203), 02zbjhq (0.20 #958, 0.20 #836, 0.17 #1199), 0bhtzw (0.20 #955, 0.20 #833, 0.17 #1196), 0d3mlc (0.20 #954, 0.20 #832, 0.17 #1195), 06yj20 (0.20 #953, 0.20 #831, 0.17 #1194), 054kmq (0.20 #952, 0.20 #830, 0.17 #1193) >> Best rule #35 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 01sgl; >> query: (?x4833, 019tzd) <- country(?x4833, ?x1558), country(?x4833, ?x1241), olympics(?x4833, ?x867), athlete(?x4833, ?x1213), member_states(?x7695, ?x1241), ?x1558 = 01mjq, ?x867 = 0l6ny >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018w8 athlete 03l26m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 37.000 0.333 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #2006-02w670 PRED entity: 02w670 PRED relation: people! PRED expected values: 02y0js => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 34): 0gk4g (0.26 #530, 0.25 #3065, 0.23 #725), 0qcr0 (0.16 #521, 0.15 #716, 0.12 #3056), 02y0js (0.13 #197, 0.09 #3057, 0.07 #3122), 0dq9p (0.12 #3072, 0.12 #537, 0.10 #732), 02k6hp (0.08 #557, 0.07 #3092, 0.06 #752), 04p3w (0.07 #3066, 0.05 #3131, 0.04 #726), 01l2m3 (0.07 #536, 0.05 #3071, 0.04 #731), 02knxx (0.06 #3087, 0.05 #747, 0.05 #3152), 01dcqj (0.04 #467, 0.04 #337, 0.03 #532), 019dmc (0.04 #569, 0.03 #764, 0.02 #3104) >> Best rule #530 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 05728w1; 07z1_q; 012vct; 01wskg; >> query: (?x5206, 0gk4g) <- nominated_for(?x5206, ?x3755), award_nominee(?x3771, ?x5206), people(?x10900, ?x5206), place_of_birth(?x5206, ?x1860) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #197 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 0cw10; *> query: (?x5206, 02y0js) <- people(?x10900, ?x5206), ?x10900 = 08g5q7, type_of_union(?x5206, ?x566) *> conf = 0.13 ranks of expected_values: 3 EVAL 02w670 people! 02y0js CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 113.000 0.263 http://example.org/people/cause_of_death/people #2005-06qn87 PRED entity: 06qn87 PRED relation: place_of_death PRED expected values: 09nyf => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 59): 030qb3t (0.22 #3137, 0.20 #216, 0.18 #5860), 04jpl (0.20 #7, 0.05 #8953, 0.05 #10898), 0rh6k (0.20 #196, 0.02 #3117, 0.01 #3313), 0k049 (0.14 #3118, 0.12 #4869, 0.11 #3508), 0f2wj (0.12 #595, 0.12 #401, 0.08 #789), 06_kh (0.12 #588, 0.12 #394, 0.08 #782), 0r3w7 (0.12 #760, 0.08 #954, 0.07 #1148), 0d6yv (0.12 #526, 0.07 #1108, 0.03 #1692), 02_286 (0.10 #4685, 0.10 #9737, 0.09 #9348), 0k_p5 (0.06 #3008, 0.06 #2813, 0.05 #1838) >> Best rule #3137 for best value: >> intensional similarity = 4 >> extensional distance = 62 >> proper extension: 01xcqc; 01gzm2; 0j_c; 03gyh_z; 036jb; 0hqcy; 0638kv; 016z51; 045cq; 01pbs9w; ... >> query: (?x7349, 030qb3t) <- people(?x4322, ?x7349), nominated_for(?x7349, ?x2423), featured_film_locations(?x2423, ?x1264), films(?x326, ?x2423) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06qn87 place_of_death 09nyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 137.000 137.000 0.219 http://example.org/people/deceased_person/place_of_death #2004-0ggh3 PRED entity: 0ggh3 PRED relation: location! PRED expected values: 01q7cb_ 02lk95 => 147 concepts (105 used for prediction) PRED predicted values (max 10 best out of 1996): 04qzm (0.39 #65453, 0.33 #2518, 0.31 #151041), 04r1t (0.39 #65453, 0.33 #2518, 0.31 #151041), 017f4y (0.33 #4674, 0.10 #9709, 0.08 #17261), 02lt8 (0.25 #796, 0.10 #33521, 0.06 #20935), 09fb5 (0.25 #51, 0.09 #60467, 0.07 #68021), 02t__3 (0.25 #1223, 0.07 #69193, 0.06 #21362), 012v1t (0.25 #1217, 0.06 #21356, 0.06 #54081), 03l26m (0.25 #2291, 0.06 #22430, 0.06 #55155), 09yrh (0.25 #913, 0.06 #21052, 0.06 #53777), 01wp8w7 (0.25 #260, 0.06 #20399, 0.06 #68230) >> Best rule #65453 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 09b8m; >> query: (?x7930, ?x1929) <- contains(?x2623, ?x7930), origin(?x1929, ?x7930), time_zones(?x7930, ?x2674), vacationer(?x2623, ?x5625) >> conf = 0.39 => this is the best rule for 2 predicted values *> Best rule #68128 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 68 *> proper extension: 015zxh; 05l5n; 02h6_6p; 01qh7; 0f25y; 0s987; 0f8j6; *> query: (?x7930, 01q7cb_) <- contains(?x94, ?x7930), location(?x12439, ?x7930), story_by(?x721, ?x12439), location_of_ceremony(?x566, ?x7930) *> conf = 0.01 ranks of expected_values: 1861, 1920 EVAL 0ggh3 location! 02lk95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 147.000 105.000 0.387 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0ggh3 location! 01q7cb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 147.000 105.000 0.387 http://example.org/people/person/places_lived./people/place_lived/location #2003-031296 PRED entity: 031296 PRED relation: award_winner! PRED expected values: 096lf_ => 115 concepts (59 used for prediction) PRED predicted values (max 10 best out of 531): 039g82 (0.81 #67615, 0.81 #86947, 0.81 #86946), 096lf_ (0.81 #67615, 0.81 #86947, 0.81 #86946), 043js (0.52 #77284, 0.49 #93387, 0.49 #80506), 084m3 (0.52 #77284, 0.49 #93387, 0.49 #80506), 01x6jd (0.49 #80506, 0.48 #77283, 0.48 #80505), 0blbxk (0.49 #80506, 0.48 #77283, 0.48 #80505), 030hbp (0.49 #80506, 0.38 #82118, 0.07 #8050), 04twmk (0.49 #80506, 0.38 #82118, 0.07 #8050), 01tj34 (0.49 #80506, 0.38 #82118, 0.07 #8050), 049dyj (0.48 #77283, 0.48 #80505, 0.44 #93386) >> Best rule #67615 for best value: >> intensional similarity = 2 >> extensional distance = 985 >> proper extension: 08849; >> query: (?x3709, ?x1784) <- award_winner(?x3709, ?x1784), type_of_union(?x3709, ?x566) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 031296 award_winner! 096lf_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 59.000 0.815 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #2002-014g91 PRED entity: 014g91 PRED relation: artists! PRED expected values: 03_d0 01fbr2 => 164 concepts (101 used for prediction) PRED predicted values (max 10 best out of 250): 03_d0 (0.64 #639, 0.36 #952, 0.26 #3776), 06by7 (0.47 #5043, 0.44 #5357, 0.43 #6612), 064t9 (0.47 #7858, 0.45 #8171, 0.44 #9110), 01fh36 (0.36 #717, 0.21 #403, 0.14 #4169), 06j6l (0.31 #4444, 0.27 #3187, 0.27 #9147), 016clz (0.31 #4084, 0.24 #11605, 0.24 #5025), 025sc50 (0.26 #9149, 0.25 #7897, 0.23 #7584), 0glt670 (0.24 #9139, 0.23 #7887, 0.22 #7574), 017_qw (0.23 #4773, 0.20 #10414, 0.18 #1006), 05bt6j (0.23 #1613, 0.22 #3496, 0.22 #7890) >> Best rule #639 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 015882; 01wgcvn; 0d9xq; 01vsy9_; >> query: (?x10879, 03_d0) <- place_of_birth(?x10879, ?x12314), artist(?x11171, ?x10879), source(?x12314, ?x958), ?x11171 = 01xyqk >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 43 EVAL 014g91 artists! 01fbr2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 164.000 101.000 0.643 http://example.org/music/genre/artists EVAL 014g91 artists! 03_d0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 101.000 0.643 http://example.org/music/genre/artists #2001-04cw0j PRED entity: 04cw0j PRED relation: award_winner! PRED expected values: 0h_9252 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 113): 0h_9252 (0.43 #58, 0.40 #198, 0.17 #4621), 0gx1673 (0.17 #4621, 0.14 #120, 0.10 #260), 0g5b0q5 (0.17 #4621, 0.14 #20, 0.10 #160), 02pgky2 (0.17 #4621, 0.14 #90, 0.10 #230), 09q_6t (0.17 #4621, 0.10 #148, 0.03 #1268), 02ywhz (0.17 #4621, 0.10 #219, 0.02 #1339), 073hmq (0.17 #4621, 0.02 #721, 0.01 #1281), 0275n3y (0.15 #355, 0.14 #75, 0.07 #635), 09gkdln (0.15 #402, 0.10 #262, 0.04 #822), 0d__c3 (0.14 #125, 0.05 #405, 0.02 #1525) >> Best rule #58 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0fvf9q; 05qd_; 026g4l_; 03m9c8; >> query: (?x3170, 0h_9252) <- award_winner(?x163, ?x3170), award(?x3170, ?x3105), award_nominee(?x7274, ?x3170), ?x7274 = 0dbpwb >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cw0j award_winner! 0h_9252 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.429 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #2000-0bz5v2 PRED entity: 0bz5v2 PRED relation: nationality PRED expected values: 07ssc => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 25): 09c7w0 (0.85 #602, 0.84 #902, 0.82 #1003), 07ssc (0.52 #315, 0.33 #6935, 0.33 #9046), 02jx1 (0.44 #333, 0.33 #6935, 0.33 #9046), 021y1s (0.33 #6935, 0.33 #9046), 0dyjz (0.33 #6935, 0.33 #9046), 059rby (0.26 #5624), 0d060g (0.09 #107, 0.05 #6942, 0.05 #6335), 0f8l9c (0.07 #222, 0.04 #423, 0.03 #523), 0j4q1 (0.06 #401), 02_286 (0.06 #401) >> Best rule #602 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0frmb1; >> query: (?x1040, 09c7w0) <- person(?x3775, ?x1040), gender(?x1040, ?x231), student(?x2999, ?x1040) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 0xnc3; *> query: (?x1040, 07ssc) <- location(?x1040, ?x739), student(?x2999, ?x1040), ?x2999 = 07tg4 *> conf = 0.52 ranks of expected_values: 2 EVAL 0bz5v2 nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 95.000 0.850 http://example.org/people/person/nationality #1999-020y73 PRED entity: 020y73 PRED relation: genre PRED expected values: 02p0szs => 61 concepts (48 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.94 #822, 0.84 #586, 0.81 #2117), 05p553 (0.42 #1647, 0.42 #1412, 0.35 #5073), 03k9fj (0.36 #949, 0.29 #11, 0.28 #479), 0lsxr (0.33 #1535, 0.29 #8, 0.23 #4131), 06n90 (0.28 #950, 0.20 #1539, 0.13 #2836), 06cvj (0.25 #1646, 0.25 #1411, 0.18 #823), 02n4kr (0.23 #1534, 0.16 #7, 0.15 #475), 060__y (0.23 #1187, 0.22 #2131, 0.22 #1305), 04xvh5 (0.22 #266, 0.22 #149, 0.15 #383), 03g3w (0.20 #140, 0.16 #257, 0.16 #725) >> Best rule #822 for best value: >> intensional similarity = 5 >> extensional distance = 322 >> proper extension: 0fq27fp; >> query: (?x2326, 07s9rl0) <- genre(?x2326, ?x3515), genre(?x2326, ?x1403), ?x1403 = 02l7c8, genre(?x7462, ?x3515), ?x7462 = 02v570 >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #144 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 39 *> proper extension: 09q5w2; 049mql; 0c38gj; *> query: (?x2326, 02p0szs) <- genre(?x2326, ?x3515), genre(?x2326, ?x225), ?x3515 = 082gq, ?x225 = 02kdv5l, film_release_region(?x2326, ?x94) *> conf = 0.12 ranks of expected_values: 27 EVAL 020y73 genre 02p0szs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 61.000 48.000 0.935 http://example.org/film/film/genre #1998-087vnr5 PRED entity: 087vnr5 PRED relation: film_crew_role PRED expected values: 0ch6mp2 089g0h => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 32): 0ch6mp2 (0.80 #145, 0.79 #417, 0.78 #589), 089g0h (0.50 #158, 0.13 #1411, 0.13 #430), 02_n3z (0.46 #140, 0.15 #1376, 0.13 #1411), 01vx2h (0.45 #150, 0.40 #422, 0.38 #594), 01xy5l_ (0.43 #153, 0.15 #1376, 0.13 #425), 0dxtw (0.38 #421, 0.38 #149, 0.38 #593), 01pvkk (0.28 #423, 0.27 #1284, 0.27 #1182), 033smt (0.21 #165, 0.13 #2036, 0.08 #1519), 02ynfr (0.20 #427, 0.19 #702, 0.19 #633), 015h31 (0.18 #147, 0.13 #2036, 0.11 #43) >> Best rule #145 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 047svrl; 01gglm; >> query: (?x8492, 0ch6mp2) <- film_crew_role(?x8492, ?x4305), nominated_for(?x541, ?x8492), country(?x8492, ?x94), ?x4305 = 0215hd >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 087vnr5 film_crew_role 089g0h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.804 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 087vnr5 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.804 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1997-0c73g PRED entity: 0c73g PRED relation: influenced_by! PRED expected values: 0459z => 143 concepts (55 used for prediction) PRED predicted values (max 10 best out of 377): 01tz6vs (0.40 #229, 0.09 #4358, 0.07 #6939), 03f47xl (0.40 #263, 0.09 #4392, 0.06 #27119), 0zm1 (0.40 #166, 0.09 #4295, 0.05 #6876), 07h1q (0.35 #4538, 0.12 #926, 0.09 #15896), 0hqgp (0.31 #7227, 0.23 #22198, 0.23 #6710), 07dnx (0.26 #4492, 0.20 #363, 0.14 #1396), 045bg (0.26 #4165, 0.20 #36, 0.11 #15523), 02wh0 (0.20 #451, 0.17 #4580, 0.14 #1484), 0683n (0.20 #340, 0.17 #4469, 0.12 #5502), 03jht (0.20 #381, 0.17 #4510, 0.06 #15487) >> Best rule #229 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 032l1; 0dw6b; >> query: (?x13248, 01tz6vs) <- gender(?x13248, ?x231), influenced_by(?x13248, ?x10605), nationality(?x13248, ?x1264), ?x10605 = 0h336, influenced_by(?x9297, ?x13248) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #465 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 032l1; 0dw6b; *> query: (?x13248, 0459z) <- gender(?x13248, ?x231), influenced_by(?x13248, ?x10605), nationality(?x13248, ?x1264), ?x10605 = 0h336, influenced_by(?x9297, ?x13248) *> conf = 0.20 ranks of expected_values: 11 EVAL 0c73g influenced_by! 0459z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 143.000 55.000 0.400 http://example.org/influence/influence_node/influenced_by #1996-03nm_fh PRED entity: 03nm_fh PRED relation: film_release_region PRED expected values: 0345h 06qd3 01mjq 03h64 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 107): 03h64 (0.89 #905, 0.84 #659, 0.77 #1398), 0345h (0.85 #386, 0.84 #633, 0.82 #2730), 01mjq (0.66 #641, 0.60 #887, 0.58 #2244), 06qd3 (0.54 #390, 0.51 #514, 0.50 #883), 06f32 (0.53 #904, 0.52 #658, 0.47 #2755), 077qn (0.43 #679, 0.35 #925, 0.28 #2282), 07t21 (0.36 #639, 0.36 #885, 0.25 #2242), 02jx1 (0.33 #12466, 0.33 #8151, 0.32 #7779), 05sb1 (0.29 #652, 0.20 #898, 0.16 #2255), 0d05w3 (0.25 #655, 0.21 #901, 0.18 #532) >> Best rule #905 for best value: >> intensional similarity = 4 >> extensional distance = 110 >> proper extension: 011yrp; 05p1tzf; 02x3lt7; 087wc7n; 0jjy0; 03bx2lk; 0dtfn; 017gm7; 0fpkhkz; 02r8hh_; ... >> query: (?x4684, 03h64) <- country(?x4684, ?x94), genre(?x4684, ?x53), film_release_region(?x4684, ?x410), ?x410 = 01ls2 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 03nm_fh film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03nm_fh film_release_region 01mjq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03nm_fh film_release_region 06qd3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03nm_fh film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1995-072r5v PRED entity: 072r5v PRED relation: music PRED expected values: 02cyfz => 108 concepts (84 used for prediction) PRED predicted values (max 10 best out of 121): 02jxmr (0.25 #74, 0.05 #284, 0.05 #915), 01cbt3 (0.25 #91, 0.03 #722, 0.02 #2618), 07q1v4 (0.25 #15, 0.02 #2120, 0.02 #646), 0146pg (0.17 #641, 0.16 #851, 0.12 #1483), 027t8fw (0.07 #1473, 0.07 #1894, 0.07 #3158), 0fvppk (0.07 #1473, 0.07 #1894, 0.07 #3158), 0284n42 (0.07 #1473, 0.07 #1894, 0.07 #3158), 02jxkw (0.07 #352, 0.07 #983, 0.06 #1194), 0150t6 (0.07 #256, 0.06 #1519, 0.06 #1308), 02bh9 (0.06 #3420, 0.06 #2156, 0.05 #4472) >> Best rule #74 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0y_9q; >> query: (?x7917, 02jxmr) <- cinematography(?x7917, ?x7249), ?x7249 = 027t8fw, film(?x556, ?x7917), nominated_for(?x500, ?x7917) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #455 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 57 *> proper extension: 05n6sq; *> query: (?x7917, 02cyfz) <- currency(?x7917, ?x170), film_distribution_medium(?x7917, ?x627), genre(?x7917, ?x53), ?x53 = 07s9rl0 *> conf = 0.05 ranks of expected_values: 13 EVAL 072r5v music 02cyfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 108.000 84.000 0.250 http://example.org/film/film/music #1994-030p35 PRED entity: 030p35 PRED relation: nominated_for! PRED expected values: 0ck27z 0cqhb3 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 175): 0bdw1g (0.69 #706, 0.69 #2589, 0.68 #1647), 0cqh6z (0.69 #706, 0.69 #2589, 0.68 #1647), 0gq9h (0.28 #10171, 0.28 #10408, 0.28 #10879), 027gs1_ (0.26 #1360, 0.26 #890, 0.26 #1595), 0cjyzs (0.25 #787, 0.24 #1257, 0.24 #1492), 0gs9p (0.25 #10173, 0.25 #10410, 0.24 #10881), 09sb52 (0.24 #13889, 0.20 #15772, 0.20 #16244), 08_vwq (0.24 #13889, 0.20 #15772, 0.20 #16244), 0ck27z (0.24 #13889, 0.20 #16718, 0.19 #17660), 0cqhb3 (0.24 #13889, 0.20 #16718, 0.19 #17660) >> Best rule #706 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 06qwh; >> query: (?x4639, ?x435) <- award(?x4639, ?x435), program(?x2062, ?x4639), nominated_for(?x190, ?x4639) >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #13889 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1088 *> proper extension: 03twd6; 02x8fs; 02gs6r; 0564x; *> query: (?x4639, ?x375) <- award_winner(?x4639, ?x1641), nominated_for(?x1641, ?x167), award(?x1641, ?x375) *> conf = 0.24 ranks of expected_values: 9, 10 EVAL 030p35 nominated_for! 0cqhb3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 78.000 78.000 0.692 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 030p35 nominated_for! 0ck27z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 78.000 78.000 0.692 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1993-032016 PRED entity: 032016 PRED relation: nominated_for! PRED expected values: 05q8pss => 64 concepts (44 used for prediction) PRED predicted values (max 10 best out of 186): 0gq9h (0.50 #61, 0.34 #1950, 0.31 #1478), 019f4v (0.50 #52, 0.29 #1469, 0.27 #1941), 0k611 (0.50 #71, 0.25 #1488, 0.25 #1960), 0p9sw (0.50 #20, 0.25 #1437, 0.23 #1909), 0gr4k (0.50 #26, 0.19 #1915, 0.17 #1443), 054krc (0.50 #67, 0.17 #1484, 0.15 #1956), 04ljl_l (0.44 #948, 0.41 #712, 0.36 #1184), 05p09zm (0.44 #1037, 0.31 #1273, 0.13 #1745), 05b4l5x (0.38 #951, 0.34 #1187, 0.32 #715), 03c7tr1 (0.36 #991, 0.23 #755, 0.23 #1227) >> Best rule #61 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0gzy02; 0bx0l; >> query: (?x3059, 0gq9h) <- film(?x5422, ?x3059), film(?x2167, ?x3059), currency(?x3059, ?x170), ?x2167 = 0b_fw, award_nominee(?x5422, ?x374) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #857 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 030cx; *> query: (?x3059, 05q8pss) <- nominated_for(?x2387, ?x3059), nominated_for(?x3064, ?x3059), ?x3064 = 05q5t0b, gender(?x2387, ?x231) *> conf = 0.23 ranks of expected_values: 20 EVAL 032016 nominated_for! 05q8pss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 64.000 44.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1992-08cyft PRED entity: 08cyft PRED relation: parent_genre! PRED expected values: 06jjbp 0kz10 => 69 concepts (38 used for prediction) PRED predicted values (max 10 best out of 287): 0133k0 (0.50 #1469, 0.36 #4539, 0.33 #191), 01b4p4 (0.50 #1436, 0.33 #158, 0.22 #3736), 01_sz1 (0.50 #1343, 0.33 #65, 0.22 #3643), 01_qp_ (0.50 #1446, 0.33 #168, 0.22 #3746), 01ym9b (0.45 #4129, 0.42 #4643, 0.40 #2083), 059kh (0.40 #1830, 0.38 #3106, 0.33 #2595), 01hydr (0.40 #1766, 0.33 #744, 0.25 #1255), 0g_bh (0.33 #2655, 0.33 #612, 0.29 #2910), 01h0kx (0.33 #2676, 0.33 #2421, 0.27 #4470), 01vw77 (0.33 #2529, 0.33 #741, 0.25 #1252) >> Best rule #1469 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 059kh; >> query: (?x3916, 0133k0) <- artists(?x3916, ?x8947), artists(?x3916, ?x7088), artists(?x3916, ?x3856), artists(?x3916, ?x2854), ?x3856 = 017vkx, ?x2854 = 0dm5l, parent_genre(?x2439, ?x3916), award_nominee(?x1974, ?x7088), award(?x7088, ?x528), profession(?x8947, ?x131) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #738 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 0m0jc; *> query: (?x3916, 0kz10) <- artists(?x3916, ?x8806), artists(?x3916, ?x3856), artists(?x3916, ?x2854), artists(?x3916, ?x2005), ?x3856 = 017vkx, ?x2005 = 05k79, artist(?x2149, ?x2854), ?x8806 = 01d_h, group(?x227, ?x2854) *> conf = 0.33 ranks of expected_values: 14, 19 EVAL 08cyft parent_genre! 0kz10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 69.000 38.000 0.500 http://example.org/music/genre/parent_genre EVAL 08cyft parent_genre! 06jjbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 69.000 38.000 0.500 http://example.org/music/genre/parent_genre #1991-0rh6k PRED entity: 0rh6k PRED relation: place_of_birth! PRED expected values: 078g3l 012ycy 02t1wn => 158 concepts (131 used for prediction) PRED predicted values (max 10 best out of 3446): 0277c3 (0.39 #209352, 0.29 #209351, 0.27 #175750), 09l3p (0.35 #289482, 0.34 #323086, 0.33 #248122), 0pyww (0.35 #289482, 0.34 #323086, 0.33 #248122), 05dbf (0.35 #289482, 0.34 #323086, 0.33 #248122), 014vk4 (0.35 #289482, 0.34 #323086, 0.33 #248122), 09b6zr (0.35 #289482, 0.34 #323086, 0.33 #248122), 025b5y (0.35 #289482, 0.34 #323086, 0.33 #248122), 02xv8m (0.35 #289482, 0.34 #323086, 0.33 #248122), 06jw0s (0.35 #289482, 0.34 #323086, 0.33 #248122), 04x1_w (0.35 #289482, 0.34 #323086, 0.33 #248122) >> Best rule #209352 for best value: >> intensional similarity = 2 >> extensional distance = 159 >> proper extension: 02qjb7z; >> query: (?x108, ?x6124) <- origin(?x6124, ?x108), instrumentalists(?x716, ?x6124) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #108550 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 70 *> proper extension: 0dlm_; *> query: (?x108, ?x51) <- capital(?x94, ?x108), nationality(?x51, ?x94) *> conf = 0.12 ranks of expected_values: 1577, 1835, 2159 EVAL 0rh6k place_of_birth! 02t1wn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 158.000 131.000 0.393 http://example.org/people/person/place_of_birth EVAL 0rh6k place_of_birth! 012ycy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 158.000 131.000 0.393 http://example.org/people/person/place_of_birth EVAL 0rh6k place_of_birth! 078g3l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 158.000 131.000 0.393 http://example.org/people/person/place_of_birth #1990-0cbv4g PRED entity: 0cbv4g PRED relation: film_format PRED expected values: 07fb8_ => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 4): 07fb8_ (0.29 #23, 0.24 #6, 0.22 #54), 0cj16 (0.24 #8, 0.21 #3, 0.16 #137), 017fx5 (0.12 #26, 0.03 #67, 0.03 #72), 01dc60 (0.01 #48) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 57 >> proper extension: 0ds35l9; 02vxq9m; 07gp9; 01k1k4; 0ds11z; 05p1tzf; 0bth54; 0fg04; 01vksx; 0b6tzs; ... >> query: (?x5293, 07fb8_) <- nominated_for(?x3019, ?x5293), film(?x2551, ?x5293), film_crew_role(?x5293, ?x137), ?x3019 = 057xs89 >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cbv4g film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.288 http://example.org/film/film/film_format #1989-022g44 PRED entity: 022g44 PRED relation: profession PRED expected values: 01d_h8 => 98 concepts (78 used for prediction) PRED predicted values (max 10 best out of 75): 09jwl (0.72 #5965, 0.33 #6690, 0.21 #4078), 01d_h8 (0.71 #5809, 0.70 #4212, 0.56 #2326), 0np9r (0.69 #1179, 0.61 #888, 0.27 #11174), 0dxtg (0.67 #5671, 0.61 #4219, 0.59 #447), 0nbcg (0.49 #5977, 0.17 #6702, 0.14 #2639), 0dz3r (0.33 #5951, 0.20 #6676, 0.10 #4064), 0kyk (0.27 #11174, 0.26 #8125, 0.15 #2057), 018gz8 (0.27 #2335, 0.21 #884, 0.18 #4366), 0cbd2 (0.27 #296, 0.25 #2037, 0.22 #5665), 01c72t (0.27 #311, 0.21 #5970, 0.14 #2052) >> Best rule #5965 for best value: >> intensional similarity = 3 >> extensional distance = 866 >> proper extension: 024zq; 018d6l; 08849; >> query: (?x4961, 09jwl) <- profession(?x4961, ?x220), profession(?x5543, ?x220), ?x5543 = 01kd57 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #5809 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 801 *> proper extension: 07f8wg; 02kxbwx; 01t6b4; 05cv94; 09pjnd; 0h1p; 04cw0j; 063472; 047q2wc; 07b3r9; ... *> query: (?x4961, 01d_h8) <- award_nominee(?x4961, ?x4128), profession(?x4961, ?x1041), profession(?x3406, ?x1041), ?x3406 = 07ym6ss *> conf = 0.71 ranks of expected_values: 2 EVAL 022g44 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 78.000 0.717 http://example.org/people/person/profession #1988-04bfg PRED entity: 04bfg PRED relation: school! PRED expected values: 061xq => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 92): 0jmj7 (0.71 #2236, 0.70 #1684, 0.70 #1040), 05m_8 (0.21 #555, 0.15 #1291, 0.14 #739), 01slc (0.16 #610, 0.12 #794, 0.12 #1346), 07l4z (0.15 #621, 0.13 #253, 0.12 #1081), 051vz (0.15 #574, 0.11 #758, 0.11 #1310), 01yjl (0.15 #582, 0.10 #1410, 0.10 #1134), 06x68 (0.14 #559, 0.10 #1663, 0.10 #1203), 07l8x (0.14 #618, 0.09 #802, 0.09 #1262), 01d6g (0.14 #623, 0.08 #1267, 0.07 #1727), 049n7 (0.13 #196, 0.11 #564, 0.08 #748) >> Best rule #2236 for best value: >> intensional similarity = 4 >> extensional distance = 162 >> proper extension: 0frm7n; >> query: (?x6602, 0jmj7) <- school(?x1632, ?x6602), category(?x6602, ?x134), team(?x5412, ?x1632), draft(?x1632, ?x1161) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #586 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 015fsv; *> query: (?x6602, 061xq) <- school(?x8111, ?x6602), state_province_region(?x6602, ?x177), season(?x8111, ?x2406), school_type(?x6602, ?x3092) *> conf = 0.11 ranks of expected_values: 16 EVAL 04bfg school! 061xq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 104.000 104.000 0.707 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #1987-02x8s9 PRED entity: 02x8s9 PRED relation: category PRED expected values: 08mbj5d => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.44 #58, 0.37 #87, 0.37 #50) >> Best rule #58 for best value: >> intensional similarity = 5 >> extensional distance = 1381 >> proper extension: 016qtt; 01vrx3g; 01wdqrx; 01p9hgt; 0244r8; 012x4t; 09mq4m; 03xmy1; 0cg9y; 02vntj; ... >> query: (?x10611, 08mbj5d) <- profession(?x10611, ?x353), profession(?x10527, ?x353), profession(?x3668, ?x353), ?x3668 = 08n9ng, ?x10527 = 020jqv >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02x8s9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.439 http://example.org/common/topic/webpage./common/webpage/category #1986-018gkb PRED entity: 018gkb PRED relation: location PRED expected values: 0tgcy => 81 concepts (70 used for prediction) PRED predicted values (max 10 best out of 111): 04jpl (0.12 #821, 0.06 #6449, 0.05 #12882), 02_286 (0.11 #42660, 0.10 #44269, 0.10 #40247), 01cx_ (0.10 #163, 0.02 #7399, 0.02 #8203), 0tz14 (0.10 #592), 0z1vw (0.10 #584), 030qb3t (0.08 #41098, 0.08 #51555, 0.08 #31447), 0k33p (0.07 #2894, 0.07 #3698, 0.05 #2090), 0cr3d (0.06 #6577, 0.05 #13814, 0.05 #1753), 0dj0x (0.06 #1579, 0.01 #6403), 0r5lz (0.06 #1066) >> Best rule #821 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0jn38; 01_wfj; >> query: (?x11161, 04jpl) <- artist(?x6474, ?x11161), ?x6474 = 0g768, artists(?x1380, ?x11161), ?x1380 = 0dl5d >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 018gkb location 0tgcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 70.000 0.118 http://example.org/people/person/places_lived./people/place_lived/location #1985-01v2xl PRED entity: 01v2xl PRED relation: major_field_of_study PRED expected values: 01zc2w => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 107): 02j62 (0.43 #3159, 0.42 #3660, 0.40 #2783), 02lp1 (0.40 #12, 0.37 #512, 0.35 #3139), 03g3w (0.38 #1528, 0.35 #2154, 0.33 #3155), 01mkq (0.36 #3143, 0.34 #3394, 0.34 #3268), 01lj9 (0.36 #1542, 0.33 #2168, 0.20 #2543), 05qjt (0.36 #1508, 0.29 #2134, 0.27 #3135), 04rjg (0.35 #3148, 0.32 #3649, 0.31 #1521), 062z7 (0.33 #3156, 0.31 #2530, 0.31 #1529), 05qfh (0.29 #1538, 0.22 #3165, 0.22 #2164), 037mh8 (0.29 #1570, 0.20 #70, 0.20 #2196) >> Best rule #3159 for best value: >> intensional similarity = 5 >> extensional distance = 155 >> proper extension: 04jr87; >> query: (?x11602, 02j62) <- category(?x11602, ?x134), school_type(?x11602, ?x3092), institution(?x1771, ?x11602), ?x1771 = 019v9k, student(?x11602, ?x8306) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1574 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 43 *> proper extension: 026036; *> query: (?x11602, 01zc2w) <- student(?x11602, ?x8306), school_type(?x11602, ?x3092), type_of_union(?x8306, ?x566), celebrities_impersonated(?x8145, ?x8306), ?x566 = 04ztj *> conf = 0.18 ranks of expected_values: 29 EVAL 01v2xl major_field_of_study 01zc2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 154.000 154.000 0.427 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1984-01ptsx PRED entity: 01ptsx PRED relation: list! PRED expected values: 0gztl 0l8sx 0168nq 018p5f 04fv0k 01yx7f 02l48d 01tmng 0841v 01frpd => 7 concepts (7 used for prediction) PRED predicted values (max 10 best out of 376): 01frpd (0.83 #971, 0.82 #1453, 0.82 #1452), 01_bp (0.83 #971, 0.82 #1453, 0.82 #1452), 01tmng (0.83 #971, 0.82 #1453, 0.82 #1452), 02l48d (0.83 #971, 0.82 #1453, 0.82 #1452), 01yx7f (0.83 #971, 0.82 #1453, 0.82 #1452), 05w3y (0.83 #971, 0.82 #1453, 0.82 #1452), 0168nq (0.83 #971, 0.82 #1453, 0.82 #1452), 0l8sx (0.83 #971, 0.82 #1453, 0.82 #1452), 0gsg7 (0.83 #971, 0.82 #1453, 0.82 #1452), 026v5 (0.83 #971, 0.82 #1453, 0.82 #1452) >> Best rule #971 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 05glt; >> query: (?x7472, ?x610) <- list(?x11727, ?x7472), list(?x9469, ?x7472), list(?x3920, ?x7472), list(?x2062, ?x7472), category(?x11727, ?x134), list(?x9469, ?x5997), ?x134 = 08mbj5d, nominated_for(?x2062, ?x5594), nominated_for(?x435, ?x5594), list(?x610, ?x5997), award(?x3920, ?x3911), award(?x972, ?x3911), nominated_for(?x3911, ?x124), ceremony(?x3911, ?x762), award_winner(?x3911, ?x541) >> conf = 0.83 => this is the best rule for 18 predicted values ranks of expected_values: 1, 3, 4, 5, 7, 8, 13 EVAL 01ptsx list! 01frpd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 7.000 7.000 0.830 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01ptsx list! 0841v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 7.000 7.000 0.830 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01ptsx list! 01tmng CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 7.000 7.000 0.830 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01ptsx list! 02l48d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 7.000 7.000 0.830 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01ptsx list! 01yx7f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 7.000 7.000 0.830 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01ptsx list! 04fv0k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 7.000 7.000 0.830 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01ptsx list! 018p5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 7.000 7.000 0.830 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01ptsx list! 0168nq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 7.000 7.000 0.830 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01ptsx list! 0l8sx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 7.000 7.000 0.830 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list EVAL 01ptsx list! 0gztl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 7.000 7.000 0.830 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #1983-027ydt PRED entity: 027ydt PRED relation: contains! PRED expected values: 09c7w0 => 171 concepts (65 used for prediction) PRED predicted values (max 10 best out of 231): 09c7w0 (0.84 #32241, 0.83 #19694, 0.79 #25968), 059rby (0.42 #33155, 0.18 #915, 0.17 #21505), 05tbn (0.35 #18125, 0.11 #224, 0.09 #1119), 05kkh (0.26 #17910, 0.06 #18807, 0.05 #32247), 07z1m (0.19 #17993, 0.05 #21577, 0.05 #18890), 02xry (0.17 #33298, 0.03 #20752, 0.03 #18961), 05fjf (0.16 #33508, 0.02 #10218, 0.02 #54625), 0d9y6 (0.15 #2992, 0.14 #3887, 0.04 #26861), 01n7q (0.13 #20667, 0.11 #32316, 0.10 #22459), 04rrd (0.13 #18018, 0.02 #21602, 0.02 #35938) >> Best rule #32241 for best value: >> intensional similarity = 5 >> extensional distance = 288 >> proper extension: 02d9nr; >> query: (?x6584, 09c7w0) <- colors(?x6584, ?x663), contains(?x4061, ?x6584), contains(?x4061, ?x10228), district_represented(?x176, ?x4061), source(?x10228, ?x958) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027ydt contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 65.000 0.841 http://example.org/location/location/contains #1982-08wjf4 PRED entity: 08wjf4 PRED relation: film PRED expected values: 0dzz6g => 98 concepts (65 used for prediction) PRED predicted values (max 10 best out of 755): 0h95927 (0.42 #3114, 0.04 #116237, 0.01 #13845), 0234j5 (0.17 #3212, 0.05 #5000, 0.02 #6789), 0b6tzs (0.17 #1927, 0.04 #116237, 0.01 #53784), 093dqjy (0.17 #2397, 0.04 #116237), 03mh_tp (0.17 #2295, 0.01 #9448), 0by17xn (0.17 #3509), 07p12s (0.17 #3463), 02gpkt (0.17 #3101), 0cz_ym (0.17 #2082), 02wgk1 (0.14 #757, 0.08 #2545, 0.07 #6122) >> Best rule #3114 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0785v8; 0292l3; 06cgy; 0170s4; 0161sp; 0f4dx2; 01_xtx; 01900g; 027bs_2; 0716t2; >> query: (?x7901, 0h95927) <- award_nominee(?x8739, ?x7901), nominated_for(?x7901, ?x781), ?x8739 = 02x0dzw >> conf = 0.42 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08wjf4 film 0dzz6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 65.000 0.417 http://example.org/film/actor/film./film/performance/film #1981-015x1f PRED entity: 015x1f PRED relation: instrumentalists! PRED expected values: 026t6 06ncr => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 123): 05148p4 (0.40 #3941, 0.36 #1209, 0.35 #3856), 0l14qv (0.33 #5, 0.29 #90, 0.14 #3415), 0l14md (0.26 #3496, 0.17 #5039, 0.17 #7), 026t6 (0.26 #3496, 0.17 #5039, 0.15 #3925), 03qjg (0.20 #559, 0.19 #1752, 0.18 #2776), 01vj9c (0.17 #5039, 0.11 #3239, 0.04 #4008), 0d8lm (0.17 #5039, 0.04 #4008, 0.03 #5211), 0fx80y (0.17 #5039, 0.04 #4008, 0.03 #5211), 03gvt (0.17 #63, 0.15 #573, 0.14 #148), 042v_gx (0.17 #8, 0.14 #93, 0.11 #3239) >> Best rule #3941 for best value: >> intensional similarity = 4 >> extensional distance = 470 >> proper extension: 03ds3; 0pmw9; >> query: (?x5048, 05148p4) <- instrumentalists(?x316, ?x5048), instrumentalists(?x316, ?x6947), ?x6947 = 01vrnsk, role(?x316, ?x74) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3496 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 371 *> proper extension: 0ph2w; 01vd7hn; 02qfhb; 08n__5; 09g0h; *> query: (?x5048, ?x1166) <- instrumentalists(?x316, ?x5048), family(?x316, ?x1166), instrumentalists(?x316, ?x3321), ?x3321 = 03bnv *> conf = 0.26 ranks of expected_values: 4, 16 EVAL 015x1f instrumentalists! 06ncr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 136.000 136.000 0.400 http://example.org/music/instrument/instrumentalists EVAL 015x1f instrumentalists! 026t6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 136.000 136.000 0.400 http://example.org/music/instrument/instrumentalists #1980-059_c PRED entity: 059_c PRED relation: location_of_ceremony! PRED expected values: 04ztj => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.68 #61, 0.62 #166, 0.56 #310), 01g63y (0.31 #634, 0.30 #629, 0.09 #2), 0jgjn (0.03 #4, 0.02 #101, 0.02 #105), 01bl8s (0.01 #104) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 017cjb; 095w_; 01f62; 06wjf; 03_xj; 0k3p; 0dprg; 0d6hn; 0ftn8; 0cp6w; >> query: (?x1138, 04ztj) <- location(?x1568, ?x1138), country(?x1138, ?x94), time_zones(?x1138, ?x2088) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 059_c location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.682 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #1979-06jzh PRED entity: 06jzh PRED relation: award_nominee PRED expected values: 045c66 => 107 concepts (59 used for prediction) PRED predicted values (max 10 best out of 865): 01kp66 (0.81 #95597, 0.81 #107255, 0.81 #81604), 0g8st4 (0.81 #95597, 0.81 #107255, 0.81 #81604), 0306ds (0.81 #95597, 0.81 #107255, 0.81 #81604), 0hvb2 (0.25 #398, 0.05 #23710, 0.05 #79670), 015vq_ (0.25 #950, 0.02 #80222, 0.02 #82555), 0c6qh (0.21 #95596, 0.17 #137566, 0.12 #540), 027kmrb (0.21 #95596, 0.17 #137566, 0.01 #19985), 01f7j9 (0.21 #95596, 0.17 #137566, 0.01 #2794), 01d8yn (0.21 #95596, 0.17 #137566), 07s93v (0.21 #95596, 0.17 #137566) >> Best rule #95597 for best value: >> intensional similarity = 3 >> extensional distance = 1048 >> proper extension: 01vvydl; 0lbj1; 01vrx3g; 023tp8; 09fqtq; 03zqc1; 01kwld; 034x61; 016khd; 01j5x6; ... >> query: (?x540, ?x539) <- film(?x540, ?x1163), award_winner(?x1163, ?x1616), award_nominee(?x539, ?x540) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #79583 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 735 *> proper extension: 043q6n_; 0hpt3; 01795t; 0c3ns; 061dn_; 02vyh; 01gb54; 07bzp; 02j_j0; 02vqpx8; ... *> query: (?x540, 045c66) <- award_nominee(?x3872, ?x540), executive_produced_by(?x5017, ?x3872), award_nominee(?x540, ?x9314) *> conf = 0.02 ranks of expected_values: 369 EVAL 06jzh award_nominee 045c66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 107.000 59.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1978-07c37 PRED entity: 07c37 PRED relation: influenced_by! PRED expected values: 03_hd => 176 concepts (76 used for prediction) PRED predicted values (max 10 best out of 404): 03sbs (0.44 #2546, 0.44 #2320, 0.20 #2037), 03_87 (0.38 #1275, 0.23 #6870, 0.22 #2803), 0dzkq (0.36 #3689, 0.33 #5215, 0.33 #4704), 04hcw (0.33 #794, 0.26 #11979, 0.24 #12488), 03f0324 (0.33 #703, 0.25 #1212, 0.22 #2740), 03cdg (0.33 #972, 0.25 #1481, 0.18 #4027), 0h25 (0.33 #416, 0.23 #7539, 0.22 #9572), 06myp (0.33 #946, 0.20 #2037, 0.18 #4001), 03jht (0.33 #886, 0.20 #2037, 0.18 #3941), 07dnx (0.33 #867, 0.18 #3922, 0.18 #3413) >> Best rule #2546 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 03s9v; >> query: (?x5797, ?x7250) <- gender(?x5797, ?x231), influenced_by(?x11830, ?x5797), religion(?x5797, ?x4641), influenced_by(?x7250, ?x11830), ?x7250 = 03sbs >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2214 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 03s9v; *> query: (?x5797, 03_hd) <- gender(?x5797, ?x231), influenced_by(?x11830, ?x5797), religion(?x5797, ?x4641), influenced_by(?x7250, ?x11830), ?x7250 = 03sbs *> conf = 0.22 ranks of expected_values: 35 EVAL 07c37 influenced_by! 03_hd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 176.000 76.000 0.444 http://example.org/influence/influence_node/influenced_by #1977-0fd3y PRED entity: 0fd3y PRED relation: parent_genre PRED expected values: 07gxw => 81 concepts (57 used for prediction) PRED predicted values (max 10 best out of 276): 06by7 (0.89 #5181, 0.60 #6634, 0.60 #817), 07gxw (0.50 #2294, 0.50 #1481, 0.44 #1972), 011j5x (0.40 #823, 0.25 #502, 0.22 #2119), 03lty (0.38 #6637, 0.33 #980, 0.25 #499), 0xhtw (0.38 #1620, 0.30 #2592, 0.17 #5178), 05r6t (0.33 #1174, 0.29 #1334, 0.26 #5217), 016clz (0.33 #1126, 0.29 #1286, 0.19 #4199), 0fd3y (0.33 #1780, 0.17 #1130, 0.16 #1771), 0jrv_ (0.33 #1066, 0.09 #2418, 0.06 #6723), 08cyft (0.30 #2295, 0.25 #1482, 0.25 #357) >> Best rule #5181 for best value: >> intensional similarity = 8 >> extensional distance = 45 >> proper extension: 01gbcf; 018ysx; >> query: (?x497, 06by7) <- parent_genre(?x2439, ?x497), parent_genre(?x497, ?x2809), artists(?x2809, ?x9262), artists(?x2809, ?x7882), artists(?x2809, ?x3403), ?x3403 = 02qwg, ?x9262 = 04n2vgk, ?x7882 = 01z9_x >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #2294 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 8 *> proper extension: 01hydr; *> query: (?x497, 07gxw) <- artists(?x497, ?x8806), artists(?x497, ?x7859), artists(?x497, ?x498), parent_genre(?x497, ?x2808), artists(?x2996, ?x498), artist(?x2931, ?x7859), role(?x7859, ?x227), parent_genre(?x301, ?x2996), ?x8806 = 01d_h *> conf = 0.50 ranks of expected_values: 2 EVAL 0fd3y parent_genre 07gxw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 57.000 0.894 http://example.org/music/genre/parent_genre #1976-0p_47 PRED entity: 0p_47 PRED relation: award_winner! PRED expected values: 09cm54 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 294): 05zvj3m (0.36 #26116, 0.32 #5137, 0.31 #24830), 07cbcy (0.36 #26116, 0.32 #5137, 0.31 #24830), 03hkv_r (0.36 #26116, 0.32 #5137, 0.31 #24830), 027986c (0.36 #26116, 0.32 #5137, 0.31 #24830), 0gkvb7 (0.36 #26116, 0.32 #5137, 0.31 #24830), 02x17s4 (0.36 #26116, 0.32 #5137, 0.31 #24830), 0hnf5vm (0.36 #26116, 0.32 #5137, 0.31 #24830), 09sb52 (0.16 #14167, 0.11 #15023, 0.10 #15879), 05p09zm (0.16 #980, 0.09 #124, 0.04 #7706), 03x3wf (0.15 #1776, 0.09 #64, 0.07 #3060) >> Best rule #26116 for best value: >> intensional similarity = 2 >> extensional distance = 2276 >> proper extension: 012ljv; 028q6; 04qvl7; 06j0md; 0411q; 06gp3f; 0qf43; 0hl3d; 01lmj3q; 089tm; ... >> query: (?x3917, ?x384) <- award_winner(?x341, ?x3917), award(?x3917, ?x384) >> conf = 0.36 => this is the best rule for 7 predicted values *> Best rule #952 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 68 *> proper extension: 02v3yy; 01l3mk3; *> query: (?x3917, 09cm54) <- award_winner(?x341, ?x3917), participant(?x3917, ?x2308) *> conf = 0.06 ranks of expected_values: 50 EVAL 0p_47 award_winner! 09cm54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 76.000 76.000 0.364 http://example.org/award/award_category/winners./award/award_honor/award_winner #1975-016ppr PRED entity: 016ppr PRED relation: artists! PRED expected values: 02ny8t => 81 concepts (46 used for prediction) PRED predicted values (max 10 best out of 268): 06by7 (0.84 #3072, 0.75 #4296, 0.61 #632), 016clz (0.65 #615, 0.38 #4279, 0.35 #3055), 0ggx5q (0.61 #1904, 0.59 #3429, 0.58 #2514), 05bt6j (0.40 #2483, 0.40 #4010, 0.40 #3398), 0xhtw (0.38 #3067, 0.37 #4596, 0.35 #4291), 02x8m (0.32 #1239, 0.24 #3967, 0.22 #5803), 01lyv (0.27 #10412, 0.19 #7667, 0.16 #9497), 0y3_8 (0.27 #4014, 0.26 #2487, 0.26 #3402), 05r6t (0.26 #688, 0.19 #4352, 0.16 #4657), 03_d0 (0.24 #1232, 0.24 #3967, 0.22 #5803) >> Best rule #3072 for best value: >> intensional similarity = 4 >> extensional distance = 77 >> proper extension: 07qnf; 02r1tx7; 05563d; 0394y; 06nv27; 02mq_y; 0123r4; 02vgh; 07m4c; 08w4pm; ... >> query: (?x10740, 06by7) <- artists(?x3996, ?x10740), group(?x2925, ?x10740), artists(?x3996, ?x2237), ?x2237 = 01vs_v8 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2569 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 04cr6qv; 02jyhv; 02twdq; *> query: (?x10740, 02ny8t) <- artists(?x3996, ?x10740), ?x3996 = 02lnbg, artist(?x3265, ?x10740), category(?x10740, ?x134) *> conf = 0.24 ranks of expected_values: 13 EVAL 016ppr artists! 02ny8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 81.000 46.000 0.835 http://example.org/music/genre/artists #1974-0bcp9b PRED entity: 0bcp9b PRED relation: nominated_for! PRED expected values: 0gr4k => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 208): 02z1nbg (0.70 #1882, 0.66 #17171, 0.66 #3998), 027571b (0.70 #1882, 0.66 #17171, 0.66 #3998), 09cn0c (0.70 #1882, 0.66 #17171, 0.66 #3998), 0gq9h (0.62 #2882, 0.62 #4058, 0.60 #3822), 0k611 (0.58 #2893, 0.54 #4069, 0.54 #3833), 0gq_v (0.55 #2842, 0.46 #3782, 0.46 #4018), 0gs9p (0.54 #3824, 0.52 #4060, 0.50 #2884), 019f4v (0.53 #4051, 0.53 #3815, 0.49 #2875), 040njc (0.44 #1417, 0.40 #4005, 0.38 #3769), 0p9sw (0.43 #3783, 0.42 #4019, 0.42 #2843) >> Best rule #1882 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 04q00lw; >> query: (?x7628, ?x1245) <- film_release_region(?x7628, ?x279), award(?x7628, ?x1245), titles(?x53, ?x7628), genre(?x7628, ?x162) >> conf = 0.70 => this is the best rule for 3 predicted values *> Best rule #1437 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 0p_th; 09p7fh; 016ks5; 03b1l8; 01fwzk; *> query: (?x7628, 0gr4k) <- nominated_for(?x749, ?x7628), award(?x7628, ?x3902), music(?x7628, ?x7955), ?x749 = 094qd5 *> conf = 0.42 ranks of expected_values: 12 EVAL 0bcp9b nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 101.000 101.000 0.698 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1973-01wyzyl PRED entity: 01wyzyl PRED relation: film PRED expected values: 016kv6 => 121 concepts (85 used for prediction) PRED predicted values (max 10 best out of 732): 02ph9tm (0.16 #2891, 0.04 #13632, 0.03 #15423), 0gyv0b4 (0.14 #1655, 0.02 #12396, 0.02 #10606), 01rxyb (0.14 #733, 0.01 #11474, 0.01 #9684), 023vcd (0.12 #3428, 0.03 #14169, 0.02 #12379), 03z20c (0.08 #2267, 0.06 #11218, 0.06 #9428), 01hvjx (0.08 #2165, 0.05 #5746, 0.03 #18278), 087pfc (0.08 #3321, 0.04 #5112, 0.02 #10482), 05fm6m (0.08 #3111, 0.02 #12062, 0.02 #10272), 0gfzfj (0.08 #3486, 0.02 #14227, 0.02 #16018), 0f2sx4 (0.08 #3176, 0.02 #13917, 0.02 #15708) >> Best rule #2891 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 0jvtp; >> query: (?x2259, 02ph9tm) <- type_of_union(?x2259, ?x566), tv_program(?x2259, ?x6884), film(?x2259, ?x5074), location(?x2259, ?x1860) >> conf = 0.16 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wyzyl film 016kv6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 85.000 0.160 http://example.org/film/actor/film./film/performance/film #1972-027ydt PRED entity: 027ydt PRED relation: institution! PRED expected values: 014mlp => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.71 #70, 0.70 #2126, 0.70 #137), 02_xgp2 (0.59 #100, 0.52 #411, 0.51 #433), 03bwzr4 (0.59 #102, 0.52 #413, 0.51 #435), 0bkj86 (0.39 #96, 0.38 #407, 0.37 #429), 07s6fsf (0.39 #90, 0.38 #224, 0.37 #423), 04zx3q1 (0.31 #2122, 0.31 #91, 0.29 #1236), 0bjrnt (0.31 #2122, 0.29 #1236, 0.16 #2073), 01ysy9 (0.31 #2122, 0.29 #1236, 0.16 #2073), 027f2w (0.24 #97, 0.24 #430, 0.23 #231), 013zdg (0.24 #229, 0.23 #95, 0.22 #428) >> Best rule #70 for best value: >> intensional similarity = 5 >> extensional distance = 71 >> proper extension: 06pwq; 0288zy; 02cttt; 01wdl3; 01ngz1; 01j_06; 017zq0; 024y8p; 0bthb; 0bx8pn; ... >> query: (?x6584, 014mlp) <- school_type(?x6584, ?x3092), colors(?x6584, ?x663), ?x663 = 083jv, currency(?x6584, ?x170), major_field_of_study(?x6584, ?x2605) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027ydt institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 175.000 175.000 0.712 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #1971-01vvyc_ PRED entity: 01vvyc_ PRED relation: award_winner! PRED expected values: 05pd94v => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 112): 056878 (0.30 #314, 0.29 #455, 0.21 #1019), 0466p0j (0.30 #358, 0.21 #499, 0.18 #1204), 019bk0 (0.21 #439, 0.21 #580, 0.20 #298), 09n4nb (0.21 #471, 0.20 #330, 0.17 #753), 02rjjll (0.21 #1133, 0.21 #992, 0.21 #569), 013b2h (0.20 #362, 0.18 #1913, 0.14 #503), 0gpjbt (0.20 #311, 0.17 #734, 0.15 #1157), 05pd94v (0.16 #7051, 0.16 #566, 0.12 #707), 01s695 (0.16 #7051, 0.14 #426, 0.11 #1836), 0hhtgcw (0.16 #7051, 0.11 #650, 0.10 #368) >> Best rule #314 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 02l840; 01s21dg; 0127s7; 03h_0_z; 013w7j; 0g824; 01t110; 0cbm64; >> query: (?x5798, 056878) <- award(?x5798, ?x1389), ?x1389 = 01c427, participant(?x5798, ?x338), origin(?x5798, ?x739) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #7051 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 703 *> proper extension: 062hgx; 01w_10; 02t_8z; *> query: (?x5798, ?x139) <- award_winner(?x5536, ?x5798), location(?x5798, ?x3415), award_winner(?x139, ?x5536) *> conf = 0.16 ranks of expected_values: 8 EVAL 01vvyc_ award_winner! 05pd94v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 109.000 109.000 0.300 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1970-019gz PRED entity: 019gz PRED relation: gender PRED expected values: 05zppz => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #99, 0.91 #61, 0.90 #85), 02zsn (0.71 #103, 0.31 #18, 0.29 #36) >> Best rule #99 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 05ty4m; 08433; 03f70xs; 046lt; 01jrvr6; 080r3; 01p1z_; 011vx3; 02dztn; 04__f; ... >> query: (?x11410, 05zppz) <- student(?x8694, ?x11410), type_of_union(?x11410, ?x566), major_field_of_study(?x8694, ?x90), influenced_by(?x10598, ?x11410) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019gz gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.923 http://example.org/people/person/gender #1969-0cmt6q PRED entity: 0cmt6q PRED relation: award_nominee PRED expected values: 05xpms => 68 concepts (29 used for prediction) PRED predicted values (max 10 best out of 631): 072bb1 (0.81 #44301, 0.81 #48966, 0.81 #46633), 0bt7ws (0.81 #44301, 0.81 #48966, 0.81 #46633), 08wq0g (0.81 #44301, 0.81 #48966, 0.81 #46633), 0cnl1c (0.81 #44301, 0.81 #48966, 0.81 #46633), 08hsww (0.81 #44301, 0.81 #48966, 0.81 #46633), 05xpms (0.67 #1983, 0.64 #4314, 0.40 #6995), 0cl0bk (0.58 #1515, 0.50 #3846, 0.40 #6995), 0cmt6q (0.57 #3821, 0.50 #1490, 0.32 #6152), 027cxsm (0.40 #6995, 0.32 #5003, 0.30 #9328), 0cj2nl (0.40 #6995, 0.30 #9328, 0.29 #5547) >> Best rule #44301 for best value: >> intensional similarity = 3 >> extensional distance = 1211 >> proper extension: 01vvycq; 09hnb; >> query: (?x6532, ?x679) <- award_nominee(?x6532, ?x7663), languages(?x7663, ?x254), award_nominee(?x679, ?x6532) >> conf = 0.81 => this is the best rule for 5 predicted values *> Best rule #1983 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0bt4r4; *> query: (?x6532, 05xpms) <- award_nominee(?x6532, ?x7752), award_nominee(?x6532, ?x7663), ?x7663 = 04zkj5, ?x7752 = 05l0j5 *> conf = 0.67 ranks of expected_values: 6 EVAL 0cmt6q award_nominee 05xpms CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 68.000 29.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1968-0c4hgj PRED entity: 0c4hgj PRED relation: locations PRED expected values: 030qb3t => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 47): 030qb3t (0.11 #34, 0.04 #4461, 0.03 #3538), 013yq (0.08 #4480), 0fsb8 (0.06 #4567), 0d9y6 (0.06 #4527), 029cr (0.06 #4484), 0f2r6 (0.06 #4446), 0kcw2 (0.05 #4605), 0f2rq (0.05 #4532), 0d9jr (0.05 #4528), 071cn (0.05 #4504) >> Best rule #34 for best value: >> intensional similarity = 12 >> extensional distance = 7 >> proper extension: 0hhtgcw; >> query: (?x6606, 030qb3t) <- award_winner(?x6606, ?x10084), award_winner(?x6606, ?x5720), award_nominee(?x6011, ?x5720), profession(?x5720, ?x1032), award_winner(?x2826, ?x5720), music(?x1372, ?x5720), nationality(?x10084, ?x94), ?x1032 = 02hrh1q, award_winner(?x2112, ?x10084), gender(?x5720, ?x231), category(?x5720, ?x134), honored_for(?x6606, ?x3510) >> conf = 0.11 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c4hgj locations 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.111 http://example.org/time/event/locations #1967-0tc7 PRED entity: 0tc7 PRED relation: politician! PRED expected values: 07wbk => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 20): 07wbk (0.50 #73, 0.50 #1, 0.44 #49), 0d075m (0.39 #195, 0.38 #123, 0.37 #219), 07w42 (0.17 #13, 0.11 #61, 0.08 #85), 07wf9 (0.11 #318, 0.10 #414, 0.10 #270), 01c9x (0.08 #124, 0.04 #268, 0.04 #316), 07wdw (0.04 #487, 0.04 #511, 0.04 #271), 02245 (0.04 #283, 0.04 #595, 0.04 #331), 07wgm (0.04 #278, 0.04 #326, 0.04 #398), 0135dr (0.04 #594), 0135cw (0.03 #585) >> Best rule #73 for best value: >> intensional similarity = 2 >> extensional distance = 10 >> proper extension: 042kg; >> query: (?x2387, 07wbk) <- jurisdiction_of_office(?x2387, ?x1227), award(?x2387, ?x102) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0tc7 politician! 07wbk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 149.000 149.000 0.500 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #1966-0d060g PRED entity: 0d060g PRED relation: religion PRED expected values: 05sfs => 206 concepts (206 used for prediction) PRED predicted values (max 10 best out of 33): 01lp8 (0.63 #3775, 0.58 #2347, 0.58 #4013), 051kv (0.57 #2350, 0.54 #3336, 0.47 #3778), 019cr (0.57 #2355, 0.54 #3341, 0.45 #3783), 0631_ (0.55 #2352, 0.54 #3338, 0.44 #3780), 04pk9 (0.53 #2363, 0.51 #3349, 0.42 #2499), 05sfs (0.51 #2348, 0.51 #3334, 0.48 #3776), 05w5d (0.51 #2367, 0.49 #3353, 0.41 #3795), 01y0s9 (0.42 #2353, 0.38 #3339, 0.35 #2489), 021_0p (0.38 #3348, 0.36 #2362, 0.31 #3790), 03_gx (0.34 #2358, 0.33 #80, 0.30 #3786) >> Best rule #3775 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 0n3g; >> query: (?x279, 01lp8) <- adjoins(?x279, ?x7387), religion(?x279, ?x492), adjoins(?x7387, ?x1905) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #2348 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 05j49; *> query: (?x279, 05sfs) <- contains(?x279, ?x7912), institution(?x620, ?x7912), partially_contains(?x279, ?x6195) *> conf = 0.51 ranks of expected_values: 6 EVAL 0d060g religion 05sfs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 206.000 206.000 0.630 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #1965-03cw411 PRED entity: 03cw411 PRED relation: film_crew_role PRED expected values: 0ch6mp2 02ynfr => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 32): 0ch6mp2 (0.73 #2063, 0.68 #231, 0.67 #81), 09vw2b7 (0.64 #2062, 0.61 #230, 0.58 #80), 01vx2h (0.48 #86, 0.38 #348, 0.32 #2068), 0dxtw (0.35 #2067, 0.32 #235, 0.30 #123), 01pvkk (0.32 #461, 0.32 #274, 0.28 #2069), 02rh1dz (0.28 #84, 0.14 #346, 0.12 #271), 02ynfr (0.19 #91, 0.17 #241, 0.16 #2073), 0215hd (0.17 #505, 0.16 #57, 0.14 #767), 01xy5l_ (0.14 #89, 0.13 #1060, 0.12 #949), 0d2b38 (0.11 #1072, 0.11 #961, 0.11 #2083) >> Best rule #2063 for best value: >> intensional similarity = 4 >> extensional distance = 844 >> proper extension: 0gs973; >> query: (?x3745, 0ch6mp2) <- genre(?x3745, ?x53), currency(?x3745, ?x170), film(?x3462, ?x3745), film_crew_role(?x3745, ?x137) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 03cw411 film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 96.000 0.730 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 03cw411 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.730 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1964-01p0vf PRED entity: 01p0vf PRED relation: artist! PRED expected values: 017l96 01t04r => 126 concepts (100 used for prediction) PRED predicted values (max 10 best out of 128): 015_1q (0.30 #1280, 0.21 #3940, 0.19 #5200), 0g768 (0.22 #878, 0.12 #3958, 0.12 #2838), 01clyr (0.19 #174, 0.12 #734, 0.09 #4374), 0n85g (0.18 #343, 0.15 #763, 0.14 #2023), 01cl2y (0.18 #731, 0.14 #31, 0.08 #3811), 011k1h (0.18 #710, 0.12 #150, 0.11 #5190), 03rhqg (0.17 #1976, 0.17 #856, 0.16 #5196), 03mp8k (0.14 #67, 0.12 #207, 0.09 #3007), 01znbj (0.14 #108, 0.02 #8685, 0.01 #1928), 01trtc (0.14 #2873, 0.14 #3853, 0.12 #3433) >> Best rule #1280 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 02bwjv; >> query: (?x7053, 015_1q) <- artist(?x7681, ?x7053), nominated_for(?x7053, ?x7911), film(?x7053, ?x7305) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #8142 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 829 *> proper extension: 0c7ct; 07qnf; 04r1t; 0167_s; 013v5j; 02r1tx7; 01l_vgt; 0p3r8; 07yg2; 03xhj6; ... *> query: (?x7053, 017l96) <- artist(?x7681, ?x7053), artists(?x302, ?x7053) *> conf = 0.09 ranks of expected_values: 22, 61 EVAL 01p0vf artist! 01t04r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 126.000 100.000 0.304 http://example.org/music/record_label/artist EVAL 01p0vf artist! 017l96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 126.000 100.000 0.304 http://example.org/music/record_label/artist #1963-026q3s3 PRED entity: 026q3s3 PRED relation: actor PRED expected values: 06_6j3 => 60 concepts (42 used for prediction) PRED predicted values (max 10 best out of 58): 05v954 (0.38 #161, 0.36 #231, 0.33 #373), 044_7j (0.33 #38, 0.25 #107, 0.14 #246), 05q_mg (0.33 #64, 0.25 #133, 0.14 #272), 0678gl (0.33 #69, 0.25 #138, 0.12 #349), 0chrwb (0.33 #10, 0.25 #79, 0.12 #290), 05j0wc (0.33 #50, 0.25 #119, 0.07 #258), 0ccqd7 (0.33 #49, 0.25 #118, 0.07 #257), 01x9_8 (0.33 #39, 0.25 #108, 0.07 #247), 01x_d8 (0.33 #22, 0.25 #91, 0.07 #230), 05dxl5 (0.33 #12, 0.25 #81, 0.07 #220) >> Best rule #161 for best value: >> intensional similarity = 14 >> extensional distance = 6 >> proper extension: 0dr1c2; >> query: (?x1334, 05v954) <- genre(?x1334, ?x2540), genre(?x1334, ?x1626), genre(?x1334, ?x812), genre(?x1334, ?x225), ?x225 = 02kdv5l, ?x2540 = 0hcr, ?x1626 = 03q4nz, film(?x256, ?x1334), genre(?x7114, ?x812), genre(?x6029, ?x812), genre(?x4786, ?x812), ?x6029 = 0b9rdk, ?x4786 = 0bbw2z6, film(?x788, ?x7114) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #298 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 15 *> proper extension: 02vw1w2; *> query: (?x1334, 06_6j3) <- genre(?x1334, ?x2540), genre(?x1334, ?x1626), genre(?x1334, ?x225), ?x225 = 02kdv5l, ?x2540 = 0hcr, genre(?x9175, ?x1626), genre(?x7789, ?x1626), genre(?x5038, ?x1626), ?x5038 = 043n0v_, ?x9175 = 02qd04y, film(?x256, ?x1334), actor(?x1334, ?x5779), ?x7789 = 0dkv90 *> conf = 0.06 ranks of expected_values: 24 EVAL 026q3s3 actor 06_6j3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 60.000 42.000 0.375 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #1962-058kh7 PRED entity: 058kh7 PRED relation: language PRED expected values: 02h40lc => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 54): 02h40lc (0.91 #1065, 0.91 #1006, 0.91 #710), 064_8sq (0.25 #22, 0.16 #1322, 0.15 #258), 04306rv (0.25 #5, 0.13 #300, 0.10 #654), 02hxcvy (0.25 #152, 0.07 #447, 0.06 #4075), 03k50 (0.25 #127, 0.06 #4075, 0.05 #1131), 01wgr (0.25 #40, 0.06 #4075, 0.04 #3348), 06nm1 (0.12 #1311, 0.11 #719, 0.10 #1015), 06b_j (0.11 #377, 0.09 #909, 0.09 #1086), 03_9r (0.10 #187, 0.08 #246, 0.06 #4075), 02bjrlw (0.10 #1064, 0.08 #1123, 0.08 #709) >> Best rule #1065 for best value: >> intensional similarity = 5 >> extensional distance = 103 >> proper extension: 0b60sq; >> query: (?x9646, 02h40lc) <- genre(?x9646, ?x1403), ?x1403 = 02l7c8, film(?x902, ?x9646), film(?x12602, ?x9646), music(?x9646, ?x460) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 058kh7 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.914 http://example.org/film/film/language #1961-03hmt9b PRED entity: 03hmt9b PRED relation: film_release_region PRED expected values: 09c7w0 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 214): 09c7w0 (0.75 #1257, 0.74 #899, 0.74 #1615), 0d0vqn (0.49 #1624, 0.43 #1803, 0.38 #1984), 0f8l9c (0.47 #1644, 0.42 #1823, 0.37 #928), 07ssc (0.47 #1636, 0.43 #11485, 0.42 #6098), 0345h (0.46 #1659, 0.42 #1838, 0.37 #2019), 05r4w (0.46 #1614, 0.40 #1793, 0.34 #1974), 06mkj (0.46 #1688, 0.41 #1867, 0.36 #972), 03rjj (0.45 #1620, 0.41 #1799, 0.37 #904), 059j2 (0.45 #1657, 0.41 #1836, 0.37 #941), 0d060g (0.42 #1623, 0.39 #1802, 0.33 #1983) >> Best rule #1257 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 0m313; 0yyg4; 011yxg; 07gp9; 0bth54; 050r1z; 011yph; 0209hj; 01hp5; 061681; ... >> query: (?x4007, 09c7w0) <- nominated_for(?x1180, ?x4007), nominated_for(?x637, ?x4007), ?x637 = 02r22gf, award(?x164, ?x1180) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hmt9b film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.748 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1960-06zpgb2 PRED entity: 06zpgb2 PRED relation: category PRED expected values: 08mbj5d => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #107, 0.20 #5, 0.19 #49) >> Best rule #107 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x10996, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06zpgb2 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #1959-0192l PRED entity: 0192l PRED relation: role! PRED expected values: 05r5c => 66 concepts (51 used for prediction) PRED predicted values (max 10 best out of 117): 02sgy (0.93 #4752, 0.93 #4636, 0.85 #3280), 05148p4 (0.85 #4746, 0.85 #4654, 0.80 #4533), 042v_gx (0.85 #1340, 0.84 #2201, 0.84 #3037), 01dnws (0.85 #1340, 0.84 #2201, 0.84 #3037), 02w3w (0.84 #2201, 0.84 #3037, 0.84 #845), 018j2 (0.83 #4181, 0.82 #4796, 0.81 #3452), 0bxl5 (0.81 #2767, 0.73 #839, 0.71 #1538), 0l14md (0.81 #3773, 0.80 #2689, 0.80 #2577), 013y1f (0.80 #2363, 0.79 #4788, 0.77 #4053), 05r5c (0.76 #3640, 0.75 #3521, 0.75 #2194) >> Best rule #4752 for best value: >> intensional similarity = 24 >> extensional distance = 25 >> proper extension: 0j871; >> query: (?x5990, ?x314) <- role(?x5990, ?x2158), role(?x5990, ?x1969), role(?x5990, ?x614), role(?x5990, ?x314), ?x614 = 0mkg, ?x314 = 02sgy, role(?x2158, ?x3991), role(?x2158, ?x2956), role(?x2158, ?x1432), ?x2956 = 0myk8, role(?x2158, ?x1166), role(?x1969, ?x2725), role(?x1574, ?x1969), instrumentalists(?x1969, ?x1001), group(?x2158, ?x4791), ?x1432 = 0395lw, instrumentalists(?x2158, ?x226), role(?x3403, ?x1969), ?x3403 = 02qwg, role(?x2865, ?x1969), ?x2725 = 0l1589, ?x1166 = 05148p4, ?x3991 = 05842k, group(?x1969, ?x1929) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #3640 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 19 *> proper extension: 06ch55; *> query: (?x5990, ?x228) <- instrumentalists(?x5990, ?x9321), artists(?x3061, ?x9321), languages(?x9321, ?x254), role(?x9321, ?x228), award_winner(?x342, ?x9321), film(?x9321, ?x5570), award(?x9321, ?x1232), artists(?x3061, ?x7459), artists(?x3061, ?x7331), artists(?x3061, ?x4840), ?x4840 = 06m61, role(?x3160, ?x228), performance_role(?x568, ?x228), role(?x2888, ?x228), role(?x745, ?x228), ?x7331 = 01vtj38, ?x7459 = 0jsg0m, ?x745 = 01vj9c, role(?x2253, ?x228), ?x2253 = 01679d, ?x3160 = 01w806h, ?x2888 = 02fsn, artist(?x3887, ?x9321) *> conf = 0.76 ranks of expected_values: 10 EVAL 0192l role! 05r5c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 66.000 51.000 0.926 http://example.org/music/performance_role/regular_performances./music/group_membership/role #1958-0319l PRED entity: 0319l PRED relation: role! PRED expected values: 02w3w => 74 concepts (45 used for prediction) PRED predicted values (max 10 best out of 112): 01vj9c (0.87 #108, 0.85 #659, 0.85 #1895), 05148p4 (0.87 #108, 0.85 #659, 0.85 #1789), 0mkg (0.87 #108, 0.85 #659, 0.85 #328), 07gql (0.87 #108, 0.85 #659, 0.85 #328), 03bx0bm (0.82 #1358, 0.81 #2913, 0.80 #3576), 042v_gx (0.82 #1556, 0.74 #3115, 0.73 #1336), 013y1f (0.81 #2581, 0.80 #3579, 0.79 #3253), 028tv0 (0.77 #1893, 0.73 #2115, 0.69 #1780), 02sgy (0.75 #2890, 0.73 #2444, 0.68 #3334), 0g33q (0.74 #3184, 0.73 #1625, 0.69 #2625) >> Best rule #108 for best value: >> intensional similarity = 22 >> extensional distance = 1 >> proper extension: 05148p4; >> query: (?x1472, ?x315) <- role(?x1472, ?x4975), role(?x1472, ?x3112), role(?x1472, ?x2206), role(?x1472, ?x4769), role(?x1472, ?x3161), role(?x1472, ?x894), role(?x1472, ?x614), role(?x1472, ?x315), ?x894 = 03m5k, role(?x3161, ?x1225), role(?x1436, ?x3161), ?x614 = 0mkg, group(?x1472, ?x997), group(?x3161, ?x3682), ?x3682 = 04qmr, ?x1225 = 01qbl, role(?x3112, ?x1663), role(?x2306, ?x3161), family(?x2620, ?x4975), ?x4769 = 0dwt5, ?x1663 = 01w4dy, ?x2206 = 07gql >> conf = 0.87 => this is the best rule for 4 predicted values *> Best rule #111 for first EXPECTED value: *> intensional similarity = 22 *> extensional distance = 1 *> proper extension: 05148p4; *> query: (?x1472, ?x645) <- role(?x1472, ?x4975), role(?x1472, ?x3112), role(?x1472, ?x2206), role(?x1472, ?x4769), role(?x1472, ?x3161), role(?x1472, ?x894), role(?x1472, ?x614), ?x894 = 03m5k, role(?x3161, ?x1225), role(?x1436, ?x3161), ?x614 = 0mkg, group(?x1472, ?x997), role(?x645, ?x3161), group(?x3161, ?x3682), ?x3682 = 04qmr, ?x1225 = 01qbl, role(?x3112, ?x1663), role(?x2306, ?x3161), family(?x2620, ?x4975), ?x4769 = 0dwt5, ?x1663 = 01w4dy, ?x2206 = 07gql *> conf = 0.65 ranks of expected_values: 47 EVAL 0319l role! 02w3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 74.000 45.000 0.873 http://example.org/music/performance_role/regular_performances./music/group_membership/role #1957-03rz2b PRED entity: 03rz2b PRED relation: currency PRED expected values: 09nqf => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.81 #141, 0.79 #29, 0.78 #8), 01nv4h (0.07 #65, 0.04 #23, 0.04 #9), 02gsvk (0.04 #69, 0.02 #76, 0.01 #251), 02l6h (0.03 #4, 0.02 #67, 0.02 #18) >> Best rule #141 for best value: >> intensional similarity = 4 >> extensional distance = 467 >> proper extension: 0c40vxk; >> query: (?x2882, 09nqf) <- film_crew_role(?x2882, ?x137), film(?x1414, ?x2882), film(?x10061, ?x2882), genre(?x2882, ?x53) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rz2b currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.814 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #1956-07xvf PRED entity: 07xvf PRED relation: genre PRED expected values: 02kdv5l 082gq => 84 concepts (72 used for prediction) PRED predicted values (max 10 best out of 112): 07s9rl0 (0.76 #6323, 0.72 #6809, 0.67 #2066), 02kdv5l (0.75 #3, 0.70 #3164, 0.70 #3285), 05p553 (0.54 #6083, 0.35 #4622, 0.35 #368), 06n90 (0.39 #13, 0.31 #134, 0.26 #1593), 01hmnh (0.36 #1109, 0.34 #1840, 0.33 #1598), 0lsxr (0.34 #1711, 0.32 #2197, 0.32 #1954), 02l7c8 (0.30 #500, 0.30 #3905, 0.30 #3784), 02n4kr (0.24 #1710, 0.23 #2196, 0.23 #1953), 082gq (0.22 #515, 0.19 #2582, 0.18 #2674), 04xvlr (0.21 #1337, 0.21 #1214, 0.21 #2067) >> Best rule #6323 for best value: >> intensional similarity = 4 >> extensional distance = 1125 >> proper extension: 0ckr7s; 018nnz; 08k40m; 0k0rf; 039zft; 05pyrb; 016ztl; 02qk3fk; 07s3m4g; 07ghv5; ... >> query: (?x7373, 07s9rl0) <- genre(?x7373, ?x812), film(?x1104, ?x7373), genre(?x2525, ?x812), ?x2525 = 02qmsr >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 95 *> proper extension: 02vw1w2; *> query: (?x7373, 02kdv5l) <- genre(?x7373, ?x812), genre(?x7373, ?x811), ?x811 = 03k9fj, film(?x300, ?x7373), ?x812 = 01jfsb *> conf = 0.75 ranks of expected_values: 2, 9 EVAL 07xvf genre 082gq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 84.000 72.000 0.757 http://example.org/film/film/genre EVAL 07xvf genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 72.000 0.757 http://example.org/film/film/genre #1955-02ht1k PRED entity: 02ht1k PRED relation: film! PRED expected values: 03n52j => 100 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1035): 086k8 (0.46 #58024, 0.45 #64243, 0.43 #45588), 0gd9k (0.17 #1379, 0.07 #3451, 0.05 #5523), 0f7h2v (0.17 #464, 0.07 #2536, 0.05 #4608), 060j8b (0.17 #1098, 0.07 #3170, 0.05 #5242), 01_njt (0.17 #1425, 0.07 #3497, 0.05 #5569), 04fhxp (0.17 #375, 0.07 #2447, 0.05 #4519), 0bxtg (0.17 #77, 0.04 #24942, 0.03 #35302), 03k545 (0.17 #1873, 0.03 #14305, 0.03 #8089), 03n52j (0.17 #950, 0.03 #13382, 0.01 #25815), 02pzck (0.17 #1727, 0.03 #7943, 0.03 #22448) >> Best rule #58024 for best value: >> intensional similarity = 5 >> extensional distance = 555 >> proper extension: 03rg2b; >> query: (?x3833, ?x2383) <- production_companies(?x3833, ?x382), nominated_for(?x2383, ?x3833), nominated_for(?x2328, ?x3833), film_crew_role(?x3833, ?x137), film(?x2328, ?x2329) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #950 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 048tv9; *> query: (?x3833, 03n52j) <- production_companies(?x3833, ?x382), film(?x3258, ?x3833), titles(?x307, ?x3833), ?x3258 = 02qx69 *> conf = 0.17 ranks of expected_values: 9 EVAL 02ht1k film! 03n52j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 100.000 57.000 0.458 http://example.org/film/actor/film./film/performance/film #1954-05qhw PRED entity: 05qhw PRED relation: country! PRED expected values: 06wrt 0486tv 03krj => 206 concepts (206 used for prediction) PRED predicted values (max 10 best out of 17): 06wrt (0.78 #361, 0.73 #157, 0.73 #140), 0486tv (0.62 #146, 0.58 #129, 0.57 #180), 02bkg (0.50 #137, 0.47 #154, 0.46 #120), 096f8 (0.50 #139, 0.46 #122, 0.43 #411), 06z68 (0.47 #176, 0.47 #159, 0.46 #142), 02y74 (0.47 #183, 0.42 #149, 0.42 #132), 0152n0 (0.40 #664, 0.40 #2044, 0.38 #147), 03krj (0.40 #664, 0.40 #2044, 0.38 #150), 018w8 (0.40 #664, 0.40 #2044, 0.35 #143), 0crlz (0.40 #664, 0.40 #2044, 0.34 #1873) >> Best rule #361 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 084n_; >> query: (?x456, 06wrt) <- adjoins(?x456, ?x344), country(?x6121, ?x456), film(?x1104, ?x6121) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 8 EVAL 05qhw country! 03krj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 206.000 206.000 0.778 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 05qhw country! 0486tv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 206.000 206.000 0.778 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 05qhw country! 06wrt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 206.000 206.000 0.778 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #1953-015l4k PRED entity: 015l4k PRED relation: olympics! PRED expected values: 02vzc 05vz3zq => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 338): 07ssc (0.85 #3288, 0.84 #2768, 0.83 #3159), 03_3d (0.78 #2623, 0.76 #2486, 0.73 #3020), 02vzc (0.76 #3576, 0.76 #3706, 0.74 #3448), 0163v (0.71 #1099, 0.67 #970, 0.62 #1354), 05vz3zq (0.71 #1262, 0.50 #475, 0.44 #3656), 0chghy (0.71 #2490, 0.68 #2762, 0.67 #2627), 0345h (0.70 #1719, 0.67 #1458, 0.67 #947), 06qd3 (0.70 #1723, 0.65 #2514, 0.64 #2379), 03gj2 (0.65 #3554, 0.57 #3167, 0.56 #3684), 0k6nt (0.63 #2775, 0.62 #3553, 0.59 #3037) >> Best rule #3288 for best value: >> intensional similarity = 30 >> extensional distance = 24 >> proper extension: 0sxrz; >> query: (?x8189, 07ssc) <- olympics(?x2513, ?x8189), olympics(?x304, ?x8189), olympics(?x3309, ?x8189), country(?x3309, ?x1273), country(?x3309, ?x410), sports(?x8189, ?x453), ?x2513 = 05b4w, ?x1273 = 04wgh, ?x410 = 01ls2, film_release_region(?x7502, ?x304), film_release_region(?x6621, ?x304), film_release_region(?x6095, ?x304), film_release_region(?x5825, ?x304), film_release_region(?x4668, ?x304), film_release_region(?x4290, ?x304), film_release_region(?x3076, ?x304), film_release_region(?x2893, ?x304), film_release_region(?x559, ?x304), ?x5825 = 067ghz, ?x4668 = 0bh8x1y, ?x6095 = 0bq6ntw, jurisdiction_of_office(?x182, ?x304), ?x3076 = 0g5838s, ?x559 = 05p1tzf, ?x2893 = 01jrbb, country(?x150, ?x304), ?x4290 = 0gtxj2q, language(?x7502, ?x254), ?x6621 = 0h63gl9, contains(?x455, ?x304) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #3576 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 35 *> proper extension: 018wrk; *> query: (?x8189, 02vzc) <- olympics(?x2513, ?x8189), olympics(?x3309, ?x8189), country(?x3309, ?x142), sports(?x8189, ?x453), film_release_region(?x9832, ?x2513), film_release_region(?x6932, ?x2513), film_release_region(?x6556, ?x2513), film_release_region(?x6270, ?x2513), film_release_region(?x4446, ?x2513), film_release_region(?x4441, ?x2513), film_release_region(?x3000, ?x2513), film_release_region(?x2644, ?x2513), film_release_region(?x2340, ?x2513), film_release_region(?x781, ?x2513), film_release_region(?x511, ?x2513), sports(?x418, ?x3309), currency(?x2513, ?x170), ?x2340 = 0fpv_3_, ?x6932 = 027pfg, ?x781 = 0gkz15s, ?x511 = 0dscrwf, ?x2644 = 01shy7, ?x6556 = 05dss7, ?x3000 = 045j3w, participating_countries(?x1608, ?x2513), combatants(?x2513, ?x583), ?x4441 = 0125xq, ?x6270 = 0g9zljd, titles(?x307, ?x9832), ?x4446 = 0db94w *> conf = 0.76 ranks of expected_values: 3, 5 EVAL 015l4k olympics! 05vz3zq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 30.000 30.000 0.846 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics EVAL 015l4k olympics! 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 30.000 30.000 0.846 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #1952-05krk PRED entity: 05krk PRED relation: major_field_of_study PRED expected values: 04x_3 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 109): 01mkq (0.70 #468, 0.68 #694, 0.67 #581), 062z7 (0.44 #592, 0.42 #25, 0.41 #479), 04x_3 (0.44 #590, 0.37 #477, 0.37 #364), 03g3w (0.40 #704, 0.39 #478, 0.38 #591), 01lj9 (0.40 #715, 0.37 #489, 0.37 #376), 02_7t (0.33 #1078, 0.33 #399, 0.33 #286), 037mh8 (0.33 #515, 0.32 #741, 0.27 #1194), 01540 (0.31 #621, 0.31 #54, 0.28 #395), 0_jm (0.30 #1640, 0.29 #2772, 0.29 #1072), 02h40lc (0.27 #571, 0.26 #345, 0.26 #232) >> Best rule #468 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 03_c8p; >> query: (?x388, 01mkq) <- organization(?x388, ?x5487), citytown(?x388, ?x6453), organization(?x346, ?x388) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #590 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 46 *> proper extension: 05rznz; *> query: (?x388, 04x_3) <- category(?x388, ?x134), organization(?x388, ?x5487), contains(?x94, ?x388) *> conf = 0.44 ranks of expected_values: 3 EVAL 05krk major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 105.000 0.696 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1951-0bdxs5 PRED entity: 0bdxs5 PRED relation: instrumentalists! PRED expected values: 0342h => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 93): 0342h (0.73 #5, 0.67 #779, 0.62 #3276), 05148p4 (0.42 #19, 0.42 #105, 0.35 #1223), 018vs (0.27 #785, 0.27 #5260, 0.27 #183), 02hnl (0.22 #807, 0.20 #205, 0.19 #1840), 0l14md (0.21 #93, 0.16 #179, 0.14 #1642), 03qjg (0.19 #50, 0.17 #1857, 0.16 #1685), 026t6 (0.19 #777, 0.16 #1638, 0.15 #1810), 0l14qv (0.13 #780, 0.13 #92, 0.11 #178), 018j2 (0.09 #5459, 0.09 #3308, 0.09 #5286), 03gvt (0.09 #1268, 0.08 #64, 0.06 #1871) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 06y9c2; 04cr6qv; 04d_mtq; >> query: (?x8693, 0342h) <- nationality(?x8693, ?x94), friend(?x8693, ?x1093), instrumentalists(?x314, ?x8693) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bdxs5 instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.731 http://example.org/music/instrument/instrumentalists #1950-02vtnf PRED entity: 02vtnf PRED relation: people! PRED expected values: 041rx => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 52): 041rx (0.29 #235, 0.23 #158, 0.21 #1160), 048z7l (0.25 #40, 0.08 #271, 0.08 #348), 033tf_ (0.21 #777, 0.19 #1240, 0.18 #1934), 0x67 (0.13 #87, 0.10 #4485, 0.10 #4562), 02ctzb (0.13 #92, 0.07 #708, 0.03 #2944), 0xnvg (0.11 #783, 0.08 #1246, 0.08 #244), 02w7gg (0.09 #850, 0.08 #927, 0.07 #2238), 03bkbh (0.08 #263, 0.07 #802, 0.06 #1265), 09vc4s (0.08 #779, 0.08 #702, 0.07 #1242), 07bch9 (0.08 #716, 0.08 #177, 0.07 #793) >> Best rule #235 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 034bgm; 01twdk; 06b_0; 036dyy; 0chw_; >> query: (?x10920, 041rx) <- film(?x10920, ?x3909), participant(?x10920, ?x2524), profession(?x10920, ?x319) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vtnf people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.289 http://example.org/people/ethnicity/people #1949-01f9zw PRED entity: 01f9zw PRED relation: artist! PRED expected values: 015_1q => 121 concepts (87 used for prediction) PRED predicted values (max 10 best out of 109): 07wg3 (0.44 #286, 0.32 #143, 0.13 #571), 015_1q (0.35 #163, 0.23 #733, 0.21 #1874), 01trtc (0.30 #217, 0.24 #74, 0.15 #360), 011k1h (0.23 #296, 0.13 #2291, 0.12 #1008), 03mp8k (0.20 #211, 0.20 #639, 0.13 #2349), 033hn8 (0.20 #157, 0.16 #2295, 0.13 #2437), 043g7l (0.20 #175, 0.13 #603, 0.12 #32), 01w40h (0.18 #29, 0.11 #742, 0.10 #172), 0181dw (0.17 #756, 0.13 #1041, 0.13 #3034), 0fb0v (0.15 #293, 0.07 #2572, 0.07 #2430) >> Best rule #286 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 01vvydl; 0gt_k; 01cwhp; 03h_fk5; 01wj18h; 0gbwp; 01vw20h; 0478__m; 03y82t6; 0c7xjb; ... >> query: (?x8856, ?x13773) <- nationality(?x8856, ?x94), origin(?x8856, ?x1523), profession(?x8856, ?x220), company(?x8856, ?x13773) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #163 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 01vvydl; 0gt_k; 01cwhp; 03h_fk5; 01wj18h; 0gbwp; 01vw20h; 0478__m; 03y82t6; 0c7xjb; ... *> query: (?x8856, 015_1q) <- nationality(?x8856, ?x94), origin(?x8856, ?x1523), profession(?x8856, ?x220), company(?x8856, ?x13773) *> conf = 0.35 ranks of expected_values: 2 EVAL 01f9zw artist! 015_1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 87.000 0.440 http://example.org/music/record_label/artist #1948-09swkk PRED entity: 09swkk PRED relation: profession PRED expected values: 0dz3r => 117 concepts (114 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.87 #15683, 0.69 #11808, 0.68 #13447), 01c72t (0.75 #471, 0.66 #1365, 0.66 #1962), 0nbcg (0.60 #3763, 0.58 #3165, 0.52 #628), 016z4k (0.55 #4332, 0.44 #5228, 0.43 #600), 0dz3r (0.54 #3733, 0.53 #3135, 0.50 #300), 01c8w0 (0.41 #903, 0.39 #1947, 0.37 #1350), 039v1 (0.41 #3768, 0.40 #3170, 0.33 #4365), 01d_h8 (0.39 #5679, 0.38 #453, 0.37 #8518), 0dxtg (0.32 #6584, 0.29 #10317, 0.29 #7930), 05vyk (0.29 #244, 0.16 #2033, 0.15 #1287) >> Best rule #15683 for best value: >> intensional similarity = 3 >> extensional distance = 2873 >> proper extension: 06v8s0; 02qjj7; 018dnt; 01yh3y; 03fghg; 02zyy4; 04h07s; 02v60l; 02h8hr; 01jb26; ... >> query: (?x4940, 02hrh1q) <- profession(?x4940, ?x1183), profession(?x7530, ?x1183), ?x7530 = 04954 >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #3733 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 160 *> proper extension: 032t2z; 01w923; 0bkg4; 023l9y; 018y81; 01ydzx; 04_jsg; *> query: (?x4940, 0dz3r) <- instrumentalists(?x716, ?x4940), ?x716 = 018vs, artists(?x4910, ?x4940) *> conf = 0.54 ranks of expected_values: 5 EVAL 09swkk profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 117.000 114.000 0.869 http://example.org/people/person/profession #1947-0123j6 PRED entity: 0123j6 PRED relation: organization! PRED expected values: 060c4 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 29): 060c4 (0.73 #1365, 0.72 #1497, 0.67 #1678), 07xl34 (0.19 #1855, 0.18 #1698, 0.17 #1613), 05k17c (0.17 #296, 0.13 #779, 0.10 #67), 0krdk (0.11 #374, 0.10 #592, 0.09 #181), 05_wyz (0.11 #374, 0.10 #592, 0.09 #181), 02211by (0.11 #374, 0.10 #592, 0.09 #181), 0dq3c (0.11 #374, 0.09 #181, 0.08 #1253), 01yc02 (0.11 #374, 0.09 #181, 0.08 #1253), 09d6p2 (0.11 #374, 0.09 #181, 0.08 #1253), 033smt (0.11 #374, 0.09 #181, 0.07 #1616) >> Best rule #1365 for best value: >> intensional similarity = 6 >> extensional distance = 398 >> proper extension: 0f1nl; 04hgpt; 02fs_d; 012mzw; 01qwb5; 04bbpm; 02x9cv; 0jpn8; 01n4w_; 02hp70; ... >> query: (?x5961, 060c4) <- state_province_region(?x5961, ?x1767), organization(?x4682, ?x5961), company(?x4682, ?x3578), organization(?x4682, ?x7457), ?x7457 = 0gsgr, ?x3578 = 08z129 >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0123j6 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.730 http://example.org/organization/role/leaders./organization/leadership/organization #1946-02d478 PRED entity: 02d478 PRED relation: nominated_for! PRED expected values: 02x1dht => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 185): 027571b (0.68 #1841, 0.66 #11510, 0.66 #9439), 027c95y (0.68 #1841, 0.66 #11510, 0.66 #9439), 0f4x7 (0.50 #24, 0.37 #5317, 0.26 #2303), 0gs9p (0.50 #61, 0.36 #5354, 0.31 #521), 09qv_s (0.50 #109, 0.26 #2303, 0.26 #2302), 02w9sd7 (0.50 #119, 0.26 #2303, 0.26 #2302), 0gq9h (0.43 #5352, 0.36 #519, 0.33 #59), 019f4v (0.34 #5344, 0.28 #1661, 0.27 #971), 04dn09n (0.33 #33, 0.30 #493, 0.28 #5326), 02x73k6 (0.33 #46, 0.26 #2303, 0.26 #2302) >> Best rule #1841 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 0kv2hv; 02q56mk; 0p4v_; 0glnm; 02rq8k8; 0y_hb; 04lhc4; 03tbg6; >> query: (?x4067, ?x2915) <- nominated_for(?x395, ?x4067), genre(?x4067, ?x53), nominated_for(?x2116, ?x4067), award(?x4067, ?x2915) >> conf = 0.68 => this is the best rule for 2 predicted values *> Best rule #41 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 03hkch7; *> query: (?x4067, 02x1dht) <- nominated_for(?x1208, ?x4067), genre(?x4067, ?x53), film_release_region(?x4067, ?x94), ?x1208 = 0sz28 *> conf = 0.17 ranks of expected_values: 52 EVAL 02d478 nominated_for! 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 85.000 85.000 0.675 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1945-04vq33 PRED entity: 04vq33 PRED relation: genre PRED expected values: 06l3bl => 71 concepts (68 used for prediction) PRED predicted values (max 10 best out of 86): 02l7c8 (0.68 #3023, 0.40 #378, 0.39 #619), 017fp (0.65 #361, 0.65 #256, 0.51 #6011), 03bxz7 (0.53 #295, 0.12 #416, 0.11 #3301), 02kdv5l (0.53 #2, 0.52 #122, 0.35 #724), 06l3bl (0.42 #38, 0.38 #158, 0.21 #399), 04xvlr (0.40 #362, 0.38 #121, 0.37 #1), 05p553 (0.34 #5533, 0.33 #3010, 0.32 #8059), 01jfsb (0.29 #7347, 0.27 #3499, 0.27 #5542), 060__y (0.27 #379, 0.24 #138, 0.21 #18), 03k9fj (0.24 #132, 0.21 #7346, 0.21 #5541) >> Best rule #3023 for best value: >> intensional similarity = 4 >> extensional distance = 547 >> proper extension: 014_x2; 02y_lrp; 034qmv; 018js4; 0sxg4; 083shs; 0b2v79; 027qgy; 02v8kmz; 09m6kg; ... >> query: (?x12679, 02l7c8) <- genre(?x12679, ?x4088), nominated_for(?x199, ?x12679), genre(?x878, ?x4088), ?x878 = 0147sh >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 01gc7; 01h7bb; 050r1z; 0cwy47; 09q5w2; 04kzqz; 083skw; 01vw8k; 049mql; 0b2qtl; ... *> query: (?x12679, 06l3bl) <- genre(?x12679, ?x4088), genre(?x12679, ?x3515), nominated_for(?x199, ?x12679), ?x4088 = 04xvh5, ?x3515 = 082gq *> conf = 0.42 ranks of expected_values: 5 EVAL 04vq33 genre 06l3bl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 71.000 68.000 0.683 http://example.org/film/film/genre #1944-022769 PRED entity: 022769 PRED relation: location PRED expected values: 0c4kv => 115 concepts (114 used for prediction) PRED predicted values (max 10 best out of 87): 0c4kv (0.71 #31298, 0.70 #64206, 0.61 #14442), 02_286 (0.26 #11269, 0.20 #8059, 0.20 #16085), 059rby (0.19 #818, 0.05 #4829, 0.05 #8038), 01qh7 (0.10 #957), 0cb4j (0.10 #832), 0cr3d (0.09 #4154, 0.08 #143, 0.07 #40270), 01cx_ (0.08 #161, 0.03 #45909, 0.02 #51525), 052p7 (0.08 #125, 0.01 #4136, 0.01 #1729), 0b2lw (0.08 #348), 0plyy (0.08 #21) >> Best rule #31298 for best value: >> intensional similarity = 3 >> extensional distance = 742 >> proper extension: 02c4s; 032md; 030dx5; 01m7f5r; 01h2_6; >> query: (?x2100, ?x12289) <- people(?x1050, ?x2100), place_of_birth(?x2100, ?x12289), location(?x2100, ?x1131) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022769 location 0c4kv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 114.000 0.712 http://example.org/people/person/places_lived./people/place_lived/location #1943-04sylm PRED entity: 04sylm PRED relation: organization! PRED expected values: 060c4 => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 37): 060c4 (0.82 #288, 0.82 #210, 0.81 #301), 0dq_5 (0.41 #529, 0.40 #256, 0.34 #698), 05k17c (0.33 #20, 0.20 #33, 0.17 #85), 07xl34 (0.22 #180, 0.22 #401, 0.22 #349), 0hm4q (0.06 #958, 0.05 #945, 0.05 #1231), 05c0jwl (0.05 #720, 0.05 #759, 0.04 #955), 0dq3c (0.02 #1512, 0.02 #92, 0.02 #1565), 01t7n9 (0.02 #1512, 0.02 #1565, 0.02 #1592), 0fkzq (0.02 #1512, 0.02 #1565, 0.02 #1592), 09n5b9 (0.02 #1512, 0.02 #1565, 0.02 #1592) >> Best rule #288 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 0473m9; 027mdh; >> query: (?x2767, 060c4) <- currency(?x2767, ?x170), state_province_region(?x2767, ?x335), school_type(?x2767, ?x1044) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04sylm organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.818 http://example.org/organization/role/leaders./organization/leadership/organization #1942-028qdb PRED entity: 028qdb PRED relation: nationality PRED expected values: 09c7w0 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.70 #5204, 0.69 #7610, 0.69 #7409), 0d060g (0.35 #6706, 0.35 #7107, 0.34 #6405), 0f8l9c (0.35 #6706, 0.35 #7107, 0.34 #6405), 01p1v (0.35 #6706, 0.35 #7107, 0.34 #6405), 02jx1 (0.26 #333, 0.20 #233, 0.20 #2534), 07ssc (0.15 #916, 0.15 #415, 0.15 #1216), 0345h (0.11 #9411, 0.06 #9813, 0.03 #331), 03gyl (0.11 #9411, 0.06 #9813), 03rk0 (0.11 #9411, 0.05 #9457, 0.05 #9557), 03_3d (0.11 #9411, 0.02 #3307, 0.01 #3907) >> Best rule #5204 for best value: >> intensional similarity = 3 >> extensional distance = 999 >> proper extension: 03b78r; >> query: (?x4206, 09c7w0) <- award_nominee(?x4206, ?x2461), award_winner(?x6487, ?x4206), location(?x2461, ?x2474) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028qdb nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.696 http://example.org/people/person/nationality #1941-0gc_c_ PRED entity: 0gc_c_ PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 34): 0ch6mp2 (0.79 #478, 0.72 #1793, 0.71 #1684), 09zzb8 (0.78 #471, 0.75 #145, 0.71 #1786), 09vw2b7 (0.70 #477, 0.67 #151, 0.66 #259), 01vx2h (0.52 #13, 0.50 #592, 0.46 #193), 0dxtw (0.43 #12, 0.42 #264, 0.42 #192), 01pvkk (0.28 #484, 0.27 #1690, 0.27 #2126), 02rh1dz (0.20 #590, 0.20 #444, 0.19 #517), 0215hd (0.19 #307, 0.19 #379, 0.19 #271), 015h31 (0.19 #10, 0.18 #589, 0.17 #226), 0d2b38 (0.17 #605, 0.17 #98, 0.15 #386) >> Best rule #478 for best value: >> intensional similarity = 4 >> extensional distance = 192 >> proper extension: 07gp9; 02hxhz; 0jyx6; 02prw4h; 05z_kps; 09z2b7; 0c8tkt; 0661m4p; 0ddjy; 04yc76; ... >> query: (?x3600, 0ch6mp2) <- featured_film_locations(?x3600, ?x1104), executive_produced_by(?x3600, ?x3744), language(?x3600, ?x254), film_crew_role(?x3600, ?x468) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gc_c_ film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.794 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1940-0157m PRED entity: 0157m PRED relation: location_of_ceremony PRED expected values: 0qxhc => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 55): 0cv3w (0.14 #273, 0.09 #1345, 0.08 #1702), 013n2h (0.12 #429, 0.10 #905, 0.10 #667), 0dclg (0.12 #384, 0.06 #1813, 0.05 #2530), 0k_q_ (0.10 #862, 0.10 #624, 0.06 #2293), 0qxhc (0.10 #831, 0.09 #1427, 0.09 #1308), 0qr8z (0.10 #1028, 0.05 #3292, 0.02 #5196), 03rk0 (0.08 #1455, 0.08 #1693, 0.06 #2409), 0f0sbl (0.08 #1755, 0.06 #2471, 0.06 #2352), 02_286 (0.06 #1799, 0.05 #2873, 0.03 #4539), 0430_ (0.06 #2501, 0.06 #2382, 0.05 #2740) >> Best rule #273 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 03f77; >> query: (?x1620, 0cv3w) <- participant(?x4196, ?x1620), student(?x3228, ?x1620), entity_involved(?x2391, ?x4196) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #831 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 06jzh; 049dyj; 01zlh5; 02m3sd; *> query: (?x1620, 0qxhc) <- people(?x3584, ?x1620), person(?x1015, ?x1620), ?x3584 = 07hwkr *> conf = 0.10 ranks of expected_values: 5 EVAL 0157m location_of_ceremony 0qxhc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 184.000 184.000 0.143 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #1939-06929s PRED entity: 06929s PRED relation: honored_for! PRED expected values: 0bq_mx => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 117): 03gwpw2 (0.20 #5, 0.09 #249, 0.08 #493), 09gkdln (0.20 #106, 0.09 #350, 0.04 #594), 05zksls (0.20 #28, 0.05 #1248, 0.04 #516), 09g90vz (0.20 #108, 0.05 #1328, 0.04 #596), 09k5jh7 (0.20 #71, 0.04 #559, 0.03 #1413), 05qb8vx (0.20 #48, 0.04 #536, 0.02 #780), 02pgky2 (0.17 #6225, 0.16 #8789, 0.15 #5248), 03gt46z (0.17 #6225, 0.16 #8789, 0.15 #5248), 0bq_mx (0.17 #6225, 0.16 #8789, 0.15 #5248), 02cg41 (0.17 #6225, 0.16 #8789, 0.15 #5248) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 04dsnp; >> query: (?x4312, 03gwpw2) <- executive_produced_by(?x4312, ?x12254), genre(?x4312, ?x2753), ?x2753 = 0219x_, person(?x4312, ?x966) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #6225 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 626 *> proper extension: 03czz87; *> query: (?x4312, ?x4617) <- award_winner(?x4312, ?x10236), titles(?x1014, ?x4312), profession(?x10236, ?x319), award_winner(?x4617, ?x10236) *> conf = 0.17 ranks of expected_values: 9 EVAL 06929s honored_for! 0bq_mx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 120.000 120.000 0.200 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #1938-0sx7r PRED entity: 0sx7r PRED relation: olympics! PRED expected values: 03gj2 => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 460): 0d0vqn (0.78 #1003, 0.75 #399, 0.72 #2244), 059j2 (0.78 #1003, 0.75 #399, 0.72 #2244), 01mjq (0.78 #1003, 0.75 #399, 0.72 #2244), 06mkj (0.78 #1003, 0.75 #399, 0.72 #398), 0ctw_b (0.78 #1003, 0.75 #399, 0.72 #398), 02vzc (0.78 #1003, 0.75 #399, 0.72 #398), 0h7x (0.78 #1003, 0.75 #399, 0.72 #398), 03_3d (0.78 #1003, 0.75 #399, 0.72 #398), 06mzp (0.78 #1003, 0.75 #399, 0.72 #398), 0d05w3 (0.78 #1003, 0.75 #399, 0.72 #398) >> Best rule #1003 for best value: >> intensional similarity = 51 >> extensional distance = 2 >> proper extension: 09n48; >> query: (?x452, ?x252) <- olympics(?x789, ?x452), olympics(?x512, ?x452), olympics(?x279, ?x452), olympics(?x94, ?x452), ?x279 = 0d060g, olympics(?x252, ?x452), sports(?x452, ?x3309), sports(?x452, ?x1037), ?x3309 = 09w1n, ?x512 = 07ssc, ?x1037 = 09_bl, ?x94 = 09c7w0, origin(?x3382, ?x789), film_release_region(?x11218, ?x789), film_release_region(?x8682, ?x789), film_release_region(?x7692, ?x789), film_release_region(?x5400, ?x789), film_release_region(?x5347, ?x789), film_release_region(?x5270, ?x789), film_release_region(?x4828, ?x789), film_release_region(?x2958, ?x789), film_release_region(?x2617, ?x789), film_release_region(?x2155, ?x789), film_release_region(?x299, ?x789), film_release_region(?x204, ?x789), country(?x11148, ?x789), country(?x5465, ?x789), country(?x4048, ?x789), ?x5400 = 0bhwhj, participating_countries(?x418, ?x789), ?x2958 = 0b_5d, ?x4828 = 02fttd, ?x5270 = 0bc1yhb, contains(?x789, ?x790), award_winner(?x2617, ?x3129), nationality(?x317, ?x789), ?x8682 = 0bmfnjs, ?x7692 = 0bt4g, ?x11148 = 01qdmh, film_crew_role(?x5347, ?x137), ?x2155 = 0407yfx, ?x11218 = 0ccck7, ?x204 = 028_yv, olympics(?x789, ?x391), nominated_for(?x10597, ?x5347), organization(?x789, ?x127), ?x299 = 01gc7, language(?x5465, ?x254), ?x4048 = 0ddcbd5, production_companies(?x5347, ?x541), films(?x7455, ?x5465) >> conf = 0.78 => this is the best rule for 13 predicted values *> Best rule #802 for first EXPECTED value: *> intensional similarity = 48 *> extensional distance = 1 *> proper extension: 0kbvb; *> query: (?x452, ?x2188) <- olympics(?x2513, ?x452), olympics(?x279, ?x452), olympics(?x205, ?x452), ?x279 = 0d060g, olympics(?x7287, ?x452), olympics(?x5114, ?x452), olympics(?x1558, ?x452), olympics(?x774, ?x452), olympics(?x252, ?x452), sports(?x452, ?x3309), medal(?x452, ?x422), ?x774 = 06mzp, country(?x3309, ?x8620), country(?x3309, ?x8197), country(?x3309, ?x3041), country(?x3309, ?x792), sports(?x2630, ?x3309), sports(?x1277, ?x3309), sports(?x418, ?x3309), ?x8197 = 06srk, olympics(?x5114, ?x2233), ?x252 = 03_3d, sports(?x784, ?x3309), combatants(?x5114, ?x3728), combatants(?x5114, ?x583), combatants(?x9814, ?x5114), ?x8620 = 016zwt, country(?x3015, ?x5114), olympics(?x3345, ?x418), olympics(?x1497, ?x2630), contains(?x5114, ?x8745), nationality(?x2693, ?x5114), ?x7287 = 05b7q, sports(?x2630, ?x8190), ?x2513 = 05b4w, ?x205 = 03rjj, participating_countries(?x418, ?x404), ?x3015 = 071t0, ?x2233 = 0l6mp, ?x1558 = 01mjq, ?x792 = 0hzlz, olympics(?x2188, ?x1277), ?x3728 = 087vz, organization(?x5114, ?x312), ?x3041 = 04w4s, ?x9814 = 025rzfc, place_of_birth(?x3336, ?x8745), ?x583 = 015fr *> conf = 0.61 ranks of expected_values: 52 EVAL 0sx7r olympics! 03gj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 23.000 23.000 0.775 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #1937-01j851 PRED entity: 01j851 PRED relation: participant! PRED expected values: 016z2j => 144 concepts (64 used for prediction) PRED predicted values (max 10 best out of 368): 016z2j (0.85 #38479, 0.85 #36523, 0.84 #5865), 037s5h (0.48 #11078, 0.46 #16299, 0.46 #12382), 02r3cn (0.25 #409, 0.11 #1060, 0.01 #23233), 014zcr (0.25 #6, 0.10 #1308, 0.07 #4567), 02dlfh (0.25 #511, 0.06 #5724, 0.05 #7027), 0151w_ (0.25 #64, 0.04 #4625, 0.04 #5277), 01gkmx (0.20 #1864, 0.04 #5123, 0.04 #7078), 022q4j (0.13 #3190, 0.08 #2538, 0.07 #3842), 0gyx4 (0.10 #5519, 0.09 #20520, 0.09 #6822), 0dvmd (0.10 #5425, 0.09 #6728, 0.07 #4773) >> Best rule #38479 for best value: >> intensional similarity = 4 >> extensional distance = 434 >> proper extension: 0gs6vr; 0kj34; 0dq9wx; >> query: (?x9573, ?x2373) <- participant(?x8667, ?x9573), participant(?x9573, ?x2373), gender(?x9573, ?x514), place_of_birth(?x2373, ?x1131) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j851 participant! 016z2j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 64.000 0.849 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #1936-06v9_x PRED entity: 06v9_x PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 29): 0ch6mp2 (0.86 #575, 0.83 #928, 0.80 #645), 01vx2h (0.55 #649, 0.47 #190, 0.45 #83), 01pvkk (0.34 #650, 0.29 #439, 0.28 #1435), 0d2b38 (0.26 #205, 0.20 #98, 0.18 #664), 02rh1dz (0.24 #46, 0.23 #153, 0.22 #648), 02ynfr (0.21 #937, 0.20 #654, 0.20 #266), 01xy5l_ (0.21 #193, 0.15 #228, 0.15 #652), 015h31 (0.19 #188, 0.15 #81, 0.13 #223), 0215hd (0.18 #657, 0.17 #198, 0.15 #940), 089g0h (0.13 #658, 0.13 #941, 0.13 #199) >> Best rule #575 for best value: >> intensional similarity = 6 >> extensional distance = 99 >> proper extension: 0dnvn3; 0ds11z; 05p3738; 0cz_ym; 01j8wk; 035s95; 0g3zrd; 019vhk; 03hmt9b; 02_sr1; ... >> query: (?x2318, 0ch6mp2) <- film_release_distribution_medium(?x2318, ?x81), film(?x2317, ?x2318), film_crew_role(?x2318, ?x468), ?x468 = 02r96rf, genre(?x2318, ?x604), ?x604 = 0lsxr >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06v9_x film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.861 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1935-05tbn PRED entity: 05tbn PRED relation: location! PRED expected values: 0bgrsl 028rk 02xc1w4 => 186 concepts (146 used for prediction) PRED predicted values (max 10 best out of 2179): 06dn58 (0.50 #6546, 0.08 #26548, 0.07 #36549), 030hcs (0.50 #5316, 0.08 #25318, 0.07 #35319), 0g824 (0.25 #6293, 0.17 #13794, 0.11 #16294), 01wp8w7 (0.25 #5257, 0.16 #25259, 0.07 #30259), 01yzhn (0.25 #7118, 0.13 #22119, 0.13 #19619), 06jw0s (0.25 #6140, 0.12 #26142, 0.12 #71144), 0738b8 (0.25 #5442, 0.12 #25444, 0.11 #15443), 0p_pd (0.25 #5047, 0.12 #25049, 0.09 #45051), 01vtmw6 (0.25 #6354, 0.12 #26356, 0.07 #38858), 02yl42 (0.25 #5701, 0.12 #25703, 0.07 #38205) >> Best rule #6546 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 059rby; 02xry; >> query: (?x3670, 06dn58) <- district_represented(?x176, ?x3670), contains(?x3670, ?x331), place_of_death(?x8970, ?x3670) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #20497 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 13 *> proper extension: 05kkh; 07ssc; 0vmt; 01n7q; 04rrx; 05k7sb; 07b_l; 03v0t; 04ly1; 081yw; ... *> query: (?x3670, 028rk) <- geographic_distribution(?x1176, ?x3670), location(?x395, ?x3670), state_province_region(?x331, ?x3670) *> conf = 0.07 ranks of expected_values: 647 EVAL 05tbn location! 02xc1w4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 146.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location EVAL 05tbn location! 028rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 186.000 146.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location EVAL 05tbn location! 0bgrsl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 186.000 146.000 0.500 http://example.org/people/person/places_lived./people/place_lived/location #1934-017lqp PRED entity: 017lqp PRED relation: nationality PRED expected values: 07ssc => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 51): 09c7w0 (0.80 #301, 0.80 #201, 0.74 #2302), 02jx1 (0.43 #33, 0.18 #133, 0.11 #934), 07ssc (0.36 #115, 0.29 #15, 0.10 #916), 06q1r (0.14 #77, 0.04 #177, 0.01 #1378), 0j5g9 (0.07 #162, 0.01 #562, 0.01 #863), 03rk0 (0.07 #2447, 0.07 #1547, 0.06 #7149), 0d060g (0.04 #307, 0.04 #3608, 0.04 #4808), 0345h (0.04 #131, 0.02 #4532, 0.02 #4432), 03_r3 (0.04 #112), 0chghy (0.02 #710, 0.02 #4311, 0.02 #1811) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 141 >> proper extension: 032xhg; 01rr9f; 0f0p0; 05fnl9; 036c_0; 0309jm; 02xp18; 0p8r1; 062ftr; 016yzz; ... >> query: (?x9406, 09c7w0) <- film(?x9406, ?x836), profession(?x9406, ?x1943), ?x1943 = 02krf9 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #115 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 0131kb; *> query: (?x9406, 07ssc) <- film(?x9406, ?x836), nominated_for(?x2506, ?x836), ?x2506 = 01kf4tt *> conf = 0.36 ranks of expected_values: 3 EVAL 017lqp nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 82.000 0.804 http://example.org/people/person/nationality #1933-023p33 PRED entity: 023p33 PRED relation: country PRED expected values: 09c7w0 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 101): 09c7w0 (0.81 #431, 0.81 #2212, 0.81 #370), 07ssc (0.22 #1554, 0.22 #755, 0.22 #1739), 03rk0 (0.14 #285, 0.09 #1022, 0.08 #961), 0f8l9c (0.13 #571, 0.10 #1618, 0.10 #388), 03rt9 (0.12 #1536, 0.10 #3317, 0.08 #1535), 0345h (0.12 #1872, 0.11 #1132, 0.11 #1994), 03_3d (0.11 #69, 0.05 #1112, 0.05 #2340), 01mjq (0.11 #97, 0.05 #281, 0.04 #774), 06t8v (0.11 #112, 0.03 #541, 0.02 #789), 0d060g (0.08 #131, 0.08 #560, 0.05 #1297) >> Best rule #431 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 01sbv9; >> query: (?x2097, 09c7w0) <- film_release_distribution_medium(?x2097, ?x627), ?x627 = 02nxhr, genre(?x2097, ?x307), nominated_for(?x13075, ?x2097), award(?x1863, ?x13075) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023p33 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.812 http://example.org/film/film/country #1932-0l14j_ PRED entity: 0l14j_ PRED relation: role PRED expected values: 01vdm0 02bxd => 90 concepts (62 used for prediction) PRED predicted values (max 10 best out of 81): 0l14qv (0.88 #306, 0.86 #305, 0.82 #2472), 07y_7 (0.88 #306, 0.86 #305, 0.82 #2472), 018vs (0.88 #306, 0.86 #305, 0.82 #2472), 0gkd1 (0.88 #306, 0.86 #305, 0.82 #2472), 0dwtp (0.88 #306, 0.86 #305, 0.82 #2472), 02pprs (0.88 #306, 0.86 #305, 0.82 #2472), 06w7v (0.88 #306, 0.86 #305, 0.82 #2472), 07gql (0.88 #306, 0.86 #305, 0.82 #2472), 0395lw (0.88 #306, 0.86 #305, 0.82 #1158), 03qmg1 (0.88 #306, 0.86 #305, 0.82 #1158) >> Best rule #306 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 0214km; >> query: (?x2944, ?x615) <- role(?x120, ?x2944), role(?x6801, ?x2944), role(?x1332, ?x2944), role(?x615, ?x2944), role(?x614, ?x2944), role(?x2944, ?x569), ?x6801 = 01c3q, instrumentalists(?x615, ?x1338), ?x1332 = 03qlv7, ?x614 = 0mkg, role(?x74, ?x615) >> conf = 0.88 => this is the best rule for 12 predicted values *> Best rule #4127 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 66 *> proper extension: 011_6p; 01399x; *> query: (?x2944, 01vdm0) <- role(?x2944, ?x74), instrumentalists(?x2944, ?x5815), group(?x2944, ?x5303), profession(?x5815, ?x563), role(?x569, ?x2944), artists(?x302, ?x5303), role(?x3991, ?x2944), role(?x433, ?x3991) *> conf = 0.87 ranks of expected_values: 13, 14 EVAL 0l14j_ role 02bxd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 90.000 62.000 0.884 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 0l14j_ role 01vdm0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 90.000 62.000 0.884 http://example.org/music/performance_role/track_performances./music/track_contribution/role #1931-0lgxj PRED entity: 0lgxj PRED relation: olympics! PRED expected values: 03_r3 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 251): 03h64 (0.87 #432, 0.69 #1044, 0.69 #1772), 035qy (0.87 #432, 0.69 #1044, 0.67 #312), 03spz (0.87 #432, 0.69 #1044, 0.67 #312), 07ylj (0.87 #432, 0.69 #1044, 0.65 #1562), 05cgv (0.87 #432, 0.69 #1044, 0.65 #1562), 05r7t (0.87 #432, 0.69 #1044, 0.65 #1562), 0165b (0.87 #432, 0.69 #1044, 0.65 #1562), 05r4w (0.87 #432, 0.69 #1044, 0.65 #1562), 06f32 (0.87 #432, 0.69 #1772, 0.65 #318), 01p1v (0.87 #432, 0.67 #312, 0.65 #318) >> Best rule #432 for best value: >> intensional similarity = 40 >> extensional distance = 1 >> proper extension: 06sks6; >> query: (?x4255, ?x87) <- olympics(?x4045, ?x4255), participating_countries(?x4255, ?x1453), participating_countries(?x4255, ?x789), participating_countries(?x4255, ?x87), ?x1453 = 06qd3, sports(?x4255, ?x3659), sports(?x4255, ?x2315), olympics(?x151, ?x4255), country(?x4045, ?x8958), country(?x4045, ?x8588), country(?x4045, ?x8033), country(?x4045, ?x5457), country(?x4045, ?x3635), country(?x4045, ?x2000), country(?x4045, ?x1790), country(?x4045, ?x1229), country(?x4045, ?x756), country(?x4045, ?x410), sports(?x2748, ?x4045), sports(?x2369, ?x4045), sports(?x584, ?x4045), ?x1229 = 059j2, ?x756 = 06npd, ?x2000 = 0d0kn, ?x2369 = 0lbbj, ?x3635 = 019pcs, ?x1790 = 01pj7, sports(?x2966, ?x4045), ?x410 = 01ls2, ?x8958 = 01ppq, ?x2748 = 0c_tl, ?x584 = 0l98s, ?x5457 = 06tw8, currency(?x8033, ?x170), ?x2315 = 06wrt, ?x2966 = 06sks6, organization(?x8033, ?x312), ?x8588 = 0jhd, ?x789 = 0f8l9c, ?x3659 = 0dwxr >> conf = 0.87 => this is the best rule for 22 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 16 EVAL 0lgxj olympics! 03_r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 61.000 61.000 0.867 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #1930-04yyhw PRED entity: 04yyhw PRED relation: film PRED expected values: 035_2h => 83 concepts (24 used for prediction) PRED predicted values (max 10 best out of 572): 03twd6 (0.20 #226, 0.02 #2015, 0.02 #3804), 01qb5d (0.20 #138, 0.02 #1927, 0.01 #5506), 01chpn (0.20 #1110, 0.02 #2899, 0.01 #4688), 08s6mr (0.20 #1319, 0.02 #3108, 0.01 #4897), 03k8th (0.20 #1720, 0.02 #3509, 0.01 #5298), 0bh8x1y (0.20 #794, 0.02 #2583), 0btbyn (0.20 #663, 0.02 #2452), 02v5_g (0.20 #792, 0.01 #16899, 0.01 #18688), 06gb1w (0.20 #735, 0.01 #16842, 0.01 #11473), 034qzw (0.20 #334, 0.01 #18230, 0.01 #30753) >> Best rule #226 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 07b2lv; 02xs5v; 030vnj; >> query: (?x14294, 03twd6) <- award(?x14294, ?x1033), film(?x14294, ?x9941), film(?x14294, ?x4651), ?x9941 = 024lt6, country(?x4651, ?x94) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #6286 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 276 *> proper extension: 048q6x; 0382m4; 01dkpb; 018qql; *> query: (?x14294, 035_2h) <- award(?x14294, ?x3066), type_of_union(?x14294, ?x566), award(?x8774, ?x3066), ?x8774 = 05xpv *> conf = 0.02 ranks of expected_values: 425 EVAL 04yyhw film 035_2h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 83.000 24.000 0.200 http://example.org/film/actor/film./film/performance/film #1929-01qvz8 PRED entity: 01qvz8 PRED relation: language PRED expected values: 02h40lc => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 34): 02h40lc (0.89 #239, 0.89 #3268, 0.88 #1544), 064_8sq (0.20 #22, 0.14 #200, 0.14 #1209), 06b_j (0.13 #23, 0.06 #378, 0.05 #674), 07zrf (0.13 #3, 0.02 #476, 0.02 #240), 06nm1 (0.12 #189, 0.11 #130, 0.11 #602), 04306rv (0.10 #360, 0.09 #715, 0.09 #774), 02bjrlw (0.07 #297, 0.06 #711, 0.06 #770), 03_9r (0.07 #10, 0.05 #1018, 0.05 #188), 04h9h (0.07 #43, 0.04 #162, 0.04 #280), 0653m (0.06 #249, 0.05 #603, 0.04 #781) >> Best rule #239 for best value: >> intensional similarity = 3 >> extensional distance = 169 >> proper extension: 027ct7c; 072r5v; 0bx_hnp; 0267wwv; >> query: (?x4709, 02h40lc) <- nominated_for(?x541, ?x4709), cinematography(?x4709, ?x7384), film_crew_role(?x4709, ?x1171) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qvz8 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.895 http://example.org/film/film/language #1928-0161sp PRED entity: 0161sp PRED relation: award_nominee PRED expected values: 04znsy => 121 concepts (60 used for prediction) PRED predicted values (max 10 best out of 1045): 04xrx (0.86 #37451, 0.81 #114691, 0.80 #7023), 0c33pl (0.86 #37451, 0.81 #114691, 0.80 #7023), 018ygt (0.18 #1466, 0.06 #8489, 0.02 #20191), 01w7nwm (0.18 #3052, 0.04 #5393, 0.03 #33480), 030vnj (0.12 #8865, 0.01 #13545), 02qwg (0.11 #5449, 0.10 #10131, 0.07 #24175), 01kv4mb (0.11 #5133, 0.07 #9815, 0.05 #35561), 0pj8m (0.11 #6464, 0.07 #11146, 0.05 #25190), 01w724 (0.11 #5295, 0.07 #9977, 0.05 #24021), 018dyl (0.11 #5678, 0.07 #10360, 0.05 #24404) >> Best rule #37451 for best value: >> intensional similarity = 3 >> extensional distance = 140 >> proper extension: 013pk3; >> query: (?x2908, ?x2415) <- artist(?x441, ?x2908), award_nominee(?x2415, ?x2908), film(?x2908, ?x781) >> conf = 0.86 => this is the best rule for 2 predicted values *> Best rule #119373 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 540 *> proper extension: 08b8vd; 0c2ry; *> query: (?x2908, ?x380) <- film(?x2908, ?x2644), participant(?x2237, ?x2908), film(?x380, ?x2644) *> conf = 0.07 ranks of expected_values: 92 EVAL 0161sp award_nominee 04znsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 121.000 60.000 0.859 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1927-01d4cb PRED entity: 01d4cb PRED relation: profession PRED expected values: 039v1 => 116 concepts (90 used for prediction) PRED predicted values (max 10 best out of 56): 02hrh1q (0.74 #6466, 0.65 #6323, 0.65 #11474), 016z4k (0.53 #3, 0.47 #289, 0.44 #2009), 0dxtg (0.31 #5321, 0.29 #10043, 0.29 #6322), 01d_h8 (0.31 #5313, 0.29 #10750, 0.28 #6314), 039v1 (0.31 #320, 0.29 #1036, 0.28 #2900), 025352 (0.28 #199, 0.15 #1202, 0.15 #1776), 02jknp (0.22 #5315, 0.21 #10752, 0.20 #6316), 03gjzk (0.22 #7900, 0.21 #12619, 0.21 #7471), 0n1h (0.20 #1874, 0.20 #2017, 0.19 #726), 01c8w0 (0.18 #1154, 0.17 #1728, 0.12 #437) >> Best rule #6466 for best value: >> intensional similarity = 4 >> extensional distance = 1013 >> proper extension: 01r42_g; 0f830f; 02pp_q_; 08w7vj; 08m4c8; 04smkr; 038g2x; 0fqyzz; 05dxl5; 02cm2m; ... >> query: (?x9128, 02hrh1q) <- award_winner(?x7594, ?x9128), gender(?x9128, ?x231), award_nominee(?x9128, ?x4563), film(?x4563, ?x463) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #320 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 98 *> proper extension: 04r1t; 01kcms4; *> query: (?x9128, 039v1) <- artists(?x7440, ?x9128), ?x7440 = 0155w *> conf = 0.31 ranks of expected_values: 5 EVAL 01d4cb profession 039v1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 116.000 90.000 0.738 http://example.org/people/person/profession #1926-01_k71 PRED entity: 01_k71 PRED relation: people! PRED expected values: 048z7l => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 31): 0x67 (0.20 #1550, 0.17 #2166, 0.17 #1935), 041rx (0.15 #1313, 0.15 #1467, 0.14 #389), 033tf_ (0.07 #3318, 0.06 #3780, 0.06 #3703), 02w7gg (0.05 #4930, 0.05 #3544, 0.05 #3621), 0xnvg (0.05 #1399, 0.05 #1168, 0.05 #1630), 013xrm (0.05 #482, 0.04 #790, 0.04 #867), 07hwkr (0.04 #2630, 0.04 #1937, 0.04 #2476), 07bch9 (0.04 #1794, 0.04 #1871, 0.04 #1486), 013b6_ (0.04 #53, 0.03 #284, 0.03 #438), 0d7wh (0.03 #787, 0.03 #864, 0.03 #402) >> Best rule #1550 for best value: >> intensional similarity = 3 >> extensional distance = 402 >> proper extension: 01pbxb; 01vvydl; 08wq0g; 0jf1b; 01wbgdv; 0157m; 012x4t; 015882; 015pxr; 0pyg6; ... >> query: (?x7168, 0x67) <- location(?x7168, ?x3689), category(?x7168, ?x134), award_nominee(?x10412, ?x7168) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1426 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 383 *> proper extension: 01zcrv; *> query: (?x7168, 048z7l) <- category(?x7168, ?x134), nominated_for(?x7168, ?x11356), award_winner(?x11356, ?x8254) *> conf = 0.02 ranks of expected_values: 20 EVAL 01_k71 people! 048z7l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 115.000 115.000 0.196 http://example.org/people/ethnicity/people #1925-02g9p4 PRED entity: 02g9p4 PRED relation: group PRED expected values: 07mvp => 76 concepts (54 used for prediction) PRED predicted values (max 10 best out of 979): 02vnpv (0.85 #6269, 0.78 #3585, 0.74 #7230), 02dw1_ (0.82 #5032, 0.80 #4263, 0.78 #3492), 07mvp (0.73 #188, 0.70 #3697, 0.62 #1141), 05563d (0.73 #188, 0.67 #3460, 0.67 #2316), 06nv27 (0.73 #188, 0.67 #2336, 0.62 #1141), 0134wr (0.73 #188, 0.67 #2389, 0.62 #1141), 0134tg (0.73 #188, 0.67 #2339, 0.62 #1141), 0dvqq (0.73 #188, 0.67 #2304, 0.62 #1141), 07bzp (0.73 #188, 0.67 #2356, 0.62 #1141), 014_lq (0.73 #188, 0.67 #2338, 0.62 #1141) >> Best rule #6269 for best value: >> intensional similarity = 24 >> extensional distance = 18 >> proper extension: 0gghm; >> query: (?x1482, 02vnpv) <- role(?x5417, ?x1482), role(?x2459, ?x1482), role(?x1433, ?x1482), role(?x745, ?x1482), role(?x1482, ?x228), ?x5417 = 02w3w, role(?x1437, ?x1433), role(?x1436, ?x1433), ?x1436 = 0xzly, role(?x2459, ?x2377), role(?x2459, ?x1655), role(?x2459, ?x1473), ?x1655 = 01hww_, ?x2377 = 01bns_, role(?x1260, ?x1433), instrumentalists(?x1482, ?x2662), role(?x74, ?x2459), award_winner(?x2662, ?x1181), ?x745 = 01vj9c, role(?x3834, ?x1437), role(?x1437, ?x645), award_winner(?x486, ?x2662), ?x3834 = 01wzlxj, ?x1473 = 0g2dz >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #188 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 1 *> proper extension: 05148p4; *> query: (?x1482, ?x997) <- role(?x2048, ?x1482), role(?x1750, ?x1482), role(?x1433, ?x1482), role(?x745, ?x1482), role(?x615, ?x1482), role(?x1482, ?x4917), role(?x1482, ?x2297), role(?x1482, ?x885), role(?x1482, ?x314), role(?x1482, ?x228), ?x1433 = 0239kh, ?x885 = 0dwtp, ?x745 = 01vj9c, instrumentalists(?x1482, ?x10239), role(?x211, ?x1482), ?x10239 = 01p95y0, ?x2297 = 051hrr, ?x314 = 02sgy, ?x228 = 0l14qv, ?x615 = 0dwsp, ?x1750 = 02hnl, ?x4917 = 06w7v, instrumentalists(?x2048, ?x4381), role(?x2048, ?x2725), ?x4381 = 0qf11, group(?x2048, ?x997), ?x2725 = 0l1589 *> conf = 0.73 ranks of expected_values: 3 EVAL 02g9p4 group 07mvp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 76.000 54.000 0.850 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1924-03fyrh PRED entity: 03fyrh PRED relation: country PRED expected values: 05b4w 0jgx 0jhd 05bmq => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 287): 07ssc (0.95 #5977, 0.87 #5652, 0.87 #4075), 015fr (0.86 #4702, 0.85 #4389, 0.85 #3766), 06mkj (0.86 #3943, 0.83 #3321, 0.83 #3165), 06npd (0.80 #2678, 0.71 #2211, 0.68 #1252), 059j2 (0.80 #1887, 0.79 #941, 0.76 #159), 01ls2 (0.80 #1887, 0.79 #941, 0.76 #159), 04g61 (0.80 #1887, 0.79 #941, 0.76 #159), 0fhnf (0.80 #1887, 0.66 #2354, 0.63 #1886), 035qy (0.79 #941, 0.76 #159, 0.75 #3621), 0hzlz (0.79 #941, 0.76 #159, 0.71 #3923) >> Best rule #5977 for best value: >> intensional similarity = 52 >> extensional distance = 41 >> proper extension: 09xp_; >> query: (?x3641, 07ssc) <- country(?x3641, ?x4743), country(?x3641, ?x1499), film_release_region(?x11022, ?x4743), film_release_region(?x9002, ?x4743), film_release_region(?x8682, ?x4743), film_release_region(?x6882, ?x4743), film_release_region(?x6283, ?x4743), film_release_region(?x6235, ?x4743), film_release_region(?x5400, ?x4743), film_release_region(?x4607, ?x4743), film_release_region(?x3830, ?x4743), film_release_region(?x3748, ?x4743), film_release_region(?x2714, ?x4743), film_release_region(?x2394, ?x4743), film_release_region(?x1386, ?x4743), film_release_region(?x1228, ?x4743), film_release_region(?x1219, ?x4743), film_release_region(?x1173, ?x4743), film_release_region(?x1108, ?x4743), film_release_region(?x1022, ?x4743), film_release_region(?x972, ?x4743), film_release_region(?x791, ?x4743), film_release_region(?x124, ?x4743), ?x3748 = 05zlld0, ?x1386 = 0dtfn, official_language(?x4743, ?x3966), ?x2394 = 0661ql3, ?x1022 = 0crfwmx, country(?x1228, ?x512), ?x3830 = 0gjcrrw, ?x6882 = 043tvp3, ?x1219 = 03bx2lk, ?x8682 = 0bmfnjs, olympics(?x3641, ?x584), ?x2714 = 0kv238, ?x124 = 0g56t9t, ?x9002 = 0ndsl1x, ?x1108 = 0jjy0, produced_by(?x11022, ?x3223), ?x6283 = 0gmd3k7, ?x6235 = 05b6rdt, ?x1173 = 0872p_c, film_festivals(?x1228, ?x11147), ?x791 = 087wc7n, country(?x5396, ?x4743), sports(?x391, ?x3641), ?x972 = 017gl1, ?x4607 = 0h03fhx, ?x5396 = 0486tv, ?x5400 = 0bhwhj, teams(?x1499, ?x4306), nationality(?x7703, ?x1499) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #941 for first EXPECTED value: *> intensional similarity = 50 *> extensional distance = 2 *> proper extension: 071t0; *> query: (?x3641, ?x429) <- country(?x3641, ?x7287), country(?x3641, ?x4743), country(?x3641, ?x4521), country(?x3641, ?x3277), country(?x3641, ?x2000), country(?x3641, ?x1917), country(?x3641, ?x1355), country(?x3641, ?x304), ?x4743 = 03spz, olympics(?x3641, ?x584), ?x3277 = 06t8v, ?x1355 = 0h7x, ?x304 = 0d0vqn, sports(?x2369, ?x3641), ?x2000 = 0d0kn, sports(?x391, ?x3641), olympics(?x2978, ?x2369), olympics(?x1967, ?x2369), olympics(?x359, ?x2369), ?x7287 = 05b7q, sports(?x2369, ?x4876), sports(?x2369, ?x2044), ?x2978 = 03_8r, ?x2044 = 06f41, ?x4876 = 0d1t3, ?x1917 = 01p1v, olympics(?x3635, ?x2369), olympics(?x550, ?x2369), olympics(?x429, ?x2369), ?x1967 = 01cgz, form_of_government(?x4521, ?x48), ?x3635 = 019pcs, film_release_region(?x7293, ?x550), film_release_region(?x3745, ?x550), film_release_region(?x3191, ?x550), film_release_region(?x3088, ?x550), film_release_region(?x2868, ?x550), film_release_region(?x1625, ?x550), film_release_region(?x1452, ?x550), film_release_region(?x1163, ?x550), ?x1163 = 0c0nhgv, ?x1625 = 01f8gz, currency(?x4521, ?x170), ?x2868 = 0dr3sl, ?x1452 = 0jqn5, ?x7293 = 027m67, ?x3088 = 06w839_, ?x359 = 02bkg, ?x3745 = 03cw411, ?x3191 = 0crc2cp *> conf = 0.79 ranks of expected_values: 12, 13, 80, 99 EVAL 03fyrh country 05bmq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 45.000 45.000 0.953 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03fyrh country 0jhd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 45.000 45.000 0.953 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03fyrh country 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 45.000 45.000 0.953 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 03fyrh country 05b4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 45.000 45.000 0.953 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #1923-02byfd PRED entity: 02byfd PRED relation: languages PRED expected values: 02bjrlw => 141 concepts (141 used for prediction) PRED predicted values (max 10 best out of 18): 02h40lc (0.90 #1256, 0.87 #572, 0.62 #40), 02bjrlw (0.12 #39, 0.05 #115, 0.04 #1255), 03k50 (0.12 #574, 0.04 #1258, 0.03 #1790), 064_8sq (0.09 #584, 0.08 #1268, 0.07 #280), 04306rv (0.03 #1257, 0.02 #573, 0.02 #155), 07c9s (0.02 #1266, 0.02 #240, 0.02 #582), 012w70 (0.02 #159, 0.01 #501, 0.01 #539), 0653m (0.02 #158, 0.01 #500, 0.01 #538), 03_9r (0.02 #157, 0.01 #1259), 02hwhyv (0.02 #173) >> Best rule #1256 for best value: >> intensional similarity = 3 >> extensional distance = 448 >> proper extension: 04h07s; 013vdl; 09r_wb; 01r4zfk; 04xhwn; 02x02kb; 0378zn; >> query: (?x8893, 02h40lc) <- film(?x8893, ?x9031), languages(?x8893, ?x2502), profession(?x8893, ?x987) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #39 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0143wl; *> query: (?x8893, 02bjrlw) <- film(?x8893, ?x9031), company(?x8893, ?x13900), participant(?x11992, ?x8893) *> conf = 0.12 ranks of expected_values: 2 EVAL 02byfd languages 02bjrlw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 141.000 0.904 http://example.org/people/person/languages #1922-0783m_ PRED entity: 0783m_ PRED relation: category PRED expected values: 08mbj5d => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.68 #6, 0.31 #13, 0.29 #58) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 465 >> proper extension: 01yznp; 0kzy0; 07q1v4; 02whj; 01r9fv; 05qw5; 0cg9y; 01vvpjj; 03j0br4; 0161c2; ... >> query: (?x2359, 08mbj5d) <- award(?x2359, ?x678), profession(?x2359, ?x1183), ?x1183 = 09jwl >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0783m_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.679 http://example.org/common/topic/webpage./common/webpage/category #1921-020_95 PRED entity: 020_95 PRED relation: award PRED expected values: 0gqyl => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 240): 0cqgl9 (0.72 #30123, 0.70 #19558, 0.70 #19166), 09cn0c (0.72 #30123, 0.70 #19558, 0.70 #19166), 0gqyl (0.69 #490, 0.37 #99, 0.31 #881), 094qd5 (0.61 #825, 0.31 #434, 0.22 #43), 03qgjwc (0.35 #564, 0.16 #955, 0.14 #173), 0ck27z (0.34 #2433, 0.33 #1260, 0.31 #2042), 0bfvw2 (0.33 #15, 0.23 #797, 0.17 #406), 0bsjcw (0.31 #193, 0.12 #21515, 0.06 #975), 0gkts9 (0.27 #159, 0.17 #550, 0.14 #941), 05pcn59 (0.24 #1640, 0.21 #467, 0.16 #858) >> Best rule #30123 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 03j90; >> query: (?x5454, ?x4225) <- award_winner(?x4225, ?x5454), award(?x488, ?x4225) >> conf = 0.72 => this is the best rule for 2 predicted values *> Best rule #490 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 04bdxl; 01j5ts; 01p7yb; 01tvz5j; 0c4f4; 06jzh; 0blbxk; 027f7dj; 01g257; 01mqz0; ... *> query: (?x5454, 0gqyl) <- nominated_for(?x5454, ?x2029), award(?x5454, ?x2257), ?x2257 = 09td7p *> conf = 0.69 ranks of expected_values: 3 EVAL 020_95 award 0gqyl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 90.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1920-0jfx1 PRED entity: 0jfx1 PRED relation: nationality PRED expected values: 09c7w0 => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 82): 09c7w0 (0.76 #3613, 0.76 #3112, 0.76 #2912), 07ssc (0.33 #15, 0.11 #6031, 0.11 #6533), 04_1l0v (0.21 #6418), 02jx1 (0.19 #433, 0.18 #233, 0.17 #333), 0498y (0.18 #902, 0.09 #2910, 0.07 #601), 01xbgx (0.17 #81, 0.01 #682, 0.01 #1183), 0j5g9 (0.17 #62, 0.01 #3874, 0.01 #3274), 0345h (0.09 #431, 0.06 #6047, 0.05 #6549), 03rk0 (0.07 #7665, 0.06 #9569, 0.06 #9969), 0d060g (0.06 #1912, 0.05 #207, 0.05 #2715) >> Best rule #3613 for best value: >> intensional similarity = 3 >> extensional distance = 293 >> proper extension: 01l1b90; 01vw87c; 09qr6; 031zkw; 01yhvv; 01wz3cx; 01wgxtl; 01jbx1; 039bpc; 0ph2w; ... >> query: (?x2444, 09c7w0) <- award(?x2444, ?x401), participant(?x2444, ?x5665), participant(?x2444, ?x117) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jfx1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.763 http://example.org/people/person/nationality #1919-0m2l9 PRED entity: 0m2l9 PRED relation: influenced_by PRED expected values: 07mvp => 116 concepts (67 used for prediction) PRED predicted values (max 10 best out of 389): 041mt (0.19 #492, 0.11 #925, 0.05 #4391), 0lrh (0.19 #507, 0.07 #940, 0.07 #2673), 014z8v (0.15 #120, 0.11 #13565, 0.11 #10526), 03hnd (0.15 #98, 0.07 #10938, 0.06 #13109), 01k9lpl (0.15 #308, 0.07 #7674, 0.07 #10714), 0l5yl (0.15 #267, 0.05 #2000, 0.04 #10673), 01s7qqw (0.14 #1895, 0.06 #9700, 0.05 #4929), 012vd6 (0.13 #5800, 0.07 #24730, 0.07 #4933), 03f70xs (0.12 #503, 0.07 #936, 0.05 #4402), 08433 (0.12 #5655, 0.11 #1754, 0.09 #4354) >> Best rule #492 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 01syr4; >> query: (?x483, 041mt) <- profession(?x483, ?x524), ?x524 = 02jknp, origin(?x483, ?x1275) >> conf = 0.19 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0m2l9 influenced_by 07mvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 67.000 0.188 http://example.org/influence/influence_node/influenced_by #1918-01v_0b PRED entity: 01v_0b PRED relation: influenced_by! PRED expected values: 0cbgl => 131 concepts (42 used for prediction) PRED predicted values (max 10 best out of 388): 07lp1 (0.33 #1453, 0.23 #4558, 0.17 #1972), 02yl42 (0.30 #2205, 0.30 #652, 0.28 #1688), 0n6kf (0.29 #192, 0.28 #1745, 0.25 #2262), 013pp3 (0.28 #1776, 0.25 #2293, 0.25 #1257), 03qcq (0.25 #1035, 0.14 #1, 0.11 #1554), 01hb6v (0.20 #611, 0.17 #1128, 0.15 #2164), 01w8sf (0.17 #1648, 0.17 #1129, 0.15 #2165), 0cbgl (0.17 #2067, 0.15 #3102, 0.15 #2584), 01hc9_ (0.17 #1917, 0.15 #2434, 0.14 #364), 019z7q (0.17 #1059, 0.15 #2095, 0.14 #25) >> Best rule #1453 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 0d4jl; 0hcvy; >> query: (?x12382, 07lp1) <- influenced_by(?x12382, ?x3541), award(?x12382, ?x7111), ?x3541 = 040_9 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2067 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 16 *> proper extension: 03q3x5; *> query: (?x12382, 0cbgl) <- award(?x12382, ?x11471), award(?x12382, ?x7111), location(?x12382, ?x4350), ?x11471 = 0g9wd99, award_winner(?x7111, ?x118) *> conf = 0.17 ranks of expected_values: 8 EVAL 01v_0b influenced_by! 0cbgl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 131.000 42.000 0.333 http://example.org/influence/influence_node/influenced_by #1917-035zr0 PRED entity: 035zr0 PRED relation: film_release_region PRED expected values: 0jgd 0b90_r 03gj2 0345h 01mjq => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 152): 03gj2 (0.89 #2324, 0.84 #1169, 0.84 #452), 0345h (0.86 #316, 0.85 #1177, 0.85 #2332), 07ssc (0.86 #1019, 0.84 #1305, 0.82 #2317), 015fr (0.84 #590, 0.81 #2318, 0.81 #302), 0jgd (0.83 #2306, 0.81 #578, 0.81 #290), 06t2t (0.79 #2358, 0.78 #1203, 0.76 #342), 0b90_r (0.79 #2307, 0.78 #1009, 0.76 #1152), 03rk0 (0.67 #337, 0.64 #481, 0.59 #625), 01mjq (0.62 #2341, 0.62 #1186, 0.60 #1763), 0ctw_b (0.62 #2325, 0.62 #165, 0.61 #1027) >> Best rule #2324 for best value: >> intensional similarity = 6 >> extensional distance = 166 >> proper extension: 0c40vxk; 0gj8t_b; 0jqn5; 0bh8yn3; 09k56b7; 06v9_x; 07x4qr; 0kv238; 0gffmn8; 03q0r1; ... >> query: (?x7538, 03gj2) <- film(?x2646, ?x7538), film_release_region(?x7538, ?x4743), film_release_region(?x7538, ?x205), ?x205 = 03rjj, ?x4743 = 03spz, award_nominee(?x2646, ?x396) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 7, 9 EVAL 035zr0 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 88.000 88.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035zr0 film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035zr0 film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035zr0 film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 88.000 88.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 035zr0 film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 88.000 88.000 0.893 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1916-08b0cj PRED entity: 08b0cj PRED relation: location_of_ceremony PRED expected values: 0fnc_ => 88 concepts (71 used for prediction) PRED predicted values (max 10 best out of 9): 04pry (0.08 #109, 0.06 #228, 0.03 #826), 06_kh (0.06 #125), 03_r3 (0.04 #369, 0.02 #1327, 0.02 #1446), 04vmp (0.03 #548, 0.03 #667, 0.02 #906), 01_d4 (0.03 #741, 0.02 #1221, 0.01 #1699), 06s_2 (0.01 #1556, 0.01 #2154, 0.01 #1914), 03s5t (0.01 #2428), 03rk0 (0.01 #2421), 02_286 (0.01 #2528) >> Best rule #109 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 01g0jn; >> query: (?x7109, 04pry) <- team(?x7109, ?x1143), athlete(?x471, ?x7109), religion(?x7109, ?x492), sports(?x358, ?x471), sport(?x59, ?x471) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08b0cj location_of_ceremony 0fnc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 71.000 0.077 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #1915-08w4pm PRED entity: 08w4pm PRED relation: artist! PRED expected values: 01w56k => 83 concepts (74 used for prediction) PRED predicted values (max 10 best out of 127): 0g768 (0.50 #435, 0.25 #301, 0.21 #837), 02p3cr5 (0.33 #26, 0.12 #1366, 0.12 #1500), 098cpg (0.33 #106, 0.08 #2949, 0.05 #1044), 03rhqg (0.32 #1622, 0.32 #1488, 0.27 #952), 0mzkr (0.28 #2302, 0.25 #292, 0.22 #6058), 015_1q (0.27 #956, 0.25 #3235, 0.25 #3369), 011k1h (0.25 #412, 0.25 #278, 0.23 #1886), 0fb0v (0.25 #409, 0.25 #275, 0.22 #6041), 02bh8z (0.25 #422, 0.25 #288, 0.09 #958), 03qy3l (0.25 #461, 0.25 #327, 0.08 #1935) >> Best rule #435 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 016z1t; 01vtj38; >> query: (?x8029, 0g768) <- artist(?x5666, ?x8029), artist(?x2241, ?x8029), ?x2241 = 02p11jq, artists(?x7329, ?x8029), ?x7329 = 016jny, place_founded(?x5666, ?x739), artist(?x5666, ?x7162), profession(?x7162, ?x131) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9931 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 826 *> proper extension: 01wphh2; *> query: (?x8029, ?x1954) <- artist(?x2241, ?x8029), artist(?x2241, ?x8344), artist(?x2241, ?x8215), artist(?x2241, ?x4873), artists(?x671, ?x8029), profession(?x4873, ?x131), gender(?x8344, ?x231), artist(?x1954, ?x8215) *> conf = 0.04 ranks of expected_values: 70 EVAL 08w4pm artist! 01w56k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 83.000 74.000 0.500 http://example.org/music/record_label/artist #1914-014g9y PRED entity: 014g9y PRED relation: profession PRED expected values: 0dxtg => 65 concepts (34 used for prediction) PRED predicted values (max 10 best out of 59): 0dxtg (0.89 #159, 0.61 #1041, 0.61 #894), 01d_h8 (0.70 #1034, 0.70 #887, 0.61 #152), 09jwl (0.68 #605, 0.67 #752, 0.66 #1341), 0nbcg (0.43 #765, 0.42 #618, 0.41 #471), 0dz3r (0.38 #737, 0.38 #590, 0.37 #443), 03gjzk (0.36 #1042, 0.36 #895, 0.30 #1189), 01c72t (0.32 #1346, 0.16 #22, 0.15 #610), 0cbd2 (0.31 #153, 0.21 #6, 0.16 #1035), 039v1 (0.27 #623, 0.27 #770, 0.26 #476), 02krf9 (0.26 #1054, 0.26 #907, 0.12 #172) >> Best rule #159 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 0l6qt; 014zcr; 0h5f5n; 0159h6; 04r7jc; 05kfs; 02kxbwx; 0yfp; 05_k56; 0207wx; ... >> query: (?x10675, 0dxtg) <- award_nominee(?x3138, ?x10675), award(?x10675, ?x601), ?x601 = 0gr4k >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014g9y profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 34.000 0.893 http://example.org/people/person/profession #1913-0d_kd PRED entity: 0d_kd PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 125 concepts (39 used for prediction) PRED predicted values (max 10 best out of 8): 09c7w0 (0.86 #25, 0.85 #144, 0.84 #182), 059rby (0.25 #216, 0.19 #282, 0.19 #281), 0d_kd (0.25 #216, 0.19 #282, 0.19 #281), 04n3l (0.25 #216, 0.19 #282, 0.19 #281), 02jx1 (0.04 #357, 0.04 #487, 0.03 #318), 07ssc (0.02 #315, 0.02 #354, 0.02 #302), 0d060g (0.02 #49, 0.01 #299, 0.01 #312), 0h7x (0.01 #78, 0.01 #90, 0.01 #117) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0fkhz; >> query: (?x12516, 09c7w0) <- contains(?x335, ?x12516), adjoins(?x12516, ?x11938), ?x335 = 059rby >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d_kd second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 39.000 0.857 http://example.org/location/country/second_level_divisions #1912-048lv PRED entity: 048lv PRED relation: participant PRED expected values: 01wxyx1 => 93 concepts (45 used for prediction) PRED predicted values (max 10 best out of 177): 02jyhv (0.14 #507, 0.02 #4355, 0.02 #4997), 029q_y (0.07 #1129, 0.03 #5618, 0.03 #3051), 046zh (0.07 #1000, 0.02 #2922, 0.02 #4205), 019pm_ (0.07 #14757, 0.05 #7697, 0.05 #4490), 0fqjhm (0.07 #14757, 0.05 #7697, 0.05 #5131), 0d02km (0.07 #14757, 0.05 #4490, 0.05 #5131), 028knk (0.07 #14757, 0.05 #4490, 0.05 #5131), 02bj6k (0.07 #14757, 0.05 #5131, 0.05 #5773), 0c4f4 (0.07 #10264, 0.06 #11545, 0.06 #8339), 0c3jz (0.05 #7697, 0.05 #4490, 0.05 #5131) >> Best rule #507 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0d02km; 09dv0sz; >> query: (?x1384, 02jyhv) <- award_winner(?x1384, ?x10139), ?x10139 = 0fqjhm, award_nominee(?x71, ?x1384) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1418 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 01gbb4; 0l786; 0432cd; 01j5sd; 034q3l; 03swmf; 02l101; 0141kz; 0428bc; 0301bq; ... *> query: (?x1384, 01wxyx1) <- award(?x1384, ?x458), location(?x1384, ?x739), ?x458 = 0789_m *> conf = 0.02 ranks of expected_values: 157 EVAL 048lv participant 01wxyx1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 93.000 45.000 0.143 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #1911-0kr5_ PRED entity: 0kr5_ PRED relation: profession PRED expected values: 01d_h8 => 118 concepts (101 used for prediction) PRED predicted values (max 10 best out of 61): 01d_h8 (0.87 #3854, 0.85 #4002, 0.84 #2522), 0dxtg (0.72 #753, 0.71 #2381, 0.71 #2677), 03gjzk (0.45 #2530, 0.45 #2086, 0.42 #2234), 0kyk (0.28 #473, 0.23 #1361, 0.13 #325), 02krf9 (0.24 #2986, 0.24 #766, 0.24 #4910), 0cbd2 (0.24 #451, 0.23 #1339, 0.17 #2967), 09jwl (0.23 #1350, 0.21 #610, 0.20 #4162), 0nbcg (0.18 #623, 0.17 #327, 0.15 #1363), 0dz3r (0.18 #594, 0.17 #298, 0.10 #1334), 016z4k (0.18 #596, 0.15 #1336, 0.13 #300) >> Best rule #3854 for best value: >> intensional similarity = 3 >> extensional distance = 339 >> proper extension: 0gg9_5q; 02xnjd; 0glyyw; 05zrx3v; 037q1z; 09zw90; 024t0y; 0g_rs_; >> query: (?x698, 01d_h8) <- profession(?x698, ?x524), produced_by(?x518, ?x698), nominated_for(?x143, ?x518) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kr5_ profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 101.000 0.868 http://example.org/people/person/profession #1910-0f4x7 PRED entity: 0f4x7 PRED relation: award! PRED expected values: 0170vn 0170qf 01_xtx 06lht1 0lkr7 0cg9f 01385g => 55 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2598): 048lv (0.79 #45434, 0.79 #48680, 0.78 #64911), 0bj9k (0.79 #45434, 0.79 #48680, 0.78 #64911), 0sz28 (0.79 #45434, 0.79 #48680, 0.78 #64911), 0bxtg (0.79 #45434, 0.79 #48680, 0.78 #64911), 02qgqt (0.79 #45434, 0.79 #48680, 0.78 #64911), 0cj8x (0.79 #45434, 0.79 #48680, 0.78 #64911), 012v9y (0.79 #45434, 0.79 #48680, 0.78 #64911), 01g42 (0.79 #45434, 0.79 #48680, 0.78 #64911), 016kb7 (0.79 #45434, 0.79 #48680, 0.78 #64911), 01wmxfs (0.79 #45434, 0.79 #48680, 0.77 #64909) >> Best rule #45434 for best value: >> intensional similarity = 4 >> extensional distance = 118 >> proper extension: 09v7wsg; >> query: (?x591, ?x157) <- nominated_for(?x591, ?x8711), award_winner(?x591, ?x157), ceremony(?x591, ?x78), nominated_for(?x3237, ?x8711) >> conf = 0.79 => this is the best rule for 14 predicted values *> Best rule #13535 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0789_m; 054ky1; *> query: (?x591, 0170qf) <- award(?x7391, ?x591), ceremony(?x591, ?x78), ?x7391 = 040z9 *> conf = 0.57 ranks of expected_values: 22, 25, 33, 73, 227, 244, 955 EVAL 0f4x7 award! 01385g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 55.000 25.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f4x7 award! 0cg9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 55.000 25.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f4x7 award! 0lkr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 55.000 25.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f4x7 award! 06lht1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 25.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f4x7 award! 01_xtx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 55.000 25.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f4x7 award! 0170qf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 55.000 25.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0f4x7 award! 0170vn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 55.000 25.000 0.788 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1909-04vr_f PRED entity: 04vr_f PRED relation: award PRED expected values: 027c924 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 185): 09sb52 (0.29 #32, 0.27 #457, 0.27 #260), 040njc (0.29 #7, 0.27 #457, 0.27 #1603), 019f4v (0.29 #52, 0.27 #457, 0.27 #1603), 04dn09n (0.29 #33, 0.27 #457, 0.27 #1603), 027c924 (0.29 #9, 0.18 #11433, 0.18 #1381), 02w_6xj (0.29 #153, 0.18 #11433, 0.13 #381), 02qvyrt (0.29 #92, 0.15 #1464, 0.11 #1695), 0gqy2 (0.27 #457, 0.27 #344, 0.27 #1603), 0gq9h (0.27 #457, 0.27 #1603, 0.27 #1601), 02pqp12 (0.27 #457, 0.27 #1603, 0.27 #1601) >> Best rule #32 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 09cr8; 026gyn_; 03hmt9b; 0bm2x; 011yg9; >> query: (?x1135, 09sb52) <- nominated_for(?x1135, ?x4359), award_winner(?x1135, ?x382), award(?x1135, ?x601), ?x601 = 0gr4k >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 09cr8; 026gyn_; 03hmt9b; 0bm2x; 011yg9; *> query: (?x1135, 027c924) <- nominated_for(?x1135, ?x4359), award_winner(?x1135, ?x382), award(?x1135, ?x601), ?x601 = 0gr4k *> conf = 0.29 ranks of expected_values: 5 EVAL 04vr_f award 027c924 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 104.000 104.000 0.286 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #1908-015q1n PRED entity: 015q1n PRED relation: major_field_of_study PRED expected values: 02h40lc => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 109): 02lp1 (0.61 #1065, 0.60 #596, 0.58 #1299), 02j62 (0.59 #497, 0.54 #1083, 0.53 #1317), 04rjg (0.52 #721, 0.50 #1307, 0.49 #1073), 062z7 (0.47 #611, 0.46 #1080, 0.45 #494), 03g3w (0.45 #493, 0.44 #844, 0.42 #1313), 05qjt (0.45 #476, 0.44 #1296, 0.42 #1413), 01tbp (0.39 #1345, 0.37 #1462, 0.37 #642), 01lj9 (0.38 #1325, 0.37 #739, 0.36 #505), 0fdys (0.37 #738, 0.36 #504, 0.33 #1324), 037mh8 (0.36 #531, 0.34 #1351, 0.33 #1468) >> Best rule #1065 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 017cy9; 02bqy; 02f4s3; >> query: (?x6271, 02lp1) <- major_field_of_study(?x6271, ?x1668), school(?x1578, ?x6271), ?x1668 = 01mkq >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #472 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 0277jc; 011xy1; *> query: (?x6271, 02h40lc) <- major_field_of_study(?x6271, ?x3489), student(?x6271, ?x1129), ?x3489 = 0193x, contains(?x94, ?x6271) *> conf = 0.36 ranks of expected_values: 11 EVAL 015q1n major_field_of_study 02h40lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 118.000 118.000 0.610 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1907-04t6fk PRED entity: 04t6fk PRED relation: currency PRED expected values: 09nqf => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.88 #64, 0.83 #29, 0.82 #141), 01nv4h (0.02 #282, 0.02 #254, 0.02 #79), 02l6h (0.02 #116, 0.01 #60, 0.01 #445), 088n7 (0.02 #126), 0kz1h (0.01 #89) >> Best rule #64 for best value: >> intensional similarity = 4 >> extensional distance = 80 >> proper extension: 0581vn8; >> query: (?x2699, 09nqf) <- nominated_for(?x2209, ?x2699), language(?x2699, ?x254), genre(?x2699, ?x225), ?x2209 = 0gr42 >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04t6fk currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.878 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #1906-0g5qmbz PRED entity: 0g5qmbz PRED relation: film_release_region PRED expected values: 03rt9 0k6nt 0d0kn => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 228): 03gj2 (0.87 #1906, 0.87 #6121, 0.86 #3069), 0k6nt (0.84 #3358, 0.83 #3793, 0.82 #599), 05b4w (0.82 #636, 0.81 #491, 0.77 #4994), 06t2t (0.82 #1214, 0.77 #1359, 0.77 #2229), 03rt9 (0.75 #2187, 0.74 #4949, 0.73 #1317), 03rj0 (0.75 #1357, 0.69 #3390, 0.68 #2227), 04gzd (0.75 #441, 0.67 #1167, 0.56 #6107), 03rk0 (0.75 #482, 0.59 #1208, 0.57 #2223), 06t8v (0.75 #504, 0.52 #1375, 0.50 #2245), 0ctw_b (0.71 #601, 0.62 #2197, 0.60 #1327) >> Best rule #1906 for best value: >> intensional similarity = 6 >> extensional distance = 53 >> proper extension: 0gtv7pk; 02x3lt7; 08720; 0crfwmx; 01c22t; 0dtfn; 03twd6; 04w7rn; 0gj9qxr; 02r8hh_; ... >> query: (?x9501, 03gj2) <- country(?x9501, ?x512), genre(?x9501, ?x1014), language(?x9501, ?x90), film_release_region(?x9501, ?x142), ?x142 = 0jgd, category(?x9501, ?x134) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #3358 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 107 *> proper extension: 0gtvrv3; *> query: (?x9501, 0k6nt) <- country(?x9501, ?x512), film_release_region(?x9501, ?x1499), film_release_region(?x9501, ?x1353), film_release_region(?x9501, ?x774), ?x1499 = 01znc_, participating_countries(?x418, ?x1353), ?x774 = 06mzp *> conf = 0.84 ranks of expected_values: 2, 5, 30 EVAL 0g5qmbz film_release_region 0d0kn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 117.000 117.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5qmbz film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 117.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g5qmbz film_release_region 03rt9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 117.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1905-05fg2 PRED entity: 05fg2 PRED relation: award_winner! PRED expected values: 05kjlr => 112 concepts (95 used for prediction) PRED predicted values (max 10 best out of 318): 0m7yy (0.31 #4059, 0.13 #28198, 0.11 #8369), 03x3wf (0.31 #3944, 0.09 #6961, 0.07 #27220), 05kjlr (0.29 #5143, 0.16 #7729, 0.11 #27587), 03j2ts (0.22 #2580, 0.20 #856, 0.15 #3873), 05qck (0.21 #4503, 0.20 #624, 0.18 #8382), 020qjg (0.20 #817, 0.14 #2110, 0.14 #1679), 06196 (0.20 #1206, 0.14 #1637, 0.06 #31898), 0ddd9 (0.20 #487, 0.10 #6521, 0.07 #19020), 027c95y (0.20 #589, 0.10 #6623, 0.06 #31898), 02r771y (0.20 #1279, 0.06 #31898) >> Best rule #4059 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 065y4w7; 01jq34; 0bdlj; 0cv_2; >> query: (?x1309, 0m7yy) <- award_winner(?x14353, ?x1309), organization(?x1309, ?x8052), disciplines_or_subjects(?x14353, ?x3878), award_winner(?x14353, ?x11596), participant(?x2499, ?x11596) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #5143 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 12 *> proper extension: 01pgzn_; *> query: (?x1309, 05kjlr) <- award_winner(?x14353, ?x1309), profession(?x1309, ?x8368), profession(?x11596, ?x8368), profession(?x11077, ?x8368), ?x11596 = 0d_w7, location(?x11077, ?x739) *> conf = 0.29 ranks of expected_values: 3 EVAL 05fg2 award_winner! 05kjlr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 95.000 0.308 http://example.org/award/award_category/winners./award/award_honor/award_winner #1904-01ct6 PRED entity: 01ct6 PRED relation: teams! PRED expected values: 094jv => 97 concepts (90 used for prediction) PRED predicted values (max 10 best out of 114): 071vr (0.33 #156, 0.25 #426, 0.06 #5567), 0wh3 (0.25 #572, 0.20 #1115, 0.17 #1385), 0d9jr (0.25 #403, 0.12 #2027, 0.09 #3380), 01_d4 (0.20 #870, 0.12 #1954, 0.10 #11160), 0nqph (0.20 #1069, 0.10 #6483, 0.09 #7568), 0h7h6 (0.20 #865, 0.05 #6279, 0.05 #7364), 068p2 (0.18 #2829, 0.12 #1747, 0.11 #2557), 0f2tj (0.12 #2316, 0.12 #1776, 0.09 #3671), 0n1rj (0.12 #2306, 0.12 #1766, 0.09 #3389), 030qb3t (0.12 #1674, 0.11 #2484, 0.09 #3569) >> Best rule #156 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 06rpd; >> query: (?x684, 071vr) <- position(?x684, ?x3346), position(?x684, ?x1517), school(?x684, ?x4603), school(?x684, ?x2959), colors(?x684, ?x332), draft(?x684, ?x465), ?x4603 = 0hd7j, ?x3346 = 02g_7z, major_field_of_study(?x2959, ?x254), team(?x2247, ?x684), ?x1517 = 02g_6j, fraternities_and_sororities(?x2959, ?x4348), organization(?x346, ?x2959), currency(?x2959, ?x170), ?x346 = 060c4, ?x332 = 01l849 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6280 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 18 *> proper extension: 05m_8; 0jm2v; 051vz; 0713r; 0jm8l; 02__x; 06wpc; 01d6g; 0x0d; *> query: (?x684, 094jv) <- team(?x2247, ?x684), category(?x684, ?x134), team(?x2247, ?x4546), draft(?x684, ?x4171), colors(?x4546, ?x663), school(?x684, ?x5621), school(?x4171, ?x546), team(?x5412, ?x684), major_field_of_study(?x5621, ?x254), citytown(?x5621, ?x13702) *> conf = 0.05 ranks of expected_values: 38 EVAL 01ct6 teams! 094jv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 97.000 90.000 0.333 http://example.org/sports/sports_team_location/teams #1903-03cf9ly PRED entity: 03cf9ly PRED relation: actor PRED expected values: 03_6y => 102 concepts (78 used for prediction) PRED predicted values (max 10 best out of 845): 02fgm7 (0.45 #23342, 0.41 #12139, 0.40 #39213), 01xndd (0.25 #3732, 0.23 #3730, 0.20 #3731), 09pl3f (0.25 #3732, 0.23 #3730, 0.20 #3731), 09pl3s (0.25 #3732, 0.23 #3730, 0.18 #17739), 044mvs (0.25 #1703, 0.20 #2635, 0.10 #3568), 01kwld (0.25 #980, 0.20 #1912, 0.10 #2845), 03_wtr (0.25 #1529, 0.20 #2461, 0.10 #3394), 07f3xb (0.25 #1053, 0.20 #1985, 0.10 #2918), 031ydm (0.25 #1274, 0.20 #2206, 0.10 #3139), 078mgh (0.25 #1566, 0.20 #2498, 0.10 #3431) >> Best rule #23342 for best value: >> intensional similarity = 4 >> extensional distance = 85 >> proper extension: 0dl6fv; >> query: (?x11895, ?x7505) <- languages(?x11895, ?x254), genre(?x11895, ?x53), ?x53 = 07s9rl0, nominated_for(?x7505, ?x11895) >> conf = 0.45 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03cf9ly actor 03_6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 78.000 0.452 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #1902-0jjy0 PRED entity: 0jjy0 PRED relation: music PRED expected values: 02jxmr => 106 concepts (77 used for prediction) PRED predicted values (max 10 best out of 105): 02jxkw (0.33 #142, 0.14 #352, 0.11 #562), 0146pg (0.29 #220, 0.13 #2327, 0.11 #430), 06fxnf (0.22 #489, 0.05 #1963, 0.04 #1753), 02bh9 (0.14 #1101, 0.10 #891, 0.07 #1311), 01tc9r (0.14 #1115, 0.07 #1325, 0.07 #1537), 0fp_v1x (0.14 #213, 0.03 #1263, 0.01 #2320), 0150t6 (0.12 #676, 0.05 #1940, 0.05 #886), 04pf4r (0.12 #698, 0.04 #2175, 0.03 #1328), 02jxmr (0.11 #494, 0.10 #2391, 0.08 #1546), 023361 (0.11 #570, 0.06 #780, 0.05 #2467) >> Best rule #142 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 0k2sk; >> query: (?x1108, 02jxkw) <- country(?x1108, ?x94), genre(?x1108, ?x53), produced_by(?x1108, ?x4552), ?x4552 = 030_3z, cinematography(?x1108, ?x10542), executive_produced_by(?x1108, ?x846) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #494 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 026p4q7; *> query: (?x1108, 02jxmr) <- country(?x1108, ?x94), genre(?x1108, ?x53), produced_by(?x1108, ?x4552), ?x4552 = 030_3z, film_crew_role(?x1108, ?x1171), titles(?x4205, ?x1108) *> conf = 0.11 ranks of expected_values: 9 EVAL 0jjy0 music 02jxmr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 106.000 77.000 0.333 http://example.org/film/film/music #1901-0cv9fc PRED entity: 0cv9fc PRED relation: award PRED expected values: 07bdd_ => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 269): 07bdd_ (0.53 #3693, 0.29 #66, 0.25 #469), 0gq9h (0.43 #78, 0.39 #3302, 0.38 #481), 040njc (0.31 #3232, 0.29 #8, 0.26 #5247), 09sb52 (0.30 #10520, 0.28 #11326, 0.25 #5683), 0gr51 (0.26 #2922, 0.23 #2116, 0.18 #1310), 0ck27z (0.24 #6945, 0.23 #8960, 0.20 #7751), 0fbtbt (0.23 #1038, 0.17 #2650, 0.13 #1441), 0gr4k (0.23 #2854, 0.21 #2048, 0.15 #1242), 04dn09n (0.20 #2865, 0.18 #2059, 0.15 #1253), 0f_nbyh (0.19 #3234, 0.14 #10, 0.14 #2428) >> Best rule #3693 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 04glx0; >> query: (?x11580, 07bdd_) <- award_nominee(?x574, ?x11580), film(?x574, ?x1150), film_release_region(?x1150, ?x47) >> conf = 0.53 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cv9fc award 07bdd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.531 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1900-05fgt1 PRED entity: 05fgt1 PRED relation: film! PRED expected values: 01fh9 => 116 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1105): 07rzf (0.20 #3957, 0.20 #1878, 0.01 #20588), 0309lm (0.20 #3682, 0.13 #14073, 0.07 #16153), 044rvb (0.20 #2180, 0.12 #4258, 0.06 #8415), 01wbg84 (0.20 #2125, 0.09 #12516, 0.06 #24994), 0bxtg (0.20 #2155, 0.07 #6311, 0.05 #16706), 04xhwn (0.20 #12379, 0.07 #8222, 0.02 #31093), 09fb5 (0.20 #57, 0.06 #8371, 0.03 #56195), 049dyj (0.20 #175, 0.05 #10567, 0.02 #31360), 01b9z4 (0.20 #3722, 0.04 #14113, 0.02 #16193), 0b_dy (0.20 #533, 0.04 #54058, 0.03 #25481) >> Best rule #3957 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 03tn80; >> query: (?x2481, 07rzf) <- film(?x5217, ?x2481), genre(?x2481, ?x53), edited_by(?x2481, ?x826), ?x5217 = 01h8f >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #6550 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 053tj7; *> query: (?x2481, 01fh9) <- production_companies(?x2481, ?x3085), produced_by(?x2481, ?x1039), film_release_region(?x2481, ?x94), ?x1039 = 04wvhz, genre(?x2481, ?x53) *> conf = 0.07 ranks of expected_values: 96 EVAL 05fgt1 film! 01fh9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 116.000 64.000 0.200 http://example.org/film/actor/film./film/performance/film #1899-01r93l PRED entity: 01r93l PRED relation: award_nominee! PRED expected values: 016zp5 => 117 concepts (77 used for prediction) PRED predicted values (max 10 best out of 1029): 09l3p (0.81 #97718, 0.81 #83758, 0.81 #69802), 02qgyv (0.81 #97718, 0.81 #83758, 0.81 #69802), 018ygt (0.81 #97718, 0.81 #83758, 0.81 #69802), 016zp5 (0.81 #97718, 0.81 #83758, 0.81 #69802), 0bq2g (0.81 #97718, 0.81 #83758, 0.81 #69802), 02p65p (0.58 #2352, 0.18 #179140, 0.12 #118653), 032_jg (0.50 #2500, 0.18 #179140, 0.12 #118653), 014zcr (0.50 #2372, 0.10 #146578, 0.04 #7025), 0c6qh (0.42 #2864, 0.18 #179140, 0.12 #118653), 0pnf3 (0.42 #4447, 0.18 #179140, 0.12 #118653) >> Best rule #97718 for best value: >> intensional similarity = 3 >> extensional distance = 370 >> proper extension: 01rr9f; 03f2_rc; 01n5309; 01j5x6; 03lt8g; 0sz28; 01vs_v8; 01pgzn_; 0jfx1; 0127m7; ... >> query: (?x4294, ?x2353) <- participant(?x4294, ?x1017), film(?x4294, ?x1444), award_nominee(?x4294, ?x2353) >> conf = 0.81 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4 EVAL 01r93l award_nominee! 016zp5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 77.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1898-0bzm81 PRED entity: 0bzm81 PRED relation: honored_for PRED expected values: 03hj3b3 => 42 concepts (28 used for prediction) PRED predicted values (max 10 best out of 556): 011yl_ (0.33 #2991, 0.33 #2604, 0.25 #3202), 0d87hc (0.33 #2944, 0.25 #3542, 0.17 #4140), 011yhm (0.33 #2797, 0.25 #3395, 0.17 #3993), 011ykb (0.33 #2791, 0.25 #3389, 0.17 #3987), 0ds2n (0.33 #2586, 0.25 #3184, 0.17 #3782), 0pv3x (0.33 #2458, 0.25 #3056, 0.17 #3654), 02kfzz (0.33 #2639, 0.25 #3237, 0.17 #3835), 07tj4c (0.33 #2961, 0.25 #3559, 0.17 #4157), 07g1sm (0.33 #2215, 0.25 #3411, 0.12 #5206), 016dj8 (0.33 #1585, 0.17 #3977, 0.12 #5174) >> Best rule #2991 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 02yxh9; >> query: (?x1747, ?x3573) <- award_winner(?x1747, ?x11364), award_winner(?x1747, ?x10416), award_winner(?x1747, ?x8719), award_winner(?x1747, ?x7615), award_winner(?x1747, ?x3002), award(?x11364, ?x3247), ?x10416 = 0cw67g, nominated_for(?x8719, ?x1386), film(?x11364, ?x3573), ?x3247 = 0bdwqv, ceremony(?x77, ?x1747), ?x3573 = 011yl_, award_nominee(?x1738, ?x11364), award_winner(?x3651, ?x3002), type_of_union(?x7615, ?x566), award_winner(?x834, ?x1738) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3588 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 2 *> proper extension: 04n2r9h; *> query: (?x1747, ?x1386) <- award_winner(?x1747, ?x11364), award_winner(?x1747, ?x10416), award_winner(?x1747, ?x8719), award_winner(?x1747, ?x7615), award(?x11364, ?x3247), ?x10416 = 0cw67g, nominated_for(?x8719, ?x1386), film(?x11364, ?x3573), ?x3247 = 0bdwqv, ceremony(?x77, ?x1747), nominated_for(?x1162, ?x3573), profession(?x8719, ?x1032), location(?x7615, ?x12656), nominated_for(?x3533, ?x3573), ?x1162 = 099c8n *> conf = 0.11 ranks of expected_values: 108 EVAL 0bzm81 honored_for 03hj3b3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 42.000 28.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #1897-04rlf PRED entity: 04rlf PRED relation: major_field_of_study! PRED expected values: 019v9k => 99 concepts (74 used for prediction) PRED predicted values (max 10 best out of 18): 04zx3q1 (0.80 #502, 0.75 #416, 0.69 #641), 019v9k (0.78 #455, 0.77 #646, 0.77 #1160), 03bwzr4 (0.70 #511, 0.67 #459, 0.62 #425), 0bkj86 (0.62 #645, 0.60 #213, 0.60 #1049), 0bjrnt (0.49 #1061, 0.44 #1173, 0.40 #193), 071tyz (0.49 #1061, 0.37 #1005, 0.36 #986), 07s6fsf (0.48 #1172, 0.44 #1173, 0.38 #172), 013zdg (0.48 #1172, 0.44 #1173, 0.38 #172), 022h5x (0.48 #1172, 0.44 #1173, 0.38 #172), 027f2w (0.48 #1172, 0.44 #1173, 0.37 #1005) >> Best rule #502 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 01mkq; 0pf2; >> query: (?x8681, 04zx3q1) <- major_field_of_study(?x9844, ?x8681), major_field_of_study(?x6637, ?x8681), taxonomy(?x8681, ?x939), major_field_of_study(?x1368, ?x8681), ?x6637 = 07vjm, ?x1368 = 014mlp, major_field_of_study(?x8681, ?x2164), contains(?x362, ?x9844) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #455 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 02ky346; 0mg1w; 09s1f; 06mq7; *> query: (?x8681, 019v9k) <- major_field_of_study(?x6637, ?x8681), major_field_of_study(?x1011, ?x8681), taxonomy(?x8681, ?x939), major_field_of_study(?x5739, ?x8681), major_field_of_study(?x6637, ?x3490), ?x3490 = 05qfh, ?x5739 = 01gkg3, student(?x1011, ?x400) *> conf = 0.78 ranks of expected_values: 2 EVAL 04rlf major_field_of_study! 019v9k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 74.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #1896-0f4y3 PRED entity: 0f4y3 PRED relation: time_zones PRED expected values: 02hcv8 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.85 #197, 0.84 #474, 0.83 #367), 02lcqs (0.24 #123, 0.24 #136, 0.22 #110), 02fqwt (0.17 #805, 0.16 #792, 0.13 #315), 02hczc (0.10 #120, 0.10 #133, 0.10 #212), 02llzg (0.08 #847, 0.07 #517, 0.07 #834), 03bdv (0.05 #823, 0.04 #849, 0.03 #901), 03plfd (0.02 #523, 0.02 #604, 0.02 #576), 0gsrz4 (0.02 #588, 0.02 #682, 0.02 #641), 042g7t (0.01 #724, 0.01 #737) >> Best rule #197 for best value: >> intensional similarity = 4 >> extensional distance = 192 >> proper extension: 05kr_; 0n5_g; 0k3ll; 0mws3; 0n5y4; 02m4d; 0nm8n; >> query: (?x10991, ?x2674) <- adjoins(?x6296, ?x10991), time_zones(?x6296, ?x2674), county_seat(?x6296, ?x6295), second_level_divisions(?x94, ?x6296) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f4y3 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.851 http://example.org/location/location/time_zones #1895-029ghl PRED entity: 029ghl PRED relation: type_of_union PRED expected values: 01g63y => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 1): 01g63y (0.29 #61, 0.28 #55, 0.28 #52) >> Best rule #61 for best value: >> intensional similarity = 3 >> extensional distance = 366 >> proper extension: 0c9d9; 06y9c2; 067jsf; 01pl9g; 02d9k; 0zjpz; 02jg92; 047hpm; 01v3bn; 02v406; ... >> query: (?x9301, 01g63y) <- profession(?x9301, ?x319), spouse(?x9301, ?x2841), type_of_union(?x9301, ?x566) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 029ghl type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 119.000 0.293 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1894-0gr51 PRED entity: 0gr51 PRED relation: nominated_for PRED expected values: 02v8kmz 01sxly 0dtfn 02_kd 0k4fz 02hfk5 041td_ 0jqd3 0kb1g => 58 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1341): 01cmp9 (0.85 #18245, 0.64 #12444, 0.59 #21146), 0404j37 (0.80 #10149, 0.77 #33363, 0.77 #11600), 0sxns (0.80 #10149, 0.77 #33363, 0.77 #11600), 0_b9f (0.80 #10149, 0.77 #33363, 0.77 #11600), 05sy_5 (0.80 #10149, 0.77 #33363, 0.77 #11600), 02rv_dz (0.80 #10149, 0.77 #33363, 0.77 #11600), 0yxm1 (0.80 #10149, 0.77 #33363, 0.77 #11600), 0h1x5f (0.80 #10149, 0.77 #33363, 0.77 #11600), 02mt51 (0.80 #10149, 0.77 #33363, 0.77 #11600), 0gt14 (0.80 #10149, 0.77 #33363, 0.77 #11600) >> Best rule #18245 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0gq_v; 094qd5; 0gqwc; 0gqyl; >> query: (?x1862, 01cmp9) <- ceremony(?x1862, ?x78), nominated_for(?x1862, ?x9138), award(?x361, ?x1862), ?x9138 = 06x77g >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #18075 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 0gq_v; 094qd5; 0gqwc; 0gqyl; *> query: (?x1862, 0k4fz) <- ceremony(?x1862, ?x78), nominated_for(?x1862, ?x9138), award(?x361, ?x1862), ?x9138 = 06x77g *> conf = 0.69 ranks of expected_values: 29, 114, 121, 205, 216, 289, 315, 397, 677 EVAL 0gr51 nominated_for 0kb1g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 58.000 23.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr51 nominated_for 0jqd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 23.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr51 nominated_for 041td_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 58.000 23.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr51 nominated_for 02hfk5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 58.000 23.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr51 nominated_for 0k4fz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 58.000 23.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr51 nominated_for 02_kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 58.000 23.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr51 nominated_for 0dtfn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 58.000 23.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr51 nominated_for 01sxly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 58.000 23.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr51 nominated_for 02v8kmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 58.000 23.000 0.846 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1893-03q5db PRED entity: 03q5db PRED relation: film! PRED expected values: 06chf => 101 concepts (57 used for prediction) PRED predicted values (max 10 best out of 133): 04pqqb (0.20 #11811, 0.18 #7410), 07rd7 (0.11 #1476, 0.05 #926, 0.05 #4494), 02vyw (0.09 #1187, 0.02 #5578, 0.02 #912), 06pj8 (0.07 #1420, 0.04 #596, 0.04 #4712), 0j_c (0.06 #3630, 0.06 #3905, 0.04 #2258), 0c1pj (0.06 #12, 0.02 #834), 01ts_3 (0.05 #438, 0.03 #2633, 0.03 #3457), 07h5d (0.05 #443, 0.03 #1541, 0.03 #1815), 015njf (0.05 #395, 0.02 #2590, 0.01 #3414), 0693l (0.04 #632, 0.03 #2004, 0.03 #2279) >> Best rule #11811 for best value: >> intensional similarity = 5 >> extensional distance = 663 >> proper extension: 05f67hw; >> query: (?x3772, ?x4854) <- language(?x3772, ?x254), country(?x3772, ?x94), ?x94 = 09c7w0, ?x254 = 02h40lc, produced_by(?x3772, ?x4854) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #4194 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 147 *> proper extension: 04grkmd; 0cmc26r; *> query: (?x3772, 06chf) <- film(?x1104, ?x3772), country(?x3772, ?x94), film_crew_role(?x3772, ?x137), ?x1104 = 016tw3 *> conf = 0.03 ranks of expected_values: 37 EVAL 03q5db film! 06chf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 101.000 57.000 0.197 http://example.org/film/director/film #1892-064p92m PRED entity: 064p92m PRED relation: profession PRED expected values: 02jknp 02hrh1q => 106 concepts (70 used for prediction) PRED predicted values (max 10 best out of 62): 02hrh1q (0.89 #9106, 0.87 #2548, 0.85 #1653), 01d_h8 (0.72 #751, 0.59 #3136, 0.57 #2987), 02jknp (0.67 #753, 0.48 #4032, 0.47 #2691), 03gjzk (0.55 #462, 0.53 #6573, 0.48 #9703), 0cbd2 (0.39 #9546, 0.36 #4776, 0.36 #9993), 0np9r (0.31 #9411, 0.15 #3151, 0.15 #3002), 0kyk (0.30 #4799, 0.24 #10016, 0.24 #10165), 015cjr (0.27 #50, 0.21 #199, 0.21 #348), 018gz8 (0.26 #2998, 0.26 #3743, 0.24 #3296), 02krf9 (0.20 #9715, 0.20 #474, 0.17 #4200) >> Best rule #9106 for best value: >> intensional similarity = 5 >> extensional distance = 1176 >> proper extension: 01vvydl; 0lbj1; 05m63c; 023tp8; 09fqtq; 0lzb8; 034x61; 016khd; 01wbgdv; 01k5t_3; ... >> query: (?x1806, 02hrh1q) <- nationality(?x1806, ?x2146), people(?x5025, ?x1806), profession(?x1806, ?x987), profession(?x2739, ?x987), ?x2739 = 02dh86 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 064p92m profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 70.000 0.885 http://example.org/people/person/profession EVAL 064p92m profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 70.000 0.885 http://example.org/people/person/profession #1891-032zg9 PRED entity: 032zg9 PRED relation: film PRED expected values: 01qb559 => 109 concepts (62 used for prediction) PRED predicted values (max 10 best out of 542): 01f7jt (0.33 #1687, 0.29 #7027, 0.29 #5247), 01f7kl (0.33 #391, 0.29 #5731, 0.29 #3951), 085bd1 (0.17 #2229, 0.17 #449, 0.14 #5789), 01pvxl (0.17 #2684, 0.17 #904, 0.14 #6244), 03kxj2 (0.17 #2136, 0.17 #356, 0.14 #5696), 0bt4g (0.17 #3108, 0.17 #1328, 0.14 #6668), 07xtqq (0.17 #1837, 0.17 #57, 0.14 #5397), 084qpk (0.17 #120, 0.14 #5460, 0.14 #3680), 02yvct (0.17 #349, 0.14 #5689, 0.14 #3909), 08rr3p (0.17 #441, 0.14 #5781, 0.14 #4001) >> Best rule #1687 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 058kqy; >> query: (?x4667, 01f7jt) <- award(?x4667, ?x880), film(?x4667, ?x9361), film(?x4667, ?x6681), ?x6681 = 04y9mm8, nominated_for(?x384, ?x9361) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #20883 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 434 *> proper extension: 02zyy4; 049k07; 04rsd2; 02lf1j; 073749; 03h2d4; 01ry0f; 05dtwm; 046chh; 041rhq; ... *> query: (?x4667, 01qb559) <- film(?x4667, ?x6681), gender(?x4667, ?x231), film_release_region(?x6681, ?x94), film_crew_role(?x6681, ?x468) *> conf = 0.02 ranks of expected_values: 188 EVAL 032zg9 film 01qb559 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 109.000 62.000 0.333 http://example.org/film/actor/film./film/performance/film #1890-01l1sq PRED entity: 01l1sq PRED relation: award_nominee PRED expected values: 02lg3y => 147 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1296): 02lg9w (0.86 #6986, 0.86 #4656, 0.85 #6985), 02lgj6 (0.86 #6986, 0.86 #4656, 0.85 #6985), 03zyvw (0.86 #6986, 0.84 #4657, 0.81 #13969), 01l1sq (0.70 #2669, 0.67 #4998, 0.35 #155950), 02lg3y (0.58 #5679, 0.50 #3350, 0.35 #155950), 072bb1 (0.34 #12203, 0.33 #9876, 0.06 #70393), 0bt4r4 (0.32 #12290, 0.31 #9963, 0.07 #105394), 0bt7ws (0.32 #12503, 0.31 #10176, 0.06 #70693), 0cnl1c (0.32 #12641, 0.31 #10314, 0.06 #70831), 05xpms (0.32 #13622, 0.31 #11295, 0.06 #71812) >> Best rule #6986 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 02lfl4; 02lgj6; 02lg9w; >> query: (?x1652, ?x447) <- award_winner(?x3154, ?x1652), ?x3154 = 02p_ycc, award_nominee(?x447, ?x1652) >> conf = 0.86 => this is the best rule for 3 predicted values *> Best rule #5679 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 02lfl4; 02lgj6; 02lg9w; *> query: (?x1652, 02lg3y) <- award_winner(?x3154, ?x1652), ?x3154 = 02p_ycc, award_nominee(?x447, ?x1652) *> conf = 0.58 ranks of expected_values: 5 EVAL 01l1sq award_nominee 02lg3y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 147.000 84.000 0.861 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1889-02v406 PRED entity: 02v406 PRED relation: film PRED expected values: 02sg5v => 121 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1098): 0f42nz (0.10 #4485, 0.07 #907, 0.04 #8063), 03z20c (0.09 #7632, 0.03 #14788, 0.03 #16577), 03nfnx (0.08 #4980, 0.03 #17503, 0.03 #22870), 04jwjq (0.07 #92, 0.05 #3670, 0.04 #7248), 03wy8t (0.07 #1586, 0.03 #5164, 0.02 #15898), 04lhc4 (0.07 #1215, 0.02 #6582, 0.02 #13738), 0661m4p (0.07 #375, 0.02 #14687, 0.02 #18265), 016dj8 (0.07 #8269, 0.05 #4691, 0.03 #22581), 013q07 (0.05 #2145, 0.05 #3934, 0.03 #21824), 03p2xc (0.05 #3033, 0.03 #1244, 0.03 #4822) >> Best rule #4485 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 021bk; 01xcfy; 0241wg; 0sw6g; 029ghl; >> query: (?x4217, 0f42nz) <- languages(?x4217, ?x254), religion(?x4217, ?x8613), spouse(?x4217, ?x2538) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02v406 film 02sg5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 74.000 0.103 http://example.org/film/actor/film./film/performance/film #1888-0fsyx PRED entity: 0fsyx PRED relation: group! PRED expected values: 018vs 05148p4 => 106 concepts (78 used for prediction) PRED predicted values (max 10 best out of 125): 05148p4 (0.86 #1588, 0.84 #1762, 0.81 #891), 018vs (0.71 #1493, 0.70 #2279, 0.70 #1058), 028tv0 (0.46 #2103, 0.45 #1754, 0.44 #883), 01vj9c (0.40 #275, 0.33 #1146, 0.32 #2105), 0l14qv (0.35 #1050, 0.33 #2096, 0.31 #1485), 03qjg (0.34 #1876, 0.32 #2138, 0.32 #2050), 05r5c (0.27 #965, 0.26 #1575, 0.25 #1749), 07gql (0.25 #908, 0.23 #995, 0.15 #1568), 02k84w (0.25 #205, 0.20 #292, 0.17 #2616), 04rzd (0.25 #380, 0.18 #554, 0.17 #2616) >> Best rule #1588 for best value: >> intensional similarity = 7 >> extensional distance = 40 >> proper extension: 01fl3; 0dtd6; 0frsw; 07yg2; 0g_g2; 06nv27; 0134tg; 07mvp; 0178_w; 02vgh; ... >> query: (?x13039, 05148p4) <- group(?x315, ?x13039), artist(?x1954, ?x13039), artists(?x302, ?x13039), ?x315 = 0l14md, group(?x2492, ?x13039), artists(?x302, ?x4909), ?x4909 = 01cblr >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0fsyx group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 78.000 0.857 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0fsyx group! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 78.000 0.857 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1887-034qbx PRED entity: 034qbx PRED relation: category PRED expected values: 08mbj5d => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.46 #3, 0.40 #6, 0.33 #8) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 04h41v; >> query: (?x6588, 08mbj5d) <- film_crew_role(?x6588, ?x1284), genre(?x6588, ?x809), film(?x722, ?x6588), ?x809 = 0vgkd, ?x1284 = 0ch6mp2 >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034qbx category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.459 http://example.org/common/topic/webpage./common/webpage/category #1886-0b1hw PRED entity: 0b1hw PRED relation: artists! PRED expected values: 01_bkd => 127 concepts (98 used for prediction) PRED predicted values (max 10 best out of 270): 0xhtw (0.78 #1253, 0.73 #9920, 0.73 #7751), 05bt6j (0.54 #7465, 0.49 #12417, 0.44 #3751), 064t9 (0.52 #4962, 0.51 #12389, 0.47 #26011), 01_bkd (0.48 #4692, 0.33 #361, 0.33 #52), 05r6t (0.40 #1934, 0.33 #80, 0.31 #3790), 05jg58 (0.40 #1973, 0.25 #3829, 0.19 #4759), 02k_kn (0.40 #7487, 0.32 #12439, 0.28 #11511), 0cx7f (0.36 #2919, 0.33 #4466, 0.33 #4156), 02yv6b (0.33 #6592, 0.33 #1642, 0.29 #715), 0155w (0.33 #1341, 0.30 #8768, 0.29 #723) >> Best rule #1253 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01s7qqw; >> query: (?x10737, 0xhtw) <- influenced_by(?x10737, ?x483), influenced_by(?x5329, ?x10737), artists(?x2249, ?x10737), ?x2249 = 03lty >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4692 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 01gx5f; 01w8n89; 0fpj4lx; 01vsyjy; 020_4z; *> query: (?x10737, 01_bkd) <- artists(?x2249, ?x10737), artists(?x302, ?x10737), ?x302 = 016clz, artist(?x12061, ?x10737), ?x2249 = 03lty *> conf = 0.48 ranks of expected_values: 4 EVAL 0b1hw artists! 01_bkd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 127.000 98.000 0.778 http://example.org/music/genre/artists #1885-0262zm PRED entity: 0262zm PRED relation: award! PRED expected values: 048_p => 59 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2322): 0c3kw (0.79 #13466, 0.71 #17270, 0.69 #60596), 01dzz7 (0.79 #13466, 0.71 #17280, 0.69 #60596), 01dhmw (0.79 #13466, 0.69 #60596, 0.69 #3369), 03772 (0.79 #13466, 0.69 #3369, 0.67 #57225), 0klw (0.79 #13466, 0.67 #57225, 0.67 #60595), 048_p (0.75 #21821, 0.71 #18456, 0.67 #15091), 01g6bk (0.71 #20021, 0.67 #16656, 0.60 #9925), 0b0pf (0.60 #8296, 0.57 #18392, 0.50 #15027), 01zkxv (0.60 #6863, 0.50 #10228, 0.43 #16959), 07zl1 (0.57 #19741, 0.50 #16376, 0.40 #9645) >> Best rule #13466 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 039yzf; >> query: (?x1375, ?x1727) <- award(?x11287, ?x1375), award(?x10275, ?x1375), award(?x8863, ?x1375), ?x8863 = 0fpzt5, ?x10275 = 03hpr, award_winner(?x1375, ?x1727), student(?x6637, ?x11287), disciplines_or_subjects(?x1375, ?x1013) >> conf = 0.79 => this is the best rule for 5 predicted values *> Best rule #21821 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 02664f; *> query: (?x1375, 048_p) <- award(?x11287, ?x1375), award(?x10275, ?x1375), award(?x8863, ?x1375), award(?x1376, ?x1375), ?x8863 = 0fpzt5, ?x1376 = 01963w, influenced_by(?x3806, ?x10275), disciplines_or_subjects(?x1375, ?x1013), religion(?x11287, ?x7131) *> conf = 0.75 ranks of expected_values: 6 EVAL 0262zm award! 048_p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 59.000 19.000 0.795 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1884-01dhpj PRED entity: 01dhpj PRED relation: nationality PRED expected values: 06mkj 0604m => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 37): 09c7w0 (0.86 #590, 0.81 #5705, 0.80 #10443), 0345h (0.40 #8175, 0.36 #1084, 0.07 #423), 084n_ (0.36 #1084), 059z0 (0.36 #1084), 01k6y1 (0.36 #1084), 06n3y (0.33 #7188), 07ssc (0.19 #998, 0.12 #1394, 0.11 #701), 02jx1 (0.17 #2789, 0.16 #3869, 0.15 #1412), 0d060g (0.12 #300, 0.08 #2567, 0.06 #8573), 0f8l9c (0.10 #1005, 0.08 #119, 0.05 #1204) >> Best rule #590 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 079vf; 03m8lq; 02_hj4; 0126y2; 049_zz; 09hd16; 062hgx; 04w1j9; 028r4y; 02x0bdb; ... >> query: (?x8129, 09c7w0) <- award_winner(?x8129, ?x5151), place_of_birth(?x8129, ?x2911), film_regional_debut_venue(?x1701, ?x2911), country(?x2911, ?x142) >> conf = 0.86 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01dhpj nationality 0604m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 165.000 0.859 http://example.org/people/person/nationality EVAL 01dhpj nationality 06mkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 165.000 0.859 http://example.org/people/person/nationality #1883-0rlz PRED entity: 0rlz PRED relation: basic_title PRED expected values: 060c4 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 17): 060c4 (0.77 #309, 0.68 #373, 0.64 #389), 0789n (0.40 #24, 0.35 #218, 0.33 #234), 0dq3c (0.38 #98, 0.33 #162, 0.28 #388), 060bp (0.24 #275, 0.19 #195, 0.15 #499), 02079p (0.22 #178, 0.22 #177, 0.17 #57), 01t7n9 (0.22 #178, 0.22 #177, 0.07 #191), 0f6c3 (0.22 #178, 0.22 #177, 0.05 #264), 09n5b9 (0.22 #178, 0.22 #177), 0p5vf (0.14 #284, 0.09 #508, 0.08 #556), 0fj45 (0.12 #110, 0.11 #142, 0.08 #174) >> Best rule #309 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 042kg; 08959; >> query: (?x5742, 060c4) <- jurisdiction_of_office(?x5742, ?x94), basic_title(?x5742, ?x900), profession(?x5742, ?x5805), taxonomy(?x5742, ?x939) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0rlz basic_title 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.773 http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title #1882-03h_9lg PRED entity: 03h_9lg PRED relation: film PRED expected values: 06wbm8q 05650n => 94 concepts (89 used for prediction) PRED predicted values (max 10 best out of 803): 017jd9 (0.28 #4339, 0.22 #775, 0.03 #110494), 017gl1 (0.25 #3705, 0.22 #141, 0.03 #128315), 017gm7 (0.25 #3772, 0.11 #208, 0.03 #128315), 0ndwt2w (0.22 #4557, 0.11 #993, 0.01 #45544), 0bt3j9 (0.12 #2666, 0.03 #128315, 0.02 #8012), 06fcqw (0.12 #2867), 0b7l4x (0.11 #1032, 0.06 #2814, 0.03 #6378), 04grkmd (0.11 #568, 0.06 #2350, 0.03 #4132), 0bh8yn3 (0.11 #255, 0.03 #128315, 0.03 #5601), 03shpq (0.11 #1439, 0.03 #128315, 0.03 #5003) >> Best rule #4339 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 031ydm; 03_wvl; 044n3h; >> query: (?x844, 017jd9) <- award_nominee(?x844, ?x628), profession(?x844, ?x319), ?x628 = 01kwld >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #4569 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 031ydm; 03_wvl; 044n3h; *> query: (?x844, 05650n) <- award_nominee(?x844, ?x628), profession(?x844, ?x319), ?x628 = 01kwld *> conf = 0.06 ranks of expected_values: 40, 373 EVAL 03h_9lg film 05650n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 94.000 89.000 0.281 http://example.org/film/actor/film./film/performance/film EVAL 03h_9lg film 06wbm8q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 94.000 89.000 0.281 http://example.org/film/actor/film./film/performance/film #1881-016s0m PRED entity: 016s0m PRED relation: artist! PRED expected values: 01cf93 => 136 concepts (75 used for prediction) PRED predicted values (max 10 best out of 123): 015_1q (0.36 #302, 0.29 #1430, 0.26 #443), 03qx_f (0.29 #356, 0.25 #74, 0.09 #779), 03rhqg (0.29 #298, 0.19 #862, 0.19 #721), 017l96 (0.25 #160, 0.25 #19, 0.12 #1429), 01cszh (0.25 #11, 0.17 #998, 0.17 #152), 01cf93 (0.25 #58, 0.17 #199, 0.14 #340), 0181dw (0.25 #42, 0.16 #1029, 0.12 #465), 03vv61 (0.25 #100, 0.08 #241, 0.07 #382), 01f_3w (0.25 #35, 0.08 #176, 0.06 #881), 01xyqk (0.25 #81, 0.08 #222, 0.06 #8890) >> Best rule #302 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 01wz3cx; >> query: (?x8921, 015_1q) <- award(?x8921, ?x10316), award(?x8921, ?x4958), ?x10316 = 02ddq4, artists(?x302, ?x8921), ceremony(?x4958, ?x139) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #58 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 01vrt_c; 015882; *> query: (?x8921, 01cf93) <- award(?x8921, ?x1479), award_winner(?x6487, ?x8921), ?x1479 = 01ckbq, currency(?x8921, ?x170) *> conf = 0.25 ranks of expected_values: 6 EVAL 016s0m artist! 01cf93 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 136.000 75.000 0.357 http://example.org/music/record_label/artist #1880-02z2xdf PRED entity: 02z2xdf PRED relation: executive_produced_by! PRED expected values: 09rvcvl => 95 concepts (59 used for prediction) PRED predicted values (max 10 best out of 308): 0bt4g (0.17 #416, 0.11 #939, 0.10 #1462), 0mbql (0.17 #373, 0.11 #896, 0.10 #1419), 01f7kl (0.17 #131, 0.11 #654, 0.10 #1177), 09gdh6k (0.17 #404, 0.11 #927, 0.10 #1450), 0k_9j (0.17 #440, 0.05 #10988, 0.02 #3578), 04sh80 (0.17 #513, 0.05 #10988, 0.01 #8372), 03tn80 (0.17 #279, 0.05 #10988, 0.01 #8372), 0cc5qkt (0.17 #194, 0.05 #10988, 0.01 #8372), 0dnqr (0.17 #161, 0.05 #10988, 0.01 #8372), 03twd6 (0.17 #71, 0.05 #10988) >> Best rule #416 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 03qmx_f; 061dn_; 03nk3t; 030_3z; >> query: (?x6944, 0bt4g) <- award_nominee(?x6944, ?x361), ?x361 = 0h5f5n, award(?x6944, ?x198) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02z2xdf executive_produced_by! 09rvcvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 59.000 0.167 http://example.org/film/film/executive_produced_by #1879-017kz7 PRED entity: 017kz7 PRED relation: genre PRED expected values: 05p553 => 61 concepts (48 used for prediction) PRED predicted values (max 10 best out of 96): 05p553 (0.67 #2485, 0.52 #3195, 0.43 #240), 02kdv5l (0.58 #710, 0.52 #2601, 0.50 #2128), 01jfsb (0.55 #2611, 0.37 #720, 0.32 #2138), 04t36 (0.52 #1653, 0.48 #3547, 0.48 #3546), 06n90 (0.43 #2139, 0.27 #485, 0.26 #1193), 02l7c8 (0.39 #960, 0.31 #1078, 0.31 #252), 04xvlr (0.29 #237, 0.28 #945, 0.18 #2246), 0lsxr (0.25 #127, 0.22 #2608, 0.18 #2372), 01g6gs (0.25 #137, 0.20 #609, 0.20 #373), 0hcr (0.20 #1438, 0.20 #730, 0.19 #1202) >> Best rule #2485 for best value: >> intensional similarity = 5 >> extensional distance = 873 >> proper extension: 0dtw1x; 087wc7n; 03mh_tp; 0gtvpkw; 0gj96ln; 094g2z; 0g9z_32; 0gwf191; 099bhp; >> query: (?x7760, 05p553) <- genre(?x7760, ?x811), genre(?x3344, ?x811), genre(?x2933, ?x811), ?x3344 = 02rrfzf, ?x2933 = 0407yj_ >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017kz7 genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 48.000 0.666 http://example.org/film/film/genre #1878-01vh18t PRED entity: 01vh18t PRED relation: award PRED expected values: 0bdwqv => 129 concepts (128 used for prediction) PRED predicted values (max 10 best out of 311): 02py7pj (0.75 #1601, 0.71 #29621, 0.71 #35227), 0gq9h (0.58 #14487, 0.23 #6081, 0.22 #2077), 07bdd_ (0.37 #14475, 0.13 #2065, 0.12 #1263), 0ck27z (0.32 #22505, 0.27 #24505, 0.21 #23705), 09sb52 (0.32 #28859, 0.27 #28458, 0.26 #26855), 01by1l (0.32 #23325, 0.10 #4515, 0.10 #29330), 040njc (0.31 #14420, 0.15 #6014, 0.13 #3212), 0gr4k (0.30 #2433, 0.29 #1632, 0.26 #2834), 01l78d (0.29 #3089, 0.26 #2688, 0.24 #1887), 04dn09n (0.28 #2444, 0.21 #2845, 0.20 #1643) >> Best rule #1601 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 018p5f; >> query: (?x9404, ?x594) <- award_winner(?x594, ?x9404), award(?x9404, ?x3105), ?x3105 = 01l29r >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #169 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 04264n; 015qyf; 022qw7; 03swmf; 0127xk; 015qq1; 076689; 02zfg3; *> query: (?x9404, 0bdwqv) <- place_of_death(?x9404, ?x2866), type_of_union(?x9404, ?x566), award(?x9404, ?x435), ?x435 = 0bp_b2 *> conf = 0.20 ranks of expected_values: 24 EVAL 01vh18t award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 129.000 128.000 0.748 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1877-078g3l PRED entity: 078g3l PRED relation: film PRED expected values: 016ky6 => 91 concepts (54 used for prediction) PRED predicted values (max 10 best out of 435): 0kfv9 (0.56 #16114, 0.56 #19695, 0.53 #57292), 0h6r5 (0.15 #679), 01xbxn (0.08 #1394, 0.02 #3184, 0.01 #4974), 0gg5kmg (0.08 #1079, 0.01 #13612), 03sxd2 (0.08 #306), 042g97 (0.04 #12533, 0.04 #21486, 0.04 #21487), 0d_wms (0.04 #12533, 0.04 #21486, 0.04 #21487), 024mxd (0.04 #12533, 0.04 #21486, 0.04 #21487), 01_mdl (0.04 #12533, 0.04 #21486, 0.04 #21487), 03wy8t (0.04 #1587, 0.03 #3377, 0.02 #5167) >> Best rule #16114 for best value: >> intensional similarity = 4 >> extensional distance = 238 >> proper extension: 01gvr1; 0hskw; 0253b6; 016yvw; >> query: (?x6299, ?x1849) <- spouse(?x8222, ?x6299), nominated_for(?x6299, ?x1849), gender(?x8222, ?x514), profession(?x6299, ?x1032) >> conf = 0.56 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 078g3l film 016ky6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 54.000 0.560 http://example.org/film/actor/film./film/performance/film #1876-0421ng PRED entity: 0421ng PRED relation: genre PRED expected values: 05p553 => 85 concepts (84 used for prediction) PRED predicted values (max 10 best out of 99): 07s9rl0 (0.74 #1221, 0.74 #1099, 0.70 #1343), 05p553 (0.68 #249, 0.36 #1469, 0.35 #1103), 01z4y (0.61 #7704, 0.53 #6481, 0.52 #6970), 02l7c8 (0.59 #262, 0.30 #1116, 0.29 #2461), 0219x_ (0.41 #272, 0.38 #516, 0.33 #28), 06cvj (0.36 #248, 0.17 #4, 0.14 #126), 0lsxr (0.33 #498, 0.29 #132, 0.22 #742), 01jfsb (0.32 #5025, 0.31 #4171, 0.30 #3927), 02kdv5l (0.30 #857, 0.26 #5014, 0.26 #3916), 03k9fj (0.29 #379, 0.29 #867, 0.21 #2579) >> Best rule #1221 for best value: >> intensional similarity = 4 >> extensional distance = 115 >> proper extension: 0267wwv; >> query: (?x5020, 07s9rl0) <- nominated_for(?x986, ?x5020), country(?x5020, ?x94), genre(?x5020, ?x2700), film_regional_debut_venue(?x5020, ?x6601) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #249 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 0m3gy; *> query: (?x5020, 05p553) <- written_by(?x5020, ?x986), film(?x986, ?x306), type_of_union(?x986, ?x566), artists(?x2480, ?x986) *> conf = 0.68 ranks of expected_values: 2 EVAL 0421ng genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 84.000 0.744 http://example.org/film/film/genre #1875-02b5_l PRED entity: 02b5_l PRED relation: genre! PRED expected values: 0m2kd 01xlqd => 49 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1837): 052_mn (0.71 #28909, 0.62 #30741, 0.56 #32573), 01p3ty (0.71 #27915, 0.62 #29747, 0.56 #31579), 0bpx1k (0.62 #29794, 0.57 #27962, 0.56 #31626), 0436yk (0.60 #22252, 0.60 #20421, 0.57 #24083), 034qmv (0.60 #20178, 0.57 #23840, 0.43 #27502), 03t97y (0.60 #22162, 0.50 #14832, 0.33 #3834), 01pvxl (0.60 #21080, 0.47 #33901, 0.44 #32068), 01hw5kk (0.60 #22687, 0.43 #28180, 0.43 #24518), 05pdd86 (0.60 #23078, 0.43 #24909, 0.40 #21247), 02w86hz (0.60 #22617, 0.43 #24448, 0.40 #20786) >> Best rule #28909 for best value: >> intensional similarity = 14 >> extensional distance = 5 >> proper extension: 04t36; 02l7c8; >> query: (?x6452, 052_mn) <- genre(?x10596, ?x6452), genre(?x9145, ?x6452), genre(?x6832, ?x6452), genre(?x5081, ?x6452), genre(?x2184, ?x6452), ?x6832 = 03cyslc, titles(?x2480, ?x10596), films(?x12759, ?x5081), crewmember(?x5081, ?x3879), music(?x5081, ?x13700), film_crew_role(?x10596, ?x137), film(?x91, ?x5081), language(?x9145, ?x254), executive_produced_by(?x2184, ?x2221) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #30945 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 6 *> proper extension: 06cvj; *> query: (?x6452, 01xlqd) <- genre(?x10596, ?x6452), genre(?x9294, ?x6452), genre(?x6832, ?x6452), genre(?x5081, ?x6452), ?x6832 = 03cyslc, titles(?x2480, ?x10596), films(?x12759, ?x5081), crewmember(?x5081, ?x3879), film_release_region(?x9294, ?x87), film(?x794, ?x10596), film(?x4832, ?x10596), titles(?x571, ?x9294), film(?x9707, ?x9294), film_release_distribution_medium(?x5081, ?x81) *> conf = 0.38 ranks of expected_values: 571, 739 EVAL 02b5_l genre! 01xlqd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 49.000 29.000 0.714 http://example.org/film/film/genre EVAL 02b5_l genre! 0m2kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 49.000 29.000 0.714 http://example.org/film/film/genre #1874-01rc6f PRED entity: 01rc6f PRED relation: school! PRED expected values: 05tg3 => 202 concepts (202 used for prediction) PRED predicted values (max 10 best out of 171): 0jmj7 (0.71 #4595, 0.70 #3647, 0.69 #6663), 0jmgb (0.33 #254, 0.11 #6291, 0.11 #6290), 07l8x (0.29 #749, 0.23 #2386, 0.19 #1784), 07l4z (0.29 #752, 0.17 #2389, 0.17 #236), 05m_8 (0.24 #3708, 0.24 #3188, 0.23 #2413), 051vz (0.23 #2432, 0.23 #2346, 0.20 #3727), 01slc (0.21 #741, 0.17 #4276, 0.17 #4707), 04vn5 (0.21 #740, 0.12 #2377, 0.11 #2807), 04wmvz (0.20 #2828, 0.17 #2398, 0.17 #245), 06wpc (0.19 #1782, 0.11 #6291, 0.11 #6290) >> Best rule #4595 for best value: >> intensional similarity = 5 >> extensional distance = 73 >> proper extension: 02zc7f; >> query: (?x8120, 0jmj7) <- school(?x8111, ?x8120), school_type(?x8120, ?x1507), fraternities_and_sororities(?x8120, ?x3697), school(?x8111, ?x5288), organization(?x5288, ?x5487) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2785 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 43 *> proper extension: 0cchk3; 0146hc; 027ybp; *> query: (?x8120, 05tg3) <- school(?x3658, ?x8120), major_field_of_study(?x8120, ?x2014), team(?x1717, ?x3658), ?x1717 = 02g_6x, school_type(?x8120, ?x1507) *> conf = 0.11 ranks of expected_values: 55 EVAL 01rc6f school! 05tg3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 202.000 202.000 0.707 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #1873-01vvpjj PRED entity: 01vvpjj PRED relation: religion PRED expected values: 0c8wxp => 160 concepts (115 used for prediction) PRED predicted values (max 10 best out of 23): 0c8wxp (0.26 #411, 0.22 #1537, 0.20 #6), 03j6c (0.17 #66, 0.06 #606, 0.05 #2726), 092bf5 (0.14 #151, 0.07 #331, 0.05 #1142), 06nzl (0.14 #105, 0.03 #691, 0.02 #1366), 0n2g (0.11 #418, 0.05 #1499, 0.04 #2401), 0kpl (0.09 #460, 0.08 #280, 0.08 #2398), 03_gx (0.07 #4522, 0.07 #2990, 0.06 #4839), 01lp8 (0.06 #1217, 0.06 #361, 0.06 #1307), 0kq2 (0.05 #423, 0.05 #1504, 0.05 #468), 0flw86 (0.04 #2480, 0.04 #2526, 0.04 #497) >> Best rule #411 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0h5g_; 02pkpfs; 0hvb2; 02xb2bt; 072twv; 016yvw; 03hy3g; 01ts_3; 0dfrq; 02f93t; ... >> query: (?x2440, 0c8wxp) <- award_winner(?x8929, ?x2440), award(?x2440, ?x1323), nationality(?x2440, ?x429), ?x429 = 03rt9 >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vvpjj religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 160.000 115.000 0.263 http://example.org/people/person/religion #1872-01vsyg9 PRED entity: 01vsyg9 PRED relation: instrumentalists! PRED expected values: 03qjg => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 113): 03qjg (0.67 #224, 0.67 #188, 0.36 #223), 05r5c (0.49 #669, 0.49 #3180, 0.48 #3920), 042v_gx (0.48 #222, 0.44 #1835, 0.42 #1614), 028tv0 (0.48 #222, 0.44 #1835, 0.42 #1614), 02sgy (0.36 #223, 0.30 #5315, 0.30 #4797), 03ndd (0.36 #223, 0.30 #5315, 0.30 #4797), 02qjv (0.36 #223, 0.30 #5315, 0.30 #4797), 0214km (0.36 #223, 0.30 #4797, 0.30 #3247), 02hnl (0.34 #543, 0.32 #397, 0.25 #1567), 06ncr (0.33 #182, 0.11 #1355, 0.11 #1942) >> Best rule #224 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01vw87c; 0m2l9; 01wl38s; 01vrncs; 01kx_81; 01wp8w7; 0gcs9; 01vsy7t; 01p0vf; 01vs4ff; ... >> query: (?x5623, ?x2798) <- gender(?x5623, ?x231), role(?x5623, ?x227), role(?x5623, ?x2798), ?x2798 = 03qjg >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vsyg9 instrumentalists! 03qjg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.667 http://example.org/music/instrument/instrumentalists #1871-023mdt PRED entity: 023mdt PRED relation: award PRED expected values: 02y_rq5 03qgjwc => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 264): 0ck27z (0.61 #9789, 0.14 #2921, 0.14 #15850), 05pcn59 (0.38 #82, 0.26 #4526, 0.24 #3718), 099tbz (0.38 #58, 0.07 #4502, 0.05 #1270), 09sb52 (0.34 #20646, 0.29 #1253, 0.27 #24686), 05p09zm (0.25 #528, 0.23 #3760, 0.21 #4568), 040njc (0.25 #8, 0.15 #412, 0.14 #816), 0f4x7 (0.25 #31, 0.12 #38786, 0.12 #7303), 099ck7 (0.25 #267, 0.12 #38786, 0.05 #1479), 0gq9h (0.25 #78, 0.11 #16643, 0.10 #482), 09qv_s (0.25 #151, 0.10 #555, 0.09 #1363) >> Best rule #9789 for best value: >> intensional similarity = 3 >> extensional distance = 358 >> proper extension: 06gp3f; 02pkpfs; 040t74; 031ydm; 07s95_l; 03_wvl; 0bbvr84; 027ht3n; >> query: (?x9207, 0ck27z) <- award(?x9207, ?x1972), award(?x1991, ?x1972), ?x1991 = 02lf70 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #15853 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 527 *> proper extension: 05f7snc; *> query: (?x9207, 02y_rq5) <- gender(?x9207, ?x514), nominated_for(?x9207, ?x603), ?x514 = 02zsn *> conf = 0.09 ranks of expected_values: 76, 81 EVAL 023mdt award 03qgjwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 114.000 114.000 0.608 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 023mdt award 02y_rq5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 114.000 114.000 0.608 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1870-09bx1k PRED entity: 09bx1k PRED relation: nationality PRED expected values: 09c7w0 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.89 #1303, 0.88 #1002, 0.84 #3512), 03gj2 (0.44 #3511, 0.38 #2404, 0.34 #6326), 02jx1 (0.44 #3511, 0.38 #2404, 0.34 #6326), 07ssc (0.44 #3511, 0.38 #2404, 0.34 #6326), 0154j (0.20 #104), 03rk0 (0.13 #846, 0.05 #11687, 0.05 #11887), 0f8l9c (0.06 #322, 0.05 #622, 0.05 #722), 0345h (0.05 #1233, 0.05 #831, 0.04 #931), 0d060g (0.04 #907, 0.04 #4526, 0.04 #3217), 0h7x (0.04 #635, 0.04 #735, 0.02 #3143) >> Best rule #1303 for best value: >> intensional similarity = 3 >> extensional distance = 171 >> proper extension: 02773nt; 02p_ycc; 0347xl; 02kmx6; 05dtwm; 01x_d8; 058vfp4; >> query: (?x5289, 09c7w0) <- award_nominee(?x5289, ?x9604), place_of_birth(?x5289, ?x3125), currency(?x3125, ?x170) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09bx1k nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.890 http://example.org/people/person/nationality #1869-015qq1 PRED entity: 015qq1 PRED relation: profession PRED expected values: 02hrh1q => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 73): 02hrh1q (0.91 #3617, 0.90 #466, 0.89 #616), 03gjzk (0.33 #9019, 0.22 #9469, 0.22 #10069), 01d_h8 (0.31 #2107, 0.31 #10059, 0.30 #11109), 0dxtg (0.31 #10067, 0.30 #9017, 0.29 #314), 02jknp (0.27 #2109, 0.23 #308, 0.23 #759), 0np9r (0.26 #923, 0.21 #172, 0.21 #8575), 0cbd2 (0.24 #1208, 0.24 #2858, 0.22 #3759), 0kyk (0.20 #31, 0.19 #1232, 0.15 #2882), 018gz8 (0.19 #168, 0.17 #3620, 0.15 #3320), 09jwl (0.18 #320, 0.16 #3922, 0.16 #10673) >> Best rule #3617 for best value: >> intensional similarity = 3 >> extensional distance = 326 >> proper extension: 06688p; 01v3s2_; 04cf09; 01wjrn; 07ymr5; 02_j7t; 012_53; 02dh86; 01_rh4; 04fhn_; ... >> query: (?x11380, 02hrh1q) <- student(?x581, ?x11380), actor(?x3725, ?x11380), currency(?x581, ?x170) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015qq1 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.915 http://example.org/people/person/profession #1868-016ztl PRED entity: 016ztl PRED relation: country PRED expected values: 03_3d => 139 concepts (110 used for prediction) PRED predicted values (max 10 best out of 187): 03_3d (0.88 #1729, 0.88 #1607, 0.84 #1299), 09c7w0 (0.84 #2707, 0.84 #2462, 0.84 #2584), 07ssc (0.27 #1430, 0.24 #4202, 0.24 #3278), 0f8l9c (0.26 #2356, 0.12 #3897, 0.12 #3403), 0ctw_b (0.23 #887, 0.13 #1930, 0.13 #1499), 01hmnh (0.22 #2399, 0.14 #367, 0.12 #430), 0345h (0.18 #2364, 0.15 #952, 0.14 #1441), 0chghy (0.13 #2349, 0.12 #629, 0.08 #937), 06mkj (0.12 #657, 0.10 #2377, 0.08 #904), 0b90_r (0.12 #621, 0.08 #868, 0.05 #2891) >> Best rule #1729 for best value: >> intensional similarity = 8 >> extensional distance = 23 >> proper extension: 015qy1; >> query: (?x5955, 03_3d) <- genre(?x5955, ?x53), actor(?x5955, ?x8439), actor(?x5955, ?x3785), film(?x296, ?x5955), ?x296 = 01kyvx, profession(?x8439, ?x1383), gender(?x3785, ?x231), ?x1383 = 0np9r >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016ztl country 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 110.000 0.880 http://example.org/film/film/country #1867-02rrfzf PRED entity: 02rrfzf PRED relation: genre PRED expected values: 02kdv5l => 77 concepts (30 used for prediction) PRED predicted values (max 10 best out of 90): 02kdv5l (0.71 #121, 0.61 #1656, 0.59 #593), 07s9rl0 (0.59 #3311, 0.57 #3074, 0.54 #1536), 01jfsb (0.50 #484, 0.50 #957, 0.49 #1075), 01hmnh (0.46 #371, 0.43 #253, 0.38 #1789), 0hcr (0.46 #259, 0.21 #1795, 0.21 #377), 02l7c8 (0.33 #3206, 0.33 #2851, 0.28 #3325), 0lsxr (0.32 #9, 0.22 #481, 0.21 #1190), 04pbhw (0.21 #172, 0.14 #1707, 0.13 #644), 082gq (0.21 #30, 0.12 #1565, 0.12 #1211), 04t36 (0.21 #242, 0.10 #714, 0.09 #3197) >> Best rule #121 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 0gj9qxr; 03_wm6; >> query: (?x3344, 02kdv5l) <- currency(?x3344, ?x170), category(?x3344, ?x134), genre(?x3344, ?x1013), ?x1013 = 06n90 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02rrfzf genre 02kdv5l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 30.000 0.714 http://example.org/film/film/genre #1866-0h95927 PRED entity: 0h95927 PRED relation: film_crew_role PRED expected values: 02r96rf => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 26): 02r96rf (0.71 #77, 0.67 #114, 0.61 #40), 09vw2b7 (0.66 #44, 0.58 #827, 0.56 #865), 01vx2h (0.35 #123, 0.33 #86, 0.28 #422), 0dxtw (0.33 #831, 0.32 #48, 0.32 #869), 01pvkk (0.32 #13, 0.27 #124, 0.27 #198), 0215hd (0.19 #57, 0.16 #20, 0.14 #168), 089g0h (0.17 #58, 0.10 #169, 0.10 #431), 02ynfr (0.16 #91, 0.14 #837, 0.14 #277), 0d2b38 (0.15 #64, 0.10 #437, 0.09 #287), 01xy5l_ (0.15 #52, 0.10 #425, 0.09 #275) >> Best rule #77 for best value: >> intensional similarity = 6 >> extensional distance = 94 >> proper extension: 02vxq9m; 0ds3t5x; 05p1tzf; 02x3lt7; 0c40vxk; 0gx9rvq; 0gkz15s; 087wc7n; 01vksx; 017gl1; ... >> query: (?x7651, 02r96rf) <- film_release_region(?x7651, ?x1497), film_release_region(?x7651, ?x789), film_release_region(?x7651, ?x252), ?x789 = 0f8l9c, ?x1497 = 015qh, ?x252 = 03_3d >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h95927 film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.708 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1865-02rl201 PRED entity: 02rl201 PRED relation: school PRED expected values: 01jq0j 021w0_ => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 676): 06pwq (0.86 #802, 0.60 #1496, 0.58 #1617), 012vwb (0.86 #802, 0.50 #1379, 0.50 #1249), 0bx8pn (0.86 #802, 0.50 #1379, 0.50 #1378), 05krk (0.86 #802, 0.50 #1268, 0.50 #1147), 01q0kg (0.86 #802, 0.43 #1385, 0.43 #1384), 017cy9 (0.86 #802, 0.41 #1501, 0.35 #1371), 022lly (0.86 #802, 0.40 #1502, 0.38 #685), 01jq34 (0.86 #802, 0.39 #1266, 0.39 #1265), 01jswq (0.86 #802, 0.39 #1266, 0.39 #1265), 01ptt7 (0.86 #802, 0.38 #685, 0.38 #684) >> Best rule #802 for best value: >> intensional similarity = 83 >> extensional distance = 1 >> proper extension: 02pq_rp; >> query: (?x1633, ?x388) <- draft(?x12042, ?x1633), draft(?x10939, ?x1633), draft(?x8995, ?x1633), draft(?x8894, ?x1633), draft(?x6823, ?x1633), draft(?x6074, ?x1633), draft(?x4487, ?x1633), draft(?x4208, ?x1633), draft(?x1632, ?x1633), draft(?x700, ?x1633), ?x6074 = 02__x, school(?x1633, ?x9131), school(?x1633, ?x8202), school(?x1633, ?x6814), school(?x1633, ?x3779), institution(?x4981, ?x6814), institution(?x1771, ?x6814), institution(?x734, ?x6814), major_field_of_study(?x6814, ?x4100), major_field_of_study(?x6814, ?x2981), major_field_of_study(?x6814, ?x1154), ?x4487 = 01ync, school(?x1010, ?x6814), school(?x799, ?x6814), school(?x4779, ?x6814), ?x734 = 04zx3q1, ?x1010 = 01d5z, ?x12042 = 05xvj, ?x1154 = 02lp1, ?x1632 = 0cqt41, contains(?x4776, ?x6814), institution(?x865, ?x9131), currency(?x6814, ?x170), school(?x4779, ?x388), season(?x8894, ?x9192), season(?x8894, ?x8517), season(?x8894, ?x3431), team(?x8520, ?x8894), adjoins(?x4776, ?x760), student(?x3779, ?x2409), contains(?x1227, ?x9131), district_represented(?x3540, ?x4776), district_represented(?x653, ?x4776), list(?x3779, ?x2197), ?x3540 = 024tcq, ?x4208 = 061xq, ?x865 = 02h4rq6, citytown(?x3779, ?x4978), colors(?x9131, ?x3189), major_field_of_study(?x9131, ?x2601), fraternities_and_sororities(?x3779, ?x3697), ?x8995 = 01d6g, jurisdiction_of_office(?x900, ?x4776), ?x10939 = 0x0d, time_zones(?x4776, ?x2674), ?x9192 = 04110b0, service_language(?x3779, ?x254), colors(?x3779, ?x4557), ?x4981 = 03bwzr4, organization(?x346, ?x9131), draft(?x2174, ?x4779), teams(?x739, ?x799), ?x1771 = 019v9k, school(?x729, ?x9131), contact_category(?x3779, ?x897), ?x4100 = 01lj9, school(?x8894, ?x466), ?x8517 = 0285r5d, location(?x397, ?x4776), teams(?x479, ?x8894), team(?x4570, ?x799), ?x3431 = 025ygqm, ?x2174 = 051vz, state_province_region(?x8202, ?x3634), ?x4570 = 03558l, institution(?x1200, ?x8202), ?x170 = 09nqf, ?x729 = 05g3b, ?x2981 = 02j62, ?x700 = 06x68, ?x8520 = 01z9v6, ?x653 = 070m6c, ?x6823 = 07l8f >> conf = 0.86 => this is the best rule for 10 predicted values *> Best rule #1379 for first EXPECTED value: *> intensional similarity = 75 *> extensional distance = 2 *> proper extension: 02qw1zx; *> query: (?x1633, ?x8479) <- draft(?x8901, ?x1633), draft(?x8894, ?x1633), draft(?x7060, ?x1633), draft(?x6074, ?x1633), draft(?x2067, ?x1633), draft(?x700, ?x1633), draft(?x580, ?x1633), draft(?x260, ?x1633), team(?x11844, ?x6074), colors(?x6074, ?x4557), school(?x1633, ?x12736), school(?x1633, ?x10945), school(?x1633, ?x5621), school(?x1633, ?x3779), sport(?x6074, ?x5063), school(?x6074, ?x6075), team(?x2010, ?x6074), ?x4557 = 019sc, school(?x7060, ?x10666), school(?x7060, ?x8479), school(?x7060, ?x4296), school(?x7060, ?x4257), school(?x7060, ?x4211), school(?x7060, ?x3149), school(?x7060, ?x2830), school(?x8894, ?x3021), school(?x8894, ?x1884), ?x2830 = 01wdj_, colors(?x8894, ?x8271), citytown(?x6075, ?x11498), ?x10945 = 01jsk6, student(?x3149, ?x287), team(?x5412, ?x260), currency(?x6075, ?x170), major_field_of_study(?x3149, ?x3995), category(?x3021, ?x134), school(?x260, ?x4209), major_field_of_study(?x8479, ?x1154), service_location(?x4211, ?x94), student(?x8479, ?x9385), school(?x2067, ?x1276), school_type(?x3021, ?x3205), organization(?x346, ?x6075), school(?x2820, ?x12736), organization(?x5510, ?x4211), team(?x12323, ?x580), student(?x4296, ?x3927), major_field_of_study(?x4296, ?x1668), teams(?x4356, ?x700), school(?x700, ?x2711), institution(?x1771, ?x4296), ?x170 = 09nqf, ?x3779 = 01pq4w, state_province_region(?x8479, ?x726), institution(?x1368, ?x10666), company(?x1907, ?x2067), state_province_region(?x6075, ?x3908), contains(?x938, ?x4296), institution(?x620, ?x4211), team(?x261, ?x260), major_field_of_study(?x4257, ?x373), ?x1884 = 0bx8pn, ?x3995 = 0fdys, colors(?x4296, ?x9464), ?x1368 = 014mlp, colors(?x580, ?x332), colors(?x8901, ?x3189), ?x1668 = 01mkq, ?x3189 = 01g5v, school(?x465, ?x5621), school_type(?x4296, ?x1507), major_field_of_study(?x5621, ?x254), company(?x7749, ?x4257), major_field_of_study(?x3021, ?x1682), ?x465 = 05vsb7 *> conf = 0.50 ranks of expected_values: 23, 60 EVAL 02rl201 school 021w0_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 17.000 17.000 0.857 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 02rl201 school 01jq0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 17.000 17.000 0.857 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #1864-0k_s5 PRED entity: 0k_s5 PRED relation: contains PRED expected values: 0k_mf => 155 concepts (35 used for prediction) PRED predicted values (max 10 best out of 2684): 0r3wm (0.83 #61755, 0.83 #82339, 0.83 #55872), 0r4qq (0.83 #61755, 0.83 #82339, 0.83 #55872), 0r8bh (0.83 #61755, 0.83 #55872, 0.82 #52932), 0r3tq (0.78 #41162, 0.70 #73514, 0.63 #85280), 0r3tb (0.78 #41162, 0.63 #85280, 0.61 #23521), 0r4h3 (0.78 #41162, 0.50 #4964, 0.33 #2025), 01zlwg6 (0.78 #41162, 0.50 #4075, 0.33 #1136), 0kpys (0.74 #52930, 0.70 #52931, 0.69 #55871), 0kv4k (0.74 #52930, 0.70 #52931, 0.69 #55871), 0m28g (0.74 #52930, 0.70 #52931, 0.69 #55871) >> Best rule #61755 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0gyh; >> query: (?x11940, ?x6834) <- contains(?x11940, ?x9887), administrative_division(?x6834, ?x9887), location(?x3806, ?x11940), adjoins(?x2949, ?x9887) >> conf = 0.83 => this is the best rule for 3 predicted values *> Best rule #4833 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 09c7w0; *> query: (?x11940, 0k_mf) <- contains(?x11940, ?x10298), contains(?x11940, ?x9887), contains(?x11940, ?x1839), ?x9887 = 0kvt9, featured_film_locations(?x3534, ?x1839), location(?x10186, ?x10298), religion(?x10186, ?x8613) *> conf = 0.50 ranks of expected_values: 64 EVAL 0k_s5 contains 0k_mf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 155.000 35.000 0.830 http://example.org/location/location/contains #1863-0d0x8 PRED entity: 0d0x8 PRED relation: religion PRED expected values: 019cr => 203 concepts (203 used for prediction) PRED predicted values (max 10 best out of 23): 019cr (0.90 #436, 0.80 #51, 0.77 #509), 03_gx (0.60 #54, 0.48 #439, 0.43 #367), 01s5nb (0.47 #61, 0.46 #446, 0.44 #277), 02t7t (0.27 #444, 0.25 #517, 0.24 #372), 03j6c (0.25 #9, 0.22 #153, 0.11 #177), 072w0 (0.25 #447, 0.20 #62, 0.19 #520), 0kpl (0.25 #2, 0.04 #98, 0.04 #194), 07w8f (0.25 #18, 0.04 #114, 0.04 #210), 0v53x (0.11 #3157, 0.11 #2964), 04t_mf (0.07 #159, 0.04 #2018, 0.04 #2042) >> Best rule #436 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05kr_; >> query: (?x3038, 019cr) <- district_represented(?x176, ?x3038), contains(?x3038, ?x2277), religion(?x3038, ?x109) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d0x8 religion 019cr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 203.000 203.000 0.896 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #1862-02wszf PRED entity: 02wszf PRED relation: position! PRED expected values: 04c9bn => 30 concepts (28 used for prediction) PRED predicted values (max 10 best out of 104): 05m_8 (0.83 #33, 0.83 #69, 0.83 #41), 01yhm (0.83 #33, 0.83 #69, 0.83 #41), 07l8f (0.83 #33, 0.83 #69, 0.83 #41), 04wmvz (0.83 #33, 0.83 #69, 0.83 #41), 04c9bn (0.83 #33, 0.83 #69, 0.83 #41), 02h8p8 (0.83 #33, 0.83 #69, 0.83 #41), 051wf (0.28 #84, 0.28 #83, 0.24 #105), 0jmj7 (0.16 #40, 0.14 #146, 0.12 #123), 0jmk7 (0.16 #40, 0.14 #146, 0.12 #123), 0jm8l (0.16 #40, 0.14 #146, 0.12 #123) >> Best rule #33 for best value: >> intensional similarity = 26 >> extensional distance = 4 >> proper extension: 01yvvn; >> query: (?x5727, ?x580) <- position(?x7060, ?x5727), position(?x4243, ?x5727), position(?x2174, ?x5727), team(?x5727, ?x580), school(?x2174, ?x546), colors(?x2174, ?x332), school(?x4243, ?x10666), school(?x4243, ?x4599), season(?x2174, ?x10017), season(?x2174, ?x3431), ?x3431 = 025ygqm, team(?x5412, ?x4243), sport(?x2174, ?x5063), team(?x8110, ?x2174), draft(?x2174, ?x3334), draft(?x2174, ?x1161), major_field_of_study(?x4599, ?x2981), colors(?x4599, ?x9778), ?x1161 = 02x2khw, school(?x7060, ?x621), ?x2981 = 02j62, draft(?x7060, ?x1633), ?x10017 = 026fmqm, institution(?x865, ?x10666), ?x3334 = 02pq_rp, category(?x7060, ?x134) >> conf = 0.83 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5 EVAL 02wszf position! 04c9bn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 30.000 28.000 0.830 http://example.org/sports/sports_team/roster./baseball/baseball_roster_position/position #1861-06151l PRED entity: 06151l PRED relation: gender PRED expected values: 05zppz => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #33, 0.72 #149, 0.71 #139), 02zsn (0.35 #4, 0.31 #2, 0.31 #30) >> Best rule #33 for best value: >> intensional similarity = 2 >> extensional distance = 974 >> proper extension: 02qjj7; 01pr_j6; 01sxd1; 0glyyw; 03p01x; 01qnfc; 0cbdf1; 06101p; 0gry51; 02r99xw; ... >> query: (?x221, 05zppz) <- profession(?x221, ?x319), ?x319 = 01d_h8 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06151l gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.807 http://example.org/people/person/gender #1860-01skmp PRED entity: 01skmp PRED relation: award PRED expected values: 05b4l5x 0bdwft 0cqgl9 => 111 concepts (107 used for prediction) PRED predicted values (max 10 best out of 279): 09sb52 (0.72 #14490, 0.58 #17299, 0.38 #1244), 09cn0c (0.72 #38137, 0.71 #13647, 0.70 #37333), 0gqy2 (0.50 #1365, 0.40 #161, 0.36 #1767), 0cqh46 (0.40 #51, 0.25 #1255, 0.18 #1657), 0bfvd4 (0.38 #914, 0.29 #513, 0.25 #1316), 0f4x7 (0.29 #432, 0.25 #1235, 0.25 #833), 04ljl_l (0.29 #404, 0.25 #805, 0.12 #1207), 05b4l5x (0.27 #1612, 0.25 #1210, 0.20 #6), 0ck27z (0.26 #13335, 0.21 #15744, 0.20 #17750), 05pcn59 (0.25 #1284, 0.23 #2087, 0.23 #14530) >> Best rule #14490 for best value: >> intensional similarity = 3 >> extensional distance = 554 >> proper extension: 06sn8m; >> query: (?x6702, 09sb52) <- award(?x6702, ?x2478), award(?x10643, ?x2478), ?x10643 = 07myb2 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1612 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0151ns; 02rrsz; *> query: (?x6702, 05b4l5x) <- film(?x6702, ?x518), ?x518 = 016z5x, award(?x6702, ?x618) *> conf = 0.27 ranks of expected_values: 8, 39, 69 EVAL 01skmp award 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 111.000 107.000 0.723 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01skmp award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 111.000 107.000 0.723 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01skmp award 05b4l5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 111.000 107.000 0.723 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1859-04rlf PRED entity: 04rlf PRED relation: major_field_of_study! PRED expected values: 01j_cy 01qd_r 03gn1x 021w0_ => 92 concepts (59 used for prediction) PRED predicted values (max 10 best out of 631): 07tgn (0.67 #12544, 0.67 #11974, 0.62 #14252), 015cz0 (0.67 #12714, 0.67 #12144, 0.62 #14422), 07wrz (0.67 #12590, 0.62 #14298, 0.50 #12020), 07tds (0.67 #12692, 0.62 #14400, 0.50 #12122), 07tg4 (0.67 #12616, 0.62 #14324, 0.50 #12046), 01bm_ (0.67 #12795, 0.62 #14503, 0.50 #12225), 07tk7 (0.67 #12430, 0.62 #14708, 0.50 #13000), 03ksy (0.67 #12642, 0.62 #17769, 0.50 #14350), 07szy (0.67 #12568, 0.62 #17695, 0.43 #13138), 01j_9c (0.67 #12538, 0.62 #17665, 0.40 #9119) >> Best rule #12544 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 05qjt; >> query: (?x8681, 07tgn) <- major_field_of_study(?x9676, ?x8681), major_field_of_study(?x6315, ?x8681), major_field_of_study(?x2196, ?x8681), major_field_of_study(?x122, ?x8681), institution(?x865, ?x2196), currency(?x2196, ?x1099), ?x6315 = 08qnnv, ?x122 = 08815, colors(?x9676, ?x7179) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #14275 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 01mkq; 0l5mz; *> query: (?x8681, 01j_cy) <- major_field_of_study(?x6434, ?x8681), major_field_of_study(?x2196, ?x8681), ?x2196 = 07w4j, major_field_of_study(?x865, ?x8681), major_field_of_study(?x3995, ?x8681), category(?x6434, ?x134), contains(?x94, ?x6434), major_field_of_study(?x388, ?x3995) *> conf = 0.50 ranks of expected_values: 21, 65, 322, 465 EVAL 04rlf major_field_of_study! 021w0_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 92.000 59.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rlf major_field_of_study! 03gn1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 92.000 59.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rlf major_field_of_study! 01qd_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 92.000 59.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 04rlf major_field_of_study! 01j_cy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 92.000 59.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1858-035dk PRED entity: 035dk PRED relation: medal PRED expected values: 02lq5w => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 2): 02lq5w (0.77 #45, 0.76 #53, 0.75 #59), 02lpp7 (0.69 #60, 0.69 #6, 0.65 #54) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 0193qj; >> query: (?x2051, 02lq5w) <- olympics(?x2051, ?x1081), capital(?x2051, ?x12331), contains(?x2467, ?x2051) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035dk medal 02lq5w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.770 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #1857-07gp9 PRED entity: 07gp9 PRED relation: film! PRED expected values: 02d6n_ => 104 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1258): 0b6mgp_ (0.48 #12484, 0.47 #33294, 0.46 #27052), 092ys_y (0.48 #12484, 0.47 #33294, 0.46 #27052), 0c94fn (0.48 #12484, 0.47 #33294, 0.46 #27052), 03_gd (0.48 #12484, 0.46 #27052, 0.45 #87398), 06rnl9 (0.48 #12484, 0.46 #27052, 0.45 #76991), 04cy8rb (0.47 #33294, 0.45 #56182, 0.45 #43697), 03r1pr (0.47 #33294, 0.45 #56182, 0.45 #43697), 03ym1 (0.40 #1013, 0.16 #3093, 0.05 #32226), 0js9s (0.40 #1155, 0.16 #3235, 0.05 #17800), 02ck7w (0.40 #941, 0.12 #3021, 0.05 #17586) >> Best rule #12484 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 07ghq; >> query: (?x324, ?x800) <- genre(?x324, ?x225), film_release_region(?x324, ?x94), award_winner(?x324, ?x800), prequel(?x6429, ?x324) >> conf = 0.48 => this is the best rule for 5 predicted values *> Best rule #12255 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 07ghq; *> query: (?x324, 02d6n_) <- genre(?x324, ?x225), film_release_region(?x324, ?x94), award_winner(?x324, ?x800), prequel(?x6429, ?x324) *> conf = 0.01 ranks of expected_values: 895 EVAL 07gp9 film! 02d6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 104.000 44.000 0.476 http://example.org/film/actor/film./film/performance/film #1856-0f6lx PRED entity: 0f6lx PRED relation: influenced_by! PRED expected values: 01wp8w7 => 169 concepts (91 used for prediction) PRED predicted values (max 10 best out of 423): 017yfz (0.40 #158, 0.33 #672, 0.17 #2731), 01vsy95 (0.40 #121, 0.33 #635, 0.17 #2694), 0f0y8 (0.27 #12867, 0.25 #13383, 0.25 #8750), 01t07j (0.20 #60, 0.17 #574, 0.14 #1088), 01m3x5p (0.20 #159, 0.17 #673, 0.11 #1702), 07lp1 (0.17 #7107, 0.13 #37067, 0.11 #41702), 0683n (0.16 #3940, 0.14 #19896, 0.12 #24532), 016lh0 (0.16 #3817, 0.04 #5874, 0.04 #15658), 07dnx (0.14 #7051, 0.07 #19919, 0.06 #28163), 086qd (0.14 #5218, 0.05 #10367, 0.04 #6248) >> Best rule #158 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0f0y8; 01lcxbb; >> query: (?x9021, 017yfz) <- artists(?x11023, ?x9021), profession(?x9021, ?x1183), ?x11023 = 0cx6f, influenced_by(?x2208, ?x9021) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #3131 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 0282x; *> query: (?x9021, 01wp8w7) <- instrumentalists(?x2309, ?x9021), place_of_death(?x9021, ?x739), influenced_by(?x2208, ?x9021), type_of_union(?x9021, ?x566) *> conf = 0.06 ranks of expected_values: 111 EVAL 0f6lx influenced_by! 01wp8w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 169.000 91.000 0.400 http://example.org/influence/influence_node/influenced_by #1855-081wh1 PRED entity: 081wh1 PRED relation: group! PRED expected values: 0342h 028tv0 => 90 concepts (63 used for prediction) PRED predicted values (max 10 best out of 125): 0342h (0.90 #1988, 0.90 #1727, 0.90 #952), 013y1f (0.54 #714, 0.42 #542, 0.42 #800), 028tv0 (0.54 #184, 0.50 #959, 0.47 #528), 03qjg (0.53 #562, 0.52 #820, 0.46 #734), 0l14qv (0.37 #522, 0.35 #780, 0.33 #694), 01vj9c (0.35 #787, 0.31 #1735, 0.31 #185), 04rzd (0.29 #718, 0.19 #804, 0.17 #861), 042v_gx (0.25 #696, 0.23 #8, 0.23 #782), 06ncr (0.23 #811, 0.16 #1759, 0.16 #553), 07y_7 (0.17 #861, 0.13 #1724, 0.11 #2847) >> Best rule #1988 for best value: >> intensional similarity = 5 >> extensional distance = 120 >> proper extension: 05xq9; 07rnh; >> query: (?x7013, 0342h) <- group(?x315, ?x7013), artists(?x1572, ?x7013), artists(?x1572, ?x13511), ?x13511 = 06lxn, parent_genre(?x114, ?x1572) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 081wh1 group! 028tv0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 63.000 0.902 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 081wh1 group! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 63.000 0.902 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1854-01mt1fy PRED entity: 01mt1fy PRED relation: profession PRED expected values: 016z4k => 88 concepts (29 used for prediction) PRED predicted values (max 10 best out of 46): 0np9r (0.69 #1493, 0.63 #1345, 0.62 #1197), 0dxtg (0.55 #3989, 0.54 #2959, 0.48 #1782), 01d_h8 (0.52 #2951, 0.51 #1627, 0.49 #2656), 02jknp (0.45 #1776, 0.33 #155, 0.28 #1629), 018gz8 (0.32 #2666, 0.32 #1637, 0.26 #1784), 09jwl (0.18 #312, 0.17 #2815, 0.16 #4140), 0cbd2 (0.15 #3982, 0.15 #2952, 0.12 #2657), 0d1pc (0.15 #3141, 0.14 #3435, 0.14 #3288), 016z4k (0.11 #151, 0.10 #2801, 0.10 #4126), 01c8w0 (0.11 #156, 0.02 #450, 0.02 #597) >> Best rule #1493 for best value: >> intensional similarity = 4 >> extensional distance = 123 >> proper extension: 0chrwb; 081jbk; 066l3y; 03crcpt; 05v954; 09wlpl; 0bn8fw; 08p1gp; 075npt; 0814k3; ... >> query: (?x4395, 0np9r) <- profession(?x4395, ?x1032), language(?x4395, ?x254), profession(?x4200, ?x1032), artists(?x671, ?x4200) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #151 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 06v8s0; *> query: (?x4395, 016z4k) <- profession(?x4395, ?x1943), profession(?x4395, ?x1032), language(?x4395, ?x254), ?x1032 = 02hrh1q, ?x1943 = 02krf9 *> conf = 0.11 ranks of expected_values: 9 EVAL 01mt1fy profession 016z4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 88.000 29.000 0.688 http://example.org/people/person/profession #1853-0y4f8 PRED entity: 0y4f8 PRED relation: artists PRED expected values: 02_fj 01gx5f 02bc74 => 66 concepts (23 used for prediction) PRED predicted values (max 10 best out of 3895): 01htxr (0.60 #3809, 0.50 #2726, 0.43 #4892), 04n32 (0.50 #3033, 0.50 #1948, 0.43 #5199), 01wd9lv (0.50 #2744, 0.40 #3827, 0.33 #577), 0pj9t (0.50 #2441, 0.40 #3524, 0.33 #274), 0dbb3 (0.50 #3112, 0.40 #4195, 0.33 #945), 0d9xq (0.50 #2625, 0.40 #3708, 0.33 #458), 02_fj (0.50 #2415, 0.40 #3498, 0.33 #248), 01n44c (0.50 #2634, 0.40 #3717, 0.33 #467), 026ps1 (0.50 #2196, 0.40 #3279, 0.33 #29), 03f2_rc (0.50 #2199, 0.40 #3282, 0.33 #32) >> Best rule #3809 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 0161rf; >> query: (?x7802, 01htxr) <- artists(?x7802, ?x12194), artists(?x7802, ?x8803), ?x8803 = 01vsy9_, artists(?x3061, ?x12194), special_performance_type(?x12194, ?x9609), profession(?x12194, ?x1032), artists(?x3061, ?x1613), ?x1613 = 01sbf2 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2415 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 0m40d; *> query: (?x7802, 02_fj) <- artists(?x7802, ?x12194), artists(?x7802, ?x8803), ?x8803 = 01vsy9_, artists(?x3061, ?x12194), special_performance_type(?x12194, ?x9609), profession(?x12194, ?x1032), artists(?x3061, ?x1749), ?x1749 = 01fl3 *> conf = 0.50 ranks of expected_values: 7, 70, 225 EVAL 0y4f8 artists 02bc74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 66.000 23.000 0.600 http://example.org/music/genre/artists EVAL 0y4f8 artists 01gx5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 66.000 23.000 0.600 http://example.org/music/genre/artists EVAL 0y4f8 artists 02_fj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 66.000 23.000 0.600 http://example.org/music/genre/artists #1852-0159h6 PRED entity: 0159h6 PRED relation: award_winner! PRED expected values: 0qf2t => 116 concepts (69 used for prediction) PRED predicted values (max 10 best out of 551): 0_b3d (0.45 #26068, 0.44 #51017, 0.42 #48748), 05cvgl (0.45 #26068, 0.44 #51017, 0.42 #48748), 02q7fl9 (0.45 #26068, 0.44 #51017, 0.42 #48748), 0462hhb (0.45 #26068, 0.44 #51017, 0.42 #48748), 0bm2nq (0.45 #26068, 0.44 #51017, 0.42 #48748), 02py4c8 (0.45 #26068, 0.44 #51017, 0.42 #48748), 0gfsq9 (0.19 #7934, 0.16 #29469, 0.16 #26069), 03ydlnj (0.19 #7934, 0.16 #29469, 0.16 #26069), 031hcx (0.19 #7934, 0.16 #29469, 0.16 #26069), 03177r (0.19 #7934, 0.16 #29469, 0.16 #26069) >> Best rule #26068 for best value: >> intensional similarity = 3 >> extensional distance = 591 >> proper extension: 03zqc1; 03f1zdw; 030znt; 02wrhj; 02k6rq; 06lgq8; 080knyg; 01hkhq; 02j9lm; 01438g; ... >> query: (?x488, ?x715) <- award_winner(?x6861, ?x488), nominated_for(?x488, ?x715), film(?x488, ?x218) >> conf = 0.45 => this is the best rule for 6 predicted values No rule for expected values ranks of expected_values: EVAL 0159h6 award_winner! 0qf2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 116.000 69.000 0.446 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #1851-030z4z PRED entity: 030z4z PRED relation: film! PRED expected values: 01zp33 => 98 concepts (50 used for prediction) PRED predicted values (max 10 best out of 672): 01zh29 (0.73 #37443, 0.71 #2081, 0.63 #62410), 03wpmd (0.61 #10403, 0.52 #58247, 0.50 #37442), 0169dl (0.17 #401, 0.03 #4562, 0.03 #35763), 01pcq3 (0.10 #132, 0.01 #47976), 08y7b9 (0.08 #1940, 0.02 #6101, 0.02 #10262), 02wgln (0.08 #315, 0.01 #8637, 0.01 #35677), 0h0wc (0.07 #4585, 0.03 #44106, 0.03 #54508), 0c6qh (0.06 #4575, 0.03 #35776, 0.02 #44096), 05g3ss (0.06 #37444, 0.05 #66573, 0.05 #56166), 0f5zj6 (0.06 #37444, 0.05 #66573, 0.05 #56166) >> Best rule #37443 for best value: >> intensional similarity = 4 >> extensional distance = 433 >> proper extension: 047svrl; >> query: (?x8657, ?x8073) <- nominated_for(?x8073, ?x8657), film_release_region(?x8657, ?x94), currency(?x8657, ?x170), participant(?x6308, ?x8073) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #1304 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 50 *> proper extension: 03_b1g; *> query: (?x8657, 01zp33) <- nominated_for(?x8073, ?x8657), film(?x8073, ?x657), languages(?x8073, ?x254), language(?x8073, ?x1882) *> conf = 0.06 ranks of expected_values: 13 EVAL 030z4z film! 01zp33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 98.000 50.000 0.733 http://example.org/film/actor/film./film/performance/film #1850-01lbcqx PRED entity: 01lbcqx PRED relation: film! PRED expected values: 0g_92 0p9gg => 61 concepts (33 used for prediction) PRED predicted values (max 10 best out of 861): 03kpvp (0.28 #11015, 0.25 #629, 0.18 #2704), 0143wl (0.25 #1066, 0.06 #11452, 0.06 #9374), 01846t (0.25 #537, 0.06 #10923, 0.06 #8845), 01cbt3 (0.25 #934, 0.06 #11320, 0.06 #9242), 0jw67 (0.25 #611, 0.06 #10997, 0.06 #8919), 0mbhr (0.25 #1831, 0.06 #12217, 0.03 #22598), 01m42d0 (0.25 #1388, 0.06 #11774, 0.03 #22155), 017lqp (0.22 #11991, 0.18 #3680, 0.17 #9913), 0c921 (0.19 #66448, 0.18 #68527, 0.17 #35303), 0gv5c (0.19 #66448, 0.18 #68527, 0.17 #35303) >> Best rule #11015 for best value: >> intensional similarity = 8 >> extensional distance = 16 >> proper extension: 02sg5v; 0ggbhy7; >> query: (?x8461, 03kpvp) <- genre(?x8461, ?x5104), ?x5104 = 0bkbm, country(?x8461, ?x512), ?x512 = 07ssc, film(?x5601, ?x8461), film(?x541, ?x8461), award(?x5601, ?x102), type_of_union(?x5601, ?x566) >> conf = 0.28 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01lbcqx film! 0p9gg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 33.000 0.278 http://example.org/film/actor/film./film/performance/film EVAL 01lbcqx film! 0g_92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 33.000 0.278 http://example.org/film/actor/film./film/performance/film #1849-0225v9 PRED entity: 0225v9 PRED relation: school! PRED expected values: 07l8x => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 93): 0jmj7 (0.75 #586, 0.67 #1144, 0.67 #2726), 05m_8 (0.25 #561, 0.23 #1212, 0.21 #1119), 05g76 (0.21 #578, 0.12 #1136, 0.09 #1508), 01slc (0.19 #1174, 0.17 #1546, 0.16 #1826), 06rpd (0.17 #632, 0.11 #1283, 0.11 #911), 05xvj (0.17 #646, 0.11 #1297, 0.10 #2414), 07l4z (0.15 #1186, 0.13 #907, 0.12 #628), 051vz (0.15 #1231, 0.14 #1510, 0.13 #1138), 01yjl (0.13 #1146, 0.13 #1425, 0.12 #588), 01d5z (0.13 #1125, 0.13 #1497, 0.12 #567) >> Best rule #586 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 0ks67; >> query: (?x12761, 0jmj7) <- organization(?x346, ?x12761), school(?x3334, ?x12761), major_field_of_study(?x12761, ?x1154), citytown(?x12761, ?x3052), ?x1154 = 02lp1 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #624 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 0ks67; *> query: (?x12761, 07l8x) <- organization(?x346, ?x12761), school(?x3334, ?x12761), major_field_of_study(?x12761, ?x1154), citytown(?x12761, ?x3052), ?x1154 = 02lp1 *> conf = 0.12 ranks of expected_values: 14 EVAL 0225v9 school! 07l8x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 186.000 186.000 0.750 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #1848-04l3_z PRED entity: 04l3_z PRED relation: category PRED expected values: 08mbj5d => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.31 #17, 0.31 #19, 0.31 #92) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 103 >> proper extension: 01z7_f; >> query: (?x975, 08mbj5d) <- producer_type(?x975, ?x632), ?x632 = 0ckd1, location(?x975, ?x10687), gender(?x975, ?x231) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04l3_z category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.314 http://example.org/common/topic/webpage./common/webpage/category #1847-03_0p PRED entity: 03_0p PRED relation: nationality PRED expected values: 09c7w0 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 23): 09c7w0 (0.77 #601, 0.74 #301, 0.74 #1901), 02jx1 (0.16 #2433, 0.15 #933, 0.15 #2033), 07ssc (0.11 #3301, 0.10 #1115, 0.09 #2415), 03rk0 (0.11 #3301, 0.06 #4147, 0.06 #4047), 0f8l9c (0.11 #3301, 0.04 #122, 0.03 #2822), 012m_ (0.11 #3301, 0.03 #391, 0.01 #1991), 0d060g (0.08 #7, 0.05 #1107, 0.05 #2107), 0d04z6 (0.08 #71), 03rjj (0.05 #705, 0.04 #605, 0.03 #805), 05vz3zq (0.03 #270, 0.02 #470) >> Best rule #601 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 02__94; 015zql; >> query: (?x5150, 09c7w0) <- gender(?x5150, ?x231), people(?x10199, ?x5150), award_winner(?x5125, ?x5150) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_0p nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.767 http://example.org/people/person/nationality #1846-02jx1 PRED entity: 02jx1 PRED relation: contains PRED expected values: 04jpl 014b4h 05l5n 0978r 0143hl 0lbfv 01jvxb 01z8f0 094vy 01clyb 0138t4 02mg7n 01rvgx 01z28b 01gln9 0m0bj 0cy07 0n96z 013t85 0cdw6 => 199 concepts (127 used for prediction) PRED predicted values (max 10 best out of 2668): 01w0v (0.86 #100862, 0.83 #332589, 0.83 #226268), 013t2y (0.84 #149936, 0.83 #73599, 0.82 #152663), 0cv5l (0.80 #57245, 0.76 #218089, 0.75 #130853), 0dbdy (0.80 #57245, 0.76 #218089, 0.75 #130853), 0jt5zcn (0.80 #57245, 0.76 #218089, 0.75 #130853), 0jcg8 (0.80 #57245, 0.76 #218089, 0.75 #130853), 0dzz_ (0.80 #57245, 0.76 #218089, 0.75 #130853), 017cjb (0.80 #57245, 0.76 #218089, 0.75 #130853), 01q1j (0.80 #57245, 0.76 #218089, 0.75 #130853), 0jmxb (0.80 #57245, 0.76 #218089, 0.75 #130853) >> Best rule #100862 for best value: >> intensional similarity = 2 >> extensional distance = 43 >> proper extension: 04w58; >> query: (?x1310, ?x3302) <- administrative_parent(?x3302, ?x1310), olympics(?x1310, ?x1608) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #256257 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 90 *> proper extension: 01rhrd; *> query: (?x1310, ?x362) <- capital(?x1310, ?x362), olympics(?x1310, ?x1608) *> conf = 0.80 ranks of expected_values: 12, 14, 17, 25, 29, 30, 39, 46, 90, 93, 110, 117, 119, 150, 160, 180, 187, 2591, 2592 EVAL 02jx1 contains 0cdw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 013t85 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 0n96z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 0cy07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 0m0bj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 01gln9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 01z28b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 01rvgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 02mg7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 0138t4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 01clyb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 094vy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 01z8f0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 01jvxb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 0lbfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 0143hl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 0978r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 05l5n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 014b4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 199.000 127.000 0.860 http://example.org/location/location/contains EVAL 02jx1 contains 04jpl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 199.000 127.000 0.860 http://example.org/location/location/contains #1845-014_x2 PRED entity: 014_x2 PRED relation: country PRED expected values: 0345h => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 20): 0d060g (0.41 #3009, 0.41 #2527, 0.37 #2588), 04hqz (0.41 #3009, 0.41 #2527), 07ssc (0.22 #3327, 0.21 #3447, 0.21 #1881), 0345h (0.11 #207, 0.11 #627, 0.11 #1892), 0f8l9c (0.09 #1884, 0.08 #1522, 0.08 #619), 0djd22 (0.06 #3071, 0.06 #3070, 0.06 #781), 01hmnh (0.06 #3071, 0.06 #3070, 0.06 #781), 07s9rl0 (0.06 #3071, 0.06 #3070, 0.06 #781), 03_3d (0.05 #67, 0.04 #3438, 0.04 #3558), 03h64 (0.05 #106, 0.02 #406, 0.02 #526) >> Best rule #3009 for best value: >> intensional similarity = 3 >> extensional distance = 1517 >> proper extension: 0c3xpwy; 07bz5; 03czz87; >> query: (?x83, ?x94) <- nominated_for(?x10051, ?x83), award(?x10051, ?x1670), nationality(?x10051, ?x94) >> conf = 0.41 => this is the best rule for 2 predicted values *> Best rule #207 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 356 *> proper extension: 0dtw1x; 026njb5; *> query: (?x83, 0345h) <- film_release_distribution_medium(?x83, ?x81), executive_produced_by(?x83, ?x4854), film_crew_role(?x83, ?x137) *> conf = 0.11 ranks of expected_values: 4 EVAL 014_x2 country 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 61.000 61.000 0.407 http://example.org/film/film/country #1844-0cd2vh9 PRED entity: 0cd2vh9 PRED relation: executive_produced_by PRED expected values: 079vf => 85 concepts (56 used for prediction) PRED predicted values (max 10 best out of 74): 07nznf (0.11 #2273, 0.10 #2526, 0.08 #2779), 0343h (0.08 #42, 0.05 #1808, 0.03 #2822), 02q_cc (0.08 #28, 0.03 #1794, 0.02 #280), 02vyw (0.08 #88, 0.02 #845, 0.02 #1350), 025n3p (0.08 #74, 0.01 #326, 0.01 #831), 079vf (0.07 #506, 0.06 #2021, 0.06 #2275), 06pj8 (0.07 #559, 0.06 #1821, 0.05 #1064), 0glyyw (0.05 #2208, 0.04 #2462, 0.03 #3221), 05hj_k (0.05 #350, 0.04 #855, 0.04 #1107), 06q8hf (0.04 #1176, 0.04 #1681, 0.04 #3705) >> Best rule #2273 for best value: >> intensional similarity = 4 >> extensional distance = 181 >> proper extension: 0g56t9t; 02v8kmz; 0bq8tmw; 01jrbb; 03kg2v; 0cmc26r; 0cc97st; 0cbn7c; >> query: (?x1640, ?x65) <- film_release_region(?x1640, ?x94), film(?x844, ?x1640), film_crew_role(?x1640, ?x137), story_by(?x1640, ?x65) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #506 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 03nfnx; *> query: (?x1640, 079vf) <- film_release_region(?x1640, ?x94), film(?x844, ?x1640), film_crew_role(?x1640, ?x137), region(?x1640, ?x512) *> conf = 0.07 ranks of expected_values: 6 EVAL 0cd2vh9 executive_produced_by 079vf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 85.000 56.000 0.109 http://example.org/film/film/executive_produced_by #1843-05zvj3m PRED entity: 05zvj3m PRED relation: award! PRED expected values: 0jnwx => 46 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1012): 0gzlb9 (0.67 #3879, 0.57 #4894, 0.06 #10983), 0h1x5f (0.57 #5983, 0.50 #6998, 0.25 #10042), 05sy_5 (0.57 #5690, 0.50 #6705, 0.25 #9749), 02krdz (0.57 #4401, 0.50 #3386, 0.09 #25382), 0kvbl6 (0.57 #4710, 0.33 #3695, 0.04 #10799), 01qvz8 (0.50 #3517, 0.43 #4532, 0.04 #10621), 0c0zq (0.43 #5971, 0.40 #8001, 0.38 #6986), 02rv_dz (0.43 #5221, 0.38 #6236, 0.33 #146), 0m313 (0.43 #5080, 0.38 #6095, 0.21 #8123), 011ywj (0.43 #5895, 0.38 #6910, 0.19 #9954) >> Best rule #3879 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 04ljl_l; 05p09zm; >> query: (?x1691, 0gzlb9) <- nominated_for(?x1691, ?x5070), award(?x8399, ?x1691), film_release_region(?x5070, ?x87), ?x8399 = 017yxq >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #12180 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 148 *> proper extension: 02q1tc5; 02pz3j5; 02pzxlw; 027qq9b; *> query: (?x1691, ?x86) <- nominated_for(?x1691, ?x86), award(?x3927, ?x1691), award(?x3258, ?x1691), actor(?x6884, ?x3927), award_nominee(?x806, ?x3258) *> conf = 0.23 ranks of expected_values: 129 EVAL 05zvj3m award! 0jnwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 46.000 31.000 0.667 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #1842-03s2dj PRED entity: 03s2dj PRED relation: nationality PRED expected values: 02jx1 => 130 concepts (70 used for prediction) PRED predicted values (max 10 best out of 52): 09c7w0 (0.87 #2998, 0.86 #3501, 0.86 #2899), 07ssc (0.72 #3700, 0.72 #4199, 0.69 #5195), 02jx1 (0.42 #3432, 0.25 #535, 0.19 #2530), 0127c4 (0.34 #3399, 0.34 #3900), 03_3d (0.12 #1206, 0.08 #1703, 0.06 #1603), 0345h (0.10 #3430, 0.05 #2528, 0.03 #5197), 03rjj (0.09 #3405, 0.03 #5197, 0.03 #5196), 03rt9 (0.08 #211, 0.07 #313, 0.06 #614), 03rk0 (0.07 #6739, 0.06 #6340, 0.05 #6440), 0chghy (0.06 #3409, 0.05 #512, 0.03 #5197) >> Best rule #2998 for best value: >> intensional similarity = 5 >> extensional distance = 339 >> proper extension: 01vvydl; 01kwld; 034x61; 0170s4; 0738b8; 01trhmt; 0blt6; 01900g; 02y_2y; 01vyv9; ... >> query: (?x12185, 09c7w0) <- actor(?x8597, ?x12185), place_of_birth(?x12185, ?x10753), nationality(?x12185, ?x279), profession(?x12185, ?x1032), location(?x12185, ?x10718) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #3432 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 375 *> proper extension: 01pkhw; *> query: (?x12185, 02jx1) <- gender(?x12185, ?x231), location(?x12185, ?x10718), nationality(?x12185, ?x279), featured_film_locations(?x1064, ?x279) *> conf = 0.42 ranks of expected_values: 3 EVAL 03s2dj nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 130.000 70.000 0.865 http://example.org/people/person/nationality #1841-0gbfn9 PRED entity: 0gbfn9 PRED relation: film_release_region PRED expected values: 03_3d 0chghy 015fr 03spz => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 205): 059j2 (0.95 #353, 0.93 #194, 0.87 #2898), 03h64 (0.88 #1501, 0.83 #1183, 0.82 #1660), 0d060g (0.88 #1438, 0.79 #1597, 0.75 #1120), 07ssc (0.87 #1130, 0.83 #1607, 0.82 #4635), 0chghy (0.86 #330, 0.86 #4471, 0.85 #171), 03_3d (0.86 #324, 0.86 #642, 0.85 #1596), 02vzc (0.86 #373, 0.85 #214, 0.83 #1645), 015fr (0.85 #1132, 0.82 #814, 0.81 #1609), 01znc_ (0.83 #1157, 0.81 #1634, 0.78 #1793), 06bnz (0.83 #1479, 0.82 #1638, 0.80 #1161) >> Best rule #353 for best value: >> intensional similarity = 6 >> extensional distance = 35 >> proper extension: 07s3m4g; >> query: (?x5576, 059j2) <- language(?x5576, ?x254), category(?x5576, ?x134), film_release_region(?x5576, ?x1355), film_release_region(?x5576, ?x172), ?x1355 = 0h7x, olympics(?x172, ?x391) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #330 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 35 *> proper extension: 07s3m4g; *> query: (?x5576, 0chghy) <- language(?x5576, ?x254), category(?x5576, ?x134), film_release_region(?x5576, ?x1355), film_release_region(?x5576, ?x172), ?x1355 = 0h7x, olympics(?x172, ?x391) *> conf = 0.86 ranks of expected_values: 5, 6, 8, 14 EVAL 0gbfn9 film_release_region 03spz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 102.000 102.000 0.946 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gbfn9 film_release_region 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 102.000 102.000 0.946 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gbfn9 film_release_region 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 102.000 102.000 0.946 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gbfn9 film_release_region 03_3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 102.000 102.000 0.946 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1840-068gn PRED entity: 068gn PRED relation: award_winner PRED expected values: 021sv1 => 39 concepts (12 used for prediction) PRED predicted values (max 10 best out of 898): 08_hns (0.40 #2273, 0.33 #4746, 0.29 #7219), 0bwh6 (0.39 #22525, 0.06 #25000, 0.06 #27474), 042kg (0.38 #9693, 0.30 #12166, 0.29 #7220), 021sv1 (0.33 #2586, 0.20 #10005, 0.20 #113), 0dj5q (0.31 #21277), 051cc (0.29 #6779, 0.25 #9252, 0.20 #11725), 02mjmr (0.29 #5511, 0.25 #7984, 0.20 #10457), 0f7fy (0.25 #8920, 0.20 #11393, 0.20 #1501), 05g7q (0.25 #9601, 0.20 #12074, 0.18 #14547), 01q9b9 (0.25 #9067, 0.20 #11540, 0.18 #14013) >> Best rule #2273 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 01kpt; 04qy5; 079sf; >> query: (?x13939, 08_hns) <- award_winner(?x13939, ?x4567), award_winner(?x13939, ?x2357), ?x2357 = 0bymv, legislative_sessions(?x4567, ?x4821), type_of_union(?x4567, ?x566), ?x4821 = 02bqm0, profession(?x4567, ?x3342) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2586 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 015cl6; *> query: (?x13939, 021sv1) <- award_winner(?x13939, ?x2357), basic_title(?x2357, ?x2358), legislative_sessions(?x2357, ?x605), profession(?x2357, ?x353), religion(?x2357, ?x962) *> conf = 0.33 ranks of expected_values: 4 EVAL 068gn award_winner 021sv1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 39.000 12.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #1839-02wtp6 PRED entity: 02wtp6 PRED relation: language PRED expected values: 02h40lc => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 59): 02h40lc (0.97 #3145, 0.96 #2194, 0.96 #2312), 04306rv (0.24 #243, 0.18 #540, 0.16 #482), 06nm1 (0.21 #129, 0.16 #722, 0.13 #369), 02bjrlw (0.14 #239, 0.14 #478, 0.13 #536), 06b_j (0.13 #557, 0.12 #499, 0.12 #260), 0653m (0.13 #370, 0.07 #844, 0.06 #12), 05zjd (0.12 #25, 0.05 #502, 0.05 #321), 03k50 (0.11 #68, 0.05 #486, 0.05 #5928), 012w70 (0.10 #131, 0.08 #371, 0.05 #845), 0jzc (0.08 #497, 0.07 #555, 0.07 #258) >> Best rule #3145 for best value: >> intensional similarity = 4 >> extensional distance = 747 >> proper extension: 016ks5; 0sxns; 02754c9; 01fwzk; 0m_h6; 09qycb; 0199wf; 03bdkd; >> query: (?x11351, 02h40lc) <- language(?x11351, ?x5607), film(?x9606, ?x11351), genre(?x11351, ?x53), student(?x5607, ?x4265) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02wtp6 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.968 http://example.org/film/film/language #1838-070mff PRED entity: 070mff PRED relation: district_represented PRED expected values: 05kkh 05fhy 07z1m 05k7sb 06btq 07b_l 081yw => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 535): 06btq (0.94 #519, 0.88 #472, 0.86 #87), 05k7sb (0.91 #541, 0.90 #495, 0.89 #585), 07z1m (0.86 #87, 0.81 #328, 0.80 #21), 07b_l (0.86 #87, 0.81 #328, 0.80 #21), 05kkh (0.86 #87, 0.81 #328, 0.80 #21), 05fhy (0.86 #87, 0.81 #328, 0.80 #21), 081yw (0.86 #87, 0.81 #328, 0.80 #21), 059s8 (0.61 #281, 0.59 #129, 0.58 #108), 0694j (0.61 #281, 0.59 #129, 0.58 #108), 0j3b (0.61 #281, 0.59 #129, 0.58 #108) >> Best rule #519 for best value: >> intensional similarity = 27 >> extensional distance = 30 >> proper extension: 01h7xx; >> query: (?x6728, 06btq) <- legislative_sessions(?x4821, ?x6728), district_represented(?x6728, ?x7518), district_represented(?x6728, ?x1227), district_represented(?x6728, ?x335), location(?x397, ?x1227), contains(?x94, ?x1227), state(?x581, ?x1227), contains(?x1227, ?x9131), legislative_sessions(?x1829, ?x4821), contains(?x335, ?x10421), contains(?x335, ?x739), legislative_sessions(?x652, ?x4821), legislative_sessions(?x2860, ?x6728), state_province_region(?x99, ?x1227), ?x7518 = 026mj, district_represented(?x4821, ?x2020), school(?x4779, ?x9131), religion(?x1227, ?x109), state_province_region(?x166, ?x335), major_field_of_study(?x9131, ?x2601), registering_agency(?x10421, ?x1982), school(?x729, ?x9131), ?x4779 = 02z6872, location(?x4234, ?x335), jurisdiction_of_office(?x7891, ?x335), award_nominee(?x4234, ?x539), featured_film_locations(?x89, ?x739) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7 EVAL 070mff district_represented 081yw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.938 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 070mff district_represented 07b_l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.938 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 070mff district_represented 06btq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.938 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 070mff district_represented 05k7sb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.938 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 070mff district_represented 07z1m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.938 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 070mff district_represented 05fhy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.938 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 070mff district_represented 05kkh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.938 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #1837-0g9lm2 PRED entity: 0g9lm2 PRED relation: executive_produced_by PRED expected values: 0fvf9q => 121 concepts (76 used for prediction) PRED predicted values (max 10 best out of 84): 06pj8 (0.11 #307, 0.08 #1009, 0.06 #1317), 02mt4k (0.11 #371, 0.07 #1128, 0.04 #1886), 02qzjj (0.11 #488, 0.06 #1498, 0.04 #2003), 05hj_k (0.10 #2374, 0.07 #1107, 0.06 #6421), 06q8hf (0.10 #2443, 0.07 #1176, 0.06 #3458), 0h5f5n (0.09 #7839, 0.06 #10114, 0.06 #14166), 01mwsnc (0.08 #877, 0.06 #1383, 0.03 #2397), 014q2g (0.08 #826, 0.06 #1332, 0.03 #2346), 01kx_81 (0.08 #791, 0.06 #1297, 0.03 #2311), 013rds (0.08 #1004, 0.02 #4552) >> Best rule #307 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0d_wms; >> query: (?x4359, 06pj8) <- person(?x4359, ?x496), language(?x4359, ?x254), nominated_for(?x4359, ?x1135), genre(?x4359, ?x53) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #6076 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 165 *> proper extension: 06fqlk; *> query: (?x4359, 0fvf9q) <- film_crew_role(?x4359, ?x137), language(?x4359, ?x254), produced_by(?x4359, ?x2689), honored_for(?x3624, ?x4359) *> conf = 0.02 ranks of expected_values: 52 EVAL 0g9lm2 executive_produced_by 0fvf9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 121.000 76.000 0.111 http://example.org/film/film/executive_produced_by #1836-09jrf PRED entity: 09jrf PRED relation: jurisdiction_of_office PRED expected values: 0bq0p9 => 176 concepts (145 used for prediction) PRED predicted values (max 10 best out of 96): 014tss (0.69 #2004, 0.65 #2061, 0.57 #1385), 09c7w0 (0.63 #1282, 0.56 #1487, 0.55 #2113), 07ssc (0.38 #722, 0.28 #929, 0.25 #878), 05kkh (0.33 #105, 0.09 #1075, 0.08 #716), 05v8c (0.33 #112, 0.08 #723, 0.06 #2164), 0d04z6 (0.33 #138, 0.08 #749, 0.05 #1522), 059rby (0.25 #159, 0.14 #310, 0.09 #1078), 02jx1 (0.19 #1072, 0.17 #153, 0.08 #731), 01zst8 (0.19 #1072, 0.17 #153, 0.02 #2966), 07z1m (0.17 #582, 0.12 #839, 0.09 #1092) >> Best rule #2004 for best value: >> intensional similarity = 5 >> extensional distance = 52 >> proper extension: 021sv1; 08f3b1; 0d0vj4; 0bwh6; 02c4s; 0203v; 0bymv; 0tc7; 02mjmr; 01k165; ... >> query: (?x13355, ?x6371) <- basic_title(?x13355, ?x12773), nationality(?x13355, ?x6371), type_of_union(?x13355, ?x566), jurisdiction_of_office(?x4689, ?x6371), combatants(?x6371, ?x1679) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2058 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 59 *> proper extension: 042kg; 014vk4; *> query: (?x13355, ?x94) <- basic_title(?x13355, ?x12773), nationality(?x13355, ?x6371), jurisdiction_of_office(?x12773, ?x4402), jurisdiction_of_office(?x12773, ?x390), currency(?x4402, ?x170), contains(?x7273, ?x4402), combatants(?x390, ?x94) *> conf = 0.02 ranks of expected_values: 92 EVAL 09jrf jurisdiction_of_office 0bq0p9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 176.000 145.000 0.691 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #1835-05mt_q PRED entity: 05mt_q PRED relation: award_nominee! PRED expected values: 06mt91 => 117 concepts (48 used for prediction) PRED predicted values (max 10 best out of 838): 06mt91 (0.81 #88448, 0.81 #107072, 0.81 #107071), 067nsm (0.81 #88448, 0.81 #107072, 0.81 #107071), 026yqrr (0.30 #3773, 0.14 #93107, 0.12 #17740), 01vw20h (0.30 #3381, 0.14 #93107, 0.11 #22003), 05mt_q (0.30 #2612, 0.14 #93107, 0.05 #21234), 01w9k25 (0.25 #4449, 0.14 #93107, 0.03 #6778), 01wgxtl (0.20 #2925, 0.14 #93107, 0.08 #21547), 01vvydl (0.20 #2343, 0.14 #93107, 0.07 #4672), 01wwvc5 (0.20 #2924, 0.14 #93107, 0.07 #21546), 01yzl2 (0.20 #3608, 0.14 #93107, 0.07 #22230) >> Best rule #88448 for best value: >> intensional similarity = 3 >> extensional distance = 636 >> proper extension: 01sl1q; 0grwj; 01pbxb; 016qtt; 01vvydl; 012d40; 028q6; 0jz9f; 0337vz; 07s3vqk; ... >> query: (?x1388, ?x527) <- award_nominee(?x1388, ?x527), category(?x1388, ?x134), award_nominee(?x3607, ?x1388) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 05mt_q award_nominee! 06mt91 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 48.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1834-09p5mwg PRED entity: 09p5mwg PRED relation: region PRED expected values: 07ssc => 90 concepts (82 used for prediction) PRED predicted values (max 10 best out of 14): 07ssc (0.65 #321, 0.58 #54, 0.58 #731), 09c7w0 (0.18 #72, 0.11 #749, 0.10 #48), 0d060g (0.18 #72, 0.11 #749, 0.10 #48), 03npn (0.03 #340), 09nm_ (0.03 #217, 0.02 #314, 0.01 #412), 059j2 (0.02 #277, 0.02 #324, 0.02 #300), 04ty8 (0.01 #507), 0162b (0.01 #506), 016zwt (0.01 #505), 06m_5 (0.01 #504) >> Best rule #321 for best value: >> intensional similarity = 7 >> extensional distance = 63 >> proper extension: 0372j5; >> query: (?x9752, 07ssc) <- film_release_region(?x9752, ?x94), ?x94 = 09c7w0, film_crew_role(?x9752, ?x1284), film_distribution_medium(?x9752, ?x2099), currency(?x9752, ?x170), titles(?x571, ?x9752), ?x1284 = 0ch6mp2 >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09p5mwg region 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 82.000 0.646 http://example.org/film/film/distributors./film/film_film_distributor_relationship/region #1833-01shhf PRED entity: 01shhf PRED relation: artist! PRED expected values: 07gqbk => 76 concepts (72 used for prediction) PRED predicted values (max 10 best out of 136): 023rwm (0.40 #1272, 0.40 #1130, 0.33 #566), 01p2b_ (0.40 #1353, 0.33 #647, 0.33 #83), 011k1h (0.35 #1563, 0.29 #1987, 0.26 #1705), 01cl2y (0.33 #172, 0.25 #1018, 0.25 #877), 015mlw (0.33 #370, 0.25 #934, 0.25 #793), 03rhqg (0.33 #439, 0.25 #862, 0.25 #3122), 0f38nv (0.33 #679, 0.25 #1102, 0.20 #1243), 02swsm (0.33 #378, 0.25 #801, 0.12 #1649), 033hn8 (0.33 #155, 0.20 #1142, 0.17 #3543), 043g7l (0.33 #32, 0.20 #1302, 0.11 #3138) >> Best rule #1272 for best value: >> intensional similarity = 11 >> extensional distance = 3 >> proper extension: 0274ck; >> query: (?x9463, 023rwm) <- artists(?x13087, ?x9463), artists(?x10306, ?x9463), artists(?x6349, ?x9463), artists(?x3753, ?x9463), artists(?x3753, ?x12246), artists(?x3753, ?x1467), ?x1467 = 01vsxdm, artist(?x4483, ?x12246), parent_genre(?x2491, ?x10306), ?x13087 = 02yw26, parent_genre(?x6349, ?x5580) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2119 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 19 *> proper extension: 018x3; *> query: (?x9463, ?x4483) <- artists(?x10306, ?x9463), artists(?x3753, ?x9463), artists(?x3753, ?x12246), artists(?x3753, ?x6406), ?x10306 = 09jw2, award(?x12246, ?x6126), group(?x227, ?x12246), artist(?x4483, ?x12246), ?x6406 = 01386_, origin(?x12246, ?x739) *> conf = 0.06 ranks of expected_values: 77 EVAL 01shhf artist! 07gqbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 76.000 72.000 0.400 http://example.org/music/record_label/artist #1832-02z9hqn PRED entity: 02z9hqn PRED relation: genre PRED expected values: 0jxy => 72 concepts (57 used for prediction) PRED predicted values (max 10 best out of 102): 03k9fj (0.79 #3796, 0.79 #3678, 0.67 #602), 05p553 (0.78 #5326, 0.65 #5444, 0.59 #1186), 0jxy (0.76 #1463, 0.76 #1108, 0.75 #1344), 01jfsb (0.54 #959, 0.50 #841, 0.49 #2732), 03q4nz (0.40 #17, 0.38 #253, 0.36 #1082), 04pbhw (0.38 #1001, 0.33 #1593, 0.33 #2419), 0btmb (0.33 #1033, 0.31 #915, 0.27 #1979), 01zhp (0.33 #666, 0.31 #785, 0.12 #1140), 02l7c8 (0.31 #5456, 0.29 #5338, 0.29 #5693), 060__y (0.23 #5575, 0.15 #4747, 0.15 #5694) >> Best rule #3796 for best value: >> intensional similarity = 9 >> extensional distance = 159 >> proper extension: 01f39b; >> query: (?x869, 03k9fj) <- genre(?x869, ?x1510), genre(?x869, ?x225), ?x1510 = 01hmnh, genre(?x5128, ?x225), genre(?x3844, ?x225), genre(?x1904, ?x225), ?x5128 = 08phg9, ?x3844 = 02_qt, ?x1904 = 09146g >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1463 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 32 *> proper extension: 0dh8v4; 05t0zfv; *> query: (?x869, 0jxy) <- genre(?x869, ?x53), actor(?x869, ?x5779), genre(?x4860, ?x53), genre(?x4457, ?x53), country(?x4860, ?x94), genre(?x273, ?x53), film(?x382, ?x4457) *> conf = 0.76 ranks of expected_values: 3 EVAL 02z9hqn genre 0jxy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 57.000 0.789 http://example.org/film/film/genre #1831-02704ff PRED entity: 02704ff PRED relation: genre PRED expected values: 05p553 => 102 concepts (66 used for prediction) PRED predicted values (max 10 best out of 92): 05p553 (0.46 #2249, 0.45 #4, 0.38 #5566), 01jfsb (0.41 #3085, 0.39 #719, 0.39 #483), 02kdv5l (0.41 #828, 0.40 #2010, 0.38 #3076), 02l7c8 (0.38 #369, 0.30 #7709, 0.29 #605), 03k9fj (0.35 #836, 0.31 #1428, 0.31 #1073), 04xvlr (0.32 #237, 0.26 #355, 0.25 #1301), 060__y (0.29 #134, 0.24 #370, 0.23 #1316), 01t_vv (0.29 #172, 0.18 #54, 0.16 #290), 03bxz7 (0.26 #291, 0.18 #1355, 0.18 #409), 01hmnh (0.22 #843, 0.21 #3448, 0.20 #961) >> Best rule #2249 for best value: >> intensional similarity = 4 >> extensional distance = 215 >> proper extension: 063y9fp; >> query: (?x5694, 05p553) <- executive_produced_by(?x5694, ?x5973), film(?x2646, ?x5694), award_nominee(?x92, ?x2646), executive_produced_by(?x7009, ?x2646) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02704ff genre 05p553 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 66.000 0.456 http://example.org/film/film/genre #1830-047jhq PRED entity: 047jhq PRED relation: profession PRED expected values: 0np9r => 146 concepts (41 used for prediction) PRED predicted values (max 10 best out of 68): 0dxtg (0.64 #748, 0.53 #2365, 0.49 #4426), 03gjzk (0.55 #308, 0.35 #896, 0.32 #5751), 02jknp (0.50 #2359, 0.49 #5450, 0.48 #5744), 018gz8 (0.38 #1192, 0.36 #751, 0.27 #310), 0kyk (0.36 #323, 0.32 #911, 0.24 #1205), 012t_z (0.27 #306, 0.12 #159, 0.09 #747), 0d1pc (0.25 #490, 0.18 #343, 0.16 #1813), 0np9r (0.23 #755, 0.18 #314, 0.18 #4580), 0d8qb (0.18 #372, 0.18 #1254, 0.14 #813), 09jwl (0.18 #2959, 0.17 #3694, 0.16 #2812) >> Best rule #748 for best value: >> intensional similarity = 6 >> extensional distance = 20 >> proper extension: 04rg6; >> query: (?x12616, 0dxtg) <- profession(?x12616, ?x4725), profession(?x12616, ?x1032), profession(?x12616, ?x319), ?x1032 = 02hrh1q, ?x4725 = 015cjr, ?x319 = 01d_h8 >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #755 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 20 *> proper extension: 04rg6; *> query: (?x12616, 0np9r) <- profession(?x12616, ?x4725), profession(?x12616, ?x1032), profession(?x12616, ?x319), ?x1032 = 02hrh1q, ?x4725 = 015cjr, ?x319 = 01d_h8 *> conf = 0.23 ranks of expected_values: 8 EVAL 047jhq profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 146.000 41.000 0.636 http://example.org/people/person/profession #1829-0652ty PRED entity: 0652ty PRED relation: nationality PRED expected values: 0d060g => 85 concepts (63 used for prediction) PRED predicted values (max 10 best out of 77): 09c7w0 (0.79 #1401, 0.79 #1301, 0.78 #801), 0d060g (0.27 #4712, 0.14 #7, 0.12 #107), 07ssc (0.27 #4712, 0.13 #1615, 0.12 #115), 0345h (0.27 #4712, 0.09 #5315, 0.09 #731), 0f8l9c (0.27 #4712, 0.09 #5315, 0.05 #1901), 03rjj (0.27 #4712, 0.04 #5013, 0.04 #3908), 02jx1 (0.25 #133, 0.14 #33, 0.13 #1633), 0h7x (0.10 #735, 0.06 #1035, 0.05 #935), 0chghy (0.09 #5315, 0.07 #210, 0.05 #1901), 03rt9 (0.09 #5315, 0.07 #213, 0.05 #1901) >> Best rule #1401 for best value: >> intensional similarity = 5 >> extensional distance = 258 >> proper extension: 085pr; 02h761; 0f7h2g; 0h25; 0ff3y; 02nygk; >> query: (?x11069, 09c7w0) <- people(?x1050, ?x11069), ?x1050 = 041rx, profession(?x11069, ?x319), profession(?x4056, ?x319), ?x4056 = 01ycck >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #4712 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1535 *> proper extension: 050t68; *> query: (?x11069, ?x205) <- film(?x11069, ?x6981), film(?x11069, ?x1118), nominated_for(?x1119, ?x1118), nominated_for(?x68, ?x1118), award(?x1118, ?x112), country(?x6981, ?x205) *> conf = 0.27 ranks of expected_values: 2 EVAL 0652ty nationality 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 63.000 0.792 http://example.org/people/person/nationality #1828-07vn_9 PRED entity: 07vn_9 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 26): 09zzb8 (0.76 #1007, 0.72 #334, 0.72 #401), 09vw2b7 (0.66 #340, 0.64 #407, 0.64 #1757), 0dxtw (0.43 #344, 0.42 #411, 0.38 #747), 01pvkk (0.34 #111, 0.30 #985, 0.30 #1051), 02vs3x5 (0.20 #22, 0.12 #2357, 0.10 #2123), 015h31 (0.14 #308, 0.12 #475, 0.12 #2357), 0215hd (0.13 #1767, 0.13 #1023, 0.12 #990), 04pyp5 (0.12 #2357, 0.12 #114, 0.10 #2123), 089g0h (0.12 #2357, 0.11 #1768, 0.11 #991), 02rh1dz (0.12 #2357, 0.11 #983, 0.11 #1049) >> Best rule #1007 for best value: >> intensional similarity = 4 >> extensional distance = 494 >> proper extension: 0bs8hvm; >> query: (?x10799, 09zzb8) <- film_crew_role(?x10799, ?x1284), genre(?x10799, ?x53), ?x53 = 07s9rl0, ?x1284 = 0ch6mp2 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #340 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 194 *> proper extension: 0gj8nq2; 09rsjpv; 03z106; 0992d9; 0b7l4x; 02q0k7v; 02bj22; *> query: (?x10799, 09vw2b7) <- film(?x1324, ?x10799), production_companies(?x10799, ?x382), films(?x3530, ?x10799), currency(?x10799, ?x170), film_crew_role(?x10799, ?x468) *> conf = 0.66 ranks of expected_values: 2 EVAL 07vn_9 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.758 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1827-02j71 PRED entity: 02j71 PRED relation: service_location! PRED expected values: 045c7b 077w0b 07xyn1 => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 119): 064f29 (0.40 #153, 0.23 #570, 0.23 #778), 06_9lg (0.35 #1432), 07zl6m (0.30 #412, 0.27 #204, 0.20 #517), 0cv9b (0.29 #944, 0.24 #1048, 0.23 #736), 0k9ts (0.27 #178, 0.26 #803, 0.24 #491), 04fv0k (0.27 #173, 0.25 #69, 0.23 #590), 069b85 (0.27 #202, 0.22 #410, 0.19 #827), 0dmtp (0.27 #152, 0.22 #360, 0.16 #465), 077w0b (0.27 #159, 0.19 #784, 0.19 #263), 05b5c (0.26 #409, 0.23 #618, 0.20 #201) >> Best rule #153 for best value: >> intensional similarity = 2 >> extensional distance = 13 >> proper extension: 04w58; >> query: (?x551, 064f29) <- administrative_parent(?x390, ?x551), vacationer(?x390, ?x2275) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #159 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 13 *> proper extension: 04w58; *> query: (?x551, 077w0b) <- administrative_parent(?x390, ?x551), vacationer(?x390, ?x2275) *> conf = 0.27 ranks of expected_values: 9, 19 EVAL 02j71 service_location! 07xyn1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 21.000 0.400 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 02j71 service_location! 077w0b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 21.000 21.000 0.400 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 02j71 service_location! 045c7b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 21.000 21.000 0.400 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #1826-0bzm__ PRED entity: 0bzm__ PRED relation: honored_for PRED expected values: 0sxfd => 28 concepts (15 used for prediction) PRED predicted values (max 10 best out of 747): 011yn5 (0.33 #326, 0.15 #1795, 0.06 #1794), 015whm (0.33 #829, 0.15 #1795), 035yn8 (0.33 #1891, 0.04 #2486, 0.03 #3087), 011yxy (0.33 #2232, 0.04 #2827, 0.03 #3428), 0gw7p (0.33 #2154, 0.04 #2749, 0.03 #3350), 01gvsn (0.33 #2362, 0.04 #2957, 0.03 #3558), 0p9rz (0.33 #1713, 0.04 #2906, 0.03 #3507), 026gyn_ (0.33 #1309, 0.04 #2502, 0.03 #3103), 0b2qtl (0.33 #1508, 0.04 #2701, 0.03 #3302), 08ct6 (0.33 #1480, 0.04 #2673, 0.03 #3274) >> Best rule #326 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 02jp5r; >> query: (?x6344, 011yn5) <- honored_for(?x6344, ?x6030), ceremony(?x1703, ?x6344), ceremony(?x591, ?x6344), ceremony(?x77, ?x6344), award_winner(?x6344, ?x6011), award_winner(?x6344, ?x406), ?x1703 = 0k611, nationality(?x6011, ?x94), profession(?x6011, ?x987), award_winner(?x6011, ?x7955), ?x77 = 0gqng, award_nominee(?x538, ?x6011), award(?x6011, ?x1232), award(?x123, ?x591), ?x406 = 09fb5, nominated_for(?x591, ?x54) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1794 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 1 *> proper extension: 0bzkvd; *> query: (?x6344, ?x351) <- honored_for(?x6344, ?x6030), ceremony(?x1703, ?x6344), ceremony(?x1245, ?x6344), award_winner(?x6344, ?x6957), award_winner(?x6344, ?x6011), award_winner(?x6344, ?x4436), ?x1703 = 0k611, ?x6011 = 02zft0, ?x1245 = 0gqwc, award_winner(?x6261, ?x4436), award_nominee(?x4436, ?x2518), award_winner(?x161, ?x4436), film(?x4436, ?x351), languages(?x6957, ?x254), profession(?x4436, ?x319), award_winner(?x11597, ?x6261), award_winner(?x458, ?x6261) *> conf = 0.06 ranks of expected_values: 58 EVAL 0bzm__ honored_for 0sxfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 28.000 15.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #1825-05w6cw PRED entity: 05w6cw PRED relation: award PRED expected values: 02p_04b => 121 concepts (105 used for prediction) PRED predicted values (max 10 best out of 276): 03qbh5 (0.52 #9928, 0.31 #2637, 0.24 #3042), 03qbnj (0.50 #2665, 0.25 #4285, 0.25 #1450), 02f6ym (0.46 #2690, 0.27 #4310, 0.26 #3095), 02f5qb (0.46 #2587, 0.21 #9878, 0.20 #1777), 01c427 (0.45 #9806, 0.35 #2515, 0.22 #4135), 09qj50 (0.40 #451, 0.29 #856, 0.20 #46), 01bgqh (0.40 #9764, 0.27 #4093, 0.27 #3283), 0cqhk0 (0.40 #37, 0.20 #16644, 0.18 #21099), 02f71y (0.38 #2614, 0.22 #4234, 0.19 #5449), 05p09zm (0.38 #1340, 0.23 #2555, 0.18 #12682) >> Best rule #9928 for best value: >> intensional similarity = 4 >> extensional distance = 178 >> proper extension: 02r3zy; 012x4t; 05crg7; 015882; 0dvqq; 03fbc; 01vrwfv; 018ndc; 04qmr; 01rm8b; ... >> query: (?x8365, 03qbh5) <- artists(?x671, ?x8365), award(?x8365, ?x6416), award(?x6461, ?x6416), ?x6461 = 01t110 >> conf = 0.52 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05w6cw award 02p_04b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 105.000 0.517 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1824-01yfm8 PRED entity: 01yfm8 PRED relation: film PRED expected values: 08gg47 => 83 concepts (38 used for prediction) PRED predicted values (max 10 best out of 395): 01chpn (0.27 #1102, 0.05 #62214, 0.05 #30216), 0c0zq (0.27 #1551, 0.05 #30216, 0.03 #55101), 0298n7 (0.13 #1340, 0.05 #30216, 0.03 #55101), 03cvvlg (0.13 #1434, 0.03 #55101, 0.03 #28438), 03h0byn (0.13 #1689), 08s6mr (0.13 #1310), 0660b9b (0.13 #990), 0kvgxk (0.13 #325), 0gvt53w (0.07 #1549, 0.05 #62214, 0.05 #30216), 0284b56 (0.07 #977, 0.05 #62214, 0.05 #30216) >> Best rule #1102 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 04bdxl; 02qgqt; 048lv; 0dlglj; 015pkc; 02bkdn; 028knk; 0h0wc; 01vvb4m; 0flw6; ... >> query: (?x7401, 01chpn) <- award_nominee(?x7401, ?x92), ?x92 = 02s2ft, film(?x7401, ?x924) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01yfm8 film 08gg47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 38.000 0.267 http://example.org/film/actor/film./film/performance/film #1823-04cj79 PRED entity: 04cj79 PRED relation: featured_film_locations PRED expected values: 04jpl => 85 concepts (72 used for prediction) PRED predicted values (max 10 best out of 71): 02_286 (0.23 #262, 0.22 #20, 0.16 #2671), 06y57 (0.23 #345, 0.03 #1792, 0.02 #3476), 030qb3t (0.22 #39, 0.07 #2690, 0.07 #2930), 04jpl (0.15 #2179, 0.11 #9, 0.08 #5788), 052p7 (0.11 #58, 0.03 #2709, 0.02 #1987), 0b90_r (0.11 #4, 0.03 #487, 0.02 #1212), 0fr0t (0.11 #84, 0.01 #567), 0rh6k (0.08 #243, 0.04 #1449, 0.04 #2652), 0cv3w (0.08 #312, 0.02 #794, 0.02 #1036), 0gkgp (0.08 #403, 0.01 #1850) >> Best rule #262 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 02z3r8t; 0gyy53; >> query: (?x3605, 02_286) <- film(?x4295, ?x3605), country(?x3605, ?x94), film_crew_role(?x3605, ?x468), ?x4295 = 09l3p >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #2179 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 212 *> proper extension: 0hz6mv2; *> query: (?x3605, 04jpl) <- country(?x3605, ?x512), country(?x3605, ?x94), ?x94 = 09c7w0, ?x512 = 07ssc *> conf = 0.15 ranks of expected_values: 4 EVAL 04cj79 featured_film_locations 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 85.000 72.000 0.231 http://example.org/film/film/featured_film_locations #1822-02_xgp2 PRED entity: 02_xgp2 PRED relation: institution PRED expected values: 07w0v 0lfgr 01ptt7 01y17m 02fjzt 02d_zc 015q1n 07ccs 02hmw9 0lvng 014xf6 02pbzv 025ttz4 020vx9 01clyb 023zl 0ymcz 013719 01hc1j 01zzy3 => 23 concepts (23 used for prediction) PRED predicted values (max 10 best out of 455): 02gr81 (0.78 #5709, 0.71 #5276, 0.62 #7008), 01hc1j (0.71 #5539, 0.67 #5972, 0.67 #4676), 01wdl3 (0.71 #5197, 0.67 #5630, 0.67 #4766), 07w0v (0.71 #5191, 0.67 #4760, 0.56 #7355), 0l2tk (0.71 #5235, 0.67 #5668, 0.56 #6101), 027xx3 (0.71 #5239, 0.67 #5672, 0.56 #6105), 0225v9 (0.71 #5580, 0.56 #6013, 0.50 #5149), 0217m9 (0.67 #4877, 0.60 #4011, 0.56 #5741), 01y17m (0.67 #4387, 0.57 #5250, 0.56 #5683), 06b19 (0.67 #5832, 0.57 #5399, 0.56 #6265) >> Best rule #5709 for best value: >> intensional similarity = 22 >> extensional distance = 7 >> proper extension: 02mjs7; >> query: (?x3437, 02gr81) <- institution(?x3437, ?x11278), institution(?x3437, ?x10036), institution(?x3437, ?x8706), institution(?x3437, ?x4342), institution(?x3437, ?x2999), institution(?x3437, ?x388), major_field_of_study(?x3437, ?x2014), major_field_of_study(?x3437, ?x1695), student(?x3437, ?x12453), student(?x3437, ?x10694), colors(?x4342, ?x332), organization(?x346, ?x10036), category(?x8706, ?x134), major_field_of_study(?x3182, ?x1695), ?x2999 = 07tg4, ?x388 = 05krk, major_field_of_study(?x1011, ?x2014), location(?x12453, ?x108), student(?x1695, ?x3806), award(?x10694, ?x435), major_field_of_study(?x732, ?x2014), student(?x11278, ?x9597) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #5539 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 5 *> proper extension: 07s6fsf; *> query: (?x3437, 01hc1j) <- institution(?x3437, ?x12257), institution(?x3437, ?x11278), institution(?x3437, ?x10036), institution(?x3437, ?x8706), institution(?x3437, ?x4342), institution(?x3437, ?x2999), institution(?x3437, ?x388), major_field_of_study(?x3437, ?x1695), student(?x3437, ?x10694), colors(?x4342, ?x332), organization(?x346, ?x10036), category(?x8706, ?x134), major_field_of_study(?x3182, ?x1695), ?x2999 = 07tg4, ?x388 = 05krk, ?x11278 = 037q2p, student(?x1695, ?x3806), currency(?x12257, ?x5696), type_of_union(?x10694, ?x566), ?x332 = 01l849 *> conf = 0.71 ranks of expected_values: 2, 4, 9, 18, 22, 23, 24, 48, 51, 105, 139, 193, 235, 237, 282, 291, 309, 324, 356 EVAL 02_xgp2 institution 01zzy3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 01hc1j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 013719 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 0ymcz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 023zl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 01clyb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 020vx9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 025ttz4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 02pbzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 014xf6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 0lvng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 02hmw9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 07ccs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 015q1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 02d_zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 02fjzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 01y17m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 01ptt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 0lfgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 02_xgp2 institution 07w0v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 23.000 23.000 0.778 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #1821-069nzr PRED entity: 069nzr PRED relation: film PRED expected values: 0fb7sd => 91 concepts (67 used for prediction) PRED predicted values (max 10 best out of 484): 05lfwd (0.37 #107248, 0.35 #98311, 0.32 #26813), 0dsx3f (0.34 #58991, 0.33 #41115, 0.32 #89375), 08phg9 (0.33 #882, 0.03 #37537, 0.03 #21450), 0hgnl3t (0.33 #761, 0.03 #37537, 0.03 #21450), 09wnnb (0.33 #1623, 0.03 #3410, 0.01 #6984), 04gknr (0.33 #137, 0.03 #1924), 0djlxb (0.33 #534, 0.01 #4108, 0.01 #14833), 02qkwl (0.33 #1389, 0.01 #4963), 04xg2f (0.33 #1552), 02r858_ (0.33 #1422) >> Best rule #107248 for best value: >> intensional similarity = 3 >> extensional distance = 1950 >> proper extension: 01nqfh_; 01vyp_; 01q415; 01q4qv; 05_pkf; 051z6rz; 013t9y; 06t8b; 0164w8; 01m7f5r; ... >> query: (?x5030, ?x4607) <- nationality(?x5030, ?x94), profession(?x5030, ?x1041), nominated_for(?x5030, ?x4607) >> conf = 0.37 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 069nzr film 0fb7sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 67.000 0.372 http://example.org/film/actor/film./film/performance/film #1820-0c8tkt PRED entity: 0c8tkt PRED relation: currency PRED expected values: 09nqf => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.89 #72, 0.84 #205, 0.83 #86), 01nv4h (0.21 #29, 0.04 #199, 0.04 #150), 0kz1h (0.21 #29, 0.01 #202), 02l6h (0.05 #152, 0.04 #40, 0.03 #61), 088n7 (0.03 #162, 0.01 #113, 0.01 #120), 02gsvk (0.01 #140) >> Best rule #72 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 04cf_l; >> query: (?x1743, 09nqf) <- prequel(?x9507, ?x1743), written_by(?x1743, ?x7522), genre(?x1743, ?x225), music(?x1743, ?x5720) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c8tkt currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.886 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #1819-0202p_ PRED entity: 0202p_ PRED relation: people! PRED expected values: 02psvcf => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 38): 0dq9p (0.22 #17, 0.17 #82, 0.12 #407), 04p3w (0.22 #11, 0.10 #401, 0.08 #791), 0gk4g (0.21 #790, 0.21 #1050, 0.20 #1375), 0dcsx (0.11 #15, 0.04 #795, 0.04 #405), 08g5q7 (0.11 #41, 0.03 #1081, 0.02 #1471), 01mtqf (0.11 #4, 0.01 #1369, 0.01 #1434), 0qcr0 (0.11 #391, 0.10 #1041, 0.10 #781), 02k6hp (0.09 #426, 0.08 #101, 0.07 #816), 0x2fg (0.08 #102, 0.01 #427), 014w_8 (0.08 #103) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0gm34; >> query: (?x11952, 0dq9p) <- nationality(?x11952, ?x94), film(?x11952, ?x8217), ?x8217 = 04v89z, type_of_union(?x11952, ?x566) >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0202p_ people! 02psvcf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 80.000 0.222 http://example.org/people/cause_of_death/people #1818-016yr0 PRED entity: 016yr0 PRED relation: nationality PRED expected values: 0b90_r => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 24): 0d060g (0.70 #1492, 0.09 #6, 0.06 #304), 07ssc (0.70 #1492, 0.09 #113, 0.09 #7991), 01xbgx (0.40 #7776), 0gx1l (0.33 #7877), 0kpys (0.33 #7877), 03h64 (0.30 #3493), 02jx1 (0.11 #825, 0.10 #924, 0.10 #8009), 03rk0 (0.06 #3638, 0.05 #8022, 0.05 #442), 03_3d (0.04 #104, 0.03 #303, 0.03 #2300), 0f8l9c (0.03 #1714, 0.03 #2216, 0.03 #2415) >> Best rule #1492 for best value: >> intensional similarity = 3 >> extensional distance = 523 >> proper extension: 066l3y; 09fp45; 06czxq; 084x96; 07glc4; >> query: (?x4327, ?x94) <- place_of_birth(?x4327, ?x1523), actor(?x8870, ?x4327), country_of_origin(?x8870, ?x94) >> conf = 0.70 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 016yr0 nationality 0b90_r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 94.000 0.704 http://example.org/people/person/nationality #1817-0xqf3 PRED entity: 0xqf3 PRED relation: category PRED expected values: 08mbj5d => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #5, 0.78 #6, 0.78 #11) >> Best rule #5 for best value: >> intensional similarity = 5 >> extensional distance = 109 >> proper extension: 0k049; 03v_5; 029cr; 0rp46; 0ply0; 0rj0z; 0sb1r; 0lhql; 0r5lz; 07bcn; ... >> query: (?x8944, 08mbj5d) <- contains(?x10054, ?x8944), adjoins(?x10054, ?x6143), location(?x7570, ?x8944), second_level_divisions(?x94, ?x10054), source(?x8944, ?x958) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0xqf3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.856 http://example.org/common/topic/webpage./common/webpage/category #1816-09nqf PRED entity: 09nqf PRED relation: currency! PRED expected values: 0vmt 03s0w 059_c 0gyh => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 44): 01nqj (0.50 #23, 0.10 #22, 0.05 #15), 06m_5 (0.50 #23, 0.10 #22, 0.05 #15), 0jdx (0.50 #23, 0.10 #22, 0.05 #15), 05b7q (0.50 #23, 0.10 #22, 0.05 #15), 02k8k (0.50 #23, 0.10 #22, 0.05 #15), 0d05q4 (0.50 #23, 0.10 #22, 0.05 #15), 03shp (0.50 #23, 0.10 #22, 0.05 #15), 0161c (0.50 #23, 0.10 #22, 0.05 #15), 06t8v (0.50 #23, 0.10 #22, 0.05 #15), 05sb1 (0.50 #23, 0.10 #22, 0.05 #15) >> Best rule #23 for best value: >> intensional similarity = 17 >> extensional distance = 2 >> proper extension: 0ptk_; >> query: (?x170, ?x6974) <- currency(?x9429, ?x170), currency(?x7911, ?x170), currency(?x1331, ?x170), currency(?x253, ?x170), currency(?x10045, ?x170), country(?x9429, ?x94), currency(?x6974, ?x170), genre(?x9429, ?x53), currency(?x99, ?x170), major_field_of_study(?x10045, ?x3561), film_crew_role(?x253, ?x137), nominated_for(?x112, ?x7911), film(?x368, ?x1331), currency(?x108, ?x170), currency(?x1961, ?x170), currency(?x1675, ?x170), teams(?x6974, ?x1598) >> conf = 0.50 => this is the best rule for 15 predicted values No rule for expected values ranks of expected_values: EVAL 09nqf currency! 0gyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.500 http://example.org/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 059_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.500 http://example.org/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 03s0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.500 http://example.org/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency EVAL 09nqf currency! 0vmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 8.000 8.000 0.500 http://example.org/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency #1815-03h_yfh PRED entity: 03h_yfh PRED relation: inductee! PRED expected values: 0g2c8 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 5): 0g2c8 (0.32 #37, 0.29 #1, 0.21 #154), 04045y (0.12 #15, 0.07 #24, 0.05 #51), 06szd3 (0.07 #218, 0.06 #263, 0.05 #200), 0qjfl (0.06 #30, 0.05 #39, 0.04 #156), 04dm2n (0.05 #44, 0.05 #53, 0.03 #179) >> Best rule #37 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 081lh; >> query: (?x7803, 0g2c8) <- profession(?x7803, ?x220), location(?x7803, ?x1860), artists(?x5905, ?x7803), celebrities_impersonated(?x3649, ?x7803) >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03h_yfh inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.316 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #1814-02725hs PRED entity: 02725hs PRED relation: production_companies PRED expected values: 03sb38 => 83 concepts (68 used for prediction) PRED predicted values (max 10 best out of 60): 0jz9f (0.31 #3086, 0.30 #3331, 0.20 #1), 03sb38 (0.30 #620, 0.27 #377, 0.20 #944), 05qd_ (0.25 #172, 0.11 #1793, 0.11 #1550), 02slt7 (0.20 #596, 0.14 #920, 0.13 #1650), 086k8 (0.20 #2, 0.14 #83, 0.08 #731), 054lpb6 (0.15 #986, 0.15 #1392, 0.14 #1149), 01795t (0.14 #102, 0.11 #264, 0.04 #1156), 05mgj0 (0.14 #144, 0.05 #630, 0.04 #1117), 02jd_7 (0.12 #230, 0.11 #311, 0.07 #392), 020h2v (0.12 #220, 0.11 #301, 0.05 #544) >> Best rule #3086 for best value: >> intensional similarity = 9 >> extensional distance = 705 >> proper extension: 02y_lrp; 0ds3t5x; 0cpllql; 0c40vxk; 09q5w2; 0bq8tmw; 0bh8yn3; 07nt8p; 0d_2fb; 03kg2v; ... >> query: (?x2289, ?x166) <- film_crew_role(?x2289, ?x2472), film_crew_role(?x2289, ?x1284), genre(?x2289, ?x53), ?x1284 = 0ch6mp2, film_crew_role(?x7081, ?x2472), film_crew_role(?x7072, ?x2472), film(?x166, ?x2289), ?x7081 = 03q8xj, ?x7072 = 02d003 >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #620 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 62 *> proper extension: 016z5x; *> query: (?x2289, 03sb38) <- country(?x2289, ?x789), genre(?x2289, ?x8681), ?x789 = 0f8l9c, disciplines_or_subjects(?x1088, ?x8681), production_companies(?x2289, ?x1104) *> conf = 0.30 ranks of expected_values: 2 EVAL 02725hs production_companies 03sb38 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 68.000 0.309 http://example.org/film/film/production_companies #1813-02ljhg PRED entity: 02ljhg PRED relation: film! PRED expected values: 01s7zw 02p7_k => 67 concepts (48 used for prediction) PRED predicted values (max 10 best out of 1069): 01hbq0 (0.25 #2019, 0.14 #4094, 0.08 #6169), 021mlp (0.25 #2008, 0.14 #4083, 0.08 #6158), 013sg6 (0.25 #1635, 0.14 #3710, 0.08 #5785), 019l3m (0.25 #1545, 0.14 #3620, 0.08 #5695), 017g2y (0.25 #1389, 0.14 #3464, 0.08 #5539), 01wbg84 (0.20 #18722, 0.07 #20752, 0.03 #12496), 02qgqt (0.19 #18694, 0.07 #20752, 0.02 #33222), 0c1pj (0.15 #4241, 0.07 #20752, 0.04 #10466), 02xs5v (0.15 #5554, 0.05 #11779, 0.05 #9704), 01l7qw (0.15 #6054, 0.04 #18505, 0.03 #12279) >> Best rule #2019 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0k4kk; >> query: (?x7757, 01hbq0) <- genre(?x7757, ?x2605), ?x2605 = 03g3w, film(?x510, ?x7757), ?x510 = 0chsq >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2500 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 01gglm; *> query: (?x7757, 01s7zw) <- film(?x510, ?x7757), ?x510 = 0chsq, currency(?x7757, ?x170) *> conf = 0.14 ranks of expected_values: 20, 294 EVAL 02ljhg film! 02p7_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 67.000 48.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 02ljhg film! 01s7zw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 67.000 48.000 0.250 http://example.org/film/actor/film./film/performance/film #1812-06p8m PRED entity: 06p8m PRED relation: contact_category PRED expected values: 03w5xm => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 3): 03w5xm (0.76 #172, 0.76 #257, 0.76 #199), 02zdwq (0.58 #63, 0.38 #69, 0.33 #18), 014dgf (0.27 #224, 0.22 #294, 0.20 #50) >> Best rule #172 for best value: >> intensional similarity = 6 >> extensional distance = 40 >> proper extension: 01yfp7; >> query: (?x11427, 03w5xm) <- category(?x11427, ?x134), industry(?x11427, ?x245), ?x134 = 08mbj5d, service_location(?x11427, ?x94), organization(?x4682, ?x11427), ?x94 = 09c7w0 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06p8m contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 201.000 201.000 0.762 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #1811-061v5m PRED entity: 061v5m PRED relation: company! PRED expected values: 0dq3c => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 36): 05_wyz (0.77 #1784, 0.60 #1052, 0.60 #923), 0dq3c (0.60 #1124, 0.57 #1081, 0.54 #1339), 01yc02 (0.50 #785, 0.48 #828, 0.48 #699), 01kr6k (0.34 #605, 0.33 #110, 0.30 #259), 0142rn (0.34 #605, 0.30 #259, 0.22 #2328), 02zdwq (0.33 #65, 0.22 #2328, 0.15 #1424), 04192r (0.22 #2328, 0.20 #38, 0.15 #1424), 02211by (0.22 #2328, 0.20 #1470, 0.19 #1642), 02y6fz (0.22 #2328, 0.18 #799, 0.17 #713), 021q0l (0.22 #2328, 0.15 #1424, 0.14 #3407) >> Best rule #1784 for best value: >> intensional similarity = 9 >> extensional distance = 58 >> proper extension: 0jbk9; 07y0n; >> query: (?x6386, 05_wyz) <- company(?x5161, ?x6386), company(?x5161, ?x12938), company(?x5161, ?x10699), company(?x5161, ?x3813), company(?x5161, ?x1492), ?x10699 = 0206k5, major_field_of_study(?x3813, ?x742), ?x12938 = 0dq23, ?x1492 = 0cv9b >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1124 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 38 *> proper extension: 0797c7; *> query: (?x6386, 0dq3c) <- currency(?x6386, ?x170), company(?x4682, ?x6386), company(?x1491, ?x6386), company(?x346, ?x6386), ?x4682 = 0dq_5, ?x170 = 09nqf, ?x1491 = 0krdk, organization(?x346, ?x99), jurisdiction_of_office(?x346, ?x47) *> conf = 0.60 ranks of expected_values: 2 EVAL 061v5m company! 0dq3c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 145.000 0.767 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #1810-028k57 PRED entity: 028k57 PRED relation: people! PRED expected values: 041rx => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 43): 041rx (0.63 #620, 0.22 #4, 0.20 #81), 033tf_ (0.16 #161, 0.12 #1163, 0.12 #931), 0x67 (0.11 #10, 0.10 #1630, 0.10 #1862), 025rpb0 (0.11 #45, 0.02 #199, 0.02 #276), 01g7zj (0.11 #52), 0dryh9k (0.11 #247, 0.04 #2485, 0.04 #1327), 013xrm (0.10 #97, 0.07 #636, 0.03 #790), 013b6_ (0.10 #130, 0.05 #669, 0.04 #438), 02w7gg (0.09 #2008, 0.07 #1236, 0.07 #1777), 07bch9 (0.09 #177, 0.05 #947, 0.05 #870) >> Best rule #620 for best value: >> intensional similarity = 2 >> extensional distance = 164 >> proper extension: 01w3v; 0mcf4; 015c1b; >> query: (?x4478, 041rx) <- religion(?x4478, ?x7131), ?x7131 = 03_gx >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 028k57 people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.627 http://example.org/people/ethnicity/people #1809-01wv9p PRED entity: 01wv9p PRED relation: profession PRED expected values: 0n1h => 148 concepts (133 used for prediction) PRED predicted values (max 10 best out of 73): 0nbcg (0.54 #1051, 0.50 #7629, 0.50 #613), 01d_h8 (0.53 #3510, 0.53 #2341, 0.47 #4680), 03gjzk (0.41 #3519, 0.36 #2350, 0.33 #4249), 0dxtg (0.35 #3518, 0.33 #2349, 0.30 #4688), 01c72t (0.34 #5428, 0.28 #10694, 0.27 #11571), 02jknp (0.30 #3512, 0.25 #4388, 0.24 #4682), 039v1 (0.29 #6904, 0.23 #472, 0.23 #8514), 0n1h (0.28 #3370, 0.28 #594, 0.25 #6880), 0d1pc (0.25 #632, 0.22 #1217, 0.20 #48), 012t_z (0.19 #157, 0.15 #887, 0.09 #5125) >> Best rule #1051 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 0152cw; 01w02sy; 017xm3; 06gd4; 01htxr; 0fpj9pm; 011lvx; 01vttb9; 01nz1q6; 01l7qw; >> query: (?x4123, 0nbcg) <- award(?x4123, ?x884), location_of_ceremony(?x4123, ?x3026), artists(?x671, ?x4123) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #3370 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 131 *> proper extension: 020hh3; *> query: (?x4123, 0n1h) <- artists(?x671, ?x4123), category(?x4123, ?x134), participant(?x4123, ?x1231) *> conf = 0.28 ranks of expected_values: 8 EVAL 01wv9p profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 148.000 133.000 0.542 http://example.org/people/person/profession #1808-01tw31 PRED entity: 01tw31 PRED relation: instrumentalists! PRED expected values: 03qjg => 128 concepts (94 used for prediction) PRED predicted values (max 10 best out of 123): 05r5c (0.61 #922, 0.58 #673, 0.57 #1672), 05148p4 (0.54 #601, 0.46 #1017, 0.46 #851), 02hnl (0.47 #449, 0.29 #532, 0.27 #366), 0l14md (0.33 #339, 0.27 #422, 0.24 #505), 026t6 (0.27 #418, 0.27 #335, 0.24 #501), 03qjg (0.25 #629, 0.23 #879, 0.23 #1460), 06ch55 (0.22 #992, 0.21 #743, 0.17 #244), 0mkg (0.20 #94, 0.08 #925, 0.07 #343), 02k84w (0.20 #119, 0.04 #701, 0.04 #617), 02k856 (0.20 #133, 0.04 #631, 0.04 #1831) >> Best rule #922 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 014g91; >> query: (?x10907, 05r5c) <- people(?x7007, ?x10907), artist(?x1543, ?x10907), role(?x10907, ?x227), artists(?x378, ?x10907) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #629 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 07mvp; *> query: (?x10907, 03qjg) <- artists(?x7440, ?x10907), artists(?x378, ?x10907), ?x378 = 07sbbz2, ?x7440 = 0155w *> conf = 0.25 ranks of expected_values: 6 EVAL 01tw31 instrumentalists! 03qjg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 128.000 94.000 0.611 http://example.org/music/instrument/instrumentalists #1807-042zrm PRED entity: 042zrm PRED relation: films! PRED expected values: 0jm_ => 124 concepts (64 used for prediction) PRED predicted values (max 10 best out of 63): 0bq3x (0.11 #30, 0.06 #344, 0.06 #502), 0nk95 (0.11 #151, 0.02 #465), 081pw (0.06 #2212, 0.05 #633, 0.05 #2999), 07jq_ (0.05 #240, 0.03 #2605, 0.03 #2921), 07c52 (0.05 #178, 0.02 #8395, 0.02 #6812), 01cgz (0.05 #177, 0.02 #5545, 0.02 #5702), 07ytt (0.05 #237), 06c97 (0.05 #206), 04rjg (0.05 #179), 06d4h (0.04 #5726, 0.04 #357, 0.04 #5884) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0b6tzs; 051ys82; 08nhfc1; 047p798; 0443v1; >> query: (?x8236, 0bq3x) <- film_release_distribution_medium(?x8236, ?x81), titles(?x53, ?x8236), featured_film_locations(?x8236, ?x3634), ?x3634 = 07b_l >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #6007 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 401 *> proper extension: 0g5qs2k; 0gj8t_b; 0gxtknx; 0gd0c7x; 0gvrws1; 0b_5d; 04grkmd; 05m_jsg; 02wgk1; 0h03fhx; ... *> query: (?x8236, 0jm_) <- film_release_distribution_medium(?x8236, ?x81), film_release_region(?x8236, ?x94), film_crew_role(?x8236, ?x137), produced_by(?x8236, ?x1039) *> conf = 0.01 ranks of expected_values: 61 EVAL 042zrm films! 0jm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 124.000 64.000 0.111 http://example.org/film/film_subject/films #1806-01cdjp PRED entity: 01cdjp PRED relation: disciplines_or_subjects PRED expected values: 0dwly => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 37): 04g51 (0.61 #293, 0.39 #487, 0.37 #371), 02vxn (0.53 #309, 0.35 #465, 0.25 #77), 02xlf (0.36 #295, 0.25 #25, 0.21 #489), 0w7c (0.35 #334, 0.17 #490, 0.11 #141), 05hgj (0.25 #24, 0.19 #294, 0.12 #100), 01hmnh (0.25 #280, 0.17 #358, 0.15 #474), 0j7v_ (0.25 #20, 0.12 #96, 0.11 #135), 0dwly (0.25 #65, 0.12 #103, 0.11 #142), 0jtdp (0.25 #8, 0.12 #84, 0.11 #123), 06n90 (0.22 #277, 0.15 #355, 0.14 #471) >> Best rule #293 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 02r0d0; >> query: (?x3676, 04g51) <- award_winner(?x3676, ?x9519), place_of_birth(?x9519, ?x2850), story_by(?x1644, ?x9519), disciplines_or_subjects(?x3676, ?x14323) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #65 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2 *> proper extension: 03hfwhq; *> query: (?x3676, 0dwly) <- award_winner(?x3676, ?x9519), ?x9519 = 01pw9v *> conf = 0.25 ranks of expected_values: 8 EVAL 01cdjp disciplines_or_subjects 0dwly CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 48.000 48.000 0.611 http://example.org/award/award_category/disciplines_or_subjects #1805-0dky9n PRED entity: 0dky9n PRED relation: edited_by! PRED expected values: 01k5y0 => 149 concepts (52 used for prediction) PRED predicted values (max 10 best out of 169): 0mbql (0.12 #1499, 0.08 #2187), 0f4yh (0.12 #1445, 0.08 #2133), 0dnqr (0.12 #1435, 0.08 #2123), 0hwpz (0.08 #2202, 0.06 #1514, 0.01 #2374), 0dfw0 (0.08 #2155, 0.06 #1467, 0.01 #3537), 03mh_tp (0.08 #2126, 0.06 #1438), 02704ff (0.08 #2169), 07bwr (0.08 #2160), 05fgt1 (0.08 #2114), 02r1c18 (0.08 #2097) >> Best rule #1499 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 027rfxc; >> query: (?x877, 0mbql) <- edited_by(?x3909, ?x877), place_of_birth(?x877, ?x1658), nationality(?x877, ?x279), language(?x3909, ?x254) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dky9n edited_by! 01k5y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 149.000 52.000 0.125 http://example.org/film/film/edited_by #1804-024tcq PRED entity: 024tcq PRED relation: legislative_sessions! PRED expected values: 0b3wk => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 6): 0b3wk (0.95 #236, 0.93 #180, 0.91 #212), 0x2sv (0.07 #244, 0.05 #194), 0h6dy (0.05 #245, 0.05 #195), 0l_j_ (0.05 #196, 0.03 #246), 0162kb (0.05 #197), 030p4s (0.02 #248) >> Best rule #236 for best value: >> intensional similarity = 32 >> extensional distance = 32 >> proper extension: 01grp0; >> query: (?x3540, ?x2860) <- legislative_sessions(?x355, ?x3540), legislative_sessions(?x3540, ?x3766), district_represented(?x3540, ?x6226), district_represented(?x3540, ?x5575), district_represented(?x3540, ?x4198), district_represented(?x3540, ?x3908), district_represented(?x3540, ?x3670), religion(?x4198, ?x109), partially_contains(?x4198, ?x13214), state_province_region(?x10572, ?x3908), time_zones(?x3908, ?x1638), contains(?x4198, ?x7067), legislative_sessions(?x652, ?x3540), jurisdiction_of_office(?x900, ?x4198), state(?x1248, ?x3908), ?x3670 = 05tbn, contains(?x3908, ?x5596), major_field_of_study(?x10572, ?x6859), featured_film_locations(?x5725, ?x3908), ?x6859 = 01tbp, currency(?x5575, ?x170), category(?x5575, ?x134), first_level_division_of(?x4198, ?x94), administrative_division(?x5193, ?x6226), ?x134 = 08mbj5d, major_field_of_study(?x5596, ?x742), state(?x9371, ?x4198), location(?x1299, ?x3908), film_release_region(?x11701, ?x6226), legislative_sessions(?x2860, ?x3766), adjoins(?x938, ?x5575), location(?x2415, ?x6226) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024tcq legislative_sessions! 0b3wk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.955 http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions #1803-0jpn8 PRED entity: 0jpn8 PRED relation: institution! PRED expected values: 02h4rq6 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 21): 02h4rq6 (0.83 #135, 0.81 #582, 0.75 #559), 019v9k (0.79 #141, 0.70 #588, 0.66 #565), 014mlp (0.75 #27, 0.75 #406, 0.73 #337), 07s6fsf (0.50 #133, 0.45 #580, 0.42 #557), 0bkj86 (0.48 #140, 0.41 #744, 0.39 #587), 027f2w (0.37 #142, 0.31 #589, 0.25 #32), 04zx3q1 (0.37 #134, 0.28 #2172, 0.28 #738), 013zdg (0.30 #7, 0.27 #139, 0.26 #609), 02m4yg (0.28 #2172, 0.17 #1515, 0.16 #2335), 01ysy9 (0.28 #2172, 0.17 #1515, 0.08 #153) >> Best rule #135 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 02bqy; >> query: (?x9071, 02h4rq6) <- fraternities_and_sororities(?x9071, ?x4348), institution(?x3437, ?x9071), contains(?x94, ?x9071), ?x3437 = 02_xgp2 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jpn8 institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.827 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #1802-033srr PRED entity: 033srr PRED relation: film! PRED expected values: 0klh7 => 130 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1405): 0p8r1 (0.29 #8905, 0.19 #29709, 0.10 #40111), 03kpvp (0.20 #2711, 0.08 #27676, 0.06 #54723), 0lpjn (0.20 #2558, 0.06 #23362, 0.06 #27523), 0147dk (0.15 #82, 0.05 #50013, 0.04 #22966), 016k6x (0.15 #889, 0.04 #9210, 0.03 #44576), 01kwsg (0.15 #837, 0.04 #23721, 0.03 #38282), 025j1t (0.15 #1075, 0.02 #17718, 0.01 #57247), 0169dl (0.13 #2480, 0.08 #27445, 0.07 #42007), 02xs5v (0.13 #3485, 0.06 #149798, 0.05 #32610), 059_gf (0.13 #3078, 0.06 #5158, 0.05 #13479) >> Best rule #8905 for best value: >> intensional similarity = 6 >> extensional distance = 22 >> proper extension: 02qm_f; 01xdxy; 016017; >> query: (?x3990, 0p8r1) <- film(?x5636, ?x3990), film(?x2156, ?x3990), genre(?x3990, ?x53), film_crew_role(?x3990, ?x137), ?x2156 = 01795t, ?x5636 = 054g1r >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #488 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 0pc62; 04gknr; 031t2d; 08rr3p; 03z20c; 0435vm; 034r25; 0h21v2; 01gwk3; 02cbg0; ... *> query: (?x3990, 0klh7) <- film_release_region(?x3990, ?x94), film_crew_role(?x3990, ?x1284), ?x1284 = 0ch6mp2, crewmember(?x3990, ?x666), genre(?x3990, ?x53), ?x666 = 0284n42 *> conf = 0.08 ranks of expected_values: 83 EVAL 033srr film! 0klh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 130.000 74.000 0.292 http://example.org/film/actor/film./film/performance/film #1801-0br1w PRED entity: 0br1w PRED relation: profession PRED expected values: 0kyk => 179 concepts (136 used for prediction) PRED predicted values (max 10 best out of 112): 02hrh1q (0.84 #8722, 0.82 #17284, 0.81 #1753), 0cbd2 (0.74 #13938, 0.70 #4357, 0.56 #1311), 02jknp (0.69 #7407, 0.68 #587, 0.67 #732), 0kyk (0.46 #4379, 0.41 #1913, 0.33 #9172), 02krf9 (0.38 #3215, 0.29 #4956, 0.28 #7860), 018gz8 (0.37 #5381, 0.33 #4511, 0.32 #2480), 0n1h (0.27 #881, 0.27 #13943, 0.26 #1316), 015h31 (0.27 #896, 0.26 #1331, 0.22 #2201), 05snw (0.22 #524, 0.04 #1539, 0.03 #6325), 0dgd_ (0.19 #754, 0.16 #609, 0.14 #1044) >> Best rule #8722 for best value: >> intensional similarity = 4 >> extensional distance = 251 >> proper extension: 0q9kd; 0184jc; 04bdxl; 01wbg84; 01qscs; 0p_pd; 0l8v5; 032xhg; 0h5g_; 03w1v2; ... >> query: (?x3806, 02hrh1q) <- award_winner(?x13130, ?x3806), religion(?x3806, ?x2694), location(?x3806, ?x3807), profession(?x3806, ?x319) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #4379 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 85 *> proper extension: 01gp_x; 080r3; 01v9724; 0c1fs; 0ldd; 02nygk; *> query: (?x3806, 0kyk) <- story_by(?x280, ?x3806), gender(?x3806, ?x231), profession(?x3806, ?x319), influenced_by(?x3806, ?x10275) *> conf = 0.46 ranks of expected_values: 4 EVAL 0br1w profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 179.000 136.000 0.838 http://example.org/people/person/profession #1800-025sf0_ PRED entity: 025sf0_ PRED relation: nutrient! PRED expected values: 0cxn2 => 58 concepts (55 used for prediction) PRED predicted values (max 10 best out of 7): 0cxn2 (0.90 #372, 0.90 #26, 0.90 #24), 06x4c (0.90 #26, 0.90 #24, 0.90 #18), 0dcfv (0.90 #26, 0.90 #24, 0.90 #18), 025tkqy (0.03 #333), 06jry (0.03 #333), 025s7j4 (0.03 #333), 01sh2 (0.03 #333) >> Best rule #372 for best value: >> intensional similarity = 103 >> extensional distance = 29 >> proper extension: 01w_3; >> query: (?x11592, 0cxn2) <- nutrient(?x10612, ?x11592), nutrient(?x9489, ?x11592), nutrient(?x9005, ?x11592), nutrient(?x7719, ?x11592), nutrient(?x6285, ?x11592), nutrient(?x6159, ?x11592), nutrient(?x6032, ?x11592), nutrient(?x5373, ?x11592), nutrient(?x1959, ?x11592), ?x6159 = 033cnk, nutrient(?x9489, ?x13944), nutrient(?x9489, ?x12902), nutrient(?x9489, ?x12083), nutrient(?x9489, ?x11784), nutrient(?x9489, ?x11758), nutrient(?x9489, ?x11409), nutrient(?x9489, ?x11270), nutrient(?x9489, ?x10709), nutrient(?x9489, ?x10195), nutrient(?x9489, ?x10098), nutrient(?x9489, ?x9949), nutrient(?x9489, ?x9915), nutrient(?x9489, ?x9840), nutrient(?x9489, ?x9733), nutrient(?x9489, ?x9619), nutrient(?x9489, ?x9490), nutrient(?x9489, ?x9426), nutrient(?x9489, ?x7894), nutrient(?x9489, ?x7720), nutrient(?x9489, ?x7431), nutrient(?x9489, ?x7364), nutrient(?x9489, ?x7362), nutrient(?x9489, ?x7219), nutrient(?x9489, ?x7135), nutrient(?x9489, ?x6586), nutrient(?x9489, ?x6033), nutrient(?x9489, ?x6026), nutrient(?x9489, ?x5549), nutrient(?x9489, ?x5526), nutrient(?x9489, ?x5451), nutrient(?x9489, ?x5010), nutrient(?x9489, ?x2702), nutrient(?x9489, ?x1304), nutrient(?x9489, ?x1258), ?x10709 = 0h1sz, ?x13944 = 0f4kp, ?x9426 = 0h1yy, nutrient(?x7719, ?x13126), nutrient(?x7719, ?x9795), nutrient(?x7719, ?x9436), nutrient(?x7719, ?x9365), nutrient(?x7719, ?x8487), nutrient(?x7719, ?x6286), nutrient(?x7719, ?x6192), nutrient(?x7719, ?x5337), nutrient(?x7719, ?x1960), ?x9619 = 0h1tg, ?x7135 = 025rsfk, ?x12902 = 0fzjh, ?x9733 = 0h1tz, ?x11270 = 02kc008, ?x6286 = 02y_3rf, ?x7431 = 09gwd, ?x11409 = 0h1yf, ?x11758 = 0q01m, ?x9840 = 02p0tjr, ?x6032 = 01nkt, ?x6192 = 06jry, ?x13126 = 02kc_w5, ?x1959 = 0f25w9, ?x9915 = 025tkqy, ?x7364 = 09gvd, ?x1258 = 0h1wg, ?x5526 = 09pbb, ?x9795 = 05v_8y, ?x1960 = 07hnp, ?x5451 = 05wvs, ?x5373 = 0971v, ?x9365 = 04k8n, nutrient(?x6285, ?x3901), ?x5549 = 025s7j4, ?x10195 = 0hkwr, ?x11784 = 07zqy, ?x6033 = 04zjxcz, ?x2702 = 0838f, ?x10098 = 0h1_c, ?x9490 = 0h1sg, ?x3901 = 0466p20, ?x5337 = 06x4c, ?x9949 = 02kd0rh, ?x6586 = 05gh50, ?x10612 = 0frq6, ?x1304 = 08lb68, ?x5010 = 0h1vz, ?x7894 = 0f4hc, ?x9005 = 04zpv, ?x7720 = 025s7x6, ?x12083 = 01n78x, ?x6026 = 025sf8g, ?x9436 = 025sqz8, ?x7362 = 02kc5rj, ?x8487 = 014yzm, ?x7219 = 0h1vg >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025sf0_ nutrient! 0cxn2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 55.000 0.903 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #1799-0407yj_ PRED entity: 0407yj_ PRED relation: language PRED expected values: 03_9r => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 29): 06nm1 (0.21 #65, 0.15 #292, 0.14 #464), 03_9r (0.14 #64, 0.11 #235, 0.08 #634), 04306rv (0.09 #286, 0.09 #458, 0.09 #629), 03k50 (0.07 #63, 0.02 #745, 0.02 #802), 0653m (0.07 #293, 0.06 #636, 0.06 #465), 06b_j (0.07 #646, 0.06 #189, 0.06 #303), 012w70 (0.06 #637, 0.06 #466, 0.05 #294), 01r2l (0.04 #305, 0.03 #477, 0.03 #648), 04h9h (0.03 #609, 0.03 #552, 0.02 #323), 032f6 (0.03 #222, 0.01 #394, 0.01 #565) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 02qm_f; 02c7k4; 01xdxy; >> query: (?x2933, 06nm1) <- nominated_for(?x400, ?x2933), language(?x2933, ?x90), film(?x3417, ?x2933), ?x3417 = 0p8r1 >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #64 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 02qm_f; 02c7k4; 01xdxy; *> query: (?x2933, 03_9r) <- nominated_for(?x400, ?x2933), language(?x2933, ?x90), film(?x3417, ?x2933), ?x3417 = 0p8r1 *> conf = 0.14 ranks of expected_values: 2 EVAL 0407yj_ language 03_9r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 59.000 59.000 0.214 http://example.org/film/film/language #1798-0g5b0q5 PRED entity: 0g5b0q5 PRED relation: ceremony! PRED expected values: 0drtkx => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 298): 0bfvd4 (0.50 #1097, 0.41 #1351, 0.33 #842), 0cjyzs (0.50 #1092, 0.41 #1346, 0.33 #837), 09qrn4 (0.50 #1181, 0.41 #1435, 0.33 #926), 09qv3c (0.50 #1053, 0.41 #1307, 0.33 #798), 0gkts9 (0.50 #1136, 0.41 #1390, 0.33 #881), 0bdw6t (0.50 #1094, 0.41 #1348, 0.33 #839), 027gs1_ (0.50 #1209, 0.35 #1463, 0.33 #954), 0fbtbt (0.50 #1176, 0.35 #1430, 0.33 #667), 0bdw1g (0.50 #1044, 0.35 #1298, 0.33 #535), 09qvf4 (0.50 #1164, 0.33 #909, 0.33 #655) >> Best rule #1097 for best value: >> intensional similarity = 7 >> extensional distance = 4 >> proper extension: 0gx_st; 02q690_; 027n06w; >> query: (?x1553, 0bfvd4) <- award_winner(?x1553, ?x5454), award_winner(?x1553, ?x5061), award_winner(?x1553, ?x3763), ?x3763 = 0c7t58, actor(?x5328, ?x5454), award_nominee(?x100, ?x5454), program_creator(?x5060, ?x5061) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1017 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 0hr3c8y; 0lp_cd3; 0gvstc3; 0hn821n; *> query: (?x1553, ?x68) <- award_winner(?x1553, ?x3763), award_winner(?x1553, ?x3223), honored_for(?x1553, ?x1988), honored_for(?x1553, ?x493), ?x493 = 080dwhx, award_nominee(?x3293, ?x3763), award_winner(?x3223, ?x902), nominated_for(?x68, ?x1988) *> conf = 0.33 ranks of expected_values: 58 EVAL 0g5b0q5 ceremony! 0drtkx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 49.000 49.000 0.500 http://example.org/award/award_category/winners./award/award_honor/ceremony #1797-09fqgj PRED entity: 09fqgj PRED relation: film! PRED expected values: 03h2d4 => 95 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1066): 09fb5 (0.17 #57, 0.04 #6276, 0.04 #8350), 015t7v (0.17 #893, 0.03 #116109, 0.03 #103670), 0686zv (0.17 #519, 0.03 #2592, 0.01 #10886), 01j5ws (0.17 #508, 0.03 #103670, 0.03 #85011), 06wm0z (0.17 #899, 0.03 #103670, 0.03 #85011), 016fjj (0.17 #629, 0.03 #103670, 0.02 #19290), 07r_dg (0.17 #1710, 0.02 #120257), 0q9kd (0.17 #4, 0.02 #10371, 0.02 #12445), 031k24 (0.17 #1400, 0.02 #11767, 0.02 #7619), 01vwllw (0.17 #542, 0.02 #10909, 0.02 #6761) >> Best rule #57 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0fzm0g; >> query: (?x10509, 09fb5) <- titles(?x811, ?x10509), film_crew_role(?x10509, ?x137), film(?x6278, ?x10509), country(?x10509, ?x94), ?x6278 = 0gx_p >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #25624 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 350 *> proper extension: 09sh8k; 06w99h3; 0gx1bnj; 0czyxs; 0gtv7pk; 0h1cdwq; 0dscrwf; 0bth54; 0fr63l; 08gsvw; ... *> query: (?x10509, 03h2d4) <- film_crew_role(?x10509, ?x2154), film(?x294, ?x10509), genre(?x10509, ?x53), ?x2154 = 01vx2h *> conf = 0.02 ranks of expected_values: 648 EVAL 09fqgj film! 03h2d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 95.000 58.000 0.167 http://example.org/film/actor/film./film/performance/film #1796-028dcg PRED entity: 028dcg PRED relation: institution PRED expected values: 08815 02s62q 07t90 0gl5_ 021996 03np_7 => 22 concepts (21 used for prediction) PRED predicted values (max 10 best out of 660): 08815 (0.86 #9065, 0.83 #7252, 0.83 #6647), 07wjk (0.83 #6711, 0.82 #6106, 0.79 #9129), 025v3k (0.82 #6167, 0.75 #6772, 0.75 #4958), 01bm_ (0.82 #6306, 0.75 #7516, 0.75 #6911), 0g8rj (0.82 #6230, 0.75 #6835, 0.75 #5021), 0gl5_ (0.82 #6304, 0.75 #6909, 0.75 #5095), 08qnnv (0.82 #6275, 0.75 #6880, 0.75 #5066), 06pwq (0.79 #9075, 0.75 #7262, 0.75 #6657), 02zd460 (0.77 #8643, 0.75 #9851, 0.75 #8039), 07tg4 (0.75 #7339, 0.75 #6734, 0.75 #4920) >> Best rule #9065 for best value: >> intensional similarity = 23 >> extensional distance = 12 >> proper extension: 013zdg; >> query: (?x8398, 08815) <- major_field_of_study(?x8398, ?x12637), major_field_of_study(?x8398, ?x373), student(?x8398, ?x516), institution(?x8398, ?x3439), major_field_of_study(?x1526, ?x373), student(?x373, ?x9153), ?x1526 = 0bkj86, student(?x3439, ?x7391), student(?x3439, ?x1984), major_field_of_study(?x3439, ?x254), taxonomy(?x12637, ?x939), contains(?x2020, ?x3439), celebrities_impersonated(?x3649, ?x7391), award(?x7391, ?x458), major_field_of_study(?x1103, ?x373), disciplines_or_subjects(?x277, ?x373), ?x1103 = 01k2wn, award_nominee(?x8473, ?x7391), award_winner(?x2779, ?x7391), politician(?x12007, ?x1984), profession(?x7391, ?x319), award(?x9153, ?x746), participant(?x9153, ?x2499) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 34, 84, 121, 495 EVAL 028dcg institution 03np_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 22.000 21.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 028dcg institution 021996 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 22.000 21.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 028dcg institution 0gl5_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 22.000 21.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 028dcg institution 07t90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 22.000 21.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 028dcg institution 02s62q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 22.000 21.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 028dcg institution 08815 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 21.000 0.857 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #1795-049n7 PRED entity: 049n7 PRED relation: team! PRED expected values: 02rsl1 017drs => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 43): 017drs (0.84 #1034, 0.83 #716, 0.82 #1168), 02s7tr (0.84 #1034, 0.83 #716, 0.82 #1168), 01sdzg (0.84 #1034, 0.83 #716, 0.82 #1168), 02sdk9v (0.70 #3140, 0.66 #3274, 0.65 #3184), 02_j1w (0.65 #3144, 0.64 #3278, 0.60 #3188), 02nzb8 (0.65 #3139, 0.63 #3273, 0.60 #3183), 02dwn9 (0.65 #2946, 0.57 #3272, 0.53 #1791), 02rsl1 (0.65 #2946, 0.57 #3272, 0.53 #1791), 02dwpf (0.65 #2946, 0.57 #3272, 0.53 #1791), 0dgrmp (0.64 #3142, 0.58 #3186, 0.53 #3276) >> Best rule #1034 for best value: >> intensional similarity = 11 >> extensional distance = 20 >> proper extension: 06wpc; >> query: (?x1160, ?x2010) <- position(?x1160, ?x5727), position(?x1160, ?x2010), draft(?x1160, ?x8499), draft(?x1160, ?x1633), ?x5727 = 02wszf, ?x8499 = 02r6gw6, draft(?x10939, ?x1633), draft(?x8111, ?x1633), ?x8111 = 07147, ?x10939 = 0x0d, school(?x1633, ?x3779) >> conf = 0.84 => this is the best rule for 3 predicted values ranks of expected_values: 1, 8 EVAL 049n7 team! 017drs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.838 http://example.org/sports/sports_position/players./sports/sports_team_roster/team EVAL 049n7 team! 02rsl1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 76.000 76.000 0.838 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #1794-05nyqk PRED entity: 05nyqk PRED relation: film_crew_role PRED expected values: 0dxtw => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 24): 02r96rf (0.81 #106, 0.77 #71, 0.76 #483), 01vx2h (0.46 #114, 0.39 #456, 0.39 #79), 0dxtw (0.46 #490, 0.44 #113, 0.44 #455), 01pvkk (0.30 #1285, 0.29 #596, 0.29 #837), 089fss (0.22 #109, 0.22 #74, 0.08 #590), 02rh1dz (0.19 #112, 0.15 #489, 0.14 #454), 0215hd (0.16 #497, 0.13 #120, 0.13 #601), 089g0h (0.14 #52, 0.13 #86, 0.13 #121), 01xy5l_ (0.13 #494, 0.13 #82, 0.13 #117), 0d2b38 (0.12 #58, 0.12 #92, 0.12 #504) >> Best rule #106 for best value: >> intensional similarity = 5 >> extensional distance = 133 >> proper extension: 0h95zbp; 03xj05; >> query: (?x9199, 02r96rf) <- film_crew_role(?x9199, ?x3197), film_crew_role(?x9199, ?x1171), genre(?x9199, ?x225), ?x3197 = 02ynfr, ?x1171 = 09vw2b7 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #490 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 349 *> proper extension: 07kb7vh; *> query: (?x9199, 0dxtw) <- film_crew_role(?x9199, ?x1171), film_crew_role(?x9199, ?x137), ?x137 = 09zzb8, produced_by(?x9199, ?x11113), ?x1171 = 09vw2b7 *> conf = 0.46 ranks of expected_values: 3 EVAL 05nyqk film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 83.000 0.815 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1793-02183k PRED entity: 02183k PRED relation: school_type PRED expected values: 05jxkf => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.61 #164, 0.60 #187, 0.59 #256), 07tf8 (0.40 #54, 0.32 #77, 0.25 #215), 05pcjw (0.33 #47, 0.28 #116, 0.27 #1105), 01rs41 (0.30 #1338, 0.30 #119, 0.30 #1108), 01_srz (0.13 #48, 0.08 #761, 0.07 #1336), 01y64 (0.10 #126, 0.10 #287, 0.03 #540), 01jlsn (0.07 #131, 0.06 #292, 0.03 #1557), 0m4mb (0.05 #125, 0.05 #286, 0.03 #1551), 0257h9 (0.05 #134, 0.04 #295, 0.02 #778), 0bpgx (0.05 #135, 0.02 #296, 0.02 #319) >> Best rule #164 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 04_j5s; >> query: (?x3416, 05jxkf) <- institution(?x7636, ?x3416), institution(?x865, ?x3416), ?x7636 = 01rr_d, institution(?x865, ?x9676), ?x9676 = 02f4s3 >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02183k school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 143.000 0.607 http://example.org/education/educational_institution/school_type #1792-07fpm3 PRED entity: 07fpm3 PRED relation: film PRED expected values: 0d6_s => 94 concepts (53 used for prediction) PRED predicted values (max 10 best out of 216): 0ddd0gc (0.41 #69717, 0.39 #28599, 0.38 #46480), 0524b41 (0.41 #69717, 0.39 #28599, 0.38 #46480), 080dfr7 (0.33 #3450), 01bl7g (0.20 #948, 0.08 #4522, 0.03 #42904), 05c9zr (0.17 #2474, 0.08 #4261), 011yph (0.17 #3659, 0.07 #5362, 0.04 #92957), 05z43v (0.17 #4928, 0.03 #42904, 0.03 #94746), 01vw8k (0.17 #4227, 0.03 #42904, 0.03 #94746), 01shy7 (0.17 #2210, 0.02 #20086, 0.02 #59414), 020bv3 (0.17 #2105, 0.02 #39647, 0.02 #41434) >> Best rule #69717 for best value: >> intensional similarity = 3 >> extensional distance = 1444 >> proper extension: 01g969; >> query: (?x3717, ?x1434) <- award_nominee(?x3717, ?x4015), nominated_for(?x3717, ?x1434), location(?x4015, ?x1227) >> conf = 0.41 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 07fpm3 film 0d6_s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 53.000 0.406 http://example.org/film/actor/film./film/performance/film #1791-05qfh PRED entity: 05qfh PRED relation: major_field_of_study! PRED expected values: 05wkw => 81 concepts (74 used for prediction) PRED predicted values (max 10 best out of 117): 05wkw (0.83 #3000, 0.82 #3718, 0.80 #2917), 0mg1w (0.83 #3000, 0.82 #3718, 0.80 #2917), 05qfh (0.60 #570, 0.60 #492, 0.55 #1283), 064_8sq (0.60 #577, 0.50 #737, 0.50 #265), 03g3w (0.57 #806, 0.40 #566, 0.36 #1279), 06bvp (0.43 #841, 0.14 #627, 0.14 #626), 01400v (0.40 #614, 0.33 #774, 0.33 #694), 04gb7 (0.40 #575, 0.33 #735, 0.25 #263), 0_jm (0.40 #586, 0.33 #746, 0.25 #274), 05qjt (0.33 #84, 0.33 #5, 0.29 #792) >> Best rule #3000 for best value: >> intensional similarity = 4 >> extensional distance = 51 >> proper extension: 0j0k; >> query: (?x3490, ?x7070) <- major_field_of_study(?x8221, ?x3490), major_field_of_study(?x3490, ?x7070), major_field_of_study(?x742, ?x8221), taxonomy(?x3490, ?x939) >> conf = 0.83 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 05qfh major_field_of_study! 05wkw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 74.000 0.831 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #1790-0gqng PRED entity: 0gqng PRED relation: award! PRED expected values: 06b_0 027jq2 => 53 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2418): 01q4qv (0.79 #53958, 0.20 #4244, 0.17 #14363), 033rq (0.79 #53958, 0.17 #15967, 0.17 #12595), 0h1p (0.79 #53958, 0.17 #14033, 0.17 #10661), 03y3dk (0.79 #53958, 0.17 #50585, 0.11 #30349), 0c12h (0.42 #15316, 0.36 #18687, 0.33 #11944), 05ldnp (0.40 #4267, 0.33 #14386, 0.29 #17757), 02kxbwx (0.40 #3551, 0.33 #13670, 0.29 #17041), 014zcr (0.40 #3423, 0.29 #16913, 0.25 #13542), 081lh (0.40 #3604, 0.27 #6977, 0.21 #17094), 026670 (0.40 #6143, 0.27 #9516, 0.17 #16262) >> Best rule #53958 for best value: >> intensional similarity = 4 >> extensional distance = 113 >> proper extension: 06196; >> query: (?x77, ?x2086) <- ceremony(?x77, ?x78), award(?x303, ?x77), award(?x1872, ?x77), award_winner(?x77, ?x2086) >> conf = 0.79 => this is the best rule for 4 predicted values *> Best rule #15719 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 10 *> proper extension: 018wng; 0gr07; *> query: (?x77, 06b_0) <- ceremony(?x77, ?x5723), ceremony(?x77, ?x5349), ceremony(?x77, ?x3173), ceremony(?x77, ?x1998), ?x1998 = 073h1t, ?x5349 = 02jp5r, ?x5723 = 0fk0xk, ?x3173 = 0bzk2h *> conf = 0.25 ranks of expected_values: 52 EVAL 0gqng award! 027jq2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 53.000 17.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqng award! 06b_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 53.000 17.000 0.786 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1789-0j3b PRED entity: 0j3b PRED relation: locations! PRED expected values: 0jnh => 137 concepts (12 used for prediction) PRED predicted values (max 10 best out of 40): 0k4y6 (0.23 #573, 0.14 #830, 0.12 #1332), 01w1sx (0.15 #590, 0.14 #847, 0.12 #1349), 01gqg3 (0.15 #584, 0.14 #841, 0.12 #1343), 0845v (0.15 #512, 0.14 #769, 0.12 #1271), 0jnh (0.15 #593, 0.12 #1352, 0.10 #850), 0cm2xh (0.10 #801, 0.08 #1303, 0.08 #1178), 05t2fh4 (0.08 #619, 0.08 #493, 0.06 #748), 01y998 (0.08 #563, 0.08 #437, 0.06 #692), 024jvz (0.08 #448, 0.06 #703, 0.04 #1083), 022840 (0.08 #435, 0.06 #690, 0.04 #1070) >> Best rule #573 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 02j7k; 017jq; >> query: (?x1144, 0k4y6) <- contains(?x1144, ?x5411), adjoins(?x94, ?x1144), form_of_government(?x5411, ?x6065), adjoins(?x1144, ?x6428) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #593 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 02j7k; 017jq; *> query: (?x1144, 0jnh) <- contains(?x1144, ?x5411), adjoins(?x94, ?x1144), form_of_government(?x5411, ?x6065), adjoins(?x1144, ?x6428) *> conf = 0.15 ranks of expected_values: 5 EVAL 0j3b locations! 0jnh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 137.000 12.000 0.231 http://example.org/time/event/locations #1788-09lxtg PRED entity: 09lxtg PRED relation: service_location! PRED expected values: 077w0b => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 137): 018mxj (0.27 #148, 0.20 #285, 0.18 #422), 0k9ts (0.22 #230, 0.18 #367, 0.16 #504), 01c6k4 (0.21 #829, 0.20 #281, 0.19 #144), 064f29 (0.16 #198, 0.16 #335, 0.12 #472), 07zl6m (0.16 #271, 0.14 #545, 0.14 #408), 077w0b (0.16 #204, 0.14 #341, 0.12 #478), 0cv9b (0.16 #286, 0.16 #834, 0.14 #423), 05b5c (0.14 #403, 0.14 #266, 0.12 #540), 0p4wb (0.14 #284, 0.14 #147, 0.12 #421), 04sv4 (0.14 #359, 0.14 #222, 0.10 #907) >> Best rule #148 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 02j71; >> query: (?x4569, 018mxj) <- adjustment_currency(?x4569, ?x170), ?x170 = 09nqf, administrative_parent(?x11662, ?x4569) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #204 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 02j71; *> query: (?x4569, 077w0b) <- adjustment_currency(?x4569, ?x170), ?x170 = 09nqf, administrative_parent(?x11662, ?x4569) *> conf = 0.16 ranks of expected_values: 6 EVAL 09lxtg service_location! 077w0b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 90.000 0.270 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #1787-04g2jz2 PRED entity: 04g2jz2 PRED relation: award! PRED expected values: 02z6l5f 013w7j => 44 concepts (5 used for prediction) PRED predicted values (max 10 best out of 3775): 014zcr (0.24 #13480, 0.19 #3422, 0.17 #6793), 0bxtg (0.24 #13480, 0.06 #13583, 0.05 #3473), 01z_g6 (0.24 #13480, 0.04 #4850, 0.04 #1480), 011zd3 (0.24 #13480, 0.04 #3967, 0.03 #7338), 02g5h5 (0.24 #13480, 0.03 #4424, 0.02 #7795), 023s8 (0.24 #13480, 0.03 #6288, 0.02 #9659), 01tvz5j (0.24 #13480, 0.03 #3448, 0.02 #6819), 01qr1_ (0.24 #13480, 0.02 #7719, 0.02 #4348), 03kcyd (0.24 #13480, 0.02 #6073, 0.02 #2703), 01t6xz (0.24 #13480, 0.02 #5253, 0.02 #1883) >> Best rule #13480 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 058vy5; >> query: (?x4838, ?x2307) <- award_winner(?x4838, ?x9451), award_winner(?x2307, ?x9451), type_of_union(?x9451, ?x566), category_of(?x4838, ?x2758) >> conf = 0.24 => this is the best rule for 15 predicted values *> Best rule #16849 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 158 *> proper extension: 047xyn; *> query: (?x4838, ?x65) <- award_winner(?x4838, ?x9451), executive_produced_by(?x1994, ?x9451), profession(?x9451, ?x319), profession(?x65, ?x319) *> conf = 0.01 ranks of expected_values: 3213, 3343 EVAL 04g2jz2 award! 013w7j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 5.000 0.239 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04g2jz2 award! 02z6l5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 5.000 0.239 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1786-06q07 PRED entity: 06q07 PRED relation: service_location PRED expected values: 0d060g => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 116): 0d060g (0.34 #5460, 0.34 #7570, 0.33 #6424), 0f8l9c (0.33 #400, 0.20 #1355, 0.14 #5473), 0chghy (0.33 #391, 0.17 #5464, 0.16 #5272), 059j2 (0.33 #405, 0.17 #2700, 0.12 #4808), 0345h (0.33 #406, 0.15 #7589, 0.14 #7298), 07ssc (0.25 #1924, 0.24 #8155, 0.22 #4412), 05v8c (0.25 #968, 0.22 #1813, 0.20 #1158), 06t2t (0.25 #989, 0.22 #1813, 0.20 #1179), 09pmkv (0.25 #975, 0.22 #1813, 0.20 #1165), 07dfk (0.19 #7564, 0.16 #8719, 0.14 #9682) >> Best rule #5460 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 01xdn1; 0178g; >> query: (?x8125, 0d060g) <- service_language(?x8125, ?x254), organization(?x4682, ?x8125), list(?x8125, ?x5997), category(?x8125, ?x134), ?x5997 = 04k4rt >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06q07 service_location 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 199.000 199.000 0.345 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #1785-04bp0l PRED entity: 04bp0l PRED relation: country_of_origin PRED expected values: 09c7w0 => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 9): 09c7w0 (0.84 #212, 0.80 #124, 0.79 #146), 06f32 (0.19 #90), 0f8l9c (0.19 #90), 07ssc (0.15 #43, 0.14 #54, 0.09 #289), 03_3d (0.09 #272, 0.08 #283), 0d060g (0.03 #149, 0.03 #273, 0.03 #284), 03rt9 (0.02 #153), 02jx1 (0.01 #280, 0.01 #291), 05v8c (0.01 #279, 0.01 #290) >> Best rule #212 for best value: >> intensional similarity = 5 >> extensional distance = 191 >> proper extension: 090s_0; 0g60z; 02_1q9; 080dwhx; 06cs95; 02_1rq; 03kq98; 072kp; 039fgy; 02py4c8; ... >> query: (?x14684, 09c7w0) <- genre(?x14684, ?x5728), nominated_for(?x6678, ?x14684), genre(?x10447, ?x5728), honored_for(?x2213, ?x10447), award_winner(?x10447, ?x1285) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bp0l country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 56.000 0.839 http://example.org/tv/tv_program/country_of_origin #1784-05k79 PRED entity: 05k79 PRED relation: group! PRED expected values: 0l14md => 90 concepts (50 used for prediction) PRED predicted values (max 10 best out of 97): 0l14md (0.62 #2696, 0.60 #2024, 0.59 #2780), 028tv0 (0.40 #1694, 0.38 #2702, 0.38 #1778), 05r5c (0.33 #7, 0.29 #1689, 0.28 #1773), 013y1f (0.33 #26, 0.17 #1624, 0.13 #446), 0239kh (0.33 #22, 0.07 #442, 0.06 #610), 03qjg (0.28 #1643, 0.27 #465, 0.26 #2483), 01vj9c (0.27 #2955, 0.26 #1611, 0.26 #2703), 0g2dz (0.25 #193, 0.20 #277, 0.09 #361), 0cfdd (0.25 #242, 0.20 #326, 0.09 #410), 07y_7 (0.21 #1600, 0.12 #2020, 0.12 #2692) >> Best rule #2696 for best value: >> intensional similarity = 6 >> extensional distance = 154 >> proper extension: 01qqwp9; 0qmpd; 016lj_; >> query: (?x2005, 0l14md) <- artists(?x2491, ?x2005), group(?x227, ?x2005), artists(?x2491, ?x7570), artists(?x2491, ?x1838), ?x1838 = 012zng, origin(?x7570, ?x3501) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05k79 group! 0l14md CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 50.000 0.622 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1783-020_4z PRED entity: 020_4z PRED relation: artists! PRED expected values: 0xhtw 0g293 => 127 concepts (61 used for prediction) PRED predicted values (max 10 best out of 291): 0xhtw (0.80 #9531, 0.55 #1800, 0.53 #2396), 064t9 (0.78 #16665, 0.53 #3581, 0.51 #5663), 0glt670 (0.46 #11037, 0.30 #16689, 0.28 #12823), 05bt6j (0.43 #931, 0.41 #1229, 0.37 #1526), 08jyyk (0.40 #61, 0.38 #359, 0.18 #3334), 017_qw (0.37 #10464, 0.36 #11653, 0.09 #14925), 0cx7f (0.31 #427, 0.20 #129, 0.14 #9051), 01lyv (0.29 #624, 0.26 #3896, 0.23 #17873), 025sc50 (0.29 #3614, 0.29 #5696, 0.28 #16698), 016jny (0.25 #1882, 0.25 #2478, 0.19 #6046) >> Best rule #9531 for best value: >> intensional similarity = 7 >> extensional distance = 210 >> proper extension: 02t3ln; >> query: (?x10437, 0xhtw) <- artists(?x7083, ?x10437), artists(?x7083, ?x5623), artists(?x7083, ?x4957), artists(?x7083, ?x3403), ?x3403 = 02qwg, ?x5623 = 01vsyg9, ?x4957 = 0g_g2 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 167 EVAL 020_4z artists! 0g293 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 127.000 61.000 0.802 http://example.org/music/genre/artists EVAL 020_4z artists! 0xhtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 61.000 0.802 http://example.org/music/genre/artists #1782-01p0w_ PRED entity: 01p0w_ PRED relation: diet PRED expected values: 07_hy => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 2): 07_jd (0.19 #7, 0.17 #3, 0.14 #13), 07_hy (0.17 #4, 0.12 #8, 0.11 #6) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 03h_fqv; 015076; >> query: (?x12422, 07_jd) <- award(?x12422, ?x1565), friend(?x12422, ?x7571), role(?x12422, ?x227), instrumentalists(?x212, ?x12422) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #4 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 018x3; *> query: (?x12422, 07_hy) <- award(?x12422, ?x1565), award_nominee(?x12422, ?x7053), group(?x12422, ?x10561), ?x1565 = 01c4_6 *> conf = 0.17 ranks of expected_values: 2 EVAL 01p0w_ diet 07_hy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 150.000 150.000 0.188 http://example.org/base/eating/practicer_of_diet/diet #1781-0psss PRED entity: 0psss PRED relation: award PRED expected values: 07h0cl => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 287): 09sb52 (0.38 #12191, 0.37 #10976, 0.33 #19482), 01by1l (0.36 #112, 0.29 #4162, 0.28 #10642), 03qbh5 (0.36 #206, 0.26 #4256, 0.20 #7496), 054krc (0.36 #2517, 0.33 #3732, 0.27 #5352), 01bgqh (0.30 #4093, 0.24 #7333, 0.23 #3283), 0l8z1 (0.29 #2493, 0.29 #3708, 0.20 #5328), 054ks3 (0.28 #4597, 0.24 #2572, 0.23 #5407), 02qvyrt (0.27 #2557, 0.24 #3772, 0.22 #5392), 0c4z8 (0.27 #71, 0.21 #4526, 0.20 #7361), 02f777 (0.27 #311, 0.10 #4361, 0.07 #5171) >> Best rule #12191 for best value: >> intensional similarity = 3 >> extensional distance = 746 >> proper extension: 01sl1q; 044mz_; 07nznf; 0q9kd; 0184jc; 04bdxl; 02s2ft; 05vsxz; 0grwj; 05bnp0; ... >> query: (?x3280, 09sb52) <- award_nominee(?x3280, ?x2443), award_winner(?x1490, ?x3280), film(?x3280, ?x4067) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #9082 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 385 *> proper extension: 01wc7p; 03kxp7; 04pp9s; 07k2p6; 0mbs8; 06r3p2; *> query: (?x3280, 07h0cl) <- award_winner(?x1490, ?x3280), gender(?x3280, ?x514), ?x514 = 02zsn *> conf = 0.03 ranks of expected_values: 207 EVAL 0psss award 07h0cl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 104.000 104.000 0.384 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1780-01x6v6 PRED entity: 01x6v6 PRED relation: music! PRED expected values: 050gkf => 121 concepts (85 used for prediction) PRED predicted values (max 10 best out of 685): 01y9r2 (0.74 #13962, 0.13 #25929, 0.07 #73816), 03bxp5 (0.74 #13962, 0.13 #25929, 0.07 #73816), 02vxq9m (0.42 #14960, 0.11 #23934, 0.06 #41884), 01s7w3 (0.06 #2853, 0.05 #8837, 0.04 #6843), 02rrfzf (0.04 #13285, 0.04 #9297, 0.04 #11291), 01_1pv (0.04 #2209, 0.04 #3206, 0.04 #4204), 07bzz7 (0.04 #2516, 0.03 #6506, 0.03 #8500), 03_gz8 (0.04 #2639, 0.02 #6629, 0.02 #8623), 03h3x5 (0.04 #2251, 0.02 #8235, 0.02 #9232), 033g4d (0.04 #2104, 0.01 #18060, 0.01 #6094) >> Best rule #13962 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 06cv1; 02rgz4; 01nqfh_; 0p5mw; 0b82vw; 04zwjd; 0kvrb; 0bs1yy; 01l9v7n; 02bh9; ... >> query: (?x6783, ?x6199) <- profession(?x6783, ?x563), music(?x385, ?x6783), nominated_for(?x6783, ?x6199) >> conf = 0.74 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01x6v6 music! 050gkf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 121.000 85.000 0.737 http://example.org/film/film/music #1779-02qyp19 PRED entity: 02qyp19 PRED relation: nominated_for PRED expected values: 011yph 09q5w2 02qr69m 047d21r 07j94 016ks5 011yhm 02yy9r => 71 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1393): 01hv3t (0.79 #9050, 0.79 #9051, 0.79 #4524), 0f4_l (0.79 #9050, 0.79 #9051, 0.79 #4524), 05sy_5 (0.79 #9050, 0.79 #9051, 0.79 #4524), 0gwjw0c (0.79 #9050, 0.79 #9051, 0.79 #4524), 011x_4 (0.79 #9050, 0.79 #9051, 0.79 #4524), 02pjc1h (0.79 #9050, 0.79 #9051, 0.79 #4524), 07024 (0.67 #3421, 0.63 #9457, 0.50 #7947), 05hjnw (0.67 #9769, 0.62 #5241, 0.42 #3733), 0k4p0 (0.67 #3849, 0.46 #5357, 0.44 #9885), 0jqj5 (0.67 #3767, 0.45 #8293, 0.44 #9803) >> Best rule #9050 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 05ztjjw; >> query: (?x68, ?x306) <- award(?x10732, ?x68), award(?x306, ?x68), nominated_for(?x68, ?x6899), nominated_for(?x2374, ?x10732), ?x6899 = 04lhc4 >> conf = 0.79 => this is the best rule for 6 predicted values *> Best rule #8511 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 05ztjjw; *> query: (?x68, 011yhm) <- award(?x10732, ?x68), nominated_for(?x68, ?x6899), nominated_for(?x2374, ?x10732), ?x6899 = 04lhc4 *> conf = 0.65 ranks of expected_values: 13, 22, 25, 26, 84, 91, 212, 234 EVAL 02qyp19 nominated_for 02yy9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 71.000 29.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyp19 nominated_for 011yhm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 71.000 29.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyp19 nominated_for 016ks5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 71.000 29.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyp19 nominated_for 07j94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 71.000 29.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyp19 nominated_for 047d21r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 71.000 29.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyp19 nominated_for 02qr69m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 71.000 29.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyp19 nominated_for 09q5w2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 71.000 29.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qyp19 nominated_for 011yph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 71.000 29.000 0.795 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1778-09bw4_ PRED entity: 09bw4_ PRED relation: film_crew_role PRED expected values: 09zzb8 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 26): 09zzb8 (0.79 #1486, 0.77 #1909, 0.76 #1416), 0dxtw (0.50 #492, 0.49 #527, 0.42 #1495), 01pvkk (0.33 #528, 0.32 #2442, 0.31 #493), 02rh1dz (0.21 #526, 0.19 #491, 0.17 #282), 015h31 (0.20 #525, 0.18 #490, 0.15 #281), 0d2b38 (0.19 #506, 0.18 #541, 0.15 #297), 0215hd (0.18 #499, 0.18 #534, 0.17 #1432), 01xy5l_ (0.18 #81, 0.18 #115, 0.16 #530), 089g0h (0.17 #500, 0.17 #535, 0.14 #86), 02_n3z (0.14 #104, 0.13 #70, 0.11 #2) >> Best rule #1486 for best value: >> intensional similarity = 5 >> extensional distance = 584 >> proper extension: 07w8fz; 0g54xkt; 0bs5k8r; 02z0f6l; 0dpl44; 02chhq; 07p12s; >> query: (?x8658, 09zzb8) <- genre(?x8658, ?x53), film_crew_role(?x8658, ?x1171), film(?x2837, ?x8658), ?x1171 = 09vw2b7, film_release_distribution_medium(?x8658, ?x81) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09bw4_ film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.790 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1777-06wrt PRED entity: 06wrt PRED relation: sports! PRED expected values: 0ldqf => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 22): 0jkvj (0.80 #20, 0.79 #365, 0.76 #643), 06sks6 (0.80 #20, 0.75 #213, 0.73 #106), 0kbws (0.62 #344, 0.60 #780, 0.59 #237), 0ldqf (0.57 #293, 0.56 #21, 0.50 #336), 0c_tl (0.56 #21, 0.50 #222, 0.46 #214), 018wrk (0.56 #21, 0.50 #301, 0.46 #214), 09n48 (0.56 #21, 0.46 #214, 0.43 #321), 018ctl (0.56 #21, 0.46 #214, 0.43 #321), 0swff (0.56 #21, 0.46 #214, 0.43 #321), 0kbvv (0.56 #21, 0.46 #214, 0.43 #321) >> Best rule #20 for best value: >> intensional similarity = 56 >> extensional distance = 1 >> proper extension: 0bynt; >> query: (?x2315, ?x391) <- sports(?x2369, ?x2315), sports(?x2043, ?x2315), sports(?x775, ?x2315), sports(?x584, ?x2315), sports(?x391, ?x2315), country(?x2315, ?x9455), country(?x2315, ?x8958), country(?x2315, ?x7747), country(?x2315, ?x4743), country(?x2315, ?x4521), country(?x2315, ?x3730), country(?x2315, ?x2316), country(?x2315, ?x1917), country(?x2315, ?x1558), country(?x2315, ?x1453), country(?x2315, ?x1355), country(?x2315, ?x1003), country(?x2315, ?x789), country(?x2315, ?x583), country(?x2315, ?x512), country(?x2315, ?x87), ?x9455 = 0jt3tjf, ?x1558 = 01mjq, ?x87 = 05r4w, ?x512 = 07ssc, ?x4743 = 03spz, ?x584 = 0l98s, ?x8958 = 01ppq, ?x1355 = 0h7x, ?x2316 = 06t2t, ?x775 = 0l998, ?x1453 = 06qd3, ?x1917 = 01p1v, ?x3730 = 03shp, film_release_region(?x3757, ?x583), film_release_region(?x2896, ?x583), film_release_region(?x1915, ?x583), film_release_region(?x1456, ?x583), film_release_region(?x607, ?x583), film_release_region(?x80, ?x583), ?x1003 = 03gj2, ?x789 = 0f8l9c, ?x1456 = 0cz8mkh, olympics(?x583, ?x1931), ?x4521 = 07fj_, countries_spoken_in(?x90, ?x583), ?x7747 = 07f1x, ?x2896 = 0645k5, jurisdiction_of_office(?x182, ?x583), ?x607 = 02x3lt7, nationality(?x6390, ?x583), ?x2369 = 0lbbj, ?x2043 = 0lv1x, ?x1915 = 0fq7dv_, ?x80 = 0b76d_m, ?x3757 = 02vr3gz >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #293 for first EXPECTED value: *> intensional similarity = 51 *> extensional distance = 5 *> proper extension: 01hp22; *> query: (?x2315, 0ldqf) <- sports(?x3971, ?x2315), sports(?x584, ?x2315), country(?x2315, ?x9455), country(?x2315, ?x3912), country(?x2315, ?x3730), country(?x2315, ?x2629), country(?x2315, ?x1892), country(?x2315, ?x608), country(?x2315, ?x252), ?x252 = 03_3d, countries_within(?x6956, ?x9455), ?x608 = 02k54, ?x1892 = 02vzc, film_release_region(?x8471, ?x2629), film_release_region(?x8292, ?x2629), film_release_region(?x6684, ?x2629), film_release_region(?x6621, ?x2629), film_release_region(?x5704, ?x2629), film_release_region(?x4707, ?x2629), film_release_region(?x4610, ?x2629), film_release_region(?x4518, ?x2629), film_release_region(?x4336, ?x2629), film_release_region(?x3748, ?x2629), film_release_region(?x2598, ?x2629), film_release_region(?x1724, ?x2629), film_release_region(?x1642, ?x2629), film_release_region(?x1173, ?x2629), administrative_area_type(?x3730, ?x2792), contains(?x3730, ?x5237), ?x4518 = 0hgnl3t, participating_countries(?x418, ?x3730), ?x1173 = 0872p_c, ?x4707 = 02xbyr, administrative_parent(?x12372, ?x2629), ?x1642 = 0bq8tmw, ?x8471 = 0cp0t91, ?x4336 = 0bpm4yw, ?x2598 = 07f_7h, ?x3748 = 05zlld0, ?x8292 = 0cmf0m0, ?x6684 = 07pd_j, ?x1724 = 02r8hh_, adjoins(?x12778, ?x3912), ?x6621 = 0h63gl9, ?x5704 = 0h95zbp, country(?x1352, ?x3730), ?x4610 = 017jd9, contains(?x6304, ?x2629), ?x584 = 0l98s, ?x1352 = 0w0d, ?x3971 = 0jhn7 *> conf = 0.57 ranks of expected_values: 4 EVAL 06wrt sports! 0ldqf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 37.000 37.000 0.800 http://example.org/user/jg/default_domain/olympic_games/sports #1776-07t21 PRED entity: 07t21 PRED relation: olympics PRED expected values: 0kbvb => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 38): 0jdk_ (0.78 #543, 0.74 #358, 0.71 #469), 0kbvb (0.74 #453, 0.73 #379, 0.72 #527), 09n48 (0.66 #2123, 0.52 #1714, 0.43 #2940), 0l6m5 (0.65 #382, 0.61 #530, 0.61 #456), 0lgxj (0.60 #24, 0.58 #211, 0.58 #470), 0l6ny (0.57 #381, 0.55 #455, 0.54 #529), 0l6mp (0.54 #352, 0.53 #463, 0.51 #389), 0ldqf (0.54 #367, 0.47 #478, 0.46 #404), 018ctl (0.52 #1714, 0.50 #306, 0.50 #8), 0lbd9 (0.51 #363, 0.50 #474, 0.50 #28) >> Best rule #543 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 03_r3; 06qd3; 03rj0; 06t2t; 06f32; >> query: (?x1471, 0jdk_) <- country(?x7108, ?x1471), film_release_region(?x124, ?x1471), ?x7108 = 0194d >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #453 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 05v8c; 07twz; *> query: (?x1471, 0kbvb) <- country(?x150, ?x1471), film_release_region(?x664, ?x1471), ?x664 = 0401sg *> conf = 0.74 ranks of expected_values: 2 EVAL 07t21 olympics 0kbvb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 154.000 154.000 0.783 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #1775-0190_q PRED entity: 0190_q PRED relation: artists PRED expected values: 04kjrv 01vs4f3 => 62 concepts (31 used for prediction) PRED predicted values (max 10 best out of 980): 0p76z (0.60 #6290, 0.57 #7366, 0.50 #4139), 02cw1m (0.60 #6254, 0.57 #8407, 0.50 #4103), 01386_ (0.60 #5959, 0.50 #3808, 0.43 #8112), 01vng3b (0.60 #5940, 0.50 #3789, 0.43 #8093), 01vsy3q (0.60 #5819, 0.50 #3668, 0.43 #7972), 07bzp (0.60 #5944, 0.50 #3793, 0.43 #8097), 01pny5 (0.60 #6430, 0.50 #4279, 0.43 #8583), 0cfgd (0.60 #6376, 0.50 #4225, 0.43 #8529), 0161sp (0.60 #5616, 0.50 #3465, 0.43 #7769), 06nv27 (0.60 #5854, 0.50 #3703, 0.43 #8007) >> Best rule #6290 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0xhtw; >> query: (?x2808, 0p76z) <- artists(?x2808, ?x7810), artist(?x2931, ?x7810), award(?x7810, ?x2634), parent_genre(?x13553, ?x2808), ?x13553 = 0b_6yv, ?x2931 = 03rhqg, award_nominee(?x7810, ?x5618) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2790 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0hdf8; *> query: (?x2808, 04kjrv) <- artists(?x2808, ?x9841), artists(?x2808, ?x8152), artists(?x2808, ?x7810), ?x7810 = 0187x8, parent_genre(?x497, ?x2808), ?x8152 = 04m2zj, ?x9841 = 02ndj5 *> conf = 0.50 ranks of expected_values: 22, 42 EVAL 0190_q artists 01vs4f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 62.000 31.000 0.600 http://example.org/music/genre/artists EVAL 0190_q artists 04kjrv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 62.000 31.000 0.600 http://example.org/music/genre/artists #1774-059kh PRED entity: 059kh PRED relation: parent_genre! PRED expected values: 05y8n7 => 76 concepts (16 used for prediction) PRED predicted values (max 10 best out of 203): 059kh (0.60 #2904, 0.60 #1338, 0.60 #1079), 03mb9 (0.60 #1900, 0.50 #340, 0.30 #2944), 06cp5 (0.60 #1370, 0.50 #591, 0.30 #2936), 0g_bh (0.56 #2708, 0.45 #3489, 0.40 #1662), 016_nr (0.50 #320, 0.40 #2924, 0.40 #1880), 0pm85 (0.50 #647, 0.40 #1426, 0.40 #1167), 01h0kx (0.50 #644, 0.40 #1423, 0.40 #1164), 0xjl2 (0.50 #556, 0.40 #1335, 0.33 #2378), 06hzq3 (0.50 #629, 0.40 #1408, 0.30 #2974), 08cg36 (0.50 #754, 0.40 #1533, 0.30 #3099) >> Best rule #2904 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 06j6l; >> query: (?x3370, 059kh) <- artists(?x3370, ?x11749), artists(?x3370, ?x9407), ?x9407 = 024qwq, parent_genre(?x2542, ?x3370), parent_genre(?x3370, ?x671), award(?x11749, ?x1389), artists(?x671, ?x211) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #608 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 05r6t; *> query: (?x3370, 05y8n7) <- artists(?x3370, ?x9407), artists(?x3370, ?x9241), artists(?x3370, ?x3856), artists(?x3370, ?x3410), ?x9407 = 024qwq, parent_genre(?x2542, ?x3370), ?x9241 = 01w5gg6, award_nominee(?x3856, ?x3290), nominated_for(?x3410, ?x708) *> conf = 0.25 ranks of expected_values: 80 EVAL 059kh parent_genre! 05y8n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 76.000 16.000 0.600 http://example.org/music/genre/parent_genre #1773-0kvt9 PRED entity: 0kvt9 PRED relation: county! PRED expected values: 0lfyx => 138 concepts (65 used for prediction) PRED predicted values (max 10 best out of 138): 0r89d (0.25 #192, 0.20 #497, 0.05 #800), 0r3tq (0.20 #506, 0.05 #809, 0.03 #1113), 0r3wm (0.20 #487, 0.05 #790, 0.03 #1094), 0r3tb (0.20 #444, 0.05 #747, 0.03 #1051), 0r111 (0.05 #849, 0.03 #1153, 0.03 #1456), 0r0ss (0.05 #845, 0.03 #1149, 0.03 #1452), 0q_xk (0.05 #750, 0.03 #1054, 0.03 #1357), 0r0f7 (0.05 #749, 0.03 #1053, 0.03 #1356), 0nbwf (0.05 #748, 0.03 #1052, 0.03 #1355), 0k_q_ (0.05 #636, 0.03 #940, 0.03 #1243) >> Best rule #192 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0d060g; >> query: (?x9887, 0r89d) <- adjoins(?x2949, ?x9887), contains(?x94, ?x9887), contains(?x2949, ?x13255), ?x13255 = 0r02m >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0kvt9 county! 0lfyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 65.000 0.250 http://example.org/location/hud_county_place/county #1772-015g_7 PRED entity: 015g_7 PRED relation: type_of_union PRED expected values: 04ztj => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.94 #308, 0.90 #64, 0.90 #84) >> Best rule #308 for best value: >> intensional similarity = 2 >> extensional distance = 3032 >> proper extension: 02s2ft; 079vf; 05d7rk; 028q6; 04qvl7; 01k7d9; 0337vz; 0cb77r; 06151l; 0lbj1; ... >> query: (?x8667, 04ztj) <- type_of_union(?x8667, ?x1873), location_of_ceremony(?x1873, ?x362) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015g_7 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.944 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1771-019rl6 PRED entity: 019rl6 PRED relation: industry PRED expected values: 03ytc => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 43): 01mw1 (0.90 #1712, 0.78 #2027, 0.75 #2477), 020mfr (0.58 #2042, 0.52 #1727, 0.45 #2492), 02vxn (0.46 #3198, 0.25 #766, 0.22 #4999), 0hz28 (0.33 #29, 0.25 #766, 0.17 #524), 01mfj (0.29 #124, 0.25 #766, 0.25 #169), 04rlf (0.25 #766, 0.22 #1589, 0.14 #238), 029g_vk (0.25 #766, 0.20 #1586, 0.17 #730), 02jjt (0.25 #766, 0.19 #3203, 0.17 #502), 019z7b (0.25 #766, 0.14 #98, 0.14 #53), 0sydc (0.25 #766, 0.12 #526, 0.08 #797) >> Best rule #1712 for best value: >> intensional similarity = 6 >> extensional distance = 50 >> proper extension: 0xwj; 08z84_; 01qxs3; 0225z1; 01swdw; 02mdty; 025txrl; 01skcy; 021gk7; 0dwcl; ... >> query: (?x7218, 01mw1) <- industry(?x7218, ?x12987), citytown(?x7218, ?x11315), industry(?x12493, ?x12987), industry(?x9469, ?x12987), ?x9469 = 04sv4, ?x12493 = 0317zz >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #194 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 06py2; *> query: (?x7218, 03ytc) <- currency(?x7218, ?x170), company(?x4682, ?x7218), company(?x1907, ?x7218), ?x1907 = 01yc02, place_founded(?x7218, ?x581), ?x4682 = 0dq_5 *> conf = 0.08 ranks of expected_values: 21 EVAL 019rl6 industry 03ytc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 133.000 133.000 0.904 http://example.org/business/business_operation/industry #1770-05s_k6 PRED entity: 05s_k6 PRED relation: film PRED expected values: 01fmys => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1608): 035s95 (0.33 #301, 0.22 #25763, 0.20 #1892), 03mh_tp (0.33 #445, 0.22 #25907, 0.17 #41829), 02gpkt (0.33 #1168, 0.20 #2759, 0.18 #13898), 047gpsd (0.33 #1060, 0.20 #2651, 0.17 #26522), 0372j5 (0.33 #1065, 0.20 #2656, 0.13 #26527), 02ylg6 (0.33 #824, 0.20 #2415, 0.13 #26286), 06x43v (0.33 #1163, 0.20 #2754, 0.12 #7527), 03rtz1 (0.33 #149, 0.20 #1740, 0.12 #6513), 02rrfzf (0.33 #480, 0.17 #25942, 0.15 #30718), 0g7pm1 (0.33 #1072, 0.14 #5845, 0.13 #26534) >> Best rule #301 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 017s11; >> query: (?x12106, 035s95) <- film(?x12106, ?x6288), film(?x12106, ?x2539), citytown(?x12106, ?x1523), ?x6288 = 01chpn, child(?x574, ?x12106), nominated_for(?x4564, ?x2539) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5056 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 01t7jy; *> query: (?x12106, 01fmys) <- child(?x9077, ?x12106), child(?x574, ?x12106), citytown(?x12106, ?x1523), ?x9077 = 0sxdg, industry(?x574, ?x373) *> conf = 0.14 ranks of expected_values: 323 EVAL 05s_k6 film 01fmys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 122.000 122.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #1769-01rwcgb PRED entity: 01rwcgb PRED relation: instrumentalists! PRED expected values: 05r5c => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 117): 05r5c (0.71 #5236, 0.54 #878, 0.50 #617), 018vs (0.45 #1579, 0.44 #448, 0.42 #535), 02hnl (0.29 #1600, 0.28 #469, 0.25 #556), 03qjg (0.25 #573, 0.24 #1617, 0.23 #660), 0l14md (0.22 #442, 0.21 #1573, 0.21 #529), 026t6 (0.20 #3, 0.17 #612, 0.17 #525), 07gql (0.20 #42, 0.16 #912, 0.14 #825), 07y_7 (0.20 #2, 0.16 #872, 0.12 #524), 06ncr (0.20 #44, 0.11 #1610, 0.11 #218), 0l14j_ (0.20 #54, 0.11 #228, 0.10 #663) >> Best rule #5236 for best value: >> intensional similarity = 5 >> extensional distance = 386 >> proper extension: 02ryx0; >> query: (?x10591, 05r5c) <- instrumentalists(?x1166, ?x10591), instrumentalists(?x1166, ?x5391), instrumentalists(?x1166, ?x3171), ?x3171 = 0p3sf, role(?x5391, ?x212) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01rwcgb instrumentalists! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.714 http://example.org/music/instrument/instrumentalists #1768-02645b PRED entity: 02645b PRED relation: student! PRED expected values: 05zl0 => 96 concepts (52 used for prediction) PRED predicted values (max 10 best out of 74): 0bwfn (0.14 #275, 0.08 #6072, 0.08 #4491), 01w5m (0.07 #632, 0.07 #1159, 0.07 #2213), 065y4w7 (0.07 #1068, 0.06 #2649, 0.06 #3176), 03ksy (0.07 #2214, 0.05 #7485, 0.05 #106), 07tgn (0.07 #2125, 0.05 #1071, 0.05 #2652), 017z88 (0.05 #1663, 0.03 #11151, 0.03 #12205), 05zl0 (0.05 #202, 0.03 #2310, 0.01 #7053), 07w0v (0.05 #20, 0.02 #4763, 0.01 #6344), 017j69 (0.05 #145, 0.02 #672, 0.02 #1726), 09r4xx (0.05 #123, 0.02 #1704, 0.02 #2231) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 05qd_; 061dn_; 03m9c8; >> query: (?x2875, 0bwfn) <- award(?x2875, ?x7606), ?x7606 = 01l78d, award_winner(?x2483, ?x2875) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #202 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 05qd_; 061dn_; 03m9c8; *> query: (?x2875, 05zl0) <- award(?x2875, ?x7606), ?x7606 = 01l78d, award_winner(?x2483, ?x2875) *> conf = 0.05 ranks of expected_values: 7 EVAL 02645b student! 05zl0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 96.000 52.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #1767-0gzh PRED entity: 0gzh PRED relation: taxonomy PRED expected values: 04n6k => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.56 #41, 0.54 #32, 0.50 #49) >> Best rule #41 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 083p7; 083q7; 083pr; 07cbs; 034rd; 0f7fy; 03_nq; 0c_md_; 038w8; 0466k4; >> query: (?x13698, 04n6k) <- jurisdiction_of_office(?x13698, ?x94), location(?x13698, ?x4061), profession(?x13698, ?x3342), basic_title(?x13698, ?x346), ?x346 = 060c4 >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gzh taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.556 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #1766-0277g PRED entity: 0277g PRED relation: major_field_of_study! PRED expected values: 07vk2 => 49 concepts (22 used for prediction) PRED predicted values (max 10 best out of 663): 06pwq (0.67 #2976, 0.60 #7120, 0.60 #6529), 09f2j (0.59 #3733, 0.54 #4324, 0.52 #3142), 01j_cy (0.59 #3596, 0.42 #2413, 0.40 #1820), 0bwfn (0.58 #4446, 0.56 #5038, 0.50 #1487), 03ksy (0.58 #6635, 0.57 #3082, 0.57 #8411), 02zd460 (0.58 #6711, 0.57 #8487, 0.54 #9078), 025v3k (0.58 #5468, 0.50 #1322, 0.36 #3690), 07szy (0.57 #3006, 0.50 #5375, 0.50 #3597), 01w3v (0.57 #2978, 0.50 #3569, 0.47 #2386), 07w0v (0.54 #4166, 0.53 #1799, 0.52 #4758) >> Best rule #2976 for best value: >> intensional similarity = 11 >> extensional distance = 19 >> proper extension: 0h5k; 04x_3; 05qfh; 0fdys; 01540; 06mnr; >> query: (?x14330, 06pwq) <- taxonomy(?x14330, ?x939), major_field_of_study(?x735, ?x14330), student(?x735, ?x10819), student(?x735, ?x5351), school(?x7643, ?x735), ?x10819 = 026m0, place_of_birth(?x5351, ?x2474), award_winner(?x384, ?x5351), produced_by(?x2386, ?x5351), school(?x465, ?x735), teams(?x479, ?x7643) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1241 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 2 *> proper extension: 03g3w; *> query: (?x14330, 07vk2) <- major_field_of_study(?x7508, ?x14330), major_field_of_study(?x2079, ?x14330), ?x7508 = 0m7yh, organization(?x346, ?x2079), institution(?x1771, ?x2079), category(?x2079, ?x134), major_field_of_study(?x2079, ?x9079), major_field_of_study(?x2079, ?x2601), major_field_of_study(?x2079, ?x1154), ?x1771 = 019v9k, ?x2601 = 04x_3, ?x9079 = 0l5mz, ?x1154 = 02lp1 *> conf = 0.50 ranks of expected_values: 44 EVAL 0277g major_field_of_study! 07vk2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 49.000 22.000 0.667 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1765-0lzb8 PRED entity: 0lzb8 PRED relation: gender PRED expected values: 05zppz => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #59, 0.80 #57, 0.80 #174), 02zsn (0.60 #44, 0.50 #147, 0.39 #56) >> Best rule #59 for best value: >> intensional similarity = 2 >> extensional distance = 262 >> proper extension: 0bbxd3; 06z9yh; >> query: (?x593, 05zppz) <- profession(?x593, ?x1943), ?x1943 = 02krf9 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lzb8 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.814 http://example.org/people/person/gender #1764-0287477 PRED entity: 0287477 PRED relation: genre PRED expected values: 02kdv5l => 90 concepts (88 used for prediction) PRED predicted values (max 10 best out of 100): 07s9rl0 (0.64 #5969, 0.62 #3578, 0.59 #239), 02kdv5l (0.62 #4178, 0.40 #5252, 0.37 #480), 05p553 (0.51 #2508, 0.39 #482, 0.38 #1196), 04xvlr (0.33 #2, 0.17 #240, 0.16 #5970), 082gq (0.33 #30, 0.17 #268, 0.10 #5998), 03bxz7 (0.33 #55, 0.12 #293, 0.10 #1127), 06n90 (0.29 #4187, 0.23 #489, 0.21 #5261), 01hmnh (0.29 #5266, 0.28 #494, 0.27 #375), 02l7c8 (0.28 #3234, 0.28 #5983, 0.27 #8726), 02n4kr (0.24 #962, 0.16 #5258, 0.14 #4184) >> Best rule #5969 for best value: >> intensional similarity = 4 >> extensional distance = 978 >> proper extension: 04xbq3; >> query: (?x6119, 07s9rl0) <- nominated_for(?x2258, ?x6119), film(?x1019, ?x6119), nominated_for(?x500, ?x6119), ceremony(?x500, ?x78) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #4178 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 741 *> proper extension: 06n90; *> query: (?x6119, 02kdv5l) <- genre(?x6119, ?x6888), genre(?x3433, ?x6888), ?x3433 = 0299hs *> conf = 0.62 ranks of expected_values: 2 EVAL 0287477 genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 88.000 0.640 http://example.org/film/film/genre #1763-0157m PRED entity: 0157m PRED relation: jurisdiction_of_office PRED expected values: 0vbk => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 49): 07b_l (0.10 #77, 0.09 #175, 0.04 #371), 0hjy (0.10 #62, 0.08 #258, 0.03 #405), 059rby (0.09 #152, 0.05 #1035, 0.05 #838), 01n7q (0.09 #163, 0.05 #506, 0.04 #359), 0d0x8 (0.09 #173, 0.04 #369, 0.03 #418), 07ssc (0.08 #1038, 0.05 #989, 0.04 #1137), 0hzlz (0.08 #257, 0.01 #1042, 0.01 #1141), 020p1 (0.08 #294, 0.01 #1079), 0164b (0.08 #292, 0.01 #1077), 01n8qg (0.08 #291, 0.01 #1076) >> Best rule #77 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 016lh0; >> query: (?x1620, 07b_l) <- profession(?x1620, ?x2225), politician(?x8714, ?x1620), currency(?x1620, ?x170) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0157m jurisdiction_of_office 0vbk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 149.000 149.000 0.100 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #1762-01vvydl PRED entity: 01vvydl PRED relation: instrumentalists! PRED expected values: 05148p4 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 109): 0342h (0.69 #1725, 0.63 #1983, 0.63 #2155), 05r5c (0.47 #2418, 0.47 #1642, 0.45 #3888), 05148p4 (0.38 #1139, 0.36 #2431, 0.35 #1741), 018vs (0.36 #2423, 0.34 #357, 0.30 #3028), 01vdm0 (0.30 #2498, 0.26 #3103, 0.25 #2324), 02hnl (0.19 #2445, 0.17 #1669, 0.16 #3050), 026t6 (0.19 #347, 0.13 #2413, 0.12 #1637), 03qjg (0.17 #568, 0.17 #1686, 0.15 #2894), 0l14md (0.16 #351, 0.14 #1641, 0.13 #2417), 03gvt (0.10 #581, 0.10 #495, 0.08 #667) >> Best rule #1725 for best value: >> intensional similarity = 3 >> extensional distance = 239 >> proper extension: 01pbxb; 07s3vqk; 05cljf; 01wl38s; 0168cl; 01vvycq; 025xt8y; 01gf5h; 01vv7sc; 018y2s; ... >> query: (?x140, 0342h) <- award_nominee(?x140, ?x527), instrumentalists(?x228, ?x140), artist(?x2299, ?x140) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1139 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 141 *> proper extension: 016qtt; 028q6; 06cc_1; 08wq0g; 02l840; 03f5spx; 016kjs; 01kx_81; 03kwtb; 0pz91; ... *> query: (?x140, 05148p4) <- award_nominee(?x140, ?x9167), instrumentalists(?x228, ?x140), currency(?x9167, ?x170) *> conf = 0.38 ranks of expected_values: 3 EVAL 01vvydl instrumentalists! 05148p4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 129.000 0.689 http://example.org/music/instrument/instrumentalists #1761-07zft PRED entity: 07zft PRED relation: artists! PRED expected values: 017_qw => 139 concepts (72 used for prediction) PRED predicted values (max 10 best out of 278): 06by7 (0.56 #20602, 0.49 #12179, 0.49 #17175), 017_qw (0.55 #5363, 0.55 #9728, 0.55 #8792), 064t9 (0.51 #3755, 0.46 #2819, 0.44 #17789), 06q6jz (0.35 #1437, 0.07 #12034, 0.06 #4553), 03_d0 (0.35 #12, 0.25 #1260, 0.25 #323), 016clz (0.30 #20586, 0.29 #9981, 0.29 #3747), 05lls (0.30 #1263, 0.11 #9679, 0.10 #10302), 025sc50 (0.27 #3793, 0.21 #10027, 0.20 #17827), 06j6l (0.25 #17825, 0.25 #16580, 0.23 #19697), 05bt6j (0.24 #10020, 0.24 #17198, 0.23 #980) >> Best rule #20602 for best value: >> intensional similarity = 3 >> extensional distance = 694 >> proper extension: 02t3ln; 02mq_y; 06br6t; 0h08p; >> query: (?x8849, 06by7) <- artists(?x6799, ?x8849), artists(?x6799, ?x3390), ?x3390 = 017j6 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #5363 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 015cxv; *> query: (?x8849, 017_qw) <- music(?x5515, ?x8849), award_winner(?x1747, ?x8849), production_companies(?x5515, ?x541), film_release_region(?x5515, ?x94) *> conf = 0.55 ranks of expected_values: 2 EVAL 07zft artists! 017_qw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 139.000 72.000 0.559 http://example.org/music/genre/artists #1760-03spz PRED entity: 03spz PRED relation: teams PRED expected values: 039_ym => 233 concepts (233 used for prediction) PRED predicted values (max 10 best out of 255): 048_lz (0.25 #413, 0.04 #8694, 0.04 #10494), 03zrc_ (0.12 #1613, 0.07 #2693, 0.07 #3053), 037mp6 (0.12 #1521, 0.03 #12683, 0.03 #14843), 03_qj1 (0.12 #1559, 0.03 #13801, 0.03 #14161), 045346 (0.12 #1573, 0.03 #15975, 0.03 #16696), 01l3vx (0.10 #1844, 0.09 #2204, 0.07 #2564), 03lygq (0.10 #2058, 0.09 #2418, 0.07 #2778), 02w64f (0.10 #2127, 0.05 #5367, 0.04 #6087), 024nj1 (0.10 #2154, 0.05 #5754, 0.04 #7554), 035l_9 (0.10 #2115, 0.05 #5715, 0.04 #7515) >> Best rule #413 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03m7d; >> query: (?x4743, 048_lz) <- combatants(?x13684, ?x4743), combatants(?x7419, ?x4743), ?x13684 = 01tffp, locations(?x7419, ?x9122) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03spz teams 039_ym CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 233.000 233.000 0.250 http://example.org/sports/sports_team_location/teams #1759-04b2qn PRED entity: 04b2qn PRED relation: genre PRED expected values: 01t_vv => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 91): 01z4y (0.61 #6215, 0.55 #821, 0.52 #4457), 060__y (0.33 #718, 0.31 #601, 0.29 #836), 02kdv5l (0.32 #1408, 0.27 #4928, 0.25 #5748), 01jfsb (0.32 #1417, 0.28 #5405, 0.28 #4937), 0219x_ (0.27 #142, 0.14 #611, 0.13 #728), 04xvlr (0.25 #469, 0.25 #1, 0.22 #1641), 03k9fj (0.25 #361, 0.23 #4936, 0.20 #1416), 082gq (0.25 #380, 0.14 #263, 0.13 #732), 01t_vv (0.25 #169, 0.12 #520, 0.12 #1927), 0lsxr (0.21 #710, 0.21 #1413, 0.20 #593) >> Best rule #6215 for best value: >> intensional similarity = 2 >> extensional distance = 1223 >> proper extension: 01qn7n; 024rwx; 05r1_t; 0ctzf1; 03y317; >> query: (?x7858, ?x2480) <- titles(?x2480, ?x7858), genre(?x631, ?x2480) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #169 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 0gbfn9; 0415ggl; 040_lv; 047p798; 049w1q; 02wtp6; 0fzm0g; *> query: (?x7858, 01t_vv) <- film(?x617, ?x7858), ?x617 = 025jfl, film(?x6804, ?x7858), genre(?x7858, ?x53) *> conf = 0.25 ranks of expected_values: 9 EVAL 04b2qn genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 76.000 76.000 0.612 http://example.org/film/film/genre #1758-0xjl2 PRED entity: 0xjl2 PRED relation: artists PRED expected values: 0244r8 => 59 concepts (18 used for prediction) PRED predicted values (max 10 best out of 3140): 0178kd (0.67 #5932, 0.60 #3789, 0.50 #574), 048tgl (0.67 #6265, 0.60 #4122, 0.50 #907), 03t9sp (0.60 #3338, 0.56 #7625, 0.50 #9771), 01323p (0.60 #3910, 0.50 #6053, 0.50 #695), 02bgmr (0.60 #3739, 0.50 #5882, 0.50 #524), 017_hq (0.60 #4221, 0.50 #6364, 0.50 #1006), 0dw4g (0.60 #3720, 0.50 #5863, 0.50 #505), 01vswwx (0.60 #3693, 0.50 #5836, 0.50 #478), 01vswx5 (0.60 #3680, 0.50 #5823, 0.50 #465), 070b4 (0.60 #2960, 0.50 #816, 0.40 #4031) >> Best rule #5932 for best value: >> intensional similarity = 11 >> extensional distance = 4 >> proper extension: 0xhtw; >> query: (?x3167, 0178kd) <- artists(?x3167, ?x13039), artists(?x3167, ?x7966), ?x7966 = 013rfk, parent_genre(?x6349, ?x3167), group(?x2309, ?x13039), group(?x1466, ?x13039), group(?x227, ?x13039), ?x227 = 0342h, ?x1466 = 03bx0bm, award(?x13039, ?x1565), ?x2309 = 06ncr >> conf = 0.67 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0xjl2 artists 0244r8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 18.000 0.667 http://example.org/music/genre/artists #1757-065zlr PRED entity: 065zlr PRED relation: nominated_for! PRED expected values: 05zvj3m => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 234): 0gr42 (0.50 #332, 0.46 #573, 0.42 #814), 07cbcy (0.45 #65, 0.20 #1993, 0.20 #18084), 0p9sw (0.42 #262, 0.38 #503, 0.37 #744), 02hsq3m (0.42 #271, 0.26 #753, 0.23 #512), 05p09zm (0.36 #96, 0.23 #578, 0.19 #3377), 057xs89 (0.33 #363, 0.31 #604, 0.26 #845), 02r22gf (0.33 #270, 0.21 #752, 0.16 #993), 05b1610 (0.31 #515, 0.26 #756, 0.22 #997), 07bdd_ (0.31 #536, 0.26 #777, 0.22 #1018), 05b4l5x (0.31 #488, 0.26 #729, 0.19 #3377) >> Best rule #332 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 05650n; 037cr1; >> query: (?x2494, 0gr42) <- film_crew_role(?x2494, ?x2848), film_crew_role(?x2494, ?x137), ?x2848 = 094hwz, ?x137 = 09zzb8, music(?x2494, ?x3371) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3377 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 175 *> proper extension: 02czd5; *> query: (?x2494, ?x154) <- nominated_for(?x4657, ?x2494), artist(?x3265, ?x4657), award(?x4657, ?x154), nominated_for(?x154, ?x103) *> conf = 0.19 ranks of expected_values: 24 EVAL 065zlr nominated_for! 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 91.000 91.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1756-01jq0j PRED entity: 01jq0j PRED relation: school! PRED expected values: 05g76 0jmj7 03lsq 0x0d 06x76 05xvj => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 78): 0jmj7 (0.70 #958, 0.69 #1660, 0.68 #568), 0289q (0.30 #113, 0.25 #35, 0.08 #815), 0jmk7 (0.25 #76, 0.12 #622, 0.09 #856), 01ypc (0.25 #1, 0.10 #79, 0.09 #781), 03lsq (0.25 #29, 0.10 #107, 0.08 #809), 07l2m (0.25 #38, 0.07 #818, 0.06 #896), 0jmbv (0.25 #45, 0.06 #591, 0.05 #825), 07147 (0.20 #134, 0.12 #212, 0.12 #836), 07l4z (0.16 #605, 0.13 #839, 0.12 #1229), 01slc (0.16 #440, 0.15 #1220, 0.14 #1454) >> Best rule #958 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 03v6t; 01rgdw; 02s8qk; 02txdf; 015fsv; 0325dj; >> query: (?x6953, 0jmj7) <- institution(?x865, ?x6953), school(?x580, ?x6953), colors(?x6953, ?x332), fraternities_and_sororities(?x6953, ?x3697) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 14, 49, 65, 70 EVAL 01jq0j school! 05xvj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 116.000 116.000 0.700 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01jq0j school! 06x76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 116.000 116.000 0.700 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01jq0j school! 0x0d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 116.000 116.000 0.700 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01jq0j school! 03lsq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 116.000 116.000 0.700 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01jq0j school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.700 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01jq0j school! 05g76 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 116.000 116.000 0.700 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #1755-02r38 PRED entity: 02r38 PRED relation: artists! PRED expected values: 0ggq0m => 113 concepts (67 used for prediction) PRED predicted values (max 10 best out of 216): 0ggq0m (0.83 #3762, 0.83 #3136, 0.80 #5952), 017_qw (0.76 #6316, 0.71 #8815, 0.57 #691), 06q6jz (0.69 #1439, 0.43 #815, 0.43 #501), 05lls (0.62 #1266, 0.37 #5955, 0.29 #328), 06by7 (0.56 #12837, 0.42 #4086, 0.41 #11584), 064t9 (0.41 #12828, 0.34 #7515, 0.30 #2200), 03_d0 (0.36 #2822, 0.35 #3448, 0.33 #4388), 016clz (0.30 #12819, 0.22 #15322, 0.20 #10004), 01wqlc (0.29 #389, 0.17 #77, 0.14 #703), 06j6l (0.25 #4427, 0.22 #2861, 0.22 #12865) >> Best rule #3762 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 011zf2; >> query: (?x10203, 0ggq0m) <- artists(?x10853, ?x10203), artists(?x10853, ?x1211), role(?x10203, ?x316), ?x1211 = 0k4gf >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r38 artists! 0ggq0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 67.000 0.830 http://example.org/music/genre/artists #1754-0gthm PRED entity: 0gthm PRED relation: film PRED expected values: 03p2xc => 96 concepts (62 used for prediction) PRED predicted values (max 10 best out of 920): 053tj7 (0.44 #17909, 0.22 #1792, 0.17 #1791), 0h1fktn (0.14 #17088, 0.12 #970, 0.04 #45733), 0prrm (0.13 #4443, 0.12 #6234, 0.10 #15187), 0bc1yhb (0.12 #911, 0.04 #4493, 0.03 #29563), 02wgk1 (0.12 #758, 0.04 #4340, 0.03 #29410), 0340hj (0.12 #236, 0.04 #3818, 0.03 #28888), 062zm5h (0.12 #858, 0.04 #4440, 0.02 #15184), 0dzlbx (0.12 #852, 0.04 #4434, 0.02 #15178), 06gb1w (0.12 #734, 0.04 #4316, 0.02 #15060), 0dnkmq (0.12 #1662, 0.04 #5244, 0.02 #15988) >> Best rule #17909 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 080knyg; >> query: (?x9854, ?x1315) <- award(?x9854, ?x7606), film(?x9854, ?x6093), person(?x1315, ?x9854), person(?x6093, ?x966) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #22736 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 01vvycq; 03cws8h; *> query: (?x9854, 03p2xc) <- profession(?x9854, ?x987), type_of_union(?x9854, ?x1873), ?x1873 = 01g63y, ?x987 = 0dxtg *> conf = 0.03 ranks of expected_values: 218 EVAL 0gthm film 03p2xc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 96.000 62.000 0.444 http://example.org/film/actor/film./film/performance/film #1753-07bbw PRED entity: 07bbw PRED relation: parent_genre PRED expected values: 03lty => 60 concepts (37 used for prediction) PRED predicted values (max 10 best out of 167): 03lty (0.95 #1331, 0.93 #1663, 0.93 #1497), 06by7 (0.50 #180, 0.43 #1001, 0.40 #509), 0xhtw (0.33 #670, 0.33 #13, 0.29 #998), 02yv6b (0.33 #65, 0.25 #229, 0.20 #558), 0pm85 (0.33 #98, 0.25 #262, 0.20 #591), 03_d0 (0.33 #2477, 0.10 #1819, 0.08 #5121), 07sbbz2 (0.25 #169, 0.20 #498, 0.20 #333), 02l96k (0.25 #235, 0.20 #564, 0.20 #399), 05r6t (0.22 #3015, 0.21 #1366, 0.20 #1698), 04_sqm (0.20 #3126, 0.20 #620, 0.17 #784) >> Best rule #1331 for best value: >> intensional similarity = 7 >> extensional distance = 37 >> proper extension: 0jf1v; 028cl7; >> query: (?x8639, 03lty) <- parent_genre(?x8639, ?x5762), artists(?x5762, ?x12228), parent_genre(?x10128, ?x5762), parent_genre(?x6805, ?x5762), ?x6805 = 04b675, parent_genre(?x5762, ?x2249), ?x10128 = 0190xp >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07bbw parent_genre 03lty CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 37.000 0.949 http://example.org/music/genre/parent_genre #1752-09xvf7 PRED entity: 09xvf7 PRED relation: gender PRED expected values: 05zppz => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #5, 0.85 #59, 0.85 #85), 02zsn (0.46 #233, 0.26 #110, 0.26 #112) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 0kn4c; 09bg4l; 01dvtx; 06whf; 036jb; 0282x; 049gc; 01gj8_; 06c97; 06crk; ... >> query: (?x13011, 05zppz) <- profession(?x13011, ?x319), nationality(?x13011, ?x94), place_of_death(?x13011, ?x191), student(?x7636, ?x13011) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09xvf7 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.857 http://example.org/people/person/gender #1751-01k0vq PRED entity: 01k0vq PRED relation: films! PRED expected values: 04jjy => 68 concepts (33 used for prediction) PRED predicted values (max 10 best out of 40): 07s2s (0.07 #569, 0.03 #1356, 0.03 #1514), 0ddct (0.04 #558, 0.03 #1188, 0.03 #1503), 0l8bg (0.04 #587, 0.03 #1374, 0.03 #1532), 0fzyg (0.04 #524, 0.03 #1941, 0.02 #997), 02vnz (0.04 #594, 0.02 #1224, 0.02 #1381), 01d5g (0.04 #1053, 0.03 #1210, 0.03 #1367), 01vq3 (0.04 #984, 0.03 #1298, 0.03 #1456), 081pw (0.03 #630, 0.03 #788, 0.03 #3310), 07_nf (0.03 #694, 0.03 #852, 0.01 #3061), 03hzt (0.03 #762, 0.03 #920) >> Best rule #569 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0g5pvv; 02mc5v; 042g97; >> query: (?x7579, 07s2s) <- film(?x1460, ?x7579), titles(?x2480, ?x7579), prequel(?x7348, ?x7579), prequel(?x7579, ?x6684) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #634 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 56 *> proper extension: 015qsq; 0kv2hv; 0c5dd; 02bg8v; 085bd1; 0bykpk; 02gd6x; 01qbg5; 02gpkt; 09sr0; *> query: (?x7579, 04jjy) <- film(?x1460, ?x7579), award(?x7579, ?x1336), film_release_region(?x7579, ?x512), nationality(?x111, ?x512) *> conf = 0.02 ranks of expected_values: 26 EVAL 01k0vq films! 04jjy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 68.000 33.000 0.067 http://example.org/film/film_subject/films #1750-04sh80 PRED entity: 04sh80 PRED relation: films! PRED expected values: 0fzyg => 85 concepts (47 used for prediction) PRED predicted values (max 10 best out of 50): 0fx2s (0.25 #232, 0.17 #73, 0.05 #702), 06d4h (0.17 #43, 0.05 #1616, 0.04 #828), 0d1w9 (0.17 #36, 0.03 #508, 0.03 #665), 04gb7 (0.12 #204, 0.04 #830, 0.03 #1460), 018h2 (0.12 #181, 0.02 #4288, 0.02 #1437), 0bq3x (0.11 #974, 0.03 #659, 0.02 #6998), 0ddct (0.08 #717, 0.01 #3562), 07s2s (0.07 #1199, 0.03 #571, 0.02 #1829), 081pw (0.06 #1260, 0.05 #1576, 0.05 #1418), 0mkz (0.05 #500, 0.01 #1128) >> Best rule #232 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0sxfd; >> query: (?x11681, 0fx2s) <- film(?x879, ?x11681), ?x879 = 01yk13, written_by(?x11681, ?x8961), film(?x2135, ?x11681) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1311 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 106 *> proper extension: 0btyf5z; 09lcsj; 04k9y6; *> query: (?x11681, 0fzyg) <- film(?x382, ?x11681), film(?x10968, ?x11681), profession(?x10968, ?x1032), music(?x11681, ?x8374), edited_by(?x11681, ?x4215) *> conf = 0.04 ranks of expected_values: 13 EVAL 04sh80 films! 0fzyg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 85.000 47.000 0.250 http://example.org/film/film_subject/films #1749-025tn92 PRED entity: 025tn92 PRED relation: school PRED expected values: 07w0v => 17 concepts (17 used for prediction) PRED predicted values (max 10 best out of 798): 01pq4w (0.71 #1034, 0.62 #1133, 0.42 #1537), 07w0v (0.55 #1416, 0.50 #1209, 0.50 #507), 0lyjf (0.52 #1299, 0.45 #1454, 0.38 #1300), 01vs5c (0.52 #1299, 0.45 #1461, 0.38 #1254), 07ccs (0.52 #1299, 0.40 #863, 0.40 #761), 01j_9c (0.52 #1299, 0.40 #807, 0.38 #1106), 01jq4b (0.52 #1299, 0.33 #55, 0.30 #1362), 0trv (0.52 #1299, 0.33 #76, 0.29 #95), 07wlf (0.52 #1299, 0.33 #21, 0.28 #499), 012mzw (0.52 #1299, 0.33 #973, 0.28 #499) >> Best rule #1034 for best value: >> intensional similarity = 55 >> extensional distance = 5 >> proper extension: 04f4z1k; >> query: (?x8133, 01pq4w) <- school(?x8133, ?x6856), school(?x8133, ?x6271), school(?x8133, ?x4296), school(?x8133, ?x2948), school(?x8133, ?x1783), institution(?x1771, ?x2948), institution(?x734, ?x2948), draft(?x12141, ?x8133), draft(?x11805, ?x8133), school(?x4469, ?x2948), school(?x2574, ?x2948), school(?x8499, ?x2948), school(?x1883, ?x2948), position(?x4469, ?x7079), position(?x4469, ?x1792), position(?x4469, ?x1240), position(?x4469, ?x180), student(?x6271, ?x1129), ?x1771 = 019v9k, ?x1883 = 02qw1zx, ?x1240 = 023wyl, major_field_of_study(?x4296, ?x11820), major_field_of_study(?x2948, ?x1682), ?x8499 = 02r6gw6, ?x7079 = 08ns5s, ?x2574 = 01y3v, student(?x4296, ?x3927), student(?x2948, ?x129), industry(?x12373, ?x11820), school(?x3333, ?x4296), school(?x1010, ?x4296), school(?x12141, ?x1087), institution(?x734, ?x9028), institution(?x734, ?x6056), position(?x3333, ?x2010), currency(?x6271, ?x170), draft(?x3333, ?x3334), season(?x3333, ?x701), ?x1010 = 01d5z, ?x180 = 01r3hr, ?x9028 = 017ztv, teams(?x108, ?x11805), position_s(?x4469, ?x1114), ?x6056 = 05zl0, team(?x1348, ?x12141), school(?x1639, ?x6271), school(?x1632, ?x6856), state_province_region(?x1783, ?x1782), ?x1792 = 05zm34, major_field_of_study(?x734, ?x9111), school_type(?x4296, ?x1507), student(?x734, ?x920), ?x9111 = 04sh3, fraternities_and_sororities(?x1783, ?x3697), organization(?x5510, ?x6856) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1416 for first EXPECTED value: *> intensional similarity = 52 *> extensional distance = 9 *> proper extension: 09l0x9; 047dpm0; *> query: (?x8133, 07w0v) <- school(?x8133, ?x6856), school(?x8133, ?x6271), school(?x8133, ?x4296), school(?x8133, ?x2948), school(?x8133, ?x1783), institution(?x1771, ?x2948), draft(?x10837, ?x8133), school(?x11361, ?x2948), school(?x4469, ?x2948), school(?x1883, ?x2948), position(?x4469, ?x2147), position(?x4469, ?x1792), position(?x4469, ?x1240), student(?x6271, ?x4395), ?x1771 = 019v9k, ?x1883 = 02qw1zx, ?x1240 = 023wyl, major_field_of_study(?x4296, ?x11820), major_field_of_study(?x4296, ?x7134), major_field_of_study(?x4296, ?x2014), major_field_of_study(?x4296, ?x1668), ?x11820 = 0w7s, major_field_of_study(?x2948, ?x10391), citytown(?x4296, ?x3983), ?x11361 = 03m1n, teams(?x4090, ?x10837), citytown(?x6271, ?x11511), school(?x1632, ?x6856), student(?x4296, ?x3927), team(?x1348, ?x10837), ?x1792 = 05zm34, sport(?x10837, ?x4833), team(?x11323, ?x4469), ?x2014 = 04rjg, ?x2147 = 04nfpk, school(?x1639, ?x6271), ?x1668 = 01mkq, organization(?x346, ?x1783), draft(?x4469, ?x465), location(?x4395, ?x3521), colors(?x6856, ?x663), currency(?x6271, ?x170), team(?x2573, ?x4469), ?x7134 = 02_7t, dog_breed(?x3983, ?x1706), school_type(?x6271, ?x1507), award_winner(?x5446, ?x3927), ?x2573 = 05b3ts, people(?x1446, ?x3927), major_field_of_study(?x10391, ?x1527), award_nominee(?x3927, ?x4046), school(?x700, ?x4296) *> conf = 0.55 ranks of expected_values: 2 EVAL 025tn92 school 07w0v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 17.000 17.000 0.714 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #1748-0bz6sq PRED entity: 0bz6sq PRED relation: film! PRED expected values: 01v42g 013pk3 => 100 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1176): 01my_c (0.20 #12454, 0.11 #41508, 0.10 #47734), 02lf1j (0.14 #2504, 0.14 #429, 0.03 #12883), 014v6f (0.14 #3042, 0.14 #967, 0.02 #17571), 02tr7d (0.14 #266, 0.07 #2341, 0.04 #10378), 016ggh (0.14 #1862, 0.07 #3937, 0.03 #8089), 0807ml (0.14 #3199, 0.07 #1124, 0.02 #11502), 04954 (0.14 #3381, 0.07 #1306, 0.01 #28287), 05bpg3 (0.14 #3033, 0.01 #38315), 0gn30 (0.12 #5097, 0.09 #13400, 0.03 #11324), 01wbg84 (0.12 #4198, 0.05 #10425, 0.03 #18727) >> Best rule #12454 for best value: >> intensional similarity = 5 >> extensional distance = 57 >> proper extension: 0gyy53; 03mh_tp; 05n6sq; 047gpsd; 047rkcm; 08984j; 03n0cd; >> query: (?x9016, ?x6937) <- film(?x541, ?x9016), film(?x1231, ?x9016), ?x541 = 017s11, executive_produced_by(?x9016, ?x6937), award_winner(?x1323, ?x1231) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #2277 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 035xwd; 03p2xc; *> query: (?x9016, 01v42g) <- film(?x752, ?x9016), film(?x541, ?x9016), film(?x926, ?x9016), award_winner(?x163, ?x541), ?x752 = 0338lq *> conf = 0.07 ranks of expected_values: 24, 513 EVAL 0bz6sq film! 013pk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 100.000 50.000 0.197 http://example.org/film/actor/film./film/performance/film EVAL 0bz6sq film! 01v42g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 100.000 50.000 0.197 http://example.org/film/actor/film./film/performance/film #1747-05169r PRED entity: 05169r PRED relation: team! PRED expected values: 0fw2d3 => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 62): 0d3f83 (0.04 #124, 0.03 #51, 0.03 #1146), 0fp_xp (0.04 #119, 0.03 #46, 0.03 #411), 0135nb (0.03 #236, 0.03 #17, 0.03 #90), 07zr66 (0.03 #426, 0.03 #134, 0.03 #207), 080dyk (0.03 #77, 0.03 #515, 0.03 #223), 0dhrqx (0.03 #552, 0.03 #260, 0.03 #41), 0g3b2z (0.03 #193, 0.03 #47, 0.02 #850), 0fw2d3 (0.03 #183, 0.03 #37, 0.02 #840), 0g9zjp (0.03 #721, 0.03 #356, 0.02 #502), 0d1swh (0.02 #392, 0.02 #830, 0.02 #903) >> Best rule #124 for best value: >> intensional similarity = 11 >> extensional distance = 135 >> proper extension: 0223bl; 03_9hm; 01n_2f; 0ytc; 035qlx; 02rqxc; 02b10g; 046f25; 0jv5x; 0284gc; ... >> query: (?x12376, 0d3f83) <- position(?x12376, ?x203), position(?x12376, ?x60), position(?x12376, ?x530), position(?x12376, ?x63), ?x530 = 02_j1w, ?x60 = 02nzb8, ?x63 = 02sdk9v, ?x203 = 0dgrmp, team(?x60, ?x12376), team(?x530, ?x12376), position(?x12376, ?x60) >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #183 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 140 *> proper extension: 04nrcg; 02rytm; 0266shh; 03_r_5; 037mp6; 024d8w; 06khkb; 03x6xl; 01x4wq; 016gp5; ... *> query: (?x12376, 0fw2d3) <- position(?x12376, ?x203), position(?x12376, ?x60), position(?x12376, ?x530), position(?x12376, ?x63), ?x530 = 02_j1w, ?x60 = 02nzb8, ?x63 = 02sdk9v, ?x203 = 0dgrmp, team(?x60, ?x12376), position(?x12376, ?x60), team(?x203, ?x12376) *> conf = 0.03 ranks of expected_values: 8 EVAL 05169r team! 0fw2d3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 35.000 35.000 0.036 http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team #1746-05c46y6 PRED entity: 05c46y6 PRED relation: nominated_for! PRED expected values: 099c8n => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 208): 099c8n (0.63 #698, 0.37 #1994, 0.31 #3723), 04dn09n (0.40 #678, 0.28 #4783, 0.26 #4999), 019f4v (0.38 #4800, 0.38 #695, 0.37 #5232), 02pqp12 (0.38 #699, 0.26 #3724, 0.25 #1995), 02qyntr (0.37 #807, 0.26 #3832, 0.26 #2103), 0gr51 (0.33 #713, 0.24 #2009, 0.22 #4818), 02qyp19 (0.33 #649, 0.18 #3674, 0.16 #4754), 027dtxw (0.32 #652, 0.21 #3677, 0.20 #1948), 09sdmz (0.32 #771, 0.16 #2067, 0.15 #3796), 0gq_v (0.31 #4770, 0.31 #5202, 0.31 #4986) >> Best rule #698 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 07w8fz; 0bnzd; 0bs5vty; 04b_jc; >> query: (?x2742, 099c8n) <- nominated_for(?x704, ?x2742), ?x704 = 09sb52, film_release_distribution_medium(?x2742, ?x81) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05c46y6 nominated_for! 099c8n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.633 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1745-0bbw2z6 PRED entity: 0bbw2z6 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 23): 0ch6mp2 (0.75 #648, 0.74 #680, 0.73 #1067), 01pvkk (0.33 #9, 0.28 #684, 0.28 #1393), 02rh1dz (0.14 #522, 0.11 #683, 0.10 #1070), 0215hd (0.14 #690, 0.12 #658, 0.11 #1431), 0d2b38 (0.10 #536, 0.10 #697, 0.09 #1438), 089g0h (0.10 #691, 0.09 #1432, 0.09 #48), 01xy5l_ (0.10 #686, 0.09 #654, 0.09 #1395), 015h31 (0.09 #521, 0.08 #1069, 0.08 #844), 02_n3z (0.09 #676, 0.08 #1255, 0.07 #1417), 04pyp5 (0.06 #656, 0.06 #1075, 0.06 #1397) >> Best rule #648 for best value: >> intensional similarity = 3 >> extensional distance = 539 >> proper extension: 02d44q; 047svrl; 0hgnl3t; >> query: (?x4786, 0ch6mp2) <- nominated_for(?x2258, ?x4786), produced_by(?x4786, ?x4946), film_crew_role(?x4786, ?x137) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bbw2z6 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.749 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1744-0cc5qkt PRED entity: 0cc5qkt PRED relation: film! PRED expected values: 03jj93 => 105 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1177): 02q_cc (0.56 #31272, 0.47 #122988, 0.44 #50037), 06pj8 (0.56 #31272, 0.47 #122988, 0.44 #50037), 0136g9 (0.47 #122988, 0.44 #50037, 0.43 #85472), 092ys_y (0.47 #122988, 0.44 #50037, 0.43 #85472), 0c94fn (0.47 #122988, 0.44 #50037, 0.43 #85472), 0146pg (0.47 #122988, 0.44 #50037, 0.43 #85472), 020h2v (0.47 #122988, 0.44 #50037, 0.43 #85472), 016yvw (0.13 #952, 0.03 #23883, 0.03 #30138), 0154qm (0.12 #2647, 0.05 #8900, 0.05 #6815), 0170pk (0.09 #2366, 0.04 #6534, 0.03 #62824) >> Best rule #31272 for best value: >> intensional similarity = 4 >> extensional distance = 177 >> proper extension: 09gdm7q; 020y73; >> query: (?x3596, ?x2135) <- films(?x5503, ?x3596), nominated_for(?x2135, ?x3596), film(?x1414, ?x3596), spouse(?x2135, ?x1802) >> conf = 0.56 => this is the best rule for 2 predicted values *> Best rule #12321 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 106 *> proper extension: 08gsvw; 0340hj; 0btyf5z; 0ddt_; 0dyb1; 0184tc; 0125xq; 062zm5h; 031hcx; 05567m; ... *> query: (?x3596, 03jj93) <- nominated_for(?x1079, ?x3596), nominated_for(?x640, ?x3596), nominated_for(?x1079, ?x6176), ?x640 = 02hsq3m, film_release_region(?x6176, ?x87) *> conf = 0.03 ranks of expected_values: 307 EVAL 0cc5qkt film! 03jj93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 105.000 65.000 0.562 http://example.org/film/actor/film./film/performance/film #1743-02js_6 PRED entity: 02js_6 PRED relation: film PRED expected values: 016017 => 141 concepts (77 used for prediction) PRED predicted values (max 10 best out of 837): 0124k9 (0.60 #69589, 0.59 #74944, 0.59 #24980), 0d68qy (0.60 #69589, 0.59 #74944, 0.59 #24980), 01fx1l (0.60 #69589, 0.59 #74944, 0.59 #24980), 0cf8qb (0.23 #3122, 0.02 #8474, 0.01 #24532), 01shy7 (0.17 #419, 0.04 #11124, 0.04 #21829), 078sj4 (0.15 #2235, 0.06 #7587, 0.02 #43270), 06v9_x (0.15 #2147, 0.02 #7499), 02_fz3 (0.15 #3162), 03f7nt (0.15 #2609), 035s95 (0.11 #5691, 0.07 #3907, 0.03 #11043) >> Best rule #69589 for best value: >> intensional similarity = 3 >> extensional distance = 643 >> proper extension: 01sl1q; 044mz_; 0184jc; 04bdxl; 02s2ft; 05vsxz; 07fq1y; 02qgqt; 02p65p; 0337vz; ... >> query: (?x12359, ?x1542) <- film(?x12359, ?x437), award_winner(?x12359, ?x5346), nominated_for(?x12359, ?x1542) >> conf = 0.60 => this is the best rule for 3 predicted values *> Best rule #3492 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 05nzw6; *> query: (?x12359, 016017) <- film(?x12359, ?x1910), ?x1910 = 011yth, nationality(?x12359, ?x94) *> conf = 0.08 ranks of expected_values: 87 EVAL 02js_6 film 016017 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 141.000 77.000 0.598 http://example.org/film/actor/film./film/performance/film #1742-01r42_g PRED entity: 01r42_g PRED relation: student! PRED expected values: 0bwfn => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 50): 0bwfn (0.06 #1856, 0.05 #4491, 0.05 #13980), 017z88 (0.05 #82, 0.04 #1136, 0.04 #609), 07tg4 (0.05 #86, 0.04 #1140, 0.04 #613), 01vg13 (0.05 #219, 0.04 #1273, 0.04 #746), 026gvfj (0.05 #111, 0.04 #1165, 0.04 #638), 027xq5 (0.05 #521, 0.04 #1575, 0.04 #1048), 09k23 (0.05 #488, 0.04 #1542, 0.04 #1015), 07wrz (0.05 #62, 0.04 #1116, 0.04 #589), 08815 (0.05 #2, 0.04 #529, 0.02 #3164), 01cf5 (0.05 #474, 0.04 #1001) >> Best rule #1856 for best value: >> intensional similarity = 3 >> extensional distance = 141 >> proper extension: 02js_6; >> query: (?x369, 0bwfn) <- award_winner(?x369, ?x1651), actor(?x1849, ?x369), student(?x5614, ?x1651) >> conf = 0.06 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r42_g student! 0bwfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.056 http://example.org/education/educational_institution/students_graduates./education/education/student #1741-02pp1 PRED entity: 02pp1 PRED relation: team! PRED expected values: 07y9k => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 8): 07y9k (0.44 #212, 0.42 #292, 0.40 #316), 021q23 (0.38 #80, 0.18 #256, 0.13 #384), 0355pl (0.33 #11, 0.32 #355, 0.31 #155), 059yj (0.29 #445, 0.27 #109, 0.16 #597), 0356lc (0.25 #481, 0.23 #505, 0.20 #313), 03zv9 (0.17 #66, 0.15 #338, 0.14 #602), 0h69c (0.16 #558, 0.15 #598, 0.11 #446), 01ddbl (0.04 #927, 0.03 #903, 0.03 #631) >> Best rule #212 for best value: >> intensional similarity = 7 >> extensional distance = 16 >> proper extension: 03y_f8; 03yl2t; 02rqxc; 03zrc_; 03ys48; 032jlh; >> query: (?x11309, 07y9k) <- teams(?x1310, ?x11309), position(?x11309, ?x530), position(?x11309, ?x60), ?x60 = 02nzb8, ?x530 = 02_j1w, current_club(?x11309, ?x5207), contains(?x1310, ?x892) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02pp1 team! 07y9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.444 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #1740-05nrg PRED entity: 05nrg PRED relation: contains PRED expected values: 0chgzm 05br2 0283sdr => 103 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2470): 012q8y (0.57 #55493, 0.50 #8173, 0.33 #5253), 01hl_w (0.57 #55493, 0.50 #7539, 0.33 #4619), 06mtq (0.57 #55493, 0.46 #61339, 0.43 #26285), 0847q (0.57 #55493, 0.46 #61339, 0.43 #26285), 0g39h (0.57 #55493, 0.46 #61339, 0.43 #26285), 04lc0h (0.57 #55493, 0.46 #61339, 0.43 #26285), 01bcwk (0.57 #55493, 0.33 #3556, 0.25 #6476), 07vk2 (0.57 #55493, 0.33 #3166, 0.25 #6086), 01l53f (0.57 #55493, 0.33 #5816, 0.25 #8736), 01j12w (0.57 #55493, 0.33 #5809, 0.25 #8729) >> Best rule #55493 for best value: >> intensional similarity = 5 >> extensional distance = 41 >> proper extension: 094jv; 05j49; 0jcpw; >> query: (?x10150, ?x901) <- contains(?x10150, ?x8378), contains(?x10150, ?x390), organization(?x8378, ?x127), contains(?x390, ?x901), contains(?x1879, ?x10150) >> conf = 0.57 => this is the best rule for 50 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 25, 39, 745 EVAL 05nrg contains 0283sdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 103.000 21.000 0.566 http://example.org/location/location/contains EVAL 05nrg contains 05br2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 103.000 21.000 0.566 http://example.org/location/location/contains EVAL 05nrg contains 0chgzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 103.000 21.000 0.566 http://example.org/location/location/contains #1739-02_3zj PRED entity: 02_3zj PRED relation: ceremony PRED expected values: 03nnm4t => 56 concepts (56 used for prediction) PRED predicted values (max 10 best out of 137): 0gkxgfq (0.78 #377, 0.75 #240, 0.24 #2333), 02q690_ (0.74 #473, 0.59 #610, 0.50 #747), 03nnm4t (0.71 #482, 0.55 #619, 0.48 #756), 0gx_st (0.68 #445, 0.50 #582, 0.44 #719), 0jt3qpk (0.67 #314, 0.62 #177, 0.24 #2333), 0gpjbt (0.52 #2497, 0.52 #2635, 0.50 #1811), 0466p0j (0.51 #2543, 0.50 #2681, 0.50 #1857), 09n4nb (0.51 #2653, 0.50 #2515, 0.49 #2791), 05pd94v (0.50 #2472, 0.50 #2610, 0.50 #1786), 02rjjll (0.50 #2475, 0.49 #2613, 0.48 #2751) >> Best rule #377 for best value: >> intensional similarity = 7 >> extensional distance = 7 >> proper extension: 02py_sj; >> query: (?x7316, 0gkxgfq) <- award(?x3310, ?x7316), nominated_for(?x7316, ?x10731), nominated_for(?x7316, ?x10669), nominated_for(?x7316, ?x2829), ?x2829 = 01b64v, actor(?x10731, ?x2194), category(?x10669, ?x134) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #482 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 29 *> proper extension: 0bfvw2; 0bp_b2; 0gkvb7; 0bdw1g; 09qvc0; 09qj50; 0fbvqf; 09qv3c; 0bdwft; 0cjyzs; ... *> query: (?x7316, 03nnm4t) <- award(?x3310, ?x7316), nominated_for(?x7316, ?x10669), nominated_for(?x7316, ?x2829), program(?x329, ?x10669), category_of(?x7316, ?x2758), award_winner(?x2829, ?x439) *> conf = 0.71 ranks of expected_values: 3 EVAL 02_3zj ceremony 03nnm4t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 56.000 56.000 0.778 http://example.org/award/award_category/winners./award/award_honor/ceremony #1738-03c7tr1 PRED entity: 03c7tr1 PRED relation: award! PRED expected values: 02lf70 01dw9z 057hz 01jw4r 01j851 01ypsj => 43 concepts (12 used for prediction) PRED predicted values (max 10 best out of 2317): 0227vl (0.70 #33137, 0.70 #36456, 0.68 #29821), 05dbf (0.70 #20454, 0.20 #7200, 0.14 #13253), 0lpjn (0.70 #20627, 0.14 #13253, 0.08 #23940), 0h0wc (0.70 #20544, 0.14 #13917, 0.12 #17230), 01kb2j (0.70 #21332, 0.11 #24645, 0.08 #37908), 0f7hc (0.60 #7946, 0.50 #11259, 0.43 #14573), 01dbk6 (0.60 #21426, 0.20 #8172, 0.14 #14799), 01hkhq (0.60 #20526, 0.10 #23839, 0.09 #33785), 02jsgf (0.60 #21004, 0.10 #24317, 0.08 #37580), 0mz73 (0.60 #22104, 0.07 #28731, 0.07 #25417) >> Best rule #33137 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 0d085; >> query: (?x1007, ?x971) <- award_winner(?x1007, ?x2763), award_winner(?x1007, ?x971), participant(?x2108, ?x2763), award_nominee(?x221, ?x2763) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #22312 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 8 *> proper extension: 0bsjcw; *> query: (?x1007, 01jw4r) <- award(?x719, ?x1007), ?x719 = 01csvq *> conf = 0.40 ranks of expected_values: 44, 113, 237, 284, 1225, 1459 EVAL 03c7tr1 award! 01ypsj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 12.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03c7tr1 award! 01j851 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 12.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03c7tr1 award! 01jw4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 43.000 12.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03c7tr1 award! 057hz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 12.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03c7tr1 award! 01dw9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 43.000 12.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 03c7tr1 award! 02lf70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 12.000 0.704 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1737-04n52p6 PRED entity: 04n52p6 PRED relation: film_release_region PRED expected values: 03_3d 047lj 0k6nt 09pmkv 059j2 07t21 06c1y 06bnz 03spz => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 183): 059j2 (0.91 #1097, 0.88 #829, 0.87 #561), 06bnz (0.88 #1241, 0.84 #1107, 0.80 #571), 03spz (0.88 #1150, 0.80 #614, 0.80 #1284), 0k6nt (0.87 #555, 0.85 #1493, 0.85 #957), 03_3d (0.86 #1211, 0.84 #1077, 0.82 #1747), 01p1v (0.78 #1113, 0.58 #1247, 0.53 #1381), 047lj (0.75 #142, 0.54 #1215, 0.53 #545), 07twz (0.75 #210, 0.43 #345, 0.40 #613), 06f32 (0.69 #1124, 0.62 #185, 0.62 #990), 09pmkv (0.67 #557, 0.65 #1227, 0.56 #1093) >> Best rule #1097 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 0872p_c; 03qnvdl; 0gj9tn5; 0j6b5; 0661m4p; 0g5838s; 0gj8nq2; 0bpm4yw; 09v71cj; 0bh8tgs; ... >> query: (?x1707, 059j2) <- film_release_region(?x1707, ?x3683), film_release_region(?x1707, ?x205), ?x205 = 03rjj, film_crew_role(?x1707, ?x137), ?x3683 = 0161c >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 7, 10, 15, 16 EVAL 04n52p6 film_release_region 03spz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04n52p6 film_release_region 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04n52p6 film_release_region 06c1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04n52p6 film_release_region 07t21 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04n52p6 film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04n52p6 film_release_region 09pmkv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04n52p6 film_release_region 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04n52p6 film_release_region 047lj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04n52p6 film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.906 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1736-03q91d PRED entity: 03q91d PRED relation: film PRED expected values: 0prrm => 64 concepts (40 used for prediction) PRED predicted values (max 10 best out of 256): 02825cv (0.04 #2931, 0.01 #26188, 0.01 #40500), 03q0r1 (0.04 #637, 0.04 #4215, 0.03 #6004), 0407yj_ (0.04 #484, 0.04 #4062, 0.03 #5851), 04gv3db (0.04 #2542, 0.02 #6120, 0.02 #4331), 034qzw (0.04 #2123, 0.02 #334, 0.02 #3912), 04hwbq (0.04 #192, 0.04 #3770, 0.04 #5559), 01xdxy (0.04 #1566, 0.04 #5144, 0.03 #6933), 05sw5b (0.04 #4393, 0.03 #815, 0.03 #6182), 0bvn25 (0.04 #1839, 0.02 #3628, 0.02 #5417), 04g73n (0.03 #4985, 0.03 #1407, 0.03 #6774) >> Best rule #2931 for best value: >> intensional similarity = 4 >> extensional distance = 254 >> proper extension: 01hxs4; 0277990; 05bnq3j; 04crrxr; 01wd3l; 0bz60q; 05rx__; 03g5_y; 02tf1y; 05nqq3; ... >> query: (?x7745, 02825cv) <- gender(?x7745, ?x231), profession(?x7745, ?x1146), nationality(?x7745, ?x94), ?x1146 = 018gz8 >> conf = 0.04 => this is the best rule for 1 predicted values *> Best rule #2650 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 254 *> proper extension: 01hxs4; 0277990; 05bnq3j; 04crrxr; 01wd3l; 0bz60q; 05rx__; 03g5_y; 02tf1y; 05nqq3; ... *> query: (?x7745, 0prrm) <- gender(?x7745, ?x231), profession(?x7745, ?x1146), nationality(?x7745, ?x94), ?x1146 = 018gz8 *> conf = 0.02 ranks of expected_values: 23 EVAL 03q91d film 0prrm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 64.000 40.000 0.043 http://example.org/film/actor/film./film/performance/film #1735-0sxkh PRED entity: 0sxkh PRED relation: film_release_region PRED expected values: 0chghy => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 224): 0chghy (0.80 #2357, 0.80 #2022, 0.79 #2524), 03rjj (0.79 #2517, 0.79 #2684, 0.78 #2350), 0k6nt (0.79 #2709, 0.78 #2542, 0.77 #2375), 07ssc (0.77 #2531, 0.76 #2029, 0.76 #2364), 0345h (0.77 #2049, 0.76 #2551, 0.76 #2718), 03h64 (0.73 #2756, 0.73 #2589, 0.71 #2422), 035qy (0.71 #2051, 0.69 #2720, 0.68 #2553), 015fr (0.71 #2533, 0.70 #2366, 0.70 #2700), 0154j (0.69 #2516, 0.69 #2014, 0.68 #2349), 05qhw (0.69 #2529, 0.67 #2362, 0.67 #2027) >> Best rule #2357 for best value: >> intensional similarity = 4 >> extensional distance = 262 >> proper extension: 0gj9qxr; 043sct5; 0h95zbp; 0g5q34q; 0gh6j94; >> query: (?x4315, 0chghy) <- film_crew_role(?x4315, ?x137), film_release_region(?x4315, ?x142), ?x142 = 0jgd, language(?x4315, ?x254) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0sxkh film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.799 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1734-073q1 PRED entity: 073q1 PRED relation: contains PRED expected values: 09pmkv 02lx0 0167v 0g8bw 01xbgx => 109 concepts (42 used for prediction) PRED predicted values (max 10 best out of 2602): 01xbgx (0.69 #5877, 0.67 #29388, 0.67 #55844), 09pmkv (0.69 #5877, 0.67 #29388, 0.67 #55844), 0chghy (0.69 #5877, 0.67 #29388, 0.67 #55844), 0d05w3 (0.69 #5877, 0.67 #55844, 0.67 #88178), 03rk0 (0.69 #5877, 0.67 #55844, 0.67 #88178), 01crd5 (0.69 #5877, 0.67 #88178, 0.63 #79359), 02j71 (0.57 #99937), 073q1 (0.54 #123468, 0.54 #123467, 0.48 #85237), 0j0k (0.54 #123468, 0.54 #123467, 0.48 #85237), 02qkt (0.54 #123468, 0.54 #123467, 0.48 #85237) >> Best rule #5877 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 0604m; >> query: (?x7456, ?x1122) <- contains(?x7456, ?x7747), contains(?x6956, ?x7747), ?x6956 = 0j0k, adjoins(?x7747, ?x1122) >> conf = 0.69 => this is the best rule for 6 predicted values ranks of expected_values: 1, 2, 14, 40, 162 EVAL 073q1 contains 01xbgx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 42.000 0.688 http://example.org/location/location/contains EVAL 073q1 contains 0g8bw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 109.000 42.000 0.688 http://example.org/location/location/contains EVAL 073q1 contains 0167v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 109.000 42.000 0.688 http://example.org/location/location/contains EVAL 073q1 contains 02lx0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 109.000 42.000 0.688 http://example.org/location/location/contains EVAL 073q1 contains 09pmkv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 42.000 0.688 http://example.org/location/location/contains #1733-01438g PRED entity: 01438g PRED relation: profession PRED expected values: 02hrh1q => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 59): 02hrh1q (0.88 #7366, 0.88 #5865, 0.88 #3015), 01d_h8 (0.36 #156, 0.35 #1056, 0.35 #1956), 09jwl (0.35 #20, 0.28 #6301, 0.24 #320), 0dxtg (0.29 #1514, 0.29 #3314, 0.28 #2714), 0dz3r (0.28 #6301, 0.27 #2, 0.19 #152), 03gjzk (0.28 #6301, 0.24 #2716, 0.24 #3316), 02jknp (0.28 #6301, 0.23 #8, 0.22 #1958), 016z4k (0.28 #6301, 0.19 #4, 0.18 #154), 0d1pc (0.28 #6301, 0.16 #652, 0.15 #202), 0np9r (0.28 #6301, 0.14 #10073, 0.14 #10523) >> Best rule #7366 for best value: >> intensional similarity = 2 >> extensional distance = 1576 >> proper extension: 05d7rk; 06688p; 01l1b90; 01vw87c; 01yznp; 01rrwf6; 01ty7ll; 033hqf; 018dnt; 09byk; ... >> query: (?x3078, 02hrh1q) <- film(?x3078, ?x6048), award(?x6048, ?x484) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01438g profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.881 http://example.org/people/person/profession #1732-07tds PRED entity: 07tds PRED relation: organization! PRED expected values: 060c4 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 29): 060c4 (0.76 #418, 0.70 #145, 0.69 #523), 0dq_5 (0.65 #360, 0.65 #295, 0.38 #347), 07xl34 (0.28 #89, 0.23 #63, 0.23 #571), 05k17c (0.17 #20, 0.12 #528, 0.10 #502), 0hm4q (0.12 #125, 0.06 #112, 0.05 #776), 05c0jwl (0.04 #747, 0.04 #695, 0.04 #721), 08jcfy (0.03 #64, 0.03 #77, 0.02 #337), 0dq3c (0.02 #482, 0.02 #105, 0.02 #1094), 0krdk (0.02 #482, 0.02 #133, 0.01 #211), 04n1q6 (0.02 #482, 0.01 #553, 0.01 #644) >> Best rule #418 for best value: >> intensional similarity = 1 >> extensional distance = 191 >> proper extension: 0fht9f; >> query: (?x4672, 060c4) <- school(?x2820, ?x4672) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tds organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.762 http://example.org/organization/role/leaders./organization/leadership/organization #1731-06rny PRED entity: 06rny PRED relation: draft PRED expected values: 05vsb7 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 16): 03nt7j (0.78 #517, 0.78 #1173, 0.77 #582), 05vsb7 (0.78 #1173, 0.77 #578, 0.77 #561), 02pq_rp (0.62 #868, 0.43 #874, 0.41 #986), 02r6gw6 (0.62 #868, 0.39 #878, 0.38 #990), 04f4z1k (0.62 #868, 0.39 #882, 0.38 #994), 047dpm0 (0.62 #868, 0.39 #883, 0.38 #995), 02z6872 (0.62 #868, 0.38 #987, 0.37 #875), 02pq_x5 (0.62 #868, 0.37 #881, 0.37 #993), 02rl201 (0.62 #868, 0.35 #871, 0.35 #983), 02x2khw (0.62 #868, 0.35 #577, 0.33 #982) >> Best rule #517 for best value: >> intensional similarity = 12 >> extensional distance = 21 >> proper extension: 01xvb; 01y3v; 084l5; 0wsr; >> query: (?x5773, 03nt7j) <- school(?x5773, ?x2171), school(?x5773, ?x621), position_s(?x5773, ?x3346), position(?x5773, ?x1114), ?x1114 = 047g8h, draft(?x5773, ?x6462), ?x3346 = 02g_7z, team(?x11323, ?x5773), company(?x4486, ?x621), institution(?x734, ?x2171), school_type(?x2171, ?x1507), school(?x6462, ?x388) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1173 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 91 *> proper extension: 0jmdb; 0jm3v; 0jmfv; 0jm2v; 0jmfb; 0jmcb; 0jmj7; 0jm6n; 0jm8l; 0jmmn; ... *> query: (?x5773, ?x465) <- school(?x5773, ?x581), team(?x2147, ?x5773), team(?x1517, ?x5773), team(?x2147, ?x5229), team(?x1517, ?x4170), colors(?x5229, ?x663), school(?x4170, ?x546), team(?x10361, ?x4170), draft(?x5229, ?x465), ?x663 = 083jv *> conf = 0.78 ranks of expected_values: 2 EVAL 06rny draft 05vsb7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 89.000 0.783 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #1730-06196 PRED entity: 06196 PRED relation: award_winner PRED expected values: 06hgj 0h25 => 62 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1882): 014ps4 (0.50 #1702, 0.36 #2468, 0.17 #9107), 01wd02c (0.37 #34560, 0.36 #2468, 0.35 #4936), 01k56k (0.37 #34560, 0.35 #4936, 0.33 #17276), 05cv8 (0.37 #34560, 0.33 #17276, 0.33 #9872), 06kb_ (0.37 #34560, 0.33 #17276, 0.33 #9872), 05jm7 (0.36 #2468, 0.25 #2467, 0.25 #834), 03772 (0.36 #2468, 0.25 #1142, 0.23 #3610), 0jt90f5 (0.36 #2468, 0.25 #480, 0.13 #24685), 0dz46 (0.36 #2468, 0.25 #1921, 0.13 #24685), 01vs4f3 (0.36 #2468, 0.15 #24684, 0.13 #24685) >> Best rule #1702 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 047xyn; >> query: (?x10270, 014ps4) <- award_winner(?x10270, ?x9132), award_winner(?x10270, ?x477), influenced_by(?x3858, ?x477), ?x3858 = 05jm7, profession(?x9132, ?x1032), people(?x743, ?x477) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #44431 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 85 *> proper extension: 05kjlr; 06zrp44; 05fmy; *> query: (?x10270, ?x1287) <- award_winner(?x10270, ?x9132), award_winner(?x10270, ?x477), influenced_by(?x3858, ?x477), peers(?x3858, ?x6723), type_of_union(?x9132, ?x566), influenced_by(?x3858, ?x1287) *> conf = 0.14 ranks of expected_values: 57, 248 EVAL 06196 award_winner 0h25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 23.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 06196 award_winner 06hgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 62.000 23.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #1729-06t6dz PRED entity: 06t6dz PRED relation: language PRED expected values: 02h40lc => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 39): 02h40lc (0.91 #297, 0.89 #4103, 0.89 #1780), 064_8sq (0.15 #1800, 0.13 #2038, 0.13 #2097), 06nm1 (0.11 #544, 0.11 #841, 0.10 #1433), 04306rv (0.11 #300, 0.10 #241, 0.09 #717), 03hkp (0.11 #15, 0.10 #653, 0.04 #668), 0880p (0.11 #46, 0.10 #653, 0.01 #400), 0t_2 (0.10 #653, 0.07 #368, 0.04 #607), 05zjd (0.10 #653, 0.02 #380, 0.02 #856), 03_9r (0.07 #69, 0.06 #246, 0.06 #663), 02bjrlw (0.07 #119, 0.06 #237, 0.06 #534) >> Best rule #297 for best value: >> intensional similarity = 4 >> extensional distance = 78 >> proper extension: 014_x2; 093l8p; >> query: (?x4788, 02h40lc) <- nominated_for(?x1336, ?x4788), film(?x1290, ?x4788), award(?x4080, ?x1336), ?x4080 = 0dl567 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06t6dz language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.912 http://example.org/film/film/language #1728-0l3h PRED entity: 0l3h PRED relation: exported_to PRED expected values: 03rjj => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 59): 09c7w0 (0.59 #346, 0.35 #862, 0.35 #232), 0j4b (0.19 #103, 0.16 #734, 0.15 #390), 0h3y (0.19 #65, 0.15 #238, 0.14 #696), 06tw8 (0.14 #904, 0.14 #732, 0.13 #388), 059j2 (0.13 #364, 0.12 #135, 0.12 #250), 03_3d (0.13 #350, 0.12 #236, 0.10 #751), 0d05w3 (0.13 #376, 0.12 #262, 0.09 #892), 03rjj (0.13 #349, 0.08 #235, 0.07 #865), 07fsv (0.12 #104, 0.10 #735, 0.09 #907), 07dzf (0.12 #99, 0.10 #730, 0.09 #902) >> Best rule #346 for best value: >> intensional similarity = 2 >> extensional distance = 37 >> proper extension: 01f08r; 0853g; >> query: (?x5622, 09c7w0) <- exported_to(?x5622, ?x1264), country(?x1679, ?x1264) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #349 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 37 *> proper extension: 01f08r; 0853g; *> query: (?x5622, 03rjj) <- exported_to(?x5622, ?x1264), country(?x1679, ?x1264) *> conf = 0.13 ranks of expected_values: 8 EVAL 0l3h exported_to 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 86.000 86.000 0.590 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #1727-01gwk3 PRED entity: 01gwk3 PRED relation: film! PRED expected values: 01gq0b => 155 concepts (103 used for prediction) PRED predicted values (max 10 best out of 1451): 03jj93 (0.22 #3982, 0.14 #1899, 0.09 #12314), 01nwwl (0.22 #2586, 0.10 #19253, 0.09 #27586), 01f6zc (0.22 #3028, 0.09 #11360, 0.08 #15527), 02dztn (0.22 #3425, 0.08 #15924, 0.07 #20092), 02_p8v (0.22 #3009, 0.08 #15508, 0.07 #19676), 0l6px (0.22 #2472, 0.07 #50397, 0.07 #19139), 06ltr (0.22 #3031, 0.07 #50956, 0.07 #19698), 0134w7 (0.22 #2244, 0.07 #50169, 0.07 #18911), 065jlv (0.22 #2397, 0.07 #50322, 0.07 #19064), 013_vh (0.22 #2746, 0.07 #50671, 0.07 #19413) >> Best rule #3982 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 01hr1; 01kf3_9; >> query: (?x6429, 03jj93) <- currency(?x6429, ?x170), films(?x11523, ?x6429), country(?x6429, ?x512), prequel(?x6429, ?x324), ?x512 = 07ssc >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #71152 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 96 *> proper extension: 07gp9; *> query: (?x6429, 01gq0b) <- film_crew_role(?x6429, ?x1171), crewmember(?x6429, ?x666), film(?x2387, ?x6429), ?x1171 = 09vw2b7, featured_film_locations(?x6429, ?x1523) *> conf = 0.02 ranks of expected_values: 1076 EVAL 01gwk3 film! 01gq0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 155.000 103.000 0.222 http://example.org/film/actor/film./film/performance/film #1726-0c41qv PRED entity: 0c41qv PRED relation: award PRED expected values: 05p1dby => 116 concepts (114 used for prediction) PRED predicted values (max 10 best out of 223): 07bdd_ (0.71 #7780, 0.69 #2908, 0.67 #3314), 05p1dby (0.59 #5386, 0.57 #7822, 0.56 #2950), 0gq9h (0.33 #3326, 0.33 #78, 0.33 #25659), 02x1z2s (0.33 #201, 0.27 #12787, 0.23 #4667), 0gq_d (0.33 #224, 0.23 #4690, 0.20 #630), 018wng (0.33 #42, 0.18 #4508, 0.14 #1666), 0gr42 (0.33 #117, 0.14 #1741, 0.14 #4583), 0p9sw (0.33 #23, 0.14 #4489, 0.08 #13422), 0gr07 (0.33 #245, 0.14 #4711, 0.07 #46292), 09sb52 (0.28 #41051, 0.21 #41457, 0.20 #41863) >> Best rule #7780 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 04wvhz; 0bgrsl; 0f7hc; 05mvd62; 016dmx; >> query: (?x7339, 07bdd_) <- award_nominee(?x574, ?x7339), production_companies(?x4565, ?x574), ?x4565 = 011wtv >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #5386 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 20 *> proper extension: 05bxwh; *> query: (?x7339, 05p1dby) <- award_nominee(?x7339, ?x574), ?x574 = 016tt2 *> conf = 0.59 ranks of expected_values: 2 EVAL 0c41qv award 05p1dby CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 114.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1725-01gvts PRED entity: 01gvts PRED relation: film! PRED expected values: 09zmys => 83 concepts (36 used for prediction) PRED predicted values (max 10 best out of 886): 019f2f (0.57 #33315, 0.51 #74960, 0.51 #58300), 04sry (0.48 #66632, 0.43 #66631, 0.43 #27068), 081lh (0.08 #2244, 0.07 #162, 0.02 #14738), 03knl (0.07 #2240, 0.01 #4323, 0.01 #10569), 012v9y (0.06 #5411, 0.04 #9575, 0.03 #7493), 0h0wc (0.05 #8754, 0.05 #6672, 0.03 #15001), 0bl2g (0.05 #55, 0.02 #16713, 0.02 #14631), 0170qf (0.05 #368, 0.02 #14944, 0.02 #10779), 02x7vq (0.05 #3063, 0.03 #981, 0.01 #21802), 09fb5 (0.05 #4223, 0.05 #8387, 0.05 #6305) >> Best rule #33315 for best value: >> intensional similarity = 4 >> extensional distance = 468 >> proper extension: 07wqr6; 0123qq; >> query: (?x7194, ?x2589) <- nominated_for(?x2589, ?x7194), religion(?x2589, ?x1985), ?x1985 = 0c8wxp, gender(?x2589, ?x514) >> conf = 0.57 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01gvts film! 09zmys CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 36.000 0.565 http://example.org/film/actor/film./film/performance/film #1724-039x1k PRED entity: 039x1k PRED relation: gender PRED expected values: 02zsn => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.93 #6, 0.91 #8, 0.89 #4), 05zppz (0.72 #173, 0.71 #169, 0.71 #37) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 01gv_f; 0g_92; >> query: (?x7615, 02zsn) <- award_winner(?x2943, ?x7615), award(?x7615, ?x375), type_of_union(?x7615, ?x566), ?x375 = 0bfvw2 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039x1k gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.929 http://example.org/people/person/gender #1723-0g5qmbz PRED entity: 0g5qmbz PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.86 #91, 0.84 #51, 0.83 #196), 07c52 (0.26 #287, 0.24 #58, 0.23 #393), 0735l (0.26 #287, 0.04 #59, 0.03 #79), 07z4p (0.23 #393, 0.20 #15, 0.16 #60), 02nxhr (0.23 #393, 0.11 #117, 0.09 #263) >> Best rule #91 for best value: >> intensional similarity = 6 >> extensional distance = 40 >> proper extension: 03f7xg; 02r858_; >> query: (?x9501, 029j_) <- film(?x8019, ?x9501), genre(?x9501, ?x1014), profession(?x8019, ?x2265), ?x2265 = 0dgd_, award_winner(?x9128, ?x8019), award(?x8019, ?x372) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g5qmbz film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #1722-02pbzv PRED entity: 02pbzv PRED relation: institution! PRED expected values: 02_xgp2 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 22): 014mlp (0.83 #896, 0.78 #968, 0.67 #353), 02h4rq6 (0.80 #164, 0.78 #280, 0.77 #304), 02_xgp2 (0.67 #550, 0.57 #383, 0.57 #290), 016t_3 (0.54 #165, 0.52 #374, 0.52 #281), 0bkj86 (0.43 #286, 0.43 #310, 0.42 #379), 07s6fsf (0.41 #278, 0.40 #371, 0.40 #325), 04zx3q1 (0.38 #2, 0.33 #48, 0.33 #25), 027f2w (0.26 #287, 0.25 #311, 0.24 #380), 02mjs7 (0.25 #5, 0.22 #51, 0.22 #28), 01rr_d (0.22 #1220, 0.17 #1395, 0.17 #1394) >> Best rule #896 for best value: >> intensional similarity = 5 >> extensional distance = 433 >> proper extension: 0kz2w; 0gkkf; 024y8p; 0yjf0; 015nl4; 01b1pf; 0ylvj; 0trv; 0373qt; 07tjf; ... >> query: (?x8820, 014mlp) <- institution(?x4981, ?x8820), institution(?x4981, ?x11870), institution(?x4981, ?x7338), ?x11870 = 02kxx1, ?x7338 = 01qgr3 >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #550 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 314 *> proper extension: 03p7gb; 01d650; 0yl_3; 0jksm; *> query: (?x8820, 02_xgp2) <- institution(?x4981, ?x8820), institution(?x4981, ?x12475), institution(?x4981, ?x388), ?x12475 = 02_jjm, ?x388 = 05krk *> conf = 0.67 ranks of expected_values: 3 EVAL 02pbzv institution! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 112.000 0.828 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #1721-01q32bd PRED entity: 01q32bd PRED relation: award PRED expected values: 02f6xy => 102 concepts (100 used for prediction) PRED predicted values (max 10 best out of 295): 03t5n3 (0.40 #250, 0.39 #655, 0.17 #3085), 02f76h (0.40 #178, 0.28 #583, 0.26 #1393), 02f6xy (0.40 #201, 0.26 #1416, 0.22 #2631), 023vrq (0.40 #327, 0.22 #732, 0.22 #3162), 02v1m7 (0.40 #113, 0.22 #518, 0.16 #2543), 01bgqh (0.35 #853, 0.34 #2473, 0.31 #3283), 03t5kl (0.33 #633, 0.30 #228, 0.28 #1443), 01d38g (0.33 #3268, 0.28 #1243, 0.23 #2053), 03qbh5 (0.31 #2636, 0.26 #3446, 0.23 #1421), 09sb52 (0.31 #25558, 0.26 #27583, 0.25 #6116) >> Best rule #250 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01wwvc5; 03j3pg9; >> query: (?x3737, 03t5n3) <- artists(?x671, ?x3737), award_nominee(?x11371, ?x3737), ?x11371 = 01wlt3k, profession(?x3737, ?x131) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #201 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01wwvc5; 03j3pg9; *> query: (?x3737, 02f6xy) <- artists(?x671, ?x3737), award_nominee(?x11371, ?x3737), ?x11371 = 01wlt3k, profession(?x3737, ?x131) *> conf = 0.40 ranks of expected_values: 3 EVAL 01q32bd award 02f6xy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 100.000 0.400 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1720-059x66 PRED entity: 059x66 PRED relation: award_winner PRED expected values: 0178rl 01mqnr => 46 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1102): 021yc7p (0.40 #1748, 0.33 #6355, 0.33 #3284), 0794g (0.35 #19970, 0.25 #495, 0.17 #3565), 0f5xn (0.35 #19970, 0.24 #6140, 0.21 #7675), 0f502 (0.35 #19970, 0.24 #6140, 0.21 #7675), 054_mz (0.35 #19970, 0.24 #6140, 0.21 #7675), 0479b (0.35 #19970, 0.07 #13822, 0.06 #19969), 02fn5 (0.35 #19970, 0.01 #3069), 09swkk (0.33 #3843, 0.25 #773, 0.22 #6914), 04ktcgn (0.25 #274, 0.22 #6415, 0.20 #7950), 02h1rt (0.25 #738, 0.22 #6879, 0.20 #8414) >> Best rule #1748 for best value: >> intensional similarity = 16 >> extensional distance = 3 >> proper extension: 073h9x; >> query: (?x1449, 021yc7p) <- ceremony(?x1323, ?x1449), ceremony(?x484, ?x1449), honored_for(?x1449, ?x7336), honored_for(?x1449, ?x7292), honored_for(?x1449, ?x1450), ?x484 = 0gq_v, film_release_region(?x7336, ?x87), nominated_for(?x2257, ?x7336), ?x1323 = 0gqz2, award(?x91, ?x2257), language(?x1450, ?x254), film(?x72, ?x7292), ?x87 = 05r4w, film_release_distribution_medium(?x7336, ?x81), award_winner(?x7336, ?x930), prequel(?x11372, ?x1450) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #6955 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 7 *> proper extension: 02yvhx; *> query: (?x1449, 0178rl) <- ceremony(?x1323, ?x1449), ceremony(?x484, ?x1449), ceremony(?x77, ?x1449), honored_for(?x1449, ?x7336), ?x484 = 0gq_v, film_release_region(?x7336, ?x87), nominated_for(?x4091, ?x7336), nominated_for(?x2257, ?x7336), ?x1323 = 0gqz2, ?x2257 = 09td7p, film(?x496, ?x7336), ?x77 = 0gqng, nominated_for(?x4091, ?x10806), award_winner(?x7336, ?x930), genre(?x7336, ?x258), film_crew_role(?x7336, ?x1171), ?x10806 = 04q827 *> conf = 0.11 ranks of expected_values: 75, 242 EVAL 059x66 award_winner 01mqnr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 15.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 059x66 award_winner 0178rl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 46.000 15.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1719-05ztjjw PRED entity: 05ztjjw PRED relation: nominated_for PRED expected values: 0ds33 0bth54 09k56b7 0661ql3 07024 02fqrf 0bpm4yw 017d93 01hv3t 07f_t4 02p76f9 0gldyz => 32 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1351): 017gl1 (0.80 #6186, 0.69 #10616, 0.67 #24279), 017gm7 (0.69 #10616, 0.67 #24279, 0.66 #25797), 04yc76 (0.69 #10616, 0.67 #24279, 0.66 #25797), 026p4q7 (0.67 #7917, 0.60 #6400, 0.25 #3367), 09gq0x5 (0.67 #7817, 0.40 #6300, 0.22 #4783), 07024 (0.67 #7988, 0.40 #6471, 0.16 #9504), 0jqn5 (0.67 #7768, 0.30 #6251, 0.11 #9284), 0pv3x (0.67 #7732, 0.20 #6215, 0.17 #9248), 0ywrc (0.61 #8020, 0.40 #6503, 0.22 #4986), 011yqc (0.61 #7777, 0.35 #6066, 0.30 #6260) >> Best rule #6186 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0gq_v; 02hsq3m; 0gr42; 02x2gy0; 02x1z2s; >> query: (?x298, 017gl1) <- nominated_for(?x298, ?x8570), nominated_for(?x298, ?x3534), nominated_for(?x298, ?x299), production_companies(?x299, ?x574), nominated_for(?x406, ?x3534), ?x8570 = 04jpg2p >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #7988 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 16 *> proper extension: 02r0csl; 0p9sw; 02g3v6; 04dn09n; 0l8z1; 019f4v; 02pqp12; 0gq9h; 0gs9p; 054krc; ... *> query: (?x298, 07024) <- nominated_for(?x298, ?x4684), nominated_for(?x298, ?x2772), nominated_for(?x298, ?x299), ?x299 = 01gc7, film_release_region(?x4684, ?x87), film_crew_role(?x2772, ?x137) *> conf = 0.67 ranks of expected_values: 6, 22, 35, 36, 98, 99, 128, 135, 138, 147, 321, 1342 EVAL 05ztjjw nominated_for 0gldyz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 02p76f9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 07f_t4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 01hv3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 017d93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 0bpm4yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 02fqrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 07024 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 0661ql3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 09k56b7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 0bth54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 05ztjjw nominated_for 0ds33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 32.000 17.000 0.800 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1718-0435vm PRED entity: 0435vm PRED relation: featured_film_locations PRED expected values: 030qb3t => 113 concepts (77 used for prediction) PRED predicted values (max 10 best out of 101): 04jpl (0.23 #249, 0.14 #489, 0.09 #729), 02_286 (0.22 #1942, 0.21 #2182, 0.20 #2664), 030qb3t (0.13 #2683, 0.11 #3888, 0.11 #1479), 0rh6k (0.11 #481, 0.09 #1923, 0.08 #2163), 0fvd03 (0.08 #455, 0.01 #935), 080h2 (0.07 #1946, 0.06 #2668, 0.06 #2186), 052p7 (0.06 #538, 0.04 #1738, 0.04 #2702), 03gh4 (0.06 #595, 0.04 #1315, 0.03 #3000), 0156q (0.05 #1001, 0.04 #761, 0.03 #3408), 06y57 (0.04 #1303, 0.03 #1543, 0.02 #3711) >> Best rule #249 for best value: >> intensional similarity = 7 >> extensional distance = 11 >> proper extension: 0209xj; 03rtz1; 084302; 01jzyf; >> query: (?x3925, 04jpl) <- genre(?x3925, ?x53), production_companies(?x3925, ?x382), film(?x3705, ?x3925), film(?x1522, ?x3925), language(?x3925, ?x254), ?x3705 = 02114t, award_nominee(?x1522, ?x5492) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #2683 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 145 *> proper extension: 02phtzk; *> query: (?x3925, 030qb3t) <- film_crew_role(?x3925, ?x1966), film_crew_role(?x3925, ?x137), produced_by(?x3925, ?x1533), production_companies(?x3925, ?x382), ?x137 = 09zzb8, film_crew_role(?x8794, ?x1966), ?x8794 = 02qydsh *> conf = 0.13 ranks of expected_values: 3 EVAL 0435vm featured_film_locations 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 113.000 77.000 0.231 http://example.org/film/film/featured_film_locations #1717-01vj9c PRED entity: 01vj9c PRED relation: group PRED expected values: 01vrwfv 07m4c 014pg1 07hgm 04k05 0pqp3 017_hq 0560w 01518s 027kwc => 86 concepts (51 used for prediction) PRED predicted values (max 10 best out of 1009): 02vnpv (0.75 #4261, 0.75 #3149, 0.73 #4814), 02dw1_ (0.67 #4740, 0.67 #3490, 0.62 #5022), 07mvp (0.67 #2122, 0.62 #3087, 0.53 #4752), 06nv27 (0.67 #2102, 0.60 #1827, 0.50 #3067), 027kwc (0.67 #2195, 0.60 #1920, 0.50 #2334), 014pg1 (0.67 #2143, 0.60 #1868, 0.50 #2282), 01v0sxx (0.67 #2173, 0.60 #1898, 0.50 #2312), 016m5c (0.67 #2191, 0.60 #1916, 0.50 #2330), 09lwrt (0.67 #2123, 0.60 #1848, 0.50 #2262), 01fmz6 (0.67 #2101, 0.60 #1826, 0.50 #2240) >> Best rule #4261 for best value: >> intensional similarity = 12 >> extensional distance = 10 >> proper extension: 0dwtp; 01s0ps; >> query: (?x745, 02vnpv) <- role(?x211, ?x745), role(?x745, ?x8957), role(?x745, ?x2205), role(?x8032, ?x745), artist(?x2931, ?x8032), role(?x214, ?x745), ?x2205 = 0dq630k, group(?x745, ?x13578), role(?x745, ?x212), artists(?x1127, ?x8032), instrumentalists(?x8957, ?x317), artist(?x5634, ?x13578) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2195 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 05148p4; 03bx0bm; *> query: (?x745, 027kwc) <- role(?x211, ?x745), role(?x745, ?x432), role(?x8032, ?x745), artist(?x2931, ?x8032), role(?x214, ?x745), role(?x74, ?x745), artists(?x1127, ?x8032), role(?x745, ?x1332), ?x432 = 042v_gx, group(?x745, ?x12449), ?x12449 = 014_xj *> conf = 0.67 ranks of expected_values: 5, 6, 32, 53, 63, 67, 71, 78, 108, 113 EVAL 01vj9c group 027kwc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 51.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01vj9c group 01518s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 86.000 51.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01vj9c group 0560w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 86.000 51.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01vj9c group 017_hq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 86.000 51.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01vj9c group 0pqp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 86.000 51.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01vj9c group 04k05 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 86.000 51.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01vj9c group 07hgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 86.000 51.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01vj9c group 014pg1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 51.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01vj9c group 07m4c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 86.000 51.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01vj9c group 01vrwfv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 86.000 51.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1716-0412f5y PRED entity: 0412f5y PRED relation: award_nominee! PRED expected values: 02zmh5 047sxrj => 72 concepts (14 used for prediction) PRED predicted values (max 10 best out of 469): 06mt91 (0.81 #25590, 0.81 #16283, 0.81 #16282), 026yqrr (0.81 #25590, 0.81 #16283, 0.81 #16282), 05mt_q (0.81 #25590, 0.81 #16283, 0.81 #16282), 03y82t6 (0.81 #25590, 0.81 #16283, 0.81 #16282), 047sxrj (0.81 #25590, 0.81 #16283, 0.81 #16282), 0288fyj (0.81 #25590, 0.81 #16283, 0.81 #16282), 02l840 (0.33 #2484, 0.33 #156, 0.25 #2328), 06x4l_ (0.33 #2971, 0.25 #2328, 0.19 #16284), 01wgxtl (0.33 #601, 0.19 #16284, 0.14 #27919), 03j3pg9 (0.33 #2086, 0.19 #16284, 0.14 #27919) >> Best rule #25590 for best value: >> intensional similarity = 4 >> extensional distance = 486 >> proper extension: 03d9d6; 09lwrt; 0187x8; 016lmg; 0cbm64; >> query: (?x3607, ?x3062) <- award_nominee(?x3607, ?x3062), artists(?x671, ?x3607), award_nominee(?x3062, ?x527), award(?x3607, ?x2139) >> conf = 0.81 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 42 EVAL 0412f5y award_nominee! 047sxrj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 72.000 14.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee EVAL 0412f5y award_nominee! 02zmh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 72.000 14.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1715-01c22t PRED entity: 01c22t PRED relation: film_release_region PRED expected values: 0154j 06bnz 05b4w => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 105): 0154j (0.85 #1653, 0.80 #1103, 0.79 #415), 06bnz (0.84 #1680, 0.72 #3468, 0.72 #1130), 05b4w (0.82 #1698, 0.75 #3486, 0.73 #1010), 0d060g (0.81 #1655, 0.77 #3443, 0.74 #4267), 04gzd (0.74 #1658, 0.57 #420, 0.55 #3446), 01p1v (0.65 #1687, 0.50 #3475, 0.45 #4299), 03rk0 (0.60 #1691, 0.50 #453, 0.44 #3479), 047lj (0.52 #422, 0.41 #1660, 0.40 #972), 05qx1 (0.50 #438, 0.47 #1126, 0.45 #1676), 07f1x (0.48 #512, 0.45 #1200, 0.44 #1750) >> Best rule #1653 for best value: >> intensional similarity = 6 >> extensional distance = 111 >> proper extension: 0g56t9t; 0gtsx8c; 02vxq9m; 0gx1bnj; 0dscrwf; 0fq27fp; 0c40vxk; 0gkz15s; 087wc7n; 0crfwmx; ... >> query: (?x1080, 0154j) <- film_release_region(?x1080, ?x1497), film_release_region(?x1080, ?x456), film_release_region(?x1080, ?x87), ?x456 = 05qhw, ?x87 = 05r4w, ?x1497 = 015qh >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 01c22t film_release_region 05b4w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01c22t film_release_region 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01c22t film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.850 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1714-05c26ss PRED entity: 05c26ss PRED relation: film_release_region PRED expected values: 0154j 0k6nt 06bnz 02vzc 05sb1 03ryn 0jgx 01crd5 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 117): 06bnz (0.88 #433, 0.87 #965, 0.85 #832), 0154j (0.85 #402, 0.84 #801, 0.84 #934), 03rt9 (0.84 #808, 0.82 #941, 0.76 #409), 02vzc (0.81 #3370, 0.79 #4701, 0.79 #2705), 0k6nt (0.80 #2818, 0.78 #4681, 0.78 #3350), 015qh (0.73 #431, 0.71 #830, 0.67 #963), 03ryn (0.73 #465, 0.51 #864, 0.42 #1663), 047lj (0.70 #407, 0.58 #806, 0.49 #1605), 09pmkv (0.70 #421, 0.51 #820, 0.44 #953), 06mzp (0.59 #947, 0.53 #2681, 0.49 #814) >> Best rule #433 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 08hmch; 04hwbq; 017gm7; 0cz8mkh; 0gj9tn5; 05qbckf; 0gd0c7x; 0407yfx; 0661m4p; 08052t3; ... >> query: (?x3839, 06bnz) <- country(?x3839, ?x94), film_release_region(?x3839, ?x2316), film_release_region(?x3839, ?x2000), ?x2316 = 06t2t, ?x2000 = 0d0kn >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5, 7, 16, 44, 45 EVAL 05c26ss film_release_region 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 91.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05c26ss film_release_region 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 91.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05c26ss film_release_region 03ryn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05c26ss film_release_region 05sb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 91.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05c26ss film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05c26ss film_release_region 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05c26ss film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05c26ss film_release_region 0154j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.879 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1713-07fj_ PRED entity: 07fj_ PRED relation: member_states! PRED expected values: 085h1 => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 13): 085h1 (0.78 #96, 0.78 #155, 0.78 #159), 018cqq (0.36 #46, 0.34 #63, 0.33 #95), 02jxk (0.27 #53, 0.25 #86, 0.24 #153), 059dn (0.25 #56, 0.23 #32, 0.21 #89), 0gkjy (0.08 #110, 0.08 #115, 0.07 #491), 07t65 (0.08 #110, 0.08 #115, 0.07 #491), 02vk52z (0.07 #491, 0.05 #403, 0.03 #586), 041288 (0.07 #491, 0.03 #586), 0b6css (0.07 #491, 0.03 #586), 0j7v_ (0.07 #491, 0.03 #586) >> Best rule #96 for best value: >> intensional similarity = 3 >> extensional distance = 67 >> proper extension: 0cdbq; >> query: (?x4521, 085h1) <- nationality(?x10965, ?x4521), contains(?x4521, ?x4522), participating_countries(?x1931, ?x4521) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07fj_ member_states! 085h1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.783 http://example.org/user/ktrueman/default_domain/international_organization/member_states #1712-0g5ptf PRED entity: 0g5ptf PRED relation: film_release_region PRED expected values: 0d0vqn => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 15): 09c7w0 (0.10 #131, 0.10 #105, 0.10 #79), 05r4w (0.04 #312, 0.02 #337, 0.01 #623), 05v8c (0.04 #321, 0.02 #346, 0.01 #859), 03_3d (0.03 #238, 0.02 #264, 0.02 #392), 07ssc (0.03 #1854, 0.03 #2160, 0.03 #2490), 0d060g (0.02 #265, 0.02 #317, 0.02 #367), 0chghy (0.02 #267, 0.02 #369, 0.02 #421), 03rjj (0.02 #285, 0.02 #387, 0.02 #439), 0345h (0.02 #297, 0.02 #323, 0.02 #1299), 01znc_ (0.02 #325, 0.01 #838, 0.01 #711) >> Best rule #131 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0cc846d; >> query: (?x10599, 09c7w0) <- featured_film_locations(?x10599, ?x205), ?x205 = 03rjj, language(?x10599, ?x90), titles(?x512, ?x10599) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #524 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 0404j37; *> query: (?x10599, 0d0vqn) <- featured_film_locations(?x10599, ?x205), film_release_region(?x7692, ?x205), olympics(?x205, ?x358), nominated_for(?x68, ?x7692) *> conf = 0.02 ranks of expected_values: 12 EVAL 0g5ptf film_release_region 0d0vqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 99.000 99.000 0.100 http://example.org/film/film/runtime./film/film_cut/film_release_region #1711-05xbx PRED entity: 05xbx PRED relation: award_winner PRED expected values: 049rl0 => 146 concepts (110 used for prediction) PRED predicted values (max 10 best out of 799): 05qd_ (0.82 #164495, 0.82 #167721, 0.81 #170951), 05xbx (0.35 #129008, 0.28 #174176, 0.25 #25050), 03mdt (0.35 #129008, 0.28 #174176, 0.25 #24737), 01j53q (0.35 #129008, 0.28 #174176, 0.25 #132234), 03jvmp (0.35 #129008, 0.28 #174176, 0.25 #132234), 026v1z (0.35 #129008, 0.28 #174176, 0.25 #132234), 01gb54 (0.33 #786, 0.29 #15295, 0.25 #5622), 0f721s (0.29 #14720, 0.29 #13107, 0.20 #32460), 05gnf (0.29 #15612, 0.28 #174176, 0.17 #25284), 09d5h (0.29 #13215, 0.17 #24500, 0.14 #14828) >> Best rule #164495 for best value: >> intensional similarity = 3 >> extensional distance = 1007 >> proper extension: 019_1h; 071dcs; 01vqrm; >> query: (?x5007, ?x902) <- award_winner(?x5007, ?x2776), award_winner(?x8733, ?x5007), award_winner(?x902, ?x5007) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #51602 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 081bls; *> query: (?x5007, ?x105) <- award_winner(?x3486, ?x5007), film(?x5007, ?x8733), award_winner(?x3486, ?x105) *> conf = 0.04 ranks of expected_values: 345 EVAL 05xbx award_winner 049rl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 146.000 110.000 0.816 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #1710-09k0h5 PRED entity: 09k0h5 PRED relation: state_province_region PRED expected values: 07dfk => 157 concepts (153 used for prediction) PRED predicted values (max 10 best out of 85): 01n7q (0.68 #3970, 0.62 #6342, 0.61 #5092), 059rby (0.52 #3725, 0.50 #3849, 0.50 #2482), 03_3d (0.45 #15748, 0.33 #15367, 0.25 #18531), 07dfk (0.45 #15748, 0.33 #15367, 0.25 #18531), 05kr_ (0.21 #5624, 0.13 #7370, 0.05 #9861), 09c7w0 (0.18 #3347, 0.01 #11591, 0.01 #10461), 06pvr (0.18 #3347), 081yw (0.12 #1550, 0.11 #4842, 0.11 #10585), 04rrd (0.12 #2999, 0.11 #4842, 0.11 #10585), 07b_l (0.11 #4842, 0.11 #10585, 0.11 #10460) >> Best rule #3970 for best value: >> intensional similarity = 7 >> extensional distance = 22 >> proper extension: 07tgn; >> query: (?x13291, ?x1227) <- child(?x13291, ?x14600), category(?x14600, ?x134), ?x134 = 08mbj5d, child(?x10436, ?x14600), organization(?x4682, ?x14600), state_province_region(?x10436, ?x1227), citytown(?x14600, ?x9559) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #15748 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 312 *> proper extension: 017v3q; *> query: (?x13291, ?x252) <- citytown(?x13291, ?x8951), citytown(?x11303, ?x8951), organization(?x4682, ?x11303), contains(?x252, ?x8951), film_release_region(?x66, ?x252), administrative_parent(?x252, ?x551) *> conf = 0.45 ranks of expected_values: 4 EVAL 09k0h5 state_province_region 07dfk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 157.000 153.000 0.679 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #1709-0c57yj PRED entity: 0c57yj PRED relation: film! PRED expected values: 01ft2l 02js9p => 82 concepts (61 used for prediction) PRED predicted values (max 10 best out of 968): 02fcs2 (0.18 #31165, 0.17 #39477, 0.14 #76868), 05dbf (0.10 #6595, 0.04 #4518, 0.02 #10750), 046zh (0.09 #7164, 0.03 #5087, 0.02 #11319), 01p7yb (0.08 #6285, 0.03 #122567, 0.02 #14594), 0bq2g (0.08 #6836, 0.01 #35925, 0.01 #4759), 01j5ts (0.08 #6261, 0.01 #49892, 0.01 #110131), 01hkhq (0.08 #6643, 0.02 #2489, 0.01 #56506), 02f2dn (0.07 #6679, 0.02 #23300, 0.02 #27456), 0n6f8 (0.06 #6440, 0.03 #2286, 0.01 #33452), 015q43 (0.06 #7131, 0.01 #36220, 0.01 #2977) >> Best rule #31165 for best value: >> intensional similarity = 4 >> extensional distance = 259 >> proper extension: 02y_lrp; 0ds33; 0bth54; 01sxly; 03s6l2; 01cssf; 0hmr4; 08gsvw; 035xwd; 02hxhz; ... >> query: (?x3859, ?x2367) <- featured_film_locations(?x3859, ?x1227), country(?x3859, ?x94), genre(?x3859, ?x53), written_by(?x3859, ?x2367) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #1226 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 016z5x; 0jym0; 0bm2g; 01qncf; 02s4l6; 016z9n; 0qmd5; 084302; 0fy66; 0kvgtf; ... *> query: (?x3859, 02js9p) <- featured_film_locations(?x3859, ?x1227), film(?x2367, ?x3859), titles(?x53, ?x3859), state_province_region(?x99, ?x1227) *> conf = 0.01 ranks of expected_values: 703 EVAL 0c57yj film! 02js9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 82.000 61.000 0.182 http://example.org/film/actor/film./film/performance/film EVAL 0c57yj film! 01ft2l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 61.000 0.182 http://example.org/film/actor/film./film/performance/film #1708-04k9y6 PRED entity: 04k9y6 PRED relation: nominated_for! PRED expected values: 0gs96 => 99 concepts (73 used for prediction) PRED predicted values (max 10 best out of 213): 0gq9h (0.47 #5020, 0.44 #4548, 0.44 #298), 0gs9p (0.42 #5022, 0.38 #3841, 0.37 #4550), 019f4v (0.41 #5011, 0.39 #3830, 0.39 #4539), 0k611 (0.35 #5031, 0.35 #4559, 0.34 #3850), 040njc (0.35 #3784, 0.33 #4965, 0.33 #4493), 0p9sw (0.31 #256, 0.28 #492, 0.27 #4506), 0gr0m (0.30 #531, 0.27 #5017, 0.27 #6611), 0gqy2 (0.30 #5079, 0.30 #3898, 0.28 #4607), 04dn09n (0.29 #4993, 0.29 #3812, 0.28 #4521), 0f4x7 (0.29 #4983, 0.29 #3802, 0.27 #4511) >> Best rule #5020 for best value: >> intensional similarity = 4 >> extensional distance = 385 >> proper extension: 0k4kk; >> query: (?x6018, 0gq9h) <- genre(?x6018, ?x53), award_winner(?x6018, ?x2534), award(?x6018, ?x3458), honored_for(?x2294, ?x6018) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #6611 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 493 *> proper extension: 0cwrr; 01h1bf; 02kk_c; 0c3xpwy; 04glx0; 05fgr_; 07bz5; 07s8z_l; 06mmr; *> query: (?x6018, ?x484) <- award_winner(?x6018, ?x6327), honored_for(?x2294, ?x6018), award_winner(?x485, ?x6327), award(?x485, ?x484) *> conf = 0.27 ranks of expected_values: 16 EVAL 04k9y6 nominated_for! 0gs96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 99.000 73.000 0.470 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1707-06pvr PRED entity: 06pvr PRED relation: location! PRED expected values: 0pz04 => 98 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1840): 094xh (0.40 #6114, 0.33 #3596, 0.20 #8633), 01yzhn (0.33 #4650, 0.20 #9687, 0.20 #7168), 03nb5v (0.33 #3841, 0.20 #8878, 0.20 #6359), 09yrh (0.33 #3431, 0.20 #8468, 0.20 #5949), 09fb5 (0.33 #2569, 0.20 #7606, 0.20 #5087), 032r1 (0.33 #2315, 0.20 #9870, 0.20 #7351), 01wp8w7 (0.33 #2778, 0.20 #7815, 0.20 #5296), 0cgbf (0.33 #3912, 0.20 #8949, 0.20 #6430), 01wy5m (0.33 #3502, 0.20 #8539, 0.20 #6020), 0p_pd (0.33 #2566, 0.20 #7603, 0.20 #5084) >> Best rule #6114 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0l2vz; >> query: (?x2632, 094xh) <- contains(?x2632, ?x10657), contains(?x2632, ?x4578), ?x4578 = 0r5wt, place_of_birth(?x158, ?x10657), location(?x3662, ?x2632) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06pvr location! 0pz04 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 63.000 0.400 http://example.org/people/person/places_lived./people/place_lived/location #1706-03_44z PRED entity: 03_44z PRED relation: teams! PRED expected values: 0ctw_b => 72 concepts (59 used for prediction) PRED predicted values (max 10 best out of 88): 052bw (0.33 #448, 0.17 #718, 0.06 #9997), 01nln (0.33 #157, 0.05 #2587, 0.04 #3127), 02m77 (0.17 #695, 0.06 #9997, 0.02 #4746), 0hyxv (0.17 #652, 0.06 #9997, 0.02 #4973), 0k33p (0.14 #1009, 0.12 #1279, 0.06 #9997), 01fbb3 (0.12 #1294, 0.06 #9997, 0.06 #2374), 0h3y (0.07 #1357, 0.06 #1627, 0.06 #1897), 06mkj (0.07 #1417, 0.06 #1687, 0.06 #1957), 06qd3 (0.07 #1396, 0.06 #1666, 0.06 #1936), 035qy (0.07 #1393, 0.06 #1663, 0.06 #1933) >> Best rule #448 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 01634x; >> query: (?x12537, 052bw) <- team(?x63, ?x12537), team(?x9088, ?x12537), team(?x6063, ?x12537), position(?x12537, ?x60), ?x60 = 02nzb8, team(?x6063, ?x8885), ?x9088 = 09l9tq, sport(?x12537, ?x471), ?x8885 = 01rlzn >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4591 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 78 *> proper extension: 04mp75; 019lvv; 03m6zs; 03w7kx; 01njml; 09cvbq; 0d3fdn; *> query: (?x12537, ?x1023) <- team(?x203, ?x12537), sport(?x12537, ?x471), team(?x10244, ?x12537), position(?x12537, ?x60), ?x203 = 0dgrmp, ?x60 = 02nzb8, nationality(?x10244, ?x1023) *> conf = 0.06 ranks of expected_values: 23 EVAL 03_44z teams! 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 72.000 59.000 0.333 http://example.org/sports/sports_team_location/teams #1705-09lxv9 PRED entity: 09lxv9 PRED relation: nominated_for! PRED expected values: 02x1z2s => 85 concepts (75 used for prediction) PRED predicted values (max 10 best out of 201): 0p9sw (0.43 #3561, 0.24 #2617, 0.23 #3797), 019f4v (0.40 #290, 0.34 #8078, 0.29 #3358), 0gr0m (0.40 #296, 0.25 #3600, 0.23 #8084), 04dn09n (0.40 #271, 0.25 #8059, 0.20 #3575), 0gqy2 (0.40 #359, 0.24 #8147, 0.19 #3427), 0gq9h (0.38 #8087, 0.33 #3367, 0.29 #3603), 02x258x (0.38 #3874, 0.17 #2458, 0.15 #2694), 0gs9p (0.34 #8089, 0.26 #3369, 0.23 #7853), 0gq_v (0.34 #8044, 0.29 #3560, 0.29 #2616), 02g2wv (0.33 #168, 0.09 #11329, 0.08 #876) >> Best rule #3561 for best value: >> intensional similarity = 3 >> extensional distance = 196 >> proper extension: 0c3xpwy; >> query: (?x8906, 0p9sw) <- nominated_for(?x929, ?x8906), crewmember(?x4565, ?x929), film(?x1051, ?x4565) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4391 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 250 *> proper extension: 0dnvn3; 03h_yy; 08hmch; 0c00zd0; 01j8wk; 01b195; 065zlr; 014zwb; 08gg47; 0gy2y8r; ... *> query: (?x8906, 02x1z2s) <- genre(?x8906, ?x225), crewmember(?x8906, ?x929), film(?x71, ?x8906), nominated_for(?x3410, ?x8906) *> conf = 0.10 ranks of expected_values: 74 EVAL 09lxv9 nominated_for! 02x1z2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 85.000 75.000 0.429 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1704-02l4pj PRED entity: 02l4pj PRED relation: award_nominee PRED expected values: 031k24 => 93 concepts (30 used for prediction) PRED predicted values (max 10 best out of 700): 04bdxl (0.77 #46462, 0.77 #13934, 0.76 #34840), 01hkhq (0.77 #46462, 0.77 #13934, 0.76 #65039), 0755wz (0.77 #46462, 0.77 #13934, 0.76 #65039), 0h0yt (0.77 #13934, 0.76 #34840, 0.76 #32515), 05k2s_ (0.76 #65039, 0.76 #25546, 0.50 #267), 015gw6 (0.76 #65039, 0.76 #25546, 0.42 #44139), 02l4pj (0.60 #768, 0.46 #5413, 0.42 #44139), 02xv8m (0.42 #44139, 0.29 #7837, 0.28 #32516), 03m8lq (0.42 #44139, 0.28 #27870, 0.28 #32516), 0ksrf8 (0.42 #44139, 0.28 #27870, 0.28 #32516) >> Best rule #46462 for best value: >> intensional similarity = 3 >> extensional distance = 859 >> proper extension: 04rcr; 01vvycq; 01kvqc; 03gr7w; 010hn; 05pq9; 02b25y; 0249kn; 0pkyh; 04cw0j; ... >> query: (?x3461, ?x91) <- award_winner(?x3461, ?x91), award_winner(?x472, ?x3461), nominated_for(?x91, ?x1064) >> conf = 0.77 => this is the best rule for 3 predicted values *> Best rule #13392 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 474 *> proper extension: 01xdf5; 018db8; 01wmxfs; 081lh; 0jdhp; 01k5t_3; 0137n0; 01l9p; 01vs_v8; 0127m7; ... *> query: (?x3461, 031k24) <- award_winner(?x3461, ?x91), award_winner(?x472, ?x3461), film(?x3461, ?x1797) *> conf = 0.02 ranks of expected_values: 408 EVAL 02l4pj award_nominee 031k24 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 93.000 30.000 0.766 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1703-0b1zz PRED entity: 0b1zz PRED relation: influenced_by PRED expected values: 05xq9 => 98 concepts (41 used for prediction) PRED predicted values (max 10 best out of 380): 05xq9 (0.25 #2336, 0.25 #154, 0.11 #1027), 02vr7 (0.25 #272, 0.13 #7863, 0.12 #10925), 07yg2 (0.25 #123, 0.13 #7863, 0.11 #17495), 0167xy (0.25 #2549, 0.11 #1240, 0.11 #3423), 07hgm (0.25 #2502, 0.11 #1193, 0.06 #3813), 014zfs (0.21 #7449, 0.17 #9637, 0.15 #10512), 01hmk9 (0.20 #7646, 0.17 #9834, 0.15 #10709), 014z8v (0.19 #12361, 0.17 #7546, 0.15 #10609), 01wp_jm (0.19 #7766, 0.15 #9954, 0.13 #10829), 0m2l9 (0.18 #1757, 0.11 #884, 0.11 #3067) >> Best rule #2336 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 05xq9; >> query: (?x5935, 05xq9) <- group(?x716, ?x5935), artist(?x1954, ?x5935), ?x716 = 018vs, influenced_by(?x5935, ?x8864) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b1zz influenced_by 05xq9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 41.000 0.250 http://example.org/influence/influence_node/influenced_by #1702-0653m PRED entity: 0653m PRED relation: titles PRED expected values: 043n0v_ 01mgw => 53 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1742): 01f85k (0.77 #12467, 0.63 #17143, 0.61 #20259), 0mb8c (0.45 #7792, 0.33 #3888, 0.25 #7005), 01f8gz (0.45 #7792, 0.33 #3329, 0.25 #6446), 05g8pg (0.45 #7792, 0.33 #2026, 0.25 #6699), 01f8f7 (0.45 #7792, 0.33 #2578, 0.25 #7251), 0ywrc (0.45 #7792, 0.33 #2000, 0.19 #23817), 027m67 (0.45 #7792, 0.25 #7309, 0.25 #5748), 01ffx4 (0.45 #7792, 0.15 #11354, 0.15 #7790), 02ryz24 (0.45 #7792, 0.15 #7790, 0.12 #7791), 05sy_5 (0.45 #7792, 0.15 #7790, 0.12 #7791) >> Best rule #12467 for best value: >> intensional similarity = 9 >> extensional distance = 11 >> proper extension: 03k9fj; 01jfsb; 02l7c8; 09blyk; >> query: (?x2890, ?x6376) <- titles(?x2890, ?x3886), prequel(?x6376, ?x3886), film_release_region(?x3886, ?x1264), film_release_region(?x3886, ?x390), ?x390 = 0chghy, language(?x3886, ?x2502), nominated_for(?x4169, ?x3886), nominated_for(?x5923, ?x3886), ?x1264 = 0345h >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #3852 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 03h64; *> query: (?x2890, 043n0v_) <- titles(?x2890, ?x7789), titles(?x2890, ?x3886), titles(?x2890, ?x3863), ?x3886 = 0198b6, nominated_for(?x11657, ?x7789), film_release_region(?x7789, ?x512), nominated_for(?x2183, ?x3863), film_crew_role(?x7789, ?x468), ?x11657 = 01f873, language(?x3863, ?x254) *> conf = 0.33 ranks of expected_values: 14, 20 EVAL 0653m titles 01mgw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 53.000 20.000 0.767 http://example.org/media_common/netflix_genre/titles EVAL 0653m titles 043n0v_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 53.000 20.000 0.767 http://example.org/media_common/netflix_genre/titles #1701-0170s4 PRED entity: 0170s4 PRED relation: diet PRED expected values: 07_hy => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 2): 07_jd (0.17 #1, 0.17 #3, 0.13 #5), 07_hy (0.05 #16, 0.04 #6, 0.03 #36) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 06y3r; >> query: (?x2415, 07_jd) <- award_winner(?x1582, ?x2415), type_of_union(?x2415, ?x566), notable_people_with_this_condition(?x8318, ?x2415) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 76 *> proper extension: 0d06m5; 0fpj9pm; *> query: (?x2415, 07_hy) <- award(?x2415, ?x102), location_of_ceremony(?x2415, ?x11561), participant(?x2415, ?x400) *> conf = 0.05 ranks of expected_values: 2 EVAL 0170s4 diet 07_hy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 126.000 126.000 0.171 http://example.org/base/eating/practicer_of_diet/diet #1700-02r0st6 PRED entity: 02r0st6 PRED relation: award PRED expected values: 05f4m9q => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 210): 05p1dby (0.49 #917, 0.40 #107, 0.03 #1727), 05f4m9q (0.25 #13, 0.16 #823, 0.09 #418), 09sb52 (0.23 #6116, 0.20 #4091, 0.20 #3686), 05b1610 (0.23 #39, 0.15 #849, 0.04 #444), 0gq9h (0.21 #887, 0.21 #77, 0.11 #1697), 040njc (0.18 #8, 0.14 #413, 0.13 #818), 019f4v (0.16 #471, 0.13 #1686, 0.12 #2091), 05p09zm (0.14 #124, 0.10 #934, 0.05 #6199), 0gs9p (0.13 #484, 0.13 #1699, 0.13 #2104), 0cjyzs (0.12 #106, 0.11 #1321, 0.09 #916) >> Best rule #917 for best value: >> intensional similarity = 2 >> extensional distance = 96 >> proper extension: 0jz9f; 017s11; 016tt2; 0338lq; 0g1rw; 0kx4m; 05qd_; 04f525m; 016tw3; 043q6n_; ... >> query: (?x14072, 05p1dby) <- award(?x14072, ?x1105), ?x1105 = 07bdd_ >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 55 *> proper extension: 09b3v; *> query: (?x14072, 05f4m9q) <- gender(?x14072, ?x231), ?x231 = 05zppz, award(?x14072, ?x1105), ?x1105 = 07bdd_ *> conf = 0.25 ranks of expected_values: 2 EVAL 02r0st6 award 05f4m9q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.490 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1699-014m1m PRED entity: 014m1m PRED relation: category PRED expected values: 08mbj5d => 184 concepts (184 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.70 #143, 0.69 #146, 0.68 #147) >> Best rule #143 for best value: >> intensional similarity = 6 >> extensional distance = 1117 >> proper extension: 0rs6x; 015zyd; 08815; 0k049; 05zjtn4; 01fq7; 06_kh; 01rtm4; 04wlz2; 05krk; ... >> query: (?x13808, 08mbj5d) <- contains(?x151, ?x13808), adjoins(?x1879, ?x151), film_release_region(?x5721, ?x151), film_release_region(?x607, ?x151), ?x5721 = 01d259, ?x607 = 02x3lt7 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014m1m category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 184.000 184.000 0.698 http://example.org/common/topic/webpage./common/webpage/category #1698-06qd3 PRED entity: 06qd3 PRED relation: form_of_government PRED expected values: 01fpfn => 187 concepts (187 used for prediction) PRED predicted values (max 10 best out of 4): 018wl5 (0.45 #165, 0.42 #193, 0.41 #69), 01fpfn (0.44 #26, 0.43 #546, 0.43 #18), 01q20 (0.36 #83, 0.36 #99, 0.32 #95), 026wp (0.16 #64, 0.15 #68, 0.15 #92) >> Best rule #165 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 084n_; >> query: (?x1453, 018wl5) <- form_of_government(?x1453, ?x48), organization(?x1453, ?x127), country(?x4430, ?x1453) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 05v8c; *> query: (?x1453, 01fpfn) <- film_release_region(?x7554, ?x1453), film_release_region(?x5230, ?x1453), ?x7554 = 01mgw, ?x5230 = 0mb8c, country(?x150, ?x1453) *> conf = 0.44 ranks of expected_values: 2 EVAL 06qd3 form_of_government 01fpfn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 187.000 187.000 0.452 http://example.org/location/country/form_of_government #1697-0c_dx PRED entity: 0c_dx PRED relation: award_winner PRED expected values: 0bv7t => 65 concepts (44 used for prediction) PRED predicted values (max 10 best out of 1953): 07w21 (0.40 #17367, 0.39 #27180, 0.36 #106263), 013pp3 (0.39 #27180, 0.38 #32120, 0.36 #106263), 0n6kf (0.39 #27180, 0.36 #106263, 0.34 #108739), 01v_0b (0.34 #108739, 0.34 #76593, 0.34 #101318), 01vdrw (0.34 #108739, 0.34 #101318, 0.33 #86477), 0210f1 (0.30 #18849, 0.29 #23791, 0.28 #21321), 0693l (0.28 #13025, 0.10 #50081, 0.10 #96373), 02kxbx3 (0.22 #13122, 0.18 #50178, 0.17 #65004), 081lh (0.22 #12540, 0.15 #49596, 0.13 #64422), 01dhmw (0.20 #22956, 0.20 #18014, 0.19 #20486) >> Best rule #17367 for best value: >> intensional similarity = 6 >> extensional distance = 28 >> proper extension: 03mv9j; >> query: (?x7111, 07w21) <- award(?x5335, ?x7111), disciplines_or_subjects(?x7111, ?x5864), student(?x2313, ?x5335), place_of_birth(?x5335, ?x6253), influenced_by(?x5335, ?x118), religion(?x5335, ?x7131) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #11072 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 047xyn; *> query: (?x7111, 0bv7t) <- award_winner(?x7111, ?x3497), award_winner(?x7111, ?x118), disciplines_or_subjects(?x7111, ?x5864), location(?x118, ?x4622), influenced_by(?x8389, ?x118), student(?x3576, ?x3497), ?x8389 = 0683n *> conf = 0.20 ranks of expected_values: 31 EVAL 0c_dx award_winner 0bv7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 65.000 44.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #1696-0h6sv PRED entity: 0h6sv PRED relation: type_of_union PRED expected values: 01g63y => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.85 #137, 0.84 #169, 0.83 #157), 01g63y (0.19 #492, 0.19 #513, 0.19 #487), 0jgjn (0.19 #492, 0.19 #513, 0.19 #487), 01bl8s (0.01 #67) >> Best rule #137 for best value: >> intensional similarity = 3 >> extensional distance = 265 >> proper extension: 02pp_q_; 083q7; 01d494; 01nrq5; 034bs; 06n9lt; 07cbs; 0627sn; 01t265; 0f7fy; ... >> query: (?x13167, 04ztj) <- award_winner(?x2324, ?x13167), people(?x10199, ?x13167), risk_factors(?x10199, ?x231) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #492 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3837 *> proper extension: 07nv3_; 0dhrqx; 02qny_; *> query: (?x13167, ?x566) <- gender(?x13167, ?x231), profession(?x13167, ?x563), profession(?x9163, ?x563), type_of_union(?x9163, ?x566) *> conf = 0.19 ranks of expected_values: 2 EVAL 0h6sv type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 128.000 0.850 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1695-02nfjp PRED entity: 02nfjp PRED relation: profession PRED expected values: 0dz3r 0nbcg => 102 concepts (47 used for prediction) PRED predicted values (max 10 best out of 56): 01d_h8 (0.76 #3708, 0.69 #2136, 0.53 #1852), 03gjzk (0.67 #13, 0.40 #1859, 0.31 #3715), 0dz3r (0.65 #570, 0.53 #1564, 0.51 #428), 0nbcg (0.62 #312, 0.58 #596, 0.58 #1306), 016z4k (0.57 #430, 0.54 #288, 0.48 #1140), 018gz8 (0.35 #1861, 0.15 #725, 0.14 #5143), 0n1h (0.31 #295, 0.30 #437, 0.21 #863), 0fnpj (0.31 #339, 0.21 #907, 0.19 #481), 0cbd2 (0.25 #2992, 0.23 #291, 0.23 #3279), 05z96 (0.23 #322, 0.08 #464, 0.07 #890) >> Best rule #3708 for best value: >> intensional similarity = 4 >> extensional distance = 734 >> proper extension: 03wdsbz; >> query: (?x5106, 01d_h8) <- award_winner(?x11702, ?x5106), profession(?x5106, ?x524), profession(?x2689, ?x524), ?x2689 = 03qmx_f >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #570 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 0c9d9; 02whj; 01wp8w7; 02zmh5; 0144l1; 016ntp; 0m_v0; 01lvcs1; 01309x; 01vrkdt; ... *> query: (?x5106, 0dz3r) <- role(?x5106, ?x227), ?x227 = 0342h, profession(?x5106, ?x1614), ?x1614 = 01c72t *> conf = 0.65 ranks of expected_values: 3, 4 EVAL 02nfjp profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 47.000 0.764 http://example.org/people/person/profession EVAL 02nfjp profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 47.000 0.764 http://example.org/people/person/profession #1694-09qs08 PRED entity: 09qs08 PRED relation: category_of PRED expected values: 0gcf2r => 43 concepts (38 used for prediction) PRED predicted values (max 10 best out of 3): 0gcf2r (0.71 #23, 0.62 #44, 0.50 #2), 0c4ys (0.34 #539, 0.25 #710, 0.25 #214), 0g_w (0.10 #259, 0.09 #323, 0.09 #345) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 027gs1_; >> query: (?x2603, 0gcf2r) <- award(?x1545, ?x2603), award(?x8132, ?x2603), profession(?x1545, ?x1032), ?x8132 = 0q9jk >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09qs08 category_of 0gcf2r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 38.000 0.714 http://example.org/award/award_category/category_of #1693-03gt46z PRED entity: 03gt46z PRED relation: award_winner PRED expected values: 02773m2 0pz7h 0c7t58 02778yp 02vqpx8 => 35 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1720): 07lt7b (0.50 #4685, 0.50 #1622, 0.40 #3153), 0pz7h (0.50 #6247, 0.50 #119, 0.27 #13911), 02773m2 (0.50 #6231, 0.50 #103, 0.19 #30661), 01j7rd (0.50 #6425, 0.25 #12557, 0.25 #297), 04ns3gy (0.50 #7451, 0.25 #1323, 0.18 #13583), 01t6b4 (0.50 #6299, 0.25 #171, 0.11 #12431), 018ygt (0.33 #7089, 0.27 #14753, 0.25 #961), 0c3p7 (0.33 #7088, 0.25 #2493, 0.25 #960), 02xs0q (0.33 #6670, 0.25 #542, 0.21 #12802), 0p_2r (0.33 #6320, 0.25 #192, 0.19 #30661) >> Best rule #4685 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 02pgky2; >> query: (?x4617, 07lt7b) <- award_winner(?x4617, ?x6072), award_winner(?x4617, ?x3572), award_winner(?x4617, ?x826), award_nominee(?x163, ?x3572), profession(?x6072, ?x353), award_winner(?x289, ?x3572), written_by(?x392, ?x3572), honored_for(?x4617, ?x1280), student(?x2171, ?x6072), student(?x1368, ?x3572), ?x826 = 02kxbwx, award(?x197, ?x289), award(?x3572, ?x68) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6247 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 4 *> proper extension: 0gvstc3; 03nnm4t; *> query: (?x4617, 0pz7h) <- award_winner(?x4617, ?x8933), award_winner(?x4617, ?x6072), award_winner(?x4617, ?x3572), award_winner(?x4617, ?x2912), award_nominee(?x163, ?x3572), profession(?x6072, ?x353), award_winner(?x277, ?x3572), written_by(?x392, ?x3572), honored_for(?x4617, ?x1280), ?x8933 = 04glr5h, currency(?x6072, ?x170), award_nominee(?x2912, ?x2143), location(?x2143, ?x11072), gender(?x3572, ?x231) *> conf = 0.50 ranks of expected_values: 2, 3, 74, 112, 134 EVAL 03gt46z award_winner 02vqpx8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 35.000 23.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03gt46z award_winner 02778yp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 35.000 23.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03gt46z award_winner 0c7t58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 35.000 23.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03gt46z award_winner 0pz7h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 35.000 23.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 03gt46z award_winner 02773m2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 35.000 23.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1692-07tp2 PRED entity: 07tp2 PRED relation: medal PRED expected values: 02lq67 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.86 #6, 0.83 #3, 0.76 #5) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; 0chghy; ... >> query: (?x9251, 02lq67) <- olympics(?x9251, ?x391), form_of_government(?x9251, ?x48), service_location(?x9968, ?x9251) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07tp2 medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.857 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #1691-01mxqyk PRED entity: 01mxqyk PRED relation: artist! PRED expected values: 016ckq => 105 concepts (66 used for prediction) PRED predicted values (max 10 best out of 95): 015_1q (0.24 #158, 0.22 #4201, 0.21 #297), 03rhqg (0.20 #433, 0.17 #1407, 0.15 #1128), 01dtcb (0.20 #463, 0.08 #1576, 0.08 #1158), 016ckq (0.16 #1294, 0.15 #1433, 0.14 #1154), 03mp8k (0.15 #1457, 0.15 #1318, 0.14 #1178), 033hn8 (0.15 #1405, 0.14 #1266, 0.11 #431), 0181dw (0.14 #1432, 0.13 #1571, 0.12 #1153), 01cszh (0.13 #1402, 0.12 #1263, 0.11 #1123), 043g7l (0.12 #1143, 0.12 #1283, 0.12 #1422), 011k1h (0.11 #427, 0.10 #4471, 0.10 #5725) >> Best rule #158 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 0dj5q; >> query: (?x11621, 015_1q) <- religion(?x11621, ?x109), award_winner(?x2139, ?x11621), ?x109 = 01lp8 >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #1294 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 114 *> proper extension: 01rm8b; 015srx; *> query: (?x11621, 016ckq) <- artists(?x3928, ?x11621), ?x3928 = 0gywn, award(?x11621, ?x567) *> conf = 0.16 ranks of expected_values: 4 EVAL 01mxqyk artist! 016ckq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 66.000 0.240 http://example.org/music/record_label/artist #1690-05w88j PRED entity: 05w88j PRED relation: student! PRED expected values: 02h7qr => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 124): 0bwfn (0.14 #799, 0.08 #3949, 0.08 #1849), 015nl4 (0.10 #2166, 0.09 #591, 0.08 #1641), 017z88 (0.09 #2706, 0.05 #1131, 0.05 #606), 053mhx (0.07 #2394, 0.05 #1869, 0.05 #819), 0217m9 (0.06 #2795, 0.01 #3845), 09f2j (0.05 #1208, 0.04 #14861, 0.03 #10659), 017zq0 (0.05 #1082, 0.02 #557, 0.02 #1607), 06182p (0.05 #297, 0.05 #1872, 0.05 #822), 0bx8pn (0.05 #44, 0.05 #569, 0.02 #1619), 09r4xx (0.05 #122, 0.03 #2747, 0.01 #8523) >> Best rule #799 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 01tsbmv; 01m4kpp; >> query: (?x9704, 0bwfn) <- award(?x9704, ?x1921), type_of_union(?x9704, ?x566), ?x1921 = 0bs0bh >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05w88j student! 02h7qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 102.000 0.136 http://example.org/education/educational_institution/students_graduates./education/education/student #1689-0gvvm6l PRED entity: 0gvvm6l PRED relation: honored_for! PRED expected values: 0n8_m93 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 103): 0hr6lkl (0.16 #134, 0.09 #622, 0.06 #256), 0n8_m93 (0.11 #225, 0.10 #2441, 0.07 #103), 0hndn2q (0.11 #154, 0.07 #642, 0.04 #276), 0gmdkyy (0.11 #146, 0.05 #634, 0.04 #1122), 0h_cssd (0.09 #144, 0.05 #632, 0.04 #266), 02hn5v (0.07 #155, 0.07 #33, 0.04 #643), 0hhtgcw (0.07 #195, 0.04 #683, 0.03 #2391), 09gkdln (0.07 #106, 0.06 #1204, 0.06 #1326), 0bzmt8 (0.07 #84, 0.04 #450, 0.04 #816), 09q_6t (0.06 #370, 0.02 #1346, 0.02 #2322) >> Best rule #134 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0ds35l9; 02vxq9m; 0ds3t5x; 0g5qs2k; 02x3lt7; 01vksx; 02d44q; 0gmcwlb; 0dtfn; 017gm7; ... >> query: (?x8176, 0hr6lkl) <- nominated_for(?x8767, ?x8176), award(?x8176, ?x372), film_release_region(?x8176, ?x1917), ?x1917 = 01p1v >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #225 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 0ds35l9; 02vxq9m; 0ds3t5x; 0g5qs2k; 02x3lt7; 01vksx; 02d44q; 0gmcwlb; 0dtfn; 017gm7; ... *> query: (?x8176, 0n8_m93) <- nominated_for(?x8767, ?x8176), award(?x8176, ?x372), film_release_region(?x8176, ?x1917), ?x1917 = 01p1v *> conf = 0.11 ranks of expected_values: 2 EVAL 0gvvm6l honored_for! 0n8_m93 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 86.000 86.000 0.159 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #1688-0m2kd PRED entity: 0m2kd PRED relation: genre PRED expected values: 02b5_l => 87 concepts (53 used for prediction) PRED predicted values (max 10 best out of 99): 05p553 (0.44 #476, 0.42 #712, 0.36 #1067), 02l7c8 (0.39 #5220, 0.38 #1315, 0.38 #251), 01jfsb (0.35 #6046, 0.30 #247, 0.30 #4861), 02kdv5l (0.32 #710, 0.30 #1893, 0.29 #2129), 06cvj (0.25 #2, 0.12 #475, 0.10 #5207), 03npn (0.25 #6, 0.10 #1188, 0.08 #952), 01t_vv (0.25 #53, 0.09 #3128, 0.09 #2771), 01q03 (0.25 #4, 0.06 #6272, 0.05 #477), 0jdm8 (0.25 #80, 0.06 #6272, 0.02 #553), 0clz1b (0.25 #20, 0.06 #6272, 0.01 #1320) >> Best rule #476 for best value: >> intensional similarity = 4 >> extensional distance = 176 >> proper extension: 0dq626; 0crfwmx; 0661m4p; 05q4y12; 09lcsj; 080nwsb; 047fjjr; 01jwxx; 0gbfn9; 01d259; ... >> query: (?x430, 05p553) <- country(?x430, ?x94), film(?x11259, ?x430), film_release_region(?x430, ?x87), sibling(?x793, ?x11259) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #6272 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1237 *> proper extension: 0n2bh; 0gfzgl; 03y3bp7; 01f3p_; 02sqkh; 05fgr_; 06dfz1; 06w7mlh; 07wqr6; 0cskb; ... *> query: (?x430, ?x225) <- titles(?x162, ?x430), nominated_for(?x1712, ?x430), titles(?x162, ?x2128), genre(?x2128, ?x225) *> conf = 0.06 ranks of expected_values: 37 EVAL 0m2kd genre 02b5_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 87.000 53.000 0.438 http://example.org/film/film/genre #1687-03f1zhf PRED entity: 03f1zhf PRED relation: languages PRED expected values: 04306rv => 160 concepts (160 used for prediction) PRED predicted values (max 10 best out of 25): 0t_2 (0.14 #77, 0.11 #182, 0.10 #252), 064_8sq (0.10 #3271, 0.09 #3306, 0.09 #3481), 03k50 (0.08 #3402, 0.08 #3472, 0.07 #3262), 02bjrlw (0.08 #457, 0.05 #3260, 0.04 #3435), 02jfc (0.07 #456, 0.01 #1999), 06nm1 (0.07 #775, 0.06 #915, 0.04 #2668), 03x42 (0.06 #521, 0.05 #591, 0.05 #626), 07c9s (0.04 #3409, 0.04 #3444, 0.04 #3479), 04306rv (0.03 #703, 0.03 #3261, 0.03 #3296), 012w70 (0.03 #707, 0.02 #917, 0.01 #1127) >> Best rule #77 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 04zkj5; >> query: (?x9762, 0t_2) <- category(?x9762, ?x134), profession(?x9762, ?x1041), ?x1041 = 03gjzk, person(?x3480, ?x9762), languages(?x9762, ?x254) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #703 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 012d40; 014zfs; 0p3r8; 01vw20h; 01vw8mh; 013w7j; 01vvyd8; 05szp; 01mbwlb; 01dpsv; *> query: (?x9762, 04306rv) <- category(?x9762, ?x134), profession(?x9762, ?x1041), ?x1041 = 03gjzk, artists(?x378, ?x9762) *> conf = 0.03 ranks of expected_values: 9 EVAL 03f1zhf languages 04306rv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 160.000 160.000 0.143 http://example.org/people/person/languages #1686-06p03s PRED entity: 06p03s PRED relation: nationality PRED expected values: 02jx1 => 158 concepts (138 used for prediction) PRED predicted values (max 10 best out of 33): 09c7w0 (0.75 #8101, 0.74 #8301, 0.74 #7401), 02jx1 (0.29 #1333, 0.29 #433, 0.25 #833), 01p1v (0.25 #44, 0.02 #1844), 07ssc (0.20 #215, 0.17 #515, 0.17 #315), 0345h (0.17 #631, 0.06 #3631, 0.04 #3231), 0d04z6 (0.17 #371, 0.01 #2871, 0.01 #4071), 035qy (0.14 #434, 0.06 #934, 0.06 #1034), 0d060g (0.10 #1407, 0.10 #1107, 0.09 #1207), 03rk0 (0.08 #3646, 0.06 #13650, 0.04 #11347), 0hzlz (0.08 #723, 0.06 #823, 0.06 #1023) >> Best rule #8101 for best value: >> intensional similarity = 3 >> extensional distance = 536 >> proper extension: 04bdxl; 0d_84; 01q_ph; 0h5g_; 01rr9f; 05hj0n; 01n5309; 05gml8; 01vlj1g; 0mdqp; ... >> query: (?x11689, 09c7w0) <- profession(?x11689, ?x220), award(?x11689, ?x1237), participant(?x2263, ?x11689) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1333 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 26 *> proper extension: 05qhnq; *> query: (?x11689, 02jx1) <- role(?x11689, ?x315), role(?x11689, ?x212), ?x315 = 0l14md, profession(?x11689, ?x220), artists(?x302, ?x11689) *> conf = 0.29 ranks of expected_values: 2 EVAL 06p03s nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 158.000 138.000 0.745 http://example.org/people/person/nationality #1685-07l50_1 PRED entity: 07l50_1 PRED relation: nominated_for! PRED expected values: 0f4x7 04kxsb => 87 concepts (78 used for prediction) PRED predicted values (max 10 best out of 192): 04kxsb (0.67 #7290, 0.66 #11051, 0.38 #331), 099c8n (0.57 #763, 0.41 #2174, 0.38 #293), 0gqyl (0.55 #786, 0.50 #316, 0.29 #2197), 09td7p (0.55 #798, 0.38 #328, 0.21 #2209), 02z0dfh (0.50 #298, 0.40 #768, 0.23 #4702), 03hl6lc (0.50 #364, 0.33 #129, 0.30 #599), 0f4x7 (0.50 #261, 0.33 #26, 0.25 #731), 02w9sd7 (0.50 #359, 0.33 #124, 0.23 #4702), 02x4sn8 (0.50 #351, 0.13 #821, 0.12 #18345), 02n9nmz (0.46 #2175, 0.34 #764, 0.20 #17638) >> Best rule #7290 for best value: >> intensional similarity = 3 >> extensional distance = 743 >> proper extension: 06mmr; >> query: (?x11619, ?x2375) <- award_winner(?x11619, ?x1222), award(?x11619, ?x2375), award_nominee(?x1222, ?x57) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 07l50_1 nominated_for! 04kxsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 78.000 0.665 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 07l50_1 nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 87.000 78.000 0.665 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1684-0dclg PRED entity: 0dclg PRED relation: location_of_ceremony! PRED expected values: 02m3sd => 165 concepts (149 used for prediction) PRED predicted values (max 10 best out of 252): 0d06m5 (0.25 #82, 0.10 #586, 0.02 #7149), 0d3qd0 (0.25 #108, 0.10 #612, 0.02 #7175), 0dvld (0.10 #651, 0.06 #1911, 0.06 #903), 03m2fg (0.10 #686, 0.06 #938, 0.04 #1190), 02yy8 (0.10 #750, 0.06 #1002, 0.04 #1254), 03l26m (0.10 #739, 0.06 #991, 0.04 #1243), 0djywgn (0.10 #697, 0.06 #949, 0.04 #1201), 05cx7x (0.10 #681, 0.06 #933, 0.04 #1185), 01p4r3 (0.10 #649, 0.06 #901, 0.04 #1153), 0c_jc (0.10 #646, 0.06 #898, 0.04 #1150) >> Best rule #82 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 0gv10; >> query: (?x2254, 0d06m5) <- location_of_ceremony(?x2357, ?x2254), legislative_sessions(?x2357, ?x605) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dclg location_of_ceremony! 02m3sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 149.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #1683-062z7 PRED entity: 062z7 PRED relation: major_field_of_study PRED expected values: 02j62 => 110 concepts (93 used for prediction) PRED predicted values (max 10 best out of 136): 02j62 (0.87 #300, 0.84 #2697, 0.82 #6114), 01400v (0.87 #300, 0.84 #2697, 0.82 #6114), 037mh8 (0.60 #1390, 0.44 #2143, 0.40 #1239), 04rjg (0.57 #1810, 0.40 #1435, 0.40 #1284), 062z7 (0.44 #2118, 0.40 #1365, 0.40 #1214), 01mkq (0.40 #1355, 0.40 #1204, 0.33 #1505), 05qjt (0.40 #1274, 0.33 #604, 0.33 #529), 02lp1 (0.40 #1427, 0.33 #81, 0.29 #1953), 05qt0 (0.40 #1306, 0.33 #784, 0.29 #1832), 0pf2 (0.40 #1218, 0.33 #1519, 0.29 #1820) >> Best rule #300 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 05qfh; >> query: (?x2606, ?x947) <- major_field_of_study(?x12302, ?x2606), major_field_of_study(?x7818, ?x2606), major_field_of_study(?x1667, ?x2606), ?x1667 = 03v6t, major_field_of_study(?x947, ?x2606), major_field_of_study(?x2606, ?x373), ?x12302 = 01cf5, category(?x7818, ?x134) >> conf = 0.87 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 062z7 major_field_of_study 02j62 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 93.000 0.867 http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study #1682-01ww_vs PRED entity: 01ww_vs PRED relation: nationality PRED expected values: 02jx1 => 129 concepts (107 used for prediction) PRED predicted values (max 10 best out of 42): 07ssc (0.84 #6722, 0.84 #8543, 0.53 #10565), 09c7w0 (0.79 #7934, 0.75 #8646, 0.74 #7431), 02jx1 (0.53 #10565, 0.51 #10363, 0.33 #233), 04jpl (0.33 #8440), 0d04z6 (0.11 #71, 0.10 #171, 0.05 #4109), 0d060g (0.11 #7, 0.08 #3111, 0.07 #907), 03rk0 (0.09 #6265, 0.09 #6466, 0.08 #6566), 06q1r (0.08 #277, 0.06 #377, 0.06 #477), 0jgd (0.08 #202, 0.05 #4109, 0.02 #602), 0ht8h (0.07 #1101, 0.03 #1302, 0.02 #5214) >> Best rule #6722 for best value: >> intensional similarity = 4 >> extensional distance = 812 >> proper extension: 07m69t; 0b5x23; >> query: (?x11633, ?x512) <- place_of_birth(?x11633, ?x9042), location(?x11633, ?x7737), location(?x1549, ?x9042), country(?x9042, ?x512) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #10565 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2208 *> proper extension: 027rfxc; *> query: (?x11633, ?x512) <- place_of_birth(?x11633, ?x9042), place_of_birth(?x6122, ?x9042), award_nominee(?x6122, ?x100), nationality(?x6122, ?x512) *> conf = 0.53 ranks of expected_values: 3 EVAL 01ww_vs nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 107.000 0.839 http://example.org/people/person/nationality #1681-0r3w7 PRED entity: 0r3w7 PRED relation: contains! PRED expected values: 09c7w0 => 96 concepts (20 used for prediction) PRED predicted values (max 10 best out of 61): 09c7w0 (0.69 #8951, 0.62 #10739, 0.60 #4479), 0kpys (0.31 #9128, 0.30 #5550, 0.30 #2865), 0gx1l (0.25 #603, 0.20 #1498, 0.14 #2393), 059_c (0.25 #71, 0.20 #966, 0.12 #11702), 0d1y7 (0.25 #747, 0.20 #1642, 0.09 #7906), 07h34 (0.20 #4706, 0.20 #3811, 0.17 #8284), 030qb3t (0.20 #4576, 0.17 #8154, 0.10 #3681), 0f8l9c (0.20 #5417, 0.14 #1837, 0.12 #13466), 01x73 (0.19 #10850, 0.18 #7273, 0.18 #6379), 059rby (0.18 #7179, 0.18 #6285, 0.14 #1810) >> Best rule #8951 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 02dtg; >> query: (?x13207, 09c7w0) <- place_of_death(?x8974, ?x13207), place_of_death(?x8006, ?x13207), time_zones(?x13207, ?x2950), gender(?x8974, ?x231), profession(?x8006, ?x1041), ?x1041 = 03gjzk >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r3w7 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 20.000 0.692 http://example.org/location/location/contains #1680-0gtv7pk PRED entity: 0gtv7pk PRED relation: film_release_region PRED expected values: 07ssc 03rj0 => 110 concepts (102 used for prediction) PRED predicted values (max 10 best out of 286): 05qhw (0.93 #5510, 0.90 #2684, 0.90 #6403), 0d060g (0.92 #3568, 0.88 #3271, 0.88 #4311), 05v8c (0.92 #3280, 0.90 #2687, 0.89 #2538), 07ssc (0.89 #4021, 0.86 #6405, 0.86 #3872), 047yc (0.88 #3291, 0.86 #2698, 0.79 #2549), 03spz (0.87 #2912, 0.85 #3654, 0.81 #4397), 02vzc (0.85 #8815, 0.85 #1977, 0.84 #8517), 01znc_ (0.81 #5538, 0.80 #3305, 0.79 #2563), 03rk0 (0.81 #2725, 0.81 #3615, 0.79 #2576), 03rj0 (0.81 #2729, 0.78 #1392, 0.77 #1985) >> Best rule #5510 for best value: >> intensional similarity = 11 >> extensional distance = 52 >> proper extension: 07qg8v; 011yqc; 01fmys; 047svrl; 02mt51; 0db94w; 0bhwhj; 02qk3fk; 0ddbjy4; 07jqjx; >> query: (?x409, 05qhw) <- film_release_region(?x409, ?x7747), film_release_region(?x409, ?x1264), film_release_region(?x409, ?x429), film_release_region(?x409, ?x344), film_release_region(?x409, ?x252), ?x1264 = 0345h, film(?x4800, ?x409), ?x252 = 03_3d, ?x7747 = 07f1x, ?x429 = 03rt9, country(?x766, ?x344) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #4021 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 26 *> proper extension: 0cc5mcj; *> query: (?x409, 07ssc) <- film_release_region(?x409, ?x7747), film_release_region(?x409, ?x1603), film_release_region(?x409, ?x1264), film_release_region(?x409, ?x429), film_release_region(?x409, ?x344), film_release_region(?x409, ?x252), ?x1264 = 0345h, film(?x4800, ?x409), ?x252 = 03_3d, ?x7747 = 07f1x, ?x429 = 03rt9, ?x344 = 04gzd, ?x1603 = 06bnz *> conf = 0.89 ranks of expected_values: 4, 10 EVAL 0gtv7pk film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 110.000 102.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gtv7pk film_release_region 07ssc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 102.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1679-01vy_v8 PRED entity: 01vy_v8 PRED relation: profession PRED expected values: 02hrh1q => 101 concepts (86 used for prediction) PRED predicted values (max 10 best out of 49): 02hrh1q (0.93 #3102, 0.92 #4131, 0.90 #5014), 01d_h8 (0.81 #888, 0.81 #1623, 0.81 #1035), 0dxtg (0.72 #895, 0.71 #1042, 0.71 #1483), 03gjzk (0.60 #456, 0.45 #1191, 0.44 #1044), 02krf9 (0.45 #467, 0.40 #320, 0.30 #8678), 09jwl (0.25 #166, 0.19 #7225, 0.17 #8697), 0cbd2 (0.25 #448, 0.17 #1036, 0.16 #889), 0kyk (0.25 #176, 0.15 #470, 0.10 #9884), 018gz8 (0.20 #311, 0.15 #1046, 0.14 #899), 015h31 (0.15 #468, 0.07 #909, 0.06 #1056) >> Best rule #3102 for best value: >> intensional similarity = 3 >> extensional distance = 460 >> proper extension: 01bpc9; >> query: (?x4242, 02hrh1q) <- actor(?x802, ?x4242), award_winner(?x4386, ?x4242), profession(?x4242, ?x524) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vy_v8 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 86.000 0.929 http://example.org/people/person/profession #1678-0d9xq PRED entity: 0d9xq PRED relation: influenced_by! PRED expected values: 012vd6 => 104 concepts (46 used for prediction) PRED predicted values (max 10 best out of 173): 017yfz (0.20 #1193, 0.07 #2225, 0.02 #8942), 012vd6 (0.20 #1252, 0.03 #2284, 0.01 #9001), 02yy8 (0.14 #494, 0.04 #3615, 0.01 #3592), 0d3k14 (0.14 #450, 0.04 #3615, 0.01 #3548), 09bg4l (0.14 #132, 0.04 #3615, 0.01 #3230), 01wcp_g (0.10 #1073, 0.03 #2105, 0.03 #2622), 01pq5j7 (0.10 #1246, 0.02 #4346, 0.02 #11063), 0hnlx (0.10 #1571, 0.02 #2087), 0ph2w (0.07 #3255, 0.07 #4806, 0.06 #5840), 040db (0.06 #7826, 0.05 #6792, 0.05 #8342) >> Best rule #1193 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 01vsqvs; >> query: (?x5101, 017yfz) <- artists(?x505, ?x5101), gender(?x5101, ?x514), ?x514 = 02zsn, people(?x8523, ?x5101) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1252 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 01vsqvs; *> query: (?x5101, 012vd6) <- artists(?x505, ?x5101), gender(?x5101, ?x514), ?x514 = 02zsn, people(?x8523, ?x5101) *> conf = 0.20 ranks of expected_values: 2 EVAL 0d9xq influenced_by! 012vd6 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 46.000 0.200 http://example.org/influence/influence_node/influenced_by #1677-0h3k3f PRED entity: 0h3k3f PRED relation: nominated_for! PRED expected values: 0gq9h => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 198): 0gq9h (0.75 #1234, 0.65 #529, 0.64 #8286), 027c95y (0.68 #8463, 0.67 #14580, 0.66 #10580), 0k611 (0.62 #1244, 0.58 #8296, 0.58 #10413), 019f4v (0.61 #8279, 0.58 #8515, 0.56 #10396), 040njc (0.50 #8234, 0.50 #4944, 0.48 #8470), 0p9sw (0.50 #725, 0.47 #8483, 0.47 #8247), 054krc (0.50 #1005, 0.36 #5002, 0.32 #8292), 0l8z1 (0.49 #4987, 0.47 #8513, 0.46 #8277), 04dn09n (0.46 #5205, 0.44 #8260, 0.43 #4970), 02qyntr (0.46 #5114, 0.42 #8640, 0.41 #8404) >> Best rule #1234 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0bmpm; 0f4yh; 0pd57; 0m_q0; 0bm2x; 0gw7p; 01k7b0; 04v89z; 072192; 0cq8nx; ... >> query: (?x8735, 0gq9h) <- nominated_for(?x1243, ?x8735), film_art_direction_by(?x8735, ?x4896), ?x1243 = 0gr0m, award_winner(?x8735, ?x4180) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h3k3f nominated_for! 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.750 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1676-06t6dz PRED entity: 06t6dz PRED relation: nominated_for! PRED expected values: 0f4x7 => 79 concepts (66 used for prediction) PRED predicted values (max 10 best out of 285): 0gq9h (0.29 #6909, 0.26 #1479, 0.23 #15120), 0gs9p (0.26 #6911, 0.21 #3842, 0.19 #11162), 02qvyrt (0.25 #96, 0.23 #15120, 0.21 #14883), 02x1z2s (0.25 #142, 0.20 #14410, 0.20 #14172), 099tbz (0.25 #46, 0.20 #14410, 0.20 #14172), 02x17s4 (0.25 #94, 0.13 #330, 0.10 #3871), 019f4v (0.24 #6900, 0.21 #3831, 0.20 #526), 07bdd_ (0.23 #15120, 0.23 #1469, 0.22 #1705), 01c9dd (0.23 #15120, 0.21 #14883, 0.20 #14410), 099vwn (0.23 #15120, 0.21 #14883, 0.20 #14410) >> Best rule #6909 for best value: >> intensional similarity = 4 >> extensional distance = 808 >> proper extension: 05y0cr; >> query: (?x4788, 0gq9h) <- nominated_for(?x704, ?x4788), genre(?x4788, ?x53), ?x53 = 07s9rl0, award_winner(?x704, ?x72) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #15120 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1445 *> proper extension: 01tspc6; *> query: (?x4788, ?x2585) <- nominated_for(?x704, ?x4788), nominated_for(?x6716, ?x4788), award(?x6716, ?x2585), nominated_for(?x2585, ?x83) *> conf = 0.23 ranks of expected_values: 13 EVAL 06t6dz nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 79.000 66.000 0.285 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1675-01y06y PRED entity: 01y06y PRED relation: major_field_of_study PRED expected values: 0_jm => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 114): 03g3w (0.78 #275, 0.47 #896, 0.42 #1392), 02j62 (0.67 #279, 0.51 #5992, 0.51 #1644), 01mkq (0.63 #511, 0.45 #1380, 0.44 #263), 04rjg (0.56 #268, 0.53 #516, 0.42 #1385), 062z7 (0.56 #276, 0.41 #5865, 0.35 #1889), 0fdys (0.56 #288, 0.37 #536, 0.33 #909), 02ky346 (0.56 #264, 0.26 #512, 0.21 #1877), 037mh8 (0.47 #566, 0.44 #318, 0.32 #1435), 05qfh (0.44 #285, 0.32 #1278, 0.32 #1898), 03nfmq (0.44 #287, 0.21 #535, 0.15 #1404) >> Best rule #275 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 07szy; 07vk2; >> query: (?x12877, 03g3w) <- student(?x12877, ?x3994), major_field_of_study(?x12877, ?x742), ?x742 = 05qjt, institution(?x7817, ?x12877), ?x7817 = 02cq61 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #4406 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 163 *> proper extension: 01zzk4; *> query: (?x12877, 0_jm) <- state_province_region(?x12877, ?x10524), contains(?x1264, ?x12877), capital(?x10524, ?x5560), contains(?x10524, ?x14052), location_of_ceremony(?x566, ?x10524) *> conf = 0.17 ranks of expected_values: 41 EVAL 01y06y major_field_of_study 0_jm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 194.000 194.000 0.778 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1674-03j90 PRED entity: 03j90 PRED relation: profession PRED expected values: 0cbd2 05z96 => 233 concepts (125 used for prediction) PRED predicted values (max 10 best out of 111): 02hrh1q (0.87 #4365, 0.83 #2715, 0.82 #15021), 0cbd2 (0.76 #7959, 0.73 #5107, 0.72 #2857), 0dxtg (0.66 #6765, 0.66 #9317, 0.64 #8116), 01d_h8 (0.56 #1806, 0.50 #1956, 0.45 #6757), 02jknp (0.56 #1808, 0.50 #1958, 0.40 #6759), 05z96 (0.56 #2894, 0.33 #7996, 0.28 #5744), 09jwl (0.50 #320, 0.31 #1220, 0.29 #1520), 0kyk (0.48 #2881, 0.45 #5131, 0.45 #7983), 03gjzk (0.34 #8118, 0.32 #8418, 0.32 #9319), 05snw (0.31 #1144, 0.21 #1444, 0.21 #2494) >> Best rule #4365 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 06tp4h; 05vk_d; >> query: (?x10870, 02hrh1q) <- notable_people_with_this_condition(?x1502, ?x10870), notable_people_with_this_condition(?x1502, ?x1503), ?x1503 = 01pw2f1 >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #7959 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 02m4t; *> query: (?x10870, 0cbd2) <- influenced_by(?x10870, ?x5004), influenced_by(?x10598, ?x5004), ?x10598 = 0mb0 *> conf = 0.76 ranks of expected_values: 2, 6 EVAL 03j90 profession 05z96 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 233.000 125.000 0.867 http://example.org/people/person/profession EVAL 03j90 profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 233.000 125.000 0.867 http://example.org/people/person/profession #1673-0ptxj PRED entity: 0ptxj PRED relation: language PRED expected values: 02h40lc => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 46): 02h40lc (0.93 #828, 0.91 #2135, 0.91 #1424), 04306rv (0.28 #300, 0.22 #359, 0.21 #477), 03hkp (0.25 #74, 0.08 #192, 0.07 #251), 0jzc (0.25 #79, 0.08 #197, 0.06 #374), 0349s (0.25 #104, 0.02 #1229, 0.02 #753), 064_8sq (0.20 #258, 0.19 #966, 0.17 #1206), 02bjrlw (0.17 #355, 0.17 #296, 0.11 #532), 03_9r (0.13 #246, 0.07 #2261, 0.07 #2202), 06nm1 (0.11 #778, 0.11 #719, 0.11 #365), 0653m (0.11 #779, 0.07 #248, 0.06 #1136) >> Best rule #828 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 0gx9rvq; 035xwd; 047gpsd; 02gqm3; >> query: (?x5212, 02h40lc) <- cinematography(?x5212, ?x5862), film(?x382, ?x5212), film(?x772, ?x5212), ?x382 = 086k8 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ptxj language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.927 http://example.org/film/film/language #1672-0frm7n PRED entity: 0frm7n PRED relation: colors PRED expected values: 083jv => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.82 #896, 0.81 #1991, 0.63 #2010), 06fvc (0.59 #1953, 0.51 #1972, 0.35 #2011), 01l849 (0.50 #236, 0.40 #198, 0.36 #795), 019sc (0.50 #1937, 0.32 #531, 0.31 #1075), 09ggk (0.33 #34, 0.21 #1989, 0.15 #814), 03vtbc (0.24 #609, 0.21 #1989, 0.21 #532), 038hg (0.23 #1744, 0.21 #1989, 0.17 #1743), 088fh (0.23 #1744, 0.21 #1989, 0.17 #1743), 0jc_p (0.23 #1744, 0.17 #1743, 0.15 #814), 09q2t (0.23 #1744, 0.14 #1950, 0.13 #914) >> Best rule #896 for best value: >> intensional similarity = 20 >> extensional distance = 31 >> proper extension: 03dkx; >> query: (?x4661, 083jv) <- colors(?x4661, ?x3189), category(?x4661, ?x134), ?x134 = 08mbj5d, colors(?x8689, ?x3189), colors(?x6803, ?x3189), colors(?x2971, ?x3189), ?x2971 = 04112r, ?x8689 = 03v9yw, colors(?x12699, ?x3189), colors(?x9108, ?x3189), colors(?x5941, ?x3189), colors(?x5539, ?x3189), colors(?x3204, ?x3189), ?x6803 = 03by7wc, contains(?x455, ?x5539), ?x5941 = 017v71, ?x9108 = 01v3k2, citytown(?x5539, ?x12756), organization(?x3484, ?x12699), institution(?x865, ?x3204) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0frm7n colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.818 http://example.org/sports/sports_team/colors #1671-017zq0 PRED entity: 017zq0 PRED relation: school! PRED expected values: 0jmj7 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 94): 0jmj7 (0.58 #687, 0.53 #593, 0.38 #1439), 01slc (0.14 #59, 0.09 #435, 0.07 #623), 05m_8 (0.13 #661, 0.12 #567, 0.08 #849), 051vz (0.12 #587, 0.12 #681, 0.06 #1339), 07l8x (0.11 #725, 0.09 #631, 0.05 #913), 07l4z (0.10 #729, 0.10 #635, 0.07 #823), 07147 (0.10 #726, 0.06 #632, 0.04 #538), 06rpd (0.09 #451, 0.07 #75, 0.06 #733), 0jmm4 (0.09 #450, 0.07 #74, 0.04 #1484), 0cqt41 (0.09 #676, 0.06 #582, 0.05 #864) >> Best rule #687 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 08qnnv; >> query: (?x1440, 0jmj7) <- major_field_of_study(?x1440, ?x4268), institution(?x865, ?x1440), fraternities_and_sororities(?x1440, ?x3697), ?x865 = 02h4rq6 >> conf = 0.58 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017zq0 school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.578 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #1670-0jj6k PRED entity: 0jj6k PRED relation: source PRED expected values: 0jbk9 => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #32, 0.94 #35, 0.92 #10) >> Best rule #32 for best value: >> intensional similarity = 5 >> extensional distance = 166 >> proper extension: 0nm3n; 0mwxz; 0n2m7; 0frf6; 0k3g3; 0drrw; 0k1jg; >> query: (?x13776, 0jbk9) <- second_level_divisions(?x94, ?x13776), time_zones(?x13776, ?x2674), ?x94 = 09c7w0, ?x2674 = 02hcv8, contains(?x2623, ?x13776) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jj6k source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.940 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #1669-0bqthy PRED entity: 0bqthy PRED relation: team PRED expected values: 02pqcfz => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 275): 02plv57 (0.75 #109, 0.75 #97, 0.71 #65), 026w398 (0.75 #114, 0.75 #102, 0.67 #58), 02pqcfz (0.67 #57, 0.62 #99, 0.62 #96), 03d5m8w (0.67 #57, 0.62 #101, 0.62 #96), 02ptzz0 (0.67 #57, 0.62 #96, 0.61 #148), 02r2qt7 (0.67 #57, 0.62 #96, 0.61 #148), 01jvgt (0.23 #121, 0.18 #32, 0.03 #25), 0fw9vx (0.23 #121, 0.18 #32, 0.03 #25), 0jmk7 (0.23 #121, 0.18 #32, 0.03 #25), 0jm9w (0.23 #121, 0.18 #32, 0.03 #25) >> Best rule #109 for best value: >> intensional similarity = 35 >> extensional distance = 6 >> proper extension: 0f9rw9; >> query: (?x13045, 02plv57) <- team(?x13045, ?x12370), team(?x13045, ?x10846), team(?x13045, ?x9833), team(?x13045, ?x9576), team(?x13045, ?x8528), team(?x13045, ?x6803), team(?x13045, ?x4938), team(?x13045, ?x4804), team(?x13209, ?x9833), team(?x12798, ?x9833), team(?x11210, ?x9833), team(?x10673, ?x9833), team(?x9956, ?x9833), team(?x8824, ?x9833), team(?x6583, ?x9833), team(?x4803, ?x9833), ?x6583 = 0b_75k, ?x12798 = 0b_770, ?x6803 = 03by7wc, ?x10673 = 0b_6mr, position(?x9833, ?x4570), ?x8528 = 091tgz, ?x4803 = 0b_6jz, ?x8824 = 05g_nr, team(?x6002, ?x9576), ?x13209 = 0b_734, ?x6002 = 0cc8q3, ?x9956 = 0bzrsh, team(?x9070, ?x12370), ?x4938 = 027yf83, ?x11210 = 0b_6q5, teams(?x3983, ?x9576), sport(?x10846, ?x12913), ?x4804 = 03d555l, team(?x5755, ?x10846) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #57 for first EXPECTED value: *> intensional similarity = 35 *> extensional distance = 4 *> proper extension: 0b_6xf; *> query: (?x13045, ?x2303) <- team(?x13045, ?x12370), team(?x13045, ?x9833), team(?x13045, ?x9576), team(?x13045, ?x9147), team(?x13045, ?x8528), team(?x13045, ?x6803), team(?x13045, ?x6003), team(?x13045, ?x5032), team(?x12798, ?x9833), team(?x10673, ?x9833), team(?x9974, ?x9833), team(?x9146, ?x9833), team(?x7042, ?x9833), team(?x6802, ?x9833), team(?x6583, ?x9833), ?x6583 = 0b_75k, ?x12798 = 0b_770, ?x6803 = 03by7wc, ?x10673 = 0b_6mr, position(?x9833, ?x4570), ?x8528 = 091tgz, ?x9576 = 02qk2d5, ?x9146 = 0b_6qj, ?x7042 = 0b_72t, colors(?x9147, ?x663), team(?x6802, ?x10171), team(?x6802, ?x2303), team(?x10594, ?x9147), ?x6003 = 02py8_w, ?x10171 = 026w398, ?x9974 = 0b_6pv, sport(?x12370, ?x12913), ?x5032 = 04088s0, ?x10594 = 0b_756, instance_of_recurring_event(?x13045, ?x10863) *> conf = 0.67 ranks of expected_values: 3 EVAL 0bqthy team 02pqcfz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 29.000 29.000 0.750 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #1668-0g5838s PRED entity: 0g5838s PRED relation: film_crew_role PRED expected values: 09zzb8 => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 23): 09zzb8 (0.80 #805, 0.78 #636, 0.77 #939), 01pvkk (0.29 #1715, 0.28 #746, 0.28 #678), 01xy5l_ (0.24 #12, 0.13 #345, 0.13 #816), 02ynfr (0.20 #818, 0.18 #952, 0.18 #649), 02rh1dz (0.18 #9, 0.17 #108, 0.16 #174), 0d2b38 (0.18 #23, 0.16 #56, 0.14 #188), 0215hd (0.18 #16, 0.16 #820, 0.15 #651), 089g0h (0.13 #821, 0.12 #955, 0.12 #384), 089fss (0.11 #72, 0.11 #105, 0.11 #39), 02vs3x5 (0.11 #153, 0.08 #54, 0.07 #287) >> Best rule #805 for best value: >> intensional similarity = 5 >> extensional distance = 589 >> proper extension: 0dnvn3; 01ln5z; 03h_yy; 02_1sj; 0170_p; 035xwd; 03ckwzc; 0963mq; 048scx; 03t97y; ... >> query: (?x3076, 09zzb8) <- film_crew_role(?x3076, ?x1284), film_crew_role(?x3076, ?x1171), ?x1171 = 09vw2b7, genre(?x3076, ?x53), ?x1284 = 0ch6mp2 >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g5838s film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.804 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1667-02bg55 PRED entity: 02bg55 PRED relation: film_release_region PRED expected values: 0b90_r 0j1z8 0k6nt 0h7x 06qd3 => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 242): 0d060g (0.88 #2668, 0.83 #4063, 0.80 #3924), 0k6nt (0.85 #3243, 0.85 #3801, 0.84 #2823), 0b90_r (0.84 #2666, 0.81 #3504, 0.80 #3922), 05v8c (0.79 #294, 0.75 #574, 0.74 #1553), 04gzd (0.75 #2671, 0.72 #4066, 0.68 #288), 07ylj (0.71 #26, 0.40 #2689, 0.38 #4084), 01mjq (0.69 #598, 0.69 #1018, 0.65 #1577), 016wzw (0.69 #616, 0.65 #1595, 0.63 #2719), 01ls2 (0.67 #2674, 0.60 #991, 0.59 #571), 047yc (0.66 #2687, 0.65 #4082, 0.64 #304) >> Best rule #2668 for best value: >> intensional similarity = 8 >> extensional distance = 71 >> proper extension: 0gkz15s; 0bh8yn3; 06wbm8q; 0h95927; >> query: (?x6520, 0d060g) <- film_release_region(?x6520, ?x1917), film_release_region(?x6520, ?x1023), film_release_region(?x6520, ?x456), film_crew_role(?x6520, ?x468), film(?x4438, ?x6520), ?x1023 = 0ctw_b, ?x456 = 05qhw, ?x1917 = 01p1v >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #3243 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 111 *> proper extension: 0gtsx8c; 0dscrwf; 0gxtknx; 0gj9tn5; 0g9wdmc; 0ch26b_; 0by1wkq; 0gd0c7x; 0gvrws1; 0gydcp7; ... *> query: (?x6520, 0k6nt) <- film_release_region(?x6520, ?x1023), film_release_region(?x6520, ?x456), film_crew_role(?x6520, ?x468), film(?x4438, ?x6520), ?x1023 = 0ctw_b, ?x456 = 05qhw, country(?x6520, ?x279) *> conf = 0.85 ranks of expected_values: 2, 3, 11, 17, 39 EVAL 02bg55 film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 118.000 118.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02bg55 film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 118.000 118.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02bg55 film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02bg55 film_release_region 0j1z8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 118.000 118.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02bg55 film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.877 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1666-091tgz PRED entity: 091tgz PRED relation: team! PRED expected values: 0b_6rk 05g_nr 0b_6q5 => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 6): 0b_6rk (0.77 #207, 0.75 #129, 0.69 #201), 0b_6q5 (0.77 #210, 0.67 #186, 0.62 #120), 05g_nr (0.67 #190, 0.62 #124, 0.58 #184), 0b_6jz (0.62 #205, 0.50 #187, 0.50 #151), 0b_6h7 (0.62 #206, 0.50 #152, 0.50 #98), 0b_6qj (0.62 #203, 0.50 #191, 0.50 #185) >> Best rule #207 for best value: >> intensional similarity = 16 >> extensional distance = 11 >> proper extension: 02pjzvh; >> query: (?x8528, 0b_6rk) <- team(?x10594, ?x8528), team(?x9908, ?x8528), team(?x6583, ?x8528), ?x9908 = 0b_6lb, locations(?x6583, ?x9417), team(?x6583, ?x10171), team(?x6583, ?x9909), team(?x6583, ?x6003), instance_of_recurring_event(?x10594, ?x10863), location(?x3758, ?x9417), teams(?x9417, ?x8541), ?x9909 = 026wlnm, ?x3758 = 02_p5w, ?x10171 = 026w398, ?x6003 = 02py8_w, place_of_birth(?x2015, ?x9417) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 091tgz team! 0b_6q5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.769 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 091tgz team! 05g_nr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.769 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 091tgz team! 0b_6rk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.769 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #1665-033cnk PRED entity: 033cnk PRED relation: nutrient PRED expected values: 0h1wg 0466p20 02y_3rf 0f4hc 014d7f 02kc008 027g6p7 0f4kp => 22 concepts (21 used for prediction) PRED predicted values (max 10 best out of 33): 02kc008 (0.75 #296, 0.67 #325, 0.67 #315), 0f4kp (0.75 #296, 0.67 #321, 0.67 #318), 0f4hc (0.75 #296, 0.67 #322, 0.67 #311), 0hkwr (0.75 #296, 0.67 #329, 0.67 #314), 014d7f (0.75 #296, 0.60 #59, 0.56 #323), 0h1wg (0.75 #296, 0.60 #269, 0.56 #324), 07zqy (0.75 #296, 0.56 #328, 0.56 #316), 02y_3rf (0.75 #296, 0.56 #327, 0.56 #310), 0466p20 (0.75 #296, 0.50 #256, 0.33 #326), 027g6p7 (0.75 #296, 0.50 #264, 0.33 #236) >> Best rule #296 for best value: >> intensional similarity = 76 >> extensional distance = 6 >> proper extension: 06x4c; >> query: (?x6159, ?x12481) <- nutrient(?x6159, ?x12454), nutrient(?x6159, ?x11592), nutrient(?x6159, ?x9915), nutrient(?x6159, ?x9733), nutrient(?x6159, ?x9365), nutrient(?x6159, ?x6192), nutrient(?x6159, ?x6026), nutrient(?x6159, ?x5549), nutrient(?x6159, ?x2702), nutrient(?x6159, ?x2018), nutrient(?x10612, ?x9733), nutrient(?x9732, ?x9733), nutrient(?x9489, ?x9733), nutrient(?x9005, ?x9733), nutrient(?x8298, ?x9733), nutrient(?x7719, ?x9733), nutrient(?x7057, ?x9733), nutrient(?x6285, ?x9733), nutrient(?x6191, ?x9733), nutrient(?x6032, ?x9733), nutrient(?x5009, ?x9733), nutrient(?x3900, ?x9733), nutrient(?x3468, ?x9733), nutrient(?x2701, ?x9733), nutrient(?x1959, ?x9733), nutrient(?x1303, ?x9733), nutrient(?x1257, ?x9733), ?x2018 = 01sh2, ?x9005 = 04zpv, ?x5009 = 0fjfh, ?x1303 = 0fj52s, ?x7057 = 0fbdb, ?x3468 = 0cxn2, ?x9732 = 05z55, ?x2702 = 0838f, ?x6285 = 01645p, ?x6192 = 06jry, ?x1257 = 09728, ?x12454 = 025rw19, taxonomy(?x9365, ?x939), nutrient(?x3900, ?x13944), nutrient(?x3900, ?x12481), nutrient(?x3900, ?x11784), nutrient(?x3900, ?x11270), nutrient(?x3900, ?x10195), nutrient(?x3900, ?x9708), nutrient(?x3900, ?x8243), nutrient(?x3900, ?x7894), nutrient(?x3900, ?x6286), nutrient(?x3900, ?x3901), nutrient(?x3900, ?x1258), ?x11784 = 07zqy, ?x4068 = 0fbw6, ?x5549 = 025s7j4, ?x7719 = 0dj75, ?x3901 = 0466p20, ?x9708 = 061xhr, ?x9489 = 07j87, ?x9915 = 025tkqy, ?x1258 = 0h1wg, ?x6191 = 014j1m, ?x6026 = 025sf8g, ?x6286 = 02y_3rf, ?x1959 = 0f25w9, ?x8243 = 014d7f, ?x8298 = 037ls6, ?x2701 = 0hkxq, ?x10612 = 0frq6, ?x10195 = 0hkwr, ?x13944 = 0f4kp, ?x6032 = 01nkt, ?x939 = 04n6k, ?x7894 = 0f4hc, ?x11270 = 02kc008, nutrient(?x3900, ?x11592), nutrient(?x4068, ?x11592) >> conf = 0.75 => this is the best rule for 11 predicted values ranks of expected_values: 1, 2, 3, 5, 6, 8, 9, 10 EVAL 033cnk nutrient 0f4kp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 21.000 0.749 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 033cnk nutrient 027g6p7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 21.000 0.749 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 033cnk nutrient 02kc008 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 21.000 0.749 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 033cnk nutrient 014d7f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 22.000 21.000 0.749 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 033cnk nutrient 0f4hc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 22.000 21.000 0.749 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 033cnk nutrient 02y_3rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 21.000 0.749 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 033cnk nutrient 0466p20 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 21.000 0.749 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 033cnk nutrient 0h1wg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 22.000 21.000 0.749 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #1664-01y9jr PRED entity: 01y9jr PRED relation: film_crew_role PRED expected values: 09zzb8 => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 23): 09zzb8 (0.75 #465, 0.75 #133, 0.74 #432), 01pvkk (0.28 #141, 0.27 #1369, 0.27 #1302), 015h31 (0.27 #41, 0.25 #8, 0.11 #140), 02ynfr (0.19 #643, 0.18 #876, 0.18 #211), 0215hd (0.15 #214, 0.14 #879, 0.13 #480), 0d2b38 (0.12 #221, 0.12 #188, 0.11 #886), 089g0h (0.11 #116, 0.11 #182, 0.11 #215), 01xy5l_ (0.11 #874, 0.10 #442, 0.10 #641), 02_n3z (0.09 #200, 0.08 #466, 0.08 #865), 04pyp5 (0.09 #47, 0.06 #146, 0.06 #445) >> Best rule #465 for best value: >> intensional similarity = 4 >> extensional distance = 655 >> proper extension: 04vvh9; 0432_5; 031ldd; 05b6rdt; 063hp4; >> query: (?x6578, 09zzb8) <- produced_by(?x6578, ?x2221), film(?x6515, ?x6578), award(?x6515, ?x678), film_crew_role(?x6578, ?x468) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01y9jr film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.749 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1663-03f19q4 PRED entity: 03f19q4 PRED relation: award_nominee! PRED expected values: 01wgxtl => 106 concepts (39 used for prediction) PRED predicted values (max 10 best out of 585): 01vw20h (0.80 #72130, 0.80 #65151, 0.79 #13959), 03j3pg9 (0.80 #72130, 0.80 #65151, 0.79 #13959), 01wgxtl (0.58 #7579, 0.56 #2926, 0.50 #5252), 03f19q4 (0.58 #8202, 0.56 #3549, 0.50 #5875), 02x_h0 (0.33 #3608, 0.30 #5934, 0.29 #1282), 01vsgrn (0.29 #12932, 0.29 #1300, 0.25 #8279), 06mt91 (0.29 #13180, 0.29 #1548, 0.22 #3874), 02wwwv5 (0.29 #13670, 0.29 #2038, 0.22 #4364), 05mt_q (0.29 #286, 0.24 #11918, 0.22 #2612), 05mxw33 (0.29 #2230, 0.22 #4556, 0.20 #6882) >> Best rule #72130 for best value: >> intensional similarity = 3 >> extensional distance = 493 >> proper extension: 01wp8w7; 01zmpg; 06449; 0gcs9; 011hdn; 017vkx; 01wn718; 01wgfp6; 0flpy; 01m3b1t; ... >> query: (?x5203, ?x1125) <- award_nominee(?x5203, ?x1125), artists(?x2937, ?x5203), award(?x5203, ?x2139) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #7579 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 016kjs; 026yqrr; *> query: (?x5203, 01wgxtl) <- award_nominee(?x11371, ?x5203), award_nominee(?x4475, ?x5203), award_nominee(?x5203, ?x4476), place_of_birth(?x11371, ?x12941), ?x4475 = 01ws9n6 *> conf = 0.58 ranks of expected_values: 3 EVAL 03f19q4 award_nominee! 01wgxtl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 39.000 0.801 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1662-0c6g1l PRED entity: 0c6g1l PRED relation: film PRED expected values: 076zy_g 0b7l4x => 94 concepts (68 used for prediction) PRED predicted values (max 10 best out of 333): 017jd9 (0.09 #780, 0.06 #76944, 0.04 #2569), 0cc7hmk (0.09 #294, 0.06 #76944, 0.03 #80524), 017gm7 (0.09 #211, 0.03 #48309, 0.02 #2000), 0gtsx8c (0.09 #12, 0.03 #48309, 0.02 #1801), 07pd_j (0.09 #1186, 0.03 #48309, 0.02 #2975), 0c0zq (0.09 #1562, 0.03 #48309, 0.02 #3351), 01k0xy (0.09 #1281, 0.03 #48309, 0.02 #3070), 03n0cd (0.09 #1495, 0.03 #48309, 0.02 #3284), 0n1s0 (0.09 #1032, 0.03 #48309, 0.02 #2821), 03k8th (0.09 #1720, 0.03 #48309, 0.02 #3509) >> Best rule #780 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 04ls53; >> query: (?x2455, 017jd9) <- nominated_for(?x2455, ?x5128), ?x5128 = 08phg9, student(?x5288, ?x2455) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #2827 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 047cqr; *> query: (?x2455, 0b7l4x) <- nominated_for(?x2455, ?x5128), nominated_for(?x4035, ?x5128), ?x4035 = 01xndd *> conf = 0.04 ranks of expected_values: 31 EVAL 0c6g1l film 0b7l4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 94.000 68.000 0.091 http://example.org/film/actor/film./film/performance/film EVAL 0c6g1l film 076zy_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 68.000 0.091 http://example.org/film/actor/film./film/performance/film #1661-097h2 PRED entity: 097h2 PRED relation: languages PRED expected values: 02h40lc => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.98 #863, 0.98 #803, 0.98 #793), 06nm1 (0.07 #75, 0.05 #235, 0.04 #516), 064_8sq (0.05 #236, 0.03 #507, 0.02 #607), 03_9r (0.04 #885, 0.04 #895, 0.04 #845), 02bv9 (0.04 #238, 0.02 #509, 0.02 #249), 04306rv (0.04 #233, 0.02 #504, 0.02 #244), 02bjrlw (0.04 #231, 0.02 #502, 0.02 #242), 05zjd (0.02 #237) >> Best rule #863 for best value: >> intensional similarity = 5 >> extensional distance = 243 >> proper extension: 02r2j8; >> query: (?x9076, 02h40lc) <- languages(?x9076, ?x3592), language(?x4422, ?x3592), genre(?x9076, ?x12120), ?x4422 = 06zn2v2, languages(?x1093, ?x3592) >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 097h2 languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.984 http://example.org/tv/tv_program/languages #1660-0hv27 PRED entity: 0hv27 PRED relation: film! PRED expected values: 017jv5 => 79 concepts (67 used for prediction) PRED predicted values (max 10 best out of 51): 016tw3 (0.24 #11, 0.13 #2658, 0.13 #3192), 086k8 (0.18 #77, 0.16 #2, 0.15 #605), 017s11 (0.14 #78, 0.14 #3, 0.14 #304), 05qd_ (0.14 #235, 0.14 #9, 0.12 #2128), 03xq0f (0.14 #306, 0.12 #533, 0.11 #1058), 016tt2 (0.13 #79, 0.11 #230, 0.11 #607), 017jv5 (0.12 #90, 0.09 #241, 0.06 #694), 01795t (0.11 #1058, 0.07 #169, 0.06 #319), 054g1r (0.11 #1058, 0.06 #1928, 0.06 #638), 03rwz3 (0.11 #1058, 0.06 #44, 0.05 #345) >> Best rule #11 for best value: >> intensional similarity = 5 >> extensional distance = 49 >> proper extension: 0j_tw; >> query: (?x6181, 016tw3) <- genre(?x6181, ?x811), film_release_region(?x6181, ?x2984), film_release_region(?x6181, ?x205), ?x205 = 03rjj, ?x2984 = 082fr >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #90 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 07bz5; *> query: (?x6181, 017jv5) <- nominated_for(?x5348, ?x6181), list(?x6181, ?x3004), award(?x6181, ?x591) *> conf = 0.12 ranks of expected_values: 7 EVAL 0hv27 film! 017jv5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 79.000 67.000 0.235 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #1659-0bsjcw PRED entity: 0bsjcw PRED relation: award_winner PRED expected values: 039x1k => 43 concepts (13 used for prediction) PRED predicted values (max 10 best out of 1259): 0l6px (0.62 #5422, 0.57 #2955, 0.07 #10354), 0lpjn (0.50 #5537, 0.29 #3070, 0.05 #8005), 0bdt8 (0.43 #3888, 0.38 #6355, 0.05 #8823), 02l3_5 (0.43 #4220, 0.25 #6687, 0.04 #11619), 0lfbm (0.38 #6441, 0.35 #29605, 0.33 #17267), 018417 (0.38 #7362, 0.29 #4895, 0.11 #9830), 019f2f (0.38 #5480, 0.29 #3013, 0.08 #24668), 0dvld (0.38 #6264, 0.14 #3797, 0.05 #11196), 0h1mt (0.35 #29605, 0.33 #17267, 0.33 #19733), 02z1yj (0.35 #29605, 0.33 #17267, 0.33 #19733) >> Best rule #5422 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 094qd5; 03c7tr1; 0gqwc; 0gqyl; 02ppm4q; 0bb57s; >> query: (?x3989, 0l6px) <- award(?x2965, ?x3989), award(?x1126, ?x3989), ?x1126 = 0h1mt, award_winner(?x3989, ?x4349), nationality(?x2965, ?x94), award_nominee(?x368, ?x2965) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #29605 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 254 *> proper extension: 02z13jg; 0gqzz; 0g_w; 02x1z2s; *> query: (?x3989, ?x4630) <- award(?x4630, ?x3989), award(?x1126, ?x3989), award_winner(?x4225, ?x4630), participant(?x1126, ?x516), award_nominee(?x1126, ?x3836) *> conf = 0.35 ranks of expected_values: 27 EVAL 0bsjcw award_winner 039x1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 43.000 13.000 0.625 http://example.org/award/award_category/winners./award/award_honor/award_winner #1658-07vqnc PRED entity: 07vqnc PRED relation: actor PRED expected values: 03wy70 01x0sy => 96 concepts (67 used for prediction) PRED predicted values (max 10 best out of 838): 0725ny (0.50 #1572, 0.25 #643, 0.11 #3430), 01r4bps (0.50 #1742, 0.25 #813, 0.09 #7317), 01tszq (0.50 #1141, 0.25 #212, 0.09 #9292), 09gb9xh (0.39 #23234, 0.38 #27882, 0.38 #32529), 01b3bp (0.25 #1855, 0.25 #926, 0.09 #9292), 02hblj (0.25 #1788, 0.25 #859, 0.09 #9292), 021yw7 (0.25 #294, 0.17 #3081, 0.17 #2152), 0sw62 (0.25 #1690, 0.09 #9292, 0.08 #2619), 0sw6y (0.25 #1786, 0.09 #9292, 0.08 #13867), 031c2r (0.25 #1795, 0.09 #9292, 0.07 #13876) >> Best rule #1572 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 05f7w84; >> query: (?x11454, 0725ny) <- genre(?x11454, ?x1510), ?x1510 = 01hmnh, actor(?x11454, ?x3785), ?x3785 = 01lly5 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9292 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 44 *> proper extension: 05r1_t; 03y317; 0h95b81; 07s8z_l; *> query: (?x11454, ?x8685) <- titles(?x7712, ?x11454), genre(?x11454, ?x809), genre(?x5583, ?x7712), titles(?x7712, ?x8846), actor(?x8846, ?x8685) *> conf = 0.09 ranks of expected_values: 65, 231 EVAL 07vqnc actor 01x0sy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 96.000 67.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 07vqnc actor 03wy70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 96.000 67.000 0.500 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #1657-01mjq PRED entity: 01mjq PRED relation: country! PRED expected values: 07rlg 09w1n 06z6r 06zgc 09f6b => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 22): 06z6r (0.91 #229, 0.88 #97, 0.87 #427), 02y8z (0.69 #138, 0.69 #204, 0.68 #72), 09w1n (0.64 #73, 0.62 #95, 0.61 #227), 07rlg (0.62 #89, 0.62 #177, 0.62 #155), 096f8 (0.62 #93, 0.58 #159, 0.55 #203), 0486tv (0.62 #190, 0.59 #80, 0.58 #102), 0d1t3 (0.59 #186, 0.54 #98, 0.54 #164), 02vx4 (0.58 #92, 0.55 #180, 0.55 #70), 0d1tm (0.58 #90, 0.55 #178, 0.50 #134), 02bkg (0.55 #69, 0.52 #179, 0.50 #157) >> Best rule #229 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 07ylj; 03f2w; >> query: (?x1558, 06z6r) <- country(?x6733, ?x1558), film_release_region(?x124, ?x1558), ?x6733 = 01sgl >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4, 11, 12 EVAL 01mjq country! 09f6b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 181.000 181.000 0.909 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01mjq country! 06zgc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 181.000 181.000 0.909 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01mjq country! 06z6r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 181.000 0.909 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01mjq country! 09w1n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 181.000 181.000 0.909 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 01mjq country! 07rlg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 181.000 181.000 0.909 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #1656-0dd6bf PRED entity: 0dd6bf PRED relation: actor PRED expected values: 04bbv7 => 91 concepts (58 used for prediction) PRED predicted values (max 10 best out of 104): 0ckm4x (0.50 #1304, 0.43 #1238, 0.42 #458), 05j0wc (0.50 #896, 0.42 #458, 0.25 #372), 066l3y (0.42 #458, 0.36 #1265, 0.30 #1065), 0cpjgj (0.42 #458, 0.36 #1269, 0.29 #1203), 044_7j (0.42 #458, 0.33 #885, 0.33 #35), 04bbv7 (0.42 #458, 0.33 #299, 0.33 #234), 08141d (0.42 #458, 0.33 #909, 0.33 #319), 05z775 (0.42 #458, 0.33 #836, 0.33 #181), 091n7z (0.42 #458, 0.33 #256, 0.30 #1107), 05q_mg (0.42 #458, 0.25 #386, 0.17 #910) >> Best rule #1304 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 016ztl; >> query: (?x7029, 0ckm4x) <- actor(?x7029, ?x4632), profession(?x4632, ?x1032), location(?x4632, ?x3670), genre(?x7029, ?x1510), student(?x11559, ?x4632), category(?x4632, ?x134) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #458 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 02pb2bp; 076xkdz; *> query: (?x7029, ?x5779) <- country(?x7029, ?x252), actor(?x7029, ?x51), film(?x296, ?x7029), genre(?x7029, ?x1510), production_companies(?x7029, ?x7030), film(?x7030, ?x1334), actor(?x1334, ?x5779) *> conf = 0.42 ranks of expected_values: 6 EVAL 0dd6bf actor 04bbv7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 91.000 58.000 0.500 http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor #1655-021bk PRED entity: 021bk PRED relation: role PRED expected values: 05r5c 04rzd => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 97): 0342h (0.55 #244, 0.54 #184, 0.52 #485), 05r5c (0.22 #428, 0.20 #790, 0.19 #549), 018vs (0.21 #72, 0.19 #794, 0.18 #252), 02hnl (0.19 #86, 0.17 #808, 0.16 #1168), 0l14md (0.14 #67, 0.12 #1149, 0.10 #488), 0l14qv (0.12 #245, 0.10 #185, 0.08 #787), 01vj9c (0.07 #73, 0.07 #554, 0.07 #1155), 02sgy (0.06 #186, 0.06 #246, 0.05 #66), 06ncr (0.05 #393, 0.04 #453, 0.04 #213), 02fsn (0.05 #459, 0.05 #99, 0.04 #580) >> Best rule #244 for best value: >> intensional similarity = 3 >> extensional distance = 47 >> proper extension: 04bpm6; 01vrkdt; 01hrqc; 01q3_2; >> query: (?x2328, 0342h) <- role(?x2328, ?x75), award_nominee(?x2328, ?x2383), nominated_for(?x2328, ?x2329) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #428 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 111 *> proper extension: 01wdqrx; 07z542; 04mn81; 01hw6wq; 01w724; 01w272y; 01s21dg; 06m61; 06h2w; 01mvjl0; ... *> query: (?x2328, 05r5c) <- role(?x2328, ?x75), award_nominee(?x2328, ?x2383), instrumentalists(?x227, ?x2328) *> conf = 0.22 ranks of expected_values: 2, 14 EVAL 021bk role 04rzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 104.000 104.000 0.551 http://example.org/music/group_member/membership./music/group_membership/role EVAL 021bk role 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 104.000 0.551 http://example.org/music/group_member/membership./music/group_membership/role #1654-0bzjvm PRED entity: 0bzjvm PRED relation: ceremony! PRED expected values: 0gr0m => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 375): 0gr42 (0.88 #3207, 0.88 #2966, 0.88 #3689), 0gr0m (0.83 #1014, 0.82 #1980, 0.82 #1496), 0gqxm (0.75 #4581, 0.75 #8672, 0.67 #599), 0gqzz (0.75 #4581, 0.75 #8672, 0.33 #278), 02x201b (0.75 #4581, 0.75 #8672, 0.33 #171), 0czp_ (0.75 #4581, 0.75 #8672, 0.22 #1157), 094qd5 (0.22 #8430, 0.20 #1690, 0.19 #1691), 054krc (0.22 #8430, 0.20 #1690, 0.18 #1689), 04dn09n (0.22 #8430, 0.20 #1690, 0.18 #1689), 04kxsb (0.22 #8430, 0.20 #1690, 0.18 #1689) >> Best rule #3207 for best value: >> intensional similarity = 22 >> extensional distance = 41 >> proper extension: 0dznvw; >> query: (?x7940, 0gr42) <- ceremony(?x2222, ?x7940), ceremony(?x1972, ?x7940), ceremony(?x1703, ?x7940), ceremony(?x591, ?x7940), ceremony(?x77, ?x7940), award_winner(?x7940, ?x8871), ?x2222 = 0gs96, ?x1972 = 0gqyl, ?x591 = 0f4x7, award_winner(?x2585, ?x8871), film(?x8871, ?x2441), nominated_for(?x1703, ?x8217), nominated_for(?x1703, ?x2914), nominated_for(?x1703, ?x1308), nominated_for(?x1703, ?x499), ?x2914 = 012mrr, ?x499 = 04v8x9, ?x8217 = 04v89z, ?x1308 = 04mzf8, award(?x1872, ?x77), award(?x1425, ?x77), award(?x763, ?x1703) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1014 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 16 *> proper extension: 02glmx; 0d__c3; *> query: (?x7940, 0gr0m) <- ceremony(?x4573, ?x7940), ceremony(?x2222, ?x7940), ceremony(?x1972, ?x7940), ceremony(?x1313, ?x7940), ceremony(?x1245, ?x7940), ceremony(?x591, ?x7940), award_winner(?x7940, ?x4190), award_winner(?x7940, ?x2507), ?x2222 = 0gs96, ?x1245 = 0gqwc, nominated_for(?x1972, ?x8827), nominated_for(?x1972, ?x5927), nominated_for(?x1972, ?x4559), nominated_for(?x1972, ?x2094), ?x4559 = 0ccd3x, ?x4573 = 0gq_d, ?x591 = 0f4x7, ?x8827 = 01fwzk, costume_design_by(?x240, ?x4190), ?x2094 = 05z7c, ?x5927 = 011ypx, award_nominee(?x2507, ?x2230), award_winner(?x1972, ?x241), ?x1313 = 0gs9p, award(?x91, ?x1972) *> conf = 0.83 ranks of expected_values: 2 EVAL 0bzjvm ceremony! 0gr0m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 37.000 37.000 0.884 http://example.org/award/award_category/winners./award/award_honor/ceremony #1653-01vtmw6 PRED entity: 01vtmw6 PRED relation: award PRED expected values: 01bgqh => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 300): 054krc (0.43 #10188, 0.41 #5744, 0.40 #8976), 01bgqh (0.43 #5295, 0.31 #9739, 0.29 #1255), 0c4z8 (0.36 #5324, 0.33 #9768, 0.29 #1284), 01ck6h (0.35 #1333, 0.29 #121, 0.16 #5373), 02wh75 (0.35 #1221, 0.12 #2029, 0.10 #11321), 02qvyrt (0.35 #10226, 0.29 #2146, 0.27 #8610), 0l8z1 (0.31 #10164, 0.30 #8952, 0.30 #5720), 0gqz2 (0.31 #5737, 0.30 #8969, 0.29 #1293), 054ks3 (0.29 #2161, 0.29 #1353, 0.26 #2565), 02x17c2 (0.29 #2239, 0.24 #1431, 0.15 #9915) >> Best rule #10188 for best value: >> intensional similarity = 3 >> extensional distance = 132 >> proper extension: 07c0j; 0134s5; 02jqjm; 0bpk2; 02cpp; 07mvp; 015cxv; 0bk1p; 01v0sxx; 0p8h0; >> query: (?x6749, 054krc) <- artists(?x378, ?x6749), award(?x6749, ?x1801), music(?x2816, ?x6749) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #5295 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 68 *> proper extension: 0dtd6; 01vrwfv; 011z3g; 0178_w; 015cqh; 013w8y; 033s6; 0mjn2; 014kyy; 027kwc; *> query: (?x6749, 01bgqh) <- artists(?x5300, ?x6749), award(?x6749, ?x1801), ?x5300 = 02k_kn *> conf = 0.43 ranks of expected_values: 2 EVAL 01vtmw6 award 01bgqh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 145.000 0.433 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1652-0crs0b8 PRED entity: 0crs0b8 PRED relation: film_release_region PRED expected values: 035qy => 104 concepts (101 used for prediction) PRED predicted values (max 10 best out of 210): 0f8l9c (0.91 #2587, 0.90 #3440, 0.90 #2245), 03h64 (0.89 #1787, 0.89 #1274, 0.85 #2297), 06mkj (0.89 #1263, 0.87 #3481, 0.86 #2628), 06bnz (0.89 #1250, 0.86 #1763, 0.82 #2615), 05qhw (0.88 #360, 0.87 #1213, 0.87 #2578), 03rjj (0.87 #3418, 0.86 #2565, 0.85 #2223), 059j2 (0.87 #1233, 0.86 #3451, 0.82 #1403), 015fr (0.87 #1216, 0.85 #1729, 0.84 #2239), 0b90_r (0.87 #1199, 0.85 #1712, 0.81 #346), 05v8c (0.87 #1215, 0.83 #1728, 0.78 #2238) >> Best rule #2587 for best value: >> intensional similarity = 5 >> extensional distance = 95 >> proper extension: 0m63c; >> query: (?x9209, 0f8l9c) <- production_companies(?x9209, ?x9518), film_release_region(?x9209, ?x1174), ?x1174 = 047yc, production_companies(?x5576, ?x9518), film_crew_role(?x5576, ?x1284) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #1406 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 49 *> proper extension: 02d44q; *> query: (?x9209, 035qy) <- country(?x9209, ?x512), film_crew_role(?x9209, ?x468), film_release_region(?x9209, ?x985), ?x468 = 02r96rf, featured_film_locations(?x9209, ?x10165), ?x985 = 0k6nt *> conf = 0.82 ranks of expected_values: 18 EVAL 0crs0b8 film_release_region 035qy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 104.000 101.000 0.907 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1651-0fs9vc PRED entity: 0fs9vc PRED relation: film_crew_role PRED expected values: 015h31 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.71 #713, 0.70 #936, 0.70 #1532), 09zzb8 (0.70 #929, 0.70 #892, 0.69 #706), 09vw2b7 (0.60 #7, 0.58 #712, 0.58 #1531), 01vx2h (0.54 #198, 0.50 #124, 0.37 #383), 0dxtw (0.50 #12, 0.38 #86, 0.34 #1536), 015h31 (0.31 #121, 0.26 #195, 0.24 #158), 01pvkk (0.30 #14, 0.27 #1538, 0.26 #905), 0d2b38 (0.27 #139, 0.21 #176, 0.15 #213), 02rh1dz (0.20 #11, 0.15 #85, 0.11 #381), 02vs3x5 (0.20 #26, 0.15 #100, 0.08 #137) >> Best rule #713 for best value: >> intensional similarity = 4 >> extensional distance = 744 >> proper extension: 06w99h3; 0g5qs2k; 0gxtknx; 0gd0c7x; 031778; 0gvrws1; 06ztvyx; 0h03fhx; 0gkz3nz; 062zm5h; ... >> query: (?x7171, 0ch6mp2) <- film(?x105, ?x7171), nominated_for(?x1053, ?x7171), film_release_distribution_medium(?x7171, ?x81), film_crew_role(?x7171, ?x468) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #121 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 0b60sq; *> query: (?x7171, 015h31) <- nominated_for(?x541, ?x7171), nominated_for(?x1053, ?x7171), ?x1053 = 0gqzz *> conf = 0.31 ranks of expected_values: 6 EVAL 0fs9vc film_crew_role 015h31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 67.000 0.708 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1650-0353xq PRED entity: 0353xq PRED relation: executive_produced_by PRED expected values: 01k_0fp => 113 concepts (81 used for prediction) PRED predicted values (max 10 best out of 129): 05hj_k (0.27 #4108, 0.13 #1599, 0.12 #13384), 06q8hf (0.23 #4176, 0.12 #13452, 0.11 #5178), 079vf (0.13 #4013, 0.08 #5015, 0.06 #7522), 06pj8 (0.13 #4066, 0.05 #14593, 0.05 #13342), 04jspq (0.11 #1651, 0.05 #7417, 0.04 #2908), 02z6l5f (0.09 #117, 0.05 #10398, 0.05 #7637), 0glyyw (0.07 #8963, 0.07 #7707, 0.06 #10969), 04pqqb (0.07 #1618, 0.03 #367, 0.03 #13403), 0343h (0.06 #4053, 0.04 #1544, 0.03 #1796), 03c9pqt (0.06 #10526, 0.05 #11780, 0.05 #7012) >> Best rule #4108 for best value: >> intensional similarity = 6 >> extensional distance = 109 >> proper extension: 083shs; 0b73_1d; 03mh_tp; 08952r; 04nnpw; 03t79f; 049xgc; 05v38p; 04y9mm8; 043mk4y; ... >> query: (?x5318, 05hj_k) <- country(?x5318, ?x512), film_crew_role(?x5318, ?x137), language(?x5318, ?x254), executive_produced_by(?x5318, ?x2170), ?x254 = 02h40lc, currency(?x2170, ?x1099) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0353xq executive_produced_by 01k_0fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 81.000 0.270 http://example.org/film/film/executive_produced_by #1649-0jpy_ PRED entity: 0jpy_ PRED relation: category PRED expected values: 08mbj5d => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #54, 0.76 #69, 0.75 #70) >> Best rule #54 for best value: >> intensional similarity = 3 >> extensional distance = 470 >> proper extension: 010bnr; >> query: (?x12472, 08mbj5d) <- source(?x12472, ?x958), ?x958 = 0jbk9, place(?x12472, ?x12472) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jpy_ category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.778 http://example.org/common/topic/webpage./common/webpage/category #1648-05p8bf9 PRED entity: 05p8bf9 PRED relation: position PRED expected values: 02_j1w => 26 concepts (26 used for prediction) PRED predicted values (max 10 best out of 5): 0dgrmp (0.91 #30, 0.88 #42, 0.87 #36), 02_j1w (0.82 #43, 0.82 #62, 0.81 #8), 03f0fp (0.54 #91, 0.52 #85, 0.50 #129), 02md_2 (0.54 #91, 0.49 #137, 0.44 #103), 02qvgy (0.52 #85, 0.50 #129, 0.50 #128) >> Best rule #30 for best value: >> intensional similarity = 21 >> extensional distance = 448 >> proper extension: 02b15h; 05jx2d; 05kjc6; 03y_f8; 02gys2; 08pgl8; 0d_q40; 02jgm0; 02q3n9c; 047g6m; ... >> query: (?x13530, ?x203) <- position(?x13530, ?x203), position(?x13530, ?x63), ?x63 = 02sdk9v, position(?x13530, ?x60), ?x60 = 02nzb8, team(?x203, ?x13445), team(?x203, ?x12780), team(?x203, ?x12269), team(?x203, ?x11339), team(?x203, ?x10956), team(?x203, ?x6871), team(?x203, ?x6566), ?x6566 = 0329t7, position(?x8689, ?x203), ?x11339 = 042rlf, ?x8689 = 03v9yw, ?x12780 = 019mdt, ?x10956 = 056zf9, ?x13445 = 042l8n, ?x12269 = 0gfnqh, ?x6871 = 05r_x5 >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 471 *> proper extension: 0568qz; 06cmd2; 042rlf; 04kn29; 05nsfc; *> query: (?x13530, ?x530) <- position(?x13530, ?x203), position(?x13530, ?x63), ?x63 = 02sdk9v, position(?x13530, ?x60), ?x60 = 02nzb8, team(?x203, ?x13502), team(?x203, ?x8515), team(?x203, ?x8455), team(?x203, ?x8106), team(?x203, ?x6926), team(?x203, ?x6566), team(?x203, ?x3032), ?x6566 = 0329t7, ?x3032 = 01j95f, ?x13502 = 025rpyx, ?x8106 = 02rxrh, ?x6926 = 06c7mk, position(?x530, ?x203), ?x8515 = 096cw_, ?x8455 = 01xbp7 *> conf = 0.82 ranks of expected_values: 2 EVAL 05p8bf9 position 02_j1w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 26.000 26.000 0.909 http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position #1647-0j1_3 PRED entity: 0j1_3 PRED relation: contains PRED expected values: 01vfwd => 101 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2698): 01zll8 (0.70 #47096, 0.59 #20602, 0.53 #8829), 03ryn (0.64 #23548, 0.57 #32377, 0.51 #20604), 0j1_3 (0.57 #23547, 0.48 #47098, 0.08 #47097), 01vfwd (0.57 #23547, 0.08 #47097, 0.07 #8451), 01bkb (0.57 #23547, 0.08 #47097, 0.07 #7735), 0chghy (0.33 #20603, 0.04 #14751, 0.03 #26526), 0rh6k (0.23 #38266, 0.14 #5891, 0.11 #8835), 02_286 (0.23 #38266, 0.07 #14785, 0.07 #5955), 0tl6d (0.23 #38266, 0.07 #7009, 0.05 #9953), 01_d4 (0.23 #38266, 0.05 #23773, 0.04 #35548) >> Best rule #47096 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 05g56; >> query: (?x14384, ?x11793) <- contains(?x14384, ?x12783), contains(?x14384, ?x11611), location(?x2669, ?x11611), administrative_division(?x11793, ?x12783) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #23547 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 36 *> proper extension: 0dg3n1; 06pvr; 02qkt; 0j0k; 07c5l; 049nq; 04swx; *> query: (?x14384, ?x11382) <- contains(?x14384, ?x13749), contains(?x14384, ?x11611), location(?x2669, ?x11611), film_release_region(?x1988, ?x11611), administrative_parent(?x13749, ?x3749), contains(?x3749, ?x11382) *> conf = 0.57 ranks of expected_values: 4 EVAL 0j1_3 contains 01vfwd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 101.000 16.000 0.703 http://example.org/location/location/contains #1646-07s846j PRED entity: 07s846j PRED relation: nominated_for! PRED expected values: 0gs9p => 89 concepts (81 used for prediction) PRED predicted values (max 10 best out of 196): 04dn09n (0.78 #6578, 0.68 #10192, 0.68 #10191), 0gr4k (0.78 #6578, 0.68 #10192, 0.68 #10191), 09d28z (0.68 #10192, 0.68 #10191, 0.67 #10621), 027c924 (0.68 #10192, 0.68 #10191, 0.67 #10621), 027c95y (0.68 #10192, 0.68 #10191, 0.67 #10621), 0gs9p (0.68 #3443, 0.68 #3655, 0.55 #897), 02rdxsh (0.67 #10621, 0.67 #6577, 0.66 #10190), 02qvyrt (0.51 #925, 0.31 #1561, 0.31 #2545), 0gq_v (0.49 #862, 0.46 #2134, 0.42 #1498), 0gs96 (0.40 #920, 0.32 #2192, 0.30 #1556) >> Best rule #6578 for best value: >> intensional similarity = 4 >> extensional distance = 596 >> proper extension: 015g28; 06w7mlh; 06mmr; >> query: (?x4047, ?x1443) <- award(?x4047, ?x1443), award_winner(?x4047, ?x163), nominated_for(?x1443, ?x155), ceremony(?x1443, ?x747) >> conf = 0.78 => this is the best rule for 2 predicted values *> Best rule #3443 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 253 *> proper extension: 02rjv2w; 01c9d; *> query: (?x4047, 0gs9p) <- nominated_for(?x1307, ?x4047), ?x1307 = 0gq9h, award_winner(?x4047, ?x163) *> conf = 0.68 ranks of expected_values: 6 EVAL 07s846j nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 89.000 81.000 0.778 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1645-05nn4k PRED entity: 05nn4k PRED relation: student! PRED expected values: 01c333 0kqj1 => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 171): 03ksy (0.33 #106, 0.12 #633, 0.06 #9578), 01w3v (0.33 #15, 0.12 #542, 0.03 #9487), 07vyf (0.33 #138, 0.12 #665, 0.02 #2769), 04b_46 (0.15 #1806, 0.08 #4963, 0.05 #6016), 0bwfn (0.13 #5010, 0.12 #801, 0.12 #10799), 08815 (0.09 #5791, 0.06 #9474, 0.06 #12106), 065y4w7 (0.08 #10539, 0.08 #5803, 0.08 #4750), 09f2j (0.07 #3316, 0.06 #4895, 0.05 #26994), 05nn2c (0.07 #9999, 0.06 #12104, 0.06 #14210), 01w5m (0.06 #4841, 0.06 #26940, 0.05 #12209) >> Best rule #106 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0cbgl; >> query: (?x4660, 03ksy) <- student(?x6732, ?x4660), ?x6732 = 0gdm1, company(?x4660, ?x3323) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #9607 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 158 *> proper extension: 0143wl; 03s9v; 0pqzh; 047g6; 01s7z0; *> query: (?x4660, 0kqj1) <- student(?x6732, ?x4660), major_field_of_study(?x6732, ?x1327), company(?x4660, ?x3323) *> conf = 0.01 ranks of expected_values: 151 EVAL 05nn4k student! 0kqj1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 109.000 109.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 05nn4k student! 01c333 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 109.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #1644-02w4v PRED entity: 02w4v PRED relation: artists! PRED expected values: 02w4v => 65 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1): 03_d0 (0.03 #6325, 0.03 #6644, 0.03 #7280) >> Best rule #6325 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 07sbbz2; 03_d0; 02vjzr; 0g293; 0h08p; >> query: (?x3108, 03_d0) <- artists(?x3108, ?x6877), artists(?x3108, ?x5543), participant(?x6059, ?x6877), student(?x6417, ?x6877), role(?x5543, ?x227), award_winner(?x6877, ?x5298) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02w4v artists! 02w4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 45.000 0.034 http://example.org/music/genre/artists #1643-0n04r PRED entity: 0n04r PRED relation: country PRED expected values: 09c7w0 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 43): 09c7w0 (0.80 #1568, 0.79 #1128, 0.79 #2061), 0h7x (0.57 #2429, 0.11 #684, 0.09 #873), 07ssc (0.29 #390, 0.25 #452, 0.24 #890), 0f8l9c (0.18 #84, 0.14 #393, 0.12 #147), 0345h (0.17 #1345, 0.16 #901, 0.15 #92), 0d060g (0.15 #73, 0.05 #382, 0.05 #1326), 0chghy (0.14 #1061, 0.05 #823, 0.04 #1330), 0ctw_b (0.14 #1061, 0.03 #24, 0.03 #212), 0d0vqn (0.11 #684, 0.10 #3981, 0.09 #873), 03_3d (0.11 #684, 0.10 #3981, 0.09 #873) >> Best rule #1568 for best value: >> intensional similarity = 4 >> extensional distance = 466 >> proper extension: 02tqm5; >> query: (?x4024, 09c7w0) <- nominated_for(?x3637, ?x4024), nominated_for(?x198, ?x4024), film(?x4240, ?x4024), student(?x3878, ?x4240) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n04r country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.799 http://example.org/film/film/country #1642-0299hs PRED entity: 0299hs PRED relation: genre PRED expected values: 01jfsb 06nbt => 126 concepts (51 used for prediction) PRED predicted values (max 10 best out of 105): 07s9rl0 (0.91 #4846, 0.89 #709, 0.86 #5201), 03k9fj (0.80 #2254, 0.51 #1428, 0.50 #2964), 01jfsb (0.75 #4384, 0.71 #2019, 0.68 #5093), 05p553 (0.74 #4730, 0.63 #1539, 0.52 #3784), 01hmnh (0.64 #371, 0.35 #4270, 0.33 #135), 02l7c8 (0.57 #251, 0.37 #1077, 0.37 #841), 060__y (0.53 #3087, 0.17 #5335, 0.17 #2495), 0hcr (0.39 #4276, 0.36 #377, 0.20 #23), 02n4kr (0.29 #2487, 0.27 #3906, 0.21 #3315), 082gq (0.29 #266, 0.22 #738, 0.21 #620) >> Best rule #4846 for best value: >> intensional similarity = 6 >> extensional distance = 377 >> proper extension: 050r1z; 0n0bp; 0209xj; 02py4c8; 0pv2t; 0p_th; 0jym0; 0c_j9x; 083skw; 02rjv2w; ... >> query: (?x3433, 07s9rl0) <- genre(?x3433, ?x225), honored_for(?x1601, ?x3433), genre(?x4452, ?x225), genre(?x2892, ?x225), ?x2892 = 05q54f5, ?x4452 = 034r25 >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #4384 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 316 *> proper extension: 015qsq; 06rmdr; 06g77c; 02qzmz6; 0277j40; 0291hr; *> query: (?x3433, 01jfsb) <- genre(?x3433, ?x225), featured_film_locations(?x3433, ?x479), genre(?x4269, ?x225), genre(?x1932, ?x225), ?x1932 = 0btyf5z, ?x4269 = 05sns6 *> conf = 0.75 ranks of expected_values: 3, 45 EVAL 0299hs genre 06nbt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 126.000 51.000 0.905 http://example.org/film/film/genre EVAL 0299hs genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 51.000 0.905 http://example.org/film/film/genre #1641-04f62k PRED entity: 04f62k PRED relation: actor! PRED expected values: 0dr1c2 => 78 concepts (27 used for prediction) PRED predicted values (max 10 best out of 168): 031kyy (0.40 #151, 0.13 #679, 0.12 #943), 02rhwjr (0.40 #258, 0.12 #1050, 0.10 #786), 08cl7s (0.20 #154, 0.10 #682, 0.09 #946), 03d3ht (0.18 #980, 0.16 #716, 0.11 #1244), 01lk02 (0.15 #956, 0.13 #692, 0.10 #428), 045nc5 (0.10 #524, 0.10 #788, 0.09 #1052), 02kwcj (0.10 #783, 0.09 #1047, 0.07 #1311), 04svwx (0.10 #765, 0.09 #1029, 0.07 #1293), 017dtf (0.09 #992, 0.07 #1256, 0.06 #728), 02v5xg (0.09 #962, 0.06 #698, 0.04 #1492) >> Best rule #151 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 03fghg; 01rddlc; 03cz4j; >> query: (?x12484, 031kyy) <- profession(?x12484, ?x1383), ?x1383 = 0np9r, special_performance_type(?x12484, ?x296), film(?x12484, ?x7029), ?x7029 = 0dd6bf >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #656 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 29 *> proper extension: 01kymm; *> query: (?x12484, 0dr1c2) <- profession(?x12484, ?x1383), ?x1383 = 0np9r, special_performance_type(?x12484, ?x296), actor(?x13050, ?x12484), gender(?x12484, ?x231) *> conf = 0.03 ranks of expected_values: 55 EVAL 04f62k actor! 0dr1c2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 78.000 27.000 0.400 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #1640-038c0q PRED entity: 038c0q PRED relation: draft! PRED expected values: 0jmdb 0jm74 0jmcv => 16 concepts (16 used for prediction) PRED predicted values (max 10 best out of 233): 01k8vh (0.93 #445, 0.78 #1061, 0.77 #520), 0jm74 (0.93 #445, 0.78 #1061, 0.77 #520), 0jmmn (0.93 #445, 0.78 #1061, 0.77 #520), 0jmfb (0.93 #445, 0.78 #1061, 0.77 #520), 0jm4v (0.93 #445, 0.78 #1061, 0.77 #520), 0jm9w (0.93 #445, 0.78 #1061, 0.77 #520), 0jmcv (0.93 #445, 0.78 #1061, 0.77 #520), 04cxw5b (0.93 #445, 0.78 #1061, 0.77 #520), 0jmdb (0.78 #1061, 0.77 #520, 0.77 #519), 0289q (0.50 #1145, 0.50 #223, 0.46 #987) >> Best rule #445 for best value: >> intensional similarity = 51 >> extensional distance = 1 >> proper extension: 092j54; >> query: (?x2569, ?x5419) <- draft(?x10409, ?x2569), draft(?x9937, ?x2569), school(?x2569, ?x10945), school(?x2569, ?x10666), school(?x2569, ?x5907), school(?x2569, ?x4980), school(?x2569, ?x3416), ?x3416 = 02183k, school(?x9937, ?x6953), ?x5907 = 01jq4b, colors(?x10666, ?x663), institution(?x620, ?x10945), school(?x580, ?x10666), student(?x10945, ?x12412), student(?x10945, ?x7795), major_field_of_study(?x10666, ?x1668), draft(?x10409, ?x8133), ?x6953 = 01jq0j, place_of_birth(?x12412, ?x479), student(?x4980, ?x691), ?x620 = 07s6fsf, major_field_of_study(?x4980, ?x12158), major_field_of_study(?x4980, ?x10046), major_field_of_study(?x4980, ?x6756), colors(?x10409, ?x4557), profession(?x12412, ?x1032), team(?x4747, ?x9937), major_field_of_study(?x10945, ?x1527), ?x10046 = 041y2, nominated_for(?x691, ?x3075), gender(?x12412, ?x231), teams(?x1523, ?x9937), school(?x8133, ?x2175), award_nominee(?x3082, ?x7795), draft(?x5419, ?x8133), award_winner(?x4817, ?x691), contains(?x94, ?x4980), ?x4817 = 0fwy0h, ?x6756 = 0_jm, award_winner(?x691, ?x3074), profession(?x691, ?x4725), award_nominee(?x7795, ?x906), ?x479 = 02dtg, category(?x4980, ?x134), student(?x10666, ?x8744), ?x2175 = 01ptt7, institution(?x4981, ?x4980), team(?x10097, ?x9937), location(?x691, ?x12929), student(?x12158, ?x8375), taxonomy(?x12158, ?x939) >> conf = 0.93 => this is the best rule for 8 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 7, 9 EVAL 038c0q draft! 0jmcv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 16.000 16.000 0.933 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 038c0q draft! 0jm74 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 16.000 16.000 0.933 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 038c0q draft! 0jmdb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 16.000 16.000 0.933 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #1639-01sxq9 PRED entity: 01sxq9 PRED relation: student! PRED expected values: 017z88 => 131 concepts (102 used for prediction) PRED predicted values (max 10 best out of 62): 017j69 (0.17 #145, 0.03 #1726, 0.02 #8050), 013807 (0.17 #411, 0.02 #1992), 06182p (0.11 #825, 0.02 #1352, 0.02 #1879), 02l9wl (0.11 #779, 0.02 #2887, 0.02 #3941), 01d34b (0.11 #783, 0.02 #13431, 0.01 #16593), 017v3q (0.11 #772), 0bwfn (0.08 #2910, 0.07 #5018, 0.06 #4491), 01w5m (0.05 #4321, 0.05 #4848, 0.05 #3794), 017z88 (0.04 #1663, 0.04 #1136, 0.03 #9568), 015nl4 (0.04 #1121, 0.04 #20096, 0.03 #28008) >> Best rule #145 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0gyx4; 016k6x; >> query: (?x1057, 017j69) <- film(?x1057, ?x3255), ?x3255 = 0_816, type_of_union(?x1057, ?x566), award_winner(?x1058, ?x1057) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1663 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 112 *> proper extension: 0721cy; 0dbc1s; 0gls4q_; 0b2_xp; 06w58f; *> query: (?x1057, 017z88) <- profession(?x1057, ?x1032), nominated_for(?x1057, ?x4517), award(?x4517, ?x870), ?x870 = 09qv3c *> conf = 0.04 ranks of expected_values: 9 EVAL 01sxq9 student! 017z88 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 131.000 102.000 0.167 http://example.org/education/educational_institution/students_graduates./education/education/student #1638-03n0q5 PRED entity: 03n0q5 PRED relation: artists! PRED expected values: 04t36 0557q => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 218): 064t9 (0.62 #959, 0.52 #4109, 0.51 #6629), 06by7 (0.44 #12939, 0.44 #13569, 0.44 #7268), 06j6l (0.38 #996, 0.31 #6666, 0.30 #7296), 0ggx5q (0.38 #1027, 0.23 #4177, 0.22 #4807), 017_qw (0.37 #2901, 0.31 #3531, 0.28 #1641), 03_d0 (0.35 #1272, 0.26 #2217, 0.25 #2532), 025sc50 (0.31 #998, 0.28 #4778, 0.28 #4148), 0gywn (0.31 #1006, 0.25 #1321, 0.22 #6991), 02lnbg (0.31 #1007, 0.24 #4787, 0.23 #4157), 02x8m (0.31 #965, 0.16 #2225, 0.15 #1280) >> Best rule #959 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0c7ct; 013v5j; 013rds; >> query: (?x2641, 064t9) <- origin(?x2641, ?x739), type_of_union(?x2641, ?x566), sibling(?x2641, ?x11729) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #487 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 01vsl3_; 016szr; 0163r3; 0bdlj; 03n0pv; *> query: (?x2641, 0557q) <- religion(?x2641, ?x7131), award_winner(?x1854, ?x2641), ?x1854 = 025m8y *> conf = 0.14 ranks of expected_values: 36 EVAL 03n0q5 artists! 0557q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 122.000 122.000 0.615 http://example.org/music/genre/artists EVAL 03n0q5 artists! 04t36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 122.000 122.000 0.615 http://example.org/music/genre/artists #1637-0c53zb PRED entity: 0c53zb PRED relation: instance_of_recurring_event PRED expected values: 0g_w => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 3): 0g_w (0.90 #99, 0.89 #157, 0.89 #149), 0c4ys (0.12 #251, 0.11 #286, 0.11 #278), 0gcf2r (0.10 #261, 0.10 #271, 0.09 #287) >> Best rule #99 for best value: >> intensional similarity = 14 >> extensional distance = 47 >> proper extension: 073hkh; 0bzk8w; 059x66; 073hmq; 0bzm81; 0dth6b; 02yv_b; 0ftlkg; 073h1t; 0bvfqq; ... >> query: (?x4445, 0g_w) <- honored_for(?x4445, ?x2112), award_winner(?x4445, ?x3519), award_winner(?x4445, ?x902), ceremony(?x1245, ?x4445), music(?x5220, ?x3519), film(?x574, ?x5220), film_release_region(?x5220, ?x94), ?x94 = 09c7w0, award_winner(?x5123, ?x3519), ?x1245 = 0gqwc, film(?x9587, ?x5220), award_nominee(?x163, ?x902), nominated_for(?x902, ?x103), award_winner(?x1105, ?x902) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c53zb instance_of_recurring_event 0g_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.898 http://example.org/time/event/instance_of_recurring_event #1636-035tlh PRED entity: 035tlh PRED relation: fraternities_and_sororities! PRED expected values: 0kw4j 07vyf 09f2j 02ln0f 01p79b 01hjy5 01qqv5 02gkxp 02hp70 => 80 concepts (31 used for prediction) PRED predicted values (max 10 best out of 57): 03wv2g (0.33 #57, 0.25 #114), 02tz9z (0.33 #56, 0.25 #113), 0ghvb (0.33 #55, 0.25 #112), 0325dj (0.33 #54, 0.25 #111), 02rv1w (0.33 #53, 0.25 #110), 02yr1q (0.33 #52, 0.25 #109), 01jpqb (0.33 #51, 0.25 #108), 0trv (0.33 #50, 0.25 #107), 01rc6f (0.33 #49, 0.25 #106), 02yxjs (0.33 #48, 0.25 #105) >> Best rule #57 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 0325pb; >> query: (?x4348, 03wv2g) <- fraternities_and_sororities(?x3090, ?x4348), fraternities_and_sororities(?x546, ?x4348), fraternities_and_sororities(?x388, ?x4348), institution(?x1200, ?x546), major_field_of_study(?x546, ?x742), ?x1200 = 016t_3, ?x388 = 05krk, school(?x1823, ?x546), student(?x546, ?x547), role(?x547, ?x214), ?x3090 = 01r3y2 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #25 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 0325pb; *> query: (?x4348, 09f2j) <- fraternities_and_sororities(?x3090, ?x4348), fraternities_and_sororities(?x546, ?x4348), fraternities_and_sororities(?x388, ?x4348), institution(?x1200, ?x546), major_field_of_study(?x546, ?x742), ?x1200 = 016t_3, ?x388 = 05krk, school(?x1823, ?x546), student(?x546, ?x547), role(?x547, ?x214), ?x3090 = 01r3y2 *> conf = 0.33 ranks of expected_values: 31 EVAL 035tlh fraternities_and_sororities! 02hp70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 31.000 0.333 http://example.org/education/university/fraternities_and_sororities EVAL 035tlh fraternities_and_sororities! 02gkxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 31.000 0.333 http://example.org/education/university/fraternities_and_sororities EVAL 035tlh fraternities_and_sororities! 01qqv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 31.000 0.333 http://example.org/education/university/fraternities_and_sororities EVAL 035tlh fraternities_and_sororities! 01hjy5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 31.000 0.333 http://example.org/education/university/fraternities_and_sororities EVAL 035tlh fraternities_and_sororities! 01p79b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 31.000 0.333 http://example.org/education/university/fraternities_and_sororities EVAL 035tlh fraternities_and_sororities! 02ln0f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 31.000 0.333 http://example.org/education/university/fraternities_and_sororities EVAL 035tlh fraternities_and_sororities! 09f2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 80.000 31.000 0.333 http://example.org/education/university/fraternities_and_sororities EVAL 035tlh fraternities_and_sororities! 07vyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 31.000 0.333 http://example.org/education/university/fraternities_and_sororities EVAL 035tlh fraternities_and_sororities! 0kw4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 31.000 0.333 http://example.org/education/university/fraternities_and_sororities #1635-0pyww PRED entity: 0pyww PRED relation: award_winner! PRED expected values: 0hn821n => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 114): 092_25 (0.12 #211, 0.04 #1191, 0.03 #1751), 027n06w (0.10 #492, 0.02 #3292, 0.02 #3712), 05c1t6z (0.08 #15, 0.08 #435, 0.06 #855), 02q690_ (0.08 #64, 0.08 #484, 0.03 #904), 09v0p2c (0.08 #82, 0.08 #502, 0.02 #922), 02wzl1d (0.08 #11, 0.06 #151, 0.04 #291), 03gyp30 (0.08 #116, 0.06 #1236, 0.05 #1796), 03nnm4t (0.08 #73, 0.05 #493, 0.04 #773), 0418154 (0.08 #107, 0.05 #527, 0.02 #947), 09g90vz (0.08 #123, 0.05 #1803, 0.04 #2783) >> Best rule #211 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 02jyhv; 065mm1; >> query: (?x4816, 092_25) <- profession(?x4816, ?x1032), film(?x4816, ?x6256), ?x6256 = 02c7k4 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #550 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 97 *> proper extension: 0dbc1s; *> query: (?x4816, 0hn821n) <- award(?x4816, ?x2016), profession(?x4816, ?x1032), ?x2016 = 0cjyzs *> conf = 0.03 ranks of expected_values: 51 EVAL 0pyww award_winner! 0hn821n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 112.000 112.000 0.125 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1634-02vq8xn PRED entity: 02vq8xn PRED relation: profession PRED expected values: 02hrh1q => 131 concepts (80 used for prediction) PRED predicted values (max 10 best out of 116): 02hrh1q (0.97 #10092, 0.95 #10388, 0.95 #10536), 0dxtg (0.82 #753, 0.71 #309, 0.65 #3718), 0np9r (0.63 #6538, 0.27 #1649, 0.25 #1205), 0kyk (0.60 #177, 0.33 #473, 0.25 #2549), 0cbd2 (0.44 #450, 0.34 #11121, 0.32 #746), 02jknp (0.43 #6080, 0.42 #3712, 0.42 #5932), 018gz8 (0.43 #312, 0.37 #2980, 0.36 #1645), 02krf9 (0.41 #3583, 0.35 #1211, 0.31 #2990), 015cjr (0.40 #197, 0.29 #345, 0.12 #1234), 0fj9f (0.22 #498, 0.14 #2870, 0.13 #1980) >> Best rule #10092 for best value: >> intensional similarity = 6 >> extensional distance = 812 >> proper extension: 06v8s0; 01sl1q; 044mz_; 02s2ft; 06qgvf; 0grwj; 01k7d9; 06688p; 06gp3f; 05bp8g; ... >> query: (?x7153, 02hrh1q) <- profession(?x7153, ?x1041), actor(?x14278, ?x7153), profession(?x6911, ?x1041), profession(?x4884, ?x1041), ?x6911 = 096hm, ?x4884 = 01pctb >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vq8xn profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 80.000 0.972 http://example.org/people/person/profession #1633-0mkz PRED entity: 0mkz PRED relation: films PRED expected values: 07cz2 => 85 concepts (31 used for prediction) PRED predicted values (max 10 best out of 1237): 04tqtl (0.40 #1733, 0.33 #3315, 0.05 #10700), 0fjyzt (0.33 #274, 0.25 #6075, 0.20 #7131), 0czyxs (0.33 #20, 0.25 #5821, 0.20 #6877), 03cp4cn (0.33 #321, 0.20 #1903, 0.17 #3485), 07w8fz (0.33 #154, 0.20 #1736, 0.17 #3318), 02dwj (0.33 #268, 0.20 #1850, 0.17 #3432), 0ggbhy7 (0.33 #149, 0.20 #1731, 0.17 #3313), 0fy66 (0.33 #180, 0.20 #1762, 0.17 #3344), 04mcw4 (0.33 #230, 0.20 #1812, 0.17 #3394), 0bw20 (0.33 #363, 0.20 #1945, 0.17 #3527) >> Best rule #1733 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 0g1x2_; 05489; >> query: (?x3389, 04tqtl) <- films(?x3389, ?x641), currency(?x641, ?x170), film(?x1678, ?x641), ?x1678 = 02zyy4, film_release_region(?x641, ?x1892), film_crew_role(?x641, ?x137), nominated_for(?x640, ?x641), combatants(?x756, ?x1892) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #528 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 01w1sx; *> query: (?x3389, ?x144) <- films(?x3389, ?x11685), films(?x3389, ?x641), ?x641 = 08720, nominated_for(?x143, ?x11685), country(?x11685, ?x2645), list(?x11685, ?x3004), language(?x11685, ?x254), genre(?x11685, ?x53), nominated_for(?x143, ?x144), award_winner(?x143, ?x4691) *> conf = 0.01 ranks of expected_values: 679 EVAL 0mkz films 07cz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 85.000 31.000 0.400 http://example.org/film/film_subject/films #1632-03gr7w PRED entity: 03gr7w PRED relation: instrumentalists! PRED expected values: 042v_gx => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 75): 05r5c (0.47 #5464, 0.47 #1793, 0.46 #5293), 03qjg (0.44 #219, 0.26 #729, 0.22 #49), 018j2 (0.40 #716, 0.17 #206, 0.12 #376), 05148p4 (0.37 #3255, 0.37 #614, 0.36 #1465), 02hnl (0.24 #628, 0.20 #3269, 0.19 #3013), 0l14qv (0.22 #5, 0.12 #600, 0.11 #515), 0mkg (0.22 #10, 0.08 #95, 0.07 #690), 0l14md (0.20 #516, 0.13 #3242, 0.12 #346), 013y1f (0.17 #710, 0.12 #370, 0.11 #30), 07xzm (0.15 #105, 0.12 #700, 0.11 #20) >> Best rule #5464 for best value: >> intensional similarity = 3 >> extensional distance = 574 >> proper extension: 02ht0ln; >> query: (?x1795, 05r5c) <- instrumentalists(?x716, ?x1795), artists(?x482, ?x1795), role(?x248, ?x716) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #3664 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 312 *> proper extension: 039cq4; *> query: (?x1795, ?x227) <- award_winner(?x1413, ?x1795), artists(?x378, ?x1413), role(?x1413, ?x227) *> conf = 0.08 ranks of expected_values: 23 EVAL 03gr7w instrumentalists! 042v_gx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 136.000 136.000 0.474 http://example.org/music/instrument/instrumentalists #1631-08gg47 PRED entity: 08gg47 PRED relation: genre PRED expected values: 03q4nz => 66 concepts (63 used for prediction) PRED predicted values (max 10 best out of 100): 02kdv5l (0.70 #2249, 0.54 #1774, 0.50 #1301), 05p553 (0.60 #4, 0.48 #359, 0.44 #831), 01jfsb (0.53 #2140, 0.52 #3207, 0.38 #2259), 017fp (0.48 #4854, 0.48 #3195, 0.48 #2602), 0jxy (0.45 #518, 0.04 #1344, 0.04 #2292), 0hcr (0.43 #497, 0.18 #1323, 0.15 #1205), 04xvlr (0.42 #1418, 0.37 #946, 0.37 #710), 02l7c8 (0.39 #4278, 0.33 #1433, 0.32 #6054), 01hmnh (0.36 #1317, 0.29 #491, 0.28 #1081), 01t_vv (0.35 #173, 0.27 #54, 0.25 #409) >> Best rule #2249 for best value: >> intensional similarity = 5 >> extensional distance = 650 >> proper extension: 015qy1; 04svwx; >> query: (?x3304, 02kdv5l) <- genre(?x3304, ?x811), genre(?x8891, ?x811), genre(?x5945, ?x811), ?x8891 = 0gwlfnb, ?x5945 = 05t0_2v >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #492 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 67 *> proper extension: 05hd32; *> query: (?x3304, 03q4nz) <- country(?x3304, ?x252), country(?x3304, ?x94), ?x252 = 03_3d, film_release_region(?x54, ?x94) *> conf = 0.23 ranks of expected_values: 14 EVAL 08gg47 genre 03q4nz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 66.000 63.000 0.704 http://example.org/film/film/genre #1630-017_qw PRED entity: 017_qw PRED relation: artists PRED expected values: 025vry 01271h 02sj1x 0641g8 07j8kh 02bn75 0pj8m 07zft 0csdzz 011k4g => 44 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1263): 0407f (0.58 #2211, 0.10 #11985, 0.09 #13939), 019f9z (0.50 #2501, 0.14 #12275, 0.13 #16183), 0pj9t (0.50 #2204, 0.08 #11978, 0.07 #12955), 09h_q (0.50 #1653, 0.07 #3605, 0.06 #25403), 025vry (0.50 #1019, 0.02 #11769, 0.02 #12746), 01kx_81 (0.42 #2033, 0.18 #20519, 0.17 #26381), 011z3g (0.42 #2505, 0.17 #14233, 0.16 #16187), 020_4z (0.42 #2810, 0.15 #13561, 0.14 #12584), 01vvycq (0.42 #1996, 0.14 #2972, 0.13 #11770), 0127s7 (0.42 #2446, 0.14 #14174, 0.13 #16128) >> Best rule #2211 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 07sbbz2; 03_d0; 02x8m; 06by7; 0glt670; 06j6l; 0gywn; 0m40d; 0h08p; 015y_n; >> query: (?x4910, 0407f) <- artists(?x4910, ?x6382), artists(?x4910, ?x4139), artists(?x4910, ?x1656), ?x6382 = 01wd9lv, award(?x4139, ?x1079), type_of_union(?x1656, ?x566), role(?x1656, ?x227) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1019 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 05lls; 01wqlc; *> query: (?x4910, 025vry) <- artists(?x4910, ?x9891), artists(?x4910, ?x9593), artists(?x4910, ?x6399), artists(?x4910, ?x6382), ?x9593 = 03f4k, ?x6399 = 0bvzp, place_of_birth(?x9891, ?x2850), profession(?x6382, ?x131) *> conf = 0.50 ranks of expected_values: 5, 112, 174, 437, 793, 795, 924, 972, 974, 1073 EVAL 017_qw artists 011k4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 33.000 0.583 http://example.org/music/genre/artists EVAL 017_qw artists 0csdzz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 33.000 0.583 http://example.org/music/genre/artists EVAL 017_qw artists 07zft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 44.000 33.000 0.583 http://example.org/music/genre/artists EVAL 017_qw artists 0pj8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 33.000 0.583 http://example.org/music/genre/artists EVAL 017_qw artists 02bn75 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 33.000 0.583 http://example.org/music/genre/artists EVAL 017_qw artists 07j8kh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 44.000 33.000 0.583 http://example.org/music/genre/artists EVAL 017_qw artists 0641g8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 33.000 0.583 http://example.org/music/genre/artists EVAL 017_qw artists 02sj1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 33.000 0.583 http://example.org/music/genre/artists EVAL 017_qw artists 01271h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 44.000 33.000 0.583 http://example.org/music/genre/artists EVAL 017_qw artists 025vry CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 44.000 33.000 0.583 http://example.org/music/genre/artists #1629-0q48z PRED entity: 0q48z PRED relation: category PRED expected values: 08mbj5d => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #44, 0.78 #10, 0.78 #28) >> Best rule #44 for best value: >> intensional similarity = 5 >> extensional distance = 691 >> proper extension: 05zjtn4; 01jssp; 05krk; 01j_9c; 01fpvz; 07w0v; 01b1mj; 01ngz1; 01j_06; 017zq0; ... >> query: (?x11221, 08mbj5d) <- contains(?x2831, ?x11221), contains(?x94, ?x11221), ?x94 = 09c7w0, district_represented(?x176, ?x2831), contains(?x8260, ?x2831) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0q48z category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.782 http://example.org/common/topic/webpage./common/webpage/category #1628-02t_st PRED entity: 02t_st PRED relation: award PRED expected values: 0cqh46 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 230): 09sb52 (0.72 #1611, 0.72 #26148, 0.72 #1208), 0ck27z (0.26 #897, 0.24 #1300, 0.20 #3312), 0cqhk0 (0.14 #1245, 0.13 #3257, 0.13 #25745), 0gqyl (0.14 #15690, 0.13 #25745, 0.12 #24940), 027dtxw (0.14 #15690, 0.13 #25745, 0.12 #24940), 02ppm4q (0.14 #15690, 0.13 #25745, 0.12 #24940), 02x73k6 (0.14 #15690, 0.13 #25745, 0.12 #24940), 099jhq (0.14 #15690, 0.13 #25745, 0.12 #24940), 03hl6lc (0.14 #15690, 0.13 #25745, 0.12 #24940), 02x8n1n (0.14 #15690, 0.13 #25745, 0.12 #24940) >> Best rule #1611 for best value: >> intensional similarity = 3 >> extensional distance = 484 >> proper extension: 0kctd; >> query: (?x7381, ?x704) <- nominated_for(?x7381, ?x8533), award_winner(?x704, ?x7381), producer_type(?x8533, ?x632) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #51 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 114 *> proper extension: 05fg2; 04z_x4v; 0bq4j6; 06w38l; *> query: (?x7381, 0cqh46) <- nationality(?x7381, ?x94), award_winner(?x704, ?x7381), student(?x1368, ?x7381) *> conf = 0.04 ranks of expected_values: 82 EVAL 02t_st award 0cqh46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 86.000 86.000 0.721 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1627-0dxmyh PRED entity: 0dxmyh PRED relation: people! PRED expected values: 0222qb => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 57): 0x67 (0.23 #3400, 0.21 #1550, 0.20 #1937), 033tf_ (0.22 #2858, 0.21 #2088, 0.20 #3012), 0xnvg (0.21 #321, 0.21 #629, 0.20 #1168), 041rx (0.20 #697, 0.20 #158, 0.19 #4318), 013b6_ (0.18 #284, 0.07 #207, 0.04 #1825), 09vc4s (0.17 #933, 0.14 #1472, 0.13 #2090), 02ctzb (0.17 #92, 0.11 #2558, 0.09 #400), 048z7l (0.17 #117, 0.07 #1657, 0.07 #194), 01qhm_ (0.16 #314, 0.12 #1161, 0.12 #1850), 07bch9 (0.12 #485, 0.11 #1718, 0.11 #331) >> Best rule #3400 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 07c0j; >> query: (?x10277, 0x67) <- participant(?x10277, ?x9228), category(?x10277, ?x134), award_winner(?x1312, ?x9228), artists(?x671, ?x9228) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #1045 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 028pzq; 01syr4; *> query: (?x10277, 0222qb) <- profession(?x10277, ?x987), participant(?x10277, ?x6577), category(?x10277, ?x134), ?x987 = 0dxtg *> conf = 0.03 ranks of expected_values: 43 EVAL 0dxmyh people! 0222qb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 168.000 168.000 0.227 http://example.org/people/ethnicity/people #1626-06tpmy PRED entity: 06tpmy PRED relation: featured_film_locations PRED expected values: 02_286 => 68 concepts (41 used for prediction) PRED predicted values (max 10 best out of 47): 02_286 (0.15 #1713, 0.14 #4124, 0.14 #5818), 030qb3t (0.09 #1008, 0.09 #2456, 0.09 #3419), 0rh6k (0.06 #2900, 0.05 #2660, 0.04 #3141), 04jpl (0.06 #3630, 0.06 #3389, 0.06 #4356), 01_d4 (0.04 #774, 0.03 #47, 0.03 #1981), 080h2 (0.04 #2441, 0.03 #2923, 0.03 #3404), 02frhbc (0.03 #165, 0.03 #407, 0.02 #892), 0cr3d (0.03 #7972, 0.03 #4830, 0.01 #1518), 052p7 (0.02 #1751, 0.02 #2717, 0.02 #3198), 02dtg (0.02 #739, 0.01 #981, 0.01 #1222) >> Best rule #1713 for best value: >> intensional similarity = 4 >> extensional distance = 315 >> proper extension: 02y_lrp; 04fzfj; 0b73_1d; 0963mq; 02qm_f; 048scx; 03t97y; 020fcn; 02pxmgz; 01kff7; ... >> query: (?x4514, 02_286) <- country(?x4514, ?x94), film_crew_role(?x4514, ?x2154), film(?x4548, ?x4514), ?x2154 = 01vx2h >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06tpmy featured_film_locations 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 41.000 0.148 http://example.org/film/film/featured_film_locations #1625-0320fn PRED entity: 0320fn PRED relation: film! PRED expected values: 01p7yb 016z2j 02nwxc => 98 concepts (40 used for prediction) PRED predicted values (max 10 best out of 840): 0bzyh (0.43 #78760, 0.41 #66322, 0.40 #33160), 07m77x (0.33 #1538, 0.09 #3610, 0.01 #34698), 01vsn38 (0.33 #1847, 0.07 #22569, 0.03 #74613), 0fby2t (0.33 #754, 0.04 #4899, 0.04 #21476), 07cjqy (0.33 #602, 0.03 #21324, 0.02 #50343), 02qgyv (0.33 #384, 0.03 #74613, 0.02 #82906), 01pjr7 (0.33 #1326, 0.02 #22048, 0.01 #51067), 0cmt6q (0.33 #1142, 0.02 #21864, 0.01 #50883), 08vr94 (0.33 #676, 0.02 #13110, 0.01 #21398), 032w8h (0.33 #280, 0.02 #21002, 0.01 #50021) >> Best rule #78760 for best value: >> intensional similarity = 4 >> extensional distance = 665 >> proper extension: 04q00lw; 08gg47; 064q5v; 03t95n; >> query: (?x4009, ?x3960) <- film(?x532, ?x4009), film(?x2549, ?x4009), titles(?x53, ?x4009), award_winner(?x4009, ?x3960) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4198 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 0353xq; 03nqnnk; *> query: (?x4009, 01p7yb) <- featured_film_locations(?x4009, ?x1523), film_regional_debut_venue(?x4009, ?x13344), film(?x532, ?x4009), written_by(?x4009, ?x3960) *> conf = 0.04 ranks of expected_values: 91, 440, 548 EVAL 0320fn film! 02nwxc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 98.000 40.000 0.434 http://example.org/film/actor/film./film/performance/film EVAL 0320fn film! 016z2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 98.000 40.000 0.434 http://example.org/film/actor/film./film/performance/film EVAL 0320fn film! 01p7yb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 98.000 40.000 0.434 http://example.org/film/actor/film./film/performance/film #1624-041xl PRED entity: 041xl PRED relation: student! PRED expected values: 0gjv_ => 174 concepts (173 used for prediction) PRED predicted values (max 10 best out of 271): 07tk7 (0.42 #2011, 0.36 #2535, 0.21 #6728), 07wrz (0.33 #62, 0.10 #5827, 0.07 #4254), 01w3v (0.33 #15, 0.05 #9448, 0.04 #12592), 03bnd9 (0.33 #432, 0.03 #6197, 0.02 #8293), 01w5m (0.21 #5345, 0.14 #4296, 0.13 #15825), 0lbfv (0.20 #746, 0.14 #1270, 0.02 #7035), 01lhdt (0.20 #783, 0.05 #4976, 0.05 #6024), 01z3bz (0.20 #968, 0.03 #5161, 0.03 #6209), 07tl0 (0.17 #1602, 0.14 #2126, 0.07 #3698), 07tgn (0.15 #5782, 0.13 #10498, 0.12 #7878) >> Best rule #2011 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 02m7r; >> query: (?x7332, 07tk7) <- student(?x2999, ?x7332), ?x2999 = 07tg4, influenced_by(?x4795, ?x7332), award_winner(?x10548, ?x4795) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #5446 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 36 *> proper extension: 033rq; 015zql; 08nz99; 0488g9; *> query: (?x7332, 0gjv_) <- student(?x2999, ?x7332), story_by(?x4772, ?x7332), type_of_union(?x7332, ?x566), people(?x6720, ?x7332) *> conf = 0.03 ranks of expected_values: 93 EVAL 041xl student! 0gjv_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 174.000 173.000 0.417 http://example.org/education/educational_institution/students_graduates./education/education/student #1623-05drr9 PRED entity: 05drr9 PRED relation: people! PRED expected values: 041rx => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 47): 0x67 (0.38 #549, 0.24 #857, 0.21 #780), 07bch9 (0.29 #177, 0.25 #23, 0.17 #408), 038723 (0.25 #69, 0.14 #223, 0.08 #454), 041rx (0.19 #1852, 0.17 #1929, 0.16 #2006), 033tf_ (0.14 #161, 0.10 #2086, 0.10 #2009), 09vc4s (0.14 #163, 0.08 #394, 0.08 #625), 02ctzb (0.11 #246, 0.08 #400, 0.08 #862), 013xrm (0.11 #251, 0.08 #405, 0.08 #636), 019kn7 (0.11 #277, 0.08 #431, 0.08 #662), 07hwkr (0.11 #1013, 0.09 #1321, 0.06 #1783) >> Best rule #549 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 06mmb; 0g2mbn; >> query: (?x5620, 0x67) <- award_nominee(?x1896, ?x5620), program(?x5620, ?x6884), celebrity(?x1503, ?x1896) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1852 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 231 *> proper extension: 02wd48; 01t94_1; 04rg6; 05b1062; *> query: (?x5620, 041rx) <- profession(?x5620, ?x1146), award(?x5620, ?x882), ?x1146 = 018gz8 *> conf = 0.19 ranks of expected_values: 4 EVAL 05drr9 people! 041rx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 102.000 102.000 0.385 http://example.org/people/ethnicity/people #1622-05zksls PRED entity: 05zksls PRED relation: honored_for PRED expected values: 0416y94 063ykwt => 32 concepts (14 used for prediction) PRED predicted values (max 10 best out of 812): 08phg9 (0.40 #2634, 0.33 #303, 0.05 #5560), 0hz55 (0.40 #2618, 0.16 #5544, 0.07 #4373), 063ykwt (0.34 #5842, 0.20 #2546, 0.09 #5472), 0vjr (0.34 #5842, 0.14 #3819, 0.13 #4404), 04xbq3 (0.34 #5842, 0.09 #5757), 0180mw (0.34 #5842, 0.07 #3888, 0.07 #5644), 01lv85 (0.34 #5842), 05lfwd (0.33 #920, 0.25 #2086, 0.25 #1502), 04p5cr (0.33 #967, 0.25 #2133, 0.25 #1549), 0cs134 (0.33 #1130, 0.25 #2296, 0.25 #1712) >> Best rule #2634 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 09qvms; 09g90vz; >> query: (?x2220, 08phg9) <- ceremony(?x8059, ?x2220), ceremony(?x2060, ?x2220), award(?x163, ?x8059), award_winner(?x2220, ?x5061), award_winner(?x2220, ?x3308), ?x3308 = 0794g, award_winner(?x2060, ?x8704), award_winner(?x2060, ?x6558), nominated_for(?x8059, ?x2933), ?x6558 = 0gs1_, award(?x5061, ?x2016), film_release_region(?x2933, ?x2513), ?x2513 = 05b4w, people(?x1050, ?x8704), location_of_ceremony(?x8704, ?x9341) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #5842 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 41 *> proper extension: 0hhtgcw; *> query: (?x2220, ?x3787) <- award_winner(?x2220, ?x9211), award_winner(?x2220, ?x5645), award_winner(?x2220, ?x5065), honored_for(?x2220, ?x8870), honored_for(?x2220, ?x6438), honored_for(?x2220, ?x2403), actor(?x3787, ?x5065), people(?x1050, ?x5065), award_nominee(?x426, ?x5065), nominated_for(?x426, ?x5674), languages(?x9211, ?x254), actor(?x8870, ?x890), people(?x3591, ?x426), nationality(?x426, ?x94), language(?x2403, ?x90), profession(?x5645, ?x1032), nominated_for(?x618, ?x6438) *> conf = 0.34 ranks of expected_values: 3, 98 EVAL 05zksls honored_for 063ykwt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 32.000 14.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 05zksls honored_for 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 32.000 14.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #1621-0clvcx PRED entity: 0clvcx PRED relation: profession PRED expected values: 02hrh1q => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 51): 02hrh1q (0.90 #915, 0.88 #765, 0.87 #1965), 03gjzk (0.33 #1516, 0.33 #2416, 0.24 #3466), 01d_h8 (0.32 #6307, 0.31 #5707, 0.30 #5557), 0dxtg (0.30 #2414, 0.29 #3314, 0.29 #1514), 018gz8 (0.27 #3601, 0.26 #10503, 0.13 #1968), 0d1pc (0.27 #3601, 0.26 #10503, 0.10 #502), 016z4k (0.27 #3601, 0.26 #10503, 0.09 #10056), 09jwl (0.23 #620, 0.22 #20, 0.16 #4521), 0nbcg (0.22 #33, 0.11 #2733, 0.11 #6034), 02jknp (0.21 #6309, 0.21 #5709, 0.20 #7810) >> Best rule #915 for best value: >> intensional similarity = 3 >> extensional distance = 248 >> proper extension: 06r3p2; >> query: (?x1435, 02hrh1q) <- award(?x1435, ?x1670), award(?x6791, ?x1670), ?x6791 = 05l4yg >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0clvcx profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.900 http://example.org/people/person/profession #1620-0_565 PRED entity: 0_565 PRED relation: place PRED expected values: 0_565 => 59 concepts (27 used for prediction) PRED predicted values (max 10 best out of 17): 0_24q (0.08 #257, 0.04 #774, 0.03 #1290), 068p2 (0.08 #105, 0.04 #622, 0.03 #1138), 0fvzz (0.08 #398, 0.03 #1431, 0.03 #1948), 0_jq4 (0.08 #317, 0.03 #1350, 0.03 #1867), 0_g_6 (0.08 #278, 0.03 #1311, 0.03 #1828), 0zlgm (0.08 #117, 0.03 #1150, 0.03 #1667), 0_3cs (0.08 #15, 0.03 #1048, 0.03 #1565), 0_7z2 (0.08 #28, 0.03 #1578, 0.01 #2094), 0mwk9 (0.06 #3100), 0l4vc (0.04 #748, 0.03 #1264, 0.01 #2297) >> Best rule #257 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0_3cs; 0_7z2; 068p2; 04hgpt; 0zlgm; 0cwx_; 0_24q; 0_g_6; 0_jq4; 0fvzz; >> query: (?x12295, 0_24q) <- contains(?x12296, ?x12295), contains(?x3670, ?x12295), ?x3670 = 05tbn, time_zones(?x12295, ?x2674), source(?x12296, ?x958) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0_565 place 0_565 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 27.000 0.083 http://example.org/location/hud_county_place/place #1619-01xqw PRED entity: 01xqw PRED relation: role PRED expected values: 03bx0bm 03_vpw => 70 concepts (50 used for prediction) PRED predicted values (max 10 best out of 98): 03bx0bm (0.82 #2237, 0.82 #2172, 0.80 #766), 07y_7 (0.82 #2239, 0.81 #1394, 0.81 #4575), 05148p4 (0.82 #2239, 0.81 #4575, 0.81 #4670), 06ncr (0.82 #2239, 0.81 #4575, 0.81 #4670), 05r5c (0.82 #2239, 0.81 #4575, 0.81 #4670), 01dnws (0.82 #2239, 0.81 #4575, 0.81 #4670), 03q5t (0.82 #2239, 0.81 #4575, 0.81 #4670), 01hww_ (0.82 #2239, 0.81 #4575, 0.81 #4670), 0l14v3 (0.82 #2239, 0.81 #4575, 0.81 #4670), 0dwsp (0.82 #2239, 0.81 #4575, 0.81 #4670) >> Best rule #2237 for best value: >> intensional similarity = 12 >> extensional distance = 20 >> proper extension: 0j871; >> query: (?x4311, ?x1466) <- role(?x4311, ?x6039), role(?x4311, ?x2460), role(?x4311, ?x645), role(?x1466, ?x6039), role(?x75, ?x6039), ?x75 = 07y_7, role(?x7237, ?x4311), ?x1466 = 03bx0bm, group(?x4311, ?x1945), ?x645 = 028tv0, role(?x615, ?x4311), ?x2460 = 01wy6 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11 EVAL 01xqw role 03_vpw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 70.000 50.000 0.818 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 01xqw role 03bx0bm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 50.000 0.818 http://example.org/music/performance_role/regular_performances./music/group_membership/role #1618-01fchy PRED entity: 01fchy PRED relation: group! PRED expected values: 0l14md 02hnl => 67 concepts (48 used for prediction) PRED predicted values (max 10 best out of 117): 02hnl (0.76 #1892, 0.69 #1325, 0.59 #270), 03bx0bm (0.65 #265, 0.61 #1320, 0.57 #1887), 0l14md (0.59 #250, 0.57 #1872, 0.56 #1305), 028tv0 (0.41 #255, 0.38 #1310, 0.36 #1877), 07y_7 (0.20 #165, 0.11 #1868, 0.10 #489), 0l14j_ (0.20 #209, 0.11 #1912, 0.06 #1345), 051hrr (0.20 #197, 0.10 #521, 0.10 #765), 02fsn (0.20 #206, 0.10 #774, 0.08 #855), 026t6 (0.20 #166, 0.07 #653, 0.06 #734), 01wy6 (0.20 #200, 0.06 #1336, 0.06 #849) >> Best rule #1892 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 089tm; 01t_xp_; 04rcr; 07qnf; 07c0j; 03g5jw; 01wv9xn; 01fl3; 05crg7; 04r1t; ... >> query: (?x9706, 02hnl) <- group(?x227, ?x9706), artists(?x11973, ?x9706), parent_genre(?x11973, ?x482) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01fchy group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 48.000 0.755 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01fchy group! 0l14md CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 48.000 0.755 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1617-0d_2fb PRED entity: 0d_2fb PRED relation: language PRED expected values: 02h40lc => 121 concepts (109 used for prediction) PRED predicted values (max 10 best out of 49): 02h40lc (0.90 #3162, 0.90 #1368, 0.90 #2505), 06nm1 (0.50 #129, 0.19 #483, 0.17 #188), 03_9r (0.43 #246, 0.33 #10, 0.17 #187), 04306rv (0.25 #123, 0.17 #182, 0.16 #715), 06b_j (0.25 #141, 0.10 #793, 0.08 #1389), 01wgr (0.25 #158, 0.06 #750, 0.04 #1109), 064_8sq (0.19 #494, 0.18 #1447, 0.17 #1270), 05zjd (0.17 #203, 0.14 #262, 0.06 #5211), 02bjrlw (0.14 #1070, 0.13 #711, 0.08 #1249), 0k0sv (0.09 #319, 0.07 #616, 0.07 #675) >> Best rule #3162 for best value: >> intensional similarity = 6 >> extensional distance = 307 >> proper extension: 0979n; >> query: (?x2339, 02h40lc) <- film(?x1104, ?x2339), country(?x2339, ?x512), ?x512 = 07ssc, film(?x3461, ?x2339), film(?x1104, ?x6121), film_crew_role(?x6121, ?x137) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d_2fb language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 109.000 0.903 http://example.org/film/film/language #1616-025m8y PRED entity: 025m8y PRED relation: ceremony PRED expected values: 01xqqp 02cg41 => 51 concepts (51 used for prediction) PRED predicted values (max 10 best out of 125): 02cg41 (0.87 #985, 0.80 #735, 0.78 #1110), 01xqqp (0.71 #956, 0.66 #1081, 0.50 #706), 0bzmt8 (0.50 #334, 0.30 #584, 0.27 #4003), 0bzm81 (0.50 #266, 0.30 #516, 0.27 #4003), 0bzm__ (0.50 #324, 0.30 #574, 0.27 #4003), 0bz6l9 (0.50 #290, 0.30 #540, 0.27 #4003), 0bzjvm (0.50 #346, 0.27 #4003, 0.26 #4129), 073h1t (0.50 #271, 0.27 #4003, 0.26 #4129), 03tn9w (0.50 #329, 0.27 #4003, 0.26 #4129), 0bzknt (0.50 #319, 0.27 #4003, 0.26 #4129) >> Best rule #985 for best value: >> intensional similarity = 3 >> extensional distance = 80 >> proper extension: 02581q; 02wh75; 02g3gj; 01d38g; 02grdc; 02g8mp; 01c9f2; 01c427; 01c4_6; 02gx2k; ... >> query: (?x1854, 02cg41) <- ceremony(?x1854, ?x486), award(?x84, ?x1854), ?x486 = 02rjjll >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 025m8y ceremony 02cg41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.866 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 025m8y ceremony 01xqqp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 51.000 0.866 http://example.org/award/award_category/winners./award/award_honor/ceremony #1615-0fqpc7d PRED entity: 0fqpc7d PRED relation: ceremony! PRED expected values: 02x17s4 => 43 concepts (42 used for prediction) PRED predicted values (max 10 best out of 367): 025mb9 (0.71 #2193, 0.18 #10792, 0.18 #6565), 02v1m7 (0.71 #2132, 0.18 #6504, 0.18 #8990), 02hdky (0.71 #2266, 0.18 #6638, 0.17 #7923), 01by1l (0.71 #2131, 0.18 #6503, 0.17 #7788), 01c4_6 (0.71 #2115, 0.18 #6487, 0.17 #7772), 02wh75 (0.71 #2057, 0.18 #6429, 0.17 #7714), 0257pw (0.71 #2298, 0.18 #6670, 0.17 #7955), 024_fw (0.71 #2218, 0.18 #6590, 0.17 #7875), 02nbqh (0.71 #2135, 0.18 #6507, 0.17 #7792), 01bgqh (0.67 #2081, 0.18 #10792, 0.17 #4877) >> Best rule #2193 for best value: >> intensional similarity = 15 >> extensional distance = 22 >> proper extension: 01mhwk; 01mh_q; 01xqqp; >> query: (?x2245, 025mb9) <- ceremony(?x4481, ?x2245), ceremony(?x3911, ?x2245), award_winner(?x2245, ?x4247), award_winner(?x2245, ?x1104), award_winner(?x1039, ?x1104), award_nominee(?x846, ?x1104), nominated_for(?x1104, ?x365), award_winner(?x3180, ?x4247), award(?x9321, ?x4481), award(?x1206, ?x4481), award_winner(?x262, ?x4247), ?x9321 = 0140t7, type_of_union(?x4247, ?x566), award_winner(?x3911, ?x617), ?x1206 = 01vrt_c >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1116 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 13 *> proper extension: 0h_9252; *> query: (?x2245, 02x17s4) <- ceremony(?x899, ?x2245), award_winner(?x2245, ?x1104), production_companies(?x7170, ?x1104), production_companies(?x4454, ?x1104), award_nominee(?x1104, ?x1039), film(?x1104, ?x12108), film(?x1104, ?x10829), film(?x1104, ?x6119), film(?x1554, ?x4454), production_companies(?x6119, ?x1478), award_nominee(?x846, ?x1104), film_release_region(?x10829, ?x87), language(?x7170, ?x254), award(?x12108, ?x3458), genre(?x10829, ?x258) *> conf = 0.27 ranks of expected_values: 139 EVAL 0fqpc7d ceremony! 02x17s4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 43.000 42.000 0.708 http://example.org/award/award_category/winners./award/award_honor/ceremony #1614-07tp2 PRED entity: 07tp2 PRED relation: vacationer PRED expected values: 01jb26 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 119): 0134w7 (0.07 #377, 0.06 #556, 0.05 #735), 03lt8g (0.07 #1992, 0.07 #2350, 0.07 #2529), 01cwhp (0.07 #946, 0.06 #1125, 0.06 #1304), 0261x8t (0.06 #1397, 0.05 #2113, 0.04 #2471), 024dgj (0.06 #1333, 0.04 #2407, 0.04 #2586), 0151w_ (0.06 #1273, 0.04 #2347, 0.03 #1452), 03_6y (0.06 #1331, 0.03 #4912, 0.03 #973), 01dw4q (0.06 #1256, 0.03 #898, 0.03 #1077), 016fnb (0.06 #1359, 0.03 #1538, 0.02 #1896), 01g23m (0.05 #807, 0.03 #986, 0.03 #1165) >> Best rule #377 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 058wp; 0dttf; >> query: (?x9251, 0134w7) <- contains(?x2467, ?x9251), time_zones(?x9251, ?x6582), ?x6582 = 0gsrz4 >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07tp2 vacationer 01jb26 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 127.000 0.071 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #1613-01zmpg PRED entity: 01zmpg PRED relation: type_of_union PRED expected values: 04ztj => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.95 #67, 0.95 #151, 0.95 #277), 0jgjn (0.16 #206) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 03wd5tk; 02756j; 01w23w; 0b5x23; >> query: (?x2273, 04ztj) <- type_of_union(?x2273, ?x1873), award(?x2273, ?x567), sibling(?x2273, ?x2274) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01zmpg type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.955 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1612-01vs_v8 PRED entity: 01vs_v8 PRED relation: award_winner! PRED expected values: 0gpjbt => 157 concepts (157 used for prediction) PRED predicted values (max 10 best out of 130): 02cg41 (0.19 #533, 0.13 #8479, 0.12 #3958), 013b2h (0.16 #8433, 0.12 #2131, 0.11 #3912), 0gpjbt (0.15 #3862, 0.10 #8383, 0.08 #1122), 0466p0j (0.14 #1579, 0.14 #483, 0.13 #8429), 056878 (0.14 #440, 0.11 #3865, 0.10 #166), 05pd94v (0.13 #8359, 0.10 #413, 0.09 #9318), 09n4nb (0.12 #44, 0.11 #318, 0.10 #181), 0bzkgg (0.12 #40, 0.10 #177, 0.03 #2369), 05c1t6z (0.12 #13, 0.08 #4260, 0.06 #287), 027hjff (0.12 #53, 0.07 #8958, 0.07 #9232) >> Best rule #533 for best value: >> intensional similarity = 2 >> extensional distance = 19 >> proper extension: 01vsxdm; 03f1d47; >> query: (?x2237, 02cg41) <- award_winner(?x3631, ?x2237), ?x3631 = 02f73p >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #3862 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 87 *> proper extension: 028q6; 0lbj1; 06cc_1; 08wq0g; 01wbgdv; 0137n0; 01wdqrx; 03kwtb; 0pgjm; 01wp8w7; ... *> query: (?x2237, 0gpjbt) <- award_winner(?x154, ?x2237), award_winner(?x2237, ?x4594), role(?x2237, ?x1750) *> conf = 0.15 ranks of expected_values: 3 EVAL 01vs_v8 award_winner! 0gpjbt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 157.000 157.000 0.190 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1611-02x2jl_ PRED entity: 02x2jl_ PRED relation: genre PRED expected values: 02qfv5d => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 102): 03mqtr (0.71 #4228, 0.70 #121, 0.61 #243), 0lsxr (0.46 #8, 0.20 #251, 0.19 #492), 05p553 (0.37 #1935, 0.35 #1575, 0.34 #3870), 0c3351 (0.35 #37, 0.06 #6403, 0.05 #4143), 02kdv5l (0.31 #1212, 0.28 #3021, 0.28 #849), 02l7c8 (0.30 #378, 0.29 #4001, 0.29 #2067), 04xvlr (0.27 #122, 0.18 #4107, 0.18 #3987), 09blyk (0.27 #31, 0.06 #274, 0.06 #4137), 03bxz7 (0.24 #176, 0.10 #4041, 0.10 #418), 017fp (0.24 #135, 0.10 #4000, 0.10 #257) >> Best rule #4228 for best value: >> intensional similarity = 2 >> extensional distance = 1104 >> proper extension: 06cs95; 03kq98; 01q_y0; 039c26; 02qjv1p; >> query: (?x11735, ?x600) <- titles(?x600, ?x11735), genre(?x280, ?x600) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #210 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 05jyb2; *> query: (?x11735, 02qfv5d) <- titles(?x3506, ?x11735), country(?x11735, ?x94), ?x3506 = 03mqtr *> conf = 0.12 ranks of expected_values: 19 EVAL 02x2jl_ genre 02qfv5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 63.000 63.000 0.714 http://example.org/film/film/genre #1610-019_6d PRED entity: 019_6d PRED relation: colors PRED expected values: 01l849 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.45 #102, 0.45 #62, 0.42 #362), 01l849 (0.24 #1041, 0.24 #221, 0.24 #1021), 06fvc (0.20 #343, 0.18 #363, 0.18 #483), 03wkwg (0.19 #155, 0.18 #115, 0.18 #75), 019sc (0.18 #67, 0.17 #1387, 0.17 #7), 06kqt3 (0.18 #97, 0.17 #17, 0.14 #37), 067z2v (0.18 #89, 0.17 #9, 0.11 #169), 036k5h (0.11 #485, 0.11 #605, 0.11 #625), 038hg (0.11 #332, 0.09 #352, 0.09 #372), 04mkbj (0.10 #430, 0.09 #610, 0.09 #110) >> Best rule #102 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01rtm4; 01k2wn; 04rwx; 07wrz; 017z88; 017j69; 03p7gb; 0bwfn; 019n9w; >> query: (?x11060, 083jv) <- registering_agency(?x11060, ?x1982), company(?x4480, ?x11060), organization(?x346, ?x11060), ?x346 = 060c4 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1041 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 248 *> proper extension: 04gd8j; *> query: (?x11060, 01l849) <- currency(?x11060, ?x170), ?x170 = 09nqf, contains(?x94, ?x11060), colors(?x11060, ?x3189) *> conf = 0.24 ranks of expected_values: 2 EVAL 019_6d colors 01l849 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 159.000 159.000 0.455 http://example.org/education/educational_institution/colors #1609-0pcc0 PRED entity: 0pcc0 PRED relation: people! PRED expected values: 0g6ff => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 50): 041rx (0.44 #381, 0.39 #613, 0.26 #5630), 0x67 (0.39 #314, 0.34 #2976, 0.32 #695), 0xnvg (0.18 #165, 0.13 #1078, 0.12 #394), 013xrm (0.13 #324, 0.12 #20, 0.10 #629), 013b6_ (0.12 #52, 0.06 #661, 0.06 #204), 06v41q (0.12 #105, 0.06 #714, 0.06 #181), 019lrz (0.12 #114, 0.06 #723, 0.03 #647), 02g7sp (0.12 #170, 0.07 #475, 0.06 #94), 09vc4s (0.12 #161, 0.06 #85, 0.04 #2975), 033tf_ (0.11 #5633, 0.11 #2745, 0.11 #5937) >> Best rule #381 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 07h1q; >> query: (?x889, ?x1050) <- peers(?x5132, ?x889), people(?x4322, ?x5132), people(?x1050, ?x5132), people(?x9943, ?x889) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #782 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 01v3vp; *> query: (?x889, 0g6ff) <- student(?x10223, ?x889), profession(?x889, ?x563), ?x563 = 01c8w0, gender(?x889, ?x231) *> conf = 0.09 ranks of expected_values: 14 EVAL 0pcc0 people! 0g6ff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 121.000 121.000 0.444 http://example.org/people/ethnicity/people #1608-063tn PRED entity: 063tn PRED relation: gender PRED expected values: 05zppz => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #92, 0.91 #94, 0.90 #120), 02zsn (0.46 #45, 0.46 #187, 0.46 #305) >> Best rule #92 for best value: >> intensional similarity = 5 >> extensional distance = 101 >> proper extension: 06cv1; 0pgjm; 02lz1s; 03n0q5; 01jpmpv; 02sj1x; 01pr6q7; 012wg; 026dx; 02w670; ... >> query: (?x9480, 05zppz) <- type_of_union(?x9480, ?x566), music(?x4688, ?x9480), nationality(?x9480, ?x1603), film_release_region(?x5644, ?x1603), ?x5644 = 0dll_t2 >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 063tn gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 180.000 180.000 0.922 http://example.org/people/person/gender #1607-0mnk7 PRED entity: 0mnk7 PRED relation: currency PRED expected values: 09nqf => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.76 #4, 0.75 #5, 0.62 #3) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0mmzt; 0mpbj; 0mndw; 0mn78; 0mpdw; 0mp08; 0mp36; 0mny8; 0mn9x; 0mnwd; ... >> query: (?x10967, 09nqf) <- source(?x10967, ?x958), time_zones(?x10967, ?x2674), contains(?x1426, ?x10967), ?x1426 = 07z1m >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mnk7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.762 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #1606-0p_2r PRED entity: 0p_2r PRED relation: profession PRED expected values: 03gjzk => 121 concepts (120 used for prediction) PRED predicted values (max 10 best out of 66): 03gjzk (0.84 #1924, 0.84 #1483, 0.83 #748), 02jknp (0.52 #1330, 0.51 #2359, 0.50 #1036), 09jwl (0.50 #17, 0.38 #3692, 0.36 #6926), 02krf9 (0.33 #25, 0.31 #1936, 0.29 #613), 018gz8 (0.33 #15, 0.25 #162, 0.23 #9115), 0nbcg (0.26 #6939, 0.25 #3705, 0.23 #9115), 016z4k (0.26 #3679, 0.22 #6913, 0.17 #4), 0dz3r (0.23 #3236, 0.23 #2942, 0.22 #2060), 0cbd2 (0.23 #9115, 0.21 #741, 0.21 #888), 0np9r (0.23 #9115, 0.17 #19, 0.14 #901) >> Best rule #1924 for best value: >> intensional similarity = 2 >> extensional distance = 215 >> proper extension: 04rtpt; >> query: (?x1422, 03gjzk) <- program(?x1422, ?x2528), award_winner(?x2528, ?x832) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p_2r profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 120.000 0.843 http://example.org/people/person/profession #1605-04gnbv1 PRED entity: 04gnbv1 PRED relation: award_winner! PRED expected values: 02f9wb => 108 concepts (49 used for prediction) PRED predicted values (max 10 best out of 663): 09r9dp (0.52 #54673, 0.51 #67540, 0.48 #35377), 07s95_l (0.32 #25729, 0.18 #38595, 0.16 #11256), 0bbvr84 (0.32 #25729, 0.18 #38595, 0.16 #11256), 06_vpyq (0.32 #25729, 0.18 #38595, 0.16 #11256), 044lyq (0.32 #25729, 0.18 #38595, 0.16 #11256), 04kr63w (0.32 #25729, 0.18 #38595, 0.16 #11256), 048q6x (0.32 #25729, 0.18 #38595, 0.16 #11256), 03v1jf (0.32 #25729, 0.18 #38595, 0.04 #25012), 027n4zv (0.32 #25729, 0.18 #38595, 0.04 #25440), 04gnbv1 (0.27 #59498, 0.26 #25728, 0.16 #11256) >> Best rule #54673 for best value: >> intensional similarity = 3 >> extensional distance = 985 >> proper extension: 04cy8rb; 086k8; 017s11; 016tt2; 0g1rw; 064nh4k; 0kx4m; 05qd_; 016tw3; 04y79_n; ... >> query: (?x4618, ?x3789) <- award_winner(?x3310, ?x4618), award_winner(?x4948, ?x4618), award_nominee(?x3789, ?x4618) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #59498 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1037 *> proper extension: 04qb6g; *> query: (?x4618, ?x3762) <- award_winner(?x3310, ?x4618), award_winner(?x8229, ?x4618), award_winner(?x8229, ?x3762) *> conf = 0.27 ranks of expected_values: 11 EVAL 04gnbv1 award_winner! 02f9wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 108.000 49.000 0.521 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #1604-0cfgd PRED entity: 0cfgd PRED relation: group! PRED expected values: 05148p4 => 87 concepts (70 used for prediction) PRED predicted values (max 10 best out of 123): 05148p4 (0.81 #1451, 0.75 #945, 0.74 #1704), 018vs (0.66 #1530, 0.64 #2121, 0.63 #2037), 0l14qv (0.62 #510, 0.50 #1437, 0.37 #1690), 05r5c (0.62 #512, 0.49 #421, 0.44 #1692), 028tv0 (0.51 #1613, 0.50 #1444, 0.48 #2036), 04rzd (0.49 #421, 0.38 #1462, 0.35 #1715), 018j2 (0.49 #421, 0.35 #1011, 0.23 #1770), 01xqw (0.49 #421, 0.25 #570, 0.13 #1771), 03gvt (0.49 #421, 0.23 #1770, 0.12 #566), 03m5k (0.49 #421, 0.11 #2445, 0.10 #1772) >> Best rule #1451 for best value: >> intensional similarity = 10 >> extensional distance = 24 >> proper extension: 01vrwfv; 01q99h; 01w5n51; 012vm6; 02cw1m; 012x1l; >> query: (?x11551, 05148p4) <- group(?x2798, ?x11551), group(?x1495, ?x11551), group(?x315, ?x11551), group(?x227, ?x11551), ?x2798 = 03qjg, ?x315 = 0l14md, artists(?x1000, ?x11551), ?x227 = 0342h, instrumentalists(?x1495, ?x211), role(?x1495, ?x74) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cfgd group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 70.000 0.808 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1603-0f3zsq PRED entity: 0f3zsq PRED relation: cinematography! PRED expected values: 0pvms => 97 concepts (56 used for prediction) PRED predicted values (max 10 best out of 344): 03cv_gy (0.82 #1371, 0.82 #1714, 0.76 #4113), 07cyl (0.82 #1714, 0.76 #4113, 0.72 #2399), 03cw411 (0.11 #1146, 0.07 #1489, 0.05 #2517), 03wy8t (0.07 #1677, 0.06 #991, 0.05 #1334), 0jvt9 (0.07 #1476, 0.05 #1133, 0.04 #3532), 084qpk (0.07 #1737, 0.06 #708, 0.06 #2079), 0kbhf (0.06 #881, 0.06 #2252, 0.04 #3281), 02b6n9 (0.06 #987, 0.05 #1330, 0.04 #1673), 08zrbl (0.06 #949, 0.05 #1292, 0.04 #1635), 01v1ln (0.06 #923, 0.05 #1266, 0.04 #1609) >> Best rule #1371 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 0854hr; >> query: (?x10542, ?x5328) <- award_winner(?x5328, ?x10542), cinematography(?x11416, ?x10542), film_crew_role(?x11416, ?x137), crewmember(?x11416, ?x7675) >> conf = 0.82 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f3zsq cinematography! 0pvms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 56.000 0.821 http://example.org/film/film/cinematography #1602-040fb PRED entity: 040fb PRED relation: seasonal_months PRED expected values: 02xx5 => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 4): 040fv (0.85 #44, 0.81 #58, 0.81 #56), 03_ly (0.85 #44, 0.81 #58, 0.81 #56), 02xx5 (0.85 #44, 0.81 #56, 0.80 #78), 040fb (0.77 #27, 0.73 #59, 0.73 #74) >> Best rule #44 for best value: >> intensional similarity = 76 >> extensional distance = 2 >> proper extension: 0ll3; >> query: (?x2140, ?x1650) <- month(?x12674, ?x2140), month(?x9605, ?x2140), month(?x9559, ?x2140), month(?x8602, ?x2140), month(?x8252, ?x2140), month(?x6703, ?x2140), month(?x3501, ?x2140), month(?x2985, ?x2140), month(?x1646, ?x2140), month(?x1523, ?x2140), month(?x1458, ?x2140), month(?x1036, ?x2140), month(?x659, ?x2140), month(?x206, ?x2140), month(?x108, ?x2140), ?x1523 = 030qb3t, location(?x10423, ?x1036), location(?x9084, ?x1036), location(?x6850, ?x1036), location(?x2818, ?x1036), state(?x1036, ?x7468), ?x659 = 02cl1, featured_film_locations(?x9069, ?x1036), featured_film_locations(?x7366, ?x1036), featured_film_locations(?x6520, ?x1036), featured_film_locations(?x4392, ?x1036), type_of_union(?x10423, ?x566), seasonal_months(?x3270, ?x2140), seasonal_months(?x1650, ?x2140), vacationer(?x1036, ?x1093), citytown(?x9309, ?x1036), time_zones(?x1036, ?x2950), participant(?x10423, ?x4407), participant(?x9084, ?x1897), gender(?x10423, ?x231), ?x3270 = 05cw8, adjoins(?x1036, ?x10586), currency(?x9084, ?x170), ?x1646 = 0156q, teams(?x1036, ?x934), ?x2985 = 03hrz, film(?x6850, ?x54), ?x9559 = 07dfk, participant(?x3503, ?x9084), ?x12674 = 0g6xq, ?x1458 = 05ywg, place_founded(?x1914, ?x1036), ?x3501 = 0f2v0, film(?x9084, ?x1701), ?x206 = 01914, profession(?x10423, ?x1032), award(?x9084, ?x1691), production_companies(?x9069, ?x902), ?x8602 = 0chgzm, award(?x6850, ?x704), industry(?x9309, ?x245), award_nominee(?x6850, ?x157), ?x108 = 0rh6k, participant(?x10805, ?x10423), organization(?x4682, ?x9309), film_crew_role(?x9069, ?x137), ?x6703 = 0f04v, award_winner(?x3499, ?x2818), film_distribution_medium(?x7366, ?x81), nationality(?x2818, ?x279), award_winner(?x9069, ?x8374), genre(?x9069, ?x600), ?x9605 = 02frhbc, film_release_region(?x6520, ?x172), participant(?x2818, ?x6917), ?x1032 = 02hrh1q, origin(?x6228, ?x1036), ?x172 = 0154j, film(?x96, ?x4392), ?x8252 = 0k3p, mode_of_transportation(?x1036, ?x4272) >> conf = 0.85 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 040fb seasonal_months 02xx5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 12.000 12.000 0.850 http://example.org/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months #1601-02q_4ph PRED entity: 02q_4ph PRED relation: nominated_for! PRED expected values: 02sj1x => 128 concepts (50 used for prediction) PRED predicted values (max 10 best out of 1084): 02645b (0.73 #60872, 0.67 #23411, 0.59 #79597), 05v1sb (0.59 #49167, 0.58 #16387, 0.58 #46827), 0fqjks (0.59 #49167, 0.58 #16387, 0.58 #46827), 0dqzkv (0.54 #30437, 0.43 #63213, 0.35 #72576), 076psv (0.53 #32779, 0.50 #21070, 0.49 #88962), 0579tg2 (0.53 #32779, 0.50 #21070, 0.49 #88962), 0c6g29 (0.50 #11704, 0.50 #9792, 0.34 #112368), 05pq9 (0.47 #74917, 0.44 #74916, 0.43 #25753), 01k7d9 (0.44 #18729, 0.34 #103006, 0.28 #23410), 0drc1 (0.44 #74916, 0.43 #25753, 0.38 #25752) >> Best rule #60872 for best value: >> intensional similarity = 5 >> extensional distance = 49 >> proper extension: 0cbl95; >> query: (?x4300, ?x2875) <- genre(?x4300, ?x3515), language(?x4300, ?x254), ?x3515 = 082gq, award(?x4300, ?x500), film(?x2875, ?x4300) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #12439 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 04mzf8; 0jymd; 0bkq7; 03mr85; *> query: (?x4300, 02sj1x) <- genre(?x4300, ?x307), country(?x4300, ?x94), film_art_direction_by(?x4300, ?x4251), ?x4251 = 05v1sb, film(?x2875, ?x4300) *> conf = 0.22 ranks of expected_values: 20 EVAL 02q_4ph nominated_for! 02sj1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 128.000 50.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #1600-02tv80 PRED entity: 02tv80 PRED relation: award_winner! PRED expected values: 02z13jg => 93 concepts (74 used for prediction) PRED predicted values (max 10 best out of 169): 01by1l (0.46 #112, 0.06 #13051, 0.06 #10893), 0gqy2 (0.37 #19848, 0.37 #19847, 0.36 #12076), 0fbvqf (0.37 #19848, 0.37 #19847, 0.36 #12076), 09sb52 (0.17 #3060, 0.17 #2197, 0.16 #1766), 0789_m (0.15 #21, 0.14 #14235, 0.09 #22008), 02grdc (0.15 #32, 0.09 #22008, 0.09 #22007), 027c95y (0.14 #14235, 0.14 #14234, 0.08 #157), 0bdwqv (0.14 #14235, 0.14 #14234, 0.06 #1032), 02z13jg (0.14 #14235, 0.14 #14234, 0.05 #912), 0gs9p (0.13 #19415, 0.11 #11644, 0.11 #28052) >> Best rule #112 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 08849; >> query: (?x6402, 01by1l) <- award_winner(?x5601, ?x6402), gender(?x6402, ?x231), films(?x5601, ?x6636) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #14235 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1299 *> proper extension: 018_q8; *> query: (?x6402, ?x458) <- award_winner(?x6402, ?x1250), award_winner(?x2915, ?x1250), award_winner(?x458, ?x1250), award(?x89, ?x2915) *> conf = 0.14 ranks of expected_values: 9 EVAL 02tv80 award_winner! 02z13jg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 93.000 74.000 0.462 http://example.org/award/award_category/winners./award/award_honor/award_winner #1599-02htv6 PRED entity: 02htv6 PRED relation: contains! PRED expected values: 09c7w0 => 138 concepts (103 used for prediction) PRED predicted values (max 10 best out of 267): 09c7w0 (0.95 #88564, 0.83 #24144, 0.82 #43818), 03rjj (0.50 #67072, 0.48 #45604, 0.10 #81406), 059rby (0.33 #20, 0.24 #76939, 0.24 #7173), 02_286 (0.33 #43, 0.17 #38491, 0.13 #7196), 02jx1 (0.29 #85064, 0.21 #91336, 0.18 #38535), 04jpl (0.27 #38470, 0.08 #33104, 0.07 #5387), 01n7q (0.26 #81474, 0.22 #84160, 0.22 #85055), 07ssc (0.21 #85009, 0.16 #39375, 0.14 #91281), 05k7sb (0.17 #133, 0.13 #14439, 0.13 #77052), 01cx_ (0.17 #196, 0.07 #38644, 0.06 #24337) >> Best rule #88564 for best value: >> intensional similarity = 4 >> extensional distance = 938 >> proper extension: 0n5j_; 0vmt; 059_c; 01n7q; 07z1m; 030qb3t; 04rrx; 0dclg; 07srw; 06btq; ... >> query: (?x12028, 09c7w0) <- contains(?x3670, ?x12028), contains(?x3670, ?x4293), ?x4293 = 02rg_4, adjoins(?x3670, ?x177) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02htv6 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 103.000 0.949 http://example.org/location/location/contains #1598-01l9p PRED entity: 01l9p PRED relation: category PRED expected values: 08mbj5d => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.54 #32, 0.45 #3, 0.42 #10) >> Best rule #32 for best value: >> intensional similarity = 3 >> extensional distance = 798 >> proper extension: 01wbsdz; >> query: (?x1735, 08mbj5d) <- award_nominee(?x3176, ?x1735), profession(?x3176, ?x131), artists(?x505, ?x3176) >> conf = 0.54 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01l9p category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.544 http://example.org/common/topic/webpage./common/webpage/category #1597-0cnl80 PRED entity: 0cnl80 PRED relation: award_nominee! PRED expected values: 043js => 60 concepts (21 used for prediction) PRED predicted values (max 10 best out of 781): 0cl0bk (0.81 #46411, 0.81 #39449, 0.81 #20880), 060j8b (0.81 #46411, 0.81 #39449, 0.81 #20880), 0bt7ws (0.81 #46411, 0.81 #39449, 0.81 #20880), 043js (0.80 #579, 0.33 #2901, 0.29 #2322), 0cnl80 (0.53 #43, 0.29 #2322, 0.28 #32486), 0bczgm (0.29 #2322, 0.28 #32486, 0.23 #46412), 03qmfzx (0.29 #2322, 0.28 #32486, 0.23 #46412), 06151l (0.29 #2322, 0.28 #32486, 0.23 #46412), 05p5nc (0.29 #2322, 0.28 #32486, 0.23 #46412), 06mfvc (0.29 #2322, 0.28 #32486, 0.23 #46412) >> Best rule #46411 for best value: >> intensional similarity = 3 >> extensional distance = 1197 >> proper extension: 07nznf; 0q9kd; 06qgvf; 01k7d9; 02bfmn; 03x3qv; 0m2wm; 02zq43; 0p_pd; 0prfz; ... >> query: (?x274, ?x7752) <- award_nominee(?x274, ?x7752), award_nominee(?x7752, ?x679), actor(?x1631, ?x7752) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #579 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0cmt6q; *> query: (?x274, 043js) <- award_nominee(?x274, ?x7752), award_nominee(?x274, ?x2602), ?x7752 = 05l0j5, award_nominee(?x2602, ?x237) *> conf = 0.80 ranks of expected_values: 4 EVAL 0cnl80 award_nominee! 043js CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 60.000 21.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1596-02xx5 PRED entity: 02xx5 PRED relation: month! PRED expected values: 06wjf 02cft 02frhbc 0h3tv => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 346): 06wjf (0.90 #8, 0.89 #87, 0.89 #37), 02frhbc (0.90 #8, 0.89 #87, 0.89 #37), 0h3tv (0.90 #8, 0.89 #87, 0.89 #37), 06mxs (0.90 #8, 0.89 #87, 0.89 #37), 02cft (0.90 #8, 0.89 #87, 0.89 #37), 0l0mk (0.90 #8, 0.89 #87, 0.88 #65), 0yfvf (0.50 #7), 0mnyn (0.50 #7), 0cr3d (0.33 #25, 0.30 #55, 0.20 #26), 02dtg (0.33 #25, 0.30 #55, 0.20 #26) >> Best rule #8 for best value: >> intensional similarity = 93 >> extensional distance = 1 >> proper extension: 04wzr; >> query: (?x4869, ?x4271) <- month(?x12674, ?x4869), month(?x11197, ?x4869), month(?x10610, ?x4869), month(?x9559, ?x4869), month(?x8977, ?x4869), month(?x8956, ?x4869), month(?x8602, ?x4869), month(?x8252, ?x4869), month(?x8174, ?x4869), month(?x6960, ?x4869), month(?x6054, ?x4869), month(?x5719, ?x4869), month(?x4826, ?x4869), month(?x3269, ?x4869), month(?x3125, ?x4869), month(?x3106, ?x4869), month(?x3052, ?x4869), month(?x2985, ?x4869), month(?x2645, ?x4869), month(?x2611, ?x4869), month(?x2474, ?x4869), month(?x2316, ?x4869), month(?x2254, ?x4869), month(?x1658, ?x4869), month(?x1649, ?x4869), month(?x1523, ?x4869), month(?x1458, ?x4869), month(?x1036, ?x4869), month(?x863, ?x4869), month(?x362, ?x4869), month(?x206, ?x4869), month(?x108, ?x4869), ?x362 = 04jpl, seasonal_months(?x4869, ?x6303), seasonal_months(?x4869, ?x4925), seasonal_months(?x4869, ?x4827), seasonal_months(?x4869, ?x3107), seasonal_months(?x4869, ?x2140), ?x108 = 0rh6k, ?x5719 = 0f2rq, ?x2645 = 03h64, ?x2611 = 02h6_6p, ?x2316 = 06t2t, ?x6303 = 0lkm, ?x11197 = 05l64, ?x2985 = 03hrz, ?x1649 = 01f62, ?x12674 = 0g6xq, ?x2254 = 0dclg, ?x2140 = 040fb, ?x3269 = 0vzm, ?x6960 = 071vr, ?x6054 = 0fn2g, ?x9559 = 07dfk, ?x1036 = 080h2, ?x4827 = 03_ly, ?x4925 = 0ll3, ?x206 = 01914, ?x3125 = 0d6lp, ?x1523 = 030qb3t, ?x863 = 0fhp9, ?x3106 = 049d1, ?x8252 = 0k3p, ?x8602 = 0chgzm, month(?x4271, ?x3107), ?x10610 = 03902, ?x8977 = 02z0j, ?x8174 = 01lfy, ?x2474 = 052p7, ?x1658 = 0h7h6, country(?x4826, ?x172), ?x1458 = 05ywg, location(?x10783, ?x3052), location(?x5488, ?x3052), location(?x5097, ?x3052), location(?x3293, ?x3052), location_of_ceremony(?x4284, ?x3052), languages(?x10783, ?x254), citytown(?x1151, ?x3052), jurisdiction_of_office(?x1195, ?x3052), place_of_birth(?x1871, ?x3052), actor(?x6375, ?x5488), award_winner(?x3293, ?x286), award_nominee(?x806, ?x5488), film(?x5097, ?x4009), nationality(?x5097, ?x94), mode_of_transportation(?x3052, ?x4272), ?x8956 = 0947l, award(?x3293, ?x277), award_winner(?x5097, ?x2275), ?x4009 = 0320fn, award_nominee(?x192, ?x5097), award_nominee(?x157, ?x3293) >> conf = 0.90 => this is the best rule for 6 predicted values ranks of expected_values: 1, 2, 3, 5 EVAL 02xx5 month! 0h3tv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 02xx5 month! 02frhbc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 02xx5 month! 02cft CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 02xx5 month! 06wjf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 12.000 12.000 0.897 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #1595-05qmj PRED entity: 05qmj PRED relation: influenced_by! PRED expected values: 05np2 043tg 03jht => 92 concepts (36 used for prediction) PRED predicted values (max 10 best out of 317): 048cl (0.60 #3199, 0.44 #4661, 0.33 #4174), 0tfc (0.50 #4353, 0.40 #2891, 0.33 #4840), 0399p (0.50 #1769, 0.40 #5178, 0.33 #3716), 052h3 (0.50 #4020, 0.33 #4507, 0.33 #613), 0420y (0.40 #2879, 0.33 #934, 0.23 #3405), 01lwx (0.40 #3371, 0.33 #4833, 0.23 #3405), 0ct9_ (0.40 #5190, 0.25 #2268, 0.25 #1781), 026lj (0.40 #2485, 0.23 #3405, 0.19 #1945), 032r1 (0.33 #4829, 0.33 #4342, 0.25 #5803), 028p0 (0.33 #3928, 0.33 #521, 0.25 #1007) >> Best rule #3199 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 042q3; 0tfc; >> query: (?x6015, 048cl) <- influenced_by(?x9600, ?x6015), influenced_by(?x5797, ?x6015), influenced_by(?x4547, ?x6015), ?x4547 = 03_hd, ?x9600 = 039n1, interests(?x5797, ?x6978) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #796 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 0gz_; *> query: (?x6015, 043tg) <- influenced_by(?x9600, ?x6015), influenced_by(?x5797, ?x6015), influenced_by(?x4547, ?x6015), influenced_by(?x3993, ?x6015), ?x4547 = 03_hd, ?x9600 = 039n1, ?x5797 = 07c37, ?x3993 = 099bk *> conf = 0.33 ranks of expected_values: 11, 17, 40 EVAL 05qmj influenced_by! 03jht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 92.000 36.000 0.600 http://example.org/influence/influence_node/influenced_by EVAL 05qmj influenced_by! 043tg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 92.000 36.000 0.600 http://example.org/influence/influence_node/influenced_by EVAL 05qmj influenced_by! 05np2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 92.000 36.000 0.600 http://example.org/influence/influence_node/influenced_by #1594-06x43v PRED entity: 06x43v PRED relation: produced_by PRED expected values: 03h304l => 111 concepts (84 used for prediction) PRED predicted values (max 10 best out of 158): 0162c8 (0.47 #7353, 0.37 #6580, 0.37 #10066), 012d40 (0.19 #21685, 0.17 #775, 0.16 #19748), 01900g (0.16 #19748, 0.13 #5805, 0.10 #16650), 02xnjd (0.14 #273, 0.05 #1822, 0.04 #2595), 0272kv (0.14 #316, 0.03 #703, 0.02 #2251), 02q42j_ (0.11 #2145, 0.03 #3692, 0.03 #20733), 0b13g7 (0.11 #2053, 0.03 #20641, 0.03 #3600), 05hj_k (0.10 #528, 0.01 #1302, 0.01 #3623), 06pj8 (0.08 #1228, 0.06 #2775, 0.04 #7033), 02q_cc (0.08 #1194, 0.05 #2741, 0.04 #6999) >> Best rule #7353 for best value: >> intensional similarity = 4 >> extensional distance = 290 >> proper extension: 02pxmgz; 0sxfd; 012mrr; 02vqsll; 0bm2x; 02pg45; 0295sy; 0bl06; 0286gm1; 01gvts; ... >> query: (?x7514, ?x1416) <- film(?x1416, ?x7514), language(?x7514, ?x254), people(?x1050, ?x1416), produced_by(?x10191, ?x1416) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #573 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 0gxsh4; *> query: (?x7514, 03h304l) <- nominated_for(?x147, ?x7514), award(?x147, ?x3508), film(?x147, ?x6205), ?x6205 = 01mszz *> conf = 0.05 ranks of expected_values: 15 EVAL 06x43v produced_by 03h304l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 111.000 84.000 0.468 http://example.org/film/film/produced_by #1593-035_2h PRED entity: 035_2h PRED relation: music PRED expected values: 02bh9 => 106 concepts (74 used for prediction) PRED predicted values (max 10 best out of 112): 01tc9r (0.14 #275, 0.14 #486, 0.07 #1540), 0146pg (0.12 #1062, 0.10 #1696, 0.06 #3172), 02sj1x (0.11 #898, 0.03 #1953, 0.02 #5959), 02bh9 (0.10 #261, 0.09 #472, 0.04 #2159), 02cyfz (0.08 #665, 0.06 #1298, 0.05 #244), 05b49tt (0.07 #12441, 0.07 #5692, 0.07 #10752), 0bj9k (0.07 #12441, 0.07 #10752, 0.07 #10753), 03_fk9 (0.07 #12441, 0.07 #10752, 0.07 #10539), 02pqgt8 (0.07 #12441, 0.07 #10752, 0.07 #10539), 0150t6 (0.07 #2154, 0.05 #3630, 0.05 #256) >> Best rule #275 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 02vp1f_; 01gc7; 07gp9; 017gl1; 0yyts; 042y1c; 02q6gfp; 026p4q7; 0kv238; 011yr9; ... >> query: (?x5294, 01tc9r) <- genre(?x5294, ?x225), award(?x5294, ?x3458), ?x3458 = 0gqxm, film(?x398, ?x5294) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #261 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 02vp1f_; 01gc7; 07gp9; 017gl1; 0yyts; 042y1c; 02q6gfp; 026p4q7; 0kv238; 011yr9; ... *> query: (?x5294, 02bh9) <- genre(?x5294, ?x225), award(?x5294, ?x3458), ?x3458 = 0gqxm, film(?x398, ?x5294) *> conf = 0.10 ranks of expected_values: 4 EVAL 035_2h music 02bh9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 106.000 74.000 0.143 http://example.org/film/film/music #1592-05_zc7 PRED entity: 05_zc7 PRED relation: nationality PRED expected values: 03rk0 => 89 concepts (54 used for prediction) PRED predicted values (max 10 best out of 31): 03rk0 (0.78 #2912, 0.27 #4924, 0.26 #2108), 09c7w0 (0.73 #501, 0.71 #4015, 0.71 #4116), 02jx1 (0.13 #1034, 0.12 #934, 0.11 #2141), 07ssc (0.13 #1116, 0.13 #916, 0.13 #1016), 0d060g (0.05 #1510, 0.05 #2215, 0.05 #5031), 05vz3zq (0.05 #671, 0.04 #771, 0.04 #871), 0d05w3 (0.04 #951, 0.03 #1051, 0.02 #651), 06q1r (0.04 #277, 0.03 #678, 0.03 #377), 0cdbq (0.03 #664, 0.03 #764, 0.02 #864), 0345h (0.03 #1332, 0.03 #732, 0.02 #3143) >> Best rule #2912 for best value: >> intensional similarity = 4 >> extensional distance = 889 >> proper extension: 07_grx; 0f1pyf; 040j2_; 03h40_7; 0bm9xk; 0459z; 011zwl; >> query: (?x10172, ?x2146) <- gender(?x10172, ?x231), location(?x10172, ?x12040), contains(?x12040, ?x7412), country(?x12040, ?x2146) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05_zc7 nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 54.000 0.784 http://example.org/people/person/nationality #1591-06fpsx PRED entity: 06fpsx PRED relation: award PRED expected values: 03hj5vf => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 164): 05zr6wv (0.26 #2109, 0.26 #2110, 0.22 #23894), 063y_ky (0.26 #2109, 0.22 #23894, 0.22 #23893), 05ztjjw (0.26 #2109, 0.22 #23893, 0.22 #4453), 05b4l5x (0.14 #1880, 0.05 #3287, 0.03 #10545), 09cn0c (0.12 #430, 0.07 #4414, 0.03 #8394), 099cng (0.12 #303, 0.05 #4287, 0.02 #773), 02r22gf (0.12 #262, 0.04 #8226, 0.04 #8928), 0gqwc (0.12 #4279, 0.05 #10600, 0.05 #8259), 05b1610 (0.11 #1906, 0.04 #736, 0.04 #2610), 05f4m9q (0.10 #1885, 0.03 #3292, 0.03 #1183) >> Best rule #2109 for best value: >> intensional similarity = 4 >> extensional distance = 147 >> proper extension: 0kfpm; 02xhpl; 02hct1; 030cx; 016zfm; >> query: (?x7702, ?x298) <- nominated_for(?x401, ?x7702), nominated_for(?x298, ?x7702), award(?x8160, ?x401), ?x8160 = 02dlfh >> conf = 0.26 => this is the best rule for 3 predicted values *> Best rule #9369 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 755 *> proper extension: 05fgr_; 06w7mlh; *> query: (?x7702, ?x277) <- titles(?x2480, ?x7702), nominated_for(?x364, ?x7702), award(?x7702, ?x1691), award(?x364, ?x277) *> conf = 0.08 ranks of expected_values: 26 EVAL 06fpsx award 03hj5vf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 111.000 111.000 0.265 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #1590-02wmbg PRED entity: 02wmbg PRED relation: location PRED expected values: 049lr => 162 concepts (113 used for prediction) PRED predicted values (max 10 best out of 190): 04jpl (0.91 #14456, 0.17 #42539, 0.16 #48958), 02_286 (0.43 #42559, 0.40 #48978, 0.31 #20094), 030qb3t (0.33 #49024, 0.27 #46617, 0.25 #22547), 04vmp (0.30 #4363, 0.17 #2759, 0.16 #6770), 07c98 (0.22 #1250, 0.05 #2052, 0.03 #3656), 0cvw9 (0.14 #4407, 0.08 #2803, 0.08 #6011), 0dclg (0.11 #919, 0.05 #4929, 0.05 #3325), 01c0h6 (0.11 #1423, 0.05 #3829, 0.05 #2225), 049lr (0.11 #1253, 0.05 #2055, 0.05 #4461), 09c6w (0.11 #1073, 0.03 #17919, 0.03 #3479) >> Best rule #14456 for best value: >> intensional similarity = 4 >> extensional distance = 131 >> proper extension: 047s_cr; >> query: (?x8530, 04jpl) <- location(?x8530, ?x4335), nationality(?x8530, ?x2146), location_of_ceremony(?x10591, ?x4335), ?x10591 = 01rwcgb >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #1253 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 09r_wb; *> query: (?x8530, 049lr) <- languages(?x8530, ?x5121), languages(?x8530, ?x254), ?x254 = 02h40lc, film(?x8530, ?x3742), ?x5121 = 07c9s *> conf = 0.11 ranks of expected_values: 9 EVAL 02wmbg location 049lr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 162.000 113.000 0.910 http://example.org/people/person/places_lived./people/place_lived/location #1589-0j3v PRED entity: 0j3v PRED relation: influenced_by! PRED expected values: 040db => 191 concepts (85 used for prediction) PRED predicted values (max 10 best out of 440): 0dzkq (0.50 #3089, 0.50 #615, 0.28 #11508), 048cl (0.50 #1275, 0.40 #2758, 0.38 #6725), 07h1q (0.50 #887, 0.33 #3857, 0.33 #3361), 039n1 (0.50 #1366, 0.33 #3841, 0.33 #377), 0h336 (0.50 #1402, 0.33 #3877, 0.33 #413), 0399p (0.50 #3780, 0.29 #7747, 0.28 #11703), 03_87 (0.44 #5199, 0.40 #2721, 0.33 #6194), 040db (0.44 #5024, 0.33 #6019, 0.33 #3042), 032l1 (0.40 #2587, 0.25 #1104, 0.17 #3579), 0683n (0.40 #2798, 0.17 #3294, 0.14 #11882) >> Best rule #3089 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03_87; 060_7; >> query: (?x2240, 0dzkq) <- location(?x2240, ?x8977), influenced_by(?x2239, ?x2240), influenced_by(?x2240, ?x1857), ?x2239 = 0453t >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5024 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 03_dj; *> query: (?x2240, 040db) <- location(?x2240, ?x8977), influenced_by(?x5148, ?x2240), influenced_by(?x4265, ?x2240), profession(?x5148, ?x6630), ?x4265 = 06whf *> conf = 0.44 ranks of expected_values: 8 EVAL 0j3v influenced_by! 040db CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 191.000 85.000 0.500 http://example.org/influence/influence_node/influenced_by #1588-06mxs PRED entity: 06mxs PRED relation: month PRED expected values: 028kb => 251 concepts (251 used for prediction) PRED predicted values (max 10 best out of 2): 02xx5 (0.91 #161, 0.90 #117, 0.90 #113), 028kb (0.89 #134, 0.89 #162, 0.89 #108) >> Best rule #161 for best value: >> intensional similarity = 2 >> extensional distance = 52 >> proper extension: 03czqs; >> query: (?x5168, 02xx5) <- month(?x5168, ?x1459), mode_of_transportation(?x5168, ?x4272) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #134 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 0g6xq; *> query: (?x5168, 028kb) <- month(?x5168, ?x4925), month(?x5168, ?x1650), ?x1650 = 06vkl, ?x4925 = 0ll3 *> conf = 0.89 ranks of expected_values: 2 EVAL 06mxs month 028kb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 251.000 251.000 0.907 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #1587-02vqsll PRED entity: 02vqsll PRED relation: featured_film_locations PRED expected values: 0rh6k => 81 concepts (65 used for prediction) PRED predicted values (max 10 best out of 80): 02_286 (0.17 #6048, 0.16 #1707, 0.16 #1947), 030qb3t (0.10 #279, 0.10 #3172, 0.10 #3656), 04jpl (0.06 #249, 0.06 #2662, 0.06 #6037), 0rh6k (0.04 #5787, 0.04 #7236, 0.04 #6029), 0h7h6 (0.04 #43, 0.03 #2696, 0.03 #283), 01_d4 (0.04 #47, 0.03 #287, 0.03 #4388), 03rjj (0.04 #6, 0.02 #486, 0.02 #967), 052p7 (0.04 #58, 0.02 #7053, 0.02 #7293), 06c62 (0.04 #130, 0.01 #2783, 0.01 #370), 01l3k6 (0.04 #190, 0.01 #430, 0.01 #670) >> Best rule #6048 for best value: >> intensional similarity = 3 >> extensional distance = 523 >> proper extension: 053tj7; >> query: (?x2989, 02_286) <- film_release_distribution_medium(?x2989, ?x81), produced_by(?x2989, ?x3568), production_companies(?x2989, ?x1478) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #5787 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 515 *> proper extension: 09xbpt; 047gn4y; 0dnvn3; 03h_yy; 04kkz8; 08hmch; 02v63m; 03s5lz; 0bh8yn3; 0m491; ... *> query: (?x2989, 0rh6k) <- film_crew_role(?x2989, ?x137), nominated_for(?x361, ?x2989), currency(?x2989, ?x170), production_companies(?x2989, ?x1478) *> conf = 0.04 ranks of expected_values: 4 EVAL 02vqsll featured_film_locations 0rh6k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 81.000 65.000 0.168 http://example.org/film/film/featured_film_locations #1586-04399 PRED entity: 04399 PRED relation: school_type! PRED expected values: 027kp3 037fqp => 141 concepts (133 used for prediction) PRED predicted values (max 10 best out of 664): 04rwx (0.40 #2330, 0.33 #4049, 0.33 #3476), 01pl14 (0.40 #2296, 0.33 #4015, 0.33 #3442), 02km0m (0.33 #812, 0.33 #240, 0.30 #2531), 03ksy (0.33 #686, 0.33 #114, 0.20 #2405), 0cwx_ (0.33 #832, 0.33 #260, 0.20 #2551), 065y4w7 (0.33 #583, 0.33 #11, 0.20 #2302), 06pwq (0.33 #580, 0.33 #8, 0.20 #2299), 06fq2 (0.33 #883, 0.33 #311, 0.20 #2602), 0kz2w (0.33 #591, 0.33 #19, 0.20 #2310), 01bm_ (0.33 #837, 0.33 #265, 0.20 #2556) >> Best rule #2330 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 01_9fk; 01_srz; 05jxkf; 01rs41; 06cs1; 01jlsn; >> query: (?x11041, 04rwx) <- school_type(?x5145, ?x11041), major_field_of_study(?x5145, ?x6756), colors(?x5145, ?x332), ?x6756 = 0_jm, institution(?x620, ?x5145) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #734 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 1 *> proper extension: 05pcjw; *> query: (?x11041, 027kp3) <- school_type(?x12530, ?x11041), school_type(?x11093, ?x11041), school_type(?x5145, ?x11041), school_type(?x4846, ?x11041), school_type(?x3543, ?x11041), ?x12530 = 02cvcd, ?x3543 = 0778p, ?x5145 = 0b1xl, contains(?x205, ?x11093), ?x4846 = 037njl, organization(?x5510, ?x11093) *> conf = 0.33 ranks of expected_values: 83, 198 EVAL 04399 school_type! 037fqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 141.000 133.000 0.400 http://example.org/education/educational_institution/school_type EVAL 04399 school_type! 027kp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 141.000 133.000 0.400 http://example.org/education/educational_institution/school_type #1585-0zdfp PRED entity: 0zdfp PRED relation: location! PRED expected values: 0h7dd => 126 concepts (108 used for prediction) PRED predicted values (max 10 best out of 655): 099p5 (0.12 #1901, 0.10 #6939, 0.10 #9458), 096lf_ (0.12 #2044, 0.08 #4563, 0.05 #7082), 01v3vp (0.12 #804, 0.08 #3323, 0.05 #5842), 04gcd1 (0.12 #416, 0.08 #2935, 0.05 #5454), 03xgm3 (0.12 #246, 0.08 #2765, 0.05 #5284), 01g1lp (0.05 #6615, 0.05 #9134, 0.04 #11653), 0kvnn (0.05 #5915, 0.05 #8434, 0.04 #10953), 0c5vh (0.05 #7391, 0.05 #9910, 0.04 #12429), 04s04 (0.05 #6673, 0.05 #9192, 0.04 #11711), 0443xn (0.05 #7513, 0.05 #10032, 0.04 #12551) >> Best rule #1901 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0d234; 0zchj; 0d23k; 0zgfm; 0zdkh; 02mf7; >> query: (?x12080, 099p5) <- contains(?x12079, ?x12080), contains(?x726, ?x12080), category(?x12080, ?x134), ?x726 = 05kj_, time_zones(?x12079, ?x2950), adjoins(?x1939, ?x12079), source(?x12080, ?x958) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #26418 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 174 *> proper extension: 0mn0v; *> query: (?x12080, 0h7dd) <- time_zones(?x12080, ?x2950), county(?x12080, ?x12079), source(?x12080, ?x958), ?x958 = 0jbk9, currency(?x12079, ?x170) *> conf = 0.01 ranks of expected_values: 159 EVAL 0zdfp location! 0h7dd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 126.000 108.000 0.125 http://example.org/people/person/places_lived./people/place_lived/location #1584-057lbk PRED entity: 057lbk PRED relation: film! PRED expected values: 058s44 => 81 concepts (55 used for prediction) PRED predicted values (max 10 best out of 1036): 058s44 (0.33 #1029, 0.20 #3111, 0.14 #5193), 014gf8 (0.33 #1011, 0.20 #3093, 0.14 #5175), 03gm48 (0.33 #154, 0.20 #2236, 0.14 #4318), 01sfmyk (0.33 #1010, 0.20 #3092, 0.14 #5174), 02tc5y (0.29 #5905, 0.20 #3823, 0.03 #41646), 01l2fn (0.20 #2345, 0.14 #4427, 0.09 #10673), 06cl2w (0.20 #3926, 0.14 #6008, 0.05 #10172), 01vxxb (0.20 #2847, 0.14 #4929, 0.04 #11175), 0dvmd (0.20 #2611, 0.14 #4693, 0.04 #27597), 02yxwd (0.20 #2828, 0.14 #4910, 0.03 #27814) >> Best rule #1029 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 09sh8k; >> query: (?x4378, 058s44) <- film(?x9780, ?x4378), ?x9780 = 023zsh, story_by(?x4378, ?x96), produced_by(?x4378, ?x7976) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 057lbk film! 058s44 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 55.000 0.333 http://example.org/film/actor/film./film/performance/film #1583-017g2y PRED entity: 017g2y PRED relation: nationality PRED expected values: 09c7w0 => 151 concepts (147 used for prediction) PRED predicted values (max 10 best out of 53): 09c7w0 (0.91 #7648, 0.85 #397, 0.85 #1090), 02jx1 (0.17 #627, 0.17 #726, 0.13 #2312), 0d060g (0.13 #205, 0.10 #106, 0.10 #7), 07ssc (0.13 #708, 0.12 #609, 0.11 #2096), 03rk0 (0.09 #3317, 0.08 #3516, 0.07 #12557), 0345h (0.05 #1615, 0.05 #2708, 0.05 #3005), 0f8l9c (0.05 #616, 0.04 #715, 0.04 #4391), 06bnz (0.04 #3014, 0.03 #1624, 0.02 #2717), 0h7x (0.03 #3009, 0.03 #7250, 0.03 #1619), 03rjj (0.03 #1690, 0.02 #6560, 0.02 #599) >> Best rule #7648 for best value: >> intensional similarity = 4 >> extensional distance = 1079 >> proper extension: 0dbpyd; 02g8h; 01v3s2_; 04shbh; 07csf4; 02wgln; 01wz3cx; 0738b8; 07_s4b; 071ynp; ... >> query: (?x8002, 09c7w0) <- student(?x8056, ?x8002), nationality(?x8002, ?x1536), institution(?x5739, ?x8056), currency(?x8056, ?x170) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017g2y nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 147.000 0.907 http://example.org/people/person/nationality #1582-0h7dd PRED entity: 0h7dd PRED relation: film PRED expected values: 0gnjh => 139 concepts (75 used for prediction) PRED predicted values (max 10 best out of 611): 0jvt9 (0.25 #9495, 0.25 #2331, 0.20 #14868), 0gxfz (0.25 #2227, 0.12 #9391, 0.10 #14764), 075cph (0.25 #2170, 0.12 #9334, 0.10 #14707), 0gcpc (0.25 #2499, 0.12 #9663, 0.10 #15036), 05ypj5 (0.25 #3525, 0.12 #10689, 0.10 #16062), 0bl06 (0.25 #2775, 0.12 #9939, 0.10 #15312), 0n04r (0.25 #2454, 0.12 #9618, 0.10 #14991), 0jwvf (0.25 #2807, 0.12 #9971, 0.10 #15344), 0gt14 (0.25 #3558, 0.12 #10722, 0.10 #16095), 0k0rf (0.25 #2678, 0.12 #9842, 0.10 #15215) >> Best rule #9495 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 023w9s; >> query: (?x6017, 0jvt9) <- award_winner(?x8259, ?x6017), ?x8259 = 0fy59t, type_of_union(?x6017, ?x566), nationality(?x6017, ?x94) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #19077 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 06c0j; *> query: (?x6017, 0gnjh) <- participant(?x1545, ?x6017), place_of_burial(?x1545, ?x8044), award(?x1545, ?x537) *> conf = 0.03 ranks of expected_values: 148 EVAL 0h7dd film 0gnjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 139.000 75.000 0.250 http://example.org/film/actor/film./film/performance/film #1581-02xs6_ PRED entity: 02xs6_ PRED relation: language PRED expected values: 02bjrlw => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 34): 06nm1 (0.17 #9, 0.14 #238, 0.14 #180), 064_8sq (0.17 #20, 0.14 #478, 0.14 #1918), 04306rv (0.11 #806, 0.11 #865, 0.11 #290), 02bjrlw (0.08 #1, 0.08 #803, 0.08 #230), 04h9h (0.08 #41, 0.03 #556, 0.03 #902), 0349s (0.08 #43, 0.03 #100, 0.01 #845), 01bkv (0.08 #57), 02hxc3j (0.08 #6), 06b_j (0.07 #250, 0.07 #479, 0.07 #1630), 0653m (0.07 #239, 0.06 #181, 0.04 #1619) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 016z5x; 049mql; >> query: (?x4991, 06nm1) <- film(?x450, ?x4991), titles(?x600, ?x4991), production_companies(?x4991, ?x788), ?x450 = 0z4s >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 016z5x; 049mql; *> query: (?x4991, 02bjrlw) <- film(?x450, ?x4991), titles(?x600, ?x4991), production_companies(?x4991, ?x788), ?x450 = 0z4s *> conf = 0.08 ranks of expected_values: 4 EVAL 02xs6_ language 02bjrlw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 83.000 0.167 http://example.org/film/film/language #1580-092vkg PRED entity: 092vkg PRED relation: nominated_for! PRED expected values: 040njc 0gr4k 02pqp12 => 92 concepts (90 used for prediction) PRED predicted values (max 10 best out of 191): 099ck7 (0.67 #4560, 0.67 #4996, 0.66 #10867), 027986c (0.67 #4560, 0.67 #4996, 0.66 #10867), 040njc (0.65 #224, 0.37 #442, 0.33 #659), 0k611 (0.65 #281, 0.35 #716, 0.33 #499), 019f4v (0.53 #267, 0.41 #702, 0.37 #485), 04dn09n (0.53 #249, 0.37 #467, 0.34 #901), 02pqp12 (0.47 #270, 0.22 #1139, 0.21 #4178), 027dtxw (0.41 #221, 0.29 #4778, 0.24 #11306), 0l8z1 (0.41 #265, 0.21 #4390, 0.20 #4173), 0gr51 (0.41 #286, 0.20 #4194, 0.20 #721) >> Best rule #4560 for best value: >> intensional similarity = 3 >> extensional distance = 461 >> proper extension: 0cwrr; 04glx0; 05fgr_; 07bz5; 06mmr; >> query: (?x1064, ?x591) <- award_winner(?x1064, ?x91), award(?x1064, ?x591), honored_for(?x1112, ?x1064) >> conf = 0.67 => this is the best rule for 2 predicted values *> Best rule #224 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0gmcwlb; *> query: (?x1064, 040njc) <- film(?x1850, ?x1064), award(?x1064, ?x2853), ?x2853 = 09qv_s *> conf = 0.65 ranks of expected_values: 3, 7, 21 EVAL 092vkg nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 92.000 90.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 092vkg nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 92.000 90.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 092vkg nominated_for! 040njc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 92.000 90.000 0.672 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1579-0yx_w PRED entity: 0yx_w PRED relation: nominated_for! PRED expected values: 0gr0m => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 201): 040njc (0.58 #1815, 0.54 #1137, 0.44 #4753), 0gr4k (0.53 #1833, 0.43 #1155, 0.42 #702), 0gqy2 (0.52 #1923, 0.39 #1697, 0.37 #1245), 018wdw (0.50 #621, 0.33 #169, 0.17 #9271), 0gqzz (0.50 #499, 0.33 #47, 0.09 #952), 02x1z2s (0.50 #585, 0.33 #133, 0.06 #6462), 0p9sw (0.48 #1828, 0.45 #4766, 0.35 #2958), 0gr0m (0.45 #1864, 0.42 #4802, 0.40 #2994), 0gs96 (0.40 #3021, 0.39 #1891, 0.29 #1665), 02pqp12 (0.40 #1185, 0.35 #1863, 0.34 #4801) >> Best rule #1815 for best value: >> intensional similarity = 5 >> extensional distance = 60 >> proper extension: 0cbl95; >> query: (?x9456, 040njc) <- nominated_for(?x398, ?x9456), award(?x9456, ?x1307), award(?x9456, ?x591), ?x1307 = 0gq9h, award_winner(?x591, ?x157) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #1864 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 60 *> proper extension: 0cbl95; *> query: (?x9456, 0gr0m) <- nominated_for(?x398, ?x9456), award(?x9456, ?x1307), award(?x9456, ?x591), ?x1307 = 0gq9h, award_winner(?x591, ?x157) *> conf = 0.45 ranks of expected_values: 8 EVAL 0yx_w nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 109.000 109.000 0.581 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1578-02vx4c2 PRED entity: 02vx4c2 PRED relation: nominated_for PRED expected values: 05pbl56 => 106 concepts (33 used for prediction) PRED predicted values (max 10 best out of 508): 02b6n9 (0.52 #9714, 0.49 #11335, 0.44 #6474), 01hqhm (0.52 #9714, 0.49 #11335, 0.44 #6474), 07kh6f3 (0.52 #9714, 0.49 #11335, 0.44 #6474), 01v1ln (0.52 #9714, 0.49 #11335, 0.44 #6474), 078sj4 (0.52 #9714, 0.49 #11335, 0.44 #6474), 0fh694 (0.52 #9714, 0.49 #11335, 0.44 #6474), 01qvz8 (0.52 #9714, 0.49 #11335, 0.44 #6474), 05pbl56 (0.49 #11335, 0.44 #6474, 0.42 #14575), 03qnvdl (0.49 #11335, 0.44 #6474, 0.42 #14575), 0660b9b (0.44 #6474, 0.42 #14575, 0.42 #11334) >> Best rule #9714 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 04qvl7; 06cv1; 0f3zf_; 079hvk; 05dppk; 0dqzkv; 07mb57; 06nz46; 06g60w; 03cx282; ... >> query: (?x7384, ?x3790) <- nominated_for(?x7384, ?x3133), award(?x7384, ?x1243), cinematography(?x3790, ?x7384), award_winner(?x3790, ?x92) >> conf = 0.52 => this is the best rule for 7 predicted values *> Best rule #11335 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 04g865; 0693l; 09myny; 0f_zkz; *> query: (?x7384, ?x3790) <- nominated_for(?x7384, ?x3133), award(?x7384, ?x1243), cinematography(?x3790, ?x7384), nominated_for(?x112, ?x3790) *> conf = 0.49 ranks of expected_values: 8 EVAL 02vx4c2 nominated_for 05pbl56 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 106.000 33.000 0.520 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #1577-0nvg4 PRED entity: 0nvg4 PRED relation: adjoins! PRED expected values: 0nvrd => 167 concepts (74 used for prediction) PRED predicted values (max 10 best out of 514): 0nvrd (0.81 #50938, 0.81 #50937, 0.80 #11751), 0nvg4 (0.50 #491, 0.26 #54076, 0.25 #50939), 0nt6b (0.50 #557, 0.26 #54076, 0.25 #50939), 0nvd8 (0.26 #54076, 0.25 #50939, 0.25 #394), 0ns_4 (0.20 #1343, 0.01 #8391, 0.01 #9174), 0vbk (0.16 #5723, 0.07 #8070, 0.06 #8853), 0k3kg (0.14 #1811, 0.07 #3380, 0.06 #11213), 0kpzy (0.14 #1867, 0.07 #3436, 0.05 #4220), 0cv1w (0.14 #2244, 0.07 #3813, 0.05 #4597), 0dc95 (0.14 #1697, 0.07 #3266, 0.05 #4050) >> Best rule #50938 for best value: >> intensional similarity = 4 >> extensional distance = 273 >> proper extension: 0mx4_; 0mw93; 0m7fm; 0n5fl; 0fr59; 0mxcf; 0mx6c; 0m2lt; 0mk7z; 0p0cw; ... >> query: (?x10134, ?x6410) <- adjoins(?x10134, ?x6410), contains(?x3818, ?x10134), currency(?x6410, ?x170), adjoins(?x6410, ?x8552) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nvg4 adjoins! 0nvrd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 74.000 0.810 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #1576-01vz80y PRED entity: 01vz80y PRED relation: written_by! PRED expected values: 01f7gh => 104 concepts (75 used for prediction) PRED predicted values (max 10 best out of 367): 062zm5h (0.54 #2635, 0.35 #1976), 02c7k4 (0.08 #7900, 0.07 #11193, 0.03 #13168), 0407yj_ (0.08 #7900, 0.07 #11193, 0.03 #13168), 01jrbb (0.08 #7900, 0.07 #11193, 0.01 #2160), 04hwbq (0.08 #7900, 0.07 #11193), 0g9z_32 (0.05 #1143, 0.02 #5751, 0.01 #9042), 03n3gl (0.05 #438, 0.02 #1097, 0.02 #3073), 01hvjx (0.05 #150, 0.02 #809, 0.02 #2785), 0gg5qcw (0.04 #1003, 0.02 #1661, 0.02 #2320), 07w8fz (0.04 #858, 0.02 #1516, 0.02 #2175) >> Best rule #2635 for best value: >> intensional similarity = 4 >> extensional distance = 94 >> proper extension: 0b_c7; 03wpmd; 081_zm; 02l5rm; 022wxh; 0gv2r; >> query: (?x7587, ?x5016) <- award_nominee(?x7587, ?x1052), gender(?x7587, ?x231), written_by(?x3053, ?x7587), film(?x7587, ?x5016) >> conf = 0.54 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01vz80y written_by! 01f7gh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 75.000 0.545 http://example.org/film/film/written_by #1575-01hcj2 PRED entity: 01hcj2 PRED relation: film PRED expected values: 09lxv9 => 143 concepts (119 used for prediction) PRED predicted values (max 10 best out of 981): 030cx (0.66 #33993, 0.64 #73355, 0.63 #64409), 01shy7 (0.18 #422, 0.17 #4000, 0.11 #5789), 0bvn25 (0.17 #3628, 0.04 #28674, 0.03 #55512), 02825cv (0.17 #4719, 0.02 #27976, 0.02 #85230), 035s95 (0.13 #2128, 0.04 #7495, 0.03 #27174), 01vw8k (0.13 #2441, 0.04 #7808, 0.02 #9597), 034qrh (0.11 #3640, 0.11 #5429, 0.09 #62), 03lrht (0.11 #3835, 0.09 #257, 0.05 #5624), 02ntb8 (0.11 #4416, 0.09 #838, 0.05 #6205), 028kj0 (0.11 #5244, 0.09 #1666, 0.05 #7033) >> Best rule #33993 for best value: >> intensional similarity = 3 >> extensional distance = 277 >> proper extension: 0162c8; >> query: (?x9545, ?x4535) <- nominated_for(?x9545, ?x4535), place_of_birth(?x9545, ?x682), participant(?x2499, ?x9545) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #5083 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 16 *> proper extension: 01xllf; *> query: (?x9545, 09lxv9) <- film(?x9545, ?x2102), ?x2102 = 034qzw *> conf = 0.06 ranks of expected_values: 153 EVAL 01hcj2 film 09lxv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 143.000 119.000 0.661 http://example.org/film/actor/film./film/performance/film #1574-0myk8 PRED entity: 0myk8 PRED relation: role! PRED expected values: 05r5c => 64 concepts (45 used for prediction) PRED predicted values (max 10 best out of 108): 03m5k (0.84 #2700, 0.83 #4052, 0.82 #4053), 02sgy (0.83 #1228, 0.71 #668, 0.64 #3263), 03q5t (0.83 #4052, 0.82 #4053, 0.82 #2476), 03bx0bm (0.80 #3190, 0.79 #3983, 0.79 #2398), 05148p4 (0.80 #1942, 0.78 #3490, 0.78 #1491), 0mkg (0.79 #2375, 0.76 #4059, 0.76 #3960), 05r5c (0.79 #2372, 0.73 #2589, 0.73 #2484), 028tv0 (0.78 #1589, 0.75 #1137, 0.70 #1816), 0bxl5 (0.75 #1192, 0.67 #1644, 0.67 #1535), 01vj9c (0.74 #3833, 0.73 #2491, 0.73 #3944) >> Best rule #2700 for best value: >> intensional similarity = 22 >> extensional distance = 14 >> proper extension: 01rhl; >> query: (?x2956, ?x6039) <- role(?x2956, ?x8014), role(?x2956, ?x4616), role(?x2956, ?x2957), ?x8014 = 0214km, role(?x4052, ?x4616), ?x2957 = 01v8y9, role(?x2956, ?x6039), role(?x2956, ?x1495), role(?x1495, ?x2310), role(?x1495, ?x1482), ?x2310 = 0gghm, role(?x6039, ?x6801), performance_role(?x130, ?x1495), instrumentalists(?x6039, ?x3030), instrumentalists(?x1495, ?x211), group(?x1495, ?x997), performance_role(?x2059, ?x1495), family(?x6039, ?x3156), role(?x217, ?x1495), ?x997 = 07qnf, ?x4052 = 050z2, ?x1482 = 02g9p4 >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #2372 for first EXPECTED value: *> intensional similarity = 29 *> extensional distance = 12 *> proper extension: 07kc_; 03ndd; *> query: (?x2956, 05r5c) <- role(?x2956, ?x8014), role(?x2956, ?x2957), role(?x2956, ?x2460), role(?x6039, ?x8014), role(?x4917, ?x8014), role(?x2309, ?x8014), role(?x2205, ?x8014), role(?x2048, ?x8014), role(?x1831, ?x8014), role(?x1495, ?x8014), ?x2205 = 0dq630k, ?x2048 = 018j2, role(?x6939, ?x8014), role(?x2956, ?x6449), role(?x2956, ?x2157), ?x6039 = 05kms, ?x4917 = 06w7v, ?x1831 = 03t22m, award_winner(?x4912, ?x6939), group(?x2956, ?x5838), artist(?x2241, ?x6939), role(?x2157, ?x316), ?x2309 = 06ncr, ?x1495 = 013y1f, role(?x1524, ?x2460), instrumentalists(?x2460, ?x6947), role(?x806, ?x2957), ?x6449 = 014zz1, ?x6947 = 01vrnsk *> conf = 0.79 ranks of expected_values: 7 EVAL 0myk8 role! 05r5c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 64.000 45.000 0.836 http://example.org/music/performance_role/regular_performances./music/group_membership/role #1573-026lyl4 PRED entity: 026lyl4 PRED relation: costume_design_by! PRED expected values: 01d2v1 => 111 concepts (42 used for prediction) PRED predicted values (max 10 best out of 199): 03kxj2 (0.65 #1190, 0.61 #991, 0.61 #1389), 015gm8 (0.11 #592, 0.11 #394, 0.07 #790), 0k419 (0.11 #583, 0.11 #385, 0.07 #781), 0jqb8 (0.11 #573, 0.11 #375, 0.07 #771), 04wddl (0.11 #572, 0.11 #374, 0.07 #770), 072192 (0.11 #570, 0.11 #372, 0.07 #768), 0h3k3f (0.11 #565, 0.11 #367, 0.07 #763), 0k4bc (0.11 #545, 0.11 #347, 0.07 #743), 01jr4j (0.11 #543, 0.11 #345, 0.07 #741), 0bm2x (0.11 #504, 0.11 #306, 0.07 #702) >> Best rule #1190 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 03wpmd; >> query: (?x12521, ?x2231) <- costume_design_by(?x2755, ?x12521), gender(?x12521, ?x514), nominated_for(?x12521, ?x2231), film(?x538, ?x2755) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 026lyl4 costume_design_by! 01d2v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 42.000 0.648 http://example.org/film/film/costume_design_by #1572-07x21 PRED entity: 07x21 PRED relation: religion! PRED expected values: 03_js => 33 concepts (23 used for prediction) PRED predicted values (max 10 best out of 3430): 099p5 (0.50 #8258, 0.40 #7191, 0.40 #6123), 06y3r (0.50 #8239, 0.25 #18921, 0.23 #19990), 0xnc3 (0.43 #11375, 0.33 #2832, 0.17 #9237), 0mb5x (0.40 #6039, 0.25 #18856, 0.23 #19925), 0jcx (0.40 #5595, 0.25 #18412, 0.23 #19481), 042q3 (0.40 #7295, 0.25 #12633, 0.17 #8362), 048cl (0.40 #5964, 0.17 #18781, 0.17 #9166), 03rx9 (0.40 #6154, 0.17 #18971, 0.17 #9356), 01pw9v (0.40 #6134, 0.17 #18951, 0.17 #9336), 01dvtx (0.40 #5662, 0.17 #18479, 0.17 #8864) >> Best rule #8258 for best value: >> intensional similarity = 13 >> extensional distance = 4 >> proper extension: 092bf5; 04pk9; >> query: (?x14017, 099p5) <- religion(?x1913, ?x14017), people(?x5741, ?x1913), place_of_birth(?x1913, ?x6555), company(?x1913, ?x5178), place_of_death(?x1913, ?x108), student(?x122, ?x1913), profession(?x1913, ?x3342), major_field_of_study(?x122, ?x3490), people(?x10199, ?x1913), institution(?x1200, ?x122), contains(?x94, ?x122), ?x3490 = 05qfh, ?x1200 = 016t_3 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3205 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 1 *> proper extension: 0n2g; *> query: (?x14017, ?x8991) <- religion(?x5254, ?x14017), religion(?x1913, ?x14017), people(?x5741, ?x1913), basic_title(?x1913, ?x12773), jurisdiction_of_office(?x1913, ?x5147), profession(?x1913, ?x3342), type_of_union(?x1913, ?x566), jurisdiction_of_office(?x12773, ?x9654), student(?x122, ?x1913), ?x9654 = 01n8qg, student(?x6919, ?x5254), peers(?x8991, ?x5254), participating_countries(?x1931, ?x5147), influenced_by(?x2608, ?x5254), olympics(?x5147, ?x358) *> conf = 0.33 ranks of expected_values: 69 EVAL 07x21 religion! 03_js CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 33.000 23.000 0.500 http://example.org/people/person/religion #1571-06jkm PRED entity: 06jkm PRED relation: profession PRED expected values: 05z96 => 117 concepts (65 used for prediction) PRED predicted values (max 10 best out of 84): 02hrh1q (0.71 #2235, 0.59 #1494, 0.58 #6089), 05z96 (0.57 #782, 0.50 #486, 0.38 #5039), 0dxtg (0.47 #8608, 0.47 #6978, 0.45 #9498), 0frz0 (0.40 #678, 0.17 #9485, 0.08 #5569), 02hv44_ (0.33 #353, 0.29 #797, 0.21 #945), 016wtf (0.33 #128, 0.25 #8892, 0.24 #7559), 01l5t6 (0.33 #259, 0.20 #703, 0.17 #9485), 025352 (0.33 #355, 0.17 #9485, 0.14 #799), 06q2q (0.33 #192, 0.17 #9485, 0.11 #5527), 080ntlp (0.33 #231, 0.17 #9485, 0.07 #971) >> Best rule #2235 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 02756j; >> query: (?x11499, 02hrh1q) <- gender(?x11499, ?x231), diet(?x11499, ?x3130), student(?x3439, ?x11499) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #782 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 04xjp; *> query: (?x11499, 05z96) <- influenced_by(?x11097, ?x11499), influenced_by(?x10090, ?x11499), ?x10090 = 03jxw, location(?x11499, ?x739), influenced_by(?x118, ?x11097) *> conf = 0.57 ranks of expected_values: 2 EVAL 06jkm profession 05z96 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 117.000 65.000 0.712 http://example.org/people/person/profession #1570-07cdz PRED entity: 07cdz PRED relation: award PRED expected values: 040njc => 122 concepts (113 used for prediction) PRED predicted values (max 10 best out of 187): 0gqyl (0.28 #1393, 0.27 #2091, 0.26 #5341), 0gr4k (0.28 #1393, 0.27 #2091, 0.26 #5341), 0f4x7 (0.28 #1393, 0.27 #2091, 0.26 #5341), 04dn09n (0.28 #1393, 0.27 #2091, 0.26 #5341), 02pqp12 (0.28 #1393, 0.27 #2091, 0.26 #5341), 0gqwc (0.28 #1393, 0.27 #2091, 0.26 #5341), 094qd5 (0.28 #1393, 0.27 #2091, 0.26 #5341), 0gq9h (0.25 #3082, 0.21 #7494, 0.21 #1222), 027dtxw (0.23 #1164, 0.21 #1862, 0.10 #2792), 0gqy2 (0.21 #1978, 0.21 #1280, 0.15 #1514) >> Best rule #1393 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 01jc6q; 0jzw; 0c_j9x; 03cw411; 0k2cb; 015qqg; 02ll45; 0y_9q; 049xgc; 0cq86w; ... >> query: (?x3510, ?x591) <- cinematography(?x3510, ?x4863), nominated_for(?x1198, ?x3510), nominated_for(?x591, ?x3510), ?x1198 = 02pqp12, film(?x1850, ?x3510) >> conf = 0.28 => this is the best rule for 7 predicted values *> Best rule #1865 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 07w8fz; 0p9tm; *> query: (?x3510, 040njc) <- cinematography(?x3510, ?x4863), nominated_for(?x1198, ?x3510), ?x1198 = 02pqp12, genre(?x3510, ?x53) *> conf = 0.21 ranks of expected_values: 11 EVAL 07cdz award 040njc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 122.000 113.000 0.276 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #1569-0k3jq PRED entity: 0k3jq PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 92 concepts (48 used for prediction) PRED predicted values (max 10 best out of 7): 09c7w0 (0.88 #282, 0.88 #104, 0.87 #167), 05k7sb (0.20 #376, 0.19 #571, 0.17 #267), 0k3jq (0.20 #376, 0.17 #267, 0.15 #419), 02jx1 (0.06 #623, 0.05 #400, 0.04 #357), 03rt9 (0.02 #232, 0.02 #296, 0.02 #338), 03rjj (0.02 #179, 0.02 #257, 0.01 #437), 0f8l9c (0.02 #370, 0.02 #442, 0.01 #299) >> Best rule #282 for best value: >> intensional similarity = 3 >> extensional distance = 174 >> proper extension: 0mn0v; >> query: (?x11677, 09c7w0) <- source(?x11677, ?x958), ?x958 = 0jbk9, county(?x13065, ?x11677) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k3jq second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 48.000 0.881 http://example.org/location/country/second_level_divisions #1568-0fxky3 PRED entity: 0fxky3 PRED relation: profession PRED expected values: 0cbd2 02hrh1q => 93 concepts (62 used for prediction) PRED predicted values (max 10 best out of 59): 0dxtg (0.89 #454, 0.88 #601, 0.87 #307), 02hrh1q (0.83 #161, 0.78 #4719, 0.76 #5308), 02jknp (0.58 #1183, 0.57 #1330, 0.55 #3388), 0cbd2 (0.32 #1329, 0.31 #1182, 0.29 #3387), 018gz8 (0.28 #16, 0.24 #1045, 0.22 #898), 0np9r (0.27 #6324, 0.25 #6766, 0.16 #2225), 0kyk (0.27 #6324, 0.25 #6766, 0.15 #1351), 015cjr (0.27 #6324, 0.25 #6766, 0.12 #930), 09jwl (0.17 #4429, 0.17 #4871, 0.16 #8108), 02hv44_ (0.13 #1379, 0.12 #1232, 0.09 #3437) >> Best rule #454 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 04l3_z; 03ft8; 0jt90f5; >> query: (?x9845, 0dxtg) <- producer_type(?x9845, ?x632), written_by(?x3752, ?x9845), type_of_union(?x9845, ?x566) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 02t_99; *> query: (?x9845, 02hrh1q) <- award_nominee(?x9845, ?x5642), film(?x5642, ?x2933), people(?x913, ?x9845), ?x2933 = 0407yj_ *> conf = 0.83 ranks of expected_values: 2, 4 EVAL 0fxky3 profession 02hrh1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 62.000 0.886 http://example.org/people/person/profession EVAL 0fxky3 profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 62.000 0.886 http://example.org/people/person/profession #1567-04gc65 PRED entity: 04gc65 PRED relation: film PRED expected values: 07sc6nw => 110 concepts (69 used for prediction) PRED predicted values (max 10 best out of 663): 063_j5 (0.33 #5051, 0.01 #15731, 0.01 #17511), 03lvwp (0.20 #1038, 0.17 #2818, 0.01 #17058), 08mg_b (0.20 #1115, 0.04 #10015, 0.02 #13575), 01738w (0.20 #1122, 0.02 #10022, 0.02 #17142), 02_1sj (0.20 #80, 0.02 #16100, 0.02 #14320), 0sxns (0.20 #1070, 0.02 #17090, 0.01 #20650), 02ht1k (0.20 #625, 0.02 #41571, 0.02 #16645), 0888c3 (0.20 #1406, 0.02 #17426, 0.01 #42352), 09k56b7 (0.20 #310, 0.02 #16330), 04fzfj (0.20 #105, 0.02 #16125) >> Best rule #5051 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 014gf8; >> query: (?x12367, 063_j5) <- people(?x1050, ?x12367), gender(?x12367, ?x231), film(?x12367, ?x810), ?x810 = 0jzw >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #14417 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 144 *> proper extension: 02qjj7; *> query: (?x12367, 07sc6nw) <- people(?x1446, ?x12367), gender(?x12367, ?x231), profession(?x12367, ?x1032), ?x1446 = 033tf_ *> conf = 0.01 ranks of expected_values: 507 EVAL 04gc65 film 07sc6nw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 110.000 69.000 0.333 http://example.org/film/actor/film./film/performance/film #1566-032sl_ PRED entity: 032sl_ PRED relation: crewmember PRED expected values: 04ktcgn => 82 concepts (74 used for prediction) PRED predicted values (max 10 best out of 31): 04ktcgn (0.27 #57, 0.12 #151, 0.06 #801), 021yc7p (0.12 #147, 0.06 #239, 0.04 #797), 092ys_y (0.12 #159, 0.06 #809, 0.03 #482), 095zvfg (0.10 #223, 0.07 #827, 0.04 #177), 051z6rz (0.09 #74, 0.06 #818, 0.05 #214), 094tsh6 (0.09 #84, 0.04 #178, 0.03 #828), 0c94fn (0.08 #150, 0.06 #800, 0.03 #1082), 02xc1w4 (0.08 #166, 0.05 #816, 0.03 #675), 0b79gfg (0.08 #807, 0.06 #109, 0.04 #157), 0bbxx9b (0.07 #810, 0.06 #112, 0.03 #623) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 03bx2lk; 09wnnb; >> query: (?x9429, 04ktcgn) <- film(?x521, ?x9429), music(?x9429, ?x2363), ?x2363 = 01hw6wq >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 032sl_ crewmember 04ktcgn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 74.000 0.273 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #1565-0h1p PRED entity: 0h1p PRED relation: award PRED expected values: 02x4sn8 => 109 concepts (75 used for prediction) PRED predicted values (max 10 best out of 269): 02pqp12 (0.72 #14745, 0.72 #17933, 0.71 #14346), 0gqng (0.72 #14745, 0.72 #17933, 0.71 #14346), 05h5nb8 (0.72 #14745, 0.72 #17933, 0.71 #14346), 0789r6 (0.72 #14745, 0.72 #17933, 0.71 #14346), 027c924 (0.72 #14745, 0.72 #17933, 0.71 #14346), 09d28z (0.72 #14745, 0.72 #17933, 0.71 #14346), 02w_6xj (0.72 #14745, 0.72 #17933, 0.71 #14346), 027b9ly (0.72 #14745, 0.72 #17933, 0.71 #14346), 0gr51 (0.55 #894, 0.47 #2488, 0.43 #2090), 0gr4k (0.50 #2424, 0.47 #2026, 0.36 #830) >> Best rule #14745 for best value: >> intensional similarity = 4 >> extensional distance = 1072 >> proper extension: 04lgymt; 01vvycq; 07c0j; 01wdqrx; 05drq5; 0gcdzz; 01p9hgt; 015882; 09mq4m; 065jlv; ... >> query: (?x2086, ?x77) <- award(?x2086, ?x7215), award_winner(?x77, ?x2086), award_winner(?x2086, ?x3862), nominated_for(?x7215, ?x467) >> conf = 0.72 => this is the best rule for 8 predicted values *> Best rule #551 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0kft; *> query: (?x2086, 02x4sn8) <- award(?x2086, ?x198), award_winner(?x13075, ?x2086), ?x13075 = 0789r6, award_winner(?x1819, ?x2086) *> conf = 0.29 ranks of expected_values: 18 EVAL 0h1p award 02x4sn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 109.000 75.000 0.717 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1564-03yvf2 PRED entity: 03yvf2 PRED relation: film_release_region PRED expected values: 05qhw 02k54 06mzp 03h64 03spz => 80 concepts (77 used for prediction) PRED predicted values (max 10 best out of 174): 03h64 (0.86 #341, 0.85 #54, 0.82 #484), 05qhw (0.81 #12, 0.79 #299, 0.76 #442), 0345h (0.81 #455, 0.81 #312, 0.78 #25), 03spz (0.80 #83, 0.78 #370, 0.76 #513), 03rt9 (0.70 #11, 0.68 #298, 0.68 #441), 016wzw (0.62 #342, 0.59 #55, 0.55 #485), 06mzp (0.60 #304, 0.59 #17, 0.55 #447), 01ls2 (0.59 #9, 0.57 #439, 0.56 #296), 03rk0 (0.59 #46, 0.56 #333, 0.51 #476), 01p1v (0.56 #329, 0.56 #42, 0.53 #472) >> Best rule #341 for best value: >> intensional similarity = 6 >> extensional distance = 61 >> proper extension: 0h3xztt; 0cmdwwg; 05ft32; 027pfg; >> query: (?x5564, 03h64) <- film_release_region(?x5564, ?x792), film_release_region(?x5564, ?x789), film_release_region(?x5564, ?x172), ?x792 = 0hzlz, ?x789 = 0f8l9c, organization(?x172, ?x127) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 7, 17 EVAL 03yvf2 film_release_region 03spz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 77.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03yvf2 film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 77.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03yvf2 film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 80.000 77.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03yvf2 film_release_region 02k54 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 80.000 77.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 03yvf2 film_release_region 05qhw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 77.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1563-0clfdj PRED entity: 0clfdj PRED relation: award_winner PRED expected values: 015rkw 02l4rh => 30 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2299): 02h1rt (0.41 #3036, 0.33 #725, 0.31 #10629), 015rkw (0.41 #3036, 0.33 #1752, 0.25 #4555), 0bzyh (0.41 #3036, 0.33 #3622, 0.25 #4555), 04wvhz (0.41 #3036, 0.33 #131, 0.21 #22782), 06dkzt (0.41 #3036, 0.33 #1219, 0.21 #22782), 05mcjs (0.41 #3036, 0.33 #986, 0.21 #22782), 0154qm (0.41 #3036, 0.31 #10629, 0.21 #22782), 0js9s (0.41 #3036, 0.31 #10629, 0.21 #22782), 09wj5 (0.41 #3036, 0.31 #10629, 0.21 #22782), 05bm4sm (0.41 #3036, 0.31 #10629, 0.21 #22782) >> Best rule #3036 for best value: >> intensional similarity = 17 >> extensional distance = 1 >> proper extension: 092t4b; >> query: (?x472, ?x617) <- award_winner(?x472, ?x9281), award_winner(?x472, ?x2533), award_winner(?x472, ?x374), ceremony(?x451, ?x472), ?x374 = 05cj4r, honored_for(?x472, ?x5067), honored_for(?x472, ?x972), nominated_for(?x617, ?x5067), profession(?x9281, ?x319), country(?x5067, ?x94), ?x972 = 017gl1, nominated_for(?x3066, ?x5067), award(?x9281, ?x68), film_release_region(?x5067, ?x151), produced_by(?x2394, ?x2533), nationality(?x2533, ?x1310), produced_by(?x638, ?x9281) >> conf = 0.41 => this is the best rule for 41 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 47 EVAL 0clfdj award_winner 02l4rh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 30.000 15.000 0.405 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0clfdj award_winner 015rkw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 30.000 15.000 0.405 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1562-0b478 PRED entity: 0b478 PRED relation: profession PRED expected values: 01d_h8 => 89 concepts (55 used for prediction) PRED predicted values (max 10 best out of 78): 01d_h8 (0.86 #2342, 0.85 #3510, 0.85 #1904), 0cbd2 (0.50 #445, 0.49 #1175, 0.48 #737), 0kyk (0.27 #757, 0.25 #1195, 0.23 #465), 02krf9 (0.26 #3820, 0.26 #3674, 0.21 #4112), 0fj9f (0.24 #198, 0.19 #344, 0.13 #52), 012t_z (0.23 #12, 0.19 #158, 0.16 #304), 018gz8 (0.22 #3664, 0.18 #3810, 0.16 #598), 09jwl (0.18 #892, 0.17 #7174, 0.16 #2206), 02hv44_ (0.15 #785, 0.15 #1223, 0.14 #493), 0np9r (0.14 #3668, 0.13 #3814, 0.09 #3230) >> Best rule #2342 for best value: >> intensional similarity = 3 >> extensional distance = 283 >> proper extension: 03qncl3; 027z0pl; 0l9k1; 030s5g; >> query: (?x4685, 01d_h8) <- profession(?x4685, ?x524), produced_by(?x5791, ?x4685), award_nominee(?x4685, ?x4702) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b478 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 55.000 0.856 http://example.org/people/person/profession #1561-0b7l4x PRED entity: 0b7l4x PRED relation: production_companies PRED expected values: 03sb38 => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 60): 017s11 (0.44 #4414, 0.44 #4413, 0.44 #1763), 03sb38 (0.29 #372, 0.26 #612, 0.22 #292), 04mwxk3 (0.17 #76, 0.06 #396, 0.04 #636), 016tt2 (0.15 #404, 0.09 #644, 0.08 #4016), 02slt7 (0.14 #588, 0.12 #268, 0.12 #348), 01gb54 (0.14 #116, 0.12 #436, 0.10 #1558), 086k8 (0.13 #1364, 0.13 #1204, 0.13 #1684), 05qd_ (0.13 #1372, 0.11 #4022, 0.11 #2498), 01795t (0.10 #420, 0.06 #660, 0.04 #1702), 05mgj0 (0.10 #302, 0.07 #622, 0.05 #782) >> Best rule #4414 for best value: >> intensional similarity = 3 >> extensional distance = 1004 >> proper extension: 016ztl; 02zk08; >> query: (?x6009, ?x1104) <- genre(?x6009, ?x53), film(?x1104, ?x6009), production_companies(?x6009, ?x1478) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #372 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 49 *> proper extension: 016z5x; *> query: (?x6009, 03sb38) <- produced_by(?x6009, ?x96), genre(?x6009, ?x53), film_release_region(?x6009, ?x94), country(?x6009, ?x789), ?x789 = 0f8l9c *> conf = 0.29 ranks of expected_values: 2 EVAL 0b7l4x production_companies 03sb38 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 85.000 0.441 http://example.org/film/film/production_companies #1560-02qvyrt PRED entity: 02qvyrt PRED relation: nominated_for PRED expected values: 0fgpvf 0209xj 0jzw 0dr_4 0ch26b_ 0ctb4g 02_kd 02phtzk 0h03fhx 017jd9 09p3_s 049xgc 0b9rdk 0hv4t 027r9t 016mhd 0gy7bj4 => 48 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1449): 0hv4t (0.82 #14241, 0.55 #11290, 0.50 #2432), 0gmcwlb (0.71 #4594, 0.71 #13450, 0.68 #32494), 0c0zq (0.68 #32494, 0.68 #32493, 0.67 #32492), 0jqn5 (0.68 #32494, 0.68 #32493, 0.67 #32492), 0f3m1 (0.68 #32494, 0.68 #32493, 0.67 #32492), 01cssf (0.68 #32494, 0.68 #32493, 0.67 #32492), 025rvx0 (0.68 #32494, 0.68 #32493, 0.67 #32492), 07cw4 (0.68 #32494, 0.68 #32493, 0.67 #32492), 07s846j (0.67 #12360, 0.67 #2027, 0.64 #9408), 0ch26b_ (0.67 #1724, 0.64 #9105, 0.58 #12057) >> Best rule #14241 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 040njc; 0f4x7; 04dn09n; 019f4v; 0gs9p; 0gqy2; 02w9sd7; >> query: (?x2379, 0hv4t) <- nominated_for(?x2379, ?x7941), nominated_for(?x2379, ?x407), titles(?x53, ?x7941), ?x407 = 07xtqq, film(?x3236, ?x7941) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10, 12, 22, 26, 27, 29, 30, 45, 46, 48, 59, 185, 259, 290, 383, 995 EVAL 02qvyrt nominated_for 0gy7bj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 016mhd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 027r9t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 0hv4t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 0b9rdk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 049xgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 09p3_s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 017jd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 0h03fhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 02phtzk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 02_kd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 0ctb4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 0ch26b_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 0dr_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 0jzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 0209xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02qvyrt nominated_for 0fgpvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 48.000 26.000 0.824 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1559-0c9t0y PRED entity: 0c9t0y PRED relation: genre PRED expected values: 02n4kr 01585b => 83 concepts (82 used for prediction) PRED predicted values (max 10 best out of 78): 07s9rl0 (0.63 #4202, 0.61 #2521, 0.61 #841), 05p553 (0.37 #4686, 0.35 #365, 0.34 #7086), 02kdv5l (0.33 #123, 0.33 #3, 0.30 #3124), 03k9fj (0.33 #12, 0.27 #2052, 0.25 #3373), 0lsxr (0.33 #129, 0.22 #9, 0.18 #1809), 02l7c8 (0.30 #496, 0.29 #4217, 0.28 #856), 01hmnh (0.22 #138, 0.19 #2058, 0.16 #3139), 04xvlr (0.22 #122, 0.17 #1682, 0.17 #1802), 060__y (0.22 #257, 0.15 #497, 0.15 #1697), 06n90 (0.22 #133, 0.13 #4694, 0.13 #2053) >> Best rule #4202 for best value: >> intensional similarity = 3 >> extensional distance = 849 >> proper extension: 04m1bm; 02rb607; 02n9bh; 04lqvly; 02hfk5; 0g9zljd; 02wk7b; 0cvkv5; 05zvzf3; 02zk08; ... >> query: (?x7187, 07s9rl0) <- genre(?x7187, ?x571), nominated_for(?x1039, ?x7187), award(?x7187, ?x350) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #1808 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 621 *> proper extension: 039zft; 06bc59; *> query: (?x7187, 02n4kr) <- produced_by(?x7187, ?x1039), film(?x5283, ?x7187), award_winner(?x5283, ?x628), titles(?x571, ?x7187) *> conf = 0.12 ranks of expected_values: 14, 66 EVAL 0c9t0y genre 01585b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 83.000 82.000 0.629 http://example.org/film/film/genre EVAL 0c9t0y genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 83.000 82.000 0.629 http://example.org/film/film/genre #1558-01y8cr PRED entity: 01y8cr PRED relation: nominated_for PRED expected values: 0bmhn => 90 concepts (49 used for prediction) PRED predicted values (max 10 best out of 134): 0pd4f (0.27 #12970, 0.24 #29186, 0.23 #24320), 0bmhn (0.27 #12970, 0.24 #29186, 0.23 #24320), 0jvt9 (0.27 #12970, 0.24 #29186, 0.23 #24320), 02bg8v (0.09 #251, 0.03 #37294), 0330r (0.09 #1414, 0.02 #48436, 0.02 #40329), 09gq0x5 (0.09 #262, 0.01 #11610), 02py4c8 (0.06 #34050, 0.05 #64856, 0.05 #99), 017jd9 (0.06 #34050, 0.05 #64856, 0.02 #12061), 01g03q (0.06 #34050, 0.05 #64856, 0.02 #12744), 05dmmc (0.06 #34050, 0.05 #64856, 0.01 #5554) >> Best rule #12970 for best value: >> intensional similarity = 3 >> extensional distance = 396 >> proper extension: 0bz5v2; 03x22w; 017lqp; >> query: (?x4279, ?x3294) <- student(?x5149, ?x4279), film(?x4279, ?x3294), award_winner(?x1193, ?x4279) >> conf = 0.27 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01y8cr nominated_for 0bmhn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 49.000 0.274 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #1557-0gqz2 PRED entity: 0gqz2 PRED relation: award! PRED expected values: 04k25 076_74 0gbwp 0kvnn 02jxmr 01vwbts 03j24kf 01bczm => 47 concepts (24 used for prediction) PRED predicted values (max 10 best out of 2513): 012ky3 (0.81 #16405, 0.79 #36094, 0.78 #3281), 0178rl (0.81 #16405, 0.79 #36094, 0.78 #3281), 0f8pz (0.81 #16405, 0.79 #36094, 0.78 #3281), 01vsgrn (0.81 #16405, 0.79 #36094, 0.78 #3281), 02cx72 (0.81 #16405, 0.79 #36094, 0.78 #3281), 016jll (0.81 #16405, 0.79 #36094, 0.78 #3281), 01cbt3 (0.81 #16405, 0.79 #36094, 0.78 #3281), 02fgp0 (0.81 #16405, 0.79 #36094, 0.78 #3281), 019x62 (0.81 #16405, 0.79 #36094, 0.78 #3281), 01k98nm (0.81 #16405, 0.79 #36094, 0.78 #3281) >> Best rule #16405 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 05q8pss; >> query: (?x1323, ?x1934) <- award(?x9008, ?x1323), award(?x7167, ?x1323), ?x7167 = 01wd9vs, award_winner(?x1323, ?x1934), artists(?x671, ?x9008), award(?x2368, ?x1323) >> conf = 0.81 => this is the best rule for 13 predicted values *> Best rule #11077 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 02x201b; *> query: (?x1323, 02jxmr) <- ceremony(?x1323, ?x78), award(?x5720, ?x1323), ?x5720 = 01l1rw, award_winner(?x1323, ?x1934), nominated_for(?x1323, ?x2097) *> conf = 0.50 ranks of expected_values: 16, 22, 24, 47, 115, 147, 345 EVAL 0gqz2 award! 01bczm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 47.000 24.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqz2 award! 03j24kf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 47.000 24.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqz2 award! 01vwbts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 47.000 24.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqz2 award! 02jxmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 47.000 24.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqz2 award! 0kvnn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 24.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqz2 award! 0gbwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 47.000 24.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqz2 award! 076_74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 47.000 24.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gqz2 award! 04k25 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 24.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1556-042v_gx PRED entity: 042v_gx PRED relation: role PRED expected values: 03q5t 0myk8 => 76 concepts (60 used for prediction) PRED predicted values (max 10 best out of 63): 01vdm0 (0.89 #2417, 0.87 #3077, 0.87 #2957), 02sgy (0.83 #1313, 0.83 #591, 0.82 #712), 042v_gx (0.83 #895, 0.82 #655, 0.81 #1553), 05r5c (0.83 #591, 0.83 #1849, 0.82 #712), 05148p4 (0.83 #591, 0.83 #1428, 0.82 #712), 03gvt (0.83 #591, 0.82 #712, 0.82 #589), 011_6p (0.82 #712, 0.82 #589, 0.82 #590), 0dwr4 (0.82 #712, 0.82 #589, 0.82 #590), 03m5k (0.82 #712, 0.82 #589, 0.82 #708), 07brj (0.82 #712, 0.82 #589, 0.82 #708) >> Best rule #2417 for best value: >> intensional similarity = 11 >> extensional distance = 35 >> proper extension: 03q5t; 03m5k; 03ndd; >> query: (?x432, 01vdm0) <- role(?x2638, ?x432), role(?x1715, ?x432), role(?x432, ?x3991), role(?x432, ?x2048), group(?x432, ?x442), role(?x6384, ?x3991), role(?x3991, ?x74), ?x2048 = 018j2, profession(?x1715, ?x131), ?x6384 = 01ldw4, award_nominee(?x1795, ?x2638) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #712 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 9 *> proper extension: 01w4dy; *> query: (?x432, ?x74) <- role(?x4583, ?x432), role(?x2888, ?x432), role(?x1225, ?x432), role(?x316, ?x432), role(?x74, ?x432), role(?x432, ?x3991), role(?x645, ?x432), ?x4583 = 0bmnm, ?x1225 = 01qbl, instrumentalists(?x74, ?x642), ?x3991 = 05842k, family(?x2888, ?x9885), role(?x115, ?x316) *> conf = 0.82 ranks of expected_values: 14, 35 EVAL 042v_gx role 0myk8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 76.000 60.000 0.892 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 042v_gx role 03q5t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 76.000 60.000 0.892 http://example.org/music/performance_role/track_performances./music/track_contribution/role #1555-01pj3h PRED entity: 01pj3h PRED relation: place_of_birth PRED expected values: 02dtg => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 101): 0281s1 (0.27 #26061, 0.27 #59163, 0.27 #45077), 0cc56 (0.14 #33, 0.02 #3553, 0.01 #8484), 0100mt (0.14 #289, 0.01 #4513), 0rd6b (0.14 #415), 02_286 (0.13 #2835, 0.12 #4243, 0.08 #34534), 030qb3t (0.12 #2870, 0.10 #4278, 0.06 #7800), 0cr3d (0.07 #1502, 0.07 #2206, 0.05 #3614), 0chrx (0.07 #1009, 0.02 #1713, 0.02 #2417), 0psxp (0.07 #915, 0.01 #5140, 0.01 #7253), 0ckhc (0.07 #1212) >> Best rule #26061 for best value: >> intensional similarity = 3 >> extensional distance = 1167 >> proper extension: 0j3v; 0dzkq; 0xnc3; 02x8mt; 047g6; 01h2_6; 011zwl; >> query: (?x11543, ?x8026) <- student(?x735, ?x11543), nationality(?x11543, ?x94), location(?x11543, ?x8026) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #3530 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 05ml_s; *> query: (?x11543, 02dtg) <- film(?x11543, ?x689), award(?x11543, ?x2071), ?x2071 = 0bdw6t *> conf = 0.03 ranks of expected_values: 18 EVAL 01pj3h place_of_birth 02dtg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 104.000 104.000 0.271 http://example.org/people/person/place_of_birth #1554-0ktpx PRED entity: 0ktpx PRED relation: titles! PRED expected values: 0djd22 => 81 concepts (54 used for prediction) PRED predicted values (max 10 best out of 62): 024qqx (0.56 #573, 0.55 #375, 0.48 #970), 01jfsb (0.49 #2200, 0.38 #2281, 0.33 #100), 07s9rl0 (0.41 #1686, 0.39 #3987, 0.39 #3888), 02l7c8 (0.38 #2281, 0.32 #4487, 0.25 #2280), 04xvlr (0.31 #202, 0.30 #1789, 0.29 #1689), 01hmnh (0.29 #918, 0.27 #521, 0.18 #1016), 01z4y (0.28 #1421, 0.27 #4023, 0.21 #3823), 01g6gs (0.25 #2280, 0.24 #3986, 0.22 #2181), 060__y (0.25 #2280, 0.24 #3986, 0.22 #2181), 07ssc (0.15 #208, 0.15 #1695, 0.12 #1396) >> Best rule #573 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 07gp9; 0ds33; 01vksx; 017gl1; 0872p_c; 020fcn; 0dtfn; 04w7rn; 0dr_4; 015x74; ... >> query: (?x5818, 024qqx) <- nominated_for(?x2209, ?x5818), ?x2209 = 0gr42, titles(?x600, ?x5818), film_crew_role(?x5818, ?x2095) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1924 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 162 *> proper extension: 02d413; 047gn4y; 016z5x; 0c40vxk; 0gjk1d; 03176f; 0ptxj; 016ky6; 02q87z6; 0c9t0y; ... *> query: (?x5818, 0djd22) <- cinematography(?x5818, ?x6549), film_release_region(?x5818, ?x94), genre(?x5818, ?x53), currency(?x5818, ?x170) *> conf = 0.02 ranks of expected_values: 39 EVAL 0ktpx titles! 0djd22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 81.000 54.000 0.556 http://example.org/media_common/netflix_genre/titles #1553-057_yx PRED entity: 057_yx PRED relation: film PRED expected values: 016z5x 04fzfj 076zy_g => 49 concepts (27 used for prediction) PRED predicted values (max 10 best out of 327): 03ntbmw (0.11 #1763, 0.03 #23170, 0.03 #24953), 01q7h2 (0.11 #1568, 0.03 #23170, 0.03 #24953), 02c7k4 (0.11 #1096, 0.03 #23170, 0.03 #24953), 0c9k8 (0.11 #484, 0.03 #23170, 0.03 #24953), 020y73 (0.11 #366, 0.03 #23170, 0.03 #24953), 035s95 (0.11 #340, 0.03 #23170, 0.03 #24953), 0260bz (0.11 #335, 0.03 #23170, 0.03 #24953), 016z5x (0.11 #70, 0.03 #23170, 0.03 #24953), 01hv3t (0.06 #1285, 0.05 #10695, 0.04 #32082), 05rfst (0.06 #971, 0.05 #10695, 0.04 #32082) >> Best rule #1763 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0z4s; 03q1vd; 02p7_k; 0410cp; 014g22; 016ks_; 03q95r; 02t_vx; 02ct_k; >> query: (?x11100, 03ntbmw) <- award_nominee(?x11100, ?x4103), ?x4103 = 02jsgf, award_nominee(?x4128, ?x11100) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #70 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 0z4s; 03q1vd; 02p7_k; 0410cp; 014g22; 016ks_; 03q95r; 02t_vx; 02ct_k; *> query: (?x11100, 016z5x) <- award_nominee(?x11100, ?x4103), ?x4103 = 02jsgf, award_nominee(?x4128, ?x11100) *> conf = 0.11 ranks of expected_values: 8 EVAL 057_yx film 076zy_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 27.000 0.111 http://example.org/film/actor/film./film/performance/film EVAL 057_yx film 04fzfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 49.000 27.000 0.111 http://example.org/film/actor/film./film/performance/film EVAL 057_yx film 016z5x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 49.000 27.000 0.111 http://example.org/film/actor/film./film/performance/film #1552-02s58t PRED entity: 02s58t PRED relation: location_of_ceremony PRED expected values: 06c62 => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 52): 0k_q_ (0.25 #29, 0.02 #2061), 0l38x (0.12 #225, 0.10 #464, 0.04 #823), 071vr (0.12 #188, 0.10 #427, 0.03 #906), 0kc40 (0.11 #341, 0.03 #1059, 0.03 #1298), 0r62v (0.10 #375, 0.06 #854, 0.03 #1092), 0f2tj (0.10 #425, 0.03 #904), 0cv3w (0.09 #512, 0.07 #752, 0.04 #2067), 06y57 (0.09 #534, 0.04 #774, 0.02 #1730), 027rn (0.09 #478, 0.04 #718, 0.02 #1674), 01cx_ (0.09 #513, 0.02 #1709, 0.02 #1949) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 06c0j; >> query: (?x8900, 0k_q_) <- participant(?x6331, ?x8900), ?x6331 = 029ql, type_of_union(?x8900, ?x566) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02s58t location_of_ceremony 06c62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 196.000 196.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #1551-018ysx PRED entity: 018ysx PRED relation: parent_genre! PRED expected values: 020ngt 0621cs => 54 concepts (39 used for prediction) PRED predicted values (max 10 best out of 321): 06hzq3 (0.60 #1466, 0.33 #926, 0.33 #384), 0621cs (0.60 #1491, 0.33 #951, 0.33 #409), 0xjl2 (0.43 #1930, 0.40 #1390, 0.33 #850), 016clz (0.43 #1897, 0.25 #1080, 0.23 #2711), 01h0kx (0.40 #4181, 0.40 #1481, 0.33 #3641), 059kh (0.40 #4094, 0.40 #1394, 0.33 #854), 08s6r6 (0.40 #1577, 0.33 #1037, 0.33 #495), 018ysx (0.40 #1560, 0.33 #1020, 0.31 #2914), 06cp5 (0.40 #1427, 0.33 #887, 0.30 #4127), 0bt7w (0.40 #1441, 0.33 #901, 0.29 #1981) >> Best rule #1466 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 06by7; >> query: (?x13294, 06hzq3) <- parent_genre(?x2996, ?x13294), parent_genre(?x13294, ?x10797), parent_genre(?x13294, ?x5934), ?x10797 = 017371, artists(?x5934, ?x7211), artists(?x5934, ?x4936), artists(?x505, ?x7211), ?x505 = 03_d0, gender(?x4936, ?x231) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1491 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 06by7; *> query: (?x13294, 0621cs) <- parent_genre(?x2996, ?x13294), parent_genre(?x13294, ?x10797), parent_genre(?x13294, ?x5934), ?x10797 = 017371, artists(?x5934, ?x7211), artists(?x5934, ?x4936), artists(?x505, ?x7211), ?x505 = 03_d0, gender(?x4936, ?x231) *> conf = 0.60 ranks of expected_values: 2, 42 EVAL 018ysx parent_genre! 0621cs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 54.000 39.000 0.600 http://example.org/music/genre/parent_genre EVAL 018ysx parent_genre! 020ngt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 54.000 39.000 0.600 http://example.org/music/genre/parent_genre #1550-01s560x PRED entity: 01s560x PRED relation: group! PRED expected values: 0l14md 05r5c 0gkd1 => 77 concepts (60 used for prediction) PRED predicted values (max 10 best out of 116): 0l14md (0.60 #1567, 0.60 #2229, 0.59 #2138), 05r5c (0.50 #172, 0.33 #90, 0.25 #1486), 02sgy (0.50 #170, 0.33 #88, 0.24 #2305), 0dwt5 (0.50 #230, 0.33 #148, 0.24 #2305), 013y1f (0.50 #188, 0.33 #106, 0.24 #2305), 01vj9c (0.33 #94, 0.28 #2482, 0.28 #2234), 0gkd1 (0.33 #159, 0.25 #241, 0.24 #2305), 03m5k (0.33 #96, 0.25 #178, 0.07 #1643), 0l14qv (0.26 #1565, 0.25 #169, 0.24 #2227), 04rzd (0.25 #192, 0.24 #2305, 0.21 #2470) >> Best rule #1567 for best value: >> intensional similarity = 8 >> extensional distance = 101 >> proper extension: 089tm; 01pfr3; 04rcr; 0150jk; 02r3zy; 067mj; 01vsxdm; 03g5jw; 05k79; 0dtd6; ... >> query: (?x10745, 0l14md) <- group(?x3716, ?x10745), group(?x716, ?x10745), group(?x227, ?x10745), ?x227 = 0342h, artist(?x2299, ?x10745), role(?x211, ?x3716), role(?x3716, ?x228), ?x716 = 018vs >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 7 EVAL 01s560x group! 0gkd1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 60.000 0.602 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01s560x group! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 60.000 0.602 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01s560x group! 0l14md CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 60.000 0.602 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1549-0ccvx PRED entity: 0ccvx PRED relation: place_of_birth! PRED expected values: 07lwsz => 113 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1265): 01wf86y (0.40 #80621, 0.35 #36403, 0.35 #31201), 01mvjl0 (0.40 #80621, 0.29 #80620, 0.26 #101426), 01htxr (0.35 #36403, 0.35 #31201, 0.35 #36404), 0126y2 (0.35 #36403, 0.35 #31201, 0.35 #36404), 03f7jfh (0.35 #36403, 0.35 #31201, 0.35 #36404), 0ddkf (0.35 #36403, 0.35 #31201, 0.35 #36404), 047sxrj (0.35 #36403, 0.35 #31201, 0.35 #36404), 01vvyd8 (0.35 #36403, 0.35 #31201, 0.35 #36404), 0mj1l (0.35 #36403, 0.35 #31201, 0.35 #36404), 02v0ff (0.35 #36403, 0.35 #31201, 0.35 #36404) >> Best rule #80621 for best value: >> intensional similarity = 3 >> extensional distance = 155 >> proper extension: 02qjb7z; >> query: (?x4253, ?x7581) <- contains(?x94, ?x4253), origin(?x7581, ?x4253), instrumentalists(?x227, ?x7581) >> conf = 0.40 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0ccvx place_of_birth! 07lwsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 82.000 0.395 http://example.org/people/person/place_of_birth #1548-01kj0p PRED entity: 01kj0p PRED relation: film PRED expected values: 05v38p => 105 concepts (66 used for prediction) PRED predicted values (max 10 best out of 902): 07cz2 (0.80 #1789, 0.76 #7154, 0.72 #16096), 05v38p (0.80 #1789, 0.72 #16096, 0.48 #66175), 01pvxl (0.15 #905, 0.02 #8059, 0.01 #15212), 02rrh1w (0.15 #1354), 0bth54 (0.15 #77), 01shy7 (0.08 #422, 0.07 #5787, 0.07 #16518), 03bx2lk (0.08 #185, 0.06 #1974, 0.05 #5550), 0kvgxk (0.08 #327, 0.06 #2116, 0.03 #3904), 02qzh2 (0.08 #690, 0.05 #4267, 0.04 #2479), 08r4x3 (0.08 #154, 0.05 #9097, 0.04 #7308) >> Best rule #1789 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0h96g; >> query: (?x2818, ?x1808) <- award(?x2818, ?x6463), nominated_for(?x2818, ?x1808), ?x6463 = 02g2yr >> conf = 0.80 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 01kj0p film 05v38p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 105.000 66.000 0.797 http://example.org/film/actor/film./film/performance/film #1547-0205dx PRED entity: 0205dx PRED relation: film PRED expected values: 0llcx => 84 concepts (67 used for prediction) PRED predicted values (max 10 best out of 693): 043t8t (0.30 #785, 0.04 #103369, 0.04 #105152), 0bs5vty (0.15 #1630, 0.04 #103369, 0.04 #105152), 02ntb8 (0.15 #834, 0.04 #103369, 0.04 #105152), 08s6mr (0.15 #1314, 0.03 #96240, 0.03 #96239), 01shy7 (0.10 #421, 0.06 #3985, 0.05 #11113), 034qzw (0.10 #332, 0.04 #103369, 0.04 #105152), 034qrh (0.10 #63, 0.04 #103369, 0.04 #105152), 033fqh (0.10 #836, 0.04 #103369, 0.04 #105152), 07gghl (0.10 #1171, 0.04 #103369, 0.04 #105152), 034qmv (0.10 #15, 0.03 #1797, 0.03 #96240) >> Best rule #785 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 02ld6x; >> query: (?x4767, 043t8t) <- award_nominee(?x4767, ?x400), ?x400 = 01q_ph, award(?x4767, ?x401) >> conf = 0.30 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0205dx film 0llcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 67.000 0.300 http://example.org/film/actor/film./film/performance/film #1546-0ndwt2w PRED entity: 0ndwt2w PRED relation: nominated_for! PRED expected values: 02r0csl 0gq_v 02hsq3m => 81 concepts (78 used for prediction) PRED predicted values (max 10 best out of 199): 099c8n (0.42 #294, 0.19 #768, 0.19 #1242), 0gq_v (0.38 #731, 0.37 #968, 0.29 #1442), 02x2gy0 (0.37 #338, 0.20 #14696, 0.20 #16594), 0gq9h (0.35 #774, 0.33 #1011, 0.33 #8122), 0gs96 (0.32 #326, 0.29 #800, 0.24 #1037), 019f4v (0.32 #291, 0.26 #8113, 0.26 #765), 0fhpv4 (0.32 #376, 0.13 #16832, 0.11 #1324), 09td7p (0.32 #329, 0.08 #1277, 0.08 #3647), 0gs9p (0.29 #8124, 0.24 #2909, 0.23 #2672), 0k611 (0.26 #311, 0.25 #8133, 0.21 #785) >> Best rule #294 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 021y7yw; 026p4q7; 011yr9; 08nvyr; 05p09dd; 04mcw4; 027m5wv; 05dptj; 04ynx7; >> query: (?x5767, 099c8n) <- film(?x382, ?x5767), film(?x3281, ?x5767), film(?x2141, ?x5767), ?x3281 = 0154qm, nominated_for(?x2141, ?x5810) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #731 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 75 *> proper extension: 01f8gz; 0c9k8; 05hjnw; 048rn; 0bykpk; 02mpyh; 04wddl; *> query: (?x5767, 0gq_v) <- film(?x382, ?x5767), written_by(?x5767, ?x3434), costume_design_by(?x5767, ?x4691), film(?x629, ?x5767) *> conf = 0.38 ranks of expected_values: 2, 12, 13 EVAL 0ndwt2w nominated_for! 02hsq3m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 81.000 78.000 0.421 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0ndwt2w nominated_for! 0gq_v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 81.000 78.000 0.421 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0ndwt2w nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 81.000 78.000 0.421 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1545-0b2v79 PRED entity: 0b2v79 PRED relation: genre PRED expected values: 0lsxr => 103 concepts (100 used for prediction) PRED predicted values (max 10 best out of 89): 0lsxr (0.59 #2250, 0.44 #361, 0.42 #125), 05p553 (0.46 #592, 0.36 #1418, 0.36 #474), 060__y (0.37 #722, 0.33 #14, 0.21 #6863), 02l7c8 (0.36 #603, 0.32 #1902, 0.31 #1665), 03k9fj (0.27 #954, 0.26 #1308, 0.25 #1072), 0219x_ (0.26 #2267, 0.12 #614, 0.10 #732), 09blyk (0.20 #383, 0.11 #147, 0.10 #265), 082gq (0.20 #1798, 0.19 #2861, 0.11 #7467), 06cvj (0.18 #591, 0.11 #1417, 0.11 #119), 02n4kr (0.15 #2249, 0.13 #1776, 0.13 #1186) >> Best rule #2250 for best value: >> intensional similarity = 4 >> extensional distance = 458 >> proper extension: 03twd6; 05p3738; 05ch98; 08c6k9; 09v42sf; 04jn6y7; >> query: (?x195, 0lsxr) <- film(?x194, ?x195), genre(?x195, ?x1316), genre(?x4312, ?x1316), ?x4312 = 06929s >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b2v79 genre 0lsxr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 100.000 0.589 http://example.org/film/film/genre #1544-0lx2l PRED entity: 0lx2l PRED relation: currency PRED expected values: 09nqf => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.43 #13, 0.43 #10, 0.42 #1), 01nv4h (0.02 #56, 0.01 #68, 0.01 #74) >> Best rule #13 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 05zbm4; 01kj0p; >> query: (?x2534, 09nqf) <- friend(?x6187, ?x2534), award_winner(?x1692, ?x2534), award_nominee(?x2534, ?x722) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lx2l currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.434 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #1543-0jmfv PRED entity: 0jmfv PRED relation: school PRED expected values: 09f2j 01jpqb => 43 concepts (37 used for prediction) PRED predicted values (max 10 best out of 191): 0j_sncb (0.50 #38, 0.36 #605, 0.33 #227), 0dzst (0.50 #147, 0.36 #714, 0.33 #336), 015q1n (0.50 #103, 0.29 #1049, 0.29 #2187), 01pl14 (0.50 #4, 0.21 #571, 0.18 #950), 0187nd (0.50 #156, 0.14 #723, 0.14 #946), 01pq4w (0.50 #52, 0.14 #619, 0.14 #946), 02zcz3 (0.50 #94, 0.14 #661, 0.12 #1040), 0bx8pn (0.33 #212, 0.30 #1726, 0.29 #590), 07t90 (0.29 #634, 0.25 #256, 0.25 #67), 0pspl (0.29 #616, 0.25 #238, 0.25 #49) >> Best rule #38 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 0jmj7; 0jmm4; >> query: (?x1347, 0j_sncb) <- draft(?x1347, ?x12852), draft(?x1347, ?x8542), draft(?x1347, ?x2569), position(?x1347, ?x4570), ?x2569 = 038c0q, ?x4570 = 03558l, ?x12852 = 06439y, ?x8542 = 09th87, school(?x1347, ?x2775), ?x2775 = 078bz >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 0jmj7; 0jmm4; *> query: (?x1347, 09f2j) <- draft(?x1347, ?x12852), draft(?x1347, ?x8542), draft(?x1347, ?x2569), position(?x1347, ?x4570), ?x2569 = 038c0q, ?x4570 = 03558l, ?x12852 = 06439y, ?x8542 = 09th87, school(?x1347, ?x2775), ?x2775 = 078bz *> conf = 0.25 ranks of expected_values: 21, 38 EVAL 0jmfv school 01jpqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 43.000 37.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 0jmfv school 09f2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 43.000 37.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #1542-07m9cm PRED entity: 07m9cm PRED relation: film PRED expected values: 0fphf3v => 100 concepts (64 used for prediction) PRED predicted values (max 10 best out of 455): 0418wg (0.22 #3973, 0.17 #5759, 0.12 #401), 09xbpt (0.22 #3619, 0.17 #5405, 0.12 #47), 06z8s_ (0.22 #3702, 0.17 #5488, 0.12 #130), 0qm8b (0.22 #2030, 0.01 #18105, 0.01 #27036), 0gj8t_b (0.17 #3753, 0.13 #5539, 0.12 #181), 026p4q7 (0.13 #5756, 0.12 #398, 0.11 #3970), 02704ff (0.12 #979, 0.11 #4551, 0.09 #6337), 03s6l2 (0.12 #83, 0.11 #3655, 0.09 #5441), 01qdmh (0.12 #1708, 0.11 #3494, 0.05 #35723), 01dc0c (0.12 #1449, 0.06 #5021, 0.05 #35723) >> Best rule #3973 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01v42g; >> query: (?x4543, 0418wg) <- award_nominee(?x4543, ?x2499), ?x2499 = 0c6qh, people(?x5540, ?x4543) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #8502 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 55 *> proper extension: 01dvtx; 01h2_6; *> query: (?x4543, 0fphf3v) <- people(?x5540, ?x4543), ?x5540 = 013xrm *> conf = 0.05 ranks of expected_values: 206 EVAL 07m9cm film 0fphf3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 100.000 64.000 0.222 http://example.org/film/actor/film./film/performance/film #1541-02vg0 PRED entity: 02vg0 PRED relation: award PRED expected values: 0bdwqv => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 241): 0bs0bh (0.72 #36603, 0.72 #37007, 0.71 #32579), 09sb52 (0.28 #5669, 0.27 #6474, 0.27 #11703), 0ck27z (0.15 #90, 0.13 #6525, 0.11 #7732), 0cqhk0 (0.13 #23329, 0.13 #35394, 0.13 #10457), 0cjyzs (0.13 #23329, 0.13 #35394, 0.13 #10457), 09qj50 (0.13 #23329, 0.13 #35394, 0.13 #10457), 09qs08 (0.13 #23329, 0.13 #35394, 0.13 #10457), 0cqhmg (0.13 #23329, 0.13 #35394, 0.13 #10457), 027gs1_ (0.13 #23329, 0.13 #35394, 0.13 #10457), 02_3zj (0.13 #23329, 0.13 #35394, 0.13 #10457) >> Best rule #36603 for best value: >> intensional similarity = 3 >> extensional distance = 2323 >> proper extension: 0kk9v; 06lxn; >> query: (?x7469, ?x1921) <- award_winner(?x1921, ?x7469), award(?x2378, ?x1921), nationality(?x2378, ?x94) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #7812 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 954 *> proper extension: 044mz_; 07nznf; 02s2ft; 05vsxz; 05d7rk; 05bnp0; 02p65p; 0lbj1; 0byfz; 03x3qv; ... *> query: (?x7469, 0bdwqv) <- student(?x1011, ?x7469), award(?x7469, ?x435), film(?x7469, ?x755) *> conf = 0.08 ranks of expected_values: 22 EVAL 02vg0 award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 112.000 112.000 0.717 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1540-01518s PRED entity: 01518s PRED relation: group! PRED expected values: 028tv0 018vs 01vj9c => 99 concepts (76 used for prediction) PRED predicted values (max 10 best out of 122): 018vs (0.76 #2279, 0.71 #2541, 0.71 #2716), 01s0ps (0.72 #3144, 0.15 #3676, 0.12 #2703), 028tv0 (0.51 #1842, 0.49 #1929, 0.47 #1230), 03qjg (0.42 #2528, 0.41 #3145, 0.39 #3320), 05r5c (0.42 #2528, 0.41 #3145, 0.39 #3320), 01vj9c (0.33 #797, 0.31 #3335, 0.31 #3424), 0l14qv (0.33 #788, 0.30 #2708, 0.29 #2184), 02fsn (0.33 #48, 0.15 #3676, 0.12 #2703), 0l14j_ (0.27 #1269, 0.15 #3676, 0.12 #3107), 06ncr (0.25 #212, 0.20 #2741, 0.17 #2915) >> Best rule #2279 for best value: >> intensional similarity = 9 >> extensional distance = 56 >> proper extension: 0dtd6; 02r1tx7; 0fcsd; 0394y; 0134tg; 07mvp; 07m4c; 0b_xm; 08w4pm; 0134wr; ... >> query: (?x12506, 018vs) <- group(?x10756, ?x12506), artists(?x9248, ?x12506), group(?x1166, ?x12506), group(?x227, ?x12506), ?x227 = 0342h, ?x1166 = 05148p4, artist(?x11715, ?x12506), artists(?x9248, ?x5227), ?x5227 = 01j59b0 >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 6 EVAL 01518s group! 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 76.000 0.759 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01518s group! 018vs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 76.000 0.759 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01518s group! 028tv0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 76.000 0.759 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1539-012x4t PRED entity: 012x4t PRED relation: artists! PRED expected values: 0509cr => 71 concepts (66 used for prediction) PRED predicted values (max 10 best out of 238): 06by7 (0.52 #3123, 0.51 #2812, 0.48 #5914), 025sc50 (0.35 #3152, 0.30 #5943, 0.25 #3772), 05bt6j (0.34 #3146, 0.33 #5937, 0.26 #2835), 016clz (0.32 #936, 0.26 #2797, 0.23 #7765), 0glt670 (0.28 #3763, 0.24 #4693, 0.23 #3143), 0ggx5q (0.27 #3180, 0.22 #5971, 0.22 #77), 02lnbg (0.27 #3160, 0.22 #5951, 0.15 #3780), 0xhtw (0.25 #947, 0.19 #7776, 0.19 #10264), 05w3f (0.24 #347, 0.13 #968, 0.10 #6552), 01lyv (0.22 #4066, 0.22 #3446, 0.22 #5306) >> Best rule #3123 for best value: >> intensional similarity = 3 >> extensional distance = 197 >> proper extension: 01l_vgt; 03xhj6; 06nv27; 02vgh; 01kcms4; 08w4pm; 01l_w0; 02cw1m; 03qkcn9; >> query: (?x1660, 06by7) <- origin(?x1660, ?x479), artists(?x671, ?x1660), ?x671 = 064t9 >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #11799 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 980 *> proper extension: 03_gx; 03d6q; 0qmpd; 0h08p; *> query: (?x1660, ?x996) <- artists(?x671, ?x1660), parent_genre(?x996, ?x671) *> conf = 0.06 ranks of expected_values: 62 EVAL 012x4t artists! 0509cr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 71.000 66.000 0.518 http://example.org/music/genre/artists #1538-02q_ncg PRED entity: 02q_ncg PRED relation: genre PRED expected values: 0hfjk => 94 concepts (91 used for prediction) PRED predicted values (max 10 best out of 100): 0hfjk (0.73 #6257, 0.72 #6741, 0.61 #8069), 02kdv5l (0.39 #842, 0.31 #1924, 0.29 #722), 01jfsb (0.39 #852, 0.34 #132, 0.32 #3256), 05p553 (0.38 #484, 0.35 #4935, 0.34 #8673), 02l7c8 (0.33 #1337, 0.32 #256, 0.32 #376), 0lsxr (0.28 #489, 0.23 #729, 0.20 #849), 060__y (0.22 #978, 0.21 #377, 0.19 #1458), 082gq (0.21 #30, 0.17 #991, 0.15 #1351), 01hmnh (0.21 #1940, 0.17 #4708, 0.16 #4949), 04xvlr (0.20 #962, 0.20 #6137, 0.19 #6621) >> Best rule #6257 for best value: >> intensional similarity = 3 >> extensional distance = 1013 >> proper extension: 0dtw1x; 0cnztc4; 0crh5_f; 0413cff; 016ztl; 03_wm6; 07s3m4g; 0564x; 02pcq92; 0d8w2n; >> query: (?x11355, ?x8280) <- titles(?x8280, ?x11355), genre(?x531, ?x8280), genre(?x148, ?x8280) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q_ncg genre 0hfjk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 91.000 0.733 http://example.org/film/film/genre #1537-047svrl PRED entity: 047svrl PRED relation: language PRED expected values: 02h40lc => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 49): 02h40lc (0.90 #2266, 0.90 #2325, 0.89 #2683), 064_8sq (0.22 #142, 0.19 #378, 0.19 #975), 06nm1 (0.16 #190, 0.13 #844, 0.11 #11), 04306rv (0.12 #1078, 0.10 #302, 0.09 #1198), 06b_j (0.12 #202, 0.08 #143, 0.06 #856), 0jzc (0.11 #20, 0.07 #199, 0.06 #973), 02bjrlw (0.08 #2324, 0.08 #834, 0.07 #180), 0t_2 (0.06 #1146, 0.06 #429, 0.06 #548), 03_9r (0.05 #10, 0.05 #903, 0.05 #3879), 02hxcvy (0.05 #34, 0.04 #272, 0.03 #509) >> Best rule #2266 for best value: >> intensional similarity = 3 >> extensional distance = 507 >> proper extension: 04cf_l; >> query: (?x2695, 02h40lc) <- currency(?x2695, ?x170), production_companies(?x2695, ?x1478), produced_by(?x2695, ?x364) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047svrl language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.898 http://example.org/film/film/language #1536-0900j5 PRED entity: 0900j5 PRED relation: genre PRED expected values: 02n4kr 01585b => 61 concepts (41 used for prediction) PRED predicted values (max 10 best out of 81): 05p553 (0.86 #1422, 0.41 #4493, 0.39 #2485), 07s9rl0 (0.70 #4489, 0.70 #2481, 0.67 #3189), 02kdv5l (0.56 #1892, 0.34 #357, 0.32 #593), 03k9fj (0.42 #129, 0.42 #11, 0.34 #247), 01hmnh (0.33 #135, 0.33 #17, 0.25 #253), 02l7c8 (0.32 #4503, 0.31 #2495, 0.31 #1432), 06n90 (0.26 #1901, 0.25 #248, 0.17 #130), 0lsxr (0.26 #362, 0.24 #1897, 0.19 #952), 0hcr (0.25 #141, 0.25 #23, 0.19 #259), 082gq (0.25 #30, 0.17 #148, 0.12 #266) >> Best rule #1422 for best value: >> intensional similarity = 4 >> extensional distance = 523 >> proper extension: 04cf_l; >> query: (?x3588, 05p553) <- genre(?x3588, ?x6452), nominated_for(?x2549, ?x3588), genre(?x8072, ?x6452), ?x8072 = 02mc5v >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1896 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 813 *> proper extension: 06n90; 09rfh9; *> query: (?x3588, 02n4kr) <- genre(?x3588, ?x10848), genre(?x2755, ?x10848), genre(?x723, ?x10848), ?x723 = 04fzfj, film(?x538, ?x2755) *> conf = 0.16 ranks of expected_values: 15, 62 EVAL 0900j5 genre 01585b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 61.000 41.000 0.859 http://example.org/film/film/genre EVAL 0900j5 genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 61.000 41.000 0.859 http://example.org/film/film/genre #1535-039cq4 PRED entity: 039cq4 PRED relation: titles! PRED expected values: 039cq4 => 107 concepts (49 used for prediction) PRED predicted values (max 10 best out of 80): 07s9rl0 (0.29 #2556, 0.27 #4097, 0.11 #2658), 09lmb (0.22 #366, 0.08 #1180, 0.06 #874), 04xvlr (0.18 #4100, 0.16 #2559, 0.08 #2148), 01z4y (0.15 #4131, 0.15 #2590, 0.08 #2179), 0215n (0.13 #685, 0.11 #1914, 0.11 #1811), 0146mv (0.13 #697, 0.03 #1926, 0.03 #1823), 05gnf (0.13 #3992, 0.09 #4813, 0.04 #2760), 03mdt (0.12 #1370, 0.11 #1574, 0.11 #1676), 0ljc_ (0.11 #395, 0.07 #700, 0.06 #903), 04t36 (0.10 #2563, 0.06 #2152, 0.03 #2665) >> Best rule #2556 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 0dr3sl; 0dgq_kn; 02c7k4; 02pxst; 01bjbk; 017n9; >> query: (?x6884, 07s9rl0) <- award(?x6884, ?x537), nominated_for(?x1896, ?x6884), artist(?x5836, ?x1896) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 039cq4 titles! 039cq4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 107.000 49.000 0.291 http://example.org/media_common/netflix_genre/titles #1534-072192 PRED entity: 072192 PRED relation: film_sets_designed! PRED expected values: 051x52f => 64 concepts (59 used for prediction) PRED predicted values (max 10 best out of 19): 07h1tr (0.15 #4, 0.02 #29, 0.02 #102), 057bc6m (0.14 #11, 0.03 #36, 0.02 #109), 0cb77r (0.10 #1, 0.05 #26, 0.02 #75), 076lxv (0.08 #2, 0.03 #27, 0.02 #76), 076psv (0.05 #6, 0.04 #31, 0.03 #56), 0579tg2 (0.05 #20, 0.01 #70), 02cqbx (0.05 #50, 0.01 #590, 0.01 #341), 012vct (0.05 #50, 0.01 #590, 0.01 #341), 02wb6d (0.05 #50, 0.01 #590, 0.01 #341), 019l68 (0.05 #50, 0.01 #590, 0.01 #341) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 57 >> proper extension: 0gcrg; 0cq8nx; >> query: (?x9100, 07h1tr) <- music(?x9100, ?x6971), film_art_direction_by(?x9100, ?x9875), genre(?x9100, ?x53) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #35 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 07bz5; *> query: (?x9100, 051x52f) <- nominated_for(?x5611, ?x9100), award(?x9100, ?x601), list(?x9100, ?x3004) *> conf = 0.02 ranks of expected_values: 14 EVAL 072192 film_sets_designed! 051x52f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 64.000 59.000 0.153 http://example.org/film/film_set_designer/film_sets_designed #1533-07k8rt4 PRED entity: 07k8rt4 PRED relation: genre PRED expected values: 02kdv5l 04pbhw => 99 concepts (80 used for prediction) PRED predicted values (max 10 best out of 84): 07s9rl0 (0.90 #7046, 0.72 #7403, 0.60 #834), 02kdv5l (0.47 #241, 0.42 #3701, 0.40 #2866), 02n4kr (0.35 #722, 0.28 #484, 0.28 #2871), 02l7c8 (0.34 #7417, 0.31 #372, 0.30 #1086), 03k9fj (0.28 #3709, 0.25 #11, 0.23 #130), 06cvj (0.25 #4, 0.19 #361, 0.12 #123), 01hmnh (0.25 #3715, 0.17 #2164, 0.17 #136), 06n90 (0.21 #250, 0.19 #3710, 0.16 #2875), 04xvlr (0.20 #4657, 0.17 #5374, 0.17 #6332), 03npn (0.19 #721, 0.16 #483, 0.13 #2870) >> Best rule #7046 for best value: >> intensional similarity = 5 >> extensional distance = 1119 >> proper extension: 015qy1; >> query: (?x4427, 07s9rl0) <- genre(?x4427, ?x604), genre(?x7103, ?x604), genre(?x1415, ?x604), ?x1415 = 09p0ct, ?x7103 = 0dpl44 >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 202 *> proper extension: 024mxd; 015bpl; *> query: (?x4427, 02kdv5l) <- genre(?x4427, ?x812), nominated_for(?x2456, ?x4427), produced_by(?x4427, ?x1417), ?x812 = 01jfsb *> conf = 0.47 ranks of expected_values: 2, 16 EVAL 07k8rt4 genre 04pbhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 99.000 80.000 0.897 http://example.org/film/film/genre EVAL 07k8rt4 genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 80.000 0.897 http://example.org/film/film/genre #1532-01kws3 PRED entity: 01kws3 PRED relation: place_of_birth PRED expected values: 0cr3d => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 80): 0h1k6 (0.28 #37352, 0.27 #10572, 0.27 #15505), 02_286 (0.12 #19, 0.10 #12704, 0.10 #723), 030qb3t (0.08 #17620, 0.07 #19031, 0.07 #21146), 01_d4 (0.07 #6408, 0.06 #2886, 0.05 #12751), 0cr3d (0.06 #94, 0.05 #11370, 0.05 #8550), 0f2tj (0.04 #248), 0d6lp (0.04 #1523, 0.03 #6456, 0.03 #2229), 01531 (0.03 #2925, 0.03 #3629, 0.03 #6447), 09c7w0 (0.03 #5639, 0.03 #4231, 0.03 #7753), 0cc56 (0.02 #2853, 0.02 #33, 0.02 #3557) >> Best rule #37352 for best value: >> intensional similarity = 3 >> extensional distance = 1657 >> proper extension: 07m69t; >> query: (?x5393, ?x11299) <- nationality(?x5393, ?x94), ?x94 = 09c7w0, location(?x5393, ?x11299) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #94 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 48 *> proper extension: 01yznp; 0jt90f5; 012_53; 046lt; 0cqt90; 014z8v; 015lhm; 01vvyd8; 05rx__; 0mbw0; ... *> query: (?x5393, 0cr3d) <- profession(?x5393, ?x1041), profession(?x5393, ?x1032), profession(?x5393, ?x353), ?x1041 = 03gjzk, ?x1032 = 02hrh1q, ?x353 = 0cbd2 *> conf = 0.06 ranks of expected_values: 5 EVAL 01kws3 place_of_birth 0cr3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 81.000 81.000 0.277 http://example.org/people/person/place_of_birth #1531-07p62k PRED entity: 07p62k PRED relation: film_crew_role PRED expected values: 02r96rf => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 27): 02r96rf (0.73 #102, 0.68 #400, 0.64 #135), 0dxtw (0.39 #109, 0.36 #975, 0.35 #407), 0d2b38 (0.23 #89, 0.17 #56, 0.15 #122), 01xy5l_ (0.23 #78, 0.17 #45, 0.13 #111), 0215hd (0.15 #82, 0.15 #115, 0.15 #413), 089g0h (0.15 #83, 0.14 #116, 0.13 #149), 02rh1dz (0.14 #108, 0.13 #141, 0.11 #406), 02_n3z (0.11 #100, 0.09 #133, 0.09 #398), 015h31 (0.10 #206, 0.10 #107, 0.09 #2635), 020xn5 (0.09 #2635, 0.08 #40, 0.08 #73) >> Best rule #102 for best value: >> intensional similarity = 3 >> extensional distance = 148 >> proper extension: 0g5q34q; 0gh6j94; 0dmn0x; >> query: (?x2207, 02r96rf) <- featured_film_locations(?x2207, ?x108), film_format(?x2207, ?x909), film_crew_role(?x2207, ?x137) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07p62k film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.733 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1530-0sxmx PRED entity: 0sxmx PRED relation: award PRED expected values: 02qt02v => 92 concepts (83 used for prediction) PRED predicted values (max 10 best out of 245): 02rdyk7 (0.31 #985, 0.27 #457, 0.26 #3893), 02rdxsh (0.27 #457, 0.26 #1148, 0.26 #968), 0gs9p (0.27 #457, 0.26 #3893, 0.25 #1147), 04dn09n (0.27 #457, 0.26 #3893, 0.25 #1147), 0gr0m (0.27 #457, 0.26 #3893, 0.25 #1147), 02x4wr9 (0.27 #457, 0.26 #3893, 0.25 #1147), 0gqy2 (0.27 #457, 0.26 #3893, 0.25 #1147), 0gr51 (0.27 #457, 0.26 #3893, 0.25 #1147), 02x4w6g (0.27 #457, 0.26 #3893, 0.25 #1147), 0gr4k (0.25 #12128, 0.22 #253, 0.15 #712) >> Best rule #985 for best value: >> intensional similarity = 4 >> extensional distance = 70 >> proper extension: 0jyx6; 0j_t1; 012mrr; 0j43swk; 0jqj5; 0g9zljd; 0gvvm6l; 0170yd; 0gvt53w; >> query: (?x4734, 02rdyk7) <- nominated_for(?x1063, ?x4734), country(?x4734, ?x512), nominated_for(?x6062, ?x4734), ?x1063 = 02rdxsh >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #12128 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 843 *> proper extension: 05h95s; *> query: (?x4734, ?x601) <- award(?x4734, ?x500), award_winner(?x4734, ?x777), award_winner(?x601, ?x777) *> conf = 0.25 ranks of expected_values: 12 EVAL 0sxmx award 02qt02v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 92.000 83.000 0.306 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #1529-09c7w0 PRED entity: 09c7w0 PRED relation: country_of_origin! PRED expected values: 01qn7n 02_1q9 02k_4g 027tbrc 01b66d 02648p 0c3xpwy 0fpxp 02r2j8 06w7mlh 0q9nj 06r4f 07wqr6 025ljp 07s8z_l 070ltt 01fszq 02py9yf 043p28m 04x4gj 026y3cf 04hs7d 045nc5 04bp0l => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 48): 027tbrc (0.25 #147, 0.17 #387, 0.14 #483), 02vjhf (0.25 #180, 0.11 #372, 0.08 #420), 02648p (0.25 #151, 0.11 #343, 0.08 #391), 02qfh (0.14 #503, 0.08 #407, 0.07 #840), 01_2n (0.14 #511, 0.08 #415, 0.07 #848), 01cjhz (0.14 #484, 0.08 #388, 0.07 #821), 0k0q73t (0.08 #424, 0.07 #520, 0.04 #664), 06f0k (0.08 #423, 0.07 #519, 0.04 #663), 03ctqqf (0.08 #422, 0.07 #518, 0.04 #662), 02qr46y (0.08 #421, 0.07 #517, 0.04 #661) >> Best rule #147 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 02j71; >> query: (?x94, 027tbrc) <- service_location(?x11727, ?x94), ?x11727 = 01hlwv >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 26 EVAL 09c7w0 country_of_origin! 04bp0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 045nc5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 04hs7d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 026y3cf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 04x4gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 043p28m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 02py9yf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 01fszq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 070ltt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 07s8z_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 025ljp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 07wqr6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 06r4f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 0q9nj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 06w7mlh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 02r2j8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 0fpxp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 0c3xpwy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 02648p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 01b66d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 027tbrc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 02k_4g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 02_1q9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin EVAL 09c7w0 country_of_origin! 01qn7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 147.000 147.000 0.250 http://example.org/tv/tv_program/country_of_origin #1528-07sp4l PRED entity: 07sp4l PRED relation: country PRED expected values: 0d060g => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 97): 0d05w3 (0.27 #42, 0.04 #160, 0.04 #455), 07ssc (0.23 #2970, 0.22 #3265, 0.21 #4276), 03h64 (0.20 #45, 0.04 #458, 0.03 #163), 03_3d (0.13 #7, 0.05 #361, 0.05 #420), 0ctw_b (0.13 #23, 0.05 #82, 0.04 #141), 0f8l9c (0.10 #1966, 0.10 #2320, 0.10 #2025), 0d060g (0.07 #8, 0.06 #67, 0.05 #126), 03rjj (0.07 #6, 0.04 #65, 0.04 #2425), 03rk0 (0.07 #38, 0.02 #274, 0.02 #3724), 059j2 (0.07 #26, 0.02 #3724, 0.01 #380) >> Best rule #42 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 0cnztc4; 07ghv5; >> query: (?x3063, 0d05w3) <- genre(?x3063, ?x1626), genre(?x3063, ?x225), genre(?x3063, ?x53), ?x53 = 07s9rl0, ?x225 = 02kdv5l, ?x1626 = 03q4nz >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #8 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 13 *> proper extension: 0cnztc4; 07ghv5; *> query: (?x3063, 0d060g) <- genre(?x3063, ?x1626), genre(?x3063, ?x225), genre(?x3063, ?x53), ?x53 = 07s9rl0, ?x225 = 02kdv5l, ?x1626 = 03q4nz *> conf = 0.07 ranks of expected_values: 7 EVAL 07sp4l country 0d060g CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 77.000 0.267 http://example.org/film/film/country #1527-0p07l PRED entity: 0p07l PRED relation: source PRED expected values: 0jbk9 => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.92 #41, 0.92 #40, 0.91 #37) >> Best rule #41 for best value: >> intensional similarity = 4 >> extensional distance = 280 >> proper extension: 0f4y_; 0mx4_; 0mw93; 0m7fm; 0n5fl; 0fr59; 0mx6c; 0mk7z; 0l2l_; 0mlyw; ... >> query: (?x14300, ?x958) <- adjoins(?x14300, ?x14360), second_level_divisions(?x94, ?x14300), ?x94 = 09c7w0, source(?x14360, ?x958) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0p07l source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.918 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #1526-016ywr PRED entity: 016ywr PRED relation: award_winner! PRED expected values: 0drtv8 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 130): 09g90vz (0.08 #949, 0.06 #1363, 0.06 #3019), 09qvms (0.07 #841, 0.07 #1255, 0.07 #2911), 092t4b (0.07 #465, 0.06 #327, 0.06 #1293), 09gkdln (0.07 #947, 0.04 #3845, 0.04 #4397), 0275n3y (0.06 #901, 0.05 #487, 0.05 #3937), 05c1t6z (0.06 #843, 0.04 #429, 0.04 #3741), 092c5f (0.06 #428, 0.05 #1256, 0.05 #2774), 03gyp30 (0.06 #942, 0.06 #804, 0.05 #3840), 02q690_ (0.06 #891, 0.04 #477, 0.04 #4341), 0gvstc3 (0.06 #862, 0.04 #3760, 0.04 #2932) >> Best rule #949 for best value: >> intensional similarity = 3 >> extensional distance = 369 >> proper extension: 027_tg; 02z6l5f; 0f3zsq; 02pbp9; >> query: (?x1867, 09g90vz) <- award_winner(?x2292, ?x1867), award_winner(?x6023, ?x1867), genre(?x6023, ?x53) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #478 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 198 *> proper extension: 02wb6yq; *> query: (?x1867, 0drtv8) <- award_winner(?x2292, ?x1867), award_winner(?x1868, ?x1867), religion(?x1867, ?x1985) *> conf = 0.04 ranks of expected_values: 42 EVAL 016ywr award_winner! 0drtv8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 110.000 110.000 0.078 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1525-0nvt9 PRED entity: 0nvt9 PRED relation: contains PRED expected values: 0s4sj => 181 concepts (53 used for prediction) PRED predicted values (max 10 best out of 2473): 01_d4 (0.86 #73623, 0.84 #76568, 0.84 #53011), 0s5cg (0.82 #76570, 0.80 #114856, 0.75 #85404), 02nvg1 (0.67 #108964, 0.43 #129579, 0.32 #138411), 03v0t (0.54 #58900, 0.51 #55956, 0.48 #156079), 0nvt9 (0.54 #58900, 0.51 #55956, 0.48 #156079), 09c7w0 (0.54 #58900, 0.51 #55956, 0.48 #156079), 0s69k (0.33 #238, 0.25 #6131, 0.25 #3186), 0s6g4 (0.33 #2160, 0.25 #8053, 0.14 #16895), 0s9b_ (0.33 #2414, 0.25 #8307, 0.12 #11254), 017j69 (0.32 #138411) >> Best rule #73623 for best value: >> intensional similarity = 3 >> extensional distance = 96 >> proper extension: 0jhz_; 0kwmc; 025rst1; >> query: (?x6410, ?x1860) <- county_seat(?x6410, ?x1860), contains(?x6410, ?x5867), location(?x510, ?x5867) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #5812 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0nvd8; *> query: (?x6410, 0s4sj) <- adjoins(?x6410, ?x11150), ?x11150 = 0nv6n, time_zones(?x6410, ?x1638), currency(?x6410, ?x170) *> conf = 0.25 ranks of expected_values: 11 EVAL 0nvt9 contains 0s4sj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 181.000 53.000 0.857 http://example.org/location/location/contains #1524-026dx PRED entity: 026dx PRED relation: profession PRED expected values: 0n1h => 104 concepts (98 used for prediction) PRED predicted values (max 10 best out of 80): 0cbd2 (0.52 #4803, 0.48 #570, 0.48 #4238), 018gz8 (0.51 #154, 0.22 #577, 0.18 #5092), 0dz3r (0.49 #707, 0.40 #6068, 0.36 #3106), 0nbcg (0.47 #729, 0.45 #6090, 0.44 #3128), 016z4k (0.35 #3108, 0.35 #6635, 0.34 #6070), 0kyk (0.35 #4820, 0.33 #4255, 0.31 #587), 01c8w0 (0.32 #854, 0.23 #1277, 0.21 #1841), 039v1 (0.27 #3133, 0.26 #6095, 0.24 #6801), 0n1h (0.24 #6782, 0.16 #715, 0.13 #6641), 09lbv (0.22 #720, 0.06 #3119, 0.06 #6081) >> Best rule #4803 for best value: >> intensional similarity = 3 >> extensional distance = 273 >> proper extension: 03qcq; 07kb5; 0hnlx; 0k4gf; 028p0; 0177s6; 026lj; 014dq7; 041mt; 01hb6v; ... >> query: (?x4703, 0cbd2) <- profession(?x4703, ?x319), influenced_by(?x8841, ?x4703), influenced_by(?x4703, ?x4915) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #6782 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 731 *> proper extension: 0gct_; 0k29f; *> query: (?x4703, 0n1h) <- profession(?x4703, ?x1183), profession(?x9087, ?x1183), ?x9087 = 0kj34 *> conf = 0.24 ranks of expected_values: 9 EVAL 026dx profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 104.000 98.000 0.520 http://example.org/people/person/profession #1523-05tbn PRED entity: 05tbn PRED relation: contains PRED expected values: 013ksx 0g0syc 0mws3 09dfcj 0mww2 0zpfy => 207 concepts (100 used for prediction) PRED predicted values (max 10 best out of 2693): 0dclg (0.83 #89125, 0.63 #146630, 0.52 #270274), 0h778 (0.83 #89125, 0.33 #555, 0.25 #6304), 01q2sk (0.71 #230024, 0.52 #230023, 0.49 #135130), 01jt2w (0.71 #230024, 0.52 #230023, 0.49 #135130), 013ksx (0.63 #146630, 0.52 #270274, 0.37 #276025), 0mww2 (0.63 #146630, 0.52 #270274, 0.37 #276025), 0mws3 (0.63 #146630, 0.52 #270274, 0.37 #276025), 0myn8 (0.63 #146630, 0.06 #13008, 0.04 #18757), 0n228 (0.63 #146630, 0.06 #12257, 0.04 #18006), 0n5dt (0.63 #146630, 0.04 #19282, 0.04 #25033) >> Best rule #89125 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 09hzw; >> query: (?x3670, ?x2254) <- state(?x2254, ?x3670), adjoins(?x177, ?x3670), country(?x3670, ?x94) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #146630 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 0k3k1; *> query: (?x3670, ?x11384) <- contains(?x3670, ?x13304), contains(?x3670, ?x8069), currency(?x8069, ?x170), adjoins(?x11384, ?x13304) *> conf = 0.63 ranks of expected_values: 5, 6, 7, 20, 774 EVAL 05tbn contains 0zpfy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 207.000 100.000 0.826 http://example.org/location/location/contains EVAL 05tbn contains 0mww2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 207.000 100.000 0.826 http://example.org/location/location/contains EVAL 05tbn contains 09dfcj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 207.000 100.000 0.826 http://example.org/location/location/contains EVAL 05tbn contains 0mws3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 207.000 100.000 0.826 http://example.org/location/location/contains EVAL 05tbn contains 0g0syc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 207.000 100.000 0.826 http://example.org/location/location/contains EVAL 05tbn contains 013ksx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 207.000 100.000 0.826 http://example.org/location/location/contains #1522-06mx8 PRED entity: 06mx8 PRED relation: locations! PRED expected values: 081pw => 136 concepts (29 used for prediction) PRED predicted values (max 10 best out of 54): 081pw (0.38 #1816, 0.35 #2727, 0.33 #2597), 05t2fh4 (0.33 #768, 0.33 #122, 0.25 #1158), 01w1sx (0.33 #349, 0.25 #1777, 0.25 #478), 0k4y6 (0.33 #332, 0.25 #461, 0.23 #1889), 0845v (0.33 #270, 0.25 #399, 0.17 #1698), 01hwkn (0.33 #369, 0.25 #498, 0.14 #2057), 03jv8d (0.33 #370, 0.25 #499, 0.14 #889), 0dr7s (0.33 #367, 0.25 #496, 0.14 #886), 0cwt70 (0.33 #357, 0.25 #486, 0.14 #876), 01_3rn (0.33 #344, 0.25 #473, 0.14 #863) >> Best rule #1816 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 06c1y; 034cm; >> query: (?x6820, 081pw) <- contains(?x6820, ?x1892), combatants(?x1892, ?x1003), locations(?x7241, ?x1892), combatants(?x326, ?x1892) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06mx8 locations! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 29.000 0.385 http://example.org/time/event/locations #1521-095w_ PRED entity: 095w_ PRED relation: vacationer PRED expected values: 01l9p => 204 concepts (87 used for prediction) PRED predicted values (max 10 best out of 155): 01xyt7 (0.22 #310, 0.14 #669, 0.13 #848), 016fnb (0.17 #466, 0.14 #645, 0.13 #4056), 0bbf1f (0.17 #423, 0.13 #781, 0.11 #1498), 019pm_ (0.17 #421, 0.13 #779, 0.11 #1496), 01k5zk (0.17 #85, 0.11 #265, 0.02 #8529), 03lt8g (0.14 #562, 0.13 #741, 0.11 #1458), 0j1yf (0.13 #751, 0.11 #1468, 0.11 #1289), 05r5w (0.13 #4026, 0.11 #256, 0.08 #436), 046zh (0.12 #1013, 0.12 #1192, 0.11 #1911), 02mjf2 (0.11 #281, 0.11 #1897, 0.09 #2792) >> Best rule #310 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 05k7sb; >> query: (?x1374, 01xyt7) <- contains(?x1003, ?x1374), location(?x11330, ?x1374), profession(?x11330, ?x1032), film_art_direction_by(?x2958, ?x11330) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #10807 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 78 *> proper extension: 01mjq; 01pj7; 02k8k; *> query: (?x1374, 01l9p) <- contains(?x1003, ?x1374), location(?x11330, ?x1374), profession(?x11330, ?x1032), film_release_region(?x11809, ?x1374) *> conf = 0.01 ranks of expected_values: 154 EVAL 095w_ vacationer 01l9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 204.000 87.000 0.222 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #1520-099ck7 PRED entity: 099ck7 PRED relation: ceremony PRED expected values: 0clfdj => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 138): 03gyp30 (0.43 #390, 0.40 #114, 0.23 #528), 09qvms (0.43 #289, 0.40 #13, 0.23 #427), 058m5m4 (0.43 #330, 0.40 #54, 0.23 #468), 0g55tzk (0.43 #410, 0.40 #134, 0.23 #548), 092t4b (0.43 #327, 0.40 #51, 0.23 #465), 0hr3c8y (0.43 #286, 0.40 #10, 0.23 #424), 027hjff (0.43 #332, 0.40 #56, 0.23 #470), 092_25 (0.43 #347, 0.40 #71, 0.23 #485), 0gpjbt (0.41 #3616, 0.33 #4307, 0.23 #4445), 09g90vz (0.40 #121, 0.29 #397, 0.21 #4832) >> Best rule #390 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 09sdmz; >> query: (?x6729, 03gyp30) <- award(?x3101, ?x6729), award(?x2670, ?x6729), award(?x1064, ?x6729), ?x3101 = 0dvmd, ?x2670 = 0blq0z, nominated_for(?x6729, ?x253) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4832 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 288 *> proper extension: 05qck; 02qkk9_; 0d085; 02py7pj; 0bqsk5; 02q3s; 01pqx6; *> query: (?x6729, ?x1553) <- award_winner(?x6729, ?x1222), award_winner(?x72, ?x1222), nominated_for(?x1222, ?x144), award_winner(?x1553, ?x1222) *> conf = 0.21 ranks of expected_values: 62 EVAL 099ck7 ceremony 0clfdj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 55.000 55.000 0.429 http://example.org/award/award_category/winners./award/award_honor/ceremony #1519-07ss8_ PRED entity: 07ss8_ PRED relation: award_winner! PRED expected values: 0gx1673 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 102): 019bk0 (0.21 #157, 0.17 #1003, 0.12 #1990), 0gpjbt (0.18 #29, 0.12 #170, 0.08 #875), 01mhwk (0.17 #182, 0.12 #1028, 0.06 #4271), 01s695 (0.16 #990, 0.12 #144, 0.09 #426), 01c6qp (0.15 #1006, 0.10 #1711, 0.09 #4249), 02rjjll (0.14 #992, 0.12 #287, 0.12 #851), 01mh_q (0.13 #1076, 0.08 #230, 0.06 #3332), 09n4nb (0.12 #189, 0.10 #1035, 0.09 #48), 0jzphpx (0.12 #180, 0.10 #1026, 0.08 #2013), 02cg41 (0.12 #1113, 0.09 #126, 0.08 #267) >> Best rule #157 for best value: >> intensional similarity = 3 >> extensional distance = 22 >> proper extension: 01wcp_g; 044gyq; 01wzlxj; 02h9_l; >> query: (?x2227, 019bk0) <- profession(?x2227, ?x131), award(?x2227, ?x4532), ?x4532 = 02f764 >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #120 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 0zjpz; *> query: (?x2227, 0gx1673) <- profession(?x2227, ?x220), participant(?x6835, ?x2227), ?x220 = 016z4k *> conf = 0.09 ranks of expected_values: 16 EVAL 07ss8_ award_winner! 0gx1673 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 90.000 90.000 0.208 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1518-01wz_ml PRED entity: 01wz_ml PRED relation: type_of_union PRED expected values: 04ztj => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.82 #73, 0.81 #77, 0.79 #89), 01bl8s (0.20 #3, 0.09 #7, 0.03 #19), 01g63y (0.14 #340, 0.14 #408, 0.14 #22), 0jgjn (0.03 #32, 0.02 #36, 0.02 #44) >> Best rule #73 for best value: >> intensional similarity = 4 >> extensional distance = 63 >> proper extension: 0f2zc; >> query: (?x3401, 04ztj) <- gender(?x3401, ?x231), inductee(?x1091, ?x3401), ?x231 = 05zppz, location(?x3401, ?x4356) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wz_ml type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.815 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1517-0fdv3 PRED entity: 0fdv3 PRED relation: music PRED expected values: 0146pg => 101 concepts (92 used for prediction) PRED predicted values (max 10 best out of 90): 01m5m5b (0.29 #398, 0.02 #3772, 0.02 #3350), 0146pg (0.21 #2539, 0.20 #10, 0.19 #1063), 01nc3rh (0.20 #182, 0.01 #1023), 06fxnf (0.14 #279, 0.07 #699, 0.05 #489), 02g40r (0.14 #395), 0150t6 (0.11 #676, 0.10 #887, 0.06 #5106), 0f276 (0.09 #3795, 0.06 #17095, 0.06 #15404), 02bh9 (0.09 #892, 0.06 #2580, 0.06 #7855), 01tc9r (0.07 #695, 0.06 #906, 0.04 #1541), 02jxkw (0.07 #772, 0.03 #3937, 0.03 #2040) >> Best rule #398 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02z3r8t; >> query: (?x1812, 01m5m5b) <- film(?x9781, ?x1812), film_crew_role(?x1812, ?x137), ?x9781 = 0f276 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #2539 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 115 *> proper extension: 035s95; 05fgt1; 04sntd; 025n07; 03mh_tp; 02rrfzf; 09g7vfw; 09rsjpv; 09lcsj; 034r25; ... *> query: (?x1812, 0146pg) <- film(?x1387, ?x1812), production_companies(?x1812, ?x847), language(?x1812, ?x254), edited_by(?x1812, ?x10262) *> conf = 0.21 ranks of expected_values: 2 EVAL 0fdv3 music 0146pg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 92.000 0.286 http://example.org/film/film/music #1516-0tj4y PRED entity: 0tj4y PRED relation: time_zones PRED expected values: 02fqwt => 171 concepts (171 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.50 #1913, 0.50 #3, 0.44 #1290), 02fqwt (0.50 #1913, 0.27 #105, 0.27 #66), 02hczc (0.23 #1730, 0.16 #2150, 0.16 #2136), 02lcrv (0.23 #1730, 0.16 #2150, 0.16 #2136), 042g7t (0.23 #1730, 0.16 #2136, 0.15 #2083), 02lcqs (0.21 #356, 0.20 #460, 0.19 #57), 02llzg (0.14 #316, 0.14 #511, 0.14 #394), 03bdv (0.08 #474, 0.07 #734, 0.06 #916), 03plfd (0.06 #400, 0.06 #569, 0.05 #686), 052vwh (0.03 #324, 0.03 #376, 0.03 #402) >> Best rule #1913 for best value: >> intensional similarity = 3 >> extensional distance = 981 >> proper extension: 01cz_1; >> query: (?x5525, ?x1638) <- contains(?x4061, ?x5525), capital(?x4061, ?x12662), time_zones(?x4061, ?x1638) >> conf = 0.50 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0tj4y time_zones 02fqwt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 171.000 171.000 0.501 http://example.org/location/location/time_zones #1515-03tcbx PRED entity: 03tcbx PRED relation: legislative_sessions PRED expected values: 02cg7g => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 40): 024tkd (0.92 #218, 0.92 #217, 0.89 #309), 02bqn1 (0.92 #218, 0.92 #217, 0.89 #309), 02cg7g (0.92 #218, 0.92 #217, 0.89 #309), 05l2z4 (0.92 #218, 0.92 #217, 0.89 #219), 02glc4 (0.92 #218, 0.92 #217, 0.89 #219), 0495ys (0.92 #218, 0.92 #217, 0.89 #219), 03tcbx (0.89 #219, 0.81 #443, 0.81 #442), 01gsvb (0.43 #1555, 0.41 #793, 0.41 #1294), 01gsvp (0.41 #793, 0.41 #1548, 0.39 #880), 01gstn (0.41 #793, 0.39 #880, 0.39 #966) >> Best rule #218 for best value: >> intensional similarity = 47 >> extensional distance = 1 >> proper extension: 03rtmz; >> query: (?x2861, ?x2976) <- legislative_sessions(?x9334, ?x2861), legislative_sessions(?x8607, ?x2861), legislative_sessions(?x5266, ?x2861), legislative_sessions(?x6933, ?x2861), legislative_sessions(?x5339, ?x2861), legislative_sessions(?x4730, ?x2861), legislative_sessions(?x3766, ?x2861), legislative_sessions(?x3765, ?x2861), legislative_sessions(?x3463, ?x2861), legislative_sessions(?x2976, ?x2861), legislative_sessions(?x1830, ?x2861), legislative_sessions(?x1137, ?x2861), legislative_sessions(?x1028, ?x2861), legislative_sessions(?x605, ?x2861), legislative_sessions(?x356, ?x2861), ?x3766 = 02gkzs, district_represented(?x2861, ?x7405), district_represented(?x2861, ?x6895), district_represented(?x2861, ?x4622), district_represented(?x2861, ?x2977), district_represented(?x2861, ?x2256), district_represented(?x2861, ?x1906), district_represented(?x2861, ?x1755), ?x4730 = 02cg7g, ?x8607 = 0226cw, ?x1906 = 04rrx, legislative_sessions(?x2860, ?x2861), ?x1830 = 03z5xd, ?x6895 = 05fjf, ?x6933 = 024tkd, ?x605 = 077g7n, ?x1028 = 032ft5, ?x356 = 05l2z4, ?x2977 = 081mh, ?x7405 = 07_f2, ?x5339 = 02glc4, ?x9334 = 02hy5d, ?x1755 = 01x73, district_represented(?x1137, ?x938), ?x2256 = 07srw, ?x938 = 0vmt, ?x3765 = 04gp1d, ?x4622 = 04tgp, legislative_sessions(?x4567, ?x1137), ?x4567 = 0d3qd0, ?x3463 = 02bqmq, ?x5266 = 016lh0 >> conf = 0.92 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 03tcbx legislative_sessions 02cg7g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 41.000 0.917 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #1514-04vq3h PRED entity: 04vq3h PRED relation: student! PRED expected values: 08815 => 111 concepts (92 used for prediction) PRED predicted values (max 10 best out of 54): 017z88 (0.11 #81, 0.06 #607, 0.05 #3237), 06182p (0.11 #297, 0.02 #823, 0.02 #3453), 02gkxp (0.11 #386), 086xm (0.11 #91), 0bwfn (0.10 #6586, 0.09 #2904, 0.08 #800), 065y4w7 (0.06 #2644, 0.05 #6326, 0.05 #26317), 09f2j (0.05 #4892, 0.05 #3314, 0.04 #2788), 015nl4 (0.05 #4800, 0.05 #592, 0.05 #3222), 03ksy (0.05 #2735, 0.04 #26408, 0.04 #6417), 017j69 (0.04 #670, 0.03 #3300, 0.03 #4878) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0q9kd; 0184jc; 06dv3; 048lv; 024bbl; 01d0fp; 031k24; >> query: (?x9998, 017z88) <- film(?x9998, ?x1496), ?x1496 = 011yqc, award_nominee(?x2296, ?x9998) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #26305 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1434 *> proper extension: 03gkn5; 05_pkf; 01dvtx; 041_y; 01d_h; 06f5j; 0cymln; 0835q; 06yj20; 02yy8; ... *> query: (?x9998, 08815) <- nationality(?x9998, ?x94), ?x94 = 09c7w0, student(?x1772, ?x9998) *> conf = 0.03 ranks of expected_values: 12 EVAL 04vq3h student! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 111.000 92.000 0.111 http://example.org/education/educational_institution/students_graduates./education/education/student #1513-01t9_0 PRED entity: 01t9_0 PRED relation: film PRED expected values: 07jnt => 147 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1568): 035s95 (0.42 #11477, 0.40 #3496, 0.33 #16265), 02bqxb (0.40 #4767, 0.33 #3171, 0.17 #12748), 0g22z (0.40 #3205, 0.33 #1609, 0.17 #11186), 0cq7kw (0.40 #3874, 0.27 #16643, 0.25 #11855), 0ndwt2w (0.40 #4084, 0.25 #12065, 0.23 #13661), 03hmt9b (0.40 #3787, 0.23 #13364, 0.18 #10172), 040b5k (0.40 #3608, 0.17 #11589, 0.15 #13185), 06tpmy (0.40 #3876, 0.17 #11857, 0.15 #13453), 08s6mr (0.40 #4371, 0.17 #12352, 0.15 #13948), 06823p (0.40 #4225, 0.17 #12206, 0.15 #13802) >> Best rule #11477 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 0jz9f; 086k8; 017s11; 016tt2; 05qd_; 030_1m; 03xsby; 024rgt; 061dn_; 032j_n; >> query: (?x10997, 035s95) <- company(?x9132, ?x10997), film(?x10997, ?x13537), film(?x10997, ?x5378), citytown(?x10997, ?x362), language(?x13537, ?x254), region(?x5378, ?x94) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #4268 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 03x_k5m; *> query: (?x10997, 07jnt) <- film(?x10997, ?x8984), film(?x10997, ?x3567), film(?x10997, ?x3306), ?x3306 = 03f7xg, genre(?x8984, ?x53), award(?x8984, ?x198), film(?x1950, ?x3567) *> conf = 0.20 ranks of expected_values: 247 EVAL 01t9_0 film 07jnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 147.000 39.000 0.417 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #1512-02dtg PRED entity: 02dtg PRED relation: location! PRED expected values: 01mxqyk => 157 concepts (99 used for prediction) PRED predicted values (max 10 best out of 2240): 02vyw (0.56 #245736, 0.49 #235705, 0.48 #72712), 01nr63 (0.56 #245736, 0.49 #235705, 0.48 #72712), 01dbgw (0.56 #245736, 0.49 #235705, 0.48 #72712), 03xx9l (0.56 #245736, 0.49 #235705, 0.48 #72712), 03pp73 (0.56 #245736, 0.49 #235705, 0.48 #72712), 05yh_t (0.56 #245736, 0.49 #235705, 0.48 #72712), 01vw917 (0.56 #245736, 0.49 #235705, 0.48 #72712), 01vsnff (0.55 #208122, 0.49 #235705, 0.48 #72712), 0m76b (0.55 #208122, 0.49 #235705, 0.48 #72712), 019f2f (0.55 #208122, 0.49 #235705, 0.48 #72712) >> Best rule #245736 for best value: >> intensional similarity = 3 >> extensional distance = 299 >> proper extension: 09f07; >> query: (?x479, ?x7625) <- place_of_birth(?x7625, ?x479), location(?x115, ?x479), people(?x2510, ?x7625) >> conf = 0.56 => this is the best rule for 7 predicted values *> Best rule #90268 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 106 *> proper extension: 0d060g; 0162v; 0mp3l; 0498y; 0vbk; 0xmlp; 0pzmf; 0j7ng; 01gc8c; 018d5b; *> query: (?x479, ?x2187) <- origin(?x2187, ?x479), award_winner(?x2186, ?x2187), location(?x115, ?x479) *> conf = 0.36 ranks of expected_values: 33 EVAL 02dtg location! 01mxqyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 157.000 99.000 0.565 http://example.org/people/person/places_lived./people/place_lived/location #1511-015grj PRED entity: 015grj PRED relation: nationality PRED expected values: 09c7w0 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.83 #301, 0.81 #1204, 0.80 #7143), 01znc_ (0.37 #5228), 07ww5 (0.37 #5228), 02jx1 (0.29 #133, 0.11 #734, 0.11 #1741), 0d060g (0.17 #7, 0.05 #2619, 0.05 #1915), 0d0vqn (0.17 #9), 07ssc (0.14 #115, 0.09 #616, 0.09 #5142), 03rk0 (0.07 #1149, 0.06 #747, 0.06 #1553), 0chghy (0.03 #1417, 0.02 #2119, 0.02 #411), 03rt9 (0.03 #714, 0.02 #1015, 0.02 #1116) >> Best rule #301 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 06sn8m; >> query: (?x968, 09c7w0) <- location(?x968, ?x1523), award(?x968, ?x435), ?x435 = 0bp_b2 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015grj nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.830 http://example.org/people/person/nationality #1510-01wy5m PRED entity: 01wy5m PRED relation: film PRED expected values: 0g3zrd 084302 03bxp5 0fzm0g => 97 concepts (59 used for prediction) PRED predicted values (max 10 best out of 682): 0djlxb (0.63 #14227, 0.49 #42680, 0.46 #26674), 02fqrf (0.63 #14227, 0.49 #42680, 0.46 #26674), 099bhp (0.10 #1608, 0.06 #3387), 047csmy (0.08 #909, 0.06 #2688, 0.01 #22248), 05sw5b (0.08 #810, 0.06 #2589), 0872p_c (0.06 #175, 0.05 #1954, 0.02 #21514), 03d8jd1 (0.06 #1714, 0.05 #3493), 0crfwmx (0.06 #151, 0.05 #1930), 0cpllql (0.06 #86, 0.05 #1865), 0g56t9t (0.06 #10, 0.05 #1789) >> Best rule #14227 for best value: >> intensional similarity = 2 >> extensional distance = 420 >> proper extension: 01qvgl; 02wb6yq; 012rng; 012dr7; 0f5zj6; 06t8b; 0dzlk; >> query: (?x4835, ?x3275) <- participant(?x4835, ?x2443), nominated_for(?x4835, ?x3275) >> conf = 0.63 => this is the best rule for 2 predicted values *> Best rule #1077 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 49 *> proper extension: 06_6j3; 03d_zl4; 034rd9; 08jtv5; 01nsyf; 031c2r; 03cz4j; *> query: (?x4835, 03bxp5) <- film(?x4835, ?x2287), actor(?x596, ?x4835) *> conf = 0.02 ranks of expected_values: 144, 434 EVAL 01wy5m film 0fzm0g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 59.000 0.628 http://example.org/film/actor/film./film/performance/film EVAL 01wy5m film 03bxp5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 97.000 59.000 0.628 http://example.org/film/actor/film./film/performance/film EVAL 01wy5m film 084302 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 59.000 0.628 http://example.org/film/actor/film./film/performance/film EVAL 01wy5m film 0g3zrd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 97.000 59.000 0.628 http://example.org/film/actor/film./film/performance/film #1509-02v3yy PRED entity: 02v3yy PRED relation: participant! PRED expected values: 0bxtg => 116 concepts (64 used for prediction) PRED predicted values (max 10 best out of 301): 0bxtg (0.83 #17807, 0.82 #17171, 0.81 #12084), 018gqj (0.19 #2545, 0.18 #3818, 0.18 #5089), 01l3mk3 (0.19 #2545, 0.18 #3818, 0.18 #5089), 014zcr (0.08 #1290, 0.07 #2563, 0.06 #9559), 01rr9f (0.08 #1305, 0.06 #669, 0.04 #1942), 029q_y (0.08 #1752, 0.05 #3025, 0.03 #4933), 011zd3 (0.08 #1427, 0.04 #3337, 0.03 #4608), 0237fw (0.07 #2075, 0.07 #3348, 0.06 #802), 0bwh6 (0.07 #7633, 0.06 #11448, 0.05 #13356), 01k98nm (0.07 #7633, 0.06 #11448, 0.05 #13356) >> Best rule #17807 for best value: >> intensional similarity = 3 >> extensional distance = 380 >> proper extension: 03ds3; 0ph2w; 03359d; 063_t; 0jbp0; 0161h5; 015076; >> query: (?x3235, ?x496) <- award_winner(?x1232, ?x3235), participant(?x398, ?x3235), participant(?x3235, ?x496) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02v3yy participant! 0bxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 64.000 0.827 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #1508-0fc_9 PRED entity: 0fc_9 PRED relation: source PRED expected values: 0jbk9 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #40, 0.92 #18, 0.92 #20) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 168 >> proper extension: 094jv; 0mp3l; 0n4mk; 034lk7; 0dzt9; 0fwc0; 0mm_4; 0mn9x; 0fr5p; 0n491; ... >> query: (?x9913, 0jbk9) <- contains(?x335, ?x9913), time_zones(?x9913, ?x2674), second_level_divisions(?x94, ?x9913), ?x2674 = 02hcv8 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fc_9 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.929 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #1507-0456xp PRED entity: 0456xp PRED relation: film PRED expected values: 0456zg => 123 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1143): 01shy7 (0.09 #4003, 0.08 #32643, 0.07 #39803), 0blpg (0.09 #6026, 0.06 #4236, 0.05 #9606), 0h7t36 (0.08 #1685, 0.01 #19585, 0.01 #46435), 03bzjpm (0.08 #12056, 0.08 #4896, 0.07 #15636), 06_wqk4 (0.08 #3706, 0.07 #9076, 0.06 #10866), 03bx2lk (0.08 #3764, 0.06 #14504, 0.05 #7344), 01l_pn (0.07 #8127, 0.06 #18867, 0.06 #4547), 0ch3qr1 (0.06 #11716, 0.06 #4556, 0.05 #8136), 027r9t (0.06 #11988, 0.05 #24518, 0.04 #15568), 03nqnnk (0.06 #1024, 0.04 #6394, 0.02 #24294) >> Best rule #4003 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 01vs_v8; 0993r; 09yrh; 01s21dg; 03h_0_z; 02dlfh; >> query: (?x1017, 01shy7) <- award(?x1017, ?x154), participant(?x1017, ?x4294), participant(?x1017, ?x1018) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #5015 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 01vs_v8; 0993r; 09yrh; 01s21dg; 03h_0_z; 02dlfh; *> query: (?x1017, 0456zg) <- award(?x1017, ?x154), participant(?x1017, ?x4294), participant(?x1017, ?x1018) *> conf = 0.02 ranks of expected_values: 677 EVAL 0456xp film 0456zg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 123.000 82.000 0.094 http://example.org/film/actor/film./film/performance/film #1506-01nm3s PRED entity: 01nm3s PRED relation: location PRED expected values: 02_286 027l4q => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 65): 030qb3t (0.22 #83, 0.17 #1689, 0.15 #25784), 02_286 (0.22 #37, 0.15 #25738, 0.15 #3249), 0cr3d (0.11 #145, 0.05 #25846, 0.05 #51544), 05fjy (0.11 #279, 0.02 #1082), 03dm7 (0.11 #578), 0b1t1 (0.11 #472), 01jr6 (0.11 #206), 01qh7 (0.11 #157), 04jpl (0.05 #2426, 0.05 #51416, 0.05 #25718), 059rby (0.04 #1622, 0.04 #25717, 0.03 #4837) >> Best rule #83 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05fnl9; 0f7h2v; 01y665; >> query: (?x4004, 030qb3t) <- award_winner(?x4004, ?x2735), film(?x4004, ?x1080), ?x2735 = 034g2b >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 05fnl9; 0f7h2v; 01y665; *> query: (?x4004, 02_286) <- award_winner(?x4004, ?x2735), film(?x4004, ?x1080), ?x2735 = 034g2b *> conf = 0.22 ranks of expected_values: 2, 58 EVAL 01nm3s location 027l4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 80.000 80.000 0.222 http://example.org/people/person/places_lived./people/place_lived/location EVAL 01nm3s location 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 80.000 80.000 0.222 http://example.org/people/person/places_lived./people/place_lived/location #1505-049mql PRED entity: 049mql PRED relation: genre PRED expected values: 03g3w => 76 concepts (61 used for prediction) PRED predicted values (max 10 best out of 128): 01jfsb (0.54 #816, 0.37 #3705, 0.33 #125), 04xvlr (0.52 #4967, 0.52 #2773, 0.50 #2541), 05p553 (0.51 #5664, 0.50 #118, 0.45 #6708), 06n90 (0.33 #2320, 0.31 #817, 0.25 #11), 06cvj (0.25 #1384, 0.25 #1269, 0.17 #2311), 060__y (0.24 #1164, 0.19 #704, 0.19 #1280), 0lsxr (0.22 #814, 0.19 #3703, 0.18 #2781), 01t_vv (0.20 #279, 0.10 #1431, 0.10 #1316), 01hmnh (0.19 #129, 0.18 #820, 0.17 #6719), 0219x_ (0.18 #253, 0.12 #23, 0.09 #4874) >> Best rule #816 for best value: >> intensional similarity = 4 >> extensional distance = 294 >> proper extension: 04tz52; 01q2nx; 01_1hw; 063y9fp; >> query: (?x4127, 01jfsb) <- production_companies(?x4127, ?x382), genre(?x4127, ?x225), ?x225 = 02kdv5l, film(?x338, ?x4127) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #481 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 126 *> proper extension: 03ckwzc; 04gknr; 02qrv7; 02r8hh_; 02q_4ph; 08tq4x; 01jwxx; 064q5v; 06rzwx; 01c9d; *> query: (?x4127, 03g3w) <- film(?x338, ?x4127), genre(?x4127, ?x3515), country(?x4127, ?x94), ?x3515 = 082gq *> conf = 0.12 ranks of expected_values: 15 EVAL 049mql genre 03g3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 76.000 61.000 0.541 http://example.org/film/film/genre #1504-0jtdp PRED entity: 0jtdp PRED relation: genre! PRED expected values: 02qr3k8 05f67hw => 60 concepts (29 used for prediction) PRED predicted values (max 10 best out of 1915): 0cc5mcj (0.73 #18848, 0.60 #13313, 0.55 #17004), 012jfb (0.63 #38748, 0.63 #42440, 0.61 #38747), 0fqt1ns (0.55 #19259, 0.50 #13724, 0.47 #26643), 0cd2vh9 (0.55 #18705, 0.50 #13170, 0.47 #26089), 0340hj (0.55 #18689, 0.50 #13154, 0.47 #26073), 02nx2k (0.55 #19690, 0.47 #23381, 0.45 #17846), 06yykb (0.55 #19867, 0.45 #18023, 0.40 #23558), 01cycq (0.55 #19841, 0.45 #17997, 0.40 #23532), 05pdd86 (0.50 #14003, 0.45 #19538, 0.45 #17694), 0436yk (0.50 #13169, 0.45 #16860, 0.40 #22395) >> Best rule #18848 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 01jfsb; 0hfjk; >> query: (?x1014, 0cc5mcj) <- genre(?x7465, ?x1014), genre(?x1315, ?x1014), region(?x1315, ?x94), ?x94 = 09c7w0, actor(?x7465, ?x1250), film_format(?x1315, ?x6392) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #19765 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 01jfsb; 0hfjk; *> query: (?x1014, 02qr3k8) <- genre(?x7465, ?x1014), genre(?x1315, ?x1014), region(?x1315, ?x94), ?x94 = 09c7w0, actor(?x7465, ?x1250), film_format(?x1315, ?x6392) *> conf = 0.18 ranks of expected_values: 1247, 1759 EVAL 0jtdp genre! 05f67hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 29.000 0.727 http://example.org/film/film/genre EVAL 0jtdp genre! 02qr3k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 29.000 0.727 http://example.org/film/film/genre #1503-01w724 PRED entity: 01w724 PRED relation: group PRED expected values: 01qqwp9 => 100 concepts (40 used for prediction) PRED predicted values (max 10 best out of 91): 01v0sx2 (0.27 #5, 0.22 #324, 0.17 #648), 01qqwp9 (0.18 #20, 0.11 #339, 0.06 #553), 04sd0 (0.09 #98, 0.04 #741, 0.03 #1273), 07c0j (0.09 #4, 0.03 #323, 0.02 #537), 0134wr (0.09 #64, 0.03 #383, 0.02 #707), 0bk1p (0.09 #72, 0.02 #285, 0.02 #1141), 015srx (0.09 #40, 0.02 #253, 0.02 #359), 07m4c (0.09 #55, 0.02 #268, 0.01 #912), 0123r4 (0.06 #362, 0.05 #686, 0.04 #1112), 06mj4 (0.05 #382, 0.03 #706, 0.03 #1132) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 03dq9; >> query: (?x2765, 01v0sx2) <- group(?x2765, ?x2567), people(?x11563, ?x2765), award_nominee(?x1089, ?x2765) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #20 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 03dq9; *> query: (?x2765, 01qqwp9) <- group(?x2765, ?x2567), people(?x11563, ?x2765), award_nominee(?x1089, ?x2765) *> conf = 0.18 ranks of expected_values: 2 EVAL 01w724 group 01qqwp9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 40.000 0.273 http://example.org/music/group_member/membership./music/group_membership/group #1502-0192l PRED entity: 0192l PRED relation: role PRED expected values: 05r5c => 70 concepts (51 used for prediction) PRED predicted values (max 10 best out of 113): 0l14md (0.86 #3283, 0.83 #3034, 0.81 #4344), 01vj9c (0.83 #3041, 0.75 #2303, 0.71 #3290), 0342h (0.83 #1562, 0.81 #3764, 0.81 #2410), 018vs (0.83 #4009, 0.80 #2793, 0.80 #3512), 05148p4 (0.82 #2927, 0.79 #3513, 0.79 #4371), 01xqw (0.80 #3512, 0.79 #3513, 0.78 #2741), 05kms (0.80 #3512, 0.79 #3513, 0.71 #1897), 06ncr (0.80 #3512, 0.79 #3513, 0.70 #4698), 01c3q (0.79 #3513, 0.70 #4698, 0.63 #1559), 03qjg (0.78 #2719, 0.75 #2349, 0.72 #459) >> Best rule #3283 for best value: >> intensional similarity = 29 >> extensional distance = 12 >> proper extension: 03qlv7; >> query: (?x5990, 0l14md) <- role(?x8172, ?x5990), role(?x2798, ?x5990), role(?x2460, ?x5990), role(?x1267, ?x5990), role(?x227, ?x5990), ?x2460 = 01wy6, role(?x5990, ?x2157), role(?x5990, ?x1750), role(?x2764, ?x2157), role(?x1831, ?x2157), role(?x960, ?x2157), performance_role(?x7683, ?x8172), ?x2798 = 03qjg, ?x960 = 04q7r, role(?x5926, ?x8172), role(?x885, ?x8172), ?x7683 = 043c4j, role(?x11443, ?x2157), ?x5926 = 0cfdd, ?x885 = 0dwtp, ?x1750 = 02hnl, role(?x2157, ?x74), ?x1831 = 03t22m, role(?x565, ?x1267), role(?x433, ?x2764), instrumentalists(?x227, ?x3126), instrumentalists(?x227, ?x1826), ?x3126 = 0161c2, award_winner(?x1826, ?x2862) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #592 for first EXPECTED value: *> intensional similarity = 35 *> extensional distance = 2 *> proper extension: 013y1f; *> query: (?x5990, ?x2048) <- role(?x8172, ?x5990), role(?x3991, ?x5990), role(?x2460, ?x5990), role(?x2377, ?x5990), role(?x1969, ?x5990), role(?x1473, ?x5990), role(?x1432, ?x5990), role(?x614, ?x5990), role(?x228, ?x5990), ?x2460 = 01wy6, role(?x5990, ?x2157), ?x2157 = 011_6p, role(?x8172, ?x2048), role(?x8172, ?x716), ?x1473 = 0g2dz, role(?x7210, ?x8172), role(?x5676, ?x8172), role(?x4078, ?x8172), role(?x3967, ?x8172), role(?x2059, ?x8172), role(?x1663, ?x8172), role(?x1268, ?x8172), ?x716 = 018vs, ?x1969 = 04rzd, ?x228 = 0l14qv, ?x4078 = 011k_j, ?x1432 = 0395lw, ?x3991 = 05842k, ?x5676 = 0151b0, ?x3967 = 01p970, ?x1663 = 01w4dy, ?x1268 = 0bm02, family(?x1482, ?x2377), ?x2059 = 0dwr4, ?x614 = 0mkg *> conf = 0.72 ranks of expected_values: 24 EVAL 0192l role 05r5c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 70.000 51.000 0.857 http://example.org/music/performance_role/regular_performances./music/group_membership/role #1501-0133x7 PRED entity: 0133x7 PRED relation: artists! PRED expected values: 02vjzr => 154 concepts (97 used for prediction) PRED predicted values (max 10 best out of 268): 06by7 (0.56 #2218, 0.55 #6616, 0.55 #4420), 05bt6j (0.56 #2241, 0.50 #45, 0.42 #4757), 0gywn (0.35 #2569, 0.35 #687, 0.31 #2256), 0xhtw (0.35 #4729, 0.32 #5984, 0.29 #644), 06j6l (0.33 #15739, 0.31 #2559, 0.30 #7583), 025sc50 (0.32 #2561, 0.30 #15741, 0.29 #2248), 02vjzr (0.32 #2646, 0.27 #2333, 0.25 #137), 01lyv (0.32 #2544, 0.22 #11959, 0.21 #16038), 016clz (0.32 #5972, 0.31 #4403, 0.28 #8165), 0dl5d (0.30 #333, 0.17 #5987, 0.15 #8180) >> Best rule #2218 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 012x1l; >> query: (?x7112, 06by7) <- gender(?x7112, ?x514), artist(?x6672, ?x7112), award(?x7112, ?x1479) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #2646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 63 *> proper extension: 0197tq; 03f2_rc; 0152cw; 01vrt_c; 01qvgl; 01r9fv; 015882; 0136p1; 01wsl7c; 010hn; ... *> query: (?x7112, 02vjzr) <- profession(?x7112, ?x220), award(?x7112, ?x4796), artists(?x671, ?x7112), ?x4796 = 01c99j *> conf = 0.32 ranks of expected_values: 7 EVAL 0133x7 artists! 02vjzr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 154.000 97.000 0.556 http://example.org/music/genre/artists #1500-03h0byn PRED entity: 03h0byn PRED relation: language PRED expected values: 02h40lc => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.93 #594, 0.90 #1905, 0.90 #2505), 06nm1 (0.33 #11, 0.17 #129, 0.14 #188), 064_8sq (0.18 #910, 0.14 #614, 0.13 #1804), 04306rv (0.17 #893, 0.14 #182, 0.13 #1070), 0jzc (0.10 #908, 0.06 #434, 0.06 #1863), 02bjrlw (0.10 #1963, 0.09 #2024, 0.09 #1662), 06b_j (0.09 #911, 0.08 #496, 0.07 #1147), 03_9r (0.06 #898, 0.05 #3346, 0.05 #1075), 06mp7 (0.06 #370, 0.02 #727, 0.02 #786), 03k50 (0.06 #304, 0.02 #4059, 0.02 #660) >> Best rule #594 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 02vxq9m; 09xbpt; 04ddm4; 06z8s_; 0gj8t_b; 03qnvdl; 050xxm; 05qbckf; 0ct5zc; 0418wg; ... >> query: (?x11022, 02h40lc) <- film(?x1596, ?x11022), award_nominee(?x1596, ?x4248), ?x4248 = 01zg98, award_winner(?x1596, ?x1802) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03h0byn language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.932 http://example.org/film/film/language #1499-03bnb PRED entity: 03bnb PRED relation: company! PRED expected values: 060c4 => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 36): 0krdk (0.88 #2741, 0.84 #3078, 0.80 #1056), 060c4 (0.79 #4679, 0.70 #4806, 0.69 #4382), 09d6p2 (0.50 #646, 0.42 #520, 0.40 #814), 01kr6k (0.40 #150, 0.33 #528, 0.32 #1495), 01rk91 (0.33 #1, 0.20 #127, 0.15 #4170), 021q0l (0.33 #8, 0.15 #4170, 0.15 #4593), 02211by (0.28 #1389, 0.21 #1894, 0.20 #1642), 0142rn (0.25 #527, 0.17 #1578, 0.16 #1914), 02y6fz (0.20 #147, 0.18 #357, 0.17 #987), 02k13d (0.15 #4170, 0.15 #4593, 0.14 #180) >> Best rule #2741 for best value: >> intensional similarity = 6 >> extensional distance = 58 >> proper extension: 06pwq; 03mdt; 05gnf; 01hlwv; 055z7; >> query: (?x10808, 0krdk) <- category(?x10808, ?x134), company(?x1907, ?x10808), company(?x1907, ?x11652), company(?x1907, ?x10926), ?x10926 = 060ppp, ?x11652 = 0hmyfsv >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #4679 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 165 *> proper extension: 059j2; *> query: (?x10808, 060c4) <- company(?x265, ?x10808), basic_title(?x966, ?x265) *> conf = 0.79 ranks of expected_values: 2 EVAL 03bnb company! 060c4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 144.000 144.000 0.883 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #1498-024lff PRED entity: 024lff PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.87 #106, 0.86 #51, 0.85 #71), 07c52 (0.10 #13, 0.05 #38, 0.04 #18), 02nxhr (0.05 #12, 0.05 #122, 0.05 #117), 07z4p (0.03 #70, 0.03 #216, 0.03 #206) >> Best rule #106 for best value: >> intensional similarity = 4 >> extensional distance = 282 >> proper extension: 03_wm6; >> query: (?x3700, 029j_) <- film_crew_role(?x3700, ?x2154), film_crew_role(?x3700, ?x1284), ?x1284 = 0ch6mp2, ?x2154 = 01vx2h >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 024lff film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.870 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #1497-0ws7 PRED entity: 0ws7 PRED relation: school PRED expected values: 065y4w7 => 74 concepts (59 used for prediction) PRED predicted values (max 10 best out of 585): 065y4w7 (0.50 #2828, 0.50 #1327, 0.47 #4526), 05krk (0.50 #756, 0.36 #1698, 0.33 #4143), 07w0v (0.50 #577, 0.29 #8306, 0.26 #9259), 01jq0j (0.42 #2371, 0.31 #3502, 0.30 #5574), 07wlf (0.33 #411, 0.25 #786, 0.18 #1728), 02y9bj (0.33 #493, 0.25 #868, 0.15 #4517), 01n6r0 (0.33 #452, 0.25 #827, 0.15 #4517), 06fq2 (0.33 #322, 0.18 #9379, 0.16 #8426), 09f2j (0.33 #264, 0.16 #9321, 0.15 #4517), 07vyf (0.33 #252, 0.15 #4517, 0.15 #378) >> Best rule #2828 for best value: >> intensional similarity = 16 >> extensional distance = 12 >> proper extension: 01lpx8; 0ftccy; >> query: (?x7078, 065y4w7) <- position(?x7078, ?x2573), position_s(?x7078, ?x180), position(?x7643, ?x2573), position(?x6976, ?x2573), position(?x5773, ?x2573), position(?x4856, ?x2573), position(?x4723, ?x2573), ?x7643 = 02c_4, colors(?x7078, ?x4557), ?x4557 = 019sc, ?x4856 = 0289q, ?x4723 = 043tz8m, position_s(?x3114, ?x2573), team(?x2573, ?x1239), ?x6976 = 04vn5, teams(?x3125, ?x5773) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ws7 school 065y4w7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 59.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #1496-05zl0 PRED entity: 05zl0 PRED relation: major_field_of_study PRED expected values: 05qjt 01mkq 0fdys => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 105): 01mkq (0.69 #1764, 0.67 #1043, 0.61 #425), 0fdys (0.57 #439, 0.47 #233, 0.47 #130), 05qjt (0.53 #213, 0.53 #110, 0.46 #1037), 01lj9 (0.43 #440, 0.41 #234, 0.41 #131), 04sh3 (0.39 #471, 0.29 #265, 0.29 #162), 0g26h (0.35 #3948, 0.35 #4466, 0.33 #4051), 04g7x (0.35 #262, 0.35 #159, 0.24 #777), 01r4k (0.35 #272, 0.35 #169, 0.24 #787), 03qsdpk (0.35 #447, 0.22 #1477, 0.22 #1683), 02_7t (0.30 #461, 0.29 #1079, 0.27 #1697) >> Best rule #1764 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 01prf3; >> query: (?x6056, 01mkq) <- organization(?x6056, ?x5487), organization(?x3439, ?x5487), citytown(?x3439, ?x3007) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 05zl0 major_field_of_study 0fdys CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.688 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05zl0 major_field_of_study 01mkq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.688 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05zl0 major_field_of_study 05qjt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 118.000 0.688 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1495-09sh8k PRED entity: 09sh8k PRED relation: nominated_for! PRED expected values: 03c7tr1 => 71 concepts (69 used for prediction) PRED predicted values (max 10 best out of 208): 02r22gf (0.33 #269, 0.20 #509, 0.20 #29), 05ztjjw (0.33 #250, 0.20 #490, 0.20 #10), 0p9sw (0.33 #261, 0.20 #21, 0.19 #981), 0k611 (0.33 #315, 0.20 #75, 0.19 #1035), 0gs9p (0.33 #306, 0.20 #66, 0.18 #1746), 0gr0m (0.33 #301, 0.20 #61, 0.16 #1021), 02qyntr (0.33 #422, 0.20 #182, 0.12 #6904), 0gq9h (0.26 #1024, 0.21 #6786, 0.20 #544), 0gq_v (0.25 #980, 0.20 #20, 0.17 #6742), 05zr6wv (0.22 #14408, 0.20 #12965, 0.20 #16) >> Best rule #269 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0qm8b; >> query: (?x136, 02r22gf) <- film(?x9780, ?x136), production_companies(?x136, ?x574), ?x9780 = 023zsh, genre(?x136, ?x225) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0dr_4; 034r25; *> query: (?x136, 03c7tr1) <- film(?x9780, ?x136), production_companies(?x136, ?x574), language(?x136, ?x2164), ?x9780 = 023zsh *> conf = 0.20 ranks of expected_values: 28 EVAL 09sh8k nominated_for! 03c7tr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 71.000 69.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1494-027pfg PRED entity: 027pfg PRED relation: cinematography PRED expected values: 03cx282 => 75 concepts (47 used for prediction) PRED predicted values (max 10 best out of 42): 0jsw9l (0.12 #51, 0.01 #556, 0.01 #1251), 079hvk (0.12 #5, 0.01 #2112), 06r_by (0.06 #149, 0.06 #86, 0.03 #465), 04qvl7 (0.06 #127, 0.05 #506, 0.04 #317), 027t8fw (0.06 #94, 0.03 #221, 0.03 #284), 0cqh57 (0.06 #98, 0.02 #603, 0.02 #666), 03rqww (0.06 #105, 0.02 #232, 0.02 #295), 02vx4c2 (0.04 #476, 0.03 #350, 0.03 #791), 08mhyd (0.04 #600, 0.03 #663, 0.03 #726), 07rd7 (0.03 #2171, 0.03 #2040, 0.03 #2105) >> Best rule #51 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 016017; >> query: (?x6932, 0jsw9l) <- film_release_distribution_medium(?x6932, ?x81), film(?x71, ?x6932), ?x71 = 0q9kd, film(?x541, ?x6932) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #142 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 46 *> proper extension: 01jzyf; 0kvgtf; 016y_f; 09qycb; *> query: (?x6932, 03cx282) <- nominated_for(?x3889, ?x6932), films(?x9203, ?x6932), award(?x84, ?x3889), edited_by(?x6932, ?x707) *> conf = 0.02 ranks of expected_values: 22 EVAL 027pfg cinematography 03cx282 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 75.000 47.000 0.125 http://example.org/film/film/cinematography #1493-07cjqy PRED entity: 07cjqy PRED relation: film PRED expected values: 047svrl => 86 concepts (66 used for prediction) PRED predicted values (max 10 best out of 963): 01jrbb (0.25 #468, 0.03 #90784, 0.03 #94345), 02xbyr (0.25 #800), 0bvn25 (0.21 #1830, 0.05 #3610, 0.03 #44551), 02825kb (0.21 #3001, 0.01 #45722, 0.01 #52842), 04gv3db (0.16 #2528, 0.03 #7868, 0.03 #11428), 07p62k (0.16 #2130, 0.02 #5690, 0.02 #7470), 06fpsx (0.16 #3108, 0.02 #38709, 0.01 #44049), 013q07 (0.12 #5693, 0.10 #7473, 0.09 #3913), 02qydsh (0.11 #3269, 0.05 #5049, 0.04 #21069), 0888c3 (0.11 #3185, 0.03 #8525, 0.03 #12085) >> Best rule #468 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01gbbz; >> query: (?x3536, 01jrbb) <- participant(?x4741, ?x3536), location(?x3536, ?x191), ?x4741 = 01s21dg >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #2206 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 17 *> proper extension: 01vsnff; 0136pk; 062ftr; 07y8l9; 0h27vc; 0cmt6q; 04zkj5; 07m77x; 01vsn38; *> query: (?x3536, 047svrl) <- award_nominee(?x3536, ?x4631), film(?x3536, ?x6480), ?x6480 = 02825cv *> conf = 0.05 ranks of expected_values: 44 EVAL 07cjqy film 047svrl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 86.000 66.000 0.250 http://example.org/film/actor/film./film/performance/film #1492-08nvyr PRED entity: 08nvyr PRED relation: production_companies PRED expected values: 04f525m => 100 concepts (50 used for prediction) PRED predicted values (max 10 best out of 68): 05qd_ (0.34 #3008, 0.32 #2671, 0.21 #919), 0fqy4p (0.34 #3008, 0.32 #2671, 0.21 #919), 086k8 (0.23 #85, 0.17 #504, 0.14 #254), 0kk9v (0.20 #35, 0.03 #954, 0.03 #1037), 02j_j0 (0.18 #1468, 0.05 #717, 0.05 #384), 03sb38 (0.17 #1475, 0.02 #391, 0.02 #2391), 01gb54 (0.15 #121, 0.12 #205, 0.10 #624), 02slt7 (0.13 #1450, 0.08 #113, 0.06 #197), 02jd_7 (0.12 #237, 0.05 #406, 0.04 #1155), 016tt2 (0.12 #923, 0.12 #1006, 0.12 #1089) >> Best rule #3008 for best value: >> intensional similarity = 4 >> extensional distance = 355 >> proper extension: 02d413; 083shs; 02vxq9m; 0b2v79; 01gc7; 011yrp; 0gzy02; 05p1tzf; 01sxly; 011yph; ... >> query: (?x4541, ?x902) <- honored_for(?x5592, ?x4541), award_winner(?x4541, ?x3281), film(?x902, ?x4541), award_winner(?x5592, ?x361) >> conf = 0.34 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 08nvyr production_companies 04f525m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 50.000 0.338 http://example.org/film/film/production_companies #1491-033tf_ PRED entity: 033tf_ PRED relation: geographic_distribution PRED expected values: 09c7w0 => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 68): 09c7w0 (0.50 #157, 0.50 #1, 0.44 #627), 0345h (0.31 #392, 0.29 #234, 0.21 #1093), 03rjj (0.29 #234, 0.20 #156, 0.16 #1877), 03rt9 (0.21 #1093, 0.19 #1094, 0.19 #1014), 06bnz (0.14 #262, 0.12 #341, 0.09 #886), 07t21 (0.14 #260, 0.12 #339, 0.06 #884), 0jhd (0.14 #304, 0.12 #383, 0.03 #928), 01c4pv (0.14 #297, 0.12 #376, 0.03 #921), 07t_x (0.14 #291, 0.12 #370, 0.03 #915), 07dvs (0.14 #286, 0.12 #365, 0.03 #910) >> Best rule #157 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 07hwkr; 07bch9; >> query: (?x1446, 09c7w0) <- people(?x1446, ?x9000), people(?x1446, ?x8793), people(?x1446, ?x4466), people(?x1446, ?x3343), nationality(?x8793, ?x94), ?x9000 = 0k9j_, award_winner(?x2349, ?x4466), participant(?x1410, ?x8793), award_winner(?x4466, ?x163), film(?x3343, ?x6114), award_winner(?x3343, ?x849) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033tf_ geographic_distribution 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.500 http://example.org/people/ethnicity/geographic_distribution #1490-02s4l6 PRED entity: 02s4l6 PRED relation: film! PRED expected values: 0k269 => 92 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1161): 01hrqc (0.50 #2083, 0.42 #2082, 0.23 #10412), 02mxbd (0.44 #52056, 0.44 #58306, 0.44 #37483), 022wxh (0.44 #52056, 0.44 #58306, 0.44 #37483), 0d500h (0.44 #52056, 0.44 #58306, 0.44 #37483), 05hj_k (0.42 #2082, 0.18 #10411, 0.11 #54139), 01q_ph (0.23 #57, 0.03 #20880, 0.03 #31293), 012d40 (0.23 #16, 0.02 #20839, 0.02 #10431), 0f7hc (0.15 #832, 0.04 #9161, 0.01 #7080), 06q8hf (0.11 #95787, 0.10 #87459, 0.09 #60389), 01swck (0.09 #4967, 0.05 #17460, 0.04 #13297) >> Best rule #2083 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0hz6mv2; >> query: (?x2287, ?x7571) <- language(?x2287, ?x254), executive_produced_by(?x2287, ?x7571), artists(?x302, ?x7571) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2695 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 0qm9n; 0cqnss; *> query: (?x2287, 0k269) <- language(?x2287, ?x254), award_winner(?x2287, ?x5613), titles(?x307, ?x2287), ?x307 = 04t36 *> conf = 0.02 ranks of expected_values: 323 EVAL 02s4l6 film! 0k269 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 92.000 72.000 0.500 http://example.org/film/actor/film./film/performance/film #1489-027kp3 PRED entity: 027kp3 PRED relation: contains! PRED expected values: 09c7w0 => 116 concepts (90 used for prediction) PRED predicted values (max 10 best out of 250): 09c7w0 (0.91 #67896, 0.85 #68789, 0.82 #69682), 03rjj (0.44 #53596, 0.11 #61649, 0.08 #66117), 04jpl (0.38 #10741, 0.25 #19675, 0.19 #29502), 02jx1 (0.34 #64404, 0.26 #10805, 0.20 #71550), 07ssc (0.30 #66138, 0.30 #67031, 0.23 #64350), 01n7q (0.27 #61715, 0.26 #63502, 0.25 #64395), 05tbn (0.26 #29702, 0.17 #2901, 0.11 #63647), 0d060g (0.25 #1799, 0.13 #61652, 0.13 #63439), 05kj_ (0.17 #2720, 0.04 #63466, 0.02 #71505), 030qb3t (0.13 #10819, 0.09 #19753, 0.06 #29580) >> Best rule #67896 for best value: >> intensional similarity = 4 >> extensional distance = 975 >> proper extension: 018mlg; >> query: (?x4794, 09c7w0) <- contains(?x739, ?x4794), place_of_death(?x340, ?x739), place_of_birth(?x65, ?x739), place_founded(?x2549, ?x739) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027kp3 contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 90.000 0.913 http://example.org/location/location/contains #1488-014pg1 PRED entity: 014pg1 PRED relation: category PRED expected values: 08mbj5d => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.87 #15, 0.86 #36, 0.85 #39) >> Best rule #15 for best value: >> intensional similarity = 9 >> extensional distance = 45 >> proper extension: 04cr6qv; 03xnq9_; 02twdq; 01vs8ng; >> query: (?x8058, 08mbj5d) <- artists(?x10290, ?x8058), artists(?x3243, ?x8058), artists(?x10290, ?x3890), artists(?x10290, ?x3321), artists(?x10290, ?x2799), ?x3321 = 03bnv, ?x2799 = 01vsl3_, ?x3243 = 0y3_8, ?x3890 = 01gg59 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014pg1 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.872 http://example.org/common/topic/webpage./common/webpage/category #1487-04180vy PRED entity: 04180vy PRED relation: music PRED expected values: 02jxmr => 85 concepts (50 used for prediction) PRED predicted values (max 10 best out of 82): 07v4dm (0.25 #819, 0.17 #1657, 0.08 #3331), 06fxnf (0.20 #1325, 0.20 #1115, 0.17 #278), 02fgpf (0.20 #30, 0.17 #448, 0.10 #1286), 03c_8t (0.17 #418, 0.08 #1674, 0.07 #2092), 01hw6wq (0.17 #247, 0.07 #1921, 0.07 #2130), 0jn5l (0.12 #933, 0.10 #1352, 0.10 #1142), 01kx_81 (0.12 #856, 0.10 #1275, 0.10 #1065), 03h610 (0.12 #704, 0.08 #1542, 0.07 #1960), 0150t6 (0.08 #2975, 0.08 #1511, 0.08 #3813), 01tc9r (0.08 #2994, 0.08 #1530, 0.06 #4042) >> Best rule #819 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 047p7fr; 05m_jsg; 06_x996; 05zpghd; >> query: (?x11686, 07v4dm) <- genre(?x11686, ?x239), ?x239 = 06cvj, country(?x11686, ?x94), film_crew_role(?x11686, ?x2472), film_crew_role(?x11686, ?x468), film(?x71, ?x11686), ?x468 = 02r96rf, ?x2472 = 01xy5l_ >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3841 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 24 *> proper extension: 014l6_; 011x_4; 058kh7; *> query: (?x11686, 02jxmr) <- genre(?x11686, ?x239), ?x239 = 06cvj, country(?x11686, ?x94), featured_film_locations(?x11686, ?x739), music(?x11686, ?x6907), film(?x71, ?x11686), film(?x7980, ?x11686) *> conf = 0.08 ranks of expected_values: 13 EVAL 04180vy music 02jxmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 85.000 50.000 0.250 http://example.org/film/film/music #1486-02581c PRED entity: 02581c PRED relation: ceremony PRED expected values: 01bx35 019bk0 0jzphpx 01mh_q => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 128): 01mh_q (0.87 #462, 0.86 #334, 0.82 #719), 019bk0 (0.86 #269, 0.84 #397, 0.82 #654), 01bx35 (0.86 #388, 0.85 #260, 0.82 #645), 0jzphpx (0.74 #289, 0.74 #417, 0.70 #674), 092868 (0.52 #513, 0.21 #3971, 0.02 #2433), 08pc1x (0.52 #513, 0.21 #3971, 0.02 #2432), 0gx1673 (0.51 #621, 0.50 #749, 0.49 #877), 05c1t6z (0.18 #1293, 0.18 #1549, 0.14 #1421), 02q690_ (0.17 #1594, 0.17 #1338, 0.13 #1722), 0gvstc3 (0.16 #1565, 0.16 #1309, 0.12 #1437) >> Best rule #462 for best value: >> intensional similarity = 6 >> extensional distance = 67 >> proper extension: 02flpc; 02flqd; 03nl5k; >> query: (?x2324, 01mh_q) <- award(?x5172, ?x2324), ceremony(?x2324, ?x5766), ceremony(?x2324, ?x3121), ?x3121 = 09n4nb, ?x5766 = 013b2h, award_winner(?x725, ?x5172) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 02581c ceremony 01mh_q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.870 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02581c ceremony 0jzphpx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.870 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02581c ceremony 019bk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.870 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 02581c ceremony 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.870 http://example.org/award/award_category/winners./award/award_honor/ceremony #1485-058vp PRED entity: 058vp PRED relation: influenced_by! PRED expected values: 019z7q => 116 concepts (47 used for prediction) PRED predicted values (max 10 best out of 469): 0dzkq (0.45 #1124, 0.25 #1624, 0.17 #2126), 0ff3y (0.40 #993, 0.25 #2497, 0.15 #6011), 019z7q (0.40 #526, 0.17 #3531, 0.14 #2005), 0683n (0.33 #2333, 0.33 #1831, 0.20 #829), 045bg (0.33 #1539, 0.27 #1039, 0.25 #2041), 04hcw (0.33 #1784, 0.27 #1284, 0.19 #4289), 041jlr (0.33 #2355, 0.20 #851, 0.15 #6011), 034bs (0.33 #1653, 0.14 #2005, 0.12 #1503), 0ct9_ (0.27 #1336, 0.25 #1836, 0.17 #13028), 01dvtx (0.27 #1149, 0.25 #1649, 0.12 #1503) >> Best rule #1124 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 05qmj; >> query: (?x5612, 0dzkq) <- influenced_by(?x1737, ?x5612), gender(?x5612, ?x231), ?x1737 = 01d494, ?x231 = 05zppz >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #526 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 084w8; 0683n; 07dnx; *> query: (?x5612, 019z7q) <- influenced_by(?x1737, ?x5612), influenced_by(?x5612, ?x5434), influenced_by(?x5612, ?x4072), profession(?x5612, ?x353), ?x4072 = 02lt8, ?x5434 = 01tz6vs *> conf = 0.40 ranks of expected_values: 3 EVAL 058vp influenced_by! 019z7q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 47.000 0.455 http://example.org/influence/influence_node/influenced_by #1484-022_lg PRED entity: 022_lg PRED relation: nationality PRED expected values: 09c7w0 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 57): 09c7w0 (0.84 #9534, 0.78 #501, 0.77 #3510), 07ssc (0.55 #1502, 0.47 #1301, 0.38 #4311), 02jx1 (0.55 #1502, 0.47 #1301, 0.38 #4311), 01w65s (0.33 #11337), 03v0t (0.33 #11337), 0h7x (0.26 #4110, 0.24 #6117, 0.21 #5012), 0d060g (0.12 #107, 0.06 #1107, 0.06 #907), 0345h (0.07 #4942, 0.07 #231, 0.06 #5546), 05bcl (0.07 #260, 0.03 #960, 0.02 #1060), 03rk0 (0.07 #6565, 0.07 #1548, 0.06 #5862) >> Best rule #9534 for best value: >> intensional similarity = 2 >> extensional distance = 1519 >> proper extension: 0bhtzw; >> query: (?x1431, ?x94) <- place_of_birth(?x1431, ?x1860), country(?x1860, ?x94) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 022_lg nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.837 http://example.org/people/person/nationality #1483-0d075m PRED entity: 0d075m PRED relation: politician PRED expected values: 021sv1 02mjmr 06bss => 165 concepts (64 used for prediction) PRED predicted values (max 10 best out of 100): 0bwh6 (0.40 #553, 0.33 #5, 0.29 #895), 0835q (0.33 #132, 0.33 #64, 0.25 #405), 014vk4 (0.33 #66, 0.25 #407, 0.25 #338), 03d9v8 (0.33 #42, 0.25 #383, 0.25 #314), 09b6zr (0.33 #18, 0.25 #359, 0.25 #290), 0gzh (0.33 #68, 0.25 #409, 0.25 #340), 01s7z0 (0.33 #67, 0.25 #408, 0.25 #339), 0444x (0.33 #63, 0.25 #404, 0.25 #335), 07hyk (0.33 #60, 0.25 #401, 0.25 #332), 0b22w (0.33 #56, 0.25 #397, 0.25 #328) >> Best rule #553 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 07w42; >> query: (?x8714, 0bwh6) <- politician(?x8714, ?x3445), politician(?x8714, ?x1159), basic_title(?x3445, ?x5402), award_winner(?x6487, ?x3445), people(?x5741, ?x1159), profession(?x3445, ?x3342) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #479 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0g8rj; 05qgd9; *> query: (?x8714, ?x236) <- citytown(?x8714, ?x108), organizations_founded(?x5254, ?x8714), ?x5254 = 07cbs, category(?x108, ?x134), location(?x236, ?x108) *> conf = 0.09 ranks of expected_values: 84 EVAL 0d075m politician 06bss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 64.000 0.400 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician EVAL 0d075m politician 02mjmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 165.000 64.000 0.400 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician EVAL 0d075m politician 021sv1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 165.000 64.000 0.400 http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician #1482-07wtc PRED entity: 07wtc PRED relation: student PRED expected values: 017lqp => 88 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1095): 03_nq (0.29 #1564, 0.11 #3658, 0.08 #7844), 016gkf (0.27 #2094, 0.15 #35592, 0.12 #43967), 0kh6b (0.14 #615, 0.11 #2709, 0.10 #4802), 01tdnyh (0.14 #889, 0.11 #2983, 0.10 #5076), 0l6qt (0.14 #16, 0.11 #2110, 0.10 #4203), 0n00 (0.14 #546, 0.11 #2640, 0.10 #4733), 041c4 (0.14 #867, 0.11 #2961, 0.10 #5054), 01lwx (0.14 #1982, 0.11 #4076, 0.10 #6169), 02m7r (0.14 #365, 0.11 #2459, 0.10 #4552), 01385g (0.14 #2036, 0.11 #4130, 0.10 #6223) >> Best rule #1564 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01mpwj; >> query: (?x11740, 03_nq) <- contains(?x455, ?x11740), company(?x5370, ?x11740), countries_within(?x455, ?x87) >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07wtc student 017lqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 36.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #1481-01vw8mh PRED entity: 01vw8mh PRED relation: artists! PRED expected values: 0glt670 => 89 concepts (83 used for prediction) PRED predicted values (max 10 best out of 197): 0glt670 (0.64 #348, 0.64 #41, 0.60 #655), 06by7 (0.62 #8927, 0.59 #9234, 0.50 #1249), 025sc50 (0.57 #357, 0.55 #50, 0.53 #664), 0gywn (0.47 #1900, 0.28 #2150, 0.24 #3129), 0ggx5q (0.43 #386, 0.40 #693, 0.36 #79), 02lnbg (0.43 #366, 0.40 #673, 0.36 #59), 05bt6j (0.36 #351, 0.33 #658, 0.32 #8335), 0y3_8 (0.29 #355, 0.27 #48, 0.27 #662), 02x8m (0.28 #2150, 0.26 #1860, 0.21 #5835), 026z9 (0.28 #2150, 0.21 #5835, 0.10 #1920) >> Best rule #348 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 02zmh5; 026yqrr; 01wwnh2; >> query: (?x4851, 0glt670) <- award_nominee(?x4851, ?x6573), ?x6573 = 067nsm, artists(?x283, ?x4851) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vw8mh artists! 0glt670 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 83.000 0.643 http://example.org/music/genre/artists #1480-03rk0 PRED entity: 03rk0 PRED relation: geographic_distribution! PRED expected values: 0d29z => 243 concepts (243 used for prediction) PRED predicted values (max 10 best out of 38): 0d29z (0.59 #629, 0.58 #591, 0.53 #287), 071x0k (0.48 #611, 0.40 #269, 0.39 #421), 01xhh5 (0.20 #286, 0.19 #628, 0.17 #438), 0g6ff (0.18 #314, 0.13 #2708, 0.12 #2138), 0g48m4 (0.13 #2699, 0.12 #3155, 0.09 #4220), 06gbnc (0.11 #204, 0.11 #166, 0.08 #242), 013b6_ (0.11 #178, 0.09 #330, 0.09 #1014), 03ts0c (0.11 #203, 0.08 #241, 0.07 #279), 012f86 (0.09 #335, 0.09 #943, 0.08 #525), 06mvq (0.09 #968, 0.08 #1196, 0.07 #1348) >> Best rule #629 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 07t_x; >> query: (?x2146, 0d29z) <- exported_to(?x2146, ?x3352), contains(?x2146, ?x1391), country(?x11181, ?x2146) >> conf = 0.59 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rk0 geographic_distribution! 0d29z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 243.000 243.000 0.593 http://example.org/people/ethnicity/geographic_distribution #1479-06fvc PRED entity: 06fvc PRED relation: colors! PRED expected values: 01r3y2 02t4yc 02s8qk 04s934 01f2xy 0gy3w 0ylzs 01g4yw 02jx_v => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 726): 01jszm (0.60 #3453, 0.33 #1791, 0.33 #551), 07lx1s (0.50 #4172, 0.40 #5003, 0.40 #3343), 01p896 (0.50 #3209, 0.40 #3622, 0.33 #4865), 027b43 (0.50 #3275, 0.40 #3688, 0.33 #4931), 06rkfs (0.50 #2795, 0.38 #4453, 0.33 #4867), 01v3k2 (0.50 #3178, 0.33 #4834, 0.33 #1929), 0lyjf (0.50 #3026, 0.33 #4682, 0.33 #1777), 0yls9 (0.50 #2251, 0.33 #1838, 0.33 #1012), 02qw_v (0.50 #3229, 0.33 #4885, 0.33 #1980), 0288zy (0.50 #2501, 0.33 #1668, 0.33 #1255) >> Best rule #3453 for best value: >> intensional similarity = 30 >> extensional distance = 3 >> proper extension: 03vtbc; >> query: (?x1101, 01jszm) <- colors(?x13785, ?x1101), colors(?x12042, ?x1101), colors(?x9835, ?x1101), colors(?x7312, ?x1101), colors(?x1823, ?x1101), colors(?x260, ?x1101), colors(?x9344, ?x1101), colors(?x8879, ?x1101), colors(?x6038, ?x1101), colors(?x5306, ?x1101), season(?x12042, ?x701), ?x9835 = 02hqt6, draft(?x12042, ?x1633), major_field_of_study(?x5306, ?x11820), position(?x7312, ?x180), school(?x12042, ?x735), team(?x2010, ?x260), position_s(?x7312, ?x1517), ?x11820 = 0w7s, draft(?x1823, ?x1161), ?x2010 = 02lyr4, season(?x260, ?x3431), position(?x7312, ?x935), position(?x13785, ?x4570), school(?x1823, ?x4296), ?x4296 = 07vyf, student(?x9344, ?x5597), school_type(?x8879, ?x1044), contains(?x252, ?x8879), citytown(?x6038, ?x6196) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3716 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 3 *> proper extension: 03vtbc; *> query: (?x1101, 02jx_v) <- colors(?x13785, ?x1101), colors(?x12042, ?x1101), colors(?x9835, ?x1101), colors(?x7312, ?x1101), colors(?x1823, ?x1101), colors(?x260, ?x1101), colors(?x9344, ?x1101), colors(?x8879, ?x1101), colors(?x6038, ?x1101), colors(?x5306, ?x1101), season(?x12042, ?x701), ?x9835 = 02hqt6, draft(?x12042, ?x1633), major_field_of_study(?x5306, ?x11820), position(?x7312, ?x180), school(?x12042, ?x735), team(?x2010, ?x260), position_s(?x7312, ?x1517), ?x11820 = 0w7s, draft(?x1823, ?x1161), ?x2010 = 02lyr4, season(?x260, ?x3431), position(?x7312, ?x935), position(?x13785, ?x4570), school(?x1823, ?x4296), ?x4296 = 07vyf, student(?x9344, ?x5597), school_type(?x8879, ?x1044), contains(?x252, ?x8879), citytown(?x6038, ?x6196) *> conf = 0.40 ranks of expected_values: 43, 144, 182, 185, 205, 207, 232, 252, 681 EVAL 06fvc colors! 02jx_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 06fvc colors! 01g4yw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 06fvc colors! 0ylzs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 06fvc colors! 0gy3w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 06fvc colors! 01f2xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 06fvc colors! 04s934 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 06fvc colors! 02s8qk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 06fvc colors! 02t4yc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 21.000 21.000 0.600 http://example.org/education/educational_institution/colors EVAL 06fvc colors! 01r3y2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 21.000 21.000 0.600 http://example.org/education/educational_institution/colors #1478-07kc_ PRED entity: 07kc_ PRED relation: role PRED expected values: 01v1d8 => 62 concepts (31 used for prediction) PRED predicted values (max 10 best out of 110): 013y1f (0.85 #2808, 0.84 #2115, 0.84 #2039), 01wy6 (0.83 #1659, 0.72 #2333, 0.71 #1326), 018vs (0.82 #878, 0.82 #1771, 0.81 #1661), 01v1d8 (0.82 #878, 0.82 #1771, 0.81 #1661), 042v_gx (0.81 #1446, 0.75 #773, 0.73 #439), 0mkg (0.78 #2347, 0.77 #1226, 0.75 #890), 04rzd (0.78 #1935, 0.77 #1776, 0.73 #1552), 07y_7 (0.77 #1776, 0.75 #1444, 0.74 #2452), 018j2 (0.77 #1776, 0.75 #810, 0.73 #1552), 0gghm (0.77 #1776, 0.70 #111, 0.69 #442) >> Best rule #2808 for best value: >> intensional similarity = 19 >> extensional distance = 24 >> proper extension: 0680x0; >> query: (?x1147, 013y1f) <- role(?x1147, ?x2205), role(?x1147, ?x1437), role(?x1147, ?x227), ?x227 = 0342h, role(?x2242, ?x1147), instrumentalists(?x716, ?x2242), profession(?x2242, ?x2659), profession(?x2242, ?x1183), ?x1183 = 09jwl, ?x1437 = 01vdm0, artist(?x2241, ?x2242), role(?x1147, ?x75), profession(?x5301, ?x2659), profession(?x5208, ?x2659), ?x5208 = 01s7qqw, group(?x2205, ?x4783), artist(?x2241, ?x2906), ?x5301 = 01vswwx, ?x2906 = 0249kn >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #878 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 6 *> proper extension: 04q7r; *> query: (?x1147, ?x716) <- role(?x1147, ?x4311), role(?x1147, ?x3215), role(?x1147, ?x1437), role(?x1147, ?x227), ?x227 = 0342h, instrumentalists(?x1147, ?x2242), role(?x716, ?x1147), role(?x3214, ?x1147), role(?x2206, ?x1147), role(?x432, ?x1147), ?x1437 = 01vdm0, role(?x4425, ?x3215), role(?x2725, ?x3215), role(?x2059, ?x3215), ?x4425 = 0979zs, ?x2059 = 0dwr4, ?x4311 = 01xqw, role(?x1472, ?x2206), ?x2725 = 0l1589, role(?x432, ?x736), ?x3214 = 02snj9, role(?x211, ?x432), instrumentalists(?x432, ?x133) *> conf = 0.82 ranks of expected_values: 4 EVAL 07kc_ role 01v1d8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 62.000 31.000 0.846 http://example.org/music/performance_role/track_performances./music/track_contribution/role #1477-05dbf PRED entity: 05dbf PRED relation: award PRED expected values: 05pcn59 0641kkh => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 284): 05p09zm (0.71 #14115, 0.71 #34118, 0.70 #35688), 027b9k6 (0.71 #14115, 0.71 #34118, 0.70 #35688), 02g2yr (0.71 #14115, 0.71 #34118, 0.70 #35688), 05pcn59 (0.38 #75, 0.32 #2427, 0.31 #1251), 01by1l (0.37 #889, 0.20 #16966, 0.13 #9122), 02x17c2 (0.35 #992, 0.13 #15685, 0.07 #17069), 01bgqh (0.33 #823, 0.18 #16900, 0.12 #9056), 0gqz2 (0.33 #858, 0.08 #16935, 0.06 #74), 03c7tr1 (0.31 #446, 0.28 #1230, 0.27 #1622), 03qbh5 (0.30 #978, 0.13 #17055, 0.11 #9211) >> Best rule #14115 for best value: >> intensional similarity = 3 >> extensional distance = 405 >> proper extension: 08wq0g; 0fqyzz; 02cm2m; 0cj36c; 04ns3gy; >> query: (?x2275, ?x749) <- award_nominee(?x2101, ?x2275), award_winner(?x749, ?x2275), influenced_by(?x2101, ?x11357) >> conf = 0.71 => this is the best rule for 3 predicted values *> Best rule #75 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0cgfb; *> query: (?x2275, 05pcn59) <- film(?x2275, ?x308), participant(?x286, ?x2275), ?x286 = 014zcr *> conf = 0.38 ranks of expected_values: 4, 95 EVAL 05dbf award 0641kkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 132.000 132.000 0.710 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 05dbf award 05pcn59 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 132.000 0.710 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1476-03gn1x PRED entity: 03gn1x PRED relation: major_field_of_study PRED expected values: 04rlf => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 104): 04rjg (0.58 #388, 0.37 #1003, 0.36 #1126), 02j62 (0.50 #399, 0.48 #1014, 0.47 #1137), 06ms6 (0.49 #1369, 0.28 #1123, 0.27 #385), 01mkq (0.46 #998, 0.46 #383, 0.45 #1121), 02ky346 (0.46 #384, 0.18 #1122, 0.13 #999), 0g26h (0.42 #411, 0.33 #42, 0.25 #1026), 03g3w (0.39 #1133, 0.39 #764, 0.38 #395), 01tbp (0.38 #429, 0.28 #1044, 0.25 #60), 02lp1 (0.37 #994, 0.36 #1117, 0.35 #379), 062z7 (0.35 #1134, 0.34 #1011, 0.34 #1503) >> Best rule #388 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 01pl14; 0h6rm; >> query: (?x8592, 04rjg) <- student(?x8592, ?x12147), major_field_of_study(?x8592, ?x947), ?x947 = 036hv, location(?x12147, ?x4253) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #439 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 01pl14; 0h6rm; *> query: (?x8592, 04rlf) <- student(?x8592, ?x12147), major_field_of_study(?x8592, ?x947), ?x947 = 036hv, location(?x12147, ?x4253) *> conf = 0.19 ranks of expected_values: 33 EVAL 03gn1x major_field_of_study 04rlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 103.000 103.000 0.577 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1475-0dgrmp PRED entity: 0dgrmp PRED relation: position! PRED expected values: 02b0y3 02b14q 09kzxt => 15 concepts (15 used for prediction) PRED predicted values (max 10 best out of 347): 01xn7x1 (0.84 #1166, 0.81 #465, 0.81 #461), 01kj5h (0.84 #1166, 0.81 #465, 0.81 #461), 039_ym (0.84 #1166, 0.81 #465, 0.81 #461), 01tqfs (0.84 #1166, 0.81 #465, 0.81 #461), 03_3z4 (0.84 #1166, 0.81 #465, 0.81 #461), 032jlh (0.84 #1166, 0.81 #465, 0.81 #461), 044l47 (0.84 #1166, 0.81 #465, 0.81 #461), 0303jw (0.84 #1166, 0.81 #465, 0.81 #461), 03y_f8 (0.84 #1166, 0.81 #465, 0.81 #461), 03zmc7 (0.84 #1166, 0.81 #465, 0.81 #461) >> Best rule #1166 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 02qvgy; >> query: (?x203, ?x3436) <- team(?x203, ?x13233), team(?x203, ?x4511), team(?x203, ?x3436), position(?x470, ?x203), position(?x993, ?x203), team(?x1142, ?x3436), team(?x208, ?x4511), teams(?x4510, ?x4511), colors(?x4511, ?x663), sport(?x13233, ?x471) >> conf = 0.84 => this is the best rule for 14 predicted values *> Best rule #465 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 1 *> proper extension: 02nzb8; *> query: (?x203, ?x993) <- team(?x203, ?x13041), team(?x203, ?x11339), team(?x203, ?x11195), team(?x203, ?x10725), team(?x203, ?x8826), team(?x203, ?x8511), team(?x203, ?x8106), team(?x203, ?x3804), ?x8106 = 02rxrh, ?x8826 = 03x6w8, ?x3804 = 02b1cn, ?x8511 = 03zrhb, position(?x9543, ?x203), position(?x12043, ?x203), position(?x993, ?x203), ?x12043 = 03jb2n, ?x11195 = 0kwv2, ?x10725 = 03l7tr, ?x11339 = 042rlf, ?x13041 = 0kqbh, ?x9543 = 07s8qm7 *> conf = 0.81 ranks of expected_values: 79, 106, 127 EVAL 0dgrmp position! 09kzxt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 15.000 15.000 0.836 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position EVAL 0dgrmp position! 02b14q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 15.000 15.000 0.836 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position EVAL 0dgrmp position! 02b0y3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 15.000 15.000 0.836 http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position #1474-011k_j PRED entity: 011k_j PRED relation: group PRED expected values: 0dvqq => 76 concepts (47 used for prediction) PRED predicted values (max 10 best out of 187): 02vnpv (0.75 #3723, 0.71 #5231, 0.71 #3535), 0134wr (0.71 #3110, 0.50 #5179, 0.50 #4612), 02mq_y (0.67 #2306, 0.56 #3808, 0.50 #5128), 07mvp (0.62 #3642, 0.62 #4962, 0.57 #5150), 0b_xm (0.62 #3660, 0.57 #5168, 0.57 #3472), 0khth (0.57 #3049, 0.54 #4930, 0.50 #5118), 047cx (0.57 #3050, 0.50 #4552, 0.50 #3611), 017_hq (0.57 #3166, 0.50 #3727, 0.50 #1097), 0dvqq (0.56 #3776, 0.50 #5096, 0.50 #3588), 02_5x9 (0.56 #3771, 0.50 #5091, 0.50 #953) >> Best rule #3723 for best value: >> intensional similarity = 24 >> extensional distance = 6 >> proper extension: 05148p4; >> query: (?x4078, 02vnpv) <- role(?x4078, ?x5676), role(?x4078, ?x4913), role(?x4078, ?x3991), role(?x4078, ?x3112), role(?x4078, ?x2764), role(?x4078, ?x316), role(?x3112, ?x3967), role(?x3112, ?x3161), role(?x3112, ?x2377), ?x2377 = 01bns_, ?x3161 = 01v1d8, ?x3967 = 01p970, role(?x2309, ?x3112), performance_role(?x4078, ?x1225), ?x4913 = 03ndd, ?x2764 = 01s0ps, group(?x4078, ?x3516), ?x316 = 05r5c, instrumentalists(?x4078, ?x4140), role(?x3171, ?x4078), role(?x5676, ?x1166), role(?x3991, ?x74), role(?x2784, ?x3991), ?x2784 = 0137g1 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #3776 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 7 *> proper extension: 05r5c; 042v_gx; *> query: (?x4078, 0dvqq) <- role(?x4078, ?x5676), role(?x4078, ?x4913), role(?x4078, ?x3112), role(?x4078, ?x2764), role(?x4078, ?x316), role(?x3112, ?x3967), role(?x3112, ?x3161), role(?x3112, ?x2377), ?x2377 = 01bns_, ?x3161 = 01v1d8, ?x3967 = 01p970, role(?x2309, ?x3112), performance_role(?x4078, ?x1225), ?x4913 = 03ndd, ?x2764 = 01s0ps, group(?x4078, ?x3516), role(?x316, ?x74), instrumentalists(?x316, ?x7794), instrumentalists(?x316, ?x4162), group(?x316, ?x997), ?x4162 = 01wy61y, ?x7794 = 01k23t, ?x5676 = 0151b0, role(?x483, ?x316) *> conf = 0.56 ranks of expected_values: 9 EVAL 011k_j group 0dvqq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 76.000 47.000 0.750 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1473-0296y PRED entity: 0296y PRED relation: parent_genre! PRED expected values: 02yw1c => 80 concepts (28 used for prediction) PRED predicted values (max 10 best out of 256): 04b675 (0.50 #598, 0.21 #2679, 0.20 #3202), 0jrgr (0.50 #678, 0.20 #938, 0.15 #3282), 0jf1v (0.50 #581, 0.20 #841, 0.14 #1102), 06cp5 (0.43 #1117, 0.38 #1376, 0.30 #1897), 03p7rp (0.43 #1190, 0.38 #1449, 0.30 #1711), 0dls3 (0.43 #1087, 0.38 #1346, 0.30 #1608), 01_bkd (0.40 #1869, 0.29 #1089, 0.25 #1348), 0g_bh (0.40 #1929, 0.22 #2969, 0.12 #6387), 03fpx (0.38 #1490, 0.29 #1231, 0.25 #710), 04f73rc (0.38 #1524, 0.29 #1265, 0.25 #744) >> Best rule #598 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 01jwt; >> query: (?x6350, 04b675) <- artists(?x6350, ?x8012), artists(?x6350, ?x1955), artists(?x6350, ?x764), ?x8012 = 01wt4wc, parent_genre(?x6349, ?x6350), ?x6349 = 08z0wx, origin(?x1955, ?x9302), instrumentalists(?x716, ?x764), ?x716 = 018vs >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #620 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 01jwt; *> query: (?x6350, 02yw1c) <- artists(?x6350, ?x8012), artists(?x6350, ?x1955), artists(?x6350, ?x764), ?x8012 = 01wt4wc, parent_genre(?x6349, ?x6350), ?x6349 = 08z0wx, origin(?x1955, ?x9302), instrumentalists(?x716, ?x764), ?x716 = 018vs *> conf = 0.25 ranks of expected_values: 41 EVAL 0296y parent_genre! 02yw1c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 80.000 28.000 0.500 http://example.org/music/genre/parent_genre #1472-0661m4p PRED entity: 0661m4p PRED relation: film_release_region PRED expected values: 0chghy 01znc_ 01mjq 01pj7 06t8v => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 223): 0chghy (0.88 #260, 0.84 #2406, 0.83 #1142), 01znc_ (0.82 #283, 0.81 #535, 0.76 #1291), 06mzp (0.73 #142, 0.56 #1150, 0.55 #1656), 06qd3 (0.71 #280, 0.62 #532, 0.59 #1288), 047lj (0.71 #261, 0.50 #1269, 0.42 #513), 01mjq (0.65 #537, 0.64 #159, 0.59 #285), 015qh (0.64 #156, 0.62 #534, 0.59 #282), 01pj7 (0.59 #288, 0.53 #1296, 0.42 #540), 04hqz (0.59 #340, 0.36 #214, 0.35 #1348), 06t8v (0.55 #182, 0.53 #308, 0.48 #1316) >> Best rule #260 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 017jd9; >> query: (?x2350, 0chghy) <- film_release_region(?x2350, ?x1475), film_crew_role(?x2350, ?x137), prequel(?x2350, ?x1673), ?x1475 = 05qx1, film(?x488, ?x2350) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 6, 8, 10 EVAL 0661m4p film_release_region 06t8v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 63.000 63.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661m4p film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 63.000 63.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661m4p film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 63.000 63.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661m4p film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0661m4p film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1471-0cjcbg PRED entity: 0cjcbg PRED relation: award! PRED expected values: 06mm1x => 39 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2540): 02cm2m (0.80 #10118, 0.78 #13491, 0.78 #23610), 06jnvs (0.80 #10118, 0.78 #13491, 0.78 #23610), 02mqc4 (0.22 #1171, 0.08 #4544, 0.06 #41645), 030znt (0.22 #329, 0.06 #30683, 0.06 #37428), 0fvf9q (0.19 #13492, 0.19 #64089, 0.18 #64088), 08hsww (0.19 #13492, 0.19 #64089, 0.18 #64088), 03qmfzx (0.19 #13492, 0.19 #64089, 0.18 #64088), 0cj2t3 (0.19 #13492, 0.19 #64089, 0.18 #64088), 027cxsm (0.19 #13492, 0.19 #64089, 0.18 #64088), 048wrb (0.19 #13492, 0.19 #64089, 0.11 #2180) >> Best rule #10118 for best value: >> intensional similarity = 5 >> extensional distance = 127 >> proper extension: 0gqng; 02581q; 02wh75; 026mg3; 0bfvw2; 0bp_b2; 0gq_v; 0p9sw; 02g3gj; 0gkvb7; ... >> query: (?x11272, ?x3895) <- award(?x2952, ?x11272), award_winner(?x11272, ?x3895), category_of(?x11272, ?x2758), award_nominee(?x2952, ?x12650), student(?x3922, ?x2952) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #10117 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 127 *> proper extension: 0gqng; 02581q; 02wh75; 026mg3; 0bfvw2; 0bp_b2; 0gq_v; 0p9sw; 02g3gj; 0gkvb7; ... *> query: (?x11272, ?x12650) <- award(?x2952, ?x11272), award_winner(?x11272, ?x3895), category_of(?x11272, ?x2758), award_nominee(?x2952, ?x12650), student(?x3922, ?x2952) *> conf = 0.15 ranks of expected_values: 52 EVAL 0cjcbg award! 06mm1x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 39.000 19.000 0.797 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1470-07g9f PRED entity: 07g9f PRED relation: nominated_for! PRED expected values: 0fbtbt => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 175): 0cqh6z (0.77 #13112, 0.70 #1639, 0.70 #4683), 0m7yy (0.70 #1639, 0.70 #4683, 0.69 #4448), 0fbtbt (0.67 #860, 0.29 #1562, 0.27 #2265), 0fbvqf (0.59 #739, 0.24 #1441, 0.24 #973), 0bdx29 (0.48 #784, 0.21 #4295, 0.20 #4061), 0gq9h (0.40 #10829, 0.37 #11063, 0.34 #11532), 0gs9p (0.38 #10831, 0.34 #11065, 0.31 #11534), 019f4v (0.37 #10820, 0.33 #11054, 0.30 #11523), 09v82c0 (0.33 #418, 0.14 #1120, 0.13 #1354), 0bfvd4 (0.33 #320, 0.11 #3831, 0.10 #1022) >> Best rule #13112 for best value: >> intensional similarity = 3 >> extensional distance = 773 >> proper extension: 08cfr1; >> query: (?x10089, ?x435) <- award(?x10089, ?x435), award(?x4882, ?x435), student(?x11378, ?x4882) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #860 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 0300ml; *> query: (?x10089, 0fbtbt) <- genre(?x10089, ?x53), nominated_for(?x435, ?x10089), ?x435 = 0bp_b2 *> conf = 0.67 ranks of expected_values: 3 EVAL 07g9f nominated_for! 0fbtbt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 91.000 91.000 0.767 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1469-0dszr0 PRED entity: 0dszr0 PRED relation: profession PRED expected values: 01d_h8 => 77 concepts (27 used for prediction) PRED predicted values (max 10 best out of 63): 0dxtg (0.71 #3668, 0.64 #13, 0.57 #159), 03gjzk (0.52 #160, 0.50 #14, 0.46 #306), 01d_h8 (0.50 #6, 0.42 #3661, 0.39 #590), 02jknp (0.36 #7, 0.33 #3662, 0.31 #445), 02krf9 (0.17 #1193, 0.16 #754, 0.16 #608), 015h31 (0.14 #25, 0.12 #317, 0.09 #171), 0kyk (0.13 #3682, 0.12 #319, 0.12 #903), 09jwl (0.13 #2649, 0.13 #163, 0.12 #309), 0d8qb (0.12 #369, 0.12 #1462, 0.04 #223), 015cjr (0.12 #339, 0.10 #777, 0.08 #1069) >> Best rule #3668 for best value: >> intensional similarity = 4 >> extensional distance = 727 >> proper extension: 0f0y8; 01jrz5j; 01bpc9; 04xjp; 02v3yy; 02cx72; 04k15; 01w524f; 01jrvr6; 081k8; ... >> query: (?x13195, 0dxtg) <- location(?x13195, ?x362), profession(?x13195, ?x1383), profession(?x7143, ?x1383), ?x7143 = 06h7l7 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 08hsww; 014v1q; *> query: (?x13195, 01d_h8) <- profession(?x13195, ?x1383), profession(?x13195, ?x353), student(?x4410, ?x13195), ?x1383 = 0np9r, ?x353 = 0cbd2 *> conf = 0.50 ranks of expected_values: 3 EVAL 0dszr0 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 27.000 0.709 http://example.org/people/person/profession #1468-012ykt PRED entity: 012ykt PRED relation: award PRED expected values: 09v478h => 105 concepts (93 used for prediction) PRED predicted values (max 10 best out of 256): 09sb52 (0.39 #443, 0.35 #847, 0.35 #8486), 0gqy2 (0.20 #165, 0.15 #567, 0.14 #11424), 09v478h (0.20 #361, 0.14 #806, 0.13 #30157), 02z0dfh (0.20 #75, 0.13 #477, 0.08 #5304), 057xs89 (0.20 #161, 0.07 #967, 0.06 #2575), 08_vwq (0.20 #271, 0.04 #1077, 0.03 #37395), 0dgshf6 (0.20 #193, 0.03 #37395), 03qgjwc (0.17 #805, 0.17 #586, 0.14 #806), 0gqwc (0.16 #1282, 0.15 #5303, 0.14 #2488), 05b4l5x (0.15 #1214, 0.10 #5235, 0.08 #7245) >> Best rule #443 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 03czrpj; 09mfvx; >> query: (?x6211, 09sb52) <- nominated_for(?x6211, ?x7554), nominated_for(?x3499, ?x7554), ?x3499 = 03qgjwc >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #361 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0jlv5; *> query: (?x6211, 09v478h) <- film(?x6211, ?x6679), ?x6679 = 0drnwh, award(?x6211, ?x749), gender(?x6211, ?x514) *> conf = 0.20 ranks of expected_values: 3 EVAL 012ykt award 09v478h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 105.000 93.000 0.386 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1467-02_xgp2 PRED entity: 02_xgp2 PRED relation: major_field_of_study PRED expected values: 01540 01r4k 05b6c 01400v => 25 concepts (25 used for prediction) PRED predicted values (max 10 best out of 92): 01540 (0.83 #240, 0.67 #753, 0.64 #1124), 01r4k (0.83 #240, 0.67 #764, 0.57 #725), 0_jm (0.72 #665, 0.71 #851, 0.70 #1098), 064_8sq (0.72 #665, 0.71 #851, 0.60 #914), 041y2 (0.70 #1098, 0.60 #914, 0.57 #725), 06mq7 (0.70 #1098, 0.60 #914, 0.57 #725), 02822 (0.70 #1098, 0.60 #914, 0.57 #725), 01400v (0.70 #1098, 0.60 #914, 0.57 #725), 04306rv (0.70 #1098, 0.60 #914, 0.57 #725), 097df (0.70 #1098, 0.60 #914, 0.53 #1223) >> Best rule #240 for best value: >> intensional similarity = 28 >> extensional distance = 1 >> proper extension: 02h4rq6; >> query: (?x3437, ?x742) <- institution(?x3437, ?x11693), institution(?x3437, ?x11555), institution(?x3437, ?x9443), institution(?x3437, ?x7278), institution(?x3437, ?x6856), institution(?x3437, ?x6132), institution(?x3437, ?x4980), institution(?x3437, ?x1884), ?x11555 = 06rjp, student(?x3437, ?x11288), student(?x3437, ?x4003), major_field_of_study(?x7278, ?x742), organization(?x7278, ?x5487), currency(?x11693, ?x170), ?x9443 = 039d4, school_type(?x7278, ?x3205), contains(?x94, ?x11693), ?x4980 = 01n6r0, ?x6856 = 0jkhr, major_field_of_study(?x3437, ?x13830), major_field_of_study(?x3437, ?x5900), ?x5900 = 0db86, profession(?x11288, ?x8290), ?x3205 = 01rs41, influenced_by(?x4003, ?x3712), ?x13830 = 0cd78, ?x6132 = 0hsb3, ?x1884 = 0bx8pn >> conf = 0.83 => this is the best rule for 2 predicted values ranks of expected_values: 1, 2, 8, 36 EVAL 02_xgp2 major_field_of_study 01400v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 25.000 25.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 02_xgp2 major_field_of_study 05b6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 25.000 25.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 02_xgp2 major_field_of_study 01r4k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study EVAL 02_xgp2 major_field_of_study 01540 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 25.000 25.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #1466-05fjf PRED entity: 05fjf PRED relation: district_represented! PRED expected values: 01gtbb 01gsvp => 214 concepts (214 used for prediction) PRED predicted values (max 10 best out of 17): 024tkd (0.77 #30, 0.67 #302, 0.62 #149), 01gtbb (0.55 #681, 0.45 #23, 0.41 #295), 01gsvp (0.55 #681, 0.45 #27, 0.37 #299), 03z5xd (0.55 #681, 0.41 #22, 0.17 #294), 03ww_x (0.55 #681, 0.41 #20, 0.17 #292), 032ft5 (0.55 #681, 0.23 #21, 0.09 #293), 0495ys (0.55 #681, 0.18 #18, 0.07 #664), 060ny2 (0.55 #681, 0.14 #28, 0.07 #300), 06r713 (0.55 #681, 0.14 #26, 0.07 #298), 04gp1d (0.55 #681, 0.14 #25, 0.07 #297) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 0g0syc; >> query: (?x6895, 024tkd) <- district_represented(?x4730, ?x6895), district_represented(?x1027, ?x6895), ?x1027 = 02bn_p, ?x4730 = 02cg7g >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #681 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 65 *> proper extension: 059ts; 05rh2; 087r4; *> query: (?x6895, ?x355) <- district_represented(?x1027, ?x6895), legislative_sessions(?x1027, ?x355) *> conf = 0.55 ranks of expected_values: 2, 3 EVAL 05fjf district_represented! 01gsvp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 214.000 214.000 0.773 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05fjf district_represented! 01gtbb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 214.000 214.000 0.773 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #1465-03jm6c PRED entity: 03jm6c PRED relation: student! PRED expected values: 0187nd 02tz9z => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 157): 065y4w7 (0.25 #14, 0.17 #2122, 0.11 #1068), 07wrz (0.25 #62, 0.09 #1643, 0.08 #2697), 07w0v (0.11 #1074, 0.09 #1601, 0.08 #2655), 02tz9z (0.11 #1523, 0.09 #2050, 0.08 #3104), 0g2jl (0.11 #1455, 0.09 #1982, 0.08 #3036), 0187nd (0.11 #1420, 0.09 #1947, 0.08 #3001), 08qnnv (0.09 #1795, 0.08 #2849, 0.03 #3903), 017z88 (0.09 #3771, 0.04 #9569, 0.03 #15367), 02bqy (0.09 #3344, 0.08 #2290, 0.02 #4398), 0bwfn (0.08 #2383, 0.07 #31900, 0.06 #7654) >> Best rule #14 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 050_qx; >> query: (?x2401, 065y4w7) <- place_of_birth(?x2401, ?x4356), award_nominee(?x2401, ?x1145), ?x4356 = 06wxw, influenced_by(?x236, ?x1145) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1523 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 049dyj; 01wyy_; 09r9dp; 02xwq9; 0646qh; 01q9b9; 0dt1cm; *> query: (?x2401, 02tz9z) <- place_of_birth(?x2401, ?x4356), award_nominee(?x2401, ?x1145), ?x4356 = 06wxw, award(?x2401, ?x3263) *> conf = 0.11 ranks of expected_values: 4, 6 EVAL 03jm6c student! 02tz9z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 138.000 138.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 03jm6c student! 0187nd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 138.000 138.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #1464-02qjv PRED entity: 02qjv PRED relation: role PRED expected values: 03gvt => 80 concepts (47 used for prediction) PRED predicted values (max 10 best out of 84): 013y1f (0.85 #2961, 0.83 #1948, 0.80 #3638), 0l15bq (0.82 #1863, 0.75 #2624, 0.75 #1443), 0l14qv (0.82 #1589, 0.81 #411, 0.81 #2434), 01wy6 (0.82 #1589, 0.81 #411, 0.81 #2434), 0dwt5 (0.82 #1589, 0.81 #411, 0.81 #2434), 02sgy (0.82 #1589, 0.81 #411, 0.81 #2434), 0dwtp (0.73 #2445, 0.73 #2191, 0.69 #247), 03m5k (0.73 #2274, 0.70 #410, 0.69 #247), 0dwsp (0.73 #3198, 0.73 #1762, 0.70 #410), 0151b0 (0.73 #1901, 0.70 #410, 0.69 #247) >> Best rule #2961 for best value: >> intensional similarity = 21 >> extensional distance = 18 >> proper extension: 07y_7; 051hrr; 0jtg0; >> query: (?x1148, 013y1f) <- role(?x1148, ?x3239), role(?x1148, ?x1432), role(?x1148, ?x1166), role(?x1148, ?x212), role(?x6938, ?x3239), role(?x2460, ?x3239), group(?x1148, ?x9757), ?x212 = 026t6, role(?x3239, ?x3703), role(?x317, ?x1148), ?x1166 = 05148p4, ?x6938 = 023r2x, ?x2460 = 01wy6, role(?x5990, ?x1432), role(?x1886, ?x1432), role(?x1574, ?x1432), ?x5990 = 0192l, artists(?x302, ?x9757), ?x1886 = 02k84w, ?x3703 = 02dlh2, ?x1574 = 0l15bq >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #410 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 2 *> proper extension: 05r5c; *> query: (?x1148, ?x74) <- role(?x1148, ?x4917), role(?x1148, ?x3239), role(?x1148, ?x1473), role(?x1148, ?x716), ?x3239 = 03qmg1, role(?x228, ?x1148), role(?x3214, ?x1148), ?x1473 = 0g2dz, ?x3214 = 02snj9, ?x4917 = 06w7v, role(?x7252, ?x716), role(?x7237, ?x716), role(?x716, ?x8014), role(?x716, ?x74), ?x7252 = 017g21, instrumentalists(?x716, ?x3166), ?x8014 = 0214km, group(?x716, ?x379), ?x7237 = 0473q, ?x3166 = 0qdyf *> conf = 0.70 ranks of expected_values: 17 EVAL 02qjv role 03gvt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 80.000 47.000 0.850 http://example.org/music/performance_role/track_performances./music/track_contribution/role #1463-02jt1k PRED entity: 02jt1k PRED relation: film PRED expected values: 0dll_t2 => 119 concepts (72 used for prediction) PRED predicted values (max 10 best out of 1120): 0jqkh (0.25 #3116, 0.04 #20998, 0.02 #128764), 078sj4 (0.14 #454, 0.12 #2242, 0.02 #27277), 03tbg6 (0.14 #1654, 0.03 #7019, 0.03 #8807), 017180 (0.14 #1188, 0.03 #4765, 0.03 #8341), 0gfzfj (0.14 #1694, 0.03 #5271, 0.02 #7059), 01jrbb (0.14 #471, 0.03 #7624, 0.02 #11201), 02v8kmz (0.14 #28, 0.02 #19698, 0.02 #128764), 0ds33 (0.14 #68, 0.02 #16162, 0.02 #128764), 04tqtl (0.14 #510, 0.02 #23757, 0.02 #128764), 0b7l4x (0.14 #1037, 0.02 #128764, 0.01 #27860) >> Best rule #3116 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02dth1; >> query: (?x1700, 0jqkh) <- film(?x1700, ?x3388), actor(?x6482, ?x1700), ?x3388 = 01rwyq >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4545 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 03d9wk; *> query: (?x1700, 0dll_t2) <- gender(?x1700, ?x514), place_of_birth(?x1700, ?x1005), diet(?x1700, ?x3130), ?x514 = 02zsn *> conf = 0.03 ranks of expected_values: 199 EVAL 02jt1k film 0dll_t2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 119.000 72.000 0.250 http://example.org/film/actor/film./film/performance/film #1462-06w33f8 PRED entity: 06w33f8 PRED relation: award PRED expected values: 0gs96 => 78 concepts (68 used for prediction) PRED predicted values (max 10 best out of 279): 0gs96 (0.79 #1333, 0.78 #928, 0.70 #1738), 09sb52 (0.30 #3685, 0.27 #5710, 0.26 #8950), 0gqwc (0.26 #3719, 0.15 #8579, 0.15 #7364), 02x2gy0 (0.26 #944, 0.25 #1349, 0.22 #1754), 027h4yd (0.26 #1187, 0.25 #1592, 0.22 #1997), 0gqyl (0.25 #3750, 0.14 #8610, 0.14 #7395), 094qd5 (0.21 #3689, 0.13 #22282, 0.13 #16608), 02ppm4q (0.20 #3802, 0.10 #8662, 0.09 #7042), 05pcn59 (0.18 #3726, 0.13 #4131, 0.13 #4941), 0bdwft (0.18 #3713, 0.12 #7358, 0.11 #8573) >> Best rule #1333 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 0bytkq; >> query: (?x1760, 0gs96) <- costume_design_by(?x3220, ?x1760), language(?x3220, ?x90), music(?x3220, ?x4428), film_release_distribution_medium(?x3220, ?x81) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06w33f8 award 0gs96 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 68.000 0.792 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1461-0bq0p9 PRED entity: 0bq0p9 PRED relation: jurisdiction_of_office! PRED expected values: 09jrf => 133 concepts (105 used for prediction) PRED predicted values (max 10 best out of 115): 0d1_f (0.27 #6633, 0.22 #6023, 0.22 #1704), 0lzcs (0.20 #61, 0.17 #519, 0.17 #367), 0948xk (0.20 #48, 0.17 #506, 0.17 #354), 03f77 (0.20 #26, 0.17 #484, 0.17 #332), 03f5vvx (0.20 #21, 0.17 #479, 0.17 #327), 02c4s (0.20 #8, 0.17 #466, 0.17 #314), 0kn4c (0.20 #6, 0.17 #464, 0.17 #312), 0dj5q (0.20 #188, 0.17 #417, 0.14 #570), 0177g (0.20 #210, 0.11 #896, 0.10 #1051), 01k165 (0.17 #398, 0.14 #551, 0.07 #1315) >> Best rule #6633 for best value: >> intensional similarity = 2 >> extensional distance = 58 >> proper extension: 0168t; 035v3; 03t1s; >> query: (?x613, 0d1_f) <- form_of_government(?x613, ?x6065), ?x6065 = 01q20 >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0bq0p9 jurisdiction_of_office! 09jrf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 105.000 0.267 http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office #1460-02vcp0 PRED entity: 02vcp0 PRED relation: place_of_birth PRED expected values: 030qb3t => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 65): 0mzww (0.40 #7047, 0.38 #4932, 0.38 #6342), 02_286 (0.07 #63400, 0.06 #3541, 0.06 #2835), 0cr3d (0.04 #94, 0.04 #27562, 0.03 #39534), 030qb3t (0.04 #63435, 0.03 #33858, 0.03 #43017), 01_d4 (0.03 #63447, 0.03 #770, 0.03 #54998), 04lh6 (0.03 #1741, 0.03 #1037, 0.01 #2445), 0dclg (0.02 #3600, 0.02 #11351, 0.02 #9943), 01cx_ (0.02 #109, 0.02 #4336, 0.02 #5041), 03b12 (0.02 #407, 0.02 #1815, 0.02 #1111), 0r2dp (0.02 #401, 0.02 #1809, 0.02 #1105) >> Best rule #7047 for best value: >> intensional similarity = 3 >> extensional distance = 213 >> proper extension: 01pr_j6; 04k15; 0kvjrw; 082brv; 082db; 04m2zj; 0dhqyw; 01vsqvs; 01rwcgb; >> query: (?x8049, ?x6987) <- instrumentalists(?x614, ?x8049), gender(?x8049, ?x514), origin(?x8049, ?x6987) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #63435 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2749 *> proper extension: 07m69t; 01nvdc; 03cxqp5; *> query: (?x8049, 030qb3t) <- nationality(?x8049, ?x94), ?x94 = 09c7w0 *> conf = 0.04 ranks of expected_values: 4 EVAL 02vcp0 place_of_birth 030qb3t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.402 http://example.org/people/person/place_of_birth #1459-0_75d PRED entity: 0_75d PRED relation: category PRED expected values: 08mbj5d => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #20, 0.82 #23, 0.82 #22) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 0f2w0; 0pzpz; 029cr; 0d8r8; 0v9qg; 02hrh0_; 0d9jr; 02m__; 01smm; 0lphb; ... >> query: (?x4201, 08mbj5d) <- place_of_birth(?x426, ?x4201), administrative_division(?x4201, ?x4202), second_level_divisions(?x94, ?x4202), contains(?x3670, ?x4202) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_75d category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.820 http://example.org/common/topic/webpage./common/webpage/category #1458-0gd0c7x PRED entity: 0gd0c7x PRED relation: edited_by PRED expected values: 0bn3jg => 92 concepts (44 used for prediction) PRED predicted values (max 10 best out of 21): 03q8ch (0.06 #430, 0.05 #1075, 0.05 #1227), 0697kh (0.06 #873, 0.05 #1060, 0.05 #969), 08h79x (0.05 #47, 0.04 #76, 0.02 #587), 0gd9k (0.05 #50), 02qggqc (0.04 #119, 0.04 #61, 0.03 #150), 06chf (0.04 #1306, 0.04 #874, 0.04 #569), 0cv9fc (0.04 #874, 0.04 #1061, 0.03 #970), 02tn0_ (0.04 #874, 0.04 #1061, 0.03 #970), 04cy8rb (0.04 #59, 0.03 #630, 0.03 #720), 02kxbwx (0.03 #848, 0.03 #1035, 0.02 #1311) >> Best rule #430 for best value: >> intensional similarity = 5 >> extensional distance = 143 >> proper extension: 01f39b; >> query: (?x1999, 03q8ch) <- film(?x8765, ?x1999), genre(?x1999, ?x811), ?x811 = 03k9fj, award_nominee(?x8765, ?x815), produced_by(?x1999, ?x2803) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #115 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 40 *> proper extension: 02ptczs; *> query: (?x1999, 0bn3jg) <- written_by(?x1999, ?x8337), genre(?x1999, ?x600), currency(?x1999, ?x170), ?x600 = 02n4kr *> conf = 0.02 ranks of expected_values: 13 EVAL 0gd0c7x edited_by 0bn3jg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 92.000 44.000 0.062 http://example.org/film/film/edited_by #1457-01bns_ PRED entity: 01bns_ PRED relation: role! PRED expected values: 0192l => 52 concepts (41 used for prediction) PRED predicted values (max 10 best out of 99): 0192l (0.91 #1290, 0.87 #1089, 0.86 #291), 0bxl5 (0.88 #1548, 0.80 #1445, 0.79 #195), 01vdm0 (0.86 #291, 0.85 #1088, 0.85 #882), 0l14j_ (0.86 #291, 0.85 #1088, 0.85 #882), 03m5k (0.86 #291, 0.85 #1088, 0.85 #882), 0395lw (0.83 #1112, 0.79 #195, 0.75 #1511), 0l15bq (0.81 #1519, 0.80 #1019, 0.79 #195), 01vj9c (0.80 #2685, 0.80 #3076, 0.79 #195), 0dwt5 (0.80 #1459, 0.79 #195, 0.75 #1163), 01dnws (0.79 #195, 0.78 #925, 0.74 #93) >> Best rule #1290 for best value: >> intensional similarity = 25 >> extensional distance = 11 >> proper extension: 04q7r; >> query: (?x2377, ?x5990) <- role(?x2888, ?x2377), role(?x2798, ?x2377), role(?x1267, ?x2377), role(?x960, ?x2377), role(?x432, ?x2377), role(?x314, ?x2377), ?x2888 = 02fsn, role(?x2377, ?x5990), role(?x2377, ?x2944), ?x2798 = 03qjg, ?x1267 = 07brj, ?x2944 = 0l14j_, instrumentalists(?x5990, ?x10802), role(?x5990, ?x2157), ?x314 = 02sgy, ?x432 = 042v_gx, role(?x960, ?x5926), role(?x960, ?x4769), role(?x960, ?x4311), ?x2157 = 011_6p, ?x4311 = 01xqw, instrumentalists(?x960, ?x248), ?x10802 = 01mxnvc, ?x5926 = 0cfdd, ?x4769 = 0dwt5 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bns_ role! 0192l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 52.000 41.000 0.909 http://example.org/music/performance_role/track_performances./music/track_contribution/role #1456-0d66j2 PRED entity: 0d66j2 PRED relation: program! PRED expected values: 02f9wb => 88 concepts (65 used for prediction) PRED predicted values (max 10 best out of 292): 0f721s (0.25 #545, 0.19 #285, 0.17 #1325), 01jw4r (0.20 #8844, 0.19 #8584, 0.18 #3640), 02j9lm (0.20 #8844, 0.19 #8584, 0.18 #3640), 0cp9f9 (0.12 #194, 0.07 #454, 0.03 #2791), 02vqpx8 (0.12 #170, 0.04 #430, 0.02 #3290), 04mx__ (0.11 #455, 0.04 #3054, 0.02 #6437), 0bbxd3 (0.11 #746, 0.04 #486, 0.03 #3867), 02773m2 (0.08 #794, 0.08 #1054, 0.05 #1573), 09_99w (0.07 #467, 0.07 #727, 0.03 #2025), 0h53p1 (0.07 #318, 0.06 #58, 0.05 #838) >> Best rule #545 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 01f3p_; 099pks; 07wqr6; 01_2n; 0cskb; 017dtf; 0123qq; 03_b1g; >> query: (?x3610, 0f721s) <- actor(?x3610, ?x2900), genre(?x3610, ?x258), genre(?x3610, ?x53), ?x258 = 05p553, ?x53 = 07s9rl0 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #668 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 26 *> proper extension: 01f3p_; 099pks; 07wqr6; 01_2n; 0cskb; 017dtf; 0123qq; 03_b1g; *> query: (?x3610, 02f9wb) <- actor(?x3610, ?x2900), genre(?x3610, ?x258), genre(?x3610, ?x53), ?x258 = 05p553, ?x53 = 07s9rl0 *> conf = 0.04 ranks of expected_values: 96 EVAL 0d66j2 program! 02f9wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 88.000 65.000 0.250 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/program #1455-03mh_tp PRED entity: 03mh_tp PRED relation: film_crew_role PRED expected values: 0dxtw 02ynfr => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 30): 09zzb8 (0.83 #218, 0.79 #146, 0.78 #983), 01vx2h (0.67 #120, 0.50 #48, 0.34 #993), 0dxtw (0.50 #119, 0.42 #992, 0.40 #776), 02ynfr (0.40 #53, 0.33 #16, 0.25 #125), 01pvkk (0.37 #229, 0.33 #301, 0.32 #157), 0215hd (0.33 #19, 0.28 #655, 0.17 #600), 089g0h (0.33 #20, 0.28 #655, 0.12 #601), 02_n3z (0.33 #2, 0.28 #655, 0.10 #39), 01xy5l_ (0.28 #655, 0.20 #51, 0.17 #123), 089fss (0.28 #655, 0.20 #43, 0.17 #115) >> Best rule #218 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 08720; 03hjv97; 08gsvw; 03r0g9; 02xs6_; 02qpt1w; 0dgq_kn; 02v_r7d; 0jqd3; 01fx6y; ... >> query: (?x3084, 09zzb8) <- production_companies(?x3084, ?x788), film_crew_role(?x3084, ?x468), film(?x541, ?x3084), ?x788 = 0g1rw >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #119 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 10 *> proper extension: 02rmd_2; 02wgk1; 012s1d; *> query: (?x3084, 0dxtw) <- film(?x11813, ?x3084), film(?x10694, ?x3084), ?x11813 = 0716t2, film_crew_role(?x3084, ?x468), country(?x3084, ?x94), location(?x10694, ?x739) *> conf = 0.50 ranks of expected_values: 3, 4 EVAL 03mh_tp film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 03mh_tp film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 77.000 77.000 0.833 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1454-032r1 PRED entity: 032r1 PRED relation: place_of_death PRED expected values: 0ptj2 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 86): 04jpl (0.25 #400, 0.20 #788, 0.17 #1179), 0l0mk (0.25 #648, 0.20 #842, 0.17 #1233), 030qb3t (0.25 #22, 0.15 #2558, 0.09 #3537), 02m77 (0.18 #1659, 0.17 #1853, 0.05 #3419), 02_286 (0.17 #1768, 0.11 #5291, 0.09 #5878), 09c7w0 (0.11 #8412, 0.10 #8608, 0.08 #8411), 07ssc (0.11 #8412, 0.10 #8608, 0.08 #8411), 0fhp9 (0.11 #8412, 0.10 #8608, 0.08 #8411), 0f04c (0.11 #1410, 0.02 #5908, 0.01 #9044), 0h7x (0.10 #8608, 0.08 #8411, 0.08 #10174) >> Best rule #400 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 07h1q; >> query: (?x11837, 04jpl) <- influenced_by(?x11837, ?x7296), ?x7296 = 04hcw, religion(?x11837, ?x1985), peers(?x10499, ?x11837) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 032r1 place_of_death 0ptj2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 159.000 159.000 0.250 http://example.org/people/deceased_person/place_of_death #1453-02624g PRED entity: 02624g PRED relation: award_nominee PRED expected values: 03mp9s => 91 concepts (42 used for prediction) PRED predicted values (max 10 best out of 947): 02gvwz (0.81 #72368, 0.81 #53692, 0.81 #63031), 0372kf (0.41 #3554, 0.05 #22230, 0.03 #5889), 04wp3s (0.41 #3627, 0.04 #22303, 0.01 #54985), 02wcx8c (0.41 #2658, 0.04 #21334, 0.01 #4993), 014zcr (0.36 #2382, 0.05 #84039, 0.05 #21058), 031k24 (0.36 #4128, 0.05 #22804, 0.03 #6463), 03pmty (0.36 #2534, 0.04 #21210, 0.02 #32882), 01d0b1 (0.36 #4260, 0.03 #22936), 015pvh (0.36 #3783, 0.03 #22459), 0bsb4j (0.36 #2902, 0.03 #21578) >> Best rule #72368 for best value: >> intensional similarity = 3 >> extensional distance = 1319 >> proper extension: 01sl1q; 07nznf; 0dbpyd; 0520r2x; 0197tq; 06gp3f; 01j5ts; 04cy8rb; 03rs8y; 054_mz; ... >> query: (?x7048, ?x1194) <- place_of_birth(?x7048, ?x12892), award_nominee(?x1194, ?x7048), award_nominee(?x7048, ?x4014) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #84039 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1546 *> proper extension: 05d6kv; 01f2w0; 09xwz; 01fsyp; *> query: (?x7048, ?x286) <- award_winner(?x2515, ?x7048), award_winner(?x2515, ?x286) *> conf = 0.05 ranks of expected_values: 206 EVAL 02624g award_nominee 03mp9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 91.000 42.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1452-01vsyjy PRED entity: 01vsyjy PRED relation: category PRED expected values: 08mbj5d => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #57, 0.83 #36, 0.83 #63) >> Best rule #57 for best value: >> intensional similarity = 7 >> extensional distance = 488 >> proper extension: 01jqr_5; 014488; >> query: (?x7272, 08mbj5d) <- artists(?x7329, ?x7272), artists(?x7329, ?x10671), artists(?x7329, ?x2906), artists(?x7329, ?x1992), ?x1992 = 01wz3cx, ?x2906 = 0249kn, group(?x227, ?x10671) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vsyjy category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.833 http://example.org/common/topic/webpage./common/webpage/category #1451-03_d0 PRED entity: 03_d0 PRED relation: parent_genre! PRED expected values: 01f9y_ 01kcty 01n5sn => 77 concepts (68 used for prediction) PRED predicted values (max 10 best out of 274): 016_nr (0.50 #1760, 0.43 #2247, 0.33 #2004), 01gbcf (0.44 #2921, 0.40 #3408, 0.33 #4866), 064t9 (0.40 #1468, 0.33 #9, 0.25 #1225), 0hdf8 (0.38 #2729, 0.33 #540, 0.25 #783), 0y3_8 (0.36 #3929, 0.33 #38, 0.25 #1254), 0133_p (0.33 #3280, 0.33 #2065, 0.33 #1821), 059kh (0.33 #1742, 0.33 #283, 0.33 #40), 0bt7w (0.33 #2999, 0.33 #81, 0.30 #3486), 01fm07 (0.33 #2041, 0.33 #1797, 0.29 #2527), 03mb9 (0.33 #1778, 0.33 #319, 0.29 #2265) >> Best rule #1760 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 064t9; 06j6l; 0gywn; >> query: (?x505, 016_nr) <- parent_genre(?x119, ?x505), artists(?x505, ?x9210), artists(?x505, ?x6715), ?x9210 = 03d2k, ?x6715 = 011z3g >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1154 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 01fbr2; 0cx6f; *> query: (?x505, 01n5sn) <- parent_genre(?x119, ?x505), artists(?x505, ?x3378), artists(?x505, ?x999), ?x3378 = 01lcxbb, profession(?x999, ?x106) *> conf = 0.25 ranks of expected_values: 69, 112, 183 EVAL 03_d0 parent_genre! 01n5sn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 77.000 68.000 0.500 http://example.org/music/genre/parent_genre EVAL 03_d0 parent_genre! 01kcty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 77.000 68.000 0.500 http://example.org/music/genre/parent_genre EVAL 03_d0 parent_genre! 01f9y_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 77.000 68.000 0.500 http://example.org/music/genre/parent_genre #1450-04jb97 PRED entity: 04jb97 PRED relation: nationality PRED expected values: 0d05w3 => 89 concepts (79 used for prediction) PRED predicted values (max 10 best out of 66): 09c7w0 (0.82 #7539, 0.77 #4763, 0.73 #2878), 0d05w3 (0.40 #2479, 0.33 #3175, 0.29 #4168), 06t2t (0.40 #2479, 0.33 #3175, 0.29 #4168), 0d060g (0.33 #3175, 0.29 #4168, 0.06 #106), 02jx1 (0.14 #2014, 0.13 #1717, 0.12 #1321), 07ssc (0.10 #7553, 0.10 #709, 0.09 #1798), 03rk0 (0.07 #1631, 0.06 #6296, 0.06 #6395), 0chghy (0.04 #3374, 0.04 #298, 0.04 #2579), 0345h (0.04 #3374, 0.04 #298, 0.04 #2579), 03rjj (0.04 #3374, 0.04 #298, 0.04 #2579) >> Best rule #7539 for best value: >> intensional similarity = 3 >> extensional distance = 3342 >> proper extension: 07qnf; 07m69t; 01ly8d; 01h2_6; 06s27s; 01nvdc; 03cxqp5; >> query: (?x8104, 09c7w0) <- nationality(?x8104, ?x2645), film_release_region(?x8370, ?x2645), ?x8370 = 07ghq >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #2479 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1033 *> proper extension: 049tjg; 01l3j; *> query: (?x8104, ?x2316) <- film(?x8104, ?x4604), country(?x4604, ?x2316), film_release_region(?x4604, ?x789), ?x789 = 0f8l9c *> conf = 0.40 ranks of expected_values: 2 EVAL 04jb97 nationality 0d05w3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 79.000 0.822 http://example.org/people/person/nationality #1449-0m9p3 PRED entity: 0m9p3 PRED relation: film! PRED expected values: 0byfz 015dqj => 79 concepts (38 used for prediction) PRED predicted values (max 10 best out of 825): 06qn87 (0.41 #20738, 0.40 #4148, 0.40 #4147), 03q1vd (0.15 #461, 0.03 #4609, 0.02 #72579), 01fwf1 (0.15 #896, 0.02 #23708, 0.02 #9193), 012c6x (0.15 #115, 0.02 #6338), 0gyx4 (0.15 #772, 0.01 #25657, 0.01 #67131), 027l0b (0.15 #474, 0.01 #23286), 04mlmx (0.15 #1432), 0kr5_ (0.15 #74654, 0.13 #66359, 0.12 #37330), 085pr (0.13 #14518), 01nwwl (0.08 #501, 0.06 #2574, 0.04 #8798) >> Best rule #20738 for best value: >> intensional similarity = 4 >> extensional distance = 275 >> proper extension: 02d44q; 07k2mq; 0372j5; >> query: (?x2423, ?x2424) <- featured_film_locations(?x2423, ?x1264), film_release_distribution_medium(?x2423, ?x81), award(?x2423, ?x1033), nominated_for(?x2424, ?x2423) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #8331 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 134 *> proper extension: 01cjhz; 0jq2r; 06f0k; *> query: (?x2423, 0byfz) <- titles(?x512, ?x2423), titles(?x53, ?x2423), ?x512 = 07ssc, titles(?x53, ?x2151), ?x2151 = 0yzvw *> conf = 0.02 ranks of expected_values: 306, 774 EVAL 0m9p3 film! 015dqj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 79.000 38.000 0.409 http://example.org/film/actor/film./film/performance/film EVAL 0m9p3 film! 0byfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 79.000 38.000 0.409 http://example.org/film/actor/film./film/performance/film #1448-01qscs PRED entity: 01qscs PRED relation: award_winner! PRED expected values: 02z13jg => 133 concepts (131 used for prediction) PRED predicted values (max 10 best out of 281): 099jhq (0.36 #28554, 0.34 #24293, 0.34 #38781), 02x73k6 (0.36 #28554, 0.34 #24293, 0.34 #38781), 099tbz (0.25 #56, 0.14 #19661, 0.11 #12843), 0f4x7 (0.20 #1308, 0.20 #882, 0.20 #456), 027b9j5 (0.20 #1502, 0.20 #1076, 0.20 #650), 0bs0bh (0.20 #1380, 0.20 #954, 0.20 #528), 0gs9p (0.16 #5617, 0.14 #14143, 0.14 #13717), 0gq9h (0.16 #11585, 0.15 #14141, 0.15 #13715), 019f4v (0.15 #5604, 0.13 #2621, 0.12 #4752), 040njc (0.14 #5546, 0.13 #11942, 0.12 #14072) >> Best rule #28554 for best value: >> intensional similarity = 3 >> extensional distance = 955 >> proper extension: 05b2f_k; >> query: (?x395, ?x112) <- award_winner(?x394, ?x395), award_winner(?x395, ?x919), award(?x395, ?x112) >> conf = 0.36 => this is the best rule for 2 predicted values *> Best rule #6440 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 118 *> proper extension: 05ty4m; 054_mz; 02lymt; 020trj; 01515w; 03dbds; 064jjy; 06rq2l; 06jz0; 044zvm; ... *> query: (?x395, 02z13jg) <- produced_by(?x2714, ?x395), award_nominee(?x192, ?x395), film(?x395, ?x394) *> conf = 0.03 ranks of expected_values: 111 EVAL 01qscs award_winner! 02z13jg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 133.000 131.000 0.365 http://example.org/award/award_category/winners./award/award_honor/award_winner #1447-02_l39 PRED entity: 02_l39 PRED relation: child! PRED expected values: 01s73z => 227 concepts (227 used for prediction) PRED predicted values (max 10 best out of 96): 0l8sx (0.53 #3008, 0.42 #3755, 0.39 #6595), 09b3v (0.47 #4111, 0.33 #6110, 0.29 #943), 02_l39 (0.47 #14032, 0.45 #14949, 0.26 #4227), 06gst (0.47 #14032, 0.45 #14949, 0.14 #1154), 086k8 (0.44 #3580, 0.27 #2997, 0.26 #3744), 05th69 (0.32 #4972, 0.20 #1718, 0.19 #6637), 01dtcb (0.30 #7614, 0.30 #1707, 0.25 #9355), 049ql1 (0.29 #1065, 0.21 #3979, 0.21 #3893), 01dfb6 (0.25 #389, 0.17 #636, 0.08 #2300), 0sxdg (0.21 #4132, 0.19 #3293, 0.17 #6131) >> Best rule #3008 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 01p5yn; >> query: (?x10957, 0l8sx) <- industry(?x10957, ?x2271), child(?x10808, ?x10957), service_language(?x10808, ?x254), company(?x4682, ?x10808), ?x4682 = 0dq_5 >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #1581 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 025txrl; *> query: (?x10957, ?x5108) <- industry(?x10957, ?x2271), child(?x10957, ?x1104), organization(?x4682, ?x1104), place_founded(?x1104, ?x682), child(?x5108, ?x1104) *> conf = 0.16 ranks of expected_values: 19 EVAL 02_l39 child! 01s73z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 227.000 227.000 0.533 http://example.org/organization/organization/child./organization/organization_relationship/child #1446-06y57 PRED entity: 06y57 PRED relation: service_location! PRED expected values: 01y81r => 201 concepts (149 used for prediction) PRED predicted values (max 10 best out of 137): 0p4wb (0.60 #9, 0.24 #1777, 0.20 #553), 018mxj (0.60 #10, 0.18 #8450, 0.17 #7090), 06_9lg (0.59 #1049, 0.48 #4318, 0.35 #1457), 01y81r (0.40 #591, 0.36 #727, 0.33 #863), 01c6k4 (0.40 #6, 0.30 #550, 0.29 #8446), 04sv4 (0.40 #84, 0.30 #628, 0.19 #1852), 05b5c (0.40 #127, 0.30 #671, 0.14 #1895), 064f29 (0.40 #60, 0.20 #604, 0.20 #5234), 07zl6m (0.40 #132, 0.20 #676, 0.14 #1900), 05w3y (0.40 #62, 0.20 #606, 0.10 #5236) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0f8l9c; 059j2; >> query: (?x5036, 0p4wb) <- origin(?x226, ?x5036), film_release_region(?x6218, ?x5036), ?x6218 = 03rg2b >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #591 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 8 *> proper extension: 02j71; *> query: (?x5036, 01y81r) <- service_location(?x10016, ?x5036), program(?x10016, ?x14197) *> conf = 0.40 ranks of expected_values: 4 EVAL 06y57 service_location! 01y81r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 201.000 149.000 0.600 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #1445-016_mj PRED entity: 016_mj PRED relation: profession PRED expected values: 02hrh1q 018gz8 => 120 concepts (119 used for prediction) PRED predicted values (max 10 best out of 89): 02hrh1q (0.90 #7605, 0.90 #8189, 0.90 #13886), 018gz8 (0.51 #4394, 0.49 #4833, 0.48 #4687), 0cbd2 (0.48 #6869, 0.44 #7307, 0.44 #8475), 09jwl (0.41 #7755, 0.36 #10676, 0.34 #9654), 0kyk (0.34 #6890, 0.33 #9492, 0.31 #7328), 0nbcg (0.33 #9492, 0.30 #7768, 0.26 #10689), 016z4k (0.28 #150, 0.27 #7743, 0.26 #4969), 0np9r (0.28 #12560, 0.27 #4398, 0.25 #4837), 02krf9 (0.28 #12560, 0.26 #5135, 0.24 #2068), 0dz3r (0.27 #7741, 0.26 #586, 0.25 #1316) >> Best rule #7605 for best value: >> intensional similarity = 3 >> extensional distance = 389 >> proper extension: 0m2wm; 01j5x6; 01q7cb_; 04cf09; 0285c; 05hdf; 0c01c; 047hpm; 01v3bn; 039crh; ... >> query: (?x1835, 02hrh1q) <- film(?x1835, ?x994), participant(?x1335, ?x1835), location(?x1835, ?x2850) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 016_mj profession 018gz8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 119.000 0.903 http://example.org/people/person/profession EVAL 016_mj profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 119.000 0.903 http://example.org/people/person/profession #1444-09p3_s PRED entity: 09p3_s PRED relation: genre PRED expected values: 07s9rl0 => 76 concepts (75 used for prediction) PRED predicted values (max 10 best out of 80): 07s9rl0 (0.82 #847, 0.82 #967, 0.80 #605), 04xvlr (0.72 #3861, 0.69 #1568, 0.61 #5671), 07ssc (0.60 #3860, 0.56 #5308, 0.56 #1567), 02l7c8 (0.47 #17, 0.40 #621, 0.38 #137), 03q4nz (0.47 #19, 0.06 #1948, 0.06 #2552), 01chg (0.42 #33, 0.01 #1841, 0.01 #1479), 05p553 (0.37 #5, 0.36 #1091, 0.34 #4950), 04t36 (0.37 #7, 0.13 #733, 0.12 #369), 02kdv5l (0.33 #486, 0.33 #365, 0.32 #245), 01jfsb (0.30 #255, 0.28 #4237, 0.28 #4597) >> Best rule #847 for best value: >> intensional similarity = 4 >> extensional distance = 215 >> proper extension: 0gzy02; 0bth54; 09q5w2; 04vr_f; 0sxfd; 0qm8b; 035yn8; 0ch26b_; 03hj3b3; 0fpv_3_; ... >> query: (?x5519, 07s9rl0) <- nominated_for(?x1313, ?x5519), country(?x5519, ?x94), ?x1313 = 0gs9p, award(?x5519, ?x289) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09p3_s genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 75.000 0.825 http://example.org/film/film/genre #1443-02mjf2 PRED entity: 02mjf2 PRED relation: film PRED expected values: 0260bz => 128 concepts (107 used for prediction) PRED predicted values (max 10 best out of 978): 02q56mk (0.22 #3980, 0.02 #16455, 0.02 #43185), 065_cjc (0.22 #4753, 0.02 #74252, 0.01 #93854), 0fvr1 (0.17 #349, 0.12 #2131, 0.03 #9259), 01l_pn (0.17 #960, 0.07 #9870, 0.06 #6306), 0ch3qr1 (0.17 #969, 0.05 #9879, 0.04 #103358), 03bzjpm (0.17 #1307, 0.05 #10217, 0.04 #17346), 01y9jr (0.17 #1155, 0.05 #10065, 0.02 #17194), 01hqk (0.17 #721, 0.04 #16760, 0.03 #27452), 01rxyb (0.17 #730, 0.03 #9640, 0.03 #27461), 0blpg (0.17 #654, 0.03 #9564, 0.03 #6000) >> Best rule #3980 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 032xhg; 015pkc; 016z2j; 01jbx1; 046qq; 086nl7; 01pgk0; >> query: (?x4400, 02q56mk) <- participant(?x4400, ?x400), film(?x4400, ?x2886), ?x2886 = 02ryz24 >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #12809 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 69 *> proper extension: 01pl9g; 01l_vgt; *> query: (?x4400, 0260bz) <- location_of_ceremony(?x4400, ?x3269), participant(?x376, ?x4400), featured_film_locations(?x1064, ?x3269) *> conf = 0.01 ranks of expected_values: 693 EVAL 02mjf2 film 0260bz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 128.000 107.000 0.222 http://example.org/film/actor/film./film/performance/film #1442-0bmc4cm PRED entity: 0bmc4cm PRED relation: film_distribution_medium PRED expected values: 0735l => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 6): 0735l (0.79 #105, 0.77 #40, 0.64 #217), 029j_ (0.18 #35, 0.18 #29, 0.13 #42), 07c52 (0.18 #35, 0.12 #48, 0.11 #13), 02nxhr (0.13 #43, 0.10 #190, 0.10 #120), 0dq6p (0.11 #9, 0.09 #31, 0.05 #433), 07z4p (0.01 #212, 0.01 #423) >> Best rule #105 for best value: >> intensional similarity = 7 >> extensional distance = 32 >> proper extension: 02bj22; >> query: (?x3135, 0735l) <- genre(?x3135, ?x812), category(?x3135, ?x134), film(?x609, ?x3135), ?x609 = 03xq0f, language(?x3135, ?x2164), genre(?x6556, ?x812), ?x6556 = 05dss7 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bmc4cm film_distribution_medium 0735l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.794 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #1441-02sjf5 PRED entity: 02sjf5 PRED relation: film PRED expected values: 0_7w6 032clf => 141 concepts (106 used for prediction) PRED predicted values (max 10 best out of 561): 017f3m (0.49 #107484, 0.41 #157637, 0.39 #105692), 01fszq (0.49 #107484, 0.41 #157637, 0.39 #105692), 0fgrm (0.17 #2579, 0.01 #27657), 03h3x5 (0.17 #2214, 0.01 #30875), 023p33 (0.17 #2124), 0k2sk (0.17 #1954), 021gzd (0.14 #4881, 0.04 #6672, 0.03 #121813), 02qzh2 (0.14 #4276, 0.03 #121813, 0.02 #6067), 0n08r (0.14 #5288, 0.03 #121813, 0.02 #8870), 0sxns (0.14 #4660, 0.03 #121813, 0.02 #8242) >> Best rule #107484 for best value: >> intensional similarity = 3 >> extensional distance = 949 >> proper extension: 02k6rq; 03h40_7; >> query: (?x1204, ?x4898) <- location(?x1204, ?x739), award_nominee(?x1204, ?x1205), nominated_for(?x1204, ?x4898) >> conf = 0.49 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 02sjf5 film 032clf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 141.000 106.000 0.486 http://example.org/film/actor/film./film/performance/film EVAL 02sjf5 film 0_7w6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 141.000 106.000 0.486 http://example.org/film/actor/film./film/performance/film #1440-05h5nb8 PRED entity: 05h5nb8 PRED relation: award_winner PRED expected values: 02fcs2 => 35 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1322): 0gyx4 (0.62 #5909, 0.13 #8376, 0.12 #10844), 04sry (0.50 #6550, 0.50 #4083, 0.24 #11485), 081lh (0.50 #5122, 0.32 #7589, 0.22 #10057), 0js9s (0.50 #6386, 0.23 #8853, 0.22 #11321), 0c921 (0.50 #6913, 0.23 #9380, 0.12 #11848), 06pj8 (0.50 #5368, 0.22 #10303, 0.19 #7835), 022_q8 (0.50 #3733, 0.20 #1267, 0.14 #11135), 0kr5_ (0.50 #5053, 0.16 #7520, 0.10 #9988), 026dx (0.50 #3535, 0.12 #6002, 0.10 #10937), 0bzyh (0.44 #22203, 0.38 #9868, 0.38 #5798) >> Best rule #5909 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 03nqnk3; >> query: (?x9171, 0gyx4) <- award_winner(?x9171, ?x10117), award_winner(?x9171, ?x8645), award_winner(?x9171, ?x5192), ?x8645 = 0jgwf, award(?x10117, ?x198), film(?x5192, ?x6005), influenced_by(?x10117, ?x2610) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #5420 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 03nqnk3; *> query: (?x9171, 02fcs2) <- award_winner(?x9171, ?x10117), award_winner(?x9171, ?x8645), award_winner(?x9171, ?x5192), ?x8645 = 0jgwf, award(?x10117, ?x198), film(?x5192, ?x6005), influenced_by(?x10117, ?x2610) *> conf = 0.12 ranks of expected_values: 171 EVAL 05h5nb8 award_winner 02fcs2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 35.000 14.000 0.625 http://example.org/award/award_category/winners./award/award_honor/award_winner #1439-06bnz PRED entity: 06bnz PRED relation: adjoins PRED expected values: 047lj => 197 concepts (129 used for prediction) PRED predicted values (max 10 best out of 481): 02vzc (0.84 #33546, 0.84 #88442, 0.83 #22111), 0j3b (0.25 #58, 0.17 #820, 0.15 #1584), 04swx (0.25 #668, 0.08 #1430, 0.08 #2194), 0j0k (0.25 #295, 0.08 #1526, 0.07 #2289), 0dg3n1 (0.25 #120, 0.05 #44978, 0.03 #13084), 03rk0 (0.21 #3925, 0.11 #26031, 0.10 #32893), 0345h (0.19 #6170, 0.19 #9216, 0.17 #12265), 0hg5 (0.17 #890, 0.15 #1654, 0.13 #2417), 0f8l9c (0.16 #8428, 0.15 #6143, 0.14 #6905), 06mkj (0.15 #1637, 0.13 #2400, 0.12 #3164) >> Best rule #33546 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 070zc; >> query: (?x1603, ?x344) <- contains(?x1603, ?x992), adjoins(?x344, ?x1603), combatants(?x3654, ?x1603) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #6887 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 0cdbq; *> query: (?x1603, 047lj) <- combatants(?x94, ?x1603), nationality(?x6766, ?x1603), award_nominee(?x6766, ?x2304) *> conf = 0.04 ranks of expected_values: 203 EVAL 06bnz adjoins 047lj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 197.000 129.000 0.845 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #1438-0k3nk PRED entity: 0k3nk PRED relation: partially_contains! PRED expected values: 05kj_ => 85 concepts (34 used for prediction) PRED predicted values (max 10 best out of 420): 03v0t (0.57 #144, 0.25 #797, 0.21 #986), 04ych (0.43 #110, 0.19 #763, 0.16 #952), 02j9z (0.39 #849, 0.32 #1131, 0.26 #1225), 05rgl (0.33 #557, 0.32 #1028, 0.30 #1121), 03s5t (0.33 #557, 0.32 #1028, 0.30 #1121), 05kj_ (0.33 #557, 0.32 #1028, 0.30 #1121), 059_c (0.33 #557, 0.32 #1028, 0.30 #1121), 059f4 (0.33 #557, 0.32 #1028, 0.30 #1121), 04_1l0v (0.33 #557, 0.32 #1028, 0.30 #1121), 02gt5s (0.33 #557, 0.32 #1028, 0.30 #1121) >> Best rule #144 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 04yf_; 02v3m7; 05lx3; 04ykz; >> query: (?x6195, 03v0t) <- partially_contains(?x94, ?x6195), contains(?x94, ?x11979), contains(?x94, ?x6846), geographic_distribution(?x1423, ?x94), ?x6846 = 01y20v, ?x11979 = 0s987 >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #557 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 01smm; *> query: (?x6195, ?x726) <- partially_contains(?x4600, ?x6195), adjoins(?x726, ?x4600), contains(?x8483, ?x6195), time_zones(?x4600, ?x2950) *> conf = 0.33 ranks of expected_values: 6 EVAL 0k3nk partially_contains! 05kj_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 85.000 34.000 0.571 http://example.org/location/location/partially_contains #1437-01vw20_ PRED entity: 01vw20_ PRED relation: profession PRED expected values: 09jwl => 82 concepts (80 used for prediction) PRED predicted values (max 10 best out of 123): 09jwl (0.75 #747, 0.69 #5567, 0.68 #6444), 0dz3r (0.69 #2, 0.48 #1608, 0.46 #732), 01d_h8 (0.41 #1757, 0.37 #4386, 0.37 #4532), 01c72t (0.31 #22, 0.29 #5572, 0.28 #6449), 03gjzk (0.29 #1765, 0.26 #4540, 0.25 #4394), 0dxtg (0.29 #1764, 0.25 #8491, 0.25 #10097), 039v1 (0.28 #5584, 0.27 #6461, 0.27 #764), 0d1pc (0.24 #1946, 0.23 #2238, 0.22 #1362), 02jknp (0.21 #2927, 0.19 #9216, 0.18 #3657), 0kyk (0.16 #466, 0.14 #320, 0.12 #28) >> Best rule #747 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 03ds3; 0157m; >> query: (?x2987, 09jwl) <- award(?x2987, ?x1389), participant(?x5536, ?x2987), instrumentalists(?x227, ?x2987) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vw20_ profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 80.000 0.747 http://example.org/people/person/profession #1436-0jltp PRED entity: 0jltp PRED relation: category PRED expected values: 08mbj5d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #18, 0.85 #49, 0.85 #36) >> Best rule #18 for best value: >> intensional similarity = 2 >> extensional distance = 83 >> proper extension: 01gv_f; 0dzf_; >> query: (?x12211, 08mbj5d) <- award(?x12211, ?x1389), ?x1389 = 01c427 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0jltp category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.906 http://example.org/common/topic/webpage./common/webpage/category #1435-025ljp PRED entity: 025ljp PRED relation: country_of_origin PRED expected values: 09c7w0 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 12): 09c7w0 (0.91 #558, 0.90 #453, 0.90 #442), 0d060g (0.49 #849, 0.33 #4, 0.14 #1168), 03_3d (0.37 #433, 0.37 #258, 0.34 #330), 07ssc (0.14 #1168, 0.14 #1156, 0.13 #1169), 02jx1 (0.14 #1168, 0.14 #1156, 0.13 #1169), 05v8c (0.14 #1168, 0.14 #1156, 0.13 #1169), 07c52 (0.03 #875, 0.03 #851, 0.02 #899), 0215n (0.03 #875), 03rt9 (0.02 #274, 0.02 #296, 0.02 #323), 03rjj (0.02 #268, 0.02 #385, 0.02 #491) >> Best rule #558 for best value: >> intensional similarity = 6 >> extensional distance = 66 >> proper extension: 063zky; 0b6m5fy; >> query: (?x9668, 09c7w0) <- program(?x6382, ?x9668), actor(?x9668, ?x13195), profession(?x13195, ?x1383), ?x1383 = 0np9r, award(?x6382, ?x2585), award_winner(?x2585, ?x248) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025ljp country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.912 http://example.org/tv/tv_program/country_of_origin #1434-0fkwzs PRED entity: 0fkwzs PRED relation: actor PRED expected values: 06tp4h 01c65z => 114 concepts (32 used for prediction) PRED predicted values (max 10 best out of 779): 0sw6y (0.36 #1776, 0.30 #852, 0.06 #13794), 02gf_l (0.36 #1490, 0.30 #566, 0.06 #13508), 01r4bps (0.27 #1733, 0.20 #809, 0.04 #12827), 031c2r (0.20 #861, 0.18 #1785, 0.04 #13803), 029cpw (0.20 #548, 0.18 #1472, 0.04 #13490), 02wrhj (0.20 #134, 0.18 #1058, 0.04 #13076), 0sw62 (0.20 #757, 0.09 #1681, 0.04 #13699), 019803 (0.20 #849, 0.09 #1773, 0.03 #13791), 0534nr (0.20 #798, 0.03 #10967, 0.03 #13740), 01w1ywm (0.20 #617, 0.03 #10786, 0.03 #13559) >> Best rule #1776 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 019nnl; 01h72l; 015w8_; 099pks; 01hvv0; >> query: (?x8554, 0sw6y) <- genre(?x8554, ?x10159), genre(?x8554, ?x258), actor(?x8554, ?x381), languages(?x8554, ?x254), ?x258 = 05p553, ?x10159 = 025s89p >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0fkwzs actor 01c65z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 32.000 0.364 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor EVAL 0fkwzs actor 06tp4h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 32.000 0.364 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #1433-023gxx PRED entity: 023gxx PRED relation: film_release_region PRED expected values: 0b90_r 0d0vqn 05qhw 015fr 0345h => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 174): 09c7w0 (0.92 #3645, 0.92 #6229, 0.92 #6380), 0d0vqn (0.92 #310, 0.91 #461, 0.90 #763), 05qhw (0.92 #317, 0.90 #770, 0.89 #468), 05r4w (0.88 #757, 0.88 #455, 0.83 #304), 05b4w (0.85 #514, 0.83 #363, 0.79 #816), 0345h (0.85 #786, 0.83 #333, 0.83 #484), 015fr (0.84 #471, 0.82 #773, 0.81 #320), 06bnz (0.83 #798, 0.80 #496, 0.77 #345), 0d060g (0.81 #762, 0.81 #460, 0.75 #309), 0b90_r (0.79 #458, 0.78 #760, 0.71 #307) >> Best rule #3645 for best value: >> intensional similarity = 3 >> extensional distance = 1007 >> proper extension: 0170z3; 014lc_; 02d413; 014_x2; 0d90m; 03qcfvw; 09sh8k; 0m313; 034qmv; 0g22z; ... >> query: (?x3081, 09c7w0) <- film_release_region(?x3081, ?x3277), administrative_area_type(?x3277, ?x2792), nominated_for(?x2596, ?x3081) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #310 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 46 *> proper extension: 011yrp; 0gkz15s; 08hmch; 01c22t; 0c0nhgv; 047msdk; 0gmcwlb; 0dtfn; 011yqc; 0bh8yn3; ... *> query: (?x3081, 0d0vqn) <- film_release_region(?x3081, ?x3277), film_release_region(?x3081, ?x1453), film_release_region(?x3081, ?x1023), ?x3277 = 06t8v, ?x1023 = 0ctw_b, ?x1453 = 06qd3 *> conf = 0.92 ranks of expected_values: 2, 3, 6, 7, 10 EVAL 023gxx film_release_region 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 68.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 023gxx film_release_region 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 68.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 023gxx film_release_region 05qhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 023gxx film_release_region 0d0vqn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 023gxx film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 68.000 68.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1432-02js9p PRED entity: 02js9p PRED relation: nationality PRED expected values: 09c7w0 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 45): 09c7w0 (0.81 #501, 0.76 #801, 0.76 #901), 0chghy (0.42 #1705, 0.20 #10, 0.10 #210), 02jx1 (0.42 #1705, 0.15 #333, 0.11 #433), 07ssc (0.42 #1705, 0.13 #315, 0.10 #215), 0d060g (0.11 #107, 0.06 #3620, 0.06 #2516), 03rk0 (0.06 #8579, 0.05 #8981, 0.05 #9381), 0f8l9c (0.04 #422, 0.04 #4821, 0.03 #322), 0d0vqn (0.04 #4821, 0.02 #309, 0.02 #409), 0345h (0.04 #4821, 0.02 #2540, 0.02 #4048), 03rt9 (0.04 #4821, 0.02 #1114, 0.02 #2522) >> Best rule #501 for best value: >> intensional similarity = 3 >> extensional distance = 169 >> proper extension: 01386_; 0dq9wx; 02jm9c; >> query: (?x7014, 09c7w0) <- location(?x7014, ?x1523), place_of_birth(?x7014, ?x4455), ?x1523 = 030qb3t >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02js9p nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.813 http://example.org/people/person/nationality #1431-02vgh PRED entity: 02vgh PRED relation: influenced_by! PRED expected values: 01r0t_j => 80 concepts (44 used for prediction) PRED predicted values (max 10 best out of 44): 0167xy (0.08 #1987, 0.07 #3020, 0.06 #3536), 01kcms4 (0.08 #1839, 0.07 #2872, 0.06 #3388), 05xq9 (0.06 #3818, 0.05 #4853, 0.03 #6406), 01vvyfh (0.06 #3762, 0.05 #4797, 0.03 #6350), 03g5jw (0.06 #4180, 0.05 #7803, 0.04 #14029), 014_xj (0.06 #4626, 0.04 #5661, 0.02 #10325), 017mbb (0.06 #4511, 0.04 #5546, 0.02 #10210), 0dw4g (0.06 #4369, 0.04 #5404, 0.02 #10068), 015f7 (0.03 #5813, 0.02 #8402, 0.02 #9959), 01trhmt (0.03 #5781, 0.02 #8370, 0.02 #9927) >> Best rule #1987 for best value: >> intensional similarity = 10 >> extensional distance = 10 >> proper extension: 07h76; >> query: (?x6986, 0167xy) <- artists(?x474, ?x6986), artist(?x3265, ?x6986), group(?x1166, ?x6986), group(?x716, ?x6986), group(?x315, ?x6986), ?x315 = 0l14md, ?x716 = 018vs, ?x3265 = 015_1q, category(?x6986, ?x134), ?x1166 = 05148p4 >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #11725 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 65 *> proper extension: 08n__5; *> query: (?x6986, 01r0t_j) <- origin(?x6986, ?x9878), contains(?x1310, ?x9878), nationality(?x57, ?x1310), featured_film_locations(?x708, ?x1310), country(?x1339, ?x1310) *> conf = 0.03 ranks of expected_values: 12 EVAL 02vgh influenced_by! 01r0t_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 80.000 44.000 0.083 http://example.org/influence/influence_node/influenced_by #1430-02pyyld PRED entity: 02pyyld PRED relation: sport PRED expected values: 039yzs => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 8): 018w8 (0.80 #251, 0.77 #279, 0.76 #337), 039yzs (0.71 #218, 0.71 #209, 0.67 #189), 02vx4 (0.52 #604, 0.52 #613, 0.51 #622), 018jz (0.38 #309, 0.37 #467, 0.27 #494), 0jm_ (0.36 #354, 0.29 #447, 0.27 #401), 03tmr (0.29 #193, 0.27 #285, 0.22 #221), 06f3l (0.11 #229, 0.04 #388, 0.02 #426), 09xp_ (0.04 #385, 0.02 #423, 0.01 #543) >> Best rule #251 for best value: >> intensional similarity = 27 >> extensional distance = 8 >> proper extension: 0jmdb; 0jml5; 0jm7n; 0jm5b; 0jmgb; >> query: (?x11789, 018w8) <- team(?x13002, ?x11789), team(?x6848, ?x11789), team(?x5755, ?x11789), team(?x1348, ?x11789), ?x1348 = 01pv51, team(?x13002, ?x12370), position(?x6847, ?x13002), position(?x1578, ?x13002), ?x5755 = 0355dz, ?x12370 = 026dqjm, ?x1578 = 0jm2v, ?x6848 = 02_ssl, team(?x12451, ?x6847), team(?x12162, ?x6847), team(?x10736, ?x6847), team(?x10441, ?x6847), team(?x8824, ?x6847), team(?x7378, ?x6847), ?x8824 = 05g_nr, ?x10441 = 0b_71r, ?x10736 = 0f9rw9, ?x12451 = 0b_6xf, colors(?x6847, ?x332), teams(?x5771, ?x6847), ?x7378 = 0bzrxn, ?x12162 = 0b_6_l, position(?x11789, ?x5755) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #218 for first EXPECTED value: *> intensional similarity = 27 *> extensional distance = 5 *> proper extension: 04088s0; *> query: (?x11789, 039yzs) <- team(?x13209, ?x11789), team(?x9974, ?x11789), team(?x9146, ?x11789), team(?x6802, ?x11789), team(?x4368, ?x11789), team(?x2302, ?x11789), ?x6802 = 0br1x_, team(?x9974, ?x12370), team(?x9974, ?x9909), ?x12370 = 026dqjm, locations(?x4368, ?x3983), locations(?x4368, ?x3786), locations(?x4368, ?x674), locations(?x9974, ?x1860), instance_of_recurring_event(?x9974, ?x10863), locations(?x9146, ?x5771), locations(?x9146, ?x5719), colors(?x11789, ?x332), ?x10863 = 02jp2w, ?x13209 = 0b_734, ?x3786 = 071cn, ?x3983 = 0fr0t, ?x674 = 0f2r6, ?x5771 = 0fpzwf, ?x5719 = 0f2rq, ?x2302 = 0b_77q, ?x9909 = 026wlnm *> conf = 0.71 ranks of expected_values: 2 EVAL 02pyyld sport 039yzs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.800 http://example.org/sports/sports_team/sport #1429-04gr35 PRED entity: 04gr35 PRED relation: award PRED expected values: 05p09zm => 111 concepts (85 used for prediction) PRED predicted values (max 10 best out of 291): 05p09zm (0.41 #934, 0.16 #13773, 0.16 #1339), 04kxsb (0.37 #936, 0.11 #1341, 0.09 #2151), 0fc9js (0.36 #216, 0.15 #621, 0.06 #1836), 09sb52 (0.36 #18674, 0.35 #851, 0.33 #20700), 05zr6wv (0.35 #827, 0.17 #1232, 0.15 #2447), 0f4x7 (0.33 #841, 0.12 #1246, 0.10 #2461), 04ljl_l (0.33 #813, 0.10 #2028, 0.08 #6484), 0gkvb7 (0.31 #432, 0.18 #27, 0.09 #1242), 02grdc (0.31 #437, 0.05 #1652, 0.03 #4487), 05pcn59 (0.30 #891, 0.14 #2511, 0.13 #7777) >> Best rule #934 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 046qq; >> query: (?x10310, 05p09zm) <- award_winner(?x3854, ?x10310), award(?x10310, ?x1312), ?x1312 = 07cbcy >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gr35 award 05p09zm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 85.000 0.413 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1428-0c1d0 PRED entity: 0c1d0 PRED relation: locations! PRED expected values: 0b_6h7 => 241 concepts (221 used for prediction) PRED predicted values (max 10 best out of 109): 0b_6rk (0.33 #172, 0.29 #1054, 0.28 #802), 0b_75k (0.28 #805, 0.19 #5344, 0.15 #1561), 0b_6pv (0.27 #1214, 0.21 #2223, 0.19 #1592), 0bzrsh (0.23 #1213, 0.22 #835, 0.21 #2222), 0b_6x2 (0.23 #1167, 0.19 #5076, 0.18 #5707), 0b_6s7 (0.23 #1200, 0.15 #5109, 0.14 #2209), 0b_6jz (0.23 #5329, 0.17 #2177, 0.16 #5708), 0b_6lb (0.22 #833, 0.18 #1211, 0.17 #2220), 0b_6v_ (0.21 #2208, 0.17 #5360, 0.17 #821), 0b_6q5 (0.18 #1228, 0.18 #4885, 0.17 #5389) >> Best rule #172 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 094jv; >> query: (?x8263, 0b_6rk) <- citytown(?x2056, ?x8263), locations(?x3797, ?x8263), place_of_birth(?x329, ?x8263), currency(?x8263, ?x170) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2182 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 0pc6x; 0qpsn; *> query: (?x8263, 0b_6h7) <- citytown(?x2056, ?x8263), locations(?x3797, ?x8263), county(?x8263, ?x10067) *> conf = 0.17 ranks of expected_values: 13 EVAL 0c1d0 locations! 0b_6h7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 241.000 221.000 0.333 http://example.org/time/event/locations #1427-0416y94 PRED entity: 0416y94 PRED relation: film_crew_role PRED expected values: 0215hd 089g0h => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 28): 0215hd (0.57 #231, 0.17 #916, 0.17 #45), 089g0h (0.54 #232, 0.17 #46, 0.14 #358), 0d2b38 (0.50 #238, 0.13 #364, 0.13 #923), 01vx2h (0.46 #226, 0.40 #414, 0.37 #2035), 0dxtw (0.44 #1409, 0.43 #2159, 0.43 #1879), 02vs3x5 (0.25 #19, 0.11 #50, 0.10 #3593), 02rh1dz (0.18 #224, 0.16 #909, 0.15 #2158), 020xn5 (0.18 #222, 0.10 #3593, 0.06 #627), 015h31 (0.14 #223, 0.10 #3593, 0.10 #2032), 033smt (0.14 #240, 0.10 #3593, 0.08 #366) >> Best rule #231 for best value: >> intensional similarity = 5 >> extensional distance = 70 >> proper extension: 08052t3; 05fgt1; 05zpghd; 02qsqmq; 0660b9b; 05pdd86; 01gwk3; 034qbx; 04y9mm8; 0642ykh; ... >> query: (?x1318, 0215hd) <- film_crew_role(?x1318, ?x2472), film_crew_role(?x1318, ?x1171), ?x1171 = 09vw2b7, titles(?x53, ?x1318), ?x2472 = 01xy5l_ >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0416y94 film_crew_role 089g0h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.569 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0416y94 film_crew_role 0215hd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.569 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1426-04bpm6 PRED entity: 04bpm6 PRED relation: category PRED expected values: 08mbj5d => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #21, 0.82 #20, 0.82 #19) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 136 >> proper extension: 0147dk; 01v40wd; 0677ng; >> query: (?x1715, 08mbj5d) <- artists(?x2937, ?x1715), artists(?x2937, ?x10012), ?x10012 = 03j3pg9 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bpm6 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.826 http://example.org/common/topic/webpage./common/webpage/category #1425-01jwxx PRED entity: 01jwxx PRED relation: film! PRED expected values: 01ycbq 0164r9 => 83 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1255): 0gr36 (0.25 #497, 0.06 #4649, 0.06 #6726), 0gm34 (0.25 #1295, 0.06 #5447, 0.06 #7524), 015q43 (0.25 #902, 0.06 #5054, 0.06 #7131), 037w7r (0.25 #1582, 0.06 #5734, 0.06 #7811), 0m0hw (0.25 #1168, 0.06 #7397, 0.02 #45677), 016ggh (0.18 #6017, 0.17 #8094, 0.07 #18476), 0h5g_ (0.18 #4225, 0.17 #6302, 0.06 #95510), 0z4s (0.18 #4219, 0.17 #6296, 0.06 #95510), 01swck (0.14 #15335, 0.03 #19487, 0.02 #83848), 05sq84 (0.14 #2311, 0.06 #4387, 0.06 #6464) >> Best rule #497 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 01fx6y; 0glbqt; >> query: (?x4971, 0gr36) <- language(?x4971, ?x254), titles(?x512, ?x4971), ?x512 = 07ssc, film(?x269, ?x4971), cinematography(?x4971, ?x4561), ?x4561 = 06nz46 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #8633 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 20 *> proper extension: 0415ggl; *> query: (?x4971, 01ycbq) <- films(?x5954, ?x4971), genre(?x4971, ?x53), film(?x269, ?x4971), ?x5954 = 0fzyg, titles(?x512, ?x4971) *> conf = 0.05 ranks of expected_values: 217, 475 EVAL 01jwxx film! 0164r9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 83.000 66.000 0.250 http://example.org/film/actor/film./film/performance/film EVAL 01jwxx film! 01ycbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 83.000 66.000 0.250 http://example.org/film/actor/film./film/performance/film #1424-048wrb PRED entity: 048wrb PRED relation: film PRED expected values: 05zpghd => 85 concepts (56 used for prediction) PRED predicted values (max 10 best out of 313): 08jgk1 (0.35 #59113, 0.35 #42992, 0.35 #41200), 031hcx (0.03 #15607, 0.02 #6650, 0.01 #22772), 0f42nz (0.03 #17032, 0.03 #18823, 0.01 #36735), 03177r (0.03 #14797, 0.01 #82871, 0.01 #21962), 02qr3k8 (0.03 #63987, 0.02 #17413, 0.02 #38907), 0bvn25 (0.02 #9008, 0.02 #5425, 0.02 #1842), 01l_pn (0.02 #9925, 0.01 #6342, 0.01 #22464), 03l6q0 (0.02 #9501, 0.01 #5918), 03bx2lk (0.02 #5560, 0.02 #18099, 0.02 #16308), 06gb1w (0.02 #6109, 0.01 #9692, 0.01 #16857) >> Best rule #59113 for best value: >> intensional similarity = 3 >> extensional distance = 1331 >> proper extension: 0b6yp2; 0854hr; 06p0s1; 02vkvcz; >> query: (?x7543, ?x1631) <- gender(?x7543, ?x231), award(?x7543, ?x2016), award_winner(?x1631, ?x7543) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #6330 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 247 *> proper extension: 0n6f8; 01qvgl; 02p21g; 01wxyx1; 02mjmr; 01gbbz; 046lt; 02wb6yq; 0d06m5; 024dgj; ... *> query: (?x7543, 05zpghd) <- gender(?x7543, ?x231), award_winner(?x944, ?x7543), participant(?x7543, ?x2143) *> conf = 0.01 ranks of expected_values: 124 EVAL 048wrb film 05zpghd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 85.000 56.000 0.351 http://example.org/film/actor/film./film/performance/film #1423-05650n PRED entity: 05650n PRED relation: film! PRED expected values: 03h_9lg 01n7qlf => 92 concepts (38 used for prediction) PRED predicted values (max 10 best out of 982): 06fxnf (0.47 #74779, 0.46 #70625, 0.44 #43616), 086k8 (0.47 #74779, 0.46 #70625, 0.44 #12459), 013t9y (0.43 #68548, 0.40 #49850, 0.32 #35307), 0p8r1 (0.33 #2661, 0.19 #8890, 0.18 #6814), 085q5 (0.19 #3795, 0.10 #10024, 0.05 #20409), 015pvh (0.19 #3177, 0.06 #7330, 0.06 #9406), 01rcmg (0.14 #3546, 0.04 #7699, 0.04 #9775), 01wy5m (0.14 #856, 0.01 #48628, 0.01 #25778), 0f0kz (0.10 #6744, 0.10 #8820, 0.05 #2591), 02lhm2 (0.10 #3040, 0.07 #963, 0.03 #23808) >> Best rule #74779 for best value: >> intensional similarity = 4 >> extensional distance = 441 >> proper extension: 03s6l2; 0pdp8; 0dgpwnk; 0blpg; 03kx49; 034hwx; 01xlqd; 09rvwmy; >> query: (?x5839, ?x382) <- genre(?x5839, ?x258), nominated_for(?x382, ?x5839), film(?x2275, ?x5839), ?x258 = 05p553 >> conf = 0.47 => this is the best rule for 2 predicted values *> Best rule #10515 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 88 *> proper extension: 0372j5; *> query: (?x5839, 03h_9lg) <- region(?x5839, ?x512), nominated_for(?x382, ?x5839), film_crew_role(?x5839, ?x137), film(?x609, ?x5839) *> conf = 0.07 ranks of expected_values: 103 EVAL 05650n film! 01n7qlf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 38.000 0.471 http://example.org/film/actor/film./film/performance/film EVAL 05650n film! 03h_9lg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 92.000 38.000 0.471 http://example.org/film/actor/film./film/performance/film #1422-06kknt PRED entity: 06kknt PRED relation: student PRED expected values: 055c8 => 117 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1798): 01pqy_ (0.33 #2980, 0.10 #7148, 0.09 #13400), 02cyfz (0.33 #2415, 0.10 #6583, 0.09 #12835), 01wwvt2 (0.33 #2446, 0.10 #6614, 0.09 #12866), 0ff3y (0.33 #4145, 0.10 #8313, 0.07 #22902), 01nzz8 (0.33 #3046, 0.10 #7214, 0.06 #17634), 01wj92r (0.33 #2528, 0.10 #6696, 0.06 #17116), 01zfmm (0.33 #2522, 0.10 #6690, 0.05 #10858), 039bp (0.33 #2234, 0.10 #6402, 0.05 #10570), 06pj8 (0.33 #2404, 0.10 #6572, 0.05 #21161), 01pj3h (0.33 #3996, 0.10 #8164, 0.05 #22753) >> Best rule #2980 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 065y4w7; >> query: (?x12063, 01pqy_) <- student(?x12063, ?x13084), student(?x12063, ?x8374), citytown(?x12063, ?x1523), ?x13084 = 01hbq0, ?x8374 = 023361 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06kknt student 055c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 117.000 88.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #1421-01v40wd PRED entity: 01v40wd PRED relation: award PRED expected values: 01c9dd => 119 concepts (117 used for prediction) PRED predicted values (max 10 best out of 284): 02f6ym (0.60 #254, 0.50 #1050, 0.38 #652), 01c9dd (0.52 #1900, 0.18 #25088, 0.16 #26283), 01c427 (0.40 #85, 0.38 #881, 0.38 #483), 01by1l (0.40 #113, 0.37 #2502, 0.33 #14049), 01bgqh (0.40 #43, 0.26 #13979, 0.26 #18760), 02f73p (0.40 #186, 0.25 #982, 0.19 #1778), 01ckbq (0.40 #89, 0.25 #885, 0.17 #1283), 03qbh5 (0.34 #2592, 0.25 #5380, 0.25 #4185), 09sb52 (0.34 #9200, 0.33 #16367, 0.33 #13579), 02f79n (0.30 #1928, 0.16 #26283, 0.15 #3919) >> Best rule #254 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 07r4c; 0g824; 06rgq; >> query: (?x3893, 02f6ym) <- participant(?x3894, ?x3893), artists(?x9630, ?x3894), ?x9630 = 012yc, instrumentalists(?x1166, ?x3893) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1900 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 01vxqyl; *> query: (?x3893, 01c9dd) <- artists(?x5630, ?x3893), award(?x3893, ?x3978), ?x3978 = 03t5b6 *> conf = 0.52 ranks of expected_values: 2 EVAL 01v40wd award 01c9dd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 119.000 117.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1420-0147w8 PRED entity: 0147w8 PRED relation: genre PRED expected values: 0lsxr 06q7n => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 95): 06q7n (0.82 #556, 0.82 #471, 0.80 #301), 05p553 (0.56 #770, 0.44 #1111, 0.41 #1706), 07s9rl0 (0.53 #1702, 0.51 #1447, 0.51 #2382), 01z4y (0.49 #784, 0.31 #1720, 0.30 #1125), 0c4xc (0.33 #809, 0.22 #1745, 0.21 #1150), 0lsxr (0.27 #350, 0.17 #945, 0.12 #1456), 0pr6f (0.25 #137, 0.10 #1073, 0.10 #3113), 03mqtr (0.25 #110, 0.09 #365, 0.07 #3657), 01t_vv (0.23 #800, 0.17 #1141, 0.16 #2416), 0hcr (0.19 #3677, 0.19 #3591, 0.18 #3762) >> Best rule #556 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 01b65l; >> query: (?x11734, 06q7n) <- nominated_for(?x2872, ?x11734), ?x2872 = 02pz3j5, country_of_origin(?x11734, ?x94), program(?x2062, ?x11734), ?x94 = 09c7w0 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 0147w8 genre 06q7n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.818 http://example.org/tv/tv_program/genre EVAL 0147w8 genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 54.000 54.000 0.818 http://example.org/tv/tv_program/genre #1419-028q6 PRED entity: 028q6 PRED relation: award_winner! PRED expected values: 05pd94v => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 116): 01c6qp (0.42 #160, 0.11 #5236, 0.11 #6082), 05pd94v (0.35 #143, 0.12 #1130, 0.10 #5501), 013b2h (0.31 #221, 0.14 #5297, 0.12 #6143), 02cg41 (0.23 #267, 0.14 #549, 0.11 #690), 01bx35 (0.19 #148, 0.13 #1135, 0.12 #1276), 02rjjll (0.18 #1556, 0.17 #1133, 0.14 #1274), 01s695 (0.16 #426, 0.16 #1131, 0.15 #144), 0gpjbt (0.16 #452, 0.15 #170, 0.13 #593), 056878 (0.15 #173, 0.10 #15795, 0.10 #11987), 01xqqp (0.15 #237, 0.10 #15795, 0.10 #11987) >> Best rule #160 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 01x15dc; >> query: (?x158, 01c6qp) <- award_nominee(?x158, ?x4343), ?x4343 = 02cx90, award(?x158, ?x159) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #143 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 01x15dc; *> query: (?x158, 05pd94v) <- award_nominee(?x158, ?x4343), ?x4343 = 02cx90, award(?x158, ?x159) *> conf = 0.35 ranks of expected_values: 2 EVAL 028q6 award_winner! 05pd94v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 143.000 0.423 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1418-0c57yj PRED entity: 0c57yj PRED relation: films! PRED expected values: 0jgxn => 98 concepts (55 used for prediction) PRED predicted values (max 10 best out of 45): 054yc0 (0.25 #290, 0.04 #759, 0.01 #5180), 04jjy (0.07 #632, 0.02 #5690, 0.02 #6796), 0fzyg (0.06 #1467, 0.06 #367, 0.05 #523), 06d4h (0.06 #356, 0.05 #512, 0.04 #5883), 081pw (0.06 #316, 0.05 #472, 0.04 #2992), 0bq3x (0.06 #343, 0.05 #499, 0.02 #1443), 0kbq (0.06 #418, 0.05 #574, 0.02 #1675), 0cm2xh (0.06 #360, 0.05 #516, 0.01 #3989), 0ktds (0.06 #462, 0.05 #618), 01vq3 (0.04 #823, 0.03 #7309, 0.02 #6830) >> Best rule #290 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0prrm; >> query: (?x3859, 054yc0) <- film(?x1814, ?x3859), written_by(?x3859, ?x2367), country(?x3859, ?x94), ?x1814 = 034np8, production_companies(?x3859, ?x7980) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c57yj films! 0jgxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 55.000 0.250 http://example.org/film/film_subject/films #1417-01w1kyf PRED entity: 01w1kyf PRED relation: location PRED expected values: 0mpbx => 91 concepts (89 used for prediction) PRED predicted values (max 10 best out of 82): 0dzt9 (0.70 #28919, 0.49 #8035, 0.48 #21690), 030qb3t (0.20 #5708, 0.16 #16150, 0.15 #10528), 02_286 (0.19 #840, 0.18 #16104, 0.18 #37), 04jpl (0.07 #17, 0.06 #1624, 0.06 #10462), 059rby (0.07 #16, 0.05 #2427, 0.05 #3231), 0cr3d (0.06 #22638, 0.06 #26653, 0.06 #28259), 0psxp (0.05 #289, 0.05 #1092, 0.04 #1896), 013yq (0.05 #119, 0.04 #1726, 0.03 #922), 0r0m6 (0.05 #218, 0.03 #1021, 0.03 #2629), 04vmp (0.05 #1157, 0.03 #354, 0.03 #2765) >> Best rule #28919 for best value: >> intensional similarity = 3 >> extensional distance = 1387 >> proper extension: 07m69t; 01h2_6; >> query: (?x5094, ?x9846) <- location(?x5094, ?x10428), nationality(?x5094, ?x94), place_of_birth(?x5094, ?x9846) >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w1kyf location 0mpbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 89.000 0.703 http://example.org/people/person/places_lived./people/place_lived/location #1416-0mnlq PRED entity: 0mnlq PRED relation: currency PRED expected values: 09nqf => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.88 #23, 0.88 #22, 0.87 #19) >> Best rule #23 for best value: >> intensional similarity = 5 >> extensional distance = 242 >> proper extension: 0mkdm; 0p07l; >> query: (?x12515, ?x170) <- second_level_divisions(?x94, ?x12515), adjoins(?x12515, ?x12233), adjoins(?x12515, ?x7421), contains(?x1426, ?x7421), currency(?x12233, ?x170) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mnlq currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.881 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #1415-04lqvlr PRED entity: 04lqvlr PRED relation: film_crew_role PRED expected values: 02r96rf => 110 concepts (108 used for prediction) PRED predicted values (max 10 best out of 32): 09vw2b7 (0.80 #172, 0.77 #741, 0.77 #305), 02r96rf (0.78 #737, 0.76 #268, 0.75 #636), 0d2b38 (0.67 #188, 0.60 #321, 0.52 #288), 01xy5l_ (0.60 #177, 0.57 #277, 0.47 #746), 089g0h (0.59 #751, 0.57 #315, 0.56 #650), 02_n3z (0.47 #166, 0.42 #600, 0.40 #634), 02vs3x5 (0.33 #87, 0.25 #120, 0.18 #936), 033smt (0.33 #190, 0.22 #624, 0.21 #759), 015h31 (0.33 #174, 0.18 #936, 0.17 #743), 0ckd1 (0.33 #169, 0.18 #936, 0.17 #302) >> Best rule #172 for best value: >> intensional similarity = 9 >> extensional distance = 13 >> proper extension: 05pdd86; 076xkps; >> query: (?x3881, 09vw2b7) <- genre(?x3881, ?x53), film_release_region(?x3881, ?x94), film_crew_role(?x3881, ?x4305), film_crew_role(?x3881, ?x2178), film_crew_role(?x3881, ?x2154), currency(?x3881, ?x170), ?x4305 = 0215hd, ?x2178 = 01pvkk, ?x2154 = 01vx2h >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #737 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 103 *> proper extension: 03g90h; 047gn4y; 026mfbr; 04q00lw; 04grkmd; 04ydr95; 05m_jsg; 03h4fq7; 047vnkj; 07_k0c0; ... *> query: (?x3881, 02r96rf) <- genre(?x3881, ?x53), film_release_region(?x3881, ?x94), film_crew_role(?x3881, ?x4305), film_crew_role(?x3881, ?x2178), currency(?x3881, ?x170), ?x4305 = 0215hd, film_crew_role(?x7792, ?x2178), ?x7792 = 02rrh1w *> conf = 0.78 ranks of expected_values: 2 EVAL 04lqvlr film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 110.000 108.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1414-03_8r PRED entity: 03_8r PRED relation: category PRED expected values: 08mbj5d => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #47, 0.25 #9, 0.06 #26) >> Best rule #47 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x2978, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_8r category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #1413-0fpj9pm PRED entity: 0fpj9pm PRED relation: artist! PRED expected values: 012b30 => 182 concepts (142 used for prediction) PRED predicted values (max 10 best out of 127): 0181dw (0.27 #9914, 0.17 #42, 0.14 #465), 01clyr (0.20 #9906, 0.11 #6239, 0.11 #3982), 015_1q (0.20 #6366, 0.20 #5378, 0.20 #14405), 01w40h (0.19 #9901, 0.17 #29, 0.11 #1157), 033hn8 (0.19 #2552, 0.13 #1847, 0.13 #3821), 03rhqg (0.18 #6221, 0.18 #1990, 0.18 #5374), 0fb0v (0.18 #148, 0.17 #7, 0.14 #994), 0n85g (0.17 #63, 0.15 #1473, 0.14 #4011), 011k1h (0.17 #574, 0.14 #2548, 0.13 #3817), 043g7l (0.17 #596, 0.10 #5390, 0.10 #4262) >> Best rule #9914 for best value: >> intensional similarity = 4 >> extensional distance = 291 >> proper extension: 02r1tx7; 08w4pm; >> query: (?x7236, 0181dw) <- artists(?x302, ?x7236), artist(?x6474, ?x7236), artist(?x6474, ?x6947), ?x6947 = 01vrnsk >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #514 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 01w02sy; 03f0fnk; 02r3cn; *> query: (?x7236, 012b30) <- type_of_union(?x7236, ?x566), ?x566 = 04ztj, role(?x7236, ?x227), spouse(?x7236, ?x2012) *> conf = 0.09 ranks of expected_values: 30 EVAL 0fpj9pm artist! 012b30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 182.000 142.000 0.273 http://example.org/music/record_label/artist #1412-037s9x PRED entity: 037s9x PRED relation: institution! PRED expected values: 014mlp => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 23): 014mlp (0.77 #841, 0.77 #259, 0.74 #284), 02h4rq6 (0.77 #357, 0.71 #1765, 0.69 #155), 019v9k (0.63 #1772, 0.62 #2731, 0.60 #2579), 03bwzr4 (0.55 #370, 0.51 #2585, 0.47 #1778), 02_xgp2 (0.50 #368, 0.48 #2583, 0.46 #1776), 016t_3 (0.45 #1766, 0.45 #358, 0.44 #2573), 0bkj86 (0.40 #363, 0.38 #1295, 0.38 #59), 013zdg (0.38 #58, 0.23 #1294, 0.20 #1595), 07s6fsf (0.37 #482, 0.37 #355, 0.34 #1763), 04zx3q1 (0.25 #356, 0.24 #2571, 0.24 #1288) >> Best rule #841 for best value: >> intensional similarity = 5 >> extensional distance = 82 >> proper extension: 01j_5k; 02m0sc; 019c57; >> query: (?x1981, 014mlp) <- currency(?x1981, ?x170), colors(?x1981, ?x1101), category(?x1981, ?x134), ?x170 = 09nqf, country(?x1981, ?x94) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 037s9x institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.774 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #1411-0lmm3 PRED entity: 0lmm3 PRED relation: teams! PRED expected values: 01z28b => 97 concepts (96 used for prediction) PRED predicted values (max 10 best out of 150): 01jp4s (0.33 #261, 0.25 #531, 0.08 #2692), 03hrz (0.25 #359, 0.08 #2520, 0.05 #3060), 061k5 (0.20 #782, 0.14 #1593, 0.12 #1863), 01n43d (0.20 #751, 0.08 #2642, 0.08 #2372), 0k33p (0.17 #1280, 0.12 #2090, 0.06 #2900), 01hvzr (0.17 #1345, 0.12 #2155, 0.06 #2965), 0h3y (0.17 #1088, 0.12 #1898, 0.06 #2708), 04jpl (0.17 #820, 0.04 #16478, 0.04 #16480), 0l3h (0.17 #947, 0.02 #5539, 0.02 #6349), 0g133 (0.14 #1538, 0.12 #1808, 0.08 #2618) >> Best rule #261 for best value: >> intensional similarity = 9 >> extensional distance = 1 >> proper extension: 01dwyd; >> query: (?x13090, 01jp4s) <- position(?x13090, ?x60), colors(?x13090, ?x4557), colors(?x13090, ?x3189), ?x3189 = 01g5v, ?x4557 = 019sc, team(?x8194, ?x13090), team(?x8194, ?x7286), colors(?x7286, ?x663), team(?x6317, ?x7286) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0lmm3 teams! 01z28b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 96.000 0.333 http://example.org/sports/sports_team_location/teams #1410-01svq8 PRED entity: 01svq8 PRED relation: nationality PRED expected values: 09c7w0 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 42): 09c7w0 (0.89 #12318, 0.85 #1701, 0.84 #6803), 02jx1 (0.41 #1933, 0.20 #833, 0.17 #1333), 06q1r (0.40 #377, 0.29 #477, 0.09 #1977), 07ssc (0.36 #1915, 0.20 #815, 0.18 #1015), 05tbn (0.33 #14632, 0.27 #14126), 0m7d0 (0.33 #14632, 0.27 #14126), 0d060g (0.22 #707, 0.14 #507, 0.10 #907), 0j5g9 (0.09 #1062, 0.01 #4663, 0.01 #4763), 0345h (0.07 #8437, 0.07 #8739, 0.07 #8939), 03rk0 (0.06 #12663, 0.06 #14978, 0.06 #13166) >> Best rule #12318 for best value: >> intensional similarity = 3 >> extensional distance = 913 >> proper extension: 05218gr; >> query: (?x13118, 09c7w0) <- place_of_birth(?x13118, ?x4499), dog_breed(?x4499, ?x1706), location(?x396, ?x4499) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01svq8 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.887 http://example.org/people/person/nationality #1409-01j8wk PRED entity: 01j8wk PRED relation: production_companies PRED expected values: 046b0s => 92 concepts (75 used for prediction) PRED predicted values (max 10 best out of 55): 046b0s (0.36 #271, 0.06 #1013, 0.04 #2424), 016tt2 (0.34 #2814, 0.32 #3728, 0.31 #2067), 0c_j5d (0.25 #170, 0.17 #6, 0.14 #88), 086k8 (0.18 #249, 0.16 #578, 0.12 #2069), 02slt7 (0.14 #112, 0.12 #194, 0.02 #2097), 04mwxk3 (0.14 #160, 0.12 #242, 0.02 #408), 05qd_ (0.13 #340, 0.12 #2077, 0.11 #2327), 016tw3 (0.13 #506, 0.12 #1001, 0.10 #2079), 0g1rw (0.11 #502, 0.09 #338, 0.09 #420), 054lpb6 (0.10 #1004, 0.08 #1999, 0.08 #2415) >> Best rule #271 for best value: >> intensional similarity = 4 >> extensional distance = 31 >> proper extension: 06zn1c; >> query: (?x2081, 046b0s) <- titles(?x812, ?x2081), country(?x2081, ?x390), film(?x574, ?x2081), ?x390 = 0chghy >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01j8wk production_companies 046b0s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 75.000 0.364 http://example.org/film/film/production_companies #1408-015pvh PRED entity: 015pvh PRED relation: participant PRED expected values: 029_3 => 57 concepts (22 used for prediction) PRED predicted values (max 10 best out of 5): 0gyx4 (0.02 #5516), 01dw4q (0.01 #19, 0.01 #1971), 0blt6 (0.01 #257), 0dvmd (0.01 #5425), 026c1 (0.01 #2095) >> Best rule #5516 for best value: >> intensional similarity = 2 >> extensional distance = 874 >> proper extension: 0jgd; 058j2; 02sch9; 02bh_v; 01nd9f; 0513yzt; >> query: (?x6255, 0gyx4) <- gender(?x6255, ?x514), ?x514 = 02zsn >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 015pvh participant 029_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 22.000 0.018 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #1407-04y0yc PRED entity: 04y0yc PRED relation: profession PRED expected values: 02hrh1q => 95 concepts (90 used for prediction) PRED predicted values (max 10 best out of 63): 02hrh1q (0.94 #7922, 0.93 #910, 0.89 #462), 01d_h8 (0.46 #901, 0.45 #155, 0.42 #1946), 0dxtg (0.30 #1954, 0.29 #1209, 0.29 #909), 0np9r (0.29 #2262, 0.27 #1067, 0.23 #2112), 02jknp (0.25 #753, 0.25 #1650, 0.24 #1799), 0d1pc (0.23 #1096, 0.21 #946, 0.21 #498), 03gjzk (0.23 #1956, 0.22 #1211, 0.19 #8817), 02t8yb (0.18 #977, 0.12 #82, 0.09 #231), 09jwl (0.17 #3308, 0.16 #6884, 0.16 #7182), 018gz8 (0.16 #1958, 0.14 #9399, 0.14 #9249) >> Best rule #7922 for best value: >> intensional similarity = 4 >> extensional distance = 1902 >> proper extension: 01vrx3g; 045931; >> query: (?x10155, 02hrh1q) <- film(?x10155, ?x5247), profession(?x10155, ?x4725), profession(?x11208, ?x4725), ?x11208 = 03h8_g >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04y0yc profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 90.000 0.944 http://example.org/people/person/profession #1406-012kyx PRED entity: 012kyx PRED relation: film_crew_role PRED expected values: 09zzb8 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 36): 0ch6mp2 (0.72 #2772, 0.72 #1617, 0.72 #3003), 09zzb8 (0.72 #2765, 0.71 #1610, 0.70 #3073), 02r96rf (0.66 #385, 0.64 #271, 0.63 #309), 01vx2h (0.51 #318, 0.47 #280, 0.44 #433), 0dxtw (0.47 #317, 0.43 #279, 0.39 #1621), 01pvkk (0.34 #128, 0.30 #281, 0.28 #2778), 02rh1dz (0.24 #316, 0.23 #278, 0.19 #431), 02ynfr (0.23 #323, 0.20 #399, 0.19 #191), 0215hd (0.19 #191, 0.13 #2785, 0.12 #173), 01xy5l_ (0.19 #191, 0.12 #16, 0.11 #283) >> Best rule #2772 for best value: >> intensional similarity = 4 >> extensional distance = 842 >> proper extension: 02d44q; 07k2mq; 0372j5; >> query: (?x6605, 0ch6mp2) <- film_crew_role(?x6605, ?x1171), language(?x6605, ?x254), ?x254 = 02h40lc, titles(?x571, ?x6605) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #2765 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 842 *> proper extension: 02d44q; 07k2mq; 0372j5; *> query: (?x6605, 09zzb8) <- film_crew_role(?x6605, ?x1171), language(?x6605, ?x254), ?x254 = 02h40lc, titles(?x571, ?x6605) *> conf = 0.72 ranks of expected_values: 2 EVAL 012kyx film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 116.000 0.719 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1405-0b9f7t PRED entity: 0b9f7t PRED relation: nationality PRED expected values: 03_3d => 69 concepts (68 used for prediction) PRED predicted values (max 10 best out of 37): 09c7w0 (0.80 #1001, 0.79 #2914, 0.78 #1809), 03_3d (0.79 #206, 0.79 #706, 0.76 #406), 0193qj (0.44 #2412), 02jx1 (0.31 #1638, 0.20 #333, 0.18 #2344), 07ssc (0.23 #1620, 0.15 #2326, 0.12 #315), 03rk0 (0.13 #1448, 0.07 #1247, 0.06 #5578), 0f8l9c (0.11 #3918, 0.08 #2333, 0.03 #1526), 03rt9 (0.11 #3918, 0.04 #1618, 0.04 #313), 0345h (0.11 #3918, 0.04 #2342, 0.03 #1535), 0chghy (0.11 #3918, 0.02 #4924, 0.02 #6836) >> Best rule #1001 for best value: >> intensional similarity = 5 >> extensional distance = 43 >> proper extension: 081jbk; 01tpl1p; >> query: (?x13156, 09c7w0) <- profession(?x13156, ?x1383), ?x1383 = 0np9r, gender(?x13156, ?x514), ?x514 = 02zsn, location(?x13156, ?x9559) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #206 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 01vs8ng; *> query: (?x13156, 03_3d) <- profession(?x13156, ?x1383), ?x1383 = 0np9r, special_performance_type(?x13156, ?x296), film(?x13156, ?x6999), ?x296 = 01kyvx *> conf = 0.79 ranks of expected_values: 2 EVAL 0b9f7t nationality 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 68.000 0.800 http://example.org/people/person/nationality #1404-04qzm PRED entity: 04qzm PRED relation: artist! PRED expected values: 01fjfv => 86 concepts (62 used for prediction) PRED predicted values (max 10 best out of 12): 01fjfv (0.27 #31, 0.23 #25, 0.18 #86), 03gfvsz (0.25 #6, 0.20 #46, 0.19 #36), 04y652m (0.22 #14, 0.15 #94, 0.12 #63), 04f73rc (0.11 #15, 0.08 #28, 0.07 #34), 0jrv_ (0.11 #13, 0.08 #26, 0.07 #32), 04n7jdv (0.02 #16, 0.02 #29, 0.01 #35), 09nwwf (0.02 #16, 0.02 #29, 0.01 #35), 05jt_ (0.02 #16, 0.02 #29, 0.01 #35), 06cp5 (0.02 #16, 0.02 #29, 0.01 #35), 01_bkd (0.02 #16, 0.02 #29, 0.01 #35) >> Best rule #31 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 07g2v; 01vw26l; >> query: (?x10427, 01fjfv) <- artists(?x6513, ?x10427), award(?x10427, ?x3103), artist(?x1954, ?x10427), ?x6513 = 06cp5 >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04qzm artist! 01fjfv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 62.000 0.267 http://example.org/broadcast/content/artist #1403-01nnsv PRED entity: 01nnsv PRED relation: student PRED expected values: 032_jg => 44 concepts (44 used for prediction) PRED predicted values (max 10 best out of 483): 042xrr (0.05 #788, 0.02 #27158, 0.02 #27159), 02_l96 (0.05 #878, 0.02 #27158, 0.01 #29249), 05sj55 (0.05 #1346, 0.01 #7613, 0.01 #9702), 04xbr4 (0.05 #2004, 0.01 #8271, 0.01 #10360), 03gkn5 (0.05 #554, 0.01 #6821, 0.01 #8910), 01_xtx (0.05 #628, 0.01 #6895, 0.01 #15251), 0fwy0h (0.05 #840, 0.01 #9196, 0.01 #11285), 04d2yp (0.05 #1944, 0.01 #18656), 01nr63 (0.05 #2001), 0pnf3 (0.05 #1745) >> Best rule #788 for best value: >> intensional similarity = 2 >> extensional distance = 18 >> proper extension: 02vk52z; 07wbk; 034h1h; 0d075m; 014dd0; 03f2fw; 07w42; 01kcmr; 0c0sl; 07k5l; ... >> query: (?x5750, 042xrr) <- citytown(?x5750, ?x108), ?x108 = 0rh6k >> conf = 0.05 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nnsv student 032_jg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 44.000 0.050 http://example.org/education/educational_institution/students_graduates./education/education/student #1402-01d_h8 PRED entity: 01d_h8 PRED relation: profession! PRED expected values: 0337vz 0h5f5n 01q_ph 01tvz5j 03f2_rc 0c1pj 02lf0c 0kr5_ 0187y5 018db8 02773m2 04l3_z 030pr 0207wx 04cf09 0pz91 043q6n_ 01pw2f1 0j582 01xcqc 09gffmz 02pb53 034np8 036c_0 0b_fw 0l56b 025tdwc 0jt90f5 0c9c0 0b_7k 0dn3n 056rgc 02xp18 02vyw 0bzyh 05hj_k 01n9d9 086sj 01vw20h 0grrq8 026l37 0b478 03sww 022g44 0534v 0362q0 054k_8 044f7 0mm1q 016bx2 0c8hct 09pl3f 01515w 01t6xz 0gs1_ 05cgy8 01t265 01520h 01vrnsk 06sn8m 04hw4b 01lz4tf 0h5jg5 0432cd 0184jw 0kjgl 02hy9p 07ftc0 0jvtp 06qgjh 09d5d5 035sc2 0gs5q 01r4zfk 02r6c_ 01v5h 03swmf 059x0w 034hck 04twmk 0738y5 026670 01p8r8 01wkmgb 03f02ct 03h40_7 0133sq 02j490 029k55 02ghq 05hjmd 01sbhvd 0b_dh 0l15n 0127xk 01vv6xv 06kbb6 03fnyk 0n839 059j4x 026gb3v 099d4 066yfh 063b4k 06w38l 07db6x 01svq8 0dszr0 05h7tk 0qkj7 01b3bp => 34 concepts (20 used for prediction) PRED predicted values (max 10 best out of 3268): 0drdv (0.71 #32427, 0.60 #19342, 0.60 #16070), 03f2_rc (0.71 #29528, 0.60 #16443, 0.50 #36070), 0pnf3 (0.71 #32037, 0.60 #18952, 0.50 #38579), 015grj (0.71 #29626, 0.50 #36168, 0.50 #23082), 01bbwp (0.71 #31892, 0.50 #25348, 0.50 #12265), 01p8r8 (0.71 #31993, 0.50 #25449, 0.50 #22178), 0c9c0 (0.67 #20233, 0.62 #26165, 0.45 #26167), 0pz7h (0.67 #19794, 0.62 #26165, 0.45 #26167), 02p21g (0.67 #19953, 0.62 #26165, 0.40 #16683), 01gbbz (0.67 #20261, 0.62 #26165, 0.40 #16991) >> Best rule #32427 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 0cbd2; >> query: (?x319, 0drdv) <- profession(?x8215, ?x319), profession(?x8070, ?x319), profession(?x6593, ?x319), profession(?x1689, ?x319), ?x6593 = 01f2f8, location_of_ceremony(?x8215, ?x3883), produced_by(?x3784, ?x8070), executive_produced_by(?x723, ?x1689) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #29528 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 0cbd2; *> query: (?x319, 03f2_rc) <- profession(?x8215, ?x319), profession(?x8070, ?x319), profession(?x6593, ?x319), profession(?x1689, ?x319), ?x6593 = 01f2f8, location_of_ceremony(?x8215, ?x3883), produced_by(?x3784, ?x8070), executive_produced_by(?x723, ?x1689) *> conf = 0.71 ranks of expected_values: 2, 6, 7, 12, 25, 43, 48, 49, 53, 76, 102, 105, 107, 134, 158, 165, 174, 177, 179, 190, 218, 270, 271, 272, 273, 281, 283, 285, 286, 287, 288, 304, 306, 307, 312, 315, 320, 335, 336, 338, 351, 353, 355, 357, 358, 360, 362, 408, 429, 475, 498, 499, 501, 520, 527, 528, 531, 533, 535, 604, 641, 693, 696, 712, 723, 736, 737, 744, 788, 810, 811, 1028, 1036, 1075, 1248, 1267, 1304, 1306, 1409, 1410, 1432, 1454, 1465, 1544, 1589, 1607, 1608, 1609, 1621, 1623, 1625, 1629, 1631, 1638, 1652, 1696, 1738, 1790, 1830, 1863, 1895, 1973, 1992, 2408, 2505, 2591, 2919 EVAL 01d_h8 profession! 01b3bp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0qkj7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 05h7tk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0dszr0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01svq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 07db6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 06w38l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 063b4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 066yfh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 099d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 026gb3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 059j4x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0n839 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 03fnyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 06kbb6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01vv6xv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0127xk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0l15n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0b_dh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01sbhvd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 05hjmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 02ghq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 029k55 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 02j490 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0133sq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 03h40_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 03f02ct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01wkmgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01p8r8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 026670 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0738y5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 04twmk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 034hck CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 059x0w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 03swmf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01v5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 02r6c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01r4zfk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0gs5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 035sc2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 09d5d5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 06qgjh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0jvtp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 07ftc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 02hy9p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0kjgl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0184jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0432cd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0h5jg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01lz4tf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 04hw4b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 06sn8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01vrnsk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01520h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01t265 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 05cgy8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0gs1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01t6xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01515w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 09pl3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0c8hct CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 016bx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0mm1q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 044f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 054k_8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0362q0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0534v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 022g44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 03sww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0b478 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 026l37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0grrq8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01vw20h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 086sj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01n9d9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 05hj_k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0bzyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 02vyw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 02xp18 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 056rgc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0dn3n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0b_7k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0c9c0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0jt90f5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 025tdwc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0l56b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0b_fw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 036c_0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 034np8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 02pb53 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 09gffmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01xcqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0j582 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01pw2f1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 043q6n_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0pz91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 04cf09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0207wx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 030pr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 04l3_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 02773m2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 018db8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0187y5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0kr5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 02lf0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0c1pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 03f2_rc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01tvz5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 01q_ph CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0h5f5n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 20.000 0.714 http://example.org/people/person/profession EVAL 01d_h8 profession! 0337vz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 34.000 20.000 0.714 http://example.org/people/person/profession #1401-034qbx PRED entity: 034qbx PRED relation: film_crew_role PRED expected values: 02ynfr => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 31): 01vx2h (0.70 #137, 0.68 #361, 0.66 #297), 089g0h (0.70 #144, 0.59 #336, 0.50 #176), 0dxtw (0.69 #296, 0.65 #232, 0.62 #360), 0215hd (0.61 #143, 0.59 #335, 0.58 #175), 0d2b38 (0.61 #150, 0.58 #182, 0.52 #342), 02_n3z (0.31 #161, 0.27 #321, 0.26 #129), 02ynfr (0.27 #364, 0.23 #300, 0.21 #236), 015h31 (0.26 #135, 0.24 #295, 0.20 #7), 020xn5 (0.26 #134, 0.14 #326, 0.13 #1301), 033smt (0.22 #152, 0.15 #184, 0.15 #344) >> Best rule #137 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 0bth54; 05qbckf; 02yvct; 0g3zrd; 05fgt1; 05zy2cy; 04ydr95; 0dzlbx; 0642xf3; 08phg9; ... >> query: (?x6588, 01vx2h) <- film_crew_role(?x6588, ?x2472), film(?x902, ?x6588), film_release_distribution_medium(?x6588, ?x81), ?x2472 = 01xy5l_, crewmember(?x6588, ?x1643) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #364 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 77 *> proper extension: 09rvwmy; *> query: (?x6588, 02ynfr) <- film_crew_role(?x6588, ?x2091), film_crew_role(?x6588, ?x137), currency(?x6588, ?x170), film(?x722, ?x6588), ?x2091 = 02rh1dz, ?x137 = 09zzb8 *> conf = 0.27 ranks of expected_values: 7 EVAL 034qbx film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 83.000 83.000 0.696 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1400-09146g PRED entity: 09146g PRED relation: film_release_region PRED expected values: 04gzd 05qhw 0ctw_b => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 151): 0d0vqn (0.90 #1308, 0.90 #1794, 0.89 #984), 03h64 (0.89 #885, 0.88 #1371, 0.87 #1047), 02vzc (0.89 #869, 0.82 #1355, 0.79 #1031), 059j2 (0.87 #1009, 0.87 #1333, 0.86 #1819), 03_3d (0.87 #820, 0.83 #1306, 0.79 #982), 05qhw (0.87 #1316, 0.86 #992, 0.82 #830), 03rjj (0.87 #1304, 0.85 #1790, 0.85 #980), 06mkj (0.86 #1847, 0.83 #1361, 0.83 #1037), 035qy (0.85 #1337, 0.82 #1013, 0.82 #1823), 0345h (0.85 #1821, 0.81 #1335, 0.80 #1011) >> Best rule #1308 for best value: >> intensional similarity = 4 >> extensional distance = 87 >> proper extension: 0g56t9t; 0c3ybss; 03g90h; 0h1cdwq; 0401sg; 087wc7n; 0bwfwpj; 08hmch; 01c22t; 0872p_c; ... >> query: (?x1904, 0d0vqn) <- film_release_region(?x1904, ?x1122), film(?x398, ?x1904), award_winner(?x591, ?x398), ?x1122 = 09pmkv >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1316 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 0g56t9t; 0c3ybss; 03g90h; 0h1cdwq; 0401sg; 087wc7n; 0bwfwpj; 08hmch; 01c22t; 0872p_c; ... *> query: (?x1904, 05qhw) <- film_release_region(?x1904, ?x1122), film(?x398, ?x1904), award_winner(?x591, ?x398), ?x1122 = 09pmkv *> conf = 0.87 ranks of expected_values: 6, 22, 25 EVAL 09146g film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 71.000 71.000 0.899 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09146g film_release_region 05qhw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 71.000 71.000 0.899 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 09146g film_release_region 04gzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 71.000 71.000 0.899 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1399-07z1m PRED entity: 07z1m PRED relation: religion PRED expected values: 0631_ 019cr => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 24): 0631_ (0.82 #454, 0.81 #529, 0.80 #554), 019cr (0.81 #531, 0.80 #556, 0.80 #456), 058x5 (0.43 #1530, 0.39 #452, 0.37 #227), 092bf5 (0.43 #1530, 0.29 #885, 0.29 #810), 072w0 (0.43 #1530, 0.23 #541, 0.22 #466), 02t7t (0.28 #264, 0.25 #489, 0.25 #514), 03j6c (0.25 #36, 0.10 #161, 0.09 #888), 0kpl (0.25 #29, 0.05 #79, 0.03 #129), 07w8f (0.25 #44, 0.05 #94, 0.03 #144), 0n2g (0.06 #156, 0.03 #1134, 0.03 #883) >> Best rule #454 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 0g0syc; >> query: (?x1426, 0631_) <- district_represented(?x2712, ?x1426), district_represented(?x653, ?x1426), ?x653 = 070m6c, legislative_sessions(?x2860, ?x2712) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07z1m religion 019cr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.816 http://example.org/location/statistical_region/religions./location/religion_percentage/religion EVAL 07z1m religion 0631_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.816 http://example.org/location/statistical_region/religions./location/religion_percentage/religion #1398-0l6vl PRED entity: 0l6vl PRED relation: sports PRED expected values: 06f41 => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 40): 06f41 (0.82 #864, 0.80 #694, 0.78 #660), 03fyrh (0.80 #445, 0.80 #618, 0.80 #856), 07jjt (0.80 #445, 0.80 #618, 0.80 #856), 0dwxr (0.80 #445, 0.80 #618, 0.80 #856), 0d1t3 (0.80 #445, 0.80 #618, 0.80 #856), 064vjs (0.80 #445, 0.80 #618, 0.80 #856), 01sgl (0.69 #103, 0.68 #136, 0.67 #686), 019w9j (0.60 #225, 0.50 #567, 0.50 #188), 07bs0 (0.54 #730, 0.49 #241, 0.47 #863), 06z68 (0.50 #189, 0.49 #241, 0.45 #377) >> Best rule #864 for best value: >> intensional similarity = 54 >> extensional distance = 15 >> proper extension: 0sxrz; >> query: (?x391, 06f41) <- sports(?x391, ?x4045), sports(?x391, ?x3015), sports(?x391, ?x2315), sports(?x391, ?x1967), olympics(?x9251, ?x391), olympics(?x7287, ?x391), olympics(?x1355, ?x391), olympics(?x1353, ?x391), olympics(?x512, ?x391), olympics(?x205, ?x391), country(?x4045, ?x12929), country(?x4045, ?x8948), country(?x4045, ?x7032), country(?x4045, ?x6827), country(?x4045, ?x6437), country(?x4045, ?x6428), country(?x4045, ?x5445), country(?x4045, ?x4302), country(?x4045, ?x2267), ?x205 = 03rjj, ?x12929 = 034tl, medal(?x391, ?x422), ?x5445 = 0167v, ?x6827 = 05cc1, adjustment_currency(?x9251, ?x170), organization(?x9251, ?x127), ?x7032 = 01c4pv, teams(?x7287, ?x7464), olympics(?x4045, ?x775), country(?x3015, ?x87), sports(?x7688, ?x4045), sports(?x2369, ?x4045), sports(?x2131, ?x4045), taxonomy(?x3015, ?x939), time_zones(?x9251, ?x6582), ?x4302 = 06vbd, ?x6437 = 06v36, ?x512 = 07ssc, ?x87 = 05r4w, ?x6428 = 0j4b, ?x7688 = 0jkvj, titles(?x1967, ?x188), ?x2315 = 06wrt, ?x2267 = 03rj0, olympics(?x2044, ?x391), ?x2369 = 0lbbj, ?x2131 = 0lk8j, film_release_region(?x1919, ?x1353), film_release_region(?x1701, ?x1353), ?x8948 = 07f5x, ?x1701 = 0bh8yn3, ?x1919 = 0_7w6, ?x939 = 04n6k, location(?x10895, ?x1355) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l6vl sports 06f41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.824 http://example.org/user/jg/default_domain/olympic_games/sports #1397-01v2xl PRED entity: 01v2xl PRED relation: institution! PRED expected values: 01rr_d => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.74 #99, 0.74 #390, 0.73 #414), 014mlp (0.74 #102, 0.73 #393, 0.72 #514), 016t_3 (0.52 #100, 0.46 #512, 0.43 #487), 02_xgp2 (0.50 #13, 0.48 #1013, 0.47 #521), 0bkj86 (0.50 #9, 0.42 #202, 0.41 #178), 071tyz (0.50 #11, 0.15 #155, 0.12 #204), 03bwzr4 (0.48 #111, 0.45 #523, 0.44 #1015), 07s6fsf (0.33 #509, 0.33 #388, 0.31 #632), 028dcg (0.32 #657, 0.30 #1590, 0.22 #116), 04zx3q1 (0.25 #1002, 0.25 #732, 0.25 #2) >> Best rule #99 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 02gr81; 017j69; 09f2j; >> query: (?x11602, 02h4rq6) <- student(?x11602, ?x8306), currency(?x11602, ?x1099), major_field_of_study(?x11602, ?x4268), colors(?x11602, ?x3315), ?x4268 = 02822 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #18 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 07tg4; 07tk7; *> query: (?x11602, 01rr_d) <- student(?x11602, ?x8306), currency(?x11602, ?x1099), ?x1099 = 01nv4h, institution(?x1771, ?x11602), ?x8306 = 0xnc3 *> conf = 0.25 ranks of expected_values: 12 EVAL 01v2xl institution! 01rr_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 138.000 138.000 0.739 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #1396-03ndd PRED entity: 03ndd PRED relation: role PRED expected values: 05842k => 66 concepts (50 used for prediction) PRED predicted values (max 10 best out of 108): 018vs (0.88 #103, 0.83 #3603, 0.83 #2505), 0l14j_ (0.88 #103, 0.83 #2505, 0.82 #218), 07y_7 (0.88 #103, 0.82 #218, 0.80 #1097), 0mkg (0.88 #103, 0.82 #218, 0.75 #3156), 0jtg0 (0.88 #103, 0.82 #218, 0.72 #1521), 01v1d8 (0.88 #103, 0.82 #218, 0.70 #1157), 05842k (0.87 #3875, 0.85 #3013, 0.84 #2686), 013y1f (0.85 #2971, 0.83 #2505, 0.83 #2429), 03m5k (0.83 #2505, 0.82 #3374, 0.82 #1849), 03q5t (0.83 #2505, 0.77 #2396, 0.74 #1524) >> Best rule #103 for best value: >> intensional similarity = 25 >> extensional distance = 1 >> proper extension: 018vs; >> query: (?x4913, ?x716) <- role(?x4913, ?x4078), role(?x4913, ?x3716), role(?x4913, ?x1969), ?x1969 = 04rzd, ?x3716 = 03gvt, role(?x9219, ?x4913), role(?x4429, ?x4913), role(?x2944, ?x4913), role(?x2785, ?x4913), role(?x894, ?x4913), role(?x716, ?x4913), ?x894 = 03m5k, ?x2785 = 0jtg0, ?x4078 = 011k_j, role(?x1292, ?x4913), ?x2944 = 0l14j_, ?x4429 = 0g33q, ?x9219 = 01399x, group(?x4913, ?x5838), role(?x1148, ?x716), family(?x716, ?x7256), instrumentalists(?x716, ?x211), role(?x214, ?x716), instrumentalists(?x4913, ?x1489), ?x1148 = 02qjv >> conf = 0.88 => this is the best rule for 6 predicted values *> Best rule #3875 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 29 *> proper extension: 03qlv7; 05kms; *> query: (?x4913, 05842k) <- role(?x4913, ?x2048), role(?x4913, ?x1969), role(?x4913, ?x1166), role(?x2638, ?x1969), group(?x1969, ?x9706), group(?x1969, ?x5279), group(?x1969, ?x4909), role(?x1969, ?x11978), role(?x1489, ?x4913), ?x1166 = 05148p4, ?x2638 = 02fn5r, role(?x1969, ?x3418), ?x11978 = 02hrlh, role(?x2865, ?x1969), role(?x1292, ?x4913), role(?x1662, ?x1969), ?x4909 = 01cblr, ?x3418 = 02w4b, ?x5279 = 06nv27, ?x9706 = 01fchy, instrumentalists(?x2048, ?x483) *> conf = 0.87 ranks of expected_values: 7 EVAL 03ndd role 05842k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 66.000 50.000 0.875 http://example.org/music/performance_role/track_performances./music/track_contribution/role #1395-0gfq9 PRED entity: 0gfq9 PRED relation: entity_involved PRED expected values: 01c83m => 48 concepts (23 used for prediction) PRED predicted values (max 10 best out of 216): 04xzm (0.50 #708, 0.15 #1811, 0.14 #1343), 01m41_ (0.40 #1211, 0.14 #1370, 0.13 #776), 011zwl (0.33 #900, 0.33 #278, 0.25 #587), 012m_ (0.33 #76, 0.25 #466, 0.25 #386), 01llxp (0.33 #259, 0.25 #568, 0.25 #412), 0lzcs (0.33 #258, 0.25 #567, 0.25 #411), 05hks (0.33 #247, 0.25 #556, 0.25 #400), 083q7 (0.33 #168, 0.25 #477, 0.25 #321), 0212ny (0.33 #138, 0.17 #917, 0.13 #776), 04jvt (0.33 #82, 0.17 #861, 0.13 #776) >> Best rule #708 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 0f6rc; >> query: (?x3654, 04xzm) <- locations(?x3654, ?x2346), entity_involved(?x3654, ?x7602), entity_involved(?x3654, ?x3918), ?x2346 = 0d05w3, combatants(?x3278, ?x7602), entity_involved(?x3278, ?x279), combatants(?x10764, ?x3918), locations(?x3278, ?x172), combatants(?x456, ?x3918) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gfq9 entity_involved 01c83m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 48.000 23.000 0.500 http://example.org/base/culturalevent/event/entity_involved #1394-0n3g PRED entity: 0n3g PRED relation: exported_to! PRED expected values: 05r7t => 151 concepts (112 used for prediction) PRED predicted values (max 10 best out of 178): 0ctw_b (0.50 #194, 0.33 #75, 0.29 #614), 07ssc (0.36 #239, 0.36 #1142, 0.33 #2924), 0d05w3 (0.35 #1667, 0.28 #1727, 0.28 #1847), 06q1r (0.33 #403, 0.33 #45, 0.25 #165), 047t_ (0.33 #98, 0.25 #217, 0.25 #158), 04sj3 (0.33 #56, 0.25 #235, 0.25 #176), 0l3h (0.33 #43, 0.25 #222, 0.17 #401), 016zwt (0.33 #54, 0.25 #174, 0.17 #412), 07fsv (0.33 #47, 0.18 #1308, 0.17 #885), 0jdd (0.25 #213, 0.25 #154, 0.15 #993) >> Best rule #194 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03_3d; >> query: (?x5411, 0ctw_b) <- contains(?x1144, ?x5411), form_of_government(?x5411, ?x6065), location_of_ceremony(?x4922, ?x5411), ?x6065 = 01q20, exported_to(?x5411, ?x94) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3234 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 51 *> proper extension: 0hkt6; *> query: (?x5411, ?x8882) <- contains(?x9729, ?x5411), contains(?x9729, ?x8882), contains(?x9729, ?x1957), religion(?x5411, ?x109), taxonomy(?x8882, ?x939), country(?x1121, ?x1957) *> conf = 0.01 ranks of expected_values: 152 EVAL 0n3g exported_to! 05r7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 151.000 112.000 0.500 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #1393-02wkmx PRED entity: 02wkmx PRED relation: nominated_for PRED expected values: 02ylg6 0gpx6 => 63 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1414): 064lsn (0.80 #6362, 0.78 #11135, 0.75 #4771), 01kqq7 (0.80 #6362, 0.78 #11135, 0.75 #4771), 0f4_l (0.80 #6362, 0.78 #11135, 0.75 #4771), 02wk7b (0.80 #6362, 0.78 #11135, 0.75 #4771), 0_b9f (0.80 #6362, 0.78 #11135, 0.75 #4771), 07cw4 (0.80 #6362, 0.78 #11135, 0.75 #4771), 02ll45 (0.80 #6362, 0.78 #11135, 0.75 #4771), 0jzw (0.80 #6362, 0.78 #11135, 0.75 #4771), 0hv1t (0.80 #6362, 0.78 #11135, 0.75 #4771), 02rb607 (0.80 #6362, 0.78 #11135, 0.75 #4771) >> Best rule #6362 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 02rdxsh; >> query: (?x372, ?x810) <- nominated_for(?x372, ?x8277), nominated_for(?x372, ?x2525), award(?x810, ?x372), film(?x7780, ?x2525), ?x8277 = 02r858_, ?x7780 = 0252fh >> conf = 0.80 => this is the best rule for 12 predicted values *> Best rule #12726 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 018wng; 054ky1; 0c_dx; *> query: (?x372, ?x814) <- disciplines_or_subjects(?x372, ?x373), award(?x3117, ?x372), award_winner(?x372, ?x767), film(?x3117, ?x814) *> conf = 0.26 ranks of expected_values: 310, 467 EVAL 02wkmx nominated_for 0gpx6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 63.000 33.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02wkmx nominated_for 02ylg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 63.000 33.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1392-05dy7p PRED entity: 05dy7p PRED relation: nominated_for! PRED expected values: 0gr0m => 136 concepts (128 used for prediction) PRED predicted values (max 10 best out of 217): 019f4v (0.50 #53, 0.49 #8585, 0.42 #10956), 0gs9p (0.50 #64, 0.40 #19501, 0.38 #10967), 04dn09n (0.50 #35, 0.36 #8567, 0.28 #10701), 02pqp12 (0.50 #58, 0.31 #8590, 0.27 #532), 0gqy2 (0.50 #121, 0.31 #19321, 0.29 #9838), 099c8n (0.50 #56, 0.29 #8588, 0.27 #1004), 027dtxw (0.50 #4, 0.21 #8536, 0.18 #2611), 0gq9h (0.50 #10965, 0.46 #19262, 0.46 #19499), 0gr0m (0.44 #9776, 0.37 #10962, 0.35 #5984), 0k611 (0.40 #8604, 0.36 #10975, 0.36 #9789) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 095zlp; 01hv3t; >> query: (?x2402, 019f4v) <- films(?x9351, ?x2402), nominated_for(?x2940, ?x2402), nominated_for(?x484, ?x2402), ?x2940 = 06449 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #9776 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 166 *> proper extension: 02r1ysd; 01kt_j; *> query: (?x2402, 0gr0m) <- nominated_for(?x484, ?x2402), nominated_for(?x185, ?x2402), cinematography(?x186, ?x185) *> conf = 0.44 ranks of expected_values: 9 EVAL 05dy7p nominated_for! 0gr0m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 136.000 128.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1391-0713r PRED entity: 0713r PRED relation: colors PRED expected values: 019sc => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 19): 01g5v (0.90 #1289, 0.89 #907, 0.69 #1361), 019sc (0.89 #1274, 0.50 #190, 0.44 #359), 083jv (0.77 #1323, 0.76 #1305, 0.74 #1535), 06fvc (0.45 #314, 0.37 #864, 0.35 #677), 02rnmb (0.36 #551, 0.33 #611, 0.33 #459), 03vtbc (0.32 #806, 0.31 #360, 0.29 #379), 06kqt3 (0.26 #1148, 0.21 #997, 0.20 #203), 03wkwg (0.25 #67, 0.11 #1553, 0.05 #441), 036k5h (0.25 #577, 0.20 #203, 0.19 #204), 038hg (0.21 #997, 0.21 #1474, 0.20 #119) >> Best rule #1289 for best value: >> intensional similarity = 14 >> extensional distance = 116 >> proper extension: 03d555l; 024nj1; >> query: (?x4243, 01g5v) <- colors(?x4243, ?x3315), colors(?x8943, ?x3315), colors(?x8479, ?x3315), colors(?x2775, ?x3315), colors(?x9983, ?x3315), colors(?x2067, ?x3315), school(?x1010, ?x8479), school_type(?x8479, ?x3092), ?x9983 = 02q4ntp, ?x2067 = 05g76, school(?x3089, ?x8479), ?x8943 = 0qlnr, student(?x2775, ?x1447), ?x3089 = 03nt7j >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1274 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 111 *> proper extension: 02plv57; 01lpx8; 02fbb5; 026w398; *> query: (?x4243, 019sc) <- colors(?x4243, ?x3315), colors(?x8565, ?x3315), colors(?x8479, ?x3315), colors(?x3354, ?x3315), ?x8479 = 01hx2t, colors(?x8186, ?x3315), colors(?x4856, ?x3315), institution(?x865, ?x3354), ?x8565 = 05q2c, position(?x4856, ?x180), ?x180 = 01r3hr, school(?x4856, ?x3416), organization(?x346, ?x3354), ?x8186 = 0jnm_ *> conf = 0.89 ranks of expected_values: 2 EVAL 0713r colors 019sc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.898 http://example.org/sports/sports_team/colors #1390-06c97 PRED entity: 06c97 PRED relation: films PRED expected values: 016z9n => 194 concepts (116 used for prediction) PRED predicted values (max 10 best out of 737): 09sr0 (0.33 #448, 0.25 #2035, 0.25 #1506), 03m5y9p (0.25 #2538, 0.20 #3596, 0.20 #3067), 03bdkd (0.25 #2607, 0.20 #3665, 0.17 #4194), 0gmgwnv (0.20 #3487, 0.17 #4016, 0.12 #6663), 04f6df0 (0.20 #3059, 0.12 #6764, 0.11 #8881), 02yvct (0.17 #3811, 0.14 #4869, 0.11 #8046), 03xj05 (0.17 #4195, 0.14 #5253, 0.11 #8430), 049xgc (0.14 #5038, 0.11 #8215, 0.07 #38412), 047bynf (0.14 #5104, 0.11 #8281, 0.02 #36882), 080lkt7 (0.14 #4994, 0.03 #27765, 0.02 #44726) >> Best rule #448 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0d3k14; >> query: (?x5572, 09sr0) <- politician(?x1912, ?x5572), films(?x5572, ?x2989), place_of_death(?x5572, ?x739), person(?x1015, ?x5572), basic_title(?x5572, ?x265) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06c97 films 016z9n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 194.000 116.000 0.333 http://example.org/film/film_subject/films #1389-09l0x9 PRED entity: 09l0x9 PRED relation: school PRED expected values: 09f2j 0jkhr 01jq0j => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1215): 07szy (0.50 #1196, 0.50 #694, 0.40 #889), 06pwq (0.50 #1393, 0.47 #1796, 0.42 #1493), 019dwp (0.50 #1332, 0.38 #1231, 0.33 #633), 02pptm (0.50 #1162, 0.33 #477, 0.33 #177), 07vyf (0.40 #1831, 0.40 #917, 0.38 #1728), 01rc6f (0.40 #953, 0.40 #853, 0.38 #1361), 0j_sncb (0.40 #904, 0.38 #1312, 0.33 #613), 05x_5 (0.40 #946, 0.38 #1354, 0.33 #655), 015q1n (0.40 #938, 0.38 #1346, 0.33 #647), 01jswq (0.40 #899, 0.33 #1101, 0.33 #608) >> Best rule #1196 for best value: >> intensional similarity = 35 >> extensional distance = 6 >> proper extension: 04f4z1k; >> query: (?x6462, 07szy) <- draft(?x5229, ?x6462), draft(?x4546, ?x6462), draft(?x4469, ?x6462), school(?x6462, ?x6814), school(?x6462, ?x1011), school(?x6462, ?x735), school(?x6462, ?x466), team(?x11323, ?x4546), ?x466 = 01pl14, fraternities_and_sororities(?x6814, ?x3697), team(?x180, ?x5229), institution(?x4981, ?x6814), institution(?x1771, ?x6814), institution(?x734, ?x6814), school(?x3333, ?x1011), major_field_of_study(?x1011, ?x6756), ?x6756 = 0_jm, major_field_of_study(?x735, ?x373), institution(?x865, ?x735), student(?x735, ?x65), colors(?x5229, ?x663), currency(?x735, ?x170), school(?x580, ?x735), school(?x4469, ?x2948), institution(?x620, ?x1011), school_type(?x1011, ?x1507), category(?x3333, ?x134), student(?x1011, ?x400), ?x4981 = 03bwzr4, ?x2948 = 0j_sncb, ?x734 = 04zx3q1, ?x580 = 05m_8, position(?x706, ?x180), major_field_of_study(?x1771, ?x5179), ?x5179 = 04gb7 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1353 for first EXPECTED value: *> intensional similarity = 33 *> extensional distance = 6 *> proper extension: 025tn92; *> query: (?x6462, 01jq0j) <- draft(?x7078, ?x6462), draft(?x5229, ?x6462), draft(?x4546, ?x6462), draft(?x3114, ?x6462), school(?x6462, ?x6814), school(?x6462, ?x466), school(?x6462, ?x388), team(?x1240, ?x5229), team(?x180, ?x5229), school(?x3114, ?x4603), sport(?x4546, ?x1083), team(?x1114, ?x3114), ?x466 = 01pl14, citytown(?x388, ?x6453), colors(?x388, ?x3364), institution(?x734, ?x6814), major_field_of_study(?x6814, ?x1154), major_field_of_study(?x388, ?x6859), school(?x4546, ?x6856), institution(?x620, ?x388), colors(?x7078, ?x663), contains(?x94, ?x388), position(?x1240, ?x706), school(?x1160, ?x6814), school(?x799, ?x6814), ?x1160 = 049n7, school(?x5229, ?x4117), ?x6859 = 01tbp, ?x734 = 04zx3q1, category(?x3114, ?x134), teams(?x739, ?x799), team(?x11620, ?x799), list(?x388, ?x2197) *> conf = 0.38 ranks of expected_values: 17, 19, 44 EVAL 09l0x9 school 01jq0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 20.000 20.000 0.500 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 09l0x9 school 0jkhr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 20.000 20.000 0.500 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school EVAL 09l0x9 school 09f2j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 20.000 20.000 0.500 http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school #1388-0fm3b5 PRED entity: 0fm3b5 PRED relation: nominated_for PRED expected values: 06823p => 41 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1800): 093dqjy (0.57 #3732, 0.19 #5323, 0.18 #8503), 095zlp (0.43 #3233, 0.26 #27095, 0.24 #30277), 0g9lm2 (0.43 #3837, 0.25 #27699, 0.23 #30881), 05c46y6 (0.43 #3574, 0.24 #5165, 0.23 #8345), 04q827 (0.43 #4672, 0.23 #11034, 0.20 #28534), 04lhc4 (0.43 #4247, 0.23 #10609, 0.19 #28109), 02d44q (0.43 #3328, 0.23 #8099, 0.19 #4919), 0j43swk (0.43 #3626, 0.20 #9988, 0.20 #27488), 011yhm (0.43 #4208, 0.20 #10570, 0.20 #28070), 0462hhb (0.43 #3917, 0.19 #7098, 0.15 #30961) >> Best rule #3732 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 09qwmm; 0gqwc; 099cng; 099flj; >> query: (?x4695, 093dqjy) <- nominated_for(?x4695, ?x3757), nominated_for(?x4695, ?x3076), nominated_for(?x4695, ?x1786), ?x3076 = 0g5838s, genre(?x1786, ?x53), award(?x5342, ?x4695), category(?x1786, ?x134), film_release_region(?x3757, ?x87) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #1024 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 1 *> proper extension: 0gq6s3; *> query: (?x4695, 06823p) <- nominated_for(?x4695, ?x5496), nominated_for(?x4695, ?x3076), nominated_for(?x4695, ?x1786), ?x3076 = 0g5838s, ?x1786 = 091z_p, disciplines_or_subjects(?x4695, ?x373), film_release_region(?x5496, ?x87), titles(?x142, ?x5496) *> conf = 0.33 ranks of expected_values: 41 EVAL 0fm3b5 nominated_for 06823p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 41.000 23.000 0.571 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1387-0f94t PRED entity: 0f94t PRED relation: contains! PRED expected values: 0cc56 => 91 concepts (57 used for prediction) PRED predicted values (max 10 best out of 446): 05k7sb (0.64 #4592, 0.09 #8161, 0.06 #19762), 02jx1 (0.55 #27745, 0.49 #31315, 0.34 #46492), 07ssc (0.37 #27691, 0.35 #31261, 0.23 #46438), 04jpl (0.30 #8945, 0.23 #13406, 0.19 #17868), 01n7q (0.27 #42910, 0.25 #46483, 0.25 #47375), 0d060g (0.26 #22321, 0.13 #42847, 0.09 #904), 05tbn (0.26 #21638, 0.04 #9146, 0.04 #18069), 0345h (0.23 #22388, 0.12 #42914, 0.04 #20604), 03rjj (0.20 #22318, 0.11 #42844, 0.03 #20534), 01531 (0.20 #187, 0.07 #2863, 0.06 #3755) >> Best rule #4592 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 0tyql; 0gv10; 0k3kg; 0k3kv; 017v71; 0k3gj; 0k3hn; 0k3l5; 0k3k1; 0v0d9; ... >> query: (?x1005, 05k7sb) <- contains(?x739, ?x1005), location(?x11499, ?x739), ?x11499 = 06jkm >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #29447 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 419 *> proper extension: 09f07; *> query: (?x1005, ?x739) <- place_of_birth(?x1700, ?x1005), location(?x4667, ?x1005), location(?x1700, ?x739) *> conf = 0.07 ranks of expected_values: 23 EVAL 0f94t contains! 0cc56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 91.000 57.000 0.641 http://example.org/location/location/contains #1386-0kz2w PRED entity: 0kz2w PRED relation: school! PRED expected values: 0jmj7 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 93): 0jmj7 (0.41 #1345, 0.41 #405, 0.38 #499), 01yhm (0.18 #20, 0.14 #396, 0.09 #1336), 05m_8 (0.18 #3, 0.11 #379, 0.11 #1319), 07l8x (0.18 #67, 0.11 #443, 0.07 #1383), 07l4z (0.18 #71, 0.11 #447, 0.06 #353), 0jmk7 (0.18 #91, 0.08 #467, 0.05 #1407), 051vz (0.14 #399, 0.09 #23, 0.08 #1245), 02d02 (0.14 #446, 0.07 #1292, 0.07 #1386), 04wmvz (0.11 #456, 0.09 #80, 0.08 #362), 0jmnl (0.11 #469, 0.09 #93, 0.08 #1409) >> Best rule #1345 for best value: >> intensional similarity = 2 >> extensional distance = 74 >> proper extension: 01mmslz; 0d06m5; 057hz; 01tj34; 01xcr4; 01bcq; 01vt9p3; 01tnbn; 01w_10; 06rgq; ... >> query: (?x1043, 0jmj7) <- organization(?x1043, ?x5487), currency(?x5487, ?x170) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kz2w school! 0jmj7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.408 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #1385-01c58j PRED entity: 01c58j PRED relation: profession PRED expected values: 015h31 => 95 concepts (89 used for prediction) PRED predicted values (max 10 best out of 85): 02hrh1q (0.96 #10888, 0.86 #6882, 0.86 #7025), 018gz8 (0.70 #2446, 0.64 #2303, 0.59 #5453), 015h31 (0.67 #166, 0.56 #452, 0.40 #3579), 0cbd2 (0.60 #5588, 0.52 #4443, 0.50 #6304), 09jwl (0.56 #1446, 0.25 #1732, 0.25 #16), 0nbcg (0.48 #1457, 0.18 #1743, 0.17 #4607), 0kyk (0.42 #5607, 0.37 #2171, 0.35 #6323), 0196pc (0.40 #3579, 0.34 #3148, 0.25 #69), 01c72t (0.36 #1450, 0.35 #5153, 0.32 #7873), 0n1h (0.35 #5153, 0.32 #7873, 0.30 #7586) >> Best rule #10888 for best value: >> intensional similarity = 5 >> extensional distance = 2599 >> proper extension: 01sl1q; 044mz_; 07nznf; 0184jc; 04bdxl; 02s2ft; 05vsxz; 06qgvf; 0grwj; 05d7rk; ... >> query: (?x1855, 02hrh1q) <- profession(?x1855, ?x1383), profession(?x9388, ?x1383), profession(?x4247, ?x1383), ?x9388 = 0309lm, ?x4247 = 02vntj >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #166 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 04gcd1; *> query: (?x1855, 015h31) <- profession(?x1855, ?x1383), profession(?x1855, ?x524), ?x1383 = 0np9r, company(?x1855, ?x6948), ?x524 = 02jknp *> conf = 0.67 ranks of expected_values: 3 EVAL 01c58j profession 015h31 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 95.000 89.000 0.960 http://example.org/people/person/profession #1384-0106dv PRED entity: 0106dv PRED relation: place! PRED expected values: 0106dv => 183 concepts (92 used for prediction) PRED predicted values (max 10 best out of 265): 0106dv (0.28 #7736, 0.28 #8252, 0.23 #11348), 07b_l (0.28 #7736, 0.28 #8252, 0.23 #11348), 09c7w0 (0.28 #7736, 0.28 #8252, 0.23 #11348), 030qb3t (0.21 #39709, 0.16 #28880, 0.08 #42290), 02_286 (0.16 #28880, 0.08 #42290, 0.07 #1044), 0mqs0 (0.08 #25787, 0.07 #25271, 0.07 #36100), 0dclg (0.07 #1073, 0.04 #2621, 0.02 #4684), 01qh7 (0.07 #1091, 0.02 #9860, 0.01 #11925), 0f2w0 (0.06 #1582, 0.05 #2099, 0.03 #3131), 010016 (0.06 #1901, 0.05 #2418, 0.03 #3450) >> Best rule #7736 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 010dft; >> query: (?x10364, ?x94) <- contains(?x10364, ?x6177), county_seat(?x10365, ?x10364), contains(?x94, ?x6177), school_type(?x6177, ?x3205) >> conf = 0.28 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0106dv place! 0106dv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 92.000 0.278 http://example.org/location/hud_county_place/place #1383-01hbq0 PRED entity: 01hbq0 PRED relation: film PRED expected values: 042g97 => 112 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1135): 05jyb2 (0.74 #12525, 0.44 #1790, 0.41 #76939), 02qr3k8 (0.51 #10234, 0.17 #1288, 0.12 #3078), 0c_j9x (0.17 #373, 0.12 #2163, 0.05 #12898), 0b4lkx (0.17 #1390, 0.12 #3180, 0.04 #10336), 03lvwp (0.17 #1043, 0.12 #2833, 0.04 #13568), 09qycb (0.17 #1645, 0.12 #3435, 0.03 #10591), 0bmhn (0.17 #1624, 0.12 #3414, 0.02 #14149), 0p7pw (0.17 #1556, 0.12 #3346, 0.02 #12291), 01f39b (0.17 #978, 0.12 #2768, 0.02 #27815), 01738w (0.17 #1129, 0.12 #2919, 0.02 #29755) >> Best rule #12525 for best value: >> intensional similarity = 3 >> extensional distance = 82 >> proper extension: 01713c; >> query: (?x13084, ?x3725) <- award(?x13084, ?x112), ?x112 = 027dtxw, nominated_for(?x13084, ?x3725) >> conf = 0.74 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01hbq0 film 042g97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 54.000 0.741 http://example.org/film/actor/film./film/performance/film #1382-02k4gv PRED entity: 02k4gv PRED relation: film PRED expected values: 02rrh1w => 138 concepts (103 used for prediction) PRED predicted values (max 10 best out of 933): 0828jw (0.68 #21470, 0.63 #19680, 0.61 #75155), 0404j37 (0.25 #1139, 0.22 #10084, 0.14 #8295), 017jd9 (0.25 #781, 0.09 #2570, 0.08 #4359), 017gm7 (0.25 #211, 0.09 #2000, 0.08 #3789), 08hmch (0.25 #152, 0.09 #1941, 0.08 #3730), 02qdrjx (0.25 #1561, 0.07 #8717, 0.06 #10506), 0bc1yhb (0.25 #912, 0.07 #8068, 0.06 #9857), 06zn2v2 (0.25 #740, 0.07 #7896, 0.06 #9685), 0466s8n (0.25 #1635, 0.07 #8791, 0.06 #10580), 0gd0c7x (0.25 #315, 0.07 #7471, 0.06 #9260) >> Best rule #21470 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 03xmy1; 02nfjp; 01pqy_; 03kxp7; 0mbs8; >> query: (?x5507, ?x5810) <- award_winner(?x5810, ?x5507), profession(?x5507, ?x4773), ?x4773 = 0d1pc >> conf = 0.68 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02k4gv film 02rrh1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 103.000 0.675 http://example.org/film/actor/film./film/performance/film #1381-01nqfh_ PRED entity: 01nqfh_ PRED relation: gender PRED expected values: 05zppz => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #55, 0.90 #59, 0.90 #33), 02zsn (0.28 #92, 0.28 #78, 0.28 #12) >> Best rule #55 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 02mz_6; 02p7xc; 0csdzz; >> query: (?x562, 05zppz) <- nationality(?x562, ?x94), music(?x1178, ?x562), film_release_region(?x54, ?x94) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01nqfh_ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.915 http://example.org/people/person/gender #1380-0wsr PRED entity: 0wsr PRED relation: team! PRED expected values: 0cv72h => 65 concepts (58 used for prediction) PRED predicted values (max 10 best out of 111): 019g65 (0.27 #303, 0.25 #415, 0.20 #2434), 0444x (0.25 #422, 0.13 #534, 0.13 #3140), 012xdf (0.25 #60, 0.09 #3087, 0.08 #3425), 02lm0t (0.25 #104, 0.08 #2234, 0.07 #2347), 03lh3v (0.25 #18, 0.06 #691, 0.06 #578), 0cg39k (0.20 #516, 0.17 #404, 0.13 #3140), 03vrv9 (0.20 #196, 0.14 #1654, 0.13 #532), 0cv72h (0.19 #822, 0.13 #3140, 0.12 #1943), 0hcs3 (0.17 #1778, 0.16 #1329, 0.15 #2226), 019y64 (0.17 #341, 0.13 #3140, 0.12 #902) >> Best rule #303 for best value: >> intensional similarity = 10 >> extensional distance = 9 >> proper extension: 043vc; >> query: (?x6645, 019g65) <- school(?x6645, ?x735), position(?x6645, ?x1792), position(?x6645, ?x180), sport(?x6645, ?x1083), team(?x935, ?x6645), draft(?x6645, ?x465), ?x180 = 01r3hr, ?x935 = 06b1q, ?x1792 = 05zm34, ?x465 = 05vsb7 >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #822 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 14 *> proper extension: 0fsb_6; *> query: (?x6645, 0cv72h) <- position_s(?x6645, ?x3346), position_s(?x6645, ?x3113), position_s(?x6645, ?x1517), team(?x2312, ?x6645), ?x1517 = 02g_6j, ?x2312 = 02qpbqj, ?x3346 = 02g_7z, colors(?x6645, ?x663), team(?x3113, ?x387), ?x387 = 02896 *> conf = 0.19 ranks of expected_values: 8 EVAL 0wsr team! 0cv72h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 65.000 58.000 0.273 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #1379-0x1jc PRED entity: 0x1jc PRED relation: source PRED expected values: 0jbk9 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.91 #25, 0.90 #15, 0.90 #14) >> Best rule #25 for best value: >> intensional similarity = 3 >> extensional distance = 400 >> proper extension: 010bnr; >> query: (?x13186, 0jbk9) <- category(?x13186, ?x134), ?x134 = 08mbj5d, place(?x13186, ?x13186) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0x1jc source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.913 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #1378-0j298t8 PRED entity: 0j298t8 PRED relation: nominated_for PRED expected values: 05pt0l => 61 concepts (23 used for prediction) PRED predicted values (max 10 best out of 1572): 05pt0l (0.77 #31921, 0.77 #3193, 0.77 #3192), 09p3_s (0.50 #4055, 0.50 #862, 0.43 #7247), 0m313 (0.50 #3205, 0.50 #12, 0.33 #25551), 0pv3x (0.50 #3357, 0.50 #164, 0.29 #6549), 095zlp (0.50 #3246, 0.50 #53, 0.29 #6438), 0hv4t (0.50 #4238, 0.50 #1045, 0.29 #7430), 0mcl0 (0.50 #3776, 0.50 #583, 0.29 #6968), 0cf08 (0.50 #4325, 0.50 #1132, 0.29 #7517), 0k4fz (0.50 #3945, 0.50 #752, 0.29 #7137), 026gyn_ (0.50 #3464, 0.50 #271, 0.29 #6656) >> Best rule #31921 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 06196; >> query: (?x13664, ?x3246) <- award(?x3246, ?x13664), award(?x1197, ?x13664), ceremony(?x13664, ?x13189), honored_for(?x13189, ?x573) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0j298t8 nominated_for 05pt0l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 23.000 0.772 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1377-076xkps PRED entity: 076xkps PRED relation: genre PRED expected values: 06n90 => 100 concepts (44 used for prediction) PRED predicted values (max 10 best out of 93): 03npn (0.81 #1452, 0.80 #3507, 0.76 #486), 07s9rl0 (0.73 #3264, 0.71 #2175, 0.66 #3386), 01hmnh (0.70 #18, 0.61 #382, 0.26 #1470), 07yjb (0.67 #241, 0.67 #1451, 0.64 #3506), 06n90 (0.60 #13, 0.33 #255, 0.28 #2913), 03k9fj (0.43 #376, 0.38 #2912, 0.38 #2065), 0btmb (0.40 #88, 0.06 #2988, 0.05 #330), 05p553 (0.39 #368, 0.36 #1094, 0.34 #3874), 02n4kr (0.37 #1338, 0.18 #128, 0.17 #3271), 0lsxr (0.31 #1339, 0.25 #4482, 0.23 #3272) >> Best rule #1452 for best value: >> intensional similarity = 6 >> extensional distance = 217 >> proper extension: 06zn1c; >> query: (?x8886, ?x571) <- film_release_distribution_medium(?x8886, ?x81), titles(?x571, ?x8886), genre(?x5002, ?x571), genre(?x1498, ?x571), ?x5002 = 03tn80, film_release_region(?x1498, ?x87) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 09sh8k; 044g_k; 05qbckf; 01hqk; 02wgk1; 012s1d; 0dc_ms; 02nx2k; *> query: (?x8886, 06n90) <- film_crew_role(?x8886, ?x2178), film_crew_role(?x8886, ?x137), ?x2178 = 01pvkk, genre(?x8886, ?x6888), ?x6888 = 04pbhw, ?x137 = 09zzb8 *> conf = 0.60 ranks of expected_values: 5 EVAL 076xkps genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 100.000 44.000 0.814 http://example.org/film/film/genre #1376-04rzd PRED entity: 04rzd PRED relation: instrumentalists PRED expected values: 05cljf 03f5spx 01vsnff 01vn35l 01wmjkb => 78 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1073): 0167v4 (0.67 #580, 0.67 #5811, 0.67 #5703), 01m15br (0.67 #580, 0.64 #2323, 0.63 #2904), 01vrx3g (0.67 #580, 0.64 #2323, 0.63 #2904), 037hgm (0.67 #580, 0.64 #2323, 0.63 #2904), 03193l (0.67 #580, 0.58 #1742, 0.58 #5810), 01p95y0 (0.67 #580, 0.58 #1742, 0.58 #5810), 016h9b (0.67 #580, 0.58 #1742, 0.58 #5810), 02l_7y (0.67 #580, 0.58 #1742, 0.58 #5810), 01vw20_ (0.67 #5387, 0.57 #7129, 0.50 #11202), 0fhxv (0.67 #5482, 0.57 #7224, 0.50 #4317) >> Best rule #580 for best value: >> intensional similarity = 14 >> extensional distance = 1 >> proper extension: 05r5c; >> query: (?x1969, ?x366) <- role(?x1969, ?x2620), role(?x1969, ?x716), group(?x1969, ?x1929), role(?x366, ?x1969), ?x716 = 018vs, ?x2620 = 01kcd, role(?x2957, ?x1969), role(?x2297, ?x1969), role(?x1662, ?x1969), role(?x367, ?x1969), instrumentalists(?x1969, ?x1001), performance_role(?x212, ?x1969), ?x2297 = 051hrr, ?x2957 = 01v8y9 >> conf = 0.67 => this is the best rule for 8 predicted values *> Best rule #5339 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 02hnl; *> query: (?x1969, 01vsnff) <- role(?x1969, ?x2620), role(?x1969, ?x716), role(?x1969, ?x645), group(?x1969, ?x1929), role(?x9117, ?x1969), ?x716 = 018vs, instrumentalists(?x1969, ?x1654), role(?x367, ?x1969), role(?x212, ?x2620), award(?x1654, ?x2238), role(?x316, ?x1969), ?x645 = 028tv0, ?x9117 = 0167v4 *> conf = 0.50 ranks of expected_values: 53, 60, 110, 124, 151 EVAL 04rzd instrumentalists 01wmjkb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 78.000 41.000 0.673 http://example.org/music/instrument/instrumentalists EVAL 04rzd instrumentalists 01vn35l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 78.000 41.000 0.673 http://example.org/music/instrument/instrumentalists EVAL 04rzd instrumentalists 01vsnff CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 78.000 41.000 0.673 http://example.org/music/instrument/instrumentalists EVAL 04rzd instrumentalists 03f5spx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 78.000 41.000 0.673 http://example.org/music/instrument/instrumentalists EVAL 04rzd instrumentalists 05cljf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 78.000 41.000 0.673 http://example.org/music/instrument/instrumentalists #1375-0h7h6 PRED entity: 0h7h6 PRED relation: teams PRED expected values: 0jmcb => 222 concepts (222 used for prediction) PRED predicted values (max 10 best out of 287): 06x6s (0.17 #699, 0.14 #1055, 0.11 #1767), 06x76 (0.17 #618, 0.14 #974, 0.11 #1686), 06x68 (0.17 #367, 0.14 #723, 0.11 #1435), 0jmk7 (0.17 #656, 0.14 #1012, 0.10 #2080), 0jnq8 (0.17 #583, 0.14 #939, 0.10 #2007), 0jmjr (0.17 #576, 0.14 #932, 0.10 #2000), 04mjl (0.17 #511, 0.14 #867, 0.10 #1935), 02pqcfz (0.17 #438, 0.14 #794, 0.10 #1862), 04112r (0.17 #407, 0.14 #763, 0.10 #1831), 07k53y (0.17 #368, 0.14 #724, 0.10 #1792) >> Best rule #699 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 02fzs; >> query: (?x1658, 06x6s) <- time_zones(?x1658, ?x2674), film_regional_debut_venue(?x1283, ?x1658), vacationer(?x1658, ?x1897) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0h7h6 teams 0jmcb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 222.000 222.000 0.167 http://example.org/sports/sports_team_location/teams #1374-01skmp PRED entity: 01skmp PRED relation: participant PRED expected values: 01x6jd => 108 concepts (64 used for prediction) PRED predicted values (max 10 best out of 222): 0jfx1 (0.11 #162, 0.02 #7882, 0.02 #805), 01pllx (0.11 #549, 0.02 #2478, 0.02 #3122), 0lx2l (0.11 #169, 0.02 #812, 0.02 #5958), 02ld6x (0.11 #185), 02t__3 (0.08 #2573, 0.06 #14805, 0.06 #10940), 01z7s_ (0.08 #2573, 0.06 #14805, 0.06 #10940), 01vw87c (0.08 #2573, 0.06 #14805, 0.06 #10940), 01vvb4m (0.08 #2573, 0.06 #6433, 0.06 #12871), 026r8q (0.06 #14805, 0.06 #10940, 0.06 #17379), 03n_7k (0.05 #10296, 0.05 #8364, 0.05 #13516) >> Best rule #162 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 05mlqj; >> query: (?x6702, 0jfx1) <- film(?x6702, ?x9527), gender(?x6702, ?x514), ?x9527 = 01rnly >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01skmp participant 01x6jd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 64.000 0.111 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #1373-01dw9z PRED entity: 01dw9z PRED relation: nationality PRED expected values: 09c7w0 => 97 concepts (97 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.79 #101, 0.75 #4402, 0.70 #5503), 02jx1 (0.21 #33, 0.15 #1733, 0.14 #1433), 07ssc (0.10 #1415, 0.09 #1715, 0.09 #815), 03rk0 (0.07 #4247, 0.07 #2447, 0.07 #6548), 035qy (0.07 #34, 0.01 #234), 0d060g (0.06 #507, 0.06 #1107, 0.05 #1607), 0f8l9c (0.04 #1722, 0.02 #822, 0.02 #3023), 03_3d (0.03 #1606, 0.02 #706, 0.02 #1406), 02hrh0_ (0.03 #2301), 03rjj (0.03 #1705, 0.02 #6207, 0.02 #4907) >> Best rule #101 for best value: >> intensional similarity = 3 >> extensional distance = 55 >> proper extension: 02yplc; >> query: (?x2683, 09c7w0) <- award(?x2683, ?x537), profession(?x2683, ?x220), ?x537 = 0gkvb7 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01dw9z nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 97.000 0.789 http://example.org/people/person/nationality #1372-0blg2 PRED entity: 0blg2 PRED relation: sports PRED expected values: 018w8 => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 42): 0dwxr (0.79 #459, 0.79 #458, 0.78 #35), 018w8 (0.79 #459, 0.79 #458, 0.78 #35), 064vjs (0.79 #459, 0.79 #458, 0.78 #35), 07jjt (0.71 #82, 0.67 #106, 0.67 #12), 01sgl (0.67 #106, 0.66 #248, 0.66 #36), 0w0d (0.45 #219, 0.39 #394, 0.38 #430), 07_53 (0.42 #24, 0.40 #236, 0.39 #411), 03krj (0.40 #240, 0.36 #98, 0.35 #415), 07bs0 (0.36 #115, 0.33 #107, 0.27 #324), 03_8r (0.35 #225, 0.33 #107, 0.30 #400) >> Best rule #459 for best value: >> intensional similarity = 8 >> extensional distance = 22 >> proper extension: 01f1jy; 015pkt; >> query: (?x2134, ?x2315) <- medal(?x2134, ?x422), sports(?x2134, ?x2315), olympics(?x1892, ?x2134), olympics(?x774, ?x2134), sports(?x2134, ?x171), ?x774 = 06mzp, ?x1892 = 02vzc, country(?x2315, ?x87) >> conf = 0.79 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0blg2 sports 018w8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 50.000 50.000 0.786 http://example.org/user/jg/default_domain/olympic_games/sports #1371-02zyy4 PRED entity: 02zyy4 PRED relation: type_of_union PRED expected values: 04ztj => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.72 #17, 0.71 #165, 0.71 #185), 01g63y (0.46 #243, 0.46 #197, 0.45 #214), 01bl8s (0.04 #15) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 05218gr; >> query: (?x1678, 04ztj) <- place_of_birth(?x1678, ?x1860), ?x1860 = 01_d4, nationality(?x1678, ?x94) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zyy4 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.722 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1370-0yjf0 PRED entity: 0yjf0 PRED relation: major_field_of_study PRED expected values: 03g3w => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 127): 02j62 (0.61 #2159, 0.60 #1783, 0.57 #1408), 01mkq (0.57 #1392, 0.55 #516, 0.54 #1642), 062z7 (0.57 #905, 0.49 #2156, 0.46 #1405), 037mh8 (0.57 #947, 0.40 #571, 0.35 #821), 04rjg (0.54 #1397, 0.52 #897, 0.52 #771), 02lp1 (0.54 #1638, 0.54 #2014, 0.52 #1388), 03g3w (0.52 #904, 0.51 #2155, 0.50 #1779), 01lj9 (0.50 #542, 0.43 #1418, 0.42 #2044), 0g26h (0.43 #795, 0.35 #545, 0.35 #921), 041y2 (0.40 #582, 0.35 #832, 0.27 #1959) >> Best rule #2159 for best value: >> intensional similarity = 7 >> extensional distance = 49 >> proper extension: 01jssp; 0lfgr; 02fgdx; 02fjzt; 033x5p; 01h8rk; 02bqy; 06bw5; 017v71; 015q1n; ... >> query: (?x1978, 02j62) <- institution(?x1771, ?x1978), institution(?x1368, ?x1978), currency(?x1978, ?x1099), student(?x1978, ?x6975), ?x1771 = 019v9k, ?x1368 = 014mlp, people(?x6655, ?x6975) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #904 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 21 *> proper extension: 050xpd; *> query: (?x1978, 03g3w) <- institution(?x1526, ?x1978), institution(?x1390, ?x1978), institution(?x1368, ?x1978), ?x1526 = 0bkj86, student(?x1978, ?x3849), ?x1368 = 014mlp, ?x1390 = 0bjrnt *> conf = 0.52 ranks of expected_values: 7 EVAL 0yjf0 major_field_of_study 03g3w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 154.000 154.000 0.608 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1369-02rqxc PRED entity: 02rqxc PRED relation: current_club PRED expected values: 023fb => 118 concepts (98 used for prediction) PRED predicted values (max 10 best out of 736): 0xbm (0.50 #310, 0.33 #165, 0.33 #20), 04ltf (0.33 #216, 0.33 #71, 0.30 #506), 0y54 (0.33 #8, 0.17 #3779, 0.17 #298), 01rly6 (0.33 #104, 0.17 #394, 0.14 #1119), 06ls0l (0.33 #54, 0.17 #344, 0.13 #1359), 0cttx (0.33 #126, 0.17 #416, 0.12 #2156), 01453 (0.33 #1, 0.17 #291, 0.07 #1161), 0466hh (0.33 #122, 0.17 #412, 0.07 #1282), 05dkbr (0.33 #73, 0.17 #363, 0.07 #1233), 01cwm1 (0.33 #64, 0.17 #354, 0.07 #1224) >> Best rule #310 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 01l3wr; >> query: (?x4805, 0xbm) <- position(?x4805, ?x63), position(?x4805, ?x60), ?x63 = 02sdk9v, ?x60 = 02nzb8, current_club(?x4805, ?x6391), team(?x8594, ?x4805), team(?x6390, ?x6391), ?x8594 = 07y9k >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #342 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 01l3wr; *> query: (?x4805, 023fb) <- position(?x4805, ?x63), position(?x4805, ?x60), ?x63 = 02sdk9v, ?x60 = 02nzb8, current_club(?x4805, ?x6391), team(?x8594, ?x4805), team(?x6390, ?x6391), ?x8594 = 07y9k *> conf = 0.17 ranks of expected_values: 20 EVAL 02rqxc current_club 023fb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 118.000 98.000 0.500 http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club #1368-06b3g4 PRED entity: 06b3g4 PRED relation: profession PRED expected values: 018gz8 => 101 concepts (78 used for prediction) PRED predicted values (max 10 best out of 50): 01d_h8 (0.54 #7117, 0.40 #6, 0.29 #9634), 0dxtg (0.53 #7125, 0.32 #757, 0.31 #609), 02jknp (0.36 #7119, 0.20 #9636, 0.19 #2973), 03gjzk (0.29 #7126, 0.27 #610, 0.26 #758), 018gz8 (0.28 #612, 0.25 #760, 0.22 #1056), 0kyk (0.25 #476, 0.12 #920, 0.11 #1216), 0cbd2 (0.20 #898, 0.17 #1343, 0.17 #454), 08z956 (0.20 #78, 0.01 #673), 09jwl (0.19 #910, 0.17 #8906, 0.17 #5058), 016z4k (0.17 #451, 0.09 #8891, 0.09 #5043) >> Best rule #7117 for best value: >> intensional similarity = 4 >> extensional distance = 1636 >> proper extension: 0c3ns; 01q415; 0cj2t3; 01q4qv; 07lwsz; 07fvf1; 0cj2nl; 06msq2; 09qc1; 02778yp; ... >> query: (?x13094, 01d_h8) <- profession(?x13094, ?x1383), nationality(?x13094, ?x94), profession(?x1052, ?x1383), ?x1052 = 05_k56 >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #612 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 225 *> proper extension: 07gkgp; *> query: (?x13094, 018gz8) <- gender(?x13094, ?x231), profession(?x13094, ?x1383), ?x1383 = 0np9r, type_of_union(?x13094, ?x566) *> conf = 0.28 ranks of expected_values: 5 EVAL 06b3g4 profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 101.000 78.000 0.539 http://example.org/people/person/profession #1367-02jxmr PRED entity: 02jxmr PRED relation: role PRED expected values: 05r5c => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 92): 0342h (0.50 #5, 0.36 #3126, 0.33 #2397), 05r5c (0.39 #1464, 0.38 #3129, 0.36 #2400), 02sgy (0.28 #6, 0.22 #1462, 0.22 #3127), 042v_gx (0.22 #1465, 0.20 #3130, 0.18 #2401), 05842k (0.17 #3200, 0.12 #79, 0.12 #2471), 018vs (0.16 #3135, 0.13 #1470, 0.13 #2406), 026t6 (0.15 #3124, 0.13 #2083, 0.11 #2395), 01vj9c (0.15 #3137, 0.13 #1472, 0.12 #2408), 013y1f (0.14 #3158, 0.12 #37, 0.12 #2429), 05148p4 (0.12 #24, 0.12 #3145, 0.12 #1480) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 30 >> proper extension: 0c9d9; >> query: (?x4428, 0342h) <- profession(?x4428, ?x131), spouse(?x4428, ?x7617), role(?x4428, ?x228) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1464 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 129 *> proper extension: 028qdb; 03_0p; *> query: (?x4428, 05r5c) <- award_winner(?x3069, ?x4428), type_of_union(?x4428, ?x566), role(?x4428, ?x228) *> conf = 0.39 ranks of expected_values: 2 EVAL 02jxmr role 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 109.000 109.000 0.500 http://example.org/music/artist/track_contributions./music/track_contribution/role #1366-01gtc0 PRED entity: 01gtc0 PRED relation: district_represented PRED expected values: 04rrd 04ly1 04tgp 0vbk => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 363): 04rrd (0.88 #1041, 0.88 #137, 0.86 #848), 04tgp (0.88 #137, 0.86 #1107, 0.86 #848), 04ly1 (0.88 #137, 0.86 #848, 0.85 #567), 02xry (0.88 #137, 0.86 #848, 0.85 #567), 0vbk (0.88 #137, 0.86 #848, 0.85 #567), 03s0w (0.88 #137, 0.86 #848, 0.85 #567), 07b_l (0.88 #137, 0.86 #848, 0.85 #567), 0824r (0.88 #137, 0.86 #848, 0.85 #567), 0d060g (0.73 #940, 0.59 #1031, 0.58 #941), 081mh (0.67 #1097, 0.59 #1031, 0.55 #753) >> Best rule #1041 for best value: >> intensional similarity = 44 >> extensional distance = 15 >> proper extension: 01gssm; 01gst9; 01gsry; 01gssz; >> query: (?x5006, 04rrd) <- district_represented(?x5006, ?x7518), district_represented(?x5006, ?x7405), district_represented(?x5006, ?x3778), district_represented(?x5006, ?x3038), district_represented(?x5006, ?x2831), district_represented(?x5006, ?x1906), district_represented(?x5006, ?x1755), district_represented(?x5006, ?x177), ?x1755 = 01x73, legislative_sessions(?x759, ?x5006), ?x177 = 05kkh, ?x3038 = 0d0x8, adjoins(?x2831, ?x2623), country(?x2831, ?x94), state(?x1106, ?x1906), district_represented(?x3463, ?x2831), district_represented(?x845, ?x2831), jurisdiction_of_office(?x10093, ?x2831), ?x3778 = 07h34, state_province_region(?x2228, ?x1906), contains(?x1906, ?x13203), contains(?x1906, ?x12411), state_province_region(?x1201, ?x2831), time_zones(?x1906, ?x1638), state(?x9556, ?x2831), adjoins(?x1906, ?x279), ?x10093 = 09n5b9, ?x279 = 0d060g, religion(?x1906, ?x109), administrative_parent(?x8350, ?x2831), major_field_of_study(?x2228, ?x1154), student(?x2228, ?x6068), ?x7405 = 07_f2, colors(?x2228, ?x332), location(?x5574, ?x1906), ?x3463 = 02bqmq, administrative_division(?x12384, ?x2831), jurisdiction_of_office(?x900, ?x1906), adjoins(?x8003, ?x13203), institution(?x865, ?x2228), source(?x12411, ?x958), ?x845 = 07p__7, location(?x115, ?x1106), ?x7518 = 026mj >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 5 EVAL 01gtc0 district_represented 0vbk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 30.000 30.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtc0 district_represented 04tgp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtc0 district_represented 04ly1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 01gtc0 district_represented 04rrd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 30.000 30.000 0.882 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #1365-017n9 PRED entity: 017n9 PRED relation: film! PRED expected values: 06chf => 73 concepts (50 used for prediction) PRED predicted values (max 10 best out of 127): 07zft (0.13 #1372, 0.07 #9342, 0.07 #10167), 0j_c (0.06 #337, 0.03 #1709, 0.01 #3906), 06pj8 (0.06 #3341, 0.05 #597, 0.05 #1420), 0js9s (0.05 #704, 0.05 #978, 0.03 #155), 04sry (0.05 #1540, 0.04 #442, 0.03 #1265), 06chf (0.05 #78, 0.04 #1450, 0.01 #2000), 0qf43 (0.05 #5, 0.02 #1377, 0.01 #2475), 07rd7 (0.05 #927, 0.03 #4772, 0.02 #3397), 01xv77 (0.05 #549, 0.04 #1922, 0.04 #1921), 0c0k1 (0.05 #549, 0.04 #1922, 0.04 #1921) >> Best rule #1372 for best value: >> intensional similarity = 3 >> extensional distance = 89 >> proper extension: 07bz5; >> query: (?x11685, ?x8849) <- award(?x11685, ?x10747), nominated_for(?x8849, ?x11685), list(?x11685, ?x3004) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #78 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 37 *> proper extension: 0qm8b; *> query: (?x11685, 06chf) <- nominated_for(?x6909, ?x11685), nominated_for(?x637, ?x11685), production_companies(?x11685, ?x382), ?x6909 = 02qyntr, ?x637 = 02r22gf *> conf = 0.05 ranks of expected_values: 6 EVAL 017n9 film! 06chf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 73.000 50.000 0.133 http://example.org/film/director/film #1364-023znp PRED entity: 023znp PRED relation: organization! PRED expected values: 060c4 => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 8): 060c4 (0.73 #197, 0.71 #236, 0.71 #288), 0dq_5 (0.29 #48, 0.26 #74, 0.25 #191), 05k17c (0.23 #20, 0.23 #33, 0.12 #267), 07xl34 (0.20 #128, 0.20 #258, 0.18 #467), 0hm4q (0.07 #112, 0.06 #99, 0.05 #151), 05c0jwl (0.06 #122, 0.05 #213, 0.04 #448), 04n1q6 (0.03 #58, 0.01 #214, 0.01 #123), 08jcfy (0.02 #168, 0.02 #220, 0.02 #455) >> Best rule #197 for best value: >> intensional similarity = 3 >> extensional distance = 267 >> proper extension: 01hhvg; 01ngz1; 06jk5_; 03v6t; 02jyr8; 0q19t; 02bjhv; 04344j; 02zccd; 01y17m; ... >> query: (?x3922, 060c4) <- category(?x3922, ?x134), colors(?x3922, ?x332), currency(?x3922, ?x170) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 023znp organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.732 http://example.org/organization/role/leaders./organization/leadership/organization #1363-0169dl PRED entity: 0169dl PRED relation: film PRED expected values: 03s6l2 078sj4 => 109 concepts (84 used for prediction) PRED predicted values (max 10 best out of 645): 07nxvj (0.62 #12384, 0.33 #63692, 0.33 #100846), 0d68qy (0.38 #113233, 0.36 #129160, 0.35 #134470), 043tz0c (0.38 #113233, 0.36 #129160, 0.35 #134470), 03hp2y1 (0.10 #1590, 0.01 #26357, 0.01 #5128), 06fpsx (0.10 #1321, 0.01 #17243, 0.01 #13705), 0bvn25 (0.05 #49, 0.03 #15971, 0.03 #12433), 0blpg (0.05 #645, 0.03 #125621, 0.03 #4183), 0g7pm1 (0.05 #1189, 0.03 #125621, 0.02 #13573), 0g0x9c (0.05 #1347, 0.03 #125621, 0.02 #4885), 02r79_h (0.05 #222, 0.03 #125621, 0.01 #3760) >> Best rule #12384 for best value: >> intensional similarity = 3 >> extensional distance = 255 >> proper extension: 09px1w; >> query: (?x2422, ?x2262) <- award_nominee(?x192, ?x2422), participant(?x286, ?x2422), award_winner(?x2262, ?x2422) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #125621 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1570 *> proper extension: 03mz9r; 0565cz; 06gh0t; 0280mv7; 02x0bdb; 01nhkxp; 01w9k25; 01wwnh2; 0fz27v; 0f87jy; ... *> query: (?x2422, ?x224) <- award_nominee(?x4282, ?x2422), type_of_union(?x4282, ?x566), film(?x4282, ?x224) *> conf = 0.03 ranks of expected_values: 118, 262 EVAL 0169dl film 078sj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 109.000 84.000 0.617 http://example.org/film/actor/film./film/performance/film EVAL 0169dl film 03s6l2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 109.000 84.000 0.617 http://example.org/film/actor/film./film/performance/film #1362-027j9wd PRED entity: 027j9wd PRED relation: nominated_for! PRED expected values: 06fxnf => 77 concepts (37 used for prediction) PRED predicted values (max 10 best out of 586): 03n08b (0.31 #60804, 0.30 #18709, 0.26 #23386), 01h910 (0.30 #18709, 0.26 #23386, 0.25 #44436), 086nl7 (0.30 #18709, 0.26 #23386, 0.25 #44436), 01wgcvn (0.30 #18709, 0.26 #23386, 0.21 #72495), 06fxnf (0.28 #32744, 0.03 #3195, 0.03 #21904), 02g8h (0.25 #44436, 0.21 #72495, 0.21 #37421), 0bbxx9b (0.23 #35083, 0.23 #39760, 0.04 #10180), 094wz7q (0.22 #749, 0.03 #3087, 0.02 #38170), 05y7hc (0.22 #1489, 0.03 #3827, 0.01 #38910), 01795t (0.14 #65481, 0.13 #14031, 0.13 #12130) >> Best rule #60804 for best value: >> intensional similarity = 3 >> extensional distance = 619 >> proper extension: 09fc83; >> query: (?x6000, ?x1461) <- film(?x1461, ?x6000), executive_produced_by(?x141, ?x1461), genre(?x6000, ?x225) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #32744 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 175 *> proper extension: 0b60sq; 02q3fdr; *> query: (?x6000, ?x4020) <- country(?x6000, ?x94), language(?x6000, ?x254), category(?x6000, ?x134), music(?x6000, ?x4020) *> conf = 0.28 ranks of expected_values: 5 EVAL 027j9wd nominated_for! 06fxnf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 77.000 37.000 0.311 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #1361-02404v PRED entity: 02404v PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #30, 0.88 #19, 0.87 #34), 02zsn (0.46 #211, 0.46 #140, 0.46 #224) >> Best rule #30 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 06p0s1; 04cw0n4; >> query: (?x7740, 05zppz) <- cinematography(?x7293, ?x7740), written_by(?x7293, ?x4169), nominated_for(?x7215, ?x7293), film_release_region(?x7293, ?x94) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02404v gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.889 http://example.org/people/person/gender #1360-086qd PRED entity: 086qd PRED relation: instrumentalists! PRED expected values: 05r5c => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 58): 0342h (0.44 #1696, 0.43 #1875, 0.40 #4551), 05r5c (0.33 #9, 0.31 #1700, 0.30 #365), 05148p4 (0.33 #22, 0.27 #289, 0.25 #378), 0l14md (0.22 #8, 0.11 #275, 0.09 #2324), 026t6 (0.22 #3, 0.09 #270, 0.07 #2319), 018vs (0.19 #2330, 0.15 #4560, 0.15 #5808), 02hnl (0.11 #2352, 0.11 #36, 0.11 #1727), 03qjg (0.11 #53, 0.11 #2369, 0.11 #1744), 04rzd (0.11 #39, 0.07 #1909, 0.06 #1730), 03gvt (0.11 #67, 0.05 #334, 0.04 #423) >> Best rule #1696 for best value: >> intensional similarity = 3 >> extensional distance = 237 >> proper extension: 013rds; >> query: (?x2138, 0342h) <- award_winner(?x2431, ?x2138), type_of_union(?x2138, ?x566), artists(?x671, ?x2138) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0407f; 01vvyfh; 01vvyvk; 03j24kf; 012vd6; *> query: (?x2138, 05r5c) <- award(?x2138, ?x567), influenced_by(?x4593, ?x2138), ?x567 = 01d38g *> conf = 0.33 ranks of expected_values: 2 EVAL 086qd instrumentalists! 05r5c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 106.000 106.000 0.444 http://example.org/music/instrument/instrumentalists #1359-045c7b PRED entity: 045c7b PRED relation: service_location PRED expected values: 02j71 => 242 concepts (211 used for prediction) PRED predicted values (max 10 best out of 146): 07ssc (0.47 #2217, 0.32 #5263, 0.25 #1846), 02j71 (0.42 #1757, 0.40 #2219, 0.29 #5265), 0345h (0.40 #2228, 0.21 #5274, 0.18 #1584), 0f8l9c (0.33 #2222, 0.27 #1578, 0.21 #5268), 01n7q (0.31 #3130, 0.17 #9309, 0.17 #9585), 06pvr (0.31 #3130, 0.15 #6451, 0.09 #11993), 0l2vz (0.31 #3130, 0.15 #6451, 0.09 #11993), 03h64 (0.31 #13844, 0.28 #11619, 0.15 #11526), 03rjj (0.27 #2209, 0.17 #1747, 0.11 #5255), 06mkj (0.27 #2237, 0.17 #1775, 0.09 #1500) >> Best rule #2217 for best value: >> intensional similarity = 5 >> extensional distance = 13 >> proper extension: 018mxj; 064f29; 069b85; 07zl6m; >> query: (?x5072, 07ssc) <- contact_category(?x5072, ?x897), service_language(?x5072, ?x2502), service_location(?x5072, ?x94), ?x2502 = 06nm1, industry(?x5072, ?x5615) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #1757 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0kc6x; 01c6k4; 0gztl; 077w0b; 0mgkg; 01yx7f; 0z07; 0plw; 055z7; *> query: (?x5072, 02j71) <- contact_category(?x5072, ?x897), service_language(?x5072, ?x2502), service_location(?x5072, ?x94), ?x2502 = 06nm1, company(?x265, ?x5072) *> conf = 0.42 ranks of expected_values: 2 EVAL 045c7b service_location 02j71 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 242.000 211.000 0.467 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #1358-02825nf PRED entity: 02825nf PRED relation: language PRED expected values: 02h40lc 012w70 02hwhyv => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 41): 02h40lc (0.89 #4176, 0.89 #4116, 0.89 #4297), 03_9r (0.15 #247, 0.12 #10, 0.09 #69), 064_8sq (0.15 #81, 0.14 #2471, 0.14 #319), 06nm1 (0.13 #427, 0.12 #486, 0.10 #1684), 04306rv (0.12 #1019, 0.11 #2335, 0.11 #1915), 02bjrlw (0.09 #837, 0.08 #1, 0.08 #1911), 06b_j (0.07 #1158, 0.07 #1993, 0.07 #141), 03k50 (0.06 #4234, 0.06 #68, 0.05 #187), 0653m (0.06 #4234, 0.06 #71, 0.05 #1982), 04h9h (0.06 #4234, 0.06 #102, 0.04 #43) >> Best rule #4176 for best value: >> intensional similarity = 4 >> extensional distance = 933 >> proper extension: 04ynx7; >> query: (?x7629, 02h40lc) <- production_companies(?x7629, ?x541), film(?x3013, ?x7629), film(?x541, ?x5271), language(?x5271, ?x90) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 16, 26 EVAL 02825nf language 02hwhyv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 104.000 104.000 0.891 http://example.org/film/film/language EVAL 02825nf language 012w70 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 104.000 104.000 0.891 http://example.org/film/film/language EVAL 02825nf language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.891 http://example.org/film/film/language #1357-092kgw PRED entity: 092kgw PRED relation: award_nominee PRED expected values: 02pq9yv => 93 concepts (41 used for prediction) PRED predicted values (max 10 best out of 1087): 06pk8 (0.80 #14048, 0.80 #81966, 0.80 #21072), 03qmx_f (0.80 #14048, 0.80 #81966, 0.80 #21072), 02pq9yv (0.80 #14048, 0.80 #81966, 0.80 #21072), 02q_cc (0.40 #170, 0.15 #72595, 0.04 #7193), 092kgw (0.30 #1309, 0.15 #72595, 0.15 #39803), 030_3z (0.30 #1078, 0.15 #72595, 0.02 #8101), 03nk3t (0.30 #1055, 0.15 #72595, 0.01 #8078), 0h5f5n (0.20 #53, 0.15 #72595, 0.02 #7076), 0c6qh (0.16 #79624, 0.15 #39803, 0.10 #542), 0154qm (0.16 #79624, 0.04 #38200, 0.04 #40544) >> Best rule #14048 for best value: >> intensional similarity = 3 >> extensional distance = 274 >> proper extension: 0162c8; 01gp_x; 03kpvp; 0fqyzz; 0b478; 0d02km; 01hrqc; 0gs5q; 025hzx; 03h40_7; ... >> query: (?x5527, ?x976) <- nominated_for(?x5527, ?x1135), produced_by(?x343, ?x5527), award_nominee(?x976, ?x5527) >> conf = 0.80 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 092kgw award_nominee 02pq9yv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 93.000 41.000 0.804 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1356-0422v0 PRED entity: 0422v0 PRED relation: nominated_for! PRED expected values: 027dtxw => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 177): 04dn09n (0.58 #4084, 0.26 #2416, 0.25 #3572), 0gq9h (0.51 #4111, 0.34 #2443, 0.31 #2681), 019f4v (0.46 #4102, 0.29 #2434, 0.27 #2672), 0gs9p (0.45 #4113, 0.30 #2445, 0.25 #3572), 0k611 (0.41 #4122, 0.25 #2454, 0.22 #2692), 0gqwc (0.38 #299, 0.22 #4109, 0.15 #2441), 0gr4k (0.35 #4075, 0.19 #2407, 0.19 #6217), 040njc (0.34 #4055, 0.20 #2387, 0.19 #2625), 03hkv_r (0.34 #4063, 0.12 #253, 0.11 #1681), 02qyntr (0.34 #4228, 0.19 #2560, 0.18 #2798) >> Best rule #4084 for best value: >> intensional similarity = 4 >> extensional distance = 303 >> proper extension: 0kxf1; >> query: (?x13027, 04dn09n) <- nominated_for(?x1033, ?x13027), film(?x396, ?x13027), nominated_for(?x1033, ?x6940), ?x6940 = 0277j40 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #4052 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 303 *> proper extension: 0kxf1; *> query: (?x13027, 027dtxw) <- nominated_for(?x1033, ?x13027), film(?x396, ?x13027), nominated_for(?x1033, ?x6940), ?x6940 = 0277j40 *> conf = 0.25 ranks of expected_values: 28 EVAL 0422v0 nominated_for! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 86.000 86.000 0.584 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1355-0gvt53w PRED entity: 0gvt53w PRED relation: nominated_for! PRED expected values: 02x1dht 04kxsb => 94 concepts (83 used for prediction) PRED predicted values (max 10 best out of 197): 09td7p (0.73 #529, 0.30 #307, 0.25 #3638), 09qv_s (0.72 #771, 0.50 #549, 0.35 #327), 0gq9h (0.70 #278, 0.54 #1832, 0.50 #722), 019f4v (0.60 #271, 0.49 #1825, 0.40 #3602), 0gs9p (0.59 #1834, 0.55 #280, 0.41 #502), 02pqp12 (0.58 #53, 0.45 #275, 0.37 #1829), 0gr51 (0.55 #1181, 0.42 #71, 0.40 #293), 04dn09n (0.55 #476, 0.49 #1142, 0.45 #254), 09sb52 (0.50 #253, 0.42 #31, 0.41 #475), 02qyntr (0.50 #164, 0.36 #1274, 0.34 #3717) >> Best rule #529 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 011ykb; >> query: (?x9432, 09td7p) <- nominated_for(?x1254, ?x9432), nominated_for(?x1162, ?x9432), nominated_for(?x157, ?x9432), ?x1162 = 099c8n, ?x1254 = 02z0dfh >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #754 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0209xj; 050gkf; 07yk1xz; 026p4q7; 019vhk; 07w8fz; 0g54xkt; 03hj5lq; 06rhz7; *> query: (?x9432, 04kxsb) <- produced_by(?x9432, ?x9754), nominated_for(?x6729, ?x9432), ?x6729 = 099ck7, film(?x157, ?x9432) *> conf = 0.47 ranks of expected_values: 11, 14 EVAL 0gvt53w nominated_for! 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 94.000 83.000 0.727 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gvt53w nominated_for! 02x1dht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 94.000 83.000 0.727 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1354-0mww2 PRED entity: 0mww2 PRED relation: contains! PRED expected values: 05tbn => 128 concepts (56 used for prediction) PRED predicted values (max 10 best out of 95): 05tbn (0.89 #37749, 0.71 #7187, 0.71 #6513), 09c7w0 (0.44 #18870, 0.43 #17070, 0.38 #5393), 04_1l0v (0.35 #19318, 0.34 #17518, 0.28 #29206), 0mww2 (0.35 #31453, 0.22 #41350, 0.19 #50348), 07c5l (0.20 #2192, 0.19 #5785, 0.08 #9379), 059rby (0.19 #33270, 0.17 #35971, 0.17 #35071), 07ssc (0.19 #4524, 0.16 #7219, 0.15 #10813), 01n7q (0.19 #5468, 0.14 #21643, 0.14 #20743), 02jx1 (0.19 #4579, 0.12 #7274, 0.12 #10868), 02qkt (0.16 #9331, 0.13 #29102, 0.12 #47097) >> Best rule #37749 for best value: >> intensional similarity = 5 >> extensional distance = 154 >> proper extension: 0lmgy; >> query: (?x12846, ?x3670) <- currency(?x12846, ?x170), ?x170 = 09nqf, contains(?x12846, ?x8241), contains(?x3670, ?x8241), state_province_region(?x331, ?x3670) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mww2 contains! 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 56.000 0.891 http://example.org/location/location/contains #1353-07rhpg PRED entity: 07rhpg PRED relation: award PRED expected values: 09sb52 => 100 concepts (84 used for prediction) PRED predicted values (max 10 best out of 253): 09sb52 (0.44 #41, 0.34 #17051, 0.34 #17456), 01by1l (0.21 #3758, 0.19 #4163, 0.19 #9023), 04kxsb (0.18 #24303, 0.15 #22276, 0.15 #23087), 027dtxw (0.18 #24303, 0.15 #22276, 0.15 #23087), 0bfvd4 (0.18 #24303, 0.15 #23087, 0.15 #32810), 02x4w6g (0.18 #24303, 0.15 #32810, 0.05 #17125), 09cm54 (0.18 #24303, 0.15 #32810), 0gqwc (0.18 #6150, 0.15 #10200, 0.15 #7770), 0gqyl (0.18 #6181, 0.14 #10231, 0.14 #3346), 01c99j (0.18 #632, 0.10 #1847, 0.08 #10352) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 0p_r5; >> query: (?x7952, 09sb52) <- film(?x7952, ?x5290), film(?x2580, ?x5290), ?x2580 = 0227tr >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07rhpg award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 84.000 0.444 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1352-0h25 PRED entity: 0h25 PRED relation: award_winner! PRED expected values: 06196 => 121 concepts (117 used for prediction) PRED predicted values (max 10 best out of 290): 01l78d (0.21 #3743, 0.20 #2447, 0.17 #3311), 03nqnk3 (0.18 #567, 0.08 #2295, 0.08 #1863), 02pqp12 (0.18 #503, 0.04 #2231, 0.03 #2663), 01by1l (0.17 #1409, 0.12 #1841, 0.08 #6593), 03x3wf (0.17 #1361, 0.09 #6545, 0.08 #7409), 0ddd9 (0.14 #920, 0.09 #488, 0.07 #6104), 0d085 (0.13 #3706, 0.10 #4138, 0.10 #2842), 025m8y (0.12 #1396, 0.08 #1828, 0.03 #10036), 0gr4k (0.12 #2193, 0.11 #3489, 0.09 #3921), 0gr51 (0.12 #2261, 0.10 #2693, 0.09 #533) >> Best rule #3743 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 0l6qt; 0h5f5n; 04r7jc; 012t1; 05183k; 03m_k0; 01d8yn; 0q59y; 0499lc; 02lfp4; ... >> query: (?x10500, 01l78d) <- profession(?x10500, ?x6421), profession(?x10500, ?x987), ?x6421 = 02hv44_, ?x987 = 0dxtg, student(?x12475, ?x10500) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2504 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 23 *> proper extension: 0gv5c; 0652ty; 05wm88; *> query: (?x10500, 06196) <- profession(?x10500, ?x6421), profession(?x10500, ?x987), ?x6421 = 02hv44_, ?x987 = 0dxtg, religion(?x10500, ?x2694) *> conf = 0.04 ranks of expected_values: 90 EVAL 0h25 award_winner! 06196 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 121.000 117.000 0.213 http://example.org/award/award_category/winners./award/award_honor/award_winner #1351-05m_8 PRED entity: 05m_8 PRED relation: season PRED expected values: 025ygws => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 7): 025ygws (0.78 #114, 0.62 #142, 0.59 #170), 05kcgsf (0.50 #113, 0.41 #141, 0.39 #169), 04110b0 (0.31 #116, 0.29 #144, 0.24 #172), 02h7s73 (0.31 #117, 0.26 #145, 0.24 #173), 03c6s24 (0.25 #118, 0.21 #146, 0.20 #174), 03c74_8 (0.22 #115, 0.18 #143, 0.17 #171), 04n36qk (0.09 #70, 0.08 #42, 0.06 #119) >> Best rule #114 for best value: >> intensional similarity = 8 >> extensional distance = 30 >> proper extension: 01ypc; 03lpp_; 06x68; 01d5z; 049n7; 0512p; 0cqt41; 01yhm; 05g76; 051vz; ... >> query: (?x580, 025ygws) <- school(?x580, ?x13753), school(?x580, ?x1675), season(?x580, ?x2406), contains(?x94, ?x13753), colors(?x13753, ?x3189), category(?x13753, ?x134), colors(?x1675, ?x663), major_field_of_study(?x1675, ?x254) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05m_8 season 025ygws CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.781 http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season #1350-0bkg4 PRED entity: 0bkg4 PRED relation: instrumentalists! PRED expected values: 05148p4 => 145 concepts (108 used for prediction) PRED predicted values (max 10 best out of 121): 05148p4 (0.57 #82, 0.57 #17, 0.48 #5068), 028tv0 (0.43 #81, 0.41 #1772, 0.40 #5067), 02sgy (0.39 #803, 0.35 #1934, 0.31 #2656), 06w7v (0.39 #803, 0.35 #1934, 0.31 #2656), 03qjg (0.36 #1007, 0.33 #446, 0.32 #2139), 03gvt (0.29 #58, 0.17 #1266, 0.14 #1046), 026t6 (0.28 #966, 0.20 #886, 0.20 #2258), 04rzd (0.22 #1239, 0.20 #914, 0.20 #433), 07y_7 (0.22 #164, 0.14 #1046, 0.08 #2577), 0l14md (0.20 #889, 0.19 #728, 0.18 #1696) >> Best rule #82 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 015196; >> query: (?x3867, ?x1166) <- artists(?x1000, ?x3867), ?x1000 = 0xhtw, currency(?x3867, ?x1099), role(?x3867, ?x1166), ?x1166 = 05148p4 >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bkg4 instrumentalists! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 108.000 0.571 http://example.org/music/instrument/instrumentalists #1349-021q2j PRED entity: 021q2j PRED relation: student PRED expected values: 06hgj => 169 concepts (144 used for prediction) PRED predicted values (max 10 best out of 1726): 07s93v (0.11 #8625, 0.09 #17002, 0.09 #19096), 0gs1_ (0.10 #1135, 0.04 #38829, 0.02 #63957), 02238b (0.10 #1216, 0.03 #30534, 0.03 #28440), 02_nkp (0.10 #2030, 0.03 #31348, 0.03 #29254), 02vwckw (0.10 #1424, 0.03 #30742, 0.03 #28648), 0b_4z (0.10 #2014, 0.03 #52272, 0.03 #35520), 01wg982 (0.10 #367, 0.03 #50625, 0.02 #38061), 03kts (0.10 #1366, 0.03 #34872, 0.02 #39060), 0pj9t (0.10 #515, 0.03 #36115, 0.02 #80089), 0488g9 (0.10 #1910, 0.02 #39604, 0.02 #52168) >> Best rule #8625 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 033q4k; 02bjhv; 01q0kg; 02zcnq; 01t0dy; 01j_5k; 014xf6; 0k__z; 021996; 021w0_; ... >> query: (?x8850, 07s93v) <- category(?x8850, ?x134), major_field_of_study(?x8850, ?x742), child(?x7545, ?x8850), colors(?x8850, ?x8047) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #207314 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 489 *> proper extension: 014b4h; 017ztv; 05ftw3; 063576; 01nmgc; 05gm16l; 02m_41; 02p72j; 024cg8; 03p2m1; *> query: (?x8850, ?x217) <- category(?x8850, ?x134), institution(?x620, ?x8850), contains(?x2850, ?x8850), location(?x217, ?x2850) *> conf = 0.01 ranks of expected_values: 1440 EVAL 021q2j student 06hgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 169.000 144.000 0.105 http://example.org/education/educational_institution/students_graduates./education/education/student #1348-0gkd1 PRED entity: 0gkd1 PRED relation: group PRED expected values: 01czx 01s560x => 89 concepts (42 used for prediction) PRED predicted values (max 10 best out of 988): 02vnpv (0.72 #7065, 0.71 #5378, 0.71 #6876), 05563d (0.70 #4325, 0.60 #4138, 0.59 #6380), 02dw1_ (0.67 #3608, 0.67 #2111, 0.67 #1923), 02t3ln (0.67 #1904, 0.52 #1114, 0.50 #2092), 0134tg (0.60 #4346, 0.60 #4159, 0.57 #2661), 0gr69 (0.60 #4377, 0.57 #2692, 0.52 #1114), 07mvp (0.60 #4367, 0.52 #1114, 0.50 #6986), 017_hq (0.57 #2765, 0.52 #1114, 0.50 #4450), 017lb_ (0.57 #2714, 0.52 #1114, 0.50 #4399), 01wv9xn (0.57 #2618, 0.52 #1114, 0.50 #4303) >> Best rule #7065 for best value: >> intensional similarity = 15 >> extensional distance = 16 >> proper extension: 04rzd; >> query: (?x7033, 02vnpv) <- role(?x7033, ?x2725), role(?x7033, ?x569), role(?x7033, ?x314), ?x2725 = 0l1589, role(?x7238, ?x7033), role(?x211, ?x7033), award(?x7238, ?x247), performance_role(?x645, ?x7033), role(?x7375, ?x314), role(?x569, ?x1750), ?x7375 = 0484q, role(?x314, ?x75), instrumentalists(?x7033, ?x642), artists(?x114, ?x7238), group(?x569, ?x2395) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #2626 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 5 *> proper extension: 01vdm0; *> query: (?x7033, 01czx) <- role(?x7033, ?x2725), ?x2725 = 0l1589, role(?x7272, ?x7033), role(?x1466, ?x7033), role(?x74, ?x7033), ?x7272 = 01vsyjy, role(?x10802, ?x1466), role(?x1291, ?x1466), role(?x300, ?x1466), performance_role(?x248, ?x1466), performance_role(?x1466, ?x645), ?x74 = 03q5t, group(?x1466, ?x7865), group(?x1466, ?x4995), ?x1291 = 01kx_81, ?x10802 = 01mxnvc, ?x4995 = 01fmz6, award_winner(?x2585, ?x300), ?x7865 = 02k5sc *> conf = 0.57 ranks of expected_values: 11, 63 EVAL 0gkd1 group 01s560x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 89.000 42.000 0.722 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 0gkd1 group 01czx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 89.000 42.000 0.722 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1347-0ylsr PRED entity: 0ylsr PRED relation: institution! PRED expected values: 0bkj86 => 209 concepts (209 used for prediction) PRED predicted values (max 10 best out of 21): 02h4rq6 (0.74 #1654, 0.74 #928, 0.73 #598), 03bwzr4 (0.65 #938, 0.53 #1906, 0.53 #1224), 02_xgp2 (0.61 #936, 0.57 #342, 0.57 #320), 0bkj86 (0.57 #316, 0.56 #1900, 0.50 #250), 04zx3q1 (0.41 #927, 0.40 #156, 0.37 #707), 07s6fsf (0.37 #882, 0.37 #1652, 0.36 #816), 027f2w (0.33 #30, 0.33 #8, 0.29 #339), 01rr_d (0.33 #16, 0.29 #347, 0.26 #721), 03mkk4 (0.29 #341, 0.23 #781, 0.21 #2781), 0bjrnt (0.26 #710, 0.24 #644, 0.22 #930) >> Best rule #1654 for best value: >> intensional similarity = 6 >> extensional distance = 165 >> proper extension: 02v992; >> query: (?x7392, 02h4rq6) <- institution(?x1771, ?x7392), institution(?x1368, ?x7392), citytown(?x7392, ?x1841), ?x1368 = 014mlp, institution(?x1771, ?x5907), ?x5907 = 01jq4b >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #316 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 07vjm; 0138t4; *> query: (?x7392, 0bkj86) <- colors(?x7392, ?x3189), institution(?x1200, ?x7392), adjoins(?x7392, ?x6548), state_province_region(?x7392, ?x2235) *> conf = 0.57 ranks of expected_values: 4 EVAL 0ylsr institution! 0bkj86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 209.000 209.000 0.743 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #1346-0dnw1 PRED entity: 0dnw1 PRED relation: award_winner PRED expected values: 016jll => 79 concepts (37 used for prediction) PRED predicted values (max 10 best out of 453): 02lfp4 (0.50 #837, 0.11 #37815, 0.06 #4125), 096hm (0.48 #31238, 0.48 #32883, 0.47 #32882), 0c4qzm (0.48 #31238, 0.48 #32883, 0.47 #32882), 057bc6m (0.48 #31238, 0.48 #32883, 0.47 #32882), 07hhnl (0.48 #31238, 0.47 #32882, 0.47 #46034), 07h1tr (0.48 #31238, 0.47 #32882, 0.47 #46034), 0146pg (0.31 #3386, 0.25 #98, 0.11 #37815), 016szr (0.25 #816, 0.15 #50966, 0.11 #37815), 02ryx0 (0.25 #976, 0.15 #50966, 0.11 #37815), 06pj8 (0.25 #338, 0.14 #3626, 0.02 #49660) >> Best rule #837 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 01k5y0; >> query: (?x6094, 02lfp4) <- award_winner(?x6094, ?x7556), ?x7556 = 01vttb9, nominated_for(?x601, ?x6094), genre(?x6094, ?x53) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #50966 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 674 *> proper extension: 02nf2c; 04p5cr; 04glx0; 08y2fn; 0dl6fv; *> query: (?x6094, ?x4850) <- award_winner(?x6094, ?x7556), award_winner(?x7556, ?x4850), award_winner(?x1821, ?x7556) *> conf = 0.15 ranks of expected_values: 20 EVAL 0dnw1 award_winner 016jll CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 79.000 37.000 0.500 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #1345-026dg51 PRED entity: 026dg51 PRED relation: award_winner! PRED expected values: 027n06w => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 77): 027n06w (0.25 #73, 0.11 #496, 0.10 #778), 0jt3qpk (0.15 #43, 0.05 #466, 0.04 #748), 0gvstc3 (0.12 #739, 0.12 #598, 0.12 #880), 05c1t6z (0.10 #438, 0.10 #720, 0.10 #579), 09v0p2c (0.09 #788, 0.09 #647, 0.09 #929), 03gt46z (0.08 #768, 0.08 #627, 0.08 #909), 0bq_mx (0.07 #838, 0.07 #697, 0.07 #979), 03nnm4t (0.07 #215, 0.07 #356, 0.06 #1061), 02q690_ (0.07 #1052, 0.07 #1334, 0.07 #1616), 0gx_st (0.05 #742, 0.05 #601, 0.05 #1024) >> Best rule #73 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 02_2v2; >> query: (?x912, 027n06w) <- award_winner(?x912, ?x5986), ?x5986 = 03clrng, tv_program(?x912, ?x589) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026dg51 award_winner! 027n06w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1344-016lmg PRED entity: 016lmg PRED relation: award PRED expected values: 02f73b => 60 concepts (55 used for prediction) PRED predicted values (max 10 best out of 258): 02f5qb (0.63 #2166, 0.31 #2970, 0.30 #3372), 02f72_ (0.57 #2239, 0.33 #3043, 0.33 #3445), 02f73b (0.55 #2297, 0.22 #3101, 0.22 #3503), 02f73p (0.51 #2197, 0.26 #3001, 0.26 #3403), 01ckcd (0.50 #334, 0.36 #3550, 0.35 #3148), 02f77l (0.50 #255, 0.28 #3069, 0.28 #3471), 03tcnt (0.50 #167, 0.23 #3383, 0.22 #2981), 01by1l (0.49 #2122, 0.36 #4132, 0.36 #2524), 02f72n (0.43 #2156, 0.33 #1352, 0.31 #2960), 01bgqh (0.38 #2053, 0.32 #4063, 0.32 #2455) >> Best rule #2166 for best value: >> intensional similarity = 3 >> extensional distance = 63 >> proper extension: 0dtd6; 016vj5; >> query: (?x8199, 02f5qb) <- artist(?x2023, ?x8199), award(?x8199, ?x3365), ?x3365 = 02f716 >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #2297 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 0dtd6; 016vj5; *> query: (?x8199, 02f73b) <- artist(?x2023, ?x8199), award(?x8199, ?x3365), ?x3365 = 02f716 *> conf = 0.55 ranks of expected_values: 3 EVAL 016lmg award 02f73b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 60.000 55.000 0.631 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1343-06rq1k PRED entity: 06rq1k PRED relation: production_companies! PRED expected values: 03z20c 02d003 02tgz4 => 94 concepts (78 used for prediction) PRED predicted values (max 10 best out of 1200): 02hxhz (0.44 #29628, 0.41 #13673, 0.41 #10254), 0gj96ln (0.41 #13673, 0.26 #22790, 0.06 #21650), 023vcd (0.41 #13673, 0.10 #20510, 0.10 #23931), 08984j (0.33 #1924, 0.25 #6481, 0.06 #54341), 02tgz4 (0.26 #22790, 0.24 #31907, 0.17 #2112), 02d003 (0.26 #22790, 0.08 #10255, 0.06 #21650), 03459x (0.25 #6082, 0.17 #1525, 0.06 #34572), 02ylg6 (0.24 #31907, 0.17 #1743, 0.16 #17091), 03z20c (0.24 #31907, 0.16 #17091, 0.05 #17408), 033g4d (0.24 #31907, 0.07 #28614, 0.06 #21650) >> Best rule #29628 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 0fb0v; 03qx_f; >> query: (?x1836, ?x821) <- organizations_founded(?x1335, ?x1836), award_nominee(?x541, ?x1335), produced_by(?x821, ?x1335), award_nominee(?x1335, ?x1942) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #22790 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 03ksy; *> query: (?x1836, ?x821) <- company(?x1335, ?x1836), award(?x1335, ?x401), award_nominee(?x541, ?x1335), executive_produced_by(?x821, ?x1335) *> conf = 0.26 ranks of expected_values: 5, 6, 9 EVAL 06rq1k production_companies! 02tgz4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 78.000 0.444 http://example.org/film/film/production_companies EVAL 06rq1k production_companies! 02d003 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 94.000 78.000 0.444 http://example.org/film/film/production_companies EVAL 06rq1k production_companies! 03z20c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 94.000 78.000 0.444 http://example.org/film/film/production_companies #1342-0bwfn PRED entity: 0bwfn PRED relation: student PRED expected values: 01r42_g 02lnhv 011zd3 0dn3n 02y_2y 02mt4k 06hgj 01r2c7 024_vw => 136 concepts (121 used for prediction) PRED predicted values (max 10 best out of 1846): 01gq0b (0.25 #261, 0.06 #6175, 0.02 #35744), 016tbr (0.25 #1635, 0.05 #13463, 0.04 #23320), 01vh08 (0.25 #1465, 0.05 #13293, 0.02 #44835), 0m76b (0.25 #1648, 0.04 #15448, 0.04 #23333), 01tnbn (0.25 #987, 0.04 #20700, 0.03 #118281), 0219q (0.25 #644, 0.04 #20357, 0.02 #116953), 0456xp (0.25 #126, 0.03 #118281), 01yd8v (0.25 #490, 0.02 #34002, 0.01 #116799), 033db3 (0.25 #1964, 0.02 #37447, 0.02 #45334), 06jz0 (0.25 #1654, 0.02 #37137, 0.02 #45024) >> Best rule #261 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 026gvfj; >> query: (?x7545, 01gq0b) <- student(?x7545, ?x2615), student(?x7545, ?x2258), ?x2258 = 0f4vbz, award_nominee(?x2615, ?x539) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #158 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 026gvfj; *> query: (?x7545, 02lnhv) <- student(?x7545, ?x2615), student(?x7545, ?x2258), ?x2258 = 0f4vbz, award_nominee(?x2615, ?x539) *> conf = 0.25 ranks of expected_values: 33, 580, 782, 836, 901, 965, 968, 1315 EVAL 0bwfn student 024_vw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 136.000 121.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0bwfn student 01r2c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 136.000 121.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0bwfn student 06hgj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 121.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0bwfn student 02mt4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 136.000 121.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0bwfn student 02y_2y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 136.000 121.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0bwfn student 0dn3n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 136.000 121.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0bwfn student 011zd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 136.000 121.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0bwfn student 02lnhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 136.000 121.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0bwfn student 01r42_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 136.000 121.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #1341-09p7fh PRED entity: 09p7fh PRED relation: genre PRED expected values: 07s9rl0 => 71 concepts (69 used for prediction) PRED predicted values (max 10 best out of 83): 07s9rl0 (0.82 #4613, 0.75 #243, 0.69 #1335), 07ssc (0.57 #728, 0.56 #1456, 0.54 #4612), 05p553 (0.45 #974, 0.44 #5589, 0.42 #1217), 03k9fj (0.43 #133, 0.33 #12, 0.28 #5597), 06n90 (0.43 #135, 0.33 #14, 0.16 #5599), 02kdv5l (0.35 #5587, 0.29 #6679, 0.25 #6193), 04xvh5 (0.33 #35, 0.29 #156, 0.15 #277), 02p0szs (0.33 #29, 0.29 #150, 0.10 #8130), 01jfsb (0.31 #6690, 0.29 #862, 0.29 #134), 06cvj (0.25 #973, 0.24 #1216, 0.13 #367) >> Best rule #4613 for best value: >> intensional similarity = 3 >> extensional distance = 1228 >> proper extension: 0vgkd; >> query: (?x2519, 07s9rl0) <- genre(?x2519, ?x1403), genre(?x7314, ?x1403), ?x7314 = 047vp1n >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09p7fh genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 69.000 0.818 http://example.org/film/film/genre #1340-05np2 PRED entity: 05np2 PRED relation: influenced_by! PRED expected values: 01hb6v => 164 concepts (68 used for prediction) PRED predicted values (max 10 best out of 404): 07g2b (0.42 #17, 0.17 #3584, 0.12 #23982), 0683n (0.33 #3901, 0.29 #844, 0.17 #334), 0c1jh (0.33 #383, 0.12 #23982, 0.11 #33180), 03cdg (0.32 #10200, 0.31 #7137, 0.25 #10711), 084w8 (0.29 #3569, 0.21 #512, 0.12 #23982), 014ps4 (0.29 #816, 0.25 #3873, 0.17 #3363), 01vdrw (0.29 #950, 0.25 #4007, 0.17 #440), 07dnx (0.26 #2904, 0.12 #3924, 0.08 #357), 04hcw (0.26 #2832, 0.09 #9974, 0.08 #285), 0bk5r (0.26 #2753, 0.08 #206, 0.08 #4282) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 03pm9; 0379s; >> query: (?x6975, 07g2b) <- influenced_by(?x6975, ?x3712), influenced_by(?x6457, ?x6975), profession(?x6975, ?x353), ?x6457 = 03_87 >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #2640 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 0d4jl; 032l1; 0c5tl; 058vp; 0mb5x; *> query: (?x6975, 01hb6v) <- religion(?x6975, ?x1985), influenced_by(?x2161, ?x6975), influenced_by(?x6975, ?x5004), ?x5004 = 081k8 *> conf = 0.16 ranks of expected_values: 54 EVAL 05np2 influenced_by! 01hb6v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 164.000 68.000 0.417 http://example.org/influence/influence_node/influenced_by #1339-01vrncs PRED entity: 01vrncs PRED relation: profession PRED expected values: 02hrh1q 0kyk 039v1 => 130 concepts (129 used for prediction) PRED predicted values (max 10 best out of 87): 02hrh1q (0.89 #14340, 0.89 #5291, 0.87 #11560), 01d_h8 (0.67 #9462, 0.50 #2228, 0.47 #1116), 01c72t (0.62 #1964, 0.40 #991, 0.39 #435), 03gjzk (0.42 #1122, 0.41 #2234, 0.31 #3068), 0kyk (0.42 #719, 0.40 #1553, 0.38 #3777), 0fj9f (0.38 #47, 0.36 #186, 0.10 #3383), 018gz8 (0.38 #1124, 0.33 #2236, 0.23 #3070), 039v1 (0.32 #1837, 0.28 #2115, 0.28 #2810), 04gc2 (0.31 #35, 0.27 #174, 0.06 #3788), 02hv44_ (0.28 #744, 0.13 #3802, 0.11 #5192) >> Best rule #14340 for best value: >> intensional similarity = 2 >> extensional distance = 2012 >> proper extension: 02zq43; 073749; 01mt1fy; 01d5vk; 0fs9jn; 03k1vm; 014zn0; 033071; 04f62k; 05m7zg; ... >> query: (?x1089, 02hrh1q) <- profession(?x1089, ?x131), film(?x1089, ?x10931) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5, 8 EVAL 01vrncs profession 039v1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 130.000 129.000 0.892 http://example.org/people/person/profession EVAL 01vrncs profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 130.000 129.000 0.892 http://example.org/people/person/profession EVAL 01vrncs profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 129.000 0.892 http://example.org/people/person/profession #1338-01x1fq PRED entity: 01x1fq PRED relation: profession PRED expected values: 01c72t => 110 concepts (89 used for prediction) PRED predicted values (max 10 best out of 71): 09jwl (0.79 #318, 0.75 #4200, 0.74 #765), 02hrh1q (0.76 #6591, 0.71 #7635, 0.69 #6889), 0dz3r (0.67 #747, 0.66 #300, 0.52 #2), 01c72t (0.66 #919, 0.61 #2859, 0.60 #3159), 016z4k (0.41 #2391, 0.40 #4184, 0.39 #1644), 0fnpj (0.38 #359, 0.37 #806, 0.29 #61), 0dxtg (0.38 #461, 0.33 #6888, 0.32 #6739), 039v1 (0.36 #4217, 0.30 #335, 0.28 #782), 01d_h8 (0.35 #7477, 0.34 #6880, 0.34 #7029), 01c8w0 (0.29 #1201, 0.29 #2843, 0.28 #3143) >> Best rule #318 for best value: >> intensional similarity = 3 >> extensional distance = 51 >> proper extension: 023l9y; 01ww_vs; >> query: (?x9891, 09jwl) <- role(?x9891, ?x228), ?x228 = 0l14qv, category(?x9891, ?x134) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #919 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 07s3vqk; 0fp_v1x; 02rgz4; 01wl38s; 0146pg; 07q1v4; 01vrncs; 03kwtb; 01vsxdm; 0244r8; ... *> query: (?x9891, 01c72t) <- music(?x5029, ?x9891), award(?x5029, ?x1033), role(?x9891, ?x228) *> conf = 0.66 ranks of expected_values: 4 EVAL 01x1fq profession 01c72t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 110.000 89.000 0.792 http://example.org/people/person/profession #1337-0c00lh PRED entity: 0c00lh PRED relation: nationality PRED expected values: 0d060g => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 30): 0d060g (0.25 #6, 0.11 #1788, 0.08 #303), 06npd (0.25 #19), 07ssc (0.18 #509, 0.13 #4672, 0.13 #311), 02jx1 (0.16 #626, 0.14 #527, 0.14 #1220), 03rk0 (0.09 #4604, 0.08 #6488, 0.08 #6587), 0345h (0.07 #1812, 0.06 #4688, 0.06 #4489), 0h7x (0.06 #1816, 0.03 #4692, 0.02 #4493), 03rt9 (0.05 #309, 0.04 #111, 0.03 #210), 03rjj (0.04 #697, 0.02 #6447, 0.02 #2975), 0f8l9c (0.04 #4679, 0.04 #4480, 0.04 #1209) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 02lf0c; 08d9z7; >> query: (?x5351, 0d060g) <- produced_by(?x2386, ?x5351), student(?x735, ?x5351), ?x2386 = 065z3_x >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c00lh nationality 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.250 http://example.org/people/person/nationality #1336-0244r8 PRED entity: 0244r8 PRED relation: origin PRED expected values: 0chgzm => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 76): 02z0j (0.11 #385, 0.04 #621, 0.03 #857), 04jpl (0.06 #11342, 0.05 #5673, 0.05 #5909), 02_286 (0.05 #4975, 0.05 #7101, 0.05 #2849), 030qb3t (0.05 #2867, 0.04 #15146, 0.04 #11370), 0hyxv (0.04 #548, 0.04 #1492, 0.03 #784), 0c8tk (0.04 #554, 0.03 #790, 0.02 #1026), 094jv (0.04 #509, 0.03 #745, 0.02 #1217), 02jx1 (0.04 #504, 0.02 #976, 0.02 #3810), 0f2tj (0.04 #590, 0.02 #1770, 0.02 #2007), 09c7w0 (0.04 #2362, 0.02 #5668, 0.02 #945) >> Best rule #385 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 016tw3; >> query: (?x1489, 02z0j) <- nominated_for(?x1489, ?x1077), ?x1077 = 09q5w2, award_winner(?x3069, ?x1489) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #7227 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 158 *> proper extension: 04qmr; 0dw4g; 03d9d6; *> query: (?x1489, 0chgzm) <- nominated_for(?x1489, ?x1077), artists(?x497, ?x1489), award_winner(?x1443, ?x1489) *> conf = 0.01 ranks of expected_values: 52 EVAL 0244r8 origin 0chgzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 118.000 118.000 0.111 http://example.org/music/artist/origin #1335-046b0s PRED entity: 046b0s PRED relation: production_companies! PRED expected values: 01j8wk 01bb9r 03cd0x 07gghl 03tbg6 => 102 concepts (79 used for prediction) PRED predicted values (max 10 best out of 1149): 060v34 (0.50 #7834, 0.46 #10073, 0.44 #12312), 0645k5 (0.50 #1420, 0.07 #3658, 0.05 #7015), 033g4d (0.50 #1242, 0.07 #3480, 0.05 #6837), 09w6br (0.33 #1054, 0.25 #2173, 0.07 #12246), 02ht1k (0.33 #406, 0.25 #1525, 0.07 #11598), 03s9kp (0.33 #1103, 0.25 #2222, 0.07 #12295), 02stbw (0.33 #249, 0.25 #1368, 0.07 #11441), 0fh694 (0.33 #97, 0.25 #1216, 0.07 #11289), 03clwtw (0.33 #772, 0.15 #7486, 0.15 #13084), 03rtz1 (0.33 #116, 0.07 #11308, 0.07 #10189) >> Best rule #7834 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 09d5h; >> query: (?x2548, ?x570) <- award_nominee(?x2548, ?x382), company(?x8503, ?x2548), nominated_for(?x2548, ?x570) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1714 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 05h4t7; *> query: (?x2548, 03cd0x) <- production_companies(?x11672, ?x2548), production_companies(?x3055, ?x2548), film(?x3708, ?x11672), ?x3055 = 0x25q *> conf = 0.25 ranks of expected_values: 57, 71, 92, 110, 789 EVAL 046b0s production_companies! 03tbg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 102.000 79.000 0.505 http://example.org/film/film/production_companies EVAL 046b0s production_companies! 07gghl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 102.000 79.000 0.505 http://example.org/film/film/production_companies EVAL 046b0s production_companies! 03cd0x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 102.000 79.000 0.505 http://example.org/film/film/production_companies EVAL 046b0s production_companies! 01bb9r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 102.000 79.000 0.505 http://example.org/film/film/production_companies EVAL 046b0s production_companies! 01j8wk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 102.000 79.000 0.505 http://example.org/film/film/production_companies #1334-07ncs0 PRED entity: 07ncs0 PRED relation: film PRED expected values: 03nm_fh => 105 concepts (80 used for prediction) PRED predicted values (max 10 best out of 291): 05p9_ql (0.60 #57271, 0.59 #89479, 0.59 #87689), 01g03q (0.60 #57271, 0.59 #89479, 0.59 #87689), 0286vp (0.13 #3015, 0.08 #1225, 0.03 #75163), 060__7 (0.13 #3250, 0.03 #75163), 01633c (0.08 #1326, 0.07 #3116, 0.03 #75163), 01l_pn (0.08 #966, 0.07 #2756, 0.03 #75163), 07p12s (0.08 #1675, 0.07 #3465, 0.03 #75163), 04z4j2 (0.08 #1627, 0.07 #3417, 0.03 #75163), 0n_hp (0.08 #1524, 0.07 #3314, 0.03 #75163), 0cqr0q (0.08 #1497, 0.07 #3287, 0.03 #75163) >> Best rule #57271 for best value: >> intensional similarity = 3 >> extensional distance = 968 >> proper extension: 02wrhj; >> query: (?x6182, ?x7317) <- nominated_for(?x6182, ?x7317), location(?x6182, ?x739), film(?x6182, ?x1721) >> conf = 0.60 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 07ncs0 film 03nm_fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 80.000 0.597 http://example.org/film/actor/film./film/performance/film #1333-025mbn PRED entity: 025mbn PRED relation: ceremony PRED expected values: 01bx35 01mhwk 013b2h => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 122): 013b2h (0.80 #574, 0.53 #700, 0.50 #196), 01bx35 (0.78 #508, 0.57 #256, 0.54 #634), 01mhwk (0.74 #537, 0.52 #663, 0.50 #285), 0bzm81 (0.22 #1513, 0.16 #647, 0.14 #269), 0bz6l9 (0.22 #1513, 0.14 #294, 0.13 #672), 0bzjvm (0.22 #1513, 0.14 #350, 0.13 #728), 073h1t (0.22 #1513, 0.14 #274, 0.13 #652), 0bzmt8 (0.22 #1513, 0.14 #338, 0.13 #716), 03tn9w (0.22 #1513, 0.14 #334, 0.12 #712), 0dthsy (0.22 #1513, 0.14 #310, 0.12 #2018) >> Best rule #574 for best value: >> intensional similarity = 5 >> extensional distance = 80 >> proper extension: 0257yf; >> query: (?x5224, 013b2h) <- ceremony(?x5224, ?x5656), ceremony(?x5224, ?x2186), ?x5656 = 0466p0j, ceremony(?x7691, ?x2186), ?x7691 = 026m9w >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 025mbn ceremony 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.805 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 025mbn ceremony 01mhwk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.805 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 025mbn ceremony 01bx35 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.805 http://example.org/award/award_category/winners./award/award_honor/ceremony #1332-0yyn5 PRED entity: 0yyn5 PRED relation: executive_produced_by PRED expected values: 0d6484 => 79 concepts (52 used for prediction) PRED predicted values (max 10 best out of 67): 04sry (0.11 #417, 0.02 #1171, 0.02 #1422), 06q8hf (0.11 #3190, 0.09 #5462, 0.05 #1927), 05hj_k (0.09 #3122, 0.09 #5394, 0.04 #3373), 0glyyw (0.06 #3212, 0.04 #5484, 0.02 #4724), 06pj8 (0.05 #3079, 0.05 #5351, 0.04 #1816), 02q42j_ (0.04 #1142, 0.04 #1645, 0.04 #890), 03c9pqt (0.04 #3270, 0.03 #5542, 0.02 #5036), 04pqqb (0.04 #3140, 0.02 #5412, 0.01 #4906), 0hskw (0.04 #2014, 0.04 #3528, 0.03 #7316), 0b13g7 (0.04 #840, 0.03 #1092, 0.03 #1343) >> Best rule #417 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 026390q; 09tqkv2; 0g9lm2; 02_06s; 03b1l8; 02wk7b; 0yx1m; >> query: (?x5584, 04sry) <- nominated_for(?x3435, ?x5584), nominated_for(?x1716, ?x5584), ?x3435 = 03hl6lc, currency(?x5584, ?x170), ?x1716 = 02y_rq5 >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #967 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 02d44q; *> query: (?x5584, 0d6484) <- nominated_for(?x3435, ?x5584), ?x3435 = 03hl6lc, award_winner(?x5584, ?x4771), film_release_distribution_medium(?x5584, ?x81) *> conf = 0.01 ranks of expected_values: 50 EVAL 0yyn5 executive_produced_by 0d6484 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 79.000 52.000 0.111 http://example.org/film/film/executive_produced_by #1331-079yb PRED entity: 079yb PRED relation: category PRED expected values: 08mbj5d => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #8, 0.73 #10, 0.73 #14) >> Best rule #8 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0hb37; >> query: (?x11650, 08mbj5d) <- contains(?x6408, ?x11650), adjoins(?x6408, ?x9274), ?x9274 = 015m08, time_zones(?x11650, ?x2864), contains(?x6408, ?x9660), location(?x1279, ?x9660) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 079yb category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.800 http://example.org/common/topic/webpage./common/webpage/category #1330-058frd PRED entity: 058frd PRED relation: location_of_ceremony PRED expected values: 030qb3t => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 4): 0cv3w (0.02 #1225, 0.02 #1106, 0.02 #1344), 0k049 (0.01 #1194, 0.01 #361), 0rsjf (0.01 #423), 03rjj (0.01 #362) >> Best rule #1225 for best value: >> intensional similarity = 3 >> extensional distance = 192 >> proper extension: 05nn4k; 0bjkpt; 06pjs; 0blpnz; >> query: (?x6086, 0cv3w) <- nominated_for(?x6086, ?x3124), produced_by(?x5570, ?x6086), award_winner(?x6086, ?x10064) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 058frd location_of_ceremony 030qb3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 104.000 0.021 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #1329-07g9f PRED entity: 07g9f PRED relation: tv_program! PRED expected values: 0821j => 86 concepts (59 used for prediction) PRED predicted values (max 10 best out of 214): 027hnjh (0.50 #186, 0.47 #3347, 0.46 #2603), 02q5xsx (0.50 #186, 0.47 #3347, 0.46 #2603), 025y9fn (0.50 #186, 0.47 #3347, 0.46 #2603), 0b05xm (0.50 #186, 0.47 #3347, 0.46 #2603), 0bbxd3 (0.50 #186, 0.47 #3347, 0.46 #2603), 0f721s (0.50 #186, 0.47 #3347, 0.46 #2603), 07g7h2 (0.33 #110, 0.05 #854, 0.05 #667), 09v6gc9 (0.33 #91, 0.05 #835, 0.05 #648), 02dth1 (0.31 #1860, 0.25 #2976, 0.25 #2046), 0cjdk (0.31 #1860, 0.25 #2976, 0.25 #2046) >> Best rule #186 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0h3mh3q; >> query: (?x10089, ?x1394) <- tv_program(?x10216, ?x10089), ?x10216 = 04h68j, nominated_for(?x435, ?x10089), program(?x1394, ?x10089) >> conf = 0.50 => this is the best rule for 6 predicted values No rule for expected values ranks of expected_values: EVAL 07g9f tv_program! 0821j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 86.000 59.000 0.500 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #1328-01cssf PRED entity: 01cssf PRED relation: nominated_for! PRED expected values: 0b_c7 0cqh57 => 111 concepts (56 used for prediction) PRED predicted values (max 10 best out of 806): 013tcv (0.80 #65434, 0.80 #63096, 0.79 #116858), 03_wtr (0.50 #25701, 0.49 #37390, 0.48 #42065), 05yh_t (0.50 #25701, 0.49 #37390, 0.48 #42065), 018swb (0.50 #25701, 0.49 #37390, 0.48 #42065), 04yj5z (0.50 #25701, 0.49 #37390, 0.48 #42065), 032w8h (0.50 #25701, 0.49 #37390, 0.48 #42065), 03q1vd (0.50 #25701, 0.49 #37390, 0.48 #42065), 02pzck (0.50 #25701, 0.49 #37390, 0.48 #42065), 03dpqd (0.50 #25701, 0.49 #37390, 0.48 #42065), 045931 (0.50 #25701, 0.49 #37390, 0.48 #42065) >> Best rule #65434 for best value: >> intensional similarity = 5 >> extensional distance = 251 >> proper extension: 015g28; >> query: (?x638, ?x3101) <- award_winner(?x638, ?x3101), award_winner(?x638, ?x1890), spouse(?x10224, ?x1890), film(?x1890, ?x414), award(?x638, ?x1180) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #32714 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 121 *> proper extension: 04zyhx; 0fsw_7; 063zky; 04y9mm8; 06bc59; 0df2zx; *> query: (?x638, ?x574) <- prequel(?x308, ?x638), film(?x804, ?x638), nominated_for(?x574, ?x308) *> conf = 0.33 ranks of expected_values: 15 EVAL 01cssf nominated_for! 0cqh57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 111.000 56.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for EVAL 01cssf nominated_for! 0b_c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 56.000 0.798 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #1327-030_1m PRED entity: 030_1m PRED relation: child! PRED expected values: 09d5h 03mdt => 102 concepts (85 used for prediction) PRED predicted values (max 10 best out of 86): 03phgz (0.33 #364, 0.33 #283, 0.33 #202), 09b3v (0.33 #2886, 0.25 #3292, 0.25 #599), 0l8sx (0.26 #1807, 0.24 #1972, 0.19 #1316), 086k8 (0.24 #2210, 0.22 #2291, 0.17 #2861), 049ql1 (0.22 #2356, 0.17 #230, 0.08 #3332), 018_q8 (0.21 #2248, 0.17 #365, 0.14 #1019), 03f2fw (0.20 #1280, 0.04 #4139, 0.03 #3894), 03xsby (0.17 #257, 0.17 #176, 0.12 #585), 0kx4m (0.17 #332, 0.12 #579, 0.07 #1068), 03d6fyn (0.17 #192, 0.11 #2318, 0.07 #1090) >> Best rule #364 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 01l50r; >> query: (?x1561, 03phgz) <- country(?x1561, ?x94), child(?x541, ?x1561), award_winner(?x541, ?x163) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1326 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 05gnf; *> query: (?x1561, 03mdt) <- film(?x1561, ?x9250), genre(?x9250, ?x53), award_winner(?x1689, ?x1561) *> conf = 0.05 ranks of expected_values: 35 EVAL 030_1m child! 03mdt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 102.000 85.000 0.333 http://example.org/organization/organization/child./organization/organization_relationship/child EVAL 030_1m child! 09d5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 85.000 0.333 http://example.org/organization/organization/child./organization/organization_relationship/child #1326-0lk90 PRED entity: 0lk90 PRED relation: category PRED expected values: 08mbj5d => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.89 #20, 0.88 #12, 0.86 #52) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 05cljf; 018y2s; 09qr6; 06w2sn5; 058s57; 033wx9; 01wgxtl; 014q2g; 0161sp; 01vw20_; ... >> query: (?x1093, 08mbj5d) <- participant(?x1093, ?x6035), artists(?x671, ?x1093), award_nominee(?x3602, ?x1093) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lk90 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.892 http://example.org/common/topic/webpage./common/webpage/category #1325-05hks PRED entity: 05hks PRED relation: people! PRED expected values: 0g6ff => 173 concepts (171 used for prediction) PRED predicted values (max 10 best out of 53): 0x67 (0.76 #5977, 0.17 #8198, 0.17 #9575), 041rx (0.44 #3826, 0.44 #2286, 0.42 #2515), 063k3h (0.40 #182, 0.18 #1170, 0.17 #1094), 0g6ff (0.38 #780, 0.33 #20, 0.25 #96), 013b6_ (0.25 #128, 0.08 #584, 0.08 #1498), 033tf_ (0.22 #3829, 0.19 #4672, 0.17 #4749), 07bch9 (0.21 #858, 0.20 #250, 0.17 #326), 03lmx1 (0.20 #242, 0.17 #318, 0.14 #394), 01qhm_ (0.20 #158, 0.08 #1070, 0.07 #4671), 013xrm (0.12 #2301, 0.11 #1921, 0.11 #1541) >> Best rule #5977 for best value: >> intensional similarity = 4 >> extensional distance = 370 >> proper extension: 080knyg; >> query: (?x10328, 0x67) <- people(?x4195, ?x10328), risk_factors(?x3984, ?x4195), people(?x4195, ?x851), student(?x1809, ?x851) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #780 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 07fzq3; *> query: (?x10328, 0g6ff) <- nationality(?x10328, ?x1603), gender(?x10328, ?x231), ?x231 = 05zppz, ?x1603 = 06bnz *> conf = 0.38 ranks of expected_values: 4 EVAL 05hks people! 0g6ff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 173.000 171.000 0.761 http://example.org/people/ethnicity/people #1324-01ffx4 PRED entity: 01ffx4 PRED relation: production_companies PRED expected values: 01w92 => 99 concepts (92 used for prediction) PRED predicted values (max 10 best out of 77): 03xsby (0.40 #1900, 0.33 #2899, 0.30 #7055), 04g2mkf (0.33 #2899, 0.30 #6971, 0.29 #7054), 027jw0c (0.20 #150, 0.03 #809, 0.02 #479), 086k8 (0.19 #248, 0.10 #825, 0.08 #3816), 030_1_ (0.19 #263, 0.07 #922, 0.05 #345), 05qd_ (0.12 #256, 0.12 #10, 0.11 #669), 054lpb6 (0.12 #261, 0.10 #426, 0.07 #674), 04rcl7 (0.12 #317, 0.07 #976, 0.05 #399), 017s11 (0.12 #249, 0.07 #2568, 0.07 #1488), 024rbz (0.12 #14, 0.01 #590, 0.01 #1250) >> Best rule #1900 for best value: >> intensional similarity = 2 >> extensional distance = 341 >> proper extension: 04bp0l; >> query: (?x3201, ?x1914) <- nominated_for(?x1914, ?x3201), film(?x1914, ?x424) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01ffx4 production_companies 01w92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 92.000 0.398 http://example.org/film/film/production_companies #1323-017180 PRED entity: 017180 PRED relation: nominated_for! PRED expected values: 02r0csl => 57 concepts (50 used for prediction) PRED predicted values (max 10 best out of 200): 02x17s4 (0.69 #2301, 0.66 #4835, 0.66 #3452), 0gqy2 (0.43 #115, 0.32 #345, 0.25 #805), 0gq9h (0.39 #289, 0.31 #1669, 0.29 #749), 0gr0m (0.37 #286, 0.31 #746, 0.29 #56), 0gr4k (0.37 #254, 0.29 #714, 0.27 #1864), 02n9nmz (0.34 #284, 0.29 #744, 0.26 #974), 019f4v (0.34 #281, 0.27 #1661, 0.27 #741), 094qd5 (0.34 #263, 0.25 #723, 0.25 #3683), 0gqwc (0.32 #287, 0.27 #747, 0.25 #3683), 0l8z1 (0.29 #279, 0.29 #49, 0.27 #739) >> Best rule #2301 for best value: >> intensional similarity = 3 >> extensional distance = 215 >> proper extension: 06mmr; >> query: (?x6721, ?x2341) <- award(?x6721, ?x2341), category(?x6721, ?x134), ?x134 = 08mbj5d >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #235 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 083shs; 08lr6s; 03s6l2; 0b73_1d; 0pv3x; 09z2b7; 02q5g1z; 0jym0; 02__34; 0yyts; ... *> query: (?x6721, 02r0csl) <- genre(?x6721, ?x1509), genre(?x6721, ?x162), ?x1509 = 060__y, ?x162 = 04xvlr, award_winner(?x6721, ?x793) *> conf = 0.18 ranks of expected_values: 57 EVAL 017180 nominated_for! 02r0csl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 57.000 50.000 0.689 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1322-0dzf_ PRED entity: 0dzf_ PRED relation: profession PRED expected values: 02hrh1q 0np9r => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 86): 02hrh1q (0.89 #5894, 0.88 #2512, 0.88 #12805), 0cbd2 (0.48 #4416, 0.47 #4563, 0.45 #5446), 03gjzk (0.46 #455, 0.43 #161, 0.43 #1043), 09jwl (0.37 #3839, 0.36 #7662, 0.36 #8250), 0kyk (0.34 #4438, 0.32 #3703, 0.31 #4585), 02jknp (0.31 #2212, 0.30 #7, 0.29 #2506), 0nbcg (0.29 #8969, 0.28 #3852, 0.28 #11175), 0dz3r (0.29 #8969, 0.28 #11175, 0.26 #3824), 0np9r (0.29 #8969, 0.28 #11175, 0.25 #5146), 01c72t (0.29 #8969, 0.28 #11175, 0.19 #610) >> Best rule #5894 for best value: >> intensional similarity = 2 >> extensional distance = 495 >> proper extension: 07nznf; 0q9kd; 0grwj; 05bnp0; 01l1b90; 0d_84; 0m2wm; 0bl2g; 0prfz; 032xhg; ... >> query: (?x4563, 02hrh1q) <- film(?x4563, ?x463), participant(?x4360, ?x4563) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 0dzf_ profession 0np9r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 115.000 115.000 0.893 http://example.org/people/person/profession EVAL 0dzf_ profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.893 http://example.org/people/person/profession #1321-03rwz3 PRED entity: 03rwz3 PRED relation: child PRED expected values: 06jntd => 123 concepts (93 used for prediction) PRED predicted values (max 10 best out of 196): 03_c8p (0.33 #112, 0.05 #2125, 0.04 #2627), 0hv0d (0.17 #287, 0.11 #625, 0.11 #457), 03jvmp (0.17 #186, 0.11 #524, 0.11 #356), 01scmq (0.15 #1660, 0.06 #5186, 0.05 #1995), 016tw3 (0.14 #1856, 0.07 #4880, 0.05 #1521), 0c41qv (0.13 #906, 0.12 #1241, 0.11 #1409), 07733f (0.10 #1656, 0.10 #1991, 0.07 #985), 02bh8z (0.10 #1543, 0.10 #1878, 0.07 #872), 024rgt (0.10 #1534, 0.10 #1869, 0.07 #863), 046b0s (0.10 #1533, 0.10 #1868, 0.07 #862) >> Best rule #112 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 06q07; >> query: (?x7526, 03_c8p) <- contact_category(?x7526, ?x6046), ?x6046 = 02zdwq, production_companies(?x5157, ?x7526) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4535 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 05gnf; *> query: (?x7526, ?x541) <- film(?x7526, ?x2160), award_winner(?x6306, ?x7526), film(?x541, ?x2160) *> conf = 0.02 ranks of expected_values: 157 EVAL 03rwz3 child 06jntd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 123.000 93.000 0.333 http://example.org/organization/organization/child./organization/organization_relationship/child #1320-0kbvv PRED entity: 0kbvv PRED relation: olympics! PRED expected values: 07ylj 06c1y 04w58 => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 235): 09c7w0 (0.92 #4596, 0.90 #5860, 0.87 #7274), 07ssc (0.71 #6494, 0.69 #6649, 0.69 #311), 0h7x (0.71 #1120, 0.69 #311, 0.60 #964), 07t21 (0.69 #311, 0.55 #1254, 0.50 #312), 01mjq (0.69 #311, 0.55 #1254, 0.45 #3007), 06c1y (0.57 #1123, 0.47 #2876, 0.40 #967), 0b90_r (0.53 #2854, 0.43 #1101, 0.40 #945), 05vz3zq (0.47 #2927, 0.29 #4670, 0.27 #5934), 019pcs (0.43 #1151, 0.40 #995, 0.33 #208), 0d04z6 (0.43 #1175, 0.40 #1019, 0.33 #77) >> Best rule #4596 for best value: >> intensional similarity = 20 >> extensional distance = 22 >> proper extension: 09n48; 0l98s; 0l998; 0l6m5; 0swbd; 0lv1x; 0blg2; 0l6mp; 0lbbj; 0nbjq; ... >> query: (?x3110, 09c7w0) <- sports(?x3110, ?x453), olympics(?x583, ?x3110), film_release_region(?x7016, ?x583), film_release_region(?x5827, ?x583), film_release_region(?x5713, ?x583), film_release_region(?x5564, ?x583), film_release_region(?x3784, ?x583), film_release_region(?x1915, ?x583), film_release_region(?x1525, ?x583), film_release_region(?x908, ?x583), country(?x150, ?x583), ?x908 = 01vksx, ?x7016 = 07g1sm, ?x1915 = 0fq7dv_, ?x3784 = 0bmhvpr, ?x5713 = 0cc97st, ?x5564 = 03yvf2, ?x1525 = 03qnvdl, ?x5827 = 0ggbfwf, contains(?x7273, ?x583) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1123 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 5 *> proper extension: 06sks6; *> query: (?x3110, 06c1y) <- olympics(?x1497, ?x3110), olympics(?x1229, ?x3110), olympics(?x456, ?x3110), olympics(?x453, ?x3110), ?x456 = 05qhw, sports(?x3110, ?x3345), olympics(?x2146, ?x3110), olympics(?x910, ?x3110), ?x1497 = 015qh, ?x1229 = 059j2, ?x910 = 019rg5, administrative_parent(?x2146, ?x551), nationality(?x111, ?x2146), administrative_parent(?x3411, ?x2146), film_release_region(?x4047, ?x2146), film_release_region(?x633, ?x2146), ?x633 = 0c40vxk, ?x4047 = 07s846j *> conf = 0.57 ranks of expected_values: 6, 16, 37 EVAL 0kbvv olympics! 04w58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 61.000 61.000 0.917 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 0kbvv olympics! 06c1y CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 61.000 61.000 0.917 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 0kbvv olympics! 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 61.000 61.000 0.917 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #1319-02sch9 PRED entity: 02sch9 PRED relation: people PRED expected values: 02756j 03fwln => 43 concepts (20 used for prediction) PRED predicted values (max 10 best out of 3373): 05g3ss (0.50 #6791, 0.33 #3351, 0.20 #10231), 01zp33 (0.50 #6199, 0.33 #2759, 0.20 #9639), 0241wg (0.50 #5582, 0.33 #2142, 0.20 #9022), 06gn7r (0.50 #6331, 0.33 #2891, 0.20 #9771), 01n8_g (0.49 #18925, 0.46 #24091, 0.44 #25814), 02vmzp (0.49 #18925, 0.46 #24091, 0.44 #25814), 040wdl (0.46 #18924, 0.44 #25814, 0.17 #24090), 01vrt_c (0.43 #10473, 0.33 #13914, 0.21 #15635), 05xpv (0.43 #11560, 0.17 #15001, 0.16 #20165), 01wk51 (0.43 #11380, 0.17 #14821, 0.16 #19985) >> Best rule #6791 for best value: >> intensional similarity = 11 >> extensional distance = 2 >> proper extension: 0bpjh3; >> query: (?x7838, 05g3ss) <- people(?x7838, ?x10750), people(?x7838, ?x6308), languages_spoken(?x7838, ?x9113), languages(?x10750, ?x1882), award_winner(?x1937, ?x10750), profession(?x10750, ?x319), type_of_union(?x10750, ?x566), ?x566 = 04ztj, participant(?x6308, ?x8073), ?x8073 = 01zh29, ?x319 = 01d_h8 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2611 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 0dryh9k; *> query: (?x7838, 02756j) <- people(?x7838, ?x10750), people(?x7838, ?x6442), people(?x7838, ?x6308), people(?x7838, ?x111), languages_spoken(?x7838, ?x9113), ?x6442 = 08d6bd, people(?x5855, ?x10750), sibling(?x10750, ?x2145), ?x111 = 05d7rk, award_winner(?x1937, ?x10750), religion(?x6308, ?x8967), gender(?x10750, ?x231), ?x8967 = 03j6c, profession(?x6308, ?x1032) *> conf = 0.33 ranks of expected_values: 17, 1549 EVAL 02sch9 people 03fwln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 20.000 0.500 http://example.org/people/ethnicity/people EVAL 02sch9 people 02756j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 43.000 20.000 0.500 http://example.org/people/ethnicity/people #1318-0kvgnq PRED entity: 0kvgnq PRED relation: nominated_for! PRED expected values: 05dppk => 90 concepts (31 used for prediction) PRED predicted values (max 10 best out of 949): 05dppk (0.65 #2338, 0.49 #56114, 0.47 #63127), 02lp3c (0.41 #25717, 0.40 #23379, 0.39 #16366), 0bytfv (0.38 #14027, 0.36 #44421, 0.33 #4677), 02q9kqf (0.23 #58452, 0.05 #1361, 0.04 #6038), 09rp4r_ (0.23 #58452, 0.05 #320, 0.03 #2658), 01f_mw (0.19 #18704), 0146pg (0.16 #14148, 0.14 #21162, 0.14 #23500), 0jz9f (0.15 #20, 0.09 #2358, 0.07 #4697), 02hfp_ (0.10 #1713, 0.09 #4051, 0.04 #6390), 04qvl7 (0.10 #21, 0.07 #14048, 0.07 #21062) >> Best rule #2338 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 09m6kg; 0k2sk; 0pv3x; 011yqc; 09cr8; 021y7yw; 02q56mk; 0mcl0; 0k2cb; 011ycb; ... >> query: (?x5752, ?x2530) <- nominated_for(?x1180, ?x5752), award_winner(?x5752, ?x5363), cinematography(?x5752, ?x2530), ?x1180 = 02n9nmz >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kvgnq nominated_for! 05dppk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 31.000 0.650 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #1317-02hnl PRED entity: 02hnl PRED relation: role! PRED expected values: 03j0br4 01bpnd 01vng3b 016vqk => 66 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1305): 01p95y0 (0.60 #2671, 0.53 #6361, 0.50 #4146), 0fq117k (0.50 #1637, 0.50 #1393, 0.50 #1148), 01vng3b (0.50 #3085, 0.50 #1613, 0.50 #1369), 03bxwtd (0.50 #3010, 0.50 #1538, 0.40 #4979), 017g21 (0.50 #3110, 0.50 #1638, 0.40 #5079), 01vsnff (0.50 #3723, 0.50 #1511, 0.40 #5198), 01w5gg6 (0.50 #1679, 0.50 #1190, 0.40 #2661), 0gcs9 (0.50 #1536, 0.50 #1047, 0.40 #2518), 017vkx (0.50 #1556, 0.50 #1312, 0.33 #3028), 03j24kf (0.50 #1580, 0.50 #1336, 0.33 #603) >> Best rule #2671 for best value: >> intensional similarity = 9 >> extensional distance = 3 >> proper extension: 04rzd; >> query: (?x1750, 01p95y0) <- role(?x885, ?x1750), group(?x1750, ?x9206), group(?x1750, ?x8029), role(?x366, ?x1750), ?x366 = 01vrx3g, artist(?x2190, ?x8029), category(?x9206, ?x134), ?x885 = 0dwtp, artists(?x1000, ?x9206) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #3085 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 4 *> proper extension: 028tv0; *> query: (?x1750, 01vng3b) <- role(?x5480, ?x1750), role(?x4913, ?x1750), group(?x1750, ?x10257), group(?x1750, ?x7868), group(?x1750, ?x4461), role(?x211, ?x1750), ?x7868 = 0knhk, ?x10257 = 01v0sxx, ?x4913 = 03ndd, role(?x3062, ?x5480), artists(?x1380, ?x4461) *> conf = 0.50 ranks of expected_values: 3, 72, 185, 278 EVAL 02hnl role! 016vqk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 66.000 53.000 0.600 http://example.org/music/group_member/membership./music/group_membership/role EVAL 02hnl role! 01vng3b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 66.000 53.000 0.600 http://example.org/music/group_member/membership./music/group_membership/role EVAL 02hnl role! 01bpnd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 66.000 53.000 0.600 http://example.org/music/group_member/membership./music/group_membership/role EVAL 02hnl role! 03j0br4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 66.000 53.000 0.600 http://example.org/music/group_member/membership./music/group_membership/role #1316-01h5f8 PRED entity: 01h5f8 PRED relation: award PRED expected values: 026mfs => 151 concepts (116 used for prediction) PRED predicted values (max 10 best out of 309): 01by1l (0.41 #2941, 0.33 #6173, 0.30 #6577), 01bgqh (0.34 #2871, 0.25 #43, 0.23 #8931), 09sb52 (0.31 #3273, 0.27 #15797, 0.24 #13777), 03qbh5 (0.30 #3034, 0.20 #6266, 0.20 #5054), 0c4z8 (0.27 #2900, 0.25 #72, 0.22 #4112), 01c99j (0.25 #227, 0.18 #1035, 0.18 #3055), 0ck27z (0.25 #93, 0.15 #10193, 0.15 #11405), 01dk00 (0.25 #141, 0.13 #545, 0.09 #949), 02f6ym (0.25 #259, 0.12 #3087, 0.12 #1067), 01c9jp (0.25 #190, 0.06 #24430, 0.05 #3018) >> Best rule #2941 for best value: >> intensional similarity = 3 >> extensional distance = 126 >> proper extension: 04dqdk; 07ss8_; 01pgzn_; 016z2j; 01trhmt; 0blq0z; 01dw9z; 01vx5w7; 016pns; 0fb1q; ... >> query: (?x11509, 01by1l) <- award_winner(?x2807, ?x11509), profession(?x11509, ?x220), ?x220 = 016z4k >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #2958 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 126 *> proper extension: 04dqdk; 07ss8_; 01pgzn_; 016z2j; 01trhmt; 0blq0z; 01dw9z; 01vx5w7; 016pns; 0fb1q; ... *> query: (?x11509, 026mfs) <- award_winner(?x2807, ?x11509), profession(?x11509, ?x220), ?x220 = 016z4k *> conf = 0.17 ranks of expected_values: 26 EVAL 01h5f8 award 026mfs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 151.000 116.000 0.406 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1315-0gz6b6g PRED entity: 0gz6b6g PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 28): 0ch6mp2 (0.79 #1048, 0.75 #1421, 0.74 #676), 09vw2b7 (0.71 #1047, 0.68 #675, 0.66 #265), 01vx2h (0.46 #271, 0.40 #1053, 0.39 #681), 0dxtw (0.45 #680, 0.42 #270, 0.40 #85), 01pvkk (0.29 #682, 0.28 #1427, 0.28 #1054), 02rh1dz (0.29 #47, 0.27 #84, 0.24 #158), 01xy5l_ (0.22 #15, 0.22 #1303, 0.21 #52), 089g0h (0.22 #21, 0.22 #1303, 0.14 #58), 0215hd (0.22 #1303, 0.21 #57, 0.20 #94), 02ynfr (0.22 #1303, 0.20 #499, 0.20 #1058) >> Best rule #1048 for best value: >> intensional similarity = 4 >> extensional distance = 587 >> proper extension: 0cvkv5; >> query: (?x2627, 0ch6mp2) <- currency(?x2627, ?x170), genre(?x2627, ?x53), film_crew_role(?x2627, ?x468), ?x468 = 02r96rf >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gz6b6g film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.795 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1314-07r1h PRED entity: 07r1h PRED relation: vacationer! PRED expected values: 04jpl 04ty8 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 98): 03gh4 (0.29 #1046, 0.22 #3347, 0.19 #77), 0f2v0 (0.19 #423, 0.13 #1029, 0.12 #60), 0b90_r (0.15 #972, 0.11 #245, 0.11 #3273), 0160w (0.13 #971, 0.11 #365, 0.07 #1213), 04jpl (0.12 #978, 0.09 #493, 0.08 #736), 06c62 (0.10 #1052, 0.08 #810, 0.06 #3353), 02_286 (0.08 #742, 0.08 #984, 0.07 #378), 0261m (0.07 #461, 0.06 #1067, 0.04 #1914), 035qy (0.06 #34, 0.06 #518, 0.06 #761), 0n3g (0.06 #74, 0.06 #195, 0.04 #1043) >> Best rule #1046 for best value: >> intensional similarity = 3 >> extensional distance = 50 >> proper extension: 04d_mtq; >> query: (?x6187, 03gh4) <- friend(?x6187, ?x262), profession(?x262, ?x319), vacationer(?x2290, ?x6187) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #978 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 04d_mtq; *> query: (?x6187, 04jpl) <- friend(?x6187, ?x262), profession(?x262, ?x319), vacationer(?x2290, ?x6187) *> conf = 0.12 ranks of expected_values: 5, 26 EVAL 07r1h vacationer! 04ty8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 119.000 119.000 0.288 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer EVAL 07r1h vacationer! 04jpl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 119.000 119.000 0.288 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #1313-0kft PRED entity: 0kft PRED relation: award PRED expected values: 02rdyk7 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 268): 0789r6 (0.72 #18369, 0.72 #28751, 0.71 #17968), 027c924 (0.72 #18369, 0.72 #28751, 0.71 #17968), 027b9ly (0.72 #18369, 0.72 #28751, 0.71 #17968), 019f4v (0.63 #2060, 0.49 #2858, 0.36 #4854), 0gq9h (0.54 #2867, 0.48 #2069, 0.29 #4863), 02qyntr (0.47 #666, 0.33 #1065, 0.05 #2262), 0k611 (0.41 #489, 0.37 #888, 0.06 #13573), 04dn09n (0.39 #2436, 0.29 #2037, 0.24 #5230), 0gr4k (0.37 #2425, 0.35 #2026, 0.28 #5219), 0gr51 (0.36 #2490, 0.32 #2091, 0.26 #5284) >> Best rule #18369 for best value: >> intensional similarity = 3 >> extensional distance = 1561 >> proper extension: 0kk9v; >> query: (?x9149, ?x1198) <- award_nominee(?x9149, ?x14003), award_winner(?x1198, ?x9149), award(?x276, ?x1198) >> conf = 0.72 => this is the best rule for 3 predicted values *> Best rule #2082 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 61 *> proper extension: 0byfz; 042l3v; 0c1pj; 0jf1b; 05kfs; 03_gd; 02kxbwx; 06pk8; 081lh; 030pr; ... *> query: (?x9149, 02rdyk7) <- award_nominee(?x9149, ?x14003), film(?x9149, ?x7978), award(?x9149, ?x1313), ?x1313 = 0gs9p *> conf = 0.30 ranks of expected_values: 11 EVAL 0kft award 02rdyk7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 96.000 96.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1312-036jv PRED entity: 036jv PRED relation: parent_genre PRED expected values: 01flzq => 54 concepts (37 used for prediction) PRED predicted values (max 10 best out of 204): 02x8m (0.50 #497, 0.38 #659, 0.33 #336), 06by7 (0.43 #2119, 0.27 #985, 0.26 #1796), 05r6t (0.30 #2156, 0.18 #1022, 0.17 #536), 0xhtw (0.27 #982, 0.23 #1144, 0.17 #496), 03lty (0.27 #2122, 0.23 #1150, 0.17 #502), 06j6l (0.19 #808, 0.19 #807, 0.17 #516), 06cqb (0.19 #808, 0.19 #807, 0.17 #484), 026z9 (0.19 #808, 0.19 #807, 0.17 #533), 0190yn (0.19 #808, 0.19 #807, 0.17 #612), 0gywn (0.19 #808, 0.19 #807, 0.10 #1333) >> Best rule #497 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 06cp5; >> query: (?x11545, 02x8m) <- artists(?x11545, ?x7601), artists(?x11545, ?x5798), artists(?x11545, ?x4877), artists(?x11545, ?x3494), award_nominee(?x5798, ?x959), ?x4877 = 03sww, ?x3494 = 01vw26l, type_of_union(?x7601, ?x566) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #645 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 06cp5; *> query: (?x11545, ?x2937) <- artists(?x11545, ?x7601), artists(?x11545, ?x5798), artists(?x11545, ?x4877), artists(?x11545, ?x3494), award_nominee(?x5798, ?x959), ?x4877 = 03sww, artists(?x2937, ?x7601), ?x3494 = 01vw26l, type_of_union(?x7601, ?x566) *> conf = 0.11 ranks of expected_values: 20 EVAL 036jv parent_genre 01flzq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 54.000 37.000 0.500 http://example.org/music/genre/parent_genre #1311-049n3s PRED entity: 049n3s PRED relation: team! PRED expected values: 03zv9 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 8): 059yj (0.17 #69, 0.12 #101, 0.11 #93), 07y9k (0.13 #141, 0.12 #113, 0.10 #245), 03zv9 (0.12 #113, 0.12 #18, 0.10 #2), 0355pl (0.12 #113, 0.09 #35, 0.08 #11), 0356lc (0.12 #113, 0.07 #138, 0.05 #242), 01ddbl (0.07 #111, 0.06 #120, 0.06 #103), 0h69c (0.06 #135, 0.05 #110, 0.05 #119), 021q23 (0.02 #321, 0.01 #297, 0.01 #361) >> Best rule #69 for best value: >> intensional similarity = 8 >> extensional distance = 52 >> proper extension: 01ct6; 01y3c; 05tfm; 07l24; 05g3v; 05tg3; 05l71; 0wsr; 0ws7; 02c_4; >> query: (?x12032, 059yj) <- team(?x60, ?x12032), colors(?x12032, ?x4557), ?x4557 = 019sc, sport(?x12032, ?x471), sport(?x11390, ?x471), athlete(?x471, ?x208), films(?x471, ?x6451), service_language(?x11390, ?x254) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #113 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 90 *> proper extension: 01ync; 07l4z; *> query: (?x12032, ?x1142) <- team(?x60, ?x12032), colors(?x12032, ?x4557), ?x4557 = 019sc, team(?x60, ?x11530), team(?x60, ?x3620), team(?x60, ?x3449), teams(?x9402, ?x3620), team(?x1142, ?x3449), colors(?x11530, ?x663), ?x663 = 083jv *> conf = 0.12 ranks of expected_values: 3 EVAL 049n3s team! 03zv9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 57.000 57.000 0.167 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #1310-0152x_ PRED entity: 0152x_ PRED relation: company! PRED expected values: 014l7h => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 43): 060c4 (0.50 #51, 0.37 #388, 0.36 #1479), 0dq_5 (0.45 #1524, 0.45 #1494, 0.45 #1429), 0krdk (0.43 #1483, 0.42 #1861, 0.42 #1388), 0dq3c (0.30 #530, 0.28 #1478, 0.26 #387), 02k13d (0.25 #48, 0.25 #14, 0.12 #62), 01yc02 (0.25 #57, 0.22 #1485, 0.21 #1959), 01rk91 (0.25 #49, 0.20 #434, 0.17 #289), 014l7h (0.25 #29, 0.15 #462, 0.12 #604), 05_wyz (0.25 #1543, 0.22 #1495, 0.21 #1400), 021q1c (0.19 #155, 0.17 #299, 0.16 #1297) >> Best rule #51 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 016tt2; 01dtcb; 0gy1_; >> query: (?x4818, 060c4) <- company(?x4817, ?x4818), service_location(?x4818, ?x94), citytown(?x4818, ?x739), organization(?x4682, ?x4818), award_nominee(?x4817, ?x690) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #29 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 09c7b; *> query: (?x4818, 014l7h) <- company(?x4817, ?x4818), award_nominee(?x4817, ?x5413), person(?x3775, ?x4817), profession(?x5413, ?x1032) *> conf = 0.25 ranks of expected_values: 8 EVAL 0152x_ company! 014l7h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 124.000 124.000 0.500 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #1309-07ssc PRED entity: 07ssc PRED relation: participating_countries! PRED expected values: 015l4k => 197 concepts (197 used for prediction) PRED predicted values (max 10 best out of 9): 0jhn7 (0.23 #825, 0.23 #847, 0.23 #683), 0l6mp (0.23 #825, 0.23 #847, 0.23 #683), 0swbd (0.23 #825, 0.23 #847, 0.23 #683), 0sx7r (0.23 #825, 0.23 #847, 0.23 #683), 0kbvb (0.23 #825, 0.23 #847, 0.23 #683), 0sxrz (0.23 #825, 0.23 #847, 0.23 #683), 0kbvv (0.20 #48, 0.17 #55, 0.14 #69), 0lv1x (0.10 #584, 0.08 #583), 015l4k (0.08 #583) >> Best rule #825 for best value: >> intensional similarity = 3 >> extensional distance = 109 >> proper extension: 027rn; 04gzd; 047lj; 047yc; 07ylj; 05qx1; 015qh; 05v10; 01pj7; 0d0kn; ... >> query: (?x512, ?x391) <- country(?x150, ?x512), olympics(?x512, ?x391), film_release_region(?x66, ?x512) >> conf = 0.23 => this is the best rule for 6 predicted values *> Best rule #583 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 07t65; *> query: (?x512, ?x391) <- combatants(?x3728, ?x512), olympics(?x3728, ?x584), olympics(?x3728, ?x391) *> conf = 0.08 ranks of expected_values: 9 EVAL 07ssc participating_countries! 015l4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 197.000 197.000 0.235 http://example.org/olympics/olympic_games/participating_countries #1308-0b1f49 PRED entity: 0b1f49 PRED relation: award_nominee PRED expected values: 03wh95l => 148 concepts (74 used for prediction) PRED predicted values (max 10 best out of 1380): 03wh95l (0.81 #132776, 0.80 #172376, 0.80 #172375), 0b1f49 (0.46 #83862, 0.28 #132778, 0.26 #69884), 0169dl (0.39 #2850, 0.17 #5179, 0.17 #520), 018ygt (0.39 #3788, 0.11 #88521, 0.08 #10776), 07yp0f (0.39 #3213, 0.11 #88521, 0.02 #10201), 0gy6z9 (0.33 #3071, 0.16 #172377, 0.11 #88521), 042xrr (0.33 #3415, 0.11 #88521, 0.06 #1085), 0dvmd (0.33 #3023, 0.11 #88521, 0.03 #131139), 02yxwd (0.33 #3315, 0.11 #88521, 0.02 #10303), 0hvb2 (0.33 #2727, 0.08 #5056, 0.06 #397) >> Best rule #132776 for best value: >> intensional similarity = 3 >> extensional distance = 819 >> proper extension: 03mz9r; 033wx9; 03jjzf; 039bpc; 01l1hr; 02wycg2; 03_1pg; 074tb5; 01wbsdz; 0133x7; ... >> query: (?x3880, ?x364) <- award_nominee(?x2499, ?x3880), award_nominee(?x364, ?x3880), participant(?x2763, ?x2499) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b1f49 award_nominee 03wh95l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 74.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1307-02_286 PRED entity: 02_286 PRED relation: featured_film_locations! PRED expected values: 06wzvr 03s5lz 0416y94 027s39y 02wgk1 06tpmy 016ky6 06cm5 02dr9j 0gd92 0jqkh 03shpq 02ptczs => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 1089): 0872p_c (0.29 #55, 0.20 #576, 0.18 #3178), 0btpm6 (0.29 #392, 0.20 #913, 0.17 #1954), 0m491 (0.29 #90, 0.20 #611, 0.17 #1652), 0gw7p (0.29 #312, 0.20 #833, 0.17 #1874), 05pxnmb (0.29 #408, 0.17 #1970, 0.10 #929), 05_5_22 (0.20 #802, 0.17 #1843, 0.14 #2364), 05sy_5 (0.20 #839, 0.17 #1880, 0.14 #318), 01cmp9 (0.20 #836, 0.17 #1877, 0.14 #315), 027qgy (0.20 #532, 0.17 #1573, 0.14 #11), 0473rc (0.16 #4484, 0.14 #320, 0.12 #3443) >> Best rule #55 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 0fpkxfd; 0g57ws5; >> query: (?x739, 0872p_c) <- film_regional_debut_venue(?x2954, ?x739), ?x2954 = 0crh5_f >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #30692 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 72 *> proper extension: 0dhdp; 0727_; *> query: (?x739, ?x153) <- place_of_birth(?x4931, ?x739), citytown(?x166, ?x739), produced_by(?x153, ?x4931) *> conf = 0.03 ranks of expected_values: 404, 407, 443, 445, 527, 728, 770, 911, 995, 1007 EVAL 02_286 featured_film_locations! 02ptczs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 03shpq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 0jqkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 0gd92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 02dr9j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 06cm5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 016ky6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 06tpmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 02wgk1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 027s39y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 0416y94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 03s5lz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations EVAL 02_286 featured_film_locations! 06wzvr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 173.000 173.000 0.286 http://example.org/film/film/featured_film_locations #1306-015wy_ PRED entity: 015wy_ PRED relation: colors PRED expected values: 09ggk => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 20): 01g5v (0.46 #183, 0.31 #243, 0.30 #123), 083jv (0.36 #942, 0.31 #181, 0.30 #902), 06fvc (0.27 #2, 0.23 #182, 0.15 #22), 019sc (0.19 #187, 0.17 #948, 0.16 #247), 088fh (0.17 #166, 0.15 #186, 0.14 #46), 036k5h (0.15 #185, 0.12 #85, 0.10 #145), 038hg (0.14 #52, 0.11 #112, 0.10 #132), 09ggk (0.09 #16, 0.08 #456, 0.06 #781), 04mkbj (0.09 #951, 0.07 #450, 0.07 #911), 067z2v (0.08 #29, 0.07 #49, 0.07 #69) >> Best rule #183 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 018m5q; 0ylsr; 01g4yw; >> query: (?x11821, 01g5v) <- contains(?x1310, ?x11821), ?x1310 = 02jx1, category(?x11821, ?x134), ?x134 = 08mbj5d, colors(?x11821, ?x332) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 08tyb_; *> query: (?x11821, 09ggk) <- citytown(?x11821, ?x362), ?x362 = 04jpl, student(?x11821, ?x3667), artists(?x284, ?x3667) *> conf = 0.09 ranks of expected_values: 8 EVAL 015wy_ colors 09ggk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 117.000 117.000 0.462 http://example.org/education/educational_institution/colors #1305-01lj9 PRED entity: 01lj9 PRED relation: major_field_of_study! PRED expected values: 0j_sncb 03ksy 0677j 0xxc 01l8t8 => 84 concepts (49 used for prediction) PRED predicted values (max 10 best out of 604): 03ksy (0.71 #14140, 0.66 #18309, 0.65 #19353), 0dzst (0.70 #9684, 0.50 #3965, 0.40 #5525), 05krk (0.62 #7286, 0.50 #9366, 0.50 #4166), 01bm_ (0.60 #9588, 0.50 #7508, 0.50 #3869), 07wjk (0.60 #9416, 0.50 #3175, 0.50 #2655), 05zl0 (0.57 #14228, 0.56 #12146, 0.56 #7988), 0bx8pn (0.56 #8361, 0.50 #9401, 0.45 #10442), 01qqv5 (0.56 #8123, 0.50 #3963, 0.33 #6042), 01nnsv (0.56 #8493, 0.45 #10574, 0.38 #7453), 07vk2 (0.56 #7847, 0.40 #9406, 0.38 #14087) >> Best rule #14140 for best value: >> intensional similarity = 12 >> extensional distance = 19 >> proper extension: 0h5k; >> query: (?x4100, 03ksy) <- major_field_of_study(?x5288, ?x4100), major_field_of_study(?x741, ?x4100), major_field_of_study(?x5288, ?x12158), major_field_of_study(?x5288, ?x5179), major_field_of_study(?x5288, ?x2606), student(?x4100, ?x1328), fraternities_and_sororities(?x5288, ?x3697), ?x5179 = 04gb7, ?x12158 = 09s1f, major_field_of_study(?x2605, ?x2606), ?x741 = 01w3v, student(?x5288, ?x460) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 14, 28, 336, 441 EVAL 01lj9 major_field_of_study! 01l8t8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 84.000 49.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01lj9 major_field_of_study! 0xxc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 84.000 49.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01lj9 major_field_of_study! 0677j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 49.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01lj9 major_field_of_study! 03ksy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 49.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 01lj9 major_field_of_study! 0j_sncb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 84.000 49.000 0.714 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1304-0dy04 PRED entity: 0dy04 PRED relation: company! PRED expected values: 04xm_ => 152 concepts (50 used for prediction) PRED predicted values (max 10 best out of 293): 03gkn5 (0.20 #3477, 0.19 #1282, 0.16 #1771), 0nk72 (0.20 #408, 0.08 #2850, 0.08 #3336), 0x3r3 (0.20 #116, 0.08 #4260, 0.07 #849), 06y7d (0.20 #231, 0.05 #1694, 0.04 #475), 01zwy (0.20 #168, 0.05 #1631, 0.04 #901), 07n39 (0.20 #189, 0.04 #433, 0.04 #677), 05fyss (0.20 #123, 0.02 #1586, 0.02 #3781), 0jcx (0.19 #547, 0.12 #303, 0.09 #2254), 04411 (0.12 #258, 0.11 #747, 0.06 #2209), 02sdx (0.12 #457, 0.07 #701, 0.07 #946) >> Best rule #3477 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 01q940; 03mp8k; 02nddq; >> query: (?x2637, 03gkn5) <- company(?x10895, ?x2637), profession(?x10895, ?x3802), languages(?x10895, ?x254) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1170 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 05d9y_; *> query: (?x2637, 04xm_) <- major_field_of_study(?x2637, ?x742), organization(?x4095, ?x2637), ?x4095 = 0hm4q *> conf = 0.06 ranks of expected_values: 24 EVAL 0dy04 company! 04xm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 152.000 50.000 0.203 http://example.org/people/person/employment_history./business/employment_tenure/company #1303-0d8lm PRED entity: 0d8lm PRED relation: performance_role! PRED expected values: 050z2 => 84 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1278): 01vn35l (0.78 #2295, 0.50 #1168, 0.40 #2801), 02s6sh (0.67 #2250, 0.56 #2375, 0.50 #1248), 050z2 (0.67 #2187, 0.50 #2693, 0.44 #2312), 02rn_bj (0.60 #725, 0.50 #1103, 0.43 #1356), 01vrncs (0.50 #1894, 0.40 #2775, 0.40 #637), 012x4t (0.33 #1024, 0.33 #145, 0.29 #1277), 043c4j (0.33 #1090, 0.33 #211, 0.29 #1343), 03mszl (0.33 #1092, 0.33 #213, 0.29 #1345), 023322 (0.33 #1118, 0.33 #239, 0.29 #1371), 01vs14j (0.33 #1020, 0.33 #141, 0.29 #1273) >> Best rule #2295 for best value: >> intensional similarity = 11 >> extensional distance = 7 >> proper extension: 02snj9; 02dlh2; >> query: (?x10811, 01vn35l) <- performance_role(?x4701, ?x10811), performance_role(?x4975, ?x10811), award(?x4701, ?x724), award_nominee(?x4701, ?x1660), role(?x2747, ?x4975), role(?x4975, ?x922), role(?x1466, ?x4975), profession(?x4701, ?x6565), ?x724 = 01bgqh, role(?x4701, ?x227), ?x6565 = 0fnpj >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2187 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 7 *> proper extension: 0395lw; *> query: (?x10811, 050z2) <- performance_role(?x4701, ?x10811), performance_role(?x315, ?x10811), award(?x4701, ?x567), profession(?x4701, ?x2348), profession(?x4701, ?x1614), artists(?x2809, ?x4701), role(?x4701, ?x3716), ?x315 = 0l14md, ?x1614 = 01c72t, ?x3716 = 03gvt, ?x2348 = 0nbcg, parent_genre(?x1380, ?x2809) *> conf = 0.67 ranks of expected_values: 3 EVAL 0d8lm performance_role! 050z2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 84.000 37.000 0.778 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #1302-0171c7 PRED entity: 0171c7 PRED relation: category PRED expected values: 08mbj5d => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.27 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14806, 08mbj5d) <- >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0171c7 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.267 http://example.org/common/topic/webpage./common/webpage/category #1301-09v92_x PRED entity: 09v92_x PRED relation: nominated_for PRED expected values: 0dkv90 => 51 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1477): 0dkv90 (0.50 #1182, 0.25 #2765, 0.21 #4751), 07w8fz (0.38 #5212, 0.33 #6797, 0.19 #16304), 0gmcwlb (0.38 #4934, 0.30 #6519, 0.23 #30289), 02yvct (0.38 #5071, 0.30 #6656, 0.20 #30426), 0m313 (0.34 #4763, 0.30 #6348, 0.24 #30118), 07024 (0.34 #5185, 0.30 #6770, 0.19 #30540), 0dr_4 (0.34 #4975, 0.24 #6560, 0.22 #30330), 0jqn5 (0.34 #4951, 0.21 #6536, 0.13 #30306), 011yl_ (0.31 #5281, 0.27 #6866, 0.21 #16373), 0_92w (0.31 #4904, 0.27 #6489, 0.14 #30259) >> Best rule #1182 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 09v0wy2; 09v51c2; >> query: (?x7215, 0dkv90) <- award(?x147, ?x7215), award(?x5826, ?x7215), ?x147 = 012d40, disciplines_or_subjects(?x7215, ?x373), nominated_for(?x7215, ?x467) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09v92_x nominated_for 0dkv90 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 51.000 25.000 0.500 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1300-0blq0z PRED entity: 0blq0z PRED relation: award_nominee! PRED expected values: 016z2j => 93 concepts (49 used for prediction) PRED predicted values (max 10 best out of 976): 0170qf (0.81 #32588, 0.81 #86123, 0.81 #86122), 016z2j (0.81 #32588, 0.81 #86123, 0.81 #86122), 0f6_dy (0.77 #100090, 0.76 #32587, 0.76 #111731), 018swb (0.77 #100090, 0.76 #32587, 0.76 #111731), 09r9dp (0.77 #100090, 0.76 #32587, 0.76 #111731), 0151w_ (0.77 #100090, 0.76 #32587, 0.76 #111731), 0ksrf8 (0.76 #111732, 0.76 #104746, 0.76 #69830), 06_bq1 (0.76 #111732, 0.76 #104746, 0.76 #69830), 02s2ft (0.76 #111732, 0.76 #104746, 0.76 #69830), 0blq0z (0.43 #584, 0.16 #74485, 0.14 #83794) >> Best rule #32588 for best value: >> intensional similarity = 3 >> extensional distance = 721 >> proper extension: 01l2fn; 01ycbq; 01vsnff; 04xrx; 0126y2; 0b_7k; 0gdh5; 01900g; 043gj; 0205dx; ... >> query: (?x2670, ?x2141) <- film(?x2670, ?x1525), award_winner(?x2670, ?x72), award_nominee(?x2670, ?x2141) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0blq0z award_nominee! 016z2j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 49.000 0.808 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1299-0343h PRED entity: 0343h PRED relation: profession PRED expected values: 012t_z => 113 concepts (112 used for prediction) PRED predicted values (max 10 best out of 105): 0cbd2 (0.52 #3606, 0.50 #4614, 0.48 #1734), 018gz8 (0.47 #3757, 0.37 #4045, 0.30 #3181), 0fj9f (0.43 #338, 0.29 #1202, 0.25 #1346), 02krf9 (0.34 #1463, 0.34 #1607, 0.27 #5351), 0kyk (0.32 #4922, 0.31 #1898, 0.31 #1754), 012t_z (0.28 #875, 0.23 #2027, 0.16 #3323), 09jwl (0.25 #7792, 0.24 #6640, 0.22 #3183), 0np9r (0.24 #3761, 0.17 #1025, 0.16 #4049), 0dz3r (0.23 #6627, 0.20 #7779, 0.17 #2450), 0nbcg (0.20 #6652, 0.19 #7804, 0.14 #891) >> Best rule #3606 for best value: >> intensional similarity = 2 >> extensional distance = 139 >> proper extension: 03j90; >> query: (?x1387, 0cbd2) <- story_by(?x8107, ?x1387), award(?x8107, ?x640) >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #875 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 34 *> proper extension: 05g7q; *> query: (?x1387, 012t_z) <- award_winner(?x1387, ?x846), organizations_founded(?x1387, ?x10503) *> conf = 0.28 ranks of expected_values: 6 EVAL 0343h profession 012t_z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 113.000 112.000 0.518 http://example.org/people/person/profession #1298-0243cq PRED entity: 0243cq PRED relation: film_crew_role PRED expected values: 02r96rf 089g0h => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 28): 09zzb8 (0.76 #996, 0.74 #596, 0.73 #1062), 02r96rf (0.73 #399, 0.73 #1064, 0.72 #998), 0ch6mp2 (0.72 #1767, 0.70 #1835, 0.68 #1868), 09vw2b7 (0.63 #1002, 0.62 #403, 0.61 #1766), 01pvkk (0.42 #77, 0.39 #176, 0.33 #110), 02rh1dz (0.33 #109, 0.26 #2260, 0.26 #175), 02ynfr (0.29 #213, 0.26 #2260, 0.20 #147), 0215hd (0.26 #2260, 0.15 #1777, 0.13 #1410), 089fss (0.26 #2260, 0.08 #567, 0.07 #900), 0263ycg (0.26 #2260, 0.03 #1844, 0.03 #1776) >> Best rule #996 for best value: >> intensional similarity = 3 >> extensional distance = 218 >> proper extension: 03wh49y; >> query: (?x4313, 09zzb8) <- story_by(?x4313, ?x4314), film_crew_role(?x4313, ?x1966), profession(?x1109, ?x1966) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #399 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 54 *> proper extension: 0gtvrv3; 03whyr; *> query: (?x4313, 02r96rf) <- story_by(?x4313, ?x4314), film(?x2156, ?x4313), category(?x4313, ?x134), film_crew_role(?x4313, ?x281) *> conf = 0.73 ranks of expected_values: 2, 17 EVAL 0243cq film_crew_role 089g0h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 79.000 79.000 0.764 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0243cq film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 79.000 0.764 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1297-02cllz PRED entity: 02cllz PRED relation: award PRED expected values: 027dtxw => 85 concepts (66 used for prediction) PRED predicted values (max 10 best out of 263): 0ck27z (0.50 #92, 0.23 #10201, 0.18 #12223), 0gqwc (0.20 #74, 0.15 #478, 0.15 #882), 02y_rq5 (0.20 #95, 0.15 #499, 0.15 #903), 02ppm4q (0.20 #157, 0.14 #17390, 0.13 #21837), 0gqyl (0.20 #105, 0.13 #21837, 0.10 #9002), 094qd5 (0.20 #44, 0.13 #21837, 0.10 #12537), 0bdwft (0.20 #68, 0.13 #21837, 0.10 #12537), 0cqgl9 (0.20 #192, 0.13 #21837, 0.10 #12537), 03nqnk3 (0.20 #135, 0.13 #21837, 0.10 #12537), 02z0dfh (0.20 #75, 0.10 #12537, 0.10 #12943) >> Best rule #92 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0l6px; 0lpjn; 07fpm3; 0dsb_yy; 05y5kf; 01x4sb; 0dgskx; 02ply6j; >> query: (?x2457, 0ck27z) <- award_nominee(?x5144, ?x2457), film(?x2457, ?x224), gender(?x2457, ?x231), ?x5144 = 017gxw >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #12942 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1031 *> proper extension: 06gp3f; 02rchht; 01lmj3q; 01r42_g; 04lgymt; 016tt2; 08wq0g; 0415svh; 02l840; 0521rl1; ... *> query: (?x2457, ?x3247) <- award_nominee(?x1549, ?x2457), award(?x1549, ?x3247), award(?x5495, ?x3247), ?x5495 = 016zp5 *> conf = 0.18 ranks of expected_values: 15 EVAL 02cllz award 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 85.000 66.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1296-01vv7sc PRED entity: 01vv7sc PRED relation: artist! PRED expected values: 02p3cr5 0n85g => 122 concepts (103 used for prediction) PRED predicted values (max 10 best out of 113): 03rhqg (0.41 #3190, 0.28 #5956, 0.26 #568), 0g768 (0.22 #590, 0.20 #1418, 0.16 #314), 0n85g (0.22 #200, 0.15 #6002, 0.11 #890), 015_1q (0.22 #6512, 0.22 #5683, 0.21 #4438), 01trtc (0.20 #3246, 0.17 #72, 0.09 #5873), 02swsm (0.20 #1473, 0.06 #921, 0.06 #1197), 01cl2y (0.19 #3205, 0.13 #5971, 0.09 #2239), 017l96 (0.18 #5959, 0.10 #6511, 0.10 #4437), 01dtcb (0.18 #3221, 0.13 #461, 0.09 #2255), 03qy3l (0.18 #1443, 0.05 #339, 0.05 #2271) >> Best rule #3190 for best value: >> intensional similarity = 3 >> extensional distance = 285 >> proper extension: 0150jk; 017mbb; 011xhx; >> query: (?x1004, 03rhqg) <- artist(?x8721, ?x1004), artist(?x8721, ?x4850), ?x4850 = 016szr >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #200 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 01cv3n; *> query: (?x1004, 0n85g) <- instrumentalists(?x1750, ?x1004), ?x1750 = 02hnl, student(?x11036, ?x1004) *> conf = 0.22 ranks of expected_values: 3, 17 EVAL 01vv7sc artist! 0n85g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 122.000 103.000 0.411 http://example.org/music/record_label/artist EVAL 01vv7sc artist! 02p3cr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 122.000 103.000 0.411 http://example.org/music/record_label/artist #1295-025tdwc PRED entity: 025tdwc PRED relation: profession PRED expected values: 01d_h8 => 75 concepts (42 used for prediction) PRED predicted values (max 10 best out of 115): 01d_h8 (0.99 #2514, 0.88 #1260, 0.88 #982), 0np9r (0.67 #157, 0.20 #1691, 0.19 #715), 014kbl (0.67 #247, 0.05 #4318, 0.04 #4598), 01c72t (0.56 #718, 0.46 #1136, 0.38 #4898), 01c979 (0.54 #632, 0.47 #911, 0.05 #4318), 016z4k (0.50 #701, 0.41 #3347, 0.37 #3625), 03gjzk (0.50 #152, 0.36 #5586, 0.29 #2521), 02krf9 (0.50 #163, 0.22 #2671, 0.21 #3228), 0n1h (0.48 #4317, 0.31 #708, 0.29 #4597), 04_tv (0.48 #4317, 0.23 #697, 0.18 #976) >> Best rule #2514 for best value: >> intensional similarity = 8 >> extensional distance = 322 >> proper extension: 02rchht; 03_gd; 03qd_; 02kxbwx; 05_k56; 016_mj; 052gzr; 0h1p; 026c1; 04gcd1; ... >> query: (?x2264, 01d_h8) <- profession(?x2264, ?x13719), profession(?x2264, ?x12763), profession(?x11208, ?x13719), disciplines_or_subjects(?x5039, ?x12763), ?x11208 = 03h8_g, type_of_union(?x2264, ?x566), gender(?x2264, ?x231), film_crew_role(?x280, ?x13719) >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025tdwc profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 42.000 0.994 http://example.org/people/person/profession #1294-03676 PRED entity: 03676 PRED relation: teams PRED expected values: 043y95 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 96): 038zh6 (0.03 #1430, 0.02 #2150, 0.02 #3590), 04k3jt (0.02 #178, 0.02 #538, 0.01 #898), 03_r_5 (0.02 #58, 0.02 #418, 0.01 #778), 04h5tx (0.02 #246, 0.02 #606, 0.01 #1686), 03yl2t (0.02 #29, 0.02 #389, 0.01 #1469), 04n8xs (0.02 #248, 0.02 #608), 044lbv (0.02 #223, 0.02 #583), 0415zv (0.02 #155, 0.02 #515), 04b4yg (0.02 #21, 0.01 #741, 0.01 #1101), 0329nn (0.02 #98, 0.01 #818, 0.01 #1538) >> Best rule #1430 for best value: >> intensional similarity = 2 >> extensional distance = 76 >> proper extension: 0k5p1; >> query: (?x7665, 038zh6) <- time_zones(?x7665, ?x5327), ?x5327 = 03bdv >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03676 teams 043y95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 74.000 74.000 0.026 http://example.org/sports/sports_team_location/teams #1293-0693l PRED entity: 0693l PRED relation: participant! PRED expected values: 06cv1 => 142 concepts (81 used for prediction) PRED predicted values (max 10 best out of 316): 07g2v (0.25 #245, 0.20 #881, 0.07 #2154), 01_f_5 (0.14 #1909, 0.09 #33750, 0.09 #29931), 01phtd (0.14 #1909, 0.09 #33750, 0.09 #29931), 0mbw0 (0.14 #1909, 0.09 #33750, 0.09 #29931), 03359d (0.14 #1909, 0.09 #33750, 0.09 #29931), 02p21g (0.14 #1909, 0.09 #33750, 0.09 #29930), 0gx_p (0.12 #420, 0.12 #1692, 0.10 #1056), 0127m7 (0.12 #170, 0.10 #806, 0.04 #2079), 0bl2g (0.12 #24, 0.10 #660, 0.04 #1933), 02ld6x (0.12 #191, 0.06 #3372, 0.04 #1463) >> Best rule #245 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0gpprt; >> query: (?x3117, 07g2v) <- film(?x3117, ?x814), award_winner(?x2532, ?x3117), ?x2532 = 02x4wr9 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0693l participant! 06cv1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 142.000 81.000 0.250 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #1292-02q1tc5 PRED entity: 02q1tc5 PRED relation: award! PRED expected values: 0275_pj => 37 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2701): 057d89 (0.78 #10060, 0.77 #13414, 0.69 #40258), 04gtdnh (0.78 #10060, 0.77 #13414, 0.69 #40258), 0275_pj (0.78 #10060, 0.77 #13414, 0.69 #40258), 025vwmy (0.78 #10060, 0.77 #13414, 0.69 #40258), 014zcr (0.20 #6758, 0.16 #10112, 0.14 #13466), 0f7hc (0.16 #8045, 0.15 #11399, 0.08 #14753), 0151w_ (0.16 #6936, 0.13 #10290, 0.09 #13644), 05ldnp (0.15 #7592, 0.11 #10946, 0.08 #14300), 04wx2v (0.14 #2685, 0.12 #6038, 0.11 #26840), 05gml8 (0.14 #149, 0.12 #3502, 0.11 #26840) >> Best rule #10060 for best value: >> intensional similarity = 4 >> extensional distance = 91 >> proper extension: 040vk98; 02grdc; 018wng; 01bgqh; 04njml; 04mqgr; 02h3d1; 03tk6z; 02x17c2; 0gq_d; ... >> query: (?x2720, ?x1056) <- award(?x439, ?x2720), award_winner(?x2720, ?x1056), tv_program(?x439, ?x3544), nominated_for(?x439, ?x416) >> conf = 0.78 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 02q1tc5 award! 0275_pj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 37.000 15.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1291-0gmdkyy PRED entity: 0gmdkyy PRED relation: instance_of_recurring_event PRED expected values: 0g_w => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 3): 0g_w (0.89 #117, 0.88 #59, 0.88 #109), 0c4ys (0.13 #196, 0.12 #204, 0.12 #213), 0gcf2r (0.09 #205, 0.09 #214, 0.09 #223) >> Best rule #117 for best value: >> intensional similarity = 16 >> extensional distance = 51 >> proper extension: 0fz2y7; >> query: (?x2082, 0g_w) <- ceremony(?x3617, ?x2082), ceremony(?x2209, ?x2082), ceremony(?x1245, ?x2082), ?x3617 = 0gvx_, honored_for(?x2082, ?x224), award_winner(?x2082, ?x5820), award_winner(?x2082, ?x2551), student(?x122, ?x2551), ?x1245 = 0gqwc, award_winner(?x2209, ?x788), nominated_for(?x2209, ?x8570), ceremony(?x2209, ?x7226), type_of_union(?x5820, ?x566), award(?x1392, ?x2209), ?x8570 = 04jpg2p, ?x7226 = 0c6vcj >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gmdkyy instance_of_recurring_event 0g_w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 29.000 29.000 0.887 http://example.org/time/event/instance_of_recurring_event #1290-010z5n PRED entity: 010z5n PRED relation: time_zones PRED expected values: 02hcv8 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.43 #614, 0.43 #601, 0.40 #172), 02lcqs (0.22 #31, 0.20 #96, 0.20 #83), 02fqwt (0.18 #248, 0.18 #53, 0.18 #183), 02hczc (0.16 #859, 0.06 #262, 0.06 #613), 042g7t (0.16 #859, 0.03 #11, 0.01 #440), 02lcrv (0.16 #859), 02llzg (0.07 #108, 0.07 #134, 0.07 #121), 03bdv (0.05 #162, 0.05 #149, 0.05 #305), 03plfd (0.01 #205, 0.01 #387, 0.01 #413) >> Best rule #614 for best value: >> intensional similarity = 2 >> extensional distance = 881 >> proper extension: 0f4y_; 0ml25; 0m2gk; 0mlyw; 0nh0f; 0nj1c; 0mmr1; 0mm0p; 0nvd8; 0n5_g; ... >> query: (?x12583, 02hcv8) <- source(?x12583, ?x958), ?x958 = 0jbk9 >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 010z5n time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.429 http://example.org/location/location/time_zones #1289-0brkwj PRED entity: 0brkwj PRED relation: tv_program PRED expected values: 0cskb => 76 concepts (58 used for prediction) PRED predicted values (max 10 best out of 55): 01b66d (0.14 #191, 0.02 #450, 0.02 #537), 0hz55 (0.11 #37, 0.09 #518, 0.09 #605), 0123qq (0.11 #75, 0.09 #518, 0.09 #605), 0cskb (0.11 #65, 0.09 #518, 0.09 #605), 01f3p_ (0.11 #24, 0.09 #518, 0.09 #605), 039c26 (0.11 #22, 0.09 #518, 0.09 #605), 0gj50 (0.11 #202, 0.03 #461, 0.03 #548), 0170k0 (0.11 #58, 0.02 #230, 0.01 #489), 025x1t (0.11 #76, 0.02 #248), 028k2x (0.11 #52, 0.02 #224) >> Best rule #191 for best value: >> intensional similarity = 3 >> extensional distance = 97 >> proper extension: 0dbpyd; 01xdf5; 050023; 026dcvf; 02773nt; 0pz7h; 0265v21; 026dg51; 057d89; 04wtx1; ... >> query: (?x8094, 01b66d) <- award_winner(?x8094, ?x2650), nationality(?x8094, ?x94), tv_program(?x8094, ?x5810) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #65 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0d7hg4; 08q3s0; *> query: (?x8094, 0cskb) <- award_winner(?x8094, ?x4023), nationality(?x8094, ?x94), ?x4023 = 09hd16 *> conf = 0.11 ranks of expected_values: 4 EVAL 0brkwj tv_program 0cskb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 76.000 58.000 0.141 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #1288-016z5x PRED entity: 016z5x PRED relation: language PRED expected values: 02h40lc => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 48): 02h40lc (0.91 #242, 0.90 #780, 0.89 #1978), 064_8sq (0.21 #82, 0.17 #142, 0.15 #800), 02bjrlw (0.18 #61, 0.09 #421, 0.09 #481), 04306rv (0.16 #65, 0.15 #125, 0.12 #425), 06nm1 (0.15 #131, 0.11 #670, 0.10 #551), 03_9r (0.09 #10, 0.06 #130, 0.05 #550), 04h9h (0.08 #163, 0.06 #222, 0.05 #103), 06b_j (0.08 #322, 0.08 #83, 0.07 #263), 0jzc (0.06 #20, 0.05 #260, 0.04 #140), 0349s (0.05 #105, 0.03 #3726, 0.02 #465) >> Best rule #242 for best value: >> intensional similarity = 4 >> extensional distance = 108 >> proper extension: 03mh94; 0jzw; 051zy_b; >> query: (?x518, 02h40lc) <- film(?x450, ?x518), genre(?x518, ?x162), produced_by(?x518, ?x519), film_production_design_by(?x518, ?x4449) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016z5x language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.909 http://example.org/film/film/language #1287-088q4 PRED entity: 088q4 PRED relation: country! PRED expected values: 06f41 01lb14 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 56): 0bynt (0.85 #3372, 0.85 #2475, 0.84 #3148), 071t0 (0.77 #1144, 0.76 #696, 0.72 #920), 03_8r (0.75 #2488, 0.71 #583, 0.71 #695), 01lb14 (0.74 #688, 0.70 #1136, 0.68 #576), 01cgz (0.71 #686, 0.70 #1134, 0.68 #2479), 06f41 (0.68 #687, 0.66 #1135, 0.61 #575), 06wrt (0.65 #689, 0.59 #1137, 0.58 #745), 03hr1p (0.61 #1145, 0.61 #585, 0.59 #697), 0194d (0.59 #1169, 0.59 #721, 0.57 #609), 0w0d (0.59 #684, 0.57 #1132, 0.54 #572) >> Best rule #3372 for best value: >> intensional similarity = 3 >> extensional distance = 145 >> proper extension: 06sff; 01nty; 0j11; >> query: (?x3432, 0bynt) <- currency(?x3432, ?x170), olympics(?x3432, ?x2966), ?x2966 = 06sks6 >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #688 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 32 *> proper extension: 0jgd; 0d060g; 07ssc; 0ctw_b; 06qd3; 01mjq; 06mkj; 06f32; 03ryn; 05r7t; *> query: (?x3432, 01lb14) <- administrative_parent(?x12404, ?x3432), olympics(?x3432, ?x778), official_language(?x3432, ?x254) *> conf = 0.74 ranks of expected_values: 4, 6 EVAL 088q4 country! 01lb14 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 128.000 128.000 0.850 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 088q4 country! 06f41 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 128.000 128.000 0.850 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #1286-03gvt PRED entity: 03gvt PRED relation: role! PRED expected values: 02qjv 04rzd 0680x0 => 79 concepts (55 used for prediction) PRED predicted values (max 10 best out of 92): 0bxl5 (0.87 #2407, 0.87 #2369, 0.85 #342), 01vj9c (0.85 #342, 0.84 #4587, 0.84 #3372), 0gkd1 (0.85 #342, 0.83 #170, 0.82 #857), 0l14md (0.85 #342, 0.83 #170, 0.82 #857), 0l15bq (0.85 #342, 0.83 #170, 0.82 #857), 01qbl (0.85 #342, 0.83 #170, 0.82 #857), 018j2 (0.85 #342, 0.83 #170, 0.82 #857), 042v_gx (0.85 #342, 0.83 #170, 0.82 #857), 0mkg (0.85 #342, 0.83 #170, 0.82 #857), 0680x0 (0.85 #342, 0.83 #170, 0.82 #857) >> Best rule #2407 for best value: >> intensional similarity = 16 >> extensional distance = 13 >> proper extension: 0979zs; >> query: (?x3716, ?x3215) <- role(?x2764, ?x3716), role(?x1437, ?x3716), role(?x780, ?x3716), role(?x3716, ?x7033), role(?x3716, ?x3215), ?x3215 = 0bxl5, role(?x211, ?x3716), role(?x219, ?x780), role(?x780, ?x1886), ?x1886 = 02k84w, role(?x6384, ?x1437), role(?x5494, ?x1437), ?x7033 = 0gkd1, ?x2764 = 01s0ps, ?x6384 = 01ldw4, ?x5494 = 018x3 >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #342 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 2 *> proper extension: 0l15bq; *> query: (?x3716, ?x614) <- role(?x8014, ?x3716), role(?x1495, ?x3716), role(?x1437, ?x3716), performance_role(?x565, ?x3716), role(?x3716, ?x432), role(?x3716, ?x614), ?x1437 = 01vdm0, role(?x6947, ?x3716), role(?x642, ?x3716), ?x8014 = 0214km, ?x1495 = 013y1f, ?x6947 = 01vrnsk, people(?x4195, ?x642), role(?x217, ?x432), role(?x432, ?x615), role(?x74, ?x432) *> conf = 0.85 ranks of expected_values: 10, 17, 38 EVAL 03gvt role! 0680x0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 79.000 55.000 0.867 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 03gvt role! 04rzd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 79.000 55.000 0.867 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 03gvt role! 02qjv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 79.000 55.000 0.867 http://example.org/music/performance_role/track_performances./music/track_contribution/role #1285-07kh6f3 PRED entity: 07kh6f3 PRED relation: production_companies PRED expected values: 01gb54 => 59 concepts (58 used for prediction) PRED predicted values (max 10 best out of 107): 086k8 (0.29 #4204, 0.14 #85, 0.10 #588), 05qd_ (0.13 #93, 0.09 #1431, 0.09 #596), 016tw3 (0.08 #1016, 0.08 #849, 0.08 #1860), 054lpb6 (0.08 #15, 0.07 #852, 0.06 #1270), 017s11 (0.08 #421, 0.07 #1007, 0.06 #1090), 016tt2 (0.08 #87, 0.08 #590, 0.07 #422), 01gb54 (0.06 #38, 0.05 #707, 0.05 #1376), 0g1rw (0.06 #426, 0.06 #91, 0.04 #1429), 030_1_ (0.06 #268, 0.05 #519, 0.04 #686), 0283xx2 (0.05 #235, 0.05 #402, 0.02 #2679) >> Best rule #4204 for best value: >> intensional similarity = 1 >> extensional distance = 1600 >> proper extension: 0522wp; >> query: (?x3790, ?x382) <- film(?x382, ?x3790) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 106 *> proper extension: 04lqvlr; 02hfk5; 03q8xj; 0cvkv5; 05zvzf3; *> query: (?x3790, 01gb54) <- film_crew_role(?x3790, ?x4305), ?x4305 = 0215hd, nominated_for(?x112, ?x3790) *> conf = 0.06 ranks of expected_values: 7 EVAL 07kh6f3 production_companies 01gb54 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 59.000 58.000 0.292 http://example.org/film/film/production_companies #1284-03_6y PRED entity: 03_6y PRED relation: actor! PRED expected values: 03cf9ly => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 131): 0cpz4k (0.12 #62, 0.05 #592, 0.03 #1122), 02gjrc (0.12 #227, 0.03 #1287, 0.02 #15077), 01b_lz (0.11 #316, 0.06 #846, 0.05 #581), 0cs134 (0.05 #478, 0.05 #743, 0.04 #1803), 01p4wv (0.05 #358, 0.05 #623, 0.03 #888), 0g60z (0.05 #269, 0.05 #534, 0.03 #799), 0gxsh4 (0.05 #489, 0.05 #754, 0.03 #1019), 099pks (0.05 #364, 0.05 #629, 0.03 #894), 02_1q9 (0.05 #270, 0.05 #535, 0.03 #800), 0147w8 (0.05 #498, 0.05 #763, 0.03 #1028) >> Best rule #62 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 016zp5; >> query: (?x3466, 0cpz4k) <- award_nominee(?x2762, ?x3466), ?x2762 = 015t56, spouse(?x8764, ?x3466) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_6y actor! 03cf9ly CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 114.000 114.000 0.125 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #1283-0c3p7 PRED entity: 0c3p7 PRED relation: nominated_for PRED expected values: 01q_y0 => 138 concepts (77 used for prediction) PRED predicted values (max 10 best out of 578): 0cfhfz (0.80 #449, 0.36 #79304, 0.35 #74446), 026gyn_ (0.79 #40456, 0.79 #92250, 0.77 #113293), 0prhz (0.36 #79304, 0.35 #74446, 0.33 #24270), 01633c (0.36 #79304, 0.35 #74446, 0.33 #24270), 01jmyj (0.36 #79304, 0.35 #74446, 0.33 #16177), 09lxv9 (0.36 #79304, 0.35 #74446, 0.33 #16177), 016yxn (0.36 #79304, 0.35 #74446, 0.33 #16177), 0bpbhm (0.36 #79304, 0.35 #74446, 0.33 #16177), 05sxr_ (0.35 #74446, 0.33 #16177, 0.32 #64732), 034qbx (0.33 #16177, 0.32 #64732, 0.32 #24269) >> Best rule #449 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 07lmxq; >> query: (?x6314, 0cfhfz) <- award_nominee(?x7337, ?x6314), award_nominee(?x2615, ?x6314), ?x7337 = 03zz8b, ?x2615 = 0306ds >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5189 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 52 *> proper extension: 02qjj7; 06y9c2; 063vn; 03f1zhf; *> query: (?x6314, 01q_y0) <- participant(?x6314, ?x851), student(?x4268, ?x6314), major_field_of_study(?x122, ?x4268) *> conf = 0.04 ranks of expected_values: 109 EVAL 0c3p7 nominated_for 01q_y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 138.000 77.000 0.800 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #1282-05g2v PRED entity: 05g2v PRED relation: contains PRED expected values: 06frc => 107 concepts (49 used for prediction) PRED predicted values (max 10 best out of 2784): 088xp (0.70 #67694, 0.68 #44146, 0.67 #55919), 01p1b (0.70 #67694, 0.68 #44146, 0.67 #55919), 07tp2 (0.70 #67694, 0.68 #44146, 0.66 #91246), 05rznz (0.70 #67694, 0.68 #44146, 0.66 #91246), 02k54 (0.68 #44146, 0.67 #55919, 0.67 #61806), 019pcs (0.68 #44146, 0.67 #55919, 0.67 #61806), 04gqr (0.68 #44146, 0.67 #55919, 0.67 #61806), 01nyl (0.68 #44146, 0.66 #91246, 0.65 #108914), 02khs (0.67 #55919, 0.67 #61806, 0.62 #79468), 019rg5 (0.67 #55919, 0.67 #61806, 0.62 #79468) >> Best rule #67694 for best value: >> intensional similarity = 4 >> extensional distance = 34 >> proper extension: 0f8l9c; >> query: (?x5903, ?x13717) <- contains(?x5903, ?x5457), adjoins(?x5457, ?x13717), form_of_government(?x13717, ?x48), official_language(?x13717, ?x254) >> conf = 0.70 => this is the best rule for 4 predicted values *> Best rule #13033 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 04q_g; *> query: (?x5903, 06frc) <- contains(?x5903, ?x13353), contains(?x5903, ?x5457), olympics(?x5457, ?x1931), time_zones(?x5457, ?x6582), films(?x13353, ?x3979) *> conf = 0.10 ranks of expected_values: 581 EVAL 05g2v contains 06frc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 107.000 49.000 0.699 http://example.org/location/location/contains #1281-01swxv PRED entity: 01swxv PRED relation: fraternities_and_sororities PRED expected values: 0325pb => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 2): 0325pb (0.61 #7, 0.59 #27, 0.39 #45), 04m8fy (0.08 #4, 0.06 #28, 0.06 #8) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 68 >> proper extension: 02zc7f; 03wv2g; >> query: (?x2959, 0325pb) <- contains(?x94, ?x2959), school(?x684, ?x2959), fraternities_and_sororities(?x2959, ?x4348), ?x94 = 09c7w0 >> conf = 0.61 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01swxv fraternities_and_sororities 0325pb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.614 http://example.org/education/university/fraternities_and_sororities #1280-015pxr PRED entity: 015pxr PRED relation: influenced_by PRED expected values: 021bk 0q5hw 02z3zp => 106 concepts (70 used for prediction) PRED predicted values (max 10 best out of 325): 014z8v (0.19 #1413, 0.07 #1845, 0.07 #2277), 01hmk9 (0.15 #1512, 0.07 #1944, 0.07 #2376), 0p_47 (0.15 #1399, 0.06 #1831, 0.06 #2263), 0ph2w (0.15 #1411, 0.05 #12950, 0.03 #980), 01k9lpl (0.14 #1601, 0.07 #2033, 0.07 #2465), 013tjc (0.14 #1666, 0.03 #1235, 0.03 #8143), 014zfs (0.10 #1317, 0.07 #3044, 0.07 #1749), 01wp_jm (0.10 #1631, 0.05 #12950, 0.04 #5949), 081k8 (0.10 #5766, 0.07 #12673, 0.06 #7925), 01s7qqw (0.08 #1456, 0.05 #12950, 0.03 #1025) >> Best rule #1413 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 0167xy; >> query: (?x2143, 014z8v) <- influenced_by(?x2143, ?x2283), celebrities_impersonated(?x3649, ?x2283) >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #12950 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 515 *> proper extension: 01d494; 02pb2bp; 099bk; 05xq9; 0lhn5; 07c37; 01kcms4; 0399p; 070b4; 02m4t; ... *> query: (?x2143, ?x397) <- influenced_by(?x2143, ?x2283), influenced_by(?x397, ?x2283) *> conf = 0.05 ranks of expected_values: 37, 38 EVAL 015pxr influenced_by 02z3zp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 106.000 70.000 0.192 http://example.org/influence/influence_node/influenced_by EVAL 015pxr influenced_by 0q5hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 106.000 70.000 0.192 http://example.org/influence/influence_node/influenced_by EVAL 015pxr influenced_by 021bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 70.000 0.192 http://example.org/influence/influence_node/influenced_by #1279-031786 PRED entity: 031786 PRED relation: genre PRED expected values: 02xlf => 93 concepts (47 used for prediction) PRED predicted values (max 10 best out of 98): 07s9rl0 (0.76 #586, 0.68 #1288, 0.66 #1524), 02kdv5l (0.71 #1994, 0.61 #3282, 0.61 #3637), 05p553 (0.59 #2464, 0.36 #2230, 0.35 #1645), 024qqx (0.50 #5521, 0.49 #5284, 0.47 #2928), 0lsxr (0.37 #945, 0.28 #4820, 0.27 #1062), 02xlf (0.36 #401, 0.33 #284, 0.14 #50), 02l7c8 (0.32 #1537, 0.29 #5416, 0.28 #5179), 017fp (0.29 #13, 0.27 #130, 0.14 #364), 04xvlr (0.29 #353, 0.25 #236, 0.19 #1525), 0219x_ (0.29 #24, 0.09 #5189, 0.09 #5426) >> Best rule #586 for best value: >> intensional similarity = 4 >> extensional distance = 40 >> proper extension: 06mmr; >> query: (?x7305, 07s9rl0) <- award_winner(?x7305, ?x4449), profession(?x4449, ?x2450), nominated_for(?x4449, ?x518), film_production_design_by(?x1493, ?x4449) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #401 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 016z5x; *> query: (?x7305, 02xlf) <- nominated_for(?x4449, ?x7305), genre(?x7305, ?x600), ?x4449 = 0d5wn3, film(?x2372, ?x7305), award_winner(?x473, ?x2372) *> conf = 0.36 ranks of expected_values: 6 EVAL 031786 genre 02xlf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 93.000 47.000 0.762 http://example.org/film/film/genre #1278-016szr PRED entity: 016szr PRED relation: award PRED expected values: 0fhpv4 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 291): 09sb52 (0.55 #23228, 0.32 #22049, 0.27 #25586), 02qvyrt (0.44 #516, 0.44 #2481, 0.37 #5232), 01by1l (0.41 #3646, 0.41 #7576, 0.33 #11899), 01bgqh (0.39 #3580, 0.31 #7510, 0.28 #15763), 0c4z8 (0.34 #3608, 0.33 #7538, 0.24 #11468), 01c92g (0.30 #3632, 0.22 #7562, 0.15 #36551), 0fhpv4 (0.26 #2548, 0.18 #5299, 0.17 #1369), 01c9jp (0.26 #3721, 0.10 #2149, 0.09 #15904), 02x1z2s (0.25 #194, 0.14 #37338, 0.12 #40876), 07bdd_ (0.25 #65, 0.09 #9104, 0.08 #10676) >> Best rule #23228 for best value: >> intensional similarity = 3 >> extensional distance = 731 >> proper extension: 017g2y; 0hpz8; 01385g; >> query: (?x4850, 09sb52) <- award(?x4850, ?x2238), award(?x2353, ?x2238), ?x2353 = 02qgyv >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2548 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 01mkn_d; 01nc3rh; 089kpp; *> query: (?x4850, 0fhpv4) <- nominated_for(?x4850, ?x1080), artists(?x4910, ?x4850), ?x4910 = 017_qw *> conf = 0.26 ranks of expected_values: 7 EVAL 016szr award 0fhpv4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 123.000 123.000 0.548 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1277-09gkx35 PRED entity: 09gkx35 PRED relation: language PRED expected values: 03x42 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 36): 064_8sq (0.20 #21, 0.17 #787, 0.17 #846), 04306rv (0.15 #238, 0.10 #770, 0.09 #62), 06nm1 (0.13 #186, 0.12 #1834, 0.12 #776), 02bjrlw (0.13 #117, 0.12 #293, 0.07 #648), 06b_j (0.13 #138, 0.07 #1846, 0.06 #1376), 0653m (0.09 #127, 0.08 #598, 0.06 #658), 0jzc (0.09 #135, 0.07 #19, 0.05 #606), 0t_2 (0.09 #129, 0.07 #13, 0.05 #71), 05qqm (0.07 #40, 0.05 #98, 0.04 #274), 02hwyss (0.07 #41, 0.04 #217, 0.04 #157) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 03cyslc; >> query: (?x3603, 064_8sq) <- film_crew_role(?x3603, ?x137), production_companies(?x3603, ?x9041), film_festivals(?x3603, ?x6828), film_release_region(?x3603, ?x1264) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 09gkx35 language 03x42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 106.000 0.200 http://example.org/film/film/language #1276-04n52p6 PRED entity: 04n52p6 PRED relation: language PRED expected values: 064_8sq => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 44): 064_8sq (0.33 #21, 0.31 #79, 0.19 #255), 04306rv (0.19 #121, 0.14 #238, 0.14 #828), 06b_j (0.19 #80, 0.13 #434, 0.11 #22), 012w70 (0.17 #246, 0.08 #362, 0.08 #129), 06nm1 (0.14 #482, 0.14 #893, 0.14 #834), 02bjrlw (0.12 #118, 0.10 #825, 0.09 #884), 0653m (0.11 #245, 0.11 #11, 0.08 #186), 0jzc (0.11 #19, 0.05 #549, 0.05 #726), 032f6 (0.11 #55, 0.03 #230, 0.02 #1294), 02hxcvy (0.11 #33, 0.02 #2270, 0.02 #325) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0c0yh4; >> query: (?x1707, 064_8sq) <- nominated_for(?x484, ?x1707), film(?x4286, ?x1707), ?x4286 = 03h2d4 >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04n52p6 language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.333 http://example.org/film/film/language #1275-01kp_1t PRED entity: 01kp_1t PRED relation: award_winner! PRED expected values: 01mhwk 013b2h => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 105): 02rjjll (0.33 #5, 0.19 #3338, 0.17 #422), 01c6qp (0.33 #19, 0.17 #436, 0.15 #1965), 056878 (0.33 #32, 0.17 #449, 0.13 #866), 02cg41 (0.33 #124, 0.14 #541, 0.12 #2070), 01s695 (0.33 #3, 0.14 #1949, 0.11 #1393), 01bx35 (0.33 #7, 0.12 #1953, 0.09 #2509), 0gpjbt (0.33 #29, 0.12 #446, 0.11 #1975), 0jzphpx (0.19 #3338, 0.12 #456, 0.11 #8903), 01mhwk (0.19 #3338, 0.11 #8903, 0.10 #1987), 01mh_q (0.19 #3338, 0.11 #8903, 0.09 #2033) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01lmj3q; >> query: (?x9528, 02rjjll) <- award_nominee(?x9528, ?x11621), award(?x9528, ?x10556), ?x10556 = 02flq1 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3338 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 454 *> proper extension: 0k8y7; *> query: (?x9528, ?x486) <- award_winner(?x11621, ?x9528), award_winner(?x11621, ?x4574), award_winner(?x486, ?x4574), artists(?x671, ?x11621) *> conf = 0.19 ranks of expected_values: 9, 13 EVAL 01kp_1t award_winner! 013b2h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 114.000 114.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01kp_1t award_winner! 01mhwk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 114.000 114.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1274-0lccn PRED entity: 0lccn PRED relation: religion PRED expected values: 03_gx => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 17): 0c8wxp (0.35 #624, 0.35 #1022, 0.34 #1066), 03_gx (0.21 #1074, 0.17 #1030, 0.12 #632), 0kpl (0.17 #628, 0.15 #1026, 0.12 #1070), 01lp8 (0.08 #1, 0.07 #619, 0.05 #1017), 03j6c (0.07 #1080, 0.06 #1036, 0.05 #638), 0flw86 (0.05 #1018, 0.05 #620, 0.05 #1062), 0kq2 (0.05 #591, 0.05 #635, 0.04 #1033), 05tgm (0.04 #70, 0.02 #203, 0.01 #380), 04pk9 (0.03 #1035, 0.03 #637, 0.02 #152), 019cr (0.03 #629, 0.02 #1071, 0.02 #1027) >> Best rule #624 for best value: >> intensional similarity = 2 >> extensional distance = 301 >> proper extension: 01w3v; 0mcf4; >> query: (?x2319, 0c8wxp) <- religion(?x2319, ?x7422), category(?x2319, ?x134) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #1074 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 611 *> proper extension: 0j5b8; 04pwg; 015c1b; *> query: (?x2319, 03_gx) <- people(?x1050, ?x2319), religion(?x2319, ?x7422) *> conf = 0.21 ranks of expected_values: 2 EVAL 0lccn religion 03_gx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.353 http://example.org/people/person/religion #1273-0353tm PRED entity: 0353tm PRED relation: language PRED expected values: 02h40lc => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 42): 02h40lc (0.93 #356, 0.93 #1131, 0.91 #1671), 06mp7 (0.59 #1307, 0.53 #653, 0.06 #193), 04306rv (0.22 #477, 0.17 #5, 0.12 #1372), 02bjrlw (0.22 #473, 0.14 #414, 0.09 #1368), 064_8sq (0.16 #494, 0.16 #1389, 0.15 #140), 06nm1 (0.12 #1378, 0.10 #1977, 0.10 #1798), 03_9r (0.11 #423, 0.09 #246, 0.08 #305), 06b_j (0.11 #200, 0.11 #495, 0.09 #259), 012w70 (0.08 #13, 0.08 #131, 0.04 #1621), 04h9h (0.08 #338, 0.05 #515, 0.05 #1351) >> Best rule #356 for best value: >> intensional similarity = 6 >> extensional distance = 27 >> proper extension: 060v34; 04fzfj; 0jjy0; 02v63m; 09k56b7; 05z7c; 05p1qyh; 06g77c; 0c34mt; 07j94; ... >> query: (?x9213, 02h40lc) <- film(?x2925, ?x9213), genre(?x9213, ?x571), film(?x2549, ?x9213), ?x571 = 03npn, participant(?x3020, ?x2925), featured_film_locations(?x9213, ?x1036) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0353tm language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.931 http://example.org/film/film/language #1272-01cz7r PRED entity: 01cz7r PRED relation: film_format PRED expected values: 07fb8_ => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.16 #61, 0.15 #103, 0.15 #51), 0cj16 (0.14 #18, 0.13 #151, 0.13 #162), 017fx5 (0.04 #69, 0.04 #106, 0.04 #49) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 271 >> proper extension: 07nt8p; 0cw3yd; 03176f; 011ypx; 01mgw; >> query: (?x7645, 07fb8_) <- film_release_region(?x7645, ?x94), executive_produced_by(?x7645, ?x1367), nominated_for(?x1104, ?x7645), ?x94 = 09c7w0 >> conf = 0.16 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cz7r film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.158 http://example.org/film/film/film_format #1271-0qm8b PRED entity: 0qm8b PRED relation: film_crew_role PRED expected values: 0dxtw => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 27): 09zzb8 (0.74 #728, 0.72 #1456, 0.71 #1129), 0dxtw (0.55 #82, 0.44 #481, 0.43 #517), 01vx2h (0.39 #482, 0.38 #518, 0.36 #83), 01pvkk (0.30 #48, 0.29 #192, 0.29 #739), 02ynfr (0.30 #52, 0.23 #451, 0.19 #487), 089fss (0.30 #42, 0.09 #78, 0.07 #1134), 0215hd (0.25 #19, 0.15 #344, 0.12 #1147), 089g0h (0.25 #20, 0.12 #491, 0.11 #527), 01xy5l_ (0.25 #14, 0.10 #339, 0.10 #194), 02rh1dz (0.21 #480, 0.20 #516, 0.18 #81) >> Best rule #728 for best value: >> intensional similarity = 4 >> extensional distance = 641 >> proper extension: 0dq626; 0gx9rvq; 04kkz8; 05p3738; 08gg47; 09rsjpv; 047fjjr; 03z106; 02dpl9; 0gy2y8r; ... >> query: (?x1586, 09zzb8) <- genre(?x1586, ?x53), film(?x629, ?x1586), film_crew_role(?x1586, ?x468), ?x53 = 07s9rl0 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #82 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0ds33; 0cfhfz; 05sy_5; 095z4q; 0g7pm1; 0gldyz; *> query: (?x1586, 0dxtw) <- genre(?x1586, ?x53), film(?x4043, ?x1586), nominated_for(?x298, ?x1586), ?x4043 = 06t74h *> conf = 0.55 ranks of expected_values: 2 EVAL 0qm8b film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.737 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1270-08815 PRED entity: 08815 PRED relation: student PRED expected values: 03x3qv 0h96g 01jrvr6 07hgkd 0fgg4 0dq2k 016kft 05cqhl 013sg6 04vq3h 03c_8t => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1440): 0c_md_ (0.25 #1616, 0.17 #3653, 0.08 #5690), 0h96g (0.25 #806, 0.08 #4880, 0.07 #8955), 03h40_7 (0.25 #1765, 0.08 #5839, 0.05 #22137), 06pwf6 (0.25 #445, 0.08 #4519, 0.04 #14705), 01mqh5 (0.25 #1835, 0.08 #5909, 0.04 #16095), 011zd3 (0.25 #334, 0.08 #4408, 0.04 #14594), 01g0jn (0.25 #1926, 0.08 #6000, 0.04 #16186), 01whg97 (0.25 #1376, 0.08 #5450, 0.04 #15636), 01xyt7 (0.25 #1011, 0.08 #5085, 0.04 #15271), 0f6_x (0.25 #559, 0.08 #4633, 0.04 #14819) >> Best rule #1616 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 07szy; 033gn8; >> query: (?x122, 0c_md_) <- institution(?x734, ?x122), student(?x122, ?x7625), ?x7625 = 03xx9l >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #806 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 07szy; 033gn8; *> query: (?x122, 0h96g) <- institution(?x734, ?x122), student(?x122, ?x7625), ?x7625 = 03xx9l *> conf = 0.25 ranks of expected_values: 2, 27, 125, 380, 452, 1098, 1142 EVAL 08815 student 03c_8t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 08815 student 04vq3h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 08815 student 013sg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 08815 student 05cqhl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 08815 student 016kft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 08815 student 0dq2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 08815 student 0fgg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 08815 student 07hgkd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 08815 student 01jrvr6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 08815 student 0h96g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 08815 student 03x3qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 84.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #1269-02jt1k PRED entity: 02jt1k PRED relation: nationality PRED expected values: 09c7w0 => 112 concepts (110 used for prediction) PRED predicted values (max 10 best out of 66): 09c7w0 (0.86 #4523, 0.86 #1101, 0.86 #1001), 01t12z (0.33 #9757, 0.30 #2607), 02_286 (0.33 #9757, 0.30 #2607), 059rby (0.33 #9757, 0.30 #2607), 02jx1 (0.17 #333, 0.15 #433, 0.14 #2237), 07ssc (0.10 #3928, 0.10 #2017, 0.09 #3828), 03rk0 (0.07 #1246, 0.07 #5674, 0.06 #9096), 0d060g (0.05 #707, 0.05 #1207, 0.05 #2211), 0345h (0.04 #1231, 0.03 #3813, 0.02 #4147), 0d0vqn (0.04 #509, 0.04 #409, 0.04 #7842) >> Best rule #4523 for best value: >> intensional similarity = 2 >> extensional distance = 814 >> proper extension: 01nqfh_; 04n7njg; 05cv94; 03cvfg; 03mz9r; 063vn; 0453t; 06pwf6; 03gkn5; 0fpj4lx; ... >> query: (?x1700, 09c7w0) <- student(?x122, ?x1700), fraternities_and_sororities(?x122, ?x4348) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02jt1k nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 110.000 0.864 http://example.org/people/person/nationality #1268-02h40lc PRED entity: 02h40lc PRED relation: languages! PRED expected values: 0kfpm 0584r4 01q_y0 026b33f 01j7mr 0557yqh 0l76z 01b66t 01fx1l 05f7w84 05p9_ql 06qwh 05h95s 034fl9 097h2 07g9f 06qxh 070ltt 0123qq 02gjrc 015pnb 06qw_ 04bp0l => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 35): 015g28 (0.57 #248, 0.56 #316, 0.50 #386), 05h95s (0.25 #136, 0.20 #185, 0.14 #251), 05f7w84 (0.25 #134, 0.20 #183, 0.14 #249), 04xbq3 (0.14 #255, 0.08 #446, 0.03 #674), 097h2 (0.14 #254, 0.08 #445, 0.03 #673), 074j87 (0.14 #262, 0.08 #453, 0.03 #681), 03czz87 (0.14 #257, 0.08 #448, 0.03 #676), 03lyp4 (0.12 #313, 0.11 #331, 0.10 #401), 045nc5 (0.12 #311, 0.11 #329, 0.10 #399), 051kd (0.12 #310, 0.11 #328, 0.10 #398) >> Best rule #248 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0t_2; >> query: (?x254, 015g28) <- language(?x2770, ?x254), languages(?x7605, ?x254), languages(?x50, ?x254), languages_spoken(?x743, ?x254), genre(?x2770, ?x225), spouse(?x10547, ?x7605) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #136 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 06nm1; *> query: (?x254, 05h95s) <- language(?x5066, ?x254), language(?x1035, ?x254), ?x5066 = 07bwr, executive_produced_by(?x1035, ?x2464), languages(?x118, ?x254), service_language(?x127, ?x254), languages(?x50, ?x254) *> conf = 0.25 ranks of expected_values: 2, 3, 5, 19, 21, 22, 23, 24, 26, 28, 31, 32, 33, 34, 35 EVAL 02h40lc languages! 04bp0l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 06qw_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 015pnb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 02gjrc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 0123qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 070ltt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 06qxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 07g9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 097h2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 034fl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 05h95s CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 06qwh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 05p9_ql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 05f7w84 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 01fx1l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 01b66t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 0l76z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 0557yqh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 01j7mr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 026b33f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 01q_y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 0584r4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 72.000 72.000 0.571 http://example.org/tv/tv_program/languages EVAL 02h40lc languages! 0kfpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 72.000 72.000 0.571 http://example.org/tv/tv_program/languages #1267-01_0f7 PRED entity: 01_0f7 PRED relation: film! PRED expected values: 01pkhw => 107 concepts (58 used for prediction) PRED predicted values (max 10 best out of 898): 09wj5 (0.72 #33280, 0.70 #31197, 0.62 #72805), 04pf4r (0.57 #89453, 0.49 #79048, 0.48 #85292), 06chf (0.13 #24953, 0.13 #29115, 0.12 #39523), 03ym1 (0.09 #33282, 0.09 #31199, 0.07 #79049), 01ps2h8 (0.09 #33282, 0.09 #31199, 0.07 #79049), 0154qm (0.09 #33282, 0.09 #31199, 0.07 #79049), 015t7v (0.09 #33282, 0.09 #31199, 0.07 #79049), 0svqs (0.09 #33282, 0.09 #31199, 0.07 #79049), 0241jw (0.09 #33282, 0.09 #31199, 0.07 #79049), 016ypb (0.09 #33282, 0.09 #31199, 0.07 #79049) >> Best rule #33280 for best value: >> intensional similarity = 4 >> extensional distance = 232 >> proper extension: 05sy0cv; >> query: (?x6531, ?x629) <- award_winner(?x6531, ?x629), award_nominee(?x230, ?x629), participant(?x629, ?x5485), film(?x629, ?x755) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #23570 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 179 *> proper extension: 02_1sj; 03ckwzc; 0963mq; 02vqhv0; 047qxs; 0j_tw; 04z257; 03wbqc4; 02rmd_2; 02j69w; ... *> query: (?x6531, 01pkhw) <- films(?x7727, ?x6531), film(?x2803, ?x6531), film_crew_role(?x6531, ?x137), genre(?x6531, ?x53) *> conf = 0.02 ranks of expected_values: 191 EVAL 01_0f7 film! 01pkhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 107.000 58.000 0.720 http://example.org/film/actor/film./film/performance/film #1266-01tx9m PRED entity: 01tx9m PRED relation: category PRED expected values: 08mbj5d => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #61, 0.91 #75, 0.90 #69) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 227 >> proper extension: 03gn1x; >> query: (?x6177, 08mbj5d) <- contains(?x94, ?x6177), major_field_of_study(?x6177, ?x3995), ?x94 = 09c7w0, currency(?x6177, ?x170) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tx9m category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 151.000 0.913 http://example.org/common/topic/webpage./common/webpage/category #1265-05631 PRED entity: 05631 PRED relation: program_creator PRED expected values: 081nh => 77 concepts (61 used for prediction) PRED predicted values (max 10 best out of 103): 0p_2r (0.33 #13, 0.17 #227, 0.11 #656), 01pfkw (0.29 #372, 0.17 #265, 0.12 #1777), 01xcr4 (0.20 #154, 0.14 #367, 0.07 #1554), 01my_c (0.20 #180, 0.07 #1580, 0.05 #2125), 03m6_z (0.18 #1073, 0.13 #1398, 0.08 #535), 01wd9lv (0.17 #282, 0.14 #389, 0.11 #711), 044f7 (0.14 #487, 0.11 #702, 0.09 #1134), 0127s7 (0.14 #536, 0.08 #535, 0.08 #2271), 021yw7 (0.14 #464, 0.04 #2308, 0.04 #3293), 0f1vrl (0.11 #1852, 0.09 #1091, 0.02 #4367) >> Best rule #13 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 039cq4; >> query: (?x13288, 0p_2r) <- actor(?x13288, ?x2435), program(?x1762, ?x13288), program(?x3397, ?x13288), country_of_origin(?x13288, ?x94), ?x94 = 09c7w0, participant(?x2763, ?x2435) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05631 program_creator 081nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 61.000 0.333 http://example.org/tv/tv_program/program_creator #1264-07z1m PRED entity: 07z1m PRED relation: location! PRED expected values: 018y2s => 193 concepts (158 used for prediction) PRED predicted values (max 10 best out of 2029): 01797x (0.25 #4576, 0.25 #2077, 0.07 #52059), 0c01c (0.25 #5470, 0.12 #10468, 0.12 #12967), 099d4 (0.25 #2346, 0.12 #12342, 0.07 #19839), 0c6qh (0.25 #2957, 0.12 #5456, 0.06 #67936), 032r1 (0.25 #2296, 0.07 #19789, 0.06 #24788), 04z0g (0.25 #1172, 0.07 #18665, 0.06 #11168), 0d05fv (0.25 #3399, 0.06 #10896, 0.06 #13395), 05dbf (0.25 #2906, 0.06 #10403, 0.06 #12902), 01vsy3q (0.25 #3485, 0.06 #10982, 0.05 #43471), 03f1zdw (0.25 #208, 0.05 #45192, 0.04 #17701) >> Best rule #4576 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 05jbn; 013gwb; >> query: (?x1426, 01797x) <- location(?x11509, ?x1426), contains(?x1426, ?x347), ?x11509 = 01h5f8 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #147455 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 0n2q0; 0lmgy; 0261m; 048fz; 09bkv; 0mskq; 0ms1n; *> query: (?x1426, ?x1165) <- contains(?x1426, ?x2298), location_of_ceremony(?x566, ?x1426), location(?x1165, ?x2298) *> conf = 0.03 ranks of expected_values: 918 EVAL 07z1m location! 018y2s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 193.000 158.000 0.250 http://example.org/people/person/places_lived./people/place_lived/location #1263-01lvrm PRED entity: 01lvrm PRED relation: contains! PRED expected values: 02j9z => 132 concepts (45 used for prediction) PRED predicted values (max 10 best out of 169): 049nq (0.83 #13436, 0.70 #2687, 0.70 #2365), 09c7w0 (0.80 #12541, 0.75 #17912, 0.74 #21494), 0d8s8 (0.80 #11641, 0.79 #13433, 0.78 #36714), 02j71 (0.44 #33133), 0345h (0.29 #13517, 0.22 #2768, 0.09 #975), 03rjj (0.26 #13446, 0.11 #2697, 0.04 #904), 02jx1 (0.24 #9936, 0.20 #9040, 0.14 #36801), 07371 (0.17 #2288, 0.02 #13935, 0.02 #21489), 01n7q (0.14 #12616, 0.11 #11720, 0.10 #30524), 03_3d (0.14 #13448, 0.01 #16132, 0.01 #15238) >> Best rule #13436 for best value: >> intensional similarity = 4 >> extensional distance = 242 >> proper extension: 02d9nr; >> query: (?x12368, ?x10382) <- citytown(?x12368, ?x10383), contains(?x7655, ?x12368), contains(?x10382, ?x7655), partially_contains(?x455, ?x10382) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #2715 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 35 *> proper extension: 02jxk; 041288; *> query: (?x12368, 02j9z) <- citytown(?x12368, ?x10383), contains(?x1229, ?x10383), time_zones(?x10383, ?x2864), ?x2864 = 02llzg, film_release_region(?x66, ?x1229) *> conf = 0.05 ranks of expected_values: 36 EVAL 01lvrm contains! 02j9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 132.000 45.000 0.825 http://example.org/location/location/contains #1262-0m3gy PRED entity: 0m3gy PRED relation: language PRED expected values: 02h40lc => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 48): 02h40lc (0.93 #1018, 0.93 #839, 0.91 #601), 06b_j (0.33 #23, 0.20 #143, 0.12 #1574), 03_9r (0.33 #10, 0.06 #1026, 0.06 #1561), 064_8sq (0.21 #501, 0.20 #142, 0.18 #560), 04306rv (0.20 #125, 0.18 #484, 0.16 #543), 06nm1 (0.14 #431, 0.14 #192, 0.13 #371), 04h9h (0.14 #224, 0.05 #761, 0.05 #2628), 02bjrlw (0.13 #539, 0.11 #302, 0.11 #242), 071fb (0.11 #259, 0.07 #378, 0.05 #438), 012w70 (0.11 #314, 0.05 #2628, 0.05 #433) >> Best rule #1018 for best value: >> intensional similarity = 5 >> extensional distance = 81 >> proper extension: 017kct; 037q31; 0kt_4; 099bhp; >> query: (?x9294, 02h40lc) <- genre(?x9294, ?x271), films(?x3530, ?x9294), film(?x12896, ?x9294), award(?x12896, ?x458), influenced_by(?x7717, ?x12896) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m3gy language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.928 http://example.org/film/film/language #1261-06myp PRED entity: 06myp PRED relation: influenced_by PRED expected values: 032l1 => 191 concepts (83 used for prediction) PRED predicted values (max 10 best out of 426): 03sbs (0.50 #1516, 0.50 #652, 0.38 #2815), 05qmj (0.50 #622, 0.36 #3647, 0.33 #1486), 0gz_ (0.50 #533, 0.33 #1397, 0.27 #3558), 032l1 (0.43 #1816, 0.33 #88, 0.30 #25975), 06jkm (0.40 #1256, 0.25 #824, 0.18 #3849), 01v9724 (0.33 #176, 0.29 #1904, 0.27 #3632), 03_dj (0.33 #408, 0.29 #2136, 0.25 #3002), 0379s (0.33 #77, 0.29 #1805, 0.15 #10864), 084nh (0.33 #391, 0.25 #2985, 0.25 #822), 0bk5r (0.33 #5612, 0.25 #12082, 0.23 #19418) >> Best rule #1516 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0c5tl; >> query: (?x10895, 03sbs) <- influenced_by(?x10895, ?x2240), influenced_by(?x8441, ?x10895), ?x8441 = 0c1fs, ?x2240 = 0j3v >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1816 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 073_6; *> query: (?x10895, 032l1) <- influenced_by(?x10110, ?x10895), influenced_by(?x8768, ?x10895), influenced_by(?x7332, ?x10895), ?x7332 = 041xl, influenced_by(?x118, ?x8768), gender(?x10110, ?x231) *> conf = 0.43 ranks of expected_values: 4 EVAL 06myp influenced_by 032l1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 191.000 83.000 0.500 http://example.org/influence/influence_node/influenced_by #1260-02kz_ PRED entity: 02kz_ PRED relation: influenced_by PRED expected values: 06lbp => 182 concepts (90 used for prediction) PRED predicted values (max 10 best out of 349): 03_87 (0.43 #11284, 0.38 #1901, 0.33 #3182), 02wh0 (0.38 #2079, 0.33 #2934, 0.33 #2505), 081k8 (0.38 #1857, 0.33 #2712, 0.29 #8256), 013tjc (0.33 #5486, 0.33 #3778, 0.29 #5059), 03f0324 (0.33 #999, 0.33 #148, 0.26 #8252), 0379s (0.33 #2209, 0.33 #78, 0.24 #8182), 02kz_ (0.33 #1017, 0.33 #166, 0.22 #3152), 01v9724 (0.33 #2303, 0.33 #1023, 0.21 #8276), 01s7qqw (0.33 #3570, 0.29 #4851, 0.27 #5278), 0ph2w (0.33 #3954, 0.27 #5235, 0.25 #3527) >> Best rule #11284 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 01d494; 0453t; >> query: (?x5336, 03_87) <- type_of_union(?x5336, ?x566), influenced_by(?x5336, ?x6370), influenced_by(?x9284, ?x6370), ?x9284 = 0gd_s >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2986 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0p8jf; *> query: (?x5336, ?x1279) <- type_of_union(?x5336, ?x566), influenced_by(?x5336, ?x9508), influenced_by(?x5336, ?x6370), ?x6370 = 0465_, influenced_by(?x9508, ?x1279) *> conf = 0.14 ranks of expected_values: 62 EVAL 02kz_ influenced_by 06lbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 182.000 90.000 0.426 http://example.org/influence/influence_node/influenced_by #1259-04cr6qv PRED entity: 04cr6qv PRED relation: gender PRED expected values: 05zppz => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.88 #119, 0.86 #73, 0.86 #131), 02zsn (0.66 #48, 0.66 #46, 0.59 #32) >> Best rule #119 for best value: >> intensional similarity = 3 >> extensional distance = 187 >> proper extension: 01zmpg; >> query: (?x5514, 05zppz) <- role(?x5514, ?x315), role(?x2205, ?x315), ?x2205 = 0dq630k >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cr6qv gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.884 http://example.org/people/person/gender #1258-04y8r PRED entity: 04y8r PRED relation: award PRED expected values: 019f4v => 114 concepts (113 used for prediction) PRED predicted values (max 10 best out of 266): 07kjk7c (0.78 #19815, 0.77 #28543, 0.71 #34885), 07z2lx (0.77 #28543, 0.71 #34885, 0.71 #26559), 019f4v (0.67 #458, 0.52 #62, 0.49 #1649), 0gr51 (0.39 #1282, 0.34 #488, 0.32 #4850), 02rdyk7 (0.34 #479, 0.26 #1273, 0.22 #2858), 0f_nbyh (0.30 #9, 0.21 #1596, 0.14 #3974), 09sb52 (0.26 #14304, 0.26 #18660, 0.25 #13908), 03hl6lc (0.25 #1361, 0.21 #4929, 0.20 #964), 02qyp19 (0.23 #1191, 0.18 #4759, 0.18 #3569), 0cjyzs (0.22 #2081, 0.17 #10405, 0.15 #12385) >> Best rule #19815 for best value: >> intensional similarity = 3 >> extensional distance = 1147 >> proper extension: 03cvfg; 01g0jn; >> query: (?x2332, ?x7850) <- award_winner(?x7850, ?x2332), profession(?x2332, ?x319), category_of(?x7850, ?x2758) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #458 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 99 *> proper extension: 03flwk; *> query: (?x2332, 019f4v) <- award_winner(?x198, ?x2332), award(?x2332, ?x1313), ?x1313 = 0gs9p *> conf = 0.67 ranks of expected_values: 3 EVAL 04y8r award 019f4v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 113.000 0.777 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1257-015mrk PRED entity: 015mrk PRED relation: artist! PRED expected values: 015_1q => 112 concepts (67 used for prediction) PRED predicted values (max 10 best out of 115): 015_1q (0.23 #873, 0.22 #20, 0.20 #588), 03rhqg (0.20 #300, 0.14 #2434, 0.14 #2007), 01trtc (0.19 #216, 0.18 #785, 0.11 #74), 0g768 (0.19 #180, 0.17 #38, 0.12 #749), 0fb0v (0.17 #7, 0.15 #149, 0.08 #718), 01dtcb (0.15 #190, 0.11 #48, 0.08 #2181), 02p11jq (0.14 #297, 0.11 #581, 0.10 #1008), 073tm9 (0.13 #748, 0.12 #179, 0.07 #321), 03mp8k (0.12 #352, 0.11 #921, 0.10 #779), 043g7l (0.12 #885, 0.11 #316, 0.08 #2307) >> Best rule #873 for best value: >> intensional similarity = 3 >> extensional distance = 134 >> proper extension: 012gq6; >> query: (?x3065, 015_1q) <- award_winner(?x3835, ?x3065), award(?x7908, ?x3835), ?x7908 = 01vs73g >> conf = 0.23 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015mrk artist! 015_1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 67.000 0.228 http://example.org/music/record_label/artist #1256-09v3jyg PRED entity: 09v3jyg PRED relation: production_companies PRED expected values: 04rcl7 => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 67): 01795t (0.54 #522, 0.44 #1673, 0.39 #920), 0kk9v (0.54 #535, 0.23 #2346, 0.19 #3098), 09b3v (0.39 #869, 0.38 #701, 0.38 #785), 086k8 (0.33 #2, 0.25 #85, 0.14 #252), 02hvd (0.33 #39, 0.25 #122, 0.14 #289), 04rcl7 (0.32 #656, 0.23 #2346, 0.21 #824), 05qd_ (0.27 #1599, 0.10 #594, 0.08 #4863), 04mwxk3 (0.25 #162, 0.14 #329, 0.12 #412), 054lpb6 (0.25 #181, 0.08 #3029, 0.08 #1018), 059x3p (0.25 #241) >> Best rule #522 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 02qm_f; >> query: (?x6931, 01795t) <- film(?x3785, ?x6931), executive_produced_by(?x6931, ?x6682), ?x6682 = 04jspq, genre(?x6931, ?x307), country(?x6931, ?x94), film(?x2156, ?x6931) >> conf = 0.54 => this is the best rule for 1 predicted values *> Best rule #656 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 29 *> proper extension: 06zn1c; *> query: (?x6931, 04rcl7) <- film(?x2156, ?x6931), genre(?x6931, ?x2540), genre(?x6931, ?x307), ?x307 = 04t36, genre(?x8794, ?x2540), genre(?x2628, ?x2540), ?x2628 = 06wbm8q, ?x8794 = 02qydsh *> conf = 0.32 ranks of expected_values: 6 EVAL 09v3jyg production_companies 04rcl7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 77.000 0.538 http://example.org/film/film/production_companies #1255-0dqcm PRED entity: 0dqcm PRED relation: languages PRED expected values: 06nm1 => 152 concepts (152 used for prediction) PRED predicted values (max 10 best out of 20): 04306rv (0.10 #106, 0.07 #4308, 0.07 #4380), 03k50 (0.08 #1927, 0.07 #2067, 0.07 #1682), 06b_j (0.07 #4308, 0.07 #4380, 0.07 #4379), 03x42 (0.04 #2557), 07c9s (0.04 #1936, 0.04 #1691, 0.04 #2076), 06nm1 (0.04 #1929, 0.03 #2069, 0.03 #389), 0x82 (0.03 #173, 0.01 #243, 0.01 #278), 0999q (0.02 #2084, 0.02 #1699, 0.02 #1944), 0t_2 (0.02 #812, 0.02 #882, 0.02 #777), 09s02 (0.02 #1712, 0.02 #2097, 0.02 #1957) >> Best rule #106 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 07_m2; >> query: (?x9095, 04306rv) <- people(?x12278, ?x9095), ?x12278 = 05l3g_, profession(?x9095, ?x1032) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #1929 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 594 *> proper extension: 02qjj7; 04xjp; 0kvnn; 0m93; 0448r; 0g7k2g; 040dv; 05nqq3; 0738y5; 023jq1; ... *> query: (?x9095, 06nm1) <- languages(?x9095, ?x90), profession(?x9095, ?x1032), nationality(?x9095, ?x512) *> conf = 0.04 ranks of expected_values: 6 EVAL 0dqcm languages 06nm1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 152.000 152.000 0.100 http://example.org/people/person/languages #1254-03hrz PRED entity: 03hrz PRED relation: month PRED expected values: 04w_7 040fv => 301 concepts (301 used for prediction) PRED predicted values (max 10 best out of 2): 04w_7 (0.95 #109, 0.94 #95, 0.94 #93), 040fv (0.83 #96, 0.83 #116, 0.83 #66) >> Best rule #109 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 02cl1; 080h2; 049d1; 06wjf; 0177z; 0chgzm; >> query: (?x2985, 04w_7) <- place_of_birth(?x1211, ?x2985), month(?x2985, ?x2140), contains(?x1264, ?x2985), ?x2140 = 040fb >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 03hrz month 040fv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 301.000 301.000 0.950 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 03hrz month 04w_7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 301.000 301.000 0.950 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #1253-030znt PRED entity: 030znt PRED relation: profession PRED expected values: 02hrh1q => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 53): 02hrh1q (0.92 #915, 0.88 #3015, 0.87 #7967), 01d_h8 (0.32 #1956, 0.32 #1356, 0.30 #3906), 09jwl (0.31 #1070, 0.18 #2870, 0.18 #5271), 0dxtg (0.27 #3914, 0.27 #4514, 0.27 #6315), 03gjzk (0.25 #4651, 0.24 #2716, 0.24 #4516), 02krf9 (0.25 #4651, 0.24 #7802, 0.22 #478), 02jknp (0.25 #4651, 0.24 #7802, 0.21 #6309), 0cbd2 (0.25 #4651, 0.24 #7802, 0.12 #9309), 0d1pc (0.25 #4651, 0.24 #7802, 0.12 #2152), 0kyk (0.25 #4651, 0.24 #7802, 0.11 #481) >> Best rule #915 for best value: >> intensional similarity = 2 >> extensional distance = 115 >> proper extension: 02rmxx; 01kgg9; >> query: (?x1343, 02hrh1q) <- award(?x1343, ?x1972), ?x1972 = 0gqyl >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030znt profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.923 http://example.org/people/person/profession #1252-09rvcvl PRED entity: 09rvcvl PRED relation: genre PRED expected values: 05p553 => 97 concepts (96 used for prediction) PRED predicted values (max 10 best out of 124): 03mqtr (0.70 #4819, 0.56 #3009, 0.54 #3370), 02l7c8 (0.39 #858, 0.38 #376, 0.37 #8212), 05p553 (0.37 #4461, 0.37 #3374, 0.36 #4943), 03k9fj (0.37 #1575, 0.25 #1935, 0.25 #4347), 01jfsb (0.37 #2539, 0.36 #1696, 0.33 #3744), 0hcr (0.36 #1587, 0.09 #1947, 0.07 #4359), 04xvlr (0.33 #241, 0.29 #361, 0.29 #1083), 02kdv5l (0.32 #2528, 0.30 #1685, 0.29 #5181), 03bxz7 (0.30 #295, 0.17 #2099, 0.17 #1137), 02n4kr (0.27 #8, 0.14 #488, 0.13 #4705) >> Best rule #4819 for best value: >> intensional similarity = 4 >> extensional distance = 692 >> proper extension: 09v42sf; >> query: (?x8723, ?x3506) <- film_crew_role(?x8723, ?x137), titles(?x3506, ?x8723), country(?x8723, ?x512), genre(?x240, ?x3506) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4461 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 687 *> proper extension: 09xbpt; 047gn4y; 0dnvn3; 03h_yy; 08hmch; 03s5lz; 0bh8yn3; 0c00zd0; 05cj_j; 0m491; ... *> query: (?x8723, 05p553) <- film_release_distribution_medium(?x8723, ?x81), production_companies(?x8723, ?x9518), nominated_for(?x4128, ?x8723), award_nominee(?x4128, ?x450) *> conf = 0.37 ranks of expected_values: 3 EVAL 09rvcvl genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 97.000 96.000 0.704 http://example.org/film/film/genre #1251-015pnb PRED entity: 015pnb PRED relation: languages PRED expected values: 02h40lc => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.90 #244, 0.89 #211, 0.89 #222), 06nm1 (0.24 #342, 0.05 #71, 0.04 #60), 0t_2 (0.24 #342, 0.03 #314, 0.03 #127), 064_8sq (0.24 #342, 0.02 #62, 0.02 #393), 02bv9 (0.24 #342, 0.02 #64, 0.02 #75), 04306rv (0.24 #342, 0.02 #58, 0.02 #69), 02bjrlw (0.24 #342, 0.02 #56, 0.02 #67), 03_9r (0.05 #490, 0.04 #545, 0.04 #556), 07qv_ (0.01 #98, 0.01 #120, 0.01 #175), 05zjd (0.01 #96) >> Best rule #244 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 01b7h8; 03cf9ly; >> query: (?x12533, 02h40lc) <- genre(?x12533, ?x258), nominated_for(?x10506, ?x12533), program(?x2062, ?x12533), state_province_region(?x2062, ?x335) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015pnb languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.899 http://example.org/tv/tv_program/languages #1250-010cw1 PRED entity: 010cw1 PRED relation: place! PRED expected values: 010cw1 => 95 concepts (52 used for prediction) PRED predicted values (max 10 best out of 95): 0xkyn (0.18 #16008, 0.09 #7743), 010cw1 (0.18 #16008, 0.09 #7743), 0xkq4 (0.18 #16008, 0.09 #7743), 0xl08 (0.09 #7743), 0pzmf (0.05 #164, 0.03 #679, 0.01 #1194), 0hptm (0.05 #157, 0.03 #672, 0.01 #1187), 0fvxz (0.05 #22, 0.03 #537, 0.01 #1052), 0h6l4 (0.05 #376, 0.03 #891), 0xn7b (0.05 #372, 0.03 #887), 0xn7q (0.05 #346, 0.03 #861) >> Best rule #16008 for best value: >> intensional similarity = 2 >> extensional distance = 288 >> proper extension: 03qzj4; >> query: (?x11407, ?x1189) <- contains(?x321, ?x11407), county(?x1189, ?x321) >> conf = 0.18 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 010cw1 place! 010cw1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 95.000 52.000 0.183 http://example.org/location/hud_county_place/place #1249-0cbd2 PRED entity: 0cbd2 PRED relation: specialization_of! PRED expected values: 025352 => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 166): 01xr66 (0.33 #36, 0.17 #765, 0.17 #661), 021wpb (0.33 #27, 0.17 #756, 0.17 #652), 0np9r (0.33 #9, 0.17 #738, 0.17 #634), 0mbx4 (0.33 #98, 0.17 #827, 0.17 #723), 0g7nc (0.33 #89, 0.17 #818, 0.17 #714), 0w7c (0.33 #34, 0.17 #763, 0.17 #659), 04cvn_ (0.33 #299, 0.12 #1254, 0.04 #1238), 064xm0 (0.20 #554, 0.12 #970, 0.12 #1254), 01kyvx (0.17 #628, 0.05 #1045, 0.04 #1151), 07s467s (0.12 #1254, 0.10 #1048, 0.08 #1154) >> Best rule #36 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 02hrh1q; >> query: (?x353, 01xr66) <- profession(?x9204, ?x353), profession(?x9030, ?x353), profession(?x7044, ?x353), profession(?x3867, ?x353), ?x7044 = 0crqcc, ?x9030 = 02rk45, ?x9204 = 06rq2l, role(?x3867, ?x227) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1254 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 23 *> proper extension: 0db79; *> query: (?x353, ?x524) <- specialization_of(?x987, ?x353), profession(?x9030, ?x987), profession(?x9030, ?x524) *> conf = 0.12 ranks of expected_values: 54 EVAL 0cbd2 specialization_of! 025352 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 31.000 31.000 0.333 http://example.org/people/profession/specialization_of #1248-0d608 PRED entity: 0d608 PRED relation: type_of_union PRED expected values: 04ztj => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.83 #17, 0.81 #97, 0.81 #61), 01g63y (0.21 #222, 0.21 #254, 0.21 #226), 01bl8s (0.02 #51, 0.02 #55, 0.02 #107) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0c_md_; 06c0j; >> query: (?x7522, 04ztj) <- student(?x1695, ?x7522), award_winner(?x102, ?x7522), person(?x9646, ?x7522) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d608 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.833 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1247-0j6cj PRED entity: 0j6cj PRED relation: role PRED expected values: 0jtg0 => 158 concepts (108 used for prediction) PRED predicted values (max 10 best out of 117): 018vs (0.39 #3952, 0.38 #3371, 0.33 #7438), 03qjg (0.39 #3952, 0.38 #3371, 0.33 #7438), 03m5k (0.39 #3952, 0.38 #3371, 0.33 #7438), 026t6 (0.34 #771, 0.30 #1445, 0.25 #387), 01vj9c (0.31 #781, 0.23 #2901, 0.21 #3675), 05842k (0.27 #937, 0.26 #2959, 0.26 #3152), 0l14qv (0.26 #2893, 0.23 #3859, 0.22 #4149), 028tv0 (0.26 #4438, 0.25 #5312, 0.25 #4826), 0214km (0.25 #92, 0.17 #668, 0.10 #1921), 04rzd (0.25 #38, 0.11 #326, 0.10 #518) >> Best rule #3952 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 028qdb; 03f4xvm; 0127gn; 03h_yfh; 01nhkxp; >> query: (?x7987, ?x716) <- artists(?x1000, ?x7987), role(?x7987, ?x316), instrumentalists(?x716, ?x7987), ?x316 = 05r5c >> conf = 0.39 => this is the best rule for 3 predicted values *> Best rule #727 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 29 *> proper extension: 01wz_ml; 0ddkf; 0f6lx; *> query: (?x7987, 0jtg0) <- artists(?x1000, ?x7987), role(?x7987, ?x227), category(?x7987, ?x134), influenced_by(?x5208, ?x7987) *> conf = 0.06 ranks of expected_values: 30 EVAL 0j6cj role 0jtg0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 158.000 108.000 0.386 http://example.org/music/artist/track_contributions./music/track_contribution/role #1246-02r6gw6 PRED entity: 02r6gw6 PRED relation: draft! PRED expected values: 03lpp_ 07l4z => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 86): 0jmhr (0.85 #1063, 0.53 #605, 0.50 #1218), 05g76 (0.82 #377, 0.70 #831, 0.61 #74), 051wf (0.82 #377, 0.70 #831, 0.61 #74), 03m1n (0.82 #377, 0.70 #831, 0.61 #74), 03lpp_ (0.82 #377, 0.70 #831, 0.61 #74), 01d5z (0.82 #377, 0.70 #831, 0.61 #1227), 07l4z (0.82 #377, 0.70 #831, 0.61 #1227), 04wmvz (0.82 #377, 0.70 #831, 0.61 #1227), 021f30 (0.61 #1227, 0.57 #227, 0.49 #1305), 02896 (0.61 #1227, 0.56 #1456, 0.50 #1218) >> Best rule #1063 for best value: >> intensional similarity = 55 >> extensional distance = 2 >> proper extension: 06439y; >> query: (?x8499, ?x11420) <- school(?x8499, ?x8120), school(?x8499, ?x7338), school(?x8499, ?x3777), school(?x8499, ?x2948), draft(?x1823, ?x8499), draft(?x1160, ?x8499), currency(?x8120, ?x170), category(?x8120, ?x134), school(?x11420, ?x8120), school(?x4469, ?x8120), school(?x4779, ?x3777), student(?x7338, ?x6562), school_type(?x3777, ?x3092), school(?x1639, ?x3777), school(?x5229, ?x7338), school(?x729, ?x7338), ?x3092 = 05jxkf, team(?x2010, ?x1160), school(?x1823, ?x10572), colors(?x3777, ?x3315), institution(?x865, ?x3777), major_field_of_study(?x7338, ?x6859), company(?x346, ?x2948), ?x4469 = 043vc, sport(?x1160, ?x5063), school(?x4779, ?x6814), school(?x4779, ?x388), ?x10572 = 0160nk, ?x388 = 05krk, school(?x1160, ?x735), student(?x2948, ?x129), award_winner(?x1480, ?x6562), ?x1639 = 07l24, institution(?x620, ?x7338), nationality(?x6562, ?x94), company(?x5510, ?x8120), award_nominee(?x6562, ?x133), team(?x6848, ?x11420), institution(?x734, ?x2948), contains(?x4758, ?x8120), school_type(?x8120, ?x4994), district_represented(?x176, ?x4758), capital(?x4758, ?x10534), ?x6859 = 01tbp, position(?x5229, ?x2573), ?x6848 = 02_ssl, adjoins(?x1025, ?x4758), fraternities_and_sororities(?x2948, ?x3697), ?x865 = 02h4rq6, fraternities_and_sororities(?x3777, ?x4348), colors(?x1823, ?x663), team(?x1114, ?x729), ?x1114 = 047g8h, ?x6814 = 03tw2s, ?x2573 = 05b3ts >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #377 for first EXPECTED value: *> intensional similarity = 69 *> extensional distance = 1 *> proper extension: 09l0x9; *> query: (?x8499, ?x1010) <- school(?x8499, ?x8120), school(?x8499, ?x3777), school(?x8499, ?x2948), draft(?x8894, ?x8499), draft(?x7060, ?x8499), draft(?x1160, ?x8499), draft(?x260, ?x8499), currency(?x8120, ?x170), category(?x8120, ?x134), school(?x11420, ?x8120), school(?x3658, ?x8120), school(?x387, ?x8120), ?x3777 = 012vwb, organization(?x5510, ?x8120), sport(?x1160, ?x5063), school(?x1883, ?x8120), school(?x465, ?x8120), team(?x2010, ?x1160), ?x1883 = 02qw1zx, ?x170 = 09nqf, school(?x1160, ?x4363), ?x465 = 05vsb7, draft(?x1160, ?x1161), major_field_of_study(?x2948, ?x10046), major_field_of_study(?x2948, ?x2601), major_field_of_study(?x2948, ?x2014), major_field_of_study(?x2948, ?x1154), ?x134 = 08mbj5d, student(?x2948, ?x10738), student(?x2948, ?x9313), colors(?x260, ?x663), institution(?x3437, ?x2948), institution(?x1200, ?x2948), position(?x387, ?x2247), ?x10046 = 041y2, school(?x387, ?x388), team(?x706, ?x3658), school(?x8894, ?x1681), team(?x5412, ?x260), ?x1154 = 02lp1, ?x3437 = 02_xgp2, draft(?x1010, ?x1161), school_type(?x8120, ?x1507), school(?x12124, ?x2948), ?x12124 = 0jmgb, major_field_of_study(?x9200, ?x2601), major_field_of_study(?x6271, ?x2601), major_field_of_study(?x331, ?x2601), ?x6271 = 015q1n, ?x1681 = 07szy, contains(?x94, ?x2948), ?x2247 = 01_9c1, ?x388 = 05krk, gender(?x9313, ?x231), school(?x7060, ?x7596), award_winner(?x3471, ?x9313), school(?x260, ?x4209), ?x331 = 01jssp, ?x9200 = 0dzst, draft(?x11420, ?x2569), teams(?x2017, ?x1160), role(?x10738, ?x227), artists(?x1572, ?x10738), ?x2014 = 04rjg, profession(?x9313, ?x319), state_province_region(?x4363, ?x1227), ?x1200 = 016t_3, type_of_union(?x9313, ?x566), major_field_of_study(?x7596, ?x742) *> conf = 0.82 ranks of expected_values: 5, 7 EVAL 02r6gw6 draft! 07l4z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 20.000 20.000 0.846 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02r6gw6 draft! 03lpp_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 20.000 20.000 0.846 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #1245-05p09dd PRED entity: 05p09dd PRED relation: film! PRED expected values: 0170qf => 78 concepts (32 used for prediction) PRED predicted values (max 10 best out of 683): 09thp87 (0.46 #64542, 0.45 #20814, 0.44 #60374), 01x6v6 (0.46 #64542, 0.44 #60374, 0.42 #39556), 0f0kz (0.07 #515, 0.06 #2596, 0.05 #60375), 05bm4sm (0.06 #24978, 0.06 #29142, 0.06 #35389), 094wz7q (0.06 #24978, 0.06 #29142, 0.06 #35389), 04ktcgn (0.06 #24978, 0.06 #29142, 0.06 #35389), 0p8r1 (0.05 #584, 0.05 #2665, 0.04 #6827), 02gvwz (0.05 #60375, 0.04 #188, 0.04 #39557), 03ym1 (0.05 #60375, 0.04 #1012, 0.04 #3093), 01wy5m (0.05 #60375, 0.04 #39557, 0.04 #62459) >> Best rule #64542 for best value: >> intensional similarity = 4 >> extensional distance = 994 >> proper extension: 0c00zd0; 047rkcm; 03p2xc; >> query: (?x4545, ?x8415) <- country(?x4545, ?x94), nominated_for(?x8415, ?x4545), titles(?x162, ?x4545), nationality(?x8415, ?x1023) >> conf = 0.46 => this is the best rule for 2 predicted values *> Best rule #10772 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 212 *> proper extension: 0d_2fb; 085bd1; 02z2mr7; 04pmnt; 02rtqvb; *> query: (?x4545, 0170qf) <- country(?x4545, ?x512), country(?x4545, ?x94), ?x94 = 09c7w0, genre(?x4545, ?x53), ?x512 = 07ssc *> conf = 0.02 ranks of expected_values: 113 EVAL 05p09dd film! 0170qf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 78.000 32.000 0.455 http://example.org/film/actor/film./film/performance/film #1244-0yyg4 PRED entity: 0yyg4 PRED relation: honored_for! PRED expected values: 073hkh => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 83): 0bvhz9 (0.12 #114, 0.02 #968, 0.02 #2310), 09p30_ (0.09 #2807, 0.09 #3541, 0.08 #4274), 02yxh9 (0.09 #2807, 0.09 #3541, 0.08 #4274), 09p3h7 (0.09 #2807, 0.09 #3541, 0.08 #4274), 073h5b (0.09 #2807, 0.09 #3541, 0.08 #4274), 0ds460j (0.09 #2807, 0.09 #3541, 0.08 #4274), 0bzknt (0.09 #2807, 0.09 #3541, 0.08 #4274), 0h_9252 (0.09 #2807, 0.09 #3541, 0.08 #4274), 0275n3y (0.09 #64, 0.02 #918, 0.02 #1162), 09gkdln (0.09 #3541, 0.08 #4274, 0.05 #350) >> Best rule #114 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 0c0yh4; 0n0bp; 09p0ct; 026gyn_; 011yth; 016z7s; 07yk1xz; 0j_t1; 019vhk; 0c9k8; ... >> query: (?x288, 0bvhz9) <- nominated_for(?x396, ?x288), genre(?x288, ?x3506), award(?x288, ?x289), ?x3506 = 03mqtr >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0yyg4 honored_for! 073hkh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 69.000 0.118 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #1243-09jw2 PRED entity: 09jw2 PRED relation: parent_genre! PRED expected values: 05r6t => 64 concepts (32 used for prediction) PRED predicted values (max 10 best out of 255): 03xnwz (0.50 #1574, 0.14 #1058, 0.14 #799), 0g_bh (0.43 #1136, 0.40 #362, 0.38 #1652), 0xv2x (0.43 #1155, 0.38 #1671, 0.25 #1413), 02t8gf (0.40 #2176, 0.33 #2434, 0.33 #629), 06cp5 (0.38 #1362, 0.33 #1877, 0.29 #1104), 0dls3 (0.38 #1589, 0.29 #1073, 0.25 #1331), 01gbcf (0.38 #1551, 0.29 #1035, 0.25 #4), 01_bkd (0.38 #1591, 0.25 #1333, 0.22 #1848), 0pm85 (0.38 #1676, 0.25 #129, 0.20 #386), 0621cs (0.38 #1683, 0.25 #136, 0.20 #393) >> Best rule #1574 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 01h0kx; >> query: (?x10306, 03xnwz) <- parent_genre(?x14354, ?x10306), parent_genre(?x7577, ?x10306), artists(?x14354, ?x8012), ?x7577 = 0bt7w, artist(?x441, ?x8012), place_of_birth(?x8012, ?x4362) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1097 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 5 *> proper extension: 016clz; *> query: (?x10306, 05r6t) <- artists(?x10306, ?x8012), artists(?x10306, ?x5126), artists(?x10306, ?x475), artists(?x14090, ?x5126), award(?x5126, ?x9462), ?x14090 = 02lw8j, parent_genre(?x2491, ?x10306), ?x475 = 01pfr3, profession(?x8012, ?x131) *> conf = 0.29 ranks of expected_values: 17 EVAL 09jw2 parent_genre! 05r6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 64.000 32.000 0.500 http://example.org/music/genre/parent_genre #1242-02qgyv PRED entity: 02qgyv PRED relation: film PRED expected values: 0cc97st => 68 concepts (50 used for prediction) PRED predicted values (max 10 best out of 498): 01hqhm (0.58 #37281, 0.41 #63919, 0.38 #28404), 025ts_z (0.23 #1482, 0.03 #55039, 0.03 #56815), 0fzm0g (0.15 #1767, 0.03 #55039, 0.03 #56815), 01jrbv (0.15 #550, 0.01 #14750, 0.01 #16525), 03kxj2 (0.15 #358), 01y9jr (0.08 #1153, 0.07 #19526, 0.05 #21302), 0gmblvq (0.08 #671, 0.07 #19526, 0.05 #21302), 0djlxb (0.08 #533, 0.07 #19526, 0.05 #21302), 06_wqk4 (0.08 #126, 0.05 #21302, 0.04 #78125), 01l_pn (0.08 #962, 0.05 #21302, 0.04 #78125) >> Best rule #37281 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 049tjg; 02wrhj; 05wjnt; 05hdf; 01pnn3; 01nrq5; 039crh; 02zrv7; 04mlh8; 01lqnff; ... >> query: (?x2353, ?x414) <- nominated_for(?x2353, ?x414), film(?x2353, ?x2362) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02qgyv film 0cc97st CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 68.000 50.000 0.584 http://example.org/film/actor/film./film/performance/film #1241-03n93 PRED entity: 03n93 PRED relation: profession PRED expected values: 02jknp => 112 concepts (53 used for prediction) PRED predicted values (max 10 best out of 102): 02hrh1q (0.88 #5859, 0.88 #7466, 0.88 #3811), 0fj9f (0.50 #492, 0.44 #1953, 0.40 #2099), 09jwl (0.41 #6448, 0.32 #5572, 0.29 #2647), 04gc2 (0.38 #1940, 0.30 #2086, 0.07 #6033), 0dxtg (0.35 #4836, 0.34 #5128, 0.34 #5274), 03gjzk (0.33 #160, 0.26 #3374, 0.26 #3082), 0cbd2 (0.33 #152, 0.25 #2051, 0.25 #1905), 01bs9f (0.33 #1550, 0.05 #3945), 0nbcg (0.32 #2660, 0.27 #6461, 0.22 #1492), 02jknp (0.28 #4831, 0.26 #5123, 0.26 #5269) >> Best rule #5859 for best value: >> intensional similarity = 3 >> extensional distance = 189 >> proper extension: 027dtv3; 06pk8; 04nw9; 016pns; 05bxwh; 0gbwp; 07swvb; 01vvyvk; 018n6m; 03y82t6; ... >> query: (?x4058, 02hrh1q) <- profession(?x4058, ?x319), award_nominee(?x4058, ?x1850), participant(?x2416, ?x4058) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #4831 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 140 *> proper extension: 0338lq; 024rdh; *> query: (?x4058, 02jknp) <- award_nominee(?x1850, ?x4058), film(?x1850, ?x327), award(?x1850, ?x1105) *> conf = 0.28 ranks of expected_values: 10 EVAL 03n93 profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 112.000 53.000 0.885 http://example.org/people/person/profession #1240-05nyqk PRED entity: 05nyqk PRED relation: language PRED expected values: 02h40lc => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 29): 02h40lc (0.90 #1125, 0.90 #1660, 0.89 #2430), 064_8sq (0.25 #22, 0.22 #199, 0.22 #140), 04306rv (0.12 #5, 0.12 #241, 0.11 #182), 03_9r (0.12 #10, 0.11 #187, 0.11 #128), 01wgr (0.12 #40, 0.11 #217, 0.11 #158), 06nm1 (0.12 #247, 0.11 #129, 0.11 #424), 02bjrlw (0.11 #60, 0.06 #296, 0.06 #1895), 06b_j (0.09 #318, 0.06 #1502, 0.06 #377), 0jzc (0.05 #315, 0.04 #433, 0.03 #1440), 012w70 (0.04 #308, 0.03 #1255, 0.03 #1492) >> Best rule #1125 for best value: >> intensional similarity = 4 >> extensional distance = 464 >> proper extension: 0ddfwj1; 0hmr4; 0b73_1d; 048scx; 092vkg; 0jjy0; 0jyx6; 0pv3x; 02prw4h; 0416y94; ... >> query: (?x9199, 02h40lc) <- film(?x6068, ?x9199), award(?x6068, ?x704), award_winner(?x496, ?x6068), executive_produced_by(?x9199, ?x1714) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05nyqk language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.903 http://example.org/film/film/language #1239-011ypx PRED entity: 011ypx PRED relation: films! PRED expected values: 04rjg => 93 concepts (40 used for prediction) PRED predicted values (max 10 best out of 56): 04gb7 (0.13 #45, 0.09 #355, 0.05 #666), 081pw (0.08 #2342, 0.07 #3751, 0.07 #4852), 06d4h (0.07 #353, 0.07 #43, 0.06 #3165), 0fx2s (0.07 #73, 0.06 #2412, 0.05 #3821), 03r8gp (0.07 #90, 0.05 #555, 0.04 #711), 05489 (0.07 #52, 0.05 #2391, 0.05 #828), 0fzyg (0.07 #54, 0.05 #3802, 0.05 #3176), 02_h0 (0.07 #100, 0.05 #410, 0.04 #721), 0g1x2_ (0.07 #27, 0.05 #337, 0.04 #648), 0nk95 (0.07 #150, 0.02 #460, 0.01 #615) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0fy34l; 078sj4; >> query: (?x5927, 04gb7) <- nominated_for(?x4091, ?x5927), ?x4091 = 09sdmz, nominated_for(?x5927, ?x2107) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #486 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 72 *> proper extension: 027qgy; 08r4x3; 0h3xztt; 07w8fz; 0btbyn; 06t6dz; 046488; 0277j40; *> query: (?x5927, 04rjg) <- nominated_for(?x4091, ?x5927), nominated_for(?x704, ?x5927), award(?x167, ?x4091), ?x704 = 09sb52 *> conf = 0.01 ranks of expected_values: 47 EVAL 011ypx films! 04rjg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 93.000 40.000 0.133 http://example.org/film/film_subject/films #1238-02vzc PRED entity: 02vzc PRED relation: member_states! PRED expected values: 02jxk => 199 concepts (199 used for prediction) PRED predicted values (max 10 best out of 9): 018cqq (0.67 #20, 0.60 #14, 0.56 #8), 02jxk (0.56 #7, 0.40 #25, 0.39 #22), 059dn (0.52 #24, 0.50 #15, 0.44 #9), 01rz1 (0.14 #130, 0.06 #301, 0.06 #300), 07t65 (0.14 #130, 0.06 #301, 0.06 #300), 02vk52z (0.14 #130, 0.06 #301, 0.06 #300), 0b6css (0.14 #130, 0.06 #301, 0.06 #300), 04k4l (0.14 #130, 0.06 #301, 0.06 #300), 0_2v (0.14 #130, 0.06 #301, 0.06 #300) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 05qtj; >> query: (?x1892, 018cqq) <- film_release_region(?x1744, ?x1892), film_release_region(?x1625, ?x1892), country(?x1744, ?x94), ?x1625 = 01f8gz >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0d0vqn; 07ssc; 0f8l9c; 0k6nt; 059j2; 06mkj; 082fr; *> query: (?x1892, 02jxk) <- film_release_region(?x6218, ?x1892), participating_countries(?x784, ?x1892), ?x6218 = 03rg2b *> conf = 0.56 ranks of expected_values: 2 EVAL 02vzc member_states! 02jxk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 199.000 199.000 0.667 http://example.org/user/ktrueman/default_domain/international_organization/member_states #1237-0h7x PRED entity: 0h7x PRED relation: countries_spoken_in! PRED expected values: 0k0sv => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 50): 02h40lc (0.44 #990, 0.40 #886, 0.37 #4736), 06nm1 (0.22 #2503, 0.20 #163, 0.19 #2451), 012v8 (0.21 #143, 0.13 #247, 0.12 #299), 064_8sq (0.20 #3710, 0.19 #4230, 0.19 #4751), 0jzc (0.20 #67, 0.17 #1315, 0.16 #2667), 02bjrlw (0.17 #1, 0.15 #521, 0.14 #729), 0349s (0.14 #141, 0.13 #245, 0.12 #297), 02hxc3j (0.13 #213, 0.13 #161, 0.12 #265), 05qqm (0.13 #244, 0.13 #192, 0.12 #296), 02bv9 (0.13 #179, 0.11 #439, 0.09 #543) >> Best rule #990 for best value: >> intensional similarity = 2 >> extensional distance = 43 >> proper extension: 0h44w; >> query: (?x1355, 02h40lc) <- location(?x10895, ?x1355), countries_spoken_in(?x732, ?x1355) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 12 *> proper extension: 07l75; 0cgs4; *> query: (?x1355, 0k0sv) <- locations(?x9939, ?x1355), ?x9939 = 03jqfx *> conf = 0.07 ranks of expected_values: 31 EVAL 0h7x countries_spoken_in! 0k0sv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 150.000 150.000 0.444 http://example.org/language/human_language/countries_spoken_in #1236-01g4bk PRED entity: 01g4bk PRED relation: nationality PRED expected values: 03_3d => 85 concepts (60 used for prediction) PRED predicted values (max 10 best out of 50): 09c7w0 (0.79 #2612, 0.77 #2211, 0.72 #2009), 03_3d (0.71 #6, 0.36 #1102, 0.29 #3823), 0193qj (0.36 #1102), 02jx1 (0.36 #834, 0.25 #1034, 0.15 #233), 07ssc (0.23 #816, 0.20 #1016, 0.15 #215), 0f8l9c (0.11 #6044, 0.10 #122, 0.08 #623), 0345h (0.11 #6044, 0.09 #331, 0.05 #532), 0d060g (0.11 #6044, 0.07 #2518, 0.06 #908), 06mkj (0.11 #6044, 0.03 #6045, 0.03 #347), 0h7x (0.11 #6044, 0.03 #6045, 0.02 #1640) >> Best rule #2612 for best value: >> intensional similarity = 4 >> extensional distance = 1067 >> proper extension: 01ry0f; >> query: (?x9747, 09c7w0) <- location(?x9747, ?x9559), type_of_union(?x9747, ?x566), contains(?x9559, ?x8951), jurisdiction_of_office(?x900, ?x9559) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 06k02; 0534v; 02bxjp; 08x5c_; 01qvtwm; *> query: (?x9747, 03_3d) <- location(?x9747, ?x9559), profession(?x9747, ?x319), gender(?x9747, ?x231), ?x231 = 05zppz, ?x9559 = 07dfk *> conf = 0.71 ranks of expected_values: 2 EVAL 01g4bk nationality 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 85.000 60.000 0.786 http://example.org/people/person/nationality #1235-01mc11 PRED entity: 01mc11 PRED relation: source PRED expected values: 0jbk9 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.93 #35, 0.92 #46, 0.91 #56) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 215 >> proper extension: 06_kh; 0s3y5; 02cl1; 0fvxz; 013jz2; 0s69k; 0ftxw; 0f04c; 0tz1x; 01m1_t; ... >> query: (?x1096, 0jbk9) <- county(?x1096, ?x6136), contains(?x335, ?x1096), contains(?x335, ?x11711), ?x11711 = 04_j5s >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01mc11 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.926 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #1234-02c638 PRED entity: 02c638 PRED relation: film! PRED expected values: 020_95 => 99 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1091): 0bwh6 (0.53 #31197, 0.52 #47842, 0.49 #47841), 0dh73w (0.53 #31197, 0.52 #47842, 0.49 #47841), 0237jb (0.53 #31197, 0.52 #47842, 0.49 #47841), 05fyss (0.53 #31197, 0.52 #47842, 0.49 #47841), 04ktcgn (0.42 #24958, 0.41 #95695, 0.41 #64491), 0b6mgp_ (0.42 #24958, 0.41 #95695, 0.41 #64491), 02qgqt (0.21 #4177, 0.04 #6256, 0.04 #29134), 0170pk (0.17 #2361, 0.08 #281, 0.07 #4440), 018db8 (0.17 #2198, 0.07 #4277, 0.04 #6356), 02ck7w (0.17 #941, 0.05 #9258, 0.03 #27978) >> Best rule #31197 for best value: >> intensional similarity = 4 >> extensional distance = 129 >> proper extension: 09xbpt; 064q5v; 05n6sq; 0322yj; >> query: (?x2116, ?x6071) <- award_winner(?x2116, ?x6071), category(?x2116, ?x134), film_crew_role(?x2116, ?x137), award_winner(?x6071, ?x6072) >> conf = 0.53 => this is the best rule for 4 predicted values *> Best rule #58246 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 216 *> proper extension: 0cwrr; 01h72l; 02rrfzf; 07ghq; 07s8z_l; 06mmr; *> query: (?x2116, ?x6072) <- award_winner(?x2116, ?x6071), category(?x2116, ?x134), award_nominee(?x6071, ?x6072), profession(?x6071, ?x353) *> conf = 0.06 ranks of expected_values: 214 EVAL 02c638 film! 020_95 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 99.000 47.000 0.530 http://example.org/film/actor/film./film/performance/film #1233-0jhd PRED entity: 0jhd PRED relation: contains! PRED expected values: 0j0k => 124 concepts (96 used for prediction) PRED predicted values (max 10 best out of 130): 0j0k (0.63 #74329, 0.60 #77915, 0.38 #3061), 09b69 (0.63 #74329, 0.60 #77915, 0.14 #2454), 04wsz (0.63 #74329, 0.12 #7660, 0.12 #10349), 02j71 (0.60 #42084, 0.60 #21491, 0.49 #84189), 04_1l0v (0.54 #16567, 0.46 #19253, 0.45 #23732), 09c7w0 (0.52 #16121, 0.44 #18807, 0.43 #23286), 0dg3n1 (0.28 #49400, 0.28 #52982, 0.28 #33282), 07ssc (0.26 #10778, 0.20 #32263, 0.07 #77946), 07c5l (0.21 #46955, 0.20 #45165, 0.20 #12931), 02jx1 (0.18 #10833, 0.12 #32318, 0.05 #41274) >> Best rule #74329 for best value: >> intensional similarity = 3 >> extensional distance = 256 >> proper extension: 0xn7q; 05bkf; 0jpkg; 0h5qxv; >> query: (?x8588, ?x6304) <- adjoins(?x8588, ?x3855), jurisdiction_of_office(?x182, ?x8588), contains(?x6304, ?x3855) >> conf = 0.63 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 0jhd contains! 0j0k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 96.000 0.634 http://example.org/location/location/contains #1232-0f4k49 PRED entity: 0f4k49 PRED relation: film_release_region PRED expected values: 03rj0 => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 125): 0f8l9c (0.90 #1899, 0.88 #2924, 0.88 #2581), 03_3d (0.83 #1878, 0.78 #2560, 0.78 #2219), 059j2 (0.82 #3277, 0.82 #2936, 0.81 #4300), 05r4w (0.82 #3239, 0.81 #2898, 0.79 #1873), 0k6nt (0.81 #1903, 0.77 #2244, 0.76 #4292), 0chghy (0.81 #2396, 0.80 #1884, 0.80 #3250), 03h64 (0.79 #3316, 0.77 #2975, 0.77 #2462), 07ssc (0.78 #1891, 0.78 #3257, 0.78 #2403), 035qy (0.76 #3280, 0.76 #2426, 0.75 #2939), 0154j (0.75 #2388, 0.74 #3242, 0.74 #2901) >> Best rule #1899 for best value: >> intensional similarity = 5 >> extensional distance = 155 >> proper extension: 0d6b7; 0gj9qxr; 040rmy; 0crh5_f; 0bmc4cm; 043sct5; 0bhwhj; 07l50vn; 05zvzf3; 08j7lh; >> query: (?x4811, 0f8l9c) <- film_release_region(?x4811, ?x1892), film_release_region(?x4811, ?x205), ?x205 = 03rjj, titles(?x53, ?x4811), ?x1892 = 02vzc >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #3308 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 233 *> proper extension: 0407yj_; 0j43swk; 0gmgwnv; 0gwjw0c; *> query: (?x4811, 03rj0) <- film_release_region(?x4811, ?x1264), film_release_region(?x4811, ?x205), ?x205 = 03rjj, ?x1264 = 0345h, film(?x968, ?x4811) *> conf = 0.57 ranks of expected_values: 21 EVAL 0f4k49 film_release_region 03rj0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 82.000 82.000 0.904 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1231-021sv1 PRED entity: 021sv1 PRED relation: award_winner! PRED expected values: 068gn => 136 concepts (131 used for prediction) PRED predicted values (max 10 best out of 272): 068gn (0.40 #1698, 0.25 #3848, 0.15 #5568), 01by1l (0.33 #543, 0.25 #973, 0.11 #4413), 02sp_v (0.33 #591, 0.25 #1021, 0.11 #4461), 02581q (0.25 #867, 0.11 #4307, 0.11 #3877), 05qck (0.20 #1913, 0.17 #3203, 0.12 #3633), 079sf (0.20 #1704, 0.12 #3854, 0.08 #5574), 04qy5 (0.20 #1592, 0.12 #3742, 0.08 #5462), 01c99j (0.17 #4954, 0.05 #19574, 0.03 #24304), 0gqyl (0.17 #4836, 0.02 #32788, 0.02 #33648), 02grdc (0.15 #5192, 0.12 #6482, 0.11 #7342) >> Best rule #1698 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0bymv; 0d3qd0; 024_vw; >> query: (?x652, 068gn) <- people(?x12136, ?x652), legislative_sessions(?x652, ?x5339), legislative_sessions(?x652, ?x4821), ?x4821 = 02bqm0, ?x5339 = 02glc4 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 021sv1 award_winner! 068gn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 131.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #1230-025v3k PRED entity: 025v3k PRED relation: list PRED expected values: 09g7thr => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 5): 09g7thr (0.52 #15, 0.46 #64, 0.44 #57), 01ptsx (0.44 #83, 0.42 #90, 0.37 #69), 04k4rt (0.36 #89, 0.35 #82, 0.34 #75), 01pd60 (0.31 #91, 0.28 #84, 0.25 #77), 026cl_m (0.03 #59, 0.02 #123) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 07vk2; 07t90; 05zl0; 012mzw; 015g1w; 02x9cv; 0373qt; 05bnq8; >> query: (?x3948, 09g7thr) <- student(?x3948, ?x1068), major_field_of_study(?x3948, ?x3878), ?x3878 = 03nfmq >> conf = 0.52 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025v3k list 09g7thr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.520 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #1229-052p7 PRED entity: 052p7 PRED relation: month PRED expected values: 05lf_ => 264 concepts (264 used for prediction) PRED predicted values (max 10 best out of 1): 05lf_ (0.93 #16, 0.92 #12, 0.88 #29) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 0h3tv; >> query: (?x2474, 05lf_) <- teams(?x2474, ?x3073), month(?x2474, ?x9905), ?x9905 = 028kb >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 052p7 month 05lf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 264.000 264.000 0.931 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #1228-02bj6k PRED entity: 02bj6k PRED relation: nominated_for PRED expected values: 0180mw => 88 concepts (39 used for prediction) PRED predicted values (max 10 best out of 302): 01y9r2 (0.32 #6483, 0.31 #38914, 0.30 #8105), 0322yj (0.32 #6483, 0.31 #38914, 0.30 #8105), 0cbv4g (0.32 #6483, 0.31 #38914, 0.30 #8105), 0gkz15s (0.32 #6483, 0.31 #38914, 0.30 #8105), 03mh94 (0.32 #6483, 0.31 #38914, 0.30 #8105), 0sxlb (0.32 #6483, 0.31 #38914, 0.30 #8105), 03vyw8 (0.32 #6483, 0.31 #38914, 0.30 #8105), 05tgks (0.32 #6483, 0.31 #38914, 0.30 #8105), 06nr2h (0.32 #6483, 0.31 #38914, 0.30 #8105), 06lpmt (0.32 #6483, 0.31 #38914, 0.30 #8105) >> Best rule #6483 for best value: >> intensional similarity = 3 >> extensional distance = 358 >> proper extension: 01q7cb_; 01pw2f1; 0285c; 045bs6; 02_j7t; 05hdf; 01pnn3; 047hpm; 03xl77; 01_rh4; ... >> query: (?x7981, ?x463) <- film(?x7981, ?x463), type_of_union(?x7981, ?x566), participant(?x7981, ?x2551) >> conf = 0.32 => this is the best rule for 13 predicted values *> Best rule #4281 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 01kt17; *> query: (?x7981, 0180mw) <- award_nominee(?x1384, ?x7981), award(?x7981, ?x2192), ?x2192 = 0bfvd4 *> conf = 0.06 ranks of expected_values: 33 EVAL 02bj6k nominated_for 0180mw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 88.000 39.000 0.318 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #1227-09sb52 PRED entity: 09sb52 PRED relation: ceremony PRED expected values: 092c5f => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 131): 0n8_m93 (0.47 #1027, 0.18 #1158, 0.17 #1289), 0bzm81 (0.47 #937, 0.18 #1068, 0.17 #1199), 02yvhx (0.47 #988, 0.18 #1119, 0.17 #1250), 0bvfqq (0.47 #948, 0.18 #1079, 0.17 #1210), 02yxh9 (0.47 #1011, 0.18 #1142, 0.16 #1273), 0bc773 (0.47 #968, 0.18 #1099, 0.16 #1230), 02yw5r (0.47 #928, 0.18 #1059, 0.16 #1190), 02hn5v (0.47 #957, 0.18 #1088, 0.16 #1219), 04110lv (0.47 #1020, 0.18 #1151, 0.16 #1282), 050yyb (0.47 #953, 0.18 #1084, 0.15 #1215) >> Best rule #1027 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 040njc; 0gq_v; 0p9sw; 02hsq3m; 099tbz; 0l8z1; 019f4v; 02n9nmz; 0gq9h; 0gs9p; ... >> query: (?x704, 0n8_m93) <- award(?x57, ?x704), ceremony(?x704, ?x873), award(?x4610, ?x704), ?x4610 = 017jd9 >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #667 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 09qwmm; 094qd5; 03c7tr1; 0bdwft; 0gqwc; 099cng; 0cqgl9; *> query: (?x704, 092c5f) <- award(?x719, ?x704), ?x719 = 01csvq, nominated_for(?x704, ?x86) *> conf = 0.22 ranks of expected_values: 85 EVAL 09sb52 ceremony 092c5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 41.000 41.000 0.471 http://example.org/award/award_category/winners./award/award_honor/ceremony #1226-02mpb PRED entity: 02mpb PRED relation: influenced_by! PRED expected values: 0klw 04x56 => 166 concepts (88 used for prediction) PRED predicted values (max 10 best out of 454): 0j0pf (0.43 #3767, 0.21 #4784, 0.12 #41721), 040db (0.36 #2109, 0.21 #4144, 0.18 #2618), 05jm7 (0.36 #3701, 0.27 #2174, 0.26 #4718), 01dzz7 (0.29 #3613, 0.21 #4630, 0.12 #41721), 034bs (0.27 #2188, 0.21 #4223, 0.20 #1171), 05qzv (0.27 #2431, 0.16 #4466, 0.14 #3449), 0d4jl (0.27 #2150, 0.16 #4185, 0.12 #6219), 01vdrw (0.27 #2472, 0.13 #13662, 0.11 #4507), 0p8jf (0.27 #2145, 0.08 #12316, 0.07 #9263), 01vs4f3 (0.27 #2378, 0.07 #3396, 0.05 #4413) >> Best rule #3767 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0g5ff; >> query: (?x8210, 0j0pf) <- influenced_by(?x5334, ?x8210), award(?x8210, ?x1375), ?x1375 = 0262zm, gender(?x8210, ?x231) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1212 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 014dq7; 0c5tl; *> query: (?x8210, 0klw) <- story_by(?x9524, ?x8210), profession(?x8210, ?x353), place_of_burial(?x8210, ?x5670), influenced_by(?x5334, ?x8210) *> conf = 0.10 ranks of expected_values: 72, 157 EVAL 02mpb influenced_by! 04x56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 166.000 88.000 0.429 http://example.org/influence/influence_node/influenced_by EVAL 02mpb influenced_by! 0klw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 166.000 88.000 0.429 http://example.org/influence/influence_node/influenced_by #1225-0hr6lkl PRED entity: 0hr6lkl PRED relation: award_winner PRED expected values: 081lh 01ycbq => 27 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1682): 018ygt (0.38 #4051, 0.36 #5593, 0.18 #7139), 0h0wc (0.35 #6537, 0.32 #8083, 0.25 #1907), 09yhzs (0.33 #445, 0.21 #3086, 0.08 #3532), 02l840 (0.33 #101, 0.14 #10911, 0.05 #9368), 01vsgrn (0.33 #859, 0.09 #11669, 0.06 #12354), 03h_9lg (0.33 #109, 0.08 #9260, 0.06 #12354), 0jfx1 (0.33 #342, 0.08 #3429, 0.07 #4971), 02qw2xb (0.33 #1134, 0.08 #4221, 0.07 #5763), 0478__m (0.33 #718, 0.07 #12350, 0.06 #12351), 02k5sc (0.33 #1143, 0.07 #11953, 0.05 #10410) >> Best rule #4051 for best value: >> intensional similarity = 17 >> extensional distance = 11 >> proper extension: 0hr3c8y; 09qvms; 092c5f; 092t4b; 058m5m4; 027hjff; 092_25; 03gyp30; 09g90vz; 0g55tzk; >> query: (?x1442, 018ygt) <- ceremony(?x6729, ?x1442), award_winner(?x1442, ?x286), honored_for(?x1442, ?x224), award_winner(?x6729, ?x262), ?x262 = 06dv3, nominated_for(?x6729, ?x11619), nominated_for(?x6729, ?x6076), nominated_for(?x6729, ?x4939), nominated_for(?x6729, ?x3573), nominated_for(?x6729, ?x2490), ?x2490 = 026p4q7, award(?x123, ?x6729), ?x11619 = 07l50_1, ?x4939 = 05hjnw, genre(?x224, ?x225), ?x3573 = 011yl_, ?x6076 = 03hj5lq >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1541 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 1 *> proper extension: 0hhtgcw; *> query: (?x1442, ?x2033) <- honored_for(?x1442, ?x9961), honored_for(?x1442, ?x6007), honored_for(?x1442, ?x86), award_winner(?x1442, ?x286), film(?x541, ?x6007), ?x86 = 0ds35l9, production_companies(?x6007, ?x9518), executive_produced_by(?x6007, ?x3744), film_crew_role(?x9961, ?x1284), film(?x5184, ?x6007), film(?x2033, ?x6007), award_nominee(?x1194, ?x5184), genre(?x9961, ?x1014), music(?x6007, ?x2945), actor(?x7119, ?x5184), award(?x2033, ?x451), award(?x5184, ?x1670) *> conf = 0.17 ranks of expected_values: 140, 565 EVAL 0hr6lkl award_winner 01ycbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 27.000 12.000 0.385 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0hr6lkl award_winner 081lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 12.000 0.385 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1224-0c78m PRED entity: 0c78m PRED relation: risk_factors! PRED expected values: 01b_5g => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 74): 02bft (0.33 #467, 0.28 #1840, 0.27 #1048), 011zdm (0.33 #33, 0.25 #293, 0.20 #996), 0hgxh (0.33 #164, 0.20 #419, 0.12 #609), 0h1wz (0.25 #381, 0.25 #317, 0.14 #571), 014w_8 (0.25 #614, 0.20 #939, 0.18 #1068), 072hv (0.25 #305, 0.08 #2330, 0.07 #1271), 097ns (0.25 #286, 0.04 #2311, 0.02 #3109), 02vrr (0.20 #976, 0.17 #461, 0.12 #1565), 0dcrb (0.20 #1019, 0.14 #1282, 0.13 #1477), 01pf6 (0.17 #498, 0.12 #754, 0.11 #881) >> Best rule #467 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 02y0js; 0gk4g; 02k6hp; >> query: (?x6710, 02bft) <- risk_factors(?x6710, ?x2510), symptom_of(?x10069, ?x6710), people(?x6710, ?x10750), risk_factors(?x7260, ?x2510), risk_factors(?x6720, ?x2510), people(?x7838, ?x10750), ?x6720 = 0m32h, profession(?x10750, ?x524), ?x7260 = 01_qc_, ?x524 = 02jknp >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1453 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 13 *> proper extension: 0fltx; *> query: (?x6710, 01b_5g) <- risk_factors(?x6710, ?x2510), symptom_of(?x13487, ?x6710), risk_factors(?x14098, ?x2510), risk_factors(?x6720, ?x2510), ?x6720 = 0m32h, ?x14098 = 01k9gb, symptom_of(?x13487, ?x11392), symptom_of(?x13487, ?x4906), ?x11392 = 0lcdk, ?x4906 = 0hg11 *> conf = 0.13 ranks of expected_values: 19 EVAL 0c78m risk_factors! 01b_5g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 57.000 57.000 0.333 http://example.org/medicine/disease/risk_factors #1223-03kxdw PRED entity: 03kxdw PRED relation: place_of_birth PRED expected values: 071cn => 90 concepts (89 used for prediction) PRED predicted values (max 10 best out of 102): 0rh6k (0.14 #2, 0.03 #3523, 0.02 #2819), 0sf9_ (0.14 #142, 0.02 #1551, 0.01 #2959), 0yc84 (0.14 #32, 0.02 #1441, 0.01 #2849), 02_286 (0.08 #7062, 0.07 #5653, 0.07 #35952), 01_d4 (0.06 #4292, 0.05 #3587, 0.04 #6405), 0cr3d (0.05 #4320, 0.04 #3615, 0.04 #7137), 0hptm (0.04 #929, 0.04 #2338, 0.04 #1634), 0cc56 (0.04 #737, 0.02 #8486, 0.02 #3554), 0dclg (0.04 #5712, 0.04 #2191, 0.02 #10644), 030qb3t (0.04 #50073, 0.04 #2167, 0.04 #35987) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 046lt; 0p_47; 01gn36; 049fgvm; 01hmk9; >> query: (?x8780, 0rh6k) <- type_of_union(?x8780, ?x566), influenced_by(?x6692, ?x8780), ?x6692 = 04l19_, award(?x8780, ?x1312) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #2248 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 01438g; *> query: (?x8780, 071cn) <- type_of_union(?x8780, ?x566), award(?x8780, ?x1312), ?x1312 = 07cbcy, gender(?x8780, ?x231) *> conf = 0.02 ranks of expected_values: 60 EVAL 03kxdw place_of_birth 071cn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 90.000 89.000 0.143 http://example.org/people/person/place_of_birth #1222-013m43 PRED entity: 013m43 PRED relation: contains! PRED expected values: 07b_l => 210 concepts (116 used for prediction) PRED predicted values (max 10 best out of 246): 09c7w0 (0.79 #42091, 0.79 #44776, 0.77 #12540), 07b_l (0.73 #89551, 0.71 #83279, 0.71 #101192), 04_1l0v (0.53 #34481, 0.49 #41643, 0.49 #40748), 02qkt (0.47 #63025, 0.40 #50489, 0.39 #77352), 02k1b (0.43 #98505, 0.32 #17012, 0.30 #48351), 01n7q (0.35 #3660, 0.33 #12615, 0.32 #14406), 06pvr (0.35 #3748, 0.29 #5539, 0.27 #1061), 02j9z (0.24 #62706, 0.23 #67187, 0.20 #77033), 0j0k (0.23 #63056, 0.21 #67537, 0.19 #33510), 07z1m (0.21 #1884, 0.08 #26953, 0.06 #25162) >> Best rule #42091 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 0mn0v; >> query: (?x3300, 09c7w0) <- jurisdiction_of_office(?x1195, ?x3300), source(?x3300, ?x958), location(?x9184, ?x3300), award(?x9184, ?x1389) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #89551 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 182 *> proper extension: 01ymvk; 0r3tb; 0lpk3; 0cf_n; 01n4w_; 0114m0; 0s6g4; 0r3w7; 03qzj4; *> query: (?x3300, ?x3634) <- contains(?x11836, ?x3300), county_seat(?x11836, ?x5719), contains(?x3634, ?x11836), time_zones(?x11836, ?x1638) *> conf = 0.73 ranks of expected_values: 2 EVAL 013m43 contains! 07b_l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 210.000 116.000 0.790 http://example.org/location/location/contains #1221-05zl0 PRED entity: 05zl0 PRED relation: list PRED expected values: 09g7thr => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 5): 09g7thr (0.71 #1, 0.58 #22, 0.48 #120), 01ptsx (0.24 #285, 0.22 #327, 0.19 #348), 04k4rt (0.18 #284, 0.14 #291, 0.14 #326), 01pd60 (0.17 #286, 0.14 #328, 0.13 #349), 026cl_m (0.03 #108, 0.03 #192, 0.01 #122) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 045c7b; >> query: (?x6056, 09g7thr) <- company(?x1737, ?x6056), organization(?x6056, ?x5487), type_of_union(?x1737, ?x566) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05zl0 list 09g7thr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.714 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #1220-09pmkv PRED entity: 09pmkv PRED relation: contains! PRED expected values: 073q1 => 164 concepts (121 used for prediction) PRED predicted values (max 10 best out of 142): 0j0k (0.72 #61832, 0.70 #55559, 0.64 #20606), 02j71 (0.65 #43909, 0.59 #44807, 0.29 #69901), 073q1 (0.59 #100369, 0.59 #102162, 0.09 #7576), 09c7w0 (0.39 #104853, 0.38 #69904, 0.34 #66318), 02j9z (0.36 #17049, 0.33 #20635, 0.31 #9883), 04_1l0v (0.33 #70351, 0.28 #66765, 0.28 #19263), 0dg3n1 (0.31 #63780, 0.30 #68263, 0.29 #84393), 03rk0 (0.29 #50321, 0.26 #58384, 0.09 #26119), 07c5l (0.26 #2185, 0.23 #60434, 0.22 #48787), 07ssc (0.21 #71727, 0.21 #82480, 0.20 #73519) >> Best rule #61832 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 05rznz; >> query: (?x1122, ?x6956) <- organization(?x1122, ?x127), form_of_government(?x1122, ?x1926), countries_within(?x6956, ?x1122) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #100369 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 189 *> proper extension: 04kbn; 04kdn; *> query: (?x1122, ?x6304) <- adjoins(?x7747, ?x1122), contains(?x6304, ?x7747), religion(?x7747, ?x492) *> conf = 0.59 ranks of expected_values: 3 EVAL 09pmkv contains! 073q1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 164.000 121.000 0.718 http://example.org/location/location/contains #1219-0dnw1 PRED entity: 0dnw1 PRED relation: film! PRED expected values: 0dqcm => 92 concepts (48 used for prediction) PRED predicted values (max 10 best out of 812): 096hm (0.47 #77075, 0.44 #91663, 0.43 #85409), 0c4qzm (0.47 #77075, 0.44 #91663, 0.43 #85409), 057bc6m (0.47 #77075, 0.44 #91663, 0.43 #85409), 07hhnl (0.44 #91663, 0.43 #85409, 0.41 #87494), 07h1tr (0.44 #91663, 0.43 #85409, 0.41 #87494), 0dqcm (0.43 #43741, 0.28 #58323, 0.08 #5727), 01vttb9 (0.43 #43741, 0.28 #58323, 0.03 #49990), 044qx (0.40 #2816, 0.33 #4898, 0.31 #6980), 0j_c (0.30 #2493, 0.27 #8740, 0.25 #4575), 04__f (0.25 #1384, 0.06 #13881, 0.05 #15963) >> Best rule #77075 for best value: >> intensional similarity = 4 >> extensional distance = 687 >> proper extension: 04969y; 04m1bm; 091z_p; 02rb607; 0bhwhj; 0hv81; 012jfb; 064lsn; 03q8xj; 02zk08; ... >> query: (?x6094, ?x9659) <- award(?x6094, ?x1079), nominated_for(?x9659, ?x6094), type_of_union(?x9659, ?x566), film(?x902, ?x6094) >> conf = 0.47 => this is the best rule for 3 predicted values *> Best rule #43741 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 394 *> proper extension: 03t97y; 03twd6; *> query: (?x6094, ?x7556) <- film(?x4349, ?x6094), award_winner(?x6094, ?x7556), featured_film_locations(?x6094, ?x739) *> conf = 0.43 ranks of expected_values: 6 EVAL 0dnw1 film! 0dqcm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 92.000 48.000 0.466 http://example.org/film/actor/film./film/performance/film #1218-04h07s PRED entity: 04h07s PRED relation: cast_members PRED expected values: 01v3s2_ => 101 concepts (48 used for prediction) PRED predicted values (max 10 best out of 54): 01v3s2_ (0.86 #20, 0.84 #25, 0.82 #16), 03q45x (0.86 #20, 0.84 #25, 0.82 #16), 04h07s (0.78 #23, 0.78 #18, 0.71 #14), 08_438 (0.04 #21), 01nfys (0.04 #21), 030vnj (0.04 #21), 07ftc0 (0.04 #21), 0252fh (0.04 #21), 06crng (0.04 #21), 059xnf (0.04 #21) >> Best rule #20 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02k21g; >> query: (?x4491, ?x905) <- cast_members(?x3927, ?x4491), cast_members(?x905, ?x4491), film(?x4491, ?x148), ?x3927 = 08vr94 >> conf = 0.86 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 04h07s cast_members 01v3s2_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 48.000 0.857 http://example.org/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members #1217-020w2 PRED entity: 020w2 PRED relation: role! PRED expected values: 0l14qv => 67 concepts (59 used for prediction) PRED predicted values (max 10 best out of 114): 013y1f (0.91 #3257, 0.90 #2901, 0.83 #1756), 05r5c (0.90 #5468, 0.80 #5583, 0.77 #6507), 0342h (0.87 #4054, 0.82 #3108, 0.79 #4289), 018vs (0.86 #5705, 0.85 #3715, 0.82 #3118), 02sgy (0.85 #4524, 0.83 #4045, 0.83 #3939), 0l14qv (0.83 #1606, 0.82 #5350, 0.81 #4171), 02w4b (0.83 #1606, 0.81 #4171, 0.81 #571), 04rzd (0.82 #6314, 0.82 #4801, 0.82 #3149), 0bxl5 (0.82 #1440, 0.77 #3171, 0.75 #2020), 03q5t (0.82 #1375, 0.75 #1722, 0.75 #911) >> Best rule #3257 for best value: >> intensional similarity = 26 >> extensional distance = 20 >> proper extension: 0bm02; >> query: (?x2675, 013y1f) <- role(?x7869, ?x2675), role(?x2888, ?x2675), role(?x7506, ?x2675), role(?x3378, ?x2675), role(?x227, ?x2675), family(?x2675, ?x2620), role(?x6039, ?x7869), role(?x4429, ?x7869), role(?x3418, ?x7869), role(?x716, ?x7869), role(?x74, ?x7869), ?x6039 = 05kms, ?x2888 = 02fsn, role(?x7869, ?x8014), role(?x7869, ?x2725), performance_role(?x7869, ?x228), ?x716 = 018vs, ?x4429 = 0g33q, ?x2725 = 0l1589, ?x8014 = 0214km, location(?x3378, ?x2879), role(?x7869, ?x2158), role(?x3239, ?x3418), artists(?x1380, ?x7506), ?x3239 = 03qmg1, role(?x74, ?x314) >> conf = 0.91 => this is the best rule for 1 predicted values *> Best rule #1606 for first EXPECTED value: *> intensional similarity = 25 *> extensional distance = 9 *> proper extension: 02w3w; *> query: (?x2675, ?x3418) <- role(?x2888, ?x2675), role(?x2048, ?x2675), role(?x1332, ?x2675), role(?x75, ?x2675), role(?x3378, ?x2675), role(?x1147, ?x2675), role(?x614, ?x2675), ?x2048 = 018j2, ?x614 = 0mkg, ?x1332 = 03qlv7, role(?x2888, ?x2309), role(?x4616, ?x2888), role(?x2377, ?x2888), role(?x74, ?x2888), role(?x2675, ?x3418), ?x75 = 07y_7, instrumentalists(?x2888, ?x425), ?x4616 = 01rhl, ?x2377 = 01bns_, role(?x4288, ?x2888), ?x74 = 03q5t, ?x2309 = 06ncr, group(?x2888, ?x2521), role(?x317, ?x1147), ?x4288 = 018dyl *> conf = 0.83 ranks of expected_values: 6 EVAL 020w2 role! 0l14qv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 67.000 59.000 0.909 http://example.org/music/performance_role/track_performances./music/track_contribution/role #1216-0156q PRED entity: 0156q PRED relation: capital! PRED expected values: 03b79 => 344 concepts (262 used for prediction) PRED predicted values (max 10 best out of 130): 0bq0p9 (0.40 #280, 0.06 #8827, 0.06 #9484), 07ssc (0.20 #277, 0.10 #670, 0.09 #801), 02jx1 (0.20 #293, 0.10 #686, 0.09 #817), 014tss (0.20 #349, 0.10 #742, 0.09 #873), 06frc (0.20 #497, 0.08 #1154, 0.07 #1942), 03rjj (0.20 #398, 0.08 #1055, 0.07 #1843), 0cdbq (0.10 #4408, 0.10 #5326, 0.09 #5721), 06t2t (0.10 #703, 0.09 #834, 0.05 #5698), 03f4n1 (0.09 #5776, 0.08 #1176, 0.06 #3544), 02psqkz (0.09 #5714, 0.07 #1902, 0.07 #1639) >> Best rule #280 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 02p3my; >> query: (?x1646, 0bq0p9) <- service_location(?x610, ?x1646), place_of_birth(?x628, ?x1646), capital(?x1264, ?x1646) >> conf = 0.40 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0156q capital! 03b79 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 344.000 262.000 0.400 http://example.org/location/country/capital #1215-07vqnc PRED entity: 07vqnc PRED relation: producer_type PRED expected values: 0ckd1 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 30): 0ckd1 (0.76 #9, 0.75 #3, 0.68 #22), 014kbl (0.02 #34), 06qc5 (0.02 #34), 033smt (0.02 #34), 0d2b38 (0.02 #34), 02vs3x5 (0.02 #34), 02zdwq (0.02 #34), 05smlt (0.02 #34), 089g0h (0.02 #34), 0215hd (0.02 #34) >> Best rule #9 for best value: >> intensional similarity = 6 >> extensional distance = 72 >> proper extension: 0ph24; >> query: (?x11454, 0ckd1) <- genre(?x11454, ?x2700), genre(?x7551, ?x2700), genre(?x6884, ?x2700), ?x6884 = 039cq4, nominated_for(?x3263, ?x11454), award_winner(?x7551, ?x773) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07vqnc producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.757 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #1214-06mmb PRED entity: 06mmb PRED relation: award_winner PRED expected values: 02g87m 049k07 => 91 concepts (39 used for prediction) PRED predicted values (max 10 best out of 431): 01x_d8 (0.82 #62947, 0.82 #9682, 0.82 #62946), 020_95 (0.56 #29049, 0.53 #43576, 0.53 #50032), 01qrbf (0.53 #43576, 0.53 #50032, 0.53 #46803), 016xh5 (0.53 #43576, 0.53 #50032, 0.53 #46803), 0h5g_ (0.53 #43576, 0.53 #50032, 0.53 #46803), 0159h6 (0.53 #43576, 0.53 #50032, 0.53 #46803), 02fz3w (0.53 #43576, 0.53 #50032, 0.53 #46803), 0175wg (0.53 #43576, 0.53 #50032, 0.53 #46803), 0993r (0.53 #43576, 0.53 #50032, 0.53 #46803), 04rsd2 (0.53 #43576, 0.53 #50032, 0.53 #46803) >> Best rule #62947 for best value: >> intensional similarity = 2 >> extensional distance = 1584 >> proper extension: 01_8w2; 01p5yn; 0khth; 035_2h; 014l4w; 07mvp; 039cq4; 03yxwq; 0gsgr; 01j53q; ... >> query: (?x2559, ?x1461) <- award_winner(?x1461, ?x2559), award_winner(?x1460, ?x1461) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #62948 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1584 *> proper extension: 01_8w2; 01p5yn; 0khth; 035_2h; 014l4w; 07mvp; 039cq4; 03yxwq; 0gsgr; 01j53q; ... *> query: (?x2559, ?x1460) <- award_winner(?x1461, ?x2559), award_winner(?x1460, ?x1461) *> conf = 0.27 ranks of expected_values: 21, 23 EVAL 06mmb award_winner 049k07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 91.000 39.000 0.820 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner EVAL 06mmb award_winner 02g87m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 91.000 39.000 0.820 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #1213-0blbxk PRED entity: 0blbxk PRED relation: film PRED expected values: 0g7pm1 => 77 concepts (44 used for prediction) PRED predicted values (max 10 best out of 369): 02prw4h (0.59 #30392, 0.48 #23241, 0.39 #21453), 017jd9 (0.39 #779, 0.03 #67937, 0.02 #6140), 017gl1 (0.39 #143, 0.03 #67937, 0.01 #12652), 017gm7 (0.32 #211, 0.01 #12720, 0.01 #5572), 0ndwt2w (0.18 #999, 0.03 #67937), 0djlxb (0.11 #534, 0.03 #67937), 049xgc (0.07 #971, 0.03 #67937), 0dlngsd (0.07 #780, 0.03 #67937), 01vw8k (0.07 #652, 0.03 #67937), 01shy7 (0.07 #422, 0.02 #21875, 0.02 #29026) >> Best rule #30392 for best value: >> intensional similarity = 3 >> extensional distance = 1348 >> proper extension: 04yywz; 02nb2s; 0151ns; 012c6x; 03_vx9; 0f0p0; 01sxq9; 0h1m9; 01xcqc; 028lc8; ... >> query: (?x1290, ?x1218) <- profession(?x1290, ?x1032), nominated_for(?x1290, ?x1218), film(?x1290, ?x1702) >> conf = 0.59 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0blbxk film 0g7pm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 44.000 0.585 http://example.org/film/actor/film./film/performance/film #1212-01w7nww PRED entity: 01w7nww PRED relation: award_winner! PRED expected values: 01cw7s => 100 concepts (98 used for prediction) PRED predicted values (max 10 best out of 278): 01cw7s (0.36 #3878, 0.33 #1725, 0.33 #1555), 03r00m (0.36 #3878, 0.32 #39587, 0.32 #14202), 01cky2 (0.36 #3878, 0.32 #39587, 0.32 #14202), 025m8l (0.36 #3878, 0.32 #39587, 0.32 #14202), 023vrq (0.36 #3878, 0.32 #39587, 0.32 #14202), 01c427 (0.36 #3878, 0.32 #39587, 0.32 #14202), 09sb52 (0.36 #3878, 0.32 #39587, 0.32 #14202), 02v1m7 (0.36 #3878, 0.32 #39587, 0.32 #14202), 02f72n (0.36 #3878, 0.32 #14202, 0.32 #14201), 02f764 (0.36 #3878, 0.32 #14202, 0.32 #14201) >> Best rule #3878 for best value: >> intensional similarity = 4 >> extensional distance = 54 >> proper extension: 0lgsq; 01bpc9; 03rl84; 01vvpjj; 0gcs9; 01817f; 01cblr; 01wqflx; 057xn_m; >> query: (?x3176, ?x704) <- award(?x3176, ?x2238), award(?x3176, ?x704), ?x2238 = 025m8l, artists(?x505, ?x3176) >> conf = 0.36 => this is the best rule for 12 predicted values ranks of expected_values: 1 EVAL 01w7nww award_winner! 01cw7s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 98.000 0.359 http://example.org/award/award_category/winners./award/award_honor/award_winner #1211-01sxly PRED entity: 01sxly PRED relation: nominated_for! PRED expected values: 0gr51 03hl6lc => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 203): 04kxsb (0.68 #2321, 0.68 #14861, 0.67 #8125), 05zvj3m (0.50 #764, 0.08 #2389, 0.07 #2156), 0gq9h (0.43 #7951, 0.43 #8417, 0.42 #9811), 02rdxsh (0.40 #512, 0.25 #280, 0.14 #2136), 019f4v (0.38 #7943, 0.37 #8409, 0.36 #9803), 0gq_v (0.33 #1873, 0.31 #7909, 0.31 #9769), 0262s1 (0.33 #209, 0.21 #9288, 0.20 #10913), 04dn09n (0.33 #3514, 0.28 #1889, 0.27 #1657), 0k611 (0.32 #7961, 0.32 #8427, 0.31 #9821), 0gr0m (0.32 #3537, 0.28 #4002, 0.27 #1912) >> Best rule #2321 for best value: >> intensional similarity = 4 >> extensional distance = 150 >> proper extension: 0291ck; >> query: (?x582, ?x112) <- written_by(?x582, ?x4988), film(?x4988, ?x148), profession(?x4988, ?x319), award(?x582, ?x112) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #2162 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 150 *> proper extension: 0291ck; *> query: (?x582, 0gr51) <- written_by(?x582, ?x4988), film(?x4988, ?x148), profession(?x4988, ?x319), award(?x582, ?x112) *> conf = 0.26 ranks of expected_values: 13, 16 EVAL 01sxly nominated_for! 03hl6lc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 98.000 98.000 0.677 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01sxly nominated_for! 0gr51 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 98.000 98.000 0.677 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1210-026mff PRED entity: 026mff PRED relation: ceremony PRED expected values: 05pd94v 02rjjll 013b2h => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 123): 05pd94v (0.84 #637, 0.76 #2, 0.75 #256), 02rjjll (0.81 #639, 0.76 #4, 0.75 #258), 013b2h (0.81 #705, 0.72 #324, 0.71 #70), 0gx1673 (0.53 #107, 0.47 #742, 0.44 #234), 09pj68 (0.41 #1780, 0.41 #1271, 0.35 #2543), 05c1t6z (0.21 #1029, 0.20 #902, 0.19 #1411), 0gvstc3 (0.18 #1044, 0.17 #1426, 0.17 #1680), 02q690_ (0.17 #1199, 0.17 #1454, 0.17 #1836), 0bzm81 (0.16 #780, 0.15 #907, 0.15 #1161), 0n8_m93 (0.16 #867, 0.15 #1248, 0.14 #994) >> Best rule #637 for best value: >> intensional similarity = 5 >> extensional distance = 68 >> proper extension: 02581q; 02wh75; 026mg3; 02g3gj; 01d38g; 01bgqh; 03x3wf; 0c4z8; 02g8mp; 01ckbq; ... >> query: (?x3094, 05pd94v) <- category_of(?x3094, ?x2421), ceremony(?x3094, ?x725), ?x725 = 01bx35, award(?x367, ?x3094), award_winner(?x3094, ?x1270) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 026mff ceremony 013b2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.843 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 026mff ceremony 02rjjll CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.843 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 026mff ceremony 05pd94v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 47.000 47.000 0.843 http://example.org/award/award_category/winners./award/award_honor/ceremony #1209-02q_4ph PRED entity: 02q_4ph PRED relation: currency PRED expected values: 09nqf => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.88 #323, 0.86 #344, 0.85 #400), 02gsvk (0.03 #230, 0.02 #447, 0.02 #671), 01nv4h (0.03 #359, 0.03 #478, 0.02 #492), 02l6h (0.01 #543, 0.01 #838), 0kz1h (0.01 #418) >> Best rule #323 for best value: >> intensional similarity = 5 >> extensional distance = 66 >> proper extension: 09q5w2; 0ptxj; 02q87z6; >> query: (?x4300, 09nqf) <- film_release_distribution_medium(?x4300, ?x81), film(?x2875, ?x4300), film(?x13497, ?x4300), nominated_for(?x4404, ?x4300), film_release_region(?x4300, ?x94) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q_4ph currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.882 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #1208-0g824 PRED entity: 0g824 PRED relation: artists! PRED expected values: 0155w => 140 concepts (93 used for prediction) PRED predicted values (max 10 best out of 226): 06by7 (0.46 #19736, 0.44 #17579, 0.44 #19119), 017_qw (0.31 #8684, 0.12 #24096, 0.12 #2832), 02lnbg (0.30 #5908, 0.29 #980, 0.29 #1904), 02x8m (0.29 #3714, 0.23 #2790, 0.21 #16034), 05bt6j (0.27 #967, 0.27 #19758, 0.25 #3123), 0ggx5q (0.26 #1924, 0.25 #1000, 0.24 #5928), 016clz (0.25 #3085, 0.24 #17563, 0.23 #19103), 01lyv (0.23 #12970, 0.20 #15434, 0.19 #16975), 0155w (0.22 #3801, 0.21 #2877, 0.16 #19203), 05r6t (0.19 #388, 0.10 #14248, 0.09 #17638) >> Best rule #19736 for best value: >> intensional similarity = 3 >> extensional distance = 474 >> proper extension: 0123r4; >> query: (?x6383, 06by7) <- artists(?x3562, ?x6383), artists(?x3562, ?x6573), ?x6573 = 067nsm >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #3801 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 114 *> proper extension: 01rm8b; 015srx; 011z3g; 046p9; 016376; 012x03; *> query: (?x6383, 0155w) <- artists(?x3928, ?x6383), award(?x6383, ?x724), ?x3928 = 0gywn *> conf = 0.22 ranks of expected_values: 9 EVAL 0g824 artists! 0155w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 140.000 93.000 0.456 http://example.org/music/genre/artists #1207-0bwgc_ PRED entity: 0bwgc_ PRED relation: type_of_union PRED expected values: 04ztj => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #29, 0.87 #33, 0.84 #13), 01g63y (0.37 #294, 0.33 #277, 0.28 #2), 0jgjn (0.02 #24) >> Best rule #29 for best value: >> intensional similarity = 2 >> extensional distance = 228 >> proper extension: 01l_vgt; 0d1_f; 0cm03; 0457w0; 0ngg; >> query: (?x11983, 04ztj) <- location_of_ceremony(?x11983, ?x10272), gender(?x11983, ?x514) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bwgc_ type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.870 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1206-0nk72 PRED entity: 0nk72 PRED relation: company PRED expected values: 02zd460 => 233 concepts (189 used for prediction) PRED predicted values (max 10 best out of 166): 01pl14 (0.33 #6, 0.14 #383, 0.09 #1513), 0c0cs (0.33 #187, 0.14 #564, 0.08 #3765), 07y0n (0.33 #163, 0.14 #540, 0.08 #3741), 07wj1 (0.33 #141, 0.14 #518, 0.07 #2213), 09kvv (0.33 #25, 0.14 #402, 0.05 #4921), 0d6qjf (0.33 #159, 0.14 #536, 0.04 #3737), 017v3q (0.33 #99, 0.14 #476, 0.04 #3677), 07ccs (0.33 #91, 0.14 #468, 0.04 #3669), 08qnnv (0.33 #90, 0.14 #467, 0.04 #3668), 01vs5c (0.33 #82, 0.14 #459, 0.04 #3660) >> Best rule #6 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 03gkn5; >> query: (?x8404, 01pl14) <- place_of_birth(?x8404, ?x3622), company(?x8404, ?x1103), company(?x8404, ?x122), ?x122 = 08815, currency(?x1103, ?x170) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 07n39; *> query: (?x8404, 02zd460) <- place_of_birth(?x8404, ?x3622), religion(?x8404, ?x7131), company(?x8404, ?x122), peers(?x8404, ?x12592) *> conf = 0.11 ranks of expected_values: 30 EVAL 0nk72 company 02zd460 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 233.000 189.000 0.333 http://example.org/people/person/employment_history./business/employment_tenure/company #1205-0167km PRED entity: 0167km PRED relation: artists! PRED expected values: 0xhtw 05bt6j => 151 concepts (87 used for prediction) PRED predicted values (max 10 best out of 289): 03_d0 (0.86 #10627, 0.25 #19063, 0.24 #23125), 0xhtw (0.42 #1577, 0.28 #9382, 0.28 #4698), 0155w (0.35 #1669, 0.19 #9474, 0.19 #7913), 01lyv (0.35 #3466, 0.28 #10337, 0.28 #6277), 016clz (0.34 #9370, 0.31 #3437, 0.31 #6248), 05bt6j (0.34 #16284, 0.32 #2228, 0.31 #3476), 06j6l (0.33 #16289, 0.30 #15041, 0.30 #14103), 025sc50 (0.31 #16291, 0.30 #2235, 0.24 #14105), 02yv6b (0.29 #1661, 0.23 #5718, 0.20 #7905), 05w3f (0.29 #1598, 0.22 #4094, 0.19 #9403) >> Best rule #10627 for best value: >> intensional similarity = 5 >> extensional distance = 158 >> proper extension: 0h08p; >> query: (?x5879, 03_d0) <- artists(?x8386, ?x5879), artists(?x8386, ?x7211), artists(?x8386, ?x2170), ?x7211 = 0135xb, ?x2170 = 0144l1 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1577 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 29 *> proper extension: 01wv9xn; *> query: (?x5879, 0xhtw) <- award_winner(?x2322, ?x5879), award(?x1521, ?x2322), award(?x1092, ?x2322), ?x1521 = 01wp8w7, ?x1092 = 02whj *> conf = 0.42 ranks of expected_values: 2, 6 EVAL 0167km artists! 05bt6j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 151.000 87.000 0.863 http://example.org/music/genre/artists EVAL 0167km artists! 0xhtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 151.000 87.000 0.863 http://example.org/music/genre/artists #1204-01rnpy PRED entity: 01rnpy PRED relation: nationality PRED expected values: 02jx1 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 27): 09c7w0 (0.77 #701, 0.74 #801, 0.72 #1101), 07ssc (0.35 #5105, 0.33 #15, 0.10 #1915), 02jx1 (0.33 #33, 0.30 #233, 0.21 #133), 0j5g9 (0.17 #62, 0.06 #6812, 0.02 #962), 03rk0 (0.07 #2748, 0.07 #5451, 0.06 #7158), 0d060g (0.06 #6812, 0.05 #107, 0.05 #207), 0ctw_b (0.06 #6812, 0.05 #127, 0.04 #327), 01xbgx (0.06 #6812, 0.05 #181, 0.04 #381), 0chghy (0.06 #6812, 0.05 #210, 0.04 #310), 03rjj (0.06 #6812, 0.04 #305, 0.02 #905) >> Best rule #701 for best value: >> intensional similarity = 5 >> extensional distance = 94 >> proper extension: 01csvq; 0h1mt; 01q9b9; 0mbs8; >> query: (?x11571, 09c7w0) <- award(?x11571, ?x375), award(?x13793, ?x375), award(?x6544, ?x375), ?x6544 = 01x209s, ?x13793 = 03p9hl >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #33 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 03b0q4; *> query: (?x11571, 02jx1) <- profession(?x11571, ?x1032), film(?x11571, ?x3111), ?x3111 = 0g68zt *> conf = 0.33 ranks of expected_values: 3 EVAL 01rnpy nationality 02jx1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 99.000 99.000 0.771 http://example.org/people/person/nationality #1203-027r9t PRED entity: 027r9t PRED relation: genre PRED expected values: 01j1n2 => 114 concepts (112 used for prediction) PRED predicted values (max 10 best out of 100): 04xvlr (0.74 #6636, 0.65 #4619, 0.64 #2601), 03k9fj (0.39 #247, 0.30 #10, 0.24 #2967), 02kdv5l (0.36 #712, 0.34 #948, 0.32 #6045), 01jfsb (0.35 #130, 0.33 #3917, 0.33 #721), 02l7c8 (0.34 #3090, 0.34 #1079, 0.33 #1553), 0lsxr (0.29 #953, 0.28 #1189, 0.25 #362), 060__y (0.25 #3091, 0.22 #962, 0.20 #2498), 04xvh5 (0.19 #3109, 0.10 #3822, 0.10 #3703), 01hmnh (0.18 #254, 0.18 #4517, 0.17 #6060), 06n90 (0.18 #249, 0.17 #1786, 0.17 #1194) >> Best rule #6636 for best value: >> intensional similarity = 4 >> extensional distance = 796 >> proper extension: 07s3m4g; 02pcq92; >> query: (?x7141, ?x53) <- titles(?x53, ?x7141), film_release_distribution_medium(?x7141, ?x81), genre(?x54, ?x53), genre(?x273, ?x53) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #2541 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 174 *> proper extension: 0d8w2n; *> query: (?x7141, 01j1n2) <- titles(?x53, ?x7141), film_release_distribution_medium(?x7141, ?x81), ?x53 = 07s9rl0, production_companies(?x7141, ?x541) *> conf = 0.07 ranks of expected_values: 27 EVAL 027r9t genre 01j1n2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 114.000 112.000 0.739 http://example.org/film/film/genre #1202-01cky2 PRED entity: 01cky2 PRED relation: ceremony PRED expected values: 01c6qp => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 126): 01c6qp (0.75 #265, 0.57 #766, 0.49 #891), 01s695 (0.58 #252, 0.54 #753, 0.45 #878), 050yyb (0.32 #376, 0.21 #3627, 0.14 #783), 0bzn6_ (0.32 #376, 0.21 #3627, 0.13 #798), 0bzknt (0.32 #376, 0.21 #3627, 0.12 #822), 09qvms (0.32 #376, 0.21 #3627, 0.08 #636), 07y_p6 (0.32 #376, 0.21 #3627, 0.06 #1710), 09p2r9 (0.32 #376, 0.21 #3627, 0.04 #706), 09bymc (0.32 #376, 0.21 #3627, 0.02 #2482), 04n2r9h (0.32 #376, 0.21 #3627, 0.02 #2413) >> Best rule #265 for best value: >> intensional similarity = 6 >> extensional distance = 10 >> proper extension: 05q8pss; >> query: (?x3835, 01c6qp) <- award(?x7553, ?x3835), award(?x2731, ?x3835), award(?x1660, ?x3835), ?x1660 = 012x4t, ?x2731 = 01wwvc5, award_winner(?x1362, ?x7553) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cky2 ceremony 01c6qp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 37.000 37.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony #1201-01xbgx PRED entity: 01xbgx PRED relation: film_release_region! PRED expected values: 0g5qs2k 0gkz15s => 105 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1317): 01vksx (0.90 #1416, 0.81 #4044, 0.57 #10615), 0dll_t2 (0.90 #2050, 0.78 #4678, 0.54 #13877), 0gtsx8c (0.90 #1326, 0.78 #3954, 0.54 #13153), 09k56b7 (0.90 #1555, 0.75 #4183, 0.52 #12068), 0gg8z1f (0.90 #2160, 0.67 #4788, 0.50 #11359), 017jd9 (0.86 #4532, 0.85 #1904, 0.70 #11103), 08hmch (0.86 #4061, 0.80 #1433, 0.69 #10632), 043tvp3 (0.86 #4859, 0.80 #2231, 0.61 #14058), 017gm7 (0.86 #4104, 0.75 #1476, 0.61 #13303), 087wc7n (0.85 #1405, 0.83 #4033, 0.56 #13232) >> Best rule #1416 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 05r4w; 09c7w0; 0b90_r; 0d060g; 0d0vqn; 03rt9; 05qhw; 07ssc; 0k6nt; 03gj2; ... >> query: (?x7748, 01vksx) <- adjoins(?x7747, ?x7748), film_release_region(?x7678, ?x7748), ?x7678 = 0gvvf4j >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #4030 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 09pmkv; *> query: (?x7748, 0gkz15s) <- adjoins(?x7747, ?x7748), film_release_region(?x5713, ?x7748), ?x5713 = 0cc97st *> conf = 0.81 ranks of expected_values: 25, 104 EVAL 01xbgx film_release_region! 0gkz15s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 105.000 59.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01xbgx film_release_region! 0g5qs2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 105.000 59.000 0.900 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1200-042g97 PRED entity: 042g97 PRED relation: country PRED expected values: 09c7w0 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 151): 09c7w0 (0.90 #1089, 0.90 #1029, 0.88 #1270), 03rjj (0.34 #3327, 0.07 #5451, 0.07 #3334), 0345h (0.24 #3354, 0.13 #268, 0.13 #1597), 0f8l9c (0.21 #3346, 0.13 #139, 0.10 #199), 06mkj (0.13 #160, 0.10 #220, 0.08 #341), 0b90_r (0.13 #125, 0.10 #185, 0.08 #306), 03_3d (0.10 #3335, 0.07 #5451, 0.07 #3395), 01hmnh (0.09 #241, 0.08 #362, 0.07 #2542), 03k9fj (0.09 #241, 0.08 #362, 0.07 #2542), 0d060g (0.09 #250, 0.07 #5451, 0.07 #129) >> Best rule #1089 for best value: >> intensional similarity = 4 >> extensional distance = 81 >> proper extension: 017180; >> query: (?x12214, 09c7w0) <- honored_for(?x3672, ?x12214), written_by(?x3672, ?x3194), nominated_for(?x7088, ?x12214), nominated_for(?x154, ?x3672) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042g97 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.904 http://example.org/film/film/country #1199-027jq2 PRED entity: 027jq2 PRED relation: award PRED expected values: 0gqng => 124 concepts (99 used for prediction) PRED predicted values (max 10 best out of 386): 02wkmx (0.75 #12596, 0.75 #9347, 0.74 #6504), 02wypbh (0.75 #12596, 0.75 #9347, 0.74 #6504), 0gs9p (0.43 #2113, 0.16 #11051, 0.16 #14301), 02rdyk7 (0.43 #2125, 0.11 #905, 0.09 #11063), 02pqp12 (0.39 #2104, 0.27 #71, 0.19 #478), 040njc (0.37 #2041, 0.27 #8, 0.19 #415), 019f4v (0.37 #2100, 0.27 #67, 0.19 #474), 05p09zm (0.33 #125, 0.29 #1345, 0.27 #3784), 0gq9h (0.33 #78, 0.25 #485, 0.15 #8612), 05pcn59 (0.29 #1708, 0.29 #1302, 0.25 #489) >> Best rule #12596 for best value: >> intensional similarity = 3 >> extensional distance = 351 >> proper extension: 01p9hgt; 0244r8; 09mq4m; 01m3b1t; 06p03s; >> query: (?x10041, ?x372) <- award_winner(?x372, ?x10041), profession(?x10041, ?x1183), ?x1183 = 09jwl >> conf = 0.75 => this is the best rule for 2 predicted values *> Best rule #2035 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 44 *> proper extension: 02pt7h_; *> query: (?x10041, 0gqng) <- award_winner(?x372, ?x10041), gender(?x10041, ?x231), award(?x2671, ?x372), ?x2671 = 04k25 *> conf = 0.11 ranks of expected_values: 66 EVAL 027jq2 award 0gqng CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 124.000 99.000 0.751 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1198-08304 PRED entity: 08304 PRED relation: student! PRED expected values: 0ylvj => 116 concepts (110 used for prediction) PRED predicted values (max 10 best out of 200): 015401 (0.33 #479, 0.17 #1004, 0.03 #2579), 01stzp (0.22 #1559, 0.05 #6285, 0.05 #22578), 07tg4 (0.20 #2710, 0.16 #6386, 0.11 #4811), 09wv__ (0.17 #692, 0.05 #4368, 0.04 #1742), 0cwx_ (0.17 #765, 0.02 #3390, 0.02 #4441), 01q2sk (0.17 #624, 0.02 #3249, 0.02 #4300), 014jyk (0.17 #929, 0.02 #3554), 0dplh (0.11 #1103, 0.05 #2678, 0.05 #22578), 01zzy3 (0.11 #1523, 0.05 #22578, 0.03 #6249), 0301dp (0.11 #1575, 0.05 #22578, 0.03 #3150) >> Best rule #479 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 023t0q; >> query: (?x6512, 015401) <- profession(?x6512, ?x2472), influenced_by(?x477, ?x6512), ?x2472 = 01xy5l_, place_of_birth(?x6512, ?x362), nationality(?x6512, ?x512) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2825 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 01vsl3_; 0178rl; 0mm1q; 01lw3kh; 016xk5; 0161h5; 02rsz0; 0168ql; *> query: (?x6512, 0ylvj) <- profession(?x6512, ?x353), nationality(?x6512, ?x1310), ?x353 = 0cbd2, gender(?x6512, ?x231), ?x1310 = 02jx1 *> conf = 0.05 ranks of expected_values: 23 EVAL 08304 student! 0ylvj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 116.000 110.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #1197-047myg9 PRED entity: 047myg9 PRED relation: currency PRED expected values: 09nqf => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.77 #78, 0.76 #85, 0.74 #232), 02l6h (0.17 #4, 0.03 #32, 0.03 #18), 01nv4h (0.10 #58, 0.09 #44, 0.08 #51), 02gsvk (0.05 #48, 0.04 #69, 0.04 #76) >> Best rule #78 for best value: >> intensional similarity = 3 >> extensional distance = 313 >> proper extension: 053rxgm; 02pxmgz; 0gxtknx; 035yn8; 02q56mk; 0j_t1; 0ddt_; 012mrr; 0j43swk; 03459x; ... >> query: (?x6387, 09nqf) <- nominated_for(?x2532, ?x6387), award(?x6041, ?x2532), ?x6041 = 037d35 >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047myg9 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.765 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #1196-03hrz PRED entity: 03hrz PRED relation: mode_of_transportation PRED expected values: 07jdr => 237 concepts (237 used for prediction) PRED predicted values (max 10 best out of 3): 07jdr (0.83 #73, 0.80 #121, 0.78 #100), 0k4j (0.08 #98, 0.04 #83, 0.03 #92), 06d_3 (0.05 #99, 0.03 #93, 0.03 #96) >> Best rule #73 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 013yq; >> query: (?x2985, 07jdr) <- jurisdiction_of_office(?x1195, ?x2985), month(?x2985, ?x4869), month(?x2985, ?x1650), ?x4869 = 02xx5, ?x1650 = 06vkl >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03hrz mode_of_transportation 07jdr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 237.000 237.000 0.826 http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation #1195-0853g PRED entity: 0853g PRED relation: location! PRED expected values: 0bg539 => 150 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1523): 02r6c_ (0.76 #47881, 0.71 #35277, 0.55 #32755), 0136g9 (0.76 #47881, 0.71 #35277, 0.54 #63002), 024n3z (0.76 #47881, 0.71 #35277, 0.54 #63002), 06dv3 (0.76 #47881, 0.71 #35277, 0.54 #63002), 09l9tq (0.67 #32756, 0.52 #20156, 0.49 #83165), 09ntbc (0.67 #32756, 0.52 #20156, 0.49 #83165), 08n__5 (0.54 #63002, 0.52 #20156, 0.44 #32754), 02bfxb (0.52 #20156, 0.44 #32754, 0.44 #35276), 023kzp (0.38 #13812, 0.16 #31451, 0.15 #41535), 05ry0p (0.38 #14757, 0.16 #32396, 0.12 #22318) >> Best rule #47881 for best value: >> intensional similarity = 6 >> extensional distance = 26 >> proper extension: 0k33p; >> query: (?x11743, ?x8812) <- place_of_birth(?x9088, ?x11743), place_of_birth(?x8812, ?x11743), place_of_birth(?x2727, ?x11743), team(?x9088, ?x4148), location(?x8812, ?x5036), award_winner(?x629, ?x2727) >> conf = 0.76 => this is the best rule for 4 predicted values *> Best rule #63004 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 0f04v; *> query: (?x11743, ?x3568) <- place_of_birth(?x1367, ?x11743), mode_of_transportation(?x11743, ?x4272), award_nominee(?x1367, ?x10025), award_nominee(?x1367, ?x3568), actor(?x5529, ?x10025) *> conf = 0.05 ranks of expected_values: 1192 EVAL 0853g location! 0bg539 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 150.000 36.000 0.764 http://example.org/people/person/places_lived./people/place_lived/location #1194-07z1_q PRED entity: 07z1_q PRED relation: award_nominee PRED expected values: 05lb65 => 119 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1157): 04psyp (0.84 #3134, 0.82 #807, 0.82 #30255), 07z1_q (0.84 #3055, 0.82 #728, 0.36 #4655), 05lb65 (0.82 #30255, 0.81 #104730, 0.81 #104729), 026zvx7 (0.82 #30255, 0.81 #104730, 0.81 #104729), 03x16f (0.82 #30255, 0.81 #104730, 0.81 #104729), 03w4sh (0.59 #1491, 0.58 #3818, 0.36 #4655), 01wb8bs (0.53 #3223, 0.47 #896, 0.36 #4655), 05lb87 (0.47 #2603, 0.41 #276, 0.36 #4655), 0308kx (0.47 #3281, 0.41 #954, 0.36 #4655), 058ncz (0.37 #2422, 0.36 #4655, 0.35 #95) >> Best rule #3134 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 038g2x; 05dxl5; >> query: (?x3272, 04psyp) <- award_nominee(?x3272, ?x10004), award_nominee(?x3272, ?x3956), ?x10004 = 04vmqg, award_nominee(?x3956, ?x515) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #30255 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 173 *> proper extension: 042kbj; 01lc5; *> query: (?x3272, ?x2579) <- award_nominee(?x3272, ?x444), people(?x4659, ?x3272), award_nominee(?x2579, ?x3272) *> conf = 0.82 ranks of expected_values: 3 EVAL 07z1_q award_nominee 05lb65 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 64.000 0.842 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1193-03ww_x PRED entity: 03ww_x PRED relation: district_represented PRED expected values: 07_f2 => 37 concepts (29 used for prediction) PRED predicted values (max 10 best out of 73): 01x73 (0.90 #1521, 0.90 #1474, 0.87 #1661), 05tbn (0.87 #1624, 0.85 #1487, 0.71 #948), 05fjf (0.85 #1523, 0.85 #1500, 0.81 #1637), 07_f2 (0.84 #1639, 0.80 #1502, 0.74 #1847), 04ych (0.77 #1605, 0.75 #1526, 0.75 #1468), 0498y (0.77 #1629, 0.75 #1492, 0.71 #953), 03s0w (0.75 #1522, 0.75 #1466, 0.71 #978), 0gyh (0.74 #1618, 0.71 #942, 0.70 #1481), 05fjy (0.71 #983, 0.71 #959, 0.70 #1498), 05fky (0.71 #980, 0.71 #955, 0.67 #833) >> Best rule #1521 for best value: >> intensional similarity = 38 >> extensional distance = 18 >> proper extension: 043djx; 01gtcc; >> query: (?x606, ?x1755) <- legislative_sessions(?x6933, ?x606), legislative_sessions(?x4821, ?x606), legislative_sessions(?x1137, ?x606), district_represented(?x606, ?x1906), legislative_sessions(?x652, ?x606), legislative_sessions(?x606, ?x1830), district_represented(?x1137, ?x6895), district_represented(?x1137, ?x1767), district_represented(?x1137, ?x1755), district_represented(?x1137, ?x1227), district_represented(?x1137, ?x1025), district_represented(?x1137, ?x961), legislative_sessions(?x5932, ?x1137), legislative_sessions(?x2860, ?x4821), district_represented(?x4821, ?x4600), location(?x1727, ?x4600), contains(?x4600, ?x1087), district_represented(?x6933, ?x1782), ?x1755 = 01x73, religion(?x1782, ?x1624), ?x961 = 03s0w, state(?x3521, ?x1782), ?x1624 = 051kv, ?x6895 = 05fjf, citytown(?x9309, ?x4600), ?x1227 = 01n7q, ?x1767 = 04rrd, partially_contains(?x1782, ?x13214), ?x1025 = 04ych, ?x1906 = 04rrx, state_province_region(?x1783, ?x1782), politician(?x8714, ?x5932), partially_contains(?x4600, ?x6195), ?x13214 = 04ykz, time_zones(?x1782, ?x2088), adjoins(?x2982, ?x1782), jurisdiction_of_office(?x900, ?x4600), contains(?x1782, ?x2986) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #1639 for first EXPECTED value: *> intensional similarity = 38 *> extensional distance = 29 *> proper extension: 01gsvp; 01h7xx; *> query: (?x606, 07_f2) <- legislative_sessions(?x6933, ?x606), legislative_sessions(?x4821, ?x606), legislative_sessions(?x1137, ?x606), district_represented(?x606, ?x448), legislative_sessions(?x652, ?x606), legislative_sessions(?x606, ?x1830), district_represented(?x1137, ?x1755), district_represented(?x1137, ?x961), legislative_sessions(?x2357, ?x1137), legislative_sessions(?x2860, ?x4821), district_represented(?x4821, ?x7058), district_represented(?x4821, ?x4600), district_represented(?x4821, ?x3634), location(?x1727, ?x4600), contains(?x4600, ?x1087), district_represented(?x6933, ?x2713), district_represented(?x6933, ?x1782), ?x1755 = 01x73, religion(?x1782, ?x8249), state_province_region(?x244, ?x4600), ?x448 = 03v1s, contains(?x961, ?x310), location(?x10473, ?x961), featured_film_locations(?x945, ?x3634), citytown(?x11503, ?x4600), state_province_region(?x216, ?x3634), state_province_region(?x1783, ?x1782), time_zones(?x3634, ?x1638), location(?x56, ?x3634), place_of_birth(?x12725, ?x961), partially_contains(?x961, ?x13214), religion(?x961, ?x962), ?x10473 = 023s8, ?x2713 = 06btq, ?x8249 = 021_0p, legislative_sessions(?x3445, ?x6933), ?x7058 = 050ks, adjoins(?x4600, ?x2768) *> conf = 0.84 ranks of expected_values: 4 EVAL 03ww_x district_represented 07_f2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 37.000 29.000 0.900 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #1192-04k05 PRED entity: 04k05 PRED relation: group! PRED expected values: 0pkyh => 139 concepts (85 used for prediction) PRED predicted values (max 10 best out of 103): 0gkg6 (0.11 #453, 0.09 #856, 0.04 #4677), 01nrz4 (0.11 #592, 0.09 #995, 0.03 #2400), 020hh3 (0.11 #563, 0.09 #966, 0.03 #2371), 01vswwx (0.05 #1303, 0.05 #1504, 0.03 #2105), 01vswx5 (0.05 #1301, 0.05 #1502, 0.03 #2103), 01vs14j (0.05 #1225, 0.05 #1426, 0.03 #2027), 01p95y0 (0.05 #1184, 0.03 #2590, 0.02 #4402), 01mwsnc (0.05 #1096, 0.03 #2502, 0.02 #4314), 014q2g (0.05 #1051, 0.03 #2457, 0.02 #4269), 01w724 (0.05 #1050, 0.03 #2456, 0.02 #4268) >> Best rule #453 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 04rcr; 0pkyh; 04b7xr; 01m7pwq; 023p29; 01y_rz; 03x82v; >> query: (?x10671, 0gkg6) <- artists(?x2249, ?x10671), award_winner(?x4608, ?x10671), ?x2249 = 03lty, award_winner(?x1088, ?x10671) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04k05 group! 0pkyh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 139.000 85.000 0.111 http://example.org/music/group_member/membership./music/group_membership/group #1191-01vtqml PRED entity: 01vtqml PRED relation: profession PRED expected values: 03gjzk 039v1 => 146 concepts (142 used for prediction) PRED predicted values (max 10 best out of 117): 0dz3r (0.56 #6587, 0.52 #1578, 0.51 #2150), 039v1 (0.50 #1033, 0.48 #604, 0.42 #1176), 0n1h (0.48 #583, 0.40 #1155, 0.36 #1299), 0np9r (0.47 #12482, 0.14 #7895, 0.12 #448), 01d_h8 (0.44 #1867, 0.43 #3870, 0.40 #5302), 0dxtg (0.34 #3878, 0.27 #12045, 0.27 #9894), 03gjzk (0.32 #3879, 0.29 #4165, 0.27 #9180), 02jknp (0.21 #11753, 0.21 #15193, 0.21 #16194), 0fnpj (0.19 #3062, 0.19 #3205, 0.17 #1631), 018gz8 (0.17 #7892, 0.17 #4167, 0.17 #3881) >> Best rule #6587 for best value: >> intensional similarity = 5 >> extensional distance = 302 >> proper extension: 01kwlwp; 04bpm6; 0770cd; 02fgpf; 01364q; 06x4l_; 0412f5y; 01wn718; 02jxmr; 01wwvd2; ... >> query: (?x3933, 0dz3r) <- profession(?x3933, ?x2348), profession(?x3933, ?x1032), ?x2348 = 0nbcg, profession(?x4634, ?x1032), ?x4634 = 04g3p5 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #1033 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 34 *> proper extension: 0lbj1; 01q7cb_; 01vrncs; 0137n0; 01kx_81; 0l12d; 0j1yf; 0285c; 0zjpz; 04mn81; ... *> query: (?x3933, 039v1) <- instrumentalists(?x227, ?x3933), group(?x3933, ?x2073), participant(?x1231, ?x3933), artists(?x1000, ?x3933) *> conf = 0.50 ranks of expected_values: 2, 7 EVAL 01vtqml profession 039v1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 146.000 142.000 0.562 http://example.org/people/person/profession EVAL 01vtqml profession 03gjzk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 146.000 142.000 0.562 http://example.org/people/person/profession #1190-0h_9252 PRED entity: 0h_9252 PRED relation: award_winner PRED expected values: 0hskw 04cw0j => 28 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2099): 0h0wc (0.61 #14257, 0.20 #4992, 0.17 #11165), 02fn5r (0.56 #12723, 0.09 #29705, 0.08 #21988), 01gq0b (0.50 #3345, 0.33 #1804, 0.20 #4891), 0bj9k (0.50 #3365, 0.33 #1824, 0.20 #4911), 020_95 (0.50 #3930, 0.33 #2389, 0.20 #5476), 01wy5m (0.50 #3842, 0.33 #2301, 0.20 #5388), 03f1zdw (0.50 #3242, 0.33 #1701, 0.20 #4788), 01d8yn (0.50 #3649, 0.33 #2108, 0.20 #5195), 02zj61 (0.50 #4605, 0.33 #3064, 0.20 #6151), 027zz (0.50 #4531, 0.33 #2990, 0.20 #6077) >> Best rule #14257 for best value: >> intensional similarity = 14 >> extensional distance = 16 >> proper extension: 092c5f; 0gmdkyy; 02hn5v; 026kq4q; 058m5m4; 092_25; 013b2h; >> query: (?x4141, 0h0wc) <- award_winner(?x4141, ?x8412), award_winner(?x4141, ?x7274), award_winner(?x4141, ?x541), award_nominee(?x7274, ?x902), ceremony(?x5841, ?x4141), ceremony(?x458, ?x4141), award_nominee(?x519, ?x541), award_winner(?x770, ?x541), nominated_for(?x541, ?x6288), nominated_for(?x902, ?x103), category(?x8412, ?x134), ?x6288 = 01chpn, award(?x538, ?x5841), award(?x541, ?x1105) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #470 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 0ds460j; *> query: (?x4141, 04cw0j) <- award_winner(?x4141, ?x7274), award_winner(?x4141, ?x541), award_nominee(?x7274, ?x902), ceremony(?x3989, ?x4141), ceremony(?x1058, ?x4141), award_nominee(?x6883, ?x541), award_winner(?x770, ?x541), ?x902 = 05qd_, award_winner(?x2022, ?x541), nominated_for(?x541, ?x821), ?x1058 = 086vfb, ?x3989 = 0bsjcw, film(?x6883, ?x1080), nominated_for(?x2022, ?x148) *> conf = 0.33 ranks of expected_values: 38, 305 EVAL 0h_9252 award_winner 04cw0j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 28.000 21.000 0.611 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0h_9252 award_winner 0hskw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 28.000 21.000 0.611 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1189-01wgcvn PRED entity: 01wgcvn PRED relation: award PRED expected values: 05ztrmj => 99 concepts (80 used for prediction) PRED predicted values (max 10 best out of 254): 01by1l (0.35 #1685, 0.34 #4050, 0.31 #4444), 01c99j (0.34 #1005, 0.17 #3370, 0.16 #3764), 02f6ym (0.34 #1037, 0.15 #3402, 0.15 #3796), 03qbnj (0.34 #1012, 0.13 #4165, 0.13 #3377), 01bgqh (0.31 #830, 0.29 #3983, 0.26 #6741), 02f5qb (0.31 #940, 0.13 #1728, 0.11 #3305), 054krc (0.31 #1267, 0.11 #4420, 0.10 #5208), 0l8z1 (0.28 #1244, 0.08 #5185, 0.07 #5973), 02f777 (0.28 #1088, 0.15 #694, 0.12 #1876), 09qvc0 (0.25 #40, 0.18 #7094, 0.13 #31535) >> Best rule #1685 for best value: >> intensional similarity = 2 >> extensional distance = 133 >> proper extension: 0m19t; 01gx5f; 01wy61y; >> query: (?x3756, 01by1l) <- artists(?x2937, ?x3756), ?x2937 = 0glt670 >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #7094 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 415 *> proper extension: 0187x8; 03vhvp; 0cbm64; *> query: (?x3756, ?x704) <- award_nominee(?x3708, ?x3756), award(?x3708, ?x704), artist(?x382, ?x3756) *> conf = 0.18 ranks of expected_values: 43 EVAL 01wgcvn award 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 99.000 80.000 0.348 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1188-0bg539 PRED entity: 0bg539 PRED relation: award PRED expected values: 09qrn4 => 125 concepts (98 used for prediction) PRED predicted values (max 10 best out of 284): 025m8l (0.50 #522, 0.40 #1731, 0.36 #5358), 02x17c2 (0.50 #621, 0.40 #1830, 0.33 #2233), 01by1l (0.45 #5351, 0.40 #1724, 0.33 #2127), 03qbh5 (0.40 #1816, 0.36 #5443, 0.33 #2219), 099vwn (0.40 #1827, 0.33 #4245, 0.33 #2230), 01bgqh (0.40 #1655, 0.33 #2058, 0.30 #12133), 03qbnj (0.40 #1844, 0.33 #2247, 0.29 #3456), 0c4z8 (0.38 #3699, 0.25 #475, 0.20 #15789), 02v1m7 (0.36 #5352, 0.20 #1725, 0.17 #2128), 02f705 (0.36 #5391, 0.20 #1764, 0.17 #2167) >> Best rule #522 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03j24kf; >> query: (?x1294, 025m8l) <- role(?x1294, ?x1750), producer_type(?x1294, ?x632), instrumentalists(?x212, ?x1294), instrumentalists(?x1750, ?x300) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #6283 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 0q5hw; 02778yp; 0362q0; 01qbjg; 03cws8h; 03wh95l; *> query: (?x1294, 09qrn4) <- profession(?x1294, ?x1032), ?x1032 = 02hrh1q, award(?x1294, ?x3906), ?x3906 = 03ccq3s *> conf = 0.20 ranks of expected_values: 49 EVAL 0bg539 award 09qrn4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 125.000 98.000 0.500 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1187-04rrx PRED entity: 04rrx PRED relation: district_represented! PRED expected values: 06f0dc 01gtdd => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 36): 06f0dc (0.82 #39, 0.80 #435, 0.79 #219), 01gtdd (0.53 #65, 0.43 #245, 0.42 #533), 01gt99 (0.47 #68, 0.44 #536, 0.43 #248), 01gtbb (0.47 #43, 0.40 #223, 0.40 #511), 01gst_ (0.42 #512, 0.41 #44, 0.40 #224), 01gsvb (0.41 #61, 0.38 #529, 0.38 #241), 01gstn (0.38 #519, 0.38 #231, 0.38 #159), 01gsvp (0.36 #522, 0.35 #54, 0.34 #162), 01gssz (0.33 #460, 0.33 #244, 0.33 #532), 01gssm (0.33 #227, 0.33 #515, 0.31 #443) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 03v1s; 05fhy; 04ych; 04ykg; 0gyh; 07b_l; 07h34; 03v0t; 04ly1; 0498y; ... >> query: (?x1906, 06f0dc) <- capital(?x1906, ?x12488), time_zones(?x1906, ?x1638), ?x1638 = 02fqwt >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04rrx district_represented! 01gtdd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.824 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 04rrx district_represented! 06f0dc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 165.000 0.824 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #1186-0jmbv PRED entity: 0jmbv PRED relation: team! PRED expected values: 03lh3v => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 113): 012xdf (0.45 #2632, 0.38 #1174, 0.33 #1622), 01gct2 (0.33 #66, 0.25 #512, 0.20 #623), 0hcs3 (0.33 #2779, 0.18 #2329, 0.14 #3672), 02lm0t (0.29 #994, 0.25 #550, 0.22 #1777), 03n69x (0.27 #2253, 0.20 #2703, 0.16 #7060), 03lh3v (0.25 #1242, 0.25 #1131, 0.25 #464), 0cymln (0.25 #1412, 0.25 #410, 0.22 #1861), 054c1 (0.25 #1430, 0.22 #1879, 0.20 #2103), 02_nkp (0.25 #323, 0.14 #1102, 0.12 #1550), 095nx (0.25 #330, 0.14 #1109, 0.12 #1557) >> Best rule #2632 for best value: >> intensional similarity = 9 >> extensional distance = 9 >> proper extension: 01lpx8; >> query: (?x6089, 012xdf) <- teams(?x6088, ?x6089), team(?x12339, ?x6089), people(?x2510, ?x12339), athlete(?x4833, ?x12339), film(?x12339, ?x12899), student(?x4955, ?x12339), gender(?x12339, ?x231), ?x231 = 05zppz, location(?x12339, ?x1131) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1242 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 6 *> proper extension: 0jm5b; *> query: (?x6089, 03lh3v) <- teams(?x6088, ?x6089), team(?x12339, ?x6089), position(?x6089, ?x6848), ?x6848 = 02_ssl, category(?x6088, ?x134), dog_breed(?x6088, ?x1706), place_of_birth(?x2794, ?x6088), country(?x6088, ?x94), film(?x2794, ?x1210), award_winner(?x2794, ?x6771) *> conf = 0.25 ranks of expected_values: 6 EVAL 0jmbv team! 03lh3v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 105.000 105.000 0.455 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #1185-095x_ PRED entity: 095x_ PRED relation: music! PRED expected values: 0372j5 => 94 concepts (73 used for prediction) PRED predicted values (max 10 best out of 75): 07bzz7 (0.11 #1546, 0.03 #4594, 0.03 #5610), 035zr0 (0.05 #1763, 0.04 #2779, 0.03 #4811), 0dgpwnk (0.05 #1352, 0.04 #2368, 0.03 #4400), 0456zg (0.05 #1834, 0.03 #5898, 0.01 #6914), 08l0x2 (0.05 #1771, 0.03 #5835, 0.01 #6851), 0401sg (0.04 #2083, 0.03 #4115, 0.01 #6147), 06pyc2 (0.04 #2999, 0.03 #5031, 0.01 #7063), 0djlxb (0.04 #2354, 0.03 #4386, 0.01 #6418), 09yxcz (0.04 #4006), 03_gz8 (0.04 #3704) >> Best rule #1546 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 014488; 07h5d; >> query: (?x8035, 07bzz7) <- profession(?x8035, ?x319), location(?x8035, ?x2254), ?x319 = 01d_h8, group(?x8035, ?x2567) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 095x_ music! 0372j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 73.000 0.105 http://example.org/film/film/music #1184-0h7x PRED entity: 0h7x PRED relation: adjoins! PRED expected values: 0345h => 156 concepts (100 used for prediction) PRED predicted values (max 10 best out of 602): 03rjj (0.86 #40637, 0.85 #51584, 0.84 #51583), 01mjq (0.86 #40637, 0.84 #51583, 0.83 #77384), 04j53 (0.86 #40637, 0.84 #51583, 0.83 #34383), 0f8l9c (0.29 #820, 0.18 #26603, 0.17 #7848), 06bnz (0.27 #7895, 0.23 #3209, 0.15 #25867), 0345h (0.24 #845, 0.20 #63, 0.17 #9437), 077qn (0.23 #3322, 0.20 #8008, 0.10 #34582), 05rgl (0.17 #1663, 0.16 #2443, 0.12 #882), 03rk0 (0.15 #26677, 0.09 #29802, 0.08 #32930), 015qh (0.14 #3203, 0.12 #861, 0.08 #10233) >> Best rule #40637 for best value: >> intensional similarity = 3 >> extensional distance = 76 >> proper extension: 09hzw; >> query: (?x1355, ?x205) <- contains(?x455, ?x1355), adjoins(?x1355, ?x205), administrative_parent(?x863, ?x1355) >> conf = 0.86 => this is the best rule for 3 predicted values *> Best rule #845 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 06q1r; *> query: (?x1355, 0345h) <- country(?x150, ?x1355), film_release_region(?x66, ?x1355), first_level_division_of(?x863, ?x1355) *> conf = 0.24 ranks of expected_values: 6 EVAL 0h7x adjoins! 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 156.000 100.000 0.856 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #1183-027kp3 PRED entity: 027kp3 PRED relation: school_type PRED expected values: 04399 052q4j => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 17): 05jxkf (0.53 #642, 0.51 #1460, 0.51 #1261), 01rs41 (0.34 #158, 0.27 #510, 0.27 #1328), 07tf8 (0.17 #118, 0.15 #206, 0.14 #184), 01_9fk (0.14 #331, 0.14 #551, 0.13 #463), 04399 (0.13 #123, 0.03 #167, 0.03 #1138), 01_srz (0.12 #156, 0.08 #90, 0.06 #861), 02p0qmm (0.04 #207, 0.04 #934, 0.03 #979), 06cs1 (0.04 #159, 0.02 #335, 0.02 #555), 04qbv (0.03 #543, 0.03 #654, 0.02 #698), 0bpgx (0.03 #195, 0.02 #305, 0.01 #1299) >> Best rule #642 for best value: >> intensional similarity = 4 >> extensional distance = 266 >> proper extension: 0269kx; 02zkz7; 057wlm; 06l32y; 016sd3; 02jx_v; 03wv2g; 019tfm; >> query: (?x4794, 05jxkf) <- colors(?x4794, ?x663), currency(?x4794, ?x170), organization(?x346, ?x4794), school_type(?x4794, ?x1044) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 21 *> proper extension: 022xml; *> query: (?x4794, 04399) <- major_field_of_study(?x4794, ?x3490), colors(?x4794, ?x663), ?x3490 = 05qfh, ?x663 = 083jv *> conf = 0.13 ranks of expected_values: 5 EVAL 027kp3 school_type 052q4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 115.000 0.526 http://example.org/education/educational_institution/school_type EVAL 027kp3 school_type 04399 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 115.000 115.000 0.526 http://example.org/education/educational_institution/school_type #1182-05842k PRED entity: 05842k PRED relation: group PRED expected values: 011xhx => 88 concepts (61 used for prediction) PRED predicted values (max 10 best out of 922): 0b_xm (0.71 #4245, 0.57 #5382, 0.50 #8605), 02vnpv (0.67 #8856, 0.64 #8668, 0.64 #8480), 05563d (0.67 #6460, 0.57 #8544, 0.57 #4565), 01fchy (0.60 #2584, 0.53 #8832, 0.50 #1457), 02cw1m (0.60 #2581, 0.50 #7884, 0.44 #9398), 03qkcn9 (0.60 #2629, 0.42 #7932, 0.36 #8689), 03k3b (0.60 #2551, 0.33 #6336, 0.33 #3872), 047cx (0.57 #5144, 0.50 #10834, 0.50 #3817), 0134wr (0.57 #4445, 0.42 #7859, 0.40 #8804), 07mvp (0.50 #9344, 0.50 #7259, 0.50 #3658) >> Best rule #4245 for best value: >> intensional similarity = 16 >> extensional distance = 5 >> proper extension: 03gvt; >> query: (?x3991, 0b_xm) <- role(?x3991, ?x645), role(?x3991, ?x1495), role(?x3991, ?x1473), role(?x3991, ?x1225), role(?x1212, ?x3991), ?x1473 = 0g2dz, role(?x7084, ?x3991), role(?x3735, ?x3991), role(?x1092, ?x3991), ?x1225 = 01qbl, ?x3735 = 0lzkm, profession(?x1092, ?x131), ?x7084 = 01vs4ff, ?x1495 = 013y1f, instrumentalists(?x1212, ?x672), artists(?x505, ?x1092) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2261 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 3 *> proper extension: 0fnpj; *> query: (?x3991, ?x379) <- split_to(?x3991, ?x315), role(?x460, ?x315), role(?x315, ?x885), role(?x315, ?x716), role(?x315, ?x316), role(?x1969, ?x315), ?x716 = 018vs, group(?x315, ?x379), ?x885 = 0dwtp, instrumentalists(?x315, ?x226), ?x316 = 05r5c, ?x1969 = 04rzd, performance_role(?x315, ?x1225) *> conf = 0.36 ranks of expected_values: 119 EVAL 05842k group 011xhx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 88.000 61.000 0.714 http://example.org/music/performance_role/regular_performances./music/group_membership/group #1181-03gh4 PRED entity: 03gh4 PRED relation: location_of_ceremony! PRED expected values: 04ztj => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.99 #366, 0.99 #435, 0.99 #299), 0jgjn (0.13 #27, 0.07 #45, 0.06 #51), 01bl8s (0.01 #125, 0.01 #155, 0.01 #149) >> Best rule #366 for best value: >> intensional similarity = 3 >> extensional distance = 284 >> proper extension: 0ydpd; 0ftxw; 049d1; 0m2rv; 0tbql; 019fh; 0h8d; 0lhql; 0gqkd; 0l0mk; ... >> query: (?x6226, 04ztj) <- location(?x2415, ?x6226), contains(?x94, ?x6226), location_of_ceremony(?x1873, ?x6226) >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03gh4 location_of_ceremony! 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 181.000 0.990 http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony #1180-0gk7z PRED entity: 0gk7z PRED relation: list PRED expected values: 09g7thr => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 4): 09g7thr (0.60 #127, 0.58 #85, 0.56 #120), 01ptsx (0.18 #509, 0.10 #110, 0.09 #782), 04k4rt (0.13 #508, 0.07 #263, 0.06 #781), 01pd60 (0.12 #510, 0.07 #237, 0.06 #216) >> Best rule #127 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 017ztv; >> query: (?x9823, 09g7thr) <- student(?x9823, ?x3461), company(?x11492, ?x9823), award_winner(?x704, ?x3461), institution(?x865, ?x9823), currency(?x9823, ?x1099) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gk7z list 09g7thr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.596 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #1179-0c4f4 PRED entity: 0c4f4 PRED relation: award PRED expected values: 0cqhk0 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 256): 09qj50 (0.72 #35963, 0.70 #23708, 0.70 #24895), 0fbtbt (0.39 #1804, 0.35 #2989, 0.29 #5359), 02x4w6g (0.31 #899, 0.17 #18174, 0.15 #19361), 0gqy2 (0.30 #552, 0.12 #20942, 0.12 #34776), 0cqhk0 (0.25 #36, 0.17 #18174, 0.15 #19361), 09qs08 (0.25 #138, 0.17 #18174, 0.15 #19361), 02z0dfh (0.25 #73, 0.15 #19361, 0.12 #20942), 0hnf5vm (0.25 #180, 0.15 #19361, 0.12 #20942), 05q5t0b (0.25 #156, 0.15 #19361, 0.12 #20942), 0gqmvn (0.25 #266, 0.15 #19361, 0.12 #20942) >> Best rule #35963 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 06lxn; >> query: (?x495, ?x757) <- award_winner(?x757, ?x495), award(?x444, ?x757) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #36 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0603qp; *> query: (?x495, 0cqhk0) <- award_nominee(?x3739, ?x495), nominated_for(?x495, ?x1045), ?x3739 = 01y0y6 *> conf = 0.25 ranks of expected_values: 5 EVAL 0c4f4 award 0cqhk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 106.000 106.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1178-04f_d PRED entity: 04f_d PRED relation: place! PRED expected values: 04f_d => 189 concepts (186 used for prediction) PRED predicted values (max 10 best out of 227): 0d9jr (0.11 #130, 0.09 #645, 0.04 #27301), 0c_m3 (0.11 #132, 0.04 #27301, 0.04 #15967), 0vzm (0.11 #71, 0.04 #27301, 0.04 #15967), 094jv (0.11 #36, 0.04 #27301, 0.04 #15967), 0dq16 (0.11 #115, 0.04 #27301, 0.04 #15967), 099ty (0.11 #42, 0.04 #27301, 0.04 #15967), 0ftvz (0.11 #51, 0.04 #27301, 0.04 #15967), 02_286 (0.09 #529, 0.06 #63891, 0.04 #70589), 06wxw (0.09 #615, 0.03 #3705, 0.03 #4220), 0c1d0 (0.09 #732, 0.03 #4337, 0.03 #4852) >> Best rule #130 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0dq16; >> query: (?x2017, 0d9jr) <- locations(?x7378, ?x2017), state(?x2017, ?x1025), ?x7378 = 0bzrxn >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #63891 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 237 *> proper extension: 0d331; 0qb62; *> query: (?x2017, ?x362) <- featured_film_locations(?x1015, ?x2017), genre(?x1015, ?x53), featured_film_locations(?x1015, ?x362) *> conf = 0.06 ranks of expected_values: 12 EVAL 04f_d place! 04f_d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 189.000 186.000 0.111 http://example.org/location/hud_county_place/place #1177-02lg9w PRED entity: 02lg9w PRED relation: profession PRED expected values: 02hrh1q => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 52): 02hrh1q (0.87 #766, 0.86 #6919, 0.86 #1066), 03gjzk (0.37 #917, 0.33 #2552, 0.25 #8706), 0dxtg (0.33 #915, 0.33 #2552, 0.28 #5417), 01d_h8 (0.33 #2552, 0.31 #5409, 0.30 #3158), 02jknp (0.33 #2552, 0.25 #8706, 0.25 #7655), 02krf9 (0.33 #2552, 0.25 #8706, 0.25 #7655), 0d1pc (0.33 #2552, 0.25 #8706, 0.25 #7655), 09jwl (0.28 #4653, 0.25 #8706, 0.25 #7655), 0cbd2 (0.28 #4653, 0.25 #8706, 0.25 #7655), 0nbcg (0.28 #4653, 0.25 #8706, 0.25 #7655) >> Best rule #766 for best value: >> intensional similarity = 3 >> extensional distance = 347 >> proper extension: 0h32q; 02tkzn; >> query: (?x1651, 02hrh1q) <- award(?x1651, ?x1670), award_winner(?x369, ?x1651), actor(?x1849, ?x1651) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lg9w profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.868 http://example.org/people/person/profession #1176-0jpkw PRED entity: 0jpkw PRED relation: major_field_of_study PRED expected values: 06ms6 04x_3 => 202 concepts (137 used for prediction) PRED predicted values (max 10 best out of 121): 01mkq (0.61 #380, 0.55 #4897, 0.48 #746), 01tbp (0.61 #425, 0.34 #4942, 0.28 #4452), 02j62 (0.56 #395, 0.55 #1737, 0.52 #4912), 062z7 (0.56 #392, 0.37 #4909, 0.37 #10286), 01lj9 (0.47 #6997, 0.44 #405, 0.35 #4922), 02ky346 (0.44 #381, 0.24 #1113, 0.24 #4408), 03g3w (0.40 #1733, 0.40 #4908, 0.40 #4174), 05qfh (0.38 #1743, 0.34 #2719, 0.34 #1865), 04x_3 (0.33 #2586, 0.33 #390, 0.31 #2708), 01540 (0.33 #426, 0.30 #4943, 0.30 #1768) >> Best rule #380 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 0ym17; >> query: (?x9880, 01mkq) <- institution(?x1771, ?x9880), ?x1771 = 019v9k, major_field_of_study(?x9880, ?x5900), contains(?x279, ?x9880), ?x5900 = 0db86 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #2586 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 55 *> proper extension: 05krk; 01j_9c; 06pwq; 01w3v; 0kz2w; 04rwx; 07vk2; 01jq34; 07wjk; 01wdj_; ... *> query: (?x9880, 04x_3) <- colors(?x9880, ?x663), major_field_of_study(?x9880, ?x742), institution(?x865, ?x9880), ?x742 = 05qjt, currency(?x9880, ?x2244) *> conf = 0.33 ranks of expected_values: 9, 18 EVAL 0jpkw major_field_of_study 04x_3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 202.000 137.000 0.611 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0jpkw major_field_of_study 06ms6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 202.000 137.000 0.611 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1175-01w5m PRED entity: 01w5m PRED relation: company! PRED expected values: 076_74 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 209): 042kg (0.33 #682, 0.05 #2330, 0.02 #2565), 0203v (0.33 #495, 0.03 #5439, 0.02 #2143), 0157m (0.33 #496, 0.02 #2144, 0.02 #5440), 034ls (0.33 #616, 0.02 #2264, 0.02 #5560), 0d06m5 (0.33 #529, 0.02 #2177, 0.02 #5473), 042fk (0.33 #703, 0.02 #2351, 0.02 #2821), 06c0j (0.33 #699, 0.02 #2347, 0.02 #2817), 038w8 (0.33 #688, 0.02 #2336, 0.02 #2806), 0d3k14 (0.33 #678, 0.02 #2326, 0.02 #2796), 07hyk (0.33 #671, 0.02 #2319, 0.02 #2789) >> Best rule #682 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 09c7w0; >> query: (?x3424, 042kg) <- company(?x2663, ?x3424), ?x2663 = 028rk, company(?x346, ?x3424) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01w5m company! 076_74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 99.000 0.333 http://example.org/people/person/employment_history./business/employment_tenure/company #1174-032md PRED entity: 032md PRED relation: influenced_by! PRED expected values: 0lrh => 140 concepts (33 used for prediction) PRED predicted values (max 10 best out of 343): 0gdqy (0.33 #1030, 0.30 #9273, 0.29 #14423), 047g6 (0.33 #995, 0.18 #3569, 0.14 #1510), 014ps4 (0.33 #1854, 0.09 #7519, 0.07 #11132), 07lp1 (0.22 #1961, 0.18 #7626, 0.12 #11239), 040db (0.22 #1619, 0.14 #10897, 0.12 #7284), 07dnx (0.22 #1905, 0.14 #3964, 0.10 #9119), 0d4jl (0.22 #1660, 0.12 #7325, 0.10 #8874), 0c1fs (0.22 #1889, 0.09 #7554, 0.07 #9103), 0lcx (0.22 #1696, 0.07 #10974, 0.07 #8910), 06jcc (0.22 #1856, 0.04 #10102, 0.04 #11134) >> Best rule #1030 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 047g6; >> query: (?x8043, ?x10354) <- peers(?x10354, ?x8043), place_of_birth(?x8043, ?x863), nationality(?x8043, ?x1355), ?x1355 = 0h7x, influenced_by(?x1431, ?x8043) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7313 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 31 *> proper extension: 03j43; 0dzkq; *> query: (?x8043, 0lrh) <- peers(?x10354, ?x8043), type_of_union(?x8043, ?x566), location(?x8043, ?x863), influenced_by(?x1431, ?x8043), nationality(?x8043, ?x1355) *> conf = 0.06 ranks of expected_values: 106 EVAL 032md influenced_by! 0lrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 140.000 33.000 0.333 http://example.org/influence/influence_node/influenced_by #1173-06z8s_ PRED entity: 06z8s_ PRED relation: executive_produced_by PRED expected values: 0glyyw => 95 concepts (68 used for prediction) PRED predicted values (max 10 best out of 90): 0glyyw (0.33 #189, 0.06 #2971, 0.05 #2718), 079vf (0.09 #255, 0.06 #1015, 0.06 #1267), 05hj_k (0.08 #604, 0.05 #12768, 0.04 #1363), 04jspq (0.08 #657, 0.03 #4205, 0.03 #3696), 02xnjd (0.06 #429, 0.02 #1189, 0.02 #1441), 06pj8 (0.06 #1068, 0.05 #815, 0.04 #1573), 014zcr (0.06 #760, 0.06 #3545, 0.03 #5065), 06t8b (0.06 #760, 0.06 #3545, 0.03 #5065), 0c6qh (0.06 #3545, 0.03 #5065, 0.03 #3798), 06q8hf (0.05 #12837, 0.04 #13847, 0.04 #10307) >> Best rule #189 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0418wg; >> query: (?x876, 0glyyw) <- genre(?x876, ?x225), film_crew_role(?x876, ?x137), nominated_for(?x8619, ?x876), ?x8619 = 02lj6p >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06z8s_ executive_produced_by 0glyyw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 68.000 0.333 http://example.org/film/film/executive_produced_by #1172-030k94 PRED entity: 030k94 PRED relation: genre PRED expected values: 01t_vv 0gs6m => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 83): 01htzx (0.30 #987, 0.25 #825, 0.25 #744), 01t_vv (0.29 #274, 0.27 #760, 0.26 #841), 0c4xc (0.27 #931, 0.27 #526, 0.25 #202), 0gf28 (0.25 #201, 0.14 #282, 0.07 #3980), 0gs6m (0.25 #195, 0.12 #357, 0.09 #438), 0l4h_ (0.25 #209, 0.07 #290, 0.07 #3980), 06cvj (0.25 #165, 0.07 #246, 0.07 #3980), 0hcr (0.24 #3425, 0.19 #3833, 0.19 #3181), 06n90 (0.24 #983, 0.21 #821, 0.21 #1551), 03k9fj (0.24 #981, 0.18 #819, 0.17 #738) >> Best rule #987 for best value: >> intensional similarity = 4 >> extensional distance = 94 >> proper extension: 017dcd; 01h72l; 05f7w84; 028k2x; 03r0rq; 05b6s5j; 01j95; >> query: (?x3169, 01htzx) <- program(?x1394, ?x3169), genre(?x3169, ?x53), genre(?x2475, ?x53), ?x2475 = 0jdgr >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #274 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 09dv8h; *> query: (?x3169, 01t_vv) <- nominated_for(?x1762, ?x3169), honored_for(?x2292, ?x3169), ?x2292 = 0gx_st *> conf = 0.29 ranks of expected_values: 2, 5 EVAL 030k94 genre 0gs6m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 90.000 0.302 http://example.org/tv/tv_program/genre EVAL 030k94 genre 01t_vv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.302 http://example.org/tv/tv_program/genre #1171-0blpnz PRED entity: 0blpnz PRED relation: award PRED expected values: 0gq9h => 93 concepts (78 used for prediction) PRED predicted values (max 10 best out of 232): 07bdd_ (0.56 #4533, 0.54 #1284, 0.53 #6563), 0gq9h (0.50 #484, 0.43 #78, 0.36 #1296), 05p1dby (0.41 #4575, 0.40 #2545, 0.40 #1732), 09sb52 (0.28 #13846, 0.23 #15064, 0.22 #23593), 040njc (0.25 #8535, 0.25 #10565, 0.25 #9347), 018wng (0.21 #2031, 0.15 #24365, 0.15 #22333), 0p9sw (0.21 #2031, 0.15 #24365, 0.15 #22333), 0gr07 (0.21 #2031, 0.15 #24365, 0.15 #22333), 0gs9p (0.17 #9419, 0.17 #10637, 0.17 #9825), 019f4v (0.17 #9406, 0.17 #10624, 0.17 #10218) >> Best rule #4533 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 030_1_; 04glx0; >> query: (?x11962, 07bdd_) <- award_winner(?x5537, ?x11962), award_nominee(?x788, ?x11962), film(?x788, ?x186) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #484 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 017jv5; 032j_n; *> query: (?x11962, 0gq9h) <- award_nominee(?x11962, ?x788), ?x788 = 0g1rw, nominated_for(?x11962, ?x3438) *> conf = 0.50 ranks of expected_values: 2 EVAL 0blpnz award 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 78.000 0.558 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1170-0bwh6 PRED entity: 0bwh6 PRED relation: currency PRED expected values: 09nqf => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.34 #4, 0.30 #31, 0.29 #16), 01nv4h (0.03 #20, 0.02 #5, 0.02 #35) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 48 >> proper extension: 0n6f8; 0hskw; 01f8ld; 015njf; 08ff1k; 01c6l; 020trj; 01_f_5; 01wk51; 01p4vl; ... >> query: (?x1365, 09nqf) <- produced_by(?x1118, ?x1365), profession(?x1365, ?x319), spouse(?x1365, ?x9817) >> conf = 0.34 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bwh6 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.340 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #1169-02cpb7 PRED entity: 02cpb7 PRED relation: student! PRED expected values: 0c_zj => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 118): 01w5m (0.12 #104, 0.04 #42185, 0.03 #59020), 0bwfn (0.09 #20788, 0.09 #23944, 0.08 #25522), 015nl4 (0.07 #13217, 0.06 #7431, 0.05 #7957), 09f2j (0.05 #2262, 0.05 #158, 0.04 #11204), 026gvfj (0.05 #2214, 0.05 #1162, 0.04 #1688), 06thjt (0.05 #397, 0.02 #2501, 0.02 #6183), 032r4n (0.05 #486), 065y4w7 (0.05 #25262, 0.05 #20528, 0.05 #39991), 03ksy (0.04 #42186, 0.04 #59021, 0.04 #49026), 0fr9jp (0.04 #1922, 0.04 #870, 0.03 #1396) >> Best rule #104 for best value: >> intensional similarity = 2 >> extensional distance = 39 >> proper extension: 01cqz5; >> query: (?x4670, 01w5m) <- religion(?x4670, ?x7422), ?x7422 = 092bf5 >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #142 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 39 *> proper extension: 01cqz5; *> query: (?x4670, 0c_zj) <- religion(?x4670, ?x7422), ?x7422 = 092bf5 *> conf = 0.02 ranks of expected_values: 39 EVAL 02cpb7 student! 0c_zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 138.000 138.000 0.122 http://example.org/education/educational_institution/students_graduates./education/education/student #1168-05zlld0 PRED entity: 05zlld0 PRED relation: country PRED expected values: 09c7w0 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 137): 09c7w0 (0.84 #63, 0.83 #926, 0.83 #3090), 07ssc (0.35 #17, 0.28 #1435, 0.28 #1311), 0345h (0.23 #6907, 0.13 #4040, 0.13 #952), 03h64 (0.23 #6907, 0.08 #4075, 0.08 #4074), 0f8l9c (0.13 #696, 0.11 #1731, 0.11 #1687), 03_3d (0.10 #6351, 0.10 #2112, 0.08 #4075), 0d060g (0.10 #6351, 0.08 #4075, 0.08 #4074), 03rjj (0.10 #6351, 0.08 #4075, 0.08 #4074), 0chghy (0.10 #6351, 0.08 #4075, 0.08 #4074), 0ctw_b (0.10 #6351, 0.08 #4075, 0.08 #4074) >> Best rule #63 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 0m5s5; >> query: (?x3748, 09c7w0) <- prequel(?x5277, ?x3748), currency(?x3748, ?x170), written_by(?x3748, ?x2442), award_winner(?x5277, ?x902) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05zlld0 country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.840 http://example.org/film/film/country #1167-0h03fhx PRED entity: 0h03fhx PRED relation: film_release_region PRED expected values: 0k6nt 0ctw_b 059j2 02vzc => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 139): 09c7w0 (0.96 #1499, 0.93 #2316, 0.93 #4768), 02vzc (0.86 #449, 0.83 #313, 0.78 #2899), 059j2 (0.85 #570, 0.84 #1250, 0.83 #434), 0k6nt (0.79 #562, 0.77 #426, 0.76 #1242), 0ctw_b (0.71 #563, 0.69 #291, 0.65 #1243), 047yc (0.69 #294, 0.67 #566, 0.64 #1246), 01mjq (0.64 #579, 0.62 #307, 0.60 #1259), 047lj (0.52 #280, 0.48 #552, 0.47 #416), 09pmkv (0.52 #293, 0.47 #565, 0.42 #1245), 0h7x (0.52 #437, 0.45 #301, 0.38 #1253) >> Best rule #1499 for best value: >> intensional similarity = 4 >> extensional distance = 177 >> proper extension: 014_x2; 0ds35l9; 0d90m; 03qcfvw; 0m313; 07gp9; 09xbpt; 01h7bb; 060v34; 0bth54; ... >> query: (?x4607, 09c7w0) <- award_winner(?x4607, ?x286), film_release_region(?x4607, ?x87), language(?x4607, ?x254), executive_produced_by(?x4607, ?x3528) >> conf = 0.96 => this is the best rule for 1 predicted values *> Best rule #449 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 62 *> proper extension: 0ndsl1x; *> query: (?x4607, 02vzc) <- award_winner(?x4607, ?x286), film_release_region(?x4607, ?x3277), film_release_region(?x4607, ?x2843), ?x2843 = 016wzw, organization(?x3277, ?x127) *> conf = 0.86 ranks of expected_values: 2, 3, 4, 5 EVAL 0h03fhx film_release_region 02vzc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.955 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h03fhx film_release_region 059j2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.955 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h03fhx film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.955 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h03fhx film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.955 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1166-01f_3w PRED entity: 01f_3w PRED relation: artist PRED expected values: 07ss8_ 03h_0_z => 46 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1870): 08w4pm (0.50 #3031, 0.50 #2211, 0.33 #1392), 01k23t (0.50 #3010, 0.50 #2190, 0.33 #1371), 0g824 (0.50 #2904, 0.50 #2084, 0.33 #1265), 01vwbts (0.50 #2782, 0.50 #1962, 0.33 #1143), 07zft (0.50 #2277, 0.33 #1458, 0.33 #638), 0jsg0m (0.50 #2984, 0.33 #1345, 0.29 #3805), 07qnf (0.50 #2503, 0.33 #864, 0.29 #3324), 03g5jw (0.50 #1721, 0.33 #902, 0.25 #2541), 018dyl (0.50 #1928, 0.33 #1109, 0.25 #2748), 01trhmt (0.50 #2602, 0.33 #963, 0.25 #1782) >> Best rule #3031 for best value: >> intensional similarity = 8 >> extensional distance = 2 >> proper extension: 01cszh; >> query: (?x5836, 08w4pm) <- artist(?x5836, ?x3397), artist(?x5836, ?x2987), artist(?x5836, ?x1206), vacationer(?x9729, ?x3397), award_nominee(?x3397, ?x4693), spouse(?x3421, ?x2987), ?x1206 = 01vrt_c, artists(?x671, ?x3397) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #940 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 0181dw; *> query: (?x5836, 07ss8_) <- artist(?x5836, ?x4842), artist(?x5836, ?x3397), artist(?x5836, ?x2987), artist(?x5836, ?x2226), ?x3397 = 015f7, ?x4842 = 0hvbj, category(?x5836, ?x134), ?x2226 = 09k2t1, instrumentalists(?x227, ?x2987) *> conf = 0.33 ranks of expected_values: 41, 244 EVAL 01f_3w artist 03h_0_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 37.000 0.500 http://example.org/music/record_label/artist EVAL 01f_3w artist 07ss8_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 46.000 37.000 0.500 http://example.org/music/record_label/artist #1165-01r97z PRED entity: 01r97z PRED relation: award PRED expected values: 07bdd_ => 83 concepts (75 used for prediction) PRED predicted values (max 10 best out of 279): 05b1610 (0.29 #232, 0.27 #9487, 0.27 #11572), 0641kkh (0.29 #232, 0.27 #9487, 0.27 #11572), 07bdd_ (0.26 #51, 0.22 #749, 0.21 #982), 07cbcy (0.21 #993, 0.20 #527, 0.17 #294), 05p1dby (0.21 #81, 0.13 #16668, 0.10 #313), 0gs96 (0.15 #1713, 0.11 #2408, 0.09 #1482), 04ljl_l (0.14 #934, 0.10 #701, 0.08 #10877), 05b4l5x (0.13 #16668, 0.12 #238, 0.11 #704), 0gq9h (0.13 #16668, 0.11 #1686, 0.09 #4000), 02x73k6 (0.13 #16668, 0.08 #10877, 0.06 #6481) >> Best rule #232 for best value: >> intensional similarity = 5 >> extensional distance = 37 >> proper extension: 06krf3; 02qzmz6; 074rg9; >> query: (?x770, ?x350) <- nominated_for(?x541, ?x770), nominated_for(?x1007, ?x770), nominated_for(?x350, ?x770), film_release_distribution_medium(?x770, ?x81), ?x1007 = 03c7tr1 >> conf = 0.29 => this is the best rule for 2 predicted values *> Best rule #51 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 37 *> proper extension: 06krf3; 02qzmz6; 074rg9; *> query: (?x770, 07bdd_) <- nominated_for(?x541, ?x770), nominated_for(?x1007, ?x770), film_release_distribution_medium(?x770, ?x81), ?x1007 = 03c7tr1 *> conf = 0.26 ranks of expected_values: 3 EVAL 01r97z award 07bdd_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 83.000 75.000 0.289 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #1164-05c9zr PRED entity: 05c9zr PRED relation: genre PRED expected values: 07s9rl0 02kdv5l => 102 concepts (86 used for prediction) PRED predicted values (max 10 best out of 89): 07s9rl0 (0.92 #5159, 0.89 #8683, 0.71 #7037), 01jfsb (0.54 #6228, 0.50 #832, 0.39 #5052), 02kdv5l (0.52 #6218, 0.50 #4104, 0.50 #3750), 05p553 (0.41 #7041, 0.38 #6572, 0.38 #3400), 02l7c8 (0.29 #8699, 0.28 #7053, 0.27 #9872), 06n90 (0.27 #3761, 0.27 #4115, 0.26 #4350), 04xvlr (0.25 #704, 0.20 #8449, 0.18 #5160), 06cvj (0.25 #706, 0.10 #5630, 0.09 #6571), 01t_vv (0.25 #754, 0.09 #1807, 0.09 #8734), 0hn10 (0.25 #713, 0.09 #1766, 0.06 #4464) >> Best rule #5159 for best value: >> intensional similarity = 5 >> extensional distance = 363 >> proper extension: 016ztl; 0cbl95; >> query: (?x4132, 07s9rl0) <- genre(?x4132, ?x7223), music(?x4132, ?x4911), production_companies(?x4132, ?x574), genre(?x6343, ?x7223), ?x6343 = 05n6sq >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 05c9zr genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 86.000 0.921 http://example.org/film/film/genre EVAL 05c9zr genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 86.000 0.921 http://example.org/film/film/genre #1163-013tcv PRED entity: 013tcv PRED relation: award PRED expected values: 019f4v 0gq9h => 126 concepts (108 used for prediction) PRED predicted values (max 10 best out of 321): 02n9nmz (0.70 #36626, 0.70 #31847, 0.69 #30652), 09sb52 (0.63 #39, 0.34 #3223, 0.31 #8795), 0gs9p (0.46 #4056, 0.42 #7638, 0.38 #872), 019f4v (0.46 #4044, 0.40 #7626, 0.31 #860), 0gr51 (0.44 #893, 0.40 #1291, 0.37 #1689), 0gq9h (0.43 #5646, 0.36 #9626, 0.35 #4054), 05pcn59 (0.32 #3262, 0.25 #8834, 0.21 #10824), 05zr6wv (0.32 #3199, 0.20 #8771, 0.19 #7179), 04dn09n (0.28 #838, 0.27 #4022, 0.24 #11584), 0gr4k (0.28 #11573, 0.26 #4011, 0.26 #827) >> Best rule #36626 for best value: >> intensional similarity = 3 >> extensional distance = 1566 >> proper extension: 026v1z; >> query: (?x9281, ?x1180) <- award_nominee(?x9281, ?x10381), award_winner(?x1180, ?x9281), award(?x10381, ?x384) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4044 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 123 *> proper extension: 045cq; 0184jw; 030vmc; 023w9s; *> query: (?x9281, 019f4v) <- award_winner(?x472, ?x9281), award(?x9281, ?x68), film(?x9281, ?x308) *> conf = 0.46 ranks of expected_values: 4, 6 EVAL 013tcv award 0gq9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 126.000 108.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 013tcv award 019f4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 126.000 108.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1162-04hhv PRED entity: 04hhv PRED relation: olympics PRED expected values: 06sks6 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 41): 06sks6 (0.88 #1963, 0.87 #1262, 0.86 #1012), 0kbws (0.54 #220, 0.46 #795, 0.45 #261), 0kbvv (0.38 #232, 0.34 #273, 0.32 #314), 0kbvb (0.37 #213, 0.32 #542, 0.32 #254), 018ctl (0.31 #214, 0.30 #995, 0.29 #90), 0jdk_ (0.31 #233, 0.25 #274, 0.24 #438), 09n48 (0.29 #209, 0.28 #702, 0.27 #990), 0swbd (0.27 #217, 0.24 #422, 0.23 #258), 0jhn7 (0.23 #234, 0.23 #275, 0.21 #316), 0sxrz (0.19 #227, 0.13 #268, 0.13 #432) >> Best rule #1963 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 027rn; 027nb; 03_3d; 03_r3; 05v8c; 04v3q; 06qd3; 06s6l; 0162v; 03rj0; ... >> query: (?x8033, 06sks6) <- contains(?x6304, ?x8033), country(?x668, ?x8033), jurisdiction_of_office(?x182, ?x8033) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04hhv olympics 06sks6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.876 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #1161-01t3h6 PRED entity: 01t3h6 PRED relation: contains! PRED expected values: 05kr_ => 120 concepts (86 used for prediction) PRED predicted values (max 10 best out of 319): 05kr_ (0.84 #30411, 0.83 #31307, 0.81 #34885), 09c7w0 (0.80 #66195, 0.66 #7158, 0.63 #67986), 07ssc (0.57 #57245, 0.56 #54561, 0.55 #54562), 02jx1 (0.48 #44808, 0.45 #45702, 0.45 #48384), 059rby (0.44 #34906, 0.41 #35800, 0.32 #38482), 01n7q (0.43 #37646, 0.42 #39434, 0.25 #57322), 04ly1 (0.33 #235, 0.04 #3812, 0.04 #7390), 04jpl (0.32 #19697, 0.25 #26855, 0.24 #28643), 030qb3t (0.22 #7255, 0.11 #19776, 0.09 #26934), 02_286 (0.20 #19718, 0.16 #26876, 0.15 #28664) >> Best rule #30411 for best value: >> intensional similarity = 3 >> extensional distance = 223 >> proper extension: 0hc8h; >> query: (?x14707, ?x1905) <- state(?x14707, ?x1905), contains(?x1905, ?x1196), country(?x1905, ?x279) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01t3h6 contains! 05kr_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 86.000 0.836 http://example.org/location/location/contains #1160-0cg9f PRED entity: 0cg9f PRED relation: award PRED expected values: 0f4x7 0bfvd4 => 118 concepts (89 used for prediction) PRED predicted values (max 10 best out of 268): 02qkk9_ (0.71 #20868, 0.68 #22074, 0.68 #20466), 0f4x7 (0.47 #2837, 0.44 #3238, 0.37 #4842), 09sb52 (0.43 #4852, 0.34 #4050, 0.33 #16089), 09sdmz (0.42 #5017, 0.36 #4215, 0.30 #7424), 027dtxw (0.40 #4816, 0.36 #4014, 0.31 #7223), 02x73k6 (0.36 #4872, 0.29 #7279, 0.28 #6878), 099jhq (0.32 #4029, 0.31 #4831, 0.21 #7238), 0bdwqv (0.32 #4983, 0.30 #4181, 0.28 #7390), 04kxsb (0.28 #4938, 0.28 #2933, 0.26 #4136), 0bfvd4 (0.25 #115, 0.22 #7334, 0.22 #6933) >> Best rule #20868 for best value: >> intensional similarity = 4 >> extensional distance = 1261 >> proper extension: 06lxn; >> query: (?x12584, ?x5180) <- award_winner(?x9372, ?x12584), award_winner(?x5180, ?x12584), award(?x406, ?x9372), category_of(?x9372, ?x2421) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #2837 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 01pfkw; 03bggl; *> query: (?x12584, 0f4x7) <- nominated_for(?x12584, ?x3311), gender(?x12584, ?x231), celebrities_impersonated(?x3649, ?x12584), honored_for(?x3332, ?x3311) *> conf = 0.47 ranks of expected_values: 2, 10 EVAL 0cg9f award 0bfvd4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 118.000 89.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0cg9f award 0f4x7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 89.000 0.713 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1159-01mqnr PRED entity: 01mqnr PRED relation: film PRED expected values: 02qzmz6 => 100 concepts (75 used for prediction) PRED predicted values (max 10 best out of 759): 04x4nv (0.59 #76780, 0.59 #80352, 0.59 #62493), 04smdd (0.59 #76780, 0.59 #80352, 0.59 #62493), 03nt59 (0.59 #76780, 0.59 #80352, 0.59 #62493), 035s95 (0.18 #338, 0.14 #2123, 0.03 #7479), 011yd2 (0.18 #353, 0.14 #2138, 0.01 #7494), 02j69w (0.18 #798, 0.14 #2583), 0g56t9t (0.18 #17856, 0.17 #8927, 0.15 #21427), 04vr_f (0.10 #3738, 0.01 #74994, 0.01 #7309), 0h1fktn (0.10 #8108, 0.05 #17037, 0.04 #13465), 03q0r1 (0.09 #634, 0.07 #2419, 0.06 #5989) >> Best rule #76780 for best value: >> intensional similarity = 3 >> extensional distance = 1270 >> proper extension: 0m2wm; 02zq43; 08w7vj; 05hdf; 06mmb; 01pctb; 03ds83; 07h565; 02cgb8; 02zrv7; ... >> query: (?x8179, ?x4347) <- nominated_for(?x8179, ?x4347), gender(?x8179, ?x231), film(?x8179, ?x590) >> conf = 0.59 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 01mqnr film 02qzmz6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 75.000 0.594 http://example.org/film/actor/film./film/performance/film #1158-0ds11z PRED entity: 0ds11z PRED relation: award_winner PRED expected values: 03v1w7 => 118 concepts (60 used for prediction) PRED predicted values (max 10 best out of 460): 07rd7 (0.57 #21330, 0.55 #50873, 0.51 #59076), 092ys_y (0.33 #6563, 0.17 #19689, 0.03 #5536), 0c9c0 (0.27 #90262, 0.20 #93545, 0.19 #64001), 05kwx2 (0.27 #90262, 0.20 #93545, 0.19 #64001), 0kszw (0.27 #90262, 0.20 #93545, 0.19 #64001), 03v1w7 (0.22 #70566, 0.20 #70565, 0.17 #73849), 014y6 (0.20 #93545, 0.19 #64001, 0.18 #14765), 03kpvp (0.17 #73849, 0.16 #75491, 0.15 #77133), 01j2xj (0.17 #73849, 0.16 #75491, 0.15 #77133), 09wj5 (0.17 #73849, 0.16 #75491, 0.15 #77133) >> Best rule #21330 for best value: >> intensional similarity = 4 >> extensional distance = 163 >> proper extension: 01jc6q; 0jzw; 024l2y; 0yyts; 02rn00y; 051zy_b; 05hjnw; 01hv3t; 0pd64; 02r858_; ... >> query: (?x485, ?x4314) <- award(?x485, ?x484), award_winner(?x485, ?x1532), film(?x4314, ?x485), films(?x3530, ?x485) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #70566 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 593 *> proper extension: 05f67hw; *> query: (?x485, ?x6369) <- produced_by(?x485, ?x6369), country(?x485, ?x94), award_winner(?x6369, ?x574) *> conf = 0.22 ranks of expected_values: 6 EVAL 0ds11z award_winner 03v1w7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 118.000 60.000 0.567 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #1157-01mk6 PRED entity: 01mk6 PRED relation: combatants PRED expected values: 015fr => 160 concepts (73 used for prediction) PRED predicted values (max 10 best out of 118): 0b90_r (0.86 #71, 0.83 #283, 0.83 #1557), 05qhw (0.86 #71, 0.83 #283, 0.83 #1557), 07ssc (0.86 #71, 0.83 #283, 0.83 #1557), 059z0 (0.86 #71, 0.83 #283, 0.83 #1557), 01mk6 (0.75 #46, 0.67 #257, 0.42 #187), 0345h (0.58 #225, 0.50 #155, 0.50 #14), 015fr (0.58 #218, 0.50 #7, 0.42 #1213), 06bnz (0.50 #229, 0.42 #159, 0.38 #18), 015qh (0.38 #1081, 0.38 #16, 0.35 #1222), 0bq0p9 (0.38 #9, 0.33 #282, 0.33 #3763) >> Best rule #71 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 059z0; >> query: (?x7430, ?x151) <- combatants(?x7430, ?x3918), combatants(?x7430, ?x279), ?x279 = 0d060g, combatants(?x151, ?x7430), ?x3918 = 02psqkz >> conf = 0.86 => this is the best rule for 4 predicted values *> Best rule #218 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 027qpc; *> query: (?x7430, 015fr) <- combatants(?x7430, ?x3918), combatants(?x7430, ?x279), ?x279 = 0d060g, combatants(?x151, ?x7430), combatants(?x3918, ?x1003) *> conf = 0.58 ranks of expected_values: 7 EVAL 01mk6 combatants 015fr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 160.000 73.000 0.858 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #1156-0h005 PRED entity: 0h005 PRED relation: place_of_death PRED expected values: 06_kh => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 31): 030qb3t (0.25 #995, 0.09 #1189, 0.08 #3911), 0f2wj (0.17 #401, 0.15 #985, 0.14 #596), 02_286 (0.15 #986, 0.14 #13, 0.04 #4487), 0r00l (0.14 #162, 0.09 #1329, 0.07 #746), 0r3w7 (0.14 #177), 0r62v (0.14 #15), 06_kh (0.07 #589, 0.05 #978, 0.02 #3312), 0k049 (0.07 #587, 0.03 #6041, 0.03 #7210), 04jpl (0.07 #591, 0.03 #1758, 0.03 #1368), 0r3tq (0.07 #733) >> Best rule #995 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 05qd_; 019fnv; >> query: (?x4572, 030qb3t) <- award_winner(?x9400, ?x4572), award_winner(?x8259, ?x4572), award(?x4572, ?x4573), ?x9400 = 0d__c3, honored_for(?x8259, ?x2721) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #589 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 01pp3p; 03s2y9; *> query: (?x4572, 06_kh) <- profession(?x4572, ?x319), award_winner(?x6323, ?x4572), ?x6323 = 05hmp6, award(?x4572, ?x4573) *> conf = 0.07 ranks of expected_values: 7 EVAL 0h005 place_of_death 06_kh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 101.000 101.000 0.250 http://example.org/people/deceased_person/place_of_death #1155-0286hyp PRED entity: 0286hyp PRED relation: films! PRED expected values: 01w1sx => 88 concepts (38 used for prediction) PRED predicted values (max 10 best out of 53): 02vnz (0.12 #440, 0.10 #754, 0.08 #281), 01w1sx (0.12 #407, 0.10 #721, 0.07 #1035), 081pw (0.11 #3, 0.08 #319, 0.06 #1264), 0fx2s (0.11 #73, 0.04 #2277, 0.04 #3223), 0fzyg (0.10 #527, 0.05 #1315, 0.05 #841), 07jq_ (0.10 #712, 0.08 #239, 0.08 #869), 0l8bg (0.08 #274, 0.08 #433, 0.06 #747), 05489 (0.07 #525, 0.06 #52, 0.05 #2099), 048n7 (0.06 #76, 0.04 #233, 0.04 #392), 0kbq (0.06 #105, 0.04 #1208, 0.03 #1836) >> Best rule #440 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0407yj_; >> query: (?x14075, 02vnz) <- film(?x12584, ?x14075), genre(?x14075, ?x5104), ?x5104 = 0bkbm, nominated_for(?x484, ?x14075) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #407 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 0407yj_; *> query: (?x14075, 01w1sx) <- film(?x12584, ?x14075), genre(?x14075, ?x5104), ?x5104 = 0bkbm, nominated_for(?x484, ?x14075) *> conf = 0.12 ranks of expected_values: 2 EVAL 0286hyp films! 01w1sx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 38.000 0.115 http://example.org/film/film_subject/films #1154-01nwwl PRED entity: 01nwwl PRED relation: award PRED expected values: 02w9sd7 => 91 concepts (74 used for prediction) PRED predicted values (max 10 best out of 247): 0f4x7 (0.26 #3999, 0.15 #3602, 0.13 #5190), 0ck27z (0.19 #6839, 0.16 #7236, 0.15 #15573), 0789_m (0.15 #3591, 0.13 #28989, 0.13 #27795), 0gqwc (0.15 #28591, 0.15 #27796, 0.14 #22632), 02w9sd7 (0.15 #28591, 0.15 #27796, 0.14 #22632), 099cng (0.15 #28591, 0.15 #27796, 0.14 #22632), 094qd5 (0.15 #28591, 0.15 #27796, 0.13 #28989), 03nqnk3 (0.15 #28591, 0.15 #27796, 0.13 #28989), 0gkvb7 (0.15 #28591, 0.15 #27796, 0.13 #28989), 027b9j5 (0.15 #28591, 0.15 #27796, 0.13 #28989) >> Best rule #3999 for best value: >> intensional similarity = 3 >> extensional distance = 541 >> proper extension: 0h1_w; 012c6x; 03ds3; 0f0p0; 09qh1; 015wfg; 02lymt; 03359d; 012dr7; 012j5h; ... >> query: (?x2938, 0f4x7) <- award(?x2938, ?x704), award(?x5283, ?x704), ?x5283 = 01ps2h8 >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #28591 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2165 *> proper extension: 01w92; *> query: (?x2938, ?x537) <- award_nominee(?x2938, ?x804), award_winner(?x537, ?x804) *> conf = 0.15 ranks of expected_values: 5 EVAL 01nwwl award 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 91.000 74.000 0.262 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1153-03c6v3 PRED entity: 03c6v3 PRED relation: nationality PRED expected values: 09c7w0 => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 38): 09c7w0 (0.91 #1204, 0.91 #703, 0.89 #904), 02jx1 (0.33 #133, 0.14 #9680, 0.11 #9782), 014wxc (0.31 #2005), 03gh4 (0.31 #2005), 03fb3t (0.31 #2005), 07b_l (0.26 #10352, 0.23 #903, 0.05 #3311), 0f2rq (0.26 #10352, 0.23 #903), 059rby (0.26 #10352, 0.23 #903), 03rk0 (0.13 #4762, 0.08 #10600, 0.08 #10901), 07ssc (0.10 #9662, 0.09 #10870, 0.09 #11070) >> Best rule #1204 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 02n9k; >> query: (?x10880, 09c7w0) <- location(?x10880, ?x108), ?x108 = 0rh6k, profession(?x10880, ?x319) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03c6v3 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 161.000 0.914 http://example.org/people/person/nationality #1152-0gyh PRED entity: 0gyh PRED relation: currency PRED expected values: 09nqf => 185 concepts (185 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.92 #27, 0.89 #42, 0.88 #66), 0ptk_ (0.04 #64, 0.03 #25, 0.03 #122), 02l6h (0.02 #200, 0.02 #90, 0.02 #99) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 35 >> proper extension: 0hjy; >> query: (?x2831, 09nqf) <- district_represented(?x3766, ?x2831), legislative_sessions(?x3766, ?x356), category(?x2831, ?x134) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gyh currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 185.000 185.000 0.919 http://example.org/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency #1151-018x3 PRED entity: 018x3 PRED relation: award_winner! PRED expected values: 03tcnt => 166 concepts (164 used for prediction) PRED predicted values (max 10 best out of 329): 01c4_6 (0.45 #12014, 0.42 #21028, 0.41 #14589), 025m8y (0.29 #98, 0.28 #3960, 0.27 #4389), 0l8z1 (0.28 #11647, 0.24 #63, 0.23 #15510), 054krc (0.25 #11671, 0.21 #15534, 0.20 #5236), 0gqz2 (0.24 #11664, 0.24 #80, 0.22 #6945), 03x3wf (0.20 #922, 0.16 #5642, 0.12 #3068), 0c4z8 (0.19 #1787, 0.15 #56216, 0.12 #3933), 054ks3 (0.18 #4001, 0.16 #4430, 0.16 #7004), 025m8l (0.18 #117, 0.14 #6982, 0.12 #11701), 025m98 (0.18 #233, 0.12 #6669, 0.12 #4095) >> Best rule #12014 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 01p7b6b; >> query: (?x5494, ?x1565) <- music(?x7854, ?x5494), award(?x5494, ?x1565), award_winner(?x725, ?x5494) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #56216 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1519 *> proper extension: 01j53q; *> query: (?x5494, ?x247) <- award_winner(?x5494, ?x5547), award_winner(?x247, ?x5547) *> conf = 0.15 ranks of expected_values: 20 EVAL 018x3 award_winner! 03tcnt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 166.000 164.000 0.448 http://example.org/award/award_category/winners./award/award_honor/award_winner #1150-03fw60 PRED entity: 03fw60 PRED relation: nationality PRED expected values: 03rk0 => 94 concepts (49 used for prediction) PRED predicted values (max 10 best out of 20): 03rk0 (0.81 #146, 0.70 #46, 0.12 #446), 09c7w0 (0.75 #3517, 0.74 #3012, 0.73 #3416), 05sb1 (0.32 #4923, 0.32 #3113, 0.32 #4219), 065zr (0.32 #3113, 0.32 #4219), 0xnt5 (0.25 #2109, 0.25 #4924, 0.25 #3516), 02jx1 (0.12 #1337, 0.11 #233, 0.11 #2944), 07ssc (0.10 #15, 0.09 #215, 0.09 #415), 0d060g (0.06 #607, 0.05 #207, 0.05 #708), 0chghy (0.02 #410, 0.02 #610, 0.02 #3626), 0f8l9c (0.02 #2231, 0.02 #4039, 0.02 #1526) >> Best rule #146 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 02jxsq; >> query: (?x7592, 03rk0) <- profession(?x7592, ?x1032), ?x1032 = 02hrh1q, award_winner(?x4687, ?x7592), ?x4687 = 03rbj2 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03fw60 nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 49.000 0.810 http://example.org/people/person/nationality #1149-05dbf PRED entity: 05dbf PRED relation: location PRED expected values: 030qb3t => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 131): 02hrh0_ (0.41 #87326, 0.41 #84922, 0.39 #34451), 030qb3t (0.38 #883, 0.36 #1684, 0.26 #2485), 02_286 (0.24 #1638, 0.23 #837, 0.19 #36), 0cc56 (0.10 #56, 0.07 #2459, 0.07 #1658), 04jpl (0.07 #2419, 0.06 #10432, 0.05 #51290), 0cr3d (0.07 #51418, 0.06 #144, 0.06 #10560), 0r0m6 (0.06 #4223, 0.05 #2620, 0.05 #7427), 01n7q (0.06 #863, 0.05 #8875, 0.05 #1664), 0k049 (0.06 #808, 0.05 #1609, 0.04 #5615), 05fjf (0.06 #1130, 0.05 #1931, 0.03 #329) >> Best rule #87326 for best value: >> intensional similarity = 1 >> extensional distance = 2612 >> proper extension: 05fh2; >> query: (?x2275, ?x5193) <- place_of_birth(?x2275, ?x5193) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #883 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 030hcs; 07ss8_; 0p_47; 0k8y7; 01pcdn; 0ddkf; 05vk_d; *> query: (?x2275, 030qb3t) <- award_winner(?x748, ?x2275), participant(?x6187, ?x2275), film(?x2275, ?x308) *> conf = 0.38 ranks of expected_values: 2 EVAL 05dbf location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 121.000 0.415 http://example.org/people/person/places_lived./people/place_lived/location #1148-0jgd PRED entity: 0jgd PRED relation: contains PRED expected values: 02tb17 => 196 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2895): 01ly5m (0.93 #85320, 0.86 #105917, 0.86 #120626), 0j3b (0.61 #223607, 0.59 #91205, 0.59 #197125), 0156q (0.47 #38242, 0.12 #3129, 0.11 #6070), 01f62 (0.47 #38242, 0.05 #23722, 0.04 #47256), 056_y (0.47 #38242, 0.04 #47670, 0.04 #53554), 02tb17 (0.47 #38242), 0jgd (0.17 #85321, 0.05 #32365, 0.03 #194190), 06n3y (0.17 #85321, 0.05 #34486, 0.02 #150051), 0bwfn (0.14 #33406, 0.13 #12812, 0.12 #3989), 06fz_ (0.14 #33390, 0.12 #3973, 0.11 #6914) >> Best rule #85320 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 022_6; 0dbdy; 03lrc; 0cv5l; >> query: (?x142, ?x2911) <- administrative_parent(?x2911, ?x142), location_of_ceremony(?x566, ?x142), contains(?x12315, ?x2911) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #38242 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 05kyr; *> query: (?x142, ?x1646) <- religion(?x142, ?x962), nationality(?x8129, ?x142), location(?x8129, ?x1646) *> conf = 0.47 ranks of expected_values: 6 EVAL 0jgd contains 02tb17 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 196.000 104.000 0.929 http://example.org/location/location/contains #1147-03vtfp PRED entity: 03vtfp PRED relation: artist PRED expected values: 016l09 => 96 concepts (49 used for prediction) PRED predicted values (max 10 best out of 842): 016lmg (0.60 #3111, 0.33 #1439, 0.12 #38284), 0kvnn (0.40 #3643, 0.38 #6989, 0.29 #4479), 047cx (0.40 #3682, 0.29 #4518, 0.25 #7028), 01w02sy (0.40 #3542, 0.29 #4378, 0.25 #6888), 01jcxwp (0.40 #3851, 0.29 #4687, 0.25 #7197), 0g824 (0.33 #1290, 0.33 #454, 0.20 #2962), 03xhj6 (0.33 #306, 0.25 #6160, 0.25 #1978), 015xp4 (0.33 #366, 0.25 #2038, 0.20 #3711), 01whg97 (0.33 #597, 0.25 #2269, 0.20 #3942), 01vsy7t (0.33 #320, 0.25 #1992, 0.20 #3665) >> Best rule #3111 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 01th4s; 01cf93; >> query: (?x11969, 016lmg) <- artist(?x11969, ?x2187), artist(?x11969, ?x1004), ?x1004 = 01vv7sc, award(?x2187, ?x247), role(?x2187, ?x212), gender(?x2187, ?x231), award_nominee(?x2187, ?x3426) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1543 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 041n43; *> query: (?x11969, 016l09) <- artist(?x11969, ?x2347), artist(?x11969, ?x2187), artist(?x11969, ?x1004), ?x1004 = 01vv7sc, ?x2187 = 01vsnff, artists(?x671, ?x2347), award_winner(?x1389, ?x2347), profession(?x2347, ?x220), category(?x2347, ?x134) *> conf = 0.33 ranks of expected_values: 18 EVAL 03vtfp artist 016l09 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 96.000 49.000 0.600 http://example.org/music/record_label/artist #1146-05zjd PRED entity: 05zjd PRED relation: countries_spoken_in PRED expected values: 0d05w3 0hg5 => 46 concepts (43 used for prediction) PRED predicted values (max 10 best out of 263): 05r4w (0.82 #172, 0.66 #3626, 0.57 #1720), 07ytt (0.62 #497, 0.45 #1014, 0.44 #843), 0162v (0.38 #568, 0.38 #396, 0.33 #742), 06mzp (0.38 #365, 0.33 #711, 0.27 #882), 01ppq (0.38 #492, 0.27 #1009, 0.25 #1354), 0697s (0.38 #588, 0.25 #416, 0.24 #1449), 0154j (0.38 #349, 0.22 #695, 0.18 #866), 01mjq (0.35 #1591, 0.22 #3496, 0.19 #3671), 0jgd (0.33 #4, 0.25 #347, 0.22 #693), 0b90_r (0.33 #5, 0.25 #348, 0.22 #694) >> Best rule #172 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 06nm1; >> query: (?x6753, ?x87) <- service_language(?x5072, ?x6753), countries_spoken_in(?x6753, ?x9459), titles(?x6753, ?x2954), ?x9459 = 034m8, official_language(?x87, ?x6753), languages(?x419, ?x6753), ?x5072 = 045c7b >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #68 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 06nm1; *> query: (?x6753, 0hg5) <- service_language(?x5072, ?x6753), countries_spoken_in(?x6753, ?x9459), titles(?x6753, ?x2954), ?x9459 = 034m8, official_language(?x87, ?x6753), languages(?x419, ?x6753), ?x5072 = 045c7b *> conf = 0.33 ranks of expected_values: 30, 48 EVAL 05zjd countries_spoken_in 0hg5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 46.000 43.000 0.818 http://example.org/language/human_language/countries_spoken_in EVAL 05zjd countries_spoken_in 0d05w3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 46.000 43.000 0.818 http://example.org/language/human_language/countries_spoken_in #1145-01qvz8 PRED entity: 01qvz8 PRED relation: film_crew_role PRED expected values: 09zzb8 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 29): 02r96rf (0.86 #40, 0.70 #4, 0.63 #725), 09zzb8 (0.82 #37, 0.73 #1883, 0.72 #2208), 01vx2h (0.55 #47, 0.41 #469, 0.31 #119), 01pvkk (0.41 #469, 0.36 #48, 0.29 #2219), 02rh1dz (0.41 #469, 0.32 #46, 0.26 #82), 02ynfr (0.41 #469, 0.23 #52, 0.20 #16), 04pyp5 (0.41 #469, 0.18 #53, 0.07 #1899), 0d2b38 (0.20 #26, 0.11 #458, 0.11 #747), 02vs3x5 (0.18 #60, 0.11 #96, 0.10 #24), 0215hd (0.15 #668, 0.12 #163, 0.12 #379) >> Best rule #40 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 031t2d; >> query: (?x4709, 02r96rf) <- nominated_for(?x154, ?x4709), music(?x4709, ?x4020), film_crew_role(?x4709, ?x1171), ?x154 = 05b4l5x >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #37 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 20 *> proper extension: 031t2d; *> query: (?x4709, 09zzb8) <- nominated_for(?x154, ?x4709), music(?x4709, ?x4020), film_crew_role(?x4709, ?x1171), ?x154 = 05b4l5x *> conf = 0.82 ranks of expected_values: 2 EVAL 01qvz8 film_crew_role 09zzb8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.864 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1144-01hvjx PRED entity: 01hvjx PRED relation: genre PRED expected values: 0cshrf => 63 concepts (61 used for prediction) PRED predicted values (max 10 best out of 101): 07s9rl0 (0.92 #4421, 0.82 #566, 0.80 #681), 01z4y (0.62 #5217, 0.61 #5446, 0.59 #4761), 02kdv5l (0.50 #229, 0.50 #116, 0.39 #683), 03k9fj (0.39 #349, 0.33 #10, 0.24 #2277), 01jfsb (0.38 #350, 0.34 #2278, 0.33 #2052), 06n90 (0.33 #125, 0.22 #351, 0.17 #2279), 03g3w (0.33 #248, 0.21 #587, 0.20 #702), 06l3bl (0.33 #260, 0.19 #714, 0.17 #827), 0cshrf (0.33 #55, 0.06 #6356, 0.03 #1076), 02l7c8 (0.33 #2962, 0.31 #6029, 0.31 #467) >> Best rule #4421 for best value: >> intensional similarity = 3 >> extensional distance = 1088 >> proper extension: 01br2w; 0dckvs; 0djb3vw; 0fq27fp; 04969y; 04dsnp; 0cnztc4; 053tj7; 04m1bm; 0d6b7; ... >> query: (?x2349, 07s9rl0) <- genre(?x2349, ?x3515), genre(?x1218, ?x3515), ?x1218 = 02prw4h >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #55 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 03n3gl; *> query: (?x2349, 0cshrf) <- genre(?x2349, ?x258), film(?x2390, ?x2349), film_release_region(?x2349, ?x94), ?x2390 = 01_x6v *> conf = 0.33 ranks of expected_values: 9 EVAL 01hvjx genre 0cshrf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 63.000 61.000 0.923 http://example.org/film/film/genre #1143-07g1sm PRED entity: 07g1sm PRED relation: film_release_region PRED expected values: 07twz => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 220): 05r4w (0.88 #618, 0.84 #4324, 0.84 #2471), 05qhw (0.84 #2174, 0.84 #2484, 0.79 #4337), 035qy (0.83 #2194, 0.81 #2504, 0.78 #4357), 0154j (0.81 #2473, 0.81 #2163, 0.79 #4326), 0d060g (0.77 #2165, 0.77 #2475, 0.73 #4328), 03rt9 (0.76 #2483, 0.73 #2173, 0.69 #4336), 03spz (0.75 #2564, 0.72 #2254, 0.70 #711), 06bnz (0.71 #2516, 0.71 #2206, 0.68 #663), 06t2t (0.67 #2222, 0.66 #4385, 0.65 #2532), 01mjq (0.64 #2204, 0.62 #2514, 0.54 #4367) >> Best rule #618 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 011yrp; 01vksx; 0c0nhgv; 0g9wdmc; 0ch26b_; 0_7w6; 0by1wkq; 01fmys; 05z7c; 02yvct; ... >> query: (?x7016, 05r4w) <- film_release_region(?x7016, ?x583), nominated_for(?x112, ?x7016), films(?x5673, ?x7016), ?x583 = 015fr >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #710 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 011yrp; 01vksx; 0c0nhgv; 0g9wdmc; 0ch26b_; 0_7w6; 0by1wkq; 01fmys; 05z7c; 02yvct; ... *> query: (?x7016, 07twz) <- film_release_region(?x7016, ?x583), nominated_for(?x112, ?x7016), films(?x5673, ?x7016), ?x583 = 015fr *> conf = 0.28 ranks of expected_values: 34 EVAL 07g1sm film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 132.000 132.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1142-01q2nx PRED entity: 01q2nx PRED relation: film! PRED expected values: 015t56 055c8 => 78 concepts (20 used for prediction) PRED predicted values (max 10 best out of 636): 0g10g (0.33 #1818), 02x08c (0.33 #1565), 01g42 (0.33 #1491), 084m3 (0.33 #1295), 0cf2h (0.33 #1095), 0jfx1 (0.20 #2476, 0.17 #4550, 0.12 #8693), 07r1h (0.20 #3156, 0.17 #5230, 0.12 #9373), 01vvb4m (0.20 #2592, 0.17 #4666, 0.04 #10881), 05dbf (0.20 #2435, 0.17 #4509, 0.03 #16938), 01cwcr (0.20 #3336, 0.17 #5410, 0.02 #11625) >> Best rule #1818 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0n0bp; >> query: (?x5275, 0g10g) <- produced_by(?x5275, ?x6369), film(?x10007, ?x5275), ?x10007 = 015dqj, film(?x902, ?x5275) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #27407 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 239 *> proper extension: 01_1hw; *> query: (?x5275, 015t56) <- produced_by(?x5275, ?x6369), executive_produced_by(?x5275, ?x1533), film(?x902, ?x5275), film(?x820, ?x5275) *> conf = 0.03 ranks of expected_values: 131, 191 EVAL 01q2nx film! 055c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 78.000 20.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 01q2nx film! 015t56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 78.000 20.000 0.333 http://example.org/film/actor/film./film/performance/film #1141-02_l96 PRED entity: 02_l96 PRED relation: sibling PRED expected values: 02tf1y => 119 concepts (77 used for prediction) PRED predicted values (max 10 best out of 87): 02tf1y (0.80 #1273, 0.78 #464, 0.74 #233), 05r5w (0.06 #232), 013v5j (0.05 #132, 0.04 #480, 0.03 #1173), 023nlj (0.05 #190, 0.02 #422, 0.02 #538), 04zn7g (0.05 #229, 0.02 #461, 0.02 #577), 06t61y (0.05 #129, 0.02 #361, 0.02 #477), 032_jg (0.05 #123, 0.02 #355, 0.02 #471), 04xhwn (0.05 #224, 0.02 #456, 0.02 #572), 018yj6 (0.05 #191, 0.02 #423, 0.02 #539), 02js6_ (0.05 #137, 0.02 #369, 0.02 #485) >> Best rule #1273 for best value: >> intensional similarity = 2 >> extensional distance = 113 >> proper extension: 02x8kk; 0dv1hh; 09m465; 0cfz_z; 0cm19f; 05h7tk; >> query: (?x5064, ?x8897) <- nationality(?x5064, ?x94), sibling(?x8897, ?x5064) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_l96 sibling 02tf1y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 77.000 0.799 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #1140-0py5b PRED entity: 0py5b PRED relation: place_of_death PRED expected values: 02_286 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 63): 030qb3t (0.16 #604, 0.16 #4101, 0.14 #3713), 0k049 (0.10 #3694, 0.09 #585, 0.07 #5055), 02_286 (0.08 #4092, 0.08 #5065, 0.08 #13), 0f2wj (0.08 #2343, 0.07 #594, 0.04 #4091), 04jpl (0.07 #589, 0.04 #3698, 0.04 #5253), 04pry (0.06 #3691, 0.05 #6412, 0.04 #1554), 0cc56 (0.06 #3691, 0.04 #1555, 0.04 #1376), 06_kh (0.05 #5057, 0.04 #393, 0.04 #587), 05qtj (0.05 #3560, 0.04 #3172, 0.03 #5310), 0rh6k (0.04 #2, 0.02 #390, 0.02 #584) >> Best rule #604 for best value: >> intensional similarity = 3 >> extensional distance = 53 >> proper extension: 0cl_m; >> query: (?x12602, 030qb3t) <- gender(?x12602, ?x231), student(?x4296, ?x12602), place_of_burial(?x12602, ?x94) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #4092 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 167 *> proper extension: 07_grx; 05hjmd; 0bm9xk; *> query: (?x12602, 02_286) <- gender(?x12602, ?x231), award_nominee(?x12602, ?x574), people(?x4322, ?x12602) *> conf = 0.08 ranks of expected_values: 3 EVAL 0py5b place_of_death 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 101.000 0.164 http://example.org/people/deceased_person/place_of_death #1139-059ss PRED entity: 059ss PRED relation: contains PRED expected values: 0j8p6 => 151 concepts (71 used for prediction) PRED predicted values (max 10 best out of 2539): 0j8p6 (0.40 #179766, 0.27 #200396, 0.27 #209236), 059ss (0.40 #179766, 0.27 #200396, 0.27 #209236), 0d060g (0.40 #179766, 0.27 #200396, 0.27 #209236), 05j49 (0.40 #179766, 0.27 #200396, 0.27 #209236), 07wlt (0.18 #16157, 0.10 #10263, 0.09 #13210), 07wm6 (0.10 #11096, 0.09 #16990, 0.09 #14043), 0dyg2 (0.10 #11562, 0.09 #17456, 0.09 #14509), 0179q0 (0.10 #11438, 0.09 #17332, 0.09 #14385), 01wj17 (0.10 #11437, 0.09 #17331, 0.09 #14384), 01zh3_ (0.10 #10867, 0.09 #16761, 0.09 #13814) >> Best rule #179766 for best value: >> intensional similarity = 4 >> extensional distance = 271 >> proper extension: 01914; 0nvd8; 0k3ll; 0mws3; 0n5y4; 0nh57; 0cc1v; 043z0; 0mlzk; 0f4zv; ... >> query: (?x5678, ?x279) <- time_zones(?x5678, ?x12963), adjoins(?x5678, ?x6842), contains(?x5678, ?x5679), contains(?x279, ?x5679) >> conf = 0.40 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 059ss contains 0j8p6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 71.000 0.400 http://example.org/location/location/contains #1138-02bfxb PRED entity: 02bfxb PRED relation: gender PRED expected values: 02zsn => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #33, 0.89 #5, 0.89 #51), 02zsn (0.49 #77, 0.25 #99, 0.25 #181) >> Best rule #33 for best value: >> intensional similarity = 4 >> extensional distance = 204 >> proper extension: 02pp_q_; 016hvl; 04b19t; 0b478; 06n9lt; 03ys2f; 03ysmg; 054187; 072vj; >> query: (?x3434, 05zppz) <- written_by(?x7012, ?x3434), film(?x447, ?x7012), film_crew_role(?x7012, ?x281), nominated_for(?x112, ?x7012) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #77 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 322 *> proper extension: 013km; *> query: (?x3434, ?x231) <- written_by(?x7012, ?x3434), film(?x3293, ?x7012), profession(?x3434, ?x319), gender(?x3293, ?x231) *> conf = 0.49 ranks of expected_values: 2 EVAL 02bfxb gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 129.000 0.903 http://example.org/people/person/gender #1137-02p2zq PRED entity: 02p2zq PRED relation: instrumentalists! PRED expected values: 05148p4 => 165 concepts (165 used for prediction) PRED predicted values (max 10 best out of 124): 0342h (0.70 #358, 0.69 #7016, 0.65 #6488), 05r5c (0.52 #7020, 0.51 #620, 0.51 #539), 05148p4 (0.45 #375, 0.43 #1971, 0.41 #2594), 018vs (0.44 #2586, 0.41 #723, 0.40 #1963), 03bx0bm (0.43 #799, 0.42 #2662, 0.40 #2303), 0l14qv (0.32 #6303, 0.31 #4441, 0.29 #2571), 013y1f (0.32 #6303, 0.31 #4441, 0.29 #2571), 01s0ps (0.32 #6303, 0.31 #4441, 0.29 #2571), 0g2dz (0.31 #4441, 0.29 #2571, 0.28 #4262), 02hnl (0.24 #2608, 0.21 #745, 0.20 #3678) >> Best rule #358 for best value: >> intensional similarity = 3 >> extensional distance = 54 >> proper extension: 024dgj; 01vtqml; 0qf11; >> query: (?x7549, 0342h) <- award_winner(?x725, ?x7549), artist(?x2039, ?x7549), group(?x7549, ?x12427) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #375 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 54 *> proper extension: 024dgj; 01vtqml; 0qf11; *> query: (?x7549, 05148p4) <- award_winner(?x725, ?x7549), artist(?x2039, ?x7549), group(?x7549, ?x12427) *> conf = 0.45 ranks of expected_values: 3 EVAL 02p2zq instrumentalists! 05148p4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 165.000 165.000 0.696 http://example.org/music/instrument/instrumentalists #1136-0627sn PRED entity: 0627sn PRED relation: location PRED expected values: 04jpl => 117 concepts (116 used for prediction) PRED predicted values (max 10 best out of 113): 0nq_b (0.49 #56298, 0.48 #42624, 0.47 #71585), 02_286 (0.14 #15315, 0.14 #12098, 0.14 #4861), 04jpl (0.12 #17, 0.09 #4037, 0.09 #3233), 05mph (0.12 #319, 0.03 #4339, 0.03 #3535), 030qb3t (0.10 #59598, 0.10 #44315, 0.10 #45924), 0cr3d (0.06 #21857, 0.06 #15423, 0.06 #20248), 0h7h6 (0.05 #4914, 0.04 #8130, 0.04 #11347), 0dclg (0.05 #921, 0.05 #1725, 0.04 #9765), 013yq (0.05 #923, 0.05 #1727, 0.03 #3335), 0fvvz (0.05 #870, 0.05 #1674, 0.03 #3282) >> Best rule #56298 for best value: >> intensional similarity = 3 >> extensional distance = 1549 >> proper extension: 0284n42; 04sx9_; 019_1h; 030znt; 01d494; 02wrhj; 02k6rq; 01hkhq; 0hwd8; 05qsxy; ... >> query: (?x5528, ?x13032) <- place_of_birth(?x5528, ?x13032), type_of_union(?x5528, ?x566), location(?x413, ?x13032) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 06nz46; 06qn87; 08t7nz; 0164w8; 06vdh8; 06kkgw; *> query: (?x5528, 04jpl) <- profession(?x5528, ?x524), type_of_union(?x5528, ?x566), cinematography(?x2475, ?x5528), people(?x1158, ?x5528) *> conf = 0.12 ranks of expected_values: 3 EVAL 0627sn location 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 117.000 116.000 0.494 http://example.org/people/person/places_lived./people/place_lived/location #1135-026rm_y PRED entity: 026rm_y PRED relation: award_winner! PRED expected values: 09sdmz => 122 concepts (106 used for prediction) PRED predicted values (max 10 best out of 242): 057xs89 (0.40 #21205, 0.37 #28843, 0.37 #30116), 09sdmz (0.40 #21205, 0.37 #28843, 0.37 #30116), 0ck27z (0.11 #13655, 0.11 #4327, 0.10 #5599), 09cn0c (0.11 #735, 0.07 #31813, 0.03 #1583), 0gqwc (0.10 #69, 0.07 #493, 0.06 #1765), 0f4x7 (0.09 #1301, 0.06 #14021, 0.06 #4269), 0cqhk0 (0.09 #4275, 0.08 #5123, 0.08 #13603), 05ztrmj (0.09 #32238, 0.08 #35633, 0.07 #33511), 05p09zm (0.09 #32238, 0.08 #35633, 0.07 #31813), 07bdd_ (0.09 #32238, 0.08 #35633, 0.07 #31813) >> Best rule #21205 for best value: >> intensional similarity = 3 >> extensional distance = 1219 >> proper extension: 01wp8w7; 04nw9; 01t2h2; 01vb403; 0h1p; 03wpmd; 09pl3s; 027l0b; 02645b; 06449; ... >> query: (?x8740, ?x3019) <- award_winner(?x112, ?x8740), award(?x8740, ?x3019), award_winner(?x8740, ?x815) >> conf = 0.40 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 026rm_y award_winner! 09sdmz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 106.000 0.397 http://example.org/award/award_category/winners./award/award_honor/award_winner #1134-0g5ff PRED entity: 0g5ff PRED relation: profession PRED expected values: 0cbd2 => 121 concepts (110 used for prediction) PRED predicted values (max 10 best out of 91): 0cbd2 (0.83 #2558, 0.83 #2258, 0.83 #907), 02hrh1q (0.68 #9778, 0.66 #13232, 0.66 #8577), 0kyk (0.65 #1231, 0.62 #2582, 0.61 #1981), 0dxtg (0.53 #2114, 0.47 #2715, 0.46 #3167), 01d_h8 (0.35 #8417, 0.34 #11271, 0.33 #4054), 018gz8 (0.34 #2118, 0.25 #2719, 0.24 #3921), 02jknp (0.34 #6614, 0.33 #4054, 0.32 #1658), 05z96 (0.33 #4054, 0.33 #944, 0.31 #6156), 03gjzk (0.33 #4054, 0.31 #6156, 0.30 #2251), 09jwl (0.20 #4074, 0.20 #4374, 0.19 #4824) >> Best rule #2558 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 0j0pf; >> query: (?x6055, 0cbd2) <- award(?x6055, ?x9285), award(?x3963, ?x9285), ?x3963 = 02g75 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g5ff profession 0cbd2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 110.000 0.833 http://example.org/people/person/profession #1133-01t_wfl PRED entity: 01t_wfl PRED relation: student! PRED expected values: 07tg4 => 141 concepts (101 used for prediction) PRED predicted values (max 10 best out of 218): 07tgn (0.22 #543, 0.11 #6329, 0.09 #17375), 07tg4 (0.20 #86, 0.13 #17444, 0.09 #2716), 018sg9 (0.20 #470, 0.02 #3100, 0.01 #4678), 0c_zj (0.20 #143, 0.02 #2773, 0.01 #4351), 01t38b (0.20 #192), 065y4w7 (0.12 #2118, 0.10 #4222, 0.08 #7904), 0bwfn (0.12 #8165, 0.10 #11321, 0.09 #10269), 02yr3z (0.11 #768, 0.05 #1820, 0.01 #5502), 01d34b (0.11 #782, 0.02 #5516, 0.02 #2886), 0ymf1 (0.11 #1050, 0.02 #3154, 0.02 #17882) >> Best rule #543 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01vz0g4; >> query: (?x11698, 07tgn) <- place_of_birth(?x11698, ?x14172), friend(?x11698, ?x2799), place_of_death(?x11698, ?x6764) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #86 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 3 *> proper extension: 04sd0; *> query: (?x11698, 07tg4) <- influenced_by(?x11698, ?x7679), ?x7679 = 0739y *> conf = 0.20 ranks of expected_values: 2 EVAL 01t_wfl student! 07tg4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 101.000 0.222 http://example.org/education/educational_institution/students_graduates./education/education/student #1132-07vyf PRED entity: 07vyf PRED relation: major_field_of_study PRED expected values: 01bt59 => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 101): 02j62 (0.62 #139, 0.52 #588, 0.47 #251), 01lj9 (0.53 #147, 0.40 #708, 0.37 #596), 03g3w (0.50 #585, 0.50 #136, 0.46 #697), 01540 (0.50 #167, 0.41 #840, 0.30 #504), 0fdys (0.50 #146, 0.37 #595, 0.33 #707), 05qjt (0.47 #120, 0.41 #793, 0.38 #681), 04x_3 (0.47 #472, 0.38 #135, 0.35 #584), 01tbp (0.44 #166, 0.33 #615, 0.33 #503), 06ms6 (0.38 #128, 0.29 #689, 0.28 #240), 02h40lc (0.35 #116, 0.30 #565, 0.30 #453) >> Best rule #139 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 01f1r4; 07tds; 02bqy; 01nnsv; 0gl5_; 0c5x_; >> query: (?x4296, 02j62) <- major_field_of_study(?x4296, ?x1154), school(?x700, ?x4296), list(?x4296, ?x2197) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #521 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 08815; 01j_9c; 06pwq; 02w2bc; 065y4w7; 01w3v; 07w0v; 07szy; 049dk; 0bx8pn; ... *> query: (?x4296, 01bt59) <- major_field_of_study(?x4296, ?x1154), school(?x700, ?x4296), company(?x4486, ?x4296) *> conf = 0.28 ranks of expected_values: 17 EVAL 07vyf major_field_of_study 01bt59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 86.000 86.000 0.618 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1131-04x56 PRED entity: 04x56 PRED relation: influenced_by PRED expected values: 0g5ff 02mpb => 125 concepts (40 used for prediction) PRED predicted values (max 10 best out of 351): 02lt8 (0.38 #119, 0.38 #983, 0.17 #6168), 03_87 (0.31 #1064, 0.21 #632, 0.19 #7113), 03f70xs (0.29 #501, 0.13 #1365, 0.11 #14258), 0g5ff (0.25 #1920, 0.11 #14258, 0.11 #7537), 081k8 (0.23 #155, 0.19 #1019, 0.15 #6636), 03f0324 (0.23 #151, 0.19 #1015, 0.15 #7064), 040db (0.23 #55, 0.19 #919, 0.14 #6968), 041h0 (0.23 #10, 0.11 #14258, 0.09 #6923), 09dt7 (0.21 #1759, 0.11 #14258, 0.08 #6944), 032l1 (0.19 #6570, 0.19 #953, 0.18 #6138) >> Best rule #119 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 09jd9; >> query: (?x10232, 02lt8) <- nationality(?x10232, ?x512), award_winner(?x4879, ?x10232), ?x4879 = 047xyn >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1920 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 041h0; 01zkxv; 09dt7; 0c3kw; 01dzz7; 0jt90f5; 04cbtrw; 085pr; 05jm7; 02g75; ... *> query: (?x10232, 0g5ff) <- profession(?x10232, ?x353), influenced_by(?x10232, ?x1029), award(?x10232, ?x8909), ?x8909 = 040_9s0 *> conf = 0.25 ranks of expected_values: 4, 100 EVAL 04x56 influenced_by 02mpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 125.000 40.000 0.385 http://example.org/influence/influence_node/influenced_by EVAL 04x56 influenced_by 0g5ff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 125.000 40.000 0.385 http://example.org/influence/influence_node/influenced_by #1130-0cmf0m0 PRED entity: 0cmf0m0 PRED relation: production_companies PRED expected values: 056ws9 => 77 concepts (64 used for prediction) PRED predicted values (max 10 best out of 66): 01gb54 (0.44 #1495, 0.44 #1663, 0.10 #951), 01795t (0.21 #188, 0.10 #603, 0.07 #1769), 0kk9v (0.16 #201, 0.08 #616, 0.04 #284), 09b3v (0.16 #199, 0.07 #1278, 0.06 #116), 04rcl7 (0.16 #238, 0.06 #155, 0.05 #1317), 056ws9 (0.16 #212, 0.05 #627, 0.04 #793), 046b0s (0.13 #24, 0.12 #107, 0.11 #356), 054lpb6 (0.12 #928, 0.08 #2012, 0.08 #264), 0c41qv (0.11 #388, 0.06 #139, 0.05 #222), 086k8 (0.11 #1247, 0.11 #1413, 0.10 #2165) >> Best rule #1495 for best value: >> intensional similarity = 4 >> extensional distance = 245 >> proper extension: 04bp0l; >> query: (?x8292, ?x4564) <- nominated_for(?x4564, ?x8292), film(?x4564, ?x3925), award_winner(?x902, ?x4564), genre(?x3925, ?x53) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #212 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 07y9w5; 08s6mr; *> query: (?x8292, 056ws9) <- film_crew_role(?x8292, ?x468), nominated_for(?x8059, ?x8292), nominated_for(?x1723, ?x8292), ?x8059 = 0drtkx, film_release_distribution_medium(?x8292, ?x81), ?x1723 = 09tqxt *> conf = 0.16 ranks of expected_values: 6 EVAL 0cmf0m0 production_companies 056ws9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 77.000 64.000 0.444 http://example.org/film/film/production_companies #1129-0241y7 PRED entity: 0241y7 PRED relation: film_crew_role PRED expected values: 015h31 01vx2h => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 34): 0ch6mp2 (0.74 #811, 0.73 #1100, 0.71 #556), 09zzb8 (0.71 #550, 0.71 #1094, 0.71 #805), 0dxtw (0.35 #1104, 0.34 #815, 0.34 #560), 01vx2h (0.30 #816, 0.29 #1105, 0.27 #561), 02ynfr (0.17 #820, 0.15 #1109, 0.15 #565), 015h31 (0.16 #46, 0.10 #155, 0.09 #1277), 0d2b38 (0.14 #63, 0.09 #830, 0.09 #1119), 0215hd (0.11 #823, 0.11 #1112, 0.09 #568), 02rh1dz (0.09 #559, 0.09 #1103, 0.09 #814), 01xy5l_ (0.09 #818, 0.09 #1107, 0.09 #1277) >> Best rule #811 for best value: >> intensional similarity = 4 >> extensional distance = 843 >> proper extension: 0gtvrv3; 0372j5; >> query: (?x6140, 0ch6mp2) <- film(?x9526, ?x6140), country(?x6140, ?x94), film_crew_role(?x6140, ?x468), religion(?x9526, ?x1985) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #816 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 843 *> proper extension: 0gtvrv3; 0372j5; *> query: (?x6140, 01vx2h) <- film(?x9526, ?x6140), country(?x6140, ?x94), film_crew_role(?x6140, ?x468), religion(?x9526, ?x1985) *> conf = 0.30 ranks of expected_values: 4, 6 EVAL 0241y7 film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 53.000 53.000 0.740 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0241y7 film_crew_role 015h31 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 53.000 53.000 0.740 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1128-05qfh PRED entity: 05qfh PRED relation: major_field_of_study! PRED expected values: 0288zy 0kz2w 01k2wn 01q0kg 02d_zc 0cwx_ 0bwfn 01jpqb 013807 06rjp => 92 concepts (73 used for prediction) PRED predicted values (max 10 best out of 695): 017j69 (0.62 #6917, 0.50 #13696, 0.50 #5873), 01bm_ (0.60 #3884, 0.60 #2840, 0.52 #11181), 07tgn (0.60 #3663, 0.60 #3141, 0.50 #8355), 02bqy (0.60 #3824, 0.60 #3302, 0.50 #1215), 0dzst (0.60 #3977, 0.60 #2933, 0.50 #1368), 07tg4 (0.60 #3730, 0.60 #3208, 0.50 #1121), 0ks67 (0.60 #3832, 0.60 #2788, 0.50 #5916), 07w0v (0.60 #3667, 0.57 #10964, 0.50 #5751), 0bwfn (0.60 #3905, 0.54 #7033, 0.50 #13812), 07wjk (0.60 #3705, 0.50 #1096, 0.50 #574) >> Best rule #6917 for best value: >> intensional similarity = 8 >> extensional distance = 11 >> proper extension: 0h5k; 0g26h; 04gb7; 02_7t; 0l5mz; 02jfc; >> query: (?x3490, 017j69) <- major_field_of_study(?x2014, ?x3490), major_field_of_study(?x8427, ?x3490), major_field_of_study(?x7991, ?x3490), major_field_of_study(?x5750, ?x3490), organization(?x346, ?x7991), ?x5750 = 01nnsv, major_field_of_study(?x196, ?x2014), student(?x8427, ?x2873) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #3905 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 01mkq; 03g3w; *> query: (?x3490, 0bwfn) <- major_field_of_study(?x2014, ?x3490), major_field_of_study(?x7991, ?x3490), major_field_of_study(?x6919, ?x3490), major_field_of_study(?x6912, ?x3490), organization(?x346, ?x7991), ?x2014 = 04rjg, ?x6912 = 0gl5_, currency(?x6919, ?x170), contains(?x94, ?x7991) *> conf = 0.60 ranks of expected_values: 9, 36, 65, 78, 127, 129, 141, 297, 319, 382 EVAL 05qfh major_field_of_study! 06rjp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 92.000 73.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qfh major_field_of_study! 013807 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 92.000 73.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qfh major_field_of_study! 01jpqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 92.000 73.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qfh major_field_of_study! 0bwfn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 92.000 73.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qfh major_field_of_study! 0cwx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 92.000 73.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qfh major_field_of_study! 02d_zc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 92.000 73.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qfh major_field_of_study! 01q0kg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 92.000 73.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qfh major_field_of_study! 01k2wn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 92.000 73.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qfh major_field_of_study! 0kz2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 92.000 73.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05qfh major_field_of_study! 0288zy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 92.000 73.000 0.615 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1127-0f8l9c PRED entity: 0f8l9c PRED relation: olympics PRED expected values: 01f1jy => 293 concepts (293 used for prediction) PRED predicted values (max 10 best out of 5): 0kbws (0.85 #232, 0.85 #154, 0.85 #217), 016r9z (0.71 #571, 0.70 #539, 0.68 #588), 09n48 (0.64 #92, 0.53 #738, 0.47 #949), 01f1jy (0.43 #53, 0.38 #353, 0.36 #93), 018wrk (0.38 #353, 0.36 #91, 0.33 #111) >> Best rule #232 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 014tss; >> query: (?x789, 0kbws) <- country(?x251, ?x789), combatants(?x789, ?x94), nationality(?x317, ?x789) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #53 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 0cgm9; *> query: (?x789, 01f1jy) <- partially_contains(?x455, ?x789), entity_involved(?x9939, ?x789) *> conf = 0.43 ranks of expected_values: 4 EVAL 0f8l9c olympics 01f1jy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 293.000 293.000 0.852 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #1126-051ys82 PRED entity: 051ys82 PRED relation: film! PRED expected values: 02mxw0 => 135 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1306): 0j_c (0.29 #410, 0.08 #58630, 0.07 #25361), 044qx (0.29 #731, 0.07 #2811, 0.06 #25682), 054bt3 (0.27 #2080, 0.18 #60300, 0.17 #108132), 06ltr (0.21 #11340, 0.08 #17579, 0.05 #67483), 0l6px (0.21 #10784, 0.08 #17023, 0.05 #41975), 065jlv (0.21 #10709, 0.08 #16948, 0.04 #66852), 0134w7 (0.21 #10556, 0.08 #16795, 0.04 #66699), 09y20 (0.21 #10644, 0.06 #16883, 0.05 #66787), 013_vh (0.17 #11059, 0.06 #17298, 0.04 #67202), 0f0kz (0.15 #8833, 0.09 #4675, 0.04 #48341) >> Best rule #410 for best value: >> intensional similarity = 3 >> extensional distance = 12 >> proper extension: 0k5g9; 02r_pp; 0k0rf; >> query: (?x6005, 0j_c) <- film(?x5192, ?x6005), nominated_for(?x857, ?x6005), film_festivals(?x6005, ?x9080) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #58682 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 123 *> proper extension: 03rtz1; 05cj_j; 01kf3_9; 0dyb1; 0d1qmz; 02jr6k; 0qmjd; 01jr4j; 0422v0; *> query: (?x6005, 02mxw0) <- film(?x5192, ?x6005), nominated_for(?x857, ?x6005), genre(?x6005, ?x53) *> conf = 0.03 ranks of expected_values: 513 EVAL 051ys82 film! 02mxw0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 135.000 90.000 0.286 http://example.org/film/actor/film./film/performance/film #1125-0127s7 PRED entity: 0127s7 PRED relation: vacationer! PRED expected values: 0cv3w => 151 concepts (151 used for prediction) PRED predicted values (max 10 best out of 98): 03gh4 (0.20 #2048, 0.17 #1063, 0.15 #79), 05qtj (0.18 #2039, 0.13 #1054, 0.11 #562), 0cv3w (0.13 #1039, 0.12 #424, 0.11 #547), 0b90_r (0.12 #1972, 0.09 #987, 0.08 #1725), 0f2v0 (0.09 #1045, 0.09 #799, 0.08 #1783), 0160w (0.09 #371, 0.08 #248, 0.08 #2), 0r0m6 (0.08 #67, 0.04 #313, 0.03 #3637), 0k3p (0.08 #93, 0.04 #339, 0.02 #1077), 06c62 (0.08 #1069, 0.07 #823, 0.06 #2301), 02_286 (0.06 #383, 0.05 #1983, 0.05 #2722) >> Best rule #2048 for best value: >> intensional similarity = 3 >> extensional distance = 74 >> proper extension: 02l840; 01ztgm; 0l12d; 01cwhp; 01ttg5; 01s21dg; 06mt91; >> query: (?x5906, 03gh4) <- award_winner(?x5906, ?x3481), location(?x5906, ?x739), vacationer(?x362, ?x5906) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1039 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 012_53; *> query: (?x5906, 0cv3w) <- participant(?x5906, ?x970), friend(?x5906, ?x3481), participant(?x5906, ?x1896) *> conf = 0.13 ranks of expected_values: 3 EVAL 0127s7 vacationer! 0cv3w CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 151.000 151.000 0.197 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #1124-0c_mvb PRED entity: 0c_mvb PRED relation: profession PRED expected values: 0kyk => 97 concepts (55 used for prediction) PRED predicted values (max 10 best out of 56): 02hrh1q (0.72 #1599, 0.68 #1743, 0.67 #2319), 0dxtg (0.70 #3619, 0.69 #878, 0.63 #7659), 09jwl (0.59 #4346, 0.57 #3191, 0.54 #3912), 0nbcg (0.44 #3923, 0.41 #4357, 0.40 #3202), 0dz3r (0.41 #2741, 0.38 #4331, 0.38 #2886), 016z4k (0.36 #3899, 0.34 #2743, 0.33 #3178), 0np9r (0.24 #7666, 0.17 #1605, 0.16 #2325), 0cbd2 (0.23 #3613, 0.20 #4769, 0.17 #872), 018gz8 (0.20 #1601, 0.20 #160, 0.20 #15), 0kyk (0.20 #171, 0.20 #26, 0.14 #3633) >> Best rule #1599 for best value: >> intensional similarity = 4 >> extensional distance = 135 >> proper extension: 0q9kd; 012_53; 0m32_; 01f8ld; 01mt1fy; 042z_g; 016z51; 022_q8; 07g7h2; 02b29; ... >> query: (?x2479, 02hrh1q) <- profession(?x2479, ?x1943), gender(?x2479, ?x231), ?x1943 = 02krf9, student(?x7545, ?x2479) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0gsg7; *> query: (?x2479, 0kyk) <- award_winner(?x2479, ?x6678), award_winner(?x3486, ?x2479), ?x6678 = 05gnf, ?x3486 = 0m7yy *> conf = 0.20 ranks of expected_values: 10 EVAL 0c_mvb profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 97.000 55.000 0.715 http://example.org/people/person/profession #1123-02p86pb PRED entity: 02p86pb PRED relation: film! PRED expected values: 029q_y => 91 concepts (47 used for prediction) PRED predicted values (max 10 best out of 816): 05kfs (0.47 #4155, 0.45 #78972, 0.41 #62345), 0146pg (0.47 #4155, 0.41 #62345, 0.41 #85205), 06r_by (0.47 #4155, 0.41 #62345, 0.41 #85205), 021yc7p (0.47 #4155, 0.41 #62345, 0.41 #85205), 013knm (0.29 #639, 0.01 #4794), 0h5g_ (0.14 #74, 0.03 #2151, 0.03 #10464), 01q_ph (0.14 #57, 0.03 #2134, 0.02 #70715), 0jfx1 (0.14 #407, 0.03 #6641, 0.03 #14952), 0h7pj (0.14 #1540, 0.02 #18164, 0.01 #76354), 02ck7w (0.14 #942, 0.02 #40425, 0.02 #9255) >> Best rule #4155 for best value: >> intensional similarity = 4 >> extensional distance = 58 >> proper extension: 0267wwv; >> query: (?x9060, ?x669) <- nominated_for(?x669, ?x9060), titles(?x162, ?x9060), production_companies(?x9060, ?x1104), ?x1104 = 016tw3 >> conf = 0.47 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 02p86pb film! 029q_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 91.000 47.000 0.466 http://example.org/film/actor/film./film/performance/film #1122-09jwl PRED entity: 09jwl PRED relation: specialization_of! PRED expected values: 04f2zj => 52 concepts (50 used for prediction) PRED predicted values (max 10 best out of 139): 01xr66 (0.33 #228, 0.25 #326, 0.17 #816), 0np9r (0.33 #202, 0.25 #300, 0.17 #790), 0mbx4 (0.33 #288, 0.25 #386, 0.17 #876), 0g7nc (0.33 #279, 0.25 #377, 0.17 #867), 0w7c (0.33 #226, 0.25 #324, 0.17 #814), 021wpb (0.33 #219, 0.25 #317, 0.17 #807), 01c979 (0.33 #43, 0.04 #1518, 0.03 #1716), 0196pc (0.33 #37, 0.04 #1512, 0.03 #1710), 0lgw7 (0.33 #19, 0.04 #1494, 0.03 #1692), 09jwl (0.33 #5, 0.04 #1480, 0.03 #1678) >> Best rule #228 for best value: >> intensional similarity = 6 >> extensional distance = 1 >> proper extension: 02hrh1q; >> query: (?x1183, 01xr66) <- profession(?x9262, ?x1183), profession(?x4537, ?x1183), profession(?x483, ?x1183), ?x483 = 0m2l9, ?x9262 = 04n2vgk, ?x4537 = 01817f >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3457 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 87 *> proper extension: 01bs9f; *> query: (?x1183, ?x220) <- profession(?x9662, ?x1183), profession(?x5391, ?x1183), profession(?x487, ?x1183), award_winner(?x487, ?x3374), profession(?x5391, ?x220), gender(?x9662, ?x231) *> conf = 0.01 ranks of expected_values: 108 EVAL 09jwl specialization_of! 04f2zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 52.000 50.000 0.333 http://example.org/people/profession/specialization_of #1121-02yv_b PRED entity: 02yv_b PRED relation: honored_for PRED expected values: 07xvf => 35 concepts (13 used for prediction) PRED predicted values (max 10 best out of 672): 09m6kg (0.11 #611, 0.10 #1797, 0.10 #1204), 011ywj (0.11 #1080, 0.10 #2266, 0.10 #1673), 017gl1 (0.11 #653, 0.10 #1839, 0.10 #1246), 04k9y6 (0.11 #959, 0.10 #2145, 0.10 #1552), 04q827 (0.11 #1162, 0.10 #2348, 0.10 #1755), 03xf_m (0.11 #985, 0.10 #2171, 0.10 #1578), 049xgc (0.11 #938, 0.10 #2124, 0.10 #1531), 02wgk1 (0.11 #866, 0.10 #2052, 0.10 #1459), 0pc62 (0.11 #633, 0.10 #1819, 0.10 #1226), 011yxg (0.11 #614, 0.10 #1800, 0.10 #1207) >> Best rule #611 for best value: >> intensional similarity = 19 >> extensional distance = 7 >> proper extension: 0bvfqq; 050yyb; 02yvhx; 02ywhz; 02pgky2; 02yxh9; 04110lv; >> query: (?x1819, 09m6kg) <- ceremony(?x3617, ?x1819), ceremony(?x3458, ?x1819), ceremony(?x2222, ?x1819), ceremony(?x1243, ?x1819), ceremony(?x1079, ?x1819), ?x1243 = 0gr0m, ?x1079 = 0l8z1, honored_for(?x1819, ?x1820), ?x3458 = 0gqxm, ?x2222 = 0gs96, nominated_for(?x1198, ?x1820), nominated_for(?x192, ?x1820), ?x1198 = 02pqp12, award_winner(?x1819, ?x262), film_crew_role(?x1820, ?x1171), ?x1171 = 09vw2b7, award_winner(?x1820, ?x919), ?x3617 = 0gvx_, country(?x1820, ?x94) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #7747 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 39 *> proper extension: 0ftlkg; 0ftlxj; *> query: (?x1819, ?x197) <- ceremony(?x3458, ?x1819), ceremony(?x1313, ?x1819), ceremony(?x1243, ?x1819), ceremony(?x1079, ?x1819), ?x1243 = 0gr0m, ?x1079 = 0l8z1, honored_for(?x1819, ?x1077), nominated_for(?x3458, ?x6981), nominated_for(?x3458, ?x2463), nominated_for(?x3458, ?x697), award(?x2871, ?x3458), ?x697 = 0209hj, film(?x398, ?x6981), award_winner(?x1819, ?x262), titles(?x1510, ?x2463), award(?x197, ?x1313), nominated_for(?x1313, ?x161), award(?x269, ?x1313) *> conf = 0.01 ranks of expected_values: 449 EVAL 02yv_b honored_for 07xvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 35.000 13.000 0.111 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #1120-03fts PRED entity: 03fts PRED relation: nominated_for! PRED expected values: 02g2yr => 73 concepts (55 used for prediction) PRED predicted values (max 10 best out of 179): 02g2wv (0.69 #1443, 0.68 #8902, 0.68 #8174), 0262s1 (0.69 #1443, 0.68 #8902, 0.68 #8174), 0gq_v (0.48 #20, 0.22 #6509, 0.22 #3865), 02r22gf (0.48 #28, 0.12 #6517, 0.12 #6035), 02hsq3m (0.43 #29, 0.20 #751, 0.14 #991), 0p9sw (0.39 #21, 0.21 #1944, 0.20 #2424), 0gq9h (0.32 #6552, 0.29 #1986, 0.29 #2466), 0gs9p (0.30 #65, 0.28 #6554, 0.25 #2468), 040njc (0.30 #7, 0.23 #2650, 0.21 #6496), 0gr42 (0.30 #90, 0.20 #812, 0.14 #1052) >> Best rule #1443 for best value: >> intensional similarity = 4 >> extensional distance = 235 >> proper extension: 02n9bh; 0gpx6; 02wk7b; 06zn1c; >> query: (?x1474, ?x5734) <- nominated_for(?x771, ?x1474), genre(?x1474, ?x258), ?x258 = 05p553, award(?x1474, ?x5734) >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #5527 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 705 *> proper extension: 0d7vtk; *> query: (?x1474, ?x1245) <- nominated_for(?x4771, ?x1474), produced_by(?x1474, ?x6718), language(?x1474, ?x254), award(?x4771, ?x1245) *> conf = 0.19 ranks of expected_values: 40 EVAL 03fts nominated_for! 02g2yr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 73.000 55.000 0.694 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1119-0cp9f9 PRED entity: 0cp9f9 PRED relation: profession PRED expected values: 02hrh1q 03gjzk => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 49): 03gjzk (0.87 #1495, 0.86 #1791, 0.86 #1939), 02hrh1q (0.68 #5490, 0.67 #11118, 0.67 #5934), 01d_h8 (0.56 #154, 0.51 #1190, 0.51 #2226), 02jknp (0.42 #4448, 0.28 #748, 0.28 #9328), 0cbd2 (0.31 #6661, 0.30 #6810, 0.28 #9328), 09lbv (0.31 #6661, 0.30 #6810, 0.28 #9328), 09jwl (0.20 #4163, 0.19 #3571, 0.19 #5643), 018gz8 (0.17 #1201, 0.17 #4457, 0.13 #2237), 0np9r (0.16 #169, 0.14 #1057, 0.13 #317), 0dz3r (0.13 #4146, 0.13 #5626, 0.13 #5182) >> Best rule #1495 for best value: >> intensional similarity = 3 >> extensional distance = 162 >> proper extension: 0dbpyd; 06j0md; 0d4fqn; 0415svh; 02773m2; 02778pf; 0284gcb; 0crx5w; 06v_gh; 09gffmz; ... >> query: (?x8229, 03gjzk) <- award_winner(?x3762, ?x8229), profession(?x8229, ?x987), program(?x8229, ?x1849) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0cp9f9 profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.872 http://example.org/people/person/profession EVAL 0cp9f9 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.872 http://example.org/people/person/profession #1118-030qb3t PRED entity: 030qb3t PRED relation: month PRED expected values: 0lkm => 162 concepts (162 used for prediction) PRED predicted values (max 10 best out of 1): 0lkm (0.85 #23, 0.76 #24, 0.73 #19) >> Best rule #23 for best value: >> intensional similarity = 2 >> extensional distance = 51 >> proper extension: 03czqs; 0g6xq; >> query: (?x1523, 0lkm) <- month(?x1523, ?x7298), ?x7298 = 04wzr >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030qb3t month 0lkm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 162.000 162.000 0.849 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #1117-02ph9tm PRED entity: 02ph9tm PRED relation: film! PRED expected values: 03xb2w => 72 concepts (35 used for prediction) PRED predicted values (max 10 best out of 996): 01hkhq (0.17 #409, 0.02 #12872, 0.01 #25337), 02r251z (0.12 #66473, 0.11 #70628, 0.09 #27006), 05ty4m (0.10 #16618, 0.01 #6277), 04xrx (0.08 #441, 0.06 #2078), 0h0wc (0.08 #420, 0.04 #52352, 0.03 #39890), 024bbl (0.08 #833, 0.04 #4988, 0.03 #52765), 0f502 (0.08 #759, 0.04 #2837, 0.02 #15299), 01wskg (0.08 #1974, 0.04 #6129), 09fb5 (0.08 #58, 0.04 #49913, 0.03 #14598), 01vvb4m (0.08 #518, 0.03 #8827, 0.02 #44142) >> Best rule #409 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 0413cff; >> query: (?x6245, 01hkhq) <- genre(?x6245, ?x53), ?x53 = 07s9rl0, film_release_region(?x6245, ?x94), person(?x6245, ?x2614), currency(?x6245, ?x170) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #2954 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 47 *> proper extension: 0crh5_f; *> query: (?x6245, 03xb2w) <- genre(?x6245, ?x53), film_crew_role(?x6245, ?x1284), ?x1284 = 0ch6mp2, production_companies(?x6245, ?x1478), film_release_region(?x6245, ?x94), ?x1478 = 054lpb6 *> conf = 0.02 ranks of expected_values: 377 EVAL 02ph9tm film! 03xb2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 72.000 35.000 0.167 http://example.org/film/actor/film./film/performance/film #1116-05b6c PRED entity: 05b6c PRED relation: major_field_of_study! PRED expected values: 02_xgp2 => 41 concepts (30 used for prediction) PRED predicted values (max 10 best out of 20): 014mlp (0.83 #458, 0.82 #90, 0.81 #174), 0bkj86 (0.82 #93, 0.75 #72, 0.75 #51), 02_xgp2 (0.80 #310, 0.75 #55, 0.74 #119), 016t_3 (0.78 #214, 0.77 #110, 0.77 #235), 019v9k (0.77 #116, 0.76 #220, 0.76 #137), 04zx3q1 (0.71 #23, 0.62 #66, 0.62 #45), 03mkk4 (0.46 #43, 0.41 #107, 0.39 #21), 01rr_d (0.46 #43, 0.41 #107, 0.39 #21), 0bjrnt (0.43 #27, 0.41 #107, 0.39 #21), 071tyz (0.43 #31, 0.41 #107, 0.39 #21) >> Best rule #458 for best value: >> intensional similarity = 17 >> extensional distance = 85 >> proper extension: 01z4y; >> query: (?x11206, 014mlp) <- major_field_of_study(?x4981, ?x11206), institution(?x4981, ?x11853), institution(?x4981, ?x10759), institution(?x4981, ?x9443), institution(?x4981, ?x6083), institution(?x4981, ?x4599), institution(?x4981, ?x3948), institution(?x4981, ?x3021), major_field_of_study(?x4981, ?x3213), ?x3021 = 027xx3, ?x9443 = 039d4, student(?x4599, ?x3273), ?x3948 = 025v3k, ?x6083 = 09s5q8, citytown(?x11853, ?x10836), ?x10759 = 023zl, ?x3213 = 0g4gr >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #310 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 72 *> proper extension: 03qh03g; *> query: (?x11206, 02_xgp2) <- major_field_of_study(?x4981, ?x11206), institution(?x4981, ?x12475), institution(?x4981, ?x8903), institution(?x4981, ?x6856), institution(?x4981, ?x3813), institution(?x4981, ?x735), institution(?x4981, ?x621), institution(?x4981, ?x581), ?x12475 = 02_jjm, ?x3813 = 07vfj, ?x6856 = 0jkhr, ?x735 = 065y4w7, ?x581 = 06pwq, ?x621 = 02w2bc, student(?x4981, ?x118), state_province_region(?x8903, ?x2020) *> conf = 0.80 ranks of expected_values: 3 EVAL 05b6c major_field_of_study! 02_xgp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 30.000 0.828 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #1115-0c_j9x PRED entity: 0c_j9x PRED relation: films! PRED expected values: 07jdr => 56 concepts (37 used for prediction) PRED predicted values (max 10 best out of 33): 081pw (0.12 #3, 0.04 #160, 0.03 #631), 0fzyg (0.04 #211, 0.04 #54, 0.02 #368), 07jq_ (0.04 #82, 0.01 #239, 0.01 #1180), 04jjy (0.04 #7, 0.01 #1419, 0.01 #3158), 03hzt (0.04 #135, 0.01 #920, 0.01 #1391), 0fx2s (0.03 #73, 0.02 #858, 0.02 #3065), 01w1sx (0.03 #91, 0.01 #2766, 0.01 #3401), 06d4h (0.03 #1455, 0.02 #3987, 0.02 #984), 05489 (0.02 #366, 0.02 #1150, 0.02 #837), 01d5g (0.02 #580) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 101 >> proper extension: 03_wm6; >> query: (?x2345, 081pw) <- language(?x2345, ?x732), titles(?x162, ?x2345), ?x732 = 04306rv >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c_j9x films! 07jdr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 56.000 37.000 0.117 http://example.org/film/film_subject/films #1114-0crd8q6 PRED entity: 0crd8q6 PRED relation: film_crew_role PRED expected values: 01vx2h => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 26): 0dxtw (0.41 #954, 0.41 #989, 0.40 #1059), 01vx2h (0.40 #185, 0.37 #10, 0.35 #80), 01pvkk (0.29 #2252, 0.28 #116, 0.28 #1202), 02ynfr (0.18 #960, 0.18 #1065, 0.18 #995), 0215hd (0.15 #963, 0.15 #998, 0.15 #1068), 02rh1dz (0.14 #953, 0.14 #988, 0.13 #1058), 01xy5l_ (0.14 #48, 0.12 #958, 0.12 #188), 015h31 (0.14 #42, 0.11 #182, 0.11 #77), 0d2b38 (0.13 #25, 0.12 #130, 0.12 #60), 089g0h (0.12 #999, 0.12 #124, 0.12 #1069) >> Best rule #954 for best value: >> intensional similarity = 3 >> extensional distance = 669 >> proper extension: 0h95zbp; 0gh6j94; >> query: (?x10191, 0dxtw) <- film_crew_role(?x10191, ?x1171), ?x1171 = 09vw2b7, language(?x10191, ?x254) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #185 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 157 *> proper extension: 0gtsx8c; 0dq626; 0661m4p; 06fqlk; 0mbql; 06_sc3; *> query: (?x10191, 01vx2h) <- film_crew_role(?x10191, ?x137), film_distribution_medium(?x10191, ?x2099), film(?x436, ?x10191) *> conf = 0.40 ranks of expected_values: 2 EVAL 0crd8q6 film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.413 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1113-02pqgt8 PRED entity: 02pqgt8 PRED relation: costume_design_by! PRED expected values: 05ldxl => 134 concepts (92 used for prediction) PRED predicted values (max 10 best out of 190): 09d38d (0.12 #374, 0.11 #563, 0.11 #752), 0f42nz (0.11 #671, 0.07 #1050, 0.06 #293), 015gm8 (0.09 #187, 0.06 #376, 0.06 #565), 0k419 (0.09 #180, 0.06 #369, 0.06 #558), 0jqb8 (0.09 #172, 0.06 #361, 0.06 #550), 04wddl (0.09 #171, 0.06 #360, 0.06 #549), 072192 (0.09 #169, 0.06 #358, 0.06 #547), 0h3k3f (0.09 #165, 0.06 #354, 0.06 #543), 0k4bc (0.09 #146, 0.06 #335, 0.06 #524), 01jr4j (0.09 #144, 0.06 #333, 0.06 #522) >> Best rule #374 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 02w0dc0; 06w33f8; 0gl88b; 0c6g29; 0dck27; 0b80__; 02cqbx; 02mxbd; 03mfqm; 0dg3jz; ... >> query: (?x4190, 09d38d) <- costume_design_by(?x8985, ?x4190), costume_design_by(?x776, ?x4190), place_of_birth(?x4190, ?x10537), production_companies(?x8985, ?x847), award(?x776, ?x112) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02pqgt8 costume_design_by! 05ldxl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 134.000 92.000 0.118 http://example.org/film/film/costume_design_by #1112-094vy PRED entity: 094vy PRED relation: contains! PRED expected values: 02jx1 => 92 concepts (30 used for prediction) PRED predicted values (max 10 best out of 111): 02jx1 (0.67 #3586, 0.67 #2774, 0.65 #15339), 09c7w0 (0.55 #26021, 0.55 #25127, 0.47 #19743), 094vy (0.25 #22435, 0.25 #26921, 0.23 #19740), 0134bf (0.25 #22435, 0.25 #26921, 0.23 #19740), 02qkt (0.25 #1242, 0.20 #2138, 0.15 #22781), 04_1l0v (0.19 #4036, 0.18 #5830, 0.15 #9420), 04jpl (0.18 #10788, 0.12 #15275, 0.11 #4506), 02j9z (0.15 #22463, 0.07 #3614, 0.07 #6307), 0345h (0.15 #23413, 0.06 #3667, 0.04 #13538), 0j5g9 (0.13 #21534, 0.05 #23331, 0.04 #4744) >> Best rule #3586 for best value: >> intensional similarity = 5 >> extensional distance = 56 >> proper extension: 0fm2_; 0c5_3; 02j7k; >> query: (?x9985, ?x1310) <- contains(?x9985, ?x10786), contains(?x1310, ?x10786), contains(?x512, ?x10786), ?x1310 = 02jx1, ?x512 = 07ssc >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 094vy contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 30.000 0.672 http://example.org/location/location/contains #1111-017d93 PRED entity: 017d93 PRED relation: nominated_for! PRED expected values: 05ztjjw => 76 concepts (68 used for prediction) PRED predicted values (max 10 best out of 293): 099ck7 (0.33 #178, 0.16 #656, 0.08 #1373), 09sb52 (0.33 #35, 0.11 #1230, 0.10 #2186), 05pcn59 (0.33 #68, 0.09 #307, 0.08 #785), 02x4w6g (0.33 #89, 0.09 #328, 0.06 #1284), 063y_ky (0.33 #102, 0.05 #1297, 0.05 #2014), 0gq9h (0.24 #7717, 0.23 #781, 0.22 #10348), 0gr4k (0.23 #744, 0.22 #3828, 0.20 #15070), 03hkv_r (0.23 #732, 0.22 #3828, 0.20 #15070), 019f4v (0.22 #3828, 0.20 #7708, 0.20 #15070), 02x17s4 (0.22 #3828, 0.20 #15070, 0.20 #13632) >> Best rule #178 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 06t6dz; >> query: (?x6298, 099ck7) <- genre(?x6298, ?x14616), film(?x3842, ?x6298), ?x14616 = 026v1nw, film_crew_role(?x6298, ?x1171) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2641 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 293 *> proper extension: 023p33; *> query: (?x6298, 05ztjjw) <- genre(?x6298, ?x307), film(?x3842, ?x6298), nominated_for(?x2585, ?x6298), category(?x6298, ?x134) *> conf = 0.09 ranks of expected_values: 76 EVAL 017d93 nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 76.000 68.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1110-039bpc PRED entity: 039bpc PRED relation: profession PRED expected values: 02hrh1q => 152 concepts (139 used for prediction) PRED predicted values (max 10 best out of 75): 02hrh1q (0.92 #611, 0.91 #4935, 0.91 #760), 09jwl (0.66 #3300, 0.63 #13592, 0.63 #11802), 016z4k (0.56 #3284, 0.48 #3135, 0.47 #1942), 0dz3r (0.47 #151, 0.47 #2, 0.47 #1940), 01d_h8 (0.45 #3436, 0.44 #6268, 0.44 #8060), 01c72t (0.38 #7182, 0.36 #7481, 0.36 #6883), 0dxtg (0.37 #6276, 0.37 #1355, 0.30 #16575), 03gjzk (0.34 #12976, 0.34 #6278, 0.33 #2998), 0np9r (0.34 #12976, 0.30 #4644, 0.29 #5688), 02krf9 (0.34 #12976, 0.15 #1369, 0.14 #326) >> Best rule #611 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 03q43g; >> query: (?x3547, 02hrh1q) <- actor(?x3413, ?x3547), student(?x735, ?x3547), spouse(?x4819, ?x3547) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039bpc profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 152.000 139.000 0.917 http://example.org/people/person/profession #1109-0c40vxk PRED entity: 0c40vxk PRED relation: language PRED expected values: 02h40lc => 98 concepts (80 used for prediction) PRED predicted values (max 10 best out of 45): 02h40lc (0.92 #1192, 0.92 #2399, 0.91 #2037), 06nm1 (0.33 #11, 0.23 #247, 0.17 #306), 064_8sq (0.23 #199, 0.19 #436, 0.19 #915), 04306rv (0.17 #5, 0.15 #182, 0.14 #778), 012w70 (0.15 #249, 0.12 #72, 0.08 #190), 06b_j (0.15 #200, 0.10 #676, 0.10 #1036), 04h9h (0.15 #279, 0.09 #816, 0.08 #220), 0jzc (0.15 #256, 0.06 #4180, 0.05 #2535), 02bjrlw (0.12 #60, 0.10 #654, 0.09 #714), 0653m (0.12 #71, 0.06 #1381, 0.06 #4180) >> Best rule #1192 for best value: >> intensional similarity = 6 >> extensional distance = 96 >> proper extension: 070fnm; 0dnqr; 01jzyf; 016y_f; 0295sy; 0286gm1; 026fs38; 0y_pg; 033pf1; 0pk1p; ... >> query: (?x633, 02h40lc) <- currency(?x633, ?x170), film(?x3580, ?x633), genre(?x633, ?x812), production_companies(?x633, ?x11557), film(?x1478, ?x633), edited_by(?x633, ?x7903) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c40vxk language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 80.000 0.918 http://example.org/film/film/language #1108-0lcx PRED entity: 0lcx PRED relation: influenced_by! PRED expected values: 01hb6v => 124 concepts (52 used for prediction) PRED predicted values (max 10 best out of 456): 0n6kf (0.50 #2751, 0.14 #4791, 0.12 #5815), 0ct9_ (0.43 #1364, 0.33 #341, 0.22 #1875), 01vdrw (0.43 #3002, 0.20 #6066, 0.18 #2489), 034bs (0.36 #2201, 0.29 #2714, 0.14 #4754), 058vp (0.36 #2282, 0.21 #2795, 0.07 #6372), 03_87 (0.36 #2307, 0.17 #771, 0.14 #2820), 0399p (0.33 #839, 0.33 #327, 0.29 #1350), 0dzkq (0.33 #125, 0.30 #6263, 0.29 #1148), 03f47xl (0.33 #260, 0.29 #2821, 0.18 #2308), 01hb6v (0.33 #93, 0.23 #5204, 0.17 #8789) >> Best rule #2751 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 084w8; 0379s; 073v6; 032l1; 014635; 03f0324; 02kz_; 01tz6vs; 040_t; 03_87; ... >> query: (?x4028, 0n6kf) <- influenced_by(?x5335, ?x4028), influenced_by(?x4028, ?x4055), gender(?x4028, ?x231), ?x5335 = 013pp3 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #93 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 040db; *> query: (?x4028, 01hb6v) <- influenced_by(?x1236, ?x4028), influenced_by(?x4028, ?x11097), influenced_by(?x4028, ?x4915), ?x4915 = 03f0324, ?x11097 = 02wh0, award(?x4028, ?x921) *> conf = 0.33 ranks of expected_values: 10 EVAL 0lcx influenced_by! 01hb6v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 124.000 52.000 0.500 http://example.org/influence/influence_node/influenced_by #1107-083pr PRED entity: 083pr PRED relation: taxonomy PRED expected values: 04n6k => 188 concepts (188 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.67 #10, 0.60 #18, 0.56 #32) >> Best rule #10 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 083p7; 083q7; 028rk; 03txms; 03_nq; 0194xc; 042d1; 0d3k14; 038w8; 06c0j; >> query: (?x1913, 04n6k) <- gender(?x1913, ?x231), people(?x10199, ?x1913), people(?x5741, ?x1913), jurisdiction_of_office(?x1913, ?x94) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 083pr taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 188.000 188.000 0.667 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #1106-0gjvqm PRED entity: 0gjvqm PRED relation: award PRED expected values: 09td7p 03qgjwc => 87 concepts (69 used for prediction) PRED predicted values (max 10 best out of 239): 09sb52 (0.72 #13117, 0.71 #13116, 0.71 #11524), 02y_rq5 (0.45 #91, 0.19 #14708, 0.18 #15900), 02ppm4q (0.43 #548, 0.32 #151, 0.15 #17890), 09td7p (0.31 #513, 0.20 #116, 0.19 #14708), 0bdwft (0.29 #67, 0.28 #464, 0.15 #17890), 02x4x18 (0.29 #128, 0.19 #525, 0.19 #14708), 0cqgl9 (0.27 #187, 0.20 #584, 0.17 #9934), 0bfvw2 (0.26 #412, 0.21 #15, 0.15 #17890), 03c7tr1 (0.20 #57, 0.09 #454, 0.07 #4030), 03qgjwc (0.20 #575, 0.17 #9934, 0.15 #17890) >> Best rule #13117 for best value: >> intensional similarity = 3 >> extensional distance = 1256 >> proper extension: 01wz_ml; >> query: (?x1253, ?x5455) <- award_winner(?x1253, ?x624), award_winner(?x5455, ?x1253), award(?x156, ?x5455) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #513 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 115 *> proper extension: 01hkck; 01rnpy; 06r3p2; *> query: (?x1253, 09td7p) <- award(?x1253, ?x1972), award(?x1253, ?x749), ?x1972 = 0gqyl, nominated_for(?x749, ?x306) *> conf = 0.31 ranks of expected_values: 4, 10 EVAL 0gjvqm award 03qgjwc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 69.000 0.723 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gjvqm award 09td7p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 69.000 0.723 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1105-01ckrr PRED entity: 01ckrr PRED relation: award_winner PRED expected values: 0frsw 07sbk => 42 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1447): 01wd9lv (0.40 #13707, 0.20 #3870, 0.17 #6328), 02k5sc (0.40 #4162, 0.10 #13999, 0.03 #54107), 09hnb (0.37 #27056, 0.37 #24597, 0.37 #24596), 01kh2m1 (0.37 #27056, 0.37 #24597, 0.37 #24596), 0144l1 (0.37 #27056, 0.37 #24597, 0.37 #24596), 01vv7sc (0.37 #27056, 0.37 #24597, 0.37 #24596), 07sbk (0.37 #27056, 0.37 #24597, 0.37 #24596), 01wp8w7 (0.37 #27056, 0.37 #24597, 0.37 #24596), 01vsyg9 (0.37 #27056, 0.37 #24597, 0.37 #24596), 03gr7w (0.37 #27056, 0.37 #24597, 0.37 #24596) >> Best rule #13707 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 02f79n; >> query: (?x4912, 01wd9lv) <- award(?x6635, ?x4912), award(?x6234, ?x4912), ?x6635 = 015cxv, artists(?x302, ?x6234) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #27056 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 95 *> proper extension: 03x3wf; 01ckbq; 02gx2k; 01ck6h; 02h3d1; 031b3h; 01ck6v; 02flpq; 024dzn; 031b91; ... *> query: (?x4912, ?x248) <- ceremony(?x4912, ?x2054), award(?x248, ?x4912), ceremony(?x6739, ?x2054), ?x6739 = 019bnn, award_winner(?x2054, ?x367) *> conf = 0.37 ranks of expected_values: 7, 26 EVAL 01ckrr award_winner 07sbk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 42.000 24.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01ckrr award_winner 0frsw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 42.000 24.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #1104-026m3y PRED entity: 026m3y PRED relation: contains! PRED expected values: 07ssc => 174 concepts (121 used for prediction) PRED predicted values (max 10 best out of 318): 07ssc (0.86 #42969, 0.76 #100237, 0.74 #102026), 09c7w0 (0.84 #84094, 0.79 #42046, 0.79 #81410), 0345h (0.49 #100205, 0.07 #34069, 0.07 #31386), 0978r (0.27 #5569, 0.23 #18086, 0.22 #18981), 02j9z (0.23 #88592, 0.09 #10755, 0.08 #5391), 07z1m (0.20 #984, 0.07 #8136, 0.07 #9924), 0mp3l (0.20 #1040, 0.04 #11768, 0.02 #8192), 01n7q (0.17 #26014, 0.14 #24223, 0.14 #27802), 059rby (0.16 #8066, 0.16 #9854, 0.13 #25062), 0d060g (0.15 #3589, 0.15 #11635, 0.12 #13423) >> Best rule #42969 for best value: >> intensional similarity = 5 >> extensional distance = 163 >> proper extension: 0dhdp; 0fm2_; 022_6; 0crjn65; 0121c1; 09tlh; 0nccd; 04p3c; 0fgj2; 013bqg; ... >> query: (?x10432, 07ssc) <- contains(?x1310, ?x10432), contains(?x362, ?x10432), ?x1310 = 02jx1, location(?x361, ?x362), location_of_ceremony(?x2092, ?x362) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026m3y contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 121.000 0.861 http://example.org/location/location/contains #1103-01664_ PRED entity: 01664_ PRED relation: taxonomy PRED expected values: 04n6k => 1 concepts (1 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.03 #1) >> Best rule #1 for best value: >> intensional similarity = 0 >> extensional distance = 14821 >> proper extension: Resource; Class; Literal; Property; subject; object; predicate; first; rest; value; ... >> query: (?x14807, 04n6k) <- >> conf = 0.03 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01664_ taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 1.000 1.000 0.030 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #1102-0bqc_ PRED entity: 0bqc_ PRED relation: organizations_founded! PRED expected values: 07hyk => 2 concepts (2 used for prediction) No prediction ranks of expected_values: EVAL 0bqc_ organizations_founded! 07hyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 2.000 2.000 0.000 http://example.org/organization/organization_founder/organizations_founded #1101-01sg7_ PRED entity: 01sg7_ PRED relation: nationality PRED expected values: 09c7w0 => 147 concepts (118 used for prediction) PRED predicted values (max 10 best out of 51): 09c7w0 (0.89 #5821, 0.88 #1303, 0.88 #1202), 0nzny (0.32 #11194, 0.31 #9269, 0.28 #11902), 0d0x8 (0.32 #11194, 0.31 #9269, 0.28 #11902), 0rh6k (0.25 #11595), 0d060g (0.22 #10785, 0.12 #5626, 0.07 #7238), 02jx1 (0.20 #3845, 0.18 #4047, 0.18 #4148), 07ssc (0.20 #3222, 0.19 #3021, 0.16 #2920), 0dclg (0.12 #4115, 0.03 #3107, 0.03 #3308), 0c_m3 (0.12 #4115), 02cl1 (0.12 #4115) >> Best rule #5821 for best value: >> intensional similarity = 3 >> extensional distance = 638 >> proper extension: 018db8; 083p7; 01wbl_r; 05_pkf; 03h502k; 048_p; 0k1bs; 01f7dd; 06jcc; 0335fp; ... >> query: (?x8996, 09c7w0) <- location(?x8996, ?x2277), place_of_birth(?x3058, ?x2277), county_seat(?x13275, ?x2277) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01sg7_ nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 118.000 0.889 http://example.org/people/person/nationality #1100-01vvyd8 PRED entity: 01vvyd8 PRED relation: profession PRED expected values: 0kyk => 106 concepts (73 used for prediction) PRED predicted values (max 10 best out of 71): 01d_h8 (0.73 #8799, 0.44 #2453, 0.43 #1013), 0dxtg (0.64 #9525, 0.57 #2315, 0.47 #1019), 09jwl (0.63 #1887, 0.59 #5778, 0.58 #5057), 016z4k (0.38 #5045, 0.38 #2163, 0.37 #723), 02jknp (0.35 #8800, 0.32 #2310, 0.30 #150), 018gz8 (0.32 #1021, 0.25 #13, 0.21 #1453), 01c72t (0.32 #5783, 0.26 #740, 0.22 #5062), 0kyk (0.31 #4059, 0.30 #4347, 0.22 #2330), 025352 (0.30 #199, 0.10 #1927, 0.07 #775), 0np9r (0.25 #9531, 0.18 #3762, 0.17 #4482) >> Best rule #8799 for best value: >> intensional similarity = 5 >> extensional distance = 1341 >> proper extension: 01t6b4; 04w1j9; 02q42j_; 0191h5; 04fyhv; 0bkf72; 0cj2k3; 0qdwr; 03qncl3; 027z0pl; >> query: (?x6231, 01d_h8) <- profession(?x6231, ?x353), profession(?x11705, ?x353), profession(?x6692, ?x353), ?x11705 = 06s1qy, ?x6692 = 04l19_ >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #4059 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 478 *> proper extension: 0c3kw; 01dzz7; 03mz9r; 040db; 0l56b; 01q415; 040_9; 0fx02; 01dvtx; 034bs; ... *> query: (?x6231, 0kyk) <- profession(?x6231, ?x353), profession(?x6231, ?x131), ?x353 = 0cbd2, profession(?x11402, ?x131), ?x11402 = 05mxw33 *> conf = 0.31 ranks of expected_values: 8 EVAL 01vvyd8 profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 106.000 73.000 0.726 http://example.org/people/person/profession #1099-0gx1bnj PRED entity: 0gx1bnj PRED relation: genre PRED expected values: 02l7c8 => 78 concepts (39 used for prediction) PRED predicted values (max 10 best out of 93): 01jfsb (0.74 #2872, 0.59 #250, 0.49 #1322), 02kdv5l (0.60 #1312, 0.54 #240, 0.44 #2862), 03mqtr (0.50 #29, 0.07 #1219, 0.06 #1100), 02l7c8 (0.46 #134, 0.33 #2280, 0.33 #2995), 03k9fj (0.45 #1321, 0.34 #249, 0.31 #606), 06cvj (0.38 #122, 0.21 #2268, 0.21 #2983), 0c3351 (0.33 #37, 0.07 #2897, 0.04 #2063), 0lsxr (0.29 #2868, 0.22 #1557, 0.19 #3346), 01hmnh (0.27 #255, 0.22 #1327, 0.20 #374), 02n4kr (0.27 #2867, 0.15 #245, 0.14 #959) >> Best rule #2872 for best value: >> intensional similarity = 5 >> extensional distance = 520 >> proper extension: 0d1qmz; 02r_pp; 01_1hw; 04ynx7; >> query: (?x343, 01jfsb) <- film(?x237, ?x343), film_release_distribution_medium(?x343, ?x81), genre(?x343, ?x1013), genre(?x11313, ?x1013), ?x11313 = 0by17xn >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #134 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 11 *> proper extension: 0h1x5f; *> query: (?x343, 02l7c8) <- film(?x4001, ?x343), film(?x237, ?x343), film_release_distribution_medium(?x343, ?x81), country(?x343, ?x94), genre(?x343, ?x53), award_winner(?x2296, ?x4001), ?x237 = 04t2l2 *> conf = 0.46 ranks of expected_values: 4 EVAL 0gx1bnj genre 02l7c8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 78.000 39.000 0.741 http://example.org/film/film/genre #1098-030h95 PRED entity: 030h95 PRED relation: type_of_union PRED expected values: 04ztj => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.85 #37, 0.85 #17, 0.84 #45), 01g63y (0.30 #22, 0.30 #18, 0.28 #46) >> Best rule #37 for best value: >> intensional similarity = 2 >> extensional distance = 351 >> proper extension: 03qkgyl; 02m30v; >> query: (?x1802, 04ztj) <- nationality(?x1802, ?x94), spouse(?x2135, ?x1802) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 030h95 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.853 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1097-09sdmz PRED entity: 09sdmz PRED relation: award! PRED expected values: 02p65p 01qscs 02js6_ 015v3r 023kzp 01tnxc => 49 concepts (21 used for prediction) PRED predicted values (max 10 best out of 2742): 01ycbq (0.79 #46432, 0.78 #46433, 0.71 #9948), 0c6qh (0.71 #3957, 0.60 #17220, 0.56 #27172), 03mg35 (0.71 #3793, 0.40 #17056, 0.38 #13741), 03f1zdw (0.71 #3598, 0.40 #16861, 0.38 #26813), 0pmhf (0.62 #13936, 0.60 #23885, 0.57 #3988), 0h0jz (0.57 #3366, 0.53 #16629, 0.50 #26581), 0bl2g (0.57 #3385, 0.46 #13333, 0.44 #26600), 016khd (0.57 #3502, 0.40 #16765, 0.38 #13450), 02m501 (0.57 #6064, 0.40 #25961, 0.38 #16012), 03h_9lg (0.57 #3497, 0.40 #16760, 0.38 #26712) >> Best rule #46432 for best value: >> intensional similarity = 5 >> extensional distance = 118 >> proper extension: 09v7wsg; >> query: (?x4091, ?x525) <- ceremony(?x4091, ?x873), nominated_for(?x4091, ?x144), award_winner(?x4091, ?x5461), award_winner(?x4091, ?x525), award_nominee(?x5461, ?x100) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #9975 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 0bp_b2; 099jhq; 02x73k6; 02x8n1n; *> query: (?x4091, 02p65p) <- award(?x5661, ?x4091), award(?x968, ?x4091), ?x968 = 015grj, nominated_for(?x4091, ?x144), nationality(?x5661, ?x512) *> conf = 0.46 ranks of expected_values: 28, 61, 62, 68, 110, 238 EVAL 09sdmz award! 01tnxc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 49.000 21.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sdmz award! 023kzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 49.000 21.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sdmz award! 015v3r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 49.000 21.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sdmz award! 02js6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 49.000 21.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sdmz award! 01qscs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 49.000 21.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09sdmz award! 02p65p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 49.000 21.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1096-0mn8t PRED entity: 0mn8t PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 134 concepts (81 used for prediction) PRED predicted values (max 10 best out of 8): 09c7w0 (0.74 #578, 0.74 #602, 0.73 #682), 07z1m (0.21 #12, 0.19 #312, 0.08 #613), 0d060g (0.04 #49, 0.03 #94, 0.03 #105), 02jx1 (0.04 #450, 0.03 #427, 0.03 #415), 07ssc (0.03 #164, 0.02 #187, 0.02 #211), 03rt9 (0.01 #824, 0.01 #742, 0.01 #662), 0f8l9c (0.01 #98), 059j2 (0.01 #884) >> Best rule #578 for best value: >> intensional similarity = 4 >> extensional distance = 362 >> proper extension: 0nppc; >> query: (?x7689, 09c7w0) <- contains(?x1426, ?x7689), currency(?x7689, ?x170), ?x170 = 09nqf, state_province_region(?x347, ?x1426) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mn8t second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 81.000 0.745 http://example.org/location/country/second_level_divisions #1095-02w86hz PRED entity: 02w86hz PRED relation: film_crew_role PRED expected values: 01vx2h => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 35): 01vx2h (0.97 #1251, 0.95 #1987, 0.93 #2206), 0ch6mp2 (0.83 #1982, 0.82 #1024, 0.81 #3048), 09zzb8 (0.79 #2303, 0.78 #2377, 0.77 #1017), 09vw2b7 (0.73 #1096, 0.72 #2532, 0.71 #1981), 015h31 (0.63 #515, 0.50 #190, 0.44 #154), 0dxtw (0.52 #517, 0.50 #2205, 0.50 #1986), 05smlt (0.40 #57, 0.30 #526, 0.25 #201), 02rh1dz (0.30 #552, 0.25 #1249, 0.24 #589), 02ynfr (0.26 #887, 0.25 #268, 0.25 #196), 0215hd (0.25 #127, 0.25 #19, 0.22 #452) >> Best rule #1251 for best value: >> intensional similarity = 10 >> extensional distance = 93 >> proper extension: 09sh8k; 034qmv; 0gx1bnj; 0dscrwf; 01h7bb; 0pc62; 0fg04; 0fr63l; 0_b3d; 05q96q6; ... >> query: (?x3742, 01vx2h) <- film_crew_role(?x3742, ?x2848), film_crew_role(?x3742, ?x2178), film_release_region(?x3742, ?x94), film_crew_role(?x5305, ?x2848), film_crew_role(?x2847, ?x2848), film_crew_role(?x1192, ?x2848), ?x1192 = 07sc6nw, ?x2847 = 05fcbk7, ?x2178 = 01pvkk, ?x5305 = 012s1d >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02w86hz film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 133.000 133.000 0.968 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1094-04k8n PRED entity: 04k8n PRED relation: nutrient! PRED expected values: 07j87 => 61 concepts (57 used for prediction) PRED predicted values (max 10 best out of 71): 07j87 (0.89 #35, 0.89 #79, 0.89 #247), 06x4c (0.89 #35, 0.89 #79, 0.89 #60), 0dcfv (0.89 #35, 0.89 #79, 0.89 #60), 04k8n (0.03 #4, 0.02 #142, 0.02 #9), 05wvs (0.03 #4, 0.02 #142, 0.02 #9), 01sh2 (0.03 #4, 0.02 #142, 0.02 #9), 0f4kp (0.03 #4, 0.02 #9, 0.02 #8), 07q0m (0.03 #4, 0.02 #9, 0.02 #8), 02kc_w5 (0.03 #4, 0.02 #9, 0.02 #8), 0fzjh (0.03 #4, 0.02 #9, 0.02 #8) >> Best rule #35 for best value: >> intensional similarity = 118 >> extensional distance = 8 >> proper extension: 05gh50; >> query: (?x9365, ?x9489) <- nutrient(?x10612, ?x9365), nutrient(?x9732, ?x9365), nutrient(?x9005, ?x9365), nutrient(?x8298, ?x9365), nutrient(?x7719, ?x9365), nutrient(?x7057, ?x9365), nutrient(?x6285, ?x9365), nutrient(?x6191, ?x9365), nutrient(?x6159, ?x9365), nutrient(?x6032, ?x9365), nutrient(?x5373, ?x9365), nutrient(?x5009, ?x9365), nutrient(?x4068, ?x9365), nutrient(?x3900, ?x9365), nutrient(?x3468, ?x9365), nutrient(?x2701, ?x9365), nutrient(?x1959, ?x9365), nutrient(?x1303, ?x9365), nutrient(?x1257, ?x9365), ?x9005 = 04zpv, ?x6032 = 01nkt, ?x1303 = 0fj52s, ?x6191 = 014j1m, ?x6159 = 033cnk, ?x3900 = 061_f, ?x2701 = 0hkxq, ?x10612 = 0frq6, ?x8298 = 037ls6, ?x9732 = 05z55, ?x5009 = 0fjfh, ?x6285 = 01645p, ?x1959 = 0f25w9, ?x3468 = 0cxn2, ?x7057 = 0fbdb, ?x7719 = 0dj75, nutrient(?x5373, ?x14210), nutrient(?x5373, ?x13545), nutrient(?x5373, ?x13498), nutrient(?x5373, ?x13126), nutrient(?x5373, ?x12902), nutrient(?x5373, ?x12454), nutrient(?x5373, ?x12083), nutrient(?x5373, ?x11758), nutrient(?x5373, ?x11592), nutrient(?x5373, ?x11409), nutrient(?x5373, ?x10709), nutrient(?x5373, ?x10453), nutrient(?x5373, ?x10098), nutrient(?x5373, ?x9915), nutrient(?x5373, ?x9795), nutrient(?x5373, ?x9733), nutrient(?x5373, ?x9619), nutrient(?x5373, ?x9490), nutrient(?x5373, ?x9426), nutrient(?x5373, ?x8413), nutrient(?x5373, ?x8243), nutrient(?x5373, ?x7894), nutrient(?x5373, ?x7720), nutrient(?x5373, ?x7652), nutrient(?x5373, ?x7431), nutrient(?x5373, ?x7364), nutrient(?x5373, ?x7219), nutrient(?x5373, ?x7135), nutrient(?x5373, ?x6517), nutrient(?x5373, ?x6192), nutrient(?x5373, ?x6160), nutrient(?x5373, ?x6033), nutrient(?x5373, ?x6026), nutrient(?x5373, ?x5549), nutrient(?x5373, ?x5526), nutrient(?x5373, ?x5451), nutrient(?x5373, ?x5374), nutrient(?x5373, ?x5010), nutrient(?x5373, ?x1960), ?x10098 = 0h1_c, ?x13498 = 07q0m, ?x10709 = 0h1sz, ?x7135 = 025rsfk, ?x10453 = 075pwf, ?x1960 = 07hnp, nutrient(?x5337, ?x8243), nutrient(?x3264, ?x8243), ?x6517 = 02kd8zw, ?x7652 = 025s0s0, ?x6192 = 06jry, ?x7364 = 09gvd, ?x12902 = 0fzjh, ?x13126 = 02kc_w5, ?x1257 = 09728, ?x5549 = 025s7j4, ?x13545 = 01w_3, ?x7720 = 025s7x6, ?x9795 = 05v_8y, ?x8413 = 02kc4sf, ?x3264 = 0dcfv, ?x5337 = 06x4c, ?x5010 = 0h1vz, ?x9915 = 025tkqy, ?x5451 = 05wvs, ?x11758 = 0q01m, ?x7219 = 0h1vg, ?x11592 = 025sf0_, ?x9733 = 0h1tz, ?x12454 = 025rw19, ?x7431 = 09gwd, ?x5374 = 025s0zp, ?x6026 = 025sf8g, ?x9426 = 0h1yy, ?x4068 = 0fbw6, ?x14210 = 0f4k5, ?x9619 = 0h1tg, ?x11409 = 0h1yf, ?x6033 = 04zjxcz, ?x5526 = 09pbb, ?x9490 = 0h1sg, ?x6160 = 041r51, ?x12083 = 01n78x, nutrient(?x9489, ?x7894) >> conf = 0.89 => this is the best rule for 3 predicted values ranks of expected_values: 1 EVAL 04k8n nutrient! 07j87 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 57.000 0.893 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #1093-02vrr PRED entity: 02vrr PRED relation: risk_factors! PRED expected values: 02bft => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 74): 02bft (0.50 #468, 0.33 #2209, 0.33 #1291), 0hgxh (0.33 #294, 0.11 #1431, 0.11 #1370), 01pf6 (0.33 #436, 0.11 #1447, 0.10 #1646), 02vrr (0.20 #1609, 0.15 #2268, 0.14 #2002), 09d11 (0.18 #1684, 0.13 #2080, 0.12 #1232), 02psvcf (0.14 #853, 0.10 #1550, 0.08 #1947), 0dcrb (0.12 #1206, 0.10 #1652, 0.10 #1581), 0lcdk (0.12 #1194, 0.10 #1508, 0.07 #2103), 01rt5h (0.12 #1251, 0.09 #1703, 0.02 #3557), 014w_8 (0.12 #1189, 0.07 #2865, 0.07 #2926) >> Best rule #468 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 0dcsx; >> query: (?x5784, 02bft) <- risk_factors(?x5784, ?x8524), notable_people_with_this_condition(?x5784, ?x3341), notable_people_with_this_condition(?x5784, ?x3336), jurisdiction_of_office(?x3341, ?x5114), type_of_union(?x3341, ?x566), profession(?x3341, ?x3342), risk_factors(?x10199, ?x8524), ?x10199 = 02k6hp, influenced_by(?x916, ?x3336) >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vrr risk_factors! 02bft CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 63.000 63.000 0.500 http://example.org/medicine/disease/risk_factors #1092-0140t7 PRED entity: 0140t7 PRED relation: instrumentalists! PRED expected values: 0342h => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 118): 0342h (0.72 #506, 0.71 #838, 0.65 #4104), 0l14md (0.41 #918, 0.39 #1420, 0.38 #1252), 0l14qv (0.32 #1168, 0.28 #2007, 0.27 #419), 02sgy (0.32 #1168, 0.28 #2007, 0.27 #419), 01vdm0 (0.32 #1168, 0.28 #2007, 0.27 #419), 07brj (0.32 #1168, 0.28 #2007, 0.27 #419), 02dlh2 (0.32 #1168, 0.28 #2007, 0.27 #419), 0cfdd (0.32 #1168, 0.28 #2007, 0.27 #419), 0bxl5 (0.32 #1168, 0.28 #2007, 0.27 #419), 06rvn (0.32 #1168, 0.28 #2007, 0.27 #419) >> Best rule #506 for best value: >> intensional similarity = 3 >> extensional distance = 98 >> proper extension: 053y0s; 01q7cb_; 01p45_v; 0285c; 02jg92; 01m65sp; 01nn6c; 01vv6_6; 01w8n89; 0phx4; ... >> query: (?x9321, 0342h) <- location(?x9321, ?x362), instrumentalists(?x212, ?x9321), group(?x9321, ?x6202) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0140t7 instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.720 http://example.org/music/instrument/instrumentalists #1091-016jfw PRED entity: 016jfw PRED relation: currency PRED expected values: 09nqf => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.26 #82, 0.25 #4, 0.25 #46), 01nv4h (0.07 #35, 0.05 #53, 0.05 #32) >> Best rule #82 for best value: >> intensional similarity = 3 >> extensional distance = 487 >> proper extension: 02zq43; >> query: (?x6129, 09nqf) <- location(?x6129, ?x11731), award_nominee(?x6129, ?x2865), artists(?x671, ?x2865) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016jfw currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.264 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #1090-01vrx3g PRED entity: 01vrx3g PRED relation: location PRED expected values: 0vbk => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 192): 0vbk (0.44 #7238, 0.41 #8043, 0.37 #8848), 02_286 (0.17 #4862, 0.15 #2450, 0.14 #37), 06wxw (0.14 #228, 0.04 #2641, 0.02 #3445), 0r0m6 (0.14 #218, 0.04 #3435, 0.03 #1826), 0f2s6 (0.14 #474, 0.03 #2082, 0.02 #2887), 0xq63 (0.14 #314, 0.03 #1922, 0.02 #2727), 0h1k6 (0.14 #562, 0.03 #2170, 0.02 #2975), 05fkf (0.12 #842, 0.05 #5667, 0.04 #3255), 0978r (0.12 #979, 0.03 #5804), 0345h (0.12 #871, 0.02 #3284, 0.02 #4088) >> Best rule #7238 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 01syr4; >> query: (?x366, ?x4758) <- category(?x366, ?x134), origin(?x366, ?x4758), film(?x366, ?x3425) >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrx3g location 0vbk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.444 http://example.org/people/person/places_lived./people/place_lived/location #1089-019y64 PRED entity: 019y64 PRED relation: team PRED expected values: 01y3v => 121 concepts (113 used for prediction) PRED predicted values (max 10 best out of 285): 084l5 (0.20 #782, 0.18 #1133, 0.13 #1835), 06rny (0.18 #1156, 0.13 #1507, 0.12 #2560), 01xvb (0.18 #1076, 0.13 #1427, 0.12 #2480), 0cqt41 (0.17 #3540, 0.10 #732, 0.10 #2838), 05l71 (0.13 #1477, 0.10 #775, 0.09 #1126), 05tfm (0.13 #1431, 0.10 #729, 0.09 #1080), 02896 (0.13 #1412, 0.10 #710, 0.09 #1061), 05tg3 (0.13 #1468, 0.09 #1117, 0.06 #2521), 01y49 (0.13 #1442, 0.09 #1091, 0.06 #3548), 0jmk7 (0.13 #3112, 0.12 #3463, 0.11 #3814) >> Best rule #782 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 02qjj7; 03n69x; 01xyt7; 0cv72h; 02bf2s; 014g_s; 03vrv9; 063g7l; >> query: (?x1177, 084l5) <- people(?x1176, ?x1177), athlete(?x1083, ?x1177), ?x1083 = 0jm_, geographic_distribution(?x1176, ?x177), team(?x1177, ?x729) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1453 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 13 *> proper extension: 0cg39k; *> query: (?x1177, 01y3v) <- team(?x1177, ?x10339), team(?x1177, ?x9172), team(?x180, ?x10339), school(?x9172, ?x466), position(?x9172, ?x1114) *> conf = 0.07 ranks of expected_values: 36 EVAL 019y64 team 01y3v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 121.000 113.000 0.200 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #1088-09qwmm PRED entity: 09qwmm PRED relation: award! PRED expected values: 03bxsw 02vntj 0dvld 0jlv5 0418ft => 41 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2537): 0h0wc (0.82 #10006, 0.82 #10005, 0.81 #6670), 03knl (0.82 #10006, 0.82 #10005, 0.81 #6670), 0dvld (0.40 #5052, 0.36 #8387, 0.29 #1717), 02d42t (0.36 #8061, 0.30 #4726, 0.29 #1391), 014zcr (0.30 #3387, 0.29 #10058, 0.27 #6722), 0l6px (0.30 #3939, 0.29 #10610, 0.27 #7274), 0170pk (0.30 #3768, 0.29 #10439, 0.27 #7103), 01l2fn (0.30 #3735, 0.29 #400, 0.27 #7070), 0171cm (0.30 #3997, 0.27 #7332, 0.21 #10668), 0z4s (0.30 #3423, 0.27 #6758, 0.21 #10094) >> Best rule #10006 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 02x4x18; >> query: (?x618, ?x495) <- nominated_for(?x618, ?x1263), award_winner(?x618, ?x495), ?x1263 = 0dgst_d >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #5052 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 8 *> proper extension: 027dtxw; 02r0csl; 099jhq; 094qd5; 0gqwc; 099cng; 02ppm4q; 09sdmz; *> query: (?x618, 0dvld) <- nominated_for(?x618, ?x1263), award_winner(?x618, ?x396), ?x1263 = 0dgst_d, ceremony(?x618, ?x873) *> conf = 0.40 ranks of expected_values: 3, 14, 33, 320, 380 EVAL 09qwmm award! 0418ft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 41.000 16.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qwmm award! 0jlv5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 41.000 16.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qwmm award! 0dvld CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 41.000 16.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qwmm award! 02vntj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 41.000 16.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09qwmm award! 03bxsw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 41.000 16.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1087-06f41 PRED entity: 06f41 PRED relation: country PRED expected values: 06mzp 0k6nt 06t2t 088q4 0jgx 04w58 0d05q4 02k8k 01nln 01ppq 03f2w => 40 concepts (35 used for prediction) PRED predicted values (max 10 best out of 365): 0h7x (0.82 #2682, 0.79 #3277, 0.73 #4767), 04g61 (0.81 #1034, 0.73 #1776, 0.72 #2664), 056vv (0.81 #1034, 0.71 #590, 0.62 #1812), 0jdx (0.81 #1034, 0.71 #590, 0.62 #1879), 0bjv6 (0.81 #1034, 0.71 #590, 0.58 #1923), 01ls2 (0.75 #1781, 0.73 #1776, 0.73 #2669), 01c4pv (0.75 #1868, 0.60 #2311, 0.60 #979), 01znc_ (0.73 #1776, 0.73 #2535, 0.72 #2664), 06mzp (0.73 #1776, 0.73 #2673, 0.72 #2664), 05vz3zq (0.73 #1776, 0.72 #2664, 0.71 #4295) >> Best rule #2682 for best value: >> intensional similarity = 40 >> extensional distance = 9 >> proper extension: 019tzd; >> query: (?x2044, 0h7x) <- country(?x2044, ?x6307), country(?x2044, ?x3730), country(?x2044, ?x2843), country(?x2044, ?x2645), country(?x2044, ?x2152), country(?x2044, ?x1536), sports(?x2432, ?x2044), sports(?x2432, ?x1967), film_release_region(?x6782, ?x2843), film_release_region(?x6376, ?x2843), film_release_region(?x4998, ?x2843), film_release_region(?x3081, ?x2843), film_release_region(?x2163, ?x2843), film_release_region(?x1108, ?x2843), film_release_region(?x781, ?x2843), film_release_region(?x385, ?x2843), film_release_region(?x343, ?x2843), ?x6376 = 01f85k, contains(?x3730, ?x5237), ?x4998 = 0dzlbx, contains(?x6304, ?x6307), ?x6782 = 07jnt, ?x343 = 0gx1bnj, countries_spoken_in(?x5121, ?x6307), ?x2645 = 03h64, administrative_parent(?x2843, ?x551), ?x3081 = 023gxx, ?x2163 = 0j6b5, participating_countries(?x418, ?x3730), ?x385 = 0ds3t5x, ?x1967 = 01cgz, organization(?x1536, ?x127), film_release_region(?x1642, ?x1536), entity_involved(?x4373, ?x3730), medal(?x2843, ?x1242), ?x2152 = 06mkj, contains(?x455, ?x1536), ?x1108 = 0jjy0, ?x1642 = 0bq8tmw, ?x781 = 0gkz15s >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1776 for first EXPECTED value: *> intensional similarity = 39 *> extensional distance = 5 *> proper extension: 07jbh; *> query: (?x2044, ?x5274) <- country(?x2044, ?x7747), country(?x2044, ?x6307), country(?x2044, ?x3730), country(?x2044, ?x2645), country(?x2044, ?x792), country(?x2044, ?x512), sports(?x7441, ?x2044), sports(?x3971, ?x2044), sports(?x2496, ?x2044), ?x3730 = 03shp, ?x512 = 07ssc, film_release_region(?x6247, ?x792), film_release_region(?x3088, ?x792), film_release_region(?x2350, ?x792), film_release_region(?x1496, ?x792), film_release_region(?x664, ?x792), form_of_government(?x792, ?x48), country(?x4310, ?x792), country(?x471, ?x792), ?x7747 = 07f1x, ?x6247 = 09v9mks, adjoins(?x792, ?x3432), olympics(?x792, ?x1931), location(?x1235, ?x792), ?x3088 = 06w839_, ?x2350 = 0661m4p, countries_spoken_in(?x254, ?x792), ?x471 = 02vx4, ?x1496 = 011yqc, sports(?x2496, ?x453), participating_countries(?x2496, ?x5274), ?x4310 = 064vjs, contains(?x792, ?x841), olympics(?x985, ?x7441), ?x664 = 0401sg, ?x2645 = 03h64, countries_within(?x6956, ?x6307), ?x3971 = 0jhn7, ?x985 = 0k6nt *> conf = 0.73 ranks of expected_values: 9, 12, 25, 28, 53, 58, 60, 64, 113, 118, 135 EVAL 06f41 country 03f2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06f41 country 01ppq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06f41 country 01nln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06f41 country 02k8k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06f41 country 0d05q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06f41 country 04w58 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06f41 country 0jgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06f41 country 088q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06f41 country 06t2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06f41 country 0k6nt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06f41 country 06mzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 40.000 35.000 0.818 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #1086-06msq2 PRED entity: 06msq2 PRED relation: gender PRED expected values: 05zppz => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #19, 0.81 #13, 0.80 #25), 02zsn (0.25 #54, 0.24 #102, 0.24 #56) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 168 >> proper extension: 01pw2f1; 0bbxd3; 07lz9l; >> query: (?x4415, 05zppz) <- profession(?x4415, ?x353), program(?x4415, ?x3626), tv_program(?x236, ?x3626) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06msq2 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.812 http://example.org/people/person/gender #1085-0fm9_ PRED entity: 0fm9_ PRED relation: time_zones PRED expected values: 02hcv8 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.86 #326, 0.80 #29, 0.63 #835), 02fqwt (0.25 #79, 0.22 #353, 0.21 #157), 02hczc (0.22 #353, 0.14 #41, 0.13 #80), 042g7t (0.22 #353, 0.03 #63, 0.02 #167), 02lcrv (0.22 #353, 0.01 #241), 02lcqs (0.21 #226, 0.21 #657, 0.21 #265), 02llzg (0.16 #56, 0.08 #238, 0.08 #747), 03bdv (0.09 #632, 0.06 #1127, 0.03 #1244), 03plfd (0.05 #62, 0.04 #244, 0.03 #623), 0gsrz4 (0.03 #60, 0.02 #829, 0.01 #908) >> Best rule #326 for best value: >> intensional similarity = 4 >> extensional distance = 228 >> proper extension: 0mm0p; 0drr3; >> query: (?x322, ?x2674) <- adjoins(?x322, ?x6135), source(?x322, ?x958), currency(?x322, ?x170), time_zones(?x6135, ?x2674) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fm9_ time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.864 http://example.org/location/location/time_zones #1084-0d4jl PRED entity: 0d4jl PRED relation: type_of_union PRED expected values: 04ztj => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.84 #82, 0.80 #37, 0.80 #222), 01g63y (0.19 #423, 0.19 #418, 0.16 #14), 01bl8s (0.19 #423, 0.19 #418, 0.02 #43), 0jgjn (0.19 #423, 0.19 #418, 0.01 #125) >> Best rule #82 for best value: >> intensional similarity = 4 >> extensional distance = 133 >> proper extension: 02pp_q_; 0jf1b; 057d89; 01g4zr; 01xcqc; 01gzm2; 01pcmd; 01_vfy; 098n5; 0q59y; ... >> query: (?x3279, 04ztj) <- profession(?x3279, ?x987), people(?x4659, ?x3279), nationality(?x3279, ?x512), ?x987 = 0dxtg >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d4jl type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 107.000 0.844 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1083-04r7p PRED entity: 04r7p PRED relation: gender PRED expected values: 02zsn => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2): 02zsn (0.88 #26, 0.78 #10, 0.46 #56), 05zppz (0.84 #71, 0.81 #51, 0.81 #53) >> Best rule #26 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 05typm; >> query: (?x6958, 02zsn) <- award(?x6958, ?x5455), ?x5455 = 0bb57s, nationality(?x6958, ?x252) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04r7p gender 02zsn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.875 http://example.org/people/person/gender #1082-01srq2 PRED entity: 01srq2 PRED relation: nominated_for! PRED expected values: 02qsfzv => 108 concepts (101 used for prediction) PRED predicted values (max 10 best out of 304): 02qrwjt (0.68 #15423, 0.67 #3082, 0.67 #9249), 02qrbbx (0.68 #15423, 0.67 #3082, 0.67 #9249), 027b9k6 (0.68 #15423, 0.67 #3082, 0.67 #9249), 02qysm0 (0.67 #3082, 0.67 #9249, 0.67 #8061), 0gqyl (0.62 #1027, 0.24 #14947, 0.24 #2449), 0gq9h (0.51 #773, 0.41 #1010, 0.35 #2906), 0gq_v (0.44 #731, 0.36 #2390, 0.32 #3339), 02ppm4q (0.43 #1063, 0.14 #3197, 0.12 #4856), 09td7p (0.41 #1040, 0.10 #4359, 0.10 #3174), 0gs9p (0.38 #1012, 0.37 #775, 0.28 #10737) >> Best rule #15423 for best value: >> intensional similarity = 2 >> extensional distance = 987 >> proper extension: 03j63k; 097h2; 019g8j; 0147w8; 0300ml; 02rq7nd; >> query: (?x7246, ?x1587) <- award(?x7246, ?x1587), award(?x276, ?x1587) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #434 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0pvms; 04jn6y7; *> query: (?x7246, 02qsfzv) <- music(?x7246, ?x3910), ?x3910 = 01tc9r, film(?x4370, ?x7246), film_crew_role(?x7246, ?x1284) *> conf = 0.08 ranks of expected_values: 93 EVAL 01srq2 nominated_for! 02qsfzv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 108.000 101.000 0.676 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1081-05c9zr PRED entity: 05c9zr PRED relation: film! PRED expected values: 01wk7ql => 119 concepts (77 used for prediction) PRED predicted values (max 10 best out of 961): 0227tr (0.29 #23308, 0.02 #69057, 0.02 #94016), 01pkhw (0.29 #21495, 0.01 #65164, 0.01 #94282), 01wbg84 (0.25 #4206, 0.14 #20843, 0.08 #31240), 02gvwz (0.25 #4346, 0.14 #20983, 0.03 #54252), 02lkcc (0.25 #4401, 0.14 #21038, 0.03 #50148), 02114t (0.25 #4794, 0.14 #21431, 0.02 #60942), 018swb (0.25 #4500, 0.04 #54406, 0.02 #71044), 05vsxz (0.25 #4168, 0.04 #60306, 0.04 #60307), 01pcq3 (0.25 #4290, 0.03 #50037, 0.03 #58357), 055c8 (0.25 #4701, 0.03 #62928, 0.03 #100363) >> Best rule #23308 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 04q24zv; >> query: (?x4132, 0227tr) <- film(?x548, ?x4132), film_crew_role(?x4132, ?x468), titles(?x1510, ?x4132), ?x468 = 02r96rf, ?x548 = 014x77 >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05c9zr film! 01wk7ql CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 119.000 77.000 0.286 http://example.org/film/actor/film./film/performance/film #1080-03ywyk PRED entity: 03ywyk PRED relation: award PRED expected values: 0cqhk0 => 114 concepts (112 used for prediction) PRED predicted values (max 10 best out of 255): 09sb52 (0.32 #1661, 0.32 #17862, 0.29 #15837), 05pcn59 (0.32 #1702, 0.25 #892, 0.25 #487), 05p09zm (0.24 #1745, 0.22 #2150, 0.19 #530), 0ck27z (0.20 #13054, 0.20 #13459, 0.20 #15484), 0cqhk0 (0.19 #1252, 0.16 #2872, 0.15 #8101), 03c7tr1 (0.19 #2084, 0.18 #1679, 0.16 #3299), 05zr6wv (0.18 #1637, 0.16 #2447, 0.15 #2042), 0gqwc (0.18 #11416, 0.15 #12631, 0.12 #14656), 0gqyl (0.18 #11447, 0.14 #12662, 0.12 #14687), 05b4l5x (0.17 #2031, 0.16 #1626, 0.13 #3246) >> Best rule #1661 for best value: >> intensional similarity = 3 >> extensional distance = 120 >> proper extension: 01wxyx1; 02wb6yq; 049qx; >> query: (?x9232, 09sb52) <- profession(?x9232, ?x1032), vacationer(?x2623, ?x9232), nominated_for(?x9232, ?x10249) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #1252 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 97 *> proper extension: 0h0jz; 0lpjn; 0dx_q; *> query: (?x9232, 0cqhk0) <- student(?x4916, ?x9232), participant(?x4411, ?x9232), actor(?x10249, ?x9232), profession(?x9232, ?x1032) *> conf = 0.19 ranks of expected_values: 5 EVAL 03ywyk award 0cqhk0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 114.000 112.000 0.320 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1079-08vr94 PRED entity: 08vr94 PRED relation: film PRED expected values: 027j9wd 0g7pm1 => 122 concepts (106 used for prediction) PRED predicted values (max 10 best out of 582): 03c7twt (0.43 #64245, 0.42 #140976, 0.41 #139190), 039cq4 (0.43 #64245, 0.41 #139190, 0.41 #148114), 03nfnx (0.25 #1398, 0.22 #6750, 0.17 #3182), 0gwgn1k (0.25 #1544, 0.22 #8680, 0.17 #3328), 0h1cdwq (0.25 #59, 0.17 #1843, 0.11 #7195), 0gldyz (0.25 #1652, 0.17 #3436, 0.11 #8788), 0640y35 (0.25 #1012, 0.17 #2796, 0.11 #8148), 02xtxw (0.25 #580, 0.17 #2364, 0.11 #7716), 034qmv (0.25 #14, 0.11 #7150, 0.02 #17856), 0gj8t_b (0.25 #179, 0.11 #7315) >> Best rule #64245 for best value: >> intensional similarity = 3 >> extensional distance = 581 >> proper extension: 01vq3nl; >> query: (?x3927, ?x86) <- actor(?x6884, ?x3927), nominated_for(?x3927, ?x86), nominated_for(?x829, ?x6884) >> conf = 0.43 => this is the best rule for 2 predicted values *> Best rule #9953 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0h96g; 01bcq; 059j1m; 012x2b; *> query: (?x3927, 027j9wd) <- film(?x3927, ?x6752), film(?x3927, ?x365), nominated_for(?x1691, ?x365), ?x6752 = 065_cjc *> conf = 0.18 ranks of expected_values: 18, 23 EVAL 08vr94 film 0g7pm1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 122.000 106.000 0.430 http://example.org/film/actor/film./film/performance/film EVAL 08vr94 film 027j9wd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 122.000 106.000 0.430 http://example.org/film/actor/film./film/performance/film #1078-05c9zr PRED entity: 05c9zr PRED relation: film_crew_role PRED expected values: 09zzb8 02rh1dz => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 29): 09zzb8 (0.77 #1352, 0.77 #1903, 0.77 #1580), 01pvkk (0.31 #1037, 0.31 #1361, 0.31 #906), 02rh1dz (0.27 #905, 0.20 #1360, 0.19 #1491), 0215hd (0.25 #399, 0.15 #1042, 0.15 #1917), 0d2b38 (0.25 #406, 0.14 #918, 0.13 #950), 01xy5l_ (0.21 #876, 0.15 #908, 0.12 #1914), 015h31 (0.19 #872, 0.15 #1132, 0.15 #904), 089g0h (0.17 #880, 0.13 #1367, 0.12 #400), 020xn5 (0.12 #391, 0.10 #2493, 0.09 #3251), 04pyp5 (0.12 #1040, 0.11 #461, 0.10 #2493) >> Best rule #1352 for best value: >> intensional similarity = 5 >> extensional distance = 264 >> proper extension: 07gp9; 01vksx; 0jqn5; 0bq8tmw; 031t2d; 02vqhv0; 0cc5mcj; 08rr3p; 0gfsq9; 0b1y_2; ... >> query: (?x4132, 09zzb8) <- film(?x574, ?x4132), crewmember(?x4132, ?x3879), film_crew_role(?x4132, ?x2848), film(?x548, ?x4132), profession(?x5287, ?x2848) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 05c9zr film_crew_role 02rh1dz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 102.000 0.774 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05c9zr film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.774 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1077-035s95 PRED entity: 035s95 PRED relation: crewmember PRED expected values: 051z6rz => 108 concepts (75 used for prediction) PRED predicted values (max 10 best out of 30): 095zvfg (0.17 #180, 0.07 #368, 0.03 #1523), 0284n42 (0.09 #193, 0.08 #146, 0.07 #669), 0b79gfg (0.09 #207, 0.05 #254, 0.04 #875), 03m49ly (0.08 #177, 0.05 #412, 0.05 #271), 051z6rz (0.08 #171, 0.05 #406, 0.03 #1368), 04ktcgn (0.08 #154, 0.05 #201, 0.04 #964), 094tsh6 (0.08 #181, 0.02 #416, 0.02 #369), 03h26tm (0.08 #149, 0.02 #337, 0.01 #911), 03crcpt (0.05 #426), 09rp4r_ (0.05 #245, 0.04 #530, 0.03 #292) >> Best rule #180 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 02_fz3; >> query: (?x2128, 095zvfg) <- film(?x7980, ?x2128), ?x7980 = 020h2v, genre(?x2128, ?x604), ?x604 = 0lsxr, film(?x489, ?x2128) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #171 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 02_fz3; *> query: (?x2128, 051z6rz) <- film(?x7980, ?x2128), ?x7980 = 020h2v, genre(?x2128, ?x604), ?x604 = 0lsxr, film(?x489, ?x2128) *> conf = 0.08 ranks of expected_values: 5 EVAL 035s95 crewmember 051z6rz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 108.000 75.000 0.167 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #1076-01ljpm PRED entity: 01ljpm PRED relation: campuses! PRED expected values: 01ljpm => 92 concepts (83 used for prediction) PRED predicted values (max 10 best out of 192): 01cf5 (0.03 #483, 0.02 #1029, 0.02 #1575), 01bk1y (0.03 #269, 0.02 #815, 0.02 #1361), 036921 (0.03 #451, 0.02 #997, 0.02 #1543), 06182p (0.03 #286, 0.02 #2470, 0.01 #3562), 03zj9 (0.03 #180, 0.02 #2364, 0.01 #3456), 017z88 (0.03 #73, 0.02 #2257, 0.01 #3349), 08qs09 (0.03 #253, 0.02 #2437, 0.01 #3529), 03bmmc (0.03 #191, 0.02 #2375, 0.01 #3467), 01p7x7 (0.03 #423, 0.02 #2607, 0.01 #3699), 032d52 (0.03 #494, 0.02 #2678, 0.01 #38781) >> Best rule #483 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 04htfd; 03lb_v; >> query: (?x6501, 01cf5) <- state_province_region(?x6501, ?x335), country(?x6501, ?x94), ?x335 = 059rby >> conf = 0.03 => this is the best rule for 1 predicted values *> Best rule #38781 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 745 *> proper extension: 0dwl2; 016tt2; 05qd_; 03xsby; 04qhdf; 024rgt; 043ljr; 055c8; 01w92; 02vyh; ... *> query: (?x6501, ?x166) <- state_province_region(?x6501, ?x335), contains(?x335, ?x322), state_province_region(?x166, ?x335) *> conf = 0.01 ranks of expected_values: 137 EVAL 01ljpm campuses! 01ljpm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 92.000 83.000 0.029 http://example.org/education/educational_institution/campuses #1075-0k7pf PRED entity: 0k7pf PRED relation: award_winner! PRED expected values: 01by1l => 111 concepts (109 used for prediction) PRED predicted values (max 10 best out of 274): 025mb9 (0.44 #3005, 0.40 #9013, 0.37 #30475), 025m8y (0.30 #529, 0.26 #2675, 0.16 #5251), 01by1l (0.26 #542, 0.25 #113, 0.24 #3118), 0gqz2 (0.25 #81, 0.23 #2656, 0.19 #510), 03x3wf (0.25 #65, 0.19 #1781, 0.11 #3499), 054ks3 (0.25 #142, 0.17 #571, 0.17 #2717), 01c9jp (0.25 #186, 0.17 #22748, 0.15 #28757), 0c4z8 (0.25 #72, 0.11 #501, 0.11 #2647), 01c92g (0.25 #98, 0.10 #3103, 0.09 #1814), 054ky1 (0.25 #110, 0.07 #968, 0.04 #18137) >> Best rule #3005 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 09bx1k; >> query: (?x3030, ?x3467) <- award_winner(?x1136, ?x3030), music(?x5139, ?x3030), award(?x3030, ?x3467) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #542 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 0146pg; 01bczm; 0bvzp; *> query: (?x3030, 01by1l) <- award_winner(?x1136, ?x3030), role(?x3030, ?x74), music(?x5139, ?x3030) *> conf = 0.26 ranks of expected_values: 3 EVAL 0k7pf award_winner! 01by1l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 109.000 0.435 http://example.org/award/award_category/winners./award/award_honor/award_winner #1074-08rr3p PRED entity: 08rr3p PRED relation: currency PRED expected values: 09nqf => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.80 #22, 0.78 #71, 0.77 #99), 02gsvk (0.03 #41, 0.01 #160), 01nv4h (0.02 #191, 0.02 #219, 0.02 #212), 02l6h (0.02 #81, 0.01 #277, 0.01 #193) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 104 >> proper extension: 0jjy0; 0c00zd0; 0m491; 0pvms; 0gyy53; 04sntd; 024mpp; 0gy2y8r; 04cv9m; 0bbw2z6; ... >> query: (?x2755, 09nqf) <- language(?x2755, ?x254), film_crew_role(?x2755, ?x137), film(?x538, ?x2755), costume_design_by(?x2755, ?x12521) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08rr3p currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.802 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #1073-050xxm PRED entity: 050xxm PRED relation: genre PRED expected values: 04t36 => 76 concepts (54 used for prediction) PRED predicted values (max 10 best out of 89): 07s9rl0 (0.65 #2323, 0.65 #2439, 0.65 #233), 02l7c8 (0.58 #2453, 0.55 #3150, 0.40 #247), 04xvlr (0.40 #234, 0.27 #2440, 0.17 #2324), 02kdv5l (0.33 #1512, 0.31 #1395, 0.30 #467), 03k9fj (0.33 #1519, 0.26 #474, 0.25 #1170), 01jfsb (0.31 #3610, 0.30 #3378, 0.30 #475), 060__y (0.28 #248, 0.19 #1525, 0.16 #2338), 06n90 (0.22 #1521, 0.14 #3611, 0.14 #3379), 0lsxr (0.19 #472, 0.18 #4072, 0.17 #3839), 082gq (0.18 #2466, 0.17 #260, 0.14 #144) >> Best rule #2323 for best value: >> intensional similarity = 3 >> extensional distance = 382 >> proper extension: 0415ggl; 04g73n; 0cvkv5; >> query: (?x1797, 07s9rl0) <- genre(?x1797, ?x239), nominated_for(?x3410, ?x1797), music(?x542, ?x3410) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #3140 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 750 *> proper extension: 01h72l; 011yfd; 02q3fdr; 015qy1; *> query: (?x1797, 04t36) <- genre(?x1797, ?x4088), language(?x1797, ?x254), genre(?x11073, ?x4088), ?x11073 = 01ry_x *> conf = 0.15 ranks of expected_values: 13 EVAL 050xxm genre 04t36 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 76.000 54.000 0.654 http://example.org/film/film/genre #1072-01p896 PRED entity: 01p896 PRED relation: student PRED expected values: 0b7t3p => 148 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1712): 0gd_s (0.29 #3689, 0.10 #16235, 0.09 #24599), 04wg38 (0.29 #3422, 0.07 #13877, 0.07 #15968), 01lwx (0.14 #1980, 0.11 #6162, 0.06 #20799), 04t969 (0.14 #1279, 0.11 #5461, 0.05 #9643), 03swmf (0.14 #3669, 0.11 #9942, 0.09 #24579), 0b_4z (0.14 #4102, 0.09 #12466, 0.07 #16648), 0ddkf (0.14 #3278, 0.09 #11642, 0.06 #22097), 0kb3n (0.14 #3552, 0.07 #14007, 0.07 #16098), 0hwqg (0.14 #3911, 0.07 #14366, 0.07 #16457), 0428bc (0.14 #3791, 0.07 #14246, 0.07 #16337) >> Best rule #3689 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 03qdm; >> query: (?x9912, 0gd_s) <- school_type(?x9912, ?x3092), state_province_region(?x9912, ?x335), student(?x9912, ?x3739), ?x335 = 059rby, colors(?x9912, ?x663), ?x3092 = 05jxkf >> conf = 0.29 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01p896 student 0b7t3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 68.000 0.286 http://example.org/education/educational_institution/students_graduates./education/education/student #1071-0gthm PRED entity: 0gthm PRED relation: gender PRED expected values: 05zppz => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.92 #35, 0.89 #61, 0.89 #29), 02zsn (0.30 #100, 0.30 #66, 0.29 #158) >> Best rule #35 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 099bk; >> query: (?x9854, 05zppz) <- religion(?x9854, ?x2694), influenced_by(?x9854, ?x1946), type_of_union(?x9854, ?x1873), student(?x2064, ?x9854) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gthm gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.918 http://example.org/people/person/gender #1070-02jjt PRED entity: 02jjt PRED relation: industry! PRED expected values: 013x0b 01p5yn 0181hw 0sxdg 01npw8 => 35 concepts (35 used for prediction) PRED predicted values (max 10 best out of 687): 02b07b (0.60 #1809, 0.50 #3886, 0.43 #3194), 0l8sx (0.60 #2091, 0.50 #711, 0.38 #3478), 0sxdg (0.60 #2169, 0.50 #789, 0.38 #3556), 05th69 (0.60 #2182, 0.50 #802, 0.38 #3569), 0300cp (0.50 #714, 0.40 #2094, 0.33 #2558), 0xwj (0.50 #514, 0.29 #3280, 0.29 #3049), 01qxs3 (0.50 #584, 0.29 #3350, 0.29 #3119), 01_4lx (0.50 #577, 0.29 #3343, 0.25 #1609), 025txrl (0.50 #622, 0.29 #3388, 0.22 #4308), 0dwl2 (0.50 #461, 0.29 #3227, 0.22 #4147) >> Best rule #1809 for best value: >> intensional similarity = 15 >> extensional distance = 3 >> proper extension: 01mw1; >> query: (?x3368, 02b07b) <- industry(?x7793, ?x3368), artist(?x7793, ?x8873), artist(?x7793, ?x7794), artist(?x7793, ?x7259), artists(?x3319, ?x7794), award(?x7794, ?x1323), origin(?x7794, ?x8653), instrumentalists(?x227, ?x7794), location(?x8873, ?x739), ?x3319 = 06j6l, artists(?x2249, ?x8873), origin(?x7259, ?x3014), award_nominee(?x7259, ?x3737), award_nominee(?x1125, ?x7259), ?x3737 = 01q32bd >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2169 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 3 *> proper extension: 0sydc; *> query: (?x3368, 0sxdg) <- industry(?x12671, ?x3368), industry(?x7793, ?x3368), industry(?x6386, ?x3368), industry(?x6230, ?x3368), citytown(?x7793, ?x242), state_province_region(?x6230, ?x335), production_companies(?x7502, ?x12671), child(?x7793, ?x7840), company(?x5161, ?x6386), ?x5161 = 09d6p2, award(?x7502, ?x5923), nominated_for(?x1864, ?x7502), artist(?x7840, ?x1125), currency(?x6386, ?x170), nominated_for(?x5039, ?x7502) *> conf = 0.60 ranks of expected_values: 3, 61, 64, 247, 265 EVAL 02jjt industry! 01npw8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 35.000 35.000 0.600 http://example.org/business/business_operation/industry EVAL 02jjt industry! 0sxdg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 35.000 35.000 0.600 http://example.org/business/business_operation/industry EVAL 02jjt industry! 0181hw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 35.000 35.000 0.600 http://example.org/business/business_operation/industry EVAL 02jjt industry! 01p5yn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 35.000 35.000 0.600 http://example.org/business/business_operation/industry EVAL 02jjt industry! 013x0b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 35.000 35.000 0.600 http://example.org/business/business_operation/industry #1069-0n5fz PRED entity: 0n5fz PRED relation: currency PRED expected values: 09nqf => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.85 #36, 0.85 #35, 0.84 #47) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 0l_q9; 0mrhq; 0lg0r; 0mnrb; >> query: (?x5463, 09nqf) <- county_seat(?x5463, ?x11903), time_zones(?x5463, ?x2674), contains(?x6895, ?x5463), source(?x5463, ?x958) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n5fz currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.851 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #1068-02y0dd PRED entity: 02y0dd PRED relation: profession PRED expected values: 0gl2ny2 => 111 concepts (99 used for prediction) PRED predicted values (max 10 best out of 73): 0gl2ny2 (0.76 #1569, 0.69 #3069, 0.66 #2919), 02hrh1q (0.73 #5866, 0.72 #4965, 0.72 #6016), 09jwl (0.67 #1220, 0.50 #470, 0.26 #1670), 01d_h8 (0.50 #606, 0.49 #2706, 0.48 #4056), 0nbcg (0.50 #633, 0.47 #1233, 0.25 #483), 0cbd2 (0.46 #1057, 0.28 #1957, 0.25 #907), 03gjzk (0.42 #916, 0.37 #2716, 0.34 #4066), 016z4k (0.40 #1204, 0.25 #904, 0.25 #604), 0dxtg (0.33 #914, 0.33 #2714, 0.32 #4064), 0dz3r (0.33 #1202, 0.26 #1652, 0.25 #602) >> Best rule #1569 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 0bn9sc; 0487c3; 080dyk; 02d9k; 083qy7; 07vfqj; 0879xc; 0d9v9q; 05c4fys; 07m69t; ... >> query: (?x11781, 0gl2ny2) <- team(?x11781, ?x8750), nationality(?x11781, ?x429), location(?x11781, ?x1591), position(?x8750, ?x60) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02y0dd profession 0gl2ny2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 99.000 0.765 http://example.org/people/person/profession #1067-028rk PRED entity: 028rk PRED relation: person! PRED expected values: 064q5v => 210 concepts (206 used for prediction) PRED predicted values (max 10 best out of 66): 0g9lm2 (0.40 #635, 0.11 #2947, 0.08 #6418), 04dsnp (0.33 #1569, 0.29 #1025, 0.25 #1229), 0f61tk (0.33 #56, 0.14 #1076, 0.08 #1756), 064q5v (0.29 #1057, 0.28 #2961, 0.25 #1601), 06929s (0.29 #1042, 0.25 #1246, 0.25 #1178), 0dtw1x (0.26 #7827, 0.22 #7691, 0.20 #8167), 0bhwhj (0.25 #1593, 0.25 #1253, 0.21 #2001), 012jfb (0.25 #1260, 0.18 #1464, 0.17 #1600), 02v570 (0.25 #1270, 0.17 #1610, 0.14 #1066), 058kh7 (0.25 #332, 0.11 #9995, 0.10 #8360) >> Best rule #635 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 06pj8; >> query: (?x2663, 0g9lm2) <- student(?x7127, ?x2663), type_of_union(?x2663, ?x566), profession(?x2663, ?x5805), organizations_founded(?x2663, ?x11089), person(?x6767, ?x2663) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1057 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0f7fy; *> query: (?x2663, 064q5v) <- entity_involved(?x12031, ?x2663), nationality(?x2663, ?x94), politician(?x1912, ?x2663), combatants(?x12031, ?x1023), person(?x6767, ?x2663) *> conf = 0.29 ranks of expected_values: 4 EVAL 028rk person! 064q5v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 210.000 206.000 0.400 http://example.org/film/film/personal_appearances./film/personal_film_appearance/person #1066-08cyft PRED entity: 08cyft PRED relation: artists PRED expected values: 015f7 048xh => 51 concepts (28 used for prediction) PRED predicted values (max 10 best out of 957): 0127s7 (0.67 #4696, 0.60 #3651, 0.50 #13058), 01s7ns (0.67 #5123, 0.60 #4078, 0.50 #7213), 0bqsy (0.67 #4520, 0.60 #3475, 0.50 #1386), 043zg (0.67 #4643, 0.60 #3598, 0.50 #1509), 03f5spx (0.62 #6326, 0.60 #2146, 0.50 #12598), 01x1cn2 (0.60 #3323, 0.57 #5414, 0.50 #12730), 01vtj38 (0.60 #3769, 0.50 #13176, 0.50 #4814), 01vvycq (0.60 #2135, 0.50 #6315, 0.50 #1091), 02vwckw (0.60 #3861, 0.50 #4906, 0.50 #1772), 01vx5w7 (0.60 #3364, 0.50 #4409, 0.50 #1275) >> Best rule #4696 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 08vlns; >> query: (?x3916, 0127s7) <- artists(?x3916, ?x9418), artists(?x3916, ?x4394), artists(?x3916, ?x1674), ?x9418 = 01w58n3, artists(?x474, ?x1674), ?x474 = 0m0jc, profession(?x4394, ?x319), participant(?x56, ?x4394) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3414 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 02lnbg; *> query: (?x3916, 015f7) <- artists(?x3916, ?x9418), artists(?x3916, ?x4740), artists(?x3916, ?x4394), artists(?x3916, ?x1674), ?x9418 = 01w58n3, artists(?x474, ?x1674), ?x474 = 0m0jc, ?x4394 = 049qx, ?x4740 = 03y82t6 *> conf = 0.60 ranks of expected_values: 19, 444 EVAL 08cyft artists 048xh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 28.000 0.667 http://example.org/music/genre/artists EVAL 08cyft artists 015f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 51.000 28.000 0.667 http://example.org/music/genre/artists #1065-06myp PRED entity: 06myp PRED relation: influenced_by! PRED expected values: 0lcx 0399p => 207 concepts (103 used for prediction) PRED predicted values (max 10 best out of 448): 0lrh (0.43 #1620, 0.29 #2128, 0.17 #610), 0nk72 (0.43 #2359, 0.25 #3370, 0.15 #8422), 0mb5x (0.43 #1846, 0.14 #2354, 0.10 #20046), 040db (0.33 #582, 0.29 #1592, 0.24 #19792), 03jht (0.33 #879, 0.29 #2397, 0.15 #8460), 03f0324 (0.33 #701, 0.29 #2219, 0.15 #8282), 03cdg (0.33 #964, 0.29 #2482, 0.11 #8545), 06myp (0.33 #938, 0.15 #38944, 0.15 #8519), 041jlr (0.33 #3389, 0.15 #8441, 0.14 #2378), 084w8 (0.33 #509, 0.14 #1519, 0.12 #35403) >> Best rule #1620 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 073_6; >> query: (?x10895, 0lrh) <- influenced_by(?x8768, ?x10895), influenced_by(?x7332, ?x10895), ?x7332 = 041xl, influenced_by(?x8768, ?x3336), influenced_by(?x3336, ?x2162) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2174 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 032l1; 02ln1; *> query: (?x10895, 0lcx) <- people(?x1050, ?x10895), religion(?x10895, ?x2694), influenced_by(?x1236, ?x10895), ?x1236 = 045bg *> conf = 0.29 ranks of expected_values: 21, 33 EVAL 06myp influenced_by! 0399p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 207.000 103.000 0.429 http://example.org/influence/influence_node/influenced_by EVAL 06myp influenced_by! 0lcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 207.000 103.000 0.429 http://example.org/influence/influence_node/influenced_by #1064-040rjq PRED entity: 040rjq PRED relation: influenced_by PRED expected values: 040db => 120 concepts (45 used for prediction) PRED predicted values (max 10 best out of 350): 03_87 (0.38 #1057, 0.33 #199, 0.21 #5779), 032l1 (0.33 #947, 0.30 #5669, 0.12 #9535), 040db (0.33 #56, 0.17 #914, 0.13 #5636), 081k8 (0.25 #1010, 0.22 #152, 0.17 #5732), 084w8 (0.25 #861, 0.13 #5583, 0.06 #9020), 02wh0 (0.23 #5957, 0.12 #1235, 0.12 #12884), 02kz_ (0.22 #167, 0.17 #1025, 0.12 #4722), 058vp (0.22 #181, 0.12 #1039, 0.12 #4722), 0h0p_ (0.22 #187, 0.09 #2333, 0.04 #1045), 07ym0 (0.22 #272, 0.08 #1130, 0.04 #2418) >> Best rule #1057 for best value: >> intensional similarity = 4 >> extensional distance = 22 >> proper extension: 01x53m; >> query: (?x12392, 03_87) <- influenced_by(?x12392, ?x4915), nationality(?x12392, ?x429), award(?x12392, ?x68), ?x4915 = 03f0324 >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01d494; *> query: (?x12392, 040db) <- influenced_by(?x12392, ?x6504), gender(?x12392, ?x231), nationality(?x12392, ?x429), ?x6504 = 03f47xl *> conf = 0.33 ranks of expected_values: 3 EVAL 040rjq influenced_by 040db CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 120.000 45.000 0.375 http://example.org/influence/influence_node/influenced_by #1063-07vk2 PRED entity: 07vk2 PRED relation: major_field_of_study PRED expected values: 02cm61 0277g => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 93): 062z7 (0.38 #248, 0.28 #700, 0.25 #813), 01lj9 (0.38 #259, 0.19 #598, 0.19 #372), 037mh8 (0.38 #286, 0.19 #399, 0.17 #738), 01mkq (0.36 #353, 0.31 #240, 0.31 #579), 05qfh (0.31 #256, 0.23 #708, 0.17 #1047), 02h40lc (0.31 #230, 0.12 #343, 0.12 #682), 0193x (0.31 #255, 0.11 #368, 0.09 #1046), 02lp1 (0.31 #350, 0.28 #689, 0.26 #2724), 0fdys (0.25 #258, 0.21 #371, 0.18 #710), 01540 (0.25 #280, 0.19 #393, 0.17 #732) >> Best rule #248 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 03cz83; >> query: (?x2013, 062z7) <- organization(?x5510, ?x2013), major_field_of_study(?x2013, ?x1327), ?x1327 = 01lhy >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #4303 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 573 *> proper extension: 01xcgf; *> query: (?x2013, ?x2605) <- school_type(?x2013, ?x3092), school_type(?x7508, ?x3092), major_field_of_study(?x7508, ?x2605) *> conf = 0.05 ranks of expected_values: 55, 73 EVAL 07vk2 major_field_of_study 0277g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 63.000 63.000 0.375 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 07vk2 major_field_of_study 02cm61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 63.000 63.000 0.375 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #1062-04fzk PRED entity: 04fzk PRED relation: participant! PRED expected values: 06mfvc => 141 concepts (89 used for prediction) PRED predicted values (max 10 best out of 371): 03m6_z (0.84 #38165, 0.81 #30400, 0.81 #35577), 06fc0b (0.29 #18750, 0.28 #14869, 0.26 #21984), 0j1yf (0.29 #18750, 0.28 #14869, 0.26 #21984), 03v1jf (0.17 #350, 0.04 #2936, 0.02 #6814), 05dbf (0.17 #140, 0.03 #6604, 0.03 #11776), 0btxr (0.17 #560, 0.02 #3146, 0.02 #9611), 015882 (0.17 #105, 0.02 #2691, 0.02 #3983), 01rh0w (0.17 #91, 0.02 #2677, 0.02 #3969), 02t_99 (0.17 #319, 0.02 #2905, 0.02 #4197), 01j2xj (0.17 #336, 0.02 #4860, 0.02 #4214) >> Best rule #38165 for best value: >> intensional similarity = 3 >> extensional distance = 501 >> proper extension: 01l_vgt; >> query: (?x4106, ?x7156) <- participant(?x6730, ?x4106), participant(?x4106, ?x7156), nominated_for(?x6730, ?x667) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #767 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0h5g_; 01mqz0; 0169dl; 01j5ws; 01x_d8; 01x9_8; 01bh6y; 015p37; *> query: (?x4106, 06mfvc) <- film(?x4106, ?x1490), participant(?x1733, ?x4106), actor(?x5286, ?x4106) *> conf = 0.10 ranks of expected_values: 24 EVAL 04fzk participant! 06mfvc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 141.000 89.000 0.842 http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant #1061-07gp9 PRED entity: 07gp9 PRED relation: currency PRED expected values: 09nqf => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.88 #57, 0.87 #50, 0.86 #113), 01nv4h (0.04 #135, 0.03 #128, 0.03 #177), 02l6h (0.03 #165, 0.03 #186, 0.03 #179), 0kz1h (0.01 #68), 02gsvk (0.01 #510) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 0d90m; 05p1tzf; 01vksx; 017gm7; 031778; 0c_j9x; 07cyl; 0198b6; 02d478; 01rxyb; ... >> query: (?x324, 09nqf) <- award(?x324, ?x298), prequel(?x6429, ?x324), nominated_for(?x323, ?x324) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07gp9 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 147.000 0.881 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #1060-073bb PRED entity: 073bb PRED relation: student! PRED expected values: 0gl5_ => 165 concepts (156 used for prediction) PRED predicted values (max 10 best out of 245): 01stzp (0.20 #509, 0.12 #5763, 0.09 #5237), 01lhdt (0.20 #258, 0.07 #1309, 0.05 #2360), 0pz6q (0.20 #371, 0.02 #12982), 01w5m (0.18 #1680, 0.16 #3256, 0.14 #2206), 01kvrz (0.16 #3678, 0.13 #5254, 0.12 #12611), 03ksy (0.15 #9564, 0.14 #630, 0.14 #6935), 06thjt (0.14 #921, 0.08 #3548, 0.08 #4074), 09r4xx (0.14 #647, 0.08 #3274, 0.05 #7478), 0lbfv (0.14 #747, 0.07 #1273, 0.04 #3374), 01w3v (0.14 #540, 0.06 #6319, 0.06 #6845) >> Best rule #509 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01h2_6; >> query: (?x1900, 01stzp) <- influenced_by(?x1900, ?x1278), people(?x6821, ?x1900), ?x6821 = 06z5s, people(?x3584, ?x1900) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #9701 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 38 *> proper extension: 073749; 01515w; *> query: (?x1900, 0gl5_) <- profession(?x1900, ?x353), location(?x1900, ?x3052), gender(?x1900, ?x514), ?x3052 = 01cx_ *> conf = 0.07 ranks of expected_values: 31 EVAL 073bb student! 0gl5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 165.000 156.000 0.200 http://example.org/education/educational_institution/students_graduates./education/education/student #1059-03rwz3 PRED entity: 03rwz3 PRED relation: film PRED expected values: 047vnkj 03clwtw => 104 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1575): 016dj8 (0.71 #21707, 0.70 #37212, 0.70 #37211), 011yn5 (0.71 #21707, 0.70 #37212, 0.70 #37211), 0c0nhgv (0.71 #21707, 0.70 #37212, 0.70 #37211), 07phbc (0.71 #21707, 0.70 #37212, 0.70 #37211), 05q_dw (0.62 #9303, 0.61 #18605, 0.58 #15504), 08984j (0.62 #9303, 0.61 #18605, 0.58 #15504), 0k54q (0.62 #9303, 0.61 #18605, 0.58 #15504), 05n6sq (0.38 #4068, 0.11 #11820, 0.09 #24225), 03mh_tp (0.29 #5086, 0.27 #6637, 0.25 #8187), 035s95 (0.28 #11148, 0.25 #3396, 0.22 #14248) >> Best rule #21707 for best value: >> intensional similarity = 3 >> extensional distance = 21 >> proper extension: 027kmrb; 01my_c; 0146mv; >> query: (?x7526, ?x1163) <- award_nominee(?x7526, ?x519), citytown(?x7526, ?x4801), nominated_for(?x7526, ?x1163) >> conf = 0.71 => this is the best rule for 4 predicted values *> Best rule #5728 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 12 *> proper extension: 0hm0k; *> query: (?x7526, 03clwtw) <- award_winner(?x7526, ?x519), industry(?x7526, ?x373), award_winner(?x5627, ?x7526) *> conf = 0.21 ranks of expected_values: 35, 224 EVAL 03rwz3 film 03clwtw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 104.000 84.000 0.706 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 03rwz3 film 047vnkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 104.000 84.000 0.706 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #1058-06sn8m PRED entity: 06sn8m PRED relation: nominated_for PRED expected values: 0kfv9 => 116 concepts (40 used for prediction) PRED predicted values (max 10 best out of 465): 024rwx (0.62 #8114, 0.45 #61671, 0.42 #63295), 02kk_c (0.62 #8114, 0.42 #63295, 0.41 #64919), 08r4x3 (0.15 #9882, 0.03 #45587, 0.02 #43965), 02k_4g (0.14 #108, 0.12 #3352, 0.11 #32569), 0h03fhx (0.14 #713, 0.12 #3957, 0.10 #5580), 01fx1l (0.14 #881, 0.12 #4125, 0.10 #5748), 01g03q (0.14 #1397, 0.12 #4641, 0.06 #9511), 09qycb (0.14 #1488, 0.12 #4732, 0.02 #33949), 0h1x5f (0.14 #1429, 0.12 #4673, 0.02 #33890), 0524b41 (0.14 #1110, 0.12 #4354, 0.01 #57910) >> Best rule #8114 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 0jbp0; >> query: (?x6962, ?x4881) <- award(?x6962, ?x8250), actor(?x4881, ?x6962), ?x8250 = 0cqhb3, people(?x1446, ?x6962) >> conf = 0.62 => this is the best rule for 2 predicted values *> Best rule #6758 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 0jbp0; *> query: (?x6962, 0kfv9) <- award(?x6962, ?x8250), actor(?x4881, ?x6962), ?x8250 = 0cqhb3, people(?x1446, ?x6962) *> conf = 0.08 ranks of expected_values: 53 EVAL 06sn8m nominated_for 0kfv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 116.000 40.000 0.625 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #1057-025vry PRED entity: 025vry PRED relation: artists! PRED expected values: 017_qw => 148 concepts (96 used for prediction) PRED predicted values (max 10 best out of 211): 017_qw (0.83 #2882, 0.67 #378, 0.65 #1004), 0ggq0m (0.83 #6588, 0.79 #6275, 0.63 #1578), 03_d0 (0.73 #12858, 0.27 #4707, 0.27 #4394), 064t9 (0.41 #19125, 0.36 #12234, 0.35 #7842), 06q6jz (0.41 #2694, 0.32 #1755, 0.21 #6452), 06by7 (0.36 #29793, 0.33 #19133, 0.33 #17881), 0l8gh (0.33 #2684, 0.23 #180, 0.21 #1745), 021dvj (0.30 #2557, 0.26 #1618, 0.20 #6315), 06j6l (0.25 #12270, 0.24 #12896, 0.22 #19161), 0gywn (0.21 #12280, 0.19 #4442, 0.18 #12906) >> Best rule #2882 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 02rgz4; >> query: (?x681, 017_qw) <- profession(?x681, ?x563), gender(?x681, ?x231), music(?x6890, ?x681), artists(?x888, ?x681), ?x563 = 01c8w0 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 025vry artists! 017_qw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 96.000 0.828 http://example.org/music/genre/artists #1056-02s4l6 PRED entity: 02s4l6 PRED relation: country PRED expected values: 07ssc => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 151): 07ssc (0.43 #1534, 0.42 #1658, 0.40 #3699), 03rt9 (0.33 #14, 0.20 #74, 0.05 #561), 04xvlr (0.25 #1580, 0.24 #1704, 0.23 #731), 0345h (0.18 #268, 0.17 #147, 0.15 #635), 03_3d (0.17 #127, 0.08 #248, 0.06 #554), 0f8l9c (0.16 #260, 0.15 #383, 0.14 #1416), 018h2 (0.12 #1579, 0.12 #1703, 0.10 #730), 0hn10 (0.12 #1579, 0.12 #1703, 0.10 #730), 04t36 (0.12 #1579, 0.12 #1703, 0.10 #730), 07s9rl0 (0.12 #1579, 0.12 #1703, 0.10 #730) >> Best rule #1534 for best value: >> intensional similarity = 7 >> extensional distance = 349 >> proper extension: 011yph; 02qrv7; 02pjc1h; 0gj9qxr; 015x74; 020bv3; 09p7fh; 01pv91; 07j8r; 01cjhz; ... >> query: (?x2287, 07ssc) <- titles(?x162, ?x2287), titles(?x162, ?x10208), titles(?x162, ?x9900), titles(?x162, ?x8682), ?x10208 = 09rfpk, film_release_region(?x8682, ?x87), ?x9900 = 0qmfk >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02s4l6 country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.427 http://example.org/film/film/country #1055-04gp1d PRED entity: 04gp1d PRED relation: legislative_sessions! PRED expected values: 0226cw 0194xc => 28 concepts (28 used for prediction) PRED predicted values (max 10 best out of 24): 0226cw (0.89 #446, 0.84 #471, 0.83 #317), 0194xc (0.74 #474, 0.71 #526, 0.71 #500), 016lh0 (0.62 #361, 0.61 #329, 0.61 #328), 012v1t (0.61 #329, 0.61 #328, 0.58 #312), 0bymv (0.61 #329, 0.61 #328, 0.58 #303), 0d06m5 (0.61 #329, 0.61 #328, 0.58 #588), 0d3qd0 (0.61 #329, 0.61 #328, 0.58 #588), 03txms (0.61 #329, 0.61 #328, 0.58 #588), 02mjmr (0.61 #329, 0.61 #328, 0.58 #562), 01lct6 (0.61 #329, 0.61 #328, 0.57 #173) >> Best rule #446 for best value: >> intensional similarity = 26 >> extensional distance = 16 >> proper extension: 06r713; >> query: (?x3765, 0226cw) <- legislative_sessions(?x6728, ?x3765), legislative_sessions(?x3540, ?x3765), ?x6728 = 070mff, district_represented(?x3765, ?x6226), district_represented(?x3765, ?x2020), district_represented(?x3765, ?x335), ?x6226 = 03gh4, ?x2020 = 05k7sb, legislative_sessions(?x3765, ?x4821), ?x335 = 059rby, district_represented(?x4821, ?x5575), district_represented(?x4821, ?x1755), district_represented(?x4821, ?x1138), district_represented(?x4821, ?x728), ?x1138 = 059_c, district_represented(?x3540, ?x4776), district_represented(?x3540, ?x3038), district_represented(?x3540, ?x2623), legislative_sessions(?x4821, ?x605), ?x3038 = 0d0x8, ?x4776 = 06yxd, ?x1755 = 01x73, ?x728 = 059f4, ?x2623 = 02xry, legislative_sessions(?x652, ?x4821), ?x5575 = 05fjy >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 04gp1d legislative_sessions! 0194xc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 28.000 0.889 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 04gp1d legislative_sessions! 0226cw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 28.000 0.889 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #1054-02q56mk PRED entity: 02q56mk PRED relation: genre PRED expected values: 0hn10 01t_vv => 101 concepts (95 used for prediction) PRED predicted values (max 10 best out of 96): 02l7c8 (0.55 #2496, 0.53 #6986, 0.53 #7458), 02kdv5l (0.45 #1064, 0.40 #710, 0.36 #3663), 0jxy (0.45 #1107, 0.02 #11148), 0hcr (0.43 #1085, 0.09 #5575, 0.07 #6993), 01jfsb (0.36 #720, 0.35 #1665, 0.35 #12), 03k9fj (0.35 #3672, 0.34 #1782, 0.32 #1073), 06n90 (0.30 #1075, 0.25 #3674, 0.21 #1784), 0lsxr (0.30 #598, 0.26 #1425, 0.24 #244), 01hmnh (0.29 #1079, 0.26 #1788, 0.25 #3678), 04xvlr (0.29 #1890, 0.29 #2244, 0.24 #1299) >> Best rule #2496 for best value: >> intensional similarity = 4 >> extensional distance = 150 >> proper extension: 0dtw1x; 03s6l2; 087wc7n; 07sc6nw; 04zyhx; 020y73; 08052t3; 05q4y12; 0gyy53; 02w86hz; ... >> query: (?x2613, 02l7c8) <- genre(?x2613, ?x11464), genre(?x146, ?x11464), film_format(?x2613, ?x909), ?x146 = 02y_lrp >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #290 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 0d_wms; *> query: (?x2613, 01t_vv) <- cinematography(?x2613, ?x7782), film_release_distribution_medium(?x2613, ?x81), honored_for(?x7141, ?x2613), country(?x2613, ?x94) *> conf = 0.18 ranks of expected_values: 15, 35 EVAL 02q56mk genre 01t_vv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 101.000 95.000 0.553 http://example.org/film/film/genre EVAL 02q56mk genre 0hn10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 101.000 95.000 0.553 http://example.org/film/film/genre #1053-01kd57 PRED entity: 01kd57 PRED relation: award_winner! PRED expected values: 01bgqh 01by1l => 151 concepts (131 used for prediction) PRED predicted values (max 10 best out of 261): 01bgqh (0.42 #22422, 0.42 #21128, 0.41 #12073), 02sp_v (0.42 #22422, 0.42 #21128, 0.41 #12073), 02nhxf (0.42 #22422, 0.42 #21128, 0.41 #12073), 02gdjb (0.42 #22422, 0.42 #21128, 0.41 #12073), 03qpp9 (0.42 #22422, 0.42 #21128, 0.41 #12073), 01by1l (0.40 #1837, 0.38 #3130, 0.31 #2699), 025m8y (0.32 #12173, 0.30 #5273, 0.29 #8724), 02f6xy (0.30 #1922, 0.12 #3215, 0.08 #2353), 03dkh6 (0.25 #1268, 0.12 #1699), 073y53 (0.25 #1259, 0.12 #1690) >> Best rule #22422 for best value: >> intensional similarity = 3 >> extensional distance = 200 >> proper extension: 01vd7hn; 03_0p; >> query: (?x5543, ?x724) <- role(?x5543, ?x227), award_winner(?x1089, ?x5543), award(?x5543, ?x724) >> conf = 0.42 => this is the best rule for 5 predicted values ranks of expected_values: 1, 6 EVAL 01kd57 award_winner! 01by1l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 151.000 131.000 0.423 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 01kd57 award_winner! 01bgqh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 131.000 0.423 http://example.org/award/award_category/winners./award/award_honor/award_winner #1052-01t8399 PRED entity: 01t8399 PRED relation: artist! PRED expected values: 05k8m5 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 113): 0g768 (0.36 #36, 0.20 #1396, 0.19 #172), 011k1h (0.27 #10, 0.22 #962, 0.16 #1778), 015_1q (0.26 #971, 0.22 #291, 0.22 #563), 01w40h (0.24 #436, 0.18 #28, 0.16 #572), 01cl2y (0.19 #166, 0.18 #30, 0.16 #982), 0181dw (0.19 #177, 0.18 #993, 0.12 #1537), 017l96 (0.18 #18, 0.18 #1514, 0.16 #1378), 033hn8 (0.18 #14, 0.16 #558, 0.15 #150), 043g7l (0.18 #31, 0.14 #983, 0.14 #575), 03mp8k (0.18 #64, 0.08 #608, 0.08 #6456) >> Best rule #36 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 04k05; >> query: (?x10744, 0g768) <- artist(?x2931, ?x10744), artists(?x7329, ?x10744), artists(?x2249, ?x10744), ?x7329 = 016jny, ?x2249 = 03lty >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01t8399 artist! 05k8m5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 113.000 0.364 http://example.org/music/record_label/artist #1051-0qxhc PRED entity: 0qxhc PRED relation: location_of_ceremony! PRED expected values: 0157m => 148 concepts (111 used for prediction) PRED predicted values (max 10 best out of 253): 01933d (0.07 #1452, 0.07 #1199, 0.06 #2211), 0h7pj (0.07 #1720, 0.05 #3744, 0.05 #3491), 02p5hf (0.07 #1997, 0.06 #2503, 0.05 #3262), 01vzxld (0.07 #1992, 0.05 #3004, 0.05 #2751), 01rwcgb (0.06 #2252, 0.06 #4782, 0.06 #5795), 02m30v (0.06 #2531, 0.05 #2784, 0.05 #3543), 0dvld (0.05 #2678, 0.04 #3943, 0.04 #4196), 03j24kf (0.05 #2642, 0.04 #4919, 0.04 #5679), 01vsy7t (0.05 #3399, 0.04 #4411, 0.04 #5170), 0fpj9pm (0.04 #422, 0.04 #929, 0.04 #1435) >> Best rule #1452 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 03gh4; >> query: (?x13060, 01933d) <- contains(?x13060, ?x8120), location_of_ceremony(?x3445, ?x13060), category(?x13060, ?x134), location_of_ceremony(?x566, ?x13060) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qxhc location_of_ceremony! 0157m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 148.000 111.000 0.071 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #1050-02wypbh PRED entity: 02wypbh PRED relation: award_winner PRED expected values: 03s9b => 52 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1601): 0bwh6 (0.60 #267, 0.50 #2729, 0.20 #10120), 0184jw (0.60 #1695, 0.50 #4157, 0.14 #11548), 0h1p (0.50 #2891, 0.40 #429, 0.19 #10282), 06pj8 (0.40 #436, 0.33 #2898, 0.24 #10289), 081lh (0.40 #189, 0.33 #2651, 0.19 #10042), 01f8ld (0.40 #660, 0.33 #3122, 0.15 #10513), 0693l (0.40 #675, 0.33 #3137, 0.14 #10528), 06b_0 (0.40 #1671, 0.33 #4133, 0.14 #11524), 06t8b (0.40 #1710, 0.33 #4172, 0.10 #11563), 01j2xj (0.40 #1112, 0.33 #3574, 0.05 #10965) >> Best rule #267 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 02rdyk7; 02w_6xj; 027b9ly; >> query: (?x10597, 0bwh6) <- award_winner(?x10597, ?x9754), award_winner(?x10597, ?x7310), ?x9754 = 026670, nominated_for(?x10597, ?x5347), ?x7310 = 04sry >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #11385 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 57 *> proper extension: 054ky1; 03nqnk3; 01l29r; 02x17c2; 04qy5; 02py7pj; 0bm70b; *> query: (?x10597, 03s9b) <- award_winner(?x10597, ?x9754), produced_by(?x9432, ?x9754), film(?x9754, ?x2090), nominated_for(?x1972, ?x9432), ?x1972 = 0gqyl *> conf = 0.08 ranks of expected_values: 111 EVAL 02wypbh award_winner 03s9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 52.000 27.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #1049-0jmdb PRED entity: 0jmdb PRED relation: draft PRED expected values: 038c0q => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 18): 0f4vx0 (0.89 #354, 0.88 #246, 0.80 #390), 025tn92 (0.87 #212, 0.85 #392, 0.85 #176), 038c0q (0.79 #884, 0.78 #1029, 0.75 #241), 09th87 (0.79 #884, 0.78 #1029, 0.75 #55), 02pq_rp (0.50 #117, 0.38 #657, 0.37 #946), 047dpm0 (0.50 #127, 0.35 #667, 0.33 #956), 02rl201 (0.50 #113, 0.31 #653, 0.30 #581), 02pq_x5 (0.50 #125, 0.30 #954, 0.29 #197), 04f4z1k (0.42 #666, 0.37 #955, 0.35 #594), 02r6gw6 (0.38 #123, 0.35 #952, 0.35 #663) >> Best rule #354 for best value: >> intensional similarity = 9 >> extensional distance = 17 >> proper extension: 0jmmn; >> query: (?x660, 0f4vx0) <- sport(?x660, ?x4833), ?x4833 = 018w8, team(?x6848, ?x660), team(?x5755, ?x660), team(?x4747, ?x660), draft(?x660, ?x8586), ?x6848 = 02_ssl, ?x4747 = 02sf_r, position(?x1347, ?x5755) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #884 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 35 *> proper extension: 02896; 01xvb; 05tfm; 0cqt41; 07l24; 051vz; 0x2p; 03b3j; 05tg3; 05l71; ... *> query: (?x660, ?x2569) <- teams(?x659, ?x660), team(?x4570, ?x660), team(?x8996, ?x660), school(?x660, ?x2497), team(?x4570, ?x10409), draft(?x10409, ?x2569), type_of_union(?x8996, ?x566) *> conf = 0.79 ranks of expected_values: 3 EVAL 0jmdb draft 038c0q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 129.000 129.000 0.895 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #1048-047msdk PRED entity: 047msdk PRED relation: film! PRED expected values: 03rl84 => 93 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1252): 05zbm4 (0.64 #112565, 0.52 #54190, 0.44 #89629), 03rl84 (0.64 #112565, 0.52 #54190, 0.44 #89629), 07swvb (0.20 #699, 0.09 #2783, 0.03 #39598), 0f5xn (0.13 #972, 0.09 #3056, 0.05 #13476), 03ym1 (0.13 #1014, 0.06 #3098, 0.03 #17686), 0gnbw (0.13 #1272, 0.06 #3356, 0.03 #5440), 02ck7w (0.13 #942, 0.06 #3026, 0.03 #11362), 0svqs (0.13 #877, 0.06 #2961, 0.02 #17549), 062dn7 (0.13 #663, 0.06 #2747, 0.02 #17335), 02gvwz (0.13 #188, 0.06 #2272, 0.02 #31448) >> Best rule #112565 for best value: >> intensional similarity = 3 >> extensional distance = 867 >> proper extension: 06dfz1; 025x1t; 0clpml; 06ys2; >> query: (?x1364, ?x2012) <- nominated_for(?x2012, ?x1364), participant(?x4929, ?x2012), profession(?x4929, ?x1032) >> conf = 0.64 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 047msdk film! 03rl84 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 93.000 63.000 0.644 http://example.org/film/actor/film./film/performance/film #1047-047rkcm PRED entity: 047rkcm PRED relation: currency PRED expected values: 09nqf => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.90 #36, 0.82 #85, 0.81 #50), 01nv4h (0.03 #58, 0.02 #163, 0.02 #268), 02gsvk (0.02 #97, 0.01 #69, 0.01 #83), 02l6h (0.01 #319, 0.01 #333, 0.01 #361) >> Best rule #36 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 03g90h; 053rxgm; 0f4_l; 02yvct; 065z3_x; 076tq0z; 04grkmd; 07kh6f3; 05m_jsg; 07k8rt4; ... >> query: (?x6762, 09nqf) <- produced_by(?x6762, ?x9316), film_crew_role(?x6762, ?x4305), ?x4305 = 0215hd, film(?x2745, ?x6762) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047rkcm currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.898 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #1046-017jd9 PRED entity: 017jd9 PRED relation: film_release_region PRED expected values: 015fr 07ylj 0h7x 077qn 07twz => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 44): 015fr (0.63 #835, 0.59 #599, 0.21 #4729), 0h7x (0.43 #610, 0.38 #846, 0.20 #20), 04gzd (0.33 #831, 0.32 #595, 0.30 #5), 082fr (0.24 #630, 0.21 #866, 0.09 #1102), 07twz (0.21 #653, 0.20 #63, 0.19 #889), 07ylj (0.20 #16, 0.20 #606, 0.19 #842), 077qn (0.18 #882, 0.17 #646, 0.10 #56), 05sb1 (0.12 #623, 0.12 #859, 0.07 #269), 012wgb (0.10 #50, 0.06 #876, 0.06 #640), 0bjv6 (0.10 #46, 0.06 #872, 0.05 #636) >> Best rule #835 for best value: >> intensional similarity = 3 >> extensional distance = 309 >> proper extension: 0dtw1x; 0gj9qxr; 0crh5_f; 043sct5; 0h95zbp; 0g5q34q; 07s3m4g; >> query: (?x4610, 015fr) <- country(?x4610, ?x94), film_release_region(?x4610, ?x1892), ?x1892 = 02vzc >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 5, 6, 7 EVAL 017jd9 film_release_region 07twz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 65.000 0.633 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017jd9 film_release_region 077qn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 65.000 0.633 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017jd9 film_release_region 0h7x CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.633 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017jd9 film_release_region 07ylj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 65.000 0.633 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 017jd9 film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.633 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1045-03j24kf PRED entity: 03j24kf PRED relation: award_winner! PRED expected values: 0gx1673 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 121): 09g90vz (0.22 #817, 0.07 #9157, 0.06 #9852), 013b2h (0.20 #78, 0.15 #4943, 0.14 #4804), 02rjjll (0.18 #143, 0.17 #282, 0.15 #4730), 01xqqp (0.18 #233, 0.10 #94, 0.10 #650), 02cg41 (0.17 #541, 0.15 #124, 0.12 #4989), 0jzphpx (0.17 #455, 0.13 #316, 0.10 #1845), 01s695 (0.16 #1254, 0.13 #420, 0.12 #1810), 01mhwk (0.15 #40, 0.09 #4627, 0.09 #4905), 019bk0 (0.15 #1266, 0.13 #432, 0.09 #4602), 0466p0j (0.14 #213, 0.13 #352, 0.12 #4661) >> Best rule #817 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 07sgfsl; 03h3vtz; >> query: (?x4701, 09g90vz) <- award_winner(?x6947, ?x4701), actor(?x4275, ?x6947), person(?x9961, ?x4701) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #396 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 21 *> proper extension: 0197tq; 01wl38s; 02l840; 01vrz41; 01kx_81; 01w60_p; 0144l1; 0lccn; 01_x6v; 01vsl3_; ... *> query: (?x4701, 0gx1673) <- award_winner(?x2799, ?x4701), role(?x4701, ?x212), person(?x9961, ?x4701) *> conf = 0.13 ranks of expected_values: 15 EVAL 03j24kf award_winner! 0gx1673 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 124.000 124.000 0.220 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1044-089tm PRED entity: 089tm PRED relation: artists! PRED expected values: 013rxq => 95 concepts (54 used for prediction) PRED predicted values (max 10 best out of 282): 064t9 (0.60 #12, 0.58 #2776, 0.57 #627), 016clz (0.58 #3385, 0.49 #4000, 0.38 #2462), 06j6l (0.47 #3118, 0.42 #354, 0.40 #968), 0gywn (0.40 #56, 0.24 #8363, 0.24 #7135), 016cjb (0.40 #74, 0.17 #1303, 0.12 #3146), 01lyv (0.39 #1260, 0.33 #3103, 0.18 #9873), 0155w (0.37 #3177, 0.28 #1334, 0.26 #1641), 05bt6j (0.36 #7734, 0.34 #4036, 0.32 #4960), 0mhfr (0.33 #1251, 0.25 #330, 0.20 #944), 05w3f (0.32 #5263, 0.24 #1878, 0.24 #4954) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 05d8vw; >> query: (?x379, 064t9) <- artist(?x2931, ?x379), artists(?x378, ?x379), origin(?x379, ?x4733), ?x4733 = 03l2n >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #15684 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 444 *> proper extension: 01vng3b; 06tp4h; *> query: (?x379, ?x837) <- artist(?x3050, ?x379), artists(?x378, ?x379), origin(?x379, ?x4733), artist(?x3050, ?x4642), artists(?x837, ?x4642) *> conf = 0.04 ranks of expected_values: 230 EVAL 089tm artists! 013rxq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 95.000 54.000 0.600 http://example.org/music/genre/artists #1043-076zy_g PRED entity: 076zy_g PRED relation: film! PRED expected values: 0c6g1l 0pmhf 057_yx => 124 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1113): 0284n42 (0.46 #106308, 0.45 #122983, 0.44 #104224), 02qggqc (0.46 #106308, 0.45 #122983, 0.44 #104224), 04pf4r (0.46 #106308, 0.45 #122983, 0.44 #104224), 03dpqd (0.22 #829, 0.02 #2912, 0.02 #40431), 020trj (0.20 #39602, 0.19 #52113, 0.18 #45859), 02tn0_ (0.15 #39601, 0.14 #52112, 0.14 #45858), 0h5g_ (0.11 #74, 0.07 #12580, 0.07 #16750), 04yj5z (0.11 #122, 0.05 #2205, 0.03 #16798), 0dvmd (0.11 #527, 0.05 #10947, 0.03 #13033), 053xw6 (0.11 #1254, 0.04 #7505, 0.03 #15845) >> Best rule #106308 for best value: >> intensional similarity = 4 >> extensional distance = 658 >> proper extension: 04nl83; 02c6d; 0b76t12; 047n8xt; 02q6gfp; 02qmsr; 0cw3yd; 0bs5k8r; 0sxkh; 0f4k49; ... >> query: (?x5155, ?x666) <- film(?x1672, ?x5155), film_crew_role(?x5155, ?x137), nominated_for(?x666, ?x5155), ?x137 = 09zzb8 >> conf = 0.46 => this is the best rule for 3 predicted values *> Best rule #442 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 04f6df0; *> query: (?x5155, 0pmhf) <- produced_by(?x5155, ?x9785), country(?x5155, ?x94), ?x9785 = 02tn0_, film(?x1672, ?x5155) *> conf = 0.11 ranks of expected_values: 13, 182, 652 EVAL 076zy_g film! 057_yx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 124.000 71.000 0.459 http://example.org/film/actor/film./film/performance/film EVAL 076zy_g film! 0pmhf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 124.000 71.000 0.459 http://example.org/film/actor/film./film/performance/film EVAL 076zy_g film! 0c6g1l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 124.000 71.000 0.459 http://example.org/film/actor/film./film/performance/film #1042-02bqvs PRED entity: 02bqvs PRED relation: genre PRED expected values: 07s9rl0 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 91): 07s9rl0 (0.62 #123, 0.59 #2569, 0.58 #4164), 02l7c8 (0.50 #17, 0.44 #139, 0.34 #871), 01jfsb (0.44 #135, 0.32 #2952, 0.31 #3321), 02kdv5l (0.34 #2942, 0.31 #3311, 0.28 #3065), 01hmnh (0.28 #6142, 0.18 #2958, 0.18 #3327), 06cvj (0.25 #4, 0.21 #3679, 0.21 #248), 011ys5 (0.25 #96, 0.19 #218, 0.02 #3771), 09q17 (0.25 #63, 0.08 #307, 0.07 #429), 0bbc17 (0.25 #102, 0.06 #224, 0.02 #1445), 03k9fj (0.25 #6135, 0.24 #3320, 0.24 #2951) >> Best rule #123 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0gx9rvq; 026mfbr; 05fgt1; 0fb7sd; 0gg5kmg; 05b6rdt; 09gmmt6; >> query: (?x8790, 07s9rl0) <- film(?x4966, ?x8790), ?x4966 = 06lht1, film_crew_role(?x8790, ?x137) >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02bqvs genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.625 http://example.org/film/film/genre #1041-040njc PRED entity: 040njc PRED relation: award! PRED expected values: 07nznf 02lf0c 030_3z 09qc1 045cq 03v1w7 03d1y3 01r2c7 026670 => 70 concepts (37 used for prediction) PRED predicted values (max 10 best out of 2501): 05kfs (0.67 #29389, 0.55 #61875, 0.50 #35886), 03xp8d5 (0.67 #30428, 0.50 #17435, 0.43 #43421), 0qf43 (0.64 #61766, 0.50 #16287, 0.45 #78016), 0c3ns (0.63 #94218, 0.62 #107219, 0.61 #110469), 0js9s (0.62 #107219, 0.61 #110469, 0.60 #94217), 0hskw (0.62 #107219, 0.61 #110469, 0.60 #94217), 02pq9yv (0.62 #107219, 0.61 #110469, 0.60 #94217), 04ld94 (0.62 #107219, 0.61 #110469, 0.60 #94217), 0gv40 (0.62 #107219, 0.61 #110469, 0.60 #94217), 07y_r (0.62 #107219, 0.61 #110469, 0.60 #94217) >> Best rule #29389 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 04dn09n; >> query: (?x198, 05kfs) <- nominated_for(?x198, ?x4541), award(?x6993, ?x198), ?x4541 = 08nvyr, ?x6993 = 01p1z_ >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #61130 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 9 *> proper extension: 03hl6lc; *> query: (?x198, 026670) <- nominated_for(?x198, ?x4347), nominated_for(?x198, ?x945), award(?x2800, ?x198), ?x4347 = 04smdd, award(?x945, ?x112), award_winner(?x1107, ?x2800) *> conf = 0.36 ranks of expected_values: 44, 75, 181, 239, 372, 375, 398, 1185, 1187 EVAL 040njc award! 026670 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 70.000 37.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 040njc award! 01r2c7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 70.000 37.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 040njc award! 03d1y3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 70.000 37.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 040njc award! 03v1w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 70.000 37.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 040njc award! 045cq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 70.000 37.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 040njc award! 09qc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 70.000 37.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 040njc award! 030_3z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 70.000 37.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 040njc award! 02lf0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 70.000 37.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 040njc award! 07nznf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 70.000 37.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1040-04vq33 PRED entity: 04vq33 PRED relation: nominated_for! PRED expected values: 0gqwc => 105 concepts (80 used for prediction) PRED predicted values (max 10 best out of 200): 0gq9h (0.61 #774, 0.61 #1248, 0.61 #63), 0gs9p (0.55 #65, 0.53 #302, 0.47 #1250), 019f4v (0.49 #2187, 0.44 #2424, 0.42 #2898), 0gr0m (0.42 #2193, 0.38 #1719, 0.36 #2904), 040njc (0.39 #2140, 0.34 #2377, 0.32 #5221), 0gr4k (0.37 #1211, 0.34 #26, 0.33 #974), 0p9sw (0.37 #2153, 0.35 #1679, 0.33 #2390), 0f4x7 (0.35 #2395, 0.34 #25, 0.33 #262), 04dn09n (0.31 #2405, 0.27 #5249, 0.26 #2642), 0l8z1 (0.31 #2896, 0.29 #2185, 0.29 #526) >> Best rule #774 for best value: >> intensional similarity = 4 >> extensional distance = 55 >> proper extension: 01wb95; 0gt1k; 0gl3hr; >> query: (?x12679, 0gq9h) <- film(?x6440, ?x12679), film_art_direction_by(?x12679, ?x199), nominated_for(?x2109, ?x12679), award_winner(?x6440, ?x6958) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #1246 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 0kb07; *> query: (?x12679, 0gqwc) <- film(?x6440, ?x12679), film_art_direction_by(?x12679, ?x199), nominated_for(?x2109, ?x12679), nominated_for(?x6440, ?x1973) *> conf = 0.29 ranks of expected_values: 11 EVAL 04vq33 nominated_for! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 105.000 80.000 0.614 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1039-02773nt PRED entity: 02773nt PRED relation: award_nominee! PRED expected values: 0277990 => 79 concepts (36 used for prediction) PRED predicted values (max 10 best out of 641): 018ygt (0.83 #4651, 0.81 #65110, 0.81 #48831), 02778pf (0.83 #4651, 0.81 #65110, 0.81 #48831), 0p_2r (0.83 #4651, 0.81 #65110, 0.81 #48831), 02773m2 (0.83 #4651, 0.81 #65110, 0.81 #48831), 0277990 (0.83 #4651, 0.81 #65110, 0.81 #48831), 02773nt (0.78 #159, 0.56 #2485, 0.44 #16274), 0266r6h (0.28 #44179, 0.19 #39528, 0.19 #76737), 04tnqn (0.28 #44179, 0.19 #39528, 0.19 #76737), 01qr1_ (0.28 #44179, 0.19 #39528, 0.19 #76737), 0gy6z9 (0.28 #44179, 0.19 #39528, 0.19 #76737) >> Best rule #4651 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01n5309; 02lk1s; 02pb53; 0277990; >> query: (?x829, ?x830) <- profession(?x829, ?x987), award_nominee(?x829, ?x1422), award_nominee(?x829, ?x830), ?x1422 = 0p_2r >> conf = 0.83 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5 EVAL 02773nt award_nominee! 0277990 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 79.000 36.000 0.826 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #1038-0g6ff PRED entity: 0g6ff PRED relation: geographic_distribution PRED expected values: 047lj 0jt3tjf => 32 concepts (32 used for prediction) PRED predicted values (max 10 best out of 204): 02jx1 (0.44 #622, 0.13 #2016, 0.11 #1947), 0d060g (0.40 #143, 0.25 #627, 0.12 #1049), 07ssc (0.30 #148, 0.25 #632, 0.16 #1054), 09pmkv (0.30 #156, 0.25 #640, 0.12 #1062), 0chghy (0.30 #146, 0.19 #630, 0.08 #1052), 0345h (0.25 #645, 0.20 #161, 0.13 #2016), 06t2t (0.20 #170, 0.19 #654, 0.09 #936), 06qd3 (0.20 #162, 0.19 #646, 0.08 #1068), 03_3d (0.20 #142, 0.19 #626, 0.08 #1048), 059j2 (0.20 #159, 0.12 #643, 0.04 #1065) >> Best rule #622 for best value: >> intensional similarity = 9 >> extensional distance = 14 >> proper extension: 04mvp8; >> query: (?x5590, ?x1310) <- people(?x5590, ?x5591), geographic_distribution(?x5590, ?x6305), geographic_distribution(?x5590, ?x94), ?x94 = 09c7w0, nationality(?x5591, ?x1310), location(?x5591, ?x1758), award(?x5591, ?x3435), nominated_for(?x3435, ?x69), jurisdiction_of_office(?x182, ?x6305) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #694 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 14 *> proper extension: 0c41n; *> query: (?x5590, ?x7430) <- geographic_distribution(?x5590, ?x2188), geographic_distribution(?x5590, ?x2000), adjoins(?x2517, ?x2188), administrative_parent(?x2188, ?x551), location(?x396, ?x2000), contains(?x455, ?x2000), adjoins(?x1499, ?x2000), adjoins(?x2517, ?x7430), taxonomy(?x2188, ?x939) *> conf = 0.12 ranks of expected_values: 44, 49 EVAL 0g6ff geographic_distribution 0jt3tjf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 32.000 32.000 0.438 http://example.org/people/ethnicity/geographic_distribution EVAL 0g6ff geographic_distribution 047lj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 32.000 32.000 0.438 http://example.org/people/ethnicity/geographic_distribution #1037-0428bc PRED entity: 0428bc PRED relation: award PRED expected values: 0bdw6t => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 300): 09sb52 (0.62 #24562, 0.40 #7275, 0.37 #1647), 0gqy2 (0.57 #1771, 0.50 #1369, 0.42 #8203), 0f4x7 (0.43 #1637, 0.32 #8069, 0.30 #1235), 027dtxw (0.43 #1611, 0.26 #7239, 0.25 #8043), 0bfvw2 (0.40 #12878, 0.29 #14, 0.23 #19712), 0cqh46 (0.37 #1658, 0.30 #8090, 0.24 #7286), 04kxsb (0.29 #1732, 0.27 #928, 0.23 #8164), 02w9sd7 (0.29 #1777, 0.18 #8209, 0.16 #7405), 0bdwft (0.27 #19765, 0.17 #2479, 0.14 #12931), 0bp_b2 (0.26 #1625, 0.25 #3233, 0.23 #8459) >> Best rule #24562 for best value: >> intensional similarity = 4 >> extensional distance = 588 >> proper extension: 01hxs4; 0c3jz; 048q6x; 01q9b9; >> query: (?x9977, 09sb52) <- award(?x9977, ?x458), gender(?x9977, ?x231), award(?x398, ?x458), ?x398 = 0bl2g >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #8149 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 99 *> proper extension: 02nb2s; 0hvb2; 016yzz; 01pkhw; 0flw6; 016k6x; 04mg6l; 01vzxmq; 0436kgz; 04954; ... *> query: (?x9977, 0bdw6t) <- award(?x9977, ?x3247), gender(?x9977, ?x231), film(?x9977, ?x4024), ?x3247 = 0bdwqv *> conf = 0.14 ranks of expected_values: 33 EVAL 0428bc award 0bdw6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 148.000 148.000 0.622 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1036-02qlp4 PRED entity: 02qlp4 PRED relation: film_crew_role PRED expected values: 02ynfr => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 24): 0dxtw (0.60 #9, 0.45 #318, 0.42 #456), 01vx2h (0.43 #319, 0.42 #663, 0.38 #181), 02ynfr (0.29 #48, 0.23 #461, 0.20 #393), 01xy5l_ (0.20 #12, 0.13 #1523, 0.12 #1385), 089fss (0.20 #5, 0.10 #39, 0.07 #1378), 02rh1dz (0.19 #42, 0.18 #179, 0.17 #661), 0d2b38 (0.19 #195, 0.17 #126, 0.16 #92), 015h31 (0.18 #75, 0.15 #109, 0.15 #178), 0215hd (0.15 #188, 0.15 #2389, 0.15 #1390), 089g0h (0.13 #397, 0.12 #1391, 0.12 #327) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 02d003; >> query: (?x10902, 0dxtw) <- executive_produced_by(?x10902, ?x3223), film(?x4662, ?x10902), ?x4662 = 016vg8, film_crew_role(?x10902, ?x137) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 19 *> proper extension: 0n2bh; 01b_lz; 02h2vv; 05p9_ql; 06k176; *> query: (?x10902, 02ynfr) <- nominated_for(?x2549, ?x10902), titles(?x8581, ?x10902), award_winner(?x5889, ?x2549), ?x5889 = 0m66w *> conf = 0.29 ranks of expected_values: 3 EVAL 02qlp4 film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 114.000 0.600 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1035-09k5jh7 PRED entity: 09k5jh7 PRED relation: award_winner PRED expected values: 0794g 04954 04znsy => 35 concepts (17 used for prediction) PRED predicted values (max 10 best out of 2121): 04znsy (0.50 #4362, 0.33 #1295, 0.17 #13568), 025jfl (0.44 #4678, 0.33 #75, 0.17 #3142), 0bxtg (0.39 #10796, 0.22 #7729, 0.21 #20007), 04sry (0.36 #7213, 0.22 #10282, 0.17 #13351), 0g5lhl7 (0.33 #5001, 0.33 #398, 0.17 #3465), 01yk13 (0.33 #3184, 0.33 #117, 0.11 #4720), 01qq_lp (0.33 #3660, 0.33 #593, 0.11 #5196), 01pcq3 (0.33 #108, 0.22 #4711, 0.17 #3175), 0lpjn (0.33 #412, 0.22 #5015, 0.17 #3479), 04y79_n (0.33 #191, 0.22 #4794, 0.17 #3258) >> Best rule #4362 for best value: >> intensional similarity = 12 >> extensional distance = 4 >> proper extension: 02cg41; >> query: (?x6108, 04znsy) <- honored_for(?x6108, ?x3455), ceremony(?x1162, ?x6108), nominated_for(?x1162, ?x8084), nominated_for(?x1162, ?x7880), nominated_for(?x1162, ?x4530), award_winner(?x6108, ?x3442), edited_by(?x8084, ?x6233), film(?x748, ?x7880), titles(?x53, ?x7880), genre(?x4530, ?x571), executive_produced_by(?x3455, ?x1052), ?x3442 = 0m_v0 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1, 15, 835 EVAL 09k5jh7 award_winner 04znsy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 35.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09k5jh7 award_winner 04954 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 35.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 09k5jh7 award_winner 0794g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 35.000 17.000 0.500 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1034-01_x6d PRED entity: 01_x6d PRED relation: type_of_union PRED expected values: 04ztj => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.77 #45, 0.77 #165, 0.76 #257), 01g63y (0.19 #46, 0.17 #122, 0.16 #134), 01bl8s (0.01 #99) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 01gp_x; 06449; 02vyw; 01900g; 026dx; 02lhm2; 04pz5c; 0p__8; 01_6dw; 052hl; ... >> query: (?x4466, 04ztj) <- award_winner(?x4466, ?x163), award_winner(?x2349, ?x4466), influenced_by(?x4466, ?x12459) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01_x6d type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 145.000 0.774 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1033-01m42d0 PRED entity: 01m42d0 PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 8): 05zppz (0.91 #36, 0.91 #48, 0.90 #15), 02zsn (0.35 #71, 0.33 #111, 0.32 #121), 0fltx (0.12 #88), 098s1 (0.12 #88), 01hbgs (0.12 #88), 0jpmt (0.12 #88), 02ctzb (0.12 #88), 0x67 (0.12 #88) >> Best rule #36 for best value: >> intensional similarity = 3 >> extensional distance = 108 >> proper extension: 03j90; >> query: (?x8010, 05zppz) <- student(?x3424, ?x8010), influenced_by(?x4536, ?x8010), award_winner(?x2071, ?x8010) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m42d0 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.909 http://example.org/people/person/gender #1032-09c7w0 PRED entity: 09c7w0 PRED relation: geographic_distribution! PRED expected values: 033tf_ 03ttfc 0g8_vp => 193 concepts (193 used for prediction) PRED predicted values (max 10 best out of 22): 0g48m4 (0.33 #67, 0.25 #221, 0.21 #463), 09vc4s (0.25 #223, 0.11 #333, 0.09 #399), 01rv7x (0.21 #693, 0.18 #363, 0.17 #429), 03ts0c (0.14 #313, 0.09 #357, 0.08 #445), 06gbnc (0.11 #336, 0.09 #358, 0.08 #424), 012f86 (0.09 #368, 0.09 #654, 0.08 #698), 01p7s6 (0.09 #367, 0.04 #653, 0.04 #807), 06mvq (0.09 #867, 0.09 #647, 0.07 #801), 0j6x8 (0.08 #435, 0.07 #545, 0.07 #523), 03ttfc (0.07 #508, 0.04 #640, 0.04 #684) >> Best rule #67 for best value: >> intensional similarity = 2 >> extensional distance = 1 >> proper extension: 03v0t; >> query: (?x94, 0g48m4) <- contains(?x94, ?x7418), ?x7418 = 03cz83 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #508 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 13 *> proper extension: 03pn9; 07ytt; *> query: (?x94, 03ttfc) <- entity_involved(?x1140, ?x94), religion(?x94, ?x109) *> conf = 0.07 ranks of expected_values: 10 EVAL 09c7w0 geographic_distribution! 0g8_vp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 193.000 193.000 0.333 http://example.org/people/ethnicity/geographic_distribution EVAL 09c7w0 geographic_distribution! 03ttfc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 193.000 193.000 0.333 http://example.org/people/ethnicity/geographic_distribution EVAL 09c7w0 geographic_distribution! 033tf_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 193.000 193.000 0.333 http://example.org/people/ethnicity/geographic_distribution #1031-0cmdwwg PRED entity: 0cmdwwg PRED relation: film! PRED expected values: 03kbb8 018fwv => 73 concepts (43 used for prediction) PRED predicted values (max 10 best out of 891): 05zbm4 (0.72 #56242, 0.55 #49992, 0.47 #56241), 06msq2 (0.47 #56241, 0.45 #83316, 0.43 #52076), 0pz91 (0.10 #6456, 0.10 #8542, 0.08 #12710), 0p_pd (0.08 #53, 0.08 #8385, 0.07 #12553), 01wbg84 (0.06 #46, 0.05 #4210, 0.04 #6292), 01swck (0.06 #799, 0.04 #2881, 0.03 #4963), 01pg1d (0.06 #1816, 0.03 #5980), 0p8r1 (0.06 #11002, 0.04 #17257, 0.04 #21421), 02w29z (0.06 #3495, 0.03 #18085, 0.02 #26413), 0bxtg (0.06 #27158, 0.04 #76, 0.03 #10493) >> Best rule #56242 for best value: >> intensional similarity = 4 >> extensional distance = 836 >> proper extension: 09rvwmy; >> query: (?x6394, ?x4371) <- genre(?x6394, ?x53), nominated_for(?x4371, ?x6394), film_crew_role(?x6394, ?x1284), film(?x4371, ?x365) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #3328 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 69 *> proper extension: 087wc7n; *> query: (?x6394, 03kbb8) <- film_release_region(?x6394, ?x7747), film_release_region(?x6394, ?x1499), film_release_region(?x6394, ?x1264), ?x1264 = 0345h, ?x1499 = 01znc_, ?x7747 = 07f1x *> conf = 0.01 ranks of expected_values: 570 EVAL 0cmdwwg film! 018fwv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 73.000 43.000 0.725 http://example.org/film/actor/film./film/performance/film EVAL 0cmdwwg film! 03kbb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 73.000 43.000 0.725 http://example.org/film/actor/film./film/performance/film #1030-05r3qc PRED entity: 05r3qc PRED relation: country PRED expected values: 0chghy => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 112): 07ssc (0.33 #376, 0.30 #256, 0.25 #436), 0345h (0.30 #267, 0.25 #387, 0.17 #207), 0f8l9c (0.12 #1520, 0.12 #559, 0.11 #619), 03rt9 (0.10 #254, 0.04 #674, 0.03 #434), 015fr (0.10 #257, 0.03 #437, 0.03 #3968), 06mkj (0.10 #340, 0.03 #640, 0.03 #3968), 03_3d (0.08 #367, 0.08 #727, 0.07 #847), 0chghy (0.08 #372, 0.06 #432, 0.06 #492), 03k9fj (0.06 #2944, 0.06 #3366, 0.06 #3427), 0d060g (0.05 #1148, 0.04 #1449, 0.04 #2411) >> Best rule #376 for best value: >> intensional similarity = 5 >> extensional distance = 10 >> proper extension: 09txzv; 07j8r; 0sxmx; 077q8x; 07l450; >> query: (?x6167, 07ssc) <- film(?x1365, ?x6167), genre(?x6167, ?x53), nominated_for(?x1365, ?x6169), ?x6169 = 077q8x, award(?x1365, ?x198) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #372 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 09txzv; 07j8r; 0sxmx; 077q8x; 07l450; *> query: (?x6167, 0chghy) <- film(?x1365, ?x6167), genre(?x6167, ?x53), nominated_for(?x1365, ?x6169), ?x6169 = 077q8x, award(?x1365, ?x198) *> conf = 0.08 ranks of expected_values: 8 EVAL 05r3qc country 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 77.000 77.000 0.333 http://example.org/film/film/country #1029-012ljv PRED entity: 012ljv PRED relation: category PRED expected values: 08mbj5d => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.79 #27, 0.79 #28, 0.76 #17) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 586 >> proper extension: 089tm; 01pfr3; 0kzy0; 0152cw; 01v0sx2; 01j4ls; 01vsxdm; 01wp8w7; 01r9fv; 01bpc9; ... >> query: (?x84, 08mbj5d) <- award_winner(?x1443, ?x84), artists(?x4910, ?x84), award(?x460, ?x1443) >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012ljv category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.791 http://example.org/common/topic/webpage./common/webpage/category #1028-044gyq PRED entity: 044gyq PRED relation: artists! PRED expected values: 01fm07 => 120 concepts (68 used for prediction) PRED predicted values (max 10 best out of 210): 064t9 (0.49 #3414, 0.47 #5577, 0.44 #4650), 06by7 (0.42 #15176, 0.42 #8986, 0.41 #2495), 016jny (0.38 #723, 0.36 #1032, 0.33 #414), 0glt670 (0.36 #3443, 0.28 #5606, 0.22 #4370), 0155w (0.33 #107, 0.25 #725, 0.21 #2580), 02w4v (0.33 #46, 0.25 #664, 0.18 #973), 05bt6j (0.33 #45, 0.22 #15199, 0.21 #5609), 0y3_8 (0.33 #49, 0.18 #976, 0.17 #358), 029fbr (0.33 #491, 0.18 #1109, 0.12 #800), 0827d (0.33 #4, 0.17 #313, 0.12 #622) >> Best rule #3414 for best value: >> intensional similarity = 3 >> extensional distance = 149 >> proper extension: 0jfx1; 01r6jt2; 016jll; >> query: (?x3493, 064t9) <- award(?x3493, ?x567), origin(?x3493, ?x2254), people(?x2510, ?x3493) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #2599 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 07q1v4; 01pbs9w; 02sjp; *> query: (?x3493, 01fm07) <- artists(?x505, ?x3493), ?x505 = 03_d0, award_winner(?x12835, ?x3493) *> conf = 0.08 ranks of expected_values: 58 EVAL 044gyq artists! 01fm07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 120.000 68.000 0.490 http://example.org/music/genre/artists #1027-027m67 PRED entity: 027m67 PRED relation: film! PRED expected values: 0139q5 => 98 concepts (66 used for prediction) PRED predicted values (max 10 best out of 1034): 02404v (0.50 #135404, 0.49 #122908, 0.48 #20829), 03cp7b3 (0.48 #20829, 0.47 #39581, 0.45 #97910), 01f7v_ (0.48 #20829, 0.47 #39581, 0.45 #97910), 0479b (0.33 #1211, 0.03 #19957, 0.02 #13707), 012q4n (0.33 #1138, 0.02 #9466, 0.01 #61546), 02f2dn (0.33 #449, 0.02 #25444, 0.01 #27527), 014x77 (0.33 #92, 0.01 #18838, 0.01 #33422), 02d45s (0.33 #1819), 0139q5 (0.29 #3761, 0.05 #10007, 0.03 #16258), 0jlv5 (0.14 #3263, 0.05 #7427, 0.03 #5345) >> Best rule #135404 for best value: >> intensional similarity = 3 >> extensional distance = 1051 >> proper extension: 01vrwfv; 01h1bf; 05gnf; 05fgr_; 06dfz1; 01b7h8; 03_b1g; 06ys2; >> query: (?x7293, ?x7740) <- nominated_for(?x7740, ?x7293), award_nominee(?x7740, ?x7739), people(?x11067, ?x7740) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3761 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0fjyzt; *> query: (?x7293, 0139q5) <- language(?x7293, ?x254), cinematography(?x7293, ?x7740), genre(?x7293, ?x53), ?x7740 = 02404v *> conf = 0.29 ranks of expected_values: 9 EVAL 027m67 film! 0139q5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 98.000 66.000 0.498 http://example.org/film/actor/film./film/performance/film #1026-0h7x PRED entity: 0h7x PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 198 concepts (198 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.89 #51, 0.88 #113, 0.88 #18) >> Best rule #51 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 01z215; 07fj_; 0l3h; 04xn_; 016zwt; 0164v; >> query: (?x1355, 0hzc9wc) <- participating_countries(?x784, ?x1355), adjoins(?x756, ?x1355), nationality(?x681, ?x1355) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h7x administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 198.000 0.887 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #1025-0j63cyr PRED entity: 0j63cyr PRED relation: film_festivals! PRED expected values: 0ddbjy4 => 63 concepts (20 used for prediction) PRED predicted values (max 10 best out of 605): 0qmfk (0.82 #1565, 0.81 #1341, 0.80 #895), 0gyh2wm (0.82 #1565, 0.81 #1341, 0.80 #895), 0gvt53w (0.82 #1565, 0.81 #1341, 0.80 #895), 0462hhb (0.25 #1001, 0.25 #778, 0.23 #1224), 0ddfwj1 (0.25 #679, 0.23 #1125, 0.21 #1348), 047p798 (0.17 #1103, 0.17 #880, 0.17 #208), 0g5qmbz (0.17 #1093, 0.17 #870, 0.17 #198), 0g9zljd (0.17 #1041, 0.17 #818, 0.17 #146), 0b76d_m (0.17 #896, 0.17 #673, 0.17 #1), 0ds6bmk (0.17 #824, 0.17 #152, 0.15 #1270) >> Best rule #1565 for best value: >> intensional similarity = 9 >> extensional distance = 12 >> proper extension: 09rwjly; >> query: (?x4903, ?x3958) <- film_festivals(?x2093, ?x4903), film_festivals(?x1927, ?x4903), film_regional_debut_venue(?x3958, ?x4903), films(?x11523, ?x1927), film_release_region(?x2093, ?x583), film_release_region(?x3088, ?x583), film_release_region(?x1547, ?x583), ?x1547 = 0168ls, ?x3088 = 06w839_ >> conf = 0.82 => this is the best rule for 3 predicted values *> Best rule #1564 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 12 *> proper extension: 09rwjly; *> query: (?x4903, ?x66) <- film_festivals(?x2093, ?x4903), film_festivals(?x1927, ?x4903), film_regional_debut_venue(?x3958, ?x4903), films(?x11523, ?x1927), film_release_region(?x2093, ?x583), film_release_region(?x3088, ?x583), film_release_region(?x1547, ?x583), film_release_region(?x66, ?x583), ?x1547 = 0168ls, ?x3088 = 06w839_ *> conf = 0.02 ranks of expected_values: 274 EVAL 0j63cyr film_festivals! 0ddbjy4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 63.000 20.000 0.816 http://example.org/film/film/film_festivals #1024-0283d PRED entity: 0283d PRED relation: parent_genre PRED expected values: 0190yn => 71 concepts (50 used for prediction) PRED predicted values (max 10 best out of 290): 06by7 (0.72 #4223, 0.67 #4689, 0.58 #6252), 03mb9 (0.57 #524, 0.22 #681, 0.20 #834), 05r6t (0.55 #5037, 0.33 #201, 0.32 #1759), 0mmp3 (0.50 #369, 0.33 #680, 0.30 #833), 06j6l (0.43 #1586, 0.38 #3302, 0.36 #2522), 016clz (0.36 #1714, 0.35 #2027, 0.20 #4992), 016_rm (0.33 #277, 0.29 #1052, 0.28 #2771), 016_nr (0.33 #194, 0.21 #2378, 0.20 #3428), 05w3f (0.33 #175, 0.20 #3428, 0.20 #4052), 03lty (0.33 #168, 0.16 #7505, 0.16 #6098) >> Best rule #4223 for best value: >> intensional similarity = 12 >> extensional distance = 52 >> proper extension: 016clz; 0m0jc; 015pdg; 016jhr; 0xhtw; 0dl5d; 0mhfr; 03lty; 05w3f; 01243b; ... >> query: (?x7280, 06by7) <- parent_genre(?x7280, ?x2937), parent_genre(?x3232, ?x7280), artists(?x7280, ?x1732), artists(?x2937, ?x11123), artists(?x2937, ?x8185), artists(?x2937, ?x6162), artists(?x2937, ?x2690), ?x6162 = 01w9wwg, origin(?x8185, ?x3501), ?x2690 = 0892sx, award_nominee(?x8185, ?x4593), profession(?x11123, ?x1032) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1050 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 12 *> proper extension: 029h7y; 016_nr; 01d_s5; 01738f; 01flzq; 0hh2s; 012yc; 016_rm; 01y2mq; 0190zg; *> query: (?x7280, 0190yn) <- parent_genre(?x7280, ?x2937), parent_genre(?x7280, ?x497), parent_genre(?x3232, ?x7280), artists(?x7280, ?x8636), ?x2937 = 0glt670, artists(?x497, ?x4701), parent_genre(?x497, ?x2808), award(?x4701, ?x567), profession(?x8636, ?x1183) *> conf = 0.21 ranks of expected_values: 20 EVAL 0283d parent_genre 0190yn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 71.000 50.000 0.722 http://example.org/music/genre/parent_genre #1023-0854hr PRED entity: 0854hr PRED relation: profession PRED expected values: 0dgd_ => 73 concepts (72 used for prediction) PRED predicted values (max 10 best out of 144): 0dgd_ (0.89 #1532, 0.88 #1683, 0.88 #1833), 02jknp (0.84 #1358, 0.50 #308, 0.45 #2109), 01d_h8 (0.80 #1356, 0.54 #2107, 0.50 #306), 02hrh1q (0.78 #4666, 0.77 #5116, 0.77 #5416), 0dxtg (0.64 #1364, 0.41 #2115, 0.33 #2565), 03gjzk (0.42 #1366, 0.25 #2567, 0.25 #4517), 0cbd2 (0.25 #307, 0.18 #1357, 0.14 #7659), 0nbcg (0.25 #33, 0.14 #1233, 0.14 #1083), 01c8w0 (0.25 #9, 0.04 #3460, 0.04 #2710), 02pjxr (0.25 #35, 0.03 #3486, 0.02 #2736) >> Best rule #1532 for best value: >> intensional similarity = 5 >> extensional distance = 45 >> proper extension: 04qvl7; 0f3zf_; 0gp9mp; 079hvk; 05dppk; 0dqzkv; 07xr3w; 07mb57; 06g60w; 03cx282; ... >> query: (?x5389, 0dgd_) <- gender(?x5389, ?x231), cinematography(?x8000, ?x5389), nominated_for(?x5389, ?x6213), film(?x398, ?x8000), titles(?x3506, ?x8000) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0854hr profession 0dgd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 72.000 0.894 http://example.org/people/person/profession #1022-0qkyj PRED entity: 0qkyj PRED relation: category PRED expected values: 08mbj5d => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.78 #37, 0.76 #23, 0.75 #16) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 470 >> proper extension: 010bnr; >> query: (?x12247, 08mbj5d) <- source(?x12247, ?x958), ?x958 = 0jbk9, place(?x12247, ?x12247) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qkyj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.778 http://example.org/common/topic/webpage./common/webpage/category #1021-0mnm2 PRED entity: 0mnm2 PRED relation: contains! PRED expected values: 0mpdw => 108 concepts (56 used for prediction) PRED predicted values (max 10 best out of 237): 09c7w0 (0.96 #14343, 0.70 #38548, 0.67 #36759), 020d5 (0.50 #17923, 0.46 #14340, 0.43 #11648), 01n7q (0.42 #15314, 0.28 #14418, 0.25 #13520), 0mnm2 (0.36 #36756, 0.35 #30477, 0.19 #35857), 04_1l0v (0.32 #12995, 0.21 #34513, 0.21 #37206), 07ssc (0.29 #2720, 0.26 #1824, 0.17 #4513), 059rby (0.19 #20631, 0.18 #10751, 0.17 #30497), 05k7sb (0.19 #5510, 0.14 #16263, 0.07 #35091), 02jx1 (0.16 #1879, 0.14 #2775, 0.13 #4568), 06pvr (0.14 #165, 0.10 #2853, 0.09 #4646) >> Best rule #14343 for best value: >> intensional similarity = 4 >> extensional distance = 79 >> proper extension: 02xry; 05tbn; 0lhql; 0b2ds; 0qpqn; 013m4v; >> query: (?x7548, 09c7w0) <- place_of_death(?x10013, ?x7548), contains(?x1426, ?x7548), contains(?x1426, ?x347), ?x347 = 04wlz2 >> conf = 0.96 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0mnm2 contains! 0mpdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 56.000 0.963 http://example.org/location/location/contains #1020-08zrbl PRED entity: 08zrbl PRED relation: nominated_for! PRED expected values: 0gr4k 0gs9p 099ck7 => 131 concepts (120 used for prediction) PRED predicted values (max 10 best out of 210): 099ck7 (0.68 #8783, 0.68 #8998, 0.68 #8782), 0fhpv4 (0.68 #8783, 0.68 #8998, 0.68 #8782), 02w_6xj (0.68 #8783, 0.68 #8998, 0.68 #8782), 02qyntr (0.56 #1655, 0.55 #2083, 0.33 #156), 0p9sw (0.51 #1515, 0.47 #4084, 0.45 #1943), 02hsq3m (0.44 #1950, 0.40 #1522, 0.35 #879), 019f4v (0.40 #1545, 0.37 #1973, 0.34 #5612), 0gs9p (0.38 #1551, 0.36 #1979, 0.34 #5618), 02x17s4 (0.38 #294, 0.22 #1365, 0.17 #722), 04dn09n (0.33 #29, 0.32 #5595, 0.29 #2998) >> Best rule #8783 for best value: >> intensional similarity = 4 >> extensional distance = 364 >> proper extension: 061681; 047n8xt; 0sxkh; 01s3vk; >> query: (?x7911, ?x6729) <- film_crew_role(?x7911, ?x1284), award(?x7911, ?x6729), ?x1284 = 0ch6mp2, nominated_for(?x6729, ?x253) >> conf = 0.68 => this is the best rule for 3 predicted values ranks of expected_values: 1, 8, 24 EVAL 08zrbl nominated_for! 099ck7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 120.000 0.680 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 08zrbl nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 131.000 120.000 0.680 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 08zrbl nominated_for! 0gr4k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 131.000 120.000 0.680 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #1019-05pdd86 PRED entity: 05pdd86 PRED relation: film_crew_role PRED expected values: 0ch6mp2 015h31 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.89 #209, 0.85 #2204, 0.82 #1824), 015h31 (0.60 #7, 0.59 #35, 0.54 #94), 0dxtw (0.51 #848, 0.48 #269, 0.46 #818), 02_n3z (0.43 #117, 0.41 #88, 0.35 #204), 02rh1dz (0.33 #239, 0.24 #325, 0.21 #847), 02ynfr (0.23 #358, 0.22 #272, 0.21 #1256), 0263ycg (0.20 #14, 0.18 #42, 0.18 #274), 02zdwq (0.18 #45, 0.16 #104, 0.16 #75), 094hwz (0.18 #39, 0.15 #242, 0.14 #1186), 089fss (0.16 #380, 0.15 #265, 0.14 #1186) >> Best rule #209 for best value: >> intensional similarity = 7 >> extensional distance = 69 >> proper extension: 01gglm; >> query: (?x6110, 0ch6mp2) <- currency(?x6110, ?x170), film_crew_role(?x6110, ?x5136), film_crew_role(?x6110, ?x137), ?x170 = 09nqf, ?x137 = 09zzb8, ?x5136 = 089g0h, language(?x6110, ?x254) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05pdd86 film_crew_role 015h31 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.887 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 05pdd86 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.887 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #1018-014l7h PRED entity: 014l7h PRED relation: company PRED expected values: 0g5lhl7 0152x_ 01y81r 017vb_ 06v99d 049rl0 => 30 concepts (30 used for prediction) PRED predicted values (max 10 best out of 959): 0300cp (0.75 #1676, 0.72 #975, 0.67 #3309), 060ppp (0.72 #975, 0.67 #3502, 0.67 #2521), 01qygl (0.72 #975, 0.67 #2470, 0.62 #1818), 087c7 (0.72 #975, 0.67 #2287, 0.62 #1635), 01s73z (0.72 #975, 0.67 #2386, 0.62 #1734), 019rl6 (0.72 #975, 0.62 #1783, 0.60 #804), 0z90c (0.72 #975, 0.62 #1794, 0.60 #815), 0vlf (0.72 #975, 0.62 #1913, 0.60 #934), 0537b (0.72 #975, 0.62 #1768, 0.60 #789), 07gyp7 (0.72 #975, 0.62 #1940, 0.60 #961) >> Best rule #1676 for best value: >> intensional similarity = 12 >> extensional distance = 6 >> proper extension: 01yc02; >> query: (?x8314, 0300cp) <- company(?x8314, ?x6678), company(?x8314, ?x1762), company(?x346, ?x1762), citytown(?x1762, ?x739), award_winner(?x10731, ?x1762), award_winner(?x1762, ?x1394), nominated_for(?x6678, ?x337), award(?x10731, ?x757), child(?x10808, ?x6678), nominated_for(?x1762, ?x782), nominated_for(?x678, ?x10731), ?x346 = 060c4 >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1357 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 5 *> proper extension: 0fkvn; 09d6p2; *> query: (?x8314, 0g5lhl7) <- company(?x8314, ?x5007), company(?x8314, ?x1762), company(?x265, ?x1762), award_winner(?x5592, ?x1762), award_winner(?x2078, ?x1762), program(?x1762, ?x50), award_winner(?x3486, ?x1762), company(?x265, ?x8641), jurisdiction_of_office(?x265, ?x142), award_winner(?x5007, ?x2776), program(?x14290, ?x2078), ?x8641 = 03y7ml *> conf = 0.29 ranks of expected_values: 198, 346 EVAL 014l7h company 049rl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 30.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 014l7h company 06v99d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 30.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 014l7h company 017vb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 30.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 014l7h company 01y81r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 30.000 30.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 014l7h company 0152x_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 30.000 30.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 014l7h company 0g5lhl7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 30.000 30.000 0.750 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #1017-0dbc1s PRED entity: 0dbc1s PRED relation: gender PRED expected values: 05zppz => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.85 #29, 0.82 #13, 0.81 #23), 02zsn (0.29 #44, 0.28 #50, 0.28 #46) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 374 >> proper extension: 024c1b; >> query: (?x7002, 05zppz) <- produced_by(?x7311, ?x7002), genre(?x7311, ?x258), language(?x7311, ?x254) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dbc1s gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.846 http://example.org/people/person/gender #1016-04qvl7 PRED entity: 04qvl7 PRED relation: award_winner! PRED expected values: 09gkdln => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 134): 03gyp30 (0.44 #117, 0.19 #706, 0.18 #9450), 02wzl1d (0.33 #11, 0.19 #706, 0.18 #9450), 02pgky2 (0.22 #90, 0.19 #706, 0.18 #9450), 03gwpw2 (0.22 #9, 0.19 #706, 0.18 #9450), 02yxh9 (0.22 #101, 0.19 #706, 0.18 #9450), 03gt46z (0.22 #63, 0.19 #706, 0.18 #9450), 0hr3c8y (0.20 #151, 0.11 #10, 0.04 #2126), 04n2r9h (0.19 #706, 0.18 #9450, 0.10 #186), 0418154 (0.19 #706, 0.18 #9450, 0.10 #249), 0n8_m93 (0.19 #706, 0.18 #9450, 0.03 #541) >> Best rule #117 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0fvf9q; 02kxbwx; 01713c; 02tr7d; 0170s4; 02kxbx3; 01kwsg; >> query: (?x185, 03gyp30) <- award_winner(?x2393, ?x185), award_winner(?x945, ?x185), ?x945 = 0b6tzs >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #9450 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1493 *> proper extension: 0cnl80; 069ld1; 0783m_; 01wk7b7; 0277990; 07_s4b; 027xbpw; 05mkhs; 04crrxr; 015dcj; ... *> query: (?x185, ?x2988) <- nominated_for(?x185, ?x1753), gender(?x185, ?x231), honored_for(?x2988, ?x1753) *> conf = 0.18 ranks of expected_values: 18 EVAL 04qvl7 award_winner! 09gkdln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 122.000 122.000 0.444 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1015-01jc6q PRED entity: 01jc6q PRED relation: honored_for! PRED expected values: 0bzkgg => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 114): 0bzkgg (0.09 #7938, 0.05 #35, 0.02 #523), 02wzl1d (0.08 #129, 0.04 #983, 0.02 #495), 04n2r9h (0.07 #524, 0.03 #1500, 0.03 #1622), 073hgx (0.05 #82, 0.04 #204, 0.02 #570), 073h1t (0.05 #21, 0.03 #265, 0.03 #387), 0bzm81 (0.05 #16, 0.03 #260, 0.03 #382), 0drtv8 (0.05 #55, 0.03 #299, 0.03 #421), 092c5f (0.05 #10, 0.02 #498, 0.02 #620), 07z31v (0.05 #25, 0.01 #3197, 0.01 #3320), 073h9x (0.05 #528, 0.04 #162, 0.02 #3419) >> Best rule #7938 for best value: >> intensional similarity = 4 >> extensional distance = 1301 >> proper extension: 04bp0l; >> query: (?x197, ?x2822) <- nominated_for(?x10146, ?x197), nominated_for(?x1034, ?x197), award_winner(?x1255, ?x10146), award_winner(?x2822, ?x1034) >> conf = 0.09 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01jc6q honored_for! 0bzkgg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.086 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #1014-02qw1zx PRED entity: 02qw1zx PRED relation: draft! PRED expected values: 01y3c 05tfm 01y3v 05g49 0ws7 01c_d => 18 concepts (18 used for prediction) PRED predicted values (max 10 best out of 377): 05g49 (0.61 #641, 0.60 #760, 0.60 #879), 05tfm (0.61 #641, 0.60 #739, 0.60 #879), 01y3c (0.61 #641, 0.60 #734, 0.60 #879), 0ws7 (0.61 #641, 0.60 #770, 0.60 #879), 03b3j (0.61 #641, 0.60 #751, 0.60 #879), 051q5 (0.61 #641, 0.60 #754, 0.60 #879), 03wnh (0.61 #641, 0.60 #764, 0.60 #879), 01y3v (0.61 #641, 0.60 #879, 0.59 #1197), 01xvb (0.61 #641, 0.60 #879, 0.59 #1197), 01c_d (0.61 #641, 0.60 #879, 0.59 #1197) >> Best rule #641 for best value: >> intensional similarity = 53 >> extensional distance = 3 >> proper extension: 02r6gw6; >> query: (?x1883, ?x1115) <- draft(?x9172, ?x1883), draft(?x7643, ?x1883), draft(?x5773, ?x1883), category(?x7643, ?x134), school(?x1883, ?x8120), school(?x1883, ?x5486), school(?x1883, ?x4296), school(?x1883, ?x2959), school(?x1883, ?x1506), ?x134 = 08mbj5d, school(?x7643, ?x1011), school(?x9172, ?x9847), school(?x9172, ?x4410), team(?x180, ?x9172), ?x9847 = 0187nd, sport(?x9172, ?x1083), currency(?x1506, ?x170), institution(?x620, ?x5486), colors(?x2959, ?x332), school_type(?x2959, ?x1507), school(?x2067, ?x1506), major_field_of_study(?x5486, ?x9079), major_field_of_study(?x5486, ?x6756), major_field_of_study(?x5486, ?x3490), major_field_of_study(?x5486, ?x2014), school(?x5773, ?x581), major_field_of_study(?x9079, ?x732), colors(?x7643, ?x663), ?x3490 = 05qfh, position(?x2067, ?x2066), position(?x2067, ?x261), major_field_of_study(?x2313, ?x9079), ?x2313 = 07wrz, ?x2014 = 04rjg, ?x8120 = 01rc6f, team(?x5412, ?x5773), athlete(?x1083, ?x445), sport(?x1115, ?x1083), ?x6756 = 0_jm, contains(?x94, ?x1506), ?x261 = 02dwn9, ?x2066 = 02s7tr, teams(?x739, ?x2067), student(?x4296, ?x7381), films(?x1083, ?x3081), major_field_of_study(?x4296, ?x5614), major_field_of_study(?x4296, ?x3213), ?x5412 = 03n69x, ?x3213 = 0g4gr, student(?x4410, ?x510), team(?x12323, ?x2067), film(?x7381, ?x463), student(?x5614, ?x396) >> conf = 0.61 => this is the best rule for 18 predicted values ranks of expected_values: 1, 2, 3, 4, 8, 10 EVAL 02qw1zx draft! 01c_d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 18.000 18.000 0.613 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02qw1zx draft! 0ws7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 18.000 18.000 0.613 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02qw1zx draft! 05g49 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 18.000 18.000 0.613 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02qw1zx draft! 01y3v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 18.000 18.000 0.613 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02qw1zx draft! 05tfm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 18.000 18.000 0.613 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft EVAL 02qw1zx draft! 01y3c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 18.000 18.000 0.613 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #1013-04t7ts PRED entity: 04t7ts PRED relation: award PRED expected values: 02x4w6g => 71 concepts (56 used for prediction) PRED predicted values (max 10 best out of 276): 01by1l (0.21 #3352, 0.10 #2947, 0.09 #2542), 05zr6wv (0.21 #422, 0.20 #827, 0.13 #9319), 02x73k6 (0.21 #465, 0.15 #3646, 0.15 #16207), 0gqy2 (0.21 #570, 0.13 #9319, 0.11 #7698), 0gqyl (0.17 #510, 0.13 #9319, 0.11 #7698), 094qd5 (0.17 #449, 0.13 #9319, 0.11 #7698), 03qgjwc (0.17 #589, 0.13 #9319, 0.11 #7698), 09qwmm (0.17 #439, 0.13 #9319, 0.11 #7698), 05p09zm (0.16 #934, 0.15 #3646, 0.15 #16207), 0f4x7 (0.16 #841, 0.13 #9319, 0.12 #436) >> Best rule #3352 for best value: >> intensional similarity = 4 >> extensional distance = 696 >> proper extension: 012ljv; 0411q; 015rmq; 0280mv7; 016ppr; >> query: (?x1324, 01by1l) <- award_nominee(?x3293, ?x1324), award_nominee(?x1250, ?x1324), award_winner(?x458, ?x1250), artist(?x6474, ?x3293) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #3646 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 696 *> proper extension: 012ljv; 0411q; 015rmq; 0280mv7; 016ppr; *> query: (?x1324, ?x458) <- award_nominee(?x3293, ?x1324), award_nominee(?x1250, ?x1324), award_winner(?x458, ?x1250), artist(?x6474, ?x3293) *> conf = 0.15 ranks of expected_values: 28 EVAL 04t7ts award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 71.000 56.000 0.213 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #1012-0lzcs PRED entity: 0lzcs PRED relation: type_of_union PRED expected values: 04ztj => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.91 #109, 0.88 #73, 0.85 #134), 01g63y (0.25 #473, 0.17 #90, 0.16 #215), 0jgjn (0.25 #473, 0.01 #233, 0.01 #221) >> Best rule #109 for best value: >> intensional similarity = 6 >> extensional distance = 42 >> proper extension: 08f3b1; 0bwh6; 01k165; 0d06m5; 09bg4l; 02xfrd; 0d05fv; 0d3qd0; 0dq2k; 012gx2; ... >> query: (?x11411, 04ztj) <- profession(?x11411, ?x12212), location(?x11411, ?x4510), nationality(?x11411, ?x512), profession(?x9680, ?x12212), politician(?x10498, ?x11411), ?x9680 = 0948xk >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0lzcs type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 131.000 0.909 http://example.org/people/person/spouse_s./people/marriage/type_of_union #1011-01mh_q PRED entity: 01mh_q PRED relation: award_winner PRED expected values: 01kwlwp 07z542 025l5 01k_mc 02g1jh 07r1_ => 37 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1190): 01vw20h (0.64 #18772, 0.60 #17261, 0.57 #20281), 01lmj3q (0.60 #16614, 0.57 #13598, 0.55 #18125), 0x3b7 (0.50 #11181, 0.50 #9674, 0.50 #6661), 02cx90 (0.50 #20250, 0.50 #17230, 0.45 #18741), 06fmdb (0.50 #9836, 0.50 #5316, 0.43 #14355), 03cfjg (0.50 #6531, 0.50 #5024, 0.40 #8036), 0ggjt (0.50 #11006, 0.50 #4979, 0.33 #12513), 0161sp (0.50 #9461, 0.50 #4941, 0.33 #10968), 018ndc (0.50 #12508, 0.50 #3461, 0.25 #6481), 0fpjd_g (0.50 #19807, 0.45 #18298, 0.44 #15278) >> Best rule #18772 for best value: >> intensional similarity = 24 >> extensional distance = 9 >> proper extension: 0466p0j; >> query: (?x6487, 01vw20h) <- award_winner(?x6487, ?x6384), award_winner(?x6487, ?x3403), ceremony(?x8076, ?x6487), ceremony(?x7691, ?x6487), ceremony(?x7594, ?x6487), ceremony(?x3903, ?x6487), ceremony(?x2561, ?x6487), ceremony(?x1479, ?x6487), ceremony(?x594, ?x6487), ceremony(?x567, ?x6487), ?x7594 = 02v703, ?x3903 = 024vjd, ?x2561 = 02hgm4, ?x7691 = 026m9w, ?x567 = 01d38g, award_nominee(?x3403, ?x2876), profession(?x3403, ?x131), artists(?x671, ?x6384), profession(?x2876, ?x2348), artists(?x378, ?x2876), artist(?x2931, ?x3403), ?x594 = 02grdc, ?x8076 = 026mml, award(?x1152, ?x1479) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #12183 for first EXPECTED value: *> intensional similarity = 28 *> extensional distance = 4 *> proper extension: 019bk0; *> query: (?x6487, 01kwlwp) <- award_winner(?x6487, ?x11026), ceremony(?x12940, ?x6487), ceremony(?x12833, ?x6487), ceremony(?x11456, ?x6487), ceremony(?x11068, ?x6487), ceremony(?x9462, ?x6487), ceremony(?x8505, ?x6487), ceremony(?x8076, ?x6487), ceremony(?x7594, ?x6487), ceremony(?x4912, ?x6487), ceremony(?x4018, ?x6487), ceremony(?x3647, ?x6487), ceremony(?x3103, ?x6487), ceremony(?x567, ?x6487), ?x7594 = 02v703, ?x4018 = 03qbh5, ?x8505 = 02fm4d, ?x8076 = 026mml, ?x11456 = 03q27t, ?x4912 = 01ckrr, ?x12940 = 02gm9n, ?x9462 = 01d38t, ?x3647 = 01c9jp, ?x12833 = 0257pw, ?x3103 = 03tcnt, ?x11068 = 02x4wb, location(?x11026, ?x3501), ?x567 = 01d38g *> conf = 0.33 ranks of expected_values: 52, 73, 251, 484, 499, 549 EVAL 01mh_q award_winner 07r1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 18.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01mh_q award_winner 02g1jh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 18.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01mh_q award_winner 01k_mc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 37.000 18.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01mh_q award_winner 025l5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 37.000 18.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01mh_q award_winner 07z542 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 37.000 18.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 01mh_q award_winner 01kwlwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 37.000 18.000 0.636 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #1010-0mwl2 PRED entity: 0mwl2 PRED relation: adjoins! PRED expected values: 0mwh1 => 150 concepts (66 used for prediction) PRED predicted values (max 10 best out of 514): 0mwh1 (0.91 #2346, 0.82 #38362, 0.82 #18779), 0l3n4 (0.29 #2753, 0.25 #407, 0.25 #40709), 0n57k (0.26 #20346, 0.26 #46193, 0.25 #46191), 0mwl2 (0.26 #20346, 0.26 #46193, 0.25 #46191), 0mwvq (0.26 #20346, 0.25 #447, 0.25 #40709), 0dclg (0.26 #20346, 0.25 #46191, 0.25 #40709), 0n5jm (0.26 #20346, 0.25 #46191, 0.25 #40709), 0n5fl (0.26 #20346, 0.25 #46191, 0.25 #40709), 0n5dt (0.26 #20346, 0.25 #46191, 0.25 #40709), 0mws3 (0.26 #20346, 0.25 #40709, 0.25 #43842) >> Best rule #2346 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 0mwxz; >> query: (?x855, ?x2744) <- adjoins(?x13414, ?x855), adjoins(?x4202, ?x855), adjoins(?x855, ?x2744), ?x4202 = 0fxyd, time_zones(?x855, ?x2674), adjoins(?x4358, ?x13414) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mwl2 adjoins! 0mwh1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 66.000 0.909 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #1009-05q7874 PRED entity: 05q7874 PRED relation: language PRED expected values: 02h40lc => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 48): 02h40lc (0.91 #415, 0.90 #356, 0.90 #179), 064_8sq (0.19 #553, 0.16 #732, 0.14 #850), 06nm1 (0.14 #542, 0.14 #247, 0.12 #188), 02bjrlw (0.12 #414, 0.11 #1, 0.08 #1544), 04306rv (0.11 #1309, 0.11 #5, 0.11 #1850), 0653m (0.11 #12, 0.04 #189, 0.04 #1494), 0349s (0.11 #45, 0.02 #163, 0.02 #1111), 06b_j (0.09 #554, 0.08 #1148, 0.07 #1327), 03_9r (0.07 #69, 0.05 #305, 0.04 #482), 03k50 (0.06 #245, 0.03 #658, 0.02 #2751) >> Best rule #415 for best value: >> intensional similarity = 4 >> extensional distance = 64 >> proper extension: 059lwy; >> query: (?x6103, 02h40lc) <- nominated_for(?x1336, ?x6103), film(?x3842, ?x6103), honored_for(?x6103, ?x1721), featured_film_locations(?x6103, ?x1523) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05q7874 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.909 http://example.org/film/film/language #1008-0qpjt PRED entity: 0qpjt PRED relation: featured_film_locations! PRED expected values: 0h1x5f => 82 concepts (63 used for prediction) PRED predicted values (max 10 best out of 135): 0kbwb (0.25 #1381, 0.08 #2855, 0.06 #3592), 093l8p (0.25 #1295, 0.04 #2769, 0.04 #4243), 0192hw (0.08 #2444, 0.06 #3181, 0.05 #13267), 08nhfc1 (0.08 #2771, 0.06 #3508, 0.02 #4245), 09fc83 (0.06 #3329, 0.06 #4066, 0.02 #6277), 0btbyn (0.06 #3232, 0.04 #2495, 0.04 #3969), 02_nsc (0.05 #13267, 0.04 #2865, 0.03 #3602), 05q4y12 (0.05 #13267, 0.04 #2415, 0.03 #3152), 05z7c (0.05 #13267, 0.04 #2359, 0.03 #3096), 0jqp3 (0.04 #2281, 0.04 #3755, 0.03 #3018) >> Best rule #1381 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0qpqn; >> query: (?x9010, 0kbwb) <- contains(?x7409, ?x9010), contains(?x938, ?x9010), ?x7409 = 0m27n, time_zones(?x9010, ?x2088), jurisdiction_of_office(?x1195, ?x9010), geographic_distribution(?x1176, ?x938) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0qpjt featured_film_locations! 0h1x5f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 82.000 63.000 0.250 http://example.org/film/film/featured_film_locations #1007-05fjy PRED entity: 05fjy PRED relation: featured_film_locations! PRED expected values: 0cc5mcj => 176 concepts (160 used for prediction) PRED predicted values (max 10 best out of 668): 061681 (0.10 #1521, 0.07 #7418, 0.07 #28055), 04j14qc (0.10 #2075, 0.06 #4286, 0.06 #5023), 03hkch7 (0.10 #1700, 0.06 #3911, 0.06 #4648), 03k8th (0.07 #8076, 0.06 #14709, 0.05 #2179), 047csmy (0.06 #4081, 0.06 #4818, 0.05 #1870), 04fzfj (0.06 #3730, 0.06 #4467, 0.05 #1519), 072x7s (0.06 #3798, 0.06 #4535, 0.05 #1587), 0g5pvv (0.06 #4134, 0.06 #4871, 0.05 #1923), 07kdkfj (0.06 #4252, 0.06 #4989, 0.05 #2041), 04dsnp (0.06 #28074, 0.05 #1540, 0.04 #27337) >> Best rule #1521 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 0c82s; >> query: (?x5575, 061681) <- country(?x5575, ?x94), adjoins(?x5575, ?x938), vacationer(?x5575, ?x4284) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05fjy featured_film_locations! 0cc5mcj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 176.000 160.000 0.100 http://example.org/film/film/featured_film_locations #1006-03kdl PRED entity: 03kdl PRED relation: profession PRED expected values: 012t_z => 179 concepts (155 used for prediction) PRED predicted values (max 10 best out of 113): 0fj9f (0.87 #5096, 0.85 #4652, 0.85 #4948), 02hrh1q (0.73 #9796, 0.73 #9648, 0.72 #16910), 01d_h8 (0.50 #1787, 0.48 #2825, 0.44 #2973), 04gc2 (0.50 #191, 0.43 #4639, 0.40 #5675), 0cbd2 (0.47 #3122, 0.40 #10822, 0.40 #749), 0kyk (0.40 #10822, 0.40 #773, 0.33 #3146), 0dxtg (0.40 #10822, 0.35 #1647, 0.33 #2981), 012t_z (0.40 #10822, 0.33 #13, 0.30 #2832), 016fly (0.40 #10822, 0.29 #521, 0.25 #224), 0g0vx (0.40 #10822, 0.22 #702, 0.20 #2779) >> Best rule #5096 for best value: >> intensional similarity = 4 >> extensional distance = 60 >> proper extension: 041wm; >> query: (?x3615, 0fj9f) <- type_of_union(?x3615, ?x566), profession(?x3615, ?x8498), jurisdiction_of_office(?x3615, ?x94), ?x566 = 04ztj >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #10822 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 231 *> proper extension: 01pfkw; 0f5mdz; 013bd1; 03qncl3; 0kr7k; 03f0qd7; 0n839; *> query: (?x3615, ?x2225) <- company(?x3615, ?x94), profession(?x3615, ?x8498), company(?x5572, ?x94), profession(?x5572, ?x2225) *> conf = 0.40 ranks of expected_values: 8 EVAL 03kdl profession 012t_z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 179.000 155.000 0.871 http://example.org/people/person/profession #1005-0c9c0 PRED entity: 0c9c0 PRED relation: student! PRED expected values: 07tl0 07tg4 => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 226): 04b_46 (0.25 #227, 0.06 #3916, 0.05 #754), 0bwfn (0.12 #275, 0.11 #802, 0.09 #7126), 01mpwj (0.12 #107, 0.08 #1161, 0.07 #1688), 0g8rj (0.12 #176, 0.08 #1230, 0.07 #1757), 01qd_r (0.12 #281, 0.02 #4497, 0.02 #8713), 02g839 (0.12 #25, 0.02 #13727, 0.02 #2133), 01hc1j (0.12 #450, 0.01 #4139, 0.01 #5193), 03ksy (0.11 #1687, 0.08 #1160, 0.06 #9592), 065y4w7 (0.10 #2649, 0.08 #4230, 0.06 #5284), 053mhx (0.06 #3984, 0.04 #6092, 0.02 #5038) >> Best rule #227 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 02kxbwx; >> query: (?x2790, 04b_46) <- award_winner(?x2790, ?x262), written_by(?x5070, ?x2790), student(?x2605, ?x2790) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #4829 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 95 *> proper extension: 099bk; *> query: (?x2790, 07tg4) <- religion(?x2790, ?x7131), student(?x2605, ?x2790) *> conf = 0.04 ranks of expected_values: 35 EVAL 0c9c0 student! 07tg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 120.000 120.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 0c9c0 student! 07tl0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 120.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #1004-066yfh PRED entity: 066yfh PRED relation: producer_type PRED expected values: 0ckd1 => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.32 #11, 0.25 #5, 0.20 #2) >> Best rule #11 for best value: >> intensional similarity = 2 >> extensional distance = 629 >> proper extension: 06w33f8; 0f1vrl; 0m32_; 01jbx1; 01mt1fy; 04l19_; 01d5vk; 011lpr; >> query: (?x12274, 0ckd1) <- profession(?x12274, ?x1041), ?x1041 = 03gjzk >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 066yfh producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 75.000 75.000 0.320 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #1003-01q20 PRED entity: 01q20 PRED relation: form_of_government! PRED expected values: 05b4w 04g61 035hm => 6 concepts (6 used for prediction) PRED predicted values (max 10 best out of 510): 02lx0 (0.60 #445, 0.41 #394, 0.40 #584), 04hqz (0.60 #486, 0.40 #625, 0.33 #221), 04g61 (0.60 #600, 0.40 #461, 0.33 #196), 0hzlz (0.50 #283, 0.41 #393, 0.40 #555), 06dfg (0.50 #349, 0.40 #621, 0.40 #482), 04xn_ (0.41 #394, 0.41 #393, 0.40 #472), 07z5n (0.41 #394, 0.41 #393, 0.40 #438), 0f8l9c (0.41 #394, 0.41 #393, 0.40 #537), 0345h (0.41 #394, 0.41 #393, 0.40 #537), 02vzc (0.41 #394, 0.41 #393, 0.34 #668) >> Best rule #445 for best value: >> intensional similarity = 116 >> extensional distance = 3 >> proper extension: 06cx9; >> query: (?x6065, 02lx0) <- form_of_government(?x10183, ?x6065), form_of_government(?x7479, ?x6065), form_of_government(?x6401, ?x6065), form_of_government(?x4493, ?x6065), form_of_government(?x4221, ?x6065), form_of_government(?x3040, ?x6065), form_of_government(?x2756, ?x6065), form_of_government(?x1122, ?x6065), form_of_government(?x512, ?x6065), administrative_parent(?x6405, ?x6401), location_of_ceremony(?x4949, ?x6401), locations(?x9798, ?x6401), exported_to(?x6401, ?x205), nationality(?x10464, ?x6401), nationality(?x6258, ?x6401), nationality(?x1435, ?x6401), country(?x2885, ?x1122), country(?x2867, ?x1122), film_release_region(?x9002, ?x1122), film_release_region(?x8193, ?x1122), film_release_region(?x8137, ?x1122), film_release_region(?x7493, ?x1122), film_release_region(?x6543, ?x1122), film_release_region(?x4610, ?x1122), film_release_region(?x4514, ?x1122), film_release_region(?x4464, ?x1122), film_release_region(?x3565, ?x1122), film_release_region(?x2695, ?x1122), film_release_region(?x2628, ?x1122), film_release_region(?x2471, ?x1122), film_release_region(?x1904, ?x1122), film_release_region(?x1173, ?x1122), film_release_region(?x664, ?x1122), film_release_region(?x542, ?x1122), film_release_region(?x186, ?x1122), entity_involved(?x12777, ?x4493), ?x2628 = 06wbm8q, ?x664 = 0401sg, award_winner(?x931, ?x6258), contains(?x1144, ?x7479), ?x4464 = 05pdh86, ?x7493 = 0btpm6, gender(?x6258, ?x231), country(?x1557, ?x3040), participating_countries(?x418, ?x7479), taxonomy(?x3040, ?x939), ?x8193 = 03z9585, award(?x4949, ?x618), organization(?x3040, ?x1062), adjoins(?x1355, ?x3040), award_winner(?x1670, ?x6258), ?x2471 = 08052t3, contains(?x1122, ?x3106), olympics(?x6401, ?x2043), olympics(?x7479, ?x3110), film(?x4949, ?x204), time_zones(?x7479, ?x11506), olympics(?x7479, ?x7688), country(?x2867, ?x1497), country(?x2867, ?x608), location(?x4949, ?x739), geographic_distribution(?x1571, ?x1122), countries_spoken_in(?x254, ?x1122), olympics(?x1122, ?x2966), jurisdiction_of_office(?x3119, ?x6401), country(?x3287, ?x6401), ?x2695 = 047svrl, country(?x150, ?x205), location_of_ceremony(?x566, ?x6401), ?x608 = 02k54, ?x1173 = 0872p_c, form_of_government(?x205, ?x4763), participating_countries(?x2553, ?x205), currency(?x10183, ?x170), countries_within(?x455, ?x2756), profession(?x10464, ?x1032), adjoins(?x728, ?x1144), contains(?x6304, ?x2756), ?x8137 = 0gtx63s, film(?x274, ?x3565), olympics(?x3040, ?x5176), executive_produced_by(?x3565, ?x3896), film(?x7980, ?x6543), ?x1032 = 02hrh1q, ?x1497 = 015qh, olympics(?x2867, ?x778), ?x9002 = 0ndsl1x, featured_film_locations(?x6543, ?x12472), award_winner(?x1904, ?x3069), ?x231 = 05zppz, contains(?x6401, ?x4030), adjoins(?x1144, ?x6428), ?x5176 = 0sx92, administrative_area_type(?x1122, ?x2792), genre(?x6543, ?x53), administrative_parent(?x9969, ?x4221), olympics(?x2885, ?x391), sports(?x358, ?x2885), nationality(?x450, ?x4221), music(?x542, ?x3410), nominated_for(?x1435, ?x1434), ?x4610 = 017jd9, film(?x541, ?x3565), film_release_distribution_medium(?x4514, ?x81), nationality(?x12651, ?x512), nationality(?x10574, ?x512), country(?x136, ?x512), region(?x54, ?x512), film_crew_role(?x3565, ?x137), ?x186 = 02vxq9m, nominated_for(?x4782, ?x3565), contains(?x512, ?x362), nominated_for(?x398, ?x1904), people(?x1050, ?x12651), award(?x10574, ?x2379), time_zones(?x4221, ?x5327) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #600 for first EXPECTED value: *> intensional similarity = 121 *> extensional distance = 3 *> proper extension: 026wp; *> query: (?x6065, 04g61) <- form_of_government(?x7479, ?x6065), form_of_government(?x6401, ?x6065), form_of_government(?x4493, ?x6065), form_of_government(?x4402, ?x6065), form_of_government(?x3040, ?x6065), form_of_government(?x2756, ?x6065), form_of_government(?x1122, ?x6065), form_of_government(?x304, ?x6065), administrative_parent(?x6405, ?x6401), location_of_ceremony(?x4949, ?x6401), locations(?x9798, ?x6401), exported_to(?x6401, ?x94), nationality(?x10562, ?x6401), nationality(?x6258, ?x6401), nationality(?x194, ?x6401), country(?x2867, ?x1122), film_release_region(?x9501, ?x1122), film_release_region(?x8193, ?x1122), film_release_region(?x7493, ?x1122), film_release_region(?x6931, ?x1122), film_release_region(?x6587, ?x1122), film_release_region(?x5791, ?x1122), film_release_region(?x4514, ?x1122), film_release_region(?x4464, ?x1122), film_release_region(?x3191, ?x1122), film_release_region(?x3088, ?x1122), film_release_region(?x2746, ?x1122), film_release_region(?x2714, ?x1122), film_release_region(?x2695, ?x1122), film_release_region(?x2628, ?x1122), film_release_region(?x2512, ?x1122), film_release_region(?x2471, ?x1122), film_release_region(?x1392, ?x1122), film_release_region(?x1386, ?x1122), film_release_region(?x972, ?x1122), film_release_region(?x664, ?x1122), entity_involved(?x12777, ?x4493), ?x2628 = 06wbm8q, ?x664 = 0401sg, award_winner(?x931, ?x6258), contains(?x1144, ?x7479), ?x4464 = 05pdh86, ?x7493 = 0btpm6, gender(?x6258, ?x231), country(?x2884, ?x3040), country(?x2266, ?x3040), country(?x1557, ?x3040), participating_countries(?x418, ?x7479), taxonomy(?x3040, ?x939), ?x8193 = 03z9585, award(?x4949, ?x618), organization(?x3040, ?x1062), adjoins(?x1355, ?x3040), award_winner(?x1670, ?x6258), ?x2471 = 08052t3, contains(?x1122, ?x3106), olympics(?x6401, ?x2043), olympics(?x7479, ?x3110), film(?x4949, ?x204), time_zones(?x7479, ?x11506), organization(?x4402, ?x127), ?x2867 = 02y8z, countries_spoken_in(?x254, ?x1122), ?x5791 = 03mgx6z, film_release_region(?x4514, ?x8958), award_nominee(?x5205, ?x6258), ?x1386 = 0dtfn, adjustment_currency(?x4402, ?x170), ?x972 = 017gl1, contains(?x6401, ?x4030), film(?x382, ?x4514), film_crew_role(?x4514, ?x468), ?x2512 = 07x4qr, ?x2695 = 047svrl, ?x6587 = 07s3m4g, administrative_area_type(?x3040, ?x2792), genre(?x4514, ?x225), ?x3088 = 06w839_, ?x8958 = 01ppq, combatants(?x12486, ?x4493), ?x2792 = 0hzc9wc, ?x1557 = 07bs0, film(?x194, ?x195), ?x2714 = 0kv238, ?x1392 = 017gm7, ?x9501 = 0g5qmbz, jurisdiction_of_office(?x3444, ?x6401), entity_involved(?x12486, ?x12487), film_release_region(?x2746, ?x1471), ?x1471 = 07t21, ?x127 = 02vk52z, sports(?x2043, ?x171), ?x3191 = 0crc2cp, film(?x902, ?x2746), film_release_region(?x5315, ?x304), film_release_region(?x5271, ?x304), film_release_region(?x1868, ?x304), film_release_region(?x1546, ?x304), film_release_region(?x1463, ?x304), film_release_region(?x1170, ?x304), film_release_region(?x1118, ?x304), film_release_region(?x599, ?x304), religion(?x2756, ?x1985), ?x5315 = 0glqh5_, medal(?x2043, ?x422), ?x2266 = 01lb14, organization(?x304, ?x312), nominated_for(?x2224, ?x599), ?x1118 = 0_92w, olympics(?x304, ?x7429), profession(?x10562, ?x353), ?x6931 = 09v3jyg, ?x5271 = 047vnkj, ?x1463 = 0gtvrv3, ?x2884 = 09wz9, ?x1868 = 0cc7hmk, nominated_for(?x1053, ?x2746), ?x1170 = 09gdm7q, participating_countries(?x2553, ?x304), ?x1546 = 0d6b7, ?x7429 = 0124ld *> conf = 0.60 ranks of expected_values: 3, 30, 57 EVAL 01q20 form_of_government! 035hm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 6.000 6.000 0.600 http://example.org/location/country/form_of_government EVAL 01q20 form_of_government! 04g61 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 6.000 6.000 0.600 http://example.org/location/country/form_of_government EVAL 01q20 form_of_government! 05b4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 6.000 6.000 0.600 http://example.org/location/country/form_of_government #1002-023tp8 PRED entity: 023tp8 PRED relation: sibling! PRED expected values: 02v60l => 111 concepts (75 used for prediction) PRED predicted values (max 10 best out of 90): 0p_r5 (0.11 #339, 0.02 #683, 0.02 #797), 0194xc (0.11 #542, 0.02 #657, 0.02 #1461), 01mqc_ (0.07 #801), 0g2lq (0.06 #410, 0.02 #640), 02tf1y (0.05 #646, 0.05 #760, 0.03 #1450), 06t61y (0.05 #472, 0.02 #587, 0.02 #816), 032_jg (0.05 #465, 0.02 #580, 0.02 #809), 026_dq6 (0.05 #539, 0.02 #654, 0.02 #883), 018yj6 (0.05 #530, 0.02 #645, 0.02 #759), 023nlj (0.05 #529, 0.02 #644, 0.02 #758) >> Best rule #339 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 02rf1y; >> query: (?x376, 0p_r5) <- film(?x376, ?x5890), ?x5890 = 02lxrv, award(?x376, ?x154) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #614 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 53 *> proper extension: 01j5ts; 0p_pd; 032_jg; 0h1mt; 0sz28; 01kvqc; 06t61y; 01zmpg; 06chf; 01fwpt; ... *> query: (?x376, 02v60l) <- people(?x1050, ?x376), profession(?x376, ?x1032), sibling(?x7617, ?x376) *> conf = 0.02 ranks of expected_values: 57 EVAL 023tp8 sibling! 02v60l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 111.000 75.000 0.111 http://example.org/people/person/sibling_s./people/sibling_relationship/sibling #1001-06wjf PRED entity: 06wjf PRED relation: film_release_region! PRED expected values: 09g7vfw => 177 concepts (62 used for prediction) PRED predicted values (max 10 best out of 1614): 06fcqw (1.00 #13269, 0.88 #31348, 0.83 #23391), 0872p_c (1.00 #13269, 0.88 #30646, 0.81 #38605), 017jd9 (1.00 #13269, 0.88 #31105, 0.81 #39064), 0gvs1kt (1.00 #13269, 0.88 #30921, 0.81 #38880), 02vxq9m (1.00 #13269, 0.88 #30528, 0.76 #38487), 04w7rn (1.00 #13269, 0.88 #30693, 0.76 #38652), 0dgst_d (1.00 #13269, 0.88 #30660, 0.76 #38619), 0bs8s1p (1.00 #13269, 0.88 #31445, 0.76 #39404), 0dzlbx (1.00 #13269, 0.83 #23206, 0.81 #31163), 05zlld0 (1.00 #13269, 0.83 #23028, 0.81 #30985) >> Best rule #13269 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 06rny; >> query: (?x4271, ?x186) <- split_to(?x2346, ?x4271), adjoins(?x252, ?x2346), film_release_region(?x186, ?x2346) >> conf = 1.00 => this is the best rule for 51 predicted values *> Best rule #30935 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 0154j; 016wzw; *> query: (?x4271, 09g7vfw) <- contains(?x2346, ?x4271), film_release_region(?x2676, ?x4271), film_release_region(?x1919, ?x4271), ?x2676 = 0f4m2z, ?x1919 = 0_7w6 *> conf = 0.81 ranks of expected_values: 87 EVAL 06wjf film_release_region! 09g7vfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 177.000 62.000 0.995 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #1000-0d90m PRED entity: 0d90m PRED relation: production_companies PRED expected values: 0c_j5d => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 64): 0c_j5d (0.40 #172, 0.22 #89, 0.14 #6), 016tt2 (0.39 #1835, 0.39 #1754, 0.33 #3260), 05s_k6 (0.33 #3260, 0.32 #1834, 0.32 #7116), 03xq0f (0.33 #3260, 0.32 #1834, 0.32 #7116), 016tw3 (0.20 #178, 0.11 #845, 0.10 #1260), 0kx4m (0.16 #258, 0.11 #1174, 0.08 #508), 0c41qv (0.15 #1806, 0.10 #222, 0.05 #1387), 05qd_ (0.14 #10, 0.11 #93, 0.11 #1676), 086k8 (0.14 #2, 0.11 #835, 0.10 #418), 01795t (0.14 #22, 0.11 #105, 0.06 #271) >> Best rule #172 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 0cc846d; 062zm5h; 0dnkmq; >> query: (?x97, 0c_j5d) <- currency(?x97, ?x170), film_crew_role(?x97, ?x468), film(?x96, ?x97), ?x96 = 079vf >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0d90m production_companies 0c_j5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.400 http://example.org/film/film/production_companies #999-0vhm PRED entity: 0vhm PRED relation: program! PRED expected values: 0cjdk => 100 concepts (69 used for prediction) PRED predicted values (max 10 best out of 58): 0gsg7 (0.33 #164, 0.26 #1425, 0.24 #656), 0cjdk (0.33 #167, 0.26 #385, 0.25 #551), 05gnf (0.27 #1272, 0.25 #614, 0.24 #504), 0ljc_ (0.25 #28, 0.20 #136, 0.20 #82), 025snf (0.25 #34, 0.20 #142, 0.20 #88), 09d5h (0.25 #3, 0.20 #57, 0.18 #1261), 0146mv (0.20 #134, 0.17 #188, 0.11 #352), 03mdt (0.19 #387, 0.15 #661, 0.14 #936), 0g5lhl7 (0.17 #168, 0.08 #660, 0.08 #1429), 0hmxn (0.17 #214, 0.03 #324, 0.03 #378) >> Best rule #164 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 017dcd; 0170k0; 043qqt5; 017dbx; >> query: (?x5219, 0gsg7) <- actor(?x5219, ?x10109), program(?x12505, ?x5219), profession(?x10109, ?x1032), ?x12505 = 03lpbx >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #167 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 4 *> proper extension: 017dcd; 0170k0; 043qqt5; 017dbx; *> query: (?x5219, 0cjdk) <- actor(?x5219, ?x10109), program(?x12505, ?x5219), profession(?x10109, ?x1032), ?x12505 = 03lpbx *> conf = 0.33 ranks of expected_values: 2 EVAL 0vhm program! 0cjdk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 69.000 0.333 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #998-033tf_ PRED entity: 033tf_ PRED relation: people PRED expected values: 02g8h 0c1pj 03pmty 09dt7 08664q 019f2f 02f8lw 02dth1 0315q3 025b5y 06s6hs 0hnp7 0432cd 01whg97 01vsy9_ 030xr_ 029ghl 019g65 04d_mtq => 23 concepts (18 used for prediction) PRED predicted values (max 10 best out of 2969): 0hfml (0.60 #5647, 0.40 #8793, 0.33 #2503), 06cgy (0.50 #3321, 0.33 #177, 0.14 #12756), 023n39 (0.40 #8709, 0.40 #7136, 0.40 #5563), 0169dl (0.40 #8142, 0.40 #4996, 0.33 #1852), 016fjj (0.40 #8313, 0.40 #5167, 0.33 #2023), 0315q3 (0.40 #8449, 0.40 #6876, 0.20 #5303), 0bx_q (0.40 #8592, 0.40 #7019, 0.20 #5446), 01tfck (0.40 #8117, 0.40 #6544, 0.20 #4971), 01d0fp (0.40 #6920, 0.40 #5347, 0.20 #8493), 01pk3z (0.40 #8574, 0.33 #2284, 0.33 #712) >> Best rule #5647 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 065b6q; 01qhm_; >> query: (?x1446, 0hfml) <- people(?x1446, ?x5246), people(?x1446, ?x2614), ?x5246 = 046zh, languages_spoken(?x1446, ?x254), vacationer(?x2983, ?x2614), award(?x2614, ?x567) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #8449 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 09vc4s; *> query: (?x1446, 0315q3) <- people(?x1446, ?x5246), people(?x1446, ?x2012), ?x5246 = 046zh, profession(?x2012, ?x220), notable_people_with_this_condition(?x8318, ?x2012), film(?x2012, ?x1246), award(?x2012, ?x757) *> conf = 0.40 ranks of expected_values: 6, 59, 68, 115, 143, 206, 210, 242, 568, 672, 695, 1340, 1426, 2691, 2713, 2832 EVAL 033tf_ people 04d_mtq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 019g65 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 029ghl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 030xr_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 01vsy9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 01whg97 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 0432cd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 0hnp7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 06s6hs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 025b5y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 0315q3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 02dth1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 02f8lw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 019f2f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 08664q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 09dt7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 03pmty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 0c1pj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 23.000 18.000 0.600 http://example.org/people/ethnicity/people EVAL 033tf_ people 02g8h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 23.000 18.000 0.600 http://example.org/people/ethnicity/people #997-0chghy PRED entity: 0chghy PRED relation: exported_to! PRED expected values: 0853g => 212 concepts (170 used for prediction) PRED predicted values (max 10 best out of 238): 0j4b (0.29 #560, 0.27 #848, 0.25 #618), 04sj3 (0.27 #858, 0.25 #1032, 0.25 #456), 06q1r (0.26 #2642, 0.25 #1484, 0.25 #1079), 0jdd (0.25 #435, 0.23 #894, 0.20 #721), 0d060g (0.25 #407, 0.20 #693, 0.20 #464), 0n3g (0.25 #440, 0.20 #726, 0.20 #497), 03_3d (0.25 #406, 0.20 #692, 0.20 #463), 0h3y (0.25 #408, 0.20 #465, 0.18 #810), 016zwt (0.25 #454, 0.20 #511, 0.15 #913), 0f8l9c (0.25 #415, 0.20 #472, 0.14 #3428) >> Best rule #560 for best value: >> intensional similarity = 2 >> extensional distance = 5 >> proper extension: 082db; >> query: (?x390, 0j4b) <- films(?x390, ?x5835), organization(?x390, ?x127) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #459 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0hkt6; *> query: (?x390, 0853g) <- religion(?x390, ?x492), films(?x390, ?x5835), award_winner(?x5835, ?x556) *> conf = 0.25 ranks of expected_values: 21 EVAL 0chghy exported_to! 0853g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 212.000 170.000 0.286 http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to #996-01xzb6 PRED entity: 01xzb6 PRED relation: artist! PRED expected values: 0fb0v => 148 concepts (106 used for prediction) PRED predicted values (max 10 best out of 113): 015_1q (0.23 #991, 0.22 #1827, 0.22 #6693), 0g768 (0.19 #175, 0.17 #1009, 0.14 #36), 033hn8 (0.17 #986, 0.14 #430, 0.12 #569), 011k1h (0.14 #426, 0.14 #565, 0.12 #3069), 0n85g (0.14 #62, 0.12 #340, 0.11 #479), 01clyr (0.12 #2536, 0.11 #2814, 0.10 #3787), 0181dw (0.12 #5881, 0.11 #1850, 0.11 #8107), 01cszh (0.12 #566, 0.11 #427, 0.09 #844), 0fb0v (0.11 #423, 0.09 #562, 0.09 #6), 01trtc (0.11 #489, 0.09 #1045, 0.09 #72) >> Best rule #991 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 0jn38; 01518s; >> query: (?x5285, 015_1q) <- category(?x5285, ?x134), artists(?x671, ?x5285), artist(?x6672, ?x5285) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #423 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 33 *> proper extension: 01q7cb_; 01wx756; *> query: (?x5285, 0fb0v) <- type_of_union(?x5285, ?x566), celebrity(?x5312, ?x5285), artists(?x671, ?x5285) *> conf = 0.11 ranks of expected_values: 9 EVAL 01xzb6 artist! 0fb0v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 148.000 106.000 0.234 http://example.org/music/record_label/artist #995-048hf PRED entity: 048hf PRED relation: gender PRED expected values: 05zppz => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.73 #15, 0.72 #209, 0.71 #33), 02zsn (0.52 #192, 0.52 #189, 0.46 #6) >> Best rule #15 for best value: >> intensional similarity = 2 >> extensional distance = 335 >> proper extension: 02d9k; 0bkg4; 016lh0; 03n69x; 0130sy; 01vsyjy; 01f492; 08gwzt; 012ycy; 0dr5y; ... >> query: (?x7842, 05zppz) <- type_of_union(?x7842, ?x566), currency(?x7842, ?x170) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 048hf gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.727 http://example.org/people/person/gender #994-031786 PRED entity: 031786 PRED relation: film! PRED expected values: 025t9b 05kwx2 053xw6 => 64 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1093): 013_vh (0.62 #74615, 0.55 #66321, 0.46 #26941), 0170qf (0.62 #74615, 0.46 #26941, 0.38 #74617), 0d5wn3 (0.46 #26941, 0.38 #74617, 0.38 #74614), 016ypb (0.30 #495, 0.11 #10857, 0.06 #6713), 0479b (0.20 #1203, 0.09 #7421, 0.09 #9493), 014gf8 (0.20 #1002, 0.06 #7220, 0.06 #9292), 012c6x (0.12 #6334, 0.12 #8406, 0.03 #31203), 0jrny (0.12 #6760, 0.12 #8832, 0.03 #31629), 04fzk (0.11 #4847, 0.10 #702, 0.09 #11064), 079vf (0.11 #4153, 0.09 #12442, 0.08 #26949) >> Best rule #74615 for best value: >> intensional similarity = 2 >> extensional distance = 886 >> proper extension: 0gfzgl; 06mr2s; >> query: (?x7305, ?x3861) <- nominated_for(?x3861, ?x7305), participant(?x3860, ?x3861) >> conf = 0.62 => this is the best rule for 2 predicted values *> Best rule #15594 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 81 *> proper extension: 07kb7vh; *> query: (?x7305, 05kwx2) <- nominated_for(?x143, ?x7305), film_distribution_medium(?x7305, ?x2099), film_crew_role(?x7305, ?x137), film(?x981, ?x7305), ?x2099 = 0735l *> conf = 0.05 ranks of expected_values: 104, 314, 326 EVAL 031786 film! 053xw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 37.000 0.625 http://example.org/film/actor/film./film/performance/film EVAL 031786 film! 05kwx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 64.000 37.000 0.625 http://example.org/film/actor/film./film/performance/film EVAL 031786 film! 025t9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 64.000 37.000 0.625 http://example.org/film/actor/film./film/performance/film #993-01xqw PRED entity: 01xqw PRED relation: role! PRED expected values: 01vsy7t 074tb5 => 59 concepts (37 used for prediction) PRED predicted values (max 10 best out of 704): 050z2 (0.55 #8102, 0.52 #4836, 0.50 #5302), 01wxdn3 (0.50 #879, 0.33 #4595, 0.29 #9725), 04bpm6 (0.42 #4254, 0.35 #9384, 0.33 #1001), 082brv (0.41 #9581, 0.39 #8183, 0.33 #4451), 05qhnq (0.39 #8226, 0.33 #2170, 0.33 #1241), 023l9y (0.39 #8127, 0.33 #4395, 0.33 #679), 0326tc (0.37 #3603, 0.35 #4067, 0.33 #1278), 0137g1 (0.35 #8033, 0.33 #4301, 0.33 #585), 02s6sh (0.33 #905, 0.29 #9751, 0.29 #8353), 0770cd (0.33 #543, 0.28 #4725, 0.27 #1471) >> Best rule #8102 for best value: >> intensional similarity = 10 >> extensional distance = 29 >> proper extension: 025cbm; >> query: (?x4311, 050z2) <- role(?x3409, ?x4311), role(?x3161, ?x4311), role(?x314, ?x4311), role(?x3161, ?x3215), role(?x314, ?x227), ?x3409 = 0680x0, role(?x1399, ?x4311), role(?x2662, ?x314), ?x2662 = 045zr, ?x3215 = 0bxl5 >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2538 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 10 *> proper extension: 07xzm; *> query: (?x4311, 01vsy7t) <- role(?x5417, ?x4311), role(?x2798, ?x4311), role(?x2460, ?x4311), ?x2798 = 03qjg, instrumentalists(?x4311, ?x5125), ?x2460 = 01wy6, people(?x4959, ?x5125), role(?x314, ?x5417), role(?x5417, ?x960) *> conf = 0.25 ranks of expected_values: 47 EVAL 01xqw role! 074tb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 59.000 37.000 0.548 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 01xqw role! 01vsy7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 59.000 37.000 0.548 http://example.org/music/artist/track_contributions./music/track_contribution/role #992-039d4 PRED entity: 039d4 PRED relation: school_type PRED expected values: 05jxkf => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 18): 05jxkf (0.49 #316, 0.49 #244, 0.48 #220), 05pcjw (0.43 #1, 0.37 #49, 0.31 #145), 07tf8 (0.34 #201, 0.23 #249, 0.22 #225), 01_9fk (0.27 #194, 0.15 #242, 0.14 #314), 01rs41 (0.24 #509, 0.24 #655, 0.22 #389), 047951 (0.14 #128, 0.12 #152, 0.05 #56), 01_srz (0.11 #1539, 0.07 #27, 0.06 #507), 02p0qmm (0.03 #634, 0.03 #442, 0.03 #466), 04399 (0.03 #398, 0.02 #518, 0.02 #206), 04qbv (0.03 #472, 0.02 #544, 0.02 #616) >> Best rule #316 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 03bwzr4; >> query: (?x9443, 05jxkf) <- major_field_of_study(?x9443, ?x1668), major_field_of_study(?x9443, ?x1527), ?x1668 = 01mkq, major_field_of_study(?x2981, ?x1527) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039d4 school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 103.000 0.493 http://example.org/education/educational_institution/school_type #991-0cvw9 PRED entity: 0cvw9 PRED relation: category PRED expected values: 08mbj5d => 201 concepts (201 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.73 #41, 0.73 #27, 0.73 #43) >> Best rule #41 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 09hzc; >> query: (?x8297, 08mbj5d) <- place_of_death(?x13492, ?x8297), administrative_division(?x8297, ?x12420), profession(?x13492, ?x524) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cvw9 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 201.000 201.000 0.729 http://example.org/common/topic/webpage./common/webpage/category #990-0c_tl PRED entity: 0c_tl PRED relation: participating_countries PRED expected values: 0d0vqn => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 221): 02k54 (0.70 #174, 0.69 #175, 0.68 #176), 0165v (0.70 #174, 0.69 #175, 0.33 #172), 0d05w3 (0.70 #174, 0.69 #175, 0.33 #231), 015fr (0.70 #174, 0.69 #175, 0.33 #196), 06bnz (0.70 #174, 0.69 #175, 0.33 #215), 047lj (0.70 #174, 0.69 #175, 0.33 #190), 0162b (0.70 #174, 0.69 #175, 0.33 #170), 059j2 (0.70 #174, 0.69 #175, 0.33 #28), 0jdx (0.70 #174, 0.69 #175, 0.33 #129), 05qhw (0.70 #174, 0.69 #175, 0.33 #17) >> Best rule #174 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 0kbws; >> query: (?x2748, ?x9072) <- olympics(?x1557, ?x2748), sports(?x2748, ?x4045), olympics(?x608, ?x2748), ?x608 = 02k54, participating_countries(?x2748, ?x94), ?x1557 = 07bs0, country(?x4045, ?x10457), country(?x4045, ?x9816), country(?x4045, ?x9072), country(?x4045, ?x5114), ?x10457 = 0162b, ?x5114 = 05vz3zq, ?x9816 = 0165v, administrative_parent(?x9072, ?x551), organization(?x9072, ?x127), medal(?x2748, ?x422) >> conf = 0.70 => this is the best rule for 152 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 16 EVAL 0c_tl participating_countries 0d0vqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 57.000 57.000 0.697 http://example.org/olympics/olympic_games/participating_countries #989-059kh PRED entity: 059kh PRED relation: parent_genre PRED expected values: 06rqw => 63 concepts (44 used for prediction) PRED predicted values (max 10 best out of 235): 011j5x (0.40 #807, 0.33 #1593, 0.33 #175), 017371 (0.38 #1518, 0.25 #1361, 0.25 #574), 03lty (0.36 #3641, 0.33 #15, 0.25 #647), 01243b (0.33 #2231, 0.27 #2860, 0.25 #500), 0xhtw (0.33 #168, 0.25 #1273, 0.25 #486), 041738 (0.33 #204, 0.22 #316, 0.18 #3150), 05w3f (0.33 #179, 0.18 #3150, 0.18 #2069), 03mb9 (0.33 #219, 0.18 #3150, 0.06 #2268), 02w4v (0.27 #2073, 0.20 #973, 0.18 #3150), 0827d (0.25 #633, 0.25 #319, 0.22 #1576) >> Best rule #807 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 0xjl2; >> query: (?x3370, 011j5x) <- parent_genre(?x2542, ?x3370), artists(?x3370, ?x8058), artists(?x3370, ?x7211), artists(?x3370, ?x2492), ?x2492 = 01tp5bj, ?x8058 = 014pg1, award(?x7211, ?x724) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #5359 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 125 *> proper extension: 01_sz1; *> query: (?x3370, ?x284) <- parent_genre(?x2542, ?x3370), artists(?x3370, ?x3767), artists(?x3370, ?x2492), artists(?x2491, ?x2492), parent_genre(?x3370, ?x283), award(?x3767, ?x724), profession(?x2492, ?x131), parent_genre(?x2491, ?x284) *> conf = 0.06 ranks of expected_values: 56 EVAL 059kh parent_genre 06rqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 63.000 44.000 0.400 http://example.org/music/genre/parent_genre #988-01lz4tf PRED entity: 01lz4tf PRED relation: nationality PRED expected values: 09c7w0 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.84 #8710, 0.79 #901, 0.78 #1101), 02jx1 (0.44 #1833, 0.27 #2934, 0.26 #1933), 07ssc (0.33 #15, 0.18 #1815, 0.17 #2916), 01n7q (0.32 #10023), 0chghy (0.20 #110, 0.08 #1210, 0.06 #610), 0jgx (0.11 #258, 0.08 #358, 0.06 #658), 06q1r (0.08 #1277, 0.04 #2277, 0.04 #2578), 035qy (0.08 #434, 0.04 #1434, 0.03 #1634), 0k6nt (0.08 #425, 0.01 #2326, 0.01 #2826), 0d060g (0.07 #507, 0.06 #2007, 0.05 #2207) >> Best rule #8710 for best value: >> intensional similarity = 4 >> extensional distance = 1099 >> proper extension: 05qsxy; 05fh2; >> query: (?x7233, ?x94) <- place_of_birth(?x7233, ?x242), location(?x241, ?x242), location_of_ceremony(?x566, ?x242), country(?x242, ?x94) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lz4tf nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 126.000 0.842 http://example.org/people/person/nationality #987-06rq2l PRED entity: 06rq2l PRED relation: profession PRED expected values: 0dxtg => 107 concepts (49 used for prediction) PRED predicted values (max 10 best out of 88): 0dxtg (0.74 #12, 0.58 #2202, 0.57 #1764), 02jknp (0.56 #590, 0.52 #882, 0.50 #3511), 03gjzk (0.53 #597, 0.50 #1765, 0.49 #1181), 0kyk (0.44 #465, 0.26 #27, 0.22 #1195), 09jwl (0.36 #5565, 0.33 #4105, 0.25 #308), 0np9r (0.31 #1186, 0.30 #2939, 0.28 #2062), 0nbcg (0.26 #5578, 0.21 #4118, 0.16 #321), 016z4k (0.24 #5553, 0.21 #4093, 0.14 #296), 0dz3r (0.24 #5551, 0.23 #294, 0.21 #148), 02krf9 (0.24 #608, 0.20 #900, 0.18 #1776) >> Best rule #12 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 0282x; >> query: (?x9204, 0dxtg) <- profession(?x9204, ?x353), executive_produced_by(?x1066, ?x9204), ?x353 = 0cbd2 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06rq2l profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 49.000 0.735 http://example.org/people/person/profession #986-02j490 PRED entity: 02j490 PRED relation: profession PRED expected values: 01d_h8 => 145 concepts (110 used for prediction) PRED predicted values (max 10 best out of 82): 01d_h8 (0.38 #2092, 0.38 #5073, 0.37 #2390), 03gjzk (0.35 #6274, 0.33 #10597, 0.25 #9702), 0dxtg (0.33 #9701, 0.32 #6273, 0.32 #12535), 09jwl (0.29 #168, 0.26 #466, 0.22 #2105), 018gz8 (0.27 #17, 0.17 #762, 0.15 #3146), 02jknp (0.24 #9695, 0.23 #2392, 0.22 #5075), 0nbcg (0.23 #181, 0.22 #479, 0.18 #32), 0np9r (0.22 #6429, 0.20 #9261, 0.20 #10006), 0dz3r (0.19 #151, 0.18 #2, 0.18 #449), 0d1pc (0.19 #498, 0.18 #1243, 0.18 #1094) >> Best rule #2092 for best value: >> intensional similarity = 3 >> extensional distance = 217 >> proper extension: 020hyj; >> query: (?x10897, 01d_h8) <- participant(?x10897, ?x7331), award_winner(?x435, ?x10897), award_winner(?x5296, ?x10897) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02j490 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 110.000 0.379 http://example.org/people/person/profession #985-0g5879y PRED entity: 0g5879y PRED relation: film! PRED expected values: 053xw6 => 86 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1014): 0c3p7 (0.63 #70853, 0.58 #112539, 0.49 #75023), 0b79gfg (0.44 #20841, 0.42 #110454, 0.42 #64601), 0h0wc (0.20 #425, 0.09 #2508, 0.08 #4592), 0f5xn (0.17 #5139, 0.09 #3055, 0.05 #7223), 026rm_y (0.17 #5681, 0.09 #3597, 0.02 #9849), 01g969 (0.10 #1673, 0.09 #3756, 0.08 #5840), 05nzw6 (0.10 #1193, 0.09 #3276, 0.08 #5360), 03_48k (0.10 #999, 0.09 #3082, 0.08 #5166), 0338g8 (0.10 #1400, 0.09 #3483, 0.08 #5567), 031k24 (0.10 #1409, 0.09 #3492, 0.08 #5576) >> Best rule #70853 for best value: >> intensional similarity = 4 >> extensional distance = 438 >> proper extension: 07wqr6; >> query: (?x2685, ?x6314) <- titles(?x162, ?x2685), nominated_for(?x6314, ?x2685), student(?x6919, ?x6314), participant(?x6314, ?x851) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #9589 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 43 *> proper extension: 03m8y5; 03n0cd; 06y611; *> query: (?x2685, 053xw6) <- film_production_design_by(?x2685, ?x6388), film_crew_role(?x2685, ?x468), ?x468 = 02r96rf, film(?x1914, ?x2685) *> conf = 0.04 ranks of expected_values: 176 EVAL 0g5879y film! 053xw6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 86.000 56.000 0.628 http://example.org/film/actor/film./film/performance/film #984-08n__5 PRED entity: 08n__5 PRED relation: artist! PRED expected values: 012b30 => 113 concepts (78 used for prediction) PRED predicted values (max 10 best out of 111): 015_1q (0.22 #1582, 0.22 #1866, 0.21 #2292), 033hn8 (0.20 #156, 0.16 #1576, 0.16 #4701), 03rhqg (0.19 #442, 0.16 #158, 0.14 #868), 01dtcb (0.17 #332, 0.13 #474, 0.08 #2178), 01cszh (0.17 #295, 0.11 #437, 0.09 #2141), 043g7l (0.17 #316, 0.10 #884, 0.10 #4009), 03mp8k (0.16 #210, 0.13 #352, 0.12 #2482), 017l96 (0.15 #19, 0.10 #4848, 0.09 #4138), 0181dw (0.15 #1605, 0.14 #895, 0.11 #2741), 01trtc (0.13 #358, 0.09 #4051, 0.09 #4193) >> Best rule #1582 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 03c7ln; 012zng; 0285c; 01tp5bj; 03xl77; 01vv6_6; 0phx4; 03f6fl0; 044mfr; 082brv; ... >> query: (?x5820, 015_1q) <- origin(?x5820, ?x11743), nationality(?x5820, ?x1023), role(?x5820, ?x227) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #92 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 04mx7s; *> query: (?x5820, 012b30) <- role(?x5820, ?x1750), role(?x5820, ?x227), ?x1750 = 02hnl, ?x227 = 0342h *> conf = 0.05 ranks of expected_values: 43 EVAL 08n__5 artist! 012b30 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 113.000 78.000 0.221 http://example.org/music/record_label/artist #983-01x66d PRED entity: 01x66d PRED relation: student! PRED expected values: 05qfh => 162 concepts (104 used for prediction) PRED predicted values (max 10 best out of 41): 04rlf (0.29 #234, 0.18 #485, 0.13 #550), 0fdys (0.18 #467, 0.14 #216, 0.13 #532), 03g3w (0.14 #208, 0.09 #459, 0.07 #524), 05qfh (0.14 #214, 0.09 #465, 0.07 #530), 04g51 (0.14 #226, 0.09 #477, 0.07 #542), 02822 (0.13 #1417, 0.13 #1732, 0.12 #1919), 0h5k (0.09 #455, 0.07 #520, 0.03 #1403), 03qsdpk (0.08 #1737, 0.08 #2176, 0.08 #2238), 05qjt (0.07 #508, 0.03 #1701, 0.03 #762), 040p_q (0.07 #552, 0.03 #1701, 0.01 #1999) >> Best rule #234 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 03gr7w; >> query: (?x1068, 04rlf) <- instrumentalists(?x75, ?x1068), student(?x1368, ?x1068), nationality(?x1068, ?x1353), artists(?x1067, ?x1068), location(?x1068, ?x1523) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #214 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 03gr7w; *> query: (?x1068, 05qfh) <- instrumentalists(?x75, ?x1068), student(?x1368, ?x1068), nationality(?x1068, ?x1353), artists(?x1067, ?x1068), location(?x1068, ?x1523) *> conf = 0.14 ranks of expected_values: 4 EVAL 01x66d student! 05qfh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 162.000 104.000 0.286 http://example.org/education/field_of_study/students_majoring./education/education/student #982-02lyr4 PRED entity: 02lyr4 PRED relation: team PRED expected values: 01d5z => 25 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1056): 03lpp_ (0.83 #10338, 0.83 #4697, 0.83 #5639), 01d5z (0.83 #5639, 0.81 #9399, 0.81 #11285), 04wmvz (0.83 #5639, 0.80 #6578, 0.80 #12240), 021f30 (0.81 #9399, 0.80 #11293, 0.80 #8461), 02gtm4 (0.81 #9399, 0.80 #11293, 0.80 #8461), 051wf (0.81 #9399, 0.80 #8461, 0.43 #15061), 02h8p8 (0.80 #5641, 0.80 #5638, 0.79 #11282), 03b3j (0.70 #13301, 0.18 #14240, 0.15 #15182), 05gg4 (0.65 #13341, 0.18 #14280, 0.16 #15055), 01y3c (0.65 #13211, 0.18 #14150, 0.16 #15055) >> Best rule #10338 for best value: >> intensional similarity = 29 >> extensional distance = 11 >> proper extension: 02sddg; >> query: (?x2010, ?x700) <- position(?x7399, ?x2010), position(?x6348, ?x2010), position(?x3333, ?x2010), position(?x2067, ?x2010), position(?x2011, ?x2010), position(?x1438, ?x2010), position(?x700, ?x2010), position(?x6348, ?x8520), position(?x6348, ?x4244), season(?x7399, ?x2406), draft(?x3333, ?x1161), school(?x3333, ?x735), position(?x2067, ?x7724), position(?x2067, ?x2066), team(?x12323, ?x2067), season(?x3333, ?x701), category(?x3333, ?x134), school(?x2067, ?x1276), colors(?x2011, ?x4557), ?x2066 = 02s7tr, school(?x7399, ?x5288), team(?x11844, ?x700), ?x4244 = 028c_8, team(?x12238, ?x7399), ?x7724 = 02rsl1, ?x8520 = 01z9v6, colors(?x7399, ?x5325), colors(?x546, ?x4557), sport(?x1438, ?x5063) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #5639 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 4 *> proper extension: 02dwpf; *> query: (?x2010, ?x12042) <- position(?x12042, ?x2010), position(?x7399, ?x2010), position(?x3333, ?x2010), position(?x2067, ?x2010), position(?x1438, ?x2010), season(?x7399, ?x3431), draft(?x3333, ?x8786), draft(?x3333, ?x3334), school(?x3333, ?x5621), school(?x3333, ?x4672), school(?x3333, ?x1011), ?x1438 = 0512p, ?x3334 = 02pq_rp, season(?x3333, ?x701), team(?x261, ?x2067), school(?x12042, ?x3948), colors(?x7399, ?x5325), ?x3431 = 025ygqm, category(?x7399, ?x134), school(?x7399, ?x5288), ?x1011 = 07w0v, ?x8786 = 02pq_x5, institution(?x620, ?x5288), student(?x5288, ?x460), ?x5621 = 01vs5c, team(?x10822, ?x12042), team(?x11844, ?x3333), organizations_founded(?x4672, ?x5487), student(?x4672, ?x264), ?x261 = 02dwn9 *> conf = 0.83 ranks of expected_values: 2 EVAL 02lyr4 team 01d5z CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 25.000 17.000 0.833 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #981-02hblj PRED entity: 02hblj PRED relation: nationality PRED expected values: 09c7w0 => 117 concepts (101 used for prediction) PRED predicted values (max 10 best out of 72): 09c7w0 (0.83 #1302, 0.83 #2002, 0.83 #1702), 0d060g (0.72 #5220, 0.70 #3107, 0.35 #5923), 0cymp (0.42 #7529, 0.38 #10157, 0.34 #7530), 059rby (0.42 #7529, 0.38 #10157, 0.34 #7530), 0chghy (0.34 #5522, 0.06 #811, 0.04 #1511), 02jx1 (0.10 #6658, 0.09 #8066, 0.09 #9075), 07ssc (0.08 #6941, 0.08 #6640, 0.07 #9258), 06mzp (0.07 #722, 0.05 #922, 0.02 #1522), 03rk0 (0.06 #9088, 0.06 #9389, 0.06 #6470), 03_3d (0.06 #4320, 0.06 #3715, 0.04 #2407) >> Best rule #1302 for best value: >> intensional similarity = 7 >> extensional distance = 22 >> proper extension: 079vf; 0bxtg; 0mdqp; 01g4zr; 0fby2t; 02v60l; 03lgg; 0f5xn; 052hl; 03b78r; ... >> query: (?x12084, 09c7w0) <- profession(?x12084, ?x1383), profession(?x12084, ?x1032), profession(?x12084, ?x319), place_of_birth(?x12084, ?x11843), ?x1383 = 0np9r, ?x1032 = 02hrh1q, ?x319 = 01d_h8 >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02hblj nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 101.000 0.833 http://example.org/people/person/nationality #980-031n5b PRED entity: 031n5b PRED relation: contains! PRED expected values: 09c7w0 => 101 concepts (77 used for prediction) PRED predicted values (max 10 best out of 210): 09c7w0 (0.88 #17054, 0.86 #16155, 0.86 #15257), 0y1rf (0.85 #14357, 0.82 #16152, 0.77 #17051), 059rby (0.76 #10766, 0.75 #53853, 0.73 #48467), 02_286 (0.50 #941, 0.33 #1838, 0.29 #2735), 07b_l (0.33 #222, 0.09 #5607, 0.06 #6503), 010016 (0.33 #699, 0.09 #6084, 0.06 #6980), 06bnz (0.25 #3695, 0.18 #4593, 0.05 #4487), 0fc2c (0.21 #35002, 0.05 #35900), 02jx1 (0.18 #4575, 0.17 #1882, 0.12 #3677), 05k7sb (0.18 #5518, 0.15 #9103, 0.14 #2825) >> Best rule #17054 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 02dqdp; >> query: (?x9612, 09c7w0) <- organization(?x3484, ?x9612), ?x3484 = 05k17c, state_province_region(?x9612, ?x335), state(?x739, ?x335), location(?x101, ?x335) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 031n5b contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 77.000 0.881 http://example.org/location/location/contains #979-016lh0 PRED entity: 016lh0 PRED relation: profession PRED expected values: 0kyk => 137 concepts (94 used for prediction) PRED predicted values (max 10 best out of 112): 0cbd2 (0.99 #9650, 0.98 #11133, 0.98 #11282), 0dxtg (0.98 #12029, 0.66 #12325, 0.66 #13509), 0d8qb (0.78 #4229, 0.34 #6226, 0.33 #8898), 02hrh1q (0.73 #10697, 0.71 #10104, 0.68 #13658), 01d_h8 (0.65 #10095, 0.50 #5935, 0.49 #12021), 0kyk (0.58 #7296, 0.47 #2107, 0.46 #8186), 04gc2 (0.56 #933, 0.54 #3008, 0.50 #3156), 06q2q (0.55 #4047, 0.34 #6226, 0.33 #8898), 02jknp (0.43 #10097, 0.40 #12023, 0.39 #12467), 016m9h (0.43 #425, 0.33 #1760, 0.25 #1908) >> Best rule #9650 for best value: >> intensional similarity = 5 >> extensional distance = 319 >> proper extension: 01xdf5; 01dw4q; 02lfcm; 04rs03; 0yfp; 04l3_z; 0m77m; 05fnl9; 05qw5; 040db; ... >> query: (?x5266, 0cbd2) <- profession(?x5266, ?x6630), profession(?x4292, ?x6630), ?x4292 = 0zm1, specialization_of(?x13369, ?x6630), type_of_union(?x5266, ?x566) >> conf = 0.99 => this is the best rule for 1 predicted values *> Best rule #7296 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 187 *> proper extension: 01_k0d; 01kp_1t; 07d3x; 01w20rx; 0m68w; *> query: (?x5266, 0kyk) <- profession(?x5266, ?x6630), profession(?x7400, ?x6630), profession(?x7341, ?x6630), category(?x5266, ?x134), gender(?x7341, ?x231), ?x7400 = 082mw *> conf = 0.58 ranks of expected_values: 6 EVAL 016lh0 profession 0kyk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 137.000 94.000 0.988 http://example.org/people/person/profession #978-0b_xm PRED entity: 0b_xm PRED relation: group! PRED expected values: 0l14md => 102 concepts (78 used for prediction) PRED predicted values (max 10 best out of 117): 0l14md (0.65 #1570, 0.63 #1492, 0.62 #84), 0l14j_ (0.50 #123, 0.20 #45, 0.12 #2470), 028tv0 (0.49 #1574, 0.48 #869, 0.44 #947), 03qjg (0.35 #978, 0.35 #1527, 0.29 #1371), 0l14qv (0.25 #83, 0.25 #2430, 0.24 #1491), 06ncr (0.25 #110, 0.15 #2457, 0.15 #2300), 02k84w (0.20 #24, 0.12 #102, 0.07 #2740), 04rzd (0.15 #962, 0.14 #1511, 0.12 #2293), 07y_7 (0.14 #1095, 0.13 #314, 0.12 #80), 07brj (0.12 #93, 0.09 #1501, 0.07 #1345) >> Best rule #1570 for best value: >> intensional similarity = 5 >> extensional distance = 94 >> proper extension: 0123r4; >> query: (?x7653, 0l14md) <- artists(?x1000, ?x7653), group(?x1466, ?x7653), group(?x227, ?x7653), ?x227 = 0342h, ?x1466 = 03bx0bm >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0b_xm group! 0l14md CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 78.000 0.646 http://example.org/music/performance_role/regular_performances./music/group_membership/group #977-0d7hg4 PRED entity: 0d7hg4 PRED relation: award_winner! PRED expected values: 0h53p1 => 98 concepts (53 used for prediction) PRED predicted values (max 10 best out of 556): 09hd16 (0.82 #49694, 0.82 #36867, 0.81 #46489), 0h53p1 (0.64 #459, 0.35 #28850, 0.29 #84959), 0h5jg5 (0.53 #73741, 0.47 #1603, 0.39 #62520), 08q3s0 (0.53 #73741, 0.47 #1603, 0.39 #62520), 047cqr (0.53 #73741, 0.47 #1603, 0.39 #62520), 0d7hg4 (0.45 #425, 0.35 #28850, 0.29 #84959), 04wvhz (0.35 #28850, 0.29 #84959, 0.28 #70535), 06pj8 (0.29 #84959, 0.28 #70535, 0.27 #84960), 09b0xs (0.28 #70535, 0.27 #84960, 0.16 #80151), 05vtbl (0.28 #70535, 0.27 #84960, 0.16 #80151) >> Best rule #49694 for best value: >> intensional similarity = 3 >> extensional distance = 884 >> proper extension: 01wz_ml; >> query: (?x2650, ?x4022) <- profession(?x2650, ?x987), award_winner(?x2650, ?x4022), place_of_birth(?x2650, ?x1274) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #459 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 047cqr; *> query: (?x2650, 0h53p1) <- profession(?x2650, ?x987), award_nominee(?x7301, ?x2650), ?x7301 = 0h5jg5 *> conf = 0.64 ranks of expected_values: 2 EVAL 0d7hg4 award_winner! 0h53p1 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 98.000 53.000 0.818 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #976-03xmy1 PRED entity: 03xmy1 PRED relation: award PRED expected values: 05q5t0b => 118 concepts (113 used for prediction) PRED predicted values (max 10 best out of 292): 05q5t0b (0.77 #40426, 0.76 #37595, 0.75 #19800), 09sb52 (0.37 #2060, 0.36 #9737, 0.35 #10141), 05pcn59 (0.33 #1696, 0.25 #8161, 0.25 #9777), 0ck27z (0.32 #20296, 0.29 #17869, 0.27 #22720), 0gkvb7 (0.25 #430, 0.10 #2854, 0.09 #2450), 05p09zm (0.22 #6588, 0.21 #5780, 0.21 #9820), 01by1l (0.22 #19506, 0.10 #5363, 0.09 #37300), 0cqhk0 (0.21 #4884, 0.17 #20241, 0.17 #21453), 01bgqh (0.20 #19437, 0.14 #42, 0.07 #37231), 0gqwc (0.19 #2093, 0.19 #17446, 0.18 #16638) >> Best rule #40426 for best value: >> intensional similarity = 3 >> extensional distance = 2183 >> proper extension: 07k2d; >> query: (?x1888, ?x3064) <- award_winner(?x3064, ?x1888), award(?x11200, ?x3064), category(?x11200, ?x134) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xmy1 award 05q5t0b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 113.000 0.766 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #975-0232lm PRED entity: 0232lm PRED relation: languages PRED expected values: 02h40lc => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.35 #626, 0.33 #2, 0.29 #1172), 06nm1 (0.03 #630, 0.02 #747, 0.02 #1020), 064_8sq (0.02 #756, 0.02 #1029, 0.02 #4032), 03_9r (0.02 #824, 0.02 #629, 0.01 #863), 03k50 (0.02 #2695, 0.02 #3085, 0.02 #3943), 02bjrlw (0.02 #313, 0.02 #742, 0.02 #391), 07c9s (0.01 #2899, 0.01 #2470, 0.01 #3016), 0t_2 (0.01 #555) >> Best rule #626 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 024yxd; 01g04k; >> query: (?x8873, 02h40lc) <- location(?x8873, ?x11359), origin(?x8873, ?x13529), profession(?x8873, ?x131), county(?x11359, ?x9751) >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0232lm languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.351 http://example.org/people/person/languages #974-07lwsz PRED entity: 07lwsz PRED relation: award_nominee PRED expected values: 01gp_x => 124 concepts (46 used for prediction) PRED predicted values (max 10 best out of 912): 04wvhz (0.81 #23345, 0.81 #102732, 0.80 #32682), 01gp_x (0.81 #23345, 0.81 #102732, 0.80 #32682), 0b05xm (0.81 #23345, 0.81 #102732, 0.80 #32682), 03772 (0.81 #23345, 0.81 #102732, 0.80 #32682), 07lwsz (0.44 #65369, 0.40 #49024, 0.28 #74708), 04511f (0.44 #65369, 0.40 #49024, 0.22 #74707), 0bbxd3 (0.44 #65369, 0.40 #49024, 0.21 #95725), 04rtpt (0.44 #65369, 0.40 #49024, 0.21 #95725), 0f721s (0.44 #65369, 0.40 #49024, 0.21 #95725), 0blt6 (0.33 #86381, 0.31 #95724, 0.29 #79377) >> Best rule #23345 for best value: >> intensional similarity = 3 >> extensional distance = 125 >> proper extension: 0g5lhl7; >> query: (?x3571, ?x1039) <- award_nominee(?x1039, ?x3571), program(?x3571, ?x4932), award_winner(?x2292, ?x3571) >> conf = 0.81 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 07lwsz award_nominee 01gp_x CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 124.000 46.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #973-0cr3d PRED entity: 0cr3d PRED relation: location! PRED expected values: 081lh 01kws3 03x400 0bz60q 01z5tr 01j5sd 02cvp8 => 124 concepts (88 used for prediction) PRED predicted values (max 10 best out of 2077): 03f4xvm (0.47 #78788, 0.47 #2388, 0.46 #74011), 07csf4 (0.47 #78788, 0.47 #2388, 0.46 #74011), 01z5tr (0.47 #78788, 0.47 #2388, 0.46 #74011), 02lgj6 (0.47 #78788, 0.47 #2388, 0.46 #74011), 02lfl4 (0.47 #78788, 0.47 #2388, 0.46 #74011), 02zft0 (0.47 #78788, 0.47 #2388, 0.46 #74011), 026yqrr (0.47 #78788, 0.47 #2388, 0.46 #74011), 05m9f9 (0.47 #78788, 0.47 #2388, 0.46 #74011), 05ccxr (0.47 #78788, 0.47 #2388, 0.46 #74011), 03xp8d5 (0.47 #78788, 0.47 #2388, 0.46 #74011) >> Best rule #78788 for best value: >> intensional similarity = 3 >> extensional distance = 190 >> proper extension: 06cn5; 0p7vt; 0rxyk; 0ftxc; 01mb87; 02lbc; >> query: (?x2850, ?x839) <- location(?x1568, ?x2850), place_of_birth(?x839, ?x2850), people(?x5801, ?x1568) >> conf = 0.47 => this is the best rule for 29 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 459, 480, 1279, 1377, 1618 EVAL 0cr3d location! 02cvp8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 124.000 88.000 0.470 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cr3d location! 01j5sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 88.000 0.470 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cr3d location! 01z5tr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 124.000 88.000 0.470 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cr3d location! 0bz60q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 88.000 0.470 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cr3d location! 03x400 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 124.000 88.000 0.470 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cr3d location! 01kws3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 88.000 0.470 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0cr3d location! 081lh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 124.000 88.000 0.470 http://example.org/people/person/places_lived./people/place_lived/location #972-02rzdcp PRED entity: 02rzdcp PRED relation: nominated_for! PRED expected values: 0cqhb3 0gkr9q => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 168): 0m7yy (0.68 #917, 0.67 #5499, 0.66 #6188), 0gq9h (0.37 #4412, 0.36 #4642, 0.33 #4871), 0gs9p (0.33 #4414, 0.32 #4644, 0.29 #4873), 019f4v (0.33 #4404, 0.32 #4634, 0.28 #4863), 04dn09n (0.29 #4582, 0.24 #8714, 0.23 #4386), 0gr4k (0.29 #4582, 0.24 #8714, 0.20 #4378), 0cqhk0 (0.29 #4582, 0.24 #8714, 0.20 #30), 0bdwqv (0.29 #4582, 0.24 #8714, 0.19 #12153), 08_vwq (0.29 #4582, 0.24 #8714, 0.19 #12153), 0gr51 (0.29 #4582, 0.24 #8714, 0.19 #12841) >> Best rule #917 for best value: >> intensional similarity = 2 >> extensional distance = 130 >> proper extension: 097h2; 019g8j; >> query: (?x3310, ?x1670) <- actor(?x3310, ?x4697), award(?x3310, ?x1670) >> conf = 0.68 => this is the best rule for 1 predicted values *> Best rule #4582 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 488 *> proper extension: 0hmr4; 044g_k; 02rjv2w; 019kyn; 07s8z_l; 01c9d; 072hx4; 06mmr; *> query: (?x3310, ?x1670) <- award_winner(?x3310, ?x10138), honored_for(?x944, ?x3310), award(?x10138, ?x1670) *> conf = 0.29 ranks of expected_values: 13, 42 EVAL 02rzdcp nominated_for! 0gkr9q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 59.000 59.000 0.685 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02rzdcp nominated_for! 0cqhb3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 59.000 59.000 0.685 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #971-0kk9v PRED entity: 0kk9v PRED relation: company! PRED expected values: 06y3r => 103 concepts (100 used for prediction) PRED predicted values (max 10 best out of 218): 06y3r (0.37 #2181, 0.20 #969, 0.17 #8962), 01w_10 (0.17 #398, 0.10 #3547, 0.10 #3063), 0frmb1 (0.17 #394, 0.10 #3543, 0.10 #3059), 02xnjd (0.17 #397, 0.10 #639, 0.09 #1124), 01xdf5 (0.17 #247, 0.10 #489, 0.09 #974), 06q8hf (0.14 #1355, 0.12 #1839, 0.11 #2567), 05hj_k (0.14 #1284, 0.12 #1768, 0.11 #2496), 0343h (0.12 #1961, 0.10 #3172, 0.10 #507), 0glyyw (0.10 #3556, 0.09 #891, 0.07 #1618), 07f7jp (0.10 #3620, 0.09 #955, 0.07 #1440) >> Best rule #2181 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 03d96s; >> query: (?x3945, ?x9105) <- child(?x3920, ?x3945), company(?x2295, ?x3945), organizations_founded(?x9105, ?x3945) >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kk9v company! 06y3r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 103.000 100.000 0.368 http://example.org/people/person/employment_history./business/employment_tenure/company #970-03xsby PRED entity: 03xsby PRED relation: award PRED expected values: 07bdd_ => 147 concepts (134 used for prediction) PRED predicted values (max 10 best out of 213): 07bdd_ (0.73 #18292, 0.60 #4926, 0.56 #2091), 04ljl_l (0.58 #28360, 0.10 #33625, 0.07 #34030), 02x1z2s (0.35 #5465, 0.33 #7895, 0.32 #5060), 09sb52 (0.34 #41763, 0.30 #44598, 0.30 #37713), 05p09zm (0.29 #33746, 0.27 #34151, 0.15 #28481), 0gq9h (0.27 #5343, 0.22 #2103, 0.21 #7368), 099tbz (0.25 #463, 0.12 #53478, 0.12 #53477), 04mqgr (0.25 #560, 0.09 #54289, 0.01 #46737), 01l29r (0.24 #5028, 0.23 #5433, 0.23 #8268), 05b4l5x (0.23 #34033, 0.07 #33628, 0.04 #47805) >> Best rule #18292 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 05zh9c; 030g9z; 03qncl3; >> query: (?x1914, 07bdd_) <- award(?x1914, ?x2022), ?x2022 = 05p1dby, award_nominee(?x1914, ?x574) >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03xsby award 07bdd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 147.000 134.000 0.726 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #969-0gvvm6l PRED entity: 0gvvm6l PRED relation: film_release_region PRED expected values: 09c7w0 03gj2 035qy 01znc_ 06t2t 077qn => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 258): 09c7w0 (0.93 #5559, 0.93 #4702, 0.92 #7987), 03gj2 (0.90 #587, 0.87 #1299, 0.78 #19), 0345h (0.86 #595, 0.85 #1307, 0.78 #2876), 035qy (0.85 #1309, 0.80 #597, 0.78 #29), 06t2t (0.82 #620, 0.77 #1332, 0.58 #2901), 01znc_ (0.76 #1316, 0.75 #604, 0.69 #2885), 015qh (0.69 #603, 0.61 #1315, 0.38 #2884), 016wzw (0.69 #624, 0.54 #1336, 0.44 #56), 04gzd (0.65 #574, 0.57 #1286, 0.41 #2855), 03rk0 (0.65 #615, 0.51 #1327, 0.35 #2896) >> Best rule #5559 for best value: >> intensional similarity = 3 >> extensional distance = 716 >> proper extension: 047gn4y; 07g_0c; 03kg2v; 04grkmd; 03wbqc4; 019kyn; 0353tm; 0m3gy; 0dc7hc; >> query: (?x8176, 09c7w0) <- film_release_region(?x8176, ?x3277), music(?x8176, ?x3774), country(?x150, ?x3277) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5, 6, 14 EVAL 0gvvm6l film_release_region 077qn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 91.000 91.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvvm6l film_release_region 06t2t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvvm6l film_release_region 01znc_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvvm6l film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 91.000 91.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvvm6l film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gvvm6l film_release_region 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.930 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #968-0381pn PRED entity: 0381pn PRED relation: award_winner! PRED expected values: 01w92 => 119 concepts (20 used for prediction) PRED predicted values (max 10 best out of 173): 05qd_ (0.26 #8178, 0.18 #13008, 0.17 #123), 05mvd62 (0.25 #2776, 0.17 #1166, 0.16 #9221), 0dbpwb (0.17 #1190, 0.12 #2800, 0.12 #7633), 0fvf9q (0.17 #14, 0.12 #1624, 0.12 #6457), 03m9c8 (0.17 #1125, 0.12 #2735, 0.12 #7568), 0g5lhl7 (0.17 #449, 0.12 #2059, 0.11 #10114), 03jvmp (0.17 #350, 0.12 #1960, 0.08 #30954), 01zcrv (0.17 #1488, 0.12 #3098, 0.08 #4708), 03lpbx (0.17 #1596, 0.12 #3206, 0.06 #27367), 0hpt3 (0.17 #310, 0.12 #1920, 0.05 #8365) >> Best rule #8178 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 016tt2; 05qd_; 030_1m; 030_1_; 03jvmp; 024rgt; 031rq5; 025hwq; >> query: (?x14079, 05qd_) <- award_winner(?x3486, ?x14079), production_companies(?x7700, ?x14079), film_release_region(?x7700, ?x2645), ?x2645 = 03h64, film_distribution_medium(?x7700, ?x2099) >> conf = 0.26 => this is the best rule for 1 predicted values *> Best rule #575 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 01gb54; 03yxwq; *> query: (?x14079, 01w92) <- category(?x14079, ?x134), award_winner(?x3486, ?x14079), ?x3486 = 0m7yy, ?x134 = 08mbj5d, production_companies(?x7700, ?x14079) *> conf = 0.17 ranks of expected_values: 13 EVAL 0381pn award_winner! 01w92 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 119.000 20.000 0.263 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #967-045r_9 PRED entity: 045r_9 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 24): 0ch6mp2 (0.71 #2532, 0.71 #2724, 0.68 #2223), 02r96rf (0.61 #2527, 0.61 #2719, 0.57 #2218), 09vw2b7 (0.58 #2531, 0.57 #2723, 0.55 #2222), 01vx2h (0.40 #51, 0.33 #89, 0.28 #2537), 0dxtw (0.35 #2536, 0.34 #2728, 0.32 #2227), 01pvkk (0.28 #2730, 0.27 #2538, 0.26 #2229), 02vs3x5 (0.25 #26, 0.05 #2241, 0.05 #2742), 06qc5 (0.20 #69, 0.17 #107, 0.06 #183), 02ynfr (0.15 #2542, 0.14 #2734, 0.14 #2233), 02rh1dz (0.11 #125, 0.09 #2535, 0.09 #2727) >> Best rule #2532 for best value: >> intensional similarity = 4 >> extensional distance = 1025 >> proper extension: 07kb7vh; >> query: (?x9616, 0ch6mp2) <- language(?x9616, ?x254), film(?x1486, ?x9616), film_crew_role(?x9616, ?x137), country(?x9616, ?x94) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 045r_9 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.715 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #966-04328m PRED entity: 04328m PRED relation: people! PRED expected values: 0dryh9k => 170 concepts (166 used for prediction) PRED predicted values (max 10 best out of 53): 0dryh9k (0.60 #555, 0.52 #786, 0.48 #709), 01rv7x (0.33 #501, 0.23 #655, 0.15 #578), 041rx (0.23 #851, 0.19 #3700, 0.16 #4547), 0x67 (0.15 #318, 0.13 #1935, 0.11 #3321), 02sch9 (0.15 #574, 0.15 #1652, 0.13 #728), 07bch9 (0.13 #1948, 0.08 #331, 0.07 #4027), 0bpjh3 (0.12 #179, 0.06 #1180, 0.05 #2258), 03kbr (0.12 #203, 0.03 #1204, 0.03 #1281), 033tf_ (0.12 #4088, 0.12 #3780, 0.11 #3318), 07hwkr (0.09 #2861, 0.09 #3246, 0.09 #3400) >> Best rule #555 for best value: >> intensional similarity = 5 >> extensional distance = 18 >> proper extension: 025p38; >> query: (?x13987, 0dryh9k) <- location(?x13987, ?x3411), country(?x3411, ?x2146), ?x2146 = 03rk0, languages(?x13987, ?x1882), ?x1882 = 03k50 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04328m people! 0dryh9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 170.000 166.000 0.600 http://example.org/people/ethnicity/people #965-04ddm4 PRED entity: 04ddm4 PRED relation: nominated_for! PRED expected values: 05f4m9q => 67 concepts (45 used for prediction) PRED predicted values (max 10 best out of 168): 05f4m9q (0.44 #11, 0.23 #2618, 0.15 #1196), 05p09zm (0.44 #93, 0.13 #2700, 0.12 #1278), 05pcn59 (0.37 #2672, 0.07 #4094, 0.06 #5279), 07cbcy (0.33 #62, 0.19 #2669, 0.15 #1247), 0gq9h (0.24 #7649, 0.22 #6697, 0.22 #6936), 0gqy2 (0.22 #8777, 0.22 #6874, 0.22 #9728), 0bp_b2 (0.22 #8777, 0.22 #6874, 0.22 #9728), 05p1dby (0.22 #81, 0.17 #1266, 0.14 #1503), 05b4l5x (0.22 #5, 0.15 #2612, 0.07 #1190), 0bdw6t (0.22 #9728, 0.20 #9016, 0.20 #7588) >> Best rule #11 for best value: >> intensional similarity = 6 >> extensional distance = 7 >> proper extension: 04fzfj; 02pxmgz; 03459x; 0dqcs3; 03tn80; 04fv5b; 0dp7wt; >> query: (?x599, 05f4m9q) <- genre(?x599, ?x812), genre(?x599, ?x571), ?x812 = 01jfsb, ?x571 = 03npn, nominated_for(?x688, ?x599), ?x688 = 05b1610 >> conf = 0.44 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04ddm4 nominated_for! 05f4m9q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 45.000 0.444 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #964-042fk PRED entity: 042fk PRED relation: profession PRED expected values: 0fj9f => 171 concepts (126 used for prediction) PRED predicted values (max 10 best out of 107): 0fj9f (0.88 #1979, 0.88 #1683, 0.88 #6273), 01d_h8 (0.84 #10076, 0.37 #7556, 0.32 #9630), 02hrh1q (0.71 #14528, 0.71 #14232, 0.71 #7565), 039v1 (0.62 #13366, 0.12 #630, 0.06 #9513), 09jwl (0.53 #13348, 0.18 #15126, 0.17 #14978), 02jknp (0.48 #10078, 0.24 #17782, 0.21 #1488), 0dxtg (0.44 #10084, 0.43 #1494, 0.38 #9922), 012t_z (0.43 #9488, 0.38 #9922, 0.35 #8735), 03gjzk (0.43 #1496, 0.21 #12456, 0.20 #15863), 0cbd2 (0.41 #4448, 0.41 #5336, 0.39 #5484) >> Best rule #1979 for best value: >> intensional similarity = 5 >> extensional distance = 15 >> proper extension: 02mjmr; >> query: (?x13098, 0fj9f) <- location(?x13098, ?x3670), legislative_sessions(?x13098, ?x2019), student(?x7816, ?x13098), district_represented(?x2019, ?x177), basic_title(?x13098, ?x346) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042fk profession 0fj9f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 171.000 126.000 0.882 http://example.org/people/person/profession #963-0g83dv PRED entity: 0g83dv PRED relation: film_format PRED expected values: 07fb8_ => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 3): 07fb8_ (0.21 #11, 0.19 #27, 0.18 #37), 017fx5 (0.12 #4, 0.04 #14, 0.04 #70), 0cj16 (0.12 #125, 0.12 #277, 0.12 #309) >> Best rule #11 for best value: >> intensional similarity = 5 >> extensional distance = 78 >> proper extension: 031t2d; >> query: (?x4158, 07fb8_) <- film_crew_role(?x4158, ?x2178), film_crew_role(?x4158, ?x1284), ?x1284 = 0ch6mp2, ?x2178 = 01pvkk, executive_produced_by(?x4158, ?x4060) >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g83dv film_format 07fb8_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.212 http://example.org/film/film/film_format #962-0571m PRED entity: 0571m PRED relation: films! PRED expected values: 04jjy => 73 concepts (32 used for prediction) PRED predicted values (max 10 best out of 49): 018h2 (0.17 #334, 0.05 #2056, 0.03 #2212), 081pw (0.12 #3, 0.10 #2037, 0.07 #2507), 06d4h (0.12 #199, 0.08 #2233, 0.07 #2077), 05489 (0.12 #52, 0.07 #1302, 0.06 #1615), 04jjy (0.09 #319, 0.03 #2197, 0.03 #2041), 02_h0 (0.06 #411, 0.06 #99, 0.04 #2133), 0bq3x (0.06 #656, 0.06 #186, 0.06 #30), 0fx2s (0.06 #73, 0.05 #2107, 0.05 #2577), 0d1w9 (0.06 #192, 0.04 #2070, 0.03 #1286), 03hzt (0.06 #134, 0.02 #2168, 0.02 #2324) >> Best rule #334 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0sxg4; 021y7yw; 04jwly; 02xtxw; 0194zl; 03prz_; 01qbg5; 04xg2f; 09y6pb; 0c0zq; ... >> query: (?x3251, 018h2) <- genre(?x3251, ?x714), film(?x133, ?x3251), award_winner(?x3251, ?x4703), ?x714 = 0hn10 >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #319 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 45 *> proper extension: 0sxg4; 021y7yw; 04jwly; 02xtxw; 0194zl; 03prz_; 01qbg5; 04xg2f; 09y6pb; 0c0zq; ... *> query: (?x3251, 04jjy) <- genre(?x3251, ?x714), film(?x133, ?x3251), award_winner(?x3251, ?x4703), ?x714 = 0hn10 *> conf = 0.09 ranks of expected_values: 5 EVAL 0571m films! 04jjy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 73.000 32.000 0.170 http://example.org/film/film_subject/films #961-01wwvd2 PRED entity: 01wwvd2 PRED relation: award_winner! PRED expected values: 01mxqyk => 85 concepts (40 used for prediction) PRED predicted values (max 10 best out of 674): 01wwvc5 (0.82 #64361, 0.82 #38608, 0.48 #46657), 02l840 (0.29 #108, 0.10 #3324, 0.05 #53092), 01wwvd2 (0.28 #57922, 0.16 #64360, 0.05 #53092), 02qlg7s (0.16 #64360, 0.05 #53092, 0.03 #49874), 01cwhp (0.16 #64360, 0.05 #53092, 0.03 #49874), 028qdb (0.16 #64360, 0.05 #53092, 0.03 #49874), 026spg (0.16 #64360, 0.05 #53092, 0.03 #49874), 02qmncd (0.16 #64360, 0.02 #5853, 0.01 #33199), 02b25y (0.16 #64360, 0.02 #5240, 0.01 #8457), 02v3yy (0.16 #64360, 0.02 #5359, 0.01 #8576) >> Best rule #64361 for best value: >> intensional similarity = 3 >> extensional distance = 1487 >> proper extension: 035_2h; 039cq4; 01j53q; >> query: (?x4467, ?x2731) <- award_winner(?x2138, ?x4467), award_winner(?x4467, ?x2731), award_nominee(?x2138, ?x2451) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #53092 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1304 *> proper extension: 0f721s; 030_1_; 0gsg7; 09d5h; 03jvmp; 0cjdk; 0g5lhl7; 03mdt; 01w92; 01_8w2; ... *> query: (?x4467, ?x215) <- award_winner(?x3835, ?x4467), award_winner(?x2138, ?x4467), award_winner(?x3835, ?x215) *> conf = 0.05 ranks of expected_values: 49 EVAL 01wwvd2 award_winner! 01mxqyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 85.000 40.000 0.819 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #960-0395lw PRED entity: 0395lw PRED relation: role! PRED expected values: 03ryks 01304j => 92 concepts (54 used for prediction) PRED predicted values (max 10 best out of 1086): 0137g1 (0.70 #12569, 0.67 #9338, 0.62 #17646), 04bpm6 (0.67 #9291, 0.67 #6985, 0.60 #19447), 01wxdn3 (0.67 #9627, 0.57 #11472, 0.56 #12396), 0140t7 (0.67 #8236, 0.56 #11923, 0.50 #18849), 01vsnff (0.67 #9311, 0.50 #15776, 0.50 #8852), 01wgjj5 (0.67 #9485, 0.50 #7179, 0.50 #5333), 0161sp (0.67 #9349, 0.50 #5197, 0.44 #12118), 0326tc (0.60 #6796, 0.57 #10948, 0.57 #10026), 01vs4ff (0.60 #13217, 0.56 #12292, 0.50 #15061), 06x4l_ (0.57 #11190, 0.57 #9807, 0.50 #15810) >> Best rule #12569 for best value: >> intensional similarity = 16 >> extensional distance = 8 >> proper extension: 018j2; >> query: (?x1432, 0137g1) <- performance_role(?x212, ?x1432), role(?x1432, ?x2957), role(?x1432, ?x1473), role(?x1432, ?x1437), role(?x1432, ?x1225), role(?x1432, ?x885), role(?x1432, ?x314), ?x1473 = 0g2dz, role(?x1225, ?x569), ?x2957 = 01v8y9, role(?x1148, ?x1432), role(?x1660, ?x1225), ?x885 = 0dwtp, role(?x1291, ?x314), role(?x217, ?x314), ?x1437 = 01vdm0 >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #19676 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 13 *> proper extension: 0dwt5; *> query: (?x1432, 03ryks) <- performance_role(?x212, ?x1432), role(?x1432, ?x4616), role(?x1432, ?x2957), role(?x1432, ?x1473), role(?x1432, ?x1225), ?x1473 = 0g2dz, role(?x1225, ?x894), ?x894 = 03m5k, role(?x1466, ?x2957), role(?x4052, ?x2957), ?x4616 = 01rhl, role(?x120, ?x1432), ?x1466 = 03bx0bm *> conf = 0.53 ranks of expected_values: 15, 60 EVAL 0395lw role! 01304j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 92.000 54.000 0.700 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0395lw role! 03ryks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 92.000 54.000 0.700 http://example.org/music/artist/track_contributions./music/track_contribution/role #959-0ptx_ PRED entity: 0ptx_ PRED relation: genre PRED expected values: 0lsxr => 70 concepts (68 used for prediction) PRED predicted values (max 10 best out of 88): 05p553 (0.44 #4, 0.33 #6624, 0.33 #6984), 01jfsb (0.35 #2419, 0.33 #2780, 0.32 #2900), 02l7c8 (0.33 #1579, 0.33 #16, 0.31 #1097), 02kdv5l (0.32 #242, 0.30 #723, 0.28 #1203), 0lsxr (0.27 #249, 0.20 #369, 0.20 #490), 04xvlr (0.25 #361, 0.20 #1805, 0.19 #482), 03k9fj (0.24 #732, 0.21 #3380, 0.21 #4705), 06cvj (0.22 #3, 0.09 #3011, 0.09 #724), 01j1n2 (0.22 #61, 0.05 #1504, 0.04 #1142), 0gsy3b (0.22 #95, 0.02 #2742, 0.01 #1296) >> Best rule #4 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 0d8w2n; >> query: (?x6111, 05p553) <- genre(?x6111, ?x53), featured_film_locations(?x6111, ?x2850), ?x2850 = 0cr3d >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #249 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 042fgh; *> query: (?x6111, 0lsxr) <- genre(?x6111, ?x53), featured_film_locations(?x6111, ?x739), honored_for(?x6111, ?x6213) *> conf = 0.27 ranks of expected_values: 5 EVAL 0ptx_ genre 0lsxr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 70.000 68.000 0.444 http://example.org/film/film/genre #958-08hsww PRED entity: 08hsww PRED relation: film PRED expected values: 07x4qr => 75 concepts (73 used for prediction) PRED predicted values (max 10 best out of 194): 08jgk1 (0.35 #82332, 0.35 #84122, 0.34 #75170), 02825cv (0.15 #2931, 0.11 #1142, 0.10 #4720), 034qzw (0.15 #2122, 0.11 #333, 0.05 #3911), 02mc5v (0.15 #3189), 08952r (0.15 #2505), 07h9gp (0.15 #2054), 06fpsx (0.11 #1337, 0.10 #4915, 0.09 #6704), 07p62k (0.11 #352, 0.09 #5719, 0.05 #3930), 0h1x5f (0.11 #1582, 0.05 #5160, 0.04 #6949), 05fm6m (0.11 #1319, 0.05 #4897, 0.04 #6686) >> Best rule #82332 for best value: >> intensional similarity = 2 >> extensional distance = 1718 >> proper extension: 042l3v; 03xsby; 0d07j8; 079hvk; 01k5zk; 0kk9v; 037hgm; 0b80__; 0hwqz; 01tnbn; ... >> query: (?x4719, ?x1631) <- award_nominee(?x4719, ?x237), nominated_for(?x4719, ?x1631) >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #2192 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 11 *> proper extension: 04s430; 0pgm3; *> query: (?x4719, 07x4qr) <- film(?x4719, ?x7311), ?x7311 = 0g9z_32 *> conf = 0.08 ranks of expected_values: 62 EVAL 08hsww film 07x4qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 75.000 73.000 0.348 http://example.org/film/actor/film./film/performance/film #957-03v3xp PRED entity: 03v3xp PRED relation: award_nominee PRED expected values: 02tr7d => 105 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1251): 016gr2 (0.81 #83821, 0.81 #125724, 0.81 #125723), 01hkhq (0.81 #83821, 0.81 #125724, 0.81 #125723), 09l3p (0.81 #83821, 0.81 #125724, 0.81 #125723), 06t61y (0.81 #83821, 0.81 #125724, 0.81 #125723), 015rkw (0.79 #370, 0.77 #107103, 0.75 #48891), 02tr7d (0.75 #104774, 0.74 #51221, 0.74 #41904), 03v3xp (0.63 #807, 0.17 #125725, 0.16 #128053), 032_jg (0.19 #41903, 0.18 #48890, 0.17 #51219), 09yrh (0.19 #41903, 0.18 #48890, 0.17 #51219), 02qgqt (0.17 #125725, 0.14 #130381, 0.11 #20) >> Best rule #83821 for best value: >> intensional similarity = 3 >> extensional distance = 459 >> proper extension: 0gv40; 01kph_c; >> query: (?x3604, ?x374) <- award_nominee(?x374, ?x3604), award_winner(?x472, ?x3604), people(?x743, ?x3604) >> conf = 0.81 => this is the best rule for 4 predicted values *> Best rule #104774 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 819 *> proper extension: 01nrgq; *> query: (?x3604, ?x374) <- award_winner(?x3604, ?x1739), award_winner(?x3604, ?x374), religion(?x1739, ?x1985) *> conf = 0.75 ranks of expected_values: 6 EVAL 03v3xp award_nominee 02tr7d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 105.000 56.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #956-02x1z2s PRED entity: 02x1z2s PRED relation: award! PRED expected values: 0jz9f => 48 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1729): 05qd_ (0.81 #16854, 0.75 #10109, 0.73 #16855), 017s11 (0.81 #16854, 0.75 #10109, 0.73 #16855), 024rgt (0.81 #16854, 0.75 #10109, 0.70 #10108), 0kk9v (0.81 #16854, 0.73 #16855, 0.70 #10108), 0g1rw (0.50 #10275, 0.46 #17018, 0.44 #13647), 04wvhz (0.50 #6983, 0.38 #10357, 0.33 #13729), 02ld6x (0.50 #4093, 0.23 #30345, 0.17 #23600), 05_k56 (0.50 #3617, 0.23 #30345, 0.17 #23600), 04jspq (0.50 #5304, 0.23 #30345, 0.17 #23600), 0fvf9q (0.38 #16878, 0.33 #20228, 0.25 #10135) >> Best rule #16854 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 02grdc; 024fz9; >> query: (?x3911, ?x541) <- award_winner(?x3911, ?x617), award_winner(?x3911, ?x541), company(?x346, ?x617), award_winner(?x762, ?x617), award(?x382, ?x3911) >> conf = 0.81 => this is the best rule for 4 predicted values *> Best rule #10137 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 01lk0l; *> query: (?x3911, 0jz9f) <- award_winner(?x3911, ?x617), company(?x346, ?x617), award_winner(?x762, ?x617), film(?x617, ?x136) *> conf = 0.38 ranks of expected_values: 13 EVAL 02x1z2s award! 0jz9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 48.000 10.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #955-0br1xn PRED entity: 0br1xn PRED relation: team PRED expected values: 02plv57 02pqcfz => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 48): 026wlnm (0.86 #123, 0.80 #155, 0.80 #139), 02q4ntp (0.80 #173, 0.80 #141, 0.77 #205), 03by7wc (0.80 #199, 0.80 #135, 0.75 #167), 02pzy52 (0.75 #174, 0.73 #94, 0.64 #126), 04088s0 (0.75 #68, 0.67 #100, 0.64 #116), 02pqcfz (0.75 #67, 0.67 #99, 0.64 #83), 02pjzvh (0.75 #37, 0.58 #101, 0.57 #197), 02plv57 (0.71 #113, 0.67 #97, 0.65 #161), 03y9p40 (0.70 #202, 0.70 #170, 0.65 #154), 02py8_w (0.67 #198, 0.65 #166, 0.60 #150) >> Best rule #123 for best value: >> intensional similarity = 9 >> extensional distance = 12 >> proper extension: 0b_71r; 0b_756; 0bqthy; >> query: (?x4937, 026wlnm) <- team(?x4937, ?x10171), team(?x4937, ?x9576), team(?x4937, ?x8528), team(?x4937, ?x4938), ?x4938 = 027yf83, ?x8528 = 091tgz, ?x9576 = 02qk2d5, team(?x7042, ?x10171), ?x7042 = 0b_72t >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #67 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 6 *> proper extension: 0cc8q3; 0br1x_; *> query: (?x4937, 02pqcfz) <- team(?x4937, ?x10171), team(?x4937, ?x9576), team(?x4937, ?x8728), team(?x4937, ?x8528), team(?x4937, ?x4938), ?x4938 = 027yf83, ?x8528 = 091tgz, ?x9576 = 02qk2d5, ?x10171 = 026w398, ?x8728 = 026xxv_ *> conf = 0.75 ranks of expected_values: 6, 8 EVAL 0br1xn team 02pqcfz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 37.000 37.000 0.857 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team EVAL 0br1xn team 02plv57 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 37.000 37.000 0.857 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #954-0gg9_5q PRED entity: 0gg9_5q PRED relation: produced_by! PRED expected values: 03mh_tp 047vnkj => 133 concepts (35 used for prediction) PRED predicted values (max 10 best out of 885): 0glqh5_ (0.33 #1452, 0.06 #7107, 0.04 #10876), 087vnr5 (0.33 #776, 0.01 #16801, 0.01 #19633), 01s7w3 (0.18 #2699, 0.10 #3641, 0.09 #7412), 072x7s (0.18 #2031, 0.10 #2973, 0.07 #3917), 08xvpn (0.18 #2735, 0.10 #3677, 0.07 #4621), 011xg5 (0.18 #2653, 0.10 #3595, 0.07 #4539), 0cc5qkt (0.18 #2206, 0.10 #3148, 0.07 #4092), 0hx4y (0.18 #2140, 0.10 #3082, 0.07 #4026), 02rb84n (0.18 #2044, 0.10 #2986, 0.07 #3930), 0jqn5 (0.18 #2016, 0.10 #2958, 0.07 #3902) >> Best rule #1452 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0gy6z9; >> query: (?x3744, 0glqh5_) <- produced_by(?x3743, ?x3744), place_of_birth(?x3744, ?x1523), ?x3743 = 047d21r, type_of_union(?x3744, ?x566), nationality(?x3744, ?x94) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3327 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 18 *> proper extension: 0343h; 03y2kr; 01s7z0; *> query: (?x3744, 047vnkj) <- executive_produced_by(?x4967, ?x3744), type_of_union(?x3744, ?x566), film_crew_role(?x4967, ?x468), profession(?x3744, ?x319), organizations_founded(?x3744, ?x1478) *> conf = 0.10 ranks of expected_values: 20, 151 EVAL 0gg9_5q produced_by! 047vnkj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 133.000 35.000 0.333 http://example.org/film/film/produced_by EVAL 0gg9_5q produced_by! 03mh_tp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 133.000 35.000 0.333 http://example.org/film/film/produced_by #953-01jr6 PRED entity: 01jr6 PRED relation: place PRED expected values: 01jr6 => 183 concepts (119 used for prediction) PRED predicted values (max 10 best out of 328): 0135g (0.23 #3612, 0.04 #1674, 0.03 #3221), 0dwh5 (0.23 #3612, 0.04 #2046), 0qxzd (0.23 #3612, 0.04 #1948), 01jr6 (0.18 #11863, 0.16 #58267, 0.16 #61364), 0kpzy (0.18 #11863, 0.16 #58267, 0.16 #61364), 06pvr (0.18 #11863, 0.16 #58267, 0.16 #61364), 01n7q (0.18 #11863, 0.16 #58267, 0.16 #61364), 09c7w0 (0.18 #11863, 0.16 #58267, 0.16 #61364), 0rh6k (0.11 #53622, 0.06 #1033, 0.04 #2064), 030qb3t (0.11 #53622, 0.03 #4674, 0.03 #3642) >> Best rule #3612 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0rqyx; >> query: (?x3976, ?x5232) <- adjoins(?x3976, ?x2552), contains(?x6815, ?x3976), county(?x5232, ?x6815) >> conf = 0.23 => this is the best rule for 3 predicted values *> Best rule #11863 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 0d23k; 0d6yv; *> query: (?x3976, ?x94) <- citytown(?x4227, ?x3976), featured_film_locations(?x974, ?x3976), contains(?x94, ?x4227) *> conf = 0.18 ranks of expected_values: 4 EVAL 01jr6 place 01jr6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 183.000 119.000 0.225 http://example.org/location/hud_county_place/place #952-07z2lx PRED entity: 07z2lx PRED relation: category_of PRED expected values: 0gcf2r => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 4): 0gcf2r (0.57 #2, 0.55 #65, 0.54 #129), 0c4ys (0.34 #720, 0.27 #741, 0.25 #573), 0g_w (0.21 #762, 0.12 #45, 0.10 #298), 04jhhng (0.02 #81, 0.02 #102, 0.02 #145) >> Best rule #2 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0ck27z; 0bdx29; 04ldyx1; 09v82c0; 07kjk7c; >> query: (?x6024, 0gcf2r) <- award(?x496, ?x6024), award(?x1434, ?x6024), ?x1434 = 0ddd0gc, nominated_for(?x6024, ?x8597) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07z2lx category_of 0gcf2r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.571 http://example.org/award/award_category/category_of #951-0gmcwlb PRED entity: 0gmcwlb PRED relation: honored_for! PRED expected values: 0hr3c8y => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 102): 02q690_ (0.09 #522, 0.07 #1230, 0.05 #3591), 05c1t6z (0.08 #483, 0.08 #1191, 0.05 #3552), 0gvstc3 (0.07 #496, 0.05 #1204, 0.04 #3092), 0gx_st (0.07 #499, 0.03 #1207, 0.03 #3568), 02hn5v (0.07 #265, 0.03 #1799, 0.03 #501), 0275n3y (0.07 #532, 0.06 #1240, 0.04 #3601), 03nnm4t (0.07 #531, 0.05 #1239, 0.04 #3600), 0drtv8 (0.07 #523, 0.05 #1231, 0.03 #1821), 0bxs_d (0.07 #568, 0.04 #1276, 0.02 #2574), 03gwpw2 (0.06 #477, 0.05 #1775, 0.05 #2483) >> Best rule #522 for best value: >> intensional similarity = 4 >> extensional distance = 105 >> proper extension: 01h1bf; >> query: (?x1370, 02q690_) <- nominated_for(?x9415, ?x1370), honored_for(?x1442, ?x1370), location(?x9415, ?x739), student(?x4268, ?x9415) >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #478 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 105 *> proper extension: 01h1bf; *> query: (?x1370, 0hr3c8y) <- nominated_for(?x9415, ?x1370), honored_for(?x1442, ?x1370), location(?x9415, ?x739), student(?x4268, ?x9415) *> conf = 0.04 ranks of expected_values: 33 EVAL 0gmcwlb honored_for! 0hr3c8y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 85.000 85.000 0.093 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #950-01q9b9 PRED entity: 01q9b9 PRED relation: people! PRED expected values: 0x67 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 49): 0x67 (0.21 #164, 0.19 #241, 0.18 #3553), 041rx (0.20 #81, 0.19 #235, 0.19 #158), 0xnvg (0.17 #629, 0.12 #1014, 0.10 #167), 02g7sp (0.12 #18, 0.06 #480, 0.06 #557), 07bch9 (0.11 #3004, 0.09 #870, 0.08 #1794), 063k3h (0.11 #3004, 0.04 #2187, 0.04 #1802), 033tf_ (0.11 #854, 0.10 #161, 0.10 #469), 048z7l (0.10 #117, 0.07 #656, 0.04 #887), 07hwkr (0.10 #89, 0.06 #397, 0.06 #2553), 013xrm (0.08 #1945, 0.07 #2099, 0.06 #2330) >> Best rule #164 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 02_wxh; 0sx5w; 0hcvy; >> query: (?x7512, 0x67) <- influenced_by(?x7512, ?x4072), actor(?x623, ?x7512), award(?x7512, ?x594) >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q9b9 people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.208 http://example.org/people/ethnicity/people #949-0155w PRED entity: 0155w PRED relation: parent_genre! PRED expected values: 0gywn 0175yg => 56 concepts (40 used for prediction) PRED predicted values (max 10 best out of 302): 01ym9b (0.50 #1816, 0.45 #2071, 0.40 #544), 07ym47 (0.40 #1833, 0.36 #2088, 0.25 #307), 016_nr (0.40 #1838, 0.36 #2093, 0.25 #312), 017371 (0.40 #902, 0.33 #139, 0.29 #1410), 0mhfr (0.40 #782, 0.33 #19, 0.29 #1290), 05jt_ (0.33 #98, 0.31 #2897, 0.29 #3151), 06cp5 (0.33 #71, 0.30 #1851, 0.27 #2106), 059kh (0.33 #39, 0.30 #1819, 0.27 #2074), 064t9 (0.33 #9, 0.29 #1280, 0.25 #263), 016jhr (0.33 #10, 0.29 #1536, 0.25 #264) >> Best rule #1816 for best value: >> intensional similarity = 8 >> extensional distance = 8 >> proper extension: 07sbbz2; 03_d0; 02x8m; 0glt670; 06j6l; >> query: (?x7440, 01ym9b) <- parent_genre(?x482, ?x7440), artists(?x7440, ?x9128), artists(?x7440, ?x5405), artists(?x7440, ?x3316), ?x3316 = 0407f, award_winner(?x9945, ?x9128), ?x9945 = 03qpp9, award(?x5405, ?x567) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #5611 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 75 *> proper extension: 0g64p; *> query: (?x7440, ?x2996) <- parent_genre(?x9935, ?x7440), parent_genre(?x5355, ?x7440), parent_genre(?x9935, ?x2996), parent_genre(?x5355, ?x505), artists(?x505, ?x2662), ?x2662 = 045zr *> conf = 0.12 ranks of expected_values: 113, 150 EVAL 0155w parent_genre! 0175yg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 56.000 40.000 0.500 http://example.org/music/genre/parent_genre EVAL 0155w parent_genre! 0gywn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 56.000 40.000 0.500 http://example.org/music/genre/parent_genre #948-0cwy47 PRED entity: 0cwy47 PRED relation: film_festivals PRED expected values: 059_y8d => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 21): 0kfhjq0 (0.09 #407, 0.07 #512, 0.07 #618), 03nn7l2 (0.08 #122, 0.05 #356, 0.05 #461), 0gg7gsl (0.07 #614, 0.05 #656, 0.05 #677), 04_m9gk (0.06 #436, 0.05 #160, 0.05 #352), 05ys0wz (0.06 #469, 0.05 #364, 0.03 #151), 0j63cyr (0.06 #616, 0.04 #658, 0.04 #679), 0bmj62v (0.05 #159, 0.05 #180, 0.03 #498), 0hrcs29 (0.04 #99, 0.04 #290, 0.03 #141), 059_y8d (0.04 #107, 0.03 #341, 0.03 #636), 0fpkxfd (0.04 #111, 0.03 #345, 0.03 #429) >> Best rule #407 for best value: >> intensional similarity = 4 >> extensional distance = 72 >> proper extension: 0bby9p5; >> query: (?x951, 0kfhjq0) <- titles(?x162, ?x951), written_by(?x951, ?x4477), film_release_region(?x951, ?x142), ?x142 = 0jgd >> conf = 0.09 => this is the best rule for 1 predicted values *> Best rule #107 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 22 *> proper extension: 09fc83; 02bqvs; *> query: (?x951, 059_y8d) <- film_release_region(?x951, ?x1264), film_release_region(?x951, ?x142), ?x1264 = 0345h, film(?x2378, ?x951), film_release_region(?x80, ?x142) *> conf = 0.04 ranks of expected_values: 9 EVAL 0cwy47 film_festivals 059_y8d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 124.000 124.000 0.095 http://example.org/film/film/film_festivals #947-042d1 PRED entity: 042d1 PRED relation: nationality PRED expected values: 09c7w0 => 179 concepts (156 used for prediction) PRED predicted values (max 10 best out of 120): 09c7w0 (0.87 #13868, 0.87 #13768, 0.87 #13568), 07z1m (0.71 #1503, 0.58 #2807, 0.55 #4118), 059rby (0.33 #9757, 0.31 #12066, 0.03 #3915), 02jx1 (0.27 #633, 0.12 #7173, 0.12 #8078), 020d5 (0.25 #89, 0.09 #689, 0.02 #2896), 07ssc (0.18 #615, 0.13 #8060, 0.11 #7155), 0345h (0.11 #1333, 0.08 #5256, 0.07 #5458), 04_1l0v (0.11 #4119), 07c5l (0.11 #4119), 03rk0 (0.11 #4266, 0.10 #10806, 0.10 #1649) >> Best rule #13868 for best value: >> intensional similarity = 3 >> extensional distance = 1086 >> proper extension: 018fmr; 092ggq; >> query: (?x10511, ?x94) <- student(?x6919, ?x10511), contains(?x94, ?x6919), ?x94 = 09c7w0 >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 042d1 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 156.000 0.870 http://example.org/people/person/nationality #946-047lj PRED entity: 047lj PRED relation: country! PRED expected values: 07jjt 0dwxr => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 31): 01lb14 (0.72 #598, 0.70 #319, 0.62 #443), 06wrt (0.66 #599, 0.65 #444, 0.62 #10), 0dwxr (0.62 #17, 0.54 #110, 0.42 #1025), 01z27 (0.61 #321, 0.54 #104, 0.50 #11), 09w1n (0.61 #325, 0.50 #604, 0.46 #449), 0194d (0.60 #615, 0.58 #460, 0.54 #119), 07rlg (0.54 #94, 0.50 #1, 0.43 #590), 07jjt (0.52 #603, 0.46 #448, 0.42 #1025), 019tzd (0.52 #331, 0.50 #21, 0.48 #610), 096f8 (0.50 #6, 0.46 #99, 0.42 #1025) >> Best rule #598 for best value: >> intensional similarity = 3 >> extensional distance = 56 >> proper extension: 05v8c; >> query: (?x404, 01lb14) <- country(?x520, ?x404), film_release_region(?x6882, ?x404), ?x6882 = 043tvp3 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #17 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0261m; *> query: (?x404, 0dwxr) <- teams(?x404, ?x11736), partially_contains(?x455, ?x404), locations(?x7241, ?x404) *> conf = 0.62 ranks of expected_values: 3, 8 EVAL 047lj country! 0dwxr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 119.000 0.724 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 047lj country! 07jjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 119.000 119.000 0.724 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #945-0h1cdwq PRED entity: 0h1cdwq PRED relation: film_release_region PRED expected values: 0d060g 03gj2 07ylj 02vzc => 73 concepts (69 used for prediction) PRED predicted values (max 10 best out of 165): 03gj2 (0.90 #978, 0.90 #429, 0.89 #1115), 0d060g (0.90 #964, 0.86 #1101, 0.79 #415), 02vzc (0.87 #861, 0.82 #2097, 0.81 #2234), 015qh (0.85 #441, 0.72 #1127, 0.72 #990), 03spz (0.80 #1038, 0.77 #1175, 0.76 #900), 0ctw_b (0.72 #430, 0.71 #1116, 0.68 #979), 01p1v (0.72 #451, 0.70 #1000, 0.68 #1137), 03rj0 (0.72 #457, 0.67 #1143, 0.66 #868), 016wzw (0.64 #463, 0.62 #1149, 0.61 #874), 07f1x (0.56 #922, 0.51 #511, 0.41 #1060) >> Best rule #978 for best value: >> intensional similarity = 10 >> extensional distance = 77 >> proper extension: 087wc7n; 0407yj_; >> query: (?x428, 03gj2) <- film_release_region(?x428, ?x2152), film_release_region(?x428, ?x1603), film_release_region(?x428, ?x1499), film_release_region(?x428, ?x512), film_release_region(?x428, ?x344), ?x512 = 07ssc, ?x2152 = 06mkj, ?x1499 = 01znc_, ?x344 = 04gzd, ?x1603 = 06bnz >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 16 EVAL 0h1cdwq film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 69.000 0.899 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h1cdwq film_release_region 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 73.000 69.000 0.899 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h1cdwq film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 69.000 0.899 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0h1cdwq film_release_region 0d060g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 69.000 0.899 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #944-0g9z_32 PRED entity: 0g9z_32 PRED relation: film! PRED expected values: 016k6x => 95 concepts (69 used for prediction) PRED predicted values (max 10 best out of 948): 0gls4q_ (0.24 #66384, 0.23 #66383, 0.20 #53939), 0fxky3 (0.23 #66383, 0.20 #53939, 0.19 #43567), 0dbc1s (0.23 #66383, 0.20 #53939, 0.19 #43567), 02mjmr (0.22 #16601, 0.16 #12453, 0.15 #8299), 0603qp (0.20 #41492, 0.18 #80902, 0.17 #29045), 0bq2g (0.13 #603, 0.08 #15130, 0.07 #4753), 0f5xn (0.11 #11345, 0.09 #966, 0.07 #5116), 0f502 (0.09 #6985, 0.09 #9062, 0.09 #760), 01ggc9 (0.09 #7947, 0.09 #10024, 0.09 #1722), 03fbb6 (0.09 #975, 0.07 #3050, 0.07 #5125) >> Best rule #66384 for best value: >> intensional similarity = 4 >> extensional distance = 383 >> proper extension: 05jf85; 0dj0m5; 0209xj; 0jzw; 084qpk; 0kv2hv; 03cvwkr; 0gjk1d; 0436yk; 06ybb1; ... >> query: (?x7311, ?x7503) <- genre(?x7311, ?x258), written_by(?x7311, ?x7503), film(?x905, ?x7311), location(?x7503, ?x1523) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #112897 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 604 *> proper extension: 0n04r; *> query: (?x7311, 016k6x) <- film_crew_role(?x7311, ?x468), produced_by(?x7311, ?x2790), film(?x3013, ?x7311), film(?x2353, ?x7311), award_winner(?x704, ?x2353), people(?x1446, ?x3013) *> conf = 0.01 ranks of expected_values: 772 EVAL 0g9z_32 film! 016k6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 95.000 69.000 0.239 http://example.org/film/actor/film./film/performance/film #943-04kf4 PRED entity: 04kf4 PRED relation: place_of_death! PRED expected values: 0k4gf => 189 concepts (71 used for prediction) PRED predicted values (max 10 best out of 747): 0372p (0.33 #1516, 0.29 #6805, 0.11 #6804), 02lt8 (0.25 #164, 0.02 #19064, 0.02 #20578), 0399p (0.20 #1156, 0.10 #3425, 0.07 #6445), 03j43 (0.20 #832, 0.10 #3101, 0.07 #6121), 0gs7x (0.20 #1411, 0.10 #3680, 0.07 #6700), 02hh8j (0.20 #1263, 0.10 #3532, 0.07 #6552), 01rgr (0.20 #1253, 0.10 #3522, 0.07 #6542), 0knjh (0.20 #1201, 0.10 #3470, 0.07 #6490), 01vh096 (0.20 #1193, 0.10 #3462, 0.07 #6482), 0ct9_ (0.20 #1174, 0.10 #3443, 0.07 #6463) >> Best rule #1516 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 019xz9; >> query: (?x5577, ?x3994) <- time_zones(?x5577, ?x2864), place_of_birth(?x3994, ?x5577), influenced_by(?x3994, ?x12259), ?x12259 = 015n8 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #19656 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 06mkj; 07g0_; *> query: (?x5577, ?x1211) <- time_zones(?x5577, ?x2864), place_of_death(?x8177, ?x5577), contains(?x1264, ?x5577), influenced_by(?x1211, ?x8177) *> conf = 0.04 ranks of expected_values: 510 EVAL 04kf4 place_of_death! 0k4gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 189.000 71.000 0.333 http://example.org/people/deceased_person/place_of_death #942-0736qr PRED entity: 0736qr PRED relation: award_nominee PRED expected values: 02d4ct => 91 concepts (26 used for prediction) PRED predicted values (max 10 best out of 669): 02d4ct (0.81 #46689, 0.80 #58361, 0.80 #58362), 0736qr (0.43 #6980, 0.28 #9338, 0.15 #32681), 043js (0.33 #579, 0.15 #32681, 0.10 #60698), 06dv3 (0.33 #43, 0.15 #32681, 0.03 #9381), 024bbl (0.33 #1109, 0.15 #32681, 0.03 #8111), 048lv (0.33 #290, 0.15 #32681, 0.02 #30635), 03k7bd (0.33 #395, 0.15 #32681, 0.01 #7397), 01jw4r (0.33 #1883, 0.15 #32681), 0zcbl (0.33 #1585, 0.15 #32681), 01520h (0.33 #1542, 0.15 #32681) >> Best rule #46689 for best value: >> intensional similarity = 3 >> extensional distance = 1123 >> proper extension: 06k02; 0k8y7; 02lymt; 03sww; 0c12h; 01hmk9; 03f7jfh; 04gr35; 0hqly; 06cl2w; ... >> query: (?x12551, ?x969) <- film(?x12551, ?x1640), nationality(?x12551, ?x94), award_nominee(?x969, ?x12551) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0736qr award_nominee 02d4ct CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 26.000 0.807 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #941-03_9r PRED entity: 03_9r PRED relation: languages! PRED expected values: 020qr4 => 76 concepts (44 used for prediction) PRED predicted values (max 10 best out of 354): 020qr4 (0.45 #3044, 0.44 #2792, 0.40 #2031), 015g28 (0.40 #2086, 0.40 #1582, 0.36 #3099), 06dfz1 (0.33 #153, 0.27 #3191, 0.25 #1168), 0fhzwl (0.33 #163, 0.25 #1178, 0.25 #669), 01b7h8 (0.33 #190, 0.25 #1205, 0.25 #696), 039cq4 (0.33 #118, 0.25 #1133, 0.25 #624), 05sy2k_ (0.33 #58, 0.25 #1073, 0.25 #564), 017dbx (0.33 #241, 0.25 #1256, 0.25 #747), 06r1k (0.33 #210, 0.25 #1225, 0.25 #716), 07gbf (0.33 #183, 0.25 #1198, 0.25 #689) >> Best rule #3044 for best value: >> intensional similarity = 9 >> extensional distance = 9 >> proper extension: 02bjrlw; 06nm1; 0jzc; 064_8sq; 01r2l; 04h9h; >> query: (?x2164, 020qr4) <- language(?x8557, ?x2164), language(?x6578, ?x2164), language(?x2699, ?x2164), nominated_for(?x500, ?x2699), major_field_of_study(?x7660, ?x2164), production_companies(?x2699, ?x541), film_crew_role(?x8557, ?x137), film_release_region(?x6578, ?x94), music(?x6578, ?x9064) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_9r languages! 020qr4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 44.000 0.455 http://example.org/tv/tv_program/languages #940-0m2rv PRED entity: 0m2rv PRED relation: place_founded! PRED expected values: 035nm => 109 concepts (83 used for prediction) PRED predicted values (max 10 best out of 44): 032dg7 (0.12 #383, 0.04 #1049, 0.03 #1160), 020h2v (0.12 #381, 0.04 #1047, 0.03 #1158), 04mkft (0.12 #365, 0.04 #1031, 0.03 #1142), 01795t (0.12 #341, 0.04 #1007, 0.03 #1118), 03qwyc (0.07 #725, 0.06 #836, 0.05 #947), 03_c8p (0.04 #1071, 0.03 #1182, 0.02 #1406), 03d6fyn (0.04 #1023, 0.03 #1134, 0.02 #1580), 01frpd (0.04 #1106, 0.03 #1217, 0.02 #1663), 01jx9 (0.04 #1026, 0.02 #1361, 0.02 #1472), 04czhj (0.03 #1219, 0.02 #1443, 0.02 #1554) >> Best rule #383 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 0b_cr; >> query: (?x3372, 032dg7) <- county(?x3372, ?x13203), category(?x3372, ?x134), place_of_birth(?x11624, ?x3372), team(?x11624, ?x4519) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0m2rv place_founded! 035nm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 83.000 0.125 http://example.org/organization/organization/place_founded #939-02gd6x PRED entity: 02gd6x PRED relation: language PRED expected values: 04306rv => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 31): 06mx8 (0.31 #395, 0.08 #508, 0.02 #3776), 064_8sq (0.23 #358, 0.21 #754, 0.20 #584), 0jzc (0.17 #74, 0.08 #356, 0.07 #469), 02hxcvy (0.17 #88, 0.08 #370, 0.05 #596), 03_9r (0.15 #347, 0.06 #1138, 0.06 #743), 04306rv (0.15 #568, 0.14 #116, 0.13 #455), 0t_2 (0.10 #238, 0.07 #464, 0.05 #577), 03hkp (0.10 #239, 0.05 #635, 0.03 #861), 06nm1 (0.09 #3616, 0.09 #3900, 0.09 #4128), 05zjd (0.08 #362, 0.07 #475, 0.05 #588) >> Best rule #395 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 0crh5_f; 043sct5; 0bs8hvm; >> query: (?x6345, ?x6820) <- genre(?x6345, ?x53), film_festivals(?x6345, ?x7988), titles(?x6820, ?x6345), titles(?x4442, ?x6345), languages_spoken(?x913, ?x4442) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #568 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 18 *> proper extension: 0fq27fp; *> query: (?x6345, 04306rv) <- genre(?x6345, ?x53), film_festivals(?x6345, ?x7988), film_release_region(?x6345, ?x304), locations(?x7988, ?x1646) *> conf = 0.15 ranks of expected_values: 6 EVAL 02gd6x language 04306rv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 106.000 106.000 0.308 http://example.org/film/film/language #938-084m3 PRED entity: 084m3 PRED relation: location PRED expected values: 052p7 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 210): 02_286 (0.33 #2449, 0.32 #7273, 0.25 #3253), 0cr3d (0.25 #145, 0.12 #4165, 0.11 #1753), 0rh6k (0.25 #4, 0.08 #3220, 0.06 #8848), 0yj9v (0.25 #653, 0.06 #4673, 0.04 #7085), 0s9z_ (0.25 #587, 0.06 #4607, 0.04 #7019), 0ftvg (0.25 #514, 0.03 #9358, 0.02 #11771), 030qb3t (0.25 #12950, 0.22 #2495, 0.22 #35465), 0r0m6 (0.22 #2630, 0.17 #3434, 0.12 #4238), 0dclg (0.20 #921, 0.11 #1725, 0.08 #3333), 0r7fy (0.20 #881, 0.02 #9726, 0.02 #10530) >> Best rule #2449 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01vrncs; 01vs_v8; 01rzqj; 086sj; 01vsy7t; 0mz73; 01pllx; >> query: (?x7489, 02_286) <- inductee(?x9953, ?x7489), participant(?x10410, ?x7489), nominated_for(?x7489, ?x782) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 084m3 location 052p7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 113.000 113.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #937-043hg PRED entity: 043hg PRED relation: award PRED expected values: 04dn09n => 101 concepts (56 used for prediction) PRED predicted values (max 10 best out of 341): 02x1dht (0.69 #22354, 0.67 #12774, 0.66 #2795), 0gs9p (0.62 #78, 0.46 #876, 0.43 #1276), 019f4v (0.62 #65, 0.28 #2061, 0.27 #1263), 02pqp12 (0.50 #69, 0.36 #867, 0.33 #1267), 0gq9h (0.50 #76, 0.32 #2072, 0.27 #1274), 04dn09n (0.38 #42, 0.37 #2038, 0.33 #1240), 040njc (0.38 #7, 0.33 #406, 0.33 #2003), 0gr4k (0.38 #31, 0.32 #2027, 0.30 #1229), 02rdyk7 (0.38 #90, 0.30 #1288, 0.29 #888), 02n9nmz (0.25 #68, 0.24 #467, 0.17 #3262) >> Best rule #22354 for best value: >> intensional similarity = 3 >> extensional distance = 1743 >> proper extension: 0kk9v; 099ks0; >> query: (?x6748, ?x899) <- award_winner(?x899, ?x6748), nominated_for(?x899, ?x54), award(?x286, ?x899) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 05kfs; 021bk; 0c00lh; 01_f_5; 06b_0; 017c87; *> query: (?x6748, 04dn09n) <- religion(?x6748, ?x1985), award(?x6748, ?x2532), place_of_birth(?x6748, ?x11360), ?x2532 = 02x4wr9 *> conf = 0.38 ranks of expected_values: 6 EVAL 043hg award 04dn09n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 101.000 56.000 0.692 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #936-011_3s PRED entity: 011_3s PRED relation: award_nominee! PRED expected values: 030hbp => 102 concepts (44 used for prediction) PRED predicted values (max 10 best out of 996): 0335fp (0.82 #6401, 0.81 #67327, 0.81 #78941), 01pgzn_ (0.81 #67327, 0.81 #78941, 0.81 #44107), 030hbp (0.81 #67327, 0.81 #78941, 0.81 #44107), 04bd8y (0.81 #67327, 0.81 #78941, 0.81 #44107), 040t74 (0.81 #67327, 0.81 #78941, 0.81 #44107), 05l4yg (0.81 #67327, 0.81 #44107, 0.81 #97517), 0jmj (0.55 #5655, 0.22 #67329, 0.14 #97518), 04yqlk (0.55 #5674, 0.22 #67329, 0.14 #97518), 015p37 (0.55 #6834, 0.22 #67329, 0.14 #97518), 022yb4 (0.45 #6490, 0.14 #97518, 0.06 #8812) >> Best rule #6401 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 04yqlk; 030hbp; 015p37; >> query: (?x3267, 0335fp) <- nominated_for(?x3267, ?x2719), award_nominee(?x3267, ?x4586), ?x4586 = 04bcb1 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #67327 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 800 *> proper extension: 02pp_q_; 01qkqwg; 0l56b; 0565cz; 0phx4; 0fwy0h; 0jn5l; 05qhnq; 0191h5; 051m56; ... *> query: (?x3267, ?x336) <- award_nominee(?x3267, ?x2352), award_nominee(?x3267, ?x336), artists(?x671, ?x2352) *> conf = 0.81 ranks of expected_values: 3 EVAL 011_3s award_nominee! 030hbp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 44.000 0.818 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #935-01czx PRED entity: 01czx PRED relation: group! PRED expected values: 05148p4 0gkd1 => 115 concepts (102 used for prediction) PRED predicted values (max 10 best out of 106): 05148p4 (0.64 #827, 0.61 #665, 0.55 #746), 06w7v (0.50 #148, 0.22 #716, 0.20 #797), 018vs (0.48 #821, 0.44 #659, 0.44 #415), 04rzd (0.25 #108, 0.17 #676, 0.16 #838), 0mkg (0.25 #89, 0.17 #657, 0.15 #738), 06ncr (0.25 #115, 0.16 #845, 0.14 #277), 07c6l (0.25 #88, 0.14 #250, 0.07 #893), 02fsn (0.25 #124, 0.12 #367, 0.11 #692), 011k_j (0.25 #142, 0.12 #385, 0.07 #893), 01hww_ (0.25 #105, 0.11 #673, 0.10 #754) >> Best rule #827 for best value: >> intensional similarity = 5 >> extensional distance = 23 >> proper extension: 04r1t; 01kcms4; 048xh; 012vm6; 016vn3; 06lxn; >> query: (?x2073, 05148p4) <- influenced_by(?x5329, ?x2073), artists(?x1000, ?x2073), group(?x645, ?x2073), artist(?x2299, ?x2073), role(?x74, ?x645) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1, 33 EVAL 01czx group! 0gkd1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 115.000 102.000 0.640 http://example.org/music/performance_role/regular_performances./music/group_membership/group EVAL 01czx group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 102.000 0.640 http://example.org/music/performance_role/regular_performances./music/group_membership/group #934-02_kd PRED entity: 02_kd PRED relation: award PRED expected values: 02pqp12 => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 199): 03hl6lc (0.26 #6534, 0.25 #7468, 0.25 #9338), 0gq9h (0.26 #6534, 0.25 #7468, 0.25 #9338), 063y_ky (0.26 #6534, 0.25 #7468, 0.25 #9338), 04dn09n (0.26 #6534, 0.25 #7468, 0.25 #9338), 02qyp19 (0.26 #6534, 0.25 #7468, 0.25 #9338), 02qyntr (0.26 #6534, 0.25 #7468, 0.25 #9338), 04kxsb (0.26 #6534, 0.25 #7468, 0.25 #9338), 027dtxw (0.26 #6534, 0.25 #7468, 0.25 #9338), 02pqp12 (0.26 #6534, 0.25 #7468, 0.25 #9338), 0gr51 (0.14 #311, 0.10 #8869, 0.10 #8868) >> Best rule #6534 for best value: >> intensional similarity = 4 >> extensional distance = 659 >> proper extension: 0g4vmj8; 03cffvv; >> query: (?x3567, ?x68) <- film(?x1950, ?x3567), film_release_distribution_medium(?x3567, ?x81), award(?x3567, ?x198), nominated_for(?x68, ?x3567) >> conf = 0.26 => this is the best rule for 9 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 9 EVAL 02_kd award 02pqp12 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 74.000 74.000 0.255 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #933-05hj_k PRED entity: 05hj_k PRED relation: religion PRED expected values: 03_gx => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 23): 03_gx (0.25 #14, 0.12 #104, 0.12 #59), 0c8wxp (0.16 #773, 0.14 #1358, 0.13 #546), 03j6c (0.13 #471, 0.09 #336, 0.07 #652), 092bf5 (0.12 #61, 0.06 #331, 0.06 #376), 0kq2 (0.12 #63, 0.04 #1100, 0.03 #604), 0kpl (0.12 #550, 0.11 #145, 0.10 #1092), 051kv (0.04 #500, 0.04 #726, 0.03 #545), 01lp8 (0.04 #451, 0.03 #541, 0.03 #632), 0631_ (0.04 #729, 0.03 #503, 0.02 #548), 02rsw (0.02 #564, 0.02 #745, 0.01 #519) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0glyyw; >> query: (?x4060, 03_gx) <- executive_produced_by(?x6731, ?x4060), film(?x2762, ?x6731), ?x2762 = 015t56 >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05hj_k religion 03_gx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.250 http://example.org/people/person/religion #932-015p37 PRED entity: 015p37 PRED relation: film PRED expected values: 03lrht => 113 concepts (92 used for prediction) PRED predicted values (max 10 best out of 1150): 0g60z (0.74 #3582, 0.66 #12537, 0.65 #19703), 0124k9 (0.74 #3582, 0.66 #12537, 0.65 #19703), 011yr9 (0.25 #2482, 0.08 #11437, 0.05 #9646), 049xgc (0.21 #8136, 0.12 #2763, 0.10 #4555), 05pxnmb (0.20 #4928, 0.17 #6719, 0.11 #8509), 034qzw (0.17 #333, 0.05 #41532, 0.03 #16454), 01bn3l (0.17 #1358, 0.03 #17479, 0.03 #22852), 02wgbb (0.17 #1357, 0.03 #17478, 0.03 #22851), 07kb7vh (0.17 #685, 0.03 #16806, 0.03 #18597), 05sxr_ (0.17 #1673, 0.03 #17794, 0.02 #23167) >> Best rule #3582 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 03k545; >> query: (?x10919, ?x337) <- nominated_for(?x10919, ?x337), religion(?x10919, ?x1985), actor(?x5286, ?x10919) >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #16378 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 01pjr7; *> query: (?x10919, 03lrht) <- nominated_for(?x10919, ?x337), special_performance_type(?x10919, ?x296), category(?x10919, ?x134) *> conf = 0.03 ranks of expected_values: 371 EVAL 015p37 film 03lrht CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 113.000 92.000 0.741 http://example.org/film/actor/film./film/performance/film #931-027pfb2 PRED entity: 027pfb2 PRED relation: nominated_for! PRED expected values: 03gt0c5 => 87 concepts (48 used for prediction) PRED predicted values (max 10 best out of 746): 04wx2v (0.51 #63239, 0.51 #96040, 0.51 #79642), 016ggh (0.51 #63239, 0.51 #96040, 0.51 #79642), 03c5f7l (0.51 #63239, 0.51 #96040, 0.51 #79642), 03rwng (0.51 #63239, 0.51 #96040, 0.51 #79642), 01gv_f (0.51 #96040, 0.51 #79642, 0.49 #105414), 015qq1 (0.51 #96040, 0.51 #79642, 0.49 #105414), 0410cp (0.50 #4682, 0.50 #3228, 0.46 #4681), 0143wl (0.49 #65580, 0.47 #7022, 0.46 #4681), 018dnt (0.49 #65580, 0.47 #7022, 0.45 #72609), 030znt (0.33 #269, 0.06 #51799, 0.05 #61165) >> Best rule #63239 for best value: >> intensional similarity = 4 >> extensional distance = 140 >> proper extension: 02_1rq; >> query: (?x4138, ?x11364) <- actor(?x4138, ?x11364), country_of_origin(?x4138, ?x94), award_winner(?x1747, ?x11364), film(?x11364, ?x697) >> conf = 0.51 => this is the best rule for 4 predicted values No rule for expected values ranks of expected_values: EVAL 027pfb2 nominated_for! 03gt0c5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 87.000 48.000 0.513 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #930-06szd3 PRED entity: 06szd3 PRED relation: inductee PRED expected values: 06j0md 0p_2r 081nh 01fs_4 01xcr4 0h953 025mb_ 01kkx2 0121rx 011lpr => 54 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1243): 028qyn (0.40 #318, 0.29 #656, 0.29 #543), 03h_fk5 (0.40 #247, 0.29 #585, 0.29 #472), 01vsy9_ (0.33 #82, 0.14 #644, 0.14 #531), 016t00 (0.29 #549, 0.20 #324, 0.14 #662), 052hl (0.26 #337, 0.01 #562), 01s7qqw (0.26 #337, 0.01 #562), 014z8v (0.26 #337, 0.01 #562), 01lc5 (0.26 #337), 013tjc (0.26 #337), 03j0d (0.26 #337) >> Best rule #318 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 0g2c8; 0qjfl; 04045y; >> query: (?x9953, 028qyn) <- inductee(?x9953, ?x11404), inductee(?x9953, ?x8006), award_winner(?x8459, ?x8006), nationality(?x8006, ?x94), location(?x8006, ?x11000), influenced_by(?x11404, ?x3542) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #337 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 0g2c8; 0qjfl; 04045y; *> query: (?x9953, ?x3542) <- inductee(?x9953, ?x11404), inductee(?x9953, ?x8006), award_winner(?x8459, ?x8006), nationality(?x8006, ?x94), location(?x8006, ?x11000), influenced_by(?x11404, ?x3542) *> conf = 0.26 ranks of expected_values: 16, 128, 135, 170, 339 EVAL 06szd3 inductee 011lpr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 54.000 25.000 0.400 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 06szd3 inductee 0121rx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 54.000 25.000 0.400 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 06szd3 inductee 01kkx2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 54.000 25.000 0.400 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 06szd3 inductee 025mb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 54.000 25.000 0.400 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 06szd3 inductee 0h953 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 25.000 0.400 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 06szd3 inductee 01xcr4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 25.000 0.400 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 06szd3 inductee 01fs_4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 25.000 0.400 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 06szd3 inductee 081nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 54.000 25.000 0.400 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 06szd3 inductee 0p_2r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 25.000 0.400 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee EVAL 06szd3 inductee 06j0md CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 54.000 25.000 0.400 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #929-01tlyq PRED entity: 01tlyq PRED relation: category PRED expected values: 08mbj5d => 7 concepts (7 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.71 #8, 0.71 #7, 0.68 #4) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 151 >> proper extension: 086k8; 016tt2; 05qd_; 04f525m; 011k1h; 0cjdk; 03rhqg; 0229rs; 01t7jy; 03mdt; ... >> query: (?x14466, ?x134) <- child(?x12007, ?x14466), child(?x12007, ?x14326), category(?x14326, ?x134), ?x134 = 08mbj5d >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tlyq category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 7.000 7.000 0.712 http://example.org/common/topic/webpage./common/webpage/category #928-0gdh5 PRED entity: 0gdh5 PRED relation: nationality PRED expected values: 09c7w0 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.81 #6950, 0.77 #9732, 0.77 #6850), 05kr_ (0.37 #10229), 02jx1 (0.21 #527, 0.16 #1022, 0.15 #1815), 07ssc (0.14 #509, 0.12 #1797, 0.11 #1004), 03rk0 (0.07 #4115, 0.06 #1927, 0.06 #342), 0345h (0.05 #1912, 0.03 #2408, 0.03 #327), 05ksh (0.05 #2677, 0.04 #2378, 0.03 #3671), 06q1r (0.04 #76, 0.03 #175, 0.03 #274), 03rjj (0.04 #5, 0.03 #203, 0.03 #2184), 0f8l9c (0.04 #2200, 0.02 #3195, 0.02 #3891) >> Best rule #6950 for best value: >> intensional similarity = 3 >> extensional distance = 1237 >> proper extension: 07_grx; 03h40_7; 0bm9xk; >> query: (?x2796, 09c7w0) <- place_of_birth(?x2796, ?x1196), nationality(?x2796, ?x279), award_nominee(?x2796, ?x1504) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gdh5 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 124.000 0.811 http://example.org/people/person/nationality #927-0ccck7 PRED entity: 0ccck7 PRED relation: film_release_region PRED expected values: 06mzp 0h7x 03h64 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 101): 09c7w0 (0.95 #3368, 0.93 #5294, 0.93 #5134), 07ssc (0.81 #2903, 0.75 #3866, 0.70 #3223), 0chghy (0.80 #2896, 0.78 #3859, 0.63 #3216), 0jgd (0.80 #2889, 0.73 #3852, 0.66 #3209), 03h64 (0.75 #2956, 0.72 #3919, 0.67 #552), 03gj2 (0.75 #2912, 0.71 #3875, 0.58 #3232), 0345h (0.74 #3884, 0.74 #2921, 0.70 #3241), 0d060g (0.71 #2891, 0.66 #3854, 0.64 #3211), 05b4w (0.68 #2953, 0.67 #3916, 0.56 #3273), 03spz (0.68 #2986, 0.53 #3949, 0.47 #3306) >> Best rule #3368 for best value: >> intensional similarity = 3 >> extensional distance = 195 >> proper extension: 03sxd2; 02vqhv0; 08k40m; 0gj8nq2; 03wbqc4; 02j69w; 03nqnnk; 0f4_2k; 0b7l4x; 01gwk3; ... >> query: (?x11218, 09c7w0) <- produced_by(?x11218, ?x12960), film_release_region(?x11218, ?x87), films(?x5069, ?x11218) >> conf = 0.95 => this is the best rule for 1 predicted values *> Best rule #2956 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 100 *> proper extension: 03twd6; *> query: (?x11218, 03h64) <- award_winner(?x11218, ?x6766), film_release_region(?x11218, ?x151), ?x151 = 0b90_r *> conf = 0.75 ranks of expected_values: 5, 19, 21 EVAL 0ccck7 film_release_region 03h64 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 84.000 84.000 0.949 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ccck7 film_release_region 0h7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 84.000 84.000 0.949 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ccck7 film_release_region 06mzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 84.000 84.000 0.949 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #926-02825cv PRED entity: 02825cv PRED relation: film_release_region PRED expected values: 0jgd 0chghy 035qy => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 129): 0f8l9c (0.92 #506, 0.90 #989, 0.90 #828), 0chghy (0.90 #334, 0.90 #495, 0.88 #817), 0k6nt (0.88 #26, 0.80 #832, 0.79 #510), 035qy (0.87 #360, 0.82 #843, 0.81 #521), 03h64 (0.87 #393, 0.81 #554, 0.81 #70), 0jgd (0.87 #810, 0.86 #488, 0.85 #4), 059j2 (0.86 #518, 0.85 #357, 0.84 #1001), 02vzc (0.85 #54, 0.85 #538, 0.82 #377), 05qhw (0.85 #15, 0.83 #338, 0.81 #821), 05r4w (0.82 #808, 0.81 #486, 0.81 #969) >> Best rule #506 for best value: >> intensional similarity = 5 >> extensional distance = 84 >> proper extension: 0dckvs; 0djb3vw; 040rmy; 0bhwhj; 064lsn; 05zvzf3; >> query: (?x6480, 0f8l9c) <- film_release_region(?x6480, ?x1453), film_release_region(?x6480, ?x172), ?x1453 = 06qd3, ?x172 = 0154j, nominated_for(?x2353, ?x6480) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #334 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 82 *> proper extension: 0crh5_f; 0bmc4cm; 07l50vn; 0gh6j94; *> query: (?x6480, 0chghy) <- film_release_region(?x6480, ?x1453), film_release_region(?x6480, ?x550), film_release_region(?x6480, ?x172), ?x1453 = 06qd3, ?x172 = 0154j, ?x550 = 05v8c *> conf = 0.90 ranks of expected_values: 2, 4, 6 EVAL 02825cv film_release_region 035qy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 68.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02825cv film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 02825cv film_release_region 0jgd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 68.000 68.000 0.919 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #925-02f6ym PRED entity: 02f6ym PRED relation: award! PRED expected values: 01r9fv 04xrx 0dl567 01vwbts 03f1d47 01t110 01wqmm8 02h9_l => 42 concepts (14 used for prediction) PRED predicted values (max 10 best out of 2323): 0kr_t (0.57 #11603, 0.56 #18279, 0.53 #21616), 01vsgrn (0.57 #11616, 0.50 #18292, 0.47 #21629), 0dw4g (0.57 #11622, 0.50 #18298, 0.47 #21635), 09889g (0.57 #11437, 0.50 #4763, 0.44 #18113), 0dvqq (0.57 #10636, 0.50 #17312, 0.42 #20649), 0j1yf (0.57 #10498, 0.50 #3824, 0.38 #17174), 0137g1 (0.57 #10755, 0.50 #4081, 0.38 #17431), 01v_pj6 (0.57 #10435, 0.50 #3761, 0.25 #17111), 0fhxv (0.57 #11337, 0.47 #21350, 0.44 #18013), 017959 (0.57 #12721, 0.44 #19397, 0.37 #22734) >> Best rule #11603 for best value: >> intensional similarity = 8 >> extensional distance = 5 >> proper extension: 02v1m7; 02f5qb; >> query: (?x6220, 0kr_t) <- award(?x8490, ?x6220), award(?x7115, ?x6220), award(?x4640, ?x6220), award(?x2796, ?x6220), ?x2796 = 0gdh5, ?x4640 = 018n6m, ?x7115 = 02z4b_8, award_nominee(?x248, ?x8490) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #7366 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 5 *> proper extension: 01c9jp; 01cw7s; *> query: (?x6220, 04xrx) <- award(?x4628, ?x6220), award(?x2796, ?x6220), award(?x2194, ?x6220), ?x2194 = 0pyg6, profession(?x2796, ?x131), instrumentalists(?x227, ?x2796), vacationer(?x739, ?x4628) *> conf = 0.57 ranks of expected_values: 12, 13, 23, 27, 213, 237, 372, 381 EVAL 02f6ym award! 02h9_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6ym award! 01wqmm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6ym award! 01t110 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6ym award! 03f1d47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 42.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6ym award! 01vwbts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6ym award! 0dl567 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 42.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6ym award! 04xrx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 42.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02f6ym award! 01r9fv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 42.000 14.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #924-050f0s PRED entity: 050f0s PRED relation: nominated_for! PRED expected values: 02qyxs5 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 192): 0p9sw (0.33 #21, 0.32 #259, 0.28 #1211), 05ztjjw (0.29 #486, 0.28 #10, 0.24 #248), 02r22gf (0.28 #267, 0.22 #1219, 0.22 #29), 02g3v6 (0.28 #260, 0.22 #22, 0.17 #2640), 0gq9h (0.28 #16012, 0.27 #16488, 0.25 #10774), 0gr42 (0.26 #566, 0.25 #1042, 0.24 #328), 019f4v (0.24 #16003, 0.24 #9337, 0.24 #16479), 0gs9p (0.24 #16014, 0.24 #16490, 0.21 #9348), 057xs89 (0.24 #359, 0.22 #121, 0.17 #1073), 0gq_v (0.22 #9302, 0.21 #15968, 0.20 #16444) >> Best rule #21 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 0df2zx; >> query: (?x1965, 0p9sw) <- category(?x1965, ?x134), film_distribution_medium(?x1965, ?x2099), written_by(?x1965, ?x2285), produced_by(?x1965, ?x3629) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #13567 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 365 *> proper extension: 011z3g; *> query: (?x1965, ?x298) <- category(?x1965, ?x134), nominated_for(?x1723, ?x1965), nominated_for(?x1723, ?x1046), nominated_for(?x298, ?x1046) *> conf = 0.06 ranks of expected_values: 85 EVAL 050f0s nominated_for! 02qyxs5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 128.000 128.000 0.333 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #923-01t0dy PRED entity: 01t0dy PRED relation: student PRED expected values: 0b7t3p => 138 concepts (88 used for prediction) PRED predicted values (max 10 best out of 1607): 0gd_s (0.33 #1592, 0.05 #5760, 0.04 #7844), 02lfns (0.33 #161, 0.04 #8497, 0.02 #35592), 0crqcc (0.33 #1215, 0.03 #36646, 0.03 #30393), 0pj9t (0.33 #514, 0.02 #35945, 0.02 #44283), 017g2y (0.25 #3460, 0.08 #9712, 0.05 #5544), 08s_lw (0.25 #3066, 0.05 #5150, 0.04 #9318), 01kt17 (0.25 #3669, 0.04 #7837, 0.04 #9921), 02nrdp (0.25 #3760, 0.04 #7928, 0.03 #28770), 02vntj (0.25 #2786, 0.04 #9038, 0.03 #27796), 04t969 (0.25 #3357, 0.04 #9609, 0.03 #28367) >> Best rule #1592 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 0204jh; >> query: (?x6417, 0gd_s) <- student(?x6417, ?x4731), ?x4731 = 01twdk, category(?x6417, ?x134), school_type(?x6417, ?x3092) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01t0dy student 0b7t3p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 138.000 88.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #922-0d05w3 PRED entity: 0d05w3 PRED relation: film_release_region! PRED expected values: 0fq27fp 07qg8v 03qnvdl 0gd0c7x 07x4qr => 256 concepts (84 used for prediction) PRED predicted values (max 10 best out of 1794): 01fmys (0.87 #37299, 0.83 #62862, 0.83 #25797), 03nm_fh (0.87 #37643, 0.79 #52981, 0.78 #26141), 017gl1 (0.83 #25669, 0.81 #37171, 0.76 #52509), 07s3m4g (0.83 #26416, 0.81 #37918, 0.73 #63481), 0jjy0 (0.81 #37189, 0.77 #28243, 0.74 #52527), 087wc7n (0.81 #37154, 0.76 #52492, 0.76 #62717), 06wbm8q (0.81 #37366, 0.76 #52704, 0.71 #62929), 0661m4p (0.81 #37336, 0.76 #62899, 0.72 #68011), 01vksx (0.81 #37165, 0.74 #25663, 0.74 #52503), 05p1tzf (0.81 #37123, 0.74 #52461, 0.73 #28177) >> Best rule #37299 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 07ww5; >> query: (?x2346, 01fmys) <- service_location(?x9517, ?x2346), country(?x1889, ?x2346), adjoins(?x252, ?x2346) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #37294 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 07ww5; *> query: (?x2346, 0gd0c7x) <- service_location(?x9517, ?x2346), country(?x1889, ?x2346), adjoins(?x252, ?x2346) *> conf = 0.77 ranks of expected_values: 19, 62, 124, 168, 205 EVAL 0d05w3 film_release_region! 07x4qr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 256.000 84.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d05w3 film_release_region! 0gd0c7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 256.000 84.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d05w3 film_release_region! 03qnvdl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 256.000 84.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d05w3 film_release_region! 07qg8v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 256.000 84.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0d05w3 film_release_region! 0fq27fp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 256.000 84.000 0.871 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #921-0136pk PRED entity: 0136pk PRED relation: role PRED expected values: 01vdm0 0dq630k => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 125): 02sgy (0.67 #312, 0.45 #210, 0.40 #6), 0l15bq (0.50 #139, 0.20 #37, 0.18 #1159), 05842k (0.38 #180, 0.29 #1098, 0.27 #1405), 01vdm0 (0.34 #1562, 0.28 #2685, 0.27 #5544), 018vs (0.33 #318, 0.25 #114, 0.20 #12), 013y1f (0.27 #240, 0.25 #342, 0.18 #1056), 03qjg (0.27 #267, 0.24 #1083, 0.22 #1390), 026t6 (0.25 #309, 0.21 #2657, 0.21 #1432), 01s0ps (0.25 #163, 0.20 #61, 0.11 #673), 04rzd (0.25 #146, 0.17 #350, 0.11 #656) >> Best rule #312 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 04bpm6; 06x4l_; 0180w8; 01wvxw1; 01d4cb; 0pk41; >> query: (?x2321, 02sgy) <- award(?x2321, ?x1801), role(?x2321, ?x4917), ?x4917 = 06w7v, artists(?x1572, ?x2321) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1562 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 01vw87c; 01kx_81; 09qr6; 01j4ls; 0j1yf; 0qf3p; 0pkyh; 01271h; 01w02sy; 02qx69; ... *> query: (?x2321, 01vdm0) <- award(?x2321, ?x1801), role(?x2321, ?x4917), participant(?x6236, ?x2321), role(?x4917, ?x75) *> conf = 0.34 ranks of expected_values: 4, 17 EVAL 0136pk role 0dq630k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 131.000 131.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 0136pk role 01vdm0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 131.000 131.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #920-0713r PRED entity: 0713r PRED relation: draft PRED expected values: 02x2khw => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 15): 02x2khw (0.81 #401, 0.79 #566, 0.75 #31), 02pq_x5 (0.79 #566, 0.75 #31, 0.74 #473), 02rl201 (0.79 #566, 0.75 #31, 0.72 #432), 0f4vx0 (0.50 #117, 0.41 #559, 0.37 #475), 09th87 (0.50 #120, 0.37 #475, 0.37 #781), 025tn92 (0.50 #561, 0.37 #781, 0.34 #782), 05vsb7 (0.41 #384, 0.37 #781, 0.36 #291), 092j54 (0.41 #391, 0.33 #635, 0.33 #316), 038981 (0.39 #563, 0.33 #28, 0.28 #308), 038c0q (0.37 #475, 0.37 #781, 0.36 #556) >> Best rule #401 for best value: >> intensional similarity = 12 >> extensional distance = 19 >> proper extension: 051wf; >> query: (?x4243, 02x2khw) <- school(?x4243, ?x9131), school(?x4243, ?x3021), major_field_of_study(?x9131, ?x10417), season(?x4243, ?x701), major_field_of_study(?x3021, ?x7134), major_field_of_study(?x3021, ?x1668), ?x1668 = 01mkq, currency(?x9131, ?x170), institution(?x620, ?x3021), ?x10417 = 01r4k, ?x7134 = 02_7t, ?x620 = 07s6fsf >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0713r draft 02x2khw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.810 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #919-063_t PRED entity: 063_t PRED relation: influenced_by! PRED expected values: 0p__8 01xwqn => 137 concepts (105 used for prediction) PRED predicted values (max 10 best out of 449): 048cl (0.20 #807, 0.06 #1318, 0.06 #2852), 039n1 (0.20 #900, 0.06 #1411, 0.04 #2945), 03_hd (0.20 #690, 0.06 #1201, 0.04 #2735), 01lwx (0.20 #986, 0.06 #1497, 0.04 #3031), 0h25 (0.20 #928, 0.04 #11674, 0.04 #14230), 014ps4 (0.18 #1841, 0.06 #21791, 0.05 #18723), 02kz_ (0.12 #1242, 0.07 #2264, 0.06 #2776), 01xwv7 (0.11 #27623, 0.10 #4511, 0.08 #8606), 0bqs56 (0.11 #27623, 0.10 #4337, 0.07 #16617), 01xwqn (0.11 #27623, 0.08 #4530, 0.05 #16810) >> Best rule #807 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 03s9v; 082db; 01hdht; 0tfc; >> query: (?x8460, 048cl) <- place_of_death(?x8460, ?x362), influenced_by(?x397, ?x8460), organization(?x8460, ?x8603) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #27623 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 353 *> proper extension: 0fpzzp; 07scx; *> query: (?x8460, ?x1835) <- influenced_by(?x4657, ?x8460), influenced_by(?x1835, ?x4657) *> conf = 0.11 ranks of expected_values: 10, 231 EVAL 063_t influenced_by! 01xwqn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 137.000 105.000 0.200 http://example.org/influence/influence_node/influenced_by EVAL 063_t influenced_by! 0p__8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 137.000 105.000 0.200 http://example.org/influence/influence_node/influenced_by #918-03q5t PRED entity: 03q5t PRED relation: family PRED expected values: 0fx80y => 82 concepts (52 used for prediction) PRED predicted values (max 10 best out of 127): 0fx80y (0.50 #279, 0.50 #215, 0.40 #376), 05148p4 (0.50 #460, 0.38 #556, 0.35 #988), 01vdm0 (0.33 #27, 0.04 #1397, 0.03 #1628), 0l14md (0.31 #779, 0.25 #102, 0.19 #948), 0d8lm (0.25 #606, 0.22 #1073, 0.20 #378), 0342h (0.25 #258, 0.20 #683, 0.20 #355), 026t6 (0.25 #99, 0.11 #648, 0.09 #1686), 01vj9c (0.20 #360, 0.14 #489, 0.12 #588), 085jw (0.17 #438, 0.17 #405, 0.11 #1068), 02qjv (0.17 #459, 0.15 #818, 0.12 #952) >> Best rule #279 for best value: >> intensional similarity = 19 >> extensional distance = 2 >> proper extension: 042v_gx; >> query: (?x74, 0fx80y) <- role(?x2059, ?x74), role(?x1432, ?x74), role(?x315, ?x74), role(?x74, ?x4311), role(?x74, ?x2048), role(?x74, ?x645), instrumentalists(?x74, ?x6208), role(?x74, ?x228), ?x2059 = 0dwr4, role(?x4429, ?x74), ?x4311 = 01xqw, ?x4429 = 0g33q, role(?x1818, ?x74), ?x1432 = 0395lw, artists(?x302, ?x6208), ?x2048 = 018j2, artist(?x2299, ?x6208), ?x645 = 028tv0, ?x315 = 0l14md >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q5t family 0fx80y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 52.000 0.500 http://example.org/music/instrument/family #917-03rk0 PRED entity: 03rk0 PRED relation: contains PRED expected values: 03gdf1 => 238 concepts (133 used for prediction) PRED predicted values (max 10 best out of 2859): 09f07 (0.86 #115274, 0.86 #100864, 0.86 #37461), 086g2 (0.86 #115274, 0.86 #100864, 0.86 #37461), 0290rb (0.86 #115274, 0.86 #100864, 0.86 #37461), 026mx4 (0.86 #115274, 0.86 #100864, 0.86 #37461), 018ckn (0.82 #178668), 0n84k (0.74 #265121, 0.04 #48479, 0.03 #65772), 0j603 (0.74 #265121, 0.02 #111252, 0.02 #154476), 02p3my (0.74 #265121), 05sb1 (0.61 #161380, 0.58 #54751, 0.07 #52140), 03gdf1 (0.47 #57634, 0.35 #279529, 0.03 #79734) >> Best rule #115274 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 07dfk; >> query: (?x2146, ?x3411) <- film_release_region(?x80, ?x2146), administrative_parent(?x3411, ?x2146), contains(?x2146, ?x1391) >> conf = 0.86 => this is the best rule for 4 predicted values *> Best rule #57634 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 024pcx; *> query: (?x2146, ?x11975) <- nationality(?x11976, ?x2146), locations(?x9532, ?x2146), student(?x11975, ?x11976) *> conf = 0.47 ranks of expected_values: 10 EVAL 03rk0 contains 03gdf1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 238.000 133.000 0.865 http://example.org/location/location/contains #916-0qf5p PRED entity: 0qf5p PRED relation: time_zones PRED expected values: 02lcrv => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 10): 02hcv8 (0.53 #29, 0.47 #94, 0.44 #315), 02lcqs (0.36 #18, 0.31 #70, 0.27 #44), 02fqwt (0.18 #209, 0.18 #196, 0.18 #157), 02lcrv (0.17 #7, 0.03 #33, 0.02 #20), 02hczc (0.14 #41, 0.09 #54, 0.09 #93), 02llzg (0.07 #82, 0.07 #251, 0.07 #186), 03bdv (0.04 #110, 0.04 #84, 0.04 #396), 03plfd (0.02 #192, 0.02 #140, 0.02 #270), 052vwh (0.02 #64, 0.01 #142, 0.01 #90), 042g7t (0.01 #89) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 0qm40; >> query: (?x11121, 02hcv8) <- state(?x11121, ?x953), administrative_parent(?x5244, ?x953), category(?x953, ?x134) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 0l_q9; 0l_tn; 0l_n1; 0l_qt; *> query: (?x11121, 02lcrv) <- contains(?x953, ?x11121), contains(?x94, ?x11121), source(?x11121, ?x958), ?x953 = 0hjy, ?x94 = 09c7w0 *> conf = 0.17 ranks of expected_values: 4 EVAL 0qf5p time_zones 02lcrv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 72.000 0.529 http://example.org/location/location/time_zones #915-01b195 PRED entity: 01b195 PRED relation: film! PRED expected values: 016srn 015wfg 02m501 => 69 concepts (41 used for prediction) PRED predicted values (max 10 best out of 613): 02vyw (0.58 #10389, 0.52 #2078, 0.44 #62337), 0gs5q (0.58 #10389, 0.52 #2078, 0.44 #62337), 01mqnr (0.47 #5585), 016ks_ (0.21 #2861, 0.01 #23642), 02fn5 (0.20 #4898, 0.14 #743, 0.02 #6975), 0c0k1 (0.14 #1504, 0.13 #5659, 0.04 #9814), 0c3p7 (0.14 #1114, 0.07 #3192, 0.07 #5269), 02bfmn (0.14 #25, 0.07 #2103, 0.07 #4180), 04vq3h (0.14 #1698, 0.07 #3776, 0.07 #5853), 07yp0f (0.14 #671, 0.07 #35330, 0.07 #4826) >> Best rule #10389 for best value: >> intensional similarity = 3 >> extensional distance = 217 >> proper extension: 05dy7p; 0bhwhj; 0bx_hnp; >> query: (?x2262, ?x3662) <- nominated_for(?x3662, ?x2262), crewmember(?x2262, ?x1622), location(?x3662, ?x2632) >> conf = 0.58 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 01b195 film! 02m501 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 41.000 0.576 http://example.org/film/actor/film./film/performance/film EVAL 01b195 film! 015wfg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 41.000 0.576 http://example.org/film/actor/film./film/performance/film EVAL 01b195 film! 016srn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 41.000 0.576 http://example.org/film/actor/film./film/performance/film #914-0ff0x PRED entity: 0ff0x PRED relation: time_zones PRED expected values: 02hcv8 => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.85 #302, 0.84 #342, 0.83 #16), 02lcqs (0.32 #84, 0.23 #136, 0.21 #608), 02fqwt (0.17 #1142, 0.16 #1116, 0.16 #1090), 02hczc (0.14 #120, 0.13 #159, 0.13 #146), 02llzg (0.10 #529, 0.09 #750, 0.09 #240), 03bdv (0.07 #137, 0.06 #189, 0.04 #1108), 03plfd (0.03 #496, 0.03 #756, 0.02 #966), 052vwh (0.03 #248, 0.02 #221, 0.02 #537), 0gsrz4 (0.03 #494, 0.02 #754, 0.02 #858), 02lcrv (0.01 #216) >> Best rule #302 for best value: >> intensional similarity = 5 >> extensional distance = 240 >> proper extension: 0h7h6; 01dbxr; >> query: (?x11752, ?x2674) <- contains(?x335, ?x11752), second_level_divisions(?x94, ?x11752), adjoins(?x11752, ?x11753), time_zones(?x11753, ?x2674), state_province_region(?x166, ?x335) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ff0x time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 159.000 159.000 0.846 http://example.org/location/location/time_zones #913-050f0s PRED entity: 050f0s PRED relation: film! PRED expected values: 0sw6g 03j9ml => 102 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1107): 03xp8d5 (0.46 #31163, 0.46 #93493, 0.45 #89340), 07fvf1 (0.46 #31163, 0.46 #93493, 0.45 #89340), 016tt2 (0.46 #31163, 0.46 #93493, 0.45 #89340), 04vlh5 (0.46 #31163, 0.46 #93493, 0.45 #89340), 04s04 (0.46 #31163, 0.46 #93493, 0.45 #89340), 03xpf_7 (0.46 #31163, 0.46 #93493, 0.45 #89340), 0721cy (0.20 #70645, 0.19 #64410, 0.19 #85186), 03xpfzg (0.20 #70645, 0.19 #64410, 0.19 #85186), 07_s4b (0.20 #70645, 0.19 #64410, 0.19 #85186), 0b2_xp (0.20 #70645, 0.19 #64410, 0.19 #85186) >> Best rule #31163 for best value: >> intensional similarity = 5 >> extensional distance = 240 >> proper extension: 09txzv; 05k2xy; 0bby9p5; 0243cq; 03yvf2; 051ys82; 03t95n; 02q5bx2; 02bj22; 03tbg6; >> query: (?x1965, ?x574) <- nominated_for(?x4385, ?x1965), nominated_for(?x574, ?x1965), category(?x1965, ?x134), award_winner(?x758, ?x4385), film_crew_role(?x1965, ?x955) >> conf = 0.46 => this is the best rule for 6 predicted values *> Best rule #15941 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 053tj7; *> query: (?x1965, 0sw6g) <- produced_by(?x1965, ?x2951), film_distribution_medium(?x1965, ?x2099), film(?x574, ?x1965) *> conf = 0.02 ranks of expected_values: 520 EVAL 050f0s film! 03j9ml CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 102.000 69.000 0.458 http://example.org/film/actor/film./film/performance/film EVAL 050f0s film! 0sw6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 102.000 69.000 0.458 http://example.org/film/actor/film./film/performance/film #912-0ddkf PRED entity: 0ddkf PRED relation: artist! PRED expected values: 03rhqg => 110 concepts (87 used for prediction) PRED predicted values (max 10 best out of 108): 01xyqk (0.19 #81, 0.05 #1491, 0.04 #786), 0n85g (0.17 #204, 0.14 #345, 0.12 #627), 0g768 (0.17 #178, 0.14 #319, 0.10 #3423), 01w40h (0.17 #169, 0.14 #310, 0.10 #1720), 043g7l (0.17 #172, 0.14 #313, 0.09 #3417), 03rhqg (0.16 #1849, 0.16 #3402, 0.15 #1567), 0181dw (0.15 #465, 0.11 #324, 0.10 #888), 0fb0v (0.14 #289, 0.12 #148, 0.11 #571), 011k1h (0.13 #856, 0.13 #433, 0.12 #574), 017l96 (0.13 #442, 0.11 #3405, 0.11 #583) >> Best rule #81 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01p7b6b; >> query: (?x6877, 01xyqk) <- profession(?x6877, ?x4654), award(?x6877, ?x724), ?x4654 = 029bkp >> conf = 0.19 => this is the best rule for 1 predicted values *> Best rule #1849 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 162 *> proper extension: 07_3qd; 04mx7s; 06br6t; *> query: (?x6877, 03rhqg) <- role(?x6877, ?x227), ?x227 = 0342h, artists(?x284, ?x6877) *> conf = 0.16 ranks of expected_values: 6 EVAL 0ddkf artist! 03rhqg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 110.000 87.000 0.190 http://example.org/music/record_label/artist #911-06sy4c PRED entity: 06sy4c PRED relation: gender PRED expected values: 05zppz => 54 concepts (54 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #11, 0.91 #9, 0.91 #25), 02zsn (0.46 #113, 0.46 #110, 0.45 #107) >> Best rule #11 for best value: >> intensional similarity = 6 >> extensional distance = 54 >> proper extension: 01sg7_; >> query: (?x8204, 05zppz) <- team(?x8204, ?x9157), team(?x8204, ?x3363), team(?x60, ?x9157), colors(?x3363, ?x1101), sport(?x3363, ?x471), ?x1101 = 06fvc >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06sy4c gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 54.000 0.911 http://example.org/people/person/gender #910-05p1tzf PRED entity: 05p1tzf PRED relation: film_release_region PRED expected values: 0jgd 06t2t 03h64 016wzw 07f1x => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 112): 03h64 (0.89 #311, 0.89 #443, 0.88 #575), 0jgd (0.83 #533, 0.82 #401, 0.82 #269), 06t2t (0.82 #571, 0.81 #439, 0.81 #307), 047yc (0.63 #414, 0.63 #546, 0.62 #282), 01p1v (0.62 #431, 0.60 #563, 0.60 #299), 06t8v (0.59 #587, 0.58 #323, 0.58 #455), 016wzw (0.58 #312, 0.58 #576, 0.56 #444), 06qd3 (0.47 #553, 0.46 #289, 0.46 #421), 06c1y (0.43 #556, 0.41 #292, 0.40 #424), 07f1x (0.42 #625, 0.41 #493, 0.41 #361) >> Best rule #311 for best value: >> intensional similarity = 6 >> extensional distance = 112 >> proper extension: 0gtsx8c; 02vxq9m; 0gx1bnj; 0ds3t5x; 0dscrwf; 02x3lt7; 0fq27fp; 0c40vxk; 0gx9rvq; 087wc7n; ... >> query: (?x559, 03h64) <- film_release_region(?x559, ?x1497), film_release_region(?x559, ?x583), film_release_region(?x559, ?x304), ?x1497 = 015qh, ?x583 = 015fr, ?x304 = 0d0vqn >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 7, 10 EVAL 05p1tzf film_release_region 07f1x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 69.000 69.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05p1tzf film_release_region 016wzw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 69.000 69.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05p1tzf film_release_region 03h64 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05p1tzf film_release_region 06t2t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05p1tzf film_release_region 0jgd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.895 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #909-035rnz PRED entity: 035rnz PRED relation: award PRED expected values: 05zr6wv 0gqy2 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 255): 05zr6wv (0.66 #1207, 0.30 #413, 0.13 #2398), 05pcn59 (0.36 #476, 0.28 #1270, 0.18 #2461), 057xs89 (0.26 #553, 0.18 #1347, 0.08 #2538), 0f4x7 (0.22 #426, 0.20 #29, 0.17 #1220), 0gqy2 (0.20 #160, 0.14 #557, 0.14 #21837), 0gq9h (0.20 #75, 0.14 #21837, 0.09 #17147), 0gr51 (0.20 #98, 0.14 #21837, 0.07 #15881), 04ljl_l (0.20 #3, 0.09 #400, 0.08 #1194), 0gs9p (0.20 #77, 0.07 #17149, 0.07 #8414), 02grdc (0.20 #30, 0.03 #34543, 0.02 #15911) >> Best rule #1207 for best value: >> intensional similarity = 3 >> extensional distance = 157 >> proper extension: 01kgxf; 02dlfh; 01f5q5; >> query: (?x4039, 05zr6wv) <- award(?x4039, ?x3508), award(?x5788, ?x3508), ?x5788 = 058s44 >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 035rnz award 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 91.000 91.000 0.660 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 035rnz award 05zr6wv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.660 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #908-05qt0 PRED entity: 05qt0 PRED relation: interests! PRED expected values: 0x3r3 => 97 concepts (77 used for prediction) PRED predicted values (max 10 best out of 83): 026lj (0.62 #536, 0.60 #236, 0.42 #838), 07kb5 (0.60 #233, 0.50 #533, 0.42 #835), 043s3 (0.60 #244, 0.50 #544, 0.33 #846), 039n1 (0.60 #256, 0.42 #858, 0.38 #556), 01bpn (0.42 #847, 0.40 #245, 0.38 #545), 0m93 (0.42 #751, 0.38 #517, 0.33 #286), 03sbs (0.40 #249, 0.38 #566, 0.38 #549), 047g6 (0.40 #262, 0.38 #562, 0.33 #864), 04hcw (0.40 #250, 0.38 #550, 0.33 #852), 0ct9_ (0.40 #255, 0.38 #555, 0.33 #56) >> Best rule #536 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0gt_hv; >> query: (?x6364, 026lj) <- interests(?x4003, ?x6364), nationality(?x4003, ?x94), company(?x4003, ?x7178), people(?x5540, ?x4003), influenced_by(?x4003, ?x7250), ?x7250 = 03sbs, currency(?x7178, ?x170) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 05r79; *> query: (?x6364, 0x3r3) <- major_field_of_study(?x3437, ?x6364), major_field_of_study(?x892, ?x6364), interests(?x3712, ?x6364), major_field_of_study(?x6364, ?x2605), ?x2605 = 03g3w, type_of_union(?x3712, ?x566), ?x3437 = 02_xgp2 *> conf = 0.33 ranks of expected_values: 15 EVAL 05qt0 interests! 0x3r3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 97.000 77.000 0.625 http://example.org/user/alexander/philosophy/philosopher/interests #907-09p0q PRED entity: 09p0q PRED relation: type_of_union PRED expected values: 04ztj => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.83 #21, 0.78 #65, 0.78 #73), 01g63y (0.19 #321, 0.12 #34, 0.11 #54), 01bl8s (0.19 #321, 0.02 #23), 0jgjn (0.19 #321) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 119 >> proper extension: 01d5vk; 03mv0b; 0gry51; >> query: (?x8662, 04ztj) <- profession(?x8662, ?x524), people(?x268, ?x8662), ?x524 = 02jknp >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09p0q type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.835 http://example.org/people/person/spouse_s./people/marriage/type_of_union #906-0gr07 PRED entity: 0gr07 PRED relation: ceremony PRED expected values: 02yv_b 0bvfqq 0bzn6_ 0bz6sb => 53 concepts (53 used for prediction) PRED predicted values (max 10 best out of 91): 0bvfqq (0.91 #660, 0.89 #569, 0.89 #478), 02yv_b (0.83 #473, 0.80 #291, 0.77 #655), 0bz6sb (0.80 #317, 0.78 #499, 0.77 #681), 0fv89q (0.78 #623, 0.77 #168, 0.76 #441), 0fz20l (0.78 #583, 0.77 #128, 0.71 #401), 0fy6bh (0.77 #125, 0.76 #398, 0.73 #307), 0bzn6_ (0.77 #130, 0.73 #676, 0.72 #494), 0c53vt (0.76 #433, 0.73 #342, 0.73 #251), 0d__c3 (0.72 #625, 0.69 #170, 0.67 #352), 0c53zb (0.72 #588, 0.69 #133, 0.65 #406) >> Best rule #660 for best value: >> intensional similarity = 5 >> extensional distance = 20 >> proper extension: 0gqxm; >> query: (?x5409, 0bvfqq) <- award(?x382, ?x5409), ceremony(?x5409, ?x5723), ceremony(?x5409, ?x5349), ?x5349 = 02jp5r, award_winner(?x5723, ?x1852) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 7 EVAL 0gr07 ceremony 0bz6sb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr07 ceremony 0bzn6_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 53.000 53.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr07 ceremony 0bvfqq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gr07 ceremony 02yv_b CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 53.000 53.000 0.909 http://example.org/award/award_category/winners./award/award_honor/ceremony #905-07qy0b PRED entity: 07qy0b PRED relation: nominated_for PRED expected values: 0hvvf => 113 concepts (53 used for prediction) PRED predicted values (max 10 best out of 681): 0473rc (0.31 #25932, 0.31 #38903, 0.31 #25931), 065zlr (0.31 #25932, 0.31 #38903, 0.31 #25931), 0296rz (0.31 #25932, 0.31 #38903, 0.31 #25931), 0cn_b8 (0.31 #25932, 0.31 #38903, 0.31 #25931), 02wgbb (0.31 #25932, 0.31 #38903, 0.31 #25931), 0kv2hv (0.31 #25932, 0.31 #38903, 0.31 #25931), 01bn3l (0.31 #25932, 0.31 #25931, 0.30 #34034), 0640y35 (0.31 #38903, 0.31 #25931, 0.30 #34034), 06w839_ (0.31 #25931, 0.30 #34034, 0.30 #38902), 043h78 (0.31 #25931, 0.30 #34034, 0.30 #38902) >> Best rule #25932 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 02vyw; 0jn5l; 02fgp0; 01c7qd; >> query: (?x3371, ?x6099) <- music(?x6099, ?x3371), genre(?x6099, ?x239), produced_by(?x6099, ?x7624), award_nominee(?x3371, ?x1314) >> conf = 0.31 => this is the best rule for 7 predicted values *> Best rule #53500 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 338 *> proper extension: 02lg9w; *> query: (?x3371, ?x1072) <- award_nominee(?x1314, ?x3371), place_of_birth(?x3371, ?x1523), written_by(?x1072, ?x1314), award_winner(?x601, ?x1314) *> conf = 0.11 ranks of expected_values: 29 EVAL 07qy0b nominated_for 0hvvf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 113.000 53.000 0.315 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #904-0bdjd PRED entity: 0bdjd PRED relation: film_crew_role PRED expected values: 02r96rf => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 30): 0ch6mp2 (0.77 #160, 0.76 #1195, 0.76 #1348), 09zzb8 (0.75 #1188, 0.75 #1341, 0.69 #153), 02r96rf (0.65 #1344, 0.65 #1191, 0.64 #42), 0dxtw (0.49 #164, 0.37 #1199, 0.37 #1352), 01vx2h (0.36 #279, 0.36 #241, 0.34 #165), 01pvkk (0.34 #166, 0.31 #472, 0.28 #1354), 02ynfr (0.27 #94, 0.20 #170, 0.19 #1205), 02rh1dz (0.23 #87, 0.23 #163, 0.22 #239), 02_n3z (0.20 #40, 0.09 #1342, 0.08 #998), 015h31 (0.17 #162, 0.16 #48, 0.13 #238) >> Best rule #160 for best value: >> intensional similarity = 4 >> extensional distance = 33 >> proper extension: 01q2nx; 02x2jl_; >> query: (?x7336, 0ch6mp2) <- film(?x496, ?x7336), featured_film_locations(?x7336, ?x108), film_crew_role(?x7336, ?x1171), ?x108 = 0rh6k >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #1344 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 526 *> proper extension: 09rvwmy; *> query: (?x7336, 02r96rf) <- film(?x496, ?x7336), featured_film_locations(?x7336, ?x108), film_crew_role(?x7336, ?x1171) *> conf = 0.65 ranks of expected_values: 3 EVAL 0bdjd film_crew_role 02r96rf CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 94.000 0.771 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #903-0333wf PRED entity: 0333wf PRED relation: location PRED expected values: 030qb3t => 137 concepts (131 used for prediction) PRED predicted values (max 10 best out of 224): 02_286 (0.29 #840, 0.22 #17706, 0.21 #40197), 030qb3t (0.28 #4098, 0.26 #24981, 0.26 #40243), 01_d4 (0.25 #102, 0.07 #3314, 0.07 #4117), 0498y (0.25 #212, 0.01 #5833, 0.01 #94798), 0d9y6 (0.25 #267), 0cr3d (0.14 #3356, 0.14 #947, 0.11 #2553), 059rby (0.14 #819, 0.10 #4031, 0.07 #3228), 0f2wj (0.14 #3246, 0.10 #4049, 0.03 #18506), 01x73 (0.14 #899, 0.01 #8129, 0.01 #4914), 0fvvz (0.11 #2475, 0.11 #1672, 0.02 #5687) >> Best rule #840 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0738b8; 046zh; 02b9g4; 06pjs; 0168dy; >> query: (?x5343, 02_286) <- place_of_birth(?x5343, ?x2277), film(?x5343, ?x2644), participant(?x5343, ?x3421), ?x2277 = 013yq >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #4098 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 0m2wm; 01q7cb_; 0892sx; 01wyz92; 073749; 086sj; 046qq; 01pctb; 02zrv7; 042ly5; *> query: (?x5343, 030qb3t) <- film(?x5343, ?x2644), ?x2644 = 01shy7, gender(?x5343, ?x231), location(?x5343, ?x2277) *> conf = 0.28 ranks of expected_values: 2 EVAL 0333wf location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 137.000 131.000 0.286 http://example.org/people/person/places_lived./people/place_lived/location #902-02482c PRED entity: 02482c PRED relation: colors PRED expected values: 083jv => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 19): 083jv (0.41 #282, 0.39 #62, 0.38 #2), 01g5v (0.30 #984, 0.29 #844, 0.29 #884), 01l849 (0.29 #101, 0.29 #41, 0.27 #161), 019sc (0.19 #888, 0.19 #1168, 0.19 #828), 06fvc (0.18 #983, 0.18 #823, 0.18 #883), 036k5h (0.17 #26, 0.13 #386, 0.12 #426), 0jc_p (0.11 #145, 0.11 #205, 0.10 #185), 038hg (0.10 #592, 0.09 #92, 0.09 #1172), 03wkwg (0.09 #15, 0.09 #255, 0.09 #55), 04mkbj (0.09 #1430, 0.09 #770, 0.09 #870) >> Best rule #282 for best value: >> intensional similarity = 4 >> extensional distance = 101 >> proper extension: 05krk; 01pl14; 052nd; 01j_9c; 06pwq; 065y4w7; 01w3v; 02cttt; 01hhvg; 04rwx; ... >> query: (?x8937, 083jv) <- colors(?x8937, ?x5325), institution(?x3437, ?x8937), citytown(?x8937, ?x6683), ?x3437 = 02_xgp2 >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02482c colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.408 http://example.org/education/educational_institution/colors #901-02_n7 PRED entity: 02_n7 PRED relation: location! PRED expected values: 0c2ry => 97 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1550): 072bb1 (0.65 #98267, 0.61 #25196, 0.54 #88186), 0227tr (0.18 #2999, 0.17 #18118, 0.17 #20637), 02ts3h (0.18 #3959, 0.15 #6478, 0.13 #8998), 01wp8w7 (0.18 #2779, 0.15 #5298, 0.13 #7818), 01yzhn (0.18 #4652, 0.15 #7171, 0.13 #9691), 01vtmw6 (0.18 #3882, 0.15 #6401, 0.13 #8921), 02yl42 (0.18 #3225, 0.15 #5744, 0.13 #8264), 0p_pd (0.18 #2567, 0.15 #5086, 0.13 #7606), 0gs1_ (0.18 #3844, 0.13 #8883, 0.09 #16442), 0738b8 (0.18 #2964, 0.13 #8003, 0.09 #15562) >> Best rule #98267 for best value: >> intensional similarity = 4 >> extensional distance = 175 >> proper extension: 09bkv; 03kjh; 0n90z; >> query: (?x6316, ?x2602) <- place_of_birth(?x2602, ?x6316), contains(?x94, ?x6316), participant(?x3852, ?x2602), location(?x2602, ?x4356) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02_n7 location! 0c2ry CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 97.000 65.000 0.651 http://example.org/people/person/places_lived./people/place_lived/location #900-0ct5zc PRED entity: 0ct5zc PRED relation: film_release_region PRED expected values: 05v8c => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 113): 03rjj (0.86 #972, 0.82 #2100, 0.78 #1133), 059j2 (0.85 #2130, 0.84 #1002, 0.82 #2452), 03gj2 (0.84 #994, 0.73 #2122, 0.72 #2444), 0345h (0.83 #1004, 0.77 #2132, 0.75 #2454), 07ssc (0.80 #983, 0.77 #1466, 0.77 #2111), 035qy (0.77 #1006, 0.73 #2134, 0.69 #1167), 0d060g (0.73 #974, 0.67 #2102, 0.66 #2424), 0154j (0.72 #971, 0.68 #2099, 0.67 #2421), 015fr (0.71 #985, 0.68 #2113, 0.67 #2435), 06bnz (0.65 #1018, 0.63 #2146, 0.60 #2468) >> Best rule #972 for best value: >> intensional similarity = 6 >> extensional distance = 119 >> proper extension: 0h1cdwq; 0djb3vw; 0gkz15s; 0bwfwpj; 08hmch; 01c22t; 0jjy0; 03bx2lk; 02c6d; 03twd6; ... >> query: (?x2342, 03rjj) <- film_release_region(?x2342, ?x1453), film_release_region(?x2342, ?x252), film_release_region(?x2342, ?x87), ?x87 = 05r4w, ?x252 = 03_3d, ?x1453 = 06qd3 >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #984 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 119 *> proper extension: 0h1cdwq; 0djb3vw; 0gkz15s; 0bwfwpj; 08hmch; 01c22t; 0jjy0; 03bx2lk; 02c6d; 03twd6; ... *> query: (?x2342, 05v8c) <- film_release_region(?x2342, ?x1453), film_release_region(?x2342, ?x252), film_release_region(?x2342, ?x87), ?x87 = 05r4w, ?x252 = 03_3d, ?x1453 = 06qd3 *> conf = 0.60 ranks of expected_values: 13 EVAL 0ct5zc film_release_region 05v8c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 95.000 95.000 0.860 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #899-01dqhq PRED entity: 01dqhq PRED relation: artists PRED expected values: 014_xj => 56 concepts (25 used for prediction) PRED predicted values (max 10 best out of 983): 0889x (0.60 #4343, 0.60 #3212, 0.57 #7557), 01czx (0.60 #4343, 0.50 #6670, 0.38 #5429), 01wt4wc (0.60 #4343, 0.38 #5429, 0.36 #7249), 0134pk (0.56 #5249, 0.46 #6335, 0.43 #4163), 0p76z (0.56 #5265, 0.43 #7438, 0.43 #4179), 01t8399 (0.56 #5312, 0.43 #7485, 0.43 #4226), 07bzp (0.46 #5998, 0.44 #4912, 0.29 #3826), 01w8n89 (0.45 #7921, 0.44 #4662, 0.43 #6835), 04k05 (0.44 #5305, 0.43 #4219, 0.38 #6391), 01vsy3q (0.44 #4785, 0.38 #5871, 0.29 #3699) >> Best rule #4343 for best value: >> intensional similarity = 7 >> extensional distance = 5 >> proper extension: 01qzt1; >> query: (?x5762, ?x2073) <- parent_genre(?x10128, ?x5762), artists(?x10128, ?x12228), artists(?x10128, ?x2073), ?x12228 = 016m5c, parent_genre(?x10128, ?x2249), parent_genre(?x11537, ?x10128), ?x2249 = 03lty >> conf = 0.60 => this is the best rule for 3 predicted values *> Best rule #4303 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 01qzt1; *> query: (?x5762, 014_xj) <- parent_genre(?x10128, ?x5762), artists(?x10128, ?x12228), ?x12228 = 016m5c, parent_genre(?x10128, ?x2249), parent_genre(?x11537, ?x10128), ?x2249 = 03lty *> conf = 0.14 ranks of expected_values: 421 EVAL 01dqhq artists 014_xj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 56.000 25.000 0.600 http://example.org/music/genre/artists #898-054_mz PRED entity: 054_mz PRED relation: produced_by! PRED expected values: 0gyv0b4 => 103 concepts (57 used for prediction) PRED predicted values (max 10 best out of 248): 02ywwy (0.04 #1706, 0.01 #3584, 0.01 #4523), 048yqf (0.04 #1790, 0.01 #3668), 048vhl (0.04 #1729, 0.01 #3607), 0kvbl6 (0.04 #1546, 0.01 #9058, 0.01 #9997), 03cp4cn (0.03 #3415, 0.02 #5293, 0.02 #9049), 0gm2_0 (0.03 #3656, 0.02 #5534, 0.02 #1778), 0b7l4x (0.03 #3381, 0.02 #1503, 0.02 #8076), 0b6l1st (0.03 #9123, 0.02 #11001, 0.02 #11940), 03bzyn4 (0.03 #9278, 0.02 #11156, 0.02 #12095), 05h43ls (0.03 #8675, 0.02 #10553, 0.02 #11492) >> Best rule #1706 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 06pwf6; 0163t3; 03f1zhf; 07bty; 01hdht; >> query: (?x459, 02ywwy) <- profession(?x459, ?x967), profession(?x459, ?x319), ?x319 = 01d_h8, ?x967 = 012t_z >> conf = 0.04 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 054_mz produced_by! 0gyv0b4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 57.000 0.040 http://example.org/film/film/produced_by #897-050ks PRED entity: 050ks PRED relation: time_zones PRED expected values: 02hcv8 => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 13): 02hcv8 (0.78 #1188, 0.68 #665, 0.64 #1502), 02fqwt (0.40 #105, 0.35 #222, 0.35 #209), 02lcqs (0.27 #83, 0.18 #434, 0.16 #958), 02hczc (0.25 #41, 0.25 #28, 0.25 #15), 042g7t (0.25 #50, 0.25 #37, 0.25 #24), 02lcrv (0.25 #46, 0.25 #33, 0.25 #20), 02llzg (0.21 #564, 0.17 #996, 0.17 #56), 03bdv (0.17 #58, 0.13 #854, 0.12 #841), 03plfd (0.08 #374, 0.06 #544, 0.06 #989), 0gsrz4 (0.06 #607, 0.05 #686, 0.05 #725) >> Best rule #1188 for best value: >> intensional similarity = 4 >> extensional distance = 217 >> proper extension: 0nh0f; 027rqbx; 0mkdm; 0fczy; 0jhz_; 0fb18; 02v3m7; 0mkv3; 0mlm_; 0mw2m; ... >> query: (?x7058, ?x2674) <- contains(?x7058, ?x13871), contains(?x7058, ?x9624), time_zones(?x13871, ?x2674), source(?x9624, ?x958) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050ks time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 168.000 168.000 0.777 http://example.org/location/location/time_zones #896-06whf PRED entity: 06whf PRED relation: influenced_by PRED expected values: 03f0324 084nh => 167 concepts (84 used for prediction) PRED predicted values (max 10 best out of 424): 081k8 (0.57 #4854, 0.50 #581, 0.29 #10402), 02wh0 (0.50 #802, 0.36 #2937, 0.30 #8489), 04xjp (0.50 #482, 0.23 #6833, 0.21 #10303), 05qmj (0.42 #10437, 0.41 #9584, 0.35 #8303), 015n8 (0.39 #6809, 0.38 #1682, 0.36 #9798), 01vh096 (0.36 #2849, 0.25 #714, 0.23 #6833), 040db (0.36 #2188, 0.13 #10677, 0.11 #17084), 0gz_ (0.33 #10349, 0.33 #6507, 0.32 #9496), 014z8v (0.33 #119, 0.20 #15064, 0.18 #16346), 01k9lpl (0.33 #303, 0.14 #13113, 0.14 #15248) >> Best rule #4854 for best value: >> intensional similarity = 5 >> extensional distance = 12 >> proper extension: 016hvl; 03f70xs; 0d4jl; 04jwp; 0448r; 0dw6b; 0dfrq; 0c1jh; >> query: (?x4265, 081k8) <- influenced_by(?x4265, ?x7250), influenced_by(?x4265, ?x1279), ?x1279 = 028p0, profession(?x4265, ?x353), nationality(?x7250, ?x1264) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #2284 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 05jm7; 03772; 016dmx; 0mb5x; 0683n; 03hpr; *> query: (?x4265, 03f0324) <- influenced_by(?x4265, ?x7250), category(?x4265, ?x134), influenced_by(?x8233, ?x7250), influenced_by(?x1029, ?x4265), ?x8233 = 0399p *> conf = 0.27 ranks of expected_values: 19, 27 EVAL 06whf influenced_by 084nh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 167.000 84.000 0.571 http://example.org/influence/influence_node/influenced_by EVAL 06whf influenced_by 03f0324 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 167.000 84.000 0.571 http://example.org/influence/influence_node/influenced_by #895-012ycy PRED entity: 012ycy PRED relation: location PRED expected values: 0rh6k => 109 concepts (105 used for prediction) PRED predicted values (max 10 best out of 278): 030qb3t (0.23 #20188, 0.18 #21797, 0.14 #20993), 02_286 (0.18 #4058, 0.12 #3254, 0.12 #20142), 0cc56 (0.17 #1665, 0.06 #7294, 0.06 #6490), 013yq (0.12 #3336, 0.08 #4944, 0.07 #2531), 0ht8h (0.12 #1173, 0.08 #1977, 0.02 #11627), 0r3wm (0.12 #506, 0.03 #6135, 0.03 #7743), 0mnz0 (0.12 #677, 0.02 #11935, 0.01 #15958), 0z2gq (0.12 #448, 0.02 #11706, 0.01 #15729), 0psxp (0.12 #289, 0.02 #11547, 0.01 #15570), 0jyw (0.12 #698, 0.02 #11956, 0.01 #17587) >> Best rule #20188 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 018db8; 08swgx; 057hz; 01d0fp; 0c3jz; 06nns1; 01xv77; 02q3bb; 05ry0p; 06jkm; ... >> query: (?x9603, 030qb3t) <- profession(?x9603, ?x131), nationality(?x9603, ?x94), ?x94 = 09c7w0, diet(?x9603, ?x11141) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #11258 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 42 *> proper extension: 08815; 06pwq; 01w3v; 07szy; 09kvv; 01w5m; 03ksy; 07tds; 02zd460; 01p5xy; ... *> query: (?x9603, ?x108) <- organizations_founded(?x9603, ?x9121), category(?x9121, ?x134), ?x134 = 08mbj5d, citytown(?x9121, ?x108) *> conf = 0.06 ranks of expected_values: 49 EVAL 012ycy location 0rh6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 109.000 105.000 0.231 http://example.org/people/person/places_lived./people/place_lived/location #894-04nw9 PRED entity: 04nw9 PRED relation: people! PRED expected values: 09vc4s => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 55): 041rx (0.38 #1560, 0.34 #3484, 0.33 #3410), 0x67 (0.25 #9, 0.21 #4675, 0.20 #675), 07hwkr (0.12 #85, 0.12 #11, 0.11 #159), 0xnvg (0.12 #12, 0.11 #2086, 0.11 #2160), 07mqps (0.12 #92, 0.07 #388, 0.04 #536), 01qhm_ (0.12 #1042, 0.11 #154, 0.09 #2080), 013xrm (0.11 #167, 0.07 #389, 0.07 #3499), 02w7gg (0.10 #4445, 0.10 #4668, 0.10 #4965), 038723 (0.10 #288, 0.02 #3102, 0.02 #1326), 09vc4s (0.09 #3192, 0.08 #1044, 0.06 #1119) >> Best rule #1560 for best value: >> intensional similarity = 3 >> extensional distance = 123 >> proper extension: 0p51w; 03bw6; 0443c; >> query: (?x1545, 041rx) <- people(?x1446, ?x1545), place_of_death(?x1545, ?x1523), award_winner(?x757, ?x1545) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #3192 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 250 *> proper extension: 02cg2v; *> query: (?x1545, 09vc4s) <- people(?x7063, ?x1545), people(?x7063, ?x2580), ?x2580 = 0227tr *> conf = 0.09 ranks of expected_values: 10 EVAL 04nw9 people! 09vc4s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 125.000 125.000 0.384 http://example.org/people/ethnicity/people #893-034qzw PRED entity: 034qzw PRED relation: nominated_for! PRED expected values: 05zvj3m 05ztrmj => 95 concepts (92 used for prediction) PRED predicted values (max 10 best out of 198): 05b1610 (0.49 #1227, 0.14 #4095, 0.14 #5291), 07bdd_ (0.48 #1248, 0.16 #4116, 0.14 #5312), 05f4m9q (0.47 #1207, 0.15 #4075, 0.14 #5271), 05zvj3m (0.36 #311, 0.19 #19363, 0.19 #16733), 03hj5vf (0.36 #364, 0.19 #16733, 0.19 #15298), 04ljl_l (0.36 #1198, 0.14 #4066, 0.13 #2632), 05p09zm (0.34 #1289, 0.19 #19363, 0.19 #16733), 0gq9h (0.33 #9863, 0.27 #9624, 0.25 #9385), 0gkvb7 (0.33 #23, 0.19 #16733, 0.19 #15298), 027gs1_ (0.33 #188, 0.11 #21038, 0.09 #1622) >> Best rule #1227 for best value: >> intensional similarity = 3 >> extensional distance = 85 >> proper extension: 06wzvr; 0fphgb; 059lwy; 06znpjr; 023vcd; >> query: (?x2102, 05b1610) <- nominated_for(?x1312, ?x2102), film(?x237, ?x2102), ?x1312 = 07cbcy >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #311 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 026mfbr; *> query: (?x2102, 05zvj3m) <- genre(?x2102, ?x258), produced_by(?x2102, ?x364), ?x364 = 05ty4m *> conf = 0.36 ranks of expected_values: 4, 35 EVAL 034qzw nominated_for! 05ztrmj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 95.000 92.000 0.494 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 034qzw nominated_for! 05zvj3m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 95.000 92.000 0.494 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #892-03f0fnk PRED entity: 03f0fnk PRED relation: artists! PRED expected values: 06by7 => 141 concepts (77 used for prediction) PRED predicted values (max 10 best out of 262): 06by7 (0.73 #2138, 0.68 #2742, 0.59 #5465), 064t9 (0.71 #7578, 0.60 #7276, 0.59 #5760), 03lty (0.62 #1540, 0.59 #2749, 0.54 #2447), 0dl5d (0.57 #926, 0.46 #2438, 0.41 #2740), 01lyv (0.45 #2151, 0.23 #5478, 0.22 #8507), 0fd3y (0.44 #1824, 0.14 #1219, 0.14 #917), 0155w (0.44 #5548, 0.32 #7971, 0.31 #8274), 05bt6j (0.43 #3065, 0.41 #2763, 0.39 #5789), 025sc50 (0.40 #7614, 0.39 #5796, 0.35 #7312), 06j6l (0.38 #7612, 0.37 #5794, 0.34 #3976) >> Best rule #2138 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01j4ls; >> query: (?x4712, 06by7) <- award(?x4712, ?x2322), artist(?x2931, ?x4712), ?x2931 = 03rhqg, ?x2322 = 01ck6h >> conf = 0.73 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03f0fnk artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 77.000 0.727 http://example.org/music/genre/artists #891-0707q PRED entity: 0707q PRED relation: disciplines_or_subjects! PRED expected values: 0265wl => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 111): 0265wl (0.76 #434, 0.67 #433, 0.67 #355), 0208wk (0.76 #434, 0.67 #275, 0.65 #215), 040_9s0 (0.76 #434, 0.67 #267, 0.60 #157), 02662b (0.76 #434, 0.65 #215, 0.60 #118), 045xh (0.76 #434, 0.65 #215, 0.60 #183), 0262x6 (0.76 #434, 0.65 #215, 0.60 #156), 0262yt (0.76 #434, 0.65 #215, 0.60 #145), 0265vt (0.76 #434, 0.65 #215, 0.59 #542), 01yz0x (0.76 #434, 0.65 #215, 0.59 #542), 040vk98 (0.76 #434, 0.65 #215, 0.59 #542) >> Best rule #434 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 0l67h; >> query: (?x10679, ?x575) <- disciplines_or_subjects(?x10678, ?x10679), disciplines_or_subjects(?x10222, ?x10679), ?x10678 = 039yzf, award_winner(?x10222, ?x8908), award_winner(?x10222, ?x3963), award_winner(?x10222, ?x3338), ?x3338 = 01dhmw, award(?x8908, ?x5050), ?x5050 = 0265wl, influenced_by(?x8908, ?x5004), type_of_union(?x8908, ?x566), influenced_by(?x3963, ?x6055), award_winner(?x575, ?x3963), profession(?x8908, ?x1032), ?x6055 = 0g5ff, nationality(?x3963, ?x94), place_of_birth(?x3963, ?x3964) >> conf = 0.76 => this is the best rule for 12 predicted values ranks of expected_values: 1 EVAL 0707q disciplines_or_subjects! 0265wl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.764 http://example.org/award/award_category/disciplines_or_subjects #890-01d6g PRED entity: 01d6g PRED relation: school PRED expected values: 0f1nl 01wdj_ => 77 concepts (60 used for prediction) PRED predicted values (max 10 best out of 644): 07w0v (0.62 #1831, 0.55 #2555, 0.50 #7317), 01jq0j (0.50 #1933, 0.44 #2114, 0.43 #1567), 01j_06 (0.50 #1104, 0.42 #3105, 0.33 #3286), 01pq4w (0.44 #2232, 0.33 #3321, 0.33 #3140), 0trv (0.43 #1590, 0.33 #496, 0.31 #3588), 09f2j (0.38 #1894, 0.33 #2256, 0.33 #2075), 0f1nl (0.36 #2757, 0.33 #3120, 0.33 #390), 01tx9m (0.36 #2642, 0.29 #1552, 0.25 #820), 01qgr3 (0.33 #1207, 0.33 #478, 0.33 #297), 02rv1w (0.33 #1245, 0.33 #335, 0.33 #154) >> Best rule #1831 for best value: >> intensional similarity = 18 >> extensional distance = 6 >> proper extension: 07147; >> query: (?x8995, 07w0v) <- draft(?x8995, ?x11905), season(?x8995, ?x9498), season(?x8995, ?x9267), position(?x8995, ?x10822), position(?x8995, ?x8520), position(?x8995, ?x2010), sport(?x8995, ?x5063), ?x9267 = 0dx84s, ?x9498 = 027pwzc, team(?x10822, ?x7725), team(?x5412, ?x8995), ?x11905 = 047dpm0, position(?x10939, ?x2010), position(?x7060, ?x2010), ?x10939 = 0x0d, ?x8520 = 01z9v6, school(?x8995, ?x466), ?x7060 = 01slc >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2757 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 9 *> proper extension: 051vz; 0x0d; *> query: (?x8995, 0f1nl) <- draft(?x8995, ?x8786), draft(?x8995, ?x4779), season(?x8995, ?x9267), season(?x8995, ?x8529), position(?x8995, ?x2010), sport(?x8995, ?x5063), ?x8786 = 02pq_x5, season(?x8894, ?x9267), season(?x8111, ?x9267), season(?x7725, ?x9267), ?x8894 = 02d02, ?x8111 = 07147, ?x8529 = 025ygws, ?x2010 = 02lyr4, school(?x8995, ?x466), ?x4779 = 02z6872, colors(?x466, ?x332), ?x7725 = 07l8x *> conf = 0.36 ranks of expected_values: 7, 73 EVAL 01d6g school 01wdj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 77.000 60.000 0.625 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school EVAL 01d6g school 0f1nl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 77.000 60.000 0.625 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #889-09p30_ PRED entity: 09p30_ PRED relation: award_winner PRED expected values: 02pv_d => 28 concepts (12 used for prediction) PRED predicted values (max 10 best out of 2491): 02pv_d (0.60 #7278, 0.50 #5747, 0.33 #1160), 0bwh6 (0.40 #6297, 0.33 #1709, 0.25 #4766), 0mz73 (0.40 #7249, 0.25 #5718, 0.08 #14894), 01wmxfs (0.40 #6219, 0.25 #4688, 0.05 #6117), 01ycbq (0.33 #281, 0.29 #7929, 0.25 #9457), 01gq0b (0.33 #261, 0.25 #4848, 0.21 #13760), 01rrd4 (0.33 #973, 0.25 #5560, 0.20 #7091), 014zcr (0.33 #28, 0.25 #4615, 0.20 #6146), 06pj8 (0.33 #298, 0.25 #4885, 0.20 #6416), 0dvld (0.33 #910, 0.25 #5497, 0.20 #7028) >> Best rule #7278 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 050yyb; >> query: (?x6238, 02pv_d) <- honored_for(?x6238, ?x1849), award_winner(?x6238, ?x7324), award_winner(?x6238, ?x1179), ceremony(?x746, ?x6238), ?x1179 = 05m883, executive_produced_by(?x1184, ?x7324), award_nominee(?x7324, ?x617), gender(?x7324, ?x231), award_winner(?x1849, ?x446), film_crew_role(?x1184, ?x137), film(?x848, ?x1184), music(?x1184, ?x3414) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09p30_ award_winner 02pv_d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 28.000 12.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #888-0c_m3 PRED entity: 0c_m3 PRED relation: location! PRED expected values: 01w60_p 07f7jp => 172 concepts (120 used for prediction) PRED predicted values (max 10 best out of 2013): 0b_4z (0.70 #10029, 0.68 #10030, 0.54 #178044), 04h6mm (0.70 #10029, 0.68 #10030, 0.50 #175534), 053vcrp (0.70 #10029, 0.68 #10030, 0.46 #97800), 07f7jp (0.70 #10029, 0.46 #97800, 0.45 #67709), 03h_fk5 (0.50 #3040, 0.29 #60186, 0.28 #168010), 01j59b0 (0.29 #60186, 0.28 #168010, 0.28 #67708), 0d05fv (0.25 #5914, 0.25 #3407, 0.15 #10931), 01p7yb (0.25 #5061, 0.25 #2554, 0.09 #15093), 01vwyqp (0.25 #5633, 0.25 #3126, 0.08 #130397), 01d0b1 (0.25 #6802, 0.25 #4295, 0.06 #270819) >> Best rule #10029 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 018djs; >> query: (?x5381, ?x3186) <- place_of_birth(?x5915, ?x5381), place_of_birth(?x3186, ?x5381), place_of_birth(?x1896, ?x5381), celebrities_impersonated(?x5915, ?x496), award(?x1896, ?x704) >> conf = 0.70 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 4, 15 EVAL 0c_m3 location! 07f7jp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 172.000 120.000 0.704 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0c_m3 location! 01w60_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 172.000 120.000 0.704 http://example.org/people/person/places_lived./people/place_lived/location #887-02x1z2s PRED entity: 02x1z2s PRED relation: nominated_for PRED expected values: 01hvjx 06ztvyx 05c26ss 09lxv9 => 40 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1543): 06wbm8q (0.68 #24684, 0.68 #24683, 0.67 #26226), 01pv91 (0.68 #24684, 0.68 #24683, 0.67 #26226), 0639bg (0.68 #24684, 0.68 #24683, 0.67 #26226), 0879bpq (0.60 #1928, 0.25 #387, 0.20 #3469), 09146g (0.50 #258, 0.40 #1799, 0.25 #33942), 03hmt9b (0.50 #3651, 0.25 #33942, 0.24 #35489), 09q5w2 (0.50 #3229, 0.23 #4771, 0.22 #27770), 0cc5qkt (0.50 #3590, 0.22 #27770, 0.15 #5132), 09gq0x5 (0.50 #3326, 0.21 #7953, 0.19 #12581), 0209hj (0.50 #3174, 0.15 #4716, 0.14 #6258) >> Best rule #24684 for best value: >> intensional similarity = 3 >> extensional distance = 215 >> proper extension: 0fqnzts; >> query: (?x3911, ?x972) <- award(?x382, ?x3911), award(?x972, ?x3911), nominated_for(?x143, ?x972) >> conf = 0.68 => this is the best rule for 3 predicted values *> Best rule #370 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 0gqzz; *> query: (?x3911, 06ztvyx) <- nominated_for(?x3911, ?x5713), award(?x382, ?x3911), ?x5713 = 0cc97st *> conf = 0.25 ranks of expected_values: 124, 163, 200, 245 EVAL 02x1z2s nominated_for 09lxv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 40.000 26.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x1z2s nominated_for 05c26ss CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 40.000 26.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x1z2s nominated_for 06ztvyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 40.000 26.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02x1z2s nominated_for 01hvjx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 40.000 26.000 0.678 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #886-02zrv7 PRED entity: 02zrv7 PRED relation: place_of_birth PRED expected values: 0gkgp => 96 concepts (89 used for prediction) PRED predicted values (max 10 best out of 144): 0s9b_ (0.33 #30291, 0.32 #29586, 0.28 #44388), 02_286 (0.17 #3541, 0.12 #2836, 0.11 #29605), 01_d4 (0.14 #66, 0.06 #6406, 0.04 #9926), 01snm (0.14 #239, 0.03 #4465, 0.02 #10803), 0cr3d (0.07 #5730, 0.06 #4320, 0.04 #43777), 02hrh0_ (0.06 #2303, 0.05 #1599, 0.03 #7938), 02dtg (0.06 #715, 0.03 #12688, 0.03 #2827), 094jv (0.06 #766, 0.03 #6401, 0.03 #4287), 0s5cg (0.06 #886, 0.03 #6521, 0.01 #9337), 0f2w0 (0.06 #767, 0.03 #9218, 0.02 #4994) >> Best rule #30291 for best value: >> intensional similarity = 4 >> extensional distance = 923 >> proper extension: 07_grx; 0grrq8; 059x0w; 014g91; 069d71; >> query: (?x6328, ?x13208) <- location(?x6328, ?x13208), nationality(?x6328, ?x94), ?x94 = 09c7w0, county(?x13208, ?x11658) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02zrv7 place_of_birth 0gkgp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 89.000 0.326 http://example.org/people/person/place_of_birth #885-07xr3w PRED entity: 07xr3w PRED relation: award_winner! PRED expected values: 0gr0m => 135 concepts (94 used for prediction) PRED predicted values (max 10 best out of 227): 0gr0m (0.72 #2235, 0.70 #3531, 0.56 #2667), 0gs9p (0.20 #513, 0.18 #35875, 0.17 #35876), 019f4v (0.20 #500, 0.18 #35875, 0.17 #35876), 040njc (0.20 #441, 0.12 #1737, 0.07 #9086), 0gr42 (0.20 #550, 0.07 #38904, 0.07 #38039), 0p9sw (0.20 #457, 0.05 #30686, 0.04 #28523), 02py7pj (0.20 #741, 0.04 #10250, 0.04 #10682), 0gq9h (0.18 #35875, 0.17 #35876, 0.17 #28956), 0gr4k (0.18 #35875, 0.17 #35876, 0.17 #28956), 0gqyl (0.18 #35875, 0.17 #35876, 0.17 #28956) >> Best rule #2235 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 06nz46; >> query: (?x3348, 0gr0m) <- cinematography(?x1973, ?x3348), profession(?x3348, ?x2265), type_of_union(?x3348, ?x566), award_winner(?x3029, ?x3348) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07xr3w award_winner! 0gr0m CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 94.000 0.722 http://example.org/award/award_category/winners./award/award_honor/award_winner #884-05zbm4 PRED entity: 05zbm4 PRED relation: award PRED expected values: 02x4w6g => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 285): 01l29r (0.67 #970, 0.06 #568, 0.04 #2176), 0ck27z (0.33 #90, 0.17 #492, 0.15 #18180), 0gqy2 (0.33 #163, 0.17 #565, 0.12 #31358), 0789_m (0.33 #20, 0.17 #422, 0.12 #31358), 0bdwqv (0.33 #171, 0.12 #31358, 0.11 #573), 04ljl_l (0.33 #3, 0.12 #31358, 0.08 #1209), 0bfvd4 (0.33 #113, 0.12 #31358, 0.07 #16193), 0cqh46 (0.33 #50, 0.12 #31358, 0.07 #1256), 08_vwq (0.33 #269, 0.12 #31358, 0.06 #671), 09qrn4 (0.33 #237, 0.12 #31358, 0.05 #1443) >> Best rule #970 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 0hskw; 0drc1; >> query: (?x949, 01l29r) <- profession(?x949, ?x106), award_nominee(?x949, ?x879), ?x106 = 05sxg2 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #23719 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1577 *> proper extension: 0f721s; 06jntd; 0283xx2; 03lpbx; *> query: (?x949, ?x899) <- award_winner(?x1364, ?x949), nominated_for(?x899, ?x1364) *> conf = 0.14 ranks of expected_values: 36 EVAL 05zbm4 award 02x4w6g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 111.000 111.000 0.667 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #883-02bn75 PRED entity: 02bn75 PRED relation: location PRED expected values: 01zmqw => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 156): 0zygc (0.70 #49845, 0.44 #58686, 0.44 #33769), 030qb3t (0.22 #83, 0.18 #24119, 0.16 #44301), 02_286 (0.18 #46667, 0.18 #35413, 0.17 #56311), 059rby (0.12 #28158, 0.05 #56274, 0.04 #44234), 01n7q (0.11 #63, 0.10 #28205, 0.03 #5690), 01_d4 (0.11 #102, 0.02 #1710, 0.02 #32263), 03b12 (0.11 #518, 0.01 #4537, 0.01 #5341), 0161c (0.11 #189, 0.01 #4208, 0.01 #5012), 0rh6k (0.07 #28146, 0.02 #26536, 0.02 #49045), 0cr3d (0.07 #56419, 0.06 #23460, 0.06 #49186) >> Best rule #49845 for best value: >> intensional similarity = 3 >> extensional distance = 1483 >> proper extension: 07h1h5; 0c8hct; >> query: (?x7857, ?x3650) <- profession(?x7857, ?x563), location(?x7857, ?x3670), place_of_birth(?x7857, ?x3650) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #1811 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 50 *> proper extension: 075npt; *> query: (?x7857, 01zmqw) <- student(?x2909, ?x7857), gender(?x7857, ?x231), ?x2909 = 017z88 *> conf = 0.02 ranks of expected_values: 67 EVAL 02bn75 location 01zmqw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 112.000 112.000 0.697 http://example.org/people/person/places_lived./people/place_lived/location #882-01x4sb PRED entity: 01x4sb PRED relation: location PRED expected values: 0psxp => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 110): 0psxp (0.70 #40924, 0.68 #19258, 0.68 #11233), 02_286 (0.23 #839, 0.19 #18492, 0.18 #32135), 04jpl (0.06 #17, 0.06 #2424, 0.06 #17670), 02m77 (0.06 #329, 0.02 #52960), 0121c1 (0.06 #112), 0cc56 (0.06 #1662, 0.06 #859, 0.05 #10487), 0cr3d (0.06 #10573, 0.06 #40264, 0.05 #32241), 01n7q (0.06 #865, 0.04 #1668, 0.04 #3273), 0vzm (0.06 #973, 0.04 #1776, 0.03 #2578), 01_d4 (0.05 #44133, 0.02 #17753, 0.02 #6518) >> Best rule #40924 for best value: >> intensional similarity = 2 >> extensional distance = 1544 >> proper extension: 05g8ky; 0fp_v1x; 07w21; 041h0; 0274ck; 04411; 07q1v4; 0d0vj4; 04l3_z; 02whj; ... >> query: (?x6259, ?x5867) <- place_of_birth(?x6259, ?x5867), location(?x6259, ?x1310) >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x4sb location 0psxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.701 http://example.org/people/person/places_lived./people/place_lived/location #881-0j_sncb PRED entity: 0j_sncb PRED relation: major_field_of_study PRED expected values: 05qjt 01lj9 01tbp => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 107): 02j62 (0.65 #562, 0.62 #133, 0.49 #1205), 03g3w (0.58 #23, 0.58 #130, 0.57 #559), 02_7t (0.54 #55, 0.42 #484, 0.32 #1020), 01lj9 (0.50 #34, 0.46 #141, 0.43 #570), 05qjt (0.50 #115, 0.43 #330, 0.38 #544), 01tbp (0.49 #586, 0.47 #372, 0.38 #50), 01540 (0.43 #587, 0.42 #158, 0.33 #373), 037mh8 (0.38 #165, 0.35 #594, 0.29 #58), 06ms6 (0.38 #122, 0.33 #15, 0.32 #551), 04sh3 (0.38 #601, 0.31 #172, 0.28 #1244) >> Best rule #562 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 04jhp; >> query: (?x2948, 02j62) <- major_field_of_study(?x2948, ?x1154), institution(?x734, ?x2948), ?x734 = 04zx3q1, ?x1154 = 02lp1 >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #34 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 015fsv; 01yqqv; *> query: (?x2948, 01lj9) <- school(?x799, ?x2948), major_field_of_study(?x2948, ?x10391), institution(?x620, ?x2948), ?x10391 = 02jfc *> conf = 0.50 ranks of expected_values: 4, 5, 6 EVAL 0j_sncb major_field_of_study 01tbp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 134.000 134.000 0.649 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0j_sncb major_field_of_study 01lj9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 134.000 134.000 0.649 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0j_sncb major_field_of_study 05qjt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 134.000 134.000 0.649 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #880-02bh8z PRED entity: 02bh8z PRED relation: artist PRED expected values: 01vvpjj 01w7nwm 01pctb 0ycp3 020hyj 03c602 063t3j => 195 concepts (182 used for prediction) PRED predicted values (max 10 best out of 835): 01vw20h (0.50 #298, 0.40 #1111, 0.25 #4364), 0150jk (0.50 #35, 0.40 #848, 0.25 #4101), 01dhjz (0.50 #658, 0.40 #1471, 0.25 #4724), 016szr (0.40 #1150, 0.38 #4403, 0.25 #337), 016376 (0.40 #1544, 0.33 #23505, 0.25 #4797), 0gbwp (0.29 #23043, 0.08 #71837, 0.08 #31992), 01k23t (0.27 #7056, 0.25 #550, 0.23 #7870), 020_4z (0.27 #7225, 0.25 #719, 0.21 #9666), 01k3qj (0.27 #7039, 0.25 #533, 0.21 #9480), 01q99h (0.27 #6939, 0.21 #9380, 0.20 #11820) >> Best rule #298 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0g768; 01dtcb; >> query: (?x3887, 01vw20h) <- artist(?x3887, ?x7259), ?x7259 = 0677ng, child(?x382, ?x3887), child(?x3887, ?x648) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #794 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0g768; 01dtcb; *> query: (?x3887, 063t3j) <- artist(?x3887, ?x7259), ?x7259 = 0677ng, child(?x382, ?x3887), child(?x3887, ?x648) *> conf = 0.25 ranks of expected_values: 62, 126, 207, 209, 514, 633 EVAL 02bh8z artist 063t3j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 195.000 182.000 0.500 http://example.org/music/record_label/artist EVAL 02bh8z artist 03c602 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 195.000 182.000 0.500 http://example.org/music/record_label/artist EVAL 02bh8z artist 020hyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 195.000 182.000 0.500 http://example.org/music/record_label/artist EVAL 02bh8z artist 0ycp3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 195.000 182.000 0.500 http://example.org/music/record_label/artist EVAL 02bh8z artist 01pctb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 195.000 182.000 0.500 http://example.org/music/record_label/artist EVAL 02bh8z artist 01w7nwm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 195.000 182.000 0.500 http://example.org/music/record_label/artist EVAL 02bh8z artist 01vvpjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 195.000 182.000 0.500 http://example.org/music/record_label/artist #879-07p__7 PRED entity: 07p__7 PRED relation: legislative_sessions! PRED expected values: 06bss 0226cw => 38 concepts (38 used for prediction) PRED predicted values (max 10 best out of 71): 0226cw (0.87 #381, 0.86 #516, 0.86 #362), 06bss (0.83 #325, 0.80 #379, 0.78 #289), 01lct6 (0.68 #212, 0.63 #140, 0.58 #336), 06hx2 (0.68 #212, 0.63 #140, 0.58 #336), 0dq2k (0.38 #393, 0.23 #471, 0.17 #592), 01mvpv (0.19 #407, 0.17 #209, 0.16 #687), 042fk (0.19 #408, 0.16 #687, 0.14 #486), 0rlz (0.16 #687, 0.14 #473, 0.13 #554), 03_nq (0.16 #687, 0.14 #480, 0.12 #402), 0424m (0.16 #687, 0.09 #474, 0.06 #555) >> Best rule #381 for best value: >> intensional similarity = 27 >> extensional distance = 13 >> proper extension: 0495ys; >> query: (?x845, 0226cw) <- district_represented(?x845, ?x3038), district_represented(?x845, ?x2831), district_represented(?x845, ?x760), legislative_sessions(?x652, ?x845), legislative_sessions(?x6933, ?x845), legislative_sessions(?x1829, ?x845), legislative_sessions(?x1027, ?x845), ?x1829 = 02bp37, legislative_sessions(?x2860, ?x845), legislative_sessions(?x845, ?x4821), state_province_region(?x1201, ?x2831), contains(?x2831, ?x4677), contains(?x3038, ?x2277), location(?x3146, ?x760), state_province_region(?x1635, ?x760), religion(?x3038, ?x109), vacationer(?x760, ?x10915), district_represented(?x759, ?x760), ?x6933 = 024tkd, first_level_division_of(?x3038, ?x94), origin(?x9623, ?x760), contains(?x760, ?x552), school(?x2820, ?x1201), ?x1027 = 02bn_p, award_winner(?x3146, ?x1413), location(?x1559, ?x2831), ?x759 = 043djx >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 07p__7 legislative_sessions! 0226cw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.867 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions EVAL 07p__7 legislative_sessions! 06bss CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 38.000 38.000 0.867 http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions #878-0grwj PRED entity: 0grwj PRED relation: list PRED expected values: 026cl_m => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 4): 01pd60 (0.06 #48, 0.06 #41, 0.04 #104), 026cl_m (0.06 #52, 0.05 #59, 0.05 #129), 01ptsx (0.03 #47, 0.03 #40, 0.03 #103), 09g7thr (0.03 #99, 0.02 #918) >> Best rule #48 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 0f721s; 0283xx2; 03lpbx; 04qb6g; >> query: (?x105, 01pd60) <- award_winner(?x3486, ?x105), ?x3486 = 0m7yy, award_winner(?x8837, ?x105) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #52 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 0dxmyh; *> query: (?x105, 026cl_m) <- actor(?x7647, ?x105), friend(?x1660, ?x105), location(?x105, ?x4978) *> conf = 0.06 ranks of expected_values: 2 EVAL 0grwj list 026cl_m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 136.000 0.065 http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list #877-05r5c PRED entity: 05r5c PRED relation: role! PRED expected values: 0myk8 0192l => 92 concepts (89 used for prediction) PRED predicted values (max 10 best out of 63): 02hnl (0.86 #225, 0.84 #787, 0.83 #788), 03qjg (0.86 #225, 0.84 #787, 0.83 #788), 013y1f (0.86 #225, 0.84 #787, 0.83 #788), 04rzd (0.86 #225, 0.84 #787, 0.83 #788), 0xzly (0.86 #225, 0.84 #787, 0.83 #788), 01xqw (0.86 #225, 0.84 #787, 0.83 #788), 06ncr (0.86 #225, 0.84 #787, 0.83 #788), 03m5k (0.86 #225, 0.84 #787, 0.83 #788), 01s0ps (0.86 #225, 0.84 #787, 0.83 #788), 05kms (0.86 #225, 0.84 #787, 0.83 #788) >> Best rule #225 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 07m2y; >> query: (?x316, ?x75) <- instrumentalists(?x316, ?x6225), instrumentalists(?x316, ?x4020), instrumentalists(?x316, ?x2945), role(?x316, ?x745), role(?x316, ?x75), ?x745 = 01vj9c, place_of_birth(?x6225, ?x11000), music(?x463, ?x4020), ?x2945 = 01271h >> conf = 0.86 => this is the best rule for 18 predicted values *> Best rule #562 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 9 *> proper extension: 0j862; *> query: (?x316, ?x214) <- role(?x5417, ?x316), role(?x4583, ?x316), role(?x432, ?x316), group(?x316, ?x997), performance_role(?x2698, ?x316), role(?x214, ?x4583), ?x432 = 042v_gx, ?x5417 = 02w3w *> conf = 0.51 ranks of expected_values: 55, 57 EVAL 05r5c role! 0192l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 92.000 89.000 0.855 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 05r5c role! 0myk8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 92.000 89.000 0.855 http://example.org/music/performance_role/regular_performances./music/group_membership/role #876-02zs4 PRED entity: 02zs4 PRED relation: company! PRED expected values: 05_wyz => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 38): 05_wyz (0.57 #836, 0.52 #318, 0.50 #749), 01yc02 (0.54 #827, 0.50 #395, 0.47 #740), 09d6p2 (0.39 #405, 0.38 #750, 0.38 #1570), 01kr6k (0.38 #196, 0.33 #240, 0.31 #845), 02y6fz (0.27 #281, 0.24 #237, 0.21 #453), 02211by (0.26 #305, 0.24 #218, 0.24 #174), 04192r (0.21 #470, 0.15 #1118, 0.15 #1721), 033smt (0.20 #27, 0.14 #70, 0.12 #5777), 09lq2c (0.19 #244, 0.19 #200, 0.14 #460), 0142rn (0.19 #1232, 0.18 #931, 0.18 #1577) >> Best rule #836 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 01kcmr; >> query: (?x266, 05_wyz) <- company(?x4682, ?x266), citytown(?x266, ?x12794), ?x4682 = 0dq_5, time_zones(?x12794, ?x2674), place_of_birth(?x115, ?x12794) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02zs4 company! 05_wyz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 179.000 0.571 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #875-0h1vz PRED entity: 0h1vz PRED relation: nutrient! PRED expected values: 09728 => 59 concepts (57 used for prediction) PRED predicted values (max 10 best out of 3): 09728 (0.90 #341, 0.89 #301, 0.89 #295), 06x4c (0.89 #40, 0.89 #33, 0.89 #78), 0dcfv (0.89 #40, 0.89 #33, 0.89 #78) >> Best rule #341 for best value: >> intensional similarity = 115 >> extensional distance = 19 >> proper extension: 014yzm; >> query: (?x5010, 09728) <- nutrient(?x10612, ?x5010), nutrient(?x9489, ?x5010), nutrient(?x9005, ?x5010), nutrient(?x8298, ?x5010), nutrient(?x7057, ?x5010), nutrient(?x6285, ?x5010), nutrient(?x6191, ?x5010), nutrient(?x6159, ?x5010), nutrient(?x6032, ?x5010), nutrient(?x5009, ?x5010), nutrient(?x3468, ?x5010), nutrient(?x2701, ?x5010), nutrient(?x1959, ?x5010), nutrient(?x1303, ?x5010), nutrient(?x7057, ?x12454), nutrient(?x7057, ?x11784), nutrient(?x7057, ?x11758), nutrient(?x7057, ?x11409), nutrient(?x7057, ?x11270), nutrient(?x7057, ?x10891), nutrient(?x7057, ?x10709), nutrient(?x7057, ?x10195), nutrient(?x7057, ?x10098), nutrient(?x7057, ?x9915), nutrient(?x7057, ?x9855), nutrient(?x7057, ?x9840), nutrient(?x7057, ?x9795), nutrient(?x7057, ?x9708), nutrient(?x7057, ?x9436), nutrient(?x7057, ?x9426), nutrient(?x7057, ?x8413), nutrient(?x7057, ?x7894), nutrient(?x7057, ?x7652), nutrient(?x7057, ?x7431), nutrient(?x7057, ?x7364), nutrient(?x7057, ?x7362), nutrient(?x7057, ?x7219), nutrient(?x7057, ?x6586), nutrient(?x7057, ?x6286), nutrient(?x7057, ?x6192), nutrient(?x7057, ?x6033), nutrient(?x7057, ?x6026), nutrient(?x7057, ?x5549), nutrient(?x7057, ?x5526), nutrient(?x7057, ?x5374), nutrient(?x7057, ?x5337), nutrient(?x7057, ?x4069), nutrient(?x7057, ?x3469), nutrient(?x7057, ?x3264), nutrient(?x7057, ?x3203), nutrient(?x7057, ?x2702), nutrient(?x7057, ?x1960), nutrient(?x7057, ?x1258), ?x6286 = 02y_3rf, ?x9795 = 05v_8y, ?x5009 = 0fjfh, ?x10612 = 0frq6, ?x1959 = 0f25w9, ?x9426 = 0h1yy, ?x9915 = 025tkqy, ?x6586 = 05gh50, ?x6285 = 01645p, ?x9708 = 061xhr, ?x11784 = 07zqy, ?x6159 = 033cnk, ?x7431 = 09gwd, ?x9005 = 04zpv, ?x9855 = 0d9t0, ?x5337 = 06x4c, ?x9840 = 02p0tjr, ?x8413 = 02kc4sf, ?x7362 = 02kc5rj, nutrient(?x3468, ?x14210), nutrient(?x3468, ?x13545), nutrient(?x3468, ?x12336), nutrient(?x3468, ?x10453), nutrient(?x3468, ?x8442), ?x6033 = 04zjxcz, ?x3203 = 04kl74p, ?x10098 = 0h1_c, ?x6192 = 06jry, ?x13545 = 01w_3, ?x3264 = 0dcfv, ?x6032 = 01nkt, ?x5526 = 09pbb, ?x7652 = 025s0s0, ?x10195 = 0hkwr, ?x11758 = 0q01m, ?x5374 = 025s0zp, ?x7219 = 0h1vg, ?x11409 = 0h1yf, ?x11270 = 02kc008, ?x14210 = 0f4k5, ?x9436 = 025sqz8, ?x12336 = 0f4l5, ?x6191 = 014j1m, ?x7364 = 09gvd, ?x10709 = 0h1sz, ?x10891 = 0g5gq, ?x1960 = 07hnp, ?x3469 = 0h1zw, ?x2702 = 0838f, ?x5549 = 025s7j4, ?x6026 = 025sf8g, ?x10453 = 075pwf, ?x7894 = 0f4hc, ?x4069 = 0hqw8p_, ?x1303 = 0fj52s, nutrient(?x9489, ?x13498), ?x2701 = 0hkxq, ?x8298 = 037ls6, ?x13498 = 07q0m, ?x1258 = 0h1wg, ?x8442 = 02kcv4x, ?x12454 = 025rw19 >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h1vz nutrient! 09728 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 57.000 0.905 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #874-03h_fqv PRED entity: 03h_fqv PRED relation: award PRED expected values: 01ck6h => 147 concepts (147 used for prediction) PRED predicted values (max 10 best out of 313): 01c4_6 (0.77 #31842, 0.76 #31841, 0.74 #33053), 01bgqh (0.64 #1655, 0.60 #446, 0.41 #2864), 01by1l (0.60 #516, 0.55 #1725, 0.42 #6158), 0c4z8 (0.60 #475, 0.45 #1684, 0.24 #2893), 02f716 (0.55 #1790, 0.24 #2999, 0.18 #6223), 09sb52 (0.50 #847, 0.50 #41, 0.35 #6489), 057xs89 (0.50 #968, 0.50 #162, 0.14 #6610), 0f4x7 (0.50 #31, 0.33 #837, 0.18 #6479), 04kxsb (0.50 #127, 0.33 #933, 0.16 #6575), 099ck7 (0.50 #269, 0.33 #1075, 0.09 #6717) >> Best rule #31842 for best value: >> intensional similarity = 3 >> extensional distance = 704 >> proper extension: 099ks0; >> query: (?x5391, ?x1565) <- category(?x5391, ?x134), award_winner(?x1565, ?x5391), award(?x1004, ?x1565) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #526 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 01hgwkr; *> query: (?x5391, 01ck6h) <- film(?x5391, ?x1481), award_winner(?x6869, ?x5391), role(?x5391, ?x212), ?x6869 = 01xqqp *> conf = 0.40 ranks of expected_values: 17 EVAL 03h_fqv award 01ck6h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 147.000 147.000 0.772 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #873-059j2 PRED entity: 059j2 PRED relation: olympics PRED expected values: 0sxrz => 217 concepts (217 used for prediction) PRED predicted values (max 10 best out of 10): 0kbws (0.76 #334, 0.76 #132, 0.76 #213), 0lgxj (0.75 #211, 0.71 #555, 0.70 #302), 018qb4 (0.75 #211, 0.71 #555, 0.70 #302), 0blfl (0.60 #66, 0.59 #363, 0.41 #1551), 0ldqf (0.59 #221, 0.57 #130, 0.52 #342), 018ljb (0.45 #149, 0.41 #220, 0.40 #69), 0sxrz (0.41 #214, 0.40 #63, 0.38 #133), 018wrk (0.40 #61, 0.31 #101, 0.24 #131), 0sx92 (0.40 #67, 0.27 #147, 0.25 #17), 0c_tl (0.25 #14, 0.20 #64, 0.20 #34) >> Best rule #334 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 084n_; >> query: (?x1229, 0kbws) <- country(?x1009, ?x1229), nationality(?x731, ?x1229), organization(?x1229, ?x127) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #214 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 014tss; *> query: (?x1229, 0sxrz) <- country(?x1009, ?x1229), combatants(?x151, ?x1229), combatants(?x326, ?x1229) *> conf = 0.41 ranks of expected_values: 7 EVAL 059j2 olympics 0sxrz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 217.000 217.000 0.762 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #872-01gg59 PRED entity: 01gg59 PRED relation: music! PRED expected values: 07vfy4 => 131 concepts (105 used for prediction) PRED predicted values (max 10 best out of 831): 09gb_4p (0.74 #14156, 0.47 #20224, 0.17 #15168), 09fn1w (0.74 #14156, 0.47 #20224, 0.17 #15168), 01s7w3 (0.07 #7948, 0.07 #8959, 0.05 #10981), 07bzz7 (0.06 #2550, 0.04 #1539, 0.03 #8616), 01v1ln (0.05 #3739, 0.02 #7783, 0.02 #8794), 035yn8 (0.04 #1181, 0.03 #2192, 0.02 #8258), 04j4tx (0.04 #1429, 0.03 #2440, 0.01 #7495), 0dtzkt (0.04 #1966, 0.01 #8032, 0.01 #9043), 02q7fl9 (0.04 #1615, 0.01 #8692, 0.01 #10714), 02rrfzf (0.04 #13471, 0.04 #12460, 0.03 #7405) >> Best rule #14156 for best value: >> intensional similarity = 3 >> extensional distance = 115 >> proper extension: 0gv07g; 01m7f5r; 07z4fy; >> query: (?x3890, ?x4444) <- profession(?x3890, ?x131), music(?x3742, ?x3890), nominated_for(?x3890, ?x4444) >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #10006 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 89 *> proper extension: 0c_drn; *> query: (?x3890, 07vfy4) <- award_nominee(?x3890, ?x4693), award(?x3890, ?x1323), ?x1323 = 0gqz2 *> conf = 0.01 ranks of expected_values: 720 EVAL 01gg59 music! 07vfy4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 131.000 105.000 0.740 http://example.org/film/film/music #871-0hkqn PRED entity: 0hkqn PRED relation: contact_category PRED expected values: 02zdwq => 149 concepts (149 used for prediction) PRED predicted values (max 10 best out of 2): 014dgf (0.45 #23, 0.33 #9, 0.32 #13), 02zdwq (0.37 #43, 0.37 #14, 0.36 #34) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0fvly; >> query: (?x12373, 014dgf) <- contact_category(?x12373, ?x897), service_language(?x12373, ?x254), service_location(?x12373, ?x551), ?x551 = 02j71 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #43 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 36 *> proper extension: 03mnk; 01yfp7; *> query: (?x12373, 02zdwq) <- organization(?x4682, ?x12373), company(?x346, ?x12373), state_province_region(?x12373, ?x1767), list(?x12373, ?x5997), contact_category(?x12373, ?x897) *> conf = 0.37 ranks of expected_values: 2 EVAL 0hkqn contact_category 02zdwq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 149.000 149.000 0.448 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #870-02681xs PRED entity: 02681xs PRED relation: award! PRED expected values: 0ggl02 0dzc16 02rxbmt => 43 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2201): 01wyq0w (0.80 #20242, 0.80 #6748, 0.79 #40486), 09z1lg (0.80 #20242, 0.80 #6748, 0.79 #40486), 01wv9p (0.40 #1157, 0.18 #26991, 0.17 #50607), 02b25y (0.40 #688, 0.05 #4063, 0.05 #7437), 0kr_t (0.20 #3375, 0.18 #53983, 0.09 #21862), 01s1zk (0.20 #3375, 0.18 #53983, 0.08 #5587), 03y82t6 (0.20 #3375, 0.18 #53983, 0.08 #21624), 01c8v0 (0.20 #3375, 0.18 #53983, 0.03 #7878), 0478__m (0.20 #3375, 0.18 #26991, 0.17 #50607), 0fpjd_g (0.20 #3375, 0.18 #26991, 0.17 #50607) >> Best rule #20242 for best value: >> intensional similarity = 5 >> extensional distance = 150 >> proper extension: 027dtxw; 0bfvw2; 03hkv_r; 0bp_b2; 0789_m; 0gr4k; 09qwmm; 0cqhk0; 0bdw1g; 09qvc0; ... >> query: (?x3666, ?x1238) <- award_winner(?x3666, ?x1238), ceremony(?x3666, ?x12139), award(?x10180, ?x3666), participant(?x10180, ?x2614), category(?x10180, ?x134) >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #3375 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 02681vq; 026rsl9; 02w7fs; *> query: (?x3666, ?x506) <- award_winner(?x3666, ?x1238), ceremony(?x3666, ?x12139), award(?x10180, ?x3666), ?x10180 = 020hyj, award_nominee(?x506, ?x1238) *> conf = 0.20 ranks of expected_values: 14, 22, 34 EVAL 02681xs award! 02rxbmt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 43.000 20.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02681xs award! 0dzc16 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 43.000 20.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02681xs award! 0ggl02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 43.000 20.000 0.802 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #869-04nm0n0 PRED entity: 04nm0n0 PRED relation: film_release_region PRED expected values: 03rjj 03_3d => 96 concepts (93 used for prediction) PRED predicted values (max 10 best out of 276): 03gj2 (0.92 #862, 0.88 #1699, 0.83 #2673), 035qy (0.92 #872, 0.77 #1709, 0.74 #5894), 03spz (0.89 #940, 0.78 #1777, 0.74 #441), 0345h (0.87 #870, 0.85 #1707, 0.83 #2673), 05r4w (0.87 #836, 0.84 #337, 0.84 #1673), 02vzc (0.86 #1896, 0.85 #2229, 0.84 #893), 03h64 (0.86 #1746, 0.77 #6098, 0.77 #5931), 05qhw (0.85 #1687, 0.84 #850, 0.84 #351), 0154j (0.85 #1676, 0.84 #839, 0.84 #340), 03rjj (0.85 #1677, 0.82 #840, 0.80 #6029) >> Best rule #862 for best value: >> intensional similarity = 9 >> extensional distance = 36 >> proper extension: 035yn8; 06v9_x; 0661m4p; 0gjc4d3; 0gtvpkw; 05c26ss; 024mpp; 062zm5h; 047vnkj; 0421v9q; ... >> query: (?x5017, 03gj2) <- film_release_region(?x5017, ?x1229), film_release_region(?x5017, ?x1023), film_release_region(?x5017, ?x789), film_release_region(?x5017, ?x512), ?x512 = 07ssc, ?x789 = 0f8l9c, featured_film_locations(?x5017, ?x1264), ?x1229 = 059j2, ?x1023 = 0ctw_b >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #1677 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 71 *> proper extension: 0h95zbp; *> query: (?x5017, 03rjj) <- film_release_region(?x5017, ?x2629), film_release_region(?x5017, ?x789), film_release_region(?x5017, ?x512), film_release_region(?x5017, ?x142), ?x512 = 07ssc, ?x789 = 0f8l9c, ?x2629 = 06f32, ?x142 = 0jgd, film_crew_role(?x5017, ?x137) *> conf = 0.85 ranks of expected_values: 10, 15 EVAL 04nm0n0 film_release_region 03_3d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 96.000 93.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 04nm0n0 film_release_region 03rjj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 96.000 93.000 0.921 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #868-01nm3s PRED entity: 01nm3s PRED relation: place_of_birth PRED expected values: 06wxw => 95 concepts (64 used for prediction) PRED predicted values (max 10 best out of 61): 0f2tj (0.27 #19013, 0.01 #1656), 0rrwt (0.25 #361), 0f25y (0.25 #348), 0rh6k (0.14 #706, 0.01 #4226, 0.01 #8452), 0qymv (0.14 #1114, 0.01 #1818), 0mpbx (0.14 #1146), 03zv2t (0.14 #1125), 02_286 (0.08 #29597, 0.07 #27484, 0.06 #18327), 0cc56 (0.04 #1441, 0.03 #2145, 0.03 #2849), 0cr3d (0.04 #9248, 0.04 #18402, 0.03 #8544) >> Best rule #19013 for best value: >> intensional similarity = 2 >> extensional distance = 1323 >> proper extension: 0c8br; 02jxsq; >> query: (?x4004, ?x6769) <- award_winner(?x435, ?x4004), location(?x4004, ?x6769) >> conf = 0.27 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01nm3s place_of_birth 06wxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 64.000 0.265 http://example.org/people/person/place_of_birth #867-01m4yn PRED entity: 01m4yn PRED relation: award PRED expected values: 02g2yr => 177 concepts (170 used for prediction) PRED predicted values (max 10 best out of 311): 09sb52 (0.31 #4496, 0.31 #7736, 0.31 #1661), 0ck27z (0.29 #33708, 0.27 #27228, 0.26 #24393), 040njc (0.25 #2033, 0.23 #1223, 0.15 #4868), 019f4v (0.20 #472, 0.15 #1282, 0.15 #2092), 0f_nbyh (0.20 #2035, 0.15 #1225, 0.07 #4870), 0gs9p (0.20 #485, 0.15 #4130, 0.08 #4940), 05f4m9q (0.20 #418, 0.12 #2848, 0.05 #2038), 02f6ym (0.20 #2284, 0.06 #34684, 0.05 #38734), 05pcn59 (0.19 #22762, 0.19 #22357, 0.19 #13447), 0cqhk0 (0.18 #18667, 0.17 #24337, 0.16 #27172) >> Best rule #4496 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 03xgm3; 01sb5r; >> query: (?x6844, 09sb52) <- place_of_birth(?x6844, ?x9660), location(?x6844, ?x726), student(?x4955, ?x6844), celebrity(?x523, ?x6844) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #59942 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2256 *> proper extension: 0kctd; *> query: (?x6844, ?x6463) <- nominated_for(?x6844, ?x814), nominated_for(?x6463, ?x814), award(?x940, ?x6463) *> conf = 0.13 ranks of expected_values: 28 EVAL 01m4yn award 02g2yr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 177.000 170.000 0.314 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #866-0blpg PRED entity: 0blpg PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.86 #81, 0.85 #91, 0.83 #156), 07z4p (0.12 #20, 0.11 #45, 0.10 #30), 07c52 (0.12 #28, 0.11 #43, 0.10 #118), 02nxhr (0.07 #22, 0.07 #102, 0.06 #112), 0735l (0.01 #29) >> Best rule #81 for best value: >> intensional similarity = 4 >> extensional distance = 180 >> proper extension: 015qsq; 02y_lrp; 0140g4; 0dnvn3; 0ds33; 0209xj; 04fzfj; 0dsvzh; 0kv2hv; 04tc1g; ... >> query: (?x3988, 029j_) <- film(?x986, ?x3988), genre(?x3988, ?x239), titles(?x307, ?x3988), nominated_for(?x2757, ?x3988) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0blpg film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.857 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #865-01y0y6 PRED entity: 01y0y6 PRED relation: profession PRED expected values: 02hrh1q 018gz8 => 104 concepts (80 used for prediction) PRED predicted values (max 10 best out of 80): 02hrh1q (0.91 #2788, 0.89 #11551, 0.89 #2349), 018gz8 (0.77 #161, 0.70 #15, 0.34 #307), 01d_h8 (0.56 #590, 0.54 #444, 0.50 #152), 0nbcg (0.48 #4118, 0.47 #2219, 0.46 #5578), 0dz3r (0.44 #2192, 0.43 #5551, 0.42 #5259), 016z4k (0.44 #4093, 0.39 #4823, 0.39 #2194), 01c72t (0.37 #1773, 0.37 #3088, 0.33 #1627), 02jknp (0.37 #591, 0.31 #3512, 0.28 #445), 0np9r (0.36 #164, 0.35 #18, 0.24 #310), 02krf9 (0.35 #754, 0.32 #3237, 0.32 #3675) >> Best rule #2788 for best value: >> intensional similarity = 3 >> extensional distance = 131 >> proper extension: 080knyg; >> query: (?x3739, 02hrh1q) <- award_nominee(?x3739, ?x495), award(?x3739, ?x678), ?x678 = 0cqhk0 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01y0y6 profession 018gz8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 80.000 0.910 http://example.org/people/person/profession EVAL 01y0y6 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 80.000 0.910 http://example.org/people/person/profession #864-0c4hx0 PRED entity: 0c4hx0 PRED relation: ceremony! PRED expected values: 0gqwc 0gr42 0gq_d => 32 concepts (31 used for prediction) PRED predicted values (max 10 best out of 358): 0gq_d (0.92 #4775, 0.91 #3064, 0.91 #5018), 0gr42 (0.91 #805, 0.82 #1779, 0.79 #4705), 0gqwc (0.91 #1755, 0.91 #4924, 0.90 #4681), 0gqz2 (0.88 #1025, 0.87 #1512, 0.86 #1268), 0gq_v (0.83 #1475, 0.82 #1231, 0.82 #3664), 0gqxm (0.78 #4142, 0.77 #6097, 0.76 #7562), 0gqzz (0.78 #4142, 0.77 #6097, 0.76 #7562), 02x201b (0.78 #4142, 0.77 #6097, 0.76 #7562), 0czp_ (0.78 #4142, 0.77 #6097, 0.76 #7562), 054krc (0.27 #3462, 0.25 #3952, 0.25 #3219) >> Best rule #4775 for best value: >> intensional similarity = 26 >> extensional distance = 60 >> proper extension: 0fzrtf; 0dznvw; >> query: (?x9667, 0gq_d) <- ceremony(?x1079, ?x9667), ceremony(?x720, ?x9667), ceremony(?x720, ?x5761), ceremony(?x720, ?x5369), ceremony(?x720, ?x3579), ceremony(?x720, ?x3332), ceremony(?x720, ?x1747), ceremony(?x720, ?x1449), ?x3332 = 0bz6l9, ?x1449 = 059x66, ?x3579 = 0bc773, ?x5761 = 02ywhz, ?x1747 = 0bzm81, nominated_for(?x1079, ?x12720), nominated_for(?x1079, ?x9993), nominated_for(?x1079, ?x8456), nominated_for(?x1079, ?x5930), nominated_for(?x1079, ?x4751), ?x5930 = 07cw4, ?x4751 = 0k2cb, award_winner(?x1079, ?x84), ?x9993 = 0kb1g, genre(?x12720, ?x53), ?x5369 = 0ftlxj, ?x8456 = 02k1pr, award_winner(?x720, ?x382) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 0c4hx0 ceremony! 0gq_d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.919 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c4hx0 ceremony! 0gr42 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.919 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0c4hx0 ceremony! 0gqwc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 32.000 31.000 0.919 http://example.org/award/award_category/winners./award/award_honor/ceremony #863-0fphgb PRED entity: 0fphgb PRED relation: film_crew_role PRED expected values: 09zzb8 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 35): 09zzb8 (0.77 #744, 0.74 #520, 0.74 #261), 02r96rf (0.69 #859, 0.69 #264, 0.68 #1081), 01pvkk (0.44 #13, 0.38 #50, 0.37 #532), 0dxtw (0.44 #271, 0.37 #159, 0.36 #1990), 01vx2h (0.34 #867, 0.33 #1089, 0.32 #755), 02ynfr (0.26 #536, 0.17 #760, 0.16 #2107), 02_n3z (0.22 #2, 0.13 #3074, 0.13 #706), 089fss (0.19 #44, 0.15 #81, 0.13 #118), 0215hd (0.13 #354, 0.13 #3074, 0.13 #706), 02rh1dz (0.13 #344, 0.13 #3074, 0.13 #706) >> Best rule #744 for best value: >> intensional similarity = 4 >> extensional distance = 109 >> proper extension: 0m313; 03s6l2; 0pc62; 0fgpvf; 0164qt; 06_wqk4; 0p9lw; 092vkg; 0bshwmp; 01_mdl; ... >> query: (?x3619, 09zzb8) <- nominated_for(?x4176, ?x3619), film_release_region(?x3619, ?x789), administrative_parent(?x790, ?x789), film_crew_role(?x3619, ?x1171) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fphgb film_crew_role 09zzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.775 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #862-035nm PRED entity: 035nm PRED relation: place_founded PRED expected values: 0m2rv => 141 concepts (131 used for prediction) PRED predicted values (max 10 best out of 55): 02dtg (0.18 #1186, 0.17 #857, 0.17 #5500), 02_286 (0.10 #473, 0.09 #1921, 0.09 #2055), 0d6lp (0.08 #420, 0.07 #485, 0.07 #550), 0y1rf (0.07 #514, 0.07 #116, 0.06 #182), 030qb3t (0.07 #78, 0.06 #144, 0.05 #2389), 01smm (0.07 #106, 0.06 #172, 0.04 #239), 06wjf (0.07 #96, 0.06 #162, 0.04 #229), 06kx2 (0.07 #126, 0.06 #192, 0.04 #259), 0f04c (0.07 #85, 0.06 #151, 0.04 #218), 01sn3 (0.07 #94, 0.06 #160, 0.04 #227) >> Best rule #1186 for best value: >> intensional similarity = 4 >> extensional distance = 43 >> proper extension: 0317zz; >> query: (?x9476, ?x479) <- industry(?x9476, ?x10787), citytown(?x9476, ?x479), taxonomy(?x10787, ?x939), place_of_birth(?x478, ?x479) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 035nm place_founded 0m2rv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 141.000 131.000 0.175 http://example.org/organization/organization/place_founded #861-033jj1 PRED entity: 033jj1 PRED relation: gender PRED expected values: 05zppz => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #7, 0.76 #29, 0.72 #15), 02zsn (0.52 #181, 0.45 #2, 0.41 #6) >> Best rule #7 for best value: >> intensional similarity = 3 >> extensional distance = 221 >> proper extension: 02vptk_; 02_nkp; >> query: (?x9815, 05zppz) <- nationality(?x9815, ?x94), currency(?x9815, ?x170), student(?x1440, ?x9815) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 033jj1 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.776 http://example.org/people/person/gender #860-0cj2t3 PRED entity: 0cj2t3 PRED relation: gender PRED expected values: 02zsn => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.83 #23, 0.83 #31, 0.81 #13), 02zsn (0.50 #113, 0.28 #26, 0.28 #50) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 860 >> proper extension: 04rs03; 01pr_j6; 01g4zr; 01c58j; 0177s6; 025tdwc; 0bymv; 0309jm; 0d4jl; 0chrwb; ... >> query: (?x2913, 05zppz) <- profession(?x2913, ?x987), ?x987 = 0dxtg, nationality(?x2913, ?x94) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #113 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2305 *> proper extension: 054lpb6; 01w806h; 02bgmr; 01t110; 02b29; 0cbm64; 04gvt5; *> query: (?x2913, ?x231) <- award_nominee(?x2913, ?x2912), award_nominee(?x4332, ?x2912), gender(?x4332, ?x231) *> conf = 0.50 ranks of expected_values: 2 EVAL 0cj2t3 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 78.000 0.832 http://example.org/people/person/gender #859-0bv8h2 PRED entity: 0bv8h2 PRED relation: film_crew_role PRED expected values: 02rh1dz => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 22): 01vx2h (0.74 #68, 0.42 #130, 0.38 #193), 0d2b38 (0.50 #82, 0.14 #22, 0.12 #207), 0dxtw (0.41 #192, 0.40 #524, 0.37 #493), 089g0h (0.39 #76, 0.14 #16, 0.13 #201), 01xy5l_ (0.35 #71, 0.14 #11, 0.11 #497), 01pvkk (0.30 #194, 0.29 #526, 0.28 #919), 02rh1dz (0.20 #191, 0.17 #128, 0.15 #66), 02ynfr (0.19 #499, 0.18 #318, 0.18 #530), 089fss (0.14 #4, 0.11 #34, 0.07 #521), 02vs3x5 (0.14 #20, 0.06 #205, 0.05 #627) >> Best rule #68 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 02725hs; 057lbk; 03hxsv; 03ydlnj; 04jpg2p; >> query: (?x3595, 01vx2h) <- film(?x4563, ?x3595), film_crew_role(?x3595, ?x8411), genre(?x3595, ?x53), ?x8411 = 033smt >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #191 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 229 *> proper extension: 0fq27fp; *> query: (?x3595, 02rh1dz) <- film_release_region(?x3595, ?x94), film_crew_role(?x3595, ?x137), crewmember(?x3595, ?x3879) *> conf = 0.20 ranks of expected_values: 7 EVAL 0bv8h2 film_crew_role 02rh1dz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 78.000 78.000 0.739 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #858-051qvn PRED entity: 051qvn PRED relation: sport PRED expected values: 02vx4 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 53): 02vx4 (0.90 #273, 0.90 #183, 0.90 #374), 0z74 (0.52 #372, 0.51 #679, 0.50 #353), 03tmr (0.36 #38, 0.20 #128, 0.13 #788), 018jz (0.20 #132, 0.14 #249, 0.12 #367), 018w8 (0.18 #41, 0.11 #746, 0.10 #131), 0jm_ (0.13 #718, 0.12 #346, 0.12 #790), 039yzs (0.05 #134, 0.04 #794, 0.04 #929), 09xp_ (0.04 #160, 0.02 #775, 0.02 #784), 01yfj (0.01 #37), 01gqfm (0.01 #37) >> Best rule #273 for best value: >> intensional similarity = 11 >> extensional distance = 50 >> proper extension: 035qgm; >> query: (?x11530, 02vx4) <- position(?x11530, ?x530), position(?x11530, ?x203), position(?x11530, ?x63), position(?x11530, ?x60), ?x530 = 02_j1w, teams(?x13724, ?x11530), ?x60 = 02nzb8, ?x63 = 02sdk9v, contains(?x1264, ?x13724), ?x203 = 0dgrmp, team(?x60, ?x11530) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 051qvn sport 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.904 http://example.org/sports/sports_team/sport #857-014gf8 PRED entity: 014gf8 PRED relation: type_of_union PRED expected values: 04ztj => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #29, 0.86 #21, 0.81 #17), 01g63y (0.23 #14, 0.20 #10, 0.20 #6), 0jgjn (0.02 #20) >> Best rule #29 for best value: >> intensional similarity = 2 >> extensional distance = 218 >> proper extension: 0lh0c; 06c0j; >> query: (?x5626, 04ztj) <- location_of_ceremony(?x5626, ?x335), location(?x101, ?x335) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014gf8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.868 http://example.org/people/person/spouse_s./people/marriage/type_of_union #856-02qvyrt PRED entity: 02qvyrt PRED relation: award_winner PRED expected values: 0146pg 01gg59 => 52 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1847): 01817f (0.58 #2462, 0.47 #12313, 0.44 #19707), 02fgpf (0.58 #2462, 0.47 #12313, 0.44 #32025), 020jqv (0.58 #2462, 0.47 #12313, 0.44 #32025), 0178rl (0.58 #2462, 0.47 #12313, 0.44 #32025), 03f2_rc (0.58 #2462, 0.47 #12313, 0.44 #32025), 01l3mk3 (0.58 #2462, 0.47 #12313, 0.44 #32025), 02fgp0 (0.58 #2462, 0.47 #12313, 0.44 #32025), 019x62 (0.58 #2462, 0.47 #12313, 0.44 #32025), 02zft0 (0.58 #2462, 0.47 #12313, 0.44 #32025), 01c8v0 (0.58 #2462, 0.47 #12313, 0.44 #32025) >> Best rule #2462 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 02r0csl; 0gqxm; >> query: (?x2379, ?x84) <- award(?x84, ?x2379), nominated_for(?x2379, ?x2928), nominated_for(?x2379, ?x2490), nominated_for(?x2379, ?x2380), ?x2380 = 02q6gfp, ?x2928 = 07024, ?x2490 = 026p4q7 >> conf = 0.58 => this is the best rule for 53 predicted values *> Best rule #20550 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 7 *> proper extension: 02681vq; 02681xs; 026rsl9; 02w7fs; *> query: (?x2379, 01gg59) <- award(?x4940, ?x2379), award(?x4537, ?x2379), award(?x2945, ?x2379), ?x4940 = 09swkk, people(?x1050, ?x4537), award_winner(?x1553, ?x2945), profession(?x2945, ?x131) *> conf = 0.33 ranks of expected_values: 81, 86 EVAL 02qvyrt award_winner 01gg59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 52.000 22.000 0.583 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 02qvyrt award_winner 0146pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 52.000 22.000 0.583 http://example.org/award/award_category/winners./award/award_honor/award_winner #855-07g7h2 PRED entity: 07g7h2 PRED relation: place_of_birth PRED expected values: 0ftxw => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 74): 030qb3t (0.11 #54, 0.05 #2166, 0.05 #6390), 02_286 (0.09 #723, 0.08 #32415, 0.08 #1427), 01_d4 (0.06 #1474, 0.06 #770, 0.04 #3586), 0cr3d (0.06 #1502, 0.05 #2910, 0.05 #4318), 094jv (0.06 #765, 0.05 #2173, 0.03 #3581), 01jr6 (0.06 #847, 0.02 #2959, 0.01 #4367), 0xl08 (0.06 #241, 0.01 #6577), 0f2rq (0.06 #205, 0.01 #6541), 0hsqf (0.06 #344), 0r8c8 (0.06 #231) >> Best rule #54 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 0cjdk; >> query: (?x6539, 030qb3t) <- award_winner(?x5060, ?x6539), ?x5060 = 05f4vxd, award_winner(?x6539, ?x5061) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #800 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 30 *> proper extension: 04n7njg; 01wyzyl; 03m_k0; 09v6gc9; 09pl3f; 01p8r8; *> query: (?x6539, 0ftxw) <- profession(?x6539, ?x1943), ?x1943 = 02krf9, tv_program(?x6539, ?x9514) *> conf = 0.03 ranks of expected_values: 28 EVAL 07g7h2 place_of_birth 0ftxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 79.000 79.000 0.111 http://example.org/people/person/place_of_birth #854-0640m69 PRED entity: 0640m69 PRED relation: currency PRED expected values: 09nqf => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.80 #43, 0.79 #78, 0.76 #64), 01nv4h (0.25 #183, 0.03 #9, 0.02 #156), 0kz1h (0.25 #183), 02gsvk (0.02 #62, 0.01 #76, 0.01 #104), 02l6h (0.01 #250, 0.01 #201, 0.01 #158) >> Best rule #43 for best value: >> intensional similarity = 4 >> extensional distance = 226 >> proper extension: 02qm_f; 0jyx6; 0fdv3; 02vqsll; 07bx6; 08s6mr; 0234j5; 011xg5; 0gzlb9; 01s7w3; >> query: (?x11980, 09nqf) <- genre(?x11980, ?x258), award_winner(?x11980, ?x1335), film_crew_role(?x11980, ?x137), executive_produced_by(?x11980, ?x9204) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0640m69 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.798 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #853-0gr42 PRED entity: 0gr42 PRED relation: nominated_for PRED expected values: 0gd0c7x 0kb57 03y0pn => 51 concepts (18 used for prediction) PRED predicted values (max 10 best out of 1756): 017gm7 (0.77 #21169, 0.75 #12095, 0.74 #10582), 02dr9j (0.77 #21169, 0.75 #12095, 0.74 #10582), 032zq6 (0.77 #21169, 0.75 #12095, 0.74 #10582), 09zf_q (0.77 #21169, 0.75 #12095, 0.74 #10582), 0hx4y (0.77 #21169, 0.75 #12095, 0.74 #10582), 0f4yh (0.77 #21169, 0.75 #12095, 0.74 #10582), 011ydl (0.77 #21169, 0.75 #12095, 0.74 #10582), 0kcn7 (0.77 #21169, 0.75 #12095, 0.74 #10582), 08ct6 (0.77 #21169, 0.75 #12095, 0.74 #10582), 06mmr (0.77 #21169, 0.75 #12095, 0.74 #10582) >> Best rule #21169 for best value: >> intensional similarity = 4 >> extensional distance = 127 >> proper extension: 02qyp19; 027dtxw; 02r0csl; 040njc; 0bfvw2; 03hkv_r; 0bp_b2; 099jhq; 0gkvb7; 02p_7cr; ... >> query: (?x2209, ?x324) <- ceremony(?x2209, ?x6606), award(?x324, ?x2209), award_winner(?x6606, ?x3519), honored_for(?x6606, ?x3510) >> conf = 0.77 => this is the best rule for 11 predicted values *> Best rule #2558 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 02hsq3m; *> query: (?x2209, 03y0pn) <- ceremony(?x2209, ?x6606), award(?x2878, ?x2209), award_winner(?x6606, ?x3519), ?x2878 = 0hx4y *> conf = 0.60 ranks of expected_values: 16, 164, 478 EVAL 0gr42 nominated_for 03y0pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 51.000 18.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr42 nominated_for 0kb57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 18.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0gr42 nominated_for 0gd0c7x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 18.000 0.773 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #852-01vtj38 PRED entity: 01vtj38 PRED relation: award PRED expected values: 05b4l5x 0gkvb7 02ppm4q 02f777 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 277): 02y_j8g (0.76 #10247, 0.71 #13795, 0.71 #43362), 02x17c2 (0.76 #10247, 0.71 #13795, 0.71 #43362), 03qbh5 (0.38 #201, 0.31 #2172, 0.26 #1778), 01by1l (0.38 #2078, 0.34 #1684, 0.32 #9959), 02f705 (0.33 #148, 0.20 #3302, 0.19 #2119), 09sb52 (0.32 #1223, 0.29 #11075, 0.28 #10681), 01c427 (0.29 #80, 0.26 #2051, 0.18 #3628), 05p09zm (0.29 #119, 0.23 #4455, 0.22 #1302), 02f71y (0.29 #178, 0.21 #2149, 0.19 #1755), 054ks3 (0.26 #1714, 0.24 #2108, 0.21 #7231) >> Best rule #10247 for best value: >> intensional similarity = 3 >> extensional distance = 193 >> proper extension: 0b82vw; 03n0q5; 01r6jt2; 012wg; 02w670; 08n__5; 025cn2; 03zz8b; 0h7pj; 01jrs46; ... >> query: (?x7331, ?x528) <- nationality(?x7331, ?x94), origin(?x7331, ?x1523), award_winner(?x528, ?x7331) >> conf = 0.76 => this is the best rule for 2 predicted values *> Best rule #300 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 0ddkf; 01l3mk3; *> query: (?x7331, 02f777) <- artists(?x474, ?x7331), award(?x7331, ?x528), participant(?x2269, ?x7331) *> conf = 0.24 ranks of expected_values: 13, 23, 37, 90 EVAL 01vtj38 award 02f777 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 139.000 139.000 0.759 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vtj38 award 02ppm4q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 139.000 139.000 0.759 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vtj38 award 0gkvb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 139.000 139.000 0.759 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vtj38 award 05b4l5x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 139.000 139.000 0.759 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #851-0cc846d PRED entity: 0cc846d PRED relation: language PRED expected values: 02h40lc => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 35): 02h40lc (0.97 #2828, 0.93 #2945, 0.92 #1643), 06mp7 (0.25 #16, 0.17 #74, 0.14 #132), 06nm1 (0.22 #244, 0.14 #595, 0.13 #1178), 04306rv (0.16 #297, 0.14 #414, 0.13 #472), 064_8sq (0.15 #839, 0.15 #723, 0.15 #897), 02bjrlw (0.10 #1642, 0.10 #1404, 0.10 #351), 03_9r (0.09 #419, 0.08 #477, 0.07 #1002), 0653m (0.08 #304, 0.08 #187, 0.05 #1004), 0jzc (0.08 #312, 0.06 #1012, 0.06 #253), 012w70 (0.08 #305, 0.05 #363, 0.05 #422) >> Best rule #2828 for best value: >> intensional similarity = 4 >> extensional distance = 1248 >> proper extension: 0d7vtk; >> query: (?x2766, 02h40lc) <- country(?x2766, ?x94), ?x94 = 09c7w0, language(?x2766, ?x7599), languages(?x5283, ?x7599) >> conf = 0.97 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cc846d language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.967 http://example.org/film/film/language #850-02k_4g PRED entity: 02k_4g PRED relation: producer_type PRED expected values: 0ckd1 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.89 #9, 0.82 #19, 0.81 #14) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 06qwh; >> query: (?x782, 0ckd1) <- nominated_for(?x1343, ?x782), nominated_for(?x4921, ?x782), ?x4921 = 0fbtbt >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02k_4g producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.895 http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type #849-0h1mt PRED entity: 0h1mt PRED relation: type_of_union PRED expected values: 04ztj => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.75 #25, 0.74 #177, 0.73 #249), 01g63y (0.30 #18, 0.27 #14, 0.26 #6) >> Best rule #25 for best value: >> intensional similarity = 2 >> extensional distance = 115 >> proper extension: 02l0sf; >> query: (?x1126, 04ztj) <- award(?x1126, ?x1972), ?x1972 = 0gqyl >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h1mt type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 130.000 0.752 http://example.org/people/person/spouse_s./people/marriage/type_of_union #848-071g6 PRED entity: 071g6 PRED relation: contains! PRED expected values: 05qhw => 12 concepts (12 used for prediction) PRED predicted values (max 10 best out of 271): 0345h (0.37 #6360, 0.29 #4521, 0.22 #7269), 09c7w0 (0.37 #7282, 0.37 #8191, 0.35 #5460), 03rjj (0.37 #10, 0.28 #7271, 0.24 #6364), 0f8l9c (0.28 #7271, 0.24 #6364, 0.14 #7276), 059j2 (0.28 #7271, 0.24 #6364, 0.14 #7276), 06mzp (0.28 #7271, 0.12 #9087, 0.10 #8185), 03gj2 (0.24 #6364, 0.14 #7276, 0.12 #9087), 06mkj (0.24 #6364, 0.12 #9087, 0.10 #8185), 0d0vqn (0.24 #6364, 0.12 #9087, 0.10 #8185), 07ssc (0.20 #3652, 0.20 #4557, 0.05 #9094) >> Best rule #6360 for best value: >> intensional similarity = 42 >> extensional distance = 924 >> proper extension: 09c7w0; 0rh6k; 0160w; 0njvn; 05kkh; 027nb; 01fq7; 0d060g; 0t015; 0n5j_; ... >> query: (?x13657, ?x1264) <- time_zones(?x13657, ?x2864), time_zones(?x3912, ?x2864), time_zones(?x1264, ?x2864), time_zones(?x205, ?x2864), film_release_region(?x6181, ?x1264), film_release_region(?x5713, ?x1264), film_release_region(?x5576, ?x1264), film_release_region(?x4811, ?x1264), film_release_region(?x4290, ?x1264), film_release_region(?x3226, ?x1264), film_release_region(?x1463, ?x1264), film_release_region(?x781, ?x1264), ?x5713 = 0cc97st, contains(?x1264, ?x196), country(?x7928, ?x1264), country(?x6365, ?x1264), nationality(?x9904, ?x1264), nationality(?x5346, ?x1264), nationality(?x3335, ?x1264), ?x7928 = 02r2j8, ?x5346 = 049gc, ?x3226 = 0gyfp9c, location(?x1221, ?x1264), ?x6365 = 03n3gl, administrative_parent(?x3912, ?x551), ?x3335 = 0jcx, country(?x12249, ?x1264), ?x781 = 0gkz15s, country(?x150, ?x1264), organization(?x1264, ?x127), film_release_region(?x251, ?x205), combatants(?x94, ?x1264), adjoins(?x1355, ?x205), ?x251 = 02vp1f_, ?x1463 = 0gtvrv3, olympics(?x205, ?x358), ?x5576 = 0gbfn9, ?x9904 = 02qx1m2, ?x6181 = 0hv27, adjoins(?x985, ?x1264), ?x4290 = 0gtxj2q, ?x4811 = 0f4k49 >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #7276 for first EXPECTED value: *> intensional similarity = 37 *> extensional distance = 936 *> proper extension: 01914; 016v46; 0d05w3; 06f32; 06wjf; 0123gq; 017236; 01l3k6; 0166c7; 01qq80; ... *> query: (?x13657, ?x1679) <- time_zones(?x13657, ?x2864), time_zones(?x14227, ?x2864), time_zones(?x13437, ?x2864), time_zones(?x9283, ?x2864), time_zones(?x1679, ?x2864), time_zones(?x1264, ?x2864), time_zones(?x774, ?x2864), administrative_parent(?x14227, ?x12932), film_release_region(?x5713, ?x1264), film_release_region(?x2783, ?x1264), ?x5713 = 0cc97st, contains(?x1264, ?x196), country(?x136, ?x1264), nationality(?x380, ?x1264), country(?x4673, ?x1264), olympics(?x1264, ?x358), film_release_region(?x1133, ?x1264), location(?x2580, ?x774), film_release_region(?x2889, ?x774), official_language(?x9283, ?x254), combatants(?x1264, ?x94), country(?x3304, ?x774), ?x4673 = 07jbh, olympics(?x774, ?x418), ?x2889 = 040b5k, form_of_government(?x1264, ?x6441), contains(?x10382, ?x13437), country(?x12249, ?x1264), location(?x1221, ?x1264), administrative_parent(?x5291, ?x774), ?x2783 = 0879bpq, taxonomy(?x774, ?x939), jurisdiction_of_office(?x346, ?x1264), currency(?x1264, ?x170), combatants(?x1679, ?x6371), adjoins(?x985, ?x1264), countries_spoken_in(?x90, ?x774) *> conf = 0.14 ranks of expected_values: 15 EVAL 071g6 contains! 05qhw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 12.000 12.000 0.373 http://example.org/location/location/contains #847-02qwg PRED entity: 02qwg PRED relation: role PRED expected values: 02sgy => 131 concepts (131 used for prediction) PRED predicted values (max 10 best out of 86): 05r5c (0.42 #320, 0.37 #3226, 0.37 #3329), 02sgy (0.33 #6, 0.31 #214, 0.25 #730), 028tv0 (0.30 #1450, 0.28 #828, 0.27 #1763), 06w7v (0.28 #2804, 0.23 #6421, 0.23 #5181), 01vdm0 (0.25 #3354, 0.24 #3251, 0.24 #757), 042v_gx (0.23 #3227, 0.22 #3330, 0.22 #321), 05842k (0.22 #391, 0.22 #183, 0.19 #287), 013y1f (0.22 #246, 0.13 #1592, 0.13 #3359), 018vs (0.18 #738, 0.17 #3541, 0.17 #3127), 026t6 (0.17 #107, 0.16 #3530, 0.15 #3116) >> Best rule #320 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 015_30; 016h4r; 016yzz; 02sjp; >> query: (?x3403, 05r5c) <- award_winner(?x247, ?x3403), role(?x3403, ?x227), languages(?x3403, ?x254) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 01vvycq; 01vs_v8; 01ttg5; 01s21dg; *> query: (?x3403, 02sgy) <- role(?x3403, ?x227), award_winner(?x3403, ?x1089), participant(?x3403, ?x4608) *> conf = 0.33 ranks of expected_values: 2 EVAL 02qwg role 02sgy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 131.000 131.000 0.417 http://example.org/music/artist/track_contributions./music/track_contribution/role #846-01qg7c PRED entity: 01qg7c PRED relation: profession PRED expected values: 02jknp => 108 concepts (90 used for prediction) PRED predicted values (max 10 best out of 62): 02jknp (0.89 #1457, 0.88 #4358, 0.54 #4068), 0dxtg (0.74 #1463, 0.70 #4364, 0.68 #3349), 0np9r (0.30 #2774, 0.24 #19, 0.15 #1759), 018gz8 (0.24 #15, 0.20 #1610, 0.19 #4947), 0cbd2 (0.23 #1601, 0.22 #1746, 0.20 #3777), 09jwl (0.20 #6834, 0.19 #6544, 0.19 #6399), 012t_z (0.14 #447, 0.12 #1172, 0.12 #2187), 0dz3r (0.13 #6819, 0.12 #6384, 0.12 #6529), 0nbcg (0.13 #6845, 0.13 #6555, 0.13 #6410), 016z4k (0.12 #6821, 0.11 #7547, 0.10 #8707) >> Best rule #1457 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 07nznf; 0q9kd; 02rchht; 014zcr; 05ty4m; 0bxtg; 03f2_rc; 0c1pj; 02lf0c; 05kfs; ... >> query: (?x9681, 02jknp) <- profession(?x9681, ?x1041), film(?x9681, ?x1372), ?x1041 = 03gjzk >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qg7c profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 90.000 0.891 http://example.org/people/person/profession #845-015cxv PRED entity: 015cxv PRED relation: group! PRED expected values: 02hnl => 68 concepts (48 used for prediction) PRED predicted values (max 10 best out of 123): 02hnl (0.78 #716, 0.77 #804, 0.77 #372), 018vs (0.65 #357, 0.62 #701, 0.62 #789), 0l14md (0.65 #351, 0.60 #783, 0.60 #695), 03bx0bm (0.65 #800, 0.65 #712, 0.64 #889), 028tv0 (0.39 #788, 0.38 #700, 0.38 #877), 03qjg (0.32 #391, 0.29 #649, 0.25 #47), 0l14qv (0.26 #349, 0.23 #1044, 0.22 #693), 0l14j_ (0.25 #137, 0.25 #51, 0.13 #395), 07c6l (0.25 #96, 0.25 #10, 0.11 #182), 07gql (0.25 #122, 0.25 #36, 0.11 #208) >> Best rule #716 for best value: >> intensional similarity = 5 >> extensional distance = 128 >> proper extension: 089tm; 01t_xp_; 01pfr3; 04rcr; 0150jk; 02r3zy; 07c0j; 067mj; 01vsxdm; 03g5jw; ... >> query: (?x6635, 02hnl) <- award(?x6635, ?x724), group(?x1472, ?x6635), role(?x1472, ?x1437), ?x1437 = 01vdm0, role(?x212, ?x1472) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015cxv group! 02hnl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 48.000 0.777 http://example.org/music/performance_role/regular_performances./music/group_membership/group #844-0kt_4 PRED entity: 0kt_4 PRED relation: currency PRED expected values: 01nv4h => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.77 #22, 0.75 #190, 0.75 #85), 01nv4h (0.25 #2, 0.12 #9, 0.06 #142), 02l6h (0.12 #11, 0.01 #88, 0.01 #46), 02gsvk (0.01 #48) >> Best rule #22 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 02vqhv0; 047qxs; 0cc846d; 0prrm; 02z2mr7; 01gwk3; 02n72k; 0mbql; 0gfzfj; >> query: (?x8984, 09nqf) <- story_by(?x8984, ?x5004), film(?x1104, ?x8984), films(?x8435, ?x8984), film(?x269, ?x8984) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 0168ls; 04vh83; *> query: (?x8984, 01nv4h) <- nominated_for(?x198, ?x8984), nominated_for(?x269, ?x8984), film(?x3028, ?x8984), ?x269 = 0byfz, ?x198 = 040njc *> conf = 0.25 ranks of expected_values: 2 EVAL 0kt_4 currency 01nv4h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.773 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #843-01vrnsk PRED entity: 01vrnsk PRED relation: profession PRED expected values: 01d_h8 09jwl 0nbcg => 141 concepts (139 used for prediction) PRED predicted values (max 10 best out of 66): 09jwl (0.82 #876, 0.82 #4596, 0.80 #303), 039v1 (0.75 #318, 0.39 #4611, 0.32 #891), 01d_h8 (0.61 #1007, 0.46 #3868, 0.45 #3296), 0nbcg (0.60 #313, 0.57 #4606, 0.56 #886), 0dz3r (0.51 #1719, 0.48 #2721, 0.47 #145), 0dxtg (0.47 #1014, 0.34 #1443, 0.33 #1300), 03gjzk (0.33 #3733, 0.32 #1444, 0.32 #3876), 0n1h (0.28 #1727, 0.28 #6297, 0.27 #3587), 0fnpj (0.28 #6297, 0.26 #17750, 0.25 #13028), 0cbd2 (0.28 #6297, 0.26 #17750, 0.25 #13028) >> Best rule #876 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 01vvydl; 058s57; 0qdyf; 01bczm; 015076; >> query: (?x6947, 09jwl) <- instrumentalists(?x212, ?x6947), profession(?x6947, ?x220), diet(?x6947, ?x3130) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 4 EVAL 01vrnsk profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 139.000 0.824 http://example.org/people/person/profession EVAL 01vrnsk profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 141.000 139.000 0.824 http://example.org/people/person/profession EVAL 01vrnsk profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 139.000 0.824 http://example.org/people/person/profession #842-0l99s PRED entity: 0l99s PRED relation: influenced_by PRED expected values: 03pm9 06kb_ => 137 concepts (86 used for prediction) PRED predicted values (max 10 best out of 327): 081nh (0.33 #65, 0.25 #496, 0.08 #1789), 01lc5 (0.33 #382, 0.25 #813, 0.08 #2106), 0407f (0.33 #86, 0.25 #517, 0.08 #1810), 0ky1 (0.25 #1650, 0.07 #31952, 0.07 #31950), 04093 (0.25 #1583, 0.07 #10646, 0.05 #33247), 02n9k (0.25 #678, 0.05 #2402, 0.04 #3695), 0mj0c (0.21 #9924, 0.20 #5608, 0.16 #8628), 03_87 (0.19 #3217, 0.15 #10124, 0.14 #11418), 026lj (0.19 #2631, 0.12 #3925, 0.09 #9537), 02wh0 (0.19 #5554, 0.13 #7711, 0.12 #11597) >> Best rule #65 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 09889g; >> query: (?x7334, 081nh) <- people(?x1158, ?x7334), sibling(?x7334, ?x3941), influenced_by(?x916, ?x7334), profession(?x7334, ?x353) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5332 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 0f0y8; 0h0p_; 03f3_p3; *> query: (?x7334, 06kb_) <- people(?x1158, ?x7334), influenced_by(?x916, ?x7334), peers(?x7334, ?x3941) *> conf = 0.05 ranks of expected_values: 114, 117 EVAL 0l99s influenced_by 06kb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 137.000 86.000 0.333 http://example.org/influence/influence_node/influenced_by EVAL 0l99s influenced_by 03pm9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 137.000 86.000 0.333 http://example.org/influence/influence_node/influenced_by #841-017fx5 PRED entity: 017fx5 PRED relation: film_format! PRED expected values: 0czyxs 0gjc4d3 07nxnw 0btpm6 => 5 concepts (4 used for prediction) PRED predicted values (max 10 best out of 1856): 011yqc (0.40 #1100, 0.40 #743, 0.33 #395), 0fgpvf (0.40 #1069, 0.40 #712, 0.33 #364), 01mgw (0.40 #1314, 0.40 #957, 0.33 #609), 05g8pg (0.40 #1172, 0.40 #815, 0.33 #467), 0bpx1k (0.40 #1153, 0.40 #796, 0.33 #448), 032_wv (0.40 #1089, 0.40 #732, 0.33 #384), 02_fm2 (0.40 #1059, 0.33 #6, 0.24 #693), 0crh5_f (0.33 #347, 0.33 #108, 0.20 #1161), 03f7nt (0.33 #531, 0.24 #693, 0.22 #692), 0btpm6 (0.33 #256, 0.24 #693, 0.20 #1402) >> Best rule #1100 for best value: >> intensional similarity = 91 >> extensional distance = 3 >> proper extension: 0hcr; >> query: (?x10390, 011yqc) <- film_format(?x5553, ?x10390), film_format(?x4664, ?x10390), film_format(?x4336, ?x10390), film_format(?x3748, ?x10390), film_format(?x3606, ?x10390), film(?x250, ?x3748), country(?x3606, ?x94), film_crew_role(?x5553, ?x1284), production_companies(?x3748, ?x902), film(?x496, ?x3606), film_release_distribution_medium(?x3748, ?x81), films(?x14013, ?x3748), film_crew_role(?x10191, ?x1284), film_crew_role(?x9858, ?x1284), film_crew_role(?x8130, ?x1284), film_crew_role(?x7532, ?x1284), film_crew_role(?x7493, ?x1284), film_crew_role(?x7275, ?x1284), film_crew_role(?x7107, ?x1284), film_crew_role(?x7080, ?x1284), film_crew_role(?x6621, ?x1284), film_crew_role(?x6451, ?x1284), film_crew_role(?x6343, ?x1284), film_crew_role(?x5534, ?x1284), film_crew_role(?x5313, ?x1284), film_crew_role(?x5293, ?x1284), film_crew_role(?x4902, ?x1284), film_crew_role(?x4811, ?x1284), film_crew_role(?x4688, ?x1284), film_crew_role(?x4607, ?x1284), film_crew_role(?x4502, ?x1284), film_crew_role(?x4500, ?x1284), film_crew_role(?x3863, ?x1284), film_crew_role(?x3093, ?x1284), film_crew_role(?x2754, ?x1284), film_crew_role(?x2494, ?x1284), film_crew_role(?x2116, ?x1284), film_crew_role(?x1595, ?x1284), film_crew_role(?x1452, ?x1284), film_crew_role(?x1450, ?x1284), film_crew_role(?x1184, ?x1284), film_crew_role(?x908, ?x1284), film_crew_role(?x821, ?x1284), film_crew_role(?x805, ?x1284), film_crew_role(?x508, ?x1284), film_crew_role(?x392, ?x1284), ?x7080 = 08984j, ?x392 = 0dnvn3, ?x4607 = 0h03fhx, ?x5534 = 05zpghd, ?x4811 = 0f4k49, ?x4502 = 02wgk1, ?x5293 = 0cbv4g, ?x908 = 01vksx, ?x7275 = 0g4vmj8, ?x9858 = 056xkh, ?x821 = 02hxhz, ?x1595 = 05pbl56, film(?x1870, ?x4664), film(?x539, ?x5553), ?x1184 = 02v63m, ?x508 = 0ds33, ?x6621 = 0h63gl9, award_nominee(?x1870, ?x100), ?x4500 = 01pj_5, film(?x541, ?x4664), film(?x5316, ?x4336), ?x7107 = 04ghz4m, ?x81 = 029j_, ?x2116 = 02c638, ?x7532 = 09gdh6k, ?x2754 = 04yc76, ?x3863 = 0dx8gj, story_by(?x4336, ?x2533), ?x805 = 03ckwzc, ?x8130 = 0bwhdbl, ?x7493 = 0btpm6, ?x4688 = 09jcj6, ?x4902 = 0dfw0, genre(?x4664, ?x53), ?x6451 = 01l2b3, award_winner(?x337, ?x1870), ?x3093 = 04tqtl, ?x10191 = 0crd8q6, ?x1452 = 0jqn5, ?x2494 = 065zlr, ?x1450 = 0pb33, film_crew_role(?x4664, ?x1078), ?x5313 = 01f6x7, ?x6343 = 05n6sq, nominated_for(?x5316, ?x351) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #256 for first EXPECTED value: *> intensional similarity = 74 *> extensional distance = 1 *> proper extension: 0cj16; *> query: (?x10390, 0btpm6) <- film_format(?x3748, ?x10390), film_format(?x3606, ?x10390), film_format(?x2394, ?x10390), film_format(?x791, ?x10390), film(?x13239, ?x3748), film(?x8638, ?x3748), film(?x4520, ?x3748), film(?x2922, ?x3748), country(?x3606, ?x94), film_release_region(?x3748, ?x7747), film_release_region(?x3748, ?x1917), film_release_region(?x3748, ?x1499), film_release_region(?x3748, ?x1475), film_release_region(?x3748, ?x583), genre(?x3748, ?x225), film_crew_role(?x3606, ?x9674), film_crew_role(?x3606, ?x2095), film_crew_role(?x3606, ?x2091), film_crew_role(?x3606, ?x1284), film_release_region(?x3606, ?x151), film(?x496, ?x3606), notable_people_with_this_condition(?x8318, ?x8638), ?x2095 = 0dxtw, profession(?x13239, ?x1383), written_by(?x3748, ?x2442), film(?x5636, ?x3748), award(?x8638, ?x1007), participant(?x8638, ?x9482), ?x94 = 09c7w0, ?x2091 = 02rh1dz, profession(?x6512, ?x9674), film(?x71, ?x791), ?x6512 = 08304, film_release_region(?x791, ?x792), location(?x13239, ?x3052), executive_produced_by(?x3748, ?x2135), film(?x5636, ?x2954), ?x2954 = 0crh5_f, child(?x3920, ?x5636), ?x1499 = 01znc_, titles(?x8581, ?x3748), ?x792 = 0hzlz, gender(?x2922, ?x231), type_of_union(?x4520, ?x566), ?x1475 = 05qx1, award_nominee(?x230, ?x2922), ?x7747 = 07f1x, profession(?x8638, ?x1032), ?x1917 = 01p1v, award_nominee(?x2922, ?x1194), film(?x2122, ?x2394), film_crew_role(?x2394, ?x1078), film_crew_role(?x9981, ?x1284), film_crew_role(?x7072, ?x1284), film_crew_role(?x6510, ?x1284), film_crew_role(?x5689, ?x1284), film_crew_role(?x5323, ?x1284), film_crew_role(?x3953, ?x1284), film_crew_role(?x1927, ?x1284), language(?x3606, ?x254), nominated_for(?x68, ?x2394), ?x231 = 05zppz, ?x7072 = 02d003, currency(?x2394, ?x170), ?x5323 = 011yn5, ?x6510 = 027gy0k, ?x2122 = 018swb, ?x1927 = 0by1wkq, ?x583 = 015fr, ?x5689 = 0c3z0, film_release_region(?x2394, ?x1203), ?x9981 = 03hp2y1, ?x151 = 0b90_r, ?x3953 = 065dc4 *> conf = 0.33 ranks of expected_values: 10, 579, 811, 1018 EVAL 017fx5 film_format! 0btpm6 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 017fx5 film_format! 07nxnw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 017fx5 film_format! 0gjc4d3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format EVAL 017fx5 film_format! 0czyxs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 5.000 4.000 0.400 http://example.org/film/film/film_format #840-0ms1n PRED entity: 0ms1n PRED relation: second_level_divisions! PRED expected values: 09c7w0 => 214 concepts (124 used for prediction) PRED predicted values (max 10 best out of 12): 09c7w0 (0.90 #968, 0.89 #493, 0.89 #1213), 07b_l (0.27 #45, 0.23 #664, 0.23 #320), 0ms6_ (0.19 #1005, 0.13 #1534, 0.09 #733), 0ms1n (0.19 #1005, 0.13 #1534, 0.09 #733), 03rt9 (0.13 #100, 0.09 #61, 0.03 #1049), 02jx1 (0.12 #119, 0.05 #142, 0.05 #1117), 03rjj (0.10 #147, 0.04 #246, 0.03 #457), 06mkj (0.05 #156, 0.02 #318), 0d060g (0.02 #196, 0.02 #284, 0.01 #359), 0f8l9c (0.02 #251, 0.02 #1363, 0.01 #419) >> Best rule #968 for best value: >> intensional similarity = 3 >> extensional distance = 163 >> proper extension: 02cl1; 0mnyn; >> query: (?x11836, 09c7w0) <- contains(?x3634, ?x11836), county(?x11050, ?x11836), time_zones(?x3634, ?x1638) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ms1n second_level_divisions! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 214.000 124.000 0.897 http://example.org/location/country/second_level_divisions #839-0cq86w PRED entity: 0cq86w PRED relation: honored_for! PRED expected values: 0bzkvd => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 104): 04n2r9h (0.10 #524, 0.06 #402, 0.05 #1500), 0dthsy (0.08 #9640, 0.07 #56, 0.05 #178), 0c4hnm (0.08 #9640, 0.02 #723, 0.02 #1455), 0bzkvd (0.08 #9640, 0.01 #2173), 0bzknt (0.08 #9640, 0.01 #1167), 0ftlxj (0.07 #59, 0.06 #303, 0.05 #181), 0clfdj (0.07 #2, 0.05 #124, 0.04 #978), 02wzl1d (0.06 #373, 0.03 #1471, 0.03 #1593), 073hmq (0.05 #137, 0.03 #259, 0.02 #625), 09p30_ (0.05 #560, 0.03 #2512, 0.03 #1414) >> Best rule #524 for best value: >> intensional similarity = 4 >> extensional distance = 37 >> proper extension: 074rg9; >> query: (?x5873, 04n2r9h) <- language(?x5873, ?x254), genre(?x5873, ?x604), ?x604 = 0lsxr, honored_for(?x1903, ?x5873) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #9640 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1276 *> proper extension: 04bp0l; *> query: (?x5873, ?x8150) <- nominated_for(?x10758, ?x5873), award_winner(?x1313, ?x10758), award_winner(?x8150, ?x10758) *> conf = 0.08 ranks of expected_values: 4 EVAL 0cq86w honored_for! 0bzkvd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 103.000 0.103 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #838-02vr7 PRED entity: 02vr7 PRED relation: instrumentalists! PRED expected values: 05148p4 07xzm => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 108): 05148p4 (0.36 #1300, 0.33 #167, 0.33 #1831), 042v_gx (0.28 #226, 0.26 #1360, 0.26 #2269), 02sgy (0.28 #226, 0.26 #1360, 0.26 #2269), 026t6 (0.17 #153, 0.12 #1286, 0.11 #2876), 0l14md (0.14 #157, 0.13 #1290, 0.11 #3033), 0l14qv (0.11 #681, 0.10 #155, 0.10 #984), 06w7v (0.11 #60, 0.07 #210, 0.05 #1343), 03gvt (0.09 #203, 0.08 #729, 0.08 #1336), 018j2 (0.09 #1310, 0.09 #1841, 0.09 #2900), 06ncr (0.08 #183, 0.08 #2680, 0.08 #1316) >> Best rule #1300 for best value: >> intensional similarity = 3 >> extensional distance = 340 >> proper extension: 05qhnq; 016lj_; 02s6sh; >> query: (?x8311, 05148p4) <- artists(?x1000, ?x8311), role(?x8311, ?x227), artist(?x382, ?x8311) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1, 27 EVAL 02vr7 instrumentalists! 07xzm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 108.000 108.000 0.360 http://example.org/music/instrument/instrumentalists EVAL 02vr7 instrumentalists! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.360 http://example.org/music/instrument/instrumentalists #837-0g60z PRED entity: 0g60z PRED relation: award PRED expected values: 0bdx29 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 184): 0bdw6t (0.43 #6388, 0.40 #4791, 0.40 #6617), 0bdx29 (0.43 #6388, 0.40 #4791, 0.40 #6617), 0fbtbt (0.43 #6388, 0.40 #4791, 0.40 #6617), 0bp_b2 (0.43 #6388, 0.40 #4791, 0.40 #6617), 09v7wsg (0.43 #6388, 0.40 #4791, 0.40 #6617), 09qvc0 (0.30 #488, 0.29 #260, 0.25 #716), 09qvf4 (0.30 #597, 0.29 #369, 0.25 #825), 0cjyzs (0.20 #534, 0.17 #762, 0.15 #990), 09qj50 (0.20 #492, 0.17 #720, 0.15 #948), 0cqhmg (0.20 #663, 0.17 #891, 0.15 #1119) >> Best rule #6388 for best value: >> intensional similarity = 3 >> extensional distance = 118 >> proper extension: 03j63k; 097h2; 019g8j; 0147w8; 0300ml; >> query: (?x337, ?x435) <- nominated_for(?x435, ?x337), languages(?x337, ?x254), award(?x337, ?x3486) >> conf = 0.43 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0g60z award 0bdx29 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 99.000 0.427 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #836-01vfwd PRED entity: 01vfwd PRED relation: contains! PRED expected values: 0j1_3 => 54 concepts (48 used for prediction) PRED predicted values (max 10 best out of 108): 0j1_3 (0.60 #1779, 0.43 #2674, 0.33 #884), 02qkt (0.54 #5717, 0.50 #6612, 0.43 #4476), 09c7w0 (0.54 #29561, 0.52 #2688, 0.51 #30456), 0j0k (0.43 #4476, 0.37 #5748, 0.34 #6643), 073q1 (0.43 #4476, 0.27 #29557, 0.27 #28660), 05nrg (0.43 #4476, 0.27 #29557, 0.27 #28660), 04_1l0v (0.38 #3135, 0.33 #7612, 0.25 #9402), 03rk0 (0.37 #10879, 0.15 #8194, 0.13 #9984), 02j9z (0.22 #4504, 0.14 #5399, 0.13 #6294), 07c5l (0.20 #4870, 0.16 #3974, 0.12 #6660) >> Best rule #1779 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 044rv; 01tmtg; >> query: (?x13628, 0j1_3) <- administrative_parent(?x13628, ?x3749), ?x3749 = 03ryn, contains(?x3749, ?x13628) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vfwd contains! 0j1_3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 54.000 48.000 0.600 http://example.org/location/location/contains #835-01vtg4q PRED entity: 01vtg4q PRED relation: instrumentalists! PRED expected values: 0l14md => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 122): 018vs (0.57 #172, 0.40 #10, 0.36 #1634), 026t6 (0.31 #164, 0.17 #1219, 0.13 #651), 0l14md (0.29 #167, 0.15 #1222, 0.13 #654), 03qjg (0.24 #207, 0.18 #1913, 0.17 #1262), 06ch55 (0.20 #157, 0.13 #563, 0.12 #806), 03gvt (0.13 #140, 0.11 #546, 0.10 #465), 04rzd (0.12 #194, 0.10 #681, 0.10 #32), 018j2 (0.12 #195, 0.10 #1657, 0.09 #3773), 06ncr (0.10 #39, 0.10 #201, 0.10 #526), 06w7v (0.10 #66, 0.08 #228, 0.07 #715) >> Best rule #172 for best value: >> intensional similarity = 4 >> extensional distance = 49 >> proper extension: 09lwrt; >> query: (?x8305, 018vs) <- artists(?x1572, ?x8305), ?x1572 = 06by7, instrumentalists(?x1750, ?x8305), ?x1750 = 02hnl >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #167 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 09lwrt; *> query: (?x8305, 0l14md) <- artists(?x1572, ?x8305), ?x1572 = 06by7, instrumentalists(?x1750, ?x8305), ?x1750 = 02hnl *> conf = 0.29 ranks of expected_values: 3 EVAL 01vtg4q instrumentalists! 0l14md CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 125.000 0.569 http://example.org/music/instrument/instrumentalists #834-04htfd PRED entity: 04htfd PRED relation: company! PRED expected values: 060c4 0krdk 09d6p2 => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 38): 060c4 (0.85 #4153, 0.79 #4331, 0.78 #1461), 0krdk (0.78 #1420, 0.77 #844, 0.77 #2304), 05_wyz (0.50 #1828, 0.48 #854, 0.45 #810), 09d6p2 (0.37 #282, 0.34 #2315, 0.33 #2843), 01kr6k (0.33 #642, 0.32 #863, 0.29 #1572), 02211by (0.23 #841, 0.23 #797, 0.22 #47), 0142rn (0.21 #289, 0.20 #333, 0.20 #4240), 04192r (0.21 #656, 0.20 #348, 0.20 #4240), 02y6fz (0.20 #4240, 0.20 #1436, 0.17 #1613), 09lq2c (0.20 #4240, 0.17 #161, 0.16 #3975) >> Best rule #4153 for best value: >> intensional similarity = 4 >> extensional distance = 154 >> proper extension: 0f8l9c; 03rj0; 01jsn5; 0j_sncb; 0pspl; 01_8w2; 04hzj; 05c74; 0ymcz; 0175rc; ... >> query: (?x6156, 060c4) <- company(?x265, ?x6156), company(?x265, ?x9198), ?x9198 = 0mgkg, jurisdiction_of_office(?x265, ?x142) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4 EVAL 04htfd company! 09d6p2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.846 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 04htfd company! 0krdk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.846 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 04htfd company! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.846 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #833-0f4vx0 PRED entity: 0f4vx0 PRED relation: draft! PRED expected values: 0jmm4 => 16 concepts (16 used for prediction) PRED predicted values (max 10 best out of 308): 0jmcb (0.62 #582, 0.61 #797, 0.57 #142), 0jmm4 (0.62 #582, 0.61 #797, 0.57 #142), 0jmdb (0.62 #582, 0.61 #797, 0.57 #142), 0jm9w (0.62 #582, 0.61 #797, 0.57 #142), 0jm7n (0.62 #582, 0.61 #797, 0.57 #142), 01jvgt (0.62 #582, 0.61 #797, 0.57 #142), 04cxw5b (0.62 #582, 0.61 #797, 0.57 #142), 0fw9vx (0.62 #582, 0.61 #797, 0.57 #142), 02ptzz0 (0.62 #582, 0.61 #797, 0.57 #142), 026wlnm (0.62 #582, 0.61 #797, 0.57 #142) >> Best rule #582 for best value: >> intensional similarity = 47 >> extensional distance = 1 >> proper extension: 02pq_rp; >> query: (?x4979, ?x660) <- draft(?x11805, ?x4979), draft(?x11420, ?x4979), draft(?x9931, ?x4979), draft(?x8228, ?x4979), draft(?x2398, ?x4979), draft(?x799, ?x4979), school(?x4979, ?x6271), school(?x4979, ?x3779), school(?x4979, ?x2152), school(?x4979, ?x1884), team(?x6848, ?x2398), teams(?x4733, ?x2398), team(?x13931, ?x9931), colors(?x8228, ?x663), organization(?x2152, ?x127), sport(?x11805, ?x4833), time_zones(?x2152, ?x2864), team(?x6848, ?x660), ?x663 = 083jv, school(?x2398, ?x2399), teams(?x108, ?x11805), ?x4733 = 03l2n, major_field_of_study(?x1884, ?x3489), major_field_of_study(?x1884, ?x2981), school(?x11420, ?x3948), student(?x1884, ?x2543), student(?x1884, ?x1815), ?x3948 = 025v3k, state_province_region(?x3779, ?x3778), award_winner(?x415, ?x2543), institution(?x620, ?x3779), teams(?x2850, ?x9931), award_nominee(?x2543, ?x438), colors(?x799, ?x3315), currency(?x3779, ?x170), fraternities_and_sororities(?x1884, ?x3697), teams(?x674, ?x11420), participant(?x3054, ?x13931), ?x2981 = 02j62, company(?x346, ?x2399), teams(?x739, ?x799), school_type(?x6271, ?x1507), major_field_of_study(?x2399, ?x1527), award_winner(?x3104, ?x2543), ?x3489 = 0193x, award_winner(?x1815, ?x851), list(?x3779, ?x2197) >> conf = 0.62 => this is the best rule for 25 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0f4vx0 draft! 0jmm4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 16.000 16.000 0.619 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft #832-018p5f PRED entity: 018p5f PRED relation: company! PRED expected values: 0krdk => 154 concepts (154 used for prediction) PRED predicted values (max 10 best out of 37): 0krdk (0.74 #2032, 0.73 #2302, 0.71 #2257), 0dq3c (0.67 #812, 0.56 #2298, 0.46 #2028), 05_wyz (0.56 #827, 0.54 #2043, 0.44 #2313), 01yc02 (0.54 #2034, 0.44 #818, 0.44 #2304), 09d6p2 (0.50 #108, 0.46 #2044, 0.44 #828), 01kr6k (0.33 #836, 0.31 #2052, 0.29 #1106), 014l7h (0.20 #342, 0.14 #477, 0.11 #5001), 02211by (0.15 #2299, 0.14 #1939, 0.14 #2254), 02y6fz (0.13 #2319, 0.12 #2544, 0.12 #2274), 0142rn (0.12 #700, 0.11 #5001, 0.11 #5000) >> Best rule #2032 for best value: >> intensional similarity = 5 >> extensional distance = 33 >> proper extension: 01s73z; 02630g; 03v52f; 0z90c; 05njw; 018c_r; 06py2; 0gy1_; >> query: (?x7390, 0krdk) <- company(?x4682, ?x7390), company(?x346, ?x7390), ?x346 = 060c4, citytown(?x7390, ?x1719), ?x4682 = 0dq_5 >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018p5f company! 0krdk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 154.000 154.000 0.743 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #831-034rd PRED entity: 034rd PRED relation: gender PRED expected values: 05zppz => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.91 #45, 0.90 #67, 0.89 #113), 02zsn (0.46 #181, 0.46 #210, 0.46 #251) >> Best rule #45 for best value: >> intensional similarity = 4 >> extensional distance = 20 >> proper extension: 06kb_; 082db; 0hfml; 0cyhq; >> query: (?x5609, 05zppz) <- profession(?x5609, ?x1682), organization(?x5609, ?x8603), type_of_union(?x5609, ?x566), ?x8603 = 02_l9 >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034rd gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.909 http://example.org/people/person/gender #830-016dmx PRED entity: 016dmx PRED relation: award PRED expected values: 07bdd_ => 118 concepts (105 used for prediction) PRED predicted values (max 10 best out of 315): 07bdd_ (0.56 #2894, 0.53 #4510, 0.27 #1278), 0gq9h (0.36 #11796, 0.33 #9371, 0.33 #2906), 09sb52 (0.31 #19841, 0.30 #20245, 0.26 #20649), 040njc (0.28 #11726, 0.26 #1220, 0.25 #9301), 0gr51 (0.24 #505, 0.14 #2121, 0.14 #4949), 05zr6wv (0.22 #825, 0.16 #421, 0.15 #2037), 019f4v (0.21 #471, 0.18 #11785, 0.17 #12999), 04dn09n (0.21 #448, 0.15 #12123, 0.14 #4892), 01by1l (0.20 #16276, 0.20 #16680, 0.19 #18700), 05ztrmj (0.20 #993, 0.15 #12123, 0.11 #589) >> Best rule #2894 for best value: >> intensional similarity = 3 >> extensional distance = 120 >> proper extension: 0kk9v; >> query: (?x8345, 07bdd_) <- nominated_for(?x8345, ?x2107), award_nominee(?x8345, ?x4564), production_companies(?x253, ?x4564) >> conf = 0.56 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016dmx award 07bdd_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 105.000 0.557 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #829-01pw2f1 PRED entity: 01pw2f1 PRED relation: profession PRED expected values: 01d_h8 => 167 concepts (165 used for prediction) PRED predicted values (max 10 best out of 96): 03gjzk (0.86 #1207, 0.85 #7614, 0.85 #9104), 0dxtg (0.66 #8656, 0.66 #7762, 0.66 #9103), 01d_h8 (0.60 #2092, 0.60 #6, 0.59 #1943), 09jwl (0.40 #466, 0.38 #2552, 0.37 #17900), 02krf9 (0.40 #1368, 0.31 #7626, 0.30 #8669), 0d1pc (0.40 #51, 0.26 #1541, 0.25 #2882), 0nbcg (0.32 #2565, 0.28 #15199, 0.28 #6886), 02jknp (0.30 #1349, 0.29 #1945, 0.28 #15199), 018gz8 (0.30 #1358, 0.28 #1209, 0.27 #1954), 0np9r (0.29 #7918, 0.28 #15199, 0.24 #1213) >> Best rule #1207 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 04n7njg; 03xp8d5; 01my_c; 047cqr; 03yf4d; >> query: (?x1503, 03gjzk) <- profession(?x1503, ?x1032), program(?x1503, ?x3413), category(?x1503, ?x134) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2092 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 41 *> proper extension: 02c0mv; 03mstc; 0488g9; *> query: (?x1503, 01d_h8) <- location(?x1503, ?x9233), people(?x3591, ?x1503), program(?x1503, ?x3413) *> conf = 0.60 ranks of expected_values: 3 EVAL 01pw2f1 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 167.000 165.000 0.862 http://example.org/people/person/profession #828-06q8qh PRED entity: 06q8qh PRED relation: language PRED expected values: 02h40lc => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.95 #2459, 0.95 #525, 0.95 #3409), 064_8sq (0.18 #254, 0.17 #196, 0.16 #545), 04306rv (0.17 #63, 0.13 #121, 0.13 #237), 06nm1 (0.16 #418, 0.12 #708, 0.12 #534), 03_9r (0.14 #591, 0.14 #649, 0.12 #358), 02bjrlw (0.13 #59, 0.13 #117, 0.08 #233), 06b_j (0.07 #836, 0.06 #313, 0.06 #952), 0653m (0.07 #70, 0.05 #128, 0.04 #419), 05zjd (0.06 #258, 0.03 #84, 0.03 #607), 012w70 (0.06 #420, 0.03 #826, 0.03 #303) >> Best rule #2459 for best value: >> intensional similarity = 4 >> extensional distance = 942 >> proper extension: 02d44q; 07k2mq; 01gglm; >> query: (?x3684, 02h40lc) <- titles(?x307, ?x3684), nominated_for(?x2551, ?x3684), language(?x3684, ?x7599), award_nominee(?x92, ?x2551) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06q8qh language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 83.000 83.000 0.950 http://example.org/film/film/language #827-03rt9 PRED entity: 03rt9 PRED relation: olympics PRED expected values: 0jdk_ => 186 concepts (186 used for prediction) PRED predicted values (max 10 best out of 31): 0l998 (0.83 #222, 0.76 #346, 0.73 #129), 0jdk_ (0.82 #143, 0.76 #794, 0.76 #763), 0l6mp (0.75 #229, 0.73 #136, 0.65 #353), 0lbd9 (0.75 #240, 0.71 #364, 0.55 #147), 0kbvv (0.73 #142, 0.71 #1613, 0.70 #1863), 0nbjq (0.73 #137, 0.67 #230, 0.65 #354), 0blg2 (0.73 #135, 0.67 #228, 0.65 #352), 018ctl (0.73 #130, 0.58 #223, 0.53 #347), 0l98s (0.67 #221, 0.64 #128, 0.59 #345), 09x3r (0.67 #225, 0.64 #132, 0.56 #783) >> Best rule #222 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 082fr; >> query: (?x429, 0l998) <- film_release_region(?x9832, ?x429), film_release_region(?x7170, ?x429), ?x7170 = 02pxst, ?x9832 = 01xlqd >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #143 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 03rjj; *> query: (?x429, 0jdk_) <- film_release_region(?x9657, ?x429), second_level_divisions(?x429, ?x1788), titles(?x53, ?x9657) *> conf = 0.82 ranks of expected_values: 2 EVAL 03rt9 olympics 0jdk_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 186.000 186.000 0.833 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #826-01j2xj PRED entity: 01j2xj PRED relation: award_winner! PRED expected values: 09pnw5 => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 130): 0h_9252 (0.33 #57, 0.03 #2857, 0.03 #8178), 0n8_m93 (0.28 #3081, 0.14 #257, 0.10 #537), 09pnw5 (0.28 #3081, 0.10 #1502, 0.08 #2902), 0418154 (0.28 #3081, 0.04 #8228, 0.04 #2347), 04n2r9h (0.28 #3081, 0.04 #744, 0.03 #1164), 092_25 (0.28 #3081, 0.03 #8192, 0.03 #8892), 02hn5v (0.28 #3081, 0.02 #2421, 0.02 #1581), 09g90vz (0.21 #263, 0.15 #543, 0.06 #403), 0hndn2q (0.18 #319, 0.09 #1019, 0.08 #2279), 02wzl1d (0.17 #11, 0.06 #291, 0.06 #2251) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 026g4l_; 03m9c8; >> query: (?x4922, 0h_9252) <- award_winner(?x1084, ?x4922), award_nominee(?x3692, ?x4922), ?x3692 = 03kpvp >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3081 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 147 *> proper extension: 01ycck; 015njf; 098n_m; 0mm1q; 05cgy8; 0flddp; 0454s1; 05bht9; 04vt98; 0htcn; ... *> query: (?x4922, ?x1084) <- film(?x4922, ?x9452), honored_for(?x1084, ?x9452) *> conf = 0.28 ranks of expected_values: 3 EVAL 01j2xj award_winner! 09pnw5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 140.000 140.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #825-04vrxh PRED entity: 04vrxh PRED relation: artists! PRED expected values: 09nwwf => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 194): 0xhtw (0.27 #1562, 0.24 #2797, 0.21 #4341), 06j6l (0.26 #3136, 0.25 #2517, 0.25 #6839), 05bt6j (0.25 #350, 0.24 #40, 0.23 #659), 01lyv (0.22 #1887, 0.22 #3431, 0.20 #4357), 0gywn (0.21 #3145, 0.19 #2526, 0.18 #5922), 03_d0 (0.18 #1866, 0.17 #939, 0.17 #321), 05w3f (0.18 #36, 0.15 #1582, 0.14 #2817), 02qdgx (0.17 #347, 0.15 #656, 0.11 #1274), 0155w (0.17 #4429, 0.17 #3812, 0.15 #6589), 03lty (0.17 #1572, 0.16 #2807, 0.14 #4351) >> Best rule #1562 for best value: >> intensional similarity = 3 >> extensional distance = 228 >> proper extension: 02fybl; 01r4zfk; >> query: (?x9882, 0xhtw) <- profession(?x9882, ?x1183), ?x1183 = 09jwl, role(?x9882, ?x316) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #444 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 85 *> proper extension: 028qdb; *> query: (?x9882, 09nwwf) <- award_winner(?x1896, ?x9882), role(?x9882, ?x316), artists(?x302, ?x9882) *> conf = 0.08 ranks of expected_values: 31 EVAL 04vrxh artists! 09nwwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.032 99.000 99.000 0.274 http://example.org/music/genre/artists #824-0gw7p PRED entity: 0gw7p PRED relation: genre PRED expected values: 02kdv5l => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 100): 02l7c8 (0.45 #722, 0.40 #368, 0.39 #250), 01jfsb (0.44 #128, 0.36 #4024, 0.35 #3433), 01g6gs (0.37 #373, 0.30 #1199, 0.25 #1553), 02kdv5l (0.33 #1, 0.33 #8383, 0.30 #4015), 03k9fj (0.33 #9, 0.22 #7210, 0.22 #5558), 06n90 (0.33 #11, 0.16 #3434, 0.15 #4025), 060__y (0.25 #3320, 0.25 #2257, 0.21 #2139), 06cvj (0.23 #592, 0.17 #356, 0.14 #1182), 01t_vv (0.23 #642, 0.16 #760, 0.13 #2176), 04xvh5 (0.22 #33, 0.20 #2275, 0.19 #3338) >> Best rule #722 for best value: >> intensional similarity = 5 >> extensional distance = 47 >> proper extension: 0sxfd; 07w8fz; >> query: (?x6004, 02l7c8) <- nominated_for(?x746, ?x6004), nominated_for(?x484, ?x6004), genre(?x6004, ?x53), ?x746 = 04dn09n, ?x484 = 0gq_v >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 01_mdl; *> query: (?x6004, 02kdv5l) <- nominated_for(?x484, ?x6004), honored_for(?x6004, ?x3510), film_art_direction_by(?x6004, ?x4168), film(?x190, ?x6004) *> conf = 0.33 ranks of expected_values: 4 EVAL 0gw7p genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 87.000 0.449 http://example.org/film/film/genre #823-04hk0w PRED entity: 04hk0w PRED relation: film! PRED expected values: 04w391 => 99 concepts (55 used for prediction) PRED predicted values (max 10 best out of 912): 01nc3rh (0.44 #14564, 0.43 #79078, 0.42 #93649), 0gn30 (0.25 #948, 0.04 #17595, 0.03 #40482), 01kb2j (0.12 #910, 0.03 #11313, 0.03 #42527), 025n3p (0.12 #490, 0.03 #10893, 0.02 #17137), 02_hj4 (0.12 #268, 0.03 #35641, 0.02 #33558), 01fh9 (0.12 #317, 0.02 #14881, 0.02 #41934), 045gzq (0.12 #2067, 0.02 #4147), 038rzr (0.12 #468, 0.02 #35841, 0.02 #8790), 01l7qw (0.12 #1909, 0.02 #10231, 0.02 #6069), 01pkhw (0.12 #699, 0.02 #9021, 0.02 #31908) >> Best rule #14564 for best value: >> intensional similarity = 5 >> extensional distance = 115 >> proper extension: 0209xj; 02py4c8; 02ppg1r; 05znbh7; 0symg; >> query: (?x12964, ?x10295) <- genre(?x12964, ?x53), ?x53 = 07s9rl0, category(?x12964, ?x134), award_winner(?x12964, ?x10295), film(?x548, ?x12964) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #687 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 03ct7jd; 03clwtw; *> query: (?x12964, 04w391) <- music(?x12964, ?x562), country(?x12964, ?x94), ?x562 = 01nqfh_, film(?x548, ?x12964), language(?x12964, ?x254) *> conf = 0.12 ranks of expected_values: 16 EVAL 04hk0w film! 04w391 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 99.000 55.000 0.444 http://example.org/film/actor/film./film/performance/film #822-0f2pf9 PRED entity: 0f2pf9 PRED relation: contains PRED expected values: 0225bv => 80 concepts (25 used for prediction) PRED predicted values (max 10 best out of 2535): 0f__1 (0.71 #50084, 0.71 #47137, 0.65 #41245), 09c7w0 (0.61 #50083, 0.53 #47136, 0.10 #44190), 0498y (0.45 #32406, 0.38 #29460, 0.23 #55975), 0f2pf9 (0.45 #32406, 0.38 #29460, 0.23 #55975), 0225bv (0.43 #13957, 0.33 #5120, 0.11 #19850), 038czx (0.43 #12723, 0.33 #3886, 0.11 #18616), 01ptt7 (0.43 #12047, 0.33 #3210, 0.11 #17940), 04344j (0.43 #12135, 0.33 #3298, 0.11 #18028), 0pqz3 (0.33 #5676, 0.29 #14513, 0.11 #20406), 0tgcy (0.33 #4506, 0.29 #13343, 0.11 #19236) >> Best rule #50084 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 04swx; >> query: (?x14493, ?x2740) <- contains(?x14493, ?x10845), administrative_division(?x2740, ?x10845), contains(?x94, ?x2740) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #13957 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0f__1; 0tct_; 0d9y6; *> query: (?x14493, 0225bv) <- contains(?x14493, ?x10845), contains(?x94, ?x14493), contains(?x4061, ?x10845), ?x4061 = 0498y *> conf = 0.43 ranks of expected_values: 5 EVAL 0f2pf9 contains 0225bv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 80.000 25.000 0.713 http://example.org/location/location/contains #821-0dt49 PRED entity: 0dt49 PRED relation: award! PRED expected values: 0167bx => 20 concepts (20 used for prediction) PRED predicted values (max 10 best out of 577): 0fhzwl (0.25 #3937, 0.04 #16213, 0.03 #13144), 02c638 (0.25 #3279, 0.03 #12486, 0.03 #18624), 07bz5 (0.25 #4017, 0.03 #13224, 0.02 #16293), 0dsvzh (0.25 #3145, 0.03 #12352, 0.02 #15421), 01mgw (0.18 #16110, 0.14 #14064, 0.12 #10995), 04nl83 (0.18 #10273, 0.14 #15388, 0.14 #13342), 0cvkv5 (0.18 #11071, 0.12 #16186, 0.11 #14140), 04b2qn (0.18 #11026, 0.11 #12049, 0.08 #16141), 0gmcwlb (0.12 #10355, 0.10 #15470, 0.08 #18539), 02tqm5 (0.12 #10548, 0.09 #13617, 0.07 #11571) >> Best rule #3937 for best value: >> intensional similarity = 44 >> extensional distance = 2 >> proper extension: 06196; 04jhhng; >> query: (?x12628, 0fhzwl) <- award(?x11392, ?x12628), disciplines_or_subjects(?x12628, ?x9111), major_field_of_study(?x11693, ?x9111), major_field_of_study(?x11632, ?x9111), major_field_of_study(?x8427, ?x9111), major_field_of_study(?x6637, ?x9111), major_field_of_study(?x6419, ?x9111), major_field_of_study(?x3665, ?x9111), major_field_of_study(?x3439, ?x9111), major_field_of_study(?x3424, ?x9111), major_field_of_study(?x2142, ?x9111), major_field_of_study(?x481, ?x9111), taxonomy(?x9111, ?x939), school_type(?x2142, ?x3092), student(?x2142, ?x2143), category(?x11693, ?x134), citytown(?x6419, ?x2953), currency(?x8427, ?x170), contains(?x1227, ?x6637), major_field_of_study(?x8427, ?x9444), major_field_of_study(?x8427, ?x1695), ?x9444 = 06q83, major_field_of_study(?x1468, ?x9111), student(?x6637, ?x395), student(?x3424, ?x117), colors(?x11632, ?x332), organization(?x346, ?x3424), ?x1227 = 01n7q, currency(?x6419, ?x1099), institution(?x865, ?x11632), company(?x9385, ?x6637), company(?x920, ?x3424), institution(?x620, ?x3424), organization(?x3484, ?x11693), contains(?x335, ?x3665), contains(?x512, ?x6419), ?x335 = 059rby, student(?x481, ?x4134), currency(?x481, ?x2244), ?x1695 = 06ms6, organization(?x3424, ?x5487), colors(?x8427, ?x663), ?x3439 = 03ksy, citytown(?x8427, ?x6703) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0dt49 award! 0167bx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 20.000 20.000 0.250 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #820-0ydpd PRED entity: 0ydpd PRED relation: time_zones PRED expected values: 02hcv8 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.74 #534, 0.44 #159, 0.43 #1135), 02lcqs (0.31 #109, 0.28 #200, 0.27 #122), 02fqwt (0.29 #1, 0.26 #14, 0.23 #27), 02hczc (0.16 #1640, 0.10 #301, 0.09 #28), 02lcrv (0.16 #1640, 0.03 #85, 0.02 #46), 02llzg (0.11 #941, 0.11 #69, 0.10 #668), 03bdv (0.07 #500, 0.06 #253, 0.05 #787), 03plfd (0.02 #856, 0.02 #947, 0.02 #674), 052vwh (0.02 #806), 042g7t (0.01 #831, 0.01 #714, 0.01 #766) >> Best rule #534 for best value: >> intensional similarity = 2 >> extensional distance = 173 >> proper extension: 0jq27; >> query: (?x553, ?x2674) <- administrative_division(?x553, ?x552), time_zones(?x552, ?x2674) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ydpd time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.739 http://example.org/location/location/time_zones #819-01f08r PRED entity: 01f08r PRED relation: vacationer PRED expected values: 015z4j => 219 concepts (77 used for prediction) PRED predicted values (max 10 best out of 214): 0261x8t (0.33 #321, 0.27 #7102, 0.19 #4781), 0bbf1f (0.33 #240, 0.20 #7021, 0.13 #4520), 01f492 (0.33 #332, 0.19 #5686, 0.16 #6400), 0bksh (0.33 #287, 0.19 #4747, 0.17 #7068), 016fnb (0.33 #283, 0.14 #1352, 0.12 #7421), 0f4vbz (0.33 #220, 0.14 #1289, 0.11 #8964), 0151w_ (0.33 #198, 0.14 #1267, 0.09 #7336), 05r5w (0.33 #253, 0.12 #4713, 0.11 #8997), 026c1 (0.33 #217, 0.12 #4677, 0.10 #6998), 01vw20_ (0.33 #241, 0.12 #4701, 0.10 #5595) >> Best rule #321 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 0cv3w; >> query: (?x3838, 0261x8t) <- vacationer(?x3838, ?x2499), contains(?x311, ?x3838), jurisdiction_of_office(?x3119, ?x3838), category(?x3838, ?x134), ?x2499 = 0c6qh >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3388 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 10 *> proper extension: 0162v; 03spz; 0165b; *> query: (?x3838, ?x3020) <- currency(?x3838, ?x170), location_of_ceremony(?x566, ?x3838), location(?x12130, ?x3838), nationality(?x12130, ?x774), participant(?x3020, ?x12130) *> conf = 0.05 ranks of expected_values: 149 EVAL 01f08r vacationer 015z4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 219.000 77.000 0.333 http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer #818-040p_q PRED entity: 040p_q PRED relation: major_field_of_study! PRED expected values: 01stj9 => 67 concepts (24 used for prediction) PRED predicted values (max 10 best out of 682): 03ksy (0.75 #2427, 0.67 #7640, 0.67 #1269), 07wjk (0.75 #2376, 0.47 #7010, 0.36 #5272), 01w3v (0.73 #4643, 0.56 #9276, 0.47 #9856), 07szy (0.73 #4671, 0.56 #2933, 0.55 #5249), 02zd460 (0.67 #3660, 0.60 #4239, 0.57 #1922), 01k2wn (0.67 #1176, 0.44 #2914, 0.38 #7547), 09f2j (0.64 #4803, 0.62 #7698, 0.62 #9436), 07tds (0.64 #4794, 0.56 #3056, 0.55 #5372), 017j69 (0.62 #2469, 0.58 #5944, 0.57 #1890), 08815 (0.62 #2315, 0.56 #2895, 0.55 #5211) >> Best rule #2427 for best value: >> intensional similarity = 9 >> extensional distance = 6 >> proper extension: 02lp1; 04sh3; >> query: (?x9093, 03ksy) <- major_field_of_study(?x2014, ?x9093), major_field_of_study(?x10910, ?x9093), major_field_of_study(?x1768, ?x9093), major_field_of_study(?x1526, ?x9093), student(?x9093, ?x1188), institution(?x1526, ?x122), student(?x1526, ?x476), ?x1768 = 09kvv, student(?x10910, ?x665) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #7526 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 13 *> proper extension: 04jq2; *> query: (?x9093, ?x99) <- major_field_of_study(?x4780, ?x9093), major_field_of_study(?x3424, ?x9093), company(?x346, ?x3424), student(?x3424, ?x117), institution(?x620, ?x3424), ?x4780 = 017cy9, organization(?x346, ?x99), company(?x920, ?x3424) *> conf = 0.12 ranks of expected_values: 548 EVAL 040p_q major_field_of_study! 01stj9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 67.000 24.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #817-05c9zr PRED entity: 05c9zr PRED relation: country PRED expected values: 0chghy => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 34): 0345h (0.29 #1380, 0.25 #317, 0.24 #3607), 0f8l9c (0.21 #3600, 0.17 #721, 0.14 #1315), 0d060g (0.14 #1363, 0.11 #1893, 0.10 #1719), 03_3d (0.12 #887, 0.10 #3589, 0.06 #2828), 06mkj (0.07 #1393, 0.04 #3620, 0.03 #2509), 0ctw_b (0.07 #1376, 0.04 #2082, 0.03 #2842), 03rt9 (0.07 #1369, 0.02 #3596, 0.02 #2193), 0h7x (0.07 #1384), 03rjj (0.07 #3588, 0.06 #1775, 0.05 #1833), 0chghy (0.06 #1781, 0.06 #2483, 0.06 #2599) >> Best rule #1380 for best value: >> intensional similarity = 6 >> extensional distance = 12 >> proper extension: 0c0yh4; >> query: (?x4132, 0345h) <- genre(?x4132, ?x811), film(?x6279, ?x4132), film(?x548, ?x4132), award(?x6279, ?x112), award_winner(?x1531, ?x6279), ?x548 = 014x77 >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #1781 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 32 *> proper extension: 0cnztc4; *> query: (?x4132, 0chghy) <- genre(?x4132, ?x811), category(?x4132, ?x134), film_release_region(?x4132, ?x94), region(?x4132, ?x512), film_crew_role(?x4132, ?x468) *> conf = 0.06 ranks of expected_values: 10 EVAL 05c9zr country 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 107.000 107.000 0.286 http://example.org/film/film/country #816-03nqnk3 PRED entity: 03nqnk3 PRED relation: award_winner PRED expected values: 0c12h => 32 concepts (7 used for prediction) PRED predicted values (max 10 best out of 2269): 06mn7 (0.60 #954, 0.29 #9757, 0.28 #9756), 0h1p (0.60 #423, 0.07 #7740, 0.06 #10180), 0jf1b (0.60 #126, 0.05 #17076, 0.04 #7443), 03ym1 (0.50 #6150, 0.40 #3712, 0.10 #12197), 0bj9k (0.50 #5290, 0.40 #2852, 0.06 #12611), 015c4g (0.50 #5850, 0.40 #3412, 0.05 #10730), 040z9 (0.40 #4051, 0.33 #6489, 0.05 #11369), 01713c (0.40 #2752, 0.33 #5190, 0.05 #17076), 0zcbl (0.40 #3965, 0.33 #6403, 0.04 #13724), 0dzf_ (0.40 #3447, 0.33 #5885, 0.04 #10765) >> Best rule #954 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 040njc; 027c924; 02pqp12; >> query: (?x2523, 06mn7) <- award_winner(?x2523, ?x7825), award_winner(?x2523, ?x7352), ?x7825 = 01p87y, award_nominee(?x4987, ?x7352), written_by(?x1842, ?x7352), award(?x7352, ?x1107) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #9757 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 175 *> proper extension: 03hj5vf; 0bm7fy; 02_3zj; 04hddx; *> query: (?x2523, ?x8645) <- award(?x8645, ?x2523), profession(?x8645, ?x319), film(?x8645, ?x3783) *> conf = 0.29 ranks of expected_values: 41 EVAL 03nqnk3 award_winner 0c12h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 32.000 7.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #815-0rxyk PRED entity: 0rxyk PRED relation: place! PRED expected values: 0rxyk => 201 concepts (135 used for prediction) PRED predicted values (max 10 best out of 233): 013yq (0.14 #6185, 0.13 #15464, 0.11 #23712), 030qb3t (0.14 #6185, 0.13 #15464, 0.11 #23712), 0rxyk (0.14 #6185, 0.13 #15464, 0.11 #23712), 0rvty (0.14 #6185, 0.13 #15464, 0.11 #23712), 0rw2x (0.07 #955, 0.06 #1470, 0.04 #1985), 0rt80 (0.07 #1007, 0.06 #1522, 0.04 #2037), 0rv97 (0.07 #748, 0.06 #1263, 0.04 #1778), 0rwq6 (0.07 #954, 0.06 #1469, 0.04 #1984), 0rwgm (0.07 #936, 0.06 #1451, 0.04 #1966), 01ktz1 (0.07 #561, 0.04 #1591, 0.01 #10354) >> Best rule #6185 for best value: >> intensional similarity = 5 >> extensional distance = 58 >> proper extension: 0d1qn; >> query: (?x11376, ?x1523) <- source(?x11376, ?x958), time_zones(?x11376, ?x2674), featured_film_locations(?x2362, ?x11376), featured_film_locations(?x2362, ?x1523), location(?x10101, ?x11376) >> conf = 0.14 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0rxyk place! 0rxyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 201.000 135.000 0.141 http://example.org/location/hud_county_place/place #814-013719 PRED entity: 013719 PRED relation: school_type PRED expected values: 05jxkf => 169 concepts (169 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.80 #294, 0.78 #462, 0.78 #222), 05pcjw (0.50 #194, 0.39 #747, 0.38 #387), 07tf8 (0.38 #202, 0.33 #323, 0.33 #33), 01rs41 (0.29 #2558, 0.28 #2462, 0.28 #2848), 01_9fk (0.25 #316, 0.22 #1158, 0.22 #244), 01jlsn (0.14 #186, 0.13 #1686, 0.11 #2335), 0m4mb (0.14 #180), 01_srz (0.13 #1686, 0.12 #581, 0.11 #2335), 06cs1 (0.13 #1686, 0.11 #2335, 0.11 #3088), 04399 (0.13 #1686, 0.11 #2335, 0.11 #3088) >> Best rule #294 for best value: >> intensional similarity = 9 >> extensional distance = 8 >> proper extension: 01c57n; >> query: (?x11640, 05jxkf) <- currency(?x11640, ?x7888), major_field_of_study(?x11640, ?x1668), ?x7888 = 0kz1h, institution(?x865, ?x11640), institution(?x865, ?x10175), institution(?x865, ?x3424), ?x3424 = 01w5m, ?x10175 = 03fgm, major_field_of_study(?x865, ?x254) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013719 school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 169.000 169.000 0.800 http://example.org/education/educational_institution/school_type #813-041rx PRED entity: 041rx PRED relation: people PRED expected values: 0h5f5n 0bl2g 0prfz 025h4z 02lf0c 0pz91 06pj8 01y_px 02vy5j 01dw9z 0klh7 01cj6y 01_x6d 03nk3t 028k57 02k21g 051wwp 05drr9 01t6xz 01_6dw 05szp 01520h 041_y 0crqcc 0mdyn 045m1_ 078jnn 0683n 01rcmg 0h953 03hhd3 01pw9v 0qdwr 01pbwwl 01gw8b 016zdd 013tjc 02vtnf 01sbhvd 01vsn38 0jt86 0d_w7 035wq7 011w20 09x8ms => 47 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1991): 05zbm4 (0.33 #1484, 0.33 #104, 0.08 #24805), 06wm0z (0.33 #1952, 0.33 #572, 0.08 #42722), 0311wg (0.33 #237, 0.21 #22285, 0.20 #23664), 01vrncs (0.33 #1497, 0.14 #24807, 0.14 #23427), 03f2_rc (0.33 #62, 0.14 #33078, 0.08 #28943), 01_ztw (0.33 #632, 0.14 #22680, 0.13 #24059), 0c01c (0.33 #268, 0.08 #24805, 0.08 #33077), 01z5tr (0.33 #888, 0.08 #24805, 0.08 #33077), 0hwqz (0.33 #2049, 0.08 #28943, 0.08 #33747), 06pj8 (0.33 #220, 0.08 #28943, 0.08 #42722) >> Best rule #1484 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 013b6_; >> query: (?x1050, 05zbm4) <- people(?x1050, ?x2319), people(?x1050, ?x1381), people(?x1050, ?x1206), ?x2319 = 0lccn, award(?x1206, ?x1479), award_winner(?x2461, ?x1381), profession(?x1381, ?x220) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #220 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 048z7l; *> query: (?x1050, 06pj8) <- people(?x1050, ?x2319), people(?x1050, ?x1537), people(?x1050, ?x710), instrumentalists(?x227, ?x2319), award_winner(?x217, ?x2319), award_nominee(?x710, ?x91), ?x1537 = 03jldb *> conf = 0.33 ranks of expected_values: 10, 16, 21, 23, 30, 63, 77, 236, 378, 545, 578, 584, 653, 782, 834, 835, 842, 845, 884, 1017, 1545, 1592, 1858 EVAL 041rx people 09x8ms CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 011w20 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 035wq7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0d_w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0jt86 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01vsn38 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01sbhvd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 02vtnf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 013tjc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 016zdd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01gw8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01pbwwl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0qdwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01pw9v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 03hhd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0h953 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01rcmg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0683n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 078jnn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 045m1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0mdyn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0crqcc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 041_y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01520h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 05szp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01_6dw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01t6xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 05drr9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 051wwp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 02k21g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 028k57 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 03nk3t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01_x6d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01cj6y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0klh7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01dw9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 02vy5j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 01y_px CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 06pj8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0pz91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 02lf0c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 025h4z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0prfz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0bl2g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 47.000 0.333 http://example.org/people/ethnicity/people EVAL 041rx people 0h5f5n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 47.000 0.333 http://example.org/people/ethnicity/people #812-01nrgq PRED entity: 01nrgq PRED relation: profession PRED expected values: 018gz8 => 69 concepts (64 used for prediction) PRED predicted values (max 10 best out of 62): 03gjzk (0.50 #14, 0.37 #902, 0.35 #1346), 01d_h8 (0.47 #6, 0.37 #154, 0.33 #2078), 09jwl (0.41 #314, 0.38 #462, 0.31 #610), 0nbcg (0.30 #327, 0.28 #475, 0.21 #623), 0np9r (0.28 #3109, 0.28 #3850, 0.22 #168), 0dz3r (0.25 #298, 0.23 #446, 0.15 #594), 018gz8 (0.24 #164, 0.13 #4902, 0.13 #16), 02jknp (0.24 #8, 0.22 #3413, 0.22 #4746), 016z4k (0.23 #300, 0.22 #448, 0.19 #596), 039v1 (0.18 #480, 0.18 #332, 0.11 #628) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 012x4t; 03xp8d5; >> query: (?x5558, 03gjzk) <- award_winner(?x758, ?x5558), profession(?x5558, ?x987), inductee(?x9953, ?x5558) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #164 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 57 *> proper extension: 04kwbt; *> query: (?x5558, 018gz8) <- nationality(?x5558, ?x94), award(?x5558, ?x693), ?x693 = 09qvc0 *> conf = 0.24 ranks of expected_values: 7 EVAL 01nrgq profession 018gz8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 69.000 64.000 0.500 http://example.org/people/person/profession #811-03gm48 PRED entity: 03gm48 PRED relation: nationality PRED expected values: 09c7w0 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 25): 09c7w0 (0.83 #1401, 0.79 #701, 0.77 #201), 07ssc (0.40 #11130, 0.29 #7717, 0.17 #15), 03rt9 (0.40 #11130, 0.03 #113, 0.02 #1313), 0jdx (0.40 #11130), 0d060g (0.29 #7717, 0.05 #107, 0.05 #2009), 0345h (0.29 #7717, 0.02 #4739, 0.02 #4338), 02jx1 (0.14 #133, 0.11 #3238, 0.11 #3739), 03rk0 (0.07 #4052, 0.07 #9168, 0.06 #3351), 03rjj (0.03 #705, 0.02 #3510, 0.02 #5614), 03_3d (0.03 #2709, 0.03 #2910, 0.02 #2008) >> Best rule #1401 for best value: >> intensional similarity = 3 >> extensional distance = 359 >> proper extension: 02x8mt; >> query: (?x965, 09c7w0) <- place_of_birth(?x965, ?x1860), student(?x2909, ?x965), registering_agency(?x2909, ?x1982) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03gm48 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.834 http://example.org/people/person/nationality #810-0170k0 PRED entity: 0170k0 PRED relation: genre PRED expected values: 025s89p => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 71): 07s9rl0 (0.82 #1546, 0.59 #2012, 0.55 #2245), 05p553 (0.54 #5, 0.53 #1164, 0.52 #469), 025s89p (0.43 #123, 0.38 #201, 0.30 #278), 01z4y (0.39 #1174, 0.35 #402, 0.35 #1252), 06n90 (0.35 #320, 0.33 #88, 0.29 #166), 01hmnh (0.33 #91, 0.29 #169, 0.23 #246), 0c4xc (0.28 #501, 0.26 #1196, 0.25 #964), 01w613 (0.27 #275, 0.08 #198, 0.08 #43), 06q7n (0.20 #1584, 0.20 #581, 0.19 #658), 06nbt (0.19 #636, 0.18 #867, 0.17 #404) >> Best rule #1546 for best value: >> intensional similarity = 4 >> extensional distance = 111 >> proper extension: 09rfpk; >> query: (?x8846, 07s9rl0) <- nominated_for(?x5832, ?x8846), genre(?x8846, ?x1844), genre(?x8644, ?x1844), ?x8644 = 06dfz1 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #123 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 19 *> proper extension: 020qr4; 0jwl2; 024rwx; 05f7w84; 0ctzf1; 05h95s; 0fkwzs; 09g_31; 04mx8h4; 05nlzq; ... *> query: (?x8846, 025s89p) <- languages(?x8846, ?x254), genre(?x8846, ?x10023), genre(?x8846, ?x4205), ?x10023 = 0pr6f, genre(?x599, ?x4205) *> conf = 0.43 ranks of expected_values: 3 EVAL 0170k0 genre 025s89p CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 87.000 87.000 0.823 http://example.org/tv/tv_program/genre #809-0blbxk PRED entity: 0blbxk PRED relation: nationality PRED expected values: 09c7w0 => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.83 #201, 0.78 #3208, 0.73 #2105), 02jx1 (0.50 #33, 0.30 #433, 0.24 #333), 07ssc (0.23 #415, 0.22 #315, 0.10 #716), 03rk0 (0.06 #3653, 0.05 #6957, 0.05 #7058), 03_3d (0.05 #406, 0.01 #1208, 0.01 #4613), 0d060g (0.05 #1108, 0.04 #2713, 0.04 #5915), 0chghy (0.05 #310, 0.02 #711, 0.02 #2014), 0j5g9 (0.05 #362, 0.01 #763), 06q1r (0.04 #477, 0.02 #377, 0.02 #978), 03rt9 (0.04 #413, 0.02 #313, 0.01 #714) >> Best rule #201 for best value: >> intensional similarity = 2 >> extensional distance = 28 >> proper extension: 0d05fv; 05v954; 0gv07g; 0jsg0m; 018fwv; 045gzq; >> query: (?x1290, 09c7w0) <- place_of_birth(?x1290, ?x108), ?x108 = 0rh6k >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0blbxk nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.833 http://example.org/people/person/nationality #808-01pcj4 PRED entity: 01pcj4 PRED relation: school_type PRED expected values: 01rs41 => 166 concepts (166 used for prediction) PRED predicted values (max 10 best out of 20): 01rs41 (0.62 #1154, 0.52 #625, 0.52 #395), 05jxkf (0.50 #1038, 0.47 #1866, 0.46 #2074), 07tf8 (0.33 #31, 0.20 #560, 0.19 #307), 0257h9 (0.33 #65, 0.05 #3115, 0.03 #479), 01_9fk (0.13 #1634, 0.13 #1611, 0.13 #1450), 01_srz (0.12 #393, 0.12 #531, 0.10 #761), 02p0qmm (0.10 #2493, 0.06 #515, 0.06 #423), 06cs1 (0.10 #2493, 0.05 #396, 0.04 #626), 01y64 (0.08 #126, 0.06 #149, 0.06 #195), 02dk5q (0.05 #466, 0.05 #3115, 0.05 #512) >> Best rule #1154 for best value: >> intensional similarity = 4 >> extensional distance = 181 >> proper extension: 02_2kg; >> query: (?x9879, 01rs41) <- state_province_region(?x9879, ?x335), school_type(?x9879, ?x1044), school_type(?x2767, ?x1044), ?x2767 = 04sylm >> conf = 0.62 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pcj4 school_type 01rs41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 166.000 0.617 http://example.org/education/educational_institution/school_type #807-02fgp0 PRED entity: 02fgp0 PRED relation: notable_people_with_this_condition! PRED expected values: 03p41 => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 7): 029sk (0.05 #67, 0.02 #819, 0.02 #465), 068p_ (0.02 #152, 0.02 #174, 0.02 #218), 03p41 (0.02 #404, 0.02 #315, 0.02 #558), 0d19y2 (0.02 #642, 0.01 #332, 0.01 #885), 01g2q (0.02 #229, 0.01 #318), 0h99n (0.01 #98, 0.01 #983), 02vrr (0.01 #135) >> Best rule #67 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 01wz3cx; 0sx5w; >> query: (?x8661, 029sk) <- profession(?x8661, ?x1183), student(?x6912, ?x8661), ?x6912 = 0gl5_ >> conf = 0.05 => this is the best rule for 1 predicted values *> Best rule #404 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 202 *> proper extension: 0584j4n; *> query: (?x8661, 03p41) <- award_nominee(?x8661, ?x1894), gender(?x8661, ?x231), place_of_death(?x8661, ?x739) *> conf = 0.02 ranks of expected_values: 3 EVAL 02fgp0 notable_people_with_this_condition! 03p41 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 126.000 0.048 http://example.org/medicine/disease/notable_people_with_this_condition #806-016yvw PRED entity: 016yvw PRED relation: film PRED expected values: 055td_ => 105 concepts (60 used for prediction) PRED predicted values (max 10 best out of 498): 0fjyzt (0.34 #62343, 0.33 #87285, 0.33 #56999), 034qbx (0.10 #1156, 0.03 #105101, 0.01 #11842), 027r9t (0.07 #1241, 0.04 #78377, 0.03 #105101), 05fm6m (0.07 #1314, 0.04 #78377, 0.03 #105101), 02cbhg (0.07 #1396, 0.03 #105101, 0.01 #3177), 06gb1w (0.07 #729, 0.02 #11415, 0.02 #13196), 0ds3t5x (0.07 #54, 0.02 #14302, 0.02 #23207), 03cvvlg (0.07 #1437), 011xg5 (0.07 #1425), 04k9y6 (0.07 #1037) >> Best rule #62343 for best value: >> intensional similarity = 3 >> extensional distance = 1240 >> proper extension: 0f0p0; 03xmy1; 01pqy_; 056ws9; 01skmp; 081bls; 0522wp; 01zh29; 02dlfh; 03mdw3c; ... >> query: (?x5363, ?x2151) <- award_winner(?x591, ?x5363), award(?x5363, ?x704), award_winner(?x2151, ?x5363) >> conf = 0.34 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 016yvw film 055td_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 105.000 60.000 0.342 http://example.org/film/actor/film./film/performance/film #805-067sqt PRED entity: 067sqt PRED relation: profession PRED expected values: 0d1pc => 106 concepts (80 used for prediction) PRED predicted values (max 10 best out of 72): 0dxtg (0.47 #3253, 0.45 #3400, 0.36 #307), 09jwl (0.37 #3698, 0.37 #6492, 0.34 #5756), 02jknp (0.33 #2216, 0.30 #301, 0.25 #890), 018gz8 (0.33 #3255, 0.17 #2224, 0.16 #3402), 0np9r (0.29 #2522, 0.23 #2081, 0.21 #6052), 0nbcg (0.27 #3711, 0.26 #6505, 0.26 #5769), 016z4k (0.26 #3685, 0.24 #5743, 0.23 #6479), 0dz3r (0.26 #3683, 0.23 #5741, 0.22 #6477), 02krf9 (0.25 #3265, 0.16 #2234, 0.16 #3412), 0kyk (0.19 #175, 0.18 #2384, 0.17 #2826) >> Best rule #3253 for best value: >> intensional similarity = 3 >> extensional distance = 313 >> proper extension: 01d5vk; >> query: (?x11782, 0dxtg) <- film(?x11782, ?x4269), profession(?x11782, ?x1041), ?x1041 = 03gjzk >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #490 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 044mfr; *> query: (?x11782, 0d1pc) <- actor(?x8775, ?x11782), category(?x11782, ?x134), participant(?x2697, ?x11782) *> conf = 0.15 ranks of expected_values: 13 EVAL 067sqt profession 0d1pc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 106.000 80.000 0.470 http://example.org/people/person/profession #804-0133x7 PRED entity: 0133x7 PRED relation: artist! PRED expected values: 01cszh 0181dw => 157 concepts (103 used for prediction) PRED predicted values (max 10 best out of 113): 017l96 (0.30 #19, 0.13 #2134, 0.12 #865), 015_1q (0.28 #1289, 0.23 #866, 0.23 #725), 011k1h (0.24 #292, 0.16 #1843, 0.16 #433), 01w40h (0.20 #29, 0.12 #311, 0.10 #1157), 0k_kr (0.20 #45, 0.07 #186, 0.06 #891), 03vtfp (0.20 #90, 0.06 #372, 0.05 #513), 033hn8 (0.19 #860, 0.17 #719, 0.13 #8054), 03rhqg (0.17 #5515, 0.16 #6361, 0.16 #4387), 043g7l (0.17 #737, 0.11 #2570, 0.10 #3557), 0mzkr (0.15 #1154, 0.10 #26, 0.09 #1859) >> Best rule #19 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 02r3zy; 0dvqq; 017j6; 0dw4g; 03vhvp; >> query: (?x7112, 017l96) <- award_nominee(?x8490, ?x7112), award(?x7112, ?x1565), origin(?x7112, ?x2541), ?x1565 = 01c4_6 >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #3004 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 124 *> proper extension: 012wg; 01n44c; *> query: (?x7112, 0181dw) <- nationality(?x7112, ?x279), award_winner(?x4488, ?x7112), place_of_birth(?x7112, ?x2541), origin(?x7112, ?x11016) *> conf = 0.14 ranks of expected_values: 12, 35 EVAL 0133x7 artist! 0181dw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 157.000 103.000 0.300 http://example.org/music/record_label/artist EVAL 0133x7 artist! 01cszh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 157.000 103.000 0.300 http://example.org/music/record_label/artist #803-01ypc PRED entity: 01ypc PRED relation: sport PRED expected values: 018jz => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 8): 018jz (0.86 #222, 0.85 #193, 0.79 #152), 0jm_ (0.81 #102, 0.47 #76, 0.40 #55), 018w8 (0.81 #102, 0.40 #55, 0.28 #197), 02vx4 (0.55 #262, 0.53 #224, 0.53 #290), 03tmr (0.11 #166, 0.11 #270, 0.10 #203), 039yzs (0.11 #270, 0.10 #145, 0.08 #52), 09xp_ (0.11 #270, 0.06 #218, 0.01 #312), 0z74 (0.11 #270) >> Best rule #222 for best value: >> intensional similarity = 8 >> extensional distance = 102 >> proper extension: 038_3y; 023fxp; >> query: (?x260, ?x5063) <- team(?x11844, ?x260), team(?x11844, ?x11361), team(?x11844, ?x7357), team(?x11844, ?x2174), sport(?x11361, ?x5063), colors(?x2174, ?x332), teams(?x1523, ?x7357), ?x332 = 01l849 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ypc sport 018jz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 36.000 36.000 0.856 http://example.org/sports/sports_team/sport #802-03bxh PRED entity: 03bxh PRED relation: influenced_by! PRED expected values: 082db => 165 concepts (62 used for prediction) PRED predicted values (max 10 best out of 397): 014ps4 (0.21 #5453, 0.08 #6481, 0.07 #8024), 03g5jw (0.18 #10331, 0.15 #9816, 0.11 #14965), 0lrh (0.18 #10392, 0.10 #16057, 0.04 #22749), 0459z (0.17 #4577, 0.17 #2520, 0.12 #30884), 082db (0.17 #2351, 0.12 #30884, 0.11 #31399), 0hr3g (0.17 #2435, 0.11 #5008, 0.11 #4492), 07dnx (0.17 #2417, 0.11 #4474, 0.08 #6532), 0167xy (0.15 #10720, 0.13 #10205, 0.09 #15354), 01vvyfh (0.14 #1171, 0.11 #4256, 0.08 #2199), 01s7qqw (0.14 #1238, 0.07 #16162, 0.06 #3809) >> Best rule #5453 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 070px; >> query: (?x5600, 014ps4) <- nationality(?x5600, ?x1310), profession(?x5600, ?x1614), ?x1310 = 02jx1, place_of_burial(?x5600, ?x4435) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #2351 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 043d4; 0hqgp; 0c73g; *> query: (?x5600, 082db) <- influenced_by(?x3774, ?x5600), artists(?x597, ?x5600), ?x597 = 0ggq0m, nationality(?x5600, ?x1310) *> conf = 0.17 ranks of expected_values: 5 EVAL 03bxh influenced_by! 082db CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 165.000 62.000 0.211 http://example.org/influence/influence_node/influenced_by #801-015fr PRED entity: 015fr PRED relation: country! PRED expected values: 0bynt 09qgm => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 24): 0bynt (0.90 #412, 0.89 #652, 0.87 #1565), 07jjt (0.85 #152, 0.60 #80, 0.56 #368), 09w1n (0.60 #153, 0.58 #201, 0.56 #321), 09_bl (0.60 #147, 0.53 #75, 0.46 #315), 02_5h (0.60 #77, 0.45 #149, 0.44 #317), 03fyrh (0.56 #587, 0.53 #83, 0.52 #443), 02y74 (0.53 #91, 0.42 #1994, 0.40 #163), 019w9j (0.53 #84, 0.40 #156, 0.38 #324), 09qgm (0.50 #154, 0.47 #82, 0.42 #202), 06zgc (0.45 #158, 0.40 #86, 0.37 #278) >> Best rule #412 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 05r4w; 0jgd; 03_3d; 0d0vqn; 04gzd; 01ls2; 03rt9; 09pmkv; 07ylj; 0h7x; ... >> query: (?x583, 0bynt) <- film_release_region(?x607, ?x583), ?x607 = 02x3lt7, country(?x150, ?x583) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 9 EVAL 015fr country! 09qgm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 138.000 138.000 0.905 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 015fr country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.905 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #800-06rq2l PRED entity: 06rq2l PRED relation: film PRED expected values: 03z20c 0ch3qr1 => 98 concepts (67 used for prediction) PRED predicted values (max 10 best out of 466): 0b6f8pf (0.63 #28575, 0.57 #48217, 0.44 #57143), 0gwgn1k (0.32 #8928, 0.12 #23215, 0.11 #26789), 0408m53 (0.32 #8928, 0.12 #23215, 0.11 #26789), 0640m69 (0.12 #14287, 0.12 #10715, 0.12 #19644), 095z4q (0.12 #14287, 0.12 #10715, 0.12 #19644), 02ph9tm (0.07 #1100, 0.04 #2885, 0.03 #46431), 0prrm (0.07 #860, 0.02 #9788, 0.02 #27649), 02f6g5 (0.06 #2064, 0.04 #279, 0.02 #7421), 034qzw (0.05 #7474, 0.03 #3902, 0.03 #21761), 07kb7vh (0.04 #2470, 0.04 #685, 0.03 #46431) >> Best rule #28575 for best value: >> intensional similarity = 3 >> extensional distance = 395 >> proper extension: 04qmr; >> query: (?x9204, ?x9920) <- award(?x9204, ?x688), participant(?x1335, ?x9204), nominated_for(?x9204, ?x9920) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #975 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 01xyt7; *> query: (?x9204, 0ch3qr1) <- participant(?x1335, ?x9204), student(?x7545, ?x9204), company(?x9204, ?x1836) *> conf = 0.04 ranks of expected_values: 22, 82 EVAL 06rq2l film 0ch3qr1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 98.000 67.000 0.630 http://example.org/film/actor/film./film/performance/film EVAL 06rq2l film 03z20c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 98.000 67.000 0.630 http://example.org/film/actor/film./film/performance/film #799-06s1qy PRED entity: 06s1qy PRED relation: award PRED expected values: 04dn09n => 128 concepts (90 used for prediction) PRED predicted values (max 10 best out of 288): 0gr4k (0.72 #833, 0.70 #1234, 0.48 #5646), 09sb52 (0.67 #12071, 0.23 #25305, 0.22 #28112), 04dn09n (0.62 #1245, 0.62 #844, 0.55 #443), 02x17s4 (0.46 #1325, 0.46 #924, 0.31 #523), 0gq9h (0.36 #8898, 0.31 #476, 0.30 #1278), 02x1dht (0.35 #454, 0.22 #1256, 0.22 #855), 0gs9p (0.35 #5692, 0.34 #4890, 0.31 #478), 040njc (0.33 #4820, 0.31 #408, 0.30 #1210), 019f4v (0.31 #466, 0.30 #5680, 0.30 #4878), 02pqp12 (0.26 #4881, 0.24 #469, 0.23 #5683) >> Best rule #833 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 0l6qt; 0qf43; 014zcr; 0h5f5n; 0159h6; 04r7jc; 05kfs; 0yfp; 05m883; 0136g9; ... >> query: (?x11705, 0gr4k) <- award(?x11705, ?x1180), written_by(?x9456, ?x11705), ?x1180 = 02n9nmz, nominated_for(?x398, ?x9456) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1245 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 0kft; *> query: (?x11705, 04dn09n) <- award(?x11705, ?x1180), written_by(?x4152, ?x11705), ?x1180 = 02n9nmz, nominated_for(?x484, ?x4152) *> conf = 0.62 ranks of expected_values: 3 EVAL 06s1qy award 04dn09n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 128.000 90.000 0.720 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #798-0qpn9 PRED entity: 0qpn9 PRED relation: contains! PRED expected values: 0vmt => 172 concepts (99 used for prediction) PRED predicted values (max 10 best out of 303): 0m27n (0.84 #65381, 0.76 #63587, 0.75 #77920), 0vmt (0.81 #59110, 0.78 #38512, 0.77 #40305), 01n7q (0.46 #13510, 0.38 #18882, 0.38 #22469), 0d35y (0.33 #269, 0.14 #3851, 0.14 #2955), 0kpys (0.30 #13613, 0.24 #6448, 0.20 #5552), 04_1l0v (0.25 #10301, 0.25 #45234, 0.19 #9406), 0345h (0.22 #31346, 0.07 #21495, 0.06 #34112), 07srw (0.19 #21641, 0.15 #27907, 0.09 #9995), 01n4w (0.17 #21680, 0.13 #27946, 0.11 #4660), 07ssc (0.16 #59141, 0.16 #60036, 0.14 #34062) >> Best rule #65381 for best value: >> intensional similarity = 4 >> extensional distance = 157 >> proper extension: 0_kq3; 0yz30; >> query: (?x7408, ?x7409) <- time_zones(?x7408, ?x2088), county(?x7408, ?x7409), currency(?x7409, ?x170), contains(?x7409, ?x9010) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #59110 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 134 *> proper extension: 0tln7; 01m20m; 029t1; *> query: (?x7408, ?x938) <- citytown(?x8706, ?x7408), contains(?x94, ?x7408), currency(?x8706, ?x170), state_province_region(?x8706, ?x938) *> conf = 0.81 ranks of expected_values: 2 EVAL 0qpn9 contains! 0vmt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 172.000 99.000 0.836 http://example.org/location/location/contains #797-01x0yrt PRED entity: 01x0yrt PRED relation: artist! PRED expected values: 01dtcb => 123 concepts (63 used for prediction) PRED predicted values (max 10 best out of 119): 015_1q (0.27 #440, 0.23 #3800, 0.21 #4641), 03mp8k (0.18 #1046, 0.13 #766, 0.13 #1186), 043g7l (0.18 #1012, 0.13 #1712, 0.12 #1992), 03rhqg (0.16 #4637, 0.15 #156, 0.15 #576), 0181dw (0.15 #1162, 0.15 #1302, 0.15 #742), 0g768 (0.15 #177, 0.15 #1017, 0.14 #597), 01trtc (0.14 #1052, 0.09 #1752, 0.08 #1192), 017l96 (0.14 #299, 0.10 #4640, 0.10 #999), 0n85g (0.14 #482, 0.12 #1322, 0.11 #1182), 033hn8 (0.13 #154, 0.11 #8000, 0.11 #714) >> Best rule #440 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 01nn6c; 024dw0; >> query: (?x8839, 015_1q) <- location(?x8839, ?x774), gender(?x8839, ?x514), artist(?x5744, ?x8839), performance_role(?x8839, ?x1574) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #607 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 83 *> proper extension: 016qtt; 0cg9y; 0dvqq; 01dw9z; 016fmf; 01vrwfv; 018ndc; 04qmr; 01rm8b; 0fcsd; ... *> query: (?x8839, 01dtcb) <- award(?x8839, ?x1389), award(?x8839, ?x1232), ?x1389 = 01c427, award_winner(?x1232, ?x248) *> conf = 0.11 ranks of expected_values: 14 EVAL 01x0yrt artist! 01dtcb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 123.000 63.000 0.273 http://example.org/music/record_label/artist #796-02zd460 PRED entity: 02zd460 PRED relation: company! PRED expected values: 0nk72 06g4_ => 117 concepts (60 used for prediction) PRED predicted values (max 10 best out of 213): 06y3r (0.50 #1145, 0.29 #1629, 0.09 #4052), 0gt_k (0.29 #761, 0.02 #10218, 0.01 #12402), 03gkn5 (0.25 #304, 0.14 #789, 0.12 #1032), 0nk72 (0.25 #406, 0.12 #4768, 0.08 #6710), 013bd1 (0.25 #425), 03rx9 (0.17 #672, 0.14 #914, 0.12 #1157), 0343h (0.17 #508, 0.14 #750, 0.03 #8508), 01_f_5 (0.17 #611, 0.14 #853, 0.01 #8611), 02vyw (0.17 #551, 0.14 #793, 0.01 #8551), 01vvyc_ (0.17 #602, 0.07 #1571, 0.01 #8602) >> Best rule #1145 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0xwj; >> query: (?x5288, 06y3r) <- company(?x8430, ?x5288), notable_people_with_this_condition(?x6656, ?x8430), place_of_death(?x8430, ?x4627) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #406 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 053mhx; *> query: (?x5288, 0nk72) <- student(?x5288, ?x4735), contains(?x94, ?x5288), ?x4735 = 02hsgn *> conf = 0.25 ranks of expected_values: 4, 118 EVAL 02zd460 company! 06g4_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 117.000 60.000 0.500 http://example.org/people/person/employment_history./business/employment_tenure/company EVAL 02zd460 company! 0nk72 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 117.000 60.000 0.500 http://example.org/people/person/employment_history./business/employment_tenure/company #795-01yznp PRED entity: 01yznp PRED relation: profession PRED expected values: 0dz96 => 117 concepts (48 used for prediction) PRED predicted values (max 10 best out of 132): 01d_h8 (0.78 #3759, 0.76 #3615, 0.74 #3038), 0dxtg (0.56 #878, 0.54 #3911, 0.52 #446), 016z4k (0.50 #148, 0.48 #6206, 0.47 #4766), 0nbcg (0.48 #5366, 0.48 #5510, 0.47 #2339), 02jknp (0.47 #296, 0.38 #3616, 0.37 #3760), 0kyk (0.45 #1614, 0.40 #26, 0.38 #459), 0n1h (0.42 #155, 0.40 #1587, 0.19 #4773), 0dz3r (0.41 #5484, 0.39 #5340, 0.38 #4764), 0np9r (0.40 #17, 0.38 #450, 0.28 #882), 06q2q (0.40 #1587, 0.05 #3217, 0.05 #3361) >> Best rule #3759 for best value: >> intensional similarity = 3 >> extensional distance = 232 >> proper extension: 01zfmm; 07lwsz; 0cj2nl; 04w1j9; 02q42j_; 06t8b; 04fyhv; 0cj2k3; 03qncl3; 027z0pl; ... >> query: (?x425, 01d_h8) <- profession(?x425, ?x353), nationality(?x425, ?x94), executive_produced_by(?x424, ?x425) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1879 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 0frmb1; *> query: (?x425, ?x987) <- nationality(?x425, ?x94), person(?x9723, ?x425), person(?x9723, ?x2283), profession(?x2283, ?x987) *> conf = 0.15 ranks of expected_values: 35 EVAL 01yznp profession 0dz96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 117.000 48.000 0.778 http://example.org/people/person/profession #794-04j0s3 PRED entity: 04j0s3 PRED relation: profession PRED expected values: 0dxtg 02krf9 => 56 concepts (49 used for prediction) PRED predicted values (max 10 best out of 118): 0dxtg (0.70 #1483, 0.68 #2808, 0.66 #2955), 02jknp (0.59 #4566, 0.41 #2213, 0.40 #2360), 0cbd2 (0.47 #4418, 0.34 #5154, 0.25 #4271), 0kyk (0.36 #4293, 0.23 #5176, 0.18 #4440), 02krf9 (0.36 #3996, 0.28 #2378, 0.27 #2820), 018gz8 (0.32 #1044, 0.30 #309, 0.29 #1485), 01c72t (0.25 #22, 0.21 #5170, 0.08 #6495), 025352 (0.25 #58, 0.05 #5001, 0.04 #5206), 0np9r (0.20 #313, 0.18 #1489, 0.17 #1342), 0d1pc (0.19 #784, 0.17 #490, 0.16 #196) >> Best rule #1483 for best value: >> intensional similarity = 6 >> extensional distance = 178 >> proper extension: 01lct6; 01svq8; >> query: (?x10233, 0dxtg) <- profession(?x10233, ?x1041), profession(?x10233, ?x319), ?x1041 = 03gjzk, people(?x5025, ?x10233), profession(?x11389, ?x319), ?x11389 = 02qdymm >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 04j0s3 profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 56.000 49.000 0.700 http://example.org/people/person/profession EVAL 04j0s3 profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 56.000 49.000 0.700 http://example.org/people/person/profession #793-020_95 PRED entity: 020_95 PRED relation: nationality PRED expected values: 09c7w0 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 67): 09c7w0 (0.86 #101, 0.81 #301, 0.78 #1), 02jx1 (0.11 #4038, 0.10 #4838, 0.10 #5841), 07ssc (0.09 #5823, 0.08 #7426, 0.08 #4020), 03rk0 (0.06 #9561, 0.05 #9863, 0.05 #9763), 0d060g (0.05 #6016, 0.05 #307, 0.05 #3311), 0f8l9c (0.04 #122, 0.03 #7812, 0.02 #3626), 03_3d (0.03 #7812, 0.03 #2710, 0.03 #2810), 0chghy (0.03 #7812, 0.02 #3214, 0.02 #3514), 03rjj (0.03 #7812, 0.02 #4110, 0.02 #7516), 0345h (0.03 #7812, 0.02 #1833, 0.02 #6040) >> Best rule #101 for best value: >> intensional similarity = 2 >> extensional distance = 55 >> proper extension: 075npt; >> query: (?x5454, 09c7w0) <- student(?x2909, ?x5454), ?x2909 = 017z88 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 020_95 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.860 http://example.org/people/person/nationality #792-03wy70 PRED entity: 03wy70 PRED relation: actor! PRED expected values: 07vqnc => 60 concepts (54 used for prediction) PRED predicted values (max 10 best out of 86): 0sw0q (0.11 #443, 0.06 #971), 02_1kl (0.11 #131, 0.02 #1188), 0180mw (0.11 #119, 0.01 #1176), 0gfzgl (0.11 #33), 0464pz (0.09 #551, 0.06 #815), 017f3m (0.06 #878), 05f4vxd (0.04 #1146), 02_1q9 (0.04 #1062, 0.01 #2121), 05f7w84 (0.03 #1693, 0.03 #1163, 0.03 #1428), 0ctzf1 (0.03 #1192, 0.02 #1722, 0.02 #1457) >> Best rule #443 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0kt64b; >> query: (?x7372, 0sw0q) <- profession(?x7372, ?x5716), ?x5716 = 021wpb, type_of_union(?x7372, ?x566), nationality(?x7372, ?x94) >> conf = 0.11 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03wy70 actor! 07vqnc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 60.000 54.000 0.111 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #791-01jtp7 PRED entity: 01jtp7 PRED relation: major_field_of_study PRED expected values: 02_7t => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 114): 01mkq (0.55 #499, 0.50 #1469, 0.36 #1953), 037mh8 (0.55 #550, 0.25 #2127, 0.22 #1520), 04rjg (0.50 #504, 0.39 #1474, 0.39 #867), 062z7 (0.50 #512, 0.36 #1482, 0.31 #2089), 02lp1 (0.49 #1465, 0.45 #858, 0.45 #495), 03g3w (0.45 #511, 0.40 #1481, 0.33 #2088), 05qfh (0.45 #519, 0.27 #1489, 0.21 #1368), 01lj9 (0.40 #523, 0.32 #886, 0.31 #1493), 04x_3 (0.35 #510, 0.26 #1480, 0.20 #1964), 0fdys (0.35 #522, 0.23 #1492, 0.23 #2099) >> Best rule #499 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 0jpkw; >> query: (?x2166, 01mkq) <- citytown(?x2166, ?x2254), major_field_of_study(?x2166, ?x742), adjoins(?x2254, ?x4202), ?x742 = 05qjt >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #1274 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 70 *> proper extension: 01p896; *> query: (?x2166, 02_7t) <- major_field_of_study(?x2166, ?x6756), ?x6756 = 0_jm, institution(?x3437, ?x2166), major_field_of_study(?x3437, ?x254) *> conf = 0.33 ranks of expected_values: 13 EVAL 01jtp7 major_field_of_study 02_7t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 161.000 161.000 0.550 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #790-01y9jr PRED entity: 01y9jr PRED relation: prequel! PRED expected values: 01l_pn => 92 concepts (42 used for prediction) PRED predicted values (max 10 best out of 17): 063fh9 (0.06 #475, 0.06 #295, 0.03 #655), 013q07 (0.06 #408, 0.06 #228, 0.03 #588), 061681 (0.06 #375, 0.06 #195, 0.03 #555), 03k8th (0.06 #356, 0.03 #716, 0.03 #896), 033qdy (0.06 #474, 0.03 #834), 02xs6_ (0.06 #449, 0.03 #809), 07cyl (0.06 #425, 0.03 #785), 0cqr0q (0.03 #688, 0.03 #868), 06gb1w (0.03 #616, 0.03 #796), 0fztbq (0.02 #1074) >> Best rule #475 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 072x7s; 07z6xs; 09r94m; 0f4_2k; 06bc59; >> query: (?x6578, 063fh9) <- language(?x6578, ?x732), film_format(?x6578, ?x909), ?x732 = 04306rv, produced_by(?x6578, ?x2221) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01y9jr prequel! 01l_pn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 92.000 42.000 0.056 http://example.org/film/film/prequel #789-014xf6 PRED entity: 014xf6 PRED relation: student PRED expected values: 0klw => 100 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1337): 0bkg4 (0.14 #2717, 0.06 #8978, 0.05 #11065), 03hnd (0.14 #2648, 0.06 #8909, 0.05 #10996), 0kh6b (0.14 #2701, 0.04 #13136, 0.04 #15223), 063vn (0.14 #2383, 0.04 #12818, 0.03 #10731), 03cd1q (0.14 #3988, 0.03 #12336, 0.02 #14423), 04pqqb (0.14 #2931, 0.03 #11279, 0.02 #13366), 01kx_81 (0.14 #2274, 0.03 #10622, 0.02 #12709), 0ff3y (0.08 #18760, 0.05 #45895, 0.04 #29197), 0d3k14 (0.06 #18546, 0.04 #14372, 0.04 #45681), 01n1gc (0.06 #13132, 0.03 #17306, 0.02 #27743) >> Best rule #2717 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0g5lhl7; 01w92; 02j_j0; >> query: (?x8223, 0bkg4) <- citytown(?x8223, ?x362), company(?x3484, ?x8223), ?x362 = 04jpl >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 014xf6 student 0klw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 100.000 91.000 0.143 http://example.org/education/educational_institution/students_graduates./education/education/student #788-01_5bb PRED entity: 01_5bb PRED relation: location! PRED expected values: 01pkhw => 150 concepts (71 used for prediction) PRED predicted values (max 10 best out of 1362): 02508x (0.23 #11179, 0.06 #41398, 0.06 #43916), 01pkhw (0.20 #792, 0.10 #3310, 0.09 #5828), 0j0pf (0.20 #1032, 0.10 #3550, 0.08 #8586), 0dx97 (0.15 #11138, 0.07 #16174, 0.06 #21211), 03j2gxx (0.15 #12283, 0.06 #22356, 0.06 #42502), 01lwx (0.15 #12434, 0.06 #22507, 0.06 #45171), 0n00 (0.15 #10741, 0.06 #20814, 0.06 #43478), 0kh6b (0.15 #10804, 0.06 #43541, 0.03 #71241), 01l2fn (0.15 #10356, 0.06 #43093, 0.03 #70793), 02lt8 (0.14 #31016, 0.13 #15906, 0.10 #38570) >> Best rule #11179 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0f3ys2; >> query: (?x13227, 02508x) <- location(?x4309, ?x13227), contains(?x512, ?x13227), ?x512 = 07ssc, company(?x4309, ?x2313) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #792 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0dt5k; *> query: (?x13227, 01pkhw) <- country(?x13227, ?x512), ?x512 = 07ssc, administrative_parent(?x13227, ?x4221), ?x4221 = 0j5g9 *> conf = 0.20 ranks of expected_values: 2 EVAL 01_5bb location! 01pkhw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 150.000 71.000 0.231 http://example.org/people/person/places_lived./people/place_lived/location #787-09v1lrz PRED entity: 09v1lrz PRED relation: nominated_for PRED expected values: 0dkv90 => 47 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1763): 027m67 (0.71 #4311, 0.67 #2715, 0.33 #1119), 01mgw (0.67 #2751, 0.57 #4347, 0.49 #13929), 065ym0c (0.57 #4627, 0.50 #3031, 0.33 #1435), 0df92l (0.57 #4092, 0.50 #2496, 0.33 #900), 0432_5 (0.57 #3903, 0.50 #2307, 0.33 #711), 01f8gz (0.57 #3419, 0.50 #1823, 0.33 #227), 0gl02yg (0.57 #4100, 0.50 #2504, 0.20 #11177), 08j7lh (0.50 #2960, 0.43 #4556, 0.10 #6152), 0dkv90 (0.43 #4384, 0.33 #2788, 0.11 #13966), 043n0v_ (0.43 #3980, 0.33 #2384, 0.09 #10366) >> Best rule #4311 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 09v4bym; >> query: (?x11702, 027m67) <- nominated_for(?x11702, ?x6376), ?x6376 = 01f85k, award(?x6211, ?x11702), nationality(?x6211, ?x1122) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4384 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 09v4bym; *> query: (?x11702, 0dkv90) <- nominated_for(?x11702, ?x6376), ?x6376 = 01f85k, award(?x6211, ?x11702), nationality(?x6211, ?x1122) *> conf = 0.43 ranks of expected_values: 9 EVAL 09v1lrz nominated_for 0dkv90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 47.000 19.000 0.714 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #786-037css PRED entity: 037css PRED relation: team! PRED expected values: 02nzb8 => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 45): 02nzb8 (0.86 #698, 0.85 #548, 0.85 #1097), 03f0fp (0.85 #548, 0.79 #1597, 0.79 #1346), 02md_2 (0.51 #4066, 0.48 #4116), 01r3hr (0.13 #2390, 0.10 #3179, 0.09 #3229), 02g_7z (0.12 #2411, 0.10 #3200, 0.08 #3250), 01_9c1 (0.12 #2404, 0.10 #3193, 0.08 #3243), 047g8h (0.12 #2394, 0.09 #3183, 0.08 #3233), 02g_6j (0.12 #2397, 0.09 #3186, 0.08 #3236), 02qpbqj (0.12 #2405, 0.10 #3194, 0.08 #3244), 06b1q (0.11 #2393, 0.09 #3182, 0.07 #3232) >> Best rule #698 for best value: >> intensional similarity = 14 >> extensional distance = 12 >> proper extension: 029q3k; >> query: (?x14056, ?x60) <- position(?x14056, ?x203), team(?x9411, ?x14056), team(?x3031, ?x14056), ?x203 = 0dgrmp, team(?x9411, ?x3702), team(?x3031, ?x6503), team(?x3031, ?x3871), team(?x3031, ?x3032), team(?x11481, ?x14056), nationality(?x3031, ?x390), team(?x60, ?x3871), colors(?x3032, ?x663), team(?x4172, ?x3871), ?x6503 = 0k_l4 >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 037css team! 02nzb8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.860 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #785-01lcxbb PRED entity: 01lcxbb PRED relation: profession PRED expected values: 01c72t => 177 concepts (133 used for prediction) PRED predicted values (max 10 best out of 102): 02hrh1q (0.74 #7288, 0.70 #19624, 0.68 #19179), 0nbcg (0.62 #1515, 0.62 #1219, 0.59 #3593), 01c72t (0.62 #2099, 0.56 #618, 0.52 #2397), 0dxtg (0.60 #19030, 0.46 #3872, 0.44 #3724), 016z4k (0.49 #3565, 0.47 #9057, 0.47 #4010), 0dz3r (0.48 #3563, 0.48 #10545, 0.47 #9203), 0cbd2 (0.48 #12629, 0.47 #5203, 0.46 #11293), 039v1 (0.44 #334, 0.42 #3598, 0.41 #9238), 05z96 (0.38 #191, 0.28 #488, 0.21 #5239), 0n1h (0.36 #903, 0.33 #1051, 0.30 #1495) >> Best rule #7288 for best value: >> intensional similarity = 4 >> extensional distance = 174 >> proper extension: 0420y; >> query: (?x3378, 02hrh1q) <- influenced_by(?x4184, ?x3378), profession(?x3378, ?x1183), profession(?x8352, ?x1183), ?x8352 = 03f7m4h >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #2099 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 48 *> proper extension: 09bx1k; *> query: (?x3378, 01c72t) <- place_of_death(?x3378, ?x8026), artists(?x505, ?x3378), artists(?x505, ?x7556), ?x7556 = 01vttb9 *> conf = 0.62 ranks of expected_values: 3 EVAL 01lcxbb profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 177.000 133.000 0.739 http://example.org/people/person/profession #784-0bczgm PRED entity: 0bczgm PRED relation: award_winner! PRED expected values: 027n06w => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 88): 027n06w (0.35 #71, 0.17 #1669, 0.13 #2643), 02q690_ (0.18 #63, 0.17 #1669, 0.13 #2643), 05c1t6z (0.18 #15, 0.13 #2643, 0.11 #154), 03nnm4t (0.18 #72, 0.13 #2643, 0.10 #211), 0418154 (0.18 #106, 0.13 #2643, 0.05 #245), 03gyp30 (0.17 #1669, 0.13 #2643, 0.12 #115), 027hjff (0.17 #1669, 0.13 #2643, 0.12 #56), 0drtv8 (0.17 #1669, 0.13 #2643, 0.06 #64), 02wzl1d (0.17 #1669, 0.13 #2643, 0.06 #11), 04n2r9h (0.17 #1669, 0.13 #2643, 0.01 #4772) >> Best rule #71 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 062ftr; 03q45x; >> query: (?x2819, 027n06w) <- profession(?x2819, ?x353), award_nominee(?x2819, ?x3895), ?x3895 = 06jnvs >> conf = 0.35 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bczgm award_winner! 027n06w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.353 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #783-088q4 PRED entity: 088q4 PRED relation: organization PRED expected values: 07t65 0gkjy => 151 concepts (148 used for prediction) PRED predicted values (max 10 best out of 48): 07t65 (0.90 #646, 0.90 #523, 0.90 #1035), 0gkjy (0.73 #269, 0.58 #1825, 0.56 #932), 0_2v (0.51 #105, 0.43 #205, 0.43 #125), 01rz1 (0.42 #284, 0.42 #504, 0.40 #565), 04k4l (0.37 #467, 0.36 #527, 0.35 #287), 018cqq (0.34 #292, 0.33 #131, 0.33 #111), 02jxk (0.27 #285, 0.26 #325, 0.24 #566), 085h1 (0.26 #543, 0.24 #666, 0.23 #768), 034h1h (0.21 #2119, 0.19 #49, 0.18 #2341), 059dn (0.10 #135, 0.08 #115, 0.07 #577) >> Best rule #646 for best value: >> intensional similarity = 3 >> extensional distance = 90 >> proper extension: 04j53; 0hdx8; >> query: (?x3432, 07t65) <- countries_spoken_in(?x254, ?x3432), member_states(?x7695, ?x3432), adjoins(?x792, ?x3432) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 088q4 organization 0gkjy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 148.000 0.902 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 088q4 organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 148.000 0.902 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #782-0r3wm PRED entity: 0r3wm PRED relation: contains! PRED expected values: 09c7w0 => 182 concepts (110 used for prediction) PRED predicted values (max 10 best out of 390): 09c7w0 (0.84 #37557, 0.82 #67070, 0.81 #6261), 0k_s5 (0.80 #65281, 0.75 #78702, 0.70 #39344), 07ssc (0.42 #72471, 0.28 #86790, 0.18 #55472), 04_1l0v (0.39 #38449, 0.34 #94810, 0.23 #54102), 06pvr (0.33 #1059, 0.30 #2847, 0.17 #24305), 0kpys (0.31 #4650, 0.25 #15379, 0.22 #6438), 059rby (0.26 #41153, 0.15 #72459, 0.10 #86778), 02jx1 (0.26 #72525, 0.17 #86844, 0.13 #55526), 030qb3t (0.21 #4570, 0.09 #15299, 0.09 #37654), 0cb4j (0.16 #35, 0.11 #6293, 0.11 #2717) >> Best rule #37557 for best value: >> intensional similarity = 5 >> extensional distance = 92 >> proper extension: 0288zy; 01hhvg; 01bzw5; 033q4k; 07vht; 027xx3; 0fnmz; 01f1r4; 01q0kg; 033x5p; ... >> query: (?x10400, 09c7w0) <- category(?x10400, ?x134), contains(?x10399, ?x10400), contains(?x1227, ?x10400), ?x1227 = 01n7q, contains(?x94, ?x10399) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0r3wm contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 182.000 110.000 0.840 http://example.org/location/location/contains #781-01z7_f PRED entity: 01z7_f PRED relation: film PRED expected values: 03wbqc4 => 93 concepts (68 used for prediction) PRED predicted values (max 10 best out of 337): 0kfv9 (0.58 #42821, 0.40 #108852, 0.38 #89220), 0h6r5 (0.21 #677, 0.03 #39251, 0.03 #114206), 03m8y5 (0.07 #407, 0.03 #39251, 0.03 #114206), 01rwyq (0.07 #549, 0.03 #39251, 0.03 #114206), 01xbxn (0.07 #1388, 0.03 #39251, 0.03 #114206), 09hy79 (0.07 #1226, 0.03 #39251, 0.03 #114206), 0286vp (0.07 #1220, 0.03 #39251, 0.03 #114206), 06cm5 (0.07 #1066, 0.03 #39251, 0.03 #114206), 0kvgtf (0.07 #619, 0.03 #39251, 0.03 #114206), 02qmsr (0.07 #406, 0.03 #39251, 0.03 #114206) >> Best rule #42821 for best value: >> intensional similarity = 3 >> extensional distance = 1049 >> proper extension: 0m2wm; 02zq43; 07lmxq; 01j5x6; 01v3s2_; 04cf09; 07hbxm; 025t9b; 02wycg2; 073749; ... >> query: (?x4328, ?x1849) <- award_nominee(?x4328, ?x71), nominated_for(?x4328, ?x1849), film(?x4328, ?x1644) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #114206 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1674 *> proper extension: 0280mv7; *> query: (?x4328, ?x508) <- award_nominee(?x368, ?x4328), film(?x368, ?x508) *> conf = 0.03 ranks of expected_values: 104 EVAL 01z7_f film 03wbqc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 93.000 68.000 0.584 http://example.org/film/actor/film./film/performance/film #780-07g2b PRED entity: 07g2b PRED relation: influenced_by PRED expected values: 081k8 => 155 concepts (89 used for prediction) PRED predicted values (max 10 best out of 405): 0j3v (0.43 #1771, 0.15 #11617, 0.15 #7763), 034bs (0.40 #2139, 0.26 #7276, 0.26 #8988), 03j43 (0.40 #2139, 0.26 #7276, 0.26 #8988), 032l1 (0.29 #1800, 0.29 #517, 0.22 #3082), 03_87 (0.29 #628, 0.22 #3193, 0.20 #200), 08433 (0.29 #449, 0.20 #21, 0.12 #1303), 042q3 (0.29 #2069, 0.17 #7207, 0.14 #17480), 05qmj (0.29 #1902, 0.17 #18166, 0.15 #17313), 039n1 (0.29 #2030, 0.14 #747, 0.12 #13699), 06myp (0.29 #2079, 0.11 #7217, 0.11 #8928) >> Best rule #1771 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 07h1q; >> query: (?x587, 0j3v) <- influenced_by(?x118, ?x587), peers(?x2080, ?x587), influenced_by(?x587, ?x7250), ?x7250 = 03sbs >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1011 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 6 *> proper extension: 0mb0; *> query: (?x587, 081k8) <- influenced_by(?x587, ?x8085), ?x8085 = 0448r, place_of_birth(?x587, ?x4356) *> conf = 0.25 ranks of expected_values: 11 EVAL 07g2b influenced_by 081k8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 155.000 89.000 0.429 http://example.org/influence/influence_node/influenced_by #779-07d3x PRED entity: 07d3x PRED relation: profession PRED expected values: 0dxtg => 157 concepts (130 used for prediction) PRED predicted values (max 10 best out of 122): 02hrh1q (0.97 #16413, 0.82 #2996, 0.78 #4486), 0cbd2 (0.88 #1795, 0.87 #2540, 0.86 #3137), 0dxtg (0.86 #4187, 0.83 #2100, 0.81 #4634), 03gjzk (0.83 #4189, 0.78 #612, 0.75 #4636), 01d_h8 (0.67 #1496, 0.67 #900, 0.61 #1645), 02jknp (0.44 #4330, 0.40 #2392, 0.40 #5522), 09jwl (0.41 #3746, 0.40 #4044, 0.37 #15077), 0dz3r (0.36 #6410, 0.25 #4473, 0.25 #11928), 0np9r (0.33 #916, 0.21 #1363, 0.18 #1214), 0nbcg (0.33 #6440, 0.29 #3758, 0.29 #4056) >> Best rule #16413 for best value: >> intensional similarity = 4 >> extensional distance = 1426 >> proper extension: 02zq43; 0bz5v2; 073749; 05qhnq; >> query: (?x9794, 02hrh1q) <- award_nominee(?x3858, ?x9794), profession(?x9794, ?x2225), profession(?x187, ?x2225), ?x187 = 04yywz >> conf = 0.97 => this is the best rule for 1 predicted values *> Best rule #4187 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 82 *> proper extension: 0bbxd3; *> query: (?x9794, 0dxtg) <- program_creator(?x5517, ?x9794), gender(?x9794, ?x231), profession(?x9794, ?x2225), ?x231 = 05zppz *> conf = 0.86 ranks of expected_values: 3 EVAL 07d3x profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 157.000 130.000 0.970 http://example.org/people/person/profession #778-047p798 PRED entity: 047p798 PRED relation: film_release_region PRED expected values: 0b90_r 047lj 0f8l9c 0k6nt 01crd5 => 57 concepts (57 used for prediction) PRED predicted values (max 10 best out of 93): 0f8l9c (0.87 #1694, 0.87 #1085, 0.87 #781), 03h64 (0.78 #824, 0.76 #1128, 0.73 #1737), 0k6nt (0.77 #785, 0.76 #1698, 0.76 #1089), 0154j (0.76 #765, 0.72 #1069, 0.68 #1678), 01znc_ (0.74 #801, 0.70 #1105, 0.65 #1714), 05qhw (0.73 #773, 0.73 #1077, 0.67 #1686), 035qy (0.71 #1098, 0.71 #794, 0.69 #1707), 0b90_r (0.68 #764, 0.64 #1068, 0.61 #1677), 03rj0 (0.56 #819, 0.55 #1123, 0.51 #1732), 05v8c (0.52 #1079, 0.52 #775, 0.48 #1688) >> Best rule #1694 for best value: >> intensional similarity = 5 >> extensional distance = 333 >> proper extension: 02vxq9m; 028_yv; 02vp1f_; 0gtv7pk; 0fq27fp; 0401sg; 0m_mm; 0jqp3; 0h3xztt; 0c0nhgv; ... >> query: (?x10475, 0f8l9c) <- film_release_region(?x10475, ?x512), film_release_region(?x10475, ?x205), ?x205 = 03rjj, participating_countries(?x358, ?x512), titles(?x512, ?x144) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 8, 23, 36 EVAL 047p798 film_release_region 01crd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 57.000 57.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047p798 film_release_region 0k6nt CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 57.000 57.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047p798 film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 57.000 57.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047p798 film_release_region 047lj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 57.000 57.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 047p798 film_release_region 0b90_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 57.000 57.000 0.872 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #777-02yl42 PRED entity: 02yl42 PRED relation: award PRED expected values: 0grw_ 039yzf => 125 concepts (115 used for prediction) PRED predicted values (max 10 best out of 296): 0bqsk5 (0.74 #25551, 0.74 #25550, 0.67 #39525), 01yz0x (0.59 #6163, 0.53 #5364, 0.48 #7360), 0262x6 (0.57 #6302, 0.50 #5503, 0.45 #7499), 0262yt (0.46 #6252, 0.44 #5453, 0.43 #1461), 0262zm (0.43 #6070, 0.41 #5271, 0.40 #7267), 01tgwv (0.43 #1558, 0.30 #2756, 0.29 #5950), 040_9s0 (0.40 #9096, 0.38 #6303, 0.30 #7899), 01ppdy (0.29 #1940, 0.22 #3593, 0.16 #14771), 09sb52 (0.29 #2036, 0.20 #20398, 0.19 #17206), 0c_dx (0.29 #1870, 0.16 #14771, 0.16 #13972) >> Best rule #25551 for best value: >> intensional similarity = 2 >> extensional distance = 745 >> proper extension: 0kc6x; 065y4w7; 0gsg7; 09d5h; 01y67v; 01jq34; 0cjdk; 0kk9v; 05xbx; 05gnf; ... >> query: (?x3663, ?x12729) <- category(?x3663, ?x134), award_winner(?x12729, ?x3663) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1908 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 0cbgl; *> query: (?x3663, 0grw_) <- influenced_by(?x3663, ?x10313), location(?x3663, ?x335), ?x10313 = 07lp1, award_winner(?x11579, ?x3663) *> conf = 0.29 ranks of expected_values: 11, 12 EVAL 02yl42 award 039yzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 125.000 115.000 0.739 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02yl42 award 0grw_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 125.000 115.000 0.739 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #776-04g2mkf PRED entity: 04g2mkf PRED relation: film PRED expected values: 09gq0x5 02qlp4 => 40 concepts (12 used for prediction) PRED predicted values (max 10 best out of 1863): 05c46y6 (0.50 #5153, 0.33 #1977, 0.33 #388), 09v71cj (0.50 #5415, 0.33 #650, 0.20 #7005), 051ys82 (0.50 #5688, 0.33 #923, 0.06 #18406), 02qk3fk (0.40 #7361, 0.33 #1006, 0.30 #10538), 03clwtw (0.40 #7463, 0.33 #4285, 0.22 #9051), 0gmcwlb (0.40 #6539, 0.33 #3361, 0.22 #8127), 091xrc (0.40 #7924, 0.33 #4746, 0.22 #9512), 0fh694 (0.40 #6480, 0.22 #8068, 0.20 #9657), 04ydr95 (0.40 #6869, 0.22 #8457, 0.20 #10046), 07jqjx (0.40 #7757, 0.22 #9345, 0.20 #10934) >> Best rule #5153 for best value: >> intensional similarity = 18 >> extensional distance = 2 >> proper extension: 0fvppk; >> query: (?x13128, 05c46y6) <- film(?x13128, ?x4158), film(?x13128, ?x3201), film(?x13128, ?x249), film_release_region(?x3201, ?x4743), film_release_region(?x3201, ?x1122), film_release_region(?x3201, ?x279), film_release_region(?x3201, ?x94), award(?x3201, ?x2599), produced_by(?x4158, ?x8041), genre(?x4158, ?x53), ?x94 = 09c7w0, genre(?x3201, ?x600), ?x249 = 0c3ybss, film_release_distribution_medium(?x3201, ?x81), ?x4743 = 03spz, ?x279 = 0d060g, nominated_for(?x2599, ?x251), country(?x3106, ?x1122) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #3427 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 1 *> proper extension: 061dn_; *> query: (?x13128, 09gq0x5) <- category(?x13128, ?x134), film(?x13128, ?x6536), film(?x13128, ?x4158), film_release_region(?x6536, ?x3749), film_release_region(?x6536, ?x3277), film_release_region(?x6536, ?x2645), film_release_region(?x6536, ?x2152), film_release_region(?x6536, ?x1353), film_release_region(?x6536, ?x789), film_release_region(?x6536, ?x512), film_release_region(?x6536, ?x172), ?x1353 = 035qy, film_crew_role(?x6536, ?x2154), ?x3277 = 06t8v, ?x2154 = 01vx2h, ?x4158 = 0g83dv, ?x2152 = 06mkj, genre(?x6536, ?x53), ?x3749 = 03ryn, ?x512 = 07ssc, film_festivals(?x6536, ?x10083), ?x789 = 0f8l9c, ?x172 = 0154j, ?x2645 = 03h64 *> conf = 0.33 ranks of expected_values: 158, 474 EVAL 04g2mkf film 02qlp4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 40.000 12.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 04g2mkf film 09gq0x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 40.000 12.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #775-018ctl PRED entity: 018ctl PRED relation: sports PRED expected values: 09_94 06zgc => 29 concepts (29 used for prediction) PRED predicted values (max 10 best out of 51): 01z27 (0.82 #687, 0.79 #540, 0.78 #837), 09_94 (0.82 #687, 0.78 #837, 0.78 #802), 09wz9 (0.79 #540, 0.78 #638, 0.76 #492), 06zgc (0.79 #540, 0.78 #638, 0.76 #492), 09_b4 (0.79 #540, 0.78 #638, 0.76 #492), 01yfj (0.79 #540, 0.78 #638, 0.76 #492), 02vx4 (0.78 #843, 0.75 #743, 0.73 #1186), 01hp22 (0.78 #845, 0.75 #745, 0.67 #1237), 0d1t3 (0.78 #870, 0.67 #571, 0.62 #1113), 0dwxr (0.78 #863, 0.67 #564, 0.62 #1106) >> Best rule #687 for best value: >> intensional similarity = 58 >> extensional distance = 5 >> proper extension: 01f1jy; >> query: (?x784, ?x2631) <- participating_countries(?x784, ?x5274), participating_countries(?x784, ?x1203), participating_countries(?x784, ?x583), sports(?x784, ?x11927), sports(?x784, ?x453), ?x453 = 03tmr, country(?x4876, ?x583), country(?x3554, ?x583), country(?x2978, ?x583), country(?x2631, ?x583), ?x2978 = 03_8r, film_release_region(?x11218, ?x583), film_release_region(?x10475, ?x583), film_release_region(?x9002, ?x583), film_release_region(?x8891, ?x583), film_release_region(?x8682, ?x583), film_release_region(?x8471, ?x583), film_release_region(?x7629, ?x583), film_release_region(?x7265, ?x583), film_release_region(?x7204, ?x583), film_release_region(?x6587, ?x583), film_release_region(?x5425, ?x583), film_release_region(?x4111, ?x583), film_release_region(?x3981, ?x583), film_release_region(?x2656, ?x583), film_release_region(?x1525, ?x583), film_release_region(?x1456, ?x583), film_release_region(?x791, ?x583), adjoins(?x583, ?x410), ?x1525 = 03qnvdl, olympics(?x583, ?x2233), ?x4876 = 0d1t3, ?x8471 = 0cp0t91, sports(?x8189, ?x2631), ?x3981 = 047tsx3, jurisdiction_of_office(?x182, ?x583), ?x2233 = 0l6mp, ?x1456 = 0cz8mkh, ?x3554 = 035d1m, ?x7629 = 02825nf, country(?x2954, ?x583), ?x791 = 087wc7n, award(?x7204, ?x5886), ?x8189 = 015l4k, ?x2656 = 03qnc6q, ?x10475 = 047p798, titles(?x53, ?x5425), film(?x8394, ?x7204), ?x9002 = 0ndsl1x, ?x8682 = 0bmfnjs, ?x4111 = 0cmc26r, combatants(?x9203, ?x5274), ?x6587 = 07s3m4g, ?x11927 = 09f6b, teams(?x1203, ?x12184), language(?x11218, ?x254), film(?x8151, ?x7265), genre(?x8891, ?x225) >> conf = 0.82 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2, 4 EVAL 018ctl sports 06zgc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 29.000 29.000 0.824 http://example.org/olympics/olympic_games/sports EVAL 018ctl sports 09_94 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 29.000 29.000 0.824 http://example.org/olympics/olympic_games/sports #774-04nm0n0 PRED entity: 04nm0n0 PRED relation: films! PRED expected values: 081pw => 93 concepts (46 used for prediction) PRED predicted values (max 10 best out of 76): 081pw (0.22 #953, 0.21 #1111, 0.18 #636), 05489 (0.22 #210, 0.14 #369, 0.11 #843), 0fx2s (0.12 #73, 0.11 #1023, 0.11 #1181), 03hzt (0.12 #135, 0.11 #1085, 0.11 #1243), 0nk95 (0.12 #151, 0.03 #2053, 0.02 #2368), 01w1sx (0.12 #724, 0.12 #566, 0.08 #1357), 018h2 (0.11 #180, 0.07 #339, 0.06 #813), 0d1w9 (0.07 #353, 0.06 #1621, 0.06 #669), 07_m9_ (0.06 #671, 0.06 #513, 0.06 #988), 0kcc7 (0.06 #777, 0.06 #619, 0.06 #1094) >> Best rule #953 for best value: >> intensional similarity = 6 >> extensional distance = 16 >> proper extension: 02rqwhl; 02qhlwd; 0hfzr; 07bwr; >> query: (?x5017, 081pw) <- language(?x5017, ?x732), executive_produced_by(?x5017, ?x3872), titles(?x53, ?x5017), ?x732 = 04306rv, country(?x5017, ?x1003), film_crew_role(?x5017, ?x137) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04nm0n0 films! 081pw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 46.000 0.222 http://example.org/film/film_subject/films #773-017yxq PRED entity: 017yxq PRED relation: award PRED expected values: 05f4m9q => 134 concepts (122 used for prediction) PRED predicted values (max 10 best out of 316): 05f4m9q (0.78 #25675, 0.77 #25674, 0.77 #23668), 019f4v (0.44 #2873, 0.42 #1269, 0.42 #2472), 05ztrmj (0.43 #13421, 0.25 #181, 0.24 #4594), 040njc (0.42 #2414, 0.41 #2815, 0.38 #1211), 0gs9p (0.42 #2484, 0.41 #2885, 0.35 #1281), 09sb52 (0.41 #2045, 0.35 #13279, 0.35 #5254), 02pqp12 (0.41 #2877, 0.39 #2476, 0.35 #1273), 057xs89 (0.38 #13397, 0.30 #2163, 0.25 #5372), 05zr6wv (0.38 #4429, 0.33 #16, 0.33 #5231), 05pcn59 (0.37 #2085, 0.33 #8505, 0.33 #4492) >> Best rule #25675 for best value: >> intensional similarity = 3 >> extensional distance = 586 >> proper extension: 06lxn; >> query: (?x8399, ?x2325) <- artists(?x2937, ?x8399), award_winner(?x2325, ?x8399), award(?x286, ?x2325) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017yxq award 05f4m9q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 122.000 0.785 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #772-0157m PRED entity: 0157m PRED relation: gender PRED expected values: 05zppz => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.90 #37, 0.90 #75, 0.89 #119), 02zsn (0.49 #353, 0.47 #150, 0.47 #138) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 01pfkw; >> query: (?x1620, 05zppz) <- company(?x1620, ?x94), profession(?x1620, ?x2225), celebrities_impersonated(?x3649, ?x1620) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0157m gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 183.000 183.000 0.900 http://example.org/people/person/gender #771-014b4h PRED entity: 014b4h PRED relation: contains! PRED expected values: 02jx1 => 166 concepts (78 used for prediction) PRED predicted values (max 10 best out of 322): 02jx1 (0.83 #9031, 0.76 #58151, 0.74 #9926), 09c7w0 (0.79 #55471, 0.79 #60840, 0.76 #17898), 07ssc (0.76 #58151, 0.74 #58183, 0.50 #926), 059rby (0.61 #36704, 0.52 #21493, 0.36 #26861), 01n7q (0.57 #51072, 0.21 #28707, 0.20 #2761), 081yw (0.31 #19066, 0.30 #19961, 0.28 #22644), 05kj_ (0.29 #4513, 0.29 #3618, 0.25 #6302), 05tbn (0.29 #4696, 0.26 #50323, 0.25 #6485), 09bkv (0.25 #1453, 0.02 #15765, 0.02 #16662), 02_286 (0.20 #21515, 0.20 #7198, 0.19 #29566) >> Best rule #9031 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 015ln1; 01g0p5; 019vsw; >> query: (?x639, 02jx1) <- school_type(?x639, ?x3092), contains(?x362, ?x639), category(?x639, ?x134), ?x362 = 04jpl >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014b4h contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 166.000 78.000 0.833 http://example.org/location/location/contains #770-0f_y9 PRED entity: 0f_y9 PRED relation: people! PRED expected values: 01qhm_ => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 49): 0x67 (0.44 #2137, 0.44 #2061, 0.40 #3201), 041rx (0.26 #4564, 0.25 #4716, 0.25 #5400), 02w7gg (0.25 #2, 0.12 #78, 0.10 #4714), 0xnvg (0.25 #12, 0.12 #88, 0.08 #4724), 05l3g_ (0.25 #60, 0.12 #136), 07hwkr (0.12 #87, 0.09 #4571, 0.08 #5407), 07bch9 (0.10 #250, 0.05 #4582, 0.05 #478), 013b6_ (0.10 #280, 0.05 #356, 0.03 #1420), 02ctzb (0.08 #166, 0.04 #4574, 0.04 #5410), 09vc4s (0.05 #616, 0.04 #2060, 0.04 #2136) >> Best rule #2137 for best value: >> intensional similarity = 3 >> extensional distance = 241 >> proper extension: 094xh; 031x_3; >> query: (?x7345, 0x67) <- location(?x7345, ?x659), artists(?x671, ?x7345), people(?x1446, ?x7345) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #6086 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 929 *> proper extension: 01xyt7; 0dj5q; 01gct2; *> query: (?x7345, 01qhm_) <- people(?x1446, ?x7345), award_winner(?x12701, ?x7345), award_winner(?x12701, ?x7398), location(?x7398, ?x739) *> conf = 0.04 ranks of expected_values: 15 EVAL 0f_y9 people! 01qhm_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 145.000 145.000 0.444 http://example.org/people/ethnicity/people #769-0c0tzp PRED entity: 0c0tzp PRED relation: place_of_birth PRED expected values: 0mzww => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 33): 010h9y (0.25 #465, 0.10 #1169, 0.07 #1873), 01_d4 (0.25 #66, 0.07 #4291, 0.03 #6404), 02_286 (0.10 #2131, 0.08 #4244, 0.07 #6357), 0sbv7 (0.10 #1326, 0.07 #2030, 0.05 #2734), 01sn3 (0.10 #853, 0.07 #1557, 0.05 #3670), 0r62v (0.10 #730, 0.07 #1434, 0.05 #3547), 09c7w0 (0.10 #705, 0.05 #3522, 0.05 #2817), 0kv5t (0.07 #2025, 0.05 #4138, 0.05 #3433), 0c_m3 (0.07 #1605, 0.05 #3718, 0.05 #3013), 0ftyc (0.07 #1592, 0.05 #3705, 0.05 #3000) >> Best rule #465 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0cb77r; 05218gr; >> query: (?x12378, 010h9y) <- award_winner(?x12378, ?x199), ?x199 = 0520r2x, nominated_for(?x12378, ?x4280) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0c0tzp place_of_birth 0mzww CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 93.000 0.250 http://example.org/people/person/place_of_birth #768-0133sq PRED entity: 0133sq PRED relation: award_winner! PRED expected values: 073h1t => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 137): 09pj68 (0.38 #102, 0.08 #792, 0.05 #1068), 09qvms (0.23 #13, 0.05 #2083, 0.05 #841), 02glmx (0.23 #80, 0.05 #770, 0.04 #2426), 0n8_m93 (0.15 #115, 0.06 #1633, 0.05 #1357), 02yvhx (0.15 #76, 0.04 #15468, 0.04 #15191), 0gvstc3 (0.12 #171, 0.06 #309, 0.06 #585), 05c1t6z (0.12 #291, 0.06 #567, 0.05 #705), 02q690_ (0.12 #340, 0.06 #616, 0.05 #3514), 03nnm4t (0.12 #349, 0.06 #625, 0.04 #1453), 0gx1673 (0.12 #393, 0.06 #669, 0.03 #807) >> Best rule #102 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 0n6f8; 04ktcgn; 05bm4sm; >> query: (?x10854, 09pj68) <- award_winner(?x2210, ?x10854), nominated_for(?x10854, ?x2539), ?x2210 = 0bvfqq, award(?x10854, ?x4573) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 14 *> proper extension: 074qgb; *> query: (?x10854, 073h1t) <- nationality(?x10854, ?x512), award(?x10854, ?x4573), ?x4573 = 0gq_d, profession(?x10854, ?x524) *> conf = 0.06 ranks of expected_values: 32 EVAL 0133sq award_winner! 073h1t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 142.000 142.000 0.385 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #767-0f4_l PRED entity: 0f4_l PRED relation: film! PRED expected values: 02p21g => 80 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1111): 01q_ph (0.14 #4196, 0.10 #10406, 0.10 #12476), 06cgy (0.13 #25092, 0.04 #23021, 0.04 #47869), 018grr (0.12 #338, 0.11 #4478, 0.08 #6548), 0c6qh (0.12 #2483, 0.08 #6623, 0.08 #10763), 059j1m (0.12 #1463, 0.08 #7673, 0.08 #3533), 0bxtg (0.11 #24918, 0.08 #2145, 0.07 #16636), 0q9kd (0.11 #33126, 0.05 #8284, 0.04 #2074), 0d_skg (0.11 #33126), 012d40 (0.11 #4156, 0.06 #10366, 0.06 #12436), 0mdqp (0.10 #10467, 0.10 #12537, 0.08 #2187) >> Best rule #4196 for best value: >> intensional similarity = 4 >> extensional distance = 26 >> proper extension: 01bl7g; >> query: (?x2177, 01q_ph) <- nominated_for(?x401, ?x2177), ?x401 = 05zr6wv, film(?x368, ?x2177), featured_film_locations(?x2177, ?x1523) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #18632 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 85 *> proper extension: 02kk_c; *> query: (?x2177, ?x1593) <- award_winner(?x2177, ?x3117), film(?x3117, ?x814), award_winner(?x1449, ?x3117), participant(?x3117, ?x1593) *> conf = 0.04 ranks of expected_values: 89 EVAL 0f4_l film! 02p21g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 80.000 37.000 0.143 http://example.org/film/actor/film./film/performance/film #766-05b2f_k PRED entity: 05b2f_k PRED relation: film_production_design_by! PRED expected values: 01q2nx => 99 concepts (49 used for prediction) PRED predicted values (max 10 best out of 156): 0f4yh (0.30 #681, 0.15 #339, 0.02 #511), 0dtfn (0.30 #681, 0.15 #339, 0.02 #511), 067ghz (0.30 #681, 0.15 #339, 0.02 #511), 03wy8t (0.06 #320, 0.04 #662, 0.04 #491), 0286hyp (0.06 #338, 0.04 #680, 0.04 #509), 0h0wd9 (0.06 #327, 0.04 #669, 0.04 #498), 06y611 (0.06 #324, 0.04 #666, 0.04 #495), 04x4nv (0.06 #316, 0.04 #658, 0.04 #487), 07g1sm (0.06 #295, 0.04 #637, 0.04 #466), 0gg8z1f (0.06 #282, 0.04 #624, 0.04 #453) >> Best rule #681 for best value: >> intensional similarity = 3 >> extensional distance = 23 >> proper extension: 07h5d; >> query: (?x8719, ?x1386) <- nominated_for(?x8719, ?x1386), nationality(?x8719, ?x512), film_art_direction_by(?x1072, ?x8719) >> conf = 0.30 => this is the best rule for 3 predicted values No rule for expected values ranks of expected_values: EVAL 05b2f_k film_production_design_by! 01q2nx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 49.000 0.297 http://example.org/film/film/film_production_design_by #765-0mrq3 PRED entity: 0mrq3 PRED relation: source PRED expected values: 0jbk9 => 164 concepts (164 used for prediction) PRED predicted values (max 10 best out of 1): 0jbk9 (0.94 #19, 0.94 #16, 0.93 #51) >> Best rule #19 for best value: >> intensional similarity = 5 >> extensional distance = 84 >> proper extension: 0m2gk; 0nvd8; 0k3ll; 0mws3; 0mlzk; 0f4zv; >> query: (?x10490, 0jbk9) <- county(?x11968, ?x10490), adjoins(?x7949, ?x10490), second_level_divisions(?x94, ?x10490), ?x94 = 09c7w0, place_of_birth(?x6290, ?x11968) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mrq3 source 0jbk9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 164.000 164.000 0.942 http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source #764-04t969 PRED entity: 04t969 PRED relation: award PRED expected values: 02x8n1n => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 222): 09sb52 (0.34 #4485, 0.33 #4081, 0.32 #5697), 0gq9h (0.33 #78, 0.09 #7755, 0.08 #8563), 018wng (0.24 #42), 0gq_d (0.19 #223, 0.02 #627), 0p9sw (0.19 #23, 0.02 #4871, 0.02 #6487), 05pcn59 (0.17 #890, 0.12 #1294, 0.10 #4526), 05p1dby (0.14 #107, 0.04 #6571, 0.04 #7784), 0gr42 (0.14 #116), 0cjyzs (0.14 #510, 0.05 #8995, 0.05 #11419), 05zr6wv (0.14 #825, 0.10 #1229, 0.08 #4461) >> Best rule #4485 for best value: >> intensional similarity = 3 >> extensional distance = 1166 >> proper extension: 01sl1q; 0184jc; 04bdxl; 06qgvf; 016qtt; 01vvydl; 012d40; 01k7d9; 0337vz; 07s3vqk; ... >> query: (?x7382, 09sb52) <- award_nominee(?x7382, ?x890), award(?x7382, ?x1670), film(?x7382, ?x5353) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #120 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 19 *> proper extension: 014l4w; *> query: (?x7382, 02x8n1n) <- award(?x7382, ?x5409), ?x5409 = 0gr07 *> conf = 0.05 ranks of expected_values: 75 EVAL 04t969 award 02x8n1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 76.000 76.000 0.341 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #763-063g7l PRED entity: 063g7l PRED relation: nationality PRED expected values: 09c7w0 => 128 concepts (127 used for prediction) PRED predicted values (max 10 best out of 89): 09c7w0 (0.90 #5011, 0.89 #8524, 0.88 #1001), 04rrx (0.38 #11648, 0.33 #11343, 0.33 #11649), 07ssc (0.33 #5511, 0.17 #615, 0.11 #1915), 0f8l9c (0.33 #5511, 0.07 #3002, 0.06 #922), 0chghy (0.33 #5511, 0.06 #1910, 0.05 #1810), 0345h (0.33 #5511, 0.04 #2231, 0.04 #2331), 03rjj (0.33 #5511, 0.03 #8323, 0.03 #5215), 05qhw (0.33 #5511, 0.03 #8323, 0.03 #2901), 02jx1 (0.18 #2533, 0.16 #2233, 0.16 #2333), 0d060g (0.12 #207, 0.10 #407, 0.08 #707) >> Best rule #5011 for best value: >> intensional similarity = 3 >> extensional distance = 399 >> proper extension: 05218gr; >> query: (?x11624, 09c7w0) <- place_of_birth(?x11624, ?x3372), county_seat(?x13203, ?x3372), jurisdiction_of_office(?x1195, ?x3372) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 063g7l nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 127.000 0.900 http://example.org/people/person/nationality #762-02301 PRED entity: 02301 PRED relation: major_field_of_study PRED expected values: 036hv 01tbp => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 117): 01mkq (0.58 #7294, 0.47 #4494, 0.42 #136), 02j62 (0.48 #4508, 0.43 #3297, 0.43 #4024), 041y2 (0.42 #319, 0.42 #198, 0.20 #77), 05qjt (0.42 #129, 0.33 #250, 0.31 #4487), 05qfh (0.42 #156, 0.33 #277, 0.30 #4514), 02_7t (0.40 #64, 0.33 #306, 0.33 #185), 09s1f (0.40 #99, 0.33 #341, 0.33 #220), 0_jm (0.40 #662, 0.25 #299, 0.25 #178), 062z7 (0.32 #4505, 0.32 #7305, 0.30 #6210), 0g26h (0.30 #646, 0.29 #4520, 0.25 #283) >> Best rule #7294 for best value: >> intensional similarity = 5 >> extensional distance = 235 >> proper extension: 03bwzr4; >> query: (?x2730, 01mkq) <- major_field_of_study(?x2730, ?x2605), major_field_of_study(?x11614, ?x2605), major_field_of_study(?x1043, ?x2605), ?x1043 = 0kz2w, ?x11614 = 07tk7 >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #3327 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 74 *> proper extension: 01cyd5; 0204jh; 02l9wl; 0fr9jp; 05nrkb; *> query: (?x2730, 01tbp) <- student(?x2730, ?x4405), award(?x4405, ?x601), nominated_for(?x4405, ?x1708), ?x601 = 0gr4k *> conf = 0.24 ranks of expected_values: 18, 23 EVAL 02301 major_field_of_study 01tbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 168.000 168.000 0.578 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 02301 major_field_of_study 036hv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 168.000 168.000 0.578 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #761-02g3w PRED entity: 02g3w PRED relation: story_by! PRED expected values: 087wc7n => 139 concepts (138 used for prediction) PRED predicted values (max 10 best out of 125): 01pv91 (0.20 #84, 0.06 #1113), 0dyb1 (0.18 #789, 0.12 #1132, 0.02 #4562), 063y9fp (0.12 #1661, 0.04 #3033, 0.02 #8522), 0bpm4yw (0.12 #1521, 0.04 #2893, 0.02 #6666), 02fqrf (0.12 #1488, 0.04 #2860, 0.01 #6633), 03x7hd (0.09 #801, 0.09 #458, 0.06 #1144), 01hvjx (0.09 #761, 0.09 #418, 0.06 #1104), 043h78 (0.09 #631, 0.06 #1660, 0.02 #3032), 02c7k4 (0.09 #908, 0.06 #1251, 0.02 #4681), 01xdxy (0.09 #985, 0.06 #1328) >> Best rule #84 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0133sq; >> query: (?x11413, 01pv91) <- profession(?x11413, ?x1966), award_winner(?x11156, ?x11413), ?x1966 = 015h31, religion(?x11413, ?x8613) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02g3w story_by! 087wc7n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 139.000 138.000 0.200 http://example.org/film/film/story_by #760-0w7c PRED entity: 0w7c PRED relation: major_field_of_study! PRED expected values: 08815 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 672): 06pwq (0.80 #6856, 0.71 #4005, 0.55 #7996), 08815 (0.71 #3994, 0.64 #7985, 0.57 #11406), 09f2j (0.70 #7017, 0.60 #17860, 0.57 #12149), 03ksy (0.67 #8668, 0.64 #11519, 0.62 #15516), 017j69 (0.64 #8140, 0.60 #17843, 0.50 #2439), 01bm_ (0.64 #8250, 0.50 #7110, 0.50 #2549), 07wrz (0.64 #8050, 0.44 #15468, 0.43 #11471), 07tg4 (0.64 #8073, 0.44 #15491, 0.43 #11494), 07szy (0.60 #6887, 0.55 #8027, 0.53 #13729), 01w3v (0.60 #6859, 0.50 #15417, 0.50 #11420) >> Best rule #6856 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 01540; >> query: (?x6760, 06pwq) <- major_field_of_study(?x8220, ?x6760), major_field_of_study(?x2909, ?x6760), ?x8220 = 0c5x_, student(?x2909, ?x4676), school_type(?x2909, ?x3205), award(?x4676, ?x678) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #3994 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 01lj9; *> query: (?x6760, 08815) <- major_field_of_study(?x6637, ?x6760), student(?x6760, ?x665), disciplines_or_subjects(?x850, ?x6760), major_field_of_study(?x1368, ?x6760), ?x6637 = 07vjm *> conf = 0.71 ranks of expected_values: 2 EVAL 0w7c major_field_of_study! 08815 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 70.000 70.000 0.800 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #759-03lh3v PRED entity: 03lh3v PRED relation: team PRED expected values: 0jmbv => 139 concepts (138 used for prediction) PRED predicted values (max 10 best out of 362): 0jmk7 (0.33 #1015, 0.19 #2785, 0.19 #2077), 0cqt41 (0.29 #3216, 0.20 #4632, 0.16 #7111), 01lpx8 (0.20 #1267, 0.18 #3391, 0.15 #6932), 0jmbv (0.15 #1526, 0.12 #464, 0.10 #4712), 0jm3b (0.15 #1652, 0.12 #590, 0.10 #1298), 0jm4b (0.12 #455, 0.11 #809, 0.10 #1163), 026dqjm (0.12 #667, 0.11 #1021, 0.10 #1375), 0jm7n (0.12 #615, 0.11 #969, 0.10 #1323), 0jmgb (0.12 #660, 0.11 #1014, 0.06 #2784), 0jm5b (0.12 #647, 0.11 #1001, 0.06 #2063) >> Best rule #1015 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 054c1; 01jz6d; >> query: (?x4834, 0jmk7) <- nationality(?x4834, ?x94), currency(?x4834, ?x170), athlete(?x4833, ?x4834), ?x4833 = 018w8, team(?x4834, ?x1578) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1526 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 063g7l; *> query: (?x4834, 0jmbv) <- people(?x2510, ?x4834), place_of_birth(?x4834, ?x13692), team(?x4834, ?x1578), type_of_union(?x4834, ?x566) *> conf = 0.15 ranks of expected_values: 4 EVAL 03lh3v team 0jmbv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 139.000 138.000 0.333 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #758-062zm5h PRED entity: 062zm5h PRED relation: featured_film_locations PRED expected values: 01sn3 => 55 concepts (46 used for prediction) PRED predicted values (max 10 best out of 93): 030qb3t (0.14 #756, 0.12 #995, 0.11 #1714), 0chgzm (0.12 #148), 0fvvz (0.12 #31), 06y57 (0.12 #581, 0.06 #1539, 0.02 #2980), 04jpl (0.08 #3366, 0.07 #4567, 0.07 #4808), 080h2 (0.07 #2180, 0.06 #2901, 0.05 #3141), 0rh6k (0.06 #2879, 0.06 #3119, 0.06 #1438), 01_d4 (0.05 #285, 0.04 #1963, 0.03 #1244), 03h64 (0.05 #300, 0.02 #1018, 0.01 #1259), 0hyxv (0.05 #324, 0.02 #1042) >> Best rule #756 for best value: >> intensional similarity = 4 >> extensional distance = 48 >> proper extension: 04cf_l; >> query: (?x5016, 030qb3t) <- nominated_for(?x401, ?x5016), ?x401 = 05zr6wv, language(?x5016, ?x254), nominated_for(?x2373, ?x5016) >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #1285 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 86 *> proper extension: 016ztl; *> query: (?x5016, 01sn3) <- film(?x4832, ?x5016), genre(?x5016, ?x225), genre(?x8162, ?x225), ?x8162 = 0bs8ndx *> conf = 0.01 ranks of expected_values: 69 EVAL 062zm5h featured_film_locations 01sn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 55.000 46.000 0.140 http://example.org/film/film/featured_film_locations #757-06bc59 PRED entity: 06bc59 PRED relation: film! PRED expected values: 0g1rw => 84 concepts (62 used for prediction) PRED predicted values (max 10 best out of 57): 06jntd (0.33 #248, 0.25 #102, 0.25 #29), 081bls (0.33 #258, 0.25 #112, 0.25 #39), 03xq0f (0.25 #78, 0.23 #443, 0.23 #297), 016tt2 (0.25 #77, 0.14 #2342, 0.14 #2709), 086k8 (0.19 #1316, 0.19 #294, 0.19 #2707), 016tw3 (0.18 #813, 0.17 #156, 0.15 #3962), 04mkft (0.17 #180, 0.12 #326, 0.10 #399), 03rwz3 (0.17 #188, 0.07 #772, 0.05 #1796), 024rgt (0.17 #165, 0.07 #457, 0.06 #1333), 017s11 (0.13 #3955, 0.12 #1903, 0.12 #1317) >> Best rule #248 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 06_sc3; >> query: (?x9786, 06jntd) <- prequel(?x3471, ?x9786), country(?x9786, ?x789), ?x789 = 0f8l9c, film(?x450, ?x3471) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #300 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 0140g4; 05nlx4; *> query: (?x9786, 0g1rw) <- prequel(?x3471, ?x9786), films(?x3530, ?x9786), genre(?x9786, ?x571), produced_by(?x9786, ?x9363) *> conf = 0.12 ranks of expected_values: 12 EVAL 06bc59 film! 0g1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 84.000 62.000 0.333 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #756-03clwtw PRED entity: 03clwtw PRED relation: production_companies PRED expected values: 0c41qv => 130 concepts (130 used for prediction) PRED predicted values (max 10 best out of 134): 0g1rw (0.60 #5904, 0.60 #6219, 0.58 #5432), 061dn_ (0.60 #5904, 0.60 #6219, 0.58 #5432), 016tt2 (0.60 #5904, 0.60 #6219, 0.58 #5432), 030_1m (0.60 #5904, 0.60 #6219, 0.58 #5432), 04f525m (0.58 #5432, 0.58 #556, 0.57 #4724), 019v67 (0.58 #5432, 0.58 #556, 0.57 #4724), 016tw3 (0.57 #1751, 0.21 #5444, 0.17 #3637), 017s11 (0.36 #1742, 0.14 #5435, 0.13 #5513), 032j_n (0.24 #781, 0.05 #2278, 0.04 #2750), 086k8 (0.23 #5434, 0.20 #83, 0.20 #5512) >> Best rule #5904 for best value: >> intensional similarity = 6 >> extensional distance = 639 >> proper extension: 07kb7vh; >> query: (?x7145, ?x574) <- production_companies(?x7145, ?x7935), company(?x10540, ?x7935), film(?x574, ?x7145), production_companies(?x136, ?x574), award_nominee(?x574, ?x541), language(?x7145, ?x254) >> conf = 0.60 => this is the best rule for 4 predicted values *> Best rule #370 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 7 *> proper extension: 0gcrg; *> query: (?x7145, 0c41qv) <- genre(?x7145, ?x225), film_format(?x7145, ?x909), film(?x788, ?x7145), ?x788 = 0g1rw, music(?x7145, ?x562) *> conf = 0.11 ranks of expected_values: 18 EVAL 03clwtw production_companies 0c41qv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 130.000 130.000 0.601 http://example.org/film/film/production_companies #755-062zjtt PRED entity: 062zjtt PRED relation: genre PRED expected values: 07s9rl0 => 111 concepts (61 used for prediction) PRED predicted values (max 10 best out of 130): 05p553 (0.75 #6163, 0.65 #6281, 0.51 #5926), 06n90 (0.74 #603, 0.67 #485, 0.37 #1429), 07s9rl0 (0.73 #6873, 0.70 #4024, 0.69 #5449), 01jfsb (0.70 #4272, 0.65 #4984, 0.63 #3441), 0hcr (0.39 #4638, 0.22 #2384, 0.20 #2977), 060__y (0.38 #135, 0.38 #17, 0.19 #4514), 02l7c8 (0.30 #1550, 0.26 #6175, 0.25 #6293), 0lsxr (0.30 #4151, 0.29 #7000, 0.28 #4269), 04xvlr (0.28 #828, 0.25 #120, 0.19 #1536), 04xvh5 (0.25 #152, 0.25 #34, 0.13 #1096) >> Best rule #6163 for best value: >> intensional similarity = 6 >> extensional distance = 409 >> proper extension: 013q0p; 03pc89; >> query: (?x4273, 05p553) <- music(?x4273, ?x4911), film(?x450, ?x4273), genre(?x4273, ?x1510), genre(?x7800, ?x1510), ?x7800 = 02wgbb, genre(?x419, ?x1510) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6873 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 488 *> proper extension: 0jqp3; 0260bz; 0879bpq; 04jpk2; 0cc5qkt; 0hfzr; 04j4tx; 0353xq; 027ct7c; 01flv_; ... *> query: (?x4273, 07s9rl0) <- music(?x4273, ?x4911), film_crew_role(?x4273, ?x137), genre(?x4273, ?x225), genre(?x3063, ?x225), genre(?x924, ?x225), ?x924 = 04gknr, ?x3063 = 07sp4l *> conf = 0.73 ranks of expected_values: 3 EVAL 062zjtt genre 07s9rl0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 61.000 0.754 http://example.org/film/film/genre #754-0kvgnq PRED entity: 0kvgnq PRED relation: currency PRED expected values: 09nqf => 77 concepts (77 used for prediction) PRED predicted values (max 10 best out of 4): 09nqf (0.80 #71, 0.79 #64, 0.78 #15), 01nv4h (0.03 #114, 0.02 #37, 0.02 #205), 02gsvk (0.03 #62, 0.02 #41, 0.02 #90), 02l6h (0.02 #32, 0.02 #46, 0.01 #39) >> Best rule #71 for best value: >> intensional similarity = 3 >> extensional distance = 306 >> proper extension: 0dtw1x; 0fq27fp; >> query: (?x5752, 09nqf) <- genre(?x5752, ?x53), film_crew_role(?x5752, ?x137), crewmember(?x5752, ?x1622) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kvgnq currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 77.000 0.795 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #753-0xzly PRED entity: 0xzly PRED relation: role! PRED expected values: 02sgy 0395lw => 58 concepts (42 used for prediction) PRED predicted values (max 10 best out of 107): 0395lw (0.87 #211, 0.85 #536, 0.85 #1221), 02sgy (0.87 #211, 0.85 #536, 0.84 #645), 0cfdd (0.87 #211, 0.85 #536, 0.84 #645), 01v1d8 (0.87 #211, 0.85 #536, 0.84 #645), 05842k (0.87 #211, 0.85 #536, 0.84 #645), 01vj9c (0.82 #2627, 0.80 #2733, 0.80 #1751), 0bxl5 (0.82 #2678, 0.77 #1259, 0.75 #3220), 042v_gx (0.80 #1638, 0.79 #2072, 0.77 #1310), 0dwtp (0.78 #884, 0.77 #1211, 0.74 #2955), 04rzd (0.77 #2652, 0.75 #3301, 0.75 #543) >> Best rule #211 for best value: >> intensional similarity = 24 >> extensional distance = 1 >> proper extension: 01vj9c; >> query: (?x1436, ?x227) <- role(?x1436, ?x716), role(?x1436, ?x615), role(?x1436, ?x314), role(?x1436, ?x227), ?x716 = 018vs, role(?x7033, ?x1436), role(?x2798, ?x1436), ?x314 = 02sgy, role(?x1534, ?x1436), role(?x1663, ?x1436), role(?x212, ?x1436), role(?x2799, ?x1436), ?x212 = 026t6, ?x1663 = 01w4dy, ?x615 = 0dwsp, profession(?x2799, ?x6565), ?x7033 = 0gkd1, instrumentalists(?x2798, ?x6461), instrumentalists(?x2798, ?x2930), ?x6461 = 01t110, ?x2930 = 0pkyh, role(?x214, ?x2798), ?x6565 = 0fnpj, group(?x2798, ?x997) >> conf = 0.87 => this is the best rule for 5 predicted values ranks of expected_values: 1, 2 EVAL 0xzly role! 0395lw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 42.000 0.873 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 0xzly role! 02sgy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 42.000 0.873 http://example.org/music/performance_role/track_performances./music/track_contribution/role #752-0lpjn PRED entity: 0lpjn PRED relation: film PRED expected values: 072zl1 0bmfnjs => 84 concepts (52 used for prediction) PRED predicted values (max 10 best out of 765): 03ctqqf (0.58 #70857, 0.39 #51368, 0.38 #58454), 031hcx (0.19 #3028, 0.03 #88578, 0.01 #15426), 03177r (0.17 #457, 0.12 #2228, 0.03 #88578), 017kct (0.17 #574, 0.06 #2345, 0.03 #88578), 09gq0x5 (0.17 #278, 0.06 #2049), 06lpmt (0.17 #676, 0.03 #4218, 0.01 #5989), 011yr9 (0.17 #683, 0.03 #88578, 0.01 #4225), 04954r (0.17 #607, 0.02 #4149, 0.01 #7692), 0295sy (0.12 #2719, 0.08 #948, 0.03 #88578), 04jpg2p (0.12 #3216, 0.08 #1445, 0.03 #88578) >> Best rule #70857 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 039crh; 01p47r; 01gc7h; 01f9mq; >> query: (?x2805, ?x12117) <- film(?x2805, ?x144), nominated_for(?x2805, ?x12117) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #88578 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1667 *> proper extension: 0565cz; 076df9; *> query: (?x2805, ?x308) <- award_nominee(?x2805, ?x5144), award_nominee(?x2805, ?x2275), award_nominee(?x5144, ?x931), film(?x2275, ?x308) *> conf = 0.03 ranks of expected_values: 224 EVAL 0lpjn film 0bmfnjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 52.000 0.583 http://example.org/film/actor/film./film/performance/film EVAL 0lpjn film 072zl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 84.000 52.000 0.583 http://example.org/film/actor/film./film/performance/film #751-01q4qv PRED entity: 01q4qv PRED relation: profession PRED expected values: 02jknp => 104 concepts (93 used for prediction) PRED predicted values (max 10 best out of 51): 02jknp (0.89 #1931, 0.87 #2079, 0.87 #2820), 02hrh1q (0.80 #11708, 0.78 #12596, 0.73 #12448), 03gjzk (0.44 #3715, 0.39 #3123, 0.39 #4603), 09jwl (0.33 #18, 0.24 #11417, 0.18 #12453), 02krf9 (0.33 #26, 0.24 #2098, 0.23 #2987), 0cbd2 (0.30 #7408, 0.28 #3115, 0.27 #1782), 0dgd_ (0.23 #622, 0.22 #1214, 0.14 #474), 0kyk (0.21 #7431, 0.15 #1805, 0.13 #4174), 02hv44_ (0.20 #205, 0.16 #1833, 0.12 #353), 018gz8 (0.18 #4161, 0.17 #4605, 0.17 #5345) >> Best rule #1931 for best value: >> intensional similarity = 4 >> extensional distance = 201 >> proper extension: 03f2_rc; 0sz28; 05drq5; 022_lg; 0127m7; 0j_c; 01ycck; 03xp8d5; 03nk3t; 01pp3p; ... >> query: (?x3177, 02jknp) <- award(?x3177, ?x198), film(?x3177, ?x5515), award_winner(?x77, ?x3177), profession(?x3177, ?x319) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01q4qv profession 02jknp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 93.000 0.887 http://example.org/people/person/profession #750-03lkp PRED entity: 03lkp PRED relation: contains! PRED expected values: 07ssc => 70 concepts (30 used for prediction) PRED predicted values (max 10 best out of 244): 07ssc (0.76 #8080, 0.75 #2712, 0.74 #8974), 09c7w0 (0.60 #23285, 0.60 #24179, 0.59 #25075), 0121c1 (0.33 #139, 0.17 #1925, 0.10 #15216), 02j9z (0.25 #7180, 0.09 #3602, 0.07 #16113), 01n7q (0.23 #11711, 0.16 #15296, 0.11 #26045), 04jpl (0.22 #6279, 0.16 #4489, 0.16 #5384), 0d060g (0.16 #9852, 0.12 #11646, 0.08 #15231), 0dg3n1 (0.14 #3728, 0.02 #17165), 0345h (0.14 #9921, 0.10 #13506, 0.10 #12611), 03rjj (0.13 #9849, 0.09 #11643, 0.09 #13434) >> Best rule #8080 for best value: >> intensional similarity = 5 >> extensional distance = 332 >> proper extension: 04jpl; 02jx1; 0zc6f; 0dbdy; 05l5n; 0jcg8; 07w4j; 0jt5zcn; 06y9v; 0978r; ... >> query: (?x13527, 07ssc) <- contains(?x1310, ?x13527), nationality(?x12651, ?x1310), ?x12651 = 01b0k1, second_level_divisions(?x1310, ?x1156), state_province_region(?x963, ?x1310) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03lkp contains! 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 30.000 0.763 http://example.org/location/location/contains #749-0581vn8 PRED entity: 0581vn8 PRED relation: country PRED expected values: 0hzlz => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 49): 09c7w0 (0.88 #712, 0.87 #594, 0.87 #1661), 07ssc (0.36 #667, 0.31 #549, 0.27 #1200), 0chghy (0.26 #71, 0.12 #189, 0.06 #722), 06f32 (0.25 #44, 0.05 #162, 0.02 #518), 0345h (0.22 #440, 0.21 #203, 0.20 #321), 0f8l9c (0.20 #314, 0.19 #255, 0.19 #433), 0d05w3 (0.11 #160, 0.03 #575, 0.03 #990), 03_3d (0.08 #1963, 0.07 #1369, 0.06 #303), 03rjj (0.05 #125, 0.04 #243, 0.03 #421), 03h64 (0.05 #163, 0.02 #519, 0.02 #2000) >> Best rule #712 for best value: >> intensional similarity = 5 >> extensional distance = 124 >> proper extension: 0g5pv3; 03ct7jd; 02q8ms8; >> query: (?x9250, 09c7w0) <- country(?x9250, ?x279), nominated_for(?x6589, ?x9250), featured_film_locations(?x9250, ?x2204), genre(?x9250, ?x225), ?x225 = 02kdv5l >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #197 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 31 *> proper extension: 08c6k9; *> query: (?x9250, 0hzlz) <- country(?x9250, ?x279), film_crew_role(?x9250, ?x1966), film_crew_role(?x9250, ?x1171), produced_by(?x9250, ?x6589), ?x1966 = 015h31, ?x1171 = 09vw2b7 *> conf = 0.03 ranks of expected_values: 15 EVAL 0581vn8 country 0hzlz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 85.000 85.000 0.881 http://example.org/film/film/country #748-01xq8v PRED entity: 01xq8v PRED relation: nominated_for! PRED expected values: 05pcn59 018wdw => 85 concepts (74 used for prediction) PRED predicted values (max 10 best out of 217): 0gs96 (0.69 #5666, 0.67 #5665, 0.67 #8265), 02g3ft (0.69 #5666, 0.67 #5665, 0.67 #8265), 02r22gf (0.48 #734, 0.28 #1914, 0.26 #2150), 02qyntr (0.48 #886, 0.25 #3482, 0.25 #3718), 0p9sw (0.46 #1907, 0.46 #727, 0.44 #2143), 019f4v (0.46 #759, 0.39 #3355, 0.38 #3591), 0l8z1 (0.43 #757, 0.25 #3353, 0.24 #993), 0gq9h (0.43 #3364, 0.42 #1004, 0.41 #768), 0k611 (0.41 #779, 0.33 #3375, 0.30 #3611), 0gr0m (0.41 #765, 0.26 #1945, 0.25 #3361) >> Best rule #5666 for best value: >> intensional similarity = 3 >> extensional distance = 508 >> proper extension: 06mmr; >> query: (?x7741, ?x1429) <- award(?x7741, ?x1429), honored_for(?x11087, ?x7741), award(?x276, ?x1429) >> conf = 0.69 => this is the best rule for 2 predicted values *> Best rule #5903 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 520 *> proper extension: 0cwrr; 01h1bf; 02kk_c; 04glx0; 01b7h8; *> query: (?x7741, ?x640) <- nominated_for(?x929, ?x7741), award_nominee(?x929, ?x930), award(?x929, ?x640), honored_for(?x11087, ?x7741) *> conf = 0.23 ranks of expected_values: 37, 44 EVAL 01xq8v nominated_for! 018wdw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 85.000 74.000 0.686 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 01xq8v nominated_for! 05pcn59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 85.000 74.000 0.686 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #747-0gh65c5 PRED entity: 0gh65c5 PRED relation: film_release_region PRED expected values: 03_3d 0chghy 03gj2 01p1v 06mkj 03spz => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 218): 06mkj (0.89 #1163, 0.88 #2142, 0.88 #1862), 03gj2 (0.88 #297, 0.87 #1134, 0.83 #436), 03_3d (0.88 #283, 0.80 #422, 0.79 #1120), 0chghy (0.88 #1123, 0.87 #1263, 0.85 #425), 0345h (0.88 #1283, 0.87 #723, 0.87 #1143), 03spz (0.80 #1196, 0.80 #359, 0.77 #1336), 01p1v (0.69 #1159, 0.61 #1299, 0.54 #1578), 0ctw_b (0.64 #1275, 0.62 #1135, 0.61 #437), 047yc (0.63 #1138, 0.57 #1278, 0.52 #2396), 016wzw (0.63 #1170, 0.47 #1310, 0.45 #2428) >> Best rule #1163 for best value: >> intensional similarity = 6 >> extensional distance = 88 >> proper extension: 028_yv; 09gdm7q; 0gmcwlb; 0dtfn; 04n52p6; 08052t3; 0gvs1kt; 05c26ss; 047fjjr; 0gtxj2q; ... >> query: (?x3606, 06mkj) <- country(?x3606, ?x94), ?x94 = 09c7w0, film_release_region(?x3606, ?x2146), film_release_region(?x3606, ?x1603), country(?x150, ?x1603), ?x2146 = 03rk0 >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 6, 7 EVAL 0gh65c5 film_release_region 03spz CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh65c5 film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh65c5 film_release_region 01p1v CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 74.000 74.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh65c5 film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh65c5 film_release_region 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0gh65c5 film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.889 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #746-03dq9 PRED entity: 03dq9 PRED relation: award_nominee! PRED expected values: 04yt7 => 83 concepts (39 used for prediction) PRED predicted values (max 10 best out of 777): 07h5d (0.81 #23323, 0.81 #74636, 0.81 #69971), 04yt7 (0.81 #23323, 0.81 #74636, 0.81 #69971), 03dq9 (0.50 #2140, 0.16 #90962, 0.02 #16132), 01tzm9 (0.17 #1658, 0.03 #34987), 02v0ff (0.07 #3247, 0.02 #24238, 0.01 #19573), 020ffd (0.07 #3763, 0.02 #20089, 0.02 #24754), 047q2wc (0.07 #3246, 0.02 #24237, 0.01 #19572), 01k70_ (0.07 #3377, 0.02 #24368, 0.01 #19703), 05yjhm (0.07 #4286, 0.02 #25277, 0.01 #20612), 05sj55 (0.07 #4066, 0.02 #25057, 0.01 #20392) >> Best rule #23323 for best value: >> intensional similarity = 3 >> extensional distance = 208 >> proper extension: 05hjmd; 0bm9xk; >> query: (?x10394, ?x4297) <- award_nominee(?x4987, ?x10394), award_nominee(?x10394, ?x4297), place_of_death(?x10394, ?x14570) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 03dq9 award_nominee! 04yt7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 39.000 0.810 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #745-020fgy PRED entity: 020fgy PRED relation: music! PRED expected values: 01jr4j 04v89z => 118 concepts (71 used for prediction) PRED predicted values (max 10 best out of 868): 09p3_s (0.76 #4034, 0.08 #19156, 0.08 #50415), 0cqnss (0.55 #9075, 0.08 #44363, 0.06 #44362), 0cq7tx (0.55 #9075, 0.06 #44362, 0.06 #55458), 09d38d (0.07 #5012, 0.03 #12069, 0.03 #13077), 01_1pv (0.06 #2233, 0.06 #3241, 0.02 #4250), 07bzz7 (0.06 #2544, 0.05 #1536, 0.05 #4561), 0gvvm6l (0.05 #5844, 0.02 #27014, 0.01 #29031), 0gnjh (0.05 #1685, 0.05 #4710, 0.02 #11767), 08rr3p (0.05 #1280, 0.03 #2288, 0.03 #3296), 0ckrnn (0.05 #1965, 0.03 #2973, 0.03 #3981) >> Best rule #4034 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 01vrkdt; 02qmncd; >> query: (?x9170, ?x278) <- award(?x9170, ?x1854), nominated_for(?x9170, ?x278), ?x1854 = 025m8y >> conf = 0.76 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 020fgy music! 04v89z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 71.000 0.761 http://example.org/film/film/music EVAL 020fgy music! 01jr4j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 118.000 71.000 0.761 http://example.org/film/film/music #744-06tw8 PRED entity: 06tw8 PRED relation: organization PRED expected values: 07t65 => 140 concepts (121 used for prediction) PRED predicted values (max 10 best out of 55): 07t65 (0.94 #583, 0.92 #101, 0.92 #162), 0j7v_ (0.58 #1506, 0.57 #1485, 0.56 #1444), 04k4l (0.57 #1485, 0.56 #1444, 0.56 #1648), 018cqq (0.55 #1283, 0.36 #110, 0.32 #2079), 085h1 (0.55 #1283, 0.32 #2079, 0.30 #1854), 01rz1 (0.52 #263, 0.48 #102, 0.45 #403), 0_2v (0.46 #325, 0.45 #225, 0.45 #185), 02jxk (0.32 #2079, 0.30 #1854, 0.30 #1956), 059dn (0.32 #2079, 0.30 #1854, 0.30 #1956), 034h1h (0.22 #1944, 0.21 #1842, 0.18 #2067) >> Best rule #583 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 05r4w; 0d0vqn; 047lj; 05qhw; 06npd; 03gj2; 047yc; 05cgv; 0h7x; 015qh; ... >> query: (?x5457, 07t65) <- adjoins(?x5457, ?x608), jurisdiction_of_office(?x265, ?x5457), organization(?x5457, ?x127), countries_within(?x2467, ?x5457) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06tw8 organization 07t65 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 121.000 0.940 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #743-0cv9fc PRED entity: 0cv9fc PRED relation: award_nominee! PRED expected values: 01f7j9 => 119 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1136): 01f7j9 (0.81 #95631, 0.81 #100296, 0.81 #104961), 03ktjq (0.81 #95631, 0.81 #100296, 0.81 #104961), 05th8t (0.36 #65306, 0.32 #65307, 0.31 #62973), 02zfdp (0.36 #65306, 0.32 #65307, 0.31 #62973), 0785v8 (0.36 #65306, 0.32 #65307, 0.31 #62973), 03mcwq3 (0.36 #65306, 0.32 #65307, 0.31 #62973), 02p7_k (0.36 #65306, 0.32 #65307, 0.31 #62973), 02rf1y (0.36 #65306, 0.32 #65307, 0.31 #62973), 0f6_dy (0.36 #65306, 0.32 #65307, 0.31 #62973), 05ml_s (0.36 #65306, 0.32 #65307, 0.31 #62973) >> Best rule #95631 for best value: >> intensional similarity = 3 >> extensional distance = 875 >> proper extension: 0kk9v; >> query: (?x11580, ?x574) <- award_winner(?x2126, ?x11580), nominated_for(?x11580, ?x3303), award_nominee(?x11580, ?x574) >> conf = 0.81 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0cv9fc award_nominee! 01f7j9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 119.000 47.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #742-083wr9 PRED entity: 083wr9 PRED relation: actor! PRED expected values: 0jwl2 => 97 concepts (60 used for prediction) PRED predicted values (max 10 best out of 134): 015w8_ (0.33 #46, 0.29 #310, 0.15 #1631), 043qqt5 (0.33 #225, 0.29 #489, 0.12 #1281), 05f7w84 (0.33 #106, 0.25 #1162, 0.23 #1691), 01hvv0 (0.33 #151, 0.25 #1207, 0.14 #415), 01h72l (0.29 #302, 0.17 #38, 0.12 #1094), 0kfpm (0.29 #805, 0.11 #1334, 0.08 #1598), 0ctzf1 (0.25 #1191, 0.17 #135, 0.14 #399), 09g_31 (0.18 #2812, 0.17 #165, 0.15 #2015), 05nlzq (0.17 #184, 0.14 #448, 0.12 #1240), 025x1t (0.17 #222, 0.14 #486, 0.12 #1278) >> Best rule #46 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 04hxyv; >> query: (?x13587, 015w8_) <- language(?x13587, ?x254), profession(?x13587, ?x1383), film(?x13587, ?x5277), film(?x13587, ?x3455), ?x5277 = 047csmy, film_crew_role(?x3455, ?x281) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #2188 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 25 *> proper extension: 01bh6y; *> query: (?x13587, 0jwl2) <- language(?x13587, ?x254), actor(?x5936, ?x13587), film(?x13587, ?x3455), ?x254 = 02h40lc, films(?x5954, ?x3455) *> conf = 0.15 ranks of expected_values: 15 EVAL 083wr9 actor! 0jwl2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 97.000 60.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #741-03x1s8 PRED entity: 03x1s8 PRED relation: school_type PRED expected values: 05jxkf => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 19): 05jxkf (0.57 #4, 0.52 #76, 0.51 #100), 01rs41 (0.33 #53, 0.30 #509, 0.30 #653), 05pcjw (0.29 #49, 0.27 #697, 0.27 #649), 01_9fk (0.19 #2, 0.14 #26, 0.13 #530), 07tf8 (0.17 #9, 0.12 #177, 0.12 #441), 01_srz (0.07 #267, 0.07 #651, 0.07 #603), 02p0qmm (0.03 #442, 0.03 #1018, 0.02 #1138), 04qbv (0.03 #520, 0.03 #40, 0.03 #304), 04399 (0.02 #566, 0.02 #62, 0.02 #134), 01y64 (0.02 #156, 0.02 #1236, 0.02 #276) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0mbwf; >> query: (?x12126, 05jxkf) <- colors(?x12126, ?x332), ?x332 = 01l849, currency(?x12126, ?x170), citytown(?x12126, ?x12384) >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03x1s8 school_type 05jxkf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.574 http://example.org/education/educational_institution/school_type #740-03f19q4 PRED entity: 03f19q4 PRED relation: award PRED expected values: 023vrq => 99 concepts (89 used for prediction) PRED predicted values (max 10 best out of 374): 02f76h (0.57 #178, 0.44 #583, 0.29 #1393), 023vrq (0.57 #327, 0.33 #732, 0.29 #1542), 03t5n3 (0.44 #655, 0.43 #250, 0.18 #18634), 03t5kl (0.43 #228, 0.33 #633, 0.24 #2253), 02f6xy (0.43 #201, 0.33 #606, 0.22 #2226), 02f716 (0.43 #177, 0.33 #582, 0.18 #18634), 02v1m7 (0.43 #113, 0.33 #518, 0.18 #18634), 01cky2 (0.33 #600, 0.29 #195, 0.22 #2220), 01bgqh (0.32 #2068, 0.30 #7333, 0.29 #2473), 01c9dd (0.29 #314, 0.24 #1934, 0.24 #1529) >> Best rule #178 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 02l840; 01wgxtl; 01vw20h; 0677ng; 03j3pg9; >> query: (?x5203, 02f76h) <- currency(?x5203, ?x170), award_nominee(?x6268, ?x5203), award_nominee(?x3737, ?x5203), ?x3737 = 01q32bd, ?x6268 = 026yqrr >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #327 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 02l840; 01wgxtl; 01vw20h; 0677ng; 03j3pg9; *> query: (?x5203, 023vrq) <- currency(?x5203, ?x170), award_nominee(?x6268, ?x5203), award_nominee(?x3737, ?x5203), ?x3737 = 01q32bd, ?x6268 = 026yqrr *> conf = 0.57 ranks of expected_values: 2 EVAL 03f19q4 award 023vrq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 99.000 89.000 0.571 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #739-0z4s PRED entity: 0z4s PRED relation: award_winner! PRED expected values: 073h9x => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 123): 0hr3c8y (0.10 #11062, 0.06 #1550, 0.04 #2810), 092c5f (0.10 #11062, 0.05 #154, 0.04 #2814), 092t4b (0.10 #11062, 0.05 #1452, 0.05 #1592), 092_25 (0.10 #11062, 0.04 #1612, 0.03 #2312), 09q_6t (0.10 #11062, 0.04 #428, 0.03 #3228), 07z31v (0.10 #11062, 0.02 #591, 0.02 #451), 0g55tzk (0.10 #136, 0.05 #276, 0.05 #1676), 0g5b0q5 (0.10 #20, 0.05 #160, 0.03 #3240), 02hn5v (0.10 #42, 0.05 #182, 0.02 #462), 0fqpc7d (0.10 #36, 0.05 #176, 0.02 #6056) >> Best rule #11062 for best value: >> intensional similarity = 2 >> extensional distance = 2022 >> proper extension: 054lpb6; 03xsby; 0181hw; >> query: (?x450, ?x873) <- award_nominee(?x450, ?x4154), award_winner(?x873, ?x4154) >> conf = 0.10 => this is the best rule for 6 predicted values *> Best rule #3270 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 837 *> proper extension: 01l79yc; *> query: (?x450, 073h9x) <- nationality(?x450, ?x512), award_winner(?x2734, ?x450), award_winner(?x7573, ?x450) *> conf = 0.01 ranks of expected_values: 104 EVAL 0z4s award_winner! 073h9x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 107.000 107.000 0.103 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #738-01y64_ PRED entity: 01y64_ PRED relation: award_nominee PRED expected values: 04y9dk => 83 concepts (41 used for prediction) PRED predicted values (max 10 best out of 858): 0dgskx (0.41 #3850, 0.03 #8524, 0.03 #13200), 03f1zdw (0.34 #2588, 0.03 #46998, 0.02 #58685), 01tspc6 (0.34 #2543, 0.01 #70121, 0.01 #46953), 0170pk (0.31 #2710, 0.02 #47120, 0.01 #58807), 0171cm (0.31 #2892, 0.02 #47302, 0.01 #70121), 0m31m (0.31 #2920, 0.01 #70121, 0.01 #47330), 0bq2g (0.28 #3135, 0.02 #47545, 0.01 #70121), 03t0k1 (0.28 #2919, 0.01 #70121, 0.01 #47329), 016xk5 (0.28 #3943, 0.01 #70121, 0.01 #48353), 03y_46 (0.28 #3685, 0.01 #70121, 0.01 #48095) >> Best rule #3850 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 07lt7b; 0151w_; 03f1zdw; 0170pk; 02wgln; 04y9dk; 0jfx1; 03t0k1; 01kj0p; 0bq2g; ... >> query: (?x4440, 0dgskx) <- award_nominee(?x1958, ?x4440), award_nominee(?x4440, ?x2805), ?x2805 = 0lpjn >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #2760 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 07lt7b; 0151w_; 03f1zdw; 0170pk; 02wgln; 04y9dk; 0jfx1; 03t0k1; 01kj0p; 0bq2g; ... *> query: (?x4440, 04y9dk) <- award_nominee(?x1958, ?x4440), award_nominee(?x4440, ?x2805), ?x2805 = 0lpjn *> conf = 0.21 ranks of expected_values: 13 EVAL 01y64_ award_nominee 04y9dk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 83.000 41.000 0.414 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #737-04gnbv1 PRED entity: 04gnbv1 PRED relation: award_winner PRED expected values: 02f9wb => 117 concepts (54 used for prediction) PRED predicted values (max 10 best out of 608): 02d6cy (0.82 #54906, 0.82 #74295, 0.82 #83992), 09r9dp (0.50 #8075, 0.49 #71065, 0.49 #69450), 03v1jf (0.32 #35529, 0.31 #77528, 0.09 #87227), 0bbvr84 (0.32 #35529, 0.31 #77528, 0.09 #87227), 027n4zv (0.32 #35529, 0.31 #77528, 0.09 #87227), 07s95_l (0.32 #35529, 0.31 #77528, 0.09 #87227), 048q6x (0.32 #35529, 0.31 #77528, 0.09 #87227), 04kr63w (0.32 #35529, 0.31 #77528, 0.09 #87227), 044lyq (0.32 #35529, 0.31 #77528, 0.02 #35097), 06_vpyq (0.32 #35529, 0.31 #77528, 0.02 #34731) >> Best rule #54906 for best value: >> intensional similarity = 3 >> extensional distance = 883 >> proper extension: 014g91; >> query: (?x4618, ?x4948) <- gender(?x4618, ?x514), award_winner(?x4948, ?x4618), award_winner(?x1265, ?x4618) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #77528 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1177 *> proper extension: 024rbz; *> query: (?x4618, ?x3051) <- award_winner(?x3310, ?x4618), honored_for(?x944, ?x3310), award_winner(?x3310, ?x3051) *> conf = 0.31 ranks of expected_values: 16 EVAL 04gnbv1 award_winner 02f9wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 117.000 54.000 0.817 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #736-01dyvs PRED entity: 01dyvs PRED relation: featured_film_locations PRED expected values: 06y57 => 54 concepts (37 used for prediction) PRED predicted values (max 10 best out of 50): 02_286 (0.30 #2421, 0.21 #260, 0.21 #20), 04jpl (0.18 #249, 0.10 #2410, 0.10 #970), 06y57 (0.15 #582, 0.06 #343, 0.05 #1545), 0rh6k (0.12 #241, 0.06 #721, 0.06 #2402), 030qb3t (0.11 #2440, 0.07 #4120, 0.06 #3880), 052p7 (0.09 #58, 0.04 #2459, 0.04 #778), 0345h (0.09 #33, 0.03 #2434, 0.02 #1234), 080h2 (0.08 #985, 0.07 #2186, 0.06 #2425), 0h7h6 (0.06 #2444, 0.03 #3644, 0.02 #4124), 0chgzm (0.05 #627, 0.01 #2549) >> Best rule #2421 for best value: >> intensional similarity = 4 >> extensional distance = 156 >> proper extension: 03ckwzc; 02847m9; 080lkt7; 02d003; 04z_3pm; 07ghq; 0353tm; 04180vy; >> query: (?x1808, 02_286) <- country(?x1808, ?x94), film(?x2818, ?x1808), category(?x1808, ?x134), featured_film_locations(?x1808, ?x5232) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #582 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 39 *> proper extension: 05fcbk7; 027x7z5; *> query: (?x1808, 06y57) <- country(?x1808, ?x390), film(?x2818, ?x1808), nominated_for(?x350, ?x1808), ?x390 = 0chghy *> conf = 0.15 ranks of expected_values: 3 EVAL 01dyvs featured_film_locations 06y57 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 54.000 37.000 0.304 http://example.org/film/film/featured_film_locations #735-02xv8m PRED entity: 02xv8m PRED relation: award PRED expected values: 0789_m => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 217): 0ck27z (0.23 #2099, 0.15 #10541, 0.15 #11345), 02z0dfh (0.15 #22111, 0.13 #34979, 0.13 #34576), 0fq9zdn (0.15 #22111, 0.13 #34979, 0.13 #34576), 0bsjcw (0.15 #22111, 0.13 #34979, 0.13 #34576), 0gqyl (0.15 #22111, 0.13 #34979, 0.08 #504), 09td7p (0.15 #22111, 0.13 #34979, 0.07 #28142), 099t8j (0.15 #22111, 0.13 #34979, 0.07 #28142), 02x4x18 (0.15 #22111, 0.13 #34979, 0.05 #532), 0bdwft (0.15 #22111, 0.13 #34979, 0.05 #16145), 0cqhk0 (0.14 #2046, 0.10 #36, 0.10 #1242) >> Best rule #2099 for best value: >> intensional similarity = 3 >> extensional distance = 547 >> proper extension: 0f3zsq; 02pbp9; >> query: (?x3876, 0ck27z) <- place_of_birth(?x3876, ?x3877), nominated_for(?x3876, ?x337), actor(?x337, ?x336) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #8059 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1004 *> proper extension: 033hqf; 02r34n; 01j4ls; 04fhxp; 01x1cn2; 012_53; 01ry0f; 058nh2; 0143wl; 012j5h; ... *> query: (?x3876, 0789_m) <- nationality(?x3876, ?x94), student(?x1809, ?x3876), film(?x3876, ?x240) *> conf = 0.06 ranks of expected_values: 68 EVAL 02xv8m award 0789_m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 106.000 106.000 0.233 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #734-047c9l PRED entity: 047c9l PRED relation: category PRED expected values: 08mbj5d => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #6, 0.78 #14, 0.78 #36) >> Best rule #6 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 0136pk; 033wx9; 01vw20_; 04gycf; 039bpc; 01v40wd; 01d1st; 09h4b5; 01vz0g4; 012xdf; ... >> query: (?x5105, 08mbj5d) <- award_nominee(?x336, ?x5105), artists(?x13359, ?x5105), participant(?x3751, ?x5105) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 047c9l category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.823 http://example.org/common/topic/webpage./common/webpage/category #733-081lh PRED entity: 081lh PRED relation: profession PRED expected values: 09jwl => 157 concepts (91 used for prediction) PRED predicted values (max 10 best out of 85): 01d_h8 (0.85 #1722, 0.82 #721, 0.81 #2294), 09jwl (0.68 #10030, 0.56 #10173, 0.56 #2733), 03gjzk (0.64 #727, 0.49 #1728, 0.45 #1871), 0nbcg (0.48 #2745, 0.45 #10042, 0.45 #10185), 016z4k (0.44 #2722, 0.37 #10019, 0.35 #10162), 0dz3r (0.42 #2720, 0.36 #10017, 0.36 #12166), 01c72t (0.33 #163, 0.29 #10035, 0.28 #10178), 0np9r (0.27 #6312, 0.25 #8441, 0.24 #3164), 039v1 (0.26 #10047, 0.21 #10190, 0.19 #12196), 0n1h (0.26 #2728, 0.25 #8441, 0.15 #10168) >> Best rule #1722 for best value: >> intensional similarity = 3 >> extensional distance = 87 >> proper extension: 01_k1z; >> query: (?x986, 01d_h8) <- profession(?x986, ?x524), currency(?x986, ?x170), ?x524 = 02jknp >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #10030 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 546 *> proper extension: 07_3qd; 0dhqyw; *> query: (?x986, 09jwl) <- nationality(?x986, ?x94), instrumentalists(?x2460, ?x986) *> conf = 0.68 ranks of expected_values: 2 EVAL 081lh profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 157.000 91.000 0.854 http://example.org/people/person/profession #732-08pth9 PRED entity: 08pth9 PRED relation: film PRED expected values: 04ynx7 => 124 concepts (81 used for prediction) PRED predicted values (max 10 best out of 490): 02rzdcp (0.58 #32185, 0.57 #62584, 0.48 #103738), 013q07 (0.14 #357, 0.03 #5721, 0.02 #7509), 0f40w (0.14 #363, 0.01 #3939), 03nx8mj (0.14 #698, 0.01 #11426), 0gzlb9 (0.14 #1460), 011ykb (0.14 #1141), 09cr8 (0.07 #285, 0.03 #3861, 0.03 #39623), 0prrm (0.07 #861, 0.02 #6225, 0.02 #8013), 02q56mk (0.07 #418, 0.02 #3994), 0gmd3k7 (0.07 #1109, 0.02 #6473, 0.02 #8261) >> Best rule #32185 for best value: >> intensional similarity = 3 >> extensional distance = 592 >> proper extension: 0c01c; 06_bq1; >> query: (?x4507, ?x3310) <- award_winner(?x3310, ?x4507), award_winner(?x1342, ?x4507), film(?x4507, ?x3859) >> conf = 0.58 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08pth9 film 04ynx7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 81.000 0.579 http://example.org/film/actor/film./film/performance/film #731-04dn09n PRED entity: 04dn09n PRED relation: award! PRED expected values: 012t1 05drq5 02vyw 05mcjs 043hg 02r6c_ 06s1qy => 55 concepts (26 used for prediction) PRED predicted values (max 10 best out of 2751): 02kxbx3 (0.80 #52850, 0.69 #85894, 0.68 #66065), 081lh (0.80 #52850, 0.69 #85894, 0.68 #66065), 01_f_5 (0.80 #52850, 0.69 #85894, 0.68 #66065), 02kxbwx (0.80 #52850, 0.69 #85894, 0.68 #66065), 05drq5 (0.80 #52850, 0.69 #85894, 0.68 #66065), 0p50v (0.80 #52850, 0.69 #85894, 0.68 #66065), 0hsmh (0.67 #16083, 0.40 #9478, 0.38 #29294), 076_74 (0.64 #37379, 0.50 #1048, 0.38 #27470), 06mn7 (0.62 #27628, 0.50 #11115, 0.50 #1206), 05cgy8 (0.60 #8489, 0.50 #28305, 0.50 #11792) >> Best rule #52850 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 05ztjjw; >> query: (?x746, ?x361) <- nominated_for(?x746, ?x10241), nominated_for(?x746, ?x1597), ?x1597 = 0dr_4, award_winner(?x746, ?x361), nominated_for(?x400, ?x10241) >> conf = 0.80 => this is the best rule for 6 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 5, 11, 27, 55, 135, 138, 544 EVAL 04dn09n award! 06s1qy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 55.000 26.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04dn09n award! 02r6c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 55.000 26.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04dn09n award! 043hg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 55.000 26.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04dn09n award! 05mcjs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 55.000 26.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04dn09n award! 02vyw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 55.000 26.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04dn09n award! 05drq5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 55.000 26.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 04dn09n award! 012t1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 55.000 26.000 0.799 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #730-0dl9_4 PRED entity: 0dl9_4 PRED relation: country PRED expected values: 07ssc => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 85): 07ssc (0.25 #1259, 0.24 #488, 0.23 #1199), 0345h (0.25 #26, 0.21 #203, 0.19 #676), 0f8l9c (0.25 #77, 0.14 #1262, 0.13 #372), 0h7x (0.25 #30, 0.01 #2848), 03gj2 (0.25 #21, 0.01 #2848), 0chghy (0.12 #70, 0.10 #661, 0.10 #306), 0d05w3 (0.12 #101, 0.08 #160, 0.05 #337), 03_3d (0.12 #66, 0.05 #480, 0.05 #243), 06mkj (0.12 #98, 0.03 #393, 0.03 #1103), 0ctw_b (0.12 #81, 0.02 #1145, 0.02 #3108) >> Best rule #1259 for best value: >> intensional similarity = 3 >> extensional distance = 186 >> proper extension: 0hz6mv2; >> query: (?x5185, 07ssc) <- genre(?x5185, ?x53), language(?x5185, ?x403), film_festivals(?x5185, ?x7988) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dl9_4 country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.250 http://example.org/film/film/country #729-0411q PRED entity: 0411q PRED relation: people! PRED expected values: 0x67 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 46): 0x67 (0.29 #549, 0.26 #2551, 0.25 #626), 041rx (0.25 #4, 0.14 #697, 0.14 #3624), 0bbz66j (0.25 #48, 0.04 #202, 0.03 #510), 033tf_ (0.21 #469, 0.16 #777, 0.16 #700), 0xnvg (0.11 #167, 0.09 #244, 0.09 #1707), 07bch9 (0.11 #485, 0.08 #716, 0.05 #2641), 09vc4s (0.07 #702, 0.06 #86, 0.05 #471), 02w7gg (0.06 #1927, 0.06 #79, 0.05 #772), 06v41q (0.06 #106, 0.05 #260, 0.04 #568), 0dbxy (0.06 #124, 0.03 #817, 0.02 #1664) >> Best rule #549 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 02wb6yq; 019f9z; 0fq117k; 01wqmm8; >> query: (?x219, 0x67) <- participant(?x219, ?x8143), artists(?x7440, ?x219), award_winner(?x2704, ?x219) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0411q people! 0x67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 123.000 123.000 0.288 http://example.org/people/ethnicity/people #728-02_sr1 PRED entity: 02_sr1 PRED relation: produced_by PRED expected values: 03h304l => 116 concepts (78 used for prediction) PRED predicted values (max 10 best out of 121): 0162c8 (0.57 #2323, 0.37 #9288, 0.36 #1162), 04zwjd (0.18 #5806, 0.17 #14317, 0.15 #3485), 012d40 (0.18 #5806, 0.17 #14317, 0.15 #3485), 01900g (0.18 #5806, 0.17 #14317, 0.15 #3485), 0jlv5 (0.17 #14317, 0.15 #3485, 0.10 #18963), 0c3p7 (0.17 #222), 0d02km (0.17 #211), 02xnjd (0.08 #660, 0.04 #1822, 0.04 #1435), 0fvf9q (0.06 #1942, 0.04 #5425, 0.04 #7747), 05bpg3 (0.06 #387, 0.02 #20898, 0.02 #23613) >> Best rule #2323 for best value: >> intensional similarity = 4 >> extensional distance = 284 >> proper extension: 0dckvs; >> query: (?x4038, ?x1416) <- film_crew_role(?x4038, ?x137), film(?x1416, ?x4038), nominated_for(?x401, ?x4038), produced_by(?x4038, ?x10715) >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #573 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 71 *> proper extension: 04cf_l; *> query: (?x4038, 03h304l) <- nominated_for(?x147, ?x4038), produced_by(?x4038, ?x10715), genre(?x4038, ?x225), prequel(?x7514, ?x4038) *> conf = 0.03 ranks of expected_values: 32 EVAL 02_sr1 produced_by 03h304l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 116.000 78.000 0.567 http://example.org/film/film/produced_by #727-0gkgp PRED entity: 0gkgp PRED relation: place_of_birth! PRED expected values: 02zrv7 => 200 concepts (125 used for prediction) PRED predicted values (max 10 best out of 1842): 01xdf5 (0.33 #125380, 0.33 #167166, 0.31 #203726), 01nd6v (0.14 #2607, 0.04 #10446, 0.04 #7832), 04zn7g (0.14 #2566, 0.04 #10405, 0.04 #7791), 01fxfk (0.14 #2509, 0.04 #10348, 0.04 #7734), 08141d (0.14 #2502, 0.04 #10341, 0.04 #7727), 02bc74 (0.14 #2494, 0.04 #10333, 0.04 #7719), 03j9ml (0.14 #2439, 0.04 #10278, 0.04 #7664), 044zvm (0.14 #2396, 0.04 #10235, 0.04 #7621), 02qzjj (0.14 #2372, 0.04 #10211, 0.04 #7597), 09g0h (0.14 #2351, 0.04 #10190, 0.04 #7576) >> Best rule #125380 for best value: >> intensional similarity = 4 >> extensional distance = 122 >> proper extension: 0cymp; 0jrxx; 0k_s5; >> query: (?x9394, ?x236) <- contains(?x9394, ?x7777), contains(?x4776, ?x9394), location(?x236, ?x9394), district_represented(?x176, ?x4776) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gkgp place_of_birth! 02zrv7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 200.000 125.000 0.335 http://example.org/people/person/place_of_birth #726-0g0syc PRED entity: 0g0syc PRED relation: district_represented! PRED expected values: 043djx 07p__7 02bqmq => 49 concepts (49 used for prediction) PRED predicted values (max 10 best out of 35): 07p__7 (0.93 #72, 0.91 #278, 0.91 #75), 02bqmq (0.93 #72, 0.91 #278, 0.91 #75), 060ny2 (0.93 #72, 0.91 #278, 0.91 #75), 04gp1d (0.93 #72, 0.91 #278, 0.91 #75), 05l2z4 (0.93 #72, 0.91 #278, 0.91 #280), 04h1rz (0.93 #72, 0.91 #75, 0.91 #74), 06r713 (0.93 #72, 0.91 #75, 0.91 #74), 043djx (0.93 #72, 0.91 #75, 0.91 #74), 01gtdd (0.93 #72, 0.91 #75, 0.91 #74), 01gtcc (0.93 #72, 0.91 #75, 0.91 #74) >> Best rule #72 for best value: >> intensional similarity = 71 >> extensional distance = 1 >> proper extension: 059rby; >> query: (?x4754, ?x2019) <- district_represented(?x11142, ?x4754), district_represented(?x6933, ?x4754), district_represented(?x5339, ?x4754), district_represented(?x5256, ?x4754), district_represented(?x4821, ?x4754), district_represented(?x4787, ?x4754), district_represented(?x4730, ?x4754), district_represented(?x3766, ?x4754), district_represented(?x3540, ?x4754), district_represented(?x2976, ?x4754), district_represented(?x2861, ?x4754), district_represented(?x1830, ?x4754), district_represented(?x1829, ?x4754), district_represented(?x1137, ?x4754), district_represented(?x1027, ?x4754), district_represented(?x952, ?x4754), district_represented(?x653, ?x4754), district_represented(?x606, ?x4754), district_represented(?x355, ?x4754), district_represented(?x176, ?x4754), ?x2976 = 03rtmz, ?x1829 = 02bp37, ?x952 = 06f0dc, ?x3766 = 02gkzs, ?x11142 = 01grq1, ?x4821 = 02bqm0, ?x1830 = 03z5xd, district_represented(?x176, ?x7058), district_represented(?x176, ?x4758), district_represented(?x176, ?x3908), district_represented(?x176, ?x3818), district_represented(?x176, ?x3670), district_represented(?x176, ?x3634), district_represented(?x176, ?x3038), district_represented(?x176, ?x2020), district_represented(?x176, ?x1767), district_represented(?x176, ?x1426), district_represented(?x176, ?x1227), district_represented(?x176, ?x448), district_represented(?x176, ?x177), ?x7058 = 050ks, ?x1227 = 01n7q, ?x3540 = 024tcq, ?x3818 = 03v0t, ?x4787 = 01grpq, ?x1767 = 04rrd, ?x2861 = 03tcbx, legislative_sessions(?x13086, ?x176), ?x5256 = 01grqd, legislative_sessions(?x10291, ?x176), legislative_sessions(?x5252, ?x176), ?x1027 = 02bn_p, ?x3634 = 07b_l, ?x606 = 03ww_x, ?x3670 = 05tbn, ?x1137 = 02bqn1, ?x1426 = 07z1m, ?x5339 = 02glc4, legislative_sessions(?x5252, ?x2019), ?x4758 = 0vbk, ?x177 = 05kkh, ?x2020 = 05k7sb, ?x448 = 03v1s, ?x3908 = 04ly1, ?x653 = 070m6c, district_represented(?x5252, ?x335), legislative_sessions(?x2860, ?x10291), ?x355 = 0495ys, ?x6933 = 024tkd, ?x3038 = 0d0x8, ?x4730 = 02cg7g >> conf = 0.93 => this is the best rule for 19 predicted values ranks of expected_values: 1, 2, 8 EVAL 0g0syc district_represented! 02bqmq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.929 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 0g0syc district_represented! 07p__7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 49.000 49.000 0.929 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 0g0syc district_represented! 043djx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 49.000 49.000 0.929 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #725-0p_47 PRED entity: 0p_47 PRED relation: influenced_by! PRED expected values: 07ymr5 03q43g => 78 concepts (67 used for prediction) PRED predicted values (max 10 best out of 319): 05rx__ (0.18 #2800, 0.17 #799, 0.05 #5307), 01n5309 (0.17 #517, 0.09 #2518, 0.06 #3019), 086qd (0.17 #567, 0.05 #2568, 0.04 #3571), 0282x (0.17 #716, 0.03 #6726, 0.03 #3720), 0bqs56 (0.14 #2743, 0.10 #5250, 0.08 #3244), 01xwv7 (0.14 #2913, 0.08 #5420, 0.07 #3916), 01wp_jm (0.09 #2897, 0.07 #14530, 0.04 #3398), 02z3zp (0.09 #2814, 0.07 #14530, 0.04 #3315), 0ph2w (0.07 #14530, 0.05 #3654, 0.05 #18539), 029_3 (0.07 #14530, 0.05 #18539, 0.05 #2650) >> Best rule #2800 for best value: >> intensional similarity = 2 >> extensional distance = 20 >> proper extension: 013qvn; 022q4j; >> query: (?x3917, 05rx__) <- influenced_by(?x236, ?x3917), participant(?x3917, ?x1817) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #3563 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 72 *> proper extension: 07s3vqk; 01xdf5; 05ty4m; 04wqr; 04rcr; 081lh; 01vrncs; 07c0j; 014zfs; 0pz91; ... *> query: (?x3917, 07ymr5) <- influenced_by(?x236, ?x3917), award_winner(?x3917, ?x2124) *> conf = 0.03 ranks of expected_values: 143, 302 EVAL 0p_47 influenced_by! 03q43g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 78.000 67.000 0.182 http://example.org/influence/influence_node/influenced_by EVAL 0p_47 influenced_by! 07ymr5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 78.000 67.000 0.182 http://example.org/influence/influence_node/influenced_by #724-0bxxzb PRED entity: 0bxxzb PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 31): 0ch6mp2 (0.81 #720, 0.73 #827, 0.71 #1183), 01vx2h (0.50 #473, 0.35 #615, 0.35 #687), 01pvkk (0.36 #81, 0.31 #117, 0.28 #1260), 0215hd (0.25 #18, 0.14 #732, 0.13 #839), 089g0h (0.25 #19, 0.12 #125, 0.11 #733), 01xy5l_ (0.25 #13, 0.11 #727, 0.10 #119), 02_n3z (0.25 #1, 0.09 #2281, 0.09 #715), 0ckd1 (0.25 #3, 0.09 #2281, 0.03 #466), 02rh1dz (0.20 #472, 0.16 #436, 0.14 #614), 02ynfr (0.19 #729, 0.18 #121, 0.16 #836) >> Best rule #720 for best value: >> intensional similarity = 4 >> extensional distance = 668 >> proper extension: 01br2w; 0cnztc4; 0gj9qxr; 091z_p; 05dy7p; 02rb607; 0crh5_f; 02phtzk; 02hfk5; 0h95zbp; ... >> query: (?x6628, 0ch6mp2) <- genre(?x6628, ?x225), country(?x6628, ?x94), film_crew_role(?x6628, ?x1171), ?x1171 = 09vw2b7 >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bxxzb film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 67.000 67.000 0.809 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #723-04yf_ PRED entity: 04yf_ PRED relation: partially_contains! PRED expected values: 03s0w => 104 concepts (56 used for prediction) PRED predicted values (max 10 best out of 954): 081mh (0.60 #395, 0.39 #1072, 0.37 #1436), 05tbn (0.60 #404, 0.33 #137, 0.33 #50), 07z1m (0.40 #380, 0.39 #1072, 0.37 #1436), 05fkf (0.40 #367, 0.33 #13, 0.30 #2245), 06yxd (0.40 #408, 0.33 #54, 0.20 #1310), 03v1s (0.39 #1072, 0.37 #1436, 0.37 #1346), 03s0w (0.39 #1072, 0.37 #1436, 0.37 #1346), 05mph (0.39 #1072, 0.37 #1436, 0.37 #1346), 0488g (0.39 #1072, 0.37 #1436, 0.37 #1346), 05fhy (0.39 #1072, 0.37 #1436, 0.37 #1346) >> Best rule #395 for best value: >> intensional similarity = 6 >> extensional distance = 3 >> proper extension: 02cgp8; >> query: (?x4540, 081mh) <- partially_contains(?x4061, ?x4540), adjoins(?x177, ?x4061), country(?x4061, ?x94), location(?x117, ?x4061), ?x177 = 05kkh, district_represented(?x176, ?x4061) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #1072 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 8 *> proper extension: 0k3nk; 02v3m7; 0db94; 0f2pf9; *> query: (?x4540, ?x177) <- partially_contains(?x4061, ?x4540), adjoins(?x177, ?x4061), country(?x4061, ?x94), location(?x117, ?x4061), contains(?x177, ?x388), religion(?x177, ?x109) *> conf = 0.39 ranks of expected_values: 7 EVAL 04yf_ partially_contains! 03s0w CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 104.000 56.000 0.600 http://example.org/location/location/partially_contains #722-05v1sb PRED entity: 05v1sb PRED relation: award_winner! PRED expected values: 0fy6bh 0c53zb 0c6vcj => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 132): 0fk0xk (0.33 #219, 0.20 #78, 0.05 #783), 0c53vt (0.25 #394, 0.12 #535, 0.05 #4936), 0fzrhn (0.20 #138, 0.05 #4936, 0.04 #10577), 0c53zb (0.17 #202, 0.07 #625, 0.06 #1330), 0fy6bh (0.17 #188, 0.07 #611, 0.05 #4936), 0fv89q (0.17 #264, 0.05 #828, 0.05 #4936), 0dthsy (0.14 #631, 0.11 #772, 0.10 #913), 0c6vcj (0.14 #666, 0.11 #807, 0.07 #948), 05hmp6 (0.14 #651, 0.06 #1074, 0.05 #1356), 0fz20l (0.14 #617, 0.05 #758, 0.03 #899) >> Best rule #219 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0dqzkv; 0dg3jz; 0cg9f; >> query: (?x4251, 0fk0xk) <- nominated_for(?x4251, ?x8711), gender(?x4251, ?x231), ?x8711 = 0kvb6p >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #202 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0dqzkv; 0dg3jz; 0cg9f; *> query: (?x4251, 0c53zb) <- nominated_for(?x4251, ?x8711), gender(?x4251, ?x231), ?x8711 = 0kvb6p *> conf = 0.17 ranks of expected_values: 4, 5, 8 EVAL 05v1sb award_winner! 0c6vcj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 98.000 98.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05v1sb award_winner! 0c53zb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 05v1sb award_winner! 0fy6bh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 98.000 98.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #721-0kyk PRED entity: 0kyk PRED relation: profession! PRED expected values: 0l6qt 0lzb8 0168cl 01vrncs 0c_mvb 0gd5z 01nn6c 02yl42 0fx02 0br1w 07h07 04mhl 0c5tl 01w1kyf 016lh0 058nh2 01ps2h8 01vvyd8 01_6dw 05cgy8 01w_10 02mpb 01j5sd 0683n 05jjl 0227vl 01hc9_ 0dz46 04znsy 04mby 04tnqn 053ksp 07zl1 03_fk9 024jwt 04_by 0ff2k 0dq9wx 0tfc 0ldd 09jd9 => 51 concepts (21 used for prediction) PRED predicted values (max 10 best out of 3927): 0pz7h (0.67 #31604, 0.55 #39239, 0.50 #43375), 0bqs56 (0.67 #33202, 0.55 #39239, 0.50 #44973), 02_wxh (0.67 #34199, 0.55 #39239, 0.50 #45970), 017r2 (0.67 #27884, 0.55 #39239, 0.43 #39657), 01j7rd (0.67 #31949, 0.55 #39239, 0.40 #43720), 01xdf5 (0.67 #31434, 0.55 #39239, 0.40 #43205), 02633g (0.67 #33837, 0.55 #39239, 0.40 #45608), 01xwv7 (0.67 #34602, 0.55 #39239, 0.40 #46373), 02xfj0 (0.67 #33665, 0.55 #39239, 0.40 #45436), 04bs3j (0.67 #31513, 0.55 #39239, 0.40 #43284) >> Best rule #31604 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 018gz8; >> query: (?x2225, 0pz7h) <- profession(?x8830, ?x2225), profession(?x7186, ?x2225), award_nominee(?x489, ?x7186), ?x489 = 0h5g_, ?x8830 = 01t94_1 >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #39239 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 03gjzk; 02krf9; 0d1pc; 01p5_g; *> query: (?x2225, ?x916) <- profession(?x12258, ?x2225), profession(?x7186, ?x2225), profession(?x3421, ?x2225), profession(?x3336, ?x2225), ?x3421 = 05r5w, influenced_by(?x916, ?x3336), award_nominee(?x380, ?x7186), location(?x12258, ?x2020) *> conf = 0.55 ranks of expected_values: 71, 99, 110, 115, 116, 144, 146, 154, 166, 167, 171, 174, 183, 184, 188, 287, 450, 453, 612, 783, 879, 904, 1003, 1035, 1063, 1074, 1078, 1312, 1412, 1588, 1803, 1814, 1875, 2031, 2046, 2063, 3161, 3245, 3499, 3743 EVAL 0kyk profession! 09jd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0ldd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0tfc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0dq9wx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0ff2k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 04_by CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 024jwt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 03_fk9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 07zl1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 053ksp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 04tnqn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 04mby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 04znsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0dz46 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 01hc9_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0227vl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 05jjl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0683n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 01j5sd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 02mpb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 01w_10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 05cgy8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 01_6dw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 01vvyd8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 01ps2h8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 058nh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 016lh0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 01w1kyf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0c5tl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 04mhl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 07h07 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0br1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0fx02 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 02yl42 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 01nn6c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0gd5z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0c_mvb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 01vrncs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0168cl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0lzb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession EVAL 0kyk profession! 0l6qt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 51.000 21.000 0.667 http://example.org/people/person/profession #720-01pkhw PRED entity: 01pkhw PRED relation: film PRED expected values: 01_0f7 => 88 concepts (62 used for prediction) PRED predicted values (max 10 best out of 766): 0bs5vty (0.53 #60659, 0.42 #78507, 0.42 #69583), 034b6k (0.10 #1672), 026gyn_ (0.10 #297), 02qrv7 (0.10 #192), 03bzjpm (0.06 #6662, 0.05 #8447, 0.05 #12015), 027r9t (0.06 #6594, 0.05 #8379, 0.04 #11947), 01shy7 (0.05 #3990, 0.05 #11127, 0.05 #12911), 08r4x3 (0.05 #3722, 0.05 #154, 0.05 #5506), 03bx2lk (0.05 #3752, 0.05 #5536, 0.04 #7321), 02r79_h (0.05 #3796, 0.04 #10933, 0.04 #12717) >> Best rule #60659 for best value: >> intensional similarity = 3 >> extensional distance = 1458 >> proper extension: 04dz_y7; >> query: (?x4053, ?x2989) <- profession(?x4053, ?x1032), ?x1032 = 02hrh1q, nominated_for(?x4053, ?x2989) >> conf = 0.53 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pkhw film 01_0f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 62.000 0.526 http://example.org/film/actor/film./film/performance/film #719-086k8 PRED entity: 086k8 PRED relation: award_nominee PRED expected values: 05hjmd => 139 concepts (93 used for prediction) PRED predicted values (max 10 best out of 1139): 0127m7 (0.81 #197120, 0.81 #208717, 0.78 #51018), 03_bcg (0.81 #197120, 0.81 #208717, 0.78 #51018), 06chvn (0.81 #197120, 0.81 #208717, 0.78 #51018), 01qg7c (0.81 #197120, 0.81 #208717, 0.78 #51018), 02ndbd (0.81 #197120, 0.81 #208717, 0.78 #51018), 06dkzt (0.81 #197120, 0.81 #208717, 0.78 #51018), 02qx1m2 (0.77 #197121, 0.76 #194800, 0.76 #204080), 056wb (0.77 #197121, 0.76 #194800, 0.76 #204080), 016tw3 (0.50 #11819, 0.25 #7180, 0.23 #9274), 048lv (0.36 #18841, 0.02 #178852, 0.02 #151029) >> Best rule #197120 for best value: >> intensional similarity = 3 >> extensional distance = 1113 >> proper extension: 06vqdf; 0cbxl0; >> query: (?x382, ?x847) <- nominated_for(?x382, ?x522), award_nominee(?x847, ?x382), award_winner(?x5647, ?x382) >> conf = 0.81 => this is the best rule for 6 predicted values *> Best rule #204079 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1167 *> proper extension: 0c01c; 06_bq1; *> query: (?x382, ?x5338) <- nominated_for(?x382, ?x5667), award_winner(?x382, ?x1300), nominated_for(?x5338, ?x5667) *> conf = 0.28 ranks of expected_values: 39 EVAL 086k8 award_nominee 05hjmd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 139.000 93.000 0.806 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #718-01vwllw PRED entity: 01vwllw PRED relation: award PRED expected values: 0bdwft 05p09zm 0cqgl9 => 122 concepts (106 used for prediction) PRED predicted values (max 10 best out of 278): 027b9k6 (0.74 #37142, 0.73 #40735, 0.72 #31544), 02z1nbg (0.74 #37142, 0.73 #40735, 0.72 #31544), 09sb52 (0.44 #1235, 0.38 #18000, 0.36 #18798), 05pcn59 (0.44 #1274, 0.24 #4466, 0.24 #7660), 05ztrmj (0.28 #1377, 0.13 #7763, 0.11 #4569), 05p09zm (0.22 #4508, 0.21 #1316, 0.21 #4109), 057xs89 (0.21 #1353, 0.10 #7739, 0.09 #10932), 0ck27z (0.20 #19248, 0.18 #16454, 0.15 #18050), 0gqyl (0.20 #100, 0.14 #2893, 0.14 #3691), 0bs0bh (0.20 #98, 0.08 #3290, 0.05 #14866) >> Best rule #37142 for best value: >> intensional similarity = 3 >> extensional distance = 1995 >> proper extension: 084w8; 0l6qt; 0411q; 05cljf; 02rchht; 089tm; 01pfr3; 0m2l9; 02mslq; 06cc_1; ... >> query: (?x3210, ?x749) <- award_winner(?x749, ?x3210), award(?x1880, ?x749), participant(?x1880, ?x286) >> conf = 0.74 => this is the best rule for 2 predicted values *> Best rule #4508 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 174 *> proper extension: 01p47r; 0202p_; *> query: (?x3210, 05p09zm) <- participant(?x1208, ?x3210), participant(?x3210, ?x1301), film(?x3210, ?x670) *> conf = 0.22 ranks of expected_values: 6, 20, 24 EVAL 01vwllw award 0cqgl9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 122.000 106.000 0.742 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vwllw award 05p09zm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 122.000 106.000 0.742 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01vwllw award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 122.000 106.000 0.742 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #717-01x6jd PRED entity: 01x6jd PRED relation: participant PRED expected values: 01skmp => 95 concepts (64 used for prediction) PRED predicted values (max 10 best out of 49): 01tnbn (0.09 #1050, 0.08 #1694, 0.01 #2980), 03q45x (0.09 #1139), 04fcx7 (0.09 #992), 01n5309 (0.09 #685), 09yrh (0.08 #1604, 0.04 #2247, 0.01 #2890), 01rr9f (0.08 #1321, 0.04 #1964, 0.01 #3250), 0237fw (0.08 #1448, 0.01 #5949, 0.01 #11093), 066m4g (0.08 #1341), 0c6qh (0.04 #2096, 0.01 #2739, 0.01 #4025), 0n6f8 (0.04 #2015, 0.01 #3301) >> Best rule #1050 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0277990; >> query: (?x12003, 01tnbn) <- award(?x12003, ?x678), award_nominee(?x1379, ?x12003), ?x1379 = 0gcdzz >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01x6jd participant 01skmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 95.000 64.000 0.091 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #716-01grmk PRED entity: 01grmk PRED relation: district_represented PRED expected values: 07z1m => 30 concepts (28 used for prediction) PRED predicted values (max 10 best out of 371): 0498y (0.84 #741, 0.84 #1288, 0.83 #681), 07_f2 (0.84 #741, 0.83 #681, 0.83 #1092), 07z1m (0.84 #741, 0.83 #681, 0.83 #1092), 07h34 (0.84 #741, 0.83 #681, 0.83 #1092), 0g0syc (0.84 #741, 0.83 #681, 0.83 #1092), 05kkh (0.77 #916, 0.77 #856, 0.60 #1258), 03v1s (0.68 #1039, 0.65 #922, 0.64 #802), 0gyh (0.63 #1113, 0.63 #1058, 0.60 #1278), 04ych (0.63 #1045, 0.60 #1265, 0.59 #928), 04tgp (0.58 #716, 0.58 #1072, 0.57 #835) >> Best rule #741 for best value: >> intensional similarity = 34 >> extensional distance = 10 >> proper extension: 01gtbb; 01gsvp; >> query: (?x10638, ?x7405) <- district_represented(?x10638, ?x7518), district_represented(?x10638, ?x6895), district_represented(?x10638, ?x4776), district_represented(?x10638, ?x3670), district_represented(?x10638, ?x2713), district_represented(?x10638, ?x1767), district_represented(?x10638, ?x760), district_represented(?x10638, ?x728), ?x7518 = 026mj, legislative_sessions(?x7715, ?x10638), legislative_sessions(?x4787, ?x10638), legislative_sessions(?x5978, ?x10638), ?x760 = 05fkf, district_represented(?x7715, ?x7405), district_represented(?x7715, ?x4061), district_represented(?x7715, ?x1426), ?x1767 = 04rrd, ?x4061 = 0498y, legislative_sessions(?x7714, ?x7715), legislative_sessions(?x9416, ?x4787), legislative_sessions(?x10511, ?x7715), ?x7714 = 01grr2, ?x728 = 059f4, legislative_sessions(?x5742, ?x4787), ?x5742 = 0rlz, ?x3670 = 05tbn, ?x4776 = 06yxd, legislative_sessions(?x2860, ?x7715), ?x9416 = 01gsry, ?x1426 = 07z1m, ?x2713 = 06btq, country(?x6895, ?x94), contains(?x6895, ?x1214), jurisdiction_of_office(?x900, ?x6895) >> conf = 0.84 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 01grmk district_represented 07z1m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 30.000 28.000 0.844 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #715-08qnnv PRED entity: 08qnnv PRED relation: colors PRED expected values: 01jnf1 => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 20): 083jv (0.42 #569, 0.40 #1430, 0.38 #275), 01l849 (0.28 #421, 0.28 #904, 0.27 #505), 06fvc (0.25 #150, 0.15 #276, 0.15 #1620), 01g5v (0.24 #1138, 0.23 #2776, 0.22 #2860), 03wkwg (0.23 #310, 0.14 #646, 0.12 #142), 019sc (0.18 #764, 0.17 #1058, 0.17 #575), 09ggk (0.17 #458, 0.15 #290, 0.14 #374), 036k5h (0.17 #447, 0.15 #762, 0.14 #510), 04mkbj (0.15 #284, 0.12 #158, 0.12 #137), 038hg (0.15 #307, 0.10 #1147, 0.10 #1441) >> Best rule #569 for best value: >> intensional similarity = 5 >> extensional distance = 22 >> proper extension: 05x_5; >> query: (?x6315, 083jv) <- institution(?x734, ?x6315), category(?x6315, ?x134), student(?x6315, ?x1400), major_field_of_study(?x6315, ?x947), ?x947 = 036hv >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #117 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 023zl; *> query: (?x6315, 01jnf1) <- institution(?x1200, ?x6315), institution(?x865, ?x6315), category(?x6315, ?x134), child(?x6315, ?x9525), ?x1200 = 016t_3, ?x865 = 02h4rq6 *> conf = 0.14 ranks of expected_values: 12 EVAL 08qnnv colors 01jnf1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 150.000 150.000 0.417 http://example.org/education/educational_institution/colors #714-01y9qr PRED entity: 01y9qr PRED relation: major_field_of_study PRED expected values: 0mg1w => 209 concepts (209 used for prediction) PRED predicted values (max 10 best out of 133): 02j62 (0.49 #2285, 0.48 #782, 0.40 #2160), 01mkq (0.45 #766, 0.45 #2269, 0.43 #16), 01tbp (0.43 #62, 0.28 #1376, 0.26 #2315), 02lp1 (0.39 #2265, 0.39 #2140, 0.34 #3391), 04rjg (0.39 #2274, 0.36 #771, 0.31 #2149), 062z7 (0.36 #2282, 0.31 #779, 0.28 #1376), 05qjt (0.36 #2261, 0.36 #758, 0.28 #383), 03g3w (0.35 #528, 0.34 #2281, 0.30 #2156), 0g26h (0.32 #2297, 0.32 #2172, 0.30 #3423), 05qfh (0.31 #788, 0.28 #1376, 0.24 #2291) >> Best rule #2285 for best value: >> intensional similarity = 5 >> extensional distance = 138 >> proper extension: 022r38; >> query: (?x6038, 02j62) <- major_field_of_study(?x6038, ?x4100), institution(?x1200, ?x6038), ?x1200 = 016t_3, major_field_of_study(?x3090, ?x4100), ?x3090 = 01r3y2 >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #66 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 5 *> proper extension: 0345gh; 0gl6x; *> query: (?x6038, 0mg1w) <- organization(?x346, ?x6038), major_field_of_study(?x6038, ?x4100), contains(?x279, ?x6038), currency(?x6038, ?x2244), ?x4100 = 01lj9 *> conf = 0.14 ranks of expected_values: 45 EVAL 01y9qr major_field_of_study 0mg1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 209.000 209.000 0.493 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #713-0888c3 PRED entity: 0888c3 PRED relation: language PRED expected values: 02h40lc => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 27): 02h40lc (0.88 #1196, 0.88 #2573, 0.88 #1314), 06nm1 (0.15 #188, 0.11 #368, 0.10 #308), 064_8sq (0.13 #1098, 0.12 #1334, 0.12 #796), 04306rv (0.12 #182, 0.09 #362, 0.09 #1020), 03_9r (0.07 #187, 0.05 #486, 0.05 #1025), 02bjrlw (0.06 #1016, 0.06 #298, 0.05 #775), 01z4y (0.05 #297, 0.04 #296, 0.02 #536), 06b_j (0.05 #200, 0.05 #380, 0.05 #1038), 0653m (0.04 #309, 0.04 #428, 0.04 #607), 0l4h_ (0.04 #296, 0.02 #536, 0.02 #357) >> Best rule #1196 for best value: >> intensional similarity = 3 >> extensional distance = 826 >> proper extension: 0cpllql; 0407yfx; 06wbm8q; 0cc97st; 01cycq; 0ds5_72; >> query: (?x8182, 02h40lc) <- nominated_for(?x806, ?x8182), nominated_for(?x2478, ?x8182), production_companies(?x8182, ?x5908) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0888c3 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 61.000 61.000 0.883 http://example.org/film/film/language #712-0khth PRED entity: 0khth PRED relation: artists! PRED expected values: 08jyyk 016jny => 101 concepts (52 used for prediction) PRED predicted values (max 10 best out of 259): 01lyv (0.67 #34, 0.56 #656, 0.47 #967), 0mhfr (0.56 #646, 0.37 #957, 0.33 #24), 0xhtw (0.50 #11863, 0.45 #2507, 0.40 #6248), 064t9 (0.50 #1258, 0.49 #5622, 0.48 #8432), 06j6l (0.47 #7532, 0.32 #5657, 0.29 #8467), 05bt6j (0.41 #3156, 0.36 #3779, 0.36 #2533), 02w4v (0.33 #44, 0.25 #666, 0.22 #16207), 09n5t_ (0.33 #215, 0.16 #1148, 0.12 #837), 0gg8l (0.31 #755, 0.16 #1066, 0.11 #133), 0gywn (0.29 #7542, 0.25 #1303, 0.22 #8477) >> Best rule #34 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 01x15dc; >> query: (?x4715, 01lyv) <- award(?x4715, ?x10316), ?x10316 = 02ddq4, award_winner(?x3735, ?x4715), award_winner(?x2186, ?x4715) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1624 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 01tp5bj; 03xnq9_; 03wjb7; *> query: (?x4715, 08jyyk) <- category(?x4715, ?x134), artists(?x2996, ?x4715), ?x2996 = 01243b *> conf = 0.29 ranks of expected_values: 11, 24 EVAL 0khth artists! 016jny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 101.000 52.000 0.667 http://example.org/music/genre/artists EVAL 0khth artists! 08jyyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 101.000 52.000 0.667 http://example.org/music/genre/artists #711-021r6w PRED entity: 021r6w PRED relation: people! PRED expected values: 02k6hp => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 45): 0gk4g (0.34 #2415, 0.27 #75, 0.24 #465), 02knxx (0.20 #32, 0.10 #357, 0.08 #1202), 034qg (0.20 #33, 0.03 #423, 0.03 #1853), 051_y (0.20 #47, 0.03 #437, 0.02 #632), 0x2fg (0.20 #38, 0.03 #428, 0.02 #623), 0qcr0 (0.17 #2406, 0.17 #781, 0.16 #456), 0dq9p (0.16 #2422, 0.16 #537, 0.15 #797), 01mtqf (0.13 #69, 0.06 #329, 0.05 #459), 01l2m3 (0.11 #146, 0.05 #796, 0.05 #211), 02y0js (0.11 #457, 0.10 #327, 0.09 #1172) >> Best rule #2415 for best value: >> intensional similarity = 3 >> extensional distance = 278 >> proper extension: 01vrx3g; 017r2; 0127gn; 03_0p; 0dbb3; >> query: (?x10587, 0gk4g) <- award(?x10587, ?x198), people(?x11563, ?x10587), risk_factors(?x14024, ?x11563) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #2442 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 278 *> proper extension: 01vrx3g; 017r2; 0127gn; 03_0p; 0dbb3; *> query: (?x10587, 02k6hp) <- award(?x10587, ?x198), people(?x11563, ?x10587), risk_factors(?x14024, ?x11563) *> conf = 0.09 ranks of expected_values: 11 EVAL 021r6w people! 02k6hp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 137.000 137.000 0.343 http://example.org/people/cause_of_death/people #710-0myk8 PRED entity: 0myk8 PRED relation: role PRED expected values: 05r5c => 91 concepts (48 used for prediction) PRED predicted values (max 10 best out of 113): 0l14j_ (0.87 #924, 0.85 #114, 0.85 #1269), 018vs (0.87 #924, 0.85 #114, 0.85 #1269), 07y_7 (0.87 #924, 0.85 #114, 0.85 #1269), 0jtg0 (0.87 #924, 0.85 #114, 0.85 #1269), 03qjg (0.80 #4599, 0.77 #4133, 0.75 #3785), 02hnl (0.80 #4582, 0.75 #2952, 0.67 #2018), 03bx0bm (0.75 #4929, 0.75 #3760, 0.71 #4455), 02w3w (0.75 #2911, 0.75 #2883, 0.69 #4162), 02sgy (0.73 #4547, 0.71 #449, 0.70 #3269), 05148p4 (0.72 #3833, 0.71 #4413, 0.71 #4330) >> Best rule #924 for best value: >> intensional similarity = 22 >> extensional distance = 2 >> proper extension: 07kc_; >> query: (?x2956, ?x7869) <- role(?x2956, ?x1225), role(?x2956, ?x1166), role(?x2956, ?x314), role(?x2956, ?x228), family(?x2956, ?x7256), role(?x7869, ?x2956), role(?x6449, ?x2956), role(?x1495, ?x2956), role(?x7410, ?x7869), role(?x7869, ?x74), role(?x1432, ?x1225), ?x1432 = 0395lw, ?x314 = 02sgy, ?x228 = 0l14qv, family(?x7256, ?x10811), ?x6449 = 014zz1, role(?x1225, ?x8172), ?x1166 = 05148p4, ?x8172 = 06rvn, role(?x1660, ?x1225), ?x74 = 03q5t, role(?x1495, ?x214) >> conf = 0.87 => this is the best rule for 4 predicted values *> Best rule #4548 for first EXPECTED value: *> intensional similarity = 23 *> extensional distance = 13 *> proper extension: 0gkd1; *> query: (?x2956, 05r5c) <- role(?x2956, ?x8014), role(?x2956, ?x4616), role(?x3657, ?x8014), role(?x3403, ?x8014), role(?x487, ?x8014), role(?x2956, ?x4429), role(?x2956, ?x1495), role(?x2956, ?x894), role(?x8014, ?x3716), role(?x8014, ?x868), role(?x8014, ?x212), ?x3403 = 02qwg, ?x3716 = 03gvt, ?x868 = 0dwvl, role(?x894, ?x2206), ?x1495 = 013y1f, ?x2206 = 07gql, ?x4616 = 01rhl, group(?x4429, ?x5838), instrumentalists(?x894, ?x1231), gender(?x487, ?x231), ?x3657 = 01w8n89, ?x212 = 026t6 *> conf = 0.67 ranks of expected_values: 26 EVAL 0myk8 role 05r5c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 91.000 48.000 0.867 http://example.org/music/performance_role/regular_performances./music/group_membership/role #709-06c44 PRED entity: 06c44 PRED relation: type_of_union PRED expected values: 04ztj => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.78 #397, 0.78 #281, 0.78 #341), 01g63y (0.29 #59, 0.27 #67, 0.20 #47), 01bl8s (0.06 #27, 0.06 #88, 0.06 #31), 0jgjn (0.05 #36) >> Best rule #397 for best value: >> intensional similarity = 3 >> extensional distance = 588 >> proper extension: 01d5vk; 03k1vm; 08nz99; 0gry51; >> query: (?x6204, 04ztj) <- profession(?x6204, ?x353), people(?x4322, ?x6204), gender(?x6204, ?x231) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06c44 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.785 http://example.org/people/person/spouse_s./people/marriage/type_of_union #708-02rk45 PRED entity: 02rk45 PRED relation: award_winner! PRED expected values: 05f4m9q => 112 concepts (105 used for prediction) PRED predicted values (max 10 best out of 242): 03hkv_r (0.37 #24101, 0.37 #24100, 0.36 #13771), 05b1610 (0.37 #24101, 0.37 #24100, 0.36 #13771), 05f4m9q (0.37 #24101, 0.37 #24100, 0.36 #13771), 0gr4k (0.34 #894, 0.17 #1326, 0.12 #2187), 04dn09n (0.33 #44, 0.27 #905, 0.14 #1337), 0gr51 (0.33 #99, 0.21 #960, 0.14 #1392), 09sb52 (0.21 #1724, 0.18 #9081, 0.17 #9511), 0gq_v (0.21 #1724, 0.09 #1293, 0.08 #6457), 02w9sd7 (0.21 #1724, 0.09 #1293, 0.08 #6457), 02rdxsh (0.21 #1724, 0.09 #1293, 0.08 #6457) >> Best rule #24101 for best value: >> intensional similarity = 2 >> extensional distance = 1462 >> proper extension: 04rcr; 0ggl02; 05crg7; 01x15dc; 0khth; 0hvbj; 01fmz6; 016890; 014l4w; 07mvp; ... >> query: (?x9030, ?x350) <- award_winner(?x4397, ?x9030), award(?x9030, ?x350) >> conf = 0.37 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 02rk45 award_winner! 05f4m9q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 105.000 0.368 http://example.org/award/award_category/winners./award/award_honor/award_winner #707-03gj2 PRED entity: 03gj2 PRED relation: member_states! PRED expected values: 059dn => 174 concepts (174 used for prediction) PRED predicted values (max 10 best out of 10): 059dn (0.38 #15, 0.35 #17, 0.35 #30), 01rz1 (0.11 #61, 0.10 #20, 0.10 #19), 07t65 (0.11 #61, 0.10 #20, 0.10 #19), 02vk52z (0.11 #61, 0.10 #20, 0.10 #19), 0b6css (0.07 #168, 0.05 #125, 0.05 #129), 04k4l (0.07 #168, 0.05 #125, 0.05 #129), 0_2v (0.07 #168, 0.05 #125, 0.05 #129), 041288 (0.07 #168), 0gkjy (0.07 #168), 0j7v_ (0.07 #168) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 27 >> proper extension: 014tss; >> query: (?x1003, 059dn) <- combatants(?x326, ?x1003), country(?x4132, ?x1003), combatants(?x1497, ?x1003) >> conf = 0.38 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03gj2 member_states! 059dn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 174.000 174.000 0.379 http://example.org/user/ktrueman/default_domain/international_organization/member_states #706-07jxpf PRED entity: 07jxpf PRED relation: crewmember PRED expected values: 027y151 => 72 concepts (41 used for prediction) PRED predicted values (max 10 best out of 35): 02lp3c (0.25 #431, 0.07 #430, 0.06 #78), 027y151 (0.09 #40, 0.07 #481, 0.03 #232), 0b79gfg (0.09 #18, 0.04 #210, 0.03 #834), 0g9zcgx (0.09 #32, 0.03 #272, 0.02 #414), 04wp63 (0.09 #89, 0.04 #424, 0.03 #234), 04ktcgn (0.07 #204, 0.04 #252, 0.04 #394), 0284n42 (0.07 #196, 0.03 #149, 0.03 #677), 03x400 (0.07 #430, 0.05 #95, 0.02 #1200), 02js6_ (0.07 #430, 0.05 #95, 0.02 #1200), 02q9kqf (0.07 #432, 0.05 #127, 0.03 #433) >> Best rule #431 for best value: >> intensional similarity = 4 >> extensional distance = 200 >> proper extension: 01s81; 03ctqqf; >> query: (?x4118, ?x6233) <- nominated_for(?x6233, ?x4118), nominated_for(?x2341, ?x4118), award_winner(?x6233, ?x6232), crewmember(?x1199, ?x6232) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 0gwjw0c; *> query: (?x4118, 027y151) <- film(?x828, ?x4118), language(?x4118, ?x254), genre(?x4118, ?x53), ?x828 = 01wmxfs *> conf = 0.09 ranks of expected_values: 2 EVAL 07jxpf crewmember 027y151 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 41.000 0.250 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #705-02sgy PRED entity: 02sgy PRED relation: group PRED expected values: 047cx => 88 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1216): 05563d (0.56 #4036, 0.55 #3127, 0.52 #7156), 07m4c (0.56 #2268, 0.50 #450, 0.44 #2085), 02t3ln (0.56 #2227, 0.50 #1137, 0.43 #1318), 01s560x (0.55 #3242, 0.50 #1058, 0.33 #5618), 06nv27 (0.55 #3147, 0.40 #3875, 0.35 #7359), 047cx (0.52 #5515, 0.48 #7719, 0.48 #7168), 0bk1p (0.51 #8219, 0.45 #3210, 0.33 #1026), 02r3zy (0.51 #8219, 0.36 #3100, 0.33 #3828), 0qmpd (0.51 #8219, 0.33 #5590, 0.33 #2301), 014_lq (0.51 #8219, 0.33 #3876, 0.33 #236) >> Best rule #4036 for best value: >> intensional similarity = 12 >> extensional distance = 14 >> proper extension: 07c6l; >> query: (?x314, 05563d) <- role(?x565, ?x314), role(?x1750, ?x314), role(?x2170, ?x314), instrumentalists(?x1750, ?x366), role(?x227, ?x314), role(?x314, ?x214), group(?x1750, ?x4942), group(?x1750, ?x4642), ?x4942 = 05xq9, ?x214 = 02pprs, artist(?x3888, ?x2170), ?x4642 = 0394y >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #5515 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 19 *> proper extension: 02snj9; *> query: (?x314, 047cx) <- performance_role(?x314, ?x212), role(?x5990, ?x314), role(?x1750, ?x314), role(?x1212, ?x314), instrumentalists(?x314, ?x8328), instrumentalists(?x314, ?x133), ?x1750 = 02hnl, award(?x133, ?x102), group(?x314, ?x442), role(?x1291, ?x314), profession(?x8328, ?x131), group(?x5990, ?x4791), role(?x615, ?x1212), award(?x1291, ?x247) *> conf = 0.52 ranks of expected_values: 6 EVAL 02sgy group 047cx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 88.000 45.000 0.562 http://example.org/music/performance_role/regular_performances./music/group_membership/group #704-0gs1_ PRED entity: 0gs1_ PRED relation: award_winner! PRED expected values: 09d28z => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 240): 0gq9h (0.37 #41777, 0.37 #36660, 0.36 #41776), 040njc (0.37 #41777, 0.37 #36660, 0.36 #41776), 0f4x7 (0.37 #41777, 0.37 #36660, 0.36 #41776), 02w9sd7 (0.37 #41777, 0.37 #36660, 0.36 #41776), 07cbcy (0.37 #41777, 0.37 #36660, 0.36 #41776), 0gqyl (0.20 #103, 0.03 #3089, 0.03 #6498), 0bdwft (0.20 #67, 0.03 #6888, 0.03 #9018), 02ppm4q (0.20 #151, 0.03 #6546, 0.03 #3137), 0gkvb7 (0.20 #27, 0.01 #10683, 0.01 #6422), 09qvf4 (0.20 #204, 0.01 #13417, 0.01 #6599) >> Best rule #41777 for best value: >> intensional similarity = 3 >> extensional distance = 2274 >> proper extension: 089tm; 04rcr; 01v0sx2; 01vsxdm; 01wv9xn; 05crg7; 0frsw; 016fmf; 01vrwfv; 0134s5; ... >> query: (?x6558, ?x591) <- award(?x6558, ?x591), award_winner(?x289, ?x6558), award_winner(?x591, ?x157) >> conf = 0.37 => this is the best rule for 5 predicted values *> Best rule #1578 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 139 *> proper extension: 032md; *> query: (?x6558, 09d28z) <- film(?x6558, ?x1454), nominated_for(?x434, ?x1454), location(?x6558, ?x242) *> conf = 0.09 ranks of expected_values: 17 EVAL 0gs1_ award_winner! 09d28z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 113.000 113.000 0.366 http://example.org/award/award_category/winners./award/award_honor/award_winner #703-0btyf5z PRED entity: 0btyf5z PRED relation: film_release_region PRED expected values: 0345h 0163v => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 154): 09c7w0 (0.93 #11368, 0.92 #11530, 0.82 #2921), 06mkj (0.88 #1197, 0.87 #2009, 0.85 #710), 0chghy (0.85 #1147, 0.82 #660, 0.81 #1959), 03_3d (0.84 #1143, 0.81 #1955, 0.79 #2279), 0345h (0.84 #1171, 0.79 #684, 0.76 #2793), 035qy (0.83 #1173, 0.71 #686, 0.71 #2309), 05qhw (0.83 #1151, 0.71 #664, 0.67 #2935), 0154j (0.83 #1141, 0.73 #654, 0.69 #2277), 05b4w (0.81 #718, 0.75 #1205, 0.68 #2017), 015fr (0.80 #1154, 0.71 #667, 0.70 #2290) >> Best rule #11368 for best value: >> intensional similarity = 5 >> extensional distance = 1315 >> proper extension: 05f67hw; >> query: (?x1932, 09c7w0) <- film_release_region(?x1932, ?x1229), film_release_region(?x6175, ?x1229), film_release_region(?x3276, ?x1229), ?x3276 = 0gjc4d3, ?x6175 = 0gg5kmg >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #1171 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 113 *> proper extension: 0gtvrv3; *> query: (?x1932, 0345h) <- film_release_region(?x1932, ?x1603), film_release_region(?x1932, ?x1229), ?x1229 = 059j2, currency(?x1932, ?x170), ?x1603 = 06bnz *> conf = 0.84 ranks of expected_values: 5, 57 EVAL 0btyf5z film_release_region 0163v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 93.000 93.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0btyf5z film_release_region 0345h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 93.000 93.000 0.926 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #702-017lb_ PRED entity: 017lb_ PRED relation: inductee! PRED expected values: 0g2c8 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 3): 0g2c8 (0.25 #19, 0.24 #55, 0.18 #208), 0qjfl (0.05 #75, 0.04 #93), 06szd3 (0.01 #839, 0.01 #830, 0.01 #821) >> Best rule #19 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 0fpj4lx; >> query: (?x8226, 0g2c8) <- artists(?x5934, ?x8226), artist(?x9492, ?x8226), artist(?x8336, ?x8226), ?x9492 = 03mp8k, ?x5934 = 05r6t, artist(?x8336, ?x4620), role(?x4620, ?x227) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017lb_ inductee! 0g2c8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.250 http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee #701-04pnx PRED entity: 04pnx PRED relation: service_location! PRED expected values: 01c6k4 => 91 concepts (24 used for prediction) PRED predicted values (max 10 best out of 143): 01c6k4 (0.50 #418, 0.35 #1935, 0.33 #1522), 03s7h (0.33 #111, 0.25 #523, 0.25 #248), 064f29 (0.25 #609, 0.25 #197, 0.24 #1576), 0k9ts (0.25 #641, 0.25 #229, 0.24 #1608), 07zl6m (0.25 #682, 0.25 #270, 0.22 #820), 05b5c (0.25 #677, 0.25 #265, 0.22 #815), 069b85 (0.25 #678, 0.25 #266, 0.22 #816), 0dmtp (0.25 #608, 0.25 #196, 0.22 #746), 01zpmq (0.25 #599, 0.25 #187, 0.22 #737), 0z07 (0.25 #652, 0.25 #240, 0.22 #790) >> Best rule #418 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 07c5l; >> query: (?x7708, 01c6k4) <- contains(?x7708, ?x10183), contains(?x7708, ?x9730), contains(?x7708, ?x7709), ?x9730 = 01p8s, ?x7709 = 05c74, ?x10183 = 0164b >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04pnx service_location! 01c6k4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 24.000 0.500 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #700-01rgr PRED entity: 01rgr PRED relation: location PRED expected values: 0dprg => 163 concepts (147 used for prediction) PRED predicted values (max 10 best out of 348): 02_286 (0.33 #98873, 0.31 #68332, 0.28 #99676), 0cc56 (0.32 #42640, 0.31 #45052, 0.28 #49070), 030qb3t (0.26 #98919, 0.22 #99722, 0.22 #102934), 04ykg (0.25 #871, 0.11 #4886, 0.09 #7296), 0b2lw (0.25 #1152, 0.11 #5167, 0.09 #7577), 0156q (0.22 #4103, 0.07 #12137, 0.07 #9727), 05ywg (0.20 #1686, 0.17 #2489, 0.11 #4095), 07gdw (0.20 #2287, 0.17 #3090, 0.02 #15140), 05k7sb (0.20 #5730, 0.04 #36260, 0.04 #38673), 0d6lp (0.19 #42751, 0.18 #45163, 0.16 #49181) >> Best rule #98873 for best value: >> intensional similarity = 4 >> extensional distance = 1058 >> proper extension: 06jzh; 059x0w; 06p0s1; >> query: (?x9595, 02_286) <- location(?x9595, ?x4627), place_of_death(?x598, ?x4627), place_of_birth(?x771, ?x4627), vacationer(?x4627, ?x436) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #17332 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 49 *> proper extension: 0d3f83; *> query: (?x9595, 0dprg) <- gender(?x9595, ?x231), ?x231 = 05zppz, nationality(?x9595, ?x789), ?x789 = 0f8l9c *> conf = 0.04 ranks of expected_values: 82 EVAL 01rgr location 0dprg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 163.000 147.000 0.330 http://example.org/people/person/places_lived./people/place_lived/location #699-08_83x PRED entity: 08_83x PRED relation: nationality PRED expected values: 07ssc => 82 concepts (63 used for prediction) PRED predicted values (max 10 best out of 14): 09c7w0 (0.78 #401, 0.76 #2611, 0.75 #301), 02jx1 (0.50 #133, 0.45 #33, 0.43 #233), 07ssc (0.42 #115, 0.36 #215, 0.31 #1104), 0kqb0 (0.31 #1104), 0dbdy (0.31 #1104), 06q1r (0.30 #3615, 0.18 #77, 0.14 #277), 03rt9 (0.30 #3615, 0.18 #13, 0.07 #213), 0d060g (0.05 #1111, 0.05 #1211, 0.05 #1814), 03rk0 (0.04 #2355, 0.03 #5463, 0.03 #6064), 03_3d (0.03 #2415, 0.02 #907, 0.01 #1008) >> Best rule #401 for best value: >> intensional similarity = 2 >> extensional distance = 338 >> proper extension: 01nzs7; >> query: (?x5205, 09c7w0) <- award_winner(?x1434, ?x5205), tv_program(?x6673, ?x1434) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #115 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 05y5kf; *> query: (?x5205, 07ssc) <- award_nominee(?x5205, ?x6258), award_nominee(?x5205, ?x1191), ?x6258 = 06ns98, profession(?x1191, ?x1032) *> conf = 0.42 ranks of expected_values: 3 EVAL 08_83x nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 63.000 0.782 http://example.org/people/person/nationality #698-01ky7c PRED entity: 01ky7c PRED relation: major_field_of_study PRED expected values: 01jzxy => 45 concepts (45 used for prediction) PRED predicted values (max 10 best out of 91): 04rjg (0.45 #250, 0.35 #598, 0.34 #366), 05qjt (0.43 #241, 0.27 #589, 0.26 #357), 01lj9 (0.39 #269, 0.33 #36, 0.27 #385), 062z7 (0.37 #374, 0.34 #258, 0.33 #25), 03g3w (0.34 #257, 0.33 #24, 0.32 #605), 01540 (0.33 #54, 0.33 #287, 0.23 #403), 05qfh (0.33 #32, 0.28 #265, 0.27 #381), 0dc_v (0.33 #39, 0.15 #272, 0.09 #620), 01lhy (0.33 #11, 0.09 #244, 0.05 #940), 0fdys (0.33 #268, 0.20 #151, 0.19 #500) >> Best rule #250 for best value: >> intensional similarity = 3 >> extensional distance = 65 >> proper extension: 01x5fb; >> query: (?x6545, 04rjg) <- list(?x6545, ?x2197), contains(?x94, ?x6545), major_field_of_study(?x6545, ?x947) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #252 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 65 *> proper extension: 01x5fb; *> query: (?x6545, 01jzxy) <- list(?x6545, ?x2197), contains(?x94, ?x6545), major_field_of_study(?x6545, ?x947) *> conf = 0.09 ranks of expected_values: 36 EVAL 01ky7c major_field_of_study 01jzxy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 45.000 45.000 0.448 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #697-0k_l4 PRED entity: 0k_l4 PRED relation: colors PRED expected values: 083jv => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 15): 083jv (0.87 #1182, 0.73 #1777, 0.73 #1374), 019sc (0.55 #1550, 0.42 #501, 0.38 #387), 01g5v (0.53 #1143, 0.37 #820, 0.36 #1375), 038hg (0.29 #107, 0.16 #639, 0.16 #1833), 01l849 (0.28 #837, 0.20 #723, 0.19 #685), 06kqt3 (0.18 #1180, 0.18 #1179, 0.14 #111), 02rnmb (0.18 #1180, 0.18 #1179, 0.09 #393), 088fh (0.18 #1180, 0.18 #1179, 0.09 #1276), 0jc_p (0.16 #1833, 0.09 #1276, 0.09 #688), 04mkbj (0.12 #143, 0.10 #162, 0.09 #181) >> Best rule #1182 for best value: >> intensional similarity = 9 >> extensional distance = 123 >> proper extension: 02fbb5; 03dkx; >> query: (?x6503, 083jv) <- colors(?x6503, ?x1101), teams(?x9976, ?x6503), sport(?x6503, ?x471), colors(?x5380, ?x1101), colors(?x3298, ?x1101), colors(?x1438, ?x1101), ?x3298 = 0jnmj, ?x1438 = 0512p, ?x5380 = 0b6p3qf >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0k_l4 colors 083jv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.872 http://example.org/sports/sports_team/colors #696-04f0xq PRED entity: 04f0xq PRED relation: company! PRED expected values: 04192r => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 37): 0dq_5 (0.87 #2383, 0.83 #1580, 0.81 #184), 01yc02 (0.52 #133, 0.51 #554, 0.44 #1571), 09d6p2 (0.45 #903, 0.44 #1777, 0.43 #522), 07xl34 (0.30 #2158, 0.11 #3896, 0.11 #3895), 02211by (0.29 #213, 0.28 #424, 0.26 #171), 0142rn (0.22 #192, 0.22 #107, 0.21 #234), 02y6fz (0.21 #232, 0.20 #1396, 0.20 #401), 09lq2c (0.20 #1396, 0.20 #27, 0.14 #238), 04192r (0.20 #1396, 0.17 #459, 0.14 #2961), 021q0l (0.20 #1396, 0.14 #2961, 0.14 #2621) >> Best rule #2383 for best value: >> intensional similarity = 7 >> extensional distance = 100 >> proper extension: 05g76; 09j_g; 01kcmr; 0gy1_; >> query: (?x7471, 0dq_5) <- company(?x4792, ?x7471), company(?x4792, ?x12350), company(?x4792, ?x8641), company(?x4792, ?x3887), ?x12350 = 018c_r, ?x8641 = 03y7ml, ?x3887 = 02bh8z >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #1396 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 55 *> proper extension: 0cwx_; 01hjy5; *> query: (?x7471, ?x346) <- category(?x7471, ?x134), list(?x7471, ?x8915), ?x134 = 08mbj5d, currency(?x7471, ?x170), list(?x1908, ?x8915), company(?x346, ?x1908) *> conf = 0.20 ranks of expected_values: 9 EVAL 04f0xq company! 04192r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 94.000 94.000 0.873 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #695-02wwwv5 PRED entity: 02wwwv5 PRED relation: profession PRED expected values: 0dz3r => 111 concepts (86 used for prediction) PRED predicted values (max 10 best out of 53): 09jwl (0.77 #5621, 0.70 #2078, 0.68 #2373), 02hrh1q (0.76 #308, 0.69 #8266, 0.68 #12384), 0dz3r (0.48 #296, 0.44 #443, 0.42 #1178), 01c72t (0.31 #171, 0.28 #4443, 0.27 #8841), 01d_h8 (0.31 #7522, 0.31 #6196, 0.31 #8257), 039v1 (0.29 #1653, 0.29 #2095, 0.27 #2390), 0dxtg (0.27 #8841, 0.25 #12383, 0.25 #9001), 0n1h (0.27 #8841, 0.25 #2070, 0.22 #2365), 03gjzk (0.27 #8841, 0.23 #7532, 0.22 #8414), 02krf9 (0.27 #8841, 0.09 #7544, 0.09 #8279) >> Best rule #5621 for best value: >> intensional similarity = 3 >> extensional distance = 806 >> proper extension: 0f0y8; 053y0s; 01rrwf6; 01nqfh_; 032t2z; 06y9c2; 0hnlx; 01q7cb_; 04n7njg; 01p45_v; ... >> query: (?x9623, 09jwl) <- profession(?x9623, ?x220), profession(?x1795, ?x220), ?x1795 = 03gr7w >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #296 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 05mt_q; *> query: (?x9623, 0dz3r) <- award(?x9623, ?x4837), award_nominee(?x827, ?x9623), gender(?x9623, ?x231), ?x4837 = 03t5kl *> conf = 0.48 ranks of expected_values: 3 EVAL 02wwwv5 profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 111.000 86.000 0.770 http://example.org/people/person/profession #694-03lmx1 PRED entity: 03lmx1 PRED relation: people PRED expected values: 01wsl7c 0783m_ 08c9b0 04mhbh 0948xk => 21 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2034): 0g824 (0.33 #889, 0.14 #4294, 0.13 #5998), 01rrd4 (0.33 #903, 0.14 #4308, 0.13 #6012), 01vwllw (0.33 #432, 0.14 #3837, 0.13 #5541), 04f7c55 (0.33 #806, 0.13 #5915, 0.12 #7619), 0227vl (0.33 #1227, 0.10 #4632, 0.10 #6336), 0gcs9 (0.33 #391, 0.10 #3796, 0.10 #5500), 06qgvf (0.33 #7, 0.10 #3412, 0.10 #5116), 04qt29 (0.33 #1269, 0.10 #4674, 0.10 #6378), 07d3z7 (0.33 #718, 0.10 #4123, 0.10 #5827), 09h4b5 (0.33 #1097, 0.10 #2800, 0.09 #11319) >> Best rule #889 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 033tf_; 0xnvg; 0d2by; 022dp5; >> query: (?x3715, 0g824) <- people(?x3715, ?x7277), people(?x3715, ?x1857), award_nominee(?x5065, ?x7277), nationality(?x7277, ?x512), film(?x7277, ?x936), religion(?x7277, ?x2694), ?x5065 = 01z_g6, profession(?x1857, ?x7397) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3405 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 27 *> proper extension: 071x0k; 078vc; 078ds; 0fk3s; 04czx7; 0c41n; *> query: (?x3715, ?x133) <- languages_spoken(?x3715, ?x13258), languages_spoken(?x5741, ?x13258), languages_spoken(?x5042, ?x13258), countries_spoken_in(?x13258, ?x512), ?x5042 = 0d7wh, ?x512 = 07ssc, people(?x5741, ?x133) *> conf = 0.05 ranks of expected_values: 952 EVAL 03lmx1 people 0948xk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 16.000 0.333 http://example.org/people/ethnicity/people EVAL 03lmx1 people 04mhbh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 21.000 16.000 0.333 http://example.org/people/ethnicity/people EVAL 03lmx1 people 08c9b0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 16.000 0.333 http://example.org/people/ethnicity/people EVAL 03lmx1 people 0783m_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 16.000 0.333 http://example.org/people/ethnicity/people EVAL 03lmx1 people 01wsl7c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 21.000 16.000 0.333 http://example.org/people/ethnicity/people #693-0d05w3 PRED entity: 0d05w3 PRED relation: school! PRED expected values: 0jmk7 => 228 concepts (228 used for prediction) PRED predicted values (max 10 best out of 93): 0jmj7 (0.58 #11321, 0.57 #11885, 0.32 #2850), 05m_8 (0.17 #11859, 0.16 #11295, 0.05 #2166), 01slc (0.13 #11915, 0.12 #11351, 0.01 #17089), 051vz (0.11 #11879, 0.11 #11315, 0.05 #2186), 0bwjj (0.11 #11951, 0.10 #11368, 0.09 #11932), 0jm8l (0.11 #11951, 0.09 #2866, 0.05 #11901), 0jmbv (0.11 #11951, 0.09 #2875, 0.04 #11910), 0jmk7 (0.11 #11951, 0.07 #11947, 0.06 #11383), 0jm6n (0.11 #11951, 0.07 #11335, 0.06 #11899), 01k8vh (0.11 #11951, 0.06 #11377, 0.05 #11941) >> Best rule #11321 for best value: >> intensional similarity = 2 >> extensional distance = 102 >> proper extension: 01pl14; 02w2bc; 01b1mj; 01wdl3; 01j_06; 01t8sr; 049dk; 02jyr8; 01ptt7; 01jsn5; ... >> query: (?x2346, 0jmj7) <- contains(?x6304, ?x2346), school(?x4979, ?x2346) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #11951 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 112 *> proper extension: 02gr81; 017j69; 0frm7n; 027mdh; 02zkz7; 08qnnv; 0trv; *> query: (?x2346, ?x799) <- school(?x4979, ?x2346), draft(?x799, ?x4979) *> conf = 0.11 ranks of expected_values: 8 EVAL 0d05w3 school! 0jmk7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 228.000 228.000 0.577 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #692-01ckhj PRED entity: 01ckhj PRED relation: film PRED expected values: 0dscrwf => 104 concepts (43 used for prediction) PRED predicted values (max 10 best out of 762): 0fpkhkz (0.32 #2018, 0.02 #5590), 01m13b (0.21 #1935), 01qb5d (0.20 #138, 0.05 #3710, 0.03 #7282), 06gb1w (0.20 #731, 0.05 #4303, 0.02 #11447), 02rlj20 (0.20 #1373, 0.05 #4945, 0.02 #6731), 0d90m (0.20 #8, 0.05 #3580, 0.01 #10724), 0bwhdbl (0.20 #1405, 0.05 #4977), 027r9t (0.20 #1244, 0.05 #4816), 05q_dw (0.20 #890, 0.05 #4462), 0260bz (0.20 #336, 0.05 #3908) >> Best rule #2018 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 07mz77; >> query: (?x12733, 0fpkhkz) <- film(?x12733, ?x3246), award(?x3246, ?x7521), nominated_for(?x746, ?x3246), ?x7521 = 02y_j8g >> conf = 0.32 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01ckhj film 0dscrwf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 104.000 43.000 0.316 http://example.org/film/actor/film./film/performance/film #691-0bsxd3 PRED entity: 0bsxd3 PRED relation: category PRED expected values: 08mbj5d => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.36 #15, 0.36 #6, 0.36 #11) >> Best rule #15 for best value: >> intensional similarity = 8 >> extensional distance = 53 >> proper extension: 031kyy; >> query: (?x13044, 08mbj5d) <- program(?x6678, ?x13044), award_winner(?x6678, ?x6447), award_winner(?x6678, ?x1686), production_companies(?x549, ?x1686), award_nominee(?x6447, ?x1422), nominated_for(?x6447, ?x3600), company(?x846, ?x1686), industry(?x1686, ?x373) >> conf = 0.36 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bsxd3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 58.000 0.364 http://example.org/common/topic/webpage./common/webpage/category #690-0fpzwf PRED entity: 0fpzwf PRED relation: location! PRED expected values: 01vrncs 06x4l_ => 160 concepts (107 used for prediction) PRED predicted values (max 10 best out of 2187): 08k881 (0.71 #260891, 0.66 #85285, 0.51 #150513), 0315q3 (0.71 #260891, 0.57 #72742, 0.51 #173090), 0blgl (0.71 #260891, 0.47 #183131, 0.47 #178113), 036jp8 (0.57 #72742, 0.47 #95318, 0.46 #85284), 03nb5v (0.51 #173090, 0.51 #150513, 0.50 #173092), 07h5d (0.51 #150513, 0.50 #173092, 0.47 #183131), 0h005 (0.47 #95318, 0.46 #85284, 0.46 #72741), 01vrncs (0.25 #179, 0.11 #52676, 0.11 #2687), 01kws3 (0.25 #1096, 0.11 #3604, 0.07 #6112), 01x66d (0.11 #52676, 0.06 #253364, 0.04 #7701) >> Best rule #260891 for best value: >> intensional similarity = 3 >> extensional distance = 326 >> proper extension: 04kf4; 0h3lt; 0rhp6; 0g251; 0p9z5; 0150n; 03902; 01c1nm; 0ljsz; 022tq4; ... >> query: (?x5771, ?x5770) <- place_of_birth(?x5770, ?x5771), location(?x5770, ?x1523), place_of_death(?x457, ?x1523) >> conf = 0.71 => this is the best rule for 3 predicted values *> Best rule #179 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2 *> proper extension: 013f9v; *> query: (?x5771, 01vrncs) <- contains(?x1274, ?x5771), ?x1274 = 04ykg, county_seat(?x10567, ?x5771) *> conf = 0.25 ranks of expected_values: 8 EVAL 0fpzwf location! 06x4l_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 107.000 0.705 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0fpzwf location! 01vrncs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 160.000 107.000 0.705 http://example.org/people/person/places_lived./people/place_lived/location #689-076xkdz PRED entity: 076xkdz PRED relation: language PRED expected values: 03_9r => 114 concepts (97 used for prediction) PRED predicted values (max 10 best out of 55): 02h40lc (0.89 #3278, 0.89 #3338, 0.89 #3218), 03_9r (0.72 #838, 0.69 #718, 0.68 #897), 064_8sq (0.27 #2678, 0.25 #140, 0.24 #2439), 02bjrlw (0.27 #2678, 0.25 #1, 0.24 #2439), 05zjd (0.27 #2678, 0.25 #85, 0.20 #262), 04306rv (0.27 #2678, 0.24 #2439, 0.17 #1425), 06nm1 (0.25 #129, 0.13 #660, 0.13 #2033), 0349s (0.24 #2439, 0.17 #1425, 0.05 #3579), 03k50 (0.17 #1425, 0.07 #658, 0.05 #2031), 02hxcvy (0.17 #1425, 0.07 #683, 0.05 #3579) >> Best rule #3278 for best value: >> intensional similarity = 5 >> extensional distance = 446 >> proper extension: 011yrp; 02v63m; 03m8y5; 0blpg; 07sgdw; 02hfk5; 07bzz7; 0ggbfwf; 02lxrv; 05dptj; ... >> query: (?x8752, 02h40lc) <- genre(?x8752, ?x258), ?x258 = 05p553, film(?x382, ?x8752), award_nominee(?x382, ?x847), award(?x382, ?x500) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #838 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 16 *> proper extension: 02z5x7l; *> query: (?x8752, 03_9r) <- genre(?x8752, ?x225), film_release_distribution_medium(?x8752, ?x81), actor(?x8752, ?x5779), genre(?x10088, ?x225), genre(?x1334, ?x225), ?x1334 = 026q3s3, film(?x788, ?x10088) *> conf = 0.72 ranks of expected_values: 2 EVAL 076xkdz language 03_9r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 97.000 0.893 http://example.org/film/film/language #688-01tv3x2 PRED entity: 01tv3x2 PRED relation: instrumentalists! PRED expected values: 018vs => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 126): 0342h (0.79 #1212, 0.78 #1125, 0.74 #1381), 05r5c (0.49 #2796, 0.48 #5058, 0.48 #955), 018vs (0.49 #1047, 0.48 #1658, 0.47 #788), 0l14qv (0.45 #1468, 0.44 #1731, 0.43 #1820), 03bx0bm (0.45 #1468, 0.44 #1731, 0.43 #1820), 028tv0 (0.45 #1468, 0.44 #1731, 0.43 #1820), 0l14md (0.33 #8, 0.24 #438, 0.24 #352), 026t6 (0.33 #3, 0.20 #89, 0.19 #1384), 01v1d8 (0.31 #1733, 0.31 #3748, 0.30 #1469), 01v8y9 (0.31 #1733, 0.31 #3748, 0.30 #1469) >> Best rule #1212 for best value: >> intensional similarity = 4 >> extensional distance = 148 >> proper extension: 011hdn; >> query: (?x6609, 0342h) <- profession(?x6609, ?x2659), artists(?x283, ?x6609), ?x2659 = 039v1, gender(?x6609, ?x231) >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #1047 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 115 *> proper extension: 03k0yw; 02ryx0; 02qtywd; *> query: (?x6609, 018vs) <- profession(?x6609, ?x131), type_of_union(?x6609, ?x566), role(?x6609, ?x227), ?x131 = 0dz3r *> conf = 0.49 ranks of expected_values: 3 EVAL 01tv3x2 instrumentalists! 018vs CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 123.000 123.000 0.787 http://example.org/music/instrument/instrumentalists #687-0pyww PRED entity: 0pyww PRED relation: film PRED expected values: 0sxns => 84 concepts (51 used for prediction) PRED predicted values (max 10 best out of 405): 072kp (0.46 #37585, 0.43 #17893, 0.39 #50114), 0f42nz (0.09 #2697, 0.01 #59969, 0.01 #90391), 0h1fktn (0.09 #969, 0.02 #18862, 0.02 #22442), 039cq4 (0.07 #34006, 0.07 #30425, 0.06 #46534), 02825cv (0.05 #1141, 0.04 #4719, 0.03 #2930), 04gv3db (0.05 #752, 0.04 #4330, 0.02 #11486), 0cc97st (0.05 #987, 0.01 #4565), 03bzyn4 (0.05 #1567), 05h43ls (0.05 #414), 0bvn25 (0.04 #3628, 0.03 #50, 0.02 #1839) >> Best rule #37585 for best value: >> intensional similarity = 3 >> extensional distance = 811 >> proper extension: 04shbh; 0854hr; 06p0s1; 02vkvcz; >> query: (?x4816, ?x631) <- location(?x4816, ?x108), award(?x4816, ?x154), award_winner(?x631, ?x4816) >> conf = 0.46 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pyww film 0sxns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 84.000 51.000 0.457 http://example.org/film/actor/film./film/performance/film #686-0556j8 PRED entity: 0556j8 PRED relation: genre! PRED expected values: 026390q 0dyb1 03nx8mj 01zfzb => 26 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1846): 0dlngsd (0.60 #11765, 0.60 #8105, 0.50 #2618), 07k8rt4 (0.60 #9893, 0.60 #8063, 0.33 #748), 03s6l2 (0.60 #7401, 0.50 #11061, 0.50 #1914), 0234j5 (0.60 #8750, 0.50 #12410, 0.50 #3263), 0b7l4x (0.60 #8363, 0.50 #12023, 0.50 #2876), 02q7yfq (0.60 #8530, 0.50 #12190, 0.50 #3043), 0g7pm1 (0.60 #8529, 0.50 #3042, 0.40 #12189), 029k4p (0.60 #8158, 0.50 #2671, 0.40 #11818), 063_j5 (0.60 #8833, 0.50 #3346, 0.40 #12493), 034hwx (0.60 #8886, 0.50 #3399, 0.40 #12546) >> Best rule #11765 for best value: >> intensional similarity = 16 >> extensional distance = 8 >> proper extension: 01drsx; >> query: (?x5231, 0dlngsd) <- genre(?x7514, ?x5231), genre(?x4847, ?x5231), genre(?x4038, ?x5231), genre(?x1076, ?x5231), language(?x7514, ?x11038), language(?x7514, ?x2890), ?x11038 = 04h9h, film(?x1561, ?x4038), ?x2890 = 0653m, film_distribution_medium(?x4847, ?x2099), film(?x609, ?x1076), nominated_for(?x102, ?x4847), award_winner(?x1076, ?x930), titles(?x2480, ?x4847), film_crew_role(?x4038, ?x2154), film(?x147, ?x4038) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #2755 for first EXPECTED value: *> intensional similarity = 18 *> extensional distance = 2 *> proper extension: 02kdv5l; *> query: (?x5231, 01zfzb) <- genre(?x7858, ?x5231), genre(?x7514, ?x5231), genre(?x6167, ?x5231), genre(?x4847, ?x5231), genre(?x2350, ?x5231), genre(?x1673, ?x5231), ?x7514 = 06x43v, nominated_for(?x1173, ?x4847), nominated_for(?x1063, ?x7858), ?x2350 = 0661m4p, film_crew_role(?x4847, ?x137), ?x1673 = 031t2d, nominated_for(?x102, ?x4847), film(?x617, ?x7858), titles(?x2480, ?x4847), currency(?x4847, ?x170), ?x6167 = 05r3qc, ?x1063 = 02rdxsh *> conf = 0.50 ranks of expected_values: 70, 218, 237, 614 EVAL 0556j8 genre! 01zfzb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 26.000 14.000 0.600 http://example.org/film/film/genre EVAL 0556j8 genre! 03nx8mj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 26.000 14.000 0.600 http://example.org/film/film/genre EVAL 0556j8 genre! 0dyb1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 26.000 14.000 0.600 http://example.org/film/film/genre EVAL 0556j8 genre! 026390q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 26.000 14.000 0.600 http://example.org/film/film/genre #685-01vn35l PRED entity: 01vn35l PRED relation: artist! PRED expected values: 01clyr => 105 concepts (74 used for prediction) PRED predicted values (max 10 best out of 117): 011k1h (0.25 #421, 0.17 #1517, 0.17 #147), 0g768 (0.22 #447, 0.17 #36, 0.15 #173), 03rhqg (0.20 #975, 0.19 #3030, 0.17 #1386), 017l96 (0.17 #19, 0.14 #1252, 0.13 #1115), 01w40h (0.17 #164, 0.11 #1534, 0.10 #849), 0181dw (0.14 #1274, 0.12 #1137, 0.12 #5110), 01clyr (0.13 #169, 0.13 #991, 0.13 #1539), 0fb0v (0.12 #1377, 0.11 #692, 0.11 #1651), 033hn8 (0.12 #1110, 0.11 #2343, 0.11 #3028), 01cl0d (0.10 #54, 0.10 #602, 0.10 #876) >> Best rule #421 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 03xhj6; 06gcn; >> query: (?x2876, 011k1h) <- artist(?x9224, ?x2876), artists(?x378, ?x2876), ?x9224 = 0n85g >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #169 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 58 *> proper extension: 0282x; *> query: (?x2876, 01clyr) <- nationality(?x2876, ?x1310), instrumentalists(?x75, ?x2876), ?x1310 = 02jx1, award(?x2876, ?x724) *> conf = 0.13 ranks of expected_values: 7 EVAL 01vn35l artist! 01clyr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 105.000 74.000 0.250 http://example.org/music/record_label/artist #684-0byb_x PRED entity: 0byb_x PRED relation: genre! PRED expected values: 02pvqmz => 30 concepts (21 used for prediction) PRED predicted values (max 10 best out of 306): 03czz87 (0.50 #558, 0.40 #852, 0.33 #264), 054gwt (0.50 #496, 0.40 #790, 0.33 #202), 099pks (0.44 #2164, 0.36 #1574, 0.32 #1868), 0275kr (0.43 #1114, 0.14 #1705, 0.12 #2295), 025ljp (0.37 #1974, 0.30 #1472, 0.21 #1680), 0584r4 (0.37 #1796, 0.29 #1502, 0.28 #2092), 01j7mr (0.36 #1532, 0.29 #941, 0.26 #1826), 06hwzy (0.33 #41, 0.30 #1472, 0.25 #1218), 0h95b81 (0.33 #208, 0.30 #1472, 0.25 #1385), 05r1_t (0.33 #121, 0.30 #1472, 0.25 #1298) >> Best rule #558 for best value: >> intensional similarity = 13 >> extensional distance = 2 >> proper extension: 03fpg; >> query: (?x13021, 03czz87) <- genre(?x4761, ?x13021), genre(?x3905, ?x13021), ?x4761 = 06mr2s, actor(?x3905, ?x6935), program(?x3817, ?x3905), program_creator(?x3905, ?x4420), award_nominee(?x221, ?x6935), vacationer(?x126, ?x6935), award_nominee(?x6935, ?x450), award(?x6935, ?x704), languages(?x3905, ?x254), profession(?x6935, ?x131), ?x131 = 0dz3r >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #219 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 09lmb; *> query: (?x13021, 02pvqmz) <- genre(?x12165, ?x13021), genre(?x9788, ?x13021), genre(?x7433, ?x13021), genre(?x4761, ?x13021), genre(?x3905, ?x13021), ?x4761 = 06mr2s, ?x3905 = 0cpz4k, ?x9788 = 01b7h8, ?x7433 = 03gvm3t, ?x12165 = 050kh5 *> conf = 0.33 ranks of expected_values: 15 EVAL 0byb_x genre! 02pvqmz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 30.000 21.000 0.500 http://example.org/tv/tv_program/genre #683-0gx1673 PRED entity: 0gx1673 PRED relation: ceremony! PRED expected values: 02nhxf 01dk00 024_41 => 62 concepts (62 used for prediction) PRED predicted values (max 10 best out of 276): 026m9w (0.89 #2078, 0.87 #1654, 0.67 #593), 01d38g (0.89 #1925, 0.87 #1501, 0.67 #440), 02ddqh (0.89 #2010, 0.87 #1586, 0.67 #525), 0248jb (0.89 #2057, 0.87 #1633, 0.67 #572), 02flq1 (0.89 #2105, 0.87 #1681, 0.67 #620), 0257wh (0.89 #2102, 0.87 #1678, 0.67 #617), 02flqd (0.89 #2096, 0.87 #1672, 0.67 #611), 02fm4d (0.89 #2090, 0.87 #1666, 0.67 #605), 02nhxf (0.87 #1547, 0.83 #1971, 0.67 #486), 02x4wb (0.87 #1684, 0.83 #2108, 0.67 #623) >> Best rule #2078 for best value: >> intensional similarity = 7 >> extensional distance = 16 >> proper extension: 0jzphpx; 01mhwk; 01xqqp; >> query: (?x8500, 026m9w) <- award_winner(?x8500, ?x827), ceremony(?x2180, ?x8500), ?x2180 = 02v1m7, award_nominee(?x527, ?x827), artist(?x6230, ?x827), people(?x2510, ?x827), award(?x827, ?x2634) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1547 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 13 *> proper extension: 01s695; 01bx35; 019bk0; 01c6qp; 0gpjbt; 013b2h; 01mh_q; 02cg41; *> query: (?x8500, 02nhxf) <- award_winner(?x8500, ?x5225), award_winner(?x8500, ?x827), ceremony(?x3103, ?x8500), ceremony(?x2180, ?x8500), ?x2180 = 02v1m7, award_nominee(?x527, ?x827), artist(?x6230, ?x827), ?x3103 = 03tcnt, place_of_death(?x5225, ?x5226) *> conf = 0.87 ranks of expected_values: 9, 21, 26 EVAL 0gx1673 ceremony! 024_41 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 62.000 62.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gx1673 ceremony! 01dk00 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 62.000 62.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony EVAL 0gx1673 ceremony! 02nhxf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 62.000 62.000 0.889 http://example.org/award/award_category/winners./award/award_honor/ceremony #682-06bzwt PRED entity: 06bzwt PRED relation: award PRED expected values: 0bp_b2 => 95 concepts (75 used for prediction) PRED predicted values (max 10 best out of 234): 0bdwqv (0.39 #1380, 0.22 #574, 0.15 #13303), 0gqy2 (0.37 #1372, 0.22 #566, 0.20 #163), 0ck27z (0.34 #2913, 0.32 #2107, 0.31 #4525), 01by1l (0.27 #1724, 0.10 #2530, 0.08 #6963), 0f4x7 (0.26 #1240, 0.15 #13303, 0.14 #17334), 0789_m (0.24 #1229, 0.15 #13303, 0.13 #18544), 0bp_b2 (0.24 #1227, 0.13 #27411, 0.13 #30236), 027dtxw (0.23 #1213, 0.13 #18544, 0.11 #407), 0cqh46 (0.22 #454, 0.22 #1260, 0.15 #13303), 02x4w6g (0.22 #517, 0.20 #114, 0.15 #13303) >> Best rule #1380 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 01tsbmv; >> query: (?x9449, 0bdwqv) <- award(?x9449, ?x2192), film(?x9449, ?x1009), ?x2192 = 0bfvd4 >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #1227 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 99 *> proper extension: 01tsbmv; *> query: (?x9449, 0bp_b2) <- award(?x9449, ?x2192), film(?x9449, ?x1009), ?x2192 = 0bfvd4 *> conf = 0.24 ranks of expected_values: 7 EVAL 06bzwt award 0bp_b2 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 95.000 75.000 0.386 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #681-0qr4n PRED entity: 0qr4n PRED relation: category PRED expected values: 08mbj5d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.82 #15, 0.82 #18, 0.81 #6) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 95 >> proper extension: 03l2n; 02_n7; >> query: (?x3832, 08mbj5d) <- administrative_division(?x3832, ?x11275), contains(?x94, ?x3832), ?x94 = 09c7w0, time_zones(?x3832, ?x2088) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qr4n category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.825 http://example.org/common/topic/webpage./common/webpage/category #680-01fsz PRED entity: 01fsz PRED relation: artists PRED expected values: 03bxh => 82 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1014): 0m19t (0.62 #2197, 0.50 #3282, 0.11 #8710), 03fbc (0.60 #3459, 0.50 #2374, 0.18 #7799), 03f5spx (0.50 #2227, 0.40 #3312, 0.12 #15256), 01w806h (0.50 #2432, 0.40 #3517, 0.07 #15461), 01gx5f (0.50 #295, 0.25 #2464, 0.21 #15493), 07r4c (0.50 #561, 0.25 #2730, 0.20 #3815), 0b_j2 (0.50 #597, 0.17 #15795, 0.10 #4936), 09hnb (0.50 #214, 0.17 #15412, 0.08 #18455), 01qkqwg (0.50 #123, 0.14 #15321, 0.10 #4462), 05563d (0.50 #311, 0.14 #15509, 0.08 #18455) >> Best rule #2197 for best value: >> intensional similarity = 6 >> extensional distance = 6 >> proper extension: 0m0jc; 0glt670; 01243b; 07d2d; 0cx7f; 012yc; >> query: (?x10523, 0m19t) <- artists(?x10523, ?x5151), artists(?x10523, ?x3667), parent_genre(?x10523, ?x597), ?x3667 = 0phx4, profession(?x5151, ?x563), award_winner(?x5151, ?x5125) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #513 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 0ggq0m; *> query: (?x10523, 03bxh) <- artists(?x10523, ?x5151), artists(?x10523, ?x3667), ?x5151 = 016k62, origin(?x3667, ?x362), people(?x743, ?x3667), role(?x3667, ?x227) *> conf = 0.25 ranks of expected_values: 189 EVAL 01fsz artists 03bxh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 82.000 21.000 0.625 http://example.org/music/genre/artists #679-02wmy PRED entity: 02wmy PRED relation: country! PRED expected values: 02wmy => 121 concepts (62 used for prediction) PRED predicted values (max 10 best out of 399): 01ly8d (0.10 #620, 0.06 #1397, 0.02 #6446), 02k_px (0.10 #619, 0.06 #1396, 0.02 #6445), 01ly5m (0.10 #443, 0.06 #1220, 0.02 #6269), 0177z (0.10 #472, 0.04 #2801, 0.04 #3189), 0bd67 (0.10 #697, 0.04 #3414, 0.03 #3803), 0mzg2 (0.10 #613, 0.04 #3330, 0.03 #3719), 0bdd_ (0.10 #547, 0.04 #3264, 0.03 #3653), 04cwcdb (0.10 #502, 0.04 #3219, 0.03 #3608), 01yl6n (0.10 #488, 0.04 #3205, 0.03 #3594), 04vg8 (0.10 #544, 0.04 #3261, 0.03 #3650) >> Best rule #620 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0hyyq; >> query: (?x9625, 01ly8d) <- locations(?x4908, ?x9625), entity_involved(?x4908, ?x512), entity_involved(?x4908, ?x142), ?x512 = 07ssc, country(?x471, ?x142) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #3494 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 26 *> proper extension: 06q1r; *> query: (?x9625, ?x177) <- form_of_government(?x9625, ?x1926), taxonomy(?x9625, ?x939), locations(?x4908, ?x9625), jurisdiction_of_office(?x900, ?x9625), jurisdiction_of_office(?x900, ?x177) *> conf = 0.01 ranks of expected_values: 380 EVAL 02wmy country! 02wmy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 121.000 62.000 0.100 http://example.org/location/administrative_division/country #678-0bz5v2 PRED entity: 0bz5v2 PRED relation: film PRED expected values: 02y_lrp => 75 concepts (52 used for prediction) PRED predicted values (max 10 best out of 421): 01j7mr (0.45 #39410, 0.38 #19705, 0.36 #51953), 0b3n61 (0.08 #1360, 0.03 #3151, 0.02 #4943), 0bvn25 (0.08 #50, 0.03 #1841, 0.01 #19755), 02qydsh (0.08 #1500, 0.03 #3291), 011ywj (0.06 #6811, 0.04 #5020, 0.02 #28308), 0prrm (0.05 #861, 0.03 #2652, 0.02 #27732), 026wlxw (0.05 #1419, 0.02 #3210, 0.02 #5002), 013q07 (0.05 #357, 0.02 #2148, 0.02 #9314), 05fm6m (0.05 #1321, 0.02 #3112, 0.01 #8486), 0g7pm1 (0.05 #1204, 0.02 #2995, 0.01 #13743) >> Best rule #39410 for best value: >> intensional similarity = 2 >> extensional distance = 870 >> proper extension: 012gbb; >> query: (?x1040, ?x3626) <- award_winner(?x3626, ?x1040), location(?x1040, ?x739) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #14 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 35 *> proper extension: 025ldg; 02ktrs; *> query: (?x1040, 02y_lrp) <- award_winner(?x2127, ?x1040), program(?x1040, ?x3626), profession(?x2127, ?x319) *> conf = 0.03 ranks of expected_values: 47 EVAL 0bz5v2 film 02y_lrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 75.000 52.000 0.449 http://example.org/film/actor/film./film/performance/film #677-02qvvv PRED entity: 02qvvv PRED relation: contains! PRED expected values: 09c7w0 => 134 concepts (117 used for prediction) PRED predicted values (max 10 best out of 224): 09c7w0 (0.85 #63485, 0.84 #27720, 0.83 #16991), 01n7q (0.31 #1866, 0.19 #6336, 0.14 #22431), 059rby (0.15 #9854, 0.12 #15218, 0.12 #11642), 0rn8q (0.14 #334, 0.10 #1228, 0.08 #2122), 0f2v0 (0.14 #217, 0.10 #1111, 0.08 #2005), 0ply0 (0.14 #208, 0.10 #1102, 0.02 #12724), 099ty (0.14 #136, 0.08 #1924, 0.04 #6394), 03s5t (0.14 #171, 0.08 #1959, 0.04 #6429), 07h34 (0.14 #229, 0.07 #12745, 0.07 #10957), 0d0x8 (0.14 #192, 0.07 #2874, 0.03 #12708) >> Best rule #63485 for best value: >> intensional similarity = 4 >> extensional distance = 335 >> proper extension: 02_2kg; 02dgq2; 026ssfj; 02vkzcx; 01wrwf; >> query: (?x3314, 09c7w0) <- contains(?x2623, ?x3314), currency(?x3314, ?x170), ?x170 = 09nqf, location(?x91, ?x2623) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02qvvv contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 117.000 0.846 http://example.org/location/location/contains #676-024c2 PRED entity: 024c2 PRED relation: people PRED expected values: 039n1 042f1 => 16 concepts (12 used for prediction) PRED predicted values (max 10 best out of 271): 016j68 (0.20 #278, 0.17 #967, 0.14 #5825), 01d0b1 (0.20 #402, 0.17 #1091, 0.13 #3463), 0b_dh (0.20 #547, 0.17 #1236, 0.13 #3463), 016dgz (0.20 #512, 0.17 #1201, 0.13 #3463), 03rx9 (0.20 #459, 0.17 #1148, 0.13 #3463), 02fgp0 (0.20 #393, 0.17 #1082, 0.13 #3463), 0ct9_ (0.20 #377, 0.17 #1066, 0.13 #3463), 015dcj (0.20 #270, 0.17 #959, 0.13 #3463), 02h48 (0.20 #4084, 0.16 #4853, 0.15 #5470), 014dq7 (0.17 #3462, 0.17 #750, 0.11 #2139) >> Best rule #278 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0h1n9; 0167bx; 0d19y2; >> query: (?x14430, 016j68) <- symptom_of(?x13373, ?x14430), symptom_of(?x9438, ?x14430), ?x9438 = 012qjw, ?x13373 = 0f3kl >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 024c2 people 042f1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 16.000 12.000 0.200 http://example.org/people/cause_of_death/people EVAL 024c2 people 039n1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 16.000 12.000 0.200 http://example.org/people/cause_of_death/people #675-0697kh PRED entity: 0697kh PRED relation: award_nominee PRED expected values: 047cqr => 106 concepts (37 used for prediction) PRED predicted values (max 10 best out of 803): 01xndd (0.87 #2339, 0.87 #2338, 0.81 #18708), 0h5jg5 (0.87 #2339, 0.87 #2338, 0.81 #18708), 047cqr (0.87 #2339, 0.87 #2338, 0.81 #18708), 0h53p1 (0.77 #70150, 0.76 #58460, 0.75 #60798), 06jrhz (0.77 #70150, 0.76 #58460, 0.75 #60798), 0884hk (0.77 #70150, 0.76 #58460, 0.75 #60798), 0697kh (0.50 #2340, 0.44 #1846, 0.28 #46772), 01rzqj (0.50 #2340, 0.33 #46771, 0.28 #46772), 059j4x (0.50 #2340, 0.33 #46771, 0.28 #46772), 04wvhz (0.50 #2340, 0.14 #14030, 0.13 #21048) >> Best rule #2339 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0brkwj; >> query: (?x8337, ?x5387) <- award_nominee(?x5387, ?x8337), award_nominee(?x2650, ?x8337), ?x2650 = 0d7hg4, award_nominee(?x2802, ?x5387) >> conf = 0.87 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0697kh award_nominee 047cqr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 106.000 37.000 0.866 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #674-09xwz PRED entity: 09xwz PRED relation: citytown PRED expected values: 0k049 => 168 concepts (168 used for prediction) PRED predicted values (max 10 best out of 231): 02_286 (0.41 #23218, 0.38 #21743, 0.35 #16221), 0r04p (0.33 #1947, 0.09 #8206, 0.06 #13733), 0d6lp (0.29 #3754, 0.20 #7434, 0.14 #10013), 01_d4 (0.20 #775, 0.20 #38, 0.17 #2618), 030qb3t (0.20 #1133, 0.18 #8129, 0.17 #39801), 0rj4g (0.20 #1332, 0.17 #3543, 0.17 #3175), 0k049 (0.20 #1107, 0.17 #3318, 0.17 #2950), 0rh6k (0.20 #738, 0.14 #16943, 0.08 #29464), 0r00l (0.20 #649, 0.13 #23852, 0.12 #5438), 01m94f (0.20 #1644, 0.04 #20059, 0.04 #22268) >> Best rule #23218 for best value: >> intensional similarity = 5 >> extensional distance = 27 >> proper extension: 0152x_; 01trtc; 02975m; >> query: (?x11706, 02_286) <- company(?x4279, ?x11706), organization(?x4682, ?x11706), ?x4682 = 0dq_5, category(?x11706, ?x134), nationality(?x4279, ?x94) >> conf = 0.41 => this is the best rule for 1 predicted values *> Best rule #1107 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0g1rw; *> query: (?x11706, 0k049) <- organizations_founded(?x2449, ?x11706), organization(?x4682, ?x11706), award_winner(?x2213, ?x11706), state_province_region(?x11706, ?x1227) *> conf = 0.20 ranks of expected_values: 7 EVAL 09xwz citytown 0k049 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 168.000 168.000 0.414 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #673-05y8n7 PRED entity: 05y8n7 PRED relation: artists PRED expected values: 01vrt_c 024qwq => 36 concepts (21 used for prediction) PRED predicted values (max 10 best out of 1047): 03t9sp (0.66 #6634, 0.57 #7715, 0.43 #9883), 02bgmr (0.50 #1614, 0.27 #5956, 0.14 #6510), 05k79 (0.50 #1234, 0.23 #6658, 0.20 #7739), 02cpp (0.50 #1630, 0.17 #5972, 0.17 #2711), 070b4 (0.40 #6249, 0.33 #821, 0.25 #1907), 07hgm (0.37 #6303, 0.25 #1961, 0.15 #7385), 01gx5f (0.33 #5723, 0.33 #295, 0.19 #6805), 02ndj5 (0.33 #899, 0.30 #6327, 0.25 #1985), 01w8n89 (0.33 #319, 0.27 #5747, 0.19 #10078), 016vn3 (0.33 #3109, 0.25 #2028, 0.23 #6370) >> Best rule #6634 for best value: >> intensional similarity = 10 >> extensional distance = 45 >> proper extension: 0827d; 0ggq0m; 08cyft; 02lnbg; 017_qw; 0ggx5q; 041738; 0l14gg; 025g__; 03ckfl9; ... >> query: (?x7960, 03t9sp) <- artists(?x7960, ?x8873), artists(?x7960, ?x8131), artists(?x3915, ?x8131), artists(?x3370, ?x8131), artists(?x3243, ?x8131), artist(?x3050, ?x8131), ?x3915 = 07gxw, ?x3243 = 0y3_8, profession(?x8873, ?x131), ?x3370 = 059kh >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #7669 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 52 *> proper extension: 05lwjc; *> query: (?x7960, 01vrt_c) <- artists(?x7960, ?x8873), artists(?x7960, ?x8131), artists(?x3915, ?x8131), artists(?x3243, ?x8131), artist(?x3050, ?x8131), ?x3915 = 07gxw, ?x3243 = 0y3_8, profession(?x8873, ?x131) *> conf = 0.20 ranks of expected_values: 93, 99 EVAL 05y8n7 artists 024qwq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 36.000 21.000 0.660 http://example.org/music/genre/artists EVAL 05y8n7 artists 01vrt_c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 36.000 21.000 0.660 http://example.org/music/genre/artists #672-0bzmt8 PRED entity: 0bzmt8 PRED relation: award_winner PRED expected values: 0kr5_ 0d5wn3 => 34 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2168): 04sry (0.60 #7238, 0.07 #11859, 0.04 #9234), 018gqj (0.40 #8624, 0.40 #5544, 0.33 #927), 016ggh (0.33 #1465, 0.25 #4617, 0.25 #4542), 0cw67g (0.33 #1406, 0.25 #4483, 0.25 #2944), 02pqgt8 (0.33 #637, 0.25 #3714, 0.20 #8334), 03q8ch (0.33 #642, 0.25 #3719, 0.20 #8339), 0bw87 (0.33 #1002, 0.25 #4079, 0.20 #8699), 03_fk9 (0.33 #1428, 0.25 #4505, 0.20 #9125), 09pjnd (0.33 #224, 0.25 #3301, 0.20 #7921), 0gyx4 (0.33 #677, 0.25 #3754, 0.20 #8374) >> Best rule #7238 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 09n4nb; >> query: (?x7100, 04sry) <- instance_of_recurring_event(?x7100, ?x3459), ceremony(?x3617, ?x7100), ceremony(?x720, ?x7100), award_winner(?x7100, ?x4969), award(?x5537, ?x720), award(?x2800, ?x720), film(?x4969, ?x1916), profession(?x4969, ?x1032), award_nominee(?x1134, ?x5537), ?x1916 = 0ch26b_, award_winner(?x3617, ?x3593), nationality(?x4969, ?x1310), award_winner(?x594, ?x4969), film(?x2800, ?x2112) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #7693 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 3 *> proper extension: 09n4nb; *> query: (?x7100, ?x3593) <- instance_of_recurring_event(?x7100, ?x3459), ceremony(?x3617, ?x7100), ceremony(?x720, ?x7100), award_winner(?x7100, ?x4969), award(?x5537, ?x720), award(?x2800, ?x720), film(?x4969, ?x1916), profession(?x4969, ?x1032), award_nominee(?x1134, ?x5537), ?x1916 = 0ch26b_, award_winner(?x3617, ?x3593), nationality(?x4969, ?x1310), award_winner(?x594, ?x4969), film(?x2800, ?x2112) *> conf = 0.06 ranks of expected_values: 592, 675 EVAL 0bzmt8 award_winner 0d5wn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 15.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0bzmt8 award_winner 0kr5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 34.000 15.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #671-0151ns PRED entity: 0151ns PRED relation: film PRED expected values: 0crc2cp => 124 concepts (77 used for prediction) PRED predicted values (max 10 best out of 990): 02_1sj (0.33 #5424, 0.33 #1860, 0.04 #41065), 035s95 (0.33 #2120, 0.17 #5684, 0.03 #35979), 07xvf (0.33 #3063, 0.17 #6627, 0.02 #49396), 04gv3db (0.33 #2531, 0.17 #6095, 0.02 #13223), 09p4w8 (0.33 #2608, 0.17 #6172, 0.02 #16865), 02ryz24 (0.33 #2247, 0.17 #5811, 0.02 #77094), 0dr_9t7 (0.33 #2525, 0.17 #6089, 0.01 #36384), 0h63gl9 (0.33 #2951, 0.17 #6515, 0.01 #40374), 026lgs (0.33 #2716, 0.17 #6280, 0.01 #40139), 0296vv (0.33 #1391, 0.08 #10301, 0.02 #17430) >> Best rule #5424 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 034g2b; 03jqw5; 02qx69; >> query: (?x558, 02_1sj) <- award(?x558, ?x154), film(?x558, ?x4694), nationality(?x558, ?x94), ?x4694 = 02j69w >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0151ns film 0crc2cp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 77.000 0.333 http://example.org/film/actor/film./film/performance/film #670-05dbf PRED entity: 05dbf PRED relation: participant PRED expected values: 02r6c_ => 125 concepts (112 used for prediction) PRED predicted values (max 10 best out of 346): 06dv3 (0.81 #36280, 0.80 #31829, 0.80 #40095), 07r1h (0.11 #1273, 0.06 #11463, 0.04 #18469), 05cljf (0.11 #1273, 0.06 #11463, 0.04 #18469), 0161sp (0.10 #26740, 0.10 #24830, 0.09 #24194), 09qr6 (0.10 #26740, 0.10 #24830, 0.09 #24194), 025n3p (0.10 #26740, 0.10 #24830, 0.09 #24194), 04shbh (0.10 #26740, 0.10 #24830, 0.09 #24194), 03f7jfh (0.09 #3821, 0.09 #1272, 0.08 #7004), 0237fw (0.09 #2068, 0.07 #3343, 0.06 #794), 01qq_lp (0.07 #22287, 0.07 #3185, 0.05 #17197) >> Best rule #36280 for best value: >> intensional similarity = 2 >> extensional distance = 495 >> proper extension: 07nznf; 0q9kd; 0184jc; 01vvydl; 0337vz; 01xdf5; 04t2l2; 0lbj1; 0h0jz; 05ty4m; ... >> query: (?x2275, ?x262) <- participant(?x262, ?x2275), film(?x2275, ?x308) >> conf = 0.81 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05dbf participant 02r6c_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 125.000 112.000 0.805 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #669-04bz7q PRED entity: 04bz7q PRED relation: nationality PRED expected values: 09c7w0 => 126 concepts (89 used for prediction) PRED predicted values (max 10 best out of 24): 09c7w0 (0.90 #1810, 0.89 #6059, 0.89 #3121), 03s5t (0.46 #8368, 0.36 #4544, 0.36 #3223), 0d060g (0.37 #7762, 0.36 #7461, 0.25 #909), 0jdx (0.37 #7762, 0.36 #7461, 0.25 #183), 02jx1 (0.37 #7762, 0.36 #7461, 0.15 #535), 07ssc (0.37 #7762, 0.36 #7461, 0.12 #216), 03rk0 (0.20 #2259, 0.12 #948, 0.06 #7909), 03_3d (0.08 #408, 0.06 #1010, 0.06 #708), 0chghy (0.07 #912, 0.04 #2223, 0.03 #2324), 0f8l9c (0.06 #2235, 0.03 #4566, 0.02 #3246) >> Best rule #1810 for best value: >> intensional similarity = 5 >> extensional distance = 120 >> proper extension: 01sl1q; 083chw; 03rs8y; 0c7ct; 03qd_; 066m4g; 05zbm4; 06b0d2; 04cf09; 01ztgm; ... >> query: (?x12683, ?x94) <- place_of_birth(?x12683, ?x2087), actor(?x9843, ?x12683), profession(?x12683, ?x1032), country(?x2087, ?x94), administrative_division(?x2087, ?x2768) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04bz7q nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 126.000 89.000 0.902 http://example.org/people/person/nationality #668-0f2tj PRED entity: 0f2tj PRED relation: jurisdiction_of_office! PRED expected values: 0pqc5 => 194 concepts (194 used for prediction) PRED predicted values (max 10 best out of 23): 0pqc5 (0.80 #327, 0.79 #465, 0.78 #718), 060c4 (0.32 #1222, 0.29 #1889, 0.28 #2441), 0f6c3 (0.31 #1733, 0.26 #2423, 0.26 #491), 0fkvn (0.30 #487, 0.30 #1729, 0.27 #1637), 060bp (0.30 #1220, 0.27 #1887, 0.26 #2048), 09n5b9 (0.27 #1737, 0.24 #2427, 0.21 #495), 0p5vf (0.14 #59, 0.13 #128, 0.11 #220), 01q24l (0.14 #14, 0.11 #198, 0.11 #290), 0fkzq (0.12 #500, 0.10 #1650, 0.09 #1742), 04syw (0.11 #260, 0.09 #1801, 0.06 #122) >> Best rule #327 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 0rh6k; 02dtg; 0f2r6; 02_286; 030qb3t; 094jv; 01_d4; 04f_d; 0dclg; 013yq; ... >> query: (?x6769, 0pqc5) <- location(?x1125, ?x6769), dog_breed(?x6769, ?x1706), origin(?x1247, ?x6769) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2tj jurisdiction_of_office! 0pqc5 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 194.000 0.800 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #667-07f1x PRED entity: 07f1x PRED relation: film_release_region! PRED expected values: 02vxq9m 011yrp 05p1tzf 0bwfwpj 0gvrws1 07f_7h 06ztvyx 0gh8zks 026njb5 09v71cj 043tvp3 05zvzf3 => 141 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1600): 08hmch (0.90 #7455, 0.89 #19697, 0.82 #13575), 02vxq9m (0.89 #19601, 0.76 #13479, 0.76 #7359), 0bpm4yw (0.87 #20081, 0.83 #7839, 0.82 #13959), 07s846j (0.87 #20046, 0.79 #7804, 0.74 #13924), 0gtsx8c (0.87 #19595, 0.72 #7353, 0.71 #13473), 043tvp3 (0.85 #14302, 0.84 #20424, 0.79 #8182), 0gj9tn5 (0.84 #19777, 0.76 #13655, 0.69 #7535), 0jjy0 (0.82 #13584, 0.82 #19706, 0.76 #7464), 05p1tzf (0.82 #13517, 0.76 #19639, 0.72 #7397), 0661ql3 (0.82 #19851, 0.79 #13729, 0.72 #7609) >> Best rule #7455 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 05r4w; 0d0vqn; 01ls2; 03_r3; 09pmkv; 07ylj; 0h7x; 01p1v; 03rk0; 06mkj; ... >> query: (?x7747, 08hmch) <- film_release_region(?x6528, ?x7747), film_release_region(?x5825, ?x7747), ?x6528 = 0dc_ms, ?x5825 = 067ghz >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #19601 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 36 *> proper extension: 0jgd; 03rt9; 01mjq; 0d0kn; 03h64; *> query: (?x7747, 02vxq9m) <- film_release_region(?x6621, ?x7747), ?x6621 = 0h63gl9 *> conf = 0.89 ranks of expected_values: 2, 6, 9, 23, 38, 70, 88, 89, 90, 126, 135, 144 EVAL 07f1x film_release_region! 05zvzf3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 043tvp3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 09v71cj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 026njb5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 0gh8zks CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 06ztvyx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 07f_7h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 0gvrws1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 0bwfwpj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 05p1tzf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 011yrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 07f1x film_release_region! 02vxq9m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 141.000 99.000 0.897 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #666-03tw2s PRED entity: 03tw2s PRED relation: institution! PRED expected values: 02h4rq6 => 140 concepts (103 used for prediction) PRED predicted values (max 10 best out of 19): 02h4rq6 (0.88 #103, 0.86 #83, 0.85 #165), 02_xgp2 (0.76 #111, 0.76 #10, 0.76 #91), 016t_3 (0.76 #104, 0.76 #3, 0.72 #84), 0bkj86 (0.71 #6, 0.69 #87, 0.68 #107), 07s6fsf (0.59 #1, 0.55 #82, 0.53 #102), 027f2w (0.59 #7, 0.53 #108, 0.52 #88), 03mkk4 (0.41 #9, 0.35 #110, 0.32 #69), 02mjs7 (0.35 #4, 0.26 #44, 0.21 #85), 0bjrnt (0.31 #147, 0.24 #5, 0.22 #331), 01rr_d (0.29 #14, 0.25 #340, 0.24 #135) >> Best rule #103 for best value: >> intensional similarity = 5 >> extensional distance = 32 >> proper extension: 08815; 03ksy; 01bm_; >> query: (?x6814, 02h4rq6) <- institution(?x1771, ?x6814), institution(?x734, ?x6814), school(?x580, ?x6814), ?x734 = 04zx3q1, ?x1771 = 019v9k >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03tw2s institution! 02h4rq6 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 103.000 0.882 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #665-0gy0n PRED entity: 0gy0n PRED relation: produced_by PRED expected values: 05prs8 => 97 concepts (79 used for prediction) PRED predicted values (max 10 best out of 183): 01xcfy (0.20 #388, 0.13 #8152, 0.12 #8151), 0150t6 (0.20 #388, 0.13 #8152, 0.12 #8151), 06pj8 (0.11 #67, 0.05 #2779, 0.04 #3168), 06chf (0.07 #487, 0.06 #1650, 0.02 #2424), 02q_cc (0.07 #808, 0.05 #33, 0.04 #4688), 0272kv (0.07 #704, 0.02 #2641, 0.02 #1867), 02lf0c (0.07 #798, 0.02 #2348, 0.01 #8176), 0j_c (0.07 #468, 0.02 #10947, 0.01 #2405), 01t6b4 (0.06 #1594, 0.04 #6640, 0.04 #7418), 05ty4m (0.06 #1563, 0.02 #8165, 0.02 #11266) >> Best rule #388 for best value: >> intensional similarity = 4 >> extensional distance = 17 >> proper extension: 04fzfj; 07j8r; 08gg47; 02gs6r; 02q3fdr; 01srq2; 0symg; 0_9l_; >> query: (?x11534, ?x2891) <- award_winner(?x11534, ?x2891), titles(?x600, ?x11534), country(?x11534, ?x252), ?x252 = 03_3d >> conf = 0.20 => this is the best rule for 2 predicted values *> Best rule #2766 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 106 *> proper extension: 053rxgm; *> query: (?x11534, 05prs8) <- award_winner(?x11534, ?x3069), nationality(?x3069, ?x1264), instrumentalists(?x227, ?x3069), nominated_for(?x3069, ?x667) *> conf = 0.04 ranks of expected_values: 29 EVAL 0gy0n produced_by 05prs8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 97.000 79.000 0.200 http://example.org/film/film/produced_by #664-0d8qb PRED entity: 0d8qb PRED relation: profession! PRED expected values: 019z7q 016hvl 01gzm2 03vrp 03v1xb 0c921 0hcvy 01g6bk => 52 concepts (21 used for prediction) PRED predicted values (max 10 best out of 4077): 0fb1q (0.75 #30214, 0.50 #20913, 0.46 #42767), 06cv1 (0.75 #25216, 0.40 #16852, 0.38 #41952), 015pxr (0.69 #42430, 0.62 #29877, 0.50 #25694), 0mb5x (0.64 #4182, 0.57 #23633, 0.50 #15267), 0d4jl (0.64 #4182, 0.57 #21881, 0.38 #30245), 045bg (0.64 #4182, 0.50 #12869, 0.43 #21235), 019z7q (0.64 #4182, 0.50 #25324, 0.40 #16960), 0ff3y (0.64 #4182, 0.50 #16616, 0.38 #29277), 025b3k (0.64 #4182, 0.50 #15675, 0.38 #32405), 0g72r (0.64 #4182, 0.50 #16540, 0.33 #12358) >> Best rule #30214 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0dxtg; 018gz8; 015cjr; >> query: (?x9081, 0fb1q) <- profession(?x7824, ?x9081), profession(?x5370, ?x9081), profession(?x4817, ?x9081), program(?x4817, ?x3075), student(?x3439, ?x7824), ?x5370 = 016gkf, student(?x865, ?x7824) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #4182 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 02hrh1q; *> query: (?x9081, ?x1236) <- profession(?x10398, ?x9081), profession(?x6708, ?x9081), profession(?x4028, ?x9081), profession(?x3710, ?x9081), ?x3710 = 03pvt, ?x10398 = 0jbp0, ?x6708 = 0g8st4, award_winner(?x921, ?x4028), influenced_by(?x1236, ?x4028), location_of_ceremony(?x4028, ?x9499) *> conf = 0.64 ranks of expected_values: 7, 21, 78, 121, 126, 298, 1054, 1302 EVAL 0d8qb profession! 01g6bk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 21.000 0.750 http://example.org/people/person/profession EVAL 0d8qb profession! 0hcvy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 52.000 21.000 0.750 http://example.org/people/person/profession EVAL 0d8qb profession! 0c921 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 21.000 0.750 http://example.org/people/person/profession EVAL 0d8qb profession! 03v1xb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 52.000 21.000 0.750 http://example.org/people/person/profession EVAL 0d8qb profession! 03vrp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 52.000 21.000 0.750 http://example.org/people/person/profession EVAL 0d8qb profession! 01gzm2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 52.000 21.000 0.750 http://example.org/people/person/profession EVAL 0d8qb profession! 016hvl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 52.000 21.000 0.750 http://example.org/people/person/profession EVAL 0d8qb profession! 019z7q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 52.000 21.000 0.750 http://example.org/people/person/profession #663-07kh6f3 PRED entity: 07kh6f3 PRED relation: music PRED expected values: 04pf4r => 80 concepts (49 used for prediction) PRED predicted values (max 10 best out of 75): 0146pg (0.17 #10, 0.07 #2340, 0.06 #851), 0bs1yy (0.08 #45, 0.02 #5129, 0.02 #255), 01njxvw (0.08 #191), 05y7hc (0.08 #126), 0151w_ (0.07 #9324, 0.07 #7842, 0.06 #8479), 018swb (0.07 #9324, 0.07 #7842, 0.06 #8479), 0f6_dy (0.06 #8479, 0.06 #8054, 0.06 #8267), 02s2ft (0.06 #8479, 0.06 #8054, 0.06 #8267), 0ksrf8 (0.06 #8479, 0.06 #8054, 0.06 #8267), 0blq0z (0.06 #8479, 0.06 #8054, 0.06 #8267) >> Best rule #10 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 016fyc; >> query: (?x3790, 0146pg) <- currency(?x3790, ?x170), film(?x2122, ?x3790), ?x2122 = 018swb >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #488 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 117 *> proper extension: 02v8kmz; 03g90h; 02z3r8t; 06_wqk4; 05sxzwc; 0b76t12; 02vqhv0; 0g3zrd; 04q00lw; 05h43ls; ... *> query: (?x3790, 04pf4r) <- film_crew_role(?x3790, ?x5136), ?x5136 = 089g0h *> conf = 0.03 ranks of expected_values: 23 EVAL 07kh6f3 music 04pf4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 80.000 49.000 0.167 http://example.org/film/film/music #662-0fby2t PRED entity: 0fby2t PRED relation: film PRED expected values: 05_5_22 => 98 concepts (66 used for prediction) PRED predicted values (max 10 best out of 838): 0g3zrd (0.40 #365, 0.33 #2143, 0.03 #92460), 03qcfvw (0.20 #9, 0.17 #1787, 0.06 #5343), 0ds11z (0.20 #64, 0.17 #1842, 0.06 #5398), 0ch3qr1 (0.20 #968, 0.17 #2746, 0.03 #11636), 02r79_h (0.20 #227, 0.17 #2005, 0.03 #10895), 03459x (0.20 #565, 0.17 #2343, 0.03 #92460), 02mmwk (0.20 #1251, 0.17 #3029, 0.03 #92460), 04xx9s (0.20 #1137, 0.17 #2915, 0.03 #92460), 0gwlfnb (0.20 #1494, 0.17 #3272, 0.03 #92460), 0h63gl9 (0.20 #1165, 0.17 #2943, 0.03 #92460) >> Best rule #365 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 01vw26l; 06lvlf; >> query: (?x4325, 0g3zrd) <- film(?x4325, ?x1642), ?x1642 = 0bq8tmw, people(?x1050, ?x4325) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #92460 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1490 *> proper extension: 076df9; *> query: (?x4325, ?x86) <- award_nominee(?x6622, ?x4325), gender(?x4325, ?x231), film(?x6622, ?x86) *> conf = 0.03 ranks of expected_values: 235 EVAL 0fby2t film 05_5_22 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 98.000 66.000 0.400 http://example.org/film/actor/film./film/performance/film #661-0dszr0 PRED entity: 0dszr0 PRED relation: gender PRED expected values: 02zsn => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.78 #53, 0.76 #13, 0.75 #87), 02zsn (0.38 #2, 0.35 #20, 0.34 #10) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 468 >> proper extension: 03qcq; 084w8; 07w21; 041h0; 01zkxv; 07g2b; 0168cl; 01vrncs; 0m77m; 045bg; ... >> query: (?x13195, 05zppz) <- profession(?x13195, ?x1146), location(?x13195, ?x362), profession(?x7752, ?x1146), ?x7752 = 05l0j5 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #2 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 032w8h; 0fb1q; 014z8v; 04cl1; 0pyww; 07663r; *> query: (?x13195, 02zsn) <- profession(?x13195, ?x1146), location(?x13195, ?x1131), ?x1146 = 018gz8, ?x1131 = 0cc56 *> conf = 0.38 ranks of expected_values: 2 EVAL 0dszr0 gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 68.000 68.000 0.781 http://example.org/people/person/gender #660-02l6dy PRED entity: 02l6dy PRED relation: award_winner! PRED expected values: 0g55tzk => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 95): 0g55tzk (0.46 #136, 0.11 #276, 0.10 #8542), 092t4b (0.10 #8542, 0.10 #7841, 0.08 #51), 09gkdln (0.10 #8542, 0.10 #7841, 0.08 #121), 03gyp30 (0.10 #8542, 0.10 #7841, 0.08 #116), 0clfdj (0.10 #8542, 0.10 #7841, 0.08 #4), 0g5b0q5 (0.10 #8542, 0.10 #7841, 0.08 #19), 0gx_st (0.10 #8542, 0.10 #7841, 0.08 #36), 04n2r9h (0.10 #8542, 0.10 #7841, 0.08 #44), 0fqpc7d (0.10 #8542, 0.10 #7841, 0.07 #175), 092c5f (0.10 #8542, 0.10 #7841, 0.04 #153) >> Best rule #136 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01wbg84; 0f830f; 08w7vj; 02tr7d; 0fx0mw; 03yj_0n; 07s8hms; 0cjsxp; 0bx0lc; 0dyztm; ... >> query: (?x6031, 0g55tzk) <- award_winner(?x6031, ?x494), award_nominee(?x6031, ?x6360), ?x6360 = 02sb1w >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02l6dy award_winner! 0g55tzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.462 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #659-09bx1k PRED entity: 09bx1k PRED relation: nominated_for PRED expected values: 04vq33 => 104 concepts (61 used for prediction) PRED predicted values (max 10 best out of 509): 0gl3hr (0.25 #996, 0.20 #2617, 0.02 #66494), 072kp (0.20 #1707, 0.04 #17920, 0.01 #69827), 0d68qy (0.20 #1995, 0.03 #31185, 0.03 #44161), 0g60z (0.20 #1662, 0.03 #27607, 0.03 #30852), 0888c3 (0.20 #4504, 0.03 #14232, 0.03 #20718), 02czd5 (0.20 #2917, 0.02 #19130, 0.01 #51570), 039cq4 (0.20 #2706, 0.02 #51359, 0.02 #70826), 04bp0l (0.20 #3242), 05pbsry (0.20 #3238), 024hbv (0.20 #3212) >> Best rule #996 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 01bh6y; 013ybx; >> query: (?x5289, 0gl3hr) <- award_winner(?x9716, ?x5289), award_nominee(?x5289, ?x11921), ?x9716 = 015cbq >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #24306 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 201 *> proper extension: 09qc1; 01fxfk; *> query: (?x5289, 04vq33) <- award_nominee(?x5289, ?x11921), nationality(?x11921, ?x94), place_of_death(?x5289, ?x1523) *> conf = 0.02 ranks of expected_values: 127 EVAL 09bx1k nominated_for 04vq33 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 104.000 61.000 0.250 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #658-06qgjh PRED entity: 06qgjh PRED relation: film PRED expected values: 06c0ns => 93 concepts (42 used for prediction) PRED predicted values (max 10 best out of 278): 06ztvyx (0.49 #50017, 0.48 #75027, 0.35 #48227), 01jmyj (0.14 #3256, 0.14 #1468, 0.11 #5043), 0k_9j (0.14 #3192, 0.11 #4979, 0.01 #6765), 0jzw (0.14 #1907, 0.11 #3694, 0.01 #14410), 0kvgtf (0.14 #2408, 0.11 #4195, 0.01 #39916), 0199wf (0.14 #3445, 0.11 #5232), 0gyv0b4 (0.14 #3440, 0.11 #5227), 01xvjb (0.14 #3295, 0.11 #5082), 0f61tk (0.14 #3257, 0.11 #5044), 0f3m1 (0.14 #3241, 0.11 #5028) >> Best rule #50017 for best value: >> intensional similarity = 3 >> extensional distance = 949 >> proper extension: 0146pg; 08wq0g; 0jf1b; 034x61; 016khd; 01j5x6; 02gvwz; 0277470; 07s8r0; 02k6rq; ... >> query: (?x8432, ?x1904) <- location(?x8432, ?x5771), award_nominee(?x5316, ?x8432), nominated_for(?x8432, ?x1904) >> conf = 0.49 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06qgjh film 06c0ns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 93.000 42.000 0.488 http://example.org/film/actor/film./film/performance/film #657-019tfm PRED entity: 019tfm PRED relation: contains! PRED expected values: 0tln7 => 130 concepts (95 used for prediction) PRED predicted values (max 10 best out of 129): 0tln7 (0.78 #4473, 0.78 #45630, 0.77 #32209), 0f2tj (0.21 #365, 0.17 #1259, 0.02 #3943), 02jx1 (0.20 #84195, 0.16 #43029, 0.14 #23346), 059rby (0.18 #4492, 0.17 #17019, 0.13 #82336), 01n7q (0.16 #6338, 0.15 #8128, 0.15 #4550), 03v1s (0.14 #1814, 0.03 #2709, 0.03 #20602), 07ssc (0.14 #84140, 0.10 #32240, 0.09 #42974), 05tbn (0.08 #17223, 0.07 #82540, 0.07 #18117), 05k7sb (0.08 #3710, 0.07 #17132, 0.07 #82449), 0d060g (0.07 #84121, 0.07 #50115, 0.07 #27747) >> Best rule #4473 for best value: >> intensional similarity = 4 >> extensional distance = 141 >> proper extension: 01fpvz; >> query: (?x14319, ?x5015) <- contains(?x94, ?x14319), citytown(?x14319, ?x5015), ?x94 = 09c7w0, category(?x5015, ?x134) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 019tfm contains! 0tln7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 95.000 0.783 http://example.org/location/location/contains #656-02c7k4 PRED entity: 02c7k4 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 59 concepts (59 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.81 #133, 0.80 #212, 0.80 #154), 02nxhr (0.04 #124, 0.04 #17, 0.04 #140), 07c52 (0.04 #90, 0.03 #50, 0.03 #70), 07z4p (0.03 #92, 0.02 #41, 0.02 #194) >> Best rule #133 for best value: >> intensional similarity = 4 >> extensional distance = 830 >> proper extension: 047svrl; 01gglm; >> query: (?x6256, 029j_) <- film(?x318, ?x6256), titles(?x1510, ?x6256), currency(?x6256, ?x170), nominated_for(?x4850, ?x6256) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02c7k4 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 59.000 59.000 0.809 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #655-01wmgrf PRED entity: 01wmgrf PRED relation: profession PRED expected values: 0dz3r => 103 concepts (102 used for prediction) PRED predicted values (max 10 best out of 61): 016z4k (0.86 #448, 0.60 #4, 0.60 #596), 09jwl (0.76 #758, 0.69 #2684, 0.68 #5798), 0nbcg (0.49 #6259, 0.49 #6408, 0.48 #5811), 0dz3r (0.46 #742, 0.41 #890, 0.41 #6230), 01d_h8 (0.34 #4302, 0.34 #3562, 0.34 #2524), 01c72t (0.29 #4912, 0.29 #5803, 0.29 #2689), 039v1 (0.29 #480, 0.28 #628, 0.28 #2702), 03gjzk (0.25 #4310, 0.25 #3570, 0.24 #5348), 0dxtg (0.25 #9654, 0.25 #11578, 0.24 #12318), 02jknp (0.21 #3120, 0.20 #3416, 0.19 #10537) >> Best rule #448 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 036px; 01x0yrt; 016j2t; >> query: (?x3122, 016z4k) <- award(?x3122, ?x2420), ?x2420 = 026mfs, place_of_birth(?x3122, ?x12941) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #742 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 01m65sp; *> query: (?x3122, 0dz3r) <- participant(?x3122, ?x4560), instrumentalists(?x227, ?x3122), category(?x3122, ?x134) *> conf = 0.46 ranks of expected_values: 4 EVAL 01wmgrf profession 0dz3r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 103.000 102.000 0.857 http://example.org/people/person/profession #654-0187y5 PRED entity: 0187y5 PRED relation: profession PRED expected values: 01d_h8 => 104 concepts (103 used for prediction) PRED predicted values (max 10 best out of 69): 0gl2ny2 (0.46 #3109, 0.36 #3689, 0.34 #4269), 01d_h8 (0.43 #296, 0.43 #151, 0.42 #1311), 0dxtg (0.42 #593, 0.30 #448, 0.29 #7843), 0fj9f (0.40 #51, 0.11 #1646, 0.11 #3531), 09jwl (0.37 #5962, 0.28 #3352, 0.26 #10006), 01445t (0.35 #745, 0.33 #890, 0.32 #1180), 0d1pc (0.29 #337, 0.26 #10006, 0.23 #1932), 015cjr (0.29 #191, 0.17 #626, 0.12 #771), 0dz3r (0.27 #3337, 0.22 #5947, 0.15 #7542), 0nbcg (0.26 #10006, 0.26 #5973, 0.23 #3363) >> Best rule #3109 for best value: >> intensional similarity = 2 >> extensional distance = 103 >> proper extension: 0dhrqx; 02qny_; 051q39; >> query: (?x703, 0gl2ny2) <- athlete(?x1083, ?x703), profession(?x703, ?x524) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #296 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 5 *> proper extension: 0408np; 01kj0p; 0gy6z9; 016vg8; 07r1h; *> query: (?x703, 01d_h8) <- award_nominee(?x703, ?x1958), participant(?x703, ?x4119), ?x1958 = 02wgln *> conf = 0.43 ranks of expected_values: 2 EVAL 0187y5 profession 01d_h8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 103.000 0.457 http://example.org/people/person/profession #653-01rv7x PRED entity: 01rv7x PRED relation: geographic_distribution PRED expected values: 04wsz => 34 concepts (34 used for prediction) PRED predicted values (max 10 best out of 209): 09c7w0 (0.70 #1008, 0.64 #1223, 0.59 #1367), 0345h (0.33 #234, 0.33 #21, 0.29 #810), 07f1x (0.33 #273, 0.33 #60, 0.21 #849), 0697s (0.33 #251, 0.33 #38, 0.17 #538), 0j1z8 (0.33 #220, 0.33 #7, 0.17 #507), 0hzlz (0.33 #226, 0.33 #13, 0.17 #513), 03spz (0.33 #49, 0.25 #333, 0.17 #262), 06qd3 (0.33 #235, 0.21 #811, 0.17 #522), 03_3d (0.33 #218, 0.21 #794, 0.17 #505), 0chghy (0.33 #221, 0.17 #508, 0.14 #797) >> Best rule #1008 for best value: >> intensional similarity = 9 >> extensional distance = 21 >> proper extension: 01qhm_; 03ttfc; 0x67; 07hwkr; 0xnvg; 0g6ff; 07bch9; 03295l; 06gbnc; 059_w; ... >> query: (?x9347, 09c7w0) <- people(?x9347, ?x3873), geographic_distribution(?x9347, ?x2146), film_release_region(?x4684, ?x2146), film_release_region(?x3292, ?x2146), film_release_region(?x3217, ?x2146), ?x3217 = 0gffmn8, nationality(?x111, ?x2146), ?x4684 = 03nm_fh, ?x3292 = 0gvs1kt >> conf = 0.70 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01rv7x geographic_distribution 04wsz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 34.000 34.000 0.696 http://example.org/people/ethnicity/geographic_distribution #652-0ndsl1x PRED entity: 0ndsl1x PRED relation: film_release_region PRED expected values: 0jgd 0154j 03rjj 04g5k => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 74): 09c7w0 (0.92 #1476, 0.92 #5639, 0.92 #5234), 03rjj (0.86 #274, 0.84 #408, 0.82 #542), 0jgd (0.84 #272, 0.82 #406, 0.79 #540), 0154j (0.80 #273, 0.77 #407, 0.74 #541), 0d060g (0.78 #276, 0.74 #410, 0.72 #544), 01ls2 (0.48 #280, 0.45 #414, 0.38 #548), 06f32 (0.47 #315, 0.45 #449, 0.42 #583), 03rk0 (0.46 #308, 0.44 #442, 0.39 #576), 06t8v (0.45 #326, 0.45 #460, 0.39 #594), 05qx1 (0.41 #297, 0.38 #431, 0.33 #565) >> Best rule #1476 for best value: >> intensional similarity = 2 >> extensional distance = 690 >> proper extension: 04969y; 016kz1; 02q3fdr; 012jfb; 02zk08; 0564x; 0cbl95; >> query: (?x9002, 09c7w0) <- film_release_region(?x9002, ?x87), award_winner(?x9002, ?x4536) >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #274 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 194 *> proper extension: 0bhwhj; 07s3m4g; 0g4pl7z; *> query: (?x9002, 03rjj) <- film_release_region(?x9002, ?x583), film_release_region(?x9002, ?x151), ?x583 = 015fr, ?x151 = 0b90_r *> conf = 0.86 ranks of expected_values: 2, 3, 4, 25 EVAL 0ndsl1x film_release_region 04g5k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 64.000 64.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ndsl1x film_release_region 03rjj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ndsl1x film_release_region 0154j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0ndsl1x film_release_region 0jgd CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.925 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #651-09g7vfw PRED entity: 09g7vfw PRED relation: genre PRED expected values: 07s9rl0 => 96 concepts (90 used for prediction) PRED predicted values (max 10 best out of 164): 07s9rl0 (0.76 #9231, 0.72 #9585, 0.71 #8637), 04xvlr (0.66 #3899, 0.16 #2598, 0.15 #2362), 03k9fj (0.58 #600, 0.53 #718, 0.48 #954), 02kdv5l (0.53 #711, 0.52 #947, 0.52 #2835), 02l7c8 (0.34 #4265, 0.34 #4383, 0.33 #4501), 04pbhw (0.33 #408, 0.25 #54, 0.21 #762), 060__y (0.33 #133, 0.21 #3912, 0.21 #487), 04xvh5 (0.33 #150, 0.14 #3929, 0.13 #386), 0lsxr (0.33 #1777, 0.27 #361, 0.26 #6036), 06n90 (0.32 #719, 0.30 #955, 0.24 #2135) >> Best rule #9231 for best value: >> intensional similarity = 5 >> extensional distance = 1327 >> proper extension: 015qsq; 0c0yh4; 05jf85; 0209xj; 02py4c8; 07ng9k; 0sxfd; 02bg8v; 016kv6; 0d1qmz; ... >> query: (?x3423, 07s9rl0) <- genre(?x3423, ?x571), titles(?x571, ?x249), genre(?x3413, ?x571), genre(?x3757, ?x571), ?x3757 = 02vr3gz >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09g7vfw genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 90.000 0.757 http://example.org/film/film/genre #650-0192hw PRED entity: 0192hw PRED relation: featured_film_locations PRED expected values: 01914 => 131 concepts (104 used for prediction) PRED predicted values (max 10 best out of 204): 02_286 (0.82 #14993, 0.76 #16869, 0.63 #14288), 04jpl (0.58 #9359, 0.58 #7255, 0.38 #15450), 0rh6k (0.46 #5838, 0.31 #7247, 0.29 #9351), 01_d4 (0.32 #5413, 0.09 #15956, 0.08 #17827), 030qb3t (0.32 #15948, 0.29 #17354, 0.28 #2602), 0qpqn (0.25 #387), 0cv3w (0.19 #5435, 0.08 #1931, 0.06 #14808), 027kp3 (0.14 #1029, 0.12 #1494, 0.08 #1959), 0ljsz (0.14 #1108, 0.12 #1573, 0.08 #2038), 080h2 (0.11 #15933, 0.11 #14763, 0.09 #17339) >> Best rule #14993 for best value: >> intensional similarity = 8 >> extensional distance = 216 >> proper extension: 038bh3; 0dpl44; >> query: (?x3257, 02_286) <- featured_film_locations(?x3257, ?x6054), country(?x3257, ?x94), place_of_death(?x11772, ?x6054), nominated_for(?x11772, ?x5212), country(?x6054, ?x7747), ?x94 = 09c7w0, place_of_birth(?x11772, ?x479), profession(?x11772, ?x987) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #5844 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 76 *> proper extension: 0bxxzb; 05y0cr; *> query: (?x3257, 01914) <- featured_film_locations(?x3257, ?x6054), featured_film_locations(?x3257, ?x4419), country(?x3257, ?x94), capital(?x7747, ?x6054), category(?x6054, ?x134), contains(?x938, ?x4419), location(?x2226, ?x4419), taxonomy(?x938, ?x939) *> conf = 0.03 ranks of expected_values: 91 EVAL 0192hw featured_film_locations 01914 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 131.000 104.000 0.821 http://example.org/film/film/featured_film_locations #649-0lfbm PRED entity: 0lfbm PRED relation: location PRED expected values: 04jpl => 109 concepts (106 used for prediction) PRED predicted values (max 10 best out of 143): 02_286 (0.22 #3254, 0.19 #8887, 0.17 #6473), 030qb3t (0.17 #18589, 0.16 #24226, 0.14 #2496), 04jpl (0.15 #7257, 0.10 #6453, 0.09 #3234), 013yq (0.08 #1728, 0.07 #6555, 0.03 #8969), 01cx_ (0.08 #1772, 0.04 #9818, 0.03 #7403), 0ftyc (0.08 #1868, 0.03 #6695, 0.01 #9109), 0tbql (0.08 #1793, 0.03 #6620, 0.01 #9034), 0ccvx (0.08 #1831, 0.02 #53331, 0.02 #58961), 0y617 (0.08 #2284), 0rh6k (0.07 #6440, 0.03 #7244, 0.03 #23343) >> Best rule #3254 for best value: >> intensional similarity = 4 >> extensional distance = 21 >> proper extension: 0btyl; 01d6jf; >> query: (?x6852, 02_286) <- people(?x3680, ?x6852), nationality(?x6852, ?x94), film(?x6852, ?x2370), spouse(?x9256, ?x6852) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #7257 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 31 *> proper extension: 07m69t; *> query: (?x6852, 04jpl) <- nationality(?x6852, ?x512), nationality(?x6852, ?x94), ?x94 = 09c7w0, ?x512 = 07ssc *> conf = 0.15 ranks of expected_values: 3 EVAL 0lfbm location 04jpl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 109.000 106.000 0.217 http://example.org/people/person/places_lived./people/place_lived/location #648-01gvyp PRED entity: 01gvyp PRED relation: nominated_for PRED expected values: 030p35 => 124 concepts (55 used for prediction) PRED predicted values (max 10 best out of 405): 0n6ds (0.36 #8106, 0.27 #32427, 0.24 #85932), 02v570 (0.33 #1152, 0.14 #2773, 0.01 #81067), 01b_lz (0.12 #5364, 0.01 #6985, 0.01 #13471), 0gj50 (0.10 #3844, 0.01 #47623), 01b66d (0.10 #3710, 0.01 #47489), 06qv_ (0.10 #4748), 027r9t (0.10 #4354), 01zfzb (0.10 #4088), 02k_4g (0.09 #4970, 0.02 #14698, 0.02 #17940), 0cfhfz (0.09 #5313) >> Best rule #8106 for best value: >> intensional similarity = 3 >> extensional distance = 71 >> proper extension: 023tp8; 01gw4f; >> query: (?x6951, ?x407) <- film(?x6951, ?x407), award(?x6951, ?x154), ?x154 = 05b4l5x >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #5582 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 30 *> proper extension: 0347xl; 02jtjz; *> query: (?x6951, 030p35) <- location(?x6951, ?x6952), nominated_for(?x6951, ?x407), award(?x6951, ?x3184), ?x3184 = 0gkts9 *> conf = 0.03 ranks of expected_values: 101 EVAL 01gvyp nominated_for 030p35 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 124.000 55.000 0.359 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #647-01mxt_ PRED entity: 01mxt_ PRED relation: artists! PRED expected values: 0hdf8 02yw0y => 152 concepts (96 used for prediction) PRED predicted values (max 10 best out of 257): 06by7 (0.64 #21, 0.63 #2805, 0.59 #2496), 0cx7f (0.62 #446, 0.31 #12831, 0.23 #6646), 02yv6b (0.55 #97, 0.26 #3811, 0.22 #2572), 016clz (0.48 #27873, 0.30 #13318, 0.29 #23838), 0155w (0.45 #105, 0.37 #2889, 0.35 #2269), 064t9 (0.43 #20756, 0.42 #27882, 0.40 #8688), 05bt6j (0.43 #27911, 0.29 #12737, 0.27 #2207), 03_d0 (0.38 #2176, 0.33 #2487, 0.27 #2796), 05w3f (0.37 #2512, 0.36 #37, 0.33 #655), 06j6l (0.32 #1284, 0.27 #2832, 0.26 #20790) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 016m5c; >> query: (?x5587, 06by7) <- artists(?x1000, ?x5587), peers(?x5587, ?x7987), ?x1000 = 0xhtw >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #12764 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 97 *> proper extension: 01t_xp_; 01wv9xn; 0dtd6; 01czx; 0167_s; 05563d; 0fcsd; 013w2r; 0l8g0; 02vgh; ... *> query: (?x5587, 0hdf8) <- artists(?x1380, ?x5587), ?x1380 = 0dl5d *> conf = 0.11 ranks of expected_values: 44, 56 EVAL 01mxt_ artists! 02yw0y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 152.000 96.000 0.636 http://example.org/music/genre/artists EVAL 01mxt_ artists! 0hdf8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 152.000 96.000 0.636 http://example.org/music/genre/artists #646-0969fd PRED entity: 0969fd PRED relation: type_of_union PRED expected values: 04ztj => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.84 #17, 0.84 #242, 0.81 #150), 01g63y (0.16 #78, 0.15 #50, 0.14 #58), 01bl8s (0.02 #91, 0.02 #103, 0.02 #107), 0jgjn (0.02 #36) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 084w8; 07w21; 04411; 0yfp; 01d494; 0453t; 0jt90f5; 01w8sf; 0jcx; 05wh0sh; ... >> query: (?x10677, 04ztj) <- gender(?x10677, ?x231), influenced_by(?x10677, ?x3428), student(?x1368, ?x10677), nationality(?x3428, ?x291) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0969fd type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.837 http://example.org/people/person/spouse_s./people/marriage/type_of_union #645-01l_pn PRED entity: 01l_pn PRED relation: production_companies PRED expected values: 03rwz3 => 56 concepts (47 used for prediction) PRED predicted values (max 10 best out of 60): 017s11 (0.48 #251, 0.30 #2423, 0.12 #250), 086k8 (0.14 #168, 0.13 #2, 0.11 #336), 026c1 (0.12 #250, 0.03 #2926, 0.03 #3099), 0bksh (0.12 #250, 0.03 #2926, 0.03 #3184), 019pm_ (0.12 #250, 0.03 #2926, 0.03 #3184), 011zd3 (0.12 #250, 0.03 #2926, 0.03 #3184), 01m7f5r (0.12 #250, 0.03 #3771, 0.03 #3269), 05qd_ (0.12 #10, 0.10 #176, 0.09 #427), 016tt2 (0.11 #170, 0.07 #421, 0.07 #836), 01gb54 (0.09 #455, 0.08 #538, 0.05 #953) >> Best rule #251 for best value: >> intensional similarity = 3 >> extensional distance = 164 >> proper extension: 04bp0l; >> query: (?x5608, ?x541) <- nominated_for(?x541, ?x5608), film(?x541, ?x7029), actor(?x7029, ?x51) >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #140 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 132 *> proper extension: 03bx2lk; 0bz3jx; 02n72k; 01jr4j; 07ghq; 029v40; *> query: (?x5608, 03rwz3) <- film(?x1206, ?x5608), language(?x5608, ?x2502), ?x2502 = 06nm1 *> conf = 0.01 ranks of expected_values: 57 EVAL 01l_pn production_companies 03rwz3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 56.000 47.000 0.476 http://example.org/film/film/production_companies #644-05c1t6z PRED entity: 05c1t6z PRED relation: honored_for PRED expected values: 0828jw 03nt59 04110lv 06y_n 07s8z_l => 44 concepts (32 used for prediction) PRED predicted values (max 10 best out of 774): 07s8z_l (0.40 #2240, 0.39 #4561, 0.33 #1672), 0dsx3f (0.40 #2084, 0.33 #945, 0.33 #374), 06y_n (0.39 #4561, 0.33 #1652, 0.33 #510), 01kt_j (0.39 #4561, 0.33 #532, 0.20 #2242), 03nt59 (0.38 #4342, 0.33 #1492, 0.33 #350), 05lfwd (0.38 #3756, 0.33 #332, 0.25 #5464), 0cs134 (0.38 #3957, 0.33 #533, 0.25 #5665), 0fhzwl (0.33 #481, 0.27 #5044, 0.25 #5613), 0266s9 (0.33 #554, 0.25 #3978, 0.20 #2264), 030k94 (0.33 #183, 0.25 #3607, 0.20 #1893) >> Best rule #2240 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 02cg41; >> query: (?x1265, 07s8z_l) <- award_winner(?x1265, ?x1896), award_winner(?x1265, ?x1285), ceremony(?x11272, ?x1265), produced_by(?x1219, ?x1285), honored_for(?x1265, ?x3310), ?x1896 = 0j1yf, award_nominee(?x1285, ?x11876), nominated_for(?x1285, ?x10595), film_release_region(?x1219, ?x87), award_winner(?x5277, ?x11876), nominated_for(?x1342, ?x3310), award(?x1986, ?x11272) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 5, 32 EVAL 05c1t6z honored_for 07s8z_l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 32.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 05c1t6z honored_for 06y_n CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 44.000 32.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 05c1t6z honored_for 04110lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 44.000 32.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 05c1t6z honored_for 03nt59 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 44.000 32.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 05c1t6z honored_for 0828jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 44.000 32.000 0.400 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #643-01s0_f PRED entity: 01s0_f PRED relation: student PRED expected values: 02r_d4 011_3s 046m59 => 170 concepts (118 used for prediction) PRED predicted values (max 10 best out of 1579): 0ff3y (0.25 #4155, 0.07 #8333, 0.05 #6244), 07g2b (0.17 #2164, 0.04 #6342, 0.03 #8431), 0btxr (0.17 #3695, 0.03 #30854, 0.02 #37121), 0kvqv (0.17 #2817, 0.02 #86383, 0.02 #65490), 04hw4b (0.10 #1233, 0.08 #3322, 0.05 #5411), 019vgs (0.10 #628, 0.04 #119083, 0.03 #29876), 033w9g (0.10 #772, 0.03 #30020, 0.02 #34198), 02l5rm (0.10 #476, 0.02 #13010, 0.02 #65238), 025b3k (0.10 #1648, 0.02 #16271, 0.02 #18360), 037d35 (0.10 #1056, 0.01 #21948, 0.01 #24037) >> Best rule #4155 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 01_f90; >> query: (?x2228, 0ff3y) <- institution(?x865, ?x2228), major_field_of_study(?x2228, ?x10380), ?x10380 = 02stgt >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #12618 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 47 *> proper extension: 04b_46; 033gn8; 02237m; *> query: (?x2228, 02r_d4) <- institution(?x3386, ?x2228), major_field_of_study(?x2228, ?x1154), ?x3386 = 03mkk4 *> conf = 0.02 ranks of expected_values: 989, 1419 EVAL 01s0_f student 046m59 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 170.000 118.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01s0_f student 011_3s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 170.000 118.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 01s0_f student 02r_d4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 170.000 118.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #642-01j5sd PRED entity: 01j5sd PRED relation: location PRED expected values: 0cr3d => 122 concepts (119 used for prediction) PRED predicted values (max 10 best out of 165): 02_286 (0.31 #57810, 0.21 #7257, 0.21 #16084), 030qb3t (0.25 #34583, 0.24 #57856, 0.23 #20943), 04jpl (0.17 #5633, 0.11 #57790, 0.07 #16064), 0cr3d (0.07 #70757, 0.07 #14587, 0.06 #78781), 0r0m6 (0.07 #1019, 0.06 #3426, 0.04 #5031), 059rby (0.06 #6434, 0.06 #16, 0.06 #57789), 01n7q (0.06 #63, 0.05 #2469, 0.05 #12902), 0dclg (0.06 #116, 0.04 #918, 0.03 #2522), 03pzf (0.06 #523, 0.02 #2127, 0.02 #6139), 02jx1 (0.05 #5687, 0.03 #71, 0.02 #1675) >> Best rule #57810 for best value: >> intensional similarity = 3 >> extensional distance = 1101 >> proper extension: 012zng; 06101p; >> query: (?x8269, 02_286) <- profession(?x8269, ?x319), location(?x8269, ?x6357), film_release_region(?x6394, ?x6357) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #70757 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1730 *> proper extension: 0f0y8; 0274ck; 0487c3; 01p45_v; 080dyk; 01ky2h; 08m4c8; 014dq7; 0g51l1; 04xjp; ... *> query: (?x8269, 0cr3d) <- profession(?x8269, ?x319), location(?x8269, ?x6357), origin(?x1321, ?x6357) *> conf = 0.07 ranks of expected_values: 4 EVAL 01j5sd location 0cr3d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 122.000 119.000 0.308 http://example.org/people/person/places_lived./people/place_lived/location #641-024mxd PRED entity: 024mxd PRED relation: story_by PRED expected values: 011s9r => 114 concepts (50 used for prediction) PRED predicted values (max 10 best out of 114): 079vf (0.35 #1947, 0.33 #436, 0.21 #2811), 0kb3n (0.33 #793, 0.33 #142, 0.31 #3457), 011s9r (0.33 #197, 0.31 #3457, 0.20 #1494), 07nznf (0.31 #3457, 0.20 #217, 0.17 #435), 0jrny (0.25 #216, 0.22 #867, 0.15 #4538), 0fx02 (0.21 #3301, 0.14 #6331, 0.09 #7197), 079ws (0.18 #2076, 0.17 #565, 0.11 #2940), 046_v (0.18 #2117, 0.11 #2981, 0.10 #3630), 02nygk (0.18 #2155, 0.11 #3019, 0.10 #3668), 04zd4m (0.17 #451, 0.06 #1962, 0.05 #2179) >> Best rule #1947 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 09sh8k; 01hr1; 0czyxs; 01hp5; 0645k5; 024mpp; 057lbk; 02wgk1; 0fqt1ns; 0dzlbx; ... >> query: (?x3672, 079vf) <- produced_by(?x3672, ?x2724), genre(?x3672, ?x6888), story_by(?x3672, ?x8209), film(?x773, ?x3672), language(?x3672, ?x90), ?x6888 = 04pbhw >> conf = 0.35 => this is the best rule for 1 predicted values *> Best rule #197 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 01_mdl; *> query: (?x3672, 011s9r) <- nominated_for(?x3672, ?x7425), language(?x3672, ?x90), nominated_for(?x154, ?x3672), film_release_region(?x3672, ?x94), written_by(?x3672, ?x3194), ?x7425 = 042fgh *> conf = 0.33 ranks of expected_values: 3 EVAL 024mxd story_by 011s9r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 114.000 50.000 0.353 http://example.org/film/film/story_by #640-07h5d PRED entity: 07h5d PRED relation: profession PRED expected values: 0dxtg => 69 concepts (69 used for prediction) PRED predicted values (max 10 best out of 47): 0dxtg (0.84 #1026, 0.83 #736, 0.83 #301), 03gjzk (0.45 #737, 0.40 #1027, 0.38 #302), 018gz8 (0.27 #6818, 0.26 #7979, 0.15 #739), 02hv44_ (0.27 #6818, 0.26 #7979, 0.12 #54), 015cjr (0.27 #6818, 0.26 #7979, 0.03 #2367), 05z96 (0.27 #6818, 0.26 #7979, 0.03 #2650), 03jgz (0.27 #6818, 0.26 #7979), 0747nrk (0.27 #6818, 0.26 #7979), 02krf9 (0.25 #169, 0.21 #894, 0.21 #1619), 09jwl (0.22 #1176, 0.21 #2482, 0.20 #3207) >> Best rule #1026 for best value: >> intensional similarity = 3 >> extensional distance = 279 >> proper extension: 0qf43; 019z7q; 04gcd1; 04k25; 01q4qv; 085pr; 0kvqv; 015njf; 0534v; 0mm1q; ... >> query: (?x7352, 0dxtg) <- nominated_for(?x7352, ?x1481), written_by(?x1842, ?x7352), profession(?x7352, ?x319) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07h5d profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 69.000 69.000 0.840 http://example.org/people/person/profession #639-0g970 PRED entity: 0g970 PRED relation: combatants! PRED expected values: 0d05w3 => 77 concepts (51 used for prediction) PRED predicted values (max 10 best out of 198): 0d05w3 (0.86 #911, 0.85 #1161, 0.84 #993), 09c7w0 (0.72 #1912, 0.60 #1251, 0.53 #831), 0154j (0.68 #1253, 0.56 #1668, 0.53 #750), 07ssc (0.64 #1258, 0.56 #1919, 0.53 #1673), 0f8l9c (0.60 #1264, 0.53 #2259, 0.51 #1925), 01mk6 (0.60 #807, 0.52 #1310, 0.44 #1725), 0d060g (0.60 #1255, 0.50 #1670, 0.46 #1916), 0b90_r (0.60 #1252, 0.47 #1667, 0.47 #749), 0hzlz (0.60 #1265, 0.47 #1680, 0.47 #762), 087vz (0.60 #1294, 0.47 #1709, 0.46 #1955) >> Best rule #911 for best value: >> intensional similarity = 6 >> extensional distance = 13 >> proper extension: 09c7w0; 05v8c; 0f8l9c; 0hzlz; 047yc; 035qy; 01z215; 03rk0; 0d05q4; 03spz; ... >> query: (?x13581, ?x2346) <- combatants(?x13581, ?x2346), locations(?x7455, ?x13581), films(?x7455, ?x6767), combatants(?x7455, ?x94), language(?x6767, ?x254), genre(?x6767, ?x1014) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0g970 combatants! 0d05w3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 51.000 0.860 http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants #638-0456xp PRED entity: 0456xp PRED relation: profession PRED expected values: 0d1pc => 140 concepts (112 used for prediction) PRED predicted values (max 10 best out of 66): 01d_h8 (0.49 #155, 0.42 #304, 0.42 #1049), 03gjzk (0.40 #164, 0.33 #4770, 0.31 #4920), 0dxtg (0.33 #4770, 0.33 #10286, 0.31 #163), 09jwl (0.33 #4770, 0.33 #10286, 0.31 #4920), 0dz3r (0.33 #4770, 0.31 #4920, 0.14 #2237), 0d1pc (0.33 #10286, 0.30 #3726, 0.29 #647), 02jknp (0.33 #10286, 0.30 #3726, 0.27 #306), 0np9r (0.33 #10286, 0.30 #3726, 0.15 #15523), 0kyk (0.33 #10286, 0.30 #3726, 0.14 #179), 0n1h (0.33 #10286, 0.30 #3726, 0.09 #8509) >> Best rule #155 for best value: >> intensional similarity = 3 >> extensional distance = 33 >> proper extension: 052hl; 0kftt; >> query: (?x1017, 01d_h8) <- nominated_for(?x1017, ?x103), spouse(?x4294, ?x1017), friend(?x1017, ?x1634) >> conf = 0.49 => this is the best rule for 1 predicted values *> Best rule #10286 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 540 *> proper extension: 0cbm64; *> query: (?x1017, ?x319) <- award(?x1017, ?x154), participant(?x1017, ?x123), profession(?x123, ?x319) *> conf = 0.33 ranks of expected_values: 6 EVAL 0456xp profession 0d1pc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 140.000 112.000 0.486 http://example.org/people/person/profession #637-0qzhw PRED entity: 0qzhw PRED relation: contains! PRED expected values: 09c7w0 => 125 concepts (89 used for prediction) PRED predicted values (max 10 best out of 315): 09c7w0 (0.81 #4475, 0.80 #5369, 0.79 #3580), 0d060g (0.44 #78754, 0.40 #68906, 0.35 #28635), 04_1l0v (0.37 #34884, 0.23 #16549, 0.22 #1344), 0kpys (0.22 #2863, 0.21 #7336, 0.20 #180), 030qb3t (0.20 #100, 0.18 #7256, 0.09 #1888), 05kr_ (0.20 #125, 0.13 #28747, 0.11 #41273), 081yw (0.20 #277, 0.11 #1171, 0.09 #2065), 0mmpz (0.20 #654, 0.11 #1548, 0.09 #2442), 07ssc (0.18 #55503, 0.18 #8976, 0.17 #59979), 02jx1 (0.18 #55557, 0.17 #60033, 0.15 #9030) >> Best rule #4475 for best value: >> intensional similarity = 4 >> extensional distance = 52 >> proper extension: 0r2l7; 0f04c; 0r5wt; 0r6rq; 01zlwg6; 0r3tb; 0qyzb; 0r6c4; 0qcrj; 0r172; >> query: (?x9300, 09c7w0) <- county(?x9300, ?x9299), contains(?x1227, ?x9300), time_zones(?x9299, ?x2950), ?x1227 = 01n7q >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0qzhw contains! 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 89.000 0.815 http://example.org/location/location/contains #636-01pl14 PRED entity: 01pl14 PRED relation: institution! PRED expected values: 019v9k => 172 concepts (170 used for prediction) PRED predicted values (max 10 best out of 19): 019v9k (0.83 #310, 0.81 #391, 0.80 #269), 03bwzr4 (0.79 #334, 0.71 #192, 0.67 #395), 0bkj86 (0.58 #349, 0.58 #329, 0.57 #491), 027f2w (0.56 #108, 0.53 #331, 0.50 #189), 07s6fsf (0.55 #509, 0.53 #324, 0.50 #712), 04zx3q1 (0.53 #325, 0.50 #22, 0.47 #264), 013zdg (0.47 #328, 0.43 #389, 0.43 #186), 028dcg (0.40 #136, 0.38 #564, 0.33 #16), 01rr_d (0.36 #195, 0.33 #398, 0.33 #276), 02mjs7 (0.36 #184, 0.33 #3, 0.26 #326) >> Best rule #310 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 017j69; 01vs5c; 01jq4b; 08qnnv; 017v3q; 01rc6f; >> query: (?x466, 019v9k) <- school(?x260, ?x466), company(?x3520, ?x466), school(?x465, ?x466) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pl14 institution! 019v9k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 172.000 170.000 0.833 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #635-018m5q PRED entity: 018m5q PRED relation: educational_institution PRED expected values: 018m5q => 140 concepts (91 used for prediction) PRED predicted values (max 10 best out of 423): 07tl0 (0.14 #25, 0.11 #29143, 0.08 #1644), 02hmw9 (0.14 #222, 0.11 #29143, 0.08 #29684), 07tg4 (0.14 #77, 0.11 #29143, 0.08 #29684), 07tlg (0.14 #478, 0.07 #2636, 0.06 #16727), 07tk7 (0.14 #438, 0.07 #2596, 0.06 #16727), 01nn7r (0.11 #29143, 0.08 #2113, 0.08 #29684), 0c_zj (0.11 #29143, 0.08 #1748, 0.08 #29684), 01f2xy (0.11 #29143, 0.08 #29684, 0.07 #29144), 01k8q5 (0.11 #29143, 0.08 #29684, 0.07 #29144), 013nky (0.11 #29143, 0.08 #29684, 0.07 #29144) >> Best rule #25 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 07tg4; >> query: (?x3671, 07tl0) <- contains(?x3301, ?x3671), ?x3301 = 0978r, major_field_of_study(?x3671, ?x2605), organization(?x2361, ?x3671), ?x2605 = 03g3w >> conf = 0.14 => this is the best rule for 1 predicted values *> Best rule #29143 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 266 *> proper extension: 02zc7f; *> query: (?x3671, ?x1369) <- colors(?x3671, ?x1101), category(?x3671, ?x134), citytown(?x3671, ?x3301), citytown(?x1369, ?x3301), organization(?x2361, ?x1369) *> conf = 0.11 ranks of expected_values: 14 EVAL 018m5q educational_institution 018m5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 140.000 91.000 0.143 http://example.org/education/educational_institution_campus/educational_institution #634-01x2tm8 PRED entity: 01x2tm8 PRED relation: languages PRED expected values: 09s02 => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 20): 09s02 (0.27 #126, 0.10 #222, 0.05 #574), 03_9r (0.25 #3, 0.11 #67, 0.03 #387), 055qm (0.10 #211, 0.04 #563, 0.03 #2628), 02bjrlw (0.10 #129, 0.05 #897, 0.05 #1057), 06mp7 (0.10 #137, 0.02 #393, 0.02 #297), 02hxcvy (0.07 #213, 0.03 #2114, 0.03 #2628), 0688f (0.07 #216, 0.03 #2114, 0.03 #2628), 06nm1 (0.05 #132, 0.03 #1252, 0.03 #1284), 0t_2 (0.05 #135, 0.03 #391, 0.02 #711), 0121sr (0.03 #219, 0.03 #2114, 0.03 #2628) >> Best rule #126 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 0jrqq; 0dfjb8; 05j12n; 02qy3py; 046rfv; 0kst7v; 06kl0k; 02hkv5; 08s0m7; >> query: (?x9253, 09s02) <- profession(?x9253, ?x319), type_of_union(?x9253, ?x566), languages(?x9253, ?x8098), gender(?x9253, ?x231), ?x8098 = 0999q >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01x2tm8 languages 09s02 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 95.000 0.273 http://example.org/people/person/languages #633-01p8s PRED entity: 01p8s PRED relation: medal PRED expected values: 02lq67 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 1): 02lq67 (0.78 #33, 0.78 #32, 0.77 #60) >> Best rule #33 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 03548; 0fv4v; >> query: (?x9730, 02lq67) <- medal(?x9730, ?x1242), adjoins(?x1475, ?x9730), adjustment_currency(?x9730, ?x170) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01p8s medal 02lq67 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.779 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal #632-0l34j PRED entity: 0l34j PRED relation: adjoins PRED expected values: 0bxqq => 162 concepts (75 used for prediction) PRED predicted values (max 10 best out of 554): 0l2vz (0.44 #1766, 0.18 #3313, 0.14 #4859), 09c7w0 (0.43 #51049, 0.30 #2320, 0.22 #773), 05rgl (0.40 #102, 0.33 #873, 0.30 #2420), 01n7q (0.33 #834, 0.30 #2381, 0.04 #34808), 0kpzy (0.33 #1838, 0.25 #44088, 0.23 #7728), 0l2sr (0.32 #3090, 0.32 #1543, 0.26 #6954), 0bxqq (0.32 #3090, 0.32 #1543, 0.26 #6954), 0l34j (0.32 #3090, 0.32 #1543, 0.26 #6954), 0l2hf (0.32 #3090, 0.32 #1543, 0.26 #6954), 03s5t (0.22 #907, 0.20 #2454, 0.03 #24889) >> Best rule #1766 for best value: >> intensional similarity = 5 >> extensional distance = 7 >> proper extension: 0d6lp; >> query: (?x4412, 0l2vz) <- currency(?x4412, ?x170), contains(?x4412, ?x4413), adjoins(?x4412, ?x7520), contains(?x2632, ?x4412), ?x2632 = 06pvr >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #3090 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 8 *> proper extension: 05rgl; *> query: (?x4412, ?x3677) <- adjoins(?x4412, ?x7520), contains(?x7520, ?x10657), adjoins(?x7520, ?x3677), ?x10657 = 0qymv, contains(?x4412, ?x4413) *> conf = 0.32 ranks of expected_values: 7 EVAL 0l34j adjoins 0bxqq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 162.000 75.000 0.444 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #631-01hw5kk PRED entity: 01hw5kk PRED relation: film! PRED expected values: 0g2lq => 69 concepts (50 used for prediction) PRED predicted values (max 10 best out of 79): 06rnl9 (0.10 #10713, 0.09 #5772, 0.09 #7695), 04wp63 (0.09 #5772, 0.09 #7695, 0.09 #13462), 06pj8 (0.07 #871, 0.05 #597, 0.04 #1147), 0343h (0.05 #3573, 0.05 #4122, 0.05 #3298), 01f7j9 (0.05 #874, 0.03 #600, 0.02 #1150), 07rd7 (0.03 #927, 0.02 #653, 0.02 #5876), 03bw6 (0.03 #167), 02qzjj (0.03 #1088, 0.02 #814, 0.01 #1364), 04sry (0.03 #991, 0.02 #443, 0.01 #5665), 0js9s (0.03 #978, 0.01 #2080, 0.01 #3178) >> Best rule #10713 for best value: >> intensional similarity = 3 >> extensional distance = 1159 >> proper extension: 0275kr; >> query: (?x4087, ?x2870) <- nominated_for(?x2870, ?x4087), award_winner(?x324, ?x2870), student(?x6988, ?x2870) >> conf = 0.10 => this is the best rule for 1 predicted values *> Best rule #2657 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 283 *> proper extension: 02z3r8t; *> query: (?x4087, 0g2lq) <- film(?x574, ?x4087), genre(?x4087, ?x1403), ?x1403 = 02l7c8, currency(?x4087, ?x170) *> conf = 0.01 ranks of expected_values: 78 EVAL 01hw5kk film! 0g2lq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 69.000 50.000 0.104 http://example.org/film/director/film #630-05sj55 PRED entity: 05sj55 PRED relation: person! PRED expected values: 02k13d => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 5): 043q4d (0.37 #59, 0.36 #52, 0.29 #44), 02k13d (0.33 #3, 0.23 #10, 0.22 #38), 0c5lg (0.11 #6, 0.07 #56, 0.06 #34), 026h21_ (0.08 #14, 0.07 #28, 0.06 #35), 09jwl (0.03 #15, 0.02 #22, 0.02 #36) >> Best rule #59 for best value: >> intensional similarity = 2 >> extensional distance = 92 >> proper extension: 01yznp; 0c7ct; 05r5w; 02w5q6; 0mdyn; 02p68d; >> query: (?x7824, 043q4d) <- program(?x7824, ?x3075), genre(?x3075, ?x10647) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #3 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 0sx5w; *> query: (?x7824, 02k13d) <- program(?x7824, ?x3075), location(?x7824, ?x739), ?x739 = 02_286 *> conf = 0.33 ranks of expected_values: 2 EVAL 05sj55 person! 02k13d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 100.000 100.000 0.372 http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person #629-03mr85 PRED entity: 03mr85 PRED relation: film_sets_designed! PRED expected values: 076psv => 63 concepts (57 used for prediction) PRED predicted values (max 10 best out of 21): 057bc6m (0.13 #11, 0.11 #61, 0.10 #36), 076lxv (0.12 #27, 0.11 #52, 0.10 #2), 07h1tr (0.11 #4, 0.10 #54, 0.09 #29), 0cb77r (0.10 #1, 0.10 #51, 0.09 #26), 076psv (0.06 #6, 0.05 #31, 0.05 #56), 0579tg2 (0.06 #20, 0.05 #45, 0.05 #70), 051ysmf (0.04 #22, 0.04 #47, 0.04 #72), 053vcrp (0.04 #15, 0.04 #40, 0.04 #65), 0fd6qb (0.03 #16, 0.03 #41, 0.02 #66), 051x52f (0.03 #10, 0.03 #35, 0.02 #60) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 04v8x9; 03hjv97; 0147sh; 0c5dd; 04mzf8; 0dtfn; 0qm98; 02r79_h; 0bcndz; 0k4kk; ... >> query: (?x12766, 057bc6m) <- language(?x12766, ?x254), film(?x5079, ?x12766), film_art_direction_by(?x12766, ?x4251), film_release_distribution_medium(?x12766, ?x81) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 69 *> proper extension: 04v8x9; 03hjv97; 0147sh; 0c5dd; 04mzf8; 0dtfn; 0qm98; 02r79_h; 0bcndz; 0k4kk; ... *> query: (?x12766, 076psv) <- language(?x12766, ?x254), film(?x5079, ?x12766), film_art_direction_by(?x12766, ?x4251), film_release_distribution_medium(?x12766, ?x81) *> conf = 0.06 ranks of expected_values: 5 EVAL 03mr85 film_sets_designed! 076psv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 63.000 57.000 0.127 http://example.org/film/film_set_designer/film_sets_designed #628-0159h6 PRED entity: 0159h6 PRED relation: nominated_for PRED expected values: 0qf2t => 99 concepts (54 used for prediction) PRED predicted values (max 10 best out of 666): 03ydlnj (0.31 #24206, 0.28 #35504, 0.27 #54866), 031hcx (0.31 #24206, 0.28 #35504, 0.27 #54866), 03177r (0.31 #24206, 0.28 #35504, 0.27 #54866), 016ywb (0.31 #24206, 0.28 #35504, 0.27 #54866), 0gfsq9 (0.31 #24206, 0.28 #35504, 0.27 #54866), 0661m4p (0.31 #24206, 0.28 #35504, 0.27 #54866), 0407yfx (0.31 #24206, 0.28 #35504, 0.27 #54866), 02_fm2 (0.31 #24206, 0.28 #35504, 0.27 #54866), 0330r (0.17 #1408, 0.07 #4636, 0.03 #41957), 03ln8b (0.17 #300, 0.03 #41957, 0.02 #29351) >> Best rule #24206 for best value: >> intensional similarity = 3 >> extensional distance = 370 >> proper extension: 0m2wm; 04wqr; 01j5x6; 01pctb; 02qfhb; >> query: (?x488, ?x218) <- award_nominee(?x488, ?x100), participant(?x7638, ?x488), film(?x488, ?x218) >> conf = 0.31 => this is the best rule for 8 predicted values No rule for expected values ranks of expected_values: EVAL 0159h6 nominated_for 0qf2t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 99.000 54.000 0.310 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #627-0c40vxk PRED entity: 0c40vxk PRED relation: country PRED expected values: 012wgb => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 123): 09c7w0 (0.85 #1009, 0.85 #759, 0.83 #1258), 07ssc (0.33 #204, 0.25 #1640, 0.23 #898), 0f8l9c (0.17 #330, 0.15 #207, 0.15 #4331), 0345h (0.16 #1223, 0.15 #1468, 0.15 #4331), 03h64 (0.15 #169, 0.10 #756, 0.10 #757), 0d060g (0.15 #4331, 0.11 #3219, 0.10 #756), 06mkj (0.15 #4331, 0.11 #3219, 0.10 #756), 03_3d (0.15 #4331, 0.11 #3219, 0.10 #756), 015fr (0.15 #4331, 0.11 #3219, 0.10 #756), 059j2 (0.15 #4331, 0.11 #3219, 0.10 #756) >> Best rule #1009 for best value: >> intensional similarity = 3 >> extensional distance = 228 >> proper extension: 0c5qvw; >> query: (?x633, 09c7w0) <- genre(?x633, ?x812), production_companies(?x633, ?x11557), cinematography(?x633, ?x7903) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #1130 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 238 *> proper extension: 01_vfy; 05whq_9; 01q4qv; 01ycck; 040z9; 081l_; 037w7r; 0gdqy; 026ck; *> query: (?x633, ?x87) <- film_festivals(?x633, ?x11852), film_festivals(?x3882, ?x11852), film_festivals(?x3745, ?x11852), genre(?x3882, ?x53), film_release_region(?x3745, ?x87) *> conf = 0.02 ranks of expected_values: 69 EVAL 0c40vxk country 012wgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 73.000 73.000 0.848 http://example.org/film/film/country #626-0__wm PRED entity: 0__wm PRED relation: category PRED expected values: 08mbj5d => 196 concepts (196 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.80 #84, 0.79 #88, 0.79 #48) >> Best rule #84 for best value: >> intensional similarity = 5 >> extensional distance = 191 >> proper extension: 0qlrh; >> query: (?x11050, 08mbj5d) <- source(?x11050, ?x958), ?x958 = 0jbk9, county(?x11050, ?x11836), adjoins(?x9712, ?x11836), time_zones(?x11050, ?x1638) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0__wm category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 196.000 196.000 0.798 http://example.org/common/topic/webpage./common/webpage/category #625-0fkhz PRED entity: 0fkhz PRED relation: time_zones PRED expected values: 02hcv8 => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 11): 02hcv8 (0.81 #16, 0.80 #29, 0.64 #314), 02lcqs (0.34 #161, 0.21 #187, 0.20 #109), 02hczc (0.24 #41, 0.12 #67, 0.11 #54), 02fqwt (0.13 #144, 0.13 #222, 0.13 #499), 02llzg (0.13 #238, 0.10 #69, 0.10 #56), 03bdv (0.09 #71, 0.07 #240, 0.07 #374), 03plfd (0.04 #244, 0.02 #364, 0.02 #338), 052vwh (0.02 #246, 0.02 #260, 0.01 #207), 0gsrz4 (0.02 #242, 0.02 #362, 0.02 #349), 042g7t (0.02 #50, 0.01 #365) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 35 >> proper extension: 0fm9_; 0drsm; 0cymp; 0dlhg; 0f6_4; 0f6_j; 0dc3_; 0fc2c; 0fc1_; 0fkhl; ... >> query: (?x12027, 02hcv8) <- contains(?x335, ?x12027), adjoins(?x334, ?x12027), ?x335 = 059rby, source(?x334, ?x958) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fkhz time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.811 http://example.org/location/location/time_zones #624-0cq8qq PRED entity: 0cq8qq PRED relation: genre PRED expected values: 0hfjk => 82 concepts (79 used for prediction) PRED predicted values (max 10 best out of 101): 02l7c8 (0.42 #17, 0.40 #497, 0.38 #1457), 02kdv5l (0.36 #242, 0.31 #7931, 0.28 #5166), 01jfsb (0.35 #253, 0.30 #4336, 0.29 #6379), 05p553 (0.34 #4327, 0.34 #5648, 0.34 #6490), 04xvlr (0.34 #1561, 0.27 #1801, 0.26 #1681), 03k9fj (0.26 #252, 0.25 #7941, 0.25 #2653), 04t36 (0.25 #6, 0.15 #1326, 0.14 #1566), 06n90 (0.25 #254, 0.14 #2655, 0.14 #4337), 04xvh5 (0.22 #1595, 0.18 #1715, 0.18 #1835), 060__y (0.22 #1578, 0.21 #738, 0.21 #1218) >> Best rule #17 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 06mmr; >> query: (?x5183, 02l7c8) <- award_winner(?x5183, ?x9710), award_winner(?x5183, ?x574), ?x574 = 016tt2, award_winner(?x7589, ?x9710) >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #8650 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1550 *> proper extension: 01qn7n; 07hpv3; 09kn9; 01cjhz; 05sy2k_; 02648p; 01p4wv; 099pks; 05r1_t; 0jq2r; ... *> query: (?x5183, ?x53) <- titles(?x4757, ?x5183), titles(?x4757, ?x6531), genre(?x6531, ?x53) *> conf = 0.06 ranks of expected_values: 38 EVAL 0cq8qq genre 0hfjk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 82.000 79.000 0.417 http://example.org/film/film/genre #623-043q6n_ PRED entity: 043q6n_ PRED relation: gender PRED expected values: 05zppz => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #23, 0.86 #25, 0.85 #33), 02zsn (0.28 #86, 0.28 #90, 0.28 #82) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 106 >> proper extension: 025n3p; 01vz80y; >> query: (?x1417, 05zppz) <- produced_by(?x1642, ?x1417), award_nominee(?x541, ?x1417), film_release_region(?x1642, ?x985), ?x985 = 0k6nt >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043q6n_ gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.870 http://example.org/people/person/gender #622-03x16f PRED entity: 03x16f PRED relation: student! PRED expected values: 01k2wn => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 132): 0bwfn (0.14 #275, 0.08 #26049, 0.08 #24997), 04b_46 (0.09 #227, 0.03 #7065, 0.03 #11799), 09f2j (0.06 #7523, 0.06 #11731, 0.05 #2789), 015nl4 (0.06 #7431, 0.05 #17951, 0.05 #15321), 08815 (0.06 #528, 0.06 #2106, 0.05 #2632), 05zl0 (0.06 #728, 0.02 #1780, 0.02 #2306), 0m4yg (0.06 #2469, 0.05 #2995, 0.03 #3521), 017z88 (0.06 #6920, 0.06 #15336, 0.05 #11654), 065y4w7 (0.05 #1066, 0.05 #24736, 0.04 #25788), 01d34b (0.05 #1308, 0.04 #4990, 0.04 #1834) >> Best rule #275 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 01dw4q; 058ncz; 03zqc1; 035gjq; 06b0d2; 05lb87; 0443y3; 026zvx7; 0gd_b_; 07z1_q; ... >> query: (?x8746, 0bwfn) <- award_nominee(?x8746, ?x444), award_nominee(?x1117, ?x8746), ?x1117 = 03lt8g >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03x16f student! 01k2wn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 98.000 0.136 http://example.org/education/educational_institution/students_graduates./education/education/student #621-015c4g PRED entity: 015c4g PRED relation: award PRED expected values: 099ck7 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 270): 027dtxw (0.72 #34612, 0.71 #24384, 0.70 #26352), 0bdwqv (0.72 #34612, 0.71 #24384, 0.70 #26352), 027986c (0.72 #34612, 0.71 #24384, 0.70 #26352), 09cm54 (0.72 #34612, 0.71 #24384, 0.70 #26352), 0gq9h (0.32 #6758, 0.26 #2039, 0.24 #4007), 05pcn59 (0.27 #470, 0.20 #863, 0.17 #7155), 01by1l (0.26 #7579, 0.12 #19666, 0.10 #15841), 07cbcy (0.26 #467, 0.12 #860, 0.09 #2433), 03c7tr1 (0.25 #448, 0.12 #19666, 0.10 #6347), 040njc (0.24 #6692, 0.22 #1186, 0.21 #1973) >> Best rule #34612 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 01lcxbb; 01wz_ml; 01h320; 034bs; 06whf; 01vsy3q; 01t265; 0d0mbj; 051cc; 0f6lx; ... >> query: (?x4436, ?x112) <- award_winner(?x112, ?x4436), award(?x92, ?x112) >> conf = 0.72 => this is the best rule for 4 predicted values *> Best rule #22418 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1484 *> proper extension: 024rbz; 01nzs7; *> query: (?x4436, ?x112) <- nominated_for(?x4436, ?x810), award_winner(?x7016, ?x4436), nominated_for(?x112, ?x7016) *> conf = 0.14 ranks of expected_values: 32 EVAL 015c4g award 099ck7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 104.000 104.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #620-01rgr PRED entity: 01rgr PRED relation: influenced_by PRED expected values: 02lt8 => 143 concepts (59 used for prediction) PRED predicted values (max 10 best out of 424): 081k8 (0.43 #156, 0.34 #5820, 0.27 #2771), 032l1 (0.43 #89, 0.33 #15365, 0.18 #8373), 048cl (0.31 #1107, 0.20 #1545, 0.18 #15511), 099bk (0.31 #983, 0.12 #548, 0.12 #7520), 04xjp (0.29 #57, 0.16 #15716, 0.15 #929), 0448r (0.29 #263, 0.15 #5927, 0.10 #10732), 03_dj (0.29 #413, 0.11 #6077, 0.09 #5204), 06jkm (0.29 #396, 0.08 #20961, 0.08 #20959), 03sbs (0.26 #15499, 0.23 #1095, 0.18 #12002), 03f0324 (0.26 #15428, 0.12 #589, 0.12 #11060) >> Best rule #156 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 058vp; 03_87; >> query: (?x9595, 081k8) <- profession(?x9595, ?x353), gender(?x9595, ?x231), ?x353 = 0cbd2, influenced_by(?x2208, ?x9595), ?x2208 = 041mt >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #8404 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 66 *> proper extension: 084w8; 081lh; 03pm9; 0lrh; 05jm7; 014635; 06whf; 0zm1; 0d5_f; 0282x; ... *> query: (?x9595, 02lt8) <- profession(?x9595, ?x353), gender(?x9595, ?x231), ?x353 = 0cbd2, influenced_by(?x2208, ?x9595), languages(?x2208, ?x254) *> conf = 0.19 ranks of expected_values: 19 EVAL 01rgr influenced_by 02lt8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 143.000 59.000 0.429 http://example.org/influence/influence_node/influenced_by #619-0g56t9t PRED entity: 0g56t9t PRED relation: film_release_region PRED expected values: 03rt9 05v8c 015fr 0f8l9c 03gj2 035qy 01znc_ 01pj7 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 126): 0f8l9c (0.88 #565, 0.88 #1387, 0.86 #428), 03gj2 (0.87 #569, 0.83 #432, 0.83 #980), 015fr (0.85 #971, 0.83 #560, 0.75 #423), 035qy (0.82 #988, 0.79 #577, 0.74 #440), 03rt9 (0.81 #557, 0.72 #968, 0.71 #420), 01znc_ (0.76 #994, 0.71 #583, 0.69 #446), 05v8c (0.65 #559, 0.65 #422, 0.61 #970), 06qd3 (0.58 #444, 0.47 #581, 0.45 #992), 016wzw (0.56 #601, 0.47 #1012, 0.42 #464), 047yc (0.55 #571, 0.52 #982, 0.39 #1393) >> Best rule #565 for best value: >> intensional similarity = 5 >> extensional distance = 76 >> proper extension: 04969y; 043sct5; 07l50vn; 0h95zbp; 0g9zljd; 07s3m4g; 05zvzf3; >> query: (?x124, 0f8l9c) <- country(?x124, ?x94), film_release_region(?x124, ?x1536), film_release_region(?x124, ?x985), ?x1536 = 06c1y, ?x985 = 0k6nt >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4, 5, 6, 7, 16 EVAL 0g56t9t film_release_region 01pj7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 65.000 65.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g56t9t film_release_region 01znc_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g56t9t film_release_region 035qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g56t9t film_release_region 03gj2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g56t9t film_release_region 0f8l9c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g56t9t film_release_region 015fr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g56t9t film_release_region 05v8c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 0g56t9t film_release_region 03rt9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.885 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #618-0_vn7 PRED entity: 0_vn7 PRED relation: location! PRED expected values: 0693l => 198 concepts (136 used for prediction) PRED predicted values (max 10 best out of 2010): 0693l (0.67 #100735, 0.57 #143535, 0.57 #12594), 020l9r (0.56 #88141, 0.52 #115840, 0.52 #163680), 09btt1 (0.52 #115840, 0.50 #161161, 0.50 #125909), 023kzp (0.22 #3735, 0.20 #1216, 0.16 #11291), 0151ns (0.20 #84, 0.17 #2603, 0.10 #15196), 01q_ph (0.20 #50, 0.14 #7605, 0.12 #10125), 03_x5t (0.20 #2090, 0.11 #4609, 0.08 #12165), 07s6prs (0.20 #336, 0.09 #7891, 0.08 #10411), 01s21dg (0.17 #3483, 0.14 #8519, 0.12 #11039), 02lt8 (0.17 #3316, 0.14 #8352, 0.12 #10872) >> Best rule #100735 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 050tt8; >> query: (?x4350, ?x3117) <- contains(?x4350, ?x7338), place_of_birth(?x3117, ?x4350), languages(?x3117, ?x254) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0_vn7 location! 0693l CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 198.000 136.000 0.665 http://example.org/people/person/places_lived./people/place_lived/location #617-02vx4 PRED entity: 02vx4 PRED relation: country PRED expected values: 01ls2 0ctw_b => 73 concepts (71 used for prediction) PRED predicted values (max 10 best out of 231): 06bnz (0.93 #5301, 0.93 #4422, 0.90 #6715), 0d05w3 (0.90 #5317, 0.89 #4438, 0.89 #3382), 07t21 (0.89 #3537, 0.87 #5295, 0.87 #2832), 07ylj (0.80 #2826, 0.79 #2651, 0.77 #2476), 01p1v (0.80 #2844, 0.79 #2669, 0.77 #2494), 015qh (0.80 #2133, 0.79 #2659, 0.75 #2308), 01mjq (0.79 #2661, 0.77 #2486, 0.74 #3541), 06f32 (0.79 #2682, 0.63 #3562, 0.60 #2857), 02vzc (0.78 #3371, 0.71 #2668, 0.70 #5306), 0h7x (0.72 #3359, 0.68 #1754, 0.67 #5294) >> Best rule #5301 for best value: >> intensional similarity = 11 >> extensional distance = 28 >> proper extension: 03rbzn; >> query: (?x471, 06bnz) <- olympics(?x471, ?x358), country(?x471, ?x2188), country(?x471, ?x774), ?x2188 = 0163v, film_release_region(?x6270, ?x774), film_release_region(?x1259, ?x774), film_release_region(?x1035, ?x774), ?x6270 = 0g9zljd, ?x1259 = 04hwbq, ?x1035 = 08hmch, olympics(?x774, ?x418) >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #3702 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 19 *> proper extension: 0d1tm; 09w1n; 019tzd; 07_53; *> query: (?x471, 0ctw_b) <- sports(?x358, ?x471), country(?x471, ?x2188), country(?x471, ?x792), olympics(?x471, ?x1931), participating_countries(?x784, ?x2188), film_release_region(?x249, ?x2188), adjoins(?x344, ?x2188), ?x792 = 0hzlz *> conf = 0.71 ranks of expected_values: 13, 41 EVAL 02vx4 country 0ctw_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 73.000 71.000 0.933 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02vx4 country 01ls2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 73.000 71.000 0.933 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #616-054lpb6 PRED entity: 054lpb6 PRED relation: film PRED expected values: 09gmmt6 => 145 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1739): 047svrl (0.76 #1598, 0.74 #1599, 0.71 #23970), 0b76d_m (0.76 #1598, 0.74 #1599, 0.71 #23970), 0cmdwwg (0.76 #1598, 0.74 #1599, 0.71 #23970), 0gys2jp (0.76 #1598, 0.74 #1599, 0.71 #23970), 0gmd3k7 (0.76 #1598, 0.74 #1599, 0.71 #23970), 0ds35l9 (0.76 #1598, 0.74 #1599, 0.71 #23970), 0h2zvzr (0.76 #1598, 0.74 #1599, 0.71 #23970), 02r1c18 (0.76 #1598, 0.74 #1599, 0.71 #23970), 047vnkj (0.76 #1598, 0.74 #1599, 0.71 #23970), 0gtsx8c (0.76 #1598, 0.74 #1599, 0.71 #23970) >> Best rule #1598 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 017s11; 0g1rw; >> query: (?x1478, ?x80) <- film(?x1478, ?x633), production_companies(?x1642, ?x1478), production_companies(?x1525, ?x1478), production_companies(?x80, ?x1478), state_province_region(?x1478, ?x1227), ?x1642 = 0bq8tmw, film_release_region(?x1525, ?x87) >> conf = 0.76 => this is the best rule for 73 predicted values *> Best rule #9028 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 9 *> proper extension: 01swmr; *> query: (?x1478, 09gmmt6) <- organizations_founded(?x3744, ?x1478), type_of_union(?x3744, ?x566), child(?x1478, ?x9481), nationality(?x3744, ?x94) *> conf = 0.09 ranks of expected_values: 710 EVAL 054lpb6 film 09gmmt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 145.000 35.000 0.755 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #615-0vbk PRED entity: 0vbk PRED relation: location! PRED expected values: 01vrx3g 0137n0 => 132 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1848): 01vrx3g (0.31 #115525, 0.25 #113013, 0.24 #120549), 0cgbf (0.20 #1390, 0.06 #18967, 0.06 #21478), 0ffgh (0.20 #1440, 0.06 #21528, 0.04 #16506), 0g2lq (0.20 #1567, 0.05 #36725, 0.03 #86952), 0f2zc (0.20 #1856, 0.05 #4367, 0.05 #6878), 025b3k (0.20 #1946, 0.05 #4457, 0.05 #6968), 0c6qh (0.20 #460, 0.05 #48175, 0.04 #18037), 0p7h7 (0.20 #929, 0.04 #87897, 0.03 #37670), 017f4y (0.20 #2150, 0.04 #22238, 0.03 #77490), 014dq7 (0.20 #346, 0.04 #20434, 0.03 #48061) >> Best rule #115525 for best value: >> intensional similarity = 3 >> extensional distance = 128 >> proper extension: 0t_gg; 02m__; 0r3tb; 0rng; 020d8d; 0l3q2; 014kj2; 0r172; 01v8c; >> query: (?x4758, ?x366) <- category(?x4758, ?x134), contains(?x94, ?x4758), origin(?x366, ?x4758) >> conf = 0.31 => this is the best rule for 1 predicted values ranks of expected_values: 1, 406 EVAL 0vbk location! 0137n0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 132.000 73.000 0.309 http://example.org/people/person/places_lived./people/place_lived/location EVAL 0vbk location! 01vrx3g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 73.000 0.309 http://example.org/people/person/places_lived./people/place_lived/location #614-0241wg PRED entity: 0241wg PRED relation: gender PRED expected values: 02zsn => 158 concepts (158 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.76 #35, 0.74 #85, 0.73 #73), 02zsn (0.55 #294, 0.48 #18, 0.47 #237) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 73 >> proper extension: 04qr6d; 02jxsq; 0cct7p; 090gpr; >> query: (?x3129, 05zppz) <- religion(?x3129, ?x8967), profession(?x3129, ?x1032), ?x8967 = 03j6c >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #294 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2567 *> proper extension: 09lhln; 0bhtzw; *> query: (?x3129, ?x231) <- place_of_birth(?x3129, ?x7412), place_of_birth(?x12038, ?x7412), gender(?x12038, ?x231) *> conf = 0.55 ranks of expected_values: 2 EVAL 0241wg gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 158.000 158.000 0.760 http://example.org/people/person/gender #613-015x1f PRED entity: 015x1f PRED relation: profession PRED expected values: 04f2zj => 124 concepts (74 used for prediction) PRED predicted values (max 10 best out of 82): 09jwl (0.82 #606, 0.80 #3404, 0.78 #3551), 02hrh1q (0.77 #9748, 0.72 #10486, 0.71 #10339), 01c72t (0.63 #3851, 0.62 #2377, 0.62 #2966), 039v1 (0.38 #1947, 0.37 #3421, 0.36 #3568), 03gjzk (0.35 #2073, 0.29 #1337, 0.18 #6940), 01d_h8 (0.34 #1327, 0.33 #4, 0.31 #9443), 0fnpj (0.33 #59, 0.29 #206, 0.27 #794), 012t_z (0.33 #11, 0.12 #158, 0.10 #305), 0kyk (0.31 #10649, 0.15 #1352, 0.13 #4299), 0dxtg (0.27 #5314, 0.27 #6938, 0.27 #9451) >> Best rule #606 for best value: >> intensional similarity = 5 >> extensional distance = 38 >> proper extension: 01vw87c; 06cc_1; 01cv3n; 025xt8y; 012x4t; 01wsl7c; 01vsnff; 0161sp; 03xl77; 01vw20_; ... >> query: (?x5048, 09jwl) <- instrumentalists(?x1750, ?x5048), instrumentalists(?x316, ?x5048), ?x316 = 05r5c, type_of_union(?x5048, ?x566), ?x1750 = 02hnl >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #683 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 38 *> proper extension: 01vw87c; 06cc_1; 01cv3n; 025xt8y; 012x4t; 01wsl7c; 01vsnff; 0161sp; 03xl77; 01vw20_; ... *> query: (?x5048, 04f2zj) <- instrumentalists(?x1750, ?x5048), instrumentalists(?x316, ?x5048), ?x316 = 05r5c, type_of_union(?x5048, ?x566), ?x1750 = 02hnl *> conf = 0.20 ranks of expected_values: 15 EVAL 015x1f profession 04f2zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 124.000 74.000 0.825 http://example.org/people/person/profession #612-01x4sb PRED entity: 01x4sb PRED relation: award PRED expected values: 0bdwft => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 214): 09sb52 (0.34 #6909, 0.34 #1253, 0.33 #6101), 05p09zm (0.27 #931, 0.23 #1335, 0.21 #1739), 05pcn59 (0.24 #1698, 0.23 #1294, 0.22 #890), 05b4l5x (0.22 #814, 0.17 #1622, 0.16 #1218), 0cqhk0 (0.19 #2461, 0.18 #3269, 0.11 #6097), 094qd5 (0.18 #12929, 0.18 #16162, 0.17 #449), 0gqwc (0.18 #12929, 0.18 #16162, 0.15 #16971), 0bdwft (0.18 #12929, 0.18 #16162, 0.15 #16971), 02ppm4q (0.18 #12929, 0.18 #16162, 0.15 #16971), 03nqnk3 (0.18 #12929, 0.18 #16162, 0.15 #16971) >> Best rule #6909 for best value: >> intensional similarity = 3 >> extensional distance = 1166 >> proper extension: 054_mz; 04kj2v; 02_340; 09h4b5; >> query: (?x6259, 09sb52) <- award_nominee(?x6259, ?x931), award(?x6259, ?x1670), film(?x6259, ?x204) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #12929 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1381 *> proper extension: 0g51l1; 0c_mvb; 0lzkm; 0280mv7; 02f9wb; 0gdhhy; 015zql; 08xz51; 0f6lx; 06y3r; ... *> query: (?x6259, ?x1670) <- award_winner(?x10640, ?x6259), profession(?x6259, ?x220), award_winner(?x1670, ?x10640) *> conf = 0.18 ranks of expected_values: 8 EVAL 01x4sb award 0bdwft CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 84.000 84.000 0.341 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #611-02681vq PRED entity: 02681vq PRED relation: award! PRED expected values: 0dzc16 => 38 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2022): 03x82v (0.85 #6738, 0.81 #6739, 0.79 #50526), 02qlg7s (0.85 #6738, 0.81 #6739, 0.79 #50526), 01wyq0w (0.85 #6738, 0.81 #6739, 0.79 #50526), 02dbp7 (0.56 #18162, 0.33 #4688, 0.33 #1318), 01wwvc5 (0.56 #17580, 0.33 #4106, 0.33 #736), 012x4t (0.56 #17264, 0.33 #3790, 0.33 #420), 0g824 (0.56 #18705, 0.33 #5231, 0.33 #1861), 05mxw33 (0.56 #19942, 0.33 #6468, 0.33 #3098), 01s7ns (0.50 #9778, 0.43 #16515, 0.11 #19883), 0415mzy (0.44 #18477, 0.33 #5003, 0.33 #1633) >> Best rule #6738 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 0c4z8; >> query: (?x884, ?x2462) <- award_winner(?x884, ?x8669), award_winner(?x884, ?x2462), award(?x12121, ?x884), award(?x6162, ?x884), ?x6162 = 01w9wwg, award_winner(?x3121, ?x8669), ?x12121 = 01qmy04 >> conf = 0.85 => this is the best rule for 3 predicted values *> Best rule #11306 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0gqng; 0gq6s3; *> query: (?x884, 0dzc16) <- award_winner(?x884, ?x2462), award(?x9220, ?x884), award(?x5342, ?x884), award_nominee(?x4258, ?x9220), ?x5342 = 02rxbmt *> conf = 0.20 ranks of expected_values: 270 EVAL 02681vq award! 0dzc16 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 38.000 19.000 0.848 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #610-01zmqw PRED entity: 01zmqw PRED relation: location! PRED expected values: 02bn75 => 62 concepts (48 used for prediction) PRED predicted values (max 10 best out of 928): 0gs1_ (0.33 #1323, 0.05 #6347, 0.04 #11371), 01p7yb (0.33 #47, 0.05 #5071, 0.04 #10095), 02lt8 (0.33 #796, 0.05 #5820, 0.04 #8332), 02p5hf (0.33 #2103, 0.04 #7127, 0.04 #12151), 02v406 (0.33 #824, 0.04 #5848, 0.04 #10872), 02sjf5 (0.33 #202, 0.04 #10250, 0.03 #5226), 023kzp (0.33 #1215, 0.03 #6239, 0.03 #13775), 0sx5w (0.33 #2139, 0.03 #7163, 0.03 #12187), 01s21dg (0.33 #963, 0.03 #5987, 0.03 #11011), 0pyww (0.33 #980, 0.03 #6004, 0.03 #11028) >> Best rule #1323 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 02_286; >> query: (?x3942, 0gs1_) <- location(?x13084, ?x3942), location(?x10989, ?x3942), ?x13084 = 01hbq0, role(?x10989, ?x212) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01zmqw location! 02bn75 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 48.000 0.333 http://example.org/people/person/places_lived./people/place_lived/location #609-03_wtr PRED entity: 03_wtr PRED relation: type_of_union PRED expected values: 04ztj => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.71 #65, 0.71 #98, 0.71 #102), 01g63y (0.47 #77, 0.20 #2, 0.16 #26) >> Best rule #65 for best value: >> intensional similarity = 3 >> extensional distance = 968 >> proper extension: 025p38; 02zrv7; 0bkmf; 03bdm4; >> query: (?x7646, 04ztj) <- nominated_for(?x7646, ?x5810), film(?x7646, ?x638), location(?x7646, ?x2850) >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03_wtr type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.714 http://example.org/people/person/spouse_s./people/marriage/type_of_union #608-01wqmm8 PRED entity: 01wqmm8 PRED relation: artist! PRED expected values: 05clg8 => 103 concepts (82 used for prediction) PRED predicted values (max 10 best out of 84): 015_1q (0.22 #300, 0.22 #159, 0.20 #3952), 03rhqg (0.15 #3948, 0.14 #5354, 0.13 #3528), 0g768 (0.12 #5375, 0.12 #3969, 0.12 #3829), 011k1h (0.11 #291, 0.11 #3943, 0.10 #150), 0181dw (0.11 #322, 0.11 #3554, 0.10 #3414), 01trtc (0.10 #353, 0.09 #212, 0.08 #4005), 017l96 (0.10 #3951, 0.09 #5357, 0.09 #3391), 0n85g (0.10 #202, 0.09 #343, 0.08 #3995), 0fb0v (0.10 #147, 0.08 #288, 0.07 #3940), 01w40h (0.08 #168, 0.07 #730, 0.07 #3961) >> Best rule #300 for best value: >> intensional similarity = 3 >> extensional distance = 260 >> proper extension: 01sfmyk; 04l19_; >> query: (?x7553, 015_1q) <- artist(?x2299, ?x7553), gender(?x7553, ?x514), people(?x2510, ?x7553) >> conf = 0.22 => this is the best rule for 1 predicted values *> Best rule #794 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 287 *> proper extension: 024y6w; *> query: (?x7553, 05clg8) <- award_nominee(?x7553, ?x2737), location(?x7553, ?x3148), artists(?x671, ?x7553) *> conf = 0.02 ranks of expected_values: 47 EVAL 01wqmm8 artist! 05clg8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 103.000 82.000 0.218 http://example.org/music/record_label/artist #607-03s6l2 PRED entity: 03s6l2 PRED relation: film_crew_role PRED expected values: 09vw2b7 0dxtw => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 23): 09vw2b7 (0.65 #381, 0.58 #858, 0.58 #347), 01vx2h (0.33 #386, 0.29 #352, 0.28 #931), 0dxtw (0.32 #385, 0.32 #930, 0.32 #862), 01xy5l_ (0.31 #47, 0.14 #388, 0.13 #354), 0215hd (0.25 #17, 0.19 #51, 0.16 #392), 089g0h (0.25 #18, 0.13 #393, 0.13 #2672), 0d2b38 (0.25 #24, 0.13 #2672, 0.12 #58), 02_n3z (0.25 #1, 0.13 #2672, 0.11 #376), 089fss (0.13 #2672, 0.12 #39, 0.06 #789), 04pyp5 (0.13 #2672, 0.07 #663, 0.05 #833) >> Best rule #381 for best value: >> intensional similarity = 4 >> extensional distance = 209 >> proper extension: 02phtzk; 03q8xj; 0g5qmbz; 02yy9r; >> query: (?x603, 09vw2b7) <- award_winner(?x603, ?x286), language(?x603, ?x254), executive_produced_by(?x603, ?x4060), film_crew_role(?x603, ?x137) >> conf = 0.65 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 03s6l2 film_crew_role 0dxtw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 84.000 84.000 0.654 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 03s6l2 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.654 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #606-0hnlx PRED entity: 0hnlx PRED relation: company PRED expected values: 065y4w7 => 108 concepts (101 used for prediction) PRED predicted values (max 10 best out of 30): 02_gzx (0.08 #919, 0.07 #1112, 0.04 #1306), 09b3v (0.03 #1992, 0.02 #3150, 0.01 #5855), 03d96s (0.03 #2054), 015_1q (0.03 #1981), 01cszh (0.03 #1968), 09c7w0 (0.02 #10650, 0.02 #11618, 0.02 #3093), 07wrz (0.02 #4673, 0.02 #8552, 0.02 #5833), 05zl0 (0.02 #8608, 0.02 #8994, 0.02 #6664), 03ksy (0.02 #5847), 017z88 (0.02 #2749, 0.02 #2942, 0.01 #3522) >> Best rule #919 for best value: >> intensional similarity = 5 >> extensional distance = 11 >> proper extension: 0c73z; >> query: (?x862, 02_gzx) <- artists(?x597, ?x862), ?x597 = 0ggq0m, profession(?x862, ?x1614), ?x1614 = 01c72t, religion(?x862, ?x7131) >> conf = 0.08 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0hnlx company 065y4w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 108.000 101.000 0.077 http://example.org/people/person/employment_history./business/employment_tenure/company #605-03jj93 PRED entity: 03jj93 PRED relation: award_winner! PRED expected values: 02z13jg => 97 concepts (75 used for prediction) PRED predicted values (max 10 best out of 244): 04ljl_l (0.60 #431, 0.36 #24967, 0.35 #6886), 07bdd_ (0.50 #66, 0.13 #23675, 0.13 #23674), 05p1dby (0.50 #107, 0.10 #16787, 0.07 #26258), 02x1z2s (0.33 #196, 0.10 #16787, 0.03 #21090), 09sb52 (0.30 #472, 0.12 #8650, 0.10 #14246), 099tbz (0.20 #489, 0.06 #8667, 0.05 #12972), 02ppm4q (0.20 #586, 0.04 #1016, 0.03 #4888), 0gq9h (0.17 #78, 0.10 #16787, 0.04 #3520), 01lk0l (0.17 #276, 0.10 #16787), 03c7tr1 (0.13 #23675, 0.13 #23674, 0.11 #32282) >> Best rule #431 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 017s11; 03rwz3; >> query: (?x11651, ?x102) <- nominated_for(?x11651, ?x5627), award(?x11651, ?x102), ?x5627 = 06bd5j, award_winner(?x1770, ?x11651) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4353 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 622 *> proper extension: 06rnl9; *> query: (?x11651, 02z13jg) <- nominated_for(?x11651, ?x5627), student(?x9844, ?x11651), type_of_union(?x11651, ?x566), award_winner(?x1770, ?x11651) *> conf = 0.02 ranks of expected_values: 115 EVAL 03jj93 award_winner! 02z13jg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 97.000 75.000 0.600 http://example.org/award/award_category/winners./award/award_honor/award_winner #604-01hb6v PRED entity: 01hb6v PRED relation: influenced_by PRED expected values: 05wh0sh 0lcx 05np2 0gthm 0969fd => 153 concepts (75 used for prediction) PRED predicted values (max 10 best out of 334): 081k8 (0.67 #2233, 0.40 #1816, 0.40 #982), 03sbs (0.60 #1043, 0.33 #2294, 0.25 #14393), 02lt8 (0.60 #1366, 0.27 #3034, 0.20 #1783), 042q3 (0.50 #2433, 0.40 #1182, 0.20 #1599), 0420y (0.50 #2471, 0.40 #1220, 0.13 #22547), 03f0324 (0.42 #3897, 0.40 #1812, 0.40 #1395), 0gz_ (0.40 #933, 0.33 #4269, 0.33 #2184), 01lwx (0.40 #1225, 0.33 #2476, 0.33 #392), 05qmj (0.40 #1016, 0.27 #14366, 0.20 #1433), 02wh0 (0.40 #1616, 0.25 #14549, 0.17 #5787) >> Best rule #2233 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 0dw6b; >> query: (?x2608, 081k8) <- influenced_by(?x2934, ?x2608), influenced_by(?x2608, ?x8390), ?x8390 = 07ym0, people(?x1050, ?x2608) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #1453 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 03f0324; *> query: (?x2608, 05np2) <- influenced_by(?x2934, ?x2608), people(?x14098, ?x2608), influenced_by(?x2608, ?x2161), ?x2934 = 04cbtrw *> conf = 0.20 ranks of expected_values: 42, 52, 101, 176, 272 EVAL 01hb6v influenced_by 0969fd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 153.000 75.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 01hb6v influenced_by 0gthm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 153.000 75.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 01hb6v influenced_by 05np2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 153.000 75.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 01hb6v influenced_by 0lcx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 153.000 75.000 0.667 http://example.org/influence/influence_node/influenced_by EVAL 01hb6v influenced_by 05wh0sh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 153.000 75.000 0.667 http://example.org/influence/influence_node/influenced_by #603-0zpfy PRED entity: 0zpfy PRED relation: contains! PRED expected values: 05tbn => 79 concepts (45 used for prediction) PRED predicted values (max 10 best out of 178): 05tbn (0.72 #1789, 0.70 #6265, 0.33 #11634), 07c5l (0.33 #11634, 0.32 #30422, 0.29 #27738), 01n7q (0.23 #1866, 0.21 #3658, 0.21 #2763), 04_1l0v (0.18 #450, 0.10 #11189, 0.10 #9401), 0d060g (0.17 #6277, 0.08 #38476, 0.05 #17907), 07ssc (0.15 #9876, 0.14 #25083, 0.14 #22400), 02xry (0.15 #3743, 0.15 #2848, 0.12 #4638), 059rby (0.12 #19703, 0.11 #17914, 0.11 #18809), 0kpys (0.11 #1969, 0.11 #3761, 0.11 #2866), 02jx1 (0.11 #9931, 0.11 #25138, 0.10 #22455) >> Best rule #1789 for best value: >> intensional similarity = 4 >> extensional distance = 157 >> proper extension: 0jbs5; >> query: (?x13092, ?x3670) <- contains(?x9948, ?x13092), location(?x7851, ?x13092), contains(?x3670, ?x9948), currency(?x9948, ?x170) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0zpfy contains! 05tbn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 45.000 0.718 http://example.org/location/location/contains #602-0fvt2 PRED entity: 0fvt2 PRED relation: nationality PRED expected values: 07ssc => 118 concepts (118 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.80 #1705, 0.80 #1605, 0.78 #2007), 07ssc (0.58 #716, 0.56 #115, 0.55 #2309), 02jx1 (0.56 #834, 0.55 #1335, 0.54 #1034), 06m_5 (0.55 #2309, 0.39 #2610, 0.25 #4923), 0345h (0.49 #8440, 0.25 #9444, 0.23 #9948), 0h7x (0.49 #8440, 0.25 #9444, 0.23 #9948), 024pcx (0.49 #8440, 0.25 #9444, 0.23 #9948), 013p59 (0.41 #6733, 0.36 #4822, 0.33 #10958), 0dbdy (0.33 #10958, 0.31 #5525, 0.28 #9747), 03rk0 (0.25 #9444, 0.23 #9948, 0.23 #9545) >> Best rule #1705 for best value: >> intensional similarity = 4 >> extensional distance = 83 >> proper extension: 03qcq; 01gp_x; 0djywgn; 04j_gs; >> query: (?x11262, 09c7w0) <- student(?x2999, ?x11262), award_nominee(?x4895, ?x11262), profession(?x11262, ?x353), influenced_by(?x11262, ?x3542) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #716 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 02m7r; *> query: (?x11262, 07ssc) <- student(?x2999, ?x11262), ?x2999 = 07tg4, award_winner(?x14213, ?x11262), gender(?x11262, ?x231) *> conf = 0.58 ranks of expected_values: 2 EVAL 0fvt2 nationality 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 118.000 118.000 0.800 http://example.org/people/person/nationality #601-01wv9xn PRED entity: 01wv9xn PRED relation: award PRED expected values: 02sp_v 02f73p => 88 concepts (68 used for prediction) PRED predicted values (max 10 best out of 275): 01ckrr (0.84 #9272, 0.81 #6449, 0.80 #23797), 02f72n (0.53 #6191, 0.46 #9014, 0.37 #6998), 02f5qb (0.50 #9024, 0.47 #6201, 0.39 #11040), 02f705 (0.50 #9021, 0.46 #11037, 0.19 #5392), 02f73b (0.48 #9155, 0.44 #6332, 0.38 #11171), 02f6yz (0.47 #6364, 0.39 #7171, 0.35 #5558), 02f72_ (0.44 #6274, 0.43 #9097, 0.39 #5468), 03tcnt (0.44 #6212, 0.33 #7019, 0.32 #5406), 01bgqh (0.42 #7298, 0.41 #6088, 0.38 #17386), 02f77l (0.42 #5494, 0.28 #7107, 0.28 #6300) >> Best rule #9272 for best value: >> intensional similarity = 5 >> extensional distance = 54 >> proper extension: 01vrt_c; 01wcp_g; 0137g1; 0gdh5; 0161sp; 0478__m; 03y82t6; 01s21dg; 043zg; 01vsgrn; ... >> query: (?x1684, ?x4912) <- origin(?x1684, ?x3301), award(?x1684, ?x9828), award_winner(?x9828, ?x7407), ?x7407 = 01dq9q, award_winner(?x4912, ?x1684) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #6232 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 30 *> proper extension: 0m2l9; 01vvycq; 01vrncs; 0gcs9; 02qwg; 01bczm; *> query: (?x1684, 02f73p) <- origin(?x1684, ?x3301), award(?x1684, ?x9828), ?x9828 = 01ckcd, award_winner(?x4912, ?x1684), artist(?x2149, ?x1684) *> conf = 0.38 ranks of expected_values: 12, 14 EVAL 01wv9xn award 02f73p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 88.000 68.000 0.842 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 01wv9xn award 02sp_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 88.000 68.000 0.842 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #600-03prz_ PRED entity: 03prz_ PRED relation: titles! PRED expected values: 018h2 => 70 concepts (36 used for prediction) PRED predicted values (max 10 best out of 60): 060__y (0.28 #2298, 0.24 #198, 0.23 #798), 02l7c8 (0.28 #2298, 0.24 #198, 0.23 #798), 0hn10 (0.28 #2298, 0.24 #198, 0.23 #798), 04xvh5 (0.28 #2298, 0.24 #198, 0.23 #798), 01z4y (0.27 #1732, 0.21 #3229, 0.20 #3430), 01jfsb (0.17 #2215, 0.16 #215, 0.14 #1619), 06l3bl (0.16 #150, 0.07 #449, 0.04 #749), 03k9fj (0.15 #115, 0.08 #315, 0.06 #1118), 017fp (0.12 #120, 0.11 #419, 0.10 #1623), 04t36 (0.11 #404, 0.07 #2204, 0.07 #503) >> Best rule #2298 for best value: >> intensional similarity = 4 >> extensional distance = 1006 >> proper extension: 0dtw1x; 0cnztc4; 0crh5_f; 0cp08zg; 0267wwv; 09rfpk; >> query: (?x5759, ?x714) <- titles(?x1510, ?x5759), genre(?x97, ?x1510), genre(?x419, ?x1510), genre(?x5759, ?x714) >> conf = 0.28 => this is the best rule for 4 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 0963mq; 092vkg; 02r79_h; 02r8hh_; 040b5k; 0dgpwnk; 0gtvpkw; 09gkx35; 02dpl9; 0cmc26r; ... *> query: (?x5759, 018h2) <- film(?x1975, ?x5759), genre(?x5759, ?x53), film(?x5959, ?x5759), ?x5959 = 024rdh *> conf = 0.03 ranks of expected_values: 34 EVAL 03prz_ titles! 018h2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 70.000 36.000 0.285 http://example.org/media_common/netflix_genre/titles #599-0ddj0x PRED entity: 0ddj0x PRED relation: film_release_region PRED expected values: 03gj2 => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 174): 03rjj (0.86 #1296, 0.85 #1135, 0.83 #2263), 03h64 (0.83 #394, 0.81 #1040, 0.77 #2329), 03gj2 (0.83 #2284, 0.82 #1156, 0.82 #349), 035qy (0.82 #1166, 0.78 #2294, 0.78 #1327), 015fr (0.82 #1148, 0.78 #1309, 0.78 #2276), 0d060g (0.78 #330, 0.77 #976, 0.76 #2265), 0154j (0.78 #2262, 0.77 #973, 0.74 #1134), 05b4w (0.75 #391, 0.75 #1037, 0.73 #2326), 0b90_r (0.72 #650, 0.71 #326, 0.69 #1133), 03spz (0.72 #1231, 0.69 #424, 0.69 #1392) >> Best rule #1296 for best value: >> intensional similarity = 6 >> extensional distance = 143 >> proper extension: 03bx2lk; >> query: (?x5578, 03rjj) <- film_release_region(?x5578, ?x1229), film_release_region(?x5578, ?x774), film_release_region(?x5578, ?x304), ?x304 = 0d0vqn, ?x774 = 06mzp, combatants(?x1229, ?x151) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #2284 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 240 *> proper extension: 047svrl; 05q4y12; *> query: (?x5578, 03gj2) <- film_crew_role(?x5578, ?x1171), film_release_region(?x5578, ?x456), ?x456 = 05qhw *> conf = 0.83 ranks of expected_values: 3 EVAL 0ddj0x film_release_region 03gj2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 94.000 94.000 0.855 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #598-03y3bp7 PRED entity: 03y3bp7 PRED relation: program! PRED expected values: 0cjdk => 77 concepts (67 used for prediction) PRED predicted values (max 10 best out of 45): 0cjdk (0.40 #62, 0.13 #756, 0.13 #580), 0ljc_ (0.33 #29, 0.04 #143, 0.04 #201), 01w5gp (0.33 #16, 0.04 #130, 0.04 #188), 05gnf (0.21 #1699, 0.21 #1059, 0.21 #885), 0gsg7 (0.20 #1861, 0.19 #1513, 0.19 #1629), 09d5h (0.16 #874, 0.14 #932, 0.13 #1048), 03mdt (0.14 #582, 0.13 #758, 0.13 #294), 07c52 (0.13 #811, 0.13 #636, 0.12 #1278), 027_tg (0.08 #123, 0.08 #181, 0.02 #1868), 0g5lhl7 (0.07 #293, 0.06 #1225, 0.06 #1459) >> Best rule #62 for best value: >> intensional similarity = 2 >> extensional distance = 3 >> proper extension: 0gj9tn5; >> query: (?x3102, 0cjdk) <- nominated_for(?x3673, ?x3102), ?x3673 = 021yw7 >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03y3bp7 program! 0cjdk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 77.000 67.000 0.400 http://example.org/tv/tv_network/programs./tv/tv_network_duration/program #597-0416y94 PRED entity: 0416y94 PRED relation: film! PRED expected values: 03_1pg 02bj6k => 83 concepts (12 used for prediction) PRED predicted values (max 10 best out of 927): 01gzm2 (0.40 #4163, 0.32 #14572, 0.28 #14571), 0d6484 (0.40 #4163, 0.28 #14571, 0.26 #24984), 0bytfv (0.40 #4163, 0.28 #14571, 0.26 #24984), 0fvf9q (0.10 #4165), 02qgyv (0.07 #385, 0.04 #2466, 0.03 #8712), 015pkc (0.07 #2360, 0.02 #19016, 0.01 #23181), 0jfx1 (0.05 #8734, 0.05 #14979, 0.04 #10815), 0h5g_ (0.05 #74, 0.03 #2155, 0.03 #4239), 01nwwl (0.05 #502, 0.03 #2583, 0.03 #4667), 0525b (0.05 #1913, 0.03 #6078, 0.02 #12321) >> Best rule #4163 for best value: >> intensional similarity = 4 >> extensional distance = 90 >> proper extension: 01gglm; >> query: (?x1318, ?x1774) <- nominated_for(?x1774, ?x1318), films(?x13555, ?x1318), film_crew_role(?x1318, ?x137), executive_produced_by(?x1318, ?x163) >> conf = 0.40 => this is the best rule for 3 predicted values *> Best rule #24288 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 271 *> proper extension: 01h1bf; 0557yqh; 05gnf; 0d7vtk; *> query: (?x1318, 02bj6k) <- nominated_for(?x1774, ?x1318), profession(?x1774, ?x353), ?x353 = 0cbd2 *> conf = 0.01 ranks of expected_values: 450 EVAL 0416y94 film! 02bj6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 83.000 12.000 0.403 http://example.org/film/actor/film./film/performance/film EVAL 0416y94 film! 03_1pg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 12.000 0.403 http://example.org/film/actor/film./film/performance/film #596-07d2d PRED entity: 07d2d PRED relation: artists PRED expected values: 01vv7sc 0bqsy => 69 concepts (33 used for prediction) PRED predicted values (max 10 best out of 1096): 01dwrc (0.69 #15547, 0.43 #11249, 0.40 #2664), 01w806h (0.67 #4547, 0.60 #3475, 0.60 #2403), 06br6t (0.67 #5175, 0.60 #4103, 0.57 #10543), 0191h5 (0.67 #4933, 0.60 #3861, 0.50 #1717), 01w5n51 (0.67 #7123, 0.57 #10343, 0.50 #8196), 01vt5c_ (0.60 #2862, 0.57 #11447, 0.50 #1790), 01vxlbm (0.60 #2481, 0.50 #15364, 0.50 #1409), 01w8n89 (0.60 #3530, 0.50 #4602, 0.50 #1386), 01vv7sc (0.60 #3279, 0.50 #4351, 0.50 #1135), 016lmg (0.60 #2895, 0.50 #5039, 0.50 #1823) >> Best rule #15547 for best value: >> intensional similarity = 7 >> extensional distance = 14 >> proper extension: 05lwjc; >> query: (?x6714, 01dwrc) <- artists(?x6714, ?x959), artists(?x6714, ?x498), artists(?x10290, ?x498), origin(?x498, ?x9929), artists(?x10290, ?x12422), ?x12422 = 01p0w_, ?x959 = 03f5spx >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #3279 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 0fd3y; *> query: (?x6714, 01vv7sc) <- artists(?x6714, ?x9262), artists(?x6714, ?x2635), artists(?x6714, ?x959), artists(?x6714, ?x498), ?x498 = 0m19t, ?x2635 = 03fbc, instrumentalists(?x1750, ?x959), artists(?x474, ?x959), ?x474 = 0m0jc, ?x1750 = 02hnl, profession(?x9262, ?x131) *> conf = 0.60 ranks of expected_values: 9, 24 EVAL 07d2d artists 0bqsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 69.000 33.000 0.688 http://example.org/music/genre/artists EVAL 07d2d artists 01vv7sc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 69.000 33.000 0.688 http://example.org/music/genre/artists #595-02lkcc PRED entity: 02lkcc PRED relation: film PRED expected values: 08952r => 77 concepts (47 used for prediction) PRED predicted values (max 10 best out of 455): 03s9kp (0.44 #1754, 0.44 #33842, 0.42 #40967), 03q0r1 (0.22 #633, 0.03 #33841, 0.03 #65910), 09w6br (0.22 #1667), 02jkkv (0.15 #3328), 04hwbq (0.11 #190, 0.08 #1971, 0.03 #33841), 0gh65c5 (0.11 #592, 0.08 #2373, 0.01 #11278), 01xdxy (0.11 #1560, 0.08 #3341), 0dyb1 (0.11 #498, 0.08 #2279), 0gxtknx (0.11 #246, 0.03 #78380, 0.03 #33841), 07024 (0.11 #478, 0.03 #78380) >> Best rule #1754 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 06fmdb; >> query: (?x1522, 03s9kp) <- profession(?x1522, ?x1032), award_nominee(?x1522, ?x7069), ?x7069 = 0z05l >> conf = 0.44 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02lkcc film 08952r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 77.000 47.000 0.444 http://example.org/film/actor/film./film/performance/film #594-03h_fqv PRED entity: 03h_fqv PRED relation: influenced_by PRED expected values: 041mt => 133 concepts (64 used for prediction) PRED predicted values (max 10 best out of 357): 01hmk9 (0.18 #2397, 0.16 #3268, 0.08 #12853), 09889g (0.14 #2330, 0.13 #1459, 0.12 #3201), 032l1 (0.12 #24398, 0.11 #22650, 0.11 #27889), 03f0324 (0.12 #24398, 0.11 #22650, 0.11 #27889), 0lrh (0.12 #24398, 0.11 #22650, 0.11 #27889), 0zm1 (0.12 #24398, 0.11 #22650, 0.11 #27889), 0lcx (0.12 #24398, 0.11 #22650, 0.11 #27889), 045bg (0.12 #24398, 0.11 #22650, 0.11 #27889), 040_9 (0.12 #24398, 0.11 #22650, 0.11 #27889), 0c1jh (0.12 #24398, 0.11 #22650, 0.11 #27889) >> Best rule #2397 for best value: >> intensional similarity = 3 >> extensional distance = 20 >> proper extension: 09qh1; 0282x; >> query: (?x5391, 01hmk9) <- friend(?x5391, ?x3056), influenced_by(?x5391, ?x1029), nationality(?x5391, ?x94) >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #24397 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 431 *> proper extension: 02m4t; 01d5g; *> query: (?x5391, ?x2162) <- influenced_by(?x5391, ?x1029), influenced_by(?x1029, ?x3336), influenced_by(?x3336, ?x2162) *> conf = 0.07 ranks of expected_values: 38 EVAL 03h_fqv influenced_by 041mt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 133.000 64.000 0.182 http://example.org/influence/influence_node/influenced_by #593-03qcfvw PRED entity: 03qcfvw PRED relation: nominated_for! PRED expected values: 05p1dby => 90 concepts (82 used for prediction) PRED predicted values (max 10 best out of 222): 02x1z2s (0.33 #141, 0.21 #1653, 0.13 #2266), 0gq9h (0.28 #295, 0.27 #7848, 0.26 #9736), 0gs9p (0.23 #7850, 0.23 #9738, 0.23 #7378), 099c8n (0.23 #289, 0.19 #1233, 0.19 #1942), 019f4v (0.22 #6187, 0.22 #7367, 0.22 #7839), 05p1dby (0.21 #1653, 0.19 #14399, 0.19 #17943), 0p9sw (0.21 #1653, 0.19 #14399, 0.19 #17943), 0m7yy (0.21 #1653, 0.03 #14636), 02hsq3m (0.21 #27, 0.17 #971, 0.14 #2152), 0k611 (0.20 #6207, 0.20 #6679, 0.20 #306) >> Best rule #141 for best value: >> intensional similarity = 4 >> extensional distance = 59 >> proper extension: 0cpllql; 0fr63l; 02qm_f; 0dr3sl; 05wp1p; 057lbk; 07nxnw; >> query: (?x103, 02x1z2s) <- film_crew_role(?x103, ?x1966), ?x1966 = 015h31, nominated_for(?x102, ?x103), nominated_for(?x902, ?x103) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1653 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 175 *> proper extension: 04bp0l; *> query: (?x103, ?x500) <- nominated_for(?x2021, ?x103), nominated_for(?x902, ?x103), award_winner(?x5277, ?x2021), company(?x346, ?x2021), award_winner(?x500, ?x902) *> conf = 0.21 ranks of expected_values: 6 EVAL 03qcfvw nominated_for! 05p1dby CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 82.000 0.328 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #592-0456zg PRED entity: 0456zg PRED relation: genre PRED expected values: 0jdm8 => 82 concepts (80 used for prediction) PRED predicted values (max 10 best out of 86): 01jfsb (0.47 #246, 0.44 #3529, 0.36 #3410), 02kdv5l (0.39 #471, 0.31 #1058, 0.31 #2346), 03k9fj (0.33 #479, 0.28 #597, 0.27 #1418), 0lsxr (0.33 #242, 0.22 #3525, 0.20 #1883), 02n4kr (0.33 #241, 0.15 #3524, 0.14 #124), 060__y (0.27 #250, 0.19 #2008, 0.18 #2477), 01hmnh (0.23 #368, 0.18 #3651, 0.18 #485), 0gf28 (0.21 #180, 0.21 #62, 0.12 #414), 04xvlr (0.18 #2463, 0.17 #705, 0.17 #1994), 06n90 (0.17 #481, 0.16 #1302, 0.16 #3411) >> Best rule #246 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 0bscw; 02r79_h; 04n52p6; 06wbm8q; 05_5rjx; 0dlngsd; 049xgc; 01y9r2; 02mpyh; 0gy7bj4; >> query: (?x8358, 01jfsb) <- film(?x4294, ?x8358), production_companies(?x8358, ?x902), ?x4294 = 01r93l, genre(?x8358, ?x53) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #315 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 0bscw; 02r79_h; 04n52p6; 06wbm8q; 05_5rjx; 0dlngsd; 049xgc; 01y9r2; 02mpyh; 0gy7bj4; *> query: (?x8358, 0jdm8) <- film(?x4294, ?x8358), production_companies(?x8358, ?x902), ?x4294 = 01r93l, genre(?x8358, ?x53) *> conf = 0.07 ranks of expected_values: 40 EVAL 0456zg genre 0jdm8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 82.000 80.000 0.467 http://example.org/film/film/genre #591-0bth54 PRED entity: 0bth54 PRED relation: nominated_for! PRED expected values: 05ztjjw 02pqp12 054krc 018wdw 02qyntr => 119 concepts (118 used for prediction) PRED predicted values (max 10 best out of 209): 02qt02v (0.70 #877, 0.67 #11836, 0.67 #12057), 04dn09n (0.58 #687, 0.46 #3537, 0.43 #3756), 02pqp12 (0.57 #4434, 0.56 #3777, 0.56 #3996), 02qyntr (0.54 #4545, 0.52 #3669, 0.52 #819), 05ztjjw (0.52 #227, 0.29 #665, 0.27 #885), 054krc (0.48 #714, 0.31 #3564, 0.30 #934), 0gs96 (0.47 #1393, 0.35 #733, 0.25 #4459), 0f4x7 (0.45 #679, 0.41 #3748, 0.41 #4405), 04kxsb (0.43 #3589, 0.42 #4465, 0.42 #3808), 0gr4k (0.42 #3749, 0.42 #3530, 0.40 #4406) >> Best rule #877 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 01gc7; 07xtqq; 0209hj; 0_92w; 035yn8; 0ch26b_; 0bx0l; 0170th; 019vhk; 07024; ... >> query: (?x573, ?x1243) <- film_crew_role(?x573, ?x137), award(?x573, ?x1243), award(?x573, ?x1107), ?x1107 = 019f4v, nominated_for(?x198, ?x573) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #4434 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 173 *> proper extension: 01jc6q; 016fyc; 04v8x9; 01sxly; 0n0bp; 0209xj; 0hmr4; 0jzw; 05j82v; 0p_th; ... *> query: (?x573, 02pqp12) <- nominated_for(?x198, ?x573), genre(?x573, ?x811), ?x198 = 040njc, award(?x573, ?x3233) *> conf = 0.57 ranks of expected_values: 3, 4, 5, 6, 13 EVAL 0bth54 nominated_for! 02qyntr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 118.000 0.703 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bth54 nominated_for! 018wdw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 119.000 118.000 0.703 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bth54 nominated_for! 054krc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 118.000 0.703 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bth54 nominated_for! 02pqp12 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 118.000 0.703 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0bth54 nominated_for! 05ztjjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 119.000 118.000 0.703 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #590-02z3zp PRED entity: 02z3zp PRED relation: location PRED expected values: 0fr0t => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 213): 01_d4 (0.59 #14462, 0.58 #7229, 0.54 #24105), 02_286 (0.32 #12891, 0.18 #8069, 0.18 #52259), 0cc56 (0.25 #860, 0.14 #57, 0.12 #4073), 0cr3d (0.18 #1751, 0.12 #947, 0.10 #22642), 01531 (0.14 #157, 0.09 #1764, 0.05 #2567), 068p2 (0.14 #233, 0.05 #3446, 0.02 #13890), 0sf9_ (0.14 #204, 0.05 #3417, 0.02 #6629), 0k049 (0.12 #811, 0.10 #2418, 0.02 #49820), 01n7q (0.12 #866, 0.08 #4079, 0.07 #4882), 01cx_ (0.12 #965, 0.06 #8194, 0.05 #2572) >> Best rule #14462 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 041h0; 0k4gf; 016hvl; 017r2; 026lj; 01hb6v; 03f70xs; 04cbtrw; 0jcx; 032l1; ... >> query: (?x8163, ?x1860) <- influenced_by(?x2127, ?x8163), people(?x1050, ?x8163), place_of_birth(?x8163, ?x1860) >> conf = 0.59 => this is the best rule for 1 predicted values *> Best rule #71500 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1610 *> proper extension: 09jrf; *> query: (?x8163, ?x3983) <- student(?x4296, ?x8163), citytown(?x4296, ?x3983) *> conf = 0.12 ranks of expected_values: 17 EVAL 02z3zp location 0fr0t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 123.000 123.000 0.593 http://example.org/people/person/places_lived./people/place_lived/location #589-01gc7 PRED entity: 01gc7 PRED relation: language PRED expected values: 04h9h => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 41): 04306rv (0.16 #988, 0.16 #931, 0.13 #643), 03_9r (0.14 #1164, 0.13 #822, 0.12 #1221), 06nm1 (0.14 #68, 0.12 #880, 0.11 #1452), 06b_j (0.14 #603, 0.10 #252, 0.08 #1005), 02bjrlw (0.11 #525, 0.07 #1733, 0.07 #1616), 0jzc (0.08 #1003, 0.07 #946, 0.04 #601), 0653m (0.06 #938, 0.06 #995, 0.05 #11), 03hkp (0.06 #596, 0.04 #653, 0.02 #187), 04h9h (0.05 #797, 0.05 #565, 0.04 #99), 07zrf (0.05 #929, 0.05 #986, 0.02 #758) >> Best rule #988 for best value: >> intensional similarity = 5 >> extensional distance = 116 >> proper extension: 04kzqz; >> query: (?x299, 04306rv) <- nominated_for(?x2214, ?x299), genre(?x299, ?x3515), genre(?x299, ?x1316), ?x3515 = 082gq, titles(?x1316, ?x89) >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #797 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 90 *> proper extension: 0c_j9x; *> query: (?x299, 04h9h) <- nominated_for(?x2214, ?x299), award(?x299, ?x637), film(?x902, ?x299), ?x902 = 05qd_ *> conf = 0.05 ranks of expected_values: 9 EVAL 01gc7 language 04h9h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 88.000 88.000 0.161 http://example.org/film/film/language #588-0r00l PRED entity: 0r00l PRED relation: place_of_birth! PRED expected values: 04xhwn => 149 concepts (71 used for prediction) PRED predicted values (max 10 best out of 2007): 0sz28 (0.64 #2606, 0.36 #151122, 0.33 #2607), 01zlh5 (0.64 #2606, 0.36 #151122, 0.30 #166750), 02s6sh (0.64 #2606, 0.36 #151122, 0.30 #166750), 01mmslz (0.64 #2606, 0.36 #151122, 0.30 #166750), 02lyx4 (0.64 #2606, 0.36 #151122, 0.30 #166750), 0l_dv (0.64 #2606, 0.36 #151122, 0.30 #166750), 04l19_ (0.64 #2606, 0.36 #151122, 0.30 #166750), 01lz4tf (0.33 #1497, 0.08 #9312, 0.07 #17125), 03n_7k (0.33 #446, 0.08 #8261, 0.07 #16074), 011lpr (0.33 #2585, 0.08 #10400, 0.07 #18213) >> Best rule #2606 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 06_kh; >> query: (?x11930, ?x2416) <- contains(?x94, ?x11930), place_of_death(?x1109, ?x11930), location(?x2416, ?x11930), location(?x1208, ?x11930), ?x1208 = 0sz28 >> conf = 0.64 => this is the best rule for 7 predicted values No rule for expected values ranks of expected_values: EVAL 0r00l place_of_birth! 04xhwn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 149.000 71.000 0.636 http://example.org/people/person/place_of_birth #587-03c5bz PRED entity: 03c5bz PRED relation: type_of_union PRED expected values: 04ztj => 85 concepts (85 used for prediction) PRED predicted values (max 10 best out of 1): 04ztj (0.94 #196, 0.94 #193, 0.94 #154) >> Best rule #196 for best value: >> intensional similarity = 2 >> extensional distance = 2742 >> proper extension: 0c11mj; 01qx13; 071pf2; 0dzkq; 0d1_f; 099bk; 0cm03; 03lh3v; 012v1t; 0457w0; ... >> query: (?x9126, 04ztj) <- nationality(?x9126, ?x94), type_of_union(?x9126, ?x1873) >> conf = 0.94 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03c5bz type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 85.000 85.000 0.942 http://example.org/people/person/spouse_s./people/marriage/type_of_union #586-0nrnz PRED entity: 0nrnz PRED relation: currency PRED expected values: 09nqf => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 1): 09nqf (0.88 #6, 0.86 #7, 0.83 #46) >> Best rule #6 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 0ml25; 0mkqr; 0nv2x; 0mxsm; 0mrhq; >> query: (?x13427, 09nqf) <- time_zones(?x13427, ?x1638), source(?x13427, ?x958), administrative_division(?x10662, ?x13427), ?x1638 = 02fqwt >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0nrnz currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 104.000 104.000 0.880 http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency #585-014dq7 PRED entity: 014dq7 PRED relation: influenced_by! PRED expected values: 040_t => 162 concepts (78 used for prediction) PRED predicted values (max 10 best out of 410): 0ph2w (0.33 #157, 0.23 #1695, 0.14 #669), 07lp1 (0.25 #1439, 0.19 #2979, 0.17 #415), 02yl42 (0.25 #1159, 0.12 #2699, 0.12 #10899), 01w8sf (0.25 #1119, 0.06 #2659, 0.05 #5736), 040rjq (0.25 #1507, 0.04 #18939, 0.04 #4588), 05rx__ (0.23 #1848, 0.07 #4415, 0.05 #16715), 0p8jf (0.19 #2676, 0.11 #5753, 0.08 #12927), 018zvb (0.19 #3001, 0.08 #1461, 0.08 #6078), 014ps4 (0.18 #4417, 0.17 #312, 0.13 #5953), 040db (0.17 #76, 0.16 #3669, 0.13 #8278) >> Best rule #157 for best value: >> intensional similarity = 4 >> extensional distance = 4 >> proper extension: 03n6r; 01t94_1; 0127xk; >> query: (?x1946, 0ph2w) <- location(?x1946, ?x739), place_of_burial(?x1946, ?x3153), ?x739 = 02_286, influenced_by(?x8720, ?x1946) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1281 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 080r3; 03f47xl; 0mb5x; 04135; 05qzv; *> query: (?x1946, 040_t) <- nationality(?x1946, ?x94), influenced_by(?x1946, ?x5612), influenced_by(?x8720, ?x1946), ?x5612 = 058vp *> conf = 0.08 ranks of expected_values: 61 EVAL 014dq7 influenced_by! 040_t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 162.000 78.000 0.333 http://example.org/influence/influence_node/influenced_by #584-06_kh PRED entity: 06_kh PRED relation: place_of_death! PRED expected values: 012c6x 07_grx 0h005 02l101 => 109 concepts (91 used for prediction) PRED predicted values (max 10 best out of 795): 0bzyh (0.20 #152, 0.10 #873, 0.09 #2314), 0579tg2 (0.20 #647, 0.10 #1368, 0.09 #2809), 0c0tzp (0.20 #646, 0.10 #1367, 0.09 #2808), 057bc6m (0.20 #394, 0.10 #1115, 0.09 #2556), 09bx1k (0.20 #228, 0.10 #949, 0.09 #2390), 076psv (0.20 #175, 0.10 #896, 0.09 #2337), 0h326 (0.20 #719, 0.10 #1440, 0.09 #2881), 05f0r8 (0.20 #713, 0.10 #1434, 0.09 #2875), 01l3j (0.20 #708, 0.10 #1429, 0.09 #2870), 067x44 (0.20 #699, 0.10 #1420, 0.09 #2861) >> Best rule #152 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0r3w7; >> query: (?x242, 0bzyh) <- place_of_death(?x8543, ?x242), place_of_death(?x8288, ?x242), participant(?x8543, ?x3627), cinematography(?x4591, ?x8288) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06_kh place_of_death! 02l101 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 91.000 0.200 http://example.org/people/deceased_person/place_of_death EVAL 06_kh place_of_death! 0h005 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 91.000 0.200 http://example.org/people/deceased_person/place_of_death EVAL 06_kh place_of_death! 07_grx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 91.000 0.200 http://example.org/people/deceased_person/place_of_death EVAL 06_kh place_of_death! 012c6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 91.000 0.200 http://example.org/people/deceased_person/place_of_death #583-051wwp PRED entity: 051wwp PRED relation: award PRED expected values: 0gq9h => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 265): 099tbz (0.70 #13275, 0.69 #4828, 0.67 #12470), 0gq9h (0.42 #2087, 0.15 #16896, 0.15 #19312), 0gqy2 (0.40 #163, 0.31 #1771, 0.30 #1369), 04kxsb (0.40 #124, 0.30 #1330, 0.19 #1732), 0bdwqv (0.40 #171, 0.26 #1779, 0.20 #1377), 0cqh46 (0.40 #51, 0.20 #1257, 0.15 #16896), 09sdmz (0.40 #205, 0.20 #1411, 0.11 #1813), 0789_m (0.35 #1628, 0.20 #20, 0.19 #2816), 040njc (0.33 #2018, 0.15 #16896, 0.15 #19312), 019f4v (0.21 #2076, 0.19 #2816, 0.19 #14482) >> Best rule #13275 for best value: >> intensional similarity = 2 >> extensional distance = 1304 >> proper extension: 014l4w; 04qb6g; >> query: (?x4928, ?x704) <- award_winner(?x374, ?x4928), award_winner(?x704, ?x4928) >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #2087 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 278 *> proper extension: 024c1b; *> query: (?x4928, 0gq9h) <- produced_by(?x8367, ?x4928), award(?x8367, ?x704) *> conf = 0.42 ranks of expected_values: 2 EVAL 051wwp award 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 82.000 0.698 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #582-0f2w0 PRED entity: 0f2w0 PRED relation: dog_breed PRED expected values: 01t032 => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 1): 01t032 (0.87 #9, 0.83 #23, 0.81 #24) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 030qb3t; 094jv; 04f_d; 0fvzg; 0cv3w; 01cx_; 0d6lp; 0vzm; 0ply0; 0f2v0; ... >> query: (?x1719, 01t032) <- country(?x1719, ?x94), dog_breed(?x1719, ?x1706), state(?x1719, ?x3634) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2w0 dog_breed 01t032 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 163.000 163.000 0.867 http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed #581-059_y8d PRED entity: 059_y8d PRED relation: film_festivals! PRED expected values: 04v8x9 0cwy47 0bbgvp => 34 concepts (26 used for prediction) PRED predicted values (max 10 best out of 1860): 0p50v (0.50 #1373, 0.33 #1376), 06mn7 (0.50 #1373, 0.33 #1376), 020fgy (0.50 #1373), 095p3z (0.50 #1373), 0jgwf (0.50 #1373), 06qn87 (0.50 #1373), 05km8z (0.50 #1373), 06nz46 (0.50 #1373), 0h0jz (0.50 #1373), 0g9zljd (0.33 #4063, 0.33 #1065, 0.33 #377) >> Best rule #1373 for best value: >> intensional similarity = 48 >> extensional distance = 1 >> proper extension: 05f5rsr; >> query: (?x3831, ?x7349) <- film_festivals(?x4699, ?x3831), film_festivals(?x3257, ?x3831), award(?x4699, ?x2209), written_by(?x4699, ?x4353), genre(?x4699, ?x811), genre(?x10225, ?x811), genre(?x8737, ?x811), genre(?x8397, ?x811), genre(?x7480, ?x811), genre(?x6533, ?x811), genre(?x6167, ?x811), genre(?x6077, ?x811), genre(?x5502, ?x811), genre(?x5002, ?x811), genre(?x4203, ?x811), genre(?x3990, ?x811), genre(?x3055, ?x811), genre(?x2878, ?x811), genre(?x1889, ?x811), genre(?x1710, ?x811), genre(?x1511, ?x811), genre(?x626, ?x811), genre(?x66, ?x811), genre(?x8870, ?x811), ?x5002 = 03tn80, ?x1710 = 05p3738, ?x1889 = 028cg00, ?x3990 = 033srr, ?x8870 = 0fhzwl, ?x2878 = 0hx4y, ?x7480 = 02vjp3, ?x5502 = 01bl7g, honored_for(?x8150, ?x4699), language(?x3257, ?x254), ?x10225 = 0466s8n, ?x626 = 0cpllql, award_winner(?x4699, ?x7349), ?x66 = 014lc_, ?x1511 = 0340hj, film_regional_debut_venue(?x3257, ?x5416), ?x4203 = 070g7, language(?x4699, ?x5671), ?x3055 = 0x25q, ?x6533 = 02n72k, ?x8397 = 0315rp, ?x8737 = 025twgf, ?x6077 = 0g5pvv, ?x6167 = 05r3qc >> conf = 0.50 => this is the best rule for 9 predicted values *> Best rule #2062 for first EXPECTED value: *> intensional similarity = 35 *> extensional distance = 1 *> proper extension: 0cmd3zy; *> query: (?x3831, ?x1474) <- film_festivals(?x5169, ?x3831), film_festivals(?x4756, ?x3831), film_festivals(?x4699, ?x3831), film_festivals(?x2168, ?x3831), award(?x4699, ?x10747), award(?x4699, ?x2209), language(?x4699, ?x254), award_winner(?x2209, ?x788), country(?x5169, ?x94), nominated_for(?x2209, ?x5570), nominated_for(?x2209, ?x508), currency(?x4756, ?x170), film_release_region(?x5169, ?x1264), film(?x1987, ?x5169), nominated_for(?x451, ?x5169), ?x5570 = 0295sy, nominated_for(?x4872, ?x5169), award(?x4756, ?x749), language(?x4756, ?x5607), award(?x382, ?x2209), ?x508 = 0ds33, nominated_for(?x488, ?x4756), genre(?x4756, ?x162), genre(?x5169, ?x53), titles(?x512, ?x4756), film_release_region(?x2168, ?x2152), film_release_region(?x2168, ?x1453), film_release_region(?x2168, ?x172), ?x172 = 0154j, nominated_for(?x112, ?x4756), ?x1453 = 06qd3, ?x2152 = 06mkj, award(?x1474, ?x10747), award(?x2182, ?x10747), film_crew_role(?x2168, ?x137) *> conf = 0.04 ranks of expected_values: 269, 279, 898 EVAL 059_y8d film_festivals! 0bbgvp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 34.000 26.000 0.500 http://example.org/film/film/film_festivals EVAL 059_y8d film_festivals! 0cwy47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 26.000 0.500 http://example.org/film/film/film_festivals EVAL 059_y8d film_festivals! 04v8x9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 34.000 26.000 0.500 http://example.org/film/film/film_festivals #580-09zf_q PRED entity: 09zf_q PRED relation: written_by PRED expected values: 05183k => 108 concepts (88 used for prediction) PRED predicted values (max 10 best out of 172): 05y5fw (0.20 #162, 0.08 #834, 0.04 #2178), 05183k (0.20 #45, 0.08 #717, 0.03 #4084), 05prs8 (0.19 #5393, 0.17 #16191, 0.16 #12811), 09pl3f (0.17 #520, 0.08 #856, 0.05 #3212), 02bfxb (0.13 #1776, 0.12 #1440, 0.05 #2787), 0js9s (0.12 #1540, 0.09 #1876, 0.07 #2887), 02kxbwx (0.09 #3388, 0.07 #2377, 0.04 #1703), 01nc3rh (0.08 #2353, 0.08 #3365, 0.08 #24284), 02vyw (0.08 #2120, 0.05 #3469, 0.03 #2458), 07s93v (0.08 #719, 0.04 #3750, 0.03 #6451) >> Best rule #162 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 02phtzk; >> query: (?x5054, 05y5fw) <- produced_by(?x5054, ?x1533), genre(?x5054, ?x225), ?x1533 = 05prs8, honored_for(?x5369, ?x5054), film_crew_role(?x5054, ?x137) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #45 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 02phtzk; *> query: (?x5054, 05183k) <- produced_by(?x5054, ?x1533), genre(?x5054, ?x225), ?x1533 = 05prs8, honored_for(?x5369, ?x5054), film_crew_role(?x5054, ?x137) *> conf = 0.20 ranks of expected_values: 2 EVAL 09zf_q written_by 05183k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 88.000 0.200 http://example.org/film/film/written_by #579-07wh1 PRED entity: 07wh1 PRED relation: company! PRED expected values: 016h4r => 80 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1008): 034ls (0.50 #611, 0.07 #4364, 0.04 #3662), 03gkn5 (0.30 #4281, 0.25 #528, 0.25 #294), 041c4 (0.25 #333, 0.14 #1505, 0.13 #2678), 0z4s (0.25 #243, 0.14 #1415, 0.07 #2118), 0hfml (0.25 #374, 0.14 #1546, 0.07 #1171), 095b70 (0.25 #350, 0.14 #1522, 0.07 #1171), 014z8v (0.25 #307, 0.14 #1479, 0.07 #1171), 03h_fk5 (0.25 #285, 0.14 #1457, 0.07 #1171), 01p45_v (0.25 #257, 0.14 #1429, 0.07 #1171), 0203v (0.25 #492, 0.11 #4245, 0.09 #6122) >> Best rule #611 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 0d6qjf; >> query: (?x13554, 034ls) <- company(?x11696, ?x13554), company(?x8375, ?x13554), place_of_birth(?x8375, ?x2850), profession(?x8375, ?x987), films(?x13554, ?x4037) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07wh1 company! 016h4r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 80.000 64.000 0.500 http://example.org/people/person/employment_history./business/employment_tenure/company #578-025n3p PRED entity: 025n3p PRED relation: place_of_birth PRED expected values: 02_286 => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 56): 02_286 (0.33 #57759, 0.30 #12681, 0.30 #1410), 030qb3t (0.33 #57759, 0.30 #12681, 0.29 #57758), 01_d4 (0.05 #4999, 0.04 #7816, 0.04 #6407), 0cr3d (0.04 #94, 0.04 #12069, 0.04 #7844), 0dclg (0.03 #5011, 0.01 #3602, 0.01 #78), 0hptm (0.03 #225, 0.02 #5158, 0.01 #7975), 04pry (0.03 #536), 0rh6k (0.03 #4935, 0.02 #7752, 0.01 #16205), 0f94t (0.02 #4961), 01531 (0.02 #809, 0.02 #12080, 0.02 #21942) >> Best rule #57759 for best value: >> intensional similarity = 2 >> extensional distance = 2264 >> proper extension: 0qkj7; >> query: (?x2858, ?x739) <- location(?x2858, ?x739), place_of_birth(?x65, ?x739) >> conf = 0.33 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 025n3p place_of_birth 02_286 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 96.000 96.000 0.331 http://example.org/people/person/place_of_birth #577-072zl1 PRED entity: 072zl1 PRED relation: country PRED expected values: 07ssc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 112): 09c7w0 (0.84 #3920, 0.82 #3014, 0.79 #1383), 07ssc (0.49 #1338, 0.45 #257, 0.44 #2286), 0ctw_b (0.44 #2286, 0.43 #2528, 0.41 #2651), 02jx1 (0.44 #2286, 0.43 #2528, 0.41 #2651), 0345h (0.23 #447, 0.21 #1348, 0.12 #87), 03rjj (0.15 #427, 0.09 #307, 0.08 #67), 03_3d (0.09 #1329, 0.07 #308, 0.06 #428), 0d060g (0.09 #429, 0.09 #1330, 0.08 #129), 01mjq (0.08 #95, 0.06 #275, 0.03 #455), 0chghy (0.07 #1334, 0.05 #373, 0.05 #253) >> Best rule #3920 for best value: >> intensional similarity = 3 >> extensional distance = 1579 >> proper extension: 0979n; >> query: (?x7320, 09c7w0) <- film(?x3327, ?x7320), country(?x7320, ?x789), award(?x3327, ?x375) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1338 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 732 *> proper extension: 05hd32; *> query: (?x7320, 07ssc) <- country(?x7320, ?x789), film_release_region(?x4607, ?x789), ?x4607 = 0h03fhx *> conf = 0.49 ranks of expected_values: 2 EVAL 072zl1 country 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 72.000 72.000 0.837 http://example.org/film/film/country #576-01n5309 PRED entity: 01n5309 PRED relation: profession PRED expected values: 02hrh1q 09jwl => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 83): 02hrh1q (0.90 #6723, 0.89 #10452, 0.89 #2996), 0dxtg (0.74 #461, 0.71 #610, 0.65 #1206), 01d_h8 (0.68 #1645, 0.58 #2241, 0.58 #1049), 03gjzk (0.53 #463, 0.52 #612, 0.50 #16), 0cbd2 (0.49 #8804, 0.45 #6566, 0.45 #7908), 0np9r (0.42 #2832, 0.40 #4174, 0.39 #5218), 09jwl (0.42 #2832, 0.40 #4174, 0.39 #5218), 0nbcg (0.42 #2832, 0.40 #4174, 0.39 #5218), 015cjr (0.42 #2832, 0.40 #4174, 0.39 #5218), 014ktf (0.42 #2832, 0.39 #5218, 0.36 #8051) >> Best rule #6723 for best value: >> intensional similarity = 3 >> extensional distance = 297 >> proper extension: 01sl1q; 04bdxl; 01l1b90; 06dv3; 01vw87c; 0d_84; 0m2wm; 0prfz; 09fb5; 0c4f4; ... >> query: (?x692, 02hrh1q) <- participant(?x692, ?x4126), participant(?x692, ?x906), film(?x692, ?x7141) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 7 EVAL 01n5309 profession 09jwl CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 125.000 125.000 0.896 http://example.org/people/person/profession EVAL 01n5309 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 125.000 0.896 http://example.org/people/person/profession #575-0g1x2_ PRED entity: 0g1x2_ PRED relation: films PRED expected values: 035xwd => 62 concepts (19 used for prediction) PRED predicted values (max 10 best out of 1142): 0ds11z (0.33 #544, 0.25 #1584, 0.25 #24), 0hfzr (0.25 #2803, 0.25 #202, 0.17 #2282), 0294mx (0.25 #1924, 0.23 #3484, 0.21 #4006), 04q01mn (0.25 #519, 0.17 #3120, 0.17 #2599), 03cw411 (0.25 #178, 0.17 #2779, 0.17 #698), 07xtqq (0.25 #21, 0.17 #541, 0.12 #1581), 049xgc (0.25 #269, 0.17 #789, 0.12 #1829), 02d478 (0.25 #190, 0.17 #710, 0.12 #1750), 03hkch7 (0.25 #148, 0.17 #668, 0.12 #1708), 0dr_4 (0.25 #71, 0.17 #591, 0.12 #1631) >> Best rule #544 for best value: >> intensional similarity = 14 >> extensional distance = 4 >> proper extension: 0bq3x; 05489; >> query: (?x3359, 0ds11z) <- films(?x3359, ?x8438), films(?x3359, ?x3566), films(?x3359, ?x2882), nominated_for(?x1253, ?x8438), honored_for(?x2882, ?x1064), film_crew_role(?x8438, ?x137), film_release_distribution_medium(?x2882, ?x81), nominated_for(?x746, ?x8438), nominated_for(?x2170, ?x3566), costume_design_by(?x8438, ?x3685), ?x746 = 04dn09n, film_release_region(?x3566, ?x985), genre(?x3566, ?x53), ?x985 = 0k6nt >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6769 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 20 *> proper extension: 0cbvg; *> query: (?x3359, ?x1318) <- films(?x3359, ?x8438), films(?x3359, ?x2882), nominated_for(?x10482, ?x8438), nominated_for(?x4666, ?x8438), film(?x7531, ?x2882), nominated_for(?x451, ?x8438), ?x451 = 099jhq, film_crew_role(?x2882, ?x137), film(?x10061, ?x2882), award_nominee(?x10482, ?x157), award_winner(?x4460, ?x4666), film(?x10482, ?x1318) *> conf = 0.04 ranks of expected_values: 454 EVAL 0g1x2_ films 035xwd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 19.000 0.333 http://example.org/film/film_subject/films #574-03jm6c PRED entity: 03jm6c PRED relation: nationality PRED expected values: 09c7w0 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 28): 09c7w0 (0.84 #2207, 0.83 #4920, 0.79 #3110), 0ndh6 (0.34 #3412, 0.33 #9751, 0.33 #9549), 04ych (0.34 #3412, 0.33 #9751, 0.33 #9549), 059_c (0.31 #4116), 02jx1 (0.17 #1133, 0.14 #1033, 0.12 #433), 07ssc (0.14 #1015, 0.11 #4317, 0.11 #3125), 0d060g (0.11 #4317, 0.09 #107, 0.08 #3016), 0345h (0.11 #4317, 0.05 #2034, 0.05 #1031), 0f8l9c (0.11 #4317, 0.04 #3031, 0.04 #1022), 0h7x (0.11 #4317, 0.02 #2741, 0.02 #3044) >> Best rule #2207 for best value: >> intensional similarity = 4 >> extensional distance = 293 >> proper extension: 01d494; >> query: (?x2401, 09c7w0) <- gender(?x2401, ?x231), ?x231 = 05zppz, place_of_death(?x2401, ?x3026), county(?x3026, ?x12569) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03jm6c nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.841 http://example.org/people/person/nationality #573-0pgjm PRED entity: 0pgjm PRED relation: place_of_birth PRED expected values: 02_286 => 107 concepts (107 used for prediction) PRED predicted values (max 10 best out of 75): 0f2tj (0.12 #952), 02_286 (0.10 #2835, 0.10 #19, 0.09 #7764), 030qb3t (0.10 #54, 0.06 #758, 0.06 #3574), 094jv (0.10 #61, 0.02 #3581, 0.02 #4285), 03pzf (0.10 #412), 0h7h6 (0.10 #58), 01_d4 (0.06 #770, 0.04 #12739, 0.04 #4290), 0cr3d (0.06 #798, 0.04 #7839, 0.03 #2910), 01531 (0.06 #809, 0.02 #1513, 0.02 #19115), 0cc56 (0.06 #737, 0.02 #12706, 0.02 #3553) >> Best rule #952 for best value: >> intensional similarity = 3 >> extensional distance = 14 >> proper extension: 0n6f8; >> query: (?x1345, 0f2tj) <- film(?x1345, ?x2907), ?x2907 = 03z20c, profession(?x1345, ?x1146) >> conf = 0.12 => this is the best rule for 1 predicted values *> Best rule #2835 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 113 *> proper extension: 01l79yc; 0fpjyd; 03f68r6; *> query: (?x1345, 02_286) <- music(?x2329, ?x1345), award(?x1345, ?x1323), type_of_union(?x1345, ?x566) *> conf = 0.10 ranks of expected_values: 2 EVAL 0pgjm place_of_birth 02_286 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 107.000 107.000 0.125 http://example.org/people/person/place_of_birth #572-04gycf PRED entity: 04gycf PRED relation: location PRED expected values: 0ply0 => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 249): 030qb3t (0.31 #2492, 0.28 #4099, 0.27 #12132), 02_286 (0.21 #20117, 0.17 #29753, 0.17 #30556), 0c4kv (0.12 #644, 0.03 #57827, 0.02 #3053), 0r62v (0.12 #47, 0.03 #57827, 0.02 #2456), 0d9jr (0.12 #268, 0.03 #57827, 0.01 #5088), 013nty (0.12 #339, 0.03 #57827), 0dc95 (0.12 #130, 0.01 #4950), 01n7q (0.08 #2472, 0.08 #3276, 0.07 #4079), 013yq (0.07 #922, 0.05 #5742, 0.05 #11365), 0ftxw (0.07 #949, 0.03 #3359, 0.03 #5769) >> Best rule #2492 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 036hf4; >> query: (?x3546, 030qb3t) <- vacationer(?x2256, ?x3546), religion(?x3546, ?x1985), location(?x3546, ?x2850) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #2585 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 60 *> proper extension: 036hf4; *> query: (?x3546, 0ply0) <- vacationer(?x2256, ?x3546), religion(?x3546, ?x1985), location(?x3546, ?x2850) *> conf = 0.02 ranks of expected_values: 146 EVAL 04gycf location 0ply0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 104.000 104.000 0.306 http://example.org/people/person/places_lived./people/place_lived/location #571-03q_w5 PRED entity: 03q_w5 PRED relation: artists! PRED expected values: 016clz 017371 => 84 concepts (51 used for prediction) PRED predicted values (max 10 best out of 285): 016clz (0.93 #7744, 0.80 #3410, 0.59 #6198), 06by7 (0.92 #8382, 0.89 #9619, 0.86 #9928), 064t9 (0.81 #14249, 0.75 #7753, 0.64 #15491), 01fh36 (0.68 #4729, 0.67 #2252, 0.62 #5659), 03lty (0.67 #9626, 0.65 #9935, 0.64 #11171), 03_d0 (0.62 #12080, 0.56 #12391, 0.56 #8372), 05bt6j (0.55 #6237, 0.55 #4068, 0.43 #7783), 05w3f (0.54 #5300, 0.50 #5921, 0.46 #5610), 0155w (0.51 #8154, 0.40 #8776, 0.35 #5988), 06j6l (0.42 #14594, 0.33 #14284, 0.31 #12117) >> Best rule #7744 for best value: >> intensional similarity = 10 >> extensional distance = 67 >> proper extension: 03f5spx; 01v_pj6; 0j1yf; 04mn81; 01wsl7c; 0qf3p; 03fbc; 0892sx; 033wx9; 0259r0; ... >> query: (?x10639, 016clz) <- artists(?x7808, ?x10639), artists(?x7083, ?x10639), artist(?x2299, ?x10639), artists(?x7083, ?x8341), artists(?x7083, ?x2865), parent_genre(?x7083, ?x837), ?x2865 = 016h9b, artists(?x7808, ?x6471), ?x8341 = 01wmjkb, ?x6471 = 0143q0 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 41 EVAL 03q_w5 artists! 017371 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 84.000 51.000 0.928 http://example.org/music/genre/artists EVAL 03q_w5 artists! 016clz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 51.000 0.928 http://example.org/music/genre/artists #570-0432_5 PRED entity: 0432_5 PRED relation: film_release_region PRED expected values: 03ryn => 97 concepts (89 used for prediction) PRED predicted values (max 10 best out of 223): 0345h (0.88 #699, 0.88 #1030, 0.80 #2351), 03rjj (0.87 #1659, 0.86 #2319, 0.82 #667), 05r4w (0.86 #2315, 0.85 #994, 0.82 #663), 06mkj (0.85 #2378, 0.85 #1057, 0.82 #2873), 0d060g (0.85 #1000, 0.72 #2321, 0.71 #669), 0chghy (0.85 #2326, 0.82 #1666, 0.79 #1005), 035qy (0.82 #701, 0.79 #2353, 0.75 #1693), 05v8c (0.82 #1011, 0.61 #1672, 0.59 #2332), 0k6nt (0.81 #3332, 0.80 #1682, 0.79 #2342), 05qhw (0.79 #2330, 0.76 #678, 0.76 #1009) >> Best rule #699 for best value: >> intensional similarity = 6 >> extensional distance = 15 >> proper extension: 0fq27fp; >> query: (?x4604, 0345h) <- film_release_region(?x4604, ?x429), film_release_region(?x4604, ?x252), ?x429 = 03rt9, film_release_region(?x4604, ?x1264), film_release_region(?x5271, ?x252), ?x5271 = 047vnkj >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #1086 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 31 *> proper extension: 0gtsx8c; *> query: (?x4604, 03ryn) <- film(?x8104, ?x4604), language(?x4604, ?x254), film_release_region(?x4604, ?x304), ?x304 = 0d0vqn, prequel(?x4604, ?x7502) *> conf = 0.45 ranks of expected_values: 36 EVAL 0432_5 film_release_region 03ryn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 97.000 89.000 0.882 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #569-0266s9 PRED entity: 0266s9 PRED relation: award_winner PRED expected values: 02bj6k => 71 concepts (59 used for prediction) PRED predicted values (max 10 best out of 675): 02bj6k (0.48 #11512, 0.46 #26315, 0.45 #65798), 01swck (0.48 #11512, 0.46 #26315, 0.45 #65798), 02661h (0.48 #11512, 0.46 #26315, 0.45 #65798), 01gvxv (0.48 #11512, 0.46 #26315, 0.45 #65798), 04bs3j (0.48 #11512, 0.46 #26315, 0.45 #65798), 01rs5p (0.48 #11512, 0.46 #26315, 0.45 #65798), 05xpms (0.45 #24669, 0.39 #34541, 0.37 #21378), 03cglm (0.37 #21378, 0.30 #3289, 0.28 #24668), 0kcdl (0.24 #14801, 0.20 #27961, 0.14 #42766), 05gnf (0.22 #1079, 0.15 #2723, 0.11 #4368) >> Best rule #11512 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 0275kr; >> query: (?x11806, ?x545) <- category(?x11806, ?x134), country_of_origin(?x11806, ?x94), nominated_for(?x545, ?x11806) >> conf = 0.48 => this is the best rule for 6 predicted values ranks of expected_values: 1 EVAL 0266s9 award_winner 02bj6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 59.000 0.476 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #568-0njcw PRED entity: 0njcw PRED relation: contains! PRED expected values: 04rrx => 42 concepts (29 used for prediction) PRED predicted values (max 10 best out of 292): 09c7w0 (0.36 #19794, 0.22 #6291, 0.22 #16192), 059rby (0.20 #20, 0.19 #917, 0.17 #1814), 01n7q (0.12 #11768, 0.12 #7262, 0.11 #10867), 05k7sb (0.11 #6421, 0.09 #4622, 0.08 #15428), 05tbn (0.11 #1121, 0.10 #3816, 0.10 #2018), 05kkh (0.11 #9, 0.10 #906, 0.09 #1803), 05fjf (0.10 #374, 0.09 #1271, 0.09 #2168), 07z1m (0.09 #3684, 0.09 #989, 0.09 #1886), 02xry (0.07 #1060, 0.07 #19954, 0.07 #3755), 04rrx (0.06 #19918, 0.05 #9889, 0.05 #8085) >> Best rule #19794 for best value: >> intensional similarity = 4 >> extensional distance = 378 >> proper extension: 0rh6k; 01fq7; 0plyy; 02dtg; 0wh3; 0yc84; 0fvxz; 0xy28; 0_7z2; 0tyql; ... >> query: (?x13188, 09c7w0) <- time_zones(?x13188, ?x2674), source(?x13188, ?x958), ?x958 = 0jbk9, ?x2674 = 02hcv8 >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #19918 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 378 *> proper extension: 0rh6k; 01fq7; 0plyy; 02dtg; 0wh3; 0yc84; 0fvxz; 0xy28; 0_7z2; 0tyql; ... *> query: (?x13188, 04rrx) <- time_zones(?x13188, ?x2674), source(?x13188, ?x958), ?x958 = 0jbk9, ?x2674 = 02hcv8 *> conf = 0.06 ranks of expected_values: 10 EVAL 0njcw contains! 04rrx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 42.000 29.000 0.363 http://example.org/location/location/contains #567-05r4w PRED entity: 05r4w PRED relation: film_release_region! PRED expected values: 053rxgm 0cnztc4 01f8gz 0bh8yn3 05qbckf 06wbm8q 0h1v19 040b5k 0crh5_f 06w839_ 02x6dqb 0c8qq 0jwmp 09g7vfw 02fqrf 024mpp 011ycb 04yg13l 047vnkj 0h95zbp 0h21v2 03mgx6z 01f85k 02qk3fk 0ds6bmk 07pd_j 06rzwx 0gh6j94 0m63c 03np63f 0cp0t91 01s9vc 0n08r => 228 concepts (166 used for prediction) PRED predicted values (max 10 best out of 1370): 06wbm8q (0.88 #37906, 0.88 #9136, 0.82 #22033), 05qbckf (0.85 #37854, 0.83 #54719, 0.80 #52735), 017gl1 (0.85 #20909, 0.83 #54639, 0.82 #5036), 024mpp (0.85 #21167, 0.79 #22159, 0.76 #30095), 047vnkj (0.83 #11405, 0.83 #55056, 0.82 #22318), 053rxgm (0.82 #5051, 0.76 #15964, 0.76 #52670), 0h95zbp (0.81 #9452, 0.75 #38222, 0.73 #5484), 040b5k (0.81 #9166, 0.73 #5198, 0.72 #11150), 09g7vfw (0.81 #21117, 0.80 #37982, 0.79 #30045), 0bh8yn3 (0.81 #20964, 0.75 #9059, 0.72 #11043) >> Best rule #37906 for best value: >> intensional similarity = 3 >> extensional distance = 38 >> proper extension: 077qn; >> query: (?x87, 06wbm8q) <- film_release_region(?x1642, ?x87), ?x1642 = 0bq8tmw, olympics(?x87, ?x778) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 17, 20, 23, 25, 28, 29, 30, 32, 33, 34, 38, 40, 41, 43, 44, 47, 54, 59, 68, 71, 94, 100, 119 EVAL 05r4w film_release_region! 0n08r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 01s9vc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0cp0t91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 03np63f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0m63c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0gh6j94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 06rzwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 07pd_j CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0ds6bmk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 02qk3fk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 01f85k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 03mgx6z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0h21v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0h95zbp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 047vnkj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 04yg13l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 011ycb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 024mpp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 02fqrf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 09g7vfw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0jwmp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0c8qq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 02x6dqb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 06w839_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0crh5_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 040b5k CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0h1v19 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 06wbm8q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 05qbckf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0bh8yn3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 01f8gz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 0cnztc4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.045 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 05r4w film_release_region! 053rxgm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 228.000 166.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #566-014x77 PRED entity: 014x77 PRED relation: people! PRED expected values: 013b6_ => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 56): 0xnvg (0.33 #11, 0.14 #455, 0.12 #381), 033tf_ (0.22 #449, 0.19 #523, 0.18 #893), 0x67 (0.21 #2751, 0.18 #5272, 0.17 #2155), 065b6q (0.17 #2, 0.08 #446, 0.06 #298), 07hwkr (0.17 #10, 0.07 #3049, 0.07 #2679), 0d7wh (0.14 #1199, 0.08 #1051, 0.08 #1347), 07bch9 (0.13 #983, 0.11 #539, 0.10 #761), 09vc4s (0.10 #451, 0.09 #229, 0.09 #895), 01qhm_ (0.10 #448, 0.09 #300, 0.08 #892), 03bkbh (0.10 #474, 0.09 #326, 0.06 #548) >> Best rule #11 for best value: >> intensional similarity = 3 >> extensional distance = 4 >> proper extension: 0gpprt; >> query: (?x548, 0xnvg) <- award(?x548, ?x749), film(?x548, ?x7012), ?x7012 = 09hy79 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1308 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 164 *> proper extension: 01w3v; 0mcf4; *> query: (?x548, 013b6_) <- religion(?x548, ?x7131), ?x7131 = 03_gx *> conf = 0.05 ranks of expected_values: 18 EVAL 014x77 people! 013b6_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 137.000 137.000 0.333 http://example.org/people/ethnicity/people #565-01bpnd PRED entity: 01bpnd PRED relation: artists! PRED expected values: 06by7 => 184 concepts (133 used for prediction) PRED predicted values (max 10 best out of 270): 064t9 (0.65 #1889, 0.61 #12200, 0.59 #10323), 06by7 (0.62 #2522, 0.60 #8145, 0.58 #648), 0xhtw (0.55 #2205, 0.38 #5641, 0.37 #1580), 0dl5d (0.45 #2208, 0.33 #5644, 0.23 #11895), 05bt6j (0.42 #1607, 0.42 #2544, 0.40 #5668), 08jyyk (0.41 #2257, 0.32 #1632, 0.25 #2569), 02yv6b (0.38 #101, 0.33 #727, 0.29 #1039), 0glt670 (0.36 #10351, 0.35 #14725, 0.34 #12228), 06j6l (0.36 #12548, 0.34 #12236, 0.33 #1299), 025sc50 (0.35 #989, 0.34 #12238, 0.33 #12550) >> Best rule #1889 for best value: >> intensional similarity = 4 >> extensional distance = 18 >> proper extension: 043zg; >> query: (?x5872, 064t9) <- artists(?x302, ?x5872), gender(?x5872, ?x231), participant(?x2444, ?x5872), location_of_ceremony(?x5872, ?x3026) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #2522 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 22 *> proper extension: 03j0br4; *> query: (?x5872, 06by7) <- artists(?x302, ?x5872), group(?x5872, ?x10745), languages(?x5872, ?x254), artist(?x2299, ?x5872) *> conf = 0.62 ranks of expected_values: 2 EVAL 01bpnd artists! 06by7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 184.000 133.000 0.650 http://example.org/music/genre/artists #564-03wbqc4 PRED entity: 03wbqc4 PRED relation: film! PRED expected values: 01z7_f => 113 concepts (70 used for prediction) PRED predicted values (max 10 best out of 1226): 046chh (0.29 #5349, 0.12 #15762, 0.11 #7432), 01wbg84 (0.29 #4212, 0.12 #14625, 0.10 #16710), 01nm3s (0.29 #4855, 0.06 #31936, 0.06 #15268), 0237jb (0.25 #2083, 0.24 #22913, 0.20 #108335), 01gbn6 (0.25 #1628, 0.10 #9958, 0.02 #34958), 01v42g (0.25 #204, 0.03 #21034, 0.02 #104374), 041c4 (0.25 #894, 0.03 #134223, 0.01 #46726), 0h0yt (0.25 #1346, 0.02 #47178, 0.02 #134675), 04yt7 (0.25 #751, 0.01 #46583), 04gc65 (0.22 #8223, 0.20 #10305, 0.05 #18638) >> Best rule #5349 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 01vfqh; 03sxd2; 05zlld0; 07bwr; 06cm5; >> query: (?x4361, 046chh) <- written_by(?x4361, ?x7761), genre(?x4361, ?x225), films(?x4272, ?x4361), film(?x4520, ?x4361), ?x4520 = 01swck >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #81256 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 174 *> proper extension: 0m313; 0g22z; 0140g4; 0b2v79; 09m6kg; 011yrp; 011yxg; 0gzy02; 01hr1; 01k1k4; ... *> query: (?x4361, ?x286) <- written_by(?x4361, ?x7761), genre(?x4361, ?x225), films(?x4272, ?x4361), film(?x4520, ?x4361), award_nominee(?x286, ?x4520) *> conf = 0.04 ranks of expected_values: 496 EVAL 03wbqc4 film! 01z7_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 113.000 70.000 0.286 http://example.org/film/actor/film./film/performance/film #563-01h5f8 PRED entity: 01h5f8 PRED relation: award_winner! PRED expected values: 026m9w => 140 concepts (107 used for prediction) PRED predicted values (max 10 best out of 304): 026m9w (0.43 #5605, 0.41 #1725, 0.39 #40547), 01by1l (0.25 #3562, 0.23 #5286, 0.22 #113), 02f73p (0.23 #28035, 0.15 #43141, 0.12 #6468), 02ddq4 (0.23 #28035, 0.15 #43141, 0.12 #6468), 02v1m7 (0.23 #28035, 0.15 #43141, 0.12 #6468), 024fz9 (0.23 #28035, 0.15 #43141, 0.07 #1500), 03x3wf (0.23 #28035, 0.15 #43141, 0.07 #5670), 01c9f2 (0.23 #28035, 0.15 #43141, 0.05 #1376), 01dpdh (0.23 #28035, 0.15 #43141, 0.05 #1424), 02gm9n (0.23 #28035, 0.15 #43141, 0.04 #33212) >> Best rule #5605 for best value: >> intensional similarity = 3 >> extensional distance = 198 >> proper extension: 01vd7hn; >> query: (?x11509, ?x2962) <- award_winner(?x11509, ?x2807), award(?x11509, ?x2962), role(?x11509, ?x1655) >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01h5f8 award_winner! 026m9w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 107.000 0.426 http://example.org/award/award_category/winners./award/award_honor/award_winner #562-0900j5 PRED entity: 0900j5 PRED relation: nominated_for! PRED expected values: 057xs89 => 73 concepts (65 used for prediction) PRED predicted values (max 10 best out of 167): 0gq9h (0.31 #2224, 0.25 #3426, 0.23 #6066), 099c8n (0.23 #2218, 0.18 #2458, 0.18 #4380), 07bdd_ (0.22 #2214, 0.20 #3174, 0.19 #3416), 019f4v (0.21 #6057, 0.19 #6297, 0.18 #10617), 0gs9p (0.21 #6068, 0.20 #2226, 0.18 #10628), 02x1z2s (0.21 #3361, 0.19 #14165, 0.19 #14649), 0gq_v (0.20 #2180, 0.18 #6022, 0.17 #6262), 05b1610 (0.19 #2193, 0.19 #3153, 0.19 #3395), 0p9sw (0.19 #2181, 0.16 #6263, 0.14 #3383), 01l29r (0.19 #14165, 0.19 #14649, 0.19 #15610) >> Best rule #2224 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 0d7vtk; >> query: (?x3588, 0gq9h) <- produced_by(?x3588, ?x11876), titles(?x571, ?x3588), nominated_for(?x2549, ?x3588), film(?x2549, ?x54) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #14648 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1500 *> proper extension: 02xhpl; 0c3xpwy; *> query: (?x3588, ?x68) <- nominated_for(?x2549, ?x3588), award_winner(?x2022, ?x2549), nominated_for(?x2549, ?x1863), nominated_for(?x68, ?x1863) *> conf = 0.12 ranks of expected_values: 59 EVAL 0900j5 nominated_for! 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.017 73.000 65.000 0.313 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #561-0d63kt PRED entity: 0d63kt PRED relation: genre! PRED expected values: 02py4c8 09tqkv2 => 42 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1828): 04jwly (0.80 #1867, 0.60 #4209, 0.56 #24289), 095zlp (0.80 #1867, 0.56 #13072, 0.55 #14939), 0d8w2n (0.60 #9313, 0.60 #5576, 0.50 #3710), 0gd92 (0.60 #8816, 0.60 #5079, 0.50 #3213), 06823p (0.60 #8662, 0.60 #4925, 0.50 #3059), 0g5q34q (0.60 #8540, 0.60 #4803, 0.50 #2937), 09y6pb (0.60 #5346, 0.50 #3480, 0.40 #9083), 0b44shh (0.60 #4641, 0.50 #2775, 0.40 #8378), 04j14qc (0.60 #5209, 0.50 #3343, 0.40 #8946), 0c0zq (0.60 #5353, 0.50 #3487, 0.40 #9090) >> Best rule #1867 for best value: >> intensional similarity = 11 >> extensional distance = 1 >> proper extension: 07s9rl0; >> query: (?x11032, ?x414) <- genre(?x6427, ?x11032), genre(?x4939, ?x11032), genre(?x3803, ?x11032), genre(?x69, ?x11032), ?x3803 = 08vd2q, ?x69 = 02d413, ?x4939 = 05hjnw, titles(?x11032, ?x2833), titles(?x11032, ?x414), ?x2833 = 04jwly, ?x6427 = 02nczh >> conf = 0.80 => this is the best rule for 2 predicted values *> Best rule #336 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 1 *> proper extension: 07s9rl0; *> query: (?x11032, 09tqkv2) <- genre(?x6427, ?x11032), genre(?x4939, ?x11032), genre(?x3803, ?x11032), genre(?x69, ?x11032), ?x3803 = 08vd2q, ?x69 = 02d413, ?x4939 = 05hjnw, titles(?x11032, ?x2833), ?x2833 = 04jwly, ?x6427 = 02nczh *> conf = 0.33 ranks of expected_values: 432, 875 EVAL 0d63kt genre! 09tqkv2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 14.000 0.797 http://example.org/film/film/genre EVAL 0d63kt genre! 02py4c8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 14.000 0.797 http://example.org/film/film/genre #560-026l1lq PRED entity: 026l1lq PRED relation: team! PRED expected values: 04nfpk => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 49): 01r3hr (0.84 #854, 0.84 #803, 0.84 #481), 047g8h (0.84 #481, 0.84 #264, 0.83 #850), 04nfpk (0.84 #481, 0.84 #264, 0.83 #850), 03h42s4 (0.84 #481, 0.84 #264, 0.83 #850), 02g_6x (0.80 #1329, 0.80 #1287, 0.78 #1169), 01_9c1 (0.79 #818, 0.76 #872, 0.75 #2392), 023wyl (0.76 #863, 0.75 #2392, 0.75 #334), 02qpbqj (0.75 #2392, 0.75 #715, 0.74 #1864), 05b3ts (0.75 #2392, 0.74 #1864, 0.74 #1862), 08ns5s (0.75 #2392, 0.74 #1864, 0.74 #1862) >> Best rule #854 for best value: >> intensional similarity = 21 >> extensional distance = 17 >> proper extension: 02vklm3; 03ttn0; 025_64l; 07kcvl; 057xlyq; 0g0z58; >> query: (?x12039, ?x180) <- position_s(?x12039, ?x3346), position_s(?x12039, ?x2147), position_s(?x12039, ?x1114), position_s(?x12039, ?x180), ?x180 = 01r3hr, category(?x12039, ?x134), ?x134 = 08mbj5d, ?x2147 = 04nfpk, ?x3346 = 02g_7z, position_s(?x3658, ?x1114), position_s(?x1516, ?x1114), position_s(?x729, ?x1114), team(?x1114, ?x5773), team(?x1114, ?x2574), position(?x1337, ?x1114), ?x5773 = 06rny, ?x3658 = 03b3j, ?x729 = 05g3b, ?x2574 = 01y3v, ?x1516 = 0ft5vs, position(?x706, ?x1114) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #481 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 8 *> proper extension: 070xg; 05l71; *> query: (?x12039, ?x935) <- position_s(?x12039, ?x2147), position_s(?x12039, ?x935), position_s(?x12039, ?x180), ?x180 = 01r3hr, colors(?x12039, ?x3189), colors(?x11278, ?x3189), colors(?x8223, ?x3189), colors(?x7363, ?x3189), colors(?x6644, ?x3189), colors(?x5750, ?x3189), colors(?x11139, ?x3189), ?x2147 = 04nfpk, company(?x3335, ?x7363), major_field_of_study(?x5750, ?x1154), ?x6644 = 01jpyb, currency(?x11278, ?x170), company(?x3484, ?x8223), team(?x5685, ?x11139), institution(?x620, ?x5750), teams(?x12884, ?x11139) *> conf = 0.84 ranks of expected_values: 3 EVAL 026l1lq team! 04nfpk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 70.000 70.000 0.842 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #559-01vrncs PRED entity: 01vrncs PRED relation: category PRED expected values: 08mbj5d => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #37, 0.83 #29, 0.82 #57) >> Best rule #37 for best value: >> intensional similarity = 2 >> extensional distance = 416 >> proper extension: 01w61th; 01kwlwp; 02r3zy; 05mt_q; 03g5jw; 01wbl_r; 05d8vw; 0pyg6; 047sxrj; 0dvqq; ... >> query: (?x1089, 08mbj5d) <- artist(?x3265, ?x1089), award_nominee(?x483, ?x1089) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vrncs category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.835 http://example.org/common/topic/webpage./common/webpage/category #558-012_53 PRED entity: 012_53 PRED relation: religion PRED expected values: 0c8wxp => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 30): 0c8wxp (0.40 #501, 0.33 #6, 0.32 #996), 03_gx (0.22 #374, 0.12 #284, 0.12 #239), 01lp8 (0.20 #451, 0.10 #1216, 0.08 #856), 0kpl (0.18 #595, 0.12 #280, 0.12 #1315), 0v53x (0.14 #164, 0.12 #299, 0.12 #209), 092bf5 (0.12 #331, 0.11 #421, 0.10 #826), 019cr (0.12 #326, 0.11 #416, 0.09 #551), 06nzl (0.12 #195, 0.10 #825, 0.07 #1275), 0kq2 (0.09 #603, 0.06 #783, 0.05 #3575), 04pk9 (0.09 #605, 0.05 #830, 0.04 #2225) >> Best rule #501 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 014zcr; 03d_w3h; 01vvb4m; 01pctb; 060j8b; 01skmp; 01k53x; 023s8; >> query: (?x2582, 0c8wxp) <- participant(?x7823, ?x2582), actor(?x3326, ?x2582), location(?x2582, ?x1523), location_of_ceremony(?x2582, ?x9660) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 012_53 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.400 http://example.org/people/person/religion #557-028_yv PRED entity: 028_yv PRED relation: film_release_region PRED expected values: 01mjq => 95 concepts (95 used for prediction) PRED predicted values (max 10 best out of 213): 0jgd (0.82 #2576, 0.78 #1516, 0.78 #2122), 07ssc (0.80 #319, 0.78 #168, 0.78 #2136), 035qy (0.80 #337, 0.73 #2154, 0.72 #2608), 015fr (0.77 #321, 0.74 #2138, 0.71 #1230), 0154j (0.75 #307, 0.73 #2124, 0.71 #1216), 05qhw (0.74 #2588, 0.73 #317, 0.72 #2134), 03spz (0.74 #397, 0.62 #2668, 0.58 #246), 0d060g (0.72 #914, 0.70 #158, 0.68 #2126), 06bnz (0.67 #348, 0.65 #2619, 0.63 #2165), 0b90_r (0.66 #2123, 0.66 #306, 0.64 #2577) >> Best rule #2576 for best value: >> intensional similarity = 3 >> extensional distance = 280 >> proper extension: 0407yfx; 0m3gy; >> query: (?x204, 0jgd) <- film_release_region(?x204, ?x1003), film(?x187, ?x204), ?x1003 = 03gj2 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #346 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 95 *> proper extension: 0bhwhj; *> query: (?x204, 01mjq) <- film_release_region(?x204, ?x5481), film_release_region(?x204, ?x1229), language(?x204, ?x90), ?x1229 = 059j2, citytown(?x3899, ?x5481) *> conf = 0.56 ranks of expected_values: 15 EVAL 028_yv film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 95.000 95.000 0.823 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #556-024pcx PRED entity: 024pcx PRED relation: nationality! PRED expected values: 03s9v => 166 concepts (86 used for prediction) PRED predicted values (max 10 best out of 4089): 0p3r8 (0.44 #284633, 0.33 #9216, 0.14 #345630), 014x77 (0.44 #284633, 0.13 #77383, 0.12 #93647), 0cbxl0 (0.44 #284633, 0.07 #81119, 0.06 #97383), 0bwgc_ (0.44 #284633, 0.07 #80942, 0.06 #97206), 0219q (0.44 #284633, 0.07 #78500, 0.06 #94764), 03s9v (0.33 #10385, 0.33 #6319, 0.20 #22583), 0465_ (0.33 #10104, 0.33 #6038, 0.20 #22302), 059xvg (0.33 #9184, 0.27 #78308, 0.18 #94572), 034rd (0.33 #5813, 0.20 #22077, 0.17 #30209), 09jrf (0.33 #8024, 0.20 #24288, 0.17 #32420) >> Best rule #284633 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 0h44w; >> query: (?x9328, ?x548) <- capital(?x9328, ?x10042), place_of_birth(?x548, ?x10042), location_of_ceremony(?x566, ?x10042) >> conf = 0.44 => this is the best rule for 5 predicted values *> Best rule #10385 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1 *> proper extension: 02jx1; *> query: (?x9328, 03s9v) <- nationality(?x5249, ?x9328), adjoins(?x1611, ?x9328), locations(?x14038, ?x9328), ?x14038 = 02n5d *> conf = 0.33 ranks of expected_values: 6 EVAL 024pcx nationality! 03s9v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 166.000 86.000 0.443 http://example.org/people/person/nationality #555-03kx49 PRED entity: 03kx49 PRED relation: film_format PRED expected values: 0cj16 => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 4): 0cj16 (0.25 #3, 0.12 #81, 0.11 #19), 07fb8_ (0.14 #53, 0.14 #58, 0.13 #43), 017fx5 (0.06 #20, 0.06 #25, 0.03 #41), 01dc60 (0.02 #10) >> Best rule #3 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 06_wqk4; 04zyhx; >> query: (?x7723, 0cj16) <- film(?x10527, ?x7723), film(?x4800, ?x7723), ?x10527 = 020jqv >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03kx49 film_format 0cj16 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 46.000 46.000 0.250 http://example.org/film/film/film_format #554-0dn3n PRED entity: 0dn3n PRED relation: religion PRED expected values: 0c8wxp => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 16): 0c8wxp (0.26 #141, 0.23 #51, 0.22 #187), 03_gx (0.14 #59, 0.13 #149, 0.09 #285), 0kpl (0.06 #145, 0.06 #1453, 0.05 #1816), 092bf5 (0.05 #61, 0.04 #332, 0.03 #151), 0kq2 (0.05 #63, 0.03 #153, 0.03 #1144), 06nzl (0.04 #105, 0.02 #331, 0.02 #601), 02rsw (0.04 #114), 07w8f (0.03 #170), 03j6c (0.03 #1827, 0.02 #1963, 0.02 #2549), 01lp8 (0.02 #407, 0.02 #857, 0.02 #317) >> Best rule #141 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 06qgvf; 0p_pd; 0jf1b; 039bp; 01j7rd; 06mmb; 0227tr; 0f6_x; 01qq_lp; 0219q; ... >> query: (?x3070, 0c8wxp) <- location(?x3070, ?x335), ?x335 = 059rby, people(?x3584, ?x3070) >> conf = 0.26 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0dn3n religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.258 http://example.org/people/person/religion #553-07h1h5 PRED entity: 07h1h5 PRED relation: team PRED expected values: 029q3k => 100 concepts (24 used for prediction) PRED predicted values (max 10 best out of 828): 0dwz3t (0.87 #2813, 0.86 #3166, 0.86 #3519), 02b153 (0.87 #2813, 0.86 #3166, 0.86 #3519), 0fvly (0.40 #1333, 0.25 #981, 0.06 #8014), 01kwhf (0.33 #64, 0.25 #767, 0.20 #1119), 027ffq (0.33 #296, 0.25 #2054, 0.17 #1703), 0272vm (0.33 #214, 0.20 #1269, 0.06 #4084), 050fh (0.33 #99, 0.17 #1506, 0.12 #1857), 01zhs3 (0.33 #141, 0.07 #7877, 0.07 #8229), 02_lt (0.33 #126, 0.06 #5753, 0.06 #6104), 01cwq9 (0.33 #129, 0.06 #3999, 0.05 #4701) >> Best rule #2813 for best value: >> intensional similarity = 7 >> extensional distance = 23 >> proper extension: 0bn9sc; 02d9k; 09ntbc; 0c11mj; 083qy7; 071pf2; 07nv3_; 0135nb; 026n047; 0879xc; ... >> query: (?x3586, ?x8678) <- team(?x3586, ?x8885), team(?x3586, ?x3587), team(?x3586, ?x8678), position(?x8885, ?x203), ?x203 = 0dgrmp, current_club(?x3587, ?x202), colors(?x8885, ?x663) >> conf = 0.87 => this is the best rule for 2 predicted values *> Best rule #1970 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 0tc7; 04pxcx; *> query: (?x3586, 029q3k) <- profession(?x3586, ?x7623), athlete(?x471, ?x3586), ?x471 = 02vx4, location(?x3586, ?x8174), time_zones(?x8174, ?x2864) *> conf = 0.12 ranks of expected_values: 64 EVAL 07h1h5 team 029q3k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 100.000 24.000 0.870 http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team #552-0dzz6g PRED entity: 0dzz6g PRED relation: nominated_for! PRED expected values: 02x4sn8 => 88 concepts (83 used for prediction) PRED predicted values (max 10 best out of 200): 03hkv_r (0.66 #5807, 0.66 #5109, 0.66 #5108), 0gq9h (0.66 #1455, 0.48 #1222, 0.31 #1919), 0gs9p (0.54 #1457, 0.47 #1224, 0.26 #4009), 019f4v (0.48 #1446, 0.43 #1213, 0.25 #4928), 02rdyk7 (0.45 #1230, 0.13 #1463, 0.13 #766), 0k611 (0.43 #1233, 0.40 #1466, 0.23 #4948), 0p9sw (0.40 #1181, 0.22 #1414, 0.18 #2574), 040njc (0.38 #1400, 0.36 #1167, 0.21 #4882), 02n9nmz (0.38 #1450, 0.22 #1217, 0.17 #521), 04dn09n (0.38 #1428, 0.21 #1195, 0.20 #4910) >> Best rule #5807 for best value: >> intensional similarity = 3 >> extensional distance = 782 >> proper extension: 03j63k; 0300ml; >> query: (?x3761, ?x1063) <- titles(?x1316, ?x3761), award(?x3761, ?x1063), nominated_for(?x1063, ?x306) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #9754 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x3761, ?x1716) <- award(?x3761, ?x1063), award(?x3251, ?x1063), award(?x3251, ?x1716) *> conf = 0.12 ranks of expected_values: 77 EVAL 0dzz6g nominated_for! 02x4sn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 88.000 83.000 0.665 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #551-011s0 PRED entity: 011s0 PRED relation: major_field_of_study! PRED expected values: 017j69 => 90 concepts (60 used for prediction) PRED predicted values (max 10 best out of 649): 06pwq (0.76 #13591, 0.75 #10051, 0.67 #17727), 02zd460 (0.75 #10231, 0.62 #15545, 0.62 #14364), 03ksy (0.69 #10746, 0.67 #10156, 0.64 #8977), 07szy (0.69 #10671, 0.58 #10081, 0.57 #13621), 01w3v (0.67 #10054, 0.57 #13594, 0.52 #14187), 017j69 (0.64 #9020, 0.57 #13739, 0.46 #16695), 0bwfn (0.64 #9159, 0.54 #10928, 0.52 #13878), 01w5m (0.57 #16651, 0.57 #14288, 0.56 #20190), 08815 (0.57 #16536, 0.55 #17716, 0.54 #10630), 07vyf (0.55 #9013, 0.46 #10782, 0.33 #13732) >> Best rule #13591 for best value: >> intensional similarity = 9 >> extensional distance = 19 >> proper extension: 02lp1; 04rjg; 03g3w; 02822; 03qsdpk; 01zc2w; 01lhf; >> query: (?x5615, 06pwq) <- major_field_of_study(?x3213, ?x5615), student(?x5615, ?x4191), major_field_of_study(?x865, ?x5615), major_field_of_study(?x620, ?x5615), ?x865 = 02h4rq6, major_field_of_study(?x4955, ?x5615), ?x4955 = 09f2j, institution(?x620, ?x5807), ?x5807 = 0ks67 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #9020 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 9 *> proper extension: 034ns; *> query: (?x5615, 017j69) <- major_field_of_study(?x1368, ?x5615), major_field_of_study(?x865, ?x5615), major_field_of_study(?x9911, ?x5615), major_field_of_study(?x2948, ?x5615), ?x1368 = 014mlp, ?x865 = 02h4rq6, ?x2948 = 0j_sncb, organization(?x346, ?x9911), student(?x5615, ?x4191) *> conf = 0.64 ranks of expected_values: 6 EVAL 011s0 major_field_of_study! 017j69 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 60.000 0.762 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #550-06nm1 PRED entity: 06nm1 PRED relation: languages! PRED expected values: 012d40 01r42_g 03lt8g 040db 039crh 0dqcm 01w58n3 => 60 concepts (40 used for prediction) PRED predicted values (max 10 best out of 3508): 0bdt8 (0.67 #3507, 0.50 #2243, 0.50 #1610), 01q8fxx (0.67 #3743, 0.50 #2479, 0.50 #1846), 03crmd (0.67 #3709, 0.50 #2445, 0.50 #1812), 0dqcm (0.50 #3641, 0.50 #2377, 0.50 #1744), 0htlr (0.50 #3204, 0.50 #1940, 0.50 #1307), 028pzq (0.50 #3642, 0.50 #2378, 0.50 #1745), 02f2p7 (0.50 #3451, 0.50 #2187, 0.50 #1554), 015q43 (0.50 #3443, 0.50 #2179, 0.50 #1546), 01h4rj (0.50 #3677, 0.50 #2413, 0.50 #1780), 01syr4 (0.50 #3675, 0.50 #2411, 0.50 #1778) >> Best rule #3507 for best value: >> intensional similarity = 8 >> extensional distance = 4 >> proper extension: 04306rv; >> query: (?x2502, 0bdt8) <- countries_spoken_in(?x2502, ?x47), language(?x8457, ?x2502), language(?x5576, ?x2502), language(?x1786, ?x2502), major_field_of_study(?x481, ?x2502), genre(?x5576, ?x53), ?x8457 = 034xyf, film(?x12856, ?x1786) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3641 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 04306rv; *> query: (?x2502, 0dqcm) <- countries_spoken_in(?x2502, ?x47), language(?x8457, ?x2502), language(?x5576, ?x2502), language(?x1786, ?x2502), major_field_of_study(?x481, ?x2502), genre(?x5576, ?x53), ?x8457 = 034xyf, film(?x12856, ?x1786) *> conf = 0.50 ranks of expected_values: 4, 26, 39, 426, 478, 581, 1250 EVAL 06nm1 languages! 01w58n3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 60.000 40.000 0.667 http://example.org/people/person/languages EVAL 06nm1 languages! 0dqcm CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 60.000 40.000 0.667 http://example.org/people/person/languages EVAL 06nm1 languages! 039crh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 60.000 40.000 0.667 http://example.org/people/person/languages EVAL 06nm1 languages! 040db CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 60.000 40.000 0.667 http://example.org/people/person/languages EVAL 06nm1 languages! 03lt8g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 60.000 40.000 0.667 http://example.org/people/person/languages EVAL 06nm1 languages! 01r42_g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 60.000 40.000 0.667 http://example.org/people/person/languages EVAL 06nm1 languages! 012d40 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 60.000 40.000 0.667 http://example.org/people/person/languages #549-02l4pj PRED entity: 02l4pj PRED relation: type_of_union PRED expected values: 04ztj => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.76 #13, 0.74 #29, 0.73 #69), 01g63y (0.45 #206, 0.43 #77, 0.31 #2) >> Best rule #13 for best value: >> intensional similarity = 2 >> extensional distance = 562 >> proper extension: 03f5vvx; 03hfxx; 06c0j; >> query: (?x3461, 04ztj) <- religion(?x3461, ?x4641), award_winner(?x704, ?x3461) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02l4pj type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.764 http://example.org/people/person/spouse_s./people/marriage/type_of_union #548-025ndl PRED entity: 025ndl PRED relation: capital PRED expected values: 02m77 => 200 concepts (151 used for prediction) PRED predicted values (max 10 best out of 378): 02m77 (0.76 #6518, 0.25 #388, 0.05 #7727), 04jpl (0.76 #6518, 0.08 #2050, 0.08 #1930), 04llb (0.33 #178, 0.25 #298, 0.17 #900), 09bkv (0.33 #50, 0.25 #410, 0.17 #892), 0fhsz (0.25 #316, 0.17 #557, 0.12 #1159), 01f62 (0.25 #253, 0.17 #494, 0.12 #1096), 0fq8f (0.17 #853, 0.17 #492, 0.12 #1094), 0d34_ (0.17 #822, 0.14 #1062, 0.06 #2386), 02z0j (0.17 #763, 0.14 #1003, 0.06 #2327), 056_y (0.14 #983, 0.11 #2428, 0.10 #1465) >> Best rule #6518 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 0160w; >> query: (?x1611, ?x6885) <- capital(?x1611, ?x14119), contains(?x6401, ?x14119), capital(?x6401, ?x6885), location_of_ceremony(?x566, ?x6401) >> conf = 0.76 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 025ndl capital 02m77 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 200.000 151.000 0.765 http://example.org/location/country/capital #547-0f502 PRED entity: 0f502 PRED relation: religion PRED expected values: 0c8wxp => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 23): 0c8wxp (0.39 #579, 0.34 #2078, 0.34 #2254), 03_gx (0.15 #2262, 0.15 #2086, 0.13 #14), 0kpl (0.15 #2258, 0.15 #2082, 0.08 #672), 03j6c (0.07 #2092, 0.07 #2268, 0.02 #3501), 01lp8 (0.07 #89, 0.06 #486, 0.05 #2073), 02rsw (0.07 #23, 0.03 #155, 0.02 #199), 092bf5 (0.06 #59, 0.05 #368, 0.04 #2263), 0kq2 (0.06 #61, 0.04 #2089, 0.04 #2265), 04pk9 (0.06 #63, 0.03 #2091, 0.02 #2267), 051kv (0.06 #49, 0.02 #226, 0.02 #2077) >> Best rule #579 for best value: >> intensional similarity = 2 >> extensional distance = 157 >> proper extension: 02cg2v; >> query: (?x4360, 0c8wxp) <- people(?x1446, ?x4360), ?x1446 = 033tf_ >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f502 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.390 http://example.org/people/person/religion #546-027986c PRED entity: 027986c PRED relation: award! PRED expected values: 0209hj 016yxn => 44 concepts (35 used for prediction) PRED predicted values (max 10 best out of 926): 0209hj (0.57 #1055, 0.50 #2049, 0.23 #3043), 03xf_m (0.50 #2617, 0.43 #1623, 0.08 #3611), 0c0zq (0.50 #2867, 0.31 #3861, 0.29 #1873), 09gq0x5 (0.50 #2156, 0.29 #1162, 0.15 #3150), 0gmgwnv (0.50 #2602, 0.29 #1608, 0.12 #32843), 07xtqq (0.43 #1026, 0.38 #2020, 0.15 #3014), 0pd64 (0.43 #1749, 0.38 #2743, 0.07 #5726), 09cr8 (0.38 #3151, 0.12 #2157, 0.12 #32843), 07cyl (0.38 #2317, 0.29 #1323, 0.12 #32843), 011yn5 (0.38 #2515, 0.29 #1521, 0.12 #32843) >> Best rule #1055 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02grdc; >> query: (?x834, 0209hj) <- award_winner(?x834, ?x4969), award_winner(?x834, ?x3056), friend(?x5391, ?x3056), ?x4969 = 016k6x >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1, 148 EVAL 027986c award! 016yxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 44.000 35.000 0.571 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 027986c award! 0209hj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 44.000 35.000 0.571 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #545-06srk PRED entity: 06srk PRED relation: organization PRED expected values: 0b6css 041288 => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 49): 041288 (0.71 #15, 0.67 #1440, 0.65 #1038), 0b6css (0.67 #1440, 0.65 #1038, 0.58 #9), 0j7v_ (0.67 #1440, 0.65 #1038, 0.32 #2291), 04k4l (0.46 #46, 0.41 #173, 0.40 #257), 0_2v (0.45 #24, 0.39 #45, 0.37 #298), 01rz1 (0.43 #43, 0.39 #296, 0.38 #22), 018cqq (0.32 #52, 0.32 #2291, 0.31 #31), 085h1 (0.32 #2291, 0.27 #64, 0.27 #317), 02jxk (0.32 #2291, 0.27 #44, 0.25 #297), 059dn (0.32 #2291, 0.11 #56, 0.10 #35) >> Best rule #15 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 01n6c; 04gqr; 01nyl; 04sj3; >> query: (?x8197, 041288) <- countries_within(?x2467, ?x8197), administrative_area_type(?x8197, ?x2792), ?x2467 = 0dg3n1 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 06srk organization 041288 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.711 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 06srk organization 0b6css CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.711 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #544-01z9v6 PRED entity: 01z9v6 PRED relation: team PRED expected values: 05g76 => 31 concepts (18 used for prediction) PRED predicted values (max 10 best out of 937): 01ypc (0.81 #7517, 0.81 #1879, 0.80 #3760), 07l8x (0.81 #7517, 0.81 #1879, 0.80 #3760), 07l4z (0.81 #7517, 0.81 #1879, 0.80 #3760), 04c9bn (0.81 #7517, 0.81 #1879, 0.80 #3760), 0132_h (0.81 #7517, 0.81 #1879, 0.80 #3760), 02h8p8 (0.81 #7517, 0.81 #1879, 0.80 #3760), 05g76 (0.76 #10335, 0.75 #8518, 0.69 #16908), 03b3j (0.65 #15153, 0.43 #13274, 0.25 #4700), 01xvb (0.65 #15066, 0.43 #13187, 0.25 #4700), 0ws7 (0.65 #15341, 0.43 #13462, 0.25 #8456) >> Best rule #7517 for best value: >> intensional similarity = 27 >> extensional distance = 4 >> proper extension: 02sg4b; >> query: (?x8520, ?x260) <- team(?x8520, ?x8995), team(?x8520, ?x7060), team(?x8520, ?x4208), team(?x8520, ?x2174), team(?x8520, ?x1823), team(?x8520, ?x1160), ?x7060 = 01slc, draft(?x4208, ?x8786), draft(?x4208, ?x8499), draft(?x4208, ?x3334), ?x8995 = 01d6g, ?x1823 = 01yhm, school(?x2174, ?x6856), school(?x2174, ?x2175), season(?x4208, ?x9267), ?x1160 = 049n7, colors(?x4208, ?x332), ?x9267 = 0dx84s, major_field_of_study(?x6856, ?x1668), ?x8786 = 02pq_x5, ?x8499 = 02r6gw6, organization(?x5510, ?x6856), school_type(?x6856, ?x1507), ?x3334 = 02pq_rp, currency(?x6856, ?x170), institution(?x865, ?x2175), position(?x260, ?x8520) >> conf = 0.81 => this is the best rule for 6 predicted values *> Best rule #10335 for first EXPECTED value: *> intensional similarity = 20 *> extensional distance = 6 *> proper extension: 02sdk9v; 0dgrmp; 02_j1w; *> query: (?x8520, ?x260) <- team(?x8520, ?x8521), team(?x8520, ?x7399), team(?x8520, ?x7060), team(?x8520, ?x4208), team(?x8520, ?x2405), team(?x8520, ?x1160), team(?x8520, ?x580), team(?x4244, ?x7060), colors(?x580, ?x332), team(?x11844, ?x4208), state_province_region(?x580, ?x1227), teams(?x1860, ?x7060), sport(?x8521, ?x5063), teams(?x2017, ?x1160), teams(?x2277, ?x2405), team(?x4244, ?x260), citytown(?x580, ?x2552), colors(?x2405, ?x8271), company(?x4682, ?x1160), category(?x7399, ?x134) *> conf = 0.76 ranks of expected_values: 7 EVAL 01z9v6 team 05g76 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 31.000 18.000 0.809 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #543-0c1ps1 PRED entity: 0c1ps1 PRED relation: profession PRED expected values: 012t_z => 78 concepts (74 used for prediction) PRED predicted values (max 10 best out of 49): 0dxtg (0.32 #7860, 0.30 #8156, 0.29 #8748), 01d_h8 (0.31 #8148, 0.30 #8444, 0.28 #7852), 02jknp (0.27 #452, 0.22 #600, 0.21 #8150), 03gjzk (0.22 #3271, 0.22 #2383, 0.21 #1495), 09jwl (0.19 #8457, 0.17 #3719, 0.17 #3571), 0np9r (0.18 #1057, 0.17 #1205, 0.14 #7422), 0cbd2 (0.17 #599, 0.17 #451, 0.15 #747), 0d1pc (0.14 #51, 0.12 #199, 0.11 #347), 018gz8 (0.12 #4013, 0.12 #1053, 0.12 #905), 016z4k (0.12 #7553, 0.10 #8442, 0.10 #2668) >> Best rule #7860 for best value: >> intensional similarity = 3 >> extensional distance = 3025 >> proper extension: 017r2; 0f1vrl; 0p8jf; 0c8hct; 0kp2_; 0454s1; 06gn7r; 04093; 0bt23; 01svq8; >> query: (?x10469, 0dxtg) <- profession(?x10469, ?x1032), profession(?x8375, ?x1032), ?x8375 = 0q9zc >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #7562 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2669 *> proper extension: 01qvgl; 01wy61y; 01lqf49; 03s2y9; 06p03s; 081t6; *> query: (?x10469, 012t_z) <- profession(?x10469, ?x1032), profession(?x6808, ?x1032), ?x6808 = 02l0sf *> conf = 0.03 ranks of expected_values: 32 EVAL 0c1ps1 profession 012t_z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 78.000 74.000 0.316 http://example.org/people/person/profession #542-03ntbmw PRED entity: 03ntbmw PRED relation: country PRED expected values: 0345h => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 40): 07ssc (0.43 #430, 0.41 #312, 0.37 #3857), 0j5g9 (0.37 #3857), 0345h (0.23 #440, 0.22 #322, 0.17 #26), 03rjj (0.15 #420, 0.12 #302, 0.09 #184), 02n4kr (0.11 #60, 0.06 #947, 0.06 #710), 04xvlr (0.11 #60, 0.06 #947, 0.06 #710), 07s9rl0 (0.11 #60, 0.06 #947, 0.06 #710), 0d060g (0.09 #422, 0.09 #304, 0.06 #186), 03_3d (0.08 #303, 0.06 #421, 0.04 #5758), 0chghy (0.07 #367, 0.05 #959, 0.05 #840) >> Best rule #430 for best value: >> intensional similarity = 2 >> extensional distance = 140 >> proper extension: 02vl9ln; >> query: (?x12403, 07ssc) <- country(?x12403, ?x789), ?x789 = 0f8l9c >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #440 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 140 *> proper extension: 02vl9ln; *> query: (?x12403, 0345h) <- country(?x12403, ?x789), ?x789 = 0f8l9c *> conf = 0.23 ranks of expected_values: 3 EVAL 03ntbmw country 0345h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 101.000 101.000 0.430 http://example.org/film/film/country #541-01mwsnc PRED entity: 01mwsnc PRED relation: role PRED expected values: 0214km => 149 concepts (81 used for prediction) PRED predicted values (max 10 best out of 120): 05842k (0.62 #792, 0.36 #2226, 0.33 #384), 018vs (0.56 #1548, 0.50 #306, 0.50 #217), 026t6 (0.50 #310, 0.38 #1024, 0.36 #2152), 05r5c (0.47 #2157, 0.43 #6958, 0.39 #6857), 02sgy (0.43 #3585, 0.41 #1131, 0.34 #2052), 01vdm0 (0.38 #746, 0.36 #2180, 0.30 #3610), 03qjg (0.38 #777, 0.29 #1227, 0.28 #2659), 03gvt (0.38 #791, 0.18 #996, 0.17 #178), 06ncr (0.37 #305, 0.32 #1637, 0.32 #409), 02k856 (0.37 #305, 0.32 #1637, 0.32 #409) >> Best rule #792 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 032t2z; 01vsy7t; 01vs4ff; 095x_; 01p95y0; >> query: (?x4918, 05842k) <- instrumentalists(?x2309, ?x4918), profession(?x4918, ?x655), artists(?x2809, ?x4918), ?x2309 = 06ncr, ?x2809 = 05w3f >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #404 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 01w9wwg; *> query: (?x4918, 0214km) <- instrumentalists(?x716, ?x4918), profession(?x4918, ?x2348), type_of_appearance(?x4918, ?x3429), ?x716 = 018vs, ?x2348 = 0nbcg *> conf = 0.17 ranks of expected_values: 23 EVAL 01mwsnc role 0214km CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 149.000 81.000 0.625 http://example.org/music/artist/track_contributions./music/track_contribution/role #540-0xrzh PRED entity: 0xrzh PRED relation: place_of_birth! PRED expected values: 04hxyv => 124 concepts (32 used for prediction) PRED predicted values (max 10 best out of 1950): 0qkj7 (0.47 #5219, 0.39 #31306, 0.38 #31307), 0lrh (0.47 #5219, 0.39 #31306, 0.35 #57399), 02g3w (0.20 #2304, 0.02 #12739, 0.01 #20565), 07n39 (0.20 #2043, 0.02 #12478, 0.01 #20304), 01g969 (0.20 #2033, 0.02 #12468, 0.01 #20294), 01nhkxp (0.20 #1991, 0.02 #12426, 0.01 #20252), 0cv9fc (0.20 #2329, 0.02 #15373, 0.01 #17981), 029ghl (0.20 #1934, 0.02 #14978, 0.01 #17586), 023qfd (0.20 #1721, 0.02 #14765, 0.01 #17373), 01vw8mh (0.20 #990, 0.02 #14034, 0.01 #16642) >> Best rule #5219 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0k_s5; >> query: (?x3807, ?x2845) <- location(?x3806, ?x3807), location(?x2845, ?x3807), ?x3806 = 0br1w, contains(?x7492, ?x3807), time_zones(?x7492, ?x2674) >> conf = 0.47 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0xrzh place_of_birth! 04hxyv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 32.000 0.465 http://example.org/people/person/place_of_birth #539-02x3y41 PRED entity: 02x3y41 PRED relation: film_crew_role PRED expected values: 02ynfr => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 26): 09zzb8 (0.84 #271, 0.83 #483, 0.80 #392), 0dxtw (0.59 #490, 0.43 #8, 0.42 #983), 089g0h (0.59 #315, 0.57 #15, 0.27 #913), 0d2b38 (0.47 #321, 0.43 #21, 0.27 #913), 01pvkk (0.44 #340, 0.41 #159, 0.40 #219), 02ynfr (0.30 #42, 0.28 #222, 0.27 #102), 02rh1dz (0.29 #7, 0.28 #489, 0.17 #307), 089fss (0.27 #913, 0.20 #95, 0.11 #396), 020xn5 (0.19 #306, 0.14 #6, 0.09 #482), 04pyp5 (0.18 #163, 0.13 #73, 0.12 #133) >> Best rule #271 for best value: >> intensional similarity = 6 >> extensional distance = 41 >> proper extension: 064lsn; >> query: (?x7843, 09zzb8) <- film_crew_role(?x7843, ?x281), genre(?x7843, ?x53), country(?x7843, ?x789), currency(?x7843, ?x170), ?x789 = 0f8l9c, produced_by(?x7843, ?x3568) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #42 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 8 *> proper extension: 0f4yh; *> query: (?x7843, 02ynfr) <- language(?x7843, ?x5359), language(?x7843, ?x254), genre(?x7843, ?x53), film_release_region(?x7843, ?x94), ?x5359 = 0jzc, written_by(?x7843, ?x7761), ?x254 = 02h40lc *> conf = 0.30 ranks of expected_values: 6 EVAL 02x3y41 film_crew_role 02ynfr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 103.000 103.000 0.837 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #538-09td7p PRED entity: 09td7p PRED relation: award! PRED expected values: 0gjvqm 02j9lm 01kp66 01kb2j 0hwqz 02l3_5 => 42 concepts (15 used for prediction) PRED predicted values (max 10 best out of 2566): 0lpjn (0.81 #13347, 0.80 #20021, 0.80 #10010), 02vntj (0.81 #13347, 0.80 #20021, 0.80 #10010), 01d0fp (0.81 #13347, 0.80 #20021, 0.80 #10010), 04qsdh (0.81 #13347, 0.80 #20021, 0.80 #10010), 014x77 (0.81 #13347, 0.80 #20021, 0.80 #10010), 02jsgf (0.62 #14478, 0.62 #11141, 0.50 #7803), 028knk (0.62 #10525, 0.50 #13862, 0.50 #7187), 01kb2j (0.62 #11476, 0.50 #8138, 0.50 #4802), 0h0wc (0.62 #10675, 0.50 #7337, 0.50 #4001), 09l3p (0.54 #11202, 0.50 #7864, 0.50 #4528) >> Best rule #13347 for best value: >> intensional similarity = 4 >> extensional distance = 11 >> proper extension: 09qwmm; 094qd5; 05zvq6g; 02z0dfh; 099cng; 02x4x18; 099t8j; 057xs89; >> query: (?x2257, ?x548) <- award_winner(?x2257, ?x548), award(?x2646, ?x2257), ?x2646 = 02f2dn, nominated_for(?x2257, ?x86) >> conf = 0.81 => this is the best rule for 5 predicted values *> Best rule #11476 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 11 *> proper extension: 09qwmm; 094qd5; 05zvq6g; 02z0dfh; 099cng; 02x4x18; 099t8j; 057xs89; *> query: (?x2257, 01kb2j) <- award_winner(?x2257, ?x548), award(?x2646, ?x2257), ?x2646 = 02f2dn, nominated_for(?x2257, ?x86) *> conf = 0.62 ranks of expected_values: 8, 30, 41, 289, 303, 507 EVAL 09td7p award! 02l3_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 15.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09td7p award! 0hwqz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 15.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09td7p award! 01kb2j CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 42.000 15.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09td7p award! 01kp66 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 42.000 15.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09td7p award! 02j9lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 15.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09td7p award! 0gjvqm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 42.000 15.000 0.813 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #537-0l12d PRED entity: 0l12d PRED relation: role PRED expected values: 01vdm0 => 113 concepts (113 used for prediction) PRED predicted values (max 10 best out of 112): 01vdm0 (0.32 #967, 0.27 #2760, 0.27 #3044), 0l14md (0.29 #847, 0.07 #946, 0.06 #2550), 03bx0bm (0.29 #847, 0.04 #3588, 0.04 #2829), 018vs (0.26 #2167, 0.26 #950, 0.24 #2450), 05148p4 (0.26 #2167, 0.24 #2450, 0.23 #4062), 06w7v (0.26 #2167, 0.24 #2450, 0.23 #4062), 01hww_ (0.26 #2167, 0.24 #2450, 0.23 #4062), 026t6 (0.24 #944, 0.19 #285, 0.16 #755), 01s0ps (0.12 #337, 0.11 #149, 0.11 #243), 03qjg (0.12 #339, 0.08 #809, 0.07 #998) >> Best rule #967 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 06br6t; >> query: (?x1656, 01vdm0) <- artists(?x302, ?x1656), role(?x1656, ?x227), ?x302 = 016clz >> conf = 0.32 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0l12d role 01vdm0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 113.000 0.320 http://example.org/music/artist/track_contributions./music/track_contribution/role #536-01qkqwg PRED entity: 01qkqwg PRED relation: category PRED expected values: 08mbj5d => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #16, 0.84 #12, 0.80 #10) >> Best rule #16 for best value: >> intensional similarity = 2 >> extensional distance = 351 >> proper extension: 03t9sp; 01fl3; 0dm5l; 01rm8b; 03xhj6; 06nv27; 015srx; 013w2r; 0123r4; 01q99h; ... >> query: (?x1720, 08mbj5d) <- artists(?x671, ?x1720), ?x671 = 064t9 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qkqwg category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.853 http://example.org/common/topic/webpage./common/webpage/category #535-01wwvc5 PRED entity: 01wwvc5 PRED relation: nationality PRED expected values: 09c7w0 => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 19): 09c7w0 (0.74 #6219, 0.73 #5516, 0.73 #5918), 02jx1 (0.21 #839, 0.19 #1139, 0.18 #1942), 07ssc (0.12 #317, 0.11 #821, 0.11 #1121), 0d060g (0.07 #1515, 0.06 #1013, 0.06 #107), 03rk0 (0.06 #10277, 0.06 #10577, 0.05 #8772), 0ftxw (0.05 #806, 0.04 #1307, 0.04 #604), 0345h (0.03 #131, 0.03 #433, 0.02 #2342), 0f8l9c (0.02 #1731, 0.02 #1831, 0.02 #4536), 06q1r (0.02 #177, 0.02 #278, 0.02 #379), 01z4y (0.02 #6922) >> Best rule #6219 for best value: >> intensional similarity = 3 >> extensional distance = 1322 >> proper extension: 07s6tbm; 02knnd; 06fc0b; 0564mx; 08qmfm; 019n7x; >> query: (?x2731, 09c7w0) <- award_nominee(?x4836, ?x2731), place_of_birth(?x2731, ?x2879), profession(?x4836, ?x220) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wwvc5 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.736 http://example.org/people/person/nationality #534-01gvsn PRED entity: 01gvsn PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.85 #156, 0.84 #171, 0.84 #66), 07c52 (0.04 #33, 0.02 #438, 0.02 #117), 02nxhr (0.03 #32, 0.03 #7, 0.03 #172), 07z4p (0.02 #175, 0.02 #323, 0.02 #119) >> Best rule #156 for best value: >> intensional similarity = 3 >> extensional distance = 521 >> proper extension: 07kdkfj; >> query: (?x10948, 029j_) <- nominated_for(?x749, ?x10948), featured_film_locations(?x10948, ?x739), language(?x10948, ?x254) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01gvsn film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 89.000 0.845 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #533-0ddkf PRED entity: 0ddkf PRED relation: artists! PRED expected values: 016jny => 140 concepts (138 used for prediction) PRED predicted values (max 10 best out of 247): 064t9 (0.65 #949, 0.58 #637, 0.54 #1571), 0ggx5q (0.50 #1014, 0.46 #1636, 0.32 #702), 02lnbg (0.50 #995, 0.46 #1617, 0.19 #4107), 016clz (0.42 #4052, 0.38 #1562, 0.32 #628), 025sc50 (0.40 #986, 0.33 #1608, 0.26 #674), 03_d0 (0.38 #1258, 0.24 #8729, 0.23 #12157), 0glt670 (0.38 #1599, 0.35 #977, 0.24 #9694), 05bt6j (0.38 #1602, 0.34 #4092, 0.32 #668), 0xhtw (0.34 #6242, 0.31 #5931, 0.29 #4065), 06j6l (0.31 #3474, 0.31 #2229, 0.28 #9701) >> Best rule #949 for best value: >> intensional similarity = 3 >> extensional distance = 18 >> proper extension: 01vw20_; 01vxlbm; >> query: (?x6877, 064t9) <- participant(?x6059, ?x6877), artist(?x3265, ?x6877), award(?x6877, ?x724) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #21811 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 466 *> proper extension: 039cq4; *> query: (?x6877, ?x671) <- award_winner(?x6877, ?x6990), profession(?x6990, ?x1032), artists(?x671, ?x6990) *> conf = 0.19 ranks of expected_values: 30 EVAL 0ddkf artists! 016jny CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 140.000 138.000 0.650 http://example.org/music/genre/artists #532-043hg PRED entity: 043hg PRED relation: film PRED expected values: 01rwyq => 93 concepts (54 used for prediction) PRED predicted values (max 10 best out of 494): 0407yj_ (0.11 #484, 0.08 #7652, 0.06 #9444), 012s1d (0.07 #922, 0.05 #8090, 0.04 #9882), 02wgk1 (0.07 #759, 0.05 #7927, 0.04 #9719), 0340hj (0.07 #237, 0.05 #7405, 0.04 #9197), 0blpg (0.07 #2449, 0.06 #4241, 0.06 #6033), 060__7 (0.06 #5047, 0.06 #6839, 0.05 #8631), 0888c3 (0.04 #10377, 0.04 #1417, 0.03 #3209), 03nfnx (0.04 #1405, 0.03 #3197, 0.03 #4989), 03bx2lk (0.04 #185, 0.03 #1977, 0.03 #3769), 085bd1 (0.04 #452, 0.03 #2244, 0.03 #4036) >> Best rule #484 for best value: >> intensional similarity = 7 >> extensional distance = 25 >> proper extension: 016hvl; 01gzm2; 015pxr; 01g1lp; 0kc6; 05wm88; 01pgk0; >> query: (?x6748, 0407yj_) <- profession(?x6748, ?x2225), profession(?x6748, ?x1032), profession(?x6748, ?x524), ?x2225 = 0kyk, ?x524 = 02jknp, ?x1032 = 02hrh1q, place_of_birth(?x6748, ?x11360) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #13095 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 90 *> proper extension: 01xcr4; *> query: (?x6748, 01rwyq) <- profession(?x6748, ?x2225), profession(?x6748, ?x524), ?x2225 = 0kyk, award_winner(?x899, ?x6748), profession(?x10573, ?x524), ?x10573 = 065d1h *> conf = 0.01 ranks of expected_values: 456 EVAL 043hg film 01rwyq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 93.000 54.000 0.111 http://example.org/film/actor/film./film/performance/film #531-092_25 PRED entity: 092_25 PRED relation: award_winner PRED expected values: 02lfns 016tb7 0gnbw => 32 concepts (22 used for prediction) PRED predicted values (max 10 best out of 2358): 058frd (0.47 #7624, 0.33 #2449, 0.33 #923), 015pkc (0.47 #7624, 0.25 #18292, 0.25 #18293), 0gnbw (0.47 #7624, 0.25 #18292, 0.25 #18293), 07s8r0 (0.47 #7624, 0.25 #18292, 0.25 #18293), 032w8h (0.47 #7624, 0.25 #18292, 0.25 #18293), 029_l (0.47 #7624, 0.25 #18292, 0.25 #18293), 05slvm (0.47 #7624, 0.25 #18292, 0.25 #18293), 01w7nww (0.47 #7624, 0.25 #18292, 0.25 #18293), 08pth9 (0.47 #7624, 0.05 #20510, 0.04 #23555), 03l3jy (0.47 #7624, 0.05 #4572, 0.01 #4571) >> Best rule #7624 for best value: >> intensional similarity = 15 >> extensional distance = 4 >> proper extension: 0hr3c8y; 07z31v; 092t4b; 0bxs_d; >> query: (?x5459, ?x4389) <- honored_for(?x5459, ?x9452), award_winner(?x5459, ?x7391), award_winner(?x5459, ?x1871), award_winner(?x5459, ?x446), currency(?x446, ?x170), nominated_for(?x68, ?x9452), film(?x7391, ?x2779), student(?x3439, ?x7391), ?x1871 = 02bkdn, award_winner(?x1169, ?x446), ceremony(?x678, ?x5459), film(?x4389, ?x9452), profession(?x7391, ?x319), place_of_birth(?x446, ?x3014), genre(?x9452, ?x53) >> conf = 0.47 => this is the best rule for 19 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 21, 1215 EVAL 092_25 award_winner 0gnbw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 32.000 22.000 0.467 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 092_25 award_winner 016tb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 22.000 0.467 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 092_25 award_winner 02lfns CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 32.000 22.000 0.467 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #530-016lj_ PRED entity: 016lj_ PRED relation: group! PRED expected values: 0l14md => 98 concepts (69 used for prediction) PRED predicted values (max 10 best out of 112): 05148p4 (0.72 #2123, 0.70 #3268, 0.69 #4067), 018vs (0.67 #800, 0.65 #2822, 0.65 #1412), 0l14md (0.62 #1144, 0.61 #2903, 0.61 #2555), 0l14qv (0.40 #442, 0.25 #966, 0.25 #268), 05r5c (0.35 #1407, 0.27 #2112, 0.25 #3257), 0192l (0.33 #78, 0.25 #341, 0.24 #3162), 01v1d8 (0.25 #1367, 0.25 #1016, 0.24 #3162), 03qjg (0.25 #222, 0.24 #3296, 0.24 #3162), 042v_gx (0.25 #272, 0.20 #446, 0.17 #883), 02snj9 (0.25 #231, 0.17 #843, 0.13 #2984) >> Best rule #2123 for best value: >> intensional similarity = 10 >> extensional distance = 76 >> proper extension: 01pfr3; 0m19t; 067mj; 03t9sp; 05k79; 0dvqq; 03fbc; 016fmf; 018ndc; 05563d; ... >> query: (?x10106, 05148p4) <- group(?x227, ?x10106), artists(?x7808, ?x10106), artists(?x3753, ?x10106), artists(?x3753, ?x5227), artists(?x3753, ?x4936), ?x4936 = 03lgg, ?x5227 = 01j59b0, parent_genre(?x3642, ?x7808), artists(?x7808, ?x8272), ?x8272 = 01mr2g6 >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #1144 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 6 *> proper extension: 0150jk; 01vsxdm; 01518s; *> query: (?x10106, 0l14md) <- group(?x1466, ?x10106), artists(?x10471, ?x10106), artists(?x3753, ?x10106), ?x3753 = 01_bkd, ?x1466 = 03bx0bm, artist(?x441, ?x10106), artists(?x10471, ?x4712), parent_genre(?x10471, ?x5436), ?x4712 = 03f0fnk *> conf = 0.62 ranks of expected_values: 3 EVAL 016lj_ group! 0l14md CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 98.000 69.000 0.718 http://example.org/music/performance_role/regular_performances./music/group_membership/group #529-09qj50 PRED entity: 09qj50 PRED relation: award! PRED expected values: 05p9_ql => 44 concepts (21 used for prediction) PRED predicted values (max 10 best out of 802): 0kfv9 (0.25 #179, 0.18 #1190, 0.06 #10122), 0180mw (0.25 #665, 0.15 #1676, 0.06 #10122), 015ppk (0.25 #711, 0.13 #1722, 0.05 #5769), 0ddd0gc (0.25 #134, 0.10 #1145, 0.03 #7219), 05lfwd (0.25 #580, 0.05 #1591, 0.02 #2602), 05f4vxd (0.22 #6072, 0.22 #11135, 0.22 #12148), 0vjr (0.22 #6072, 0.22 #11135, 0.22 #12148), 023ny6 (0.22 #6072, 0.22 #11135, 0.22 #12148), 05p9_ql (0.22 #6072, 0.22 #11135, 0.22 #12148), 0431v3 (0.22 #6072, 0.22 #11135, 0.22 #12148) >> Best rule #179 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 0cqhk0; 0bdwft; 0ck27z; 0bdx29; 0bb57s; 0cqhmg; >> query: (?x757, 0kfv9) <- award(?x10491, ?x757), ?x10491 = 030hbp, ceremony(?x757, ?x1265) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #6072 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 165 *> proper extension: 0fms83; *> query: (?x757, ?x758) <- award(?x444, ?x757), participant(?x444, ?x1117), nominated_for(?x757, ?x758) *> conf = 0.22 ranks of expected_values: 9 EVAL 09qj50 award! 05p9_ql CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 44.000 21.000 0.250 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #528-083qy7 PRED entity: 083qy7 PRED relation: athlete! PRED expected values: 02vx4 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 5): 02vx4 (0.90 #154, 0.89 #235, 0.89 #134), 0jm_ (0.19 #186, 0.17 #196, 0.17 #206), 018w8 (0.13 #259, 0.13 #269, 0.12 #219), 018jz (0.08 #149, 0.07 #250, 0.07 #260), 03tmr (0.02 #244, 0.02 #254, 0.02 #264) >> Best rule #154 for best value: >> intensional similarity = 6 >> extensional distance = 60 >> proper extension: 02zbjwr; >> query: (?x2666, 02vx4) <- team(?x2666, ?x7122), team(?x2666, ?x3216), position(?x7122, ?x530), ?x530 = 02_j1w, team(?x10244, ?x7122), current_club(?x1598, ?x3216) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 083qy7 athlete! 02vx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.903 http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete #527-0gbfn9 PRED entity: 0gbfn9 PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 5): 029j_ (0.88 #11, 0.85 #36, 0.82 #159), 02nxhr (0.57 #164, 0.12 #22, 0.09 #27), 07c52 (0.10 #141, 0.09 #136, 0.07 #8), 07z4p (0.07 #138, 0.07 #143, 0.06 #10), 0735l (0.01 #9, 0.01 #19) >> Best rule #11 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 02bj22; >> query: (?x5576, 029j_) <- film(?x376, ?x5576), genre(?x5576, ?x239), production_companies(?x5576, ?x617), ?x239 = 06cvj >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gbfn9 film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.875 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #526-01pfr3 PRED entity: 01pfr3 PRED relation: split_to! PRED expected values: 077rj => 85 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1): 01vsxdm (0.02 #1007, 0.02 #1205) >> Best rule #1007 for best value: >> intensional similarity = 7 >> extensional distance = 41 >> proper extension: 03f5spx; 01vv7sc; 0ftps; 01v_pj6; 04mn81; 01vs_v8; 06k02; 01271h; 01w806h; 0qdyf; ... >> query: (?x475, 01vsxdm) <- artists(?x8187, ?x475), artists(?x474, ?x475), artist(?x648, ?x475), artists(?x8187, ?x7810), ?x7810 = 0187x8, ?x474 = 0m0jc, award(?x475, ?x247) >> conf = 0.02 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01pfr3 split_to! 077rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 27.000 0.023 http://example.org/dataworld/gardening_hint/split_to #525-05qjt PRED entity: 05qjt PRED relation: student PRED expected values: 042kg => 72 concepts (55 used for prediction) PRED predicted values (max 10 best out of 295): 0kn4c (0.50 #1669, 0.33 #261, 0.30 #3551), 04z0g (0.33 #595, 0.33 #361, 0.25 #2004), 03j2gxx (0.33 #449, 0.25 #2092, 0.25 #1857), 0n00 (0.33 #304, 0.25 #1947, 0.25 #1712), 0dx97 (0.33 #351, 0.25 #1994, 0.25 #1759), 0djywgn (0.33 #875, 0.25 #1579, 0.17 #2754), 0ky1 (0.33 #1375, 0.20 #2313, 0.12 #3020), 03dq9 (0.33 #1372, 0.20 #2310, 0.12 #3017), 06c0j (0.33 #699, 0.11 #3519, 0.10 #4460), 01zh29 (0.33 #624, 0.11 #3444, 0.10 #4385) >> Best rule #1669 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 03g3w; >> query: (?x742, 0kn4c) <- major_field_of_study(?x7716, ?x742), major_field_of_study(?x7178, ?x742), major_field_of_study(?x6919, ?x742), major_field_of_study(?x4390, ?x742), organization(?x346, ?x7716), ?x4390 = 0h6rm, ?x7178 = 03hdz8, currency(?x7716, ?x170), major_field_of_study(?x734, ?x742), major_field_of_study(?x742, ?x1668), contains(?x94, ?x7716), student(?x6919, ?x2127) >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05qjt student 042kg CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 55.000 0.500 http://example.org/education/field_of_study/students_majoring./education/education/student #524-0bdw1g PRED entity: 0bdw1g PRED relation: award! PRED expected values: 02j9lm 05typm => 46 concepts (19 used for prediction) PRED predicted values (max 10 best out of 2680): 0c3p7 (0.78 #26769, 0.78 #26768, 0.69 #50193), 01csrl (0.78 #26769, 0.78 #26768, 0.69 #50193), 01934k (0.78 #26769, 0.78 #26768, 0.69 #50193), 059fjj (0.78 #26769, 0.78 #26768, 0.69 #50193), 03x16f (0.78 #26768, 0.69 #50193, 0.69 #43495), 0l6px (0.64 #7302, 0.50 #612, 0.11 #13995), 01j5ts (0.64 #6733, 0.50 #43, 0.10 #3389), 0h0wc (0.57 #7363, 0.50 #673, 0.12 #23422), 028knk (0.57 #7206, 0.50 #516, 0.12 #56887), 0dvld (0.57 #8427, 0.50 #1737, 0.12 #21811) >> Best rule #26769 for best value: >> intensional similarity = 3 >> extensional distance = 146 >> proper extension: 02v1ws; >> query: (?x686, ?x1641) <- award_winner(?x686, ?x1641), type_of_union(?x1641, ?x566), category_of(?x686, ?x2758) >> conf = 0.78 => this is the best rule for 4 predicted values *> Best rule #8016 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 12 *> proper extension: 09sb52; 0cqh6z; 02z0dfh; 02y_rq5; 0gqyl; 09td7p; 099t8j; 02ppm4q; 0gkts9; 0cqgl9; *> query: (?x686, 05typm) <- award(?x8612, ?x686), award(?x6744, ?x686), nominated_for(?x686, ?x337), participant(?x6744, ?x4360), ?x8612 = 01jw4r *> conf = 0.14 ranks of expected_values: 231, 381 EVAL 0bdw1g award! 05typm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 46.000 19.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0bdw1g award! 02j9lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 46.000 19.000 0.784 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #523-01m59 PRED entity: 01m59 PRED relation: entity_involved! PRED expected values: 07_nf => 2 concepts (2 used for prediction) PRED predicted values (max 10 best out of 1): 07_nf (0.02 #17) >> Best rule #17 for best value: >> intensional similarity = 2 >> extensional distance = 442 >> proper extension: 027rn; 05r4w; 09c7w0; 0rh6k; 0160w; 0b90_r; 0154j; 05kkh; 03rjj; 03_3d; ... >> query: (?x14639, 07_nf) <- taxonomy(?x14639, ?x939), ?x939 = 04n6k >> conf = 0.02 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01m59 entity_involved! 07_nf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 2.000 2.000 0.016 http://example.org/base/culturalevent/event/entity_involved #522-0mgkg PRED entity: 0mgkg PRED relation: organization PRED expected values: 03mbdx_ => 242 concepts (225 used for prediction) PRED predicted values (max 10 best out of 9): 034h1h (0.25 #3197, 0.24 #3245, 0.24 #2145), 03mbdx_ (0.06 #1113, 0.05 #1840, 0.05 #1987), 07t65 (0.03 #3921, 0.03 #4269, 0.03 #4294), 02vk52z (0.02 #3920, 0.02 #4268, 0.02 #4293), 018cqq (0.02 #3932, 0.02 #4280, 0.02 #4305), 0b6css (0.02 #3931, 0.02 #4279, 0.02 #4304), 06nvzg (0.01 #2376), 0_2v (0.01 #3924, 0.01 #4272, 0.01 #4297), 01rz1 (0.01 #3922, 0.01 #4270, 0.01 #4295) >> Best rule #3197 for best value: >> intensional similarity = 5 >> extensional distance = 119 >> proper extension: 01jssp; 0cwx_; 0373qt; 019q50; 01l8t8; 01x5fb; >> query: (?x9198, 034h1h) <- list(?x9198, ?x5997), state_province_region(?x9198, ?x4600), list(?x266, ?x5997), company(?x346, ?x266), ?x346 = 060c4 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1113 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 34 *> proper extension: 018mxj; 064f29; 03rwz3; 059wk; 01dycg; 069b85; *> query: (?x9198, 03mbdx_) <- industry(?x9198, ?x14344), contact_category(?x9198, ?x897), ?x897 = 03w5xm, organization(?x4682, ?x9198), citytown(?x9198, ?x5267) *> conf = 0.06 ranks of expected_values: 2 EVAL 0mgkg organization 03mbdx_ CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 242.000 225.000 0.248 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #521-01vh08 PRED entity: 01vh08 PRED relation: type_of_union PRED expected values: 01g63y => 104 concepts (104 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.76 #5, 0.76 #17, 0.74 #41), 01g63y (0.46 #337, 0.45 #320, 0.44 #346) >> Best rule #5 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 036hf4; 01l1ls; >> query: (?x9036, 04ztj) <- location(?x9036, ?x191), film(?x9036, ?x5212), gender(?x9036, ?x231), ?x191 = 0k049 >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #337 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2386 *> proper extension: 07t3x8; *> query: (?x9036, ?x566) <- location(?x9036, ?x191), location(?x5462, ?x191), type_of_union(?x5462, ?x566), award(?x5462, ?x401) *> conf = 0.46 ranks of expected_values: 2 EVAL 01vh08 type_of_union 01g63y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 104.000 0.762 http://example.org/people/person/spouse_s./people/marriage/type_of_union #520-06m_5 PRED entity: 06m_5 PRED relation: organization PRED expected values: 0j7v_ => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 49): 0b6css (0.62 #9, 0.50 #30, 0.42 #392), 01rz1 (0.54 #1, 0.41 #533, 0.40 #575), 018cqq (0.54 #10, 0.36 #117, 0.33 #31), 0j7v_ (0.51 #323, 0.27 #938, 0.26 #1428), 041288 (0.45 #334, 0.38 #1035, 0.37 #949), 04k4l (0.39 #704, 0.38 #535, 0.38 #343), 02jxk (0.38 #2, 0.24 #576, 0.23 #534), 0gkjy (0.31 #1026, 0.30 #686, 0.27 #1154), 059dn (0.23 #14, 0.17 #35, 0.10 #163), 085h1 (0.23 #1063, 0.23 #1127, 0.21 #1447) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 05kyr; >> query: (?x8420, 0b6css) <- nationality(?x4895, ?x8420), written_by(?x4699, ?x4895), influenced_by(?x1683, ?x4895) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #323 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 65 *> proper extension: 0h44w; *> query: (?x8420, 0j7v_) <- countries_spoken_in(?x5121, ?x8420), countries_spoken_in(?x254, ?x8420), ?x254 = 02h40lc, languages(?x3873, ?x5121) *> conf = 0.51 ranks of expected_values: 4 EVAL 06m_5 organization 0j7v_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 150.000 150.000 0.615 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #519-02q4mt PRED entity: 02q4mt PRED relation: profession PRED expected values: 02hrh1q => 95 concepts (63 used for prediction) PRED predicted values (max 10 best out of 48): 02hrh1q (0.92 #3984, 0.92 #3249, 0.91 #7806), 01d_h8 (0.78 #741, 0.78 #888, 0.78 #1329), 0cbd2 (0.33 #7, 0.30 #154, 0.28 #1183), 0dgd_ (0.29 #617, 0.09 #911, 0.08 #1352), 0np9r (0.26 #6048, 0.15 #9136, 0.15 #8841), 02hv44_ (0.25 #56, 0.19 #203, 0.17 #497), 02krf9 (0.24 #1790, 0.23 #760, 0.23 #1348), 018gz8 (0.18 #2221, 0.15 #3545, 0.14 #6044), 01c72t (0.16 #1934, 0.08 #2964, 0.08 #6198), 09jwl (0.16 #6193, 0.16 #5752, 0.16 #8398) >> Best rule #3984 for best value: >> intensional similarity = 3 >> extensional distance = 1124 >> proper extension: 01sl1q; 05vsxz; 01j5ts; 02zq43; 01qscs; 0p_pd; 0z4s; 054_mz; 07lmxq; 018dnt; ... >> query: (?x11873, 02hrh1q) <- film(?x11873, ?x7784), profession(?x11873, ?x524), honored_for(?x7515, ?x7784) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02q4mt profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 95.000 63.000 0.925 http://example.org/people/person/profession #518-01wd9lv PRED entity: 01wd9lv PRED relation: instrumentalists! PRED expected values: 026t6 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 63): 0342h (0.63 #2270, 0.59 #3850, 0.41 #1485), 05r5c (0.45 #3853, 0.41 #2273, 0.34 #1314), 05148p4 (0.31 #2286, 0.31 #3866, 0.27 #978), 02hnl (0.29 #122, 0.16 #3880, 0.12 #2300), 018vs (0.27 #3858, 0.21 #2278, 0.18 #1493), 06w7v (0.17 #594, 0.06 #855, 0.06 #1117), 03qjg (0.14 #138, 0.13 #2316, 0.13 #3896), 06ch55 (0.14 #169, 0.05 #1388, 0.05 #1999), 0dwr4 (0.14 #127), 03gvt (0.13 #587, 0.08 #1022, 0.07 #761) >> Best rule #2270 for best value: >> intensional similarity = 3 >> extensional distance = 304 >> proper extension: 0bg539; 0h7pj; 0cj2w; >> query: (?x6382, 0342h) <- profession(?x6382, ?x131), award_nominee(?x568, ?x6382), instrumentalists(?x228, ?x6382) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #3848 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 600 *> proper extension: 01tszq; 03llf8; 0948xk; 09g0h; *> query: (?x6382, 026t6) <- profession(?x6382, ?x131), instrumentalists(?x228, ?x6382) *> conf = 0.11 ranks of expected_values: 11 EVAL 01wd9lv instrumentalists! 026t6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 119.000 119.000 0.627 http://example.org/music/instrument/instrumentalists #517-011ysn PRED entity: 011ysn PRED relation: award PRED expected values: 05h5nb8 => 91 concepts (78 used for prediction) PRED predicted values (max 10 best out of 206): 0gs9p (0.23 #10120, 0.23 #10119, 0.23 #1414), 0gr0m (0.23 #10120, 0.23 #10119, 0.23 #1414), 0gq9h (0.23 #10120, 0.23 #10119, 0.23 #1414), 02x258x (0.23 #10120, 0.23 #10119, 0.23 #1414), 0p9sw (0.23 #10120, 0.23 #10119, 0.23 #1414), 0k611 (0.23 #10120, 0.23 #10119, 0.23 #1414), 0gr4k (0.23 #10120, 0.23 #10119, 0.23 #1414), 02x17s4 (0.23 #10120, 0.23 #10119, 0.23 #1414), 02rdyk7 (0.23 #10120, 0.23 #10119, 0.23 #1414), 099c8n (0.23 #10119, 0.23 #1414, 0.22 #18363) >> Best rule #10120 for best value: >> intensional similarity = 4 >> extensional distance = 863 >> proper extension: 01fs__; >> query: (?x3496, ?x1313) <- language(?x3496, ?x254), award_winner(?x3496, ?x3069), nominated_for(?x1313, ?x3496), award(?x269, ?x1313) >> conf = 0.23 => this is the best rule for 9 predicted values *> Best rule #18126 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1597 *> proper extension: 02_1q9; 02_1rq; 0358x_; 01p9hgt; 0464pz; 0584r4; 01xr2s; 03ln8b; 01kv4mb; 027tbrc; ... *> query: (?x3496, ?x384) <- nominated_for(?x1162, ?x3496), nominated_for(?x1162, ?x7150), nominated_for(?x406, ?x7150), nominated_for(?x384, ?x7150) *> conf = 0.02 ranks of expected_values: 158 EVAL 011ysn award 05h5nb8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 91.000 78.000 0.233 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #516-014zcr PRED entity: 014zcr PRED relation: currency PRED expected values: 09nqf => 153 concepts (153 used for prediction) PRED predicted values (max 10 best out of 2): 09nqf (0.48 #1, 0.43 #4, 0.43 #25), 01nv4h (0.02 #23, 0.02 #80, 0.02 #92) >> Best rule #1 for best value: >> intensional similarity = 3 >> extensional distance = 19 >> proper extension: 05zbm4; 05k2s_; 086sj; 0hqcy; 02hy9p; >> query: (?x286, 09nqf) <- award_nominee(?x192, ?x286), participant(?x444, ?x286), executive_produced_by(?x964, ?x286) >> conf = 0.48 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014zcr currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 153.000 153.000 0.476 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #515-02_1kl PRED entity: 02_1kl PRED relation: tv_program! PRED expected values: 026n9h3 => 71 concepts (51 used for prediction) PRED predicted values (max 10 best out of 239): 070w7s (0.33 #432, 0.12 #8862, 0.09 #623), 0265v21 (0.33 #398, 0.12 #8862, 0.04 #589), 057d89 (0.33 #401, 0.05 #1554, 0.05 #1746), 04wtx1 (0.25 #404, 0.12 #8862, 0.09 #595), 025vw4t (0.25 #494, 0.09 #685, 0.05 #876), 026n998 (0.25 #431, 0.04 #622, 0.04 #1584), 026n9h3 (0.17 #506, 0.14 #1729, 0.14 #2306), 026dg51 (0.17 #399, 0.12 #8862, 0.04 #590), 02rghbp (0.17 #428, 0.12 #8862, 0.04 #619), 0265vcb (0.17 #426, 0.12 #8862, 0.04 #617) >> Best rule #432 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 01b65l; >> query: (?x7175, 070w7s) <- nominated_for(?x3545, ?x7175), languages(?x7175, ?x254), ?x3545 = 02pzxlw, award(?x7175, ?x4115) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #506 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 01b65l; *> query: (?x7175, 026n9h3) <- nominated_for(?x3545, ?x7175), languages(?x7175, ?x254), ?x3545 = 02pzxlw, award(?x7175, ?x4115) *> conf = 0.17 ranks of expected_values: 7 EVAL 02_1kl tv_program! 026n9h3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 71.000 51.000 0.333 http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program #514-0jrqq PRED entity: 0jrqq PRED relation: people! PRED expected values: 04gfy7 => 124 concepts (124 used for prediction) PRED predicted values (max 10 best out of 51): 041rx (0.37 #916, 0.33 #688, 0.24 #3730), 0x67 (0.21 #3432, 0.21 #3813, 0.20 #3508), 033tf_ (0.15 #691, 0.13 #3733, 0.13 #3810), 02w7gg (0.13 #914, 0.11 #686, 0.10 #3500), 0g5y6 (0.11 #341, 0.02 #3763, 0.02 #1709), 07hwkr (0.10 #3282, 0.08 #2750, 0.07 #3130), 048z7l (0.09 #951, 0.06 #1027, 0.05 #1179), 0xnvg (0.08 #697, 0.08 #3816, 0.08 #925), 0dryh9k (0.07 #3286, 0.07 #3742, 0.06 #1688), 07bch9 (0.07 #783, 0.07 #1619, 0.06 #3293) >> Best rule #916 for best value: >> intensional similarity = 3 >> extensional distance = 99 >> proper extension: 04t2l2; 014zcr; 0h5f5n; 01q_ph; 02lfcm; 0159h6; 03f2_rc; 0c1pj; 0jf1b; 05kfs; ... >> query: (?x3873, 041rx) <- written_by(?x1246, ?x3873), award_winner(?x350, ?x3873), people(?x9347, ?x3873) >> conf = 0.37 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0jrqq people! 04gfy7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 124.000 0.366 http://example.org/people/ethnicity/people #513-01ffx4 PRED entity: 01ffx4 PRED relation: film_release_region PRED expected values: 059j2 06mkj 06t2t => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 126): 06mkj (0.90 #987, 0.87 #1299, 0.86 #1920), 059j2 (0.89 #1893, 0.86 #2203, 0.84 #2358), 03rjj (0.86 #1867, 0.84 #2177, 0.83 #2332), 035qy (0.84 #1896, 0.76 #2206, 0.73 #1430), 015fr (0.84 #1879, 0.75 #2189, 0.75 #1258), 0k6nt (0.82 #955, 0.82 #1267, 0.81 #1577), 0b90_r (0.77 #1866, 0.69 #2176, 0.68 #1245), 01znc_ (0.76 #970, 0.76 #1903, 0.73 #2213), 06bnz (0.76 #1908, 0.69 #2218, 0.66 #2373), 06t2t (0.74 #1925, 0.65 #2235, 0.63 #2390) >> Best rule #987 for best value: >> intensional similarity = 4 >> extensional distance = 65 >> proper extension: 0bh8yn3; 0ndsl1x; >> query: (?x3201, 06mkj) <- film_release_region(?x3201, ?x2513), production_companies(?x3201, ?x2549), ?x2513 = 05b4w, award_winner(?x3201, ?x6794) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 10 EVAL 01ffx4 film_release_region 06t2t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 78.000 78.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ffx4 film_release_region 06mkj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01ffx4 film_release_region 059j2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.896 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #512-02z1nbg PRED entity: 02z1nbg PRED relation: award! PRED expected values: 026390q 093l8p 0h3k3f 01fx4k => 43 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1060): 0bnzd (0.40 #1695, 0.33 #700, 0.07 #2690), 02nczh (0.40 #1648, 0.33 #653, 0.06 #2643), 03c_cxn (0.40 #1506, 0.33 #511, 0.05 #2501), 07cyl (0.33 #337, 0.20 #1332, 0.09 #2327), 04qw17 (0.33 #178, 0.20 #1173, 0.06 #2168), 05jzt3 (0.33 #82, 0.20 #1077, 0.06 #2072), 011yxy (0.33 #721, 0.20 #1716, 0.04 #3706), 01pgp6 (0.33 #170, 0.20 #1165, 0.04 #4150), 0g9wdmc (0.33 #167, 0.20 #1162, 0.03 #2157), 01fx4k (0.33 #907, 0.20 #1902, 0.03 #6968) >> Best rule #1695 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 02ppm4q; 027571b; >> query: (?x3902, 0bnzd) <- award_winner(?x3902, ?x9604), award_winner(?x3902, ?x6278), ?x6278 = 0gx_p, award(?x898, ?x3902), award_nominee(?x9604, ?x1343) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #907 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 09cn0c; *> query: (?x3902, 01fx4k) <- award_winner(?x3902, ?x11433), award_winner(?x3902, ?x6278), ?x6278 = 0gx_p, award(?x898, ?x3902), ?x11433 = 01bj6y *> conf = 0.33 ranks of expected_values: 10, 236, 306, 985 EVAL 02z1nbg award! 01fx4k CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 43.000 22.000 0.400 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02z1nbg award! 0h3k3f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 43.000 22.000 0.400 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02z1nbg award! 093l8p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 43.000 22.000 0.400 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 02z1nbg award! 026390q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 43.000 22.000 0.400 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #511-0cbn7c PRED entity: 0cbn7c PRED relation: language PRED expected values: 02h40lc => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 44): 02h40lc (0.91 #2220, 0.90 #4275, 0.89 #2042), 04306rv (0.20 #5, 0.13 #184, 0.13 #1325), 06nm1 (0.20 #11, 0.13 #1029, 0.11 #547), 071fb (0.20 #18, 0.04 #256, 0.02 #614), 064_8sq (0.19 #439, 0.17 #2240, 0.16 #1040), 02bjrlw (0.13 #120, 0.08 #1321, 0.08 #537), 06mp7 (0.13 #195, 0.06 #4454, 0.03 #1276), 06b_j (0.12 #381, 0.10 #500, 0.09 #559), 0653m (0.09 #310, 0.05 #970, 0.05 #1211), 03_9r (0.08 #1028, 0.06 #308, 0.06 #2894) >> Best rule #2220 for best value: >> intensional similarity = 4 >> extensional distance = 282 >> proper extension: 0286gm1; 0kt_4; 0m_h6; 03bdkd; >> query: (?x7864, 02h40lc) <- nominated_for(?x382, ?x7864), genre(?x7864, ?x53), nominated_for(?x1307, ?x7864), ?x1307 = 0gq9h >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cbn7c language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.908 http://example.org/film/film/language #510-085wqm PRED entity: 085wqm PRED relation: genre PRED expected values: 02kdv5l => 69 concepts (66 used for prediction) PRED predicted values (max 10 best out of 166): 07s9rl0 (0.62 #361, 0.60 #3016, 0.59 #2173), 02kdv5l (0.56 #724, 0.50 #1087, 0.49 #603), 024qqx (0.53 #3136, 0.49 #3859, 0.49 #6874), 03k9fj (0.41 #854, 0.38 #612, 0.36 #1216), 04xvlr (0.38 #362, 0.17 #3017, 0.16 #3740), 0lsxr (0.34 #1093, 0.20 #3988, 0.20 #129), 05p553 (0.33 #3623, 0.33 #6398, 0.33 #3020), 06n90 (0.31 #613, 0.29 #253, 0.26 #734), 01hmnh (0.29 #860, 0.29 #739, 0.27 #618), 02l7c8 (0.29 #3031, 0.27 #2188, 0.27 #7731) >> Best rule #361 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 016fyc; 03bxp5; 0symg; >> query: (?x10397, 07s9rl0) <- film(?x8269, ?x10397), ?x8269 = 01j5sd, genre(?x10397, ?x571) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #724 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 172 *> proper extension: 0d_2fb; 0crs0b8; *> query: (?x10397, 02kdv5l) <- film(?x2387, ?x10397), film_crew_role(?x10397, ?x2154), film_crew_role(?x10397, ?x2095), ?x2154 = 01vx2h, ?x2095 = 0dxtw *> conf = 0.56 ranks of expected_values: 2 EVAL 085wqm genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 69.000 66.000 0.625 http://example.org/film/film/genre #509-01h6pn PRED entity: 01h6pn PRED relation: combatants PRED expected values: 04g61 => 56 concepts (35 used for prediction) PRED predicted values (max 10 best out of 497): 06mzp (0.56 #604, 0.55 #1211, 0.38 #1210), 09c7w0 (0.51 #2427, 0.49 #2669, 0.49 #2549), 0chghy (0.50 #369, 0.50 #128, 0.42 #2072), 0ctw_b (0.50 #379, 0.33 #138, 0.33 #18), 06mkj (0.50 #881, 0.21 #2340, 0.19 #2099), 06v9sf (0.38 #1210, 0.27 #603, 0.25 #241), 0bxjv (0.38 #1210, 0.27 #603, 0.05 #2484), 0193qj (0.33 #185, 0.33 #65, 0.25 #426), 02vzc (0.33 #153, 0.33 #33, 0.25 #394), 0bq0p9 (0.33 #133, 0.32 #2077, 0.31 #2438) >> Best rule #604 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 03jqfx; 0727h; >> query: (?x5530, ?x774) <- entity_involved(?x5530, ?x774), first_level_division_of(?x5535, ?x774), olympics(?x774, ?x358), film_release_region(?x7554, ?x774), film_release_region(?x6376, ?x774), ?x7554 = 01mgw, country(?x359, ?x774), ?x6376 = 01f85k >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #241 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 4 *> proper extension: 06k75; 0c3mz; 0bqtx; *> query: (?x5530, ?x151) <- combatants(?x5530, ?x456), combatants(?x5530, ?x279), ?x279 = 0d060g, olympics(?x456, ?x391), film_release_region(?x3201, ?x456), combatants(?x456, ?x151), ?x3201 = 01ffx4, olympics(?x456, ?x1081), country(?x150, ?x456) *> conf = 0.25 ranks of expected_values: 33 EVAL 01h6pn combatants 04g61 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 56.000 35.000 0.556 http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants #508-016jfw PRED entity: 016jfw PRED relation: celebrities_impersonated! PRED expected values: 01n5309 => 94 concepts (48 used for prediction) PRED predicted values (max 10 best out of 4): 03m6t5 (0.07 #20, 0.07 #28, 0.05 #68), 0pz04 (0.03 #89, 0.02 #146, 0.02 #163), 018grr (0.02 #19, 0.01 #27, 0.01 #51), 01n5309 (0.02 #82, 0.01 #156, 0.01 #165) >> Best rule #20 for best value: >> intensional similarity = 3 >> extensional distance = 52 >> proper extension: 03wd5tk; 02756j; 0b5x23; >> query: (?x6129, 03m6t5) <- sibling(?x6129, ?x2865), location(?x6129, ?x11731), type_of_union(?x6129, ?x566) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #82 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 176 *> proper extension: 0126rp; 02dlfh; 01xwqn; *> query: (?x6129, 01n5309) <- category(?x6129, ?x134), profession(?x6129, ?x987), ?x987 = 0dxtg *> conf = 0.02 ranks of expected_values: 4 EVAL 016jfw celebrities_impersonated! 01n5309 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 94.000 48.000 0.074 http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated #507-0prrm PRED entity: 0prrm PRED relation: film! PRED expected values: 03n08b 01jb26 03q91d => 70 concepts (47 used for prediction) PRED predicted values (max 10 best out of 788): 06q8hf (0.36 #12392, 0.16 #14458, 0.11 #47511), 05hj_k (0.36 #12392, 0.16 #14458, 0.11 #47511), 0f6_x (0.33 #2683, 0.02 #15076, 0.02 #17141), 079vf (0.26 #10334, 0.05 #12400, 0.03 #22729), 02xs5v (0.25 #5522, 0.18 #7588, 0.15 #9653), 0c0k1 (0.25 #3559, 0.03 #86750, 0.02 #11820), 0gr36 (0.25 #2554, 0.01 #29406), 032_jg (0.18 #6336, 0.15 #8401, 0.14 #140), 02s2ft (0.17 #4137, 0.12 #6203, 0.10 #8268), 04gc65 (0.17 #6088, 0.12 #8154, 0.10 #10219) >> Best rule #12392 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 0dr3sl; >> query: (?x5024, ?x4060) <- film(?x5785, ?x5024), produced_by(?x8130, ?x5785), executive_produced_by(?x5024, ?x4060), story_by(?x9213, ?x5785) >> conf = 0.36 => this is the best rule for 2 predicted values *> Best rule #33048 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 422 *> proper extension: 011yrp; 08720; 01vksx; 0m_mm; 0_b3d; 07qg8v; 0g9wdmc; 02rx2m5; 047qxs; 02725hs; ... *> query: (?x5024, ?x906) <- film(?x9656, ?x5024), films(?x14068, ?x5024), people(?x2510, ?x9656), award_nominee(?x906, ?x9656) *> conf = 0.04 ranks of expected_values: 264 EVAL 0prrm film! 03q91d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 47.000 0.356 http://example.org/film/actor/film./film/performance/film EVAL 0prrm film! 01jb26 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 70.000 47.000 0.356 http://example.org/film/actor/film./film/performance/film EVAL 0prrm film! 03n08b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 70.000 47.000 0.356 http://example.org/film/actor/film./film/performance/film #506-08966 PRED entity: 08966 PRED relation: month PRED expected values: 05lf_ => 195 concepts (195 used for prediction) PRED predicted values (max 10 best out of 1): 05lf_ (0.87 #39, 0.87 #48, 0.86 #51) >> Best rule #39 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 01_d4; 052p7; 0vzm; >> query: (?x6458, 05lf_) <- contains(?x774, ?x6458), month(?x6458, ?x1459), contains(?x6458, ?x6811), time_zones(?x774, ?x2864) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08966 month 05lf_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 195.000 195.000 0.871 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #505-0161sp PRED entity: 0161sp PRED relation: award_nominee! PRED expected values: 04znsy => 124 concepts (64 used for prediction) PRED predicted values (max 10 best out of 1179): 0pmhf (0.15 #74543, 0.11 #123467, 0.10 #81533), 05vzw3 (0.12 #5748, 0.08 #8079, 0.05 #10408), 01dwrc (0.12 #1354, 0.06 #6012, 0.03 #29305), 03f5spx (0.12 #4853, 0.04 #7184, 0.03 #21158), 0dvqq (0.12 #513, 0.04 #7502, 0.02 #70396), 03g5jw (0.12 #330, 0.02 #9648, 0.02 #70213), 0187x8 (0.12 #1732, 0.01 #25025, 0.01 #90851), 03d9d6 (0.12 #1328, 0.01 #24621, 0.01 #90851), 016lmg (0.12 #1817, 0.01 #25110), 02qwg (0.10 #19401, 0.07 #10085, 0.07 #28718) >> Best rule #74543 for best value: >> intensional similarity = 3 >> extensional distance = 268 >> proper extension: 08m4c8; 049_zz; 06s6hs; >> query: (?x2908, ?x2596) <- award_winner(?x342, ?x2908), profession(?x2908, ?x131), participant(?x2908, ?x2596) >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0161sp award_nominee! 04znsy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 124.000 64.000 0.148 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #504-03gt46z PRED entity: 03gt46z PRED relation: honored_for PRED expected values: 02rv_dz 0d68qy 02rzdcp => 43 concepts (38 used for prediction) PRED predicted values (max 10 best out of 679): 0d68qy (0.60 #8529, 0.57 #4341, 0.56 #6733), 02rzdcp (0.52 #7780, 0.50 #8576, 0.50 #7380), 039cq4 (0.52 #7780, 0.50 #2204, 0.45 #6582), 02hct1 (0.52 #7780, 0.45 #6582, 0.38 #11372), 01h72l (0.52 #7780, 0.45 #6582, 0.38 #11372), 08jgk1 (0.52 #7780, 0.38 #11372, 0.38 #4878), 0kfv9 (0.52 #7780, 0.38 #11372, 0.37 #11975), 0557yqh (0.52 #7780, 0.38 #11372, 0.37 #11975), 01b66d (0.52 #7780, 0.38 #11372, 0.37 #11975), 0170k0 (0.52 #7780, 0.38 #11372, 0.37 #11975) >> Best rule #8529 for best value: >> intensional similarity = 23 >> extensional distance = 8 >> proper extension: 02wzl1d; 03nnm4t; >> query: (?x4617, 0d68qy) <- award_winner(?x4617, ?x10236), award_winner(?x4617, ?x9500), award_winner(?x4617, ?x8229), award_winner(?x4617, ?x829), ?x8229 = 0cp9f9, nationality(?x10236, ?x94), award(?x10236, ?x3617), award(?x9500, ?x2016), type_of_union(?x10236, ?x566), award_nominee(?x9500, ?x830), honored_for(?x4617, ?x945), tv_program(?x9500, ?x2528), award_nominee(?x117, ?x10236), nominated_for(?x2016, ?x8536), nominated_for(?x2016, ?x8132), nominated_for(?x2016, ?x4588), ?x4588 = 0l76z, ?x8132 = 0q9jk, award_winner(?x2016, ?x201), ?x8536 = 016tvq, award_winner(?x829, ?x1422), producer_type(?x829, ?x632), tv_program(?x829, ?x6884) >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 40 EVAL 03gt46z honored_for 02rzdcp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 38.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 03gt46z honored_for 0d68qy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 43.000 38.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 03gt46z honored_for 02rv_dz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 43.000 38.000 0.600 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #503-089pg7 PRED entity: 089pg7 PRED relation: origin PRED expected values: 0cv3w => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 85): 0cv3w (0.25 #62, 0.14 #298, 0.10 #770), 09c7w0 (0.25 #1, 0.14 #237, 0.08 #1417), 030qb3t (0.20 #742, 0.14 #978, 0.10 #506), 02_286 (0.14 #1196, 0.14 #960, 0.12 #2376), 04jpl (0.14 #242, 0.11 #3074, 0.11 #1894), 01jr6 (0.14 #311, 0.10 #783, 0.10 #547), 0k9p4 (0.10 #865, 0.10 #629, 0.07 #1101), 0_xdd (0.10 #798, 0.10 #562, 0.07 #1034), 013yq (0.08 #2405, 0.07 #1697, 0.03 #2641), 0d9jr (0.08 #1514, 0.05 #2222, 0.03 #2458) >> Best rule #62 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 0161sp; >> query: (?x7781, 0cv3w) <- award(?x7781, ?x6126), award(?x7781, ?x4892), ?x6126 = 02f77l, ?x4892 = 02f72_, artists(?x302, ?x7781), instrumentalists(?x227, ?x7781) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 089pg7 origin 0cv3w CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.250 http://example.org/music/artist/origin #502-0bs0bh PRED entity: 0bs0bh PRED relation: award_winner PRED expected values: 015rhv 016zp5 => 44 concepts (10 used for prediction) PRED predicted values (max 10 best out of 1510): 0171cm (0.50 #533, 0.30 #7928, 0.20 #2997), 0170pk (0.50 #353, 0.30 #7748, 0.20 #5282), 01713c (0.50 #315, 0.30 #7710, 0.20 #5244), 01qscs (0.50 #57, 0.30 #7452, 0.20 #4986), 026rm_y (0.50 #1859, 0.20 #9254, 0.20 #6788), 03v3xp (0.50 #775, 0.20 #8170, 0.10 #7394), 016zp5 (0.50 #1236, 0.20 #8631, 0.10 #13556), 0l6px (0.45 #12803, 0.40 #15268, 0.25 #483), 0zcbl (0.40 #6468, 0.25 #1539, 0.20 #8934), 01kwsg (0.40 #5992, 0.25 #1063, 0.10 #8458) >> Best rule #533 for best value: >> intensional similarity = 6 >> extensional distance = 2 >> proper extension: 027dtxw; 09sb52; >> query: (?x1921, 0171cm) <- award_winner(?x1921, ?x399), award(?x2708, ?x1921), award(?x1250, ?x1921), award_nominee(?x10282, ?x2708), ?x10282 = 0356dp, ?x1250 = 01tcf7 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1236 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 2 *> proper extension: 027dtxw; 09sb52; *> query: (?x1921, 016zp5) <- award_winner(?x1921, ?x399), award(?x2708, ?x1921), award(?x1250, ?x1921), award_nominee(?x10282, ?x2708), ?x10282 = 0356dp, ?x1250 = 01tcf7 *> conf = 0.50 ranks of expected_values: 7, 52 EVAL 0bs0bh award_winner 016zp5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 44.000 10.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 0bs0bh award_winner 015rhv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 44.000 10.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #501-01tsbmv PRED entity: 01tsbmv PRED relation: gender PRED expected values: 05zppz => 92 concepts (92 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.93 #5, 0.91 #7, 0.89 #3), 02zsn (0.51 #113, 0.47 #108, 0.46 #188) >> Best rule #5 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 02qgqt; 02p65p; 0h0jz; 0z4s; 01yk13; 03gm48; 015grj; 01nwwl; 01438g; 01846t; ... >> query: (?x11684, 05zppz) <- award(?x11684, ?x2192), people(?x6736, ?x11684), film(?x11684, ?x103), student(?x2486, ?x11684), ?x2192 = 0bfvd4 >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01tsbmv gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 92.000 92.000 0.929 http://example.org/people/person/gender #500-01wdqrx PRED entity: 01wdqrx PRED relation: nationality PRED expected values: 09c7w0 => 98 concepts (98 used for prediction) PRED predicted values (max 10 best out of 26): 09c7w0 (0.85 #902, 0.85 #1504, 0.85 #301), 059rby (0.27 #8512), 02jx1 (0.21 #33, 0.20 #1436, 0.18 #133), 07ssc (0.12 #1418, 0.12 #115, 0.11 #415), 0d060g (0.06 #807, 0.05 #407, 0.05 #207), 03rk0 (0.05 #9259, 0.05 #7256, 0.05 #9159), 01531 (0.05 #1403, 0.05 #1904, 0.04 #901), 02_286 (0.05 #1403, 0.05 #1904, 0.04 #901), 0chghy (0.05 #1212, 0.02 #4117, 0.02 #4818), 03rt9 (0.04 #113, 0.02 #1416, 0.02 #13) >> Best rule #902 for best value: >> intensional similarity = 3 >> extensional distance = 167 >> proper extension: 01gct2; 0443c; >> query: (?x1282, 09c7w0) <- people(?x2510, ?x1282), ?x2510 = 0x67, award_winner(?x4958, ?x1282) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wdqrx nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 98.000 0.852 http://example.org/people/person/nationality #499-05v8c PRED entity: 05v8c PRED relation: participating_countries! PRED expected values: 0lgxj => 140 concepts (140 used for prediction) PRED predicted values (max 10 best out of 41): 0lgxj (0.82 #222, 0.71 #417, 0.65 #652), 0kbws (0.82 #3454, 0.81 #3493, 0.76 #208), 09n48 (0.65 #628, 0.62 #120, 0.60 #745), 016r9z (0.65 #215, 0.56 #176, 0.53 #254), 0blfl (0.59 #223, 0.50 #301, 0.50 #184), 0sx8l (0.56 #168, 0.56 #285, 0.53 #246), 0c_tl (0.47 #217, 0.38 #178, 0.35 #256), 06sks6 (0.41 #218, 0.38 #101, 0.38 #140), 0jdk_ (0.31 #181, 0.29 #259, 0.28 #298), 0l6mp (0.21 #1250, 0.18 #2932, 0.10 #1720) >> Best rule #222 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 05r4w; 09c7w0; 0jgd; 03rjj; 03_3d; 0d0vqn; 0chghy; 07ssc; 0f8l9c; 0k6nt; ... >> query: (?x550, 0lgxj) <- film_release_region(?x5849, ?x550), combatants(?x7455, ?x550), ?x5849 = 02h22 >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05v8c participating_countries! 0lgxj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 140.000 0.824 http://example.org/olympics/olympic_games/participating_countries #498-01bczm PRED entity: 01bczm PRED relation: gender PRED expected values: 05zppz => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.81 #35, 0.80 #31, 0.79 #57), 02zsn (0.41 #6, 0.38 #4, 0.38 #18) >> Best rule #35 for best value: >> intensional similarity = 3 >> extensional distance = 218 >> proper extension: 01m7f5r; >> query: (?x5550, 05zppz) <- place_of_birth(?x5550, ?x13347), role(?x5550, ?x212), nationality(?x5550, ?x279) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01bczm gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.814 http://example.org/people/person/gender #497-0psss PRED entity: 0psss PRED relation: nationality PRED expected values: 02jx1 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 65): 09c7w0 (0.74 #2974, 0.73 #10811, 0.72 #9718), 0345h (0.37 #7536, 0.25 #30, 0.05 #228), 0k6nt (0.37 #7536, 0.25 #24, 0.05 #3766), 03rjj (0.37 #7536, 0.05 #3766, 0.04 #5949), 0d0vqn (0.37 #7536, 0.05 #3766, 0.04 #5949), 05qhw (0.37 #7536, 0.05 #3766, 0.04 #5949), 0d060g (0.16 #106, 0.06 #5061, 0.06 #4268), 02jx1 (0.15 #925, 0.14 #2015, 0.12 #1718), 07ssc (0.11 #213, 0.10 #2493, 0.09 #908), 03rk0 (0.06 #10358, 0.06 #10259, 0.05 #11251) >> Best rule #2974 for best value: >> intensional similarity = 3 >> extensional distance = 782 >> proper extension: 06v8s0; 03f5vvx; 066l3y; 09fp45; 027rfxc; 04j0s3; 0814k3; 090gk3; 0ldd; 090gpr; ... >> query: (?x3280, 09c7w0) <- gender(?x3280, ?x514), ?x514 = 02zsn, nationality(?x3280, ?x789) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #925 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 336 *> proper extension: 01npcy7; *> query: (?x3280, 02jx1) <- type_of_union(?x3280, ?x1873), ?x1873 = 01g63y *> conf = 0.15 ranks of expected_values: 8 EVAL 0psss nationality 02jx1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 117.000 117.000 0.736 http://example.org/people/person/nationality #496-01vtg4q PRED entity: 01vtg4q PRED relation: special_performance_type PRED expected values: 01pb34 => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 5): 01pb34 (0.15 #23, 0.08 #129, 0.07 #73), 014kbl (0.04 #25), 01kyvx (0.02 #176, 0.01 #489, 0.01 #509), 02t8yb (0.02 #79, 0.02 #89, 0.01 #158), 09_gdc (0.01 #500, 0.01 #536, 0.01 #505) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 0443c; >> query: (?x8305, 01pb34) <- location(?x8305, ?x1860), people(?x4322, ?x8305), type_of_union(?x8305, ?x566), inductee(?x1091, ?x8305) >> conf = 0.15 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vtg4q special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 136.000 136.000 0.154 http://example.org/film/actor/film./film/performance/special_performance_type #495-028_yv PRED entity: 028_yv PRED relation: film! PRED expected values: 01pj5q => 96 concepts (58 used for prediction) PRED predicted values (max 10 best out of 895): 02sjp (0.45 #85135, 0.43 #83058, 0.42 #62292), 0252fh (0.25 #1350, 0.06 #53986, 0.02 #11732), 01qqtr (0.25 #1546, 0.06 #53986, 0.01 #7775), 03q1vd (0.25 #461, 0.06 #53986, 0.01 #31603), 02zfg3 (0.25 #2032, 0.06 #53986), 0gyx4 (0.25 #771, 0.06 #53986), 01p7yb (0.25 #52, 0.05 #2129, 0.03 #4205), 02114t (0.25 #634, 0.03 #4787, 0.03 #8940), 02kxwk (0.25 #762, 0.02 #19448, 0.02 #13220), 0bl2g (0.25 #54, 0.02 #31196, 0.02 #81035) >> Best rule #85135 for best value: >> intensional similarity = 3 >> extensional distance = 850 >> proper extension: 01j95; >> query: (?x204, ?x9163) <- award_winner(?x204, ?x9163), nominated_for(?x9163, ?x195), location(?x9163, ?x6959) >> conf = 0.45 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 028_yv film! 01pj5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 96.000 58.000 0.450 http://example.org/film/actor/film./film/performance/film #494-0hpv3 PRED entity: 0hpv3 PRED relation: major_field_of_study PRED expected values: 0fdys => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 100): 02j62 (0.42 #1531, 0.29 #4907, 0.29 #2156), 01mkq (0.38 #1516, 0.35 #2266, 0.31 #141), 03g3w (0.37 #1528, 0.22 #653, 0.21 #6157), 02lp1 (0.31 #137, 0.31 #1512, 0.28 #2262), 04rjg (0.28 #1521, 0.27 #771, 0.25 #146), 037mh8 (0.25 #1570, 0.19 #195, 0.18 #2320), 0g26h (0.25 #169, 0.22 #1544, 0.20 #3919), 0fdys (0.25 #165, 0.19 #2290, 0.18 #1540), 04sh3 (0.25 #203, 0.15 #1578, 0.14 #2328), 05qjt (0.25 #1508, 0.20 #2258, 0.19 #2883) >> Best rule #1531 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 027xx3; 01r3y2; 03ksy; 0k9wp; 01q8hj; 034q81; 06rjp; 050xpd; 0lk0l; >> query: (?x9560, 02j62) <- contains(?x335, ?x9560), major_field_of_study(?x9560, ?x2606), ?x2606 = 062z7 >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #165 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 03v52f; 07xyn1; *> query: (?x9560, 0fdys) <- country(?x9560, ?x94), list(?x9560, ?x2197), category(?x9560, ?x134) *> conf = 0.25 ranks of expected_values: 8 EVAL 0hpv3 major_field_of_study 0fdys CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 68.000 68.000 0.415 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #493-013y1f PRED entity: 013y1f PRED relation: instrumentalists PRED expected values: 0k4gf 01lvcs1 01p95y0 => 93 concepts (56 used for prediction) PRED predicted values (max 10 best out of 1092): 01qgry (0.72 #4178, 0.71 #2387, 0.69 #5971), 01vsksr (0.72 #4178, 0.67 #594, 0.64 #7165), 01p45_v (0.72 #4178, 0.67 #594, 0.64 #4177), 0j6cj (0.71 #2388, 0.71 #2387, 0.68 #4180), 0kzy0 (0.71 #2388, 0.71 #2387, 0.68 #4180), 01vvycq (0.71 #8995, 0.62 #11386, 0.60 #13777), 05qhnq (0.71 #2387, 0.69 #5971, 0.68 #4180), 04bpm6 (0.71 #2387, 0.69 #5971, 0.68 #4180), 06449 (0.71 #2387, 0.69 #5971, 0.68 #4180), 016ntp (0.71 #2387, 0.69 #5971, 0.68 #4180) >> Best rule #4178 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 018vs; >> query: (?x1495, ?x2269) <- role(?x2269, ?x1495), group(?x1495, ?x11700), group(?x1495, ?x11425), role(?x2158, ?x1495), role(?x1495, ?x780), ?x2158 = 01dnws, role(?x130, ?x1495), ?x780 = 01qzyz, gender(?x2269, ?x231), ?x11700 = 017_hq, location(?x2269, ?x4978), ?x11425 = 02vnpv >> conf = 0.72 => this is the best rule for 3 predicted values *> Best rule #7960 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 4 *> proper extension: 0bxl5; *> query: (?x1495, 01lvcs1) <- group(?x1495, ?x4791), role(?x1495, ?x3296), role(?x1495, ?x1647), role(?x1495, ?x745), ?x745 = 01vj9c, role(?x1818, ?x1495), ?x1818 = 0770cd, role(?x228, ?x1495), performance_role(?x1495, ?x1433), role(?x4162, ?x1647), role(?x2309, ?x1495), ?x3296 = 07_l6, origin(?x4791, ?x3052) *> conf = 0.50 ranks of expected_values: 102, 386, 600 EVAL 013y1f instrumentalists 01p95y0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 93.000 56.000 0.716 http://example.org/music/instrument/instrumentalists EVAL 013y1f instrumentalists 01lvcs1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 93.000 56.000 0.716 http://example.org/music/instrument/instrumentalists EVAL 013y1f instrumentalists 0k4gf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 93.000 56.000 0.716 http://example.org/music/instrument/instrumentalists #492-07jqjx PRED entity: 07jqjx PRED relation: written_by PRED expected values: 053ksp => 79 concepts (61 used for prediction) PRED predicted values (max 10 best out of 108): 01v9724 (0.29 #1010, 0.21 #4042, 0.21 #6403), 06b_0 (0.18 #4043, 0.15 #3032, 0.15 #10111), 0184dt (0.14 #74, 0.03 #1420, 0.03 #2096), 05183k (0.14 #45, 0.02 #3413, 0.02 #1055), 05_k56 (0.08 #370, 0.07 #707, 0.04 #1044), 0237jb (0.08 #570, 0.02 #1244, 0.02 #3939), 05mcjs (0.08 #536, 0.01 #2895), 0p8jf (0.08 #425), 06dkzt (0.06 #1951, 0.03 #2625, 0.02 #2962), 02bfxb (0.05 #1106, 0.05 #2118, 0.04 #769) >> Best rule #1010 for best value: >> intensional similarity = 5 >> extensional distance = 26 >> proper extension: 011yrp; 05qbckf; 0crc2cp; 05zlld0; 0cbn7c; >> query: (?x9657, ?x5435) <- genre(?x9657, ?x53), film(?x382, ?x9657), story_by(?x9657, ?x5435), film_release_region(?x9657, ?x1355), ?x1355 = 0h7x >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3000 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 93 *> proper extension: 035xwd; 01719t; 0260bz; 016z9n; 026fs38; 04f6df0; 0f61tk; 064ndc; *> query: (?x9657, 053ksp) <- genre(?x9657, ?x162), film(?x382, ?x9657), produced_by(?x9657, ?x7670), ?x162 = 04xvlr, country(?x9657, ?x205) *> conf = 0.02 ranks of expected_values: 40 EVAL 07jqjx written_by 053ksp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 79.000 61.000 0.294 http://example.org/film/film/written_by #491-0g1rw PRED entity: 0g1rw PRED relation: award_nominee! PRED expected values: 017s11 => 126 concepts (87 used for prediction) PRED predicted values (max 10 best out of 983): 0htcn (0.81 #90744, 0.81 #202439, 0.81 #193129), 012vby (0.81 #90744, 0.81 #202439, 0.81 #193129), 027vps (0.81 #90744, 0.81 #202439, 0.81 #193129), 016z1c (0.77 #179168, 0.77 #160550, 0.76 #202441), 05qd_ (0.33 #180, 0.27 #11813, 0.23 #7158), 043q6n_ (0.33 #296, 0.25 #2622, 0.13 #11929), 016tt2 (0.33 #112, 0.20 #11745, 0.15 #7090), 024rgt (0.33 #550, 0.20 #12183, 0.15 #14510), 03m9c8 (0.33 #1556, 0.14 #10862, 0.13 #13189), 0l6wj (0.33 #1837, 0.13 #13470, 0.11 #29759) >> Best rule #90744 for best value: >> intensional similarity = 4 >> extensional distance = 143 >> proper extension: 0181hw; >> query: (?x788, ?x4785) <- award_nominee(?x788, ?x4785), award_nominee(?x788, ?x1850), nominated_for(?x4785, ?x2612), film(?x1850, ?x327) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #16394 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 18 *> proper extension: 01_8w2; *> query: (?x788, 017s11) <- award_winner(?x1850, ?x788), organization(?x4682, ?x788) *> conf = 0.20 ranks of expected_values: 54 EVAL 0g1rw award_nominee! 017s11 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 126.000 87.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #490-0g54xkt PRED entity: 0g54xkt PRED relation: film! PRED expected values: 086k8 => 69 concepts (48 used for prediction) PRED predicted values (max 10 best out of 74): 016tw3 (0.67 #86, 0.59 #162, 0.27 #679), 04rtpt (0.47 #2044, 0.44 #2497, 0.44 #2120), 01gb54 (0.27 #679, 0.08 #784, 0.06 #1844), 017s11 (0.23 #154, 0.19 #78, 0.14 #605), 086k8 (0.19 #604, 0.17 #907, 0.16 #1589), 05qd_ (0.18 #9, 0.16 #611, 0.14 #914), 016tt2 (0.18 #4, 0.12 #759, 0.11 #909), 0jz9f (0.18 #1, 0.11 #227, 0.10 #377), 03xq0f (0.11 #1517, 0.11 #760, 0.10 #1820), 025tlyv (0.10 #134, 0.09 #210, 0.02 #814) >> Best rule #86 for best value: >> intensional similarity = 4 >> extensional distance = 19 >> proper extension: 0c9t0y; >> query: (?x3222, 016tw3) <- produced_by(?x3222, ?x1039), ?x1039 = 04wvhz, genre(?x3222, ?x714), production_companies(?x3222, ?x6560) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #604 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 371 *> proper extension: 0bq8tmw; 058kh7; *> query: (?x3222, 086k8) <- produced_by(?x3222, ?x1039), award_nominee(?x1039, ?x4564), genre(?x3222, ?x714), film(?x4564, ?x253) *> conf = 0.19 ranks of expected_values: 5 EVAL 0g54xkt film! 086k8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 69.000 48.000 0.667 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #489-0263cyj PRED entity: 0263cyj PRED relation: team! PRED expected values: 0b_6h7 => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 17): 0cc8q3 (0.82 #533, 0.79 #329, 0.77 #312), 0b_6rk (0.78 #260, 0.67 #396, 0.64 #328), 0b_6zk (0.73 #392, 0.73 #528, 0.71 #324), 0b_72t (0.73 #400, 0.71 #332, 0.71 #145), 0b_6xf (0.71 #339, 0.71 #169, 0.71 #152), 0b_71r (0.71 #166, 0.71 #149, 0.67 #268), 0br1x_ (0.71 #144, 0.69 #433, 0.68 #535), 0b_6_l (0.71 #355, 0.68 #542, 0.60 #406), 0b_6v_ (0.71 #164, 0.67 #402, 0.67 #266), 0b_75k (0.71 #160, 0.67 #398, 0.64 #330) >> Best rule #533 for best value: >> intensional similarity = 10 >> extensional distance = 20 >> proper extension: 026w398; >> query: (?x9147, 0cc8q3) <- team(?x9146, ?x9147), team(?x8824, ?x9147), team(?x8824, ?x9576), team(?x8824, ?x6003), team(?x8824, ?x5032), ?x6003 = 02py8_w, colors(?x9147, ?x663), ?x9576 = 02qk2d5, locations(?x9146, ?x659), ?x5032 = 04088s0 >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #310 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 11 *> proper extension: 02py8_w; 091tgz; 03d5m8w; 02q4ntp; *> query: (?x9147, 0b_6h7) <- sport(?x9147, ?x12913), colors(?x9147, ?x1101), team(?x2302, ?x9147), colors(?x481, ?x1101), colors(?x1438, ?x1101), school(?x1438, ?x3948), draft(?x1438, ?x1161), ?x3948 = 025v3k *> conf = 0.46 ranks of expected_values: 16 EVAL 0263cyj team! 0b_6h7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 116.000 116.000 0.818 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #488-03rbj2 PRED entity: 03rbj2 PRED relation: award_winner PRED expected values: 03t8v3 => 44 concepts (17 used for prediction) PRED predicted values (max 10 best out of 1531): 0f5zj6 (0.34 #22105, 0.30 #22107, 0.29 #31930), 06gn7r (0.33 #2455, 0.31 #4911, 0.30 #22107), 045n3p (0.33 #2455, 0.31 #4911, 0.29 #29472), 0b5x23 (0.33 #2455, 0.31 #4911, 0.29 #29472), 0cct7p (0.31 #4911, 0.29 #29472, 0.29 #29471), 0cvbb9q (0.31 #4911, 0.29 #29472, 0.29 #29471), 015npr (0.30 #22107, 0.29 #31930, 0.29 #31928), 044prt (0.30 #22107, 0.29 #31930, 0.29 #31928), 04c636 (0.30 #22107, 0.29 #29472, 0.29 #29471), 038b_x (0.30 #22107, 0.29 #29472, 0.29 #29471) >> Best rule #22105 for best value: >> intensional similarity = 7 >> extensional distance = 201 >> proper extension: 02nbqh; 0gqmvn; 0c_dx; 03ncb2; 03r00m; >> query: (?x4687, ?x6308) <- award(?x14156, ?x4687), award(?x8975, ?x4687), award(?x6308, ?x4687), religion(?x8975, ?x8967), people(?x5025, ?x14156), type_of_union(?x8975, ?x566), person(?x5247, ?x6308) >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #14739 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 188 *> proper extension: 02rdxsh; 099c8n; 09tqxt; 03m73lj; 02qysm0; 054knh; 02qwzkm; *> query: (?x4687, ?x12209) <- nominated_for(?x4687, ?x4444), nominated_for(?x4687, ?x2617), genre(?x2617, ?x53), film_crew_role(?x2617, ?x137), film(?x12209, ?x4444) *> conf = 0.03 ranks of expected_values: 327 EVAL 03rbj2 award_winner 03t8v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 44.000 17.000 0.336 http://example.org/award/award_category/winners./award/award_honor/award_winner #487-0453t PRED entity: 0453t PRED relation: student! PRED expected values: 01w5m => 166 concepts (108 used for prediction) PRED predicted values (max 10 best out of 296): 01z3bz (0.33 #445, 0.09 #2545, 0.01 #19874), 07tgn (0.32 #21546, 0.17 #22596, 0.15 #4217), 0bwfn (0.26 #48584, 0.17 #53309, 0.13 #56459), 03ksy (0.21 #22685, 0.20 #4306, 0.18 #48417), 01w5m (0.16 #3780, 0.15 #5355, 0.12 #6931), 07tg4 (0.16 #3236, 0.09 #6912, 0.09 #9013), 02zd460 (0.15 #21699, 0.08 #37451, 0.05 #3320), 08815 (0.15 #6828, 0.13 #24156, 0.12 #527), 09f2j (0.14 #48470, 0.08 #22738, 0.07 #56345), 065y4w7 (0.13 #24693, 0.10 #28368, 0.09 #29943) >> Best rule #445 for best value: >> intensional similarity = 5 >> extensional distance = 1 >> proper extension: 07dnx; >> query: (?x2239, 01z3bz) <- student(?x7154, ?x2239), place_of_death(?x2239, ?x5193), type_of_union(?x2239, ?x566), ?x7154 = 01lhdt, influenced_by(?x2239, ?x2240) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3780 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 17 *> proper extension: 0kn4c; 063vn; *> query: (?x2239, 01w5m) <- student(?x5638, ?x2239), place_of_death(?x2239, ?x5193), gender(?x2239, ?x231), student(?x3995, ?x2239), student(?x1368, ?x2239) *> conf = 0.16 ranks of expected_values: 5 EVAL 0453t student! 01w5m CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 166.000 108.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #486-06m61 PRED entity: 06m61 PRED relation: category PRED expected values: 08mbj5d => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.91 #3, 0.90 #8, 0.90 #7) >> Best rule #3 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 01bmlb; >> query: (?x4840, 08mbj5d) <- award(?x4840, ?x1801), ?x1801 = 01c92g, profession(?x4840, ?x1183), ?x1183 = 09jwl >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06m61 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 94.000 0.915 http://example.org/common/topic/webpage./common/webpage/category #485-01lyv PRED entity: 01lyv PRED relation: artists PRED expected values: 0168cl 01p45_v 01n8gr 01kstn9 01c8v0 01m15br 094xh 015cxv 0f_y9 028hc2 06rgq 0bdxs5 01h5f8 => 52 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1011): 020_4z (0.80 #7600, 0.33 #2762, 0.33 #1795), 0407f (0.60 #7005, 0.33 #2167, 0.33 #1200), 01wg25j (0.60 #7452, 0.33 #2614, 0.33 #1647), 01vsksr (0.60 #7269, 0.33 #2431, 0.33 #1464), 0178_w (0.57 #5367, 0.50 #4399, 0.50 #3432), 01vwyqp (0.57 #5064, 0.50 #7000, 0.33 #2162), 094xh (0.57 #5245, 0.50 #3310, 0.33 #4277), 07qnf (0.57 #4889, 0.50 #2954, 0.33 #3921), 08w4pm (0.57 #5476, 0.50 #3541, 0.33 #4508), 0qf11 (0.57 #5159, 0.40 #7095, 0.33 #2257) >> Best rule #7600 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 07sbbz2; 02x8m; 05w3f; 06j6l; 02yv6b; >> query: (?x2664, 020_4z) <- artists(?x2664, ?x8246), artists(?x2664, ?x2807), artists(?x2664, ?x2321), artists(?x2664, ?x1291), artists(?x2664, ?x217), gender(?x8246, ?x231), participant(?x2321, ?x6236), award_winner(?x217, ?x1181), award_winner(?x1088, ?x2807), ?x1291 = 01kx_81 >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #5245 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 5 *> proper extension: 05bt6j; 02k_kn; *> query: (?x2664, 094xh) <- artists(?x2664, ?x8272), artists(?x2664, ?x8246), artists(?x2664, ?x2566), artists(?x2664, ?x2321), gender(?x8246, ?x231), ?x2321 = 0136pk, artist(?x4081, ?x8272), participant(?x2566, ?x702), profession(?x2566, ?x1032) *> conf = 0.57 ranks of expected_values: 7, 22, 53, 109, 112, 124, 179, 224, 240, 398, 407, 418, 441 EVAL 01lyv artists 01h5f8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 0bdxs5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 06rgq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 028hc2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 0f_y9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 015cxv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 094xh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 01m15br CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 01c8v0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 01kstn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 01n8gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 01p45_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 52.000 24.000 0.800 http://example.org/music/genre/artists EVAL 01lyv artists 0168cl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 24.000 0.800 http://example.org/music/genre/artists #484-018swb PRED entity: 018swb PRED relation: people! PRED expected values: 01_qc_ => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 34): 0gk4g (0.21 #201, 0.20 #265, 0.20 #393), 0dq9p (0.13 #208, 0.11 #592, 0.10 #400), 02y0js (0.12 #1, 0.07 #257, 0.06 #193), 04p3w (0.10 #586, 0.07 #394, 0.06 #266), 01l2m3 (0.07 #271, 0.05 #207, 0.03 #1295), 01dcqj (0.07 #203, 0.02 #1291, 0.01 #1227), 02k6hp (0.07 #292, 0.06 #228, 0.05 #420), 0m32h (0.05 #406, 0.04 #598, 0.03 #278), 02knxx (0.04 #223, 0.03 #415, 0.03 #607), 01mtqf (0.04 #259, 0.03 #195, 0.01 #1219) >> Best rule #201 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 03_0p; >> query: (?x2122, 0gk4g) <- award_winner(?x2122, ?x92), award(?x2122, ?x112), people(?x268, ?x2122) >> conf = 0.21 => this is the best rule for 1 predicted values *> Best rule #411 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 144 *> proper extension: 01k7d9; 0h1_w; 0f0p0; 08433; 0h1m9; 02sjf5; 02lkcc; 01xcqc; 028lc8; 04y9dk; ... *> query: (?x2122, 01_qc_) <- film(?x2122, ?x394), award(?x2122, ?x112), place_of_death(?x2122, ?x13818) *> conf = 0.04 ranks of expected_values: 12 EVAL 018swb people! 01_qc_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 109.000 109.000 0.205 http://example.org/people/cause_of_death/people #483-032t2z PRED entity: 032t2z PRED relation: group PRED expected values: 01wv9xn => 94 concepts (42 used for prediction) PRED predicted values (max 10 best out of 65): 01wv9xn (0.20 #8, 0.17 #116, 0.14 #224), 06nv27 (0.20 #33, 0.04 #573, 0.03 #897), 02r1tx7 (0.17 #124, 0.14 #232, 0.08 #664), 07m4c (0.17 #165, 0.14 #273, 0.04 #705), 01qqwp9 (0.12 #669, 0.09 #885, 0.05 #1644), 01v0sxx (0.09 #949, 0.05 #517, 0.03 #1925), 0123r4 (0.08 #584, 0.07 #1017, 0.06 #1125), 0134pk (0.07 #408, 0.04 #624, 0.03 #1057), 047cx (0.07 #354, 0.03 #894, 0.02 #1870), 0mjn2 (0.07 #410, 0.01 #3338, 0.01 #3555) >> Best rule #8 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 01wv9xn; >> query: (?x642, 01wv9xn) <- artists(?x5379, ?x642), artist(?x3265, ?x642), ?x3265 = 015_1q, ?x5379 = 08jyyk >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 032t2z group 01wv9xn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 94.000 42.000 0.200 http://example.org/music/group_member/membership./music/group_membership/group #482-02b1mr PRED entity: 02b1mr PRED relation: team! PRED expected values: 0dgrmp => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 47): 02_j1w (0.89 #484, 0.89 #592, 0.89 #587), 02sdk9v (0.86 #747, 0.86 #696, 0.86 #962), 0dgrmp (0.81 #533, 0.81 #532, 0.80 #640), 03f0fp (0.58 #2077, 0.52 #2390, 0.50 #2761), 02md_2 (0.52 #2390, 0.49 #2813, 0.48 #2865), 02qvgy (0.50 #2761, 0.50 #2760, 0.01 #2732), 02qpbqj (0.14 #1575, 0.14 #983, 0.13 #1626), 01_9c1 (0.14 #1574, 0.13 #1625, 0.13 #1676), 02g_7z (0.14 #1581, 0.13 #1632, 0.13 #1683), 01r3hr (0.14 #1558, 0.13 #1609, 0.13 #1660) >> Best rule #484 for best value: >> intensional similarity = 15 >> extensional distance = 45 >> proper extension: 02279c; 04b4yg; 0182r9; 01k2yr; 0j2pg; 02_cq0; 0xbm; 02b10g; 050fh; 01rl_3; ... >> query: (?x13787, 02_j1w) <- position(?x13787, ?x63), position(?x13787, ?x60), ?x63 = 02sdk9v, ?x60 = 02nzb8, team(?x7622, ?x13787), team(?x7622, ?x6892), team(?x7622, ?x2355), gender(?x7622, ?x231), athlete(?x471, ?x7622), team(?x5763, ?x2355), position(?x6892, ?x530), team(?x203, ?x6892), nationality(?x7622, ?x1310), profession(?x7622, ?x7623), location(?x7622, ?x362) >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #533 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 45 *> proper extension: 02279c; 04b4yg; 0182r9; 01k2yr; 0j2pg; 02_cq0; 0xbm; 02b10g; 050fh; 01rl_3; ... *> query: (?x13787, ?x203) <- position(?x13787, ?x63), position(?x13787, ?x60), ?x63 = 02sdk9v, ?x60 = 02nzb8, team(?x7622, ?x13787), team(?x7622, ?x6892), team(?x7622, ?x2355), gender(?x7622, ?x231), athlete(?x471, ?x7622), team(?x5763, ?x2355), position(?x6892, ?x530), team(?x203, ?x6892), nationality(?x7622, ?x1310), profession(?x7622, ?x7623), location(?x7622, ?x362) *> conf = 0.81 ranks of expected_values: 3 EVAL 02b1mr team! 0dgrmp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 55.000 55.000 0.894 http://example.org/sports/sports_position/players./sports/sports_team_roster/team #481-02r8hh_ PRED entity: 02r8hh_ PRED relation: language PRED expected values: 04306rv => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 51): 04306rv (0.22 #753, 0.18 #407, 0.16 #524), 03_9r (0.18 #644, 0.17 #9, 0.10 #412), 0jzc (0.15 #539, 0.12 #422, 0.06 #711), 02bjrlw (0.14 #750, 0.10 #693, 0.08 #980), 06nm1 (0.14 #66, 0.12 #759, 0.11 #1339), 06b_j (0.10 #770, 0.10 #541, 0.09 #424), 05zjd (0.07 #24, 0.04 #659, 0.03 #4194), 04h9h (0.05 #1020, 0.04 #790, 0.04 #154), 0653m (0.05 #874, 0.04 #1223, 0.04 #1398), 03hkp (0.04 #417, 0.04 #534, 0.03 #4194) >> Best rule #753 for best value: >> intensional similarity = 7 >> extensional distance = 164 >> proper extension: 02qrv7; 042fgh; >> query: (?x1724, 04306rv) <- language(?x1724, ?x13310), language(?x1724, ?x5607), language(?x1724, ?x254), film(?x1208, ?x1724), ?x254 = 02h40lc, ?x5607 = 064_8sq, official_language(?x3730, ?x13310) >> conf = 0.22 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02r8hh_ language 04306rv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 88.000 0.217 http://example.org/film/film/language #480-0ymff PRED entity: 0ymff PRED relation: educational_institution PRED expected values: 0ymff => 190 concepts (109 used for prediction) PRED predicted values (max 10 best out of 285): 02hmw9 (0.20 #222, 0.05 #4537, 0.03 #6693), 01jvxb (0.20 #236, 0.03 #6707, 0.02 #8324), 07tgn (0.19 #48581, 0.12 #1093, 0.07 #43721), 0ymff (0.19 #48581, 0.08 #47500, 0.06 #33468), 01722w (0.19 #48581), 08815 (0.14 #541, 0.05 #3239, 0.04 #5395), 0g8rj (0.14 #702, 0.05 #3400, 0.04 #5556), 05zl0 (0.14 #728, 0.05 #3426, 0.04 #5582), 0677j (0.14 #852, 0.05 #3550, 0.02 #7323), 0gdm1 (0.14 #752, 0.05 #3450) >> Best rule #222 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 02hmw9; 01jvxb; 0vkl2; >> query: (?x10393, 02hmw9) <- currency(?x10393, ?x1099), major_field_of_study(?x10393, ?x254), ?x1099 = 01nv4h, colors(?x10393, ?x3621), language(?x54, ?x254) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #48581 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 291 *> proper extension: 06xpp7; 02d9nr; 053mhx; 02_gzx; 0342z_; *> query: (?x10393, ?x892) <- contains(?x1310, ?x10393), student(?x10393, ?x5249), citytown(?x10393, ?x1841), gender(?x5249, ?x231), student(?x892, ?x5249) *> conf = 0.19 ranks of expected_values: 4 EVAL 0ymff educational_institution 0ymff CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 190.000 109.000 0.200 http://example.org/education/educational_institution_campus/educational_institution #479-0djkrp PRED entity: 0djkrp PRED relation: production_companies PRED expected values: 025jfl => 79 concepts (50 used for prediction) PRED predicted values (max 10 best out of 62): 025jfl (0.49 #330, 0.49 #252, 0.44 #1986), 05qd_ (0.20 #92, 0.18 #175, 0.16 #423), 017s11 (0.14 #3, 0.10 #85, 0.09 #416), 016tt2 (0.14 #4, 0.08 #251, 0.08 #2074), 01gb54 (0.12 #533, 0.11 #698, 0.07 #1940), 086k8 (0.11 #2320, 0.11 #2072, 0.11 #993), 016tw3 (0.10 #1914, 0.10 #3248, 0.10 #2330), 054lpb6 (0.08 #1338, 0.08 #1917, 0.08 #2085), 0l6qt (0.08 #331, 0.07 #2486, 0.07 #165), 05sq84 (0.08 #331, 0.07 #165, 0.05 #1571) >> Best rule #330 for best value: >> intensional similarity = 5 >> extensional distance = 35 >> proper extension: 02d44q; 0gh8zks; >> query: (?x9145, ?x617) <- language(?x9145, ?x254), film(?x617, ?x9145), ?x617 = 025jfl, nominated_for(?x164, ?x9145), nominated_for(?x2375, ?x9145) >> conf = 0.49 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0djkrp production_companies 025jfl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 50.000 0.486 http://example.org/film/film/production_companies #478-046_v PRED entity: 046_v PRED relation: profession PRED expected values: 0dxtg => 97 concepts (37 used for prediction) PRED predicted values (max 10 best out of 96): 0dxtg (0.84 #2953, 0.83 #894, 0.81 #1336), 03gjzk (0.77 #455, 0.74 #896, 0.72 #1338), 02hrh1q (0.74 #748, 0.62 #2366, 0.56 #601), 09jwl (0.49 #2665, 0.48 #1489, 0.47 #1930), 0nbcg (0.47 #619, 0.45 #1502, 0.43 #1943), 016z4k (0.41 #2651, 0.41 #1916, 0.39 #2063), 01d_h8 (0.39 #3682, 0.39 #3535, 0.39 #447), 0kyk (0.39 #1647, 0.38 #3264, 0.28 #4587), 0dz3r (0.35 #2649, 0.33 #1473, 0.32 #1914), 02jknp (0.32 #3683, 0.32 #3536, 0.31 #3095) >> Best rule #2953 for best value: >> intensional similarity = 3 >> extensional distance = 164 >> proper extension: 0cj2nl; 06msq2; >> query: (?x10439, 0dxtg) <- nationality(?x10439, ?x94), tv_program(?x10439, ?x9649), profession(?x10439, ?x353) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 046_v profession 0dxtg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 37.000 0.843 http://example.org/people/person/profession #477-01wmxfs PRED entity: 01wmxfs PRED relation: award_nominee! PRED expected values: 0f7hc => 112 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1459): 0837ql (0.81 #51181, 0.81 #46527, 0.80 #44199), 01vw37m (0.81 #51181, 0.81 #46527, 0.80 #44199), 0f7hc (0.81 #51181, 0.81 #46527, 0.80 #44199), 01vw20h (0.17 #3382, 0.15 #5707, 0.09 #24317), 05vsxz (0.17 #2333, 0.15 #4658, 0.03 #48862), 0pmhf (0.17 #2890, 0.15 #5215, 0.02 #72117), 04mg6l (0.17 #3646, 0.15 #5971, 0.02 #90727), 014v6f (0.17 #3605, 0.15 #5930, 0.02 #90727), 04v7kt (0.17 #4626, 0.15 #6951, 0.01 #37195), 04qsdh (0.17 #4102, 0.15 #6427, 0.01 #36671) >> Best rule #51181 for best value: >> intensional similarity = 3 >> extensional distance = 356 >> proper extension: 04nw9; 01933d; >> query: (?x828, ?x193) <- participant(?x91, ?x828), award_nominee(?x828, ?x193), film(?x828, ?x857) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 01wmxfs award_nominee! 0f7hc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 47.000 0.814 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #476-01q7cb_ PRED entity: 01q7cb_ PRED relation: role PRED expected values: 03bx0bm => 155 concepts (155 used for prediction) PRED predicted values (max 10 best out of 106): 0342h (0.44 #2262, 0.43 #4982, 0.43 #5048), 03bx0bm (0.42 #2281, 0.36 #90, 0.32 #3078), 05148p4 (0.31 #2276, 0.27 #85, 0.21 #4996), 02hnl (0.18 #97, 0.18 #362, 0.15 #2288), 028tv0 (0.18 #80, 0.17 #809, 0.17 #212), 018vs (0.18 #81, 0.16 #3333, 0.13 #2272), 05r5c (0.18 #75, 0.15 #2266, 0.15 #4986), 03qjg (0.18 #110, 0.12 #442, 0.09 #5021), 0l14qv (0.18 #72, 0.06 #4983, 0.06 #5049), 0l14md (0.13 #2265, 0.12 #1530, 0.12 #339) >> Best rule #2262 for best value: >> intensional similarity = 4 >> extensional distance = 89 >> proper extension: 01w806h; 017vkx; 01vvyfh; 05qhnq; 01w03jv; >> query: (?x970, 0342h) <- artists(?x3753, ?x970), group(?x970, ?x10427), artists(?x3753, ?x9706), ?x9706 = 01fchy >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #2281 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 89 *> proper extension: 01w806h; 017vkx; 01vvyfh; 05qhnq; 01w03jv; *> query: (?x970, 03bx0bm) <- artists(?x3753, ?x970), group(?x970, ?x10427), artists(?x3753, ?x9706), ?x9706 = 01fchy *> conf = 0.42 ranks of expected_values: 2 EVAL 01q7cb_ role 03bx0bm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 155.000 155.000 0.440 http://example.org/music/group_member/membership./music/group_membership/role #475-04ych PRED entity: 04ych PRED relation: district_represented! PRED expected values: 01gtcq => 212 concepts (212 used for prediction) PRED predicted values (max 10 best out of 34): 01gstn (0.50 #355, 0.48 #1769, 0.48 #219), 01gtcq (0.48 #1769, 0.48 #220, 0.47 #356), 01gst9 (0.48 #1769, 0.48 #225, 0.44 #361), 01gssm (0.48 #1769, 0.44 #215, 0.44 #351), 01gssz (0.48 #1769, 0.44 #232, 0.44 #368), 01gsrl (0.48 #1769, 0.44 #216, 0.41 #352), 01grpc (0.48 #1769, 0.41 #354, 0.41 #218), 01grr2 (0.48 #1769, 0.41 #227, 0.38 #363), 01grq1 (0.48 #1769, 0.41 #235, 0.38 #371), 01gsry (0.48 #1769, 0.37 #231, 0.35 #367) >> Best rule #355 for best value: >> intensional similarity = 3 >> extensional distance = 32 >> proper extension: 0125q1; >> query: (?x1025, 01gstn) <- state(?x2017, ?x1025), location(?x1029, ?x1025), influenced_by(?x117, ?x1029) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1769 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 71 *> proper extension: 0nj07; *> query: (?x1025, ?x605) <- adjoins(?x1025, ?x6521), contains(?x1025, ?x4356), district_represented(?x605, ?x6521) *> conf = 0.48 ranks of expected_values: 2 EVAL 04ych district_represented! 01gtcq CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 212.000 212.000 0.500 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #474-045qmr PRED entity: 045qmr PRED relation: actor PRED expected values: 04j5fx => 78 concepts (57 used for prediction) PRED predicted values (max 10 best out of 821): 05bp8g (0.40 #1881, 0.14 #14003, 0.13 #15867), 02gf_l (0.33 #1504, 0.20 #4301, 0.19 #23890), 01qvtwm (0.33 #5548, 0.20 #3684, 0.17 #16737), 01nsyf (0.33 #5483, 0.20 #10147, 0.17 #12944), 01kwh5j (0.33 #5352, 0.20 #3488, 0.17 #12813), 01d_4t (0.33 #1618, 0.20 #4415, 0.14 #6281), 02_p5w (0.33 #1236, 0.20 #4033, 0.14 #5899), 01rcmg (0.33 #1588, 0.20 #4385, 0.14 #6251), 01rw116 (0.33 #1746, 0.20 #4543, 0.14 #6409), 01yh3y (0.33 #1043, 0.20 #3840, 0.14 #5706) >> Best rule #1881 for best value: >> intensional similarity = 14 >> extensional distance = 3 >> proper extension: 02kwcj; >> query: (?x8444, 05bp8g) <- program(?x14343, ?x8444), ?x14343 = 01bfjy, genre(?x8444, ?x225), genre(?x7806, ?x225), genre(?x7514, ?x225), genre(?x6533, ?x225), genre(?x6014, ?x225), genre(?x3507, ?x225), film(?x541, ?x7514), nominated_for(?x7739, ?x6014), film_production_design_by(?x6533, ?x5532), ?x7806 = 0b3n61, nominated_for(?x350, ?x3507), film_release_region(?x6014, ?x142) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #10145 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 8 *> proper extension: 05hd32; 088tp3; *> query: (?x8444, 04j5fx) <- genre(?x8444, ?x5937), genre(?x8444, ?x2540), genre(?x8444, ?x258), ?x2540 = 0hcr, ?x5937 = 0jxy, genre(?x7141, ?x258), genre(?x5534, ?x258), genre(?x3752, ?x258), genre(?x3084, ?x258), genre(?x428, ?x258), ?x5534 = 05zpghd, ?x428 = 0h1cdwq, film(?x396, ?x7141), film_release_region(?x7141, ?x1790), ?x3752 = 0cn_b8, nominated_for(?x68, ?x7141), film(?x541, ?x3084) *> conf = 0.10 ranks of expected_values: 64 EVAL 045qmr actor 04j5fx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 78.000 57.000 0.400 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #473-0gl3hr PRED entity: 0gl3hr PRED relation: genre PRED expected values: 02l7c8 => 103 concepts (102 used for prediction) PRED predicted values (max 10 best out of 93): 07s9rl0 (0.93 #8604, 0.88 #9209, 0.71 #10420), 02l7c8 (0.44 #379, 0.39 #621, 0.39 #137), 01jfsb (0.36 #1828, 0.36 #3403, 0.34 #3161), 01g6gs (0.35 #263, 0.33 #142, 0.30 #505), 02kdv5l (0.30 #1819, 0.29 #4606, 0.28 #5212), 03k9fj (0.25 #5220, 0.23 #7399, 0.23 #6431), 04xvlr (0.21 #365, 0.19 #1091, 0.19 #9210), 060__y (0.20 #1106, 0.17 #1227, 0.17 #1348), 0lsxr (0.20 #1945, 0.19 #3036, 0.19 #3520), 01hmnh (0.17 #5227, 0.16 #7406, 0.15 #986) >> Best rule #8604 for best value: >> intensional similarity = 3 >> extensional distance = 1079 >> proper extension: 0ddfwj1; 0fq27fp; 02z9hqn; 09gq0x5; 09tqkv2; 0fpmrm3; 0p_qr; 05jyb2; 0yx7h; 02ppg1r; ... >> query: (?x6243, 07s9rl0) <- genre(?x6243, ?x307), genre(?x3425, ?x307), ?x3425 = 0qm9n >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #379 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 59 *> proper extension: 03cw411; *> query: (?x6243, 02l7c8) <- language(?x6243, ?x254), costume_design_by(?x6243, ?x2068), film(?x3017, ?x6243), cinematography(?x6243, ?x10741) *> conf = 0.44 ranks of expected_values: 2 EVAL 0gl3hr genre 02l7c8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 103.000 102.000 0.931 http://example.org/film/film/genre #472-015h31 PRED entity: 015h31 PRED relation: specialization_of PRED expected values: 0196pc => 61 concepts (53 used for prediction) PRED predicted values (max 10 best out of 45): 0cbd2 (0.33 #65, 0.20 #226, 0.17 #291), 09jwl (0.26 #588, 0.23 #620, 0.22 #717), 02hrh1q (0.20 #231, 0.17 #296, 0.17 #263), 0n1h (0.17 #261, 0.13 #844, 0.12 #978), 06q2q (0.16 #821, 0.11 #1450, 0.09 #1383), 01c979 (0.11 #410, 0.11 #444, 0.06 #670), 04_tv (0.11 #429, 0.09 #462, 0.08 #655), 09j9h (0.06 #896, 0.01 #1325, 0.01 #1357), 05t4q (0.06 #633, 0.05 #827, 0.05 #795), 015cjr (0.04 #889, 0.04 #923, 0.03 #1121) >> Best rule #65 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 0dxtg; >> query: (?x1966, 0cbd2) <- profession(?x11413, ?x1966), profession(?x8713, ?x1966), profession(?x3456, ?x1966), profession(?x2426, ?x1966), profession(?x1109, ?x1966), ?x2426 = 081nh, ?x1109 = 01g4zr, ?x11413 = 02g3w, ?x3456 = 05jcn8, ?x8713 = 02q6cv4 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1534 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 76 *> proper extension: 0mb31; *> query: (?x1966, ?x319) <- profession(?x4466, ?x1966), profession(?x2426, ?x1966), award(?x2426, ?x720), profession(?x4466, ?x319), place_of_birth(?x4466, ?x4733), religion(?x4466, ?x2694), award_winner(?x1869, ?x4466) *> conf = 0.03 ranks of expected_values: 18 EVAL 015h31 specialization_of 0196pc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 61.000 53.000 0.333 http://example.org/people/profession/specialization_of #471-02mdty PRED entity: 02mdty PRED relation: citytown PRED expected values: 0cc56 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 271): 07dfk (0.45 #2050, 0.38 #2785, 0.38 #3152), 04jpl (0.29 #7726, 0.15 #11413, 0.15 #11780), 059rby (0.23 #9192, 0.20 #11406, 0.10 #35701), 0cc56 (0.23 #9192, 0.20 #11406, 0.07 #30541), 09c7w0 (0.23 #9192, 0.05 #37546, 0.02 #34963), 0d060g (0.23 #9192, 0.05 #37546, 0.02 #34963), 03rjj (0.23 #9192, 0.05 #37546, 0.02 #34963), 030qb3t (0.20 #11406, 0.13 #11433, 0.12 #11800), 0h7h6 (0.20 #11406, 0.09 #1133, 0.07 #8485), 05qtj (0.20 #11406, 0.07 #30541, 0.05 #10299) >> Best rule #2050 for best value: >> intensional similarity = 5 >> extensional distance = 31 >> proper extension: 08t9df; 03d6fyn; 02qdyj; 01dycg; 01qckn; 08z84_; 027lf1; 03_c8p; 0225z1; 025txrl; ... >> query: (?x12011, 07dfk) <- industry(?x12011, ?x245), ?x245 = 01mw1, citytown(?x12011, ?x739), place_of_birth(?x65, ?x739), origin(?x217, ?x739) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #9192 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 209 *> proper extension: 07vht; *> query: (?x12011, ?x205) <- citytown(?x12011, ?x739), citytown(?x7545, ?x739), citytown(?x2730, ?x739), adjoins(?x3415, ?x739), contains(?x205, ?x7545), colors(?x2730, ?x4557) *> conf = 0.23 ranks of expected_values: 4 EVAL 02mdty citytown 0cc56 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 105.000 105.000 0.455 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #470-0136pk PRED entity: 0136pk PRED relation: artists! PRED expected values: 02w4v => 128 concepts (72 used for prediction) PRED predicted values (max 10 best out of 279): 064t9 (0.67 #5274, 0.67 #2799, 0.62 #3108), 02lnbg (0.50 #2843, 0.50 #1296, 0.48 #3152), 0ggx5q (0.50 #2862, 0.48 #3171, 0.41 #3789), 02w4v (0.50 #662, 0.47 #2519, 0.35 #6230), 016clz (0.47 #4955, 0.46 #4027, 0.40 #1243), 0glt670 (0.44 #2825, 0.38 #6846, 0.38 #3134), 0xhtw (0.43 #3421, 0.33 #1875, 0.31 #5587), 03lty (0.42 #1885, 0.22 #956, 0.21 #4978), 06j6l (0.41 #4379, 0.36 #6853, 0.31 #5307), 09n5t_ (0.40 #2689, 0.38 #832, 0.15 #6709) >> Best rule #5274 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0lbj1; 06cc_1; 01vrz41; 012x4t; 015_30; 02b25y; 0259r0; 01vsl3_; 01vn35l; 02_fj; ... >> query: (?x2321, 064t9) <- award(?x2321, ?x1801), type_of_union(?x2321, ?x566), ?x1801 = 01c92g >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #662 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 03h_fk5; 0m_v0; *> query: (?x2321, 02w4v) <- award(?x2321, ?x10316), type_of_union(?x2321, ?x566), ?x10316 = 02ddq4, artists(?x1572, ?x2321) *> conf = 0.50 ranks of expected_values: 4 EVAL 0136pk artists! 02w4v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 128.000 72.000 0.667 http://example.org/music/genre/artists #469-0ctw_b PRED entity: 0ctw_b PRED relation: country! PRED expected values: 04lc0h => 209 concepts (146 used for prediction) PRED predicted values (max 10 best out of 629): 04lc0h (0.40 #12760, 0.25 #63817, 0.22 #57127), 0gs0g (0.35 #17013, 0.28 #40710, 0.25 #49830), 020p1 (0.35 #17013), 0f8j6 (0.25 #2422, 0.07 #4248, 0.04 #7893), 0kqb0 (0.25 #2419, 0.07 #4245, 0.04 #7890), 01d66p (0.25 #2415, 0.07 #4241, 0.04 #7886), 0jgvy (0.25 #2405, 0.07 #4231, 0.04 #7876), 0dj0x (0.25 #2403, 0.07 #4229, 0.04 #7874), 0cv5l (0.25 #2394, 0.07 #4220, 0.04 #7865), 01z26v (0.25 #2392, 0.07 #4218, 0.04 #7863) >> Best rule #12760 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 01mzwp; >> query: (?x1023, ?x12901) <- contains(?x1023, ?x12901), combatants(?x94, ?x1023), place_of_birth(?x6063, ?x12901) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ctw_b country! 04lc0h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 209.000 146.000 0.397 http://example.org/base/biblioness/bibs_location/country #468-018dh3 PRED entity: 018dh3 PRED relation: category PRED expected values: 08mbj5d => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.81 #7, 0.78 #3, 0.77 #2) >> Best rule #7 for best value: >> intensional similarity = 5 >> extensional distance = 50 >> proper extension: 036k0s; 01kxnd; 02dj3; 05gm16l; 0xxc; >> query: (?x12836, 08mbj5d) <- contains(?x1905, ?x12836), contains(?x279, ?x12836), district_represented(?x3473, ?x1905), adjoins(?x1905, ?x177), ?x279 = 0d060g >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 018dh3 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.808 http://example.org/common/topic/webpage./common/webpage/category #467-06pwq PRED entity: 06pwq PRED relation: company! PRED expected values: 099p5 => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 207): 0d06m5 (0.33 #547, 0.33 #61, 0.25 #790), 028rk (0.33 #48, 0.20 #1020, 0.08 #1263), 0203v (0.33 #25, 0.20 #997, 0.04 #8533), 042kg (0.33 #219, 0.20 #1191, 0.04 #4110), 0157m (0.33 #26, 0.20 #998, 0.03 #8534), 034ls (0.33 #150, 0.20 #1122, 0.03 #8658), 042fk (0.33 #240, 0.20 #1212, 0.02 #5832), 06c0j (0.33 #236, 0.20 #1208, 0.02 #5828), 038w8 (0.33 #226, 0.20 #1198, 0.02 #5818), 0d3k14 (0.33 #215, 0.20 #1187, 0.02 #5807) >> Best rule #547 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 07vsl; >> query: (?x581, 0d06m5) <- category(?x581, ?x134), company(?x12453, ?x581), ?x12453 = 014vk4 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4074 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 26 *> proper extension: 049dk; 0bqxw; 035gt8; *> query: (?x581, 099p5) <- major_field_of_study(?x581, ?x2314), school_type(?x581, ?x1044), ?x2314 = 0h5k *> conf = 0.04 ranks of expected_values: 97 EVAL 06pwq company! 099p5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 117.000 117.000 0.333 http://example.org/people/person/employment_history./business/employment_tenure/company #466-029pnn PRED entity: 029pnn PRED relation: gender PRED expected values: 05zppz => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #9, 0.85 #3, 0.80 #13), 02zsn (0.31 #52, 0.31 #68, 0.30 #106) >> Best rule #9 for best value: >> intensional similarity = 3 >> extensional distance = 60 >> proper extension: 022769; >> query: (?x8257, 05zppz) <- award(?x8257, ?x1312), ?x1312 = 07cbcy, location(?x8257, ?x191) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 029pnn gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.871 http://example.org/people/person/gender #465-05nrkb PRED entity: 05nrkb PRED relation: registering_agency PRED expected values: 03z19 => 99 concepts (99 used for prediction) PRED predicted values (max 10 best out of 1): 03z19 (0.86 #7, 0.85 #12, 0.85 #10) >> Best rule #7 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 01dq5z; 03fmfs; 01p7x7; 02pdhz; >> query: (?x9479, 03z19) <- school_type(?x9479, ?x1962), student(?x9479, ?x3705), currency(?x9479, ?x170), people(?x1050, ?x3705) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05nrkb registering_agency 03z19 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 99.000 99.000 0.864 http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency #464-01ync PRED entity: 01ync PRED relation: colors PRED expected values: 09ggk => 126 concepts (126 used for prediction) PRED predicted values (max 10 best out of 17): 06fvc (0.45 #1524, 0.45 #1149, 0.40 #601), 02rnmb (0.43 #389, 0.40 #132, 0.39 #235), 01l849 (0.35 #310, 0.32 #941, 0.32 #464), 01g5v (0.31 #841, 0.30 #2073, 0.30 #1115), 0jc_p (0.27 #651, 0.25 #23, 0.25 #4), 0680m7 (0.20 #53, 0.03 #1061, 0.02 #736), 036k5h (0.17 #224, 0.15 #1744, 0.14 #1027), 07plts (0.17 #224, 0.15 #1744, 0.14 #1027), 06kqt3 (0.17 #224, 0.15 #1744, 0.14 #1027), 038hg (0.17 #224, 0.14 #1027, 0.11 #1498) >> Best rule #1524 for best value: >> intensional similarity = 8 >> extensional distance = 144 >> proper extension: 02plv57; >> query: (?x4487, 06fvc) <- colors(?x4487, ?x5325), teams(?x659, ?x4487), colors(?x7643, ?x5325), colors(?x3298, ?x5325), ?x3298 = 0jnmj, draft(?x7643, ?x465), position_s(?x7643, ?x2573), ?x2573 = 05b3ts >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #953 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 60 *> proper extension: 0hm2b; *> query: (?x4487, 09ggk) <- team(?x2010, ?x4487), teams(?x659, ?x4487), contains(?x94, ?x659), place_of_birth(?x1775, ?x659), ?x94 = 09c7w0, colors(?x4487, ?x663) *> conf = 0.06 ranks of expected_values: 13 EVAL 01ync colors 09ggk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 126.000 126.000 0.452 http://example.org/sports/sports_team/colors #463-05cgv PRED entity: 05cgv PRED relation: teams PRED expected values: 03ylxn => 163 concepts (163 used for prediction) PRED predicted values (max 10 best out of 167): 03_qrp (0.14 #527, 0.08 #887, 0.05 #1967), 044l47 (0.14 #406, 0.08 #766, 0.05 #1846), 0ckf6 (0.08 #1039, 0.03 #2839, 0.02 #3199), 01z1r (0.08 #871, 0.03 #2671, 0.02 #3031), 02_lt (0.08 #844, 0.03 #2644, 0.02 #3004), 03_3z4 (0.07 #1411, 0.04 #2491, 0.02 #3211), 03lygq (0.07 #1338, 0.04 #2418, 0.02 #3498), 01352_ (0.07 #1380, 0.04 #2460, 0.02 #3540), 03zkr8 (0.07 #1384, 0.04 #2464, 0.02 #3544), 03zrhb (0.07 #1254, 0.04 #2334, 0.02 #5574) >> Best rule #527 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0fngf; >> query: (?x1241, 03_qrp) <- featured_film_locations(?x5044, ?x1241), administrative_parent(?x1241, ?x551), ?x5044 = 0413cff >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05cgv teams 03ylxn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 163.000 163.000 0.143 http://example.org/sports/sports_team_location/teams #462-0mdqp PRED entity: 0mdqp PRED relation: award_winner! PRED expected values: 0738b8 => 122 concepts (78 used for prediction) PRED predicted values (max 10 best out of 847): 05ty4m (0.81 #64405, 0.81 #107879, 0.81 #117545), 0738b8 (0.81 #64405, 0.81 #107879, 0.81 #117545), 01q_ph (0.53 #125598, 0.50 #77285, 0.37 #49909), 030vnj (0.53 #125598, 0.50 #77285, 0.37 #49909), 06cgy (0.53 #125598, 0.50 #77285, 0.37 #49909), 0bq2g (0.53 #125598, 0.50 #77285, 0.37 #49909), 0205dx (0.53 #125598, 0.50 #77285, 0.37 #49909), 039bp (0.53 #125598, 0.50 #77285, 0.37 #49909), 01j5ts (0.53 #125598, 0.50 #77285, 0.37 #49909), 0bksh (0.53 #125598, 0.50 #77285, 0.37 #49909) >> Best rule #64405 for best value: >> intensional similarity = 3 >> extensional distance = 527 >> proper extension: 09h_q; >> query: (?x794, ?x364) <- people(?x1050, ?x794), nationality(?x794, ?x94), award_winner(?x794, ?x364) >> conf = 0.81 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 0mdqp award_winner! 0738b8 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 78.000 0.815 http://example.org/award/award_winner/awards_won./award/award_honor/award_winner #461-01lc5 PRED entity: 01lc5 PRED relation: award PRED expected values: 0gq9h => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 306): 0gq9h (0.75 #1278, 0.60 #75, 0.50 #476), 07bdd_ (0.49 #11692, 0.41 #6078, 0.27 #6880), 04dn09n (0.40 #8062, 0.09 #7661, 0.08 #2448), 03hl6lc (0.40 #8195, 0.05 #6190, 0.05 #27444), 05p1dby (0.39 #11733, 0.36 #6119, 0.27 #6921), 02qyp19 (0.38 #8021, 0.06 #7620, 0.05 #6016), 0gr4k (0.37 #8051, 0.14 #833, 0.10 #27300), 0gs9p (0.32 #8097, 0.25 #1280, 0.16 #31281), 019f4v (0.27 #8084, 0.16 #3673, 0.14 #866), 09sb52 (0.26 #10866, 0.23 #29314, 0.23 #23698) >> Best rule #1278 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 02hy9p; >> query: (?x11265, 0gq9h) <- award_nominee(?x11265, ?x1850), award(?x11265, ?x198), ?x1850 = 017jv5 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01lc5 award 0gq9h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.750 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #460-01wn718 PRED entity: 01wn718 PRED relation: artist! PRED expected values: 033hn8 => 124 concepts (69 used for prediction) PRED predicted values (max 10 best out of 110): 017l96 (0.40 #18, 0.17 #578, 0.11 #3101), 03rhqg (0.31 #295, 0.29 #435, 0.20 #15), 04fcjt (0.23 #309, 0.21 #449, 0.07 #1149), 0fb0v (0.23 #287, 0.20 #7, 0.17 #567), 01trtc (0.22 #632, 0.15 #1332, 0.13 #1612), 015_1q (0.22 #4230, 0.22 #6340, 0.21 #1419), 073tm9 (0.20 #36, 0.17 #596, 0.10 #736), 033hn8 (0.20 #13, 0.11 #1133, 0.11 #3096), 02y21l (0.20 #95, 0.06 #655, 0.05 #795), 01jv1z (0.20 #5, 0.06 #565, 0.05 #705) >> Best rule #18 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 02r3cn; >> query: (?x3977, 017l96) <- artists(?x9630, ?x3977), artists(?x5934, ?x3977), ?x9630 = 012yc, location(?x3977, ?x1860), ?x5934 = 05r6t >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #13 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 02r3cn; *> query: (?x3977, 033hn8) <- artists(?x9630, ?x3977), artists(?x5934, ?x3977), ?x9630 = 012yc, location(?x3977, ?x1860), ?x5934 = 05r6t *> conf = 0.20 ranks of expected_values: 8 EVAL 01wn718 artist! 033hn8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 124.000 69.000 0.400 http://example.org/music/record_label/artist #459-03hnd PRED entity: 03hnd PRED relation: influenced_by! PRED expected values: 03j0d => 183 concepts (86 used for prediction) PRED predicted values (max 10 best out of 464): 02465 (0.38 #2436, 0.09 #8429, 0.09 #8929), 01dvtx (0.36 #3642, 0.10 #18635, 0.07 #21629), 0dzkq (0.28 #3618, 0.13 #29972, 0.12 #38464), 04hcw (0.28 #3777, 0.12 #18770, 0.11 #13770), 047g6 (0.28 #3959, 0.09 #11955, 0.09 #18952), 01_k0d (0.25 #2261, 0.14 #18486, 0.13 #29972), 0lrh (0.25 #2100, 0.09 #8093, 0.09 #8593), 05qw5 (0.25 #2066, 0.07 #8059, 0.06 #8559), 03cdg (0.25 #14485, 0.24 #15488, 0.22 #11989), 013pp3 (0.24 #13705, 0.21 #2714, 0.17 #17706) >> Best rule #2436 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 06hgj; 046_v; >> query: (?x3542, 02465) <- influenced_by(?x8753, ?x3542), profession(?x3542, ?x7397), ?x8753 = 0yxl, place_of_death(?x3542, ?x362) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #2388 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 06hgj; 046_v; *> query: (?x3542, 03j0d) <- influenced_by(?x8753, ?x3542), profession(?x3542, ?x7397), ?x8753 = 0yxl, place_of_death(?x3542, ?x362) *> conf = 0.12 ranks of expected_values: 111 EVAL 03hnd influenced_by! 03j0d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 183.000 86.000 0.375 http://example.org/influence/influence_node/influenced_by #458-05zl0 PRED entity: 05zl0 PRED relation: school_type PRED expected values: 05pcjw => 143 concepts (143 used for prediction) PRED predicted values (max 10 best out of 23): 05jxkf (0.50 #892, 0.49 #1204, 0.46 #1228), 05pcjw (0.48 #97, 0.44 #73, 0.35 #409), 01rs41 (0.33 #53, 0.28 #1710, 0.26 #1013), 07tf8 (0.31 #81, 0.29 #105, 0.27 #681), 01_9fk (0.21 #1226, 0.20 #458, 0.20 #674), 02p0qmm (0.09 #274, 0.07 #490, 0.06 #226), 01_srz (0.06 #627, 0.06 #243, 0.06 #1902), 01y64 (0.05 #588, 0.04 #1417, 0.04 #492), 06cs1 (0.05 #102, 0.05 #462, 0.04 #1417), 04399 (0.04 #1417, 0.04 #1238, 0.03 #3244) >> Best rule #892 for best value: >> intensional similarity = 4 >> extensional distance = 98 >> proper extension: 019q50; >> query: (?x6056, 05jxkf) <- institution(?x3437, ?x6056), institution(?x1200, ?x6056), ?x1200 = 016t_3, ?x3437 = 02_xgp2 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #97 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 0f8l9c; *> query: (?x6056, 05pcjw) <- company(?x346, ?x6056), ?x346 = 060c4, organization(?x6056, ?x5487) *> conf = 0.48 ranks of expected_values: 2 EVAL 05zl0 school_type 05pcjw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 143.000 143.000 0.500 http://example.org/education/educational_institution/school_type #457-0dnvn3 PRED entity: 0dnvn3 PRED relation: titles! PRED expected values: 04btyz => 130 concepts (112 used for prediction) PRED predicted values (max 10 best out of 61): 01jfsb (0.43 #3457, 0.43 #3374, 0.36 #6009), 07s9rl0 (0.37 #407, 0.36 #8984, 0.35 #8470), 07ssc (0.36 #4893, 0.11 #5401, 0.10 #7962), 04xvlr (0.35 #714, 0.31 #511, 0.29 #613), 0lsxr (0.33 #9191, 0.26 #2335, 0.25 #9086), 024qqx (0.29 #994, 0.29 #1096, 0.23 #1500), 05p553 (0.26 #2335, 0.25 #9086, 0.25 #3456), 09blyk (0.21 #2843, 0.17 #3400, 0.10 #5745), 02n4kr (0.21 #2843, 0.17 #3368, 0.13 #2247), 01hmnh (0.17 #27, 0.14 #839, 0.12 #4194) >> Best rule #3457 for best value: >> intensional similarity = 4 >> extensional distance = 160 >> proper extension: 0dckvs; 0fy66; 05dss7; 08j7lh; >> query: (?x392, ?x812) <- film(?x3572, ?x392), titles(?x2480, ?x392), genre(?x392, ?x812), ?x812 = 01jfsb >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3435 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 160 *> proper extension: 0dckvs; 0fy66; 05dss7; 08j7lh; *> query: (?x392, 04btyz) <- film(?x3572, ?x392), titles(?x2480, ?x392), genre(?x392, ?x812), ?x812 = 01jfsb *> conf = 0.04 ranks of expected_values: 30 EVAL 0dnvn3 titles! 04btyz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 130.000 112.000 0.426 http://example.org/media_common/netflix_genre/titles #456-04f2zj PRED entity: 04f2zj PRED relation: specialization_of PRED expected values: 09jwl => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 44): 09jwl (0.42 #200, 0.36 #232, 0.33 #135), 0n1h (0.33 #4, 0.25 #68, 0.25 #36), 06q2q (0.09 #922, 0.09 #889, 0.08 #1120), 0cbd2 (0.08 #484, 0.08 #551, 0.08 #323), 02hrh1q (0.05 #456, 0.05 #263, 0.04 #684), 015cjr (0.05 #598, 0.05 #273, 0.04 #727), 01c979 (0.04 #833, 0.04 #865, 0.04 #964), 01c8w0 (0.04 #228, 0.02 #293, 0.02 #517), 04_tv (0.04 #949, 0.04 #1248, 0.03 #395), 09j9h (0.03 #1163, 0.03 #1196, 0.02 #313) >> Best rule #200 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 0dz3r; 01d_h8; 0n1h; 01c72t; 039v1; 01b30l; >> query: (?x11254, 09jwl) <- profession(?x8341, ?x11254), profession(?x2575, ?x11254), artists(?x9750, ?x2575), instrumentalists(?x227, ?x2575), instrumentalists(?x2206, ?x8341), ?x2206 = 07gql, ?x9750 = 016zgj >> conf = 0.42 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04f2zj specialization_of 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 40.000 40.000 0.417 http://example.org/people/profession/specialization_of #455-01s21dg PRED entity: 01s21dg PRED relation: artists! PRED expected values: 05bt6j => 121 concepts (77 used for prediction) PRED predicted values (max 10 best out of 256): 0glt670 (0.56 #39, 0.42 #6141, 0.40 #344), 05bt6j (0.51 #3092, 0.46 #1262, 0.41 #3397), 0ggx5q (0.47 #380, 0.39 #4346, 0.32 #1906), 02lnbg (0.41 #4327, 0.40 #361, 0.34 #6158), 025sc50 (0.40 #353, 0.39 #6150, 0.39 #4319), 06j6l (0.38 #6148, 0.37 #4317, 0.33 #3097), 08jyyk (0.37 #674, 0.30 #979, 0.11 #7691), 01lyv (0.34 #7965, 0.31 #3084, 0.25 #1254), 03_d0 (0.30 #1537, 0.23 #8858, 0.21 #3062), 02vjzr (0.28 #3181, 0.19 #6537, 0.15 #1656) >> Best rule #39 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 01wgxtl; 04vrxh; >> query: (?x4741, 0glt670) <- profession(?x4741, ?x131), award_nominee(?x4741, ?x1989), ?x1989 = 04mn81 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #3092 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 37 *> proper extension: 01ydzx; 01nn3m; *> query: (?x4741, 05bt6j) <- profession(?x4741, ?x131), artists(?x2823, ?x4741), ?x2823 = 02qdgx *> conf = 0.51 ranks of expected_values: 2 EVAL 01s21dg artists! 05bt6j CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 121.000 77.000 0.556 http://example.org/music/genre/artists #454-02b25y PRED entity: 02b25y PRED relation: currency PRED expected values: 09nqf => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 3): 09nqf (0.33 #31, 0.30 #88, 0.30 #46), 01nv4h (0.11 #14, 0.07 #38, 0.04 #41), 02l6h (0.01 #42) >> Best rule #31 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 07ss8_; 0127s7; 03h_0_z; 02z4b_8; 01x0yrt; >> query: (?x2584, 09nqf) <- award(?x2584, ?x1389), profession(?x2584, ?x1183), ?x1389 = 01c427 >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02b25y currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 132.000 132.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #453-02lkcc PRED entity: 02lkcc PRED relation: people! PRED expected values: 0gk4g => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 30): 0gk4g (0.13 #670, 0.13 #1924, 0.12 #538), 0dq9p (0.08 #545, 0.07 #1931, 0.07 #2723), 0qcr0 (0.08 #661, 0.07 #1915, 0.06 #2641), 0d19y2 (0.06 #253, 0.05 #319, 0.02 #715), 03p41 (0.06 #219, 0.05 #285), 02y0js (0.05 #530, 0.05 #2642, 0.05 #2774), 04p3w (0.05 #605, 0.04 #2651, 0.04 #2783), 02knxx (0.04 #692, 0.03 #1946, 0.03 #2738), 02k6hp (0.04 #565, 0.03 #631, 0.03 #1951), 01l2m3 (0.04 #544, 0.03 #2656, 0.03 #2788) >> Best rule #670 for best value: >> intensional similarity = 2 >> extensional distance = 275 >> proper extension: 01vq3nl; >> query: (?x1522, 0gk4g) <- place_of_death(?x1522, ?x1523), nominated_for(?x1522, ?x11996) >> conf = 0.13 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02lkcc people! 0gk4g CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 108.000 0.134 http://example.org/people/cause_of_death/people #452-04vs9 PRED entity: 04vs9 PRED relation: countries_spoken_in! PRED expected values: 07c9s => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 55): 02h40lc (0.80 #56, 0.68 #886, 0.67 #775), 06nm1 (0.19 #616, 0.18 #396, 0.16 #1169), 0jzc (0.17 #459, 0.16 #238, 0.15 #127), 071fb (0.17 #347, 0.14 #291, 0.07 #1340), 04306rv (0.13 #503, 0.12 #61, 0.10 #5), 05zjd (0.12 #298, 0.10 #132, 0.09 #243), 02hwhyv (0.10 #136, 0.07 #192, 0.07 #247), 06b_j (0.08 #461, 0.06 #571, 0.06 #793), 02hxcvy (0.08 #141, 0.07 #252, 0.06 #473), 02bjrlw (0.08 #112, 0.06 #57, 0.06 #1162) >> Best rule #56 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 0g8bw; >> query: (?x9072, ?x254) <- countries_spoken_in(?x5607, ?x9072), ?x5607 = 064_8sq, official_language(?x9072, ?x254) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #182 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 54 *> proper extension: 0853g; *> query: (?x9072, 07c9s) <- contains(?x2467, ?x9072), exported_to(?x9072, ?x8781) *> conf = 0.05 ranks of expected_values: 19 EVAL 04vs9 countries_spoken_in! 07c9s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 63.000 63.000 0.800 http://example.org/language/human_language/countries_spoken_in #451-0265wl PRED entity: 0265wl PRED relation: disciplines_or_subjects PRED expected values: 0707q => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 35): 05hgj (0.50 #260, 0.38 #123, 0.36 #225), 02vxn (0.42 #553, 0.42 #519, 0.38 #588), 014dfn (0.33 #58, 0.25 #126, 0.20 #933), 08_lx0 (0.29 #587, 0.20 #933, 0.19 #624), 02n4kr (0.29 #587, 0.19 #624, 0.19 #623), 0707q (0.29 #96, 0.25 #164, 0.20 #198), 0w7c (0.25 #470, 0.20 #574, 0.20 #540), 0dwly (0.20 #933, 0.19 #624, 0.19 #623), 0j7v_ (0.20 #933, 0.19 #624, 0.19 #623), 01tz3c (0.20 #933, 0.19 #624, 0.19 #623) >> Best rule #260 for best value: >> intensional similarity = 8 >> extensional distance = 12 >> proper extension: 040_9s0; 058bzgm; >> query: (?x5050, 05hgj) <- award_winner(?x5050, ?x1287), award(?x10438, ?x5050), award(?x10275, ?x5050), award(?x1752, ?x5050), ?x1752 = 01dzz7, profession(?x10438, ?x353), disciplines_or_subjects(?x5050, ?x1013), influenced_by(?x10275, ?x2161) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #96 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 0262zm; 039yzf; *> query: (?x5050, 0707q) <- award_winner(?x5050, ?x1287), award(?x10438, ?x5050), award(?x10275, ?x5050), award(?x3663, ?x5050), ?x10275 = 03hpr, influenced_by(?x3663, ?x1089), category(?x10438, ?x134), location(?x3663, ?x335) *> conf = 0.29 ranks of expected_values: 6 EVAL 0265wl disciplines_or_subjects 0707q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 48.000 48.000 0.500 http://example.org/award/award_category/disciplines_or_subjects #450-0cy__l PRED entity: 0cy__l PRED relation: genre PRED expected values: 07s9rl0 => 88 concepts (85 used for prediction) PRED predicted values (max 10 best out of 91): 07s9rl0 (0.83 #121, 0.66 #3127, 0.63 #1444), 05p553 (0.42 #3131, 0.37 #2650, 0.35 #2891), 01jfsb (0.37 #253, 0.35 #373, 0.34 #1216), 02kdv5l (0.31 #2889, 0.31 #483, 0.30 #2648), 0lsxr (0.31 #249, 0.24 #369, 0.22 #9), 03k9fj (0.30 #492, 0.26 #4699, 0.25 #5299), 06cvj (0.24 #3130, 0.12 #124, 0.09 #4211), 04t36 (0.22 #7, 0.12 #127, 0.10 #3133), 060__y (0.21 #137, 0.18 #3143, 0.16 #2060), 04xvlr (0.19 #843, 0.19 #122, 0.19 #1445) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0sxfd; 0g9wdmc; 03hj3b3; 01gvts; 0h3k3f; 0k419; >> query: (?x5509, 07s9rl0) <- award(?x5509, ?x1245), nominated_for(?x382, ?x5509), ?x1245 = 0gqwc >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cy__l genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 88.000 85.000 0.833 http://example.org/film/film/genre #449-02g2wv PRED entity: 02g2wv PRED relation: award! PRED expected values: 0blt6 07r1h 0jbp0 => 44 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2338): 0bxtg (0.69 #50658, 0.68 #27017, 0.68 #37145), 0flw6 (0.68 #27017, 0.68 #50657, 0.67 #37144), 019vgs (0.68 #27017, 0.68 #50657, 0.67 #37144), 0170s4 (0.50 #634, 0.20 #4009, 0.09 #7384), 0237fw (0.50 #643, 0.12 #4018, 0.10 #7393), 014zcr (0.42 #52, 0.17 #6802, 0.15 #50659), 07r1h (0.42 #1804, 0.17 #5179, 0.10 #8554), 03ym1 (0.42 #1677, 0.15 #5052, 0.09 #8427), 015grj (0.42 #223, 0.12 #3598, 0.07 #6973), 018ygt (0.42 #1848, 0.12 #5223, 0.06 #8598) >> Best rule #50658 for best value: >> intensional similarity = 4 >> extensional distance = 225 >> proper extension: 05qck; 02qkk9_; 02py7pj; >> query: (?x5734, ?x2373) <- award_winner(?x5734, ?x2373), award_winner(?x2373, ?x1672), participant(?x2373, ?x91), award_nominee(?x2373, ?x192) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #1804 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 02x17c2; *> query: (?x5734, 07r1h) <- award(?x6917, ?x5734), award(?x2373, ?x5734), ?x2373 = 016z2j, participant(?x2818, ?x6917) *> conf = 0.42 ranks of expected_values: 7, 1038, 1442 EVAL 02g2wv award! 0jbp0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 16.000 0.691 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02g2wv award! 07r1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 44.000 16.000 0.691 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 02g2wv award! 0blt6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 44.000 16.000 0.691 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #448-01z_jj PRED entity: 01z_jj PRED relation: category PRED expected values: 08mbj5d => 167 concepts (167 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.86 #15, 0.84 #34, 0.84 #11) >> Best rule #15 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 01rtm4; 0hhjk; >> query: (?x13656, 08mbj5d) <- currency(?x13656, ?x170), state_province_region(?x13656, ?x108), registering_agency(?x13656, ?x1982), place_of_birth(?x236, ?x108) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01z_jj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 167.000 167.000 0.864 http://example.org/common/topic/webpage./common/webpage/category #447-06vsbt PRED entity: 06vsbt PRED relation: location PRED expected values: 030qb3t => 81 concepts (81 used for prediction) PRED predicted values (max 10 best out of 54): 030qb3t (0.21 #1686, 0.20 #884, 0.20 #82), 02_286 (0.17 #15278, 0.17 #1641, 0.17 #45763), 04jpl (0.08 #23279, 0.07 #33705, 0.07 #17), 0cr3d (0.08 #33832, 0.06 #45870, 0.06 #16989), 01n7q (0.07 #63, 0.03 #2469, 0.03 #3272), 0rh6k (0.07 #4, 0.02 #23266, 0.02 #33692), 0r0m6 (0.07 #217, 0.02 #1821, 0.02 #2623), 06y57 (0.07 #255, 0.01 #5870, 0.01 #5068), 06wxw (0.07 #227, 0.01 #23489, 0.01 #11457), 052p7 (0.07 #126, 0.01 #23388) >> Best rule #1686 for best value: >> intensional similarity = 2 >> extensional distance = 417 >> proper extension: 029_3; 0gv40; 02lymt; 01c6l; 07pzc; >> query: (?x5505, 030qb3t) <- award_nominee(?x1342, ?x5505), participant(?x8875, ?x5505) >> conf = 0.21 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06vsbt location 030qb3t CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 81.000 81.000 0.212 http://example.org/people/person/places_lived./people/place_lived/location #446-01pcmd PRED entity: 01pcmd PRED relation: award_nominee! PRED expected values: 0hskw => 107 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1119): 0hskw (0.89 #2333, 0.82 #76971, 0.81 #123617), 024rgt (0.62 #552, 0.06 #2885, 0.04 #21542), 07ym6ss (0.62 #771, 0.04 #40421, 0.04 #17097), 03mdt (0.62 #764, 0.04 #17090, 0.02 #40414), 070j61 (0.62 #1699, 0.03 #41349, 0.02 #18025), 0m66w (0.62 #1379, 0.02 #41029, 0.02 #17705), 046b0s (0.50 #551, 0.02 #40201, 0.02 #16877), 01pcmd (0.47 #58310, 0.31 #100294, 0.30 #156271), 09hd6f (0.47 #58310, 0.02 #41782, 0.02 #58109), 01pw2f1 (0.47 #58310) >> Best rule #2333 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 046b0s; 024rgt; 03mdt; >> query: (?x2136, ?x2733) <- award_nominee(?x4589, ?x2136), award_nominee(?x2136, ?x2733), ?x4589 = 03fg0r >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01pcmd award_nominee! 0hskw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 107.000 68.000 0.889 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #445-04gd8j PRED entity: 04gd8j PRED relation: category PRED expected values: 08mbj5d => 137 concepts (137 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.90 #32, 0.90 #79, 0.90 #50) >> Best rule #32 for best value: >> intensional similarity = 4 >> extensional distance = 207 >> proper extension: 01hhvg; 07lx1s; 03v6t; 02jyr8; 02bjhv; 07vht; 02zccd; 018m5q; 01lnyf; 02897w; ... >> query: (?x9865, 08mbj5d) <- citytown(?x9865, ?x242), contains(?x94, ?x9865), currency(?x9865, ?x170), colors(?x9865, ?x663) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04gd8j category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 137.000 0.904 http://example.org/common/topic/webpage./common/webpage/category #444-0htlr PRED entity: 0htlr PRED relation: actor! PRED expected values: 04sskp => 120 concepts (82 used for prediction) PRED predicted values (max 10 best out of 95): 01fx1l (0.39 #17259, 0.37 #19386, 0.36 #19919), 0191n (0.31 #798, 0.29 #1065, 0.22 #18852), 03cvwkr (0.31 #798, 0.29 #1065, 0.22 #18852), 05631 (0.20 #257, 0.08 #1323, 0.07 #1588), 0vjr (0.10 #360, 0.08 #627, 0.07 #894), 0hz55 (0.10 #352, 0.08 #619, 0.07 #886), 0q9jk (0.10 #420, 0.08 #687, 0.07 #954), 0124k9 (0.10 #286, 0.08 #553, 0.07 #820), 04f6hhm (0.08 #690, 0.07 #957, 0.02 #2019), 0gvsh7l (0.08 #688, 0.07 #955) >> Best rule #17259 for best value: >> intensional similarity = 4 >> extensional distance = 627 >> proper extension: 01pfkw; >> query: (?x914, ?x5594) <- nominated_for(?x914, ?x5594), award(?x914, ?x154), program(?x1394, ?x5594), honored_for(?x8128, ?x5594) >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0htlr actor! 04sskp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 120.000 82.000 0.386 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #443-01vrkdt PRED entity: 01vrkdt PRED relation: artist! PRED expected values: 0n85g => 108 concepts (78 used for prediction) PRED predicted values (max 10 best out of 115): 03mp8k (0.65 #204, 0.16 #2290, 0.14 #1734), 033hn8 (0.29 #2239, 0.14 #1683, 0.12 #153), 01q940 (0.29 #189, 0.10 #467, 0.06 #606), 0181dw (0.26 #2265, 0.24 #179, 0.18 #735), 03rhqg (0.25 #3355, 0.23 #433, 0.18 #711), 01cszh (0.24 #150, 0.10 #1680, 0.10 #428), 016ckq (0.22 #41, 0.08 #1710, 0.06 #180), 017l96 (0.21 #575, 0.11 #3358, 0.11 #853), 0g768 (0.20 #3374, 0.12 #6993, 0.12 #1982), 01w40h (0.19 #444, 0.13 #1278, 0.12 #583) >> Best rule #204 for best value: >> intensional similarity = 4 >> extensional distance = 15 >> proper extension: 011z3g; 016ppr; >> query: (?x3962, 03mp8k) <- artist(?x5666, ?x3962), artists(?x3562, ?x3962), ?x3562 = 025sc50, ?x5666 = 043g7l >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #1730 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 0hvbj; 01yzl2; 016890; 01dwrc; 016376; 015bwt; *> query: (?x3962, 0n85g) <- artist(?x3265, ?x3962), artists(?x3562, ?x3962), ?x3562 = 025sc50 *> conf = 0.11 ranks of expected_values: 29 EVAL 01vrkdt artist! 0n85g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 108.000 78.000 0.647 http://example.org/music/record_label/artist #442-02py9yf PRED entity: 02py9yf PRED relation: country_of_origin PRED expected values: 09c7w0 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 42): 09c7w0 (0.92 #369, 0.91 #211, 0.91 #391), 07ssc (0.22 #323, 0.18 #97, 0.18 #86), 0d060g (0.22 #323, 0.18 #81, 0.13 #674), 03_3d (0.22 #323, 0.17 #643, 0.14 #766), 03rt9 (0.22 #323, 0.13 #674, 0.12 #413), 03rjj (0.22 #323, 0.13 #674, 0.12 #413), 02jx1 (0.13 #674, 0.12 #413, 0.08 #992), 05v8c (0.13 #674, 0.12 #413, 0.03 #536), 04jpl (0.13 #674, 0.12 #413), 0d0vqn (0.12 #413, 0.02 #561, 0.02 #362) >> Best rule #369 for best value: >> intensional similarity = 8 >> extensional distance = 64 >> proper extension: 0sw0q; >> query: (?x10669, 09c7w0) <- nominated_for(?x7316, ?x10669), genre(?x10669, ?x811), titles(?x2008, ?x10669), ?x2008 = 07c52, genre(?x6214, ?x811), genre(?x2362, ?x811), ?x6214 = 0k5fg, ?x2362 = 05p1qyh >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02py9yf country_of_origin 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.924 http://example.org/tv/tv_program/country_of_origin #441-05fhy PRED entity: 05fhy PRED relation: district_represented! PRED expected values: 024tcq 070mff => 220 concepts (220 used for prediction) PRED predicted values (max 10 best out of 54): 070mff (0.84 #415, 0.83 #847, 0.78 #577), 024tcq (0.82 #396, 0.81 #828, 0.78 #558), 02bn_p (0.69 #816, 0.68 #384, 0.65 #546), 02bp37 (0.59 #388, 0.59 #550, 0.58 #820), 03rl1g (0.58 #811, 0.55 #1405, 0.55 #379), 043djx (0.58 #815, 0.55 #1405, 0.54 #275), 02bqm0 (0.57 #567, 0.55 #1161, 0.55 #1405), 02bqmq (0.55 #1405, 0.54 #556, 0.53 #1150), 01h7xx (0.55 #1405, 0.52 #852, 0.49 #2378), 01gt99 (0.55 #1405, 0.49 #2378, 0.49 #320) >> Best rule #415 for best value: >> intensional similarity = 4 >> extensional distance = 42 >> proper extension: 0g0syc; >> query: (?x1024, 070mff) <- district_represented(?x952, ?x1024), district_represented(?x653, ?x1024), ?x653 = 070m6c, ?x952 = 06f0dc >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 05fhy district_represented! 070mff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 220.000 220.000 0.841 http://example.org/government/legislative_session/members./government/government_position_held/district_represented EVAL 05fhy district_represented! 024tcq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 220.000 220.000 0.841 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #440-0gd_s PRED entity: 0gd_s PRED relation: location PRED expected values: 059rby => 137 concepts (91 used for prediction) PRED predicted values (max 10 best out of 242): 0f94t (0.61 #26557, 0.57 #12871, 0.55 #13677), 01n7q (0.25 #2475, 0.17 #5695, 0.17 #6499), 02frhbc (0.20 #1273, 0.08 #2881, 0.05 #5297), 02_286 (0.17 #12103, 0.15 #11298, 0.15 #12908), 01xd9 (0.12 #8129, 0.04 #19396, 0.04 #17788), 0cr3d (0.11 #10602, 0.11 #14627, 0.10 #11406), 030qb3t (0.10 #54803, 0.10 #64459, 0.10 #53998), 0h7h6 (0.10 #1698, 0.08 #2502, 0.04 #5722), 0f25y (0.10 #2063, 0.06 #4479, 0.05 #5283), 0c4kv (0.10 #2253, 0.06 #4669, 0.03 #7885) >> Best rule #26557 for best value: >> intensional similarity = 4 >> extensional distance = 179 >> proper extension: 07h1q; 0459z; 015n8; >> query: (?x9284, ?x1005) <- influenced_by(?x1752, ?x9284), place_of_birth(?x9284, ?x1005), gender(?x9284, ?x231), location(?x4667, ?x1005) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #3219 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 10 *> proper extension: 04r68; 048_p; *> query: (?x9284, ?x94) <- student(?x2730, ?x9284), award(?x9284, ?x12418), ?x12418 = 045xh, contains(?x94, ?x2730) *> conf = 0.09 ranks of expected_values: 16 EVAL 0gd_s location 059rby CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 137.000 91.000 0.608 http://example.org/people/person/places_lived./people/place_lived/location #439-033rq PRED entity: 033rq PRED relation: place_of_death PRED expected values: 06c62 => 123 concepts (123 used for prediction) PRED predicted values (max 10 best out of 45): 030qb3t (0.17 #1575, 0.15 #4293, 0.14 #4681), 0k049 (0.14 #197, 0.11 #1556, 0.09 #3497), 02_286 (0.10 #3701, 0.10 #207, 0.10 #3896), 0f2wj (0.08 #1371, 0.06 #206, 0.04 #5836), 05qtj (0.04 #2781, 0.04 #3170, 0.02 #6859), 04jpl (0.04 #3695, 0.04 #201, 0.04 #3890), 0k_p5 (0.04 #282, 0.02 #4359, 0.02 #6107), 027l4q (0.04 #332, 0.01 #1691, 0.01 #2467), 06_kh (0.04 #4276, 0.03 #6024, 0.03 #6800), 04vmp (0.03 #1467, 0.03 #2825, 0.03 #3214) >> Best rule #1575 for best value: >> intensional similarity = 3 >> extensional distance = 143 >> proper extension: 0h1_w; 019z7q; 0f0p0; 0h1m9; 028lc8; 01t07j; 01vyp_; 018swb; 01_vfy; 0gr36; ... >> query: (?x8573, 030qb3t) <- award(?x8573, ?x198), people(?x4322, ?x8573), award_winner(?x5429, ?x8573) >> conf = 0.17 => this is the best rule for 1 predicted values *> Best rule #877 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 96 *> proper extension: 058nh2; 09xx0m; *> query: (?x8573, 06c62) <- award(?x8573, ?x1862), nationality(?x8573, ?x205), ?x1862 = 0gr51 *> conf = 0.01 ranks of expected_values: 42 EVAL 033rq place_of_death 06c62 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 123.000 123.000 0.172 http://example.org/people/deceased_person/place_of_death #438-05p3738 PRED entity: 05p3738 PRED relation: film_crew_role PRED expected values: 09vw2b7 => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 34): 09vw2b7 (0.71 #572, 0.71 #608, 0.68 #465), 0dxtw (0.41 #505, 0.38 #576, 0.38 #648), 0215hd (0.38 #18, 0.16 #620, 0.16 #584), 02ynfr (0.30 #50, 0.25 #15, 0.19 #617), 01pvkk (0.29 #577, 0.29 #613, 0.28 #1660), 089g0h (0.25 #19, 0.13 #585, 0.13 #621), 02rh1dz (0.20 #44, 0.17 #325, 0.15 #504), 04pyp5 (0.20 #51, 0.11 #822, 0.09 #2476), 015h31 (0.17 #324, 0.12 #8, 0.12 #503), 01xy5l_ (0.13 #615, 0.13 #579, 0.12 #13) >> Best rule #572 for best value: >> intensional similarity = 5 >> extensional distance = 664 >> proper extension: 01br2w; 0djb3vw; 04dsnp; 05dy7p; 02n9bh; 04lqvlr; 04lqvly; 02hfk5; 02h22; 064lsn; ... >> query: (?x1710, 09vw2b7) <- genre(?x1710, ?x53), currency(?x1710, ?x170), film_crew_role(?x1710, ?x1284), ?x1284 = 0ch6mp2, ?x170 = 09nqf >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05p3738 film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.712 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #437-0m0bj PRED entity: 0m0bj PRED relation: contains! PRED expected values: 02jx1 => 84 concepts (19 used for prediction) PRED predicted values (max 10 best out of 196): 02jx1 (0.66 #981, 0.64 #1880, 0.55 #86), 09c7w0 (0.60 #6282, 0.58 #7179, 0.58 #8075), 04jpl (0.15 #1816, 0.12 #917, 0.08 #22), 0345h (0.15 #2774, 0.11 #3670, 0.09 #4566), 03rk0 (0.13 #2829, 0.08 #4621, 0.08 #5518), 0d060g (0.13 #3602, 0.10 #4498, 0.10 #5395), 01n7q (0.11 #8149, 0.10 #9045, 0.10 #9942), 0f8l9c (0.10 #2739, 0.07 #3635, 0.06 #4531), 03rjj (0.10 #3599, 0.08 #4495, 0.08 #5392), 059rby (0.08 #8988, 0.08 #9885, 0.08 #10781) >> Best rule #981 for best value: >> intensional similarity = 5 >> extensional distance = 333 >> proper extension: 01l5rz; >> query: (?x13528, 02jx1) <- contains(?x512, ?x13528), contains(?x512, ?x9420), contains(?x512, ?x7213), ?x9420 = 01pr6n, location(?x1194, ?x7213) >> conf = 0.66 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0m0bj contains! 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 19.000 0.657 http://example.org/location/location/contains #436-02nt3d PRED entity: 02nt3d PRED relation: film! PRED expected values: 03ywyk => 88 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1049): 09yrh (0.63 #85291, 0.63 #81128, 0.63 #87374), 0gv07g (0.55 #10401, 0.45 #83210, 0.44 #97775), 026l37 (0.33 #812, 0.01 #11213), 01jmv8 (0.33 #1500), 0kryqm (0.33 #1207), 0lx2l (0.20 #2498, 0.10 #91536, 0.06 #81129), 048lv (0.20 #2298, 0.10 #91536, 0.02 #12701), 04t2l2 (0.20 #2106, 0.07 #8348, 0.06 #4188), 01wmxfs (0.20 #2207, 0.06 #4289, 0.04 #12610), 032xhg (0.20 #2142, 0.03 #4224, 0.03 #6304) >> Best rule #85291 for best value: >> intensional similarity = 3 >> extensional distance = 859 >> proper extension: 01f3p_; >> query: (?x6198, ?x4536) <- nominated_for(?x4536, ?x6198), participant(?x6515, ?x4536), film(?x6515, ?x2814) >> conf = 0.63 => this is the best rule for 1 predicted values *> Best rule #7819 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 69 *> proper extension: 0jqb8; 058kh7; *> query: (?x6198, 03ywyk) <- produced_by(?x6198, ?x71), genre(?x6198, ?x239), ?x239 = 06cvj, film(?x157, ?x6198) *> conf = 0.01 ranks of expected_values: 706 EVAL 02nt3d film! 03ywyk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 88.000 58.000 0.632 http://example.org/film/actor/film./film/performance/film #435-0czyxs PRED entity: 0czyxs PRED relation: film_format PRED expected values: 017fx5 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 4): 07fb8_ (0.30 #11, 0.23 #22, 0.22 #1), 0cj16 (0.22 #3, 0.18 #50, 0.16 #24), 017fx5 (0.15 #40, 0.14 #9, 0.13 #62), 0hcr (0.05 #566) >> Best rule #11 for best value: >> intensional similarity = 6 >> extensional distance = 28 >> proper extension: 02q52q; 0407yj_; 0cn_b8; 0gyv0b4; >> query: (?x383, 07fb8_) <- genre(?x383, ?x225), film(?x382, ?x383), story_by(?x383, ?x8753), film(?x101, ?x383), region(?x383, ?x512), ?x512 = 07ssc >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #40 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 53 *> proper extension: 07kb7vh; *> query: (?x383, 017fx5) <- language(?x383, ?x254), nominated_for(?x507, ?x383), ?x254 = 02h40lc, region(?x383, ?x512), film_crew_role(?x383, ?x468), ?x468 = 02r96rf *> conf = 0.15 ranks of expected_values: 3 EVAL 0czyxs film_format 017fx5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 102.000 102.000 0.300 http://example.org/film/film/film_format #434-0nh0f PRED entity: 0nh0f PRED relation: contains! PRED expected values: 04ykg => 46 concepts (8 used for prediction) PRED predicted values (max 10 best out of 69): 09c7w0 (0.46 #5388, 0.39 #1796, 0.34 #5385), 04ykg (0.34 #5385, 0.22 #7181, 0.22 #7180), 0nh0f (0.34 #5385, 0.22 #7181, 0.22 #7180), 04_1l0v (0.25 #451, 0.18 #2244, 0.17 #6732), 07c5l (0.25 #395, 0.03 #2188, 0.02 #5780), 07b_l (0.21 #1118, 0.16 #2015, 0.14 #5607), 03v0t (0.18 #1129, 0.07 #2026, 0.06 #5618), 01n7q (0.12 #3667, 0.11 #4565, 0.10 #2769), 059rby (0.10 #4507, 0.09 #2711, 0.07 #3609), 0824r (0.09 #1148, 0.07 #5637, 0.03 #2045) >> Best rule #5388 for best value: >> intensional similarity = 2 >> extensional distance = 205 >> proper extension: 01pl14; 0pmpl; 0s69k; 03h2c; 04yf_; 0b2h3; 03l6bs; 04gxf; 013nws; 0164b; ... >> query: (?x6697, 09c7w0) <- time_zones(?x6697, ?x1638), ?x1638 = 02fqwt >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #5385 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 164 *> proper extension: 0cb4j; 0nvrd; 02xry; 01cx_; 0d6lp; 0mnm2; 0mkdm; 0f2nf; 0fc_9; 0l2q3; ... *> query: (?x6697, ?x94) <- contains(?x6697, ?x4674), contains(?x94, ?x4674), time_zones(?x6697, ?x1638), currency(?x6697, ?x170) *> conf = 0.34 ranks of expected_values: 2 EVAL 0nh0f contains! 04ykg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 46.000 8.000 0.464 http://example.org/location/location/contains #433-01vsps PRED entity: 01vsps PRED relation: award_winner! PRED expected values: 0bzk8w => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 136): 0hhtgcw (0.25 #227, 0.14 #368, 0.12 #650), 02cg41 (0.17 #831, 0.06 #972, 0.05 #1113), 0fz0c2 (0.14 #388, 0.12 #670, 0.12 #529), 09q_6t (0.14 #290, 0.12 #572, 0.12 #431), 09p3h7 (0.14 #353, 0.12 #635, 0.12 #494), 0hn821n (0.14 #413, 0.12 #695, 0.12 #554), 059x66 (0.14 #300, 0.12 #582, 0.12 #441), 073h1t (0.14 #309, 0.12 #591, 0.12 #450), 0418154 (0.14 #390, 0.12 #672, 0.12 #531), 0bxs_d (0.14 #397, 0.12 #679, 0.12 #538) >> Best rule #227 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0pz91; 0d05fv; >> query: (?x4379, 0hhtgcw) <- celebrities_impersonated(?x3649, ?x4379), place_of_birth(?x4379, ?x4962), produced_by(?x5080, ?x4379), contains(?x4962, ?x8820) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1416 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 02knnd; 014dq7; 0ly5n; 013qvn; 0c_md_; 044bn; 0pqzh; *> query: (?x4379, 0bzk8w) <- celebrities_impersonated(?x3649, ?x4379), place_of_birth(?x4379, ?x4962), gender(?x4379, ?x231), place_of_death(?x4379, ?x4151) *> conf = 0.04 ranks of expected_values: 49 EVAL 01vsps award_winner! 0bzk8w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 105.000 105.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #432-02qgyv PRED entity: 02qgyv PRED relation: award PRED expected values: 02x8n1n => 87 concepts (71 used for prediction) PRED predicted values (max 10 best out of 293): 0cjyzs (0.71 #104, 0.14 #15964, 0.13 #27538), 027dtxw (0.30 #1600, 0.25 #403, 0.14 #15964), 0gr51 (0.29 #896, 0.20 #1295, 0.14 #2492), 02x73k6 (0.28 #1654, 0.25 #457, 0.14 #15964), 09sdmz (0.28 #1797, 0.17 #600, 0.14 #15964), 0f4x7 (0.27 #1626, 0.14 #15964, 0.13 #27538), 0bdwqv (0.27 #1763, 0.14 #15964, 0.13 #27538), 0bfvd4 (0.25 #511, 0.22 #1708, 0.14 #15964), 05pcn59 (0.25 #478, 0.14 #2074, 0.14 #15964), 057xs89 (0.25 #555, 0.14 #15964, 0.13 #27538) >> Best rule #104 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 02773m2; 0crx5w; 0b1f49; 0b7t3p; >> query: (?x2353, 0cjyzs) <- award_nominee(?x364, ?x2353), award_winner(?x3078, ?x2353), ?x364 = 05ty4m >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #515 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 02qgqt; 02p65p; 01yb09; 04t7ts; 02wgln; 019pm_; 0gy6z9; 016vg8; 01kb2j; 023kzp; *> query: (?x2353, 02x8n1n) <- award_nominee(?x703, ?x2353), film(?x2353, ?x414), ?x703 = 0187y5 *> conf = 0.17 ranks of expected_values: 20 EVAL 02qgyv award 02x8n1n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 87.000 71.000 0.714 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #431-01gkg3 PRED entity: 01gkg3 PRED relation: major_field_of_study PRED expected values: 04rjg => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 154): 05qjt (0.82 #1403, 0.82 #1296, 0.79 #1621), 02j62 (0.82 #1316, 0.78 #1749, 0.78 #1179), 05qfh (0.82 #1322, 0.78 #1105, 0.73 #1429), 062z7 (0.82 #1421, 0.78 #1097, 0.73 #1314), 0g4gr (0.80 #1208, 0.67 #1100, 0.67 #992), 04rjg (0.78 #1089, 0.78 #640, 0.76 #531), 036hv (0.78 #1082, 0.78 #640, 0.74 #861), 037mh8 (0.78 #1133, 0.73 #1457, 0.73 #1350), 0fdys (0.78 #1108, 0.73 #1432, 0.73 #1325), 03g3w (0.78 #1096, 0.73 #1313, 0.67 #638) >> Best rule #1403 for best value: >> intensional similarity = 31 >> extensional distance = 9 >> proper extension: 071tyz; >> query: (?x5739, 05qjt) <- institution(?x5739, ?x5844), major_field_of_study(?x5739, ?x12907), major_field_of_study(?x5739, ?x6760), major_field_of_study(?x5739, ?x5740), major_field_of_study(?x1771, ?x12907), major_field_of_study(?x865, ?x12907), major_field_of_study(?x12907, ?x10391), specialization_of(?x12907, ?x3802), major_field_of_study(?x3178, ?x12907), profession(?x12258, ?x3802), profession(?x3864, ?x3802), ?x865 = 02h4rq6, disciplines_or_subjects(?x850, ?x6760), student(?x6760, ?x6324), student(?x6760, ?x2965), ?x3178 = 01vc5m, major_field_of_study(?x6760, ?x10332), major_field_of_study(?x2981, ?x12907), ?x1771 = 019v9k, ?x12258 = 019fz, major_field_of_study(?x3437, ?x5740), award_nominee(?x406, ?x6324), award_winner(?x1135, ?x6324), ?x3437 = 02_xgp2, award_winner(?x678, ?x6324), award_winner(?x6324, ?x832), genre(?x802, ?x10391), major_field_of_study(?x2327, ?x6760), major_field_of_study(?x10391, ?x1527), award_nominee(?x2965, ?x368), ?x3864 = 03f5vvx >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #1089 for first EXPECTED value: *> intensional similarity = 31 *> extensional distance = 7 *> proper extension: 04zx3q1; 016t_3; *> query: (?x5739, 04rjg) <- institution(?x5739, ?x5844), major_field_of_study(?x5739, ?x12907), major_field_of_study(?x5739, ?x6760), major_field_of_study(?x1771, ?x12907), major_field_of_study(?x865, ?x12907), major_field_of_study(?x12907, ?x2014), specialization_of(?x12907, ?x3802), major_field_of_study(?x3178, ?x12907), profession(?x2397, ?x3802), ?x865 = 02h4rq6, disciplines_or_subjects(?x850, ?x6760), student(?x6760, ?x6324), student(?x6760, ?x4214), student(?x6760, ?x4043), ?x3178 = 01vc5m, major_field_of_study(?x6760, ?x10332), major_field_of_study(?x2981, ?x12907), ?x1771 = 019v9k, award_winner(?x406, ?x6324), student(?x5739, ?x2639), film(?x6324, ?x667), award(?x6324, ?x102), award_nominee(?x539, ?x4043), film(?x4043, ?x508), actor(?x4881, ?x4214), participant(?x3195, ?x6324), ?x2981 = 02j62, award_nominee(?x4043, ?x192), type_of_union(?x4043, ?x566), location(?x4214, ?x335), award_nominee(?x830, ?x6324) *> conf = 0.78 ranks of expected_values: 6 EVAL 01gkg3 major_field_of_study 04rjg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 21.000 21.000 0.818 http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study #430-03wbqc4 PRED entity: 03wbqc4 PRED relation: featured_film_locations PRED expected values: 01qcx_ => 124 concepts (105 used for prediction) PRED predicted values (max 10 best out of 130): 030qb3t (0.20 #1236, 0.16 #1954, 0.16 #997), 04jpl (0.17 #728, 0.14 #5997, 0.14 #3361), 0f94t (0.10 #21, 0.08 #261, 0.02 #1219), 0100mt (0.10 #140, 0.08 #380), 0rh6k (0.07 #1917, 0.07 #1678, 0.07 #2156), 0d6lp (0.07 #1987, 0.04 #3903, 0.02 #7017), 03gh4 (0.06 #2030, 0.03 #3946, 0.03 #2748), 035p3 (0.06 #2148, 0.03 #4064, 0.03 #712), 06y57 (0.05 #1779, 0.04 #2257, 0.04 #2018), 0345h (0.05 #1230, 0.04 #3384, 0.04 #1469) >> Best rule #1236 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 09fc83; >> query: (?x4361, 030qb3t) <- currency(?x4361, ?x170), films(?x4272, ?x4361), executive_produced_by(?x4361, ?x3744), featured_film_locations(?x4361, ?x739) >> conf = 0.20 => this is the best rule for 1 predicted values *> Best rule #1407 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 53 *> proper extension: 09fc83; *> query: (?x4361, 01qcx_) <- currency(?x4361, ?x170), films(?x4272, ?x4361), executive_produced_by(?x4361, ?x3744), featured_film_locations(?x4361, ?x739) *> conf = 0.02 ranks of expected_values: 73 EVAL 03wbqc4 featured_film_locations 01qcx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 124.000 105.000 0.200 http://example.org/film/film/featured_film_locations #429-01wy61y PRED entity: 01wy61y PRED relation: nationality PRED expected values: 03_3d => 126 concepts (73 used for prediction) PRED predicted values (max 10 best out of 33): 09c7w0 (0.74 #2004, 0.71 #6414, 0.70 #4610), 02jx1 (0.33 #33, 0.32 #633, 0.29 #433), 03_3d (0.33 #6515, 0.32 #7323, 0.32 #7322), 049yf (0.33 #6515, 0.32 #7323, 0.32 #7322), 07ssc (0.22 #15, 0.18 #615, 0.13 #1816), 0d060g (0.12 #407, 0.10 #1307, 0.10 #707), 06q1r (0.11 #77, 0.07 #777, 0.04 #5712), 018qd6 (0.07 #1401), 03rt9 (0.06 #413, 0.04 #5712, 0.04 #5611), 03rk0 (0.05 #2949, 0.04 #5712, 0.04 #5611) >> Best rule #2004 for best value: >> intensional similarity = 8 >> extensional distance = 113 >> proper extension: 01q7cb_; 01vw20_; 0gy6z9; 016ksk; 01vw20h; 03f19q4; 01vvzb1; 01vsgrn; 01vvyc_; 01wbsdz; ... >> query: (?x4162, 09c7w0) <- gender(?x4162, ?x514), artists(?x2937, ?x4162), artists(?x671, ?x4162), artists(?x671, ?x7162), artists(?x671, ?x3321), ?x7162 = 0ffgh, award(?x3321, ?x724), ?x2937 = 0glt670 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #6515 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 444 *> proper extension: 02lg9w; 080knyg; 05typm; 02vkvcz; *> query: (?x4162, ?x252) <- gender(?x4162, ?x514), ?x514 = 02zsn, place_of_birth(?x4162, ?x4163), contains(?x252, ?x4163), location(?x8947, ?x4163) *> conf = 0.33 ranks of expected_values: 3 EVAL 01wy61y nationality 03_3d CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 126.000 73.000 0.739 http://example.org/people/person/nationality #428-0170s4 PRED entity: 0170s4 PRED relation: award PRED expected values: 0bdwqv => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 266): 02x73k6 (0.42 #5541, 0.20 #53, 0.15 #27837), 027dtxw (0.24 #5491, 0.20 #3, 0.18 #26660), 0ck27z (0.20 #14197, 0.20 #14589, 0.17 #1260), 0bdwqv (0.20 #161, 0.19 #5649, 0.18 #26660), 02w9sd7 (0.20 #551, 0.19 #5647, 0.15 #27837), 0cqhk0 (0.20 #33, 0.13 #1209, 0.12 #14146), 0789_m (0.20 #17, 0.13 #5505, 0.06 #12954), 0bp_b2 (0.20 #408, 0.08 #5504, 0.06 #1584), 0bs0bh (0.20 #95, 0.08 #5583, 0.04 #13032), 0cjyzs (0.20 #98, 0.05 #15388, 0.05 #19700) >> Best rule #5541 for best value: >> intensional similarity = 3 >> extensional distance = 184 >> proper extension: 02t__l; >> query: (?x2415, 02x73k6) <- award(?x2415, ?x2183), award(?x4400, ?x2183), ?x4400 = 02mjf2 >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #161 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 3 *> proper extension: 0zjpz; *> query: (?x2415, 0bdwqv) <- participant(?x3329, ?x2415), ?x3329 = 02f8lw *> conf = 0.20 ranks of expected_values: 4 EVAL 0170s4 award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 114.000 114.000 0.425 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #427-04twmk PRED entity: 04twmk PRED relation: film PRED expected values: 04sh80 => 107 concepts (69 used for prediction) PRED predicted values (max 10 best out of 340): 02k_4g (0.59 #69872, 0.59 #44791, 0.59 #71664), 030k94 (0.59 #69872, 0.59 #44791, 0.59 #71664), 058kh7 (0.07 #1580, 0.05 #3372), 0gmgwnv (0.07 #1080, 0.04 #121828, 0.01 #26165), 012gk9 (0.07 #1511, 0.04 #121828), 0221zw (0.07 #569, 0.04 #121828), 03l6q0 (0.07 #544, 0.02 #2336, 0.02 #5918), 025s1wg (0.07 #1707, 0.02 #3499, 0.01 #5290), 0421v9q (0.07 #1159, 0.02 #2951), 016z9n (0.07 #370, 0.02 #29037, 0.02 #41578) >> Best rule #69872 for best value: >> intensional similarity = 3 >> extensional distance = 1315 >> proper extension: 01wxyx1; 01wk7b7; 02nfjp; 03m6pk; 0gd9k; 04jb97; 016ynj; 06r3p2; >> query: (?x9435, ?x782) <- film(?x9435, ?x3588), award(?x9435, ?x435), nominated_for(?x9435, ?x782) >> conf = 0.59 => this is the best rule for 2 predicted values *> Best rule #3542 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 39 *> proper extension: 0l5yl; *> query: (?x9435, 04sh80) <- award(?x9435, ?x5235), ?x5235 = 09qrn4, nationality(?x9435, ?x94) *> conf = 0.02 ranks of expected_values: 99 EVAL 04twmk film 04sh80 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 107.000 69.000 0.589 http://example.org/film/actor/film./film/performance/film #426-05qx1 PRED entity: 05qx1 PRED relation: contains! PRED expected values: 04pnx => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 80): 02qkt (0.61 #1241, 0.60 #3029, 0.60 #347), 02j71 (0.60 #57269, 0.59 #58167, 0.49 #54583), 04pnx (0.60 #59065, 0.30 #2212, 0.21 #57270), 06n3y (0.60 #59065, 0.26 #2512, 0.21 #57270), 0dg3n1 (0.35 #8203, 0.32 #13570, 0.31 #10887), 02j9z (0.33 #2710, 0.30 #28, 0.26 #9866), 09c7w0 (0.31 #23260, 0.30 #61756, 0.26 #29523), 04_1l0v (0.31 #23707, 0.25 #29970, 0.23 #41600), 0j0k (0.29 #4848, 0.28 #3954, 0.24 #12898), 05nrg (0.21 #57270, 0.12 #21140, 0.10 #4141) >> Best rule #1241 for best value: >> intensional similarity = 4 >> extensional distance = 39 >> proper extension: 0jgd; 0j1z8; 047yc; 06qd3; 06c1y; 06t2t; 03h64; 016wzw; >> query: (?x1475, 02qkt) <- film_release_region(?x6078, ?x1475), film_release_region(?x559, ?x1475), film(?x665, ?x559), ?x6078 = 04pk1f >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #59065 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 756 *> proper extension: 0mlyw; 0mm0p; 0ntwb; 09dfcj; 0l2mg; 0mvxt; *> query: (?x1475, ?x7273) <- adjoins(?x1475, ?x9730), contains(?x7273, ?x9730) *> conf = 0.60 ranks of expected_values: 3 EVAL 05qx1 contains! 04pnx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 71.000 0.610 http://example.org/location/location/contains #425-025jfl PRED entity: 025jfl PRED relation: film PRED expected values: 08nhfc1 => 109 concepts (69 used for prediction) PRED predicted values (max 10 best out of 1561): 012kyx (0.73 #10891, 0.63 #28001, 0.62 #28002), 0372j5 (0.33 #1043, 0.21 #7268, 0.12 #8823), 0404j37 (0.33 #992, 0.21 #7217, 0.12 #8772), 03nfnx (0.33 #1219, 0.19 #8999, 0.17 #10554), 02lk60 (0.33 #693, 0.19 #8473, 0.17 #10028), 0dr3sl (0.33 #398, 0.19 #8178, 0.17 #9733), 0d_2fb (0.33 #322, 0.14 #6547, 0.12 #8102), 0cn_b8 (0.33 #539, 0.14 #6764, 0.12 #8319), 05zlld0 (0.33 #538, 0.12 #8318, 0.11 #9873), 04k9y6 (0.33 #912, 0.12 #8692, 0.11 #10247) >> Best rule #10891 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 05d6q1; >> query: (?x617, ?x616) <- production_companies(?x616, ?x617), film(?x617, ?x136), award_winner(?x762, ?x617) >> conf = 0.73 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 025jfl film 08nhfc1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 109.000 69.000 0.727 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #424-0bbw2z6 PRED entity: 0bbw2z6 PRED relation: language PRED expected values: 064_8sq => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 36): 064_8sq (0.27 #19, 0.25 #130, 0.15 #187), 04306rv (0.24 #3, 0.11 #114, 0.10 #336), 0jzc (0.08 #17, 0.06 #128, 0.03 #1629), 0653m (0.06 #232, 0.04 #673, 0.04 #342), 03_9r (0.05 #1003, 0.05 #3673, 0.05 #3729), 012w70 (0.04 #233, 0.03 #674, 0.03 #784), 04h9h (0.03 #262, 0.03 #703, 0.03 #427), 06mp7 (0.03 #124, 0.02 #622, 0.01 #68), 0349s (0.03 #41, 0.02 #152, 0.02 #209), 02ztjwg (0.03 #28, 0.02 #471, 0.02 #582) >> Best rule #19 for best value: >> intensional similarity = 3 >> extensional distance = 72 >> proper extension: 01pvxl; >> query: (?x4786, 064_8sq) <- language(?x4786, ?x5671), ?x5671 = 06b_j, film(?x2258, ?x4786) >> conf = 0.27 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0bbw2z6 language 064_8sq CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 71.000 71.000 0.270 http://example.org/film/film/language #423-0b455l PRED entity: 0b455l PRED relation: award PRED expected values: 0gr51 => 96 concepts (93 used for prediction) PRED predicted values (max 10 best out of 241): 05b1610 (0.71 #10010, 0.71 #9208, 0.71 #8807), 02g3gw (0.71 #10010, 0.71 #9208, 0.71 #8807), 01l78d (0.43 #285, 0.05 #1886, 0.05 #686), 09sb52 (0.30 #4041, 0.30 #6443, 0.29 #5243), 01lk0l (0.29 #275), 0gr51 (0.25 #2498, 0.25 #898, 0.25 #498), 04dn09n (0.25 #443, 0.24 #843, 0.24 #1643), 040njc (0.24 #2809, 0.17 #2409, 0.17 #409), 0gs9p (0.19 #2477, 0.19 #1677, 0.19 #2077), 02n9nmz (0.18 #468, 0.17 #868, 0.17 #1668) >> Best rule #10010 for best value: >> intensional similarity = 3 >> extensional distance = 1229 >> proper extension: 018ndc; 0hvbj; >> query: (?x10064, ?x688) <- award_winner(?x688, ?x10064), award(?x10064, ?x384), award_winner(?x3692, ?x10064) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #2498 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 307 *> proper extension: 043hg; *> query: (?x10064, 0gr51) <- written_by(?x781, ?x10064), award(?x10064, ?x384), genre(?x781, ?x225) *> conf = 0.25 ranks of expected_values: 6 EVAL 0b455l award 0gr51 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 96.000 93.000 0.715 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #422-01yb09 PRED entity: 01yb09 PRED relation: gender PRED expected values: 05zppz => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.80 #27, 0.80 #11, 0.79 #23), 02zsn (0.53 #117, 0.46 #172, 0.31 #30) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 638 >> proper extension: 0kn4c; 063vn; 03f5vvx; 06c97; 03txms; 02mx98; 09py7; 01mvpv; 01k31p; >> query: (?x1251, 05zppz) <- profession(?x1251, ?x353), profession(?x3563, ?x353), ?x3563 = 09bg4l >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01yb09 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.798 http://example.org/people/person/gender #421-0cfywh PRED entity: 0cfywh PRED relation: profession PRED expected values: 02hrh1q => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 68): 02hrh1q (0.86 #3315, 0.85 #2715, 0.85 #3165), 03gjzk (0.55 #1366, 0.33 #916, 0.33 #466), 01d_h8 (0.41 #1506, 0.40 #3006, 0.40 #1206), 0dxtg (0.40 #1364, 0.34 #2114, 0.33 #1214), 02jknp (0.33 #1208, 0.33 #758, 0.33 #458), 02krf9 (0.22 #478, 0.20 #1378, 0.17 #928), 028kk_ (0.21 #1577, 0.12 #227, 0.08 #3077), 09jwl (0.20 #1220, 0.16 #7371, 0.16 #6020), 0cbd2 (0.19 #3607, 0.16 #4357, 0.15 #5107), 0np9r (0.17 #1672, 0.16 #1822, 0.13 #2872) >> Best rule #3315 for best value: >> intensional similarity = 2 >> extensional distance = 83 >> proper extension: 0gcdzz; 03d8njj; 02r99xw; >> query: (?x14088, 02hrh1q) <- people(?x5025, ?x14088), ?x5025 = 0dryh9k >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0cfywh profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 128.000 128.000 0.859 http://example.org/people/person/profession #420-0h336 PRED entity: 0h336 PRED relation: organization PRED expected values: 02_l9 => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 8): 02_l9 (0.17 #88, 0.17 #64, 0.07 #736), 05g9h (0.10 #287, 0.03 #551, 0.02 #671), 02hcxm (0.07 #298, 0.07 #346, 0.06 #370), 03lb_v (0.07 #310, 0.07 #334, 0.04 #454), 01prf3 (0.04 #452, 0.03 #572, 0.02 #596), 01r3kd (0.01 #752), 07t65 (0.01 #3291), 02vk52z (0.01 #3290) >> Best rule #88 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 028p0; 04jwp; 07ym0; 042q3; >> query: (?x10605, 02_l9) <- influenced_by(?x8659, ?x10605), influenced_by(?x10605, ?x7250), ?x8659 = 0dw6b, gender(?x10605, ?x231), profession(?x10605, ?x2225) >> conf = 0.17 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h336 organization 02_l9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 138.000 138.000 0.167 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #419-0jsf6 PRED entity: 0jsf6 PRED relation: award PRED expected values: 0gq_v => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 230): 0f4x7 (0.27 #9765, 0.27 #9531, 0.27 #9764), 02qvyrt (0.27 #9765, 0.27 #9531, 0.27 #9764), 054krc (0.27 #9765, 0.27 #9531, 0.27 #9764), 0gs9p (0.27 #9765, 0.27 #9531, 0.27 #9764), 0gr4k (0.27 #9765, 0.27 #9531, 0.27 #9764), 04kxsb (0.27 #9765, 0.27 #9531, 0.27 #9764), 0gqy2 (0.27 #9765, 0.27 #9531, 0.27 #9764), 04dn09n (0.27 #9765, 0.27 #9531, 0.27 #9764), 0gs96 (0.27 #9765, 0.27 #9531, 0.27 #9764), 0gqyl (0.27 #9765, 0.27 #9531, 0.27 #9764) >> Best rule #9765 for best value: >> intensional similarity = 3 >> extensional distance = 566 >> proper extension: 02rq7nd; >> query: (?x6213, ?x601) <- nominated_for(?x601, ?x6213), honored_for(?x2988, ?x6213), award(?x164, ?x601) >> conf = 0.27 => this is the best rule for 11 predicted values *> Best rule #15353 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 995 *> proper extension: 034fl9; 0qmk5; *> query: (?x6213, ?x484) <- nominated_for(?x591, ?x6213), award_winner(?x6213, ?x6096), award_winner(?x484, ?x6096) *> conf = 0.22 ranks of expected_values: 18 EVAL 0jsf6 award 0gq_v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 105.000 105.000 0.274 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #418-02hp70 PRED entity: 02hp70 PRED relation: institution! PRED expected values: 014mlp => 108 concepts (79 used for prediction) PRED predicted values (max 10 best out of 22): 02h4rq6 (0.81 #50, 0.74 #74, 0.72 #285), 014mlp (0.71 #288, 0.71 #357, 0.71 #546), 03bwzr4 (0.56 #61, 0.54 #85, 0.45 #554), 016t_3 (0.49 #51, 0.48 #75, 0.43 #544), 0bkj86 (0.48 #80, 0.43 #103, 0.38 #549), 04zx3q1 (0.33 #73, 0.27 #96, 0.23 #212), 07s6fsf (0.33 #48, 0.32 #541, 0.32 #306), 027f2w (0.28 #81, 0.23 #104, 0.22 #1251), 013zdg (0.27 #79, 0.22 #1251, 0.21 #102), 022h5x (0.25 #20, 0.22 #1251, 0.19 #67) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 02jztz; >> query: (?x11397, 02h4rq6) <- school_type(?x11397, ?x1507), major_field_of_study(?x11397, ?x1527), colors(?x11397, ?x663), ?x1507 = 01_9fk >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #288 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 172 *> proper extension: 015zyd; 08815; 01rtm4; 01jssp; 05krk; 052nd; 01j_9c; 01fpvz; 02w2bc; 065y4w7; ... *> query: (?x11397, 014mlp) <- institution(?x1771, ?x11397), ?x1771 = 019v9k, state_province_region(?x11397, ?x728), student(?x11397, ?x6398) *> conf = 0.71 ranks of expected_values: 2 EVAL 02hp70 institution! 014mlp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 108.000 79.000 0.814 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #417-057__d PRED entity: 057__d PRED relation: film_release_distribution_medium PRED expected values: 029j_ => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 4): 029j_ (0.87 #26, 0.85 #152, 0.84 #172), 02nxhr (0.05 #37, 0.04 #137, 0.04 #22), 07c52 (0.04 #53, 0.04 #255, 0.03 #403), 07z4p (0.04 #55, 0.03 #92, 0.03 #312) >> Best rule #26 for best value: >> intensional similarity = 5 >> extensional distance = 69 >> proper extension: 083shs; 0dsvzh; 09z2b7; 047qxs; 0kvgxk; 021y7yw; 0mcl0; 03hmt9b; 07jxpf; 032zq6; ... >> query: (?x8633, 029j_) <- film(?x4564, ?x8633), film_crew_role(?x8633, ?x137), genre(?x8633, ?x1509), ?x1509 = 060__y, music(?x8633, ?x6251) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 057__d film_release_distribution_medium 029j_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.873 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium #416-02pt27 PRED entity: 02pt27 PRED relation: artists! PRED expected values: 0dl5d => 104 concepts (45 used for prediction) PRED predicted values (max 10 best out of 234): 06by7 (0.92 #1264, 0.89 #2197, 0.88 #7174), 064t9 (0.67 #944, 0.67 #323, 0.62 #3436), 06j6l (0.52 #5957, 0.42 #2224, 0.39 #3781), 03lty (0.44 #649, 0.33 #28, 0.22 #3451), 0126t5 (0.44 #707, 0.10 #1329, 0.09 #2262), 02yv6b (0.40 #3833, 0.33 #2276, 0.19 #8811), 016clz (0.39 #8405, 0.35 #10271, 0.34 #1870), 0glt670 (0.34 #5950, 0.27 #7505, 0.25 #9374), 03_d0 (0.33 #322, 0.28 #2188, 0.24 #3745), 05r6t (0.33 #83, 0.22 #704, 0.12 #7859) >> Best rule #1264 for best value: >> intensional similarity = 5 >> extensional distance = 47 >> proper extension: 0134s5; 0d193h; 018gm9; 015srx; 01q99h; 0178kd; 02vgh; 01323p; 046p9; 02cw1m; ... >> query: (?x9693, 06by7) <- artists(?x7440, ?x9693), artist(?x2299, ?x9693), ?x2299 = 033hn8, artists(?x7440, ?x1556), ?x1556 = 03qmj9 >> conf = 0.92 => this is the best rule for 1 predicted values *> Best rule #950 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 28 *> proper extension: 01vvycq; 09hnb; *> query: (?x9693, 0dl5d) <- artists(?x3061, ?x9693), role(?x9693, ?x227), ?x3061 = 05bt6j, instrumentalists(?x1166, ?x9693), ?x1166 = 05148p4 *> conf = 0.30 ranks of expected_values: 23 EVAL 02pt27 artists! 0dl5d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 104.000 45.000 0.918 http://example.org/music/genre/artists #415-07f5x PRED entity: 07f5x PRED relation: time_zones PRED expected values: 03bdv => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 12): 02hcv8 (0.28 #1070, 0.28 #1083, 0.28 #966), 0gsrz4 (0.20 #8, 0.19 #21, 0.12 #924), 02llzg (0.19 #43, 0.18 #615, 0.18 #303), 03bdv (0.19 #19, 0.16 #6, 0.12 #924), 02lcqs (0.15 #278, 0.12 #1033, 0.12 #1059), 02fqwt (0.12 #924, 0.12 #430, 0.12 #469), 03plfd (0.12 #924, 0.11 #49, 0.10 #114), 042g7t (0.12 #924, 0.05 #271, 0.05 #297), 052vwh (0.12 #924, 0.02 #922, 0.02 #831), 02hczc (0.11 #54, 0.08 #275, 0.07 #899) >> Best rule #1070 for best value: >> intensional similarity = 2 >> extensional distance = 720 >> proper extension: 013ksx; 029jpy; 0rj0z; 0mkdm; 0cm5m; 0bwtj; 013d_f; 0nvvw; 0s3pw; >> query: (?x8948, 02hcv8) <- adjoins(?x8948, ?x2051), contains(?x2467, ?x8948) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #19 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 57 *> proper extension: 05g2v; 0ftn8; 0lnfy; 0fnyc; 0dbks; 0c1xm; 01pxqx; 0fnc_; *> query: (?x8948, 03bdv) <- contains(?x2467, ?x8948), ?x2467 = 0dg3n1 *> conf = 0.19 ranks of expected_values: 4 EVAL 07f5x time_zones 03bdv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 90.000 0.280 http://example.org/location/location/time_zones #414-01gg59 PRED entity: 01gg59 PRED relation: award PRED expected values: 02qvyrt => 129 concepts (104 used for prediction) PRED predicted values (max 10 best out of 305): 054ks3 (0.60 #2520, 0.47 #1329, 0.32 #3711), 01by1l (0.43 #8049, 0.37 #4476, 0.37 #11622), 01bgqh (0.43 #11555, 0.34 #7982, 0.30 #4409), 02qvyrt (0.38 #4887, 0.36 #2902, 0.36 #3696), 0c4z8 (0.37 #2452, 0.33 #1261, 0.32 #4437), 09sb52 (0.34 #24657, 0.28 #22272, 0.28 #9965), 025m8y (0.31 #2875, 0.29 #2081, 0.26 #4860), 01c92g (0.25 #4461, 0.18 #1285, 0.17 #888), 03qbh5 (0.25 #8141, 0.24 #1392, 0.24 #4568), 01ck6h (0.22 #1309, 0.17 #4485, 0.14 #2500) >> Best rule #2520 for best value: >> intensional similarity = 3 >> extensional distance = 68 >> proper extension: 03f2_rc; 02fgpf; 02cyfz; 01l9v7n; 0m_v0; 01tc9r; 02dbp7; 01jrvr6; 07j8kh; 01l1rw; ... >> query: (?x3890, 054ks3) <- artists(?x284, ?x3890), award(?x3890, ?x1323), ?x1323 = 0gqz2 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #4887 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 102 *> proper extension: 0dpqk; 01mh8zn; 02fgp0; 03975z; *> query: (?x3890, 02qvyrt) <- award(?x3890, ?x462), music(?x3742, ?x3890), award_winner(?x139, ?x3890) *> conf = 0.38 ranks of expected_values: 4 EVAL 01gg59 award 02qvyrt CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 129.000 104.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #413-0241jw PRED entity: 0241jw PRED relation: award PRED expected values: 04kxsb => 75 concepts (75 used for prediction) PRED predicted values (max 10 best out of 258): 09sb52 (0.88 #444, 0.71 #40, 0.71 #8890), 099tbz (0.71 #8890, 0.70 #10912, 0.70 #10507), 0ck27z (0.32 #901, 0.30 #1305, 0.19 #4133), 05zr6wv (0.29 #17, 0.24 #421, 0.18 #826), 05ztrmj (0.18 #588, 0.15 #16973, 0.15 #16974), 09qv_s (0.18 #555, 0.15 #16973, 0.15 #16974), 099ck7 (0.18 #671, 0.15 #16973, 0.15 #16974), 0gqy2 (0.15 #16973, 0.15 #16974, 0.13 #25466), 0f4x7 (0.15 #16973, 0.15 #16974, 0.13 #25466), 0bdwqv (0.15 #16973, 0.15 #16974, 0.13 #25466) >> Best rule #444 for best value: >> intensional similarity = 3 >> extensional distance = 15 >> proper extension: 0f0kz; 02p7_k; >> query: (?x1846, 09sb52) <- award_nominee(?x1846, ?x230), ?x230 = 02bfmn, award_winner(?x704, ?x1846) >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #16973 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1553 *> proper extension: 01jq34; 09h_q; *> query: (?x1846, ?x693) <- award_winner(?x1846, ?x5661), award(?x5661, ?x693), award_winner(?x693, ?x1223) *> conf = 0.15 ranks of expected_values: 13 EVAL 0241jw award 04kxsb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 75.000 75.000 0.882 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #412-0cc56 PRED entity: 0cc56 PRED relation: citytown! PRED expected values: 02mdty => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 611): 017ztv (0.33 #435, 0.02 #7709, 0.02 #8519), 03qdm (0.28 #53390, 0.27 #25878, 0.26 #38824), 04d5v9 (0.28 #53390, 0.27 #25878, 0.26 #38824), 02lwv5 (0.28 #53390, 0.27 #25878, 0.26 #38824), 03dm7 (0.28 #53390, 0.27 #25878, 0.26 #38824), 03_fmr (0.25 #1375, 0.12 #2183, 0.02 #7033), 01vg13 (0.25 #1108, 0.12 #1916, 0.02 #6766), 03bmmc (0.25 #1078, 0.12 #1886, 0.02 #6736), 09r4xx (0.25 #970, 0.07 #63100, 0.03 #5819), 0204jh (0.25 #1012, 0.03 #5861, 0.01 #12331) >> Best rule #435 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 01lfy; >> query: (?x1131, 017ztv) <- location(?x6361, ?x1131), location(?x2965, ?x1131), ?x6361 = 0807ml, award_nominee(?x368, ?x2965) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #5479 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 25 *> proper extension: 09c7w0; 059rby; 0s3y5; 0fhp9; 015zxh; 06mkj; 02xry; 0lhql; 0f2rq; 0b2ds; ... *> query: (?x1131, 02mdty) <- location(?x406, ?x1131), adjoins(?x10856, ?x1131), place_of_death(?x1047, ?x1131) *> conf = 0.04 ranks of expected_values: 125 EVAL 0cc56 citytown! 02mdty CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 112.000 112.000 0.333 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #411-06nm1 PRED entity: 06nm1 PRED relation: language! PRED expected values: 04fzfj 0b6tzs 034qzw 0f4_l 0571m 04954r 02vr3gz 03tps5 0qmhk 07cw4 02q7yfq 02rrh1w 0fh2v5 02jxrw 02wwmhc => 86 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1711): 0dr_4 (0.71 #22499, 0.60 #11358, 0.57 #24090), 0c_j9x (0.71 #22609, 0.60 #11468, 0.57 #24200), 02jxrw (0.68 #33425, 0.66 #35019, 0.60 #41387), 05v38p (0.68 #33425, 0.66 #35019, 0.60 #41387), 02wgk1 (0.68 #33425, 0.66 #35019, 0.60 #41387), 017jd9 (0.68 #33425, 0.66 #35019, 0.60 #41387), 017gl1 (0.68 #33425, 0.66 #35019, 0.60 #41387), 0qm9n (0.68 #33425, 0.66 #35019, 0.60 #41387), 05rfst (0.68 #33425, 0.66 #35019, 0.60 #41387), 0b6tzs (0.68 #33425, 0.66 #35019, 0.60 #41387) >> Best rule #22499 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 04306rv; 06mp7; >> query: (?x2502, 0dr_4) <- language(?x89, ?x2502), languages(?x1991, ?x2502), award_nominee(?x369, ?x1991), countries_spoken_in(?x2502, ?x47), spouse(?x2849, ?x1991), service_language(?x234, ?x2502) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #33425 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 17 *> proper extension: 0999q; *> query: (?x2502, ?x339) <- languages(?x4702, ?x2502), countries_spoken_in(?x2502, ?x2843), film_release_region(?x3854, ?x2843), award_nominee(?x521, ?x4702), ?x3854 = 03q0r1, film(?x4702, ?x339) *> conf = 0.68 ranks of expected_values: 3, 10, 105, 228, 250, 338, 709, 770, 842, 909, 964, 1224, 1551, 1556, 1596 EVAL 06nm1 language! 02wwmhc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 02jxrw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 0fh2v5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 02rrh1w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 02q7yfq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 07cw4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 0qmhk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 03tps5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 02vr3gz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 04954r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 0571m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 0f4_l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 034qzw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 0b6tzs CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 86.000 53.000 0.714 http://example.org/film/film/language EVAL 06nm1 language! 04fzfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 86.000 53.000 0.714 http://example.org/film/film/language #410-0dwt5 PRED entity: 0dwt5 PRED relation: role PRED expected values: 0j871 0g33q => 86 concepts (58 used for prediction) PRED predicted values (max 10 best out of 95): 0j871 (0.84 #2734, 0.80 #1689, 0.67 #1164), 02sgy (0.75 #2170, 0.75 #3105, 0.74 #5067), 028tv0 (0.75 #2926, 0.71 #3490, 0.71 #1601), 0g2dz (0.75 #2190, 0.71 #1618, 0.67 #3696), 042v_gx (0.75 #3105, 0.74 #5067, 0.74 #2543), 03gvt (0.75 #3105, 0.70 #2511, 0.62 #2545), 01v1d8 (0.75 #3105, 0.66 #560, 0.64 #1978), 018j2 (0.74 #4161, 0.73 #3705, 0.68 #4253), 01dnws (0.73 #2861, 0.68 #4163, 0.67 #3707), 06w7v (0.71 #1947, 0.71 #1755, 0.71 #5161) >> Best rule #2734 for best value: >> intensional similarity = 21 >> extensional distance = 8 >> proper extension: 03f5mt; >> query: (?x4769, ?x2592) <- role(?x4769, ?x2798), role(?x4769, ?x1969), role(?x4769, ?x745), role(?x4769, ?x316), role(?x4769, ?x315), role(?x4769, ?x227), role(?x4769, ?x212), role(?x4769, ?x74), ?x227 = 0342h, ?x2798 = 03qjg, instrumentalists(?x4769, ?x642), ?x745 = 01vj9c, ?x316 = 05r5c, ?x1969 = 04rzd, role(?x868, ?x74), ?x315 = 0l14md, ?x868 = 0dwvl, instrumentalists(?x212, ?x226), performance_role(?x2876, ?x212), role(?x212, ?x736), role(?x2592, ?x4769) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 44 EVAL 0dwt5 role 0g33q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 86.000 58.000 0.838 http://example.org/music/performance_role/regular_performances./music/group_membership/role EVAL 0dwt5 role 0j871 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 58.000 0.838 http://example.org/music/performance_role/regular_performances./music/group_membership/role #409-04yc76 PRED entity: 04yc76 PRED relation: featured_film_locations PRED expected values: 0rh6k => 76 concepts (61 used for prediction) PRED predicted values (max 10 best out of 56): 02_286 (0.27 #4571, 0.22 #259, 0.15 #5289), 030qb3t (0.14 #996, 0.13 #4590, 0.12 #517), 04jpl (0.12 #4560, 0.11 #248, 0.10 #3602), 0cv3w (0.11 #308, 0.10 #786, 0.03 #1743), 052p7 (0.11 #296, 0.06 #535, 0.02 #3171), 01_d4 (0.11 #285, 0.04 #4597, 0.03 #763), 0d6lp (0.06 #549, 0.04 #1028, 0.02 #4622), 0h7h6 (0.06 #521, 0.03 #3396, 0.03 #4594), 03pzf (0.06 #653, 0.02 #1610, 0.02 #1132), 0dc95 (0.06 #538, 0.02 #1495, 0.02 #1017) >> Best rule #4571 for best value: >> intensional similarity = 4 >> extensional distance = 401 >> proper extension: 026njb5; 072r5v; 0581vn8; 0dmn0x; 09rfh9; >> query: (?x2754, 02_286) <- nominated_for(?x401, ?x2754), film_release_region(?x2754, ?x94), genre(?x2754, ?x239), featured_film_locations(?x2754, ?x1767) >> conf = 0.27 => this is the best rule for 1 predicted values *> Best rule #1197 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 101 *> proper extension: 053tj7; *> query: (?x2754, 0rh6k) <- genre(?x2754, ?x239), film_format(?x2754, ?x909), category(?x2754, ?x134) *> conf = 0.05 ranks of expected_values: 16 EVAL 04yc76 featured_film_locations 0rh6k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 76.000 61.000 0.273 http://example.org/film/film/featured_film_locations #408-014ps4 PRED entity: 014ps4 PRED relation: influenced_by PRED expected values: 040db 03j0d => 149 concepts (73 used for prediction) PRED predicted values (max 10 best out of 425): 032l1 (0.43 #6860, 0.24 #7284, 0.18 #9826), 04093 (0.40 #706, 0.23 #4089, 0.14 #2398), 03f0324 (0.40 #3110, 0.20 #7345, 0.17 #6921), 09dt7 (0.38 #4683, 0.36 #4259, 0.33 #31), 040db (0.33 #1745, 0.33 #55, 0.30 #3015), 03j0d (0.33 #328, 0.27 #3710, 0.25 #4980), 06bng (0.33 #272, 0.25 #6196, 0.25 #4924), 04x56 (0.33 #335, 0.20 #757, 0.10 #3295), 014ps4 (0.33 #237, 0.18 #3619, 0.14 #4465), 06jcc (0.33 #239, 0.18 #3621, 0.10 #2776) >> Best rule #6860 for best value: >> intensional similarity = 4 >> extensional distance = 44 >> proper extension: 051cc; 0969fd; >> query: (?x7828, 032l1) <- influenced_by(?x7828, ?x6810), award_winner(?x4879, ?x7828), influenced_by(?x5336, ?x6810), ?x5336 = 02kz_ >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1745 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 04x56; *> query: (?x7828, 040db) <- award(?x7828, ?x5050), ?x5050 = 0265wl, influenced_by(?x7828, ?x477), influenced_by(?x3858, ?x7828), ?x3858 = 05jm7 *> conf = 0.33 ranks of expected_values: 5, 6 EVAL 014ps4 influenced_by 03j0d CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 149.000 73.000 0.435 http://example.org/influence/influence_node/influenced_by EVAL 014ps4 influenced_by 040db CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 149.000 73.000 0.435 http://example.org/influence/influence_node/influenced_by #407-058bzgm PRED entity: 058bzgm PRED relation: disciplines_or_subjects PRED expected values: 03npn 01hmnh => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 39): 02xlf (0.73 #491, 0.63 #419, 0.62 #239), 01hmnh (0.62 #226, 0.60 #262, 0.55 #478), 014dfn (0.51 #1020, 0.49 #947, 0.47 #652), 0707q (0.51 #1020, 0.49 #947, 0.47 #652), 0l67h (0.51 #1020, 0.49 #947, 0.47 #652), 05h83 (0.51 #1020, 0.49 #947, 0.42 #799), 0jtdp (0.49 #947, 0.42 #799, 0.41 #762), 08_lx0 (0.47 #652, 0.21 #1134, 0.19 #1210), 02vxn (0.42 #984, 0.39 #1021, 0.38 #1098), 0w7c (0.23 #860, 0.20 #1006, 0.19 #1043) >> Best rule #491 for best value: >> intensional similarity = 8 >> extensional distance = 20 >> proper extension: 027x4ws; 039yzf; 03mv9j; >> query: (?x11579, 02xlf) <- award(?x12009, ?x11579), award(?x1752, ?x11579), award(?x12009, ?x9285), award(?x12009, ?x1288), influenced_by(?x1752, ?x1287), gender(?x12009, ?x231), ?x9285 = 0265vt, ?x1288 = 02662b >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #226 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 6 *> proper extension: 040_9s0; *> query: (?x11579, 01hmnh) <- award(?x12009, ?x11579), award(?x5086, ?x11579), ?x12009 = 01g6bk, influenced_by(?x5086, ?x3338), peers(?x5086, ?x576), location(?x5086, ?x9969), disciplines_or_subjects(?x11579, ?x5864), ?x5864 = 04g51 *> conf = 0.62 ranks of expected_values: 2 EVAL 058bzgm disciplines_or_subjects 01hmnh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 64.000 64.000 0.727 http://example.org/award/award_category/disciplines_or_subjects EVAL 058bzgm disciplines_or_subjects 03npn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 64.000 64.000 0.727 http://example.org/award/award_category/disciplines_or_subjects #406-066m4g PRED entity: 066m4g PRED relation: award_winner! PRED expected values: 027hjff => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 109): 027hjff (0.75 #197, 0.73 #337, 0.71 #57), 09qvms (0.17 #11203, 0.16 #6442, 0.13 #4201), 03gwpw2 (0.17 #11203, 0.16 #6442, 0.13 #4201), 02q690_ (0.17 #11203, 0.16 #6442, 0.13 #4201), 09pj68 (0.17 #11203, 0.16 #6442, 0.13 #4201), 0drtv8 (0.17 #11203, 0.12 #626, 0.02 #906), 092t4b (0.13 #892, 0.06 #1452, 0.05 #2992), 092c5f (0.13 #854, 0.06 #2954, 0.05 #1134), 0clfdj (0.12 #844, 0.04 #564, 0.03 #2944), 0bvhz9 (0.10 #689) >> Best rule #197 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 05np4c; 025b5y; >> query: (?x849, 027hjff) <- award_winner(?x5809, ?x849), ?x5809 = 06s6hs, participant(?x2221, ?x849) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 066m4g award_winner! 027hjff CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.750 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #405-02vqhv0 PRED entity: 02vqhv0 PRED relation: crewmember PRED expected values: 027rwmr => 75 concepts (50 used for prediction) PRED predicted values (max 10 best out of 27): 0b79gfg (0.25 #18, 0.11 #110, 0.08 #804), 0g9zcgx (0.17 #169, 0.11 #123, 0.06 #817), 05bm4sm (0.12 #209, 0.11 #255, 0.07 #301), 0c94fn (0.11 #287, 0.06 #379, 0.06 #195), 0284n42 (0.10 #790, 0.06 #557, 0.04 #1308), 03m49ly (0.08 #820, 0.07 #356, 0.05 #587), 095zvfg (0.08 #823, 0.05 #590, 0.05 #359), 027y151 (0.07 #825, 0.03 #592, 0.02 #1722), 03h26tm (0.07 #283, 0.06 #191, 0.05 #237), 051z6rz (0.07 #814, 0.03 #956, 0.03 #674) >> Best rule #18 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 02mt51; 02vyyl8; >> query: (?x2024, 0b79gfg) <- genre(?x2024, ?x53), film(?x2534, ?x2024), ?x2534 = 0lx2l, featured_film_locations(?x2024, ?x362) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #190 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 14 *> proper extension: 02q3fdr; *> query: (?x2024, 027rwmr) <- genre(?x2024, ?x2540), ?x2540 = 0hcr, currency(?x2024, ?x170), film(?x2182, ?x2024), film_release_region(?x2024, ?x94) *> conf = 0.06 ranks of expected_values: 13 EVAL 02vqhv0 crewmember 027rwmr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 75.000 50.000 0.250 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #404-08swgx PRED entity: 08swgx PRED relation: type_of_union PRED expected values: 04ztj => 74 concepts (74 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.74 #37, 0.74 #85, 0.72 #81), 01g63y (0.24 #26, 0.22 #30, 0.22 #22) >> Best rule #37 for best value: >> intensional similarity = 3 >> extensional distance = 267 >> proper extension: 03j90; >> query: (?x2844, 04ztj) <- student(?x122, ?x2844), location(?x2844, ?x739), languages(?x2844, ?x254) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 08swgx type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 74.000 74.000 0.740 http://example.org/people/person/spouse_s./people/marriage/type_of_union #403-06bnz PRED entity: 06bnz PRED relation: teams PRED expected values: 03262k => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 173): 01l3vx (0.14 #404, 0.07 #1124, 0.06 #2205), 098knd (0.14 #673, 0.06 #2474, 0.04 #3914), 03_3z4 (0.14 #691, 0.06 #2492, 0.04 #3932), 0jnm2 (0.14 #718, 0.04 #3959), 0fjzsy (0.14 #491, 0.04 #3732), 01kkg5 (0.14 #393, 0.04 #3634), 086x3 (0.09 #1080, 0.07 #1440, 0.06 #2161), 02w64f (0.07 #1407, 0.06 #2488, 0.04 #3928), 020wyp (0.07 #1413, 0.06 #2134, 0.04 #4654), 0cnk2q (0.07 #1081, 0.06 #1802, 0.04 #4322) >> Best rule #404 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 01smm; >> query: (?x1603, 01l3vx) <- location_of_ceremony(?x566, ?x1603), partially_contains(?x6956, ?x1603), ?x566 = 04ztj >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 06bnz teams 03262k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 177.000 177.000 0.143 http://example.org/sports/sports_team_location/teams #402-0l8v5 PRED entity: 0l8v5 PRED relation: nationality PRED expected values: 0d060g => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 89): 09c7w0 (0.83 #2481, 0.81 #6056, 0.79 #5460), 02jx1 (0.30 #9935, 0.14 #1222, 0.13 #527), 0d060g (0.30 #9935, 0.08 #603, 0.06 #2190), 05kr_ (0.30 #9935, 0.01 #4663), 04jpl (0.30 #9935), 01n7q (0.25 #1291, 0.01 #4663), 0kpys (0.25 #1291), 03rk0 (0.10 #3418, 0.09 #3517, 0.08 #4014), 03rjj (0.05 #1593, 0.04 #898, 0.04 #1395), 0f8l9c (0.04 #716, 0.04 #1411, 0.03 #417) >> Best rule #2481 for best value: >> intensional similarity = 3 >> extensional distance = 441 >> proper extension: 02d9k; 01vv126; 015z4j; 01pcrw; 01m65sp; 01vxlbm; 0ph2w; 01817f; 01pcvn; 02r3cn; ... >> query: (?x413, 09c7w0) <- location(?x413, ?x6196), nationality(?x413, ?x512), participant(?x9257, ?x413) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #9935 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2257 *> proper extension: 02rgz4; 0bn9sc; 0d0vj4; 04jzj; 028p0; 01_4z; 012zng; 083qy7; 043s3; 034rd; ... *> query: (?x413, ?x279) <- location(?x413, ?x6196), nationality(?x413, ?x512), contains(?x279, ?x6196) *> conf = 0.30 ranks of expected_values: 3 EVAL 0l8v5 nationality 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 116.000 116.000 0.826 http://example.org/people/person/nationality #401-03sbb PRED entity: 03sbb PRED relation: profession! PRED expected values: 01mv_n 019fz => 65 concepts (22 used for prediction) PRED predicted values (max 10 best out of 4126): 01zmpg (0.67 #38743, 0.62 #51448, 0.60 #26042), 042kg (0.67 #46014, 0.50 #24843, 0.33 #16376), 0144l1 (0.62 #51419, 0.60 #26013, 0.50 #38714), 03j24kf (0.62 #52321, 0.50 #39616, 0.42 #81963), 02fybl (0.62 #53145, 0.50 #40440, 0.40 #27739), 0m93 (0.60 #36268, 0.60 #32035, 0.40 #57439), 01wl38s (0.60 #25542, 0.50 #50948, 0.50 #38243), 01vtqml (0.60 #26614, 0.50 #52020, 0.50 #39315), 04k15 (0.60 #26556, 0.50 #51962, 0.50 #39257), 0dpqk (0.60 #27015, 0.50 #39716, 0.43 #48185) >> Best rule #38743 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 0gbbt; >> query: (?x10252, 01zmpg) <- profession(?x8433, ?x10252), profession(?x6351, ?x10252), profession(?x4724, ?x10252), profession(?x425, ?x10252), specialization_of(?x10252, ?x3802), ?x6351 = 01vsksr, executive_produced_by(?x424, ?x425), student(?x11349, ?x4724), influenced_by(?x8433, ?x3969), people(?x7185, ?x3969) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #25082 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 2 *> proper extension: 0fj9f; *> query: (?x10252, 019fz) <- profession(?x8433, ?x10252), profession(?x8299, ?x10252), nationality(?x8299, ?x94), influenced_by(?x8299, ?x8390), ?x8433 = 06bng, gender(?x8299, ?x231), location(?x8299, ?x739) *> conf = 0.50 ranks of expected_values: 67, 2643 EVAL 03sbb profession! 019fz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 65.000 22.000 0.667 http://example.org/people/person/profession EVAL 03sbb profession! 01mv_n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 65.000 22.000 0.667 http://example.org/people/person/profession #400-0cq7kw PRED entity: 0cq7kw PRED relation: nominated_for! PRED expected values: 0c0tzp => 112 concepts (38 used for prediction) PRED predicted values (max 10 best out of 839): 07djnx (0.76 #37360, 0.76 #67713, 0.01 #27818), 02gyl0 (0.33 #32690, 0.26 #65378, 0.25 #18682), 03cdg (0.26 #9341, 0.24 #18683, 0.24 #14013), 05qd_ (0.17 #7005, 0.17 #4843, 0.16 #7004), 086k8 (0.16 #7004, 0.15 #4669, 0.15 #2392), 016tt2 (0.16 #7004, 0.11 #84060, 0.11 #11678), 09d5h (0.16 #7004, 0.11 #84060, 0.09 #11677), 0146pg (0.15 #23472, 0.08 #121, 0.06 #11799), 01b9ck (0.12 #35025, 0.03 #9603, 0.02 #7266), 03_bcg (0.12 #35025, 0.02 #3749, 0.01 #27100) >> Best rule #37360 for best value: >> intensional similarity = 4 >> extensional distance = 292 >> proper extension: 03twd6; 02x8fs; 07ghq; >> query: (?x4504, ?x6857) <- genre(?x4504, ?x53), film_release_region(?x4504, ?x94), award_winner(?x4504, ?x6857), featured_film_locations(?x4504, ?x362) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #4612 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 89 *> proper extension: 085ccd; 09g7vfw; 0435vm; 05zpghd; 063zky; 03bzjpm; 026hh0m; 091xrc; *> query: (?x4504, 0c0tzp) <- genre(?x4504, ?x53), film_release_region(?x4504, ?x94), production_companies(?x4504, ?x382), ?x382 = 086k8 *> conf = 0.03 ranks of expected_values: 63 EVAL 0cq7kw nominated_for! 0c0tzp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 112.000 38.000 0.763 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #399-026lgs PRED entity: 026lgs PRED relation: award_winner PRED expected values: 03h26tm => 84 concepts (50 used for prediction) PRED predicted values (max 10 best out of 497): 02mjf2 (0.43 #9847, 0.43 #21335, 0.42 #21334), 092ys_y (0.43 #9847, 0.43 #21335, 0.42 #21334), 0bbxx9b (0.43 #9847, 0.42 #21334, 0.41 #4924), 06pj8 (0.22 #3621, 0.19 #8544, 0.07 #67289), 02r4qs (0.16 #52520, 0.07 #67289), 0js9s (0.16 #18052, 0.12 #19693, 0.09 #34468), 01ggc9 (0.15 #78777, 0.14 #47593, 0.13 #62365), 016ks_ (0.14 #47593, 0.13 #82061, 0.13 #62365), 06bzwt (0.14 #47593, 0.13 #62365, 0.12 #65648), 035kl6 (0.14 #47593, 0.12 #65648, 0.12 #62364) >> Best rule #9847 for best value: >> intensional similarity = 5 >> extensional distance = 46 >> proper extension: 025x1t; >> query: (?x5418, ?x3782) <- nominated_for(?x8508, ?x5418), nominated_for(?x3782, ?x5418), company(?x8508, ?x741), student(?x865, ?x8508), award_winner(?x3486, ?x8508) >> conf = 0.43 => this is the best rule for 3 predicted values *> Best rule #16557 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 112 *> proper extension: 05dy7p; *> query: (?x5418, 03h26tm) <- nominated_for(?x500, ?x5418), production_companies(?x5418, ?x382), crewmember(?x5418, ?x3782), award_winner(?x5418, ?x2182) *> conf = 0.03 ranks of expected_values: 81 EVAL 026lgs award_winner 03h26tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 84.000 50.000 0.428 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #398-02j4sk PRED entity: 02j4sk PRED relation: gender PRED expected values: 05zppz => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 5): 05zppz (0.89 #45, 0.88 #43, 0.85 #49), 02zsn (0.46 #230, 0.46 #227, 0.45 #218), 0fltx (0.12 #71), 01hbgs (0.12 #71), 0c58k (0.12 #71) >> Best rule #45 for best value: >> intensional similarity = 3 >> extensional distance = 88 >> proper extension: 0cg9y; >> query: (?x10219, 05zppz) <- celebrities_impersonated(?x3649, ?x10219), ?x3649 = 03m6t5, profession(?x10219, ?x1032) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02j4sk gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 114.000 114.000 0.889 http://example.org/people/person/gender #397-04mcw4 PRED entity: 04mcw4 PRED relation: crewmember PRED expected values: 04wp63 => 127 concepts (97 used for prediction) PRED predicted values (max 10 best out of 37): 04wp63 (0.12 #181, 0.04 #603, 0.04 #790), 06rnl9 (0.10 #156, 0.03 #437, 0.02 #765), 0284n42 (0.10 #425, 0.08 #753, 0.05 #97), 04ktcgn (0.10 #433, 0.08 #293, 0.07 #761), 03m49ly (0.08 #455, 0.07 #783, 0.03 #80), 027y151 (0.08 #460, 0.06 #788, 0.03 #225), 0g9zcgx (0.07 #77, 0.06 #452, 0.06 #312), 02xc1w4 (0.07 #447, 0.06 #775, 0.03 #212), 095zvfg (0.07 #786, 0.06 #458, 0.02 #1018), 0bbxx9b (0.06 #769, 0.05 #441, 0.05 #301) >> Best rule #181 for best value: >> intensional similarity = 3 >> extensional distance = 49 >> proper extension: 0199wf; >> query: (?x4551, 04wp63) <- music(?x4551, ?x669), ?x669 = 0146pg, genre(?x4551, ?x225) >> conf = 0.12 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04mcw4 crewmember 04wp63 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 97.000 0.118 http://example.org/film/film/other_crew./film/film_crew_gig/crewmember #396-01gb54 PRED entity: 01gb54 PRED relation: production_companies! PRED expected values: 07kh6f3 => 132 concepts (120 used for prediction) PRED predicted values (max 10 best out of 1109): 01gc7 (0.50 #1115, 0.15 #6583, 0.12 #4397), 0ds3t5x (0.46 #30594, 0.45 #2185, 0.43 #30593), 02ryz24 (0.46 #30594, 0.45 #2185, 0.43 #30593), 047csmy (0.46 #30594, 0.43 #30593, 0.25 #1657), 0dr3sl (0.45 #2185, 0.43 #30593, 0.38 #24039), 02rb84n (0.45 #2185, 0.42 #36055, 0.42 #9837), 02mmwk (0.45 #2185, 0.42 #9837, 0.37 #22944), 04k9y6 (0.45 #2185, 0.38 #24039, 0.37 #22944), 01dvbd (0.45 #2185, 0.38 #24039, 0.37 #22944), 057__d (0.45 #2185, 0.37 #22944, 0.36 #24038) >> Best rule #1115 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0fvppk; >> query: (?x4564, 01gc7) <- film(?x4564, ?x7470), production_companies(?x1904, ?x4564), ?x7470 = 02yxbc >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01gb54 production_companies! 07kh6f3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 132.000 120.000 0.500 http://example.org/film/film/production_companies #395-0132k4 PRED entity: 0132k4 PRED relation: artist! PRED expected values: 01cf93 => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 110): 043g7l (0.30 #553, 0.12 #3205, 0.09 #6104), 02zn1b (0.30 #553, 0.07 #1390, 0.07 #1114), 0229rs (0.30 #553, 0.07 #3192, 0.06 #845), 011k1h (0.18 #3186, 0.14 #3738, 0.13 #424), 01clyr (0.16 #3207, 0.13 #445, 0.13 #1826), 0181dw (0.15 #1421, 0.11 #8185, 0.11 #9427), 016ckq (0.13 #455, 0.11 #1560, 0.10 #1974), 0fb0v (0.13 #421, 0.09 #4978, 0.08 #4840), 017l96 (0.13 #1812, 0.11 #1398, 0.11 #1260), 0g768 (0.13 #7490, 0.13 #6110, 0.12 #35) >> Best rule #553 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 03dq9; >> query: (?x6996, ?x2039) <- profession(?x6996, ?x131), place_of_death(?x6996, ?x3892), group(?x6996, ?x8429), artist(?x2039, ?x8429) >> conf = 0.30 => this is the best rule for 3 predicted values *> Best rule #56 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 013zyw; 05fyss; *> query: (?x6996, 01cf93) <- profession(?x6996, ?x7998), ?x7998 = 01d30f, category(?x6996, ?x134), nationality(?x6996, ?x94) *> conf = 0.12 ranks of expected_values: 14 EVAL 0132k4 artist! 01cf93 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 135.000 135.000 0.295 http://example.org/music/record_label/artist #394-09z1lg PRED entity: 09z1lg PRED relation: category PRED expected values: 08mbj5d => 68 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #14, 0.87 #13, 0.87 #12) >> Best rule #14 for best value: >> intensional similarity = 3 >> extensional distance = 221 >> proper extension: 01wbsdz; >> query: (?x9631, 08mbj5d) <- origin(?x9631, ?x6559), award_nominee(?x3200, ?x9631), artist(?x9492, ?x9631) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09z1lg category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 68.000 68.000 0.883 http://example.org/common/topic/webpage./common/webpage/category #393-01n2m6 PRED entity: 01n2m6 PRED relation: category PRED expected values: 08mbj5d => 73 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.75 #53, 0.73 #68, 0.73 #30) >> Best rule #53 for best value: >> intensional similarity = 4 >> extensional distance = 86 >> proper extension: 086k8; 06wcbk7; 033hn8; 043ljr; 01gfq4; 081g_l; 030jj7; 073tm9; 01th4s; 026s90; ... >> query: (?x10352, 08mbj5d) <- artist(?x10352, ?x9848), artists(?x671, ?x9848), award_winner(?x9848, ?x1378), location(?x9848, ?x14308) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n2m6 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 73.000 73.000 0.750 http://example.org/common/topic/webpage./common/webpage/category #392-059xvg PRED entity: 059xvg PRED relation: film PRED expected values: 0353tm => 66 concepts (14 used for prediction) PRED predicted values (max 10 best out of 299): 02b6n9 (0.09 #1575, 0.09 #3368, 0.07 #5160), 01wb95 (0.09 #623, 0.07 #6000, 0.06 #7792), 01c22t (0.09 #165, 0.04 #1958, 0.04 #3750), 03kx49 (0.09 #1344, 0.04 #3137, 0.04 #4929), 0dfw0 (0.09 #841, 0.04 #2634, 0.04 #4426), 0fdv3 (0.09 #283, 0.04 #2076, 0.04 #3868), 0f3m1 (0.09 #1457, 0.04 #3250, 0.04 #5042), 0184tc (0.09 #660, 0.04 #2453, 0.04 #4245), 0ddt_ (0.09 #475, 0.04 #2268, 0.04 #4060), 0ddjy (0.09 #378, 0.04 #2171, 0.04 #3963) >> Best rule #1575 for best value: >> intensional similarity = 6 >> extensional distance = 9 >> proper extension: 03q5dr; >> query: (?x3664, 02b6n9) <- nationality(?x3664, ?x512), nationality(?x3664, ?x94), ?x512 = 07ssc, ?x94 = 09c7w0, profession(?x3664, ?x1032), actor(?x14357, ?x3664) >> conf = 0.09 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 059xvg film 0353tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 66.000 14.000 0.091 http://example.org/film/actor/film./film/performance/film #391-01gvyp PRED entity: 01gvyp PRED relation: award_winner! PRED expected values: 0gqwc => 113 concepts (75 used for prediction) PRED predicted values (max 10 best out of 235): 0gkts9 (0.37 #15529, 0.36 #863, 0.33 #15097), 05b4l5x (0.37 #15529, 0.36 #863, 0.33 #15097), 0l8z1 (0.26 #1359, 0.04 #7827, 0.03 #31483), 0gqwc (0.20 #1370, 0.12 #937, 0.11 #23721), 09sb52 (0.20 #2630, 0.14 #3063, 0.13 #4356), 02y_rq5 (0.17 #1391, 0.04 #958, 0.04 #2684), 02z1nbg (0.16 #1490, 0.08 #1057, 0.06 #2783), 09cn0c (0.16 #1614, 0.04 #2907, 0.04 #1181), 01by1l (0.14 #112, 0.05 #5720, 0.04 #26419), 05p09zm (0.14 #124, 0.03 #4439, 0.02 #3577) >> Best rule #15529 for best value: >> intensional similarity = 4 >> extensional distance = 1156 >> proper extension: 01sl1q; 044mz_; 07nznf; 012ljv; 02s2ft; 05bnp0; 028q6; 0fvf9q; 04qvl7; 01k7d9; ... >> query: (?x6951, ?x154) <- award_winner(?x749, ?x6951), place_of_birth(?x6951, ?x6952), gender(?x6951, ?x514), award(?x6951, ?x154) >> conf = 0.37 => this is the best rule for 2 predicted values *> Best rule #1370 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 138 *> proper extension: 04r7jc; 0l12d; 02fgpf; 01q415; 03h4mp; 04ls53; 09swkk; 02ryx0; 01ts_3; 019x62; ... *> query: (?x6951, 0gqwc) <- award_winner(?x749, ?x6951), award(?x396, ?x749), nominated_for(?x749, ?x2458), ?x2458 = 021y7yw *> conf = 0.20 ranks of expected_values: 4 EVAL 01gvyp award_winner! 0gqwc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 113.000 75.000 0.365 http://example.org/award/award_category/winners./award/award_honor/award_winner #390-015whm PRED entity: 015whm PRED relation: nominated_for! PRED expected values: 094qd5 02w9sd7 => 88 concepts (77 used for prediction) PRED predicted values (max 10 best out of 209): 027c95y (0.66 #5386, 0.66 #11012, 0.66 #11011), 0gqyl (0.44 #77, 0.26 #1248, 0.25 #2184), 0gq9h (0.44 #3570, 0.40 #4507, 0.35 #2868), 0gs9p (0.39 #3572, 0.36 #4509, 0.33 #61), 03hl6lc (0.33 #126, 0.26 #829, 0.24 #1297), 0gr4k (0.33 #25, 0.26 #3536, 0.23 #2834), 0gs96 (0.33 #87, 0.23 #2194, 0.23 #3598), 0gr51 (0.32 #1246, 0.30 #1012, 0.24 #2182), 0gq_v (0.31 #3530, 0.30 #2126, 0.30 #4467), 0k611 (0.31 #3581, 0.30 #4518, 0.26 #4986) >> Best rule #5386 for best value: >> intensional similarity = 3 >> extensional distance = 581 >> proper extension: 06mmr; >> query: (?x3943, ?x2915) <- award_winner(?x3943, ?x8460), people(?x1050, ?x8460), award(?x3943, ?x2915) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #11482 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 1056 *> proper extension: 05h95s; *> query: (?x3943, ?x112) <- award_winner(?x3943, ?x4436), award_winner(?x112, ?x4436) *> conf = 0.24 ranks of expected_values: 19, 42 EVAL 015whm nominated_for! 02w9sd7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 88.000 77.000 0.658 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 015whm nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 88.000 77.000 0.658 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #389-01vdm0 PRED entity: 01vdm0 PRED relation: role PRED expected values: 0dwtp 0l14j_ => 65 concepts (62 used for prediction) PRED predicted values (max 10 best out of 50): 01vdm0 (0.90 #1326, 0.88 #1462, 0.84 #1238), 0395lw (0.86 #886, 0.85 #843, 0.83 #1062), 05r5c (0.85 #88, 0.82 #1719, 0.82 #746), 03bx0bm (0.85 #88, 0.70 #305, 0.69 #526), 028tv0 (0.85 #88, 0.70 #305, 0.69 #526), 0dwtp (0.83 #794, 0.82 #1719, 0.82 #746), 0bxl5 (0.82 #1719, 0.82 #746, 0.81 #920), 07kc_ (0.82 #1719, 0.82 #746, 0.81 #920), 011k_j (0.82 #1719, 0.82 #746, 0.81 #920), 03t22m (0.82 #1719, 0.82 #746, 0.81 #920) >> Best rule #1326 for best value: >> intensional similarity = 12 >> extensional distance = 19 >> proper extension: 01dnws; 01v8y9; >> query: (?x1437, 01vdm0) <- role(?x7614, ?x1437), role(?x1715, ?x1437), award(?x7614, ?x4018), role(?x1437, ?x3161), role(?x1437, ?x314), role(?x1437, ?x316), award_nominee(?x2335, ?x1715), ?x314 = 02sgy, ?x3161 = 01v1d8, award(?x5904, ?x4018), ?x5904 = 01k_mc, instrumentalists(?x1437, ?x226) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #794 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 10 *> proper extension: 0l14qv; 016622; 02dlh2; 011k_j; *> query: (?x1437, 0dwtp) <- role(?x7614, ?x1437), role(?x2865, ?x1437), role(?x1715, ?x1437), award(?x7614, ?x567), role(?x1437, ?x4769), role(?x1437, ?x3409), role(?x1437, ?x1663), role(?x1437, ?x314), role(?x1437, ?x316), award_nominee(?x2335, ?x1715), ?x314 = 02sgy, ?x1663 = 01w4dy, role(?x3409, ?x614), profession(?x2865, ?x220), ?x4769 = 0dwt5 *> conf = 0.83 ranks of expected_values: 6, 24 EVAL 01vdm0 role 0l14j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 65.000 62.000 0.905 http://example.org/music/performance_role/track_performances./music/track_contribution/role EVAL 01vdm0 role 0dwtp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 65.000 62.000 0.905 http://example.org/music/performance_role/track_performances./music/track_contribution/role #388-07cn2c PRED entity: 07cn2c PRED relation: place_of_birth PRED expected values: 03902 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 62): 0cr3d (0.25 #798, 0.03 #9955, 0.03 #10659), 0h7h6 (0.25 #58, 0.01 #37329, 0.01 #30991), 02_286 (0.11 #5652, 0.10 #4243, 0.09 #1427), 0jpy_ (0.09 #1942, 0.08 #3350, 0.08 #2646), 0s5cg (0.09 #1589, 0.08 #2997, 0.08 #2293), 0chgzm (0.09 #1718, 0.08 #2422, 0.06 #3830), 01smm (0.08 #3050, 0.08 #2346, 0.05 #4458), 0k_p5 (0.08 #3029, 0.08 #2325, 0.05 #4437), 0hx5f (0.08 #3353, 0.08 #2649, 0.05 #4761), 02dtg (0.06 #3530, 0.05 #7756, 0.05 #4234) >> Best rule #798 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 07csf4; >> query: (?x4134, 0cr3d) <- award_winner(?x4134, ?x3096), award_winner(?x4134, ?x1657), gender(?x1657, ?x231), ?x3096 = 02s5v5 >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 07cn2c place_of_birth 03902 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 89.000 89.000 0.250 http://example.org/people/person/place_of_birth #387-016wvy PRED entity: 016wvy PRED relation: profession PRED expected values: 01c72t => 102 concepts (73 used for prediction) PRED predicted values (max 10 best out of 65): 0nbcg (0.62 #2953, 0.59 #2368, 0.58 #1344), 01c72t (0.58 #2799, 0.56 #1190, 0.56 #3383), 039v1 (0.45 #1641, 0.42 #2958, 0.40 #3250), 05z96 (0.45 #478, 0.09 #1501, 0.08 #624), 0n1h (0.37 #2057, 0.28 #2203, 0.28 #886), 0cbd2 (0.36 #443, 0.17 #589, 0.16 #10560), 0fnpj (0.36 #2397, 0.28 #934, 0.24 #1373), 01d_h8 (0.31 #8945, 0.28 #9974, 0.27 #9387), 0dxtg (0.30 #10128, 0.29 #10421, 0.29 #10567), 0kyk (0.27 #465, 0.13 #5738, 0.13 #8234) >> Best rule #2953 for best value: >> intensional similarity = 4 >> extensional distance = 139 >> proper extension: 0lbj1; 01vrx3g; 032t2z; 012x4t; 01w923; 01zmpg; 01w02sy; 0qdyf; 01vsykc; 0bkg4; ... >> query: (?x10144, 0nbcg) <- profession(?x10144, ?x131), artist(?x3050, ?x10144), instrumentalists(?x716, ?x10144), ?x716 = 018vs >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #2799 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 135 *> proper extension: 02sj1x; 0c_drn; *> query: (?x10144, 01c72t) <- music(?x6967, ?x10144), nationality(?x10144, ?x512), gender(?x10144, ?x231), region(?x54, ?x512) *> conf = 0.58 ranks of expected_values: 2 EVAL 016wvy profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 102.000 73.000 0.617 http://example.org/people/person/profession #386-09r94m PRED entity: 09r94m PRED relation: film_crew_role PRED expected values: 01vx2h => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 24): 09vw2b7 (0.78 #1135, 0.76 #1265, 0.74 #1329), 01vx2h (0.42 #556, 0.40 #330, 0.40 #1139), 01xy5l_ (0.27 #2069, 0.16 #12, 0.14 #1399), 02_n3z (0.27 #2069, 0.16 #1, 0.10 #289), 02vs3x5 (0.27 #2069, 0.07 #116, 0.06 #599), 0263ycg (0.27 #2069, 0.04 #1756, 0.03 #625), 089fss (0.21 #551, 0.09 #776, 0.09 #1392), 015h31 (0.17 #328, 0.13 #811, 0.12 #1037), 0215hd (0.16 #15, 0.16 #1402, 0.16 #529), 02rh1dz (0.16 #1138, 0.16 #555, 0.15 #1332) >> Best rule #1135 for best value: >> intensional similarity = 6 >> extensional distance = 263 >> proper extension: 0c38gj; >> query: (?x5331, 09vw2b7) <- produced_by(?x5331, ?x3568), film_crew_role(?x5331, ?x468), film_crew_role(?x5331, ?x137), ?x468 = 02r96rf, production_companies(?x5331, ?x1104), ?x137 = 09zzb8 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #556 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 133 *> proper extension: 0gbtbm; 085wqm; *> query: (?x5331, 01vx2h) <- genre(?x5331, ?x53), film_crew_role(?x5331, ?x3197), ?x3197 = 02ynfr, nominated_for(?x68, ?x5331) *> conf = 0.42 ranks of expected_values: 2 EVAL 09r94m film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 136.000 136.000 0.777 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #385-06lbpz PRED entity: 06lbpz PRED relation: genre! PRED expected values: 015qsq 01k60v 0dpl44 => 42 concepts (8 used for prediction) PRED predicted values (max 10 best out of 1864): 02jkkv (0.75 #9099, 0.50 #3484, 0.40 #7226), 03s6l2 (0.62 #7573, 0.50 #3829, 0.50 #1958), 0sxlb (0.62 #9136, 0.50 #3521, 0.40 #7263), 0sxfd (0.62 #7708, 0.50 #2093, 0.40 #5835), 07tj4c (0.62 #9259, 0.50 #3644, 0.40 #7386), 02rtqvb (0.62 #9339, 0.50 #3724, 0.40 #7466), 025rxjq (0.62 #8892, 0.50 #3277, 0.40 #7019), 05dptj (0.62 #8858, 0.50 #3243, 0.40 #6985), 05vxdh (0.62 #8288, 0.50 #2673, 0.40 #6415), 0ktpx (0.60 #6649, 0.50 #4778, 0.50 #2907) >> Best rule #9099 for best value: >> intensional similarity = 12 >> extensional distance = 6 >> proper extension: 06cvj; 05p553; 0lsxr; 02l7c8; >> query: (?x12626, 02jkkv) <- genre(?x6100, ?x12626), genre(?x4626, ?x12626), currency(?x6100, ?x170), story_by(?x6100, ?x4808), nominated_for(?x591, ?x6100), film(?x9257, ?x4626), ?x591 = 0f4x7, film_crew_role(?x4626, ?x137), featured_film_locations(?x4626, ?x6769), ?x9257 = 01gkmx, film_release_distribution_medium(?x6100, ?x81), music(?x6100, ?x3519) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #4514 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 01jfsb; *> query: (?x12626, 01k60v) <- genre(?x6100, ?x12626), genre(?x4626, ?x12626), currency(?x6100, ?x170), ?x4626 = 038bh3, nominated_for(?x500, ?x6100), ?x170 = 09nqf, film_release_distribution_medium(?x6100, ?x81), music(?x6100, ?x3519), nominated_for(?x574, ?x6100), ?x81 = 029j_, film(?x3002, ?x6100), award_winner(?x458, ?x3002) *> conf = 0.50 ranks of expected_values: 197, 444, 1181 EVAL 06lbpz genre! 0dpl44 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 8.000 0.750 http://example.org/film/film/genre EVAL 06lbpz genre! 01k60v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 42.000 8.000 0.750 http://example.org/film/film/genre EVAL 06lbpz genre! 015qsq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 8.000 0.750 http://example.org/film/film/genre #384-0gdm1 PRED entity: 0gdm1 PRED relation: student PRED expected values: 01xcqc => 133 concepts (58 used for prediction) PRED predicted values (max 10 best out of 1222): 06jkm (0.12 #1906, 0.10 #10254, 0.06 #3993), 01my4f (0.12 #1192, 0.10 #7453, 0.06 #9540), 0d3k14 (0.11 #3937, 0.10 #8111, 0.08 #14373), 06hx2 (0.11 #3154, 0.10 #7328, 0.06 #15678), 0194xc (0.11 #3724, 0.10 #7898, 0.04 #22509), 0hnjt (0.11 #2908, 0.10 #7082, 0.04 #13344), 0gs7x (0.11 #4024, 0.06 #1937, 0.05 #6111), 0gd5z (0.11 #2469, 0.06 #382, 0.05 #4556), 024y6w (0.11 #3535, 0.06 #16059, 0.05 #18146), 03qd_ (0.11 #2189, 0.05 #6363, 0.04 #12625) >> Best rule #1906 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 017hnw; 02kj7g; >> query: (?x6732, 06jkm) <- state_province_region(?x6732, ?x2020), ?x2020 = 05k7sb, citytown(?x6732, ?x12099), student(?x6732, ?x3961) >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0gdm1 student 01xcqc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 133.000 58.000 0.125 http://example.org/education/educational_institution/students_graduates./education/education/student #383-08sk8l PRED entity: 08sk8l PRED relation: film! PRED expected values: 030hbp => 83 concepts (53 used for prediction) PRED predicted values (max 10 best out of 948): 08x5c_ (0.33 #1948, 0.14 #4028, 0.09 #8188), 043hg (0.26 #18725, 0.21 #68651, 0.20 #70733), 0154qm (0.17 #563, 0.14 #2643, 0.10 #4723), 03cglm (0.17 #1046, 0.14 #3126, 0.04 #7286), 0jlv5 (0.17 #1180, 0.14 #3260, 0.04 #7420), 012ykt (0.17 #1092, 0.14 #3172, 0.04 #7332), 04bdlg (0.17 #1922, 0.14 #4002, 0.04 #8162), 0c0k1 (0.17 #1508, 0.10 #5668, 0.05 #11909), 02yxwd (0.17 #745, 0.10 #4905, 0.04 #6985), 01nwwl (0.17 #504, 0.10 #4664, 0.04 #6744) >> Best rule #1948 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 047wh1; 0drnwh; >> query: (?x6346, 08x5c_) <- film(?x4233, ?x6346), genre(?x6346, ?x811), film_crew_role(?x6346, ?x1776), ?x1776 = 020xn5, edited_by(?x6346, ?x4215) >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 08sk8l film! 030hbp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 83.000 53.000 0.333 http://example.org/film/actor/film./film/performance/film #382-0js9s PRED entity: 0js9s PRED relation: award PRED expected values: 03hkv_r => 90 concepts (77 used for prediction) PRED predicted values (max 10 best out of 262): 040njc (0.72 #19328, 0.71 #27226, 0.71 #19327), 02g3ft (0.72 #19328, 0.71 #27226, 0.71 #19327), 02w_6xj (0.72 #19328, 0.71 #27226, 0.71 #19327), 09d28z (0.72 #19328, 0.71 #27226, 0.71 #19327), 04dn09n (0.57 #832, 0.57 #437, 0.33 #3590), 02qyp19 (0.55 #396, 0.52 #791, 0.21 #1973), 03hkv_r (0.42 #410, 0.39 #805, 0.25 #3563), 02x1dht (0.38 #448, 0.36 #843, 0.14 #3601), 09sb52 (0.37 #9500, 0.30 #14234, 0.29 #8318), 02x4wr9 (0.18 #2099, 0.17 #522, 0.16 #917) >> Best rule #19328 for best value: >> intensional similarity = 3 >> extensional distance = 1563 >> proper extension: 02pp_q_; 01qkqwg; 0191h5; 01jllg1; 051m56; >> query: (?x6589, ?x1307) <- award_nominee(?x846, ?x6589), award_winner(?x1307, ?x6589), award(?x71, ?x1307) >> conf = 0.72 => this is the best rule for 4 predicted values *> Best rule #410 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 51 *> proper extension: 043hg; 01p1z_; *> query: (?x6589, 03hkv_r) <- award(?x6589, ?x3435), ?x3435 = 03hl6lc, award_winner(?x198, ?x6589) *> conf = 0.42 ranks of expected_values: 7 EVAL 0js9s award 03hkv_r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 90.000 77.000 0.724 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #381-02d4ct PRED entity: 02d4ct PRED relation: award_nominee! PRED expected values: 0736qr => 68 concepts (45 used for prediction) PRED predicted values (max 10 best out of 998): 04wp3s (0.81 #20830, 0.81 #76380, 0.81 #57864), 02lkcc (0.81 #20830, 0.81 #76380, 0.81 #57864), 0736qr (0.81 #20830, 0.81 #76380, 0.81 #57864), 06lht1 (0.29 #46293, 0.07 #104160, 0.07 #94902), 02nwxc (0.29 #46293, 0.03 #5962, 0.02 #85643), 0382m4 (0.29 #46293, 0.03 #29103, 0.03 #17529), 02yj7w (0.29 #46293, 0.02 #17050, 0.02 #28624), 02j9lm (0.29 #46293, 0.02 #16845, 0.02 #28419), 06gp3f (0.29 #46293, 0.02 #16235, 0.02 #9291), 05p5nc (0.29 #46293, 0.02 #29324, 0.02 #10806) >> Best rule #20830 for best value: >> intensional similarity = 2 >> extensional distance = 417 >> proper extension: 0knjh; >> query: (?x2374, ?x286) <- award_nominee(?x2374, ?x286), participant(?x3195, ?x2374) >> conf = 0.81 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 02d4ct award_nominee! 0736qr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 68.000 45.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #380-03q43g PRED entity: 03q43g PRED relation: gender PRED expected values: 05zppz => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.82 #27, 0.82 #33, 0.80 #37), 02zsn (0.53 #96, 0.48 #55, 0.46 #14) >> Best rule #27 for best value: >> intensional similarity = 2 >> extensional distance = 451 >> proper extension: 05xq9; 01kcms4; 070b4; 07h1q; 0167xy; 01d5g; >> query: (?x6569, 05zppz) <- influenced_by(?x6569, ?x11357), location(?x11357, ?x739) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03q43g gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 78.000 0.823 http://example.org/people/person/gender #379-01_wfj PRED entity: 01_wfj PRED relation: group! PRED expected values: 0326tc => 78 concepts (45 used for prediction) PRED predicted values (max 10 best out of 143): 02whj (0.33 #17, 0.25 #421, 0.20 #827), 01wg6y (0.33 #168, 0.25 #572, 0.20 #978), 01wwvt2 (0.17 #1258, 0.04 #3711, 0.04 #3915), 0191h5 (0.17 #1350, 0.04 #3803, 0.04 #4007), 02bh9 (0.12 #1689, 0.08 #2305, 0.05 #3328), 0phx4 (0.08 #2310, 0.04 #3944, 0.03 #4351), 01w806h (0.08 #2295, 0.04 #3929, 0.03 #4336), 04n65n (0.08 #2371, 0.02 #4614, 0.01 #5020), 0770cd (0.08 #2266, 0.02 #4509, 0.01 #4915), 023322 (0.08 #4056, 0.04 #3852, 0.04 #5275) >> Best rule #17 for best value: >> intensional similarity = 12 >> extensional distance = 1 >> proper extension: 05563d; >> query: (?x9999, 02whj) <- artists(?x10290, ?x9999), artists(?x2809, ?x9999), artists(?x2808, ?x9999), artists(?x1380, ?x9999), artists(?x1000, ?x9999), ?x10290 = 03ckfl9, ?x1380 = 0dl5d, ?x2809 = 05w3f, group(?x1166, ?x9999), parent_genre(?x497, ?x2808), ?x1000 = 0xhtw, ?x1166 = 05148p4 >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01_wfj group! 0326tc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 45.000 0.333 http://example.org/music/group_member/membership./music/group_membership/group #378-063g7l PRED entity: 063g7l PRED relation: film PRED expected values: 053rxgm 0gffmn8 => 81 concepts (33 used for prediction) PRED predicted values (max 10 best out of 650): 056xkh (0.33 #1593, 0.14 #6942, 0.09 #10508), 0bq6ntw (0.33 #1057, 0.14 #6406, 0.09 #9972), 0gwlfnb (0.33 #1499, 0.14 #6848, 0.09 #10414), 03t97y (0.33 #160, 0.14 #5509, 0.09 #9075), 03pc89 (0.14 #6802, 0.11 #8585, 0.09 #10368), 01738w (0.14 #6476, 0.09 #10042, 0.07 #11825), 09lxv9 (0.14 #6849, 0.09 #10415, 0.07 #12198), 01gglm (0.14 #6749, 0.09 #10315, 0.07 #12098), 0963mq (0.14 #5486, 0.09 #9052, 0.07 #10835), 03z20c (0.11 #7606, 0.09 #9389, 0.07 #11172) >> Best rule #1593 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 014g_s; >> query: (?x11624, 056xkh) <- team(?x11624, ?x4519), film(?x11624, ?x8214), ?x8214 = 026wlxw, profession(?x11624, ?x1032) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #37617 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 126 *> proper extension: 019tzd; *> query: (?x11624, 053rxgm) <- athlete(?x1083, ?x11624), films(?x1083, ?x3081) *> conf = 0.02 ranks of expected_values: 308 EVAL 063g7l film 0gffmn8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 81.000 33.000 0.333 http://example.org/film/actor/film./film/performance/film EVAL 063g7l film 053rxgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 81.000 33.000 0.333 http://example.org/film/actor/film./film/performance/film #377-07h1h5 PRED entity: 07h1h5 PRED relation: nationality PRED expected values: 0k6nt => 117 concepts (80 used for prediction) PRED predicted values (max 10 best out of 62): 0k6nt (0.85 #2615, 0.85 #2717, 0.85 #2820), 09c7w0 (0.81 #5046, 0.80 #3732, 0.80 #4743), 03rjj (0.66 #5550, 0.58 #1508, 0.46 #3124), 07ssc (0.42 #615, 0.33 #115, 0.28 #817), 02jx1 (0.33 #835, 0.33 #633, 0.33 #133), 05bcl (0.33 #660, 0.33 #60, 0.17 #962), 0j5g9 (0.33 #262, 0.25 #562, 0.12 #964), 0f8l9c (0.21 #1830, 0.10 #2132, 0.06 #2637), 0345h (0.13 #1839, 0.08 #631, 0.04 #1337), 0chghy (0.12 #912, 0.11 #812, 0.10 #1316) >> Best rule #2615 for best value: >> intensional similarity = 5 >> extensional distance = 376 >> proper extension: 067pl7; >> query: (?x3586, ?x985) <- place_of_birth(?x3586, ?x8174), mode_of_transportation(?x8174, ?x8731), country(?x8174, ?x985), ?x8731 = 01bjv, month(?x8174, ?x1459) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07h1h5 nationality 0k6nt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 80.000 0.852 http://example.org/people/person/nationality #376-013hxv PRED entity: 013hxv PRED relation: teams PRED expected values: 026wlnm => 163 concepts (150 used for prediction) PRED predicted values (max 10 best out of 156): 0bszz (0.03 #3600, 0.02 #356, 0.02 #1077), 0jnng (0.02 #4577, 0.02 #10704, 0.02 #1694), 0jmk7 (0.02 #303, 0.02 #664, 0.02 #1024), 0jnq8 (0.02 #229, 0.02 #590, 0.02 #950), 0jmjr (0.02 #222, 0.02 #583, 0.02 #943), 04mjl (0.02 #156, 0.02 #517, 0.02 #877), 02pqcfz (0.02 #82, 0.02 #443, 0.02 #803), 04112r (0.02 #51, 0.02 #412, 0.02 #772), 07k53y (0.02 #12, 0.02 #373, 0.02 #733), 0jmfv (0.02 #25, 0.02 #386, 0.02 #1106) >> Best rule #3600 for best value: >> intensional similarity = 3 >> extensional distance = 77 >> proper extension: 05g2v; 073q1; 04pnx; 0157g9; 048fz; 09b69; 06n3y; 0jcpw; >> query: (?x6158, 0bszz) <- contains(?x94, ?x6158), contains(?x6158, ?x9200), organization(?x9200, ?x5487) >> conf = 0.03 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 013hxv teams 026wlnm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 163.000 150.000 0.025 http://example.org/sports/sports_team_location/teams #375-016_v3 PRED entity: 016_v3 PRED relation: parent_genre! PRED expected values: 01y2mq => 49 concepts (20 used for prediction) PRED predicted values (max 10 best out of 242): 01h0kx (0.40 #398, 0.29 #664, 0.25 #931), 059kh (0.40 #311, 0.25 #844, 0.18 #1646), 0y3_8 (0.40 #309, 0.25 #842, 0.14 #575), 0grjmv (0.40 #388, 0.25 #921, 0.14 #654), 01ym9b (0.38 #1110, 0.29 #574, 0.20 #1377), 016_nr (0.25 #1133, 0.24 #1666, 0.20 #1400), 0263q4z (0.25 #232, 0.14 #766, 0.13 #1569), 0283d (0.25 #1156, 0.14 #620, 0.13 #1423), 07ym47 (0.25 #1128, 0.13 #1395, 0.12 #1661), 06kcjr (0.25 #164, 0.12 #965, 0.10 #1070) >> Best rule #398 for best value: >> intensional similarity = 7 >> extensional distance = 3 >> proper extension: 064t9; 06by7; 025sc50; >> query: (?x8184, 01h0kx) <- artists(?x8184, ?x6715), artists(?x8184, ?x6264), artists(?x8184, ?x1125), ?x1125 = 016kjs, award_winner(?x192, ?x6264), ?x6715 = 011z3g, award(?x6264, ?x704) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #735 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 5 *> proper extension: 03mb9; *> query: (?x8184, 01y2mq) <- artists(?x8184, ?x11709), artists(?x8184, ?x8185), artists(?x8184, ?x1125), award_nominee(?x1125, ?x140), ?x8185 = 02vwckw, ?x11709 = 03f0qd7 *> conf = 0.14 ranks of expected_values: 77 EVAL 016_v3 parent_genre! 01y2mq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 49.000 20.000 0.400 http://example.org/music/genre/parent_genre #374-01wgjj5 PRED entity: 01wgjj5 PRED relation: artist! PRED expected values: 015_1q 043g7l => 125 concepts (125 used for prediction) PRED predicted values (max 10 best out of 112): 011k1h (0.74 #1114, 0.50 #148, 0.44 #2908), 015_1q (0.42 #4711, 0.27 #1536, 0.24 #2916), 03rhqg (0.33 #14, 0.20 #290, 0.17 #566), 01cf93 (0.33 #56, 0.14 #470, 0.09 #1160), 041bnw (0.33 #66, 0.14 #480, 0.07 #894), 017l96 (0.30 #1121, 0.25 #155, 0.17 #2915), 03mp8k (0.27 #2962, 0.25 #202, 0.23 #1582), 043g7l (0.25 #167, 0.24 #995, 0.20 #2927), 0k_kr (0.25 #180, 0.17 #1146, 0.07 #870), 0n85g (0.25 #198, 0.14 #888, 0.13 #1716) >> Best rule #1114 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 0cg9y; 01vvzb1; 02cpp; 02vcp0; 0bk1p; 0fb2l; 0p76z; 01vzxld; 0167xy; >> query: (?x5883, 011k1h) <- artist(?x4797, ?x5883), artist(?x2299, ?x5883), artist(?x2299, ?x1953), ?x1953 = 019g40, ?x4797 = 02p3cr5 >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #4711 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 350 *> proper extension: 089tm; 0147dk; 03f2_rc; 01w61th; 01q7cb_; 01v0sx2; 01fl3; 04mn81; 01wz3cx; 01wbl_r; ... *> query: (?x5883, 015_1q) <- artist(?x2299, ?x5883), artist(?x2299, ?x1953), ?x1953 = 019g40, artists(?x302, ?x5883) *> conf = 0.42 ranks of expected_values: 2, 8 EVAL 01wgjj5 artist! 043g7l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 125.000 125.000 0.739 http://example.org/music/record_label/artist EVAL 01wgjj5 artist! 015_1q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 125.000 125.000 0.739 http://example.org/music/record_label/artist #373-01gc7 PRED entity: 01gc7 PRED relation: film_release_region PRED expected values: 06qd3 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 190): 07ssc (0.86 #654, 0.83 #2085, 0.80 #3035), 03rjj (0.86 #2548, 0.83 #3023, 0.83 #2706), 06bnz (0.86 #842, 0.70 #2748, 0.68 #2590), 03h64 (0.84 #864, 0.81 #706, 0.77 #2296), 015fr (0.84 #813, 0.77 #655, 0.77 #2719), 0b90_r (0.84 #798, 0.71 #2704, 0.70 #640), 0d060g (0.84 #802, 0.70 #644, 0.70 #2708), 06t2t (0.79 #859, 0.67 #701, 0.66 #2765), 0154j (0.77 #799, 0.74 #3338, 0.73 #2072), 03rt9 (0.75 #810, 0.70 #2716, 0.67 #652) >> Best rule #654 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 0dgst_d; 0g57wgv; >> query: (?x299, 07ssc) <- nominated_for(?x143, ?x299), film_release_distribution_medium(?x299, ?x81), film_release_region(?x299, ?x4737), ?x4737 = 07twz >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #676 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 0dgst_d; 0g57wgv; *> query: (?x299, 06qd3) <- nominated_for(?x143, ?x299), film_release_distribution_medium(?x299, ?x81), film_release_region(?x299, ?x4737), ?x4737 = 07twz *> conf = 0.60 ranks of expected_values: 21 EVAL 01gc7 film_release_region 06qd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 110.000 110.000 0.860 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #372-03cf9ly PRED entity: 03cf9ly PRED relation: genre PRED expected values: 02n4kr 01jfsb => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 85): 01hmnh (0.60 #96, 0.45 #260, 0.41 #670), 03k9fj (0.59 #1986, 0.46 #666, 0.44 #995), 01htzx (0.55 #261, 0.49 #1991, 0.46 #671), 05p553 (0.53 #2393, 0.52 #1072, 0.51 #1236), 0hcr (0.44 #2817, 0.40 #919, 0.39 #673), 01z4y (0.41 #836, 0.39 #1248, 0.39 #2652), 02n4kr (0.40 #89, 0.31 #992, 0.23 #1398), 0c4xc (0.32 #861, 0.30 #1769, 0.30 #2677), 01jfsb (0.31 #996, 0.23 #1398, 0.22 #421), 0jxy (0.30 #440, 0.24 #686, 0.23 #932) >> Best rule #96 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 0d_rw; >> query: (?x11895, 01hmnh) <- genre(?x11895, ?x571), tv_program(?x6001, ?x11895), ?x571 = 03npn, written_by(?x2441, ?x6001) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #89 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0d_rw; *> query: (?x11895, 02n4kr) <- genre(?x11895, ?x571), tv_program(?x6001, ?x11895), ?x571 = 03npn, written_by(?x2441, ?x6001) *> conf = 0.40 ranks of expected_values: 7, 9 EVAL 03cf9ly genre 01jfsb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 93.000 93.000 0.600 http://example.org/tv/tv_program/genre EVAL 03cf9ly genre 02n4kr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 93.000 93.000 0.600 http://example.org/tv/tv_program/genre #371-01ktz1 PRED entity: 01ktz1 PRED relation: time_zones PRED expected values: 02hcv8 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 9): 02hcv8 (0.50 #16, 0.50 #3, 0.43 #900), 02lcqs (0.22 #44, 0.20 #187, 0.19 #122), 02fqwt (0.19 #118, 0.19 #27, 0.18 #508), 02hczc (0.16 #1158, 0.08 #483, 0.07 #353), 02lcrv (0.16 #1158, 0.01 #33), 02llzg (0.11 #82, 0.08 #108, 0.08 #212), 03bdv (0.06 #539, 0.05 #604, 0.05 #240), 03plfd (0.02 #439, 0.02 #842, 0.02 #283), 042g7t (0.02 #141, 0.01 #219, 0.01 #37) >> Best rule #16 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 0f1nl; >> query: (?x2410, 02hcv8) <- contains(?x3038, ?x2410), contains(?x94, ?x2410), ?x94 = 09c7w0, ?x3038 = 0d0x8 >> conf = 0.50 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01ktz1 time_zones 02hcv8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.500 http://example.org/location/location/time_zones #370-08swgx PRED entity: 08swgx PRED relation: award_nominee! PRED expected values: 014gf8 => 119 concepts (59 used for prediction) PRED predicted values (max 10 best out of 910): 015t56 (0.82 #2321, 0.82 #16244, 0.81 #136918), 0btpx (0.82 #2321, 0.82 #16244, 0.81 #136918), 01p4vl (0.82 #2321, 0.82 #16244, 0.81 #136918), 0z4s (0.82 #2321, 0.82 #16244, 0.81 #136918), 04w391 (0.82 #2321, 0.82 #16244, 0.81 #136918), 014gf8 (0.82 #2321, 0.82 #16244, 0.81 #136918), 03_6y (0.82 #2321, 0.82 #16244, 0.81 #136918), 08swgx (0.41 #636, 0.31 #78907, 0.16 #116034), 02qgyv (0.34 #493, 0.16 #116034, 0.15 #136919), 02wgln (0.31 #411, 0.15 #136919, 0.12 #16245) >> Best rule #2321 for best value: >> intensional similarity = 4 >> extensional distance = 30 >> proper extension: 02s2ft; 02p65p; 06151l; 0c4f4; 0187y5; 01tcf7; 01yb09; 04t7ts; 015pkc; 01pgzn_; ... >> query: (?x2844, ?x450) <- award_nominee(?x2844, ?x5925), award_nominee(?x2844, ?x450), ?x5925 = 023kzp, profession(?x2844, ?x1032) >> conf = 0.82 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 6 EVAL 08swgx award_nominee! 014gf8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 119.000 59.000 0.821 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #369-0g26h PRED entity: 0g26h PRED relation: major_field_of_study! PRED expected values: 03v6t 07szy 02bb47 033x5p 09f2j 0gl5_ 01qgr3 01q7q2 021996 02pptm => 60 concepts (29 used for prediction) PRED predicted values (max 10 best out of 560): 07szy (0.69 #2501, 0.67 #3490, 0.63 #3984), 09f2j (0.68 #4089, 0.67 #3595, 0.62 #2606), 08815 (0.67 #3460, 0.63 #3954, 0.62 #2471), 07tds (0.61 #3587, 0.58 #4081, 0.54 #2598), 01bm_ (0.56 #3679, 0.54 #2690, 0.53 #4173), 07tgn (0.56 #3468, 0.53 #3962, 0.38 #2479), 0dzst (0.54 #2782, 0.47 #3276, 0.44 #3771), 0g8rj (0.54 #2623, 0.47 #3117, 0.42 #4106), 07tg4 (0.53 #4023, 0.50 #3529, 0.46 #2540), 01mpwj (0.50 #3549, 0.47 #4043, 0.46 #2560) >> Best rule #2501 for best value: >> intensional similarity = 9 >> extensional distance = 11 >> proper extension: 05qjt; 01mkq; 04x_3; 03g3w; 02j62; 0jjw; 02822; 02_7t; 02jfc; >> query: (?x4321, 07szy) <- major_field_of_study(?x6177, ?x4321), major_field_of_study(?x546, ?x4321), major_field_of_study(?x865, ?x4321), major_field_of_study(?x1527, ?x4321), citytown(?x6177, ?x10364), school_type(?x6177, ?x3205), ?x546 = 01j_9c, ?x865 = 02h4rq6, school(?x580, ?x6177) >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 38, 47, 49, 50, 112, 113, 186, 262 EVAL 0g26h major_field_of_study! 02pptm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 60.000 29.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g26h major_field_of_study! 021996 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 60.000 29.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g26h major_field_of_study! 01q7q2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 60.000 29.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g26h major_field_of_study! 01qgr3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 60.000 29.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g26h major_field_of_study! 0gl5_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 60.000 29.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g26h major_field_of_study! 09f2j CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 29.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g26h major_field_of_study! 033x5p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 60.000 29.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g26h major_field_of_study! 02bb47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 60.000 29.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g26h major_field_of_study! 07szy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 60.000 29.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0g26h major_field_of_study! 03v6t CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 60.000 29.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #368-027dpx PRED entity: 027dpx PRED relation: role PRED expected values: 0l14md => 118 concepts (101 used for prediction) PRED predicted values (max 10 best out of 118): 0342h (0.56 #1714, 0.51 #656, 0.48 #1182), 03bx0bm (0.44 #675, 0.42 #805, 0.39 #1997), 05148p4 (0.32 #279, 0.31 #149, 0.31 #1728), 05r5c (0.31 #139, 0.24 #269, 0.23 #660), 026t6 (0.28 #1707, 0.28 #2239, 0.27 #1574), 028tv0 (0.27 #404, 0.22 #995, 0.19 #665), 018vs (0.25 #145, 0.20 #1192, 0.20 #275), 05842k (0.20 #1708, 0.19 #1575, 0.15 #1376), 0l14md (0.19 #856, 0.19 #724, 0.14 #1185), 03qjg (0.12 #173, 0.12 #694, 0.11 #1752) >> Best rule #1714 for best value: >> intensional similarity = 4 >> extensional distance = 191 >> proper extension: 017mbb; >> query: (?x5437, 0342h) <- role(?x5437, ?x1750), artist(?x441, ?x5437), artists(?x1000, ?x5437), instrumentalists(?x1750, ?x300) >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #856 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 66 *> proper extension: 01sbf2; 03j0br4; 045zr; 0137g1; 050z2; 023l9y; 09889g; 01v0fn1; 03h_fqv; 018x3; ... *> query: (?x5437, 0l14md) <- instrumentalists(?x212, ?x5437), role(?x5437, ?x3991), profession(?x5437, ?x131), ?x3991 = 05842k *> conf = 0.19 ranks of expected_values: 9 EVAL 027dpx role 0l14md CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 118.000 101.000 0.560 http://example.org/music/group_member/membership./music/group_membership/role #367-03rt9 PRED entity: 03rt9 PRED relation: countries_within! PRED expected values: 02j9z => 194 concepts (180 used for prediction) PRED predicted values (max 10 best out of 17): 02j9z (0.45 #148, 0.45 #156, 0.44 #95), 012wgb (0.30 #502, 0.27 #580, 0.24 #248), 02qkt (0.30 #502, 0.27 #580, 0.24 #248), 0dg3n1 (0.29 #376, 0.25 #460, 0.23 #406), 0j0k (0.25 #101, 0.23 #122, 0.23 #138), 059g4 (0.18 #20, 0.14 #194, 0.14 #186), 07ssc (0.16 #595, 0.06 #784), 0jtf1 (0.01 #677, 0.01 #182), 0hkq4 (0.01 #677, 0.01 #182), 03rt9 (0.01 #677, 0.01 #182) >> Best rule #148 for best value: >> intensional similarity = 3 >> extensional distance = 42 >> proper extension: 05r4w; 04gzd; 047yc; 015qh; 01pj7; 02vzc; 03rj0; 06t8v; 07f1x; >> query: (?x429, 02j9z) <- olympics(?x429, ?x391), film_release_region(?x1927, ?x429), ?x1927 = 0by1wkq >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rt9 countries_within! 02j9z CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 194.000 180.000 0.455 http://example.org/base/locations/continents/countries_within #366-02vr7 PRED entity: 02vr7 PRED relation: award PRED expected values: 02f6xy => 113 concepts (93 used for prediction) PRED predicted values (max 10 best out of 275): 054ks3 (0.60 #1717, 0.28 #137, 0.21 #927), 0c4z8 (0.40 #1652, 0.21 #1257, 0.21 #6787), 01by1l (0.36 #6824, 0.32 #504, 0.31 #9589), 01bgqh (0.32 #6758, 0.31 #43, 0.27 #7153), 09sb52 (0.28 #17816, 0.27 #23346, 0.26 #22556), 054krc (0.27 #1666, 0.18 #28837, 0.16 #28441), 02gdjb (0.27 #1793, 0.11 #8508, 0.09 #213), 099vwn (0.24 #1790, 0.16 #28441, 0.14 #32790), 0l8z1 (0.24 #1644, 0.13 #29233, 0.13 #35161), 02wh75 (0.24 #2379, 0.19 #2774, 0.13 #1589) >> Best rule #1717 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 01_x6v; 01l9v7n; 01r6jt2; 02w670; 01c7p_; 01m5m5b; >> query: (?x8311, 054ks3) <- role(?x8311, ?x227), award(?x8311, ?x1323), ?x1323 = 0gqz2 >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #590 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 36 *> proper extension: 0cg9y; 01x1cn2; *> query: (?x8311, 02f6xy) <- artist(?x4483, ?x8311), ?x4483 = 0mzkr, profession(?x8311, ?x131) *> conf = 0.21 ranks of expected_values: 11 EVAL 02vr7 award 02f6xy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 113.000 93.000 0.600 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #365-05kh_ PRED entity: 05kh_ PRED relation: spouse PRED expected values: 01pl9g => 103 concepts (58 used for prediction) PRED predicted values (max 10 best out of 33): 01cpqk (0.06 #230, 0.02 #620, 0.01 #1011), 03p9hl (0.03 #387, 0.01 #1948), 0c2ry (0.03 #145, 0.01 #1706), 0btyl (0.03 #137, 0.01 #1698), 0bmh4 (0.03 #82, 0.01 #1643), 01pqy_ (0.03 #183), 028knk (0.02 #450, 0.01 #841), 01p7yb (0.02 #397, 0.01 #788), 01jfrg (0.02 #608), 03xmy1 (0.02 #442) >> Best rule #230 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 0d05fv; 0bkmf; >> query: (?x5601, 01cpqk) <- award_winner(?x9185, ?x5601), film_release_distribution_medium(?x9185, ?x81), celebrities_impersonated(?x3649, ?x5601) >> conf = 0.06 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05kh_ spouse 01pl9g CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 103.000 58.000 0.056 http://example.org/people/person/spouse_s./people/marriage/spouse #364-0cqgl9 PRED entity: 0cqgl9 PRED relation: nominated_for PRED expected values: 043mk4y 05z43v 0dl6fv => 52 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1437): 026gyn_ (0.83 #3178, 0.77 #7945, 0.77 #6357), 0b6m5fy (0.83 #3178, 0.77 #7945, 0.77 #6357), 0g9lm2 (0.55 #2246, 0.17 #13364, 0.17 #3834), 0bnzd (0.45 #2672, 0.12 #1082, 0.11 #4260), 04qw17 (0.45 #1850, 0.11 #3438, 0.10 #5028), 011yg9 (0.36 #2503, 0.25 #913, 0.19 #4091), 0h95927 (0.36 #2747, 0.25 #1157, 0.18 #4335), 05c46y6 (0.36 #1981, 0.25 #391, 0.17 #3569), 05v38p (0.36 #2597, 0.25 #1007, 0.11 #4185), 0c0zq (0.36 #2965, 0.20 #4553, 0.18 #6143) >> Best rule #3178 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 09qwmm; 09sb52; 094qd5; 0fq9zdn; 05zvq6g; 0gqwc; 05pcn59; 02y_rq5; 07h0cl; >> query: (?x3722, ?x715) <- award(?x3461, ?x3722), award(?x715, ?x3722), ?x3461 = 02l4pj, nominated_for(?x3722, ?x531) >> conf = 0.83 => this is the best rule for 2 predicted values *> Best rule #1180 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 6 *> proper extension: 0bfvw2; 0bdwft; 0gqyl; 0gkts9; 09qvf4; *> query: (?x3722, 043mk4y) <- award(?x8045, ?x3722), award(?x3461, ?x3722), award(?x715, ?x3722), ?x8045 = 04qsdh, award_winner(?x374, ?x3461) *> conf = 0.12 ranks of expected_values: 304, 307, 358 EVAL 0cqgl9 nominated_for 0dl6fv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 20.000 0.826 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0cqgl9 nominated_for 05z43v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 20.000 0.826 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0cqgl9 nominated_for 043mk4y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 52.000 20.000 0.826 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #363-02m0b0 PRED entity: 02m0b0 PRED relation: institution! PRED expected values: 03mkk4 => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.71 #95, 0.67 #556, 0.67 #950), 016t_3 (0.70 #578, 0.62 #50, 0.48 #96), 019v9k (0.63 #1071, 0.63 #77, 0.62 #54), 02_xgp2 (0.54 #334, 0.52 #636, 0.51 #288), 03bwzr4 (0.48 #106, 0.46 #60, 0.46 #336), 07s6fsf (0.38 #116, 0.38 #47, 0.35 #93), 013zdg (0.33 #76, 0.33 #53, 0.32 #122), 028dcg (0.33 #65, 0.28 #2614, 0.26 #134), 04zx3q1 (0.33 #324, 0.33 #417, 0.31 #626), 03mkk4 (0.26 #80, 0.24 #126, 0.22 #287) >> Best rule #95 for best value: >> intensional similarity = 4 >> extensional distance = 29 >> proper extension: 01314k; 02km0m; >> query: (?x10497, 02h4rq6) <- major_field_of_study(?x10497, ?x4268), currency(?x10497, ?x170), ?x4268 = 02822, category(?x10497, ?x134) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #80 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 25 *> proper extension: 02g839; 01d34b; 02xwzh; *> query: (?x10497, 03mkk4) <- student(?x10497, ?x8544), story_by(?x2886, ?x8544), award_nominee(?x222, ?x8544), film(?x8544, ?x3251) *> conf = 0.26 ranks of expected_values: 10 EVAL 02m0b0 institution! 03mkk4 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 133.000 133.000 0.710 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #362-015p37 PRED entity: 015p37 PRED relation: profession PRED expected values: 0dxtg => 115 concepts (108 used for prediction) PRED predicted values (max 10 best out of 87): 0np9r (0.71 #2215, 0.70 #1044, 0.64 #1484), 03gjzk (0.55 #306, 0.33 #746, 0.33 #1332), 0dxtg (0.47 #6737, 0.45 #305, 0.36 #745), 02jknp (0.45 #6731, 0.28 #739, 0.26 #1178), 09jwl (0.37 #7617, 0.32 #7325, 0.30 #3529), 016z4k (0.28 #3370, 0.23 #7604, 0.22 #7312), 0kyk (0.26 #12274, 0.25 #8915, 0.20 #1785), 02krf9 (0.26 #12274, 0.25 #8915, 0.15 #1196), 0xzm (0.26 #12274, 0.25 #8915, 0.02 #1716), 0nbcg (0.26 #7630, 0.23 #7338, 0.23 #3396) >> Best rule #2215 for best value: >> intensional similarity = 3 >> extensional distance = 110 >> proper extension: 065mm1; >> query: (?x10919, 0np9r) <- gender(?x10919, ?x514), language(?x10919, ?x254), profession(?x10919, ?x319) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #6737 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 882 *> proper extension: 03z0l6; 01g5kv; *> query: (?x10919, 0dxtg) <- nationality(?x10919, ?x94), profession(?x10919, ?x319), ?x319 = 01d_h8 *> conf = 0.47 ranks of expected_values: 3 EVAL 015p37 profession 0dxtg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 108.000 0.714 http://example.org/people/person/profession #361-05_swj PRED entity: 05_swj PRED relation: music! PRED expected values: 0gfzfj => 112 concepts (90 used for prediction) PRED predicted values (max 10 best out of 767): 04gcyg (0.25 #792, 0.01 #5868), 07vn_9 (0.25 #959), 0b4lkx (0.25 #795), 0hmm7 (0.25 #196), 019nnl (0.21 #5076, 0.07 #36543, 0.06 #55834), 01s7w3 (0.05 #6964, 0.04 #10009, 0.04 #7979), 09d3b7 (0.05 #4904, 0.02 #3889, 0.02 #8965), 02ht1k (0.04 #5445, 0.04 #7475, 0.03 #6460), 08l0x2 (0.04 #2784, 0.02 #9890, 0.02 #8875), 0140g4 (0.04 #2044, 0.02 #6105, 0.01 #9150) >> Best rule #792 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0197tq; 07hgkd; >> query: (?x6891, 04gcyg) <- nationality(?x6891, ?x94), award_winner(?x6891, ?x11469), ?x11469 = 03cd1q >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05_swj music! 0gfzfj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 112.000 90.000 0.250 http://example.org/film/film/music #360-07bs0 PRED entity: 07bs0 PRED relation: country PRED expected values: 01ls2 06mzp 059j2 0d05w3 06t8v 07t_x => 41 concepts (41 used for prediction) PRED predicted values (max 10 best out of 304): 0d05w3 (0.88 #4981, 0.86 #4603, 0.86 #4471), 06qd3 (0.87 #4797, 0.86 #4630, 0.86 #4456), 07ylj (0.86 #4449, 0.85 #4265, 0.80 #4790), 0b90_r (0.82 #4081, 0.82 #3738, 0.80 #3568), 04g5k (0.78 #3302, 0.68 #837, 0.67 #3382), 035qy (0.76 #3380, 0.75 #2728, 0.73 #4101), 07f1x (0.76 #3380, 0.75 #2652, 0.71 #2483), 06t8v (0.76 #3380, 0.75 #2925, 0.70 #3381), 0d04z6 (0.76 #3380, 0.71 #4515, 0.71 #2108), 03shp (0.76 #3380, 0.71 #2087, 0.71 #4425) >> Best rule #4981 for best value: >> intensional similarity = 38 >> extensional distance = 15 >> proper extension: 0194d; >> query: (?x1557, 0d05w3) <- sports(?x358, ?x1557), country(?x1557, ?x2188), country(?x1557, ?x1892), country(?x1557, ?x1558), country(?x1557, ?x252), country(?x1557, ?x142), film_release_region(?x5706, ?x142), film_release_region(?x4448, ?x142), film_release_region(?x3784, ?x142), film_release_region(?x3201, ?x142), film_release_region(?x2628, ?x142), film_release_region(?x2394, ?x142), film_release_region(?x1915, ?x142), film_release_region(?x467, ?x142), film_release_region(?x80, ?x142), ?x80 = 0b76d_m, ?x4448 = 01k60v, currency(?x142, ?x170), form_of_government(?x142, ?x6377), ?x467 = 0dckvs, ?x1892 = 02vzc, ?x1915 = 0fq7dv_, ?x2394 = 0661ql3, olympics(?x142, ?x775), ?x3201 = 01ffx4, ?x2188 = 0163v, ?x5706 = 0284b56, ?x1558 = 01mjq, film_release_region(?x3784, ?x1229), jurisdiction_of_office(?x265, ?x142), ?x170 = 09nqf, country(?x2911, ?x142), ?x1229 = 059j2, film_release_region(?x7700, ?x252), religion(?x142, ?x962), ?x2628 = 06wbm8q, ?x7700 = 0cp08zg, nominated_for(?x198, ?x3784) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 8, 11, 23, 30, 39 EVAL 07bs0 country 07t_x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 41.000 41.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07bs0 country 06t8v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 41.000 41.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07bs0 country 0d05w3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 41.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07bs0 country 059j2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.038 41.000 41.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07bs0 country 06mzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 41.000 41.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 07bs0 country 01ls2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 41.000 41.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #359-0gs973 PRED entity: 0gs973 PRED relation: film! PRED expected values: 0241jw => 69 concepts (49 used for prediction) PRED predicted values (max 10 best out of 756): 0c9xjl (0.30 #968, 0.02 #42512, 0.02 #15508), 0f5xn (0.20 #966, 0.07 #9275, 0.07 #11352), 079vf (0.20 #8, 0.04 #14548, 0.04 #16626), 016z2j (0.20 #389, 0.04 #19085, 0.03 #23240), 0bq2g (0.20 #602, 0.02 #42146, 0.02 #29684), 02114t (0.20 #632, 0.02 #21405, 0.02 #44254), 0h0yt (0.15 #3419, 0.04 #4155, 0.03 #97632), 051wwp (0.15 #2950, 0.04 #4155, 0.03 #68550), 03c9pqt (0.14 #16618, 0.12 #18696, 0.12 #22851), 0h5g_ (0.12 #4229, 0.11 #6306, 0.04 #29156) >> Best rule #968 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 05qbckf; 0bbw2z6; 023g6w; >> query: (?x5290, 0c9xjl) <- film(?x3274, ?x5290), film_crew_role(?x5290, ?x137), film_release_distribution_medium(?x5290, ?x81), ?x3274 = 01chc7 >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #2372 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 11 *> proper extension: 04gcyg; 03wjm2; *> query: (?x5290, 0241jw) <- film(?x5743, ?x5290), film(?x2493, ?x5290), ?x2493 = 01hkhq, genre(?x5290, ?x53), award_nominee(?x5743, ?x100) *> conf = 0.08 ranks of expected_values: 67 EVAL 0gs973 film! 0241jw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 69.000 49.000 0.300 http://example.org/film/actor/film./film/performance/film #358-0djywgn PRED entity: 0djywgn PRED relation: type_of_union PRED expected values: 04ztj => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.88 #49, 0.87 #89, 0.87 #29), 01g63y (0.21 #26, 0.19 #14, 0.14 #175) >> Best rule #49 for best value: >> intensional similarity = 2 >> extensional distance = 133 >> proper extension: 01l1sq; 0d7hg4; 03h_fk5; 01vsykc; 017xm3; 01wv9p; 02bwc7; 01vw20h; 02778yp; 06czyr; ... >> query: (?x8566, 04ztj) <- award_nominee(?x8566, ?x100), location_of_ceremony(?x8566, ?x739) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0djywgn type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.881 http://example.org/people/person/spouse_s./people/marriage/type_of_union #357-02_fm2 PRED entity: 02_fm2 PRED relation: language PRED expected values: 02h40lc => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 50): 02h40lc (0.95 #832, 0.92 #951, 0.92 #1010), 06nm1 (0.33 #11, 0.12 #1137, 0.12 #1256), 02bjrlw (0.33 #1, 0.08 #1661, 0.08 #831), 012w70 (0.33 #13, 0.03 #1554, 0.03 #665), 064_8sq (0.21 #733, 0.14 #377, 0.14 #2216), 03_9r (0.13 #602, 0.11 #187, 0.09 #840), 04306rv (0.13 #1665, 0.12 #2021, 0.11 #776), 06b_j (0.11 #853, 0.09 #912, 0.09 #260), 05zjd (0.05 #203, 0.04 #618, 0.02 #797), 0jzc (0.05 #1680, 0.04 #2036, 0.04 #2570) >> Best rule #832 for best value: >> intensional similarity = 4 >> extensional distance = 93 >> proper extension: 011yrp; 04t6fk; 0ctb4g; 02fqrf; 04vh83; 0d61px; 04mcw4; 026qnh6; 09p4w8; 0dt8xq; ... >> query: (?x218, 02h40lc) <- story_by(?x218, ?x6400), films(?x10489, ?x218), genre(?x218, ?x811), film(?x488, ?x218) >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02_fm2 language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.947 http://example.org/film/film/language #356-0373qt PRED entity: 0373qt PRED relation: institution! PRED expected values: 0bkj86 => 43 concepts (43 used for prediction) PRED predicted values (max 10 best out of 18): 019v9k (0.77 #44, 0.68 #240, 0.66 #182), 03bwzr4 (0.77 #50, 0.50 #227, 0.43 #30), 02_xgp2 (0.72 #48, 0.51 #186, 0.50 #225), 0bkj86 (0.60 #23, 0.47 #102, 0.43 #181), 04zx3q1 (0.36 #40, 0.35 #99, 0.34 #20), 027f2w (0.36 #45, 0.32 #25, 0.23 #164), 013zdg (0.34 #101, 0.28 #42, 0.26 #22), 03mkk4 (0.27 #106, 0.17 #47, 0.17 #125), 01rr_d (0.24 #93, 0.22 #73, 0.21 #33), 0bjrnt (0.19 #100, 0.19 #21, 0.14 #61) >> Best rule #44 for best value: >> intensional similarity = 3 >> extensional distance = 45 >> proper extension: 01jssp; 01pl14; 065y4w7; 07w0v; 04rwx; 01j_cy; 0f1nl; 01jswq; 078bz; 01wdj_; ... >> query: (?x8930, 019v9k) <- major_field_of_study(?x8930, ?x6859), state_province_region(?x8930, ?x6357), ?x6859 = 01tbp >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #23 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 45 *> proper extension: 08815; 05krk; 052nd; 01j_9c; 06pwq; 01w3v; 0bx8pn; 07wrz; 07wjk; 02301; ... *> query: (?x8930, 0bkj86) <- major_field_of_study(?x8930, ?x3995), state_province_region(?x8930, ?x6357), ?x3995 = 0fdys *> conf = 0.60 ranks of expected_values: 4 EVAL 0373qt institution! 0bkj86 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 43.000 43.000 0.766 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #355-015gm8 PRED entity: 015gm8 PRED relation: music PRED expected values: 01l1rw => 89 concepts (52 used for prediction) PRED predicted values (max 10 best out of 101): 06kkgw (0.10 #843, 0.07 #842, 0.07 #4642), 015gy7 (0.10 #843, 0.07 #842, 0.07 #4642), 02sj1x (0.09 #899, 0.08 #477, 0.05 #267), 01dvms (0.07 #842, 0.07 #4642, 0.07 #5064), 0146pg (0.07 #1063, 0.07 #1484, 0.06 #1274), 07hhnl (0.07 #7390, 0.06 #3586, 0.06 #6967), 07h1tr (0.07 #7390, 0.06 #3586, 0.06 #6967), 02wb6d (0.07 #338, 0.05 #548, 0.04 #758), 015wc0 (0.06 #597, 0.05 #387, 0.04 #1019), 01x6v6 (0.05 #123, 0.04 #1176, 0.02 #3498) >> Best rule #843 for best value: >> intensional similarity = 4 >> extensional distance = 88 >> proper extension: 0bm2g; 019kyn; 0ptxj; 011ywj; 02mpyh; 0h3k3f; >> query: (?x11597, ?x6261) <- award_winner(?x11597, ?x6261), award(?x11597, ?x1243), film_release_region(?x11597, ?x94), place_of_death(?x6261, ?x739) >> conf = 0.10 => this is the best rule for 2 predicted values *> Best rule #734 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 88 *> proper extension: 0bm2g; 019kyn; 0ptxj; 011ywj; 02mpyh; 0h3k3f; *> query: (?x11597, 01l1rw) <- award_winner(?x11597, ?x6261), award(?x11597, ?x1243), film_release_region(?x11597, ?x94), place_of_death(?x6261, ?x739) *> conf = 0.02 ranks of expected_values: 37 EVAL 015gm8 music 01l1rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 89.000 52.000 0.099 http://example.org/film/film/music #354-027r8p PRED entity: 027r8p PRED relation: type_of_union PRED expected values: 04ztj => 64 concepts (64 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.74 #21, 0.72 #17, 0.71 #117), 01g63y (0.16 #34, 0.16 #46, 0.15 #14), 0jgjn (0.02 #16, 0.01 #20, 0.01 #32) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 02mslq; 01pw2f1; 03cvfg; 03rl84; 02mhfy; 01z0rcq; 0d3qd0; 016z1t; 01n44c; 044mfr; ... >> query: (?x3747, 04ztj) <- nationality(?x3747, ?x94), ?x94 = 09c7w0, notable_people_with_this_condition(?x11990, ?x3747) >> conf = 0.74 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027r8p type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 64.000 64.000 0.736 http://example.org/people/person/spouse_s./people/marriage/type_of_union #353-02630g PRED entity: 02630g PRED relation: company! PRED expected values: 09d6p2 => 114 concepts (114 used for prediction) PRED predicted values (max 10 best out of 36): 01yc02 (0.52 #258, 0.48 #1523, 0.44 #889), 09d6p2 (0.52 #309, 0.45 #57, 0.42 #1532), 01kr6k (0.45 #65, 0.32 #149, 0.31 #275), 02211by (0.28 #254, 0.24 #1139, 0.23 #885), 0142rn (0.21 #883, 0.21 #274, 0.19 #905), 02y6fz (0.21 #883, 0.20 #104, 0.18 #230), 04192r (0.21 #883, 0.16 #499, 0.14 #2695), 021q0l (0.21 #883, 0.14 #2695, 0.14 #3201), 01rk91 (0.21 #883, 0.14 #2695, 0.14 #3201), 09lq2c (0.21 #883, 0.14 #2695, 0.14 #3201) >> Best rule #258 for best value: >> intensional similarity = 4 >> extensional distance = 27 >> proper extension: 0jvs0; >> query: (?x6638, 01yc02) <- company(?x265, ?x6638), list(?x6638, ?x8915), currency(?x6638, ?x170), ?x8915 = 01pd60 >> conf = 0.52 => this is the best rule for 1 predicted values *> Best rule #309 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 29 *> proper extension: 056ws9; 01c7j1; 02p10m; *> query: (?x6638, 09d6p2) <- organization(?x4682, ?x6638), organization(?x1491, ?x6638), company(?x4792, ?x6638), ?x4682 = 0dq_5, ?x4792 = 05_wyz, company(?x1491, ?x266) *> conf = 0.52 ranks of expected_values: 2 EVAL 02630g company! 09d6p2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 114.000 0.517 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #352-043js PRED entity: 043js PRED relation: nationality PRED expected values: 09c7w0 => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 35): 09c7w0 (0.82 #1002, 0.82 #1104, 0.82 #6120), 0d060g (0.30 #10538, 0.10 #107, 0.07 #507), 07ssc (0.30 #10538, 0.10 #615, 0.10 #2119), 0f8l9c (0.30 #10538, 0.05 #922, 0.03 #4434), 02jx1 (0.14 #733, 0.12 #33, 0.12 #2137), 03rt9 (0.12 #13, 0.04 #613, 0.03 #713), 03rk0 (0.11 #1850, 0.10 #3053, 0.06 #12389), 0345h (0.07 #2205, 0.04 #531, 0.02 #4443), 03rjj (0.07 #2205, 0.03 #705, 0.03 #805), 0chghy (0.07 #2205, 0.03 #3418, 0.02 #2315) >> Best rule #1002 for best value: >> intensional similarity = 3 >> extensional distance = 302 >> proper extension: 03c9pqt; >> query: (?x2657, ?x94) <- place_of_birth(?x2657, ?x3052), nominated_for(?x2657, ?x782), country(?x3052, ?x94) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043js nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.819 http://example.org/people/person/nationality #351-029b9k PRED entity: 029b9k PRED relation: student! PRED expected values: 01pq4w => 127 concepts (92 used for prediction) PRED predicted values (max 10 best out of 180): 01w5m (0.10 #105, 0.09 #1160, 0.06 #10646), 01jq0j (0.10 #248, 0.03 #3938, 0.01 #9208), 01jt2w (0.10 #283, 0.02 #2392, 0.02 #3446), 01b1mj (0.10 #22, 0.02 #3185, 0.02 #3712), 06thjt (0.09 #1453, 0.05 #8304, 0.03 #11466), 0bwfn (0.06 #802, 0.06 #22413, 0.05 #25048), 07szy (0.06 #567, 0.02 #9000, 0.02 #3203), 01rtm4 (0.06 #531, 0.02 #6329, 0.01 #9491), 032r4n (0.06 #1542, 0.03 #2069, 0.01 #11555), 03ksy (0.06 #2742, 0.05 #10647, 0.04 #19081) >> Best rule #105 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 01lqnff; 063g7l; 01my95; >> query: (?x8924, 01w5m) <- location(?x8924, ?x335), category(?x8924, ?x134), athlete(?x14205, ?x8924) >> conf = 0.10 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 029b9k student! 01pq4w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 127.000 92.000 0.100 http://example.org/education/educational_institution/students_graduates./education/education/student #350-0136p1 PRED entity: 0136p1 PRED relation: artists! PRED expected values: 02x8m => 134 concepts (78 used for prediction) PRED predicted values (max 10 best out of 209): 05bt6j (0.33 #10915, 0.32 #9103, 0.29 #9707), 0glt670 (0.32 #3664, 0.29 #8194, 0.28 #6986), 02x8m (0.32 #3643, 0.25 #321, 0.17 #8173), 03_d0 (0.29 #616, 0.29 #1522, 0.28 #3636), 016clz (0.28 #9065, 0.26 #1213, 0.24 #18130), 0xhtw (0.24 #9077, 0.19 #22373, 0.18 #18142), 017_qw (0.23 #1568, 0.19 #2776, 0.18 #3078), 0155w (0.23 #3723, 0.21 #401, 0.16 #9159), 02w4v (0.21 #346, 0.16 #648, 0.12 #4272), 02k_kn (0.18 #4289, 0.16 #2779, 0.16 #10933) >> Best rule #10915 for best value: >> intensional similarity = 2 >> extensional distance = 351 >> proper extension: 0123r4; >> query: (?x1974, 05bt6j) <- artists(?x671, ?x1974), ?x671 = 064t9 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #3643 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 116 *> proper extension: 0qmny; *> query: (?x1974, 02x8m) <- artist(?x1954, ?x1974), artists(?x3928, ?x1974), ?x3928 = 0gywn *> conf = 0.32 ranks of expected_values: 3 EVAL 0136p1 artists! 02x8m CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 134.000 78.000 0.326 http://example.org/music/genre/artists #349-064_8sq PRED entity: 064_8sq PRED relation: major_field_of_study! PRED expected values: 0g8rj 01rgn3 => 81 concepts (73 used for prediction) PRED predicted values (max 10 best out of 738): 09f2j (0.75 #6582, 0.73 #5999, 0.70 #5417), 01w5m (0.69 #7103, 0.58 #15256, 0.56 #7686), 06pwq (0.69 #7000, 0.52 #15153, 0.51 #26211), 0bwfn (0.64 #6120, 0.62 #7868, 0.60 #5538), 03ksy (0.62 #7104, 0.50 #29228, 0.50 #7687), 017j69 (0.55 #5982, 0.50 #7730, 0.50 #6565), 01w3v (0.54 #7003, 0.42 #6421, 0.40 #5256), 02zd460 (0.50 #3104, 0.48 #26391, 0.47 #29304), 07wjk (0.50 #2978, 0.46 #7054, 0.44 #7637), 05zl0 (0.50 #2558, 0.46 #7217, 0.35 #8965) >> Best rule #6582 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 04rjg; 0l5mz; >> query: (?x5607, 09f2j) <- major_field_of_study(?x254, ?x5607), major_field_of_study(?x122, ?x5607), languages(?x118, ?x254), language(?x8457, ?x254), language(?x4174, ?x254), ?x4174 = 07nxvj, ?x8457 = 034xyf >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6604 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 10 *> proper extension: 04rjg; 0l5mz; *> query: (?x5607, 0g8rj) <- major_field_of_study(?x254, ?x5607), major_field_of_study(?x122, ?x5607), languages(?x118, ?x254), language(?x8457, ?x254), language(?x4174, ?x254), ?x4174 = 07nxvj, ?x8457 = 034xyf *> conf = 0.42 ranks of expected_values: 20, 104 EVAL 064_8sq major_field_of_study! 01rgn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 81.000 73.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 064_8sq major_field_of_study! 0g8rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 81.000 73.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #348-015qt5 PRED entity: 015qt5 PRED relation: award_winner! PRED expected values: 0f4x7 => 125 concepts (80 used for prediction) PRED predicted values (max 10 best out of 271): 0f4x7 (0.43 #31, 0.42 #9475, 0.41 #4307), 02w9sd7 (0.43 #166, 0.42 #9475, 0.41 #4307), 0bdwqv (0.42 #9475, 0.41 #4307, 0.41 #4306), 09qvc0 (0.42 #9475, 0.41 #4307, 0.41 #4306), 0789_m (0.42 #9475, 0.41 #4307, 0.41 #4306), 027c95y (0.29 #156, 0.16 #4031, 0.10 #19106), 09cm54 (0.29 #96, 0.11 #3971, 0.08 #19046), 09sb52 (0.25 #18991, 0.14 #24592, 0.12 #472), 0ck27z (0.18 #17318, 0.11 #27653, 0.10 #27223), 054ky1 (0.18 #3984, 0.14 #109, 0.12 #6140) >> Best rule #31 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 015d3h; 0l786; 0gnbw; >> query: (?x4266, 0f4x7) <- award(?x4266, ?x2192), award_winner(?x3173, ?x4266), celebrities_impersonated(?x3649, ?x4266), ?x2192 = 0bfvd4 >> conf = 0.43 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 015qt5 award_winner! 0f4x7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 125.000 80.000 0.429 http://example.org/award/award_category/winners./award/award_honor/award_winner #347-06nm1 PRED entity: 06nm1 PRED relation: major_field_of_study! PRED expected values: 07tgn => 86 concepts (63 used for prediction) PRED predicted values (max 10 best out of 668): 08815 (0.75 #4698, 0.70 #8222, 0.60 #2937), 07szy (0.69 #11197, 0.67 #10610, 0.40 #2977), 06pwq (0.62 #11167, 0.58 #10580, 0.52 #30542), 01w5m (0.60 #8337, 0.60 #3052, 0.54 #11272), 07tds (0.60 #3103, 0.58 #10736, 0.54 #11323), 01j_cy (0.60 #2976, 0.58 #10609, 0.54 #11196), 01bm_ (0.60 #3210, 0.50 #10843, 0.50 #8495), 015cz0 (0.60 #3125, 0.50 #10758, 0.46 #11345), 0lfgr (0.60 #2982, 0.50 #1808, 0.42 #10615), 0cwx_ (0.60 #3206, 0.50 #8491, 0.38 #4967) >> Best rule #4698 for best value: >> intensional similarity = 7 >> extensional distance = 6 >> proper extension: 0fdys; 03qsdpk; >> query: (?x2502, 08815) <- major_field_of_study(?x6056, ?x2502), major_field_of_study(?x3149, ?x2502), ?x3149 = 02fy0z, major_field_of_study(?x5974, ?x2502), student(?x2502, ?x9156), student(?x6056, ?x445), language(?x136, ?x5974) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2952 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 04x_3; 02j62; 037mh8; *> query: (?x2502, 07tgn) <- major_field_of_study(?x6056, ?x2502), major_field_of_study(?x3149, ?x2502), ?x3149 = 02fy0z, major_field_of_study(?x2314, ?x2502), student(?x2502, ?x9156), ?x6056 = 05zl0 *> conf = 0.40 ranks of expected_values: 50 EVAL 06nm1 major_field_of_study! 07tgn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 86.000 63.000 0.750 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #346-03_80b PRED entity: 03_80b PRED relation: place_of_death PRED expected values: 0f485 => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 13): 030qb3t (0.12 #22, 0.08 #216, 0.04 #4683), 0rd5k (0.12 #50), 0k049 (0.04 #197, 0.02 #780, 0.02 #391), 0f2wj (0.04 #206, 0.02 #400, 0.02 #595), 02_286 (0.04 #2536, 0.03 #6421, 0.03 #5256), 06_kh (0.02 #199, 0.01 #2917), 04jpl (0.02 #2530, 0.01 #3695, 0.01 #3890), 04vmp (0.02 #6603, 0.02 #14372, 0.02 #14955), 05qtj (0.01 #3947, 0.01 #4335, 0.01 #3752), 0r3w7 (0.01 #371) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 6 >> proper extension: 01kt17; >> query: (?x5816, 030qb3t) <- award_nominee(?x5816, ?x2733), profession(?x5816, ?x319), ?x2733 = 0hskw >> conf = 0.12 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 03_80b place_of_death 0f485 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 88.000 0.125 http://example.org/people/deceased_person/place_of_death #345-0jjw PRED entity: 0jjw PRED relation: major_field_of_study! PRED expected values: 07w0v 02zccd 03bmmc => 48 concepts (22 used for prediction) PRED predicted values (max 10 best out of 643): 01w3v (0.69 #1770, 0.60 #2942, 0.60 #1185), 01w5m (0.64 #2457, 0.60 #1287, 0.57 #3628), 07wrz (0.64 #2405, 0.60 #1235, 0.46 #1820), 07szy (0.62 #1797, 0.60 #2969, 0.57 #2382), 02zd460 (0.62 #1947, 0.55 #3119, 0.53 #5462), 052nd (0.60 #1182, 0.31 #1767, 0.25 #2929), 017j69 (0.57 #2500, 0.50 #3671, 0.48 #4258), 08815 (0.57 #2344, 0.50 #2, 0.44 #5274), 0bwfn (0.55 #4983, 0.53 #3810, 0.53 #5569), 07tds (0.54 #1922, 0.45 #3094, 0.44 #5437) >> Best rule #1770 for best value: >> intensional similarity = 10 >> extensional distance = 11 >> proper extension: 036hv; 01mkq; 02ky346; 062z7; 0193x; 0fdys; 01540; 02jfc; >> query: (?x3440, 01w3v) <- major_field_of_study(?x4955, ?x3440), major_field_of_study(?x3439, ?x3440), major_field_of_study(?x817, ?x3440), major_field_of_study(?x3440, ?x3490), ?x4955 = 09f2j, major_field_of_study(?x2014, ?x3440), taxonomy(?x3440, ?x939), ?x3439 = 03ksy, major_field_of_study(?x865, ?x3440), student(?x817, ?x158) >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #4704 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 31 *> proper extension: 0w7c; 04rlf; *> query: (?x3440, 07w0v) <- major_field_of_study(?x4955, ?x3440), major_field_of_study(?x3440, ?x3490), student(?x4955, ?x6844), student(?x4955, ?x3841), ?x3841 = 07s8hms, company(?x4309, ?x4955), institution(?x620, ?x4955), company(?x6844, ?x13515) *> conf = 0.39 ranks of expected_values: 53, 411, 527 EVAL 0jjw major_field_of_study! 03bmmc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 22.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0jjw major_field_of_study! 02zccd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 48.000 22.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 0jjw major_field_of_study! 07w0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 48.000 22.000 0.692 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #344-0bpx1k PRED entity: 0bpx1k PRED relation: produced_by PRED expected values: 06cgy => 89 concepts (54 used for prediction) PRED predicted values (max 10 best out of 180): 034bgm (0.53 #8874, 0.46 #2702, 0.45 #10033), 016xh5 (0.17 #773, 0.12 #386, 0.12 #9646), 016tw3 (0.17 #773, 0.12 #386, 0.12 #9646), 058frd (0.12 #599, 0.02 #1370, 0.01 #1756), 04wvhz (0.12 #2352, 0.06 #423, 0.06 #5051), 0j_c (0.09 #2396, 0.04 #5095, 0.02 #8568), 02q_cc (0.06 #420, 0.05 #2349, 0.03 #7747), 0g2lq (0.06 #651, 0.04 #2580, 0.03 #4894), 05ty4m (0.06 #399, 0.03 #5027, 0.02 #2328), 03h304l (0.06 #572, 0.02 #10218, 0.02 #11376) >> Best rule #8874 for best value: >> intensional similarity = 3 >> extensional distance = 403 >> proper extension: 026p_bs; >> query: (?x2881, ?x2648) <- titles(?x512, ?x2881), film(?x2648, ?x2881), produced_by(?x2881, ?x3568) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #2372 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 92 *> proper extension: 0j_tw; *> query: (?x2881, 06cgy) <- film(?x1104, ?x2881), ?x1104 = 016tw3, film(?x2648, ?x2881) *> conf = 0.02 ranks of expected_values: 53 EVAL 0bpx1k produced_by 06cgy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 89.000 54.000 0.531 http://example.org/film/film/produced_by #343-0m66w PRED entity: 0m66w PRED relation: gender PRED expected values: 02zsn => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.84 #59, 0.84 #65, 0.83 #11), 02zsn (0.75 #6, 0.73 #4, 0.71 #2) >> Best rule #59 for best value: >> intensional similarity = 2 >> extensional distance = 344 >> proper extension: 01vsps; 0342vg; 02x20c9; >> query: (?x5889, 05zppz) <- nominated_for(?x5889, ?x2586), produced_by(?x9496, ?x5889) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #6 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 04xrx; *> query: (?x5889, 02zsn) <- award_winner(?x5889, ?x2548), award_winner(?x1007, ?x5889), ?x1007 = 03c7tr1 *> conf = 0.75 ranks of expected_values: 2 EVAL 0m66w gender 02zsn CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 138.000 138.000 0.841 http://example.org/people/person/gender #342-03rj0 PRED entity: 03rj0 PRED relation: administrative_area_type PRED expected values: 0hzc9wc => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1): 0hzc9wc (0.90 #16, 0.89 #19, 0.86 #8) >> Best rule #16 for best value: >> intensional similarity = 3 >> extensional distance = 95 >> proper extension: 0167v; >> query: (?x2267, 0hzc9wc) <- country(?x1121, ?x2267), currency(?x2267, ?x170), countries_within(?x455, ?x2267) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03rj0 administrative_area_type 0hzc9wc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 65.000 0.897 http://example.org/base/aareas/schema/administrative_area/administrative_area_type #341-026t6 PRED entity: 026t6 PRED relation: role! PRED expected values: 01lvcs1 028qdb 01vwbts 03wjb7 02bc74 => 71 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1347): 082brv (0.67 #6233, 0.50 #9036, 0.50 #7032), 03ryks (0.67 #4264, 0.50 #1864, 0.48 #13072), 0326tc (0.57 #5100, 0.50 #7500, 0.50 #5900), 01gx5f (0.57 #4937, 0.50 #5737, 0.50 #3740), 01w9mnm (0.57 #5119, 0.50 #5919, 0.50 #3922), 0lzkm (0.56 #6149, 0.50 #6948, 0.50 #4151), 01wxdn3 (0.56 #6352, 0.50 #4354, 0.50 #1954), 0l12d (0.56 #6061, 0.50 #4063, 0.43 #4861), 0m_v0 (0.56 #6142, 0.50 #1744, 0.43 #4942), 01lvcs1 (0.56 #6144, 0.30 #6943, 0.27 #8547) >> Best rule #6233 for best value: >> intensional similarity = 8 >> extensional distance = 7 >> proper extension: 01vj9c; >> query: (?x212, 082brv) <- role(?x4052, ?x212), role(?x211, ?x212), performance_role(?x212, ?x228), role(?x212, ?x1472), ?x4052 = 050z2, artists(?x671, ?x211), ?x1472 = 0319l, place_of_death(?x211, ?x405) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #6144 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 7 *> proper extension: 01vj9c; *> query: (?x212, 01lvcs1) <- role(?x4052, ?x212), role(?x211, ?x212), performance_role(?x212, ?x228), role(?x212, ?x1472), ?x4052 = 050z2, artists(?x671, ?x211), ?x1472 = 0319l, place_of_death(?x211, ?x405) *> conf = 0.56 ranks of expected_values: 10, 102, 125, 250, 424 EVAL 026t6 role! 02bc74 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 71.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 026t6 role! 03wjb7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 71.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 026t6 role! 01vwbts CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 71.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 026t6 role! 028qdb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 71.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role EVAL 026t6 role! 01lvcs1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 71.000 53.000 0.667 http://example.org/music/artist/track_contributions./music/track_contribution/role #340-05_z42 PRED entity: 05_z42 PRED relation: genre PRED expected values: 0m1xv => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 79): 01z4y (0.75 #99, 0.37 #342, 0.35 #1234), 0c4xc (0.58 #122, 0.25 #1257, 0.24 #1176), 07s9rl0 (0.56 #1054, 0.56 #1380, 0.53 #1217), 0hcr (0.42 #100, 0.22 #2291, 0.19 #2373), 09lmb (0.35 #273, 0.07 #354, 0.07 #2221), 06n90 (0.33 #175, 0.23 #823, 0.21 #661), 01hmnh (0.33 #178, 0.17 #826, 0.16 #664), 0vgkd (0.33 #91, 0.13 #901, 0.13 #1145), 01t_vv (0.28 #438, 0.24 #357, 0.21 #924), 01w613 (0.25 #209, 0.22 #290, 0.08 #128) >> Best rule #99 for best value: >> intensional similarity = 3 >> extensional distance = 10 >> proper extension: 019nnl; 01h72l; >> query: (?x5698, 01z4y) <- award_winner(?x5698, ?x236), genre(?x5698, ?x2700), ?x2700 = 06nbt >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #76 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0cmdwwg; *> query: (?x5698, 0m1xv) <- nominated_for(?x4415, ?x5698), ?x4415 = 06msq2, award(?x5698, ?x882) *> conf = 0.20 ranks of expected_values: 12 EVAL 05_z42 genre 0m1xv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 67.000 67.000 0.750 http://example.org/tv/tv_program/genre #339-0kcdl PRED entity: 0kcdl PRED relation: state_province_region PRED expected values: 059rby => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 57): 059rby (0.75 #6665, 0.67 #1854, 0.66 #2841), 01n7q (0.46 #2362, 0.37 #1991, 0.36 #2239), 02_286 (0.16 #11495, 0.04 #8766, 0.04 #8891), 0cc56 (0.16 #11495, 0.02 #7282), 0d060g (0.16 #11495), 03rjj (0.16 #11495), 09c7w0 (0.16 #11495), 05fjf (0.11 #1060, 0.04 #2541, 0.03 #3525), 02xry (0.11 #1025, 0.04 #2506, 0.03 #3490), 015jr (0.08 #1313, 0.08 #1437, 0.05 #2053) >> Best rule #6665 for best value: >> intensional similarity = 2 >> extensional distance = 93 >> proper extension: 07t65; 01xdn1; 04sylm; 0gvbw; 01hb1t; 05njyy; 0jvs0; 095kp; 04b_46; 03m9c8; ... >> query: (?x11249, 059rby) <- citytown(?x11249, ?x739), ?x739 = 02_286 >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kcdl state_province_region 059rby CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 110.000 0.747 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #338-05b5c PRED entity: 05b5c PRED relation: contact_category PRED expected values: 03w5xm => 156 concepts (156 used for prediction) PRED predicted values (max 10 best out of 2): 03w5xm (0.90 #70, 0.89 #109, 0.87 #59), 014dgf (0.33 #169, 0.33 #13, 0.28 #125) >> Best rule #70 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 07xyn1; >> query: (?x13349, 03w5xm) <- company(?x4682, ?x13349), ?x4682 = 0dq_5, industry(?x13349, ?x245), contact_category(?x13349, ?x6046) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05b5c contact_category 03w5xm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 156.000 156.000 0.900 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category #337-01svq8 PRED entity: 01svq8 PRED relation: profession PRED expected values: 01d_h8 02hrh1q 018gz8 => 140 concepts (79 used for prediction) PRED predicted values (max 10 best out of 86): 02hrh1q (0.91 #897, 0.85 #9281, 0.85 #10313), 01d_h8 (0.63 #2066, 0.58 #1478, 0.56 #5007), 018gz8 (0.60 #458, 0.49 #752, 0.44 #164), 0cbd2 (0.57 #8391, 0.52 #5155, 0.50 #5596), 0kyk (0.56 #177, 0.36 #2384, 0.34 #8413), 02jknp (0.44 #11337, 0.42 #11484, 0.37 #2068), 09jwl (0.37 #4578, 0.37 #10759, 0.36 #3843), 0np9r (0.35 #10447, 0.33 #6178, 0.30 #9858), 015cjr (0.35 #10447, 0.33 #6178, 0.30 #9858), 02krf9 (0.35 #10447, 0.28 #5027, 0.27 #2969) >> Best rule #897 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 020ffd; >> query: (?x13118, 02hrh1q) <- profession(?x13118, ?x987), type_of_union(?x13118, ?x566), person(?x8144, ?x13118), award(?x13118, ?x537) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 01svq8 profession 018gz8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 79.000 0.907 http://example.org/people/person/profession EVAL 01svq8 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 79.000 0.907 http://example.org/people/person/profession EVAL 01svq8 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 79.000 0.907 http://example.org/people/person/profession #336-04qp06 PRED entity: 04qp06 PRED relation: special_performance_type PRED expected values: 01pb34 => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 4): 01pb34 (0.25 #3, 0.20 #35, 0.18 #19), 01kyvx (0.03 #194, 0.01 #352, 0.01 #364), 02t8yb (0.02 #20, 0.01 #75), 09_gdc (0.01 #84, 0.01 #90) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 03wpmd; >> query: (?x13506, 01pb34) <- award(?x13506, ?x4687), award_winner(?x4443, ?x13506), ?x4443 = 0b6k___, religion(?x13506, ?x8967), languages(?x13506, ?x1882) >> conf = 0.25 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04qp06 special_performance_type 01pb34 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 122.000 122.000 0.250 http://example.org/film/actor/film./film/performance/special_performance_type #335-016ypb PRED entity: 016ypb PRED relation: film PRED expected values: 0cc846d => 69 concepts (55 used for prediction) PRED predicted values (max 10 best out of 399): 017gm7 (0.58 #48028, 0.48 #1988, 0.44 #3766), 017gl1 (0.58 #48028, 0.44 #3699, 0.43 #1921), 0645k5 (0.58 #48028, 0.41 #16004, 0.36 #24896), 0dr_4 (0.26 #3802, 0.05 #2024, 0.03 #33793), 0djlxb (0.14 #2307, 0.08 #12447, 0.06 #32013), 01vw8k (0.14 #2423, 0.07 #4201, 0.03 #26675), 04z4j2 (0.10 #3394, 0.04 #5172, 0.03 #26675), 03lvwp (0.10 #2810, 0.04 #4588, 0.03 #26675), 0btpm6 (0.10 #3070), 02r858_ (0.08 #12447, 0.06 #32013, 0.05 #3191) >> Best rule #48028 for best value: >> intensional similarity = 2 >> extensional distance = 1401 >> proper extension: 0h1_w; 0gm34; 015qq1; 01dbgw; >> query: (?x2922, ?x972) <- film(?x2922, ?x1173), nominated_for(?x2922, ?x972) >> conf = 0.58 => this is the best rule for 3 predicted values *> Best rule #7555 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 375 *> proper extension: 06v8s0; 01g4zr; 04n7njg; 01c58j; 01gx5f; 059xvg; 08n9ng; 0chrwb; 01s7qqw; 081jbk; ... *> query: (?x2922, 0cc846d) <- profession(?x2922, ?x1383), ?x1383 = 0np9r *> conf = 0.01 ranks of expected_values: 387 EVAL 016ypb film 0cc846d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 69.000 55.000 0.584 http://example.org/film/actor/film./film/performance/film #334-0f2df PRED entity: 0f2df PRED relation: film PRED expected values: 0ckrnn => 123 concepts (58 used for prediction) PRED predicted values (max 10 best out of 864): 035xwd (0.22 #5479), 032016 (0.20 #503, 0.17 #2291, 0.12 #4079), 0bx0l (0.20 #348, 0.17 #2136, 0.12 #3924), 02psgq (0.20 #940, 0.17 #2728, 0.12 #4516), 0h1v19 (0.20 #438, 0.12 #4014, 0.03 #12954), 083skw (0.20 #416, 0.12 #3992, 0.02 #11144), 031778 (0.17 #2103, 0.12 #3891, 0.09 #9255), 03176f (0.17 #2494, 0.12 #4282, 0.08 #7858), 0jvt9 (0.17 #2327, 0.12 #4115, 0.06 #11267), 02qr3k8 (0.17 #3077, 0.12 #4865, 0.05 #12017) >> Best rule #5479 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 06b_0; >> query: (?x1567, 035xwd) <- people(?x6734, ?x1567), film(?x1567, ?x327), ?x6734 = 03ts0c >> conf = 0.22 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0f2df film 0ckrnn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 123.000 58.000 0.222 http://example.org/film/actor/film./film/performance/film #333-0bvfqq PRED entity: 0bvfqq PRED relation: honored_for PRED expected values: 078sj4 05sy_5 => 36 concepts (24 used for prediction) PRED predicted values (max 10 best out of 778): 017gl1 (0.33 #52, 0.25 #641, 0.10 #2422), 0pc62 (0.33 #33, 0.25 #622, 0.10 #2403), 011ywj (0.33 #477, 0.25 #1066, 0.10 #2847), 011yxg (0.33 #14, 0.25 #603, 0.10 #2384), 09m6kg (0.33 #11, 0.25 #600, 0.10 #2381), 0dr3sl (0.33 #167, 0.25 #756, 0.10 #2537), 0344gc (0.33 #48, 0.25 #637, 0.10 #2418), 01718w (0.33 #468, 0.25 #1057, 0.10 #2838), 0194zl (0.33 #293, 0.25 #882, 0.10 #2663), 0dr_4 (0.25 #1269, 0.25 #677, 0.10 #2458) >> Best rule #52 for best value: >> intensional similarity = 18 >> extensional distance = 1 >> proper extension: 02yvhx; >> query: (?x2210, 017gl1) <- ceremony(?x6860, ?x2210), ceremony(?x1243, ?x2210), ceremony(?x591, ?x2210), ceremony(?x500, ?x2210), ceremony(?x484, ?x2210), ?x1243 = 0gr0m, award_winner(?x2210, ?x1983), ?x484 = 0gq_v, honored_for(?x2210, ?x4939), honored_for(?x2210, ?x3845), ?x1983 = 04ktcgn, ?x591 = 0f4x7, ?x6860 = 018wdw, ?x500 = 0p9sw, award(?x4939, ?x112), film(?x6977, ?x4939), award_nominee(?x6977, ?x91), nominated_for(?x2156, ?x3845) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #11836 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 48 *> proper extension: 0hhtgcw; *> query: (?x2210, ?x349) <- honored_for(?x2210, ?x3008), award_winner(?x2210, ?x10854), award_winner(?x2210, ?x286), nominated_for(?x1053, ?x3008), produced_by(?x4607, ?x286), participant(?x5197, ?x286), participant(?x969, ?x286), award_nominee(?x192, ?x286), film(?x286, ?x349), student(?x735, ?x5197), genre(?x3008, ?x1510), award(?x286, ?x68), nationality(?x10854, ?x512), award_nominee(?x286, ?x427), award_nominee(?x969, ?x1676) *> conf = 0.04 ranks of expected_values: 228, 526 EVAL 0bvfqq honored_for 05sy_5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 36.000 24.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0bvfqq honored_for 078sj4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 36.000 24.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #332-0642xf3 PRED entity: 0642xf3 PRED relation: film! PRED expected values: 02gvwz => 114 concepts (63 used for prediction) PRED predicted values (max 10 best out of 1234): 0bdxs5 (0.25 #1506, 0.08 #5663, 0.03 #16057), 0p8r1 (0.25 #2663, 0.08 #35929, 0.06 #52564), 0q9kd (0.25 #4, 0.05 #6239, 0.04 #14555), 04fzk (0.25 #707, 0.04 #11100, 0.04 #4864), 02t_st (0.25 #1288, 0.04 #5445, 0.03 #26236), 01ycbq (0.25 #2404, 0.04 #10719, 0.03 #27354), 01kgv4 (0.25 #1183, 0.04 #5340, 0.03 #28211), 03fbb6 (0.25 #978, 0.04 #5135, 0.03 #28006), 083wr9 (0.25 #4130, 0.04 #12445, 0.03 #16603), 0f276 (0.25 #1667, 0.04 #5824, 0.03 #18298) >> Best rule #1506 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 07ykkx5; >> query: (?x5081, 0bdxs5) <- film_crew_role(?x5081, ?x137), genre(?x5081, ?x6452), genre(?x5081, ?x1509), ?x1509 = 060__y, film(?x1672, ?x5081), ?x6452 = 02b5_l, profession(?x1672, ?x353) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #52166 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 175 *> proper extension: 0bmc4cm; 076xkdz; *> query: (?x5081, 02gvwz) <- nominated_for(?x2456, ?x5081), film(?x574, ?x5081), film_release_region(?x5081, ?x94), genre(?x5081, ?x811), ?x811 = 03k9fj *> conf = 0.03 ranks of expected_values: 330 EVAL 0642xf3 film! 02gvwz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 114.000 63.000 0.250 http://example.org/film/actor/film./film/performance/film #331-02g7sp PRED entity: 02g7sp PRED relation: people PRED expected values: 065jlv 02xs5v 0ky1 => 31 concepts (9 used for prediction) PRED predicted values (max 10 best out of 3643): 0g824 (0.36 #6037, 0.29 #4320, 0.17 #9467), 06cgy (0.33 #1910, 0.33 #195, 0.24 #7058), 01fwj8 (0.33 #1927, 0.33 #212, 0.24 #7075), 032_jg (0.33 #1823, 0.33 #108, 0.18 #5256), 02184q (0.33 #3089, 0.33 #1374, 0.18 #8237), 01tzm9 (0.33 #2726, 0.33 #1011, 0.12 #7874), 0479b (0.33 #2656, 0.33 #941, 0.12 #7804), 01m65sp (0.33 #2146, 0.33 #431, 0.12 #7294), 022_q8 (0.33 #2503, 0.27 #5936, 0.18 #7651), 014x77 (0.33 #1783, 0.18 #5216, 0.14 #3499) >> Best rule #6037 for best value: >> intensional similarity = 11 >> extensional distance = 9 >> proper extension: 0g6ff; >> query: (?x5056, 0g824) <- people(?x5056, ?x11104), people(?x5056, ?x4701), people(?x5056, ?x294), film(?x294, ?x1763), award_winner(?x2192, ?x294), instrumentalists(?x227, ?x4701), location_of_ceremony(?x4701, ?x362), nominated_for(?x294, ?x3157), story_by(?x2795, ?x11104), influenced_by(?x2161, ?x11104), gender(?x294, ?x231) >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #8578 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 15 *> proper extension: 0bhsnb; *> query: (?x5056, ?x72) <- people(?x5056, ?x5869), people(?x5056, ?x294), award_winner(?x2192, ?x294), nationality(?x294, ?x1310), award_winner(?x401, ?x5869), award(?x72, ?x2192), gender(?x5869, ?x231), ?x1310 = 02jx1, location(?x5869, ?x6885) *> conf = 0.04 ranks of expected_values: 1677, 2375, 3471 EVAL 02g7sp people 0ky1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 9.000 0.364 http://example.org/people/ethnicity/people EVAL 02g7sp people 02xs5v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 31.000 9.000 0.364 http://example.org/people/ethnicity/people EVAL 02g7sp people 065jlv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 31.000 9.000 0.364 http://example.org/people/ethnicity/people #330-026hxwx PRED entity: 026hxwx PRED relation: genre PRED expected values: 02kdv5l 03k9fj => 62 concepts (60 used for prediction) PRED predicted values (max 10 best out of 85): 07s9rl0 (0.61 #3604, 0.58 #3725, 0.58 #2164), 01z4y (0.53 #3724, 0.50 #962, 0.48 #4926), 02kdv5l (0.37 #603, 0.33 #3, 0.28 #2646), 03k9fj (0.37 #612, 0.29 #492, 0.25 #132), 02l7c8 (0.36 #377, 0.30 #257, 0.28 #3620), 060__y (0.33 #18, 0.25 #138, 0.14 #3621), 082gq (0.33 #32, 0.25 #152, 0.10 #6731), 03g3w (0.33 #26, 0.25 #146, 0.10 #6731), 0l4h_ (0.30 #313, 0.27 #433, 0.15 #6610), 01q03 (0.30 #245, 0.27 #365, 0.15 #6610) >> Best rule #3604 for best value: >> intensional similarity = 3 >> extensional distance = 1335 >> proper extension: 09rfh9; >> query: (?x6500, 07s9rl0) <- genre(?x6500, ?x7323), titles(?x7323, ?x750), titles(?x2480, ?x6500) >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #603 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 0522wp; *> query: (?x6500, 02kdv5l) <- film(?x574, ?x6500), category(?x6500, ?x134), region(?x6500, ?x512) *> conf = 0.37 ranks of expected_values: 3, 4 EVAL 026hxwx genre 03k9fj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 60.000 0.609 http://example.org/film/film/genre EVAL 026hxwx genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 62.000 60.000 0.609 http://example.org/film/film/genre #329-01vsy7t PRED entity: 01vsy7t PRED relation: artists! PRED expected values: 0grjmv 09jw2 => 110 concepts (110 used for prediction) PRED predicted values (max 10 best out of 218): 01lyv (0.77 #1241, 0.31 #2449, 0.24 #8200), 06j6l (0.42 #2159, 0.36 #2461, 0.29 #3674), 02yv6b (0.39 #5542, 0.26 #2512, 0.16 #3725), 0155w (0.36 #2520, 0.24 #3733, 0.24 #3427), 017_qw (0.34 #303, 0.25 #6111, 0.11 #13971), 0gywn (0.33 #2169, 0.29 #2773, 0.28 #3684), 025sc50 (0.33 #2161, 0.27 #2765, 0.26 #9727), 016cjb (0.31 #1281, 0.09 #2187, 0.08 #12171), 08jyyk (0.29 #5511, 0.21 #3694, 0.21 #3388), 016clz (0.26 #3938, 0.26 #4846, 0.25 #5452) >> Best rule #1241 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 01lmj3q; 016sp_; 0ggjt; 016srn; 03cfjg; 0x3b7; 01k_r5b; 01l47f5; 05sq20; 01hmk9; ... >> query: (?x4620, 01lyv) <- award_winner(?x4620, ?x2300), profession(?x4620, ?x131), ?x2300 = 01ww2fs >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #157 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 5 *> proper extension: 041rx; *> query: (?x4620, 09jw2) <- split_to(?x7027, ?x4620), artists(?x4910, ?x7027) *> conf = 0.14 ranks of expected_values: 37, 55 EVAL 01vsy7t artists! 09jw2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 110.000 110.000 0.769 http://example.org/music/genre/artists EVAL 01vsy7t artists! 0grjmv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 110.000 110.000 0.769 http://example.org/music/genre/artists #328-016yr0 PRED entity: 016yr0 PRED relation: award_nominee! PRED expected values: 069ld1 => 83 concepts (29 used for prediction) PRED predicted values (max 10 best out of 897): 0276jmv (0.81 #60712, 0.81 #67718, 0.81 #67719), 05mlqj (0.81 #60712, 0.81 #67718, 0.81 #67719), 07q0g5 (0.81 #60712, 0.81 #67718, 0.81 #60711), 069ld1 (0.46 #178, 0.33 #30352, 0.28 #42030), 04t969 (0.46 #1664, 0.33 #30352, 0.15 #49036), 01yk13 (0.38 #175, 0.33 #30352, 0.30 #37359), 022yb4 (0.38 #1858, 0.33 #30352, 0.28 #42030), 04lp8k (0.33 #30352, 0.31 #1661, 0.30 #37359), 02jtjz (0.33 #30352, 0.31 #882, 0.28 #42030), 01j5x6 (0.33 #30352, 0.31 #179, 0.28 #42030) >> Best rule #60712 for best value: >> intensional similarity = 3 >> extensional distance = 1572 >> proper extension: 04cy8rb; 08wr3kg; 0584j4n; 07fzq3; 09dv0sz; 09hd6f; 03h40_7; 05hjmd; 06mm1x; >> query: (?x4327, ?x9384) <- award_nominee(?x4327, ?x9384), nominated_for(?x4327, ?x8870), type_of_union(?x9384, ?x566) >> conf = 0.81 => this is the best rule for 3 predicted values *> Best rule #178 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 01j5x6; *> query: (?x4327, 069ld1) <- award_nominee(?x4327, ?x5645), actor(?x8870, ?x4327), ?x8870 = 0fhzwl *> conf = 0.46 ranks of expected_values: 4 EVAL 016yr0 award_nominee! 069ld1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 29.000 0.809 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #327-021996 PRED entity: 021996 PRED relation: institution! PRED expected values: 016t_3 028dcg => 189 concepts (189 used for prediction) PRED predicted values (max 10 best out of 18): 02_xgp2 (0.79 #117, 0.71 #63, 0.55 #536), 016t_3 (0.62 #110, 0.54 #147, 0.51 #202), 027f2w (0.62 #114, 0.30 #2028, 0.29 #1876), 04zx3q1 (0.47 #109, 0.41 #201, 0.35 #146), 013zdg (0.32 #205, 0.30 #2028, 0.29 #1876), 0bjrnt (0.30 #2028, 0.29 #1876, 0.26 #112), 01rr_d (0.30 #2028, 0.29 #1876, 0.26 #121), 028dcg (0.30 #2028, 0.29 #1876, 0.24 #215), 03mkk4 (0.30 #2028, 0.29 #1876, 0.24 #190), 02m4yg (0.30 #2028, 0.29 #1876, 0.17 #1481) >> Best rule #117 for best value: >> intensional similarity = 4 >> extensional distance = 32 >> proper extension: 052nd; 065y4w7; 01j_cy; 07wjk; 07wlf; 01q460; 03ksy; 025v3k; 0h6rm; 0gjv_; ... >> query: (?x8427, 02_xgp2) <- colors(?x8427, ?x332), institution(?x620, ?x8427), major_field_of_study(?x8427, ?x9111), ?x9111 = 04sh3 >> conf = 0.79 => this is the best rule for 1 predicted values *> Best rule #110 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 32 *> proper extension: 052nd; 065y4w7; 01j_cy; 07wjk; 07wlf; 01q460; 03ksy; 025v3k; 0h6rm; 0gjv_; ... *> query: (?x8427, 016t_3) <- colors(?x8427, ?x332), institution(?x620, ?x8427), major_field_of_study(?x8427, ?x9111), ?x9111 = 04sh3 *> conf = 0.62 ranks of expected_values: 2, 8 EVAL 021996 institution! 028dcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 189.000 189.000 0.794 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 021996 institution! 016t_3 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 189.000 189.000 0.794 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #326-0gj96ln PRED entity: 0gj96ln PRED relation: film_crew_role PRED expected values: 01vx2h => 83 concepts (83 used for prediction) PRED predicted values (max 10 best out of 27): 0ch6mp2 (0.77 #1277, 0.77 #198, 0.77 #1626), 09zzb8 (0.75 #1153, 0.74 #1658, 0.74 #1619), 09vw2b7 (0.66 #1664, 0.66 #1159, 0.65 #1625), 01vx2h (0.40 #280, 0.39 #356, 0.38 #853), 0dxtw (0.36 #1164, 0.36 #1669, 0.36 #1630), 01pvkk (0.27 #1283, 0.27 #1322, 0.25 #510), 0215hd (0.24 #173, 0.16 #861, 0.15 #1639), 089g0h (0.24 #174, 0.13 #862, 0.13 #212), 015h31 (0.21 #315, 0.19 #811, 0.11 #1046), 0d2b38 (0.21 #180, 0.15 #868, 0.15 #333) >> Best rule #1277 for best value: >> intensional similarity = 6 >> extensional distance = 260 >> proper extension: 09p35z; 01q2nx; 047gpsd; 01gglm; 0fsd9t; 03hp2y1; >> query: (?x6168, 0ch6mp2) <- executive_produced_by(?x6168, ?x881), film(?x1942, ?x6168), film_crew_role(?x6168, ?x468), language(?x6168, ?x254), category(?x1942, ?x134), profession(?x1942, ?x319) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #280 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 43 *> proper extension: 014lc_; 0gtsx8c; 017gl1; 08hmch; 01c22t; 0jjy0; 0gmcwlb; 0dtfn; 017gm7; 0bq8tmw; ... *> query: (?x6168, 01vx2h) <- executive_produced_by(?x6168, ?x881), film_release_region(?x6168, ?x1353), film_release_region(?x6168, ?x1023), film_release_region(?x6168, ?x1003), film_release_region(?x6168, ?x512), ?x1353 = 035qy, ?x1023 = 0ctw_b, ?x1003 = 03gj2, ?x512 = 07ssc *> conf = 0.40 ranks of expected_values: 4 EVAL 0gj96ln film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 83.000 83.000 0.775 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #325-0kjgl PRED entity: 0kjgl PRED relation: award PRED expected values: 09qvc0 07cbcy => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 252): 054ky1 (0.71 #14404, 0.70 #24811, 0.70 #24410), 027c95y (0.71 #14404, 0.70 #24811, 0.70 #24410), 027986c (0.71 #14404, 0.70 #24811, 0.70 #24410), 040njc (0.25 #1609, 0.24 #4409, 0.22 #808), 0gqyl (0.25 #101, 0.15 #21207, 0.12 #30013), 02x17c2 (0.25 #215, 0.15 #21207, 0.08 #22008), 02lp0w (0.25 #246, 0.15 #21207, 0.08 #22008), 054ks3 (0.25 #137, 0.14 #537, 0.08 #22008), 0gqwc (0.25 #71, 0.12 #30013, 0.12 #3672), 094qd5 (0.25 #42, 0.12 #30013, 0.10 #3643) >> Best rule #14404 for best value: >> intensional similarity = 3 >> extensional distance = 1241 >> proper extension: 03yxwq; 03lpbx; >> query: (?x7946, ?x591) <- award_winner(?x7946, ?x3139), award_nominee(?x156, ?x3139), award_winner(?x591, ?x7946) >> conf = 0.71 => this is the best rule for 3 predicted values *> Best rule #30013 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 2257 *> proper extension: 07nznf; 0q9kd; 06qgvf; 0grwj; 05bnp0; 016qtt; 0jz9f; 04qvl7; 04yywz; 01k7d9; ... *> query: (?x7946, ?x384) <- nominated_for(?x7946, ?x3826), nominated_for(?x384, ?x3826) *> conf = 0.12 ranks of expected_values: 61, 67 EVAL 0kjgl award 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 89.000 89.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0kjgl award 09qvc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 89.000 89.000 0.706 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #324-02vm9nd PRED entity: 02vm9nd PRED relation: award! PRED expected values: 01f7j9 => 55 concepts (25 used for prediction) PRED predicted values (max 10 best out of 3082): 016tt2 (0.33 #128, 0.27 #3376, 0.25 #6880), 01f7j9 (0.33 #572, 0.27 #3376, 0.25 #7324), 086k8 (0.33 #68, 0.27 #3376, 0.25 #6820), 05nn4k (0.33 #1354, 0.27 #3376, 0.25 #8106), 030_1m (0.33 #391, 0.27 #3376, 0.25 #7143), 047q2wc (0.33 #1112, 0.27 #3376, 0.25 #7864), 02v0ff (0.33 #4489, 0.27 #3376, 0.23 #6752), 03h304l (0.33 #1444, 0.25 #8196, 0.23 #6752), 07r1h (0.33 #1806, 0.25 #8558, 0.12 #38940), 026c1 (0.33 #580, 0.25 #7332, 0.12 #14082) >> Best rule #128 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 05p1dby; >> query: (?x2750, 016tt2) <- award(?x7904, ?x2750), award(?x11580, ?x2750), award(?x5781, ?x2750), award(?x703, ?x2750), ?x11580 = 0cv9fc, ?x5781 = 03ktjq, award_nominee(?x157, ?x703) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #572 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 1 *> proper extension: 05p1dby; *> query: (?x2750, 01f7j9) <- award(?x7904, ?x2750), award(?x11580, ?x2750), award(?x5781, ?x2750), award(?x703, ?x2750), ?x11580 = 0cv9fc, ?x5781 = 03ktjq, award_nominee(?x157, ?x703) *> conf = 0.33 ranks of expected_values: 2 EVAL 02vm9nd award! 01f7j9 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 55.000 25.000 0.333 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #323-0vm39 PRED entity: 0vm39 PRED relation: contains! PRED expected values: 02gt5s => 89 concepts (36 used for prediction) PRED predicted values (max 10 best out of 202): 02gt5s (0.75 #20575, 0.74 #23261, 0.67 #19680), 02qkt (0.24 #14656, 0.23 #16448, 0.19 #7497), 01n7q (0.23 #20652, 0.22 #21548, 0.22 #22444), 02j9z (0.19 #9867, 0.14 #14338, 0.13 #16130), 07ssc (0.15 #25080, 0.07 #27763, 0.07 #30442), 0j0k (0.13 #14687, 0.12 #16479, 0.09 #15583), 0kpys (0.13 #11806, 0.13 #20754, 0.11 #21650), 059rby (0.13 #29537, 0.08 #17912, 0.07 #13437), 05fjf (0.12 #11999, 0.11 #13790, 0.10 #18265), 04_1l0v (0.11 #13867, 0.10 #18342, 0.07 #29074) >> Best rule #20575 for best value: >> intensional similarity = 4 >> extensional distance = 198 >> proper extension: 0k049; 02dtg; 01tlmw; 0f2r6; 0288zy; 0_3cs; 0yc84; 0xkq4; 0fvxz; 0r1yc; ... >> query: (?x8969, ?x11993) <- contains(?x8968, ?x8969), category(?x8969, ?x134), contains(?x11993, ?x8968), county(?x7321, ?x8968) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0vm39 contains! 02gt5s CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 89.000 36.000 0.751 http://example.org/location/location/contains #322-0bm02 PRED entity: 0bm02 PRED relation: role PRED expected values: 02dlh2 => 59 concepts (48 used for prediction) PRED predicted values (max 10 best out of 112): 01vj9c (0.90 #3029, 0.84 #3849, 0.84 #2794), 013y1f (0.87 #2194, 0.87 #2116, 0.85 #2419), 01p970 (0.85 #448, 0.85 #447, 0.84 #1251), 018vs (0.83 #3125, 0.83 #3027, 0.80 #2211), 0l14md (0.82 #1622, 0.80 #2090, 0.79 #2899), 042v_gx (0.81 #2667, 0.77 #3022, 0.75 #922), 0342h (0.80 #5353, 0.78 #4887, 0.76 #4072), 0g2dz (0.77 #1995, 0.75 #1290, 0.73 #2114), 0mkg (0.77 #1975, 0.73 #1505, 0.71 #694), 0l14j_ (0.77 #2026, 0.73 #1556, 0.71 #450) >> Best rule #3029 for best value: >> intensional similarity = 27 >> extensional distance = 28 >> proper extension: 04q7r; >> query: (?x1268, 01vj9c) <- role(?x3161, ?x1268), role(?x1495, ?x1268), role(?x1267, ?x1268), ?x1267 = 07brj, role(?x10144, ?x1495), role(?x7053, ?x1495), role(?x3168, ?x1495), role(?x2725, ?x1495), role(?x1332, ?x1495), role(?x716, ?x1495), role(?x645, ?x1495), group(?x1495, ?x8226), ?x2725 = 0l1589, instrumentalists(?x1495, ?x483), ?x716 = 018vs, artists(?x1000, ?x3168), ?x1000 = 0xhtw, role(?x1433, ?x1268), artists(?x2491, ?x8226), participant(?x7053, ?x12422), ?x3161 = 01v1d8, role(?x1332, ?x2675), artist(?x2149, ?x8226), group(?x645, ?x3420), profession(?x10144, ?x131), ?x3420 = 0134s5, group(?x7570, ?x8226) >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #450 for first EXPECTED value: *> intensional similarity = 30 *> extensional distance = 2 *> proper extension: 026t6; *> query: (?x1268, ?x1574) <- role(?x8172, ?x1268), role(?x2798, ?x1268), role(?x1969, ?x1268), role(?x1495, ?x1268), role(?x1432, ?x1268), role(?x1267, ?x1268), role(?x1225, ?x1268), role(?x615, ?x1268), role(?x316, ?x1268), role(?x314, ?x1268), ?x1267 = 07brj, ?x1495 = 013y1f, role(?x1268, ?x227), ?x316 = 05r5c, ?x1969 = 04rzd, role(?x1268, ?x885), ?x8172 = 06rvn, ?x1432 = 0395lw, ?x1225 = 01qbl, role(?x1660, ?x1268), ?x2798 = 03qjg, role(?x615, ?x7449), role(?x315, ?x615), ?x227 = 0342h, ?x314 = 02sgy, group(?x615, ?x6475), role(?x1574, ?x615), ?x7449 = 01vnt4, instrumentalists(?x615, ?x1338), ?x6475 = 07mvp *> conf = 0.71 ranks of expected_values: 33 EVAL 0bm02 role 02dlh2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 59.000 48.000 0.900 http://example.org/music/performance_role/track_performances./music/track_contribution/role #321-0mj0c PRED entity: 0mj0c PRED relation: nationality PRED expected values: 09c7w0 => 143 concepts (136 used for prediction) PRED predicted values (max 10 best out of 138): 09c7w0 (0.88 #6318, 0.82 #5316, 0.82 #7926), 07ssc (0.88 #6318, 0.40 #516, 0.38 #3410), 0345h (0.50 #132, 0.36 #3542, 0.31 #1736), 0h7x (0.43 #938, 0.28 #7824, 0.25 #1439), 02jx1 (0.25 #434, 0.21 #4546, 0.20 #534), 059j2 (0.25 #230, 0.02 #4442, 0.02 #4943), 0f8l9c (0.24 #9633, 0.22 #1026, 0.20 #1126), 02_286 (0.23 #1505, 0.20 #1004, 0.20 #101), 05k7sb (0.23 #11941, 0.22 #11737, 0.09 #11940), 01cx_ (0.23 #11941, 0.22 #11737) >> Best rule #6318 for best value: >> intensional similarity = 4 >> extensional distance = 74 >> proper extension: 0dv1hh; 09m465; >> query: (?x3941, ?x94) <- gender(?x3941, ?x231), sibling(?x7334, ?x3941), ?x231 = 05zppz, nationality(?x7334, ?x94) >> conf = 0.88 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0mj0c nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 143.000 136.000 0.878 http://example.org/people/person/nationality #320-05cgv PRED entity: 05cgv PRED relation: olympics PRED expected values: 0l98s => 161 concepts (161 used for prediction) PRED predicted values (max 10 best out of 36): 06sks6 (0.77 #741, 0.72 #921, 0.71 #597), 0l6mp (0.69 #339, 0.55 #231, 0.53 #735), 0lgxj (0.67 #347, 0.58 #59, 0.55 #563), 0l998 (0.67 #330, 0.45 #726, 0.42 #222), 0jkvj (0.61 #356, 0.43 #752, 0.42 #68), 0l98s (0.61 #329, 0.42 #725, 0.39 #905), 09x3r (0.58 #45, 0.50 #333, 0.47 #297), 0lbd9 (0.58 #351, 0.42 #747, 0.36 #171), 0ldqf (0.56 #355, 0.38 #751, 0.35 #247), 0blg2 (0.53 #338, 0.38 #302, 0.35 #230) >> Best rule #741 for best value: >> intensional similarity = 3 >> extensional distance = 58 >> proper extension: 09c7w0; 059j2; 035qy; 015qh; 01mjq; 06t8v; 04w8f; 087vz; 03shp; >> query: (?x1241, 06sks6) <- olympics(?x1241, ?x3971), ?x3971 = 0jhn7, film_release_region(?x2050, ?x1241) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #329 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 34 *> proper extension: 0d060g; 05v8c; 06mzp; 035dk; 07twz; 05vz3zq; 0193qj; 01mk6; *> query: (?x1241, 0l98s) <- olympics(?x1241, ?x2369), ?x2369 = 0lbbj *> conf = 0.61 ranks of expected_values: 6 EVAL 05cgv olympics 0l98s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 161.000 161.000 0.767 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #319-03k9fj PRED entity: 03k9fj PRED relation: titles PRED expected values: 05q96q6 05szq8z => 65 concepts (24 used for prediction) PRED predicted values (max 10 best out of 1755): 027pfg (0.42 #7528, 0.40 #22088, 0.34 #4515), 02q_x_l (0.42 #7528, 0.39 #9034, 0.37 #9035), 06zsk51 (0.42 #7528, 0.39 #9034, 0.33 #8798), 07ng9k (0.42 #7528, 0.39 #9034, 0.32 #4514), 03177r (0.42 #7528, 0.34 #4515, 0.33 #7529), 02q5bx2 (0.42 #7528, 0.34 #4515, 0.33 #8731), 09gb_4p (0.42 #7528, 0.34 #4515, 0.33 #3646), 0dcz8_ (0.42 #7528, 0.34 #4515, 0.33 #7332), 0415ggl (0.42 #7528, 0.34 #4515, 0.33 #3815), 0b73_1d (0.42 #7528, 0.34 #4515, 0.33 #3116) >> Best rule #7528 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 01hmnh; >> query: (?x811, ?x66) <- genre(?x8359, ?x811), genre(?x2869, ?x811), genre(?x721, ?x811), genre(?x626, ?x811), genre(?x66, ?x811), genre(?x50, ?x811), ?x721 = 0fr63l, ?x626 = 0cpllql, ?x8359 = 015ynm, ?x2869 = 03177r >> conf = 0.42 => this is the best rule for 315 predicted values *> Best rule #6794 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 01hmnh; *> query: (?x811, 05szq8z) <- genre(?x8359, ?x811), genre(?x2869, ?x811), genre(?x721, ?x811), genre(?x626, ?x811), genre(?x50, ?x811), ?x721 = 0fr63l, ?x626 = 0cpllql, ?x8359 = 015ynm, ?x2869 = 03177r *> conf = 0.33 ranks of expected_values: 768, 1082 EVAL 03k9fj titles 05szq8z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 65.000 24.000 0.420 http://example.org/media_common/netflix_genre/titles EVAL 03k9fj titles 05q96q6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 65.000 24.000 0.420 http://example.org/media_common/netflix_genre/titles #318-05kkh PRED entity: 05kkh PRED relation: partially_contains PRED expected values: 0lm0n => 161 concepts (132 used for prediction) PRED predicted values (max 10 best out of 31): 0lm0n (0.33 #455, 0.33 #261, 0.33 #1006), 0k3nk (0.33 #14, 0.12 #91, 0.11 #130), 06c6l (0.33 #30, 0.08 #107, 0.07 #146), 04yf_ (0.21 #207, 0.19 #440, 0.18 #285), 02cgp8 (0.20 #64, 0.14 #220, 0.12 #453), 02m4d (0.20 #65, 0.04 #103, 0.04 #142), 04ykz (0.14 #501, 0.14 #268, 0.13 #307), 026zt (0.11 #1593, 0.07 #1951, 0.05 #1712), 0lcd (0.09 #1585, 0.08 #93, 0.07 #132), 02v3m7 (0.07 #223, 0.05 #301, 0.04 #418) >> Best rule #455 for best value: >> intensional similarity = 3 >> extensional distance = 46 >> proper extension: 0694j; >> query: (?x177, 0lm0n) <- state(?x4945, ?x177), jurisdiction_of_office(?x900, ?x177), currency(?x177, ?x170) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05kkh partially_contains 0lm0n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 161.000 132.000 0.333 http://example.org/location/location/partially_contains #317-013t9y PRED entity: 013t9y PRED relation: gender PRED expected values: 05zppz => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.89 #29, 0.88 #19, 0.88 #25), 02zsn (0.46 #219, 0.33 #2, 0.30 #76) >> Best rule #29 for best value: >> intensional similarity = 5 >> extensional distance = 130 >> proper extension: 0hky; >> query: (?x6629, 05zppz) <- award(?x6629, ?x1307), award(?x6629, ?x601), award(?x11554, ?x601), ?x11554 = 03cdg, award_winner(?x1307, ?x163) >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013t9y gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 112.000 0.894 http://example.org/people/person/gender #316-014zcr PRED entity: 014zcr PRED relation: participant! PRED expected values: 0f4vbz => 131 concepts (86 used for prediction) PRED predicted values (max 10 best out of 274): 0f4vbz (0.81 #16694, 0.80 #11744, 0.80 #23492), 0151w_ (0.20 #11742, 0.08 #11741, 0.07 #16693), 032_jg (0.17 #55, 0.08 #1292, 0.06 #11743), 043zg (0.17 #348, 0.08 #1585, 0.06 #11743), 01pllx (0.17 #523, 0.08 #1760, 0.06 #11743), 01y665 (0.17 #207, 0.08 #1444, 0.06 #11743), 04bs3j (0.17 #38, 0.08 #1275, 0.06 #11743), 0gd9k (0.17 #481, 0.08 #1718, 0.06 #11743), 02238b (0.17 #434, 0.08 #1671, 0.06 #11743), 02lkcc (0.17 #98, 0.08 #1335, 0.06 #11743) >> Best rule #16694 for best value: >> intensional similarity = 3 >> extensional distance = 266 >> proper extension: 0411q; 0157m; 016kkx; 03d0ns; 05g7q; 027zz; >> query: (?x286, ?x1554) <- award_winner(?x426, ?x286), participant(?x286, ?x1554), participant(?x969, ?x286) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 014zcr participant! 0f4vbz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 86.000 0.807 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #315-01797x PRED entity: 01797x PRED relation: profession PRED expected values: 039v1 => 151 concepts (97 used for prediction) PRED predicted values (max 10 best out of 71): 0nbcg (0.56 #1800, 0.54 #767, 0.52 #1358), 0dz3r (0.48 #886, 0.46 #1182, 0.45 #1330), 01d_h8 (0.43 #4132, 0.39 #1481, 0.35 #5899), 039v1 (0.37 #35, 0.34 #1805, 0.33 #772), 01c72t (0.34 #3560, 0.33 #9605, 0.33 #4591), 0n1h (0.34 #601, 0.31 #452, 0.26 #748), 0dxtg (0.29 #1489, 0.28 #4140, 0.26 #14017), 03gjzk (0.28 #4141, 0.25 #1490, 0.21 #5908), 02jknp (0.23 #10180, 0.20 #4134, 0.20 #11508), 0d1pc (0.20 #490, 0.15 #639, 0.14 #2264) >> Best rule #1800 for best value: >> intensional similarity = 3 >> extensional distance = 176 >> proper extension: 018y81; >> query: (?x10396, 0nbcg) <- role(?x10396, ?x227), artists(?x1572, ?x10396), ?x1572 = 06by7 >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #35 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 33 *> proper extension: 05crg7; 0dvqq; 0frsw; 02lbrd; 04qmr; 0134tg; 0l8g0; 015cxv; 0b_xm; 07sbk; ... *> query: (?x10396, 039v1) <- award_winner(?x9945, ?x10396), award(?x10396, ?x4912), ?x4912 = 01ckrr, artists(?x671, ?x10396) *> conf = 0.37 ranks of expected_values: 4 EVAL 01797x profession 039v1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 151.000 97.000 0.556 http://example.org/people/person/profession #314-06zn2v2 PRED entity: 06zn2v2 PRED relation: film_release_region PRED expected values: 03_3d 0d0vqn 01mjq 06bnz => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 178): 0d0vqn (0.93 #1321, 0.90 #1613, 0.89 #2928), 03_3d (0.80 #881, 0.78 #1612, 0.77 #2343), 06bnz (0.75 #1210, 0.74 #1648, 0.72 #1502), 05b4w (0.75 #933, 0.74 #1956, 0.74 #1664), 03spz (0.71 #963, 0.69 #1548, 0.67 #1256), 03rt9 (0.68 #1328, 0.66 #889, 0.65 #2351), 047yc (0.59 #1486, 0.50 #1632, 0.49 #1194), 0ctw_b (0.57 #2361, 0.52 #899, 0.51 #1338), 04gzd (0.56 #1470, 0.55 #1616, 0.52 #2347), 01mjq (0.55 #2377, 0.53 #915, 0.50 #2523) >> Best rule #1321 for best value: >> intensional similarity = 5 >> extensional distance = 107 >> proper extension: 0jqp3; 053rxgm; 011yqc; 0ch26b_; 01fmys; 07f_7h; 03qnc6q; 0g5879y; 040b5k; 0j43swk; ... >> query: (?x4422, 0d0vqn) <- film(?x72, ?x4422), country(?x4422, ?x94), film_release_region(?x4422, ?x1353), produced_by(?x4422, ?x6718), ?x1353 = 035qy >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 10 EVAL 06zn2v2 film_release_region 06bnz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06zn2v2 film_release_region 01mjq CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 100.000 100.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06zn2v2 film_release_region 0d0vqn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 06zn2v2 film_release_region 03_3d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 100.000 0.927 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #313-0h1q6 PRED entity: 0h1q6 PRED relation: type_of_union PRED expected values: 04ztj => 70 concepts (70 used for prediction) PRED predicted values (max 10 best out of 4): 04ztj (0.87 #21, 0.87 #29, 0.86 #25), 01g63y (0.19 #277, 0.14 #42, 0.14 #54), 0jgjn (0.19 #277, 0.02 #48), 01bl8s (0.19 #277, 0.01 #23, 0.01 #27) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 84 >> proper extension: 01wj9y9; 03h_yfh; >> query: (?x12298, 04ztj) <- profession(?x12298, ?x1032), celebrities_impersonated(?x3649, ?x12298), location(?x12298, ?x362) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h1q6 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 70.000 70.000 0.872 http://example.org/people/person/spouse_s./people/marriage/type_of_union #312-0r0m6 PRED entity: 0r0m6 PRED relation: location_of_ceremony! PRED expected values: 0z4s 01cv3n 01svw8n 09yrh 0kryqm => 130 concepts (105 used for prediction) PRED predicted values (max 10 best out of 278): 02m30v (0.25 #745, 0.25 #495, 0.10 #991), 03j24kf (0.25 #605, 0.06 #1593, 0.05 #2582), 01rwcgb (0.25 #719, 0.06 #2203, 0.05 #965), 0h7pj (0.25 #445, 0.05 #2918, 0.05 #3164), 01vsy7t (0.25 #603, 0.05 #849, 0.04 #1095), 014v1q (0.25 #737, 0.05 #983, 0.04 #1229), 0436kgz (0.25 #655, 0.05 #901, 0.04 #1147), 02g0rb (0.25 #650, 0.05 #896, 0.04 #1142), 02_j7t (0.25 #542, 0.05 #788, 0.04 #1034), 01nglk (0.25 #482, 0.05 #978, 0.04 #1224) >> Best rule #745 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 02k54; >> query: (?x4151, 02m30v) <- featured_film_locations(?x951, ?x4151), ?x951 = 0cwy47, contains(?x4151, ?x6455) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0r0m6 location_of_ceremony! 0kryqm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 105.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony EVAL 0r0m6 location_of_ceremony! 09yrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 105.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony EVAL 0r0m6 location_of_ceremony! 01svw8n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 105.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony EVAL 0r0m6 location_of_ceremony! 01cv3n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 105.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony EVAL 0r0m6 location_of_ceremony! 0z4s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 130.000 105.000 0.250 http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony #311-04tnqn PRED entity: 04tnqn PRED relation: profession PRED expected values: 02hrh1q 0kyk => 100 concepts (99 used for prediction) PRED predicted values (max 10 best out of 64): 02hrh1q (0.92 #607, 0.91 #459, 0.89 #10227), 0cbd2 (0.45 #747, 0.45 #1043, 0.44 #895), 0dxtg (0.45 #162, 0.45 #310, 0.39 #902), 0kyk (0.35 #177, 0.30 #325, 0.30 #769), 01d_h8 (0.35 #1190, 0.34 #2374, 0.33 #1782), 0np9r (0.26 #11841, 0.15 #10232, 0.14 #10972), 02krf9 (0.26 #11841, 0.09 #8018, 0.09 #8610), 02jknp (0.23 #1192, 0.22 #8000, 0.21 #2376), 09jwl (0.21 #5790, 0.18 #4606, 0.18 #2682), 0nbcg (0.15 #5803, 0.13 #4323, 0.12 #4619) >> Best rule #607 for best value: >> intensional similarity = 3 >> extensional distance = 384 >> proper extension: 01m65sp; 044mfr; 02rmxx; 01kmd4; 0163t3; 02_wxh; 04bbv7; 01tpl1p; 07bsj; 01j5sv; ... >> query: (?x9656, 02hrh1q) <- people(?x2510, ?x9656), actor(?x2528, ?x9656), profession(?x9656, ?x1041) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4 EVAL 04tnqn profession 0kyk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 100.000 99.000 0.920 http://example.org/people/person/profession EVAL 04tnqn profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 100.000 99.000 0.920 http://example.org/people/person/profession #310-0jxy PRED entity: 0jxy PRED relation: genre! PRED expected values: 017dtf 06xkst => 68 concepts (26 used for prediction) PRED predicted values (max 10 best out of 260): 0ctzf1 (0.71 #3761, 0.60 #1525, 0.57 #4041), 06r1k (0.67 #3293, 0.60 #4975, 0.50 #1061), 05f7w84 (0.60 #4856, 0.50 #2336, 0.40 #1776), 028k2x (0.50 #4893, 0.50 #3211, 0.43 #4051), 09g_31 (0.50 #4912, 0.50 #3230, 0.43 #4070), 0dk0dj (0.50 #4356, 0.50 #2677, 0.43 #3514), 03g9xj (0.50 #4945, 0.50 #3263, 0.40 #4663), 0123qq (0.50 #4978, 0.50 #3296, 0.40 #2181), 06xkst (0.50 #1052, 0.40 #1886, 0.40 #1608), 017dtf (0.50 #1039, 0.40 #4953, 0.39 #6146) >> Best rule #3761 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 01jfsb; >> query: (?x5937, 0ctzf1) <- genre(?x3844, ?x5937), genre(?x1419, ?x5937), genre(?x596, ?x5937), genre(?x8610, ?x5937), genre(?x8610, ?x2540), actor(?x596, ?x11470), film_release_distribution_medium(?x596, ?x81), ?x2540 = 0hcr, actor(?x8610, ?x4944), country(?x3844, ?x94), ?x1419 = 02vw1w2, film(?x11470, ?x1184) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #1052 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 2 *> proper extension: 07s9rl0; *> query: (?x5937, 06xkst) <- genre(?x5936, ?x5937), genre(?x5286, ?x5937), genre(?x596, ?x5937), genre(?x8610, ?x5937), ?x8610 = 01lk02, production_companies(?x596, ?x3920), film_release_region(?x5286, ?x252), film_release_distribution_medium(?x5286, ?x81), currency(?x5286, ?x12281), nominated_for(?x5287, ?x596), ?x5936 = 02q3fdr, actor(?x5286, ?x1382) *> conf = 0.50 ranks of expected_values: 9, 10 EVAL 0jxy genre! 06xkst CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 68.000 26.000 0.714 http://example.org/tv/tv_program/genre EVAL 0jxy genre! 017dtf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 68.000 26.000 0.714 http://example.org/tv/tv_program/genre #309-02cx90 PRED entity: 02cx90 PRED relation: artists! PRED expected values: 0gg8l 02mscn => 123 concepts (106 used for prediction) PRED predicted values (max 10 best out of 217): 064t9 (0.50 #2819, 0.48 #2508, 0.46 #4689), 06by7 (0.46 #959, 0.45 #3450, 0.44 #2205), 0glt670 (0.33 #2847, 0.22 #4717, 0.21 #3158), 017_qw (0.31 #1935, 0.12 #8164, 0.12 #5673), 06j6l (0.30 #2854, 0.27 #4724, 0.25 #5969), 025sc50 (0.28 #2856, 0.24 #4726, 0.22 #2545), 016clz (0.28 #317, 0.26 #629, 0.23 #9041), 05bt6j (0.25 #2539, 0.22 #6276, 0.21 #4720), 03_d0 (0.25 #12, 0.22 #324, 0.21 #4998), 0gywn (0.23 #4734, 0.22 #2864, 0.22 #2553) >> Best rule #2819 for best value: >> intensional similarity = 3 >> extensional distance = 213 >> proper extension: 03fbc; >> query: (?x4343, 064t9) <- award_nominee(?x7258, ?x4343), artist(?x2241, ?x7258), origin(?x4343, ?x13450) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1691 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 180 *> proper extension: 01r6jt2; 03k0yw; 0149xx; 02w670; 02ryx0; 0kftt; 05mxw33; 02qtywd; *> query: (?x4343, 0gg8l) <- award_nominee(?x158, ?x4343), role(?x4343, ?x75), award_winner(?x139, ?x4343) *> conf = 0.08 ranks of expected_values: 40, 81 EVAL 02cx90 artists! 02mscn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 123.000 106.000 0.498 http://example.org/music/genre/artists EVAL 02cx90 artists! 0gg8l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 123.000 106.000 0.498 http://example.org/music/genre/artists #308-05l71 PRED entity: 05l71 PRED relation: school PRED expected values: 07w0v => 114 concepts (104 used for prediction) PRED predicted values (max 10 best out of 186): 0lyjf (0.53 #6442, 0.43 #7381, 0.42 #8507), 0bx8pn (0.43 #955, 0.33 #22, 0.20 #14477), 01vs5c (0.38 #1961, 0.33 #4399, 0.30 #7396), 06pwq (0.33 #6, 0.29 #939, 0.21 #12770), 0jkhr (0.33 #111, 0.29 #1044, 0.17 #4047), 01ptt7 (0.33 #25, 0.29 #958, 0.17 #4711), 01jsn5 (0.33 #27, 0.29 #960, 0.07 #14107), 05krk (0.33 #4, 0.25 #190, 0.24 #5814), 01pl14 (0.33 #5, 0.25 #191, 0.22 #7314), 05x_5 (0.33 #116, 0.25 #302, 0.17 #3679) >> Best rule #6442 for best value: >> intensional similarity = 6 >> extensional distance = 17 >> proper extension: 05g3b; 03lsq; >> query: (?x4170, 0lyjf) <- position(?x4170, ?x180), position(?x4170, ?x1240), draft(?x4170, ?x3089), ?x1240 = 023wyl, school(?x4170, ?x546), ?x3089 = 03nt7j >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #12774 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 68 *> proper extension: 01ypc; 05m_8; 0jm3v; 01ync; 01slc; 07l4z; 04wmvz; *> query: (?x4170, 07w0v) <- team(?x180, ?x4170), school(?x4170, ?x546), draft(?x4170, ?x465), colors(?x4170, ?x4557), currency(?x546, ?x170), student(?x546, ?x547), major_field_of_study(?x546, ?x742) *> conf = 0.26 ranks of expected_values: 124 EVAL 05l71 school 07w0v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 114.000 104.000 0.526 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #307-0640y35 PRED entity: 0640y35 PRED relation: film! PRED expected values: 034np8 => 118 concepts (46 used for prediction) PRED predicted values (max 10 best out of 1257): 0b2_xp (0.24 #4157, 0.18 #91462), 0f7hc (0.20 #830, 0.04 #7065, 0.04 #29930), 01qr1_ (0.20 #605, 0.03 #21391, 0.02 #17233), 04pqqb (0.18 #12470, 0.14 #35335, 0.12 #83147), 0f0kz (0.15 #2594, 0.14 #19223, 0.13 #15065), 03ym1 (0.15 #3089, 0.11 #11402, 0.10 #19718), 015t56 (0.15 #2548, 0.08 #10861, 0.04 #6705), 0lx2l (0.15 #2498, 0.07 #44069, 0.04 #46148), 0svqs (0.15 #2952, 0.06 #11265, 0.05 #19581), 02gvwz (0.15 #2266, 0.06 #10579, 0.04 #29288) >> Best rule #4157 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 08fbnx; >> query: (?x5847, ?x8164) <- genre(?x5847, ?x1510), country(?x5847, ?x94), film(?x2437, ?x5847), ?x1510 = 01hmnh, prequel(?x428, ?x5847), written_by(?x5847, ?x8164) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #43941 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 153 *> proper extension: 0gkz15s; 01vksx; 02v63m; 0gj8t_b; 031t2d; 02vqhv0; 01hqhm; 0gfsq9; 051zy_b; 023p7l; ... *> query: (?x5847, 034np8) <- genre(?x5847, ?x258), film(?x9545, ?x5847), participant(?x9545, ?x513), award_winner(?x4535, ?x9545), notable_people_with_this_condition(?x11990, ?x9545) *> conf = 0.03 ranks of expected_values: 653 EVAL 0640y35 film! 034np8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 118.000 46.000 0.235 http://example.org/film/actor/film./film/performance/film #306-01my_c PRED entity: 01my_c PRED relation: award_nominee! PRED expected values: 01pfkw => 113 concepts (59 used for prediction) PRED predicted values (max 10 best out of 1068): 01pfkw (0.81 #104880, 0.81 #65251, 0.81 #130522), 04lgymt (0.33 #86231, 0.32 #88564, 0.05 #32725), 016732 (0.33 #86231, 0.32 #88564, 0.01 #34170), 0127m7 (0.33 #525, 0.29 #5185, 0.12 #7515), 01f7j9 (0.33 #461, 0.29 #5121, 0.12 #7451), 0147dk (0.33 #98, 0.29 #4758, 0.12 #7088), 086k8 (0.33 #62, 0.14 #4722, 0.12 #7052), 01my_c (0.32 #88564, 0.12 #20973, 0.03 #41189), 04cw0j (0.29 #5371, 0.20 #14693, 0.19 #19353), 017s11 (0.29 #4766, 0.20 #14088, 0.19 #18748) >> Best rule #104880 for best value: >> intensional similarity = 4 >> extensional distance = 449 >> proper extension: 01dq9q; 07sbk; >> query: (?x6937, ?x1367) <- category(?x6937, ?x134), award_nominee(?x6937, ?x7040), award_nominee(?x6937, ?x1367), film(?x7040, ?x365) >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01my_c award_nominee! 01pfkw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 113.000 59.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #305-04l590 PRED entity: 04l590 PRED relation: teams! PRED expected values: 0c1d0 => 57 concepts (52 used for prediction) PRED predicted values (max 10 best out of 148): 061k5 (0.17 #242, 0.04 #3223, 0.04 #4578), 01n43d (0.17 #211, 0.04 #3192, 0.04 #4547), 09pmkv (0.14 #304, 0.08 #1661, 0.04 #3014), 0135k2 (0.14 #460, 0.08 #1817, 0.04 #3170), 0sjqm (0.11 #2711, 0.11 #2710, 0.11 #2709), 03pzf (0.11 #2711, 0.11 #2710, 0.11 #2709), 0f1sm (0.11 #2711, 0.11 #2710, 0.11 #2709), 071cn (0.11 #2711, 0.11 #2710, 0.11 #2709), 0n1rj (0.11 #687, 0.10 #958, 0.09 #1228), 0hptm (0.11 #689, 0.10 #960, 0.09 #1230) >> Best rule #242 for best value: >> intensional similarity = 18 >> extensional distance = 4 >> proper extension: 0gxkm; 0ytc; 01_gv; 0lmm3; >> query: (?x14124, 061k5) <- colors(?x14124, ?x3621), colors(?x14124, ?x663), team(?x3724, ?x14124), team(?x2918, ?x14124), ?x3621 = 088fh, team(?x13270, ?x14124), team(?x3724, ?x14258), team(?x3724, ?x2919), team(?x2918, ?x12541), sport(?x2919, ?x453), colors(?x12541, ?x8047), teams(?x9445, ?x12541), colors(?x14258, ?x1101), ?x1101 = 06fvc, colors(?x7545, ?x663), colors(?x11673, ?x663), ?x11673 = 02gtm4, student(?x7545, ?x157) >> conf = 0.17 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04l590 teams! 0c1d0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 57.000 52.000 0.167 http://example.org/sports/sports_team_location/teams #304-073hkh PRED entity: 073hkh PRED relation: honored_for PRED expected values: 0yyg4 0k2cb => 32 concepts (17 used for prediction) PRED predicted values (max 10 best out of 971): 07024 (0.33 #176, 0.10 #2376, 0.05 #4334), 011yr9 (0.33 #250, 0.10 #2376, 0.05 #4408), 0170xl (0.33 #568, 0.10 #2376, 0.05 #4726), 0m313 (0.33 #3, 0.10 #2376, 0.05 #4161), 0170yd (0.33 #482, 0.10 #2376, 0.05 #4640), 032zq6 (0.33 #249, 0.10 #2376, 0.05 #4407), 011yrp (0.33 #13, 0.10 #2376, 0.05 #4171), 0bdjd (0.25 #1030, 0.16 #4753, 0.16 #5944), 02lxrv (0.25 #945, 0.10 #2376, 0.07 #8936), 0bt4g (0.25 #1045, 0.10 #2376, 0.07 #8936) >> Best rule #176 for best value: >> intensional similarity = 16 >> extensional distance = 1 >> proper extension: 02ywhz; >> query: (?x78, 07024) <- honored_for(?x78, ?x582), ceremony(?x3458, ?x78), ceremony(?x1703, ?x78), ceremony(?x1323, ?x78), ?x1703 = 0k611, ?x3458 = 0gqxm, award_winner(?x78, ?x6718), award_winner(?x78, ?x2715), award_winner(?x78, ?x930), ?x1323 = 0gqz2, award_winner(?x6718, ?x2900), award_winner(?x2366, ?x930), profession(?x6718, ?x319), award_nominee(?x1676, ?x6718), participant(?x3100, ?x2715), ?x3100 = 02g0mx >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #4753 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 19 *> proper extension: 02yw5r; 0bzm81; 02yv_b; 073h1t; 0gmdkyy; 0bvfqq; 050yyb; 02hn5v; 0bc773; 02yvhx; ... *> query: (?x78, ?x2366) <- honored_for(?x78, ?x582), ceremony(?x3458, ?x78), ceremony(?x1703, ?x78), ceremony(?x1323, ?x78), ?x1703 = 0k611, ?x3458 = 0gqxm, award_winner(?x78, ?x6718), award_winner(?x78, ?x2715), award_winner(?x78, ?x930), ?x1323 = 0gqz2, award_winner(?x6718, ?x2900), award_winner(?x2366, ?x930), profession(?x6718, ?x319), award_nominee(?x1676, ?x6718), participant(?x3100, ?x2715), place_of_birth(?x3100, ?x1523) *> conf = 0.16 ranks of expected_values: 25, 926 EVAL 073hkh honored_for 0k2cb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.040 32.000 17.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 073hkh honored_for 0yyg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 32.000 17.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #303-02xry PRED entity: 02xry PRED relation: origin! PRED expected values: 03h502k => 168 concepts (148 used for prediction) PRED predicted values (max 10 best out of 358): 02s2wq (0.25 #273, 0.17 #787, 0.16 #37560), 06nv27 (0.25 #217, 0.17 #731, 0.07 #2277), 01wv9p (0.25 #168, 0.17 #682, 0.06 #1713), 015bwt (0.25 #474, 0.17 #988, 0.06 #2019), 0cbm64 (0.25 #409, 0.17 #923, 0.04 #2469), 0d193h (0.25 #172, 0.17 #686, 0.04 #6348), 0136p1 (0.25 #59, 0.17 #573, 0.03 #1089), 03j0br4 (0.25 #89, 0.17 #603, 0.03 #1634), 0837ql (0.25 #202, 0.17 #716, 0.03 #1747), 0153nq (0.25 #514, 0.17 #1028, 0.03 #2059) >> Best rule #273 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 0jgk3; >> query: (?x2623, 02s2wq) <- contains(?x2623, ?x3892), adjoins(?x2831, ?x2623), ?x3892 = 0rj0z >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02xry origin! 03h502k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 168.000 148.000 0.250 http://example.org/music/artist/origin #302-04kxsb PRED entity: 04kxsb PRED relation: award_winner PRED expected values: 016xh5 0gyy0 => 50 concepts (22 used for prediction) PRED predicted values (max 10 best out of 1597): 0151w_ (0.40 #2634, 0.38 #7539, 0.35 #7355), 0js9s (0.40 #3888, 0.38 #8793, 0.25 #11244), 0sz28 (0.40 #5129, 0.35 #7355, 0.31 #7354), 01vvb4m (0.40 #5554, 0.35 #7355, 0.31 #7354), 04__f (0.40 #6611, 0.35 #7355, 0.31 #7354), 0d6d2 (0.40 #6657, 0.35 #7355, 0.31 #7354), 06cgy (0.40 #5201, 0.35 #7355, 0.31 #7354), 0cj8x (0.40 #5541, 0.35 #7355, 0.31 #7354), 01g42 (0.40 #6729, 0.35 #7355, 0.31 #7354), 016ywr (0.40 #5276, 0.35 #7355, 0.31 #7354) >> Best rule #2634 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 094qd5; 019f4v; 0gq9h; >> query: (?x2375, 0151w_) <- award(?x253, ?x2375), nominated_for(?x2375, ?x10362), nominated_for(?x2375, ?x5515), ?x5515 = 0qmhk, ?x10362 = 0h0wd9 >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #7355 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 0789_m; *> query: (?x2375, ?x8634) <- award(?x11396, ?x2375), award(?x8634, ?x2375), ?x11396 = 016z68, award_winner(?x591, ?x8634), film(?x8634, ?x592) *> conf = 0.35 ranks of expected_values: 56, 464 EVAL 04kxsb award_winner 0gyy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 50.000 22.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 04kxsb award_winner 016xh5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 50.000 22.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #301-0345h PRED entity: 0345h PRED relation: olympics PRED expected values: 0lbbj 0lbd9 => 230 concepts (230 used for prediction) PRED predicted values (max 10 best out of 22): 09n48 (0.60 #121, 0.58 #1065, 0.57 #482), 0lbbj (0.50 #128, 0.21 #429, 0.20 #469), 0jhn7 (0.48 #373, 0.47 #172, 0.47 #573), 0l6m5 (0.47 #164, 0.44 #923, 0.44 #922), 0124ld (0.44 #923, 0.44 #922, 0.41 #1085), 015pkt (0.44 #923, 0.44 #922, 0.41 #1085), 016r9z (0.42 #1064, 0.42 #1527, 0.41 #1166), 0sxrz (0.42 #170, 0.37 #471, 0.35 #331), 0ldqf (0.40 #138, 0.35 #339, 0.33 #118), 0l6ny (0.37 #163, 0.30 #123, 0.21 #444) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 8 >> proper extension: 0j5g9; >> query: (?x1264, 09n48) <- nationality(?x12186, ?x1264), contains(?x1264, ?x196), film_art_direction_by(?x3003, ?x12186) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #128 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 0j5g9; *> query: (?x1264, 0lbbj) <- nationality(?x12186, ?x1264), contains(?x1264, ?x196), film_art_direction_by(?x3003, ?x12186) *> conf = 0.50 ranks of expected_values: 2, 13 EVAL 0345h olympics 0lbd9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 230.000 230.000 0.600 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics EVAL 0345h olympics 0lbbj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 230.000 230.000 0.600 http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics #300-039xcr PRED entity: 039xcr PRED relation: type_of_union PRED expected values: 04ztj => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.90 #61, 0.84 #105, 0.83 #73), 01g63y (0.16 #14, 0.14 #174, 0.14 #46), 01bl8s (0.03 #15, 0.02 #31, 0.01 #103) >> Best rule #61 for best value: >> intensional similarity = 4 >> extensional distance = 136 >> proper extension: 01l3j; >> query: (?x10058, 04ztj) <- film(?x10058, ?x10614), cinematography(?x10614, ?x3237), film_sets_designed(?x200, ?x10614), nominated_for(?x574, ?x10614) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 039xcr type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.899 http://example.org/people/person/spouse_s./people/marriage/type_of_union #299-07sgdw PRED entity: 07sgdw PRED relation: music PRED expected values: 01l1rw => 80 concepts (43 used for prediction) PRED predicted values (max 10 best out of 69): 023361 (0.25 #150, 0.12 #572, 0.06 #783), 01l9v7n (0.25 #469, 0.12 #680, 0.04 #891), 02bn75 (0.25 #144), 01l1rw (0.17 #314), 05_pkf (0.17 #272), 02bh9 (0.12 #473, 0.06 #684, 0.04 #3006), 019x62 (0.12 #552, 0.06 #763), 02fgpf (0.12 #452, 0.01 #874, 0.01 #3829), 0146pg (0.12 #854, 0.11 #1274, 0.09 #1064), 0gn30 (0.07 #3799, 0.06 #5909, 0.05 #3377) >> Best rule #150 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 03rg2b; >> query: (?x4749, 023361) <- film(?x1548, ?x4749), film(?x919, ?x4749), ?x1548 = 0j582, award_nominee(?x192, ?x919) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #314 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0j_tw; 0ds1glg; *> query: (?x4749, 01l1rw) <- film(?x919, ?x4749), ?x919 = 04sx9_, language(?x4749, ?x90) *> conf = 0.17 ranks of expected_values: 4 EVAL 07sgdw music 01l1rw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 80.000 43.000 0.250 http://example.org/film/film/music #298-0lgw7 PRED entity: 0lgw7 PRED relation: profession! PRED expected values: 01n8gr 01vsksr 0f13b 0hsn_ => 61 concepts (6 used for prediction) PRED predicted values (max 10 best out of 3811): 026dx (0.67 #14188, 0.62 #18414, 0.60 #9962), 06cv1 (0.67 #12802, 0.62 #17028, 0.60 #8576), 04dz_y7 (0.67 #16622, 0.62 #20848, 0.60 #12396), 0405l (0.67 #16291, 0.62 #20517, 0.60 #12065), 0gdqy (0.67 #16078, 0.62 #20304, 0.60 #11852), 031bf1 (0.67 #15943, 0.62 #20169, 0.60 #11717), 012vct (0.67 #15030, 0.62 #19256, 0.60 #10804), 01xv77 (0.67 #14704, 0.62 #18930, 0.60 #10478), 01vqrm (0.67 #13846, 0.62 #18072, 0.60 #9620), 04k25 (0.67 #13459, 0.62 #17685, 0.60 #9233) >> Best rule #14188 for best value: >> intensional similarity = 10 >> extensional distance = 4 >> proper extension: 0dgd_; >> query: (?x4354, 026dx) <- profession(?x10075, ?x4354), profession(?x7951, ?x4354), profession(?x6928, ?x4354), profession(?x1678, ?x4354), ?x10075 = 0kc6, artists(?x3108, ?x7951), award(?x7951, ?x724), parent_genre(?x1572, ?x3108), ?x6928 = 018ty9, gender(?x1678, ?x231) >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #6290 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 2 *> proper extension: 0dz3r; 01c72t; *> query: (?x4354, 01vsksr) <- profession(?x9009, ?x4354), profession(?x7951, ?x4354), profession(?x5283, ?x4354), ?x7951 = 01vt5c_, place_of_birth(?x9009, ?x5237), location(?x5283, ?x739), award_winner(?x5283, ?x628), languages(?x5283, ?x90) *> conf = 0.50 ranks of expected_values: 331, 339, 471, 779 EVAL 0lgw7 profession! 0hsn_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 61.000 6.000 0.667 http://example.org/people/person/profession EVAL 0lgw7 profession! 0f13b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 61.000 6.000 0.667 http://example.org/people/person/profession EVAL 0lgw7 profession! 01vsksr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 6.000 0.667 http://example.org/people/person/profession EVAL 0lgw7 profession! 01n8gr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 61.000 6.000 0.667 http://example.org/people/person/profession #297-03s9b PRED entity: 03s9b PRED relation: influenced_by PRED expected values: 0d5_f => 133 concepts (59 used for prediction) PRED predicted values (max 10 best out of 363): 0ff2k (0.25 #402, 0.10 #1275, 0.03 #6076), 0d6b7 (0.25 #36), 05qmj (0.17 #4996, 0.11 #17225, 0.10 #15914), 02wh0 (0.17 #5187, 0.10 #17416, 0.10 #11297), 03sbs (0.15 #5026, 0.10 #17255, 0.09 #17694), 048cl (0.15 #5038, 0.07 #17267, 0.06 #17706), 0w6w (0.15 #5236, 0.03 #11346, 0.03 #17465), 081k8 (0.14 #11069, 0.13 #10195, 0.12 #9321), 02lt8 (0.14 #10159, 0.13 #11033, 0.12 #9285), 032l1 (0.13 #4892, 0.13 #11002, 0.12 #15810) >> Best rule #402 for best value: >> intensional similarity = 2 >> extensional distance = 2 >> proper extension: 02pb2bp; >> query: (?x6957, 0ff2k) <- influenced_by(?x6957, ?x12355), film_festivals(?x12355, ?x9932) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #14540 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 233 *> proper extension: 014_lq; 07r1_; 0bk1p; 07hgm; 0b1hw; 014_xj; *> query: (?x6957, 0d5_f) <- influenced_by(?x6957, ?x12355), award_winner(?x77, ?x6957), award(?x6957, ?x198) *> conf = 0.02 ranks of expected_values: 304 EVAL 03s9b influenced_by 0d5_f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 133.000 59.000 0.250 http://example.org/influence/influence_node/influenced_by #296-0gbwp PRED entity: 0gbwp PRED relation: award PRED expected values: 0gqz2 01ckbq => 138 concepts (138 used for prediction) PRED predicted values (max 10 best out of 300): 03t5kl (0.48 #2111, 0.22 #211, 0.21 #7431), 03t5b6 (0.44 #2089, 0.12 #8549, 0.11 #189), 01c9dd (0.41 #2191, 0.13 #37623, 0.12 #8651), 09sb52 (0.39 #8019, 0.31 #10299, 0.31 #6119), 01bgqh (0.35 #11441, 0.32 #7261, 0.30 #8401), 02f705 (0.33 #147, 0.17 #3947, 0.15 #8507), 02f75t (0.33 #2140, 0.13 #37623, 0.12 #8600), 0c4z8 (0.27 #11469, 0.24 #7289, 0.22 #3869), 02f6xy (0.26 #2087, 0.23 #7407, 0.22 #187), 03t5n3 (0.26 #2131, 0.15 #8591, 0.13 #7451) >> Best rule #2111 for best value: >> intensional similarity = 3 >> extensional distance = 25 >> proper extension: 01vvydl; 01wmxfs; 016kjs; 05mt_q; 04mn81; 047sxrj; 01wgxtl; 0126y2; 016pns; 01w7nwm; ... >> query: (?x3997, 03t5kl) <- currency(?x3997, ?x170), award(?x3997, ?x9295), ?x9295 = 023vrq >> conf = 0.48 => this is the best rule for 1 predicted values *> Best rule #16418 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 302 *> proper extension: 03_0p; *> query: (?x3997, 0gqz2) <- award_nominee(?x2732, ?x3997), award(?x3997, ?x528), instrumentalists(?x1166, ?x3997) *> conf = 0.13 ranks of expected_values: 47, 152 EVAL 0gbwp award 01ckbq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 138.000 138.000 0.481 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 0gbwp award 0gqz2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 138.000 138.000 0.481 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #295-035dk PRED entity: 035dk PRED relation: time_zones PRED expected values: 03bdv => 179 concepts (179 used for prediction) PRED predicted values (max 10 best out of 13): 03bdv (0.63 #1943, 0.63 #1772, 0.58 #1864), 02hcv8 (0.35 #1932, 0.33 #395, 0.32 #851), 02lcqs (0.24 #450, 0.19 #1347, 0.17 #1399), 02llzg (0.23 #135, 0.22 #95, 0.21 #566), 02fqwt (0.20 #1122, 0.20 #1161, 0.16 #393), 0gsrz4 (0.20 #205, 0.19 #283, 0.16 #2191), 03plfd (0.16 #2191, 0.14 #141, 0.12 #23), 02hczc (0.12 #447, 0.10 #1162, 0.10 #120), 042g7t (0.07 #102, 0.07 #182, 0.06 #430), 05jphn (0.05 #52, 0.05 #65, 0.04 #91) >> Best rule #1943 for best value: >> intensional similarity = 2 >> extensional distance = 579 >> proper extension: 0nv2x; 0mrf1; >> query: (?x2051, ?x5327) <- adjoins(?x7360, ?x2051), time_zones(?x7360, ?x5327) >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 035dk time_zones 03bdv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 179.000 179.000 0.630 http://example.org/location/location/time_zones #294-024rgt PRED entity: 024rgt PRED relation: film PRED expected values: 051zy_b 0n1s0 => 129 concepts (37 used for prediction) PRED predicted values (max 10 best out of 1728): 03z20c (0.75 #4647, 0.63 #43357, 0.63 #44907), 0900j5 (0.75 #4647, 0.63 #43357, 0.63 #44907), 06_wqk4 (0.75 #4647, 0.63 #43357, 0.63 #44907), 0fphf3v (0.75 #4647, 0.63 #43357, 0.63 #44907), 0crd8q6 (0.75 #4647, 0.63 #43357, 0.63 #44907), 02q0k7v (0.75 #4647, 0.63 #43357, 0.63 #44907), 01ffx4 (0.75 #4647, 0.63 #43357, 0.63 #44907), 0n_hp (0.75 #4647, 0.63 #43357, 0.63 #44907), 013q07 (0.75 #4647, 0.63 #43357, 0.63 #44907), 04nl83 (0.75 #4647, 0.63 #43357, 0.63 #44907) >> Best rule #4647 for best value: >> intensional similarity = 4 >> extensional distance = 1 >> proper extension: 017s11; >> query: (?x2549, ?x54) <- production_companies(?x8987, ?x2549), production_companies(?x54, ?x2549), ?x8987 = 02tgz4, citytown(?x2549, ?x1523) >> conf = 0.75 => this is the best rule for 14 predicted values *> Best rule #3599 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 017s11; *> query: (?x2549, 051zy_b) <- production_companies(?x8987, ?x2549), ?x8987 = 02tgz4, citytown(?x2549, ?x1523) *> conf = 0.33 ranks of expected_values: 127 EVAL 024rgt film 0n1s0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 37.000 0.753 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 024rgt film 051zy_b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 129.000 37.000 0.753 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #293-081l_ PRED entity: 081l_ PRED relation: award PRED expected values: 0gvx_ => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 319): 02wypbh (0.76 #22126, 0.75 #19308, 0.74 #22530), 0789r6 (0.76 #22126, 0.75 #19308, 0.74 #22530), 0gs9p (0.60 #882, 0.40 #2491, 0.37 #14158), 09sb52 (0.45 #6475, 0.32 #21361, 0.27 #17336), 019f4v (0.40 #870, 0.38 #9318, 0.36 #2881), 0gq9h (0.37 #9328, 0.33 #15362, 0.26 #18579), 040njc (0.36 #3225, 0.36 #9260, 0.31 #14088), 0gr51 (0.33 #500, 0.20 #902, 0.20 #9350), 02qyp19 (0.33 #403, 0.20 #805, 0.11 #14081), 0gqng (0.33 #404, 0.20 #806, 0.10 #2415) >> Best rule #22126 for best value: >> intensional similarity = 3 >> extensional distance = 692 >> proper extension: 089tm; 01pfr3; 04rcr; 02r3zy; 07c0j; 01v0sx2; 01vsxdm; 0ggl02; 03g5jw; 05crg7; ... >> query: (?x8019, ?x372) <- category(?x8019, ?x134), award_winner(?x372, ?x8019), award(?x8019, ?x1587) >> conf = 0.76 => this is the best rule for 2 predicted values *> Best rule #39032 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1454 *> proper extension: 01nzs7; 06jntd; *> query: (?x8019, ?x3617) <- award_winner(?x9501, ?x8019), nominated_for(?x3617, ?x9501), ceremony(?x3617, ?x602), award(?x574, ?x3617) *> conf = 0.13 ranks of expected_values: 44 EVAL 081l_ award 0gvx_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.023 129.000 129.000 0.762 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #292-05x_5 PRED entity: 05x_5 PRED relation: major_field_of_study PRED expected values: 0193x 02_7t => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 105): 04rjg (0.62 #467, 0.59 #354, 0.58 #241), 01tbp (0.50 #391, 0.47 #504, 0.45 #278), 05qfh (0.47 #480, 0.47 #367, 0.44 #819), 0g26h (0.47 #713, 0.44 #374, 0.42 #261), 03g3w (0.46 #585, 0.46 #811, 0.45 #472), 01lj9 (0.45 #484, 0.41 #823, 0.41 #371), 041y2 (0.42 #297, 0.40 #523, 0.38 #410), 01540 (0.41 #392, 0.38 #505, 0.32 #844), 09s1f (0.41 #430, 0.33 #543, 0.32 #317), 02_7t (0.39 #283, 0.35 #509, 0.34 #396) >> Best rule #467 for best value: >> intensional similarity = 2 >> extensional distance = 38 >> proper extension: 08815; 07w0v; 03ksy; 025v3k; 04hgpt; 0g8rj; 0ks67; 01bm_; >> query: (?x6973, 04rjg) <- organization(?x6973, ?x5487), school(?x387, ?x6973) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #283 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 29 *> proper extension: 06mkj; 0d05w3; *> query: (?x6973, 02_7t) <- organization(?x6973, ?x5487), school(?x465, ?x6973), draft(?x387, ?x465) *> conf = 0.39 ranks of expected_values: 10, 20 EVAL 05x_5 major_field_of_study 02_7t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 80.000 80.000 0.625 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study EVAL 05x_5 major_field_of_study 0193x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 80.000 80.000 0.625 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #291-09g_31 PRED entity: 09g_31 PRED relation: genre PRED expected values: 05p553 => 83 concepts (60 used for prediction) PRED predicted values (max 10 best out of 151): 07s9rl0 (0.86 #2246, 0.76 #1927, 0.76 #3373), 05p553 (0.72 #1850, 0.72 #2170, 0.66 #2012), 01hmnh (0.52 #1380, 0.51 #2485, 0.34 #4653), 01z4y (0.52 #1861, 0.51 #2485, 0.46 #2181), 06nbt (0.51 #2485, 0.45 #255, 0.34 #4653), 095bb (0.51 #2485, 0.40 #193, 0.34 #4653), 0215n (0.51 #2485, 0.39 #802, 0.34 #4653), 0c4xc (0.51 #2485, 0.38 #1885, 0.34 #4653), 0vgkd (0.51 #2485, 0.34 #4653, 0.34 #4734), 02lvfq (0.51 #2485, 0.34 #4653, 0.34 #4734) >> Best rule #2246 for best value: >> intensional similarity = 10 >> extensional distance = 124 >> proper extension: 047m_w; >> query: (?x8628, 07s9rl0) <- country_of_origin(?x8628, ?x94), genre(?x8628, ?x811), genre(?x7917, ?x811), genre(?x7760, ?x811), genre(?x5936, ?x811), genre(?x972, ?x811), ?x972 = 017gl1, ?x5936 = 02q3fdr, ?x7760 = 017kz7, ?x7917 = 072r5v >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #1850 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 92 *> proper extension: 02nf2c; 01cjhz; 06f0k; *> query: (?x8628, 05p553) <- country_of_origin(?x8628, ?x94), genre(?x8628, ?x2540), titles(?x7712, ?x8628), genre(?x2933, ?x2540), ?x2933 = 0407yj_ *> conf = 0.72 ranks of expected_values: 2 EVAL 09g_31 genre 05p553 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 83.000 60.000 0.857 http://example.org/tv/tv_program/genre #290-03_x5t PRED entity: 03_x5t PRED relation: film PRED expected values: 0bq6ntw => 118 concepts (112 used for prediction) PRED predicted values (max 10 best out of 857): 02ntb8 (0.29 #838, 0.02 #6196, 0.01 #29414), 012mrr (0.20 #2263), 035s95 (0.14 #340, 0.04 #5698, 0.04 #48223), 0gvrws1 (0.14 #320, 0.04 #5678, 0.01 #21752), 01j5ql (0.14 #1200, 0.03 #121451, 0.01 #13702), 06sfk6 (0.14 #762, 0.03 #121451, 0.01 #13264), 095z4q (0.14 #1148, 0.02 #4720, 0.02 #10078), 0ds35l9 (0.14 #6, 0.02 #12508, 0.02 #7150), 0bscw (0.14 #217, 0.02 #5575, 0.01 #23435), 02z3r8t (0.14 #107, 0.02 #39399, 0.01 #50116) >> Best rule #838 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 063g7l; >> query: (?x10371, 02ntb8) <- location(?x10371, ?x1523), film(?x10371, ?x8063), ?x8063 = 01718w >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #11775 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 129 *> proper extension: 04bgy; 0mbhr; *> query: (?x10371, 0bq6ntw) <- profession(?x10371, ?x4773), film(?x10371, ?x365), ?x4773 = 0d1pc *> conf = 0.02 ranks of expected_values: 378 EVAL 03_x5t film 0bq6ntw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 118.000 112.000 0.286 http://example.org/film/actor/film./film/performance/film #289-01vrt_c PRED entity: 01vrt_c PRED relation: artists! PRED expected values: 02qdgx 02lnbg 0ggx5q 02yv6b 05y8n7 => 112 concepts (112 used for prediction) PRED predicted values (max 10 best out of 249): 03lty (0.45 #27, 0.25 #4542, 0.24 #4241), 02lnbg (0.38 #1859, 0.36 #3063, 0.32 #3966), 0xhtw (0.36 #16, 0.35 #4531, 0.28 #4230), 0ggx5q (0.34 #1879, 0.33 #3083, 0.28 #5493), 0155w (0.28 #6122, 0.18 #100, 0.15 #13949), 059kh (0.28 #4259, 0.18 #45, 0.09 #4560), 0jmwg (0.27 #104, 0.23 #4318, 0.06 #10237), 02k_kn (0.27 #4575, 0.13 #6082, 0.12 #1866), 03p7rp (0.27 #173, 0.09 #4387, 0.06 #10237), 06cp5 (0.27 #85, 0.07 #4299, 0.07 #4600) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 0fpj4lx; 012ycy; 015196; >> query: (?x1206, 03lty) <- artists(?x5934, ?x1206), currency(?x1206, ?x170), ?x5934 = 05r6t >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1859 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 56 *> proper extension: 0g476; *> query: (?x1206, 02lnbg) <- award(?x1206, ?x1479), artists(?x302, ?x1206), participant(?x5906, ?x1206) *> conf = 0.38 ranks of expected_values: 2, 4, 19, 22, 92 EVAL 01vrt_c artists! 05y8n7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 112.000 112.000 0.455 http://example.org/music/genre/artists EVAL 01vrt_c artists! 02yv6b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 112.000 112.000 0.455 http://example.org/music/genre/artists EVAL 01vrt_c artists! 0ggx5q CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 112.000 112.000 0.455 http://example.org/music/genre/artists EVAL 01vrt_c artists! 02lnbg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 112.000 112.000 0.455 http://example.org/music/genre/artists EVAL 01vrt_c artists! 02qdgx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 112.000 112.000 0.455 http://example.org/music/genre/artists #288-027c95y PRED entity: 027c95y PRED relation: award! PRED expected values: 0yyg4 06cm5 => 41 concepts (21 used for prediction) PRED predicted values (max 10 best out of 990): 03hkch7 (0.62 #4213, 0.25 #6866, 0.25 #14723), 0p_th (0.50 #4066, 0.10 #5046, 0.10 #6029), 0gmgwnv (0.50 #4527, 0.08 #5507, 0.07 #6490), 0yzvw (0.50 #4120, 0.07 #5100, 0.07 #6083), 011yl_ (0.50 #4261, 0.06 #6224, 0.05 #7206), 02yvct (0.40 #2167, 0.33 #206, 0.07 #5107), 06cm5 (0.38 #4523, 0.25 #6866, 0.25 #14723), 07cw4 (0.38 #4493, 0.25 #6866, 0.25 #14723), 09gq0x5 (0.38 #4086, 0.20 #2126, 0.17 #3106), 016ks5 (0.38 #4524, 0.20 #2564, 0.05 #19627) >> Best rule #4213 for best value: >> intensional similarity = 4 >> extensional distance = 6 >> proper extension: 05zr6wv; 0f4x7; 027986c; 09cm54; 09qv_s; 02w9sd7; >> query: (?x2915, 03hkch7) <- award(?x2370, ?x2915), award_winner(?x2915, ?x3056), ?x3056 = 01vvb4m, award_winner(?x2370, ?x1197) >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #4523 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 05zr6wv; 0f4x7; 027986c; 09cm54; 09qv_s; 02w9sd7; *> query: (?x2915, 06cm5) <- award(?x2370, ?x2915), award_winner(?x2915, ?x3056), ?x3056 = 01vvb4m, award_winner(?x2370, ?x1197) *> conf = 0.38 ranks of expected_values: 7, 280 EVAL 027c95y award! 06cm5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 41.000 21.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award EVAL 027c95y award! 0yyg4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 41.000 21.000 0.625 http://example.org/award/award_winning_work/awards_won./award/award_honor/award #287-035dk PRED entity: 035dk PRED relation: country! PRED expected values: 01cgz => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 55): 06z6r (0.86 #1076, 0.85 #911, 0.83 #1846), 06f41 (0.73 #234, 0.57 #674, 0.54 #1499), 071t0 (0.71 #2222, 0.71 #902, 0.71 #572), 01cgz (0.70 #233, 0.69 #1828, 0.68 #1388), 01lb14 (0.67 #235, 0.60 #400, 0.57 #1500), 06wrt (0.64 #236, 0.55 #676, 0.52 #1061), 03hr1p (0.61 #243, 0.60 #683, 0.56 #1068), 064vjs (0.61 #252, 0.54 #1517, 0.47 #1077), 07gyv (0.58 #226, 0.53 #1491, 0.53 #886), 03fyrh (0.58 #248, 0.52 #1073, 0.50 #83) >> Best rule #1076 for best value: >> intensional similarity = 3 >> extensional distance = 62 >> proper extension: 0j5g9; >> query: (?x2051, 06z6r) <- teams(?x2051, ?x8102), contains(?x2051, ?x12330), countries_spoken_in(?x254, ?x2051) >> conf = 0.86 => this is the best rule for 1 predicted values *> Best rule #233 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 0193qj; *> query: (?x2051, 01cgz) <- olympics(?x2051, ?x2369), capital(?x2051, ?x12331), ?x2369 = 0lbbj *> conf = 0.70 ranks of expected_values: 4 EVAL 035dk country! 01cgz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 135.000 135.000 0.859 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #286-0z1vw PRED entity: 0z1vw PRED relation: place_of_birth! PRED expected values: 03f1d47 => 69 concepts (23 used for prediction) PRED predicted values (max 10 best out of 926): 083p7 (0.38 #23511, 0.31 #36575, 0.31 #36574), 01jz6d (0.10 #2517, 0.04 #5129, 0.04 #7742), 0736qr (0.10 #2470, 0.04 #5082, 0.04 #7695), 01jgkj2 (0.10 #1910, 0.04 #4522, 0.04 #7135), 0jn5l (0.10 #1130, 0.04 #3742, 0.04 #6355), 033_1p (0.10 #2080, 0.04 #7305, 0.03 #9917), 0f14q (0.10 #2070, 0.04 #7295, 0.03 #9907), 04r68 (0.10 #1038, 0.04 #6263, 0.03 #8875), 05dxl5 (0.10 #778, 0.04 #6003, 0.03 #8615), 0ckcvk (0.10 #2023, 0.04 #7248, 0.03 #12472) >> Best rule #23511 for best value: >> intensional similarity = 4 >> extensional distance = 137 >> proper extension: 013f1h; >> query: (?x11595, ?x1157) <- time_zones(?x11595, ?x2674), ?x2674 = 02hcv8, place_of_birth(?x5126, ?x11595), location(?x1157, ?x11595) >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0z1vw place_of_birth! 03f1d47 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 69.000 23.000 0.379 http://example.org/people/person/place_of_birth #285-055c8 PRED entity: 055c8 PRED relation: student! PRED expected values: 01fsv9 06kknt => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 56): 0bwfn (0.08 #2384, 0.07 #2911, 0.06 #1856), 065y4w7 (0.08 #1595, 0.05 #2650, 0.05 #2123), 015nl4 (0.06 #1121, 0.04 #8500, 0.03 #15352), 03ksy (0.04 #9066, 0.03 #26988, 0.02 #27516), 04b_46 (0.04 #2336, 0.03 #1808, 0.03 #2863), 017z88 (0.04 #7988, 0.04 #3772, 0.03 #4826), 07tg4 (0.03 #1667, 0.03 #2722, 0.03 #2195), 01w5m (0.03 #1686, 0.03 #26987, 0.03 #30678), 09f2j (0.03 #12809, 0.03 #8065, 0.03 #4903), 08815 (0.03 #1056, 0.02 #15814, 0.02 #15287) >> Best rule #2384 for best value: >> intensional similarity = 3 >> extensional distance = 194 >> proper extension: 0gdhhy; 02x20c9; >> query: (?x3186, 0bwfn) <- nominated_for(?x3186, ?x2107), gender(?x3186, ?x231), executive_produced_by(?x9069, ?x3186) >> conf = 0.08 => this is the best rule for 1 predicted values *> Best rule #1521 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 133 *> proper extension: 05bnp0; 02p65p; 06dv3; 0byfz; 014zcr; 0h0jz; 0p_pd; 0bl2g; 09fb5; 0z4s; ... *> query: (?x3186, 06kknt) <- award(?x3186, ?x591), profession(?x3186, ?x319), ?x591 = 0f4x7 *> conf = 0.01 ranks of expected_values: 29 EVAL 055c8 student! 06kknt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.034 106.000 106.000 0.082 http://example.org/education/educational_institution/students_graduates./education/education/student EVAL 055c8 student! 01fsv9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 106.000 106.000 0.082 http://example.org/education/educational_institution/students_graduates./education/education/student #284-01s0ps PRED entity: 01s0ps PRED relation: role PRED expected values: 026t6 => 89 concepts (61 used for prediction) PRED predicted values (max 10 best out of 89): 07y_7 (0.91 #2264, 0.87 #2269, 0.82 #1847), 018vs (0.91 #2264, 0.83 #2956, 0.83 #1091), 04rzd (0.83 #3395, 0.82 #2550, 0.82 #3311), 0bxl5 (0.83 #1091, 0.82 #1845, 0.81 #3283), 02sgy (0.83 #1091, 0.82 #1845, 0.81 #3283), 0dwt5 (0.83 #1091, 0.82 #1845, 0.81 #3283), 05148p4 (0.83 #1091, 0.82 #1845, 0.81 #3283), 0dwvl (0.83 #1091, 0.82 #1845, 0.81 #3283), 02qjv (0.83 #1091, 0.82 #1845, 0.81 #3283), 06w7v (0.83 #1091, 0.82 #1845, 0.81 #3283) >> Best rule #2264 for best value: >> intensional similarity = 10 >> extensional distance = 13 >> proper extension: 07c6l; >> query: (?x2764, ?x75) <- role(?x2764, ?x1433), role(?x158, ?x2764), role(?x75, ?x2764), role(?x3869, ?x2764), role(?x2048, ?x75), ?x2048 = 018j2, ?x1433 = 0239kh, group(?x75, ?x1751), role(?x5883, ?x75), ?x5883 = 01wgjj5 >> conf = 0.91 => this is the best rule for 2 predicted values *> Best rule #82 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 05r5c; *> query: (?x2764, ?x615) <- role(?x2764, ?x1267), role(?x2764, ?x1147), role(?x8978, ?x2764), role(?x2747, ?x2764), ?x2747 = 01qdjm, ?x8978 = 01wg6y, ?x1147 = 07kc_, group(?x1267, ?x997), role(?x75, ?x2764), role(?x6947, ?x1267), instrumentalists(?x1267, ?x1521), ?x6947 = 01vrnsk, role(?x3062, ?x1267), role(?x1267, ?x615) *> conf = 0.79 ranks of expected_values: 15 EVAL 01s0ps role 026t6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 89.000 61.000 0.907 http://example.org/music/performance_role/track_performances./music/track_contribution/role #283-085q5 PRED entity: 085q5 PRED relation: profession PRED expected values: 0cbd2 => 125 concepts (39 used for prediction) PRED predicted values (max 10 best out of 71): 0kyk (0.81 #904, 0.61 #1634, 0.41 #2510), 01d_h8 (0.62 #2196, 0.55 #4972, 0.55 #5411), 0cbd2 (0.50 #883, 0.47 #3073, 0.46 #2489), 03gjzk (0.49 #2788, 0.40 #5273, 0.39 #5419), 025352 (0.48 #4437, 0.27 #1663, 0.25 #203), 02jknp (0.47 #4242, 0.46 #4827, 0.46 #4974), 018gz8 (0.43 #2790, 0.36 #5275, 0.36 #4982), 01c72t (0.26 #4402, 0.14 #1628, 0.12 #3964), 0nbcg (0.25 #4410, 0.11 #1636, 0.10 #1344), 09jwl (0.24 #4398, 0.12 #5423, 0.12 #2792) >> Best rule #904 for best value: >> intensional similarity = 7 >> extensional distance = 46 >> proper extension: 0h5f5n; 01bpc9; 04xjp; 03pm9; 03m_k0; 0c2dl; 0d5_f; 0n6kf; 01tz6vs; 0h0p_; ... >> query: (?x10121, 0kyk) <- profession(?x10121, ?x6421), profession(?x10121, ?x1383), ?x6421 = 02hv44_, profession(?x11007, ?x1383), profession(?x7609, ?x1383), ?x11007 = 0c408_, ?x7609 = 01j7z7 >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #883 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 46 *> proper extension: 0h5f5n; 01bpc9; 04xjp; 03pm9; 03m_k0; 0c2dl; 0d5_f; 0n6kf; 01tz6vs; 0h0p_; ... *> query: (?x10121, 0cbd2) <- profession(?x10121, ?x6421), profession(?x10121, ?x1383), ?x6421 = 02hv44_, profession(?x11007, ?x1383), profession(?x7609, ?x1383), ?x11007 = 0c408_, ?x7609 = 01j7z7 *> conf = 0.50 ranks of expected_values: 3 EVAL 085q5 profession 0cbd2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 125.000 39.000 0.812 http://example.org/people/person/profession #282-0p7pw PRED entity: 0p7pw PRED relation: genre PRED expected values: 0hn10 => 81 concepts (62 used for prediction) PRED predicted values (max 10 best out of 92): 06cvj (0.56 #122, 0.53 #3, 0.25 #838), 07ssc (0.55 #358, 0.53 #835, 0.52 #3819), 01jfsb (0.37 #2040, 0.33 #1801, 0.33 #2159), 02kdv5l (0.29 #2030, 0.28 #2865, 0.27 #2627), 03k9fj (0.28 #607, 0.22 #5501, 0.22 #4546), 04xvlr (0.24 #359, 0.20 #955, 0.20 #478), 060__y (0.23 #970, 0.23 #374, 0.21 #493), 0lsxr (0.21 #2036, 0.20 #1320, 0.19 #1797), 02b5_l (0.20 #48, 0.19 #167, 0.04 #883), 04xvh5 (0.20 #392, 0.18 #511, 0.10 #1465) >> Best rule #122 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 047vp1n; >> query: (?x9383, 06cvj) <- genre(?x9383, ?x12008), language(?x9383, ?x254), film(?x6585, ?x9383), ?x12008 = 0gsy3b >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #9 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 13 *> proper extension: 06cgf; *> query: (?x9383, 0hn10) <- genre(?x9383, ?x12008), film_release_distribution_medium(?x9383, ?x81), ?x12008 = 0gsy3b, film_release_region(?x9383, ?x94) *> conf = 0.13 ranks of expected_values: 33 EVAL 0p7pw genre 0hn10 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.030 81.000 62.000 0.562 http://example.org/film/film/genre #281-0b4lkx PRED entity: 0b4lkx PRED relation: nominated_for! PRED expected values: 027dtxw 0gs9p 02qyntr => 86 concepts (78 used for prediction) PRED predicted values (max 10 best out of 195): 02x73k6 (0.67 #8637, 0.66 #10235, 0.66 #1590), 09d28z (0.67 #8637, 0.66 #10235, 0.66 #1590), 027c924 (0.67 #8637, 0.66 #10235, 0.66 #1590), 0gs9p (0.65 #1647, 0.50 #1192, 0.44 #965), 0f4x7 (0.55 #1158, 0.55 #931, 0.33 #1613), 02qyntr (0.52 #1304, 0.50 #1077, 0.42 #1759), 0gr0m (0.46 #1643, 0.38 #507, 0.34 #961), 02qvyrt (0.40 #994, 0.34 #1221, 0.29 #1676), 054krc (0.37 #970, 0.29 #1197, 0.29 #1652), 0l8z1 (0.35 #955, 0.31 #1637, 0.31 #1182) >> Best rule #8637 for best value: >> intensional similarity = 4 >> extensional distance = 738 >> proper extension: 0cwrr; 05h95s; 05fgr_; 05sy0cv; 06mmr; >> query: (?x8000, ?x289) <- award_winner(?x8000, ?x1250), award(?x8000, ?x289), award(?x1250, ?x112), award_nominee(?x157, ?x1250) >> conf = 0.67 => this is the best rule for 3 predicted values *> Best rule #1647 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 151 *> proper extension: 016kz1; 02zk08; *> query: (?x8000, 0gs9p) <- award_winner(?x8000, ?x1250), nominated_for(?x1703, ?x8000), film_release_distribution_medium(?x8000, ?x81), ?x1703 = 0k611 *> conf = 0.65 ranks of expected_values: 4, 6, 18 EVAL 0b4lkx nominated_for! 02qyntr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 86.000 78.000 0.666 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0b4lkx nominated_for! 0gs9p CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 86.000 78.000 0.666 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 0b4lkx nominated_for! 027dtxw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 86.000 78.000 0.666 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #280-07jwr PRED entity: 07jwr PRED relation: people PRED expected values: 02n9k => 78 concepts (47 used for prediction) PRED predicted values (max 10 best out of 2225): 0chsq (0.38 #7473, 0.17 #31214, 0.12 #8830), 0jrny (0.33 #15696, 0.33 #15021, 0.25 #9603), 0gr36 (0.33 #1461, 0.33 #781, 0.12 #8240), 0gyy0 (0.33 #15287, 0.30 #17323, 0.25 #9869), 01938t (0.33 #956, 0.25 #8415, 0.25 #2991), 02nrdp (0.33 #1133, 0.25 #3168, 0.12 #8592), 0byfz (0.33 #686, 0.25 #2721, 0.12 #8145), 0b22w (0.33 #15398, 0.23 #21512, 0.22 #16073), 02dth1 (0.33 #824, 0.20 #17094, 0.15 #21172), 04__f (0.33 #1697, 0.18 #20003, 0.18 #19324) >> Best rule #7473 for best value: >> intensional similarity = 10 >> extensional distance = 6 >> proper extension: 0dcsx; >> query: (?x4291, 0chsq) <- people(?x4291, ?x4072), people(?x7185, ?x4072), risk_factors(?x4291, ?x13122), profession(?x4072, ?x353), place_of_death(?x4072, ?x1705), featured_film_locations(?x7726, ?x1705), place_of_birth(?x1092, ?x1705), people(?x7185, ?x4647), ?x4647 = 043gj, student(?x5486, ?x4072) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #31197 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 16 *> proper extension: 0dcqh; *> query: (?x4291, ?x65) <- people(?x4291, ?x4072), symptom_of(?x4905, ?x4291), influenced_by(?x8768, ?x4072), location(?x4072, ?x739), gender(?x8768, ?x231), profession(?x4072, ?x353), place_of_birth(?x65, ?x739), contains(?x739, ?x1005), citytown(?x166, ?x739) *> conf = 0.02 ranks of expected_values: 1064 EVAL 07jwr people 02n9k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 78.000 47.000 0.375 http://example.org/people/cause_of_death/people #279-031f_m PRED entity: 031f_m PRED relation: music PRED expected values: 0ftqr => 98 concepts (59 used for prediction) PRED predicted values (max 10 best out of 82): 02rgz4 (0.67 #1897, 0.50 #2108, 0.40 #1266), 04ls53 (0.17 #1760, 0.07 #5338, 0.06 #3865), 02bh9 (0.11 #6366, 0.08 #2786, 0.07 #5942), 06fxnf (0.11 #5328, 0.08 #7227, 0.05 #10387), 016szr (0.11 #5340, 0.06 #7239, 0.02 #8716), 02cyfz (0.10 #5714, 0.09 #5925, 0.04 #7402), 04pf4r (0.07 #5327, 0.06 #7226, 0.03 #11227), 023361 (0.07 #5619, 0.04 #6252, 0.03 #10048), 07qy0b (0.07 #5308, 0.04 #5518, 0.03 #6364), 0146pg (0.07 #6112, 0.06 #12010, 0.06 #7801) >> Best rule #1897 for best value: >> intensional similarity = 9 >> extensional distance = 4 >> proper extension: 0564x; >> query: (?x9698, 02rgz4) <- actor(?x9698, ?x10363), actor(?x9698, ?x6066), country(?x9698, ?x252), profession(?x10363, ?x987), nominated_for(?x6066, ?x9350), award_nominee(?x6066, ?x92), genre(?x9698, ?x225), place_of_birth(?x10363, ?x1860), language(?x6066, ?x254) >> conf = 0.67 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 031f_m music 0ftqr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 98.000 59.000 0.667 http://example.org/film/film/music #278-04vq33 PRED entity: 04vq33 PRED relation: nominated_for! PRED expected values: 09bx1k => 67 concepts (24 used for prediction) PRED predicted values (max 10 best out of 617): 08ff1k (0.48 #35037, 0.44 #30364, 0.04 #1196), 02cqbx (0.22 #25690, 0.21 #32700, 0.15 #1248), 057bc6m (0.22 #25690, 0.21 #32700, 0.09 #28027), 076psv (0.22 #25690, 0.21 #32700, 0.09 #28027), 0cb77r (0.22 #25690, 0.21 #32700, 0.09 #28027), 0579tg2 (0.22 #25690, 0.21 #32700, 0.09 #28027), 0dg3jz (0.22 #25690, 0.21 #32700, 0.09 #28027), 0dck27 (0.22 #25690, 0.21 #32700, 0.09 #28027), 0f7h2g (0.22 #25690, 0.21 #32700, 0.09 #28027), 04r7p (0.22 #25690, 0.21 #32700, 0.09 #28027) >> Best rule #35037 for best value: >> intensional similarity = 4 >> extensional distance = 245 >> proper extension: 0m313; 0g22z; 01br2w; 0140g4; 01jc6q; 028_yv; 09m6kg; 0c0yh4; 0yyg4; 011yxg; ... >> query: (?x12679, ?x5438) <- nominated_for(?x484, ?x12679), films(?x11183, ?x12679), genre(?x12679, ?x53), produced_by(?x12679, ?x5438) >> conf = 0.48 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04vq33 nominated_for! 09bx1k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 24.000 0.484 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #277-0j210 PRED entity: 0j210 PRED relation: performance_role! PRED expected values: 031x_3 => 49 concepts (38 used for prediction) PRED predicted values (max 10 best out of 1062): 02rn_bj (0.50 #355, 0.40 #617, 0.40 #487), 043c4j (0.50 #342, 0.40 #474, 0.33 #733), 01r0t_j (0.42 #1389, 0.36 #1648, 0.33 #227), 02s6sh (0.38 #1533, 0.33 #246, 0.33 #117), 01vn35l (0.33 #164, 0.33 #35, 0.25 #1326), 0l12d (0.33 #147, 0.33 #18, 0.25 #274), 01vrncs (0.33 #139, 0.31 #1426, 0.29 #785), 050z2 (0.33 #52, 0.31 #1468, 0.25 #308), 02qwg (0.33 #174, 0.29 #820, 0.20 #563), 07r4c (0.33 #205, 0.25 #1367, 0.21 #1626) >> Best rule #355 for best value: >> intensional similarity = 24 >> extensional distance = 2 >> proper extension: 02hnl; >> query: (?x3238, 02rn_bj) <- performance_role(?x3238, ?x14165), role(?x3238, ?x3239), role(?x3238, ?x1267), role(?x3238, ?x227), ?x3239 = 03qmg1, ?x1267 = 07brj, ?x227 = 0342h, group(?x3238, ?x3516), performance_role(?x1466, ?x3238), ?x3516 = 05563d, group(?x1466, ?x13142), role(?x7162, ?x1466), role(?x5745, ?x1466), role(?x1953, ?x1466), performance_role(?x2876, ?x1466), ?x2876 = 01vn35l, role(?x1466, ?x3703), artists(?x2937, ?x1953), ?x13142 = 0jg77, ?x2937 = 0glt670, award(?x7162, ?x462), performance_role(?x3703, ?x1225), group(?x5745, ?x1271), role(?x3703, ?x314) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1520 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 11 *> proper extension: 018l5l; *> query: (?x3238, 031x_3) <- performance_role(?x1817, ?x3238), award_nominee(?x4239, ?x1817), artists(?x9007, ?x1817), people(?x1816, ?x1817), award_winner(?x3094, ?x1817), location(?x1817, ?x938), artists(?x9007, ?x2409), award_winner(?x342, ?x4239), award_winner(?x4239, ?x367), ?x2409 = 010hn, award_winner(?x2518, ?x4239), award_winner(?x2139, ?x4239), artist(?x3240, ?x1817), award(?x1817, ?x724), artists(?x8798, ?x4239), award(?x4239, ?x341), award(?x2124, ?x3094) *> conf = 0.08 ranks of expected_values: 317 EVAL 0j210 performance_role! 031x_3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 49.000 38.000 0.500 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #276-01zk9d PRED entity: 01zk9d PRED relation: contains! PRED expected values: 06q1r => 174 concepts (60 used for prediction) PRED predicted values (max 10 best out of 258): 07ssc (0.74 #35014, 0.68 #13490, 0.67 #14387), 06q1r (0.71 #35881, 0.59 #26915, 0.56 #17045), 09c7w0 (0.71 #9873, 0.68 #12565, 0.58 #15255), 0l1k8 (0.68 #17942, 0.50 #41265, 0.33 #8077), 02jx1 (0.57 #35069, 0.46 #13545, 0.43 #14442), 01zk9d (0.50 #41265, 0.33 #8077, 0.33 #8076), 01n7q (0.33 #9948, 0.26 #17123, 0.17 #22510), 04jpl (0.33 #22, 0.13 #8995, 0.12 #5407), 04_1l0v (0.30 #24675, 0.28 #37231, 0.27 #36333), 06pvr (0.25 #10036, 0.17 #17211, 0.11 #22598) >> Best rule #35014 for best value: >> intensional similarity = 5 >> extensional distance = 75 >> proper extension: 0fgj2; 0125q1; 02j7k; 09bkv; 0n048; 048kw; 0dzz_; 02hvkf; >> query: (?x14595, 07ssc) <- contains(?x14595, ?x12276), contains(?x6401, ?x12276), contains(?x6401, ?x12726), time_zones(?x6401, ?x5327), ?x12726 = 09vzz >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #35881 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 75 *> proper extension: 0fgj2; 0125q1; 02j7k; 09bkv; 0n048; 048kw; 0dzz_; 02hvkf; *> query: (?x14595, ?x6401) <- contains(?x14595, ?x12276), contains(?x6401, ?x12276), contains(?x6401, ?x12726), time_zones(?x6401, ?x5327), ?x12726 = 09vzz *> conf = 0.71 ranks of expected_values: 2 EVAL 01zk9d contains! 06q1r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 174.000 60.000 0.740 http://example.org/location/location/contains #275-0fy59t PRED entity: 0fy59t PRED relation: honored_for PRED expected values: 04mzf8 => 49 concepts (37 used for prediction) PRED predicted values (max 10 best out of 813): 0cq8qq (0.25 #315, 0.24 #11947, 0.24 #3582), 0kb1g (0.25 #540, 0.20 #1136, 0.14 #2331), 01lsl (0.25 #512, 0.20 #1108, 0.14 #2303), 0j80w (0.25 #294, 0.18 #4179, 0.14 #1488), 0bl06 (0.25 #342, 0.16 #10752, 0.16 #17324), 014kkm (0.25 #307, 0.16 #10752, 0.16 #17324), 0bcndz (0.20 #692, 0.16 #10752, 0.16 #17324), 03cw411 (0.20 #814, 0.10 #2607, 0.09 #3204), 03hkch7 (0.20 #782, 0.10 #2575, 0.09 #3172), 02qpt1w (0.20 #942, 0.10 #2735, 0.09 #3332) >> Best rule #315 for best value: >> intensional similarity = 21 >> extensional distance = 2 >> proper extension: 05hmp6; 0fz0c2; >> query: (?x8259, 0cq8qq) <- award_winner(?x8259, ?x6017), award_winner(?x8259, ?x4926), award_winner(?x8259, ?x4240), award_winner(?x8259, ?x2449), award_winner(?x8259, ?x382), ceremony(?x4573, ?x8259), ceremony(?x3066, ?x8259), ceremony(?x2209, ?x8259), honored_for(?x8259, ?x2721), profession(?x6017, ?x1032), participant(?x6017, ?x9862), ?x4573 = 0gq_d, ?x4926 = 01pp3p, nominated_for(?x4240, ?x3104), location(?x6017, ?x7166), ?x3066 = 0gqy2, ?x2449 = 072twv, participant(?x4240, ?x3002), award_winner(?x591, ?x4240), ?x2209 = 0gr42, film(?x382, ?x83) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #10752 for first EXPECTED value: *> intensional similarity = 17 *> extensional distance = 19 *> proper extension: 02yw5r; 02pgky2; *> query: (?x8259, ?x1745) <- award_winner(?x8259, ?x6017), award_winner(?x8259, ?x4926), award_winner(?x8259, ?x3519), ceremony(?x4573, ?x8259), ceremony(?x484, ?x8259), honored_for(?x8259, ?x2721), profession(?x6017, ?x1032), participant(?x6017, ?x9862), ?x4573 = 0gq_d, type_of_union(?x4926, ?x566), ?x484 = 0gq_v, film(?x4926, ?x3294), award(?x4926, ?x1107), award_nominee(?x4926, ?x1850), ?x1107 = 019f4v, gender(?x6017, ?x514), award_winner(?x1745, ?x3519) *> conf = 0.16 ranks of expected_values: 34 EVAL 0fy59t honored_for 04mzf8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 49.000 37.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #274-0m0jc PRED entity: 0m0jc PRED relation: parent_genre! PRED expected values: 07d2d => 71 concepts (34 used for prediction) PRED predicted values (max 10 best out of 289): 016y3j (0.50 #389, 0.40 #649, 0.33 #129), 016_nr (0.50 #842, 0.15 #2147, 0.12 #3190), 07lnk (0.40 #546, 0.36 #1590, 0.25 #286), 0283d (0.40 #605, 0.27 #1649, 0.25 #865), 07gxw (0.38 #1041, 0.20 #567, 0.17 #1871), 06by7 (0.38 #1041, 0.12 #1040, 0.11 #8380), 059kh (0.38 #822, 0.23 #2127, 0.18 #1606), 06cp5 (0.38 #855, 0.22 #3203, 0.18 #1639), 016_rm (0.38 #975, 0.15 #2280, 0.11 #1236), 07ym47 (0.38 #837, 0.12 #3185, 0.09 #4179) >> Best rule #389 for best value: >> intensional similarity = 10 >> extensional distance = 2 >> proper extension: 0fd3y; >> query: (?x474, 016y3j) <- artists(?x474, ?x11689), artists(?x474, ?x5544), artists(?x474, ?x3168), artists(?x474, ?x1004), parent_genre(?x3232, ?x474), ?x1004 = 01vv7sc, award(?x3168, ?x1479), ?x11689 = 06p03s, parent_genre(?x474, ?x1572), category(?x5544, ?x134) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #856 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 6 *> proper extension: 064t9; 02x8m; 06by7; 0glt670; 06j6l; 0gywn; *> query: (?x474, 07d2d) <- artists(?x474, ?x6289), artists(?x474, ?x1004), parent_genre(?x3232, ?x474), parent_genre(?x474, ?x1572), ?x6289 = 0x3n, gender(?x1004, ?x231), nationality(?x1004, ?x94) *> conf = 0.12 ranks of expected_values: 63 EVAL 0m0jc parent_genre! 07d2d CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 71.000 34.000 0.500 http://example.org/music/genre/parent_genre #273-0fqt1ns PRED entity: 0fqt1ns PRED relation: film_crew_role PRED expected values: 09vw2b7 01vx2h => 71 concepts (71 used for prediction) PRED predicted values (max 10 best out of 32): 09zzb8 (0.80 #141, 0.73 #212, 0.73 #570), 09vw2b7 (0.68 #146, 0.67 #217, 0.66 #111), 02ynfr (0.56 #155, 0.22 #15, 0.21 #226), 01vx2h (0.45 #80, 0.40 #221, 0.40 #150), 01pvkk (0.29 #151, 0.27 #580, 0.27 #1578), 02_n3z (0.22 #2, 0.12 #72, 0.12 #142), 0215hd (0.22 #123, 0.20 #158, 0.16 #53), 0d2b38 (0.21 #165, 0.14 #236, 0.12 #376), 02rh1dz (0.21 #79, 0.17 #432, 0.16 #468), 01xy5l_ (0.19 #118, 0.13 #153, 0.12 #224) >> Best rule #141 for best value: >> intensional similarity = 4 >> extensional distance = 73 >> proper extension: 034qmv; 028_yv; 03ckwzc; 0fh694; 09q5w2; 09gdm7q; 020fcn; 032_wv; 01719t; 04jkpgv; ... >> query: (?x4664, 09zzb8) <- film(?x96, ?x4664), genre(?x4664, ?x53), film_crew_role(?x4664, ?x1078), ?x1078 = 089fss >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #146 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 034qmv; 028_yv; 03ckwzc; 0fh694; 09q5w2; 09gdm7q; 020fcn; 032_wv; 01719t; 04jkpgv; ... *> query: (?x4664, 09vw2b7) <- film(?x96, ?x4664), genre(?x4664, ?x53), film_crew_role(?x4664, ?x1078), ?x1078 = 089fss *> conf = 0.68 ranks of expected_values: 2, 4 EVAL 0fqt1ns film_crew_role 01vx2h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 71.000 71.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0fqt1ns film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 71.000 71.000 0.800 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #272-06mkj PRED entity: 06mkj PRED relation: country! PRED expected values: 07jjt 064vjs 07jbh 0486tv => 223 concepts (223 used for prediction) PRED predicted values (max 10 best out of 19): 07jbh (0.84 #524, 0.80 #372, 0.76 #734), 064vjs (0.76 #466, 0.74 #657, 0.73 #733), 0486tv (0.70 #146, 0.63 #298, 0.58 #527), 07jjt (0.66 #559, 0.65 #712, 0.64 #807), 03fyrh (0.65 #522, 0.62 #312, 0.60 #141), 09_bl (0.56 #591, 0.52 #518, 0.50 #804), 096f8 (0.56 #591, 0.52 #517, 0.50 #555), 0d1tm (0.56 #591, 0.50 #553, 0.50 #191), 09wz9 (0.56 #591, 0.50 #196, 0.45 #520), 09f6b (0.56 #591, 0.44 #130, 0.43 #206) >> Best rule #524 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 016wzw; >> query: (?x2152, 07jbh) <- film_release_region(?x7275, ?x2152), country(?x689, ?x2152), ?x7275 = 0g4vmj8 >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3, 4 EVAL 06mkj country! 0486tv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 223.000 223.000 0.839 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06mkj country! 07jbh CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 223.000 223.000 0.839 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06mkj country! 064vjs CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 223.000 223.000 0.839 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 06mkj country! 07jjt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 223.000 223.000 0.839 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #271-07s6fsf PRED entity: 07s6fsf PRED relation: institution PRED expected values: 08815 065y4w7 017zq0 07wrz 02dj3 08qnnv 01r3w7 0trv 0160nk 03np_7 => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 514): 065y4w7 (0.80 #7540, 0.80 #6536, 0.79 #7038), 08815 (0.78 #6026, 0.75 #5524, 0.74 #8034), 0pspl (0.78 #6116, 0.75 #5614, 0.67 #7622), 01s0_f (0.78 #6077, 0.75 #5575, 0.60 #6579), 01bm_ (0.75 #5737, 0.70 #6741, 0.67 #6239), 05zl0 (0.75 #5700, 0.70 #6704, 0.67 #6202), 08qnnv (0.70 #6715, 0.67 #6213, 0.67 #5209), 013807 (0.70 #6895, 0.67 #6393, 0.62 #5891), 06fq2 (0.70 #6783, 0.67 #6281, 0.62 #5779), 01n6r0 (0.70 #6666, 0.67 #6164, 0.62 #5662) >> Best rule #7540 for best value: >> intensional similarity = 22 >> extensional distance = 13 >> proper extension: 01rr_d; >> query: (?x620, 065y4w7) <- institution(?x620, ?x11768), institution(?x620, ?x7545), institution(?x620, ?x546), institution(?x620, ?x388), colors(?x7545, ?x663), student(?x7545, ?x12804), student(?x7545, ?x12462), student(?x7545, ?x6324), student(?x7545, ?x3747), student(?x7545, ?x275), ?x6324 = 018ygt, award_nominee(?x221, ?x275), major_field_of_study(?x7545, ?x373), place_of_birth(?x12462, ?x5381), school_type(?x11768, ?x3092), nationality(?x12804, ?x94), film(?x3747, ?x5976), currency(?x388, ?x170), student(?x388, ?x643), award(?x275, ?x678), school(?x1823, ?x546), award_nominee(?x275, ?x2602) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 7, 23, 28, 68, 121, 155, 237, 261 EVAL 07s6fsf institution 03np_7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 24.000 24.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07s6fsf institution 0160nk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 24.000 24.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07s6fsf institution 0trv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 24.000 24.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07s6fsf institution 01r3w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 24.000 24.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07s6fsf institution 08qnnv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 24.000 24.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07s6fsf institution 02dj3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 24.000 24.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07s6fsf institution 07wrz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 24.000 24.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07s6fsf institution 017zq0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 24.000 24.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07s6fsf institution 065y4w7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 07s6fsf institution 08815 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 24.000 24.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #270-0f4yh PRED entity: 0f4yh PRED relation: genre PRED expected values: 02kdv5l => 133 concepts (131 used for prediction) PRED predicted values (max 10 best out of 102): 07s9rl0 (0.80 #5864, 0.79 #5620, 0.75 #4154), 024qqx (0.61 #733, 0.54 #13690, 0.53 #8551), 01jfsb (0.52 #11255, 0.44 #2822, 0.44 #501), 02kdv5l (0.50 #491, 0.50 #11245, 0.37 #3911), 02l7c8 (0.42 #1360, 0.39 #2581, 0.38 #2337), 0lsxr (0.40 #620, 0.31 #1841, 0.28 #2696), 05p553 (0.38 #3913, 0.36 #9290, 0.35 #5379), 01g6gs (0.33 #1243, 0.31 #2586, 0.31 #2342), 06n90 (0.33 #14, 0.31 #258, 0.25 #502), 01hmnh (0.33 #19, 0.28 #3927, 0.25 #5393) >> Best rule #5864 for best value: >> intensional similarity = 3 >> extensional distance = 269 >> proper extension: 011yfd; 0j8f09z; >> query: (?x3535, 07s9rl0) <- nominated_for(?x1307, ?x3535), language(?x3535, ?x254), ?x1307 = 0gq9h >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #491 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 14 *> proper extension: 0199wf; *> query: (?x3535, 02kdv5l) <- film(?x8704, ?x3535), language(?x3535, ?x254), ?x8704 = 0c0k1 *> conf = 0.50 ranks of expected_values: 4 EVAL 0f4yh genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 133.000 131.000 0.797 http://example.org/film/film/genre #269-01wg6y PRED entity: 01wg6y PRED relation: artist! PRED expected values: 09d5h => 136 concepts (116 used for prediction) PRED predicted values (max 10 best out of 126): 015_1q (0.25 #1674, 0.23 #5124, 0.23 #2088), 0n85g (0.22 #60, 0.17 #336, 0.14 #1854), 02p11jq (0.20 #841, 0.17 #13, 0.10 #2083), 01cl2y (0.18 #857, 0.17 #29, 0.10 #1685), 01dtcb (0.17 #45, 0.13 #1011, 0.09 #183), 0fb0v (0.17 #7, 0.09 #3181, 0.08 #3043), 011k1h (0.16 #148, 0.15 #976, 0.12 #1114), 0229rs (0.15 #430, 0.08 #844, 0.07 #2224), 0181dw (0.15 #592, 0.14 #730, 0.14 #1972), 041bnw (0.14 #894, 0.10 #480, 0.07 #2136) >> Best rule #1674 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 011_vz; >> query: (?x8978, 015_1q) <- artist(?x2299, ?x8978), role(?x8978, ?x228), award_winner(?x9034, ?x8978), artists(?x302, ?x8978) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01wg6y artist! 09d5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 136.000 116.000 0.248 http://example.org/music/record_label/artist #268-05c74 PRED entity: 05c74 PRED relation: form_of_government PRED expected values: 01fpfn => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 4): 01fpfn (0.46 #30, 0.44 #58, 0.42 #126), 018wl5 (0.34 #109, 0.34 #57, 0.33 #97), 01q20 (0.31 #111, 0.29 #99, 0.28 #59), 026wp (0.08 #40, 0.07 #32, 0.07 #56) >> Best rule #30 for best value: >> intensional similarity = 3 >> extensional distance = 81 >> proper extension: 0j11; >> query: (?x7709, 01fpfn) <- film_release_region(?x1150, ?x7709), contains(?x7273, ?x7709), form_of_government(?x7709, ?x48) >> conf = 0.46 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05c74 form_of_government 01fpfn CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.458 http://example.org/location/country/form_of_government #267-06w58f PRED entity: 06w58f PRED relation: award PRED expected values: 027gs1_ => 68 concepts (55 used for prediction) PRED predicted values (max 10 best out of 214): 03ccq3s (0.43 #604, 0.20 #199, 0.18 #1009), 05b1610 (0.40 #39, 0.29 #444, 0.24 #849), 0cqhk0 (0.29 #847, 0.29 #442, 0.20 #37), 0fbtbt (0.29 #2259, 0.27 #2664, 0.17 #3474), 09qrn4 (0.29 #645, 0.20 #240, 0.12 #19449), 09sb52 (0.24 #14222, 0.23 #5308, 0.23 #15842), 027gs1_ (0.24 #3647, 0.23 #1621, 0.18 #9318), 0ck27z (0.23 #6170, 0.23 #6575, 0.23 #4955), 0drtkx (0.20 #299, 0.14 #704, 0.11 #19855), 09qvc0 (0.18 #850, 0.12 #19449, 0.11 #19855) >> Best rule #604 for best value: >> intensional similarity = 3 >> extensional distance = 5 >> proper extension: 0603qp; 0q9zc; 0fxky3; >> query: (?x10575, 03ccq3s) <- award_nominee(?x10575, ?x7002), ?x7002 = 0dbc1s, award(?x10575, ?x2016) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #3647 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 274 *> proper extension: 0gsg7; 0cjdk; *> query: (?x10575, ?x2016) <- award_winner(?x10575, ?x201), award_winner(?x2016, ?x201), program(?x201, ?x2293) *> conf = 0.24 ranks of expected_values: 7 EVAL 06w58f award 027gs1_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 68.000 55.000 0.429 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #266-0lcx PRED entity: 0lcx PRED relation: influenced_by PRED expected values: 032l1 06myp => 170 concepts (79 used for prediction) PRED predicted values (max 10 best out of 379): 05qmj (0.62 #1484, 0.47 #6640, 0.33 #2770), 0gz_ (0.48 #2682, 0.47 #6552, 0.42 #1825), 03sbs (0.48 #2800, 0.43 #6670, 0.38 #1514), 081k8 (0.40 #1017, 0.33 #586, 0.33 #154), 03_dj (0.40 #1270, 0.33 #839, 0.33 #407), 028p0 (0.40 #893, 0.33 #462, 0.33 #30), 05np2 (0.40 #1077, 0.33 #646, 0.33 #214), 03_87 (0.40 #11375, 0.33 #201, 0.25 #16529), 0448r (0.40 #1123, 0.33 #692, 0.21 #6879), 04xjp (0.33 #488, 0.33 #56, 0.22 #3064) >> Best rule #1484 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 07kb5; 045bg; 099bk; 0372p; 04hcw; 02ln1; >> query: (?x4028, 05qmj) <- religion(?x4028, ?x2694), influenced_by(?x1236, ?x4028), interests(?x4028, ?x6364), influenced_by(?x4028, ?x13901), ?x13901 = 0w6w >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #521 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 03_87; *> query: (?x4028, 032l1) <- religion(?x4028, ?x2694), influenced_by(?x5335, ?x4028), influenced_by(?x4008, ?x4028), influenced_by(?x4028, ?x4055), ?x4008 = 07h07, ?x5335 = 013pp3 *> conf = 0.33 ranks of expected_values: 12, 39 EVAL 0lcx influenced_by 06myp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 170.000 79.000 0.625 http://example.org/influence/influence_node/influenced_by EVAL 0lcx influenced_by 032l1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 170.000 79.000 0.625 http://example.org/influence/influence_node/influenced_by #265-062qg PRED entity: 062qg PRED relation: place_of_birth! PRED expected values: 01jmv8 => 156 concepts (68 used for prediction) PRED predicted values (max 10 best out of 1873): 0178kd (0.28 #130628, 0.27 #148917, 0.27 #141078), 0154qm (0.20 #630, 0.17 #5857, 0.17 #3244), 0879xc (0.20 #1420, 0.17 #6647, 0.17 #4034), 049qx (0.20 #884, 0.17 #6111, 0.17 #3498), 018009 (0.20 #859, 0.17 #6086, 0.17 #3473), 02404v (0.20 #1603, 0.17 #6830, 0.17 #4217), 06lgq8 (0.20 #377, 0.17 #5604, 0.17 #2991), 03h_9lg (0.20 #128, 0.17 #5355, 0.17 #2742), 013tcv (0.20 #1931, 0.17 #7158, 0.17 #4545), 071pf2 (0.20 #580, 0.17 #5807, 0.17 #3194) >> Best rule #130628 for best value: >> intensional similarity = 3 >> extensional distance = 182 >> proper extension: 0mn0v; >> query: (?x8823, ?x6368) <- place_of_birth(?x2443, ?x8823), origin(?x6368, ?x8823), artists(?x302, ?x6368) >> conf = 0.28 => this is the best rule for 1 predicted values *> Best rule #67927 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 89 *> proper extension: 017cjb; 0jcg8; 020skc; 03hrz; 09b8m; 01d88c; 06wjf; 0177z; 0ptj2; 0ftlx; ... *> query: (?x8823, ?x72) <- place_of_birth(?x2443, ?x8823), country(?x8823, ?x390), administrative_parent(?x390, ?x551), medal(?x390, ?x422), nationality(?x72, ?x390) *> conf = 0.02 ranks of expected_values: 1081 EVAL 062qg place_of_birth! 01jmv8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 156.000 68.000 0.278 http://example.org/people/person/place_of_birth #264-0kvjrw PRED entity: 0kvjrw PRED relation: instrumentalists! PRED expected values: 05r5c => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 117): 05r5c (0.64 #1139, 0.52 #3401, 0.51 #1922), 018vs (0.35 #4886, 0.33 #4799, 0.30 #4712), 07gql (0.33 #651, 0.25 #303, 0.21 #825), 03f5mt (0.29 #431, 0.25 #344, 0.20 #605), 07y_7 (0.29 #350, 0.25 #263, 0.16 #785), 0l14md (0.25 #877, 0.25 #268, 0.17 #1921), 02hnl (0.25 #295, 0.18 #4907, 0.17 #3862), 0l14j_ (0.25 #315, 0.17 #663, 0.14 #402), 07c6l (0.25 #271, 0.17 #619, 0.14 #358), 06ncr (0.25 #305, 0.17 #653, 0.14 #392) >> Best rule #1139 for best value: >> intensional similarity = 6 >> extensional distance = 31 >> proper extension: 0149xx; 02ryx0; 0ckcvk; >> query: (?x5476, 05r5c) <- profession(?x5476, ?x6476), profession(?x5476, ?x563), instrumentalists(?x227, ?x5476), ?x563 = 01c8w0, profession(?x6771, ?x6476), award_winner(?x2794, ?x6771) >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0kvjrw instrumentalists! 05r5c CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.636 http://example.org/music/instrument/instrumentalists #263-016dgz PRED entity: 016dgz PRED relation: notable_people_with_this_condition! PRED expected values: 03p41 => 111 concepts (111 used for prediction) PRED predicted values (max 10 best out of 7): 03p41 (0.02 #117, 0.02 #51, 0.02 #162), 0h99n (0.02 #77, 0.02 #413, 0.01 #302), 0d19y2 (0.02 #134, 0.01 #381, 0.01 #470), 0dq9p (0.02 #134, 0.01 #381, 0.01 #470), 029sk (0.02 #515, 0.01 #559, 0.01 #293), 068p_ (0.01 #43, 0.01 #65), 01g2q (0.01 #233, 0.01 #256) >> Best rule #117 for best value: >> intensional similarity = 2 >> extensional distance = 241 >> proper extension: 03qhyn8; >> query: (?x10724, 03p41) <- people(?x6260, ?x10724), nominated_for(?x10724, ?x10349) >> conf = 0.02 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016dgz notable_people_with_this_condition! 03p41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 111.000 111.000 0.021 http://example.org/medicine/disease/notable_people_with_this_condition #262-073h9x PRED entity: 073h9x PRED relation: honored_for PRED expected values: 01zfzb => 47 concepts (32 used for prediction) PRED predicted values (max 10 best out of 992): 017gl1 (0.33 #647, 0.10 #2428, 0.09 #3024), 011ywj (0.33 #1075, 0.10 #2856, 0.09 #3452), 0344gc (0.33 #643, 0.10 #2424, 0.09 #3020), 09m6kg (0.33 #606, 0.10 #2387, 0.09 #2983), 01718w (0.33 #1066, 0.10 #2847, 0.09 #3443), 011yxg (0.33 #609, 0.10 #2390, 0.09 #2986), 0pc62 (0.33 #627, 0.10 #2408, 0.09 #3004), 0dr3sl (0.33 #762, 0.10 #2543, 0.09 #3139), 0194zl (0.33 #889, 0.10 #2670, 0.09 #3266), 0yx_w (0.33 #518, 0.04 #7661, 0.04 #8850) >> Best rule #647 for best value: >> intensional similarity = 15 >> extensional distance = 1 >> proper extension: 02yvhx; >> query: (?x3254, 017gl1) <- instance_of_recurring_event(?x3254, ?x3459), award_winner(?x3254, ?x11314), award_winner(?x3254, ?x9313), ?x11314 = 0bn3jg, ceremony(?x5409, ?x3254), ceremony(?x1245, ?x3254), ceremony(?x591, ?x3254), profession(?x9313, ?x319), award(?x9313, ?x198), ?x1245 = 0gqwc, ?x319 = 01d_h8, ?x591 = 0f4x7, produced_by(?x2565, ?x9313), honored_for(?x3254, ?x324), ?x5409 = 0gr07 >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #6547 for first EXPECTED value: *> intensional similarity = 15 *> extensional distance = 17 *> proper extension: 0bzjvm; *> query: (?x3254, ?x5320) <- instance_of_recurring_event(?x3254, ?x3459), award_winner(?x3254, ?x11314), award_winner(?x3254, ?x9033), award_winner(?x3254, ?x5319), award_winner(?x3254, ?x3782), nominated_for(?x11314, ?x1077), student(?x1368, ?x5319), ceremony(?x77, ?x3254), award(?x9033, ?x618), honored_for(?x3254, ?x324), award(?x3782, ?x637), award_winner(?x5320, ?x5319), film(?x5319, ?x751), award_nominee(?x3782, ?x5653), film(?x9033, ?x4352) *> conf = 0.20 ranks of expected_values: 35 EVAL 073h9x honored_for 01zfzb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 47.000 32.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #261-044qx PRED entity: 044qx PRED relation: gender PRED expected values: 05zppz => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.87 #15, 0.85 #91, 0.84 #69), 02zsn (0.42 #60, 0.41 #34, 0.38 #74) >> Best rule #15 for best value: >> intensional similarity = 1 >> extensional distance = 124 >> proper extension: 0d9kl; 057ph; 0dng4; >> query: (?x4240, 05zppz) <- celebrities_impersonated(?x3649, ?x4240) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 044qx gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.865 http://example.org/people/person/gender #260-01clyr PRED entity: 01clyr PRED relation: child! PRED expected values: 01dtcb => 37 concepts (37 used for prediction) PRED predicted values (max 10 best out of 70): 02bh8z (0.50 #446, 0.25 #362, 0.22 #865), 049ql1 (0.33 #235, 0.25 #405, 0.25 #319), 016tw3 (0.33 #177, 0.25 #347, 0.25 #261), 03mp8k (0.20 #556, 0.14 #640, 0.03 #1058), 07gqbk (0.20 #578, 0.14 #662, 0.02 #1080), 01dtcb (0.20 #967, 0.14 #1385, 0.13 #2390), 0l8sx (0.14 #686, 0.11 #852, 0.11 #769), 01dfb6 (0.14 #727, 0.11 #893, 0.11 #810), 01bfjy (0.14 #669, 0.01 #1339, 0.01 #2008), 043g7l (0.10 #956, 0.03 #1039, 0.03 #1123) >> Best rule #446 for best value: >> intensional similarity = 12 >> extensional distance = 2 >> proper extension: 0g768; >> query: (?x5744, 02bh8z) <- artist(?x5744, ?x8849), artist(?x5744, ?x6639), artist(?x5744, ?x2600), artist(?x5744, ?x1563), artist(?x5744, ?x1136), profession(?x1563, ?x1614), ?x2600 = 0qf3p, ?x8849 = 07zft, artists(?x671, ?x1136), award_winner(?x139, ?x1563), award_nominee(?x6129, ?x6639), category(?x6639, ?x134) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #967 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 8 *> proper extension: 05b0f7; *> query: (?x5744, 01dtcb) <- artist(?x5744, ?x10744), artist(?x5744, ?x1563), artists(?x1000, ?x10744), profession(?x1563, ?x1614), student(?x3424, ?x1563), profession(?x10744, ?x1183), instrumentalists(?x1166, ?x1563), ?x3424 = 01w5m *> conf = 0.20 ranks of expected_values: 6 EVAL 01clyr child! 01dtcb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 37.000 37.000 0.500 http://example.org/organization/organization/child./organization/organization_relationship/child #259-01p85y PRED entity: 01p85y PRED relation: nationality PRED expected values: 0d060g => 136 concepts (136 used for prediction) PRED predicted values (max 10 best out of 77): 09c7w0 (0.83 #4165, 0.81 #4662, 0.81 #397), 07ssc (0.33 #3188, 0.10 #2492, 0.10 #2096), 0d060g (0.20 #7, 0.06 #6056, 0.05 #403), 03rk0 (0.10 #5995, 0.08 #8280, 0.05 #12845), 0chghy (0.03 #2091, 0.03 #2487, 0.03 #1100), 0345h (0.02 #8266, 0.02 #2706, 0.02 #1319), 03rjj (0.02 #798, 0.02 #5955, 0.02 #8240), 0f8l9c (0.02 #8257, 0.02 #2103, 0.02 #3988), 03rt9 (0.02 #1896, 0.02 #1499, 0.02 #607), 0j5g9 (0.02 #754, 0.02 #854, 0.01 #1944) >> Best rule #4165 for best value: >> intensional similarity = 3 >> extensional distance = 440 >> proper extension: 0p3r8; 016cff; 0dxmyh; 01my95; >> query: (?x8741, 09c7w0) <- participant(?x8741, ?x2813), nationality(?x8741, ?x1310), location(?x8741, ?x739) >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #7 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 3 *> proper extension: 0d608; *> query: (?x8741, 0d060g) <- film(?x8741, ?x2084), participant(?x5889, ?x8741), ?x2084 = 048qrd *> conf = 0.20 ranks of expected_values: 3 EVAL 01p85y nationality 0d060g CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 136.000 136.000 0.830 http://example.org/people/person/nationality #258-04vjh PRED entity: 04vjh PRED relation: organization PRED expected values: 041288 => 78 concepts (78 used for prediction) PRED predicted values (max 10 best out of 48): 01rz1 (0.47 #121, 0.43 #61, 0.43 #101), 041288 (0.40 #416, 0.37 #274, 0.37 #396), 04k4l (0.34 #164, 0.32 #204, 0.32 #224), 0_2v (0.31 #343, 0.30 #364, 0.29 #545), 0j7v_ (0.30 #285, 0.27 #245, 0.27 #265), 018cqq (0.22 #109, 0.22 #89, 0.22 #129), 085h1 (0.21 #361, 0.21 #382, 0.07 #10), 02jxk (0.21 #102, 0.21 #82, 0.20 #42), 034h1h (0.18 #1152, 0.03 #88, 0.02 #651), 059dn (0.10 #113, 0.10 #93, 0.09 #133) >> Best rule #121 for best value: >> intensional similarity = 3 >> extensional distance = 66 >> proper extension: 06sff; >> query: (?x10451, 01rz1) <- country(?x1121, ?x10451), organization(?x10451, ?x127), time_zones(?x10451, ?x5327) >> conf = 0.47 => this is the best rule for 1 predicted values *> Best rule #416 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 134 *> proper extension: 0j11; *> query: (?x10451, 041288) <- olympics(?x10451, ?x2966), ?x2966 = 06sks6, official_language(?x10451, ?x5359) *> conf = 0.40 ranks of expected_values: 2 EVAL 04vjh organization 041288 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 78.000 78.000 0.471 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #257-021r7r PRED entity: 021r7r PRED relation: group PRED expected values: 07bzp => 182 concepts (69 used for prediction) PRED predicted values (max 10 best out of 88): 02r1tx7 (0.11 #124, 0.10 #449, 0.06 #341), 081wh1 (0.11 #917, 0.05 #485, 0.04 #701), 070b4 (0.11 #181, 0.01 #2242, 0.01 #2893), 07bzp (0.10 #261, 0.06 #370, 0.05 #586), 01qqwp9 (0.10 #562, 0.08 #2407, 0.08 #670), 06nv27 (0.10 #574, 0.05 #1223, 0.04 #2527), 01v0sx2 (0.08 #1412, 0.07 #2065, 0.07 #1303), 07c0j (0.06 #329, 0.05 #545, 0.03 #2390), 07hgm (0.06 #404, 0.02 #1269, 0.01 #2139), 01vsxdm (0.06 #332, 0.02 #1197, 0.01 #2393) >> Best rule #124 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 02w670; >> query: (?x7437, 02r1tx7) <- origin(?x7437, ?x1860), profession(?x7437, ?x131), ?x1860 = 01_d4, gender(?x7437, ?x231) >> conf = 0.11 => this is the best rule for 1 predicted values *> Best rule #261 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 8 *> proper extension: 01wv9xn; 0394y; 0qmny; 0jltp; 03qkcn9; *> query: (?x7437, 07bzp) <- origin(?x7437, ?x1860), artists(?x7436, ?x7437), ?x7436 = 02l96k *> conf = 0.10 ranks of expected_values: 4 EVAL 021r7r group 07bzp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 182.000 69.000 0.111 http://example.org/music/group_member/membership./music/group_membership/group #256-098s2w PRED entity: 098s2w PRED relation: honored_for! PRED expected values: 02yw5r => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 112): 03gwpw2 (0.06 #485, 0.04 #965, 0.04 #1565), 09gkdln (0.06 #584, 0.04 #944, 0.04 #1064), 05c1t6z (0.05 #1692, 0.05 #1812, 0.05 #1571), 02q690_ (0.05 #1735, 0.05 #1855, 0.05 #1614), 04n2r9h (0.05 #516, 0.04 #1596, 0.04 #1717), 09k5jh7 (0.05 #550, 0.03 #910, 0.03 #1030), 05qb8vx (0.05 #528, 0.02 #648, 0.02 #888), 0gvstc3 (0.04 #1708, 0.04 #1828, 0.04 #1587), 0275n3y (0.04 #1744, 0.04 #1864, 0.04 #1623), 03nnm4t (0.04 #1743, 0.04 #1863, 0.04 #1622) >> Best rule #485 for best value: >> intensional similarity = 3 >> extensional distance = 158 >> proper extension: 07l50vn; >> query: (?x6493, 03gwpw2) <- honored_for(?x5459, ?x6493), film_release_distribution_medium(?x6493, ?x81), featured_film_locations(?x6493, ?x3670) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #2163 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 670 *> proper extension: 097h2; 019g8j; 0147w8; *> query: (?x6493, ?x873) <- award(?x6493, ?x2257), award_winner(?x2257, ?x548), ceremony(?x2257, ?x873) *> conf = 0.02 ranks of expected_values: 54 EVAL 098s2w honored_for! 02yw5r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 76.000 76.000 0.062 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #255-02knxx PRED entity: 02knxx PRED relation: people PRED expected values: 012c6x 03s9b => 27 concepts (20 used for prediction) PRED predicted values (max 10 best out of 1858): 0b22w (0.40 #2442, 0.33 #1128, 0.33 #472), 01gzm2 (0.40 #2022, 0.33 #52, 0.10 #6623), 0ly5n (0.40 #2097, 0.33 #127, 0.10 #6698), 01938t (0.33 #273, 0.27 #3557, 0.23 #4214), 0gyy0 (0.33 #1022, 0.25 #1679, 0.20 #2993), 0jrny (0.33 #762, 0.25 #1419, 0.20 #2733), 016gkf (0.33 #861, 0.25 #1518, 0.20 #2832), 024qwq (0.33 #1074, 0.25 #1731, 0.20 #3045), 05v45k (0.33 #1249, 0.25 #1906, 0.20 #3220), 0cgbf (0.33 #941, 0.25 #1598, 0.20 #2912) >> Best rule #2442 for best value: >> intensional similarity = 8 >> extensional distance = 3 >> proper extension: 04psf; >> query: (?x9771, 0b22w) <- people(?x9771, ?x5366), award(?x5366, ?x1862), award(?x5366, ?x1313), ?x1862 = 0gr51, award(?x197, ?x1313), produced_by(?x5980, ?x5366), nominated_for(?x1313, ?x144), ceremony(?x1313, ?x78) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2627 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 3 *> proper extension: 04psf; *> query: (?x9771, ?x269) <- people(?x9771, ?x5366), award(?x5366, ?x1862), award(?x5366, ?x1313), ?x1862 = 0gr51, award(?x269, ?x1313), award(?x197, ?x1313), produced_by(?x5980, ?x5366), nominated_for(?x1313, ?x144), ceremony(?x1313, ?x78) *> conf = 0.05 ranks of expected_values: 637, 1150 EVAL 02knxx people 03s9b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 27.000 20.000 0.400 http://example.org/people/cause_of_death/people EVAL 02knxx people 012c6x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 27.000 20.000 0.400 http://example.org/people/cause_of_death/people #254-01v1d8 PRED entity: 01v1d8 PRED relation: role PRED expected values: 0l14j_ => 81 concepts (56 used for prediction) PRED predicted values (max 10 best out of 104): 03qlv7 (0.82 #4857, 0.81 #1135, 0.81 #5585), 02bxd (0.82 #4857, 0.81 #1135, 0.81 #5585), 07y_7 (0.81 #3601, 0.66 #417, 0.64 #4231), 0mkg (0.81 #4338, 0.80 #4237, 0.76 #3607), 03qjg (0.76 #3648, 0.76 #5218, 0.75 #1596), 026t6 (0.75 #3184, 0.67 #207, 0.66 #417), 07xzm (0.71 #2687, 0.71 #1464, 0.67 #2480), 01xqw (0.71 #3668, 0.69 #2633, 0.67 #3050), 03q5t (0.71 #3600, 0.68 #2767, 0.66 #417), 06w7v (0.71 #1522, 0.67 #1313, 0.66 #417) >> Best rule #4857 for best value: >> intensional similarity = 18 >> extensional distance = 27 >> proper extension: 0192l; >> query: (?x3161, ?x645) <- role(?x2957, ?x3161), role(?x2798, ?x3161), role(?x1437, ?x3161), role(?x569, ?x3161), instrumentalists(?x3161, ?x5478), instrumentalists(?x3161, ?x3893), instrumentalists(?x569, ?x642), role(?x315, ?x2957), family(?x2957, ?x227), role(?x645, ?x3161), ?x2798 = 03qjg, role(?x3161, ?x1147), origin(?x5478, ?x2673), award(?x3893, ?x2180), ?x1437 = 01vdm0, award_nominee(?x5478, ?x1818), role(?x7772, ?x2957), artists(?x302, ?x5478) >> conf = 0.82 => this is the best rule for 2 predicted values *> Best rule #1083 for first EXPECTED value: *> intensional similarity = 19 *> extensional distance = 4 *> proper extension: 04q7r; *> query: (?x3161, 0l14j_) <- role(?x4311, ?x3161), role(?x2957, ?x3161), role(?x2798, ?x3161), role(?x228, ?x3161), role(?x212, ?x3161), ?x2957 = 01v8y9, instrumentalists(?x3161, ?x140), role(?x1466, ?x3161), ?x212 = 026t6, ?x4311 = 01xqw, ?x2798 = 03qjg, group(?x3161, ?x3682), role(?x75, ?x228), role(?x433, ?x228), role(?x130, ?x228), performance_role(?x11364, ?x1466), instrumentalists(?x228, ?x211), student(?x2486, ?x11364), role(?x1663, ?x1466) *> conf = 0.67 ranks of expected_values: 21 EVAL 01v1d8 role 0l14j_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 81.000 56.000 0.821 http://example.org/music/performance_role/regular_performances./music/group_membership/role #253-03y9ccy PRED entity: 03y9ccy PRED relation: profession PRED expected values: 03gjzk 02krf9 => 108 concepts (106 used for prediction) PRED predicted values (max 10 best out of 60): 03gjzk (0.84 #3144, 0.84 #2697, 0.84 #2399), 02hrh1q (0.76 #2994, 0.75 #4037, 0.75 #2249), 01d_h8 (0.64 #900, 0.55 #1943, 0.54 #1794), 02jknp (0.42 #6266, 0.39 #753, 0.33 #1796), 02krf9 (0.40 #176, 0.30 #3603, 0.30 #2858), 018gz8 (0.39 #762, 0.22 #2252, 0.21 #1805), 0np9r (0.35 #766, 0.25 #10880, 0.25 #8047), 0cbd2 (0.30 #752, 0.28 #9985, 0.25 #10880), 09jwl (0.30 #3297, 0.25 #4787, 0.25 #3446), 0kyk (0.24 #924, 0.17 #775, 0.12 #6288) >> Best rule #3144 for best value: >> intensional similarity = 3 >> extensional distance = 197 >> proper extension: 0grwj; 02lf0c; 0bg539; 043q6n_; 03jldb; 0gz5hs; 07_s4b; 027xbpw; 01pfkw; 03772; ... >> query: (?x3727, 03gjzk) <- award_nominee(?x3727, ?x4671), program(?x3727, ?x2009), award_winner(?x8128, ?x4671) >> conf = 0.84 => this is the best rule for 1 predicted values ranks of expected_values: 1, 5 EVAL 03y9ccy profession 02krf9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 108.000 106.000 0.844 http://example.org/people/person/profession EVAL 03y9ccy profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 108.000 106.000 0.844 http://example.org/people/person/profession #252-0p2n PRED entity: 0p2n PRED relation: contains! PRED expected values: 06n3y => 26 concepts (23 used for prediction) PRED predicted values (max 10 best out of 63): 09c7w0 (0.46 #15423, 0.46 #14510, 0.44 #7257), 06n3y (0.46 #16328, 0.40 #19054, 0.40 #10879), 04pnx (0.38 #15419, 0.38 #16324, 0.38 #7254), 07c5l (0.38 #19049, 0.38 #16324, 0.38 #7254), 059g4 (0.33 #12249, 0.33 #7717, 0.33 #2275), 02j71 (0.33 #8153, 0.24 #19042, 0.22 #6340), 02qkt (0.29 #3968, 0.25 #13043, 0.25 #347), 0jgd (0.22 #4528, 0.22 #2712, 0.14 #4518), 016wzw (0.22 #4528, 0.17 #4519, 0.14 #4518), 07ylj (0.22 #4528, 0.14 #4518, 0.14 #2718) >> Best rule #15423 for best value: >> intensional similarity = 12 >> extensional distance = 11 >> proper extension: 01smm; >> query: (?x12972, 09c7w0) <- partially_contains(?x1203, ?x12972), contains(?x12315, ?x1203), contains(?x7708, ?x1203), contains(?x7273, ?x1203), taxonomy(?x7273, ?x939), ?x939 = 04n6k, adjoins(?x9459, ?x1203), time_zones(?x7708, ?x2674), ?x2674 = 02hcv8, partially_contains(?x12315, ?x789), geographic_distribution(?x13662, ?x12315), location(?x5283, ?x12315) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #16328 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 11 *> proper extension: 01smm; *> query: (?x12972, ?x12315) <- partially_contains(?x1203, ?x12972), contains(?x12315, ?x1203), contains(?x7708, ?x1203), contains(?x7273, ?x1203), taxonomy(?x7273, ?x939), ?x939 = 04n6k, adjoins(?x9459, ?x1203), time_zones(?x7708, ?x2674), ?x2674 = 02hcv8, partially_contains(?x12315, ?x789), geographic_distribution(?x13662, ?x12315), location(?x5283, ?x12315) *> conf = 0.46 ranks of expected_values: 2 EVAL 0p2n contains! 06n3y CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 26.000 23.000 0.462 http://example.org/location/location/contains #251-013cr PRED entity: 013cr PRED relation: profession PRED expected values: 02jknp => 115 concepts (50 used for prediction) PRED predicted values (max 10 best out of 61): 03gjzk (0.74 #598, 0.72 #14, 0.71 #452), 01d_h8 (0.53 #1612, 0.37 #4095, 0.36 #444), 02jknp (0.44 #1614, 0.26 #4097, 0.25 #4535), 0cbd2 (0.28 #591, 0.27 #737, 0.24 #7), 02krf9 (0.24 #24, 0.22 #462, 0.20 #608), 09jwl (0.21 #2499, 0.20 #2061, 0.18 #1185), 0kyk (0.20 #757, 0.15 #1925, 0.14 #1341), 0d1pc (0.17 #1216, 0.13 #2676, 0.13 #1800), 015cjr (0.15 #47, 0.11 #193, 0.06 #1653), 0nbcg (0.13 #2511, 0.12 #2073, 0.11 #1197) >> Best rule #598 for best value: >> intensional similarity = 3 >> extensional distance = 111 >> proper extension: 01gp_x; 03bx_5q; 04crrxr; 055sjw; 03p01x; 03yf4d; 01s7z0; >> query: (?x1401, 03gjzk) <- student(?x6894, ?x1401), profession(?x1401, ?x987), tv_program(?x1401, ?x6884) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #1614 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 384 *> proper extension: 079vf; 025p38; 049k07; 07ymr5; 02_j7t; 0bymv; 05wjnt; 02lf1j; 03kpvp; 02wr2r; ... *> query: (?x1401, 02jknp) <- film(?x1401, ?x1402), profession(?x1401, ?x987), ?x987 = 0dxtg *> conf = 0.44 ranks of expected_values: 3 EVAL 013cr profession 02jknp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 115.000 50.000 0.743 http://example.org/people/person/profession #250-02z6gky PRED entity: 02z6gky PRED relation: locations PRED expected values: 010h9y => 60 concepts (49 used for prediction) PRED predicted values (max 10 best out of 181): 013yq (0.69 #2262, 0.50 #3181, 0.50 #2997), 0156q (0.54 #1879, 0.50 #771, 0.50 #588), 0d9jr (0.40 #1020, 0.36 #1572, 0.36 #1203), 0fsb8 (0.40 #1059, 0.36 #1611, 0.36 #1242), 0d9y6 (0.35 #3044, 0.31 #2309, 0.29 #2492), 029cr (0.33 #2817, 0.30 #976, 0.27 #1528), 071cn (0.33 #2837, 0.30 #996, 0.27 #1548), 030qb3t (0.32 #3350, 0.29 #4270, 0.29 #4087), 0f2rq (0.31 #2314, 0.30 #3049, 0.28 #2865), 0h7h6 (0.31 #2064, 0.31 #1881, 0.30 #773) >> Best rule #2262 for best value: >> intensional similarity = 10 >> extensional distance = 14 >> proper extension: 0jhn7; >> query: (?x14041, 013yq) <- locations(?x14041, ?x674), location(?x10086, ?x674), film(?x10086, ?x1586), student(?x4750, ?x10086), award_winner(?x1784, ?x10086), award_winner(?x873, ?x10086), county_seat(?x673, ?x674), citytown(?x2760, ?x674), locations(?x11210, ?x674), ?x11210 = 0b_6q5 >> conf = 0.69 => this is the best rule for 1 predicted values *> Best rule #2923 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 16 *> proper extension: 0b_6jz; *> query: (?x14041, 010h9y) <- locations(?x14041, ?x674), location(?x10086, ?x674), film(?x10086, ?x1586), student(?x4750, ?x10086), award_winner(?x1784, ?x10086), award_winner(?x873, ?x10086), county_seat(?x673, ?x674), citytown(?x2760, ?x674), actor(?x8533, ?x10086) *> conf = 0.17 ranks of expected_values: 47 EVAL 02z6gky locations 010h9y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.021 60.000 49.000 0.688 http://example.org/time/event/locations #249-09f6b PRED entity: 09f6b PRED relation: country PRED expected values: 07ylj 0345h 01mjq 06t8v => 33 concepts (33 used for prediction) PRED predicted values (max 10 best out of 802): 0345h (0.91 #4490, 0.89 #5069, 0.86 #4683), 0163v (0.87 #3541, 0.80 #3346, 0.80 #1799), 0ctw_b (0.84 #971, 0.82 #2542, 0.81 #969), 0d0vqn (0.84 #971, 0.81 #969, 0.81 #3878), 02vzc (0.84 #971, 0.81 #969, 0.81 #3878), 06qd3 (0.84 #971, 0.81 #969, 0.81 #3878), 06mkj (0.84 #971, 0.81 #969, 0.80 #1354), 015fr (0.84 #971, 0.81 #969, 0.79 #970), 059j2 (0.84 #971, 0.81 #969, 0.79 #970), 06t8v (0.84 #971, 0.81 #969, 0.79 #970) >> Best rule #4490 for best value: >> intensional similarity = 53 >> extensional distance = 30 >> proper extension: 02vx4; 0d1t3; 01gqfm; >> query: (?x11927, 0345h) <- sports(?x1617, ?x11927), country(?x11927, ?x1603), country(?x11927, ?x456), country(?x11927, ?x390), ?x390 = 0chghy, olympics(?x304, ?x1617), ?x456 = 05qhw, film_release_region(?x11395, ?x1603), film_release_region(?x9501, ?x1603), film_release_region(?x8495, ?x1603), film_release_region(?x6168, ?x1603), film_release_region(?x6078, ?x1603), film_release_region(?x4355, ?x1603), film_release_region(?x3276, ?x1603), film_release_region(?x3252, ?x1603), film_release_region(?x2627, ?x1603), film_release_region(?x1916, ?x1603), film_release_region(?x1785, ?x1603), film_release_region(?x1701, ?x1603), film_release_region(?x1496, ?x1603), film_release_region(?x1202, ?x1603), ?x9501 = 0g5qmbz, ?x1785 = 0gj9tn5, country(?x6733, ?x1603), country(?x5177, ?x1603), country(?x1967, ?x1603), country(?x1352, ?x1603), ?x1202 = 0gj8t_b, ?x6168 = 0gj96ln, ?x6733 = 01sgl, ?x4355 = 08tq4x, ?x6078 = 04pk1f, ?x1916 = 0ch26b_, ?x11395 = 05ypj5, ?x3252 = 0gh8zks, countries_spoken_in(?x403, ?x1603), olympics(?x1603, ?x778), ?x1701 = 0bh8yn3, nationality(?x889, ?x1603), ?x5177 = 06zgc, country(?x1352, ?x5186), country(?x1352, ?x3277), country(?x1352, ?x1203), administrative_area_type(?x1603, ?x2792), administrative_parent(?x1603, ?x551), ?x1496 = 011yqc, ?x5186 = 06sff, ?x1203 = 07ylj, ?x3276 = 0gjc4d3, ?x1967 = 01cgz, ?x3277 = 06t8v, ?x2627 = 0gz6b6g, person(?x8495, ?x3183) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1, 10, 21, 33 EVAL 09f6b country 06t8v CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 33.000 33.000 0.906 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09f6b country 01mjq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 33.000 33.000 0.906 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09f6b country 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 33.000 33.000 0.906 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 09f6b country 07ylj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.033 33.000 33.000 0.906 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #248-0421st PRED entity: 0421st PRED relation: type_of_union PRED expected values: 04ztj => 82 concepts (82 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.71 #1, 0.71 #113, 0.71 #121), 01g63y (0.27 #22, 0.27 #18, 0.25 #14) >> Best rule #1 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 02mxw0; 04264n; 037w7r; 05xd_v; 04gc65; >> query: (?x7754, 04ztj) <- nationality(?x7754, ?x94), place_of_birth(?x7754, ?x4733), film(?x7754, ?x2719), ?x2719 = 0j_t1 >> conf = 0.71 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0421st type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 82.000 82.000 0.714 http://example.org/people/person/spouse_s./people/marriage/type_of_union #247-07zhd7 PRED entity: 07zhd7 PRED relation: location PRED expected values: 019fh => 126 concepts (112 used for prediction) PRED predicted values (max 10 best out of 175): 02_286 (0.23 #33790, 0.22 #71555, 0.19 #34593), 030qb3t (0.17 #71601, 0.16 #38656, 0.13 #36245), 01_d4 (0.10 #101, 0.07 #905, 0.03 #1708), 04f_d (0.10 #107, 0.07 #911, 0.02 #9751), 0k049 (0.10 #8, 0.02 #38582, 0.02 #71527), 03rjj (0.10 #9, 0.02 #4027), 01x73 (0.10 #95, 0.01 #4917), 0cr3d (0.08 #71663, 0.07 #58803, 0.07 #61214), 0f2wj (0.07 #837, 0.03 #4051, 0.02 #38607), 05tbn (0.07 #991, 0.02 #3402, 0.02 #9831) >> Best rule #33790 for best value: >> intensional similarity = 4 >> extensional distance = 641 >> proper extension: 01ty7ll; 03jm6c; 01wz_ml; 02xwq9; >> query: (?x12188, 02_286) <- place_of_birth(?x12188, ?x362), vacationer(?x362, ?x827), location(?x3069, ?x362), music(?x224, ?x3069) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #74734 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1656 *> proper extension: 027lfrs; *> query: (?x12188, ?x792) <- place_of_birth(?x12188, ?x362), contains(?x362, ?x639), location(?x1235, ?x362), location(?x1235, ?x792) *> conf = 0.02 ranks of expected_values: 88 EVAL 07zhd7 location 019fh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 126.000 112.000 0.227 http://example.org/people/person/places_lived./people/place_lived/location #246-0dq626 PRED entity: 0dq626 PRED relation: film_crew_role PRED expected values: 01pvkk => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 28): 01vx2h (0.57 #247, 0.45 #386, 0.45 #667), 01pvkk (0.33 #10, 0.30 #1185, 0.29 #1956), 02rh1dz (0.24 #246, 0.15 #701, 0.15 #420), 06qc5 (0.20 #95, 0.10 #1910, 0.02 #299), 01xy5l_ (0.20 #250, 0.16 #912, 0.14 #670), 02ynfr (0.19 #1713, 0.18 #879, 0.16 #914), 0215hd (0.17 #255, 0.17 #917, 0.17 #1089), 015h31 (0.17 #314, 0.17 #349, 0.16 #419), 0d2b38 (0.16 #924, 0.15 #160, 0.15 #262), 089g0h (0.15 #883, 0.13 #1124, 0.12 #952) >> Best rule #247 for best value: >> intensional similarity = 8 >> extensional distance = 44 >> proper extension: 0cnztc4; >> query: (?x377, 01vx2h) <- genre(?x377, ?x258), film_crew_role(?x377, ?x1171), region(?x377, ?x512), ?x1171 = 09vw2b7, genre(?x4048, ?x258), genre(?x1192, ?x258), ?x4048 = 0ddcbd5, ?x1192 = 07sc6nw >> conf = 0.57 => this is the best rule for 1 predicted values *> Best rule #10 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 1 *> proper extension: 05zlld0; *> query: (?x377, 01pvkk) <- genre(?x377, ?x53), film_crew_role(?x377, ?x137), region(?x377, ?x512), film(?x4277, ?x377), production_companies(?x377, ?x2156), ?x4277 = 046qq *> conf = 0.33 ranks of expected_values: 2 EVAL 0dq626 film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 90.000 0.565 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #245-02f77l PRED entity: 02f77l PRED relation: award! PRED expected values: 017mbb => 32 concepts (16 used for prediction) PRED predicted values (max 10 best out of 2119): 081wh1 (0.85 #30200, 0.81 #16776, 0.81 #20132), 0dw4g (0.67 #11696, 0.64 #8341, 0.51 #15052), 01vs_v8 (0.60 #3936, 0.57 #7291, 0.54 #14002), 07r1_ (0.60 #5407, 0.57 #8762, 0.53 #12117), 0gbwp (0.60 #4463, 0.50 #7818, 0.47 #11173), 0478__m (0.60 #4675, 0.50 #8030, 0.47 #11385), 01pfr3 (0.60 #3449, 0.47 #10159, 0.43 #6804), 09889g (0.60 #4798, 0.43 #8153, 0.40 #11508), 01bczm (0.60 #4987, 0.33 #1632, 0.32 #15053), 01v_pj6 (0.60 #3777, 0.33 #422, 0.29 #7132) >> Best rule #30200 for best value: >> intensional similarity = 7 >> extensional distance = 76 >> proper extension: 02q3s; >> query: (?x6126, ?x7013) <- award_winner(?x6126, ?x7013), award_winner(?x6126, ?x5493), award_winner(?x6126, ?x3682), artists(?x302, ?x3682), award(?x5493, ?x247), artist(?x8738, ?x7013), award_nominee(?x5493, ?x1566) >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #5978 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 3 *> proper extension: 02f72n; 02f72_; *> query: (?x6126, 017mbb) <- award_winner(?x6126, ?x646), award(?x10813, ?x6126), award(?x6699, ?x6126), award(?x1467, ?x6126), ?x1467 = 01vsxdm, artist(?x8738, ?x10813), instrumentalists(?x212, ?x6699) *> conf = 0.20 ranks of expected_values: 147 EVAL 02f77l award! 017mbb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 32.000 16.000 0.854 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #244-018n6m PRED entity: 018n6m PRED relation: award_winner! PRED expected values: 019bk0 => 128 concepts (128 used for prediction) PRED predicted values (max 10 best out of 109): 02rjjll (0.25 #5, 0.13 #557, 0.11 #1661), 019bk0 (0.25 #16, 0.09 #568, 0.08 #5122), 0466p0j (0.19 #75, 0.09 #5181, 0.09 #4491), 05pd94v (0.17 #2, 0.10 #4418, 0.09 #5108), 056878 (0.17 #32, 0.07 #5138, 0.07 #4862), 0gpjbt (0.14 #29, 0.11 #581, 0.09 #1685), 0jzphpx (0.14 #39, 0.08 #177, 0.08 #729), 01c6qp (0.11 #19, 0.09 #4159, 0.09 #5677), 01mhwk (0.11 #41, 0.08 #731, 0.08 #5147), 01s695 (0.09 #5109, 0.09 #4419, 0.09 #4143) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 34 >> proper extension: 03f5spx; 0770cd; 01w7nwm; 01wqmm8; 03t852; >> query: (?x4640, 02rjjll) <- award_winner(?x3121, ?x4640), award(?x4640, ?x3835), ?x3835 = 01cky2 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #16 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 34 *> proper extension: 03f5spx; 0770cd; 01w7nwm; 01wqmm8; 03t852; *> query: (?x4640, 019bk0) <- award_winner(?x3121, ?x4640), award(?x4640, ?x3835), ?x3835 = 01cky2 *> conf = 0.25 ranks of expected_values: 2 EVAL 018n6m award_winner! 019bk0 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 128.000 128.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #243-01r42_g PRED entity: 01r42_g PRED relation: type_of_union PRED expected values: 04ztj => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.77 #29, 0.73 #129, 0.73 #113), 01g63y (0.24 #14, 0.20 #34, 0.20 #18) >> Best rule #29 for best value: >> intensional similarity = 3 >> extensional distance = 331 >> proper extension: 01xyt7; >> query: (?x369, 04ztj) <- award_winner(?x1670, ?x369), people(?x1050, ?x369), religion(?x369, ?x14146) >> conf = 0.77 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01r42_g type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 102.000 0.772 http://example.org/people/person/spouse_s./people/marriage/type_of_union #242-02xry PRED entity: 02xry PRED relation: state_province_region! PRED expected values: 022fj_ => 168 concepts (113 used for prediction) PRED predicted values (max 10 best out of 776): 0j_sncb (0.65 #27471, 0.59 #28959, 0.33 #36389), 0c5v2 (0.23 #11879, 0.23 #19301, 0.21 #23755), 0rn0z (0.23 #11879, 0.23 #19301, 0.21 #23755), 0rmby (0.23 #11879, 0.23 #19301, 0.21 #23755), 0jgm8 (0.23 #11879, 0.23 #19301, 0.21 #23755), 0rk71 (0.23 #11879, 0.23 #19301, 0.21 #23755), 0rj4g (0.23 #11879, 0.23 #19301, 0.21 #23755), 0rrwt (0.23 #11879, 0.23 #19301, 0.21 #23755), 0jrxx (0.23 #11879, 0.23 #19301, 0.21 #23755), 0rhp6 (0.23 #11879, 0.23 #19301, 0.21 #23755) >> Best rule #27471 for best value: >> intensional similarity = 3 >> extensional distance = 78 >> proper extension: 0g14f; >> query: (?x2623, ?x4904) <- contains(?x2623, ?x4904), state_province_region(?x13197, ?x2623), colors(?x4904, ?x663) >> conf = 0.65 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02xry state_province_region! 022fj_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 168.000 113.000 0.652 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #241-050z2 PRED entity: 050z2 PRED relation: profession PRED expected values: 0dz3r 09jwl => 177 concepts (147 used for prediction) PRED predicted values (max 10 best out of 100): 02hrh1q (0.85 #19039, 0.80 #4993, 0.80 #21084), 09jwl (0.84 #5436, 0.82 #312, 0.79 #458), 0dz3r (0.62 #148, 0.55 #295, 0.50 #587), 016z4k (0.52 #4691, 0.51 #5421, 0.48 #7763), 01c8w0 (0.42 #2350, 0.39 #1618, 0.29 #4842), 01d_h8 (0.38 #152, 0.36 #299, 0.36 #4985), 0n1h (0.36 #304, 0.26 #450, 0.26 #4698), 0dxtg (0.33 #13, 0.29 #20353, 0.28 #8797), 02jknp (0.33 #8, 0.25 #154, 0.21 #20348), 03gjzk (0.33 #15, 0.23 #5871, 0.22 #4994) >> Best rule #19039 for best value: >> intensional similarity = 4 >> extensional distance = 1603 >> proper extension: 02wlk; >> query: (?x4052, 02hrh1q) <- award_winner(?x5123, ?x4052), profession(?x4052, ?x2659), profession(?x8319, ?x2659), ?x8319 = 02_t2t >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #5436 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 164 *> proper extension: 01cv3n; 01p45_v; 011hdn; 04d_mtq; *> query: (?x4052, 09jwl) <- artists(?x284, ?x4052), profession(?x4052, ?x2659), ?x2659 = 039v1 *> conf = 0.84 ranks of expected_values: 2, 3 EVAL 050z2 profession 09jwl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 177.000 147.000 0.847 http://example.org/people/person/profession EVAL 050z2 profession 0dz3r CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 177.000 147.000 0.847 http://example.org/people/person/profession #240-01cqz5 PRED entity: 01cqz5 PRED relation: type_of_union PRED expected values: 04ztj => 173 concepts (173 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.75 #186, 0.74 #168, 0.74 #76), 01g63y (0.36 #2, 0.36 #536, 0.29 #6), 0jgjn (0.04 #25) >> Best rule #186 for best value: >> intensional similarity = 4 >> extensional distance = 183 >> proper extension: 07cbs; 0163r3; >> query: (?x11755, 04ztj) <- gender(?x11755, ?x231), ?x231 = 05zppz, place_of_death(?x11755, ?x6054), religion(?x11755, ?x7422) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01cqz5 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 173.000 173.000 0.746 http://example.org/people/person/spouse_s./people/marriage/type_of_union #239-03p7gb PRED entity: 03p7gb PRED relation: school_type PRED expected values: 01rs41 => 217 concepts (217 used for prediction) PRED predicted values (max 10 best out of 19): 01rs41 (0.63 #3139, 0.60 #1569, 0.60 #993), 05jxkf (0.62 #1913, 0.59 #2166, 0.52 #2652), 07tf8 (0.33 #54, 0.28 #1089, 0.26 #4312), 01y64 (0.26 #4312, 0.20 #149, 0.12 #126), 01_srz (0.26 #4312, 0.12 #1382, 0.12 #991), 02dk5q (0.26 #4312, 0.09 #1110, 0.08 #1225), 03ss47 (0.26 #4312, 0.05 #403, 0.03 #817), 01_9fk (0.25 #93, 0.20 #208, 0.20 #24), 02p0qmm (0.25 #101, 0.17 #55, 0.10 #216), 06cs1 (0.20 #28, 0.17 #51, 0.12 #97) >> Best rule #3139 for best value: >> intensional similarity = 7 >> extensional distance = 218 >> proper extension: 020yvh; >> query: (?x4755, 01rs41) <- school_type(?x4755, ?x1044), school_type(?x14216, ?x1044), school_type(?x13089, ?x1044), school_type(?x4262, ?x1044), ?x4262 = 017y26, ?x13089 = 043q2z, ?x14216 = 022r38 >> conf = 0.63 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03p7gb school_type 01rs41 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 217.000 217.000 0.627 http://example.org/education/educational_institution/school_type #238-01wdcxk PRED entity: 01wdcxk PRED relation: profession PRED expected values: 02hrh1q 01c72t => 145 concepts (73 used for prediction) PRED predicted values (max 10 best out of 72): 02hrh1q (0.98 #6448, 0.81 #5132, 0.73 #4255), 0nbcg (0.91 #7051, 0.72 #764, 0.70 #5442), 01c72t (0.72 #756, 0.66 #1049, 0.64 #609), 016z4k (0.63 #2052, 0.58 #441, 0.56 #589), 0kyk (0.50 #2370, 0.16 #615, 0.13 #1348), 0dxtg (0.35 #1624, 0.34 #1478, 0.31 #1770), 0n1h (0.32 #449, 0.25 #3084, 0.25 #4106), 01d_h8 (0.30 #4246, 0.25 #5123, 0.25 #6439), 0fnpj (0.23 #938, 0.20 #2838, 0.18 #1231), 02hv44_ (0.21 #1521, 0.20 #1667, 0.19 #1813) >> Best rule #6448 for best value: >> intensional similarity = 5 >> extensional distance = 266 >> proper extension: 044mz_; 05bnp0; 02p65p; 01tvz5j; 04bs3j; 014x77; 02g87m; 02lkcc; 01t07j; 01gq0b; ... >> query: (?x10094, 02hrh1q) <- type_of_union(?x10094, ?x1873), profession(?x10094, ?x6476), profession(?x13574, ?x6476), ?x13574 = 01kym3, ?x1873 = 01g63y >> conf = 0.98 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3 EVAL 01wdcxk profession 01c72t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 145.000 73.000 0.978 http://example.org/people/person/profession EVAL 01wdcxk profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 145.000 73.000 0.978 http://example.org/people/person/profession #237-05148p4 PRED entity: 05148p4 PRED relation: role! PRED expected values: 0kzy0 013pk3 => 72 concepts (66 used for prediction) PRED predicted values (max 10 best out of 908): 03c7ln (0.62 #2457, 0.50 #673, 0.50 #225), 0167v4 (0.62 #2639, 0.50 #3308, 0.42 #4203), 02jg92 (0.60 #1560, 0.60 #1377, 0.50 #443), 01wl38s (0.50 #2907, 0.50 #2461, 0.50 #229), 01p45_v (0.50 #2483, 0.50 #251, 0.40 #3152), 0bg539 (0.50 #2477, 0.40 #1584, 0.38 #2255), 032t2z (0.50 #230, 0.40 #1344, 0.37 #1335), 04s5_s (0.50 #889, 0.40 #1780, 0.33 #4012), 01vrx3g (0.50 #675, 0.38 #2459, 0.33 #4023), 0fq117k (0.50 #819, 0.38 #2603, 0.30 #3272) >> Best rule #2457 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 03qjg; >> query: (?x1166, 03c7ln) <- role(?x565, ?x1166), role(?x248, ?x1166), role(?x74, ?x1166), role(?x1166, ?x227), group(?x1166, ?x1060), instrumentalists(?x1166, ?x130), role(?x1166, ?x1225), ?x1060 = 02r3zy >> conf = 0.62 => this is the best rule for 1 predicted values *> Best rule #679 for first EXPECTED value: *> intensional similarity = 10 *> extensional distance = 2 *> proper extension: 01vj9c; *> query: (?x1166, 0kzy0) <- family(?x228, ?x1166), role(?x565, ?x1166), role(?x1166, ?x2309), role(?x1166, ?x1212), group(?x1166, ?x10265), group(?x1166, ?x498), ?x498 = 0m19t, ?x2309 = 06ncr, ?x1212 = 07xzm, ?x10265 = 01dpts *> conf = 0.25 ranks of expected_values: 148 EVAL 05148p4 role! 013pk3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 72.000 66.000 0.625 http://example.org/music/group_member/membership./music/group_membership/role EVAL 05148p4 role! 0kzy0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 72.000 66.000 0.625 http://example.org/music/group_member/membership./music/group_membership/role #236-034qbx PRED entity: 034qbx PRED relation: film! PRED expected values: 01dw9z 01wb8bs => 129 concepts (80 used for prediction) PRED predicted values (max 10 best out of 1408): 01vy_v8 (0.32 #56101, 0.28 #29091, 0.25 #22857), 017r13 (0.25 #1107, 0.06 #7341, 0.04 #15651), 0gx_p (0.25 #1106, 0.05 #116362, 0.03 #93506), 01fwpt (0.25 #590, 0.04 #21369, 0.04 #19291), 01mmslz (0.25 #397, 0.03 #10785, 0.02 #17019), 01gbn6 (0.25 #1623, 0.03 #12011, 0.02 #18245), 0kjrx (0.25 #1417, 0.03 #5573, 0.02 #24274), 046lt (0.25 #503, 0.02 #21282, 0.02 #19204), 026r8q (0.25 #1278, 0.02 #22057, 0.02 #19979), 012gq6 (0.25 #595, 0.02 #29686, 0.02 #10983) >> Best rule #56101 for best value: >> intensional similarity = 5 >> extensional distance = 263 >> proper extension: 0d1qmz; 07p12s; >> query: (?x6588, ?x4242) <- film(?x4242, ?x6588), film(?x722, ?x6588), currency(?x6588, ?x170), type_of_union(?x4242, ?x566), film(?x4242, ?x1080) >> conf = 0.32 => this is the best rule for 1 predicted values *> Best rule #8990 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 64 *> proper extension: 034xyf; 06cgf; *> query: (?x6588, 01wb8bs) <- genre(?x6588, ?x8467), film(?x722, ?x6588), ?x8467 = 0gf28 *> conf = 0.02 ranks of expected_values: 980 EVAL 034qbx film! 01wb8bs CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 129.000 80.000 0.318 http://example.org/film/actor/film./film/performance/film EVAL 034qbx film! 01dw9z CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 129.000 80.000 0.318 http://example.org/film/actor/film./film/performance/film #235-01wc7p PRED entity: 01wc7p PRED relation: program PRED expected values: 06hwzy => 151 concepts (132 used for prediction) PRED predicted values (max 10 best out of 21): 06hwzy (0.40 #86, 0.37 #376, 0.32 #192), 01s81 (0.20 #53, 0.11 #107, 0.06 #1028), 0304nh (0.14 #10, 0.07 #90, 0.07 #36), 01j7mr (0.13 #88, 0.10 #378, 0.08 #352), 0cpz4k (0.11 #169, 0.07 #511, 0.07 #35), 01b7h8 (0.08 #153, 0.07 #99, 0.06 #363), 03gvm3t (0.07 #41, 0.03 #175, 0.02 #332), 0124k9 (0.07 #28, 0.02 #398, 0.02 #424), 01h1bf (0.06 #351, 0.04 #509, 0.03 #87), 026bfsh (0.06 #381, 0.06 #171, 0.03 #91) >> Best rule #86 for best value: >> intensional similarity = 3 >> extensional distance = 28 >> proper extension: 06mmb; >> query: (?x5848, 06hwzy) <- film(?x5848, ?x3524), person(?x3480, ?x5848), nominated_for(?x5848, ?x4517) >> conf = 0.40 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01wc7p program 06hwzy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 151.000 132.000 0.400 http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program #234-0173b0 PRED entity: 0173b0 PRED relation: artists PRED expected values: 0232lm 01wkmgb 0889x => 65 concepts (22 used for prediction) PRED predicted values (max 10 best out of 910): 01q7cb_ (0.75 #4335, 0.67 #1127, 0.35 #7539), 089tm (0.71 #3228, 0.33 #21, 0.27 #1069), 07bzp (0.71 #3766, 0.33 #559, 0.27 #1069), 011_vz (0.67 #1905, 0.62 #5113, 0.50 #6182), 04qzm (0.67 #1994, 0.62 #5202, 0.38 #6271), 04qmr (0.67 #1384, 0.62 #4592, 0.29 #7796), 01dw_f (0.67 #2814, 0.33 #676, 0.29 #3883), 01vw20_ (0.62 #4522, 0.57 #3452, 0.50 #1314), 0fpj4lx (0.62 #4598, 0.50 #5667, 0.50 #1390), 027kwc (0.57 #4252, 0.33 #3206, 0.33 #3183) >> Best rule #4335 for best value: >> intensional similarity = 8 >> extensional distance = 6 >> proper extension: 01738f; 04n7jdv; >> query: (?x11040, 01q7cb_) <- artists(?x11040, ?x1970), parent_genre(?x11040, ?x3061), parent_genre(?x11040, ?x2249), ?x2249 = 03lty, profession(?x1970, ?x131), participant(?x3034, ?x1970), artists(?x3061, ?x7781), group(?x227, ?x7781) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #1023 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 1 *> proper extension: 0xhtw; *> query: (?x11040, 0889x) <- artists(?x11040, ?x4658), artists(?x11040, ?x3933), parent_genre(?x11040, ?x2249), artists(?x2249, ?x3420), artists(?x2249, ?x717), ?x3420 = 0134s5, ?x4658 = 018gm9, ?x3933 = 01vtqml, ?x717 = 0150jk *> conf = 0.33 ranks of expected_values: 149, 162, 234 EVAL 0173b0 artists 0889x CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 65.000 22.000 0.750 http://example.org/music/genre/artists EVAL 0173b0 artists 01wkmgb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 65.000 22.000 0.750 http://example.org/music/genre/artists EVAL 0173b0 artists 0232lm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 65.000 22.000 0.750 http://example.org/music/genre/artists #233-0f9rw9 PRED entity: 0f9rw9 PRED relation: team PRED expected values: 02ptzz0 => 27 concepts (27 used for prediction) PRED predicted values (max 10 best out of 347): 03y9p40 (0.88 #233, 0.75 #223, 0.75 #129), 02qk2d5 (0.81 #210, 0.79 #200, 0.75 #222), 026xxv_ (0.79 #188, 0.75 #220, 0.69 #208), 02pzy52 (0.71 #192, 0.71 #107, 0.70 #155), 02pqcfz (0.71 #100, 0.66 #170, 0.65 #215), 02py8_w (0.70 #176, 0.70 #162, 0.69 #219), 04088s0 (0.66 #170, 0.65 #215, 0.60 #175), 02ptzz0 (0.66 #170, 0.65 #215, 0.60 #77), 026dqjm (0.66 #170, 0.65 #215, 0.60 #182), 0263cyj (0.66 #170, 0.65 #215, 0.44 #140) >> Best rule #233 for best value: >> intensional similarity = 34 >> extensional distance = 15 >> proper extension: 0b_734; >> query: (?x10736, 03y9p40) <- team(?x10736, ?x9909), team(?x10736, ?x8528), team(?x10736, ?x6847), team(?x10736, ?x6803), team(?x10736, ?x5551), team(?x10736, ?x4804), ?x9909 = 026wlnm, team(?x6848, ?x6847), team(?x5755, ?x6847), team(?x10673, ?x5551), team(?x10441, ?x5551), team(?x9956, ?x5551), team(?x8824, ?x5551), team(?x7378, ?x5551), team(?x6802, ?x5551), team(?x5897, ?x5551), team(?x4803, ?x5551), team(?x3797, ?x5551), ?x3797 = 0b_6zk, team(?x2302, ?x4804), ?x7378 = 0bzrxn, ?x2302 = 0b_77q, ?x10441 = 0b_71r, ?x6848 = 02_ssl, ?x8528 = 091tgz, ?x6802 = 0br1x_, ?x9956 = 0bzrsh, colors(?x4804, ?x332), ?x10673 = 0b_6mr, ?x8824 = 05g_nr, ?x4803 = 0b_6jz, ?x5755 = 0355dz, ?x6803 = 03by7wc, ?x5897 = 0b_6rk >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #170 for first EXPECTED value: *> intensional similarity = 34 *> extensional distance = 8 *> proper extension: 0b_6qj; *> query: (?x10736, ?x3798) <- team(?x10736, ?x8528), team(?x10736, ?x6847), team(?x10736, ?x5551), team(?x10736, ?x4804), ?x6847 = 02r2qt7, team(?x12162, ?x5551), team(?x10673, ?x5551), team(?x9956, ?x5551), team(?x9908, ?x5551), team(?x8992, ?x5551), team(?x8824, ?x5551), position(?x5551, ?x1579), team(?x9974, ?x8528), team(?x4937, ?x8528), team(?x2302, ?x8528), position(?x8528, ?x4747), ?x2302 = 0b_77q, ?x8992 = 0b_6s7, sport(?x8528, ?x12913), colors(?x4804, ?x3189), ?x9974 = 0b_6pv, ?x4937 = 0br1xn, ?x9908 = 0b_6lb, teams(?x6088, ?x4804), team(?x8824, ?x9833), team(?x8824, ?x5032), team(?x8824, ?x3798), ?x10673 = 0b_6mr, ?x5032 = 04088s0, ?x3189 = 01g5v, ?x9833 = 03y9p40, ?x9956 = 0bzrsh, locations(?x12162, ?x1719), instance_of_recurring_event(?x8824, ?x10863) *> conf = 0.66 ranks of expected_values: 8 EVAL 0f9rw9 team 02ptzz0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 27.000 27.000 0.882 http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team #232-012qjw PRED entity: 012qjw PRED relation: symptom_of PRED expected values: 0hgxh 0h3bn => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 86): 097ns (0.72 #265, 0.53 #274, 0.42 #423), 02psvcf (0.72 #265, 0.50 #191, 0.42 #794), 0hgxh (0.72 #265, 0.42 #794, 0.40 #244), 02k6hp (0.71 #397, 0.62 #520, 0.62 #498), 01l2m3 (0.68 #913, 0.35 #369, 0.30 #786), 01psyx (0.68 #913, 0.27 #475, 0.22 #221), 072hv (0.56 #171, 0.53 #274, 0.47 #420), 07jwr (0.56 #171, 0.50 #329, 0.50 #181), 02bft (0.56 #171, 0.47 #420, 0.45 #321), 0c58k (0.53 #274, 0.45 #223, 0.42 #423) >> Best rule #265 for best value: >> intensional similarity = 35 >> extensional distance = 3 >> proper extension: 02tfl8; >> query: (?x9438, ?x7007) <- symptom_of(?x9438, ?x13131), symptom_of(?x9438, ?x11739), symptom_of(?x9438, ?x11064), symptom_of(?x9438, ?x10480), symptom_of(?x9438, ?x9119), symptom_of(?x9438, ?x8675), symptom_of(?x9438, ?x6781), symptom_of(?x9438, ?x6655), risk_factors(?x5784, ?x6655), risk_factors(?x6655, ?x8524), ?x13131 = 0d19y2, risk_factors(?x8675, ?x11659), risk_factors(?x8675, ?x8523), ?x8524 = 01hbgs, symptom_of(?x13099, ?x6781), symptom_of(?x6780, ?x6781), ?x10480 = 0h1n9, ?x6780 = 0j5fv, people(?x6781, ?x2145), people(?x6655, ?x6975), risk_factors(?x13099, ?x10199), symptom_of(?x10717, ?x9119), risk_factors(?x7006, ?x11739), symptom_of(?x4905, ?x11659), ?x8523 = 0c58k, location(?x2145, ?x8297), ?x10717 = 0cjf0, symptom_of(?x13373, ?x11739), symptom_of(?x9510, ?x11739), ?x13373 = 0f3kl, award_winner(?x1937, ?x2145), symptom_of(?x9510, ?x7007), symptom_of(?x3679, ?x9510), people(?x13099, ?x8858), ?x11064 = 01n3bm >> conf = 0.72 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 37 EVAL 012qjw symptom_of 0h3bn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 21.000 21.000 0.722 http://example.org/medicine/symptom/symptom_of EVAL 012qjw symptom_of 0hgxh CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 21.000 21.000 0.722 http://example.org/medicine/symptom/symptom_of #231-06rq2l PRED entity: 06rq2l PRED relation: award PRED expected values: 07bdd_ => 103 concepts (103 used for prediction) PRED predicted values (max 10 best out of 294): 05p09zm (0.45 #1339, 0.18 #15391, 0.15 #2959), 09sb52 (0.41 #16241, 0.33 #16646, 0.32 #20292), 05pcn59 (0.40 #486, 0.33 #81, 0.31 #1701), 05zr6wv (0.40 #422, 0.33 #17, 0.31 #1637), 05ztrmj (0.40 #590, 0.33 #185, 0.31 #1805), 05zvj3m (0.40 #498, 0.33 #93, 0.22 #903), 07bdd_ (0.36 #1280, 0.23 #11000, 0.19 #4520), 0gq9h (0.35 #8582, 0.34 #8987, 0.33 #9392), 03c7tr1 (0.33 #58, 0.20 #463, 0.18 #15391), 040njc (0.29 #8513, 0.28 #8918, 0.26 #9728) >> Best rule #1339 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 017s11; 016tw3; >> query: (?x9204, 05p09zm) <- category(?x9204, ?x134), award_nominee(?x9204, ?x1335), ?x1335 = 0pz91 >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #1280 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 9 *> proper extension: 017s11; 016tw3; *> query: (?x9204, 07bdd_) <- category(?x9204, ?x134), award_nominee(?x9204, ?x1335), ?x1335 = 0pz91 *> conf = 0.36 ranks of expected_values: 7 EVAL 06rq2l award 07bdd_ CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 103.000 103.000 0.455 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #230-09fb5 PRED entity: 09fb5 PRED relation: religion PRED expected values: 0c8wxp => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 23): 0c8wxp (0.29 #323, 0.28 #277, 0.27 #231), 03_gx (0.12 #830, 0.10 #2598, 0.08 #694), 0kpl (0.06 #826, 0.06 #3186, 0.05 #2094), 092bf5 (0.05 #241, 0.05 #287, 0.04 #379), 019cr (0.04 #101, 0.03 #191, 0.02 #146), 0v53x (0.04 #119, 0.03 #209, 0.02 #164), 0kq2 (0.04 #108, 0.02 #607, 0.02 #381), 02rsw (0.04 #114, 0.01 #159), 03j6c (0.03 #837, 0.02 #2877, 0.02 #4551), 06nzl (0.03 #559, 0.02 #741, 0.02 #105) >> Best rule #323 for best value: >> intensional similarity = 3 >> extensional distance = 133 >> proper extension: 02qjj7; >> query: (?x406, 0c8wxp) <- people(?x743, ?x406), participant(?x5665, ?x406), participant(?x241, ?x406) >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09fb5 religion 0c8wxp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 135.000 135.000 0.289 http://example.org/people/person/religion #229-057__d PRED entity: 057__d PRED relation: film_production_design_by PRED expected values: 0d5wn3 => 85 concepts (69 used for prediction) PRED predicted values (max 10 best out of 17): 0bytkq (0.06 #196, 0.05 #291, 0.03 #132), 03cp7b3 (0.04 #88, 0.02 #311, 0.02 #26), 0dh73w (0.04 #39, 0.03 #8, 0.02 #134), 02x2t07 (0.03 #342, 0.03 #277, 0.03 #119), 0cdf37 (0.03 #206, 0.02 #301, 0.02 #78), 0bqytm (0.03 #222, 0.03 #317, 0.02 #94), 01l79yc (0.03 #222, 0.03 #317, 0.02 #94), 0d5wn3 (0.02 #136, 0.02 #200, 0.02 #167), 05b2gsm (0.02 #143, 0.02 #335, 0.02 #270), 03wdsbz (0.02 #93, 0.02 #221, 0.01 #316) >> Best rule #196 for best value: >> intensional similarity = 3 >> extensional distance = 130 >> proper extension: 06g60w; >> query: (?x8633, 0bytkq) <- nominated_for(?x5014, ?x8633), award(?x5014, ?x1243), ?x1243 = 0gr0m >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #136 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 119 *> proper extension: 0prhz; 071nw5; 016z43; *> query: (?x8633, 0d5wn3) <- titles(?x53, ?x8633), music(?x8633, ?x6251), production_companies(?x8633, ?x574), ?x53 = 07s9rl0 *> conf = 0.02 ranks of expected_values: 8 EVAL 057__d film_production_design_by 0d5wn3 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 85.000 69.000 0.061 http://example.org/film/film/film_production_design_by #228-01wxyx1 PRED entity: 01wxyx1 PRED relation: type_of_union PRED expected values: 04ztj 01g63y => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.75 #125, 0.74 #262, 0.73 #290), 01g63y (0.55 #185, 0.25 #446, 0.24 #22) >> Best rule #125 for best value: >> intensional similarity = 3 >> extensional distance = 277 >> proper extension: 0p51w; 02sj1x; 03bw6; >> query: (?x2108, 04ztj) <- award_winner(?x7767, ?x2108), gender(?x2108, ?x231), religion(?x2108, ?x1985) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01wxyx1 type_of_union 01g63y CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.749 http://example.org/people/person/spouse_s./people/marriage/type_of_union EVAL 01wxyx1 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 116.000 0.749 http://example.org/people/person/spouse_s./people/marriage/type_of_union #227-0k_q_ PRED entity: 0k_q_ PRED relation: place_of_death! PRED expected values: 05233hy => 183 concepts (23 used for prediction) PRED predicted values (max 10 best out of 578): 029cpw (0.20 #324, 0.05 #4868, 0.04 #7895), 022p06 (0.10 #977, 0.07 #1735, 0.05 #3247), 02rf51g (0.10 #1487, 0.07 #2245, 0.05 #3757), 08gyz_ (0.10 #1457, 0.07 #2215, 0.05 #3727), 0bkmf (0.10 #1239, 0.07 #1997, 0.05 #3509), 03bw6 (0.10 #1095, 0.07 #1853, 0.05 #3365), 06hzsx (0.10 #1087, 0.07 #1845, 0.05 #3357), 03n6r (0.10 #1000, 0.07 #1758, 0.05 #3270), 0638kv (0.10 #969, 0.07 #1727, 0.05 #3239), 03h4mp (0.10 #902, 0.07 #1660, 0.05 #3172) >> Best rule #324 for best value: >> intensional similarity = 3 >> extensional distance = 3 >> proper extension: 0lbp_; >> query: (?x2495, 029cpw) <- category(?x2495, ?x134), place_of_burial(?x4943, ?x2495), place_of_birth(?x4943, ?x8989) >> conf = 0.20 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0k_q_ place_of_death! 05233hy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 183.000 23.000 0.200 http://example.org/people/deceased_person/place_of_death #226-06m_5 PRED entity: 06m_5 PRED relation: jurisdiction_of_office! PRED expected values: 060bp => 175 concepts (175 used for prediction) PRED predicted values (max 10 best out of 21): 0pqc5 (0.84 #334, 0.82 #554, 0.57 #1038), 060bp (0.68 #1233, 0.65 #2047, 0.64 #1343), 0f6c3 (0.43 #733, 0.33 #1481, 0.32 #2229), 09n5b9 (0.39 #737, 0.29 #2233, 0.28 #1485), 0fj45 (0.38 #3191, 0.17 #19, 0.14 #195), 0fkvn (0.37 #1477, 0.36 #729, 0.32 #1543), 0p5vf (0.27 #474, 0.25 #298, 0.25 #56), 0dq3c (0.24 #90, 0.24 #68, 0.21 #112), 04syw (0.21 #1458, 0.20 #1656, 0.19 #1898), 09d6p2 (0.20 #30, 0.06 #294, 0.05 #382) >> Best rule #334 for best value: >> intensional similarity = 3 >> extensional distance = 36 >> proper extension: 0q_0z; >> query: (?x8420, 0pqc5) <- featured_film_locations(?x9805, ?x8420), jurisdiction_of_office(?x346, ?x8420), origin(?x7951, ?x8420) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1233 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 97 *> proper extension: 04fh3; *> query: (?x8420, 060bp) <- adjoins(?x2146, ?x8420), countries_within(?x6956, ?x8420), jurisdiction_of_office(?x346, ?x8420) *> conf = 0.68 ranks of expected_values: 2 EVAL 06m_5 jurisdiction_of_office! 060bp CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 175.000 175.000 0.842 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #225-03j1p2n PRED entity: 03j1p2n PRED relation: award_winner! PRED expected values: 02g3gj => 96 concepts (94 used for prediction) PRED predicted values (max 10 best out of 264): 025m8l (0.39 #16819, 0.37 #24154, 0.37 #24586), 03qbh5 (0.39 #16819, 0.37 #24154, 0.37 #24586), 03qpp9 (0.39 #16819, 0.37 #24154, 0.37 #24586), 01by1l (0.29 #1406, 0.26 #2268, 0.22 #3563), 01c99j (0.24 #1517, 0.10 #2588, 0.09 #27179), 0c4z8 (0.24 #1365, 0.09 #3522, 0.08 #5677), 01c92g (0.24 #1391, 0.07 #2686, 0.06 #3548), 09sb52 (0.18 #12116, 0.16 #11254, 0.13 #12979), 02f73p (0.18 #1478, 0.17 #616, 0.15 #25884), 054ks3 (0.18 #1435, 0.15 #25884, 0.15 #25883) >> Best rule #16819 for best value: >> intensional similarity = 3 >> extensional distance = 949 >> proper extension: 0k8y7; >> query: (?x7859, ?x2238) <- award_winner(?x342, ?x7859), award_winner(?x2237, ?x7859), award(?x7859, ?x2238) >> conf = 0.39 => this is the best rule for 3 predicted values *> Best rule #888 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 0c7ct; 0136p1; 01x1cn2; 01vsykc; 01vxlbm; 01wgfp6; 02vwckw; 03f0qd7; *> query: (?x7859, 02g3gj) <- artists(?x7267, ?x7859), profession(?x7859, ?x131), ?x7267 = 03mb9 *> conf = 0.12 ranks of expected_values: 36 EVAL 03j1p2n award_winner! 02g3gj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 96.000 94.000 0.392 http://example.org/award/award_category/winners./award/award_honor/award_winner #224-057xs89 PRED entity: 057xs89 PRED relation: award_winner PRED expected values: 048lv 0j5q3 => 47 concepts (15 used for prediction) PRED predicted values (max 10 best out of 1625): 06cgy (0.50 #2773, 0.46 #7397, 0.45 #9865), 026rm_y (0.50 #4326, 0.46 #7397, 0.45 #9865), 01pj5q (0.50 #4142, 0.46 #7397, 0.45 #9865), 01ycbq (0.50 #2882, 0.22 #10281, 0.05 #12745), 055c8 (0.50 #3152, 0.11 #10551, 0.06 #19727), 02t__l (0.50 #2661, 0.06 #10060, 0.02 #12524), 0gy6z9 (0.46 #7397, 0.45 #9865, 0.42 #7398), 01vvb4m (0.46 #7397, 0.45 #9865, 0.42 #7398), 019pm_ (0.46 #7397, 0.45 #9865, 0.42 #7398), 0dvmd (0.46 #7397, 0.45 #9865, 0.42 #7398) >> Best rule #2773 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02x73k6; 0gqy2; >> query: (?x3019, 06cgy) <- nominated_for(?x3019, ?x13027), nominated_for(?x3019, ?x11534), award(?x71, ?x3019), titles(?x600, ?x11534), ?x13027 = 0422v0 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #7399 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 6 *> proper extension: 02g3v6; *> query: (?x3019, ?x57) <- nominated_for(?x3019, ?x708), award(?x2280, ?x3019), ?x708 = 0fg04, award_nominee(?x57, ?x2280) *> conf = 0.38 ranks of expected_values: 67, 1095 EVAL 057xs89 award_winner 0j5q3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 47.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner EVAL 057xs89 award_winner 048lv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.015 47.000 15.000 0.500 http://example.org/award/award_category/winners./award/award_honor/award_winner #223-020qr4 PRED entity: 020qr4 PRED relation: languages PRED expected values: 03_9r => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 52): 03_9r (0.67 #73, 0.67 #33, 0.60 #25), 02hwhyv (0.22 #275, 0.10 #368, 0.03 #197), 01r2l (0.22 #275, 0.03 #197, 0.02 #342), 0t_2 (0.19 #228, 0.19 #215, 0.19 #355), 07qv_ (0.19 #228, 0.19 #215, 0.19 #355), 01lqm (0.03 #197, 0.02 #342, 0.02 #288), 01jb8r (0.03 #197, 0.02 #342, 0.02 #288), 012v8 (0.03 #197, 0.02 #342, 0.02 #288), 0880p (0.03 #197, 0.02 #342, 0.02 #288), 0349s (0.03 #197, 0.02 #342, 0.02 #288) >> Best rule #73 for best value: >> intensional similarity = 17 >> extensional distance = 10 >> proper extension: 02kwcj; >> query: (?x419, 03_9r) <- program(?x2159, ?x419), genre(?x419, ?x5937), genre(?x419, ?x2540), genre(?x419, ?x53), ?x5937 = 0jxy, languages(?x419, ?x6753), ?x2540 = 0hcr, language(?x1108, ?x6753), genre(?x11324, ?x53), genre(?x3981, ?x53), genre(?x2882, ?x53), genre(?x2783, ?x53), service_language(?x127, ?x6753), ?x2783 = 0879bpq, film_crew_role(?x2882, ?x137), film_release_region(?x3981, ?x87), film_format(?x11324, ?x6392) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 020qr4 languages 03_9r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.667 http://example.org/tv/tv_program/languages #222-0mx3k PRED entity: 0mx3k PRED relation: adjoins! PRED expected values: 0d22f => 130 concepts (52 used for prediction) PRED predicted values (max 10 best out of 397): 0d22f (0.82 #4714, 0.82 #30673, 0.82 #25158), 0mxhc (0.82 #4714, 0.82 #30673, 0.82 #25158), 0mx0f (0.33 #454, 0.25 #22797, 0.25 #10216), 0mx3k (0.33 #555, 0.25 #22797, 0.25 #10216), 0mx48 (0.33 #480, 0.25 #22797, 0.25 #10216), 0mx2h (0.25 #22797, 0.25 #10216, 0.25 #10215), 0mx4_ (0.18 #39335, 0.17 #40124, 0.07 #822), 0mx6c (0.15 #897, 0.10 #1685, 0.09 #3254), 0mx5p (0.11 #1452, 0.06 #1575, 0.06 #3025), 0mxbq (0.11 #1250, 0.06 #1575, 0.06 #2038) >> Best rule #4714 for best value: >> intensional similarity = 5 >> extensional distance = 55 >> proper extension: 0l2l_; 0kq39; 0kvt9; 0n6nl; 0l2nd; >> query: (?x11062, ?x3067) <- second_level_divisions(?x94, ?x11062), time_zones(?x11062, ?x2950), ?x2950 = 02lcqs, adjoins(?x11062, ?x3067), ?x94 = 09c7w0 >> conf = 0.82 => this is the best rule for 2 predicted values ranks of expected_values: 1 EVAL 0mx3k adjoins! 0d22f CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 130.000 52.000 0.822 http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins #221-0sw6g PRED entity: 0sw6g PRED relation: award PRED expected values: 0cqh46 => 114 concepts (113 used for prediction) PRED predicted values (max 10 best out of 259): 0ck27z (0.61 #490, 0.21 #11263, 0.15 #22744), 0gqy2 (0.25 #162, 0.15 #22744, 0.14 #31924), 05pcn59 (0.17 #9257, 0.16 #6065, 0.14 #7661), 02z0dfh (0.17 #74, 0.15 #22744, 0.14 #31924), 0cqh46 (0.17 #50, 0.14 #449, 0.05 #10823), 0gr4k (0.17 #33, 0.07 #28731, 0.06 #17190), 03hkv_r (0.17 #16, 0.07 #28731, 0.05 #6400), 0cqhk0 (0.15 #2830, 0.15 #22744, 0.13 #32324), 04kxsb (0.15 #22744, 0.15 #1320, 0.14 #31924), 0gkvb7 (0.15 #22744, 0.15 #2820, 0.13 #36316) >> Best rule #490 for best value: >> intensional similarity = 3 >> extensional distance = 26 >> proper extension: 06sn8m; >> query: (?x8061, 0ck27z) <- profession(?x8061, ?x1032), award(?x8061, ?x8250), ?x8250 = 0cqhb3 >> conf = 0.61 => this is the best rule for 1 predicted values *> Best rule #50 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 10 *> proper extension: 02t_vx; 01phtd; 01pg1d; *> query: (?x8061, 0cqh46) <- profession(?x8061, ?x1032), film(?x8061, ?x5013), ?x5013 = 011ycb *> conf = 0.17 ranks of expected_values: 5 EVAL 0sw6g award 0cqh46 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 114.000 113.000 0.607 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #220-01w1kyf PRED entity: 01w1kyf PRED relation: award_winner! PRED expected values: 09qftb => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 134): 02rjjll (0.25 #5, 0.05 #4485, 0.04 #5185), 0gpjbt (0.25 #29, 0.05 #4509, 0.04 #7029), 092t4b (0.13 #472, 0.03 #1172, 0.03 #2572), 0ftlkg (0.12 #306, 0.02 #10502, 0.01 #2546), 09qftb (0.11 #252, 0.06 #532, 0.03 #1512), 09n4nb (0.11 #188, 0.05 #4528, 0.04 #5228), 01bx35 (0.11 #147, 0.05 #4487, 0.04 #5187), 0drtv8 (0.11 #206, 0.04 #486, 0.02 #10502), 0bzm81 (0.11 #162, 0.02 #10502, 0.02 #4502), 0clfdj (0.09 #424, 0.03 #2524, 0.03 #3224) >> Best rule #5 for best value: >> intensional similarity = 3 >> extensional distance = 2 >> proper extension: 03f5spx; 02jg92; >> query: (?x5094, 02rjjll) <- location(?x5094, ?x10428), type_of_union(?x5094, ?x566), ?x10428 = 0fwc0 >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #252 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 084x96; *> query: (?x5094, 09qftb) <- place_of_birth(?x5094, ?x9846), profession(?x5094, ?x319), ?x9846 = 0dzt9 *> conf = 0.11 ranks of expected_values: 5 EVAL 01w1kyf award_winner! 09qftb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 122.000 122.000 0.250 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #219-012t_z PRED entity: 012t_z PRED relation: specialization_of! PRED expected values: 02n9jv => 46 concepts (31 used for prediction) PRED predicted values (max 10 best out of 134): 01xr66 (0.33 #146, 0.25 #992, 0.25 #674), 0g7nc (0.33 #199, 0.25 #727, 0.25 #620), 0np9r (0.33 #116, 0.25 #644, 0.25 #537), 0mbx4 (0.33 #205, 0.25 #733, 0.25 #626), 0w7c (0.33 #144, 0.25 #672, 0.25 #565), 021wpb (0.33 #135, 0.25 #663, 0.25 #556), 0sydc (0.33 #286, 0.03 #1239), 01p5_g (0.25 #692, 0.20 #904, 0.12 #1010), 0d2ww (0.25 #691, 0.20 #903, 0.12 #1009), 04j5jl (0.25 #413, 0.12 #1049, 0.03 #1261) >> Best rule #146 for best value: >> intensional similarity = 10 >> extensional distance = 1 >> proper extension: 02hrh1q; >> query: (?x967, 01xr66) <- profession(?x11098, ?x967), profession(?x7540, ?x967), profession(?x6086, ?x967), profession(?x4593, ?x967), profession(?x3291, ?x967), ?x3291 = 01jbx1, ?x11098 = 0cgfb, ?x6086 = 058frd, ?x4593 = 0478__m, ?x7540 = 034ls >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 012t_z specialization_of! 02n9jv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 46.000 31.000 0.333 http://example.org/people/profession/specialization_of #218-02s62q PRED entity: 02s62q PRED relation: institution! PRED expected values: 028dcg => 159 concepts (159 used for prediction) PRED predicted values (max 10 best out of 21): 014mlp (0.74 #259, 0.72 #369, 0.71 #697), 02h4rq6 (0.66 #418, 0.64 #579, 0.63 #740), 02_xgp2 (0.47 #357, 0.44 #703, 0.44 #588), 0bkj86 (0.47 #354, 0.41 #585, 0.39 #700), 03bwzr4 (0.47 #359, 0.39 #590, 0.38 #452), 028dcg (0.43 #65, 0.16 #457, 0.16 #272), 016t_3 (0.43 #695, 0.42 #580, 0.40 #442), 07s6fsf (0.33 #416, 0.33 #93, 0.32 #116), 04zx3q1 (0.26 #578, 0.25 #347, 0.25 #693), 013zdg (0.25 #353, 0.24 #100, 0.21 #54) >> Best rule #259 for best value: >> intensional similarity = 3 >> extensional distance = 93 >> proper extension: 02kth6; 01c333; 01j_5k; 017y6l; 01b7lc; 02c9dj; 019c57; >> query: (?x2056, 014mlp) <- student(?x2056, ?x123), institution(?x1771, ?x2056), country(?x2056, ?x94) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #65 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 12 *> proper extension: 01xk7r; 02m0sc; *> query: (?x2056, 028dcg) <- student(?x2056, ?x123), institution(?x3386, ?x2056), country(?x2056, ?x94), ?x3386 = 03mkk4 *> conf = 0.43 ranks of expected_values: 6 EVAL 02s62q institution! 028dcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 159.000 159.000 0.737 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #217-02bkg PRED entity: 02bkg PRED relation: country PRED expected values: 09c7w0 07ssc => 48 concepts (47 used for prediction) PRED predicted values (max 10 best out of 458): 09c7w0 (0.92 #8588, 0.92 #8399, 0.91 #7839), 07ssc (0.89 #5987, 0.88 #7851, 0.85 #8226), 059j2 (0.89 #5996, 0.81 #184, 0.81 #185), 05qhw (0.85 #7665, 0.84 #7292, 0.83 #6919), 02k54 (0.83 #4686, 0.82 #4127, 0.81 #184), 01p1v (0.81 #184, 0.81 #185, 0.80 #5449), 0jgd (0.81 #184, 0.81 #185, 0.80 #5413), 01mjq (0.81 #184, 0.81 #185, 0.80 #5441), 05r4w (0.81 #184, 0.81 #185, 0.80 #2979), 02vzc (0.81 #184, 0.81 #185, 0.80 #2979) >> Best rule #8588 for best value: >> intensional similarity = 38 >> extensional distance = 49 >> proper extension: 037hz; >> query: (?x359, 09c7w0) <- sports(?x1081, ?x359), country(?x359, ?x2188), country(?x359, ?x1453), country(?x359, ?x789), country(?x359, ?x583), olympics(?x94, ?x1081), sports(?x1081, ?x171), organization(?x1453, ?x127), contains(?x1453, ?x2079), exported_to(?x1453, ?x5457), film_release_region(?x8137, ?x1453), film_release_region(?x6932, ?x1453), film_release_region(?x4047, ?x1453), film_release_region(?x1259, ?x1453), ?x1259 = 04hwbq, nationality(?x4389, ?x1453), olympics(?x2188, ?x418), country(?x2266, ?x2188), country(?x520, ?x2188), combatants(?x172, ?x583), film_release_region(?x6175, ?x583), film_release_region(?x4668, ?x583), film_release_region(?x1785, ?x583), film_release_region(?x141, ?x583), olympics(?x87, ?x1081), administrative_area_type(?x583, ?x2792), ?x520 = 01dys, titles(?x53, ?x6932), ?x4668 = 0bh8x1y, ?x4047 = 07s846j, ?x6175 = 0gg5kmg, ?x1785 = 0gj9tn5, ?x141 = 0gtsx8c, ?x8137 = 0gtx63s, film(?x4314, ?x6932), member_states(?x7695, ?x789), ?x2266 = 01lb14, contains(?x789, ?x790) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02bkg country 07ssc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 47.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country EVAL 02bkg country 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 47.000 0.922 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #216-096gm PRED entity: 096gm PRED relation: citytown! PRED expected values: 02qdyj => 232 concepts (84 used for prediction) PRED predicted values (max 10 best out of 593): 02pbzv (0.39 #38062, 0.38 #1619, 0.36 #37250), 01stzp (0.20 #1542, 0.08 #3161, 0.06 #6399), 01c0cc (0.20 #813, 0.08 #2432, 0.06 #5670), 0h6rm (0.20 #996, 0.06 #5853, 0.06 #6662), 06rjp (0.12 #2215, 0.06 #6262, 0.05 #8689), 04399 (0.12 #2181, 0.06 #6228, 0.05 #8655), 04jr87 (0.12 #1894, 0.06 #5941, 0.05 #8368), 02vk52z (0.12 #1621, 0.06 #5668, 0.05 #8095), 0dn_w (0.12 #2392, 0.06 #6439, 0.05 #8866), 01z_jj (0.12 #2369, 0.06 #6416, 0.05 #8843) >> Best rule #38062 for best value: >> intensional similarity = 5 >> extensional distance = 57 >> proper extension: 022_6; >> query: (?x4962, ?x8820) <- location_of_ceremony(?x566, ?x4962), contains(?x4962, ?x8820), place_of_birth(?x8002, ?x4962), contains(?x1536, ?x4962), place_of_death(?x8002, ?x1523) >> conf = 0.39 => this is the best rule for 1 predicted values *> Best rule #36729 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 54 *> proper extension: 0hzlz; *> query: (?x4962, 02qdyj) <- location_of_ceremony(?x566, ?x4962), contains(?x4962, ?x8820), place_of_birth(?x4379, ?x4962), place_of_death(?x4379, ?x4151), gender(?x4379, ?x231) *> conf = 0.02 ranks of expected_values: 456 EVAL 096gm citytown! 02qdyj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 232.000 84.000 0.385 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #215-04y9mm8 PRED entity: 04y9mm8 PRED relation: nominated_for! PRED expected values: 05zrvfd => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 210): 099c8n (0.42 #1022, 0.34 #2468, 0.20 #3432), 0k611 (0.33 #1039, 0.28 #2485, 0.21 #2244), 05zvj3m (0.33 #74, 0.12 #315, 0.11 #4894), 02r0csl (0.29 #969, 0.28 #2415, 0.16 #9401), 019f4v (0.29 #1019, 0.26 #2465, 0.21 #5357), 02pqp12 (0.29 #1024, 0.23 #2470, 0.16 #9401), 02r22gf (0.29 #993, 0.21 #2439, 0.19 #3403), 0gq_v (0.29 #984, 0.21 #2430, 0.17 #2189), 02qyntr (0.29 #1147, 0.21 #2593, 0.16 #9401), 02qvyrt (0.29 #1063, 0.19 #2509, 0.19 #3473) >> Best rule #1022 for best value: >> intensional similarity = 6 >> extensional distance = 22 >> proper extension: 04dsnp; >> query: (?x6681, 099c8n) <- film_release_distribution_medium(?x6681, ?x81), titles(?x571, ?x6681), currency(?x6681, ?x170), genre(?x6681, ?x258), executive_produced_by(?x6681, ?x7324), ?x7324 = 06q8hf >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #26040 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1556 *> proper extension: 0431v3; 05h95s; *> query: (?x6681, ?x154) <- titles(?x571, ?x6681), titles(?x571, ?x10590), titles(?x571, ?x570), award(?x10590, ?x11230), nominated_for(?x154, ?x570) *> conf = 0.05 ranks of expected_values: 129 EVAL 04y9mm8 nominated_for! 05zrvfd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 115.000 115.000 0.417 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #214-02vy5j PRED entity: 02vy5j PRED relation: people! PRED expected values: 041rx => 80 concepts (80 used for prediction) PRED predicted values (max 10 best out of 37): 041rx (0.20 #4, 0.16 #158, 0.15 #81), 0x67 (0.10 #934, 0.10 #2476, 0.09 #1474), 06mvq (0.10 #34, 0.08 #111, 0.07 #1387), 09zyn5 (0.10 #73, 0.08 #150, 0.07 #1387), 06gbnc (0.10 #27, 0.08 #104, 0.07 #1387), 063k3h (0.10 #31, 0.08 #108, 0.06 #1696), 033tf_ (0.09 #161, 0.09 #315, 0.08 #238), 0fqz6 (0.08 #119, 0.07 #1387, 0.06 #1696), 09vc4s (0.07 #1387, 0.06 #1696, 0.04 #240), 01qhm_ (0.07 #1387, 0.06 #1696, 0.04 #237) >> Best rule #4 for best value: >> intensional similarity = 4 >> extensional distance = 8 >> proper extension: 04sx9_; 0dlglj; 030h95; 04smkr; 01zg98; 028r4y; 0crvfq; 0kjgl; >> query: (?x2282, 041rx) <- award_winner(?x5628, ?x2282), award_winner(?x2200, ?x2282), ?x2200 = 01tfck, ?x5628 = 07h565 >> conf = 0.20 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02vy5j people! 041rx CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 80.000 80.000 0.200 http://example.org/people/ethnicity/people #213-04t9c0 PRED entity: 04t9c0 PRED relation: film! PRED expected values: 0zcbl => 97 concepts (27 used for prediction) PRED predicted values (max 10 best out of 805): 081lh (0.71 #22854, 0.56 #18695, 0.53 #20775), 03tdlh (0.29 #1622), 0jfx1 (0.27 #2483, 0.04 #21183, 0.03 #17024), 0f0kz (0.27 #2592, 0.03 #21292, 0.02 #12978), 0170qf (0.18 #2444, 0.03 #4521, 0.02 #29452), 016xk5 (0.18 #3320, 0.02 #9552), 05kwx2 (0.18 #3172, 0.02 #11480, 0.01 #21872), 01w1kyf (0.14 #909, 0.05 #7140, 0.02 #25840), 01nwwl (0.14 #503, 0.04 #10887, 0.03 #12965), 02x7vq (0.14 #981, 0.03 #5134, 0.03 #11365) >> Best rule #22854 for best value: >> intensional similarity = 4 >> extensional distance = 343 >> proper extension: 0gxsh4; 0clpml; 06ys2; >> query: (?x5353, ?x986) <- nominated_for(?x986, ?x5353), award(?x986, ?x68), award_winner(?x1130, ?x986), celebrity(?x719, ?x986) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #11606 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 108 *> proper extension: 033pf1; *> query: (?x5353, 0zcbl) <- genre(?x5353, ?x239), film(?x2531, ?x5353), produced_by(?x5353, ?x4562), film_production_design_by(?x5353, ?x12092) *> conf = 0.02 ranks of expected_values: 298 EVAL 04t9c0 film! 0zcbl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 97.000 27.000 0.712 http://example.org/film/actor/film./film/performance/film #212-016hvl PRED entity: 016hvl PRED relation: people! PRED expected values: 012hw => 135 concepts (135 used for prediction) PRED predicted values (max 10 best out of 51): 0gk4g (0.25 #2610, 0.22 #3390, 0.21 #4950), 012hw (0.20 #51, 0.06 #246, 0.05 #311), 034qg (0.20 #33, 0.03 #1073, 0.02 #2048), 0dq9p (0.19 #602, 0.15 #2227, 0.14 #2292), 02k6hp (0.14 #427, 0.10 #2182, 0.09 #102), 0qcr0 (0.12 #2146, 0.12 #1691, 0.12 #2081), 02y0js (0.11 #912, 0.10 #2212, 0.09 #67), 01l2m3 (0.11 #406, 0.08 #926, 0.08 #2226), 04p3w (0.09 #2156, 0.09 #2221, 0.08 #3261), 06z5s (0.08 #155, 0.08 #480, 0.06 #935) >> Best rule #2610 for best value: >> intensional similarity = 4 >> extensional distance = 223 >> proper extension: 05hjmd; 0436zq; >> query: (?x1278, 0gk4g) <- people(?x12333, ?x1278), place_of_birth(?x1278, ?x5481), nationality(?x1278, ?x205), citytown(?x3899, ?x5481) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #51 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 012z8_; 0gzh; *> query: (?x1278, 012hw) <- profession(?x1278, ?x353), people(?x12333, ?x1278), influenced_by(?x1900, ?x1278), ?x12333 = 051_y *> conf = 0.20 ranks of expected_values: 2 EVAL 016hvl people! 012hw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 135.000 135.000 0.253 http://example.org/people/cause_of_death/people #211-081wh1 PRED entity: 081wh1 PRED relation: artists! PRED expected values: 0xhtw 02yv6b => 109 concepts (81 used for prediction) PRED predicted values (max 10 best out of 280): 0xhtw (0.88 #1268, 0.75 #10342, 0.75 #6899), 064t9 (0.65 #11280, 0.64 #20334, 0.62 #4393), 016clz (0.57 #3133, 0.57 #317, 0.50 #1879), 06j6l (0.46 #11315, 0.38 #987, 0.36 #9431), 05bt6j (0.43 #6298, 0.39 #5361, 0.38 #982), 0glt670 (0.43 #9423, 0.35 #11307, 0.31 #4420), 025sc50 (0.43 #11317, 0.36 #9433, 0.34 #11629), 0gywn (0.40 #11325, 0.31 #997, 0.28 #4438), 011j5x (0.40 #32, 0.15 #970, 0.14 #344), 05r6t (0.40 #5316, 0.33 #710, 0.28 #4151) >> Best rule #1268 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 01wt4wc; >> query: (?x7013, 0xhtw) <- artists(?x11040, ?x7013), category(?x7013, ?x134), ?x11040 = 0173b0, artist(?x1954, ?x7013) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 11 EVAL 081wh1 artists! 02yv6b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 109.000 81.000 0.875 http://example.org/music/genre/artists EVAL 081wh1 artists! 0xhtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 81.000 0.875 http://example.org/music/genre/artists #210-084302 PRED entity: 084302 PRED relation: film! PRED expected values: 01wy5m => 108 concepts (44 used for prediction) PRED predicted values (max 10 best out of 755): 0184dt (0.71 #6236, 0.48 #2079, 0.47 #70681), 0jbp0 (0.43 #5912, 0.02 #12148, 0.02 #22538), 02xv8m (0.40 #667, 0.17 #2746, 0.03 #91473), 042ly5 (0.29 #5420, 0.07 #87314, 0.05 #31177), 01pcq3 (0.29 #4289, 0.07 #87314, 0.05 #31177), 03knl (0.29 #4314, 0.07 #87314, 0.02 #10550), 03ym1 (0.29 #5166, 0.03 #91473, 0.02 #11402), 02pk6x (0.29 #5154, 0.02 #11390, 0.02 #21780), 01k53x (0.29 #5790, 0.02 #12026), 02t_y3 (0.29 #5839) >> Best rule #6236 for best value: >> intensional similarity = 5 >> extensional distance = 5 >> proper extension: 0c3zjn7; >> query: (?x3196, ?x2533) <- language(?x3196, ?x254), nominated_for(?x2533, ?x3196), nominated_for(?x844, ?x3196), film(?x382, ?x3196), ?x844 = 03h_9lg >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #13327 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 77 *> proper extension: 0413cff; *> query: (?x3196, 01wy5m) <- language(?x3196, ?x254), genre(?x3196, ?x53), featured_film_locations(?x3196, ?x362), ?x362 = 04jpl *> conf = 0.03 ranks of expected_values: 220 EVAL 084302 film! 01wy5m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 108.000 44.000 0.706 http://example.org/film/actor/film./film/performance/film #209-018j2 PRED entity: 018j2 PRED relation: instrumentalists PRED expected values: 04m2zj 01l7qw 02qx5h => 85 concepts (39 used for prediction) PRED predicted values (max 10 best out of 1035): 01gg59 (0.67 #5937, 0.56 #11101, 0.50 #9380), 03c7ln (0.64 #4008, 0.64 #2861, 0.63 #10320), 0326tc (0.64 #4008, 0.64 #2861, 0.62 #8602), 02fn5r (0.64 #4008, 0.64 #2861, 0.62 #8602), 050z2 (0.63 #10320, 0.58 #7452, 0.51 #1717), 01w923 (0.63 #10320, 0.58 #7452, 0.51 #1717), 017f4y (0.63 #10320, 0.58 #7452, 0.51 #1717), 01309x (0.63 #10320, 0.58 #7452, 0.51 #1717), 03ryks (0.63 #10320, 0.58 #7452, 0.51 #1717), 01m15br (0.63 #10320, 0.58 #7452, 0.51 #1717) >> Best rule #5937 for best value: >> intensional similarity = 17 >> extensional distance = 4 >> proper extension: 07_l6; >> query: (?x2048, 01gg59) <- instrumentalists(?x2048, ?x4568), instrumentalists(?x2048, ?x2170), role(?x7869, ?x2048), role(?x4311, ?x2048), role(?x2157, ?x2048), role(?x1473, ?x2048), ?x4311 = 01xqw, role(?x212, ?x2048), role(?x211, ?x2048), award_nominee(?x4568, ?x506), group(?x2048, ?x997), ?x7869 = 0l14v3, role(?x2048, ?x75), ?x75 = 07y_7, role(?x1473, ?x433), group(?x2170, ?x10257), ?x2157 = 011_6p >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #6730 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 4 *> proper extension: 0l14j_; *> query: (?x2048, 04m2zj) <- instrumentalists(?x2048, ?x4568), instrumentalists(?x2048, ?x2242), role(?x4311, ?x2048), role(?x2459, ?x2048), role(?x780, ?x2048), ?x4311 = 01xqw, role(?x716, ?x2048), role(?x211, ?x2048), award_nominee(?x4568, ?x506), group(?x2048, ?x997), award(?x4568, ?x159), ?x2242 = 09prnq, ?x2459 = 021bmf, artist(?x5666, ?x4568), ?x716 = 018vs, role(?x780, ?x75) *> conf = 0.50 ranks of expected_values: 37 EVAL 018j2 instrumentalists 02qx5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 39.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018j2 instrumentalists 01l7qw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 39.000 0.667 http://example.org/music/instrument/instrumentalists EVAL 018j2 instrumentalists 04m2zj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 85.000 39.000 0.667 http://example.org/music/instrument/instrumentalists #208-01yhvv PRED entity: 01yhvv PRED relation: actor! PRED expected values: 0hz55 => 102 concepts (67 used for prediction) PRED predicted values (max 10 best out of 83): 0hz55 (0.33 #87, 0.01 #16193), 026bfsh (0.15 #1158, 0.03 #2483, 0.03 #4875), 01f3p_ (0.12 #317, 0.02 #2438, 0.02 #2968), 02py4c8 (0.06 #542, 0.05 #807, 0.01 #16193), 053x8hr (0.06 #733, 0.05 #998, 0.01 #16193), 0828jw (0.06 #635, 0.05 #900, 0.01 #13107), 02qfh (0.06 #698, 0.05 #963), 0fkwzs (0.06 #692, 0.05 #957), 0c3xpwy (0.06 #631, 0.05 #896), 0dl6fv (0.05 #966) >> Best rule #87 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 01rzqj; >> query: (?x1410, 0hz55) <- participant(?x1410, ?x140), award_nominee(?x4933, ?x1410), ?x4933 = 03_1pg >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01yhvv actor! 0hz55 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 67.000 0.333 http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor #207-0x25q PRED entity: 0x25q PRED relation: film_crew_role PRED expected values: 09vw2b7 01vx2h => 79 concepts (79 used for prediction) PRED predicted values (max 10 best out of 25): 09vw2b7 (0.72 #261, 0.71 #5, 0.68 #421), 01vx2h (0.43 #8, 0.41 #104, 0.41 #360), 01pvkk (0.29 #425, 0.29 #361, 0.29 #9), 094hwz (0.29 #12, 0.10 #44, 0.09 #108), 0215hd (0.20 #271, 0.15 #79, 0.15 #111), 0d2b38 (0.20 #278, 0.14 #22, 0.14 #374), 089g0h (0.18 #112, 0.17 #272, 0.12 #432), 01xy5l_ (0.17 #267, 0.14 #11, 0.12 #107), 015h31 (0.15 #359, 0.15 #103, 0.10 #521), 06qc5 (0.14 #25, 0.10 #57, 0.04 #185) >> Best rule #261 for best value: >> intensional similarity = 3 >> extensional distance = 102 >> proper extension: 0bq8tmw; 02mpyh; >> query: (?x3055, 09vw2b7) <- nominated_for(?x2922, ?x3055), executive_produced_by(?x3055, ?x8503), film_format(?x3055, ?x909) >> conf = 0.72 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0x25q film_crew_role 01vx2h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.721 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 0x25q film_crew_role 09vw2b7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 79.000 79.000 0.721 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #206-0ctw_b PRED entity: 0ctw_b PRED relation: service_location! PRED expected values: 0226k3 => 158 concepts (144 used for prediction) PRED predicted values (max 10 best out of 136): 01c6k4 (0.42 #1775, 0.38 #5588, 0.38 #1503), 07zl6m (0.33 #540, 0.27 #948, 0.22 #404), 069b85 (0.33 #400, 0.26 #672, 0.23 #944), 01zpmq (0.33 #321, 0.23 #865, 0.21 #593), 05b5c (0.33 #399, 0.23 #943, 0.21 #1624), 018mxj (0.32 #1779, 0.32 #1371, 0.29 #2868), 06_9lg (0.30 #10313, 0.27 #5271, 0.05 #11131), 0p4wb (0.27 #825, 0.27 #417, 0.24 #1506), 077w0b (0.27 #473, 0.23 #881, 0.22 #337), 064f29 (0.27 #467, 0.23 #875, 0.22 #331) >> Best rule #1775 for best value: >> intensional similarity = 2 >> extensional distance = 29 >> proper extension: 04v3q; >> query: (?x1023, 01c6k4) <- film_release_region(?x6684, ?x1023), ?x6684 = 07pd_j >> conf = 0.42 => this is the best rule for 1 predicted values *> Best rule #408 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 7 *> proper extension: 088q1s; *> query: (?x1023, 0226k3) <- combatants(?x12789, ?x1023), capital(?x1023, ?x11743), ?x12789 = 02h2z_ *> conf = 0.11 ranks of expected_values: 51 EVAL 0ctw_b service_location! 0226k3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 158.000 144.000 0.419 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #205-013w7j PRED entity: 013w7j PRED relation: film PRED expected values: 0344gc => 154 concepts (147 used for prediction) PRED predicted values (max 10 best out of 1100): 013q07 (0.14 #2148, 0.06 #9312, 0.05 #25431), 02qr3k8 (0.10 #96213, 0.04 #4872, 0.03 #71139), 01shy7 (0.09 #39826, 0.09 #43408, 0.08 #34453), 03lrht (0.08 #3840, 0.02 #18168, 0.02 #28914), 0f42nz (0.08 #95832, 0.03 #110160, 0.03 #119115), 056xkh (0.07 #3392, 0.05 #8765, 0.03 #23093), 016dj8 (0.07 #2906, 0.04 #10070, 0.04 #4697), 03nx8mj (0.07 #2490, 0.04 #25773, 0.03 #32937), 0640m69 (0.07 #3554, 0.04 #14300, 0.02 #39374), 0f2sx4 (0.07 #3177, 0.04 #13923, 0.02 #53325) >> Best rule #2148 for best value: >> intensional similarity = 2 >> extensional distance = 12 >> proper extension: 06c0j; >> query: (?x6151, 013q07) <- participant(?x1125, ?x6151), organizations_founded(?x6151, ?x14234) >> conf = 0.14 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 013w7j film 0344gc CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 154.000 147.000 0.143 http://example.org/film/actor/film./film/performance/film #204-04lhc4 PRED entity: 04lhc4 PRED relation: nominated_for! PRED expected values: 027pdrh => 67 concepts (26 used for prediction) PRED predicted values (max 10 best out of 806): 02rmfm (0.42 #25671, 0.40 #11669, 0.29 #58356), 02r34n (0.42 #25671, 0.40 #11669, 0.24 #58355), 0h0wc (0.14 #2864, 0.12 #5197, 0.11 #9865), 086k8 (0.13 #39732, 0.12 #46737, 0.03 #44402), 0146pg (0.10 #11788, 0.09 #30458, 0.07 #32791), 0jz9f (0.10 #2353, 0.09 #4686, 0.09 #19), 05qd_ (0.10 #39847, 0.10 #46679, 0.09 #46852), 016tt2 (0.10 #46679, 0.09 #39782, 0.08 #46787), 014zcr (0.10 #46679, 0.07 #28005, 0.06 #7042), 02qgqt (0.10 #46679, 0.07 #28005, 0.06 #7017) >> Best rule #25671 for best value: >> intensional similarity = 4 >> extensional distance = 66 >> proper extension: 08cfr1; >> query: (?x6899, ?x1188) <- genre(?x6899, ?x6674), award(?x6899, ?x289), ?x6674 = 01t_vv, film(?x1188, ?x6899) >> conf = 0.42 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 04lhc4 nominated_for! 027pdrh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 67.000 26.000 0.418 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #203-01wbg84 PRED entity: 01wbg84 PRED relation: film PRED expected values: 02hxhz 04jm_hq => 98 concepts (67 used for prediction) PRED predicted values (max 10 best out of 595): 080dwhx (0.52 #14154, 0.49 #21238, 0.47 #28318), 0kfv9 (0.52 #14154, 0.49 #21238, 0.47 #28318), 0d68qy (0.52 #14154, 0.49 #21238, 0.47 #28318), 0h6r5 (0.24 #4210, 0.24 #2441, 0.10 #5979), 05c5z8j (0.20 #6022, 0.06 #4253, 0.06 #2484), 04ghz4m (0.17 #1231, 0.03 #61940, 0.01 #15385), 0284b56 (0.17 #977, 0.03 #61940, 0.01 #31064), 0fpmrm3 (0.17 #420, 0.03 #61940), 0gxtknx (0.17 #243, 0.03 #61940), 03sxd2 (0.12 #3839, 0.12 #2070, 0.05 #5608) >> Best rule #14154 for best value: >> intensional similarity = 3 >> extensional distance = 147 >> proper extension: 01507p; >> query: (?x368, ?x1849) <- actor(?x493, ?x368), nominated_for(?x368, ?x1849), religion(?x368, ?x1985) >> conf = 0.52 => this is the best rule for 3 predicted values *> Best rule #10735 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 135 *> proper extension: 01d494; 01xyt7; *> query: (?x368, 02hxhz) <- award_winner(?x1670, ?x368), company(?x368, ?x1836) *> conf = 0.01 ranks of expected_values: 436, 566 EVAL 01wbg84 film 04jm_hq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 98.000 67.000 0.515 http://example.org/film/actor/film./film/performance/film EVAL 01wbg84 film 02hxhz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 98.000 67.000 0.515 http://example.org/film/actor/film./film/performance/film #202-01swdw PRED entity: 01swdw PRED relation: industry PRED expected values: 020mfr => 177 concepts (177 used for prediction) PRED predicted values (max 10 best out of 42): 020mfr (0.80 #1623, 0.67 #3096, 0.62 #2528), 02vxn (0.40 #1466, 0.39 #1939, 0.39 #3987), 019z7b (0.37 #5361, 0.28 #4082, 0.23 #4603), 02jjt (0.37 #5361, 0.26 #3276, 0.23 #4603), 0hz28 (0.28 #4082, 0.23 #4603, 0.22 #4130), 01mf0 (0.28 #4082, 0.23 #4603, 0.22 #4130), 01mfj (0.28 #4082, 0.23 #4603, 0.22 #4130), 06xw2 (0.28 #4082, 0.23 #4603, 0.22 #4130), 07c1v (0.28 #4082, 0.23 #4603, 0.22 #4130), 029g_vk (0.28 #4082, 0.23 #4603, 0.22 #4130) >> Best rule #1623 for best value: >> intensional similarity = 7 >> extensional distance = 23 >> proper extension: 049vhf; 02rfft; >> query: (?x11823, 020mfr) <- industry(?x11823, ?x245), ?x245 = 01mw1, place_founded(?x11823, ?x11889), contains(?x252, ?x11889), country(?x150, ?x252), olympics(?x252, ?x418), film_release_region(?x66, ?x252) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01swdw industry 020mfr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 177.000 177.000 0.800 http://example.org/business/business_operation/industry #201-03gk2 PRED entity: 03gk2 PRED relation: countries_spoken_in! PRED expected values: 01wgr => 170 concepts (170 used for prediction) PRED predicted values (max 10 best out of 82): 02h40lc (0.93 #4049, 0.69 #2574, 0.55 #3829), 02bjrlw (0.77 #5457, 0.77 #5456, 0.76 #2354), 04306rv (0.77 #5457, 0.77 #5456, 0.76 #3009), 04h9h (0.77 #5457, 0.77 #5456, 0.76 #3009), 06nm1 (0.36 #2306, 0.28 #4812, 0.26 #2961), 083tk (0.33 #30, 0.22 #738, 0.20 #304), 0h407 (0.33 #45, 0.22 #753, 0.20 #319), 02ztjwg (0.29 #518, 0.25 #681, 0.25 #626), 0jzc (0.28 #2095, 0.27 #2203, 0.22 #1871), 0880p (0.25 #638, 0.18 #1295, 0.17 #421) >> Best rule #4049 for best value: >> intensional similarity = 4 >> extensional distance = 69 >> proper extension: 0160w; 0h44w; >> query: (?x1778, 02h40lc) <- countries_spoken_in(?x7658, ?x1778), languages(?x9173, ?x7658), service_language(?x610, ?x7658), ?x9173 = 01x53m >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #634 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 6 *> proper extension: 0d0vqn; 03gj2; 01pj7; 02vzc; 03pn9; 04g61; *> query: (?x1778, 01wgr) <- contains(?x455, ?x1778), combatants(?x1777, ?x1778), official_language(?x1778, ?x90), ?x455 = 02j9z, capital(?x1778, ?x8977), countries_spoken_in(?x5607, ?x1778) *> conf = 0.12 ranks of expected_values: 21 EVAL 03gk2 countries_spoken_in! 01wgr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.048 170.000 170.000 0.930 http://example.org/language/human_language/countries_spoken_in #200-050zr4 PRED entity: 050zr4 PRED relation: award PRED expected values: 09sb52 => 124 concepts (117 used for prediction) PRED predicted values (max 10 best out of 284): 09sb52 (0.85 #1241, 0.72 #39222, 0.70 #14805), 0gqwc (0.47 #2874, 0.43 #4874, 0.41 #4074), 02ppm4q (0.46 #4953, 0.45 #2953, 0.43 #5353), 094qd5 (0.33 #845, 0.32 #2845, 0.31 #4845), 0bdwft (0.31 #5268, 0.30 #4868, 0.27 #4068), 09qwmm (0.29 #2834, 0.20 #4834, 0.19 #4034), 0bfvw2 (0.29 #4015, 0.27 #4815, 0.26 #5215), 0gkts9 (0.25 #165, 0.20 #4165, 0.19 #4965), 05zr6wv (0.24 #1217, 0.13 #38820, 0.11 #4417), 02x4x18 (0.23 #930, 0.23 #2930, 0.21 #4130) >> Best rule #1241 for best value: >> intensional similarity = 3 >> extensional distance = 39 >> proper extension: 05ty4m; 017149; 0187y5; 01yb09; 07vc_9; 04t7ts; 02bkdn; 01gq0b; 02wgln; 05dbf; ... >> query: (?x8346, 09sb52) <- award(?x8346, ?x995), award_nominee(?x8346, ?x2353), ?x2353 = 02qgyv >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 050zr4 award 09sb52 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 124.000 117.000 0.854 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #199-07hgkd PRED entity: 07hgkd PRED relation: student! PRED expected values: 08815 => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 88): 01lhdt (0.33 #260, 0.12 #787, 0.02 #2368), 01g0p5 (0.12 #734, 0.02 #5477, 0.02 #2315), 031ns1 (0.12 #1045, 0.02 #2626), 035yzw (0.12 #979), 017z88 (0.10 #2190, 0.08 #3771, 0.08 #8514), 09f2j (0.09 #2794, 0.08 #2267, 0.07 #3321), 065y4w7 (0.08 #1068, 0.06 #2122, 0.04 #3176), 02g839 (0.08 #1079, 0.04 #2133, 0.04 #2660), 017rbx (0.08 #1396, 0.03 #2977, 0.03 #9828), 052nd (0.08 #1063, 0.03 #2644, 0.02 #3171) >> Best rule #260 for best value: >> intensional similarity = 3 >> extensional distance = 1 >> proper extension: 0hr3g; >> query: (?x4866, 01lhdt) <- music(?x7947, ?x4866), ?x7947 = 04gcyg, artists(?x4910, ?x4866) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #26354 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1738 *> proper extension: 07m69t; *> query: (?x4866, 08815) <- nationality(?x4866, ?x94), place_of_birth(?x4866, ?x3689), ?x94 = 09c7w0 *> conf = 0.02 ranks of expected_values: 36 EVAL 07hgkd student! 08815 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 91.000 91.000 0.333 http://example.org/education/educational_institution/students_graduates./education/education/student #198-0dw3l PRED entity: 0dw3l PRED relation: instrumentalists! PRED expected values: 0342h 02fsn => 118 concepts (55 used for prediction) PRED predicted values (max 10 best out of 126): 0342h (0.92 #517, 0.82 #261, 0.80 #1292), 05r5c (0.57 #3782, 0.56 #180, 0.53 #265), 05148p4 (0.42 #1308, 0.39 #3794, 0.39 #704), 03bx0bm (0.40 #2059, 0.40 #770, 0.39 #1114), 026t6 (0.40 #512, 0.36 #88, 0.32 #171), 01vj9c (0.40 #512, 0.32 #171, 0.31 #1287), 03qjg (0.38 #49, 0.29 #475, 0.27 #561), 013y1f (0.29 #458, 0.25 #32, 0.12 #544), 02w3w (0.19 #583, 0.04 #3859, 0.04 #1628), 0l14qv (0.19 #432, 0.19 #177, 0.14 #603) >> Best rule #517 for best value: >> intensional similarity = 5 >> extensional distance = 24 >> proper extension: 01vd7hn; >> query: (?x8048, 0342h) <- instrumentalists(?x1969, ?x8048), nationality(?x8048, ?x94), ?x1969 = 04rzd, ?x94 = 09c7w0, gender(?x8048, ?x231) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1, 41 EVAL 0dw3l instrumentalists! 02fsn CNN-1.5+0.5_MA 0.000 0.000 0.000 0.025 118.000 55.000 0.923 http://example.org/music/instrument/instrumentalists EVAL 0dw3l instrumentalists! 0342h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 118.000 55.000 0.923 http://example.org/music/instrument/instrumentalists #197-0blgl PRED entity: 0blgl PRED relation: profession PRED expected values: 0n1h => 90 concepts (39 used for prediction) PRED predicted values (max 10 best out of 71): 02hrh1q (0.73 #5607, 0.71 #5312, 0.71 #5165), 01d_h8 (0.62 #4275, 0.59 #1918, 0.50 #6), 03gjzk (0.57 #14, 0.50 #1926, 0.47 #3105), 02jknp (0.43 #1919, 0.36 #7, 0.32 #301), 0n1h (0.39 #2796, 0.39 #2501, 0.30 #3091), 0kyk (0.32 #2825, 0.30 #3857, 0.19 #176), 015h31 (0.31 #3828, 0.29 #27, 0.27 #321), 02krf9 (0.31 #3828, 0.29 #26, 0.25 #5447), 0np9r (0.31 #3828, 0.25 #5447, 0.18 #314), 018gz8 (0.20 #1928, 0.17 #3107, 0.17 #898) >> Best rule #5607 for best value: >> intensional similarity = 3 >> extensional distance = 804 >> proper extension: 01vvydl; 01kwld; 0146pg; 08wq0g; 034x61; 016khd; 01wbgdv; 02gvwz; 0277470; 01g257; ... >> query: (?x11463, 02hrh1q) <- award_winner(?x11463, ?x8211), profession(?x11463, ?x353), location(?x11463, ?x7328) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2796 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 224 *> proper extension: 05gnf; *> query: (?x11463, ?x353) <- award_winner(?x11463, ?x8211), people(?x268, ?x8211), profession(?x8211, ?x353) *> conf = 0.39 ranks of expected_values: 5 EVAL 0blgl profession 0n1h CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 90.000 39.000 0.730 http://example.org/people/person/profession #196-04_m9gk PRED entity: 04_m9gk PRED relation: film_festivals! PRED expected values: 05c46y6 05zpghd => 78 concepts (18 used for prediction) PRED predicted values (max 10 best out of 231): 0ddfwj1 (0.25 #1738, 0.25 #1522, 0.15 #3249), 0gyfp9c (0.25 #1582, 0.17 #1798, 0.14 #930), 0prpt (0.20 #1512, 0.17 #645, 0.14 #861), 0468g4r (0.20 #1510, 0.17 #643, 0.14 #859), 02wwsh8 (0.20 #1476, 0.17 #609, 0.14 #825), 02y_j8g (0.20 #1461, 0.17 #594, 0.14 #810), 018cvf (0.20 #1515, 0.17 #646, 0.14 #863), 0b76d_m (0.17 #1732, 0.17 #1516, 0.17 #431), 03cw411 (0.17 #1812, 0.17 #1596, 0.17 #511), 0gvvm6l (0.17 #1908, 0.17 #1692, 0.14 #1040) >> Best rule #1738 for best value: >> intensional similarity = 8 >> extensional distance = 10 >> proper extension: 0gg7gsl; 059_y8d; 0kfhjq0; 0fpkxfd; 0g57ws5; 09rwjly; 0bmj62v; 0cmd3zy; >> query: (?x11147, 0ddfwj1) <- film_festivals(?x5496, ?x11147), film_festivals(?x1498, ?x11147), film_release_region(?x5496, ?x279), genre(?x5496, ?x53), honored_for(?x4224, ?x5496), nominated_for(?x4695, ?x5496), ?x279 = 0d060g, film_crew_role(?x1498, ?x137) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3298 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 18 *> proper extension: 0bx_f_t; 03nn7l2; 02z6gky; *> query: (?x11147, 05c46y6) <- film_festivals(?x6900, ?x11147), film_festivals(?x5496, ?x11147), film_release_region(?x5496, ?x279), genre(?x5496, ?x53), honored_for(?x4224, ?x5496), nominated_for(?x4695, ?x5496), nationality(?x199, ?x279), currency(?x6900, ?x170), contains(?x279, ?x481) *> conf = 0.05 ranks of expected_values: 206 EVAL 04_m9gk film_festivals! 05zpghd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 18.000 0.250 http://example.org/film/film/film_festivals EVAL 04_m9gk film_festivals! 05c46y6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 78.000 18.000 0.250 http://example.org/film/film/film_festivals #195-0194zl PRED entity: 0194zl PRED relation: film_crew_role PRED expected values: 01pvkk => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 23): 02r96rf (0.74 #75, 0.73 #111, 0.71 #549), 0dxtw (0.45 #46, 0.42 #118, 0.40 #82), 01vx2h (0.34 #447, 0.33 #557, 0.31 #737), 01pvkk (0.29 #774, 0.28 #630, 0.28 #558), 02ynfr (0.19 #52, 0.17 #562, 0.17 #16), 0215hd (0.17 #91, 0.15 #308, 0.15 #127), 089fss (0.17 #6, 0.13 #42, 0.09 #114), 02_n3z (0.17 #1, 0.09 #253, 0.09 #763), 0263ycg (0.17 #18, 0.03 #270, 0.03 #343), 01xy5l_ (0.14 #303, 0.12 #450, 0.12 #776) >> Best rule #75 for best value: >> intensional similarity = 4 >> extensional distance = 45 >> proper extension: 01h7bb; 0b6tzs; 0fh694; 020fcn; 01719t; 09gq0x5; 0fy34l; 0260bz; 02c638; 011yd2; ... >> query: (?x4963, 02r96rf) <- film(?x2805, ?x4963), nominated_for(?x451, ?x4963), ?x451 = 099jhq, titles(?x53, ?x4963) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #774 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 727 *> proper extension: 03g90h; 02_1sj; 02z3r8t; 03ckwzc; 04gknr; 03t97y; 07sc6nw; 07g_0c; 05p3738; 028cg00; ... *> query: (?x4963, 01pvkk) <- film(?x2805, ?x4963), currency(?x4963, ?x170), titles(?x53, ?x4963), film_crew_role(?x4963, ?x137) *> conf = 0.29 ranks of expected_values: 4 EVAL 0194zl film_crew_role 01pvkk CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 72.000 72.000 0.745 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #194-01zg98 PRED entity: 01zg98 PRED relation: award PRED expected values: 0bdwqv => 99 concepts (83 used for prediction) PRED predicted values (max 10 best out of 268): 0ck27z (0.31 #9790, 0.27 #11002, 0.26 #12214), 01by1l (0.20 #9002, 0.14 #1323, 0.12 #1727), 03c7tr1 (0.19 #1673, 0.16 #2077, 0.15 #22228), 05pcn59 (0.19 #1696, 0.16 #2504, 0.15 #2908), 01bgqh (0.18 #8933, 0.14 #1254, 0.09 #13377), 0gqyl (0.18 #21823, 0.18 #508, 0.18 #104), 0bdwqv (0.18 #21823, 0.18 #576, 0.18 #172), 099tbz (0.18 #21823, 0.18 #460, 0.18 #56), 0f4x7 (0.18 #21823, 0.18 #435, 0.18 #31), 05b4l5x (0.18 #21823, 0.17 #1622, 0.17 #18994) >> Best rule #9790 for best value: >> intensional similarity = 2 >> extensional distance = 601 >> proper extension: 01zmpg; >> query: (?x4248, 0ck27z) <- award_nominee(?x4248, ?x395), actor(?x3326, ?x4248) >> conf = 0.31 => this is the best rule for 1 predicted values *> Best rule #21823 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1505 *> proper extension: 0cjdk; 03yf3z; 01_8w2; 0khth; 0gsgr; 04k05; *> query: (?x4248, ?x704) <- award_winner(?x2282, ?x4248), award_winner(?x704, ?x2282), award_winner(?x919, ?x2282) *> conf = 0.18 ranks of expected_values: 7 EVAL 01zg98 award 0bdwqv CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 99.000 83.000 0.313 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #193-01_lhg PRED entity: 01_lhg PRED relation: teams! PRED expected values: 03rjj => 61 concepts (61 used for prediction) PRED predicted values (max 10 best out of 93): 035qy (0.33 #43, 0.06 #853, 0.05 #1393), 0k6nt (0.20 #297, 0.06 #567, 0.06 #1107), 05r4w (0.20 #271, 0.06 #541, 0.06 #1081), 06t8v (0.06 #638, 0.06 #1178, 0.06 #908), 05bcl (0.06 #654, 0.06 #1194, 0.04 #5678), 0154j (0.06 #545, 0.06 #1085, 0.04 #5678), 0bjv6 (0.06 #636, 0.06 #1176, 0.04 #5677), 0d0kn (0.06 #603, 0.06 #1143, 0.04 #5677), 06c1y (0.06 #591, 0.06 #1131, 0.04 #5677), 04v3q (0.06 #571, 0.06 #1111, 0.04 #5677) >> Best rule #43 for best value: >> intensional similarity = 8 >> extensional distance = 1 >> proper extension: 035qgm; >> query: (?x4485, 035qy) <- team(?x63, ?x4485), current_club(?x4485, ?x10389), current_club(?x4485, ?x8326), ?x10389 = 08vk_r, sport(?x4485, ?x471), position(?x4485, ?x203), position(?x8326, ?x60), ?x63 = 02sdk9v >> conf = 0.33 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 01_lhg teams! 03rjj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 61.000 0.333 http://example.org/sports/sports_team_location/teams #192-0124ld PRED entity: 0124ld PRED relation: olympics! PRED expected values: 0h7x => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 372): 06mzp (0.89 #1798, 0.83 #2078, 0.81 #560), 07ssc (0.82 #1963, 0.81 #1683, 0.81 #2643), 05b4w (0.81 #560, 0.80 #839, 0.79 #1449), 0mhhw (0.81 #2079, 0.81 #2217, 0.75 #1387), 0h7x (0.78 #1156, 0.75 #2661, 0.71 #2522), 0k6nt (0.75 #1691, 0.71 #1415, 0.71 #2512), 0154j (0.75 #1258, 0.67 #1533, 0.61 #703), 03_3d (0.70 #2357, 0.70 #1811, 0.68 #141), 03gj2 (0.68 #1972, 0.68 #141, 0.67 #1553), 05qhw (0.68 #141, 0.64 #842, 0.60 #1543) >> Best rule #1798 for best value: >> intensional similarity = 77 >> extensional distance = 14 >> proper extension: 0lk8j; >> query: (?x7429, ?x774) <- olympics(?x774, ?x7429), olympics(?x1175, ?x7429), olympics(?x1892, ?x7429), olympics(?x789, ?x7429), olympics(?x279, ?x7429), medal(?x7429, ?x422), ?x1892 = 02vzc, sports(?x7429, ?x453), participating_countries(?x7429, ?x512), ?x789 = 0f8l9c, film_release_region(?x8137, ?x774), film_release_region(?x7897, ?x774), film_release_region(?x7554, ?x774), film_release_region(?x7494, ?x774), film_release_region(?x7275, ?x774), film_release_region(?x4690, ?x774), film_release_region(?x3619, ?x774), film_release_region(?x3453, ?x774), film_release_region(?x3135, ?x774), film_release_region(?x2961, ?x774), film_release_region(?x2889, ?x774), film_release_region(?x2655, ?x774), film_release_region(?x2501, ?x774), film_release_region(?x2394, ?x774), film_release_region(?x2163, ?x774), film_release_region(?x1927, ?x774), film_release_region(?x1364, ?x774), film_release_region(?x1080, ?x774), ?x2655 = 0fpmrm3, contains(?x774, ?x1220), nationality(?x9665, ?x774), nationality(?x3335, ?x774), organization(?x774, ?x3750), organization(?x774, ?x1062), olympics(?x774, ?x7688), olympics(?x774, ?x2496), olympics(?x774, ?x1081), ?x7688 = 0jkvj, ?x4690 = 0gkz3nz, ?x7275 = 0g4vmj8, executive_produced_by(?x3135, ?x8652), service_location(?x896, ?x774), ?x8652 = 09d5d5, ?x2163 = 0j6b5, ?x9665 = 01syr4, influenced_by(?x3335, ?x1857), ?x3750 = 0_2v, ?x2889 = 040b5k, ?x1062 = 01rz1, ?x2501 = 040rmy, film_release_region(?x3453, ?x1790), film_release_region(?x3453, ?x410), film_release_region(?x3453, ?x390), film_release_region(?x3453, ?x252), genre(?x3135, ?x53), ?x279 = 0d060g, ?x8137 = 0gtx63s, ?x1790 = 01pj7, ?x7897 = 03np63f, film_festivals(?x3453, ?x13775), teams(?x774, ?x11564), official_language(?x774, ?x90), ?x252 = 03_3d, ?x3619 = 0fphgb, ?x1927 = 0by1wkq, ?x390 = 0chghy, ?x2394 = 0661ql3, ?x2496 = 0sxrz, ?x1081 = 0l6m5, ?x410 = 01ls2, ?x1080 = 01c22t, ?x7494 = 0dgrwqr, award(?x7554, ?x77), country(?x359, ?x774), profession(?x3335, ?x353), ?x1364 = 047msdk, ?x2961 = 047p7fr >> conf = 0.89 => this is the best rule for 1 predicted values *> Best rule #1156 for first EXPECTED value: *> intensional similarity = 85 *> extensional distance = 7 *> proper extension: 0swbd; *> query: (?x7429, 0h7x) <- olympics(?x94, ?x7429), olympics(?x5177, ?x7429), olympics(?x1892, ?x7429), olympics(?x279, ?x7429), medal(?x7429, ?x422), film_release_region(?x11125, ?x1892), film_release_region(?x8471, ?x1892), film_release_region(?x8236, ?x1892), film_release_region(?x7897, ?x1892), film_release_region(?x7832, ?x1892), film_release_region(?x7493, ?x1892), film_release_region(?x7293, ?x1892), film_release_region(?x7170, ?x1892), film_release_region(?x6215, ?x1892), film_release_region(?x6095, ?x1892), film_release_region(?x5980, ?x1892), film_release_region(?x5825, ?x1892), film_release_region(?x4841, ?x1892), film_release_region(?x4448, ?x1892), film_release_region(?x4355, ?x1892), film_release_region(?x3938, ?x1892), film_release_region(?x3830, ?x1892), film_release_region(?x3784, ?x1892), film_release_region(?x3491, ?x1892), film_release_region(?x3471, ?x1892), film_release_region(?x3311, ?x1892), film_release_region(?x3252, ?x1892), film_release_region(?x3076, ?x1892), film_release_region(?x3000, ?x1892), film_release_region(?x2893, ?x1892), film_release_region(?x2471, ?x1892), film_release_region(?x2037, ?x1892), film_release_region(?x1546, ?x1892), film_release_region(?x1470, ?x1892), film_release_region(?x1071, ?x1892), film_release_region(?x303, ?x1892), ?x6095 = 0bq6ntw, ?x3938 = 024mpp, ?x8471 = 0cp0t91, olympics(?x1892, ?x7688), olympics(?x1892, ?x2369), olympics(?x1892, ?x2134), official_language(?x1892, ?x4442), ?x2471 = 08052t3, ?x7170 = 02pxst, ?x3471 = 07cyl, ?x1546 = 0d6b7, ?x279 = 0d060g, ?x2369 = 0lbbj, ?x1071 = 02d44q, ?x3491 = 0gtvpkw, service_location(?x555, ?x1892), ?x7293 = 027m67, ?x3252 = 0gh8zks, ?x2037 = 0gvrws1, ?x4355 = 08tq4x, ?x4841 = 0k4fz, nominated_for(?x484, ?x3311), ?x11125 = 0gy4k, ?x3784 = 0bmhvpr, ?x7897 = 03np63f, ?x1470 = 03twd6, country(?x2266, ?x1892), country(?x1352, ?x1892), ?x7688 = 0jkvj, ?x2266 = 01lb14, ?x7493 = 0btpm6, ?x7832 = 0fphf3v, ?x5825 = 067ghz, ?x1352 = 0w0d, ?x3830 = 0gjcrrw, produced_by(?x3311, ?x6488), ?x2893 = 01jrbb, ?x3076 = 0g5838s, ?x94 = 09c7w0, ?x3000 = 045j3w, ?x4448 = 01k60v, ?x5177 = 06zgc, ?x8236 = 042zrm, ?x6215 = 0jyb4, ?x303 = 011yrp, contains(?x455, ?x1892), ?x2134 = 0blg2, ?x5980 = 0hv81, nominated_for(?x3690, ?x3311) *> conf = 0.78 ranks of expected_values: 5 EVAL 0124ld olympics! 0h7x CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 21.000 21.000 0.889 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #191-02vptk_ PRED entity: 02vptk_ PRED relation: profession PRED expected values: 012t_z => 118 concepts (107 used for prediction) PRED predicted values (max 10 best out of 60): 02hrh1q (0.71 #4667, 0.69 #7518, 0.69 #2716), 01d_h8 (0.46 #2407, 0.44 #2107, 0.44 #2707), 0dxtg (0.41 #2115, 0.40 #2415, 0.37 #2715), 03gjzk (0.34 #2117, 0.34 #2717, 0.33 #616), 05s9tm (0.33 #114, 0.25 #264), 02jknp (0.26 #2409, 0.24 #2109, 0.24 #2709), 09jwl (0.25 #4372, 0.25 #6922, 0.24 #7222), 012t_z (0.25 #313, 0.20 #463, 0.17 #613), 0fj9f (0.25 #206, 0.05 #11914, 0.05 #8910), 0dz3r (0.20 #4354, 0.20 #7204, 0.20 #6904) >> Best rule #4667 for best value: >> intensional similarity = 4 >> extensional distance = 378 >> proper extension: 01vvydl; 01t6b4; 058s57; 0l56b; 0170s4; 01trhmt; 0gbwp; 01900g; 0f7hc; 0863x_; ... >> query: (?x10959, 02hrh1q) <- currency(?x10959, ?x170), nationality(?x10959, ?x94), ?x170 = 09nqf, ?x94 = 09c7w0 >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #313 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 2 *> proper extension: 027kmrb; 0jvs0; *> query: (?x10959, 012t_z) <- state_province_region(?x10959, ?x335), gender(?x10959, ?x231), ?x231 = 05zppz, citytown(?x10959, ?x739), ?x739 = 02_286 *> conf = 0.25 ranks of expected_values: 8 EVAL 02vptk_ profession 012t_z CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 118.000 107.000 0.705 http://example.org/people/person/profession #190-0x44q PRED entity: 0x44q PRED relation: time_zones PRED expected values: 02hczc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 8): 02hczc (0.57 #28, 0.40 #2, 0.33 #15), 02hcv8 (0.48 #159, 0.43 #250, 0.43 #276), 02lcqs (0.22 #187, 0.19 #70, 0.19 #83), 02fqwt (0.19 #105, 0.19 #131, 0.18 #144), 02lcrv (0.17 #692, 0.17 #706, 0.17 #811), 02llzg (0.05 #801, 0.05 #696, 0.05 #815), 03bdv (0.03 #593, 0.03 #671, 0.03 #803), 03plfd (0.01 #702, 0.01 #807, 0.01 #913) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0x1jc; >> query: (?x13598, 02hczc) <- contains(?x2049, ?x13598), ?x2049 = 050l8, category(?x13598, ?x134), ?x134 = 08mbj5d >> conf = 0.57 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0x44q time_zones 02hczc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.571 http://example.org/location/location/time_zones #189-05d9y_ PRED entity: 05d9y_ PRED relation: institution! PRED expected values: 04zx3q1 => 190 concepts (112 used for prediction) PRED predicted values (max 10 best out of 23): 02h4rq6 (0.83 #308, 0.66 #2209, 0.65 #1318), 014mlp (0.70 #1399, 0.70 #996, 0.70 #311), 03bwzr4 (0.70 #321, 0.45 #1331, 0.44 #271), 02_xgp2 (0.65 #319, 0.52 #294, 0.50 #244), 019v9k (0.61 #1000, 0.61 #315, 0.60 #1403), 016t_3 (0.52 #309, 0.45 #564, 0.45 #538), 0bkj86 (0.48 #314, 0.43 #999, 0.38 #1402), 04zx3q1 (0.39 #307, 0.26 #992, 0.22 #1317), 027f2w (0.39 #316, 0.24 #291, 0.22 #1001), 013zdg (0.35 #313, 0.25 #646, 0.24 #568) >> Best rule #308 for best value: >> intensional similarity = 5 >> extensional distance = 21 >> proper extension: 017lvd; 02ckl3; >> query: (?x6223, 02h4rq6) <- major_field_of_study(?x6223, ?x9079), category(?x6223, ?x134), ?x9079 = 0l5mz, organization(?x4095, ?x6223), ?x134 = 08mbj5d >> conf = 0.83 => this is the best rule for 1 predicted values *> Best rule #307 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 21 *> proper extension: 017lvd; 02ckl3; *> query: (?x6223, 04zx3q1) <- major_field_of_study(?x6223, ?x9079), category(?x6223, ?x134), ?x9079 = 0l5mz, organization(?x4095, ?x6223), ?x134 = 08mbj5d *> conf = 0.39 ranks of expected_values: 8 EVAL 05d9y_ institution! 04zx3q1 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 190.000 112.000 0.826 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #188-0djlxb PRED entity: 0djlxb PRED relation: genre PRED expected values: 07s9rl0 => 102 concepts (101 used for prediction) PRED predicted values (max 10 best out of 102): 07s9rl0 (0.68 #2533, 0.68 #2049, 0.67 #2291), 0219x_ (0.53 #4701, 0.53 #8672, 0.53 #4100), 016z4k (0.53 #4701, 0.53 #8672, 0.53 #4100), 05p553 (0.42 #605, 0.33 #7353, 0.33 #10720), 02kdv5l (0.41 #363, 0.37 #723, 0.30 #1687), 02l7c8 (0.38 #617, 0.33 #17, 0.31 #2549), 01jfsb (0.37 #1697, 0.36 #3509, 0.35 #373), 04xvlr (0.33 #2, 0.26 #122, 0.22 #1324), 0hn10 (0.33 #10, 0.08 #610, 0.06 #130), 01lrrt (0.33 #52, 0.03 #172, 0.02 #1013) >> Best rule #2533 for best value: >> intensional similarity = 3 >> extensional distance = 442 >> proper extension: 0d_wms; >> query: (?x3275, 07s9rl0) <- honored_for(?x944, ?x3275), country(?x3275, ?x1264), nationality(?x380, ?x1264) >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0djlxb genre 07s9rl0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 102.000 101.000 0.680 http://example.org/film/film/genre #187-05z55 PRED entity: 05z55 PRED relation: nutrient PRED expected values: 0h1zw 06jry 09gwd 0h1tg 02p0tjr 02kc_w5 => 22 concepts (21 used for prediction) PRED predicted values (max 10 best out of 43): 025sf8g (0.82 #529, 0.80 #135, 0.79 #36), 06x4c (0.80 #135, 0.79 #36, 0.77 #25), 06jry (0.80 #135, 0.79 #36, 0.77 #25), 0hqw8p_ (0.80 #135, 0.79 #36, 0.77 #25), 09gwd (0.80 #135, 0.79 #36, 0.77 #25), 0h1tg (0.80 #135, 0.79 #36, 0.77 #25), 02p0tjr (0.80 #135, 0.79 #36, 0.77 #25), 04kl74p (0.80 #135, 0.79 #36, 0.77 #25), 014yzm (0.80 #135, 0.79 #36, 0.77 #25), 0h1zw (0.80 #135, 0.79 #36, 0.77 #25) >> Best rule #529 for best value: >> intensional similarity = 85 >> extensional distance = 9 >> proper extension: 06x4c; >> query: (?x9732, 025sf8g) <- nutrient(?x9732, ?x12454), nutrient(?x9732, ?x9915), nutrient(?x9732, ?x9365), nutrient(?x9732, ?x7219), nutrient(?x9732, ?x5549), nutrient(?x9732, ?x2702), nutrient(?x9732, ?x2018), nutrient(?x10612, ?x7219), nutrient(?x9489, ?x7219), nutrient(?x9005, ?x7219), nutrient(?x8298, ?x7219), nutrient(?x7719, ?x7219), nutrient(?x7057, ?x7219), nutrient(?x6285, ?x7219), nutrient(?x6191, ?x7219), nutrient(?x6159, ?x7219), nutrient(?x6032, ?x7219), nutrient(?x5373, ?x7219), nutrient(?x5009, ?x7219), nutrient(?x4068, ?x7219), nutrient(?x3900, ?x7219), nutrient(?x3468, ?x7219), nutrient(?x2701, ?x7219), nutrient(?x1959, ?x7219), nutrient(?x1303, ?x7219), nutrient(?x1257, ?x7219), ?x6285 = 01645p, ?x3468 = 0cxn2, ?x3900 = 061_f, ?x10612 = 0frq6, ?x2701 = 0hkxq, ?x8298 = 037ls6, ?x6191 = 014j1m, ?x2018 = 01sh2, taxonomy(?x9365, ?x939), ?x6032 = 01nkt, ?x9489 = 07j87, ?x2702 = 0838f, ?x5373 = 0971v, nutrient(?x7719, ?x13126), nutrient(?x7719, ?x12868), nutrient(?x7719, ?x11784), nutrient(?x7719, ?x9855), nutrient(?x7719, ?x9840), nutrient(?x7719, ?x9795), nutrient(?x7719, ?x9619), nutrient(?x7719, ?x8487), nutrient(?x7719, ?x8243), nutrient(?x7719, ?x6286), nutrient(?x7719, ?x6192), nutrient(?x7719, ?x5337), nutrient(?x7719, ?x4069), nutrient(?x7719, ?x3469), nutrient(?x7719, ?x3264), nutrient(?x7719, ?x3203), ?x5337 = 06x4c, ?x9795 = 05v_8y, ?x9840 = 02p0tjr, ?x9855 = 0d9t0, ?x5009 = 0fjfh, ?x6159 = 033cnk, ?x1257 = 09728, ?x939 = 04n6k, ?x4069 = 0hqw8p_, ?x8243 = 014d7f, ?x4068 = 0fbw6, ?x13126 = 02kc_w5, ?x12868 = 03d49, ?x5549 = 025s7j4, ?x6286 = 02y_3rf, ?x8487 = 014yzm, ?x9915 = 025tkqy, ?x9619 = 0h1tg, ?x6192 = 06jry, ?x7057 = 0fbdb, ?x1303 = 0fj52s, ?x1959 = 0f25w9, ?x3264 = 0dcfv, ?x12454 = 025rw19, ?x3469 = 0h1zw, nutrient(?x9005, ?x10453), ?x11784 = 07zqy, ?x3203 = 04kl74p, ?x10453 = 075pwf, nutrient(?x5009, ?x9365) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #135 for first EXPECTED value: *> intensional similarity = 132 *> extensional distance = 1 *> proper extension: 01645p; *> query: (?x9732, ?x3203) <- nutrient(?x9732, ?x13944), nutrient(?x9732, ?x13498), nutrient(?x9732, ?x12902), nutrient(?x9732, ?x12454), nutrient(?x9732, ?x11758), nutrient(?x9732, ?x11592), nutrient(?x9732, ?x11409), nutrient(?x9732, ?x11270), nutrient(?x9732, ?x10891), nutrient(?x9732, ?x10709), nutrient(?x9732, ?x10098), nutrient(?x9732, ?x9949), nutrient(?x9732, ?x9915), nutrient(?x9732, ?x9733), nutrient(?x9732, ?x9490), nutrient(?x9732, ?x9436), nutrient(?x9732, ?x9426), nutrient(?x9732, ?x9365), nutrient(?x9732, ?x8442), nutrient(?x9732, ?x8413), nutrient(?x9732, ?x7894), nutrient(?x9732, ?x7720), nutrient(?x9732, ?x7652), nutrient(?x9732, ?x7364), nutrient(?x9732, ?x7362), nutrient(?x9732, ?x7219), nutrient(?x9732, ?x6586), nutrient(?x9732, ?x6160), nutrient(?x9732, ?x6033), nutrient(?x9732, ?x5549), nutrient(?x9732, ?x5526), nutrient(?x9732, ?x5451), nutrient(?x9732, ?x5374), nutrient(?x9732, ?x5010), nutrient(?x9732, ?x2702), nutrient(?x9732, ?x2018), nutrient(?x9732, ?x1960), nutrient(?x9732, ?x1304), nutrient(?x9732, ?x1258), ?x7219 = 0h1vg, ?x9426 = 0h1yy, ?x5010 = 0h1vz, nutrient(?x9489, ?x2018), nutrient(?x8298, ?x2018), nutrient(?x7719, ?x2018), nutrient(?x7057, ?x2018), nutrient(?x6191, ?x2018), nutrient(?x5337, ?x2018), nutrient(?x5009, ?x2018), nutrient(?x4068, ?x2018), nutrient(?x3900, ?x2018), nutrient(?x3468, ?x2018), nutrient(?x3264, ?x2018), nutrient(?x2701, ?x2018), nutrient(?x1303, ?x2018), nutrient(?x1257, ?x2018), ?x6160 = 041r51, ?x2701 = 0hkxq, ?x7362 = 02kc5rj, ?x7894 = 0f4hc, ?x8298 = 037ls6, ?x1258 = 0h1wg, ?x1960 = 07hnp, ?x9733 = 0h1tz, ?x1304 = 08lb68, ?x1303 = 0fj52s, ?x7057 = 0fbdb, ?x12454 = 025rw19, ?x10709 = 0h1sz, ?x5374 = 025s0zp, ?x7720 = 025s7x6, ?x11592 = 025sf0_, ?x7719 = 0dj75, ?x11758 = 0q01m, ?x11270 = 02kc008, ?x5337 = 06x4c, ?x5526 = 09pbb, ?x5451 = 05wvs, nutrient(?x10612, ?x9436), nutrient(?x6032, ?x9436), nutrient(?x5373, ?x9436), nutrient(?x1959, ?x9436), ?x9949 = 02kd0rh, ?x6191 = 014j1m, ?x3264 = 0dcfv, ?x8413 = 02kc4sf, ?x2702 = 0838f, ?x13498 = 07q0m, ?x6032 = 01nkt, ?x8442 = 02kcv4x, ?x10098 = 0h1_c, ?x1257 = 09728, ?x4068 = 0fbw6, ?x9489 = 07j87, ?x10612 = 0frq6, ?x5373 = 0971v, ?x7652 = 025s0s0, nutrient(?x3468, ?x13126), nutrient(?x3468, ?x11784), nutrient(?x3468, ?x10195), nutrient(?x3468, ?x9840), nutrient(?x3468, ?x7431), nutrient(?x3468, ?x6286), nutrient(?x3468, ?x6192), nutrient(?x3468, ?x6026), nutrient(?x3468, ?x4069), nutrient(?x3468, ?x3469), nutrient(?x3468, ?x3203), ?x9365 = 04k8n, ?x7431 = 09gwd, ?x10195 = 0hkwr, ?x12902 = 0fzjh, ?x6586 = 05gh50, ?x1959 = 0f25w9, ?x6033 = 04zjxcz, ?x5549 = 025s7j4, ?x4069 = 0hqw8p_, ?x6026 = 025sf8g, ?x11784 = 07zqy, ?x10891 = 0g5gq, ?x3469 = 0h1zw, ?x9490 = 0h1sg, ?x9915 = 025tkqy, ?x11409 = 0h1yf, ?x7364 = 09gvd, ?x6192 = 06jry, ?x13126 = 02kc_w5, ?x5009 = 0fjfh, ?x9840 = 02p0tjr, ?x3900 = 061_f, ?x6286 = 02y_3rf, ?x13944 = 0f4kp *> conf = 0.80 ranks of expected_values: 3, 5, 6, 7, 10, 13 EVAL 05z55 nutrient 02kc_w5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 22.000 21.000 0.818 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 05z55 nutrient 02p0tjr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 21.000 0.818 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 05z55 nutrient 0h1tg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 21.000 0.818 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 05z55 nutrient 09gwd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 22.000 21.000 0.818 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 05z55 nutrient 06jry CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 22.000 21.000 0.818 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient EVAL 05z55 nutrient 0h1zw CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 22.000 21.000 0.818 http://example.org/food/food/nutrients./food/nutrition_fact/nutrient #186-04xhwn PRED entity: 04xhwn PRED relation: type_of_union PRED expected values: 04ztj => 50 concepts (50 used for prediction) PRED predicted values (max 10 best out of 2): 04ztj (0.78 #21, 0.73 #13, 0.72 #25), 01g63y (0.17 #6, 0.16 #62, 0.16 #42) >> Best rule #21 for best value: >> intensional similarity = 3 >> extensional distance = 16 >> proper extension: 01r7pq; 026dd2b; 01gw8b; >> query: (?x12566, 04ztj) <- actor(?x9098, ?x12566), actor(?x9098, ?x3261), ?x3261 = 01nrq5 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04xhwn type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 50.000 50.000 0.778 http://example.org/people/person/spouse_s./people/marriage/type_of_union #185-0243cq PRED entity: 0243cq PRED relation: film! PRED expected values: 05zjx => 85 concepts (25 used for prediction) PRED predicted values (max 10 best out of 1177): 02bh9 (0.39 #29180, 0.32 #37520, 0.32 #43774), 02_p5w (0.20 #646, 0.12 #6898, 0.07 #8982), 0p8r1 (0.19 #6838, 0.08 #11006, 0.08 #4754), 07rd7 (0.11 #16674, 0.10 #29181, 0.10 #35434), 02gf_l (0.10 #7522, 0.10 #1270, 0.08 #9606), 01v3vp (0.10 #710, 0.06 #6962, 0.03 #17384), 03f2_rc (0.10 #86, 0.04 #16760, 0.03 #20929), 0kftt (0.10 #1470, 0.03 #7722, 0.03 #9806), 0p_47 (0.10 #675, 0.03 #6927, 0.02 #21518), 0gx_p (0.10 #1112, 0.02 #24039, 0.02 #17786) >> Best rule #29180 for best value: >> intensional similarity = 4 >> extensional distance = 197 >> proper extension: 0d1qmz; >> query: (?x4313, ?x3410) <- currency(?x4313, ?x170), ?x170 = 09nqf, nominated_for(?x3410, ?x4313), story_by(?x4313, ?x4314) >> conf = 0.39 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0243cq film! 05zjx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 85.000 25.000 0.390 http://example.org/film/actor/film./film/performance/film #184-0c73z PRED entity: 0c73z PRED relation: artists! PRED expected values: 06q6jz => 165 concepts (107 used for prediction) PRED predicted values (max 10 best out of 232): 06q6jz (0.80 #1750, 0.69 #2687, 0.41 #6755), 064t9 (0.73 #31904, 0.72 #32217, 0.42 #11899), 017_qw (0.57 #4439, 0.51 #16015, 0.42 #9764), 06by7 (0.45 #4082, 0.42 #31912, 0.42 #32225), 03_d0 (0.33 #12, 0.33 #8769, 0.30 #10024), 0155w (0.33 #109, 0.28 #7613, 0.25 #9493), 0gywn (0.33 #59, 0.23 #11945, 0.22 #13822), 02w4v (0.33 #45, 0.20 #4106, 0.17 #3480), 07ym47 (0.33 #71, 0.07 #2256, 0.04 #8828), 01ydtg (0.33 #180, 0.04 #9250, 0.04 #12692) >> Best rule #1750 for best value: >> intensional similarity = 5 >> extensional distance = 8 >> proper extension: 0hnlx; 0k1wz; >> query: (?x11497, 06q6jz) <- artists(?x10853, ?x11497), artists(?x597, ?x11497), gender(?x11497, ?x231), ?x10853 = 0l8gh, ?x597 = 0ggq0m >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0c73z artists! 06q6jz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 165.000 107.000 0.800 http://example.org/music/genre/artists #183-09fb5 PRED entity: 09fb5 PRED relation: award PRED expected values: 07cbcy 04kxsb => 117 concepts (117 used for prediction) PRED predicted values (max 10 best out of 272): 04kxsb (0.71 #30257, 0.70 #23487, 0.70 #20699), 02w9sd7 (0.71 #30257, 0.70 #23487, 0.70 #20699), 02x73k6 (0.71 #30257, 0.70 #23487, 0.70 #20699), 027dtxw (0.71 #30257, 0.70 #23487, 0.70 #20699), 099ck7 (0.71 #30257, 0.70 #23487, 0.70 #20699), 027b9j5 (0.71 #30257, 0.70 #23487, 0.70 #20699), 027c95y (0.71 #30257, 0.70 #23487, 0.70 #20699), 09cm54 (0.71 #30257, 0.70 #23487, 0.70 #20699), 02z13jg (0.71 #30257, 0.70 #23487, 0.70 #20699), 027986c (0.71 #30257, 0.70 #23487, 0.70 #20699) >> Best rule #30257 for best value: >> intensional similarity = 2 >> extensional distance = 1587 >> proper extension: 04rcr; 02r3zy; 03g5jw; 05crg7; 0dvqq; 03fbc; 0249kn; 018ndc; 017j6; 01w92; ... >> query: (?x406, ?x112) <- award_winner(?x112, ?x406), award_nominee(?x1870, ?x406) >> conf = 0.71 => this is the best rule for 10 predicted values ranks of expected_values: 1, 12 EVAL 09fb5 award 04kxsb CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 117.000 117.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 09fb5 award 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 117.000 117.000 0.705 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #182-03flwk PRED entity: 03flwk PRED relation: nationality PRED expected values: 09c7w0 => 78 concepts (33 used for prediction) PRED predicted values (max 10 best out of 20): 09c7w0 (0.90 #2625, 0.89 #703, 0.88 #401), 01x73 (0.30 #1814, 0.30 #2725, 0.28 #1107), 02jx1 (0.28 #333, 0.27 #133, 0.14 #33), 07ssc (0.27 #115, 0.24 #315, 0.14 #15), 05kyr (0.09 #168, 0.08 #268, 0.03 #368), 0chghy (0.08 #210, 0.03 #310, 0.02 #612), 0345h (0.08 #231, 0.03 #1341, 0.02 #1441), 0d060g (0.06 #1012, 0.06 #1517, 0.05 #911), 03rk0 (0.05 #2568, 0.04 #648, 0.03 #2265), 03rjj (0.03 #305, 0.02 #1415, 0.02 #506) >> Best rule #2625 for best value: >> intensional similarity = 4 >> extensional distance = 993 >> proper extension: 01vw917; >> query: (?x5100, 09c7w0) <- place_of_birth(?x5100, ?x12912), source(?x12912, ?x958), contains(?x12912, ?x1103), contains(?x94, ?x12912) >> conf = 0.90 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03flwk nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 78.000 33.000 0.897 http://example.org/people/person/nationality #181-03_gz8 PRED entity: 03_gz8 PRED relation: film! PRED expected values: 03m6pk => 89 concepts (47 used for prediction) PRED predicted values (max 10 best out of 876): 0154qm (0.56 #2081, 0.46 #8322, 0.45 #87377), 0jfx1 (0.20 #406, 0.04 #12888, 0.04 #10808), 09wj5 (0.13 #101, 0.04 #4262, 0.03 #8423), 0171cm (0.13 #425, 0.04 #14987, 0.03 #97779), 01tsbmv (0.13 #1897, 0.03 #6058, 0.02 #10219), 01l2fn (0.13 #263, 0.02 #31469, 0.02 #10665), 02q42j_ (0.11 #56171, 0.11 #39529, 0.11 #35368), 06ltr (0.08 #3027, 0.07 #5107, 0.07 #7187), 0lpjn (0.07 #2560, 0.07 #479, 0.06 #15041), 01f7dd (0.07 #1209, 0.05 #7450, 0.04 #5370) >> Best rule #2081 for best value: >> intensional similarity = 4 >> extensional distance = 13 >> proper extension: 017180; >> query: (?x6362, ?x3281) <- film_crew_role(?x6362, ?x137), film(?x1738, ?x6362), nominated_for(?x3281, ?x6362), ?x1738 = 0170pk >> conf = 0.56 => this is the best rule for 1 predicted values *> Best rule #91538 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 955 *> proper extension: 05sy0cv; *> query: (?x6362, ?x1286) <- award(?x6362, ?x2222), award(?x3441, ?x2222), film(?x1286, ?x3441) *> conf = 0.02 ranks of expected_values: 420 EVAL 03_gz8 film! 03m6pk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 89.000 47.000 0.557 http://example.org/film/actor/film./film/performance/film #180-08nhfc1 PRED entity: 08nhfc1 PRED relation: nominated_for! PRED expected values: 02xj3rw => 116 concepts (105 used for prediction) PRED predicted values (max 10 best out of 233): 02x4w6g (0.69 #937, 0.69 #4218, 0.68 #13828), 09qv_s (0.69 #937, 0.69 #4218, 0.68 #13828), 027986c (0.69 #937, 0.69 #4218, 0.68 #13828), 0gq9h (0.47 #3341, 0.46 #1233, 0.45 #3576), 0gr0m (0.43 #59, 0.31 #1230, 0.29 #1933), 0gs9p (0.42 #3343, 0.40 #3578, 0.40 #1235), 019f4v (0.41 #1224, 0.41 #3332, 0.40 #3567), 0gq_v (0.40 #1190, 0.37 #1659, 0.36 #1893), 0k611 (0.36 #3352, 0.35 #1244, 0.34 #1713), 040njc (0.34 #1178, 0.34 #3286, 0.33 #1647) >> Best rule #937 for best value: >> intensional similarity = 4 >> extensional distance = 92 >> proper extension: 06w99h3; 02qm_f; 031778; 0407yfx; 06wbm8q; 0dr3sl; 01jrbb; 02ctc6; 03q0r1; 0g9yrw; ... >> query: (?x7635, ?x591) <- award(?x7635, ?x591), nominated_for(?x3442, ?x7635), genre(?x7635, ?x8681), major_field_of_study(?x122, ?x8681) >> conf = 0.69 => this is the best rule for 3 predicted values *> Best rule #17816 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1025 *> proper extension: 0lcdk; 0542n; 087z2; *> query: (?x7635, ?x143) <- award(?x7635, ?x834), award(?x2376, ?x834), award(?x2376, ?x143) *> conf = 0.12 ranks of expected_values: 93 EVAL 08nhfc1 nominated_for! 02xj3rw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 116.000 105.000 0.690 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #179-05567m PRED entity: 05567m PRED relation: genre PRED expected values: 02kdv5l => 79 concepts (77 used for prediction) PRED predicted values (max 10 best out of 87): 07s9rl0 (0.67 #7766, 0.66 #2628, 0.64 #3104), 02kdv5l (0.59 #3346, 0.39 #242, 0.35 #2391), 09q17 (0.53 #5733, 0.53 #4181, 0.53 #6330), 01z4y (0.53 #5733, 0.53 #4181, 0.53 #6330), 03k9fj (0.50 #2160, 0.38 #3354, 0.36 #607), 01jfsb (0.50 #251, 0.35 #3355, 0.34 #2400), 06n90 (0.27 #3356, 0.23 #2162, 0.21 #252), 06cvj (0.25 #2631, 0.22 #4, 0.22 #2750), 0hcr (0.24 #2171, 0.16 #3365, 0.12 #618), 04xvh5 (0.22 #33, 0.17 #152, 0.14 #272) >> Best rule #7766 for best value: >> intensional similarity = 3 >> extensional distance = 1503 >> proper extension: 0vgkd; >> query: (?x9303, 07s9rl0) <- genre(?x9303, ?x1510), genre(?x4130, ?x1510), ?x4130 = 06lpmt >> conf = 0.67 => this is the best rule for 1 predicted values *> Best rule #3346 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 775 *> proper extension: 06n90; 01h72l; *> query: (?x9303, 02kdv5l) <- genre(?x9303, ?x1510), genre(?x9201, ?x1510), ?x9201 = 056k77g *> conf = 0.59 ranks of expected_values: 2 EVAL 05567m genre 02kdv5l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 79.000 77.000 0.668 http://example.org/film/film/genre #178-02rrh1w PRED entity: 02rrh1w PRED relation: film! PRED expected values: 02k4gv => 79 concepts (46 used for prediction) PRED predicted values (max 10 best out of 709): 03pmzt (0.29 #497, 0.01 #2575, 0.01 #6733), 01f7dd (0.14 #1206, 0.03 #3284, 0.02 #7442), 0jfx1 (0.14 #406, 0.03 #21188, 0.02 #2484), 035rnz (0.14 #695, 0.02 #2773, 0.02 #9009), 01nwwl (0.14 #503, 0.02 #2581, 0.02 #58700), 02s2ft (0.14 #7, 0.02 #2085, 0.02 #35340), 05lb87 (0.14 #213, 0.01 #4370, 0.01 #6449), 0fsm8c (0.14 #275, 0.01 #4432), 01z7_f (0.14 #756, 0.01 #25695, 0.01 #27774), 07myb2 (0.14 #1787, 0.01 #22569, 0.01 #8023) >> Best rule #497 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0sxmx; >> query: (?x7792, 03pmzt) <- film(?x4470, ?x7792), film_release_region(?x7792, ?x94), ?x4470 = 02y_2y, titles(?x812, ?x7792) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #3059 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 89 *> proper extension: 0g22z; 0yyg4; 026p_bs; 03cvwkr; 0gjk1d; 026390q; 02tqm5; 0_816; 051zy_b; 01jzyf; ... *> query: (?x7792, 02k4gv) <- produced_by(?x7792, ?x1417), genre(?x7792, ?x604), genre(?x7792, ?x53), ?x53 = 07s9rl0, film(?x851, ?x7792), ?x604 = 0lsxr *> conf = 0.01 ranks of expected_values: 577 EVAL 02rrh1w film! 02k4gv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 79.000 46.000 0.286 http://example.org/film/actor/film./film/performance/film #177-02cqbx PRED entity: 02cqbx PRED relation: costume_design_by! PRED expected values: 0k5g9 0p9tm => 145 concepts (145 used for prediction) PRED predicted values (max 10 best out of 190): 0bl06 (0.62 #2061, 0.57 #1125, 0.33 #111), 029jt9 (0.62 #2061, 0.57 #1125, 0.12 #726), 0bj25 (0.62 #2061, 0.57 #1125, 0.12 #936), 0gw7p (0.62 #2061, 0.57 #1125, 0.12 #936), 0k4kk (0.62 #2061, 0.57 #1125, 0.12 #936), 01gvsn (0.33 #179, 0.14 #553, 0.12 #927), 0291ck (0.33 #170, 0.14 #544, 0.12 #918), 0cqnss (0.33 #96, 0.14 #470, 0.12 #844), 0cq7tx (0.33 #83, 0.14 #457, 0.12 #831), 02q_4ph (0.33 #82, 0.14 #456, 0.12 #830) >> Best rule #2061 for best value: >> intensional similarity = 3 >> extensional distance = 24 >> proper extension: 0ft7sr; >> query: (?x5611, ?x1746) <- costume_design_by(?x5095, ?x5611), nominated_for(?x198, ?x5095), nominated_for(?x5611, ?x1746) >> conf = 0.62 => this is the best rule for 5 predicted values No rule for expected values ranks of expected_values: EVAL 02cqbx costume_design_by! 0p9tm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 145.000 145.000 0.623 http://example.org/film/film/costume_design_by EVAL 02cqbx costume_design_by! 0k5g9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 145.000 145.000 0.623 http://example.org/film/film/costume_design_by #176-0g83dv PRED entity: 0g83dv PRED relation: music PRED expected values: 012ljv => 95 concepts (59 used for prediction) PRED predicted values (max 10 best out of 45): 01m7f5r (0.25 #160, 0.06 #580), 03h610 (0.12 #287, 0.03 #3463, 0.03 #497), 08c9b0 (0.12 #293, 0.03 #503, 0.01 #3469), 01tc9r (0.12 #275, 0.03 #8104, 0.02 #9375), 06jzh (0.07 #8886, 0.07 #8462, 0.06 #10581), 0fbx6 (0.07 #8886, 0.07 #8462, 0.06 #10581), 0417z2 (0.06 #592, 0.02 #1015), 01l79yc (0.06 #534, 0.01 #6038, 0.01 #5403), 07j8kh (0.06 #521, 0.01 #3487, 0.01 #5177), 02bh9 (0.05 #894, 0.04 #1318, 0.04 #1105) >> Best rule #160 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 02_1rq; >> query: (?x4158, 01m7f5r) <- nominated_for(?x4254, ?x4158), nominated_for(?x540, ?x4158), ?x540 = 06jzh, award_nominee(?x57, ?x4254), place_of_birth(?x4254, ?x4627) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #3809 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 415 *> proper extension: 0dtw1x; 0bmc4cm; 02h22; 0hz6mv2; *> query: (?x4158, 012ljv) <- executive_produced_by(?x4158, ?x4060), film(?x166, ?x4158), country(?x4158, ?x94) *> conf = 0.01 ranks of expected_values: 39 EVAL 0g83dv music 012ljv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 95.000 59.000 0.250 http://example.org/film/film/music #175-01mwsnc PRED entity: 01mwsnc PRED relation: artists! PRED expected values: 0xhtw 06by7 => 121 concepts (45 used for prediction) PRED predicted values (max 10 best out of 270): 06by7 (0.78 #7131, 0.78 #13648, 0.73 #5899), 0xhtw (0.54 #5894, 0.44 #325, 0.38 #10855), 064t9 (0.42 #11779, 0.39 #13639, 0.39 #6198), 016clz (0.38 #3715, 0.38 #9293, 0.37 #2787), 03_d0 (0.33 #2793, 0.30 #3411, 0.20 #10540), 0gywn (0.33 #56, 0.30 #11823, 0.23 #1915), 0mhfr (0.33 #333, 0.25 #4355, 0.25 #6210), 02x8m (0.33 #18, 0.21 #3419, 0.20 #2801), 02w4v (0.33 #4374, 0.26 #6229, 0.17 #13670), 0glt670 (0.31 #11806, 0.13 #10569, 0.11 #348) >> Best rule #7131 for best value: >> intensional similarity = 5 >> extensional distance = 164 >> proper extension: 01hw6wq; 06m61; 0167km; 01mvjl0; 0137hn; 01dw_f; 01w5gg6; 01vzz1c; >> query: (?x4918, 06by7) <- role(?x4918, ?x227), profession(?x4918, ?x655), artists(?x3319, ?x4918), artists(?x3319, ?x12670), ?x12670 = 0ql36 >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 01mwsnc artists! 06by7 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 45.000 0.783 http://example.org/music/genre/artists EVAL 01mwsnc artists! 0xhtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 45.000 0.783 http://example.org/music/genre/artists #174-02681xs PRED entity: 02681xs PRED relation: award_winner PRED expected values: 03x82v => 41 concepts (11 used for prediction) PRED predicted values (max 10 best out of 1560): 03x82v (0.67 #7212, 0.55 #7427, 0.55 #4950), 01w61th (0.55 #7427, 0.55 #4950, 0.54 #4949), 02qlg7s (0.55 #7427, 0.55 #4950, 0.54 #4949), 09swkk (0.55 #4950, 0.54 #4949, 0.44 #12377), 04lgymt (0.55 #4950, 0.54 #4949, 0.40 #2474), 020hyj (0.55 #4950, 0.54 #4949, 0.39 #17328), 0cc5tgk (0.55 #4950, 0.54 #4949, 0.39 #17328), 0415mzy (0.55 #4950, 0.54 #4949, 0.39 #17328), 01s7ns (0.55 #4950, 0.54 #4949, 0.38 #7425), 02bgmr (0.54 #4949, 0.38 #7425, 0.36 #17330) >> Best rule #7212 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02681vq; >> query: (?x3666, 03x82v) <- award(?x11182, ?x3666), award(?x8599, ?x3666), ceremony(?x3666, ?x12139), artists(?x9225, ?x11182), ?x9225 = 0g293, ?x8599 = 01nkxvx >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02681xs award_winner 03x82v CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 41.000 11.000 0.667 http://example.org/award/award_category/winners./award/award_honor/award_winner #173-01nv4h PRED entity: 01nv4h PRED relation: currency! PRED expected values: 0140t7 => 8 concepts (8 used for prediction) PRED predicted values (max 10 best out of 2312): 016tbr (0.33 #448, 0.28 #1539, 0.25 #1997), 0f4vbz (0.33 #108, 0.28 #1539, 0.25 #1657), 01vs_v8 (0.33 #107, 0.28 #1539, 0.25 #1656), 0c6qh (0.33 #124, 0.28 #1539, 0.25 #1673), 0261x8t (0.33 #337, 0.28 #1539, 0.25 #1886), 01f492 (0.33 #383, 0.28 #1539, 0.25 #1932), 0127s7 (0.33 #295, 0.28 #1539, 0.25 #1844), 019pm_ (0.33 #139, 0.28 #1539, 0.25 #1688), 02mjmr (0.33 #136, 0.28 #1539, 0.25 #1685), 018grr (0.33 #97, 0.28 #1539, 0.25 #1646) >> Best rule #448 for best value: >> intensional similarity = 56 >> extensional distance = 1 >> proper extension: 09nqf; >> query: (?x1099, 016tbr) <- currency(?x1369, ?x1099), currency(?x6451, ?x1099), currency(?x5185, ?x1099), currency(?x2550, ?x1099), currency(?x1481, ?x1099), currency(?x12187, ?x1099), currency(?x10071, ?x1099), currency(?x5846, ?x1099), currency(?x13618, ?x1099), currency(?x12726, ?x1099), currency(?x11987, ?x1099), currency(?x6675, ?x1099), currency(?x639, ?x1099), currency(?x6401, ?x1099), institution(?x865, ?x10071), contains(?x1310, ?x11987), major_field_of_study(?x10071, ?x1154), titles(?x512, ?x2550), film_release_region(?x2550, ?x789), colors(?x6675, ?x1101), institution(?x1368, ?x5846), ?x1154 = 02lp1, nominated_for(?x2880, ?x2550), nominated_for(?x2375, ?x2550), genre(?x5185, ?x53), ?x2375 = 04kxsb, ?x789 = 0f8l9c, ?x1368 = 014mlp, contains(?x362, ?x639), film(?x7352, ?x1481), nominated_for(?x7068, ?x2550), film(?x2033, ?x1481), student(?x10071, ?x3542), genre(?x1481, ?x1013), nominated_for(?x2880, ?x9432), nominated_for(?x2880, ?x3111), nominated_for(?x2880, ?x414), nominated_for(?x13042, ?x5185), award(?x156, ?x2880), ?x13042 = 02qrbbx, film(?x166, ?x2550), institution(?x1390, ?x11987), ?x414 = 095zlp, ?x9432 = 0gvt53w, film_crew_role(?x6451, ?x137), major_field_of_study(?x1369, ?x2605), company(?x5652, ?x5846), currency(?x639, ?x5696), colors(?x13618, ?x332), institution(?x1305, ?x13618), citytown(?x12726, ?x4030), student(?x12726, ?x2320), ?x3111 = 0g68zt, ?x1101 = 06fvc, ?x332 = 01l849, category(?x12187, ?x134) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #1020 for first EXPECTED value: *> intensional similarity = 52 *> extensional distance = 2 *> proper extension: 0kz1h; *> query: (?x1099, ?x71) <- currency(?x11158, ?x1099), currency(?x9989, ?x1099), currency(?x6837, ?x1099), currency(?x1369, ?x1099), currency(?x1098, ?x1099), currency(?x7538, ?x1099), currency(?x4864, ?x1099), currency(?x13856, ?x1099), currency(?x6908, ?x1099), currency(?x13491, ?x1099), currency(?x6132, ?x1099), currency(?x2999, ?x1099), currency(?x1978, ?x1099), nominated_for(?x601, ?x4864), institution(?x620, ?x6908), film(?x166, ?x4864), organization(?x5510, ?x6132), list(?x6132, ?x2197), student(?x6837, ?x488), film_release_region(?x7538, ?x1523), school_type(?x1369, ?x5931), location(?x71, ?x1523), category(?x9989, ?x134), major_field_of_study(?x9989, ?x1668), place_of_death(?x457, ?x1523), contains(?x1310, ?x1369), place_of_birth(?x338, ?x1523), major_field_of_study(?x1978, ?x13501), student(?x2999, ?x164), contains(?x1523, ?x682), colors(?x13491, ?x4557), organization(?x2361, ?x1098), film(?x1669, ?x4864), origin(?x250, ?x1523), ?x4557 = 019sc, institution(?x734, ?x2999), genre(?x4864, ?x258), nominated_for(?x276, ?x4864), language(?x4864, ?x254), film_crew_role(?x7538, ?x137), ceremony(?x601, ?x7226), state_province_region(?x9989, ?x11933), nominated_for(?x601, ?x11483), ?x7226 = 0c6vcj, place_founded(?x541, ?x1523), state_province_region(?x11158, ?x2235), award_winner(?x601, ?x647), colors(?x11158, ?x663), major_field_of_study(?x13856, ?x2605), award(?x286, ?x601), ?x11483 = 01f69m, institution(?x1390, ?x6132) *> conf = 0.16 ranks of expected_values: 1083 EVAL 01nv4h currency! 0140t7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 8.000 8.000 0.333 http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency #172-01n8gr PRED entity: 01n8gr PRED relation: artists! PRED expected values: 01lyv => 116 concepts (60 used for prediction) PRED predicted values (max 10 best out of 283): 01lyv (0.81 #656, 0.74 #1278, 0.69 #345), 02w4v (0.44 #356, 0.25 #1289, 0.19 #5340), 0mhfr (0.38 #646, 0.38 #335, 0.32 #1268), 016clz (0.38 #317, 0.33 #5, 0.29 #8418), 05bt6j (0.32 #10012, 0.31 #355, 0.28 #1911), 06j6l (0.31 #4721, 0.29 #10640, 0.29 #7526), 0xhtw (0.28 #1572, 0.28 #9985, 0.26 #6558), 0glt670 (0.27 #9698, 0.26 #7518, 0.26 #9076), 025sc50 (0.26 #9086, 0.25 #9708, 0.24 #10642), 02vjzr (0.25 #448, 0.15 #759, 0.11 #10416) >> Best rule #656 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 05w6cw; 01x0yrt; 01fkxr; 0mjn2; >> query: (?x3358, 01lyv) <- artist(?x3240, ?x3358), artists(?x10833, ?x3358), award(?x3358, ?x724), ?x10833 = 06924p >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n8gr artists! 01lyv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 116.000 60.000 0.808 http://example.org/music/genre/artists #171-042v_gx PRED entity: 042v_gx PRED relation: role PRED expected values: 01wy6 => 84 concepts (59 used for prediction) PRED predicted values (max 10 best out of 80): 02hnl (0.83 #299, 0.82 #3877, 0.82 #521), 02sgy (0.83 #299, 0.82 #3877, 0.82 #521), 07y_7 (0.83 #299, 0.82 #3877, 0.82 #521), 0l1589 (0.83 #299, 0.82 #3877, 0.82 #521), 03_vpw (0.83 #299, 0.82 #3877, 0.82 #521), 0192l (0.83 #299, 0.82 #3877, 0.82 #521), 02snj9 (0.83 #299, 0.82 #3877, 0.82 #521), 042v_gx (0.83 #2160, 0.72 #2085, 0.71 #1863), 037c9s (0.76 #891, 0.74 #297, 0.71 #147), 03q5t (0.74 #1857, 0.68 #145, 0.57 #294) >> Best rule #299 for best value: >> intensional similarity = 13 >> extensional distance = 3 >> proper extension: 0dwsp; >> query: (?x432, ?x1147) <- role(?x1433, ?x432), role(?x1225, ?x432), role(?x228, ?x432), group(?x432, ?x442), role(?x211, ?x432), role(?x1291, ?x432), performance_role(?x432, ?x645), role(?x432, ?x75), instrumentalists(?x432, ?x133), ?x1433 = 0239kh, ?x1225 = 01qbl, role(?x1147, ?x432), ?x228 = 0l14qv >> conf = 0.83 => this is the best rule for 7 predicted values *> Best rule #148 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 1 *> proper extension: 0342h; *> query: (?x432, ?x75) <- role(?x1433, ?x432), group(?x432, ?x442), role(?x8282, ?x432), role(?x6049, ?x432), role(?x1413, ?x432), role(?x1291, ?x432), performance_role(?x432, ?x645), role(?x432, ?x75), instrumentalists(?x432, ?x133), ?x1433 = 0239kh, ?x8282 = 01q_wyj, ?x1413 = 01p9hgt, ?x6049 = 082brv *> conf = 0.65 ranks of expected_values: 42 EVAL 042v_gx role 01wy6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.024 84.000 59.000 0.834 http://example.org/music/performance_role/regular_performances./music/group_membership/role #170-0gtsx8c PRED entity: 0gtsx8c PRED relation: film! PRED expected values: 028k57 => 58 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1165): 028k57 (0.33 #786, 0.10 #47715, 0.04 #36048), 04zn7g (0.33 #2044, 0.10 #47715, 0.02 #62239), 01l2fn (0.25 #2335, 0.10 #47715, 0.10 #4409), 02m501 (0.25 #3755, 0.10 #47715, 0.05 #7903), 0301yj (0.25 #3868, 0.10 #47715, 0.04 #18668), 02s2ft (0.25 #2081, 0.10 #47715, 0.04 #18668), 02bkdn (0.25 #2372, 0.10 #47715, 0.04 #18668), 028knk (0.25 #2398, 0.10 #47715, 0.04 #18668), 011zd3 (0.25 #2446, 0.10 #47715, 0.03 #16964), 016fjj (0.25 #2705, 0.10 #47715, 0.02 #62239) >> Best rule #786 for best value: >> intensional similarity = 7 >> extensional distance = 1 >> proper extension: 01k0vq; >> query: (?x141, 028k57) <- film(?x7117, ?x141), film(?x6066, ?x141), film(?x1773, ?x141), ?x6066 = 01x_d8, ?x1773 = 049k07, award_winner(?x6297, ?x7117), film_crew_role(?x141, ?x468) >> conf = 0.33 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gtsx8c film! 028k57 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 58.000 30.000 0.333 http://example.org/film/actor/film./film/performance/film #169-02_l9 PRED entity: 02_l9 PRED relation: organization! PRED expected values: 07ym0 0h336 => 36 concepts (36 used for prediction) PRED predicted values (max 10 best out of 1201): 07ssc (0.75 #4190, 0.69 #4486, 0.56 #5080), 02vzc (0.67 #4241, 0.67 #1563, 0.62 #4537), 059j2 (0.67 #4214, 0.67 #1536, 0.62 #4510), 0k6nt (0.67 #4205, 0.67 #1527, 0.62 #4501), 0d0vqn (0.67 #4178, 0.67 #1500, 0.62 #4474), 03rjj (0.67 #4174, 0.67 #1496, 0.62 #4470), 03rt9 (0.67 #4187, 0.62 #4483, 0.50 #5077), 05b4w (0.67 #1584, 0.58 #4262, 0.54 #4558), 06mkj (0.67 #1571, 0.58 #4249, 0.54 #4545), 0h7x (0.67 #1542, 0.58 #4220, 0.54 #4516) >> Best rule #4190 for best value: >> intensional similarity = 7 >> extensional distance = 10 >> proper extension: 01rz1; 02jxk; 0_2v; 0j7v_; 018cqq; 059dn; >> query: (?x8603, 07ssc) <- organization(?x12258, ?x8603), organization(?x7386, ?x8603), organization(?x5742, ?x8603), films(?x7386, ?x2376), entity_involved(?x8416, ?x5742), organizations_founded(?x12258, ?x4672), genre(?x2376, ?x53) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #2083 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 4 *> proper extension: 05g9h; *> query: (?x8603, ?x916) <- organization(?x10654, ?x8603), influenced_by(?x10313, ?x10654), influenced_by(?x8659, ?x10654), influenced_by(?x7509, ?x10654), place_of_birth(?x8659, ?x8745), influenced_by(?x10654, ?x5004), religion(?x7509, ?x962), influenced_by(?x10313, ?x916) *> conf = 0.04 ranks of expected_values: 346, 373 EVAL 02_l9 organization! 0h336 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 36.000 36.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization EVAL 02_l9 organization! 07ym0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 36.000 36.000 0.750 http://example.org/organization/organization_member/member_of./organization/organization_membership/organization #168-0bzjgq PRED entity: 0bzjgq PRED relation: award_winner PRED expected values: 0829rj => 41 concepts (28 used for prediction) PRED predicted values (max 10 best out of 1713): 0151w_ (0.33 #4750, 0.18 #9374, 0.18 #7833), 0146pg (0.29 #6239, 0.17 #10863, 0.17 #3157), 012ky3 (0.29 #6785, 0.17 #3703, 0.14 #17570), 01wd9vs (0.29 #7223, 0.17 #4141, 0.08 #11847), 05dppk (0.29 #6518, 0.17 #3436, 0.08 #11142), 01l3mk3 (0.27 #4613, 0.17 #4232, 0.15 #7698), 0bwh6 (0.27 #4613, 0.17 #3257, 0.15 #7698), 02zft0 (0.27 #4613, 0.15 #7698, 0.14 #12319), 01qq_lp (0.27 #4613, 0.15 #7698, 0.14 #12319), 0cgzj (0.27 #4613, 0.15 #7698, 0.14 #12319) >> Best rule #4750 for best value: >> intensional similarity = 21 >> extensional distance = 4 >> proper extension: 02hn5v; 02jp5r; 0n8_m93; >> query: (?x8478, 0151w_) <- ceremony(?x1703, ?x8478), ceremony(?x1243, ?x8478), ceremony(?x1079, ?x8478), ceremony(?x720, ?x8478), ceremony(?x484, ?x8478), ceremony(?x77, ?x8478), ?x720 = 018wng, ?x1243 = 0gr0m, award_winner(?x8478, ?x1250), award_nominee(?x1250, ?x1958), profession(?x1250, ?x1032), award_winner(?x458, ?x1250), ?x484 = 0gq_v, ?x1703 = 0k611, spouse(?x1250, ?x1607), award_nominee(?x2353, ?x1958), languages(?x1958, ?x90), ?x1079 = 0l8z1, film(?x1958, ?x224), ?x2353 = 02qgyv, ?x77 = 0gqng >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #7701 for first EXPECTED value: *> intensional similarity = 21 *> extensional distance = 5 *> proper extension: 0bzm__; *> query: (?x8478, ?x157) <- ceremony(?x1703, ?x8478), ceremony(?x1243, ?x8478), ceremony(?x720, ?x8478), ceremony(?x500, ?x8478), ceremony(?x484, ?x8478), ?x720 = 018wng, ?x1243 = 0gr0m, award_winner(?x8478, ?x1250), award_winner(?x8478, ?x538), award_nominee(?x1250, ?x1958), award_nominee(?x1250, ?x157), profession(?x1250, ?x1032), award_winner(?x458, ?x1250), ?x484 = 0gq_v, ?x1703 = 0k611, music(?x2755, ?x538), award_nominee(?x538, ?x6011), film(?x1958, ?x224), award(?x382, ?x500), ?x6011 = 02zft0, nominated_for(?x500, ?x144) *> conf = 0.08 ranks of expected_values: 266 EVAL 0bzjgq award_winner 0829rj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 41.000 28.000 0.333 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #167-017yfz PRED entity: 017yfz PRED relation: artist! PRED expected values: 041n43 => 119 concepts (119 used for prediction) PRED predicted values (max 10 best out of 110): 03rhqg (0.45 #571, 0.29 #1683, 0.26 #2101), 015_1q (0.35 #1270, 0.32 #1687, 0.27 #575), 0fb0v (0.22 #980, 0.17 #1119, 0.07 #1953), 0g768 (0.20 #871, 0.17 #1288, 0.13 #2679), 01clyr (0.18 #589, 0.12 #1979, 0.09 #2119), 0mcf4 (0.17 #1309, 0.09 #3395, 0.07 #3951), 02p3cr5 (0.17 #1000, 0.13 #1139, 0.07 #1695), 016ckq (0.16 #2684, 0.11 #1710, 0.06 #1015), 03mp8k (0.15 #2708, 0.13 #900, 0.11 #1734), 01w40h (0.13 #2670, 0.11 #1001, 0.10 #6702) >> Best rule #571 for best value: >> intensional similarity = 4 >> extensional distance = 9 >> proper extension: 01vvyfh; >> query: (?x4142, 03rhqg) <- artists(?x505, ?x4142), ?x505 = 03_d0, gender(?x4142, ?x231), influenced_by(?x4142, ?x120) >> conf = 0.45 => this is the best rule for 1 predicted values *> Best rule #2198 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 02z4b_8; *> query: (?x4142, 041n43) <- artists(?x505, ?x4142), ?x505 = 03_d0, profession(?x4142, ?x2348), ?x2348 = 0nbcg *> conf = 0.04 ranks of expected_values: 64 EVAL 017yfz artist! 041n43 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 119.000 119.000 0.455 http://example.org/music/record_label/artist #166-01vvyd8 PRED entity: 01vvyd8 PRED relation: type_of_union PRED expected values: 04ztj => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 3): 04ztj (0.87 #57, 0.87 #77, 0.86 #41), 01g63y (0.18 #10, 0.17 #34, 0.17 #130), 0jgjn (0.01 #40, 0.01 #52, 0.01 #60) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 173 >> proper extension: 01l_vgt; >> query: (?x6231, 04ztj) <- location_of_ceremony(?x6231, ?x3415), location(?x772, ?x3415), place_of_birth(?x666, ?x3415) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01vvyd8 type_of_union 04ztj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.874 http://example.org/people/person/spouse_s./people/marriage/type_of_union #165-0969fd PRED entity: 0969fd PRED relation: influenced_by PRED expected values: 081k8 => 140 concepts (55 used for prediction) PRED predicted values (max 10 best out of 323): 02wh0 (0.23 #9900, 0.17 #2543, 0.15 #7736), 03sbs (0.22 #9741, 0.21 #1087, 0.21 #7577), 05qmj (0.21 #1057, 0.20 #9711, 0.17 #2786), 03_87 (0.21 #1067, 0.18 #6691, 0.18 #7124), 039n1 (0.21 #1190, 0.14 #2487, 0.12 #2919), 0gz_ (0.20 #9622, 0.15 #8323, 0.15 #2697), 032l1 (0.19 #9610, 0.16 #5716, 0.13 #7446), 0j3v (0.18 #9581, 0.14 #927, 0.14 #2224), 081k8 (0.17 #2317, 0.16 #9674, 0.13 #7077), 042q3 (0.16 #9882, 0.14 #2525, 0.10 #7285) >> Best rule #9900 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 0gz_; >> query: (?x10677, 02wh0) <- influenced_by(?x10677, ?x10110), profession(?x10677, ?x353), interests(?x10110, ?x713) >> conf = 0.23 => this is the best rule for 1 predicted values *> Best rule #2317 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 27 *> proper extension: 09bg4l; 0ct9_; 06myp; 02wh0; *> query: (?x10677, 081k8) <- people(?x3799, ?x10677), influenced_by(?x10677, ?x3428), company(?x10677, ?x3424) *> conf = 0.17 ranks of expected_values: 9 EVAL 0969fd influenced_by 081k8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 140.000 55.000 0.225 http://example.org/influence/influence_node/influenced_by #164-05tk7y PRED entity: 05tk7y PRED relation: nominated_for PRED expected values: 07f_t4 => 49 concepts (14 used for prediction) PRED predicted values (max 10 best out of 136): 0828jw (0.46 #8114, 0.41 #12982, 0.02 #7402), 01l_pn (0.30 #3246, 0.26 #16231, 0.24 #22727), 07f_t4 (0.30 #3246, 0.26 #16231, 0.24 #22727), 047p7fr (0.30 #3246, 0.26 #16231, 0.24 #22727), 01y9jr (0.18 #1051), 03y0pn (0.10 #2743), 011yg9 (0.10 #2557), 0d68qy (0.06 #373, 0.03 #5240, 0.03 #6862), 08r4x3 (0.06 #144, 0.02 #5011, 0.02 #13126), 01g03q (0.06 #1396, 0.02 #7885, 0.02 #12756) >> Best rule #8114 for best value: >> intensional similarity = 3 >> extensional distance = 598 >> proper extension: 045w_4; 026dd2b; >> query: (?x1550, ?x5810) <- award_nominee(?x1550, ?x380), award_nominee(?x380, ?x381), actor(?x5810, ?x1550) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #3246 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 19 *> proper extension: 05vsxz; 02zq43; 0159h6; 0h5g_; 03f1zdw; 01yhvv; 09y20; 01l2fn; 07hbxm; 02cllz; ... *> query: (?x1550, ?x2029) <- award_nominee(?x1550, ?x380), ?x380 = 0m2wm, film(?x1550, ?x2029) *> conf = 0.30 ranks of expected_values: 3 EVAL 05tk7y nominated_for 07f_t4 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 49.000 14.000 0.465 http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for #163-01xllf PRED entity: 01xllf PRED relation: film PRED expected values: 02rrfzf => 50 concepts (31 used for prediction) PRED predicted values (max 10 best out of 524): 09g8vhw (0.33 #325, 0.18 #3887, 0.14 #2106), 01shy7 (0.29 #2203, 0.03 #28920, 0.02 #48511), 0h95927 (0.18 #4881, 0.17 #1319, 0.02 #13786), 06_wqk4 (0.17 #7250, 0.17 #126, 0.15 #10812), 03q0r1 (0.17 #634, 0.14 #2415, 0.09 #4196), 033fqh (0.17 #837, 0.14 #2618, 0.09 #4399), 0fpgp26 (0.17 #6872, 0.14 #3310), 051ys82 (0.17 #1034, 0.09 #4596, 0.08 #16030), 0407yj_ (0.17 #482, 0.09 #4044, 0.08 #7606), 02sfnv (0.17 #895, 0.09 #4457, 0.08 #8019) >> Best rule #325 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 06cgy; 01pgzn_; 01yf85; 029k55; >> query: (?x10126, 09g8vhw) <- profession(?x10126, ?x319), film(?x10126, ?x6806), film(?x10126, ?x2102), ?x6806 = 02q7yfq, region(?x2102, ?x512) >> conf = 0.33 => this is the best rule for 1 predicted values *> Best rule #544 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 4 *> proper extension: 06cgy; 01pgzn_; 01yf85; 029k55; *> query: (?x10126, 02rrfzf) <- profession(?x10126, ?x319), film(?x10126, ?x6806), film(?x10126, ?x2102), ?x6806 = 02q7yfq, region(?x2102, ?x512) *> conf = 0.17 ranks of expected_values: 15 EVAL 01xllf film 02rrfzf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 50.000 31.000 0.333 http://example.org/film/actor/film./film/performance/film #162-043d4 PRED entity: 043d4 PRED relation: place_of_death PRED expected values: 0fhp9 => 127 concepts (127 used for prediction) PRED predicted values (max 10 best out of 67): 0fhp9 (0.29 #389, 0.29 #208, 0.25 #791), 0d58_ (0.17 #128, 0.12 #517, 0.10 #711), 02_286 (0.17 #13, 0.08 #2344, 0.08 #1567), 04jpl (0.14 #201, 0.12 #396, 0.11 #2143), 0k049 (0.14 #975, 0.12 #392, 0.10 #586), 030qb3t (0.14 #994, 0.10 #605, 0.10 #1382), 0cpyv (0.14 #262, 0.04 #2788, 0.04 #3370), 04swd (0.13 #1286, 0.06 #1868, 0.04 #3034), 05qtj (0.13 #2395, 0.11 #3366, 0.09 #5697), 06pr6 (0.10 #688, 0.02 #3795, 0.02 #3989) >> Best rule #389 for best value: >> intensional similarity = 4 >> extensional distance = 5 >> proper extension: 0h336; >> query: (?x7559, ?x863) <- influenced_by(?x11512, ?x7559), influenced_by(?x7559, ?x5600), place_of_death(?x11512, ?x863), ?x863 = 0fhp9 >> conf = 0.29 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 043d4 place_of_death 0fhp9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 127.000 127.000 0.286 http://example.org/people/deceased_person/place_of_death #161-0f2s6 PRED entity: 0f2s6 PRED relation: category PRED expected values: 08mbj5d => 144 concepts (144 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.83 #27, 0.82 #36, 0.81 #3) >> Best rule #27 for best value: >> intensional similarity = 3 >> extensional distance = 107 >> proper extension: 016v46; 02_n7; 0fttg; 01mgsn; >> query: (?x9713, 08mbj5d) <- administrative_division(?x9713, ?x9712), place_of_birth(?x838, ?x9713), time_zones(?x9713, ?x1638) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0f2s6 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 144.000 144.000 0.835 http://example.org/common/topic/webpage./common/webpage/category #160-05dptj PRED entity: 05dptj PRED relation: genre PRED expected values: 04xvh5 => 82 concepts (66 used for prediction) PRED predicted values (max 10 best out of 165): 04xvlr (0.70 #709, 0.60 #2723, 0.57 #2722), 01z4y (0.70 #709, 0.57 #2722, 0.51 #4858), 04xvh5 (0.44 #32, 0.17 #622, 0.17 #504), 03k9fj (0.33 #10, 0.30 #128, 0.29 #837), 01jfsb (0.33 #3328, 0.31 #2137, 0.30 #1192), 02kdv5l (0.33 #3319, 0.31 #711, 0.31 #1183), 0lsxr (0.26 #243, 0.21 #597, 0.20 #1188), 01t_vv (0.22 #52, 0.18 #1587, 0.18 #1705), 0hcr (0.20 #139, 0.11 #21, 0.08 #1674), 04t36 (0.20 #122, 0.10 #1893, 0.09 #1775) >> Best rule #709 for best value: >> intensional similarity = 5 >> extensional distance = 143 >> proper extension: 08cfr1; >> query: (?x7671, ?x162) <- genre(?x7671, ?x1509), genre(?x7671, ?x53), ?x1509 = 060__y, ?x53 = 07s9rl0, titles(?x162, ?x7671) >> conf = 0.70 => this is the best rule for 2 predicted values *> Best rule #32 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 7 *> proper extension: 09fqgj; 0fzm0g; *> query: (?x7671, 04xvh5) <- film_crew_role(?x7671, ?x137), country(?x7671, ?x94), film(?x11985, ?x7671), ?x11985 = 01vh3r *> conf = 0.44 ranks of expected_values: 3 EVAL 05dptj genre 04xvh5 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 82.000 66.000 0.700 http://example.org/film/film/genre #159-03t5n3 PRED entity: 03t5n3 PRED relation: award! PRED expected values: 04n2vgk => 41 concepts (20 used for prediction) PRED predicted values (max 10 best out of 2321): 016kjs (0.82 #23526, 0.79 #20165, 0.79 #36969), 01vw20h (0.82 #23526, 0.79 #20165, 0.79 #36969), 06mt91 (0.82 #23526, 0.79 #20165, 0.79 #36969), 026yqrr (0.82 #23526, 0.79 #20165, 0.79 #36969), 04mn81 (0.82 #23526, 0.79 #20165, 0.79 #36969), 0gbwp (0.71 #14552, 0.60 #7830, 0.50 #24635), 016pns (0.71 #10885, 0.38 #17607, 0.33 #4163), 0478__m (0.62 #18125, 0.60 #8042, 0.57 #14764), 01vs_v8 (0.62 #24106, 0.60 #7301, 0.57 #14023), 02z4b_8 (0.60 #8780, 0.53 #22224, 0.50 #25585) >> Best rule #23526 for best value: >> intensional similarity = 3 >> extensional distance = 13 >> proper extension: 0c4z8; 01c427; 031b3h; 01cw7s; >> query: (?x5799, ?x1125) <- award(?x6383, ?x5799), ?x6383 = 0g824, award_winner(?x5799, ?x1125) >> conf = 0.82 => this is the best rule for 5 predicted values *> Best rule #10082 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 3 *> proper extension: 02f716; 02f72_; *> query: (?x5799, ?x1989) <- award(?x6383, ?x5799), award(?x4851, ?x5799), award(?x2732, ?x5799), ?x6383 = 0g824, ?x2732 = 01wgxtl, award_nominee(?x4851, ?x1989) *> conf = 0.26 ranks of expected_values: 235 EVAL 03t5n3 award! 04n2vgk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 41.000 20.000 0.815 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #158-04ydr95 PRED entity: 04ydr95 PRED relation: executive_produced_by PRED expected values: 03v1w7 => 88 concepts (83 used for prediction) PRED predicted values (max 10 best out of 104): 02lfwp (0.25 #746, 0.14 #1000, 0.01 #1508), 05hj_k (0.25 #352, 0.08 #1878, 0.07 #1114), 06q8hf (0.25 #421, 0.06 #1947, 0.05 #2453), 079vf (0.11 #2036, 0.05 #5858, 0.04 #7886), 0glyyw (0.07 #6550, 0.04 #8580, 0.03 #8073), 06pj8 (0.07 #1071, 0.07 #2089, 0.06 #1326), 0343h (0.04 #2076, 0.03 #5898, 0.02 #7673), 03c9pqt (0.03 #1263, 0.03 #9913, 0.03 #2533), 0gg9_5q (0.03 #1106, 0.02 #6959, 0.02 #7466), 05txrz (0.03 #1120, 0.02 #3412, 0.01 #1375) >> Best rule #746 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0gxtknx; 01xq8v; >> query: (?x3532, 02lfwp) <- film(?x5391, ?x3532), ?x5391 = 03h_fqv, film_release_region(?x3532, ?x94), crewmember(?x3532, ?x5664) >> conf = 0.25 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 04ydr95 executive_produced_by 03v1w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 88.000 83.000 0.250 http://example.org/film/film/executive_produced_by #157-04pk1f PRED entity: 04pk1f PRED relation: category PRED expected values: 08mbj5d => 86 concepts (86 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.41 #12, 0.36 #2, 0.35 #15) >> Best rule #12 for best value: >> intensional similarity = 4 >> extensional distance = 120 >> proper extension: 04nlb94; >> query: (?x6078, 08mbj5d) <- film_crew_role(?x6078, ?x137), genre(?x6078, ?x258), nominated_for(?x143, ?x6078), film_distribution_medium(?x6078, ?x2099) >> conf = 0.41 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04pk1f category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 86.000 86.000 0.410 http://example.org/common/topic/webpage./common/webpage/category #156-0187x8 PRED entity: 0187x8 PRED relation: group! PRED expected values: 0l14md => 55 concepts (55 used for prediction) PRED predicted values (max 10 best out of 95): 0l14md (0.64 #258, 0.57 #679, 0.55 #426), 028tv0 (0.40 #263, 0.38 #431, 0.36 #684), 0l14j_ (0.30 #132, 0.21 #300, 0.11 #721), 01vj9c (0.27 #685, 0.25 #432, 0.24 #264), 07y_7 (0.20 #86, 0.19 #254, 0.11 #675), 06ncr (0.20 #120, 0.14 #709, 0.14 #288), 01v1d8 (0.20 #136, 0.07 #304, 0.04 #725), 013y1f (0.19 #277, 0.13 #698, 0.13 #445), 042v_gx (0.12 #7, 0.12 #259, 0.10 #680), 02sgy (0.12 #5, 0.12 #257, 0.05 #509) >> Best rule #258 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 01t_xp_; 067mj; 0dtd6; 01czx; 0167_s; 05563d; 0fcsd; 013w2r; 02vgh; 01kcms4; ... >> query: (?x7810, 0l14md) <- group(?x227, ?x7810), artists(?x1380, ?x7810), ?x1380 = 0dl5d >> conf = 0.64 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0187x8 group! 0l14md CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 55.000 55.000 0.643 http://example.org/music/performance_role/regular_performances./music/group_membership/group #155-01fbr2 PRED entity: 01fbr2 PRED relation: artists PRED expected values: 01qdjm 014g91 => 36 concepts (14 used for prediction) PRED predicted values (max 10 best out of 1101): 07z542 (0.50 #1179, 0.33 #106, 0.15 #3330), 0fpjd_g (0.40 #3334, 0.25 #1183, 0.14 #2256), 01dhjz (0.40 #4059, 0.25 #1908, 0.09 #13973), 01gg59 (0.37 #4630, 0.15 #3557, 0.11 #10006), 01vsy95 (0.35 #3506, 0.25 #1355, 0.10 #4579), 03j0br4 (0.35 #3421, 0.11 #5567, 0.09 #6645), 02mslq (0.35 #3256, 0.09 #5402, 0.09 #13973), 04zwjd (0.33 #137, 0.25 #1210, 0.15 #3361), 0f6lx (0.33 #826, 0.25 #1899, 0.10 #4050), 032nwy (0.30 #3250, 0.25 #1099, 0.09 #13973) >> Best rule #1179 for best value: >> intensional similarity = 9 >> extensional distance = 2 >> proper extension: 0gt_0v; >> query: (?x5355, 07z542) <- artists(?x5355, ?x12304), artists(?x5355, ?x3378), artists(?x5355, ?x3171), ?x12304 = 02pbrn, instrumentalists(?x1495, ?x3171), ?x1495 = 013y1f, ?x3378 = 01lcxbb, artist(?x8489, ?x3171), profession(?x3171, ?x353) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #1289 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 2 *> proper extension: 0gt_0v; *> query: (?x5355, 01qdjm) <- artists(?x5355, ?x12304), artists(?x5355, ?x3378), artists(?x5355, ?x3171), ?x12304 = 02pbrn, instrumentalists(?x1495, ?x3171), ?x1495 = 013y1f, ?x3378 = 01lcxbb, artist(?x8489, ?x3171), profession(?x3171, ?x353) *> conf = 0.25 ranks of expected_values: 34, 37 EVAL 01fbr2 artists 014g91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.028 36.000 14.000 0.500 http://example.org/music/genre/artists EVAL 01fbr2 artists 01qdjm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 36.000 14.000 0.500 http://example.org/music/genre/artists #154-0gmf0nj PRED entity: 0gmf0nj PRED relation: category PRED expected values: 08mbj5d => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.88 #8, 0.86 #7, 0.85 #71) >> Best rule #8 for best value: >> intensional similarity = 5 >> extensional distance = 86 >> proper extension: 07w6r; >> query: (?x12208, 08mbj5d) <- citytown(?x12208, ?x2277), administrative_division(?x2277, ?x3038), state(?x3037, ?x3038), country(?x3038, ?x94), contains(?x3038, ?x2410) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0gmf0nj category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 142.000 142.000 0.875 http://example.org/common/topic/webpage./common/webpage/category #153-01xq8v PRED entity: 01xq8v PRED relation: currency PRED expected values: 09nqf => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.86 #50, 0.85 #106, 0.84 #120), 01nv4h (0.17 #2, 0.03 #289, 0.03 #30), 088n7 (0.04 #21, 0.03 #28), 02gsvk (0.02 #69, 0.02 #76, 0.01 #41), 02l6h (0.02 #74, 0.01 #32, 0.01 #151) >> Best rule #50 for best value: >> intensional similarity = 4 >> extensional distance = 96 >> proper extension: 05jf85; >> query: (?x7741, 09nqf) <- language(?x7741, ?x90), nominated_for(?x640, ?x7741), ?x640 = 02hsq3m, film(?x450, ?x7741) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01xq8v currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.857 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #152-02mw6c PRED entity: 02mw6c PRED relation: colors PRED expected values: 02rnmb => 180 concepts (180 used for prediction) PRED predicted values (max 10 best out of 18): 01g5v (0.58 #255, 0.56 #1011, 0.40 #273), 083jv (0.46 #199, 0.39 #1045, 0.38 #1387), 06fvc (0.38 #200, 0.32 #254, 0.28 #308), 09ggk (0.33 #32, 0.25 #104, 0.10 #536), 09q2t (0.25 #66, 0.25 #48, 0.20 #120), 04mkbj (0.25 #62, 0.20 #152, 0.20 #116), 02rnmb (0.25 #47, 0.07 #515, 0.07 #2053), 088fh (0.16 #257, 0.13 #221, 0.10 #275), 038hg (0.15 #208, 0.10 #280, 0.10 #1630), 03wkwg (0.12 #409, 0.11 #481, 0.10 #157) >> Best rule #255 for best value: >> intensional similarity = 5 >> extensional distance = 17 >> proper extension: 02hmw9; 026m3y; 07tlg; >> query: (?x11350, 01g5v) <- student(?x11350, ?x3028), colors(?x11350, ?x332), contains(?x1310, ?x11350), ?x1310 = 02jx1, nominated_for(?x3028, ?x972) >> conf = 0.58 => this is the best rule for 1 predicted values *> Best rule #47 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 2 *> proper extension: 0173s9; *> query: (?x11350, 02rnmb) <- school_type(?x11350, ?x12633), state_province_region(?x11350, ?x10603), colors(?x11350, ?x332), ?x12633 = 01jlsn *> conf = 0.25 ranks of expected_values: 7 EVAL 02mw6c colors 02rnmb CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 180.000 180.000 0.579 http://example.org/education/educational_institution/colors #151-021q1c PRED entity: 021q1c PRED relation: company PRED expected values: 01k7xz 03ksy 025v3k 017j69 02gnh0 => 32 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1993): 0300cp (0.55 #4240, 0.53 #6188, 0.42 #7156), 060ppp (0.53 #6373, 0.45 #4425, 0.42 #7341), 09c7w0 (0.50 #1286, 0.20 #7434, 0.20 #2906), 03s7h (0.47 #6390, 0.46 #8657, 0.45 #8335), 019rl6 (0.47 #6287, 0.45 #4339, 0.37 #7255), 087c7 (0.47 #6148, 0.45 #4200, 0.37 #7116), 02r5dz (0.47 #6206, 0.37 #7174, 0.36 #8151), 07xyn1 (0.47 #6313, 0.37 #7281, 0.36 #4365), 0z90c (0.45 #4351, 0.40 #6299, 0.32 #8244), 0vlf (0.45 #4468, 0.40 #6416, 0.32 #7384) >> Best rule #4240 for best value: >> intensional similarity = 12 >> extensional distance = 9 >> proper extension: 02k13d; 0dq_5; 014l7h; >> query: (?x3131, 0300cp) <- company(?x3131, ?x3424), company(?x3131, ?x3132), company(?x3131, ?x581), list(?x3424, ?x2197), company(?x346, ?x3424), organization(?x5510, ?x3132), citytown(?x3424, ?x739), ?x346 = 060c4, category(?x581, ?x134), ?x739 = 02_286, citytown(?x581, ?x13529), company(?x920, ?x3424) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #2326 for first EXPECTED value: *> intensional similarity = 13 *> extensional distance = 7 *> proper extension: 02md_2; *> query: (?x3131, 03ksy) <- company(?x3131, ?x6056), company(?x3131, ?x3132), company(?x3131, ?x581), major_field_of_study(?x581, ?x3440), major_field_of_study(?x581, ?x1695), category(?x3132, ?x134), ?x3440 = 0jjw, school(?x580, ?x581), student(?x581, ?x1299), school_type(?x581, ?x1044), student(?x1695, ?x3806), contains(?x94, ?x6056), major_field_of_study(?x734, ?x1695) *> conf = 0.33 ranks of expected_values: 53, 410, 431, 481, 557 EVAL 021q1c company 02gnh0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 30.000 0.545 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 021q1c company 017j69 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 30.000 0.545 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 021q1c company 025v3k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 30.000 0.545 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 021q1c company 03ksy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 32.000 30.000 0.545 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company EVAL 021q1c company 01k7xz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 32.000 30.000 0.545 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #150-03zqc1 PRED entity: 03zqc1 PRED relation: award_nominee PRED expected values: 03w4sh => 117 concepts (73 used for prediction) PRED predicted values (max 10 best out of 1212): 0443y3 (0.83 #4646, 0.81 #111512, 0.81 #153327), 07z1_q (0.83 #4646, 0.81 #146357, 0.81 #146358), 048hf (0.83 #4646, 0.81 #146357, 0.81 #146358), 01rs5p (0.83 #4646, 0.81 #146357, 0.81 #146358), 06hgym (0.83 #4646, 0.81 #146357, 0.81 #146358), 03x16f (0.83 #4646, 0.81 #146357, 0.81 #146358), 03zqc1 (0.80 #4741, 0.50 #2417, 0.17 #94), 030znt (0.79 #116161, 0.77 #116160, 0.76 #137067), 03w4sh (0.44 #3808, 0.35 #6132, 0.17 #1485), 027n4zv (0.39 #4166, 0.02 #13457, 0.02 #18102) >> Best rule #4646 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 05dxl5; >> query: (?x516, ?x336) <- award_nominee(?x3051, ?x516), award_nominee(?x336, ?x516), award_winner(?x516, ?x2578), ?x3051 = 0gd_b_ >> conf = 0.83 => this is the best rule for 6 predicted values *> Best rule #3808 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 16 *> proper extension: 05dxl5; *> query: (?x516, 03w4sh) <- award_nominee(?x3051, ?x516), award_winner(?x516, ?x2578), ?x3051 = 0gd_b_ *> conf = 0.44 ranks of expected_values: 9 EVAL 03zqc1 award_nominee 03w4sh CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 117.000 73.000 0.835 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #149-0gj96ln PRED entity: 0gj96ln PRED relation: film! PRED expected values: 017s11 => 58 concepts (58 used for prediction) PRED predicted values (max 10 best out of 58): 03xq0f (0.50 #80, 0.23 #230, 0.18 #605), 01795t (0.35 #243, 0.25 #93, 0.13 #618), 017s11 (0.33 #153, 0.33 #3, 0.25 #78), 03rwz3 (0.25 #119, 0.08 #494, 0.07 #869), 054g1r (0.21 #260, 0.14 #335, 0.08 #1386), 05qd_ (0.20 #309, 0.17 #234, 0.17 #159), 016tw3 (0.17 #311, 0.14 #686, 0.13 #2337), 086k8 (0.17 #152, 0.15 #1728, 0.14 #3228), 01gb54 (0.17 #179, 0.12 #329, 0.08 #254), 024rgt (0.17 #170, 0.05 #470, 0.05 #395) >> Best rule #80 for best value: >> intensional similarity = 7 >> extensional distance = 2 >> proper extension: 02bj22; >> query: (?x6168, 03xq0f) <- film(?x6577, ?x6168), film(?x4625, ?x6168), film(?x4263, ?x6168), ?x4263 = 02yplc, friend(?x6577, ?x1462), people(?x1050, ?x4625), award(?x4625, ?x757) >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #153 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 4 *> proper extension: 03rtz1; 03tbg6; *> query: (?x6168, 017s11) <- film(?x4263, ?x6168), film(?x1787, ?x6168), music(?x6168, ?x5508), profession(?x4263, ?x1032), ?x1787 = 02jm0n, language(?x6168, ?x254) *> conf = 0.33 ranks of expected_values: 3 EVAL 0gj96ln film! 017s11 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 58.000 58.000 0.500 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #148-0g9z_32 PRED entity: 0g9z_32 PRED relation: produced_by PRED expected values: 0fvf9q => 87 concepts (72 used for prediction) PRED predicted values (max 10 best out of 181): 0603qp (0.55 #11141, 0.53 #5378, 0.52 #11911), 030g9z (0.20 #304, 0.11 #1456), 02mc79 (0.20 #275, 0.11 #1427), 0fvf9q (0.20 #390, 0.05 #5000, 0.04 #9610), 0mdqp (0.20 #411, 0.03 #3099, 0.02 #2716), 076_74 (0.20 #131, 0.01 #3588, 0.01 #7047), 0gyx4 (0.20 #540, 0.01 #11681), 02tn0_ (0.17 #1093, 0.01 #3782, 0.01 #4935), 0c6qh (0.17 #849, 0.01 #3538), 040rjq (0.17 #1137) >> Best rule #11141 for best value: >> intensional similarity = 4 >> extensional distance = 442 >> proper extension: 02q3fdr; 0hv81; 0g5qmbz; >> query: (?x7311, ?x5643) <- produced_by(?x7311, ?x2790), film(?x5643, ?x7311), award(?x5643, ?x2016), country(?x7311, ?x94) >> conf = 0.55 => this is the best rule for 1 predicted values *> Best rule #390 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 3 *> proper extension: 0g_zyp; *> query: (?x7311, 0fvf9q) <- film(?x8163, ?x7311), film_release_distribution_medium(?x7311, ?x81), ?x81 = 029j_, ?x8163 = 02z3zp *> conf = 0.20 ranks of expected_values: 4 EVAL 0g9z_32 produced_by 0fvf9q CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 87.000 72.000 0.555 http://example.org/film/film/produced_by #147-0fpj4lx PRED entity: 0fpj4lx PRED relation: category PRED expected values: 08mbj5d => 134 concepts (134 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #42, 0.84 #31, 0.82 #79) >> Best rule #42 for best value: >> intensional similarity = 5 >> extensional distance = 197 >> proper extension: 04l19_; >> query: (?x3740, 08mbj5d) <- profession(?x3740, ?x1032), artist(?x2299, ?x3740), place_of_birth(?x3740, ?x739), profession(?x13842, ?x1032), ?x13842 = 095nx >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fpj4lx category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 134.000 134.000 0.849 http://example.org/common/topic/webpage./common/webpage/category #146-0ywrc PRED entity: 0ywrc PRED relation: titles! PRED expected values: 04xvlr => 112 concepts (34 used for prediction) PRED predicted values (max 10 best out of 64): 04xvlr (0.45 #3, 0.36 #1656, 0.27 #1946), 024qqx (0.27 #75, 0.13 #1144, 0.11 #1436), 01z4y (0.24 #1879, 0.21 #2754, 0.21 #2950), 03bxz7 (0.20 #291, 0.16 #2917, 0.16 #2526), 01jfsb (0.18 #2254, 0.16 #2936, 0.15 #794), 07c52 (0.17 #414, 0.15 #2651, 0.14 #2553), 01hmnh (0.11 #1385, 0.11 #2260, 0.10 #606), 09blyk (0.11 #527, 0.09 #2278, 0.08 #1306), 04t36 (0.09 #394, 0.07 #3216, 0.06 #1076), 02n4kr (0.09 #205, 0.07 #2248, 0.06 #2930) >> Best rule #3 for best value: >> intensional similarity = 5 >> extensional distance = 9 >> proper extension: 049xgc; >> query: (?x3157, 04xvlr) <- nominated_for(?x746, ?x3157), nominated_for(?x640, ?x3157), ?x746 = 04dn09n, ?x640 = 02hsq3m, titles(?x53, ?x3157) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0ywrc titles! 04xvlr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 112.000 34.000 0.455 http://example.org/media_common/netflix_genre/titles #145-06qwh PRED entity: 06qwh PRED relation: languages PRED expected values: 02h40lc => 84 concepts (84 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.93 #179, 0.92 #146, 0.91 #135), 03_9r (0.17 #498, 0.12 #170, 0.10 #203), 0653m (0.17 #498), 06nm1 (0.13 #160, 0.12 #49, 0.08 #72), 0t_2 (0.10 #17, 0.05 #249, 0.04 #293), 064_8sq (0.08 #162, 0.06 #51, 0.05 #173), 02bv9 (0.06 #53, 0.05 #164, 0.05 #175), 04306rv (0.06 #47, 0.05 #158, 0.05 #169), 02bjrlw (0.06 #45, 0.05 #156, 0.05 #167), 05zjd (0.06 #52, 0.04 #75, 0.03 #163) >> Best rule #179 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 0dsx3f; >> query: (?x7488, 02h40lc) <- nominated_for(?x4921, ?x7488), award(?x10160, ?x4921), ?x10160 = 06y9bd, award(?x687, ?x4921), ceremony(?x4921, ?x2126) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06qwh languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 84.000 84.000 0.929 http://example.org/tv/tv_program/languages #144-06j8q_ PRED entity: 06j8q_ PRED relation: profession PRED expected values: 03gjzk => 137 concepts (72 used for prediction) PRED predicted values (max 10 best out of 54): 03gjzk (0.68 #607, 0.62 #1051, 0.59 #163), 0dxtg (0.63 #606, 0.56 #310, 0.55 #1050), 02jknp (0.53 #304, 0.51 #600, 0.51 #1044), 01d_h8 (0.52 #302, 0.51 #154, 0.49 #598), 018gz8 (0.27 #461, 0.24 #165, 0.21 #313), 0np9r (0.23 #169, 0.22 #465, 0.19 #317), 09jwl (0.19 #1648, 0.18 #6385, 0.18 #6681), 0cbd2 (0.16 #2524, 0.14 #599, 0.14 #1043), 016z4k (0.12 #1633, 0.11 #6370, 0.11 #6666), 0nbcg (0.11 #1660, 0.11 #6397, 0.11 #6693) >> Best rule #607 for best value: >> intensional similarity = 4 >> extensional distance = 132 >> proper extension: 06j0md; 01f7j9; 0bgrsl; 078jt5; 03m_k0; 02q5xsx; 06jnvs; 04m_zp; 03fg0r; 09v6gc9; ... >> query: (?x10696, 03gjzk) <- profession(?x10696, ?x1943), award(?x10696, ?x783), award_winner(?x687, ?x10696), ?x1943 = 02krf9 >> conf = 0.68 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06j8q_ profession 03gjzk CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 72.000 0.679 http://example.org/people/person/profession #143-0cfz_z PRED entity: 0cfz_z PRED relation: location PRED expected values: 0cw4l => 128 concepts (57 used for prediction) PRED predicted values (max 10 best out of 150): 04vmp (0.26 #11269, 0.17 #4025, 0.17 #3574), 0hj6h (0.26 #11269, 0.08 #3825, 0.05 #6240), 02_286 (0.20 #841, 0.17 #16136, 0.17 #2452), 01b8jj (0.18 #2202, 0.07 #7838, 0.07 #8642), 04jpl (0.17 #8066, 0.07 #12092, 0.07 #11287), 04ykg (0.12 #4898, 0.11 #68, 0.08 #6508), 030qb3t (0.12 #9742, 0.11 #12963, 0.10 #8938), 0cvw9 (0.11 #398, 0.08 #3618, 0.06 #5228), 0fpzwf (0.11 #282, 0.06 #5112, 0.04 #6722), 06_kh (0.11 #11, 0.04 #7256, 0.02 #8866) >> Best rule #11269 for best value: >> intensional similarity = 5 >> extensional distance = 48 >> proper extension: 01j5ts; 023tp8; 0kr5_; 032_jg; 0151w_; 0h1mt; 040wdl; 06t61y; 013v5j; 01n8_g; ... >> query: (?x12098, ?x7412) <- sibling(?x11260, ?x12098), profession(?x12098, ?x1032), nationality(?x12098, ?x2146), ?x1032 = 02hrh1q, location(?x11260, ?x7412) >> conf = 0.26 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0cfz_z location 0cw4l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 128.000 57.000 0.258 http://example.org/people/person/places_lived./people/place_lived/location #142-07y9k PRED entity: 07y9k PRED relation: team PRED expected values: 01cw24 035qgm 02pp1 035s37 03262k => 9 concepts (9 used for prediction) PRED predicted values (max 10 best out of 781): 01rlz4 (0.42 #140, 0.38 #142, 0.38 #141), 01rlzn (0.42 #140, 0.38 #142, 0.38 #141), 0cj_v7 (0.42 #140, 0.38 #142, 0.38 #141), 02b2np (0.42 #140, 0.38 #142, 0.38 #141), 0j2pg (0.42 #140, 0.38 #142, 0.38 #141), 0182r9 (0.42 #140, 0.38 #142, 0.38 #141), 01xn7x1 (0.42 #140, 0.38 #142, 0.38 #141), 014nzp (0.42 #140, 0.38 #142, 0.38 #141), 01dtl (0.42 #140, 0.38 #142, 0.38 #141), 0199gx (0.42 #140, 0.38 #142, 0.38 #141) >> Best rule #140 for best value: >> intensional similarity = 22 >> extensional distance = 1 >> proper extension: 0h69c; >> query: (?x8594, ?x4116) <- team(?x8594, ?x13154), team(?x8594, ?x11736), team(?x8594, ?x11268), team(?x8594, ?x10788), team(?x8594, ?x9254), team(?x8594, ?x3587), team(?x3586, ?x3587), team(?x60, ?x11736), sport(?x11268, ?x471), colors(?x9254, ?x663), teams(?x2152, ?x13154), teams(?x985, ?x3587), film_release_region(?x9941, ?x985), ?x9941 = 024lt6, sports(?x358, ?x471), athlete(?x471, ?x208), teams(?x404, ?x11736), company(?x4486, ?x10788), film_release_region(?x7524, ?x2152), sport(?x4116, ?x471), country(?x471, ?x94), ?x7524 = 01cm8w >> conf = 0.42 => this is the best rule for 272 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 110, 134, 155, 185, 197 EVAL 07y9k team 03262k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 9.000 9.000 0.417 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team EVAL 07y9k team 035s37 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 9.000 9.000 0.417 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team EVAL 07y9k team 02pp1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 9.000 9.000 0.417 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team EVAL 07y9k team 035qgm CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 9.000 9.000 0.417 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team EVAL 07y9k team 01cw24 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 9.000 9.000 0.417 http://example.org/sports/sports_league/teams./sports/sports_league_participation/team #141-01kd57 PRED entity: 01kd57 PRED relation: award PRED expected values: 01by1l => 129 concepts (115 used for prediction) PRED predicted values (max 10 best out of 285): 03tcnt (0.81 #8824, 0.80 #23667, 0.79 #12034), 01by1l (0.65 #1716, 0.49 #2117, 0.47 #4122), 0c4z8 (0.47 #2077, 0.42 #1676, 0.27 #6087), 01c92g (0.40 #2103, 0.31 #899, 0.27 #1301), 054krc (0.39 #4900, 0.24 #3296, 0.15 #889), 0gqz2 (0.35 #3289, 0.35 #4893, 0.27 #1685), 054ks3 (0.35 #2147, 0.31 #4954, 0.31 #3350), 02qvyrt (0.34 #4939, 0.31 #3335, 0.17 #2934), 02x17c2 (0.33 #1422, 0.31 #1020, 0.21 #2224), 01ckrr (0.33 #229, 0.18 #4240, 0.17 #3037) >> Best rule #8824 for best value: >> intensional similarity = 4 >> extensional distance = 169 >> proper extension: 0lbj1; 01vrx3g; 0qdyf; 01bczm; >> query: (?x5543, ?x3103) <- instrumentalists(?x227, ?x5543), category(?x5543, ?x134), award_winner(?x3103, ?x5543), role(?x5543, ?x314) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #1716 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 24 *> proper extension: 01wwvd2; 02_jkc; *> query: (?x5543, 01by1l) <- award_winner(?x725, ?x5543), award(?x5543, ?x1827), ?x1827 = 02nhxf *> conf = 0.65 ranks of expected_values: 2 EVAL 01kd57 award 01by1l CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 129.000 115.000 0.805 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #140-024pcx PRED entity: 024pcx PRED relation: capital PRED expected values: 0bwtj => 199 concepts (153 used for prediction) PRED predicted values (max 10 best out of 117): 04jpl (0.38 #2168, 0.33 #124, 0.33 #4), 01q0l (0.25 #520, 0.25 #400, 0.20 #1001), 0d34_ (0.25 #459, 0.20 #939, 0.20 #699), 02z0j (0.25 #401, 0.20 #881, 0.20 #641), 05qtj (0.25 #2184, 0.17 #1463, 0.10 #2664), 0156q (0.25 #252, 0.10 #2656, 0.10 #2536), 0fhsz (0.20 #795, 0.17 #1277, 0.14 #1878), 01f62 (0.20 #733, 0.17 #1215, 0.14 #1816), 0k3p (0.20 #874, 0.14 #1837, 0.08 #2919), 07g0_ (0.20 #892, 0.14 #1855, 0.04 #4380) >> Best rule #2168 for best value: >> intensional similarity = 5 >> extensional distance = 6 >> proper extension: 0h44w; >> query: (?x9328, 04jpl) <- capital(?x9328, ?x10042), contains(?x362, ?x10042), place_of_birth(?x3754, ?x10042), location_of_ceremony(?x3849, ?x10042), vacationer(?x6226, ?x3754) >> conf = 0.38 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 024pcx capital 0bwtj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 199.000 153.000 0.375 http://example.org/location/country/capital #139-02fn5r PRED entity: 02fn5r PRED relation: award_nominee PRED expected values: 01k_r5b => 116 concepts (49 used for prediction) PRED predicted values (max 10 best out of 736): 02lk95 (0.81 #56014, 0.81 #72351, 0.81 #72352), 0ggjt (0.76 #114361, 0.76 #109691, 0.75 #112026), 01kv4mb (0.76 #114361, 0.76 #109691, 0.75 #112026), 0pmw9 (0.76 #114361, 0.76 #109691, 0.75 #112026), 01lmj3q (0.76 #114361, 0.76 #109691, 0.75 #112026), 0p_47 (0.76 #114361, 0.76 #109691, 0.75 #109692), 03cfjg (0.75 #112026, 0.39 #7002, 0.33 #3102), 01k_r5b (0.33 #3577, 0.25 #5911, 0.11 #8245), 02fn5r (0.33 #2909, 0.12 #5243, 0.11 #7577), 01xzb6 (0.33 #3580, 0.01 #19918, 0.01 #31587) >> Best rule #56014 for best value: >> intensional similarity = 4 >> extensional distance = 361 >> proper extension: 03fbc; 018ndc; 06mj4; >> query: (?x2638, ?x1795) <- award_nominee(?x12659, ?x2638), award_nominee(?x1795, ?x2638), religion(?x12659, ?x1985), artist(?x4868, ?x12659) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #3577 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1 *> proper extension: 03yf3z; *> query: (?x2638, 01k_r5b) <- award_nominee(?x12659, ?x2638), ?x12659 = 01dpsv, type_of_union(?x2638, ?x566) *> conf = 0.33 ranks of expected_values: 8 EVAL 02fn5r award_nominee 01k_r5b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 116.000 49.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #138-043djx PRED entity: 043djx PRED relation: legislative_sessions! PRED expected values: 01h7xx => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 49): 01gstn (0.83 #203, 0.83 #202, 0.82 #636), 01h7xx (0.83 #203, 0.83 #202, 0.78 #910), 043djx (0.83 #203, 0.83 #202, 0.78 #910), 01grr2 (0.83 #203, 0.83 #202, 0.78 #910), 01gsrl (0.83 #203, 0.83 #202, 0.78 #910), 01grrf (0.83 #203, 0.83 #202, 0.78 #910), 01gssz (0.83 #203, 0.83 #202, 0.78 #910), 01gssm (0.83 #203, 0.83 #202, 0.78 #910), 01gst9 (0.83 #203, 0.83 #202, 0.78 #910), 01gsry (0.83 #203, 0.83 #202, 0.78 #910) >> Best rule #203 for best value: >> intensional similarity = 43 >> extensional distance = 2 >> proper extension: 070m6c; 07p__7; >> query: (?x759, ?x6021) <- district_represented(?x759, ?x7518), district_represented(?x759, ?x7058), district_represented(?x759, ?x6895), district_represented(?x759, ?x4776), district_represented(?x759, ?x4758), district_represented(?x759, ?x4105), district_represented(?x759, ?x3778), district_represented(?x759, ?x3634), district_represented(?x759, ?x2831), district_represented(?x759, ?x1755), district_represented(?x759, ?x1025), district_represented(?x759, ?x961), district_represented(?x759, ?x448), district_represented(?x759, ?x335), district_represented(?x759, ?x177), legislative_sessions(?x759, ?x5006), ?x3634 = 07b_l, legislative_sessions(?x2860, ?x759), ?x2831 = 0gyh, ?x6895 = 05fjf, ?x3778 = 07h34, ?x177 = 05kkh, ?x1755 = 01x73, ?x1025 = 04ych, location_of_ceremony(?x566, ?x4105), religion(?x4105, ?x2591), religion(?x4105, ?x962), legislative_sessions(?x5006, ?x6021), contains(?x4105, ?x5581), ?x7518 = 026mj, ?x7058 = 050ks, ?x448 = 03v1s, ?x2591 = 0631_, ?x2860 = 0b3wk, jurisdiction_of_office(?x3959, ?x4105), ?x962 = 05sfs, ?x335 = 059rby, ?x4758 = 0vbk, major_field_of_study(?x5581, ?x1154), student(?x5581, ?x5582), ?x961 = 03s0w, school(?x2820, ?x5581), ?x4776 = 06yxd >> conf = 0.83 => this is the best rule for 10 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 043djx legislative_sessions! 01h7xx CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 31.000 31.000 0.833 http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions #137-04lhc4 PRED entity: 04lhc4 PRED relation: nominated_for! PRED expected values: 02pqp12 => 97 concepts (87 used for prediction) PRED predicted values (max 10 best out of 207): 02pqp12 (0.69 #51, 0.54 #718, 0.48 #273), 027b9k6 (0.68 #11559, 0.67 #7999, 0.67 #7998), 027571b (0.68 #11559, 0.67 #7999, 0.67 #7998), 02z1nbg (0.68 #11559, 0.67 #7999, 0.67 #7998), 02w_6xj (0.68 #11559, 0.67 #7999, 0.67 #7998), 027c924 (0.68 #11559, 0.67 #7999, 0.67 #7998), 04dn09n (0.67 #253, 0.61 #698, 0.49 #5584), 0k611 (0.66 #728, 0.62 #61, 0.52 #283), 09sb52 (0.54 #30, 0.50 #697, 0.35 #1585), 0l8z1 (0.54 #46, 0.34 #713, 0.28 #2045) >> Best rule #51 for best value: >> intensional similarity = 6 >> extensional distance = 11 >> proper extension: 09r94m; >> query: (?x6899, 02pqp12) <- nominated_for(?x6909, ?x6899), nominated_for(?x1587, ?x6899), nominated_for(?x1162, ?x6899), ?x1162 = 099c8n, ?x1587 = 02rdyk7, ?x6909 = 02qyntr >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04lhc4 nominated_for! 02pqp12 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 97.000 87.000 0.692 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #136-02y7t7 PRED entity: 02y7t7 PRED relation: company! PRED expected values: 0dq3c => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 35): 0dq_5 (0.93 #2014, 0.93 #1481, 0.91 #1305), 0dq3c (0.58 #2442, 0.56 #313, 0.50 #179), 05_wyz (0.50 #193, 0.48 #1040, 0.44 #327), 09d6p2 (0.42 #551, 0.38 #640, 0.38 #194), 01kr6k (0.38 #202, 0.33 #1093, 0.33 #559), 02211by (0.31 #626, 0.29 #760, 0.28 #1555), 04192r (0.28 #1555, 0.25 #260, 0.21 #3023), 0142rn (0.28 #1555, 0.25 #245, 0.21 #3023), 01rk91 (0.25 #90, 0.17 #3248, 0.17 #491), 02y6fz (0.22 #377, 0.17 #3609, 0.17 #3248) >> Best rule #2014 for best value: >> intensional similarity = 11 >> extensional distance = 94 >> proper extension: 049n7; 046b0s; 01xdn1; 0kk9v; 01r3kd; 01gb54; 034f0d; 0jvs0; 09j_g; 018p5f; ... >> query: (?x3379, 0dq_5) <- company(?x1907, ?x3379), company(?x1907, ?x12452), company(?x1907, ?x9517), company(?x1907, ?x8931), company(?x1907, ?x3920), company(?x1907, ?x2067), ?x12452 = 0vlf, ?x9517 = 04fv0k, ?x3920 = 09b3v, ?x2067 = 05g76, ?x8931 = 01qygl >> conf = 0.93 => this is the best rule for 1 predicted values *> Best rule #2442 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 127 *> proper extension: 09c7w0; 01w3v; 030_1m; 0gsg7; 09d5h; 01cyd5; 02hcxm; 0hm0k; 073tm9; 04gvyp; ... *> query: (?x3379, ?x4682) <- company(?x1907, ?x3379), company(?x1907, ?x12452), company(?x1907, ?x11080), company(?x1907, ?x9517), company(?x1907, ?x3920), company(?x1907, ?x3578), ?x12452 = 0vlf, ?x9517 = 04fv0k, list(?x3578, ?x7472), production_companies(?x148, ?x3920), organization(?x4682, ?x11080) *> conf = 0.58 ranks of expected_values: 2 EVAL 02y7t7 company! 0dq3c CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 120.000 120.000 0.927 http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company #135-0cc5mcj PRED entity: 0cc5mcj PRED relation: executive_produced_by PRED expected values: 0gg9_5q => 82 concepts (60 used for prediction) PRED predicted values (max 10 best out of 78): 09pl3f (0.17 #2506, 0.09 #3259, 0.09 #3511), 09pl3s (0.17 #2506, 0.09 #3259, 0.09 #3511), 0697kh (0.17 #2506, 0.09 #3259, 0.09 #3511), 06q8hf (0.12 #2420, 0.10 #4935, 0.09 #4180), 05hj_k (0.11 #2352, 0.10 #4867, 0.09 #4112), 02q42j_ (0.10 #135, 0.04 #2390, 0.02 #4150), 059x0w (0.10 #202), 014zcr (0.10 #9), 079vf (0.05 #253, 0.05 #1253, 0.04 #4772), 0c0k1 (0.05 #4266, 0.02 #5021, 0.02 #6781) >> Best rule #2506 for best value: >> intensional similarity = 4 >> extensional distance = 169 >> proper extension: 09fc83; >> query: (?x2441, ?x2442) <- nominated_for(?x2771, ?x2441), written_by(?x2441, ?x2442), executive_produced_by(?x2441, ?x2135), genre(?x2441, ?x53) >> conf = 0.17 => this is the best rule for 3 predicted values *> Best rule #4859 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 427 *> proper extension: 0g5pv3; 018nnz; 03l6q0; 0h1fktn; 02ljhg; 02mc5v; 03d8jd1; *> query: (?x2441, 0gg9_5q) <- film(?x8871, ?x2441), film(?x609, ?x2441), student(?x5357, ?x8871), executive_produced_by(?x2441, ?x2135) *> conf = 0.03 ranks of expected_values: 18 EVAL 0cc5mcj executive_produced_by 0gg9_5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 82.000 60.000 0.171 http://example.org/film/film/executive_produced_by #134-03cvv4 PRED entity: 03cvv4 PRED relation: nationality PRED expected values: 09c7w0 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 21): 09c7w0 (0.78 #704, 0.76 #3424, 0.76 #201), 0mlzk (0.26 #4733), 081yw (0.26 #4733), 07z1m (0.26 #4733), 02jx1 (0.10 #938, 0.10 #4362, 0.10 #5166), 07ssc (0.09 #3842, 0.09 #1423, 0.08 #1020), 0h7x (0.08 #35, 0.01 #3458, 0.01 #3560), 03rk0 (0.06 #9491, 0.06 #447, 0.06 #5179), 0d060g (0.05 #1515, 0.05 #207, 0.05 #3329), 0chghy (0.03 #3837, 0.03 #1317, 0.03 #1115) >> Best rule #704 for best value: >> intensional similarity = 3 >> extensional distance = 404 >> proper extension: 0277990; 07q0g5; 06hgym; 076df9; 01gw8b; >> query: (?x9972, 09c7w0) <- location(?x9972, ?x9973), award_nominee(?x9972, ?x2813), actor(?x6706, ?x9972) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03cvv4 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 106.000 0.776 http://example.org/people/person/nationality #133-02rmd_2 PRED entity: 02rmd_2 PRED relation: film_crew_role PRED expected values: 0ch6mp2 => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 33): 09zzb8 (0.84 #621, 0.79 #766, 0.78 #1678), 0ch6mp2 (0.81 #628, 0.81 #1685, 0.81 #1757), 02r96rf (0.72 #1681, 0.71 #40, 0.70 #624), 01pvkk (0.32 #632, 0.28 #2350, 0.28 #1249), 02ynfr (0.25 #16, 0.25 #1640, 0.19 #636), 089fss (0.25 #7, 0.16 #1055, 0.12 #2851), 02rh1dz (0.25 #1640, 0.23 #47, 0.16 #631), 01xy5l_ (0.25 #1640, 0.19 #86, 0.17 #50), 04pyp5 (0.25 #1640, 0.16 #1055, 0.12 #2851), 05smlt (0.25 #1640, 0.12 #2851, 0.09 #3578) >> Best rule #621 for best value: >> intensional similarity = 5 >> extensional distance = 242 >> proper extension: 02z9rr; 07p12s; >> query: (?x4372, 09zzb8) <- film_release_distribution_medium(?x4372, ?x81), currency(?x4372, ?x170), film_crew_role(?x4372, ?x2095), ?x2095 = 0dxtw, country(?x4372, ?x94) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #628 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 242 *> proper extension: 02z9rr; 07p12s; *> query: (?x4372, 0ch6mp2) <- film_release_distribution_medium(?x4372, ?x81), currency(?x4372, ?x170), film_crew_role(?x4372, ?x2095), ?x2095 = 0dxtw, country(?x4372, ?x94) *> conf = 0.81 ranks of expected_values: 2 EVAL 02rmd_2 film_crew_role 0ch6mp2 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 101.000 101.000 0.836 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #132-05yzt_ PRED entity: 05yzt_ PRED relation: place_of_death PRED expected values: 0k_mf => 94 concepts (94 used for prediction) PRED predicted values (max 10 best out of 16): 030qb3t (0.15 #22, 0.08 #604, 0.07 #216), 02_286 (0.07 #1177, 0.07 #401, 0.06 #983), 04jpl (0.05 #7, 0.03 #589, 0.03 #783), 0k049 (0.03 #3, 0.02 #585, 0.02 #779), 06c62 (0.03 #101, 0.01 #295, 0.01 #489), 0nbwf (0.03 #117, 0.01 #699, 0.01 #893), 0281rb (0.03 #85, 0.01 #667, 0.01 #861), 0q34g (0.03 #173), 04swd (0.03 #120), 0f2wj (0.03 #1370, 0.02 #982, 0.01 #206) >> Best rule #22 for best value: >> intensional similarity = 3 >> extensional distance = 37 >> proper extension: 07zhd7; >> query: (?x8532, 030qb3t) <- profession(?x8532, ?x563), award_winner(?x3001, ?x8532), ?x563 = 01c8w0 >> conf = 0.15 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 05yzt_ place_of_death 0k_mf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 94.000 94.000 0.154 http://example.org/people/deceased_person/place_of_death #131-03fyrh PRED entity: 03fyrh PRED relation: sports! PRED expected values: 0jkvj => 46 concepts (46 used for prediction) PRED predicted values (max 10 best out of 27): 0jdk_ (0.88 #973, 0.88 #768, 0.86 #896), 0jhn7 (0.80 #693, 0.79 #129, 0.79 #1138), 0l6ny (0.79 #129, 0.79 #1138, 0.78 #883), 0l98s (0.79 #129, 0.79 #1138, 0.78 #883), 0l6vl (0.79 #129, 0.79 #1138, 0.78 #883), 0lbd9 (0.79 #129, 0.79 #1138, 0.78 #883), 06sks6 (0.79 #129, 0.78 #883, 0.78 #806), 0kbws (0.69 #396, 0.69 #395, 0.61 #730), 0jkvj (0.67 #257, 0.61 #604, 0.61 #603), 016r9z (0.67 #457, 0.61 #604, 0.61 #603) >> Best rule #973 for best value: >> intensional similarity = 41 >> extensional distance = 24 >> proper extension: 02vx4; >> query: (?x3641, 0jdk_) <- sports(?x778, ?x3641), country(?x3641, ?x6105), country(?x3641, ?x4521), country(?x3641, ?x1603), country(?x3641, ?x404), ?x778 = 0kbvb, country(?x5396, ?x4521), country(?x3659, ?x1603), country(?x779, ?x1603), film_release_region(?x6684, ?x1603), film_release_region(?x6543, ?x1603), film_release_region(?x6078, ?x1603), film_release_region(?x4355, ?x1603), film_release_region(?x4041, ?x1603), film_release_region(?x3498, ?x1603), film_release_region(?x3151, ?x1603), film_release_region(?x1988, ?x1603), film_release_region(?x1035, ?x1603), ?x6684 = 07pd_j, participating_countries(?x1931, ?x4521), ?x779 = 096f8, contains(?x1603, ?x992), form_of_government(?x6105, ?x4763), ?x4763 = 01fpfn, service_location(?x6016, ?x1603), ?x1035 = 08hmch, ?x3151 = 0gtsxr4, ?x3498 = 02fqrf, ?x6016 = 01zpmq, ?x6078 = 04pk1f, adjoins(?x4521, ?x2856), nationality(?x889, ?x1603), ?x4041 = 0gy2y8r, adjoins(?x2513, ?x1603), ?x1988 = 09k56b7, ?x4355 = 08tq4x, ?x5396 = 0486tv, film_release_region(?x9194, ?x404), ?x3659 = 0dwxr, ?x9194 = 0fpgp26, ?x6543 = 0421v9q >> conf = 0.88 => this is the best rule for 1 predicted values *> Best rule #257 for first EXPECTED value: *> intensional similarity = 44 *> extensional distance = 4 *> proper extension: 0w0d; *> query: (?x3641, 0jkvj) <- sports(?x5395, ?x3641), sports(?x778, ?x3641), country(?x3641, ?x4521), country(?x3641, ?x3730), country(?x3641, ?x3277), country(?x3641, ?x1603), country(?x3641, ?x1471), country(?x3641, ?x789), country(?x3641, ?x142), ?x778 = 0kbvb, country(?x7687, ?x4521), country(?x4833, ?x4521), ?x1603 = 06bnz, ?x4833 = 018w8, ?x3277 = 06t8v, teams(?x4521, ?x3436), ?x7687 = 03krj, sports(?x3729, ?x3641), ?x3730 = 03shp, ?x1471 = 07t21, administrative_parent(?x4521, ?x551), ?x5395 = 018qb4, film_release_region(?x9565, ?x142), film_release_region(?x8955, ?x142), film_release_region(?x7379, ?x142), film_release_region(?x6492, ?x142), film_release_region(?x4446, ?x142), film_release_region(?x2868, ?x142), film_release_region(?x1916, ?x142), film_release_region(?x511, ?x142), ?x511 = 0dscrwf, ?x4446 = 0db94w, ?x8955 = 0g4pl7z, ?x7379 = 032clf, organization(?x4521, ?x312), film_release_region(?x886, ?x142), olympics(?x142, ?x1931), currency(?x142, ?x170), ?x6492 = 0ds6bmk, ?x2868 = 0dr3sl, ?x9565 = 0hz6mv2, ?x1916 = 0ch26b_, ?x3729 = 0jdk_, ?x789 = 0f8l9c *> conf = 0.67 ranks of expected_values: 9 EVAL 03fyrh sports! 0jkvj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 46.000 46.000 0.885 http://example.org/user/jg/default_domain/olympic_games/sports #130-046_v PRED entity: 046_v PRED relation: story_by! PRED expected values: 0dzlbx 062zm5h => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 226): 0ccd3x (0.25 #154, 0.02 #4203, 0.02 #5892), 0fzm0g (0.25 #337, 0.01 #3711, 0.01 #4386), 02rtqvb (0.25 #336, 0.01 #3710, 0.01 #4385), 025scjj (0.25 #295, 0.01 #3669, 0.01 #4344), 0p9rz (0.25 #287, 0.01 #3661, 0.01 #4336), 0kt_4 (0.25 #280, 0.01 #3654, 0.01 #4329), 0k2m6 (0.25 #256, 0.01 #3630, 0.01 #4305), 016ywb (0.25 #234, 0.01 #3608, 0.01 #4283), 0prh7 (0.25 #171, 0.01 #3545, 0.01 #4220), 0prhz (0.25 #163, 0.01 #3537, 0.01 #4212) >> Best rule #154 for best value: >> intensional similarity = 4 >> extensional distance = 2 >> proper extension: 0py5b; >> query: (?x10439, 0ccd3x) <- story_by(?x2766, ?x10439), film(?x7981, ?x2766), country(?x2766, ?x94), ?x7981 = 02bj6k >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #1522 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 07nznf; 02l5rm; 03hbzj; 09v6tz; *> query: (?x10439, 0dzlbx) <- story_by(?x136, ?x10439), place_of_birth(?x10439, ?x739), nationality(?x10439, ?x94), ?x739 = 02_286 *> conf = 0.05 ranks of expected_values: 74, 82 EVAL 046_v story_by! 062zm5h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 108.000 108.000 0.250 http://example.org/film/film/story_by EVAL 046_v story_by! 0dzlbx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 108.000 108.000 0.250 http://example.org/film/film/story_by #129-02v3yy PRED entity: 02v3yy PRED relation: profession PRED expected values: 0nbcg => 132 concepts (91 used for prediction) PRED predicted values (max 10 best out of 59): 02hrh1q (0.87 #3146, 0.86 #4488, 0.86 #4787), 0nbcg (0.57 #480, 0.56 #629, 0.45 #1076), 09jwl (0.54 #914, 0.47 #1063, 0.45 #3449), 016z4k (0.43 #898, 0.40 #4, 0.36 #451), 0dxtg (0.43 #163, 0.37 #6118, 0.33 #5819), 01c72t (0.42 #770, 0.40 #472, 0.40 #621), 01d_h8 (0.41 #2690, 0.41 #3286, 0.40 #2988), 0dz3r (0.38 #449, 0.38 #598, 0.36 #3431), 02jknp (0.37 #6118, 0.33 #5819, 0.33 #4772), 03gjzk (0.37 #6118, 0.33 #5819, 0.33 #4772) >> Best rule #3146 for best value: >> intensional similarity = 3 >> extensional distance = 269 >> proper extension: 04shbh; 01bpnd; 078jnn; 031sg0; 0sx5w; >> query: (?x3235, 02hrh1q) <- participant(?x4397, ?x3235), award(?x3235, ?x1232), spouse(?x5043, ?x4397) >> conf = 0.87 => this is the best rule for 1 predicted values *> Best rule #480 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 40 *> proper extension: 01sbf2; 013423; *> query: (?x3235, 0nbcg) <- award_nominee(?x3234, ?x3235), award_winner(?x1232, ?x3235), ?x1232 = 0c4z8 *> conf = 0.57 ranks of expected_values: 2 EVAL 02v3yy profession 0nbcg CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 132.000 91.000 0.867 http://example.org/people/person/profession #128-05v38p PRED entity: 05v38p PRED relation: nominated_for! PRED expected values: 0gq9h => 67 concepts (67 used for prediction) PRED predicted values (max 10 best out of 181): 09td7p (0.66 #2522, 0.66 #4128, 0.65 #1605), 0gq9h (0.30 #742, 0.28 #2347, 0.26 #1430), 040njc (0.29 #464, 0.20 #693, 0.20 #11689), 05zvq6g (0.27 #2523, 0.20 #11689, 0.19 #6649), 02y_j8g (0.27 #2523), 02pqp12 (0.26 #510, 0.19 #739, 0.15 #1427), 0gs9p (0.25 #2349, 0.24 #1432, 0.24 #515), 02qyntr (0.24 #629, 0.19 #858, 0.16 #2463), 019f4v (0.23 #2340, 0.23 #735, 0.22 #1423), 027dtxw (0.22 #462, 0.20 #11689, 0.19 #6649) >> Best rule #2522 for best value: >> intensional similarity = 3 >> extensional distance = 843 >> proper extension: 06mmr; >> query: (?x6445, ?x2257) <- award(?x6445, ?x2257), award_winner(?x6445, ?x4254), award_winner(?x1008, ?x4254) >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #742 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 157 *> proper extension: 03cffvv; 0cbl95; *> query: (?x6445, 0gq9h) <- language(?x6445, ?x5607), nominated_for(?x1958, ?x6445), ?x5607 = 064_8sq, genre(?x6445, ?x53) *> conf = 0.30 ranks of expected_values: 2 EVAL 05v38p nominated_for! 0gq9h CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 67.000 67.000 0.661 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #127-03tmr PRED entity: 03tmr PRED relation: sport! PRED expected values: 02r7lqg => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 489): 02r7lqg (0.47 #5581, 0.47 #5580, 0.38 #857), 032yps (0.47 #5581, 0.47 #5580, 0.38 #857), 04l5b4 (0.47 #5581, 0.47 #5580, 0.38 #857), 01tz_d (0.47 #5581, 0.47 #5580, 0.38 #857), 0gx159f (0.47 #5581, 0.47 #5580, 0.38 #857), 0gvt8sz (0.47 #5581, 0.47 #5580, 0.38 #857), 02c_4 (0.37 #5582, 0.33 #615, 0.29 #3426), 07l2m (0.37 #5582, 0.33 #537, 0.25 #3428), 02d02 (0.37 #5582, 0.20 #2793, 0.17 #5373), 01_1kk (0.33 #409, 0.29 #3426, 0.25 #2122) >> Best rule #5581 for best value: >> intensional similarity = 26 >> extensional distance = 4 >> proper extension: 039yzs; >> query: (?x453, ?x11368) <- sport(?x10690, ?x453), sport(?x8892, ?x453), sport(?x8541, ?x453), sport(?x3298, ?x453), teams(?x479, ?x8892), team(?x3724, ?x3298), colors(?x8892, ?x663), colors(?x3298, ?x12067), teams(?x9417, ?x8541), team(?x5234, ?x10690), place_of_birth(?x478, ?x479), origin(?x1660, ?x479), category(?x479, ?x134), featured_film_locations(?x1015, ?x479), team(?x11825, ?x8541), location(?x115, ?x479), contains(?x479, ?x2228), administrative_division(?x479, ?x7387), contains(?x94, ?x479), teams(?x1274, ?x10690), colors(?x1506, ?x12067), colors(?x8885, ?x12067), ?x8885 = 01rlzn, team(?x3724, ?x11368), jurisdiction_of_office(?x1195, ?x9417), location(?x3758, ?x9417) >> conf = 0.47 => this is the best rule for 6 predicted values ranks of expected_values: 1 EVAL 03tmr sport! 02r7lqg CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 39.000 39.000 0.473 http://example.org/sports/sports_team/sport #126-04lc0h PRED entity: 04lc0h PRED relation: country PRED expected values: 0ctw_b => 148 concepts (57 used for prediction) PRED predicted values (max 10 best out of 37): 09c7w0 (0.60 #1644, 0.59 #1558, 0.59 #1818), 0ctw_b (0.57 #87, 0.57 #28, 0.50 #174), 04lc0h (0.25 #3808, 0.23 #4591, 0.23 #2596), 012ts (0.25 #3808, 0.23 #4591, 0.23 #2596), 05nrg (0.23 #4591, 0.23 #2596, 0.22 #4678), 07ssc (0.19 #278, 0.16 #795, 0.11 #451), 0d060g (0.19 #270, 0.09 #787, 0.06 #356), 02jx1 (0.06 #295, 0.06 #468, 0.05 #812), 03rk0 (0.05 #1516, 0.04 #4291, 0.04 #4378), 0345h (0.04 #380, 0.03 #2195, 0.03 #1329) >> Best rule #1644 for best value: >> intensional similarity = 4 >> extensional distance = 149 >> proper extension: 0tlq9; >> query: (?x13174, 09c7w0) <- category(?x13174, ?x134), contains(?x13174, ?x12293), currency(?x12293, ?x7888), school_type(?x12293, ?x3092) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #87 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 01wx74; *> query: (?x13174, ?x1023) <- location_of_ceremony(?x566, ?x13174), contains(?x1023, ?x13174), ?x1023 = 0ctw_b, ?x566 = 04ztj *> conf = 0.57 ranks of expected_values: 2 EVAL 04lc0h country 0ctw_b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 148.000 57.000 0.596 http://example.org/base/biblioness/bibs_location/country #125-04cbbz PRED entity: 04cbbz PRED relation: genre PRED expected values: 01jfsb => 90 concepts (89 used for prediction) PRED predicted values (max 10 best out of 113): 07s9rl0 (0.76 #3151, 0.72 #8605, 0.68 #4727), 01jfsb (0.60 #1587, 0.48 #2921, 0.48 #3648), 03k9fj (0.59 #981, 0.43 #2070, 0.41 #1828), 05p553 (0.46 #1457, 0.43 #731, 0.41 #2062), 06n90 (0.37 #1225, 0.29 #2786, 0.28 #862), 01hmnh (0.35 #382, 0.33 #988, 0.29 #1230), 0lsxr (0.32 #3999, 0.30 #2301, 0.29 #2786), 0556j8 (0.32 #3999, 0.30 #2301, 0.29 #2786), 01lrrt (0.30 #2301, 0.29 #2786, 0.12 #52), 01drsx (0.30 #2301, 0.29 #2786, 0.12 #44) >> Best rule #3151 for best value: >> intensional similarity = 2 >> extensional distance = 155 >> proper extension: 0c0wvx; >> query: (?x5441, 07s9rl0) <- genre(?x5441, ?x3515), ?x3515 = 082gq >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #1587 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 76 *> proper extension: 02z3r8t; 03t97y; 02v63m; 03l6q0; 02mc5v; 01_1hw; 0cqr0q; 01jnc_; *> query: (?x5441, 01jfsb) <- prequel(?x5441, ?x6918), language(?x5441, ?x254), featured_film_locations(?x5441, ?x8654), film(?x1020, ?x5441) *> conf = 0.60 ranks of expected_values: 2 EVAL 04cbbz genre 01jfsb CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 90.000 89.000 0.758 http://example.org/film/film/genre #124-02t_tp PRED entity: 02t_tp PRED relation: award_nominee PRED expected values: 03nkts => 89 concepts (42 used for prediction) PRED predicted values (max 10 best out of 781): 0418ft (0.29 #60898, 0.29 #93683, 0.28 #96027), 03nkts (0.29 #60898, 0.29 #93683, 0.28 #96027), 05m883 (0.29 #60898, 0.29 #93683, 0.28 #96027), 02t_tp (0.29 #60898, 0.29 #93683, 0.28 #96027), 06cgy (0.18 #98371, 0.17 #4683, 0.17 #2668), 01900g (0.18 #98371, 0.16 #98372, 0.15 #30452), 0306ds (0.18 #98371, 0.16 #98372, 0.15 #30452), 0f5xn (0.18 #98371, 0.16 #98372, 0.15 #30452), 01j5ws (0.18 #98371, 0.16 #98372, 0.15 #30452), 04wvhz (0.16 #91339, 0.02 #2553, 0.01 #7237) >> Best rule #60898 for best value: >> intensional similarity = 3 >> extensional distance = 1401 >> proper extension: 02lq10; 0c01c; 04mlh8; 0q1lp; >> query: (?x2587, ?x8183) <- nominated_for(?x2587, ?x8267), nominated_for(?x8183, ?x8267), film(?x2587, ?x3251) >> conf = 0.29 => this is the best rule for 4 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 02t_tp award_nominee 03nkts CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 89.000 42.000 0.290 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #123-015fsv PRED entity: 015fsv PRED relation: major_field_of_study PRED expected values: 02stgt => 133 concepts (133 used for prediction) PRED predicted values (max 10 best out of 120): 01mkq (0.78 #641, 0.52 #1267, 0.49 #1017), 0g26h (0.54 #295, 0.50 #920, 0.47 #670), 04rjg (0.53 #646, 0.37 #1272, 0.35 #1022), 02lp1 (0.50 #637, 0.46 #262, 0.41 #1764), 01lj9 (0.47 #667, 0.35 #1293, 0.33 #1168), 02j62 (0.45 #657, 0.37 #1283, 0.36 #1158), 062z7 (0.45 #654, 0.33 #1280, 0.33 #1030), 05qfh (0.45 #663, 0.33 #1039, 0.32 #1289), 03g3w (0.42 #653, 0.29 #1279, 0.29 #1029), 041y2 (0.40 #707, 0.25 #1333, 0.24 #1083) >> Best rule #641 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 06pwq; 01w3v; 01w5m; 09f2j; 01nnsv; 0ks67; 0gl5_; 0trv; 0g2jl; >> query: (?x9249, 01mkq) <- citytown(?x9249, ?x11359), fraternities_and_sororities(?x9249, ?x4348), institution(?x1526, ?x9249), ?x1526 = 0bkj86 >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #1377 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 73 *> proper extension: 024y8p; 0b1xl; *> query: (?x9249, ?x254) <- citytown(?x9249, ?x11359), fraternities_and_sororities(?x9249, ?x4348), institution(?x1526, ?x9249), major_field_of_study(?x1526, ?x254) *> conf = 0.11 ranks of expected_values: 56 EVAL 015fsv major_field_of_study 02stgt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 133.000 133.000 0.775 http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study #122-024rbz PRED entity: 024rbz PRED relation: film PRED expected values: 01dvbd 0900j5 0b44shh => 91 concepts (45 used for prediction) PRED predicted values (max 10 best out of 1723): 02mt51 (0.75 #6208, 0.72 #1552, 0.67 #26386), 05q4y12 (0.75 #6208, 0.72 #1552, 0.67 #26386), 0gx1bnj (0.75 #6208, 0.72 #1552, 0.67 #26386), 02r1c18 (0.74 #24834, 0.69 #24833, 0.68 #34153), 0209xj (0.71 #34154, 0.69 #24833, 0.68 #34153), 06823p (0.60 #5657, 0.33 #2553, 0.09 #25835), 0g22z (0.40 #4669, 0.33 #13, 0.10 #9327), 02bqxb (0.40 #6185, 0.33 #1529, 0.10 #10843), 035zr0 (0.40 #5792, 0.33 #2688, 0.06 #25970), 0gpx6 (0.40 #5823, 0.33 #2719, 0.06 #26001) >> Best rule #6208 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 086k8; 016tt2; >> query: (?x1414, ?x343) <- production_companies(?x343, ?x1414), film(?x1414, ?x3491), film(?x1414, ?x2889), ?x2889 = 040b5k, film_crew_role(?x3491, ?x137) >> conf = 0.75 => this is the best rule for 3 predicted values *> Best rule #431 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 1 *> proper extension: 016tw3; *> query: (?x1414, 01dvbd) <- production_companies(?x343, ?x1414), film(?x1414, ?x3512), film(?x1414, ?x2889), ?x3512 = 04grkmd, nominated_for(?x143, ?x2889) *> conf = 0.33 ranks of expected_values: 83, 469, 857 EVAL 024rbz film 0b44shh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 91.000 45.000 0.748 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 024rbz film 0900j5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 91.000 45.000 0.748 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film EVAL 024rbz film 01dvbd CNN-1.5+0.5_MA 0.000 0.000 0.000 0.012 91.000 45.000 0.748 http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film #121-0jtg0 PRED entity: 0jtg0 PRED relation: role! PRED expected values: 0j6cj => 72 concepts (35 used for prediction) PRED predicted values (max 10 best out of 1058): 050z2 (0.73 #8131, 0.71 #4857, 0.71 #3457), 02s6sh (0.71 #3705, 0.67 #2774, 0.64 #8379), 01wxdn3 (0.71 #3680, 0.67 #2749, 0.60 #7418), 082brv (0.71 #3538, 0.67 #2607, 0.50 #2142), 01wsl7c (0.71 #4757, 0.43 #10361, 0.43 #9895), 01vn35l (0.67 #2465, 0.57 #3396, 0.50 #6667), 03ryks (0.60 #1707, 0.46 #9642, 0.45 #7776), 023l9y (0.57 #4882, 0.57 #3482, 0.55 #8156), 0326tc (0.57 #10156, 0.57 #5018, 0.50 #2687), 04bpm6 (0.57 #4742, 0.57 #3342, 0.50 #2411) >> Best rule #8131 for best value: >> intensional similarity = 21 >> extensional distance = 9 >> proper extension: 0mbct; >> query: (?x2785, 050z2) <- role(?x2785, ?x4769), role(?x2785, ?x716), role(?x2785, ?x3967), role(?x2785, ?x2309), role(?x2785, ?x1574), ?x3967 = 01p970, ?x1574 = 0l15bq, role(?x1165, ?x2785), role(?x4769, ?x8172), role(?x4769, ?x2310), role(?x4769, ?x2297), role(?x4769, ?x868), ?x868 = 0dwvl, ?x2297 = 051hrr, ?x2310 = 0gghm, role(?x1472, ?x2309), ?x8172 = 06rvn, role(?x5926, ?x2785), role(?x565, ?x4769), instrumentalists(?x2309, ?x120), role(?x677, ?x716) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #5019 for first EXPECTED value: *> intensional similarity = 16 *> extensional distance = 5 *> proper extension: 02sgy; 042v_gx; 0dwtp; *> query: (?x2785, 0j6cj) <- role(?x2785, ?x1466), role(?x2785, ?x228), instrumentalists(?x2785, ?x12266), instrumentalists(?x2785, ?x3321), ?x228 = 0l14qv, ?x1466 = 03bx0bm, group(?x2785, ?x1945), artist(?x5744, ?x12266), role(?x2785, ?x2725), role(?x3328, ?x2785), profession(?x12266, ?x131), artists(?x284, ?x3321), place_of_birth(?x3321, ?x9026), ?x2725 = 0l1589, artists(?x1000, ?x12266), ?x3328 = 016622 *> conf = 0.57 ranks of expected_values: 15 EVAL 0jtg0 role! 0j6cj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 72.000 35.000 0.727 http://example.org/music/artist/track_contributions./music/track_contribution/role #120-014gf8 PRED entity: 014gf8 PRED relation: profession PRED expected values: 02jknp => 99 concepts (98 used for prediction) PRED predicted values (max 10 best out of 53): 09jwl (0.37 #3694, 0.37 #3106, 0.36 #3400), 0dxtg (0.31 #3836, 0.29 #3983, 0.28 #8834), 0nbcg (0.27 #3707, 0.26 #3413, 0.26 #3119), 02jknp (0.25 #154, 0.22 #4712, 0.22 #7064), 03gjzk (0.24 #7953, 0.24 #2955, 0.24 #3543), 016z4k (0.23 #1327, 0.23 #3680, 0.23 #3092), 0dz3r (0.23 #1325, 0.22 #3678, 0.22 #3090), 0np9r (0.20 #2520, 0.18 #1490, 0.17 #1196), 018gz8 (0.16 #1192, 0.14 #1486, 0.13 #1928), 0cbd2 (0.15 #5740, 0.14 #1035, 0.14 #10150) >> Best rule #3694 for best value: >> intensional similarity = 2 >> extensional distance = 1131 >> proper extension: 0f1vrl; 023l9y; >> query: (?x5626, 09jwl) <- category(?x5626, ?x134), profession(?x5626, ?x319) >> conf = 0.37 => this is the best rule for 1 predicted values *> Best rule #154 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 015rhv; 033tln; 02gyl0; 02_p8v; 031y07; 0l786; 07nx9j; 03xx9l; 02dztn; 02661h; ... *> query: (?x5626, 02jknp) <- award(?x5626, ?x1921), film(?x5626, ?x136), ?x1921 = 0bs0bh *> conf = 0.25 ranks of expected_values: 4 EVAL 014gf8 profession 02jknp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 99.000 98.000 0.373 http://example.org/people/person/profession #119-03rk0 PRED entity: 03rk0 PRED relation: participating_countries! PRED expected values: 018ctl => 268 concepts (268 used for prediction) PRED predicted values (max 10 best out of 36): 0kbws (0.84 #6116, 0.83 #5141, 0.79 #6152), 018ctl (0.71 #1882, 0.70 #1630, 0.70 #1270), 0sx8l (0.42 #228, 0.42 #662, 0.39 #1887), 0blfl (0.40 #1648, 0.40 #891, 0.39 #1900), 0c_tl (0.30 #346, 0.30 #165, 0.29 #671), 0kbvv (0.26 #5561, 0.25 #5056, 0.24 #6500), 0swbd (0.26 #5561, 0.25 #5056, 0.24 #6500), 01f1kd (0.25 #142, 0.11 #214, 0.10 #287), 019n8z (0.25 #137, 0.11 #209, 0.10 #282), 0sx92 (0.25 #134, 0.11 #206, 0.10 #279) >> Best rule #6116 for best value: >> intensional similarity = 3 >> extensional distance = 137 >> proper extension: 06jnv; >> query: (?x2146, 0kbws) <- form_of_government(?x2146, ?x1926), participating_countries(?x2553, ?x2146), olympics(?x94, ?x2553) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #1882 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 49 *> proper extension: 01d8l; *> query: (?x2146, 018ctl) <- olympics(?x2146, ?x778), contains(?x2146, ?x1391), combatants(?x10413, ?x2146) *> conf = 0.71 ranks of expected_values: 2 EVAL 03rk0 participating_countries! 018ctl CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 268.000 268.000 0.842 http://example.org/olympics/olympic_games/participating_countries #118-02237m PRED entity: 02237m PRED relation: student PRED expected values: 01w5gg6 => 115 concepts (53 used for prediction) PRED predicted values (max 10 best out of 1798): 09hnb (0.50 #428, 0.03 #48476, 0.02 #65192), 01k_mc (0.25 #3119, 0.10 #7297, 0.02 #28188), 01h8f (0.25 #2989, 0.10 #7167, 0.02 #28058), 019vgs (0.25 #2715, 0.10 #6893, 0.02 #27784), 037lyl (0.25 #660, 0.04 #34085, 0.03 #36174), 03kts (0.25 #1363, 0.04 #34788, 0.03 #36877), 01x1fq (0.25 #1686, 0.02 #30933, 0.02 #35111), 06h2w (0.25 #930, 0.02 #30177, 0.02 #34355), 02ryx0 (0.25 #1037, 0.02 #34462, 0.02 #36551), 05yzt_ (0.25 #1479, 0.02 #34904, 0.02 #36993) >> Best rule #428 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 04sylm; 03fgm; >> query: (?x10449, 09hnb) <- major_field_of_study(?x10449, ?x7070), student(?x10449, ?x7951), artists(?x2936, ?x7951), type_of_union(?x7951, ?x566), ?x2936 = 029h7y >> conf = 0.50 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 02237m student 01w5gg6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 115.000 53.000 0.500 http://example.org/education/educational_institution/students_graduates./education/education/student #117-02cbhg PRED entity: 02cbhg PRED relation: genre PRED expected values: 01fc50 => 110 concepts (107 used for prediction) PRED predicted values (max 10 best out of 105): 01jfsb (0.43 #132, 0.38 #2757, 0.36 #1443), 05p553 (0.38 #2629, 0.38 #3105, 0.37 #242), 03k9fj (0.37 #250, 0.35 #1085, 0.34 #966), 02kdv5l (0.37 #240, 0.34 #1910, 0.34 #2746), 0lsxr (0.31 #128, 0.26 #604, 0.26 #8), 01hmnh (0.27 #1091, 0.24 #972, 0.20 #17), 060__y (0.23 #2165, 0.23 #2404, 0.22 #731), 06n90 (0.20 #1087, 0.19 #2758, 0.18 #968), 02n4kr (0.20 #7, 0.18 #127, 0.14 #1558), 03bxz7 (0.19 #412, 0.18 #5971, 0.16 #531) >> Best rule #132 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 0gcrg; 02yy9r; >> query: (?x8084, 01jfsb) <- cinematography(?x8084, ?x7327), film_crew_role(?x8084, ?x468), film_format(?x8084, ?x909), language(?x8084, ?x254) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #2006 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 155 *> proper extension: 0c0wvx; *> query: (?x8084, 01fc50) <- genre(?x8084, ?x3515), ?x3515 = 082gq *> conf = 0.04 ranks of expected_values: 46 EVAL 02cbhg genre 01fc50 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 110.000 107.000 0.429 http://example.org/film/film/genre #116-0hhtgcw PRED entity: 0hhtgcw PRED relation: honored_for PRED expected values: 0bh8yn3 05sy2k_ 0c1sgd3 09v8clw => 78 concepts (75 used for prediction) PRED predicted values (max 10 best out of 541): 0d68qy (0.30 #1317, 0.27 #5419, 0.22 #11288), 01j7mr (0.30 #1381, 0.20 #3725, 0.16 #12522), 039cq4 (0.30 #1577, 0.16 #3921, 0.12 #19751), 06mr2s (0.30 #1452, 0.16 #3796, 0.12 #12593), 04xbq3 (0.30 #1677, 0.12 #4021, 0.09 #12818), 02gl58 (0.30 #1715, 0.12 #4059, 0.07 #11686), 027tbrc (0.30 #1314, 0.12 #3658, 0.07 #11285), 0l76z (0.21 #2613, 0.13 #5543, 0.13 #3199), 02rzdcp (0.20 #1364, 0.17 #5466, 0.15 #17193), 01b_lz (0.20 #1366, 0.16 #3710, 0.12 #12507) >> Best rule #1317 for best value: >> intensional similarity = 6 >> extensional distance = 8 >> proper extension: 0jzphpx; 013b2h; >> query: (?x6297, 0d68qy) <- award_winner(?x6297, ?x1335), producer_type(?x1335, ?x632), category(?x1335, ?x134), influenced_by(?x692, ?x1335), award_winner(?x401, ?x1335), film(?x692, ?x7141) >> conf = 0.30 => this is the best rule for 1 predicted values *> Best rule #39270 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 110 *> proper extension: 0ftlxj; *> query: (?x6297, ?x2907) <- award_winner(?x6297, ?x1335), award_nominee(?x2035, ?x1335), award_winner(?x102, ?x2035), honored_for(?x6297, ?x86), film(?x1335, ?x821), nominated_for(?x1335, ?x2907) *> conf = 0.10 ranks of expected_values: 91, 103 EVAL 0hhtgcw honored_for 09v8clw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.011 78.000 75.000 0.300 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0hhtgcw honored_for 0c1sgd3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 75.000 0.300 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0hhtgcw honored_for 05sy2k_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 78.000 75.000 0.300 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0hhtgcw honored_for 0bh8yn3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.010 78.000 75.000 0.300 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #115-0fz27v PRED entity: 0fz27v PRED relation: producer_type PRED expected values: 0ckd1 => 76 concepts (76 used for prediction) PRED predicted values (max 10 best out of 1): 0ckd1 (0.37 #23, 0.33 #2, 0.33 #1) >> Best rule #23 for best value: >> intensional similarity = 3 >> extensional distance = 455 >> proper extension: 04nw9; 01vb403; 02ts3h; 01c1px; 02q6cv4; 0227vl; >> query: (?x10360, 0ckd1) <- award_nominee(?x10360, ?x1541), profession(?x10360, ?x1041), ?x1041 = 03gjzk >> conf = 0.37 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0fz27v producer_type 0ckd1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 76.000 76.000 0.365 http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type #114-02q5g1z PRED entity: 02q5g1z PRED relation: film! PRED expected values: 02kxwk => 129 concepts (57 used for prediction) PRED predicted values (max 10 best out of 1147): 01j2xj (0.47 #110348, 0.46 #16652, 0.46 #68704), 0fvf9q (0.47 #110348, 0.46 #16652, 0.46 #68704), 0dvmd (0.47 #110348, 0.46 #16652, 0.46 #68704), 01gb54 (0.47 #110348, 0.46 #16652, 0.46 #68704), 04qvl7 (0.47 #110348, 0.46 #16652, 0.46 #68704), 0fqy4p (0.47 #110348, 0.46 #16652, 0.46 #68704), 02w0dc0 (0.47 #110348, 0.46 #16652, 0.46 #68704), 0bxtg (0.33 #77, 0.06 #6322, 0.05 #8404), 0309lm (0.33 #1606, 0.05 #3687, 0.03 #9933), 044rvb (0.33 #102, 0.03 #43819, 0.02 #70888) >> Best rule #110348 for best value: >> intensional similarity = 4 >> extensional distance = 543 >> proper extension: 01br2w; 06w99h3; 0c3ybss; 0m2kd; 05p1tzf; 02x3lt7; 05jzt3; 01vksx; 0b6tzs; 09p0ct; ... >> query: (?x1753, ?x163) <- film_crew_role(?x1753, ?x1284), nominated_for(?x163, ?x1753), currency(?x1753, ?x170), ?x1284 = 0ch6mp2 >> conf = 0.47 => this is the best rule for 7 predicted values *> Best rule #102017 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 509 *> proper extension: 0g60z; 080dwhx; 0kfpm; 02k_4g; 0358x_; 019nnl; 0ddd0gc; 08jgk1; 0464pz; 0kfv9; ... *> query: (?x1753, ?x164) <- honored_for(?x2988, ?x1753), nominated_for(?x163, ?x1753), nominated_for(?x484, ?x1753), award_nominee(?x164, ?x163) *> conf = 0.04 ranks of expected_values: 224 EVAL 02q5g1z film! 02kxwk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 129.000 57.000 0.467 http://example.org/film/actor/film./film/performance/film #113-0mzy7 PRED entity: 0mzy7 PRED relation: category PRED expected values: 08mbj5d => 150 concepts (150 used for prediction) PRED predicted values (max 10 best out of 1): 08mbj5d (0.85 #33, 0.84 #19, 0.83 #40) >> Best rule #33 for best value: >> intensional similarity = 4 >> extensional distance = 99 >> proper extension: 0ydpd; 0pzpz; 0tbql; 0ggh3; 0fsb8; 02d6c; 0h1k6; 0kcw2; >> query: (?x10904, 08mbj5d) <- administrative_division(?x10904, ?x6776), source(?x6776, ?x958), location(?x4790, ?x10904), contains(?x94, ?x10904) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0mzy7 category 08mbj5d CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 150.000 150.000 0.851 http://example.org/common/topic/webpage./common/webpage/category #112-0qm9n PRED entity: 0qm9n PRED relation: film_release_region PRED expected values: 0chghy => 82 concepts (75 used for prediction) PRED predicted values (max 10 best out of 115): 07ssc (0.80 #1031, 0.78 #1369, 0.77 #1200), 0chghy (0.80 #1024, 0.78 #1362, 0.77 #1193), 03_3d (0.77 #1018, 0.77 #1356, 0.77 #1187), 0345h (0.76 #1050, 0.74 #1388, 0.73 #1219), 03rjj (0.75 #1354, 0.75 #1185, 0.75 #1016), 03h64 (0.68 #1089, 0.67 #1427, 0.65 #1258), 015fr (0.64 #1033, 0.62 #1371, 0.62 #1202), 01znc_ (0.64 #1399, 0.63 #1061, 0.60 #1230), 05qhw (0.64 #1029, 0.62 #1367, 0.62 #1198), 035qy (0.61 #1390, 0.61 #1221, 0.59 #1052) >> Best rule #1031 for best value: >> intensional similarity = 4 >> extensional distance = 174 >> proper extension: 07s3m4g; >> query: (?x3425, 07ssc) <- titles(?x53, ?x3425), film_release_region(?x3425, ?x142), ?x142 = 0jgd, film(?x1104, ?x3425) >> conf = 0.80 => this is the best rule for 1 predicted values *> Best rule #1024 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 174 *> proper extension: 07s3m4g; *> query: (?x3425, 0chghy) <- titles(?x53, ?x3425), film_release_region(?x3425, ?x142), ?x142 = 0jgd, film(?x1104, ?x3425) *> conf = 0.80 ranks of expected_values: 2 EVAL 0qm9n film_release_region 0chghy CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 82.000 75.000 0.795 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #111-01f6ss PRED entity: 01f6ss PRED relation: colors PRED expected values: 06kqt3 => 183 concepts (183 used for prediction) PRED predicted values (max 10 best out of 20): 01g5v (0.40 #243, 0.33 #203, 0.30 #1063), 083jv (0.39 #1721, 0.37 #1001, 0.37 #1061), 06fvc (0.33 #242, 0.17 #1062, 0.17 #302), 019sc (0.29 #247, 0.22 #107, 0.20 #367), 038hg (0.22 #92, 0.20 #132, 0.17 #52), 088fh (0.19 #246, 0.12 #306, 0.08 #326), 09ggk (0.12 #156, 0.12 #256, 0.08 #216), 03wkwg (0.11 #215, 0.07 #635, 0.06 #435), 036k5h (0.11 #705, 0.10 #1505, 0.10 #1125), 04mkbj (0.09 #570, 0.09 #1510, 0.08 #1630) >> Best rule #243 for best value: >> intensional similarity = 5 >> extensional distance = 40 >> proper extension: 024cg8; >> query: (?x13618, 01g5v) <- colors(?x13618, ?x332), currency(?x13618, ?x1099), institution(?x3437, ?x13618), ?x1099 = 01nv4h, student(?x3437, ?x1737) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #1137 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 205 *> proper extension: 02mw6c; *> query: (?x13618, 06kqt3) <- student(?x13618, ?x3717), category(?x13618, ?x134), organization(?x11157, ?x13618), school_type(?x13618, ?x3092), colors(?x13618, ?x332) *> conf = 0.03 ranks of expected_values: 20 EVAL 01f6ss colors 06kqt3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 183.000 183.000 0.405 http://example.org/education/educational_institution/colors #110-0571m PRED entity: 0571m PRED relation: film! PRED expected values: 039wsf => 61 concepts (33 used for prediction) PRED predicted values (max 10 best out of 795): 026dx (0.49 #16639, 0.44 #56165, 0.42 #47844), 01x1fq (0.49 #16639, 0.44 #56165, 0.39 #66569), 01vsn38 (0.06 #26812, 0.02 #12251, 0.01 #14331), 0151ns (0.05 #4252), 0f5xn (0.05 #11367, 0.03 #21767, 0.03 #15528), 06cgy (0.05 #14809, 0.04 #12728, 0.03 #10648), 05dbf (0.05 #14559, 0.03 #12843, 0.02 #23243), 09fb5 (0.04 #2136, 0.04 #16696, 0.04 #18776), 01wbg84 (0.04 #46, 0.04 #10444, 0.03 #25005), 01swck (0.04 #799, 0.03 #21597, 0.02 #9118) >> Best rule #16639 for best value: >> intensional similarity = 4 >> extensional distance = 212 >> proper extension: 0dc7hc; >> query: (?x3251, ?x4703) <- film(?x133, ?x3251), nominated_for(?x4703, ?x3251), genre(?x3251, ?x604), ?x604 = 0lsxr >> conf = 0.49 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0571m film! 039wsf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 61.000 33.000 0.487 http://example.org/film/actor/film./film/performance/film #109-02y_lrp PRED entity: 02y_lrp PRED relation: film! PRED expected values: 0d_84 0bz5v2 => 112 concepts (27 used for prediction) PRED predicted values (max 10 best out of 951): 04q5zw (0.50 #2079, 0.49 #51968, 0.45 #49889), 05qd_ (0.50 #2079, 0.49 #51968, 0.45 #49889), 027z0pl (0.50 #2079, 0.49 #51968, 0.43 #49888), 016k6x (0.50 #2079, 0.49 #51968, 0.43 #49888), 06cgy (0.20 #250, 0.05 #4407, 0.03 #27270), 02lkcc (0.20 #242, 0.03 #8556, 0.03 #4399), 015t56 (0.20 #469, 0.03 #2548, 0.02 #25409), 032_jg (0.20 #139, 0.03 #2218, 0.02 #8453), 02_hj4 (0.20 #268, 0.03 #2347, 0.02 #31445), 01kgv4 (0.20 #1181, 0.03 #3260) >> Best rule #2079 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 04tqtl; 051ys82; 02q7yfq; >> query: (?x146, ?x902) <- film_crew_role(?x146, ?x137), nominated_for(?x8716, ?x146), nominated_for(?x902, ?x146), country(?x146, ?x94), ?x8716 = 01yf85 >> conf = 0.50 => this is the best rule for 4 predicted values *> Best rule #54048 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 501 *> proper extension: 018nnz; 070g7; 0prrm; 0h1fktn; 02n72k; 02mc5v; 063y9fp; 03d8jd1; *> query: (?x146, ?x1365) <- film(?x3186, ?x146), executive_produced_by(?x146, ?x4946), award_nominee(?x1365, ?x3186) *> conf = 0.03 ranks of expected_values: 181, 540 EVAL 02y_lrp film! 0bz5v2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 112.000 27.000 0.500 http://example.org/film/actor/film./film/performance/film EVAL 02y_lrp film! 0d_84 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 112.000 27.000 0.500 http://example.org/film/actor/film./film/performance/film #108-03v1s PRED entity: 03v1s PRED relation: district_represented! PRED expected values: 01gst_ => 181 concepts (181 used for prediction) PRED predicted values (max 10 best out of 26): 01gst_ (0.60 #187, 0.53 #83, 0.50 #239), 01gssm (0.57 #190, 0.47 #86, 0.38 #242), 01gsrl (0.57 #191, 0.47 #87, 0.38 #269), 01grpc (0.53 #193, 0.53 #89, 0.35 #271), 01grqd (0.53 #90, 0.47 #194, 0.30 #272), 01grr2 (0.53 #198, 0.40 #94, 0.35 #276), 01grq1 (0.50 #205, 0.47 #101, 0.33 #283), 01gsry (0.50 #202, 0.33 #98, 0.33 #280), 01grp0 (0.47 #199, 0.47 #95, 0.30 #277), 01grnp (0.47 #186, 0.40 #82, 0.30 #264) >> Best rule #187 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 0d0kn; >> query: (?x448, 01gst_) <- contains(?x448, ?x3589), religion(?x448, ?x109), time_zones(?x3589, ?x2674), ?x2674 = 02hcv8 >> conf = 0.60 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 03v1s district_represented! 01gst_ CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 181.000 0.600 http://example.org/government/legislative_session/members./government/government_position_held/district_represented #107-013kcv PRED entity: 013kcv PRED relation: citytown! PRED expected values: 07w3r => 140 concepts (116 used for prediction) PRED predicted values (max 10 best out of 591): 07w3r (0.55 #16985, 0.51 #2425, 0.36 #27500), 035wtd (0.08 #170, 0.02 #1786, 0.02 #2595), 01vs5c (0.08 #241), 064f29 (0.07 #1122, 0.05 #4357, 0.05 #3547), 01w5m (0.06 #71178, 0.05 #79272, 0.02 #946), 0fr9jp (0.06 #71178, 0.05 #79272, 0.02 #1267), 08qnnv (0.06 #71178, 0.05 #79272), 017j69 (0.06 #71178, 0.05 #79272), 017z88 (0.05 #79272, 0.02 #920, 0.02 #2537), 01bm_ (0.05 #79272, 0.02 #1947, 0.02 #2756) >> Best rule #16985 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 0plyy; 0_ytw; 03v_5; 0mp3l; 013hxv; 0d23k; 0f67f; 0fvwg; 0b2ds; 0mnsf; ... >> query: (?x859, ?x2150) <- contains(?x859, ?x2150), category(?x859, ?x134), source(?x859, ?x958) >> conf = 0.55 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 013kcv citytown! 07w3r CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 140.000 116.000 0.551 http://example.org/organization/organization/headquarters./location/mailing_address/citytown #106-01pbs9w PRED entity: 01pbs9w PRED relation: place_of_death PRED expected values: 0f2wj => 122 concepts (122 used for prediction) PRED predicted values (max 10 best out of 33): 030qb3t (0.13 #9561, 0.13 #8978, 0.11 #1384), 02_286 (0.11 #403, 0.08 #13, 0.07 #5272), 0k049 (0.06 #198, 0.05 #393, 0.05 #8959), 0fhp9 (0.06 #209, 0.04 #1182, 0.03 #1570), 01_d4 (0.05 #585, 0.05 #419, 0.04 #1946), 0r3w7 (0.05 #567, 0.03 #2514, 0.02 #2903), 0r3tq (0.05 #539, 0.03 #2486, 0.02 #2875), 0r15k (0.05 #522, 0.01 #2469, 0.01 #2858), 0167q3 (0.05 #489, 0.01 #2436, 0.01 #2825), 0rh6k (0.05 #392, 0.01 #2339, 0.01 #2728) >> Best rule #9561 for best value: >> intensional similarity = 3 >> extensional distance = 569 >> proper extension: 07c37; >> query: (?x5757, 030qb3t) <- gender(?x5757, ?x231), ?x231 = 05zppz, people(?x4322, ?x5757) >> conf = 0.13 => this is the best rule for 1 predicted values *> Best rule #1374 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 02qmncd; 01lc5; 06lk0_; *> query: (?x5757, 0f2wj) <- award(?x5757, ?x1079), ?x1079 = 0l8z1, profession(?x5757, ?x131) *> conf = 0.04 ranks of expected_values: 12 EVAL 01pbs9w place_of_death 0f2wj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 122.000 122.000 0.130 http://example.org/people/deceased_person/place_of_death #105-016h9b PRED entity: 016h9b PRED relation: nationality PRED expected values: 02jx1 => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 31): 02jx1 (0.87 #1687, 0.86 #1886, 0.39 #4304), 09c7w0 (0.79 #2086, 0.78 #2186, 0.77 #6954), 03rk0 (0.27 #342, 0.24 #738, 0.21 #441), 0d060g (0.10 #5272, 0.09 #997, 0.07 #1992), 03_r3 (0.07 #606, 0.07 #507, 0.02 #1401), 0d04z6 (0.07 #268, 0.01 #1558, 0.01 #2553), 012m_ (0.07 #387, 0.05 #486, 0.03 #783), 01b8jj (0.05 #2583, 0.05 #4670, 0.05 #4570), 0f8l9c (0.05 #5286, 0.03 #6674, 0.03 #2802), 06q1r (0.04 #4348, 0.03 #1564, 0.02 #967) >> Best rule #1687 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 0dv1hh; 09m465; >> query: (?x2865, ?x512) <- sibling(?x2865, ?x6129), nationality(?x6129, ?x512), gender(?x6129, ?x231) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 016h9b nationality 02jx1 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 121.000 121.000 0.873 http://example.org/people/person/nationality #104-059lwy PRED entity: 059lwy PRED relation: honored_for PRED expected values: 037xlx => 114 concepts (43 used for prediction) PRED predicted values (max 10 best out of 160): 0q9sg (0.90 #466, 0.88 #3110, 0.86 #2799), 01771z (0.90 #466, 0.88 #3110, 0.86 #2799), 07sgdw (0.88 #3110, 0.86 #2799, 0.85 #4360), 037xlx (0.73 #1026, 0.70 #870, 0.67 #404), 059lwy (0.73 #1054, 0.70 #898, 0.67 #432), 0cf08 (0.17 #6085, 0.11 #5457), 08984j (0.17 #6085), 0dr_4 (0.11 #5457, 0.01 #2365, 0.01 #2834), 011yqc (0.11 #5457, 0.01 #2362, 0.01 #2831), 02fqxm (0.11 #5457) >> Best rule #466 for best value: >> intensional similarity = 5 >> extensional distance = 4 >> proper extension: 0946bb; 04cbbz; 0cwfgz; 06c0ns; >> query: (?x6746, ?x2749) <- honored_for(?x6746, ?x2165), language(?x6746, ?x254), film_release_distribution_medium(?x6746, ?x81), nominated_for(?x2749, ?x6746), ?x2165 = 06ybb1 >> conf = 0.90 => this is the best rule for 2 predicted values *> Best rule #1026 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 9 *> proper extension: 07sgdw; *> query: (?x6746, 037xlx) <- honored_for(?x6746, ?x3330), language(?x6746, ?x254), ?x3330 = 0946bb, nominated_for(?x102, ?x6746), country(?x6746, ?x94) *> conf = 0.73 ranks of expected_values: 4 EVAL 059lwy honored_for 037xlx CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 114.000 43.000 0.900 http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for #103-0h5jg5 PRED entity: 0h5jg5 PRED relation: profession PRED expected values: 01d_h8 => 98 concepts (97 used for prediction) PRED predicted values (max 10 best out of 51): 01d_h8 (0.85 #3262, 0.84 #2818, 0.84 #2078), 02hrh1q (0.67 #8895, 0.66 #9635, 0.65 #7711), 02jknp (0.51 #2376, 0.50 #2080, 0.49 #3116), 02krf9 (0.38 #470, 0.32 #1210, 0.32 #1802), 01c72t (0.28 #6957, 0.27 #11103, 0.27 #10954), 0cbd2 (0.27 #11103, 0.27 #10954, 0.26 #12290), 0196pc (0.26 #12290, 0.26 #11548, 0.25 #12141), 018gz8 (0.19 #4012, 0.16 #1940, 0.14 #1644), 09jwl (0.18 #5346, 0.17 #6530, 0.17 #7123), 0np9r (0.13 #4016, 0.12 #1944, 0.12 #1500) >> Best rule #3262 for best value: >> intensional similarity = 2 >> extensional distance = 334 >> proper extension: 024c1b; >> query: (?x7301, 01d_h8) <- produced_by(?x1012, ?x7301), film_release_region(?x1012, ?x87) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0h5jg5 profession 01d_h8 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 98.000 97.000 0.848 http://example.org/people/person/profession #102-04y9dk PRED entity: 04y9dk PRED relation: award_nominee PRED expected values: 01y64_ => 103 concepts (60 used for prediction) PRED predicted values (max 10 best out of 786): 012q4n (0.81 #74824, 0.81 #114577, 0.81 #88854), 016zp5 (0.16 #137964, 0.12 #1296, 0.10 #88853), 0154qm (0.16 #137964, 0.12 #737, 0.10 #88853), 01kb2j (0.16 #137964, 0.10 #88853, 0.07 #121594), 02f2dn (0.16 #137964, 0.10 #88853, 0.07 #121594), 04y9dk (0.16 #137964, 0.10 #88853, 0.07 #121594), 0kjrx (0.16 #137964, 0.10 #88853, 0.07 #121594), 0170qf (0.16 #137964, 0.10 #88853, 0.07 #121594), 01vh3r (0.16 #137964, 0.10 #88853, 0.07 #121594), 01k5zk (0.16 #137964, 0.10 #88853, 0.07 #121594) >> Best rule #74824 for best value: >> intensional similarity = 3 >> extensional distance = 1120 >> proper extension: 02pt6k_; 08qmfm; >> query: (?x1975, ?x6444) <- student(?x12374, ?x1975), award_nominee(?x1975, ?x1958), award_nominee(?x6444, ?x1975) >> conf = 0.81 => this is the best rule for 1 predicted values *> Best rule #137964 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1764 *> proper extension: 09r_wb; *> query: (?x1975, ?x986) <- film(?x1975, ?x2111), film(?x1975, ?x306), nominated_for(?x986, ?x306), film_release_region(?x2111, ?x94) *> conf = 0.16 ranks of expected_values: 19 EVAL 04y9dk award_nominee 01y64_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.053 103.000 60.000 0.811 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #101-0k6nt PRED entity: 0k6nt PRED relation: olympics PRED expected values: 0lbbj => 190 concepts (190 used for prediction) PRED predicted values (max 10 best out of 20): 06sks6 (0.78 #875, 0.78 #775, 0.75 #371), 0lbbj (0.63 #88, 0.59 #148, 0.55 #188), 018ctl (0.61 #622, 0.60 #663, 0.59 #744), 09n48 (0.61 #622, 0.60 #663, 0.59 #744), 016r9z (0.61 #622, 0.60 #663, 0.59 #744), 0blfl (0.61 #622, 0.60 #663, 0.59 #744), 0sx7r (0.54 #43, 0.47 #83, 0.45 #143), 0kbvv (0.54 #52, 0.41 #192, 0.41 #272), 015pkt (0.54 #60, 0.41 #200, 0.38 #1285), 0swff (0.47 #90, 0.46 #50, 0.41 #150) >> Best rule #875 for best value: >> intensional similarity = 3 >> extensional distance = 44 >> proper extension: 04ty8; >> query: (?x985, 06sks6) <- country(?x7108, ?x985), ?x7108 = 0194d, member_states(?x2106, ?x985) >> conf = 0.78 => this is the best rule for 1 predicted values *> Best rule #88 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 17 *> proper extension: 0jgd; 0b90_r; 03_3d; 0h7x; 06qd3; 01znc_; *> query: (?x985, 0lbbj) <- film_release_region(?x7864, ?x985), film_release_region(?x1080, ?x985), ?x7864 = 0cbn7c, ?x1080 = 01c22t *> conf = 0.63 ranks of expected_values: 2 EVAL 0k6nt olympics 0lbbj CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 190.000 190.000 0.783 http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics #100-0j_sncb PRED entity: 0j_sncb PRED relation: school! PRED expected values: 0jm4v => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 82): 06wpc (0.40 #55, 0.17 #703, 0.16 #865), 07l4z (0.40 #61, 0.14 #1357, 0.14 #1276), 049n7 (0.40 #11, 0.12 #659, 0.12 #1226), 02d02 (0.40 #60, 0.12 #708, 0.11 #870), 01d6g (0.40 #63, 0.11 #2513, 0.11 #1359), 0713r (0.40 #32, 0.11 #2513, 0.11 #2057), 05xvj (0.40 #77, 0.11 #2513, 0.10 #1778), 01yhm (0.24 #343, 0.15 #667, 0.14 #1315), 05m_8 (0.22 #1218, 0.22 #1299, 0.20 #3), 07l8x (0.20 #57, 0.15 #705, 0.14 #1353) >> Best rule #55 for best value: >> intensional similarity = 4 >> extensional distance = 3 >> proper extension: 065y4w7; 01qgr3; 06fq2; >> query: (?x2948, 06wpc) <- company(?x346, ?x2948), school(?x11361, ?x2948), ?x11361 = 03m1n, contains(?x94, ?x2948) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #2513 for first EXPECTED value: *> intensional similarity = 2 *> extensional distance = 112 *> proper extension: 06mkj; 0d05w3; *> query: (?x2948, ?x1347) <- school(?x8133, ?x2948), draft(?x1347, ?x8133) *> conf = 0.11 ranks of expected_values: 73 EVAL 0j_sncb school! 0jm4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.014 116.000 116.000 0.400 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #99-01vvycq PRED entity: 01vvycq PRED relation: award PRED expected values: 02x201b => 121 concepts (121 used for prediction) PRED predicted values (max 10 best out of 310): 01cky2 (0.77 #33175, 0.77 #26692, 0.77 #24022), 01bgqh (0.44 #1184, 0.44 #803, 0.35 #2327), 09sb52 (0.38 #9954, 0.33 #11859, 0.32 #12622), 01ckrr (0.30 #2121, 0.28 #1740, 0.24 #3647), 03qbh5 (0.29 #571, 0.25 #190, 0.24 #4003), 01ck6h (0.29 #494, 0.25 #113, 0.24 #3926), 02f72_ (0.28 #4026, 0.22 #976, 0.21 #9535), 02f716 (0.28 #3978, 0.21 #9535, 0.21 #9534), 03qbnj (0.28 #1361, 0.28 #980, 0.25 #2504), 02x17c2 (0.28 #966, 0.25 #2490, 0.22 #1347) >> Best rule #33175 for best value: >> intensional similarity = 2 >> extensional distance = 1280 >> proper extension: 01gct2; >> query: (?x702, ?x2563) <- award_winner(?x2563, ?x702), category_of(?x2563, ?x2421) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #40822 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 2476 *> proper extension: 019y64; 0cmpn; *> query: (?x702, ?x1443) <- award_winner(?x1854, ?x702), award_winner(?x1854, ?x1800), award_winner(?x1443, ?x1800) *> conf = 0.07 ranks of expected_values: 119 EVAL 01vvycq award 02x201b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.008 121.000 121.000 0.775 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #98-0416y94 PRED entity: 0416y94 PRED relation: honored_for! PRED expected values: 05zksls 09gkdln => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 103): 04n2r9h (0.06 #157, 0.06 #520, 0.06 #762), 09q_6t (0.06 #125, 0.04 #488, 0.02 #972), 02ywhz (0.06 #188, 0.04 #551, 0.02 #1035), 0275n3y (0.06 #548, 0.04 #3694, 0.04 #5630), 0hhtgcw (0.06 #556, 0.03 #2613, 0.02 #3581), 03gwpw2 (0.06 #2546, 0.05 #3635, 0.05 #4361), 05c1t6z (0.05 #3641, 0.05 #5577, 0.04 #4730), 09bymc (0.05 #346, 0.05 #2645, 0.03 #3976), 0hr6lkl (0.05 #2553, 0.03 #11497, 0.03 #3884), 02q690_ (0.05 #5620, 0.05 #3684, 0.05 #4531) >> Best rule #157 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 0d1qmz; 02fqxm; >> query: (?x1318, 04n2r9h) <- currency(?x1318, ?x170), genre(?x1318, ?x53), films(?x13555, ?x1318), nominated_for(?x1318, ?x7590) >> conf = 0.06 => this is the best rule for 1 predicted values *> Best rule #3735 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 381 *> proper extension: 03czz87; *> query: (?x1318, 09gkdln) <- award_winner(?x1318, ?x2551), honored_for(?x6108, ?x1318), nominated_for(?x1774, ?x1318), titles(?x53, ?x1318) *> conf = 0.05 ranks of expected_values: 11, 29 EVAL 0416y94 honored_for! 09gkdln CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 109.000 109.000 0.061 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for EVAL 0416y94 honored_for! 05zksls CNN-1.5+0.5_MA 0.000 0.000 0.000 0.036 109.000 109.000 0.061 http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for #97-04_j5s PRED entity: 04_j5s PRED relation: currency PRED expected values: 09nqf => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.67 #163, 0.67 #157, 0.63 #115), 01nv4h (0.08 #146, 0.06 #230, 0.06 #224), 0ptk_ (0.03 #15, 0.01 #237, 0.01 #255), 0kz1h (0.02 #29), 02l6h (0.01 #190, 0.01 #274, 0.01 #112) >> Best rule #163 for best value: >> intensional similarity = 3 >> extensional distance = 296 >> proper extension: 02g839; 022xml; 031n8c; 02_2kg; 021l5s; 0352gk; 0269kx; 02zc7f; 038czx; 02gn8s; ... >> query: (?x11711, 09nqf) <- contains(?x94, ?x11711), school_type(?x11711, ?x1044), ?x94 = 09c7w0 >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04_j5s currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.671 http://example.org/education/university/local_tuition./measurement_unit/dated_money_value/currency #96-0456zg PRED entity: 0456zg PRED relation: film! PRED expected values: 0456xp 01qr1_ => 79 concepts (30 used for prediction) PRED predicted values (max 10 best out of 1129): 01kx_81 (0.46 #25001, 0.45 #60421, 0.45 #29166), 06fxnf (0.46 #25001, 0.45 #60421, 0.45 #29166), 081lh (0.29 #2244, 0.11 #4329, 0.11 #6414), 09l3p (0.29 #748, 0.10 #2830, 0.04 #9085), 053xw6 (0.29 #1253, 0.05 #3335, 0.04 #7505), 057_yx (0.29 #1841, 0.05 #3923, 0.02 #18513), 07s8r0 (0.29 #263, 0.05 #2345, 0.02 #6515), 02x7vq (0.19 #3062, 0.08 #5147, 0.07 #7232), 02yxwd (0.14 #744, 0.05 #2826, 0.04 #9081), 02qgqt (0.14 #18, 0.05 #2100, 0.04 #10440) >> Best rule #25001 for best value: >> intensional similarity = 5 >> extensional distance = 185 >> proper extension: 027qgy; 047q2k1; 0pv2t; 026390q; 03m4mj; 0sxfd; 01_1pv; 0yyts; 0kcn7; 0bmpm; ... >> query: (?x8358, ?x1291) <- genre(?x8358, ?x258), genre(?x8358, ?x53), nominated_for(?x1291, ?x8358), ?x258 = 05p553, ?x53 = 07s9rl0 >> conf = 0.46 => this is the best rule for 2 predicted values No rule for expected values ranks of expected_values: EVAL 0456zg film! 01qr1_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 30.000 0.459 http://example.org/film/actor/film./film/performance/film EVAL 0456zg film! 0456xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 79.000 30.000 0.459 http://example.org/film/actor/film./film/performance/film #95-0n5d1 PRED entity: 0n5d1 PRED relation: county! PRED expected values: 0xq63 => 191 concepts (113 used for prediction) PRED predicted values (max 10 best out of 164): 0xq63 (0.78 #10435, 0.67 #10434, 0.65 #917), 0xpq9 (0.33 #122, 0.09 #733, 0.03 #2569), 0xpp5 (0.33 #91, 0.09 #702, 0.03 #2538), 02_286 (0.10 #314, 0.08 #925, 0.08 #1231), 0mn0v (0.10 #335, 0.08 #1252), 0mn8t (0.10 #431, 0.05 #2266, 0.02 #4109), 0xr0t (0.09 #869, 0.03 #2705, 0.02 #3321), 0h6l4 (0.09 #855, 0.03 #2691, 0.02 #3307), 0xn7b (0.09 #851, 0.03 #2687, 0.02 #3303), 0xn7q (0.09 #837, 0.03 #2673, 0.02 #3289) >> Best rule #10435 for best value: >> intensional similarity = 4 >> extensional distance = 143 >> proper extension: 0nm87; >> query: (?x10054, ?x8944) <- contains(?x10054, ?x8944), contains(?x6895, ?x10054), source(?x8944, ?x958), second_level_divisions(?x94, ?x10054) >> conf = 0.78 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0n5d1 county! 0xq63 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 191.000 113.000 0.783 http://example.org/location/hud_county_place/county #94-0123qq PRED entity: 0123qq PRED relation: languages PRED expected values: 02h40lc => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 8): 02h40lc (0.91 #90, 0.88 #112, 0.88 #167), 03_9r (0.07 #158, 0.05 #202, 0.05 #15), 06nm1 (0.05 #16, 0.03 #203, 0.03 #104), 0t_2 (0.04 #105, 0.03 #215, 0.03 #94), 064_8sq (0.02 #62, 0.02 #117, 0.01 #139), 02bv9 (0.02 #64, 0.01 #97), 04306rv (0.02 #58, 0.01 #91), 02bjrlw (0.02 #56, 0.01 #89) >> Best rule #90 for best value: >> intensional similarity = 5 >> extensional distance = 92 >> proper extension: 03_8kz; >> query: (?x11203, 02h40lc) <- actor(?x11203, ?x11676), program_creator(?x11203, ?x9842), program(?x7587, ?x11203), nationality(?x11676, ?x94), profession(?x11676, ?x1032) >> conf = 0.91 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0123qq languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.915 http://example.org/tv/tv_program/languages #93-0vmt PRED entity: 0vmt PRED relation: contains PRED expected values: 0qr4n 0qpn9 => 194 concepts (110 used for prediction) PRED predicted values (max 10 best out of 2859): 0m24v (0.84 #43937, 0.84 #5859, 0.83 #120094), 0qpqn (0.84 #108376, 0.82 #61509, 0.75 #2930), 0ny57 (0.84 #108376, 0.82 #61509, 0.75 #2930), 0vmt (0.61 #84940, 0.06 #3015, 0.05 #8874), 09c7w0 (0.61 #84940, 0.03 #29295, 0.03 #26367), 0qr4n (0.49 #17578, 0.06 #3412, 0.05 #9271), 02xpy5 (0.46 #123025, 0.46 #67368, 0.40 #146454), 0trv (0.46 #123025, 0.46 #67368, 0.40 #146454), 021q2j (0.19 #4183, 0.11 #10042, 0.09 #15900), 03bmmc (0.19 #3704, 0.11 #9563, 0.09 #15421) >> Best rule #43937 for best value: >> intensional similarity = 3 >> extensional distance = 43 >> proper extension: 0lwkz; >> query: (?x938, ?x5449) <- administrative_parent(?x5449, ?x938), contains(?x94, ?x938), time_zones(?x938, ?x2088) >> conf = 0.84 => this is the best rule for 1 predicted values *> Best rule #17578 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 31 *> proper extension: 02j71; *> query: (?x938, ?x3832) <- administrative_parent(?x11275, ?x938), time_zones(?x11275, ?x2088), contains(?x11275, ?x3832) *> conf = 0.49 ranks of expected_values: 6, 1337 EVAL 0vmt contains 0qpn9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 194.000 110.000 0.845 http://example.org/location/location/contains EVAL 0vmt contains 0qr4n CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 194.000 110.000 0.845 http://example.org/location/location/contains #92-02nfhx PRED entity: 02nfhx PRED relation: profession PRED expected values: 02hrh1q => 146 concepts (128 used for prediction) PRED predicted values (max 10 best out of 82): 02hrh1q (0.92 #3889, 0.92 #3591, 0.91 #3144), 01d_h8 (0.47 #304, 0.34 #4774, 0.33 #2539), 0gl2ny2 (0.38 #1856, 0.36 #1707, 0.36 #1260), 09jwl (0.35 #3000, 0.31 #3298, 0.27 #169), 0nbcg (0.33 #32, 0.25 #3012, 0.24 #3310), 03gjzk (0.33 #7767, 0.33 #8512, 0.27 #165), 0dxtg (0.31 #13282, 0.30 #7765, 0.30 #8510), 0dz3r (0.30 #2982, 0.24 #3280, 0.19 #2386), 018gz8 (0.27 #167, 0.17 #2551, 0.16 #4339), 02jknp (0.24 #9994, 0.23 #13276, 0.22 #5074) >> Best rule #3889 for best value: >> intensional similarity = 4 >> extensional distance = 344 >> proper extension: 01r42_g; 01hxs4; 01_j71; 018z_c; 018n6m; 06czyr; 02rmxx; 02tf1y; 02_wxh; 01tpl1p; ... >> query: (?x7732, 02hrh1q) <- people(?x2510, ?x7732), profession(?x7732, ?x1581), actor(?x758, ?x7732), gender(?x7732, ?x231) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02nfhx profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 146.000 128.000 0.919 http://example.org/people/person/profession #91-0d05w3 PRED entity: 0d05w3 PRED relation: service_location! PRED expected values: 01c6k4 0p4wb 0cv9b => 249 concepts (245 used for prediction) PRED predicted values (max 10 best out of 167): 01c6k4 (0.45 #5957, 0.43 #1763, 0.36 #4198), 018mxj (0.38 #2848, 0.36 #3524, 0.36 #3389), 064f29 (0.36 #4252, 0.32 #3304, 0.29 #2898), 06_9lg (0.35 #16476, 0.31 #16882, 0.02 #32758), 07zl6m (0.29 #2969, 0.29 #1888, 0.28 #2564), 0cv9b (0.29 #1768, 0.29 #551, 0.22 #5827), 05b5c (0.29 #666, 0.28 #2559, 0.24 #4182), 04sv4 (0.29 #624, 0.28 #2517, 0.24 #2922), 0k9ts (0.29 #1848, 0.24 #4283, 0.24 #2929), 069b85 (0.29 #667, 0.24 #2965, 0.23 #3641) >> Best rule #5957 for best value: >> intensional similarity = 3 >> extensional distance = 31 >> proper extension: 07c5l; >> query: (?x2346, 01c6k4) <- contains(?x2346, ?x12999), time_zones(?x12999, ?x11859), service_location(?x9517, ?x2346) >> conf = 0.45 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6, 17 EVAL 0d05w3 service_location! 0cv9b CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 249.000 245.000 0.455 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 0d05w3 service_location! 0p4wb CNN-1.5+0.5_MA 0.000 0.000 0.000 0.067 249.000 245.000 0.455 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location EVAL 0d05w3 service_location! 01c6k4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 249.000 245.000 0.455 http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location #90-034m8 PRED entity: 034m8 PRED relation: jurisdiction_of_office! PRED expected values: 060c4 0fkvn => 148 concepts (148 used for prediction) PRED predicted values (max 10 best out of 20): 060c4 (0.85 #487, 0.74 #883, 0.74 #1280), 0dq3c (0.44 #2401, 0.38 #2711, 0.38 #2688), 01gkgk (0.44 #2401, 0.38 #2711, 0.38 #2688), 0pqc5 (0.39 #1568, 0.38 #1656, 0.37 #1106), 0f6c3 (0.32 #1747, 0.31 #1197, 0.28 #2033), 0fkvn (0.29 #1193, 0.29 #1743, 0.28 #1589), 09n5b9 (0.27 #1201, 0.27 #1751, 0.24 #2037), 0p5vf (0.23 #78, 0.23 #100, 0.22 #122), 04syw (0.17 #1306, 0.16 #1262, 0.16 #1350), 01zq91 (0.14 #146, 0.13 #168, 0.11 #344) >> Best rule #487 for best value: >> intensional similarity = 2 >> extensional distance = 71 >> proper extension: 02wm6l; >> query: (?x9459, 060c4) <- form_of_government(?x9459, ?x48), ?x48 = 06cx9 >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1, 6 EVAL 034m8 jurisdiction_of_office! 0fkvn CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 148.000 148.000 0.849 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office EVAL 034m8 jurisdiction_of_office! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 148.000 148.000 0.849 http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office #89-0yzbg PRED entity: 0yzbg PRED relation: cinematography PRED expected values: 08mhyd => 90 concepts (41 used for prediction) PRED predicted values (max 10 best out of 38): 08mhyd (0.07 #95, 0.03 #413, 0.03 #223), 06r_by (0.05 #214, 0.04 #404, 0.04 #468), 06p0s1 (0.05 #121, 0.01 #503), 08z39v (0.05 #111, 0.01 #493), 04qvl7 (0.04 #446, 0.02 #1092, 0.02 #1028), 02vx4c2 (0.04 #352, 0.02 #415, 0.02 #225), 0bkf72 (0.04 #127, 0.04 #255, 0.04 #509), 0dzf_ (0.04 #127, 0.04 #255, 0.04 #509), 07b3r9 (0.04 #127, 0.04 #255, 0.04 #509), 027t8fw (0.04 #286, 0.02 #158, 0.01 #861) >> Best rule #95 for best value: >> intensional similarity = 5 >> extensional distance = 42 >> proper extension: 09p7fh; 0571m; 0yxm1; 015qqg; 0qmhk; 01cmp9; 0gmgwnv; 0p9tm; >> query: (?x7243, 08mhyd) <- nominated_for(?x4383, ?x7243), nominated_for(?x2379, ?x7243), nominated_for(?x746, ?x7243), ?x746 = 04dn09n, ?x2379 = 02qvyrt >> conf = 0.07 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 0yzbg cinematography 08mhyd CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 41.000 0.068 http://example.org/film/film/cinematography #88-012g92 PRED entity: 012g92 PRED relation: film PRED expected values: 0n08r => 111 concepts (60 used for prediction) PRED predicted values (max 10 best out of 506): 011ywj (0.18 #1435, 0.04 #19325, 0.04 #5013), 011yg9 (0.18 #1029), 05v38p (0.09 #1136, 0.09 #4714, 0.06 #2925), 01m13b (0.09 #149, 0.06 #1938, 0.04 #5516), 02r8hh_ (0.09 #264, 0.06 #2053, 0.04 #5631), 0bz3jx (0.09 #1139, 0.06 #2928, 0.04 #6506), 0gtvrv3 (0.09 #224, 0.06 #2013, 0.04 #5591), 0g83dv (0.09 #691, 0.06 #2480, 0.04 #6058), 031hcx (0.09 #1274, 0.06 #3063, 0.04 #6641), 035w2k (0.09 #855, 0.04 #4433, 0.03 #13378) >> Best rule #1435 for best value: >> intensional similarity = 3 >> extensional distance = 9 >> proper extension: 01tspc6; 01hkhq; 02f2dn; 02l4pj; 0fbx6; 02f2p7; 0dvld; 02l4rh; 0cbkc; >> query: (?x12218, 011ywj) <- award_winner(?x1008, ?x12218), ?x1008 = 05zvq6g, award_nominee(?x12218, ?x8888) >> conf = 0.18 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 012g92 film 0n08r CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 111.000 60.000 0.182 http://example.org/film/actor/film./film/performance/film #87-04cy8rb PRED entity: 04cy8rb PRED relation: gender PRED expected values: 05zppz => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.79 #17, 0.72 #5, 0.72 #3), 02zsn (0.26 #94, 0.25 #168, 0.25 #160) >> Best rule #17 for best value: >> intensional similarity = 3 >> extensional distance = 64 >> proper extension: 0prjs; 0j582; 026v_78; 0gqrb; 0p9qb; >> query: (?x323, 05zppz) <- award_winner(?x1048, ?x323), titles(?x4757, ?x1048), ?x4757 = 06l3bl >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 04cy8rb gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 129.000 0.788 http://example.org/people/person/gender #86-026lgs PRED entity: 026lgs PRED relation: film_release_region PRED expected values: 07ssc 047yc 0345h 06t2t => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 104): 0345h (0.86 #472, 0.83 #1955, 0.83 #918), 06mkj (0.86 #1977, 0.85 #494, 0.84 #2273), 07ssc (0.83 #457, 0.81 #1940, 0.78 #2236), 05b4w (0.73 #2281, 0.73 #948, 0.73 #1985), 06bnz (0.71 #2263, 0.71 #1967, 0.61 #484), 03spz (0.68 #2017, 0.67 #534, 0.64 #2313), 06t2t (0.66 #2278, 0.66 #1982, 0.60 #499), 05v8c (0.61 #904, 0.59 #458, 0.59 #1941), 01mjq (0.54 #1965, 0.52 #2261, 0.51 #482), 04gzd (0.52 #1935, 0.49 #2231, 0.45 #452) >> Best rule #472 for best value: >> intensional similarity = 3 >> extensional distance = 86 >> proper extension: 0gtv7pk; 0bwfwpj; 08hmch; 0jjy0; 0gj8t_b; 07g_0c; 0gmcwlb; 03qnvdl; 01fmys; 0661m4p; ... >> query: (?x5418, 0345h) <- featured_film_locations(?x5418, ?x108), film_release_region(?x5418, ?x583), ?x583 = 015fr >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1, 3, 7, 12 EVAL 026lgs film_release_region 06t2t CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 87.000 87.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 026lgs film_release_region 0345h CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 026lgs film_release_region 047yc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 87.000 87.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 026lgs film_release_region 07ssc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 87.000 87.000 0.864 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #85-05yvfd PRED entity: 05yvfd PRED relation: nationality PRED expected values: 03rk0 => 131 concepts (108 used for prediction) PRED predicted values (max 10 best out of 73): 03rk0 (0.89 #647, 0.85 #804, 0.85 #748), 09c7w0 (0.76 #3027, 0.74 #1310, 0.74 #3230), 05sb1 (0.51 #3127, 0.49 #3330, 0.10 #9432), 0bq0p9 (0.35 #2012, 0.02 #9329, 0.01 #1127), 055vr (0.29 #3026, 0.29 #3637, 0.28 #4146), 016zwt (0.25 #186, 0.10 #9432, 0.10 #8714), 02jx1 (0.23 #1943, 0.14 #2249, 0.13 #9022), 07ssc (0.18 #1925, 0.12 #215, 0.10 #6783), 0d060g (0.08 #1417, 0.08 #1717, 0.08 #2223), 0f8l9c (0.07 #1932, 0.05 #3149, 0.03 #2945) >> Best rule #647 for best value: >> intensional similarity = 5 >> extensional distance = 44 >> proper extension: 0cfywh; >> query: (?x9465, 03rk0) <- people(?x7838, ?x9465), place_of_birth(?x9465, ?x11801), languages_spoken(?x7838, ?x9113), people(?x7838, ?x12616), ?x12616 = 047jhq >> conf = 0.89 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05yvfd nationality 03rk0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 131.000 108.000 0.891 http://example.org/people/person/nationality #84-07fq1y PRED entity: 07fq1y PRED relation: film PRED expected values: 0340hj => 106 concepts (65 used for prediction) PRED predicted values (max 10 best out of 821): 0340hj (0.67 #3811, 0.62 #2025, 0.06 #7383), 09cr8 (0.61 #7431, 0.10 #5645, 0.06 #105398), 0c0yh4 (0.59 #55373, 0.57 #75024, 0.57 #50013), 0298n7 (0.25 #3135, 0.22 #4921), 0gg5qcw (0.25 #875, 0.20 #6235, 0.11 #8021), 0j43swk (0.25 #500, 0.01 #11218, 0.01 #13004), 09gq0x5 (0.25 #284, 0.01 #30649, 0.01 #12788), 083shs (0.22 #3593, 0.12 #1807, 0.04 #8951), 0g0x9c (0.22 #4937, 0.12 #3151), 01pvxl (0.22 #4481, 0.12 #2695) >> Best rule #3811 for best value: >> intensional similarity = 3 >> extensional distance = 7 >> proper extension: 015gsv; >> query: (?x156, 0340hj) <- film(?x156, ?x5305), ?x5305 = 012s1d, award(?x156, ?x1132) >> conf = 0.67 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07fq1y film 0340hj CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 106.000 65.000 0.667 http://example.org/film/actor/film./film/performance/film #83-0g5pv3 PRED entity: 0g5pv3 PRED relation: language PRED expected values: 02bjrlw => 142 concepts (141 used for prediction) PRED predicted values (max 10 best out of 45): 06b_j (0.40 #1885, 0.33 #251, 0.23 #308), 04306rv (0.40 #1885, 0.31 #290, 0.30 #176), 02bjrlw (0.40 #1885, 0.22 #115, 0.20 #173), 064_8sq (0.40 #1885, 0.21 #364, 0.20 #992), 06nm1 (0.40 #1885, 0.20 #182, 0.19 #1494), 012w70 (0.40 #1885, 0.11 #126, 0.10 #184), 02hwhyv (0.40 #1885, 0.10 #201, 0.08 #258), 02ztjwg (0.12 #31, 0.11 #145, 0.10 #203), 0jzc (0.12 #76, 0.11 #133, 0.08 #248), 02hwyss (0.12 #98, 0.10 #213, 0.08 #270) >> Best rule #1885 for best value: >> intensional similarity = 4 >> extensional distance = 121 >> proper extension: 03rtz1; 01dc0c; >> query: (?x1262, ?x90) <- music(?x1262, ?x3134), nominated_for(?x2160, ?x1262), language(?x2160, ?x90), film_release_region(?x2160, ?x512) >> conf = 0.40 => this is the best rule for 7 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 0g5pv3 language 02bjrlw CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 142.000 141.000 0.396 http://example.org/film/film/language #82-07cyl PRED entity: 07cyl PRED relation: film_crew_role PRED expected values: 02vs3x5 => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 33): 02r96rf (0.82 #156, 0.80 #615, 0.80 #234), 09zzb8 (0.81 #1108, 0.74 #421, 0.72 #1527), 0ch6mp2 (0.80 #239, 0.79 #1116, 0.77 #429), 09vw2b7 (0.75 #1115, 0.70 #238, 0.68 #467), 01vx2h (0.53 #165, 0.49 #624, 0.47 #472), 02rh1dz (0.35 #164, 0.25 #394, 0.20 #509), 01pvkk (0.32 #1121, 0.31 #1540, 0.30 #1045), 02ynfr (0.32 #56, 0.28 #208, 0.22 #170), 015h31 (0.18 #163, 0.18 #393, 0.18 #241), 089fss (0.18 #45, 0.09 #1114, 0.07 #1266) >> Best rule #156 for best value: >> intensional similarity = 4 >> extensional distance = 47 >> proper extension: 04zyhx; 04y9mm8; >> query: (?x3471, 02r96rf) <- film_crew_role(?x3471, ?x2095), prequel(?x4991, ?x3471), film(?x450, ?x3471), crewmember(?x3471, ?x6232) >> conf = 0.82 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 7 *> proper extension: 07ghq; *> query: (?x3471, 02vs3x5) <- film_release_region(?x3471, ?x7430), film_release_region(?x3471, ?x2513), film_release_region(?x3471, ?x1355), ?x2513 = 05b4w, ?x7430 = 01mk6, medal(?x1355, ?x422) *> conf = 0.11 ranks of expected_values: 18 EVAL 07cyl film_crew_role 02vs3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 108.000 108.000 0.816 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #81-021r7r PRED entity: 021r7r PRED relation: artists! PRED expected values: 03_d0 02x8m 05w3f => 159 concepts (100 used for prediction) PRED predicted values (max 10 best out of 249): 05bt6j (0.77 #16701, 0.29 #23801, 0.24 #30599), 05w3f (0.62 #345, 0.50 #2194, 0.50 #37), 0dl5d (0.54 #2176, 0.52 #13896, 0.45 #4952), 064t9 (0.53 #9880, 0.52 #16671, 0.51 #13273), 06j6l (0.50 #3747, 0.36 #5905, 0.32 #9915), 0126t5 (0.50 #702, 0.33 #2243, 0.22 #4709), 0glt670 (0.49 #9907, 0.38 #13300, 0.31 #12684), 03_d0 (0.46 #6793, 0.43 #4634, 0.40 #5560), 01lyv (0.44 #3732, 0.27 #5890, 0.20 #33), 03lty (0.38 #335, 0.36 #643, 0.33 #2184) >> Best rule #16701 for best value: >> intensional similarity = 5 >> extensional distance = 243 >> proper extension: 0150jk; 07qnf; 03t9sp; 01fl3; 0dtd6; 0frsw; 016fmf; 04qmr; 01rm8b; 0fcsd; ... >> query: (?x7437, 05bt6j) <- artists(?x6210, ?x7437), artists(?x6210, ?x7549), artists(?x6210, ?x1407), ?x7549 = 02p2zq, award(?x1407, ?x4488) >> conf = 0.77 => this is the best rule for 1 predicted values *> Best rule #345 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 11 *> proper extension: 067mj; 07bzp; 01l_w0; 0p76z; *> query: (?x7437, 05w3f) <- artists(?x7083, ?x7437), artists(?x6210, ?x7437), artists(?x1000, ?x7437), ?x6210 = 01fh36, ?x1000 = 0xhtw, ?x7083 = 02yv6b *> conf = 0.62 ranks of expected_values: 2, 8, 18 EVAL 021r7r artists! 05w3f CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 159.000 100.000 0.771 http://example.org/music/genre/artists EVAL 021r7r artists! 02x8m CNN-1.5+0.5_MA 0.000 0.000 0.000 0.062 159.000 100.000 0.771 http://example.org/music/genre/artists EVAL 021r7r artists! 03_d0 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 159.000 100.000 0.771 http://example.org/music/genre/artists #80-024t0y PRED entity: 024t0y PRED relation: profession PRED expected values: 025sppp => 147 concepts (86 used for prediction) PRED predicted values (max 10 best out of 86): 0dxtg (0.64 #2791, 0.64 #596, 0.62 #4107), 02jknp (0.57 #737, 0.56 #4835, 0.50 #7904), 015cjr (0.41 #923, 0.31 #1362, 0.30 #2973), 018gz8 (0.40 #1767, 0.39 #2060, 0.35 #1036), 0cbd2 (0.33 #152, 0.27 #590, 0.18 #9072), 09jwl (0.33 #162, 0.27 #9374, 0.26 #5282), 014ktf (0.33 #98, 0.06 #2144, 0.05 #1851), 03sbb (0.33 #231, 0.03 #3595, 0.03 #1692), 05ll37 (0.33 #69, 0.02 #1822, 0.02 #2115), 02krf9 (0.30 #7191, 0.27 #608, 0.27 #8506) >> Best rule #2791 for best value: >> intensional similarity = 4 >> extensional distance = 53 >> proper extension: 0q9kd; 02qgqt; 02p65p; 014zcr; 0h5f5n; 05ty4m; 01q_ph; 0z4s; 05kfs; 0mdqp; ... >> query: (?x12254, 0dxtg) <- executive_produced_by(?x3133, ?x12254), student(?x6315, ?x12254), people(?x1050, ?x12254), major_field_of_study(?x6315, ?x742) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #3577 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 62 *> proper extension: 01q7cb_; 01pfkw; 01bzr4; 08xz51; 044prt; *> query: (?x12254, 025sppp) <- gender(?x12254, ?x231), profession(?x12254, ?x967), ?x231 = 05zppz, ?x967 = 012t_z *> conf = 0.06 ranks of expected_values: 32 EVAL 024t0y profession 025sppp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.031 147.000 86.000 0.636 http://example.org/people/person/profession #79-01xcfy PRED entity: 01xcfy PRED relation: award PRED expected values: 0fm3kw => 96 concepts (96 used for prediction) PRED predicted values (max 10 best out of 288): 02g2yr (0.71 #25611, 0.70 #22809, 0.70 #19604), 027571b (0.71 #25611, 0.70 #22809, 0.70 #19604), 027b9k6 (0.71 #25611, 0.70 #22809, 0.70 #19604), 05ztrmj (0.36 #580, 0.33 #180, 0.14 #22408), 09sb52 (0.33 #40, 0.32 #2040, 0.31 #3640), 0gq9h (0.33 #9676, 0.09 #475, 0.07 #17677), 0f4x7 (0.27 #431, 0.17 #31, 0.15 #28015), 099ck7 (0.27 #663, 0.17 #263, 0.15 #28015), 04kxsb (0.27 #522, 0.17 #122, 0.14 #22408), 05pcn59 (0.26 #2479, 0.25 #3679, 0.24 #2879) >> Best rule #25611 for best value: >> intensional similarity = 3 >> extensional distance = 2002 >> proper extension: 01wz_ml; 0f6lx; >> query: (?x2891, ?x6463) <- gender(?x2891, ?x514), award_winner(?x6463, ?x2891), award(?x940, ?x6463) >> conf = 0.71 => this is the best rule for 3 predicted values *> Best rule #2289 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 55 *> proper extension: 0fthdk; *> query: (?x2891, 0fm3kw) <- languages(?x2891, ?x254), participant(?x2891, ?x3604), nominated_for(?x2891, ?x2892) *> conf = 0.02 ranks of expected_values: 230 EVAL 01xcfy award 0fm3kw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 96.000 96.000 0.712 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #78-032f6 PRED entity: 032f6 PRED relation: countries_spoken_in PRED expected values: 0d05q4 => 39 concepts (37 used for prediction) PRED predicted values (max 10 best out of 256): 0hzlz (0.60 #729, 0.50 #906, 0.40 #552), 0697s (0.60 #779, 0.40 #602, 0.33 #75), 0h44w (0.40 #844, 0.40 #667, 0.33 #140), 03spz (0.40 #802, 0.40 #625, 0.33 #98), 07dzf (0.40 #809, 0.40 #632, 0.33 #105), 06tw8 (0.40 #812, 0.40 #635, 0.33 #108), 03__y (0.40 #791, 0.40 #614, 0.33 #87), 03rk0 (0.40 #762, 0.33 #58, 0.31 #3777), 0162b (0.40 #870, 0.33 #166, 0.20 #693), 05sb1 (0.33 #176, 0.31 #881, 0.28 #6383) >> Best rule #729 for best value: >> intensional similarity = 12 >> extensional distance = 3 >> proper extension: 0jzc; 02hxcvy; >> query: (?x13310, 0hzlz) <- countries_spoken_in(?x13310, ?x6305), language(?x4998, ?x13310), languages_spoken(?x3584, ?x13310), country(?x4045, ?x6305), ?x4998 = 0dzlbx, adjoins(?x6305, ?x7032), ?x4045 = 06z6r, contains(?x6305, ?x13440), administrative_parent(?x6305, ?x551), olympics(?x6305, ?x1931), medal(?x6305, ?x422), jurisdiction_of_office(?x182, ?x6305) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #176 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 02h40lc; *> query: (?x13310, ?x7032) <- countries_spoken_in(?x13310, ?x6305), language(?x4998, ?x13310), language(?x1724, ?x13310), languages_spoken(?x3584, ?x13310), country(?x4045, ?x6305), country(?x3641, ?x6305), ?x4998 = 0dzlbx, adjoins(?x6305, ?x7032), ?x4045 = 06z6r, contains(?x6305, ?x13440), administrative_parent(?x6305, ?x551), ?x1724 = 02r8hh_, ?x3641 = 03fyrh, administrative_area_type(?x6305, ?x2792) *> conf = 0.33 ranks of expected_values: 12 EVAL 032f6 countries_spoken_in 0d05q4 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.083 39.000 37.000 0.600 http://example.org/language/human_language/countries_spoken_in #77-05c74 PRED entity: 05c74 PRED relation: country! PRED expected values: 0bynt => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 52): 0bynt (0.88 #531, 0.88 #375, 0.87 #479), 03_8r (0.72 #490, 0.70 #438, 0.69 #542), 07gyv (0.67 #59, 0.52 #423, 0.51 #475), 01lb14 (0.57 #379, 0.57 #483, 0.56 #431), 03hr1p (0.57 #439, 0.56 #387, 0.55 #491), 07jbh (0.55 #396, 0.54 #604, 0.53 #500), 0w0d (0.54 #377, 0.51 #429, 0.51 #481), 06wrt (0.50 #432, 0.47 #380, 0.47 #536), 0194d (0.49 #462, 0.43 #514, 0.42 #410), 064vjs (0.47 #498, 0.45 #446, 0.45 #394) >> Best rule #531 for best value: >> intensional similarity = 3 >> extensional distance = 91 >> proper extension: 027rn; 05r4w; 09c7w0; 0jgd; 0b90_r; 0154j; 03rjj; 03_3d; 0d060g; 0d0vqn; ... >> query: (?x7709, 0bynt) <- currency(?x7709, ?x170), country(?x1967, ?x7709), film_release_region(?x1150, ?x7709) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 05c74 country! 0bynt CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.882 http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country #76-01wg3q PRED entity: 01wg3q PRED relation: performance_role PRED expected values: 0l14qv => 122 concepts (95 used for prediction) PRED predicted values (max 10 best out of 27): 03bx0bm (0.16 #194, 0.16 #506, 0.16 #106), 0l14qv (0.14 #48, 0.06 #180, 0.04 #2681), 026t6 (0.11 #134, 0.06 #178, 0.06 #1296), 0d8lm (0.11 #130, 0.03 #441, 0.03 #218), 0l14md (0.10 #182, 0.08 #941, 0.06 #360), 0342h (0.10 #179, 0.07 #311, 0.07 #354), 013y1f (0.10 #195, 0.06 #239, 0.06 #954), 02sgy (0.06 #353, 0.05 #359, 0.04 #623), 05r5c (0.06 #315, 0.05 #95, 0.04 #585), 03gvt (0.05 #126, 0.04 #346, 0.03 #392) >> Best rule #194 for best value: >> intensional similarity = 5 >> extensional distance = 29 >> proper extension: 0b68vs; 01w923; 0144l1; 0892sx; 014q2g; 01vsl3_; 01vn35l; 016ntp; 02qwg; 0bkg4; ... >> query: (?x8754, 03bx0bm) <- profession(?x8754, ?x2659), artist(?x2931, ?x8754), nationality(?x8754, ?x1310), ?x2659 = 039v1, ?x1310 = 02jx1 >> conf = 0.16 => this is the best rule for 1 predicted values *> Best rule #48 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 5 *> proper extension: 04rcr; *> query: (?x8754, 0l14qv) <- artist(?x12752, ?x8754), artists(?x505, ?x8754), award(?x8754, ?x4912), ?x12752 = 07gqbk *> conf = 0.14 ranks of expected_values: 2 EVAL 01wg3q performance_role 0l14qv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 122.000 95.000 0.161 http://example.org/music/artist/contribution./music/recording_contribution/performance_role #75-01vksx PRED entity: 01vksx PRED relation: film_release_region PRED expected values: 047lj 06npd 0ctw_b 02vzc 07twz => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 113): 02vzc (0.82 #2535, 0.80 #2010, 0.80 #2799), 06t2t (0.76 #438, 0.72 #174, 0.69 #2018), 05v8c (0.64 #141, 0.58 #1062, 0.56 #1985), 0ctw_b (0.63 #412, 0.52 #1992, 0.44 #148), 06c1y (0.59 #422, 0.40 #158, 0.33 #2002), 07twz (0.52 #203, 0.37 #467, 0.27 #1124), 07f1x (0.47 #489, 0.36 #225, 0.33 #2069), 01pj7 (0.45 #427, 0.36 #163, 0.35 #2007), 047lj (0.44 #138, 0.41 #402, 0.37 #1059), 09pmkv (0.39 #413, 0.36 #149, 0.30 #1993) >> Best rule #2535 for best value: >> intensional similarity = 3 >> extensional distance = 205 >> proper extension: 07ghq; 0m3gy; >> query: (?x908, 02vzc) <- titles(?x811, ?x908), film_release_region(?x908, ?x87), ?x87 = 05r4w >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1, 4, 6, 9, 14 EVAL 01vksx film_release_region 07twz CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 90.000 90.000 0.821 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01vksx film_release_region 02vzc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.821 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01vksx film_release_region 0ctw_b CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 90.000 90.000 0.821 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01vksx film_release_region 06npd CNN-1.5+0.5_MA 0.000 0.000 1.000 0.100 90.000 90.000 0.821 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region EVAL 01vksx film_release_region 047lj CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 90.000 90.000 0.821 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #74-07c1v PRED entity: 07c1v PRED relation: taxonomy PRED expected values: 04n6k => 90 concepts (90 used for prediction) PRED predicted values (max 10 best out of 1): 04n6k (0.70 #44, 0.68 #47, 0.67 #19) >> Best rule #44 for best value: >> intensional similarity = 4 >> extensional distance = 28 >> proper extension: 02h40lc; 05qjt; 036hv; 02lp1; 01mkq; 02ky346; 01jzxy; 0h5k; 03g3w; 062z7; ... >> query: (?x14555, 04n6k) <- major_field_of_study(?x3439, ?x14555), ?x3439 = 03ksy, major_field_of_study(?x1200, ?x14555), ?x1200 = 016t_3 >> conf = 0.70 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07c1v taxonomy 04n6k CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 90.000 90.000 0.700 http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy #73-06jzh PRED entity: 06jzh PRED relation: languages PRED expected values: 02h40lc => 101 concepts (101 used for prediction) PRED predicted values (max 10 best out of 10): 02h40lc (0.39 #470, 0.37 #119, 0.34 #1328), 0x82 (0.25 #37), 06nm1 (0.06 #2462, 0.02 #279, 0.02 #357), 064_8sq (0.06 #561, 0.06 #483, 0.05 #249), 02bjrlw (0.03 #235, 0.02 #469, 0.02 #391), 03k50 (0.02 #1174, 0.02 #1408, 0.02 #2467), 04306rv (0.02 #237, 0.01 #549, 0.01 #471), 03_9r (0.01 #161), 0t_2 (0.01 #321, 0.01 #789, 0.01 #360), 07c9s (0.01 #1417, 0.01 #2003, 0.01 #2476) >> Best rule #470 for best value: >> intensional similarity = 3 >> extensional distance = 306 >> proper extension: 03f1zhf; 01vv6xv; >> query: (?x540, 02h40lc) <- location(?x540, ?x5719), people(?x3584, ?x540), participant(?x540, ?x8927) >> conf = 0.39 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06jzh languages 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 101.000 101.000 0.390 http://example.org/people/person/languages #72-01f9r0 PRED entity: 01f9r0 PRED relation: genre! PRED expected values: 072x7s 02z0f6l 07l450 => 45 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1884): 011ywj (0.71 #19890, 0.60 #14359, 0.57 #23577), 03s6l2 (0.71 #18517, 0.60 #12986, 0.50 #14828), 03f7nt (0.65 #5528, 0.60 #13744, 0.50 #8215), 01flv_ (0.65 #5528, 0.50 #15826, 0.50 #8455), 0hfzr (0.65 #5528, 0.50 #15463, 0.50 #6249), 027r7k (0.65 #5528, 0.50 #9139, 0.43 #23886), 025rvx0 (0.65 #5528, 0.50 #15759, 0.43 #21293), 0260bz (0.65 #5528, 0.50 #5873, 0.43 #22463), 0jsf6 (0.65 #5528, 0.50 #8479, 0.40 #14008), 07024 (0.65 #5528, 0.50 #6020, 0.40 #9705) >> Best rule #19890 for best value: >> intensional similarity = 11 >> extensional distance = 5 >> proper extension: 01t_vv; >> query: (?x10122, 011ywj) <- genre(?x5001, ?x10122), genre(?x4498, ?x10122), genre(?x3246, ?x10122), ?x4498 = 043tz0c, country(?x3246, ?x94), award(?x3246, ?x3245), ?x94 = 09c7w0, film(?x157, ?x5001), film_crew_role(?x5001, ?x137), nominated_for(?x3751, ?x5001), genre(?x4881, ?x10122) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #13165 for first EXPECTED value: *> intensional similarity = 11 *> extensional distance = 3 *> proper extension: 01jfsb; *> query: (?x10122, 072x7s) <- genre(?x7768, ?x10122), genre(?x4498, ?x10122), genre(?x3246, ?x10122), ?x4498 = 043tz0c, country(?x3246, ?x1023), award(?x3246, ?x13664), ?x13664 = 0j298t8, film(?x2551, ?x3246), honored_for(?x7767, ?x7768), ?x1023 = 0ctw_b, produced_by(?x7768, ?x2789) *> conf = 0.40 ranks of expected_values: 539, 897, 988 EVAL 01f9r0 genre! 07l450 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 45.000 27.000 0.714 http://example.org/film/film/genre EVAL 01f9r0 genre! 02z0f6l CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 45.000 27.000 0.714 http://example.org/film/film/genre EVAL 01f9r0 genre! 072x7s CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 45.000 27.000 0.714 http://example.org/film/film/genre #71-0j0pf PRED entity: 0j0pf PRED relation: influenced_by PRED expected values: 04mhl 048_p => 106 concepts (44 used for prediction) PRED predicted values (max 10 best out of 440): 0g5ff (0.34 #2743, 0.19 #4019, 0.19 #3168), 03f0324 (0.33 #150, 0.22 #1425, 0.13 #10370), 07lp1 (0.33 #339, 0.22 #1614, 0.12 #17888), 06dl_ (0.33 #48, 0.22 #1323, 0.06 #3024), 02wh0 (0.33 #373, 0.20 #5052, 0.11 #1648), 01v9724 (0.33 #175, 0.15 #9544, 0.12 #3151), 03j2gxx (0.33 #374, 0.12 #17888, 0.11 #1649), 03j0d (0.33 #328, 0.11 #1603, 0.10 #5434), 073v6 (0.33 #87, 0.11 #1362, 0.09 #3063), 013pp3 (0.33 #167, 0.11 #1442, 0.06 #3143) >> Best rule #2743 for best value: >> intensional similarity = 5 >> extensional distance = 30 >> proper extension: 06hmd; 01y8d4; >> query: (?x5086, 0g5ff) <- influenced_by(?x5086, ?x9425), gender(?x5086, ?x231), award(?x9425, ?x1375), student(?x1675, ?x9425), ?x1375 = 0262zm >> conf = 0.34 => this is the best rule for 1 predicted values *> Best rule #5532 for first EXPECTED value: *> intensional similarity = 6 *> extensional distance = 48 *> proper extension: 0gppg; *> query: (?x5086, ?x476) <- gender(?x5086, ?x231), award(?x5086, ?x8880), nationality(?x5086, ?x512), award(?x7828, ?x8880), award(?x476, ?x8880), ?x7828 = 014ps4 *> conf = 0.03 ranks of expected_values: 232, 235 EVAL 0j0pf influenced_by 048_p CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 106.000 44.000 0.344 http://example.org/influence/influence_node/influenced_by EVAL 0j0pf influenced_by 04mhl CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 106.000 44.000 0.344 http://example.org/influence/influence_node/influenced_by #70-04mkbj PRED entity: 04mkbj PRED relation: colors! PRED expected values: 019m9h => 21 concepts (21 used for prediction) PRED predicted values (max 10 best out of 368): 04l5d0 (0.90 #2179, 0.78 #363, 0.63 #5087), 0j5m6 (0.90 #2179, 0.78 #363, 0.63 #5087), 01ct6 (0.90 #2179, 0.78 #363, 0.63 #5087), 03lpp_ (0.90 #2179, 0.78 #363, 0.63 #5087), 03915c (0.90 #2179, 0.78 #363, 0.63 #5087), 05tfm (0.90 #2179, 0.78 #363, 0.63 #5087), 0jmk7 (0.90 #2179, 0.78 #363, 0.63 #5087), 051vz (0.90 #2179, 0.78 #363, 0.63 #5087), 0jm9w (0.90 #2179, 0.78 #363, 0.63 #5087), 04b5l3 (0.90 #2179, 0.78 #363, 0.63 #5087) >> Best rule #2179 for best value: >> intensional similarity = 43 >> extensional distance = 3 >> proper extension: 038hg; >> query: (?x7179, ?x580) <- colors(?x10945, ?x7179), colors(?x9676, ?x7179), colors(?x3948, ?x7179), colors(?x6537, ?x7179), currency(?x3948, ?x170), institution(?x1771, ?x9676), institution(?x865, ?x9676), institution(?x620, ?x9676), colors(?x3948, ?x332), school_type(?x3948, ?x1507), major_field_of_study(?x3948, ?x3490), major_field_of_study(?x3948, ?x2606), major_field_of_study(?x3948, ?x1154), ?x865 = 02h4rq6, major_field_of_study(?x9880, ?x1154), major_field_of_study(?x8850, ?x1154), major_field_of_study(?x3813, ?x1154), major_field_of_study(?x1681, ?x1154), major_field_of_study(?x741, ?x1154), colors(?x5651, ?x332), ?x620 = 07s6fsf, colors(?x580, ?x332), ?x3813 = 07vfj, ?x1681 = 07szy, ?x8850 = 021q2j, student(?x3948, ?x1068), major_field_of_study(?x11768, ?x3490), major_field_of_study(?x8016, ?x3490), ?x741 = 01w3v, major_field_of_study(?x10945, ?x1527), ?x11768 = 01hc1j, ?x9880 = 0jpkw, major_field_of_study(?x2606, ?x373), major_field_of_study(?x5900, ?x1154), ?x8016 = 02yxjs, state_province_region(?x9676, ?x3818), major_field_of_study(?x12293, ?x2606), ?x1771 = 019v9k, school(?x700, ?x10945), school_type(?x10945, ?x1044), ?x5651 = 027mdh, institution(?x734, ?x3948), ?x12293 = 01pj48 >> conf = 0.90 => this is the best rule for 41 predicted values *> Best rule #543 for first EXPECTED value: *> intensional similarity = 39 *> extensional distance = 1 *> proper extension: 083jv; *> query: (?x7179, 019m9h) <- colors(?x10666, ?x7179), colors(?x9676, ?x7179), colors(?x3948, ?x7179), colors(?x6537, ?x7179), currency(?x3948, ?x170), institution(?x865, ?x9676), school_type(?x3948, ?x1507), major_field_of_study(?x3948, ?x9111), major_field_of_study(?x3948, ?x6756), major_field_of_study(?x3948, ?x1154), major_field_of_study(?x3948, ?x742), ?x865 = 02h4rq6, ?x1154 = 02lp1, school(?x580, ?x3948), ?x6537 = 01s0t3, ?x1507 = 01_9fk, organization(?x5510, ?x9676), student(?x3948, ?x1068), major_field_of_study(?x11640, ?x6756), major_field_of_study(?x10910, ?x6756), major_field_of_study(?x5486, ?x6756), major_field_of_study(?x2948, ?x6756), major_field_of_study(?x1011, ?x6756), major_field_of_study(?x3354, ?x9111), major_field_of_study(?x2327, ?x9111), ?x5486 = 0g8rj, major_field_of_study(?x2830, ?x742), contains(?x94, ?x9676), state_province_region(?x3948, ?x1274), ?x2830 = 01wdj_, ?x2948 = 0j_sncb, ?x10910 = 013807, ?x3354 = 01q460, ?x1011 = 07w0v, ?x2327 = 07wjk, major_field_of_study(?x734, ?x742), ?x10666 = 01dzg0, team(?x2010, ?x580), ?x11640 = 013719 *> conf = 0.33 ranks of expected_values: 250 EVAL 04mkbj colors! 019m9h CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 21.000 21.000 0.896 http://example.org/sports/sports_team/colors #69-08849 PRED entity: 08849 PRED relation: award_winner! PRED expected values: 05f3q => 132 concepts (132 used for prediction) PRED predicted values (max 10 best out of 221): 02grdc (0.40 #1328, 0.33 #32, 0.09 #19043), 05p09zm (0.33 #557, 0.20 #1421, 0.06 #6606), 0fc9js (0.33 #214, 0.20 #1510, 0.02 #19225), 03tcnt (0.33 #1029, 0.11 #4485, 0.05 #21336), 054ky1 (0.33 #3998, 0.09 #28194, 0.07 #21281), 03rbj2 (0.27 #4974, 0.23 #5838, 0.16 #11887), 0gq9h (0.22 #3966, 0.10 #21249, 0.09 #12175), 0gqwc (0.22 #3963, 0.04 #28159, 0.03 #28593), 0c4z8 (0.22 #3960, 0.03 #21243, 0.03 #34207), 054ks3 (0.22 #4030, 0.03 #31253, 0.03 #32549) >> Best rule #1328 for best value: >> intensional similarity = 5 >> extensional distance = 3 >> proper extension: 0157m; 014vk4; >> query: (?x11617, 02grdc) <- religion(?x11617, ?x492), jurisdiction_of_office(?x11617, ?x10569), award_winner(?x10552, ?x11617), religion(?x512, ?x492), film_release_region(?x66, ?x512) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #39755 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 1086 *> proper extension: 03h26tm; 030_1m; *> query: (?x11617, ?x3846) <- award_winner(?x11617, ?x10552), student(?x6127, ?x10552), award_winner(?x3846, ?x10552), nationality(?x10552, ?x792) *> conf = 0.17 ranks of expected_values: 17 EVAL 08849 award_winner! 05f3q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.059 132.000 132.000 0.400 http://example.org/award/award_category/winners./award/award_honor/award_winner #68-0klh7 PRED entity: 0klh7 PRED relation: film PRED expected values: 033srr => 121 concepts (86 used for prediction) PRED predicted values (max 10 best out of 838): 016dj8 (0.15 #1105, 0.06 #2880, 0.04 #8206), 0k_9j (0.15 #1393, 0.04 #8494, 0.04 #10269), 08mg_b (0.15 #1113, 0.04 #8214, 0.03 #17089), 0q9sg (0.12 #2535, 0.04 #11411, 0.04 #13186), 0n6ds (0.12 #3389, 0.03 #12265, 0.02 #14040), 0gffmn8 (0.12 #2294, 0.03 #11170, 0.02 #12945), 053rxgm (0.12 #1951, 0.03 #10827, 0.02 #12602), 01cssf (0.08 #89, 0.06 #3639, 0.02 #7190), 03nfnx (0.08 #1390, 0.06 #3165, 0.04 #15591), 01jrbv (0.08 #547, 0.06 #2322, 0.03 #11198) >> Best rule #1105 for best value: >> intensional similarity = 3 >> extensional distance = 11 >> proper extension: 0157m; 01gy7r; 03l3ln; 0hfml; 0432cd; >> query: (?x2849, 016dj8) <- film(?x2849, ?x1230), student(?x8398, ?x2849), company(?x2849, ?x13490) >> conf = 0.15 => this is the best rule for 1 predicted values *> Best rule #11303 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 74 *> proper extension: 016bx2; 0fpj9pm; 011lvx; 0167v4; 01npcy7; *> query: (?x2849, 033srr) <- type_of_union(?x2849, ?x1873), location_of_ceremony(?x2849, ?x13064), spouse(?x2849, ?x1991) *> conf = 0.01 ranks of expected_values: 704 EVAL 0klh7 film 033srr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 121.000 86.000 0.154 http://example.org/film/actor/film./film/performance/film #67-01gf5h PRED entity: 01gf5h PRED relation: award_winner! PRED expected values: 03r0g9 => 132 concepts (90 used for prediction) PRED predicted values (max 10 best out of 163): 05lfwd (0.18 #4058, 0.05 #97713, 0.01 #83587), 01v1ln (0.15 #96576, 0.14 #95439, 0.13 #89758), 024lff (0.15 #96576, 0.14 #95439, 0.13 #89758), 03r0g9 (0.15 #96576, 0.14 #95439, 0.13 #89758), 0164qt (0.15 #96576, 0.14 #95439, 0.13 #89758), 04vr_f (0.12 #3526, 0.05 #97713, 0.01 #60332), 0b1y_2 (0.08 #3732, 0.05 #97713), 053x8hr (0.07 #101125, 0.07 #101124), 08gsvw (0.07 #101125, 0.07 #101124), 03ln8b (0.05 #97713, 0.04 #3636, 0.02 #74075) >> Best rule #4058 for best value: >> intensional similarity = 2 >> extensional distance = 47 >> proper extension: 01fsyp; >> query: (?x1001, 05lfwd) <- award_winner(?x5592, ?x1001), ?x5592 = 0275n3y >> conf = 0.18 => this is the best rule for 1 predicted values *> Best rule #96576 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 1244 *> proper extension: 01w92; 05xbx; 03yxwq; 01zcrv; 04rqd; 03lpbx; *> query: (?x1001, ?x835) <- award_winner(?x7027, ?x1001), award(?x7027, ?x1854), award_winner(?x835, ?x7027) *> conf = 0.15 ranks of expected_values: 4 EVAL 01gf5h award_winner! 03r0g9 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 132.000 90.000 0.184 http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner #66-04g73n PRED entity: 04g73n PRED relation: film! PRED expected values: 028d4v => 90 concepts (52 used for prediction) PRED predicted values (max 10 best out of 797): 01mkn_d (0.47 #37344, 0.45 #64316, 0.44 #58092), 01795t (0.47 #37344, 0.45 #64316, 0.44 #58092), 0p8r1 (0.44 #2655, 0.34 #6803, 0.12 #8877), 0bxtg (0.40 #77, 0.11 #2151, 0.10 #10448), 02_p5w (0.32 #4788, 0.09 #8936, 0.07 #6862), 02gf_l (0.27 #5411, 0.14 #7485, 0.09 #9559), 015pvh (0.22 #3172, 0.14 #7320, 0.05 #9394), 019vgs (0.22 #2729, 0.10 #6877, 0.05 #8951), 01rs5p (0.22 #3860, 0.07 #8008, 0.06 #10082), 01mylz (0.22 #4014, 0.07 #8162, 0.06 #10236) >> Best rule #37344 for best value: >> intensional similarity = 4 >> extensional distance = 441 >> proper extension: 047q2k1; 0b76t12; 02ppg1r; 0f42nz; 0ddj0x; 02754c9; 052_mn; 0bs5vty; 04b_jc; 09rvwmy; >> query: (?x8112, ?x2156) <- nominated_for(?x2156, ?x8112), film(?x806, ?x8112), genre(?x8112, ?x258), ?x258 = 05p553 >> conf = 0.47 => this is the best rule for 2 predicted values *> Best rule #6612 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 27 *> proper extension: 0g4pl7z; *> query: (?x8112, 028d4v) <- production_companies(?x8112, ?x2156), genre(?x8112, ?x258), ?x2156 = 01795t, nominated_for(?x1723, ?x8112) *> conf = 0.03 ranks of expected_values: 192 EVAL 04g73n film! 028d4v CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 90.000 52.000 0.471 http://example.org/film/actor/film./film/performance/film #65-09qc1 PRED entity: 09qc1 PRED relation: gender PRED expected values: 05zppz => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 2): 05zppz (0.86 #23, 0.85 #31, 0.85 #59), 02zsn (0.46 #119, 0.29 #80, 0.28 #104) >> Best rule #23 for best value: >> intensional similarity = 4 >> extensional distance = 192 >> proper extension: 0dzkq; >> query: (?x4732, 05zppz) <- people(?x10035, ?x4732), type_of_union(?x4732, ?x566), ?x566 = 04ztj, place_of_death(?x4732, ?x6959) >> conf = 0.86 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 09qc1 gender 05zppz CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.856 http://example.org/people/person/gender #64-02q87z6 PRED entity: 02q87z6 PRED relation: film_crew_role PRED expected values: 02vs3x5 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 29): 0dxtw (0.44 #9, 0.38 #1311, 0.37 #315), 01vx2h (0.38 #1448, 0.36 #1312, 0.33 #10), 01pvkk (0.34 #317, 0.31 #625, 0.28 #2038), 089fss (0.33 #5, 0.30 #39, 0.11 #175), 0215hd (0.22 #17, 0.20 #51, 0.15 #1455), 0d2b38 (0.22 #24, 0.20 #58, 0.14 #160), 089g0h (0.22 #18, 0.20 #52, 0.12 #1456), 02rh1dz (0.18 #76, 0.13 #1446, 0.11 #1310), 04pyp5 (0.12 #629, 0.11 #905, 0.09 #732), 01xy5l_ (0.12 #422, 0.12 #1451, 0.11 #1315) >> Best rule #9 for best value: >> intensional similarity = 4 >> extensional distance = 7 >> proper extension: 0ktpx; >> query: (?x5964, 0dxtw) <- titles(?x3613, ?x5964), nominated_for(?x5964, ?x392), ?x3613 = 09blyk, film_crew_role(?x5964, ?x137) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #158 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 41 *> proper extension: 080lkt7; *> query: (?x5964, 02vs3x5) <- titles(?x162, ?x5964), ?x162 = 04xvlr, film_crew_role(?x5964, ?x137), cinematography(?x5964, ?x185) *> conf = 0.12 ranks of expected_values: 11 EVAL 02q87z6 film_crew_role 02vs3x5 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.091 87.000 87.000 0.444 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #63-02z0dfh PRED entity: 02z0dfh PRED relation: nominated_for PRED expected values: 0_b9f 0p_tz 01f8f7 06fpsx 02r858_ 04b_jc => 42 concepts (16 used for prediction) PRED predicted values (max 10 best out of 1374): 0k4p0 (0.80 #4593, 0.76 #3062, 0.72 #6124), 0jqj5 (0.80 #4593, 0.76 #3062, 0.72 #6124), 04b_jc (0.80 #4593, 0.76 #3062, 0.72 #6124), 06q8qh (0.80 #4593, 0.76 #3062, 0.72 #6124), 03lvwp (0.80 #4593, 0.76 #3062, 0.72 #6124), 02c638 (0.67 #1830, 0.50 #3361, 0.33 #299), 05hjnw (0.67 #2269, 0.44 #3800, 0.18 #11463), 0209xj (0.67 #1623, 0.39 #3154, 0.33 #92), 0gmcwlb (0.67 #1707, 0.39 #3238, 0.20 #10901), 07s846j (0.67 #2112, 0.39 #3643, 0.17 #11306) >> Best rule #4593 for best value: >> intensional similarity = 5 >> extensional distance = 16 >> proper extension: 0gr4k; 09qwmm; 09sb52; 094qd5; 0gqwc; 099cng; 02y_rq5; 02x4w6g; 02x17s4; 02x4x18; ... >> query: (?x1254, ?x144) <- award(?x144, ?x1254), nominated_for(?x1254, ?x7087), nominated_for(?x1254, ?x813), ?x7087 = 0bnzd, film_release_distribution_medium(?x813, ?x81) >> conf = 0.80 => this is the best rule for 5 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3, 259, 291, 537, 642, 852 EVAL 02z0dfh nominated_for 04b_jc CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 42.000 16.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02z0dfh nominated_for 02r858_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 42.000 16.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02z0dfh nominated_for 06fpsx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 16.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02z0dfh nominated_for 01f8f7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 42.000 16.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02z0dfh nominated_for 0p_tz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 42.000 16.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for EVAL 02z0dfh nominated_for 0_b9f CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 42.000 16.000 0.798 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #62-064vjs PRED entity: 064vjs PRED relation: sports! PRED expected values: 0l98s => 40 concepts (40 used for prediction) PRED predicted values (max 10 best out of 29): 06sks6 (0.85 #948, 0.83 #823, 0.82 #122), 0kbvb (0.82 #122, 0.81 #935, 0.81 #60), 0kbws (0.82 #122, 0.81 #60, 0.79 #247), 0l98s (0.75 #374, 0.67 #467, 0.67 #404), 0nbjq (0.75 #383, 0.67 #476, 0.67 #413), 0lk8j (0.75 #382, 0.67 #475, 0.67 #412), 018qb4 (0.67 #239, 0.67 #177, 0.56 #485), 0c_tl (0.67 #234, 0.51 #92, 0.51 #463), 0lv1x (0.62 #381, 0.56 #754, 0.56 #474), 0sxrz (0.57 #632, 0.56 #414, 0.55 #539) >> Best rule #948 for best value: >> intensional similarity = 44 >> extensional distance = 24 >> proper extension: 0d1tm; 06wrt; 035d1m; 0dwxr; 06z68; 018w8; >> query: (?x4310, 06sks6) <- olympics(?x4310, ?x775), country(?x4310, ?x2513), country(?x4310, ?x1536), country(?x4310, ?x512), ?x512 = 07ssc, sports(?x1081, ?x4310), film_release_region(?x9002, ?x2513), film_release_region(?x7832, ?x2513), film_release_region(?x7554, ?x2513), film_release_region(?x7016, ?x2513), film_release_region(?x6095, ?x2513), film_release_region(?x5825, ?x2513), film_release_region(?x5473, ?x2513), film_release_region(?x5425, ?x2513), film_release_region(?x4828, ?x2513), film_release_region(?x3854, ?x2513), film_release_region(?x3812, ?x2513), film_release_region(?x1956, ?x2513), film_release_region(?x1701, ?x2513), film_release_region(?x908, ?x2513), ?x7554 = 01mgw, ?x1956 = 05qbckf, ?x5425 = 02prwdh, currency(?x2513, ?x170), olympics(?x2513, ?x418), ?x908 = 01vksx, ?x7016 = 07g1sm, ?x5825 = 067ghz, ?x4828 = 02fttd, country(?x520, ?x2513), ?x1701 = 0bh8yn3, ?x520 = 01dys, country(?x1009, ?x2513), ?x3812 = 0c3xw46, ?x5473 = 0hv8w, ?x3854 = 03q0r1, film_release_region(?x5576, ?x1536), ?x5576 = 0gbfn9, ?x6095 = 0bq6ntw, ?x9002 = 0ndsl1x, ?x1081 = 0l6m5, organization(?x2513, ?x127), nationality(?x4379, ?x1536), ?x7832 = 0fphf3v >> conf = 0.85 => this is the best rule for 1 predicted values *> Best rule #374 for first EXPECTED value: *> intensional similarity = 41 *> extensional distance = 6 *> proper extension: 0d1t3; *> query: (?x4310, 0l98s) <- olympics(?x4310, ?x775), country(?x4310, ?x4059), country(?x4310, ?x2513), country(?x4310, ?x2346), country(?x4310, ?x512), country(?x4310, ?x456), country(?x4310, ?x279), ?x512 = 07ssc, sports(?x4255, ?x4310), sports(?x3971, ?x4310), ?x2513 = 05b4w, ?x279 = 0d060g, ?x456 = 05qhw, country(?x206, ?x2346), adjoins(?x2146, ?x2346), nationality(?x754, ?x2346), locations(?x3654, ?x2346), film_release_region(?x6882, ?x2346), film_release_region(?x1927, ?x2346), film_release_region(?x8193, ?x4059), film_release_region(?x6621, ?x4059), film_release_region(?x6543, ?x4059), film_release_region(?x4441, ?x4059), film_release_region(?x3606, ?x4059), film_release_region(?x2037, ?x4059), film_release_region(?x1202, ?x4059), ?x6543 = 0421v9q, ?x2037 = 0gvrws1, ?x1202 = 0gj8t_b, currency(?x2346, ?x170), contains(?x2346, ?x7351), ?x4441 = 0125xq, ?x6621 = 0h63gl9, ?x6882 = 043tvp3, ?x3971 = 0jhn7, ?x1927 = 0by1wkq, ?x3606 = 0gh65c5, countries_spoken_in(?x8650, ?x4059), teams(?x4059, ?x978), ?x8193 = 03z9585, ?x4255 = 0lgxj *> conf = 0.75 ranks of expected_values: 4 EVAL 064vjs sports! 0l98s CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 40.000 40.000 0.846 http://example.org/olympics/olympic_games/sports #61-017gm7 PRED entity: 017gm7 PRED relation: currency PRED expected values: 09nqf => 72 concepts (72 used for prediction) PRED predicted values (max 10 best out of 5): 09nqf (0.88 #57, 0.84 #50, 0.82 #134), 02l6h (0.03 #102, 0.03 #109, 0.03 #123), 01nv4h (0.02 #247, 0.02 #275, 0.02 #359), 088n7 (0.01 #133, 0.01 #154), 0ptk_ (0.01 #66, 0.01 #73) >> Best rule #57 for best value: >> intensional similarity = 3 >> extensional distance = 70 >> proper extension: 03twd6; 07ghq; >> query: (?x1392, 09nqf) <- film_release_region(?x1392, ?x47), prequel(?x4610, ?x1392), award_winner(?x1392, ?x1846) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 017gm7 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 72.000 72.000 0.875 http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency #60-0jm74 PRED entity: 0jm74 PRED relation: school PRED expected values: 02y9bj => 76 concepts (55 used for prediction) PRED predicted values (max 10 best out of 622): 065y4w7 (0.60 #1704, 0.40 #1891, 0.33 #8), 01ptt7 (0.56 #1534, 0.33 #25, 0.24 #4536), 078bz (0.50 #788, 0.44 #1354, 0.33 #2857), 0lyjf (0.50 #638, 0.40 #447, 0.33 #72), 07t90 (0.42 #2889, 0.33 #65, 0.22 #1574), 05krk (0.38 #758, 0.33 #1324, 0.33 #569), 09f2j (0.35 #3836, 0.33 #74, 0.26 #5150), 0f1nl (0.35 #3790, 0.33 #28, 0.26 #5104), 01vs5c (0.35 #5163, 0.29 #3849, 0.20 #462), 01jsn5 (0.33 #1536, 0.33 #969, 0.33 #27) >> Best rule #1704 for best value: >> intensional similarity = 10 >> extensional distance = 8 >> proper extension: 01lpx8; >> query: (?x7136, 065y4w7) <- colors(?x7136, ?x4557), teams(?x1860, ?x7136), ?x4557 = 019sc, team(?x11924, ?x7136), student(?x1884, ?x11924), place_of_birth(?x1933, ?x1860), featured_film_locations(?x195, ?x1860), location(?x827, ?x1860), location_of_ceremony(?x566, ?x1860), award_winner(?x1998, ?x1933) >> conf = 0.60 => this is the best rule for 1 predicted values *> Best rule #116 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 0jmj7; *> query: (?x7136, 02y9bj) <- school(?x7136, ?x331), draft(?x7136, ?x8542), draft(?x7136, ?x8133), ?x331 = 01jssp, position(?x7136, ?x6848), position(?x7136, ?x1348), ?x1348 = 01pv51, ?x8542 = 09th87, ?x8133 = 025tn92, position(?x6847, ?x6848), position(?x6128, ?x6848), ?x6847 = 02r2qt7, ?x6128 = 0jm64, team(?x6848, ?x660) *> conf = 0.33 ranks of expected_values: 24 EVAL 0jm74 school 02y9bj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 76.000 55.000 0.600 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #59-06b7s9 PRED entity: 06b7s9 PRED relation: currency PRED expected values: 09nqf => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 6): 09nqf (0.79 #127, 0.73 #50, 0.60 #190), 01nv4h (0.12 #198, 0.11 #170, 0.11 #114), 02l6h (0.05 #116, 0.04 #333, 0.04 #137), 0ptk_ (0.03 #115, 0.02 #276, 0.02 #234), 0kz1h (0.02 #117, 0.01 #376, 0.01 #341), 02gsvk (0.01 #160, 0.01 #174, 0.01 #202) >> Best rule #127 for best value: >> intensional similarity = 4 >> extensional distance = 262 >> proper extension: 04wlz2; 05krk; 01pl14; 01j_9c; 02w2bc; 065y4w7; 0288zy; 02cttt; 01hhvg; 07w0v; ... >> query: (?x12277, 09nqf) <- category(?x12277, ?x134), school_type(?x12277, ?x3092), contains(?x94, ?x12277), ?x94 = 09c7w0 >> conf = 0.79 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06b7s9 currency 09nqf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 115.000 115.000 0.792 http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency #58-09qv3c PRED entity: 09qv3c PRED relation: ceremony PRED expected values: 07y9ts => 39 concepts (39 used for prediction) PRED predicted values (max 10 best out of 131): 0gpjbt (0.53 #808, 0.48 #938, 0.33 #2889), 09n4nb (0.51 #824, 0.47 #954, 0.33 #2905), 0466p0j (0.51 #850, 0.46 #980, 0.32 #2931), 05pd94v (0.50 #782, 0.46 #912, 0.32 #2473), 02rjjll (0.50 #785, 0.46 #915, 0.32 #2866), 07y9ts (0.50 #453, 0.33 #63, 0.22 #583), 02cg41 (0.50 #897, 0.46 #1027, 0.32 #2978), 056878 (0.49 #810, 0.46 #940, 0.32 #2891), 01c6qp (0.49 #798, 0.45 #928, 0.32 #2879), 019bk0 (0.47 #795, 0.43 #925, 0.30 #2486) >> Best rule #808 for best value: >> intensional similarity = 4 >> extensional distance = 133 >> proper extension: 0249fn; 02flpq; >> query: (?x870, 0gpjbt) <- award(?x9656, ?x870), award_winner(?x9656, ?x906), gender(?x9656, ?x231), category_of(?x870, ?x2758) >> conf = 0.53 => this is the best rule for 1 predicted values *> Best rule #453 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 24 *> proper extension: 09v7wsg; *> query: (?x870, 07y9ts) <- nominated_for(?x870, ?x758), award_winner(?x870, ?x71), ceremony(?x870, ?x5585), ?x5585 = 03nnm4t *> conf = 0.50 ranks of expected_values: 6 EVAL 09qv3c ceremony 07y9ts CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 39.000 39.000 0.526 http://example.org/award/award_category/winners./award/award_honor/ceremony #57-0hhtgcw PRED entity: 0hhtgcw PRED relation: award_winner PRED expected values: 03y82t6 06mt91 02s_qz => 62 concepts (47 used for prediction) PRED predicted values (max 10 best out of 1293): 01j7rd (0.71 #4891, 0.67 #1828, 0.25 #18668), 04ns3gy (0.67 #2852, 0.57 #5915, 0.16 #19692), 0gcs9 (0.60 #439, 0.45 #9627, 0.29 #3502), 01vw20h (0.60 #692, 0.40 #9880, 0.22 #6816), 02xs0q (0.57 #5135, 0.50 #2072, 0.19 #18912), 05bnq3j (0.57 #5315, 0.50 #2252, 0.16 #19092), 0j1yf (0.50 #1795, 0.43 #4858, 0.29 #3327), 0cp9f9 (0.50 #2713, 0.43 #5776, 0.22 #19553), 0p_2r (0.50 #1722, 0.43 #4785, 0.16 #18562), 02778qt (0.50 #1987, 0.43 #5050, 0.12 #18827) >> Best rule #4891 for best value: >> intensional similarity = 6 >> extensional distance = 5 >> proper extension: 0lp_cd3; >> query: (?x6297, 01j7rd) <- honored_for(?x6297, ?x9788), honored_for(?x6297, ?x8775), award_winner(?x6297, ?x827), country_of_origin(?x9788, ?x94), ?x8775 = 07zhjj, genre(?x9788, ?x5728) >> conf = 0.71 => this is the best rule for 1 predicted values *> Best rule #4594 for first EXPECTED value: *> intensional similarity = 8 *> extensional distance = 5 *> proper extension: 02yw5r; 0gpjbt; 0bc773; 02cg41; *> query: (?x6297, ?x1715) <- honored_for(?x6297, ?x86), award_winner(?x6297, ?x7865), award_winner(?x6297, ?x827), award(?x7865, ?x1389), artists(?x6210, ?x7865), ?x6210 = 01fh36, award_winner(?x1715, ?x827), location(?x827, ?x1860) *> conf = 0.20 ranks of expected_values: 80, 240 EVAL 0hhtgcw award_winner 02s_qz CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 62.000 47.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0hhtgcw award_winner 06mt91 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.013 62.000 47.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner EVAL 0hhtgcw award_winner 03y82t6 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 62.000 47.000 0.714 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #56-01756d PRED entity: 01756d PRED relation: artists PRED expected values: 01w5n51 0j6cj 017_hq => 65 concepts (27 used for prediction) PRED predicted values (max 10 best out of 1089): 0197tq (0.70 #12, 0.31 #1079, 0.31 #1082), 06br6t (0.58 #9547, 0.57 #8467, 0.18 #21453), 01w5n51 (0.54 #8266, 0.48 #9346, 0.25 #6099), 0259r0 (0.50 #220, 0.31 #1079, 0.31 #1082), 0dl567 (0.50 #357, 0.31 #1079, 0.31 #1082), 02z4b_8 (0.50 #635, 0.31 #1079, 0.31 #1082), 025ldg (0.50 #370, 0.31 #1079, 0.31 #1082), 0qf11 (0.50 #380, 0.31 #1082, 0.30 #1083), 01vvycq (0.40 #46, 0.31 #1079, 0.31 #1082), 01vvyfh (0.40 #341, 0.31 #1079, 0.31 #1082) >> Best rule #12 for best value: >> intensional similarity = 7 >> extensional distance = 8 >> proper extension: 01lyv; 02qdgx; >> query: (?x1748, 0197tq) <- artists(?x1748, ?x2824), parent_genre(?x1748, ?x3061), artists(?x3061, ?x6368), artists(?x3061, ?x3682), award_winner(?x7535, ?x6368), category(?x3682, ?x134), ?x2824 = 02w4fkq >> conf = 0.70 => this is the best rule for 1 predicted values *> Best rule #8266 for first EXPECTED value: *> intensional similarity = 9 *> extensional distance = 26 *> proper extension: 03ckfl9; 03w94xt; *> query: (?x1748, 01w5n51) <- artists(?x1748, ?x4942), artists(?x1748, ?x1749), group(?x1166, ?x1749), artists(?x9342, ?x1749), artists(?x9063, ?x1749), ?x9342 = 0grjmv, ?x9063 = 0cx7f, influenced_by(?x4942, ?x1136), instrumentalists(?x1166, ?x130) *> conf = 0.54 ranks of expected_values: 3, 54, 362 EVAL 01756d artists 017_hq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.019 65.000 27.000 0.700 http://example.org/music/genre/artists EVAL 01756d artists 0j6cj CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 65.000 27.000 0.700 http://example.org/music/genre/artists EVAL 01756d artists 01w5n51 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 65.000 27.000 0.700 http://example.org/music/genre/artists #55-03np_7 PRED entity: 03np_7 PRED relation: institution! PRED expected values: 07s6fsf 028dcg => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 19): 014mlp (0.75 #1332, 0.71 #85, 0.70 #1435), 02_xgp2 (0.46 #415, 0.45 #495, 0.44 #154), 0bkj86 (0.42 #150, 0.37 #88, 0.36 #67), 07s6fsf (0.35 #405, 0.32 #485, 0.30 #525), 04zx3q1 (0.29 #145, 0.28 #1537, 0.22 #246), 028dcg (0.28 #1537, 0.25 #17, 0.24 #98), 071tyz (0.28 #1537, 0.08 #69, 0.07 #90), 02m4yg (0.28 #1537, 0.07 #238, 0.06 #157), 01ysy9 (0.28 #1537, 0.06 #503, 0.06 #79), 01gkg3 (0.28 #1537, 0.05 #2360, 0.02 #197) >> Best rule #1332 for best value: >> intensional similarity = 5 >> extensional distance = 481 >> proper extension: 014b4h; 01v3ht; 07b2yw; 01nmgc; 0xxc; 01fsv9; 019_6d; 0yl_w; 03q6zc; >> query: (?x12795, 014mlp) <- institution(?x1771, ?x12795), institution(?x1771, ?x6816), institution(?x1771, ?x3821), ?x3821 = 0kw4j, ?x6816 = 017y6l >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #405 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 207 *> proper extension: 01w_sh; *> query: (?x12795, 07s6fsf) <- student(?x12795, ?x1408), colors(?x12795, ?x663), institution(?x865, ?x12795), ?x865 = 02h4rq6 *> conf = 0.35 ranks of expected_values: 4, 6 EVAL 03np_7 institution! 028dcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 129.000 129.000 0.745 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 03np_7 institution! 07s6fsf CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 129.000 129.000 0.745 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #54-01ky2h PRED entity: 01ky2h PRED relation: profession PRED expected values: 0nbcg => 152 concepts (105 used for prediction) PRED predicted values (max 10 best out of 87): 02hrh1q (0.76 #2828, 0.76 #4013, 0.75 #3865), 01d_h8 (0.71 #6, 0.36 #2227, 0.33 #302), 02jknp (0.71 #8, 0.33 #304, 0.23 #2229), 0nbcg (0.67 #8482, 0.52 #772, 0.51 #6549), 0dxtg (0.57 #14, 0.33 #310, 0.31 #5049), 016z4k (0.49 #5336, 0.43 #6522, 0.43 #6078), 0cbd2 (0.48 #10239, 0.45 #155, 0.42 #1932), 0dz3r (0.45 #1187, 0.44 #5927, 0.43 #6668), 0n1h (0.43 #12, 0.33 #308, 0.28 #901), 0kyk (0.40 #325, 0.36 #177, 0.29 #29) >> Best rule #2828 for best value: >> intensional similarity = 4 >> extensional distance = 102 >> proper extension: 01vlj1g; 04yj5z; 0yfp; 013cr; 01mqz0; 01gzm2; 08hp53; 030h95; 0gt_k; 0311wg; ... >> query: (?x1832, 02hrh1q) <- location(?x1832, ?x739), ?x739 = 02_286, people(?x2510, ?x1832), award_winner(?x2561, ?x1832) >> conf = 0.76 => this is the best rule for 1 predicted values *> Best rule #8482 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 636 *> proper extension: 025tdwc; 01d1yr; *> query: (?x1832, 0nbcg) <- profession(?x1832, ?x1614), profession(?x7578, ?x1614), ?x7578 = 01k3qj *> conf = 0.67 ranks of expected_values: 4 EVAL 01ky2h profession 0nbcg CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 152.000 105.000 0.760 http://example.org/people/person/profession #53-06kcjr PRED entity: 06kcjr PRED relation: parent_genre PRED expected values: 0glt670 => 48 concepts (48 used for prediction) PRED predicted values (max 10 best out of 124): 0glt670 (0.92 #673, 0.35 #996, 0.33 #512), 016_nr (0.33 #48, 0.20 #209, 0.19 #693), 02x8m (0.31 #659, 0.22 #498, 0.22 #807), 06by7 (0.29 #1306, 0.28 #1467, 0.26 #1950), 06j6l (0.22 #518, 0.22 #807, 0.21 #1002), 06cqb (0.22 #485, 0.22 #807, 0.15 #5645), 0827d (0.22 #486, 0.20 #163, 0.14 #324), 026z9 (0.22 #807, 0.20 #213, 0.15 #5645), 0190yn (0.22 #807, 0.15 #5645, 0.12 #776), 0gywn (0.20 #202, 0.14 #363, 0.11 #525) >> Best rule #673 for best value: >> intensional similarity = 4 >> extensional distance = 24 >> proper extension: 025tjk_; >> query: (?x11692, 0glt670) <- parent_genre(?x11692, ?x13432), artists(?x13432, ?x140), ?x140 = 01vvydl, parent_genre(?x13432, ?x1127) >> conf = 0.92 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06kcjr parent_genre 0glt670 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 48.000 48.000 0.923 http://example.org/music/genre/parent_genre #52-01vrz41 PRED entity: 01vrz41 PRED relation: artist! PRED expected values: 01trtc => 117 concepts (78 used for prediction) PRED predicted values (max 10 best out of 99): 015_1q (0.24 #2876, 0.21 #3965, 0.21 #4785), 03rhqg (0.20 #422, 0.18 #1103, 0.18 #558), 0g768 (0.17 #1122, 0.14 #1530, 0.14 #2483), 043g7l (0.15 #435, 0.13 #299, 0.09 #2885), 01trtc (0.14 #1293, 0.13 #1701, 0.12 #1429), 0181dw (0.14 #1535, 0.13 #1399, 0.13 #1263), 03mp8k (0.14 #470, 0.13 #334, 0.10 #1287), 01clyr (0.13 #1118, 0.10 #2479, 0.08 #437), 0n85g (0.13 #602, 0.12 #1147, 0.11 #2644), 011k1h (0.13 #1098, 0.12 #417, 0.11 #281) >> Best rule #2876 for best value: >> intensional similarity = 3 >> extensional distance = 274 >> proper extension: 0khth; 07mvp; 04k05; >> query: (?x1231, 015_1q) <- award(?x1231, ?x462), artist(?x1954, ?x1231), award_winner(?x5059, ?x1231) >> conf = 0.24 => this is the best rule for 1 predicted values *> Best rule #1293 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 123 *> proper extension: 07c0j; 04mn81; 01vs_v8; 01pgzn_; 01vx5w7; 01w02sy; 01wmgrf; 03bnv; 04qmr; 01svw8n; ... *> query: (?x1231, 01trtc) <- award(?x1231, ?x462), artist(?x1954, ?x1231), participant(?x2647, ?x1231) *> conf = 0.14 ranks of expected_values: 5 EVAL 01vrz41 artist! 01trtc CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 117.000 78.000 0.236 http://example.org/music/record_label/artist #51-0gyh PRED entity: 0gyh PRED relation: state_province_region! PRED expected values: 03x1s8 => 187 concepts (116 used for prediction) PRED predicted values (max 10 best out of 758): 058cm (0.27 #64242, 0.23 #19407, 0.21 #1493), 0plxn (0.27 #64242, 0.23 #19407, 0.21 #1493), 0q6lr (0.27 #64242, 0.23 #19407, 0.21 #1493), 0q8p8 (0.27 #64242, 0.23 #19407, 0.21 #1493), 0q8jl (0.27 #64242, 0.23 #19407, 0.21 #1493), 0lphb (0.27 #64242, 0.23 #19407, 0.21 #1493), 0q8s4 (0.27 #64242, 0.23 #19407, 0.21 #1493), 0qc7l (0.27 #64242, 0.23 #19407, 0.21 #1493), 0kwmc (0.23 #19407, 0.21 #1493, 0.20 #23140), 0fttg (0.23 #19407, 0.21 #1493, 0.20 #23140) >> Best rule #64242 for best value: >> intensional similarity = 3 >> extensional distance = 158 >> proper extension: 07cfx; 0dhdp; 022_6; 0dbdy; 05l5n; 0jcg8; 0jt5zcn; 02h6_6p; 09tlh; 0vzm; ... >> query: (?x2831, ?x11387) <- contains(?x2831, ?x11387), country(?x2831, ?x94), category(?x11387, ?x134) >> conf = 0.27 => this is the best rule for 8 predicted values No rule for expected values ranks of expected_values: EVAL 0gyh state_province_region! 03x1s8 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 187.000 116.000 0.271 http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region #50-07hgm PRED entity: 07hgm PRED relation: group! PRED expected values: 01vj9c => 100 concepts (100 used for prediction) PRED predicted values (max 10 best out of 91): 018vs (0.72 #637, 0.36 #1527, 0.36 #459), 0l14md (0.67 #630, 0.44 #185, 0.38 #1787), 03bx0bm (0.61 #649, 0.44 #204, 0.40 #1806), 0l14qv (0.39 #628, 0.22 #183, 0.21 #450), 028tv0 (0.33 #636, 0.33 #191, 0.29 #1793), 05r5c (0.28 #631, 0.21 #453, 0.16 #1076), 01vj9c (0.22 #638, 0.22 #193, 0.18 #282), 03qjg (0.22 #227, 0.18 #316, 0.17 #2542), 0l14j_ (0.22 #231, 0.11 #676, 0.06 #2583), 06ncr (0.17 #40, 0.11 #663, 0.08 #4581) >> Best rule #637 for best value: >> intensional similarity = 4 >> extensional distance = 16 >> proper extension: 01fl3; 0fcsd; 047cx; 0ycp3; 06gcn; >> query: (?x9497, 018vs) <- artists(?x9063, ?x9497), ?x9063 = 0cx7f, artist(?x2149, ?x9497), group(?x1656, ?x9497) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #638 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 16 *> proper extension: 01fl3; 0fcsd; 047cx; 0ycp3; 06gcn; *> query: (?x9497, 01vj9c) <- artists(?x9063, ?x9497), ?x9063 = 0cx7f, artist(?x2149, ?x9497), group(?x1656, ?x9497) *> conf = 0.22 ranks of expected_values: 7 EVAL 07hgm group! 01vj9c CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 100.000 100.000 0.722 http://example.org/music/performance_role/regular_performances./music/group_membership/group #49-01n5309 PRED entity: 01n5309 PRED relation: nationality PRED expected values: 09c7w0 => 139 concepts (139 used for prediction) PRED predicted values (max 10 best out of 49): 09c7w0 (0.87 #402, 0.84 #1504, 0.83 #2707), 03rt9 (0.32 #314, 0.03 #10125, 0.03 #3821), 0d060g (0.17 #7, 0.09 #207, 0.08 #708), 07ssc (0.15 #4926, 0.14 #215, 0.13 #4425), 02jx1 (0.11 #2438, 0.11 #4443, 0.11 #3039), 05bcl (0.09 #361), 0345h (0.07 #3940, 0.07 #3839, 0.07 #5745), 03rk0 (0.06 #9870, 0.05 #9167, 0.05 #13382), 03rjj (0.05 #306, 0.03 #406, 0.03 #10125), 04hqz (0.05 #380) >> Best rule #402 for best value: >> intensional similarity = 3 >> extensional distance = 29 >> proper extension: 08jtv5; 035wq7; 02hg53; >> query: (?x692, 09c7w0) <- location(?x692, ?x2850), ?x2850 = 0cr3d, actor(?x6884, ?x692) >> conf = 0.87 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01n5309 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 139.000 139.000 0.871 http://example.org/people/person/nationality #48-0cvkv5 PRED entity: 0cvkv5 PRED relation: nominated_for! PRED expected values: 094qd5 => 108 concepts (86 used for prediction) PRED predicted values (max 10 best out of 244): 02y_j8g (0.71 #3212, 0.67 #230, 0.66 #8945), 0468g4r (0.71 #3212, 0.67 #230, 0.66 #13075), 0gq9h (0.47 #5792, 0.41 #4647, 0.40 #4876), 0gs9p (0.46 #5794, 0.37 #4649, 0.36 #6481), 02qyp19 (0.43 #1, 0.38 #231, 0.28 #1607), 0gqng (0.43 #2, 0.38 #232, 0.27 #461), 019f4v (0.41 #5555, 0.40 #4639, 0.39 #5784), 094qd5 (0.38 #3017, 0.21 #5768, 0.16 #6455), 03hl6lc (0.36 #1733, 0.24 #2192, 0.17 #5860), 0k611 (0.36 #4197, 0.35 #5802, 0.34 #4886) >> Best rule #3212 for best value: >> intensional similarity = 5 >> extensional distance = 160 >> proper extension: 0372j5; >> query: (?x8496, ?x7521) <- nominated_for(?x7774, ?x8496), award(?x1880, ?x7774), film_crew_role(?x8496, ?x137), ?x1880 = 06x58, award(?x8496, ?x7521) >> conf = 0.71 => this is the best rule for 2 predicted values *> Best rule #3017 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 160 *> proper extension: 0372j5; *> query: (?x8496, 094qd5) <- nominated_for(?x7774, ?x8496), award(?x1880, ?x7774), film_crew_role(?x8496, ?x137), ?x1880 = 06x58, award(?x8496, ?x7521) *> conf = 0.38 ranks of expected_values: 8 EVAL 0cvkv5 nominated_for! 094qd5 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 108.000 86.000 0.712 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #47-0320jz PRED entity: 0320jz PRED relation: people! PRED expected values: 06v41q => 142 concepts (142 used for prediction) PRED predicted values (max 10 best out of 58): 0x67 (0.29 #3610, 0.27 #5635, 0.21 #309), 0xnvg (0.23 #162, 0.17 #237, 0.11 #462), 041rx (0.22 #3530, 0.22 #6530, 0.21 #8180), 033tf_ (0.21 #232, 0.18 #3308, 0.18 #3158), 048z7l (0.12 #38, 0.07 #413, 0.06 #3039), 03295l (0.12 #22, 0.03 #697, 0.03 #922), 02w7gg (0.10 #6528, 0.09 #7353, 0.08 #8178), 065b6q (0.09 #378, 0.06 #603, 0.05 #678), 01qhm_ (0.09 #156, 0.08 #1282, 0.08 #981), 07hwkr (0.08 #236, 0.08 #86, 0.07 #3612) >> Best rule #3610 for best value: >> intensional similarity = 3 >> extensional distance = 363 >> proper extension: 0f0y8; 01ky2h; 01wz_ml; 01vsy3q; 024zq; 0lsw9; 01w9ph_; 04bbv7; 020_4z; 05vzql; ... >> query: (?x1897, 0x67) <- category(?x1897, ?x134), people(?x1816, ?x1897), location(?x1897, ?x1755) >> conf = 0.29 => this is the best rule for 1 predicted values *> Best rule #102 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 11 *> proper extension: 01q415; *> query: (?x1897, 06v41q) <- award(?x1897, ?x154), location(?x1897, ?x3634), ?x3634 = 07b_l *> conf = 0.08 ranks of expected_values: 13 EVAL 0320jz people! 06v41q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 142.000 142.000 0.288 http://example.org/people/ethnicity/people #46-0n2g PRED entity: 0n2g PRED relation: religion! PRED expected values: 01wp8w7 09qh1 06kb_ 01wd02c 082xp => 36 concepts (8 used for prediction) PRED predicted values (max 10 best out of 4224): 0mb5x (0.43 #2754, 0.40 #1716, 0.30 #5866), 03xnq9_ (0.40 #1499, 0.29 #3576, 0.29 #2537), 04v7k2 (0.40 #2052, 0.29 #4129, 0.29 #3090), 049m19 (0.40 #1973, 0.29 #4050, 0.29 #3011), 07_m9_ (0.40 #1425, 0.29 #3502, 0.29 #2463), 0dj5q (0.40 #1584, 0.29 #3661, 0.29 #2622), 04hcw (0.40 #1629, 0.29 #2667, 0.20 #5779), 0q9kd (0.40 #1038, 0.29 #2076, 0.20 #5188), 01vrncs (0.40 #1099, 0.29 #2137, 0.17 #3113), 02ln1 (0.40 #1720, 0.29 #2758, 0.17 #7941) >> Best rule #2754 for best value: >> intensional similarity = 12 >> extensional distance = 5 >> proper extension: 0flw86; 0kpl; >> query: (?x4641, 0mb5x) <- religion(?x13167, ?x4641), religion(?x11412, ?x4641), religion(?x5301, ?x4641), artists(?x302, ?x5301), origin(?x5301, ?x6357), profession(?x11412, ?x353), nationality(?x11412, ?x429), influenced_by(?x11097, ?x11412), award(?x13167, ?x2324), nationality(?x11097, ?x1264), participant(?x5301, ?x2258), influenced_by(?x118, ?x11097) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #1592 for first EXPECTED value: *> intensional similarity = 12 *> extensional distance = 3 *> proper extension: 03_gx; *> query: (?x4641, 01wd02c) <- religion(?x13167, ?x4641), religion(?x11412, ?x4641), religion(?x5301, ?x4641), artists(?x302, ?x5301), origin(?x5301, ?x6357), profession(?x11412, ?x353), nationality(?x11412, ?x429), influenced_by(?x11097, ?x11412), award(?x13167, ?x2324), nationality(?x11097, ?x1264), participant(?x5301, ?x2258), vacationer(?x3288, ?x5301) *> conf = 0.20 ranks of expected_values: 64, 863, 1993, 2300, 3582 EVAL 0n2g religion! 082xp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 36.000 8.000 0.429 http://example.org/people/person/religion EVAL 0n2g religion! 01wd02c CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 36.000 8.000 0.429 http://example.org/people/person/religion EVAL 0n2g religion! 06kb_ CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 8.000 0.429 http://example.org/people/person/religion EVAL 0n2g religion! 09qh1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 36.000 8.000 0.429 http://example.org/people/person/religion EVAL 0n2g religion! 01wp8w7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 36.000 8.000 0.429 http://example.org/people/person/religion #45-019f4v PRED entity: 019f4v PRED relation: award! PRED expected values: 030pr 04y8r 09ftwr 0b_7k 06chf 06mn7 0gyx4 026dx 045cq 04ld94 03d1y3 06t8b 013tcv 06jz0 => 62 concepts (31 used for prediction) PRED predicted values (max 10 best out of 2955): 0gyx4 (0.80 #13165, 0.79 #62551, 0.79 #85595), 0c1pj (0.80 #13165, 0.79 #62551, 0.79 #85595), 01f7j9 (0.80 #13165, 0.79 #62551, 0.79 #85595), 025jbj (0.80 #13165, 0.79 #62551, 0.79 #85595), 0p51w (0.80 #13165, 0.79 #62551, 0.79 #85595), 0gv40 (0.80 #13165, 0.79 #62551, 0.79 #85595), 030pr (0.80 #13165, 0.79 #62551, 0.79 #85595), 03f2_rc (0.80 #13165, 0.79 #62551, 0.79 #85595), 06mn7 (0.60 #7782, 0.50 #1200, 0.40 #4491), 022_q8 (0.60 #8190, 0.50 #1608, 0.40 #4899) >> Best rule #13165 for best value: >> intensional similarity = 6 >> extensional distance = 4 >> proper extension: 02x73k6; >> query: (?x1107, ?x538) <- award(?x276, ?x1107), nominated_for(?x1107, ?x5013), nominated_for(?x1107, ?x3943), ?x3943 = 015whm, award_winner(?x1107, ?x538), ?x5013 = 011ycb >> conf = 0.80 => this is the best rule for 8 predicted values ranks of expected_values: 1, 7, 9, 11, 17, 19, 33, 68, 72, 145, 154, 200, 634, 1115 EVAL 019f4v award! 06jz0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 013tcv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 06t8b CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 03d1y3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.007 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 04ld94 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.002 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 045cq CNN-1.5+0.5_MA 0.000 0.000 0.000 0.001 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 026dx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 0gyx4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 06mn7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 06chf CNN-1.5+0.5_MA 0.000 0.000 0.000 0.071 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 0b_7k CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 09ftwr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.016 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 04y8r CNN-1.5+0.5_MA 0.000 0.000 1.000 0.125 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award EVAL 019f4v award! 030pr CNN-1.5+0.5_MA 0.000 0.000 1.000 0.167 62.000 31.000 0.803 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #44-06dn58 PRED entity: 06dn58 PRED relation: award_nominee! PRED expected values: 03yj_0n => 65 concepts (26 used for prediction) PRED predicted values (max 10 best out of 553): 03yj_0n (0.81 #811, 0.81 #18589, 0.81 #39502), 040981l (0.81 #18589, 0.81 #39502, 0.81 #41826), 08w7vj (0.75 #171, 0.52 #2495, 0.29 #13942), 0cjsxp (0.75 #867, 0.48 #3191, 0.29 #13942), 0f830f (0.75 #107, 0.44 #2431, 0.29 #13942), 02lfns (0.72 #2555, 0.69 #231, 0.29 #13942), 0dyztm (0.69 #1365, 0.48 #3689, 0.29 #13942), 026v437 (0.69 #1456, 0.36 #3780, 0.22 #4648), 03w1v2 (0.62 #87, 0.48 #2411, 0.29 #13942), 0fx0mw (0.62 #721, 0.44 #3045, 0.29 #13942) >> Best rule #811 for best value: >> intensional similarity = 4 >> extensional distance = 14 >> proper extension: 040981l; >> query: (?x7776, 03yj_0n) <- award_nominee(?x5830, ?x7776), award_nominee(?x561, ?x7776), award_nominee(?x494, ?x561), ?x5830 = 0bx0lc >> conf = 0.81 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 06dn58 award_nominee! 03yj_0n CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 65.000 26.000 0.812 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #43-02nx2k PRED entity: 02nx2k PRED relation: film_crew_role PRED expected values: 02r96rf 02ynfr => 110 concepts (95 used for prediction) PRED predicted values (max 10 best out of 25): 02r96rf (0.88 #261, 0.88 #131, 0.85 #1974), 02ynfr (0.29 #270, 0.25 #629, 0.24 #564), 015h31 (0.25 #8, 0.24 #233, 0.21 #330), 01xy5l_ (0.25 #10, 0.19 #268, 0.19 #1342), 0d2b38 (0.25 #22, 0.19 #1342, 0.19 #1175), 0215hd (0.25 #15, 0.19 #1342, 0.19 #1175), 033smt (0.25 #24, 0.19 #1342, 0.19 #1175), 02zdwq (0.25 #19, 0.19 #1342, 0.17 #2831), 089g0h (0.19 #1342, 0.19 #1175, 0.17 #1142), 02_n3z (0.19 #1342, 0.19 #1175, 0.17 #2831) >> Best rule #261 for best value: >> intensional similarity = 4 >> extensional distance = 50 >> proper extension: 053rxgm; 035w2k; 043tvp3; >> query: (?x6897, 02r96rf) <- genre(?x6897, ?x812), film_crew_role(?x6897, ?x2091), ?x812 = 01jfsb, ?x2091 = 02rh1dz >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 02nx2k film_crew_role 02ynfr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 95.000 0.885 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role EVAL 02nx2k film_crew_role 02r96rf CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 110.000 95.000 0.885 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #42-02kxbx3 PRED entity: 02kxbx3 PRED relation: award_winner! PRED expected values: 02pgky2 => 105 concepts (105 used for prediction) PRED predicted values (max 10 best out of 133): 02pgky2 (0.28 #5070, 0.09 #87, 0.05 #5893), 03gyp30 (0.28 #5070, 0.04 #3812, 0.04 #3675), 09gkdln (0.28 #5070, 0.04 #3680, 0.04 #3817), 04n2r9h (0.28 #5070, 0.02 #1002, 0.02 #1139), 0bzlrh (0.18 #100, 0.05 #5893, 0.04 #237), 02jp5r (0.09 #66, 0.07 #203, 0.05 #5893), 02ywhz (0.09 #76, 0.07 #213, 0.05 #5893), 073h1t (0.09 #25, 0.07 #162, 0.05 #5893), 0bzm81 (0.09 #20, 0.07 #157, 0.05 #5893), 0hndn2q (0.09 #38, 0.07 #312, 0.07 #997) >> Best rule #5070 for best value: >> intensional similarity = 3 >> extensional distance = 1147 >> proper extension: 06jntd; >> query: (?x3572, ?x762) <- award_winner(?x945, ?x3572), honored_for(?x762, ?x945), nominated_for(?x198, ?x945) >> conf = 0.28 => this is the best rule for 4 predicted values ranks of expected_values: 1 EVAL 02kxbx3 award_winner! 02pgky2 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 105.000 105.000 0.277 http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner #41-038rzr PRED entity: 038rzr PRED relation: award PRED expected values: 07cbcy => 107 concepts (85 used for prediction) PRED predicted values (max 10 best out of 298): 05pcn59 (0.36 #1694, 0.13 #32654, 0.10 #6933), 09sb52 (0.35 #1653, 0.29 #4474, 0.28 #15358), 04kxsb (0.33 #126, 0.17 #1738, 0.07 #11009), 09sdmz (0.33 #206, 0.16 #1818, 0.07 #29024), 05zr6wv (0.30 #1629, 0.08 #6868, 0.08 #15334), 057xs89 (0.26 #1773, 0.18 #23380, 0.18 #21767), 0l8z1 (0.25 #467, 0.13 #26202, 0.12 #34267), 0gq9h (0.25 #481, 0.09 #10961, 0.08 #11767), 07bdd_ (0.25 #469, 0.06 #10949, 0.05 #11755), 05p1dby (0.25 #511, 0.05 #10991, 0.04 #12604) >> Best rule #1694 for best value: >> intensional similarity = 3 >> extensional distance = 75 >> proper extension: 05bnp0; 01wmxfs; 03h_9lg; 026c1; 0f4vbz; 05r5w; 0fby2t; 058s44; 01xv77; 0451j; ... >> query: (?x2745, 05pcn59) <- film(?x2745, ?x2036), award(?x2745, ?x3508), ?x3508 = 05ztrmj >> conf = 0.36 => this is the best rule for 1 predicted values *> Best rule #1691 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 75 *> proper extension: 05bnp0; 01wmxfs; 03h_9lg; 026c1; 0f4vbz; 05r5w; 0fby2t; 058s44; 01xv77; 0451j; ... *> query: (?x2745, 07cbcy) <- film(?x2745, ?x2036), award(?x2745, ?x3508), ?x3508 = 05ztrmj *> conf = 0.16 ranks of expected_values: 24 EVAL 038rzr award 07cbcy CNN-1.5+0.5_MA 0.000 0.000 0.000 0.042 107.000 85.000 0.364 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #40-065jlv PRED entity: 065jlv PRED relation: nationality PRED expected values: 03rt9 => 87 concepts (87 used for prediction) PRED predicted values (max 10 best out of 18): 03rt9 (0.83 #501, 0.80 #703, 0.33 #7828), 09c7w0 (0.74 #2613, 0.74 #2211, 0.74 #1506), 02jx1 (0.43 #133, 0.42 #233, 0.36 #333), 07ssc (0.36 #15, 0.26 #215, 0.21 #115), 06q1r (0.14 #177, 0.05 #477, 0.03 #678), 0d060g (0.08 #407, 0.05 #710, 0.04 #1411), 0f8l9c (0.07 #22, 0.07 #422, 0.05 #222), 04xn_ (0.07 #74, 0.05 #274, 0.04 #374), 03rk0 (0.06 #7672, 0.05 #7874, 0.05 #8074), 0chghy (0.02 #913, 0.02 #813, 0.02 #1314) >> Best rule #501 for best value: >> intensional similarity = 2 >> extensional distance = 307 >> proper extension: 01c59k; 01_k0d; 084x96; >> query: (?x1951, ?x429) <- place_of_birth(?x1951, ?x10062), second_level_divisions(?x429, ?x10062) >> conf = 0.83 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 065jlv nationality 03rt9 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 87.000 87.000 0.828 http://example.org/people/person/nationality #39-0b76d_m PRED entity: 0b76d_m PRED relation: film_crew_role PRED expected values: 09vw2b7 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 22): 0ch6mp2 (0.73 #218, 0.73 #576, 0.73 #648), 09vw2b7 (0.63 #719, 0.62 #6, 0.62 #575), 01vx2h (0.40 #80, 0.39 #45, 0.38 #150), 01pvkk (0.30 #544, 0.28 #580, 0.28 #1261), 02ynfr (0.24 #85, 0.22 #155, 0.20 #50), 0215hd (0.19 #265, 0.19 #229, 0.16 #301), 0d2b38 (0.17 #60, 0.17 #95, 0.17 #25), 089fss (0.15 #40, 0.14 #75, 0.13 #145), 089g0h (0.14 #230, 0.14 #266, 0.14 #124), 033smt (0.12 #27, 0.10 #62, 0.10 #97) >> Best rule #218 for best value: >> intensional similarity = 4 >> extensional distance = 155 >> proper extension: 09tqkv2; 05q7874; 04gp58p; >> query: (?x80, 0ch6mp2) <- genre(?x80, ?x53), film_crew_role(?x80, ?x137), film_festivals(?x80, ?x2686), film(?x1223, ?x80) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #719 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 809 *> proper extension: 02y_lrp; 02_fm2; 0qm8b; 0g3zrd; 0d_2fb; 07w8fz; 02ht1k; 01qvz8; 0gs973; 0415ggl; ... *> query: (?x80, 09vw2b7) <- genre(?x80, ?x53), film_crew_role(?x80, ?x137), production_companies(?x80, ?x541), film(?x1223, ?x80) *> conf = 0.63 ranks of expected_values: 2 EVAL 0b76d_m film_crew_role 09vw2b7 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 63.000 63.000 0.732 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #38-02ht1k PRED entity: 02ht1k PRED relation: language PRED expected values: 02h40lc => 91 concepts (91 used for prediction) PRED predicted values (max 10 best out of 38): 02h40lc (0.95 #476, 0.92 #837, 0.91 #777), 06nm1 (0.20 #129, 0.18 #425, 0.18 #485), 02bjrlw (0.17 #60, 0.12 #415, 0.11 #654), 03_9r (0.17 #69, 0.09 #246, 0.05 #365), 04306rv (0.16 #419, 0.15 #300, 0.15 #360), 064_8sq (0.15 #615, 0.15 #1332, 0.15 #675), 06b_j (0.11 #318, 0.11 #378, 0.08 #676), 05qqm (0.10 #159, 0.01 #1471), 012w70 (0.08 #308, 0.07 #368, 0.06 #666), 0jzc (0.06 #673, 0.06 #795, 0.06 #553) >> Best rule #476 for best value: >> intensional similarity = 4 >> extensional distance = 76 >> proper extension: 03t97y; 03twd6; 014nq4; 024mpp; 0640y35; 01gwk3; 033qdy; 063fh9; 04y9mm8; 02fj8n; ... >> query: (?x3833, 02h40lc) <- film_crew_role(?x3833, ?x1171), prequel(?x8182, ?x3833), film(?x806, ?x3833), ?x1171 = 09vw2b7 >> conf = 0.95 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 02ht1k language 02h40lc CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 91.000 91.000 0.949 http://example.org/film/film/language #37-01qdjm PRED entity: 01qdjm PRED relation: profession PRED expected values: 09jwl => 137 concepts (104 used for prediction) PRED predicted values (max 10 best out of 73): 09jwl (0.82 #3409, 0.82 #2230, 0.80 #2819), 02hrh1q (0.78 #9453, 0.75 #7539, 0.74 #4441), 0dz3r (0.49 #2212, 0.48 #443, 0.47 #886), 016z4k (0.47 #3541, 0.45 #1771, 0.45 #1624), 039v1 (0.41 #477, 0.39 #2835, 0.39 #2246), 0dxtg (0.35 #14, 0.31 #10775, 0.31 #8568), 01d_h8 (0.34 #4432, 0.32 #6646, 0.32 #8413), 0fnpj (0.27 #500, 0.22 #795, 0.22 #353), 03gjzk (0.24 #11514, 0.24 #10483, 0.24 #8570), 0n1h (0.22 #4141, 0.20 #3254, 0.20 #1338) >> Best rule #3409 for best value: >> intensional similarity = 3 >> extensional distance = 247 >> proper extension: 01cv3n; 03qd_; 0bg539; 01p45_v; 04gycf; 02qfhb; 0jn5l; 04d_mtq; 015196; >> query: (?x2747, 09jwl) <- role(?x2747, ?x1332), instrumentalists(?x1831, ?x2747), profession(?x2747, ?x1614) >> conf = 0.82 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01qdjm profession 09jwl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 137.000 104.000 0.819 http://example.org/people/person/profession #36-09889g PRED entity: 09889g PRED relation: role PRED expected values: 03bx0bm => 115 concepts (115 used for prediction) PRED predicted values (max 10 best out of 45): 0342h (0.38 #864, 0.30 #1194, 0.19 #4108), 03bx0bm (0.27 #1213, 0.25 #883, 0.20 #1743), 05148p4 (0.15 #1256, 0.15 #878, 0.14 #4241), 05r5c (0.15 #1256, 0.14 #4241, 0.14 #4171), 0l14md (0.15 #1256, 0.14 #4241, 0.14 #4171), 026t6 (0.15 #1256, 0.14 #4241, 0.14 #4171), 026g73 (0.15 #1256, 0.14 #4241, 0.14 #4171), 028tv0 (0.11 #410, 0.10 #1203, 0.08 #80), 02hnl (0.10 #890, 0.08 #1750, 0.07 #1220), 018vs (0.10 #874, 0.07 #4187, 0.07 #4118) >> Best rule #864 for best value: >> intensional similarity = 3 >> extensional distance = 79 >> proper extension: 0lzkm; >> query: (?x4960, 0342h) <- artists(?x671, ?x4960), award_winner(?x462, ?x4960), group(?x4960, ?x1271) >> conf = 0.38 => this is the best rule for 1 predicted values *> Best rule #1213 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 86 *> proper extension: 020hh3; *> query: (?x4960, 03bx0bm) <- artists(?x671, ?x4960), instrumentalists(?x212, ?x4960), participant(?x1126, ?x4960) *> conf = 0.27 ranks of expected_values: 2 EVAL 09889g role 03bx0bm CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 115.000 115.000 0.383 http://example.org/music/group_member/membership./music/group_membership/role #35-026hh0m PRED entity: 026hh0m PRED relation: film_crew_role PRED expected values: 0dxtw => 109 concepts (109 used for prediction) PRED predicted values (max 10 best out of 29): 0dxtw (0.69 #1379, 0.65 #200, 0.60 #364), 02ynfr (0.37 #140, 0.31 #76, 0.25 #464), 0d2b38 (0.35 #214, 0.30 #279, 0.23 #637), 02rh1dz (0.32 #199, 0.29 #363, 0.27 #264), 033smt (0.29 #216, 0.25 #281, 0.13 #812), 0215hd (0.29 #207, 0.24 #272, 0.21 #630), 01xy5l_ (0.29 #202, 0.21 #267, 0.19 #625), 089g0h (0.23 #208, 0.23 #273, 0.20 #631), 089fss (0.22 #5, 0.14 #37, 0.13 #812), 02_n3z (0.19 #193, 0.17 #258, 0.13 #812) >> Best rule #1379 for best value: >> intensional similarity = 7 >> extensional distance = 422 >> proper extension: 0170z3; 0b76d_m; 028_yv; 09m6kg; 011yxg; 0dq626; 0ds11z; 0ds33; 04nl83; 03h_yy; ... >> query: (?x10158, 0dxtw) <- film_crew_role(?x10158, ?x1966), production_companies(?x10158, ?x382), genre(?x10158, ?x53), film_crew_role(?x7263, ?x1966), film_crew_role(?x103, ?x1966), ?x103 = 03qcfvw, ?x7263 = 0292qb >> conf = 0.69 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 026hh0m film_crew_role 0dxtw CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 109.000 109.000 0.693 http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role #34-04_lb PRED entity: 04_lb PRED relation: contains! PRED expected values: 059s8 => 97 concepts (28 used for prediction) PRED predicted values (max 10 best out of 121): 09c7w0 (0.90 #15240, 0.89 #16137, 0.88 #17037), 05kr_ (0.46 #3707, 0.43 #2812, 0.39 #5498), 01n7q (0.44 #6346, 0.31 #9036, 0.17 #14418), 059s8 (0.40 #658, 0.29 #1553, 0.05 #2448), 059j2 (0.34 #2686, 0.22 #10752, 0.11 #11650), 0165b (0.34 #2686, 0.11 #11650, 0.07 #12548), 0161c (0.34 #2686, 0.11 #11650, 0.07 #12548), 0l3h (0.34 #2686, 0.11 #11650), 027nb (0.34 #2686, 0.11 #11650), 02jx1 (0.23 #14427, 0.15 #13531, 0.08 #20704) >> Best rule #15240 for best value: >> intensional similarity = 6 >> extensional distance = 984 >> proper extension: 018mm4; >> query: (?x11505, 09c7w0) <- contains(?x279, ?x11505), contains(?x279, ?x1658), location(?x9819, ?x1658), location(?x6917, ?x1658), ?x9819 = 02184q, ?x6917 = 0479b >> conf = 0.90 => this is the best rule for 1 predicted values *> Best rule #658 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 3 *> proper extension: 074r0; 02w70; 0lg0r; *> query: (?x11505, 059s8) <- category(?x11505, ?x134), time_zones(?x11505, ?x11506), ?x11506 = 042g7t, ?x134 = 08mbj5d, contains(?x279, ?x11505) *> conf = 0.40 ranks of expected_values: 4 EVAL 04_lb contains! 059s8 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.250 97.000 28.000 0.905 http://example.org/location/location/contains #33-0jml5 PRED entity: 0jml5 PRED relation: school PRED expected values: 01jq34 => 49 concepts (44 used for prediction) PRED predicted values (max 10 best out of 191): 0bx8pn (0.50 #591, 0.38 #1347, 0.33 #2299), 015q1n (0.45 #859, 0.33 #102, 0.25 #291), 0dzst (0.33 #147, 0.31 #1471, 0.30 #715), 01jsn5 (0.33 #28, 0.31 #1163, 0.26 #2275), 078bz (0.33 #34, 0.28 #3073, 0.27 #791), 0j_sncb (0.33 #38, 0.27 #795, 0.25 #227), 01pl14 (0.33 #5, 0.27 #762, 0.16 #6852), 0jkhr (0.33 #112, 0.27 #869, 0.16 #2008), 065y4w7 (0.33 #8, 0.27 #6855, 0.26 #6472), 0lyjf (0.33 #73, 0.21 #5575, 0.20 #6537) >> Best rule #591 for best value: >> intensional similarity = 12 >> extensional distance = 8 >> proper extension: 0jm3b; 0jmk7; >> query: (?x5483, 0bx8pn) <- sport(?x5483, ?x4833), draft(?x5483, ?x12852), draft(?x5483, ?x8542), ?x12852 = 06439y, position(?x5483, ?x4570), position(?x5483, ?x1348), ?x8542 = 09th87, position(?x7136, ?x4570), ?x4833 = 018w8, ?x7136 = 0jm74, team(?x13931, ?x5483), ?x1348 = 01pv51 >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #26 for first EXPECTED value: *> intensional similarity = 14 *> extensional distance = 1 *> proper extension: 0jmj7; *> query: (?x5483, 01jq34) <- school(?x5483, ?x4599), team(?x6848, ?x5483), team(?x5755, ?x5483), team(?x4570, ?x5483), draft(?x5483, ?x8586), draft(?x5483, ?x8133), draft(?x5483, ?x2569), ?x4570 = 03558l, ?x8586 = 038981, ?x8133 = 025tn92, ?x5755 = 0355dz, ?x6848 = 02_ssl, ?x2569 = 038c0q, ?x4599 = 07t90 *> conf = 0.33 ranks of expected_values: 39 EVAL 0jml5 school 01jq34 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 49.000 44.000 0.500 http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school #32-080h2 PRED entity: 080h2 PRED relation: month PRED expected values: 06vkl 0ll3 0lkm => 212 concepts (212 used for prediction) PRED predicted values (max 10 best out of 3): 0ll3 (0.88 #92, 0.87 #110, 0.82 #35), 0lkm (0.86 #24, 0.86 #93, 0.85 #111), 06vkl (0.83 #109, 0.82 #34, 0.82 #22) >> Best rule #92 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 03czqs; 03khn; 0g6xq; >> query: (?x1036, 0ll3) <- country(?x1036, ?x279), month(?x1036, ?x7298), ?x7298 = 04wzr >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2, 3 EVAL 080h2 month 0lkm CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 212.000 0.881 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 080h2 month 0ll3 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 212.000 0.881 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 080h2 month 06vkl CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 212.000 212.000 0.881 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #31-07n68 PRED entity: 07n68 PRED relation: group! PRED expected values: 05148p4 => 66 concepts (60 used for prediction) PRED predicted values (max 10 best out of 121): 05148p4 (0.88 #1168, 0.85 #1257, 0.84 #992), 03bx0bm (0.65 #1263, 0.62 #1174, 0.61 #1441), 0l14md (0.65 #1423, 0.62 #1156, 0.62 #1245), 0l14qv (0.43 #449, 0.35 #1243, 0.33 #1154), 028tv0 (0.37 #1873, 0.32 #1429, 0.31 #1251), 03qjg (0.29 #1197, 0.27 #1286, 0.26 #1021), 05r5c (0.27 #717, 0.26 #981, 0.26 #1424), 01vj9c (0.26 #987, 0.25 #1163, 0.23 #1252), 06ncr (0.22 #1635, 0.21 #1012, 0.21 #1811), 013y1f (0.21 #1001, 0.18 #737, 0.17 #1177) >> Best rule #1168 for best value: >> intensional similarity = 12 >> extensional distance = 22 >> proper extension: 01fl3; >> query: (?x13505, 05148p4) <- artists(?x9063, ?x13505), artists(?x2491, ?x13505), ?x9063 = 0cx7f, group(?x1750, ?x13505), group(?x227, ?x13505), ?x1750 = 02hnl, ?x227 = 0342h, artists(?x2491, ?x8058), artists(?x2491, ?x1838), ?x1838 = 012zng, group(?x75, ?x8058), artist(?x2241, ?x8058) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07n68 group! 05148p4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 66.000 60.000 0.875 http://example.org/music/performance_role/regular_performances./music/group_membership/group #30-022p06 PRED entity: 022p06 PRED relation: location PRED expected values: 02_286 => 129 concepts (129 used for prediction) PRED predicted values (max 10 best out of 165): 081m_ (0.46 #37849, 0.45 #80485, 0.43 #47507), 04f_d (0.25 #108, 0.20 #4140, 0.20 #914), 01_d4 (0.25 #102, 0.12 #2521, 0.10 #4134), 030qb3t (0.24 #16210, 0.24 #12179, 0.17 #12984), 05tbn (0.20 #994, 0.17 #5833, 0.17 #5026), 07ssc (0.17 #1638, 0.15 #7282, 0.11 #3250), 02_286 (0.17 #1649, 0.15 #46739, 0.12 #25816), 06y57 (0.17 #1868, 0.11 #3480, 0.08 #5901), 02jx1 (0.17 #1683, 0.11 #3295, 0.08 #5716), 07cfx (0.17 #1656, 0.11 #3268, 0.08 #5689) >> Best rule #37849 for best value: >> intensional similarity = 2 >> extensional distance = 302 >> proper extension: 07h1q; 05fh2; >> query: (?x4943, ?x8989) <- place_of_birth(?x4943, ?x8989), capital(?x456, ?x8989) >> conf = 0.46 => this is the best rule for 1 predicted values *> Best rule #1649 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 4 *> proper extension: 0hnp7; 0cf2h; 03bw6; 01t94_1; *> query: (?x4943, 02_286) <- place_of_burial(?x4943, ?x1227), ?x1227 = 01n7q, place_of_birth(?x4943, ?x8989) *> conf = 0.17 ranks of expected_values: 7 EVAL 022p06 location 02_286 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 129.000 129.000 0.464 http://example.org/people/person/places_lived./people/place_lived/location #29-02v406 PRED entity: 02v406 PRED relation: student! PRED expected values: 04rwx => 163 concepts (122 used for prediction) PRED predicted values (max 10 best out of 235): 012lzr (0.25 #857), 04rkkv (0.17 #2400, 0.01 #13936, 0.01 #20752), 04b_46 (0.14 #2844, 0.12 #3893, 0.09 #4941), 0bwfn (0.14 #2892, 0.12 #3941, 0.08 #10758), 02bq1j (0.14 #2784, 0.12 #3833, 0.04 #5405), 01w5m (0.14 #1676, 0.11 #6395, 0.07 #11639), 07wrz (0.14 #1633, 0.07 #2681, 0.06 #3730), 0lyjf (0.14 #1203, 0.07 #2775, 0.03 #4872), 01jzyx (0.14 #1744, 0.07 #3317, 0.05 #4365), 026036 (0.14 #1962, 0.07 #3535, 0.05 #4583) >> Best rule #857 for best value: >> intensional similarity = 5 >> extensional distance = 2 >> proper extension: 013t9y; >> query: (?x4217, 012lzr) <- location(?x4217, ?x5036), location(?x4217, ?x4600), student(?x2013, ?x4217), ?x5036 = 06y57, jurisdiction_of_office(?x900, ?x4600) >> conf = 0.25 => this is the best rule for 1 predicted values *> Best rule #8952 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 87 *> proper extension: 02r_d4; 05ml_s; 03lt8g; 0n6f8; 01wyzyl; 0jt90f5; 02tqkf; 01_rh4; 0391jz; 0blt6; ... *> query: (?x4217, 04rwx) <- location(?x4217, ?x739), film(?x4217, ?x4538), student(?x865, ?x4217), nominated_for(?x102, ?x4538) *> conf = 0.01 ranks of expected_values: 232 EVAL 02v406 student! 04rwx CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 163.000 122.000 0.250 http://example.org/education/educational_institution/students_graduates./education/education/student #28-04d2yp PRED entity: 04d2yp PRED relation: profession PRED expected values: 05z96 => 93 concepts (77 used for prediction) PRED predicted values (max 10 best out of 72): 09jwl (0.64 #2239, 0.60 #3423, 0.18 #6827), 0dz3r (0.40 #3406, 0.39 #2222, 0.11 #7847), 01d_h8 (0.39 #450, 0.34 #154, 0.34 #4742), 016z4k (0.34 #2224, 0.32 #3408, 0.11 #6812), 0dxtg (0.34 #606, 0.31 #458, 0.30 #1346), 02jknp (0.33 #452, 0.26 #600, 0.24 #4744), 03gjzk (0.25 #5491, 0.23 #5935, 0.23 #7860), 039v1 (0.24 #3440, 0.21 #2256, 0.04 #6844), 0n1h (0.22 #2232, 0.17 #3416, 0.09 #900), 0cbd2 (0.21 #2671, 0.20 #3263, 0.17 #4299) >> Best rule #2239 for best value: >> intensional similarity = 4 >> extensional distance = 194 >> proper extension: 01ydzx; >> query: (?x11861, 09jwl) <- profession(?x11861, ?x2348), profession(?x11861, ?x1032), ?x1032 = 02hrh1q, ?x2348 = 0nbcg >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #2706 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 282 *> proper extension: 063vn; 052h3; 0zm1; 0hgqq; 0641g8; 080r3; 0h0p_; 0c1fs; 01k47c; 06y3r; ... *> query: (?x11861, 05z96) <- gender(?x11861, ?x231), people(?x268, ?x11861), student(?x2486, ?x11861) *> conf = 0.07 ranks of expected_values: 23 EVAL 04d2yp profession 05z96 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.043 93.000 77.000 0.638 http://example.org/people/person/profession #27-01vsykc PRED entity: 01vsykc PRED relation: artists! PRED expected values: 0ggx5q => 108 concepts (108 used for prediction) PRED predicted values (max 10 best out of 196): 06by7 (0.44 #11079, 0.42 #6164, 0.41 #13845), 016clz (0.34 #621, 0.24 #313, 0.23 #5), 05bt6j (0.25 #5266, 0.24 #6495, 0.23 #660), 02lnbg (0.25 #673, 0.13 #6508, 0.13 #5279), 01lyv (0.24 #4949, 0.22 #3413, 0.22 #6177), 0xhtw (0.23 #17, 0.19 #11674, 0.17 #11075), 0glt670 (0.22 #12636, 0.21 #12022, 0.21 #10485), 03_d0 (0.19 #11674, 0.18 #3391, 0.17 #6770), 0155w (0.19 #11674, 0.16 #11162, 0.15 #8091), 02w4v (0.19 #11674, 0.14 #45, 0.13 #5267) >> Best rule #11079 for best value: >> intensional similarity = 3 >> extensional distance = 470 >> proper extension: 07_3qd; 04mx7s; >> query: (?x3290, 06by7) <- instrumentalists(?x227, ?x3290), artist(?x2931, ?x3290), artists(?x671, ?x3290) >> conf = 0.44 => this is the best rule for 1 predicted values *> Best rule #692 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 63 *> proper extension: 02fybl; *> query: (?x3290, 0ggx5q) <- profession(?x3290, ?x220), ?x220 = 016z4k, participant(?x3291, ?x3290) *> conf = 0.18 ranks of expected_values: 34 EVAL 01vsykc artists! 0ggx5q CNN-1.5+0.5_MA 0.000 0.000 0.000 0.029 108.000 108.000 0.436 http://example.org/music/genre/artists #26-07nv3_ PRED entity: 07nv3_ PRED relation: nationality PRED expected values: 0chghy => 129 concepts (126 used for prediction) PRED predicted values (max 10 best out of 59): 0chghy (0.88 #3816, 0.85 #3412, 0.85 #4019), 09c7w0 (0.84 #3716, 0.83 #6644, 0.82 #5634), 02jx1 (0.28 #2036, 0.28 #933, 0.28 #1936), 07ssc (0.23 #515, 0.18 #1015, 0.17 #1217), 03rk0 (0.17 #3458, 0.09 #3660, 0.08 #4065), 015fr (0.15 #217, 0.15 #317, 0.14 #517), 0j5g9 (0.15 #262, 0.10 #362, 0.09 #962), 0ctw_b (0.14 #427, 0.12 #627, 0.09 #927), 05bcl (0.08 #1362, 0.06 #1262, 0.05 #1963), 0d060g (0.08 #3218, 0.07 #3419, 0.07 #4126) >> Best rule #3816 for best value: >> intensional similarity = 5 >> extensional distance = 454 >> proper extension: 03r1pr; 01s21dg; 0dq9wx; >> query: (?x3551, ?x390) <- place_of_birth(?x3551, ?x8602), profession(?x3551, ?x7623), featured_film_locations(?x570, ?x8602), country(?x8602, ?x390), state(?x8602, ?x9494) >> conf = 0.88 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 07nv3_ nationality 0chghy CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 126.000 0.879 http://example.org/people/person/nationality #25-03xkps PRED entity: 03xkps PRED relation: award PRED expected values: 09qvc0 => 106 concepts (106 used for prediction) PRED predicted values (max 10 best out of 261): 0bdwqv (0.72 #34815, 0.71 #16803, 0.70 #18004), 0ck27z (0.31 #6889, 0.28 #3289, 0.20 #9689), 0cqhk0 (0.18 #3236, 0.18 #6836, 0.13 #9636), 02x8n1n (0.18 #115, 0.11 #515, 0.05 #4116), 0gqyl (0.15 #1701, 0.13 #901, 0.12 #4102), 0gs9p (0.15 #4476, 0.09 #8876, 0.09 #875), 0gqwc (0.14 #27208, 0.13 #1670, 0.12 #70), 03hl6lc (0.14 #27208, 0.13 #34413, 0.08 #4575), 094qd5 (0.14 #27208, 0.09 #4043, 0.07 #8843), 02qvyrt (0.14 #27208, 0.07 #922, 0.04 #2122) >> Best rule #34815 for best value: >> intensional similarity = 2 >> extensional distance = 2328 >> proper extension: 099ks0; >> query: (?x3808, ?x3247) <- award_winner(?x3247, ?x3808), award(?x192, ?x3247) >> conf = 0.72 => this is the best rule for 1 predicted values *> Best rule #38 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 15 *> proper extension: 02v406; 03k48_; *> query: (?x3808, 09qvc0) <- religion(?x3808, ?x8613), ?x8613 = 04pk9, film(?x3808, ?x3784) *> conf = 0.12 ranks of expected_values: 49 EVAL 03xkps award 09qvc0 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.020 106.000 106.000 0.716 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #24-03cp7b3 PRED entity: 03cp7b3 PRED relation: costume_design_by! PRED expected values: 0gys2jp => 101 concepts (41 used for prediction) PRED predicted values (max 10 best out of 15): 05dy7p (0.07 #1059), 0p9rz (0.05 #1604, 0.04 #1808, 0.04 #2419), 02754c9 (0.05 #1562, 0.04 #1766, 0.04 #2377), 0b9rdk (0.05 #1550, 0.04 #1754, 0.04 #2365), 011yrp (0.05 #1429, 0.04 #1633, 0.04 #2244), 027m67 (0.03 #4683, 0.02 #4275, 0.02 #5700), 01f85k (0.03 #4683, 0.02 #4275, 0.02 #5700), 0198b6 (0.03 #4683, 0.02 #4275, 0.02 #4479), 0gys2jp (0.03 #4683, 0.02 #4275, 0.02 #4479), 0f61tk (0.03 #2613) >> Best rule #1059 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 05728w1; 05v1sb; 03mdw3c; >> query: (?x9086, 05dy7p) <- award_winner(?x6376, ?x9086), gender(?x9086, ?x231), film_production_design_by(?x1625, ?x9086), profession(?x9086, ?x2450) >> conf = 0.07 => this is the best rule for 1 predicted values *> Best rule #4683 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 462 *> proper extension: 07hbxm; 01846t; 01pkhw; 03m6pk; *> query: (?x9086, ?x3886) <- nationality(?x9086, ?x2645), nominated_for(?x9086, ?x3886), official_language(?x2645, ?x254) *> conf = 0.03 ranks of expected_values: 9 EVAL 03cp7b3 costume_design_by! 0gys2jp CNN-1.5+0.5_MA 0.000 0.000 1.000 0.111 101.000 41.000 0.071 http://example.org/film/film/costume_design_by #23-055c8 PRED entity: 055c8 PRED relation: nationality PRED expected values: 09c7w0 => 129 concepts (126 used for prediction) PRED predicted values (max 10 best out of 82): 09c7w0 (0.85 #6832, 0.81 #5926, 0.79 #807), 0345h (0.35 #8843, 0.03 #8241, 0.02 #8272), 0hzlz (0.35 #8843, 0.03 #8241), 0msyb (0.33 #11551), 07h34 (0.33 #11551), 02jx1 (0.10 #4450, 0.10 #8775, 0.10 #9076), 07ssc (0.09 #8757, 0.09 #5639, 0.09 #9058), 03rk0 (0.06 #11798, 0.06 #5066, 0.06 #7582), 0d060g (0.05 #1114, 0.05 #813, 0.05 #913), 03_3d (0.03 #8241, 0.03 #4523, 0.02 #4222) >> Best rule #6832 for best value: >> intensional similarity = 3 >> extensional distance = 1314 >> proper extension: 02y8bn; 069d71; >> query: (?x3186, 09c7w0) <- gender(?x3186, ?x231), location(?x3186, ?x13861), source(?x13861, ?x958) >> conf = 0.85 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 055c8 nationality 09c7w0 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 129.000 126.000 0.847 http://example.org/people/person/nationality #22-01z9_x PRED entity: 01z9_x PRED relation: location PRED expected values: 030qb3t => 116 concepts (116 used for prediction) PRED predicted values (max 10 best out of 144): 0f2tj (0.40 #4021, 0.40 #13671, 0.39 #20908), 030qb3t (0.13 #4104, 0.12 #53155, 0.12 #46721), 02_286 (0.13 #6470, 0.12 #46675, 0.12 #53109), 0cr3d (0.05 #20248, 0.05 #13011, 0.04 #3361), 04jpl (0.04 #17, 0.04 #821, 0.04 #5646), 01531 (0.03 #3374, 0.03 #6591, 0.03 #1766), 05jbn (0.03 #4274, 0.03 #1861, 0.03 #13924), 059rby (0.03 #53088, 0.03 #4841, 0.03 #46654), 094jv (0.03 #93, 0.03 #897, 0.02 #5722), 0cc56 (0.03 #46695, 0.03 #53129, 0.03 #6490) >> Best rule #4021 for best value: >> intensional similarity = 3 >> extensional distance = 114 >> proper extension: 06lxn; >> query: (?x7882, ?x6769) <- award_winner(?x6939, ?x7882), origin(?x7882, ?x6769), people(?x2510, ?x6939) >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #4104 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 115 *> proper extension: 01yznp; 03ds3; 01wk7b7; 02wk4d; 015076; *> query: (?x7882, 030qb3t) <- award(?x7882, ?x4018), instrumentalists(?x227, ?x7882), film(?x7882, ?x9507) *> conf = 0.13 ranks of expected_values: 2 EVAL 01z9_x location 030qb3t CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 116.000 116.000 0.402 http://example.org/people/person/places_lived./people/place_lived/location #21-03cmsqb PRED entity: 03cmsqb PRED relation: film! PRED expected values: 03pmzt => 86 concepts (62 used for prediction) PRED predicted values (max 10 best out of 718): 016tt2 (0.43 #99909, 0.38 #56201, 0.38 #43711), 01f7dd (0.22 #1210, 0.04 #9535, 0.04 #3291), 01g969 (0.22 #1673, 0.01 #26647, 0.01 #20403), 016ypb (0.16 #2581, 0.07 #10906, 0.06 #8825), 0pz91 (0.12 #2293, 0.10 #8537, 0.08 #14780), 02gf_l (0.12 #3350, 0.07 #11675, 0.05 #5432), 06rq2l (0.12 #3659, 0.06 #9903, 0.05 #14065), 0127m7 (0.11 #408, 0.05 #4571, 0.05 #10814), 03kbb8 (0.11 #1247, 0.05 #5410, 0.04 #9572), 03xpsrx (0.11 #487, 0.04 #2568, 0.03 #4650) >> Best rule #99909 for best value: >> intensional similarity = 3 >> extensional distance = 1086 >> proper extension: 02r2j8; >> query: (?x7968, ?x574) <- titles(?x2480, ?x7968), film(?x3308, ?x7968), nominated_for(?x574, ?x7968) >> conf = 0.43 => this is the best rule for 1 predicted values *> Best rule #4661 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 35 *> proper extension: 053rxgm; 0ddt_; 049mql; 05fm6m; 09y6pb; *> query: (?x7968, 03pmzt) <- nominated_for(?x350, ?x7968), ?x350 = 05f4m9q, film_crew_role(?x7968, ?x1171), ?x1171 = 09vw2b7 *> conf = 0.03 ranks of expected_values: 266 EVAL 03cmsqb film! 03pmzt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 86.000 62.000 0.432 http://example.org/film/actor/film./film/performance/film #20-0g68zt PRED entity: 0g68zt PRED relation: nominated_for! PRED expected values: 0f4x7 => 78 concepts (59 used for prediction) PRED predicted values (max 10 best out of 167): 0gq9h (0.40 #298, 0.33 #1243, 0.32 #535), 0gq_v (0.36 #255, 0.30 #1200, 0.21 #2382), 019f4v (0.33 #53, 0.29 #526, 0.28 #1234), 04dn09n (0.33 #34, 0.22 #507, 0.21 #979), 02pqp12 (0.33 #58, 0.18 #1003, 0.16 #531), 09td7p (0.33 #93, 0.10 #1038, 0.07 #2220), 0f4x7 (0.32 #261, 0.22 #498, 0.18 #1206), 0l8z1 (0.31 #1232, 0.22 #51, 0.20 #11336), 0gs9p (0.29 #537, 0.25 #1245, 0.24 #2427), 0k611 (0.29 #1254, 0.27 #546, 0.23 #782) >> Best rule #298 for best value: >> intensional similarity = 4 >> extensional distance = 23 >> proper extension: 0b005; 0qmk5; 024hbv; >> query: (?x3111, 0gq9h) <- nominated_for(?x12584, ?x3111), award_winner(?x9372, ?x12584), nominated_for(?x112, ?x3111), ?x9372 = 024dzn >> conf = 0.40 => this is the best rule for 1 predicted values *> Best rule #261 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 23 *> proper extension: 0b005; 0qmk5; 024hbv; *> query: (?x3111, 0f4x7) <- nominated_for(?x12584, ?x3111), award_winner(?x9372, ?x12584), nominated_for(?x112, ?x3111), ?x9372 = 024dzn *> conf = 0.32 ranks of expected_values: 7 EVAL 0g68zt nominated_for! 0f4x7 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 78.000 59.000 0.400 http://example.org/award/award_category/nominees./award/award_nomination/nominated_for #19-0bt4g PRED entity: 0bt4g PRED relation: film_distribution_medium PRED expected values: 02nxhr => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 4): 0735l (0.65 #28, 0.62 #24, 0.61 #49), 02nxhr (0.26 #30, 0.25 #47, 0.25 #22), 07z4p (0.03 #4, 0.02 #25, 0.02 #29), 07c52 (0.01 #31) >> Best rule #28 for best value: >> intensional similarity = 4 >> extensional distance = 134 >> proper extension: 0372j5; >> query: (?x7692, 0735l) <- nominated_for(?x68, ?x7692), film(?x4366, ?x7692), nominated_for(?x930, ?x7692), film_distribution_medium(?x7692, ?x81) >> conf = 0.65 => this is the best rule for 1 predicted values *> Best rule #30 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 163 *> proper extension: 053tj7; 0bh8yn3; 034qbx; *> query: (?x7692, 02nxhr) <- genre(?x7692, ?x258), film_release_region(?x7692, ?x94), film_distribution_medium(?x7692, ?x81) *> conf = 0.26 ranks of expected_values: 2 EVAL 0bt4g film_distribution_medium 02nxhr CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 88.000 88.000 0.647 http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium #18-01rr9f PRED entity: 01rr9f PRED relation: participant PRED expected values: 048lv => 124 concepts (80 used for prediction) PRED predicted values (max 10 best out of 599): 029q_y (0.83 #28908, 0.83 #30792, 0.83 #32048), 0lbj1 (0.83 #28908, 0.83 #30792, 0.83 #32048), 048lv (0.83 #28908, 0.83 #30792, 0.83 #32048), 01hxs4 (0.39 #5024, 0.06 #10048, 0.05 #3766), 0mm1q (0.20 #367, 0.14 #995, 0.09 #2250), 04bdxl (0.20 #5, 0.14 #633, 0.09 #1888), 02v60l (0.10 #5652, 0.09 #628, 0.05 #9420), 01j5ws (0.10 #5652, 0.08 #3767, 0.07 #5023), 014zcr (0.10 #18, 0.10 #3156, 0.07 #646), 01515w (0.10 #405, 0.07 #5023, 0.07 #1033) >> Best rule #28908 for best value: >> intensional similarity = 3 >> extensional distance = 550 >> proper extension: 01l1b90; 0d_84; 04bs3j; 03ds3; 01q7cb_; 0456xp; 0h1m9; 02lnhv; 0n6f8; 01qvgl; ... >> query: (?x513, ?x248) <- participant(?x513, ?x8146), award(?x8146, ?x9343), participant(?x248, ?x513) >> conf = 0.83 => this is the best rule for 3 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 3 EVAL 01rr9f participant 048lv CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 124.000 80.000 0.829 http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant #17-0dgq80b PRED entity: 0dgq80b PRED relation: film_release_region PRED expected values: 0chghy => 88 concepts (88 used for prediction) PRED predicted values (max 10 best out of 125): 09c7w0 (0.94 #3765, 0.93 #6332, 0.93 #7702), 03h64 (0.91 #251, 0.75 #3159, 0.74 #3501), 03gj2 (0.88 #202, 0.77 #3110, 0.70 #3452), 02vzc (0.86 #1260, 0.82 #234, 0.81 #3655), 03_3d (0.85 #179, 0.79 #1205, 0.77 #3258), 059j2 (0.85 #3118, 0.81 #3460, 0.79 #3289), 0chghy (0.82 #3092, 0.79 #184, 0.78 #1210), 0k6nt (0.81 #3280, 0.80 #3622, 0.79 #3451), 0jgd (0.80 #3083, 0.78 #1543, 0.76 #175), 05v8c (0.79 #191, 0.54 #3099, 0.49 #1217) >> Best rule #3765 for best value: >> intensional similarity = 3 >> extensional distance = 243 >> proper extension: 025twgf; 063y9fp; 02gqm3; >> query: (?x10623, 09c7w0) <- genre(?x10623, ?x225), film_release_region(?x10623, ?x87), story_by(?x10623, ?x13308) >> conf = 0.94 => this is the best rule for 1 predicted values *> Best rule #3092 for first EXPECTED value: *> intensional similarity = 5 *> extensional distance = 211 *> proper extension: 0gtsx8c; *> query: (?x10623, 0chghy) <- film_release_region(?x10623, ?x304), film_release_region(?x10623, ?x279), ?x304 = 0d0vqn, film(?x541, ?x10623), ?x279 = 0d060g *> conf = 0.82 ranks of expected_values: 7 EVAL 0dgq80b film_release_region 0chghy CNN-1.5+0.5_MA 0.000 0.000 1.000 0.143 88.000 88.000 0.939 http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region #16-01531 PRED entity: 01531 PRED relation: place_of_birth! PRED expected values: 02t_v1 => 65 concepts (65 used for prediction) PRED predicted values (max 10 best out of 1402): 081lh (0.34 #2575, 0.34 #46350, 0.33 #41200), 01vvyvk (0.34 #2575, 0.34 #46350, 0.33 #41200), 01r7pq (0.34 #2575, 0.34 #46350, 0.33 #41200), 01817f (0.34 #2575, 0.34 #46350, 0.33 #41200), 0677ng (0.34 #2575, 0.34 #46350, 0.33 #41200), 0pj9t (0.34 #2575, 0.34 #46350, 0.33 #41200), 043zg (0.34 #2575, 0.34 #46350, 0.33 #41200), 017yfz (0.34 #2575, 0.34 #46350, 0.33 #41200), 01wj5hp (0.34 #2575, 0.34 #46350, 0.33 #41200), 02lt8 (0.34 #2575, 0.34 #46350, 0.33 #105571) >> Best rule #2575 for best value: >> intensional similarity = 3 >> extensional distance = 40 >> proper extension: 0cb4j; 0fhp9; 05ksh; 017cjb; 0h7h6; 0dbdy; 09tlh; 0d6lp; 0hyxv; 05qtj; ... >> query: (?x3014, ?x890) <- place_of_birth(?x434, ?x3014), location(?x890, ?x3014), second_level_divisions(?x94, ?x3014) >> conf = 0.34 => this is the best rule for 24 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 20 EVAL 01531 place_of_birth! 02t_v1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.050 65.000 65.000 0.343 http://example.org/people/person/place_of_birth #15-034q81 PRED entity: 034q81 PRED relation: institution! PRED expected values: 014mlp => 120 concepts (120 used for prediction) PRED predicted values (max 10 best out of 20): 014mlp (0.80 #90, 0.77 #133, 0.73 #218), 02h4rq6 (0.75 #153, 0.73 #131, 0.70 #238), 019v9k (0.75 #93, 0.64 #158, 0.61 #221), 03bwzr4 (0.56 #141, 0.54 #248, 0.52 #163), 07s6fsf (0.38 #151, 0.36 #129, 0.33 #236), 04zx3q1 (0.35 #87, 0.35 #152, 0.34 #237), 0bjrnt (0.33 #6, 0.22 #28, 0.19 #984), 027f2w (0.32 #159, 0.31 #244, 0.28 #94), 013zdg (0.28 #92, 0.27 #157, 0.25 #135), 03mkk4 (0.28 #96, 0.19 #1550, 0.18 #161) >> Best rule #90 for best value: >> intensional similarity = 4 >> extensional distance = 38 >> proper extension: 015zyd; 0j_sncb; 02fy0z; 02gkxp; >> query: (?x9810, 014mlp) <- category(?x9810, ?x134), major_field_of_study(?x9810, ?x5614), ?x5614 = 03qsdpk, institution(?x1200, ?x9810) >> conf = 0.80 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 034q81 institution! 014mlp CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 120.000 120.000 0.800 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #14-0pc56 PRED entity: 0pc56 PRED relation: place PRED expected values: 0pc56 => 160 concepts (81 used for prediction) PRED predicted values (max 10 best out of 138): 0mzy7 (0.07 #302, 0.04 #817, 0.03 #1849), 0r2dp (0.07 #287, 0.04 #802, 0.03 #1318), 0nbwf (0.07 #224, 0.04 #739, 0.03 #1255), 071vr (0.07 #176, 0.04 #691, 0.03 #1207), 0d7k1z (0.07 #143, 0.04 #658, 0.03 #1174), 0r3wm (0.07 #283, 0.03 #1830, 0.03 #1314), 0r0m6 (0.07 #95, 0.03 #1126, 0.03 #2672), 0r62v (0.07 #17, 0.03 #1048, 0.03 #2594), 0r2l7 (0.07 #34, 0.03 #1065, 0.02 #3127), 01zqy6t (0.07 #456, 0.03 #1487, 0.02 #3549) >> Best rule #302 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 0k_mf; >> query: (?x11934, 0mzy7) <- contains(?x1227, ?x11934), ?x1227 = 01n7q, citytown(?x4363, ?x11934), school(?x1160, ?x4363) >> conf = 0.07 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0pc56 place 0pc56 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 160.000 81.000 0.071 http://example.org/location/hud_county_place/place #13-0j5q3 PRED entity: 0j5q3 PRED relation: award PRED expected values: 057xs89 => 89 concepts (89 used for prediction) PRED predicted values (max 10 best out of 257): 05b4l5x (0.73 #27761, 0.71 #18102, 0.70 #22528), 09sb52 (0.37 #1649, 0.35 #41, 0.30 #10094), 05p09zm (0.27 #2133, 0.27 #525, 0.21 #1329), 05pcn59 (0.26 #2091, 0.24 #1287, 0.23 #483), 05zr6wv (0.20 #1625, 0.19 #419, 0.19 #17), 094qd5 (0.17 #1653, 0.12 #4065, 0.11 #2055), 05ztrmj (0.17 #2193, 0.15 #183, 0.12 #3399), 0cjyzs (0.17 #5333, 0.13 #9756, 0.13 #10560), 07cbcy (0.17 #2088, 0.12 #3294, 0.12 #2490), 0ck27z (0.15 #494, 0.13 #10145, 0.12 #1298) >> Best rule #27761 for best value: >> intensional similarity = 3 >> extensional distance = 2274 >> proper extension: 06lxn; >> query: (?x7056, ?x9770) <- award_winner(?x9770, ?x7056), award(?x5246, ?x9770), award_nominee(?x5246, ?x192) >> conf = 0.73 => this is the best rule for 1 predicted values *> Best rule #2169 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 82 *> proper extension: 06w2sn5; 01wv9p; 0bx_q; *> query: (?x7056, 057xs89) <- participant(?x7056, ?x1736), award(?x7056, ?x1007), friend(?x2796, ?x7056) *> conf = 0.14 ranks of expected_values: 13 EVAL 0j5q3 award 057xs89 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.077 89.000 89.000 0.727 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #12-011xg5 PRED entity: 011xg5 PRED relation: genre PRED expected values: 06n90 => 102 concepts (102 used for prediction) PRED predicted values (max 10 best out of 96): 05p553 (0.66 #7882, 0.66 #8003, 0.50 #4), 02kdv5l (0.57 #730, 0.49 #3031, 0.48 #5093), 01hmnh (0.51 #5956, 0.37 #2563, 0.34 #5109), 01jfsb (0.48 #254, 0.44 #861, 0.44 #1588), 06n90 (0.37 #741, 0.28 #3042, 0.26 #983), 02l7c8 (0.31 #2077, 0.30 #7046, 0.30 #7288), 060__y (0.30 #502, 0.25 #138, 0.19 #1472), 04xvlr (0.27 #1092, 0.24 #243, 0.22 #607), 0hcr (0.25 #24, 0.21 #2569, 0.21 #3053), 0556j8 (0.25 #43, 0.19 #164, 0.07 #1862) >> Best rule #7882 for best value: >> intensional similarity = 3 >> extensional distance = 878 >> proper extension: 087wc7n; 0crfwmx; 026q3s3; 02pb2bp; 08k40m; 0cks1m; 05pyrb; 0bh72t; 0dr1c2; 08cfr1; ... >> query: (?x8349, 05p553) <- genre(?x8349, ?x811), genre(?x1080, ?x811), ?x1080 = 01c22t >> conf = 0.66 => this is the best rule for 1 predicted values *> Best rule #741 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 61 *> proper extension: 025twgf; *> query: (?x8349, 06n90) <- genre(?x8349, ?x811), ?x811 = 03k9fj, nominated_for(?x8349, ?x4235) *> conf = 0.37 ranks of expected_values: 5 EVAL 011xg5 genre 06n90 CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 102.000 102.000 0.662 http://example.org/film/film/genre #11-0c6qh PRED entity: 0c6qh PRED relation: award PRED expected values: 0gqy2 => 119 concepts (113 used for prediction) PRED predicted values (max 10 best out of 292): 05p09zm (0.76 #2347, 0.72 #39912, 0.71 #2739), 09cm54 (0.76 #2347, 0.72 #39912, 0.71 #2739), 05b4l5x (0.52 #4308, 0.17 #3135, 0.17 #787), 057xs89 (0.52 #4062, 0.28 #541, 0.16 #932), 094qd5 (0.35 #7080, 0.17 #2385, 0.15 #12526), 0gqwc (0.32 #7109, 0.17 #5934, 0.16 #8284), 02x4x18 (0.27 #7165, 0.15 #12526, 0.13 #36390), 0gqyl (0.26 #7138, 0.15 #12526, 0.13 #36390), 02x8n1n (0.25 #111, 0.13 #36390, 0.13 #39519), 0bs0bh (0.25 #94, 0.13 #36390, 0.13 #39519) >> Best rule #2347 for best value: >> intensional similarity = 3 >> extensional distance = 100 >> proper extension: 01xyt7; 01g0jn; >> query: (?x2499, ?x704) <- award_winner(?x704, ?x2499), vacationer(?x1353, ?x2499), participant(?x2499, ?x91) >> conf = 0.76 => this is the best rule for 2 predicted values *> Best rule #4066 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 130 *> proper extension: 02p59ry; *> query: (?x2499, 0gqy2) <- award(?x2499, ?x3508), award(?x7610, ?x3508), ?x7610 = 0451j *> conf = 0.18 ranks of expected_values: 18 EVAL 0c6qh award 0gqy2 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.056 119.000 113.000 0.759 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award #10-0bkj86 PRED entity: 0bkj86 PRED relation: institution PRED expected values: 05krk 0kz2w 07tds 0345gh 01h8rk 037fqp 02kzfw 0yls9 0885n 0ylsr 01bk1y 011xy1 02x9cv 0373qt 01_f90 => 24 concepts (24 used for prediction) PRED predicted values (max 10 best out of 499): 07wlf (0.75 #6823, 0.67 #7793, 0.64 #7308), 07tds (0.75 #6883, 0.61 #9306, 0.58 #7853), 0pspl (0.73 #7338, 0.67 #4433, 0.62 #6853), 05krk (0.71 #5324, 0.60 #3872, 0.58 #9682), 01s0_f (0.67 #4393, 0.62 #6813, 0.58 #7783), 0hsb3 (0.67 #5002, 0.62 #6938, 0.58 #7908), 0l2tk (0.67 #4407, 0.60 #3923, 0.50 #8281), 01qgr3 (0.67 #4565, 0.57 #6502, 0.55 #7470), 09vzz (0.67 #4785, 0.57 #6722, 0.50 #9143), 01vs5c (0.67 #4492, 0.57 #6429, 0.50 #6912) >> Best rule #6823 for best value: >> intensional similarity = 24 >> extensional distance = 6 >> proper extension: 027f2w; >> query: (?x1526, 07wlf) <- institution(?x1526, ?x8937), institution(?x1526, ?x7660), institution(?x1526, ?x5539), institution(?x1526, ?x3485), institution(?x1526, ?x1809), institution(?x1526, ?x1768), institution(?x1526, ?x1675), major_field_of_study(?x1526, ?x3490), student(?x1526, ?x476), school_type(?x8937, ?x3092), school(?x2820, ?x8937), ?x1675 = 01j_cy, ?x1768 = 09kvv, major_field_of_study(?x4599, ?x3490), student(?x7660, ?x2390), major_field_of_study(?x7660, ?x2164), contains(?x94, ?x8937), contains(?x2982, ?x7660), major_field_of_study(?x1809, ?x1154), company(?x5796, ?x3485), category(?x5539, ?x134), colors(?x1809, ?x3189), ?x4599 = 07t90, currency(?x7660, ?x170) >> conf = 0.75 => this is the best rule for 1 predicted values *> Best rule #6883 for first EXPECTED value: *> intensional similarity = 24 *> extensional distance = 6 *> proper extension: 027f2w; *> query: (?x1526, 07tds) <- institution(?x1526, ?x8937), institution(?x1526, ?x7660), institution(?x1526, ?x5539), institution(?x1526, ?x3485), institution(?x1526, ?x1809), institution(?x1526, ?x1768), institution(?x1526, ?x1675), major_field_of_study(?x1526, ?x3490), student(?x1526, ?x476), school_type(?x8937, ?x3092), school(?x2820, ?x8937), ?x1675 = 01j_cy, ?x1768 = 09kvv, major_field_of_study(?x4599, ?x3490), student(?x7660, ?x2390), major_field_of_study(?x7660, ?x2164), contains(?x94, ?x8937), contains(?x2982, ?x7660), major_field_of_study(?x1809, ?x1154), company(?x5796, ?x3485), category(?x5539, ?x134), colors(?x1809, ?x3189), ?x4599 = 07t90, currency(?x7660, ?x170) *> conf = 0.75 ranks of expected_values: 2, 4, 29, 40, 49, 60, 118, 124, 165, 168, 192, 274, 307, 358, 391 EVAL 0bkj86 institution 01_f90 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 0373qt CNN-1.5+0.5_MA 0.000 0.000 0.000 0.037 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 02x9cv CNN-1.5+0.5_MA 0.000 0.000 0.000 0.005 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 011xy1 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.027 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 01bk1y CNN-1.5+0.5_MA 0.000 0.000 0.000 0.018 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 0ylsr CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 0885n CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 0yls9 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 02kzfw CNN-1.5+0.5_MA 0.000 0.000 0.000 0.003 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 037fqp CNN-1.5+0.5_MA 0.000 0.000 0.000 0.004 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 01h8rk CNN-1.5+0.5_MA 0.000 0.000 0.000 0.022 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 0345gh CNN-1.5+0.5_MA 0.000 0.000 0.000 0.006 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 07tds CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 0kz2w CNN-1.5+0.5_MA 0.000 0.000 0.000 0.009 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution EVAL 0bkj86 institution 05krk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.333 24.000 24.000 0.750 http://example.org/education/educational_degree/people_with_this_degree./education/education/institution #9-021yw7 PRED entity: 021yw7 PRED relation: award_nominee PRED expected values: 0d9_96 => 114 concepts (43 used for prediction) PRED predicted values (max 10 best out of 1353): 02q6cv4 (0.83 #60798, 0.82 #56120, 0.80 #23385), 0d9_96 (0.83 #60798, 0.82 #56120, 0.80 #23385), 021yw7 (0.41 #39752, 0.06 #10176, 0.05 #12516), 08qmfm (0.21 #16031, 0.17 #20708, 0.12 #23046), 01vz80y (0.18 #4038, 0.14 #6376, 0.03 #36775), 02cm2m (0.18 #3246, 0.11 #908, 0.08 #24293), 0fvf9q (0.18 #2360, 0.11 #22, 0.08 #23407), 05m9f9 (0.17 #10575, 0.16 #12915, 0.15 #17592), 0169dl (0.17 #9875, 0.16 #12215, 0.15 #16892), 046mxj (0.16 #15308, 0.12 #19985, 0.08 #22323) >> Best rule #60798 for best value: >> intensional similarity = 3 >> extensional distance = 104 >> proper extension: 03qcq; 0gs5q; >> query: (?x3673, ?x1537) <- story_by(?x1785, ?x3673), award_nominee(?x1537, ?x3673), profession(?x3673, ?x319) >> conf = 0.83 => this is the best rule for 2 predicted values Best rule for first EXPECTED value is the SAME ranks of expected_values: 2 EVAL 021yw7 award_nominee 0d9_96 CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 114.000 43.000 0.827 http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee #8-027xx3 PRED entity: 027xx3 PRED relation: organization! PRED expected values: 060c4 => 93 concepts (93 used for prediction) PRED predicted values (max 10 best out of 9): 060c4 (0.76 #119, 0.76 #106, 0.76 #197), 07xl34 (0.23 #24, 0.21 #76, 0.21 #544), 05k17c (0.14 #150, 0.13 #59, 0.13 #137), 0hm4q (0.05 #632, 0.05 #645, 0.05 #567), 05c0jwl (0.04 #486, 0.04 #512, 0.04 #564), 0dq_5 (0.03 #1193, 0.02 #61, 0.02 #74), 02wlwtm (0.02 #26), 08jcfy (0.02 #350, 0.02 #519, 0.02 #532), 04n1q6 (0.01 #162, 0.01 #344, 0.01 #487) >> Best rule #119 for best value: >> intensional similarity = 3 >> extensional distance = 191 >> proper extension: 0fht9f; >> query: (?x3021, 060c4) <- school(?x2820, ?x3021), school(?x2820, ?x4556), institution(?x4981, ?x4556) >> conf = 0.76 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 027xx3 organization! 060c4 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 93.000 93.000 0.762 http://example.org/organization/role/leaders./organization/leadership/organization #7-01c99j PRED entity: 01c99j PRED relation: ceremony PRED expected values: 056878 => 31 concepts (31 used for prediction) PRED predicted values (max 10 best out of 126): 056878 (0.75 #400, 0.75 #2126, 0.73 #275), 0gx1673 (0.75 #2126, 0.55 #355, 0.50 #480), 0hndn2q (0.21 #2502, 0.17 #32, 0.14 #157), 09qftb (0.21 #2502, 0.17 #98, 0.14 #223), 09pnw5 (0.21 #2502, 0.17 #88, 0.14 #213), 026kqs9 (0.21 #2502, 0.17 #77, 0.14 #202), 09pj68 (0.21 #2502, 0.17 #90, 0.14 #215), 026kq4q (0.21 #2502, 0.17 #37, 0.14 #162), 0n8_m93 (0.21 #2502, 0.16 #853, 0.14 #228), 0bzm__ (0.21 #2502, 0.14 #200, 0.14 #825) >> Best rule #400 for best value: >> intensional similarity = 4 >> extensional distance = 10 >> proper extension: 026m9w; >> query: (?x4796, 056878) <- award(?x4080, ?x4796), ?x4080 = 0dl567, ceremony(?x4796, ?x139), award_winner(?x4796, ?x2138) >> conf = 0.75 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01c99j ceremony 056878 CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 31.000 31.000 0.750 http://example.org/award/award_category/winners./award/award_honor/ceremony #6-01f492 PRED entity: 01f492 PRED relation: profession PRED expected values: 02hrh1q => 181 concepts (176 used for prediction) PRED predicted values (max 10 best out of 79): 02hrh1q (0.99 #24311, 0.91 #9254, 0.91 #4634), 0gl2ny2 (0.65 #4091, 0.59 #5134, 0.53 #1856), 01d_h8 (0.49 #7457, 0.48 #3135, 0.48 #2688), 0nbcg (0.45 #1075, 0.36 #5514, 0.33 #14457), 03gjzk (0.44 #2698, 0.43 #5380, 0.43 #3145), 0dxtg (0.39 #10594, 0.39 #5378, 0.38 #11190), 02jknp (0.36 #5514, 0.33 #14457, 0.33 #19077), 09jwl (0.36 #5514, 0.33 #14457, 0.33 #19077), 0kyk (0.36 #5514, 0.33 #14457, 0.33 #19077), 01p5_g (0.36 #5514, 0.33 #14457, 0.33 #19077) >> Best rule #24311 for best value: >> intensional similarity = 3 >> extensional distance = 2517 >> proper extension: 045931; >> query: (?x8206, 02hrh1q) <- profession(?x8206, ?x1581), profession(?x11924, ?x1581), ?x11924 = 054c1 >> conf = 0.99 => this is the best rule for 1 predicted values ranks of expected_values: 1 EVAL 01f492 profession 02hrh1q CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 181.000 176.000 0.992 http://example.org/people/person/profession #5-0crfwmx PRED entity: 0crfwmx PRED relation: genre PRED expected values: 02kdv5l => 66 concepts (64 used for prediction) PRED predicted values (max 10 best out of 89): 07s9rl0 (0.64 #485, 0.56 #364, 0.54 #7506), 03k9fj (0.64 #618, 0.58 #1223, 0.50 #255), 05p553 (0.63 #731, 0.59 #610, 0.56 #368), 01hmnh (0.50 #141, 0.45 #625, 0.45 #1230), 02kdv5l (0.44 #850, 0.41 #3270, 0.37 #1092), 02l7c8 (0.41 #3285, 0.26 #4134, 0.26 #4981), 01jfsb (0.35 #861, 0.32 #1708, 0.31 #1103), 01t_vv (0.33 #419, 0.29 #540, 0.06 #4777), 04t36 (0.33 #7, 0.16 #1217, 0.12 #733), 0gf28 (0.33 #66, 0.14 #550, 0.11 #429) >> Best rule #485 for best value: >> intensional similarity = 4 >> extensional distance = 12 >> proper extension: 06zsk51; >> query: (?x1022, 07s9rl0) <- film(?x4376, ?x1022), film(?x241, ?x1022), ?x241 = 01j5ts, award_nominee(?x6693, ?x4376) >> conf = 0.64 => this is the best rule for 1 predicted values *> Best rule #850 for first EXPECTED value: *> intensional similarity = 4 *> extensional distance = 52 *> proper extension: 0g5qs2k; 0gtvrv3; 05qbckf; 0gd0c7x; 09v9mks; 0m63c; 0ds5_72; 0gwf191; *> query: (?x1022, 02kdv5l) <- film(?x241, ?x1022), film_release_distribution_medium(?x1022, ?x81), film_release_region(?x1022, ?x1471), ?x1471 = 07t21 *> conf = 0.44 ranks of expected_values: 5 EVAL 0crfwmx genre 02kdv5l CNN-1.5+0.5_MA 0.000 0.000 1.000 0.200 66.000 64.000 0.643 http://example.org/film/film/genre #4-0j_tw PRED entity: 0j_tw PRED relation: written_by PRED expected values: 01pjr7 => 63 concepts (63 used for prediction) PRED predicted values (max 10 best out of 102): 030vmc (0.36 #8415, 0.33 #11447, 0.33 #7741), 03thw4 (0.20 #141, 0.12 #814, 0.04 #2160), 02lf0c (0.19 #7740, 0.15 #11786, 0.14 #12795), 0c12h (0.17 #525, 0.07 #1197, 0.04 #2207), 06l6nj (0.08 #649, 0.04 #985, 0.03 #1321), 0343h (0.08 #381, 0.03 #1053, 0.02 #1727), 081lh (0.05 #4403, 0.04 #703, 0.03 #1039), 098n5 (0.04 #772, 0.03 #1108, 0.01 #2118), 02rk45 (0.04 #950, 0.03 #1286, 0.01 #2296), 01v80y (0.04 #948, 0.03 #1284, 0.01 #2294) >> Best rule #8415 for best value: >> intensional similarity = 4 >> extensional distance = 494 >> proper extension: 014zwb; 0bpbhm; 02psgq; 043mk4y; >> query: (?x2104, ?x9164) <- genre(?x2104, ?x258), language(?x2104, ?x90), film_crew_role(?x2104, ?x2095), film(?x9164, ?x2104) >> conf = 0.36 => this is the best rule for 1 predicted values No rule for expected values ranks of expected_values: EVAL 0j_tw written_by 01pjr7 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.000 63.000 63.000 0.357 http://example.org/film/film/written_by #3-0d9jr PRED entity: 0d9jr PRED relation: month PRED expected values: 040fv 04wzr => 259 concepts (259 used for prediction) PRED predicted values (max 10 best out of 2): 04wzr (0.93 #88, 0.93 #78, 0.92 #32), 040fv (0.88 #53, 0.85 #39, 0.85 #71) >> Best rule #88 for best value: >> intensional similarity = 4 >> extensional distance = 41 >> proper extension: 03hrz; >> query: (?x5267, 04wzr) <- month(?x5267, ?x1459), contains(?x94, ?x5267), place_of_birth(?x275, ?x5267), location(?x1165, ?x5267) >> conf = 0.93 => this is the best rule for 1 predicted values ranks of expected_values: 1, 2 EVAL 0d9jr month 04wzr CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 259.000 259.000 0.930 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month EVAL 0d9jr month 040fv CNN-1.5+0.5_MA 1.000 1.000 1.000 1.000 259.000 259.000 0.930 http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month #2-03qmx_f PRED entity: 03qmx_f PRED relation: profession PRED expected values: 03gjzk => 104 concepts (78 used for prediction) PRED predicted values (max 10 best out of 47): 02hrh1q (0.74 #2659, 0.71 #3835, 0.70 #3100), 03gjzk (0.62 #308, 0.48 #1778, 0.44 #161), 0dxtg (0.61 #1335, 0.60 #1629, 0.55 #306), 02hv44_ (0.25 #11471, 0.04 #8732, 0.04 #6379), 0np9r (0.21 #8696, 0.15 #314, 0.10 #4136), 09jwl (0.17 #4428, 0.17 #2517, 0.17 #6193), 018gz8 (0.17 #310, 0.12 #8692, 0.11 #1780), 01c72t (0.17 #8699, 0.10 #1934, 0.09 #4433), 0cbd2 (0.15 #8829, 0.15 #6329, 0.15 #3534), 0nbcg (0.12 #4440, 0.12 #6205, 0.11 #2529) >> Best rule #2659 for best value: >> intensional similarity = 2 >> extensional distance = 974 >> proper extension: 02knnd; >> query: (?x2689, 02hrh1q) <- award_nominee(?x2135, ?x2689), spouse(?x2135, ?x1802) >> conf = 0.74 => this is the best rule for 1 predicted values *> Best rule #308 for first EXPECTED value: *> intensional similarity = 3 *> extensional distance = 238 *> proper extension: 06v8s0; 02hhtj; *> query: (?x2689, 03gjzk) <- profession(?x2689, ?x1943), ?x1943 = 02krf9, nationality(?x2689, ?x6401) *> conf = 0.62 ranks of expected_values: 2 EVAL 03qmx_f profession 03gjzk CNN-1.5+0.5_MA 0.000 1.000 1.000 0.500 104.000 78.000 0.738 http://example.org/people/person/profession #1-01y2mq PRED entity: 01y2mq PRED relation: parent_genre PRED expected values: 016_v3 => 59 concepts (57 used for prediction) PRED predicted values (max 10 best out of 189): 06by7 (0.50 #2145, 0.49 #2802, 0.30 #3294), 016_nr (0.33 #47, 0.20 #701, 0.14 #1027), 02x8m (0.32 #668, 0.24 #1322, 0.15 #1979), 016_rm (0.28 #786, 0.18 #1440, 0.14 #4263), 064t9 (0.26 #1319, 0.13 #9354, 0.08 #991), 06j6l (0.25 #360, 0.16 #523, 0.15 #1998), 03_d0 (0.19 #2795, 0.15 #1974, 0.13 #2138), 05r6t (0.18 #3332, 0.17 #3660, 0.17 #4151), 0gywn (0.17 #1841, 0.16 #1677, 0.13 #2005), 03lty (0.16 #3297, 0.15 #3625, 0.15 #8055) >> Best rule #2145 for best value: >> intensional similarity = 5 >> extensional distance = 76 >> proper extension: 01gbcf; 01h0kx; 018ysx; >> query: (?x13077, 06by7) <- parent_genre(?x13077, ?x2937), parent_genre(?x11692, ?x13077), artists(?x11692, ?x4851), artists(?x2937, ?x702), ?x702 = 01vvycq >> conf = 0.50 => this is the best rule for 1 predicted values *> Best rule #2129 for first EXPECTED value: *> intensional similarity = 7 *> extensional distance = 53 *> proper extension: 0fd3y; 0xhtw; 01ym9b; 01fbr2; 0gt_0v; 0126t5; 01fh36; 0cx6f; 09qxq7; 01n4bh; ... *> query: (?x13077, ?x671) <- artists(?x13077, ?x1125), award_winner(?x5656, ?x1125), artists(?x671, ?x1125), award_nominee(?x1125, ?x5203), award_nominee(?x1125, ?x1206), ?x1206 = 01vrt_c, origin(?x5203, ?x1005) *> conf = 0.06 ranks of expected_values: 39 EVAL 01y2mq parent_genre 016_v3 CNN-1.5+0.5_MA 0.000 0.000 0.000 0.026 59.000 57.000 0.500 http://example.org/music/genre/parent_genre AVERAGE MEASURES: AVG ALGO COUNT Hits@1 Hits@3 Hits@10 MRR $nb_concepts $nb_concepts_used $max_measure CAT AVG CNN-1.5+0.5_MA 40932 0.222 0.322 0.446 0.296 93.194 73.495 0.613 all AVG CNN-1.5+0.5_MA 20466 0.313 0.433 0.567 0.398 94.966 79.017 0.644 d-fwd AVG CNN-1.5+0.5_MA 20466 0.131 0.210 0.325 0.195 91.422 67.974 0.581 d-gbwd AVG CNN-1.5+0.5_MA 29867 0.262 0.370 0.499 0.341 94.737 76.819 0.610 k-val AVG CNN-1.5+0.5_MA 10380 0.122 0.204 0.323 0.187 93.676 67.593 0.661 k-ana AVG CNN-1.5+0.5_MA 20 0.750 0.750 0.750 0.753 49.400 41.800 0.696 p-http://example.org/award/award_category/category_of AVG CNN-1.5+0.5_MA 20 0.400 0.400 0.500 0.424 31.900 29.900 0.516 p-http://example.org/award/award_category/category_of! AVG CNN-1.5+0.5_MA 15 0.600 0.800 0.933 0.719 50.067 49.800 0.726 p-http://example.org/award/award_category/disciplines_or_subjects AVG CNN-1.5+0.5_MA 15 0.200 0.200 0.400 0.248 72.733 72.733 0.556 p-http://example.org/award/award_category/disciplines_or_subjects! AVG CNN-1.5+0.5_MA 858 0.023 0.061 0.119 0.062 51.541 22.319 0.762 p-http://example.org/award/award_category/nominees./award/award_nomination/nominated_for AVG CNN-1.5+0.5_MA 858 0.103 0.220 0.503 0.218 88.952 83.029 0.629 p-http://example.org/award/award_category/nominees./award/award_nomination/nominated_for! AVG CNN-1.5+0.5_MA 217 0.037 0.083 0.171 0.086 45.982 21.456 0.501 p-http://example.org/award/award_category/winners./award/award_honor/award_winner AVG CNN-1.5+0.5_MA 217 0.143 0.253 0.507 0.252 116.866 104.230 0.426 p-http://example.org/award/award_category/winners./award/award_honor/award_winner! AVG CNN-1.5+0.5_MA 323 0.644 0.759 0.842 0.714 49.220 49.133 0.780 p-http://example.org/award/award_category/winners./award/award_honor/ceremony AVG CNN-1.5+0.5_MA 323 0.607 0.768 0.858 0.701 38.232 37.068 0.842 p-http://example.org/award/award_category/winners./award/award_honor/ceremony! AVG CNN-1.5+0.5_MA 317 0.038 0.076 0.145 0.081 36.227 21.902 0.556 p-http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner AVG CNN-1.5+0.5_MA 317 0.221 0.363 0.612 0.335 109.192 109.088 0.297 p-http://example.org/award/award_ceremony/awards_presented./award/award_honor/award_winner! AVG CNN-1.5+0.5_MA 121 0.099 0.165 0.298 0.161 36.140 25.471 0.473 p-http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for AVG CNN-1.5+0.5_MA 121 0.066 0.223 0.504 0.204 89.190 89.190 0.231 p-http://example.org/award/award_ceremony/awards_presented./award/award_honor/honored_for! AVG CNN-1.5+0.5_MA 16 0.375 0.562 0.750 0.514 90.938 39.438 0.650 p-http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for AVG CNN-1.5+0.5_MA 16 0.312 0.625 0.688 0.468 98.000 43.688 0.652 p-http://example.org/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for! AVG CNN-1.5+0.5_MA 1067 0.132 0.276 0.520 0.253 111.890 102.371 0.609 p-http://example.org/award/award_nominee/award_nominations./award/award_nomination/award AVG CNN-1.5+0.5_MA 1067 0.015 0.037 0.119 0.050 47.812 20.778 0.777 p-http://example.org/award/award_nominee/award_nominations./award/award_nomination/award! AVG CNN-1.5+0.5_MA 214 0.234 0.523 0.790 0.413 103.126 49.350 0.767 p-http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee AVG CNN-1.5+0.5_MA 214 0.210 0.481 0.771 0.385 98.393 46.832 0.778 p-http://example.org/award/award_nominee/award_nominations./award/award_nomination/award_nominee! AVG CNN-1.5+0.5_MA 132 0.129 0.258 0.341 0.207 105.417 52.932 0.539 p-http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for AVG CNN-1.5+0.5_MA 132 0.091 0.159 0.280 0.155 86.356 39.826 0.634 p-http://example.org/award/award_nominee/award_nominations./award/award_nomination/nominated_for! AVG CNN-1.5+0.5_MA 41 0.098 0.341 0.659 0.286 109.805 55.732 0.702 p-http://example.org/award/award_winner/awards_won./award/award_honor/award_winner AVG CNN-1.5+0.5_MA 41 0.073 0.341 0.610 0.252 109.000 55.098 0.721 p-http://example.org/award/award_winner/awards_won./award/award_honor/award_winner! AVG CNN-1.5+0.5_MA 118 0.119 0.254 0.407 0.221 92.720 87.110 0.360 p-http://example.org/award/award_winning_work/awards_won./award/award_honor/award AVG CNN-1.5+0.5_MA 118 0.051 0.102 0.237 0.107 49.093 23.059 0.541 p-http://example.org/award/award_winning_work/awards_won./award/award_honor/award! AVG CNN-1.5+0.5_MA 24 0.083 0.167 0.542 0.200 85.250 47.917 0.483 p-http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner AVG CNN-1.5+0.5_MA 24 0.250 0.500 0.708 0.391 111.000 74.583 0.405 p-http://example.org/award/award_winning_work/awards_won./award/award_honor/award_winner! AVG CNN-1.5+0.5_MA 10 0.200 0.500 1.000 0.458 102.200 50.100 0.851 p-http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for AVG CNN-1.5+0.5_MA 10 0.100 0.200 0.900 0.310 103.300 53.000 0.783 p-http://example.org/award/award_winning_work/awards_won./award/award_honor/honored_for! AVG CNN-1.5+0.5_MA 27 0.000 0.000 0.000 0.003 92.926 53.185 0.389 p-http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee AVG CNN-1.5+0.5_MA 27 0.704 0.926 0.963 0.806 113.926 112.444 0.230 p-http://example.org/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee! AVG CNN-1.5+0.5_MA 48 0.875 1.000 1.000 0.931 153.542 153.542 0.596 p-http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list AVG CNN-1.5+0.5_MA 48 0.042 0.146 0.250 0.108 6.146 6.146 0.690 p-http://example.org/award/ranked_item/appears_in_ranked_lists./award/ranking/list! AVG CNN-1.5+0.5_MA 20 1.000 1.000 1.000 1.000 128.100 128.100 0.889 p-http://example.org/base/aareas/schema/administrative_area/administrative_area_type AVG CNN-1.5+0.5_MA 20 0.000 0.000 0.000 0.000 1.000 1.000 0.000 p-http://example.org/base/aareas/schema/administrative_area/administrative_area_type! AVG CNN-1.5+0.5_MA 25 0.920 0.920 0.960 0.929 153.000 92.960 0.776 p-http://example.org/base/aareas/schema/administrative_area/administrative_parent AVG CNN-1.5+0.5_MA 25 0.040 0.080 0.120 0.090 46.560 36.080 0.445 p-http://example.org/base/aareas/schema/administrative_area/administrative_parent! AVG CNN-1.5+0.5_MA 8 0.000 0.000 0.000 0.005 119.750 70.000 0.509 p-http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated AVG CNN-1.5+0.5_MA 8 0.625 0.625 1.000 0.702 120.250 65.875 0.345 p-http://example.org/base/americancomedy/celebrity_impressionist/celebrities_impersonated! AVG CNN-1.5+0.5_MA 14 0.714 0.929 1.000 0.829 134.214 81.286 0.703 p-http://example.org/base/biblioness/bibs_location/country AVG CNN-1.5+0.5_MA 14 0.071 0.071 0.143 0.097 199.000 164.643 0.382 p-http://example.org/base/biblioness/bibs_location/country! AVG CNN-1.5+0.5_MA 7 0.857 1.000 1.000 0.929 149.286 120.714 0.479 p-http://example.org/base/biblioness/bibs_location/state AVG CNN-1.5+0.5_MA 7 0.143 0.143 0.429 0.189 161.571 144.286 0.262 p-http://example.org/base/biblioness/bibs_location/state! AVG CNN-1.5+0.5_MA 20 0.050 0.050 0.100 0.071 62.650 43.050 0.533 p-http://example.org/base/culturalevent/event/entity_involved AVG CNN-1.5+0.5_MA 20 0.150 0.300 0.300 0.246 109.200 109.200 0.357 p-http://example.org/base/culturalevent/event/entity_involved! AVG CNN-1.5+0.5_MA 11 0.636 1.000 1.000 0.818 124.182 124.182 0.157 p-http://example.org/base/eating/practicer_of_diet/diet AVG CNN-1.5+0.5_MA 11 0.000 0.000 0.000 0.005 13.818 13.818 0.333 p-http://example.org/base/eating/practicer_of_diet/diet! AVG CNN-1.5+0.5_MA 3 0.333 0.667 1.000 0.528 12.000 12.000 0.842 p-http://example.org/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months AVG CNN-1.5+0.5_MA 3 0.000 1.000 1.000 0.444 12.000 12.000 0.791 p-http://example.org/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months! AVG CNN-1.5+0.5_MA 5 0.000 0.000 0.000 0.010 159.600 124.400 0.544 p-http://example.org/base/locations/continents/countries_within AVG CNN-1.5+0.5_MA 5 0.600 1.000 1.000 0.767 200.400 175.200 0.508 p-http://example.org/base/locations/continents/countries_within! AVG CNN-1.5+0.5_MA 41 0.390 0.610 1.000 0.556 49.780 49.780 0.821 p-http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team AVG CNN-1.5+0.5_MA 41 0.146 0.561 0.902 0.369 82.512 82.512 0.785 p-http://example.org/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team! AVG CNN-1.5+0.5_MA 21 0.952 1.000 1.000 0.976 177.571 177.571 0.776 p-http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed AVG CNN-1.5+0.5_MA 21 0.190 0.190 0.524 0.257 5.000 5.000 0.502 p-http://example.org/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed! AVG CNN-1.5+0.5_MA 1 1.000 1.000 1.000 1.000 136.000 90.000 0.816 p-http://example.org/base/popstra/celebrity/canoodled./base/popstra/canoodled/participant AVG CNN-1.5+0.5_MA 1 0.000 0.000 0.000 0.000 75.000 31.000 0.082 p-http://example.org/base/popstra/celebrity/canoodled./base/popstra/canoodled/participant! AVG CNN-1.5+0.5_MA 11 0.364 0.545 0.636 0.470 125.455 75.091 0.563 p-http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant AVG CNN-1.5+0.5_MA 11 0.545 0.636 0.636 0.597 130.273 80.727 0.661 p-http://example.org/base/popstra/celebrity/dated./base/popstra/dated/participant! AVG CNN-1.5+0.5_MA 32 0.406 0.500 0.562 0.461 123.500 80.500 0.615 p-http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant AVG CNN-1.5+0.5_MA 32 0.406 0.531 0.562 0.471 123.062 71.375 0.651 p-http://example.org/base/popstra/celebrity/friendship./base/popstra/friendship/participant! AVG CNN-1.5+0.5_MA 31 0.000 0.032 0.129 0.048 180.323 141.484 0.286 p-http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer AVG CNN-1.5+0.5_MA 31 0.097 0.258 0.516 0.208 130.774 130.258 0.246 p-http://example.org/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer! AVG CNN-1.5+0.5_MA 3 0.333 1.000 1.000 0.611 106.667 68.667 0.600 p-http://example.org/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members AVG CNN-1.5+0.5_MA 3 0.333 1.000 1.000 0.667 94.333 58.000 0.744 p-http://example.org/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members! AVG CNN-1.5+0.5_MA 20 0.950 1.000 1.000 0.975 149.450 149.450 0.694 p-http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category AVG CNN-1.5+0.5_MA 20 0.050 0.050 0.250 0.092 16.250 12.950 0.400 p-http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category! AVG CNN-1.5+0.5_MA 21 0.762 0.857 0.905 0.814 142.095 142.095 0.742 p-http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language AVG CNN-1.5+0.5_MA 21 0.190 0.333 0.524 0.298 68.429 54.952 0.464 p-http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language! AVG CNN-1.5+0.5_MA 38 0.368 0.500 0.632 0.472 144.842 130.868 0.706 p-http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location AVG CNN-1.5+0.5_MA 38 0.184 0.316 0.526 0.295 159.947 148.447 0.487 p-http://example.org/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location! AVG CNN-1.5+0.5_MA 60 0.983 1.000 1.000 0.992 119.333 119.333 0.442 p-http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency AVG CNN-1.5+0.5_MA 60 0.000 0.000 0.000 0.001 8.000 8.000 0.333 p-http://example.org/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency! AVG CNN-1.5+0.5_MA 29 0.000 0.000 0.069 0.018 95.793 70.379 0.504 p-http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club AVG CNN-1.5+0.5_MA 29 0.034 0.172 0.483 0.178 96.655 68.586 0.406 p-http://example.org/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club! AVG CNN-1.5+0.5_MA 27 0.852 0.889 1.000 0.894 94.000 94.000 0.836 p-http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season AVG CNN-1.5+0.5_MA 27 0.778 0.926 0.963 0.853 12.926 12.926 0.840 p-http://example.org/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season! AVG CNN-1.5+0.5_MA 14 0.214 0.214 0.214 0.221 30.000 24.000 0.620 p-http://example.org/broadcast/content/artist AVG CNN-1.5+0.5_MA 14 0.786 1.000 1.000 0.893 119.500 107.286 0.260 p-http://example.org/broadcast/content/artist! AVG CNN-1.5+0.5_MA 41 0.415 0.707 0.854 0.566 142.732 141.439 0.656 p-http://example.org/business/business_operation/industry AVG CNN-1.5+0.5_MA 41 0.024 0.073 0.122 0.069 49.585 49.098 0.599 p-http://example.org/business/business_operation/industry! AVG CNN-1.5+0.5_MA 1 1.000 1.000 1.000 1.000 147.000 147.000 0.843 p-http://example.org/business/business_operation/revenue./measurement_unit/dated_money_value/currency AVG CNN-1.5+0.5_MA 1 0.000 0.000 1.000 0.143 8.000 8.000 0.333 p-http://example.org/business/business_operation/revenue./measurement_unit/dated_money_value/currency! AVG CNN-1.5+0.5_MA 85 0.047 0.094 0.200 0.097 39.988 33.553 0.701 p-http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company AVG CNN-1.5+0.5_MA 85 0.329 0.588 0.894 0.499 143.247 143.141 0.640 p-http://example.org/business/job_title/people_with_this_title./business/employment_tenure/company! AVG CNN-1.5+0.5_MA 1 0.000 1.000 1.000 0.500 127.000 116.000 0.807 p-http://example.org/celebrities/celebrity/celebrity_friends./celebrities/friendship/friend AVG CNN-1.5+0.5_MA 1 1.000 1.000 1.000 1.000 109.000 80.000 0.808 p-http://example.org/celebrities/celebrity/celebrity_friends./celebrities/friendship/friend! AVG CNN-1.5+0.5_MA 402 1.000 1.000 1.000 1.000 111.323 111.323 0.710 p-http://example.org/common/topic/webpage./common/webpage/category AVG CNN-1.5+0.5_MA 402 0.000 0.000 0.000 0.000 1.000 1.000 0.000 p-http://example.org/common/topic/webpage./common/webpage/category! AVG CNN-1.5+0.5_MA 10 0.000 0.000 0.000 0.009 82.900 36.100 0.160 p-http://example.org/dataworld/gardening_hint/split_to AVG CNN-1.5+0.5_MA 10 0.100 0.100 0.100 0.111 78.900 45.100 0.149 p-http://example.org/dataworld/gardening_hint/split_to! AVG CNN-1.5+0.5_MA 287 0.056 0.132 0.216 0.117 26.115 24.606 0.767 p-http://example.org/education/educational_degree/people_with_this_degree./education/education/institution AVG CNN-1.5+0.5_MA 287 0.474 0.714 0.948 0.629 135.105 122.199 0.762 p-http://example.org/education/educational_degree/people_with_this_degree./education/education/institution! AVG CNN-1.5+0.5_MA 63 0.190 0.317 0.556 0.300 27.016 26.349 0.750 p-http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study AVG CNN-1.5+0.5_MA 63 0.444 0.746 0.968 0.615 65.302 51.524 0.806 p-http://example.org/education/educational_degree/people_with_this_degree./education/education/major_field_of_study! AVG CNN-1.5+0.5_MA 27 0.000 0.000 0.074 0.022 25.259 24.222 0.502 p-http://example.org/education/educational_degree/people_with_this_degree./education/education/student AVG CNN-1.5+0.5_MA 27 0.556 0.741 1.000 0.669 136.259 131.815 0.322 p-http://example.org/education/educational_degree/people_with_this_degree./education/education/student! AVG CNN-1.5+0.5_MA 13 0.154 0.308 0.385 0.238 150.000 93.154 0.164 p-http://example.org/education/educational_institution/campuses AVG CNN-1.5+0.5_MA 13 0.077 0.385 0.385 0.249 144.692 93.077 0.157 p-http://example.org/education/educational_institution/campuses! AVG CNN-1.5+0.5_MA 90 0.211 0.511 0.878 0.414 144.478 144.478 0.445 p-http://example.org/education/educational_institution/colors AVG CNN-1.5+0.5_MA 90 0.000 0.000 0.000 0.006 20.178 20.100 0.549 p-http://example.org/education/educational_institution/colors! AVG CNN-1.5+0.5_MA 80 0.500 0.762 0.975 0.652 139.988 139.988 0.571 p-http://example.org/education/educational_institution/school_type AVG CNN-1.5+0.5_MA 80 0.000 0.000 0.013 0.009 23.350 21.762 0.492 p-http://example.org/education/educational_institution/school_type! AVG CNN-1.5+0.5_MA 321 0.125 0.252 0.555 0.251 131.355 129.296 0.581 p-http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study AVG CNN-1.5+0.5_MA 321 0.047 0.087 0.184 0.097 74.396 47.994 0.726 p-http://example.org/education/educational_institution/students_graduates./education/education/major_field_of_study! AVG CNN-1.5+0.5_MA 311 0.003 0.010 0.023 0.012 124.990 85.167 0.225 p-http://example.org/education/educational_institution/students_graduates./education/education/student AVG CNN-1.5+0.5_MA 311 0.039 0.119 0.241 0.110 116.965 111.781 0.210 p-http://example.org/education/educational_institution/students_graduates./education/education/student! AVG CNN-1.5+0.5_MA 13 0.077 0.462 0.538 0.263 150.692 96.615 0.168 p-http://example.org/education/educational_institution_campus/educational_institution AVG CNN-1.5+0.5_MA 13 0.077 0.308 0.538 0.206 150.538 89.462 0.165 p-http://example.org/education/educational_institution_campus/educational_institution! AVG CNN-1.5+0.5_MA 11 0.455 0.455 0.636 0.491 69.000 57.818 0.769 p-http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study AVG CNN-1.5+0.5_MA 11 0.273 0.545 0.636 0.429 77.273 70.273 0.782 p-http://example.org/education/field_of_study/students_majoring./education/education/major_field_of_study! AVG CNN-1.5+0.5_MA 34 0.029 0.029 0.118 0.048 72.029 44.529 0.402 p-http://example.org/education/field_of_study/students_majoring./education/education/student AVG CNN-1.5+0.5_MA 34 0.206 0.294 0.382 0.281 117.382 112.235 0.220 p-http://example.org/education/field_of_study/students_majoring./education/education/student! AVG CNN-1.5+0.5_MA 5 0.800 0.800 1.000 0.840 135.200 135.200 0.778 p-http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency AVG CNN-1.5+0.5_MA 5 0.000 0.000 0.000 0.007 8.000 8.000 0.751 p-http://example.org/education/university/domestic_tuition./measurement_unit/dated_money_value/currency! AVG CNN-1.5+0.5_MA 21 0.714 1.000 1.000 0.857 133.714 133.714 0.449 p-http://example.org/education/university/fraternities_and_sororities AVG CNN-1.5+0.5_MA 21 0.000 0.000 0.095 0.026 37.238 16.238 0.429 p-http://example.org/education/university/fraternities_and_sororities! AVG CNN-1.5+0.5_MA 1 1.000 1.000 1.000 1.000 115.000 115.000 0.671 p-http://example.org/education/university/local_tuition./measurement_unit/dated_money_value/currency AVG CNN-1.5+0.5_MA 1 0.000 0.000 0.000 0.001 8.000 8.000 0.420 p-http://example.org/education/university/local_tuition./measurement_unit/dated_money_value/currency! AVG CNN-1.5+0.5_MA 12 1.000 1.000 1.000 1.000 122.833 122.833 0.474 p-http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language AVG CNN-1.5+0.5_MA 12 0.000 0.000 0.000 0.000 72.000 55.000 0.333 p-http://example.org/film/actor/dubbing_performances./film/dubbing_performance/language! AVG CNN-1.5+0.5_MA 836 0.023 0.044 0.072 0.044 105.634 70.096 0.444 p-http://example.org/film/actor/film./film/performance/film AVG CNN-1.5+0.5_MA 836 0.020 0.044 0.077 0.043 87.285 48.196 0.433 p-http://example.org/film/actor/film./film/performance/film! AVG CNN-1.5+0.5_MA 16 0.875 0.938 1.000 0.922 118.000 118.000 0.245 p-http://example.org/film/actor/film./film/performance/special_performance_type AVG CNN-1.5+0.5_MA 16 0.000 0.000 0.000 0.004 7.750 5.938 0.479 p-http://example.org/film/actor/film./film/performance/special_performance_type! AVG CNN-1.5+0.5_MA 19 0.053 0.053 0.105 0.062 114.263 79.158 0.381 p-http://example.org/film/director/film AVG CNN-1.5+0.5_MA 19 0.105 0.158 0.421 0.167 86.316 57.053 0.312 p-http://example.org/film/director/film! AVG CNN-1.5+0.5_MA 17 0.235 0.235 0.588 0.296 78.588 54.824 0.131 p-http://example.org/film/film/cinematography AVG CNN-1.5+0.5_MA 17 0.000 0.000 0.000 0.000 100.118 41.882 0.314 p-http://example.org/film/film/cinematography! AVG CNN-1.5+0.5_MA 13 0.308 0.462 0.692 0.425 82.615 63.769 0.102 p-http://example.org/film/film/costume_design_by AVG CNN-1.5+0.5_MA 13 0.000 0.000 0.077 0.009 108.846 64.231 0.277 p-http://example.org/film/film/costume_design_by! AVG CNN-1.5+0.5_MA 131 0.580 0.748 0.893 0.681 91.458 89.947 0.695 p-http://example.org/film/film/country AVG CNN-1.5+0.5_MA 131 0.000 0.000 0.000 0.006 190.954 160.260 0.519 p-http://example.org/film/film/country! AVG CNN-1.5+0.5_MA 38 0.553 0.974 1.000 0.739 96.711 96.711 0.688 p-http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium AVG CNN-1.5+0.5_MA 38 0.026 0.026 0.132 0.059 7.605 7.026 0.563 p-http://example.org/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium! AVG CNN-1.5+0.5_MA 10 1.000 1.000 1.000 1.000 91.200 90.400 0.785 p-http://example.org/film/film/distributors./film/film_film_distributor_relationship/region AVG CNN-1.5+0.5_MA 10 0.000 0.000 0.000 0.000 167.000 70.000 0.333 p-http://example.org/film/film/distributors./film/film_film_distributor_relationship/region! AVG CNN-1.5+0.5_MA 8 0.125 0.250 0.375 0.203 107.250 73.750 0.417 p-http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor AVG CNN-1.5+0.5_MA 8 0.500 0.500 0.750 0.559 120.250 109.875 0.329 p-http://example.org/film/film/dubbing_performances./film/dubbing_performance/actor! AVG CNN-1.5+0.5_MA 13 0.308 0.462 0.769 0.424 95.615 66.231 0.150 p-http://example.org/film/film/edited_by AVG CNN-1.5+0.5_MA 13 0.000 0.000 0.000 0.001 112.385 50.846 0.182 p-http://example.org/film/film/edited_by! AVG CNN-1.5+0.5_MA 151 0.967 0.987 1.000 0.979 90.974 90.974 0.835 p-http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency AVG CNN-1.5+0.5_MA 151 0.000 0.000 0.000 0.000 8.000 7.914 0.347 p-http://example.org/film/film/estimated_budget./measurement_unit/dated_money_value/currency! AVG CNN-1.5+0.5_MA 73 0.205 0.288 0.479 0.289 88.068 60.000 0.188 p-http://example.org/film/film/executive_produced_by AVG CNN-1.5+0.5_MA 73 0.055 0.055 0.288 0.106 116.274 72.973 0.225 p-http://example.org/film/film/executive_produced_by! AVG CNN-1.5+0.5_MA 100 0.140 0.300 0.510 0.251 87.430 65.980 0.263 p-http://example.org/film/film/featured_film_locations AVG CNN-1.5+0.5_MA 100 0.010 0.020 0.070 0.026 182.190 156.120 0.268 p-http://example.org/film/film/featured_film_locations! AVG CNN-1.5+0.5_MA 3 0.667 1.000 1.000 0.778 102.333 76.000 0.301 p-http://example.org/film/film/film_art_direction_by AVG CNN-1.5+0.5_MA 3 0.000 0.000 0.333 0.067 100.000 85.000 0.167 p-http://example.org/film/film/film_art_direction_by! AVG CNN-1.5+0.5_MA 18 0.333 0.444 0.833 0.449 82.611 82.611 0.131 p-http://example.org/film/film/film_festivals AVG CNN-1.5+0.5_MA 18 0.000 0.000 0.000 0.009 50.500 26.056 0.457 p-http://example.org/film/film/film_festivals! AVG CNN-1.5+0.5_MA 39 0.769 0.974 1.000 0.865 90.590 90.590 0.286 p-http://example.org/film/film/film_format AVG CNN-1.5+0.5_MA 39 0.000 0.000 0.026 0.004 5.000 4.462 0.403 p-http://example.org/film/film/film_format! AVG CNN-1.5+0.5_MA 10 0.300 0.500 0.600 0.420 94.400 63.500 0.111 p-http://example.org/film/film/film_production_design_by AVG CNN-1.5+0.5_MA 10 0.100 0.100 0.100 0.100 104.200 63.500 0.330 p-http://example.org/film/film/film_production_design_by! AVG CNN-1.5+0.5_MA 722 0.241 0.439 0.733 0.391 88.546 73.529 0.709 p-http://example.org/film/film/genre AVG CNN-1.5+0.5_MA 722 0.015 0.029 0.058 0.032 56.801 33.684 0.732 p-http://example.org/film/film/genre! AVG CNN-1.5+0.5_MA 314 0.669 0.783 0.895 0.745 90.025 88.223 0.711 p-http://example.org/film/film/language AVG CNN-1.5+0.5_MA 314 0.016 0.038 0.086 0.045 74.675 59.567 0.749 p-http://example.org/film/film/language! AVG CNN-1.5+0.5_MA 92 0.033 0.109 0.239 0.112 86.033 55.424 0.222 p-http://example.org/film/film/music AVG CNN-1.5+0.5_MA 92 0.011 0.022 0.054 0.022 121.674 77.674 0.381 p-http://example.org/film/film/music! AVG CNN-1.5+0.5_MA 26 0.192 0.385 0.500 0.310 91.462 66.308 0.158 p-http://example.org/film/film/other_crew./film/film_crew_gig/crewmember AVG CNN-1.5+0.5_MA 26 0.000 0.000 0.000 0.005 97.423 60.038 0.271 p-http://example.org/film/film/other_crew./film/film_crew_gig/crewmember! AVG CNN-1.5+0.5_MA 606 0.564 0.781 0.949 0.697 90.856 90.746 0.721 p-http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role AVG CNN-1.5+0.5_MA 606 0.012 0.036 0.104 0.047 36.310 25.820 0.746 p-http://example.org/film/film/other_crew./film/film_crew_gig/film_crew_role! AVG CNN-1.5+0.5_MA 11 0.000 0.000 0.000 0.010 101.818 72.000 0.262 p-http://example.org/film/film/personal_appearances./film/personal_film_appearance/person AVG CNN-1.5+0.5_MA 11 0.273 0.273 0.545 0.341 132.364 126.091 0.205 p-http://example.org/film/film/personal_appearances./film/personal_film_appearance/person! AVG CNN-1.5+0.5_MA 18 0.056 0.111 0.111 0.083 90.500 45.222 0.123 p-http://example.org/film/film/prequel AVG CNN-1.5+0.5_MA 18 0.000 0.056 0.111 0.028 94.167 46.222 0.110 p-http://example.org/film/film/prequel! AVG CNN-1.5+0.5_MA 60 0.033 0.133 0.333 0.124 89.783 59.417 0.290 p-http://example.org/film/film/produced_by AVG CNN-1.5+0.5_MA 60 0.017 0.067 0.183 0.055 113.767 77.417 0.324 p-http://example.org/film/film/produced_by! AVG CNN-1.5+0.5_MA 84 0.202 0.310 0.536 0.311 91.214 73.976 0.372 p-http://example.org/film/film/production_companies AVG CNN-1.5+0.5_MA 84 0.095 0.119 0.214 0.129 122.917 81.012 0.444 p-http://example.org/film/film/production_companies! AVG CNN-1.5+0.5_MA 20 0.200 0.450 0.800 0.379 98.450 97.700 0.234 p-http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue AVG CNN-1.5+0.5_MA 20 0.100 0.150 0.300 0.165 96.850 79.300 0.388 p-http://example.org/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue! AVG CNN-1.5+0.5_MA 158 0.968 1.000 1.000 0.982 88.215 88.215 0.827 p-http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium AVG CNN-1.5+0.5_MA 158 0.013 0.013 0.025 0.029 8.468 8.089 0.348 p-http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium! AVG CNN-1.5+0.5_MA 1447 0.349 0.596 0.827 0.510 86.300 84.012 0.890 p-http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region AVG CNN-1.5+0.5_MA 1447 0.087 0.221 0.388 0.191 185.614 117.602 0.879 p-http://example.org/film/film/release_date_s./film/film_regional_release_date/film_release_region! AVG CNN-1.5+0.5_MA 9 0.000 0.333 0.556 0.189 86.333 86.333 0.114 p-http://example.org/film/film/runtime./film/film_cut/film_release_region AVG CNN-1.5+0.5_MA 9 0.000 0.000 0.000 0.007 224.222 157.778 0.258 p-http://example.org/film/film/runtime./film/film_cut/film_release_region! AVG CNN-1.5+0.5_MA 31 0.161 0.323 0.387 0.259 86.774 53.194 0.186 p-http://example.org/film/film/story_by AVG CNN-1.5+0.5_MA 31 0.032 0.161 0.194 0.082 138.935 109.290 0.214 p-http://example.org/film/film/story_by! AVG CNN-1.5+0.5_MA 26 0.000 0.038 0.192 0.054 90.346 56.115 0.274 p-http://example.org/film/film/written_by AVG CNN-1.5+0.5_MA 26 0.115 0.154 0.231 0.147 119.192 80.346 0.335 p-http://example.org/film/film/written_by! AVG CNN-1.5+0.5_MA 160 0.000 0.006 0.013 0.011 115.919 80.331 0.629 p-http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film AVG CNN-1.5+0.5_MA 160 0.200 0.356 0.644 0.334 89.325 70.194 0.506 p-http://example.org/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film! AVG CNN-1.5+0.5_MA 8 0.000 0.000 0.000 0.009 100.000 71.000 0.341 p-http://example.org/film/film_set_designer/film_sets_designed AVG CNN-1.5+0.5_MA 8 0.000 0.250 0.625 0.188 85.375 71.875 0.152 p-http://example.org/film/film_set_designer/film_sets_designed! AVG CNN-1.5+0.5_MA 77 0.013 0.013 0.026 0.017 81.740 49.416 0.308 p-http://example.org/film/film_subject/films AVG CNN-1.5+0.5_MA 77 0.182 0.312 0.468 0.276 92.156 39.416 0.188 p-http://example.org/film/film_subject/films! AVG CNN-1.5+0.5_MA 3 1.000 1.000 1.000 1.000 127.000 127.000 0.116 p-http://example.org/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance AVG CNN-1.5+0.5_MA 3 0.000 0.000 0.000 0.000 1.000 1.000 0.000 p-http://example.org/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance! AVG CNN-1.5+0.5_MA 11 0.000 0.000 0.000 0.003 12.091 12.091 0.576 p-http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film AVG CNN-1.5+0.5_MA 11 0.545 0.909 1.000 0.750 85.727 85.727 0.286 p-http://example.org/film/special_film_performance_type/film_performance_type./film/performance/film! AVG CNN-1.5+0.5_MA 105 0.552 0.752 0.962 0.677 22.495 22.200 0.767 p-http://example.org/food/food/nutrients./food/nutrition_fact/nutrient AVG CNN-1.5+0.5_MA 105 0.762 0.924 1.000 0.849 52.019 49.210 0.917 p-http://example.org/food/food/nutrients./food/nutrition_fact/nutrient! AVG CNN-1.5+0.5_MA 91 0.022 0.055 0.121 0.064 30.418 25.835 0.645 p-http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office AVG CNN-1.5+0.5_MA 91 0.527 0.670 0.923 0.642 151.505 151.505 0.717 p-http://example.org/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office! AVG CNN-1.5+0.5_MA 12 1.000 1.000 1.000 1.000 19.833 19.833 0.845 p-http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions AVG CNN-1.5+0.5_MA 12 1.000 1.000 1.000 1.000 34.000 34.000 0.919 p-http://example.org/government/governmental_body/members./government/government_position_held/legislative_sessions! AVG CNN-1.5+0.5_MA 117 0.667 0.786 0.974 0.759 33.949 32.026 0.900 p-http://example.org/government/legislative_session/members./government/government_position_held/district_represented AVG CNN-1.5+0.5_MA 117 0.778 0.829 0.966 0.827 180.598 180.598 0.798 p-http://example.org/government/legislative_session/members./government/government_position_held/district_represented! AVG CNN-1.5+0.5_MA 13 0.308 0.462 0.923 0.455 35.846 35.846 0.858 p-http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions AVG CNN-1.5+0.5_MA 13 0.231 0.462 1.000 0.422 33.615 33.615 0.857 p-http://example.org/government/legislative_session/members./government/government_position_held/legislative_sessions! AVG CNN-1.5+0.5_MA 11 0.000 0.000 0.000 0.004 146.636 79.364 0.475 p-http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician AVG CNN-1.5+0.5_MA 11 0.545 0.818 0.818 0.673 138.636 138.636 0.392 p-http://example.org/government/political_party/politicians_in_this_party./government/political_party_tenure/politician! AVG CNN-1.5+0.5_MA 16 0.688 0.688 0.875 0.737 153.250 153.250 0.552 p-http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title AVG CNN-1.5+0.5_MA 16 0.000 0.188 0.188 0.103 42.562 42.562 0.480 p-http://example.org/government/politician/government_positions_held./government/government_position_held/basic_title! AVG CNN-1.5+0.5_MA 9 0.222 0.222 0.556 0.273 154.111 128.111 0.554 p-http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office AVG CNN-1.5+0.5_MA 9 0.111 0.222 0.222 0.172 167.778 150.222 0.370 p-http://example.org/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office! AVG CNN-1.5+0.5_MA 30 0.467 0.767 0.833 0.624 135.567 135.567 0.628 p-http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions AVG CNN-1.5+0.5_MA 30 0.633 0.800 1.000 0.753 34.933 34.900 0.772 p-http://example.org/government/politician/government_positions_held./government/government_position_held/legislative_sessions! AVG CNN-1.5+0.5_MA 4 1.000 1.000 1.000 1.000 65.750 65.750 0.854 p-http://example.org/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position AVG CNN-1.5+0.5_MA 4 0.000 0.250 1.000 0.181 24.250 13.250 0.764 p-http://example.org/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position! AVG CNN-1.5+0.5_MA 235 0.034 0.094 0.230 0.102 142.162 63.311 0.398 p-http://example.org/influence/influence_node/influenced_by AVG CNN-1.5+0.5_MA 235 0.047 0.089 0.234 0.105 142.055 63.928 0.406 p-http://example.org/influence/influence_node/influenced_by! AVG CNN-1.5+0.5_MA 2 0.000 0.000 0.500 0.100 180.000 95.000 0.532 p-http://example.org/influence/influence_node/peers./influence/peer_relationship/peers AVG CNN-1.5+0.5_MA 2 0.000 0.000 0.500 0.125 161.000 84.500 0.085 p-http://example.org/influence/influence_node/peers./influence/peer_relationship/peers! AVG CNN-1.5+0.5_MA 25 0.080 0.120 0.280 0.142 55.400 50.320 0.686 p-http://example.org/language/human_language/countries_spoken_in AVG CNN-1.5+0.5_MA 25 0.120 0.200 0.400 0.222 149.640 149.640 0.513 p-http://example.org/language/human_language/countries_spoken_in! AVG CNN-1.5+0.5_MA 3 0.667 0.667 0.667 0.667 101.667 61.333 0.503 p-http://example.org/location/administrative_division/country AVG CNN-1.5+0.5_MA 3 0.000 0.000 0.000 0.008 203.000 162.667 0.481 p-http://example.org/location/administrative_division/country! AVG CNN-1.5+0.5_MA 11 0.182 0.182 0.455 0.236 128.273 105.455 0.349 p-http://example.org/location/country/capital AVG CNN-1.5+0.5_MA 11 0.091 0.182 0.273 0.171 192.455 139.455 0.319 p-http://example.org/location/country/capital! AVG CNN-1.5+0.5_MA 43 0.628 0.907 1.000 0.772 136.605 136.605 0.509 p-http://example.org/location/country/form_of_government AVG CNN-1.5+0.5_MA 43 0.000 0.023 0.093 0.035 5.860 5.860 0.507 p-http://example.org/location/country/form_of_government! AVG CNN-1.5+0.5_MA 16 0.500 0.688 1.000 0.636 132.000 132.000 0.547 p-http://example.org/location/country/official_language AVG CNN-1.5+0.5_MA 16 0.000 0.062 0.125 0.055 73.125 68.688 0.555 p-http://example.org/location/country/official_language! AVG CNN-1.5+0.5_MA 46 0.000 0.000 0.000 0.000 196.239 193.761 0.172 p-http://example.org/location/country/second_level_divisions AVG CNN-1.5+0.5_MA 46 0.978 1.000 1.000 0.986 131.848 66.087 0.854 p-http://example.org/location/country/second_level_divisions! AVG CNN-1.5+0.5_MA 7 0.714 0.714 1.000 0.751 97.000 61.714 0.426 p-http://example.org/location/hud_county_place/county AVG CNN-1.5+0.5_MA 7 0.286 0.429 0.429 0.359 132.857 71.286 0.530 p-http://example.org/location/hud_county_place/county! AVG CNN-1.5+0.5_MA 48 0.146 0.312 0.625 0.268 122.917 76.729 0.225 p-http://example.org/location/hud_county_place/place AVG CNN-1.5+0.5_MA 48 0.188 0.458 0.604 0.322 114.583 71.771 0.197 p-http://example.org/location/hud_county_place/place! AVG CNN-1.5+0.5_MA 84 1.000 1.000 1.000 1.000 128.048 128.048 0.918 p-http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source AVG CNN-1.5+0.5_MA 84 0.000 0.000 0.000 0.000 95.000 95.000 0.000 p-http://example.org/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source! AVG CNN-1.5+0.5_MA 55 0.400 0.600 0.782 0.532 149.600 80.109 0.704 p-http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins AVG CNN-1.5+0.5_MA 55 0.491 0.582 0.836 0.576 146.891 82.291 0.667 p-http://example.org/location/location/adjoin_s./location/adjoining_relationship/adjoins! AVG CNN-1.5+0.5_MA 330 0.055 0.112 0.236 0.111 172.430 107.279 0.754 p-http://example.org/location/location/contains AVG CNN-1.5+0.5_MA 330 0.561 0.733 0.855 0.667 124.218 73.864 0.748 p-http://example.org/location/location/contains! AVG CNN-1.5+0.5_MA 13 0.308 0.462 0.615 0.398 172.154 115.846 0.351 p-http://example.org/location/location/partially_contains AVG CNN-1.5+0.5_MA 13 0.000 0.231 0.615 0.162 114.923 72.769 0.416 p-http://example.org/location/location/partially_contains! AVG CNN-1.5+0.5_MA 137 0.905 0.971 1.000 0.942 129.781 129.781 0.728 p-http://example.org/location/location/time_zones AVG CNN-1.5+0.5_MA 137 0.066 0.109 0.146 0.108 12.635 12.635 0.816 p-http://example.org/location/location/time_zones! AVG CNN-1.5+0.5_MA 4 1.000 1.000 1.000 1.000 159.250 159.250 0.891 p-http://example.org/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency AVG CNN-1.5+0.5_MA 4 0.000 0.000 0.000 0.000 8.000 8.000 0.500 p-http://example.org/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency! AVG CNN-1.5+0.5_MA 3 0.667 0.667 1.000 0.750 175.000 175.000 0.557 p-http://example.org/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency AVG CNN-1.5+0.5_MA 3 0.000 0.000 0.333 0.064 8.000 8.000 0.505 p-http://example.org/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency! AVG CNN-1.5+0.5_MA 13 0.000 0.000 0.308 0.086 176.231 151.538 0.381 p-http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to AVG CNN-1.5+0.5_MA 13 0.154 0.308 0.538 0.292 189.077 142.769 0.367 p-http://example.org/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to! AVG CNN-1.5+0.5_MA 60 0.667 0.750 0.900 0.733 175.617 175.617 0.755 p-http://example.org/location/statistical_region/religions./location/religion_percentage/religion AVG CNN-1.5+0.5_MA 60 0.417 0.567 0.767 0.524 40.800 35.400 0.769 p-http://example.org/location/statistical_region/religions./location/religion_percentage/religion! AVG CNN-1.5+0.5_MA 46 1.000 1.000 1.000 1.000 125.674 125.674 0.832 p-http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency AVG CNN-1.5+0.5_MA 46 0.000 0.000 0.000 0.000 8.000 8.000 0.000 p-http://example.org/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency! AVG CNN-1.5+0.5_MA 2 0.000 0.000 0.000 0.010 173.000 161.500 0.399 p-http://example.org/location/us_county/county_seat AVG CNN-1.5+0.5_MA 2 0.000 0.000 0.000 0.000 193.000 186.000 0.159 p-http://example.org/location/us_county/county_seat! AVG CNN-1.5+0.5_MA 124 0.008 0.008 0.032 0.018 71.081 55.637 0.545 p-http://example.org/media_common/netflix_genre/titles AVG CNN-1.5+0.5_MA 124 0.403 0.573 0.806 0.531 89.677 56.403 0.535 p-http://example.org/media_common/netflix_genre/titles! AVG CNN-1.5+0.5_MA 15 0.067 0.067 0.067 0.068 42.067 27.267 0.395 p-http://example.org/medicine/disease/notable_people_with_this_condition AVG CNN-1.5+0.5_MA 15 0.333 0.533 0.800 0.451 125.533 125.533 0.098 p-http://example.org/medicine/disease/notable_people_with_this_condition! AVG CNN-1.5+0.5_MA 13 0.154 0.538 0.692 0.362 59.538 59.538 0.787 p-http://example.org/medicine/disease/risk_factors AVG CNN-1.5+0.5_MA 13 0.154 0.231 0.462 0.265 43.154 43.154 0.640 p-http://example.org/medicine/disease/risk_factors! AVG CNN-1.5+0.5_MA 31 0.129 0.194 0.484 0.236 26.161 26.161 0.706 p-http://example.org/medicine/symptom/symptom_of AVG CNN-1.5+0.5_MA 31 0.290 0.710 0.935 0.518 61.839 61.000 0.766 p-http://example.org/medicine/symptom/symptom_of! AVG CNN-1.5+0.5_MA 17 0.235 0.353 1.000 0.413 205.000 149.412 0.840 p-http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants AVG CNN-1.5+0.5_MA 17 0.176 0.412 0.882 0.372 195.471 138.647 0.835 p-http://example.org/military/military_combatant/military_conflicts./military/military_combatant_group/combatants! AVG CNN-1.5+0.5_MA 34 0.059 0.147 0.324 0.140 62.088 51.559 0.637 p-http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants AVG CNN-1.5+0.5_MA 34 0.265 0.382 0.794 0.392 160.441 160.441 0.638 p-http://example.org/military/military_conflict/combatants./military/military_combatant_group/combatants! AVG CNN-1.5+0.5_MA 14 0.214 0.429 0.429 0.336 114.143 95.357 0.309 p-http://example.org/music/artist/contribution./music/recording_contribution/performance_role AVG CNN-1.5+0.5_MA 14 0.000 0.214 0.286 0.094 77.429 54.714 0.561 p-http://example.org/music/artist/contribution./music/recording_contribution/performance_role! AVG CNN-1.5+0.5_MA 34 0.118 0.235 0.382 0.211 104.912 103.647 0.253 p-http://example.org/music/artist/origin AVG CNN-1.5+0.5_MA 34 0.029 0.029 0.088 0.044 176.235 140.353 0.250 p-http://example.org/music/artist/origin! AVG CNN-1.5+0.5_MA 152 0.171 0.263 0.566 0.271 122.934 105.033 0.505 p-http://example.org/music/artist/track_contributions./music/track_contribution/role AVG CNN-1.5+0.5_MA 152 0.039 0.066 0.151 0.081 81.737 51.572 0.689 p-http://example.org/music/artist/track_contributions./music/track_contribution/role! AVG CNN-1.5+0.5_MA 664 0.017 0.039 0.131 0.053 62.702 29.958 0.670 p-http://example.org/music/genre/artists AVG CNN-1.5+0.5_MA 664 0.182 0.330 0.518 0.296 118.328 74.386 0.677 p-http://example.org/music/genre/artists! AVG CNN-1.5+0.5_MA 97 0.155 0.289 0.526 0.268 60.402 44.629 0.708 p-http://example.org/music/genre/parent_genre AVG CNN-1.5+0.5_MA 97 0.031 0.113 0.216 0.103 63.340 37.948 0.495 p-http://example.org/music/genre/parent_genre! AVG CNN-1.5+0.5_MA 22 0.091 0.318 0.591 0.252 130.409 57.455 0.170 p-http://example.org/music/group_member/membership./music/group_membership/group AVG CNN-1.5+0.5_MA 22 0.000 0.000 0.045 0.008 97.955 49.182 0.207 p-http://example.org/music/group_member/membership./music/group_membership/group! AVG CNN-1.5+0.5_MA 29 0.103 0.414 0.586 0.276 125.828 118.828 0.441 p-http://example.org/music/group_member/membership./music/group_membership/role AVG CNN-1.5+0.5_MA 29 0.034 0.069 0.207 0.077 75.345 51.828 0.626 p-http://example.org/music/group_member/membership./music/group_membership/role! AVG CNN-1.5+0.5_MA 6 0.167 0.167 0.833 0.259 79.500 62.667 0.426 p-http://example.org/music/instrument/family AVG CNN-1.5+0.5_MA 6 0.000 0.333 0.333 0.162 58.667 51.667 0.417 p-http://example.org/music/instrument/family! AVG CNN-1.5+0.5_MA 153 0.007 0.039 0.085 0.037 77.732 52.379 0.689 p-http://example.org/music/instrument/instrumentalists AVG CNN-1.5+0.5_MA 153 0.301 0.490 0.693 0.429 132.118 119.595 0.609 p-http://example.org/music/instrument/instrumentalists! AVG CNN-1.5+0.5_MA 2 0.000 0.000 0.000 0.029 78.500 57.000 0.844 p-http://example.org/music/performance_role/guest_performances./music/recording_contribution/performance_role AVG CNN-1.5+0.5_MA 2 0.000 0.000 0.000 0.022 78.500 61.000 0.690 p-http://example.org/music/performance_role/guest_performances./music/recording_contribution/performance_role! AVG CNN-1.5+0.5_MA 167 0.126 0.210 0.479 0.219 77.006 57.359 0.708 p-http://example.org/music/performance_role/regular_performances./music/group_membership/group AVG CNN-1.5+0.5_MA 167 0.503 0.689 0.838 0.621 92.461 75.503 0.748 p-http://example.org/music/performance_role/regular_performances./music/group_membership/group! AVG CNN-1.5+0.5_MA 40 0.125 0.200 0.425 0.203 75.100 56.600 0.853 p-http://example.org/music/performance_role/regular_performances./music/group_membership/role AVG CNN-1.5+0.5_MA 40 0.025 0.075 0.400 0.121 71.300 56.700 0.860 p-http://example.org/music/performance_role/regular_performances./music/group_membership/role! AVG CNN-1.5+0.5_MA 53 0.038 0.189 0.566 0.181 76.000 56.245 0.875 p-http://example.org/music/performance_role/track_performances./music/track_contribution/role AVG CNN-1.5+0.5_MA 53 0.094 0.245 0.566 0.226 73.943 57.019 0.873 p-http://example.org/music/performance_role/track_performances./music/track_contribution/role! AVG CNN-1.5+0.5_MA 266 0.004 0.026 0.041 0.022 101.489 65.838 0.526 p-http://example.org/music/record_label/artist AVG CNN-1.5+0.5_MA 266 0.064 0.188 0.447 0.183 116.857 89.673 0.383 p-http://example.org/music/record_label/artist! AVG CNN-1.5+0.5_MA 16 1.000 1.000 1.000 1.000 38.812 38.812 0.893 p-http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal AVG CNN-1.5+0.5_MA 16 1.000 1.000 1.000 1.000 3.000 3.000 0.859 p-http://example.org/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal! AVG CNN-1.5+0.5_MA 23 0.000 0.000 0.087 0.047 51.304 50.522 0.830 p-http://example.org/olympics/olympic_games/participating_countries AVG CNN-1.5+0.5_MA 23 0.522 0.826 1.000 0.695 158.000 158.000 0.720 p-http://example.org/olympics/olympic_games/participating_countries! AVG CNN-1.5+0.5_MA 13 0.077 0.769 1.000 0.384 46.154 46.154 0.862 p-http://example.org/olympics/olympic_games/sports AVG CNN-1.5+0.5_MA 13 0.000 0.385 1.000 0.296 48.154 48.154 0.859 p-http://example.org/olympics/olympic_games/sports! AVG CNN-1.5+0.5_MA 31 0.452 0.710 0.806 0.589 143.387 143.387 0.782 p-http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics AVG CNN-1.5+0.5_MA 31 0.000 0.032 0.097 0.063 51.516 50.161 0.818 p-http://example.org/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics! AVG CNN-1.5+0.5_MA 38 0.895 1.000 1.000 0.947 150.974 150.974 0.827 p-http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal AVG CNN-1.5+0.5_MA 38 0.395 0.500 0.579 0.473 3.000 3.000 0.807 p-http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal! AVG CNN-1.5+0.5_MA 63 0.159 0.349 0.778 0.326 173.254 173.254 0.775 p-http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics AVG CNN-1.5+0.5_MA 63 0.127 0.286 0.556 0.250 48.476 45.381 0.822 p-http://example.org/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics! AVG CNN-1.5+0.5_MA 258 0.178 0.260 0.422 0.260 45.283 44.140 0.871 p-http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country AVG CNN-1.5+0.5_MA 258 0.384 0.578 0.841 0.523 153.194 153.194 0.816 p-http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country! AVG CNN-1.5+0.5_MA 8 0.750 0.750 0.875 0.789 43.250 43.250 0.729 p-http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics AVG CNN-1.5+0.5_MA 8 0.375 0.625 0.750 0.494 53.625 53.625 0.716 p-http://example.org/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics! AVG CNN-1.5+0.5_MA 3 0.667 1.000 1.000 0.833 111.667 111.667 0.727 p-http://example.org/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency AVG CNN-1.5+0.5_MA 3 0.000 0.000 0.000 0.002 8.000 8.000 0.767 p-http://example.org/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency! AVG CNN-1.5+0.5_MA 22 1.000 1.000 1.000 1.000 141.955 141.955 0.837 p-http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency AVG CNN-1.5+0.5_MA 22 0.000 0.000 0.000 0.000 109.000 109.000 0.000 p-http://example.org/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency! AVG CNN-1.5+0.5_MA 20 0.000 0.150 0.150 0.077 161.000 139.650 0.354 p-http://example.org/organization/organization/child./organization/organization_relationship/child AVG CNN-1.5+0.5_MA 20 0.150 0.200 0.400 0.235 110.450 93.450 0.318 p-http://example.org/organization/organization/child./organization/organization_relationship/child! AVG CNN-1.5+0.5_MA 55 0.291 0.364 0.618 0.384 123.436 107.400 0.474 p-http://example.org/organization/organization/headquarters./location/mailing_address/citytown AVG CNN-1.5+0.5_MA 55 0.091 0.145 0.164 0.121 182.200 133.745 0.481 p-http://example.org/organization/organization/headquarters./location/mailing_address/citytown! AVG CNN-1.5+0.5_MA 7 0.857 0.857 0.857 0.857 117.714 105.857 0.546 p-http://example.org/organization/organization/headquarters./location/mailing_address/country AVG CNN-1.5+0.5_MA 7 0.000 0.000 0.000 0.000 178.429 177.143 0.318 p-http://example.org/organization/organization/headquarters./location/mailing_address/country! AVG CNN-1.5+0.5_MA 39 0.564 0.744 0.846 0.667 138.077 128.538 0.645 p-http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region AVG CNN-1.5+0.5_MA 39 0.026 0.051 0.103 0.048 185.667 131.487 0.479 p-http://example.org/organization/organization/headquarters./location/mailing_address/state_province_region! AVG CNN-1.5+0.5_MA 16 0.000 0.188 0.312 0.093 156.125 124.188 0.312 p-http://example.org/organization/organization/place_founded AVG CNN-1.5+0.5_MA 16 0.125 0.188 0.188 0.149 173.188 149.938 0.203 p-http://example.org/organization/organization/place_founded! AVG CNN-1.5+0.5_MA 9 0.222 0.222 0.222 0.229 135.556 109.000 0.384 p-http://example.org/organization/organization_founder/organizations_founded AVG CNN-1.5+0.5_MA 9 0.000 0.000 0.222 0.036 111.333 57.556 0.390 p-http://example.org/organization/organization_founder/organizations_founded! AVG CNN-1.5+0.5_MA 96 0.771 0.906 1.000 0.845 123.938 117.865 0.751 p-http://example.org/organization/organization_member/member_of./organization/organization_membership/organization AVG CNN-1.5+0.5_MA 96 0.188 0.302 0.469 0.281 72.531 38.198 0.730 p-http://example.org/organization/organization_member/member_of./organization/organization_membership/organization! AVG CNN-1.5+0.5_MA 70 0.014 0.014 0.014 0.015 44.314 43.371 0.601 p-http://example.org/organization/role/leaders./organization/leadership/organization AVG CNN-1.5+0.5_MA 70 0.743 0.943 1.000 0.843 131.843 131.843 0.759 p-http://example.org/organization/role/leaders./organization/leadership/organization! AVG CNN-1.5+0.5_MA 94 0.000 0.000 0.011 0.003 58.894 40.670 0.442 p-http://example.org/people/cause_of_death/people AVG CNN-1.5+0.5_MA 94 0.149 0.309 0.553 0.270 124.117 124.117 0.233 p-http://example.org/people/cause_of_death/people! AVG CNN-1.5+0.5_MA 9 0.333 0.556 1.000 0.516 109.111 106.556 0.150 p-http://example.org/people/deceased_person/place_of_burial AVG CNN-1.5+0.5_MA 9 0.000 0.000 0.000 0.001 86.778 41.444 0.328 p-http://example.org/people/deceased_person/place_of_burial! AVG CNN-1.5+0.5_MA 78 0.103 0.333 0.551 0.256 115.179 110.538 0.230 p-http://example.org/people/deceased_person/place_of_death AVG CNN-1.5+0.5_MA 78 0.000 0.000 0.000 0.001 149.115 107.308 0.173 p-http://example.org/people/deceased_person/place_of_death! AVG CNN-1.5+0.5_MA 17 0.176 0.235 0.412 0.239 34.000 33.059 0.487 p-http://example.org/people/ethnicity/geographic_distribution AVG CNN-1.5+0.5_MA 17 0.235 0.529 0.706 0.402 161.588 160.882 0.402 p-http://example.org/people/ethnicity/geographic_distribution! AVG CNN-1.5+0.5_MA 29 0.207 0.379 0.586 0.328 35.724 35.724 0.694 p-http://example.org/people/ethnicity/languages_spoken AVG CNN-1.5+0.5_MA 29 0.172 0.276 0.483 0.266 53.517 52.310 0.612 p-http://example.org/people/ethnicity/languages_spoken! AVG CNN-1.5+0.5_MA 251 0.004 0.008 0.024 0.012 36.275 27.884 0.455 p-http://example.org/people/ethnicity/people AVG CNN-1.5+0.5_MA 251 0.347 0.506 0.705 0.464 117.243 114.892 0.357 p-http://example.org/people/ethnicity/people! AVG CNN-1.5+0.5_MA 53 0.019 0.019 0.019 0.020 5.000 5.000 0.333 p-http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony AVG CNN-1.5+0.5_MA 53 1.000 1.000 1.000 1.000 145.679 145.679 0.702 p-http://example.org/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony! AVG CNN-1.5+0.5_MA 24 0.125 0.167 0.250 0.162 128.375 98.375 0.239 p-http://example.org/people/person/employment_history./business/employment_tenure/company AVG CNN-1.5+0.5_MA 24 0.125 0.125 0.250 0.157 119.833 90.333 0.300 p-http://example.org/people/person/employment_history./business/employment_tenure/company! AVG CNN-1.5+0.5_MA 436 0.807 1.000 1.000 0.904 110.268 110.268 0.842 p-http://example.org/people/person/gender AVG CNN-1.5+0.5_MA 436 0.000 0.000 0.000 0.001 32.472 32.472 0.412 p-http://example.org/people/person/gender! AVG CNN-1.5+0.5_MA 98 0.704 0.837 0.969 0.786 122.776 122.776 0.435 p-http://example.org/people/person/languages AVG CNN-1.5+0.5_MA 98 0.020 0.020 0.133 0.046 59.796 42.541 0.486 p-http://example.org/people/person/languages! AVG CNN-1.5+0.5_MA 494 0.692 0.881 0.957 0.793 114.200 106.842 0.808 p-http://example.org/people/person/nationality AVG CNN-1.5+0.5_MA 494 0.020 0.061 0.077 0.045 183.962 150.982 0.644 p-http://example.org/people/person/nationality! AVG CNN-1.5+0.5_MA 170 0.129 0.224 0.359 0.209 104.853 101.606 0.254 p-http://example.org/people/person/place_of_birth AVG CNN-1.5+0.5_MA 170 0.018 0.035 0.065 0.033 155.624 100.476 0.346 p-http://example.org/people/person/place_of_birth! AVG CNN-1.5+0.5_MA 305 0.134 0.252 0.377 0.218 120.095 109.403 0.407 p-http://example.org/people/person/places_lived./people/place_lived/location AVG CNN-1.5+0.5_MA 305 0.030 0.049 0.066 0.044 174.928 121.390 0.466 p-http://example.org/people/person/places_lived./people/place_lived/location! AVG CNN-1.5+0.5_MA 1311 0.456 0.690 0.886 0.599 117.651 91.832 0.783 p-http://example.org/people/person/profession AVG CNN-1.5+0.5_MA 1311 0.002 0.005 0.018 0.010 43.944 24.625 0.688 p-http://example.org/people/person/profession! AVG CNN-1.5+0.5_MA 127 0.394 0.614 0.874 0.548 120.858 119.488 0.336 p-http://example.org/people/person/religion AVG CNN-1.5+0.5_MA 127 0.000 0.000 0.016 0.005 42.047 32.071 0.444 p-http://example.org/people/person/religion! AVG CNN-1.5+0.5_MA 7 0.429 0.429 0.429 0.436 123.857 63.429 0.511 p-http://example.org/people/person/sibling_s./people/sibling_relationship/sibling AVG CNN-1.5+0.5_MA 7 0.286 0.429 0.571 0.383 110.143 59.571 0.344 p-http://example.org/people/person/sibling_s./people/sibling_relationship/sibling! AVG CNN-1.5+0.5_MA 35 0.029 0.057 0.257 0.088 125.829 121.886 0.127 p-http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony AVG CNN-1.5+0.5_MA 35 0.029 0.029 0.086 0.039 174.514 131.486 0.212 p-http://example.org/people/person/spouse_s./people/marriage/location_of_ceremony! AVG CNN-1.5+0.5_MA 4 0.500 0.500 0.500 0.500 100.000 54.500 0.425 p-http://example.org/people/person/spouse_s./people/marriage/spouse AVG CNN-1.5+0.5_MA 4 1.000 1.000 1.000 1.000 125.500 60.500 0.817 p-http://example.org/people/person/spouse_s./people/marriage/spouse! AVG CNN-1.5+0.5_MA 346 0.939 1.000 1.000 0.970 111.717 111.717 0.775 p-http://example.org/people/person/spouse_s./people/marriage/type_of_union AVG CNN-1.5+0.5_MA 346 0.000 0.000 0.003 0.002 4.864 4.592 0.374 p-http://example.org/people/person/spouse_s./people/marriage/type_of_union! AVG CNN-1.5+0.5_MA 13 0.154 0.308 0.385 0.240 33.692 31.077 0.293 p-http://example.org/people/profession/specialization_of AVG CNN-1.5+0.5_MA 13 0.000 0.000 0.000 0.009 53.769 45.000 0.319 p-http://example.org/people/profession/specialization_of! AVG CNN-1.5+0.5_MA 9 0.333 0.333 0.444 0.366 84.889 61.556 0.812 p-http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team AVG CNN-1.5+0.5_MA 9 0.444 0.556 0.667 0.530 88.444 85.556 0.676 p-http://example.org/soccer/football_player/current_team./sports/sports_team_roster/team! AVG CNN-1.5+0.5_MA 10 0.700 1.000 1.000 0.850 51.500 51.500 0.859 p-http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position AVG CNN-1.5+0.5_MA 10 0.000 0.000 0.000 0.011 14.600 11.700 0.846 p-http://example.org/soccer/football_team/current_roster./soccer/football_roster_position/position! AVG CNN-1.5+0.5_MA 12 0.667 1.000 1.000 0.833 69.000 69.000 0.862 p-http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position AVG CNN-1.5+0.5_MA 12 0.000 0.000 0.000 0.021 13.583 12.417 0.821 p-http://example.org/soccer/football_team/current_roster./sports/sports_team_roster/position! AVG CNN-1.5+0.5_MA 16 0.000 0.062 0.125 0.057 104.500 88.188 0.454 p-http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team AVG CNN-1.5+0.5_MA 16 0.188 0.250 0.562 0.292 90.375 86.938 0.412 p-http://example.org/sports/pro_athlete/teams./sports/sports_team_roster/team! AVG CNN-1.5+0.5_MA 43 0.535 1.000 1.000 0.744 88.116 88.116 0.824 p-http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft AVG CNN-1.5+0.5_MA 43 0.163 0.512 0.860 0.391 17.744 17.744 0.748 p-http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft! AVG CNN-1.5+0.5_MA 86 0.035 0.105 0.267 0.112 88.198 69.988 0.572 p-http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school AVG CNN-1.5+0.5_MA 86 0.198 0.233 0.302 0.247 143.186 143.186 0.462 p-http://example.org/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school! AVG CNN-1.5+0.5_MA 17 0.471 0.471 0.471 0.471 70.941 53.588 0.528 p-http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete AVG CNN-1.5+0.5_MA 17 0.471 0.824 0.882 0.632 102.235 102.235 0.540 p-http://example.org/sports/sport/pro_athletes./sports/pro_sports_played/athlete! AVG CNN-1.5+0.5_MA 16 0.000 0.000 0.062 0.025 35.438 35.438 0.384 p-http://example.org/sports/sports_league/teams./sports/sports_league_participation/team AVG CNN-1.5+0.5_MA 16 0.875 1.000 1.000 0.927 92.250 92.250 0.533 p-http://example.org/sports/sports_league/teams./sports/sports_league_participation/team! AVG CNN-1.5+0.5_MA 42 0.000 0.024 0.167 0.058 17.667 17.667 0.662 p-http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school AVG CNN-1.5+0.5_MA 42 0.071 0.214 0.690 0.231 132.024 132.024 0.353 p-http://example.org/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school! AVG CNN-1.5+0.5_MA 22 0.045 0.182 0.409 0.160 23.273 15.545 0.845 p-http://example.org/sports/sports_position/players./sports/sports_team_roster/team AVG CNN-1.5+0.5_MA 22 0.500 0.955 1.000 0.688 68.682 68.682 0.857 p-http://example.org/sports/sports_position/players./sports/sports_team_roster/team! AVG CNN-1.5+0.5_MA 77 0.377 0.792 0.935 0.588 88.026 88.026 0.729 p-http://example.org/sports/sports_team/colors AVG CNN-1.5+0.5_MA 77 0.000 0.000 0.013 0.014 21.091 20.987 0.546 p-http://example.org/sports/sports_team/colors! AVG CNN-1.5+0.5_MA 1 0.000 0.000 1.000 0.250 107.000 107.000 0.864 p-http://example.org/sports/sports_team/roster./american_football/football_historical_roster_position/position_s AVG CNN-1.5+0.5_MA 1 0.000 0.000 0.000 0.023 33.000 24.000 0.831 p-http://example.org/sports/sports_team/roster./american_football/football_historical_roster_position/position_s! AVG CNN-1.5+0.5_MA 3 0.000 0.667 1.000 0.389 88.667 88.667 0.884 p-http://example.org/sports/sports_team/roster./american_football/football_roster_position/position AVG CNN-1.5+0.5_MA 3 0.000 0.000 0.333 0.090 31.667 25.667 0.871 p-http://example.org/sports/sports_team/roster./american_football/football_roster_position/position! AVG CNN-1.5+0.5_MA 3 0.667 1.000 1.000 0.833 73.000 73.000 0.877 p-http://example.org/sports/sports_team/roster./baseball/baseball_roster_position/position AVG CNN-1.5+0.5_MA 3 0.000 0.000 1.000 0.189 30.000 23.667 0.861 p-http://example.org/sports/sports_team/roster./baseball/baseball_roster_position/position! AVG CNN-1.5+0.5_MA 2 1.000 1.000 1.000 1.000 102.000 102.000 0.857 p-http://example.org/sports/sports_team/roster./basketball/basketball_roster_position/position AVG CNN-1.5+0.5_MA 2 0.000 0.000 0.000 0.036 22.000 16.000 0.820 p-http://example.org/sports/sports_team/roster./basketball/basketball_roster_position/position! AVG CNN-1.5+0.5_MA 53 0.943 0.981 1.000 0.966 92.415 92.415 0.854 p-http://example.org/sports/sports_team/sport AVG CNN-1.5+0.5_MA 53 0.170 0.208 0.302 0.208 63.698 60.396 0.570 p-http://example.org/sports/sports_team/sport! AVG CNN-1.5+0.5_MA 53 0.000 0.000 0.000 0.000 172.660 167.774 0.159 p-http://example.org/sports/sports_team_location/teams AVG CNN-1.5+0.5_MA 53 0.000 0.000 0.000 0.012 71.000 66.321 0.254 p-http://example.org/sports/sports_team_location/teams! AVG CNN-1.5+0.5_MA 11 0.818 0.818 0.818 0.818 30.455 30.455 0.707 p-http://example.org/time/event/instance_of_recurring_event AVG CNN-1.5+0.5_MA 11 0.000 0.000 0.000 0.007 35.364 35.364 0.258 p-http://example.org/time/event/instance_of_recurring_event! AVG CNN-1.5+0.5_MA 34 0.176 0.324 0.471 0.281 59.824 54.235 0.512 p-http://example.org/time/event/locations AVG CNN-1.5+0.5_MA 34 0.088 0.235 0.559 0.214 163.206 137.176 0.328 p-http://example.org/time/event/locations! AVG CNN-1.5+0.5_MA 60 0.967 1.000 1.000 0.983 223.383 223.383 0.908 p-http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month AVG CNN-1.5+0.5_MA 60 0.967 1.000 1.000 0.983 11.900 11.900 0.901 p-http://example.org/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month! AVG CNN-1.5+0.5_MA 20 0.950 1.000 1.000 0.975 218.500 218.500 0.844 p-http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation AVG CNN-1.5+0.5_MA 20 0.200 0.500 1.000 0.429 70.600 70.600 0.560 p-http://example.org/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation! AVG CNN-1.5+0.5_MA 7 0.000 0.000 0.000 0.010 29.143 20.714 0.390 p-http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person AVG CNN-1.5+0.5_MA 7 0.571 1.000 1.000 0.762 126.286 126.286 0.404 p-http://example.org/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person! AVG CNN-1.5+0.5_MA 26 0.038 0.038 0.154 0.066 125.385 100.346 0.496 p-http://example.org/tv/tv_network/programs./tv/tv_network_duration/program AVG CNN-1.5+0.5_MA 26 0.115 0.269 0.615 0.271 82.000 73.269 0.421 p-http://example.org/tv/tv_network/programs./tv/tv_network_duration/program! AVG CNN-1.5+0.5_MA 11 0.727 0.818 0.818 0.758 127.273 116.909 0.334 p-http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program AVG CNN-1.5+0.5_MA 11 0.000 0.000 0.000 0.010 81.273 45.545 0.356 p-http://example.org/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program! AVG CNN-1.5+0.5_MA 22 1.000 1.000 1.000 1.000 105.000 105.000 0.535 p-http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type AVG CNN-1.5+0.5_MA 22 0.000 0.000 0.000 0.000 32.000 28.000 0.000 p-http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type! AVG CNN-1.5+0.5_MA 4 0.000 0.000 0.500 0.107 103.750 67.500 0.386 p-http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/program AVG CNN-1.5+0.5_MA 4 0.000 0.000 0.000 0.045 94.000 67.500 0.361 p-http://example.org/tv/tv_producer/programs_produced./tv/tv_producer_term/program! AVG CNN-1.5+0.5_MA 32 0.781 0.875 0.969 0.847 79.250 79.031 0.892 p-http://example.org/tv/tv_program/country_of_origin AVG CNN-1.5+0.5_MA 32 0.031 0.062 0.062 0.048 163.781 154.375 0.257 p-http://example.org/tv/tv_program/country_of_origin! AVG CNN-1.5+0.5_MA 94 0.287 0.521 0.755 0.444 80.819 76.819 0.700 p-http://example.org/tv/tv_program/genre AVG CNN-1.5+0.5_MA 94 0.053 0.117 0.287 0.130 57.202 41.926 0.637 p-http://example.org/tv/tv_program/genre! AVG CNN-1.5+0.5_MA 24 1.000 1.000 1.000 1.000 81.542 81.542 0.908 p-http://example.org/tv/tv_program/languages AVG CNN-1.5+0.5_MA 24 0.042 0.167 0.167 0.124 72.167 70.833 0.567 p-http://example.org/tv/tv_program/languages! AVG CNN-1.5+0.5_MA 2 0.000 0.000 0.000 0.000 70.000 38.500 0.333 p-http://example.org/tv/tv_program/program_creator AVG CNN-1.5+0.5_MA 2 0.000 0.000 0.000 0.000 118.000 88.000 0.101 p-http://example.org/tv/tv_program/program_creator! AVG CNN-1.5+0.5_MA 91 0.000 0.022 0.055 0.025 81.165 52.088 0.457 p-http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor AVG CNN-1.5+0.5_MA 91 0.077 0.176 0.330 0.161 108.077 80.176 0.256 p-http://example.org/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor! AVG CNN-1.5+0.5_MA 11 1.000 1.000 1.000 1.000 73.818 73.818 0.773 p-http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type AVG CNN-1.5+0.5_MA 11 0.000 0.000 0.000 0.000 25.000 18.000 0.000 p-http://example.org/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type! AVG CNN-1.5+0.5_MA 9 0.222 0.556 0.889 0.457 113.778 104.889 0.177 p-http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program AVG CNN-1.5+0.5_MA 9 0.000 0.000 0.222 0.043 86.556 65.111 0.364 p-http://example.org/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program! AVG CNN-1.5+0.5_MA 9 0.000 0.333 1.000 0.268 139.889 121.444 0.506 p-http://example.org/user/alexander/philosophy/philosopher/interests AVG CNN-1.5+0.5_MA 9 0.000 0.111 0.333 0.107 52.778 39.556 0.644 p-http://example.org/user/alexander/philosophy/philosopher/interests! AVG CNN-1.5+0.5_MA 11 0.182 0.545 1.000 0.451 48.727 48.727 0.821 p-http://example.org/user/jg/default_domain/olympic_games/sports AVG CNN-1.5+0.5_MA 11 0.091 0.182 1.000 0.263 45.455 45.455 0.841 p-http://example.org/user/jg/default_domain/olympic_games/sports! AVG CNN-1.5+0.5_MA 24 0.125 0.208 0.208 0.188 132.917 20.667 0.401 p-http://example.org/user/ktrueman/default_domain/international_organization/member_states AVG CNN-1.5+0.5_MA 24 0.917 1.000 1.000 0.958 142.792 142.792 0.785 p-http://example.org/user/ktrueman/default_domain/international_organization/member_states! AVG CNN-1.5+0.5_MA 50 1.000 1.000 1.000 1.000 110.480 110.480 0.602 p-http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy AVG CNN-1.5+0.5_MA 50 0.000 0.000 0.000 0.000 1.000 1.000 0.000 p-http://example.org/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy!